From e130c0c417bae71915553fc36e6200564b39c504 Mon Sep 17 00:00:00 2001
From: husein zolkepli <husein.zol05@gmail.com>
Date: Mon, 28 Jun 2021 14:18:46 +0800
Subject: [PATCH] release 4.5

---
 docs/Api.rst                                  |    6 +
 docs/load-dependency.ipynb                    | 5589 ++++++++++++----
 ...load-knowledge-graph-from-dependency.ipynb |  400 +-
 docs/speech-toolkit.rst                       |    5 +
 example/dependency/load-dependency.ipynb      |  391 +-
 ...load-knowledge-graph-from-dependency.ipynb |  400 +-
 load-dependency.ipynb                         | 5956 +++++++++++++++++
 malaya/dependency.py                          |  113 +-
 malaya/knowledge_graph.py                     |   10 +-
 malaya/model/bert.py                          |    5 +-
 malaya/model/xlnet.py                         |    5 +-
 malaya/train/__init__.py                      |  155 -
 malaya/train/model/__init__.py                |    3 -
 malaya/train/model/alxlnet/__init__.py        |    1 -
 malaya/train/model/bigbird/__init__.py        |    1 -
 malaya/train/model/bigbird/attention.py       | 1279 ----
 malaya/train/model/bigbird/beam_search.py     |  277 -
 malaya/train/model/bigbird/decoder.py         |  681 --
 malaya/train/model/bigbird/encoder.py         |  515 --
 malaya/train/model/bigbird/modeling.py        |  516 --
 malaya/train/model/bigbird/optimization.py    |  182 -
 malaya/train/model/bigbird/utils.py           |  806 ---
 malaya/train/model/pegasus/__init__.py        |    1 -
 malaya/train/model/pegasus/base.py            |   59 -
 malaya/train/model/pegasus/layers/__init__.py |   13 -
 .../train/model/pegasus/layers/attention.py   |  135 -
 .../train/model/pegasus/layers/beam_search.py |  301 -
 malaya/train/model/pegasus/layers/decoding.py |  208 -
 .../train/model/pegasus/layers/embedding.py   |   73 -
 malaya/train/model/pegasus/layers/timing.py   |   68 -
 .../model/pegasus/layers/transformer_block.py |  123 -
 malaya/train/model/pegasus/transformer.py     |  198 -
 .../train/model/product_key_memory/layer.py   |   22 -
 .../train/model/product_key_memory/model.py   |   33 -
 pretrained-model/fnet/README.md               |    4 +-
 pretrained-model/fnet/sentiment-base.ipynb    |  494 ++
 pretrained-model/fnet/test-fnet.ipynb         |  561 ++
 pretrained-model/performer/fast_attention.py  |  529 ++
 pretrained-model/performer/model.py           |  230 +
 .../performer/test-performer.ipynb            |   33 +
 pretrained-model/performer/util.py            |  195 +
 session/dependency/albert-base.ipynb          | 2606 +++++---
 session/dependency/albert-tiny.ipynb          | 2563 ++++---
 session/dependency/alxlnet-base.ipynb         | 2639 +++++---
 session/dependency/bert-base.ipynb            | 2884 ++++----
 session/dependency/tiny-bert.ipynb            | 2736 ++++----
 session/dependency/xlnet-base.ipynb           | 2928 ++++----
 47 files changed, 22562 insertions(+), 14370 deletions(-)
 create mode 100644 load-dependency.ipynb
 delete mode 100644 malaya/train/__init__.py
 delete mode 100644 malaya/train/model/__init__.py
 delete mode 100644 malaya/train/model/alxlnet/__init__.py
 delete mode 100644 malaya/train/model/bigbird/__init__.py
 delete mode 100644 malaya/train/model/bigbird/attention.py
 delete mode 100644 malaya/train/model/bigbird/beam_search.py
 delete mode 100644 malaya/train/model/bigbird/decoder.py
 delete mode 100644 malaya/train/model/bigbird/encoder.py
 delete mode 100644 malaya/train/model/bigbird/modeling.py
 delete mode 100644 malaya/train/model/bigbird/optimization.py
 delete mode 100644 malaya/train/model/bigbird/utils.py
 delete mode 100644 malaya/train/model/pegasus/__init__.py
 delete mode 100644 malaya/train/model/pegasus/base.py
 delete mode 100644 malaya/train/model/pegasus/layers/__init__.py
 delete mode 100644 malaya/train/model/pegasus/layers/attention.py
 delete mode 100644 malaya/train/model/pegasus/layers/beam_search.py
 delete mode 100644 malaya/train/model/pegasus/layers/decoding.py
 delete mode 100644 malaya/train/model/pegasus/layers/embedding.py
 delete mode 100644 malaya/train/model/pegasus/layers/timing.py
 delete mode 100644 malaya/train/model/pegasus/layers/transformer_block.py
 delete mode 100644 malaya/train/model/pegasus/transformer.py
 delete mode 100644 malaya/train/model/product_key_memory/layer.py
 delete mode 100644 malaya/train/model/product_key_memory/model.py
 create mode 100644 pretrained-model/fnet/sentiment-base.ipynb
 create mode 100644 pretrained-model/fnet/test-fnet.ipynb
 create mode 100644 pretrained-model/performer/fast_attention.py
 create mode 100644 pretrained-model/performer/model.py
 create mode 100644 pretrained-model/performer/test-performer.ipynb
 create mode 100644 pretrained-model/performer/util.py

diff --git a/docs/Api.rst b/docs/Api.rst
index f0931351..16697a09 100644
--- a/docs/Api.rst
+++ b/docs/Api.rst
@@ -27,6 +27,12 @@ malaya.constituency
 .. automodule:: malaya.constituency
     :members:
 
+malaya.coref
+---------------------
+
+.. automodule:: malaya.coref
+    :members:
+
 malaya.dependency
 ------------------
 
diff --git a/docs/load-dependency.ipynb b/docs/load-dependency.ipynb
index 4f3e3f5a..d1a09942 100644
--- a/docs/load-dependency.ipynb
+++ b/docs/load-dependency.ipynb
@@ -31,15 +31,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.98 s, sys: 944 ms, total: 5.92 s\n",
-      "Wall time: 6.56 s\n"
+      "CPU times: user 5.15 s, sys: 925 ms, total: 6.07 s\n",
+      "Wall time: 6.8 s\n"
      ]
     }
    ],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -135,91 +135,86 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>advmod</td>\n",
-       "      <td>adverbial modifier</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
        "      <td>compound</td>\n",
        "      <td>compound</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>9</th>\n",
        "      <td>compound:plur</td>\n",
        "      <td>plural compound</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>10</th>\n",
        "      <td>conj</td>\n",
        "      <td>conjunct</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>11</th>\n",
        "      <td>cop</td>\n",
        "      <td>cop</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>12</th>\n",
        "      <td>csubj</td>\n",
        "      <td>clausal subject</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>13</th>\n",
        "      <td>dep</td>\n",
        "      <td>dependent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>14</th>\n",
        "      <td>det</td>\n",
        "      <td>determiner</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>15</th>\n",
        "      <td>fixed</td>\n",
        "      <td>multi-word expression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>16</th>\n",
        "      <td>flat</td>\n",
        "      <td>name</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>17</th>\n",
        "      <td>iobj</td>\n",
        "      <td>indirect object</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>18</th>\n",
        "      <td>mark</td>\n",
        "      <td>marker</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>19</th>\n",
        "      <td>nmod</td>\n",
        "      <td>nominal modifier</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>20</th>\n",
        "      <td>nsubj</td>\n",
        "      <td>nominal subject</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>21</th>\n",
        "      <td>obj</td>\n",
        "      <td>direct object</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>22</th>\n",
        "      <td>parataxis</td>\n",
        "      <td>parataxis</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>23</th>\n",
        "      <td>root</td>\n",
        "      <td>root</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>24</th>\n",
        "      <td>xcomp</td>\n",
        "      <td>open clausal complement</td>\n",
        "    </tr>\n",
@@ -237,27 +232,26 @@
        "5             aux                  auxiliary\n",
        "6            case               case marking\n",
        "7           ccomp         clausal complement\n",
-       "8          advmod         adverbial modifier\n",
-       "9        compound                   compound\n",
-       "10  compound:plur            plural compound\n",
-       "11           conj                   conjunct\n",
-       "12            cop                        cop\n",
-       "13          csubj            clausal subject\n",
-       "14            dep                  dependent\n",
-       "15            det                 determiner\n",
-       "16          fixed      multi-word expression\n",
-       "17           flat                       name\n",
-       "18           iobj            indirect object\n",
-       "19           mark                     marker\n",
-       "20           nmod           nominal modifier\n",
-       "21          nsubj            nominal subject\n",
-       "22            obj              direct object\n",
-       "23      parataxis                  parataxis\n",
-       "24           root                       root\n",
-       "25          xcomp    open clausal complement"
+       "8        compound                   compound\n",
+       "9   compound:plur            plural compound\n",
+       "10           conj                   conjunct\n",
+       "11            cop                        cop\n",
+       "12          csubj            clausal subject\n",
+       "13            dep                  dependent\n",
+       "14            det                 determiner\n",
+       "15          fixed      multi-word expression\n",
+       "16           flat                       name\n",
+       "17           iobj            indirect object\n",
+       "18           mark                     marker\n",
+       "19           nmod           nominal modifier\n",
+       "20          nsubj            nominal subject\n",
+       "21            obj              direct object\n",
+       "22      parataxis                  parataxis\n",
+       "23           root                       root\n",
+       "24          xcomp    open clausal complement"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -270,12 +264,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### List available transformer Dependency models"
+    "### List available transformer Dependency models\n",
+    "\n",
+    "```python\n",
+    "def available_transformer(version: str = 'v2'):\n",
+    "    \"\"\"\n",
+    "    List available transformer dependency parsing models.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    \"\"\"\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -316,51 +326,51 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>bert</th>\n",
-       "      <td>426.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.855</td>\n",
-       "      <td>0.848</td>\n",
-       "      <td>0.920</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>0.820450</td>\n",
+       "      <td>0.79970</td>\n",
+       "      <td>0.98936</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tiny-bert</th>\n",
-       "      <td>59.5</td>\n",
-       "      <td>15.7</td>\n",
-       "      <td>0.718</td>\n",
-       "      <td>0.694</td>\n",
-       "      <td>0.886</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>17.50</td>\n",
+       "      <td>0.795252</td>\n",
+       "      <td>0.72470</td>\n",
+       "      <td>0.98939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>albert</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>13.2</td>\n",
-       "      <td>0.811</td>\n",
-       "      <td>0.793</td>\n",
-       "      <td>0.879</td>\n",
+       "      <td>60.8</td>\n",
+       "      <td>15.30</td>\n",
+       "      <td>0.821895</td>\n",
+       "      <td>0.79752</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tiny-albert</th>\n",
-       "      <td>24.8</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>0.708</td>\n",
-       "      <td>0.673</td>\n",
-       "      <td>0.817</td>\n",
+       "      <td>33.4</td>\n",
+       "      <td>8.51</td>\n",
+       "      <td>0.786500</td>\n",
+       "      <td>0.75870</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>xlnet</th>\n",
-       "      <td>450.2</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>0.931</td>\n",
-       "      <td>0.925</td>\n",
-       "      <td>0.947</td>\n",
+       "      <td>480.2</td>\n",
+       "      <td>121.00</td>\n",
+       "      <td>0.848110</td>\n",
+       "      <td>0.82741</td>\n",
+       "      <td>0.92101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alxlnet</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>14.3</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>0.886</td>\n",
-       "      <td>0.942</td>\n",
+       "      <td>61.2</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>0.849290</td>\n",
+       "      <td>0.82810</td>\n",
+       "      <td>0.92099</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -368,23 +378,23 @@
       ],
       "text/plain": [
        "             Size (MB)  Quantized Size (MB)  Arc Accuracy  Types Accuracy  \\\n",
-       "bert             426.0                112.0         0.855           0.848   \n",
-       "tiny-bert         59.5                 15.7         0.718           0.694   \n",
-       "albert            50.0                 13.2         0.811           0.793   \n",
-       "tiny-albert       24.8                  6.6         0.708           0.673   \n",
-       "xlnet            450.2                119.0         0.931           0.925   \n",
-       "alxlnet           50.0                 14.3         0.894           0.886   \n",
+       "bert             455.0               114.00      0.820450         0.79970   \n",
+       "tiny-bert         69.7                17.50      0.795252         0.72470   \n",
+       "albert            60.8                15.30      0.821895         0.79752   \n",
+       "tiny-albert       33.4                 8.51      0.786500         0.75870   \n",
+       "xlnet            480.2               121.00      0.848110         0.82741   \n",
+       "alxlnet           61.2                16.40      0.849290         0.82810   \n",
        "\n",
        "             Root Accuracy  \n",
-       "bert                 0.920  \n",
-       "tiny-bert            0.886  \n",
-       "albert               0.879  \n",
-       "tiny-albert          0.817  \n",
-       "xlnet                0.947  \n",
-       "alxlnet              0.942  "
+       "bert               0.98936  \n",
+       "tiny-bert          0.98939  \n",
+       "albert             1.00000  \n",
+       "tiny-albert        1.00000  \n",
+       "xlnet              0.92101  \n",
+       "alxlnet            0.92099  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -393,15 +403,6 @@
     "malaya.dependency.available_transformer()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/models-accuracy.html#Dependency-parsing\n",
-    "\n",
-    "**The best model in term of accuracy is XLNET**."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -409,13 +410,19 @@
     "### Load xlnet dependency model\n",
     "\n",
     "```python\n",
-    "def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
+    "def transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
     "    \"\"\"\n",
     "    Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    model : str, optional (default='bert')\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    model : str, optional (default='xlnet')\n",
     "        Model architecture supported. Allowed values:\n",
     "\n",
     "        * ``'bert'`` - Google BERT BASE parameters.\n",
@@ -442,11 +449,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:running dependency-v2/albert using device /device:CPU:0\n"
+     ]
+    }
+   ],
    "source": [
-    "model = malaya.dependency.transformer(model = 'xlnet')"
+    "model = malaya.dependency.transformer(model = 'albert')"
    ]
   },
   {
@@ -462,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -470,12 +485,12 @@
      "output_type": "stream",
      "text": [
       "WARNING:root:Load quantized model will cause accuracy drop.\n",
-      "INFO:root:running dependency/xlnet-quantized using device /device:CPU:0\n"
+      "INFO:root:running dependency-v2/albert-quantized using device /device:CPU:0\n"
      ]
     }
    ],
    "source": [
-    "quantized_model = malaya.dependency.transformer(model = 'xlnet', quantized = True)"
+    "quantized_model = malaya.dependency.transformer(model = 'albert', quantized = True)"
    ]
   },
   {
@@ -502,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -523,63 +538,75 @@
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        "<title>3</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"186.5,-394.18 183,-384.18 179.5,-394.18 186.5,-394.18\"/>\n",
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        "<title>1</title>\n",
-       "<text text-anchor=\"middle\" x=\"155\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">1 (Dr)</text>\n",
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-       "<g id=\"edge4\" class=\"edge\">\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
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-       "<g id=\"node6\" class=\"node\">\n",
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-       "<g id=\"edge5\" class=\"edge\">\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"152.66,-304.16 144.7,-297.18 146.57,-307.6 152.66,-304.16\"/>\n",
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        "<!-- 6 -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
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-       "<text text-anchor=\"middle\" x=\"350\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (berhenti)</text>\n",
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        "</g>\n",
        "<!-- 3&#45;&gt;6 -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
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-       "<text text-anchor=\"middle\" x=\"323\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M192.92,-347.8C199.82,-335.82 209.16,-319.62 216.98,-306.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"220.14,-307.59 222.1,-297.18 214.07,-304.09 220.14,-307.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"223\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"317\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (.)</text>\n",
+       "</g>\n",
+       "<!-- 3&#45;&gt;15 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>3&#45;&gt;15</title>\n",
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-       "<title>14</title>\n",
-       "<text text-anchor=\"middle\" x=\"155\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (memandu.)</text>\n",
-       "</g>\n",
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-       "<title>1&#45;&gt;14</title>\n",
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-       "<title>13</title>\n",
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        "<title>5</title>\n",
-       "<text text-anchor=\"middle\" x=\"261\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (supaya)</text>\n",
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        "<title>9</title>\n",
-       "<text text-anchor=\"middle\" x=\"441\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (tidur)</text>\n",
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        "<title>6&#45;&gt;9</title>\n",
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        "<title>8</title>\n",
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-       "<!-- 10 -->\n",
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-       "<title>10</title>\n",
-       "<text text-anchor=\"middle\" x=\"441\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (sebentar)</text>\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"321\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (mengantuk)</text>\n",
        "</g>\n",
-       "<!-- 9&#45;&gt;10 -->\n",
+       "<!-- 9&#45;&gt;12 -->\n",
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-       "<text text-anchor=\"middle\" x=\"463.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "<title>9&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M321,-173.8C321,-162.16 321,-146.55 321,-133.24\"/>\n",
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-       "<title>12</title>\n",
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+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"440\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (memandu)</text>\n",
        "</g>\n",
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+       "<!-- 9&#45;&gt;14 -->\n",
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+       "<title>9&#45;&gt;14</title>\n",
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-       "<!-- 11 -->\n",
+       "<!-- 10 -->\n",
        "<g id=\"node14\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"219\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (sebentar)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;10 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>12&#45;&gt;10</title>\n",
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+       "<text text-anchor=\"middle\" x=\"289\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>11</title>\n",
-       "<text text-anchor=\"middle\" x=\"555\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (sekiranya)</text>\n",
+       "<text text-anchor=\"middle\" x=\"329\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (sekiranya)</text>\n",
        "</g>\n",
        "<!-- 12&#45;&gt;11 -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
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+       "</g>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"440\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;13 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>14&#45;&gt;13</title>\n",
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       ],
       "text/plain": [
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+       "<graphviz.files.Source at 0x14fdb9f90>"
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      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
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@@ -720,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
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+       "<title>14</title>\n",
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-       "<!-- 14 -->\n",
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-       "<title>14</title>\n",
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+       "<title>10</title>\n",
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+       "<title>11</title>\n",
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       "text/plain": [
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+       "<graphviz.files.Source at 0x1471f5cd0>"
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+     "execution_count": 8,
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@@ -936,14 +975,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:running dependency/alxlnet using device /device:CPU:0\n"
+      "INFO:root:running dependency-v2/alxlnet using device /device:CPU:0\n"
      ]
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        "</g>\n",
-       "<!-- 13 -->\n",
+       "<!-- 11 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"329\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (sekiranya)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;11 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>12&#45;&gt;11</title>\n",
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        "<title>13</title>\n",
-       "<text text-anchor=\"middle\" x=\"513\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (ketika)</text>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x1488e55d0>"
+       "<graphviz.files.Source at 0x1769cdc10>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "alxlnet = malaya.dependency.transformer(model = 'alxlnet')\n",
-    "tagging, indexing = malaya.stack.voting_stack([model, alxlnet, model], string)\n",
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], string)\n",
     "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
    ]
   },
@@ -1160,18 +1211,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "# https://www.astroawani.com/berita-malaysia/terbaik-tun-kita-geng-najib-razak-puji-tun-m-297884\n",
     "\n",
-    "s = \"Najib yang juga Ahli Parlimen Pekan memuji sikap Ahli Parlimen Langkawi itu yang mengaku bersalah selepas melanggar SOP kerana tidak mengambil suhu badan ketika masuk ke sebuah surau di Langkawi pada Sabtu lalu\""
+    "s = \"\"\"\n",
+    "KUALA LUMPUR: Dalam hal politik, jarang sekali untuk melihat dua figura ini - bekas Perdana Menteri, Datuk Seri Najib Razak dan Tun Dr Mahathir Mohamad mempunyai 'pandangan yang sama' atau sekapal. Namun, situasi itu berbeza apabila melibatkan isu ketidakpatuhan terhadap prosedur operasi standard (SOP). Najib, yang juga Ahli Parlimen Pekan memuji sikap Ahli Parlimen Langkawi itu yang mengaku bersalah selepas melanggar SOP kerana tidak mengambil suhu badan ketika masuk ke sebuah surau di Langkawi pada Sabtu lalu.\n",
+    "\"\"\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
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+       "<title>11&#45;&gt;1</title>\n",
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+       "<title>11&#45;&gt;8</title>\n",
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-       "<!-- 4 -->\n",
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+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>9</title>\n",
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+       "<!-- 11&#45;&gt;9 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>11&#45;&gt;9</title>\n",
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+       "<title>10</title>\n",
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+       "</g>\n",
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+       "<title>29</title>\n",
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+       "</g>\n",
+       "<!-- 11&#45;&gt;29 -->\n",
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+       "<title>11&#45;&gt;29</title>\n",
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+       "</g>\n",
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+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">42 (berbeza)</text>\n",
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+       "<!-- 11&#45;&gt;42 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>11&#45;&gt;42</title>\n",
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+       "<title>2</title>\n",
+       "<text text-anchor=\"middle\" x=\"58\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (LUMPUR)</text>\n",
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+       "<title>5&#45;&gt;3</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"58.66,-735.56 48.61,-732.21 54.38,-741.1 58.66,-735.56\"/>\n",
+       "<text text-anchor=\"middle\" x=\"108.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
-       "<!-- 6 -->\n",
+       "<!-- 4 -->\n",
        "<g id=\"node8\" class=\"node\">\n",
+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>5&#45;&gt;4</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M141.7,-782.8C135.98,-770.93 128.26,-754.93 121.76,-741.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"124.78,-739.66 117.28,-732.18 118.48,-742.7 124.78,-739.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>6</title>\n",
-       "<text text-anchor=\"middle\" x=\"201\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (Pekan)</text>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
        "</g>\n",
        "<!-- 5&#45;&gt;6 -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
        "<title>5&#45;&gt;6</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M201,-347.8C201,-336.16 201,-320.55 201,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"204.5,-307.18 201,-297.18 197.5,-307.18 204.5,-307.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"210\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M159.92,-782.8C166.82,-770.82 176.16,-754.62 183.98,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"187.14,-742.59 189.1,-732.18 181.07,-739.09 187.14,-742.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
        "</g>\n",
-       "<!-- 9 -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>9</title>\n",
-       "<text text-anchor=\"middle\" x=\"377\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (Ahli)</text>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
        "</g>\n",
-       "<!-- 8&#45;&gt;9 -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>8&#45;&gt;9</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M377,-521.8C377,-510.16 377,-494.55 377,-481.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"380.5,-481.18 377,-471.18 373.5,-481.18 380.5,-481.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"386\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M601.31,-869.8C567.25,-855.74 519.11,-835.88 483.74,-821.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
        "</g>\n",
-       "<!-- 14 -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>14</title>\n",
-       "<text text-anchor=\"middle\" x=\"579\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (mengaku)</text>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
        "</g>\n",
-       "<!-- 8&#45;&gt;14 -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>8&#45;&gt;14</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M409.9,-525.15C442.13,-511.59 491.66,-490.75 528.8,-475.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"530.58,-478.17 538.44,-471.07 527.87,-471.72 530.58,-478.17\"/>\n",
-       "<text text-anchor=\"middle\" x=\"499\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
        "</g>\n",
-       "<!-- 10 -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>10</title>\n",
-       "<text text-anchor=\"middle\" x=\"308\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (Parlimen)</text>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
        "</g>\n",
-       "<!-- 9&#45;&gt;10 -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>9&#45;&gt;10</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M363.04,-434.8C353.12,-422.59 339.65,-405.99 328.52,-392.28\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"330.96,-389.73 321.94,-384.18 325.53,-394.15 330.96,-389.73\"/>\n",
-       "<text text-anchor=\"middle\" x=\"356\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
-       "<!-- 12 -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>12</title>\n",
-       "<text text-anchor=\"middle\" x=\"401\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (itu)</text>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
        "</g>\n",
-       "<!-- 9&#45;&gt;12 -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>9&#45;&gt;12</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M381.86,-434.8C385.17,-423.05 389.64,-407.24 393.42,-393.84\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"396.8,-394.75 396.15,-384.18 390.06,-392.85 396.8,-394.75\"/>\n",
-       "<text text-anchor=\"middle\" x=\"399.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"724.56,-828.29 729.95,-819.17 720.05,-822.94 724.56,-828.29\"/>\n",
+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"874.4,-821.02 864.02,-818.86 870.79,-827.02 874.4,-821.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"938.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M988,-869.8C975.7,-857.36 958.91,-840.36 945.22,-826.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"947.49,-823.83 937.97,-819.18 942.51,-828.75 947.49,-823.83\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1062.27,-829.31 1065.27,-819.15 1056.58,-825.22 1062.27,-829.31\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
        "</g>\n",
-       "<!-- 13 -->\n",
+       "<!-- 12 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
-       "<title>13</title>\n",
-       "<text text-anchor=\"middle\" x=\"482\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (yang)</text>\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;13 -->\n",
+       "<!-- 13&#45;&gt;12 -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>14&#45;&gt;13</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M559.37,-434.8C545.04,-422.24 525.42,-405.05 509.53,-391.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"511.43,-388.13 501.6,-384.18 506.81,-393.4 511.43,-388.13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"552\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
        "</g>\n",
        "<!-- 15 -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
+       "<g id=\"node17\" class=\"node\">\n",
        "<title>15</title>\n",
-       "<text text-anchor=\"middle\" x=\"579\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (bersalah)</text>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;15 -->\n",
+       "<!-- 13&#45;&gt;15 -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>14&#45;&gt;15</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M579,-434.8C579,-423.16 579,-407.55 579,-394.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"582.5,-394.18 579,-384.18 575.5,-394.18 582.5,-394.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"597.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">ccomp</text>\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
-       "<!-- 17 -->\n",
-       "<g id=\"node17\" class=\"node\">\n",
-       "<title>17</title>\n",
-       "<text text-anchor=\"middle\" x=\"692\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;17 -->\n",
+       "<!-- 13&#45;&gt;16 -->\n",
        "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>14&#45;&gt;17</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M601.87,-434.8C618.87,-422.01 642.26,-404.42 660.95,-390.36\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"663.28,-392.98 669.17,-384.18 659.07,-387.39 663.28,-392.98\"/>\n",
-       "<text text-anchor=\"middle\" x=\"662\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"442.14,-742.38 439.73,-732.06 435.18,-741.63 442.14,-742.38\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
        "</g>\n",
-       "<!-- 21 -->\n",
-       "<g id=\"node18\" class=\"node\">\n",
-       "<title>21</title>\n",
-       "<text text-anchor=\"middle\" x=\"813\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;21 -->\n",
+       "<!-- 13&#45;&gt;17 -->\n",
        "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>14&#45;&gt;21</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M625.62,-437.2C644.18,-431.16 665.67,-423.96 685,-417 710.45,-407.84 738.46,-396.98 761.78,-387.73\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"732\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
-       "</g>\n",
-       "<!-- 11 -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>11</title>\n",
-       "<text text-anchor=\"middle\" x=\"308\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (Langkawi)</text>\n",
-       "</g>\n",
-       "<!-- 10&#45;&gt;11 -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>10&#45;&gt;11</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M308,-347.8C308,-336.16 308,-320.55 308,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"311.5,-307.18 308,-297.18 304.5,-307.18 311.5,-307.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"317\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"538.2,-742.72 537.45,-732.15 531.45,-740.88 538.2,-742.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"541\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 16 -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>16</title>\n",
-       "<text text-anchor=\"middle\" x=\"609\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (selepas)</text>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
        "</g>\n",
-       "<!-- 17&#45;&gt;16 -->\n",
+       "<!-- 17&#45;&gt;14 -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>17&#45;&gt;16</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M675.2,-347.8C663.05,-335.36 646.46,-318.36 632.93,-304.5\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"635.26,-301.88 625.77,-297.18 630.25,-306.78 635.26,-301.88\"/>\n",
-       "<text text-anchor=\"middle\" x=\"668\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
        "</g>\n",
        "<!-- 18 -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
+       "<g id=\"node21\" class=\"node\">\n",
        "<title>18</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;18 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
        "<title>17&#45;&gt;18</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M694.02,-347.8C695.39,-336.16 697.23,-320.55 698.8,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"702.29,-307.52 699.98,-297.18 695.33,-306.7 702.29,-307.52\"/>\n",
-       "<text text-anchor=\"middle\" x=\"707\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
        "<!-- 20 -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>20</title>\n",
-       "<text text-anchor=\"middle\" x=\"809\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
        "</g>\n",
-       "<!-- 21&#45;&gt;20 -->\n",
+       "<!-- 17&#45;&gt;20 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>21&#45;&gt;20</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M812.19,-347.8C811.64,-336.16 810.91,-320.55 810.28,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"813.77,-307 809.81,-297.18 806.78,-307.33 813.77,-307\"/>\n",
-       "<text text-anchor=\"middle\" x=\"834.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
        "</g>\n",
        "<!-- 25 -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>25</title>\n",
-       "<text text-anchor=\"middle\" x=\"902\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
        "</g>\n",
-       "<!-- 21&#45;&gt;25 -->\n",
+       "<!-- 17&#45;&gt;25 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>21&#45;&gt;25</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M838.86,-347.98C846.32,-342.59 854.25,-336.37 861,-330 868.96,-322.48 876.8,-313.44 883.46,-305.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"886.3,-307.17 889.69,-297.13 880.78,-302.87 886.3,-307.17\"/>\n",
-       "<text text-anchor=\"middle\" x=\"890\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
-       "</g>\n",
-       "<!-- 23 -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>23</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
-       "</g>\n",
-       "<!-- 18&#45;&gt;23 -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>18&#45;&gt;23</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702,-260.8C702,-249.16 702,-233.55 702,-220.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.5,-220.18 702,-210.18 698.5,-220.18 705.5,-220.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"731\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
        "</g>\n",
-       "<!-- 19 -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>19</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
-       "</g>\n",
-       "<!-- 23&#45;&gt;19 -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>23&#45;&gt;19</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702,-173.8C702,-162.16 702,-146.55 702,-133.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.5,-133.18 702,-123.18 698.5,-133.18 705.5,-133.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"710.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
-       "</g>\n",
-       "<!-- 22 -->\n",
+       "<!-- 21 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
-       "<title>22</title>\n",
-       "<text text-anchor=\"middle\" x=\"795\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;22 -->\n",
-       "<g id=\"edge24\" class=\"edge\">\n",
-       "<title>25&#45;&gt;22</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M880.35,-260.8C864.39,-248.13 842.5,-230.73 824.88,-216.74\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"826.63,-213.66 816.62,-210.18 822.27,-219.14 826.63,-213.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"864\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
        "<!-- 24 -->\n",
-       "<g id=\"node26\" class=\"node\">\n",
+       "<g id=\"node28\" class=\"node\">\n",
        "<title>24</title>\n",
-       "<text text-anchor=\"middle\" x=\"885\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;24 -->\n",
-       "<g id=\"edge25\" class=\"edge\">\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
        "<title>25&#45;&gt;24</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M898.56,-260.8C896.23,-249.16 893.11,-233.55 890.45,-220.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"893.83,-219.29 888.44,-210.18 886.96,-220.67 893.83,-219.29\"/>\n",
-       "<text text-anchor=\"middle\" x=\"909.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
        "</g>\n",
-       "<!-- 28 -->\n",
-       "<g id=\"node27\" class=\"node\">\n",
-       "<title>28</title>\n",
-       "<text text-anchor=\"middle\" x=\"977\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (surau)</text>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;28 -->\n",
-       "<g id=\"edge26\" class=\"edge\">\n",
-       "<title>25&#45;&gt;28</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M917.18,-260.8C928.05,-248.47 942.87,-231.68 955.04,-217.89\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"957.85,-219.99 961.85,-210.18 952.6,-215.36 957.85,-219.99\"/>\n",
-       "<text text-anchor=\"middle\" x=\"953\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 32 -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
-       "<title>32</title>\n",
-       "<text text-anchor=\"middle\" x=\"1075\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;32 -->\n",
-       "<g id=\"edge27\" class=\"edge\">\n",
-       "<title>25&#45;&gt;32</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M937.01,-260.8C964.18,-247.45 1002.01,-228.86 1031.19,-214.52\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1032.91,-217.58 1040.34,-210.03 1029.82,-211.3 1032.91,-217.58\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1008\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 26 -->\n",
-       "<g id=\"node29\" class=\"node\">\n",
-       "<title>26</title>\n",
-       "<text text-anchor=\"middle\" x=\"807\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
        "</g>\n",
-       "<!-- 28&#45;&gt;26 -->\n",
-       "<g id=\"edge28\" class=\"edge\">\n",
-       "<title>28&#45;&gt;26</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M940.97,-174.61C928.56,-168.85 914.61,-162.26 902,-156 882.84,-146.49 861.77,-135.46 844.35,-126.19\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"914\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
        "<!-- 27 -->\n",
        "<g id=\"node30\" class=\"node\">\n",
        "<title>27</title>\n",
-       "<text text-anchor=\"middle\" x=\"893\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (sebuah)</text>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
        "</g>\n",
-       "<!-- 28&#45;&gt;27 -->\n",
-       "<g id=\"edge29\" class=\"edge\">\n",
-       "<title>28&#45;&gt;27</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M960,-173.8C947.7,-161.36 930.91,-144.36 917.22,-130.5\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"919.49,-127.83 909.97,-123.18 914.51,-132.75 919.49,-127.83\"/>\n",
-       "<text text-anchor=\"middle\" x=\"949.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 30 -->\n",
+       "<!-- 28 -->\n",
        "<g id=\"node31\" class=\"node\">\n",
-       "<title>30</title>\n",
-       "<text text-anchor=\"middle\" x=\"1000\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
-       "</g>\n",
-       "<!-- 28&#45;&gt;30 -->\n",
-       "<g id=\"edge30\" class=\"edge\">\n",
-       "<title>28&#45;&gt;30</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M981.65,-173.8C984.83,-162.05 989.11,-146.24 992.74,-132.84\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"1006\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
-       "</g>\n",
-       "<!-- 31 -->\n",
-       "<g id=\"node33\" class=\"node\">\n",
-       "<title>31</title>\n",
-       "<text text-anchor=\"middle\" x=\"1101\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pada)</text>\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
        "</g>\n",
-       "<!-- 32&#45;&gt;31 -->\n",
-       "<g id=\"edge33\" class=\"edge\">\n",
-       "<title>32&#45;&gt;31</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1080.26,-173.8C1083.86,-162.05 1088.69,-146.24 1092.79,-132.84\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"1102\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 29 -->\n",
-       "<g id=\"node32\" class=\"node\">\n",
-       "<title>29</title>\n",
-       "<text text-anchor=\"middle\" x=\"1000\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
        "</g>\n",
-       "<!-- 30&#45;&gt;29 -->\n",
-       "<g id=\"edge31\" class=\"edge\">\n",
-       "<title>30&#45;&gt;29</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1003.5,-46.18 1000,-36.18 996.5,-46.18 1003.5,-46.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1012\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
        "<!-- 33 -->\n",
-       "<g id=\"node34\" class=\"node\">\n",
+       "<g id=\"node36\" class=\"node\">\n",
        "<title>33</title>\n",
-       "<text text-anchor=\"middle\" x=\"1101\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
        "</g>\n",
        "<!-- 31&#45;&gt;33 -->\n",
-       "<g id=\"edge32\" class=\"edge\">\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
        "<title>31&#45;&gt;33</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1101,-86.8C1101,-75.16 1101,-59.55 1101,-46.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1104.5,-46.18 1101,-36.18 1097.5,-46.18 1104.5,-46.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1116.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1219.27,-611.28C1244.62,-602.95 1272.28,-593.67 1278,-591 1294.65,-583.24 1312.15,-572.91 1326.69,-563.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1328.83,-566.42 1335.32,-558.05 1325.03,-560.55 1328.83,-566.42\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1336.9,-479.43 1330.13,-471.28 1330.34,-481.87 1336.9,-479.43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1340.6,-695.8C1328.01,-683.36 1310.82,-666.36 1296.79,-652.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
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+       "</g>\n",
+       "<!-- 63 -->\n",
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+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
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+       "</g>\n",
+       "<!-- 61 -->\n",
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+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.5,-568.18 1455,-558.18 1451.5,-568.18 1458.5,-568.18\"/>\n",
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+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
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+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.19,-567.51 1629.21,-558.18 1619.47,-562.34 1624.19,-567.51\"/>\n",
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+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549.8,-521.8C1549.66,-510.16 1549.48,-494.55 1549.32,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.82,-481.13 1549.2,-471.18 1545.82,-481.22 1552.82,-481.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1648,-521.8C1648,-510.16 1648,-494.55 1648,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.5,-481.18 1648,-471.18 1644.5,-481.18 1651.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1667.63,-521.8C1681.96,-509.24 1701.58,-492.05 1717.47,-478.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1720.19,-480.4 1725.4,-471.18 1715.57,-475.13 1720.19,-480.4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1726.18,-347.8C1712.44,-335.24 1693.63,-318.05 1678.39,-304.12\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
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+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1766.25,-347.8C1781.9,-335.13 1803.39,-317.73 1820.68,-303.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1823.21,-306.19 1828.78,-297.18 1818.81,-300.75 1823.21,-306.19\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1819\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 74 -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1719\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">74 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;74 -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>76&#45;&gt;74</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1815.12,-260.9C1804.89,-255.52 1793.82,-249.32 1784,-243 1771.6,-235.02 1758.59,-225.31 1747.49,-216.58\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1798.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 75 -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1812\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">75 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;75 -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>76&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1833.68,-260.94C1829.3,-255.56 1824.96,-249.35 1822,-243 1818.71,-235.94 1816.5,-227.82 1815.02,-220.21\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1844.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 77 -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">77 (suhu)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;77 -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>76&#45;&gt;77</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1859.92,-260.8C1866.82,-248.82 1876.16,-232.62 1883.98,-219.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1887.14,-220.59 1889.1,-210.18 1881.07,-217.09 1887.14,-220.59\"/>\n",
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+       "</g>\n",
+       "<!-- 80 -->\n",
+       "<g id=\"node80\" class=\"node\">\n",
+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">80 (masuk)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;80 -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>76&#45;&gt;80</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1888.04,-260.8C1917.83,-247.33 1959.4,-228.54 1991.22,-214.15\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1971\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 78 -->\n",
+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"1852\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">78 (badan)</text>\n",
+       "</g>\n",
+       "<!-- 77&#45;&gt;78 -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1879.69,-173.83C1874.65,-168.53 1869.62,-162.4 1866,-156 1861.99,-148.91 1859.07,-140.59 1856.96,-132.8\"/>\n",
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+       "</g>\n",
+       "<!-- 79 -->\n",
+       "<g id=\"node82\" class=\"node\">\n",
+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"1946\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">79 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;79 -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>80&#45;&gt;79</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2012.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
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+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">83 (surau)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;83 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>80&#45;&gt;83</title>\n",
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+       "</g>\n",
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+       "<title>85</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">85 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;85 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;85</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2058.84,-173.8C2074.2,-161.13 2095.28,-143.73 2112.24,-129.74\"/>\n",
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+       "</g>\n",
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+       "<title>87</title>\n",
+       "<text text-anchor=\"middle\" x=\"2245\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">87 (Sabtu)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;87 -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>80&#45;&gt;87</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2077.91,-174.61C2112.42,-160.44 2162.33,-139.95 2198.61,-125.05\"/>\n",
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+       "</g>\n",
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+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"1961\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">81 (ke)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;81 -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>83&#45;&gt;81</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2017\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node87\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">82 (sebuah)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;82 -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>83&#45;&gt;82</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2052.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 84 -->\n",
+       "<g id=\"node88\" class=\"node\">\n",
+       "<title>84</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">84 (di)</text>\n",
+       "</g>\n",
+       "<!-- 85&#45;&gt;84 -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>85&#45;&gt;84</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2153\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 86 -->\n",
+       "<g id=\"node89\" class=\"node\">\n",
+       "<title>86</title>\n",
+       "<text text-anchor=\"middle\" x=\"2232\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">86 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;86 -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>87&#45;&gt;86</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2242.37,-86.8C2240.59,-75.16 2238.2,-59.55 2236.17,-46.24\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"2252\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 88 -->\n",
+       "<g id=\"node90\" class=\"node\">\n",
+       "<title>88</title>\n",
+       "<text text-anchor=\"middle\" x=\"2316\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">88 (lalu)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;88 -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>87&#45;&gt;88</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2259.37,-86.8C2269.67,-74.47 2283.69,-57.68 2295.21,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2297.93,-46.09 2301.65,-36.18 2292.56,-41.61 2297.93,-46.09\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2299.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x15fad6450>"
+       "<graphviz.files.Source at 0x168e76690>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tagging, indexing = malaya.stack.voting_stack([model, alxlnet, model], s)\n",
-    "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dependency graph object\n",
-    "\n",
-    "To initiate a dependency graph from dependency models, you need to call `malaya.dependency.dependency_graph`."
+    "d_object, tagging, indexing = model.predict(s)\n",
+    "d_object.to_graphvis()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<malaya.function.parse_dependency.DependencyGraph at 0x1f5db7910>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "graph = malaya.dependency.dependency_graph(tagging, indexing)\n",
-    "graph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### generate graphvis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1657,760 +2345,3110 @@
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        "<!-- Title: G Pages: 1 -->\n",
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-       " viewBox=\"0.00 0.00 1143.00 740.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 736)\">\n",
+       "<svg width=\"2356pt\" height=\"1088pt\"\n",
+       " viewBox=\"0.00 0.00 2355.50 1088.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 1084)\">\n",
        "<title>G</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-736 1139,-736 1139,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-1084 2351.5,-1084 2351.5,4 -4,4\"/>\n",
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        "<title>0</title>\n",
-       "<text text-anchor=\"middle\" x=\"328\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">0 (None)</text>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\">0 (None)</text>\n",
        "</g>\n",
-       "<!-- 7 -->\n",
+       "<!-- 11 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>7</title>\n",
-       "<text text-anchor=\"middle\" x=\"328\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (memuji)</text>\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (melihat)</text>\n",
        "</g>\n",
-       "<!-- 0&#45;&gt;7 -->\n",
+       "<!-- 0&#45;&gt;11 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>0&#45;&gt;7</title>\n",
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-       "<text text-anchor=\"middle\" x=\"339\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">root</text>\n",
+       "<title>0&#45;&gt;11</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M489,-1043.8C489,-1032.16 489,-1016.55 489,-1003.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"492.5,-1003.18 489,-993.18 485.5,-1003.18 492.5,-1003.18\"/>\n",
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        "</g>\n",
        "<!-- 1 -->\n",
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        "<title>1</title>\n",
-       "<text text-anchor=\"middle\" x=\"287\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">1 (Najib)</text>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">1 (KUALA)</text>\n",
        "</g>\n",
-       "<!-- 7&#45;&gt;1 -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>7&#45;&gt;1</title>\n",
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-       "<text text-anchor=\"middle\" x=\"326\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "<!-- 11&#45;&gt;1 -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>11&#45;&gt;1</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-962.98C421.28,-956.25 388.21,-947.36 359,-939 320.79,-928.07 277.84,-915.03 245.33,-905.01\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"374\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
        "</g>\n",
        "<!-- 8 -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>8</title>\n",
-       "<text text-anchor=\"middle\" x=\"377\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (sikap)</text>\n",
+       "<text text-anchor=\"middle\" x=\"358\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (jarang)</text>\n",
        "</g>\n",
-       "<!-- 7&#45;&gt;8 -->\n",
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-       "<title>7&#45;&gt;8</title>\n",
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-       "<text text-anchor=\"middle\" x=\"365\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "<!-- 11&#45;&gt;8 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>11&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-958.12C434.76,-952.81 421.98,-946.34 411,-939 400.15,-931.75 389.44,-922.23 380.52,-913.44\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
        "</g>\n",
-       "<!-- 4 -->\n",
+       "<!-- 9 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>9</title>\n",
+       "<text text-anchor=\"middle\" x=\"445\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (sekali)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;9 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>11&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M477.52,-956.85C473.97,-951.26 470.16,-944.96 467,-939 463.06,-931.58 459.21,-923.31 455.87,-915.66\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"459.07,-914.24 451.92,-906.42 452.63,-916.99 459.07,-914.24\"/>\n",
+       "<text text-anchor=\"middle\" x=\"489.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"534\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;10 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>11&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M501.05,-956.91C504.77,-951.32 508.74,-945 512,-939 516.08,-931.5 520.01,-923.1 523.39,-915.35\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"532\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"643\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (mempunyai)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;29 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M520.16,-956.8C544.14,-943.56 577.45,-925.18 603.33,-910.9\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"605.08,-913.93 612.15,-906.03 601.7,-907.8 605.08,-913.93\"/>\n",
+       "<text text-anchor=\"middle\" x=\"591\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">42 (berbeza)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;42 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>11&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M531.23,-967.04C624.77,-951.63 849.29,-914.65 952.04,-897.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"952.89,-901.13 962.19,-896.05 951.75,-894.22 952.89,-901.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>4</title>\n",
-       "<text text-anchor=\"middle\" x=\"145\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Ahli)</text>\n",
+       "<title>2</title>\n",
+       "<text text-anchor=\"middle\" x=\"58\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (LUMPUR)</text>\n",
        "</g>\n",
-       "<!-- 1&#45;&gt;4 -->\n",
+       "<!-- 1&#45;&gt;2 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>1&#45;&gt;4</title>\n",
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+       "<title>1&#45;&gt;2</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.48,-869.8C145.65,-856.78 116.85,-838.78 94.16,-824.6\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"144\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 2 -->\n",
+       "<!-- 5 -->\n",
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-       "<title>2</title>\n",
-       "<text text-anchor=\"middle\" x=\"31\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (yang)</text>\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"150\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (hal)</text>\n",
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+       "<title>1&#45;&gt;5</title>\n",
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        "</g>\n",
-       "<!-- 3 -->\n",
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-       "<title>3</title>\n",
-       "<text text-anchor=\"middle\" x=\"110\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (juga)</text>\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"222\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (,)</text>\n",
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+       "<title>1&#45;&gt;7</title>\n",
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-       "<title>5</title>\n",
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+       "<title>3</title>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (:)</text>\n",
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-       "<!-- 6 -->\n",
+       "<!-- 4 -->\n",
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+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
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+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>6</title>\n",
-       "<text text-anchor=\"middle\" x=\"201\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (Pekan)</text>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
        "</g>\n",
        "<!-- 5&#45;&gt;6 -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
        "<title>5&#45;&gt;6</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M201,-347.8C201,-336.16 201,-320.55 201,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"204.5,-307.18 201,-297.18 197.5,-307.18 204.5,-307.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"210\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M159.92,-782.8C166.82,-770.82 176.16,-754.62 183.98,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"187.14,-742.59 189.1,-732.18 181.07,-739.09 187.14,-742.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
        "</g>\n",
-       "<!-- 9 -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>9</title>\n",
-       "<text text-anchor=\"middle\" x=\"377\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (Ahli)</text>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
        "</g>\n",
-       "<!-- 8&#45;&gt;9 -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>8&#45;&gt;9</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M377,-521.8C377,-510.16 377,-494.55 377,-481.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"380.5,-481.18 377,-471.18 373.5,-481.18 380.5,-481.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"386\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M601.31,-869.8C567.25,-855.74 519.11,-835.88 483.74,-821.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
        "</g>\n",
-       "<!-- 14 -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>14</title>\n",
-       "<text text-anchor=\"middle\" x=\"579\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (mengaku)</text>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
        "</g>\n",
-       "<!-- 8&#45;&gt;14 -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>8&#45;&gt;14</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M409.9,-525.15C442.13,-511.59 491.66,-490.75 528.8,-475.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"530.58,-478.17 538.44,-471.07 527.87,-471.72 530.58,-478.17\"/>\n",
-       "<text text-anchor=\"middle\" x=\"499\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
        "</g>\n",
-       "<!-- 10 -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>10</title>\n",
-       "<text text-anchor=\"middle\" x=\"308\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (Parlimen)</text>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
        "</g>\n",
-       "<!-- 9&#45;&gt;10 -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>9&#45;&gt;10</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M363.04,-434.8C353.12,-422.59 339.65,-405.99 328.52,-392.28\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"330.96,-389.73 321.94,-384.18 325.53,-394.15 330.96,-389.73\"/>\n",
-       "<text text-anchor=\"middle\" x=\"356\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
-       "<!-- 12 -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>12</title>\n",
-       "<text text-anchor=\"middle\" x=\"401\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (itu)</text>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
        "</g>\n",
-       "<!-- 9&#45;&gt;12 -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>9&#45;&gt;12</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M381.86,-434.8C385.17,-423.05 389.64,-407.24 393.42,-393.84\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"396.8,-394.75 396.15,-384.18 390.06,-392.85 396.8,-394.75\"/>\n",
-       "<text text-anchor=\"middle\" x=\"399.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"724.56,-828.29 729.95,-819.17 720.05,-822.94 724.56,-828.29\"/>\n",
+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"874.4,-821.02 864.02,-818.86 870.79,-827.02 874.4,-821.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"938.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M988,-869.8C975.7,-857.36 958.91,-840.36 945.22,-826.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"947.49,-823.83 937.97,-819.18 942.51,-828.75 947.49,-823.83\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1062.27,-829.31 1065.27,-819.15 1056.58,-825.22 1062.27,-829.31\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
        "</g>\n",
-       "<!-- 13 -->\n",
+       "<!-- 12 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
-       "<title>13</title>\n",
-       "<text text-anchor=\"middle\" x=\"482\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (yang)</text>\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;13 -->\n",
+       "<!-- 13&#45;&gt;12 -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>14&#45;&gt;13</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M559.37,-434.8C545.04,-422.24 525.42,-405.05 509.53,-391.12\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"552\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
        "</g>\n",
        "<!-- 15 -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
+       "<g id=\"node17\" class=\"node\">\n",
        "<title>15</title>\n",
-       "<text text-anchor=\"middle\" x=\"579\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (bersalah)</text>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;15 -->\n",
+       "<!-- 13&#45;&gt;15 -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>14&#45;&gt;15</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M579,-434.8C579,-423.16 579,-407.55 579,-394.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"582.5,-394.18 579,-384.18 575.5,-394.18 582.5,-394.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"597.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">ccomp</text>\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
-       "<!-- 17 -->\n",
-       "<g id=\"node17\" class=\"node\">\n",
-       "<title>17</title>\n",
-       "<text text-anchor=\"middle\" x=\"692\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;17 -->\n",
+       "<!-- 13&#45;&gt;16 -->\n",
        "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>14&#45;&gt;17</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M601.87,-434.8C618.87,-422.01 642.26,-404.42 660.95,-390.36\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"662\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
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-       "<!-- 21 -->\n",
-       "<g id=\"node18\" class=\"node\">\n",
-       "<title>21</title>\n",
-       "<text text-anchor=\"middle\" x=\"813\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
        "</g>\n",
-       "<!-- 14&#45;&gt;21 -->\n",
+       "<!-- 13&#45;&gt;17 -->\n",
        "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>14&#45;&gt;21</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M625.62,-437.2C644.18,-431.16 665.67,-423.96 685,-417 710.45,-407.84 738.46,-396.98 761.78,-387.73\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"732\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
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-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>11</title>\n",
-       "<text text-anchor=\"middle\" x=\"308\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (Langkawi)</text>\n",
-       "</g>\n",
-       "<!-- 10&#45;&gt;11 -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>10&#45;&gt;11</title>\n",
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-       "<text text-anchor=\"middle\" x=\"317\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
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-       "<!-- 16 -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>16</title>\n",
-       "<text text-anchor=\"middle\" x=\"609\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (selepas)</text>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
        "</g>\n",
-       "<!-- 17&#45;&gt;16 -->\n",
+       "<!-- 17&#45;&gt;14 -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>17&#45;&gt;16</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M675.2,-347.8C663.05,-335.36 646.46,-318.36 632.93,-304.5\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"635.26,-301.88 625.77,-297.18 630.25,-306.78 635.26,-301.88\"/>\n",
-       "<text text-anchor=\"middle\" x=\"668\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
        "</g>\n",
        "<!-- 18 -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
+       "<g id=\"node21\" class=\"node\">\n",
        "<title>18</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;18 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
        "<title>17&#45;&gt;18</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M694.02,-347.8C695.39,-336.16 697.23,-320.55 698.8,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"702.29,-307.52 699.98,-297.18 695.33,-306.7 702.29,-307.52\"/>\n",
-       "<text text-anchor=\"middle\" x=\"707\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
        "<!-- 20 -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>20</title>\n",
-       "<text text-anchor=\"middle\" x=\"809\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
        "</g>\n",
-       "<!-- 21&#45;&gt;20 -->\n",
+       "<!-- 17&#45;&gt;20 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>21&#45;&gt;20</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M812.19,-347.8C811.64,-336.16 810.91,-320.55 810.28,-307.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"813.77,-307 809.81,-297.18 806.78,-307.33 813.77,-307\"/>\n",
-       "<text text-anchor=\"middle\" x=\"834.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
        "</g>\n",
        "<!-- 25 -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>25</title>\n",
-       "<text text-anchor=\"middle\" x=\"902\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
        "</g>\n",
-       "<!-- 21&#45;&gt;25 -->\n",
+       "<!-- 17&#45;&gt;25 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>21&#45;&gt;25</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M838.86,-347.98C846.32,-342.59 854.25,-336.37 861,-330 868.96,-322.48 876.8,-313.44 883.46,-305.12\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"886.3,-307.17 889.69,-297.13 880.78,-302.87 886.3,-307.17\"/>\n",
-       "<text text-anchor=\"middle\" x=\"890\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
-       "</g>\n",
-       "<!-- 23 -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>23</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
-       "</g>\n",
-       "<!-- 18&#45;&gt;23 -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>18&#45;&gt;23</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702,-260.8C702,-249.16 702,-233.55 702,-220.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.5,-220.18 702,-210.18 698.5,-220.18 705.5,-220.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"731\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
-       "</g>\n",
-       "<!-- 19 -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>19</title>\n",
-       "<text text-anchor=\"middle\" x=\"702\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
-       "</g>\n",
-       "<!-- 23&#45;&gt;19 -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>23&#45;&gt;19</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M702,-173.8C702,-162.16 702,-146.55 702,-133.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"705.5,-133.18 702,-123.18 698.5,-133.18 705.5,-133.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"710.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
        "</g>\n",
-       "<!-- 22 -->\n",
+       "<!-- 21 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
-       "<title>22</title>\n",
-       "<text text-anchor=\"middle\" x=\"795\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;22 -->\n",
-       "<g id=\"edge24\" class=\"edge\">\n",
-       "<title>25&#45;&gt;22</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M880.35,-260.8C864.39,-248.13 842.5,-230.73 824.88,-216.74\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"826.63,-213.66 816.62,-210.18 822.27,-219.14 826.63,-213.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"864\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
        "<!-- 24 -->\n",
-       "<g id=\"node26\" class=\"node\">\n",
+       "<g id=\"node28\" class=\"node\">\n",
        "<title>24</title>\n",
-       "<text text-anchor=\"middle\" x=\"885\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;24 -->\n",
-       "<g id=\"edge25\" class=\"edge\">\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
        "<title>25&#45;&gt;24</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M898.56,-260.8C896.23,-249.16 893.11,-233.55 890.45,-220.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"893.83,-219.29 888.44,-210.18 886.96,-220.67 893.83,-219.29\"/>\n",
-       "<text text-anchor=\"middle\" x=\"909.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
        "</g>\n",
-       "<!-- 28 -->\n",
-       "<g id=\"node27\" class=\"node\">\n",
-       "<title>28</title>\n",
-       "<text text-anchor=\"middle\" x=\"977\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (surau)</text>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;28 -->\n",
-       "<g id=\"edge26\" class=\"edge\">\n",
-       "<title>25&#45;&gt;28</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M917.18,-260.8C928.05,-248.47 942.87,-231.68 955.04,-217.89\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"957.85,-219.99 961.85,-210.18 952.6,-215.36 957.85,-219.99\"/>\n",
-       "<text text-anchor=\"middle\" x=\"953\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 32 -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
-       "<title>32</title>\n",
-       "<text text-anchor=\"middle\" x=\"1075\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
        "</g>\n",
-       "<!-- 25&#45;&gt;32 -->\n",
-       "<g id=\"edge27\" class=\"edge\">\n",
-       "<title>25&#45;&gt;32</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M937.01,-260.8C964.18,-247.45 1002.01,-228.86 1031.19,-214.52\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1032.91,-217.58 1040.34,-210.03 1029.82,-211.3 1032.91,-217.58\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1008\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 26 -->\n",
-       "<g id=\"node29\" class=\"node\">\n",
-       "<title>26</title>\n",
-       "<text text-anchor=\"middle\" x=\"807\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
        "</g>\n",
-       "<!-- 28&#45;&gt;26 -->\n",
-       "<g id=\"edge28\" class=\"edge\">\n",
-       "<title>28&#45;&gt;26</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M940.97,-174.61C928.56,-168.85 914.61,-162.26 902,-156 882.84,-146.49 861.77,-135.46 844.35,-126.19\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"914\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
        "<!-- 27 -->\n",
        "<g id=\"node30\" class=\"node\">\n",
        "<title>27</title>\n",
-       "<text text-anchor=\"middle\" x=\"893\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (sebuah)</text>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
        "</g>\n",
-       "<!-- 28&#45;&gt;27 -->\n",
-       "<g id=\"edge29\" class=\"edge\">\n",
-       "<title>28&#45;&gt;27</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M960,-173.8C947.7,-161.36 930.91,-144.36 917.22,-130.5\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"919.49,-127.83 909.97,-123.18 914.51,-132.75 919.49,-127.83\"/>\n",
-       "<text text-anchor=\"middle\" x=\"949.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 30 -->\n",
+       "<!-- 28 -->\n",
        "<g id=\"node31\" class=\"node\">\n",
-       "<title>30</title>\n",
-       "<text text-anchor=\"middle\" x=\"1000\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
-       "</g>\n",
-       "<!-- 28&#45;&gt;30 -->\n",
-       "<g id=\"edge30\" class=\"edge\">\n",
-       "<title>28&#45;&gt;30</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M981.65,-173.8C984.83,-162.05 989.11,-146.24 992.74,-132.84\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"1006\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
-       "</g>\n",
-       "<!-- 31 -->\n",
-       "<g id=\"node33\" class=\"node\">\n",
-       "<title>31</title>\n",
-       "<text text-anchor=\"middle\" x=\"1101\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pada)</text>\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
        "</g>\n",
-       "<!-- 32&#45;&gt;31 -->\n",
-       "<g id=\"edge33\" class=\"edge\">\n",
-       "<title>32&#45;&gt;31</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1080.26,-173.8C1083.86,-162.05 1088.69,-146.24 1092.79,-132.84\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1096.17,-133.76 1095.75,-123.18 1089.47,-131.71 1096.17,-133.76\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1102\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
        "</g>\n",
-       "<!-- 29 -->\n",
-       "<g id=\"node32\" class=\"node\">\n",
-       "<title>29</title>\n",
-       "<text text-anchor=\"middle\" x=\"1000\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
        "</g>\n",
-       "<!-- 30&#45;&gt;29 -->\n",
-       "<g id=\"edge31\" class=\"edge\">\n",
-       "<title>30&#45;&gt;29</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1000,-86.8C1000,-75.16 1000,-59.55 1000,-46.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1003.5,-46.18 1000,-36.18 996.5,-46.18 1003.5,-46.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1012\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
        "</g>\n",
        "<!-- 33 -->\n",
-       "<g id=\"node34\" class=\"node\">\n",
+       "<g id=\"node36\" class=\"node\">\n",
        "<title>33</title>\n",
-       "<text text-anchor=\"middle\" x=\"1101\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
        "</g>\n",
        "<!-- 31&#45;&gt;33 -->\n",
-       "<g id=\"edge32\" class=\"edge\">\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
        "<title>31&#45;&gt;33</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1101,-86.8C1101,-75.16 1101,-59.55 1101,-46.24\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1104.5,-46.18 1101,-36.18 1097.5,-46.18 1104.5,-46.18\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1116.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1219.27,-611.28C1244.62,-602.95 1272.28,-593.67 1278,-591 1294.65,-583.24 1312.15,-572.91 1326.69,-563.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1328.83,-566.42 1335.32,-558.05 1325.03,-560.55 1328.83,-566.42\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1336.9,-479.43 1330.13,-471.28 1330.34,-481.87 1336.9,-479.43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1340.6,-695.8C1328.01,-683.36 1310.82,-666.36 1296.79,-652.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1383.86,-695.88C1391.53,-690.44 1399.8,-684.22 1407,-678 1415.92,-670.29 1425.04,-661.15 1432.91,-652.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1435.51,-655.16 1439.74,-645.44 1430.37,-650.4 1435.51,-655.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1432\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 63 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
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+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.5,-568.18 1455,-558.18 1451.5,-568.18 1458.5,-568.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1464\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1553.99,-608.8C1553.3,-597.16 1552.39,-581.55 1551.6,-568.24\"/>\n",
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+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.19,-567.51 1629.21,-558.18 1619.47,-562.34 1624.19,-567.51\"/>\n",
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+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549.8,-521.8C1549.66,-510.16 1549.48,-494.55 1549.32,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.82,-481.13 1549.2,-471.18 1545.82,-481.22 1552.82,-481.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1648,-521.8C1648,-510.16 1648,-494.55 1648,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.5,-481.18 1648,-471.18 1644.5,-481.18 1651.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1667.63,-521.8C1681.96,-509.24 1701.58,-492.05 1717.47,-478.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1720.19,-480.4 1725.4,-471.18 1715.57,-475.13 1720.19,-480.4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1726.18,-347.8C1712.44,-335.24 1693.63,-318.05 1678.39,-304.12\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
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+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1766.25,-347.8C1781.9,-335.13 1803.39,-317.73 1820.68,-303.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1823.21,-306.19 1828.78,-297.18 1818.81,-300.75 1823.21,-306.19\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1819\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 74 -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1719\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">74 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;74 -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>76&#45;&gt;74</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1815.12,-260.9C1804.89,-255.52 1793.82,-249.32 1784,-243 1771.6,-235.02 1758.59,-225.31 1747.49,-216.58\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1798.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 75 -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1812\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">75 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;75 -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>76&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1833.68,-260.94C1829.3,-255.56 1824.96,-249.35 1822,-243 1818.71,-235.94 1816.5,-227.82 1815.02,-220.21\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1844.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 77 -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">77 (suhu)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;77 -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>76&#45;&gt;77</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1859.92,-260.8C1866.82,-248.82 1876.16,-232.62 1883.98,-219.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1887.14,-220.59 1889.1,-210.18 1881.07,-217.09 1887.14,-220.59\"/>\n",
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+       "</g>\n",
+       "<!-- 80 -->\n",
+       "<g id=\"node80\" class=\"node\">\n",
+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">80 (masuk)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;80 -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>76&#45;&gt;80</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1888.04,-260.8C1917.83,-247.33 1959.4,-228.54 1991.22,-214.15\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1992.66,-217.34 2000.33,-210.03 1989.78,-210.96 1992.66,-217.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1971\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 78 -->\n",
+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"1852\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">78 (badan)</text>\n",
+       "</g>\n",
+       "<!-- 77&#45;&gt;78 -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1879.69,-173.83C1874.65,-168.53 1869.62,-162.4 1866,-156 1861.99,-148.91 1859.07,-140.59 1856.96,-132.8\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1895\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 79 -->\n",
+       "<g id=\"node82\" class=\"node\">\n",
+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"1946\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">79 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;79 -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>80&#45;&gt;79</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2019.38,-173.8C2005.79,-161.24 1987.18,-144.05 1972.11,-130.12\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"2012.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node83\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">83 (surau)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;83 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>80&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2038,-173.8C2038,-162.16 2038,-146.55 2038,-133.24\"/>\n",
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+       "</g>\n",
+       "<!-- 85 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>85</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">85 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;85 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;85</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2058.84,-173.8C2074.2,-161.13 2095.28,-143.73 2112.24,-129.74\"/>\n",
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+       "</g>\n",
+       "<!-- 87 -->\n",
+       "<g id=\"node85\" class=\"node\">\n",
+       "<title>87</title>\n",
+       "<text text-anchor=\"middle\" x=\"2245\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">87 (Sabtu)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;87 -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>80&#45;&gt;87</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2077.91,-174.61C2112.42,-160.44 2162.33,-139.95 2198.61,-125.05\"/>\n",
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+       "</g>\n",
+       "<!-- 81 -->\n",
+       "<g id=\"node86\" class=\"node\">\n",
+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"1961\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">81 (ke)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;81 -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>83&#45;&gt;81</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2022.42,-86.8C2011.25,-74.47 1996.04,-57.68 1983.55,-43.89\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"2017\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node87\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">82 (sebuah)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;82 -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>83&#45;&gt;82</title>\n",
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+       "</g>\n",
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+       "<g id=\"node88\" class=\"node\">\n",
+       "<title>84</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">84 (di)</text>\n",
+       "</g>\n",
+       "<!-- 85&#45;&gt;84 -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>85&#45;&gt;84</title>\n",
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+       "<!-- 86 -->\n",
+       "<g id=\"node89\" class=\"node\">\n",
+       "<title>86</title>\n",
+       "<text text-anchor=\"middle\" x=\"2232\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">86 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;86 -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>87&#45;&gt;86</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2242.37,-86.8C2240.59,-75.16 2238.2,-59.55 2236.17,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2239.6,-45.53 2234.63,-36.18 2232.68,-46.59 2239.6,-45.53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2252\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 88 -->\n",
+       "<g id=\"node90\" class=\"node\">\n",
+       "<title>88</title>\n",
+       "<text text-anchor=\"middle\" x=\"2316\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">88 (lalu)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;88 -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>87&#45;&gt;88</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2259.37,-86.8C2269.67,-74.47 2283.69,-57.68 2295.21,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2297.93,-46.09 2301.65,-36.18 2292.56,-41.61 2297.93,-46.09\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2299.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x1f5dbc190>"
+       "<graphviz.files.Source at 0x16ab2e590>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "graph.to_graphvis()"
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], s)\n",
+    "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Get nodes"
+    "### Dependency graph object\n",
+    "\n",
+    "To initiate a dependency graph from dependency models, you need to call `malaya.dependency.dependency_graph`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "defaultdict(<function malaya.function.parse_dependency.DependencyGraph.__init__.<locals>.<lambda>()>,\n",
-       "            {0: {'address': 0,\n",
-       "              'word': None,\n",
-       "              'lemma': None,\n",
-       "              'ctag': 'TOP',\n",
-       "              'tag': 'TOP',\n",
-       "              'feats': None,\n",
-       "              'head': None,\n",
-       "              'deps': defaultdict(list, {'root': [7]}),\n",
-       "              'rel': None},\n",
-       "             1: {'address': 1,\n",
-       "              'word': 'Najib',\n",
+       "<malaya.function.parse_dependency.DependencyGraph at 0x16ab39c10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph = malaya.dependency.dependency_graph(tagging, indexing)\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### generate graphvis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.44.1 (20200629.0846)\n",
+       " -->\n",
+       "<!-- Title: G Pages: 1 -->\n",
+       "<svg width=\"2356pt\" height=\"1088pt\"\n",
+       " viewBox=\"0.00 0.00 2355.50 1088.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 1084)\">\n",
+       "<title>G</title>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-1084 2351.5,-1084 2351.5,4 -4,4\"/>\n",
+       "<!-- 0 -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>0</title>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\">0 (None)</text>\n",
+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (melihat)</text>\n",
+       "</g>\n",
+       "<!-- 0&#45;&gt;11 -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>0&#45;&gt;11</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"492.5,-1003.18 489,-993.18 485.5,-1003.18 492.5,-1003.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"500\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\">root</text>\n",
+       "</g>\n",
+       "<!-- 1 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>1</title>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">1 (KUALA)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;1 -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>11&#45;&gt;1</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-962.98C421.28,-956.25 388.21,-947.36 359,-939 320.79,-928.07 277.84,-915.03 245.33,-905.01\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"374\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 8 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>8</title>\n",
+       "<text text-anchor=\"middle\" x=\"358\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (jarang)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;8 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>11&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-958.12C434.76,-952.81 421.98,-946.34 411,-939 400.15,-931.75 389.44,-922.23 380.52,-913.44\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 9 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>9</title>\n",
+       "<text text-anchor=\"middle\" x=\"445\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (sekali)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;9 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>11&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M477.52,-956.85C473.97,-951.26 470.16,-944.96 467,-939 463.06,-931.58 459.21,-923.31 455.87,-915.66\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"489.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"534\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;10 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>11&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M501.05,-956.91C504.77,-951.32 508.74,-945 512,-939 516.08,-931.5 520.01,-923.1 523.39,-915.35\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"526.67,-916.57 527.35,-906 520.22,-913.84 526.67,-916.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"532\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"643\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (mempunyai)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;29 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M520.16,-956.8C544.14,-943.56 577.45,-925.18 603.33,-910.9\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"605.08,-913.93 612.15,-906.03 601.7,-907.8 605.08,-913.93\"/>\n",
+       "<text text-anchor=\"middle\" x=\"591\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">42 (berbeza)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;42 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>11&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M531.23,-967.04C624.77,-951.63 849.29,-914.65 952.04,-897.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"952.89,-901.13 962.19,-896.05 951.75,-894.22 952.89,-901.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>2</title>\n",
+       "<text text-anchor=\"middle\" x=\"58\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (LUMPUR)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;2 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>1&#45;&gt;2</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.48,-869.8C145.65,-856.78 116.85,-838.78 94.16,-824.6\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"95.82,-821.51 85.48,-819.18 92.11,-827.44 95.82,-821.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 5 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"150\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (hal)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;5 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>1&#45;&gt;5</title>\n",
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+       "<text text-anchor=\"middle\" x=\"184\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 7 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"222\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (,)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;7 -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>1&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M199.67,-869.8C203.54,-858.05 208.75,-842.24 213.16,-828.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"216.54,-829.77 216.34,-819.18 209.89,-827.58 216.54,-829.77\"/>\n",
+       "<text text-anchor=\"middle\" x=\"225.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 3 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>3</title>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (:)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;3 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>5&#45;&gt;3</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M122.98,-784.15C113.38,-778.33 102.59,-771.55 93,-765 80.89,-756.73 67.96,-747.11 56.77,-738.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"58.66,-735.56 48.61,-732.21 54.38,-741.1 58.66,-735.56\"/>\n",
+       "<text text-anchor=\"middle\" x=\"108.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 4 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>5&#45;&gt;4</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M141.7,-782.8C135.98,-770.93 128.26,-754.93 121.76,-741.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"124.78,-739.66 117.28,-732.18 118.48,-742.7 124.78,-739.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>6</title>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;6 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>5&#45;&gt;6</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M159.92,-782.8C166.82,-770.82 176.16,-754.62 183.98,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"187.14,-742.59 189.1,-732.18 181.07,-739.09 187.14,-742.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
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+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
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+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;12 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;15 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;16 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"442.14,-742.38 439.73,-732.06 435.18,-741.63 442.14,-742.38\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;17 -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"538.2,-742.72 537.45,-732.15 531.45,-740.88 538.2,-742.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"541\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;14 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;20 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;25 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;24 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>25&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 33 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>33</title>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;33 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;33</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1219.27,-611.28C1244.62,-602.95 1272.28,-593.67 1278,-591 1294.65,-583.24 1312.15,-572.91 1326.69,-563.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1328.83,-566.42 1335.32,-558.05 1325.03,-560.55 1328.83,-566.42\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1336.9,-479.43 1330.13,-471.28 1330.34,-481.87 1336.9,-479.43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1340.6,-695.8C1328.01,-683.36 1310.82,-666.36 1296.79,-652.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1383.86,-695.88C1391.53,-690.44 1399.8,-684.22 1407,-678 1415.92,-670.29 1425.04,-661.15 1432.91,-652.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1435.51,-655.16 1439.74,-645.44 1430.37,-650.4 1435.51,-655.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1432\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 63 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1501.56,-695.8C1511.18,-683.59 1524.27,-666.99 1535.08,-653.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1538.02,-655.2 1541.46,-645.18 1532.52,-650.86 1538.02,-655.2\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1535\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1455,-608.8C1455,-597.16 1455,-581.55 1455,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.5,-568.18 1455,-558.18 1451.5,-568.18 1458.5,-568.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1464\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1553.99,-608.8C1553.3,-597.16 1552.39,-581.55 1551.6,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1555.09,-567.95 1551.01,-558.18 1548.1,-568.36 1555.09,-567.95\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1562\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1573.82,-608.8C1587.56,-596.24 1606.37,-579.05 1621.61,-565.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.19,-567.51 1629.21,-558.18 1619.47,-562.34 1624.19,-567.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1616\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549.8,-521.8C1549.66,-510.16 1549.48,-494.55 1549.32,-481.24\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1726.18,-347.8C1712.44,-335.24 1693.63,-318.05 1678.39,-304.12\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1754\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1766.25,-347.8C1781.9,-335.13 1803.39,-317.73 1820.68,-303.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1823.21,-306.19 1828.78,-297.18 1818.81,-300.75 1823.21,-306.19\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1819\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 74 -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1719\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">74 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;74 -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>76&#45;&gt;74</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1815.12,-260.9C1804.89,-255.52 1793.82,-249.32 1784,-243 1771.6,-235.02 1758.59,-225.31 1747.49,-216.58\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1798.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 75 -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1812\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">75 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;75 -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>76&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1833.68,-260.94C1829.3,-255.56 1824.96,-249.35 1822,-243 1818.71,-235.94 1816.5,-227.82 1815.02,-220.21\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1844.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 77 -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">77 (suhu)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;77 -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>76&#45;&gt;77</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1859.92,-260.8C1866.82,-248.82 1876.16,-232.62 1883.98,-219.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1887.14,-220.59 1889.1,-210.18 1881.07,-217.09 1887.14,-220.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1887\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 80 -->\n",
+       "<g id=\"node80\" class=\"node\">\n",
+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">80 (masuk)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;80 -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>76&#45;&gt;80</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1888.04,-260.8C1917.83,-247.33 1959.4,-228.54 1991.22,-214.15\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1992.66,-217.34 2000.33,-210.03 1989.78,-210.96 1992.66,-217.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1971\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 78 -->\n",
+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"1852\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">78 (badan)</text>\n",
+       "</g>\n",
+       "<!-- 77&#45;&gt;78 -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1879.69,-173.83C1874.65,-168.53 1869.62,-162.4 1866,-156 1861.99,-148.91 1859.07,-140.59 1856.96,-132.8\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1895\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 79 -->\n",
+       "<g id=\"node82\" class=\"node\">\n",
+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"1946\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">79 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;79 -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>80&#45;&gt;79</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2019.38,-173.8C2005.79,-161.24 1987.18,-144.05 1972.11,-130.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1974.31,-127.39 1964.59,-123.18 1969.56,-132.53 1974.31,-127.39\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2012.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node83\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">83 (surau)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;83 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>80&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2038,-173.8C2038,-162.16 2038,-146.55 2038,-133.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2041.5,-133.18 2038,-123.18 2034.5,-133.18 2041.5,-133.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 85 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>85</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">85 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;85 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;85</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2058.84,-173.8C2074.2,-161.13 2095.28,-143.73 2112.24,-129.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2114.7,-132.24 2120.19,-123.18 2110.25,-126.84 2114.7,-132.24\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2105\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 87 -->\n",
+       "<g id=\"node85\" class=\"node\">\n",
+       "<title>87</title>\n",
+       "<text text-anchor=\"middle\" x=\"2245\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">87 (Sabtu)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;87 -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>80&#45;&gt;87</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2077.91,-174.61C2112.42,-160.44 2162.33,-139.95 2198.61,-125.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2200.07,-128.23 2207.99,-121.2 2197.41,-121.76 2200.07,-128.23\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2163\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 81 -->\n",
+       "<g id=\"node86\" class=\"node\">\n",
+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"1961\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">81 (ke)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;81 -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>83&#45;&gt;81</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2022.42,-86.8C2011.25,-74.47 1996.04,-57.68 1983.55,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1985.87,-41.24 1976.56,-36.18 1980.68,-45.94 1985.87,-41.24\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2017\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node87\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">82 (sebuah)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;82 -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>83&#45;&gt;82</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2039.82,-86.8C2041.05,-75.16 2042.71,-59.55 2044.12,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2047.61,-46.49 2045.18,-36.18 2040.65,-45.75 2047.61,-46.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2052.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 84 -->\n",
+       "<g id=\"node88\" class=\"node\">\n",
+       "<title>84</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">84 (di)</text>\n",
+       "</g>\n",
+       "<!-- 85&#45;&gt;84 -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>85&#45;&gt;84</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2141,-86.8C2141,-75.16 2141,-59.55 2141,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2144.5,-46.18 2141,-36.18 2137.5,-46.18 2144.5,-46.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2153\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 86 -->\n",
+       "<g id=\"node89\" class=\"node\">\n",
+       "<title>86</title>\n",
+       "<text text-anchor=\"middle\" x=\"2232\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">86 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;86 -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>87&#45;&gt;86</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2242.37,-86.8C2240.59,-75.16 2238.2,-59.55 2236.17,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2239.6,-45.53 2234.63,-36.18 2232.68,-46.59 2239.6,-45.53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2252\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 88 -->\n",
+       "<g id=\"node90\" class=\"node\">\n",
+       "<title>88</title>\n",
+       "<text text-anchor=\"middle\" x=\"2316\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">88 (lalu)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;88 -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>87&#45;&gt;88</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2259.37,-86.8C2269.67,-74.47 2283.69,-57.68 2295.21,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2297.93,-46.09 2301.65,-36.18 2292.56,-41.61 2297.93,-46.09\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2299.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x17ae39450>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(<function malaya.function.parse_dependency.DependencyGraph.__init__.<locals>.<lambda>()>,\n",
+       "            {0: {'address': 0,\n",
+       "              'word': None,\n",
+       "              'lemma': None,\n",
+       "              'ctag': 'TOP',\n",
+       "              'tag': 'TOP',\n",
+       "              'feats': None,\n",
+       "              'head': None,\n",
+       "              'deps': defaultdict(list, {'root': [11]}),\n",
+       "              'rel': None},\n",
+       "             1: {'address': 1,\n",
+       "              'word': 'KUALA',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'flat': [2], 'obl': [5], 'punct': [7]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             11: {'address': 11,\n",
+       "              'word': 'melihat',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 0,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nsubj': [1],\n",
+       "                           'advmod': [8, 9],\n",
+       "                           'case': [10],\n",
+       "                           'advcl': [29],\n",
+       "                           'dep': [42]}),\n",
+       "              'rel': 'root'},\n",
+       "             2: {'address': 2,\n",
+       "              'word': 'LUMPUR',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             3: {'address': 3,\n",
+       "              'word': ':',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             5: {'address': 5,\n",
+       "              'word': 'hal',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [3], 'case': [4], 'compound': [6]}),\n",
+       "              'rel': 'obl'},\n",
+       "             4: {'address': 4,\n",
+       "              'word': 'Dalam',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             6: {'address': 6,\n",
+       "              'word': 'politik',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             7: {'address': 7,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             8: {'address': 8,\n",
+       "              'word': 'jarang',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             9: {'address': 9,\n",
+       "              'word': 'sekali',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             10: {'address': 10,\n",
+       "              'word': 'untuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             12: {'address': 12,\n",
+       "              'word': 'dua',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'nummod'},\n",
+       "             13: {'address': 13,\n",
+       "              'word': 'figura',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nummod': [12],\n",
+       "                           'punct': [15],\n",
+       "                           'compound:plur': [16],\n",
+       "                           'flat': [17]}),\n",
+       "              'rel': 'obj'},\n",
+       "             29: {'address': 29,\n",
+       "              'word': 'mempunyai',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'obj': [13, 31], 'punct': [37], 'mark': [38]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             14: {'address': 14,\n",
+       "              'word': 'ini',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             17: {'address': 17,\n",
+       "              'word': 'Perdana',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'det': [14],\n",
+       "                           'flat': [18],\n",
+       "                           'punct': [19],\n",
+       "                           'appos': [20],\n",
+       "                           'conj': [25]}),\n",
+       "              'rel': 'flat'},\n",
+       "             15: {'address': 15,\n",
+       "              'word': '-',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             16: {'address': 16,\n",
+       "              'word': 'bekas',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound:plur'},\n",
+       "             18: {'address': 18,\n",
+       "              'word': 'Menteri',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             19: {'address': 19,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             20: {'address': 20,\n",
+       "              'word': 'Datuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {'flat': [21]}),\n",
+       "              'rel': 'appos'},\n",
+       "             21: {'address': 21,\n",
+       "              'word': 'Seri',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 20,\n",
+       "              'deps': defaultdict(list, {'flat': [22]}),\n",
+       "              'rel': 'flat'},\n",
+       "             22: {'address': 22,\n",
+       "              'word': 'Najib',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 21,\n",
+       "              'deps': defaultdict(list, {'flat': [23]}),\n",
+       "              'rel': 'flat'},\n",
+       "             23: {'address': 23,\n",
+       "              'word': 'Razak',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 22,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             24: {'address': 24,\n",
+       "              'word': 'dan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 25,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'cc'},\n",
+       "             25: {'address': 25,\n",
+       "              'word': 'Tun',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {'cc': [24], 'flat': [26]}),\n",
+       "              'rel': 'conj'},\n",
+       "             26: {'address': 26,\n",
+       "              'word': 'Dr',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 25,\n",
+       "              'deps': defaultdict(list, {'flat': [27]}),\n",
+       "              'rel': 'flat'},\n",
+       "             27: {'address': 27,\n",
+       "              'word': 'Mahathir',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 26,\n",
+       "              'deps': defaultdict(list, {'flat': [28]}),\n",
+       "              'rel': 'flat'},\n",
+       "             28: {'address': 28,\n",
+       "              'word': 'Mohamad',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 27,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             30: {'address': 30,\n",
+       "              'word': \"'\",\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 31,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             31: {'address': 31,\n",
+       "              'word': 'pandangan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {'punct': [30], 'amod': [33]}),\n",
+       "              'rel': 'obj'},\n",
+       "             32: {'address': 32,\n",
+       "              'word': 'yang',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 7,\n",
-       "              'deps': defaultdict(list, {'fixed': [4]}),\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'nsubj'},\n",
-       "             7: {'address': 7,\n",
-       "              'word': 'memuji',\n",
+       "             36: {'address': 36,\n",
+       "              'word': 'sekapal',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 0,\n",
-       "              'deps': defaultdict(list, {'nsubj': [1], 'obj': [8]}),\n",
-       "              'rel': 'root'},\n",
-       "             2: {'address': 2,\n",
+       "              'head': 33,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nsubj': [32], 'punct': [34], 'cc': [35]}),\n",
+       "              'rel': 'conj'},\n",
+       "             33: {'address': 33,\n",
+       "              'word': 'sama',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 31,\n",
+       "              'deps': defaultdict(list, {'conj': [36]}),\n",
+       "              'rel': 'amod'},\n",
+       "             34: {'address': 34,\n",
+       "              'word': \"'\",\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             35: {'address': 35,\n",
+       "              'word': 'atau',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'cc'},\n",
+       "             37: {'address': 37,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             38: {'address': 38,\n",
+       "              'word': 'Namun',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             39: {'address': 39,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             42: {'address': 42,\n",
+       "              'word': 'berbeza',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [39, 54, 89],\n",
+       "                           'nsubj': [40],\n",
+       "                           'advcl': [44],\n",
+       "                           'dep': [55]}),\n",
+       "              'rel': 'dep'},\n",
+       "             40: {'address': 40,\n",
+       "              'word': 'situasi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {'det': [41]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             41: {'address': 41,\n",
+       "              'word': 'itu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 40,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             43: {'address': 43,\n",
+       "              'word': 'apabila',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 44,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             44: {'address': 44,\n",
+       "              'word': 'melibatkan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {'mark': [43], 'obj': [45]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             45: {'address': 45,\n",
+       "              'word': 'isu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 44,\n",
+       "              'deps': defaultdict(list, {'compound': [46], 'nmod': [48]}),\n",
+       "              'rel': 'obj'},\n",
+       "             46: {'address': 46,\n",
+       "              'word': 'ketidakpatuhan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 45,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             47: {'address': 47,\n",
+       "              'word': 'terhadap',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             48: {'address': 48,\n",
+       "              'word': 'prosedur',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 45,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'case': [47],\n",
+       "                           'compound': [49],\n",
+       "                           'amod': [50],\n",
+       "                           'appos': [52]}),\n",
+       "              'rel': 'nmod'},\n",
+       "             49: {'address': 49,\n",
+       "              'word': 'operasi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             50: {'address': 50,\n",
+       "              'word': 'standard',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'amod'},\n",
+       "             51: {'address': 51,\n",
+       "              'word': '(',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 52,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             52: {'address': 52,\n",
+       "              'word': 'SOP',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {'punct': [51, 53]}),\n",
+       "              'rel': 'appos'},\n",
+       "             53: {'address': 53,\n",
+       "              'word': ')',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 52,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             54: {'address': 54,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             55: {'address': 55,\n",
+       "              'word': 'Najib',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [56], 'nsubj': [59], 'acl': [62]}),\n",
+       "              'rel': 'dep'},\n",
+       "             56: {'address': 56,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 55,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             57: {'address': 57,\n",
        "              'word': 'yang',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 4,\n",
+       "              'head': 59,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'nsubj'},\n",
-       "             4: {'address': 4,\n",
+       "             59: {'address': 59,\n",
        "              'word': 'Ahli',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 1,\n",
+       "              'head': 55,\n",
        "              'deps': defaultdict(list,\n",
-       "                          {'nsubj': [2], 'advmod': [3], 'flat': [5]}),\n",
-       "              'rel': 'fixed'},\n",
-       "             3: {'address': 3,\n",
+       "                          {'nsubj': [57], 'advmod': [58], 'flat': [60]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             58: {'address': 58,\n",
        "              'word': 'juga',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 4,\n",
+       "              'head': 59,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'advmod'},\n",
-       "             5: {'address': 5,\n",
+       "             60: {'address': 60,\n",
        "              'word': 'Parlimen',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 4,\n",
-       "              'deps': defaultdict(list, {'flat': [6]}),\n",
+       "              'head': 59,\n",
+       "              'deps': defaultdict(list, {'flat': [61]}),\n",
        "              'rel': 'flat'},\n",
-       "             6: {'address': 6,\n",
+       "             61: {'address': 61,\n",
        "              'word': 'Pekan',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 5,\n",
+       "              'head': 60,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'flat'},\n",
-       "             8: {'address': 8,\n",
+       "             62: {'address': 62,\n",
+       "              'word': 'memuji',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 55,\n",
+       "              'deps': defaultdict(list, {'obj': [63]}),\n",
+       "              'rel': 'acl'},\n",
+       "             63: {'address': 63,\n",
        "              'word': 'sikap',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 7,\n",
-       "              'deps': defaultdict(list, {'flat': [9], 'acl': [14]}),\n",
+       "              'head': 62,\n",
+       "              'deps': defaultdict(list, {'flat': [64], 'acl': [69]}),\n",
        "              'rel': 'obj'},\n",
-       "             9: {'address': 9,\n",
+       "             64: {'address': 64,\n",
        "              'word': 'Ahli',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 8,\n",
-       "              'deps': defaultdict(list, {'flat': [10], 'det': [12]}),\n",
+       "              'head': 63,\n",
+       "              'deps': defaultdict(list, {'flat': [65]}),\n",
        "              'rel': 'flat'},\n",
-       "             10: {'address': 10,\n",
+       "             65: {'address': 65,\n",
        "              'word': 'Parlimen',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 9,\n",
-       "              'deps': defaultdict(list, {'flat': [11]}),\n",
+       "              'head': 64,\n",
+       "              'deps': defaultdict(list, {'flat': [66]}),\n",
        "              'rel': 'flat'},\n",
-       "             11: {'address': 11,\n",
+       "             66: {'address': 66,\n",
        "              'word': 'Langkawi',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 10,\n",
-       "              'deps': defaultdict(list, {}),\n",
+       "              'head': 65,\n",
+       "              'deps': defaultdict(list, {'det': [67]}),\n",
        "              'rel': 'flat'},\n",
-       "             12: {'address': 12,\n",
+       "             67: {'address': 67,\n",
        "              'word': 'itu',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 9,\n",
+       "              'head': 66,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'det'},\n",
-       "             13: {'address': 13,\n",
+       "             68: {'address': 68,\n",
        "              'word': 'yang',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 14,\n",
+       "              'head': 69,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'nsubj'},\n",
-       "             14: {'address': 14,\n",
+       "             69: {'address': 69,\n",
        "              'word': 'mengaku',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 8,\n",
-       "              'deps': defaultdict(list,\n",
-       "                          {'nsubj': [13],\n",
-       "                           'ccomp': [15],\n",
-       "                           'xcomp': [17],\n",
-       "                           'acl': [21]}),\n",
+       "              'head': 63,\n",
+       "              'deps': defaultdict(list, {'nsubj': [68], 'xcomp': [70]}),\n",
        "              'rel': 'acl'},\n",
-       "             15: {'address': 15,\n",
+       "             70: {'address': 70,\n",
        "              'word': 'bersalah',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 14,\n",
-       "              'deps': defaultdict(list, {}),\n",
-       "              'rel': 'ccomp'},\n",
-       "             16: {'address': 16,\n",
+       "              'head': 69,\n",
+       "              'deps': defaultdict(list, {'xcomp': [72]}),\n",
+       "              'rel': 'xcomp'},\n",
+       "             71: {'address': 71,\n",
        "              'word': 'selepas',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 17,\n",
+       "              'head': 72,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'case'},\n",
-       "             17: {'address': 17,\n",
+       "             72: {'address': 72,\n",
        "              'word': 'melanggar',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 14,\n",
-       "              'deps': defaultdict(list, {'case': [16], 'obj': [18]}),\n",
+       "              'head': 70,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'case': [71], 'obj': [73], 'advcl': [76]}),\n",
        "              'rel': 'xcomp'},\n",
-       "             18: {'address': 18,\n",
+       "             73: {'address': 73,\n",
        "              'word': 'SOP',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 17,\n",
-       "              'deps': defaultdict(list, {'compound': [23]}),\n",
+       "              'head': 72,\n",
+       "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'obj'},\n",
-       "             19: {'address': 19,\n",
+       "             74: {'address': 74,\n",
        "              'word': 'kerana',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 23,\n",
+       "              'head': 76,\n",
        "              'deps': defaultdict(list, {}),\n",
-       "              'rel': 'det'},\n",
-       "             23: {'address': 23,\n",
-       "              'word': 'badan',\n",
+       "              'rel': 'mark'},\n",
+       "             76: {'address': 76,\n",
+       "              'word': 'mengambil',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 18,\n",
-       "              'deps': defaultdict(list, {'det': [19]}),\n",
-       "              'rel': 'compound'},\n",
-       "             20: {'address': 20,\n",
+       "              'head': 72,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'mark': [74],\n",
+       "                           'advmod': [75],\n",
+       "                           'obj': [77],\n",
+       "                           'advcl': [80]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             75: {'address': 75,\n",
        "              'word': 'tidak',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 21,\n",
+       "              'head': 76,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'advmod'},\n",
-       "             21: {'address': 21,\n",
-       "              'word': 'mengambil',\n",
-       "              'lemma': '_',\n",
-       "              'ctag': '_',\n",
-       "              'tag': '_',\n",
-       "              'feats': '_',\n",
-       "              'head': 14,\n",
-       "              'deps': defaultdict(list, {'advmod': [20], 'advcl': [25]}),\n",
-       "              'rel': 'acl'},\n",
-       "             22: {'address': 22,\n",
+       "             77: {'address': 77,\n",
        "              'word': 'suhu',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 25,\n",
-       "              'deps': defaultdict(list, {}),\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {'compound': [78]}),\n",
        "              'rel': 'obj'},\n",
-       "             25: {'address': 25,\n",
-       "              'word': 'masuk',\n",
+       "             78: {'address': 78,\n",
+       "              'word': 'badan',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 21,\n",
-       "              'deps': defaultdict(list,\n",
-       "                          {'obj': [22], 'mark': [24], 'obl': [28, 32]}),\n",
-       "              'rel': 'advcl'},\n",
-       "             24: {'address': 24,\n",
+       "              'head': 77,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             79: {'address': 79,\n",
        "              'word': 'ketika',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 25,\n",
+       "              'head': 80,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'mark'},\n",
-       "             26: {'address': 26,\n",
+       "             80: {'address': 80,\n",
+       "              'word': 'masuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {'mark': [79], 'obl': [83, 85, 87]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             81: {'address': 81,\n",
        "              'word': 'ke',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 28,\n",
+       "              'head': 83,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'case'},\n",
-       "             28: {'address': 28,\n",
+       "             83: {'address': 83,\n",
        "              'word': 'surau',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 25,\n",
-       "              'deps': defaultdict(list,\n",
-       "                          {'case': [26], 'det': [27], 'nmod': [30]}),\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [81], 'det': [82]}),\n",
        "              'rel': 'obl'},\n",
-       "             27: {'address': 27,\n",
+       "             82: {'address': 82,\n",
        "              'word': 'sebuah',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 28,\n",
+       "              'head': 83,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'det'},\n",
-       "             29: {'address': 29,\n",
+       "             84: {'address': 84,\n",
        "              'word': 'di',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 30,\n",
+       "              'head': 85,\n",
        "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'case'},\n",
-       "             30: {'address': 30,\n",
+       "             85: {'address': 85,\n",
        "              'word': 'Langkawi',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 28,\n",
-       "              'deps': defaultdict(list, {'case': [29]}),\n",
-       "              'rel': 'nmod'},\n",
-       "             31: {'address': 31,\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [84]}),\n",
+       "              'rel': 'obl'},\n",
+       "             86: {'address': 86,\n",
        "              'word': 'pada',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 32,\n",
-       "              'deps': defaultdict(list, {'amod': [33]}),\n",
+       "              'head': 87,\n",
+       "              'deps': defaultdict(list, {}),\n",
        "              'rel': 'case'},\n",
-       "             32: {'address': 32,\n",
+       "             87: {'address': 87,\n",
        "              'word': 'Sabtu',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 25,\n",
-       "              'deps': defaultdict(list, {'case': [31]}),\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [86], 'amod': [88]}),\n",
        "              'rel': 'obl'},\n",
-       "             33: {'address': 33,\n",
+       "             88: {'address': 88,\n",
        "              'word': 'lalu',\n",
        "              'lemma': '_',\n",
        "              'ctag': '_',\n",
        "              'tag': '_',\n",
        "              'feats': '_',\n",
-       "              'head': 31,\n",
+       "              'head': 87,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'amod'},\n",
+       "             89: {'address': 89,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
        "              'deps': defaultdict(list, {}),\n",
-       "              'rel': 'amod'}})"
+       "              'rel': 'punct'}})"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2428,47 +5466,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(('memuji', '_'), 'nsubj', ('Najib', '_')),\n",
-       " (('Najib', '_'), 'fixed', ('Ahli', '_')),\n",
+       "[(('melihat', '_'), 'nsubj', ('KUALA', '_')),\n",
+       " (('KUALA', '_'), 'flat', ('LUMPUR', '_')),\n",
+       " (('KUALA', '_'), 'obl', ('hal', '_')),\n",
+       " (('hal', '_'), 'punct', (':', '_')),\n",
+       " (('hal', '_'), 'case', ('Dalam', '_')),\n",
+       " (('hal', '_'), 'compound', ('politik', '_')),\n",
+       " (('KUALA', '_'), 'punct', (',', '_')),\n",
+       " (('melihat', '_'), 'advmod', ('jarang', '_')),\n",
+       " (('melihat', '_'), 'advmod', ('sekali', '_')),\n",
+       " (('melihat', '_'), 'case', ('untuk', '_')),\n",
+       " (('melihat', '_'), 'advcl', ('mempunyai', '_')),\n",
+       " (('mempunyai', '_'), 'obj', ('figura', '_')),\n",
+       " (('figura', '_'), 'nummod', ('dua', '_')),\n",
+       " (('figura', '_'), 'punct', ('-', '_')),\n",
+       " (('figura', '_'), 'compound:plur', ('bekas', '_')),\n",
+       " (('figura', '_'), 'flat', ('Perdana', '_')),\n",
+       " (('Perdana', '_'), 'det', ('ini', '_')),\n",
+       " (('Perdana', '_'), 'flat', ('Menteri', '_')),\n",
+       " (('Perdana', '_'), 'punct', (',', '_')),\n",
+       " (('Perdana', '_'), 'appos', ('Datuk', '_')),\n",
+       " (('Datuk', '_'), 'flat', ('Seri', '_')),\n",
+       " (('Seri', '_'), 'flat', ('Najib', '_')),\n",
+       " (('Najib', '_'), 'flat', ('Razak', '_')),\n",
+       " (('Perdana', '_'), 'conj', ('Tun', '_')),\n",
+       " (('Tun', '_'), 'cc', ('dan', '_')),\n",
+       " (('Tun', '_'), 'flat', ('Dr', '_')),\n",
+       " (('Dr', '_'), 'flat', ('Mahathir', '_')),\n",
+       " (('Mahathir', '_'), 'flat', ('Mohamad', '_')),\n",
+       " (('mempunyai', '_'), 'obj', ('pandangan', '_')),\n",
+       " (('pandangan', '_'), 'punct', (\"'\", '_')),\n",
+       " (('pandangan', '_'), 'amod', ('sama', '_')),\n",
+       " (('sama', '_'), 'conj', ('sekapal', '_')),\n",
+       " (('sekapal', '_'), 'nsubj', ('yang', '_')),\n",
+       " (('sekapal', '_'), 'punct', (\"'\", '_')),\n",
+       " (('sekapal', '_'), 'cc', ('atau', '_')),\n",
+       " (('mempunyai', '_'), 'punct', ('.', '_')),\n",
+       " (('mempunyai', '_'), 'mark', ('Namun', '_')),\n",
+       " (('melihat', '_'), 'dep', ('berbeza', '_')),\n",
+       " (('berbeza', '_'), 'punct', (',', '_')),\n",
+       " (('berbeza', '_'), 'nsubj', ('situasi', '_')),\n",
+       " (('situasi', '_'), 'det', ('itu', '_')),\n",
+       " (('berbeza', '_'), 'advcl', ('melibatkan', '_')),\n",
+       " (('melibatkan', '_'), 'mark', ('apabila', '_')),\n",
+       " (('melibatkan', '_'), 'obj', ('isu', '_')),\n",
+       " (('isu', '_'), 'compound', ('ketidakpatuhan', '_')),\n",
+       " (('isu', '_'), 'nmod', ('prosedur', '_')),\n",
+       " (('prosedur', '_'), 'case', ('terhadap', '_')),\n",
+       " (('prosedur', '_'), 'compound', ('operasi', '_')),\n",
+       " (('prosedur', '_'), 'amod', ('standard', '_')),\n",
+       " (('prosedur', '_'), 'appos', ('SOP', '_')),\n",
+       " (('SOP', '_'), 'punct', ('(', '_')),\n",
+       " (('SOP', '_'), 'punct', (')', '_')),\n",
+       " (('berbeza', '_'), 'punct', ('.', '_')),\n",
+       " (('berbeza', '_'), 'dep', ('Najib', '_')),\n",
+       " (('Najib', '_'), 'punct', (',', '_')),\n",
+       " (('Najib', '_'), 'nsubj', ('Ahli', '_')),\n",
        " (('Ahli', '_'), 'nsubj', ('yang', '_')),\n",
        " (('Ahli', '_'), 'advmod', ('juga', '_')),\n",
        " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n",
        " (('Parlimen', '_'), 'flat', ('Pekan', '_')),\n",
+       " (('Najib', '_'), 'acl', ('memuji', '_')),\n",
        " (('memuji', '_'), 'obj', ('sikap', '_')),\n",
        " (('sikap', '_'), 'flat', ('Ahli', '_')),\n",
        " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n",
        " (('Parlimen', '_'), 'flat', ('Langkawi', '_')),\n",
-       " (('Ahli', '_'), 'det', ('itu', '_')),\n",
+       " (('Langkawi', '_'), 'det', ('itu', '_')),\n",
        " (('sikap', '_'), 'acl', ('mengaku', '_')),\n",
        " (('mengaku', '_'), 'nsubj', ('yang', '_')),\n",
-       " (('mengaku', '_'), 'ccomp', ('bersalah', '_')),\n",
-       " (('mengaku', '_'), 'xcomp', ('melanggar', '_')),\n",
+       " (('mengaku', '_'), 'xcomp', ('bersalah', '_')),\n",
+       " (('bersalah', '_'), 'xcomp', ('melanggar', '_')),\n",
        " (('melanggar', '_'), 'case', ('selepas', '_')),\n",
        " (('melanggar', '_'), 'obj', ('SOP', '_')),\n",
-       " (('SOP', '_'), 'compound', ('badan', '_')),\n",
-       " (('badan', '_'), 'det', ('kerana', '_')),\n",
-       " (('mengaku', '_'), 'acl', ('mengambil', '_')),\n",
+       " (('melanggar', '_'), 'advcl', ('mengambil', '_')),\n",
+       " (('mengambil', '_'), 'mark', ('kerana', '_')),\n",
        " (('mengambil', '_'), 'advmod', ('tidak', '_')),\n",
+       " (('mengambil', '_'), 'obj', ('suhu', '_')),\n",
+       " (('suhu', '_'), 'compound', ('badan', '_')),\n",
        " (('mengambil', '_'), 'advcl', ('masuk', '_')),\n",
-       " (('masuk', '_'), 'obj', ('suhu', '_')),\n",
        " (('masuk', '_'), 'mark', ('ketika', '_')),\n",
        " (('masuk', '_'), 'obl', ('surau', '_')),\n",
        " (('surau', '_'), 'case', ('ke', '_')),\n",
        " (('surau', '_'), 'det', ('sebuah', '_')),\n",
-       " (('surau', '_'), 'nmod', ('Langkawi', '_')),\n",
+       " (('masuk', '_'), 'obl', ('Langkawi', '_')),\n",
        " (('Langkawi', '_'), 'case', ('di', '_')),\n",
        " (('masuk', '_'), 'obl', ('Sabtu', '_')),\n",
        " (('Sabtu', '_'), 'case', ('pada', '_')),\n",
-       " (('pada', '_'), 'amod', ('lalu', '_'))]"
+       " (('Sabtu', '_'), 'amod', ('lalu', '_')),\n",
+       " (('berbeza', '_'), 'punct', ('.', '_'))]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2486,7 +5580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2495,7 +5589,7 @@
        "False"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2519,16 +5613,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<networkx.classes.multidigraph.MultiDiGraph at 0x1f5dbc390>"
+       "<networkx.classes.multidigraph.MultiDiGraph at 0x16ab39d50>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2540,12 +5634,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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UvmulSxU32WIi6+f3MF4+jSU/g4ixb6Nr2tEhitQxKviWCL3UBrVidMUT2DXuqs3UDaJQ+gaR/8cPBCbcg+HiIQwXj6Br2sHlecL91eSf3kta0l4KD23AUpSHj48PH330EbIs18gHqN5z95hUYQqd1WQol7mh0PohlxQ7vhvMVr5fs51XH+hNQUEBRUVFBAUFkZOTg5+fHxqNplrj2dWJXRTJnYobgDaqHQHdRpH1k3NjTyGdWb3UuNF1J0tozk0nO3EeJalJoFLj16o3IYOnISmUAPXyCSzLMkVFRVy5csXtJycnx/H32aihEFpeHUxSqgi77yVy1n9K/q4f0DRqgV+bPqB0XeDQPETDhl/eR5+Xh8Vsq2Dz9fXlzJkzSJLklY9Coah0H8Br1yv72WiquLhDodZhNRY5jVlLipA0zupqx89cICMjw2HMCwoKiIuLo6ioCLPZjK+vr+Pj5+fn9N0b4zqdzuuGvXSYyr7YWjb8IinV16oXy8TxhXRm9VLjRtedLGF24jyUvsFEPfUNVoOe9KUvUbDvVwK7/cWxj6snsCzLrF27li5duhAZGXnD83O1qBcXqmNgrC8YCpyMpCfGVKlUEhIS4vZz22230aBBA0JCQlhyXsPOVNfluZrwZk6txtO+eQ6/9oNc7tsoNJjMzEw2btzIc889x7Fjx2jSpAkff/zxDf8+dYWDS/cDrqvzANRhTdEf3uj4bi0xYL6ShiYsxmm/B+8dSdMeAbz66qsYjUaaNGnCuXPnADCbzRQVFbn86PV6l+N5eXlcvnzZ4/2NRiM+Pj5ujXRVDLp9bGuG5oZ/X3voxdNwj8BzatToViRLaM5LJzB+pC2h31+DT7N4TFkXnfYp+wQ+evQoU6dOZffu3SxYsIBp06a5vXZxcXE5z7L052yehWNEk6ONtHk9pbxIq8nILElCmXGC0LQ9RGqMToazRYsWbo2qK91ad6RsPcOe9JMuX5tLMs6hbtAEWbZSsG815sIr+HcYXG4/e7WZJEkMHjyY/fv3s3nzZjIyyrfSqc/YRdMNJSaXZcS+LXtyZfOX6JN24tsigbydS1CHxzriuWD7rdo2CWb62P9j6tSpvP76607dNFQqFYGBgQQGBrqaglewWCwUFxdX2bhnZWW53RY47CmM1opFjypDSGdWHzVqdCta/AjsNgr9sW1oYzpgNRRSfHYPwX0nlNtPAr7amsSOz19lzZo1mEwmJEli2bJlHDlyxK3nKcsyISEhDq+y9Cc9sCV7fZpiQUJ2IdNib6AoN+lAYdPOPFhNi3pj4qOYveGky236I5spPLgO2WpBG92OiIdmuuy8W7baTJIk7rjjjnL71Xfsv1WemzLi4L7jCbv337aOE6veR9OoJWF/+afTOUr/VsHBwcyePbsmbwEApVKJv78//v7+Xjvn1EW7OeuiX11VEdKZ1UONGt2KZAl10e0pPLCW5A8eANmKX/tB+LTsWW4/g9nKmt8PsmnlSiRJciyupKenM2LECOLj4916nK5iZ/ZFPbOl9hf17ELgZfN0AULumErIHVMrPL6iarObDcdvZXFfRuwT25km0xa43HYz/1bu+tVV/TxCOrM6qFGj606WUJatpC97hYDOw4mcOAurqZjsXz8id8tXhAwsb2hCwhvz1VdfMXv2bA4dsqVBFRQU8Oyzz5bbF2ytxlu1aoWfn5/TuKtFvYvvj3Gem7mEgC530WDo446x6lzUcycE7gllNQludsRv5ZrS/ercqbhJCiWy2YTN3wfZarZtU6qRJElIZ1YjNVp+5O4JbC0uwJKfSUDXkUgqNUqfQPw7Dqb4jOtquB2b1/Pwww87DC7Y4m8fffQRBw8exGp19lqHDRtGy5Yt2b17t9O4q0W9mL8vd3yinvoGSaXB92qCfWnsi3rexq5JoFVV7T9NWU2CWwH7b+WjFr9VaUr3q8vb+T0XZ40mf9dy9Ec3c3HWaPJ2fg9A6mfTuThrNJaCbDKWvmL7O88WlqiKKJKgatSop+u2Y6xvEKqgCAr2rybw9tHIJcUUHt7olNxuR6dS8Pwzj3G5o4533nmHkpISVCoVRUVFvPzyyxQWFiLLMr6+voSHhxMdHU12djayLNO3b1/++c9/8sorr5BrsFTYawyg6MRvKH2D0Ea3K7etutNqLFYPpMWuolZKvHhXm5s6h9kd9nsWpa7XKB2mqkjFLeqJL12O38yhl7pAjXq6FXWMDRv9IsVn95Ly0ThSP52GpFTRYNCj5faTgfvjo3n99dfZtWsXMTExWCwWNm/eTH5+PhaLhVOnTvHhhx8yaNAg8vKupRUZjUZmzpxJcHAwD7/xKRaL63CHncLDG/Frf4fbPMrq6EjriDFXweje6u0/JvSIZem0HgxrG4FWpUBX5i1Bp1KgVSkY1jaCpdN63NQG186TA1qgUymv69ibOfRSF6hxwRtXgi6e4kqkpKioiCVLljBlyhSUyvL/yJYtW8aECRNQq9U0aNCA/v3707ZtWzYbYjhlCnF7LXNeBqkLHqXx9M9QB7vP/723cxNmP9iZ7OxsFi9ezGeffca7777LyJEjq3x/rhTG8vf+4rKMsyy3kmpWRVRFv+Fm53okQm2hl1vzrcmb1CnBG28vfvj6+vLII4+4PaZ9+/a8+uqrjB49mjZt2jjGTy/azakK0moKj2xCG9W2QoMLcOz0ORISHuPQoUMolUpkWcZgMHh4R864ijFXVMZZGlG6aaMq+g03OyL0UjepcaPrLfFqT2nbti1t27YtN15ZWo3+yCaCeoypcB+AQ3v/IK3M28Df/vY35syZQ+PGjWncuDFNmjQp93fZogl3hSPuyjjL4s0Ys5DavHmY0COWjlHB1yWdKageakXwpi48gd0t6gEYUo5jKcx2mbVQGq1KYvy9Q5FbK/jkk0/Q6/XIskx2djZWq5WcnBwuXryIRqPBarVSVFREZmYmly9fxtfX18kYN7vLfTWdp9xo6eatIrV5qz1Urkc6U1B91Jq0Y20/gSuu/tqIb8teKLS+lZxF4v/u7U3oxDt45ZVXeP3111mwYAGXLl3i0qVLnDhxwumTmppKfn4+bdu2JTY2loiICIKCgtBqtVzIM1eomuUJN1K6eStIbd4qDxV3iNBL3aBOdI6orSewtxf1wCbAU5FqVH5+fjljfOLECTJaj0bb3H089sq2b7DkZ7ldSLMzqHU4X0xOqNK93AoLLqKDgqAmqVMLaa6orSdwdVQ0VSbTFxgYSEJCAgkJzobx6e/3s/LgpSrPo9z5q1i66U5qszLqk9Sm0G8W1CVu6RTPulTR1KZRoMsqNNlqsZVnlirjlK2uHxJKrMhXUjh16hRmc8U5yHbsGRP5e3/h8sJnuPDePWStci38krtjCRfeGUnx+QNA9VXleZMbfagcSsmtnokJblnqhKdbm9SFRT1wH2OuSEWrLLIsc2TVFwx99yCXL1+mWbNmtGrVitatW9OqVSvHJzQ0FHDOmKgsNc105TJFJ3ag9G9Q6np1X+y69EPFVb6zOTed1AWPIKl1jmMCe9xHcO+xIg1PUC3c8kYXan9RD9wrjFVUxlkaSYKh7Rqz4J2fAJt+8OnTpx0x482bNzN//nxOnDiBWq2mVatW6LqMRA7uBFSempaTOJ+QAVPIXjff+bo4Z0zs3r2bF198EX9/f3788cfr+CW8R1UeKtHPLnV0KbFTHx4qgvqHMLpXqQtpNd6MMfv4+NChQwc6dHDuo2aXwTxx4gTv7cigpLDyVUR90g4kpRqfuATA2ejaMibyWbNmDa+99hpHjhyhqKiI+Pj4Kt+Dtymt3+xpvnNZRAcFgbcRRrcMtZlWUxOFI5IkERkZSWRkJIvO74ZKxK6txiJyty4i4sE33e5zLjWdux66y2ls7969+Pj4oFarHR+VSlWl7+7GKhovvW1HcYzHaXip8x4GSUIX24WQgQ87mlqKDgoCbyOMbh2jJmPMnohd5+74Dr92d6AKjnC7T0SDQJ588km+//57srOzAdBqtWRnZ2MymTCZTJjNZsffrr5XZazsuMFgcDl+OfYuoOJca4VvIJGTZ6OJaI61OJ+cxPlk/TKLiAdnOvYRHRQE3kQY3TpITcWYK6rKs2O4cBBLQTYF+38FwFqUT9ZP7xDYYwxBPcagUynYvf4n9n83t9yxmZmZxMTEVEu3W08qyp5Zup8zBypOw1NofNA2snVgVvqF0GDIDFI+mYjVWOQojhEdFATeRBjdOkpNxJhLZ0y46zAQMfYtsFyLMV9e9Cwhgx7Fp7ktZisD43o058ASidKFNoGBgXTv3h3AkZfcvXt3EhISaNjw+pomVrWizJOHSjnsz4er9yI6KAi8jTC6dZzqjDGXzpjI9TQ1TVKg0Pmj0Pg4xK6fm3AnveI7MnToUPR6PQBRUVGcOnWKrl27EhYWRnJyMlu3bmXv3r2EhoY6GeKuXbtW2pjxesqUPXmolKSdRqH1Q9WgMVZDITnrP0Mb0wGFztbaSXRQEHibOlEGLKg9XGn4ekpZDd+jR4/St29f1Go1aWlpFBQUsGnTJtasWcPatWuRJIlhw4bRoUMHNBoNR44c4c8//+Tw4cM0a9bM4Ql3797dsQ/cWJnytlNZrD+ezpVt3zo9VACCeo9FHRrFla1fYy3KRaHxRRfbmZCBU1H6hzhKvd+8p8MtJZAjuHEqKgMWRlfgVe2FlJQUMjIy6Nq1q9O4LMskJSU5DPDvv/9Ot27dGD58OIMHDwZsOb72z5kzZ2jfvj0tewxml19PTHLV48I+aiUzR7Xj5ZVHr+uholUq6BITzP7kXKBsOMMWV7+ZBXIE148wuoJKqWlBGL1ez5YtW1i7di1r1qyhqKiI4cOHc+eddzJ48GDUajX79+/n1Q2pnNFryF6/AMP5A1gNhaiCIwnpPxmfuG7IFhNZP7+H8fJpLPkZRIx9G13Tjo65DmsbQZ8WDav8UFEpbOl1ZqssBHIEVaYio3tLay8IrlHTfcb8/PwYMWIEc+bM4fTp02zbto2uXbuycOFCmjZtyrBhwzhx/hKp1iBkWUYV0JDIce8Q/exSgvtNJHPlu5hz0wHQRrWj4d1/R+nn3H7JXlF2Z/tGvHhXG3zUSipLpJAkUCkkJEnCZKnY4NqvYRfIWbzr/A38IoJbBeHpCspROmMiI7eAtORzPDC0d42JXRsMBrZv3866ixZ+OS+7zD649MVfCeo9Fr/WvR1jKXMn03Dk3x2eLoBaAU8NaM7fhrThUEpupWl4XWOC2Xcx97q0jUWfOoGdOi/tKKhblM6Y+Otf/8p3c+cy+4GDNbZopNPpGDJkCL8u3Y/RXD7P1qK/giknFU1YTKXnMlnhrblf8fn/rXEs0j1wZzxJhiBOZRaVS8N7YcVhSixWtwI5hUc3k7O2VE6yLCObjURO+RCpUQshkCOoFGF0BW7JyC9mRVIBoSP/j9EfJjJiqIU2jWpu1T7fUF6eUraYyfp5Fv4dBqEOjfboPHeNuo+/dnyQ3bt38+effzJ37lzOnj1Lhw4dSEhIoEtCAt3Du2OxWisVyPFvNxD/dgMd3wsPbSDvt+/RRMQJgRyBRwijKyiHvQhhU1I6qs6jUKu1mIGVBy+x7mjNtbUpW6Ysy1ayVr0PShUNhjzu8XmCfbXEx3cmPj6exx+3HVdYWMi+ffvYvXs3v/76K6+99hqGZn0I6PUQ4LlATuGRjfi1v8NRdScEcgSVIYyuwImyWQyS2tljq8leaaUrymRZJnv1x1j0uYTf/xqS0rN/uu4qyvz9/enXrx/9+vVzjD313R5+OZzu8fzMeRkYk48SetfTjjEhkCOoDJG9IHBwLV+34rQxqJlV+zHx1yrBctbNxZSdTPiYV1CUeRDIZpOtwgyQrWZbd42rN1CVijK9qWrN8gqPbEQb1RZ1cKTTuBDIEVSE8HQFwLW2Num7VrpcQCrJukj2qg8wX7kMgCayBSFDpkPDmGrrlWYvU16z6zCFB9aCUk3KnImO7Q2GP4l/u4GkfjYdS75NojJj6SsANHn8C9QhEQxsFeZxfNUT1bXS6I9sIqjnAy7OIwRyBO4RRlcAXGtr424BSeXfgLB7XkAZFA6ylYJ9v5K18j80fuSTam1rYxd2b/r8Krf7RD3xpctxd81D3VEVgRxDyjEshTn4turtNF46nCHLMllZWWRmZtK6dWsUCvFiKRDhBQHObW18W/XCt2VPFD6BTvsodP6ogiOuLRhJCofXW3rV3pvIskxxahIvDG9VI81DS4czKmsIqj+8Ed+WvRzyj3asssxL44cQFBSERqMhOjqadu3ace7cuSrNX3DzIoyuwKmtTWVcnP0gF9+7l5z1nxLY837HuH3V3hscOXKE559/nrCwMHr27El73ZWqVZRhJX/rIhRnf8No9PxBYA9nSJKtIejFWaPJ37Uc/dHNXJw1mryd3wMgm0vQJ+3Ar8OgcteO1ehRWwwUFBRgNpsxGo1ERkbSrFmzKv8OgpsTEV4QkJSW73EFVsyzS7GWGNAf2YgyMNwx7o1Ve1mW6dq1KydOnHB0gQgLC6Nr1650BY+F3XuH6Jn0zlIe3rWC6dOnM2XKFP7+978TGxtb6Rzs4YyKGoJKKg0xzy4tN65TKUlJ/ILLly87Cbfn5ubSoUMHJk+ezIQJE2jcuHEVfxnXeCrmLqhbCKMrcFmEUBEKjQ7/LneS8tF4tI/NR+kXfPU8N7ZqL0kSEydO5N///jdmsxmlUskjjzzi2O6psLvVamWGnx96vZ6SkhLmzZuHxWJh3rx5lc7hRvvUDXvqG5o3b05hYaFj286dO9Hr9SxcuJB27drRo0cPJk+ezKhRo/Dx8anaj0TVxdwFdQsRXhBUedUecJS/WgqyS53nxlftExISUKlUaLVaNBoNY8eOLbePvUx59oOd+WJyArMf7Mz0fnEO706hUNC/f3/AZsh79uzJRx995PEcJvSIrVI4w0etdMhchoWF8fXXX+Pr64tarSYgIIBnnnkGrVbLF198QWpqKhMmTODLL7+kSZMmTJs2jZ07d1KRBkppFu86z0Of72L98XSMZmu5NxTD1bHEY+k89PkuIcJTBxGersC5CMFNhwXDhUMofQJRh8cim4zkbvsGhc4fdUNbKW5FbW08fQ3esWMHo0eP5qeffuLYsWP897//LddC3lPuueceVq9ezaRJk1i1ahV//PEHffr08XhON9Kn7t5772XEiBFs2LCB8+fPs3z5cu6991769+/P22+/zfjx4xk/fjwpKSksXryYRx55BKvVyqRJk5g4cSJNmzZ1OcesQiPHLuVjtlZuoEvnUQNCdrIOIVTGBGQVGun97iaMZiu52910WAhrSu62xVgKspBUGrSNWxLcfzKacNsCkVal4Ld/3eFkRCt+DXYWAdcnH+Oee+5h8eLFDB06FLDFeK+3qWVhYSE7duxg+PDhJCYmMnHiRH777TcKNaEez8n+an49feqKioq4fPkycXFxjvnMmjWLOXPm8Oijj/LCCy8QHBzsuM8///yTRYsWsWzZMlr2HEJQj/s5U6xDkiSnOcpmE9mJ81xqC5cmd8cS8nZ8S/hDb9LgtnihflbDCBFzQaVM+2YP64+nV1qJ5gq7WHjpPN2qiKJrFBL52xby1b+nMnz48OuYfeWsXr2aYyUN+GJfbo0Jtbvi0qVLvPzyy6xatYqXX36Z6dOno1ZfC8t8teM0b69JwmSRQSof/bOWGMj/4wf8OwxGGRRG8Zk9ZP38Ho2nfoIqOAIA05XLZP74JtZim1iRb7PO5f77CKoXIWIuqJQnB7RAp1Je17FlixCqWk5stMgE9JtMVnDr67q+J+Q0aMsX+67Ueolz48aN+eKLL1i/fj0///wz7du3Z+XKlciyzOJd5/lP4ilMVsmlwQXbImZw3/FXc6YV+LbojiooAmPaacc+OYnzCRkwBRQqx/1URx614PoQMV0BcOOr9vZX14PJucz8+RCpv37i9hW4+PwBchIXYMnPRNO4JQ1HPAtB4dVWTlzZnMy56aQueARJrXMcE9jjPug9ttrm1LFjR9atW8fatWt57rnneH/hcjI6jCPDTRk2gNVk4MqmLylK2oFsNaMJa0bYvc87aQvrk3YgKdX4xCUA8x3HCvWzuoMwugIH9lfpG+mVNnfLaQwlJkd7HfsrcObKd2k89RMkjY7MFW8Teuff8G3Rndxti8lc+S6NJr1fbeXElc3JTvSzS5EUzt6+qzlt2rSJvXv38o9//OOG5iVJEnfeeSdDhgzhobmbSE43uS3DBshZ+wmy1ULjx+aj0PljTDvtpC1sNRaRu3UREQ++We5aQv2s7iCMrsCJG1m1t5cTS2qdU2FB6Vdgq6EATcMY/FrbMgmC+oyj4ONxmLKTUYdGe10E3JM5aSPd6zOUfjXPvnSBGTNmsHPnTgICAtwaXaPRSEFBgdMnPz/f7fdsfQl7G49CVqjc6viaspMpOvUHUU8uQqH1RZatFOz+yUlbOHfHd/i1u8MR2y2LUD+rGwijKyiHp0UIZXFXTly6vU7BvtWow6+VxCo0OlTBkZRkXkQdGu3112BP5mQndd7DIEnoYrsQMvBhlL5BgO3VfPC0lzjyvw+xWCzIsozZbGb48OEujStAQEAAAQEBBAYGOv4uOxYWFkbz5s3Zb2iIMlOJuYI3C+Olk6iCwsnd/i2FRzaBbEUVEErk5NkObWHDhYNYCrIp2P8rANaifLJ+eofAHmMI6jHmuvKoRdWb9xFGV+CW0r3SPMFVOXHZ9jpWk8FhzOwotH7IJcWA91+DPZpTSTGRk2ejiWiOtTifnMT5ZP0yi4gHZzrmZFXZ5qxUKjGbzciyzNNPP+3SoGq1VTNGzyzdjzmjfC+40lgKsjFlXsC3ZS/8WvXEmHIcU246lrwMFFdzpSPGvgWWa6I8lxc9S8igR/FpHl9hHrUrRNVb9SGyFwReo2w5sav2Ogq1DquxyGk/a0kRksan1Hm89xrs0Zw0Pmgb3YakUKL0C6HBkBkYzu13mmffQcNISUnhhRdeICgoCKvVyuDBg+nTpw+dOnWiefPmhIWFVdngupqjKySVBhQq/NrfQeHBREy5aWAxcemrp7j4/hgKj25G6ROI0j/E8UFSoND5o9D4UGwwcHDFpxw4cKDSa4mqt+pFGF2B1yhdTly6vU7Yvf92vAKrw5piyrgmc2gtMWC+kub0mu9NEXBP5lQOyXGA05wiIiJ44403SE9PZ+fOnU75td6aozvU4bEAqILCafr8Kpr+YwU+cd0IGfAwMX9f7tQs007UE1/iE9sZSYK+cQ1oGKBj1KhRdOrUiQ8++ID09PKtiepa95CbEWF0BV7DVk5s+yflrr2Ob8uelGRdQJ+0E9lcQt7OJajDYx2dfav6GuyNORkvncCUnYIsW7EU55Oz/jO0MR1Q6Pxczkmr1dKrV69qmaM7HV9ddHtUgWHk/b4M2WrBkHIMw8XD+DTvWun5dSol/xzZiTfeeINz587x4YcfcujQIVq1asXdd9/N8uXLMRqNTt1DLi98hgvv3UPWqtlO59If307q549z8YP7ufT5DIpO/k6xycpbq5M4lJLrtd/kZkZUpAm8hr2cWJ+dRur8qaBUO6Vg2dvrXMvTzUDTyJana19xd1VOXN1zkiQFV7Z+jbUoF4XGF11sZ0IGTrW9olfDnNzNsaIy7OC+4ynJvED2mo8xZZ5HFRhOcL+JjmwHd9jyqNu4rKwrLCzkxx9/ZOHChRw6dIiBryxhT1oJ+qTfQJIcaWv2XGFzQRap8x8l/L6X0DWPt1XD/fQOTWZ8gco/WFS9laKiijSxkCbwGnYR8PXHrRW21/GJ7UyTaQvKjUsSVepp5s05+bXt73JcwqbFUJ0r9dfmmF6hjq8mrCmNJr3v0Tk9KWX29/dn0qRJTJo0iSOnLzB64TFH9xAon7ZmKchGofNzFLn4tkhAUmsx515G6Rfs9XS/mxURXhB4FW+WE3uLG5mTxWRg8QsT6dq1KzNmzODLL790pIV5kxuZY2l0KgValYJhbSNYOq2Hx9oRO1LNlcpYaiJboA6NpujUH8hWC0Unf0dSqVGH2VIAvdk95GZGeLoCr+KtcuK6Mqc4w2lWp55gf4rM/v37WbBgAS1atKBfv351Yo4qBbRvHESov9Yj9TN3eNI9RFIo8Wt/B1k/v4dsLkFSqml4z/MoNLbyaVH15hnC6Aq8jjfKiWt1Ttg83ASfHN57fgrNP3uDoiJb+tjdd9/tdYN7XXP08u/mSdpa8fkD5G7+iohx/w9NZBwlaafJXD4T1QOvo4lofvU8ouqtMkR4QVAtTOgRy9JpPRjWNgKtSoFO5fxP7Xpfg2tiToNbh5H+7fN898YMOnXqxODBg1EoFERHR/Pnn38yc+ZMLKWKEGpjjt7+3TxJWytJP4s2up0tp1lSoG3UEk3jVhSfP1DqPN5L97tZEZ6uoNq43nLimp5TVoGeNT/9SIc20Sx8dQah/lp+ei6XLIuF9PR0fv31Vxo0aMCWLVvQarWMGzeObdu2sXjxYiIiXOsceHuO1f27edI9RNvoNvJ3Lack/SyaiOaUpJ3BmHyUgK53Ad5P97tZESljglueZcuWMXbsWFQqFevWrWPAgAH06tWL33//HYDbbruN1atX06KFbZHPbDbz+uuv8+WXX7J48WIGDixfmFDf8DRtLX/vLxTs/hlLUS5Kn0ACuo4g8PbRQPWn1tUnRMqYQFABK1aswGq1UlJSwr333suxY8fo3Lkzu3btol+/flgsFpo1uybSo1KpmDlzJn379mXcuHE8/vjjvPTSSyiVN559UFt4mrYWGH83gfF3lxuvjnS/mxUR0xXc0siyzLp16xzf9Xo9Tz31FH//+9/5448/2LhxIyqVivfee6/csUOHDmXv3r1s3ryZoUOHkpaWVpNT9zp1Md3vZkQYXcEtTWFhIWq1mvbt26PT6fjqq6/46KOPiIuLIyEhAaVSyddff80HH3yAq1Bb48aN2bBhA7179yY+Pp5NmzbVwl14B3vamo+6amahOtP9bkaE0RXc0gQEBJCens7hw4cJDg6mb9++NGnSxGmf6Oho5syZw/jx49Hr9eXOoVKpeOONN1i0aBETJkzgtddeq7bshupmQo9YXryrDT5qZaXFEpIEPmql2zJjgWvEQppAcJURI0bw2GOPcc8997jcPnnyZHQ6HZ9++qnbc1y+fJnx423x0G+//ZZGjRq53K+ui4MfSsmtsHuI2WLBeG4fX/3jIQZ3bVl7E62jiBbsAoEHvPzyywDMnDnT5fb8/Hw6d+7M7NmzGTVqlNvzWCwWZs6cyWeffcbXX3/N4MGDHdsqFge3tUKqS+Lg7tLWpPN/Mn3yOPz9/fnll18YMGBAbU+1TiGMrkDgAX8cPMbyvcmY/MLdep+//fYbo0ePZv/+/W69WDubNm1i4sSJPPLII7z66qss2Z1cp6r0boTvvvuOqVOnYjQa8fHx4bXXXuMf//gHUmUxiVsEYXQFggqoqvf56quvsmvXLtasWYNCUfGySFpaGhMmTMCv83BO+ra7Dj2KuhkvXbhwIX/961/R6/Wo1WpUKhUXLlwgLCystqdWJxB5ugKBG2ydEtx7n/ZYZuKxdLadzOLFu1rz8ssv07dvX+bMmcPTTz9d4fkjIyN578tlPDh/B5dWzsZw/gBWQyGq4EhC+k92yCTqj28nd8e3WAqyUQU0JLj/JGjZk7dWJ9ExKrjOZQaYTCaMRiMajYYOHTqwfv16QkJCanta9QKRvSC4Zbne1jTf70lh8eLFvPnmmxw+fLjS68zfehajyYQqoCGR494h+tmlBPebSObKdzHnpmMuyCLrl/dpcMejRD+7jOCBU8n6eRYWfS4Gs4V5W0576Y69R//+/ZkzZw4nTpzg7NmzGI3G2p5SvUF4uoJbkoPJucz8+RCpv37i0vs0piaRu30xJWmnQVKgi+lAyJDpFPs34K3VSSyd1oP33nuPcePGsXv3bnQ6ncvrZBUa2XoyE0mtc6ry8m3RHVVQBMa006gCG9Y7cfCWLVvSsqUta2HcuHHMnj2bd999t5ZnVT8Qnq7glmTultMYStx7n1ZDIf6dh9Nkxpc0eeJLJI0P2b9+CODwPidPnkzr1q154YUX3F5n+V7Xot4W/RVMOalowmLqvTj4c889x+eff05ubm5tT6VeIIyu4JajrPepCo5AkhRO3qdPXDf8WvdBofVFodYRED8SY+pxwBZq2Hwikxx9CZ9++inLly8nMTHR5bVciYPLFjNZP8/Cv8Mg1KHRTuLgF9+7l6yfZ9Fg2F8rFAe3WCxYrZ4vylUnsbGxjBgxgnnz5tX2VOoFwugKbjk88T7LYkw+irrhtXG799mgQQMWLVrEww8/TFZWVrnjyoqDy7KVrFXvg1JFgyGPA87i4DH//ImI8f+PnDUfU5J+ttR5TKSnp/P1118zcuRIAgMDeeWVV67n9r1OVqGRFnc/zrwDRUz+chfPLN3Pgq1nyC4UcV5XiJiu4JbDE++zNCUZ58jbuYSw+15yjJX2Pu+44w7GjRvHo48+yooVK5xyVUuLg8uyTPbqj7Hocwm//zUkpW1baXFwwEkc3N6RYde2TUROeR61Wo3JZEKr1dK0aVMv/ipVp2yqnSquJ1tPZQOgU6Uxe8PJOlXoUVcQnq7glsMT79OO6colMpa9Ssjgaeii25c5z7XWNG+++SYXLlzgv//9r2Msq9BIbpEJxVUbnLNuLqbsZMLHvIJCfW1RTNvoNowpxxyerV0cXBMeC9hyhccM7kWTJk2w59WXlJTw1FNP0a1bNx599FHmzJnD9u3bycvLu7Efx0MW7zrPQ5/vYv3xdIxma7mHmOHqWOKxdB76fBeLd52vkXnVB4SnK7jl8MT7BDDnZZC+5CWCej+Ef/s7XJznWmsarVbLd999x4gRI0gYPoZ5W844PECrbDtX4YG1oFSTMmei47gGw5/Ev91AgvqMJXPF/3OIgwf1vB+fZl0BsFitvPXo3ViK8hyavbIsc/DgQXJycjhw4AAHDhzg22+/5fDhw0RGRtKpUyc6d+7s+ERHR3utWuxaql3lMeXSqXZAnSz0qGmE0RXccpRuTWP3PiMeetPJ+zQXZJG+5N8ExI8koMtd5c7hqjVNmzZteG3xRsZ+/ke5YgtVUDhNn1/ldk4ViYNrsk/TMEBHRnG+Q70sMDCQli1bIkkSPXv2dOxvsVg4deoUBw8e5MCBA8yfP58DBw5gNBqdDHFs6w4c0ftzKlNfJcGdg8m5vLU6qUqVdQDFJmudLfSoaUQZsOCWw96aRp+dRur8qaBUIymuiXc3GP4k5iuXydvxHZLaOf825u/LAdetaariAXqKj1qJcsscjm5f7TQeGBhIs2bNmDFjBuPHj8ff37/C86Snp3Pw4EHW7Ulic5qaHF0jrFar04NGrQBJkhjYOtxtHHbaN3tIPJxC1rp5LvObC49uJmft3GsHyDKy2UjklA/RNWrBsLYRLJjgsjr2pkKUAQsEpbjWmsZaofcZ3Gecy3FXrWnsHmDyj//BcP4gVpMBpV8IgT3uI6DTMLfFFir/Bm6vbxcHH/Lkl8TFxTm0fCVJ4vLly+zYsYP58+fzwgsvMHbsWGbMmEH79u1dnisiIoKMwJasNlgx+FtABkWZJhG2Z4XM2sOXSTycQndVMg/GN6Fz5840a9aMbH0JW09mYrVYHPnNyqAwis/sIXPluzSe+gn+7Qbi3+5az7jCQxvI++17NBFxjlS7ulboUdOIhTTBLYm3W9PM3XIag9lCYI/7aTLjS2L+73+Ej3mZ3G3fYEw7XWGxRVnKioNHRETw0Ucf4evri0ajoVGjRowaNYqYmBhWrFjBoUOHaNiwIcOGDaNv375899135cpyq1LyLCkUyAo1e6xNeX/lHwwYMIDg4GD+tWAFAAqN+/zmshQe2Yhf+zsc8WSTxcrflx+8pdPJhNEV3JJ4szWNvdhClkET1hRJZV9gk5CQMF+5XGGxhR2tUkKrUjCsbQRLp/VwWnSaOnUqHTt25O677+b8+fOMGDGCPn368PzzzxMcHMzrr7/O+fPneeaZZ/jyyy+Jjo7mX//6F2fPnnWUPCevnE3KvIe5+MH9XPryKYrPXAsdWk0GstfNI/mjcVyc/QBpi/+FBSU5TQey6rdDnD17Fp9GLcplKYD7/GZzXgbG5KP4lVqEtMqw7WQmvd7dxPTFeziYnFul3/9mQBhdwS2Lt1rTlC22yF43j4uz7uPS54+j9G/g0FQoTdliC4UEvVo05Ld/3cGCCd3KLTZJksTmzZtZsmQJarWaZ555hsOHD5Oamkrbtm353//+h0ql4r777mPDhg3s2LEDk8nE7bffzju/7K+w5BkgZ+0nWA0FNH5sPtFPLyFk8GPAtZLn0NBQiszlXeSK8psLj2xEG9UWdXCk07hV5pZOJxNGV3BLM6FHLEun9WBY2wi0KgU6lfP/EjqVwq33aadssUXosCeI/r9lRIx/F5+WPZGUaqf97cUWIQMfdoxZZQj20VQY69TpdKjV187VqFEjvvnmGxYvXswbb7zB0KFDSUpKAmyCNB988AHHziaz77KhwpJnU3YyRaf+IHT4Uyh9g5AUSrSRtvBJ6Ths6VQ72zb3+c0A+iOb8O8wyO39lE4nu5UMr1hIE9zydIwKZsGEbm5b04zpWnEaVdliCwBJoUQX3Q790c0U7F9NYLe/AJ4XW1SFfv36sW/fPubOnUvfvn155JFHeOmll/D39+eHfakujykdEjBeOokqKJzc7d+iP7oZpX8IQb3H4de6t+1esJU8l061c5ffLJtNZCfOo/j0bqxFueT9+RNKvxB84rpRknWR7FUfYL5yGQBNZAtChkyHhjG3VDqZ8HQFgquE+muZ3i+O2Q925ovJCcx+sDPT+8VVutJe1gN0wmp1GJmqFFtUFXvI4dChQ04hB09Kni0F2ZgyL6DQ+hL110U0GPI42b/OxpSVDFwreR4TH+U4h7vqOtlqy2zQNe2Ib7uBhAyY7AhjqPwbEHbPC0Q98z1RT3+Hz223k7XyP1evUTd1g6sDYXQFghvE5gEqsOhz0R/birWkGNlqofjsXvTHt6KL7XxdxRbXQ9mQQ+KWHU7bXYUEJJUGFCqCej+EpFSji+mALqYDxef2OY7LN5gcqXaWfFt1XUn6WVLmTOTi+2O4+P4YCo9uRqHREdTzforP7sW/wyCnMIZC5381vGELoEuSwvFAKh3GuNkR4QWB4AYZEx/F7A0nQZIo2L+G7HXzQLaiCgonZNBj+N52O7k7vsOcm0beju/I2/Gd41h7sYUMjOka5eYKVccecrj7reUkXbVj7kIC6qsaD06UWVm0e+FPDmjB9lNZFeY3SyoNMc8uBVxnNlyc/SBySTHIMkGlhN3tYYzp/eKu447rD8LoCgQ3SOlii8jx77jcJ7jPuCoVW3gDtVrNPQO788H6E5RYZLclz7ro9qgCw8j7fRlBPR/AeOkEhouHHQt9pb1we6qdJ5V37jIbYp5dirXEgP7IRpSB4Y5xV7rBNyMivCAQeAFvF1t4izHxUUiS5BDccRUSkJQqwu57ieIze0ie/QA5a+bQcMSzDkNZ1gv3JNWusswGhUaHf5c7yV71ARZ9rmP8ehcT6xPC0xUIvEBVPMDSaJVSuWILb+JpybMmrCmNJr1fbtzuhcvAgq1nSErLdwjkPNAtiuScInaeycZksWK9msZbkXKbE1d1GSwF2Sj9goEbW0ysLwijKxB4CXsOb0Ut3e1IEsjmEi6t/Zyv/zTT9OWX6dOnj9fkF0tjj8MWmyxVPlajUJBbZKL3u5sAnDIhdCoFMtArLpS8YhMHknOxyrgNYxSf24/SJxB1eCyyyUjutm9Q6PxRN4x2nM8bi4l1HaEyJhB4mUMpuczbcprNJzKRsMUq7dgN1cBWYUQXJvHqX6dgsVjw8/OjcePGrFixgnbt2nl9TtejgKa6qjpmtsqVPkC0SgUmixVjboZb5TZJqSZ322IsBVlIKg3axi0J7j8ZTbitAacr5bb6ilAZEwhqEE+LLc6dC+VNtRqLxYLBYCA/Px8/P79qmVOVvHBAqZCQJDBZKlHHwZbuZTBbUUiV6wb7te7j+prVtJhYFxFGVyCoJuzFFu6IjY3Fx8cHq9WK1Wrl/fffJzY2ttrmM6FHLB2jgt164VqlRInZjDX9NKomrT0yuKWxVm13J6pzMbGuIYyuQFBLSJLEX/7yF8xmM9OmTWPMmDF06tTJrSauN/DEC5/xlS+7L9mSe7N+meVSHxhsXYxzEhdgyc9E07ilLeMhKBxJqpoBdqXcdjMjjK5AUIssXLjQ8ffs2bO5++67+eOPPwgPD3d/kBdw54VnFRo5mGHCbjMDe9xP6J1PI6nUmLKTSfvuBTQRcagCw8hc8Tahd/4N3xbdyd22mMyV79Jo0vsoJQmNUsJosVYaC5YsZiZ1CL2leqeJPF2BoI4wfvx4xo8fz+jRo8uJkNcUZWUq3ekDF538HU3DGPxa90FSaQjqMw5TxjlM2cmoFBIPJUR7pNyW8f2/efHBfowePZqTJ0/W0F3WLsLTFQjqEG+88Qb3338/06ZNY+HChdWSQlYRrgRystfNQ394I7LZiCYiDp+4buRu/Rr11awDsBU7qIIjKcm8iCE0mrxis0eLiYfnB/PbRZmVK1eyZs0a7r//fhYtWlTj912TCKMrENQhFAoFX3/9Nf369eO9997jn//8Z41e35VMZeiwJ2gwZDrG1CQMFw8jKdW2GK9vkNN+Cq2fTVOBa5VllS0m9u7dm99//x2r1WbotVrtTW1wQYQXBII6h5+fHytXruTjjz/mp59+qtFru5OptOsDWwqyKNi/GoVah9VY5LSPtaQISeNz9Ty2kITVauXUqVMsXbqU/fv3lztvly5dUKlUaDQamjRpwty5c8vtc7MhjK5AUAeJiopixYoVPPbYYxw4cKDGrmuXqXTLVX1gdVhTTBnnrg2XGDBfSUMTFoNOpeDYb4nExMTg6+tLly5dmDBhAsuWLSt3uj59+jBo0CCOHTtGmzZtePHFF6vjtuoUwugKBHWUhIQE5s6dy6hRo0hLS6uRa5YWKq9IH9i3ZU9Ksi6gT9qJbC4hb+cS1OGxqEOjkYE2PgWkpKRgNBrR6/UoFAqCg4MpLCx0ul50dDRr1qwhLi6Or776iqVLl7JmzZobvo+sQiMLtp7hmaX7mbpoN88s3c+CrWfqhF6vKAMWCOo4r7/+OmvWrGHLli3odLpqv960b/aw/ng6Zn0emSv+HyUZ5xz6wAHxdxPQeThQOk83A02jq3m6IREMaxvBggnd+OWXX3jooYcoKipCpVLRu3dv9u7dS7du3Rg6dChDhw6lS5cuKBTXfL/t27dz//33s3fvXpo0aVLluR9MzmXultNsPZkJuNaKGNAqjCf6t6BTdPAN/U4VUVEZsDC6AkEdR5Zlxo4di1KpZPHixdW+0HQwOZeHPt91XQI5PmolS6f1cBQ67Nq1i8GDB5OQkMDmzZvR6/Vs3bqVxMREEhMTyczMZPDgwQ4j3KRJE2bOnMnGjRvZuHEjSqVNvyGr0MjyvSlOKmetIwO5P/5a/zqbvoRnYkM6lZIX72pdbfnBwugKBPWc4uJi+vfvz6hRo2ok7nk9Ajm2yrLyLerPnTuHyWSiZcuW5Y5JTk5m/fr1JCYmsmHDBiIjIxk8eDBbtmxhxIgRPPD4PzzyXJuF+rHo9/Nema83EEZXILgJuHTpErfffjsffvgh9913X7Vfr6Y9R4vFwv79+0lMTGTVqlUcNYbQYNBjyEoVNhke71PWM/cWwugKBDcJ+/btY9iwYaxbt46uXbtW+/U8lal8YkALrxquxbvO8+avx52uVxH21u+G8wewGgpRBUcS0n8yPnE2u1dwcB35vy/Hor+CNqotoXc9jSogFEnCEYP2JsLoCgQ3ET/++CNPP/00f/zxB40bN66Ra1ZWWeZNriembC0xkP/HD/h3GIwyKIziM3vI+vk9Gk/9BHNeOpkr3yVi7NuoGzQmZ8NnmLKSHf3sqkPHV+jpCgQ3EaNHjyYpKYlRo0axdetWfH19Ac8Wm66XyirLvMncLafJ2PUThYc3UpJ5Hr82/Wk48lnHdndea3CpzsKlW7+XXErCt3UfNGFNAQjq9RCpcydjunIZdUijGu9CLIyuQFAPeeGFFzh27BhPPvkkz7wxu4LFpjRmbzhZI2lS3iCr0MjWk5ko/UMJ6vUgxef2IZtKHNsNFw6Ru/VrJ6816+f3ynVhLt36veRSEs5BadvfpswLqEMa1XgXYlEcIRDUQyRJ4r///S9hvUbz0Oe7WH88HaPZWk6sxnB1LPFYOg99vovFu87XzoQ9xK5y5tuqF74te6LwCXTaXnxmt8NrlZRqgno9hDH5CKYrlx37lG39rmseT1HSDkoyzmE1Gcnb+T0gIZuvFUrUZBdi4ekKBPWU5QfS+CVZ5VHsU5ah2GThrdXHAeqsfq0rlbNyVOC1umr97hPbmeA+48hc8TZWYzGBCX9B0vqgDAh1nKUmuxALoysQ1EMOJufy1uokkn/8j8vODrLFRNbP72G8fBpLfgYRY99G17QjxSYrb61OomNUcJ3s1OBK5aw0uubxZK38DwFd7kQV0tjJa62o9XtA/EgC4kcCYMpJJe+3pajDYm3nrOEuxMLoCgT1kLlbTmMwW9x2dtCENUUb1Y6AbqPI+sk53mkwW5i35bTX06S8gTuVMzsVea3uWr/L5hJMVy6hbtgUS34m2WvmENDtLyh1/rbtwJiuUW6u6H2E0RUI6hn2xSZZxrEib+NaZwdtZAsCE0bZhhXOSzeyDJtPZJJdaKxz3XdtKmdpFYYYXHmtktaPwgNrQakmZc5Ex74Nhj+Jb1wCWT/Pwpx7GUnjg3+HwQT3nQDUThdiYXQFgnpG2ZY6rjo7VEZNp0l5ypj4KGZvOIlstYD9I1uRzSWgUILV4tJr1YY3q7D1e+NHPnE5XhtdiIXRFQjqGWUXm1x1dqiMmk6T8pSG/lr6twxj2WcfkLdjiWNcf3QzQb3HEpgwyq3XWlVqqwuxMLoCQT3D1WKTvbOD/uhmCvavJrDbXzw4T82lSVWFJwe0YPupSQT3Ge9yuzuv1Y4EVNQBviZUxipC5OkKBPWMChebrnZ28Ow8NZcmVRU6RQfz4l2t8VFXzTz5qBXM6NecYe0q70K8dFqPWkubE56uQFDPsC82FeXlYLhwEJ8W3ZFUGgznD6A/vpWGf7E1s5TNJuw+n2w12+KiSjWSJNV4mlRVsRvE61U5q0mtiKoiBG8EgnpGVqGR3u9uoij/SoWdHVLmTcWSn+F0bJPHv0AVHFEtIi/VQW2pnN0oQmVMILjJsLfUqcgDdEd1yRlWJ3XZc3WFUBkTCG4ybItNWdfVUkcpW3mgfbD3J1WN1KTKWXUjFtIEgnrI9S42aZUS6WvnMyS+FTExMcyYMYP169dX0ywFrhBGVyCop0zoEcuLd7XBR62ksl6VkmRrTfPyyLZ08MnFarWSnJzMggULeOCBBzCZ6mb62M2IMLoCQT1mQo9Ylk7rwbC2nqdJ/fvf/8bf36Y7oFQqWblyJWp13UwfuxkRC2kCwU2Cp4tNFouFiIgICgsL6dOnD/n5+axatYrw8PBanP3NhVhIEwhuATxdbFIqlbz33nsoFAomTZrEa6+9Rq9evVizZg233XZbDcz01kYYXYHgFuThhx92/P36668TFRVFv379+Omnn7j99ttrcWY3PyKmKxAIeOyxx/jvf//LyJEj+fnnn2t7Ojc1wugKBAIARowYwerVq3n88cdZsGBBbU/npkWEFwQCgYOEhAS2b9/OnXfeSXJyMm+++SZSZflogiohPF2BQOBEXFwcO3fuZOPGjUyZMoWSkpLKDxJ4jDC6AoGgHGFhYWzatInc3FxGjhxJfn5+bU/ppkEYXYFA4BJfX19++OEH4uLi6N+/P5cuXartKd0UCKMrEAjcolKpmDdvHg888AC9evXi+PHjtT2leo9YSBMIBBUiSRIvvPACUVFRDBgwgB9++IE+ffqU2y+r0MjyvSkkpeWTbzATqFPROjKQ++PrpvxibSGMrkAg8IiJEycSGRnJ6NGjmTdvHmPGjAHgYHIuc7ecZuvJTACnppk6VRqzN5xkQKswnujfgk7RwbUx9TqFMLoCgcBjhgwZQmJiIiNHjiQtLY3gbiMrbKlj7/SQeCydbSezaq0ZZF1CxHQFAkGV6Ny5Mzt37mR7Gry1+jjFpop7mAHIMhSbLLy1+jiLd52vkXnWVYSnKxAIqkyuIohDihYk//guhvMHsZoMKP1CCOxxHwGdhlGSdZHsVR84OhNrIlsQMmQ6NIzhrdVJdIwKrlM9zWoSYXQFAkGVmbvlNAazhcAe9xN659NIKjWm7GTSvnsBTUQc6uBIwu55AWVQOMhWCvb9StbK/9D4kU8wmC3M23K6XvVo8yYivCAQCKpEVqGRrSczkWXQhDVFUtkF0CUkJMxXLqPQ+aMKjnCUEEuSwuH1yjJsPpFJdqGxlu6gdhGerkAgqBLL96Y4fc9eNw/94Y3IZiOaiDh84q55sBdnP4hcUgyyTFDf8Y5xCVi+L+WmaTZZFYTRFQgEVSIpLd8pLSx02BM0GDIdY2oShouHkZTXWv/EPLsUa4kB/ZGNKAOvdaYwmK0kXS6o0XnXFUR4QSAQVIl8g7ncmKRQootuh6Ugi4L9q522KTQ6/LvcSfaqD7Doc0ud59ZshimMrkAgqBKBugpekK1WR+zWCVlGNhuxFGSXOs+t2QxTGF2BQFAlWkcGolUpsOhz0R/birWkGNlqofjsXvTHt6KL7Uzxuf2UpJ1BtlqwGou4svG/KHT+qBtGA7Yuxa0bBdTyndQOIqYrEAiqxJj4KGZvOAmSRMH+NWSvmweyFVVQOCGDHsP3ttvRJ+0gZ/2nWAqykFQatI1bEv7A60gqDQAyMKZrVO3eSC0hjK5AIKgSDf219G8ZxvrjViLHv+NyH7/WffBrXV4UB0CSYGCrsFtWBEeEFwQCQZV5ckALdCrldR2rUyl5YkALL8+o/iCMrkAgqDKdooN58a7W+KirZkJ81ApevKv1LVsCDCK8IBAIrhO7WlhFKmN2JMnm4QqVMWF0BQLBDTChRywdo4KZt+U0m09kInFNzhFsWQoythjuEwNa3NIerh1hdAUCwQ3RMSqYBRO6kV1oZPm+FJIuF5BvMBGoU9O6UQBjuorOEaURRlcgEHiFUH/tLamlUFXEQppAIBDUIMLoCgQCQQ0ijK5AIBDUIMLoCgQCQQ0ijK5AIBDUIMLoCgQCQQ0ijK5AIBDUIMLoCgQCQQ0iyRUUTEuSlAlcqLnpCAQCwU1BU1mWw1xtqNDoCgQCgcC7iPCCQCAQ1CDC6AoEAkENIoyuQCAQ1CDC6AoEAkENIoyuQCAQ1CD/H0hxvGwrTRa9AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2563,16 +5657,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "OutMultiEdgeDataView([(1, 7), (2, 4), (3, 4), (4, 1), (5, 4), (6, 5), (8, 7), (9, 8), (10, 9), (11, 10), (12, 9), (13, 14), (14, 8), (15, 14), (16, 17), (17, 14), (18, 17), (19, 23), (20, 21), (21, 14), (22, 25), (23, 18), (24, 25), (25, 21), (26, 28), (27, 28), (28, 25), (29, 30), (30, 28), (31, 32), (32, 25), (33, 31)])"
+       "OutMultiEdgeDataView([(1, 11), (2, 1), (3, 5), (4, 5), (5, 1), (6, 5), (7, 1), (8, 11), (9, 11), (10, 11), (12, 13), (13, 29), (14, 17), (15, 13), (16, 13), (17, 13), (18, 17), (19, 17), (20, 17), (21, 20), (22, 21), (23, 22), (24, 25), (25, 17), (26, 25), (27, 26), (28, 27), (29, 11), (30, 31), (31, 29), (32, 36), (33, 31), (34, 36), (35, 36), (36, 33), (37, 29), (38, 29), (39, 42), (40, 42), (41, 40), (42, 11), (43, 44), (44, 42), (45, 44), (46, 45), (47, 48), (48, 45), (49, 48), (50, 48), (51, 52), (52, 48), (53, 52), (54, 42), (55, 42), (56, 55), (57, 59), (58, 59), (59, 55), (60, 59), (61, 60), (62, 55), (63, 62), (64, 63), (65, 64), (66, 65), (67, 66), (68, 69), (69, 63), (70, 69), (71, 72), (72, 70), (73, 72), (74, 76), (75, 76), (76, 72), (77, 76), (78, 77), (79, 80), (80, 76), (81, 83), (82, 83), (83, 80), (84, 85), (85, 80), (86, 87), (87, 80), (88, 87), (89, 42)])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2583,16 +5677,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33))"
+       "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89))"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2603,48 +5697,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{1: 'Najib',\n",
-       " 2: 'yang',\n",
-       " 3: 'juga',\n",
-       " 4: 'Ahli',\n",
-       " 5: 'Parlimen',\n",
-       " 6: 'Pekan',\n",
-       " 7: 'memuji',\n",
-       " 8: 'sikap',\n",
-       " 9: 'Ahli',\n",
-       " 10: 'Parlimen',\n",
-       " 11: 'Langkawi',\n",
-       " 12: 'itu',\n",
-       " 13: 'yang',\n",
-       " 14: 'mengaku',\n",
-       " 15: 'bersalah',\n",
-       " 16: 'selepas',\n",
-       " 17: 'melanggar',\n",
-       " 18: 'SOP',\n",
-       " 19: 'kerana',\n",
-       " 20: 'tidak',\n",
-       " 21: 'mengambil',\n",
-       " 22: 'suhu',\n",
-       " 23: 'badan',\n",
-       " 24: 'ketika',\n",
-       " 25: 'masuk',\n",
-       " 26: 'ke',\n",
-       " 27: 'sebuah',\n",
-       " 28: 'surau',\n",
-       " 29: 'di',\n",
-       " 30: 'Langkawi',\n",
-       " 31: 'pada',\n",
-       " 32: 'Sabtu',\n",
-       " 33: 'lalu'}"
+       "{1: 'KUALA',\n",
+       " 2: 'LUMPUR',\n",
+       " 3: ':',\n",
+       " 4: 'Dalam',\n",
+       " 5: 'hal',\n",
+       " 6: 'politik',\n",
+       " 7: ',',\n",
+       " 8: 'jarang',\n",
+       " 9: 'sekali',\n",
+       " 10: 'untuk',\n",
+       " 11: 'melihat',\n",
+       " 12: 'dua',\n",
+       " 13: 'figura',\n",
+       " 14: 'ini',\n",
+       " 15: '-',\n",
+       " 16: 'bekas',\n",
+       " 17: 'Perdana',\n",
+       " 18: 'Menteri',\n",
+       " 19: ',',\n",
+       " 20: 'Datuk',\n",
+       " 21: 'Seri',\n",
+       " 22: 'Najib',\n",
+       " 23: 'Razak',\n",
+       " 24: 'dan',\n",
+       " 25: 'Tun',\n",
+       " 26: 'Dr',\n",
+       " 27: 'Mahathir',\n",
+       " 28: 'Mohamad',\n",
+       " 29: 'mempunyai',\n",
+       " 30: \"'\",\n",
+       " 31: 'pandangan',\n",
+       " 32: 'yang',\n",
+       " 33: 'sama',\n",
+       " 34: \"'\",\n",
+       " 35: 'atau',\n",
+       " 36: 'sekapal',\n",
+       " 37: '.',\n",
+       " 38: 'Namun',\n",
+       " 39: ',',\n",
+       " 40: 'situasi',\n",
+       " 41: 'itu',\n",
+       " 42: 'berbeza',\n",
+       " 43: 'apabila',\n",
+       " 44: 'melibatkan',\n",
+       " 45: 'isu',\n",
+       " 46: 'ketidakpatuhan',\n",
+       " 47: 'terhadap',\n",
+       " 48: 'prosedur',\n",
+       " 49: 'operasi',\n",
+       " 50: 'standard',\n",
+       " 51: '(',\n",
+       " 52: 'SOP',\n",
+       " 53: ')',\n",
+       " 54: '.',\n",
+       " 55: 'Najib',\n",
+       " 56: ',',\n",
+       " 57: 'yang',\n",
+       " 58: 'juga',\n",
+       " 59: 'Ahli',\n",
+       " 60: 'Parlimen',\n",
+       " 61: 'Pekan',\n",
+       " 62: 'memuji',\n",
+       " 63: 'sikap',\n",
+       " 64: 'Ahli',\n",
+       " 65: 'Parlimen',\n",
+       " 66: 'Langkawi',\n",
+       " 67: 'itu',\n",
+       " 68: 'yang',\n",
+       " 69: 'mengaku',\n",
+       " 70: 'bersalah',\n",
+       " 71: 'selepas',\n",
+       " 72: 'melanggar',\n",
+       " 73: 'SOP',\n",
+       " 74: 'kerana',\n",
+       " 75: 'tidak',\n",
+       " 76: 'mengambil',\n",
+       " 77: 'suhu',\n",
+       " 78: 'badan',\n",
+       " 79: 'ketika',\n",
+       " 80: 'masuk',\n",
+       " 81: 'ke',\n",
+       " 82: 'sebuah',\n",
+       " 83: 'surau',\n",
+       " 84: 'di',\n",
+       " 85: 'Langkawi',\n",
+       " 86: 'pada',\n",
+       " 87: 'Sabtu',\n",
+       " 88: 'lalu',\n",
+       " 89: '.'}"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2656,12 +5806,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 1 Axes>"
       ]
@@ -2702,7 +5852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2711,7 +5861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2721,16 +5871,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(33, 2)"
+       "(89, 2)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2745,12 +5895,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -2780,13 +5930,6 @@
     "        textcoords = 'offset points',\n",
     "    )"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/load-knowledge-graph-from-dependency.ipynb b/docs/load-knowledge-graph-from-dependency.ipynb
index 6a08cf06..7621253d 100644
--- a/docs/load-knowledge-graph-from-dependency.ipynb
+++ b/docs/load-knowledge-graph-from-dependency.ipynb
@@ -44,8 +44,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.11 s, sys: 927 ms, total: 6.04 s\n",
-      "Wall time: 6.12 s\n"
+      "CPU times: user 5.14 s, sys: 883 ms, total: 6.03 s\n",
+      "Wall time: 6.61 s\n"
      ]
     }
    ],
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "infrared-discipline",
    "metadata": {},
    "outputs": [
@@ -82,8 +82,8 @@
     }
    ],
    "source": [
-    "quantized_model = malaya.dependency.transformer(model = 'xlnet', quantized = True)\n",
-    "alxlnet = malaya.dependency.transformer(model = 'alxlnet')"
+    "quantized_model = malaya.dependency.transformer(version = 'v1', model = 'xlnet', quantized = True)\n",
+    "alxlnet = malaya.dependency.transformer(version = 'v1', model = 'alxlnet')"
    ]
   },
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"422.82,-481.59 420.87,-471.18 415.9,-480.53 422.82,-481.59\"/>\n",
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        "<title>17</title>\n",
-       "<text text-anchor=\"middle\" x=\"692\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
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        "<!-- 14&#45;&gt;17 -->\n",
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-       "<text text-anchor=\"middle\" x=\"640\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"504.71,-479.67 511.04,-471.18 500.79,-473.87 504.71,-479.67\"/>\n",
+       "<text text-anchor=\"middle\" x=\"500.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
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        "<!-- 10&#45;&gt;11 -->\n",
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-       "<text text-anchor=\"middle\" x=\"317\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
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+       "<text text-anchor=\"middle\" x=\"236.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
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        "<!-- 16 -->\n",
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        "<!-- 17&#45;&gt;16 -->\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M517.68,-434.8C503.94,-422.24 485.13,-405.05 469.89,-391.12\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"508.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
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        "<title>18</title>\n",
-       "<text text-anchor=\"middle\" x=\"750\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
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        "<!-- 17&#45;&gt;18 -->\n",
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        "<title>17&#45;&gt;18</title>\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M536.5,-434.8C536.5,-423.16 536.5,-407.55 536.5,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"540,-394.18 536.5,-384.18 533,-394.18 540,-394.18\"/>\n",
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        "<title>21</title>\n",
-       "<text text-anchor=\"middle\" x=\"679\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
+       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
        "</g>\n",
-       "<!-- 18&#45;&gt;21 -->\n",
+       "<!-- 17&#45;&gt;21 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>18&#45;&gt;21</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M735.63,-521.8C725.33,-509.47 711.31,-492.68 699.79,-478.89\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"727\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "<title>17&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M557.75,-434.8C573.4,-422.13 594.89,-404.73 612.18,-390.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"614.71,-393.19 620.28,-384.18 610.31,-387.75 614.71,-393.19\"/>\n",
+       "<text text-anchor=\"middle\" x=\"603.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
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        "<title>23</title>\n",
-       "<text text-anchor=\"middle\" x=\"787\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;23 -->\n",
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        "<title>18&#45;&gt;23</title>\n",
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-       "<text text-anchor=\"middle\" x=\"800\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M536.5,-347.8C536.5,-336.16 536.5,-320.55 536.5,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"540,-307.18 536.5,-297.18 533,-307.18 540,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"565.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
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        "<title>20</title>\n",
-       "<text text-anchor=\"middle\" x=\"645\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
+       "<text text-anchor=\"middle\" x=\"634.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
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        "<!-- 21&#45;&gt;20 -->\n",
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        "<title>21&#45;&gt;20</title>\n",
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-       "<text text-anchor=\"middle\" x=\"680.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M640.08,-347.8C639.13,-336.16 637.84,-320.55 636.74,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"640.22,-306.85 635.91,-297.18 633.25,-307.43 640.22,-306.85\"/>\n",
+       "<text text-anchor=\"middle\" x=\"661\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
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        "<title>25</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
+       "<text text-anchor=\"middle\" x=\"727.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
        "</g>\n",
        "<!-- 21&#45;&gt;25 -->\n",
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        "<title>21&#45;&gt;25</title>\n",
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-       "<text text-anchor=\"middle\" x=\"728\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
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+       "<text text-anchor=\"middle\" x=\"716.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
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        "<title>19</title>\n",
-       "<text text-anchor=\"middle\" x=\"643\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
+       "<text text-anchor=\"middle\" x=\"545.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;19 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>25&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M690.67,-260.8C661.96,-247.39 621.94,-228.7 591.19,-214.34\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"656\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
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        "<!-- 22 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
        "<title>22</title>\n",
-       "<text text-anchor=\"middle\" x=\"646\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"637.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;22 -->\n",
-       "<g id=\"edge25\" class=\"edge\">\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
        "<title>25&#45;&gt;22</title>\n",
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        "<title>24</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
+       "<text text-anchor=\"middle\" x=\"727.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;24 -->\n",
-       "<g id=\"edge26\" class=\"edge\">\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
        "<title>25&#45;&gt;24</title>\n",
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-       "<text text-anchor=\"middle\" x=\"752.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"731,-220.18 727.5,-210.18 724,-220.18 731,-220.18\"/>\n",
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        "<!-- 28 -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
+       "<g id=\"node27\" class=\"node\">\n",
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+       "<text text-anchor=\"middle\" x=\"819.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (surau)</text>\n",
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        "<!-- 25&#45;&gt;28 -->\n",
-       "<g id=\"edge27\" class=\"edge\">\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
        "<title>25&#45;&gt;28</title>\n",
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-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>22&#45;&gt;19</title>\n",
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        "<!-- 32 -->\n",
-       "<g id=\"node27\" class=\"node\">\n",
+       "<g id=\"node28\" class=\"node\">\n",
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-       "<text text-anchor=\"middle\" x=\"738\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
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-       "<title>24&#45;&gt;32</title>\n",
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+       "<text text-anchor=\"middle\" x=\"937.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
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-       "<title>31</title>\n",
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-       "<g id=\"edge33\" class=\"edge\">\n",
-       "<title>32&#45;&gt;31</title>\n",
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-       "<text text-anchor=\"middle\" x=\"750\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 25&#45;&gt;32 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;32</title>\n",
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+       "<text text-anchor=\"middle\" x=\"854.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
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        "<title>26</title>\n",
-       "<text text-anchor=\"middle\" x=\"821\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
+       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
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        "</g>\n",
        "<!-- 28&#45;&gt;27 -->\n",
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        "<title>28&#45;&gt;27</title>\n",
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        "</g>\n",
        "<!-- 30 -->\n",
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        "<title>30</title>\n",
-       "<text text-anchor=\"middle\" x=\"1014\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
+       "<text text-anchor=\"middle\" x=\"912.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
        "</g>\n",
        "<!-- 28&#45;&gt;30 -->\n",
        "<g id=\"edge30\" class=\"edge\">\n",
        "<title>28&#45;&gt;30</title>\n",
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+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"1013.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 32&#45;&gt;31 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>32&#45;&gt;31</title>\n",
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        "<!-- 29 -->\n",
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        "<title>29</title>\n",
-       "<text text-anchor=\"middle\" x=\"1014\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
+       "<text text-anchor=\"middle\" x=\"912.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
        "</g>\n",
        "<!-- 30&#45;&gt;29 -->\n",
        "<g id=\"edge31\" class=\"edge\">\n",
        "<title>30&#45;&gt;29</title>\n",
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        "</g>\n",
        "<!-- 33 -->\n",
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        "<title>33</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"1013.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
        "</g>\n",
        "<!-- 31&#45;&gt;33 -->\n",
        "<g id=\"edge32\" class=\"edge\">\n",
        "<title>31&#45;&gt;33</title>\n",
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-       "<text text-anchor=\"middle\" x=\"753.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
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+       "<text text-anchor=\"middle\" x=\"1036\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x1401cd950>"
+       "<graphviz.files.Source at 0x14f0e3310>"
       ]
      },
      "execution_count": 4,
@@ -551,13 +551,14 @@
     "### Parse knowledge graph from dependency\n",
     "\n",
     "```python\n",
-    "def parse_from_dependency(tagging, indexing,\n",
-    "                          subjects=[['flat', 'subj', 'nsubj', 'csubj']],\n",
-    "                          relations=[['acl', 'xcomp', 'ccomp', 'obj', 'conj', 'advcl'], ['obj']],\n",
-    "                          objects=[['obj', 'compound', 'flat', 'nmod', 'obl']],\n",
-    "                          get_networkx=True):\n",
+    "def parse_from_dependency(tagging: List[Tuple[str, str]],\n",
+    "                          indexing: List[Tuple[str, str]],\n",
+    "                          subjects: List[List[str]] = [['flat', 'subj', 'nsubj', 'csubj']],\n",
+    "                          relations: List[List[str]] = [['acl', 'xcomp', 'ccomp', 'obj', 'conj', 'advcl'], ['obj']],\n",
+    "                          objects: List[List[str]] = [['obj', 'compound', 'flat', 'nmod', 'obl']],\n",
+    "                          get_networkx: bool = True):\n",
     "    \"\"\"\n",
-    "    Generate knowledge graphs from dependency parsing.\n",
+    "    Generate knowledge graphs from dependency parsing, we suggest use dependency parsing v1.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -600,13 +601,16 @@
     {
      "data": {
       "text/plain": [
-       "{'result': [{'subject': 'Najib Ahli Parlimen Pekan',\n",
-       "   'relation': 'memuji sikap mengaku melanggar SOP mengambil masuk',\n",
+       "{'result': [{'subject': 'Najib',\n",
+       "   'relation': 'memuji sikap mengaku melanggar SOP',\n",
+       "   'object': 'badan'},\n",
+       "  {'subject': 'Najib',\n",
+       "   'relation': 'memuji sikap mengaku melanggar mengambil masuk',\n",
        "   'object': 'suhu'},\n",
-       "  {'subject': 'Najib Ahli Parlimen Pekan',\n",
+       "  {'subject': 'Najib',\n",
        "   'relation': 'memuji sikap',\n",
        "   'object': 'Ahli Parlimen Langkawi'}],\n",
-       " 'G': <networkx.classes.multidigraph.MultiDiGraph at 0x140338c10>}"
+       " 'G': <networkx.classes.multidigraph.MultiDiGraph at 0x14a51a850>}"
       ]
      },
      "execution_count": 6,
@@ -626,7 +630,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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OMGTIEFq0aPEeqxMRkbeh7Qyv8KLQMxqNREREEBQUxK1bt+jbty8rVqzgt99+4+rVqwCsXbuWa9euvedqRUTkdSj43tCVK1fw8/PDw8ODTp06sWHDBnLmzMnPP/9M1qxZCQwMJG/evNrnJyKSSCn43lBISAjdunVj3759lC5dmh07dhAREUG2bNmYOXMmnTt3xsvLi2rVqrFo0SLOnTtn6ZJFRCQOBd8bKly4ML/++isdOnRg5syZRERE4OjoyJw5c9i7dy+NGjUiderUzJ07l969exMYGGjpkkVEJA4tbnlDsQtezp07R2RkJHnz5mXnzp0sXLiQRo0a0axZM+bOnct3333HrFmzqFSpklZ6iogkIgq+dzR//nxWrlzJJ598QpMmTdi4cSPdunVj+/bt5MmTh5iYGGxtbS1dpoiI/D9dueUdffDBBzg7O9OkSRMWLVrEgAEDiIqKYuHChZw6dYqYmBi+/PJLypYta+lSRUQEdXzvJO4Q5oIFCxg0aBCTJ09m9erV9OzZExcXF0JDQ3FzcyNv3rzY29tbuGIREVHwxZODBw8SERFB/vz56dixI2vXrjUfCw8P54svvqB8+fI0a9bMglWKiIiGOuOByWSidOnSAFy7dg2j0QjA3bt3sbW1JSgoiC5duvDJJ59gNBp1hRcREQvSdoZ4EHfFZnR0NI6Ojly6dIlp06bRvXt3Pv30U06dOsXy5cs5ePAgMTExFqxWRMS6aagzAfj7+2NjY0OfPn3o06cPxYoVo06dOty8eZMKFSowY8YMS5coImK11PElgLRp0/Lo0SP27NlDREQEDg4O+Pj4kD59eiZNmgTA2LFj2bFjh/kx+vwhIvJ+aI4vgeTKlYslS5Ywb948WrZsiZeXF+vXr8fZ2ZmBAwfy008/0aBBAyIjI6lTp442uIuIvCcKvgRiMpmoUKECLi4uBAYG8uOPP5I5c2aGDh3Ktm3b2Lt3L97e3tSpU4dUqVJRpkwZS5csImIVFHwJxGAwYDQaKVGiBIsXL8bBwYEvv/yS//znP/z5559ky5YNgIIFCxIVFWXhakVErIeCLwHZ2DydQnVwcGDcuHEsX76cnj17ki1bNkJCQrhw4QJOTk6kSpXKwpWKiFgPBd97MHHiRGbOnMkvv/zCxIkTmT17NiEhIRw5coTcuXNTqFAhAF3XU0TkPdB2hvfgzp07REZGkiVLFnbs2MHkyZMpVqwYWbNmpV27dgBERUWZL2l27tw58uXLZ8GKRUSSLwXfexQbbrH38IsVGRmJg4MDAN988w1bt25lxowZ5k5QRETij4LPAuJe3Do6Oho7u6cjzl9++SWHDh0if/78XL16lRYtWtCmTRtLlioikuxoA7sFxN2zFxt6Q4cO5a+//mLChAlMnjyZHj16MG3aNCIjI83n6jOKiMi7U/AlAl999RWHDx9m+PDhFC5cmCdPnnDp0iVCQkKYNWsWq1atAtAmdxGReKChzkTg7NmzREdHU7hwYUJDQ9mwYQMLFiygWrVq1K1bl3r16jF//nxKlSpl6VJFRJI8bWewMJPJRP78+QEIDQ1l1apVrFixgtatW9OyZUsAvLy8uH37tiXLFBFJNjTUaWFxhy9DQ0NZunQpjRs3Nodely5d8PLyomHDhgDPXOVFzbqIyJvTUGcic//+fby8vADo3r07MTEx9O7dG5PJxI4dOwgJCaFAgQLmIBQRkTejoc5EJk2aNMDT0Ltz5w5jx47l6NGjrFixgkuXLrFixQpatGiBwWCgQYMGFq5WRCTpUceXSJ0/fx5bW1uyZctG586dyZ8/P6tWreKbb74BYM2aNfz8888YDAbs7Oye2RsoIiIvp+BLhOKG2K1bt2jZsiV+fn48fvyYGjVqYDQaad++PV27duXJkye4ublZuGIRkaRDi1sSobidm4ODA5GRkezYsQN3d3c2btxIxYoVqVGjBpcuXSJLliz88ccf5vP1OUZE5NXU8SUBu3fvpnv37gwbNsw8v3flyhVKly5Njhw58PX1pXjx4jRt2tTSpYqIJHrq+BI5k8mEr68vM2fOJCIiAoPBwIULFyhVqhT9+vVj//79NG/enH79+nH+/HlLlysikuhpVWciZzAYMJlMlC1blrJly/L3339TqFAhRo4cyaBBgzAajYSEhODr60u6dOksXa6ISKKnji8JiDvn9+DBA7799lsGDRoEwPHjx1m3bh3Zs2cnRYoUREZGEhwcbKlSRUQSPc3xJSFGoxEbm/99Vjl48CBr1qwhKiqKH3/8ke+//57Tp09z9epVunTpQtu2bQG01UFEJA4NdSYhcUMvJCSEdevW8eDBA6ZNm8aQIUNYu3YtS5cu5cGDB/z6668ULVqUQoUKKfREROJQ8CVRrq6udOjQATc3N/7880+2bt3K0aNHcXFxAWDr1q2ULFmSQ4cOUaRIEYWfiMj/0xxfEpYjRw7Spk1LQEAA5cuXN4feo0ePmD9/Pt26daNo0aLPhJ5GtkXE2mmOLwkLCgri8uXLeHl5Ua5cOX799VfCw8MZMGAANWrUYOrUqQCMHj2a7Nmzm+/4ICJizdTxJWE2Njb06tWLEydOsGXLFnbs2EGbNm1o1aoVU6dOxWg0cv36dRo2bMjPP//M/PnzLV2yiIjFqeNL4q5cuULz5s0pW7Yshw8fpnr16owYMYLp06ezZ88ebt++Tfv27albty59+vRh5syZODk5ARAYGEiqVKks/A5ERN4vBV8ycPfuXW7cuIHJZKJUqVKYTCbat29PjRo1qFKlCu3atSM6OhpHR0dWr16Ng4MDCxYsYNKkSezcuRN3d3ctfhERq6GhzmTAx8eHDz74gFKlSgFw/fp1/Pz8SJEiBT4+PlSqVIkrV67w/fff4+DgwOLFi/npp5/4+uuvSZEihUJPRKyKOr5kJnaz+t69e5k1axbnzp3jypUrLFu2jEqVKrFs2TJGjRrFuHHjqFmz5jOPERGxBur4kpnYa3uWL1+e/v37Y29vz6JFi6hUqRLr169n4MCBFCpUCBcXF/bs2cOmTZsUeiJiVdTxJVOxXVzsjWrnzp3LpEmTuH//PjVr1iRdunQ4OjoSGRlJr1698PLysnTJIiLvhYLPCjx48ICqVavy66+/cvz4cTJnzkzdunUBuHjxIqNHj2bAgAEUKFDAwpWKiCQ8DXVagTRp0rBz504+/PBD9u3bh9FoBODq1atMnDgRLy8vwsLCAMzH9HlIRJIrBZ+V8PDwAJ5ezsxgMBAUFMT48eNJkyYNH3/8MSVLlgTg7NmzAJr3E5FkS8FnJezsnl6PfPTo0WTJkoWdO3diMBho0KABRYsWBeCLL76gQoUKhIWFqeMTkWRLd2ewIiaTicKFCwOwcuVKTCYTxYsXB2DYsGEsXbqUEydO4OzsbMkyRUQSlDo+KxJ3+LJ69eocOnSIdevWma/t+ccff5AlSxaio6Ofe6w6QBFJLrSq0wrF3sn90qVLtG/fnuPHj3Pu3DkyZcpEdHS0eVgUns4JpkyZ0nLFiojEM3V8Vij2Tu45cuTg559/5sKFCy8MvYMHD1K1alV27Nhh/pk+J4lIUqeOz4rFdn7Ac6F39OhRmjVrRo4cOciQIQM1atSgdevWlipVRCTeqOOzYrGhBzwTesePH6d79+5069aNHTt28MUXX/DTTz9x/fp18zn6vCQiSZWCT55x7NgxWrVqRfPmzRk0aBAAGzduJDQ0lJMnT7Jr1y5A+/xEJOlS8Mkz7O3t6dixIwMHDgTgxx9/5O+//6Zt27akTZuWjz/+mE2bNlm4ShGRt6d9fGIWu88vdq/fqFGjuHr1Kg0bNqRixYq4u7vTvn17oqKiLFypiMjbU8cnZnGHL+/cucO1a9eoU6cOlStXxt3dne3bt7Nz504yZ8783GM15yciSYVWdcpLPXr0CAcHB1xcXNi+fTv9+/dn/PjxFCtWjJUrVxIcHEy+fPlo0KCBpUsVEXlt6vjkpVKmTImLiws7d+6kU6dOfP/992TLlo3q1auzbt06ypQpw7hx45g7d66lSxUReW0KPvlXXl5eDB8+nDp16vD111/TvXt3AG7dusX69es5cuQIMTExANy+fZvbt29bslwRkVfSUKe8UtxN7pGRkbRp04YpU6ZgY2NDkyZN8Pf3p2TJkixYsIDbt28zZcoULl26xLx583BwcNC2BxFJdNTxySvF3eQeFBREYGAgx44dI02aNKxevRpfX1+GDRvG48ePmTFjBhcvXmTEiBE4Ojoq9EQkUVLHJ29k7969dO3alc6dO9O7d29MJhO3bt1i9uzZnDp1ih9++IGsWbOazzeZTApAEUlU1PHJazMajZQvX54ZM2bg7++P0WjE39/fHHpjx44la9asrFixgpUrVwJPt0jos5WIJCbq+OSNxJ3zCw4OZtSoUdy4cYOJEyfi7e3No0ePCA4OpkmTJvTq1YtPPvnEwhWLiDxLV26RNxJ3zi84OJhr164xYcIE/Pz8WL16NSEhITRu3Jjly5fTrl07ateujaenp4Y7RSTRUMcn7yQiIgJHR0c+++wz6tSpQ7Zs2Rg0aBCFChUiPDycKVOmAGBra2vhSkVEntIcn7wTBwcHgoODOXnyJOHh4ZQoUYIJEyawePFicufOja2tLWvXruXkyZPmx+izlohYkjo+iRd79uxh6tSpuLu74+fnR6NGjRgzZgw7d+5kyJAheHh40Lt3bz766CNLlyoiVk5zfPLOjEYjlSpVImfOnFSrVo0mTZowatQotm7dyurVqylRogTt2rWja9eumEwm6tevb+mSRcSKqeOTeHXz5k0yZszI+vXr2bx5M9mzZ6d///7Y2NgwZMgQDAYD3333HQaDQQteRMQiNMcn8SpjxowEBQWxZcsW0qVLx4ABA7CxsWHNmjVs2rSJ2rVrY2Nj80zo6bOXiLxP6vgkQdy9e5e0adNiMBhYv349M2bMoG3bttStW5ejR4/y119/kSVLFmrWrGnpUkXEyqjjkwTh4+ODwWBgw4YNjB8/npYtW5I7d25mz55N//79uX79Oj179mTZsmWWLlVErIyCTxJUrly56Ny5My1btuTs2bP8+eefTJo0ie+++47FixezatUqjh49aukyRcSKKPgkwRiNRvLkyUPr1q2Jiopi6tSp1K9fn7JlyxIREcHvv//OmTNnWLt2LdHR0RiNRkBzfiKSsBR8kmBiL2/25MkTDAYDrq6uuLq6ArBt2zauX79Op06dGDFiBHZ2djx48ABAqz1FJEFpcYskKJPJxJgxY8iRIweVKlWiefPmpE+fnps3b1K/fn0GDRrEoEGDuH79OpcvX6ZLly506NDB/FiFoIjEN21glwRlMBj47LPPqFmzJqGhoRQpUoTw8HAaNWrEgAED6Ny5MydOnGD58uXcvXuX//znPxQrVozixYsr9EQkQWioUxKcj48PmzZt4tChQ6RJk4YGDRrQrVs3du/ezZkzZzh8+DBZsmThww8/JFWqVJQsWZLTp0+b5/xEROKTOj55LzJkyMAvv/yCnd3//sgFBwdTokQJ8/c3btxg0aJFDB8+nEKFCj3zeA17ikh80RyfvHf379/n4sWLZMuWjXLlyjFhwgQCAwMZPHgw7du3Z/To0RiNRr755hty5szJp59+aumSRSQZ0VCnvHfOzs7079+fffv2sWnTJnbt2kW3bt0YNGgQo0ePJiQkhMuXL/Ppp58yffp0ZsyYYemSRSQZUccnFnHr1i0aN25MyZIlOXbsGM2aNWPAgAFMnDiR/fv34+/vT5s2bWjatCn9+/dn2rRpODo6WrpsEUkGFHxiMQ8fPuTu3bvExMSY79jesWNHmjdvTtWqVWnRogX37t3Dx8eH1atX6y7uIhIvNNQpFpM6dWry589vXsji7+/P/v37AXB1daVw4cKEhoYyadIkbG1tmT17Nhs3bjQ/Xp/ZRORtqOOTRCF21ebhw4eZPn06Bw8eJCgoiDVr1lCiRAkmTZpE3759qV27Np999hlNmjSxdMkikkRpO4MkCgaDAaPRSKlSpXB3d6dv377Mnj2bEiVKMHPmTKZOncry5cvJlSsXvXr1okyZMqRPn97SZYtIEqSOTxKV2M4vLCwMZ2dnpkyZwuzZs5k0aRLlypUDMB/T3j4ReRsKPkm0AgICqF27NuPHj8fX1xej0YjBYHgu7DZs2EB4eDg5cuSgWLFiFqpWRJIKLW6RRMvT05Pff//dHHo2NjbPhd7EiRNp1aoV58+fp1WrVvj5+VmoWhFJKjTHJ4la7G2MYm9xFMtkMhEcHMzatWtZvnw5NWvWpHDhwnTu3JlVq1aRL18+S5QrIkmAgk8StZfN4RkMBjw8PMiXLx8TJ06kTJkyfPTRR0RHR/Pw4UNA1/cUkRfTUKckOQcOHODu3bsAdOvWDXd3dw4ePIjJZKJOnTrmRTAKPRF5EQWfJCmPHj1i6NChbNu2DYBChQoRExPD4cOHzQtflixZwsiRIxk7diwHDx60cMUiktgo+CRJSZkyJaNGjeKnn35iyZIlmEwm7t69S8qUKYmMjKRs2bJs3ryZhw8f4u7uTuvWrfnjjz8sXbaIJCLaziBJSuzqzsOHD9OnTx8yZsxIWFgYGzZswNfXl4wZM7Jw4ULz+evWrWP06NFs2bKFlClTWq5wEUk0FHyS5MSGX2BgIAaDgZQpU7JlyxbmzZvH4sWLAYiOjsbOzo7w8HDq1KnDkiVL8Pb2tnDlIpIYaKhTkpzYrQ2pUqUyd3FHjx7Fy8sL+F/oAWzevBlnZ2dz6N27d+/9FywiiYqCT5IFe3t7smXLBmAOvQsXLuDn50f58uUB+P7776latSp79+61WJ0iYnkKPkkWqlevzpQpU1i+fDnh4eGsXLmSadOmERAQQL9+/Zg5cyb79u2jTZs29OrVS1d4EbFimuOTJC92zu/QoUP07dsXV1dXHB0dqVSpEj169GDTpk3MmjWLoUOHUq5cOebPn8+CBQtYu3YtLi4uli5fRN4zXblFkjwbGxuMRiMffvghW7duJTIyEk9PTwBmzpzJhAkT+PDDD80b2318fMiWLRtOTk6WLFtELETBJ8lC7IKXFClSmH82cuRIjh49yoQJExg7dix9+vQhb968zJ07l4YNGz53/U8RsQ76my/Jkr+/P8eOHaNXr17UqFGDjRs34ujoSHBwMO3bt2fIkCGWLlFELERzfJJshYSE4Ozs/NLOLjIyEgcHB/P3uqi1iHVQxyfJlqur60tDLzo62hx6sdfzNBgM6HOgSPKn4BOrExMTY97r161bNwYMGMDhw4cBhZ+INdBQp1iVmJgYbG1tAejYsSN3796lRIkSHDx4kIYNG9KtWzcLVygiCU3BJ1apa9euREZGMmzYMLJmzcrmzZv55ZdfWLt2rbkb1JyfSPKkoU6xOt27d+fhw4fm0Hv48CEHDx7k5s2bfP/998yePRvQjWxFkit1fGJ1rl27BkCWLFl49OgRS5YsYePGjXTs2JEKFSpQsWJFZs2axYcffmjhSkUkIWgDu1gVk8lElixZAAgKCmLOnDns2bOHAQMG4OvrC4CtrS2PHz+2YJUikpA01ClWJe7wZVRUFHv37qV79+74+voSHBxMixYtqFChAtWqVQMgLCzMfL4GR0SSBw11ilV78uQJbm5uBAUF0aVLF7Jly0bbtm25f/8+W7duJTo6mrx58/LZZ59ZulQRiSca6hSr5uLigslkomfPnri4uNCtWze2bt3KypUrCQgIYMeOHdSuXRtXV1datGhh6XJFJB6o4xMBbt68ib29Pe7u7rRp04a6deuyePFiOnbsiIODAwcPHmTcuHHmfYDa6iCSdKnjE6tnMpnImDEjAFeuXCEkJIQOHTrwySefUL16dYKCgujXrx/w9OLX6dOnV+iJJGFa3CJWL26Iubq6cv/+fVatWoW9vT2bNm2iRYsW1K9fn127dpExY0Z+//138/kaMBFJejTUKfL/Yocvjx49Stu2bRk0aBCtW7fGwcGB33//nbp161K/fn0yZsxI3rx56dixo6VLFpG3oOATicNoNGJjY8OZM2e4du0adevWZdu2bTRt2pS5c+fSuHFjTp06RcOGDdm+fTs5cuSwdMki8oY0xycSh42NDUajkYIFC1KwYEF27txJrVq1WL58OY0bNyY0NJSbN29SuXJlMmTI8MxjteBFJGnQHJ/IP8S9h5+XlxcrV66kSZMmREVFceTIETZu3EjRokVxcnIiODiY27dvA7q2p0hSoaFOkZeIHfaEp1d52bNnD2vWrCFjxowMHjyYLl26EBQUxF9//UWXLl3MtzRS5yeSuKnjE3mJuJ1fZGQkmzdvxtXVlcGDB9OyZUsuXbrEL7/8woIFC/jzzz+fuZO7iCRemuMTeQ2urq4MHDiQNGnSsHPnTu7du8euXbsA8PT0BKBs2bKcOXOGvHnzPhOaIpK4KPhEXlO6dOkAePDgAfnz5zf//L///S8rV65k0qRJ5M+fn+joaHPwadhTJPHRx1KR13T//n3++OMPKleuzPbt21mxYgU///wzFStWZMiQIfTs2ZPIyEgGDx7MjBkzAA17iiRGWtwi8ppCQ0OpUqUKffr0oXjx4kyaNIk5c+bwn//8h3bt2nH//n3u37+Pq6srbdq04eOPP6Z79+6WLltE/kFDnSKvycXFhXXr1tGwYUMKFy7MiRMnmDRpEu3atWPMmDEcPXqUe/fu8fHHH7Nu3ToGDx5MZGQkDg4Oli5dROJQxyfyhh4/fkxAQAAxMTHkzJnTfC+/zp07U6VKFRo2bMj58+cpUKAAK1aseOaxmvMTsTzN8Ym8IXd3d7Jly0bOnDkBCAoK4siRIwQFBQGQOXNmUqRIwbRp0wCYMGECq1atAp7O+emzpohlqeMTeQexHdzJkyf5z3/+w7Zt27Czs2PDhg3kyZOH7777jmHDhlG/fn0+/vhj6jVuxumH4dwLiyEixoijrQ3ezrYUTu2Ei70+h4q8D5rjE3kHBoMBo9FIkSJFGDhwIAEBAYwcOZI8efIwYcIEli9fzu+//076fEX4afVOrp4OwNbGhug4HzcvPAK/O6FkT+FA2bTOpHO1t9j7EbEG6vhE4kHs5c1iF7OMHj2a1atXM2PGDIwZ8rDrVgiRMcZ/3dhuZ4AqGVwp7uX8nioXsT4aW5FEY/To0eZ73F2/fh03NzdiYmIA8PX1ZebMmfH2WgaDgYsXL77w2Ny5cylfvrz5ezc3Ny5fvvzK54sNNAcHBwICAti4cSNTp07FmCEPO28+IdrEv4be8m96sumX0ey6FcKs9TvIkyfPG76rxOPq1asYDAaio6MtXYrIcxR8Em+yZs2Kt7c3ISEh5p/NnDkTX1/f13r8l19+aQ63zJkz8+TJE2xtbd+6nitXrmBjY2O+ePTbevLkCdmzZ3/hMV9fX5ycnHBzc8PT05PGjRsTFRXFzp07yZCvCDtvPiGGN1vFGW2CwExF2f3nmXeqOz7s3r0bGxsb3NzccHd3J0+ePMyZM8fSZYm8EwWfxKuYmBh+/vlnS5cBwPz580mVKhVLly4lIiIiwV7nl19+4cmTJ1y4cIFHjx7Rr18/7O3t2XcnhGjj680kGP+/s40VbYIDd8MSotw3lj59ep48eUJwcDDff/89nTp14uzZs5YuS+StKfgkXn3++ef88MMPPHr06IXH+/TpQ6ZMmUiRIgUlSpTAz8/PfGz48OG0adMGePFQ2aVLlyhVqhQpUqSgQYMGPHz48KV1mEwm5s+fz3fffYe9vT3r169/7pwdO3aQK1cuUqZMSY8ePV66zeBVw6JxpU6dmiZNmnDmzBnCjQZGdPuE0TULMrxidn7t8BH+l/4yn7v8m56sGf05c3q1ZFjZLFw+uveZ57p8dB+flclLaJQReNpNjx8/nsKFC+Pq6kqHDh3w9/endu3auLu7U61aNQIDA82PP3jwIGXLliVlypQUKVKE3bt3m4/5+voydOhQypUrh7u7OzVq1CAgIOBf35/BYKBhw4akSpWKs2fPYjQaGTt2LDly5CBNmjQ0b978pf9PVq5cSdasWTlz5gyXLl2iSpUqpEmTBk9PT1q3bv3Mn5esWbPyww8/ULhwYTw8PGjRogXh4eH/Wp/I61LwSbwqWbIkvr6+/PDDDy88/sEHH3DixAkePnxIq1ataNas2Wv/ozZ//nxmz57NnTt3sLOzo3fv3i89d+/evdy8eZOWLVvSvHlz5s2b99w5GzZs4MiRI5w6dYply5axdevW13uTLxEQEMDKlSspVqwYpx+Gk7dcVQauOczXO86RPm9hln7V9ZnzT2xZSeUO/Ri+9wpZin74wuc89fB/vzcrV65k+/btXLhwgfXr11O7dm1Gjx7N/fv3MRqNTJo0CYBbt25Rt25dvv76ax4+fMgPP/xAkyZNuH//vvm5Fi1axJw5c7h37x6RkZEv/f8Vl9FoZPXq1Tx69IhChQoxefJk1qxZw549e7h9+zapUqWiR48ezz1uzpw5fPHFF+zYsYOCBQtiMpkYMmQIt2/f5ty5c9y4cYPhw4c/85hly5axZcsWrly5wqlTp5g7d+6/1ifyuhR8Eu9GjBjB5MmTn/mHNlabNm1IkyYNdnZ2DBgwgIiICM6fP/9az9u2bVsKFiyIq6srI0eOZNmyZebFL/80b948ateuTapUqWjVqhVbtmzh3r17z5wzePBgUqZMSebMmalcuTInTpx44/cK0Lt3b3NnlS5dOn766SfuhcVQvEFrHF3dsHNwpFrXQdy58F/CHwebH5e/Um2yFv0QGxsb7B2dnnteE3A/7H/vr1evXqRNm5YMGTJQoUIFPvzwQ4oVK4aTkxONGjXizz//BGDhwoXUqVOHOnXqYGNjQ/Xq1SlZsiSbNm0yP1f79u3JnTs3zs7ONG/e/JXv/fbt26RMmRJPT0++/fZbFixYQJ48eZg2bRqjRo0iY8aMODo6Mnz4cFasWPFMlz5x4kTGjx/P7t27zRv+c+bMSfXq1XF0dMTLy4v+/fuzZ8+e535P06dPT+rUqfnoo4/e+v+NyItoH5/Eu4IFC1KvXj3Gjh1Lvnz5njn2ww8/MGvWLG7fvo3BYCA4OPi1htkAMmXKZP46S5YsREVFERAQQNq0aZ85LywsjOXLl5sXypQpU4bMmTOzaNEi+vbtaz7Px8fH/LWLiwtPnjx507cKwKRJk8yrUc01BD5ky6QRnN6xnpDAAAyGp58xQx49wMk9BQAeadP/63NHxBjNX8d9n87Ozs99H1v/tWvXWL58+TPDu1FRUVSuXNn8/Zu89/Tp03Pz5s3nfn7t2jUaNWr0zGpVW1tb/P39zd+PHz+eYcOGkTFjRvPP/P396dOnD35+fjx+/Bij0UiqVKmeee5/1nf79u2X1ifyptTxSYL49ttvmTFjBrdu3TL/zM/Pj3HjxrFs2TICAwN59OgRHh4er30Jrxs3bpi/vn79Ovb29uabwMa1evVqgoOD6d69Oz4+Pvj4+HDr1q0XDncmlIMbVnB29xY6TF3BN39cZtDG4wDPvNfXuWanw1v8Dc2UKRNt27bl0aNH5l8hISEMHjz4zZ/sX15n8+bNz7xOeHg4GTJkMJ+zbds2vvvuO1auXGn+2ZdffonBYOD06dMEBwezcOFCXcZN3isFnySInDlz0qJFC/O8Ezy9uLOdnR1eXl5ER0czYsQIgoODX/Esz1q4cCFnz54lNDSUYcOG0bRp0xdud5g3bx6fffYZp0+f5sSJE5w4cYJ9+/Zx8uRJTp8+HS/v79/YRYZi5+CAi0dqosJD2frLqDd+DgNwaOcWzp0790aPa9OmDevXr2fr1q3ExMQQHh7O7t27X9i1vYuuXbvy1Vdfce3aNeDp/QrXrl37zDkFChRgy5Yt9OjRg3Xr1gFP/xy4ubnh4eHBrVu3GD9+fLzWJfJvFHySYIYNG/bMnr6aNWtSq1YtcufOTZYsWXBycnpm+PLftG3blnbt2uHj40N4ePgzoRrr1q1b7Ny5k759+5q7PR8fH0qUKEGtWrXeW9c3sMtnpEqXiTG1CjGhSXkyFyrxxs9hAi79sfm5odx/kylTJtauXcvo0aPx8vIiU6ZMjB8/HqPR+O8PfgN9+vShfv361KhRA3d3d0qXLs2hQ4eeO69IkSJs2LCBTp06sXnzZr755huOHz+Oh4cHdevWpXHjxvFal8i/0SXLJNEYNmwYN2/eZPbs2ZYuJV6svBzM30GRb/VYk9HI40unGVitGKlTpzZfEk1E3p3+JkmiYDKZOHv2LNmyZbN0KfGmbFpn7N7y1nummGg+rVCU1KlTExMTo9ATiUf62ySJQvHixbl58yadOnWydCnxJp2rPVUyuL5x+BmjIqmY1oE86dIQExPz3DymBmlE3o2GOkUS2PH7Yey6FfLMrYhexGQ0YoyOopKPI+WzeD4TeteuXSM0NNS8PUR3chd5e+r4RBJYcS9nWuf2ILeHA7YGnusA7Qxga4DMTkaaZXF+LvTu3r3LkiVL6N+/Pxs2bABebyuEiLyYOj6R9yg0ysiph+Hcj3MHdi9nWwqldsTV/mnQxQ29GzdusHbtWlauXMkHH3zA0aNHady4MT179rTk2xBJ0nTlFpH3yMXehtJpXV55TmzoXb16lU2bNnHq1CkGDhxI3bp1OXny5NNLot27h7e3N6BhT5E3pY5PJBGKjo5m9uzZ7N69m7Zt21K7dm0A6tWrx+XLl2nVqhXZsmWjdevWFq5UJOlRxyeSCNnZ2VG3bl2yZMlCzZo1AWjSpAk2NjYsW7YMDw8PypYtS9q0aalWrZqFqxVJWhR8IomQyWQiQ4YM5uteVq9eHRcXF5YvX46joyM3btygYsWKpEuX7rnHadhT5NW0qlMkEYobXtevXzffSd7R0ZHz58+zbNkyPD09SZ06Nffv3zffkshgMGifn8i/0ByfSCL2zw7u7NmzbNq0ifPnz9OjRw+KFi3KtWvXGDBgABUrVnzlzXlF5CkNdYokYnFDLzIykh07dnDs2DEmTJjAjRs3mD59Ot7e3ixfvpyKFSuSPn16mjZtasGKRRI/dXwiScjt27e5f/8+Dg4OFCxYkPHjx7NmzRpq1aqFjY0NmTNnplWrVpYuUyRRU/CJJBFxhz3nz5/PkSNHmDx5Mo8fP6ZmzZq4ubkxevRoSpYsSUREBI6Ojs89TkQ01CmSZMQNr1KlSvH1119ToEABHjx4QL58+ShSpAglS5akZ8+emEwm8ufPT48ePRR6Iv+gVZ0iSYzRaCRv3rxs27aNy5cvs2TJEvLmzUuPHj04cOAAx48f5/PPP2fhwoVMmTLF0uWKJDrq+ESSGBsbG3P49enTh0yZMtGrVy8AsmfPTqpUqYiMjGTGjBlMmTLlhbc2ErFmmuMTSaLizt1Nnz6dx48fM2DAACZOnIibmxsdO3YE4K+//iIoKIgPP/zQkuWKJBrq+ESSqLhzd+XKlaN169aUKFGCGzducPr0aTp27MjRo0dZsWIFgYGB5MuXDzc3N2xsbLTgRayaOj6RJM5oNGJjY8OJEycYMmQIbm5u9OvXD09PTxYsWEBISAjdunUjV65chIaG4uLiouFPsWoKPpFkIDb8goKCcHZ2xsHBgeHDhxMYGEjPnj3JlSsXN2/epFu3bgwePJhy5coBcPjwYUqVKmXh6kXeLw11iiQDNjZPF2h7eHgATy9ttmHDBtauXUuGDBm4ffs2X3zxBbly5aJAgQIAFChQgNy5c7N69WqL1S1iCQo+kWTIw8ODyMhILl68SGhoKIMHDyZnzpx8+eWXeHh4UKZMGfLkycOqVauA/3WMItZAf9JFkpnYWxotXryYKVOmUKpUKdzc3BgyZAgeHh5Ur179mdADFHpiVTTHJ5IMxXZw165dY/HixfTq1QtXV1fq1avHpk2bGDFiBFevXsXFxYVSpUrRpk0b4Om1QNOnT2/h6kUSloJPJJmKDb/Y/+bOnZsCBQqQKlUqateujbOzM87Ozjx+/Jg6deowc+ZMVqxYwbp163BxcVEXKMmW5vhEkqnY4LKxseHKlSsUL16cJUuW8Mknn5ApUyZKly5tPnfDhg0sW7bMvPk9LCwMZ2dnS5UukqD0kU4kmTMajWTLlo0lS5YAEBISYl79GTvgU6ZMGWbOnEnRokWJioqiVatWbN682WI1iyQkBZ9IMhfb+UVHRxMcHEx4eDghISHA06u/GI1G0qRJQ86cOQGwt7cnc+bM2uYgyZaGOkWshJ2dHSlSpGD27NkYjUbzz/85l7dhwwaio6OpV6/e+y5R5L1Q8IlYmbRp07702I4dO9i1axe5cuXSFV0k2dJQp4gV++ei7hMnTuDs7EzTpk3x8fGxUFUiCUvBJ2KFYoc6Y+/QsGnTJn744QcmT55MlixZyJgxoyXLE0lQGuoUsUI2NjbcvXuXDRs2kC5dOrZv307KlCnp2bOneeGLSHKlDewiVurJkyfUrFmTTJkyUbZsWZo0aUKGDBksXZZIglPwiVixiIgIwsPDcXR0xMnJ6bUfd/bsWVKmTKnLm0mSpOATkdfm7+/PvHnzWLBgAaGhoezdu5d06dJZuiyRN6I5PhF5pfv37+Pg4EB4eDgLFy5k//79bNu2jXv37uHp6Wnp8kTemFZ1isgrHTp0iKxZszJ06FD279/PyJEjSZcuHUWKFMHe3p4FCxZw7949S5cp8trU8YnIKxUvXpwaNWrQs2dP0qRJ88wCmOnTp7N7926yZ8+Ot7e3BasUeX0KPhF5qYCAAH777Teio6PJnDkzKVOmBJ5ufH/w4AEXLlygU6dOFC1a1KJ1irwJLW4RkZeaPXs2v/32Gz///DMFCxZ87vjjx49xd3fn/v37eHl5YTKZzJviRRIrzfGJyAtFR0eza9cuSpcuTcGCBZ+5vFnslV/c3d2ZO3cuRYsW5dy5cxgMhucugyaS2Cj4ROSF7OzsGDx4MHZ2dkRFRZk7OZPJZL6jw4wZMxg5ciQVK1Zk2LBh7Ny5Ux2fJHoa6hSRV3rZ8OWsWbMYO3YsM2bMoGLFipw7d45jx47RvHlznJycNOwpiZY6PhF5pReF17Rp0/j666+ZO3cuvr6+hIWF4e3tzcWLF/n111/ZunWrhj0l0dKqThF5Y3ny5GH58uWUK1cOgD179jBhwgQCAwNZtGgRzZo1w87OjqpVq6rzk0RHHZ+IvBGTyUTlypUpX7488HShy5QpUyhTpgwODg7cvXuXb775hrVr15pD7/Dhw9y6dcvClYs8peATkTfyz+7t2rVrREVFMWLECJYvX07//v2ZOXMm+fPnx2AwsG/fPrp06cLOnTstVLHIsxR8IvJO7O3tCQgIYN++fWTIkIFVq1aRMWNGqlSpwvHjx/n666/p1q0bjRo1snSpIoBWdYpIPFi/fj0DBw5kwoQJ1KlTB4BTp07Rp08fmjdvTrdu3TAajdjY2Jj/K2IpWtwiIu/so48+wsnJidu3bwNw4MABvvzyS5o2bUr79u2JiYlh9OjRlCtXjipVqmjBi1iUOj4RiVeHDh2id+/edO7cmXbt2hEREUFkZCQXL16kQ4cOjBkzhjp16ij8xGI03iAi8cpoNNKmTRtatWrFtm3bqFmzJm3btuXBgwesWLGCuXPnEhwcbA69yMhIC1cs1kYdn4gkiMePH1O/fn3atm1LvXr1qFu3Lm5ubhQrVoyffvqJiIgIFixYgKurKy1atNC8n7w3muMTkQRx7949AgICKF++PN7e3tSuXZu9e/fStm1boqOjqVatGhcvXuTKlSsKPXmv9KdNRBJEjhw5GDFiBN26daNUqVLs2bOHXr16UaxYMSZMmMDt27dJnTo1hw4dAiAmJoabN29auGqxBgo+EUkwjRo1YuzYsRQoUIDevXvTqFEjxowZw4IFCzh+/DjTpk3j+PHjABw/fpyZM2dauGKxBprjE5EEd+fOHby8vFiwYAETJ05k6dKl5M2bF3h637/69evz66+/kilTJgtXKtZAHZ+IJLh06dIBcObMGfr27ftM6NWoUQNXV9dnQk+fxyUhKfhE5L2IvVvDV199RVBQEFFRUVStWpX8+fOzZMkSwsLCOHDgAJcuXdItjSRBaahTRN6rs2fPki9fPsqWLUvx4sX5/vvvWblyJRs2bCAwMJD79+8zatQo6tWrZ+lSJZlSxyci70XsZ+z8+fNz5MgRMmfOzJQpUzh9+jSnTp2iYcOG7Nixg3nz5jF16lQCAwMtXLEkVwo+EXkv4l6erFSpUixduhSAhQsXEhMTQ8uWLQHw9/cnODgYO7v/bTPWwJTEJwWfiFhMeHg4MTExfPTRR9ja2nLy5En2799P8+bNcXd3Z9OmTRw9elTX9JR4peATEYtxcnKiTp06dOrUiS+//JIBAwYAULlyZdavX89HH33EsGHDOHLkCKDOT+KHFreIiMXt3buX//73v+TKlYtixYpx/Phx+vTpQ4cOHahSpQoff/wx06dPp3z58pYuVZIBBZ+IWMyLbk20evVqvvvuOwYNGkSLFi0AaNy4MTVr1qRLly4ALFiwgNq1a+Pp6fnea5akT0OdImIx/wy9u3fvMmTIEPr160eLFi2Iiopi0aJFpEqVinLlygHwn//8hy1btvD3339bomRJBnR3BhFJNHx8fNi1axfp06cnOjqalStXcuTIEUqWLEnOnDl58OABV65coXPnzhQrVszS5UoSpaFOEUlUjEYjNjY2/Pbbb+zdu5fChQvz6aef4uLiAjy9z5+joyMODg7mc0XehP7EiEiiEhtkRYoUIW/evHzyySe4uLhgNBoBcHd358KFC8+cK/Im1PGJSKIV29H9s7NbsGABKVKkoEGDBhasTpIqBZ+IJEkPHz4kderUz/zMaDQSGRmJk5OThaqSpEDjBCKSJBw5coTLly+bv/9n6MXExLBx40YmTpzI48eP33d5koQo+EQkSbCxsWHv3r1ERka+8Lifnx8HDhzA1tYWBweH91ydJCUa6hSRJCMsLAxnZ+cXHrty5Qr79++nWbNmODg4EBMTg62t7XuuUJICBZ+IJHlxQ27RokUULlyYggULvvDKMCIa6hSRJM1oNJpDb+7cufzyyy9MnTqVQ4cO6U7u8kIKPhFJsuJuc5g6dSq7d++mSpUq+Pr60rhxYw4cOKCOT56jS5aJSJIVG3qTJk3i1KlTNG7cmEqVKuHh4cHvv//OgwcPePDgAWnSpNGwp5ip4xORJO3q1ascPnyYWrVqUaVKFTw8PPDz82P+/Pls376dNm3asGPHDoWemGlxi4gkeXfv3sXV1RV3d3f27t1Lnz59aNeuHb169WLnzp18++23rFmz5rm9f2KdNNQpIkmej48PAH/88QedOnWid+/e9OjRA4CtW7eSLVs2hZ6YaahTRJINo9FIt27dzKE3evRoQkND6dOnD/C0MwwICADQak8rpqFOEUmWvv32W/z9/albty7u7u4sXrwYo9HIhQsXGDx4MDVr1tSCFyuljk9Ekp0LFy5w5coV6tevT6FChVi3bh3Lly+nXLlyLF26lK+//prTp08r9KyU5vhEJNnJnTs348aNI3Xq1EyePJnz58/z+++/06VLFyIiIqhduzYxMTHm89X5WRd1fCKSLHl7e2NnZ4ezszONGzemUKFCzJgxg1mzZrF//34yZ87MlStXOHLkiELPyqjjE5FkLWvWrAwcOJA8efJQtmxZVqxYwYMHDzCZTNSqVYucOXPy008/kSdPHnV+VkIdn4gka7Vq1WL48OH06tWLHTt2kDFjRlKkSEHlypX54IMPaN26NR9//DG7du1S6FkJreoUkWQrbgd39uxZcufOzc2bN2nYsCFNmjRh6NChAAwYMAA3Nze++eYbbGxs1Pklcwo+EbEaYWFhFClShGbNmjFq1Cjg6ab30aNH4+npiaurK40aNaJWrVoWrlQSkoJPRKzKX3/9Rd68eYGnd23fuHEjRqORgQMHcvnyZXr37s2mTZvw9PS0cKWSUDTHJyJWIfYzfmzo7d69mxUrVmBjY0O/fv3w9vbG1taWqKgo810f4j5Okg8Fn4hYhX/O2Xl6emJnZ0fPnj1Jly4dBw4coGXLlvTu3ZvUqVOzZcsW/vvf/2quLxnSUKeIWK2IiAgcHR3Zt28fHTp04PPPP6dDhw5s2bKF8ePH06RJE7p3727pMiWeKfhExGrFxMQQHBxMoUKF+Pzzz+nTpw9bt25l6tSp1KxZk27dugHg7+9P2rRpLVytxBcFn4hYvcuXL5M9e3auXbtGs2bN+PTTT813eJg+fTpHjhxh1KhReHt7s23bNgoVKkS6dOksXLW8Lc3xiYjViv3cny1bNgDWrl1Lrly5zKE3b948jh07RunSpfH29iY0NJQUKVJw/vx5i9Us707BJyJWK3bhSux/CxUqxJ9//smZM2cYMWIEfn5+lC9fng4dOrBs2TLq1q1LoUKF8PX15cyZM9y8edOS5ctbUvCJiPy/ypUrM378eAYMGMBvv/1GlSpVaNu2LWvWrGH69Ol07twZV1dXIiIiuHfvHsePH7d0yfIWNMcnIsKzlzc7fvw4p06dol27dqxatYpJkybRu3dvGjdujJ+fH5999hnHjh0jRYoUFq5a3oaCT0Tk//3zGp2rV69m0qRJ9OrVi8aNG3Pw4EHatWtHr169zPOAkvQo+ERE/mHQoEEEBgZy/PhxvvrqKxo3bsyhQ4do1aoVX375JR06dLB0ifIOFHwiIv9w6tQpFi1aRMuWLSlatCj79++ncePGjBkzhvbt27/0cbqrQ9Kg4BMR+Rdbtmzh2rVrdOnSxfyz2JC7ceMGu3btolChQhQvXlzhlwQo+ERE3pLJZKJRo0Z4enpy8OBBJk6cSLVq1SxdlvwLbWcQEXlDRqORyMhI7t27x99//823337L7NmzmTRpEnfu3AFg/fr1BAQEWLhSeRF1fCIib+jixYsEBARQunRpJk6ciKOjI926dTPf6y8kJIS//vqLR48eUbVqVUuXK/+gjk9E5A3dvXuXTz/9lHPnzlGyZEm2bdsGPL3X34wZM+jSpQt58uShatWqrFy50twFSuKg4BMReUPly5dn5MiRNG3alOXLlxMSEgLA7Nmz2bdvH7Vq1cLNzY05c+bQsWNHrl+/buGKJS4NdYqIvIG4qzYPHz6M0WikSJEi7Nq1i99++41GjRrRrFkz5s6dy3fffcfs2bOpWLGiVnsmIgo+EZE39M8Qmzt3LmvXrqVNmzY0adKEjRs30qNHD7Zt20bu3LkxGo3Y2Ngo/BIJO0sXICKS1PwzvPLnz4+TkxNNmjRh8eLF9OvXD6PRyKJFi/jrr7+IiIhg5MiRFCxY0EIVS1zq+ERE4smcOXMYNmwYY8aMYePGjfTu3RsbGxuCgoLw8fGhQIEC2NraqvOzMHV8IiLxJG3atCxYsIAcOXKwcuVKypQpYz4WFhZGv379aNSoEZUrV1b4WZBWdYqIxJM6derg6+tLSEiI+e7u9+7dIygoiAcPHtC8eXP69evH77//rtCzIAWfiEg8i4iIwNbWlps3b/Kf//yHzp0706JFC6Kjo5k5cyZbtmxBs0yWo6FOEZF4VqRIEX766ScMBgMXL16kX79+ZMyYkebNmxMWFkalSpWe6fhiV33K+6HfaRGRBJAlSxbu3r3Lzp078fDwIGPGjGTIkIGUKVPy/fffAzBhwgT+/PNP81YHeT+0qlNEJAGtWrWKDRs2sGPHDtKkScO6devIlCkTAwYMYMKECTRt2pSBAwdSqlQpLXh5TzTUKSKSAGJDrHHjxqRIkYI7d+4wbdo0MmXKxLBhw9i6dSu7du3CycmJNm3asGbNGvLnz2/psq2Cgk9EJAHE7dyqVatGuXLlcHZ25osvvmDWrFkcO3aMLFmyAJA7d24ePXoEwJMnTxg0aBBjxozBw8PDEqUne5rjExF5D5ydnfnhhx9Yvnw5/fv3J0uWLISEhHDkyBE8PDzw8fEhJCSE9u3bY2tri6urq6VLTrbU8YmIvAdjx45l3rx5jBs3jjlz5rBu3Tr8/f3x8/Mjd+7cZM+enfPnz1OkSBH69++PnZ0dBw8eJHv27Hh7e1u6/GRFi1tERN6Dc+fO4ezsTNasWVm0aBHz58+nWLFi5MiRg44dO5rPi7vAZcmSJUycOJEdO3bg5uZmqdKTHQWfiMh7FB0djZ2dHU+ePHkmzGJiYrC1tX3m3CtXrvD555/zyy+/4OPj875LTbY0xyci8h7Z2T2dYfrnHF5s6BmNRgAuXbpEhw4dyJUrl0IvnqnjExFJJGKHOUNCQqhQoQLVq1c3b3bXHr/4o45PRMRC/tl3GAwGwsLCmDdvHtmzZzeHntFoVOjFI63qFBGxkNgwCwgIwNPTk8uXL1O/fn3SpEnDo0ePzIGn63jGLwWfiIgFBQUF8fnnn5MnTx62b99OnTp1GDduHEeOHCEsLEz7+RKA5vhERCzs+PHjbNy4ER8fHzp16vTc8aioKOzs7DTcGU8UfCIiiUDcWxPF3dpgMpm4evUq48aN45dffnluy4O8OQ0ci4gkAnHn8eKGm8FgwNvbm/Pnz9OhQwdiYmIsUV6youATEUnkXF1dWb9+PdevX1f4xQMFn4hIEhAbfteuXaNjx44Kv3eg4BMRSSJcXV3ZsGEDV69eVfi9AwWfiEgSovB7dwo+EZEkRuH3bhR8IiJJkMLv7Sn4RESSKIXf21HwiYgkYXHDr1OnTubbGsnLKfhERJK42PC7fPkyHTt2VPj9CwWfiEgy4OrqysaNGxV+r0HBJyKSTCj8Xo+CT0QkGVH4/TsFn4hIMqPwezUFn4hIMqTwezkFn4hIMhUbfpcuXVL4xaHgExFJxlxdXdm0aZPCLw4Fn4hIMqfwe5aCT0TECij8/kfBJyJiJRR+Tyn4RESsSNwFL9Z6bU8Fn4iIlXFzc2Pjxo1cvHjRKsNPwSciYoWsOfwUfCIiVspaw0/BJyJixawx/BR8IiJWLjb8/v77b6sIPwWfiIjg5ubGpk2b+Pvvv+ncuTNGo5Ho6Gj+/PNPS5cW7wwmk8lk6SJERCRxePLkCXXq1CFnzpw8ePCATZs2ERAQgIeHh6VLizcKPhERecajR4/IkSMHQUFBODk5MXXqVNq2bWvpsuKNgk9ERJ7RokUL1qxZQ2RkJAAVK1Zkz549z5wTEmXk9MNw7oXFEBFjxNHWBm9nWwqndsLFPnHPotlZugAREUlcSpcuzaFDh7h37x7h4eH4+fkRGBhIqlSpuBMSxX7/MC4HR2IAouO0Thcegd+dULKncKBsWmfSudpb6i28kjo+ERF5ob/++ot58+YxZcoUFi5cSMYy1dl1K+SZsHsZOwNUyeBKcS/nhC/0DSXuflREJIlp164dX3/99UuPGwwGLl68CEDXrl0ZOXJkgte0e/duMmbMaP6+QIEC7N69+18flzdvXsaMGUNwcPAbhR487QR33Qrh+P2wt6w64Sj4RETegq+vL6lSpSIiIuKtn2PatGkMHTr0hcfmzp2Lra0tbm5upEiRgqJFi7Jhw4a3fq24/vvf/+Lr6/va598JiXqj0IsVG353QqNeeDxr1qzs2LHjzZ40Hij4RETe0NWrV/Hz88NgMLBu3boEe50yZcrw5MkTHj16RIcOHWjevDmBgYFv9BzR0dHvXMd+/7A3Dj3z65vgwN3E1fUp+ERE3tD8+fMpXbo07dq1Y968ec8dDwwMpG7duri7u/Phhx9y6dKlFz7Pvw2LxrKxseGzzz4jLCyMS5cuMWfOHPLly4e7uzvZs2fn119/NZ8bO6z5/fff4+PjQ/v27Z97vrid1vDhw2nWrBlt2rTB3d2dQoUKceHCBcaMGYO3tzcZM2Viy9at5seGPw5m5bd9GF2jAGNqFmLblNEYY2IAOLZuMdM+q8umCd/wbaWcjKtXgvP7dnApOJLQqNe/GkxgYCD16tXDy8uLVKlSUa9ePW7evGk+7uvry9ChQylXrhzu7u7UqFGDgIAA8/H58+e/+vfztSsRERHg6T+srVu3pnXr1mzduhV/f/9nji9ZsoRvvvmGwMBAcubMyVdfffVOrxcdHc3MmTNxc3MjV65ceHt7s2HDBoKDg5kzZw79+vXj+PHj5vPv3r3Lw4cPuXbtGtOnT//X51+/fj1t27YlMDCQYsWKUbNmTYxGI7du3aJt38Gs/G6g+dzlw3thY2vHwLWH6bV4F38f3M2R1QvNx2+cOY5nlpwM3XWeip/2ZOW3fcFk4tTD8Nd+v0ajkfbt23Pt2jWuX7+Os7MzPXv2fOacRYsWMWfOHO7du0dkZCQ//PADAGfPnqV79+6vfH4Fn4jIG9i7dy/Xrl2jefPmlChRghw5crBo0aJnzmnUqBGlSpXCzs6O1q1bc+LEibd6rYMHD5IyZUp8fHxYvHgxq1evxsPDg7p165IjRw4MBgOVKlWiRo0a+Pn5mR9nY2PDt99+i6OjI87O/76qskKFCtSsWRM7OzuaNWvG/fv3GTx4MPb29hSp2YjA29cJexzE4wf3OL93B/UGfoeDsytuqb0o17orp7atNj9XSp+MlGrcFhtbW4rXa8HjAH8eBdzjfljMa7/vNGnS0KRJE1xcXHB3d+err756bh9h+/btyZ07N87OzjRv3tz8e7xixQo++uijVz6/9vGJiLyBefPmUaNGDTw9PQFo1aoV8+bNo1+/fuZzfHx8zF+7uLjw5MmTt3qt0qVLs3fv3ud+vnnzZr799lsuXLiA0WgkNDSUQoUKmY97eXnh5OT02q+TNm1a89fOzs54enpia2v79Af2jgBEhoYQfP8uxugoRtcoaD7fZDLikTaD+Xt3T2/z1w7OLgBEhIYQEfP6Q52hoaH069ePLVu2mOc0Hz9+TExMjLmul/0e3759m0yZMr3y+RV8IiKvKSwsjGXLlhETE2P+hzciIoJHjx5x8uRJihQpkuA1RERE0KRJE+bPn0+DBg2wt7enYcOGxN2SbTAY4u31HG3/NzDokTY9tg6OfL3rPLZ2bxYfcZ/n3/z444+cP3+eQ4cO4ePjw4kTJyhWrBivs+08Xbp0nD9//pXnaKhTROQ1rVmzBltbW86ePcuJEyc4ceIE586do0KFCv+6oCK+REZGEhERgZeXF3Z2dmzevJlt27Yl2Ot5Oduav07h5UOu0r5smjCM8CePMRqNPLhxhcvH9r3yOewMzz5PXFFRUYSHh5t/RUdH8/jxY5ydnUmZMiUPHz7k22+/fe16mzZtyvr16195joJPROQ1zZs3j/bt25M5c2Z8fHzMv3r27Mlvv/0WL1sH/o27uzuTJk2iefPmpEqVikWLFlG/fv0Ee71CqZ8dMm0+YgoxUVFMaFqOEb45+W3QZzwOuPfK5zABhVO/eOi1Tp06ODs7m38NHz6cvn37EhYWhqenJ6VLl6ZWrVqvXW+BAgWYPHnyK8/RJctEROSVVl4O5u+gyLd+fG4PBxpnTxGPFb0bdXwiIvJKZdM6Y/eW04Z2Bijj836v16mhThEReSfpXO2pksH1jcMv9kLV6Vze710a1q5d+8rjGuoUEZHXcvx+WLK4O4OCT0REXtud0CgO3A3j0gvux2dneLqQJUcKB8r4OL/3Tu91KfhEROSNhUYZOfUwnPtx7sDulUTuwK7gExERq5K4Y1lERCSeKfhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSq/B+6LS0xDATmLgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -779,7 +783,7 @@
     {
      "data": {
       "text/plain": [
-       "(35, 58)"
+       "(34, 58)"
       ]
      },
      "execution_count": 9,
@@ -821,7 +825,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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0qUmJQMb9u3jxorF90aJFWV6nc+fOAff2OomIiDypnpzfJkREREQkx9ytOu369eu88MILNGnShNmzZ/PJJ5/g6+tL06ZN2bdvHwDlypUjKSmJQ4cOARmre37++edYLBZ69erFokWLWLduHYMHD8bOzs44t7+/vxHMxMXF4ejoSK5cuUhMTGTZu2/c87jdvH3xyJ2XfWu/wWI2s/u7hUReOocN4Ods+89uyiNy/PhxfvzxR1JSUnBycsLZ2RkbGxs6dOjAxIkTuXHjBjdv3mTChAlZFhIAqF27NgsXLqRly5bs2rULW1tbnm3Wku8/epOUhHiiIy6ydeEsyjRu/bfj8M4XSP7iz/DDx1MhPZWrR3azatUqY/sfX6eRI0c+8HshIiLyuFGwJiIiIiJ/KjNMy6xOS0lJMR7btm0b1atXZ/fu3YSGhvLRRx+xb98+Bg0axLx58wCoWLEifn5+7N27l8jISIKDg/nss89IS0ujUqVKFC5cGMiYRnh7ZdOIESOYOHEiXl5eREVFERQURP78+SlRogQNa1a9r+fQcsy7/DLvI96oU4RrZ44TVKoCVqCUj9MDuEMPX0pKCsOHD8fX15c8efJw/fp13nrrLUaPHk358uUpVaoUTz/9NGXLlmX06NF3HF+/fn0+//xzwsLC2Lt3L5/O/AgHZxemhpXn4+5NKd2wFeWad7zLle/UbtIsLhz8jXG1i7Bw2kTatWuHo6MjAC+++GKW16ly5coP9D6IiIg8jkxW1WaLiIiIyG0sFkuWVT0hYzGCgQMHcuvWLVq3bk2vXr2YPn06n332Gbly5cLV1ZX+/fvTsGFDIKMv2IwZM2jYsCHLli3jxo0btGvXDm9v7wcyxmVnbnEyNjXbxxfxdKBlQY8HMpYn0YO6f+3ataNYsWKMHz/+AY5ORETkyaFgTURERORfKiHNwqGoZK4nmUkxW3C0tSG3sy2lfJxwsf/7iQspKSl88MEHmEwm8ubNi9lsplixYrRv356NGzfyww8/sGbNGsaOHUv58uWBjD5fBQoUYOTIkRw7dozly5djsViwscl6PavVmiW4u19XEtJYeDKW9Gz8Jmtngo5FPMnrYp/t6z/psnv/Lh7Zh7unF6/WKcWhrT/x/PPPs337dsqUKfNwBioiIvKYU7AmIiIi8i9zJSGNbdeSOHMrFRNkCU/sTGAFCno4UNXfmbyud4ZLCQkJfPDBB9y8eZOoqCicnJz4+uuvOXbsGP7+/nTr1o0qVapQtGhRvv76a3x9fXn11VeZOnUqR44c4bPPPiN37twPPEz7o703kvjxcsJ9hUN2Jqib35Wyfs4PbBxPquzcvxM/b2DtlGHcio4iICCAESNG0K1bt4c3SBERkcecgjURERGRf5H7CUv+KmR69tln8fb2ZunSpdy6dYsBAwZQtWpVevbsyZo1a/jiiy/48MMPiY6O5uOPP2b79u3UqFGD3r17U7hwYSNEe9Bh2h89qOf7X6X7JyIi8s9o8QIRERGRJ8Tx48d55plncHd354MPPrhje2ZIsnPFYma/1ORvz5duhR8vJ7D3RpLxmNlsBqBz587Y2dkRFxeHk5MTderU4ccffwSgSZMmXLhwgS1btlCiRAmmTZvGrl27mDZtmrEYQWaY9jBDNYA93y1g05sDKeLpgK0pI/y5nZ0JbE0ZPcE6FvFUKPQHZf2caexn/cv7Z7JaCLBP1/0TERG5CwVrIiIiIk+IKVOmUKdOHeLi4ujfv3+WbVcS0u57Wh9khGsbz8dyOS4F+F8Q1rx5c65evcrhw4dxcHCgUqVKREdHs379egAmTpxIjRo1ALCzswPuXNnzYUpJSeGVV16hV69e7Ni4mpYFPehb0ofqeV0o6e1IIQ97Sno7Uj2vC31L+tCyoMd/uqfa3VitViZOnEjJgNw8m8v6p/dvYc+m9KwQwrkDu3N6yCIiIo8du5wegIiIiIjcm/Pnz9O+ffu7btt2LSlbjfwBLCYT3x08R99qRbGxscFiseDt7U25cuVYu3YtFStWJDAwkBYtWuDhkbGSZoMGDe44j62tbfYGcJ9OnjxJWFgY586dw2q14uXlBYCLvQ2V/V0eyRiedGlpafTs2ZNFixZhY2PDrVu3CPT0vOv9y+XhyqHkZOrWrcv48eMZMmTIQ69EFBEReVKoYk1ERETkCVC3bl1++ukn+vXrh5ubGwcOHODFF1/Ez8+PAkFBzJn2FhaL5a7Hrp0ezuyXmpAcd4vJTcpyaucWY9um2VP4Zkw/bjl6cvj4STp16oSfnx9eXl6sXbuWn3/+maioKFxcXOjVqxdVq1Y1js2pVr1vvfUWp06dIiUlo8ous2JO7k1MTAzVqlVjyZIlpKWl4ezsTHx8/J/ub2+fUemXnJzMmDFj/jTcFRER+S/SbyEiIiIiT4Aff/yR2rVr06lTJ3r06MGLL75IbGwsZ86cYdPvl3i5dVNcff2p8Hwn4xiLxcJ3k14j5solXvroGxyc/7yay5yezvTP5hMbG8vFixdxdHRk//79BAYG4ufnl+Wcmat95lTV0qeffkrx4sUZPnw4dnZ2j6xS7t/i/PnznDp1yghibW1tiYuL+9P9bw9svby8aNmy5UMfo4iIyJNCwZqIiIjIE8ZsNvPVV1+xf/9+3N3dsfULoHqnPuxb840RrJnT0/lqxMtYzOm8+P5C7Owd/vKcdo5OxKaauXHjBqdOnaJUqVKULVsWk8mUJUzL/G9OsrGx4ddff2XKlCnkyZOHy5cv5/SQniilS5fmypUrhIaGYrVaiYiIIDEx8U/3d3d3J1++fBQrVowyZcrQrl27RzhaERGRx5uCNREREZEclpBm4VBUMteTzKSYLTja2pDb2ZZSPk642N8ZZF2/fp20tDSCgoIASDFb8MobwK3rV4x9Ii+e5eqJI/SZt+FvQ7VMLXoO4JS3A+3btycmJoZOnToxadIkYyrg42L37t389ttvLF68GGdnrVKZHVu3bsXT05NDhw6xf/9+SpYs+af7Llq0CHt7e65evUqpUqUYNmxYlipGERGR/7Kc/5OjiIiIyH/UlYQ0lp25xcwjUWy9ksjR6BRO30rjaHQKW68k8tGRKJaducWVhLQsx+XOnRt7e3s2btzI2bNncbS1IfbqZTxy5/3fPiGFaRX+AXNfbc+Nc6eMxx2cXUhNTjI+j4u8bnzs4erMuHHjOHr0KNu2bWP16tXMmzfvId6B7Bk7diwjR45UqPYPTJ8+nYEDB2JjY0PZsmVxdHT8032dnZ2xs7MjICCAdu3a8e677z7CkYqIiDzeFKyJiIiI5IC9N5JYeDKWk7GpmK3csaJnuhXMVjgZm8rCk7HsvZERhpnNZtavX4+fnx/dunWjQ4cOxF04ydaFsyjTuHWWczzTsCUN+o3is1daEXnxLAB5iz7FwQ3LMaelcenofg7/sAoAOxNc2PMLhw4dwmw24+Hhgb29/WMx9fN227dv58iRI/To0SOnh/LEOn78OLt27aJTp05/v/MfjBgxgk8++YTIyMiHMDIREZEnz+P1m5KIiIjIv8C5c+cwmUykp6ffdfveG0n8eDnhjjDtz6Rb4cfLCcSlWdi5cyfDhg3jzTffpGnTpuzfv5++zZ/lmedaUq55xzuOLRfWnro9X+fTXi2JjrhAg1eGE3npHBNqF2LT7MmUbpjRiN4KuCZG07p1azw8PChevDi1atWic+fO2b0ND8WYMWMYPXr0X1ZYyV97//336dWrV7Yq/goUKECrVq2YPn36QxiZiIjIk8dkzal10kVERP6l7rdfljy+vvrqK6ZPn87hw4dxdXUlJCSELl268Morr/zlipjnzp0jJCSEtLQ07OyytrS9kpDGwpOx9xyq3c7OBHXdk3itx4t06dKFrl27smnTJj777DMaDJvCNYsTZHOlziKeDrQs6JGtYx+VLVu28NJLL/H7778/dn3fnhRRUVGEhoZy9OhR8ubN+/cH3MW5c+coV64cJ0+exMfH5wGPUERE5Mmi3+5FREQekOz2y5LH07Rp0xgwYABDhgzh6tWrXLt2jdmzZ/Prr7+Smpqa7fNuu5aUrVANIM1s4ZzJk4YNG3Ly5EkA6tWrh9VqJf3EHuxssheq2ZmgSp7Hu1+Z1WplzJgxjBs3TqHaPzBnzhyaNWuW7VANIDg4mBYtWvDee+89uIGJiIg8oRSsiYiIPADZ7Zclj6fY2FjGjh3LzJkzad26Ne7u7phMJsqUKcPChQtxdHRkzZo1lClTBg8PDwIDAwkPD7/jPJ9//jn58uUjb968vPPOOySkWThzK5VvxvVj40dvGvud2f0rbzUsZXw+uUlZfp73Ee+3rUV4zYIsGtaDtJRkTDY2nI5N5fS580yfPh0/Pz8+/fRTvv76a35c/jXVfO04uXUjH3SoQ3iNEN5uVJpNs6cY542OuMCIsn7sWfUVbzd+hjfqFmXLZ+9SN78reV0e77Dq+++/5/r163TseOd0V7k3aWlpzJgxg0GDBv3jc40cOZKZM2cSExPzzwcmIiLyBFOwJiIichfBwcFs2rTpnvbNbr+sP4Zr93NNebi2b99OSkoKzZs3/9N9XF1dmTdvHjExMaxZs4ZZs2bx3XffZdnnp59+4uTJk2zcuJHJkyfz2fK19zyGQ9+voNtHXzN01R6unjzK3lVfAXD0l40sW7aMsLAw+vTpw+bNmwEIDw+nSoAXlQJz0Tb8A8b8dJIu7y9i59K5HPkp63XP79/Ja99up9fsZfwwZxrON8/d87hyQma1Wnh4OLa2tjk9nCfW0qVLKVSoEM8888w/PlfBggUJCwvj/fff/+cDExEReYIpWBMREfkHriSk3VeolikzXLuSqGmhj6ObN2/i6+ubpT9a1apV8fLywtnZmZ9//pnatWvz9NNPY2NjQ6lSpejQoQNbtmzJcp7MaYu+vr5069aNVcuWYL7Hr5Wq7Xvi4ZcHF09vitd8jojjhwE49MNq6rTuRP/+/XnppZeMSjlbW1vS09N5+fnnGNasOsVzuRBQtCTPNGzB2T3bspy7fq8hlPT3ZEhYNZ4pXZoDBw5k/2Y9AmvWrCExMZG2bdvm9FCeWFarlenTpz+QarVMI0eOZMaMGcTGxj6wc4qIiDxpFKyJiIj8A/+kX1a6FbZf1ZTQx1GuXLm4efNmllU9t23bRkxMDLly5cJiyVids06dOvj5+eHp6cns2bO5efNmlvN4eXnxwQcf8PnnnxMUFMTNq1fueQzuvrmNj+2dnElNTADg1o2rePnno1q1agQFBREYGGjsZ2dnx86dO3mhSQN6VQplYq1Qdi37EvvEGAp52FPY0wGAYTWL0bKgB3ld7HFxcSE+Pj5b9+lRsFqtjB07lvHjx2Njo19ds2vbtm1ER0fTtGnTB3bOwoUL06hRIz788MMHdk4REZEnjX47ERER+QsWi4W3336b0NBQcuXKRdu2bYmKigLg6MkztA71ZPeKRbzdqDTjaxVi59K5XDyyj/fb1mJ8zVBWvD3MOFfkxbPMebkFE+oU4Y26RflqVG8OX75BYprF2Gf//v2UKlUKT09P2rVrR3Jy8iN/zgJVqlTB0dGRFStW/Ok+L7zwAs2aNePixYvExsbSu3dv/rjYekxMDIUKFSI2NpYDBw7gmyejYbyDkwupyf8LVeMir9/z2Dx8/Ym9/r+A7uLFi385rld69ya/qx2tQz2pF+AG8EStTrt8+XIAWrRokcMjebJNnz6dAQMGPPBwctSoUbz//vvcunXrgZ5XRETkSfHk/FYlIiKSAz788EO+++47tmzZQkREBN7e3vTt2xeA4zEpAFw8vJfXvttJh7fnsPqd0Wz+bDrdZy9l4NKtHPp+JWf2/ApkVN7UfmkAIzccYtCyX4m9epnvZ0/hYNT/wrMlS5awfv16zp49y8GDB5k7d+4jf86SUWk2btw4+vTpw9KlS4mLi8NisbB//34SEjIqx+Li4vDx8cHJyYldu3axaNGiO87zxhtvULBgQXx8fFi+fDnV69QjPTWFvEWf5sSvm0iMjSbu5jV+XfjxPY/tmQbN+WnpAo4dO0ZiYiJvvPFGlu33Mq4nhcViYdy4cbzxxhuYTNlb8VTg7NmzbN68ma5duz7wcxctWpQGDRrw0UcfPfBzi4iIPAkUrImIiPyF2bNnM2nSJAICAnB0dCQ8PJylS5eSnp7OzRQzAHV7voa9oxNFqtTB3tmF0s+1wM3HD8/ceQkuU4mI3w8B4FugIIUr18bOwRE3b1+qd3qF03u2cSPJbFyvf//+5MuXDx8fH8LCwti/f39OPG0Bhg4dyrvvvsuUKVPw9/fH39+fXr16MXnyZKpWrcrMmTMZPnw4zs7OjB079q79v2rVqkXDhg157733GDJkCK937wRAmSZtyFO4JFOaluXzPm0p1eD5ex5X0er1ePXV/tSpU4dChQpRuXJlABwdHQGYOXMmY8eOxd3dnQkTJjzRfcmWLFmCq6srjRs3zumhPNE+/PBDXnrpJdzc3B7K+UePHs306dMf6ynFIiIiD4vd3+8iIiLy33X+/HlatGiRZfqUra0t165dI9WcMYXTzcfP2Gbv6IRbrtt6Yzk6k5r0/xVOkddZPXUU5/btICUxHqvFgrOHFynm/00FzZMnj/Gxi4sLERERD+25yd/r2LEjHTt2vONxi8VC69at8fT0ZMGCBYwaNYoiRYoAcOvWLYKDg41poS+//DIAqampONjb4HDrGuk+Abww+dMs56zeqbfx8bA1e7Nsq9d7qPFxqIcDLUePZNzokQAcO3YMGxsb8ubNmGbaunVrWrdufdfnc/u4MmWuKvq4SU9PZ9y4ccyYMUPVav/ArVu3+PLLL9m3b99Du0bx4sWpW7cuH330EcOGDfv7A0RERP5FVLEmIiLyFwIDA1m3bh0xMTHGv+TkZPLnz4+D7f39GN04YxKYTAxY8jPhv5yl7cRZWK1WHO/zPJIzLBYLFktGCJoZtNavXx9bW1tOnToFZFRYffnll3ftN+XgkLFwQIdKJbDBcsf2e2Fngip5nFm+fDkpKSlER0czbNgwwsLCsqxg+m+wcOFC/P39qVevXk4P5Yn2+eefU69ePQoUKPBQrzNmzBjeffddY6q0iIjIf4V+kxcREfkLvXv3ZtSoUZw/fx6AGzduGA3tfR1t7+tcKYnxOLq44uTmQez1K/wybwYmwM/5/s4jOcPGxgYbGxvS09NZsmQJv//+OwAjR46kdu3aADRs2JBXX30VDw+PPz1PoIcTDQp4YnefRVh2Jqib35W8LvZ8/PHH5M6dm9DQUGxtbZk1a1Z2n9ZjKS0tjQkTJqi32j9kNpv54IMPGDRo0EO/VsmSJalVq9a/7mtRRETk7/y7/rQpIiLygA0YMACr1UqDBg2IiIggd+7ctGvXjubNm1PUy/G+zvXsy0NYMqYv42sWJFdgCGWatOXXhbMp5eP0kEYv2WW1WrFYLNja/i/0vHLlCosXL2bHjh1cvHiRgIAAXnnlFerWrWvs81eB2u3K+jkD8OPlBNKtf7Mz/wvVMo9bv379fTybJ8/cuXMJCQmhVq1aOT2UJ9qKFSvw9/c3+vA9bGPGjKFBgwb06dMHFxeXR3JNERGRnGay/rHRhoiIiNyzZWducTI2NdvHF/F0oGXBewtj5OFKT0/Hzs4Oq9WapUoqMTERFxcXli5dStu2bTl48CBPPfUUb7zxBsnJyQwePJhcuXJlOdeJEyewt7cnJCTkL695JTGN7VeTOH0rFRNkCdnsTGAlo6dalTzO5HWxf4DP9vGVkpJCkSJF+Oqrr6hSpUpOD+eJVrNmTfr16/dIF7Bo1aoV1atXfyRVciIiIo8DTQUVERH5B6r6O9/3lL5Mmf2yJOfNnz+ft99+GwCTyURMTAwDBgygUqVKDBo0iFOnTtG4cWNCQ0O5dOkSAFWrViUhIYHdu3cDGIsCJCQk8O2335KWlva3183rYk/Lgh70LelD9bwulPR2pJCHPSW9Hame14W+JX1oWdDjPxOqAXz66aeULFlSodo/tGfPHs6fP0/Lli0f6XXHjBnD1KlTSUpKeqTXFRERySmqWBMREfmH9t5IuucpfZn+OLVPHi2r1cqFCxcICgoC/leVZjabsbW1ZcaMGRw6dIj333+fIUOGYLFY6NOnD99//z27d+9mwYIFREZGMmvWLGP1SpPJdEe1m9yfpKQkChUqxMqVKylXrlxOD+eJ1qlTJ5555hlef/31R37t559/nrp169K/f/9Hfm0REZFHTRVrIiIi/1BZP2fq5ne958o1hWo5Ly0tjXHjxpGYmMiNGzewWq306tWLiRMnAnDw4EFCQkJwcnJi6NCh5M2bl507dxIWFsbRo0e5ceMGuXLlIjAwEDs7OyIjIwGyhGr62+X9mzVrFhUrVlSo9g9dvnyZtWvX0qNHjxy5/tixY5k8eTLJyck5cn0REZFHScGaiIjIA1DWz5mORTwp4mGPrYk7QjY7E9iaMnqqdSziqVDtEbNYLFgsFiBjpUQHBwcuX75MqVKlqFWrFkePHqVhw4b88MMPpKenExoaiqurKykpKQQGBnLx4kXi4uIIDQ0ld+7czJkzB4C2bdsyevRofH1977imKtfuT3x8PFOmTGH8+PE5PZQn3kcffUSnTp3w8vLKkeuXLVuWsmXL8tlnn+XI9UVERB4lrQoqIiLyAFitVvp0bMv27ds5czGCg1HJ3Egyk2K24Ghrg5+zLaV8nHCx19+0HpW4uDjc3d0BsLHJuO8xMTGkpKTg5eVFqVKlOHr0KEeOHMHR0ZEKFSowcuRI9u3bx9NPP82SJUtYv349zZs3Jz093QgpZsyYQe7cuQFwds4ISDUF9J+bMWMGtWrVolSpUjk9lCdaYmIic+bMYceOHTk6jrFjx9KyZUt69OiBo+P9raAsIiLyJNFv9yIiIv9Qeno6L7zwAitXrszo1WVvQ2V/F8KC3Wkd6klYsDuV/V0Uqj1C8+fPp2/fvsTGxgLw22+/0aFDB+rUqcPw4cM5ffo006ZNIyQkhNWrVxvHtWzZkjlz5tC4cWOeffZZZs+eTfHixUlISKBhw4YAFCpUCA+PrCu5KlT7Z27dusW7775LeHh4Tg/liTdv3jyqVatGaGhojo6jQoUKPP3003z++ec5Og4REZGHTYsXiIiI/ANJSUk0b96crVu3kpSUhJ2dHampqQpacoDZbMZkMmFjY8OlS5d48cUXeffdd3nmmWd4++23KVasGM8//zwtW7bE2dmZuXPnMm/ePJYsWcKGDRsAuHLlCiVKlODw4cPkz5+fs2fP4uLigr+/fw4/u3+3CRMmcPLkSebPn5/TQ3miWSwWSpQowccff0ytWrVyejjs3LmTNm3acOrUKRwcHHJ6OCIiIg+F/nQuIiLyDyxYsIDvv/+epKQkIGPKYWaVlDxatra22NjYYLFYCAgIoFChQqxZswaAoUOH4uXlRdWqVbl27Rrx8fFs3LiRLl26cPbsWVavXs2IESPw9PRk6NChxMfHAxASEoK/v3+WHm3yYEVHR/PBBx8wbty4nB7KE2/9+vU4OztTs2bNnB4KAJUqVaJEiRLMnTs3p4ciIiLy0ChYExER+Qd69OjB9u3b8fT0xNHRkbS0NK5evZrTw/rXs1qtWVbdNJvNfPjhh4SFhTFr1ixu3bpFz549+eWXX7h8+TJms5lPPvmEiRMn8uuvv+Lk5MTq1auxs7Nj+vTpLFy4kJs3b5KamsqIESMoWrRoluvZ2NgYfdrkwZo2bRrPP/88hQoVyumhPPGmT5/OoEGDHquK2bFjx/Lmm2+SlpaW00MRERF5KPQbooiIyD+QOfXQz8+PyMhIvvvuuxzvbfRvdfXqVcxmM5Bx300mEydPnuTixYt89tlnnDlzhnHjxnH58mX69etHhQoVcHR05JdffsHGxoYLFy5w8+ZNABISErh+/ToHDhygSZMmLF68mDlz5hgLFGReRx6uGzduMGvWLMaMGZPTQ3niHTp0iCNHjtC+ffucHkoWVatWpXDhwsybNy+nhyIiIvJQKFgTERH5hz7//HO6deuGq6srzZo1w97ePqeH9K+RnJwMwDfffMOECROIiIgAYMWKFbz33nv06NGDiIgIVq1aRaNGjVi5ciUrVqygcOHCANSvX5+ffvqJ+Ph4Bg4cyDvvvEPx4sWpUKECM2bMoHTp0sa1bp/uaWtr+4if6X/TlClTaNeuHUFBQTk9lCfee++9R58+fR7LXmZjx45l0qRJqloTEZF/JS1eICIi8g8kJiYSEBDAwYMHCQgIyOnh/GvcunWLfv36ARmrHF6/fp3XX3+d7t27U6tWLYKDgylbtizz5s3Dzc2N559/nsOHDzNy5EjatGmDu7s7ZrOZW7duUatWLWbOnEn16tU5dOgQxYoVU/j5GLh69SolSpTg0KFD5M+fP6eH80S7fv06RYsW5eTJk/j6+ub0cEhIs3AoKpnrSWZSzBYcbW1YPu9TaoT481LnF3J6eCIiIg+UXU4PQERE5En27bffUrlyZYVqD1h6ejoHDhxg8+bNAOTOnZtq1aphZ5fxq0v//v1Zt24dbm5uREdH8+yzz+Lt7c1LL70EwKZNmzh//jzdu3dn2LBhFCxYEICnn34ayJjqaWNj81j1ovqveeutt3jxxRcVqj0As2fPpk2bNjkeql1JSGPbtSTO3ErFBKTf9uf7Yk07EpGWxtLTsVTL40JeV4XbIiLy76CKNRERkX+gbt269OnTh9atW+f0UP5VoqOj6dWrF23atKFNmzZ3bL958yYVK1Zk69at5MuXj9jYWJo1a0bhwoU5duwYKSkpjBw5kpYtW+bA6OXvXLp0iVKlSnH06FHy5MmT08N5oqWkpBAcHMwPP/xAiRIlHsg5M/sX3m1BiYULF/Lll1+ycePGLI/vvZHEj5cTsoRpd2W1YGdjQ938rpT1c34g473duXPnCAkJIS0tzQjiRUREHiYFayIiItl05swZKlWqxKVLl3B0dMzp4fyrpKSkMH/+fLZu3crcuXONx61WKxaLBVtbW9q3b0/p0qUZMWIEAHFxcRw4cACLxULNmjWznM9qtao67THyyiuv4O7uzpQpU3J6KE+8uXPn8tVXX7F+/foHds6/Ctbu5p5DtdvYmXgo4ZqCNRERedS0eIGIiEg2zZ07l44dOypUewgcHR0pU6YM8fHxnD9/3ng8czVQgG7duvHRRx+R+TdCd3d3qlevboRqt6/sqVDt8XHu3DmWLFnC0KFDc3ooTzyr1cr06dMZNGhQjo3hSkLafYdqkDFN9MfLCVxJ1IIGIiLyZFOwJiIikg1ms5m5c+fSrVu3nB7Kv07mypw///wztra25MqVC8AI0GxsMn59ee6551i6dOkdoVnmflrZ8/H0xhtv0KdPnxzvB/YkCA4OZurUqZQqVQpXV1e6d+/OtWvXaNSoEe7u7pQvX56UlBQaNGjAjh07qFq1Kl5eXpQuXdroTwhQu3ZtRo8eTdWqVXFzcyMsLIzIyEg6duyIh4cHFSpU4Ny5c1muvXbtWgoWLIivry9Dhgwx/r+cO3cu1atXN/bL5+bAr9/M5Z3mFRlfM5QVbw3l9gkxu76dz7stqzKuejDTW1Xj8rEDAGz+4n3eCqtAwdw+lChRguXLlxvHZF7j9ddfx9vbm5CQENatW2dsP3v2LDVr1sTd3Z169erRt29fOnXqdNd7uGzZMoKDgzl8+DCnT5+mbt265MqVC19fXzp27EhMTEyW+/3OO+9QqlQpPD09adeunbEysYiIyJ9RsCYiIpINmzZtInfu3JQuXTqnh/KvY2NjQ1RUFLt27cJqtdK3b1+ioqIwmUxZ3rBbrVYqV658x/GqTnt8nTx5khUrVjB48OCcHsoTY9myZXz//fecOHGCVatW0ahRI958801u3LjB+fPnKVKkCBERETRp0oTRo0cTFRXFO++8Q6tWrbhx44Zxnq+++or58+dz+fJlTp8+TZUqVejWrRtRUVEUL16c8ePHZ7nu8uXL2b17N3v37mXFihV8/vnnd4wtIS0jbDv280b6LvieAV9v4dD3Kzmx7UcADn2/gh8+nkKbNz4i/JezdJ4+HxdPHwByBQTT67NVhP98muGjxtCpUyeuXLlinHvnzp0ULVqUmzdvMnToULp37278///CCy9QsWJFIiMjCQ8PZ/78+Xe9d1988QXDhg1j06ZNPPXUU1itVkaMGEFERATHjh3j4sWLhIeHZzlmyZIlrF+/nrNnz3Lw4MEsU9FFRETuRsGaiIhINnz++efGCpTy4Pn4+LB48WJmzJiBnZ0dGzZsAP5XzQYK0J5E48ePZ8CAAXh7e+f0UJ4Yr776Kv7+/uTPn58aNWpQqVIlypQpw4ULF0hJScFqtbJgwQIaN25M48aNsbGxoX79+pQvX561a9ca5+nWrRuhoaF4enrSqFEjQkNDqVevHnZ2drRp04Z9+/Zlue6wYcPw8fGhQIECDBw4kMWLF98xtkNRGdVctbv1x9ndE6+8ARSsUI0rJw4D8NvyBdTs0o/AkmUwmUz4FiiId75AAJ6u3xwPvzzY2thQpF5zChcuzK5du4xzBwUF0bNnT2xtbenSpQtXrlzh2rVrXLhwgd9++40JEybg4OBA9erVadas2R1je++995g6dSqbN282esUVKlSI+vXr4+joiJ+fH4MHD2bLli1Zjuvfvz/58uXDx8eHsLAw9u/fn41XTURE/kvU0VNEROQ+RUZGsn79embPnp3TQ/nX8/T0pEaNGsyYMYP9+/fTr18/AgMDc3pYkg1Hjx5l48aNzJw5M6eH8kTx9/c3PnZ2djY+f//996lVqxZJSUmcP3+eb775hlWrVhn7pqWlUadOnb89T+bn8fHxWa57+/9nQUFBRERE3DG260kZfQzdcuU2HrN3ciY1MQGA2GsR+ASE3PV57V39NVsXzCI64iK2JhPJifHcvHnT2H77arEuLi4AxMdn7OPj42M8ljnWixcvZjn/1KlTGTt2LAEBAcZj165dY8CAAfzyyy/ExcVhsVjuCHn/eN27PW8REZHbqWJNRETkPi1atIgmTZqo6uYRcHR0pGvXrrRu3RpXV1f15XqChYeH89prr+Hh4ZHTQ3niRUVFsWjRImrXrg1kBEudO3cmJibG+JeQkMDw4cOzfY3bg6oLFy6QL1++O/ZJMVvueOx2nv75iLp09o7HoyMu8u0bg2k27G3G/HSCufvOG1M1/07evHmJiooiMTHxrmPNtHHjRiZOnMiyZcuMx0aOHInJZOLQoUPcunWLBQsW3NM1RURE/oqCNRERkfukaaCPTuab3kGDBjF27FicnZ1zeESSHQcOHODnn3+mX79+j+yaCWkWdlxLZOW5OL45HcvKc3HsuJZIYtpfh0FPgjlz5hAWFoaXlxcAnTp1YtWqVWzYsAGz2UxycjKbN2/m0qVL2b7G1KlTiY6O5uLFi7z//vu0a9fujn0cbf/6rUSFFp34Zf5MLh89gNVq5eaFM0RHXCQ1ORGTyYSrd0ZQ/su3izh8+PA9jSsoKIjy5csTHh5Oamoq27dvz1Kpl6lkyZKsX7+evn37snLlSgDi4uJwc3PD09OTy5cvM3Xq1Hu6poiIyF/RVFAREZH7sG/fPqKjo6lbt25OD+U/QX3U/h3GjRvH8OHDcXV1fejXupKQxrZrSZy5lYoJSL+tIOlEDPxyJZGCHg5U9Xcmr6v9Qx/Pg2Y2m5kxYwYrV65kz549QEbF2ooVKxg6dCgdOnTA1taWihUrMmvWrGxfp3nz5pQrV47Y2Fi6du1K9+7d79gnt/Nfr7z7dP3mJMZG89WoXty6fgXvfAVo+8ZH5CtWiuqdXmFW10aYTCbC2nWkWrVq9zy2hQsX0rVrV3LlykXFihVp164dZrP5jv1Kly7N6tWradKkCfb29owbN44XX3wRT09PChUqROfOnZk+ffo9X1dERORuTFbVP4uIiNyzV199FV9fX8aNG5fTQxF5IuzevZvnn3+eU6dO4eTk9FCvtfdGEj9eTsgSpv0ZOxPUze9KWb8nqwpy8eLFfPzxx2zevDmnh0JCmoWZR6Iw/4N3E+bUFIpe20frZk2zfY527dpRrFixO1Y2FREReRQ0FVREROQeJScns3jxYrp27ZrTQxF5YowZM4aRI0c+lFDt3LlzmEwm0tPT7ylUWz7pdX6YMw3IqGT78XICe28kPfBxPSxWq5Xp06czaNCgnB4KAK72NhT0cPhH5/BMi+PN8LH31evst99+4/Tp01gsFtavX8+KFSt4/vnn/9E4REREskvBmojIf8y/ue/Qw7ZixQrKlClDUFBQTg9F5JEKDg5m06ZN933ctm3bOHr06F2nET5IVxLS7qlSrcWod3i252vG55nh2pXEtIcyrrlz51K9evUHdr7t27cTFRVF06bZr+560Kr6O2OXzRnbdiZo8UxBzGYzq1evvufjrl69Su3atXFzc6N///7MmjWLMmXKZG8QIiIi/5B6rImI/Ef82/sOPQpatEDk/owZM4YxY8bg6Oj4UK+z/VrSPU3/vJt0K2y/mkTLgo//973p06czYMAAbG3/urfZo5TX1Z66+V3veQpupsypuPndHBg7diwTJkygadOm99RXMSwsjLCwsH8wahERkQdHFWsiIv8Be28ksfBkLCdjUzFbuePNT7oVzFY4GZvKwpOxT9TUqEflwoULRq8okf+Szp07c+HCBcLCwnBzc2PKlCkA7Nixg6pVq+Ll5UXp0qWz9PyqXbs2nTt35tdff2XgwIGEhYURGRlJx44d8fDwoEKFCpw7d87Y32Qy8cEHH1CwYEF8fX0ZMmQIFktGFa3FYmHixIkEBQWRO3duXnzxRWJjY7OMcdlXi3i78TO8UbcoP3367p8+l2/G9WPjR28CkHQrhrn9X2Bi3WJ0eqYAjZo0MVbR/Omnn3j66aeN4+rXr0+FChWMz2vUqMF3330HwNtvv01oaCju7u6UKFGC5cuXA3Ds2DF69+7N9u3bcXNzM1bwXLNmDWXKlMHDw4PAwEDCw8ON82ZObf3yyy8pUKAAvr6+TJo0ydj2008/0a1bt797yR65sn7O1M3vii33lqz9sb9dixYtSE5OZt26dQ9zmCIiIg+FgjURkX+5+2nmDdnrOxQeHk6nTp2yOcInw5dffkn79u1xdn6yGp2L/FPz58+nQIECrFq1ivj4eIYOHcrly5dp0qQJo0ePJioqinfeeYdWrVpx48YN47ilS5fyxhtvcPnyZU6fPk2VKlXo1q0bUVFRFC9e/I5G88uXL2f37t3s3buXFStW8PnnnwMZ0ynnzp3LTz/9xJkzZ4iPj6dfv35Zjj23fyevfbudHrOX8cOcaVw/c+Jvn5fFYqF88w4MXbOXkev2k2LjaJy3cuXKnDx5kps3b5KWlsbBgweJiIggLi6OpKQkdu/eTY0aNQAIDQ3ll19+ITY2lnHjxtGpUyeuXLlC8eLFmT17NlWqVCE+Pp6YmBgAXF1dmTdvHjExMaxZs4ZZs2YZIV2mrVu3cvz4cX744QcmTJjAsWPH+PDDD+nWrRtubm739fo9ChaLhSXTxvN+x/rktUnB1sQd00PtTGBrgiKeDnQs4pll0QgbGxvGjBnD+PHj76vXmoiIyONAwZqIyL/YvfYd+qPMcO29jz97oP2BnlQWi4UvvvhC00BF/t+CBQto3LgxjRs3xsbGhvr161O+fHnWrl0LQHR0NG5ubgwePBhPT08aNWpEaGgo9erVw87OjjZt2rBv374s5xw2bBg+Pj4UKFCAgQMHsnjxYgAWLlzI4MGDKViwIG5ubrz11lt89dVXpKenG8fWfXkI9k7O5C3yFHmLlOTKySN/+xxcvXx46tkwHJxdsHNxo/krr7FlyxYAnJ2dqVChAj///DN79uyhdOnSVKtWjV9//ZUdO3ZQuHBhcuXKBUCbNm3Ily8fNjY2tGvXjsKFC7Nr164/vW7t2rV5+umnsbGxoVSpUnTo0MG4bqZx48bh7OxM6dKlKV26NDt27GDu3Lm8+uqr9/DqPFrXrl2jRo0aTJs2jasnDlPTI5W+JX2onteFkt6OFPKwp6S3I9XzutC3pA8tC3qQ1+XOabetW7cmPj6ejRs35sCzEBERyT71WBMR+RfbdlvfIavVitVqxcbm3v6mkm6Fc7GpxudWqxWTycTp06cZMmQIJUuW5I033uDSpUucOnXKeJNrsVju+RpPijNnztC5c2fKli2b00MReSycP3+eb775hlWrVhmPpaWlUadOHaxWK2fPnqVDhw5GLzBnZ2f8/f2NfZ2dnYmPj89yzsDAQOPjoKAgIiIiAIiIiMiyYEhQUBDp6elcu3bNeMwtV27jY3snZ1ITs577blKTElkzbQwntv1IUlwMNphISojDbDZja2tLrVq12Lx5MwEBAdSqVQtvb2+2bNmCo6MjtWrVMs4zb9483n33XWNqa3x8PDdv3vzT6+7cuZPhw4dz+PBhUlNTSUlJoU2bNln2yZMnj/Gxi4sLGzZsoF69ehQoUOBvn9ejdPDgQWrUqEFiYiLp6em4uLhgY2ODi70Nlf1d7utcNjY2jB49mvHjx9OgQYN76rUmIiLyOPh3vfMREXlMBQcH884771CqVCk8PT1p164dycnJxvbVq1fzzDPP4OXlRdWqVTl48KCxbe/evZQpUwZ3d3fatGlDu3btGD16NJBRFdK0aVP8/Pzw9vamadOmRo+ghDQLQ9s2YsOMSczu1pixVQsQdekcu1cs4t2WVRlXPZgpYeXZufTLPx33jeR0LP8fzJlMJiZPnkzZsmVZtWoVCxYs4IcffuDYsWMcPnyYlJQUIOPNUZs2bciTJw+enp7UrFmTI0eOYLVaMZvNdO3aldatW1O8eHHc3NyoVq0aERERDBgwAG9vb4oVK8a+ffuwWCxGj6WcFhQUxKhRo/RGT/6z/vi1HxgYSOfOnYmJiTH+JSQkMHz4cNasWYPFYqFcuXL3dY2LFy8aH1+4cIF8+fIBkC9fPs6fP59lm52dXZagLjt+WTCLG+dP0WfeesJ/OctbSzL6e2VORcwM1n7++Wdq1apFrVq12LJlC1u2bDGCtfPnz9OzZ09mzJhBZGQkMTExPPXUU8Y57vY944UXXqBZs2ZcvHiR2NhYevfu/ZfTH61WKz/88AODBg36R8/3YXB2diYkJCTL8/wnf1hp27Yt0dHR/PDDDw9ieCIiIo+EgjURkUdkyZIlrF+/nrNnz3Lw4EHmzp0LwL59+3jppZf4+OOPiYyMpFevXjRr1oyUlBRSU1Np0aIFXbt2JSoqig4dOhiNsSGjOqxbt26cP3+eCxcu4OzsbPQIOhSVEdztW/sNLUa/S/gvZ/HOG4ibjy9d3l9I+C9naR3+PmveHcPlYwf+dNwJ6Rnh1vHjx/nwww954YUX+OCDD/jpp58IDg4mOTkZR0dHHBwcuHTpEnv37qV8+fJs3ryZK1euUKZMGTp27IjJZDKqVzZs2EBAQABnzpzB0dGRatWqUa5cOW7evEnr1q0ZPHgwu3btMqaCAaxcuZJRo0YBGW80LRYLZrP5jvDtYfTnsbe3x8HB4YGfV+RJ4e/vz5kzZ4zPO3XqxKpVq9iwYQNms5nk5GQ2b97MhQsXGDNmDMHBwfcdsEydOpXo6GguXrzI+++/T7t27QDo0KED06dP5+zZs8THxzNy5EjatWuHnd3/Jl78sZ/XvUhNiMfe0Qknd09Sb0Xz7YzJWbZXrVqV48ePs2vXLipWrEjJkiU5f/48O3fupGbNmgAkJCRgMpnw8/MD4IsvvuDw4cPGOfz9/bl06RKpqf+r/o2Li8PHxwcnJyd27drFokWL/nKckZGRuLu7U7ly5ft/kg9Z4cKFWbt2LY6OjhQpUoTExMQsr8v9srW1NarW1GtNRESeFArWREQekf79+5MvXz58fHwICwtj//79AHzyySf06tWLSpUqYWtrS5cuXXB0dGTHjh3s2LGD9PR0+vfvj729PS1btqRixYrGOXPlykWrVq1wcXHB3d2dUaNGGb16rieZsQLlwtrjH1oMWzs7bO3tKVajAbkCMyoMCparRuHKtTm3b8ddx2yxQtr/l6zZ2tqSkpLClStXcHBwIDg4mNDQUEqUKEGxYsWwt7fnk08+oX379hw+fJiZM2eyb98+7OzsOHDgAFWqVGHlypUAlC5dmqJFi5I7d26qVatGcnIyycnJbN26lebNm7Nv3z7jTXumxo0bG5V6JpMJGxsbbG1t73jzfnvlROYbs02bNjFhwgQiIyP/9PVJSkri3LlzJCYmkpaWRlKSVkYVyTRixAgmTpyIl5cX77zzDoGBgaxYsYI333wTPz8/AgMDmTp1KmvXrsXGxgZfX9/7vkbz5s0pV64czzzzDE2aNKF79+4AvPTSS3Tu3JmaNWsSEhKCk5MTH374YZZjsxPBVOvYi7SUZCbWLcqHLzakddNGWba7urpStmxZSpYsaQTrVapUMVYnBShRogSvvfYaVapUwd/fn0OHDlGtWjXjHHXr1qVkyZLkyZPHuCczZ85k7NixuLu7M2HCBNq2bfuX47x06RL169fPxjN8NCZPnkyPHj34/fffOXToEHnz5v1H52vXrh3Xrl3jp59+ekAjFBERebjUY01E5BH5Y8+czP5B58+f58svv8zyRjE1NZWIiAhMJhP58+fPEhbd3ocoMTGRQYMGsX79eqKjo4GMagiz2UyKOaOSy9M/f5ZxHP91Ez98/A43L5zGarGQlpyEf6HifzpuizUjoCpUqBDDhg1jypQp/PDDD3z//fdMnTqV0NBQQkJCOHXqFCkpKRQrVgw/Pz9WrFjB7NmzjeqF3r178+uvv5KamoqLi4uxuqatra1xPzZu3Ejz5s2Jj49ny5YtHD9+nGLFijFnzhyOHTtGTEwMQ4cO5dq1ayxcuJBjx47RoEEDwsLCcHJy4uzZs8b5XVxc8PT0xM7OjqioKBISEoznlNlDCf7XO+7s2bPMmjWLjh07Eh8fz+LFixk9ejQhISF39I2zWCzGcbc/nvnYHz8WedI1b96c5s2bZ3msUqVKWZrum81mSpcuzeTJk2nSpEmWfSdOnJjl83r16nHq1KksjzVu3Jj+/fvfcW0bGxvGjh3L2LFj79gWHByM1Wpl2ZlbnPz/npAvz1nxp8/DarFia58Rknn45TH2LeLpQMuCHrza55Us+2/fvj3L50uXLr3jnJMmTWLSpEl3vZ6DgwNr1qzJ8ljr1q1p3br1XffPfD6Z9uzZg4eHBx999NGfPqecdOnSJebPn8+xY8cwmUw89dRT//icdnZ2jB49mgkTJlC3bt0HMErJlJBm4VBUMteTMn5HcLS1IbezLaV8nHCxV72FiEh26TuoiEgOCwwMZNSoUVl6FSUmJtKhQwfy5s3L5cuXs7zRur0P0bRp0zh+/Dg7d+7k1q1b/Pzzz0BGqONom/Et/vZsJz01hYVDXqLGi30Y9f1Rxv18mqLV68FfTLmxMWVUgc2dO5c33niD/v37c+7cOUwmEyNHjgQy3lBnjs3Dw4N169bxySefULt2bby8vICMN5/r168nJSWF9PR0XF1dAcidOzfu7u40a9YMR0dHfvzxRwBKlSrFsGHD+P3336lRowY7d+4kMTERgDfffJNjx45RpUoV5s2bZ7xxfe211+jUqRMjRoygXLlyxpvgpKQkPDw8jDAt878Z98dkPAcnJyc8PDxITk7Osn9meJb5OtxLtdyaNWv45Zdfshx3t+mrmdNaMxeXEHlSLVmyBDc3Nxo3bvzIr13V3/lvp4Oa09O5cf4U3vmyLgBgZ4IqeZwf4uiyb/r06bz66qv/aHrlw/Tmm2/So0ePf9zv7o9eeOEFLl26dMdqqZI9VxLSWHbmFjOPRLH1SiJHo1M4fSuNo9EpbL2SyEdHolh25hZXEtJyeqgiIk8kBWsiIjmsZ8+ezJ49m507d2K1WklISGDNmjXExcVRpUoVbG1tmTFjBunp6axYsYJdu3YZx8bFxeHs7IyXlxdRUVGMHz/e2Jbb2ZY/vs80p6WRnpqCq3cubOzsOP7rJk7u2PynY7Mxgb1NxlmqVKlCnz59OHPmDNHR0Tg7OxvBUmagFBcXh52dHY6Ojvj4+ODt7U316tUBjADM3d2d+Ph4PDw8AFi/fj0RERHMnj2bI0eOGKuLRkVF4ebmZowlNjaW0NBQDh8+TGRkJC+//DIvvfQSFStWZPv27aSlpREbG0vTpk1ZsGABI0aMYMeOHVy/fp0uXbrQr18/vLy82LBhAwsXLiQ2NjbLc/Xw8OCpp57C39+fhIQEY0GFgIAA3njjDRITEzGZTOzZs4ehQ4fSrVs3li1bhtlsJi0tjRMnTnDkyBGOHTtGVFQUS5Ys4dChQ1nuz90CucyqN5PJ9JcVbreHbgrh5HGTnp5OeHg4EydOzJFKzbyu9tTN7/qX4dqbDUri7ObBU882NR6zM0Hd/K7kdbF/BKO8P5cvX2bt2rX07Nkzp4dyV+fPn+frr79m6NChD/zcdnZ2jBo1igkTJjzwc//X7L2RxMKTsZyMTcVsxVgpPFO6FcxWOBmbysKTsey9oTYIIiL3S8GaiEgOK1++PHPmzKFfv354e3tTqFAhY2EDBwcHvv32Wz777DO8vLxYsGABTZs2xdHREYCBAweSlJSEr68vlStXpmHDhsZ5n/ZxuuNajq5uhA15k8XDejKhViH2r/uW4jWf+8vxudpl/KhISUlh7dq1zJs3j3LlynH9+nXeeuutLPteuHCBhg0bEhQURM2aNVmxYgU+Pj4A+Pr6GtM/U1NTyZUrFzExMezdu5eQkBA++eQTwsLCjH1iY2PJlSuXce7Y2Fh8fHy4cOECQUFBRr+ip59+Gjs7OyIiInB1dTUafPv5+WFjY2NMAfX09ASgZMmSlClTBienrPcnKCiILl26GOM6ffo0Q4cO5dKlS2zcuJHNmzcb01XLly9Ply5dWLduHb/99hvx8fH07t2bPn360LdvX15++WV2797NhAkTaNWqFefPn+fEiRP07t2bRo0aMW3aNG7dugXA1q1b2bJlCz///DNHjx4lLS3N6PN2u8ywwmKx/G0I90eZFXF/fOxuj4tkx8KFC8mTJw/PPvtsto7PnG7+T5T1c/7LcG3Mj8d5aeY3ODi7AP8L1cr6PZ7VajNnzqRjx45G1e/jZuLEifTu3Ttb/fTuRadOnTh79ixbt259KOd/HDVq1Igvv/zzlbrv194bSfx4OeGOMO3PpFvhx8sJ9x2uPehxi4g8aR7PunIRkX+Zc+fOZfk8PDw8y+cNGzbMEordrnz58sZCB5DR1ygsLAyAfPnysXnz5iz79+rVC8j4Bj9lyTqj71CmKu26U6Vd93sad7tOXWg59lUgY2rmt99+S5cuXfjxxx+zrJKZWVXw2muv8fTTT9O+fXsAzp49S79+/ShVqhTNmzend+/ezJ07l+eeew6LxYKnp6fRDHzZsmUsWrSIsLAw0tPTadmyZZbQJzExERcXFwoVKsTbb79tPM9NmzaRL18+PD09SUxMNAK0zFX4MkPITAEBAX/6fNPT07Gzs+PGjRs0a9aM0NBQIGO6bmJiIsuWLWPJkiWULVuWypUr8/3331OoUCGCg4OJjIxk1KhRRiPyxo0bM23aNBo1asTp06eZP38+zZo1o1SpUrz99tv8/PPPNG3alMGDB+Ph4UGBAgXYv38/L7/8MidOnGDu3LmMHTuWgQMHsn79eo4cOULbtm0JDAzkyy+/pHDhwlStWjXL+C9fvoyTkxNOTk64uLhkqZTLdLfecH8nM4CztbXl8OHDnDlzhqpVq+Lr62sEfYB6yv1HpaWlMX78eObOnZvjXwOlvO2ZNvYNfCvWI6BMFUxkrdCxM2UsdBDq4UCVPM6PZaUaZHy/mzNnDtu2bcvpodzV6dOnWb58OSdOnHho17C3t2fkyJGMHz+e77///qFd53Gybt26B3auKwlp9xWqZcoM1/K62vHxlEmcOnWKBQsW/OUxD3LcIiJPIgVrIiKPuS1btlC0aFF8fX1ZuHAhBw8e/NMQ7o+q+jtz9lbqff9iDXfvO1SwYEF8fHxo1KgRn332GcHBwVm2T5s2LcvnISEhrFmzhpSUFG7dumWEXJMnT8bf3x+TycSAAQNYtmwZW7dupUePHoSGhmKxWOjdu7fRwPrw4cPcvHkTe3t7AgMDqVSpEiNGjCAgIIDjx4/TvXt3XFxcuHTpklHdkZiYiI2NzR2VaX8ls6dacnKyMaUVMsIod3d3vv/+eyNwi4yM5JVXXqFWrVrExMQQGhpqNO5OSUkhISEBb29vIKMqbdmyZezevZsyZcqwYcMG7OzsqF27NqmpqfTo0YP27dszaNAg5s+fz8qVKxkwYAADBgygRYsWxMXFceHCBWOa7Pz582ndurURrKWlpWFvb0+zZs3IlSsXzs7O/P7778ycOZNffvmFBQsWMGbMGF544QXs7e3Zt28fW7ZsISUlhfr161O2bFnS09OJiYnBzs7OCOcy3R7C+fv7Y7FYjJ5P9xLQZU5dzQxKM4O9vXv34ubmRpEiRe75NZLHzxdffEFoaCg1a9bM0XFcuXKFpk2bsnfvXp7Z8TO/7trDwahkbtzWqN3vCWnUPn/+fKpUqfKPq/gelokTJ9KvXz+jIvlhefHFF5k4cSLbtm274w8J8te2XUvK1s9+yAjXtl/9+6q1zO/r9/OHGhGRfyN9FxQRecwdP36c0qVL4+XlxbRp01i6dCl58+a9p2Pvpe/Q3fxZ3yGTycSsWbMYM2bMXaf/ZC5i8EeOjo74+fkZfdWeeeYZ4zlUr16d6dOnM336dEaMGEHbtm2xsbGhQYMG7Nq1i8OHDwPw66+/UqlSJQCGDRtG06ZN8fPzY9KkSZQpUwaLxULNmjXJnTs3kBGSOTo6ZgnHAD777DM2bNjwp2OFjGBsw4YNxmIJ27ZtIygoiMqVK3PlyhVefPFFBg0axPDhw6lSpYqxAIG7u7txraSkJCPku3DhAq1atWLQoEFUqFCB8PBwo6rPxcWFp59+2rhP7dq1w8nJiYCAAK5fv47ZbCY9Pd1Y6RQypgjny5cvy+sCGYs01K5dmxUrVtChQwdee+01GjVqxFdffcXixYu5cOECly9fNoI9e3t7PvroI86fP09ERARt27alffv2lC9fno4dO7JmzRpeeuklypYta6yOePHiRaKiovDy8mLnzp3s27eP3bt3s2fPHtLT00lJSbnrNNbM/nK395i7du0aly9fzrKYw9+xWq2YzWbjnv+bJKRZ2HEtkZXn4vjmdCwrz8Wx41oiiWmP7/NMSUlh4sSJvPHGGzk6jp9++onixYsb1b3e3t642NtQ2d+FsGB3Wod6EhbsTmV/l8c+VLNYLLz33nsMGjQop4dyVydOnGD16tUMHDjwoV/LwcGBkSNHPra91iZPnkz+/Plxd3enaNGi/PDDD0DGa/j2228TGhpKrly5aNu2LVFRUUDGH246depErly58PLyokKFCly7dg2A2rVr8+mnn971WuHh4bRu3Zp27drh7u5O2bJlOXDggLE9IiKCVq1a4efnR3BICPNmzzC2bZo9hUXDurNkTF/GVQ9meuvqXDq639i+Ze4HvPXc04yrHsy0FpU5tfNn1q5bx5tvvsnXX3+Nm5sbpUuXNsY4atQoqlWrhouLC2fOnMky7rlz51KtWjUGDRqEl5cXBQsWZNu2bcydO5fAwEBy586dZdromjVrKFOmDB4eHgQGBmap6M9cKOnLL7+kQIEC+Pr6/ukKvCIiOUkVayIij7mXX36Zl19+OdvHZ/YPutcpIX/Xdyh//vzkz5//rttuX23zr2ROR4Ss/b/+aori7UGer68vHTp0yLLdycmJTz75xPi8S5cuWbZnXq9GjRrY29vfdayZ+9jb21O1alXatGlDREQEw4YNIzQ0lODgYLZu3UqHDh3IkycP6enpjB49msTERKKjo42VTp2cnIiJiTGCtZo1azJhwoQ7wof4+HjS09ONQC45ORlHR0fs7e1JS0sjKSkJNzc3EhMTcXJyMqrEbt26ZRxz+7hjYmJo1qwZALly5aJu3boUKVIEb29vjh8/DsCyZctYtWoVrVq1onz58nz44YfUrVuXGjVqcPbsWb744gtq165NjRo1+PLLL3nnnXf4+uuvWbp0KRUqVGDnzp3s37+fmjVr8sEHHxAZGUnevHn56aefGDVqFGfOnGHJkiXUrFmTadOm4ePjw+DBg3FwcCAyMpKgoCD69++Ph4cHwcHBRi+8Cxcu4OHhgYODgxEg/lHm103ma3cv0w4zw7fMqay3P3Z7r7qYmBhcXFyyTHF+VK4kpLHtWhJnbqXeMXXxRAz8ciWRgh4OVPV3Jq/r4zV1cc6cOTz99NNGb8OcMm3aNJKTk43X9kkOXTds2ICTkxO1atXK6aHc1YQJExg4cOAj6/3WtWtXJk6cyM6dO40/rjwOjh8/zowZM/jtt9/Ily8f586dM/5g8+GHH/Ldd9+xZcsW/Pz86N+/P3379mXx4sV8+eWXxMbGcvHiRRwdHdm/f7/RW/TvrFixgsWLF7NgwQLef/99nn/+eU6cOIGtrS1hYWE0b96cxYsXs2r/SV5u2YRcQYUoUrUuAMe2bKDjO1/QOvwDNs58k5VvD6fPvPXcOHeK7V9/Rt8F3+Phl4foiAtYzGb8C4TwYv8hJF89f8dU0Pnz57Nu3TqKFi161z6dO3fupEePHkRGRjJu3Djat29PWFgYp06dYsuWLbRq1YpWrVrh5uaGq6sr8+bNo2TJkhw+fJj69evzzDPP8Pzzzxvn27p1K8ePH+fEiRNUrFiRli1bUrx48Wy+ciIiD97j/Sc7ERF5IMr6OdOxiCdFPB2wNXFHBZudCWxNUMTTgY5FPB96M+/bA5G7VTLdi7s13/+rKrRMRYoUISQk5C/3efPNN5k2bRpbt27lyJEj9O7dG3t7e5ycnBg3bhzNmzcnNDSUQoUK4ebmhpeXF7Vq1TIq8gAaNGhAvXr1GDhwIFWrVqVatWrUr1+fdu3a0alTJ44cOUJiYiIRERHGcbGxsTg4OGBnZ0dSUhJmsxkXFxc8PT05c+aMsZjDyZMnswRrmffNbDZn6THn7e1thHFxcXH4+Phw4MABKlasSExMDF9//TUNGjQgf/78REVFUbJkSQoXLgyAl5cXzZs3p0CBAjRq1IhTp06RkJCAvb09uXPnxsbGBnt7e55++mm++OILli5dyrBhw2jevDmnT59m9+7d/P7770BGBUNaWhotW7bk119/ZeXKlQD069ePpUuXAlC1alX69u1L69atKVSoEHv27MnymmSGasuWLWP8+PGsXbuWtWvXEh0dDWT0x4uOjiY5OTnLMTY2NsbX2O33K3Ml1swA5pVXXmHOnDnG19Pt/zWbzUaVXKYHtfDDk7xiX1JSEm+99dZjUU20atUqXn/9dRwdHbGzs3uig7Xp06czaNCgHO9XdzdHjx7l+++/p3///o/smg4ODowYMeKx+Dq7na2tLSkpKcaiM8HBwUZfztmzZzNp0iQCAgJwdHQkPDycpUuXkp6ejr29PZGRkZw6dQpbW1vKlSuX5WfHXylXrhytW7fG3t6ewYMHk5yczI4dO/jtt9+4ceMGY8eOxcHBAfvcBajQohMHNyw3jg16phLFqtfHxtaWMk3acuXkEQBMNjakp6Zy/cxxzGlpeOcrQK7AENKtkJB+9/+PunbtSsmSJY3K5z8KCQmhW7du2Nra0q5dOy5evMjYsWNxdHSkQYMGODg4cOrUKSCjAu7pp5/GxsaGUqVK0aFDB7Zs2ZLlfOPGjcPZ2ZnSpUtTunTpLJV6IiKPA1WsiYj8R+R1sadlQXsS0yxPbN+h290thLvXirm/c3tF3R8rCQoUKECBAgWyPBYaGnrHm74ZM2ZgtVpJSUnB3t6eIUOGcODAASIjI0lMTCR//vzY2NjQs2dPo/IjKCjICK2Sk5NJTU3F0dGRhg0bMn36dJo2bUqJEiW4ceNGlqquzLFGRUUZPY8yr3t7rzRnZ2dCQkKwWCyMHTs2y3gzg6zMwC4tLc2YrpuWlkZaWhpOTk7Ex8cbYd3Nmzfp3LkzkFHld3tPqHz58pGYmEhCQgLu7u706tWLIkWKkJyczDfffEPHjh3JlSuXMaU1PT2d2rVr07NnT6ZOncqyZcsoVqwYrq6uxuuxevVqvvrqKwoUKMCuXbuYMGECmzdvpmbNmixbtoz58+dz6dIlihQpwkcffYSfnx8tWrSgUqVK/P777/To0QOz2cyhQ4ewtbWldOnSVKxY0QjafHx8sizE0LVrVwICApg4ceIdXx9/FgJ/+umnxMXF8fLLLxMbG8trr73GU089xcCBA42KxkzZXbEPMsLyzZs306lTJy5dugRAcHAwn376KfXq1bu3E96jX375hR49ehhVj5lmzZpFxYoVKVeu3AO9XnatW7eOxYsX4+zsTEpKSk4PJ1sOHz7M4cOHjanij5vx48fz2muvZQn2H4WXXnqJN998k99++40KFSo80mv/mUKFCvHee+8RHh7OkSNHeO6553j33XfJly8f58+fp0WLFlm+T9ja2nLt2jU6d+7MxYsXad++PTExMXTq1IlJkybdNaD6o8DAQONjGxsbAgICiIiIwGQyERERYfwsSbNYSTebCS7zv0pS91y5jY8dnJxJT0nGnJ6Ob4GCNH19Ips+nsK108cpUqUOTV57Aw+/PJgtVu72U/X2cdyNv7+/8XHmz9A/PhYfHw9kVLcNHz6cw4cPk5qaSkpKCm3atMlyvjx58hgfu7i4GMeKiDwunpx3UCIi8kA8qX2HHqW/qxTJrJa7vSrmbhUyJpPJCLbc3d2pXr06zZs3p0OHDnh5eeHh4cG4ceOM/cPDw2nSpAkAefPm5dChQ9jb2+Pm5sbcuXMZOHAgzZo1Y+nSpXd9Y1O6dGkjvElLS8NkMhlv1qKionBycuLFF19kz549jBs3jhkzZvDee+8RGxtLfHw8qampuLm5Gcdnnis5ORl7e3scHBxITU01Qr309HTj+SUnJ2NjY2Mcb7FYcHR0JDExEUdHR4KDg7FYLJQoUcLoNRQfH29cIzExkdatWwNQvHhxrly5csfrMGPGDDp06MC0adMYNmwYLi4u+Pn5cerUKWbNmsWMGTPYv38/ISEhLF+eUamxadMmbt68SVhYGAEBAcZ9uX79OtOmTTNW7N2wYQM9e/YkJSWFUaNG8eqrGavhbtmyhdatW9OmTRt++uknYxXUNWvWkD9/fuzt7fntt9+Masnk5GTeeecd3NzciI+PZ+zYscydO5eLFy9meS7/dMW+K4lpf7/zA1KjRo07QrX4+HimTJny2FQRJSYmsnDhQp5//nkaNmxI8+bNc3pI2fLee+/Rp0+fHJmS/HcOHTrEli1b6Nu37yO/tqOjI8OGDcvxXn5/9MILL7B161bOnz+PyWRi2LBhQEbwtG7dOmJiYox/ycnJxveMcePGcfToUbZt28bq1auZN2/ePV3v9u8jFouFS5cukS9fPgIDAwkJCTGutfjgJcZvPUe3D7+6p/M+06gVvT9fw7A1+8BkYt37Gf9f29ne/feCB1lN+cILL9CsWTMuXrxIbGwsvXv3fiDVwCIij5LeRYmIiNynzAqn26sR7nV1zD8GcrdPX/3jm4nbKxgKFy5MvXr1qF69Oi1btrxrxcju3buNj8eNG8drr71mnHfnzp1ARsXdyJEjgYwm5FevXsXZ2RlHR0dCQkKwsbEhPT2dtLQ0Y+GHmJgYoqOjMZlMXLlyhdTUVACuXr1qBACZ/eIygzaz2YyrqytJSUmcPXuW69evY2Njw7p16wgKCsJkMpGenm5Mgbo9lMucdpl5/cw3cZcvX6ZKlSqkpKTg7OyMu7s7np6ebNy4kRs3btCyZUvKli3Lt99+y7Fjx4CMN599+vShVatWBAcHU6ZMGQIDA6lVqxaJiYls2LCBc+fOERkZiclkolGjRqSlpTFmzBggI+QbO3YsAwYM4KOPPuLmzZsAhIWFcevWLZycnGjSpInRNP/q1avGa+rg4GD0Afrjgg6PYsW+h2nGjBnGFK7HgaurK8WKFXssp0/eq+vXr7Ns2TJ69eqV00O5q3HjxjF06NA7Ki8flR49erBnzx727t2bI9f/o+PHj/Pjjz+SkpKCk5MTzs7Oxs+B3r17M2rUKM6fPw/AjRs3WLFiBZCx0MahQ4cwm814eHhgb29/z20Q9uzZw7fffkt6ejrvvfcejo6OVK5cmYoVK+Lu7s7kyZNJSkoilwPcPH2Mi0f2/e05b5w7xeldv5CemoKdoyP2jk6YbEzYmSAgbx7OnTv3UKdWZ7YpcHJyYteuXSxatOihXUtE5GFRsCYiIvL/UlNTH+oUk8zFGf44Pej27X/GarUa//7sTc4fH8+csmkymbJMn6pUqRLjx4/ngw8+4O2338bBwYHKlSvz8ccfG8dt3rzZWAWuevXqxrYXXniBpk2bYjab6dChgzGV08HBgYCAAAASEhJISUkxKtb8/f158803eeGFF1izZg19+vQhNTWV69evG9P2Mvu3Wa1Wo0Lu9ntjsVgICAjg2LFjODo6cvXqVeLi4nB2dsbZ2ZnGjRuzd+9e9u7dy7Fjx5g8ebLx3AsWLGjct1dffZW1a9fy5ZdfcuvWLSIjI5k3bx6enp44ODhw+fJlJk2aZKwuGxUVxcyZM1m5ciUbNmzg6tWrQMZUJjc3N4YNG4aTkxPffPMN6enpJCcn37WiZ9y4cfj5+REUFMTY8W9wKiajF9ym2VP4etQrxn7RERcYUdYPc3o6ALtXLOLdllUZVz2YKWHl2bk0YzW907dSSf6T/kcAu3btokqVKnh5eZE3b1769etnBKKZ92XmzJkULlwYd3d3xowZw+nTp6latSoeHh60bdvW2H/z5s3GawsZvQCnTZuWZfU++ecOHz7MrFmz8PPzy+mh3GHv3r3s3LmTV1555e93fkicnJwYOnToY1MlmZKSwvDhw/H19SVPnjxcv36dt956C4ABAwbQrFkzGjRogLu7O5UrVzb+uHH16lVat26Nh4cHxYsXp1atWsaU+r/TvHlzvv76a7y9vZk/fz7ffvut8b1y9erVRsVu05IF+GbCIFLib/3tOdNTU1j/4Ru8Ubcob9YvSXzUTRq+OgYr8OqLGYsE5cqVi7Jly2bvRv2NmTNnMnbsWNzd3ZkwYQJt27Z9KNcREXmYTFbV2oqIiAAZFRHdu3enSpUqOT2UB+72vnG3h3O3r8R6+z7/VFpaGvb29uzatYsBAwbwxRdfsHv3bgoUKEDNmjVJSEjgvffe4+WXX8bOzo7nnnuOXbt2kZaWxsKFC9m6dSuffvpplnOuWrWK9957j+eee47Y2FhmzpxJZGQkx48fp127dqxcuZJ8+fKRkpJCamoqTk5OBAUFGVVmERERhISEGP23Xn31VQIDA/nkk08wmUxUqlSJRYsWceXKFfz8/AgLC+PIkSPGipPvvvsuH374IZUrV8bd3R0/Pz8+/vhjXnrpJapUqcLXX39NUFAQv/76K8HBwZw9e5bg4GA8PT2pVKkSy5YtIzIykprP1qdyp76Ufb4Tm2ZPIfLiWdpNmgVkBGtTmpZj4q4r2NrZ8fsvG/ELLoxPQDBn925j7qsd6PXZKoJKlMblzG7e7N/jrj3W9uzZQ1paGuXLl+fSpUs0atSIXr16MXDgQON1b9asGfPnz+fixYuULVuW6tWrM2fOHHLlykWVKlUYNmwYXbp0uaOX2/jx4zl9+vQ9T1/7Jx7k1+Tjzmw2/+XKyDkpLCyMBg0aGFOkc0pSUhIFCxZk3bp1PPPMMzk6lkctPDycU6dO3bFC559ZduYWJ2NT/37HP1HE04GWBe9tUQURkf+6x+8nt4iISA44fvw4q1evpnz58jk9lIfi9nDCZDLddSXWewkw/moV1tu3ZU5jvX79OomJiRQrVoxOnTpRs2ZNLBYLrq6ujBo1Cj8/P7y9vdm1a5dxXNeuXZk5c+Yd127cuDGvvfYa3t7eODs7ky9fPmxsbChWrBjh4eE8//zzlC9fnmbNmhEbG0t0dLTRzw0yKvEqVKjAO++8w7vvvsvatWu5fPky58+fJ1euXIwePZpcuXIRFhaGjY0N0dHRuLi40KpVK1q3bk10dDQeHh5GpVymKlWqcPr0ab7//nuKFy9O/vz5jW3p6ekkJCRw9epVfvnlF4KDg2navR+713zzt/caoFiNBuQKDMmovCtXjcKVa3Nu3w7SrRCT+ucVa+XKlaNy5crY2dkRHBxMr1697lhpb+jQoXh4eFCyZEmeeuopGjRoQMGCBfH09KRRo0bs23fnNLKoqCg+/PDDOxa/eBAyw96VK1dSvnx5I/D88ccf+e6774wVX81m8x2rsv7x78RP4qqg97sy8qMSGxtLnz59crRaLZOzszNDhgx57HqtPY6q+jvfsQL4vbIzQZU8D3d1cBGRfxOtCioiIgJ88cUXvPjii/e0Mtt/2e3h2x9XYb1bMFenTh2++SYjRDKbzVmmwloslrt+DNy1efv+/fs5cOAA+fLlIyEhwWhQb7VaadmyJS1btgQywiw7OzvS09M5fPiwcbyfnx/jx49n+fLleHt7M3z4cJYuXUqdOnXw8PDA09OTXr16MWvWLEaMGEH+/Pk5deoU7du35+eff+bq1atUqlSJiRMnEhMTg5ubG2azmTJlyrBhwwbmzp17x5Sus2fPYjabefbZZ/H29gbAO08At65fuaf7ffzXTfzw8TvcvHAaq8VCWnIS/oUy+rb9vG07V65coWDBgri7u3P16lWGDx9OUFAQFouFffv2cfPmTdLT07FYLAQFBfHVV18ZveyuX7/O77//jpubG/b29vj6+hrXdXZ2Nqa93m7atGk8//zzxuqvD1Jmf79vvvmGFi1a0KFDxjS0gwcPEh0dTe3atXFycrrr6r+3V2OaTCZjmurYsWOxs7PLErSZTCZMJhO///47zs7OBAQEYGtry5YtW/Dy8jKmQEuGzKD1cdG7d2+mTp3KoUOHHpsef4+jvK721M3vet+LpNiZoG5+V/K66GehiMi9UrAmIiL/eenp6cybN48ffvghp4fyr+Pq6kqRIkWAO4O4+138wcPDA7PZzLFjx8ifPz/9+vXLcmxmqJLZW87Ozo4SJUoYx5tMJp599lmeffZZIGNa2euvv47ZbMbNzY0yZcqQkpJCTEwMTZs2Zc6cOTRv3pxWrVrRvXt3TCYTFStW5NatW5w9e5aVK1ditVoJDAzE19eXtWvX3jF9NTExEZPJRM+ePY2xxFy9jEfuvAA4OLuQlvy/hQjibl43Pk5PTWHhkJdoM2EGJWo1wtbenvmDX4T/r86qVqkiB5f68/333xMfH0/Dhg1p1aoVhQsXZuzYsYSGhtKtWzfS0tL45ZdfOH78ON99953RR3D06NGkp6cTHx/PtWvX2LVrF/369cPNzY20tDRsbGw4cuQIqampREZG0qZNG1atWkXXrl2ZMGECbm5uf/rP3d3d+Ph+wuobN25gNpupXbu2sUCH2WzGxcUFLy8vVqxYgclkIjk5GX9/fypWrMjZs2eJi4vD19eX/Pnz4+TkREpKCl5eXkbgdvvXV+bKrvPnz6dixYoEBQUBcOvWrfsaa+aUaltbW1asWEFaWhpNmzY1FvD4o/j4eKKjo8mbN6/Rvy5zhV25dy4uLrz22mu88cYbLFmyJKeH88hkp6dhWb+MqrN7DdcyQ7XM40RE5N4oWBMRkf+8devWERwcbKzgKI+nwoULM3r06D/dfi9TWTOnEJpMJr799ltsbW05dOgQ9vb2xvFt27bl22+/BcDf35+GDRtmOYeHhwcffPABK1euxNHRkRdffJFq1aoRHR2Nm5sb6f+/8EDmmNzc3Bg1ahRffPEFsbGxrP7iIyp26A1A3qJPseXLD4m5cgknNw82f/G+caw5LY301BRcvXNhY2fH8V83cXLHZvxDi2FnAg/njNUEQ0NDAXB0dKRChQrUq1ePKVOmUKtWLcaMGcPx48f55ptvKFSoEF999ZUxrhUrVhiVZ9WrV6d79+60b9+e+Ph4xo8fT0REBK+//jq//vorb7/9NgkJCVSqVImCBQsSHx/PpUuXiIuLIz4+/k//xcXFYWtre0fwNn/+fCNwvV1qaipWqzVLCNuyZUsjiJo9ezYJCQmULVuWSpUqsWLFCnbs2MHly5eJiYnh9ddf57nnniMhIYHcuXNja2vLl19+yfz584mKiqJevXqEh4dz9epVvvvuO7Zt28bevXvp27cvBQoUMKr2kpOTjWrDwMBAPD097/r1ljnOY8eOYbVajenRt1dgZn697du3jwULFjBmzBhWrlzJ2bNnGTFiBD4+Pn/5dXp7Veft146MjMTFxeU/Gc698sorTJ06lSNHjlCyZMmHfr2ENAuHopK5nmQmxWzB0daG3M62lPJxwsX+8Zu+e7uyfs7kdbVj+9UkTt9KxQRZQjY7E1iBUA8HquRxVqWaiEg2KFgTEZH/vM8//5yXXnopp4chj8DtlUvz58+nW7duFChQIMs+/fr1o3///tSrV89YiTVTZvj2x/5ymeHW7RYvXsy3335L9+7duXHjBoULF8bJyYkuL/XAvXlHrEDhyrUpVb8577erhYuXD7W69ufYlvUAOLq6ETbkTRYP60l6agrFaj5H8ZrPARlvhAt63DldNtM777zDyy+/zJQpUyhTpgzt2rXjxx9//Mt7YzKZjFVWvby8SE5OpmrVqqSmpuLo6MjOnTs5ePBglh5yf8dqtZKSknJH4PbHc2SGSGfOnMFqtRrTZq1WKyEhIcZ+tra21KpVy+ixVaNGDTp16sSUKVOYO3cuCxcupGbNmlitVlxdXQFo0qQJjRo1ws/Pjx49evDDDz/QqFEj3N3dCQ0NxcXFhdy5c9O8eXO6devGyy+/zMyZM1m3bh2xsbEUKVKEN998kwIFCtCyZUvs7OyIiYkhMTGR2bNn89RTT5GWloa3t/ddK+RuD3zd3NyMijY3N7csK7Xe7m4VnH987OOPP6ZFixZZqjL/7DW4/b82NjZGCGhra3vHNNo/unXrFi4uLkYl6OPA1dWVwYMHM3HiRBYvXvzQrnMlIY1t15I4c5dA6kQM/HIlkYIeDlT1dyav6+MbSOV1sadlQXsS0yzMWL6RNAdXij9dCkdbG/yekIBQRORxplVBRUTkP+3atWsULVqUCxcu4OGhFdDkwbl06RIXL16kWLFiRlCU6UlbsW/AgAHY2Ngwffr0h3aNiRMnMmnSJD799FM6duyYZVtm37zKlSszceJE6tWrx9atWxk5ciR2dnbY2tqSnJxMYGAg8+bN4+WXX6ZmzZp07dqVbdu28d5775GUlMTOnTt59dVXGTNmDM8++ywTJkygWrVqAEaFX6FChejcuTMff/wxJUqUoHnz5jz33HP06dOH0NBQevfuzZAhQ+jVqxeFCxdm4MCBxhTWgIAAvv/+e+zs7KhatSqOjo7Gczhy5Ag7duygU6dOzJw5k927dxMVFcXBgwcZMmQIvXv3xsnJieXLl7No0SKuXbtGt27d6NKlC7Gxsfz444/GdNLg4GDGjRtHu3bt6NChAwEBAUDGdFMHBwcjVMyOPwZsdevWpVevXrRr14733nuPjz76iEmTJtG2bdtsX+NBiIuLIzQ0lC1btjyUauO9N5L+dVMoIyIiCAoKwsPDg8jIyJwejojIv8bj86cnERGRHLBgwQJatGihUE0euICAACPw+KOq/s6cvZV6X03FMz3qFfsuXrzI/PnzOXbs2EO9zujRo8mTJw9btmyhSZMmeHl5Gdsyq7XMZrMRGuXNmxcnJydWr159x2IXsbGxuLm5kZyczOuvv87gwYOpXLky48ePN6ZvJiQkkJycbIR28fHxeHp6sm3bNurWrWtMCw0LC+PixYtcu3YNX19f6tWrB0ClSpX4/fffiY2NJW/evMa1vby87liMA6BkyZLGtMXExETOnDnD119/TYECBShZsiR16tQhPT2dAwcOMGrUKPLnz8/AgQMpXbo0fn5+vP7669SuXRsfHx9u3bpFYmIi69at48aNG4waNYpNmzYxb948Ll++TNmyZZkyZQp+fn68+OKLODk5ceHCBQoVKkT//v1ZtWoVBw8e5LXXXqNUqVIcO3aMxMRESpYsiZOTE8ePH8fX15dcuXIBGQt/AAwcOBAvLy+2bdtG9erVyZcv311fyzfffJMzZ87c0XMwU3BwMJ9++qlxL7PD3d2dgQMHMmnSJBYsWJDt89zN3htJtGlUj2cat6ZCi853bI+5conprasx7ucz2Njakm7N6GMGPLbhmtVqpX379pjNZuLj4zlx4sRdp2OLiMj9U82viIj8Z1mtVk0DlRyRuWKf3d+3hcsiJ1bsmzRpEj179sTf3/+hX6tChQpERESQlpZ21+3nzp0zqsBCQ0NxdnY2qr/27t3Lb7/9BmT0anN1deXmzZvEx8fTpEkTAgICOHLkiHGuxMREChQoYExxNJvNODk54efnx+nTp43eZRERETg5ORlVXJn91jI//+MUyQoVKlCpUqW7LoSQOYU4Ojqatm3bGpWMISEhREZGsmzZMr7++mtGjRrFmDFjWL58OXv27CE5ORk3NzeGDBnCtGnTGDt2LLlz52b58uVMnz6dY8eOGR8fOHAAq9XKhg0bSEtLY+PGjRQuXJiPP/6YEydOUKZMGUaOHMnixYupXLkyL774ImvXruWLL74gOjoagA4dOvD9998DGQHm7SvGBgYGkpiYmKWX4B+NHDnyT0O1B6lfv35s2LCBEydOANC1a1ccHBxwc3PDx8eH+vXr8/vvv9/XOa8kpPHj5QT+KvP2yhvA+F/PY3NbL8DMcO1K4t2/dnPazJkz2bt3rzG9PbPf4p85d+6c0SPSzc2N4OBg3n77bWO7yWTC1dUVNzc3fH196dChAzExMcb2zFV83dzc8PT0pGbNmhw6dOhhPT0RkRylYE1ERP6zdu3aRWpqKtWrV8/poch/UFk/5/sK13JiutnZs2f55ptvGDJkyCO5nslkIioqyqiQypRZ/TVhwgSCg4ONxz/77DP279/P8OHD6du3L+fPnzfOY29vT0BAABUrVqRy5co0a9aMpKQk41z169dn3LhxjB8/HoArV65gMpmoWrUqFy5c4KeffuL06dOsXLmSatWq4ejoSEpKilExl5aWhr29/X2tJHp75V1KSopRaWexWHB0dOTixYsMHDiQsWPH0rp1a1asWEHDhg1JSkoiJCTECLhiYmJISUkxgrDNmzdz8uRJ2rdvT/ny5dm8eTPnz58nLi4OPz8/mjVrRlBQEIUKFcLOzo41a9YQFxdHsWLF2LVrFz/88AO5cuUy+r95enoa1WoLFy6kVKlSxnNIS0sz7u8fRUVFcfnyZWJjY+/oQ/gweHh40L9/fyZOnGg8NnToUGNxjdy5c9O1a9c7jrNarSQlJd3xOMC2a0mkpP15aPhX0q2w/erdz5vTpk6dmuU1mTt37j0dFxMTQ3x8PIsXL2bChAmsX7/e2HbgwAHi4+M5c+YM0dHRd6xcOmPGDOLj44mKiqJ27dp07nxn9Z+IyL+BgjUREfnPyqxWu5fVJEUehrJ+znQs4kkRTwdsTdwRstmZwNaU0VOtYxHPRz7N7I033qBPnz5ZKpYepqJFi+Lu7k6tWrW4fPnyHdtfeeWVLKto+vr6MnfuXDZt2sT27dtp3bo1ACtXrjSmGX744YesXLmSadOmsXz5crp06QLA4MGDqVmzJu7u7sa58+bNi6urK+Hh4cyaNYvOnTvzyiuvULNmTdLS0rJMRc2cjvnHgGnp0qXs3r37rs8v83tNSkqKEW5l9lkrVKgQderUYevWrVSqVIl69epRv359AgMDMZvNmEwmo1rP1taWhIQEgoKCAHBwcKB9+/bs27eP3bt3c+7cOUaOHImtrS12dnZGZZytrS0ODg6kpaWRlpaGxWKhQYMGXLhwAXt7e3x8fPjoo4/Yvn27cZ9WrVrFM888g5eXF1WrViU6OpqTJ0+SO3duJk+eTP78+XF3d6do0aKMHDmSChUqkDt3buzs7IyFIfz8/HBwcMDe3p5ChQpx48YNJk+eTN++fRk6dCj169fH19cXNzc3KleuzKJFi9i8eTMrV67EZDIxffp0AgIC8PX1ZdKkSVnuaf/+/Vm7di2nTp3K8riLiwsvvPAChw8fBjIqD1u1aoWfnx/58uXD3d2d8PBwEhMTCQ8Pp3Xr1rR/oSMdS+Vn76qMBRGir1xidrfGjKsezGd92pAQndGXLDriAiPK+mH+/6q9T3o2Z+NHbzKra2M6PJWXxk2aEhkZSceOHfHw8KBChQqcO3cuy9fBBx98QMGCBfH19WXIkCHGqq/h4eF06tTJ2DezciyzQrB27dqMGTOGatWq4e7uToMGDbh586axf5s2bciTJ49RJZZZpXny5Elq166No6MjhQsXJiIigkqVKnH69Om7fq3+UZUqVShZsqRxP2/n4eFBs2bNOHr06F2PtbW1pX379n+6XUTkSaceayIi8p+UmJjIN998o6kpkuNuX7HvYFQyN5LMpJgtOb5i38mTJ1m1ahUnT558ZNd0dHRk7dq1xkqkf3S3lSszAwm4+2qazs7ORgB1u/z589O7d2/j89urberWrUvdunWz7O/r65vl+8Urr7xCcnLyHcGan5+fUfn1Z9zd3alatSrNmjXj+vXrTJs2jdy5c9O5c2d2795N3bp1cXZ2xtHRkU8++YT4+HiuX79unNfV1RWTycTGjRspUaIETZo0oUePHjRp0oTQ0FCioqJwcHAwVgJ1ds4IZDPDGVtbW1JSUkhLS2PTpk2EhIQQGxsLwHfffYe/vz9Llixh3759dO/enVWrVlG+fHkWLFjAiBEjCAkJITAwEIDdu3eTL18+zp07h9lsZvbs2YSHh3Pq1Ck++eQTdu/eTaNGjZg9ezYhISF8+OGHnD17ljJlyhAUFMT69es5evQozZs3x2Kx8PPPPzNkyBAKFy5sNNgfPXo0tra2xMfHM3r0aKZOnYqXlxfu7u7GVMNnn30WOzs7vLy8SElJwd7envXr15M3b14WLFhAeHg41atX57vvvmP16tVMnjyZt956iw8//JCaNWuyevVqJs5ZQNnXp5OSksK+tUs5sP5bun74FV7++fji1fb8Mv8jGvYfe9fX9MCG5bz00RI8vHPxefcmVKlShZkzZ/Lll1/y0ksvMX78eL744gtj/+XLl7N7927i4+OpV68eRYsWpUePHn/5dZNp0aJFrFu3jsDAQBo1asQ777xjTNNs1KgRn3/+OQ4ODgwbNoyOHTuyf/9+7O3tjdVgv/jiC8qWLUuXLl0YNWrU304LtVqtbNu2jSNHjlCmTJk7tkdHR/Pdd99RuXLlux6fmprKwoUL/3S7iMiTTsGaiIj8Jy1btowqVaqQP3/+nB6KCAAu9jZU9nfJ6WEYxo8fz4ABA7IsIvAo2NraGkHQH92tuvRuYdq9ygyeTCZTlgUHMvtQ/dX5XV1d77r6Zq1atf72upMnT77r4/b29kyaNIkzZ84QFxeHxWLBy8uLQoUK8frrrxtho42NDa+++iqffvopFSpUYNSoUQwePJiuXbuSlpaGs7Mz8+bNw8nJiaioKKNf3PXr17FarbRs2dIIqnr27Em3bt0YOnQoAJUrV+aHH37A09OT999/n169elGpUiUAunTpwujRo4mLi2POnDl069aNo0eP4ufnl2WKbiYXFxc2b95MWFiY0cuyYsWKeHt706BBA+rVq8eMGTOYN28ezz77LJAxJbdAgQKcP3+eS5cuERISwvHjxwkICMBqtVKhQgVefvll6tevT3x8PPHx8URERNC1a1eKFi3KgQMHOHr0KLa2tvj7+1OhQgVjUYfz588zcOBALl26hNVqJTU1laioKL777jt8fHwoXrsxR6NTsHfK+Por16w9fkGhADxdvznHtqy/4zlmKt+sA7kCQwAoXbMeKZdPGVWTbdq0YcyYMVn2HzZsGD4+Pvj4+DBw4EAWL158z8Fat27djIUH2rZty8qVK41tt/cMDQ8Px9vbm9jYWDw9Pbl16xYhISFUrFgRgI4dOzJ48OC/vJavry8mk4k8efLw9ttvG68TQNmyZbH5P/buOiyq9P3j+HuGEgTBAEVEsBUbsTswMLAbu9ZuXRWwO9YOLOxY1+5uRUXEJhQMDBQEpJmZ3x/8Zr6yursGcAZ4Xte117UycT4zHGbOuc/z3I9cTlRUFMWKFUtROITk0YRjx44lNjaWbNmy8ddff33X6xMEQchoRGFNEARByJI2btzI0KFDpY4hCFrp0aNHnD59mtWrV0sdBUju6xUdHY2pqWmqTt3+8rm+LKDJZLI0nSL+rZF3ajlz5qRSpUopfmZpaUm7du1S/Oy3337jt99+0/y7Y8eOdOzY8avt3LlzR7MSqrowsmvXLqpWrcq+ffs0PchatGjBpUuX+Pz5My4uLpiZmREcHIynpyfLly/XPGdsbCylSpWiefPm/PHHH0ydOpWHDx/SpEkTFi9e/NVKoSEhIZrRbZBckFT3bwMIDg6mTZs2Kd5/HR0d3r17p/l3vnz5ADTN9OVyOYUKFUqxnXv37rFr1y4mTJiQoucawJ49ezh37hx3794FkqfiqrejXpXVyMiIeIUyxeNMclto/l8/myEJsdH8E+Pc/+sLqGOQLcViH4aGhnz+/DnF/b98T2xsbAgJCfnH5/479fsBycVL9XMrFAomT57M3r17CQ0N1bynHz58wNTUlKioqBQXk7587D/58OHDVwt0qHl7e1O0aFESExNZtWoVtWvX5tGjR5qRlcuWLaNfv34olUquXr1Kq1atuHjxYoqefVKKTlRyPyyO91+MEraQcJSwIAgZl/jEEARBEtGJSm68i+FQUBR7AyM4FBTFjXcxxCQq//vBgvCLAgMDefjwIS1btpQ6iiBoJXd3d8aMGaPpPyY1FxcXPDw8Mk0/xJ95HeoRdF/+W6lUpvj53+8jk8lS9McrVqyYpthiYmKSorG/eoGKIUOG4OnpSb58+bC2tmby5Ml8+vRJ89+sWbOwt7cHoGvXrly5coXg4GBkMhkTJkz4KrelpSUvX77U/DsmJkYzxROSC0zHjx9PsY24uLgfHk08cuRIgoODU6xM+eU2ChUqpHn+rVu30qBBA06ePElgYCCVKlVCT08PA53UOTX6ngVJvnxPXrx4oSlIZs+enZiYGM1tb9++/e7t7tixg4MHD3LmzBkiIiI0fd3U+0VUVJSm315q0tPTo1+/fjx//vybPdjkcjm1a9emaNGinDp1KtW3/6PeRCey71kkqx6GceVNDI/C4wmMTORReDxX3sSw8mEY+55F8iZaO1d4FQRB+4jCmiAI6UoczAjaYPPmzXTr1k2zIp8gCP/j4+PDlStXGDJkiNRRAHj8+DHnz59PMToro4qNjf3H1Sj/y9+LcTKZDLlcnuLnP1qw+7IQp+5Vpy7YAfTv3581a9Zw8+ZNYmJiaNq0KZ6enjg7O/P06VPOnTtHfHw82bJlw9DQ8JvTZtu3b8+RI0e4cuUKCQkJuLm5peiLN2jQICZPnqxZ0TU0NJSDBw/+0OsAyJUrFyVKlODatWtf3ValShVMTEyYN28esbGxtGnThqVLl5IjR44U75mFoc53r9L7T3RlfNdopwULFhAeHs7Lly9ZunQpnTp1AqBChQpcunSJFy9eEBERwZw5c75721FRURgYGJA7d25iYmKYNGlSitsjIyNTLP6RWtR92wwNDSlcuPA373P9+nUePXpE6dKlU337P8I7NJbt/hH4RySgUCWv5PqlJBUoVOAfkcB2/wi8Q7VzlVdBELSLKKwJgpBuxMGMoA0UCgWbN29O0YdGEIT/cXd3Z8KECd/sHyaFWbNmMXLkSIyNjaWO8kuio6Np2bKlZpEAbfCtqbDqgh2Ag4MDHh4eDB06lPz583Pv3j1KlixJmTJliI+PZ+LEieTJk4d8+fLx/v37bxaBSpcuzcqVK+natSuWlpbkzJmTAgUKaG4fMWIErVq1onHjxpiYmFCtWjVu3rz5U6/Hzs6Ox48f8+LFixQ/19HR4ciRI/j4+FCoUCHy5MlDv379vvpdlM2Vjb8dmvwwFWCR7b+77Tg7O1OpUiUqVKhA8+bN6du3LwCOjo506tSJcuXKUalSJVq0aPHd2+7Rowc2NjZYWVlhZ2eXYrEAlUqV6iPWypcvj7GxMTlz5sTT05P9+/enKNwNHToUY2NjjI2NcXFxYebMmTRr1izVtv+jvENjOfc6+qvjz3+SpIJzr6M1x6N/X7FVEARBTab6+5hxQRCENPDlwcymoZ0o16QNlVp2/tfH6MqggVV27M2/3cT6W9QrkW3btu1XIwuZ1MmTJ5kyZQq3bt2SOoogaJ1bt27Rpk0bAgIC/nNly/Tg5+dHzZo1CQwMJEeOHFLH+SXLli3jypUr7NmzR+ooKahHj6V1X7n0MnHiRKKioli5cuVPPX7fs0j8IxJ+evvFTfVpW/jf91WZTIa/vz9Fixb96e38qLdv31KuXDnev3+fbttML7a2tqxfv16zWMS3vIlOZLt/xHcX1b6kK4NuxU1ZO3+WOMYUBOGbxIg1QRDS3JvoxBRXCHuv2P2fRTVIvlLYoVkjFq5am8YJhaxk48aNYrSaIPwDNzc3Jk+erBVFNYDZs2czfPjwDF9UUygULF26lFGjRkkdJYWkpCRKlCjBxYsXM0VRDWDSpElMmTIlxXTTH1Ejr+FPTwfVlUH1fN9/MTA9BQQEpGshT9tcexf7U0U1SD4evf5WzKIQBOGfiVVBBUFIc+qDGZVKhUql+mYPln+iAp79wpVjQfjSx48fOXnyJGvXimKtIPzdtWvXePz48U/1t0oLgYGBHDlyhICAAKmj/LLDhw9jbm5O9erVpY6SwtatWylQoAD169eXOkqqyZEjxy8VYi2z69HAKvsPTRmE/42ytzTS++ltp6XMWlhzcXHhxYsXtGzZEh0dHdzc3Lh16xaXL18mNjaW8uXLs2jZSp5hCcBe96HoZzMi/M1Lnntfx6JwCTrPWkNu6+RVZg8vmMTDc0eJ+xxJbuvCtBg7k0L21QmMTCBRoSIhIYEePXqwf/9+ChYsiKenJw4ODlK+BYIgaAExYk0QMjFbW1vmzJmDnZ0dOXPmpHfv3sTFxQFw5MgRKlSogJmZGTVq1MDX1zfF4xYsWEC5cuXInj07ffv25d27dzRr1gwTExMaNWpEeHi45v43btygRo0amJmZUb58eS5cuKC5rXbdeqya7c6a3k641ShI2Ksg1vV35tb+rQDcObSTNX2ac2yJO9PqFmV+i0o8vXoGgJMrZhF09wYeU8dhbGzM0KFDgeR+LNbW1uTIkYNKlSpx+fLlFK9bfdBjYmJC6dKluX37tua2uXPnUqRIEUxMTLCzs2P//v2a2zZv3kytWrUYO3YsOXPmpFChQhw/fjyVfhuCNtixYwfNmzfHzMxM6iiCoHVcXV1xdXXVmkU95syZw+DBgzPF3+uSJUu0brRaYmIiM2bMYPr06VJH0Tr25oY0sMr+3SPXfrR1hUqlSvciV2YtrG3dupWCBQty+PBhPn/+zPjx42nWrBn+/v68f/8ee3t7unXvxpe/ynunDtBwwDjcLgSQ27oQp1bO1txWwK4iw3aex/W8PxWatWPHhL4kxschA97FJnHo0CE6d+7Mp0+faNWqlebYVBCErE0U1gQhk9u+fbtmOXk/Pz9mzpzJ3bt36dOnD2vXruXjx48MHDiQVq1aER8fr3ncvn37OH36NH5+fhw+fJhmzZoxe/ZsQkNDUSqVLFu2DIDXr1/TvHlzpkyZQlhYGAsXLqRdu3aEhoYCEJ2oxPvoXtpMWczUy8/JaWn9VcaXD7zJY1MU13NPqdNzKPumjUSlUtFk6GRsK1aj9YQ5nAl8z4oVKwCoXLkyPj4+hIWF0bVrVzp06KApGAL/etBTpEgRLl++TEREBO7u7nTv3p03b95obr958yYlSpTgw4cPjB8/nr59+yJaUWYOKpWKDRs2iGmggvAN58+fJzg4mB49ekgdBYCgoCD279/PyJEjpY7yy7y9vXn+/Dnt2rWTOkoKmzZtomjRotSuXVvqKFrJ3tyQbsVNKW6qj46Mr4psujLQkSX3VOtW3PSH+sFKIbMW1r6lT58+mJiYYGBgwNSpU/F/eJ/PkZGa20vXd8K6jD06urpUaNaON34PNLdVbN6B7Ga50NHVpbbLYJISEvgQHECSCqKTlNSqVQsnJyd0dHRwcXHh3r17UrxEQRC0jCisCUImN3ToUKytrcmVKxeTJ09m586drFu3joEDB1K1alV0dHTo2bMnBgYG3LhxQ/O4YcOGkTdvXqysrKhduzZVq1alYsWKZMuWjTZt2nD37l0Atm3bhpOTE05OTsjlchwdHXFwcODYsWMAJCpV2LfsTN4iJdHR1UVH7+spEmb5ClClrQtyHR3sW3Qi6sM7Pn/8X3NdJRAaq9D8u3v37uTOnRtdXV3GjBlDfHw8T58+1dz+bwc9HTp0IH/+/Mjlcjp16kSxYsXw8vLS3G5jY0P//v0178ubN2949+7dr/8iBMndvXuXiIiITDXlSRBSg0qlwtXVFXd3d/S+8Rkthblz5zJw4MAUKwxmVEuWLGHYsGHo6mpPB5b4+HhmzpyZZUerfe8FM0sjPdoWzsGQ0rmoZWlE6ZwGFM2hR+mcBtSyNGJI6Vy0LZxDa6d/fimrFNYUCgUTJ06kSJEi5MiRA1tbWwCiP33U3Mckt4Xm//WzGREfE63596UtK1nctgZT6xRmWp0ixH+OJDo8LPm5lSry5cunua+RkRFxcXEkJSWl8asSBEHbac83vCAIacLa+n8jxGxsbAgJCSE4OBhPT0+WL1+uuS0hIYGQkBDNv/Pmzav5f0NDw6/+/fnzZwCCg4PZu3cvhw8f1tyemJioKV4oVSpM81r9a0aTPF8c4BgaARAfE43JF/eJV/yvCfHChQvZsGEDISEhyGQyIiMj+fDhg+b2fzro0dXVZcuWLSxevJigoCAAPn/+/K+PVd9HyPg2btxI7969f6jHnyBkBadOneLjx4907dpV6igAvHz5kj179uDn5yd1lF8WEhLCkSNHNKO8tcWGDRsoW7Ys1apVkzpKmlCpVMhkMnbv3k379u2JiYnh6dOnHD16lBs3btCwYUPGjh373c9npCenWl6jNEyctlQqVaYurH258MaOHTs4ePAgZ86cwdbWloiICHLmzPldxdTn3te55LmCfmv2YVGkJHK5nGl1i5Lc8Rd05JljgQ9BEFKfKKwJQib38uVLzf+/ePGC/PnzY21tzeTJk5k8efIvP7+1tTUuLi54eHh883a5TMavLDSmPlgy0Ekuhly+fJn58+dz9uxZSpcujVwu/+4DpuDgYPr378/Zs2epXr06Ojo6VKhQQUz1zALi4uLYtWsXd+7ckTqKIGgV9Wi1qVOnoqOjI3UcAObNm0e/fv3IkyeP1FF+2cqVK+nWrRs5c+aUOopGbGwss2fP5sCBA1JHSTPqY4dVq1axfv16HBwcePz4MQULFqRfv36sWrWK4cOHa00/wbT28eNH5HJ5phgB+i158+bl2bNnAERFRWFgYEDu3LmJiYlh0qRJwNdTeb8lPuYzch0dsufMjVKRxPn1y4iPjtI8PruunLj/eA5BELImcdleEDK5lStX8urVK8LCwpg1axadOnWif//+rFmzhps3b6JSqYiOjubo0aNERUX98PN3796dw4cPc/LkSRQKBXFxcVy4cIFXr14BoCeX/dIHjXEucz69DsbcMPmELyoqCl1dXczNzUlKSmL69OlEftE3499ER0cjk8kwNzcHkvvLPHjw4D8eJWQGBw4cwN7eHhsbG6mjCIJWOXLkCHFxcXTo0EHqKEBy384dO3YwZswYqaP8spiYGNatW8eIESOkjpLCunXrcHBwyJQrGaovlEVGRnLz5k2KFy/O4MGDsbCwwN3dnWXLltGuXTsOHDiQZYpqkPmngf7+++/MnDkTMzMzwsLCsLGxwcrKCjs7O82ozO+5hFq8egOK12jAojbVmN+8Irr6BppZFyogr6EYkyIIwreJTwdByOS6du1K48aNCQkJwdnZmSlTpmBkZISHhwdDhw7F398fQ0NDatWqRZ06dX74+a2trTl48CDjx4+nS5cu6OjoUKVKFVavXg1Adj35dx3M/JMaXQbwp/tQmv3liYuLC0uWLKFp06YUL16c7NmzM2rUqBTTXf+NnZ0dY8aMoXr16sjlcnr06EHNmjV/IZ2QUWzcuFEsWiAIf6NUKnFzc2P69OlaM0V6wYIF9OrVK0X7gYxq69atVK9enWLFikkdRSMmJoa5c+dy4sQJqaOkCZlMhkKhYOHChdy5cwc7OzucnJwwMDBIcT8TExMuXrxIjRo1tKavYFrK7IU1Z2dnnJ2d//H2Hj16sO9ZJP4RCXSYtiLFbYUdavL7CV8A5Do6tJ+6jPZT/zd1u26vYQAUyaFP2xnTUjzW1tZWzHoQBAEAmUp8GghCpmVra8v69etp1KiRpDnUBzM/q7ipPm0L50jFREJWEhwcTKVKlXj16hXZsmWTOo4gaI19+/YxZ84cbt26laJHkVTevn2LnZ0dDx8+xNLSUuo4v0SpVFK6dGlWrVqlVQumLFy4kJs3b7J3716po6SZnTt3smjRIg4ePIiV1f96vKr7rt29e5dNmzbx9u1bJkyYQKVKlSRMmz6mTp2KUqnMsotVALyJTmS7fwRJP3HmqyuDbsVNM8QiFYIgSEOMWBMEIc3VyGvI88iEnz6YqZ5Pu5ewF7Sbp6cnnTt3FkU1QfiCQqHAzc2NBQsWaEVRDZKLPt27d8/wRTVIXhDCwMCAevXqSR1FIyoqigULFnD27Fmpo6Qpe3t7rKysNEU1pVKJXC7X7OdyuZzSpUszYMAAChcuLGXUdBMQEEDjxo2ljiEpy+x6NLDKzrnX0T90PKorgwZW2UVRTRCEfyUKa4IgpDlxMCNIRalUsmnTJvbt2yd1FEHQKrt37yZHjhw0a9ZM6igAhIaGsnHjRnx9faWOkiqWLFnCqFGjtKZoCbBixQoaNGhAmTJlpI6SpkqUKMH27ds1/1ZPc1YqlahUKsqXL0/58uU1tysUCq1ZuCOtBAQEMHjwYKljSM7ePPlC7fcej6qPQ9WPEwRB+CdiKqggCOnGOzT2uw9m5ColDQtkp5JF9rQPJmRa586dY/To0dy9e1erTnAFQUpJSUnY2dmxatUqyVsFqP3+++9ERESwatUqqaP8socPH9KoUSOCgoK+6u0llcjISIoWLcqlS5coWbKk1HHSlUqlQqlUaopnERERzJkzh6CgIHbt2iVxuvSRJ08eHj16hIWFhdRRJBUWFkbr1q2Zu2o9b43yERiZgAxSHJfqypIXKiiSQ5/q+QzFxV1BEL6LGLEmCEK6sTc3xDK7Ltffxv7nwczYto1YGBLExo0bad68uSiKCD9FvWiB2H8E4X+2bduGpaUlDRs2lDoKAB8/fmTdunXcvXtX6iip4o8//mDw4MFaU1SD5ExNmzbNckU1SF7QQEdHR7M6+v79+zE2NsbBwYEnT55QsmRJzXTRzCg8PJyEhATNiuhZ1cGDB+nevTufP39GFvWRtmWKE5OoxDcsjtBYBfEKJQY6cswNdSiXKxtGeplzfxAEIW2IEWuCIEjivw5mypcvj6+vL0ZGRhQtWpTly5f/1KqlQtb16dMnbG1tCQwMJHfu3FLHEQStkJiYSIkSJdi8ebPWfKa6urry7t071q1bJ3WUXxYaGkrx4sXx8/PTmkJGeHg4xYoV48aNG5l6Zch/M3nyZDZs2EDTpk2ZMGECefPm5eDBg5w8eTLTj1q7ffs2AwYMwNvbW+ookggPD6dv376cPHmSmJgYDAwMePToUZbprycIQvoQI9YEQZCEkZ6canmN/vH22rVr4+vrS0xMDA8ePKBTp068fv06015RFlLfrl27aNy4sSiqCcIXNm3aRNGiRbWmqBYeHs7q1au5deuW1FFSxZo1a2jfvr3WFNUAFi9ejLOzc5YsqqlHotWrV49OnTpRrlw5IHk6dO/evcmbNy9xcXFky5ZNs2poZhMQEJAlf/dqV65c4cCBAyl+liOHWGleEITUJQprgiBopQoVKmBgYEBSUhL58uXj9u3boqgm/JCNGzcyffp0qWMIgtaIj49n5syZ7N27V+ooGsuWLaNVq1YUKlRI6ii/LD4+nlWrVnHmzBmpo2h8/PiRVatWcefOHamjSEJ93ODo6Kj5WUhICIcPH+bcuXNYWFjw8eNHXFxcMmVRDURhrWXLlly/fp3atWujo6NDfHw8pqamUscSBCGTEYU1QRC0Urly5UhKSmLSpEmsX7+ehw8fki9fPqljCRlEbGwsderUSXEyJQhZnYeHB+XKlaNq1apSRwGSm8gvX76cGzduSB0lVezatYuyZctSunRpqaNoLFy4kA4dOmBrayt1FK0QFRXFyJEj0dHR4cyZM3h7e9OrVy9sbGyoU6dOpuy1FhAQoDUjVKWyb98+unfvTuvWrdm+fTt6emJBAkEQUpfosSYIgtb6+PEjuXPn5vTp0/Tu3RsfHx/y5MkjdSwhg4iPj9eq5uGCIKWYmBiKFi3KkSNHsLe3lzoOALNmzeLJkyds3bpV6ii/TKVSUbFiRebMmUOzZs2kjgPA+/fvKVWqFD4+PlhbW0sdRyvcvHmTxYsXs3v3boYPH06tWrWQy+UcP36cDRs2oFAoNKuHZha1atVi9uzZWba4FhAQQLVq1Xjw4IG4QCsIQprJXJdkBEHIVNS9sRwdHenSpQt9+/ZFXAsQvpcoqgnC/6xevZpq1appTVEtKiqKpUuXMnnyZKmjpIoLFy4QHx9PkyZNpI6iMX/+fLp27SqKal+4d++eZsrnkCFDmDp1Kt7e3jRq1Agg0xXVQEwFHTt2LOPGjRNFNUEQ0pQorAmCkCHMmjWLV69esXr1aqmjCIIgZCifP39m/vz5TJs2TeooGqtWraJhw4aULFlS6iipYsmSJYwcOVJrphG+efOGTZs28fvvv0sdRav07duXR48e4evrS4kSJWjfvj2vXr3C2dkZgCNHjtCnTx+JU6aeqKgooqKisLS0lDqKJM6cOcP9+/cZOXKk1FEEQcjkRI81QRAyBH19fXbu3EmNGjWoU6cOZcqUkTqSoCXUoxgza+NpQfhVy5cvp379+pQtW1bqKABER0ezePFizp07J3WUVOHv78/169fZtWuX1FE05s6dS8+ePcmfP7/UUbSGeppnv379OHz4MEWKFNEscHPp0iWWLl1KUlIStWvXJioqChMTE4kT/7rAwECKFCmSJb8fk5KSGDlyJAsXLhQj2AVBSHOisCYIQoZRvHhx5s+fT5cuXfDy8sLQ0FDqSIIW+PKEITP2xxGEXxEREcGSJUu4dOmS1FE01qxZQ506dbSqyf+vWLZsGQMGDMDIyEjqKAC8evWKrVu38vjxY6mjaBX1aMLhw4drvisOHTrEtm3biIqKomXLlrRq1Yq8efNmmub2WXka6Lp167CwsKB169ZSRxEEIQsQixcIgpChqFQqOnXqRN68eVm+fLnUcQQJXbhwgaCgIBISEqhVqxZ2dnYEBQURFhZGxYoVATGKTRCmTZvGs2fP8PT0lDoKkLyIQpEiRThx4gTly5eXOs4v+/TpE4ULF+bBgwdaMzps8ODBGBsbM3/+fKmjaK2AgAD69+9Pzpw5adKkCc2bN8fKygqlUklMTAwmJiaZYoXQuXPnEhYWluX2hfDwcEqWLMnp06cpV66c1HEEQcgCxIg1QRAyFJlMxtq1a6lQoQJNmjShRYsWUkcSJBAbG0vPnj1xcXEhMTERb29vPn/+zIULF+jTp4/WNGgXBCmFhYWxfPlybt68KXUUDQ8PD6pVq5YpimqQ/HqaN2+uNUW14OBgdu/ezdOnT6WOotVsbW3p1q0bTZo0IV++fMTGxqJQKDh58iRHjx5l1apVmeLCTEBAAJUrV5Y6RrqbNm0abdu2FUU1QRDSjRixJghChnT58mU6dOjA3bt3s2xT3qzsxYsXnD17lt69e+Pn58ezZ88IDQ0lPDycI0eO0KRJE+rWrYuDg4PUUQVBMpMmTSI0NBQPDw+powAQFxdHkSJFOHz4cKYoficlJVG4cGEOHDigNa+nf//+WFhYMGvWLKmjaC2VSqUpmoWEhDBu3Dj8/PyoU6cO8+bNo169eixZsoTKlStn+FFr9erVw9XVlYYNG0odJd08fvyYOnXq8OjRI8zNzaWOIwhCFiEKa4IgZFju7u5cu3aNkydPZugDXyF17dq1i0ePHhEeHs6YMWOwtbWVOpIgpLvQ0FBKliyJt7c3NjY2UscBYOXKlZw4cYLDhw9LHSVV7N69m1WrVnHx4kWpowDJjeqrVq2Kn58fuXLlkjqO1lOpVAwfPpzY2FgWL17Mb7/9RteuXXn9+jXv3r3D1dU1RREuIypQoABXr17Vms+A9NCsWTMaN27MqFGjpI4iCEIWIs5EBUHIsFxdXYmJiWHx4sVSRxHSUVBQEFOmTCE0NDTFz9XXidq3b8+4ceMIDg7m+PHjUkQUBMnNmzePzp07a80JdXx8PHPnzsXV1VXqKKlmyZIlWnXyPmPGDIYOHSqKat9BqVQik8nIlSsXpqam5MiRg759+9KjRw927NhB06ZNgYzdpzM2NpaPHz9SoEABqaOkm2PHjvHs2TOGDBkidRRBELIY0WNNEIQMS1dXl+3bt1O5cmXq169PpUqVpI4kpJMHDx5QqVIlJkyYoDmAVp8A6erqYmJiQtmyZWnWrJmUMQVBEm/evGHjxo3cv39f6iganp6elClThipVqkgdJVVcv36d0NBQWrZsKXUUAPz8/Dh69CgBAQFSR8kQ1N8XEyZMoH379tSvX5+cOXPSvHlzunbtSuXKlbl06RK6urq8f/8+Q64s+ezZM2xtbbPMStmJiYmMHj2aJUuWoK+vL3UcQRCyGFFYEwQhQ7O1tWX58uV06dIFb29vjI2NpY4kpDFbW1sOHDhAUlIS79+/B/hmHxzRY0jIqubMmUOvXr2wsrKSOgqQfMI7e/Zsdu7cKXWUVLNkyRJGjBihNUWLadOmMXLkSExNTaWOkiHIZDKUSiVGRkYMHDiQ9evX4+zsTLNmzbCwsODo0aMMHDgQXV1dtmzZgkKh0Jrf9fcKCAigaNGiUsdINytXrqRQoUI4OTlJHUUQhCxI9FgTBCFT6N27N3K5nA0bNkgdRUgHSUlJ6Or+87WhjN4XRxB+1suXL6lQoQKPHz/GwsJC6jgAbNy4kZ07d3L69Gmpo6SK4OBg7O3tCQoKwsTEROo4PHr0iHr16hEYGKgVeTIK9fdEUlISz549o3jx4pw8eZJZs2YRGxtL4cKF0dPTY9u2bRnyO2XRokW8evWKJUuWSB0lzYWGhmJnZ8elS5coVaqU1HEEQciCRI81QRAyhWXLlnHp0iX27NkjdRQhDSkUCgAuXLiAi4sLw4YNw8vLi6SkpBT3y2gnQIKQWmbNmqVZGVIbJCUlMWvWLNzc3KSOkmqWL19Or169tKaINW3aNMaOHas1eTKKL9sHFC1alBkzZjB58mS6devGrVu32L17N+/fv+fDhw/IZDJevHghceIfk5VGrLm5udG1a1dRVBMEQTJixJogCJnG7du3cXJy4tatW1rTsFtIG0WKFGHSpEncunULLy8v8uXLR7169fjtt9/EyaWQZT179ozKlSvj5+dH7ty5pY4DwJYtW9i0aRPnz5+XOkqqiIqKwtbWljt37mjFisO+vr40adKEgIAAsmfPLnWcDCshIYGmTZuycuVKSpUqRWJiInp6ekDygjn6+vpMnDiRLVu2SJz0+zk6OjJ27FiaNGkidZQ05evri6OjI48fPxYLdwiCIBkxYk0QhEzDwcGBsWPH0q1bt69GMAkZn1KpBODq1atUq1aNvn37smbNGry9venZsyfnz58nMTFR4pSCIJ0ZM2YwZMgQrSmqKRQKZs6cmalGq23atIkGDRpoRVENYOrUqYwfP14U1X6BUqlEX1+fkiVLsnfvXgD09PTYvXs3VapUwdHRkTx58uDp6Slx0h+TFUasqVQqRo0ahZubmyiqCYIgKTFiTRCETEWpVNK4cWPq1KmTqU7mhP9ZvXo1GzduZMCAATRu3FiMThQEkleFrFmzJv7+/piZmUkdB4AdO3awatUqLl++nCmmZysUCkqUKMGWLVuoUaOG1HHw9vamZcuWBAQEYGhoKHWcDEvdP009Sm3nzp0sXrwYAwMDhg8fTseOHb+6r7aLj4/H1NSUz58//2s/0ozuwIEDTJkyBR8fn0z9OgVB0H7iE0gQhExFLpezZcsW7O3tadiwITVr1pQ6kpCKFAoFxsbGNGjQAC8vL4KCgihcuDBFixalVq1aGW7VNkFILdOmTWPEiBFaU1RTj1b7448/MkQh4nscOXKE3LlzU716damjAODu7s7vv/8uimq/SL1/6unpsWLFChYsWMCiRYto2rQpL1++5Pbt2wCULl0aQ0PDb65CrW2CgoKwtrbO1MWm+Ph4xo4dy5o1azL16xQEIWMQI9YEQciUDh06xPDhw/Hx8dGaE00hdT1+/JhLly5x//59jIyMmD9/vtSRBEESDx8+pEGDBgQEBGhNj8E9e/awePFirl+/nmkKa/Xq1WPQoEF07txZ6ih4eXnRrl07/P39yZYtm9RxMjz1SLSEhAT09fUBWLNmDQEBAdy5cwdDQ0Ps7OxYuHBhhiisHT16lBUrVnD8+HGpo6SZ+fPnc/XqVQ4ePCh1FEEQBFFYEwQh8xoyZAgfP35k586dmebELqvz9PQkKCiIyMhIGjRoQOPGjUlMTCQkJISiRYtmiBMeQUhtHTp0oEqVKowbN07qKEDylPzy5cszb948nJycpI6TKu7evYuzszOBgYGapvZSatq0Ka1bt2bQoEFSR8lU1AW2DRs2sHnzZn7//XeqVatGrly5KFq0KOfOnaNgwYJSx/xPS5cuJSAggOXLl0sdJU28ffuWMmXKcP36dYoVKyZ1HEEQBLF4gSAImdfChQt58OBBhms4LKT05aIFGzduxMbGhiNHjrB+/XocHR2ZMWOG5kRHFNWErMbHx4erV68yZMgQqaNoHDhwAAMDA5o1ayZ1lFSzZMkShg4dqhVFtatXr/LkyRP69OkjdZRMR91r7fLly8yZMwcnJydy5cpFVFQUJUuW5P3791JH/C6ZfeGCyZMn07t3b1FUEwRBa4gzEEEQMi1DQ0N27tzJuHHj8Pf3lzqO8JPUow3XrFnD2LFjAWjcuDFr167FxMQEuVyumbojCFmNu7s7EyZMwMjISOooQPKIn+nTp+Pm5pZpRgq/efOGI0eO0L9/f6mjAODm5oarq6v43EsDKpUKPT099PX1OXXqFJ8+feL48eNMmjQJKysrKlSoIHXE75KZC2ve3t4cO3aMKVOmSB1FEARBQxTWBEHI1MqWLYu7uztdu3YlISFB6jjCT5DJZKhUKsqUKUOdOnU4ffo0DRs2xMLCAgsLC2rXrg38b2SbIGQVXl5eeHt7M3DgQKmjaBw+fBiAli1bSpwk9axcuZKuXbuSM2dOqaNw4cIFgoOD6dGjh9RRMiV1h5xZs2bx4sUL6taty9GjR8mfPz+zZs1CoVDg7e0NaPd3TmYtrKlUKkaMGMGMGTMwNTWVOo4gCIKG6LEmCEKmp1KpaNWqFXZ2dsybN0/qOMIPUPdMi4qKIikpCRMTEw4ePMjevXtp1qwZEyZMICAgAGNjY6mjCkK6a9q0Kc7Ozvz2229SRwGSP2srV67MpEmTaNu2rdRxUkVsbCw2NjZcvXpV8mlnKpWKunXr0q9fP1FYS0PqPmtRUVHExcUByavcXrx4kcmTJ6NQKLh16xZ58uTR3FebJCYmYmJiQkREBAYGBlLHSVV79uxhzpw53L59W6wCLgiCVhEj1gRByPRkMhkbN25k27ZtnDlzRuo4wg9QX/txcXHh7du36Orq0qBBAwoXLszdu3fZuXMnxsbGKBQKiZMKQvpS99nq27ev1FE0jh8/Tnx8PK1bt5Y6SqrZunUrVatWlbyoBnD27FnevXtH165dpY6SqakLZSYmJhgbG3P06FF69OjB8uXLGTBgAMOHD2f16tXA/76jtMmLFy+wtLTMdEW12NhYxo8fz9KlS0VRTRAErSNGrAmCkGWcPn2a3r174+PjQ548eaSOI/yAtWvX8v79e1xdXTU/8/LyomLFilrRTFwQ0luDBg3o3r271jSwV6lUVK9endGjR9OxY0ep46QKlUpF6dKlWbFiBQ0aNJA8S82aNRk2bBhdunSRNEtWsmDBAjw9PXF3d6dDhw4AxMXFUbJkSW7duoW5ubnECb928uRJFi5cyOnTp6WOkqpmzJiBr68ve/fulTqKIAjCV3SlDiAIgpBeHB0d6dq1K3369OHgwYNaN31DSGnRokVkz56dTp060b59e2bOnElERASHDh3i7Nmz+Pv7c/XqValjCkK6O3/+PC9fvtSq6YCnT58mMjKSdu3aSR0l1Zw8eRI9PT3q168vdRROnDhBREREpilaajv1FM9hw4Yxbtw4zc/v3r3LwYMHqVatmoTp/l1m7K/2+vVrli5dyu3bt6WOIgiC8E1iKqggCFnKzJkzef36tWYah6CdlEolpqam3Lp1i+7du7NgwQJOnz5NqVKlCAkJoUePHly+fBlATAMVshSVSoWrqyvu7u7o6mrH9VH1SqBTpkzJVFO0lixZwqhRoyS/CKNSqXBzc2PatGmZ6v3VZurfuXrl1Vu3buHh4cHu3buJjo5m2LBhmJubEx0dzbp167h3756UcVPIjIW1iRMnMmjQIGxtbaWOIgiC8E3acUQmCIKQTvT19dm5cyc1a9akTp06lClTRupIwjfI5XJcXFxo0aIFT58+5dKlS1hbW2NiYoKpqSmFChVCLpejUqnEiaaQpZw6dYqwsDCtmg544cIFQkND6dSpk9RRUs3Dhw/x9fXl0KFDUkfh8OHDJCQkZJoFITISuVxOdHQ0O3bsAKBUqVLUq1eP4sWL8/DhQ+bPn4+xsbFW9OBTCwgIoF69elLHSDU3btzg/PnzPHnyROoogiAI/0j0WBMEIUvatGkTixcvxsvLC0NDQ6njCN/hzZs3XL16lQMHDlCgQAHmzp0rdSRBSFcqlYoqVaowbtw4rZoSWL9+fXr37q1VU1N/1YABAyhQoABubm6S5lAqldjb2zNt2jScnZ0lzZIVqaeEXrp0CQsLC0qWLKm5LTo6mqtXr1KyZEksLS21pt9nqVKl+PPPPyldurTUUX6ZUqmkRo0aDB48OFN9vgiCkPmIwpogCFmSSqWic+fOWFhYsHz5cqnjCF9Qn8hERkayYsUK9u/fT+3atRk5ciQFCxYEICIiAlNTU5RKJXK56GogZA2HDh3C1dWVu3fvas1+f+nSJfr06cOTJ0+0Zmrqr/rw4QPFihXj6dOnWFhYSJpl3759zJ49m9u3b0s+JVVIpv6O0kYKhQJjY2PCwsIyxUXDrVu3smLFCq5fv56mn3nRiUruh8XxPlZBvEKJgY4cC0MdyuXKhpGednzWCoKg3cQnhSAIWZJMJmPt2rUcPnyYI0eOSB1H+IL6es+yZcu4desWS5Ys4d27d1SsWJFy5cqxdetWTE1NAbSmuCAIaU2pVGr6bGnTfj9jxgwmTZqUaYpqAGvWrKFdu3aSF9WUSiXu7u5Mnz5daws5WYlSqQT+138tKSmJ7du3SxnpK69evSJPnjyZoqj2+fNnfv/9d/744480+8x7E53IvmeRrHoYxpU3MTwKjycwMpFH4fFceRPDyodh7HsWyZvoxDTZviAImUfmOQoSBEH4QWZmZmzbto327dtz9+5dLC0tpY4k8L9i2bt37xg7diw1a9akVq1aAKxatYrXr18DiNFqQpby119/oaurq1XTAa9du0ZAQAAuLi5SR0k18fHxrFq1ilOnTkkdhb1792JsbIyTk5PUUQS+vpCjq6uLSqXi6dOnlChRQqJUKWWmhQvmzZtH3bp1qV69epo8v3doLOdeR5P0D3O31D/3j0jgeWQCDayyY2+e8QuWgiCkDTEVVBCELG/q1KlcvXqVkydPikKNlnj+/DkjRozA2NiYUaNGUahQIfLkyZPiPto8HUcQUpNKpeLhw4d8/vyZatWqSR1Ho1mzZrRu3ZqBAwdKHSXVbNmyhW3btkleWFMoFJQpU4alS5fSuHFjSbMI/3PixAkUCgXNmzcHICEhAV1dXa05dli7di23b9/Gw8ND6ii/JDg4mEqVKuHj40OBAgVS/fm/LKo9977OXzNGMWb/jX99jK4M4q4f5tL+nd/1+TB16lQCAgLYtm1basUWBEGLace3gCAIgoSmTJlCbGwsixcvljqK8IWaNWuir6+Ph4cHnp6eHDt2jBcvXmhuF0U1IauQyWTY2dlRtWpVqaNoeHl58eDBA3r16iV1lFSjUqlYsmQJo0aNkjoKO3fuJE+ePDg6OkodRfiCg4NDipWo9fX1taaoBplnxNr48eMZPnx4mhTV3kQnphipVsi++n8W1SB5BFu26i3xPHA01TMJgpDxac83gSAIgkR0dXXZvn078+fP586dO1LHEYBChQoxbtw4PDw8cHFxITw8nGXLlqUorAlCViKXy7WqmDxjxgwmTpyIgYGB1FFSzcWLF4mLi6NJkyaS5khKSmLatGmit5oWypMnz3/uH48ePSI0NDSdEqWUGQprly9f5saNG4wdOzZNnv/au9h/nP75X5JUcP1tbOoGEgQhUxCFNUEQBMDGxoZly5bRpUsXPn/+LHWcLEmhUABw9epVFixYwIgRI1i4cCE6OjrMmDGDrVu3atU0OEFIbRmlO4e3tzfe3t707dtX6iipasmSJYwcOVLyEUhbt27F2tqa+vXrS5pD+LZ/KnZ++PCBHTt2sG3bNvbt25fOqZJl9MKaQqFgxIgRzJs3DyMjo69ut7W1Zc6cOdjZ2ZEzZ0569+5NXFwcAEeOHKFChQqYmZlRo0YNfH19Uzxu4cKFlClbju7lC7BjQj8S45Mf9+z2VeY0Lae574VNS1nQqjLutWxZ0q4mD8/9b4TanUM7GdPOkZjE5IUsRowYgbW1NTly5KBSpUpcvnw5Rd6EhAR69OiBiYkJpUuX5vbt26n3ZgmCoFVEYU0QBOH/de7cmZo1azJixAipo2RJ6pPZWbNmYWJigq+vLw8ePGDu3Lk4Ozvj7e2dqVYeFIS/+/sJu7YW2mbMmMH48ePJli2b1FFSTUBAANeuXZN8IYbExERmzJjBtGnTJM0h/Lhs2bLh7+9PsWLFJJnCq1QqCQwMpEiRIum+7dSyefNmjIyM6NSp0z/eZ/v27Zw8eZLAwED8/PyYOXMmd+/epU+fPqxdu5aPHz8ycOBAWrVqRXx8vOZxe/bsYc62/fx+5A5v/R/hfXjXN58/dwFbBm44jPulZzQcMJbdUwYTGfo2xX18w5KLcpUrV8bHx4ewsDC6du1Khw4dNIU+gEOHDtG5c2c+ffpEq1atGDp06K+8PYIgaDFRWBOEDCw6UcmNdzEcCopib2AEh4KiuPEuRnMlTfhxy5cv5/Lly+zZs0fqKFmOTCYjKCiIjx8/MmjQICIjI5k2bRqOjo7o6upSqFAhQHuLDYLwK1auXMn+/fvx8fEhMjISQDN6Vpv2+Xv37nHjxg369+8vdZRUtXTpUvr37//NUTLpadOmTRQtWpTatWtLmkP4fiqVCoVCgbGxMe7u7vTu3ZsiRYqgVKbvsdibN28wNTXF2Ng4XbebWiIjI5kyZQp//PHHv06BHjp0KNbW1uTKlYvJkyezc+dO1q1bx8CBA6latSo6Ojr07NkTAwMDbtz4X++04cOHozK1QD9HTkrVaULI0wfffP6yjs7kMM+HXC6nXJM25ClYiFcP72puVwGhsckj7Lt3707u3LnR1dVlzJgxxMfH8/TpU819a9WqhZOTEzo6Ori4uHDv3r1ffJcEQdBW4tK/IGRAb6ITufYulmeRCcggRa8Iv09w+U0MhXPoUyOvIZbZ9aSKmSEZGxuzY8cOnJycqFq1KjY2NlJHylLevHnD8OHD8fX1pUCBAhQtWpSOHTty+PBhihcvDohFC4TMZ+HChXh4eFCxYkVy5sxJhQoVyJcvH9OmTWPXrl2afV8bzJw5k7Fjx0pegEpNnz59Yvv27dy/f1/SHPHx8cycOVNc2MlA1KtT6+jo8O7dO44cOcLly5dZs2ZNuo/ozOjTQGfNmkWzZs1wcHD41/tZW1tr/t/GxoaQkBCCg4Px9PRk+fLlmtsSEhIICQnR/DtfvnyEK5KLnXrZDL8ahabmfWQ3V7atJjzkZfLzxEYT/eljivvE///zLFy4kA0bNhASEoJMJiMyMpIPHz6k2KaakZERcXFxJCUlidH3gpAJib9qQchgvlwi/FvUP/ePSOB5ZAINrLJjb26YfgEzAQcHB8aOHUu3bt24cOGCOABKR9WrV6dSpUrIZDKUSiVt2rRBX1+f0qVLA8n9V75ckU0QMoPWrVszdOhQsmXLxq5du7hw4QLZs2dHqVTy4cMHrK2tMTSU/nP8wYMHXLp0ic2bN0sdJVV5eHjg5OSElZWVpDnWr19P2bJlRS/JDEQmk/HmzRv27duHl5cXoaGhyOVyDh48SKdOndL1OysgICDDTgMNCAhgw4YNPHjw7VFkX3r58qXm/1+8eEH+/PmxtrZm8uTJTJ48GUheiKRevXoMGzaMbdu2ERkZiY+PD8WL/vvKyuEhL/lrxmj6rdlHwXKVkevosKxzPfjbqGEDHTmXL19m/vz5nD17ltKlSyOXy8mZM6dWjTAWBCH9iKmggpCKSpcuzYULF9Ls+f+rqPZ3SSo49zoa71CxgtGPGjt2LNmyZWPWrFlSR8n01NNl4uLiiIyM5PHjx8TExLBp0ybKlSuHk5MT06dPB5C8qbggpLbIyEiKFi2qGd3SuXNnli1bxqJFi+jfvz+bN29mz549RERESJw0eUTJ6NGjyZ49u9RRUk1SUhLLly9n1KhRkuaIjY1l9uzZms86IWM4cuQIgwYNIigoCGdnZ2bPns3ChQuZPHkyMTEx6XohKCOPWBs7dixjx45NMcLrn6xcuZJXr14RFhbGrFmz6NSpE/3792fNmjXcvHkTlUqFra0tcrmcjx8/cuzYMcLDw/n9998xzyZH918GvSfExSCTycieMw8Atw/u4F3gkxT3kQHmhjpERUWhq6uLubk5SUlJTJ8+XTONXxCErEcMwxCEVPTw4cM0e+430Yk/VFRTUxfXLLPrYmkkpoV+L7lczpYtW7C3t6dRo0bUrFlT6kiZllKpRC6XM2bMGJ48eUKRIkUwMzOjUqVK9OnTB2tra01BTUwDFTIThULBmDFjKFGiBN27dydfvnwolUr09fUB6NmzJ3/99ZemsDZ8+HDJsj558oSzZ8+ybt06yTKkhb/++gtbW1sqVaokaY61a9dSuXJlyXMI30c9BbRSpUpERkZSuXJlbG1t0dNLPs4qWLAgQUFB2NnZpVumgIAA2rVrl27bSy1nz57F19eXXbu+vZjA33Xt2pXGjRsTEhKCs7MzU6ZMQU9Pj9GjR9O+fXvevn2rWWUcQE9PD6VSyZYtWyiX25Arb//5YnPewiWo1f03Vvdqhkwup2LzjtiUr/LV/crlyoZBkyY0bdqU4sWLkz17dkaNGpVimqogCFmLTCXGqwpChrDvWST+EQk//fjipvq0LZwjFRNlDYcPH2bYsGH4+PhgZmYmdZxMJy4uTjNSp2rVqmzdupUXL17g5+dHcHAwISEhTJgwgTJlykicVBBSX0xMDJcuXeLKlSsYGhpqpjHB/07cAebPn8/169fZv3+/VFFxcXGhZMmSKTJmBtWrV2f8+PG0adNGsgzR0dEULVqUEydOUL58eclyCD9HfXFIpVJx/PhxZs2axYcPH5g5cyZt2rRBV1c3xd9zWrG3t2fdunX/2aNMmyQlJVGxYkWmTZtG27Zt//P+tra2rF+/ntq1a3Pr1i0uXrzIxYsXuXHjBoULF6Zu3brUrVuX2rVr4+bmxrp166hbty6HDh3SLOrw9+PpQK/L7JsxivGHb//n9m8f2M7TU/u4f/3Sz79oQRAyJTGnRhBSka2tLWfOnAHAy8sLBwcHcuTIQd68eRk9ejQAFy5coECBAv/4uL87evQo5StUpFs5K+Y2K8+ZNfNT3O59ZDfznCoyvX5xznosYl5zewJuXgTg5QNvVvVsxrQ6RehVtRiDBg8hIeF/BxMymYxVq1ZRrFgxTExMcHV1JTAwkBo1apAjRw46duyouX94eDgtWrTA3NycnDlz0qJFC169epU6b5wWa9myJS1atGDQoEGib0Ya+OOPP7C3t2fMmDHUqFGD4sWL06hRIwYPHkzfvn1p166dKKoJmZaRkRGOjo5MnjyZgQMHAv+bGi2TyVCpVCiVSho3bsycOXMkyxkQEMCJEycYOnSoZBnSwo0bN3j//j2tWrWSNMfq1aupVauWKKplUHK5nL/++otWrVrh5uZGx44duXr1Kl5eXsyePRtI+5V9VSpVhuyx5uHhgbm5+X8WtuPi4rhw4QKfPn1i7Nix5MmTh5EjRxIWFsbQoUMJDg7Gx8eHpUuX0rZtW8zNzenXrx+TJk3i9OnTKVZKrZHXMMV00LeBj8mVv+B35Q199pQyxTPWeywIQvoQU0EFIY2MGDGCESNG4OLiwufPn7+rIeu3ZM+enXF/rCUkhy2v/R+zcXAHLEuUoXR9J949e8rBOePpvWI3BcrYc2rFLCJD32geK5fr0GLMDKzsKhAdGsKOkV1ZtWoVI0eO1Nzn5MmT3Llzh5cvX2Jvb8+1a9fYtm0buXPnpnr16uzcuZOePXuiVCrp3bs3e/bsQaFQ0KdPH4YOHcqBAwd+8Z3SfgsWLKBy5cp4enrSq1cvqeNkKgMHDsTa2ppz586xa9cuHj9+zLhx42jYsCHFixfXqtUQBSE1qZua379/Hx0dHcqWLQuk7CMok8mQyWRUqFBBopTJZs+ezdChQzE1NZU0R2pbsmQJw4cPl3RBlKioKBYsWMC5c+ckyyD8ukuXLlGzZk0mTpyo+VnJkiU5dOgQKpUqzfuDhoaGoq+vT86cOdN0O6kpPDycqVOncvr06a9G80VHR3P9+nXNiDRvb2/KlCmDSqWiQ4cO3/V5VKlSpW9OrbbMrkcDq+ycex3N/vmTeHzxJB2mLf/GM6S0bXQPYt8EsfqvP3/shQqCkCWIqaCCkIrUQ9QbNWpEnTp1qF+/PsOGDSNPnjya+1y4cIHu3bunGO315eO+5VBQFI/C4wE4vGAyMpmMFmNncnbdQt4/96PLnOSeNwmxMUyvW5Rey3dStGrdr54nYP96Pt6/qZlOJJPJuHLliqZ/WKVKlejYsSMTJkwAYMyYMSgUCv7444+vnsvHx4f69esTHh7+E+9UxnP//n0aNGjAtWvXKFasmNRxMqXHjx+zZ88eTp8+TVJSErVq1WL27NmaflOCkBnVrVuXfv364eLiwqtXr8iTJ49merQ2eP78OZUrV8bf3z9DnbT/lxcvXlCxYkWCgoIwMTGRLMecOXO4f/8+O3bskCyD8PPU00C/dO3aNQ4ePMiNGzeYMGECTk5OaT4V9Nq1a4wePZobN26k2TZS26hRo4iNjWXNmjVERUVx9epVTSHN19eXChUqaKZ21qhRI8Wos9TwIwuC6cqggVV27M2lX51ZEATtJEasCUIa2bBhA25ubpQsWZJChQrh7u5OixYtfvh5bt68yaQRYwl6+pikpAQUCQmUaZQ8bSUy9C1mea0099U3NMLI7H8nPqHBgRxd5Mrrxz4kxsWiUiio4pDy6l3evHk1/29oaPjVv9++fQsk9wIaNWoUJ06c0BTToqKi0nUpeSmVLVuWqVOn0qVLF65duyaKPalAfaIRHx9PSEgIpUqVwt3dXTN1Q7zPQmal3vcPHDiAgYEBLi4uXLx4kUGDBpEzZ06WLl1K5cqVpY4JJBd+fvvtt0xVVANYvnw5vXr1krSoFhERwZIlS7h8+bJkGYRf82VR7dGjR6xcuZKYmBjs7OyYPXu25sJlWvdXy2grgnp5ebFx40a6d+9OlSpVePToEQ4ODtStW5eZM2dSrVo1jIyM0jSDvbkhltl1uf42lsDIBGSQosimKwMVUCSHPtXzGYoFwARB+FeisCYIaaRYsWLs3LkTpVLJX3/9Rfv27fn48SPZs2cnJiZGcz+FQkFoaOg/Pk/Xrl2p17Ufnf7YiZ5BNg4vmEzMpzAATPLk5UNwgOa+iXGxxHz63wiyg7PHkb9kWbrMWYdBdmMC9q/nybkjP/V6Fi1axNOnT7l58yb58uXDx8eHihUrZqm+Y4MHD+bEiRO4uroyb948qeNkeOoTjREjRuDr68v79+9p27YtHTt2xMnJCScnJ4B0afosCOnlyxEuBgYGlCtXjhUrVnD37l02bNjAnTt32L59u1YU1oKDg9m3bx9+fn5SR0lVnz9/ZuPGjdy5c0fSHEuXLqVZs2aUKFFC0hxC6siWLRumpqZ07NgRQ0NDXr16xeTJk8mfPz9Vq1bFwcEhzS5GanthLSwsjEuXLqUYkVa4cGHy5cvHwoULqVKliiQjdS2N9GhbWI+YRCW+YXGExiqIVygx0JFjbqhDuVzZMNITLckFQfhv4pNCENLItm3bCA0NRS6Xa1aTlMvlFC9enLi4OI4ePUpiYiIzZ84kPj7+H58nKiqK/Oa5McyWjZcPvLl34i/NbWUbteTxpZME3/MiKTGBM2sXoOJ/ha74mM8YZDdB3yg7YUH+nN6x8adfT1RUFIaGhpiZmREWFsa0adN++rkyKplMxsaNG9m2bds/LjYhfB+FQgHAjh07iIuLY9euXejr6/PhwweqVKlC27ZtSUxMBNL+Sr8gpKfdu3dz//59EhMTady4MR8/fuT69euaBTy8vLw0hRb1QgZSmTdvHgMGDCB37tyS5khtBgYG7NixA1tbW8kyhIeHs2zZMlxdXSXLIKSuwoULM2HCBKpWrcqePXvYsGEDHh4elC9fnj59+qBUKtNshL+2Fdbev3/Pn3/+ybBhwyhXrhyFChVizZo1WFhY0K1bNwoXLsyDBw9wdXWlTp06kk9/N9KTUy2vES1tTWhfxJSWtiZUy2skimqCIHw3MWJNENLIiRMnGD16NDExMdjY2LBr1y4MDQ0xNDRk1apV9OvXD4VCwfjx479aJfRLq1atYvSYMbz7fTSF7GtQ1rEVcVGRAOQtUpJW4+ew8/cBJMTGULPrQIxz5kFHL3n6nNPIqeyfNYZLnsvJX7IsXTt14vLF8z/1ekaOHEnXrl3JkycP+fPnZ8yYMVli4YK/Mzc31yxicPfuXczNzaWOlCGpTy527NjBmDFj8PT0ZMiQIQwZMoSCBQuSmJiInp7eN/vXCEJGpVAoMDMzo2zZssyYMQMrKyvWrl2rmfK8a9cuAgIC2Lp1K4Ck+/6rV6/YtWsXT58+lSxDWtHT08PR0VHSDIsXL8bZ2VmriiHCrzM1NWX//v08evSITZs20bFjR+zt7WnYsCHr1q3TrDCe2heMpC6svXnzRjMa7eLFi4SEhFCrVi3q1q3L+vXrsbe3R1dXl8TERMqVK8eSJUtEqwdBEDIVsXiBIGQQ+55F4h+R8K/3iY/5zPS6RRlz4Ca5rGxS3FbcVJ+2hXOkZcQsZfz48Tx58oSDBw+KEVW/4N69exQtWhQ3NzcMDQ1xc3Ojffv2jB07ljp16ojCmpDpqFQqlEolHh4e+Pn58fnzZxwcHGjTpg06OjoEBQVhb28vef/K4cOHo6+vz8KFCyXLkFl9/PiR4sWLc+fOHUlHzQlpY9iwYdjZ2fHbb7+xatUqDh48SO3atSlWrBidOnVKk23mypULPz+/FItlpaWXL1+mKKR9/PiR2rVraxYbKF++/Dc/v5YuXcrx48c5fvy4OHYSBCFTEYU1Qcgg3kQnst0/4qvVix5fPEmRKrUBFUcXu/PywR2G7TiX4oBFVwbdipuKxqupKCEhgRo1atCnTx8GDx4sdZwMaf/+/VhbW+Pg4IC/vz8zZ85EpVLx6NEjbt++LXU8QUgzT548oUSJEty/fx9vb298fHwICwujW7duNGnSROp4vHnzhtKlS/Po0SPy5csndZxfoo09GidOnMinT59Ys2aN1FGEVKS+EHT58mXGjRunWaGzSZMmWFlZsXHjxq/umxrCwsIoXLgw4eHhabavBwUFceHCBU0h7fPnz9SpU0dTSCtTpsx/vp4PHz5gZ2fHhQsXsLOzS5OcgiAIUhGFNUHIQL61NPi+6SN5cOYwKlQUKFUB59/nY277v+kAYonwtOPn50fNmjU5f/48ZcqUkTpOhqBUKjl27Bj58+dn1KhR7N+/n1y5chEbG8vDhw9JSkqidOnSmJiYiNFqQqaiHoHm6enJ8ePH2bVrl+a2V69eceDAAapXr06lSpX+5VnSx+jRo1Eqlfzxxx9SR0k16pGCUq9i/f79e0qVKoWPjw/W1taSZhHSTrt27WjZsiW9evUiOjoaIyMj7t69y6JFi9i+fXuqbsvLy4vBgwen2gUplUpFQECApoh26dIlEhISNEW0unXrUqpUqR8u4g0ePBhdXV2WLVuWKjkFQRC0iSisCUIG863i2j8RRbW0t2nTJhYvXoyXlxeGhuJ9/i9xcXEcOnQIV1dX3rx5w4wZM2jXrp2mz+Do0aOZNm0aJiYmEicVhLRRqlQp9u/fT8GCBZk5cyYHDx5k6dKlNGrUSOpoALx7945SpUrx4MED8ufPL3Wcn3blyhXevHmDgYEBdevWxdTUlISEBGQyGXK5XLIC25gxY0hISGD58uWSbF9IW+oLQu/fv8fQ0BATExPOnTvHokWLePjwIZaWlixdupQqVaqk2nTvHTt2cOjQoRTF+h+hUql48uRJikKaXC5PUUgrVqzYL42Gu3//Pg0bNuTJkyfkypXrp59HEARBW4nCmiBkQG9iErn+NpbAyARkkKLIpisDFVAkhz7V8xmK6Z9pTKVS0blzZywsLMSJ0ndQT8uaPHky2bJl48WLF9y6dQtbW1uKFi2Kr68vp06dkjqmIKQJf39/xo0bh4eHB1OnTiVXrlwUKVKEY8eO4eHhgampqdQRefToEQcOHGDSpElSR/lpISEhVKtWja5du/L582dkMhmFChVi9+7dDBw4kD59+gDpP01UPcU2oxcthe9z9+5dBg4cSEJCAn369KF58+a8f/+eDRs2sH79+lTbzvTp00lISGDmzJnfdX+lUsnDhw9TFNIMDQ2pV6+eppBWqFChVPvbUKlUODo60rp1a4YOHZoqzykIgqBtRGFNEDKwmEQlvmFxXLzjS3bTXNhYWWJuqEO5XNnEEuHp6NOnT1SoUIHly5fTsmVLqeNovcjISKpUqYKvry/6+voEBQVx+PBhHj58SLdu3ahdu7bkjdsFIbW8e/eOR48eUb9+fQBGjBjBn3/+ybBhw5g4cSI+Pj4MHTqUK1euSJw0WVJSEnK5PENPw75//z4PHjygS5cuXL9+ncePH/Po0SP8/PywtLRk5MiRlCpVKt1zjRgxAh0dHRYvXpzu2xbS361bt7h69SpDhgxBT+9/Fzk/f/6MoaFhqn3H9ejRgwYNGtCrV69v3q5QKLh//76mR9rly5cxMzNLMSLNxsbmm49NDQcPHmTy5Mn4+Pigq6ubZtsRBEGQkiisCUIGFxsbi5mZGebm5rx8+VLrmjRnFVeuXKF9+/Z4e3uLkQj/QD1FZv/+/Zw+fZpVq1ZJHUkQ0tyhQ4ewsbFBX1+fhw8f0r59eyIjI8mRIweRkZHUr1+fqVOn0rJlS1FQTiNJSUno6uoSGhrK3Llz+fz5M+XLl2fQoEHpVkB89eoV5cuX59GjR+TNmzddtiloj7QcHVmjRg3mz59PrVq1gOT93cfHRzMi7cqVK1hYWKQopFlZWaVJlr+Lj4+ndOnSrF69GkdHx3TZpiAIghTEZQNByOBWrlyJUqkkNDSU8+fP06BBA6kjZUm1atVi0KBB9OzZk5MnT2bo0R5pRf2e+Pj4sGnTJrJly8bAgQMpUaKExMkEIe20atUKlUrFH3/8wePHj/H19aVq1ao4ODhgbGxMjx49NCNdpSiqaeOqmb9i7969vHr1ilGjRml+pquri1KpxNzcnKFDh/Lo0SM8PDxo165duhW5Zs+eTb9+/URRLYv6t7+xX12ox9/fn4iICObNm8fFixe5du0aBQoUoG7dunTv3p1169ZJtrrv0qVLsbOzE0U1QRAyPTFiTRAysOjoaKysrIiIiACgYsWKeHt7S5wq60pKSqJevXo4Ozszbtw4qeNorWfPnnHr1i2uX79OYGCg5kp69+7dRUFSyFTUI6V27NiBjo4O1tbWnD9/nvfv35M7d26qVKlC06ZNgfQvcKlHzWU2Fy9exN3dnZcvX/LXX39Rvnz5r+6TlJREnTp1uHLlSrp85gQHB2Nvb8/Tp0/JkydPmm9PyNzi4+O5desWFy9e5OzZs5w/f57y5ctrRqPVqVNHK/azd+/eUbp0aa5fv06xYsWkjiMIgpCmRGFNEDKwpUuXMmbMGCD5amhSUhJXr16lRo0aEifLuoKDg6lcuTLHjh3DwcFB6jhaQ100iI6OJiwsDIVCgUwmIywsjMuXLxMcHMyiRYukjikIqUa9zyuVSpo2bcrWrVvJmzcv8fHxXLt2jdOnT5MnTx5Gjx6drrn8/PyYP38+pqamFCxYkGbNmlG8eHEuX75M1apVkcvlGbYPUnx8PAYGBgAEBARQuHDhrwpn6um2r1690qxGnNb69+9P3rx5v7u5vJB5PH36lNDQUCpXroyBgcFPFdBjY2O5efOmZmrnrVu3KFGiBHXr1qVAgQJs2LCBBw8epNEr+Hn9+vXDzMyMhQsXSh1FEAQhzYnCmiBkYOpVnbZv3461tTW1atWiffv2kg35F5Lt3r0bV1dXvL29MTY2ljqOVhkwYADPnz/nzZs3nDp1ivz585OYmIhCoSBbtmyZblqaIOzYsYOtW7fSv39/mjdvrin8xMfHa3pkpud+P3HiRMLDw2nQoAH37t3jxYsXJCQksG/fPhQKRbpkSCu9evVi0qRJmJiYYGlpqfm5lJ8rgYGBVK1aFT8/P3LlyiVJBiH9fPz4kSdPnlCzZk38/f1xdXUlMjKSxo0bM3LkyO/uo6hQKFi0aBFHjhzB29ubMmXKaEak1axZU7OC8J49e9izZw9//vlnWr+0H3L37l2cnJx48uSJVqx2LAiCkNZEYU0QMoEBAwbg4ODAgAEDpI4i/L8+ffoAsHHjRomTSE/dP2b37t0cOXKERYsW4ejoyL1793j69Cn79+9n7NixGXaUjCD8m+PHj7NkyRIMDAwYMGAA1apVI1euXJItUvD+/XssLCyA5NE0z549Izw8nD179mBvb0+7du0oXbq0JNl+VWhoKObm5pQuXRpLS0tGjx6Nk5OTpJl69epFoUKFcHd3lzSHkDYSExN58+YNefLkwcjISDMV+fTp05qVQL28vBgyZAi3bt365nN8q8daQkICPj4+fPr0iRo1avzjRbrZs2cTGRnJ3LlzU/eF/QKVSkXdunVxcXGhf//+UscRBEFIF6KZjSBkAqI+rn2WLVvGlStX2L17t9RRJKc+YfD19aVNmzZs3rwZZ2dnAK5evcqNGzfQ1dUV+7GQaahHfnl4eFCyZElOnDhBixYtWL58OWPGjOHgwYOSjA578+aNpqgGUKJECZo1a0bXrl0ZM2YMMTExLF26lBs3bqR7tl+lUCgwNzcHkhdIadu2LZMmTaJKlSqsXr2amJiYdM/k5+fH0aNHGTlyZLpvW0gbKpUKhUKh+b6Ki4tjxYoVmturVauGQqHg9evXmp8VKVIElUpFcHDwd29HX1+fKlWq0Lhx438d+R4QEEDRokV/4pWknT///JPIyEjNBUZBEISsQBTWBCETENPntI+xsTE7duxg2LBhP3QwnZm1bduWmzdvsn79etq3bw8kH4C7uLgAyVftBSGjU6lUmtFowcHB3Lx5E7lczsCBAzl69CgVK1bk1q1b6T5iLTw8HAcHBzp37szTp081WdVq1qzJ0KFDsba2Ztu2bRmu0K1+Pz08PPDz82Pw4MH4+Pjg5ubG+vXrGT9+fLpnmjZtGiNHjhRT4TIg9f7v4+PDnDlzuHjxIpDcz1ZHR0dzzGVkZMThw4dp2bIlJUqUICIiggEDBqCvr695rty5c2NiYsLJkyeB5MUz/u7Vq1c/lVPbCmuxsbGMGzeOpUuXSjYqVxAEQQqisCYImYAorGknBwcHxo0bR7du3b55IJ3VVKpUieLFi2NlZcWmTZto3LgxhQoVol27dgDiIFzIFLZv387p06cBqF+/PteuXQOST6b19PQoU6YMM2bMANK3mJwzZ06eP39OiRIlWLt2LUCK7w25XE6BAgUwMDDAwsIiQ36nvH37luPHjzN58mSmTJnClStXaNGiBXfu3GHBggVA+o3wfvToEWfOnGH48OHpsj0hdXz69ClFYXn06NEolUpKlSoFJBeyli5diqurK+/evePly5cULFiQ3Llzc+zYMSwsLHBxcSF//vzA//7GJ0+ezLp166hZsybv3r1LsU25XE7OnDl/Kq+2FdYWLVqEg4MDdevWlTqKIAhCuhI91gQhE+jTpw81a9akb9++UkcR/kapVNK4cWNq166dpXvs+Pv7c+bMGSpXrkxUVBRJSUkUKVKEAgUKoK+vL4rDQqbw/v17Zs2ahVwux8rKioIFC7J7925sbGwICwvj1atXFC9enFWrVqV7tm81Tf9Wb6fo6Gj09fU1/aEyIi8vL/bu3cvx48extrZm0qRJ1K5dO10zdOzYEQcHB0lGygnfT6VSaYpfOjo6hISEcP/+fRo2bEhkZCQdO3akatWqtGnThqioKDw8PChRogQ6OjpEREQwdepUrly5wp49e1izZo3m7+bvf1svX76kefPm+Pr6flem7/k+jI6OxtzcnM+fP3/1dyyF169fU65cOW7fvk2hQoWkjiMIgpCupP8UFgThl4mihPaSy+Vs2bKF1atXc/XqVanjpCv1ycrFixfp06cPp06d4vfff2f9+vWEhYVRuHBhzXQZsf8KmYGFhQWurq7UqVOH8PBwLly4wNOnT/Hx8WHIkCEcPXpUU1RLz9Fq6qJaWFgYDx8+1Pz87yfjKpWK7NmzZ6iFRNS96t68ecOJEydQKpVUqVKFBQsWsHHjRl6/fp3uRQdfX18uX77MkCFD0nW7wvf5skeaemqnuuicP39+cuXKxa1btzh+/Dg3btzg2LFjGBoaUqlSJdauXUvLli15+fIlR44c4dSpU5QvX55nz57x/PlzzTb+vs+ZmZkRExPDmzdv/jHX2bNnNZm+R2BgIIULF9aKohrA77//zqBBg0RRTRCELCnjHDkJgvCvRGFCe+XPnx8PDw+6deuGj48PZmZmUkdKF+p98s8//6RHjx6a1cF27drFihUrsLe3p1ixYlJGFIRUpVKpyJMnD23atKFmzZpcu3YNY2NjZDIZFy5cIHfu3BQuXBj4+sQ7LamLBn369CEwMBCVSsWwYcPo3Llziv5f6r/ZjPR9on5tV65cYe/evZw5c4YqVarQpk0bbG1tKVSoEDVr1kzXTO7u7owfP57s2bOn63aFr33rwqN6n1GpVLx7945169bh5eVFtWrVmDJlCseOHePly5esX78ePT09zp8/T+nSpVEqlaxevZpTp07RoEEDzMzMuHv3Lm3atMHAwIDXr19TpEgRkpKSMDAwSLFNExMTypQpw9WrV2nfvv1XuRITE7l27RolSpTAysrqu/4GtWka6M2bNzl79qymf6MgCEJWox2XOARB+CViRrf2a9myJS1atGDgwIFZ5vclk8k0IwNevnzJhw8fAOjcuTNmZmaalQezyvshZH4ymQylUolKpcLCwoLWrVszZswYypUrx+vXr/91db+0oh4Z99dffwFw//59Zs2axZ9//knz5s1ZsGBBhu0BeevWLWbMmEF0dDQdOnRgwoQJWFlZcenSJZycnGjatCl16tQB0m+E4J07d/Dy8mLQoEHpsj3h298hX45Ig//9/v39/Tly5AidO3dmx44drF27lvj4eIYPH06vXr0AqFGjBmFhYQAUKFCA4OBgwsLCkMvlzJ07l0WLFjFixAjCwsJ48OABMTEx9O/fnwULFpA7d25OnTqVIktSUhI7d+4kMTERGxubFLnUdHR0cHV1pUCBAt9d2NaWwppSqWTEiBHMnj1bks84QRAEbSBGrAlCJiCmgmYMCxYsoEqVKnh6emoO4DO7Bw8eEBgYyL179zA1NcXS0pKYmBiioqLo0qULkLFGxwjCt9y/fx9DQ0OKFi2qGYmWlJSEjo4OefPmpVu3bjRv3lyS0arqPN7e3pQrVw4AZ2dnnJ2dOXjwIMeOHctQUz+/pFKpuHbtGvv376dp06YMHjyYUaNG8fr1a4KCgoiLi9M0UU+vEYJTp07l999/x9DQMF22l5XFxsbi7+9PuXLlSEhISLESp7rIfePGDV68eEHnzp2JjIykdevWVKhQgf79+1OvXj3KlSuHm5sbefLkwdzcHAAbGxuSkpLw8/OjePHimJiYcP/+ferWrUulSpVYvHgxtra2moULnjx5Qrt27ahQoQL58+f/6ncfHR1NeHg4AwYMwN7e/puvRS6Xo1Kp2LFjB2ZmZjRv3vybfRG/FBAQ8I/Pl5527NiBUqnUrPAtCIKQFYnFCwQhE+jRowcNGzakZ8+eUkcR/sODBw+oX78+V69epXjx4lLHSVPqgm98fDw+Pj789ddfPHr0iA8fPuDk5ESbNm00/WwEISPz9PSkSZMmmgJbrVq1NLclJSVJXrh69uwZ06ZN4+nTp3Tt2pUaNWpQqVKlFEXtby1kkFHcunWLDRs2cPXqVcqXL8/QoUOpVq0akL4Xnm7evEmHDh3w8/MjW7Zs6bLNrOzIkSNMnDiRBw8eACn34ZCQEFxcXMiVKxfW1taYmZkxefJkBgwYgLGxMUuXLgVg586dXLx4EUNDQy5dukTv3r3p1asXEyZMoGbNmnTt2pXffvsNCwsLpk2bxpMnT/D09CQpKYmuXbtSvnz5b/Yq/N59TqVSkZSUxJUrV4iPj8fMzIwJEyZw8eLF/3xsgwYNmDRpEo0aNfqRty1VRUdHU7JkSXbv3k2NGjUkyyEIgiA1UVgThEzAxcUFR0dHevToIXUU4TusXLmSTZs2ce3atRRX2DML9clNWFgYN2/exNfXFwcHB+rWrUtsbCwnT57k1KlTfPz4EVdXVypUqCB1ZEH4JdHR0ejp6fHHH3/g5+eHjY0NLVq0oGLFilJH0/jw4QMXL17ULKJSoEABGjVqpBnFltGoC5bv37/HwsICSF55cdOmTRw+fBgdHR3OnDmTrlPTmjZtSps2bRg4cGC6bTOzU0+t/tbIrZCQEJydnenXrx9//fUXCoWC2bNnU6VKFSZPnkyNGjVwdHRk2bJlLF26lBMnTnD8+HE+ffqEm5vbV9+/np6enDhxgjVr1rB06VJevnyJh4cHhw4dAqBVq1bfzPgzxVv16depU6fw8vJCV1eX3bt3c/fuXdq2bUvfvn1p0aLFvz5HwYIFuXTpEra2tj+07dTk5uZGQEAAO3bskCyDIAiCNhCFNUHIBFxcXGjcuLEYhp9BqFQqnJ2dKVWqFPPmzZM6TqpTF9aGDRvG48ePadSoEQ8fPiQiIoKKFSvSq1cvrK2tuXz5MnXq1PnXqS6CoO3UJ9XLli3D2toafX19bt26xZs3b7C2tqZVq1aSFK/U08hevnxJaGgoz58/x8nJCZVKxbFjxzh+/DhDhw7VquLfz6hSpQphYWHMnj2bjh07AhATE8PVq1dxdHRMtxFrV69epXv37jx9+jRTXjBJb9/ze0tKSqJly5bo6Ogwd+5crl69yoULFxg9ejR//vknK1asoFq1ahQpUoTmzZvj7OzMiRMn2LRpE6tXryZXrlxER0eza9cugoKCuHbtGo6OjkycOFGzyMc/9TBTKBTI5fJf3rcqVaqEm5sbzs7OLF++nJs3byKXyxk/fjxlypT5x8fFxsaSM2dOoqOjJfsODQ4OplKlSty9exdra2tJMgiCIGiLjNlUQxCEFER9PGORyWRs3LiRChUq4OjoKOk0jrSgnhajUqnYsGEDNjY2vH79msePH3Po0CFu3LhBoUKFqF+/vsRJBeHXqIvIL1++ZPfu3ZrRYKVLl+bevXtcuHABX19fSQpr6pPt3r17U7duXY4ePcry5ctxdnbWjIbJDNMVvby82LBhA66urgwdOpTRo0czduxYHB0dgfTr4ejm5oarq6soqv2Evxep1EW1hIQE7ty5w+bNmylVqhTdu3cnT548mr87XV1dChQowPv37ylTpgwFChQgNjaW7du3U6dOHfz9/TWLdkDyqM3q1aszadIkXrx4Qa5cuciePTt+fn7kyZOHefPm4eDgAECRIkVSZPz7VOlfLWapC99Tpkzh7NmzxMbGcuHCBa5cucKgQYP+tagG8Pz5c2xsbCS9MDVhwgSGDRsmimqCIAiIVUEFIVMQixdkPHny5GHz5s307NmT0NBQqeOkGvXKa6GhoVhZWbF//36io6OxsrKiUaNGKUaViIKwkNGpT7RPnz5Nrly5CAgIAMDW1hZnZ2eGDRtGhw4dgPTd39Xb+vPPPzEzM2PQoEF8/vyZUaNGsWvXLqpWrcqzZ8/SLU9aUa/c2LdvX54+fcqSJUtwd3dP96L9hQsXCA4OFqPGv4NSqfxqhVYdHR1kMhkRERF8/PgRmUzGsWPH6N69O2vXrqVYsWI8evSIsWPHap5DrXnz5pr9wNDQEHNzcxITE6lZsya3b9/m0KFD7N69m759+7J582ZMTU1p06ZNiinC8+bNY9y4cZqimtqXf7Op3X9QXRBr06YNgYGBdO/enfz58+Pl5cW0adOIi4vj2LFj//j4oKAgSVcEvXz5MteuXWPcuHGSZRAEQdAmYsSaIGQCorCWMTVq1Ihu3brRt29fDh48mKl+h1OmTOHx48eEhIQQGBhIvXr1qFy5MtbW1prXmZler5D1BAYGYm1tjUqlwt/fn6ioKNatW0fLli0pWbIk5ubmFC5cWHP/9Nzf1dsKDg5m4sSJ7Nq1i3r16uHs7MzDhw/R1dXFzs4u3fKkhZCQEDw8PKhRowbFihUjf/78dOvWjWfPnmlGq6XHggwqlQo3Nzfc3NzQ09NL021lNB8+fOD58+fY2dmRPXt24OsCVVRUFFu3bmXjxo0AODo6Mnr0aMqWLUt4eDiVK1dm7Nix+Pv7axYF+XKUloODA0+ePOHJkyeULFmSCxcuUKNGDfLmzcvBgwdZtWoVUVFRVK1aVXNRx9XV9aus35ramdZ/s+pRawsXLmTZsmUUKVKEz58/s2LFCubNm0d4eDinT5+mevXqX60QmitXLskKa0qlkpEjRzJv3jyMjIwkySAIgqBtxIg1QcgERGEt45o5cyYhISGsWrVK6iipQi6XEx4ezsOHD7l06RIXLlzAxsYGT09P2rdvz5MnT6SOKAipYt++fcTFxREUFMTs2bPx8PBALpfj4eHBhg0buHLliqT5Pn78SPPmzbGzs8PIyIhChQoBcPHiRU2z84w8avTTp0+8f/+erVu34uHhwb59+1i2bBk7d+7UrAiaHqucnj17lvfv39O1a9c031ZGoFAoSEpKAsDf35+PHz9qCkJhYWFs27aN4cOH4+npCSQXr4oWLcrp06c5f/48ISEhrFy5EmtrawoUKEDhwoVJTEykWLFiKBQKgoODkclkmn23QIECmJqaMmTIEJycnHj06JGmAFexYkU8PDzYtWsXo0aNwsrKSpPzn0bNpSf1+1KqVCny5s3L0qVLcXJy4sCBA0yfPp21a9cyY8aMFPdVq1ixomTF8c2bN5MtWzY6d+4syfYFQRC0kRixJgiZhCisZUz6+vrs2LGDmjVrUrdu3f/sq6LN1AXeM2fOoFKpCAkJoUCBAowdO5axY8dy5coVSpUqJXVMQfhlSUlJdOzYEV1dXWbMmIG5uTlt27Zl7ty53Lt3jxUrVmBjY5PuuVQqlWZV0uHDhzN9+nSMjIyoWLEi3bt31xT71CN3MvL3hp2dHStXruT169fs37+fS5cuoa+vz4QJE4D0G63m6uqKu7s7urpZ85D67xf2viwAVa9encePH/Pu3TssLS2ZN28ecXFx1KlThyNHjmBmZoazszM1a9Zk/PjxXL58GWNjYz5//kx0dDRFihQhMDCQqKgocuXKRfny5Tly5AhDhgxBoVBo3vP58+ejo6NDpUqVKFCgwFe5FAoFQIoRaelRdP0RFy9exNPTkxkzZtC8eXPNz9++fUtERASmpqYpXpNKpaJBgwbpnjMyMpIpU6Zw6NChDP358auiE5XcD4vjfayCeIUSAx05FoY6lMuVDSM97dq3BEFIH2JVUEHIBDp37oyzszNdunSROorwkzZt2sTixYvx8vLC0NBQ6ji/5MCBAyxYsICEhARq165NixYtNCcA6XGyKwjpJTY2lrt37+Ll5cX9+/fJlSsXbdq0oUaNGpp9PT1HFL9584ajR49y8uRJHj58yI0bN8iRI4fm9osXL1KxYkVy5Mjx1dSyjCA+Ph4DAwN27tzJ/fv3iY6OpnLlyrRs2RJTU9N0z3P8+HHGjRvHvXv3Mtx7+bPUpw3f2qc/fvzIgQMHuHbtGlWqVGHgwIG0adMGJycn+vXrR0xMDK9eveLcuXPMmzePxo0bs3DhQvbt24evry9Llizh8ePHTJw4kZkzZ/LixQsOHTrEqFGjKFmyJNOnTycwMBBPT89//bvKTN8zoaGh7N+/nwEDBnx1mxSvc+LEibx7945Nmzal63a1xZvoRK69i+VZZAIyIOmLs2hdGaiAwjn0qZHXEMvsYmq4IGQlmeNbRxCyODEVNOPr1asXpUuXzhSNgFu3bs2xY8dYuHAhuXPnZs2aNTRu3JgPHz5kmpMdIetSj35JSEhAoVBQokQJRo4cyfDhw8mXLx8LFy7k5MmTmn09vT6bVSoVlpaW2Nvb4+fnR758+Vi5ciXbt2/n06dPPHv2jNu3b2sKbRmxEGRgYAAkr8BpampK2bJl8fb2ZtiwYUycOJHnz5+nWxZ1b7WpU6dmyPfyeykUihTTJmUyWYp9+syZM9y9exdI7l12/fp1mjZtSp06dQCoVKkSHz9+5PPnz7x8+ZKJEycSHh7OhAkTUCgUfPr0iRcvXhAUFATA1atXCQwM5MKFC5QrVw4DAwPN+zt58uQUU0j/nlMto3/PBAQEsGbNGg4fPoyBgQFnzpzhyJEjQMrXmd7HfYGBgaxfv57Zs2en63a1hXdoLNv9I/CPSEChSllUg+R/K1TgH5HAdv8IvENjpQkqCIIksua4dUHIZERhLeOTyWSsWbOGChUq0KRJE1q2bCl1pB+ivnJ++/ZtLl68iFKppFWrVowaNYqXL1/y/Plz8uTJI3VMQfhl6pP23r17I5fLCQ0NxcHBgSFDhjBmzBgaNGhA2bJl0z3Xn3/+iZ6eHs7Ozhw5coSkpCT279/PtWvXePjwIX/++Se//fYbkDG/M37//Xfi4+Np3bo1PXv2ZMKECURHR/Pq1Sv8/Py4cuUK2bJlS7c8hw8fJjExkbZt26bbNqXw96LhixcvOHLkCC1btsTa2potW7ZQsmRJ8ufPT2RkJJUrV8bR0VEz8rpMmTIcP36cxMRELl68SGRkJJMmTdKs4urj40P//v0ZNmwYtra2lCtXjqlTp1K4cGGsra1ZtmzZP2b5t5wZlb+/P4MGDUKpVJI3b14CAwOZMWMGv/32Gy1atEjxOtP7b3js2LGMGTMGS0vLdN3u99i8eTPr169Ps96W3qGxnHsd/VUx7Z8kqeDc62gA7M2/noWQVnlnz57Ns2fPWL9+fao+ryAI/00U1gQhE8iIJ0nC18zMzNi+fTvt2rXD29ub/PnzSx3pu8nlcpKSkhg0aBB9+/Zl7NixODs7Y2RkhKGhIU2bNpU6oiD8MvXKg35+fgQEBLBz507evXvHn3/+iYuLC+XLl2fGjBmS9NuqXLky+fPnZ+7cuVy7do3Ro0czevRoAgMDuXv3LhUrVqRDhw5Axuyt1r59e6ZMmcLGjRsxMzOjevXqNGzYkBIlSlC8eHGqVq2KhYVFumRRKpW4ubkxbdq0DD86SqVSoVKpUrwO9THF+/fvuXDhAps2bcLJyYmBAwfy6dMnrl+/jq2tLdbW1jRo0AAvLy9MTExo3rw5W7Zs4enTp4SEhODo6EjXrl3Zu3cvQUFBVK9enT/++IOFCxdy69YtKlWqxMuXL2nVqhVr164F+OYFmKx0jPP06VMSExO5dOkSAOXLl0cul1OkSBHCw8MxNTVN9ynmAOfOnePevXvs3Lkz3bYptaCgIAoVKsSLTzH/WFQ7s2Y+5zcuQVdPH7mOLhaFS+A0aho25StrimuW2XWxNEr9aaEXLlyge/fuvHr1SvOzSZMmpfp2BEH4Phn7aEAQBCBrHXRmdjVr1uS3336jZ8+eX61apq3UPXf27dtHjRo1GDRoEPb29hQvXhw/Pz86depEXFycxCkF4dcolUp69uxJrVq12Lp1K927d6dw4cI4ODgwdepUpk+fjlwuR19fX5J8tra26Onp0bp1a5ycnFixYgXdunUjMDCQ9u3ba4pqGbW1bqVKlTh+/Dj+/v60bNmSNm3aULVqVY4cOYJMJku3ohrA/v370dXVpVWrVum2zdTyramd6qKaehVPmUzGypUradeuHXfv3qVTp048ePCAGTNmUK5cOQoWLIifnx+QXNANCQnh5cuXdOnShePHj7Nw4UKGDBnCpk2byJkzJ/Hx8dy9e5dy5coxbdo0Xr16hYuLCxs3bmTIkCFAckEtT548qFQqlEpliv00Kx3ftGjRAqVSyeHDhzly5AjGxsbs27eP3r17kzNnTuRyOa9evdKsvJoekpKSGDlyJAsWLEjXUaFfbl9K19/F/utItXKOrZl2NZgpZ59gW6Eq28f10uy/SSq4/lZMCRWErEAU1gQhk8hKB56Z3eTJk4mNjWXRokVSR/kuX668ZmNjg4uLi+aE8/r16xQuXJhs2bJlmEKhIHyLXC7n5s2b9O/fn5MnT7Js2TIOHTqEnp4eJiYmVK9enVmzZqGrqyvZvi6TyShatCi//fYby5cvp27duixfvpy+ffumuE9GFRcXh7m5OcuXLycyMpIePXrQu3dv6tevn24ZlEol7u7uTJ8+XevfS3WR6su+XDo6OilGp7169YrRo0dTunRpBg4cyLJly0hKSqJ27drExsbSoEEDevXqRbdu3Th27BgAVlZWvH79GoDSpUvz5MkTgoKCCAsL49y5c6xcuZKlS5fSu3dvAPr166f5HXXs2JE//viDFi1afHOhHnWhT9vf27Sg/j1NmDCB8ePH4+rqSsuWLfHw8KBGjRo8e/aMAQMGULx4cU6dOpVuudavX0/u3LnTddqzra0t8+bNo1y5cmTPnp2kpCTmzp1LkSJFMDExwc7Ojv379//j48eNG0etWrXw9PSkUqVKKW5bvHgxzs7OABw9elSzoIu1tTVTp07V3E/dJ9ClgjXuNW0IvnfrXzPr6Olh37ITUR/eE/MpjAublrKgVWW6lM1PqVL/nvfJkyc4OjqSK1cuSpQowZ49ezS3HTt2DDs7O0xMTLCysmLhwoVER0fTrFkzQkJCMDY2xtjYmJCQEKZOnUr37t2B5BF3MpkMT09PChYsSJ48eZg1a9a/vgZBEH6emAoqCJlARh2BIHybrq4u27dvp3LlytSvXx8HBwepI32Xrl274ubmxs2bN+nQoQNr165l165dTJ48WepogvDLEhMT0dPTo1KlSnh5ebF8+XImTJjAunXrGDVqFA0bNtSMVkvP6YHq/oY+Pj4cP36cGzduULNmTfr06cOAAQNo3rw54eHhKe6bkagzh4SEsHbtWq5cuULDhg1xcnJiyJAhDBkyRDMVKj1e3549ezA2NqZZs2Zpup2foVQqUywu8PeFBuLi4ti7dy87duygcuXKTJ8+HRMTE9q1a8fixYsJCgqie/fuFClShG7dumFsbIyFhQVKpZIqVaoQERHBx48fqVKlCnv27OH+/fvI5XI+f/6Mj48P5cqV49SpUyQmJjJkyBAcHR0BvmoFoC48Z7R9Ma2p+6e1bNmS169fU7duXUqVKsX79+8ZMWIER48exdHRkUePHmFra5sumcLDw5k6dSonT55M92Lnzp07OXr0KHny5EFXV5ciRYpw+fJl8uXLx969e+nevTsBAQEper4plUoGDhzIixcvOHXqFDo6OowaNYrHjx9TqlQpALZu3cqUKVMAyJ49O1u2bKF06dI8ePAAR0dHKlSoQOvWrbl06RKFChVi+qVAVDr/fcqclBDPnUO7MM1nRfacucldwJaBGw5jlseChFsnvpkXIDo6GkdHR6ZPn87x48e5f/8+jo6OlClTBjs7O/r27cuePXuoXbs24eHhPH/+nOzZs3P8+PGvpoJ+y5UrV3j69Cl+fn5UqVKFtm3bat4LQRBSj/hGE4RMQEwFzXxsbGxYsWIFXbt25fPnz1LH+U8KhQKVSsWkSZMYNmwYe/fu5dGjRwwePJhGjRoB4iRKyNj09PR48OCBpofNsGHDePz4MXXq1KFDhw4cOHBAklzqz35XV1fy58+PpaUlW7ZsoVq1aowbNw4DAwPKlCkDZMy/QfWFo8mTJ2NpaUmpUqXw8PBg4MCBdOzYkYsXL1KgQAEg7V+fQqFg6tSpzJgxQyu+c/9+Ue3vI71evHjBwYMHad68OQ4ODqxdu5bnz58zaNAgvL292b17N6amplhbW+Pk5ES7du3Q0dHB29sbHR0dbGxsuHv3LiqVimzZsmFra8uxY8dwcHCgSZMmdOnSBXd3d9zc3OjUqROWlpbMnTuXRYsW0axZsxS9Br/MKpfLM+S+mB7U79OgQYPQ19enf//+2Nvbs27dOvbv38/q1as1RbX0GBk7ffp0nJ2dKV++fJpv6++GDx+OtbW1ZmRjhw4dyJ8/P3K5nE6dOlGsWDG8vLw0909MTKRLly6EhYVx+PBhjIyMMDAwoFOnTmzbtg2Ahw8fEhQURIsWLQCoV68eZcuWRS6XU65cObp06cLFixdT5PivBQvunz7ItDpFmNusAq8f38NlYfLKtWUdnclhng+lTE75Jm2+yqt25MgRbG1t6d27N7q6ulSsWJF27dqxd+9eIPm759GjR0RGRpIzZ07s7e1/6H10d3fH0NCQ8uXLU758ee7du/dDjxcE4fuIbzVByAREYS1z6tixI7Vq1WL48OFSR/lPy5cvp1mzZvz22280aNCAbdu2sXTpUk1fJ0HIqBITE3n8+DFxcXHY2dmRI0eOFCcm48ePJyQkhNatW0uSTyaT4e3tTVhYGD179uTWrVtcvnyZESNGsH37dvbt2ydJrtSio6PD58+fuX//PgMGDCAwMJBDhw6xbNkybty48Z+jNVLTjh07MDc311wsSG9BQUHMmTMHSC7yffm9n5SUxMWLF1m4cCHv379HpVKxbNky5syZw4gRI9i6dSvjxo2jRo0aODs74+joyP3794mJiWHFihXUqlWLO3fuMHLkSIKDgwkPD8fBwYHr169remTWqVOH58+fAzB69Gju3bvHn3/+yYABA1KMoPp7HzfI2FOQ09OX71OnTp2wsLBgypQpNG7cGA8PD1xcXFi6dCmQ9oXkJ0+esG3bNmbMmJGm2/kn1tbWKf69ZcsWKlSogJmZGWZmZjx48IAPHz5obg8ICODgwYO4u7un6HXZs2dPduzYgUqlYuvWrXTs2BEDAwMAbt68Sf369TE3N8fU1JQ1a9akeM7vUdbRGfdLgUw5+5j+6/ZjZZdchPQ+sptlnesxrU4ROpcr8FVeteDgYG7evKl5XeqFrN6+fQsk9689duwYNjY21K1bl+vXr/9Qvnz58mn+38jIKENcrBWEjEgU1gQhExCFtcxr2bJlXLlyhd27d0sd5SvqK+sHDx5k/fr1zJs3DwsLCxo3bkyhQoUYPXq06KsmZHh79+5l7ty57Nu3j6CgIKytrblx4wYPHjxg2LBhNG7cmIMHD0qa0cjIiIULF3L16lUsLCzImTMn9erVo1evXgwYMADI2C0DPn36xJAhQ3jz5g1xcXGULVuWqlWrUr58eZycnNIlQ1JSEtOmTUv33mrv379n8ODBfPr0CV1dXc3Ueh0dHa5fv87du3eB5D5m69atQ1dXl0GDBvHixQvq1KlDzpw5KVWqFKVKlaJatWo8e/YMgCJFihAREUFYWBgJCQman1+7dk1zol+3bl1y5MihyeLm5oabmxsqlQoDAwN0dHRQKpVfLTbw9z5uwo9R91o7f/48s2bN4tGjRzRu3JgZM2YwaNAgzaqraW3MmDFMnDgxXRcG+dKXf2fBwcH079+fFStW8PHjRz59+kSZMmVS7HelSpVi06ZNNGvWjKdPn2p+Xq1aNfT19bl8+TI7duzAxcVFc1vXrl1p1aoVL1++JCIigkGDBmme81f+zsNDXvLXjNG0mjAX1/N+7PJ99VVeNWtra+rWrcunT580/33+/JnVq1cDyQuEHDx4kPfv39O6dWs6duz4y/kEQUh94ltPEDIJ8QWbORkbG7Nz506GDRtGUFCQ1HFSUO9zOXLkYPz48ZQvX5558+ZpeiHp6+sjl8sz9Am9IDRr1ozq1atz+vRpPD09CQkJYcmSJbi7u2NnZ8eCBQvo1KlTuudSn3y/evUKmUyGg4MDpUqVIn/+/GzZsoURI0ago6ODTCbLkBdfQkNDmT17Nh8/fqRAgQL07t0bc3NzKlSoQLNmzWjVqhVmZmbkzJkzXQr4W7dupWDBgumyUMKXr8fCwoKFCxdiampKgQIFKFCggGbE5NSpUwkLC2PTpk04ODgwY8YMihUrxuHDhzl79iz58uWjZMmSBAcHA+Dk5MSRI0eA5MKaSqXizp07jBgxgpCQEAoXLsynT59YuHAh9vb2lCtXjgULFpA9e/YU2b7cl9TTOjPa/qXN1L3WTExMCAwMJDAwkO7du2NqakrNmjVZsmQJuXPnTtMMJ06cwN/fn2HDhqXpdr5XdHQ0MpkMc3NzADZt2sSDBw++ul+XLl2YPXs2jRo1IjAwUPPzHj16MHToUPT09KhVq5bm51FRUeTKlYts2bLh5eXFjh07NLeZm5sjl8uJDAn+4bwJcTHIZDKy58yDrgxuHtj+zbyQvBKsn58fW7duJTExkcTERG7dusXjx49JSEhg+/btREREoKenR44cOTRF67x58/Lx40ciIiJ+OJ8gCKlPFNYEIRMQhYvMrVKlSowbN47u3btLvuy8mnqfi4uLw9/fn7Vr1+Lh4cH9+/dRKBQ0btyYuXPnSpxSEH6NSqUiZ86cDBo0iAULFlCwYEHNiKl27drh5OQkSe8h+N/J99KlSzl27JjmpKtmzZpcvnyZ1q1b4+bmJkm21PDp0ydOnjxJvXr1GDNmDA8fPkRfXx9XV1c6duxIkyZNmDlzJpD2F5YSEhKYPn0606dPT/XnVvenVFOpVMjlcuLi4jh9+jSXLl1i0aJFLFiwAIDy5ctz584dVqxYgYODAw0bNiQxMZEJEyYwfPhwDh06xK5du+jTpw8WFhbo6Ojw8uVLIPkE/sSJE0ByH09zc3OUSiWFChVi27ZtPHv2DA8PDxo0aKAZpfStPm5C+rl//z42NjaYmpri7+8PJBc3hw4dyuXLlzX/Tk2JiYmMGjWKxYsXp5hSKSU7OzvGjBlD9erVyZs3L/fv36dmzZrfvG/Pnj1xc3OjQYMGmguSLi4uPHjwQLNiptqqVatwc3PDxMSE6dOna0aDQfJI4HETJ7GyV3Om1SnCC9/b3503b+ES1Or+G6t7NWNaIzsigp7+Y14TExNOnTrFrl27yJ8/P/ny5WPChAnEx8cDyUV9W1tbcuTIwZo1a9i+fTsAJUuWpEuXLhQuXBgzM7N0GcUoCMI/k6nEGbkgZHitWrWib9++muXDhcxHqVTSpEkTatWqhbu7u9RxNKvvLV68mF27dmmmWshkMvLly0fZsmVp3Lix1DEFIVW8ffuW8PBwChQogJ6eHrt37+bQoUOULVuWqVOnpnseX19fSpUqhZ6eHq9fv6Z3794cO3ZM0yz+zp07lCxZkuzZs2fI0WpfGjlyJIcPH+bNmzfUrVuXCRMmUK9evXTNsHHjRs6dO6dpgP6z/m01zKioKHR0dDAyMmLKlCmcOHGCsmXLMnDgQG7dusXt27fx9PRk69at9O7dm+7duzNnzhwsLS25cuUKPXv2TDFCJyQkBEtLS8aPH4+xsTGurq7I5XLat2/P5s2bMTY2/uGMgjR8fX25d+8ep0+fRqVS0alTJ3R1dVm+fDlHjx5N9e0tW7aMo0ePcuLEiQz92fGl2NhYLCws8Pb2plixYj/02H3PIvCPSPzpbRc31adt4Rz/fUdBEDK0/147WBAErZfRT5yE/yaXy9myZQsVK1akYcOGKaYySJUHIDIyks2bN2NnZ4e3tze+vr54e3tjZWUlaT5B+FUKhQIdHR327NnD9u3byZUrFxEREdStW5fevXvj7OysaUStLjSnl+XLl3Pp0iV69OjBuHHjqFWrFl5eXnh5eXH//n0eP37MhQsXgIzZJkD9fl66dAl/f3+ePHlCQkICixYtokmTJpQsWZLjx4+TP3/+NM+iUqno1asXvXr1+qnHwv9+B3/fRxITE9m+fTt//PEHOXLkwNnZmTFjxlChQgVWrFjBuXPnyJEjB7lz5+bgwYO8e/eOJk2aoFQqSUpKYubMmYwePZpatWpRoUIFxowZQ+nSpbl27RoRERFs3bqVpk2bYmRkpHlP//zzz68y/n1qp6BdcubMydatW+nXrx/Zs2fH3d2d27dvM3/+fIKCglIsHPGrPnz4wMyZM7lw4UKG/Oz4J6tXr6Zy5cr/WVR7/PgxBw8e5O7du9y/fx9/f38q1HWk08It/7k66LfoyqB6PsOfTC0IQkYiCmuCkAmIwlrWYGlpiYeHB927d8fHxwczMzNJ89y/f5+FCxcSHx/PnDlzsLe3x97enlatWqGnpweIfVPIuNRTLefNm8fOnTtxc3Mjd+7c3Lp1i0OHDtG3b1+6du0KpH8xwsPDg8uXL7Nt2zaqVq3Ko0eP2LZtG5MmTaJ3796ULFkSfX19TXEwo1G/n5cvX8bc3Bw9PT309PRwc3PDxsaG06dPp0tRDZKLYj/7Gfbl48LDwzl16hS+vr40a9aMWrVq8eLFC+7fv8+dO3d49+4dffv2JVu2bHTv3p3Y2Fhy5MiBQqHQFAMeP35MvXr1sLKyYtq0afz111+MGDGCxo0b88cff3D48GGuXLmCvb09zZs3J1u2bDRs2DBFJpVKpZlu+veMgnbKnz8/QUFBVK9eHWtra/bu3UuZMmUoU6ZMqi8s4O7uTqdOnbCzs0vV55WSra0tKpWKAwcO/Od9jx49yuTJk1OM3BzbvyfFrLJz7nX0DxXXdGXQwCo7lkZ6P5lcEISMRBTWBCGTEAfHWUPLli05deoUAwcOZNeuXZL93lUqFcWLF2f8+PF4enqyYcMGOnfuzPDhwylatGiqrKolCFK7cuUKderUoUCBAjx8+BAvLy9OnTqFp6cnJUuWBNK/eKweeXT+/Hl+//13goOD2bVrFxcvXiQqKory5ctjYmICkCGLal/q1asXY8eO5fjx4zRt2hSVSsW5c+do1qwZoB2F+38qXqoLaR8+fCB37tysWLECOzs7rKysGD58OJs3byYsLIy//vqLgIAAXr16haWlJYUKFcLU1BRzc3Pu3r1LxYoVgeTFBry8vKhXrx6Wlpbs3buXiRMnYm9vT0xMDPnz52fw4MHfzPjl+/QrhUIh/an3r2nTpvH777/z6dMnHj58SKNGjZg5cyZGRkaa3++vjpx98OABe/fu5fHjx6n4CqT3Iws/DR06lAULFvD+/Xsguadhx44dNX8z31tcUxfV7M3FaDVByCpEYU0QMgHRKjFrmT9/PlWqVGHz5s307t1bkgwymQwDAwPc3Nxwc3Pj9u3bLF++nOLFi7N161a6desmSS5BSE0VK1bExsaGp0+fUrlyZQwNDbGwsCApKQl7e3sgfYvH6hPn4OBgjh07hpubG7a2ttSoUYPbt28zbdo0lEolI0aMSLdMacnKyoouXbrg6urKH3/8Qf78+Xn+/DkdOnQA0v69/3vhLikpSbMKppq6qBYSEkL+/Pk1U1Zv3LhB3rx5adGiBRERETx79oxTp05hZGREQkICx44dw8TEhOrVq9OhQwfatGmTYtu1atVi//79msJauXLlND3Utm3bhpmZGSqV6qsRaUqlUjMi7ctimpAxqfevjh07snTpUvLly8fevXtxcHAAYPv27YSEhDBu3Lhf+j2rVCpGjRqlGZmbFT179gwXFxeKFClCVFQUKpWKlStXat5Xe3NDLLPrcv1tLIGRCcggRZFNIKAVrAABAABJREFUVwYqoEgOfarnMxQj1QQhixGFNUHIBLThqr2QfgwNDdm5cyf169enZs2aFC9eXJIcCoUCSJ4q4eDggKenJ56eniQmJjf5FfulkBGpi1f37t0jLi4OBwcH8ufPj5+fHyVLliRXrlwMHToU+OfRSmlFXdDZsWMHMpmMiIgITE1N0dPTo3r16hw/flxzoSUj/v2pMysUCs6fP8+9e/c0RcPbt29jbGxMvnz50NfXT5fXJ5PJ8Pf3p1OnTnh7e2sWh/iSu7s7p0+fRi6X4+TkxNixY7GwsODZs2esX78ec3Nzrl69SsOGDXny5An29vbUqFEDLy8vSpYsiampKW/evAGSF524ffs2AwcOpHHjxhw+fFiznUGDBmn2tb9/5n85Ukn0SMt81J8zhw8fxtzcHID9+/ezbNkyvLy8KF++PG3btqVIkSI/PWrt8OHDhISEMGjQoNSOr/VUKhWenp6MGzeOSZMmMWLECLZu3cqZM2eoXr16ivtaGunRtrAeMYlKfMPiCI1VEK9QYqAjx9xQh3K5smGkJ/4GBSErEoU1IcsKDg7GysrqmwfKGU1GPIESfk2ZMmWYNm0aXbt25dq1a+jr66fLdtUH7fHx8RgYGKT4uUKhQE9PT/M3JfZJISNSn5Ru2rQJHx8f2rRpg6OjI1euXOH06dPkzp1bM1pNiqmWCoWCQoUKAdCiRQu6dOmCi4sLJiYmKab5ZcS/P/V32cKFC/nzzz9p2bIl8+fPJzExkfbt29OqVStNb8nUfn3/NH29WLFihIeH4+Pjg4+PD+fPn2fUqFFUqFABX19fLCwsOHbsGGZmZlhbW1OwYEFKly5NrVq1ePnyJebm5uTOnZscOXJo+p/p6elx8+ZNpk2bRq5cuVi/fj0rV67k/9g767Aqlv+Pvzh0N4iooIJiFyoGYrcotmK3ol67+9qtYHd3N4qdWKBiCwoSSkj3Ofv7w9/Zrxj36hVFdF/Pc58rZ/fszO6ZmZ15zyf09fWpW7cuycnJdO/enR49eoj1+Litffjel8S03xvlb29ubs7BgwdZvHgx6enpdO/eHW9vb7y9vRk/fvx/Dg+RlpbG8OHDWbZs2W8xJ/4WoqOj6du3L0+ePMHHx4fSpUsD0LVrV7p27frF7+moy3Cy1PlZ1ZSQkMgF/Fmjp4TE/3P27Fl69OhBhQoV2LdvH2/fvuX69evUqVMHXV3dnK7eNyMJa38m/fv35+TJk0yYMIG5c+f+8PKUu+bp6en06tWLTZs2iQv5D92jpLYokVtRtnFPT08iIiIoVqwYQUFBLF68mLJly9KoUSNR1MqpcVdVVZX27dtTs2ZNrl27xsmTJzl69CgNGzZk8ODBP70+2YlyDAkLC2PNmjWULVuW8PBwbty4webNm3nx4gV///13tpap3Cz43G+pPFa2bFkmTZpEsWLFKFiwIH///TcDBgwgNDQUT09PDh8+zLt37yhZsiQlSpRAW1sbTU1NQkJCKF++PNbW1ujp6bF27Vo0NTXZu3ev6MJZo0YNSpcujVwu/8QF75/amDTO/pnExMTg5uaWxd3bxMQEf39/0R35W1m6dCkODg7Ur18/O6v6y+Pt7U2PHj1o27YtW7duRUtLK6erJCEhkYuRhDWJP47k5GQOHz7MoEGDGD58OAB+fn7s37+fMmXKoKurm8WV5nt2gpMyFNyPSeXtB6biFj/AVFwS1v5MVFRUWL9+PWXLlqV+/frUrVv3h5Z3/vx5du7cSXR0NEWLFs3SN2JjY1EoFJiYmPzQOkhI/EiUliGbNm3i5MmTmJmZERISwsqVK1m9ejUhISH07dsXW1vbHImt9ubNG9avX09AQADVqlXD3d2dSpUqcfHiRbE/5tb3gbLePj4+3Lt3j2fPnlG2bFmsrKxwdXWldu3aopXsf7lHQRBQKBTiRoAS5b/9/Px49eoVVatWxdzcXMyeCVCzZk3WrVvHhg0bMDU1ZenSpaxYsYIZM2YQExPD9evXs2RpTk1NRVtbm8DAQAD09fUpWbIk4eHhhIeH4+HhQcOGDcXzld9V1lHZDnPj7yjxY1C2+Q+tGB8/foyvry8XLlzgr7/+EkW1b3EHffPmDXPmzOHq1as/pN6/IikpKYwdO5Z9+/axcePGHz53kpCQ+DOQbMcl/jhiY2OJi4ujWLFi4mevXr0if/78mJubk56ensUK50MEQRDjSoWFhREaGvrZMsKTMtgXGM/ygBguhyfz8F0aL+IzePgujcvhySwLiGFfYDzhSRnZdl/SBPzPxMzMjE2bNtGtWzciIyN/aFk1a9akXLlynD17lpUrV+Lh4YG/vz8AK1asYOPGjT+0fAmJn0FcXByFCxdm3bp1JCYmkj9/fmbMmEHevHkJDAxk7ty5pKam5kjdhgwZgpGREQqFgvPnz2NgYIC2tjYdOnSgXbt2OVKn7EL5DtPS0kJXV5cDBw7g4+NDWloaMpkMAwMDUVj7t/edUhDLzMzMcn1VVdUs7/XIyEjWrl1Lq1atGDduHJ6enowbN048X1lOtWrVUFFRES3K2rVrx61btyhSpAgWFhZcuXIFuVzO1atXmTx5MhoaGlhZWaGurk5ycjIA+fLlQyaT0aNHD1q0aPFZ6xhlHSUkPubDNh8VFcWyZcvYvHkzd+/exdHRkebNmyOXy9mzZw8dOnTg8uXLX3Xdffv20bVr1xyL1fqz8ff3p2LFioSFheHv7y+JahISEtmGZLEm8ceg3O17+/Yt0dHRODg4iMecnZ2Ji4tDR0cHV1dXzMzMUCgU1KhRg7Zt2xIVFYWFhQU6OjripHflypVERkayePFiNDU1RcHNPyaNs6HJX0zHrfz8WVw6QfHp2ZKOW8oK+mdTp04d3N3d6dmzJ4cOHfphIquqqioDBgzAxMQEJycnZs6cSZ06dciTJ4+YUAFyr8WMhASAoaEhQ4cOZc2aNWzYsAEDAwPevXuHrq4uW7ZsoXbt2j9d/JDJZERFRREVFUX//v05deoUQ4cOBWDMmDHUrl2b9u3bA7lzk+XDMaNatWocPXoUuVyOIAhfFfPpw+8PGzaMokWL0rdv3yzfDQ4OZsOGDVy7dg17e3vGjRuHkZERd+/eJSIigitXrhATE0Pjxo159OgRxYoVE0U4R0dHEhMTOXv2LLVr1yY6OppixYqRmprKqlWrWL9+PRMnTkRHRwc3NzfS09MZOHBgFhEvT5482NraEhoaSoECBbLz8Un8YRgaGvLy5Uusra2pU6cOpUqVAuDq1atcuHABV1dXbG1tv+paXbt2zRIv9XdFoVCwYMEC5s6dy8KFC+nUqVOuHCslJCR+XVQEaUUu8QcxadIkLl++TNOmTRkyZMhnTeUrVqxIxYoVad26NSYmJly7dg1fX1/8/f2xtrZm5cqVWFtb07dvX8qWLUu/fv3El/OdyBTOhiZ9UVT7HGoq/Ku4FhwcTPHixYmLi/vsgq5QoUIULFgQHx+fry9Y4rciPT2datWq0a1bNzw8PLL9+sqFa3p6OvHx8ZiZmYnHfHx80NPTo3LlypKoJpErUbpORUZGEhsbi729PadPn+b27dv4+flhYGDAuHHjWLduHZmZmcyaNeun1k8QBOLi4li+fDmvX78mODiYo0ePAlC6dGlOnTqFlZXVT61TdqIcN/5LRsMbN24QHBxMmzZtAJgzZw4BAQFiTLapU6cyc+ZMHjx4wL1792jYsCHPnj1j48aNHDlyhI0bN3Lu3DnWrl2Luro67du3p23btrRo0QKZTCbG3XN1dSUxMZEGDRqwefNmhg0bRs+ePYH3ca/09PQ+SSLzpYQIEhL/lQ83iS0sLD45/vr1a8zMzNDU1CQ2NhZjY+McqOWvRUhICF26dCEzM5MtW7Z8tegoISEh8S1IrqASfxTFixdHVVWVOnXqZJm8KxQK4H38taioKEaPHi3Gc5k9ezZjx47lzp071KpVizNnzgDvX9Q2NjaoqKgwb948Bo6fyqmXsV8lqgXeusKshu8zD2UKcDY0ifDkL7uFFihQgMTExC9aSShFQIk/Fw0NDbZv386UKVN48OBBtl9fRUWFpKQk+vXrR8OGDalcuTJ///03169fp0qVKlSuXFk8T0IiN6GMpZmSkkLnzp1p0KABdevWRUVFhf79+7Nz505Wr16Nnp4ez549Y8qUKT+9jioqKhgZGVGmTBnu3btHxYoV2bRpE127dsXZ2RkrKyvxPZYb+bfslnFxccD75BIf36e2tjajRo3i4cOH7Nmzh0aNGolj4KpVq6hWrZpo2VOnTh3WrFnD7NmzuXnzJiEhIdjb26Ours6LFy8AKFy4ME+ePBHdfZXl1ahRAzs7OwoVKoSXl1eWWFcmJiZoaGiI2ZE/vC9pTJTITpTtSSmqfWwfkS9fPrS0tHj79i2DBg366fX71di5cycVKlSgfv36nD9/XhLVJCQkfhiSsCbxR9G+fXtq1KjB7t27gf9NmJUTlYCAAIyNjbGxsSExMZHTp09jbGxMv379KFu2LKtXr+bq1atkZGSQlJSEjY0NAE2bNqV0886oyP7dPUj+QcwXJZkCXItIEev0rYakOjo6f4Qpv8Q/Y29vz7x582jfvj0pKSnZdl3lQnHdunWYmJiwatUqEhMTCQoKombNmkycOFFyR5bItSjbrqenJw4ODgQGBtK6dWvGjh1Lly5d2LdvH/A+nuGGDRt+6lir7HvR0dH4+Pjg6OjI4MGDiYiI4N69e1StWpUZM2b8tPr8LJSilp+fH8WLF6dfv34kJSWJMdJSU1OJjY0F4NixY7x+/ZohQ4YQFRVF3rx5UVFRYe/evSQlJdG2bVtUVFSIi4tj9erVFChQgIsXL1KpUiV8fHwoVqwYWlpaohhXtGhRbt26RXx8PIDoTjpixAhWr15NmzZtqFWr1mcFM5lMJsVIk/ghfEk4/7AdfhhT0NLSksDAQO7duyd+9ie9p2NjY3F3d2fKlCmcOHGCsWPHSn1TQkLihyIJaxJ/FHFxcbx+/TpLfDX434Rl+vTpPH36FH19fSpVqsTNmzdp3LgxXbp0QUdHh0qVKrFjxw6KFi1KUlISlpaWpKamsmDxUia0a8DkGoWY28yRG3s3iddWWqdd2LiUGfWKs3NcHzYMak9CZASTq9kwuZoN8ZERPItNZdqMWdjb22NmZkbbtm2JiYlBEARevnyJiooKb968ASAoKAgXFxf09fWpV68eUVFRP+8hSvzSdO3alZIlSzJy5Mhsu6ZyMnr06FE6derE1q1bmTRpEuvXr6dPnz6Ym5uLblwSErkNmUxGeno6T58+FWMV9evXj5s3b1KtWjXOnz8PvH9PaGt/XzzMb0Hpgvj27VtcXV2ZNm0arq6uvHr1ikmTJrFgwQL69u0rZpT8ngzWvwppaWl4eXlx4sQJAF6+fEnLli3ZunUrurq6rFy5kjp16lCuXDnGjh1LcHAwY8eOxcHBgfHjx9O/f3/MzMwoWbIkbdu2JX/+/Ojr6wNw7tw5YmJiGDlyJFpaWkRFRXHq1ClMTEzQ1NQkKCgIgBYtWjBnzhzy5MkDZBUulBtff5JAIfHzUSgUREdHEx0dTVpaGhcvXiQj48teDUpBTSkCz58/n7x584oWlkqUbVmZUON35fz585QpUwYjIyPu3LlDhQoVcrpKEhISfwC5fxYmIfENGBoaEh8f/0n2ROWCRENDgwEDBhAXF8fkyZPZs2cPAQEBREVF4evrS+nSpYmOjqZp06bcu3ePzMxMVq5cSWgadF28hSmXgmg9ZQnHFk4k9JG/eP3E6Lckx8Uy+thd2k5bRnfPneib52HqlVdMvfIKA/M8XN25hu279zB48GBOnz6NlpYWHh4eWSb1t27dAqBjx45UqFCBqKgoJk6cyKZNm5CQgPcT55UrV3Ls2DGOHDmSrdeeOXMmdnZ2ZGZm8uLFCxQKBQ8fPqRevXpi2RISuZFbt27x7t07Nm/ezKZNm3jy5AkAo0aNwtPTM0fqdPbsWcLCwvDy8sLFxYULFy6wc+dOAgMDcXFxISQkJEfqlV18TojX1NSkb9++NG/enISEBLZs2cLmzZtxcHAgLCwMBwcHli5dyqNHj4iMjGTu3LkA1KtXT3wPyuVyqlWrRtGiRcnMzMTd3Z03b97g6OiIsbExjRo1wtXVlZo1a6KnpwfA+PHjxc0IAwMD7OzsPltnmUwmuXdK/FDOnTuHk5MTDRo0oF+/fnh4eLB27VquX7+epc+kpaURExMD/E9Qe/r0Kba2tuzdu5ft27ezefNmsY0rEQSB6dOnk5mZ+dsJxGlpaYwePZqOHTuyYsUKli1bho6OTk5XS0JC4g9Bygoq8cfRsmVLli5dSvXq1alUqRLwXhAQBIE9e/aI57Vr145Zs2ahr6+Pl5cXAO/evUNdXR1NTU3MzMw4duwYvr6+1O85kjST/AAUqlANe6eavLx7HetiZf7/+jLq9huFmsaXXYiu7dlEq/7DGD16CIMHD+bSpUsEBwezZcsWIiIigPdZxYKDg7l58yZnzpxBU1OTGjVq0KxZMwD27t2LQqGgbdu22f/gJHINRkZGbN26lVatWnHnzh3y5s37n6+lDJR88OBBihcvjp6eHt27d2fq1Kk0atQIFRUVcTdYWmxK5FaqVq1K6dKl2b17N35+fjx9+hQbGxuaNWsmJgX4mRZhwcHBeHl5YWdnR3JyMjY2NmRmZlKwYEFWrFhB9+7duXv3Lvnz5/9pdfpelNZeSgvYj5/njRs3UFFRITMzk927d9OkSRNq1KiBqqoqI0eOJG/evOjp6eHp6Unnzp1JTU0lMTGRhIQEmjdvTq9evYD3FrYlSpRAT0+PCRMmsGrVKho3bszUqVOZNm0ahw4dokqVKpQuXVosW4pRKvGrcOHCBRo3bszIkSPx9fUlJCSEUqVKkZaWRkZGhuiKHhYWxsyZM1m2bBnz589n0aJFVK9enebNm7NkyRIA0bryw7728uVLwsLCRDHuvyQM+RV5+PAh7u7uFChQAD8/v88mdpCQkJD4kUjCmsQfR5s2bcTsYR+ioqLCxo0bWbx4MS9fvgQgMTGRQYMGUbduXby8vPjrr78AKFmyJIUKFSIqKooSJUpw/vhh7nofISr4BYJCQUZqCpZ2xcRr6xqboq6p9Y/1io14zYYpw1EoFKxevVqMrePv74+W1vvvWltb8/LlS4yNjdHV1RW/a2NjQ2BgIEFBQZ8VN5QTJylD2Z9DtWrV6N+/P126dMHb2/s/T5yVbSUwMJCrV68ybtw4ypcvz+zZs9HQ0MDAwAD4n9uahERuQTkunj17luvXr5OQkMD48eMpX748Fy9e5ObNmzRv3jxH6lagQAGWL1/O4cOHuXTpEidOnMDGxgYbGxvy5cvH9evXGTt2LECuycT78Rh069YtwsLCcHV1BWD//v1kZGQwbNgw0tPT0dLSolevXrx+/ZqgoCAqVqzIkydPxN9GJpNRqlQpnj17hrOzM0lJSaxbt46wsDC6d++Onp4eYWFh9O3bl8aNG4siZN++fcU6/C6igsTvw6BBg1BVVUVXV5datWp98byCBQsik8moUqUKmpqanDp1imfPnnHt2jXgf+/kj8cGVVVVTp06xV9//cWUKVNyfdZQQRDw8vJi2rRpzJw5k169euWK8VBCQuL3Q5pNSEj8P69evaJv3754eXkRHR1NbGwsJUuWRC6XIwgCYWFhmJubA+/jWGVkZJA3b148PDw4sWw2zl0GMP70QyZffEHR6nXhQxP7j1/yn3nnG1rmpc3Iv1mzZg2xsbEcOHAADw8PADGGjpmZGa9evSImJoZnz56J3w0ODiYjI4Pk5GTs7e1JTU3l4cOHJCUlAf9b0HzJhUWKjfV7Mn78eNLS0liwYMF3X6tly5ZERUXRokULrl+/TrFixShcuLDYJyRRTSI3obTiiIiIYODAgVSvXp05c+Zw//59ypYty+DBg5k+fTqWlpY5Vj9ra2v69++Pl5cXnTp14uTJk0ybNo1OnTrh4eFBkSJFUCgUv9Qi8uN3iXKD6N27d9y4cQN3d3fGjx8PwNu3b/Hy8uL69evAexfMsmXLYmFhgZ6eHo8ePUJbWxtNTU1ev34NQGRkJAqFAlVVVR49esTLly+5evUqMpkMLy8vbty4gbW1Nebm5pw7d468efOSmZmZxbLvwxhpkqgm8athaGgozvngf33oc7H9Fi1axPHjx7l69Srly5enXbt2jBkzBvj0naz8boECBbh8+TIZGRn07NkzV8foDQsLo1GjRmzdupWrV6/Su3fvX2o8lJCQ+LOQZhQSEv9PUlISKioqolCwYcMGHjx4IMZUefv2LUuXLiUjI4Ndu3bx6NEjGjdujKqqKvKMdAyMTZGpqfHkyhmeXT//j2XpmViQHPeO1IR48TOn1t04vWk5ZmZmwPsEBS9fvhT/VtKuXTvKlSvH8uXLSU9P5/Llyxw5cgQdHR369euHq6srx48fp3///vTq1Yv8+fPj5eXFixcv2LZtGwEBAZ9Mzj63uFBO5iRyL2pqamzdupV58+aJ8fn+C4IgYGtry/r16/Hw8ODgwYNZMo1JSOQ2lIuvxYsXM3DgQDERTJUqVXjw4AEjR47E0NAwR+unXEibmJjQu3dvFi5cSJcuXbCyshLd/XNyEalQKD55TyjfJWFhYTx8+BBVVVVu3ryJg4MDJ06coGLFiiQkJDBkyBAaN27M0KFDmTBhAvA+4HjFihXR0NDAwsKC8PBwBEGgUKFCREZGEhMTQ/Xq1SlQoAD29vaMHj2akSNH4uLiArwX/1evXk2PHj1EdzmFQiG6vCn50gZTZmYmcrlcDAQvbThJ5AQzZ85k5syZrF27Nosl+OfarY6ODpaWlgiCILZbZdINeJ9dV9mOld9VKBQULlyYJUuW8PbtWzEpVm5j//79lC9fHicnJy5fvoy9vX1OV0lCQuIPR3IFlZD4f4oXL87w4cOpUqUKMpmMLl26UK1aNfF45cqVefbsGWZmZlhaWrJ3715MTU0BmLdwMZNH9yYzPQ2HGg0oVqPBP5ZlUdCeMg3cmOvqiCCXM3TfFap16M2VTUsZOnQo7du3R0tLiyJFimBhYfHJxGf37t107doVExMTqlSpQpcuXYiNjRWtK0JCQtDT02Pq1KkIgkCPHj0IDQ0lOTmZuXPnsmTJEmrWrElqaiqnT58mNTWVSpUqYWNjI7rGfMkCSXKdyV3Y2Njg5eVFhw4duHv37ieBjP+NpKQkRo8eTUJCgjiB3blzJ2fOnGHq1Kk0adLkB9VcQuLHU7p0aUxMTBg7dqzoWnn06FHevXsnuuDnJMqYYzKZDFNTUzp27Ejz5s3R1dX9qS6gSpHvn2KkAdy+fZv+/fujpaVF0aJF6dChAzVr1kRfX59ixYrRrl07Hjx4wJAhQ0RrkyVLlnD06FHi4uJEK2sbGxvOnz9PaGgoNjY2XLt2jZcvX1K+fHlmzJhBZmYm+fLl+6T8f4vj9iGpqanIZDISEhKIjo6mUKFCWd570ntO4mejjKOYkpICvLc6U1ql/lNfV1FRySIgK+dpKSkpaGlpZRkrlO06MDAQZ2dnSpQo8QPvKPtRCvMXLlzgwIEDVKlSJaerJCEhIQGAivC7pYSRkPgBbNy4kbVr13L58uUvnrMvMJ5ncen/uYyCOirIb5+iY8eOovteeno6U6ZM+eprKCdTgwcPplSpUnTu3Bk1NTXs7OzYtGkTLi4uuLu7U7NmTXr37s20adPQ1NQkLCyM+/fvM3nyZFxcXFizZg3W1tbkz58fKysrzMzMPiuoKT8LDQ0lIiKCYsWKSRmYflF69uyJIAisX7/+q85X7pQfPHiQd+/eERMTg5aWFiVLluT58+ekp6dz+vRpRo4cKU1sJXItT548wd3dneDgYJ4+fcrVq1eZMGECu3fvxs7O7qdvJPyTWKYU2H5GfTIzM7l+/Trx8fE0btz4k+OCIHD8+HG2b9+OlZUVs2fPRk1NjWHDhtG/f3/RouzGjRucOXOGRo0aMWDAANzc3IiJiWHAgAG0bduWli1bcvXqVRYsWMCLFy84cuQI+fPn5/bt2xw6dAh3d3dsbW1JS0vDwMDgk+ejtMb5mmei/G5GRgb+/v44OjoC77OvLl++nJs3b+Lj4/PFjKASEj8LpfXZx9aW/4ZCoRA3TNPS0sQN0y+NKampqb/EBsLXcu3aNTp16kStWrVYtGgR+vr6OV0lCQkJCRFpO05CIpuoaqmN2n80HlBTgRr5DejYsSMKhQJNTU3GjRvHiBEjspwXHR3NuHHjuHHjxmevI5PJyMzMJDExEXNzc7S0tIiIiMDQ0FC0vtPX10dfX58bN26wZs0aFAoFQ4cOpUWLFhw/fhyAlStXsm7dOhYvXkzp0qU5cuQIMpmMJUuW8OrVqyzlwfsFS3BwMLGxsQDMnz+fhg0bcvr06f/2QCSynSVLlnD58mV27dr1VecrLTc2btyIs7Mzw4cPx8PDAxcXF3r27En//v3Jnz8/q1at+pHVlpDIVpRCTEZGBnFxcZiYmHDp0iXat29P7dq1OXnyJH379s1RUS01NZXRo0fz999/s3LlSu7cuQO8X2T/rPoIgsDt27fF+Gdv377l6tWr9OvXj0KFCrFhwwauXbtGmzZt8PPzY8OGDSQlJeHr60uzZs2oXLkyjx8/plu3bqipqVGhQgXOnTsHgK6uLsWLF+fSpUvA+4ysXbp0IT09XYyFVqFCBaZNm0bRokXR1NQUk6R8LBB8i9Co/O7Bgwfx9PTkypUrJCcnU7t2bfbu3SuKqRISOY2amhre3t60adOGkSNHsnHjRkJDQ//1e2lpaQDExcVx+PBhVqxYwcmTJ7/o7plbRLWMjAwmT56Mm5sb8+fPZ+3atZKoJiEh8cshuYJKSGQTVrrq1LbW5WxoEpnfYAeqpgK1rXWx0lH/JC36x257pqamTJ06lYyMjC9eT5ncQJnp6fnz5xgYGKCmpsa7d+9ISUlBX1+foKAgypQpQ2JiIh4eHkRHR1O0aFEAkpOT6dSpE25ubuzdu5cjR47QrFkzTp06hb29PTY2NgA0adKEBQsWoKKiQuXKlcmbNy+7d+8mLCyMTp06ZYn1AVmtMRITE9HV1ZUCzf4k9PT02LFjB40aNaJy5crY2tp+8dy0tDQxRlHr1q2ZO3cuq1evBt5bsm3cuJEePXpQt27db3YtlZDISZTjzYABA4iLiyMzMxN7e3vGjBmDpaVlFlfAnz02KcfHefPmcffuXWrWrMnr1695/PgxFy5coGrVqlSuXDlbynr06BF6enpZgvp/iLq6OgULFiQkJITU1FRWrFiBj48P7u7u9O/fnw4dOtC6dWtatGhBdHQ0T58+xc/PDycnJ/T09D6xtG7cuDEDBgwQr12yZElOnTolHrezs/tsjKTscndViqQLFy5k165dyGQydu7cSVBQEBUqVKBQoULZ9mwlJP4rSkvxZcuWcf78eRwdHYmPj2fz5s2cO3eOhQsXiiFIPubMmTOkpKTQrFkz2rRpQ+PGjVm/fj1bt27l3r17ODk5UalSJbS1tX/yXX0fT58+pVOnTpiYmHD37l2srKxyukoSEhISn0WyWJOQ+Aq6dev2j26gSsqba1PbWverLdeUolp58/cTna9ZQKirq/+ju6WmpiaNGzfG2toagDt37oi7ki9evCA9PR0bGxu0tbXJyMhgxowZHDt2jOvXr7Np0yaCg4NRKBS4ubmRmZmJkZERYWFhAAwZMoRjx44B4O3tTXJyMjY2NmzcuBEvLy/OnTvH+PHjOXLkCA8ePKBUqVIEBAQQHR0t3p/SxWH69OkEBQUB7xdP69aty7VBdHMLFSpUYNSoUXTq1En8HT7HwoULmTp1Kjdv3qR69eqiVc/MmTNp1KgRPj4+qKio0KxZM2rVqvUT70BC4vtQUVEhIiKCR48eMWvWLMaPH4++vj79+vWjV69evHr1Skzu8rOFNZlMRkZGBuHh4ezfv59x48bRuXNnKleu/I9xL78GQRCyBOOfO3cuZ8+e/cdxoEKFCvTo0QMtLS0cHR0xMDCgUqVKlClTBnd3dzFTZ5kyZcTMgk5OThw5coTExERu3LjBlClT8PHxwcnJiTx58pCYmIhMJsPNzU20dj19+jSurq64u7t/Uofs+g2UG1YPHjzg6NGjXL58mRIlSnD27FnmzZvH+fPns6UcCYns4Pr167i5uTF69GhmzJjB2bNnSUhIYN++fcDnE2sULlyYxo0bs2LFCsaNG8fTp08ZNWoU48aN482bN6xevVq0aMsNCILA6tWrRYvWEydOSKKahITEL41ksSYhkc2UN9fGSleNaxEpvIhPRwWyWLCpqYAAFDbQoEoebax01LO1/Hz58tGnTx/x7y5duogB5tPS0rC1tUVPT4+KFStiamrKwoULKVeuHJaWlhQtWhR/f3/RmklFRYWXL1+K1m+6uroEBgYSFxfHypUrmTBhAurq6igUCooVK0atWrVo0aIFampqODo6IggCS5Ys4eHDhyQkJDBv3jzq16/P7du3WbBgAUOGDAHeuy1MnjyZevXqfXI/XxO4V+LrGTZsGKdOnWL69Omfjd+Xnp6OtbU1gYGBrF69Gn19fQ4fPoyXlxcrV65k8+bNoiXihxnLJCR+dZTWT1FRUTRr1oz8+fNja2uLg4MDjx494tixYxgaGuboWHPixAk2btxIyZIlGTBgAEWLFqVo0aLI5fL/5AaqtNT6eAwtV64cISEhJCQkYGxszJs3b9DS0sqSCdXa2lrcoLGzs8PU1FRcmNeoUYMjR44AULJkSTIzMwkNDaV169a8ffuWRo0aoaamRtmyZSlcuDAaGhqcOXNGvLaKioo4fhQvXhwfH59/tKLNDnbu3MmBAwdo3rw5zZs3p1+/frRq1YpNmzZ9NhGChMTPRtnHy5Urx6NHjwgKCsLU1BRVVVU0NTVFr4LPjVEFCxYEoESJEkRHR7N582auXr1K8+bNWbhwIY8fP8bIyOinJj35r7x9+5ZevXoREhLCxYsXKV68eE5XSUJCQuJfkYQ1CYkfgJWOOi0LqZOcoeBeTCqRKXLS5Ao0VWWYa6tS2kQLHfUfYzD6sbWFmZmZmHygWrVqWTKdenh4sG3bNg4dOkSZMmVYunQpFy9eFF0NMjIyePHihTiZ09DQwM7OjpkzZ6KqqkqdOnV49uwZKSkpWFpaEh0dTWZmJtWrV6d58+bI5XKmT5+OhYUF/v7+TJ06lfr163P8+HHR4qlatWp4eHhgaWlJgQIFPrmfLy0mc8Pk8FdEJpOxefNmypcvT926dalevXqW4xoaGnTp0oXQ0FACAwO5d+8eNWvWRC6X8/z5cywsLERhTRLVJHILSoHp8ePHTJ48mQsXLpCYmEiPHj0oWLAgjo6OlC5dGg0NjRwdW5o1a0ZCQgIpKSlkZGSgrv5+4+Vr+ppcLkdFRSXLmKn89/379zl27BglSpSgWbNmuLi4EBwcjLGxMUFBQWzZsoV27dplEdbgf+Osvb096urqBAcHU7lyZUqWLElUVBSBgYEUKlQIc3Nz0tLSkMvlDBgwgF69eqGhofFJHT+MW6e8J6V497lys5MaNWrQpUsXhg4dyr59+xg9ejQlSpT4JJaphEROoWzzp0+fJigoiEePHmFra4u/vz958+YF3nseFCxYUDz3w76SlpZG/vz5mTBhAtevX+fAgQPMnz+fatWq0aVLlyxl/KocO3aM3r1706VLF/bu3fvZcURCQkLiV0QS1iQkfiA66jKcLLMvS+bnFk4f86VJk0wm+2Sx8rHQBu8t3JTiXEZGBq9fvxazwlWsWJGpU6cSGRnJkiVLAAgLC0NFRYV8+fLx5s0b0tPTxcXZ4cOHWbVqFREREcTFxWFpaQm8D1ZdpkwZbt68SXR0NNeuXRNN/Ddu3MiaNWvYsWMHkZGRXLp0ifLly1O2bFkxgPU/3afEv2NlZcWaNWvo1KkTAQEB6OrqiseUbcTa2ho9PT2cnZ2Jj4/nypUrnD9/XkxeIQmbErkJZVudN28etWvXZvTo0Xh6ejJgwADq1atHy5Yts1jq/kyUfUn5f1VV1a+KXbhjxw7i4+Pp27cv8D+hSqFQkJmZiYaGBt7e3owcOZJatWoRGxvLhQsX0NPTo1atWpQpUwZ4b+kyadKkz5ahrJdMJsPY2Jj79+9Tv359jI2Nad68OfHx8cB719IP30saGhooFArxu8pn+rUbJdn1GyiFvJSUFMzMzJgxYwbDhg1j9erVuLu7Y2dnx5YtW9DS0pLGM4lfhvXr1/Pu3TtevHhBUFAQGhoaBAUFMW3aNAoWLMiaNWvEc5XtdsaMGfj6+vL27VsUCgWrVq1ixowZrFmzhkKFCuXUrXw1ycnJjBgxguPHj7Nz505q1KiR01WSkJCQ+CYkYU1CIhcRGxtLaGgoefLkwcTE5JtTsX+8cFAufD4U60qVKiUe19fXZ8uWLaSnp4ufRUdH07t3b5ycnAAIDAwkKSkJS0tL7ty5g7q6OjY2NkRERIhB7tu2bcvff/9NSEgIAGfPnhVN+01NTXn06BFRUVEsWLAAf39/jh07hpaWFlFRUSQkJLBkyRJ0dHTYtGkTSUlJ3L59G5lMhrm5OXZ2dqJVx8fPqlWrVhw9ehQNDY0sizsJaNq0KadOnSI4OJhixYoB/1vcvnz5ko0bN3Lz5k10dHRo0qQJHTt2pFGjRqSmpuZwzSUkvh0VFRUCAwN59OgRU6ZMIX/+/GzZsoXTp08zZ84cDA0N6dmzZ47V7cP/f4ng4GCePXtGnTp1gPcWWB9mxjt37hwTJ04kMzOTBg0aMHHiRCwtLQkICODkyZNYWVnx999/c+HCBapUqfLVGQGV40KDBg1IT08Xg58vWLBAPP65jZtvcV39EWOzsl7p6ekMGzaM+Ph41NTUmDFjBjNmzKB79+74+PjkumDuEr8/VlZWxMfHY29vj6OjI2ZmZuI8JzMzU+wvFy9exNLSEnt7e44ePcqqVasoXbo0q1atonXr1qxYsYL+/fvn5K18Fbdu3aJTp05UrFgRf3//TyxnJSQkJHIDUvICCYlchKmpKaVLl8bCwgI1NTXS09OJj4/PInx9C8qA2B8ugD4XFFdDQ4MDBw7Qr18/ChcuTNu2bcVjlSpVwtXVFVNTU968eYNMJkNHRwdjY2PU1NTECeDx48cxMjIC3gfmVbogyuVyQkND8fX1RS6Xs3nzZoyMjNDS0kJdXR1nZ2emTp2Kubk527ZtQyaT4eXlxfjx4xk9ejSVK1fm7NmzYn2U1nb37t0jOjoabW1tVFVVPysqfu5e/yTmzp2bZSdb+Tw8PT1RV1enVq1aREVFsXnzZgoUKMDYsWPFxbgkUkrkNsLDwxEEgSZNmrB582YEQaBevXqcOXOGbt26Af8bP3IahUKBXC7PUh81NTWmTJlCamoqN2/eJG/evPTu3ZvExETS0tJYvHgxo0aN4sqVK/j4+LBhwwYKFChAmTJliIiIAKB48eLExsYSHBycpTy5XM7Dhw/FJAQfonw/1K1bl8aNG4sZg5Xf+1ph8GejfHaTJk3C0tKSevXq8fjxY9G62sjISLT2k5DIaZTt9f79+3h4ePDXX38xZMgQRo0ahZubGytXrgTIsqF6/vx5nJ2dcXd3x9HREQcHB+RyOX379mX69On4+PhkufavhlwuZ+bMmTRu3JgpU6awZcsWSVSTkJDItUjCmoRELkQul7NixQoKFixImTJlsLW1pU+fPiQkJHz3tb9kZWBubo6hoSELFizI4pJZokQJXF1dUVVVpWvXrkyfPh1LS0s0NTWpUaMG8+fPx83NjejoaDFWW3p6uhgvRBAEHjx4wOTJkwkJCREzhe7YsQNPT0+WLl3K6NGj2bRpExYWFsTHx5OSkkKfPn04fPgwNWrU4NSpU+K1lOLQo0ePMDAwYP369cydO1d0YfzwPj++V+X3f9VJaHajra2dxdpP6Up26tQphg4dyqVLl5g7dy67d++mXr16lC9fHnjf/iQkchsVK1Zk3759jBgxgtOnT9OhQwfWrl2b5ZycEofi4uKYNWuWmIFZuenxYSblJ0+ecOvWLSpUqMD06dNRUVHh7t273L59m6ioKNLS0ihWrBiqqqoMHz6c58+f8+bNG5ycnMQFdoECBZDJZDx79gz4X18OCgri1KlTpKSk/GM9P96M+C9xFv8pE2l2IpPJyMzMJCgoiL/++oubN28yYMAAANatW8fChQt/Sj0kJL4GZd86cuQIefPmpXr16piZmdGqVSvi4uLEJFIfMmnSJG7cuIGqqip79uxh7ty5Yp8MDg4mICAA+PVEb3g/5ri4uHDmzBlu375N+/btc7pKEhISEt+FJKxJSOQilBOv48ePc/z4cfz8/AgKCuLly5fkyZOHiRMnAj9G+KhevTpz5swR46Qp+ViE0tH5X0y5v/76ixs3bnDgwAEuXrxIixYtgPdJEyZOnEj79u0JCwsThTV4n7kNYP/+/Tg5ObF//36OHTuGlZUVBQoUIDg4mHz58lGpUiXxmSjjsynjAQE8ePCAtLQ0BEHg6tWrrFq1SlzQnTp1inHjxrF+/XrCw8PF+ipdYj+chCotR35X67aPxcWYmBhmzJiBXC4nKSkJLS0tzMzMCA0NpW7duoCUtEAid6KhoUHevHlp3749U6dOpV69ely6dIn09PSf3qY/tkgzNDRk3759ouB17949Ro8ejaOjI7179+bVq1cUL16cypUrM2zYMA4dOgS8dwe9ceMG7969o1SpUqJlmqWlJSEhIRgYGODo6MjFixcByJs3L4Ig8Pr1a+B/fdnOzo6hQ4eSP3/+f6z3f8lM+iG+vr5iXX4kyvFaTU2NFi1a0K1bN3x9fenatSsABw8exNXVFfh1rXkk/kxevnyJs7MzKioquLi40KJFC6pXry5m/VQiCAKCIFCwYEG2bt3K4cOHOXnyJI6OjnTs2JE3b97g6ekJ/FqbYYIgsHnzZipVqoSbmxtnzpz513FHQkJCIjcgxViTkMiFpKSkYGpqirm5Oenp6WhoaFCyZElOnDjx0+vyTzuhcrlcFKqUmSQB2rdvT/v27UXha/ny5QAsWrSIGjVq4OjoiKurK3v37kVLS4vbt28TFxeHvb09hw4dQk1NjXz58gHv07I7OzuLQaqV9bl9+zY9e/akZ8+e9OjRg2rVqhESEoK3tzeZmZmUKlUKX19fgoKC+Pvvvzl37hzPnz9HVVUVa2trKlWqhLGx8T8G21YoFP+aTCK3oIyPtHXrVmrVqoW+vj6tW7dmzpw5ooWisbFxlqx+EhK5CblcjqqqKhoaGhQqVIi8efPSsmVLMdD+j2zXyvFCKWR9WJayXuXKlcPf3x8XFxdevnxJsWLFmDZtGgsWLGDq1KmsX7+eVq1acf78eTEeXP369dm0aRMeHh7o6emxdu1anJ2dSU1NJSYmhrx581K6dGnRMs/a2pqJEydiYmLyxXpmp3VLRkYGMTExmJmZoaqqSmhoKA4ODuLx0NBQDAwMssSK+16Uz1MQBLZv306lSpU4f/485ubmjBkzhtDQUGxtbXFycpKSsEj8MijHhiJFimBgYICDgwM7d+5EVVWVJ0+eUKFCBfFcZRv39vbm4sWLFCpUCGdnZy5fvsyePXvw8PBg3Lhx2NraIghClo2D1NRUNDU1c6Tdx8TE0K9fPx4+fMiZM2fEBCoSEhISvwOSsCYhkYtQToTy5s3L27dv2bFjB/Xq1ePRo0ccOnSIsmXLZjkvp/mSFYhSkFLG6mnZsiVyuRw1NTWuXr1KZmYmSUlJvHz5El9fXywtLbGyskJNTY2nT58il8vR1dUlOTmZpKQk8uXLJy5UlWW+efOGhg0bisLj06dPsbCwYOPGjairq9O2bVvatm1LmzZt6N69O1FRUSxcuJA6deqIloBPnz4lKioKFRUVOnbsSL169cRylNn7PkZphaKmpkZCQkK2Lhh/JCoqKrx7944dO3YwePBgANzc3EhKSqJw4cJMmDABkKw7JHIvyv6amZmJmpoaWlpaYszA7BbVLl68iEwmo0qVKqJL54dZO2/evMnKlSsJDw+na9eudOjQgSpVqnDt2jVSUlJwdXXl0qVLdOvWjRs3bmBlZYWfnx+1atVi9erVYjkuLi5i3+zZsyejRo2icuXKpKSkMGvWLAAqVKjAlStXgPf990uiGmT/u2P//v1cv36devXqUaVKFdzc3LIcX7duHX369MnWcfL06dOEh4fz8OFDjI2NcXd3Z+jQoZw5cwZVVVVq1aqFo6NjtpUnIZGdjBgxAoVCQZEiRXj8+DE7duxg+fLlWcYo5VgyduxYatSowd27d/H398fBwYHWrVuL2do/R2ZmJj4+PjRp0uSn3I+S06dP0717d9q0acPmzZu/OnmKhISERK5BkJCQyJVcu3ZNcHV1FUqWLCm4uLgImzZtyukq/WcUCsVXn/vgwQPh/PnzgiAIwuPHj4XGjRsLAQEBWa7z+vVrIV++fMKJEycEQRCE+/fvC7a2toJCoRCMjIyECxcuCJMmTRLat28vODk5CfHx8cKcOXOErl27iuUcPXpUsLCwEHbu3Cls3LhRqFmzpvDq1StBEATh+vXrQu/evQUPDw/B29tbkMvln63r6tWrheXLlwuCIAhLliwRrl279m0P5ieRmZkpCIIgnD17VnB1dRVu3bolpKWl5XCtJCT+O8o2/fDhQ2HXrl2Ch4eH4OvrKx5XKBTfNO78G76+vkKDBg2EXbt2CYIgCDNnzhQmT54svHv3TkhNTRXOnj0rLFiwQOjatatw+fJlYdKkScLp06cFX19fYdCgQcKqVauE4OBgoU6dOkJYWJgQExMjuLq6CsePHxdiYmKEdu3aCVu3bhUEQRBKlCghnDhxQtiyZYsgCIJQpEgR4fLly4IgCEJkZKTw8uXLz9YxO+/3awkJCRGWLl0qdOvWTXjy5Mknx+/cuZOt5SkUCiE4OFho3bq1oKWlJYwcOTLLcT8/PyElJSVby5SQyC7kcrmwYcMGIT4+PsvnH/Zd5b/9/PyEcePGCYIgCGFhYcLmzZuFESNGCD169BACAwO/WMbdu3eFcuXK/bTxICUlRRgyZIhgbW0teHt7/5QyJSQkJHICyWJNQiKXUrlyZTHOTm7nczurwgeulsr/4H2yBHhv9VG0aFEOHDjwibXJo0ePxF3Zw4cPk5CQwKBBg0hISKBs2bLY29tTo0YN8fzExERiYmKoVq0aAGlpaYSGhtKwYUPatWsHwLZt23jy5AkpKSksWrSIUaNGERQUxObNmylWrBj58uWjevXqdOjQgYSEBDp27EiXLl3EDF4zZ85k/fr1n71P4f/dkXLK0lC5+33lyhViYmLw8vKiadOmFC5cmPz582Nqapoj9ZKQ+K8o2/TAgQNp3749T548oVWrVpQqVYoJEyZQpUqVbClH6UJ69uxZ9PT0xIzJVapUYceOHchkMq5du0bjxo0ZO3Ys8+bNIzAwkD179mBlZcWJEyd48eIFNjY25M+fH0EQCAwMxNbWlrt379KoUSPgvWu7hYUFHTt2ZObMmXh5eaGmpkaTJk24fv06xsbGCIKAmZkZZmZmWeqmJCfGl3z58jFo0CBSUlLQ1tb+pF7lypXL1vIEQSB//vxUrlwZOzs7QkNDKVeuHO3bt2fo0KEMGzaMNWvWZMmGLCHxq/DixQs2btxIt27dRMvatLQ01NXVUVFREftOVFQUN2/e5MiRIzRu3Jhq1arRuXNnAgICuHfv3ifx2D6kdOnSyOVyjh07RtOmTX/o/dy7dw93d3ccHBzw9/eX5hISEhK/NZKwJiGRi1BOqs6cOcOQIUOwsrIiMzMTmUxGTEwMbdq0Ydy4cWL8jdzMl1wtlSKUcmGmoaGR5TuCIFC3bl0ePnzIq1evOHToEDVr1hQXvD169KBbt27Uq1ePPHnyUKFCBfT19UlPTxcnfampqURERFCgQAHgvVtp7dq1CQgI4ObNm5w+fRpTU1Ps7OwIDw/n9OnTuLu7c/XqVZo1a0ZKSgoxMTG0bNmSZcuWUblyZTIzM8WECx+6iH5OUPuZYpu/vz/nz5+nTp06TJgwgZYtW3L8+HEOHz6MlpYW+fPnZ+jQoejq6v7QekhIZDfHjh1DXV2drl27smLFCs6ePcvIkSNxdnbmwIEDNGvW7LvLUI5DBgYGWVwsK1asiJaWFvr6+jg5OWFtbY2zszPm5ub4+/ujpqbG69evGTNmDJUqVRLHuoIFCxIQEEC5cuVo3Lgxzs7OADg5OeHs7ExSUhKurq5i4P0P+XisyOlYiMox7O3bt6SkpJCUlETx4sXFegnZHN9MEARkMhmRkZGsX7+ea9euYWhoyLlz51i4cCHr16+nTp06FCpUSIqtJvFLoWyPz549E8cRZftUU1MT+4zy/3369MHY2BhnZ2caNmxI69atWbFiBSVKlBA3H7/UxmUyGZMmTWLq1Kk0adLkh/QDhULBokWLmD17NgsWLKBz585Sf/sFScpQcD8mlbcpctLkCjRVZVhoq1LaRAsddSmWroTEtyIJaxISuQjlpMrR0ZENGzagrq6Oqqoqjx8/5vjx49jZ2WU573fk3yZnyuPGxsYYGxuLceeUuLm5YWBgwMWLF7l37x5FixYlPT2dqKgoDA0NAUhKSuLu3bsYGRkB73eRHz16RJMmTXj69CmTJk2iWLFiXL58GQcHB2xtbXn48CFmZmaMHj0agIiICAwNDcmXLx/h4eGoqqpiZmbGoUOHmDVrFrNmzaJGjRosW7YMMzMzypUrR7FixcR7+JzY9iOSJZiamlKoUCHOnj2LQqGgdOnSFC9enKSkJLy9vQkMDJRENYlciYaGBtOnT2f37t0ULVoUOzs7unXrRtWqVbNFVPsQBwcHTp06RUZGBurq6ujq6uLk5ASAlpYWpqampKWlAZAnTx4cHBxo1KiRaDl3/vx5atSoQYkSJbhy5Qp9+vRh0qRJ+Pv7U7p0aaytrT8p88PkML8aykV9SEgIrq6uFCxYEBsbGywtLXFycqJixYo/bFzZunUrDRs2FMfzWrVqUatWLSIiItDT0/shZUpIfA/KPmxmZkZaWhpbtmyhRYsW6OjoiKK7sk89f/4cuVzOunXrAJgyZQoeHh7o6Oiwb98+MY7hP40Lbm5uTJkyhRMnTtC4ceNsvZeQkBC6du1Keno6vr6+/2g9J5EzhCdlcPVNCoHx6agAmR+Ezn0aC5fCkylkoEFVS22sdNVzqpoSErkOSViTkMiFGBkZUbFiReD9ZKtUqVJkZmZy/vx52rZtmyX73J+M8FEmPgA9PT2aN29O8+bNxc/kcjnTp08Xs1+GhYVhbW3N06dPadu2LbGxsdSqVYu2bdvi4eGBQqGgfv361K9fX7zGokWLqFy5svj3/fv30dfXx9ramrNnz5KUlMSaNWs4fvw4R44cITk5mRkzZmBubo6fnx979uxhzpw55M+fn3379mFiYoK6ujolSpQgb968X7TgUygUoqXGf1lg58uXj7x582Z5ToIgoKurmyXQuGThIZEbULpPvXjxAlNTU0qUKEFaWhpmZmY8fvyYRYsW0alTJyB727SVlRUymYznz5+LAvmH9alatSrnzp2jYcOGlCxZkpo1a7JgwQJmz55NYGAgZcqUoXTp0vTs2ROFQgG8T1KTN29e8Vofu3b+ymO88tnu2LEDV1dXevfuzZUrV3j06BF79uzh4sWLTJw4MVvHFBUVFdLT03n69CmbNm3C0tKSwYMHiy6oH2amlsYyiV8NhUJBpUqVGDBgAJs3b8bExIS6deuK/VzZZg8fPszLly+ZNm0aXbt2xcbGht27d3PlyhUKFy78VWXJZDImTpzItGnTaNSoUbb1h127djFo0CD++usvRo8eLYbCkPh1uBOZwtnQpCxi2ocoP38Wl05QfDq1rXUpb6798yooIZGLkUY8CYlcSGpqKu/evUNVVRWZTEZ6erqY9U3if/ybGKW0/lJVVRXdPgGioqJITU1l1qxZxMTEkJ6eTq1atQDo1asXEydOxN3dHRsbG5ydnWnUqBG3bt0SLVTgfZw3pZWJv78/ycnJ+Pr6snHjRgwNDdm9ezfLly9n6NChdOvWjf379zN9+nTWrVvHrl27kMvl6OjoEBYWxt9//423tzfv3r2jc+fOonsYfN46Ufj/zJ1fa+Emk8k+G4vpQ+FBWohK/Ooo4yrq6uoyceJEli1bhqamJoUKFSI2NpZZs2Zha2tLr169gO9r00qRS9lH9u3bh5mZGba2tlnOU/arWrVqMWbMGObMmQPAgAEDqF+/PgEBAVSvXv2LsYc+7IO5yRJZJpORkZHBlStXGDVqFPny5aNdu3ZERERw5coVjIyMRNf97BxbNDQ0+Pvvv6lVqxbe3t507NiRRo0a0bVrVzELtYTEr4hyPGnSpAkuLi7o6Ohk6fPKMadx48YYGBhw69YtNm7cSMWKFalWrRrVqlX7pqzdrVu3ZurUqXh7e9OgQYPvqntcXBwDBw7E19eXY8eOiRu/Er8W/yaqKbl9eAc3D26l3/pjnA1NApDENQmJryD3zNIkJCRES4YTJ05gZ2eHs7MzlStXpk6dOmhpaTFp0iTg17Zk+BVQimlfWqgqFAq0tLQoV64c9erVo0mTJujo6ADvg20vWbKEZs2akZmZKX7H19eXMmXKiH/7+fmJi+wzZ84wYcIEMd4PwJEjR6hbty56enpMmjSJTZs2UbZsWYKDg9HV1aVbt27s3bsXJycnRo8eTatWrdDS0mLz5s0ABAYG0qdPHxo0aMCECRN4/vy5WLbSlfRz9/gtE29JTJPITSQkJJCRkcGoUaN4/fo1iYmJwHtrsi1bttC7d29Wr14N/G8s/VpevXrF5s2b6dmzJ7Vr1yY1NRV430cePXrExYsXiYyMZNu2bcD/+pmy/9WtW5e2bdtmGTPs7Oxo3rw5pqamYlzFj8nNfTAoKIjAwECaNWvGrFmziIuLI0+ePLRq1Yo6deoA2XN/yt8yOTmZy5cvc+/ePUqWLMmECRNwd3fnxIkTrFy58rvLkZD4USj7vpOTEw4ODjx8+FD8TPl/FRUV4uPjkcvl9OrVi8GDB6Oqqsr+/fvFcedb+pNMJmPChAlMnTr1m+YFH3Px4kXKlCmDnp4ed+7ckUS1X5TwpIyvEtU+JlOAs6FJhCdn/JiKSUj8RqgI3zOaSkhISPzmfJiZ9J9ITExEW1tbFDWHDBlCnTp1aNasGRoaGgQEBPDkyRO2bNnC9u3bGTlyJPny5WPYsGFZrrN//36uX79O165dKVGiBM2bN6dMmTJMmzYNb29v9u/fz5gxY7CxsSEiIgKFQsGyZcuwsLBgyJAh7Nu3T9wxjoiIoHnz5pQvXz5LGZ+zElEuTnMyO6mExPcSGxtLnz590NbWxtDQEC0tLdq0aYNCoWDbtm0sXbr0X6+hzMx54cIF8b+UlBRcXFzE/0qUKPFJPzl58iTLli3j0KFDX21d9ru7WAuCwNmzZ9m+fTt3796lVKlSzJw5U3Rvzw6Uljy9e/dGT0+Pbdu20a9fP6ZNm0ZycjJxcXEYGhqKmyMSEr8q0dHRREVFUbhwYdGNUtm+J02aRHh4ONHR0ejp6dG7d2+cnZ05deoUpqamODo6fvN4IpfLKVmyJJ6entStW/eb6pqens7kyZPZtGkTa9asoUmTJt/0fYmfy77AeJ7FpX/VuR9arCkpYqhBy0IGP6p6EhK/BZIrqIRELuTly5dcuHABXV3dLLuZMpmM1NRUihYtSoUKFXK4lr8H/2TVJgiCKKR9HBR78eLF4r+PHz9OwYIFsbe358CBAyxevJiBAwcyatQoEhISqFatGvDequXt27doa2tjYWEBwJMnTxg8eDAA4eHhGBgYoK+vz+PHj/Hy8gLg9evXvH37FoVCQVhYGPfv36dq1aqEhYWxaNEiVqxYQWRkJPv27cPFxYWKFSvy7t07tLW10dLS+ux9Pn/+nBMnTtCqVasscZ4kJH5FlItPIyMjdu/eDcDVq1c5e/Ysy5Yt48KFC6JF78exygRB4MWLF5w9e1YU0gRBEEW0sWPHUrRo0X9dsBYuXJikpCQCAgIoWbLkZ8Xrj4Xr301UUz7byMhIrl27RmZmJlpaWkybNo3o6GiWLFlCdHT0Z5Mx/FdkMhkhISHcu3ePGzducP/+fdF1f9euXTRq1EgS1SRyBaamplncwpXxU1+8eMHRo0c5fvw4bdq0wdramnHjxlGiRAmmTJkixg/81vFEVVWV5cuXs3btWurUqfPV33/06BHu7u7ky5cPPz8/cb4i8fNRZpNVJi/r1q0b+fLlY/r06URFRdGtWzcuX75MuqCCZaGi9F57GJlMxvkNS7h5YCuJMZEYWVpT32McJWpnFUePL5rMzYPb0NY3xG3sHBr2bYuOuowNGzYwd+5cXr9+jbm5OaNHj6Zv3745cfsSEr8UkrAmIZGLUC5agoODGT9+PHp6elSrVo379+/z5s0bXFxcSElJoVGjRpKw9oP5N4uUDxfvdevWFS3ClJm8AAYOHMjx48dZvHgxxYoVo27dugQGBqKtrS1OrkNDQ0WLs6ioKPT09DA1NaVq1aqMHz8eMzMzNm3aJLp9+vv706ZNG3r16kWvXr2oU6cODx48wMDAgHv37okJFubOnYtcLmfu3Ln4+vpy5swZ8ubNS82aNbG1tcXW1hY3N7dPBMOvteCTkPiZKPvaihUrePHiBebm5vTr14+qVasSHBzMu3fvRFftD/tueno6GzZsYObMmbi4uFC7dm2mTp1K4cKFv7mN29vb06hRI5o0aUK/fv0YN27cZ+v4MZmZmZ/EOcytKJ+Zh4eHaD1jb2+Pv78/I0eOzDL+ZSeBgYHUr1+flStXoqWlRa1atYiPj2f27NnfHT9KQuJnEBMTQ1xcHDY2NuJYoOxPp06dYsSIEbx48QINDQ08PT0ZPnw49+/fJyoqKktijm+lZs2aVKpUCblc/q/JBgRBYPny5UyePJmZM2fSu3dvaS7wC7NgwQLy5cvH0QevuBKeTOC9W+LvZZrPlr7rjqBnasGD04fYNWEAIw7dwMD8fVsKeXCH8k3bM/HsE3z3b2bv1CH0at2MKnl0sbCw4OjRoxQqVIiLFy/SqFEjKlas+Il3hITEn4YkrElI5ELevHlDq1atWLJkifjZqlWrCA0NZdq0ad8cP0gi+/nYIkb594euGjVr1qRmzZpZvjdw4ECSk5ORyWS8efMGAwMDjI2NSU1N5dWrVxQoUIB3794RHx9Ply5dADh48KC4Y/z69Ws6deqEXC5HRUWF0NBQbG1tCQgIQFNTU8waFhMTQ6VKlQgLC2P58uXY2Nhw8eJFbt26xfz58zl06BDq6uq0bNmSyMhIBEHAwsLis/cBkuAmkXPI5XJUVVVZs2YNPj4+uLq6MmLECFauXEmPHj3w8PDIkpzkQ2QyGY0aNfru3XZlfxg4cCC9evXC2Nj4X+srCAKxsbE8evSIqlWrflf5vwLKZ3D//n2ePHmCv78/crmcmzdvMnbsWNTV1Rk+fPgPGSNcXFy4ePEiixcvZvbs2aSnpzNnzhxq1aolZj3+HYRLid8PZb+5evUqLi4un33Hdu7cGYVCwaZNm2jatCkAxsbGNGvWjJIlS35X+SoqKujq6v7reREREXTv3p3o6GiuXr1KkSJFvqtciR+Puro64eHhBDwPQjDMR8HyVcRjpeo1F/9duoEb5zcs4XXAXYrXbASAUZ58VGrZGYDyTdtxaNYonoWEUyWPXRa3XxcXF+rXr8+lS5ckYU3ij0eaZUhI5EJiY2NJSnqfqSctLQ14Pwl78+aN+G+JX4cvuX4pFArkcnkWIbRAgQI4ODigUCiwtLQkNDRUPNasWTOqVq2KgYEBDRs2pEKFCnTq1IlLly6RJ08eEhMTSUtLw8rKClVVVV6/fo1CoSBPnjyEhYWhp6eHmZkZAJGRkeTNmxcDAwMuXLhApUqVWL9+PV5eXmhpaXH16lWeP3+OQqHgypUrdOvWjeLFi9O/f3/i4+NRUVEhLi5ObIcymUzM8qdEEIRP7u/DYxIS2YHSHXv58uUsWbKE27dvM336dFavXo2XlxcdO3b84nfV1NS+KLp9C8p+ra2t/VlRLSMjg/T0dMLDw5k1axYdOnRg/fr1GBoaiqJabu8TymeQkpKClZUVz58/R1VVFScnJxYtWsT169ezVVRTjisZGRkoFAratm2Lo6Mjhw4dwtXVleDgYKZNm5albhISvyplypTJInAp2+yqVasIDw/H0NAQZ2dn1qxZw7hx41i+fDktWrQAsm/s+NKm7MGDBylbtiwVK1bkypUrkqiWSxg5ciR2dnZM6tKCuc0cOb/hf5vxd47uYmn7mkytUZipNQrz5sVjkmKjxeP6Zv9z79XQfu9KHxefALxPoObk5ISJiQlGRkYcP36cqKion3RXEhK/LpLFmoRELkI50apQoQL79++nd+/eNGnShBs3bnDjxg0GDhyY5TyJX5t/sqCQyWRZdqy1tLSoV6+eeHzdunWiC3CbNm2wsLDg9evXyGQyDAzeB5j18/NDW/t9ivS0tDQiIyPR0NAgIiKCN2/ekCdPHvT09JgzZw6nTp3i3r17eHh4YGBgQGJiIoULF0Ymk9GgQQMaN26MhoYGvXv35vHjx1SqVIkLFy6wdOlSoqOjqVKlCvPmzUNXV5fo6GhMTU3FzKQfo7QeOXDgAJUrV5ZiuEl8N1FRUYwaNQp9fX2uXbvGhAkTMDc3p1OnTvTq1Qv4n6XYz0TZh/v378+QIUPYvXs3urq6uLm50adPH3bt2oW3tzeQu8ft2NhYjIyMAKhUqRLlypVjwYIFVKlShTx58rBjxw6KFy8OfBrj7r8ik8l49uwZy5Yt49ixY3Tr1o0hQ4agr6+PgYEBBgYGYhzS3PxsJX5vlO3zQ4tw5Vh18eJFduzYwenTp6lVqxa9evVi48aNBAQEsG/fPvLnz5+t49rH/TIxMZEhQ4Zw7tw59u/f/1tY1v5u6OjokJycLP4dERFBvnz5ANDX12fBggW4DJrC2Zt+rO3bknzFy2GavyD7/x5Gr5X7KFC6IjJVVZa2rwn/ItBqqKqQlpZGq1at2Lx5M82bN0ddXZ0WLVrk+o0hCYnsQLJYk5DIRSgXB+XLl2fDhg2ULFkSb29v9PX1WbZsGS1btgT+Pf6XRO7g48XghxMXmUxGmTJlqF+/Ps2bN6dKlSo4ODhw7NgxMd6KmpqaaLJfuHBh7t+/z+LFi5k+fTqRkZGYmpqSnp5O27ZtGTduHAEBAcyYMYOEhARSUlLIkycPCQkJTJ48GScnJypUqMC5c+cICAgA3seOO3PmDFevXkVbW5s7d+4AMHjwYP766y+aN29Oq1atePHiRZb7ULbPt2/filY87u7uPHnyBPjyrrmEBPyvH3zYH8LDw3F1dUVXV5datWpx5MgRFi5cyKVLlyhWrBjATxfV4H0ffvv2LYUKFaJIkSIcP34cd3d32rZtS69evZg1axbwfiGdmxk/fjx+fn4EBAQgl8uZOnUqpUuX5uHDh8ybN49ChQqJMeey4/2kHCvGjRuHubk5+/btIz4+ngEDBuDj44OVlZVo/SOJahK/Msr2qYxvpkyKlJyczMiRI2natCnFixfn1atXeHh4kJGRQZcuXahS5b1b3/eMa//0rvXz86Ns2bIoFAr8/PwkUe0XpWzZsmzfvh25XM7Jkye5cOGCeOzo0aM8f/4ccy0ZevoGyFRlqMhkpKcmv3cBNn7vwXDr0HbevHj8r2WZaKmSnp5OWloa5ubmqKmpceLECXFzSELiT0eyWJOQyKXkyZOHv/76i+fPnxMTEyMuHiV+X74ktH0Yw02Z5ROgcePGNG7cGIBq1aqxaNEi7t69i5GREb1790ZVVZWVK1dy6dIl6tatKy5GY2JiUFdXp0CBAhw4cABvb2/8/Py4d+8eixcvRkdHh5SUFJYuXcru3bvR0tIiNTWVfPnyUblyZdLT00lMTGTJkiWsW7cOT0/PLFlSldjY2GBra4uvry/R0dEULVoU+HThrRQdlO6mEn8WqampYrbH3r17o6mpmcUKae/evezdu5edO3cC7zcetm3bRpEiRZgzZw7w863VUlJSWLlyJUOHDsXCwoJx48aRkpJC9erVuX79OnK5nDNnzjB//nwgZ0S/7GTZsmUANG/enIcPHzJo0CA6duyIiYlJFuvb7LAe8/HxYcOGDdSuXRt1dXV69uxJnjx5KF26NL6+vqKr7T/FuZOQ+FVQ9gflGKD8e8uWLZQqVYoRI0aQlpbGrVu3GDt2LF5eXjx58oQePXr8p/LS0tLYuHEjjRo1+qIbvEKhQFdXl7lz54obthK/JkuWLKFr164sW7aMFi1a4OrqKs4Nnz17xsCBA4mMjERdz5DKbXpQuGJ1AKp36s+Kbo1Qkcko16QtNmUq/WtZDoaa6Ovrs3TpUtq2bUtaWhrNmjXD1dX1h96jhERuQUWQbDclJHIlSnea1atXExAQwJIlS3LE1Uni1+Vr2kNISAjnz5/n5s2blChRgr59+7Jjxw5OnjzJokWL2L17NxcuXGDHjh1s3bqVFStWsH79ei5cuMC2bdu4cOECt2/fZtmyZTg7O1OrVi0mTpxInz59cHZ25ty5cwwYMIBHjx5lcQF7/PgxxYsXR6FQcPDgQZYuXUqJEiXQ1dWlc+fOlChR4h/r/eFOu2Sh+XuRnJzMtWvXuHDhgti+SpQowdq1aylRogQymYywsDA0NDTEmIE1atRg7969YhKPV69eoaurKx7/2QQHBxMUFIStrS1Tpkxh2LBhlCpViiNHjrB48WKsra1p0KAB7u7uuX7cVtY/ICCAEiVKcP78eRYuXMijR4+oX78+vXv3pmzZstlWXlpaGjt37uTcuXM8f/6cYsWKMWTIEHE8sbGx4eHDh6JLvIREbuTMmTNiJl1lUPilS5fi5+dHVFQUCxcuxM7O7puuqVAoGDRoEHfv3gVg06ZN2Nvbf/Fc6d2au0hISMDU1JSMjIwscW937dqFSvn6PItL/8/XLmKoQctC0pgqIfFPSCOmhEQu50txrCQkPm4XCoVCTJigJH/+/HTu3JmlS5eKmRFbtmzJzJkzMTY2pl69eqSkpFCyZEl27tyJkZERurq6JCQk4ODgALzPRBocHIy9vT2vXr1CX18fS0tLAG7cuJEltpJSEPPz88PFxQWAmzdvEhwcTLt27UhMTGThwoXAe1fRnj170rFjR+bPn09ERIRYb5lMJv73IV9KliDx65KYmMipU6cYN24c1apVw8LCgkmTJpGZmcm4ceMIDw/nxo0blCpVSvy99+7dS5kyZRg7dizJycm0bt2aZ8+ecfz4caZMmYK7u3uW9vKzKVCgAC4uLsTFxWFtbc3EiRMZPnw4ZcuWxdvbm82bN+Pu7g7kbms1uVzO8+fPAejRowc3btygZs2aHD58mJMnT5KZmYmnp2e2lXf58mU2b96Mq6sry5cvp1+/fqSmprJ27Vq6detGt27dGDBgAAYGBtI4IJGrqVu3Lh06dGDt2rVMmTKFa9eu4enpybx58zAyMhJDL3wtDx8+RCaTMWvWLK5evYqjoyPLly//YtB5SVTLfejr61Op0nvLM+V8q2jRorRo0YKqltqo/UdjYTUVqJJHOxtrKiHxeyJZrElI5HLCw8NJTEz84q6jhMS/oVAoxLgu/0RsbCxv3ryhQIECREVF0bNnT169ekW5cuUIDAxk3759HDhwgIiICAYPHkyePHlo3bo1Tk5OjBgxQhT0VFVVGTRoEGpqaixatIj+/ftjaWnJlClTePDgAVOnTmXixImULl2aJ0+ekJKSwtatW7GysmL48OFcuXKFhQsX4ubmhiAINGrUCENDQ9TV1bPUNz09nSNHjlCxYkXy5csnLRR+EeLi4rh8+bJokRYQEECFChVwcXHBxcWFKlWqoKOj86/XuX37Njt27MDHx4d3796RkJDAqFGj0NLSombNmpQpU+Yn3M2nKC24fHx8OHToEDNmzODhw4ecPHkSX19fihYtSocOHahYsWKO1C87iYqKYvLkyRw8eBA9PT0x9pmSt2/filaE2eEGevjwYXx9fUlNTWXYsGHkzZuXt2/fcvDgQU6ePImBgQGrVq1CU1Pzu8qRkPgZfNgnlONGUFAQt27donz58igUCu7fv8/FixcJCQnBzc0NZ2dnGjZsyMOHD7+6Px05coQTJ07g4eEhWoPHxMTg7u5OzZo1AShYsCBt27b9Ifcp8eOJjY1l+fLlzJ8/n/j4eORyORYWFgQEBIiW23ciUzgbmkTmN6z81VSgtrUu5c0lYU1C4t+QYqxJSORCAgICCA0NpX79+kRGRjJ9+nSKFi3KiBEjMDQ0zOnqSeQy/klwEgRBjOFmZGQkZv7Lnz8/3t7eZGZmEhMTw/Pnz8mbNy+ampoUKlRIDBx++vRp+vXrJ5ajtCLx9/fHw8MDQRAIDw8XkyyoqKhgZWVFUlISr1+/ZuPGjWhpaREeHk54eDgA9+7d49atW1SuXBmZTMb9+/c5efIkx48fp2jRogwePJgaNWpw8+ZNDh8+TLly5T57j8p7U5YrxW/7McTExHDp0iVRSHv69CkVK1bExcWFOXPmULlyZTF77degXIDeunWLTp060bJlS7Zs2cKZM2fQ1tZm4MCBYlvLCTFVKVBv376dli1boq+vj6OjI0WKFOH58+ecOXOGpUuXMm3aNAoWLPjT65edmJmZsWzZMuRyOXfv3qVYsWI0aNCA8ePHc/36daZMmcLt27eB7Eki4OrqSrFixXj58qWYTdjCwoI+ffrg6urKy5cv0dTUlNzYJHIdynGjU6dO6OjoEB0dTfv27alTpw6LFy9GEAQUCgVTp05l2rRpX9WfDh06RP78+SlTpgyPHj3i8OHD2Nvbo6amhomJCTNmzODGjRtMnz6dgwcP/uA7lPgRhIeHs2jRItatW0fTpk25ePEiHTp04MmTJ5w4cSJLOASlOPa14pokqklIfBuSxZqERC5CuVjw8vIiKCiIBQsWMHLkSAC0tbWRy+XMmDEj18fskfixKBQKMjMzUVVV/c/t5ENB6p9YtWoVXbt2FZMqKNuwTCbj8ePH6OrqUqpUKfbt20etWrVYtmwZjx8/ZvDgwQwdOpRq1apRqFAhVqxYQalSpfD09KRnz56YmJgwb9484L3Qkpqaiq6uLmvWrCE2NhYPDw/GjRvH6tWrKVSoEAMHDqRv375fvbhXCoDS4vzbiYqK4uLFi6KQFhgYSJUqVUSLNEdHx/9sUaS08EhLS6NmzZrs3LkTGxsb0tPTuXXrFpMnT6ZgwYKsXr06m+/q2wgMDGTgwIFYWFgwZcoUbG1txfrL5XL69OlDwYIFmThxYo7W83tR/h5xcXEYGhry5MkTFi1ahI+PD0WLFqVLly60bdv2hwpdXzsWSUj8qij7x+7duzl37hwrVqzg/v37rFixgrdv3+Lo6Ei/fv0wMjIiOjoaU1PTf71meno6V69excnJCS0tLR49ekRiYuInlrIPHjxg5syZLFmyBHNz8x91ixLZzPPnz5k3bx579uyhc+fODBs2DBsbG+C9NXd4eDhNmzb97HfDkzO4FpHCi/h0VCCLyKamAgJQ2ECDKnm0sdJR/+w1JCQkPkWyWJOQyKXo6enx4sULUlNTmTRpEseOHePevXs5XS2JXxi5XM7Lly9JT09HJpNhZWUlWvbo6ellyd73b3wuQ6mKisonC2hl3DYlyjJmzpxJkSJF8PX1pUSJEnh6erJp0yYCAwOZPHky6urqhISEMHbsWACOHz8uWqg8fPiQ6dOni9e8du0aK1asIDw8HIVCgbGxMf369aNMmTIMGTKEhg0bYmFhkaV+e/fu5dGjRxgbG6NQKGjRokWWDGn/ZOEmWbdlJSIiIouQ9vr1a6pVq4aLiwurVq2ifPnyn7jp/leUz3337t0kJyejpvZ+GqOhoUHVqlU5efIkycnJQM4G305OTqZ69er4+fmxYcMGSpQoQaVKlbC1tUVNTQ03N7ccc1XNLpTP99y5czx79gxdXV3Kly/PypUrSUpKIjY2Fmtra+DHCtRSX5TI7chkMjIyMjhx4gQxMTEkJiZSqlQpli9fztGjRzl58qRoLf5vopqyX2poaFCzZk3RXX7t2rWfPb948eKkpqaybt06xowZk923JpHN3Llzhzlz5nD27Fn69+/PkydPPhFEK1So8I/XsNJRp2UhdZIzFNyLSSUyRU6aXIGmqgxzbVVKm2ihoy5tKkpIfCuSxZqERC5COWE6fPgw69evJzo6mnLlyrF06VIWLFhAeHg48+fPlyzWJLKgbA+rV6/mwIEDJCYmUrVqVQwMDBg0aNAPzZ73NeKGXC7nxYsX3Lhxgzx58lCvXj0yMzNp3bo1CQkJFC9enN27dzNr1ix69OiBvb0958+fFxftFhYW7Ny5E1tbW3bu3Mndu3fZtm0bkyZNwtramkGDBn1Sn0WLFrF+/Xratm3L06dP0dLSYvbs2ZiamnLjxg0uX76Mubk5DRo0EBMxfA6le44yA9efQGhoqCiiXbhwgbdv3+Ls7CxapJUpU0YUvH4Ux48fZ86cOaSkpNCtWze6du0quh/nFB+29ZSUFLS1tXn69CmHDh0iPDyc9PR0Bg0aRNGiRbMl3lhOoqx/TEwM9erVo0WLFixevJiGDRuSL18+atasSfXq1dHX18/pqkpI5ArevHnDvn37OHbsGIULF6ZOnTo0b948yzn/9j79cO5369YtHB0duX37NnPnzqVp06Z07tw5yzWU54eEhNCsWTNOnTr1j+87iZxBEATOnz/P7NmzCQgIYNiwYfTu3VsaXyUkfjEkYU1CIpfy8OFDQkJCqF69Ojo6Oty6dQsdHR0xMK2ExMfY29tz7tw5TExM8Pf3Z968eVhaWrJw4cJvinGVHfzTAkF5LDExkdu3bxMWFkZaWhrFihUjX758VKhQgbCwMGQyGbGxsTg6OvLo0SPU1dXp378/giCwcuVKevToQcOGDWnRogUaGhpZyhg8eDC6urrMmjULeB+4+dixY8THx3P69GnMzc15+PAhcrmc2bNnk5SUxOHDh9HX18fMzIzKlSv/oyCpTAjxvYJbUoaC+zGpvP1gR9niJ+8ov3r1KouQFhsbS40aNUQhrVSpUj9FyP9YjIqKiuLKlSscPXqUyMhIateuzeDBg394Pf6NoUOHkpCQwN69e7l48SKlS5fG39+fa9euifEGczvKPjpz5kxUVVVp27YtPXv2ZNy4cfTp04fq1auzZs2abEsioCwvIyMDmUwmbRxJ/BYo27VCoSAlJQVdXV38/f25cOECT548QVVVlX79+omZtb+WwYMHc+HCBfbv34+1tTXHjx9n/fr1zJ8/HwcHh0/ev0ePHmXUqFEcO3bsk7iPqampYigHiZ+LQqHg0KFDzJ49m9jYWEaPHo27u7uUnEVC4hdFcgWVkMiFxMXFoa6uTtGiRXn79i0qKiqYmJhgYmKS01WT+EUJDQ2lSJEiqKuro6OjQ5UqVdi/fz8uLi4kJyf/dGHtY1HtwyQJymN6enq4uLh88t179+6J5+jo6NC7d29sbW0pX748cXFxODk5IZfL0dTUxMjI6BNRLS0tjdjYWOrXry/GmitSpAjp6emsWrWK+/fv07t3b/r160ebNm148eIF6urqjBs3Dnd3d54+fYqRkRHu7u4cPnwYVVVVpk2blsUd40uupAqFAlVVVdLT00lKSsLY2Pizzyc8KYOrb1II/EwMlKexcCk8mUIGGlS11MZKN/tioAiCQGBgYBYhLSUlRRTRhg4dSvHixX+6i6VSVIuPj2f58uU8fPiQihUr4u7uToUKFTh37hwxMTE/tU4folyoXrt2jbt37+Lj48PZs2cxNDQkJSUFNTU1UVTL7dZq8L/2HR8fT48ePfD09KR3797UrVuXPn36oFAosjWJgPIae/bsoUyZMtIGksRvgbJdDxs2jKioKO7evcuWLVvo2rUrt2/f5syZM/9qlfTheKJQKMTEBjdv3kRDQwNBEGjcuDEPHz5k3rx5rFu37pM+aWtri4+PD1ZWVp9ce+LEiXTq1CnXu67nJtLT09m+fTtz5sxBT0+PMWPG0KJFC2lDQULiF0cS1iQkchHKRcrRo0cZPnw4efPmJTMzkzdv3hAZGcn8+fMZNmyY5Aoq8QlWVlZUqFCBHj160LNnT6ytrXn9+jVpaWmYmprm+GL/S3HLPszcqVwMWFhYiMc1NDQYPXo0Q4cOJTQ0lOjoaFJSUlBVVaVMmTJ06NCBvn37MnXqVDHO15s3b4iIiMDPz4+mTZvy5MkT0tPTUSgUREVF0bt3b4KCgjh+/DgWFhYkJCSQmpqKjY0NS5YsAd5nRbW1taVXr17Mnj2bc+fO0bZtW+Li4hg/fjxv376lcOHCjBo1ShTPVFRUxH6ZmJjI4sWL6dChA8WKFUMul4vP4G5U6j9m7VJ+/iwunaD49O/K2iUIAk+fPs0ipAmCIAppY8eOpWjRojkuBCnb54gRI7C3t0dfX5/Dhw8zaNAg1NTU6NSpk5hwIifasrJt7tu3j7/++otNmzbh7OyMjY0Nvr6+TJo0iRMnTvwW8fn8/PwwMTGhQIECzJ49G4BSpUqxZMkS1NXV2b59O9u2bQO+P7baw4cP0dTUJF++fKKVRrFixcTjERER5MmT57vKkJDICZTzuSNHjvDs2TMOHz6MtbU1hoaGGBoa4uzsTI0aNf7Rrf7juZ4gCDx//hxnZ2dWrVpFXFwcPj4+LFu2jDZt2jBixAi2b99Ox44ds1ynZMmSn72+Mkv333//zd69e7PnxiW+SGJiImvXrmXBggU4ODjg5eVF7dq1c/07Q0LiT0FyBZWQ+A3IyMhg6dKlmJiY0L179xwN2i3x6/Ds2TMuXbpEjx49xM9Wr17Ns2fPuH37Nnnz5qV///5Uq1bth4qxCoWChIQEwsLCyMzMpFixYtkag+ufhJSoqChiY2Oxs7MTzzt79ixeXl5oa2vj4ODAy5cvkclkrF69mt69e9O0aVNatGghXiM9PZ1t27Zx5coV1q5dy7Nnz+jXrx+enp4UL16crVu3cuXKFTw9PWnfvj3Dhg1DX1+fM2fOoKmpyYABA5gxYwapqanExcVRrlw52rVrh4aGxifP4U5kyj+Kap9DTQUc5FE0cyxGRkbGPz5bQRCIjIzkzp07nDhxgj179qCuri4KaS4uLhQuXPiXnMjHxcXRpk0bvL29cXd3p23btjRv3pyRI0dSunRpOnfunNNVxM/Pj927d3Py5EmuXr2KlpYWvXr1omDBgowfPz7Xj80KhQI3NzcyMzNp2LAhDRs2pHDhwsjlcpYuXcqtW7coWrQoU6ZM+W6BUy6XM2DAADQ1NalVqxYtWrTIcr3Q0FB27drFsGHDsuPWJCRyhIEDB9KiRQvu3LnDq1evWLZsGSdOnGD9+vXs2LHjX9+VISEhzJ49m7p16+Li4sKLFy9YvHgxdnZ2ODk5sW/fPgwMDFi4cCHPnz/Hzs7um+qXnJxM4cKF8fb2plSpUt9zqxJfIDo6Gk9PT5YtW0bNmjUZPXo0jo6OOV0tCQmJb0SyWJOQyKUoFy2ZmZmoq6ujoqLCw4cPxWMSEmpqahQsWJAzZ84wZMgQJk2aRLt27cjMzERfXx9BEEQrkOwU1QRBICMjgwsXLlCvXj3u37/Prl27uH79Onp6elhaWrJmzZpsK+/DxfbHi3kzMzPMzMyyHHv27BlFixalevXqXLx4EScnJ9q1a4eKigoNGzZkzZo1REREYGRkRLly5bCysiIkJERMZf/s2TOMjY3Jly8f6enpREVFYWlpSVBQEAcOHCA6Opo7d+6QmJhIvnz5GDBgAC9fvuThw4eEh4fj6elJRkYGBw4c4MSJE6xevfq9y6CGHm9tKqJQ+TbhJVOAq2+Sv/pZmZiYUKNGDerUqcPixYt/SRHtYwRBQE1NjTp16jBw4EDevn0rBvY+ffo0PXv2FM/LyfspVqwY6urqPHz4kClTppCens6zZ89YsWIFkPszWMpkMg4dOsSRI0fYvn07Bw8exMnJCTc3N/r06cPw4cOzrSxVVVWWLl3Knj17ePHixSfPTkNDQ3QVz+nfXULiv9KmTRv27dvHtWvXuHTpEgBbtmyhcuXKqKmpfdK2P/z70KFDjB07lgEDBnDu3DmOHTvGjBkzRItRgKtXrxIfH49cLhdFtW8R+HV0dBg+fDh///03u3fvzq7bluC9KLpgwQI2b95My5YtuXLlCkWKFMnpaklISPxHJGFNQiIXoZxQ3bp1i6NHj2JgYICKigpv377l4sWLdOvWDcj9izeJ7KFgwYJiIOLBgwfj5eXFpEmTaNSoES1atMDFxeWHLEhVVFTQ0NCgZ8+eVK5cGZlMhrm5Obt27cLc3Jy3b99ma3kfl/0llAuJ4OBg9PT0aNKkCU2aNMlyTuPGjVFVVeXs2bM8e/YMAwMD8ubNy/3792nQoAEAd+/exdraGl1dXVJSUggODqZq1apcvXoVNzc3tmzZQoECBTA0NKRatWpiooX69euzfft24L0Lm9KlzcfHh5iYGKoPnIK6ALLP3II8MxPVf7BcyJT/zw3y335TNTW1H561M7tRUVFBV1eXcuXKcezYMWrWrMmOHTs4f/48ZcuW/WxA7p+B0tLzzZs3PHjwgOvXr9OpUyc8PDxYs2YNJUuWpF+/fqirq/8WLvrKe2jWrBnNmjXjxYsXLF++nDFjxmBjY0PXrl2pUaMG8P3vIWWctk6dOomfKTeNVFRUMDc3F+MaSu88idzCh+OzIAiUL1+eXbt2kZaWxvbt2/H19SU8PJwRI0Z89vvK70ZFRfHmzRu8vb2JiYnB09OTkiVLMnfuXBYsWMDVq1cZPnw4dnZ2rFu3LsvY863jZP/+/Zk3bx4BAQFSfMNs4NGjR8ydO5dDhw7Ro0cP7t+/L2Y5l5CQyL3krpm1hIQEAJmZmSQnJ6OhoYFcLsfc3BxPT0/Kly8PfH9cG4ncj3IB/PTpU2bOnMnGjRvp06cPT548YdWqVXTo0AFvb+8vxlb5HjIzM4mIiKB+/fp06NABQRCoW7eueNzCwgJvb2/s7e0/yUD2M5gxYwYJCQlZ4popFys6Ojq4ubnh5uYmni8IAlOnThUX8enp6djb26OqqkpcXByPHz/G1dUVAwMDtLS00NDQQFdXl169enHo0CGePHmCtrY2T58+pUuXLkyYMIGwsDBKlChBv379OHr0KKpqaiRsWUOT4X8j09Im8NYVdk3oT9X2vbi8bSV2lWvSZpoXFzd5cvPAVlIS4rCr5EyLcfPRMTRG5f/7fEqmgIG6Cvv27WP48OEcOXIETU1N+vXrh7+/PyoqKjRo0IBly5ZhZGT005/9t6Jsx+Hh4dy6dYvy5cszePBgfHx8iI+Pp2TJknTo0CHH6qcca3v27ImhoSGWlpY4Ozvj4uKCl5dXloQWuV1UUybeePv2LWfOnOHRo0eUKlWKBQsWEB4ezvLly4mPjwe+34JM+bs/fvyYkJAQsc99aM0hWalJ5EaU7Xbr1q2EhYWhra2Nl5cXJ0+e5Pr161SrVo0hQ4YAn8ZQS0tLY9++fcycOZMtW7aIyXQGDBjA+vXrSUxMZOrUqWzbto2mTZuKG2mfu9a3oKury7Bhw5g+fTo7duz47mfwp3Ljxg1mz57N1atXGTRoEM+fP5eSjklI/EZIMdYkJHIh/v7+pKWlUalSpSyfpaamUrly5RysmcSvwunTp7lx4wb+/v6UKVOGCRMmkJGRIQbw/5H4+/uzYsUK4uLiWLZsmThxVApZixcv5saNG3Ts2FF05/uVUCgU4uLn30TqxMREDh06RO3atbGysmL48OH4+vri5+eHq6srN2/epF+/foSFhbFt2zauX7+Ora0tbm5uqKmpER4eTokSJXDu1I8Z40djUdiBhoMmEnjrCuv6t6J6pwHU6z8aQaHAd/9m/E8ewH3eenSNTTkydxypSQl0mLWad2HBzG1agQX7TyN79UBcqNnZ2fH8+XOCgoKoUaMG8fHxtGrVinLlyrF48WLg17X2UbbXwMBA2rZti7GxMfHx8bRu3ZouXbpgaWmZo/VTWsiFh4fz119/ZXGTGjp0KAULFmTw4ME5WMPsRbkwHzp0KGpqarx+/RojIyNWrFhBaGjoD7G4qF69Ojo6Otja2mJgYECRIkWoUaMGDg4O2V6WhMTPws/Pj3r16jFv3jxOnTrFixcvGD9+/L++D58+fYqjoyMzZsxg0KBBAKxfv57bt2+zbNkyLl68yPz586lcuTJjx44V31/ZYc2bkJBA4cKFuXjxotT/vgFBEPD29mb27NkEBQUxYsQIevTogY6OTk5XTUJCIpuRzFokJHIRcrkceC+aHDx4EIDU1FTgvTvZ4cOHs5wn8edSsWJFEhISOHXqFHv27GHBggWEhoYC0KlTJ1atWvVDylXumJuYmLB+/fosu7GqqqokJSWRmJjIoEGDqFmz5g+pw/cik8lQVVXNshD5cA/qw3/r6enh7u6OlZUVAHPmzMHLywtNTU1MTU3p3Lkzp0+fJiQkBAcHB1F8ePv2LUePHqV169YYGxujapIHl+5DuHfqoHhtFRUZdfuNQk1DE3UtbW7s3UR9j3EYWuZFTUOTOn1H8sDnCPLMTPE7e7duYsaMGZw/fx51dXVmzZrFmjVrSElJIS0tDXNzc4YNG8bFixc/m6EyICCAU6dOfXKfOcHBgwcJDAxk2bJldOjQgdOnT7N7926ePXtGrVq1iIiIyNH6KdvHrFmziIqKws/PTzzWvn17Nm3aREZGRg7VLvtRVVUlOTmZU6dOMW/ePKKjo2ndujUAnp6eeHt7Z0s5yvfX9evXKV26NN7e3gwYMAAHBwcCAwOZP38+jx8/zpayJCRygrS0NBYvXky3bt3YsWMHEydOZOHChVSrVo2kpKQvjr3p6em4u7sD8OrVK+C9+Lx+/XpGjx5N9+7d6dq1K+PHj8/y/soOLwZ9fX2GDBnC9OnTv/tafwJyuZzdu3dToUIFhg8fTs+ePXn27BkDBw6URDUJid8UyRVUQiIXER4ezuHDh0XLCLlcTmJiIqqqqty8eZP27dvncA0lfhWMjIyYM2cOlStXxtzcHC8vL7y8vChXrhwREREsXLgQyH53Kg0NDZ4+fcq4cePQ1tbOslOuUCjQ19dn0qRJWb6TGzIlfviMPn5eH9ZfTU2NMmXKoKenh6urK/b29qxdu5bKlStnyc5qaWlJSkoKY8aMQRAEVJctRy4ICB+I4rrGpqhraol/x0a8ZuuIrqiofLhgUiUxJlL8+/65E5QrWRx1dXUMDAyoVasWt2/fxsPDg8TERORyORkZGWhra3P06FE0NTWpXLkyBgYGABw/fpwnT57QoEEDrl69SteuXRk4cKDomvSzePXqFdu3b+fcuXOkpqZiZmZGamoqNjY2rF69mg4dOvDgwQPy5MnzU+v1OcqWLcuNGzfo27cvI0eOJE+ePJw6dYpGjRr9NrHVlCQnJ9O6dWu2bduGiooKderUITU1lYMHD2ZbG1E+q5kzZ4r9qmzZspQtW5Y7d+7w6NEjyWJGItehHAeuXLnCtm3bePnyJQ4ODpQsWZJmzZpRt25dbt26ha6u7ifCWlpaGgEBAWhoaDB+/HhGjhyJmZkZNjY2FClShJMnTxIQEMCePXvEkCA/4r06cOBAChcuzNOnT6Ug+18gNTWVzZs3M3fuXCwsLJg6dSpNmjT55ec4EhIS348krElI5CJ0dHQoV64cFy9eJCMjA2dnZ6Kjo1FVVaVbt26UK1cOyP2xfCS+D6VYlpSURLVq1TA3N8fZ2ZmEhAQuXryIra0tFhYWPyRGkYaGBtu2bcPY2DjLxP7DslJSUujUqRNFihRh1qxZuX7C+XH9lYsiQRCwsbGhYMGCnDx5kg0bNojnzJs3jxMnTnDz5k10dXW5hykP36VlvfBHv42hZV5aTV6CbdlP3b3fhQUDMGDOcpYN7cGJEydo1aoVt27dYt++fRgaGtKgQQM8PT0ZOnQo69ev59y5c1y/fp3ixYszf/58DA0NefXqFU5OTgBUq1aNsWPHcufOHWJiYn5qLBg9PT127tzJ0aNHOXToED4+Ptja2pI/f34sLS25efMmCxYs+Gn1+Sd69OhBjx492LBhA7NnzyY8PJzatWuzbt064PeKeenr64uZmRmjRo3CxcWFu3fvcuDAARwdHcmTJ0+2LeYFQaBNmzbMmTOHihUrMmTIENzd3SlfvrwoHEgx1iRyC4IgiPOyQYMGUbt2bTQ0NNi+fTtOTk5UqFCBQoUK4ezs/Nnvq6mpcfHiRfz8/Jg3bx5t2rTh+PHjBAQEcP78eTZu3ChmyFUoFF8VxuC/YGBgwODBg5kxYwabNm3K9uvnZuLj41m5ciWLFy+mXLlybNiwgerVq0tjlITEH4QUY01CIpciLSokPoeyXbx9+5bBgwcTERGBhoYGNWrUwMXFhcKFC5M3b96fWqcPLXa8vb0ZMWIEKioq1KxZk4YNG4rBlX8nbG1tWbt2LXXr1uXFixe8e/cOR0dHMjMzUVdXJygoiEWLFhEeHo6XlxcvFLocvxdI6LNHFKlaW0xeMPbkPfGal7eu5OGFE7SZ6oVx3vwkvosi2P8mxWs2EmOszd55nBc+Bzh8+DAWFha0atWK7du3k5CQQNWqVVm8eDFOTk7ExcWRkJAAQMmSJdm6dStly5blzJkz2NvbY2NjA8DevXu5du0aAwcO/GmJJm7fvs3q1aupUqUKLVu2BGD37t1cunSJ169fA+/dmbt3757j1o6CICCXy7NkWd2/fz8LFizg3bt3LFy4kIYNG+ZY/bID5ZiitJbeunUrR48e5ebNmxw+fJguXbrQqlUrChQokO3vpbi4OM6fP8+KFSuIjIzk77//pnHjxtl2fQmJn4FynNqyZQuPHz9mxowZxMTEsG3bNm7duoWBgQGjRo0if/78n3xXGWvy8ePH7Nixg8zMTGbMmIG3tzeHDh2ifv36PzVWaVxcHHZ2dly7dg07O7ufVu6vyps3b1iyZAmrV6+mQYMGjBo1ijJlyuR0tSQkJHKA32cbVULiDyItLY1Vq1ZRoUIFChcujL29Pbq6usyfPx+QYqz9ySgUCgCWLl0qZlCMjo4mPDyc1q1bs3bt2p9eJ6WoNmbMGAYOHMjIkSM5c+YMbdu2ZcuWLSQmJv70Ov1MChcujKOj4yefz5kzBzs7O5ycnGhQxIrV/VoR+er5F69TtWMfirk0ZL1HGyZXt2VF10aE3L+d5Zyoe1cpVaoU27Zt4/79+1SqVIn9+/ejqqrKoUOHaNq0KVZWVmhqapL5/7HZXr9+LS6Q6tatK4pq8N793MDAAENDw+x4FF+Fubk5devW5eXLlzx+/BgDAwN69erFwoUL6dy5M3nz5qVdu3ZAziReuHfvf2KniooKampqCIIgPs+WLVty5coVRo4c+VtkfFM+46dPn4pW0Q0aNGDs2LHcvXuXoUOHUqBAgSzn/heU762rV68ycOBA3N3dWb16Nebm5uzdu5cePXpkETAlJHILMpmMjIwMdu7cyYoVKzh79iwmJiYMGjSIMWPGYG9v/1lRDSA6OhoABwcHGjRoQEhICMuXL6d+/fp4eXnRvHnznxoP09DQkIEDBzJz5syfVuavSFBQkBj7MTY2Fl9fX7Zt2yaJahISfzCSxZqERC5Cueu5dOlSQkNDkclkqKmp4ebmxqpVq2jXrh21a9eWrNkkaNKkCStXrmTixIl06NCBBg0a0KlTJ2rVqkXPnj1/uqXP3Llz2bVrF7t27cLOzk7chffx8cHOzk4Uc/7ktrsvMJ5ncen/+fu22pBw6QBWVlbUq1ePzp07o6mpiZ2dHXv27KFr16707dsXDw8PevbsSc2aNQkKCsLJyYk3b96IouyH7WLJkiW8fv2aefPmfff9fSuvX78mX758QNZ4QYmJiejp6eWItZqfnx/Hjx9n2LBhXLt2jQoVKojx6ZRkZmb+NgKQsj++efOGMmXKYGBgwJQpU+jYseMPK7NEiRKMHTuWhIQEUlJSePjwIWPGjJGsYyRyPbGxsSxdupS9e/dSuXJlpk2bJia+gc/HRRs4cCBmZmZMmTKFhIQEvL29MTc3p0aNGkDOvDPfvXuHvb09vr6+FCpU6KeWndPcu3ePOXPmcPLkSfr27ctff/2V4xmqJSQkfg0kizUJiVyEUgdXpj0vWrQoqamplC9fngIFCnDp0iXgf1ZLEn8Wyt9dLpczaNAgTE1N0dPTIygoCIDnz5+LMVx+9kR81KhR3L59W1wc+/j40LVrV8aMGYOnpycTJ078qfXJKRQKBenp6cTFxYkWTkqqWmqj9h9/FjUVcClgSO/evWnatCmamprMmzePSpUqYWZmRqdOnShUqBCvXr0iISEBXV1d4L11kNLaSBCETxZ11apVY9euXWzevPm/Vew/oBznlKIavBf7BEFAEAT09PTEz342ZcuWZcyYMZw/f56BAwcybtw4zp07R3x8vHjO7yKqARw5coSAgAAsLS0JDw9n9OjRzJs3j4oVK7Jw4UKSkpKypRzlb37jxg1sbGzo1KkT/fv3p0ePHujo6LBixYrfKsOqxO+Psk1/OM4bGRkxadIkDh06hK6uLhUqVBCTUUHWMU0ulxMdHc3IkSOZPXs2N2/eRF9fHzc3N1FUg5yx2jU2NmbAgAF/lNXapUuXaNKkCQ0bNqRMmTIEBgYyc+ZMSVSTkJAQkYQ1CYlchHICpampiUKhIH/+/ISEhODt7c2DBw+kpAV/OCtWrOD06dMkJSXRsGFDdHR06NixI2vWrKFSpUrY2tpSpEiRHLcKe/bsGcuXLycqKor8+fMzf/58fHx88PX1RUVF5bcUhq9du0br1q0xNzdn/vz5ZGRkfCLAWOmqU9ta95vFNTUVqG2ti5WOehaXIBsbGwYNGoSHhwdjxoyhVatWODg4MH/+fIoWLQpASEgIFSpUAPjEnejly5fs3buXSpUqoaGh8R/u+r/xpbapoqKSo+1W+XxkMhkNGzbk/Pnz5M+fn2nTpjF69GiOHTtGampqjtUvu4mIiGD37t0sWbKETZs2oaKiQs+ePbl79y5Tp05l69at7N27N1vKUv6uefPmJS4ujkWLFhEZGYmRkRHu7u48efIEdXX1bClLQuJnoBwL1NTUUCgUWUJ0FCxYkMWLF7Nr164vJixQKBRs3boVGxsbxo8fz+3b793+f5VkKEOGDOHAgQO8fPkyp6vyw1AoFBw9epTq1avTvXt3mjdvTmBgIKNGjfqp4REkJCRyB5IrqIRELuT169ckJSVhb2/Prl27WLduHfXq1aNfv37Sy/4PJT09nVGjRvHgwQMMDQ1xcXGhTp06ODg48O7dO5KTk9HT08PExCTHA74vW7aMoKAg5s+fj5ubGy4uLiQkJGBvb0/79u2B91aZ+vr6OVbH7EAul3Po0CHmz59PeHg4Q4YMoUePHv96X3ciUzgbmkTmV7ydlaJaeXPtT44pLbyUfPybf6kdKJNNeHt7c+TIETw8PHBwcMjxdpPTKO//yZMnJCUlYW1tjaWlpZgN7tixYxw9ejTXt9uPOX/+PIIgUKtWLdGFOzsZP3483bt3F61Zz58/z+HDh9HS0iIuLo4nT57Qs2dPOnTo8Me3QYncw8CBAzl69Ch79+4VY2xmZmaiqqr6VRsEz58/Z/bs2TkSF/VrGT9+PFFRUaxatSqnq5KtZGRksGvXLubMmYOampq4MfU7WSNLSEhkP5KwJiGRC7l9+zaCIODo6ChaH925cwdBEETrE4k/k4yMDI4ePcrx48d5+vQppUqVom7dulStWhULC4ucrh7wfjIOMGPGDIKDg2nevDk2NjasXbsWMzMztm3bxtq1a9m7dy+mpqY5XNtvJykpiQ0bNrB48WLMzMwYPnw4bm5u3zQpD0/O4FpECi/i01GBLCKbmgoIQGEDDark0cZK578LHV8SKgRBwNPTk9evXzNo0KAvBtbOTpRjWWJiIhEREb9UTC3lcwoJCaFnz56oq6sTFRVF06ZNady4MRUqVCA9PR0NDY3fQvx5+vQpd+/eFZNEfMyHmX6/l2vXrlGlShXq169PixYt6NGjB/7+/jx//pygoCDKly8vZQKVyJXMmTOHOXPmUK9ePVauXImxsTHw73EYMzIyqFevHu3bt6dfv37imJLT1uYfEx0dTZEiRbh7964YUiA3k5yczPr165k/fz4FCxZkzJgx1K9f/5d65hISEr8ukrAmIZGLUC5m/v77bwAmTpxIamoqWlpaTJ8+HRUVFcaPH5+tix6J3MGHv3lUVBRGRkZERESwc+dO9u3bR4sWLRg9enSO1lG5KIiMjKR+/frMmjWLhg0bcuPGDdLS0qhatSqjR4/myZMndOzYETc3N7S1P7XE+lUJCwvDy8uLNWvW4OzszPDhw6latep3TcqTMxTci0klMkVOmlyBpqoMc21VSptooaP+48Sb5s2b8+rVKzw9PalevfoPX1ikpaWhqakJwLp16yhQoAD16tX7oWV+C8qFrYeHBxUrVqRAgQKMGjWKSpUqceLECXr06MGYMWN+G3fFv/76i8TERNatWyd+9iMW9cpxSy6Xc/r0af7++28UCgUjRoygZcuWWcr71UQFCYkvsXbtWnr16gW832jp378/hw4dYsiQIUydOvUfv5uens6JEyfIzMykVatWP6O638WYMWOIj49n+fLlOV2V/8y7d+9YtmwZnp6e4jzEyckpp6slISGRy5CENQmJXERwcDCHDx9m/fr1aGlpUb16dZKSklBVVeXGjRt0796dfv36ScLaH4ZywRkUFMTcuXPJyMggNjY2S/wjpWtlTlvTKNvmoUOHOH36NCNGjMDW1hY/Pz+mTJmCubk5Q4YMoUSJEjlWx2/l3r17LFy4kEOHDuHu7s6QIUN+KWur/0pSUpKY5OBHcuzYMSIjI3F2dqZgwYLExMRgamoqiijXrl3DwcFBtPbIKRISEujevTvr16+nbdu2zJ49m7Jly9K4cWNatmxJr169fhvxx9vbm6lTp1KyZEnatGlD3bp1xWOfyx77X/hQTK1Xrx7e3t6oqKiwb98+5s2bR0xMDAsXLqRp06bfVY6ExM9CEATi4uJYt24d/fv3Z8eOHfTs2RN4/54YOHAgd+/eZe/evTRo0OCrrpnT7+x/IzIykqJFi3Lv3r0sCWdyA2FhYSxatIh169bh6urKqFGjKF68eE5XS0JCIpfy647UEhISn6Cjo0P58uUpWLAgBQoUwNnZmUqVKlGxYkVWrVpF3759ASRR7Q9DudD19PSkcOHCWFtbi7H2fHx8OHfunBj3Kacn6P/H3nnH5bj+D/z9PKXSkjKy9yZl75k4VvY+9ihkZa+EiKMkGeGYR8jeHNnkkL2pEAqptHfP9fuj3/N8dXCs9CT3+/U6r6Pnvu/P9bnXdV/X5/oM5bNpbW2Ns7MzJUuWxMPDg/Hjx9OsWTOcnZ2pUqUKN27cUB2THdd/hBAcP34cKysr2rRpQ/ny5QkMDMTDwyNHGNWALDGqQbqHxrlz53B3d+fNmzfky5dPZZyKiIjgyJEjajWqKd+vyMhIXF1dkcvlmJqaEhAQAKR7iLZr105t+v0IrKys8PT0pFKlSqxevZq5c+eqKtrK5XLkcjnx8fHf1cbNmzfp3r07HTt2VN1zhUJB165d+eeff3BxcSF//vyZdEYSEj8emUyGkZER9vb2+Pv7M3v2bFq2bMmZM2cwMzPj3LlzrFq16ovyML5fLCU7kz9/foYMGcKiRYvUrcoX8/jxY4YNG0bVqlVJSUnh5s2bbNy4UTKqSUhIfBeSx5qExE+IMim5XC4nJSWFwMBADA0NKVy4sLpVk1AjLVq04NSpU/Tr149+/frRpk0bBg8eTJUqVbC3t8+W3jSzZ8/G19eXWbNm0bRpU4QQpKSkMHv2bLS1tXF0dMxWeiclJeHl5YWrqysA9vb29O7dW+V5I/FtpKSkcOHCBZo3b/7B/Y6OjsbQ0FAtz4GyzbCwMJo2bcqNGzfQ0tLiwIEDTJ8+HX19ferWrcuyZcuyvWfJtxATE0NgYCBTp06lWrVq+Pv706RJE9Vizl9//fVd8n18fBg4cCC6urrMmDGDbt26oaenx+HDh9HR0aFly5aZdCYSEj+ej/UBa9euZfHixTRo0IA5c+ZQqlSpjx6bnb5zX8ubN2+oVKkSd+/ezdbj0GvXruHs7MyZM2cYNWoUo0ePJl++fOpWS0JCIocgGdYkJH4ilGF0ixcvJikpiVmzZuHg4MDKlSupU6cOkyZNolmzZj/1AE3i20hMTGTz5s34+Phw79497t27B4C5uTlHjhyhcOHC2fK5ePPmDcnJyeTNm5c1a9bg5+dHjRo1mDRpEnXq1MHZ2ZkWLVqoXffw8HBWr17NihUrqFatGvb29rRq1SrbXc+fmX9XnMwOhiqlDqtWreLVq1cqzy25XE5ISAhRUVEUL14cPT29bKHvj8Lc3BxbW1tMTU05fPgwjx49wtHRkWbNmn2TPOXQUyaT4evrixCCGTNm8O7dO0aPHo27uzsLFy6UwkAlfkpWr17N27dvyZcvH61ataJIkSKMHz+eo0ePcvv27U9Wb1f3d+57sLe3Jy0tDTc3N3WrkgEhBKdPn2bhwoU8fPgQe3t7hg4dir6+vrpVk5CQyGFIhjUJiZ8IpWFt9uzZ5M+fn549ezJ//nzGjRvHnj17SExMZObMmVKOtV+I9yf+devWZcuWLdy/fx8dHR10dXUxMjJi1apV2X7Sv27dOtatW4ejoyOenp5YW1vz+vVr9PX1sbGxUdvzHBAQwNKlS/Hy8qJTp05MmDCBatWqqUWXnIayn/Lx8WHTpk0UK1aM4sWL06RJk2wVkpOSkkKHDh04e/YsK1asYPDgwepWKctQTvQfPnxIxYoVSU1NJSkpCS0trW8u1KCUKYTg8ePHKBQKKlSogFwu5/jx4xw6dAgzMzOGDRuWyWcjIfHjCAwMpEyZMmzdupVVq1bRsGFD8uTJw9OnT+nXrx9NmzYlODiYIkWKZPvv8bfw+vVrKleuzL179yhUqJC61UGhULBv3z6cnZ2Jjo5mypQp9O3bFy0tLXWrJiEhkUORDGsSEj8Ryomog4MDenp65M2bl7t377Js2TIcHR3JlSsX06dPlwxrvxjR0dH89ttvXLx4kaioKK5du0ZUVBTGxsY0bNgQTU3NbD+QnzRpEklJSbi7uxMUFETt2rUpW7YsW7dupWDBgujq6rJnzx4sLS0xNDT8oboIIbh48SIuLi5cuHCB4cOHM3r06GwxWciJtGjRgubNm6Ovr09YWBhJSUkUL16cdu3aUaZMGXWrB6QXcti5cycbNmwAYPjw4fTt21fNWmUtmeVNo/w+LVq0iOvXr/PgwQMqVaqElZUVPXr0yJB/6mf24JH4dQgKCuL333+nWbNm3L17lyVLllC6dGmCg4PZvXs3586dY8OGDV+UW+1nZty4ccjlclWqBHWQnJzMX3/9xeLFizE0NGTatGlYW1tn6/GPhIREzkAyrElI/IQEBwczc+ZM7ty5w9KlS2ncuDEODg6UKVOG/v37Z3sjikTmoJx0bt++nf379zN58mQsLCzUrdZXoTyH6OhorK2tsbCwICAggHz58tG3b19atmzJhAkTVNXVZsyYQe7cuX+ILqmpqezdu5clS5YQFhbGhAkTGDhwYJYl8f+VUPZRZ86cYc+ePbi7uwNw9+5drly5wpUrVxg8eDB16tRRi37K5zIlJYVbt26RnJyMsbExRYsW5fDhwyxatIgOHTrg6OioFv2yiqSkJDQ1NTN9oSYpKYnatWtz+/ZtrK2tMTExwd/fH21tbSZOnEibNm0ytT0JiczmfaNvbGwsly9f5vTp0+zYsYNWrVqxcuVK1b5169Zl+fLlauvPsoqQkBCqVq3KgwcPKFiwYJa2HRsby5o1a3B1daVKlSpMnTqVZs2aSYZ5CQmJLEMyrElISEj8pCiNE0uWLMHDwwMTExOGDBlCz549MTExUbd6X4zSg+XGjRtMmzaNYsWKMWnSJMqXL8/SpUtZunQpaWlpnD59mvLly2d6+zExMaxfvx43NzcKFy6Mvb091tbWktdnFjB+/HiWLVvGokWLmDRpkup3ZeihulA+k46Ojjx79ozr16/TunVrFi9eTEpKCmlpaaSmpqKvr59jFzKePHlCr169uHDhQqaHT+3Zs4ebN2/y+++/069fPy5fvsylS5eYOHEiW7ZsoXTp0pnanoREZvHy5UuKFi36we9CCCIiIjh27BibN28mT548tGvXDl1dXf7880+OHTv2Wdk5IdrAzs4OHR0d/vjjjyxpLywsjOXLl7Ny5UqaN2/O1KlTqVGjRpa0LSEhIfE+kmFNQkJC4ifkzp07BAQEYGFhQcmSJQHYuXMnf/75JwEBAVStWpWtW7f+dN5Wfn5+lCxZErlczqBBgwgPD2fXrl2cOHGCY8eO4eXlRWBgIE+fPsXS0vK72nr58iXLly/nzz//pHnz5tjb21OvXr1MOhOJLyEtLY2dO3eyceNGoqKiGDVqFP369VO3WkB6+Gft2rW5f/8+bdq0Yfjw4XTp0oXt27dTv359SpQooW4Vfyjjxo1DW1ubRYsWZYq84OBg4uPjKV26NHK5nOjoaB48eMCCBQs4cOAAu3btYt++fd9daVRC4kfx4sULdu7cyZgxY9DU1AT+57n2/PlzUlNTKV26NK9evcLb2xtnZ2fy5MnDjh07qF69+icNZ2lpaapq79+auzC78PLlS8zMzHj06BH58+f/Ye08f/4cFxcXtmzZQrdu3Zg0aRLlypX7Ye1JSEhIfA7JsCYh8R5xKQruRCQSmpBGUpoCbQ05BXJrYGasg26unOeRIPHz4u3tzd69e9HX18fOzg4zMzPVtpcvX3Lo0CFsbGzUqOG3k5iYyKBBgyhRogQODg4ZQj8fPnyIoaEhJ06cYMCAAd8k/+bNm7i4uHD48GF+//13xo0bR6lSpTJLfYkvJCAggLi4OHR0dDA0NOTy5cssWLCAcuXKsXXrVnWrx/Xr19m8eTNt27ZlwYIFnDlzhrS0NKpWrcq+ffuoUKGCulX8YURFRVGqVClu3bpFsWLFMkWmo6Mjx48fp3fv3lhbW1OsWDFSUlIYO3Ysvr6+AHh6elKvXj0pt5pEtiQuLg5NTU20tbWB/3mNHzt2jIULF+Lv70+jRo2YN28eFSpU4NatW7x8+ZJ27dp9UmZiYiK5cuX66T3V3mfkyJEYGhri7Oyc6bLv3bvH4sWLOXToEEOGDGHcuHEULlw409uRkJCQ+Fokw5qEBPAqLgXfNwk8iU5GBqS+91ZoykAApQ21aFAwN4X0fu7VRImcQ0pKCr6+vtSrVw9tbW1SU1ORy+U/fVhadHQ0bdu2xc3NjVq1agHpE/3+/ftz584d7ty589WeeAqFgmPHjuHi4sKjR4+ws7Nj+PDh5M2b90ecgsQnUHpsbNmyhR07dhAcHEzz5s0pVKgQo0aNQkNDg1evXlGyZEm1hkWlpqaiqamJi4sLq1atYtSoUYwfPx4PDw/OnTuHt7d3jjb+LF26lCtXrrBt27ZMlevj48PatWuJioqiXbt2dO7cGRMTE0JCQkhMTKRKlSo5+rpK/Px8LPS7R48eDB06FCsrK+zt7dm/fz+dOnVi5syZGBkZAR8vxJGYmMhff/3F5s2bqVChAmvXrs2q0/ihPH/+HAsLCx49ekS+fPkyRealS5dwdnbm8uXLjBkzBltbW+n7LSEhka2QDGsSvzzX3yZwKjgugzHtU2jKoEURPWrk/zHJ078GPz8/zpw5Q3x8PHK5nKioKLp27Ur9+vWliUkOZ8uWLdSqVYtKlSoBHw7Yf+b7r9R9+fLlPHnyhKVLl3L48GEmTJhAu3btcHJyyuDB9rn8VsqJi6urK1paWtjb29OzZ89Mzxkl8XVUqVKFgwcPUqxYMc6fP4+HhwdVq1Zl7ty5atUrOjoaQ0NDtmzZgo6ODlWrVsXDwwMNDQ0uX75MyZIlmT17NlWqVMmxudXS0tIoW7YsO3bsyLRk6/82kl6+fBk3NzdiY2Pp2LEj7dq1k7xOJH4qlM90eHg4Bw8exNTUVFV048mTJwwcOJAePXowevToD459v2hPWFgYAQEBKBQK2rRpk2P6FRsbG0xMTHBycvpmGUIIjh8/jrOzM8+ePWPSpEkMHjz4hxUwkpCQkPgeJMOaxDeRWSGTJUuWZN26dd+dK+lb+RqjmpIvMa7p6+tz+/btTE/ArBxweXp6snHjRpo3b46pqSkKhYLo6GjatWtHzZo1f2rDisTnqVKlCm5ubrRq1UrdqvxwJkyYwPHjx1mwYAFNmzbl6dOnXLt2jSJFivDbb78BHzckhoWFsXLlSlauXEmNGjWwt7enRYsW0nuRDQgODsbW1hYXFxdVTpxXr15hY2PD2rVrKVCggFr0iouLY9myZTx69Ih//vmHmzdvkjt3bt6+fUtQUBAmJiYULVr0p8+B9Dl2796Nq6srFy9ezBR5yvczLi6OEydO8OLFC6pWrUrz5s05fPgwS5cupUmTJsyePTtT2pOQ+F6UU6N/fy/+/a1JTEykRo0a5MuXD7lcTpcuXejUqRPFixf/z+OUHrE5mWfPnlGzZk38/f0xNjb+qmNTU1PZtWsXzs7OpKWlMXXqVHr06JHj+14JCYmfG8mwJvFVZHbIpDoNa6/iUtjqH/VVRjUlmjLoWz4PhXSz9iOvXCGdMmUKhQoVYty4cZIR7RdDoVDg7u6Ot7c3FhYWtGvXjjZt2mRY4c4JlcUgfXD9+++/M3/+fPLnz4+bmxuvXr3i1atXJCUlUa9ePRwcHDK8A0IIAgMDqVOnDl26dGH8+PFUqVJFzWci8fbtW/LkyaPyFJw/fz737t3j999/p27duty6dYspU6bg5+enVj1jY2Np164dDx8+xNramhEjRlCzZk0AXFxcGDlyZI73lmjUqBHjxo2jW7dumSJPuSA0depUoqOjiY6ORgiRIY9ebGws+vr60vdMQu287zH2999/8/TpUxo1apThO6LcZ8uWLdy+fZtZs2Zx7Ngx/Pz8iIuLo3r16gwdOhSZTPaB91lqaipr1qyhfv36lCtXDn19/Sw9v6xk2LBhFCpU6Is9kRMTE9m4cSN//PEHhQoVYtq0abRt21bqEyQkJH4KJMOaxBfzI0Im1WlY2/0kGv+o5G8+vnweLbqUNsxEjT6PcjC3adMm3r59i62tLXp6etJk5BfEz8+PixcvcunSJZo3b06XLl0ICwujcuXKQHo+kvr166tZy2/n38ZBd3d3rl69ysiRI6lXrx5paWk0a9aM3bt3f+DhlJycTFJSEgYGBlmttsQnmDdvHg0bNqRIkSKqqpDu7u4EBQVx5swZSpcuzejRo2nRooXaDcOHDh3CxMSEc+fOsWPHDipXrkzx4sV5/Pgxu3btUpteWYGfnx/du3cnICAgUz1qIiIiaNy4Mffu3aNNmzbY2tpibW2Np6cnderUwcLCItPakpDIDLZv3878+fNp164ddnZ2FC1aNMP2Z8+eMXjwYPr27cuQIUNISUnhwYMHnD17luTkZOzt7T8qNzw8nBYtWmBgYEDevHnR0dEhNTWVJUuWUKZMmaw4tSzjyZMn1KlTB39////MhxYVFcXq1atxc3OjZs2aTJ06lUaNGmWhphISEhLfz88fxC+RJXxtyGSqAMvqFVi16/AXt/HgwQNKlSrFtm3bOHToEObm5hgZGdGgQQNu376t2q9kyZL88ccfmJmZoaenx5AhQ3jz5g2//fYbBgYGWFpa8u7dO9X+3bt3x9TUlDx58tCkSRPu3btHXIqCJ9HJ7HQYzf6Fk9k4pjcOjUqyon9rwl88BeBdyHOm1chPWmqqStaaYdb47d0CwOW7D2ncpCl58uQhX7589OzZU7WfTCYjICAAgMOHD2NhYYGhoSHFihVjzpw5Gc77Y/opGThwIKNGjaJdu3YYGBhQr149AgMDiYiIYPLkyZQpUwYzMzNq1qxJwYIF+euvv4B0A5xEzkS5FlK7dm2GDh3K5MmTcXd3Z8qUKUyePJnevXvTqVMnPDw81Kzp9/G+YSUqKoqLFy9iY2NDvXr1ANixYweampofDQ3R0tKSjGrZjFmzZtGiRQvmzZtHpUqV8PLyYujQocyYMYODBw+yYsUKWrRoAaAWo1paWhqQPlkuVqwYqampDBgwgH379lGuXDmCgoJwdHQEcnb/unTpUsaMGZPpYWpJSUl069aNnTt3oqGhgbW1NWlpaSxduvSrC5FISGQ2ync6LS2N5ORkhgwZwp49e9izZw+LFi2iYMGCGfYXQqgKBS1atIi//vqLXLlyYWZmxsCBAxkxYkQGue+jp6fHhQsXWLt2LbNnz2bo0KG0adOG/Pnz//gTzWJKly5Nx44dWbZs2Ue3v379mmnTplG6dGnu3LnD8ePHOXTokGRUk5CQ+CmRDGsSn+VVXMpX5yGD9LDQG2GJvIpP+ey+169fp3Xr1ixfvpyKFSsyePBgPD09CQ8PZ8SIEXTs2JGkpCTV/rt37+bEiRM8fvyYgwcP8ttvv7FgwQLevn2rCpVT8ttvv+Hv709oaCg1atSgb9++3IlIROnfdevvfbQcPonZZwIwKVaKv1cs+KLz+3ulM1UaNufdu3e8fPkSOzu7j+6np6fH5s2biYyM5PDhw6xatYp9+/b9p37vs337dhwcHHj37h3lypVjxowZDB48mKCgIK5du8aRI0c4cOAAly9fplOnTgA5IvGtxMd53zNRX18fMzMzChYsyPjx43FycqJFixbUq1fvAwPuz0yePHlISkri9u3bpKSksH37dvz8/LCxsSFv3ryEhoaqW0WJ/0A5ufT19eWvv/5i2bJlrFq1CktLS44ePUqxYsUoVKiQWnXU0NBACMGQIUNYuXIlAwcOJCgoiOLFi+Pg4MDWrVtVoWA5tX99+fIlx48fZ8iQIZkiT7kIEB4eztWrVwEYPnw4VatWBdLDgevVq0f58uVztLFSInuTlpameqdlMhlaWlqqfJ5hYWEIIVQLOMrnVCaTUbx4cXx8fFi2bBlubm5YWVlx+vRpDAwMVOGdH+srXr58iZeXF5cvX6Z27do0adKEESNGYGiYtREQWcWMGTPw8PAgKipK9VtgYCC2trZUqlSJmJgYrl69yl9//YWZmZkaNZWQkJD4PnLm6FAiU/F9k/BNecgAFMCl1wn/uc/58+fp2LEjmzdvpn379qxZs4YRI0ZQt25dNDQ0GDBgANra2vzzzz+qY+zs7ChYsCBFihShcePG1K1bFwsLC3R0dOjcuTM3btxQ7Tt48GAMDAzQ1tZmzpw53Lp1i6dvIlTnVKV5W4pVrYGGpibmv3Xl1eO7X3RuMs1cPH0WREhICDo6Op9cYWvWrBnVqlVDLpdjZmZG7969OXv27H/q9/4ApHPnztSpUwdNTU369u3LzZs3yZMnD8WKFUNfX5/U1FRSU1NJS0tDiuz+tVAO+Pfs2YOZmRlmZmYMGzaMqVOnqpLC/+woJzLu7u4cOHCABg0acPLkScqWLUvLli3ZvHkz9erVw8fHJ8P+EtmDd+/ecebMGSIiIhg2bBhPnjyhXbt2+Pr6MmXKFBYvXsygQYPUqqOy33R3d6d27do4OztjaGhI3bp1CQsLY9OmTaS+57mcU/Hw8OD3338nT548mSrX1dUVf39/HB0d+eOPP3j37h2lSpUiOjpatQAgpTKQUBcaGhq8fv2agQMHMmPGDPz8/Ojfvz8NGjT4YEylNJR5eHjg6OjI/v37adSoEVevXqVNmzaMGDGCmJiYT7YVHBzM7NmzOXjwIMuXLwfSw6/Hjh37405QzZQpU4Z27drh7u7OrVu36NOnD3Xr1sXExIRHjx7h4eFBqVKl1K2mhISExHcjGdZyMIsWLaJIkSIYGBhQoUIFTp48iUKhwNnZmTJlymBiYkKPHj2IiIgA0kNgZDIZGzZsoFixYuTNm5dlK1Zy9tJllvVoimOTMux3nqKSH/7iKWuHd2Zu8/LMa1GB7TNsSIiJ+kCPwOhkzvn+Q/369TEyMqJQoUKMHj2a5OT0/GarV68mODiY+/fvU65cOdauXaua2GhqaiKTyQgMDOT58+cqme+75efOnfuDv2NjYwFU1YTKlCmDoaEhJUuWBNIrBioxMPlffiYtHV2S4uO+6Pr+NnY2aQoFderUoUqVKqxfv/6j+12+fJnmzZuTP39+8uTJw+rVq1Xtf4l+pqamqn/r6uoSGxvLs2fP+P333ylQoAANGzakZs2alCtXjiNHjgCSceFXI2/evDk2z55cLictLY3ixYuzefNmdu3axYoVK+jWrRvLli1j2rRpNGvWjB07dhASEoJcLpee/2yEQqFg586dFCtWDAMDgwyVkrt06cLdu3dVYULKcMysRvneJCcnY2FhweTJk7GxsQFg586d7Ny5M8dX8IuLi2PdunWf9Lz+FmQyGW/evOHdu3eULVsWgJ49e6ryJbq4uFCyZMkc23dJZF/e72vOnz9Phw4dsLKyInfu3MyYMYMzZ87g5ORErVq1VM+m8ruyePFi/Pz8CAsLY8yYMUyfPp2jR48yYcIE7t27h4GBwQd9mdJAd+nSJYyNjdm8ebMqZ1t8fDyPHz/+QK+cghACKysr5s2bR5s2bbCwsODJkyfMnz9fbRWgJSQkJH4EkmEth6JcBfLz8yMmJobjx49TsmRJli9fzr59+zh79iwhISHkzZuXUaNGZTj28uXL+Pv7s2PHDibbT+DUuqUMWb2LcbsucOfEAZ5cuwikfyybDR7L9ON3GL/7IlGvg/FZvfgDXWTAk9j0XCphYWFcunSJkydPsnLlSiDdsAbpFdeuXbtGly5dEEJQu3ZtHj9+TGRk5DeHinh5ebF//358fHyIiori2bNnAGh9wZOfK7cuACmJ//O4iw3/X8iZQb6CjF3kQUhICJ6enowcOVKVV+19+vTpQ8eOHXnx4gVRUVHY2NioBlmf0u9TnmfKQdfSpUtp1aoV06dPZ86cOYSHhzN27FhVXilpkvJrcOvWLeLj44Gcfc+VObfy5ctHiRIluHjxIgMGDODevXvcvn2b9evXU6pUKUaPHg3k7Gvxs2FiYsKqVavo3bs3Ojo6mJmZ4ebmBsDy5csZPHiwKgRK3ZVsra2t2bVrF76+vjRr1gyFQsGmTZuYOHEikDMnvUo2bdpE48aNMy15urJfunnzJl5eXixevJhbt25hYGCAjo4OJiYmqn2l91Uiq4iLS1841dDQIC0tjfDwcAA2bNhAjRo1OHDgAHp6enh7e6OlpZUhh6dcLic+Pp4DBw6wZs0aNDQ06Nu3L7lz52bw4MG4u7ur9v9UX5aYmEj+/PnZtGkTJUqUAFCFnOc0FAoFBw4coGHDhsyZM4caNWpgY2PDpEmTcmzY689KXIqCf97Ec+BZDDsDozjwLIZ/3sQTnyItUkpIfA2SYS2HoqGhQVJSEvfv3yclJYWSJUtSpkwZVq9ejZOTE0WLFlWFHu7atStDmMusWbPQ0dHBysoK7dy6mLXujL5xfvIUKERJi7qEPLwDQL7ipSlXrxmaWtro581Ho362PL3u+4EuqQJMyptRr149NDU1KVmyJCNGjFCFQyqNQZqamixYsIDJkycjl8upWLEipUqVQlNTkwoVKnD58uWvvg4xMTFoa2tjYmJCfHw806dPB8BYRwPNz4zl9fPmw7BAIW4c2YkiLY2r+7YS/vKZavs9n/2Id6+BdI+hj5VVV+pgbGyMjo4OV65cwcvL67P6fQ65XI6RkRE6Ojoqj8O0tDSePk0vvCCFhP4aDB48mEuXLqlbjUwlISGBPXv2kJDw6RDyJUuW0LBhQ/bs2YOJiQkvXrzgzZs3VKpUCUifqIeEhJCS8vn8jhI/jvf7oVWrVnHmzBmWL1/OwYMHqVOnDrt27VLl81K3l2FycjLly5dn2LBhmJubs3DhQlq0aEHz5s1p1qwZoH7D349CoVDg5ubG+PHjM0XeP//8w+rVq7l79y6tW7cmJCSEpk2b0rlzZwYOHMitW7cypR0Jia/hwIEDqgVegHHjxnHkyBEaNWqEqakpEydOxMPDg7Vr16pSa/x7TBcWFoatrS1v3rzh6tWrLFiwgMWLF1O5cmVVYZ2Pjb+UxuOWLVuSN29edu7ciZaWFgsXLmT//v106dIlw34/MykpKWzevJlq1aoxZ84cxo8fz8OHD9m4cSMrVqz4z1BZiazlVVwKu59Es/JeBBdexXP/XRKB0Sncf5fEhVfxrLgXwe4n0byKk8ZSEhJfgmRYy6GULVsWNzc35syZQ4ECBejVqxchISEEBQXRuXNnjIyMMDIyolKlSmhoaPDmzRvVse+HVebSzo3+e6GSubRzk5yQvuIXEx7KtqnDWNi6GnMal8J7pi3xkREf1edpwGPat2+PqakphoaGTJ8+PUO4I8DWrVs5evQo+/fvV4U15s2bl7Jly/Lw4UNVeOfX0L9/f0qUKEGRIkUyDHwq5NHmS0xPXWa5cn7zCuY1L8+bJ48oYVZbte35nev0aGCGpqYmtWvXpnDhwiovvPdZuXIls2fPxsDAgLlz59KjR4/P6vcplIMuIyMj4uLiqFWrFr6+vowZM4abN2+SL1++LzgriZzAjRs3CA8Pp3nz5upWJVMIDQ3FwcGBkiVLsmHDBl69evXBBEVpfNm5cyezZs0C0hPib926FZlMRteuXYH0SryjRo1ShUZLZD3K8L74+HicnZ2ZNWsWW7dupVy5cpw8eZINGzbg6elJw4YNAfUUBFB6oJ04cYKRI0fSu3dvSpQowZIlS3BwcGDbtm2qHGDqNvz9SI4cOYKBgQGNGzfOFHmampo8efKEJUuWcPPmTXR1dXFycuL69evkzZuX9u3bZ6jcLSHxIwkKCiI0NJQ8efIQHBzMqVOnSE1N5fXr1zRq1AiZTEZYWBh3796lXr16GBkZMW/ePLS1tYH/vftJSUkUL16cvn37oqOjg6amJq6urqxcuRJdXV3q1KkD/LdxrFChQgwYMIC+ffvy+vVrUlJScHV1pXXr1sDPXRglPj4ed3d3ypYty6ZNm3Bzc+PatWt0794dDQ0NKlasiKWl5UfHyRJZz/W3CWz1j8I/Kpk0wQe5tFMFpAnwj0pmq38U19/+d75sCQkJkAnJtSXHEx0dzYgRI9DU1OTKlSusX79eNZl5n2fPnlGqVClSUlJU+WTyFSpCl7krKV0rff8dM2zJX6osLYbas9txLMmJCVhPXYRunrzcO32EA4umMu3YbQAWtatB19lLKVu3KdtGd6NV/Vo4ODhgYGCAm5sbu3bt4sKFC0D6QMTf31+Vh6VRo0YMHTqUgQMHAjBz5kxev37NunXrMu267H4SjX9U8jcfr3j9hBlt66r+1tDQYNSoUZ8sK56ZpKSkkJaWho6ODlevXuXkyZM0btyYBg0a/PC2JbIHY8aMwdjY+Kev/vngwQNcXV3ZtWsXPXr0YPz48VSsWPE/j1EoFKSmpuLl5cX9+/fJlSsXrVu3pkmTJrx8+ZL58+fTokULGjVqROHChbPoTCTeJy0tDQ0NDezs7DA0NOTZs2dcvXqVhg0bUq9ePZo0afLZ+5xVlCpViiVLlnDx4kUOHDhAo0aNGD58+C/Tn1paWjJw4ED69ev33bLi4uLQ09MD0pOy16xZE7lcTmpqqmpcodxHoVD81IYEiexPUlISCxYswN/fHy8vL9atW4e/vz8WFhZcu3aNP/74Q7WvtbU1MTExtGjRgsmTJ6OlpZVB1qxZs0hISGDs2LEUK1aMM2fO4O7uTlxcHH/88QdmZmaffabDwsI4dOgQBgYGFChQAAsLC1UF0Z+ViIgIVqxYgYeHBw0bNmTKlCnUrVv3o/vev3+f5s2bExgY+NOf98/M9bcJnAqO+8CYNq1Gfuz3XSZf8dLsdZqIYYFCtBxmD4CmDFoU0aNG/txAem7CoUOH8ujRo6xWX0Ii2yKNaHIojx494tSpUyQlJaGjo0Pu3LmRy+XY2NgwY8YMgoKCAHj79i379+//pBy5DDQ+sfiWFB+Ltq4eOvqGRIW+4vxmj4/upymDlPhYDA0N0dfX5+HDh6xateq7z/F7aVAw92fDQT+FpgwGNbVgwoQJ6OjoAOkTSYVCwatXrzJRy4wo7eCnTp3i3r17ANSoUYMpU6bg7++vqowo2ctzNomJiXh5eakMzz8bQghOnTpFu3btaNasGUWLFuXx48d4enp+kbFFLpejpaVFQkICefLkwcbGhiZNmgBQtGhRZs2aRdu2bSWjmhrR0NAgIiKCa9eu4eTkRHJyMlOnTqVkyZI4ODhw7do1teqn7COjoqLo27cvXbt2xdXVlTt37lCsWDHatWuXobp0TuX27ds8ePAggyf199ClSxdVVevatWurjAyampoIIUhLS1MZ3iSjmsSPRKFQoK2tTa9evciVKxdr166lX79+aGpqsm7dOv766y+WLl3Krl27SEtLY/369cyYMYNp06apjGpKr9bTp0/j6+vLjh076NOnD5s2bcLCwoI9e/awa9cuzMzMPho6+j6pqamMGzeOnTt3sm/fPpycnKhUqRLnz5/PkuuR2bx8+RJ7e3vKli3L06dPOXPmDHv27PmkUQ2gcuXKNGvWLFvMAX5VXsWlfNSo9m86z1iiMqpBugfbqeA4XsWnh4U2btxYMqpJSPwLaVSTQ0lKSmLq1Knky5cPU1NTQkNDWbhwIWPHjqVjx45YWVlhYGBAvXr1/jN3WS657JMhky2HTyL4wW0cm5Rm05jeVGnR/qP7CcDNZQleXl4YGBgwbNgwevbs+f0n+Z0U0stFiyJ6X21cU67aFNLNxR9//EH9+vXR0NDA0tISIQRVqlRh4MCB3L59O9N1Vk4G79+/z6pVq3j+/DlyuZwdO3awfft2VeiCRM7mwIEDmJubq6rI/iykpKTw119/UbNmTUaNGkWnTp149uwZDg4O5M+f/4vlKN+DoUOHMnXqVIoVK5bh9yJFiqjyYeXkEL7sijJnZ3h4OLa2tgQGBhIeHs6gQYOYPXs21apVUxlC1bUIoAzX2rBhA+fOnWPcuHHcu3eP3LlzM2/ePEJDQ7GwsFCLblmJm5sbo0aN+sA751u4du0ajx8/Vi3c/RuZTJZj89RJZD/kcjnJyclUqlSJvn37smfPHvz9/Rk6dCjFihXD0tISHR0dvL29sbS0ZP/+/bRs2TLDM6r899SpU1myZAkvXrxg/PjxLFq0iA4dOuDn5/fFRaNevnzJrVu3OHz4MFu2bOHYsWN4eXmp0hr8LDx69IghQ4aojIm3bt1i/fr1qhynn2PWrFm4uLioCpxIZC2+bxI+a1T7FKkCLr1OyJCXW0JC4n9IoaASn+V7Qya13oUQsGs11apVw9DQEENDQypUqED58uUzUctv51Mu0R/j367QkB5q265dOzw8PKhevToRERF4enqyfPlyqlatir29PVZWVpmWlFaZu2j+/PnExMSQJ08erl69ytSpU6lTp45qu0TOpU2bNvTv358+ffqoW5UvIjIykjVr1rB8+XLKlSuHvb09v/32W6Z5rCjDDpXcu3ePffv2MWPGjEyRL/Hl+Pr6cuLECfr27asK7Y+IiKBHjx5UqFABXV1dXr58ybZt29TWVynb3bhxI25ubgwYMIA3b94QERFB2bJladWqFRYWFjk+VFFZ8MPf3z9Dlc7vYfXq1ezbt4+yZcsyaNAgatasqdqW06+nhPp5v09JSUmhUaNGjBkzhr59++Li4oKfnx9btmxh//79+Pr60r59e1q0aEFCQgK5c+f+qMwnT54wdOhQ3NzcMDMzA9KNZJ06dSIiIgJnZ+f/9Ph88+YNjx8/5t27d+zYsYMpU6ZQuHBh8uXLx6VLl5g9ezYnTpz44DuW3fDz88PZ2Znz588zatQoRo8e/c39Rrdu3WjQoAETJkzIZC1zJsqib1u2bOHVq1d06tSJVatWoaOjw6FDh5g5cybPnj2jcuXKrF69WvWclixZktGjR7N582aCgoKwtGpNzYmuyLXSI23ObfLgwtZVgAyrkdPYPXecKhR0p8No8hQojNWo6Ty5epEdM21p0GsoF7aupn1rK0YMG0q/fv14+fIlAM7Ozqxdu5bQ0FCKFSuGk5MTnTt3BmDjxo2sW7eOevXq8eeff2JkZMTKlSv57bff1HI9JSR+FNIIR+KzfG/I5NsrPqxatYrRo0czYsQIunXrhq2tbeYq+R3UyJ+bvuXzUD6PFhoyPjhXzf8Phy2fR4u+5fNkMKoBGBoacv78eapXrw6AsbEx06ZN4+nTp/Tt25dJkyZRrVo11q9fT1JS0nfpGh0dzatXr3j16hUTJ04kJSWFvXv3MnbsWCpXrkxCQoJkVMvhPH/+HD8/P9WAJTvz7Nkzxo0bR+nSpbl9+zb79+9XhYBm5gT735ORKlWq0KZNG+Li4jKtDYkvI3fu3Ny+fZsxY8bg4uLCkydPMDY2ZtmyZWhoaPD27VsWLlwIqN9bLTQ0FA8PD8aPH8/AgQNp1aoVr1694tChQ0DOD1VctWoVPXr0yDSjGsCQIUNYsGABhQoVwsXFhdevX/P6dXr1bLlc/t3fQAmJ/0L5bisUCnLlysXEiRNxcnIiIiKCYcOGoa+vj6urK926dUNLS4uXL18ihFCl9PhYn1S6dGmsrKzw8vIiKCiIxMREgoKCqFq1KmvWrOHWrVv/6Rl99epVFi5cyN69e3n16hWTJk3Cy8uLsWPHMmvWLPr3759B9+yEEAIfHx8sLS3p2rUrTZo04enTpzg4OHxXvzFr1iz++OOP/6z+LZGRrVu3cvz4cQIDA3n8+DHz58/nxo0bDB48GE9PT8LDwxkxYgQdO3bM0M96e3tz7Ngxnj59yrVbt7l2YDsAjy6e5PyWlQxeuYuJ+y8TcOXcf7YfGx5KfFQkM47cYITTh7mky5Qpw/nz54mKisLBwYF+/fplSI1z+fJlKlSoQFhYGJMnT2bIkCFS2hqJHIfksSbxRXyNV5cSpXeXRT4d6tevz5UrVxBCoKmpiY+PD02bNv1xCn8j8SkKbkck8jYhjaQ0BdoacvLn1sDMWAfdXN82yRJCcPLkSVxcXLh58yajRo3C1tb2mwYlM2bMwMPDg4IFC5KamopMJlOF17x48YL169fTq1cvyTMgBzNv3jxevXqVrStrXblyBRcXF3x8fBgyZAh2dnaqcM0fhY+PD9ra2p+tbCh5dGYNp06dYtOmTYSEhNCxY0esra0pXry4aru678Px48fp3Lkzffv2Zc2aNSpdHj58SJ48eShUqFCO7kcTExMpWbIkZ86cyfQiEgqFgpSUFIYMGUJMTAxyuZySJUsyfPhwFi5ciKen5ye9gyQkvhalp1daWhpRUVFMmjSJ0aNHq0K5x4wZw4sXL9i7dy9XrlxhwoQJzJ8/nzp16qCrq/tRmcr+KSUlhcTERGQyGVOmTCE0NBRdXV1u3rzJ2rVruXLlCn5+fmzatOmT+gkhiIiIICIigufPnxMYGMjz58+Ji4vD398fGxsb2rdvn636m7S0NPbu3YuzszPx8fFMmTKF3r17Z0rIuJLOnTvTrFkzxo4dm2kycyolS5Zk6tSp2NjYAOmVnO3s7LCysiJfvnzMmzdPtW+FChVYs2YNTZs2pWTJksyfP19VmKbLiHEEh0fSecYSds0Zg75xPtqMmQ3A26BAXDvX+6TH2vqR3XE4/4Rc2jpUyauNwbNrGTzW/o25uTmOjo5YW1uzceNG5s+fT0BAAJBeQVZPT49Xr15hamr6Iy+dhESWoqluBSR+DpReWt8aMunt7U3lypVJTEzE1NSUvn37snDhQvr27ZttBhIAurnk1Cv48YHWtyKTybC0tMTS0pJ79+7h6upK2bJl6dWrF+PHj/+qkFgnJyecnJw+u192uqYSmYdCoWDDhg14e3urW5UPSEtL4+DBg7i4uPDixQvGjh3L2rVrMTQ0zJL2K1WqxOXLl/8znObixYuEhYVhbW2dJTr9arxvLGvUqBEtWrTA19cXNzc3jh49SuvWrbGzs0Mul6vduNm6dWt27NiBm5sbderUYfTo0QwYMCCDkSkn96NeXl7UqFHjh1RmlcvlaGtro6OjQ926dalTpw4HDhzAxsaGxo0bS0Y1iUwhPj4eXV1dVX+voaGBsbExurq6rFmzhrlz55I/f34WLFigCkEbMmQIkydPVoWlw4dGfqWB6+bNmyqvy0aNGuHu7s6TJ0949+4durq6GBsb06tXL65cufKfenp5eREfH0+RIkVo27YtLVu2VG1LSUlRebtlh/4mKSmJv/76i8WLF5M3b15mzZpFhw4dfohus2fPpn379owYMULlMSjxad5fnCxRogQhISEEBQWxadMmli9frtqWnJxMSEiI6u/3DVca2jokx6d78ke/fU2RStVV2/IWKvqf7evlNSGXdvp9SkpTYPCv7Zs3b8bV1ZVnz54BEBsbS1hY2Ef1UL57sbGx/9mmhMTPhvp7cYmfhu8JmSxevDguLi5oampy6dIlvL29cXd3p379+ly6dCmLz0R9VKlShT///JMHDx6QP39+GjVqhLW1NefOnftql+iAgAB27tzJhg0b2Lx5MytXrlStBknkTM6ePYuBgUGGvEXqJj4+npUrV1KxYkUWLFjA6NGjCQgIYPz48VlmVIP0ggUdOnT4qFEtLS2NQ4cO8ffff+Pr65thsCeROSgUCmQyGc+fP2fAgAFMmjSJ3r1706BBA7y9vRk5ciRaWlpqnTwqK/y9ffuWoKAgTE1NOXnyJFOmTGHt2rUUK1ZMNSnIyQghcHNzY/z48T+0nWXLlmFnZ0fdunWZOXMmf//9N46Ojj+0TYmcjxCChw8fqnI4hoWF0aZNG8aOHcuOHTtYtmwZISEh7NmzB4VCgb6+PhYWFkybNo2IiAg6duxIoUKFVGOufxv5lX2Ug4MDFStWxMXFhcePH1O1alXOnDlDnTp1qFq1Krlz58bT05N8+fL9p75paWlERERw/fp1VcVcpTEtV65c2aLoVExMDC4uLpQuXZqdO3fi6enJpUuXsLa2/mF9toWFBbVq1WLdunU/RH5O48WLF6p/P3/+nMKFC1OsWDFmzJhBZGSk6r/4+Hh69+79URma8v8964b5ChL1Jlj1d+Trj3ueqXjvPdHWyPhMBAUFMWzYMDw8PAgPDycyMpKqVatKoZ4SvxySx5rEV1FINxddSuf6ppDJ4cOH06lTJwoWLEjRokW5fPkyf/31F926daNp06YsWrToh4eLZRdMTU2ZO3cuU6dOZfPmzQwbNgxDQ0Ps7e3p2rUruXLl+uhxypXU9evXs3LlSsqVK6caIIaHh1OzZk3Kli2r9jAriR/D+vXrGTx4cLa4t69fv8bDwwNPT08aNmzI+vXradSokVp1+9R7o6GhgY6ODqVKlaJjx44YGxtnsWY5H+V9nzZtGo0bN+bly5eEhoYC8ODBA1q3bq26P+rqn5RG12HDhlGlShUOHz7MggUL6NatG926dePs2bM/XaXdb+HkyZMoFAosLS0zXfb791ZPT0/1t+SlJpFZyGQyKlasSI0aNbCyssLMzAwrKytMTU1Zv349UVFRLFmyhJEjR5KUlMTNmzcpWbIk586dy9D3f6wPet9brUSJEowePZo8efKwdetWzpw5w6hRowgPD2fq1KnkzZuXVq1afVbfPn36EBcXR3x8PHny5AGyh3capC8yuLu7s2rVKiwtLTl06FCWVkOePXs2nTp1YtiwYdnCwJidWbFiBe3bt0dXVxcnJyd69uxJly5d6Ny5M5aWltSpU4f4+HjOnDlDkyZNVNVq30dPU66yj1WzsmbXnDFYtO9J3kLFOOm55Iv00JRB/twaJL73W1xcHDKZTFXdfcOGDdy9e/d7T1lC4qcje/TsEj8dypDJDiUN6FYmDx1KGlCvoO5/5iGTyWQULFhQ9bdcLqd///48evSIMmXKYG5ujoODwy+VcFxXVxcbGxsePHjA7NmzWb16NWXLlsXFxUW1svk+ytWfS5cuMXLkSLZt24arqytLly5l8+bN1K1bF8ieSXAlvo+oqCgOHjxI37591arH3bt3GTx4MJUqVSIiIoKLFy+yb98+GjdunC2fO6VngKWlJf379ycyMjLD7xKZg0wmIzIykpiYGGxsbLh69aqqSM369evZvn17hn2zGmXfuW3bNvT09BgyZAipqak0a9aM6Oho9u3bR5MmTbJcL3WwdOlSxo0b90Puw7lz5zJ8u7JjnyDxc/LvPvvAgQMEBgZy584dJkyYQJ8+fXB3d2fNmjXkyZOH2bNnExcXh6amJnPmzKFixYqf9aBRGrxWrVrF0aNH8fT0JCoqCiEEzZo14969e0ycOPGj+nwKTU1NVd7G7MKzZ8+ws7OjQoUKvH37ln/++Yft27dnqVENoGbNmlSvXp3169dnabs/I3369MHKyorSpUtTpkwZZs6cSa1atVi7di2jR48mb968lC1blo0bN35SRoHc//OnqdDQkoZ9RrBuRGeWWNehTO1GX6SHAMyMM4buVq5cGXt7e+rXr0/BggW5c+cODRs2/JbTlJD4qZGKF0hkG4KCgpgyZQoXL15k4cKF9OnTJ9us6mUlV69exdXVlePHjzNw4EDGjRun8uRTrqYuWrQIY2Njhg0b9slwBomchaenJz4+PuzcuTPL21ZWBnNxceHWrVuMGjUKGxubz4bAqJv3k0E/evSIVatWERwcrCoA8l/52CS+npcvX+Lh4cGlS5cwMjJi//79REZG0qhRI3x8fDA1NVW7N627uztmZmbcvn2b8PBwHB0dOXLkCEuXLuXEiRNq0yurePjwIU2bNuXZs2eZ7kWWlJREuXLl2LVrF3Xq1MlU2RISSq5fv46GhgbVq1fn7NmztGjRgrdv32JsbExaWho2NjYMHjyY+vXrZzjuc8UB/t03/f333yxevJjy5cszYMAAqlatip6e3g87r6zg7t27LFq0iCNHjjBs2DDGjh2rdoPflStX6NatG/7+/pLX2icoWbIk69atyxQv491PovGPSv7m48vn0aJL6axL8yEh8TPx61ktJLItJUqUYPv27Wzfvp1ly5bRoEED/vnnH3WrleXUqlULLy8vbty4gUwmo379+qrcQEojmo6ODvb29pibm9OpUyc6d+5MgwYNOHXqVIb9JHIOyjDQrCQ5OZlNmzZhbm7OuHHj6NGjB0+fPmXmzJnZ3qgG//M+8Pb2Zv78+YSEhGBkZMSYMWMAJKNaJqDsa+7evYuHhwe2trYULFiQXLlyMXnyZMaNG0erVq0wNTVV5WFTJ40bN2bChAnMmTOHoUOHAuDh4cGAAQOA/+Vhy6ksW7aMESNG/JDQzLVr12JmZiYZ1SQyDSFEBs+wmTNnMmDAAAYPHoy3tzdNmzZl9OjRtGzZkoiICEJCQrhy5coHaQE+Z1RT9k2RkZF4e3vj5OSEkZERe/fuJX/+/IwcOZINGzb8sPP80fj6+tKxY0csLS2pXLkygYGBODs7q92oBlCnTh2qVKnyn55WEplHg4K5P8iR/aVoyqC+qRTWLyHxKSSPNYlsiUKhYMuWLUyfPp1mzZrh7Oz8y+Rf+zfR0dHo6ellMAL4+/sTHByMlpaWakCYkJCAmZkZBQoUUKO2Ej+Cu3fv0qZNG4KCgrLEGBQREYGnpyceHh4qF//WrVur3SjytURFRTF//nzevXtH06ZNqV+/PmXLlqVVq1Z06NBBZWCT+HaUE9aVK1eSmJjIhAkTuH37Nk+ePOH06dN07tyZevXqoaOjo3ZvNSWnTp3C29ubx48fo6uri46ODrt27VK3Wj+ciIgIypQpw4MHDzJUaMsMEhISKFu2LAcPHqRGjRqZKlvi1+R9Y9jjx4+Ry+Vs2LABJycndu3axcGDBxk8eDBNmzbF3Nyc+Ph4LC0tKV26tCpc80tR9k3Dhw/HxMSE169f8+7dO/bt2wekezynpqZSpUqVb+7HXr16hb6+/kdzX/0IhBAcPXoUZ2dnXr58yaRJkxg4cGC2zHd46dIlevfuzZ1HAdyLTCb0vfzNBT6Tv/lXILM81oKCgnB1deWNljG1+o4i9SssAJoyaFFEL0NhOgkJiYxIhjWJbE1sbCyLFi1i5cqVjB49msmTJ//0rviZiRQG+mtgb2+Pjo4OTk5OX3VcfHw8y5cvp1q1ajRr1gxdXd3/nBQEBgbi5ubG1q1b6dChAxMmTKB69eof3fdn4O3btyxevJhWrVpRr149DA0NiY2NpXv37gwaNIj69etTrFgxKST0O3nw4AG//fYbHTt2xN3dXd3qZEB5b2/cuMGuXbsoWbIkFSpUQENDg7x585KcnEylSpXQ1tb+rFfLz46zszMPHz78IZ4hS5cu5dy5c+zduzfTZUv8WiQkJGQw/vz5559MnTqV2rVr8+jRIwIDAwFwdHTk3bt3TJ8+nZiYGAYMGMCOHTsoUqQI8PVFUm7fvs3IkSO5cOECTZs2Zfbs2bRs2ZKNGzfSqlUrldyvRdmvrFixAktLSypUqACk5yNUFqDKTFJTU/H29mbRokUATJ06le7du6OpmX3r1b2KS2HX9QASDfIjgwwGH01Zel6v0oZaNCiYm0J6Hy9SJPFxkpKSWLduHWvWrOHRo0ckJSVhZmbGBp9/OBUc90XGNcmoJiHxZeTcEaREjkBfX5958+Zx/fp1Hj16RMWKFdm6desvn3j8zZs32NraYmRkhLa2Ntra2sjlcry9vQEpMXtOIjk5mb/++otBgwZ98THx8fEAHDt2jCtXrrBv3z7Wrl0LfLoS2tChQ6lbty76+vrcuXOHTZs2/dRGNSEE+fPnZ8aMGbRq1QpDQ0P8/Pzo2rUrr169Iioqij59+vD27Vs0NDSk8Olv4Pbt20RGRlKhQgWGDh2Kj48PPXr04MaNG+pWTYXSYDphwgSioqI4c+YMXl5e+Pn58e7dO0qVKqXK65OTjWopKSl4eHgwfvz4TJcdFxfH4sWLcXR0zHTZEr8Wd+/exc3NjYSEBBITE3FycuLkyZPcuXOHHTt2ULx4cZydnQEYOXIkoaGhrF27ljJlynDhwgWKFCmCEOKbvMry5ctHmzZt8PDwoHz58rRs2ZLY2FhcXFy+a0yl7Ffq1KmTwTj3zz//ZGroeUJCAqtWraJ8+fKsXr2ahQsXcvPmTXr37p2tjWrX3yaw1T+KOP38pAk+MPSkCkgT4B+VzFb/KK6/TVCPoj8pjx8/ZuzYsdy+fZukpCS0tbWZMmUKNfLnpm/5PJTPo4WGjA/CQzVloCFLz6nWt3weyagmIfEF5NxRpESOQpl/bdu2bSxduvSXzb+mHITNnj2bxo0b07dvX7Zv387Dhw+xs7OjbNmygOTBlpM4fPgwFStWVN3bz3H69GmaN28OQJcuXdi9ezetW7fmwYMHqpX+fw/mU1JSGDlyJM+ePWPhwoXfvDKfnVC+A0ZGRshkMhYtWsSIESNo2rQpN2/eZNiwYVSoUIFVq1Zl2F/iyzh79iyOjo5s2bKF4OBgZs6cybVr1yhbtiy9evWibdu2hIaGqltNAEJCQihVqhQeHh5s3bqVLl268Pz5c5ydnQkKClK3elnCzp07KV++/A8xlq9YsYLGjRtjZmaW6bIlfi0MDQ0ZN24c4eHh6Ojo8PbtW0JCQoiIiMDAwIDFixezY8cO/vnnH1Xus86dO6uOV6bG+Jr+XLmoki9fPoKCghg3bpyq8IEyDUKxYsW+ybh24cIF7t69y4sXL6hduzb6+vqqbZMnT6Zo0aJfLfPfREZG0rp1a4yNjTl69Chbtmzh3LlztG3bVnUdSpYsiY+Pz3e3ldlcf5vwxV5TkG5kOxUcpzKunT9/XuUBCNn3PP8LGxsb5s2bB8CZM2cy5Zl4n2rVqrFu3TrVsyCEoF27dgAU0s1Fl9KGjKpiTKNCulTJq01Zw1xUyatNo0K6jKpiTJfShhTSlbwEJSS+BMmwJvFT0ahRI65cuYKtrS1du3alb9++vHjxQt1qZTmpqakULVoUQ0NDwsPDKVWqFDExMTx8+BCQihfkJNavX8/kyZP/cx+FQqG6582bNycmJoZr166ptleqVAl9fX0OHToEfOiZo62tTY0aNTIM+nMSaWlpXLp0CVdXV6ZPnw7Atm3buHXrFg0aNFCzdj8nTZs2ZeLEiQQFBfHq1SsAcufOzYIFC/Dz88Pc3Jw8efKoWct0nj17RmhoKK6urgBYWVnh6urKlClTfgljkBCCpUuX/hBvtZiYGJYsWYKDg0Omy5b4NXj/+1W8eHGePXvG+PHjOXLkCLNmzaJo0aJcv36dmJgYateuTe/evVWFRxo1akTlypVVsr7G6/T9VBovX75UpR7ZvHkzmzZtokmTJiQnJzN//nzVfl9DREQEy5Yto169epQuXRp9fX0KFizIwIEDiY2N/SpZH+PVq1dMmTKFMmXKULBgQfz8/Dhw4AANGzb8btlfy5w5c5DJZFy+fPmLj3kVl/JVRjUlSuPaq/gUGjduzKNHj75S289z7949rKysMDY2xsjIiJo1a3LkyJFMbwdg9erVzJo167vlyGQyAgICPvj99evXODs7M2TIEHLlykWNGjU++Dbr5pJTr6AuHUoa0K1MHjqUNKBeQd1fOq+dhMS3IL0xEj8dcrmcAQMG8OjRI0qVKoW5uTmOjo6q8LdfAT09PRISEqhevTqnTp1i5cqV+Pv7o6urq27VJDKRiIgInJycaNu27Ue3K0Ne5HI5MpmM1NRUAAYPHsyVK1dU+1WoUIFWrVqpQmm+ZvD7s6PMsbVv3z6aNWtGWFgYjo6OnD17Fnt7eywtLTMYoqUw6s/z999/ExYWRv369VmyZImqCqRCoSAtLQ1DQ0MWLFiAtra22oz8yvt45MgR+vTpg5mZGbt27aJRo0Zs3rwZSJ+U/wpcvHiRqKgolZdCZrJ8+XJatWpFlSpVMl22RM7n/e/XmzdvgHSvI0tLS/bv309KSgr9+vXDx8eHq1evAjBx4kRWr16dKW0DzJs3j/bt22NnZ8eaNWswNTXl1KlTHDhwgA0bNqCjo/NNFY2NjY3x8vIib968rFu3jtjYWK5fv87Vq1dVxrr3UX6/P0dAQAA2NjZUqVKFhIQErl27xubNm6latepX6ZdZCCHYvHkzxsbGqr71S/B9k/DVRjUlqQIuvIz5toO/gA4dOtCqVStev35NaGgo7u7uGBoaZno7P7oKdVhYGJaWlvTt25e1a9dy4cKFbJcHVUIiJyEZ1iR+WvT19Zk/fz7Xrl3j/v37VKhQIcfnX1PmC5o9eza1atWid+/e9OjRg5s3bzJt2jQ6dOgAfLhqm5KSQnh4eJbrK/F96OnpYWZm9skBvTLk5cGDB8yaNUvlkWZvb4+tra1qPw0NDVq3bk337t1p2rQp9erV++EDuuyC8p0RQvD8+XNsbW2Jjo6mU6dOFClShD///JNJkybh6uqqmuTl5D7ke0lMTGTw4MEZ+hjlhFAul3+Qr07dIbbnz59n8eLFLFiwgNOnTzN69Gg8PT1/iJEpu7J06VLGjh2b6TnkoqKiWLp0KbNnz85UuRK/Bu8bq5SLHJMnT+bhw4f06tULExMTVqxYQZs2bShTpgxeXl48f/4cuVyeKUZxuVxOVFQUFy9e5MCBA/z+++8kJSVx8OBBXF1def36dYZ9v4VcuXKhoaFB4cKF8fPzIyIigsaNG3Pr1i0gvX9csWIF5cqVo1y5cgAcOnQIc3NzjIyMaNCgAbdv3wbgxo0bVK9enfLly7NhwwaMjY2xtramZMmSzJkzh379+qna3bJlCyVKlMDExOSDokcKhQJnZ2fKlCmDiYkJPXr0ICIiAkj37pXJZGzatInixYuTL1++zxZNOn/+PK9evcLd3Z3t27eTnJys2rZx40YaNmzI+PHjMTIyonTp0vj6+uL553qGNKjE/JaVuHZwu2r/1OQkjix1wLmtOU6WldnrNJGUxPSwzydXL7KwjRlnN7rj1KoyzhNsOOZz6pOhkw8ePKBUqVJs27YNgO7du2NqakqePHlo0qQJ9+7d++hxYWFhPH36lGHDhqGlpYWWlhYNGzZUPXNVq1bl4MGDqv1TUlLIly+fKrfof7UzcOBAbG1tadu2LXp6epw+fZqBAwcyc+bMj+ri7u5O5cqVefnyJVeuXKF+/foYGRlRqFAhRo8erbrWTZo0AaB69ero6+uzY8cO3r17h5WVFR07dlTJr1OnDrVr1/7UrZSQkPhOJMOaxE9PyZIl2bFjR4b8azndIydfvnyYmJgghKBTp06sWbOGtm3bfrKyoRCCcuXKMWrUKPz9/bNYW4lvRZlUHdLvodIYpjRcPHv2DA8PD8aMGUNaWhr29vbs3bsXmUyGECKDgUihUJA7d25y585NcnLyL1cFUyaTUbx4cbp27crw4cOpXLky+/fvZ+LEiZQpU4bk5GRGjBgB5Owk9t9LQkICdevWZcyYMZw7dw5AlRg7O1Uplsvl3L9/n5s3b/L3338TEBCApqYmvXr14syZM6pV+5xuYH769Clnz55lwIABmS7bzc2Ndu3aZchxJCHxpcjlcgIDA3FxcUFHR4dt27ZhamrKrFmzyJUrF927d+ft27ds3ryZ8ePH06VLF4oXL54pbSv7qsjISKpWrUrx4sVp06YNo0aNonbt2jx9+pRcub4vr5Syb4mPj8fZ2Zm1a9eybNkydu7cSVhYGOfPnwdg3759XL58mfv373Pjxg0GDx6Mp6cn4eHhDB8+HCsrK6ysrGjTpg1BQUE8fPiQpKQkfHx8KFmy5Aft3r9/H1tbW7Zs2UJISAjh4eG8fPlStX358uXs27ePs2fPEhISQt68eRk1alQGGRcuXODRo0ecPHmSuXPn8uDBg0+e56ZNm+jQoQM9evQAyGB0Arh8+TJmZmaEh4fTp08fevXqxd8XLjP1wBV6zF/JgUVTSYpPD4095j6PsKBAxmw7zcT9V4gOfcXJtUtUsmLDQ4mPimTK4Rt0m+nCk+hkPsb169dp3bo1y5cvp3fv3gD89ttv+Pv7ExoaSo0aNejbt+9HjzUxMaFs2bL069ePffv2qTwplfTv35+//vpL9feRI0coVKgQFhYWX9SOl5cXM2bMICYm5j8NxHPnzmXjxo2cPXuWokWLoqGhwdKlSwkLC+PSpUucPHmSlStXAqi+xbdu3SI2Npa2bdvy22+/0bRpU5ycnLLFN1lC4pdASEjkINLS0sSGDRtE4cKFRb9+/cSLFy/UrdIP4cWLF8LOzk5UqFBBlCpVSpQrV07kzp1bHDx4UAiRfh3+zatXr8SMGTNEvnz5hLW1tTh37pxQKBRZrbrEN/Dv+6S8v6tXrxY1atQQXl5eQggh1q5dK9q2bSuSk5M/KufixYtixIgR4tSpUz9W4WyI8popr2WfPn3EmDFjhKenp+jZs6dITk4WLVu2FDdv3lSnmj8Fr1+/FosXLxZdunQRdnZ2Ijw8PMP2j/U/WYVCoRAHDhwQYWFhIjIyUixYsED07NlTzJgxQ+zbt08EBgaqVb+sZty4cWLSpEmZLjc8PFyYmJiIgICATJctkXNJTU3N8Pfq1atF7ty5xe7du4UQQrx9+1aMHDlSTJ48WQghxLp168T06dNFUlJSpumgfP9PnTolWrRoIQoVKiSqVasm7ty5o9onODj4u9tRnmvu3LmFlpaW0NfXF/nz5xeWlpbCyclJPHjwQADCx8dHdYyNjY2YOXOmSEtLE3v37hV169YVuXLlEpMmTRL37t0T+fPnFydOnPjgG+/g4CD69u0rhBDC0dFR9OzZU7UtNjZW5MqVS5w4cUIIIUTFihUztBkSEiI0NTVFSkqKePr0qQAyjJ1r164ttm3b9tFzjIuLEwYGBmLv3r1CCCGGDx8uOnbsqNq+YcMGUbZsWdXft2/fFoDYdCVALLz+Viy8/lbkzpNX2G07JRZcCxW5dHTFxP1XVNtsNhwReQsXFwuvvxXD1uwTGpq5xNxLL1TbnbYdFkWKFFHJL1GihJg9e7YoUqSIOH369Cfvzbt37wQgIiMjP7r9xYsXYtSoUaJ06dJCJpOJxo0bi8ePHwsh0p8NfX19ERUVJYQQomvXrmLRokVf1M6AAQPE77//nmGfAQMGiBkzZgghhDh9+rQoXLiwGD9+vGjYsOEn9RNCiKVLl4pOnTqp/gaEv7+/iI2NFY0bNxY2NjbSGF9CIouRluUlchRyuZyBAwfy8OFDihcvTvXq1XNU/rX3q4Jqampy7tw5bty4weXLl3nx4gWtW7cGPu5xY2pqyvz58wkKCqJ169YMGTKEunXrsmPHji/O7SGRdYj3PM5kMhlJSUksXLiQevXq8ccff3D79m169+6Nubm5Ksx36NChvHnzRrUS/r4sgKJFi/Lu3TuMjY2z9mSyAcp3QiaTqTz2HBwcGD58ODo6OlhbW1O7du0MVROlkNCPU7BgQWxsbJg+fTpCCE6cOMG6detUef38/f2Jjo5Wi253797Fx8cHV1dX/P39mTZtGvPnz0dfX5+9e/eyePFinj59qhbdspro6Gg2bdqEnZ1dpst2dXWlU6dOlClTJtNlS+RcNDQ0CA8PZ82aNTx//pwRI0bQv39/Dh8+DKR7448aNQpfX1+8vb0ZNGgQTk5OaGlpZZoOym/B6tWrGTlyJCEhIXTq1IkmTZrQq1cvYmNjKVy4cKa1o6mpydatW4mJiSE0NJQTJ04wffp0KlasCJDBCy8oKAgXFxdy5cpFt27duHPnDpqamlhYWFC5cmXc3NyYM2cOBQoUoFevXoSEhHzQbkhICMWKFVP9raenh4mJSYY2OnfujJGREUZGRlSqVAkNDY0Mnlmmpqaqf+vq6n6y2MLevXvR1NRU5YHt27cvR48e5e3bt6p9ChYsqPp37ty50/9vnE/1Wy5tHZLj44h7F0ZKYjwefS1xbFIGxyZl2GDXk7h3Yf87l7wm5NLWUf2dkvbhN3r16tU0aNCAZs2aqX5LS0tj6tSplClTBkNDQ5WnX1hY2AfHQ/pYycPDg8DAQIKCgtDT06N///4AFC5cmIYNG7J7924iIyM5evSoyivtS9p5/958jMjISNasWcO0adMyFBl4/Pgx7du3x9TUFENDQ6ZPn/6B/klJSap+ecWKFZKnmoREFiMZ1iRyJAYGBjg5Oanyr1WsWBEvL68cUy1TS0uLRo0aUaBAAQwMDMibNy8mJiZfFLqgq6uLra0tDx8+ZMaMGaxYsYIyZcrg6uqqtsmwRDppaWl4eXnx8uVLZDIZcrmcyMhIVbWyyMhItm7dSlJSEvPmzUNfX59atWoRHBxMYGAgAJ06dcLFxSWDXOXgasmSJRkGe78qWlpa5M6dW1UhdN26dejr66uuy65duwApJPS/MDAwoGbNmkB6RTh/f3/mzp1Lv379aN269UcnfFlB+fLl+f333ylTpoxqMl62bFmmTp3KrFmzqFy58i9jDPrzzz+xsrL67ETuawkLC2PVqlWfzAskIfEp9uzZQ8uWLQkMDMTR0ZFly5bh4uLChQsX2L17NwDlypVjypQplChRQtUHZ/Yih5+fH+/evVMVfJo7dy5PnjwhLCyMJUuWfOboL0P53U1MTGTSpEnMmTOHnTt3cvv2bWJiYj7YD9KNLmPGjOH48eOkpKQQFxdHfHy8KpyxT58+XLhwgaCgIGQyGVOmTPmg3UKFCvHixQvV3/Hx8Rly7BYrVoyjR48SGRmp+i8xMZEiRYp89Tlu2rSJ2NhYihcvjqmpKd27dyclJQUvL6//PE5b48Nvq66RCbl0cjNu1wUczgXicC6QOeee4Hgx6H87/ctQlOsjclavXs3z588zVEH28vJi//79+Pj4EBUVxbNnzwC+aE5QrFgxRo0axd27d1W/DRgwgL/++oudO3dSv3591bX7knY+Z+zKmzcvhw4dYtCgQVy8eFH1u62tLRUrVlQtXC1YsOAD/UePHk2+fPlYt26dNH6RkFAD0lsnkaNR5l/bunUrLi4uP33+tcDAQK5du4aZmRlHjx5lx44d3Lp1i6tXr3Lp0iUiIyO/WJZcLsfa2ppz586xa9cu/Pz8KFWqFBMnTuT58+c/7iQkPklERAQ+Pj5s3LgRgCtXrtChQwdV/i87OzucnZ3Zu3cvr1+/ZteuXbRv3564uDjVcz169GjMzc1JSkpSyVVOSpR5Xv5dav1XQnktVq9ezdWrV1m/fj2ampp4e3vTv39/hg0bxoABA/Dx8QG+bOD9K6K8Li1btuTgwYPMmzePOXPm0LBhQ+bPn6/yxshqtLW1qVWrFr///jtmZmbA/+55mTJlGDNmTAb9cyppaWm4u7tnmFxmFn/88Qc9evT45Q30Ev/Nv9+xpKQk/Pz8OHPmDN26dcPX1xdtbW309PT4448/mDBhAsHBweTKlYv27dtTt25d1bGZbSR49uwZBQoUYPPmzXh5eREUFISRkRE+Pj7MmTPno/p/C0IITExMaNKkCTExMXh7ezNkyBAsLS0/uv+wYcPYunUrBgYGAMTFxXH48GFiYmJ49OgRp06dIikpCR0dHXLnzv3R69KtWzcOHTrEhQsXSE5OZvbs2RkMkzY2NsyYMYOgoHSD1du3b9m/f/9Xn1twcDAnT57k0KFD3Lx5k5s3b3Lr1i2mTJny2eqgBXJroPkv+5JcLqd2534cdplJbES6x1tU6Cse+576qAxNGRhpfXj+BgYGHDt2jHPnzjF16lQAYmJi0NbWxsTEhPj4eNWi2sd49+4dDg4OBAQEoFAoCAsLY/369dSrV0+1T6dOnbh+/TrLli1TebJ9bTv/RbNmzdi6dStdunRReYLHxMRgaGiIvr4+Dx8+ZNWqVar9U1NT0dbWJjk5mc2bN/9yOXQlJLILkmFN4pegcePG+Pn5MXz4cDp37szvv/+eIZnrz8K2bduwsbFh3759PH36lDVr1mBvb8/06dMZMWIEd+7cAb5+QFi7dm22bdvG9evXEUJgYWFBnz59VOXtJX4M4r2CBJCeNLdbt25cuXKF2NhY9uzZw+jRozE2NqZgwYJ4enpSvHhxbty4wW+//cbu3bspVqwYefLk4dmzZ8TGxmJkZISTk1OGwgfKwbdydf5XRi6Xk5aWhkwmw9vbW2V8OXToEBYWFkRHR+Pi4sLGjRs5c+YMMplMCgn9CMpV906dOlG2bFm0tLSoVasWtra2GarTqYv3vXeVz//XeA387Ozfv59ChQplME5kBm/evGHdunXMmDEjU+VK5Czer/ipXPCTy+Wq5Pz29va4ublhY2NDSEgIHTt2xNramocPH2aJft27d2f+/PnUr1+fy5cvs3HjRnbu3ElKSkqGFAzfi0wmQ1tbm+7duzNw4EBWrFiBr68vR48e/ej+tWrVYu3atYwePZq8efNStmxZ1UJbUlISU6dOJV++fJiamhIaGsrChQs/kFGlShVWrFhBnz59KFSoEHnz5s1QOXPs2LF07NgRKysrDAwMqFev3jctOG/ZsgVzc3OsrKwwNTVV/TdmzBhu376dwcPr31Qz1uFjo9Q2Y2ZjUrQUKwf8xpzGpfjTpitvgwI+KkMApQ0/HiJsZGTEiRMnOHr0KLNmzaJ///6UKFGCIkWKULly5QxGsn+jpaXFs2fPsLS0xNDQkKpVq6Ktra26D5Ae0tq1a1eePn1Kly5dVL9/TTufo1WrVqxfv54OHTpw/fp1lixZgpeXFwYGBgwbNoyePXsC6Yso/fv3p2zZsjx58oT8+fPj7e39ze1KSEh8OzKR05dtJST+RUxMDM7OzqxevZqxY8cyceJEyeDwL6Kjo1m3bh3Lli2jZMmS2Nvb0759+/9cNRZC5PjJamaSlpamWlUUQvD27VsKFCjAmzdvVPlX3r17x9SpUzE0NCQiIgIrKyu8vb0pXbo0tra2+Pj4sG3bNkqXLv1B3jSFQiGFAnwhNjY2nDlzBkdHR9VgddmyZRw+fJjjx49Lz/VHSEtLIyEhAX19/Qy/S/1A9qBx48aMGTOG7t27Z6rcCRMmkJqaqqqqKiHxPuJflYFnz57NhQsX6NmzJ4MGDWLVqlVs2rQJHx8fjI2NCQkJwdHRkcmTJ2dZiPa/v42XLl3i4MGDGBsbM3HixK+W9/TpU8LCwqhdu/ZH+7/k5GSmTZuGpqYmGhoadOzY8bsMLjmF3U+i8Y/6eFXPL6F8Hi26lDbMRI2+jrlz5/L48eMMFUKzGoVCwdChQwkKCuLQoUOqHHYSEhLqQTKsSfyyPH36lClTpvDPP/+waNEievXq9VNNCK9fv865c+eIjIxELpcTFRXFsGHDMjUEKzU1lV27duHi4kJ0dDTjx49n0KBBGbyh/s2qVauoUqUKTZo0yTQ9cgphYWF0794dLy8vChUqRGRkJH/88Ycq70yRIkWYNm0aW7ZsYdq0aSQkJHDkyBHKli2LiYkJo0aNIiIigitXrtCpUycGDRpE1apVVfIlo8a3MWvWLAYNGkTp0qURQhAWFsbq1asxMjL6IYnfcwITJkzA3Nw8QxiMRPbg6tWrdOvWjYCAADQ1NTNNbkhICFWrVuXevXsUKlQo0+RK5Aze//6kpKTw999/c+DAAXr27MmCBQsYOHAghQsX5tChQwQGBtK+fXtWrlyJtbW1KvwyK3VNS0tTvR8JCQkkJiaSN2/er/qO3rp1iz59+iCEYM2aNTRq1OiDfZThhA8fPuTIkSNcu3aNP/74A3Nz88w8pZ+OV3EpbPWPIvUbZqGaMuhbPg+FdD+fV/hHEBERgYWFBVu2bFHbWFcIwahRo7hz5w7Hjh1DT09PLXpISEj8D8mwJvHLc+7cOcaNG4eOjg5ubm7UqVNH3Sp9EuVK64oVK7hy5Qr//PMPzZo1482bN7x584a1a9dStWrVTDewCCE4f/48rq6uLFmyhLJly36wT2JiIm5ubuzcuZNy5cqxfv16yRPw/3nfO23AgAGULl0aBwcH9u3bx82bN5k0aRK+vr5YW1sTEBCAhoYGkydPJiYmhgoVKuDn50fz5s3p1asX4eHhCCEyPcTrV+T9+wLphuSAgABWrlzJ06dPmT9/PtWrVycqKoo8efJIXoD/T3BwMGZmZty7dy9D9bjsQGxsLFeuXKF58+a/rJG5b9++1KhRA3t7+0yVa2dnh5aW1gfFUSQk3u8bly1bxoEDBzAyMmLUqFG0aNGC7du3c+rUKXr27EndunXZsmULISEh/PbbbzRo0AD4cQtDyn4+ISEBIQTR0dGqfkuZiuFrclKFhoZy+PBhOnToQL586dUtPTw8uHbtGq6uruTNm/c/jx8wYABWVlb07dv3l/+mXH+bwKnguK8yrmnKoEURPWrkV4931tq1axk3bhy///47q1evVosOQggmTpzIhQsXOHHiBIaG6vPck5CQ+B+/bm8uIfH/NGnSBD8/P4YNG0anTp3o378/wcHB6lbroyjt4GfOnMHOzg4rKyssLS3Zt28f5cqV+2FFB2QyGU2aNGHfvn2ULl36o3r5+/tz6dIltm7dyvbt29HV1WX//v1MmzaNqKioH6LXz4Jy0O7n50elSpU4ePAgCoWC9u3bM3ToUIYPH84ff/xB5cqVmTJlCgULFqRWrVoYGhqycOFCtmzZQlJSEqamptSpU4e6desihJByf30n70+m0tLSOHDgAGPHjkVfX5/t27dTvXp1Xr16xcSJE/H391flZ/vVWbBgAYMHD842RrWwsDBV3+jr60tYWJhqgv6r3a/g4GCOHj3K0KFDM1Xuixcv2Lp160erEEpIyOVyEhIS+PPPP7l48SIODg5oaWmxaNEiAHr16kWRIkXYvXs3L1++xNbWlnnz5tGgQQOEED/U21rZz3fv3h0bGxtmzpzJypUriY6ORkND44uNasrvbVBQEHfv3uX8+fOqbb179+bFixdcu3btk8eGh4fj5+fHixcvVJEFv6rxX0mN/LlpUUTvg0IGH0OhUKjdqAbpBSbi4uLUZlSDdC/7U6dOcezYMcmoJiGRjZAMaxISpA+8Bg0axKNHjyhatChmZmbMmzeP+Ph4dav2UXR0dID0ZPRv36ZXT1KWTIcfW/HuY6urMpmMU6dOYWJikiEU9fTp05QqVUo1cP0VJrnvFyRQ3ofU1FS6du2KnZ0d8fHxREZGsnHjRjQ1NfHx8UFfX5+///6bNWvWsHPnThISEmjZsiUKhYLnz59TqFAh5s6dm8HVXyaT/dIr3ZmNhoYGwcHB9OzZkzlz5rBx40b++usvoqKiaNeuHaNGjVLt9ysTFBTE9u3bmTx5srpVAeDs2bM4Oztz5swZVR7Cbt26qbYfPHiQ+/fvq1HDrMXDw4Pff/890yv/LliwgOHDh1OgQIFMlSuRc/D29mbNmjVYWFjQpEkTPD09EUKwePFiIN0gIZfL0dL6X8J5pUHtRxmYlN/iI0eOoKGhgb29Pa1ateLhw4fY29uzdevWz8pQKBQIIVTfWzMzM8qXL8+DBw9ISkpSVf5s1aoVXl5e9OnTh/Xr12eQIZfLMTQ0xMnJia5du6qK5vzqhjVIN671LZ+H8nm00JDxgZFNUwYaMnh10xfTF9fUalTLDsyfP599+/bx999/f9Y7UkJCIosREhISH/DkyRPRrVs3UaxYMeHl5SUUCoW6VRJCCJUeGzZsEJcvXxYBAQGiRYsWQktLS3Tt2lU8e/Ysy3WaPHmyqFu3rhg2bJj4+++/VXq+efNGTJ8+XTx48OCDY7LL9cxs0tLSVP+Oj48XMTExQgghbt68KXr27Kna9ueff4oGDRoIIdKv3/z580ViYqJYvXq10NLSEhs2bPio/Jx63dTNv6/r/PnzxYABA4SDg4MwNzcXoaGhok+fPuLYsWNq0jD7MGTIEDF9+nR1q6EiJCREODs7i759+4orV65k2Jaamio8PT1FYmKimrTLWmJjY0W+fPlEQEBApsp9+vSpMDY2Fm/fvs1UuRI5i7S0NDFlyhQxadIk1Vjk0qVLoly5cuLChQtCCCFSUlKyXK/ExETRq1cvcfToUSGEENHR0eLWrVvCx8dHREREfLGcqKgosXz5cvH48WMRExOjOlb53U9KShInTpwQderUEf7+/h8cn5aWlmGMIPEhcclp4tLrOHHgabTYGRApDjyNFpdex4m45DRx6NAhUa1atV/6Gi5ZskSUL19evHr1St2qSEhIfATJsCYh8R+cPXtWWFhYiPr1638waVMniYmJqsGFQqEQsbGxIjY2Vi2GlwcPHohWrVqJx48ff7BNqePChQtFjx49xKVLlz7Y/rMbi5YsWSICAwNVf79580YsXLhQlC5dWgwePFg8e/ZMPHnyRBQpUkQIkX7O7969EzVr1hRXrlwRvr6+Yvjw4aJgwYJi3Lhx4uHDhxnk/8qDSHUQHx8vWrZsKY4fPy6EEGL9+vWiSZMmYuDAgSI0NFS138/+3H4L/v7+wsTERISHh6tblQ94f6Lxq74zK1euFJ06dcp0uUOGDBEzZ87MdLkSPx+ferdSU1OFEELcv39f9OvXT2zatEm1sLRkyRKxd+9e1b5Z1XdGRUUJhUKh6rcKFSokrl69qtr+qXP59+9BQUGia9euonfv3sLc3Pw/3wUPDw8xZswY1fX4NykpKeLRo0ffcDYSCoVC1KpVS+zatUvdqqgFDw8PUapUKfHixQt1qyIhIfEJpDgiCYn/QJl/bejQoVhbWzNgwAC15l9ThjUMGDCAw4cPA+nVt/T09Bg6dKjqN5GFNUmMjIwwNTXl7t27H+iqDJ3w9vZGoVDg5ubGlClTePfunWq7TCb76XKFpaWlqe5FtWrVKFKkCJCe36lWrVro6upy8eJFDA0NWb16Nfny5aNGjRqsXLkSuVyOQqEgV65cTJs2jfr16+Ps7MzFixdZunQpFSpUyNCWFO6ZdQghyJ07N1ZWVqxZs4bjx4+TkJDAkydPqFu3Lvnz5+fq1aukpKT8lM/t9zJ37lzs7OwwNjZWtyrA//q52NhYcufOzZs3b4D/vTOpqalq0y2rUfav48aNy1S5AQEB7Nu3jwkTJmSqXImfj/cT7a9evZpDhw4REREBpIfICyGoVKkSHTp04OTJk1y8eBEAe3t7OnXqpJKTFeGPe/bsYf/+/SgUCsqWLUtYWBi7d++mQoUKqpQZ//62Kvvz939PS0tj06ZNVK5cGU9PTzp27MjVq1fx9/fPcIyyL2rRogXXr1/nwYMHH9UrKSkJX19flWyJL0cmk+Hg4MDcuXN/uW/vn3/+yeLFizl16hRFixZVtzoSEhKfQJqxSUh8Bg0NDQYPHsyjR48oXLiwKv9aQkJClusSGRnJy5cvCQ8PJyAggIcPH3Lt2jUePHhAcHCwWvI/3bhxg7dv39KyZcuPbt+4cSO6urosW7aM7du3Y2Njw8aNG+nQoQOjR48mJCRENZDdvXs327dvz0r1v5oHDx6wbds21bVu1aoVQ4YMISQkBDMzM5KSkjA2NsbU1JROnTqRmJjI5cuXmTRpEsuWLWPdunXY2dnRpk0bOnfuTFpaGkZGRpQpUwaFQvHLDRizI5MnT6Z79+7069ePTZs2sWPHDmxsbPj7779xdHTE3d0dSJ+AZaURW508fPiQY8eOZbrh5lsR/5+bKSAgACsrK2xtbXFzc2Pz5s28ePECAE1NTTVrmXUcPXoUPT09mjRpkqly582bh52dnZTLRwK5XE5oaCjjx49n+/bteHp6smzZMp48eQL8z8jUo0cPChQooNb8Ye3bt6dbt26sXLmS06dPo1AoqF+/Pvr6+qoctf9GOQ45duwYK1asIDU1FQ0NDXbu3Enz5s0xMDDAxsYGc3NzvLy8gP8ZCZX/r1SpEiYmJly9ehX4cJFTT0+PgQMHAlK+zm+hXbt25MqVi/3796tblSxj69atODg44OPjQ8mSJdWtjoSExH8gGdYkJL4QAwMDFi5ciJ+fH7du3aJixYps3749SyfWjx8/Zvr06dy4cYPNmzczduxYZsyYwejRo+nYsSP169cHsmZFWHneDx48oGDBghgaGmYwCikHjWvWrGHkyJEULlyYFy9esGTJEo4ePcqRI0coXbo006ZNUx2zYcMGwsPDs7VxqWLFisycORNHR0caNmxIcnIy7969Y+3atejr6zN69Gj27dsHQM2aNSlcuDBnz56lcePGbN26ldu3b1OnTh0cHBwYNWoUGhoaqvsll8slDzU1IpPJVM91hw4dGDBgAOfPn6dy5cosX76c9evXI5fLuXz5ssq49qswZ84cJkyYkOlJ8b+XtWvX0rp1a0aPHk2xYsW4ffs2S5cuZePGjb+M0RNg6dKljB8/PlP7/ocPH3LkyJFsY0yVyHref4fevHlDq1atkMlknDlzBkdHR1JSUti9ezfJycloaGiQkpICpBe7sLKyUovOKSkpaGlpoampyejRo2nUqNEH31XxkaraFy9exMbGBnd3d3bv3s3kyZOJjIykf//+KkNOoUKFSEtL48KFC4SGhmb4ZigUCs6ePcudO3coXLgw8OFYLCUlBW9vb5UOEl+HTCZj9uzZODo6ZutxYmaxe/duJk6cyN9//025cuXUrY6EhMTnyProUwmJnMGZM2eEubm5aNCgQZbnXzt8+LB4/vx5lrb5KXx8fETlypXFxYsXVb8pc6gcPHhQlClTRpVvxMfHR7Rr107Url1bdO/eXTg5OYlRo0aJyMhIcerUKfHbb799cF6pqalqy2f173YVCoV4+fKlMDc3FxUqVBA3b94UQgjh6+srKlWqJBITE1Xb79+/L4QQYu/evWLw4MEffUYUCsUvmasru/N+fpxLly6JSZMmCRsbG7F//34hhBARERGiTp064t27d0KInJ9v7fbt26JgwYKqnEnZheDgYNGxY0fx5s0bIUR6/qLLly+LRYsWiX379qlZu6zj1q1bolChQiIpKSlT5fbu3Vs4OTllqkyJn4P384y937/Z2tqKihUrqv7etWuXGD9+vPDy8vqoHHX2jZ/Kc/ax3GqhoaGia9euomnTpkIIIV6+fCkGDx4stm/fLq5fvy5q1aql+oaPGTNGdO/eXWzZsuUDORERESI6Ovqj7SYnJ4vt27eLy5cvCyFy/nfjR6FQKISFhUWGvH05kYMHD4oCBQqIGzduqFsVCQmJL0RyjZCQ+EaaNm3K1atXGTx4MB07dmTAgAGEhIT80DaVOTm2bNmiymkm1Lzq2bJlS+7du0edOnVUv73vgTVt2jSV99qjR48wMDDgypUrDBo0iDt37vD8+XP09PTw9vamadOmFCtWDF9fXzZu3KgKxcjqkBLlSuj77SoUCmQyGfnz58fT0xMAMzMzAOrXr0/+/PnZsmULRYoUoVGjRsydOxdIz7ni5ORE7dq1PypfneEyEh/n/RCdV69ekZCQwLhx4+jYsSOQ7h0kl8u5du0aQI7Pt+bg4MDkyZPR19dXtyoZePz4MRcvXqR58+b4+PigqalJnTp1mDRpEu3bt1e3elnGsmXLGDVqFFpaWpkm8969e5w8eRI7O7tMkynx86D08Dpw4ADDhw/H2dkZAHd3d4oXL878+fMBaN26Nfnz5+fWrVtERkZ+IEcd327lmOjfoZbv51BLSkpi1qxZDB48mE2bNpE/f3569OhBdHQ08fHxFClShPLly3P37l1KlSrFsGHDcHJyolixYhQpUgQTE5OPhpTmzZsXAwMD1d9CCFJTU3n58iVLliwBwMLCAsj6a5NTUOZac3R0VPv490dx4sQJBg8ezKFDhzA3N1e3OhISEl+Keu16EhI5g6ioKDFlyhRhbGws5s2bJ+Lj439IO8oVzvXr14tx48aJf/75R4SFhYnQ0FDx5s0btZSy/xrOnTsnBg0a9MHvAQEBomnTpuLevXtCCCHu3bsn7O3tRfny5cWff/4pkpOThRBCXLlyRXh7e39yJTqzOXPmjDh58mSG35Rtt2vXTixYsED1+86dO0WVKlWEEELcvXtXLF68WFqRziEo73lwcLBo1aqVqF+/vli8eLFwcHAQEyZMULN2P5arV6+KwoUL/7A+7XsJDw8XK1asEA0bNhRt27ZVeTH8KpVB37x5I4yMjMTbt28zVW63bt3E4sWLM1WmRPbn/W/WxIkTRbNmzcTdu3dFjRo1hI2NjRBCiGvXrokaNWqIc+fOCSGEeP78uYiNjVWLvp/j9u3bIjIyUvW3sh8bOnSoGDp0qLh48aIoX7682LRpk7hz546wtbUVa9asEUIIcfPmTdGvXz/h7e0thBAiJiZGJCYmivv374vmzZurPM/+C2UV8ISEBCGEyPZjtJ8FhUIhzM3Nc6Rn8pkzZ0T+/PnF+fPn1a2KhITEVyIZ1iQkMpHAwEDRtWtXUaJECbF9+/ZMN6woJ/gDBw4U+fLlE+XKlROlSpUS5cqVE7q6uqqBbnYx6Pxbj7i4ODFo0CBhbm4u7OzsVKEV9vb2YurUqUIIIZ49eya8vb1FSEiIePLkiRgwYIBQKBTC19dXVKlSRRw8eFAl79y5cyIkJOS7dExLS/tAz/Pnz4vevXuLhg0biurVq4uNGzeqtikHxqdOnRLVqlUTycnJ4vTp00IIIdq2bSsCAgK+qv34+Phsc78kPs3WrVtF4cKFxcSJE1W/PXnyRJQpU0YEBgaqUbMfS9u2bcXy5cvVrYYKpcHs3r17Ytu2beL48ePi8uXL4vXr12L58uWiVq1aqhDdX4E5c+aI4cOHZ6rMmzdvClNTUxEXF5epciV+Dt68eaMKW4yOjhZr1qwR5ubmokSJEmLt2rVCCCEWL14s6tatm+G47PYdO3funOjUqZM4e/asECI9tK5Pnz4iODhYtGnTRrWQd+DAATFu3Dhx5coVsX37dtGtWzeVjCVLlghfX18hRPrCip2dnahXr95nwxDj4+OFq6uraNiwoWjVqpUYOHCg6NWrl/jzzz9/zMn+guzdu1dYWFhku+fue/D19RX58+f/YEFXQkLi50AyrElI/ABOnz4tzM3NRcOGDYWfn5+61cl2vHjxQixZskQ8evRIhIaGijp16oh//vlHBAcHi1q1aglbW1vRoEEDUbZsWdGhQwchhBBLly4VWlpaon379ipD1u7du1WD5m/hY14tZ8+eFc2bNxd2dnZCiPTBW9euXcWjR48+2HfUqFGiSZMmonr16iI4OPiD7Z8b8MXHx4vDhw9/o/YSWcmePXtUngtCpHslOjs7i5kzZ6pRqx+Lr6+vKFasmEhMTFS3KkKI/72vL168EI0aNRIdO3YUw4cPF5MmTRJHjx4VQgiVrr+Cx1pCQoIoWLCgKp9jZmFtbS2WLl2aqTIlsh8KhULEx8eLXr16iWfPngkhhLh+/bpo3769ePXqlRAifUHB0tJSCJGeT01PT0/cvXtXKBQK8eLFC7Xp/in+nZPVzs5OuLq6iqSkJDFp0iSxZ88eERsbK0aMGKEaO6SlpYm6desKX19f8eLFC9GmTZsMC3jvy/7cIoqy7c2bNwtbW1vh6ekpmjVrJrZu3SrMzc1VOQt/hf7pR6NQKET16tXFgQMH1K1KpnDt2jVRoEAB1bdMQkLi50PKsSYh8QNo1qwZV69eZdCgQXTo0IGBAwdmev61rVu30qVLFx49ekR8fDz79+8nNDQ0U9vIbBQKBQqFgqJFi2Jvb0/58uV58eIFjRs3xsLCAl9fXwwNDVm5ciUXL16kevXqNG7cGIB//vkHR0dH+vfvT3R0NHfv3qVgwYI0adLkA/kfQ/x/Lg5lnjpIz7USEBDAyJEjWbRoEY8fP1bpEhUVBUCnTp3Q0NDgypUrqopnShkLFy5k8+bN3Lx5U1UF7H35/5VDRaFQULVqVYoVK/bV11Ei6+ncuTPdu3cnISGBM2fOsG3bNoKCgmjZsiUAt27dUu0rckjel9mzZzNr1iy0tbXVrQrwv/dp2bJlWFpasn//fhwcHChdujRTp07l/v37Kl1/heq627Ztw8LCgkqVKmWazGvXruHn58eIESMyTaZE9kQmk5E7d260tbUZP348ALGxsVSuXBlTU1MAUlNTVdUItbS0KFy4MI8ePUImk1G0aNFsl1tSmZP13bt3ALRq1YonT55w5MgRYmNjadWqFbq6uujq6nL27FnevHmDXC6nWLFiCCEoUqQIq1atypCjUbyXs6106dL/2b7yejx58oR69eqhr69P06ZN6dOnD6NGjSI5OfkHnfmvh7JC6Jw5c376b+6dO3do27Ytnp6etGnTRt3qSEhIfCM5f+QpIaEmNDQ0GDJkCI8ePcLU1JRq1arh5OREQkLCN8tUDtpmzJiBv78/L168IDg4GF1dXdauXcvt27eB7Duxl8vlyOXyDPrVqFGDJUuWoKWlRa5cuQgNDWXv3r3MnTuXkJAQWrVqxfPnz7ly5QrDhw+ne/fudOzYkaFDh3L69GkA4uLiSElJUcn/N4GBgbx+/Rr4X0Lj1NRUdu3aRY8ePTAzM6NMmTIMGTKE5ORk2rRpg46ODtevXwegTZs2HD16lODg4AwyDAwMKFGiBPA/g9q/EyZ/ihMnTmBsbEy1atW++jpKqI/jx4+zfv16dHR0mDdvHs2aNSMhIYFdu3apklNn1/fvazh37hxPnjxh4MCB6lZFhUwmQwhBwYIFefv2LcnJyRQuXBgbGxsaNGjAjRs31K1iliGEYOnSpSqDSGYxe/Zspk+fTu7cuTNVrkT24v0FoI0bN/L8+XO2bt3K8+fPVYY0gPz585OQkECzZs1wdXVl586ddOnSRbVd3QZs5Xko+9zDhw9jZWWFjY0NGzdupEOHDqSmprJgwQKuX7/O/fv3kclkTJs2jYiICAYNGkSNGjXQ1NTEzMwMmUxGyZIlM7TxNUUGlN9/PT09ChYsiKGhITdv3uSvv/5iz549FC1aNHNOXAJIX/hMTU3l8OHD6lblm3n48CGtW7dm2bJldOrUSd3qSEhIfA9q8pSTkPjlCAgIEF26dBElSpQQO3bs+Ka8EMoca506dRIPHz4UQ4YMEUeOHBFCCNGxY0dx4sQJIUT2y3XyX/w7JOLMmTNixIgRokWLFmLUqFFCiPRwDmVophBC+Pn5iWrVqom4uDgRExMjkpKSVMmBP4atra3o16+fEEKId+/eCWtra3H37l3x9OlTkZSUJHx8fESLFi1EoUKFhLu7u0hJSRHTpk0TixYtEkIIER0dLXr37v3RcNBvpUePHmLlypWZJk8ia/D39xfr1q0TQggxefJkYWNjo7qPNWrUENeuXRNC/Fzv4L9RKBSiSZMmGXILqpvr16+r/v306VPRvXt34eLiInbu3Cnu3bsnSpcurQrT+pmv/Zfi4+MjqlSpkqnneunSpWwV+iuR+bz/vKSkpIgnT54IIdK/qSVKlBCVK1cWI0eOFJs2bRJ3794Vr169EoGBgWLNmjUZcu5lx1DGkJAQ0axZM7Fnzx7xzz//iDp16ogdO3aIGzduiLZt24phw4aJ4cOHi5YtWwo3NzcRFRUlTpw4IW7cuPFD9ElNTRXJycnCw8NDdOrUSUyZMkWVMuJX6KOyil27dolatWr9lNc0ICBAFC1aNFt9ayUkJL4dybAmIZHFnD59WlSvXl00atRIXL169auOVQ5me/fuLa5fvy769u0r/v77byGEEG3atMl2xQu+h7CwMHH79m0hhBBDhgwRO3fuVG2bPXu2ePbsmUhLS/tohdDw8HCVwVEIIe7cuSPMzc1FWFiYuHr1qhg5cqRq2549e0TTpk2Fn5+fOHLkiLCwsBBCpOeW6d69uwgKCvoh55YnT55fKsl6TmPVqlWiVq1aws/PTzRt2lRs3bpVODo6CldX15/+/Ttx4oQoX758tqlg9+TJE2FraytSUlLEoUOHhBBC3LhxQzg6OopevXqJ9u3bi9WrVwshsueE/0fQrl07VSL5zMLKykp4enpmqkyJ7MnFixdFjRo1RP369cWWLVuEEEJs2bJF5M2bV3h6ego7OzvRqVMn0blzZ/H06VPVcVlVkftraNmypTh+/Lg4cOCAGDx4sOr3kydPiooVKwoh0quAbtmyRURERIjz588LR0fHDNVCFQpFpvQdmzZtEseOHRM3b94UQUFBIjo6WqSlpWXbqqk5gbS0NFG1alXVt+Fn4dmzZ6JEiRKqb5eEhMTPj6a6PeYkJH41mjVrxrVr19iwYQPt27enTZs2ODk5qXJ0/RfKsIu5c+fi5OTE0aNHiYuLY+TIkYwYMYJ69eoBXxe6kN1QKBTIZDJMTEwwMTEBwNramjFjxqCvr4+lpSWTJ09GV1f3k+dpbGzMhAkT0NfXx8/PD0tLS+rWrcuWLVvQ0NBQ5cVKTEzkwoULDBkyhFq1arF582bu3buHt7c3HTp0oEaNGhQvXhwhBDKZDIVCkSmhL15eXrRr1w4jI6PvliWhHh49eoS1tTW1atXCzc2Nrl27Ur9+fWbPno1MJuP169fcu3ePRo0aZZscZV+CEIKZM2cyZ84cNDWzxxChVKlSrFy5ksePH9O/f3/q16/PpEmTmDRpErlz5yYpKYlcufvyhIYAAQAASURBVHIBP3ff96U8evSIK1eusHPnzkyTeeHCBR4/fpytQn8lfgybNm1i7969rF+/nqCgIHbs2IGuri79+vVj9+7dhIaG4u7ujkKhIDo6OsN36ktTHWQmyhQYym9vfHw8L1++pHz58gDUqlWL0NBQatSowYkTJ1THNWvWDCMjI0JCQqhRowaXL1+mbdu2NGrUiEaNGqn2U37fv7fvSExM5NChQ8TFxREXF4dcLkdPT49ChQpRpEgRTE1NpdyFPwC5XI6DgwOOjo60bdv2p/gGBAcH07JlS8aPHy89ExISOQgpx5qEhBrQ0NBg6NChPHr0iAIFClCtWjUWLFjwxfnXypYti6enJwcOHKBfv34cP36ciRMnqiaXPzNyufyDgVGHDh14+vQprVu3RlNTEz09vQz7vJ8vJjU1FQB9fX3atm1LUlISJUqUoFevXjg7O7NkyRKKFi1KUlISOjo6GBgYcOHCBXr37s3ff/+Nh4cHtWrVwsDAgIoVKwL/m6xnVj6Z9evXM3jw4EyRJZG1iP/P5TNu3DgOHDjAtGnT2LZtG9WrV6ddu3aUL1+eCRMmYG1tTXBwMJGRkepV+CtRJvnu2bOnulUB/ne9o6KiCAsLIzw8nNatWzNy5Ei6dOnC9u3b0dbWVr2jP8Ok6ntZtmwZI0aMyNQ8aMpCFVpaWpkmU0L9vP9tBAgPD+fy5cvcvXuX6tWr07FjR+rXr8+pU6d49OgRK1euxNPTk8ePHyOTyTAyMlJbgYL3DWpyuVw1Ptq1axeDBg3Cx8dHta+enp6q6IKzszOQnse0QoUKFC5cmAEDBrBkyRKMjY0/kJ9ZfYaOjg7e3t4cPnwYHx8fdu7ciaOjI/Xr1yc1NZWnT58COSMHZ3ajS5cuJCQkcOzYMXWr8llCQ0OxtLRk2LBhjB07Vt3qSEhIZCIyIfXwEhJqJzAwkEmTJnH9+nUWL15M9+7dPzvYu3jxIkeOHCEyMpKqVavSs2fPDIPGnIRyRfl9zpw5g0wmo2nTpqrffH19VSvDO3bsYPTo0URHRwPpq8nDhg3j0qVLdO/enQcPHjBw4EBKlixJeHg4J06cYOzYsRQqVOiHnsuNGzfo3LkzT548UXviZ4lvIy0tDQ0NDa5fv86sWbPQ19fHzs6OunXrsn79evbu3cv9+/fZuHEjLVq0ULe6X4wQglq1ajF9+nS6du2qbnWA9Pfc2NiYHTt2EBMTg7u7u2qbp6cn7u7uHDt27JeprhsREUGZMmW4f/9+pvVVp0+fZtiwYTx8+DDbeClKfD/ve1gfP36csmXLUqZMGV68eMHQoUOxtrZm5MiRxMTE4ODggEwmY9GiRcTGxmYrb+q3b99ib2/Ps2fPGDt2LF27duXUqVMsXrwYR0dH9u/fT1paGosWLeLChQts3bqVBw8ekJCQgJ2dHf369VNdi4+NJTIL5XRKJpMREBDAjh07aNWqFXXq1Pkh7UlkxNvbGxcXF/75559su8ASHh5O8+bN6dKlC3PmzFG3OhJAXIqCOxGJhCakkZSmQFtDToHcGpgZ66CbSxqjS3wdkmFNQiIbcfr0acaPH4+BgQFubm7UrFkzw3bl4NDd3Z2dO3fSvn17KlSowJ49e4iIiMDd3f2z5eBzAuvXr2fOnDns27cPCwsLtm3bxurVqzEwMEBDQ4PRo0djZWVF586dKVWqFK6urgDs2bOHFStWcPLkSc6dO8eSJUuYMWMGdevWVcl+f3D8I7CzsyNfvnw4ODj8EPkSWcvOnTupX78+ycnJuLi4ADBgwAD27duHXC5n/vz5bN68GTMzM8zNzdWr7GdQVuO9du1atjH6nj9/nqFDh/Ls2TM8PT0zhCpGRESoFhN+5IQ5O+Hs7MyDBw/YtGlTpsgTQtCkSROGDx/O77//nikyJbIPYWFh2NrakpSUhImJCXnz5sXV1ZVDhw6xatUq5syZQ+3atbl06RLPnj2jd+/egHreJ+WChbJ9hUKBra0tBgYGlCpVilKlSrFp0ybatWvHgAED8Pb25tChQ2hoaFCgQAEWLlyo6reuX79OjRo1slR/5fhs06ZNXL16lQcPHtCyZUumTZvGunXrKFKkCL/99luW6vQroVAoqFatGi4uLrRp00bd6nxAZGQklpaWtGzZEmdn51/ie5WdeRWXgu+bBJ5EJyMDUt+zhmjKQAClDbVoUDA3hfR+/mggiawhe4ycJSQkAGjevDnXrl2jf//+tG/fnsGDBxMaGqrarjT6HDlyhLlz5zJlyhTat2/P5s2bSU1N5fnz5xn2y0mcOXOGkydPApArVy6GDh3KzZs3SUhIIDExkWXLlnH48GGio6P5448/iIuLw9HRkY0bN6pkNG7cmKioKE6ePEmTJk04cODAB0a1zMi18ikSExPZtm2blMcoB6B8x7p3707RokVZu3YtpUuXZsCAAdSqVYsFCxYwdOhQJk+ezNq1awkODlazxv+NQqFg9uzZzJs3L9sY1SD9nf3zzz/p378/y5cvp2HDhuzdu5eYmBjMzc0JCAgAfo0Q0JSUFDw8PBg/fnymyfTx8eHt27f06dMn02RKqI/3Qz8VCgVubm40a9aMAwcOkJKSgpubG/v27aN9+/Y0atSIWbNmkZqaSv369VVGNVDP+6Q0ql25coXXr1+joaGBlpYWZ86cYdiwYbRr147u3buzfft2AHr06KHyQk9OTkYul6vOX2lU+3co7I9E+U04efIknTp1olq1auTNmxdIjzAIDAwEUFtobU5HLpcze/ZsHB0ds90YOCYmhrZt29KwYUPJqJYNuP42ga3+UfhHJZMmMhrVIP3vNAH+Ucls9Y/i+tsvS9MjIZF9Rs8SEhJA+uBSGZaTP39+rK2tSUpKAv432C1TpgxhYWEAaGpqkpiYSL58+TAwMFCb3j8K5QBJCEHt2rWBdCPbH3/8wZ49e9DQ0KBfv348fPgQMzMzatasSVpaGocOHVJ5CXXq1IlatWpx7949jh8/TsuWLVWD2/cH3v8e7GT24Gz//v1YWFhQokSJTJUrkfW8/6y8ffuWw4cPY2ZmRp06dZDL5Vy7dg1bW1uio6NZuXIllpaWatT28+zcuRM9PT3atWunblUykJaWRqNGjVi7di3Xrl1j9OjRzJ07l+7du9OtWzfKli37y0xUd+3aRbly5TLN81EIwaxZs5gzZ45aktL/H3tnHVbF9jXglwOoiISBIBYGdit2o5iYKAYYiN3dBYotit3dgY0YiIqC3YpBKSgignSeM98ffGd+oN57DSRk3ufxUc/M7Fkzs3PtFRLpR2xsLIIgoKqqiiAICIKATCZjwoQJdO3alebNm1OnTh0OHTrEoEGDiI6OxtbWlo4dO6JQKNKMsxmB0iItNZ6enjRs2JDZs2ezZs0aDh48iJ2dHWFhYURERADQqFEjtLS0ROVakyZNWL58Obdv3wa+Ta6QkfVauSERExNDhQoVCA8Pp1GjRgAEBwdTqVIlIGdsAmQWFhYWREREcOHChcwWRSQ2NhZzc3OqVKnCqlWrpO+fydz/FIdbUMw3yrR/IlkAt6AYSbkm8UNIrqASElmct2/fUqhQIfLmzSu6GkyePJnVq1dTqVIlDA0N8fT0pESJEpQuXZpPnz6xZcsWKlasmNmipyuJiYkcPnwYmUyGgYEBx48fp0GDBvTt2xcAKysr+vXrh5mZGQMHDsTHx4cLFy7w5csX3NzcqFevHmXKlPmhe8nlcgRBSBNvKD0ygiplS20ZIJG9UVo52tnZUahQIUaMGMG6devYsWMHNWrUYMaMGWncs9Mrs2x6kpycTJUqVXBycsLMzCyzxQH+5xZ29OhRbt68iUKhoGnTppiZmZEvXz4+fPhA/vz5yZMnT5Z8p+mNIAjUq1ePWbNm0alTp3Qp89y5c0yZMoXHjx//9e/vb+bo0aNcv36dVatW4enpycSJE2natCmmpqaYmZnh7u7O7t272b59O58/f8bIyIh27dpx+PDhTJH3e26miYmJODo6Ym5uTpkyZejRowdJSUm4uLjQp08fSpUqxcKFC0lOTmbjxo2cO3eOc+fOASkhHm7dupVlLIFu376Nvb09bm5uTJ8+nfDwcAIDA1mzZg2FCxfObPH+eg4ePIiTkxM3btzI9PoQHx9P586d0dfXZ+fOnVI/m8l8iEli3+uIH1aqpUZNBfqW06FI3vRxCx0wYADFihVjwYIF6VKeRNZAUqxJSGRDnjx5Qnh4uLg7Df/baY6Pj6d+/frZ1nrt6yDDoaGhjB07lo0bNzJ37lwKFSrEjBkzOHDgALdv32bQoEFUqFCB4cOHU7RoUczNzVm0aBGqqqosXbo0jXXYj8SNEQSBDx8+oKury6NHjyhevDjFihX77ecKCAigVq1aBAYGpms2P4nMJXWd8vX1Zdy4cWhpaaFQKIiJiUFDQwMdHR2MjIyYMWNGJkv7fXbv3s2WLVu4du1api9E4H/vNC4ujpo1a7Jx40Z69+6NiYkJWlpatG7dmjZt2vzxRCNZiRs3bjBgwABevnyZLoszpQXw9OnTs0yiColf4/379/Tt2xdzc3MePHhAmzZtSE5OZvHixbi7u/PixQs2b95M165dcXFxwcTEhA4dOmSo5bTSQk1pQZaUlISTkxNhYWFMmTIFHR0dmjdvjq6uLsHBwdStW5dp06ZhaGjI06dP6dChAw8fPiR//vyEhYWRnJxM4cKFef78OePGjcPGxoZevXpl2PP8EwkJCRw9epS6dety8uRJoqKiCA8PZ/LkyTkmwUpmI5fLxY2i1q1bZ5ociYmJWFhYkCdPHvbv3y8lhskCHPON5HVE4i9fX04nF91Ka//necnJyf/5vSXF2t+JpDqXkMiGVK1alaZNm1KtWjXKly9PhQoVKF++PA0bNqR169bZTqmW2hVFJpORmJgoLvALFSrEmzdvCA0NxdzcnPfv33P//n2aNGmCQqHg0aNHqKmpMXToUDw9PRk5ciR9+/bl0KFDP61UgxQ3DUNDQ+RyOQ4ODkyfPh0bGxvOnDmT5ryfjd2ya9cuevXqJSnV/jJS1yk9PT1q1qyJg4MDurq6mJmZ4eTkxODBg9HW1iYuLsWVICvtZyUlJTF//nzs7e2zhFIN/vdON23aRM+ePSlfvjxly5Zl3bp1fPz4kf379+e4nX9HR0fGjh2bbs996tQpkpOT6dq1a7qUJ5GxKN0oBUHA0NCQcePGcejQIfT09LCysmLAgAF07NiRUaNG0aJFC1q3bs3u3bvR19dnxIgRlCxZMsPij8nlclRUVFBVVeXLly+Eh4ezYMEC3rx5w+vXr5k1axYBAQE0adKEqKgobt68iZOTE4aGhty8eZMqVapgYmLCo0ePAChQoIBo+VWhQgUuXLjwjVItI2Orpcbf359t27ZhbGzM0KFDmTBhAk5OTpJSLQNRVVUVXdwza6xNTk6mb9++qKiosG/fPkmp9oMYGRmxbNkyqlWrhqamJoMGDeLjx4+0a9cOLS0tWrVqRXh4OABeXl40bNgQXV1dqlevjru7u1jOzp07KV26tJj4ZN++fYTHxGNdsyTBr5+L50WHfWJOwxJEh4dy79QBNtqkDYUxvZYeoW99AfC+foHR7Ruhra1N8eLF02R19ff3R0VFhW3btlGiRAkxG3yPHj0wMDBAR0eHpk2b8uzZMwA2b97Mvn37WLp0Kfny5cPc3PxPvE6JTCBnzUwlJLI5yknCxYsXqVatGsbGxtSvX5+GDRtSunRpceKZlRbu/4Zy8iuTyVBRUREXC126dMHe3p73798TEREhBiKuWrUq+fLlw8PDg2LFilG2bFlu3ryJr68vderUYd++fXh5eYmLxdQxXH5UaaCU6fTp08hkMtH9bMGCBfj4+HDp0iXg52K3CIJA165d0zXouETWQqFQoKWlxfz58zE0NOTjx4906dIFfX19TExMGDVqFLdv3yYgIAAVFZVMW/h9za5duzAyMqJ58+aZLQqQ0v6U7bZkyZL069eP/fv306lTJ4oXL06/fv2oWrUq+vr6mSxpxuHn54e7u3u6JT1RJqqws7PLcQrKv4HUrs/KMb9z58507tyZgIAAPn36BMCiRYsIDAzE3t4eGxsbjhw5wuLFiwHEeGx/ikePHrFmzRrxPl++fGHZsmUYGxszaNAgHjx4wIYNG3ByciIqKor79+/TuHFjBEHAzc2NW7duYW5uzo4dO0hISODIkSPf7aO+V38zY/6j7LOSkpIoV64cly9fRktLCx0dnQyXRQIsLS35/PmzmPAqIxEEgUmTJhEVFcXhw4dRV5cySv4Mx44d4+LFi7x69YrTp0/Trl07HBwc+PTpEwqFAicnJ4KCgujQoQOzZs0iLCyM5cuX0717dz59+kRMTAxjxozBxcVFVNTXqFGDl9EKarTpyoNzR8V7PTp/nDImTciXv9B/ypVLQ5Ne9us4//I9Z8+eZcOGDZw4cSLNOVevXuXFixe4uroC0K5dO16/fk1ISAi1atUSQ9cMGTKEvn37MmXKFKKjozl9+nT6vUCJTEWaUUlIZCOUkzcHBwccHBwIDg7Gx8eHV69eER0dLSqgsorlyX+hnNi/fPmS7t27M2PGDIKDg9m0aRPq6upMnDgRHR0dvLy8UFVVpVChQlSpUoVXr17x8uVLTE1NqV+/PoUKpQyKyr9TK+x+RSa5XM6VK1fo0qULffv2pVevXsTHx7N06VIOHjyIiYkJDx48SHPd94IxK1FRUaFq1ao/HONNIvuRuq69efOGhIQEihUrRlhYGOHh4YSFheHn58eQIUMA0rhxZxYJCQnY29tjb2+fqXKkZsWKFRw4cIDw8HC6du1K2bJlRXfQOXPmsHz5cnr27AnknOx6a9aswcbGhnz58qVLecePHydXrlzSLnk2RSaTERkZSdeuXZk2bRrjx4/H09OTKVOmkJSUxIEDB4iLi0NdXR0nJyfRUjZ37tzAj1tv/w5BQUH4+flx584d/Pz86NevH0+fPuXt27eMHTuW27dvExoaioGBAWXKlOHhw4eYmJgwevRoTpw4wZgxY+jYsSNbtmwhd+7caTbe/guldVxGouz/79+/z969exk6dCgNGzakT58+jBs3Di8vrwyVJ6ejtFrLjAyhCoWCuXPn4uzsLLY5iR9n9OjR6OvrU7RoUZo0aUK9evWoWbMmefLkoWvXrjx48IC9e/fSvn172rdvj0wmo3Xr1tSpU0eMuyiTyXj69ClxcXEUKVKEypUrExInp0ZHSx65HhfrxIOzR6jZoccPyVW6TiP0ylbic4JAtWrV6N27N1evXk1zzrx589DU1BQ9U2xsbNDS0iJ37tzMmzePR48eiYlYJP5OJMWahEQ2InVWUG3tFD//7LK4FAQhjZWOIAj4+/szcOBAdu/eTb169dDR0WHo0KHkz5+fadOmERERwc6dOylQoACPHz8GoF69emhpaREVFUWlSpWwtrYW34WSX51UK9/l+fPnRWVY3rx5efDgAQkJCSxYsICtW7eSP39+3r59C6SY/IeHh6OiovKfirzsovCU+D0qVqyIiYkJPj4+zJ8/n549e2JjY4OlpSUVKlQQrUYyuz5s27aNypUr07Bhw0yVIzWamprs2bOHoUOHcvDgQUJDQ2nZsiXr168nMTGRQYMGUa9ePTHr4d9OZGQku3btYtSoUelSnlwuZ+7cudjZ2WV6/ZP4caKjo8V/h4WFMXz4cNq3b8+ZM2e4dOkSq1at4vXr1zg4OODs7MyNGzdQKBTUrVsXBwcH4H/Knz/53ZUL1jp16lCkSBHevHmDkZERRYsWFcflZs2a0aFDB1asWAFA165defjwIdevX6dr1644OTlx69Ythg4dCvxvXM7K7T0+Pp7Y2Fj69etHdHQ0165dw8HBgWbNmhEeHi5aEWYVS+WcQK9evQgJCcHNze2PlP9PCjtVVVV0dXWlsB+/SGprdA0NjW/+Hx0dTUBAAEeOHEFXV1f84+HhwYcPH9DU1OTQoUNs3LiRIkWK0KFDB7y9vUmQKyhRtTa58mjge/cGIX6v+fzOj4rN2v6QXG+f3GPLkC70rVUKHR0dNm7cSGhoaJpzUrt8y+Vypk2bJq7XjIyMAL65RuLvIuuOUhISEt+gnFg6ODiwaNEi0b1w7Nix9O/fn4CAgEyW8J9JvYt88+ZNVFRUKF68OC4uLgBMmTKFIUOGULBgQbZt2wakWK8EBARw48YNMYGAsbExixYtok6dOmLZ6bUjqXy/Fy5coFixYqKF2c6dO2ndujV6eno8f/4cTU1NihcvTnR0NIsXL8bGxoZOnTpx69Yt4H+TZ2kSnfNQLgLnzZvHnTt3ePfuHXv27KFt27YMHjyYwoULi8kNMpO4uDgcHByws7PLVDm+ZuTIkbi4uNCxY0f27t3L4MGD2b9/P9WrV2fx4sWMHTs2s0XMULZv306rVq0oUaJEupR3+PBhtLW1adv2xxYTEpnP1atXOXHiBFFRUSxduhRNTU3mzJmDmZkZLVq0oH79+pQuXZrt27dTuXJlOnTowN27d9Mo0P6E1c73ylTes3DhwlhZWdG7d29UVFQwNTUld+7cvHnzBoDBgwdz7tw5YmJiqFKlCh07dqRKlSrA/8bh37E8z2j279/P7t27OXbsGLGxsRgaGtK8eXOGDh3Krl27ROvQjLaky8moqqoya9asdLda8/X1JSQk5F+tKKVNiz9L8eLFsba25suXL+KfmJgYpk2bBkCbNm24ePEiHz58oEKFCgwePJjcqin9SK2Oljw8d4QHZw9TpZU56rnzAJBLIy9J8XHiPaJCP6a556GZw6jYtC07PF8QERHBsGHDvqlXqb/7/v37OXnyJJcuXSIiIgJ/f3/gf/2mVEf+TrL+aCUhISGiHMTHjRuHjo4OHTt2xMTEBBMTExo2bJhurkLpwdcTDoVCweLFi6lVqxarV6/GwcGB0NBQJk+ejI+PD4D4TBcvXgRSLH/mzp1L9erVef48JeCocrKdekBL7wFq8eLFDBgwgPz58+Pr68vp06cZM2YMAGfOnKFatWqiBdvhw4dxdnbG1taWVatWkZycLE6elX9LCracQ+pFYExMDJ8/f8bAwAAbGxtu3rzJ0aNHRfcFJZlRPzZt2kSdOnXSKKizAspA5506deLMmTNYWlpy8OBB+vbty/nz58XzcsKkVC6X4+TklG6xGZOTk5k3b16WSlQh8c8o+4WaNWuyfPly6tSpw927d8mdOzfGxsacPXuWLl26sGXLFoyMjDhz5gybNm1i0qRJTJs2Lc03Ts/vHRcXx+rVq7l///6/nlekSBHxvh06dCApKYn79+8TFxdH/fr1KVGihOi6NXjw4G9CJXxPCZWY+OsZ/f4kgiAQGBjIjRs3xLmJQqFAoVCQnJycydLlXHr37k1wcHCawPa/w86dO7GwsKBFixZcunRJzGAvkbFYWVlx+vRpXF1dkcvlxMfH4+7uTmBgIB8/fuTkyZPExMSQO3du8uXLh0wmo7CGKmoqUKN9D55dOcfDc0ep1aGnWKZBuSp89HnJ+5dPSEqI59KmZWnumRATTT4dXYrqanL79m3279//rzJGRUWRO3duChYsSGxs7DdZ4fX19fH19U2/lyKRJZAUaxIS2ZDIyEgmTZrEoEGDsLKywsrKiqFDh1KwYMHvnv+9gV/525+aqH69y+zr6ysGKba1teXgwYMcO3aM/v37c+vWLd6+fYuamhr169cnJiaGgwcPite2adOGXLlyAf+bbP+phaEgCGhoaIgWIlFRUWKsp+DgYJ49e0bVqlWRy+X4+flhYGBA8+bNcXd3R0NDg6CgIK5du0bdunW5du0a8fHxosxfu8NK/N0MGjSIunXr0rZtW8qUKUPr1q05fPgwpUqV4ty5c6KrU0ZbMcTExLB48eIsZa2mbBf79+9n3LhxDBw4kMWLF9OrVy9OnTqFjY1Nmiy/OYGTJ0+ir69P/fr106W8/fv3o6+vj6mpabqUJ/HnkMvlYr9w79492rVrh7q6uug+KZPJ+PjxIwcOHCApKQlPT08sLS3p3r27WEZ6W8Uq5wwaGhq8evWK27dvExkZCaTESX316tU/XqehoUHDhg25dOmSuJg8dOgQPXr0SHPeP6F8ltRKKn9/f3FTLrMZNGgQCxYsYOXKlWhqagIp30gmk0kZITMRNTU10Wrtd1EoFBQqVIjz58+zYMECLCwsOHv2LCoqKimuhgkJ6SCxxI9QvHhxTp48iYODA3p6ehQvXpxly5aJyuyVK1diaGhIgQIFuHr1Khs2bKBqgTwIgK5BUQwrVAMVFYxqNRDL1CtZBtPBE9k23IIVXephVKNemnt2nr4E1w1LaF1WHzs7OzHW6z/Rr18/SpYsSdGiRalUqdI34/igQYN4/vw5urq6dOnSJb1ejUQmoyJIqnYJiWyDMujwunXrCA0NxcLCAgMDAxQKBYIgULBgwZ9apIeFhTFlyhSioqLYtGkTurq6vyxb6oUAgLu7O3v27KFBgwbY2tpy48YNBg4cSO3atfHz86N///7Y2tqirq7OwIED0dXVxdHRkeTkZG7fvo2xsTGFChVi9+7d2NnZ4erqStmyZX9ZvvTg3LlzHD16lNmzZ/Ps2TOWL1+Ou7s73t7ebN26FR8fHw4ePMjIkSN58eIFZmZmbN68mc2bN9OuXbtvlI2ps7tJ/F0ov21cXBzDhw+ndOnSzJo1C5lMxrhx43BycqJZs2bMnz+fpk2bZmhdWLJkCffu3ePw4cMZcr+foU6dOqxfv54pU6ZgY2NDv379uH37NnXr1s1s0TKcJk2aMHr06P+cwP8ISUlJVKxYka1bt2aZDLAS/87Hjx85dOgQHh4eHD58mBkzZvD582dmzZolxvLp2bMn/v7+NG7cmJUrVwLpn5xAEIRvYhpeuXKF48ePM3PmTAoUKICjoyNdunShfPny371eRUWF8PBwzpw5g4WFhRh/6t9kFQSB5ORk/P39SUhIEF1FlSxcuBALC4vv3jMjUW6m9e7dW/xN+VzZIT7c305ycjIVK1Zky5Ytv9z3vX37loIFC4pKU4ATJ05gZWVFx44d8ff35/Lly2mOS2Q9jvlG8joikaPzxqCtZ4DZyBn/fVEqyunkoltp7f8+USLHIinWJCSyEUrlVdeuXfH09ERLSwu5XI6amhofPnzA09OTKlWq/NDEOiwsjGXLlhEYGEiTJk3o1KkTBgYGPyVPUlLSN6nEPT09KVCgAPPnz6d+/frs2bOH2bNnU6xYMSZOnEibNm3EOAgxMTHExMTw4cMHhg4dyvXr178pLzAwEENDw0ybmH6t8Pjy5Qv58uUjMDCQqVOnsmzZsjTxj/z8/DA1NeX06dNUrlyZefPm8eHDB4oVK4anpyerV6/G2NiYhIQE9u3bR2JiIsOGDcuMR5P4wyjba3h4OPnz5ycgIAALCwtkMhlbt25FR0eH9u3bc/DgwW8WjX+KyMhIypYti7u7O5UqVcqQe/4o165d49ixY6xevZp69eqJMQvbtm2Lk5MT5cqVy2QJM467d+/SvXt3fHx80sXiZdu2bezfv5/Lly+ng3QSfwJlf6Ecv01NTQkLC2Pfvn1UqlSJuLg4LCws6Nq1K927d+fy5ct0796djx8/imN3eivoU88lQkJCuHLlCpaWlgBiv/YnSH3fT58+oaen983vUVFRaGlp/ZH7/wynTp1i1qxZuLm5oaqq+t13EhcXh7+/PxUrVswECSV27drFzp07uXLlyk9fGxISwtixY8UslAULFhTr4fPnz7lw4QJhYWHY2dmhUChQUVGRXO2zKB9iklhz9QmOli0YfcCNAkV/3ApeTQX6ltOhSF71/z5ZIscibaFISGQjlBZhzs7OBAcH8/r1a3x9fXn16hVRUVHi4vxHBvV79+7x/Plz7O3tGTJkCAYGBly4cIFx48YRFRX1r9eGhYXh6OjI0aNHgZSMWJcuXaJJkyYMHTqUfv360bFjR8aMGYO1tTWenp5ER0djbm6Op6cn8fHx7NixgzZt2uDh4UH16tXx8vJKo1RT6vyLFSuWqbu9X99bV1cXNTU1SpQoQZUqVejcuTNdunTh0qVLQIo1UMeOHalcuTLv378nISEBf39/pk6dSoMGDdiwYQOCIBAcHMzly5dFK8GkpCQpVsdfhrK95s+fn3v37lGqVCnMzMy4desWVatWpUSJEpQqVeo/YxWlJ6tXr8bMzCzLKdUgJeB5aGgo1apVw8rKCoCDBw+iUChylFINYNWqVYwePTpdlGqJiYnY29tnKddfiW9RVVUlPj6eZ8+eAbB582aioqL48OEDkOKCOX36dC5fvoypqSkHDhwAEJVq6ZUpN7ULqYqKCkFBQWzYsIGuXbuyfPlypkyZwtOnT8mfP/8vhTb4kXEutTJPEAS+fPki/i4IAgqFIkso1QCqVq1KgwYN6N27NwMGDKBHjx5cuHCBZ8+esWHDBq5fv86CBQtYsmRJZouaY+nbty/v3r3j6tWrP31t4cKFsbW1xdXVlXv37gH/q4eVKlWiSZMmLF68mC1btiCTySSlWhbGacEcHHs0oXn/kT+tVGtZVFNSqkn8J5LFmoRENkQQBLZv346LiwurVq0iX758uLu706JFC3R0dP712rVr1xIbG4u3tzcVKlRgypQp4rHdu3fTpUsXtLS0/nVyEBcXx86dOwkODmbWrFksWrSIy5cvM3bsWLp168bgwYOJi4tj7969vH37ltmzZ9O1a1fatm3LrFmz8Pb2RlVVlVGjRtG6dWux3K/dSbMDcrmcAwcOUKtWLYoVK0bJkiVFV9YrV65w9OhRunTpQuvWrdm8eTN79+4VLXM2bdrEgAED6NOnD/C/3fgHDx5Qs2bNTH4yifTm4cOH1KhRA4DXr19z/fp1bt68ydy5c9Okaf9ThIeHY2xsjKenJ8bGxn/8fj+C0sJm7969QErslIULF2JoaEjBggW5d+8e06dPp02bNtmyf/gVgoKCqFq1Kr6+vr/lnq9k06ZNODs7p0n+IJH1OHfuHAsWLKB69erkypWLyZMnc+bMGfbs2YObmxu5c+cGUurHly9fqFy5crre/58s3W1sbPD09OT58+d8+fKFBQsWYGhoyMSJE9Oc9/btW86cOUO7du0oVarUL8uh7BN27drFqlWrqFq1KqVLl6ZBgwaYmppm2Zhlvr6+hIWF4ePjw/r16ylSpAiFCxfmw4cPKBQKhg8fTqtWrTJbzBzLjh07xLb0I3zdHh49ekS5cuVEN2b4X129e/cuHTp04OTJk+kWE1PizyAIAg9C43ELiiH5BzQgSqVaLT2N/z5ZIscjWaxJSGQjUmcF/fjxI69fvyY0NBRdXV1WrVolBgX+t2QFycnJbNu2jb59+4qZLpUBPy0tLdHW1v7PHTcNDQ2qV6+OhoYGcXFxtGnTBi0tLXG3fMqUKTx+/JjAwEBKlChBmTJluHDhAuHh4SxfvlxMQ51aqQbZKxW9MhGBqqoqVlZWVKpUieDgYIYPH46xsTHh4eE8evQIDQ0NWrZsCcDWrVvFDH9OTk5i0OkaNWrg6ekpvvcjR47w9OnTTHs2ifRF2W5r1KhBXFwcN27c4NChQ7i7u9OxY8cMUaoBrFy5ks6dO2cZpRr8zyLUy8sLIyMjMe5chw4dyJcvHxs2bKBNmzZA9uoffod169ZhZWWVLkq1+Ph4FixYkC7BuyXSH+W4/PHjR3bs2MHx48dp27Yt58+f586dOwwbNoyCBQsyefJk8ZqiRYuKSrXfTYYTGBhISEgI8D8rMVdXV6ZNm8axY8cAGDBgACEhIcTHx5M/f37q1KnDu3fvePDgAfC//k1TU5N69er9llJNaXWnUCg4ceIETk5OWFtbkzdvXs6fP4+DgwMPHz78jSdOf5TPX7p0aerUqYOlpSWFChWiY8eOLFu2jPXr13Ps2DFJqZbJWFlZERAQwPXr13/ofGV78PT05MOHDxgZGaGhoZHGolM5fikzbCvbkkTmsHHjRnx8fIiNjf3Hc1RUVKilp0HfcjqU08mFqkqK8iw1aiqgqpISU61vOR1JqSbxw2TNbR8JCYl/5dmzZ8yYMYN79+6JWT1TLzq/pxhT/ta+fXvu3LlDxYoVyZMnD/C/yYFyRxxSYonp6OiIAXi/djExMTGhQYMGqKioUK9ePUqWLMmHDx+IiorC2NiY+vXrs2zZMlavXk337t158eKFGCdFW1tbdOfIrotlFRWVb2QvV64cDg4OAISGhvLixQsaNmyIqqoqrq6uKBQKzM3NuXz5MjKZjN27d1O8eHFsbGx4+PAhDRo04Pr160ydOjWN5WFOsdT5W0nddkJDQ9m4cSMFChRg+vTpYsyd1G0sOjqauLg4sb2kB6Ghoaxfv150ZclKHDp0iIsXL1KxYkUaN25MgwYpmbpSZwzMKcTGxrJlyxa8vLzSpbytW7dSvXp16tWr998nS2QIqdu6clwODQ1FoVBw9OhRdu3axaJFi+jatSuCILB161bq1q3L+PHjv1Fa/eq4oLTG2bt3L02aNKFw4cJAinXjjh07GDhwIAsXLiQoKIjevXvTvn171q1bx6RJkzAxMeHx48e4urpSs2ZN8VkKFiz4j5nJfxTl+1i7di0KhYImTZoAKZsS169f5+rVq1ku++L33G/Xr1+Pvr4+QLr24xK/jrq6OjNnzmT+/Pli6I7/YseOHcyfP59q1apRt25dunbt+l1L0adPn1KrVi06deqU3mJL/CBDhgzh5s2buLi40LdvX1q1akWBAgX+8fwiedXpVlqd2CQFj8Pi+RQnJ0GuILeqDD0NVaoVyENedcn+SOLnkGqMhEQ2RENDg+joaBITE0W3CLlc/k3g/69JSEjgwYMHTJkyBUNDw388b//+/Tg4OPDp0yfg+xNHdXV1VFRUSE5OBqBVq1Y8evQIHx8fICWmxZs3b1AoFFSuXBkLC4s0LhzfU0xld1LvZBobG7NkyRIxo9+MGTPo1asXampqHDp0iBo1alC8eHEiIiIoWbIkSUlJAPTq1Ytz584BKdYmkLJ4CggIYMeOHeI3kcieFC9eHGtra5YvX07FihVFy0eZTEZSUhKOjo6sX7+e27dvi98/PVi2bBk9e/bEyMgo3cr8HeLi4vD19SUhIYEmTZqIWQ3HjRvH27dvM1u8TGP37t00atSIMmXK/HZZcXFxLFq0SIqtloVIrVTbsGED8+fP58mTJxgaGhIfH8/OnTu5c+cO3bp1IzQ0FCcnJwoXLszjx49/yxJMSXR0NPfu3RMVWNOmTUMQBNHSxsXFhTlz5jB06FCWLVvGhw8fuHXrFra2tmJM1bJly1K6dGn09fX/mJJLQ0ODq1ev0rFjRwICAtDT06Nbt25MmjQpWyiJlUo1iayFtbU1vr6+eHh4/Oe558+f59atW9y7d49p06aRlJTEvn37ePz48TfnVq5cGXt7+z8hssQPsnDhQp4+fUrPnj05f/48Li4ufPjwgbNnz/7rdXnVZdTXz4u5kRYWZXQwN9Kivn5eSakm8UtItUZCIhuhnJAvXryYmTNncuPGDRYtWkSpUqXo06fPf2acUldXp1OnTlSrVu2bY3K5XFQMNWzYUHQ1hZTduEOHDhEREfHNdUplWbt27QgNDeXWrVskJibSrFkzzp49m6PSzKd+VkEQ0NXVFa0AR4wYwZAhQ/D09OTQoUM0bdoUAG9vbyIjIylXrhzu7u6ULFmS3r174+Pjw6RJk2jWrBmHDh3Cw8MDT09P/P39xfIlshfKb2ZmZoa6ujpyuVxUMCckJHDy5EmWLl3KuXPnqFSpUhoL0t8hODiYLVu2MHPmzHQpLz04cuQICxYswNnZmYIFC7Jt2zYuXbpEZGQkpqam9OvXj7i4uMwWM0NRKBSsWrVKdBf/XTZu3EjdunWpVatWupQn8fvIZDK+fPnC2LFjuXDhAp8/f2bx4sVs2LABa2trjIyMuHTpEufPn8fU1FQcc5VW3r9Lrly5mD59Ot7e3qxbt47Q0FAcHR1FpYCxsTGBgYEANG7cmPDwcCIiImjSpAkKhYLNmzcDKe6hAwcOTLc+6msGDx7M8+fPMTExoVOnTsycOZOkpKQMc5v/XVJvsklkHZRWa/8VZy02NpYVK1Zw7do1ChYsSMOGDWnXrh25cuVi8+bN3yjXpIQFmcfixYu5f/++aBnat29fmjVrxrVr12jZsiVr1qzJZAklchJS8gIJiWxKQkICt27d4vPnz9SpU+eXJ5xfuxkqXURWrlyJiYkJTZo04e7du6iqqv5jQH3lLrybmxvGxsZpZJHcGNPy5s0bFi5cSExMDI0bN8bFxYX69eszd+5c2rdvT69evTAxMWHFihVoaGhga2uLg4MDWlpaVK9enREjRojvUxCEdMsEJ5F5vH79muPHjxMeHo62tjZbtmzh4sWLlC1bNl3KHzduHIIgsHr16nQpLz1ITEzE2dkZd3d3Jk+eTOnSpcVj/v7+7N69mzlz5mSihBnPuXPnmDVrVhqLol8lJiZGjG35vY0UiYwj9RgYHR3NnDlzOHv2LC9fvgTg4sWLnDt3jvbt2xMeHs65c+cICgpi0qRJYnzBX0Wp4JHJZKIczZs359GjR3To0IF169YREBBA79698fDwYO/evbx//56hQ4diZGTE4MGDMTExYciQITx8+JCSJUuSP39+sfx/Snjwsyhle/XqFSdOnCAiIoIqVapQp04dYmNjmTx5MrVq1WLx4sW/fa+M4PTp01SqVCldLE8l0pfk5GSSkpLSJCGAb+vyu3fv6NatG9WrV2fr1q1ASixQd3d3bG1tKVSoUIbKLfEtYWFh7Nixg2fPnjF58uQ0xgUxMTHs2rWL+Ph4Ro8e/Z8ePRIS6YGkWJOQyKa8fPmS169fEx8fj0wmIy4uDnNzc7S1tX+pPA8PD1atWkWTJk0YPnw4uXLlSmeJcwYJCQmoq6t/Y7329eLj2rVr7N27l7p162Jra8ujR4/o27cvnp6ebN++nbi4OAYMGICBgQGTJk3i7t27ODo6UrZsWZydnalfvz7lypXL6MeTSGfCwsJwdHQkKSmJ2rVr06NHD7y9vUlISKB69erA7y1eAwMDqVatGs+fP8fAwCA9Rf9lvLy8qFev3jfxG1MrAZR8L77j30rr1q2xtramX79+v13W0qVLuXv3LocPH04HySR+F4VCwZcvXyhQoACXL1+mX79+bNy4EXNzc0JCQpgwYQKWlpaYm5ujUCgQBEFUxqVHGwgJCSE2NpZixYqxadMmlixZgp+fn3iPDh060LZtW/r06cOCBQt4+fIlOjo6BAQEsH37dipUqPDb7+BHqFOnDp07d+bz589i8o7p06eTO3du4uPjyZMnT7boE1q1asWUKVMwMzPLbFEkfgDlGOvu7o6vry958uShT58+BAcHM3jwYPT09Ni+fTuAWA/TS6ks8Xt8+PCBEydOUK1aNRo1apTmuxw8eBAXFxe6du1Khw4dJOWaxB8na49MEhISaVDqwefOnUvv3r3Zu3evGEvA2dn5P12nlPGcUuPt7Y2FhQVHjhyhSZMmeHh4sHbtWjG+09e696ioqB+SMScSFRWFmZmZmFBCSerJl/L9N23alM2bN2NrawukxIdo3rw5WlpaBAUFkZycjIGBAZGRkairq9O6dWsqVqyIXC5HLpfTt29f+vXr900Wqt/NEieRsdy7d4/bt28zZMgQMVh/VFSUmFUWfs/NxMHBgUGDBmUZpVpISAg9e/YUn0lpSaP8t0wmS9OHZPUFdHrx5MkTnj17Rq9evX67rKioKFasWMG8efN+XzCJXyJ1P3z9+nVq1KhBnz592L59O6ampsyYMYOtW7fy6dMnChcuTFxcHF++fAFS6ryqqup3Fc3/xbVr15g6dSqRkZEA+Pn5YWNjg5mZGdOmTcPNzY2RI0fSsmVLZsyYIV43depUtm7diqamJsuWLWPYsGG0a9eOGzdu/HGlmrK9X79+HT09PWbPns2qVavo3r07169fZ/369cD/kitlhz7hzZs36WZxLPHnUVFRwc3NTczCa2try7x58zAwMGDHjh0EBgaKGx7Keigp1TKP1P1rkSJFGD58OI0aNQLSfpdevXrRokUL9uzZ86+ZQiUk0ousPzpJSEiIKCegbm5u7N69m4MHD7J161a2bNnC0aNH/zVgrnIXR1VVldjYWIKCgoCUGGnOzs60bt2asWPH0rdvXwIDA7/JSpeUlMSiRYvw8PD4V+VZTp5sHD58mAIFCojZVr9HaisE+N833bZtG4sWLQL+p2AAOHHiBO/fv6dp06aoqanx5MkTSpcuzZ07d9DR0RG/ozL5QWo3UYmsT6VKlXjx4oWYBXb8+PG0a9eOZ8+eMXfuXA4ePAj8WsyegIAADh06xJQpU9JV5t/hy5cvFClShHnz5omJTr6uszmxD1m1ahUjRoxIF0thJycnWrVqRaVKldJBMomfJbW1WXh4OLdv32bNmjXY29sze/Zsnjx5gpWVFaqqqrRo0YLBgwcTExPzjdvnzyiQlP2DkZER165d48WLFwA4OzvTtGlTHj58iI6ODgsXLsTHx4epU6dy/Phx8fqmTZuSN29ejhw5gpqaGp06daJfv36oqKj80c0aZZxJgEKFCvHp0ydOnDhBcnIyVatWZebMmTx79gzIPv1CQkICwcHBlChRIrNFkfhBvnz5wrx58zhw4ADa2tqUK1eO69evM3z4cAoVKsShQ4eYNWsWkH3q4d+Ksn999eoVM2bMYPXq1eKmhHIerDwPUqxHb926JcaOlJD4k0iuoBIS2QilcmzatGk0bdqUtm3bihYe3xvs7927x6ZNm8SAw7GxscyZM4fTp09jbGxM9+7dGThwIMOHD0dFRYX169cTFBTEli1b0NTUZPLkycD/XFHev3//r9lE/0nenEKjRo2YNm0a5ubmP3Wd8j0p/7537x5DhgyhaNGivHz5kjZt2uDk5ESXLl0oWLAgwcHBYqa0CRMm0LhxYxwdHblw4QI9evRg1KhRfyyotET6oYwrdPv2bXR0dNi3bx9eXl6ULl2awoULM2LECFq0aMHVq1cpXLjwT5dva2uLgYEBCxYs+APS/zqPHz9m586dvHnzhnr16jFx4sQ0yuic1m+EhIRQvnx5Xr9+/dtxeyIiIihbtiweHh6UL18+nSSU+FnevXuHhYUFuXLlIjExkTNnzqCnp8fChQu5ePEiFy9e5MWLF9jZ2VGqVCmWLVsG/HzdTx2/TXntpEmTUFFRYcmSJQiCwKtXrxgxYgTq6uro6OjQqFEjxo0bR58+fVBTU+PmzZusXLmS5s2bpwklkRHtsG/fvqxdu1aM27Zz506ePn2KgYEBZcuWZc2aNfTq1YvBgwdnCxdQSPEC6NSpE69evcpsUST+hdTzWi0tLaKjo4mIiODw4cPMnj0bSOlPX758Sd26ddPM0SQyB+U3+/jxI506daJp06ZER0fz6NEjXFxc0NHR+Sau84sXL3j8+DGWlpaZKLlEjkGQkJDIdoSGhgoRERFCVFTUN8eioqKEpKQk8f8xMTFCfHy8IAiCcPbsWcHCwkIQBEFwcXER+vbtKxw4cEB48uSJULVqVSEyMlIQBEHYs2eP0L9/f+HevXs/LZtCoRAEQRAiIyOFM2fOCImJiWl+/1t58eKFYGBgkObd/y63b98WnJychCNHjgjh4eFCkSJFxGN79+4VzM3Nhbdv3wrr168XrKyshKCgIGHAgAHCwIEDvykrPDxc8PHx+eu/Q3Zm0qRJgqenpyAIglC/fn3hypUrwoQJE4SQkJCfLuv169dCwYIFhbCwsPQWM10ICQkRLl26JNja2gqXLl0SduzYITx//lwQBEF4+PChEBMTk8kSZhzz588XBg8enC5lzZ07V+jfv3+6lCXxcyj71l27dgnTpk0T9u7dK9y6dUuwsrISli5dKp5namoqDB8+XBAEQTh8+LDQrFkzwc/P76fvkxpnZ2fh0KFDgiAIgq+vr9CsWTPh4cOHgiCk1Inly5cLgiAIy5cvF2rVqiW8fPlSiIiIEA4cOCBcvXr1P8v/Uzx79kyIiYkRatasKbi6ugqxsbHCwYMHhXnz5gk9evQQVq9enWGypBenT58W2rVrl9liSPwDqedo4eHhQtu2bYVHjx4JgiAIiYmJQmxsbJrz5XJ5hson8e+8fftWGD16tODg4CD+Nnr0aKFRo0biGkbZh0nzXYmMJutv/UhISIgI/29gqqmpiba2Nvny5fvmnPXr1+Pt7Q1AcHAwly9fpm3btgCEhoaKbh1t27alRYsWeHt7U7x4capVq4ajoyMADRo0wMrKiipVqvywbMnJySQmJiIIAidOnKBChQoIgiAGC/3w4cOvP3g2YMeOHfTr1w81NbXfLkv5jUxMTBg9ejRdunRBTU0NExMTJkyYgLOzM/v27RMzsFarVo3AwECeP3/Ojh07WLlyJYAY6y0yMhJPT08OHDgg7rZ+HQdOIvN59eqV2HaXLVtGy5Ytef78OXp6ej/txjB//nzGjBmTJoNfVkJPTw9TU1NiYmKYPHkyDx48YMKECfTv359OnTr9ZyzHv4WEhAQ2bNjAuHHjfrussLAw1q5dK1pbSPx5UrtoK/vWq1evcvDgQZo3b07dunXp0aMHb9684eTJkwBs375dtEBt0qQJnTt3TuPC9G+kdp2ElBhqdevW5ejRozx79oyRI0diaGhIrVq1RFfPuLg4Hjx4AMDbt28xMDAgMDAQbW1tevXqRdOmTdPcIyMtcipVqkTevHkZNGgQ48aNY9SoUdSrV4+5c+dy+PBhRo0aBfyaK3xmIcVXy9qMHz+eDh06IAgCurq6dOvWjcqVKwOgrq7+TbbQr+N+SmQ8qd9/VFQUt2/f5tGjR3z+/BlICX9QpUoV6tWrR3JystiHSdaFEhmNpFiTkMiGpHabUmYQUw48uXLlomPHjtSvX581a9bQokUL3r17h6+vL7q6umJMJwBtbW3u3r2Ljo4OFhYWPHz4EIAyZcrQqlWrH473I5fL2bFjB46OjshkMm7evElsbCzr1q0TA7CvW7eO8+fPp+NbyDokJSWxe/duBg4cmC7lfR2HTU1NjXz58rF582bU1dW5cuUKoaGhdO3aFUhxQZ01axaHDx/m0aNHYjY15fdzdHSkf//+BAQEiPdwdXUVFXASmYvyO8+ePZulS5dy584dGjdujLe3N2fPnuXKlSvMnz8fPz8/4L8TVLx48QJXV9d0Udb8aVq1asWhQ4dwcHBgzpw5mJiYsGLFin+NF/k3ceDAAapXr54u8dBWrlxJ165dKVOmTDpIJvEjKF0Tz507x7p16/D19WXbtm0UK1YMZ2dnIKV/rl69OgcPHuTNmzeUKFFCTCxhYGDA+PHjMTY2/sd7vHjxgrCwMCBlbJDL5Tg6OrJ3716uXLnCnDlz2Lt3L76+vty+fZugoCD69u2Ll5cXr169wsbGhvDwcCpUqIC6ujo7duygZcuWYvmZoTTw9/cHUpItWFpaMnLkSO7fv4+mpiZt2rRhxIgRJCQkpElykl2QFGtZmzVr1qCmpsbmzZsRBAFra+s0roPfQ1LQZB4KhQIVFRU+fvxIeHg4lSpVYv/+/YSGhrJv3z4iIiIA2LhxI6tXr06XzW0JiV8ms0zlJCQkfp/UJupKd89Vq1YJBQoUEHbt2iUes7W1FaZNmya8e/dOmDJlitCvXz9BEARhwYIFwtSpU3/p3vHx8UJ4eLggCCluib169RK8vLyEL1++CMbGxsKjR4+E3bt3Cw8fPhQePnwoJCQkiNf+bebZp06dEho2bPhH75H6nSkUCsHNzU0QhBT3wf379wuCIAht27YVv2fq8y9cuCCYmJgILVq0EBYuXCi6FgYHBwuCkOIa8bd9k+yGsi1v3LhRWLRokSAIKe6cy5YtEzp16iQ0bNhQ6NOnj9jO/+179ezZUyxDIuuiUCiEatWqCefPn//tsj59+iQUKFBA8Pf3TwfJJH6GoUOHCmZmZoKjo6PQtm1b4fLly8KdO3eE0qVLC8+ePRMEIcW9ed++fWL7FYQfczH79OmT0LlzZ+HixYuCIAjC1q1bhYEDBwrDhw8XoqOjhUWLFgkGBgZCvXr1hBkzZoiuUIIgCL179xZmz54tCIIgvH//Xvjy5Yt4LDP7e7lcLuzYsUOoWbOmYGxsLJw+fTrN8VevXglWVlZCaGhoJkn4e7Rp00Y4e/ZsZosh8RUKhSJNvf/a5VMi66H8Xrdv3xYaNWokDBw4UFiwYIEQGhoqvHz5UjAzMxNWrFiRpm9LfZ2EREaTfbaAJCRyOIIgEBsby+bNmwkODgZSdnEfP35M7969GTZsGLdu3WLs2LGMGTOGa9euiddOmTIFZ2dnNDQ0GD9+POrq6jRt2pS7d+8yfPjwNPf5N2sYIdXOtkwmY/78+ZiZmTF58mQqVqxIvXr1mDVrFg0aNKBatWpYW1tjbGxM48aN08ij3P37k9nG/hQxSQq8PsZyyj+KIz4RnPKP4sLrYAYMGfZH76sMnKvcvWvRogUAzZs3Z+vWrTRp0gRATJygfMcfP37ExcUFKysr3NzcsLa25uLFi1hZWaGvr098fDxqamppypfIeJTfa+jQoUybNg03Nze2b9/O58+f6du3Lzdu3KBQoUKMGDEizflf8/jxY65evSq6UGU3hBzkcnPlyhWSk5MxMzP77bKWLVuGpaUlJUuWTAfJJP6Jr8esd+/eUaBAAVxdXQkPDyciIgI9PT3q1KnDwIED6d27NwDVq1enT58+5M6dW6zj/2SFdeLECapXr86zZ88oVKgQjRs35urVqwBoaGhw5swZ+vTpg6amJhUrVqRq1aocPHiQhQsXoqWlxblz54iMjGTs2LFiOIciRYqIgb2FTA7ALpPJGDBgAF26dCE2NpY1a9Zw/fp18fjTp0/Zvn07BQsWzJbjkWSxlvVQulArFAqxTn3t8vlv5KRxKSuhoqLCq1evGDVqFOvXr6dy5crs2LGDBQsWoK2tjaOjIwcOHBAtYFNfJyGRGUhZQSUkshl169ZlypQpWFhYcPr0aTZs2MCwYcOQy+UsXbqUHTt2UKhQIUxMTHB3dxcXWu3bt6dy5cosW7aM2NhY5HI5WlpavyRD6on5hw8fcHV1pVq1aujp6dG6dWs8PT3F2E6jRo0iPj6erVu34ufnh7u7O1paWlhYWKTPC8kgPsQkcfNjHL6RiagAyal6TpmgQEUmo7R2Lhrqa1BEUz3D5Xvy5AkqKirfxMW7ceMGixYtYsWKFZQvX57g4GDmzZtHrVq1sLCwYObMmZiYmNCwYUMqVKiQ4XJLfEtiYiIzZ84kX758tG/fHhMTEwAxrfyIESPQ09P7bpa8rl270rRpU8aPH58Zov8QCoWCz58/kz9//hzttmFubk6nTp0YPHjwb5Xz8eNHKlasyOPHjylWrFg6SSfxNanb28ePH9HX18fX15eOHTuSP39+KlasyKpVq8iXL5+oXBk1ahT29vbo6ur+8GJv8uTJyOVy0VX/3bt3PHnyhPbt2wNQq1Yt5s2bR6dOnQgICGD58uUEBQVhZWXFpk2bSE5OZtu2bRgZGf2R95BehISEoKGhwd69e1m5ciVNmzalSZMmzJ07N03YguxEUlISWlpaREZG/nAoDYk/i7LdxsTEoKqqmiaUys+WIZExKDN7Jicn8+DBA6Kjo9HW1sbGxoZJkyZx+vRptLS0mD9/Ppqamlk2lqxEzkPqJSQksjDfsyAaOHAgp06dIj4+npYtW3LgwAHkcjmrV6/m8+fPHDlyhEKFCtGiRQs2bNjAsWPHOHbsGKtXrxaVLnnz5kVLSyvN7t3PoFwgKBQKihQpwoABA6hVqxaRkZGUKFECX19fICU2jIuLC3PnzsXd3Z0hQ4Zw9+5d1qxZw4ABA4iOjhbLVMaKy4rc/xTHvtcRvI5IRC6kVaoBKFRkyAV4HZHIvtcR3P8Ul2GyKS0oqlatmkappvyuN27coFy5cpQvX57k5GSePHnCu3fv6Nu3L25ubty8eZOHDx9iaWnJxIkTiY+P/275EhmDIAjkypWLsWPHYmtrKyrVhg8fzvjx4/n06ROTJk3Cx8cHmUyWpv3K5XJKlizJsGF/1nrydzly5AjdunX7z7g2fzOvXr3i9u3bWFlZ/XZZS5YswcrKSlKq/SGUfaBMJiMgIIC2bdvSs2dPnJ2dyZ8/P61atcLQ0JCtW7eSL18+9uzZw4oVK4iLi2Pt2rXkz5//pywoateuja+vr9i2ixcvLirVIGUOsHbtWgBKliyJg4MDnTt35vjx4wwaNIjLly9nWaWa8plu3rzJ3bt3efv2LcOHD+fhw4fkz5+fe/fusXPnTiB7jj0BAQEYGhpKSrUshEwmIyEhgbdv3/6jUk059zxw4ADJyclERkZy69YtZs+eTatWrViyZElGipxjSUhIAFJiSb58+ZIhQ4ZQq1YtGjVqxMmTJ1mwYAHW1tZUrFiRsLAwAgMDRaVaVl0/SOQsJMWahEQWRDlAqKioIJPJiIyMxM3NDYB+/foRGBjIjRs30NTU5MmTJ2zfvp1du3axdu1aLly4wJs3b3BwcBDdLGQyGcbGxvTv3z/NfWQy2W/twn19beXKlenUqRNjx44FYNKkSdjY2JA7d25cXFzo0qUL69at4+rVqwQEBPDu3TsA4uPjkclkqKioZLnJ9P1PcbgFxXyjTPsnkgVwC4rJMOXaPyknlN/m06dPXL9+HV9fXyIiIrh48SLNmjUTk0z06tULJycnHj16JCZFAMSsjKqqqgQEBLB69epsa0WQnVAuwIsVK0bRokV5/vw51atX5/Hjx2LWx5o1a4oWaanboKqqKsuWLfspF5eMRqFQYG9vz8yZM3O0u8bq1asZMmTIb3+r9+/fs3PnTqZPn55OkkmkRqFQiH1sXFwce/bswcrKinHjxrF3716uX79Ox44dSUpKwtbWlkGDBrF+/Xqsra3Fb/uzCz4TExO0tLQ4ceKEKEPqv0ePHk1AQACXL18GQEtLi/79+7N371569uwJZE2llFwuF8NXKLNbd+3alRkzZuDt7c3y5ctxcnISwxxkR8W75AaadVC2F7lcztu3b//Vqkk5Fm3atIl27dqxYMECFi1aRHR0NOPGjePKlSui0kfizxAVFcWSJUs4duwYkJLlWkNDA1VVVXLlykVUVBQLFy7k2bNnuLq6MnjwYOrXry9en5PnExJZh5zrgyEhkQVRmj8rB4jExESmTp3K1atXqV69Ojdu3GD48OGYm5tz7NgxmjdvTkBAAOHh4ZQsWZLQ0FD8/Pw4deoUY8aMYenSpb9k9v47jBo1iuHDhxMYGIi3tzeHDh0iODiYZ8+e0a9fPwDu379P7ty50dHRISAggN69ezNmzBh69eqFqqpqpseAUfIhJumnlGpKlMq1IppqFMmb8W6hqVm2bBmHDx/m6tWrlCpVips3b3L48GGuXLmCXC4Xs8MdP34chUJBsWLFePr0KUuXLuXp06f069cPHR0dvL29+fTpEyVLlpTcIjKQQYMGYWpqKrqFvXr1Cm9vb2rUqAH8r89QktVdK52dncmbNy9t2rTJbFEyjbCwMPbv38/z589/u6xFixYxcOBAihQpkg6SSaRGEATRhaxHjx7kypWLoKAgrl+/Tp48eXjz5g3u7u4MHDiQTZs24eHhQUBAAFu2bEEmk4nj2M+OZcWLF6d27dp4enrSrVs3sa+VyWRie+/Zsyd37tzB1NQ0zbXK41lRKaWUycHBATs7O1RUVPD29iYmJgZLS0tsbW0ZM2YMefPmzWRJfx1JsZY1SD0utmvXjrlz51KmTBmSkpJQV//fnEzZRiMiInjx4gXGxsZ06tQJf39/rK2tqVq1KgAtW7Ykd+7cmfIsOYWkpCTkcjmenp7o6uqSlJREvnz5xOPK7PYTJ05kwIABohVvVlkvSEiApFiTkMh03r9/j0wmw8DAQJwIeHl58fLlS0qXLo2+vj73799n//792NvbU7ZsWSwtLenbty+PHj2iXr16bNq0ic6dOxMSEsLUqVOxsrJCTU1NXGR/vfj+kwiCgKqqKsWKFePBgwfky5ePsLAwEhISqFy5MpDiBlKrVi0gxY3p48ePvH79mpo1a7JixQpR2bN//34qVqxIzZo1M0T2r7n5Me6nlWpKkgXwDI6jW+nMU6wpJxxKK4agoCD69+9PkSJFWLhwIaVKlRIVNFu3bmXSpEncvXuXTZs2YWhoyKpVq5g0aRJyuRwTExNq164N/M9KKiPrVU5Dqby8cuWKqBy/cOECV69eRVNTU3T3DAoKokSJEuL5WXmCqVAosLOzY8GCBVlazj/Nli1bMDc3/21l2Lt379i/fz8vXrxIJ8kkIEX5W61aNcqUKcODBw/Yv38/9evXx8rKCmtra2bNmsXy5csZOXIkkydPZv/+/YwbN47u3buLZfxO35grVy5u3LhBixYtiI+PJ0+ePGL7Vva99vb23702q/bHycnJqKmpiZszDRo0oG3bthw/fhx9fX2ePn2Knp5etlaqgaRYyyoo28HYsWMxMTGhUaNGQIryJjVKL4nly5dz7949qlatipmZ2TdKtLx583Lx4kWaNGmS4ZvVOQFBEChQoACjRo1i27ZtXLt2jc+fP/Pu3TuePn1KcHAwZcqUYeHChWhra6OtrS1el5PnEhJZD8nkQEIik0hOTgZg+vTpuLq6kpCQwIMHDxg9ejTr1q2jWLFifP78ma1bt9KxY0e2bNmCvb09vXv3xtDQkGrVqnH48GHKli3L2rVr6dSpE+fPn2f06NHfmLxn5GQ7dfw15eBXtmxZtLS06NSpE8OGDeP8+fO0bt2az58/c/XqVVxcXJg9ezYNGzYUs4MFBgZy6tQp3r59m+FuLYsXL6Z0mTL0qWqIY/dGPHM7C8C9UwfYOLA9JxdPZV7T0qzs1oA3t/6X7fTuyf2s7NaQuY2NWGpeh33btxCblHlZzZTfQumKVLRoUQYPHkxCQgKNGjWiRo0a5M6dGy8vL8LCwmjVqhU3b96kbNmyTJgwgQIFCqCnp0dAQABNmzYlKioKJycnMcNr6nr18ePHjH/AvxjlAlpdXZ2EhARWr17N6dOnyZs3L5MmTUJHR4fu3bvTo0cPEhISvom3lhU5ffo0MpmMjh07ZrYomUZSUhJr165Nl+QSCxcuZPDgwRQuXDgdJJMA8PHx4c6dO5w8eRKAY8eO4e7uTt26dSldujTbt2/n8uXLuLi4kDdvXvr27UvBggXTjLnKzaVf5cqVK3z+/Jl79+4xceJE4H/9wdeLyOwQV8jHx4eFCxfi4+ODnp4eDg4OQEqm0mvXrvHx40diYmLEhEbZ4Zn+CUmxlnUICAjg+PHjPH78WPxNXV39G+XakSNHOHfuHNu2bWPJkiWiUk1ZD+/du8fIkSPZvn07z549y7gHyCEolWNxcXHo6ekxYsQINDU1efz4MT4+PmzduhVHR0cmTJjA3bt3JaWaRJZGUqxJSGQwd+7coWbNmri4uADQoUMH7ty5Q3x8PIIgsH37dpo3b46pqSnFihXDwMCAnj17cuXKFSwsLAgODiY0NJT+/fsTHBxMdHQ01apVY9CgQejo6GSZJACpXQULFCjA8ePH6dKlCyYmJqxfv54WLVqwYsUKunXrRrly5QgODiZPnjwYGBgAcO7cOSpUqED9+vUzfBe+TJkyrD5+AfvrvpgOmcShWSOI/BQMwLun9ylYrBSzL7+k1dAp7J08gNiIcADyFShE/9X7mHfdD4t5qzm9YjZH3D0zVPbv8bWCLU+ePPTp04dWrVoBsHPnTurVq4eBgQHv378nNjaWwoULExERgbq6Om3atKF8+fKoqqpSunRpFixYgLm5OX5+fgC8fPmS4cOHc+vWrcx5wL8YVVVVcufOjYaGBtWqVWPmzJl4e3tTp04dQkJCaNCgAXPnzgW+jXmYlRAEATs7O+bMmZOjJ8NHjx6lTJkyv22F6+/vz5EjR5g0aVI6SSYBKX1/y5YtCQkJwd/fn9GjR1OtWjV8fHz4/Pkz5cuXZ8KECYwcOZLIyEgaNmzIpEmT0gSr/5n6HRMTg7e3d5rFfosWLXBzc2PcuHEEBgb+62I+O7QlQRC4f/8+48ePZ/v27YSFhaGlpYW1tTVLliyhQ4cO9OjRQ5y/ZIdn+ickxVrmkXoDVqFQULJkSc6dO0dAQIAYg1Imk6VxBYWUZCHFihUTLYiVG1TKepg7d25q1qzJ7NmzqVixYkY8So5BqRy7cOECffv2xcHBgTt37jBx4kQ6dOhAu3btMDc35+zZszg7O9OlSxfx2uzcT0j8vWTdWbiExF+GUqnx9OlTihQpgrm5OQAdO3akXr16yGQyatWqRbNmzYiIiACgcOHCtGzZktOnT/P582eWLFlC586defToEbVq1WLnzp1pYhAoY8JktQFHOVGxsbFh0KBBlChRgiNHjnD16lXmzZsHpGSvVCgUGBsbAymxpYYNG4a+vn6Gy9ujRw8EncIoVGRUa9OVQiVKEfjsAQCa+QvRqO9QVNXVqdamK3oly+J9/SIAFZqYUbB4KVRUVChduxHG9Zvj4eGR4fL/E18r2JRs3LiRBQsWAKCpqSkG6T148CB+fn60aNECuVyOu7s7UVFRnD17lgoVKhASEgKkKEGNjY0pUKAAkDLBzQrK3b8BZdsZMmQIgwcPZvHixfTv3x97e3suXbrEqlWrcHFx4cCBA5ks6b9z7tw5kpKS6Ny5c2aLkmkIgoCjo2O6WKvZ29szYsQIChUqlA6SScD/+sXGjRszatQojIyM0NfXx8LCgtu3b3P79m3kcjnW1tbY2NgQExMjXvOz/V1YWBh2dnaUKlWKdevWpdk8UpZVqlQp6tWrh42NDRYWFoSHh6fTk2YsZcuW5eTJk/Tv35/jx48zatQoXF1dad26NefPn+fAgQOiZV5Wm7v8DHK5HH9/f0qXLp3ZouQ4UicZmTFjBrNmzWL//v1UrVqVY8eOcfv2bSZMmPDda42Njdm3b5/4f+UGlUKhQC6XU6VKFWxtbalYsSJ58+bNkolBshuplZeurq5MnjyZkSNH4uPjw9SpU3F2dmbChAnky5eP48ePi3NNCYmsjhRjTUIig1BOGMuXL09SUhIfP35EX1+fvHnzYm1tLZ43ZMgQFi5cyIQJEyhRogTTpk3D3t4eKysrDAwM2LRpkxgXC0gTSD6rTkq/Z0nTsGFDtmzZIqbVfvToEYaGhjRr1gxIsdQxMDDg3r17JCcnU69evW/KiIqKQktLK93l3b17N3MWL+Pju7cAJMbFEPPlMzKZKtqFi6R5z7pFihEVmmLN9vLGJS5vWk7oWx8EhYKk+Dgq/n9cuaxEavmVO4bK92hpacmIESNo1aoVQUFBtGnThvr169OqVSuMjY2Jj49n3rx5FCpUiPr161O+fHkePnxIhw4dMDY2/iY4sJTo4PdQvruHDx8yfvx43r9/j5WVleg2BdCgQYMsvZhTWqvNnj07R9cFT09PwsLCftsV9s2bN5w8eZLXr1+nk2QS8L9+MU+ePBQrVkz8vV27djx9+lSMB1arVi1mzZr13Wv/i8DAQFauXMnOnTvp2rUr165do0KFCt8tK1++fEybNo0iRYpQunTpf81qmFVJ7a7VvXt3unfvztatW1m8eDGXLl3C0tKSOnXqiOdn1TnMj/Du3TsKFy4sxeDKBJTjytChQwHo3LkzHTt25OPHj4wfP55t27bRtWtXtm/fjo2NzTfXf71BnVpR9/nzZxwcHMR+N6vGMcwuBAcHs2XLFoYPH06hQoV4+vQphw8fxt/fn0ePHjFixAhWrVqFhoYG06ZNw8/PTwp3IJFtyLkzXAmJTKJMmTJUrFiRQ4cOAf/buVH+3aVLFwRB4OjRo0BK0NRFixbh7OzMjh07qFGjRprd8ey6UC1atKjoivjs2TMCAwNp0aKFmBVUiY6ODr6+vqIVn5LExEQOHDjAunXr0jW2VEBAAIMHD2bY/BXMvvKKudd80C9TAf5fpsiQD2nk+xIchFYhA5ITE9g32YYm/UYw8+Jz5l7zoXzjVmT1KdjXC5ly5cpx6dIlNm/ezIwZM2jevDkRERH4+PiwYcMGduzYweLFiylUqBD16tUTM+Tly5eP3bt3Y2try9q1a8XyUu/+SvwaiYmJ2NjY0Lp1a1atWsWXL1/EY6NGjeLevXvfuB9lJYvBCxcuEB0dnSa4e07E0dGRsWPH/vbCzM7OjjFjxmRLRUtWICEh4YctIJTtaOjQoRQsWPCXF3je3t7Y2NhQrVo1AB49esS2bdu+Uap9fW+ZTMbAgQPFDafshFwuR0VFhRcvXjB06FDGjRvHlClTsLW15cCBA8TExLB///7MFjPdkNxAM57U84rr16+TkJDA+vXrOXToEAMHDmTDhg1Mnz4dIyMjLl269F2l2teoqKigqqrKp0+fGDNmDCYmJly5coVChQqJ8dqk+cyv8+bNG4KDg1mzZg0xMTGMHj0aHR0dli5dys6dO7GxsSF37tzMnTuX8PDwf+0jJSSyGtlzRS4hkY3R09OjXr16PHr0KI01j0wmE03MLS0tOXHiRJrrlJnBsnsMku/RrVs3Jk6cKO5cp36+smXL0rt3b3R0dNJc8+DBA/z8/ChRogSfP39mw4YNxMfH/7YsMTExqKioUMqwMGoqKQkJPvp4/+94eCg3D2xGnpTEk4sn+eT3ivKNWyFPSiI5MQHN/AWRqanx8sYlXnu5k1c9e3WzSjfO0qVLY21tTefOncmVKxcmJib06tVLDCRbpUoVihUrxunTp/Hw8GDv3r34+/szePBgLC0tefr0KQMGDBDrcWoFcFZS+mQHcuXKxeXLl5kxYwbt2rXj4cOH9OrVCxMTEz58+MDNmze5cuUKe/fu5dSpU0DWsfwQBIH58+cza9asbLsJkB74+/tz5coVBg4c+FvleHt74+LiwtixY9NJspyFQqFg5syZ7N+/X1RQBwYGcuHCBRITE785X0VFRUzEs3jx4jSWbD/CnTt36N69O02bNsXIyIjXr1+zcuVKihcv/p/XZpU2/KsoFcjjx4+ncePGxMbG8vnzZyAliPz69etZtGgR8HcoKiTFWsaTekxp0qQJTk5ObNy4kaSkJLZt28b8+fNZsmQJzs7OFCxYEPixujZr1iwqV65MTEwM58+fx93dHVNTU+zs7L65r8SPkdrVvkGDBvj6+rJ69WoSExPR0dFBU1OTggULcvfuXQwMDNizZ48Yc1lCIrsguYJKSGQgyh1od3d3ihcvTmJioqgwk8lk4kR08uTJ373+bxzMla4ilf/fZVL5LuLi4tDQ0PjH63LlykWBAgXo0KEDK1aswM/Pj/fv3/+2S1ylSpWYOHEiw81bkiCoULNDT0pWryseL16lFqFvfbE3LU++Anr0WbodTd2U2GLmkx04MHUwyYkJVGjahkpN21A4T/bqZpV1UPldVFRU0NDQYP/+/axbtw4/Pz8UCgU9evTg6tWrANSsWROZTMacOXPEcnLnzo2NjQ2rV6/m7NmzLFu2DF1dXSBlwSgIgtgeJP6b/Pnzk5ycjJqaGmfOnOHz589ERUVRsWJF2rVrR2RkJD169GDFihUoFAq6dOmCXC7PdLcVNzc3Pn/+TM+ePTNVjsxmzZo1DBw4MI3L0a9gZ2fHhAkTvtlokPhvlGNLz549Wbt2LdWrV6dFixbcvn0bfX39NAkIUvP1psB/KbwEQeDSpUssXryY169fM2nSJHbv3o2mpma6Pk924NWrV+TPnx9ra2vWrl3Ltm3bAHBycqJNmzY0bNgQ+DvmNpJiLXPYvXs3c+fOxc/PD21tbTQ1NdHU1CQ5ORlfX19mz55N165dxfP/ra4p+4hWrVrRq1cvqlSpAqRYjffp04cCBQqIc1MpK+XPoXxXBw8eZP369ZQoUQI3NzcSExOZM2cOZcuWxcbGhpcvX7Js2TIpUYREtkRFkEwHJCQylIcPHzJ16lQMDQ1RVVVl69at35yjHLBzcnyqc+fO8fLlS2xtbf81jpq3tzfz5s2jXbt29O/fH0i/NNzHfCN5HfE/K4Z7pw5w58Rehm0/+0PXl9PJRbfS2r8tR2bzdT18+PAhNWrUYPDgwWLm1g0bNrB9+3Zy5cqFl5cXZ86cIX/+/PTs2ZORI0eydetWChcuzK5du7CwsMiRi8z0IHXdTk5OplevXpQoUYKVK1cCKW6XDg4OnDt3jrx582amqAA0a9YMW1vbNHEkcxpRUVEYGRnx4MEDSpQo8cvlPH36FFNTU968efNHYkv+zXzdhx07doyqVatSrly5dLuHXC7H2dmZxYsXExsby9SpU+nTp883WQj/dlL3US9fvmTRokX4+vrSoEEDlixZwosXL+jSpQt37txBWzv7j49KunTpQr9+/ejWrVtmi/JX8735nYWFBb6+vnh5eeHv78+SJUt4/fo1qqqqXL58WfQI+ZWNpvfv33Py5EkuXrxI4cKFqVWrFkOGDEmvx8lRREREiBsbxsbGuLq6cvr0acqVK8eYMWP49OkTMTExGBkZSYpLiWxJzlyxS0hkEMqd69QuijVq1MDV1ZUZM2YQERGBp6eneK4S5WDyT0o1QRD49OkTe/fuZd68edjZ2TFt2jSioqL+4NNkLO3bt6d3797fVQ6kflc7d+7Ex8eHs2fP0rhxY7y9vdNtMG6or4HaLxalpgINDP7Z4i478XWcNGXyjGnTpolZbWNjY/Hx8eHz588MGDAAfX19oqKiqFevHjExMRQoUID79+9ja2vL8uXLGTVqFN7e3v90S4l/IHXdDg8PR11dXczoCinxo+rWrSu2m3PnzuHj45PhcgJcvXqVoKAgevfunSn3zyps376dVq1a/ZZSDWD+/PlMmjRJUqr9BMrwCjKZjOfPn7Njxw5iYmLo3r075cqV+8Yt/dWrVz89jiYkJLB161YqVqzI8uXLmT17Nk+fPqV///45TqmWGkdHR27fvs3AgQMRBIHY2FiOHz/O5MmTGTZsGNra2n9VhsU3b95QpkyZzBbjr0c5Bt65c4cXL14AcPToUapWrUqNGjUoWbIkmzZtYtmyZbi6uiKTydIkI/gZoqKiGDNmDB4eHly7do3p06dz4sQJzp07B/wdLsx/kq/7VxUVFUJDQ3ny5AkALVu2RF9fn82bN7NgwQIKFiyIkZGReK6ERHZDUqxJSPwBEhIS2L59O1WrVmXChAn4+/uLA4zy76JFi9KgQQMmTJiAtbU1oaGh/1mu8tqJEycyaNAgAgMD0dXVRVtbG21t7b/Ous3AwOC7kyHlezh58iTBwcHY2tpy+PBh2rdvz6lTp76Z7Pzq5L2Ipjoti2r+tHJNTQVaFtWkSN6/a1H1df0qU6YM5cuXR01NDXNzcw4dOkRAQAB6enqMHj2aefPm0blzZ1q1aoWamhqrVq2ic+fOWFpaoqamxowZM6R4a79B/vz58fX1xcXFBYVCgbOzM0OHDqV27dokJSWxatUqtm7dys2bNzNFPjs7O2bOnImaWvZyh05P5HI5q1evZvz48b9VzsOHD/Hw8GDkyJHpJFnOQDl+nDp1it69e7Nnzx6mTJnCpUuXgLSLt3v37nHs2LEfLjsmJobly5dTunRpjh07xpYtW/D09KRz585/3Vj8oyitTGJiYnjz5g21a9emWbNmzJs3DyMjIw4cOIC1tbXYHjLbVT29UCgU+Pr6Soq1DCI0NBQHBwdOnjzJy5cvAdi1axf58+enZMmSxMbGUq9ePXLlyoVcLv/l9vjy5UtUVVXZt28fgwYN4tKlS4wYMUJMuiHNX/6Z1BZnISEhhIeHo62tzfjx43F1dcXDwwN1dXVq165N3bp16dGjR47tNyX+HnLubFdC4g+gDKK/bt06qlWrxsqVK2ndunWaybvy33nz5mX8+PFoampSqFAh9PT0fvg+Hh4enDp1KkcG9lTG5YqLi8PFxYVq1arRpUsXIOXd3rx5E5lMRnJyMjdv3qRp06a/NXmvpZdideYWFENt817U7vTv1jdKpZryupyCMii7n58fSUlJ9OnTBw0NDa5du8bhw4cJDg7Gw8MDT09P9PX1GTVqFHPmzMHPz+8f4+IpFaTSZOtb5HI5ampqrFixgunTp3PkyBGePXvG8ePHqVChAjNmzCAwMBBLS8tMcU26ceMGvr6+WFlZZfi9sxKnTp2icOHC1K9f/7fKmTdvHlOnTs0S7r1ZndQLOkEQmDdvHi4uLly9ehVtbW2WLl3KxYsXKVmyJMbGxqKbaO3ataldu/Z/lh8bG8uSJUtYt24drVu35syZM9SsWfNPP1a2QPne7ezsOHnyJPXr16dSpUqYmppiamqa5ty/ydXr/fv36Ojo/HYMRYn/Zt++fchkMlauXIm9vT2nTp2iffv2VK5cmX79+nH+/Pk05//O/O/58+ckJCQAKRm427RpQ8eOHenYseNvl/23o2zbGzdu5PTp08THxzNmzBiqV69OZGQkQ4cOxdTUlBMnTrBv3z7Kly+fyRJLSPw+0mpFQiIdePPmDSNHjqRs2bL4+Phw4cIFXF1dMTMz+8eJo3JSOXToULp37/5T92vYsCFPnjwRMzjmpF0z5fvcuXMnXl5eqKqqoq+vT0xMDDdv3mTMmDGcPXsWBwcHDh8+TM2aNXF2dk5Txs++r1p6GtRMCsLvljuqKnxjwaamAqoqKTHV+pbTyXFKtdR1sFSpUnh5eWFhYYGOjg4dO3akZs2arFixglatWqGvr09SUhIPHjwgJiZGVKopr799+zZubm7ExcUhk8lEpVpOquP/ROp3oKqqiiAING7cGGdnZzZs2MDjx49JSEhg5MiR+Pn5UbBgQQ4dOiS6o2Wk24q9vT0zZszI0a5wkOIO97vWanfv3uXu3bsMHTo0naT6e5HL5WliEKqoqFCvXj0eP36Mr68vMpmM1q1bo6qqirOzM/Hx8d8o7v+pr1FaHxYrVoyPHz/i5eXFgQMHJKXa/6PcOImPj2fGjBlYWFgwb948Fi9e/I8ZV/8WfHx8pMQFGUTJkiXZuHEjOjo6jB8/nlevXrF582Z69+7N4cOH2b17d7q5Gffr14+AgAA8PT0pXrw4/fv3JyQkRFSsOTs706tXL5KSkn77Xn8j+/btY//+/Zw+fZoCBQowadIknjx5Qv/+/dmzZw9NmjThxIkTNGnSRJrjSfwVSBZrEhK/iCAI3LhxgxUrVuDh4cGQIUN4/vw5RYoU+aHrf2dS+fTpU5ycnChVqhSamprIZDK+fPnCvXv3xJTifzuNGzcmLCyMW7du8fLlS+zs7ChTpgzx8fEsX76ciIgIjh49ypw5c5g6dSrNmzcnf/78AGJiCGXWyx/h6PaNFC9alJGVu/M4LJ5PcXIS5Apyq8rQ01ClWoE85FXPmXsVqd+hMkBwt27d6NatG2FhYSQnJ7N7925cXFyAlAC2p0+fpkOHDgBitsvExEQ8PDzYsmULRkZGGBoasnLlSnR0dNJYoPxNC7KfITk5OY2iSvkeChUqRFhYGFOmTCE0NJT27dtjYmJCuXLlsLKyYs6cOdjZ2WWY5d+tW7d48eKFmEwkp3Lv3j38/f1/euPka+bOncv06dP/NUuyRAqqqqokJSUxdepUAFq1aoWZmRnz589n2rRpXLhwgdq1a4su1A8ePKBBgwZpyvi6f1EoFNy7d4+ePXtiaWnJs2fPfniczykoFAouXLhAQEAA9evXZ/jw4axatYp+/foxbdo0Dh48yLRp0+jVq1dmi/pHkDKCZhyNGzfGxMSEu3fvYmZmxqRJk/D29ubBgwcsXboULS0tBEH4bWsy5Vxm9OjRXLx4kcqVK4v9ipubG2vWrAGgefPmxMXF5fhNJPh2fqZQKNi8eTNbt24lMjJSHMvCwsIYNGgQtWrV+u51EhLZFSkrqITET5KcnMzx48dZsWIFnz9/Zvz48QwYMCBDsxwGBweTnJws7vCoqKggl8spXrx4jnObW758OWfOnKF27drMmTOHPXv2EBMTQ758+di9ezdGRka8evWKa9eu4ePjw927d+nRowc6OjoAPHr0iOLFi1OgQIF/vIdCoaB9+/Zs3bqVYsWKZdSjZWu+zsIXFBRE0aJFEQSBM2fOsGTJEi5evIiGhoZ47qNHj1i1ahXFihXD3t6efv36YWJigqGhIYaGht8sgHMSCQkJTJw4kdWrV393wRAREcHSpUtp1qwZJiYmohLZ1taWhg0bYmNjA0B8fDx58uT5o7J27NiRDh06MHz48D96n6yOlZUV1atXZ/Lkyb9chpeXFz179uT169fkzp07HaX7ezh27JiovAwPD8fa2pry5ctTrlw5zp49i6mpKZaWlowZM4aKFSsyf/58YmNjefv2LRUqVPjHchUKBQkJCUyYMIGSJUsybNgwdHV1M+ipsiceHh48ffqUYcOGpVks79ixg5CQEFEx8bcxffp08uXLx8yZMzNblL8Sd3d31qxZw8CBA2nQoAEeHh7MmzdPdO9Oza9m//w3lHOU48ePs2/fPuLi4ujSpQudOnWiUKFCOTqOqJLU7f3GjRs0atQISImHZ2Njw/r16ylWrBidO3cGUvqEf5t3/w0cOXKE+/fvM2rUKIoWLZrZ4khkBIKEhMQPERkZKTg6OgpGRkZCo0aNhOPHjwvJycmZJs+zZ8+E9evXCytWrBBu3LghyOXyTJMlM1AoFOK/k5OTxefftm2bMGzYMEEQBCE+Pl6YOXOmMHfuXCE0NFQYMWKEULhwYaF9+/bC3LlzhejoaKF69erCli1b/vNecXFxf+5h/nJSfytBEIQzZ84Iy5YtEwRBEJKSkgRBSPmGu3btEkaMGCEEBQUJgiAIHTt2FBo1aiTs2rVLKFu2rLBkyZJvyk5OTv6m/L+RtWvXCu3atfvuMWXdj4iIEN+Fv7+/ULNmTaFu3bpCWFiYIAiC8OLFC6FLly7CgQMH/picd+/eFYoWLSrEx8f/sXtkB4KCgoT8+fML4eHhv1VO69athU2bNqWPUH8h0dHRwtKlS8X/e3t7C40bNxb/f+7cOWHcuHHCixcvhAcPHggGBgbCw4cP05Txvf4jNjZWePjwobBnzx4hNjb2zz3AX4CHh0eab6BEoVB8d470N85VLCwshIMHD2a2GH8VX7dLR0dHYe7cuUKtWrWE8+fPC40aNRJcXV0FQRD++Fzcz89PaNq0qdCtWzdhy5YtQlBQkKBQKISkpCQhNDQ0Q2TIyqT+Vq6uroKurq5gbm4u/mZtbS1MmTJFOHjwoNChQwfh+fPnmSHmHycyMlJwcXERXr9+LQiCIGzYsEFo166dMGLEiEyWTCKjkBRrEhL/wbt374TJkycLBQoUEHr06CF4eXlltkjCjh07hKpVqwqTJk0SFi1aJFSvXl2YM2eOkJCQkNmiZThfT76uXLkiNGnSRHBzcxN/i42NFdzd3YWGDRsKFy9eFIKCgoRDhw4JR48eFUxNTcXz/sYJf1ZHORl9/vy5MHXqVMHJyUkQhBTFUNmyZYWXL18KgpAyQbGzsxOve/HiRY6p77GxsYKhoaFw586dHzrf0dFRKFiwoDB16lTxt1evXgktW7YU7O3t/5SYgiAIQufOncVvmJOZMWOGMGrUqN8q49q1a4KRkVGOqec/Q0xMjPhvhUIhWFlZCe/evRNiYmKEbt26CWfOnBGPN23aVDh+/LggCCmK339DLpcLcXFxwoMHD0Slv8S/M2DAAGH48OHi/xUKRY7Y7EhNjRo1/rNuSfw8ly5dEhYvXiwcOXJEnCucOnVKmDt3rmBsbPyPm01/gl27dgnv3r0TEhIShLCwMCEhIUG4dOmS0K9fvwyTIauzevVqoUOHDsLu3buFihUrCj169BAEQRCuX78uDB06VKhdu7Zw69YtQRC+v6GRHZHL5eLa4dWrV8LEiRPTbF6+fftWMDU1Fd6+fZtZIkpkIDnLZ0wi04lJUuD1MZZT/lEc8YnglH8UXh9jiU3KuKDaP8qDBw+wsrKiWrVqJCYmcvfuXQ4fPky9evUyTSbh/10/16xZw+nTp1m2bBnTpk3j4cOHnD59mi9fvmSabJnF13EZmjdvztSpU5k9ezYdOnTgw4cPfPnyhRMnTtC6dWtatWqFoaEhjRs3ZunSpcydOxeADx8+5Dg32swidRB9pctGSEgI7969o0WLFkBK0PfmzZtTrlw5goOD+fLlC2pqaigUCkaOHMmePXto0KABkydPJiYmBoA7d+4wY8YMnj17lvEP9QfZuHEjJiYm1KlT54fOP378OIcOHWLx4sVAyvs2NjZm7dq1zJo1C0jJVhkbG5uucj58+JDbt29ja2ubruVmN2JjY9myZQtjx479rXLmzJnDnDlzyJUrVzpJ9nfg7e3NypUrgRR3oujoaNTU1JgyZQqqqqrUqVMHT09PHjx4AIC+vr4YqkGZ9fN7iTzi4+OJj48nd+7c1KhRQ3Lv+kGGDh3KixcvGD16NN7e3j8Vu/RvQBAE3rx5Q5kyZTJblL8CZdv09vZmxIgRhIWFcebMGWxsbIiNjcXc3JzZs2fz9OlTkpKSxNitfwrlvLtfv36oqakxYMAAmjdvzpgxY2jSpAnBwcG4urqmkT0nEh8fz4ULFxg3bhzW1tY8f/4cPz8/evfuTePGjdm4cSMuLi7UrVtXjHGcnUmdtV65dihatCglS5bE399fPK948eKoqanh7e2dGWJKZDDSKlIiQ/gQk8Qx30jWPwvD40Msz8MT8IlM4nl4Ah4fYln3LIxjvpF8iMnczDoKhYKzZ8/SsmVLzM3NqVatGr6+vqxatYpSpUplqmypUVVVJTk5Gfhf557TJrP/RocOHfDw8GDGjBkUKVIENzc3Xr16xYgRI4CUGBw7duwgNDSUxMREevXqRceOHbl27VqazETpkVVK4lu+p8Bs1qwZjo6OVKlShfj4eLZu3Sp+r9evX+Pr60vLli3ZuXMnO3bsoHfv3ly7dg11dXV8fX2BlOyJCQkJ4oL4b/h+MTExLF26FDs7u/88V/m8165dw9TUFIVCkSbuScWKFcVzjx8/zqhRo9JV1gULFjBp0qQcH2RfqfT9nWDmV65cITAwEGtr63SU7O+gQoUKuLq6YmxszL59+0hMTGTHjh28ePGCQ4cOiTFPhw8fToMGDdDS0sLMzCxNGV/3QXK5nDx58pA3b15pHP1J6tevz6pVq9DT02PSpElcuHAhzfG/oR/+N0JCQsiTJ48Ufy+dUMZbdXJyYsmSJSxZsoR58+aRL18+hgwZQkREBKqqquTKlYvSpUsTHBz8R+VJ3R8sW7aM3Llzc/fuXWQyGQcOHKBfv364u7t/c+7fztdKxDx58mBkZERUVJT42/bt2zl79iwTJ04EQE9PD0EQst0mtkKh+KYfk8lkJCcnc+bMGTE7bd68eTEzM6Nnz57A//q+OnXqcPr0abZu3cqbN28yXH6JjEPajpP449z/FIdbUAzJ/5AmQ/n764hE/CITaVlUk1p6Gbswi4+PZ8+ePTg6OpI7d24mTpxIz549s5ylgHLQHj16NN26dcPMzIz8+fNz5MgROnfuLAbkz+koA80qg6cqFAo6dOhA4cKFAXj27Bn79+/Hz8+Pjx8/YmtrS+PGjYmLi8PFxYWnT5+K1g8SGUfhwoWRy+UoFArWrFlDzZo1iYqK4u7du+TLl4/atWszcOBAxo8fz/jx49HW1iYkJAQ9PT309fV59eoV1atXp3z58sD/rOH+RDDjjGLt2rU0bdqUatWq/ee5qZ/x3yavd+/eRUNDgxo1aqSXmDx9+hQPDw927dqVbmVmRxQKBatWrWLDhg2/XIYgCMyZM4e5c+dKVlP/j7INKxXFjRs3ZuvWrezYsUPMhL1hwwb69+9PnTp1mD59Ol26dEEul1OlShXg+5nnlGNFdu0fsgrVq1enWLFiFClShJMnT+Lj40PevHnp378/CoWCkJCQvzaTqpQRNH1I3T6DgoLYt28f2tradOnShRIlSjB9+nQWLFiAjY0NBw8e5NOnTyQnJ3+jNP8TKPuJokWL8uXLF9TV1enfvz/t2rWjUqVKrFixAshZijXl/OL8+fMYGBhQqlQpWrVqxcKFCylVqhQ1atTgzZs3WFlZcfHiRS5cuICZmVm2eEdCSqgs8Rm/N5eytbVFTU0NVVVV4uLiWLRoEQMHDqRp06biOcrrpk+fzpIlS1i+fDk1a9bMmIeQyBSkGZtEuuDv70+pUqVISkpKsxD4L6Xa1yQL4BaU4tZVS08Dd3d3rKysCAwMTFd59+3bx65du9i3bx/r169nw4YN1K5dm7Vr19KiRYss3/FbW1tTr149rly5Qnx8PJs2bcLExCTLy51RfD0Iprb6kMvlzJgxAwsLCwRBoFy5cqKLXZ48eahVqxYuLi7Url0bJycnUTnn6+uLlpYWenp6GfcgORBVVVXy5s3LwIEDgRSld3h4ODVr1uTTp0/ky5ePhQsXAuDi4sKuXbvo3bs3N2/eRBAE6tWrR0hICMuWLaNkyZKMGjXqG4VTdmknkZGRrFixgqtXr/70tV8/oyAIyOVyjh07xu3btzEwMMDU1DS9RGXBggVMmDAhQ7MjZ0VcXV3JkycPzZo1++UyLl68yKdPn+jdu3c6SpZ9USgUYhs+c+YMdevWZdGiRdSoUYPRo0dz8eJFChUqRP369bG0tMTCwoLnz5+nsdD8OkuxkuxmOZGVKViwINra2pw4cYIyZcrg4eHBsWPHePv2LWPHjhX79L8NSbH2+yjb58ePH8mdOzft27fnypUrWFhYULVqVfr27UuxYsWYM2cOkZGRqKurY2hoyPr16zMkW7Kyn5gwYQJdunShUaNGFChQgM6dO9OrVy/q1avH+fPnUVNTIyQkhN69e2ebecbPkrovXbVqFStXrqRt27Z8+vSJ7du3ExERwbRp09DU1OTly5dcvXqVxYsXZ/ms1q6urqiqqtKyZUtkMpn4/RISEjhz5gwHDhxAW1ubwYMH06BBAwoWLIi7uzvu7u5oaGgwf/58Tp8+TdOmTUlOTkZNTS1NHXjy5Am7du0SwxFI/J1IM4q/FCMjIy5dupSpMnyISRKVavdOHWBGHX3mNirJvCalcOrVnBfXLnz3OqVy7UNs+riF+vv7o6KiIrpOQopZrnJ3pVGjRrx48UJ0Ac3Kg+Hnz58B+PTpEzo6OnTv3p2+fftSunRpQkNDM1m67EFYWBjq6urMnz+fmJgYbty4AcD9+/c5cOAATk5OzJgxg2nTpvHq1SsAPDw8OHv2rBizR+LPktodV09PDzs7OywtLSlSpAi1atXC3t4egHbt2nHw4EHU1dW5ceMGZcqUoUKFCujo6DBo0CBev35N48aNefnypViesn1nh1goq1atok2bNmkUBD9DatcFhUJBeHg4rq6ulCpVigEDBqTbYvDFixe4ubmJrrs5GUdHR8aPH//L44jSWm3evHk53opKGQNQJpNx+/ZtatSowalTp7C2tsbV1RVLS0uqVq3K9OnTAXB2dsbOzg5HR8dvypIUaH8eQRCoW7cuN2/eZMKECcyfP5+ePXtia2v71yrVQFKs/S5Ky6BXr17Ro0cPHBwc6NGjBwYGBmzfvh07OzvRAtjQ0JAKFSqI43dGKmuU9xw9ejT6+vr07NmTZcuW0aZNG86ePSvW85IlS/7V7s/KvvTgwYOEhoby4MEDli9fTt26denbty8dOnTg8OHD2NnZ4enpyaFDh/D09KRWrVqZLHlalO6dym916dIl3N3dCQ8PR6FQsGXLFry8vHj8+DFXrlxh/PjxtG3bloULFxIdHU337t1JSEhAQ0ODpKQkGjZsyOPHjwHSGJgIgoCmpiaqqqr4+Phki7mnxK8jWaxJAIja9fTk5se4NJZqJarVYdj2sygUCrwOb+PANFumn3+Mhrbut/II4BkcR4F0lEcQBK5evcqKFSvw8vJiyZIlbNiwIVtNuAcOHMjx48cxNzcnKCiIXLlyiZOSyMhIXr9+LbmD/gd6eno4OzsDYGpqyt69e4mPj2fmzJmYmJigqqpK586dUVNTo27dusTHx9O/f39UVFTYv3+/WE52snzKbqR+r8r3rK6uDsDw4cNZunQplStXpnfv3syYMYMbN24QHx9P27Zt8fPz48yZM7x//54RI0awa9cuwsPDgZSFd+PGjdHT08vy7T48PBwnJye8vLx+uYx3795hZGSEi4sLnp6ePH/+nNDQULZv356OksLChQsZN24c+fLlS9dysxtPnz7l6dOnWFpa/nIZ586dIzo6WozRklMZO3YsxYoVY8yYMURGRrJ69Wo2bNhAzZo1KVu2LBs3bqRgwYJs3ryZNm3a0KZNGz59+kTTpk1p06ZNZoufI1FRUUkTi7Zs2bI5QuH05s0bOnbsmNliZFtUVFSIioqif//+LFu2DF9fX1xdXYmJiaF58+bs2rWLbt260apVK4yNjYHMUZQr72lqaoqRkRFlypThzJkzLFq0CLlcTrt27fj48aPo5fC3oZyLKRQK4uPjsbOzQ1NTk0mTJqGlpcWwYcMQBAFzc3P27dtH5cqVefLkCWfOnOHs2bNoaWll9iOk4es61L59e/bu3UtUVBQ+Pj5cvnyZ7t27o6WlRalSpdi9ezcnT54kICCAs2fPYmlpSXR0tKhYNzY2RhAEnjx5QtWqVcX3pfy7evXq3Lt3jz59+kjrh7+YrL2ykPht3rx5Q7NmzdDR0aFQoUJpJvwqKiqsW7cOY2NjcbAaO3YsxYsXR1tbm9q1a3P9+nXxfIVCweLFiylTpgwFCxakZ8+ehIWFffe+MUkKfCMTv3tMJpNRp3MfkuLj+Bzoz92T+1nZrSFzGxux1LwOt46mxOjxiUwkPjmtZv/9+/d0794dPT09SpUqhZOTk3js9u3b1KlTB21tbfT19ZkwYQKA6O+eJ08eWrRoQYUKFbC3t2fbtm1ix6qiosLGjRsxNjZGV1eXkSNHprGaySqcOnUKNTU1vLy8ePfuHT4+Pvj6+vLmzRtCQkIkpdoPoNwtUigUtGzZkk6dOvHu3TuKFi3KwIEDsbe3Z/fu3QQGBjJ69GgiIiIwMzOjXLlyzJ07l+fPnwP/U/78zTuTWQHle1b+Xb16dfbt24eLiwutW7dGoVBw+fJlChcujLGxMa1btyY2Nlbs7y5cuMCXL1/4+PEjTk5OTJ8+nX79+nHx4sVv7pWV2vyKFSvo3LnzLy9Mo6Ki6NChAzY2Npw9exZ1dXVMTU3FPi+96u2rV69wdXVN90QI2ZFVq1YxfPjwX7aiUFqrzZ8/P8srfv8UyjbYpUsXXF1duXv3Lnp6eqxbtw4VFRXq1q3L/PnzKV68OGvXriUhIYETJ04wf/587t+/L8Zbk8hY4uLiiIuL+8YaIyv1qX8KyWLt94mNjaVZs2YULFiQtWvXsnz5coyNjfHy8qJ+/fo8e/ZMXKdkBUqVKsWSJUuwt7dn0KBBeHl5sWXLFmQyGUFBQUCKJfffQuosnp8/fyZv3rzcunULDQ0NlixZgoqKCvnz52fo0KFYWlqSN29eAKpWrcrp06d/e20Sk6TA62Msp/yjOOITwSn/KLw+xhKb9N/WX3K5PE0/pPz3y5cvGTNmDGZmZmzdupVatWohl8vx9vZm586ddO/enQIFCqCurs769et5+/YtO3bsoFevXqJlWvXq1Tl37hwABQoUoGTJkuJ3V74vmUyGp6cnu3btEjMHS0q1vxfJYu0vZ/bs2ZiZmXHlyhUSExO5e/dumuMnTpwQO0cAExMT5syZg46ODqtXr6ZHjx74+/uTJ08e1qxZw4kTJ7h69Sp6enqMGTOGkSNHcuDAgW/u+yQsnn/qNuTJydxx3kuuvJoUKl6a6M8h9F+9jwLFjPC7f5Odo3tTrHINSlaqnkY5p1AoMDc3p3Pnzhw4cIDAwEBatWpF+fLladOmDWPHjmXs2LFYW1sTHR2Nl5cXy5cvF11Ajx49SufOnZHJZOzcufMbuc6cOcOdO3eIjIykdu3amJub07Zt21978X8I5S6Hqakpx44dQ1dXV/ytdevWHDlyRMpM9R+kVqbmyZOHXr16kZycTHJyMl27dsXW1pbdu3djbm5OmTJlWLlyJblz5+bcuXPEx8fz7t07Vq1ahYGBAb169crx7loZjXLhVqJECUqUKIEgCHTo0IG8efPy+fNntLW1mTZtGpDiEn/48GEaNWrEgQMH0NLSol27diQlJbFgwQLKlClD6dKlxbKVu7GZnWE3NDSUDRs2cO/evV8uQ0tLi/Xr1+Pt7Y2+vj4NGzakcOHCDB06FCDd6q2DgwOjR49GW1s7XcrLroSEhHDs2DHRffxXOHnyJAqFgq5du6ajZNmH1ElGWrRogZeXF05OThgZGVG0aFGuXbtG7969GTRoEIcOHWLGjBmcOXOG3r17U79+/W/KkMg4pk2blib+pZK/fQEpCAKvX7+WFGs/ibKdKuN16evr8/LlS+rWrcuJEycwNTXF39+fKVOm4OTkJCbaySqWPjKZjCtXruDo6EjDhg1JSkpCXV2dEydO8OzZM7S1tXFwcGDPnj2ZLWq6oJw329nZ8fTpU0qUKEGzZs1wdnbG1NQUNTU17O3tKViwIGPHjhXnUr+bGOZDTBI3P8bhG5mICqTxgnr1Ba5/iKW0di4a6mtQRDPFq+HrOZzy/kFBQSQkJFC6dGkSExOZP38+DRo0YNiwYZQsWRJNTU0KFCiAnZ0dWlpaLF26VLzX5cuXmTBhAqVLlyYsLIwnT57w5csXWrdujYeHBwDa2tps2LDhu95fpUuXxsXFJUsphyX+DDlzSzQHoa6uTkBAAO/fvydPnjw0btw4zfHp06dToEABUbFmZWVFwYIFUVNTY+LEiSQkJIjxiTZu3MjChQspVqwYuXPnZt68eRw9ejRN7DIlIXHybxIWvHtyj/lNy+BgVplH552xXrGLPFraVGhiRsHipVBRUaF07UYY12+O/wMvkgX4kpiyiBYEgS1btvDy5UvatGkjptkePHgwBw8eFJ/1zZs3PHz4kLlz59KzZ0/u3bvHxo0bATA3N/9XK4Bp06ahq6vL+fPn0dPT4+bNm0CKlVxSUvrEe/tdEhMTiYuLo3jx4oSFhREeHk5ISAghISG8f/8+R+wOpxepJ2dqamrs3LmTtWvXEhMTw/Pnz7G3t+f+/fu8ePFCbDd58uShZMmSmJqacvDgQdq3by/GtouPj8fNzY2AgIBMeZ6cgkwmS9OOVVRUMDMzo3HjxmhqalK9enVMTEyYOnUq9vb2NGrUiFy5cnH9+nVsbGzEYMNRUVH4+/sDcO/ePfbs2UN0dHSaoLWZxdKlS7G0tMTIyOi3ymnWrBlDhw6lS5cuYkZc5btLSkr67f7Cx8eHM2fOMGbMmN8q529g48aNWFhY/HJyE4VCwdy5c3O0tZqqqioxMTGcOHGCuLg4pk+fTu7cudm8eTOCIFCiRAn27NnD6dOn2blzJyNHjqRPnz5p3pekVMt4Xrx4wf79+xk3blxmi5LhhIWFoaKiQoEC6Rm45O9GEARUVVWJiopi3LhxrFy5kuDgYMaMGYOlpSVnzpzBy8sLS0tLunbtmiZ7dWaPzfA/a+/69etz7NgxIGX9sW/fPkxMTES34L9FqaZk+fLlPHr0iKVLl+Ll5YWnpycFCxbExcWFnTt3snz5ciCtpdbvcP9THPteR/A6IhG5wDdrymQB5AK8jkhk3+sI7n+KE++bup5s2bKFZs2a0aVLF2bNmsWDBw/IlSsXL168QBAEYmJixHAhDRs2RE9PD0EQmDx5smiN1qVLF1auXEn37t15+/YtXbp0ITIyksGDB7N3717xXmpqat+dV+nr60tKtRyCZLH2l7N06VJmz55N3bp1yZ8/PxMnTsTGxkY8Xrx48TTnL1++nG3btvH+/XtUVFSIjIwUFQcBAQF07dr1m0nsx48f05Rx/fp1erZthyAI6BYpzvijKdr84lVrM2z72W9kfHnjEpc3LSf0rQ+CQkFSfBz6ZVOCdUfGxBEZGUnRokUJCwsjISGBli1bivGW5HI5TZo0AWDcuHGMHz8ee3t7ChcuzPLly7GxsREXz/+FgYEB3t7erFmzhrx58xIbG4tCoWD06NGMHj2a5s2bix1m6ixlGWnhoqqqSmJiIqtWrUJdXZ08efKQmJjIly9fuHTpkuQK+osod0EbN25M48aNadmyJQUKFMDFxQVBELCwsABSslDev38fDQ0Ndu/ejZOTE58+faJQoUL4+fmxa9cu2rVrR8mSJUlMTCRXrlyZ/GQ5izx58rBz506uXLnCy5cvMTQ0pHPnzpw6dQpdXV3Kli0rulJ//PiRli1bsm/fPnbs2IGuri5btmyhR48e9OvXL01bykgrmODgYLZt2ya6GvwJBEHg/v37vH79Gisrq18uZ9GiRYwYMSLHW8kmJCSwYcOG30oYdPz4cXLlyoW5uXk6Spb1SW2BcubMGebPn0/dunU5d+4cbdq0wcHBASsrK1xcXGjfvj0fPnxgzZo1DBw4UMyamlWsWHIqEyZMYMaMGTkyY7bSDVSqfz+G0opJLpdjbW2NkZER165d48OHD5iZmTF69GjWr1/PyZMnsba2FkMMZKU2rlwDzZs3D4B9+/bh6OhI3rx5mTFjRhqL46wk9++gXPMsXbqU3bt3Y2hoiIODAzExMeTPn5+7d+/+0HMaGRmxdetWWrVq9a/n3f8UJya/A1jSoRbd5zhStl4zrmxzJCwogO5zVonnK5Peffr0iS9PPPHw8KBly5Z07dqVsmXLsmvXLoyMjLCwsGDt2rVs3bqVadOm4ebmhp+fH/fv36dWrVrMmjWLs2fPMnDgQKKjo9m8eTP79u1jz549YkilNm3a/Gu4h7/he0v8OpJi7S/HwMCALVu2ACmZDVu1akXTpk1Fs/XUHcD169dZunQply9fpnLlyshkMvLnzy8qk4oXL8727du/G5gztfKqSZMmHH72gefhCf8pX3JiAvsm29DDbi2VmrVDVV2dPRP6wf/f8+mjB0RFRREVFSVeU7duXapVq4ahoSH6+vq8ffsWExMTPnz4wLhx4xg0aBCXL1/GysoKS0vLH+7klEEnK1asSN68ecmTJw+RkZFoamqKi+yvTYvh212Z+Ph4LCwscHR0xNjYGIVCIQ6uv7uDo6am9k0AUA0NDdHiUOLXUH5X5XcyMTEBUiYTp0+fxsvLi7i4OJYtW0aPHj149uwZLVq0QKFQULVqVSpWrIiXlxfa2tr06tULAGtra2rUqCFmrJP48ygn7S1atKBFixZiPEY7Oztq1aolWoDNnTuX8ePH8/z5c65du8awYcOwsLDg+vXr9OjRAy0tLaysrEST/tRK9D9tTbR48WKsra0pWrToH7uHiooKWlpajB8/njp16lChQoWfLiMgIABnZ2dev379ByTMXhw8eJBq1apRuXLlX7peLpczd+5cli9fnmMm5cp5hfJ5w8LCcHNz4+zZs/j6+mJjY0Pt2rUpVqwYAwcOxMnJiTJlyjB+/HhGjBghLmwyok1K/DPnzp3D19eXkSNHZrYomYIUX+3HSe3+GRQURLVq1bCzsyMmJgY7OzvOnz9Pjx492LRpU5rrslobT91H79u3j5kzZ7Jy5UratGnDmzdvuH79OnK5nBo1aqCrq5vl5P8RvpZZ6dJZu3Zt2rZty+HDhwFYuXIlurq6jB49GkifTcgPMUlplGpf02LQ+O/+nizAnUgZig+fcXNzIzQ0lK5du9KkSRMcHR3ZvXu3+D08PDywtLQU444/f/6cPn364OjoyJcvXwgODqZnz56YmJgQGxuLTCZLY5Ty9fglIaFEUqz95Rw5coQGDRpQrFgx8ufP/6/KnaioKNTU1NDT0yM5OZnFixcTGRkpHh82bBgzZ85k165dlCxZkk+fPnHz5k06d+78TVmFNVR59eVb092vkSclkZyYgGb+gsjU1Hh54xKvvdzRL1MBNRVo26wRHrt1SExMJCEhAUEQyJcvH4ULF+bSpUuiu6auri5hYWHMmjWL9evXi+mPJ0+ejIGBASoqKuzZswcTExMMDQ2/a6qblJTEly9fKFSoELGxseJvFStWpGjRorx8+ZJevXphaWnJ06dPmTp1KhoaGri5uWFkZESVKlUwNDTk8+fPREdHi4vy773v2NhYtmzZwqhRo1BVVSU6Ohp7e3sWLVoknv+37HRlJ75+39bW1piYmODn50eePHmoUKECw4cPB1JiS12+fFms/507d6Z58+bitf369WPp0qVMnTo1202qsiup2w4gWlCsXbuWL1++kC9fPgICArh48SKnTp1i7969FC9eXHT1ffr0KeXLl6djx468e/eO/fv3c+HCBcaNG/eNte6fIDAwkN27d4sJMv4klSpVYsGCBfTp0wdPT8+fDri/ePFihg4dmuNdoARBwNHRkcWLF/9yGYcPH0ZbWzvLxfT8kyj72tevXzN79myGDBnC48ePcXBw4NatWyxcuJCuXbvy+fNn+vfvj6enJ8HBwZQvX57cuXOLCz+pb808kpKSmDBhAitXrsyx1tmSYu3HSK1wady4Mdra2ri5uVG7dm06d+7M3LlzsbOzY+3atejr66fJLpsV27hyft63b1/69u0LwObNm3n9+jXPnj1DTU0NXV3d/2PvrMOi2to+fM8MXdKCBSbYCgZ2e2zBLkzswO5WFBPF7u6OY3fXsVsURVJKehhg9vcH3+wXBDtAnfu6uJjZs+PZvdZvPcHGjRt/y3a86pjPnTuXyMhIunbtSpMmTXjx4gVKpZLQ0FBWrVrF4cOHOXjwoLjcj/DsvxKS8Nm+48fQ0NKiSLNOlC2Yl1mzZokpc/bv38+DBw+A1OvvzZs3lC1blj179nDp0iX+++8/3N3dgVSxUHX9feiFq3rv/I7nVM2vIfs9rdT8UG7evEnFihUxMDCgWbNmLFy4MF2y7rT8888/NGjQgCJFimBjY4OOjk66UFF3d3eaNWtG/fr1MTQ0xMnJievXr2e6rpKmOnzJc1Fb34CmI2awbVRPptYoxN2jeyla/R8ABKBQDm0MDAx48uQJ1atXRxAEXr58yYQJEzh//jw2Njbs37+fgIAAsSxySEgI8fHxDB06FDs7O+Li4ihRogS9e/emdOnS2NjYfLTj6uvrS86cOYHUB6iFhQVjxozB0tKSgIAAQkJCsLKyYuTIkQQEBLB27VrevXvH5s2b8fLyAiAkJAQ9PT1MTU0JCwtjypQpjB8/no0bN4phtXfu3GHdunXiS0hDQ4NWrVplyB31ISkpKSiVSvG/ys60AqiaH4dSqcTe3p6GDRtiZ2fHuXPn6NixI2PHjmXNmjVi5dmkpCRMTU3T5cS6fv06VatWzTb5+f4m0oZmC4KAnp4euXLlAlI9rQYPHiwKSW/evMHKygpIvS87duyIQqGgf//+xMbGMmHCBPbs2cOIESM4d+5cuu0olcoMVfC+Bw8PD9zc3ER7fja9evXCxsaGsWPHftVyb9++ZefOneL1/zdz7tw5FAoF//zzzzctn5yczOTJk5k2bdof31gPDw9n165dvHv3DkjNS7ds2TLq1atH7dq1KVWqFGfPnuX06dO4uLgQERHBnDlzePfuHUuXLqVGjRriurJjZ/tvY8mSJdja2tKoUaOsNiXLUAtrX4aqrevt7U2VKlU4duwYy5cvZ+LEiRw4cAA9PT2mTJlCp06d0olq2ZW07QtI9Vxbs2YNNWvWZMuWLRw8eJB79+59cYhkdiFte8bT05MzZ84QERHBxIkTefjwIR06dEBPT4/evXtz69YtDh06RM6cOb+qyvjNmzcpVqwYJiYmdOvWDblcDsDBgwcpVboMnUrnZVnXRgQ9f5Tp8qeWz2bHuL7i9y0ju+NRrxiTqxdghVtzLt+5T50GjUlOTqZu3brs2rWLkJAQgoKC8PDw4L///uPevXvI5XIiIyMpW7Ys+/bto1u3bgCfvP7U7x01n0PtsfaHogrNrFu3brrKJmn50GtLJpOxdu1a1q5dK04bOXKk+FkqlTJ06NBMO1O2trbp1qevKaWAkRYvolKrejo2a49js/aZ2lGpbQ8qte2RYXpBIy0alKmNv78/jx8/pkCBAty+fZvq1auzb98+7Ozs0s2fNoHkh8yaNSvd9w/dnC9dukSLFi1wdHRk9uzZ6cKwVNV+fHx8cHR0pGvXrkCql8GRI0fYsGEDb968YceOHRw/fhxDQ0P09PSQyWQIgkCpUqWQy+WcP3+emJgY3NzcWLFiBU+fPqVMmTJ07tyZli1bcu3aNcqXL48gCPj4+BATE0P+/PkxMTERbclsNOj169eMHTuWSZMmUbRo0Uz3X6lUEhERgb6+vjps9BO8e/cODQ2NTL07c+XKxePHjzl58iTLli2jYMGCNG7cGEDM+afi4sWLvH37lk6dOn21J5CaH8uHjdrq1atTvXp1AAoWLIi3tzcbN27Ex8eHa9eu0bdvX+7fv4+WlhYeHh5IpVJu3rzJli1bRI+iffv2UbFiRVGs+xG8fv2anTt3isVifgUSiYTVq1dTpkwZ6tev/8Xi0OzZs+nRowfm5uY/2cLsj5eXF4MHD/7mztPWrVvJmTMnderU+cGWZT+CgoLYtm0b+vr6NGrUiCdPnrBv3z569Eh9/zdu3Jh3794xefJkKlasyIwZM6hXr55YeENN9iE0NBQPDw/Onz//WwkHPxofHx/69OmT1WZkW06ePElYWBjt27dn9+7dbN26VXzWde/eHUNDQyZMmCB6Ranesb9LxIbKxkuXLjFx4kSxTRgVFUXevHnF6JffgbT9otevXyOTydi7dy86OjrMmzePI0eO4OzszJw5c4D/eSF+bfjnli1bOH78OPr6+jRt2pTp06fTsmVL3NzcmLlhF+GW9tz8dxcbh7gybN9VNLQ+3Ya2q1yHVpO8kWlqcnThVLaN7UPrqjeIjIwkKSkJc3NzXF1dqVu3LikpKeTOnZtBgwZhbm7+VxZcUfNzUQtran4alXPq4hut+CaXXg0JOOXU5fTp08ybN4/bt2/Tr18/fHx8fkiC3A9HHapUqcK4ceNE8Suzef38/ERvv1evXhEeHk6RIkVYuXIlfn5+SKVSzM3NefPmDUZGRujq6pKYmEhycjKCIKCtrc3x48fp378/NWvWRKFQsG7dOnR0dFi9ejV79+5l4MCBHD16lGXLlhEWFoa+vj7jx4+nZs2a7N69mxMnTlCwYEFiY2Np2bIlZcqUISIiAmNjY1EwyyyfQ3h4OIsXL8ba2po+ffqIBRdU/6VSqThS9TePyCxevFjMpfYhquNar149qlSpQmxsLJA+xCFtIyt//vyUKlXq1xmv5otQCeURERHky5ePI0eOsG7dOs6dO0fbtm0pW7YsXbt2pVSpUkilUqKiojAxMcHBwYE6deoQGRmJv78/06dPp3jx4nh7e6dL3v+t+VSmTZtG3759f7lYZWZmxsaNG+nYsSN37979rIgRGBjIli1bePLkyS+yMPvy4sULrl27xo4dO75p+aSkJKZMmcLatWt/i07ktxAbG8vAgQMZOXIkJUqUwMXFhVOnTlGrVi0WLlzI7du3OXr0KMWLF6dmzZrkz5+fVatWcezYMTw9PalXr15W74KaTJg4cSIdOnSgWLFiWW1KlqL2WPs4crmcN2/e8Pz5cwwMDHBxceHly5diblMnJydat26NUqnM8D75XZ6Hqve9hYUF+/bto1y5cly/fp0LFy6QK1cuKlWqlNUmfhFp27HdunXj/v37+Pv7ExISwty5cxk2bBjz589n5cqV6Ovr4+BUhQcRibxLSCExRYm2TIqlroxSpjroaX66/dOvXz8xGmrcuHEMHDiQgIAAqlatSpSGIUqpDMem7Ti3dgF+D25RwDFjXu+0lHPuKH6u22ckU2sU4viFq5iamuLn5wfAsGHDcHd3Z//+/axevTpdNNavLE6l5s9HLayp+WlY62tSO7f+J5NQZoaGBPT9H9K42xASExMZOnSoOGryM2nQoAHHjh3j9evX2NvbZxgxCwgIEJNTFyhQgLCwMFxdXWnWrFm69Zw4cQJLS0tkMhlz587lyZMnlCxZkocPH2JlZUVycjJ+fn5YWVmJYtjbt2+pUKECsbGx7Nq1izp16jB48GC8vLxYvXo1NWvW5MyZM1y9epU2bdqwfPly1q9fz4IFCxAEgQIFCqCvrw9kLowlJiaKL/+083yqCIMKleCWNrzud0zG+jlSUlJYv349hw4dyvR31f4qlUr09PTEc6c6hqrrJS4uDm1tbZ4+ffpXVknL7qi8C+/cuYO7uztVqlTBzs4OuVyOq6srANbW1uTLlw9IDek9ffo0HTp0AFLFlOrVq9OnTx88PDzw8fGhXLlyvHv3DktLy2+6L96+fcuBAweyrBBArVq16Nq1K926dePw4cOf7NTMmTOHrl27iiHzfzMLFy6kV69e3+wFvHHjRmxtbdOFOP5pbNiwgSdPnoje1K6urvzzzz/iMVu4cCGtWrWibt26lClThgIFCjBz5kxxeUEQEAThj3vf/M7cv3+fvXv3/vXi+vv375HL5WqPyo+go6NDy5YtOXLkCPHx8chkMkaMGMH06dPZtm0biYmJVK1aVUwg/zuieldOnDiRwYMH06hRIypWrEjevHkZPnw4ycnJXLlyhRo1amRbL7zExEQxsmLHjh0IgsB///3H9evXcXd3Z9asWYwaNYqhQ4dimDs/wVYlWPooAgnp82g/fw8Xg+IpYKRF5Zy6WOtrIgiCWFFUhY2NTbrPAQEBHDx4kOjoaI6eOAmS1Gd9SnIS0aEhn7RdmZLCiSUePDh1iLjIMCT/v2xiooJhw4aJEUYGBgbA/94nac+FWlRT8yNRC2tqfioOFqmN5y8W11KSOLl8Fkmv7jN9+nQaNmz4yxrUiYmJvHv3jhIlSqSbrnr4+vv7p0tOX69ePfbu3SsWPDAwMKB69eqEhYWRJ08eYmJiOHv2LBMmTKB27dro6Ojw/PlzNDQ0CAgIoHDhwuK63r59i4ODAw8fPsTS0lLcTtGiRfH398fX15ekpCRatmxJ3bp10dTUZMuWLTx8+JDy5cuLVSzPnDnDnTt36NChA9bW1uL6NTQ0yJMnD8WLF+fixYusW7eOHDlycOvWLfr27UvVqlU5ffo0efPmxcnJSXwJQXrBTTWy8ykRLu1v2bUhkRmnTp3C0tKS0qVLf3I+1f6l3S/VcXn37h3jx48nPj6ehw8fcu/ePXF9ycnJREVFYWZm9vN2Qs0XU6dOHR4+fMiyZcsQBIENGzaQL18+kpKScHV1pV27dhw/fpz4+Hi0tbVxcXFh0KBBJCQk8OzZM/FaNzc3p3Tp0sybN4/o6GiqVatG27Ztv6qxZmlpycOHD9OFff9qpkyZQpUqVVi0aBGDBg3KdJ7g4GA2bNjAo0eZ5z75m4iMjGTr1q3ffCwUCgXTpk1jy5YtP9iy7IHq2e/k5MS2bdt4+vQp9vb2KJVKLC0txQ6Xg4MDvXv3pmPHjty7d08s+pN2Hb/LO+RvQBAEhgwZwsSJE//6wiUvX76kUKFC6uvzE5iYmNCiRQtRSJdKpUycOBFPT09WrlyJhYUFZcqUyVojvwNV5IeGhgZz5sxBoUhNf5OQkMDZs2cZN24csbGxXLx4ETs7u2w3KH3+/HkuXLjAgAEDCAwMZMmSJVhZWREdHU3btm0ZMWIEmzdvJi4uDueBY4iyq0Z4bOb51FR9vBfvE/GNVlA7tz4OFrrp2kKJiYmMHDmSJk2a8PTpU/z8/NDR0aF8+fI4OjqSUqwaxsXKfbH9d4/u4fG5Y/RYthuTXPmQx0YztUYhNKUCmzZtolChQunCcUNCQtTvFDU/lexzd6v5Y3Gw0KVjkRwUyaGFTJLqkZYWGQJCchLPLhzn5fZFePbvwpkzZ2jcuPEvfQHlyZMHPT09Ro0aRWBgoPjgVdlw/Phx0WsFoGfPnlSuXJlz586xYMECnj59CkBYWBg6OjoYGhpSokQJvL29Wbx4MatWrRIT2ScmJqbz+AgNDcXKygpLS0t8fX3FzkVQUBAaGhpIpVJiY2NFj7n4+Hj09fXTeZBBarWbzp07ZxBvrKys6NGjB/b29gQHB/P48WPKlSvHsGHD2LBhA4sXL8bHx4fRo0dz4MABIDXka9y4cTg6OtK2bVuuXbuGTCbjzp07XLt2jdevXxMQEJAut55UKhVHgyBzd36VrRERERw5ciTbjHrb29uzZ8+eb1pWdY3s3r0bPT09Jk+ezN27d9OJdBKJhA4dOtCwYcNss89qoG/fvvTr10/0qNm+fTsPHjzg/v371KxZkxw5cuDi4kJoaCi+vr6MGzeOCxcu4OHhwevXr2nTpg1Pnjzh33//RSaTcezYMebNm5duG0+ePKF27doZwsxVaGtr/7KCBR9DU1OTbdu2MW3aNO7fv5/pPPPmzaNjx47pRPu/lVWrVtGkSZNvPhZr167F3t6eKlU+Hebyu6J69hcpUoRmzZoxc+bMdJ5naXNYjho1isaNG5OQkJDpOtRkHw4cOEBISAi9e/fOalOyHHUY6JeR1qNX1f4bPXo07du3/61FNRWq55iuri5aWlrs3LkTV1dXli9fztChQ5k9ezZLliwBst8zTaFQkJSUxLp16yhevDiDBg1CKpVy+vRplEoldnZ2qZVBDay+3EFCIiFZSHWoOP7Yj7lz52JnZ4eXlxcJCQlERUUBqbmvp0yZQsGCBbl06RLz588n6uUjJIISRUIcTy+eIDEu9pObSoyPRUNLC70cpiTJ4zm+2AMA/2cP6d69O40aNWLv3r3Ex8fj4+PDmjVrvveQqVHzSdTCmppfgrWeJi0KGNG/uClVrfUobqKNWUoc4Q+uc3rFbN4fXM60ljVYNXcGZcuWzTI7p02bhpOTU6aCnlQqTZekXiKR0KtXL9auXcuJEyfo1asXAOvXrxeTMQ8dOpQGDRoQGxtL//79adiwIcnJyXTu3JnFixdTsmRJsdKpjo4OBQoUQCKRcPbsWUJCQti0aRN2dnbkzp2b0NBQsahCbGwsUqkUIyMj0TYALS0tLCws0NLSymB/cnIykOp5V716dRo0aICzszPv378nV65ceHh40KRJE1H08fDwoHDhwvz33380atRIrIi4fPly+vfvz/z58ylfvrwYtpM2V5BEIuH69es8fPgQSM21odq+ytaQkBD27NnDvXv3PnlOVJUXf2T1xcywsbFJ56L+Nfj7+1O7dm2OHTtG/fr1yZMnD5C+QIhMJuPQoUPUr1+f6tWrM2jQICIiIn6I7Wp+HNWqVWP//v2ULl2aM2fOUL16dZydncWw3rlz53L58mW8vb2pWrUqlpaWnDx5kho1arB48WI2btzIkCFDgP91Inbu3ImNjQ2GhobpKvpmNwoWLMj8+fNp165dhqTLoaGhrFmzhlGjRmWRddmHpKQkFi1a9M2Jj+VyOR4eHkydOvXHGpYNMTQ0xNnZGUEQMgxcSCQSsZrc7NmzMTQ0zAoT1XwhiYmJDBs2DC8vr3SehX8ramHt60mbz7dFixYA2fZ9+C3s3LkTb29v+vTpw/nz5xkwYABdu3bl0KFDPH78GIlEkqFwXFZSr149atasKaa+aNWqFXXq1OHAgQPI5XJiY2MpUKYCNvXbfHW+7GQBbsVICVOkFiy4evUqcrlcdFDYvHkzRkZGdOnShU6dOrFv3z5uHNzO5BqFmdu8Av8d2v7ZbTg0aYOxdV5mNiiJV8uq5CvpCMDgds40b96coUOHoqWlRc6cOenSpQsdO3b8zBrVqPlOBDVqfiHJycnC3r17hSpVqgg2NjaCl5eXEB0dndVmfTPJycni39eSkpIiBAcHC4IgCBcvXhTCw8MFQRCEu3fvCm3bthUcHR2F2bNnC/Hx8UJCQoJgY2MjvHjxQhAEQVi8eLHQt29fcZkvQalUCoIgCBMmTBDmzp0rxMTECIIgCLVq1RIuXbokCIIgTJ48WfDy8hKuXLkiVKlSRShQoIDQokULoUaNGoKjo6MQGhoquLm5Cb179xYEQRCePHkitG3bVvD19RXWrFkjjBkzRlxvw4YNhUWLFgnJycnCzJkzhUqVKgn29vbCypUrxf3s37+/cPnyZXFdb9++FRQKRTp7v2b/VOfia5f9ESiVyi/eblhYmNC3b1/BwsJC8Pb2FvdZTfbh3bt3wsOHD9NNe/nypTB48GBh8uTJgkQiEe7cuSP4+PgIffv2Ffbu3SsIgiAkJSWlWyYkJEQoX768MGzYMOHu3bu/zP5vRalUCh06dBD69OmTbvro0aMzTPtb2bZtm1C9evVvXt7b21to0qTJD7Qo+9OxY0dh7969QlhYmCAIqe8/Nb8Xnp6eQrNmzbLajGxD165dhdWrV2e1GdmC6OjodO2fsLAwITIyMusM+sWo9v3DNuCtW7eEqVOnCm3bthXb+z8TGxsbYcaMGULRokUFY2NjoWvXrkJCQoIgCIJw6NAhoXTp0kKOHDmESpUqpWuP2NjYCJ6enkLJkiUFLS0tYdWqVYKurq7g5uYm5ClkJ+gY5hAcm7YTpl59K8y8HSq0muwt2JSpIMy8HSr+AcKw/deFmbdDhS4LtwjWdiUEbX0DwdgipzBp0iTh7NmzQrly5QRfX18BELp06SLs3LlTmD17tqCpqSloa2sLpqamQu0u/YWZt0KEmbdDhZ4r9wtGltZCoyFTBH0Tc8HQ3FJoOWlhuu1++LfnZdRPP85q1HwM9ZCTml9CXFwc69evx8vLCzMzM4YNG0aLFi1++1HPz+VREtKERUL6fGVSqVQMB61atao4vXTp0mzfnn6kRhAETpw4IVYlLVeuHFZWVqLHmoo5c+ZQuHBhmjVr9tEwWlUZcC0tLeRyOfHx8WIYWnR0tBjaZGNjw7Jly9DW1sbPzw9BEDA3N+fp06dMmDABACMjI2JjY4mJiaF79+40btyYyMhIzp07h62tLd27d2fkyJFYWVlx9uxZtLW1admypVgpSS6XY2pqSmBgIEuXLuXOnTvExMTg6elJgwYNOHv2LH5+fkRHR6OpqUmLFi2Qy+VIJBJy5syZzjNPIpF89nyoRkYFQchQdOB7+Zp1mJmZsXTpUry9vfH39+f69evky5dPHDVUk/VYWFikKz4h/H+REC8vLxQKBXZ2dpQpUwYvLy/kcjl169YFyPBMW7t2LYGBgTg5OdGrVy/Kli3L8uXL082TkpKCVCrNFmEiEomEpUuXUrZsWfbv34+zszPh4eGsXLmS27dvZ7V5WY4gCHh5eTFmzJhvWj4hIYGZM2dy+PDhH2xZ9kQQBK5cucKVK1fQ0tJi69atrFixAlNT02yXb0jNxwkODmbOnDlcvXo1q03JNvj4+IjJ0f9mHj9+zPr16+ncuTPFihUjLi4OLy8vevbsma5i9p+M6t2tak9ev36de/fu4ePjA0Dv3r3JmTMnUVFRbNq0CXt7e7HN8KPZsmULx48fR19fn6ZNmzJ9+nRatmxJ9+7dOXToEOXKlWPz5s00b96cZ8+eiYULtm3bxuHDh7GwsEBXV5dx48Zx+cpVOi/agUxHj42DO3J29Xzq9x/7WRu0dPVpM3UJlgXteffiEUvd22FnZwfA8+fPgdT0O7dv36ZChQocO3aMkydP8uTJE45uX03Jf1ywsk9NpRIb/g55bDRjjt3nxfVzbB3Zg+K1GqFrZJxhuxoSqGT1bcWE1Kj5EfzeqoaabE9QUBCLFy9m5cqVVK1alfXr11OlSpVs0YH8FXxpksy0HQzh/5M6p81BI5FIKFKkiDh/xYoVqVixYob19OnTR+ykZ2YLIFat1NTU5N27d/j6+ooCwrt37yhWrBiOjo7cuHEDbW1tihQpQpEiRURRKioqShTi5HI5CQkJYhLjuLg4EhMTWbBgAUOHDkVPT4+zZ8+iUCjYsWMHenp6XLp0iQkTJhAbG4sgCBgYGJArVy4mTZqEmZkZx44d4+jRo1SuXJl3794xe/ZsGjVqhJGREeHh4ezatYsTJ04QHh7O6NGj6dy5MxKJhHXr1vH8+XNMTU1p0qSJmDMrLZ86Lh+eD1UD6Wd2/DQ0NLC1tcXW1hZIDdf9EoFQza9HlaRYIpGgpaVFu3btgFRRPH/+/BgaGmYQaePi4jh06BCLFy/G2dkZHR0ddu/eDaRWlLtz5w4VKlQQK/r+KJH3e8mRIwdbtmzB2dmZcuXKsWLFClq0aPHNodJ/ElevXiUsLIymTZt+0/LLli3DyckJBweHH2xZ1qNUKjly5AgWFhaULVsWLS0tJBIJlStX5tWrV4SEhODh4YGHhwdubm7o6uqKzz412Ztx48bRrVu3dEWX/nbUoaCpFChQQKyq3r9/f/Lnz0/Pnj3/yveFKtT1wIEDxMbGUqpUKWrWrEmhQoV4/fo1kyZNwtTUNF17/kczYMAA8ubNC6TetwMHDiQ8PJzevXuL/YYuXbowY8YMrl27JlalHjRoEPny5RPb+rq6ujTp0gsTq9wkC1CzxxAOzR77RcJagXL/yx2as6A91Ro5c+PGDQoWLMiRI0cAcHR05M2bN9SqVQtjY2Nq164NgLOzMxqv7qBRNFVYk2poUrvncGQaGthXrYeWrj6hr33IVyp9kQMNCdTOrY+1niZq1GQVamFNzU/hwYMHzJ8/n/3799OhQweuXLmibpB9grTizZcIK8JHigN8SY6a1atXk5SUhEQiwcjIiGXLlmFkZIQgCJQsWRIbGxu0tLRYsWIFrq6uYiGG3r1707RpU/z8/DA3NwdSc72lpKSI31u3bs348eNJTEwUX5IRERHcvHkTCwsLIiIiiIqKwsbGhgMHDohVFU+ePMmiRYvw8/NDoVBgZGSEhoYGsbGxlChRAg8PD9E7zd3dnREjRpCcnIyzszP16tXj8ePH3Lp1iyJFivDq1SuePn1K4cKF0dDQECt2dunSBTs7OzFx6tixY3n9+jUxMTGULl063bH7UjFNEARRyJRKpQQGBpKUlESuXLnS5eP7Un53D84/nbTit+reS1uV98P78ciRI8hkMpydnUlJSeH169fkypWLyMhIUlJSuHXrFgMGDKBx48aMHTsWY2Nj3r9/z+jRo+nXrx+lSpX6tTuYhkqVKjFw4EA6dOjAw4cPuXXrVpbZkp1YsGAB7u7u3yR+x8XFMXv2bE6cOPETLMs6kpKS2LFjB7NmzUJDQ4PevXtTtGjRDLk+zczM6NWrF5MnT6Z58+asW7dOLaz9Bty+fZsjR46IBZrUpLZ9oqKi/upCLqqIDB0dHWbOnMm6devEtmBaUe3OnTvky5fvr6iILvx/gZbWrVujq6uLvb29+JuNjQ1ubm7Y2tr+1OtGJaqpthkYGMijR4+4desWixYtAlIHQZKSkggMDMywXNr2r465tZhbzcQ6L9GhwV9kg9+D/zi+aBohPk9JTlagVCTStk0bWrduzbNnzwBo2rQpGhoaHD16lClTpvD8+XOUSiXx8fGMKlmS2rn1eXkL9HKYIEvTNtbU0UWREJdueypRzcFC7a2mJmtR++Cr+WGowhX/+ecf6tevT6FChfDx8WHJkiVqUe0H873lolWij66uLi1atBCFgpEjR4ru6bVr1+bIkSMsWbKEIUOGUK5cOZKSkujdu7fYeIqPj0dTU1N0JW/RogV79uxh1KhR6OjoANC+fXu8vb0BMDU1xcjICKlUSnx8PHp6eujo6DBo0CDGjBnD3bt3WbduHdra2mhraxMWFoajo6MoJL548YI+ffpQuXJl6tSpw9mzZ9HT00NDQ4O7d+9StmxZFi5ciIuLiyhSqY7T3bt3ef36NZUrV+bu3btMmzaNrVu3MmzYMMaMGSNWbDxy5AgdOnSgZcuW7Ny5M922Q0NDkcvl6c6DqmorwMGDB5k1axaRkZFA+uIFM2bMEDsmKo84Nb8nae+9j53HlJQU5s+fT/v27YHUcBlfX1+KFi2KiYkJ0dHRtGjRgkePHhEeHs6jR48AOHr0KEqlUkzqrvqfFYwZMwY/Pz/y588vhqH/zbx584bTp0/TrVu3b1p+yZIlVK9ePUsF0x9JfHw8S5YsoUiRIqxZs4Y5c+bQu3dvxo8fz/nz5zMMAGloaFCiRAk2bNjAnTt3/tiKqH8SgiDg7u7O1KlTyZEjR1abk214+fIlBQsW/KtDmVUe/SEhIWhpadG7d28MDQ3FQlWQepwOHTqEgYFBFlr661A968qWLSuKammfg9WqVSNv3rw/dRD17du34mc/Pz/MzMx4+/YtrVu3xt/fn9evXzN48GDevHkjtk/S2p6WoAB/8fP7YH+MLFKjVbR09UiS/6+Kc0xYSLrldozrQ9HqDRh19C6TL7yiVpsuCIJAy5YtxeIFkBr10rJlS4YPH05ISAjv37+nUaNGCIKAg4UudfPooyGVIJOkimdp0ZCATAJFcmjRsUgOtaimJlugdo9Q890kJiaybds25s+fj1KpZNiwYRw8eFAUW9Rkb9KGoX6Y88bMzAwzMzNKlCghTps1a5b42cnJiZMnT4rf379/T6FChWjSpIko1o0ePZrevXtTqlQppFIpDRo0wNPTk8DAQBITEwEoVKgQwcHBBAQEsGDBAgwMDJDJZCQkJGBmZoa2tjaCIDB//nxKlSrFtm3bUCqV6OjoYGxsTKVKlWjfvj379u3j+PHjDBw4UAxXVe1PQkICbm5uVKhQgcjISBYsWMCdO3eQSCRYWFgwffp07t27x9GjRxk7diwKhYKFCxfi4OBAzpw5qV69OjVq1ODp06cUKFCA8ePHs3r1akJCQpg9ezYFCxZEqVRibm5OREQEJiYm6bzWWrRoQa5cudLZ9LHzoWqIqUNCsz8fE7gFQcDV1ZW+ffsCcO3aNZKSkmjTpg3jxo3Dx8eHN2/eoKWlhbGxMW/evKFy5cqcPXuWsmXLitWRVdfA2bNnsbCwSHcv/mxUXhmxsbHcuHGDChUq/LJtZ0cWLVpEt27dvql6ZUxMDHPnzhWrK//OREZGijkiK1WqxLZt2yhQoAA9evQgMDCQixcvZhqKr0IV+qwm+7Nz504xh6qa//E3h4Gm9dheu3YtS5YsoW7dulSuXJnmzZujoaEhzlOwYEFGjx6daaX6Px3VMUibf23Dhg24uLj8VJF6yZIlNGnSBD09PTw8POjcuTO6urp4enrSuHFjWrduzaBBg7h+/TrVq1f/5Pvs5JbVmJevg6aOLmfXeFGqXnMArIqUIOTlMwKfPcDCtjCnVsxJt1xiXCy6OYzR1Nbh7cPbXD+6jyYN/gHSDxYmJSWRmJiIhYWF6L124sQJsZ1jpqOBrkxC/+Km3I+QE5qQgqYUbAw0qWqtRylTHfQ0/15xW032Q301qvlmIiIimDFjBvnz52fr1q3MmTOHBw8e0K1bN7Wo9pNJSEgQc7ElJyd/V7nyDwsqZEZar5y0L0XV9MjISAYOHEjv3r2ZMWNGumVz5MjB9u3buX//PmfOnGHYsGEANGzYUBy5Gjp0KJMnT8bNzQ1LS0txNC8wMFDct8TERKytrZHL5YSFheHp6Ymenh6QmptswIABDB8+nMePH7Nly5Z0NsjlcpKTkylZsqQ4mlq3bl0kEgkpKSkYGhoilUrZtm0bhw4dYsaMGezYsYNz585x6dIlYmJiiI2NZerUqdy9e5egoCC8vb3p0qULtra2bNiwAUg9L3fu3GHgwIHY29uzdOlSBEEgPj6enTt3oqOjw6tXr7hz5w7379/H19c33eiu6hzIZLIvEtWUSqX4pyZ7oaGhQb9+/YBUUSUqKorcuXOjqanJ8uXL8fLy4tq1a7i6uhIXF0eJEiW4efMmMpmMMmXK8ODBAzw8PNi7dy8A06dP5/79+wCEhIR8dLs/ksWLF9OkSROWL19Ohw4dRK/Ov5GYmBjWrVvHwIEDv2l5b29v6tWrR7FixX6wZb+OwMBARowYQcGCBXnx4gVnz55l//79hIWFUbp0aUqWLMnVq1c/Kaqp+X1ISEhg5MiRLFy4UD3I8wF/s7CmEoouXbrEiRMnmD17NkZGRpw7d46NGzeKgpKqrfg3imrwv+OkOg6qQdxXr1791O126NCB+vXrU6BAAQoWLMj48eOZMGECs2bNYs6cOZiYmFCyZEnWr18PwPHjxz/qGd+wZRvW9mvNnKblMM1jSy23oQBY2BSkTs9hrOnbinnOFbEtkz7nc/Mxszi1bBaTqtpydtVc6jZrIf6W9lliaGiIt7c3bdq0wcTEhK1bt9KsWbMMduhpSnHKqUdTW0P0NKRUstLDKaeeWlRTk+2QCOp4JDVfycuXL/Hy8mLLli00b96coUOH/jGhLb8L5cqVE6usZQeUSiUvX74kPDwcJyen715fQkICurq6vHjxAk1NTTEPz5MnT+jWrRvm5uZYWlpy4cIFfHx8mDt3Lo8fP6ZgwYIEBwdTtmxZunfvLnrgvXnzhkaNGvHo0SPkcjmrVq0iKCiIGTNmEBQURJ06dbh69SpjxoyhdOnSFC1aFF9fX5KSknB0dERbW5t27dpx//595HI53bt3559//qFLly48fvyY7t27c+3aNSZMmMCFCxc4f/480dHRlCpVijt37hAdHU3lypXx9/dnzZo1LFy4kFKlSvHo0SMqV67M4sWLkUqlvHr1ijlz5mBgYEDBggXp1asXiYmJhIaGYm5uLgqJX0LairSqKqh37tzB2tpa9OZT8/PIrBBBdHQ02tratG/fnpo1a1KvXj1mz56NhoYGK1euZOLEiRw8eBAHBwckEgklSpSgUqVK6OvrM3LkSHbu3ImRkRFbt25l1qxZrF27FkdHx59if0xMDAULFuTChQvY29vTs2dPFAqFKCL/bXh7e3Pp0iV27tz51cu+f/+ewoULc/ny5Z+atPpn8fz5c+bMmcOePXvo3LkzQ4cOJV++fMTFxTFs2DCOHTvGpk2bqFatWlabquYHMm3aNO7fv8+uXbuy2pRsR8+ePXF0dKRPnz5ZbUqW4OvrS40aNejZsycTJkwgLCyMffv28eTJEwoXLkyvXr3UYixw+fJlfHx86NKlC/Dx/MjfSnx8PDo6OuKguK2tLatXr6Z27dritAMHDlCpUiUMDQ3R1dUlOTlZHLz28fHh2LFjDBgwIMO6lUol8ckCyx5HkvIdSoFMAv2Lm6pFMDV/BepQUDVfzJUrV5g3bx7nz5+nV69ePHr0SAxtU/NrqV+/PnPmzKF9+/aiG7dEIhFzn/1qpFIphQsX/qpcemmFB5UIpGoI6Oqm5kr4cH1Fixbl2rVr4ve4uNQEpo0bN8bExIR3797h4OAg5kBSrf/t27di2GlCQgLBwcFiA8ff3x+ZTEaOHDmws7PjypUr9O7dm+rVq4vbOXfunOi6HxUVhUwmE5Pzvn//XmykJCYmpstZ4eTkxJMnTzA0NCRPnjxIJBISExMxNjZmy5YtCIJA/vz5iY6OJikpibZt29KzZ0/kcjmBgYFIpVIePXrEhAkTCA8PR0dHhx07dmBtbc25c+cICAhAoVCgq6tL9erV092PmeXh8/T0pE6dOvTo0SNdo1fl/Qip5zI7VKb83fkwD5uqWAjApEmTmDZtGrdu3eLevXvMmjULHx8ffH19KVCgADExMWL1UEi95qZPny4u36FDB548ecLVq1d/mrC2bNky6tSpI+aJWbBgAY6OjmzdujVdjpS/gZSUFBYuXMjmzZu/afkFCxbQuHHj305U+++///D09OTcuXP079+f58+fi++Ymzdv0rFjR5ycnLh37546/9YfRkBAAAsXLlQXLfkIPj4+tG3bNqvNyDLy58+Pm5sbixcvpkGDBpQvX562bduyefNmsSiUsbFxVpuZ5VSoUIGYmBixiNaPaFulbS9nFmWiKqgF0KxZM/z9/SlWrBiVKlXC1dVVLFYmkUgoVKiQKKp92CaPj4/nxo0bFLAtx4v3ifCNthc00lKLamr+GtTCmppPkpyczL59+5g/fz7v3r1j8ODBbNiw4a9JRJpd2bt3LwqFgjVr1gCpnfikpCR8fX1/m1HCtA2MLy3G8KEAp8rVU7Ro0UzDj1TrdHJyEnMb6erqUr9+fVFIiouLE0M6nJ2duX79Oo0aNSJHjhyUKVOGgQMH8vbtWzFfWkREBFKpVPQeCwkJwcLCAkgVQKKjo4HU8FgdHR0kEgnh4eHi/PHx8WKBCH9/f0qVKoW/vz8PHz4kb9689OrVK90+lCpViv3796OhocGBAwfw9PRk4cKF3Lx5kxUrVtCtWzdu3rzJv//+y6ZNm8Sy997e3hQoUIAmTZpgamqKi4sLBgYGWFpaIpPJ0o1afmklWrXg9m18eNxKly7N7t27iY2N5eDBg9SqVYtJkyZRuHBhSpYsKVaNVHlu5siRg3LlUkvLC4KAQqHgxo0bdO3aFciYG/F7iYuLY/78+Zw+fVqcpq+vz7Zt26hfvz5OTk5/VTGDQ4cOYWFhQaVKlb562YiICBYvXsyNGzd+gmU/HkEQOHv2LJ6enjx+/Jhhw4axbt068Z2fnJyMp6cn3t7eLFq06K8WF/5kVLlR1VVbM+dvCwVVvWMeP37My5cvsbOzY+TIkeTLlw83NzeWLFlC1apV6dq1K3K5XC2q/T+ampo0aNDgo79/Tbsqbc421TLa2toZlpfJZAiCwNChQ6lduzaDBw9m+/bt3Lp1i3Xr1tG9e3cMDQ0zbDutqCaRSDh8+DAWFhZUzqmLb7RCrA76NWhIoJKVuqiAmr8HtbCmJlNiYmJYu3YtCxcuxMrKiuHDh+Ps7PzbiDZ/On9r2fuPCXAqwU0lun04j4aGBnny5AFAR0eHGjVqAKkjezVr1qRmzZpAamlyLy8vHjx4QEBAAKampujp6WFoaEjVqlUBCA8PJyUlRQzDffbsGVKpVBQ2Q0NDgdRk8z4+PlSqVInNmzeL5dXj4+NFQTAsLAwdHR2USiVhYWE4ODiI8+jp6aFQKDh8+DBz584lPj6euLg4ihcvDqR6xzk5OTFu3DjCw8OpWrUqcrmcmzdvMn/+fE6dOsW9e/cYOXIkjRs3pnHjxigUCszMzAgODmbq1KloaWkxb948hg0bRnBwMFZWVnTr1o3SpUujVCrx9/fH2toaTU3Nr278wf8KMaiqh6lJRalUIpFIMDAwEL2/qlevjo6ODhUrVmTZsmX4+vqSP3/+dMupjuX58+eJiooSvSN/9LFdsWIF1apVE681FWXLlmXMmDF07NiRixcv/tTKZtkJLy8vhgwZ8k3Lzps3DxcXl2wvRCqVSvbv34+npyfR0dGMGjWKjh07pks38OrVK1xdXdHR0eH27dviM1XNn8W1a9c4c+YMz549y2pTsiUJCQmEhoaSN2/erDbll6BqV507d45evXpRoUIFgoODqVu3Lm3atEEmk9GpUyfWrl1L7dq1vyplxd/Op9pVycnJ6bzcVP/9/f2xsrJCQ0Mjg2f869evAXjw4AGrV68WozdatWqFTCbj6tWreHt7M2zYMHR0dDJsU9U2AWjXrp04vXZufc4ExH2VuKYhSV3OWk/z8zOrUfOH8He0itV8MQEBASxatIjVq1dTs2ZNtmzZ8k2j9Gp+DmfOnAFSR8HU+Wz+x5d6vKlQCRSqkb20y+bMmZOcOXOmm9/Z2RlnZ2cEQaBq1aqUK1dOFBUaNmxIlSpVSEhIwMXFhRcvXlCiRAlsbW2ZMye1UtLjx4/FDmpQUJBY8SgkJAQNDQ1sbGyQSqVcv34dQGyY3rx5k3Xr1rF06VLKlCnDkiVLRC8ihUIhejH5+vri4ODA8+fPuXPnDk2bNsXa2hptbW1Kly6NjY0NcXFxJCUlcevWLTZs2ICdnR0jRoxAoVDQtWtXZDIZV65cYeXKlSxZsoSXL1/i6OjIkCFD2LFjB8WKFWPbtm3pCpMIgkBwcDDa2tqYmpqK50LFpwSftEKoIAgcPnyY0qVLkzdv3j/OM04VVqESVFXHJe2116hRI3H+Ro0aMXv2bJYtW5bp+pYsWSIm+E3refgjSEhIYM6cORw7dizT3wcPHsyJEyeYMmUK06ZN+2Hbza7cvn2bV69e0aJFi8/P/AFhYWEsX76c27dv/wTLfgwKhYLNmzeLCcjHjBlD8+bN0927qmp2I0aMYOzYsbi7u6uF8j8UpVKJu7s7M2bMUEcmfARfX19sbW3/+IFmhUKBlpYWEokEuVzO+vXrWbx4MfXr1+fs2bOcOnWKy5cv4+rqSkpKijoc/DuJiopi7dq1NGjQgKJFi6ZLMRIUFISWlhazZ89mxowZGd75qlBTSD1vJUuWZP/+/fTv359169bRrVs3WrduTXx8PECmohpkbLOp1utgkep19qXimkpUUy2nRs3fglpY+02JS1LyIELOu4QUElOUaMukWOrKvrn08L1795g3bx6HDx+mU6dOqXH12XyE/W9k27ZtKJVKDA0N1cLad5CZ+3taVKKPSrBThUGo5k3bKCldurT4uW/fvplur0ePHigUCpKTk6lduza5c+cGUkPujI2NkUgkNGnSRKwSaWFhQYcOHTA0NMTY2JjExERevnzJjh07cHJyQhAEEhMTxZxboaGhaGtro62tTWxsLJaWlgDExsZia2uLrq6umFtu5cqVuLu7i0mXExMTuX37Ng8ePCA+Pp7//vuPqKgo3r9/j4WFBQ0bNmTIkCFMmDCBTZs24ebmJh6bmJgYli5dSnJyMjNnzuTly5dMnTqVGTNmYG5uzurVqwkPD8fe3p7q1atjZWVFYmKiGL6gOp6RkZGsWLECd3d38uXLl+H4qTzfgN+yM7Nv3z7Onj2Lt7f3Z689gCFDhvDu3TsgvfgmkUiIjY3F2tqawMBAgB/uNbZq1SqcnJzSXddpkUqlrF+/nrJly1K3bl3R+/NPxcvLi4EDB4qh4F/D7Nmzadu2rZiPMTsRGxvLqlWrmD9/PkWLFmXp0qXUqlUrwzUZHh5O7969efbsGadPn1YXKvrDUeX+dHV1zWpTsi1/QxioIAh069aN8ePHU7RoUXR0dFAoFNy6dYv69etTq1Yt/P39Wb9+Pa1btxbTEqhTRnw5giCwcOFC9u/fz86dO7G0tOTmzZsUKVKEokWLsmXLFoKDg9m/fz9du3ZFR0eHOXPmZHgXKZVKZDIZCQkJuLu78/79e2rUqEGdOnVYu3YtgwYNQiKR0LVrV7GIgmr7H54rVX421fS07S0HC12s9TW4GpzAy2gFEkgnsmlIQCA1p1olK121p5qavxK1sPabERSXxJWQBF5l8lB7/h4uBsVTwEiLyjl1sdbP+FBbsmQJ169fF0tiHzt2jHnz5vHkyRMGDhzIwoULMTEx+WX7o+brWLVqVVab8FfwoQfc5zyv0oY+ZrZ8wYIFxc9t2rQR523ZsiUtW7YEwMjIiEmTJnHlyhWCgoJISkqiXLlylCpVitatW9OsWTPkcjk5cuQgOjqagIAASpYsCaQWZ1AqleTPnx+FQiGGA7x48YKzZ89Su3ZtAgMDKVSoEAULFuTRo0eiPevWrePs2bP07NmTY8eOkSdPHmJjY/Hz88POzg4nJydSUlLImzcvz58/B1IbXxoaGigUCjQ0NEQhLyQkhNjYWBQKBUeOHOHs2bNUrlyZixcvYm5ujomJCdOmTePAgQPo6+vTvn17sSFobm5OeHg4Fy9eJHfu3OmE/S/xjlEJb6pQBqlUKn7Oyoa+qkBFxYoVvziPoEQiEY/ph8sMGDCAyMhI8ufPn6GDl5SUhCAI31wtWC6XM3v2bA4ePPjJ+aysrFizZg2dO3fm7t27f+w7IzAwkMOHD+Pt7f3Vy4aEhLB69Wru37//Eyz7dsLCwli0aJEopO3fv/+jBTBOnDhB9+7dadOmDZs3b/6ol4OaP4PY2FjGjBnDrl271B6Jn+BvENYkEgkLFixAS0uLBQsWMHjwYDp37syJEyc4cuQIjRo1onLlyuzatYuEhATx2aAW1b4ciUTCgQMHGDJkiPi+L168OM+fP6dx48ZcunSJ8+fPs2XLFsqWLUtiYmKmAzxSqZSUlBRat26Ng4MDzZs358SJEwQGBtKjRw88PT1xdXWlSJEiVK5cOd32P+RzA5fWepq0KKBJfJKS+xFyQtM4d1h8h3OHGjV/Cmph7TfidmjCJ91wVdNfRCnwjVZkcMM9ffo0I0aMQBAE7O3t2bJlCxoaGgwbNox27dp9c2dMza/j9evXn00mHBAQIHpEqcnIw4cPKVSo0A/rJH6JAPeh+Paxak4FCxZMJ8IBjBgxghEjRgCpYXpKpRItLS3c3d3FkFVLS0scHR2RSqV06tSJgQMHUqZMGYoUKYK5uTlWVlY8ePAALS0tBg8ejJeXFzNnzmTMmDH8999/VKtWjUaNGhEVFcWbN2/IkSMHAQEBYl645ORk3r9/n0FAiYuLIz4+XswFFhoaiqGhIWZmZvj6+vLu3bt03j5KpZJRo0Yxffp0bt++zdatW/Hz80OhUBASEsLp06cJCAggNjaWOXPmULFiRa5evcq4ceMwNDTEycmJIUOGZHruMhth/dj5UHnAfVgZ9Wd0CiQSCWXLlv2q+T9EFY4RGBjInTt3OHXqFBYWFqKQq5rnwIEDuLu7M2DAAHr37i2G534pa9eupUyZMmKuv0/RqFEjXFxc6NmzJ7t27fojO1RLliyhY8eO3yQcqjoz2SUPmZ+fH/PmzWPTpk20atWKy5cvf7RKaUJCAmPGjGHPnj2sX79eLLai5s9m1qxZ1KhRQ53+4zP4+PhQrFixrDbjp5GUlISmpiZaWlokJiYyc+ZMUlJS6NmzJ0+ePGHVqlWsXr2aFy9eMHr06D92YOVnExsbS758+UhISBCn9ejRQ0y30aZNG65evUqpUqUQBCFdGg6AOXPmMHToULFtkJSUxKRJk5DJZBQqVIgZM2aIobrHjh37qBf6t6CnKcUppzqXnho1H6IW1n4TPieqfUiykBoLD6nuu69evcLZ2Vl8gC9atIjg4GAUCsU3hbioyRoaN27M3bt301Vz/JAmTZpw+/btP7Kj+73cuXMHFxcXXr169Uu3+7V5x9KKPmm9rnR1/yeUly9fXvzd2dlZnJ4rVy727duHjo4OkyZNIiIiAlNTU/Lnz098fDz58uVjwoQJ9OjRg/Lly1O3bl3mz5/Pq1evuHnzJmZmZhgYGPDs2TOx2qlKWMusIx4TEyNWAAsMDMTAwACZTEaLFi2IjIxk4MCBODk50aVLF4KCgli6dCn//vsvkBrG6OLiIjYYW7ZsSYMGDZg0aRKrVq2iYsWKFC5cmCVLliCVStmwYQNbt26le/fuCIIghkDs3LmTmTNn0qxZM27fvs2gQYOws7PjxIkTODg4ULZs2c+eg4/dL2nDUDMrjPEruHv3Lh4eHmhpaVG5cmVRVEu7HzKZjFatWmFnZ8f8+fMpVKgQHTp0YPDgwV/kXZGYmIinpye7du36Yrs8PT2pWLEia9aswc3N7Zv2LbsSHx/PqlWruHz58lcvGxgYyIYNG9J5hmYVjx8/Zvbs2Rw6dIgePXrw8OFDcuXK9dH57927R8eOHSlWrBj37t37anFWze/J69evWbp0KXfv3s1qU7I9Pj4+Yo7LP42rV69y5swZ2rVrR/v27Tl8+DCXL1+mefPmpKSkMGzYMNq2bct///2HtbW1mOdVzdejoaFBhQoVuHTpEu3atUOpVGJlZSX+Xq1aNYoWLfpRr/ucOXOKqUry5s2Lubk53t7e9OnTBzs7O+rUqcPNmzfp1KmTKKqlzcWmRo2aH49aWMsm2Nrasnr16kxHhoPikr66Ggv8T1zLIUmibJkyxMbGir+FhYUBarft3w17e3uqVq2KhYUFMpmMXLly4ebmhre3NxEREYSGhlKoUCH1ef0Ia9asoVu3btk2zCWzBtTnvOBUIY8qMW7z5s1s3boVpVKJnp4eY8aMEQsyqMS4XLlycfToUTFXW+HChQkICKBevXqiN5iJiQl2dnZAajJcPz8/6tWrl86mvHnzEhMTg5+fHwBHjhwhd+7cJCUlYWtry7Rp07h27RqDBg2iRIkS7Nixg9jYWO7evcvFixdZvnw5GhoahISEULRoUXF74eHhYqL/Z8+esWnTJvT09Dh9+jRVqlShe/fu6by1nj59SmxsLO3bt0cikTBjxgycnZ25cuUK+/fvZ9q0aZQtW5Y7d+4wZ84cfHx8qFmzpjja/uzZM+RyOYaGhpibm6dLwvzh8f/Qsy1tEQYVPzoE1dHRkbFjxxIREYGTk9Mn5y1ZsiTr1q0jOTmZoKAgMX9eqVKlKFCgwEdt2rBhA8WKFaNixYpfbJeOjg7btm2jRo0aVK1aFXt7+6/ar+zM5s2bcXJyonDhwl+97MyZM+nWrZvo8ZkVXLt2DU9PT65evcqgQYPw8fH5pGeJUqlk3rx5zJ49m/nz59OpUyf1e+QvYuTIkQwaNOivqXT5PfzJoaCVKlViypQpzJgxg6lTp2JpaYmlpSXHjh2jWbNmhISEMG/evHTivDqv2reho6NDgQIFOH/+vOglqEKpVKKhocG6desytEFUg2qdO3dmypQpnDx5krNnz9K2bVsuXbrErFmzGDhwILt27aJq1arpzo1aVFOj5ueiFtZ+A66EJGQQ1VKSk5F9QdLqZAFuhSdRvnx5LCwsSEpKIjY2lqioKLECoZrfhz179vDkyRMiIyNJTk7m7t271KpVi3nz5mFvb4+uru4Pdff+k5DL5Wzfvp3//vsvq035IXzMA6tXr160b99eTG6vEqsyE3skEgk6OjqZijXTp08XP5uYmLB27VqxSpyqkqdUKsXZ2ZlZs2Zx9OhRQkNDqVGjBvHx8cyZMwe5XE6RIkWwsbHB0NCQ2NhYMZH7vXv3CAgIIGfOnDx79gw9PT1R1Hv9+jXNmjUTqyqWLFmSOnXqcOPGDbFBL5PJSE5OBv7nQWBvb0+tWrW4fPkyxYoVw93dnZ49e3LmzBmsrKzYsWMH3bt3p27dukycOJH9+/fTpUsXpk+fztOnTylQoAAPHjxgypQptGnThocPH7JhwwbCw8PFXHe5c+cmJiYGqVSKvr7+V4mh31OE4UPPgM+JwxoaGuTNm5e8efOKAmpcXBxaWlpoamqms1mpVJInTx5Wr179VTYBFCtWjOnTp9O+fXuuXbuWIVzld0QQBBYsWMCSJUu+elk/Pz+2bt3KkydPfoJln0YQBI4fP46npyevX79mxIgRbN26Vawy/DHevn1L586dSU5O5ubNm59NN6Dmz+LChQtcv36d9evXZ7Up2R6FQkFgYGC2LEjyPaRtH3Tq1ImEhATOnz9Pr169MDQ0JG/evBw/fpzatWvTvHlzqlevLi6rFtW+HpXH/fbt26lQoUKGyCFVO+HDtBenT5+mRIkSmJqaMnfuXCZNmsStW7dwc3Nj8eLF6Ovrs2HDBnr16kXBggUZNWrUL9snNWrUQPZ02/iL8fHxoUaNGuTIkQNzc3Natm7Dq2gFAGMcLLi6Yw1zm1dgnnOqV8GhOWPxbFiaydXys6hDHXxvXxXXpVQqObduIf3qluPu3bsolUpWrVrF8ePH2b59e5bsn5rvp2jRolSuXJnq1aszaNAgnJ2d6dmzJ9WqVaNcuXLq0N6PsH//fhwcHP64BnFmGBoaYmdnJ4pq8OkwRlUjL63wk9YjDFLzuKXtoEskEgRBoGXLlty4cYN///2Xixcv0q9fP8zNzalRowa5cuXi8ePHDBgwADs7O9zc3Ni/fz/FihXj6tWrKJVKDAwMePnyJYCY59HX1xdbW1t8fHwQBIF+/frh6OiIRCLB0NAwg/0BAQFizpt3795Rrlw5Mc9gQEAAuXLl4vr16+zZs4dRo0bRtWtX1q5dy40bN4iIiCAiIoLWrVuzc+dOunbtypUrV1AqleTMmZMWLVrQrVs3Xrx4wZEjRwBYvXo1Li4u9OrVi/z58zNr1ixSUlIAePPmDTt27OD8+fO8efMGuVye7hzIZLJ0Yb6ZnQtI9dpTiZXfi0pANTAwQEtLK1MhsFGjRt+cD6xXr14ULlyYsWPHfret2YHjx4+jpaVFzZo1v3rZGTNm0LNnTzEZ9a8gOTmZ7du3U7ZsWUaMGEHPnj158eIF/fv3/6yotn37dhwdHalfvz7nzp1Ti2p/GSkpKQwePJhZs2Z99lpRkzrokydPnj+qnaWqBJmUlERAQADt27fn/PnzWFhY0LRpU1JSUggNDeXSpUvcv38/naim5tuQSCScPHmS169f8/DhQzZv3gz8r03wsYEzhUJBhQoVcHJyEuc9dOgQwcHBuLu7U7t2bTZu3MjKlStZsGABgNg2UaNGzc9H7bGWzZgwYQL169fn7NmzKBQKNpy4REya3x+fO0q/jcfR1E4dxchTrCy1ew5Hx8CIK9tWsnVUD0Yevo2mtg5Xt6/i8dmj9F19gPpF87LFYzT9+/dn1apVbNiwIWt2UM0PZ+PGjVltwm/BmjVr6NGjR1abkS35Gq+rD5dTCUESiSSdt1L9+vWpX79+uvkdHR25du0a8fHxKBQKwsPDMTU1FedTecQZGRmRM2dOSpcuzYwZM6hTpw7Vq1fn8uXLDBw4MIMdISEhomAaGxubToALDAzE1taWq1ev0qNHD9q2bUtoaCitWrXCxsaG6OhozMzMqFChgmiDvr4+0dHRaGlpcePGDZKSkggJCeHatWv06NFDFCGHDBnC4MGDmTRpEv/99x/29vbMnz+fZ8+eYWtry7lz53B2dsbT05OjR4+yfPly5HI5tWvXxs3NDTMzM169eoWmpiYmJiYYGBiI58HIyIhChQqJwpwq/CM5OVnMsQikO/5pP/9KJBIJO3bsICEhgYSEhHS5AH9HvLy8GDJkyFcfR19fX3bt2iVWz/3ZyOVyNmzYwOzZs7G2tsbDw4NGjRp9kd3v379nwIAB3Lp1i6NHj360MqiaP5v169ejp6dH27Zts9qU34I/LQxUlac0MTGRpk2bYmxsjIGBAUOGDGHNmjX06dOH+vXrExUVRaNGjWjRogWgDv/8ETRo0IAGDRqwefNmDh8+TKdOncR214fHVnW8GzZsSPHixbl16xadOnUSfz9+/LjY5jp58iRmZmbicurwTzVqfh1qYS2boampyZs3bwgMDCRPnjxYl6pIZGSi+HvNbu7o5fhfnpSyjVuLn6u59uPM6vmEvfHBukgJru/eQLNRnhhY5iJKqUGPHj2oVKkSBw8eJCkp6Zfulxo1Wcnr16+5c+dOuiT/alL53gbyp5L+q9b/YcNOT08PPT09sehBlSpV0v1+9er/PG9Xr17Nq1evUCqVlClTJt28qkZocHCw6KEWFBREQkKC6H3x+vVrjIyMqFSpEtOnT6d///5iFVNI7SjFx8eLFVZjYmLQ1tbG2NiY9u3bky9fPvLnz58ul52vry/NmzenUKFCaGpq8u7dOwB2795NSkoKe/bsQV9fHycnJ9HGChUqYGdnh5aWFlOnTuX8+fO0aNGCMWPG8O7dO/Lnz8+///7LzJkz6d69OwEBATx69ChDmO6TJ0/E0ByZTJYhtPdz50Llufhh8YPvRSKR/BEeL48ePeL+/fscPHjwq5edPn06/fr1Ezs1P4uoqCiWL1/OggULcHR0ZMOGDVStWvWLlz937hxdunQRC938CedNzdcTHR3N+PHjOXTokFok+UL+JGFN9T4QBIEOHTrQokUL6tWrh6OjI0qlEjc3N5YvX8758+dJTk6mTp064rLq6+XbyKy9VbJkSZYtW8arV6/Inz9/ht9VBQcSExPR1tZm/fr17N27VxTlVIMiJ06cYN26demWVZ8nNWp+LWphLZsxe/ZsJkyYQIUKFTAxMaG6a19s/mkn/p7DKn01rwsbl3Br/xaiw4KRICExLoa4yAgA3gf7s3l4FyQSKRIEEmJTfd/i4+PF5Tt37kyOHDkwMjLK8GdoaJhhmiqnkJrsw+dcx9WkJmZv3759hnwVav7X8FLlXjQyMkrnOfWtfM31+KHIk/a7KnkyQOXKlTPdRkBAgOgt16pVK6Kjo0XRrmLFilhbW1O8eHGcnJyoW7cuhoaG6OnpsWzZMqKjowkNDRWrH0ZFRYlVN8+cOcPDhw+xsLAgMDCQiIgIZDIZkZGRWFpaioJhZGQk1tbWbNq0iWrVqon22djYUKBAAQBevnzJ6tWr0dLS4uzZs+TNm5cWLVqQkpKCnZ2d2IFZvXo1DRo0IDg4mK1bt5IvXz6srKxEb7SnT59y9epVRo0ahSAIeHp60qVLF4oXL87JkydJSUmhWLFiFChQQDw+mZ2LzKapQoJVRTEgVWiytLQUK8T+6SxYsIB+/fp9da44Hx8fDhw4wIsXL36SZakC8sKFC1m5ciUNGzbk+PHjlCpV6ouXT0xMZOLEiWzatInVq1fTqFGjn2armuyPh4cHDRo0UFd2/Ar+FGFt+fLl2NjYUK9ePd69e0f9+vVp06YNTZs2Zfr06fj5+TF48GAmTJhA06ZNxffBjx6Q+dvIrF1VunRphg0bhrW19UdFtbdv3zJ58mSsra1xcXGhT58+KBQKOnXqxPLlyxk6dChTpkyhW7dugPo8qVGTVaiFtWyGlZUVq1atAuDSpUvUrlOXQUUrYJ6vwP/P8b+Hru/tq1zYsBi35XuwLGiPVCplSo1CQGoHLEfOXLSctBDbMhUpbqJF8MkdjB49mvj4eDG8qH79+sTHxxMdHU10dDRBQUHi55iYGPGz6k8ul2NgYPDFQlxm01TTVfmU1Hwf3t7e+Pv7M3fu3Kw2JVuiVCpZt24d+/bty2pTsjWqEMj9+/czatQoKleuzJQpU35JTroPG4CZVeJU/c+ssZhWBClSpEi6344fPy5+HjduHN26dSMoKIiIiAgsLCxITk6mcePGonCkqqYqlUpxcXGhS5cuFCtWjFOnTlGpUiUgNfTUzMwMqVRKSkoKCoWCHDlyYGJigq+vr1jR1NfXFwsLC+Li4hg0aBD9+/cnd+7cvH37Vgzn9PPzY9CgQQDkz5+f0NBQUlJSSE5OxtjYWBQVVQ3uqKgojI2NuXjxItu3b6dOnTpiVem3b9/y/v17Vq9ezaRJk3B0dMTZ2Zk8efKQkpKCpqYm48aN4+XLl4SEhFCtWjXMzc3F4yORSDJ4F7q7u9OuXTvc3NzSjbanrYYaEBAgPutVx1Dl4fc7Ne5DQ0PZvXv3N4VyTp06lUGDBn2y8ua38vLlS+bOncv27dvp0KEDt27dSud1+SU8fvyYjh07ki9fPu7evftLc8CpyX74+PiwevVqHj58mNWm/Fb4+PhkSHHwOxIfH8+8efOQSCQ0aNCA3r17c+TIEezs7BgwYABv3rzh5MmTSKXSdM/w3+l5/jugeqe6uLhkKrrJZDL8/f1p2bIlo0eP5uDBg/Tt25dhw4YxaNAgdHV1Wb9+PU2bNqVJkybicurzpEZN1qAW1rIZu3btolKlSuTJkwcTExMkUgmasswfkInxsUhlMvRNzFCmJHN2tTeJcf/LyFaxZVdOLJlB+6mLschlh5OzMyYmJjx58oRp06aRlJREp06d0uXr+RzJycnExsZmENw+FOGCgoJ49uxZpuKc6k9DQ+OrhLiPTdfX1/+rXyKqfE9qMiciIoKlS5dStmzZrDYl26Nq4DVq1Ahvb28cHR3p3LkzEydOFD3AssqutP8/xYehFqoRX0htbObJkyddkn4bG5t0lbNmzZolCnlTp07lwoULREdHU716dbEKqaWlpShIRUVFERAQgIGBAa1bt2bo0KFYWVmRI0cOHj16hLm5OQEBAcTExODq6gqkhgyqRKjY2FgxDFUulyOXyzE2NubevXsA5MiRI92+KxQKDh8+zPnz5/Hw8MDJyYmbN2+yZMkSBg8eTLNmzZg+fToHDhzA0dGRu3fvkj9/fvE8TpkyhVy5cnH48GGuXr3KhAkTMDQ0ZMOGDezZs4fY2Fj69etHixYtxGILaSvLymQy8RirbJo7dy4dO3YUPV9cXFxo37497dq1Y8uWLRw/fpxBgwZlG8+YuCQlDyLkvEtIITFFibZMiqWujFMbNtCyZcuv9s57+vQpR48excfH54faee/ePWbNmsWJEyfo3bs3T58+Fa+VL0UQBBYvXszUqVOZMWMGbm5uaq9zNQwfPpwRI0ZgbW2d1ab8VvzuHmuqwY6hQ4diZGTE7NmzSUpKomnTplhbW3PixAl27drF7t27cXZ2pmnTpllt8h9NZm2bpKQkdu7cSaNGjTAxMWHPnj24u7tTqVIl8Z2/fv16pFIp3bt3x9XVVYzGSNveUaNGza9HLaxlM27evMngwYOJiooiZ86czJ3vhTyPLSlCxnmLVKpNkcq1mefihJauHlU69CFHztzi75U79EJAYGW/1niHh2BpaUnbtm2ZMWMGDRs2pHz58l9tn4aGBsbGxt/dyRYEAblc/lmBLjo6muDg4EyFOdW8CQkJGbzovtZ7TvX5d/SiU4kAP2vdv3snzNzcXB3y9JVoa2szYsQIOnfuzIQJE7Czs2Py5Mn07Nnzq4T4rODD6zWzRuaHSf4/9KxSTbe0tKRVq1YZlt+6dav4OUeOHFy/fh2pVErJkiUZOHAgFy5cICUlBXt7ewoUKICFhQUFCxakWrVq2Nra4uvri46ODoIg8O7dO1HIiY2NRUNDA0NDQ6Kjo9HV1RULOqT1WLO3t+fSpUtiGODly5cpUqQI165d4+DBg8TGxoq56KKioujfvz+FChWiatWqXLt2jaVLlzJ+/HiKFCnCiBEjOHPmDK9evWLBggXIZDJGjx6Ng4MD+fLlIzw8HBsbG86cOcOWLVuYNm0auXLlYtasWdSoUQNNTU02btxIzpw5MTMzI3/+/ISHh4s576pVq8bZs2c5cuQIRYsWFb35soKguCSuhCTwKlqBBEhO8+h8/h406rhSo5GSoLgkrPW/vOrflClTGDp0qCiCfg+CIHDx4kU8PT25e/cuQ4YMYfny5aIQ+zUEBQXRrVs3IiMjuXLlCoULF/5u+9T8/pw+fZr79++rq8N/JcnJyfj5+X21t2h24cP3nJubG5aWlsydO5ekpCSaNWvGnDlz2LJlC7a2tkyaNCnT5dT8XPz9/Zk3bx4hISG4u7szYMAAkpKS6NWrF1OnTqVx48Y0aNCAtWvXYm9vT8mSJQF1oQI1arID2buH9Bfx+vVrAOrWrcvs2bPT/bbnVTQvohTMvB2abrpUJqPVZG9aTfYWp9Xo+r+KeVKplGqd+tKjvzstCqRvlJcrV+6nCjKfQyKRoKuri66u7lePwH/Ih150H/OSCwkJ4cWLF58U82Qy2Xd5z6XNRferGiI/SvxSNZ7OnTvHxIkTadWqFYMGDeLWrVsEBwdTp04ddHV1xRxMv7vgpubz5MyZk5UrV9KvXz+GDBnC0qVL8fLyom7dullt2nfxNRVQ03qDquZLe8/JZDKKFSsGpOZ6Cw4OpmXLlrx48YI7d+5gbW2NIAhs3LiRFy9eEBcXR8+ePbG0tCQ+Pp66deuKAxUREREoFApxXfHx8elsVT3rypUrR4sWLWjatCmnT5/Gzs6OS5cusWbNmgz2Jycnix4WUqmUunXrkpSUJBZdMDExYd++fVy5coWbN2+SL18+9u/fT8eOHcmRIwcKhQIzMzPu3r0rin4AFy5cwNbWlly5cqFUKrl48SKPHj1i06ZN6OvrkzdvXgDy5ctHmzZt2L17N/Hx8VkmrN0OTeBMQFw6MS0tyQIglRGQJGPLiyhq59bHweLz1U0fPnzImTNnxBQO34pSqeTw4cN4enoSGhrKyJEj2bt37zfnhdy7dy/9+vWjT58+jBs3Dk3NLxcK1fy5JCcnM3jwYObOnavOOfqV+Pn5YWVl9dX5F7MDadMoDB8+HG1tbTQ1NRk8eDCCIODt7Y1SqaRdu3a0bNlSfF6oPaB+LUqlkvz587Nx40aGDBmCUqmkb9++6OrqEhcXx7Nnz2jcuDHa2tq0a9dOFNVAXahAjZrsgFpY+w2onFMX32jFRzsEn0JDApWsPt85+J35GV50HxPnVNNDQkI+6Wmn8qL7HnHuS73ofpSwpqoOtXPnTsqXL0/79u0BuHPnDs+fP8fJyQldXd3PihBSqZQVK1YQEhLCmDFj0NTUJCUlBSBdUvTQ0FCkUimmpqZIJBJCQkLQ0tL6KTmK1HwfZcqU4cyZM+zfv5/evXtTvHhx5s2b91d4wGR2vX94v6nuwZSUFB48eMDZs2cxNjZm27Zt4vzGxsaZegmn9RqpW7euOE/Lli2JiIhIN69cLkcmk6Gvr4+zszO3bt2iW7duTJs2DblczooVK7C1tcXAwID8+fOjra0tdgLlcjkJCQkYGxujqakpDiRoaGgQFRWFt7c31tbWvH37lvr161OsWDGio6MB0NfXJzIyEi0tLVFY09TUxMLCgmrVqqFUKvn3339FO9PmtYPU4g7a2tq/pPrk69evyZ8/P0lJSaJ35edEtQ9JFuBMQBzAZ8W1yZMnM2LECNGz8FPUrFmTTp064ebmxpYtW9iwYQP//vsv27dvZ9asWWhpaTF69Ghatmz5zZ3ZmJgYBg8ezPnz59m3b5+YF1CNGoBVq1Zhbm6Oi4tLVpvy2/E7h4Gq3lk9e/ZEJpNRp04dhg4dSqlSpWjRogUymYxRo0Zhbm5OzZo1AbUH1K9G1X5OSUmhRIkSeHt7i/lX3d3dGTRoEOPGjWP9+vViugX4MyJL1Kj5U1ALa78B1vqa1M6t/1UdA0gV1Wrn1sdaTz1S/SX8SC+6lJQUYmJivkigU3nRfWxemUz2ydDWp0+fkpycjLe39ydFuy/xogsLCyM+Pp7mzZuL4WlJSUkYGhpiZmbGlStXkMvlSKVSjI2NKVasGFFRUSQnJ2NkZCSOgIeEhJCQkCB6RaZtnKkaAbt378bCwkIMtTtw4ABaWlp07dr1o/alrVqoWo9UKuXVq1coFAoKFy6sbgj+JNLmX1uxYgXVqlWjY8eOTJgwIUvzr2UHVI3afPnysWjRoo/Ol9ZL+GNhqKpwwgIFCogVRVUYGBgwdepU8fvw4cO5c+cOefLkYcqUKSxcuJBDhw5hYGDAkiVLCAgIEPPCRUVFERISgpWVFZAabqKibNmyHDlyBG9vb0qXLi1Ov3nzpigYJSQkiCJdTEwMSUlJmJqa8v79e3FfkpOTMw0VFgSB8PDwT3qr1axZk3v37hEcHPxDPUKC4pI++e6MCgnk0Jyx+N6+SkpyEsY5c1PNtR+OzdpzJiAOa32Nj75D7969y+XLl9m4ceNX2+Xi4kJERASFChWiYMGCzJ8/n3r16n1XB+nq1at06tSJWrVqcefOHVEEVaMGUgXuyZMnc+LECXVH/Bv4HYW1tO+X8PBwLCwsmDFjBj169OCff/6hRYsWBAYG0qRJE/LkyUOZMmXEZX+Xa+Tp06eYmJh8d7s9K1Gdp2fPnrFs2TIcHBxwcHBg+fLl9OrVCz09PTp37szBgwd58eIFFStWTLecGjVqsgdqYe03QTVq/qXimkpU+5JQFjU/HplM9sO86BITEz8pzvn5+REZGflJgS4mJkYMwzIyMqJixYqsX78+Q8dLoVCgVCrThQ3Vq1ePuLg4JBIJXl5e+Pn5UaJECcqUKUNgYCD//vsvd+/eRSKRMHHiROrXr09sbKzobXf48GF27txJfHw8NWrUwM3NDaVSyfbt27GyssLMzIyyZcvi6ur62fxdmVUthFRRLjQ0lFGjRpEjR44M1QslEgkPHjzg33//pUmTJpQoUYLk5GTRS08qlSKRSMSwP3VD5eNoa2szaNAgBg4cSEpKCgkJCcTFxaGnp/fbNMSzisyOz8eutS8ZhTY2NqZWrVoAODo6ZhB4zMzMePXqFQBGRkZ069ZNvOfj4+NxcnICoEuXLgwbNowaNWqgr69PuXLlmDp1Kn5+fuI9oaenx8uXL4HUjszDhw/JmTMnwcHB6Oqmvmc+dv86Ojri4eHBli1baN++fbp9ViqVDB06lAsXLqCvr8+CBQvo3LkzlpaWmd7rXxuadCUk4ZPvzJ0T+mFVuDij/r2NTFObEJ/HxIS/A1I9164GJ9CiQObC2qRJkxg9evRXeeLFxcUxbdo0Fi9eTJUqVdi5c6fYSfpWkpKSmD59OitWrGDZsmVqbyQ1mTJlyhScnZ3TiedqvpzfTVhLK7rMnz+fYsWK8fjxYwoVKkTTpk3x8vICYM6cOTRt2pTatWtnWC67c/fuXXr16kVISMgPeZZmFVKplMePH9OuXTvc3d05deoU+/btY9++fcyePZshQ4YQHR3N6NGj1aKaGjXZGLWw9hvhYKGLtb4GV4MTeJlJ8mUNCQhAQSMtKlnpZhhlVz+Efz8kEgk6Ojro6OhgaWmZ6TzR0dGEh4dnyM33ISkpKWIuuuTkZLEzDP/rxL9580bMqQSp10zacD+JRIKTkxMLFy4EoFGjRrRt25YlS5awe/duDhw4QNWqVREEQfROKVasGP369UNXV5cFCxZw6dIlypQpQ3x8PP7+/ixcuJA1a9bQtm1batSowbBhwzh27BheXl48f/6c1q1bM2bMGExMTJg1axZyuZz4+Hg0NTXp06cPefLkISUlBVNTU7HDnZkgER8fT3h4uOjBk5kI8DX3h8r7KG2lRJU3nVQqJTExkcePH5MnT54MVQb/BNd9iUSSLufW30pcXBx79uyhdevW6e6p7+Vrrw/V9QeIQjGAqakpALq6umJYoFKppFy5cmL4po2NDWvWrOHVq1cEBQWJ966ZmRkNGjQAwMnJieXLl1OtWjUMDAzImTMnMpmMxMRErKysuHXrFrly5SJXrlwZ7Hr06BFaWlo8e/Yswz2mUCi4c+cOFhYW6OnpMWPGDObPn09ERAQWFhYkJiZiaGiIUqkkODiYfv36YWRkxI4dOwgMDMTY2JgePXowefLkdOtdu3YtkyZPJi4phaqd+lG9c/9Mj5v/ozs0HjYNLd3Ufc5lXyrd75N7d6LvgxvIExIoXbo0y5Yto3jx4ty6dYtjx47xzz//iPOuX7+e1atXc+nSJQBOnjzJwIEDCQoKwsXFBR8fH8aOHUvbtm1xd3fnyJEj390RfP78OZ06dcLU1FTM6adGzYc8ffqULVu28Pjx46w25bfFx8eH6tWrZ7UZX4zqWXvy5EkuXbrE0KFD0dXVZdq0aaK4OmzYMB48eCCKbGmXy+6EhoayYcMGmjZtyoQJE4DUlAc7duygTZs2P/R9/LNQ9cuSkpK4desW8+fPx97eHi8vL1auXAmkDkzNnz9fHCRT8bucJzVq/ibUwtpvhrWeJi0KaBKfpOR+hJzQhBQSU5Roy6RY6MooZaqDnmbmD1upVIpCofgtK1+q+ThfKtLIZDJy5MiRaeU6iUTC7NmzmTx5MosWLRKrDape3KoQr5CQEDp16gTA9evXiYmJYf78+Sxfvpy4uDhy5syJUqkkPj5e9OIQBEHswF+9epV8+fJRr149cubMibu7O/Xq1QMgMTGR/PnzExcXx8yZMxk+fDhNmzalYcOG7N+/n27durFu3ToqVapE27ZtmTNnDjY2NvTs2ZOyZcsikUgwMDAgLCwMiUQi5m5LG3Ink8mwtrbmwIEDvH79msjISF6+fMmYMWNQKBRcu3aNggULUr16dTEcTZULzsjIKJ0nX9rk9arvaT1pwsLCGD9+PEOGDBGT/avO1fjx4ylevDgdOnRAEARSUlLQ0NDg8uXLvHz5kgYNGmBpaUlKSoq4HXUjKnsyefJk4uLi6Ny5c5ba8TFvzrSkDZ/+sDJqjhw5KFu2LGXLlhXnr1mzJjVr1kSpVFKkSBEuX75MWFiYGAqqCjHv2rUrEyZMoH379nTu3DndIE5ISAjXr1+na9euopdjWjt1dHQICAhg0qRJVKxYEScnJ54/f46pqSkhISH06dOHs2fP0q9fP3R0dPD39+fatWtIpVL09PQICgpi2rRpbNy4kWLFiomhq6tWrWLquj08jU5iRa8W5LIrQaGKNTIck7wly3HQcxSV2vXEplR5jK3zpPvdvkodvJavomoeY0aNGkXHjh25e/cukyZNIl++fB99n4aFhdGiRQumT5/OvXv32L59O3K5HE9PT0aOHMn69es/c0Y/jSAIrFq1irFjxzJ58mT69+//24v1an4eQ4cOZcyYMRkGedR8Ob+Lx9rTp09JSkqiWLFiXL58mZYtW+Lu7g6Avb09gwYNwsPDgyNHjhATE8ORI0eA369Qwb1793j58qWYhwxg3759XLx4kerVq2f76q2q9+S9e/e4ceMGsbGxLF26FIVCwerVq3FycuLBgwdcu3aNnj17Uq5cuaw2WY0aNZ9BLaz9puhpSnHK+enwk/DwcMzMzIiLi+P58+fs2LGDCxcu0LlzZ/r06fOLLFXzs/lR3k8jR47ExMSEGzdu4OLiInq6wP9EHYVCgZFRaoVZExMTTExMOHz4sCjWqWwJDw8XvUyGDh1KxYoVKV68OA8fPhRtjY6OJjExUWxcxMTEYGFhwenTpylXrhwODg4A1K9fnzdv3hAZGYmhoSEdO3akbt26vH37lufPn/Pu3Tvq1Kkj2nrs2DGSk5Np1apVuoTiFSpUoGjRohgbG/P48WMOHDhAz549kcvljB07ltKlS5OYmMjKlStZtGgRVapU4fz586xZs4YXL16gqanJnDlzqFixIl27dsXc3JzIyEgCAgJYvHgxt27d4tSpU7Rt25Z69eqhVCrJmTMnQUFBPHnyBAMDA7FSooeHh2iXyvMLICgoiGvXrlGlSpWPhsJ9jvj4eB4+fIiNjQ05c+YkKiqKU6dOUb58efLlyyd61am2rebbCQ4OZs2aNTx48CCrTfki0p7vzM69IAjpqsel9cBULWNhYZGhc+7u7i523CD1eaG6rzU0NFAoFOLz5EOB+NKlS7x584Y2bdpgbm5OwYIF2bp1K0OGDCFPnjyYm5vTsmVL5syZk+k+JScn06dPH+Lj42nXrh0PHjxgx44d5M+fn+fvIrAoURHHZu25d2xvpsJax9lrOL/emzOr5hH6+gVWhYriMsGLvMVTBUaH5h2Jl6UWgZg8eTImJiacPHmSBw8eYGtr+9FjvXjxYrS0tPDw8KB///74+flRpkyZdM/Vb+Xdu3e4ubnx9u1bLly4IFalVaMmM44ePcrLly/Zv39/Vpvy25KSkoKvr2+GvJfZDUEQWL9+PXK5nJ49e1K9enXc3d3Zt28fPXr0wNbWlmbNmtGwYUPkcjl6enrIZLLfSlRbs2YNp06dwsDAACcnJ1FAS0lJ4fz589SvX5/cuXOL82fXaB2pVIqfnx/z58+nQ4cO1KxZk5MnT1KwYEGcnJzw9/fH1dWVAQMGZLWpatSo+UKy35NGzQ+jdevWeHl5sWnTJmbOnIlcLmfcuHHcunVLDIdT8/vzI8MKnZycCAgIQKFQZPr7mzdvRPf6IkWKEBsby/79+3n37h3h4eE8e/YMQPRmCQoK4vnz54wcOZLmzZsD//PwiomJIW/evGKDRy6Xo6+vL4a0qTzG9PT0xIqt2tra5MmT6lGiKpSgWp/KA6dTp0507do1Q5U+Vd47SE3EXq1aNbp3746npyfXr1+nSpUqeHp6Urp0aR4+fIhcLmfSpEl06NCBq1ev0rNnT7HKY3BwMEFBQbi7u2NnZ0f//v2JioqiePHizJgxg9evX6OlpcWrV6/YuXMnQ4cOpUOHDty/fx+A6tWri52cM2fO0KRJE5o0acK5c+cwMTHB2NgYX19fli5dyqJFizh8+DByuRxADIkLDw/P9DwlJSWxZcsWsRKrjo4OZmZmoredKkwws2tGFUp44MAB5s6dS3BwMJDaYFX9qY7zs2fP8PHxEbfzNzJz5kw6d+6crhH/O5NWRIP0IaWfQnVtqFB1ZI4ePUrr1q1JTEz8aMjjhg0bqF+/Pubm5gB06NCBDRs2pJtHJUiruH79OrVq1cLCwgIzMzM2b96MTCajWbNmdOzYEYB169ZRoXJVAIyt8xAdGpzp9nWNjGkwaCJDdl9i3MnHWNuVYPPQzqnPoZQUjnlPpWf1UhgZGYlC2pQpUxg/fnyGzpogCLx//566deuyYMEC8ubNi6+vL5MmTcLc3DzDfnwL//77L2XKlKFYsWJcv35dLaqp+SRJSUkMGTKEefPmqaMVvoOAgADMzMx+SWXjb8Xf3x+JRML06dPR19fn2LFjAEybNo2mTZvi7OzM8+fPgdSqzoaGhmIai99FVIPU9tOzZ89o3749Q4cOBf7nbTd69GhcXFwICQkR22vZSVSLiori3LlzREVFER8fT7169YiIiKBKlSpIJBIGDRpEUFAQNWvWpGXLlvTo0QM3N7esNluNGjVfSPZ52qj5oSQnJ2NqasrLly+5e/cunTt3ZsGCBTRu3JjVq1d/Nkm8mt+HH52vKyIiQqwcqELVMOnXr186L41Nmzaxb98+GjduTI0aNbh165a4DolEQu7cuSldujSNGzemf//+3Lt3T1y2ZMmSHDp0iOPHjwPw/v17NDQ0qFy5MufOnSM2NhaAjRs3UqJECfT19UlKShIFM4VCgaampijAfckxUAlHsbGxFClSBLlcTmJiInnz5hVzyZmZmaGhocGlS5eIi4tj9OjRVKhQgdmzZ4uN0tjYWNq0aUOpUqVo0KABCoWCZs2aMWTIEPz9/QkLCwNSxacxY8Zw9OhRypYty969ewHEKqrx8fFMnDiRdu3aMXv2bO7cuUNkZCS6urrExsYik8mQy+Vs2LBBzNu0d+9eunTpQoMGDXBwcODFixfiOgGmTp3K5s2b6dKlC2vXriUkJISkpCRRrHv48CE3b97k6tWrREREZHqeo6OjCQwMFI+XTCYT/1THec2aNaxfv57o6OjPHnfVsVf9pRXoQkJCRCET4PXr14SGhn7ROrMSf39/Nm/ezOjRo7PalM8iCALXrl0jMjLyp6xfdW2oUF1HDg4OjB49mkGDBokhVGnv07i4OHbs2MH58+exsrLCysoKLy8v7t27l+5Z8eG93aFDB5o1a8bbt2+JioqiT58+6SquArx9+xZtWaod74MDMLJI/0zLDH0TM6q59ic6NJj4qEjuHt3D43PHmLblIFFRUbx+/RpIHWDo1q0b+vr6xMfHk5KSwu7du5kwYQKvXr3C1dWVefPmoaOjI+arEwSBt2/ffukhzUB8fDz9+vWjf//+bNu2DU9PT7VQouazLFmyBBsbGxo3bpzVpvzWZPcw0Hv37lG3bl1CQ0PR0NDAw8OD3r17i7/PnDmTVq1aUaNGDbEdo+J381y3tramatWq+Pn5ZRhgtbW1RVNTk4kTJzJlyhT69evHxo0b070fsnIw8MKFCyxevJjLly+jp6eHp6cnjx8/5vTp02hpafHPP/+wa9culixZwqZNmxg4cCBAhvebGjVqsidqdeUP5M2bN+zevRulUsnEiRMxMzNL1+lJSUlh2bJlVKtWTV0d6g/hRzWMihQpgoGBAfXq1WPDhg0ZEpGPHz8+3ffcuXNnGl5y8OBBUfCaNWsWDx48EMMzVaFQQ4cOxdvbm8ePH/PPP/+IYZoWFhYMHDiQFi1aoFAoaNiwIY0aNUIul+Pn5yd2VCMiIoiJiUmX9wxSBQ8jIyMxZDUtaXPGaWpqoqWlRVxcHEqlUhyJTkxMRENDgxw5cpArVy4OHDggLq9qkL1//14UGBUKBcWKFRPt0NHRQUtLi/fv31OmTBkKFiwIQOPGjdm1axeQ6p1nbm7O0aNHKVmyJM2bN8fQ0BAHBwfRI7Bo0aIEBAQgk8l49eoVe/bsoW7duixatIixY8fyzz//kJiYKG5XJZY3aNCAa9eu0bp1a0qXLs27d++YNGkSu3fv5tSpU2zatIl8+fLx6tUrChUqhJeXF2ZmZmzYsIFFixahoaGBnZ0defPmxcjIiJs3b7Jx40bkcjn29vb06tULQ0NDdHR0xMq3crmciIgIrK2txVx2aUkbXvght27d4vDhw4wZM4Y8efIwefJkrl27xoEDB7Czs8t0meyAh4cHbm5uGUTo7Mi5c+fo3bv3L09cnjNnznTJ/T9k9+7dJCYm4uDgQL9+/ahTpw5SqZQ2bdqwceNG5s2bl+lyMTExmJqaoqOjw40bN9i6dSv169dPN8+0adNoO2o6oS/9+e/gNtpMX5rpuo4unErZxq2xsC1McqKc67vWYZY3P/rGpiTGx6KhpUUBawvi4+MZO3YsAP3790dTU5MSJUqwZMkSFi1ahJ6eHkqlEgcHB7p06UJYWBiDBw9m7969NGvWjCVLlogeoF/LrVu36NSpE+XLl+fevXuZ5slUo+ZDwsLC8PDw4Pz587+deJLdyO7C2suXLzE0NBTD9AVBwMDAIF14//jx4zE1NcXExCSLrf0+7t27x4sXL+jfP7UgjWpwWeUp/fLlSy5cuMDw4cMpU6YMr1+/5unTpwQHB1OrVq0s8c4LCQlBV1eXpk2bEhsby5EjR6hQoQIuLi4oFAqGDh2Kvr6+mI+3ePHi4rJ/QrErNWr+FtTC2h9GQkICEyZMQKFQMHz4cLGSpGq0w8/PjxkzZiCTycQE9Wp+b37kSJa2tjb//vsviYmJmYY8ZJarQpWHKe2LP22lSBsbG2xsbDKsq0yZMqxdu1b8ripwANC9e3e6du1KXFwcWlpaaGun5jg6ePCg2CisV68ekZGRooCnYteuXRQuXJgmTZp8dD9DQkJITk5GKpWK1UlVgl14eDgpKSmULVuW4OBgDh8+LLrph4aGUrhwYVJSUsT5k5KSAMTjlZKSgoGBAfHx8bx9+1YcUX379q0omqnCXiMjI8mZM6dol62tLbGxsejp6TFu3DiCgoLQ0NAgODgYXV1dFAoFXbp0Yc6cOdy4cQNXV9cMeZ5sbGywsLCgW7duaGpq8vDhQ8zNzTE0NERDQwNtbW1Wr16Njo4OuXLlIjQ0FIVCgZeXFxs2bKBYsWJUrlwZU1NTtLS0MDExoU2bNiQlJbFr1y4uXLhA48aNiY2NpUCBAoSGhrJkyRKePHnCmjVrWL58OXv37iUyMpLmzZszYcIEDAwM6N+/Pw4ODrx8+ZKEhASGDx9O7ty5EQQBQ0NDdHV1kUqlrF+/nvr16/PkyZNsK6z5+vqyc+dOMfQ5uzN16lTGjRuXrTyVk5OT2bBhA71798bJyYl58+YxY8YMhgwZQq9evRg+fDizZs3KdNmlS5cybNgwBgwYQI0aNWjTpg3v378H/nc/PnjwgO0OhdEzNqWaaz+KVKqV6bqS5PFsHtaFmLAQNLV1yVPCgc5emwFwaNIGn6tncS5bGFNTUzp06ACkPnvmzZvHxo0bSUpKIj4+nrJly+Li4sKpU6cAMDc3Z9euXQwaNIhu3brh6upKlSpVvuoYpaSkMGvWLBYsWIC3tzft2rX7quXV/N1MnDiR9u3bq8OFfwDZXVhr3LgxJ0+epHv37sydOxdTU1NRkEkrOvXr1w/4/QoVwP8Epjt37mBpaSm2Dz4UnaZNm0azZs3o27cvkNovmjZtGtbW1gwfPpyBAwfi6uqKTCbD29tbbI/8rOMRFhZGtWrVWLNmDdWqVaN9+/bUrl0bc3NzBEGgbdu2KJVKunTpwrp16zIMEn24fx8KiWrUqMk+ZJ9Wtpofwtu3bzl37hxv3rzJNFG1lZUVNWvWxMnJ6Y/JC/S386NHszQ1NTN4ganI7CX+NS/2tLaqRlI/bPilXW9agU5DQ4PYD6ndAAEAAElEQVTy5cuL38uUKZPpNoYMGfJZO2bMmIGBgQESiQRDQ0M8PDzEENPq1atTuHBhNDQ02Lx5M3379mXSpEkkJyczefJkChcuTFhYmCikvX//nvfv34thWX5+fmhpaREYGIiPjw///vsvxYsXZ9u2bWL1qsTERLS1talYsSJLly5l8uTJQKqnn6pRNXv2bKKiotDT02PVqlVcvHgRpVJJ9+7dadSoEXv37qVSpUpcv36dfPnyifv2/v17kpOTiY6OxszMDLlcTnJyMtra2kRGRlKnTh3R807lYfbvv//SsGFDihQpgqamJjVq1BCPh4WFBfv27SMpKQk/Pz8OHjxI48aNEQSBN2/e4OHhgY6ODjt37gSgd+/eDB06FJlMhrOzM3fv3qVq1aqcOnUKhUJB/fr12bVrF1u3bmXo0KEULFgQhUKRLieevr5+tm70T5s2jX79+ol5wbIzFy5cwM/PTxSFsgsymQxTU1Ny5MhBhw4daN++PefPn2fevHncuHGDvn37EhERkWn1zFatWtGqVat0054+fcrIkSPZuHEjVatWpXv37rRu3Zrj75S8iMo8ZyRAs1GeH/1NS0ePsUvW0bG4FYIg4OTkRPPmzfnnn3+oW7cuR44cSVdFFRDvZUj1Hv0w7EpF165d6dq160e37evri6urK1paWvz3338/JD+bmr+HBw8esHv3bp4+fZrVpvwR+Pj4ZLtnaFq0tbUZNmwYK1euZPjw4SxatEjMWfth7kwgW79fP0bafsy5c+e4d+8epUqVQiKRiF55r1694tKlS5w5cwaAK1euMHPmTIKDg9m0aRO3b99m69atdO3alZSUFI4cOUKXLl3E4/MzvMPi4+PR19dP5ymY1ulBIpHQvn17EhISiImJyXQdCoUCf39/xo4di42NDbNmzVKLamrUZEPUwtofRpEiRXB3d8ff359cuXKle3kmJSWhra1N+/btiYmJUedn+UP4ndzEPxR7037/lkZCZvv+JcdDFZ4JqSJOjx49xO9pK+YWLlxY9EBJy+PHj0VRpUmTJlSsWFH0Bpo+fTpmZmbExsYyZswYLl26xIoVK6hbt65YwAFSPdxy585NjRo1cHJyIk+ePCQkJIgeN82aNWPq1KnY2tqyZcsWrKys0NHR4dSpU9jb29OjRw82bdpEaGhoOmFNKpWiUCgwMzMDUu97lVialJSEVCoVvehSUlLQ0tISvQ5V+2BoaIiWlhZaWlp06tSJEiVKYGpqSr58+cSGX3JyMnv27MHW1paDBw+K29+1axerVq1CqVTy4sULmjdvTtWqVdHQ0KBdu3bUqVOHQoUKMWbMGPr27UvRokUpWrRounOXmJgo2vghz54948GDBxgaGoohv6o/AwODn95hePHiBQcPHhRz22V3pk2bxtixYz8qlmcVEomExYsXU7ZsWerWrUuNGjWoWbMmNWvW5OnTp3h5eWFnZ0erVq0YOnSoeI2kJSYmhp07d7J27VpevXpFly5dOH/+vOjJEJekxFAzDgnwLX69EkHJzJ7t8G/ekCtXrnDr1i169OjBtWvXfpr3iiAIbNq0iWHDhjF69GiGDBmi7kCp+SoEQWDw4MFMnDjxh1ShVZP9PdYA8ufPj5ubG7NmzSI8PBxIbd/8aZ5NrVq1onr16uLgKPyvbbl582aqVKkiton8/f3JkycPLVq0oEqVKmhqamJmZsb79+/Zt28fOXLkoGHDhunWk5KS8sWFez6HIAjky5ePXr16MWbMGMaOHUulSpXEdUulUrHd071790zXkZiYiL+/P3fu3MHZ2ZmqVauK7brfqf2vRs3fgFpY+wMZNmxYuu+qBOSqjtXatWsZOnQoq1atonXr1n/cS/dv429+sWa2399yLD70pEu7jsyOryqPCSAmXVcxePBgAOzt7Zk0aVKm23v58qX4efr06bx8+RKFQoG+vr4obs2YMYMVK1bw6tUr+vbtKyb037x5M48fPyYpKYkGDRrg6OiYbt2FChUiMDCQzp0706JFC3R0dMRiEmFhYZiamoqVwOLj49HS0qJSpUosWbKEGTNmALBnzx6xkbd3717RG23SpEmi8CeXy2ndujUXL17k6tWrVKpUiTdv3jBjxgyuXr2KoaEhffv2FZ8/crlcDD/X0NBAoVCI4llaz0UAOzs77t+/T+3atTMIZT4+Pmzbto2YmBiio6PT/cXFxaGrq5tBcDMyMspUiPvUdG1t7UyvpSlTpuDu7v5b5Km5cuUKL168wNXVNatNyRQrKyvWrFmDq6srd+/eFUUAe3t7VqxYwfTp01m2bBm1atXC0dGRYcOGUbNmTa5evcratWvZu3cvNWrUYNSoUTRs2FB8xwXFJXElJIFX0YpvFtU0JGCvGUd+M0PGjRuHTCYTRcqfRUREBH369OHx48ecOnVKnQNVzTdx4MABgoOD0w0Sqfl2BEHg5cuX6QbksiMymQw9PT22bdtGUlISCoWCOXPmkDdvXlGI+VNQeXx9yMSJE8VCSACPHj3CzMyMvn370rdvX7Zu3UpgYCDGxsbs3r2bLl26iJ9fvXpFnz59Ms3P+62o2hBt2rRBEAQ8PT3x8vKiQIECGeb5GNra2hQsWJCCBQvy9OlT5syZQ8mSJXFzc/tr2/5q1GRX1MLaX4BKNLt//z6DBg0iOjqaJk2acPbsWapWrYq1tXUWW6jme1G/XFMFZJVX5teSWdj0x75/iR2qe07lCfahcJQWfX39TPMd2tnZMX/+/AzTMwuNS4uxsTEbNmzA398fW1tbTExMxMpSFStWFPO8JSYmUrhwYQwMDMibNy/Ozs7Y2dmRO3ducuXKJVYZ7dKlCy4uLuTNm5d79+6JIeTh4eE0a9aMunXrMmTIELZu3UpUVBTGxsZYWlry/v17zpw5Q8OGDcWCBiovWblcjoaGhigifnhcnJ2dadasGc+fP2fFihXpfmvcuPFHK9wplUri4uIyCG7R0dHphLjw8HB8fX0zFedU86akpGQQ26RSKdevX6ddu3YMHz78i0Q7Q0PDLAu7mTZtGmPGjMnW3smNGjWiRYsW9OzZk927d6e7FiwsLJg4cSIjR45kyZIltG/fnqioKPGafvLkSYbiEbdDEzgTEEfy96SeFASigv3Z9fgulTv0oXVHV0YOHsT8+fOxsLD4KR2akydP0q1bN1q3bs3GjRs/6rGpRs2nSExMZPjw4SxdujRb5VT8nQkKChKf5dkduVzO5s2bKVOmDBs3bsTNzQ17e3ty5crFqFGjstq8n4qq7ZX22dm+fXt69erFpk2bcHV1FcN5t27dira2NrVr1wbA0dGRZ8+eUaFCBaZMmULLli3R0NDg7NmzCIJArVq1vuuZb2ZmRvv27Xnw4AHh4eEYGxuny4P3uX26ceMGu3btIiQkhCJFimBra6t2ilCjJhsiEdQ1fP9oVA/tvn37iolNR48ejVQqZfny5QDqUc3fnMmTJyMIAlOmTMlqU7KUKlWq4OHhQc2aNbPalB+GytsrbZhCZsUifhSJiYnI5XIx/52JiQlBQUGcOHECqVSKjY0NUVFRNGnShCVLllCpUiUcHR3Zs2cPO3bsYNu2bbi6unL79m3y5cuHUqmkZ8+eNGrUCGtra8LDw9HW1ubEiROMHTuWW7duZbAhPj4eFxcXhg0bliGJ768kMTExg/A2btw48uTJQ82aNT8q2n04PTY2NoMX3dd6z6mm6+jofPF5v3HjBi1btsTHx+ebxOZfiVwux8nJiQEDBuDm5iZOT05O5ujRo6xZs4bz58/j7OxM8eLFOXbsGE+fPmXgwIH06tVL9B5MK6q9D/LHq1UVJl14hfQLhE0Jqe9LpaBEKv3f/BoSUCQlYaSIpoyhwODunUhMTMTExIQLFy78kH0fM2YMu3btYt26ddSrV++716nm72X27NlcunQpXXi+mu/jwoULjB07lkuXLmW1KZkSHx8PkKHgVEhICPv37+fGjRtMnz79rx1EP3PmDHPmzEGpVDJt2jQqVKhA06ZN6dixI+3atePEiRP8999/dOjQgdevX7N//368vLw4e/YsPXv2ZN++fZQsWRKA58+fU6RIkW+yIz4+HhsbG2rUqEFKSgoTJkzAwcHhs96EiYmJjB49GlNTU6pWrYqdnR25cuX6JhvUqFHzc1ELa38JEyZMoEOHDmKemtjYWGJjY3n48CGOjo6YmJj8llWC1KSG50kkknRJs/827t27R9OmTfH19VVfw5mgEthVj3uVOPOzRjwFQRCLJmhoaIgJ2CtUqADAmzdvuHLlCu3bt8+wbHx8PA0bNmTNmjXZKqfNvXv3aNCgAT4+PmJF2C8hMy+6zIS4T4lzqs8fetFlJsSppq1fv55y5crRpk2bDPNmpRfdx3j8+DE1atTg4sWLSKVS1q5dy8aNG7G1taVHjx60adMmncfI3bt3mT9/PocPH6ZTp070GjGOY+Gyb/NUUxVS+cy9oCGBmta6nFzrzfz58/Hw8KBnz57fLHLfv3+fjh07Ymdnx4oVK8S8iGrUfAshISEUL16cq1evUrhw4aw2549h7dq1XLhw4bPe4lmBn58fderU4cSJE+TPnz/TedRt+1RU4bybN29m/fr1bNmyhdu3b9O/f3/c3d3Zt28fT548oUOHDnh5edGzZ082bdrEgAEDGD16NObm5ri7u9OtWzfKlClDXJKSBxFy3iWkkJiiRFsmxVJXRilTHfQ0M75L3r9/z5UrV3BwcGDbtm0cOnSIatWqkZyczLRp0z7aFtuyZQsrVqzg8OHD6cJUFyxYQLt27TJ4batRoybrUAtrfziZvVCfPn3K+vXr2bp1K126dOH69ev8+++/6kSYvykTJ05EJpN9NJ/X34C7uzvGxsZ/vdfezyKtIJdWpEubl071+/eQmJjIxYsXGTNmDCdPnsTY2Pi71vcjad68ObVq1RJz6GUFab3oPiXE+fj4sH//fv755x9R1Es7f1ovum/1nlP9fY0X3aeIi4ujX79+7NmzBwMDA1xdXenevXumRQvSEhAQwNKlS8nTpCtROj8+792rW5fZMb4vY47dB1LFtdq59dEKeUmrVq14+/Ytjx8/xsbGBltbW1avXk3dunU/uU6lUsmCBQuYOXMmc+fOpXPnzur3rprvxs3NDWNjY+bOnZvVpvxRjB07Fl1dXSZMmJDVpqQjMDCQGjVq0K9fvy+qhv638mE/6MWLF7x+/Zp69eoxZswYNDU1mTp1Km/fvmXgwIFi+gsHBweGDx/O3bt3admyJQYGBhQqVIiIZGm6HJ5pB3M0JKk5PQsYaVE5py66SXFoa2uLKThUhIeHc+jQIU6ePMmQIUMoV65cBrtVbSw/Pz9at27NqVOnMDQ0xNvbm3nz5qFUKjl69CjFixdXvz/UqMkmqBMw/OF8KKrFxMQwYMAAihYtiq6uLsOHD2f58uWMHz+eWbNmibmQ1Pw+/O1iaGJiIlu3buXGjRtZbcofS2Y56D6Vl+5LyOy6vX//PitXrqRv374YGxtnmEflBWdgYPDV2/sebt68yX///ceOHTt+6XY/RFtbG21tbbEi7cdo0aIFM2fOxN3dPdPf03rRfUqgi4yM5M2bN5/0qlN50X3Ke+7DvwEDBuDi4sLJkycJCQnBysqK0NBQqlevTq5cuYiMjGTVqlVcvnyZ5cuXizkIbW1t6d+/P5s2beLly5e0a9eOGTNmcPO//5i7YCF5SzjScfYadI2MObpgMhc2LmH6jSBkGhqs7Nmc4BePUsM9k5MpUK4qraYsYnYTB5QpKeQsaE/H2WswyZVaTW6MgwVNRnhwfp03seHvOOI1mQbuE0mWSpm1bDW+x7bz8OFD5s6di62tLR06dCAgIABnZ2e6dOnC4sWLM70v3r59S9euXUlMTOTGjRsf9TJRo+ZruH37NocPH+bZs2dZbcofh4+PDy4uLlltRjrevXtH3bp16dat228lqq1fv57Vq1d/U1jt69evyZ8/P0lJSV+VP/DDPk3hwoVFj848efKwdOlS8uXLx+bNm8mVKxf//PMPW7ZsQU9Pjw4dOoh52czMzFiw5zghxrYf9YxWTX8RpcA3WkEVcxmltQQMDAy4f/++WLTAzMyMrl270rFjx4+GgapSf+TLl49hw4ZRtWpVXrx4QdGiRVm5ciX//PMP8L+BzazuC5w7d45OnTrh7++fZTaoUZPVqIW1v4zAwEAsLCxYtGgR+/bto2PHjvTp04fDhw8THx+fIUeDmt+Dv1lYO3DgAKVKlVJ3UH8zMrtmy5cvL1Yg/XAeQRBQKpUUL16c8ePH07179182CDBx4kTGjh37WySUv3//PlevXmXz5s0fnUcqlf6wZNwKheKLwljTCnRRUVGsXbsWiURCSkoKr169QhAETp06RWJiIlKplPz58/P+/XucnJxwcXHBxMSE9+/fs2jRIvr164eenh4TJ07kwoULDJm7lFpTVrN6QHsub1tF3d4jyFPCAQBlSgoyDQ1SkhQkxsWhY2jEiIO3WNG9CUtc66OIj2PUkTucWDKT0yvm0GrKInHfHp89gvO4ueybPozH549iYVuQ8i6uKAWIUSjR0NBg9OjRjBkzhn///ReZTMb06dPx8PCgadOmNGjQIN2x2rFjBwMHDsTd3Z1Ro/6PvbOOqmJr4/Bz6E4REZEyEAVExUIUC7u7A7FQsQMD22t3KxbWtQsTCwtRQBQTbAxARLrP9wffmSsKJojKedZyCczM3u/MmTOz92+/MU6aXF5KniAWi3Fzc2P69OloamoWtDl/HaGhob9VWoLo6GgaNmxIu3btfqhKsYmJCW/fvkVWVhZVVVWaNGnCihUrfmjBytHRke7du2fLjfkn4erqSs2aNdm9ezcqKio4OTmhoqLCqFGjsLCwEPabNm0aGTKyrF27lmZjZn/TmDtdDJejMlBUFBEfH5/jPl+r1Crpx97eHjU1Nc6ePUv16tWF7adOneLy5ctMmzYt34S1HxU0pUgpjEi/IYWM2NhY7t27B0CbNm3Yt28fs2fPZsKECaioqJCQkMC0adNwcXGR5uj4Qyjs0dyenp44OzsXtBlS8hmRSISKigr79+9n+PDhrFixgiVLllC3bt187ffKlSvcvXuXgwcP5ms/ecXMmTMZPXr0L1skUVBQQFdX96v5wdLT0zl58iQbN24kNTWVatWqMXfuXBwcHDh+/DhDhw6lbt26qKmpYWtry6hRo1i9ejXDhw+nZMmSFC9eHJFIhLW1Nc+ePSMuLg5lZWUSEhKIRgk5BSXK12tK2HVfAIqVLg/Am4chGFlVIjkulmKlyqGoqsb78OeUsa9P2HVfdEqYEOp3kTB/X+Ii3/I44Cp1emVV0a3dayjyikrIyMpi33UAt04cIDU5iQublqFZ1IATZ87Sr3dPAA4ePEibNm3w8PCgVKlSBAYGCsLahw8fGDJkCNevX+fYsWPY2dnl18chpRCyZ88e4uLipO/BfEAsFhMWFvbbCGsfPnygUaNGNGzYkOnTp/9wO0eOHKFBgwaEh4fTqFEjZs6cyT///PPNx0sKHP3JSOy3tbXF1taWd+/ekZaWxps3b1BTU0NPTw/Iygn7z7z5VGvfm8bDp35XHylp6ZwNT8BAVQ4DlS+LaDkh8VozNDTk8uXLpKenM2nSJGJjYzExMaFv3764u7vTv39/DA0NC9xrTYqUwo60Tm8hQiwWY2dnR9WqVRk8eDAAixYtws3NjRYtWnDo0CE6dOhAeHg4qampBWytlG+lML9Inz9/jr+//28XpiEl/6hcuTIXL14UvNbatm1LWFhYvvU3efJkJk+e/NtX1gQICQnhwoULv1Wl59DQUNzd3TE2NmbGjBk0adIEIyMjJk2aRO3atRGJRBgbG/Pq1StevXrFunXrcHNzIz4+nq5du/L+/Xusra0ZOnQompqaDB8+nNWrV+Pl5UX9+vXp378/ZStkhYrKKyqRkpgAgNz/PQGeBF4FICk+lmKlLTGuWI0nAVeRV1QiLTUZ00o1UNMpQiPXiWgblqT91KUcW5SVS0mrmKFwHtoGJXj9KISAI7uo08cNOQUFHsf+954sUaIEmpqaLFq0iOfPn7N27VqePn3KxYsXsbGxQU1NjYCAAKmoJiVPSUpKYuzYsSxZskSaxiMfiIqKQk5OTqg8XJDEx8fTtGlTqlWrxvz58/Nk3GdoaEiTJk24c+cOANeuXaNmzZpoaWlhY2PD+fPnhX0dHR2ZOHEi9vb2qKio0KNHD3x9fRkyZAhqamoMGTKEp0+fIhKJSE9Pz3bchg0bhN/FYjFDhgxBU1MTCwsLfHx8hG0mJiacOXNG+H3q1Kl07949m83bt2+nZMmSFClShFmzZgl/v379OjVq1EBLSwsDAwOGDBmSbS4jEolYs2YNpUuXRktLiyFDhgBZqRHEYjG6uroUK1YMbW1tihUrxrFjx7h+/Tq2trZUdGqZTVS7cXA7i9rWZFqdUngO7sD7Vy+EbRMq6XF190YWtKrKwtbVSBdDcVUFQkNDATh27Bi2trZoaGhgZGSUrfBYcnIy3bt3R1dXFy0tLezs7IiMjEQsFrNp0yYMDAyYN28eu3btYu3atTx69IiePXvi7u5OiRIlmD9/PkWLFsXAwICDBw/i7e1NmTJl0NHRYfbs2d98rb6Et7c3lpaWqKurY2homGtOx3/++Qdzc3PU1dWxtLTkwIEDwrbNmzdjb2/PiBEj0NLSwszMjCtXrrB582aMjIwoWrQoW7Zs+SZ7pEj5nZAKa4UIyerMokWLUFNT486dOxQtWhQHBwdGjBjBqlWr6NSpEytWrKB8+fIFbK2Ub6UwC2tbtmyhc+fOnyWGlfJ3IxKJ6NChA/fu3aNKlSpUrVqVsWPHEhsbm6f9nD9/nmfPntGrV688bTe/mDVrFiNHjvyuqqX5QWJiIlu3bsXR0ZGaNWuSkpLCqVOnuHbtmlBF88WL/yYiz58/p3jx4hgZGTFx4kRiYmKIi4ujUqVKzJkzJ8fqsR+jKJv7UOZp4DUAkuNj0TU2w8S2uiC2xb+LwrRyTSwcnNAomlVZzayyPaWrOwIQ8yY8qxGxGF+vNWSmp+Oy9iBKqmqIgeiUjM/6MzY2pkmTJpiammJpaUmLFi1Yvnw5q1evLvDPRcrfx8KFC6lSpQqOjo4Fbcpfye8SBpqUlETLli0pV64cy5Yty7Mx34sXL/D29sbW1pbw8HCaNWvGpEmTiI6OZsGCBbRr147IyEhh/23btrFu3Tri4uLYvHkzDg4OrFixgvj4eFasWPFNffr5+WFubk5UVBTTpk2jbdu2REdHf7PNly5d4sGDB/j4+DB9+nQhCkdWVpbFixcTFRXF1atX8fHxYdWqVdmOPXr0KP7+/gQHB/Pvv/9y8uRJZGRksl1PRUVFGjRogKWlJW3atEFFXYNWkxYL2++eP845zyV0X7CZST73MalUnV3uA7L1c/f8cQZvPcmIvf/lkktKywRAVVWVrVu3EhMTw7Fjx1i9erXgEb9lyxY+fPjAixcvePfuHWvWrEFZWRmRSETRokWpV68ebm5u7Nmzh+fPn9O9e3fOnz+PtbU1b968ITk5mfDwcKZPn46LiwteXl7cvHkTX19fZsyYwZMnT775WuWGs7Mza9euJS4ujjt37lCvXr0c9zM3N8fX15cPHz7g4eFB9+7def36tbDdz88Pa2tr3r17R9euXencuTP+/v6Ehobi5eXFkCFDcg2hlSLld0UqrBUiZGRkyMzMRE1NjTlz5lChQgXWrVtHt27dkJOTY9myZfTq1euP8MyQ8h+FVVjLzMxk06ZN9O3bt6BNkVJAKCkp4e7uzp07d4iMjKRs2bKsX7+ejIzPBY/vRSwWM3nyZDw8PL6aB+V34P79+5w+fVrwRv7ViMVirl+/zsCBAylRooSQT+zly5csXLjws8WalStX8vLlS6Kjo5k1axadOnXCxcWFNWvW4Ofnh5ycHBs2bGDKlClcuXLli30XVZZFLpdH4LOg6yR+eE9mWhrqOnoY21Tl+S1/0lKSSY6LwbRSDR5cPsPhuRN4H/6cabXNeXApy2PCd+tKUhLiSYyN4fGNS9g264CSuobQ9vP4dJIyPg+HiouL4969e1SrVg1zc3MWL14sTGikSMkrwsPDWbx4MfPnzy9oU/5afgdhLSUlhTZt2mBgYMDatWuRkfn5qVvr1q3R0tKiVq1a1KlTB3d3d7y8vGjatClNmzZFRkaGhg0bUqVKFby9vYXjevfuTfny5ZGTk/vh92LRokUZPnw48vLydOrUibJly3Ls2LFvPt7DwwNlZWVsbGywsbHh1q1bQJY3e/Xq1ZGTk8PExIQBAwZw4cKFbMeOHz8eLS0tSpYsSd26dQkKCvqsfYkH28OHD0lISGCW57/IfeQN6rd3M4593ShqVgZZOTkc+47g9cM72bzWHPu4oaKpjbzSf4u+ga9jsrY5OmJlZYWMjAzW1tZ06dJFsFNeXp53794RGhqKrKwslStXRkNDg8zMTJo1a8bChQuJioriypUraGtrExERgZWVFZUrV0ZeXp6JEyciLy9P586diYqKws3NDXV1dcqXL4+lpeV3XavckJeX5+7du8TGxqKtrU2lSpVy3K9Dhw4UL14cGRkZOnXqROnSpbMVGTM1NaVPnz7IysrSqVMnXrx4wZQpU1BUVMTJyQkFhf+8/KRI+VOQCmuFDMkLWZJk+d9//2XChAnMmTOHsmXLkp6ezrRp0zh06BBAnkxQpeQ/hVFYO3/+POrq6rm+1KUUHgwMDNi0aRNHjx5l69atVK5cOVsIy5dISMvk2ttEDj+NY0/YBw4/jePa20TOXbpKVFSUUBHsd2f27NnCIPpXEhUVxZIlS4QJgpGREcHBwRw7dox27dqhoKCQ43Fdu3bFyckJMzMzzM3NmTRpElWqVGH9+vUMGTIEbW1tnJycKFu2LH369CExMTFXG6x0lMgt209yfCz+B7ahqJYliCmpqaOup8+rB7eRV1JGQ68Y28f0xaphS7QNS+JxMYyytRoAUM6xMQdmjSIjNQWrBi24dfIgT4P8srWfnpnVc8i7ZCCrmMrx48epVKkSZ8+e5fr16zRp0gQ7OztWrVpFZmbm91xeKVJyZcKECQwYMEBauCcfKWhhLS0tjU6dOqGmpsaWLVvyLNz34MGDxMTE8OzZM1atWoWysjLPnj1jz549aGlpCf8uXbqUzcvIyMjop/s2NDTMNmaVpAL4VooVKyb8rKKiIng1PXz4kObNm1OsWDE0NDRwd3cnKirqm479GIkHW8uWLenTpw9jerYnMvw/0Szm9UuOzp/ItNrmTKttzgzH0ojFYmIj/7tOmsWKf9ZukkyWEOnn50fdunXR09NDU1OTNWvWCHb26NGDRo0a0blzZ4oXL87YsWNJS0tDRkYGb29v2rdvz65du3B3d+ft27f06NGDadOmkZqairKysnA+kkJL+vr6Qv8fb/+Wa5Ub+/btw9vbG2NjY+rUqcPVq1dz3G/r1q1UrFhRuJfu3LmTrY9PbfuSvVKk/ClIhbVCiCQkdMiQIWzfvp0aNWrg5eXFhAkT8PHxwdnZmUmTJpGSkiLN2fEH8KcnkP1RJEULCqOoKCVnJPnX3N3d6d27N23btuXx48c57vs6IY19j2NZFRLNpdeJ3H2fQlhsGnffp3DpdSI31cowYbcPEcm/vxASGhqKt7c3Q4cO/SX9ZWRkcPz4cTp06ECpUqW4efMmy5cv59GjR0ycOJESJUp8tQ07Ozvu3r1LTEwMW7ZsEYotNG7cGH9/f2JiYnj9+jXXr1/Hzs6OkSNH8vTpUxo0aCC04eXlxdSpU1GVl8FMQwG7Nj3ot2YfANrFSzInIJIS5W255LWGei4jsWvTAwCTitWICHuAZd2mZKSlkZ6agnlVB0Yfus6Dy2d4dO08AGXtG9B59lrU9YrRde5GOs9ew/bRfShqVpaBnv95WMwJiOShgj7vYmI5deoUwcHBeHt7IxKJkJOTY8yYMfj6+rJ161YaNGgg9V6T8tNcu3YNHx8fJkyYUNCm/NUUpLCWnp5O9+7dycjIYMeOHflekdHIyIgePXoQExMj/EtISGD8+PHCPp+Otz79XRLu/vFCyJs3b7LtEx4enm3cKkkFIDn+S8d+iUGDBmFhYcGjR4+IjY1l9uzZPz0+Xrx4MbZ1G7FhYFs+RGQJZ5r6xWk9cSEeF8OEfzOuvsDYpupHR34+Lk39v59C165dadmyJS9evODDhw8MHDhQsFNeXh4PDw/u3r3LlStXhMXClJQU2rdvz+jRowkPD+fJkye0aNECRUVFVqxYQY8ePXj//j3btm0Dvj4v+JlrZWdnx6FDh4iIiKB169Z07Njxs32ePXuGi4sLK1as4N27d8TExFChQoVCO1+RUniQCmuFEMmLUEtLC319fdavX8+yZcsoVqwY48ePR09PDycnJ3bt2lXAlkr5FgpjKGhMTAxHjx6lW7duBW2KlN8MkUhEx44duXfvHpUrV8bOzo5x48Zly78WEJnE9kcfePQhlQwxpH8y1ksXQwbwKkOB7Y8+EBCZ9GtP4juZPXu2kAw6P3n8+DGTJ0/GxMSEKVOmUL9+fZ49e8a2bdtwdHTMkxClnFi5ciWnTp3Klvz4U5RfPyAjNeWzv5tWrkF8dCQmFasJfzOxrU58dCSmtjVQVFWjxZjZ7BznwvQ6pQg6vl/YL8TnKIfn/jepLF3dkXYeS9g6vDvh925l6ycDEXLKqtRyasq7d+8+SwRdrlw5Ll++TNOmTbGzs2PlypVS7zUpP0RmZibDhw9n9uzZv9xDtbBRUMJaZmYmffv25f379+zZsydXz9+8pHv37hw5coSTJ0+SkZFBcnIy58+f5+XLl7keo6+vn23xSk9PD0NDQ7y8vMjIyMDT0/Oz4kIREREsW7aMtLQ09uzZw71792jatCkAFStWZNeuXaSlpXHjxg327t37zfbHxcWhoaGBmpoa9+/fZ/Xq1d95BXJm8IyFmFWxZ+PAtsS9i6Ba+95c2LSUt2H3AUiOi+X26UNfbSc68i3R0dHExcWho6ODkpIS169fZ8eOHbx7946UlBTOnTvH7du3ycjIQENDA3l5eWRkZEhNTSUlJQU9PT20tbUJCgrC29ub3bt3c+TIEVxdXSlatCibN2/mzZs3X30X/+i1Sk1NZfv27Xz48AF5eXk0NDRy7CshIQGRSCRUVt20aZNQIEOKlL8ZqbAmBV9fX9zc3HBzc2P8+PHY2toSFBREtWr/TUQSEhIK0EIpX6IwCms7d+7EyckJXV3dgjZFyi/GxMQEZWVl1NTU0NbWplmzZtkS4UtQVlZm4sSJ3L59m4iICMqWLYunpycBkUmcDU/4TEzLjXQxnA1P+KK41rt3byZNmvSZnR9XN8svnjx5wqFDh3Bzc8uX9pOSkti+fTv16tWjWrVqxMXFcezYMfz9/Rk4cGC+i3kAmpqa7Nixg4EDB+Y4yTt48CC9WjfFJOXNZ7nWGg+dzJyASAzL2Qh/s3ZqzZyASKq1zypKUaOTMxPP3MXjYhidZq5CVbuIsK+SugYTTgQLv1s4ODHxzF0My9lgVsU+27Zxx25iWK8NgwcPRkdHB0dHRyZPnszJkyeJi4tDVlaW0aNHc+nSJeGa5uZRKUVKbuzYsYPMzEx69OhR0Kb89RSEsCYWixk0aBDPnz/n4MGDQlhffmNkZMShQ4eYPXs2enp6GBkZMX/+/C8uALi5ubF37160tbUZNmwYAOvXr2f+/Pno6uoSEhJCzZo1sx1TrVo1Hj16RJEiRZg4cSJ79+4VxnIzZswgLCwMbW1tPDw8visVw4IFC9ixYwfq6uq4uLjQqVOnH7gKn6OvIkeHyYsoUb4SGwe1x8S2OrV7DWXnhP5MdTBlSUcHHlz2+Wo7CulJ3Lp1i1WrVjFlyhTU1dWZPn06jo6OPHnyhBMnTvDmzRvat2+PhoYG5cqVo06dOvTo0QN1dXWWLVtGx44d0dbWxsvLCy0tLWrWrMnJkyepXbs28vLyTJky5ZsWbH7mWm3btg0TExM0NDRYs2YN27dv/2wfS0tLRo0aRY0aNdDX1+f27dvY29t/cx9SpPypiMRSv8xCS2ZmJjIyMpw7d47Zs2fTsWNHbt++TVhYGL169aJjx45s2bKFJ0+eIC8vz4gRI4RwHSm/D2PGjEFPT4+xY8cWtCm/DDs7O2bOnEmjRo0K2hQpvxgTExM2bNhAgwYNSE5OZvDgwURHRwtVtXLjxo0brN6xl3I9R3+zqPYxciLoVkYTA5XPEzb37t2bEiVKMHPmzBztzE/69+9P0aJFs/X9s4jFYgICAti4cSO7d+/Gzs4OZ2dnWrZsWaDFbWbNmsWZM2c4c+aMkKZg3bp1eHh4cOTIEapUqfLdwmlOzG1WCYcegzi+ZBoZ6WnIKyohIyuHx8Uw1rm0wrZpeyGs9Obhnfgf9BJCQ2VF4Fpeh7TEOK5cucKlS5fw9fUlICCAsmXLUqtWLRwcHKhRowa7du1izpw5TJ06lcGDB+ebx5+Uv4eEhAQsLCzYvXv3Z4KFlLwlOjoaU1NTYmJiftnipVgsZvjw4fj7+3Py5EmpR+JvQEJaJqtCosmhTs03kZmZycQq+twPfUJZc5Ns29LS0ti6dSuPHz9m9OjRaGtrC8fk9j740mJ6QkICK1euxMXFBW1t7UK58C5FSkEiHcUVYmRkZBCLxdStW5eWLVsyb948UlJSmDJlCh07duTy5ctMnDiRTZs2MXToUKmo9ptS2F6cwcHBvH37Nt8FCym/P0pKSrRv3567d+8CWdXTRo8eTcmSJdHX12fgwIEkJWV5msXHx3Nw5zbObV3FzPrlmO1UnhuHdght7fEYwqE5Y9k8rAsetUxY2bMR7178lwvr1eNHNHZyQkdHh7Jly/Lvv/8CWcLO9u3bmTdvHmpqarRo0YIePXrw/PlzWrRogZqaGvPmzcuX83/27Bl79+5lxIgRedLeu3fvWLZsGRUrVqR9+/YYGBgQFBTEiRMn6NChQ4FXjB4/fjyZmZnMmzcPsVjM9OnTmTt3LhcvXqRKlSoAVNJTplsZTcpoKiAr4jMPNjlRlvhVRlOBakWVcq0mWtS0DK3d51PSugrTLj/D42JYzjt+gggIjk5GU1OTJk2aMGvWLC5evChcW0mhjQoVKrBmzRrq1KnDkiVLqF69urQCmpSvMnfuXBwcHKSi2i8gLCyMUqVK/VJRbfz48Vy6dAlvb2+pqPabIMnh+aO8Db2HgqISpkafFzSQl5enVq1adOrUCbFYjL+/P8AXF1k+vR8fP37M7NmzWbx4MdHR0cjJyTFt2jSg8OZgliKloMjfTJhS/hgGDRqEvr4+bdq0ITU1lf79+3Pp0iXc3NzYtm0bSUlJaGhoFLSZUnKhMAlrnp6e9O7dW1pYQwqJiYns3r2b6tWrA1nCS1hYGEFBQcjLy9O1a1emT5/OnDlzSErPJCbyLcnxsUw4Ecwjv/PsGOtM+bpNUdbQAuDWqYP0Wb6L4hbW7PEYwqmVs+nyz3pSkxLwHNwep0Hj8D19krD7ITRs2JAKFSrQv39/rly58pnHmq+vb757rM2dO5f+/fv/VEh0RkYGPj4+bNy4kZMnT9KsWTMWL16crznTfhRZWVm8vLyoXLkyN27c4PHjx1y+fDlbpTcAAxV52prJk5iWSXB0MpFJGaRkZKIoK4OesizWOkqoyGedm6aCLGdexJEpEpFTwunvJV0MkUmfV9NWVFTE3t4ee3t7QSC8c+cOvr6+yMvLc+LECcqUKYONjQ29evWidu3a2NjYSJ9zUgSePXvGypUrCQoKKmhTCgW/Ogx02rRpHD9+nHPnzqGlpfXL+pXydWrqK/MkNvW7PaHv+Bxh/8xRdOzRK9f3admyZQGYM2cOPj4+VKhQgSpVqtC9e3dhn4yMjBzfBWKxmKioKF6+fImOjg6DBg3i6NGjWFtbExMTg5aWVqFbfJcipSCRCmuFHJFIhFgsRk5Ojo4dOxIaGkq7du2oUqUKPj4+GBgYoKurS1RUFEWLFmX8+PEMHz4cAwODgjZdyv8pTCtSKSkpbN++HT8/v4I2RUoB0rp1a+Tk5EhISEBPT4+TJ08iFotZt24dwcHB6OjoAODu7k7Xrl2ZM2cOT2JTkZGTp57LaGTl5LCo1RAFZVUin4ZS0jrL26l83aYYVagEQMUm7fBeNAWA+xdPoW1gRNVWXbkbm051W1vatWvHnj178PDwKJBr8PLlS3bt2sX9+/d/6PinT5+yadMmNm/eTJEiRXB2dmbNmjVCKMrvip6eHubm5nh7e/Po0aPPRLWPUZGXobr+lz2tSyul03tYZ0at3MabTEXy4mmakvH1HDcyMjJYW1tjbW2Nq6srYrGYs2fPMnDgQObOncvy5cuJioqievXqODg44ODgQNWqVVFWVs4DC6X8iYwdO5ahQ4diZGRU0KYUCn6lsDZ37lx2797N+fPnpbljf0MMVOWpZ6j63WkGKjZowchenVCPCc+xqqsk5PPRo0fcunWLMmXK0KxZM4YOHUqJEiUoWbIkZmZmuS6wiEQiKlWqRNWqWRVJXVxcGDJkCBUqVODdu3e5FheQ8n0kpGVyOzqZiI8W6Yp+skgnRQpIhTUpZPd28vX1pXr16qxdu1ZIgNm3b1/WrFlDYmIiHTt2/CXJqqV8O4VpNerw4cNYWVlhZmZW0KZIKUAOHjxIgwYNyMjI4NChQ9SpU4egoCASExOpXLmysJ9YLCYjI8t76H1qJiqa2sh+NLiVV1ImNem/wizqukWFnxWUVEhJzNr2/vVLXtwJYLKDObIiEXIykJ6eXqDJw+fNm0ffvn0pWrTo13f+P8nJyRw4cABPT08CAwPp2rUrhw4domLFivlnaB7y4cMHWrVqhZGREZaWlri7u7N169afanP58uVUKFmMXjaG7H8cy8MPH1XzzOG5qqCsQmryf4Us4t5FfLaPouz3D7RFIhH169fn/v37LFu2jFmzZjFu3DgsLCy4fPkyY8eO5c6dO9jY2AhCW82aNQURWcrfja+vL1evXmXTpk0FbUqhITQ0FEdHx3zvZ+nSpaxfv54LFy6gr6+f7/1J+TEq6WUtanyruCYngnqGKlTSUwG90jnuIxG99u7dS+nSpenTpw9mZma0b9+e6dOnU6ZMGYKCgpg/fz4ODg7CcR+P+yWCXWZmJitWrOD27dukpaVhbm4OwMOHD0lJScHKyuqHz72w8johjStvk3gcm4qI7BXkH8aA7+tEzDQUqKmvjIHq5/l3pRQ+pDKrFOA/r6c6depw+PBh4uLikJGRISgoCEdHR1avXk1qaiqVK1eW5lr7zShMwpqnpyfOzs4FbYaU3wRZWVnatm2LrKws165dQ1lZmZCQEGJiYoiJieHDhw/ExcXx8uVL3r3/8MP9aBYzxLRyTTwuhrE58BkxMTHEx8cLJepz+v7l53fy9evXeHl5MXr06G/aPzAwUFgB37RpE87Ozrx8+VLIp/Yn8OrVK2rXro2VlRU7d+5k2bJl+Pv751iR7Fv58OEDixcvFrwOi6vKZcu5pqajx4e3r0lP+09sMyhTgZCzx0hNSiTq+WNuHMzev5wI9JR/PHxTVlaWESNGcOXKFby9vVmyZAmDBg3Cz8+PiIgIZsyYgYqKCkuWLMHY2BgrKysGDx7Mjh07cqyOK+XPJzMzk+HDhzN37lzp+OsX8is81tatW8fixYvx8fHB0NAwX/uS8vN8Tw7PbmU0s0S1XJDMux49esT9+/exs7PDxMSElJQUzp8/T+vWrYU8nIcPH852rGR8IVk4hCyRTlFRkcqVK1OjRg0SExOZMWMG1atX/yUVyv82AiKT2P7oA48+pJIh5jMxNV0MGWJ49CGV7Y8+fLFyvJTCg9RjTQqQ9ZDOyMjAzMyMM2fOoK6uzqhRozh06BAjRoxgwIAByMnJFSoR50+hsHwmL168wM/Pj/379xe0KVJ+E8RiMYcPH+b9+/eUL1+efv360a9fP1q1asXjx4+5du0at27dQlFRkTp93H64n3IOTpxcNoOAo/9StnNn0tLSCAoKQk1NjXLlyqGvr8/jx4+zHZPT3/KKBQsW0LNnzy+GQb5//57t27fj6elJdHQ0ffr04ebNmxgbG+eLTfnJw4cPadSoES4uLkyYMAGRSISqqio7duzAycmJGjVq/JAX65IlS2jatKmQ48ZKRwnf14nCdvOqDuibl2V2w/KIZGSYfPYBtboN5OXdIGY1tMSgtCUVm7Qn9PoF4RgxYK2j9NPnXKZMGS5cuMDy5cupXr06kydPZujQodSvX5/69esDCPehr68ve/bswc3NDVVVVaHyqIODAxYWFtJQoD+czZs3o6SkROfOnQvalEJFfgtrW7duZcaMGZw7d+6PfC4XVr4nh+eXkIzbfXx8UFFRoUKFCsjIyLBv3z5EIhHDhg0D/ltUArhy5Qq+vr4oKyszbNiwHENEJe0qKSkxduxYxo4dW+CFh/40vre6eLo4y5MR/vNslFI4EYkLU4ImKd9Mp06diIiIwNPTE7FYzIULF7h69SpFihSha9euVKhQIddkmlJ+LcOHD8fExIThw4cXtCn5ysyZMwkPDxe8hKQUTkxMTHj79i2ysrKIxWK0tLQoV64cCQkJBAcHo6CgQGpqKhkZGejp6dG/f38mTZrE6gMncR/Ul/EngoW25jarRLspiylVrQ57PIagWbQ4Tq7uADy+cZndkwYx4f/7Rz4NxXvRZN7cDUKGTGxsbFi0aBEVK1bk0aNHdOjQgadPn+Lo6MjBgwc5dOgQQ4cOJTY2lkmTJn2zd9nXiIiIwMLCgjt37lC8ePYqY5mZmZw9exZPT0+8vb1p3Lgxzs7O1K9f/48VV65fv06rVq2YOXNmjt6qixcvZvfu3UIRgG8lOjqaMmXK4OfnJ4TMAOy6H8XTn1h4LqOpQFuzvC308+jRI/r27Qtkee2WLp1zWJFYLObBgwf4+vpy6dIlfH19iY2Nxd7eXhDbKlWqhILCj1e4k/JriY2NxcLCgkOHDmFnZ1fQ5hQaYmNjKV68OHFxcfmycLl7925GjBiBj48P5cqVy/P2pfxZvHjxAkNDQ9LT03FwcMDV1ZWePXty4sQJDh06ROfOnUlOTmbUqFFs3bqVzZs38/DhQzw9PbONAyRT+pzu2cKyCP+zvE5IY/ujD99dqAKyPBa7ldHEQEUaFlpYkQprUrIhSaQZERFB0aJFSU5OZvr06SgpZa3A6+rqsnbtWoKDg7/SkpRfhZubG6ampn+1sJaZmUnp0qXZvXs3VapUKWhzpPxioqOjCQoKIigoiMDAQAIDA3n8+DFlypTB1tYWW1tbKlasiI2NTa45IBPSMlkVEk3GT7zxZEXgWl7nlySrzS1Z7vENS3n3OpwVK1YI+z5//pxNmzaxadMmtLS0cHZ2plu3bn98/q2TJ0/SvXt3Nm7cSMuWLXPcJzMzk6ZNm2JnZ8eMGTO+ue2JEyfy9u1bNmzYAGQVRvHw8ODM9SA6L/L67QbVmZmZLF++nBkzZjBp0iSGDh36TQtb4eHhgsjm6+vL48ePsbOzE4S2GjVqoKamluf2Sskbxo0bx9u3b9m8eXNBm1KoCAwMpHfv3ty6dSvP2z506BADBgzg1KlTWFtb53n7Uv4cPhW7nj9/zpgxY9i9ezcAI0aMwMTEhKpVq7J161b2799P//79ad++PXv37qVHjx6UKVOGnTt30qVLl1zblfLt7Hscy6OPc61+J58uronFYsRi8R+7uCnl+5CGgkrJhuSLr6enB2Qldg4ICGDp0qVCuMzJkye5fPky9vb2BWanlP8oDC/Qixcvoqqqmi0xvZS/D7FYzIsXLwgMDMwmor1//x4bGxtsbW2pW7cuI0eOxNLS8rvCG1TlZTDTUPipAZO5hkK+i2pfS5ar2rQflioyPItJxO/UUTZu3MiNGzfo3Lkz+/fvp1KlSvlq36/Cy8uLUaNGcfDgwS++a2RkZNiyZQsVK1akQYMG1KlT56ttR0VFsWbNGgICAgC4c+cO3bt3x9TUlOO7t/GC76/+lpWoWjXfVqplZGRwc3OjadOm9O3bl71797Jp06ZcvdckGBoa0qlTJzp16gRATEyMEE40ffp0AgICKFeunBA6WqtWre8qiCEl/wgLC2Pjxo3cvn27oE0pdORXGOiJEydwcXHh+PHjUlFNymdj95IlSwqimp+fH48fP6Z9+/bo6OgQHBzM/fv32bp1K3379iU1NZXmzZtz9OhRnJ2def78OTIyMowYMSLHCqSFFRMTE4YMGcLWrVt59uwZjRs3ZsuWLSgpKXH06FEmTZrE06dPsbS0ZPHyVTyWLQFA+L1b7Js+nHcvnlCmZj1EIhmKlDTDydWdpNgYdk8azMs7AWRkpGNsU5U2ExegqV+csNhUatdxxKGWPefPnycgIIDbt2/j6+vLvHnzePnyJXp6eowbN44BAwYU8NWRktdIv3lSckQkEiEWi3n06BGjR4+mbNmypKSksGvXLl6+fEmJEiUK2kQp/6cwCGsbN27E2dn5rz/PwkR6ejoPHz4UxDOJmKagoCB4oHXr1o358+djbm6eJ6t9NfWVeRKb+sPeSDWK5W/ujK/l9cj6u4jH8Rk8jH5HaHAYvXr14uDBgygr/z15PRYuXMjSpUs5e/Ys5cuX/+r++vr6bNy4kR49ehAUFPRVT7158+bRqVMnjIyMWLhwIf/88w/z5s2jd+/eiEQi9P6/3/dVf1P9JblVSpcuzYULF1ixYgU1atRg4sSJuebayQktLS2aNm1K06ZNgaxKsf7+/ly6dIkNGzbQt29f9PX1BZHNwcEBMzMz6bO3ABg9ejSjRo3CwMCgoE0pdOSHsHb27Fl69uzJoUOHpIuEUnLk4xQ71apVY+HChRQtWpTXr1+jq6uLtrY2bm5uuLm5cenSJapVq4ajoyN9+vShcePGjBgxgoiICObPn59ru4WRf//9lxMnTqCkpIS9vT2bN2+mWrVq9O3blyNHjlClShW8vLxo3boVbvuukI4Ir1G9qdV9INU79OXexZPsmtCf2r2GAFke5FVadaHr3A2IMzPZO20Yh+eOp8eirYiA+LRMtm3bxvHjxylbtqyQpuHo0aOYmZlx8eJFmjRpgp2d3V+zGColC2koqJQckYg1mzdvZsOGDQwdOpT79++TkJBA9erVadu2LW/evPli8mwpv4YhQ4ZQtmxZhg4dWtCm5AsfPnzA2NiY0NBQihQpUtDmSPkBEhMTuX37djZPtDt37mBoaEjFihWzhXPm9zPle5PSws8JJ+XLl2flypU4Ojr+VnblB+fPn6d79+68fPnyh47PzMxk7NixHD9+nBMnTmBkZPRdxw8fPpwXL16wd+/eXIWgN2/eYGlpibe3NxMmTCA9PZ2tW7diamr62b6vE9O4+iaJsBy8B+VEWYUKzDUUqFFMuUByqoSGhuLs7Ex6ejqbNm2iTJkyP91mRkYGd+7cEUJHfX19AbIVRLCysirUk7RfwdmzZ+nXrx93794VUnFI+XU4OztTvXp1XFxc8qS9y5cv06ZNG/bs2fNNXrVSpHxMWloa/fr148mTJ/Tr149GjRqhr6/P7t27WbhwIdevXwfg9OnT7Nu3j6VLl6KoqEhQUBClS5dGVVUVKJwCm4mJCTNnzqR79+4AjB07ltjYWCArOuvjFBKGZqVp5r4AESJ2uQ9g/IlgYSyxpm8zzCrbC3l4P+bVg9us798GjwuhAGwf1IY2TnWZPn16rna1bt2aunXr4ub244W1pPx+SD3WpOSI5EHSu3dvkpOT8fb2ply5clhbW1OrVi2WLFnCggUL2LBhA40bNxZys0kpGP5mb4KdO3fSsGFDqaj2h/Du3btsHmiBgYE8ffoUCwsLQUDr3r07NjY2qKur/3L7Kukp07yyBc3dF2FWLWuCc+vkAQ7OHkOPRVswq5w97PBnxauQkJCv7vM6Ie27RTX4rxKVgarcZ8KOSCTi0aNH+VrVLi9JS0ujb9++PH78GF9f3x/KDzd37lyqVavGhg0bcp0Q//PPP9jZ2dGiRQtGjx7N6NGjc51o5FX1t/yiVKlSnDt3jpUrV1KzZk3c3d1xc3P7qYmTrKwsNjY22NjYMGTIEMRiMU+ePBEKIqxcuZI3b95Qo0YNwautatWqUvEnD0lPT2f48OHMnz9fel0LiNDQUGEi/rP4+/vTpk0bvLy8pKKalB9CXl6eLVu2cOLECdauXYuGhgatW7dm6dKlwqJ6YmIi165dQ0tLi4yMDMaPH09AQADPnz9n0qRJtGnTRhDYoHBEu0j4eMFWRUWF0NBQDh48iEgkYsGCBcjLyyMjI0NSSiqxkW8RiURoFDXIdn009Q2Fn1OTEjm2cDIPr5wlKS4GgJSEeDIzMpCRlSVTLP5sYfD48eNMmzaNhw8fkpmZSWJiIlZWVvl74lJ+OVJhTUquSMSygQMHMnDgQACCgoIYOnQoQUFBtGvXjgMHDlC7dm1UVFSk4loB8bc7nXp6en5x1UdKwSAWi3n27Nln+dBiY2MFL7SGDRsyduxYypUr91tVIlSQEVG/hCrymgrs9trK0UVT6L1sB8Y2VQHITEtDRkZEGR2VL3ojpaen50kukytvk34oPBWyxLWrb5Joa/bzHlN5dT7fS3x8PO3bt0deXp7Tp0+joqLyQ+0oKiqyY8cOateujYODAxYWFtm237lzh1WrVmFqasrp06epWLHiN7WrIi9Ddf0fsym/kZGRYejQoULutX379uHp6SnkRP1ZRCIRZmZmmJmZ0atXLyCrMu3ly5fx9fVl1KhR3L17F1tbW8Grzd7eHi0trTzpvzCyfv16dHR0aNu2bUGbUmjJq1DQoKAgmjdvzsaNG3FycsoDy6QURiTzq8aNG9O4cWMArly5wps3b+jRowcAz5494+zZs2zdupXp06cTGxvL5s2bUVRUxMXFhXnz5uHn5yekjRCJRGRmZiISiQqNwCZBUVFRKCqQnp5OWloaCgoK7LodzoO4DB7fvExsxOts4uOHt+HoljABwNdrNZHPQhm89QTqRfR59eA2y7vUE+ZjMp9c05SUFNq1a8fWrVtp1aoV8vLytG7d+q+fvxVGpCqIlFyRiGRisZjU1FSGDx9Onz59KFeuHPfv32fp0qXIycnh7v65W6yUX8ffvOp0+/ZtXr16RcOGDQvalEJNWloat2/fZtu2bYwcOZK6deuio6ODvb09GzduJCMjg169enHhwgXev3/PhQsXWLJkCb1798bGxua3EtUk6CrJEXl6Jz7LprJi92GaOjpQSkMeE9lkTkwbhIe9CUMdyrFy9jQyMjIA2Lx5M/b29owYMQJdXV2mTp1KWFgY9erVQ1dXlyJFitCtWzdiYmKEfkxMTDhz5gwA169fp0aNGmhpaWFgYMCQIUN4n5DM49isggpH5rvzTxMbpjqYsrxrfZ4EXBXaObNmHjvGOfPvZFc8apmwuH0tXt4NAiAsNpXEtMxcz/X69etUqVIFDQ0N9PX1GTlyJABPnz5FJBKxceNGSpYsSb169QDo0KEDxYoVQ1NTk9q1a2fzuktJSWH06NGULFkSfX19Bg4cSFJS0g9/DpGRkdSrVw9DQ0MOHDjww6KaBEtLS2bNmkWXLl1ISUkR/n7ixAmqVauGtbU1t27d+mZR7U/B3Nycc+fO0aVLF+zt7Vm4cKFw3+Y1RYsWpU2bNixatAh/f3/evn3L1KlTUVRUZNGiRRgZGWFtbY2rq6uQl1XKt/H+/XumTp3KkiVL/tr3+u9OQkIC0dHRGBoafn3nLxASEkKTJk1YuXIlLVq0yCPrpBRGJPOxj5/pNWvW5OrVrDFCcnIyO3bswNLSEl1dXU6dOsWkSZPQ19dHV1eXsLAwGjRogLKyMn5+fkyePJm3b98iIyNTKJ4zjx8/xsvLi7Fjx+Ll5cWhQ4eEbenp6YhEIgYMGMDja+dIT4ynpLUdIllZru7eQEZ6OnfPH+flnUDhmNSEeOQVlVBS1yTxw3t81i0QtsmJQF4m+zVNTU0lJSUFPT095OTkOH78OKdOncr/E5fyy5EKa1K+ikgkEibGXl5eeHh4AFmDhpiYGMHdVUZGJt8G8lJy528W1jw9Pendu3ehywlRkCQkJHD16lVWrVqFi4sLdnZ2aGlp0aFDB44dO4a+vj4TJkzgwYMHhIeHc/ToUWbOnEm7du0wMzP7Y7xWV69ezZQpU/Dx8aFPo1q0MFGnvbkmuz2GUsagCEpKipw8eZJTp06xYcMG4Tg/Pz/MzMx4+/YtEydORCwWM2HCBF69esW9e/d48eIFU6dOzbFPWVlZFi9eTFRUFFevXsXb25uOzgNITk4GoISlLUN3nmPyuUdUbNKOHeOcSUtJFo6/d+Ek1o1a43EhjHJ1GnH4n/EAiIDg6OScugQQkh3HxsYSFhZGx44ds22/cOEC9+7d4+TJkwA0adKER48eERERQaVKlejWrZuw7/jx43n48CFBQUGEhoYSHh7+wx6lT548oVatWjg5ObFhw4Y885br378/pqamTJgwgYSEBFxdXXF2dhYGtH9reJ2MjAxDhgzh+vXrHDlyhFq1anH//v1871dNTY0GDRowbdo0fHx8iI6OZv369ZiZmbFr1y4qVqyIqakpPXv2ZN26ddy7d0+6Up8L06dPp3Xr1n+d8Psn8fjx459+lz169AgnJycWLFhA+/bt89A6KYWZT8fC+vr6ws9paWl07tyZmJgYLC0tUVVVRVZWlsePH/P27VsmTJgAgJGREWpqatSpU4fVq1d/9izOzMx9ke53RiwW8/LlS7y9vZk7dy7dunXDysqKZ8+eMWvWLI4ePYqmpiZ2dnY0atSI5s2bIyMjg7y8PEpKSuzatYtLB3YgBuTkFei+YDM3Dm5neh1zAr33YOHghKxCViV6+24DSEtJZma9sqzq1ZgyNer9Zweg9kmKCHV1dZYtW0bHjh3R1tZmx44dtGzZ8hdeHSm/CmnxAilf5dNkl9HR0Zw9e5Zbt24JnmwGBgZcvHgRT09Ppk6diomJScEZXMgYOHAgNjY2DBo0qKBNyVNSU1MpUaIEV69exdzcvKDNKTBevHhBcnIypUuXzvO2IyIisoVxBgUF8fz5cywtLbMVFLC2tkZNTS3P+y8oTExMiI6Opm7duhw4cECYQL19+5aSJUsSExND7969adiwIaqqqqxbt45z586xefNmpkyZwvPnz3Nt++DBg0ybNo3AwEChrw0bNtCgQQMgqxjHrl278PT05MGDBxS3sKbn6v05tjWtTin6rz+IQZkKnFkzj6dBfvRbsy/L1scPWNGtATOuvgCgvLYiLUz+y1n3cY612rVrU7duXYYOHZotV+HTp08xNTUlLCwMMzOzHG2IiYlBW1ubmJgYNDQ0UFNTIzg4WPhOXr16la5du/LkyZPvKl5w69YtmjVrxvjx4xkyZMhX9/9e3r17J4Qg16tXDxkZGYoXL87s2bPzvK/fkczMTFavXo2Hhwfjx49nxIgRBbZAkZmZyYMHD4RiCJcuXSI+Ph57e3uhIIKtrS3y8r++AMTvxP3793FwcCAkJISiRYsWtDmFlgMHDrB58+ZsXi3fw5MnT6hTpw4eHh44OzvnsXVSpHyZtLQ0GjVqRIMGDejXrx9NmzbF0dGRBQsW4O/vz7lz56hbty6lS5dm6NChrF69+rPx3bt373j79i2WlpYFdBZfJj4+npCQEIKDgwkODub27dsEBwejoKCAlZUV1tbWwr9y5crluJi2detWTpw4wbp167Kd/77HsTz6kPrZ/it7NqJau15UadX1i7aV0VSgrZnGz5+klD8SaY41KV/l48H4kydP8PT0JCEhAQMDA5o3b46BgQHe3t7s3r2bGjVqFEiOnsLO3+ixduTIEcqXL1/oRLUHDx7w+vVr7O3tefbsGYMHD+bdu3ds2rQJa2vrH/JQlCQg/7SoQGJiopAPrWnTpkycOBELC4tCMcFdvXo1M2fOpF+/fmzcuBGRSMSzZ89IS0vDwMCAtLQ09u/fj7KycrYktJ8mpH379i1ubm74+voSFxdHZmYm2tra2fYRi8VcuHCBxYsXc+zYMWRlZf8Ltc9IF/a7uHUlNw5uJzbqDSJEpCTEkfA+WtiurvvfZFtBSZn0lGQy0tORlZMjJSP3VeaNGzcyZcoULCwsMDU1xcPDg+bNm+d4ThkZGUycOJE9e/YQGRkp2BkVFUVKSgqJiYlUrlw527l9r6fy+fPn6dixIytWrPjMey4vSEtLY/ny5ULulCFDhtCkSRMePnyY5339rsjIyODq6kqTJk1wdnZm3759bNq06bO8c7/KlnLlylGuXDn69+8PwMuXLwWRbcuWLTx58oSqVasKQlu1atX+KjH/Wxg1ahTjx4+XimoFzM/kV3v58iX169dn3LhxUlFNyi9DkoMtJSWF9+/fs3PnTpYuXcqQIUN4+vQpY8aMwdfXl4kTJ1K7dm2mTZtGUFAQlpaWqKmp8eLFCw4cOMDdu3eZNGkST58+ZeXKlbi6ulKrVq0CO6+MjAweP36cTTwLDg7m1atXQkE9KysrWrVqhZWVVTYPvq/Rs2dPevbs+dnfa+or8yQ2lYc3LqNnXAoVLV2Cju/lzaO7lKlZL4eW/kNOBDWK/R5V2qUUDFIFRMp3YWxsjKKiInZ2djRr1kwQ3RwcHChevDjm5uYFUumvMPO3Op1u3Ljxrx+YSrxBAwMDOXz4MCNGjMDPz4979+5RpkwZSpUqxalTp+jVqxf79u3D2tr6q22mpaVx9+7dbALarVu3UFdXFzzQnJ2dsbW1xdjY+K8UZb8FfX19fHx8qFOnDoMHD2b16tUYGRmhqKhIVFQU8fHxlCxZklevXmWb4H96vdzd3RGJRNy+fRsdHR0OHjwoeGCFh4fz4cMHevfujZaWFqmpqbi4uDB37lzU1dVZsmQJ67bvBuBJwFUubllBvzX7KGpugYyMDNPqlCIrsODrKMrmHrZUunRpdu7cSWZmJvv376d9+/a8e/cux3PasWMHhw4d4syZM5iYmPDhwwe0tbURi8UUKVIEZWVlQkJCfjj/0L59+xg0aBC7du0ScrrlJQ8ePKBHjx7o6Ohw584dVq5cSdu2bXF1dUVXVzfP+/vdMTMzw8fHhzVr1lCrVi3GjRvHyJEjCzy8vkSJEnTp0oUuXboAWbnFrly5gq+vL1OmTCEoKIjy5csLlUdr1aqFnp5egdqcnxw/fpxHjx5x4MCBgjal0BMaGvpDobhv3ryhfv36DB48GFdX17w3TIqUXJAsgMXHx9OpUycsLS3p3bs37u7uDBkyBH19fY4ePUqZMmWYOXMmkDVv69u3L48ePWLMmDHo6+tjampKv379qFKlCiVLlsTePqtKuiQ8ND9Tfbx79y6beHb79m1CQkLQ09MTvNA6d+7MrFmzKF26dL45cRioylPPUJVr+8LYOc6F1KQEdAxN6DZvIxp6xXI9TlJBPrdiV1IKB1JhTco3I1kRGT16tOBWK/GeUVdXl+YE+cUkpGVyOzqZ8p1dkVHX4PDTOIoqy2Kto4SK/J+R5yo3Xr58ybVr19i7d29Bm5JnhIeHc+zYMRwcHChXrhzwnzeoRPCSVOC7c+cOUVFRFC9eHMhKUuvj4wN8nlNPLBYTHBzMsmXLCAwM5P79+5iYmAieaM2bN6dixYrZQgClZFG8eHFBXBsxYgSLFy/GycmJUaNGMWPGDKpVq8aWLVuoUKECderUybGNuLg4NDU10dTUJDw8nHnz5pGUlETz5s25fPky6enpQrhjtWrVKFasGGpqaty/f5/Vq1ejpKWLnAhSEuORkZVFVVuXzIx0zm1YRkpC3Dedh5wI9JRzF0q8vLxo1KgRenp6QrXG3AbIcXFxKCoqoqurS2JiYrbiNDIyMri4uDBixAhWrFhB0aJFCQ8P586dOzRq1Oirdkq8BE+ePImtre03ndu3IhaLhdDHadOmMWjQIEQiEV27dmXevHlCJbTCiIyMDIMHD/7Me03yHPod0NbWplmzZjRr1gyApKQk/P398fX1Zd26dfTu3RsDAwPBo61WrVqYmpr+FQsDaWlpjBw5koULF/6WhV4KG6Ghod+dFy0qKooGDRrQvXt3Ro8enU+WSZHyZXR1dblw4QLLly9n3rx59O7dW1i8UFZW5ty5c6xduxY/Pz/S0tKwsbHhwoULmJiYsGDBAuTk5AgNDcXf35+JEyciEol49eqVMBbNi5zOqamp3L9//zMvtPj4eEFAq1y5Mn369KFChQpoamr+9HX5XirpKeM+bBDV2/X8portElGtkl7hHWdIyUIqrEn5ZiQTMYmoJhHaJKxZswYnJ6dcc/VIyRteJ6Rx5W0Sj2NTEQHpWsVJBKLfp/AwBnxfJ2KmoUBNfWUMVP/MlZOtW7fSsWPHn64QWBDcunWLqKgo6tevny0/oZqaGo0bN8bAwACAV69ecf/+fU6dOkXZsmV5//49cnJyuLq6IhaLs+UprFq1KqtWrfrsOwdZ3kaShKwuLi5YW1v/kdetoChZsiRnz56ldu3aKCkpsXXrVsaPH4+lpSXv3r3j5s2brFq1KtfjPTw86NmzJ+rq6qipqZGUlERqaiodOnRg9+7dlC9fnnLlyiESiViwYAH9+/dn3rx52Nra0qlTJ874nEUMlKlRjzI167GwTXUUlFWw7zoQTf1v8woTA9Y6uSfkP3HiBCNHjiQxMRFjY2N27dqVq9DUs2dPTp48iaGhITo6OsyYMYPVq1cL2+fOncv06dOpXr06UVFRGBoaMmjQoC8Ka2KxGA8PD3bu3Imvr2+evyNevXpF3759iY6O5tKlS5QtW1bYNnv2bIYNG8aiRYto2rQpNjY2edr3n4SpqSlnzpxh7dq11K5dmzFjxjBy5MjfMn2DsrIytWvXpnbt2kCWd29wcDCXLl3i6NGjjBs3DhkZGUFkc3BwoEKFCgXuifcjrFq1CiMjo2zh2VIKju8NBX3//j0NGzakZcuWTJo0KR8tkyLl2xg6dChDhw7N9reuXbtStWpVdu7cSXBwMF26dKFcuXJ4eHjQoEED5OTkePv2Ldra2ujo6ODg4MCDBw+YOHEiMjIyTJ48GSsrKyArBcajR4++GCYqFot59eqVIJxJhLRHjx5hYmIi5EAbPHgwVlZWv10ERSU9ZQxU5bj6JokwyXzrI5FNTpQ19jLXUKBGMWWpp5oUQFq8QEoeEhgYSEpKCtWrVy9oU/5aAiKTOBue8FevoIjFYpo0acKMGTOws7MraHNyJS4ujvv372Nubo6Ojg5paWnIy8uzaNEivL29OXPmDAApKSkoKiqSmprKmTNniIuLo2HDhnTo0AE1NTU6depExYoVOXHiBO/fv2fGjBmf9ZWZmYm7uztnzpyhQ4cOjB079rcagPytREZGYmFhwZs3b3LMOxcXF8fu3bvZuHEjz549o1evXvTt2zdboYmSJUvi5eUlCAQ5kVuy3G/ld06Wm56ezuDBgwkICMDb2zvP80ft2bOHIUOGMHjwYNzd3bN9TiEhIdStW5ewsDAOHTrErFmzuHnzplR4JqtwhbOzM/Hx8WzatOm3TVKdG2KxmMePH2criBAREUHNmjUFoc3Ozg5FRcXvbnvBggXs37+fmTNnUq9ePdLT0/NNfIyKisLS0pJz585Rvnz5fOlDyreTnJyMlpYW8fHx3/SZx8bG0rBhQ2rWrMmiRYuk72UpvyVisRixWCwszL5//57U1FSKFClChw4dmDRpEpUqVeLff//lzJkzdOzYkQYNGvD69WsMDAz4999/Wb9+PXv27EFLS4tt27Zx/vx5Bg8enC3nqoTk5GQyMzPZuHEj586dw9TUVPBGK1eu3B/nQZ6YlklwdDKRSRmkZGSiKCuD3l8SISQlb/n9liml/DFERERw7Ngx+vTpA/BZaE9euAxL+Q+RSMS4Q35oGX2bt0e6GM6GJwD8UeKaWCzmwIEDOVbxKUgyMzOFe1pGRob79+8za9Ysxo0bR40aNZCXlxdEQU9PT7y8vJg/fz4aGhqsXLmSChUqEBISwsuXL+nUqROGhoZoaGjQtWtWhaHAwEBCQ0NJTU39LBxIRkYGCwsL3r59S7t27aTfq1+Enp4eZmZm+Pr6CvnAxGIxly9fZuPGjRw4cIC6desyceJEGjdu/NlELDIyksjIyK9WSZYky/0WwfxTfudkuUlJSXTp0oXExETOnTuXp/k3Y2JiGDp0KNevX+fIkSNUrVr1s32mTZvG6NGjUVdXp3v37oLn3po1a/LMjj8VExMTTp8+zbp166hTpw6jR49m1KhRv6X3Wk6IRCLMzc0xNzend+/eQJYXxaVLl7h06RLDhw/n3r17VKpUSQgfrVmz5lfDiu7du8eePXsYN26cIJCPGzcOe3t7WrVqlececR4eHnTq1Ekqqv0mPHnyBGNj42/6HiQkJNCsWTMqV64sFdWk/NaIRCJEIpEgsH1cYKlVq1a0adOGNm3acPbsWZo3b06DBg0YO3YsL1++5NmzZ7Rs2RIrKytevHhBSkoKd+/excbGRvBg+3S+Jxm/5+Q59yeiIi9DdX3pgpyUryOVWaX8MEWLFkVGRoaIiIgct0sesm/evPmVZv1yTExMBO8kgF27dqGtrc2FCxfyrI/XCWkA3z3xlohrrxPT8syW/EZGRgZlZeUCG6SKxWIyMzOFZK0f2/VxNcdSpUqhpaVFXFxWHqypU6cydOhQypQpQ3x8PA8ePODo0aO4uLgwdepU0tPTKVOmDElJSWRkZKCvr4+VlRUJCVnip56eHunp6bx69Spbv5KKi8eOHcPW1vaHq5VJyZnExEQg9yIgHTt25NChQ7x+/Zq5c+diYWGBi4sL5cuX58GDBxw4cIDmzZt/NhHz9/cXytmXLFnyizZIkuXKfect/zsny33//j1OTk6oqKhw9OjRPBXVzp07h42NDRoaGgQEBOQoqt26dQtfX99sScRXrVrF6dOnpQni/4+MjAwDBw7E39+f06dPU7NmTe7evVvQZv0w+vr6tGvXjsWLF3Pjxg3evHnDlClTkJOTY968eRgaGlKxYkVWrFhBaurnHqJpaWncuXMHU1NT2rZti5GREenp6URHR6OlpZVNVMuLYI/ExETMzc2ZNWvWT7clJW8IDQ39pkrkSUlJtGrVitKlS7NixQqpqCblt+TT55RkYfhjevXqhZ+fH5UrV8bGxoaGDRty9uxZzp49y+rVq1m5ciX+/v74+flhYGCAn58fIpEIe3t7FBQUePbsWbb7/9OxsxQphQmpsCblh5A8rLt27ZpraM+7d+84duwYK1as4OnTp7/QuoJjy5YtuLq6cuzYsVyTnf8IV94m/fCx6WK4+ubHj/+byWkAIBl4fDz4iIiIwNPTk4EDBzJ06FASEhLQ1tZGUVGR5ORktm/fzoMHD5g3bx6ysrIULVqU0qVLY2RkRL169UhJSSE0NJSSJUuSlpbG27dvKVWqFK9evRKEHT09PWRlZXn06FGOttWqVYuLFy/mareU7+Po0aNYW1szduxY0tLScpwYicViKleuzPr167G0tOTRo0ds3ryZu3fvMnr06C+WdrezsyMmJoZ58+Z9kz2V9JSpZ6gKGemIv+Hz/Z1DvcPDw6lduzZVqlTBy8srzxKyJycnM2rUKLp3786aNWtYuXIlqqqqOe7r4eHB2LFjs23X0NBg+/btDBw4kJcvX+aJTX8DEu81Z2dn6tSpwz///EN6enpBm/XTqKur07BhQ6ZPn865c+eIjo5mzZo1VK5cOdvzXTKekZGRISQkBAsLC2Hbq1ev0NDQwNzcnJiYGC5fvpzr8+J7n8sqKiq4ubmhofF7hnEXRr4lv1pqairt27enaNGirF+/Pl8rJUqR8jN8TfCVLCQXK1aMHj16sHbtWurUqYO2trZQ+VtPTw8DAwNq1aqFrq4u586d48WLF/j5+dG7d2/69evHpUuXhLGs5PsgzTQlpTAifRtI+SEkD+uc8g5JuH37Nj4+PmhpaRWKSldr165l1KhRnDx5kpo1axIWFka9evXQ1dWlSJEidOvWjZiYGGF/SRUea2trNDU16dSpE8nJycL2+fPnY2BggEHx4uzeuilbX+tcWuF/YJvw+83DO1nTt5nw+4RKevjt3cyCVlWZVtucBROGk5Ca5fmUkZHBqFGjKFKkCKampsJqq2QiJakWp66ujpmZGWvXrhXaPX/+PCVKlGDhwoUULVoUAwMDNm3Kbtvvzscv+08HxOnp6Vy+fJnVq1ezatUqVqxYQXp6Ojdv3uTFixe0a9eO4sWLs3DhQtLS0jA1NWXfvn0cO3aMMWPGCLmbzM3Nef/+PQCampoUK1aMkJAQjIyMUFFR4cmTJ5QpU4anT58SFRUFgJaWFkWKFCEpKSmbbTIyMqSlpfHq1SvU1NSArw+WpOSMr68vV65cAaBcuXKcOHGC+/fvs3z5coDPxARJuJmamhpHjx5lw4YN1KhRI9+ufwVNWfaO7Y2eOBFZEZ95sMmJQFaUlVOtWxnN31JUu3fvHvb29vTo0YNFixbl2aQzKCiIKlWq8OzZM4KDg2nSpEmu+968eRN/f38GDhz42bbq1aszbNgwevToIXiDSsm61wcMGMCNGzfw8fGhRo0ahISEFLRZeYqCggLVq1enRo0a2TxMRSIRZ8+eZcyYMbx48UJYFEtPT6dkyZLMnz8fY2Njdu7cSbt27Rg0aBCdO3fm8ePHvHz5UvDKl9zrL1++xMnJiRcvXnzVpj+x2MLfzNeEtbS0NDp37oyioiJbtmyRfn5S/mgkC8mSEFEVFRVkZGQoX748xsbGNGnShFGjRnHu3DmGDRuGj48P0dHRnDx5kri4OHr27Mnp06epUaMGGzdupH379nh7ewttS5FS2JAKa1LyjWrVqjFgwABGjx5N8eLF/2ovm9WrVzNlyhR8fHyoUqUKkCXgTJgwgVevXnHv3j1evHjB1KlTsx3377//cuLECZ48eUJwcDCbN28Gsqr4LViwgNOnT7Pj0i0e+V38bpvuXTyFq9dp3HZfIPjUYdbtOwLA+vXrOX78OEFBQQQEBHDw4MFsxxUtWpSjR48SGxvLpk2bGDFiBAEBAcL2N2/e8OHDB8LDw9m4cSOurq6CiPQz5Mf9kdOKmeRlHxUVxeHDh7l69aoQFrRlyxYmTJjAmzdv2LFjB+fPnycmJoYmTZrQsWNH7t+/z+HDhzlx4gShoaFUrlyZ0NBQAgMD8ff3F/qoVKkSQUFBACgqKmJkZMTVq1cpUqQIYrGYu3fvUrZsWSwsLIQQOVNTU2bMmEHLli0/s/n06dOEh4czbNiwbOcg5eu8fv2aPXv2kJ6ezpUrVzh+/DjR0dGYm5tTvHhxXF1d2bBhA0COeXWMjY0ZM2YMp0+fzndbt27dio5cJv2qmOBaXodaBiqU11aklIY85bUVqWWggmt5HdqaafyW4Z9Xr16lbt26TJ8+Pc8KbGRkZDB37lycnJwYN24ce/bsQVdX94vHTJkyBXd391wTJI8fPx6xWPzN3oSFCWNjY06dOoWLiwuOjo7MmTPnr/Be+xpJSUkcOXKEli1bUrduXeC/54Hk/6tXr1KnTh0mT57M+vXrOXv2LKtWraJ58+bUrVuX8+fPA1nCrpaW1mfelJI8nVJ+X8LCwnIV1jIyMujZsycpKSns2rXriwvLUqQUJLk9Z1JTU3NcUJLkYJOgoKDAsmXLOHv2LB07dqRVq1YUK1aMEydOYGFhQdWqVWnSpImQezYjI4PevXvj7u7OkiVLWLp06Wd9/M1zQClSJEiFNSl5juSBrqysTFJSEitXrgQ+9w76mzh9+jTVq1cXEnlCVg6uhg0boqioiJ6eHiNHjvws79qwYcMoXrw4Ojo6tGjRQhBj/v33X/r06UOFChWIk1Gi3oCx322TY59hKKtromVQAjM7e4KCbgltu7m5UaJECbS1tRk/fny245o1a4a5uTkikYg6derg5OSEr6+vsF1eXp4pU6YgLy9P06ZNUVNT48GDB99lW1xcHOHh4dy9e5eUlBSAbKtm38rH+3963MfJVB8+fCjkLjt16hQjRozA2dmZ9evXM27cOA4dOgRk5TEbPHgw06ZNY8CAASgrK/PixQuio6OZP38+79+/Z+zYsVSqVImAgACMjY2xsLBg3Lhx3Lt3jy5dupCSkkK1atXw8fEBsiZldnZ2FC9eHAB3d3d69OhB8eLFmTBhAiVKlPjM7o8RiUQ0bdoULy8vKlWq9M3XprAjGTzGxMRQqlQp5OTkaNmyJU+fPuX169fCfm3atOH169dcv36d9+/fCx6DH9OtW7fPBOi8Ji0tjdmzZzNlyhTgv2S5LUzUaW+uSQsTdarrq/y2FaiOHTtGy5Yt8fT0pGfPnnnS5pMnT3B0dOTEiRPcuHGDHj16fFWsu3r1Krdv36Zfv3657iMrK8u2bdtYsmQJfn5+eWLr34RIJKJ///7cuHGDc+fOUaNGDe7cuVPQZuUrzZo1Y+rUqTl+zyVjF39/f8aOHYuxsTExMTEsXboUU1NTbty4gbOzM7duZb1j/fz8KFu2LDo6Ojx69Ei4djIyMsL9GxQUxPHjx4XJZk4536T8enIT1jIzM3F2diYyMpJ9+/YViigMKb8/mZmZhIaGsn//fqZNm0a7du1o1qxZjuMYgEePHtG9e3cga4yUm9e2JERUSUmJtm3bMmvWLF68eEFGRgZ16tShS5cugniWlJSEu7s7nTp14ubNmzRq1EgICw0MDOTq1avA3z0HlCJFgvQul5JnSAQBkUjE+/fvWbRoEVOmTOHIkSNcunQJ4K8NvVm9ejUPHz6kX79+wnV4+/YtnTt3Fqo/du/eXQj7k1CsWDHhZxUVFeLj44GsvC5GRkYApGRkom2QXXz5FtR0/8t9J6+knGPbQLafAY4fP0716tXR0dFBS0sLb2/vbHbr6upm8+z52O5vpXPnzgwfPpz169ezcuVKDh48SN++fXF1df0uL5ePV9lEIhFpaWlCMQGRSMTWrVupUaMGzs7OzJ8/H39/f4yNjbl27RqNGzfmyJEjNG3alKNHj/LmzRvMzMwED7LKlSujqalJfHw8ISEhnD9/nilTptC6dWsCAgJ4/vw5xYoV4+3bt5QsWZJZs2ZhZ2eHi4sLVatWZc6cOUJ582bNmjF69GgASpQoIVRMyklIlHqj/RySiaokRKdcuXJC2fdy5coxYsQIypYtC/wX+rl06VLGjRtH/fr1s4luEgwMDIiKisrXXJHbt2/HxMSEWrVq5Vsf+cXmzZtxdnYWvk8/i1gsxtPTk6pVq9KmTRt8fHy+WgBCgoeHB5MmTUJRUfGL+xkZGbFq1Sq6du1KbGzsT9v8N2JsbMzJkyfp378/devWZfbs2X+t91pSUhIvX74UvCE/9a6QhHVaW1uTmZnJpUuX0NHR4eDBg1SqVIm5c+dy/PhxIGshp2LFikBWSoxJkyZRtmxZJk+eLAhoCxcu5OnTp8Jkc9u2bcyePftXnKqUj0hIy+Ta20QOP41jT9gHxm7z5p2GIYlp/33+YrEYV1dXHj9+zKFDh367iuVSCgfR0dFcuHCB5cuX4+LiQrVq1dDQ0KBBgwZs2rSJ1NRUOnbsyIIFC3K8R8ViMeXLlyc+Pp79+/ezdetWbt68KWz7mE9DRCErfc3ChQupUaMG9erV49mzZwCMGjWKwMBANm3ahL+/P0uXLhXev0uWLGHq1KmMGTOGnTt35uflkSLlt+DPqKsu5Y9AIggcPnyYI0eOoKmpScOGDdHR0cHNzY3Tp0+jo6NTwFbmD/r6+vj4+FCnTh0GDx7M6tWrcXd3RyQScfv2bWEAPmTIkG9qz8DAQBjIK8rKEPMme6JtBWUVUpP/W5GKe5dzZdaPkeRrMjAwyJa4++M8MCkpKbRr146tW7fSqlUr5OXlad26dZ6Gr7x58waRSMScOXPw9/fn/v37nDp1isjISJSVlQkMDMTMzAxNTc0vtpOQkMDNmzd5/fo15cqV48CBA6xcuZJOnToxefJkEhMTefz4MQcPHkRfX5++ffsyefJk9u7di42NjTCBqlWrFj4+PmRmZqKqqkpQUBDNmjXDyMgIHx8f7O3tad68OZqamowePZqXL19SqlQpXrx4gYaGBl27dsXMzAxVVVVGjhwp2NetW7ds9mZmZn62YicV0fKGj70TJdd47969BAUFMWHCBEJCQrh58yZ2dnbZvP4kAnHbtm3ZuHEjZ86cyfEZJRKJcHd359ChQ7i5ueW5/enp6cyaNYv169fnedv5iSSccvXq1Zw/fz5b0vcfJSIigv79+/P06VPOnTtHhQoVvvlYX19fQkND6dOnzzft365dO06ePMmQIUPYunXrj5r8VyMSiXBxcaFRo0b069eP/fv3s2nTpmze2X8DysrKhIWFYWtrC/w30ZQ8t48fP46Ojo4Q/peamkrx4sWFyeKdO3dISUkhMTGRyMhI4btQq1YtGjRogIaGBm3atOHt27eEhITg7e3N3bt3efbsGR4eHvTt2zfH92xO7w0pP8/rhDSuvE3icWwqIj6quK6ozeU3SVx6k4SZhgI19ZWZP3kcgYGBnDp1KtdiKVKk5BWpqak8ePCA4OBgbt++TXBwMMHBwcTGxmJlZYW1tTW2trb06tWLChUqoKWl9U3tSsZJM2fOZNKkSSQkJDBmzBji4uKIjY3FwMDgs5DQT6t9SsQ2AwMD2rZty/v370lISKB3797o6+vTunVrMjMzqV27NsHBwTx58oSWLVtiY2PD8OHD0dHRoVGjRnl9yaRI+W2QCmtS8pSrV6/i6elJ48aNcXBwoHTp0igoKLBz504uXLhAmzZtCtrEfKN48eKCuDZixAji4uLQ1NREU1OT8PBw5s+f/81tdezYkT59+tCzZ080lItydt2CbNsNylQg5Owx7Fp3JzbyDTcObkdNVy/X9kQghJB17NiRpUuX0qxZM1RVVZk7d66wX2pqKikpKejp6SEnJ8fx48c5derUd01uv4Sk+tCRI0cQiUSUKlWK5ORk4uLi0NHRYeLEiWzZsoUSJUowePBgoRjAxyQmJuLp6YmXlxelSpUiISGB169fM3r0aMLCwvjnn3/w9PSkW7duLFq0iJMnT5Kenk7x4sXp0aMHkFUoIDo6GgAbGxvevXuHSCSiSZMmuLq6UrRoUcLCwjA1NSUyMhJ1dXU8PT05fPgwXbt2xdHRUajk1rVr12z2SQYvn06IpJOjvEWykvpxeBVkeYsMGTIENTU12rdvj6qqKtWrV+fWrVsEBwdjbW2dTYgTi8Woqanx5s0bjh8/Trdu3UhPT/8s31qrVq3o3r17vghru3btwsDAIE8rCec3mZmZjBw5Eh8fHy5fvixUEPsZjhw5woABA+jZsye7d+/+qtfZx4jFYiZPnszkyZO/K/fR4sWLqVKlCtu3b/9MDJfyHyVLluTkyZNs3LiRevXqMXz4cMaOHftX5ZmqWLEi//zzD1ZWVoLnqOS5bWlpyZgxY4R9y5Qpw9GjRwkMDMTW1lZ4R/r4+KCrq4uRkRHXrl1jwYIFPHnyhPT0dD58+EBcXBz169dHU1OThQsXcvfuXZSVlSlRogTXrl2jRIkSvH37lszMTAwMDD57b0g86T6dAEv5dgIikzgbnkC6GB7fuMzuSYOYcCIYgMXta9Fq/FzMqtjz6EMqT2JTyShehuPHJ0urt0rJU8RiMa9evcomnt2+fZuHDx9iYmIiiGgDBw7E2toaY2Pjn/rOS54lNjY2eHp6IhKJOH/+PJ07d8bAwAAFBQXat29P3bp1c+xHcrxk2+DBgwHo0KEDI0eOxNvbG39/fzp06ECVKlUYOHAgzZs3Z9CgQaiqqtK9e3euX7+eq7CWkZEhLQYi5Y9HKqxJyVNq1KjBpEmTMDMzEzw/PD098fPzo0uXLty7d49y5cr9tQ/QkiVLcvbsWWrXrk2VKlUICAhAU1OTUqVK0aNHDxYvXvxN7TRp0oThw4dTr149RDIyOPQfR6D3XmF7rW4DeXk3iFkNLTEobUnFJu0JvX7hCy1CUaWsr7uLiwsPHz7E2toaDQ0Nhg0bxvnz55GVlUVdXZ1ly5bRsWNHUlJSaNGiRY7J9L+Fj8ULCTIyMty4cUMo8ACgpKQkuK0PGTKEu3fvsmrVKpSVlXF1df2sPSUlJdLT03n//j1eXl7Ex8dTpUoVIYyzWrVq7NixQ5gEnTp1SgjvlKCjo8O7d+9ISkpCXV2dmJgYQkJCaNCgAdOnT+fs2bM4Ojpy//59YXJva2sreDN86Vw/9ZySknd8ep1FIhFJSUmcOnUKMzMzrKysePXqFWKxmP379wtVdsuUKUPx4sV5+PAh7969y5b8PiMjAzk5OSZMmMDq1avp1q1bjs+mYsWKcfPmzc+O/1kyMjKYOXOmUJ33TyAlJYXevXsTHh6Or6/vN6+Y50Z8fDwjR47k9OnT7N69GwcHh+9u49y5c7x69UoQz78VVVVVduzYgZOTEzVq1MDMzOy7+y4siEQi+vXrh5OTE/3792f//v1s3rz5r/FeGzx4MM7Ozjkm2f40RFsSDjVkyBAiIiLo0qUL06dP5/Lly5QqVQpZWVnWrFlD2bJl2bt3L/v37+fff/9FVVWVM2fOYGRkhKOjI46OjgQEBKCrq0uJEiW4c+cO06dP58GDBygrK7Nw4ULs7e2FMdPH7xXJ81ASjvqt4dJ/IyYmJrx9+xZZWVnk5eWpWbMma9as+SzVxceiWk6M2Hsp2+/pYjBq0J4n6Upo55fxUv56EhISCAkJ+cwLTVZWFmtra6ytrWnQoAEjRozA0tIy18I7eYWenh7v3r3j2LFj9O3bF3t7e4oVK0b//v0JCgpi5MiROY7hc6J58+Y0b96cEydOULJkSRo3bsydO3cICwtj2LBhgpfnwYMHmTBhAvCfiJaWlsbr168pWbKkMO6SeulK+ZORCmtS8hyJaHL37l1mzZpFWFgY06dPR1tbm759+3L16tW/SlT7NO+SqalptvDKjxk1alSux31aMXT8+PFCYYF9j2Op1Oo/bwpVbV2cV+3Jtn+Dgf8VOJgTEJltm/uSdbQ1y1ptlZOTY/HixYLId/z4cYoXLy68QF1dXbMJWh/j6OiYLYw0p/OIjo4mPDwcKyurzzx/JN5E+vr69OrVi7Zt22Y7tkSJEpQoUQIvL6/PxDAJMjIyWFlZCRNgNTU1jI2NhSIIhoaGxMfHIxKJqF+/PkuXLqV79+6Eh4dz/Phxxo8fT5EiRXj48CEfPnxAWVmZ9evXY2NjA2RNmLS1tbl37x5isVgIHfw414Q0pPPXkpGR8ZlnWkJCArNmzeLYsWNUq1aNqKgopk2bRo0aNQgPD6dly5YYGBjw+PFjRo0aRdOmTVmxYgWBgYE0aNBAGDTKycmRlpbGs2fPsLGxydFbDbJyttWvX59jx47lWXJ+yApZ1dHRoX79+nnWZn4SFxdH27ZtUVNT4+TJkz89Abhy5Qo9e/akTp063Lp164e8QiTeah4eHjl+dl/D1tYWd3d3unbtiq+v71/lhZUflCxZkuPHj+Pp6Um9evVwc3Nj3Lhxf8V1y81LMqfFwEGDBjFo0CDi4+OFytgpKSmUL18eVVVVFBQUhCT3Bw8eREZGBmNjY1asWIGdnZ3QztmzZ7GzsyMmJgZPT08MDQ35999/2bRpEytXrsTe3p7jx4/j4eFB69atkZeXp2PHjpiZmZGens62bdtQUVEp1MIaZHm8NmjQgOTkZAYPHszQoUOzFaN4nZD2RVEtN9LFcDY8AQNVud+yGrOU34fMzEyePHkiCGcSIe3ly5dYWFgIXmjNmzfH2toafX39ArN1//79pKen065dO+FvNWvW5M2bN8C3j2slz8bGjRvTuHFjACZPnkzFihUxNTUFwMvLC4DWrVsD/+XAjYyMZPDgwaiqqtK0aVN69eoljK//VgcMKX83UklYSr5w4cIF6tWrR/HixfH19WXQoEE0bdqUjIwMzpw5U9Dm/XHU1FcWcqR9L3IiqFHsv8lvUlIS3t7epKenEx4ezrRp034qRFcsFpORkSGs8u/fv59BgwZl9f3JJLdMmTKcOHGCfv36CTnUPs4rI/l51apV2TxPPl0509fXR05OjidPngBQpEgRHj16JPyclpaGv78/27dvJyUlhc6dOzNlyhSUlZXJyMigW7du/PPPPxQrVozMzEzq169PkSJFAAgODmb16tWEhoYyZswYKleuDPyXzFW6kvbrkZWVFYqi7Nu3j0ePHqGqqkrNmjW5du0aM2fOJDAwkLVr15Kens6VK1fYtGkTM2bMoFWrVixZsoTSpUsjKysrFOL4+H66desWSUlJDB8+/IvCTOvWrYUKsnlBZmYmM2bMYMqUKX+EOPv27VscHR0xMzNj7969PyWqpaamMnHiRNq1a8eCBQvYuHHjD4danTx5kvfv39O5c+cftsfNzQ1tbW2mTZv2w20UJkQiEc7OzgQEBHD58mWqVatGcHBwQZuVb+Q0wcvIyBBCySWeUbNmzaJXr14AdOnShevXr1O/fn0ePnwoTDIfP36Mo6Oj0M7FixepU6cOd+7cQUFBQRDu9fT0UFJSIi4ujvPnz5ORkUGjRo24du2akFri/v37qKurC6JaTt52APfu3SMi4uu5WP8GlJSUaN++PXfv3gWyxM7Ro0djXdqMafUtOTBrNGnJOVdNnNusEqF+Wd7/L+4EsKpXE6bVNmd6w/L0HegqFJ94+vQpIpEoWzEPR0dHNmzYkM9nJ+V34eNiAv3796d69epoaGhQr149PD09SU1NpUOHDhw8eJAPHz4QEBDAli1bGDVqFA0bNiwwUU0yzq5UqRIhISEAvH79mmnTpjFhwgTq1q0LwMuXL7lx48ZX2/vY00zCjBkzmDhxojBGmDNnjuBYIPnOpKenc+nSJQICAujSpQuHDh1i6NChgrAnFdWk/IlIPdak5At16tTB29tb8Pbx9fVl48aNmJub/1CYT2HHQFWeeoaq373aKieCeoaq2VZZxWIxHh4edOrUCWVlZZo1a8b06dO/qb3MzMzP8lqJRKJsL8CqVauydu1aIGvynJGRkW0CrqWlRYsWLYTfJR5CH+eMUVNTyya4JSYmoqqqKqxgSSqW3rlzB1NTU4oUKcKNGzfo0aMH2tratGzZEllZWVRUVJgyZQozZszI9ZwkQplEvJOE50gpGHLyTgsKChIEj7NnzzJq1CimTJlCgwYNmDhxItevX6ddu3a8efMGPz8/6tWrR0REBFeuXOHs2bN07twZOTk5ZsyYkWNRjCpVqmQLT86NZs2aMWzYMJKSkvIkVOPAgQOoqKj8Ecl8w8LCaNSoEd27d8fDw+OnhMC7d+/SvXt3DA0NCQoK+qkJhsRbberUqT81EJeRkWHz5s1UrFiRhg0b/lH57goSIyMjvL292bRpE/Xr12fYsGGMHz/+r/Be+xo53W8fLwLVrVtXmKS+fftWqJ7dsmVLxo8fz5EjR1i7di23bt3in3/+QSwWc+vWLSGNwYcPH9DX1yc6OpqwsDBGjhxJ1apV6devH0eOHOHly5cYGxvTt29fQVjLaeEnOjqaf//9F3V1dUaOHCk8Y+Hv9LZOTExk9+7dVK9eHcjy/n/4KJQhO8+CrDy73Afgs34BjYdO/mI7MjKyNB81A0PLisRGvGLz0M4sWb6SsaNG/IrTkPKbICkm8HEI58fFBKysrKhYsSI9e/b8rmICBYVIJEIsFlO5cmWqV69OmzZtCAwMpHPnzpw6dQoLCwtOnDjBzZs3uXfvHvPmzaN48eJfbVfyTJGEckquw7NnzyhTpgwdO3YE/ltwf/PmDT4+PowbN47WrVtjZmZGq1atMDU15ciRI7Rr1+6bC75JkfK7IBXWpOQ5koFlpUqViIqKwtPTk5CQECpVqkSrVq1QVFTMNdxKSu5U0suayH+ruCYR1STHSVBRUcHf3z/X42JiYsjIyEBXV/czV+ycBu2RkZHs37+fW7ducfLkSXx8fBCLxXz48IHU1FT09LKKKkhetikpKQQGBpKWloaDg4MwAfs0r4JkwJ+QkIC/vz+Ojo5kZmYiKyuLqqoq+vr6vH37FkAIXRWJRGhoaAi/i8XibO2LxeJcJ99/4wTjT+DTz13y+SQkJKCiooJIJGLv3r2YmpqyaNEi5syZw4kTJxg+fDiRkZH4+vpy/fp1IMsjsly5ctjY2ODu7s6bN2/o2bOnMKD7WqXZr1GkSBFsbW05c+ZMNnH4R8jMzGT69OnMmjXrt7/3AgICaN68OVOmTGHgwIE/3E5mZibLly9n5syZzJ49m379+v30uR89epSUlBTat2//U+1Aliesp6cnPXr0ICgo6K+tYp3XiEQi+vbti5OTEy4uLlSrVo1NmzYJ4fWFiY/v54yMDMHTWV9fXxCQe/XqRZ06dXj69CkJCQmYmppSrlw53r9/T0ZGBg8ePMDGxoZly5YxcuRIlJSUePbsGQ0aNADg/fv3aGpqIi8vj7q6OrVr1wZgx44dxMfH07lz52zenxERESQnJ1OtWjUgZ0EwMzPzjy+I0Lp1a+Tk5EhISEBPT4+TJ08iFotZt24dW8768VRRm3QxOPYdzu6JA78qrBla/nf/ahcvSfV2PTnmc04qrP2liMViXr9+nS2EMzg4mIcPH2JsbCzkQhswYIBQTOBPjWCQzNNWrFhBREQEioqKREdH8+jRIw4cOMDr16/R19fHxcXlm0S1j/n0mhgbG3PgwAHgv/DO9PR0bty4QUREBAsWZBVnW758OXXq1KFnz55YWlqyZs0a+vXrJ+RgliANEZXyOyNVNqTkOR8PzPbs2UNISAgdO3ZEQ0OD69evM2XKFKytrbGwsKB58+bSh+R3UElPGQNVOa6+SSLs0zLxZIlpYsBcQ4EaxZS/OR/I1atXefjwIb169WLgwIG0atWKLl26fPa53Lx5k02bNuHn50erVq0YNmwYMTExzJw5k4EDB3Lnzh2UlZXR1tZm0aJFTJw48bO++vbti5mZGUuWLEFDQ4OOHTsycuRIjIyMckyWKicnx759+3B0dBTs0dTUZP78+UL1zTJlynzWj7Qq5+/P06dPGTFiBHPmzMHCwoL09HT27NnD8uXLKVOmDHXr1qVHjx68fv1ayMc3duxYTpw4gb+/PzY2NiQlJXHkyBFiY2MpUqSI8DmvWLHiswFZXtCqVSsOHTr008LakSNHkJWVpVmzZnlkWf7g4+NDly5dWLNmzWc5Eb+Hly9f0rt3bxITE7l69SqlSpX6adsyMzOZMmUK06ZNy7Pvd5MmTWjXrh0uLi7s3bv3jxYafjUlSpTA29ubzZs307BhQ4YMGcKECRMKhfdaTnxpXGNiYoKJiQkA58+fB0BbWxtXV1eGDh2KiooKbdq0oVOnTpw7dw4ZGRmKFy9Oamoqr1+/RkND4zNPz65du/L27VshWbgEXV1dqlSpgr29PTt27CAqKgpDQ0NMTEyEVAd/w/vx4MGDNGjQgIyMDA4dOkSdOnUICgoiMTGR3g1rkfF/L3gxYsQZGV9tL/JZGMcWTib8XhBpyUlkZmRQukLFfD4LKb8CSTGBTytyikQibGxssLKyEqof/4piAr8ayfddRkYGeXl5rly5wu3btwkMDCQ0NJQZM2bQtGlTYf9vLWSQEx/P8ST/R0dHs3v3bqpVq4a6ujqBgYGEhYWxatUqihQpgqGhIYqKinz48IEPHz5w/fp1Hj9+zMCBA7+rWrgUKb8aqbAmJV+QPIT79OlD3bp1KV26NDNmzOD8+fNEREQwe/ZsGjVqRI0aNdDV1f2ph3Zhw0BFnrZm8iSmZRIcnUxkUgYpGZkoysqgpyyLtY4SKvLfN0h+9uwZQUFB9OjRA3Nzc169esWKFSs4ceIEnTt3pnXr1qipqXHhwgVq1qzJihUrcHZ2ZuXKlUIp8OLFiwuDj6pVq2ar8iMJH3358iWhoaFs376dM2fOMGnSJMaPH8/SpUu5evWqsKIuQZKzYfny5QCfebRJ2pX08fE99DdMFP5GPvbiUFVVxcjIiGPHjmFhYUFISAiXLl3Cy8sLsVhM2bJlqVq1KvHx8WRmZpKYmIiKigqampps3bqVzZs3M2nSJObOnYuJiQkrV67MVrlVLBYLXo55RatWrZgzZ85PLQiIxWKmT5/+2+dW2717N0OHDmXPnj0/FRq5c+dO3NzchCT3eeWtfODAAWRkZISEyHnFP//8Q7Vq1diwYQMuLi552vbfjuS937BhQwYMGEDVqlXZtGkTFStWLGjTfks+XgASiUS0atWKVq1akZaWJuzz6tUrwfvvxYsXvHz5Emtr6xzbyymsWk9PT0hQ7u/vT0BAAFWrVmXkyJEsWbKEpKQkQkNDadOmTY4VXiVpGX7nZ9XHyMrK0rZtWwYMGMC1a9eyqqsev8p7Fb3vaufQ7DEUt7Ciy5x1KKqqcWn7GkLPHwMQxMvExETBO1CSG0rK78OnxQQkQtqLFy+wsLAQvNCaNWsmFBP4U+7zvCI5ORkvLy/Kli0rLI4fO3aMq1evUqdOnWzFnn6EnMZJRYsWZeTIkVhYWACwcOFCateuTalSpUhJSeHs2bMUKVKEIkWK0LBhQ4YOHUpkZCQVK1Zk/fr12ao0/w3etlL+HqTCmpR8QfKAU1JSwsLCgsOHD3Py5En27NlDz549SUxMpGPHjqxatYrJkydLH4g/gIq8DNX1VXLc9vFLcNq0aZQtW5b27dsjJyeX4wtSUhU0KiqK0qVLs3fvXszMzOjbty+enp5ERETQpEkTLly4gKamJmfOnOHkyZN07tyZ2NhYqlatyqtXr4T2LC0tadeunfBClfR35MgRmjZtip+fH5qamjRr1gwNDQ3OnTv3magGWeLYl1anPj4P6T30eyMRoj4eZOnq6lKnTh22b9/OqFGjOHfuHOrq6ixfvpyrV6/Stm1bzM3NadiwIQcOHEBJSYlatWqhra3Ny5cvSUpKolOnTrRt2zabV4zkHv80/19eYGZmhoGBAVevXs02uPseJMVDWrZsmae25SXLly9n7ty5nDlzJtdJ/NeIjo7G1dWVW7ducfz4ccE7Ji/IzMzEw8ODuXPn5vl3X1FRkZ07d1K7dm0cHByEwb+Ub6dEiRIcPXqULVu20LBhQ1xdXXF3dxeqZP7tiMViIiMj0dXV/eIz6NMFIMnCw8fPs27dutGtW1ZVcCUlJcqXLy/ck5Lnampqaq7XViLexcTEEBkZSadOnRg8eDDNmzenQ4cOLFy4kJSUFFxdXfH29kZNTY3Y2Fhu3bqFlZWVkCvpYxHwa6kVChKxWMzhw4d5//495cuXx8XFhW2zJ+I4chZqOnp8iHjN29B7lKlZ74vtpCTGo6iqjoKKKhFPHuG3d7OQ2kJPTw9DQ0O8vLwYMGAAW7ZsISws7FecnpRceP/+/Wd50EJCQtDR0RGqcbZv357p06dTunTpQutJ+ymGhoZMnjxZWFRfvnw5Hz584OrVq+zYsYMGDRrky0K1nZ0dYrGY+Ph41NXVadGiBXJycoSGhnLx4kVmzpzJvHnzuHr1KkWLFmX+/Pk0btxYGHtdv36dihUrCs+9T6NUpEgpCKTCmpRfwt27d2nbti0lSpRg9OjRtG/fHicnJ6EapdRj7ef5dOVbgpKSEq9evSI5ORk1NbUcr7OVlRUlSpSgaNGimJqaEhMTQ4MGDWjZsiWpqan4+PhQvnx5Pnz4QK1atahZs6YQyikWizE2Nuby5ctCe7a2ttmqZUno2LEjKSkpBAcHU6RIEUJCQti2bRvFihUDPr8PpPfFn4tYLObcuXNcvHiRqVOnCvfm0aNH2bx5M82aNaNdu3ZUr14dLy8vAgMD0dHRYdmyZUyaNAkPDw9hQte9e3dUVVWZMWMGcXFxuLq6MmfOHO7fv0+lSpWQl5cXvBs/LXyQH0jCQX9EWJN4q02aNOm3HASKxWImTpzIvn37uHTpkhCu9r2cPn2avn370q5dO27evJnnoTT//vsvampq2cJV8pJy5coxa9YsunTpwrVr16ThJz+ASCSid+/eNGzYkP79+1O1alWhQMTfzo4dO1iyZAl+fn7fdVxu+c8kzwpDQ0MGDBggbJP83dvbm7CwMHr27CmIPxIkz8N79+6hoKAgPLfevn2Lubk5PXr0IDk5mbp16/LhwwcSExNZsGCBIFRYW1uzbt06ZGRkiIyMRE9PL1dBsCCfaS1atBAqSBsbG7NlyxbKly/P3LlzGTBuMqt7NSEh5h0aegZU69D7q8Ja0+FTOTBrFBe3LKd4WSsqOrUm4tYVYfv69esZPHgw7u7uODs7U7Nmzfw+RSlAWloaDx48+MwLLSYmRhDQKlasSI8ePbIJw1Jyx9LSktOnTxMXF4eioiIyMjK0atVKEL8kaVfy+vstEolQU1Nj9erVQFYxs6NHjyISiShXrhydO3cmODiYoKAgunXrhp+fHy4uLiQnJzN//nzU1NQwMzNjyJAhaGtrA2QbC0qR8quRCmtS8hXJA7lx48b06dOHPn360LRpU65evUpERAT29va8evVKSI4pFVJyJy4uDnV1dcRicbYQSAmS35OSkrhz5w46OjqYm5tTqlQpQkJCyMzMJCoqCh8fH6pUqYK5ublwrLa2tvBSMjAwwMzMjKJFiwJZq0rbtm3DyMgIY2Nj9PT0sLe3B+DatWtUqFCBokWLCqXtgVxXA3V1dYEsD7kLFy7wzz//EBERwdixY3M8Z+m98GciGYDJyMhw+fJloqOj0dHRYdGiRQQGBjJu3Dg2bdrExYsXWbVqFXZ2duzdu5dhw4axZs0aLC0t0dLSIjg4mIULFzJ79my6dOlC/fr1KVKkCL6+vtjb22NkZCT0+SsHUa1bt6ZTp07Mmzfvu+/RU6dOER8fL4Rm/U6kp6czYMAA7ty5w6VLlz6boH8LiYmJjB8/ngMHDrBp0yYh4Xpe2zl16lSWLVuWr88IFxcXTpw4wYQJE1i0aFG+9fO3Y2hoyNGjR9m6dStOTk6CGPEne68lpGVyOzqZiI9SMRT9fyoGcWoS48ePZ9euXXnyXPq4jU/DMiX/t27dmoSEhBzzSkrGVbdv30ZPT094D/v5+Qkh3ufPn6dy5cpCpb4DBw5w48YNkpKSWL58OXfu3EFdXZ2KFSsyduxY7ty5Q/v27YXF0Y8FwcDAQNatW8esWbOyFQDJz/Hd06dPc92mpKTEqgVzqdBnHBmfFH4yq2LPhBPBwu/jjgUIP5tWrsnI/VeF32VF4Fr+v/Np0qQJT548+Xnj/xIk7/19+/Zx7Ngxhg0b9lMiuqSYwKdeaB8XE7CysqJ///5/fDGB3wFPT086depEYmIiO3fu5OLFi9y7dw8HBwdWrlyJjIxMvnyHPxbs5OTkaNasGTVq1ADAwcGB58+f06FDBzp06IC3tzfVq1fnwIEDREZG0rhxYwICAujfvz/Lli1DTU0NdXV1QDqflFIwSIU1KfmKZJWjYsWKNGjQgOnTpzN9+nRmzJgBwNatWxk4cCArV66kT58+BWzt70N8fDzJycloa2sjKyvL/Pnz0dHRwdnZOVsugdjYWJSUlFBQUMDb25vly5dTtGhRAgICsLGxYfHixbRr145WrVohJydHVFQUGhoawsD6YyQvIWNjYyBLAKhevTolSpQgKCgIc3NzXF1d+eeff9i9ezdv3rxBRUWFffv2YWdnx/z584WwlI9FNclL08fHh/Pnz/Pu3TtcXFyYM2cOUVFRFClSRNhX+hL8O5AMwOLj49HR0RHChuPi4ujYsSNBQUFCvr60tDRq167NP//8g7q6OsOHD2f58uWMGTOGxMRE2rdvL6w4v3nzhtq1a1O6dGkGDx78Q8JPXmBra0tKSgr37t3D0tLym48Ti8VMmzbtt/RWS0xMpFOnTqSnp3P27NnPEqB/Czdv3qR79+7Y2toSHBwsiPV5zc6dO9HT06Nhw4b50r4EkUjE+vXrsbW1xcnJicaNG+drf38zIpGIXr160aBBAwYMGICdnR2bN2/OlhPxT+B1QhpX3ibxOIfiQQ9jwPd1ImmvHtOwfVdhASov+dI7MrfvrORZ8/DhQ3R1dYXqyAEBAYwZMwbI8mbT09MjJSWF58+fY2VlRZcuXXj+/Dnp6enUqFEDWVlZihUrRo0aNVBUVGTlypWIRCKmTp1KhQoVWLx4MUWKFOHp06eEh4d/VlVXUuFZUr3zV6IqL4OZhgKPPqT+cBvmGgrfnb/2b+Zj4cLV1RVHR0c6dOiAqqqqkLdLU1MTU1PTr7aVmJhISEjIZ15oIpFIyINWr1493NzcsLS0REUl5zQoUr4fybh96dKlpKen0759ewwMDOjZsyctWrSgR48ezJs3j/Hjx+eLWPXpWKhcuXLCzxYWFri7uzNo0CAaNGhA06ZNCQ0N5caNG3Tt2hVnZ2ecnZ0xMzNj6dKl+Pv7U7lyZTw8PFBVVSUwMBB3d3cOHjwo9TqX8kuQCmtS8h3JQ3j+/Pncv38fLS0t/Pz8GDlyJOnp6fTr14979+5x9+5dLC0tC12VUEmCdYlgJhKJuH79Ojdv3hQGvRKRAbLc4NetW8eSJUswMDCgadOmjB8/npSUFM6fP09UVBSqqqq4uLiwf/9+BgwYIAxiixQpQpMmTXK0Q/I5KSoqoq2tzbFjx4iOjubSpUuMGTMGGRkZqlatyty5c3n9+jVly5bNlig5N5FDRkaGxMREunfvzrRp00hOTqZbt27o6enh5OTEmDFjkJOT++2EBik/zsGDB3F1dcXJyYnw8HB8fX3p3Lkzp0+f5tixYzg5OXH48GHBa9LCwgJNTU02bNjAsGHDcHBwICIiQkjYLaF8+fL4+voWmKAmQZJk/ODBg98lrJ09e5Z3797RsWPHfLTu+3n37h0tWrSgVKlSbNy48btzz6Snp/PPP/+wbNkyli5dSpcuXfLJ0qzn37Rp09iwYcMvEeJ1dXXZunUrXbt2JTAwMMfk8FK+HUNDQ44cOcK2bdto1KgRAwcOZNKkSX+E91pAZBJnwxOyiWkfI/m7TDFTyvUaTUBkEpX0fp9qgvPnzycmJgY1NTXevXtHfHy8EL54+/ZtbG1tsbW1xc3NjYMHD2JoaEhaWhr379/HysoKFxcXOnfujKOjI6ampvz77788fPiQoKAgunfvzsGDB3FxceHJkydkZGSwYsUKLC0tcXR0FMYBz549E8YjkpCtX5V4vKa+Mk9iU3P9/L6EnAhqFPt9PstfhSTsUktLixIlSmQbn3/8menp6REaGgpA48aNKVOmDEuWLCE0NBRTU9PPBJn09HRmz54tCGkvX76kbNmyghdaYS4m8KuRfJ5Fixbl0qVLaGtrs3HjRmF7vXr1uH79OvDrwyuHDBlClSpVmD9/PpcuXWL9+vVcvHgROTk57OzsAFi7di3p6enMmDGDd+/e0bx5c4YNG4aqqirz58/HxsYGRUVF0tPThUgKKVLyC+ndJSXf+filaGFhQf/+/enWrRsdO3bEz8+PZcuWUaJECQYNGgR8uUT938CdO3eYPXs2cXFxAEKC9Y9zQ8XHxzNv3jzc3Nxwd3cnKipK8CR78uQJXl5e+Pn5cezYMfbu3cvx48extbWlWLFiQi6jihUr8uLFC1JSUr7ZNslAt2LFigwcOJCaNWuyaNEiXF1dkZeXRywWU7p0aWrXrv3ZBFMSovIxfn5+PHv2jPPnz9OhQwf69+/P/PnzCQ4Oxs3NjcDAQOmL7g8kIyMj122pqals376d/fv3s2nTJgYMGMDjx4958+YNVapUoWXLlsycORNzc3OuXbvGli1bKFKkCB07dhRWKg0MDARRLSMjQ7i3ZGVlC1xUk9C6dWsOHjz4XcdIcqv9Ts+4Fy9e4ODgQK1atdi8efN3i2qhoaE4ODhw4cIFAgIC8lVUgywv55IlS+Lo6Jiv/XyMo6Mjffr0oXfv3sIzUsqPIxKJ6NmzJ0FBQQQGBlKlShUCAgK+fmAB8jVRLRsiERliEWfDEwiITMp3274VkUgkeJHq6uri7++PlpYWqampWFlZYWFhgbKyMo0aNWLDhg08ePAAeXl5wePI399fCO1++PAhlSpVEnIcRkZGCs/pnTt3oqqqilgsZvz48Rw6dAjIGvu0atVKsEfy7o+MjGTcuHGcPn06X8/fQFWeeoaqyH2nTiMngnqGqhio/P3J7sViMRkZGcJzLioqinv37gkLu5J318OHDzl//rzwd0tLS96/fy+0o6SkhKKiolAp9dPxYUZGBvLy8rRv354DBw7w4cMHAgMD2bJlC6NHj8bJyYlixYpJRbVfjEgkIiQkBMiqRnz+/Hn+/fdfYUH+S2O/vEZyz1SvXp19+/axcOFCEhISuH79Ovr6+oK3s2RRT15engcPHlC2bFlkZWUJCwvj0qVLVKhQgVevXmVbwI+MjPxl5yGlcCH1WJPyy7G0tGT8+PGYmZkBkJCQgLKy8g9X2PudySkfmoyMDEePHqV79+6oq6sTHByMj48Pt27dwsnJia5du/LkyRNSU1MxNjbG0dGRIkWKoKWlxatXr3j8+DHm5uaoqamhoKBA//79uXbtGhUrVqR48eKEhIRgZWWFrq4uT548ISIiAiMjI2HF8MaNGzx8+BAnJ6dsYZgS24Bcw3Ilg5yc3MFzGgCtWrWKp0+fYmxsTEZGBrdu3aJcuXIoKCjQtm1b2rZtm2t7Un5fJIPrU6dOUaJECcFrSywWo6CgwKtXr3j9+jWQtXp98+ZNjhw5wsiRI5kyZQqdO3cmKSmJp0+f0qVLF8RicbYJV059/W7Url2b0NBQwsPDMTQ0/Or+Fy5cIDw8PN+Fp+8hJCSEJk2aMHz4cEaOHPldx4rFYtavX8/EiROZMmUKrq6u+S6Qp6amMmPGDLZv356v/eTE1KlTcXBwYPny5bi5uf3y/v9GihcvzuHDh/Hy8qJx48a/hffapUuXGDt2LCEhIcjKylKuXDkmzVlAiEbp7/Z0ShfD2fAEDFTlfjtR5uN3roKCAiNGjBC2DR48mClTptCzZ08UFRWZP38+5ubmvHnzhkqVKgFZYfmysrKUKVMGgOfPn2Nvb8+jR49QVFRk8uTJWFlZERUVxfXr12nTpg0TJkzAwcGBqVOnEhwczIEDByhVqhRaWlokJiZ+9qz/2JM/r5B4EH6rSCoR1X4nz8O8QBIp8Wmxn08raRsYGFC/fn3evXtHQkICkydPJiMjg5s3byIvLy94E7Vu3TqbJ7a+vj56enpcunSJFi1afFZAQFFRkQkTJuT7eUr5Puzt7bG1taVJkybo6emhrKxM06ZNhQrmknvjV4zZJe1L7lNJCPvUqVNJSEgAsuYYCgoKtG7dmoyMDK5duyZUbnd1dUVHR4fnz59TvXp1Ro0aJby7b9y4wfPnz+nRo4c0rFhKniJ1E5Hyy5CsgA0fPhwzMzPS09N58uQJixcvZvfu3Tg5OWXbPycPqN+RJ0+e8OHDB+Dz1ZyPq2RJ9ilTpgwqKirCism+fftITU2ld+/e7N69m3PnzuHm5oa5uTktWrQQBrLa2tqEhYURHx+Pqakp4eHhAKSkpJCSkoKmpibGxsbCyr+WlhZRUVFCUl/J9VdXVxeSw3+JzMxMMjMzc/wcvvWFumXLFlauXEmZMmWIiopi0qRJrFy5kuDgYOF6fE97Un4tmZmZOa5Qrl+/Hjs7O2bPns27d++ECrAikYiMjAzat2/PyZMngawQkYiICPbv34+BgQGbN2+mVatW9OnTh1u3bjF+/Phsgu2fgry8PE2bNuXw4cPftP/06dOZOHHiL88tlBuXL1+mXr16zJkz57tFtTdv3tCiRYv/sXfWYVGl7x++h5YQxABUbAQkBEFExO7C7lZsxe5WzLW7u3vtLhRbQBAURVQUJUWQhjm/P/jN+YLorrq0c1/XXi4zJ55z5sTzft4n2LRpE7du3WLkyJE5EnW6fft2TExMsqV21b+hrKzM/v37cXV1xdPTM8f3X1CRSCT06tULLy8vPD09sbW15fHjx7liS3R0NK1atWLkyJFERkby4cMHZs2ahe9X4bfSByFNXLv7Ke9Ercn4tvt2+mdvyZIl2bp1K/fv3+f06dNiU4NmzZpRqFAhIiIieP78OVpaWqioqBAYGCiKbP7+/mhqamJhYUFycjLW1tZidH5ERATt27fn48ePDBs2jPj4eG7fvs369etRU1PD1NSUyMhIXrx4AWTu8PxtNNXvUq14IXpU1sZQJZWUxIRMEWxKkrRGBZW1VehRWTvfimoy8UF2ztKfN5mAlv78BgYG8ujRI0aOHEmfPn348OEDUqmUrVu3snjxYjQ0NAgKCuLTp0/cvn2bq1evsnnzZhISEjKJ4YqKiowcORIfHx8qVaokRq7JybvIro+lS5cya9YsnJ2dmTZtGiNHjuSvv/5CQUGBrVu35rhd3/oW+vr6YhmR58+fM336dCBtUuTDhw/Uq1ePDx8+cP78ebZt28bUqVPZuXMnAQEBQFqjk7p164qi2o/GOXLk/A5yYU1OjpH+4ZicnMzRo0cZPXo0QUFBHDx4kPLly3P//n2eP38O5E2xRRAEdu3aRd++fYmJiSEhIYGxY8fi4eEB/G82JzAwkBcvXhAaGkrPnj2pVq0aXbt25e7duygpKYkiGcDUqVOpUaMG9+7d4+7du5w5cwaASpUqceHCBXHfVapU4dGjR9StWxd/f3/OnTsHwIsXL9DS0kJdXZ0SJUrw8OFDIK0eVe/evTExMclgm7GxMVZWVv86wJelafzu7yBL4TM3N2f69OlcuHCBMWPG8PLlS5ydnVm8ePFvbVdO9iNzMhQUFFBUVCQ1NZWgoCDx+4CAAKZOncqNGzeoXbt2hmtJUVGRdu3acfr0ac6cOcOuXbtQVlZGX19fvOa7detG27ZtgYypnnnxnv8n2rRpI6Y5/RO3b9/m9evX9OzZMwes+ndOnTpFu3bt2LNnDz169PildU+cOIGVlRXW1tbcvXs3Q6Hh7CQhIYH58+czd+7cHNnf96hQoQIrVqygW7duYgqUnKzBwMCAv//+m0mTJtGiRQumT5/+S2UMsgJ/f38g7fmkqKhIoUKFqFW/EdKSlYkICmTLoHbMrV+ZeQ2MOThtCPExaZNDt3atZe/4vhm2dWrJFE7/NRWAg3t2YmJiipaWFhUqVGDTpk05elz/xrdRYbIJFUEQ0NbWRklJCXNzc3bu3AmAuro6jo6OYkfRs2fPoq2tjbKyMnfu3BGjlaVSKY8ePaJMmTJ8/vyZuLg4ypcvz549e7C1tWXRokVs2LCBoKAgNDQ00NPTw9vbm2nTpmFpacnAgQP5+vVrBjtlZTP+KwbqynQ1KUYnvVQcDdQxK6JKpcLKmBVRxdFAneFmurSvUDjPRRr+E+mFM3d3d7FGr+ycyc5bcnIyr1+/Zv78+UyaNImUlBQEQaB9+/YsWbIEIyMjqlWrRrt27UhISMDExAQlJSVSU1OpUKEC9vb2fP36FQUFBYoWLcrTp2ldVb8VJwICAqhQoQJ37txBX19fLl7kcWTXh66uLvb29tSpU4cjR45QvHhxDhw4wMiRI8V7WyKR5Infc/Xq1fTq1QsANzc3BEGgQYMGrFy5kjp16mBjYwMgPpsgbcJ/2rRpqKmpERERIY5zZFGccuT8F+TCmpxcQVlZGXd3d9q1a8fGjRtZtmwZHTp0YPv27XTu3FkcrOa1h1xycjJr166lQYMGqKuro6amRqlSpQgNDQXSUihHjBjBgAED8PDwYOfOnbRr144nT57Qt29fFi1axIcPHzA3NxcFxOvXr7N8+XIMDAwYM2YM0dHRxMXFYWNjg5+fn7hvOzs7Ll++TPHixRk8eDA3btygXLlyREREiIPjWbNmsWzZMgAMDQ1p0KDBd2tS5cQLUTYbeuDAAdavX8/OnTspWrQo69ev5+zZs3Tt2hXIe7/xn0r6qAXZIOvDhw/0798fc3Nz5s+fL4qhysrKzJkzh6lTpzJ69Gj2798vCg0pKSmUK1eObdu2cf78eY4cOYKLiws7d+4URV743+/+7ax5fqJZs2a4u7tniL78HvPmzWPq1Km/XL8sO9i6dSuDBw8Wm0j8LNHR0fTr148JEyZw4sQJ5s2bl6Mpe1u2bMHKygo7O7sc2+f36NmzJzY2Nr8c5Sfn35FIJPTo0QNPT0+8vb1zPHqtcuXKKCoq0qdPH86fP8/nz5/xjkxAQtrzsV7/UUy96M2YY3f48ukDVzYuAcCqRUf83a+LQltqSgpPL56kWssuAGjpFsN11xGio6PZsWMHY8aMydM15WQTKumfy+kjlwsVKkTz5s1p2rQpAK1atWLlypUkJSUhkUhQUlJiypQpjBw5koCAAAYMGMCNGzcwNTVFQUGByMhIatSoAcDHjx+pU6cO5cuXR0FBASsrK/bu3cvDhw/R0tIS/aQzZ87QokULJkyYwKVLlzLZHB0dzenTp0VfTBZF/U8oKipiXL4s9nrqtC6nRceK2rQup4W9nnqe7v4pi7BJPykFGSevHRwcWLduHQAvX75k69at9OrVi/LlyzN79mz27t2LRCLh5cuXrFy5EolEgrW1Ndra2ri4uDBq1CgEQeDu3bvo6+uTmJhIeHg4hoaGREdHi9FwZmZmYmF72TtdZlNYWBhFihQRa6/l1/f8n4ZEIiEwMFAsW3P27FmePHnCqlWr8PT0FMcYuc2319vQoUMZNGgQ4eHhrFq1ipIlS4rLzp8/ny5duhASEkJoaCj169cnNjaWZs2asXfvXvz8/DJkGMmR87vIryA5OY7MQVuxYgV9+/bF1dWV4OBgzp8/z6ZNm1i9erWYXqWgoJAnZkVkhIeHo6urS+3atcUIMAcHB4oWLSou4+vry7Fjx+jatStPnjxh5syZNG3alPnz56OlpYWqqipGRka8ffuWlJQU7ty5Q4kSJejTpw/VqlXj+vXrBAcH06JFC968eUODBg2YM2cOLVu2REtLC0jr0rN582YCAwM5cOAA5cqVA9LSRdO3lP7RuctuB0f2Gx85coRDhw7x+PFjli9fzurVqxk6dCiXLl3CwsICyPkuQ3L+R/rrQxa1EBsby+rVq1m5ciWRkZE4ODjw9OlTunTpwty5cwkNDWXWrFksWLCA1q1bU6RIEc6fP8+tW7eA/0VGNm3alDVr1nDmzBlx1jC9iFoQfnctLS1q167N+fPnf7jMvXv3eP78OX369MlByzIjCAKurq4sWLCAmzdvih21foZbt25RtWpVVFRU8PT0pGbNmtloaWbi4uJYuHBhrkarpWf9+vVcvnyZ48eP57YpBRIDAwNOnjzJ5MmTczR6rXDhwty+fRuJRMLAgQMpXrw4Lj068jk8lGJlKmBkXw8lFVU0ixTDsedQAp+4p61XXJ/y1ezxvpzmt/i7X0NDR5dSVdIasBg5NkFVrywSiYS6devSpEkT3Nzcsv14spLv1T+TUa5cOapXr46Kigrz58/n5MmTlC9fnpIlSzJnzhyxs27lypXR0NDg06dPREZGAmkNDWJiYrCwsMDX15fJkydTs2ZN7O3tuXnzJn5+frx79445c+awaNEiDAwMOHDgQCb73Nzc2LVrFyVKlADSCq+PHj1ajLDJS37kryCLHvz23fltKuf79+85cOAAjx49QiqVkpCQgJmZGYGBgQQFBbFo0SIaNmxIYGAg58+fJyQkRJwYe/78uViLt1KlSmLqbtWqVfHy8sLQ0BA1NTXevXuHsbEx4eHhREdHA2litCzLQnaOZf+am5sTHx8vj+7Nh5QvXx4/Pz+uX7+OnZ0dsbGxPH/+HA0NDbEmWW4LpTIfUmZH0aJFsbCwQEVFhQULFhAdHc3IkSOZMWMGkZGRTJgwgaNHj6Kurk6TJk3EWq2BgYF07NiR4cOHZyp9Ip/4l/Or5I1iL3L+KGQOWmJiIklJSTx+/JjZs2eL4pSxsTHu7u4sWrQoQ/2lvICysjIVKlTg3r17lC9fHqlUSvfu3cXvq1WrRkxMDEWKFOHjx48YGhpSpEgRli5dioaGhricqakpBw8eJCIigurVq7No0SKmT5+Ot7c3JiYmvHz5kubNmzN79mw+fPiAvb09BgYGHDp0SNyGrLvXPxX4za1zJ3vh7d69m0WLFnH27FmKFSuGo6MjU6ZMoWrVqmLodV76ff8UUlNTM6X5Jicnc/DgQc6dO4euri5Dhw7FxMQEBQUFsbGAoaEhCxYsYOXKlWI3uJCQEHx9fUWxJf02FRQURMekoHZ/laWDyiIwv2XevHlMnjw5Vwuyp6amMmrUKG7fvs2dO3cwMDD4qfUSExOZOXMme/bsYfPmzbRq1SqbLf0+GzZswN7eXuwCltsULlyYffv20aZNG+zs7ChdunRum1TgkEWvNWzYkCFDhmBjY8POnTuxtbXN1v2ampqKKY/Pnz+nZadunFk6nVbjXTnz1zTeeNwjMe4rglRKocI64nrVWnfl/pEd2LXvhee5I1i37CR+9+LOFbZvXUafdwFIpVLi4uLEiaX8Svpn+bfvcUNDQwYNGpRh+aFDh4qdzRs0aMCOHTtQUFDg77//RkFBgWrVqtGxY0fKly+Ph4cH9+7dY/Xq1Whra6OoqIi6ujo3btzIECkq229wcDBjxowhIiKC4cOH4+zsjKWlJStXrhSXTV8IPasbImQFsjS0b6NmvvfO9PDw4PPnz5w7d447d+4wf/58zp07h7+/P1+/fmXOnDnUrl2bsmXL8vz5c6ysrDA2Nhab7NjZ2Yn+tq6uLmpqagQEBFC1alV27NhBu3bt0NLSQkdHh7CwMLHJ1dOnT3F0dCQkJERM0XV2dubly5cAYkkIBQUFYmNjGTx4MPHx8b80iSMnbyAIAsbGxsTHxxMYGIi3tzfu7u7o6OjkmZIWP0JbW5vx48cTEhLCvHnzKFu2LGfOnOHt27f4+fnRuHFjUlNTOXToENOnT6dNmzYMGDCACRMmEBoaioGBgfhskd1/8rGKnJ+l4I1y5OQLvn79yl9//UXhwoUJCQkhIiICiUQiPvQAateuLS6fV2YbdXR0xG6b6ZGlHVSrVo23b98CaUXb7ezs8PT0RENDg+TkZG7evMmDBw8wNjbmzZs3hIWF4eTkRO/evdHQ0GDy5MkZWlvb2dnRrl07cSD8vdmT/1IHLbuQSCR8/fqVsmXLUq5cOS5dusT48eNp3bo1RkZGWFpa5raJfxw+Pj4cPXoU+F/6ZWBgICtXruT9+/coKysjCAJPnz6lR48emJubo6SkxNWrV7GysuLcuXMsXrxYFHdPnDiBjY0NW7dupUuXLmLHpm8pqIKaDCcnJy5cuEBSUlKm7x49eoSXlxf9+/fPBcvSSExMpGvXrjx79oybN2/+tKjm7e2NnZ0d/v7+eHl55ZqoJntXzJkzJ1f2/yPs7e1xcXGhV69e323wISdr0NfX58SJE0ybNo2WLVsyderUHKu9ZmJiQqOOPQgJeM6ltfNBImHU4VvMdguks+uGDH5JlXrN+fjSl0+v/Hjudgmr5h0BSElKZN+E/rQf5EJISAhRUVG0aNEiz/g0WcH3/I9v0xSbNGmCsbExAN27d2fixImEhIRgZ2eHnZ0dqamplC5dWnyPXLp0iZiYGMqVK0epUqVYv349Hz9+ZMqUKWK6p2y/JUqUoGfPnvTt25dOnTrx9etXVq5cycyZM0W/S1Yj9Hv+ksxWWRRWdpGQkCCekx81E/hWsLx06RIjRoygd+/eYsrl/PnzmTJlCvb29vTo0YPhw4dTpUoVTp06hYmJiRhJW7lyZTw8PChSpAjGxsaEhIQAYGNjI9ZFK1q0KEWKFOHly5dYW1sTGBjIunXr6NixI56enowePZqUlBRatGiBubk5lStXZvv27eIkh6mpqdgxMj0aGhr079+fLVu2iJkWcvIPsnvk7t27LF++nHPnzlGrVi1WrVpFbGxsno7kkqVK6+npsXbtWpydnSlRogQnT54kKSmJxo0bs2PHDipVqkS9evWAtIhPb29vihcvTkBAAIsXL2bAgAFcu3YN+N/5KEjPbTnZQ8Ed7cjJ02hqauLn58fatWvZtWsXhw4dombNmtSqVYvOnTvj5+dHrVq1uH//PpA7kVffK2T56NEjfHx8xNpEMidINlNnZWXF58+fiY6ORklJiXbt2tGkSRNq166Nra2tmNKirq7OiRMnqFKlCgBDhgxhypQp1KxZM1Pr5/yaPqepqcmCBQtQVVXF0dGR9u3bs379enx9fXFwcAByP5T8T0EqlXL79m1OnTrFly9fiI6OZvDgwXTr1o23b9+yZs0azp49S/PmzXFwcOD9+/dAWlcxb29vypQpI6YChISEiE00zpw5w5kzZ2jfvn0uH2Huoa+vj6mpKTdu3Mj0naurK5MmTcqQnp2TfPnyhWbNmgFw4cKFH4qf6ZFKpSxbtowGDRowZswYjh8//t06jTnF2rVrqVevXp6M8Jk8eTKCIMgbsWQzEomEbt264eXlhZ+fH9WqVROb9GQlz58/Z9myZeLzLygoiLtnj1HWwobEuK+oqmugplmYL6Efcdu9NsO6yqpqWDRqzaGpQyhtVg0dg7QoxtTkZFKSEiljUAIlJSXOnz//3RphBY1va7Sl92OUlJTEiPy5c+cya9YsFBUVGTx4MNevX8fe3h4/Pz9SUlKoUqUKHh4emJmZsXDhQvbt28ejR4+A/w1yg4OD8ff3p0WLFtSrVw8zMzNevXpFlSpVeP/+PXPmzGHkyJGYm5szcOBAIiIiMtmamJjI1KlT2b9/f4ZtZxU3btxgypQp4qRs+gmnxMREgoODWbhwIWPGjMHb2xtIm9y4evUqrVu3plWrVhw8eBAfHx/atGmDpqYmHTt2pEuXLjRp0kQU9x0dHcW6dBYWFnh7e6OmpkaRIkXETvI2NjbiPgoXLoyqqiovXrzAwMCAokWLUr16dbp06cKWLVvQ09OjUKFCtG7dmho1aiCRSNDU1MxwbD+aWGjVqpU8mjefY2BggKmpKZs3b8bIyIiGDRvSqlUrnJ2d2bFjB/Dj3z+3kInn347fBg8ezMiRI5FIJGzevJmOHTuKou/SpUvp168fAQEBTJs2jeDgYOrXr8+MGTNYv369uA3ZMy2vHbOcvIM8FVROrrFx40Zq167N58+fOX36NDVq1GDbtm1UqVKFjx8/smjRIg4fPpyhHld2kZqair+/Px4eHnh6euLh4UF8fDznz58XH7xSqZRt27YRFBTEw4cPsbGxydQKXhaZFhAQgLW1NUpKSsyZMwdnZ2cMDQ0z7FPWLjq9Dd+bTf0nMU0QBGJiYihcuPB/PQVZzvHjx6lXrx5KSkpMnDgRTU1NPD09WbFiBZB2vN/WbZGTNaRPv5T9a2Njg4+PD/fu3aNRo0Y4OztTvXp1Tp48ybx58wgKCqJly5aUL18ef39/vnz5gra2Ns2aNePEiRPMmjWLcePGide+DJkA/Sf/lm3atOHkyZMZmgF4enry4MGD79YDygk+fvxI8+bNcXR0ZNWqVT/1+7x9+5Y+ffoglUp58OAB5cuXzwFLf0x0dDTLly/n5s2buWrHj1BUVBQ7HDZs2FAsyC4ne9DX1+f48eMcPHiQVq1aMWDAAGbNmpVlwrWWlhb3799n+fLlREVFoaOjQ9MWLSnfZwqRnz5weMZw5tSpQFHD8li37MztfRszrF+tVRcenthLh1mrxM9UNTRpM3EBs4f0Zmq/RFq3bv3dCJ+Czrd+jOwdlT4t09TUlKtXrwIQGhpKQEAAgiAwffp0wsPDsbKywszMTHwuydYLDw8nKiqKypUri+sGBwdTo0YN/Pz8UFJSYvLkydjb21O/fn3u3LmDk5MT7u7u7NixAw0NDaytrVFWVhbTHr8VBRUUFEhOTkZJSemXJgRlKWTm5uZ8+vSJmJgYQkNDOXPmDL6+vuzfv5/WrVtTqVIllJSUiI+PZ8mSJezZs4dbt25x7949qlatyvHjx/H29sbe3h4jIyOxbpmioiIGBgZ8+vQJSMuakAn9ZmZmYgMDNTU1Xrx4AaQJbiYmJsTGxqKhocHQoUPR0NBATU2NlJQUSpcuLUbyfPubfc8f/ZPf/QUdU1NTTE1NOXnyJBMnTmT06NH07duXa9euMX36dPr165dnf/9voz8LFSqEhYUFycnJzJo1Czs7OxQUFPD09OTp06fs3buX0aNH06RJEzp16oSWlhbdu3cnMDCQ+Ph4Vq5cSbVq1WjatGmePWY5uY9EkMc1yslFAgICCAgIQCKRiCmg+/btY+3atVSuXJnFixejr68vLp8Vee7x8fF4e3uLApqHhwc+Pj7o6elhbW2NtbU1VlZWWFtbY2BgkGl/3t7eTJkyhaVLl2JiYiLaJPs3MjISXV3d7+77W8HjvxIREYGLi4tYhDO3kTlely9fZsmSJVy+fJnIyEiOHDlCiRIlaNeuXW6bWGD53r0RFRXFgwcPaNSoEfHx8axevZrY2FhcXV35+vUrvXv3Jjk5mW7durFjxw6WLl1KTEwM+/bto0uXLqJz/eHDBzQ1NX8q4ulP5MWLFzRo0ICgoCDx3u7YsSO1atVizJgxOW7Py5cvadq0Kf3792fatGn/+swUBIE9e/Ywbtw4JkyYwLhx4/KE4zh37lxevnzJnj17ctuUf+T48eNMmDABDw+PPDnJURAJCQlh6NChvHjxgh07dmRrt9hjr6N5+SVzqve3RH18z/IODky99Aw1zf+lv1XWVqF9Bfl18W/IUri+9+x5/fo1Dx8+pEyZMtSsWTPD++7mzZssXLiQCxcuAGmd1lesWMGpU6dYvHgxEomEYcOGoampSZUqVdixYweGhoZ06NCB7t27o6amxpEjRzAxMWHChAkoKiry5MmTDCIewOnTpzlz5gyTJ0+mfPnyv+SPCoJAUlISysrKYgR5VFQULVu2ZMaMGRw/fhx/f3/evXvHxIkTGTFiBF5eXmzYsIG+fftSqVIl7OzsKFmyJGFhYTg6OuLt7Y2SkhI7duzA09OTNWvWAKCurs7Lly9RVlamR48enD17lsDAQJKTkzEzM/tHm+fNm0fDhg2xt7fPVxkScrIeqVTKx48fKV68OIMHD6ZPnz7Uq1dPvO5nzpxJnTp1aNSoUb6eKO/fvz/Kysps2rSJfv360bFjR1q0aEFSUpI4aZOYmMidO3dYtmwZioqKrFmzRqwZCT8WneX8ecgj1uTkKhUrVhQjtwRB4ODBg4wfP54NGzbQtm1bIC0d4/Xr19StW/eXtx8ZGZlBQPP09CQgIABjY2NRQOvWrRtVq1b9adGgePHiqKur8+HDB0xMTMTPZc7Kj0Q1yPpUzvDw8GxJifmvHDhwgEGDBiEIArNnz+bTp0/o6upSpUoVsc6KnKwlvbOckJDAxIkTcXd3p1atWqxfv16MrDl48CCvX7/mzZs3FCtWjM2bN5OUlMSKFSvYt28f8+bN4/Tp0xmuVVnR47xa+Dm3MTY2RktLi8ePH1O9enV8fHy4ffs2u3fvznFbHj16ROvWrZk3bx7Ozs7/unx4eDhDhgzhxYsXXLlyhapVq+aAlf/O58+fWb16NXfv3s1tU/6V9u3bc/HiRYYPH57nRcCCgp6eHseOHePw4cM4OTnRt29fZs+ejZqaWpbvy0GvEIHRSaT8wzS0VCrFbe8GLJu0zSCqKUmgpn6hLLepIPJP/lGFChWoUKGC+Hf6d1DhwoXx9/dn8ODBDBgwAB8fHwwNDZFKpYSHh2NiYoKmpiaRkZGoqalRqlQp9u7dS8OGDRk5ciQAe/bsQV1dHX19fZ4/fy420NLQ0GDr1q0YGhry8uVL9PT0xGssvQ2pqamZGg+kRyKRiIN0BQUF6tWrR0pKCkpKSsyfP5+OHdNq8hUtWhRNTU0iIiKoWLEipUuXZvz48eJ2nj9/jomJCZGRkfj7+2Nubk7hwoVRUFDg8+fPFClShMOHD6Ojo4OGhgaXL18GyOR3yWIqvn2Xz5gx44e/gZw/C0EQmDFjBqtWrRKjQCHtmlm1ahXz58/n8uXLNGrUKN+KapCWPSVL7dTR0eHDhw8Z7tdPnz5x9epVvn79ytmzZxk2bBjv3r2jbNmyvH37lrJly4r3fX4WGOVkDXJ5VU6eQda8YMSIEbRt25bk5GSuXbvGzp072bFjh/iwCw8Pz7SuIAi8e/eOv//+m9mzZ9O2bVuxcP6sWbN48+YNDRo0YM+ePURFReHp6cmOHTsYNWoUderU+aVIHH19ferUqcOQIUOYNGlSrooMycnJKCsr59r+v0XWBVJJSYnHjx/TsGFDKlWqxM6dO4mIiODx48eAvABodrFs2TI+fPjAx48fMTExwd3dnREjRnD69GmOHTuGiYkJ2traeHh4oK2tzf3797lx4wYzZszAzs6OuLg4lJWVWbx4MXXq1Mm0/bzYKCOv0LZtW06ePAmk1VYbN25cpnqJ2c2lS5do0aIFGzdu/ClR7fz581StWpVy5crx8OHDPCOqASxfvhwnJyeMjIxy25SfYvny5Tx69Ii9e/fmtil/DBKJhC5duuDl5cXLly+pVq2aWJc1KzHQUKZBKQ2UfvDoS4qPZU7t8ry6f5NGQyaJnytJoEEpDQzU8847Oj/zvYLpgiBgbW3Ns2fPGDhwILq6uigpKVG9enXevn1LcHCwWCPS3d2dUqVKoampSWxsrFieIzw8HHt7eypWrIiysjL6+vr07NmT69evU6NGDTw9PYG0DItSpUphYGCAv7+/2AAAyNR4QGbbPyGrzauoqIitra1YTmTjxo3UqlWLJk2a4ODgwKhRo+jWrRtWVlacP38eABcXF7HTdLt27Vi1ahVFihQhNTWVVq1aZehC/z175BNkcv4NRUVFdHV1Wb58OQsWLODp06f06dMHY2Njli1bxs6dO/PFxNc/IZVKUVFREe+lZs2asXbtWnr37o2Pjw8AHTp04MaNG/j6+mJkZMT9+/fFhifdunVjyZIlXLlyBZCnRcuRp4LKySPIQot9fHxwdnZm5MiR+Pv7ExMTg5aWFk5OTtjY2LBnzx6OHz/OokWLMszABQcH4+DggKmpaYZ0zooVK2ZLeG5sbCxPnz6lUqVKuVrY28PDg379+omOX17h06dP7N69m8jISBYtWsTnz59xdHTEw8NDfIHJ+XW+NxsWFxcnCjj169dn0qRJREREsHjxYooUKYKSkhLdunXD2dmZ5ORkdu3ahaenJ2vXruXgwYNs3rwZa2trpk6dStGiRcXtytuL/xr37t3D2dmZI0eOUK9ePQICAjIVec5O9u3bx9ixYzl+/Di1atX6x2VjY2OZMGEC586dY+fOnd+tp5ObhIeHY2xszKNHj3K9ztuv4OnpSePGjbl//36G6Bo52Y8gCBw+fJhRo0bRp08f5syZk+XRa0/C4rn6/iupAvAvz0aZqFatuDxaLbv50bsqJiaGhw8fUrFiRcqWLcvw4cNJSkpiy5YtTJw4ESUlJRYsWMD169dZu3at2Ghm0aJFREZGEhwcTHJyMsOGDcPZ2ZnBgwfTvXt33r59y/Xr1zE0NGTJkiXMnTuX5ORkHBwcaNy4MSoqKpls+p1IlvQdROPi4khJSaFIkSK/fT7kyPlVYmNjqV+/Po0aNeLo0aMUKlSI6dOn06lTJwBSUlL4+++/6dChQy5bmnVIpVJ2795NhQoVMDExoXbt2mJ9wnXr1nH//n1WrVrFjRs3WLBgAW3atMHNzY2UlBROnjyJlpaWeA/KI9j+POTCmpw8gyxH/ciRI6xcuZJy5crRtm1b2rVrx+vXr9m4cSNfv37F3t6eRo0aUaZMmUzr/mk8ePCA4cOH56l00C1btlCnTh0qVqyIkpISUqmUI0eO4OXlxYIFC+Qvmt/g2+v769evaGpqMnr0aL5+/cqcOXMoWbIkEydOpE2bNpQtWxYTExOeP38uzsoHBQVRrFgxnjx5wrZt2xg/frzYlfaf9iXn55BKpZQqVYrq1atjb2/P1KlTc2zfy5cvZ+XKlZw/fx4zM7N/XPb+/fv06tWLmjVrsnr16jxZN2/y5MlERUWxcePGf184j7Fy5UoOHjyIm5tbnoom/lMIDQ1l2LBhPHv2jJ07d2ZpQwlBEOjsPAy7roOguCESyJAeqiQBAahYWIWa+oXkkWq5xI/eYe/fvyc5OZny5ctz48YNJk2ahIWFBe/evSM5OZlLly4xfPhwFBUV2bBhAx8+fGD27Nk0a9YMKysrevfujYKCAk2bNqVx48bUqFGDpKQknj59yosXLzh16hQ2NjZMnDiR5ORkrly5Qp06db4bPZYVyAU0OTnBhw8f8PPzIz4+ntatW/P+/Xtu3bqFrq4u9vb2jBkzBltbW4YPH57v/ftv7f/8+TO9e/dGQ0ODGjVqsHv3bpydnRk+fDgtW7aka9eu9OrVC4Du3bszefJkLC0tM20nv58XOT+PXFiTk2dI7yRs2rQJJycndHV12blzJzdv3sTc3JzmzZtTpUqVLOsClt+5c+cOEyZMwN3dPbdNAdJmt7p3786bN28wNjamffv2NGvWDIlEgrKyMurq6nJn8CcQBAFBEMTUWgUFBRISErh58yaTJk2icuXKTJo0CSMjI7Zu3crDhw85cOAADRs2ZOXKlVhYWFC/fn1q1apF8+bNefDgASdOnGDmzJk0aNCA5OTkDPfQjzrSyvk1unbtyunTp/n48WOOFLGXSqVMnjyZM2fOcOHChQyTDd+SnJyMq6srGzduZN26dWJNn7xGSEgIpqameHl5ZeqknB+QSqW0bNkSGxsbXF1dc9ucP5YjR44wcuRIevfuzdy5c7Mkeu3s2bOMHTsWb29vUiRKPI1MICw+lcRUKaqKChQvpIilrhrqyvKJibzCP/kbwcHBuLu7Ex0dTUxMDIMHD2br1q08ePCAqVOnsn37dh48eCCmvF2+fJknT57g6upKq1atAHj37h1nz56lePHihIWFcfXqVY4ePcqVK1do06YNY8eO5cSJE3Tq1IlZs2b90M5/atwgR05eITU1lQkTJhAWFsbXr1+pUKECU6ZMoXbt2vj5+eW2ednG5s2befbsGa9fv2bLli3cu3ePffv2sWbNGvT19Xn//j0NGzbk9u3bhIaGsnv3btzd3Rk0aJAovMn5M5ALawWI2GQp3pEJhKZz9ErkQUdPEATevHmDp6cn8fHxtG/fXnR608803rx5k71796KsrEzTpk2pUaMG+vr63L17l5o1a+bmIeQZbty4waxZs7h582Zum5KByMhI9u/fz6ZNmwgKCmLJkiUMGjQot83KF3xvIPDixQuaNGlC7969ady4MW/fvmX//v3s2rWLEiVK0Lx5c/r168eOHTvo27cvXbp04cWLF7i5uXH48GHKly/PwIEDsbW1/cf9yPlvNGrUiJcvX/L27dts31dycjIDBgzg5cuXnDlzJkMa77e8ePGCnj17UqxYMbZv346BgUG22/e7jB07luTkZLHDXX4kJCQEa2trDhw48FtNd+RkDaGhoQwfPhwfHx927NiBvb39b28rKSkJCwsLVqxYQYsWLbLQSjn/Rnh4OMrKymhra4sF/3+Hn3nnhYeHs2DBArGekr+/P+fOnWPUqFFYWFhQuHBhLly4wLZt21BSUqJVq1YYGxujqqrK0aNHsbOzY+/evcyfP5+bN2+ye/du/P392bJlCzNmzKBy5crivhITE4mOjv5uORG50CYnL/Lo0SO6dOnC06dP0dDQoHbt2ixYsIDdu3ezaNGif/RD8iPfRpr5+Phgbm5OrVq1cHZ2plevXigpKTFmzBiSk5OZNWsWw4YNw8LCggYNGrBv3z7evXvHsmXLxGZ3snGu7FmWX8bucn4OeVfQAsDH2GTcQ+J5HZ2UKTXBPwrcPsZRobAKDnqFMNDI2dSE5ORknj9/nqErp6enJ+rq6mIdtAYNGqCnp5epo5Kvry/ly5end+/elC5dmpiYGOLj4zl27BgPHz7ExcXljw+vzWvNC27cuIG5uTnFihVjxIgRjBgxgi5duoh11eRphpn59pxIJBLevXvHzp07+fDhAxMmTMDY2JjSpUuTmppKnTp1+Pz5M7dv32bfvn2MGTOG+fPns3//fu7du8fcuXOBtC5gxsbGDBgw4LuDCbmolrUEBATg4eFBSkoKoaGhlChRItv2FRsbS6dOnVBUVOTq1as/bJIgCALr169n1qxZzJs3jyFDhuTp3z04OJidO3fy7Nmz3DblP6Gnp8f27dvp1asXnp6e/9gpWk72UaJECY4cOcKRI0do27YtvXr1Yu7cuRQq9Ot1z9auXUuFChXkoloOcPbsWY4fP87s2bMxNDRk1KhRdOrUCScnJ1FUS01NJSwsjMKFC3Pw4EFiYmKoXr06dnZ2KCkpER8fj5qaWobn3feefbKGCLJ3cLFixVi+fLn43bt371BQUEBfXx9DQ0MaNmzI+fPnOXz4MDVr1iQwMJAzZ84AafVOZSLZ1atXGT58OPr6+qiqqpKUlMSbN2+oXLmy6LcGBgayYMECvnz5QkJCAp07d6ZLly6oqal9Vzz8t86jcuRkN7a2tlhZWYlpzhKJhEaNGjFz5swC+Z6TjS9lfrq5uTkA8+fPp0qVKigpKfHp0yfOnj3LzZs32bRpEzY2NgwcOJCiRYvi5+fHoUOHvtt4ZejkmZg074K0aKk8N3aX8/vIhbV8zpOweK59iP1hG3jZ5y+/JBEYnZStxXS/fv3K06dP8fT0FIU0X19fypQpg5WVFdbW1kyZMgUrK6t/HHTKZhUHDRqEVCrl+fPnNGrUCCMjI2rUqMGSJUuwsLCgc+fO6OvrZ8ux5BfygrAm+71evHjB0aNHcXNzo2LFipibm1O5cmUiIiJo06YNgNwh/A7fnpP379/j4uJCgwYNKF++PDNnzmTixIn07t2by5cvA1C4cGEaNmzI/v37GTNmDNbW1lhaWnLo0CGSk5OB/xU+lkgkmQYPcrKehQsXMnz4cJ4/f86ZM2fo379/tuwnPDycli1bYmZmxubNm38YvREcHEz//v2JjIzE3d09Q6REXmXhwoX069cvT0fU/SzNmjWjQ4cODBw4kKNHj+ZpQbOg06lTJ+rVq8eIESOwtrZmx44dvxT1HhoaysKFC7l161Y2WilH5kusWrUKR0dH0U8sW7Ys4eHhKCgosHjxYkJDQ/Hy8qJx48YYGhoSFRVFnz59uHv3LkOGDGH69On4+/ujo6ODnZ0dgiAQFxeHmppaponY73XylA2iFRQUKFeuHADTp08Xlxk1ahQnT56kY8eOlChRgj59+lClShV2797Nhg0bgLS6VHZ2dkDaMzs8PFwclMueBZ8/f+bp06dMmjSJSpUqMXv2bD59+oSPjw+vX79m7dq1VK9eXdzvt7bLlrWxsfmppgZy5PwXZPfF9OnTadeuHcHBwXTq1Il169ZhYWGBIAh8+PCBUqVKFbigh2+fE+kbPu3fv59y5cphYGBAaGgoNWvWFEXGK1euMGLECLGesey83AgIoUyHoSSrqaUV5vyGnBy7y8la5KOsXKZcuXJim95f5d9EtW9JEeDah1iehMX/1v7SExoaysWLF1m8eDFdu3bF2NiYEiVK4OLigoeHB9bW1qxevZrQ0FCeP3/OwYMHmTRpEk2aNPnXSA6Z06GoqEhycjLz58+nT58+nD59mgsXLnDmzBns7e25f//+fz6O/E5eENZkXL58GUtLS1RVVXn8+DFr166ladOmFC9enCJFinx3xuZPQ+a0p+fp06esXLlSjNK5efMmpqamNGvWjGPHjvHq1Su+fv1Khw4d8PDwICQkBEVFRWxtbXn//j13795FIpGgpKSEk5MTERERQNp9JLuXZIMEOdnDmzdvOHHiBKNHj6ZNmzacPHky2/ZTq1YtGjZsKKYifY8jR45gbW1NzZo1uXPnTr4Q1d69e8e+ffuYNGlSbpuSZSxatIiAgAC2bt2a26b88RQvXpxDhw7h6upK+/btGT9+PPHxP+cLzZgxgx49emBqaprNVv7ZSCQSPn/+jIaGBk2bNhXrgFpbW4s1K8PDw7ly5QqrVq1i0qRJPHz4kCNHjjB//nxmzZqFt7c34eHhCILAsmXLSExMZM+ePSxbtgxFRUWSkpJITk4mNTX1uz6JRCJBUVExkxCevmqOjY0N8+bNQ0VFhb1791KnTh0SExMZNGgQFStW5NGjR3z9+hVDQ0MEQSAgIAB1dXVKliwJ/G+Q/vr1a2rWrEnbtm2pXr06Xl5epKamcuDAAWxtbcWmVJ8/f2bevHkMHDiQ3bt38/XrVwC8vLzYt28fPj4+QNqgPTU1FXmFHznZgey6tba2ZuzYsbi5ubFv3z7Mzc05cuQI5ubm9OjRA8gsAhcU3r17h6amJqmpqeJnY8eO5ciRI0DapPeTJ0+QSCScOXOGt2/f0r17d3HZXbt2YVXDAfeIVJTVCgH/PuGWlWP39KTXHxYsWICzs3OWbv9PRR6xlk/5GJv8S6KaDNkNaqChJHaskkgkzJ07l/Xr1xMYGJihyK8gCLx+/TpDFJqnpydxcXFiFFqLFi2YNm0aJiYmWS7yqKur8/XrV0qXLg1Anz59GDZsGB06dMDJySmDnX9iREBeENYkEgmfPn3i77//FiOqfH19+fDhA+XKlSuQ4eG/QlBQEHp6eqioqIhi19evX3n79i2RkZG4urpStGhRzp07x65du9DS0mLdunX4+/vTs2fPDEXmq1SpwqZNm5g5cyalSpVizZo1VK1aleTkZLH997x583LxaP9MFi1axODBg9HV1aVFixYMHTqU2NjYLO0G9/TpU1q0aMHEiRNxcXH57jJRUVGMHDmSBw8ecObMmQzRDnmdBQsWMGjQoGxNoc1pVFVVOXDgAHXq1MHR0VEuzOQBOnbsSN26dRk5ciRWVlbs2LEDBweHHy7v6enJyZMnef78eQ5a+ecikUgwMjLi4cOH1KhRA0EQ6NSpk/h99erVefToEWZmZnz+/BlNTU10dHRo06YNI0aMoEKFCgD4+/sTExNDeHg4Ojo6XLp0iVatWnHixAlOnTrFsmXLaNSoEaGhoejo6CAIwj82xUrvX6Yv31CqVCkGDBiQYdmAgAAWLFgAQEpKCu7u7mLtKVnESkxMDB8/fqR48eIUKlSI4OBgNDQ0mDhxIgCWlpa8fv0aQGx8EBcXx9GjR9HW1qZNmza8f/8eAwMDjIyMgB+LGX+qfywn65FdS7JSPCdOnGDz5s2EhobSuHFjFBUVOXbsGB06dMjTUWvlypUTJ6mVlZVxcHBg48aN/9owqUyZMqKwDWmRaz179sTZ2Znk5GT69esnNs25desW7dq1Eyc2U1NTiU4W+JKUioKyyi/Z+72xe1aSk13sCzpyYS2f4h4SL4pqqSkpKP5CMdcUAe5+iqd9BWViY2OBtEGNRCLhxIkTJCYmZqiHVrhwYbEemrOzM9bW1pQtWzbbX9Syh/LSpUsZNWoU27dvp3Tp0nTs2JHhw4cjkUg4cuQIHTp0+GOjcXJbWJM5mN7e3ujo6BAWFkbx4sWpUqWKGPos40/8jR4/fszOnTtp164dDRo0wNfXl+vXr7Nq1SpKlixJSkoKx48fp0SJEvTq1YtLly5hYGBAu3btGDduHJaWlgAcPnyYZs2aMXz4cB48eACkDdrTpzPVr18fZ2dndHR0cuNQ/1iCgoI4fPgw/v7+ABQpUgQ7OzsuXbpEu3btsmQfN2/epFOnTqxZs4YuXbp8d5lr167Rr18/WrdujYeHxw/rruVFAgMDOXLkiHgOCxKmpqbMnz+fbt26cf/+fXlH6zxA8eLFOXjwoDgA7N69O66urplqrwmCwOjRo5k9e7Y81S6H0NbWpmjRogQFBQH/G8jL/EFzc3PCwsIA0NLSwtramjt37ohNQqKionjz5g0WFhYEBQXx/v17WrduzbVr1xg/fjy1a9dm3bp1on/yO0J+el9G1sE7/ecVK1akYsWKACgrK9O1a1eSkpLE5QE+fvxIYGAgNjY2ALi5uVG2bFnU1NSIiIggMjISPT09Xrx4wbVr11BTU6NBgwZUrVqVJUuW0KZNGz58+ICOjg76+vocPnyY48ePY2ZmRrt27TA3NxfPncxXT18eQo6c3yH9tTN69GguX77M2LFjxeZkT58+pVevXnTo0CHPimoyTp8+TaNGjUhISGDYsGGMHDnyP2UbLF68mJSUFM6ePcvKlSt58+YNLi4uGZ4Xr74k8bsBpenH7nLyLn/eSDcP8vDhQ6pUqUKRIkXo168fCQkJAJw5cwYrKyt0dHRwcHDg6dOnQFr3z4GO5tzcuZpVnesyq1ZZUlNSuLlzNQubWjDLsRzL2tnz6n5aPZAgnyes79OcOXUqsqCJGX8vmsSLiK/cvPtAHIQnJCQQHx/PsGHD2LhxI6dOnWLGjBkEBAQQFBTE6dOn6d27N+3ataNcuXI58mJWVFREKpViYmLC2LFjUVNTo1mzZixcuBBFRUWGDx/O5MmTOX36NMAfGf6e28Ka7IVx9uxZXr16haurK9evX+fNmzeiI/knY2hoSNmyZfHx8SE1NZWpU6dy+vRp7ty5w+nTp/nw4QNPnjwBoHbt2vj4+FCmTBlsbGwYOnQoo0ePplq1auzfv5/o6GiaNWvGzJkzM+xDdt3b2NjIRbVcYPHixTg7O1OsWDHxs7Zt22ZZOujx48fp1KkTBw4c+K6olpCQwNixY+nduzebNm1i7dq1+UpUA5g3bx7Dhg0rcB3FZAwcOJCKFSsyZcqU3DZFTjo6dOiAt7c3wcHBWFlZcefOnQzfHz9+nMjISAYOHJhLFhY8QkJCuHz5MsuWLWPx4sXExMRk+N7Hx4cHDx7QrFkz4H8+hmyQbmpqSlJSEmFhYSgpKdGmTRsqVKhA+/btqVWrFjVr1sTf3x9FRUXmzZtHhQoVkEgkrFy5kmvXrjFnzhwcHR0zRNJLpdIMqV2/gqyZQPrB87cpplWqVMHKygpATN/X0NDAyMhIrLt29OhRUewLCwsjJCQEGxsbbty4QceOHdm8eTNfv37l0qVLVK9endTUVMLDw8X0UkdHR6ZPn46amhqurq5io4OjR48SEBBAbGxsBpFNxo9SYuXI+RGy62XixIk8f/5cFNV8fX05evQopqamREdH56aJv4SamhodO3bE19cXSBvPyNLPDQ0NmT17trjsmzdvkEgkpKSkMG3aNNzc3BgxYgSampq8efOG9+/fo6CggIeHB87OzpQvX5569eqxefNmEqQSwuJTMmR/TqlWnLuHt7O0jR2zHMtxaf1CIoIC2dC3BbNrl2f/pAGkJKeNpeKjo5jWt6NYXqdVq1a8f/9e3FZgYCB169ZFS0uLxo0bM2LECHr27Cl+v2fPHsqWLUvRokWZP39+hnMwe/ZscdmEhAR69uxJ0aJF0dHRoXr16oSEhGT1aS+wyCPW8gD79u3j4sWLaGho0Lp1a1xdXenQoQP9+/fn9OnT2NrasnfvXpycnHjx4gXeUWkOgNeF4/RZvR8NHV0i37/h7qFtDN97mcLF9fkc/A7p/zsKCgqKtBo3j1JVrIgODWbHiK7cO7ydFEtzlJSUSElJQVlZmeTkZDp06ICjoyNbt26lUaNGuXla/t/2NGeladOmVKpUiYoVK7J+/XoOHz5MtWrVcHZ2Zv/+/ejp6WFvb//HdZ3MbWFNNiPq6urK69evOXDgANu3b8fAwABjY2N69uxZ4CM0/umaK1GiBJaWluJse5MmTbh165bYPWzgwIHs3buXZs2a0aRJE27evIm/vz8uLi40bNiQCxcucOjQITHVAzLPOstnn3OPDx8+sH//fvz8/DJ83qZNG2bPni22U/9dNm7cyNy5c7lw4QLVqlXL9L2Hhwe9evXC1NQULy+vfClMvXz5klOnTvHy5cvcNiXbkEgkbNmyBSsrK5o0aSKKBnJyn2LFinHgwAFRwO7atSuurq4oKCgwfvz4f6xlKOfHxMfH4+vri7e3N0+fPhX/S01NxdLSEgsLC+zs7DKly2/atIkXL17g5+eHo6NjBv9G9q5NSEjA19eXunXroqioyKZNm3Bzc8PAwIDKlSuL78S2bdtm2LZUKhV9lvTv7Kz2Gb/XEOHb93SpUqUYPXq0+HeXLl3ElDFZpJ2hoSG+vr4UL16ckiVLZphUu3fvHikpKZiYmJCQkMDZs2d58eIF5cuXR1VVlVu3bmFiYsKqVasoW7Ysvr6+6Orqsnv3blGMA3n6qJxfR3Z9y0r0PHv2jMePH+Pv709sbCzjx48XayLmB+Li4jh06BD29vZAmui9e/duzMzM8PHxoXHjxlhZWWV6nsyfP587d+6IqaCQJrxt27aN1atXZ5joVlBQwDsygVRpZgH/5d3rjNh3lS8hH1jTvSHvvB7SxXU96tq6bOjbHK8Lx7Fp3RWpVEp1p27037Ufm2Kq9O/fnxEjRoiTuN27d6dmzZpcunSJ+/fv07JlS7FxnK+vL0OHDuXcuXPUqFGDKVOmZBDl0rNr1y6+fPlCUFAQqqqqeHp6/lYn7T8VubeQBxgxYoSY1z1t2jRGjhxJREQEgwcPpkaNGkBabbEFCxZw7949vpSthgA4dB2Ijn4pACQKCqQkJRH6+gUaOkUpUrKMuP1SVaqK/1+kZBnsOvTm1eO7OA8fRVxcnOhAXr9+PU+LUiVKlKBRo0bo6ekxZ84cbG1t0dDQYM2aNcydO5dz587lafuzg6SkpFwT1mROblBQEFevXgXSuvq9ePGCy5cvExwcXKBFtfj4eBYvXkyjRo1wdHT84XJNmjQR/9/BwYH79+8TFBRE2bJl6dChA4cPH+bz589iV6GEhARSUlIwMzPDzMwM+F+6iYKCgtzZzUP89ddf9O3bFz09vQyfyyIVb9++naF71M8iCALv3r1j1apVYpfd9KSmpvLXX3+xfPlyli9fTo8ePfLtdTF37lxGjRpV4FPtZIPa7t274+HhkemakZO7tG/fnjp16uDi4oKVlRX16tXD2tqaBg0a5LZpeRpBEHj79q0onMmEtDdv3mBkZISlpSWWlpY0btwYS0tLSpYs+Y/PqrVr1/L8+XMmTZqEg4MDVlZWmVIar169KnbrhDRxKP1zNr0olP7/c8s//N7xfjtBlr6WqqOjI0WKFKF06dIMHjyYrl270rp1axwcHDAyMqJjx44EBgairq6Ora0tPXv2RFdXl2rVqnH58mVev36NtrY2r169QkdHh06dOok16A4dOsSYMWN49uwZS5YsQSqV0rFjRxwdHTNMzKQ/fzLfQ46c9EgkEt6/f8+GDRtQUVGhXLly1K9fHwsLC3GZvCzQtm3bFiUlJWJjYylevDgXL14EMnb8tLS0pFu3bty8eTOTsPZPaGpqZvosND4ViUJmIbtOnxGoaWqhpmmCXkUTjGrWQ7d0OQAq12pI8HNvbFp3RUNHlyoNWxMjUUVLS4tp06ZRv359IK2pwsOHD7ly5QqqqqrUqVOH1q1bi/s4evQorVq1ok6dOkBalsDatWu/a7uysjIRERG8evUKS0tLMV1dzs8hF9byAOmLJZYtW5bg4GDevn3Lrl27WLNmjfhdUlISwcHBKJW2AkD7/0U1gGJlKtBqvCtXNi0hJOAFlWvWp+W4eRQurk/Y2wDOLpvBBz9PkhPikaamUsrEksRUqfjA69+/v1hsdefOndl/0L+B7EFibGyMvr6++KIPCQmhcePGuWxd7pCbEWsKCgrExcXRoUMHBg4ciIuLC3Xq1MHY2JgyZcqIMxx5+cX6q8i6eioqKlKoUCG+fv3K06dPqVq1KlpaWkRFRZGcnEzx4sUzrSeRSLCyskJbWxsfHx+sra0xNjamePHinDp1ij59+rBw4cJMv+e3gwo5eYNPnz6xe/dusZvrt8jSQX9HWJNIJJQoUQIPD48MzWQgrZNc7969UVFR4dGjR5QpU+YHW8n7+Pn5cfHiRdatW5fbpuQI9erVo1+/fvTt25ezZ8/KB6t5jGLFirF//362bdvGwIED6du3L3FxcfkutTq7+PLlC97e3hmi0Ly9vSlcuLAYhebk5MT06dMxNjZGReXXCnTL0NXVRVNTU0zRTf8OFAQhQwS3jPR+Rvp3ZV59b35rV/rI90KFCmFrayt+t23bNm7dusW1a9fQ0tIC0qKEZNFmjx8/5vDhw1hYWFC0aFH27t2LoaEh+/btw97eHjs7OyBt8F25cmVCQ0OZP38+xsbG2NnZsWTJEvbv34+rqytGRkZ4eXmRlJRE9erVM/keP4r6k/NnUrp0aWrVqoWJiQnW1tbi56mpqRkmgvNiNtHJkydp1KgRqamp/P3339StWxdfX1/evn3L5MmT8fHxISkpicTExAwNVH6XxNTvp1xr6v5vvKCsppbxb1U1YiJCAUiKj+Psshksu3eNxJgvAMTExJCamkpwcDBFihTJEAFctmxZsVZlcHBwBq1BQ0PjhxkOvXr1IigoiK5duxIVFUXPnj2ZP39+rjfKyy/krav8D0V24UPai69kyZIYGhoybdo0oqKixP/i4uLo1q0bqoppP9u3L2ar5h0Ysv0sk856gETC+VVzAfh7wQRKlDdi/MkHzHYLpMnwqQgI4na+RUNDg7i4OPHvT58+ZfUh/zKy2b369etTsmRJFBQUePjwIfXr1+fBgwe0b9/+u8sXdHJLWJPVWNi9ezd169aldu3aODo6UqFCBXx9fRkxYoS4TF51bH+F9M5k+tSJxo0b8/btW1JSUoiKimLp0qV8+PAh0/oSiUQ8H9WqVcPNzU0Mwz548CB9+vRBEASUlZUzXbsF4fwVRJYuXUrPnj0xMDD47vdt27bl7Nmz330Wffnyhfj4ePG779W4KVSoUKYOzdu2baNGjRp06NCBK1eu5GtRDdLqeowdOzZfpY38V2bPns3nz59ZvXp1bpsi5wfcunULFxcXEhMTqVq1Krdv385tk3KUlJQU/Pz8OHToENOmTcPJyYly5cpRqlQpxo0bx5MnT6hSpQrz588nMDCQ9+/fc+7cORYvXkyPHj2wsLD4bVEN0rITHBwcGDt2LFOmTMkwIP/R+/BHUWFJSUn5oobYP4kOWlpatGzZkmXLljFixAgABgwYQLdu3QAYNGgQPXv2ZNSoUSxatAhlZWWKFy/Oq1evMDQ0FAfQjx49olWrVhw4cAAzMzNGjBhB8+bN0dPTQ01NDSMjI5YvX87s2bMZPHgwffr0Yfbs2Tx+/Bj4n1iiqKiYqYFDfjjHcrIWmf/SrVs3UVRLSUkRJ58lEgmhoaEsWbKE7du356ap/4iioiLt27dHUVGR27dv0717d5ycnAgKCuLLly8MGTLkh2PKb587MmHre2PoH425fxa3vRsIe/uKZSevEx0dzYULF4C03+HevXt8/vxZbEgIaXqCDAMDgwxaQ1xcHBEREd/dj7KyMrNmzcLX1xd3d3fOnDnD7t27/5PtfxLyiLU8wLp162jVqhXq6urMnz+fLl260L59e9q1a0ejRo2ws7MjLi6OGzduUKdOHUoUUuRbFyLszSuiQz9S1soOJVVVlFXVkP5/Lndi3FdUNbRQUdcgNPAl94/uRLNIUYoXShMI9PT0eP36NZUqVQKgatWqPHv2DE9PT0xMTDIUbswtvp0xGz58OJcvX2bSpEkMHDiQq1ev4uXlhbKyMs2bN/9jxIjk5OT/5MD+LjKnKjo6GicnJ7Zt20aPHj2AtDSN5ORkFBQU8uQs1c/yvRSSyMhI1q9fT0xMDHPnzqVp06bUr19f/A1cXV1/uD3ZtmTFlmX1VGROr7xmWv4hLCyM7du3iw1lvoe5uTm7d+8WnUwZfn5+dOzYkbp162JmZsbw4cNRUFD4x9b0MTEx9OrVizdv3nD9+nWx4HV+xtvbm5s3b7Jt27bcNiVHUVZWZv/+/dSoUYN69eqJRc3l5A3u37/P5cuXefHiBVpaWpw8eZLOnTvTuXNnFixYUOCi10JCQjKkcD59+pTnz59TqlQpMQqtb9++WFpaUqFChRx7n/ft2xdjY+NMafC/wt27d5k3bx7nz5/PQstyB5lwJTv/5cuXF78bM2YMtWrV4uXLl5iZmREdHU10dDQBAQG0aNECFRUVIiMjSUlJoUKFCoSEhFCxYkUxXa1IkSLY2Njw/v179u/fz6ZNm7CxsWH27NkcPnyYFi1akJSUhIuLCz4+Ptja2jJmzBjKli0L8FMR9QUpc0FOGul/T1mzDFk9yrCwMM6dO8fVq1fR1dWldOnS/7nmbHYhCAKnTp3i8+fPmJqaEhMTg66uLmpqajx48ID9+/dnKOmSHtn4WUbx4sUpVaoUe/fuZfDgwezatYuAgAAAShRSROE/3AJJsV9RVlXDsKg2rq6urFq1Ckjzpdzd3SlbtiwzZsxg0aJFPHjwgNOnT+Pk5ASkpZrXqFGD27dvY2dnx8yZM38ohl+/fp1ixYpRpUoVChcujLKycr4dx+UGee8K/wPp3r07TZo0ITg4mDZt2jB9+nTU1dXZsmULI0aM4OXLlxQqVAhHR0fq1KmDha5apm2kJCVyYc08QgP9UVRSpoxlddrPWA5Ai9GzOTF/HLd2raGksQWWTdry+qEblv+/ndmzZ9OnTx/i4+PZvHkznTt3ZubMmTRq1IhChQqxcOFCNm3alKPn5J9QUFBAV1eXq1evoqKiQpcuXdDU1ERVVRV3d3eio6Pp0qVLvhZ1fpbcbl7Qo0cPOnfuzN27d3n06BGvX79m//79LF++PNds+q9IpdJMjmJMTAwTJ04kKSmJYsWKERAQwLx58xg0aBBlypT5R1FEhmx7RYoUEescyMmfLF++nK5du4rFe7+HRCLBxsYm03Xx+fNn5syZg5KSEpcuXWLLli0MHDjwH68fRUVFOnfuTIcOHQpM3cJZs2YxYcKE79YiKehUqFCBFStW0K1bNx4/flzgxJr8ilQqZdSoUSxYsEBMuWvbti21a9dm1KhRVK1ale3bt1O7du1ctvTXkRX8/7YWWnJyslgHzdHRkaFDh2JmZpbr96WGhsZ/bqC1ZcsWmjdvnkUW5S7/1hDBzs5OTPmEtNIx06ZNE8Wv8+fPixHQpUuX5v79+wwYMIDg4GC8vLxo3749b968oVSpUtjY2CCVSqlWrRoPHz7E3NwcFRUVVq1aRUREBLt27eLixYsMGjSIe/fuERAQQLFixTA2NqZcuXIZbHNxccHc3JxBgwb9ET75n4rMf4mIiODgwYPcv3+fFy9eYGhoSIsWLejatWsuW5iZ1q1bi5F1ZcuWZdeuXZiZmbF+/XrGjRvHiBEjqFu3Lp07dyYqKuq72xg1ahR9+vRhw4YN9OrVi9WrV7NlyxaGDRvG1KlTGTBgAA4ODgDfHbv/CrV6DObQ1ME0NSmFmpoao0aNwtXVldGjR1OzZk0GDRrEnDlz0NXVpWbNmvTq1YsvX9JSRs3MzFi3bh3du3cnNjaWsWPH/tB//fTpE0OGDOH9+/doamrSpUsXevXq9Z9s/5OQCH9KzlwB49jraF5+Sfrt9Strq9C+Qv5Lv/lWwJg6dSpaWlr0798fPT09Xrx4waxZs9i7dy9KSkoF/kU+c+ZMFBUVmTVrVq7ZEBoayrZt29i5cyempqY0adKEYcOG5Zo9v4LMATx16pQ4syPD19eXsLAwqlevjrq6OjY2NlhbW7N161Y8PT3Zs2cP1atXp2vXrhkcyaCgIN6+fUuNGjXkNQkKIBEREVSuXBkPD49fSsW8cuUKdevWFa+Jr1+/cvbsWU6ePEn58uV59eoVa9eupVixYgX6mQXw5MkTWrVqxatXr/5oUalXr16oq6vnqYmrP5m9e/eyatUq7t+//9178O+//2bo0KF06tSJBQsWZOpomReQNRP4thunrJmAhYWFKKT9TDOB/EpSUhIGBgZ4eXn94wRIQUHWZAC+L8J9/fqVN2/eYGFhgZeXF4MHD6ZUqVKoqKjw8uVLzp8/z61bt1i1ahW3bt0iISGBPXv24O7uzo4dO7hx4wbLly9HXV0dfX19PDw8uHnzJgcOHGD8+PE0adIEf39/Tpw4QYkSJYC0tMCGDRuyePFisePit3YVxGvvT+XQoUPs2bMHFRUVevbsiY6ODklJSUyaNAkvL6/cNi9HefToEVu2bCE6OpoqVarQu3dvihUrxoWQ1N8fuwtS9CQJPN62hP79+1OtWjU+ffqEiYkJq1evpnfv3ri5uaGnp0flypWZPXs2r169Yu/evVl7cHL+EXnEWj7FQa8QgdFJpPyGLKokgZr6+bN1rkxUS0hIICQkhJs3b7Jnzx6xw9r79++5ePEi+/bto0+fPgV+gJqcnJypuHlOsX//fh4+fEh4eDjt27fHz8+P6OjoDC2m8zIyR1QikeDp6UmTJk1QU1Pj8+fPTJo0idu3b2NiYoK2tjZr165l1KhR4gDYyMgIfX19nj9/TkJCAmpqauK2Hj58iLa29r9GsMnJn6xcuZIOHTr8cn2zAwcO4Orqyo0bN4C0rlEtWrSgTJkybNq0iaCgIHFAUtCZNWsWU6ZM+aNFNUgrA2Ftbc3x48cz1QmVk7N8/fqVyZMnc/jw4R/6DW3atMkUvZab0cfR0dGigJb+X01NTVE4c3JyYtq0aZiYmORK2Yjc4vr16xgbG/8Rohr8czqmRCJBS0tL7Naor6/PjRs3iIiIYNCgQbRv355ChQphZGSEjo4OV65cIS4ujs2bN+Pi4sLjx49ZtmwZ/fv3R19fn9WrV4sNml6/fo2DgwM7duzItN+PHz8SFRXFpUuXuHPnDs2aNRM7ncvsgoydR6Ojo/+ompsFAZnva2JigpKSEk5OTjRv3lxsYLZkyRJ8fX2pUqVKLluac4SGhrJ161Yx3XLmzJnMnTsX57GTf3/srqCAdvgbIiMjxQYup0+fplWrVjg6Oor7nTp1Kvr6+pQqVSrD+nIhO2co2KpDAcZAQ5kGpTRQ+sV7REkCDUppYKCefyNpIiIi6N69OwYGBhQqVIigoCAEQWDlypWMGDGCNm3aiM7u58+f2bhxYy5bnH3kZCpo+uDWgwcPsnjxYmxtbWnatCk7duxg/Pjx+UZU+/vvv/Hz8xMHUGPGjOHEiRNAWret169f4+vry/79+1FSUmLz5s00atSIpKQkfHx80NDQwNjYmODgYB48eAD87/y0b9+ehg0bFnhR90/k8+fPbNiwgSlTpvz0OjLHatu2bRQtWpS5c+eK32lpaVGzZk1cXFwoWrQobm5uWW5zXuP+/ft4enoycODA3DYl1ylcuDD79u1j6NChYjMTObnD4sWLqVOnjpi28yN0dXXZs2cPy5cvp1u3bri4uGQoGJ0dpKSk8Pz5cw4fPsz06dNxcnKifPnylCxZkrFjx/L48WNMTExwdXXl9evXfPjwgfPnz4vNBCwtLf8oUQ3g6NGjdOzYMbfNyJMMHTqU+vXrM336dARBoGnTpmhqamJhYYGzszPbt2/n1KlTlChRAjMzM6KioihZsiTt2rWjZs2aCIKAubk5ycnJRERE0Lp1ayAtoyQ99+7d4927d9jZ2fHixQvGjBkDpL0Tb9y4wdWrV4mNjc3QYXTMmDHs3LmTlJQU4M9pRJafkYk1VatWRSKRYGRkRKFChXj16hVdunRBQUEh3zda+lUsLS3R1dUV/y5btiwTJkz4z2P3YippqZpaWlqEhYVx5MgRGjVqRIUKFcTmh25ublStWhVfX98M25B1VZY3Gsle5BFr+ZhqxdNmA659iP0p9Vt2Y8rWy68ULVqUokWLsnz5crZt28aAAQN4/PgxNjY2rFy5kqZNmwKwY8cO+vbtW6Ajh3JSWJNIJKxbt46hQ4cSERGBq6ur6FA1bNiQkSNHcvr0afGzvET66DSJREJkZCSjRo2iefPmfPnyhYEDBzJo0CBat25NSkoKurq6REVFoaOjQ7Nmzbh//z6qqqrUrVuXrVu3snLlSszMzHj37h3FihUDyNQlSz4zVPBYvXq1OKj9GWSp67KU9GPHjpGQkJBpOX19fRo1asS+ffsoXbr0T28/PzJz5kymTZuWa5G2eQ17e3tcXFzo2bMnV69eLdDvq7zKmzdvWL9+PZ6enj+9jpOTE46OjowePRpLS0u2b99O3bp1/7MtoaGhmeqg+fn5UbJkSTEKrU+fPmIzAfn1kpmUlBROnjzJ9OnTc9uUPMnx48f5+PEjd+/epWrVqmKDiF27dgGwcOFCVq9ejY6ODpUqVUJNTY2lS5diYmJC69atOX78ODdv3iQgIIC4uDhMTEyAzCmojx49omvXrjRr1gxjY2OmTp2Kl5cXpqamREREcPz4cSZOnEj79u2ZNm0anz9/JiUlheLFi4tF7mV+1JgxY5gyZQolSpSQ+1d5EJmv07lzZ6ZOnQqAiooKTZo0YfDgwRlqNhbk3+/r168sWbKEdevW4eTkxMGDB5FIJBw7dkz0ef7L2D1Ow5aqVavi6OhIUlISEokEJycnLl++zMSJEylTpgxhYWGoqqri5OTEmDFj8Pb25uTJkzRo0IBatWoV2HOfV5ALa/mcasULYaChxN1P8QREJyGBDDeqkgQEoGJhFWrqF8rXkWrpWb16NU2aNOHRo0d8/vyZmTNnMnjwYNTV1bl16xYTJ04kISGBdu3aFejIiJwU1r58+cKuXbuYPn06ampqNG/eXBTRDAwMiIiIEGcs89KLUyZqpLcnISGBW7duYWhoiIuLC6VLl8bGxoYLFy5QsmRJypcvz5MnT2jQoAHFihXj/v37FC1alObNmzNnzhwAKlasyIgRI767z7xy7HKyji9fvrBmzRru3bv3U8u/fftWLBx97NgxChcuTNOmTVFTU8tU+7FkyZJ06tSJSZMmER0dnS325wVu376Nv78//fv3z21T8hSTJ0/m8uXLLF68WByUyMk5Jk6ciIuLC4aGhr+0nq6uLrt37+b06dN0796d9u3bs3Dhwp8q/J+QkICfn1+GOmje3t4kJSWJ3TgdHBwYMmRInmgmkJ+4desW5cqVE5+/cjJjYGCQKf3c2tqanTt3snfvXiwtLZkxY4aYlnn+/HmCgoKIiooSGxucPXuWyMhIUZj71u95+vQpQ4YMAcDf318UxVRUVKhQoQLr16/H19eXEydOEBsbi5+fH4qKilSqVAmAmzdvYmdnR1BQEI8fP6ZQoUIkJyfj6uqKu7s7mzZtokKFCtl9quT8BDKBv1u3btja2vLo0SNq1arF9evXWbJkCYaGhmhqatKjR48C6R+npqayY8cOZs6cSYMGDXjy5Ally5ZFUVGRcuXKYWNjk2H53x27q6urs2LFCiIiIjh16hSQ9h5avnw5AwYMYMSIEZw4cYJ169ahr68PgKGhIba2tkyfPp2yZcuyevVqNDU1RR80L43XCgJyYa0AYKCuTPsKysQlS3kamUBYfCqJqVJUFRUoXkgRS1011JULVlpaoUKFOH36NG/evMHU1BRVVVXi4uIYOnQoV69eZfz48QwaNCi3zcx2clJY09bW5sGDB8TGxrJ9+3aWLVuGjo4OrVu3pnTp0tSsWZO2bdsCuSsshYSE8NdffzFq1CgMDQ1RUFDA39+f9evXU6hQIUaOHEmfPn24cuUKtra2WFlZAdCzZ0927drFoUOHuHfvHrNnz0ZXV5eDBw9Sr149IC0yr2HDhhn2J38p/RmsXbuW5s2bi07/P7Fx40bevn1L7969GTNmDOrq6ri7uzN+/HjGjx//3TRhf39/nj9/niF9oKAxc+ZMZsyY8celpf0bioqK7NmzB1tbWxo2bEiNGjVy26Q/hlu3bnH//n127tz529to3bo1tWrVYsyYMVStWpVt27aJ7wxBEHj37l2GCLSnT58SGBhIpUqVxCi0MWPGYGlpSalSpeTvk/+IPA3096hatSorVqzI8JksFVMikWBoaIihoSHLli0DoGXLllSpUoWiRYtm2tanT58ICgoSU6uDg4NRUFDAysqKyZMn8/79e16/fk1ISAjlypUjKiqKwMBADA0NefHiBVu3biUoKAipVEp8fDyVK1cWO/VOmzaNadOmcerUKUaPHp2NZ0TO72BkZISRkRE7d+7k2rVrPHnyhL59+3Lv3j1SUlLo06dPpkZ0+ZmLFy8yfvx4ihQpwt9//0316tXF77Zu3frD9X5n7C5L4yxatCj9+vVDKpUilUqpVKkST58+JTg4mLNnz1K+fHmaNGmCt7c3V69excDAQKzzm5SUhIKCAq9fv6ZChQri+6agN/vLMQQ5cgoAr169EooWLSr07t1bCA4OFj+XSqWCIAhCcnJybpmWrfTp00fYvn17juwrJSVFSElJyfDZw4cPhV69egkSiUSYPn26IAj/O+c5iVQqFVJTU8W/a9WqJZw8eVIQBEHYt2+f0KRJE2H79u3C0qVLhU6dOgmpqanC9evXBSsrKyEpKUkQBEFISkoSKleuLDx//lwQBEFYuXKl0K5dO2HKlClCVFRUhv2l35ecgk90dLRQvHhxwc/P76eWv3LlijB+/Hhh1KhRwoQJEwRBEAQfHx+hWrVqwrZt2767zpcvX4SwsLAsszmvce3aNaFixYri/SYnM8eOHRMqVKggfPnyJbdN+SNISUkRrKyshAMHDmTJ9r58+SIsWbJE0NHREczNzQV7e3tBW1tbKFmypNCsWTNhwoQJwp49ewQvLy8hMTExS/YpJyMpKSmCnp6e8PLly9w2pUCSmpr6Uz7eo0ePBEdHR0EQBCEuLk6YMWOGMG/ePEEQBEFBQUF8Dxw6dEjo0qWLkJiYKMyZM0cwNzcXunTpIuzbt09c5uvXr8K7d+8ybH/48OHC/PnzBUEQMvmlcnKfkJAQoXXr1oKbm5swZ84cYc6cOcKXL1+EmjVr5rZpWcbTp0+Fpk2bCpUqVRKOHz+eo2Ofb/cVHR0tjB49WmjTpo1QrVo1YefOnUJcXJygqakpbNy4UejZs6dQsWJFoVmzZoKPj4/g5eUlNGnSRFi1apVw9erVTNuPj4/PqUMpcMgj1uQUCCpWrMjZs2fFmX7h/6OIpFIpioqKYr2GtWvX/jB9Lz+SkxFrstml1NRUBEFAUVERW1tbdu/eze7du/n69SuQ8xFc30v1dHJy4vz58zg5OdGiRQtatmzJo0ePOHjwIK9evWL//v307NlTbEZgbW2NsrIytra2bNmyhaVLlzJy5EhGjRr13X3KZ3X+LNavX0/Dhg3FWjI/QnYtNmzYkOTkZPbs2cOXL18IDw/HzMyM5cuX4+LiQqFChejWrZu4niAIBboTmiAIzJw5k1mzZuXY8yo/0r59ey5evMjw4cPZs2dPbptT4NmxYwcaGhp06dLll9ZLSUnh1atXmaLQQkNDMTMzo1WrVvj7+/P27Vt27twpRnLLyX7u3LmDgYHBT0UWy/l1ftb3sbGxEZvxJCUlUbZsWbS0tEhMTKR58+bs2bMHY2Nj1qxZg4ODA8rKygQEBGBvb4+npyfdu3cXt6WhoYGGhob4d0pKCj4+PuI7VO6P5T2CgoKIjY3F0dERe3t7qlevTkpKCjVr1iQ+Pl7sGJof+fTpEzNmzODvv/9m+vTpDBkyJMej8NOPdwRBQEtLixUrVpCYmMj169epVKkS7u7u2NvbM3jwYAYPHsxff/2Fn58fZmZmLFy4kPj4eBITExk/fjyOjo4sX74cJSUlnj59yujRozl58mSB9kuzC/nTSE6+R/j/UPVv02dSUlJEMej06dOUKlWKQ4cOkZiYWGA6DeWUsCYLP05MTBSFSplwmZycDCDWgMluJ0cQhAzdpxQUFAgNDWXVqlWsX78egB49evD8+XOePXuGjo4O586dY/Xq1cyePZsBAwaIDt+QIUOYOXMmZcuWZePGjWzcuBFXV9cMxyETEuX8mcTGxrJ8+fJ/LIQtuz4UFBR4+/YtAM2aNWPIkCGUKlWKv//+m5iYGOrWrcvEiRMzdf8s6Klfly9fJiwsLMNgSc73Wb58OY8ePWLv3r25bUqB5suXL0yfPp1Vq1b94/0XGhrK1atXWbFiBf369cPW1hZtbW1atWrFgQMHUFBQoHfv3ly6dIno6GgePHjAnj17uH//Plu2bGHEiBEMHz5cnHiSk73I00DzBuk7D2prazNgwAA6d+6Mqqoq48aN4+TJk2zdupUSJUpQrlw5AgICSEpKonv37tSuXfsfU7Pj4uLw8vKiVq1aQMF/f+ZHbGxsiI+P5/z58ygpKdGnTx8uXbpEr1698q2oFhcXx7x58zAzM0NbW5sXL17g4uKS66Ut0nf7VFVVpVmzZlSqVImKFSsSFhbGoEGDGD16NKdPn6Zt27YEBgby7NkzJkyYwIQJE3Bzc8PPz4/Pnz8DsG/fPuzt7SlcuDCBgYGMGzeOsLCwXD3G/IQ8Yk1Ovufbl2pKSgpKSkooKSkRGhpKz549uX79OhMmTGDBggW5ZGX2kBPCmiAIosjUt29ftm7dKs4eSiSSHBH2oqOjUVNTQ0VFBYlEIgqmYWFhBAUFMWTIEFq0aIGKigrDhw9n1apVGBkZceXKFczNzbl58ybVqlWjZs2aeHh4sHXrVq5cucKIESOwtrZGR0cHc3Pz7+67oNSBkPN7bNy4kTp16mBmZvbDZWTPoCNHjrBo0SKaNWuGkpISc+bMISIigqtXr6KkpET37t3p0aMHPXr0yCnzcx1BEJgxYwazZ8+W30s/gYaGBgcOHKBx48Y4ODjIi3NnE66urrRs2VIsKp2+mUD6KLTExESxDpqDgwODBw/G3Nz8p5oJtGzZEm9vb8aOHYulpSXbtm2jfv362X1ofyxSqZRjx45x7dq13Dblj+fbCdb0mQz169cX74Po6GikUinv3r3DxMQEU1NTVFRUWLx4Mc2bN6dEiRKZfPy4uDiqVq3Khg0b6Nu3bwahJiezOOR8H1n9tNGjR7NixQrq1KnD6NGjady4MXp6euzatYuqVatiZWWVL2qtSaVSdu/ezfTp06lVqxYPHz7Mc+9liUSS6T4pV64cDx48YMeOHQQGBooTQgsXLsTIyIiqVasCsH//fj59+kTx4sWJjY0FEDO7ZI3c1q9fz6xZs3L2oPIpcmFNToFDlva5bNky5syZQ5s2bTh+/Dhnzpzh5s2b1K1bt8AUacwJJ+Lhw4fs2rWL+Ph41NTUMoTkJyQkEB8fny0F12WOmJ+fH/Pnz2fixIlYWlry9u1bfH19mTdvHi4uLnh5ebF48WIqVarEwIED+fr1K+Hh4bRq1Yq9e/fSq1cvGjRowIoVKwgPD+f58+cMGDBALNrp6OiYYX9y5MiIi4tj6dKlPxyopX+OnD9/nkWLFnH8+HE2bNjA0aNHiY2NZenSpXz58oVr165hZWUlOjN/yvV27tw54uLi6Ny5c26bkm+wsrJi2rRpdO/eHTc3N/lAMQsRBIFbt26xadMmhg8fTteuXfH29ub169dUqlQJCwsLLC0tGT16dJY0EyhSpAg7duzg3Llz9O7dm9atW7NkyRJ5l89s4N69e+jq6mJsbJzbpsj5hvT3kCzjQFFRUUw109HRwdLSEoDixYvTsGFDvL29adSokfiulP27YcMGVFVVCQkJISYmRhTW4uPjmTx5Mrdv38bR0ZHatWvj6OgodkeUkzPIhLLOnTtTvXp1FBUVCQoKws/PjylTpnD58mU6duzInj178ryodu3aNcaNG0ehQoU4cuQINWvWzG2TfprU1FRUVFQYPHgwgiDw8uVLPnz4wO3btxk5ciRlypQBYP78+axcuRJIm9hbvHhxhu3Url2bokWL5gsRNE+QkwXd5MjJbqRSqRAbGyuUL19eMDMzEx4+fCh+5+rqKrRq1SoXrct6mjRpIpw/fz5b95Gamirs3LlT0NHREQoXLiwMGzZMePLkiSAIgrBr1y5h6tSpWbq/lJQUYe7cuUKtWrWEyMhIQRAEYfDgwcLBgwcFQRCEFi1aCHZ2dsK1a9cEQRCEMWPGCIULFxbatGkjbNu2TWwskJycLNSrV084d+6cIAiCsGnTJmHBggXC+/fvM+0zNxouyMn7rFy5Umw+8C3prxl3d3fh0qVLwqdPn4R9+/YJ9vb2wvXr14UGDRoIixcvFgRBEO+ZPwmpVCpUq1ZNOHbsWG6bku9ITU0VmjVrJkybNi23Tcm3fPnyRbhz546wYcMGYdiwYYKjo6Ogra0tqKqqCpUrVxYmTJgg7N69W/D09BQSEhKy3Z7Pnz8L/fr1E8qVK/fdgtFy/htjxowRZs+endtmyPkNfqUhVN26dYXr168LgpDZd0tMTBTu3LkjLFq0SGjZsqWgo6MjVKpUSejXr5+wfft2wd/fX+7v5SCJiYnCjh07BGdnZ6FNmzbCmjVrhHfv3glOTk5iI6e8+Hv4+voKrVq1EsqXLy8cPnw4T9r4s3x7b7m7uwtBQUGCIAjCli1bBDMzM/E7WSMQ2TpXr14VRo8eLZw+fTqHrM3/yCPW5BQoJBIJ6urqbNiwgaZNmwJpNZJ8fHwICQnJUDC8IJATEWsKCgr06dMHAAcHB5YvX07Tpk0pXrw4mpqaYuv1rIoCVFRU5ObNm8yYMYMiRYoAiFGGAEOHDmXy5MlUq1aNxMREzM3Nad68OQcPHhS3ceHCBZo1a0adOnUIDQ0FYNCgQeL3giBkSHH9EyKH5PwaCQkJ3L9//7tF5IV00WZz5swhLCyMBQsWoKSkxLlz59i5cyfGxsZs3LiRI0eO0KZNG6ytrTOtW9A5efIkUqlUXrz9N1BQUGDnzp1YW1vTqFEj6tWrl9sm5VlSU1PFZgKy/7y9vQkJCcHMzEyMQuvYsSPh4eFMmTKFp0+foqqqmqN26ujosH37ds6fP0+fPn1o1aoVS5YsQUtLK0ftKIgIgsDRo0c5f/58bpsi5zf41nf8nj/57t07QkNDiYqKEqPbvn2Xqqio4ODggIODA5MmTUIqlfLs2TPc3Ny4fPkyM2fOJDk5WYxoq127NlWrVpVH4mQTKioqPHz4kKpVq+Ls7IyamhoAc+fOJT4+nuvXr7N+/Xo2bdqULZkvv0poaCizZ8/myJEjTJ48maNHj+b4eyKr+fY+Sh91t23bNiZMmACQISJNQUEBqVSKh4cHGhoa2Nra5pzB+Ry5sCanQCEbtMpEtVevXuHh4cH169dRUVGhVq1aJCcnIwgCKioqvyQGJSYmEhsbmyce/jKyW1iTnc/k5GScnJwoUqQIGzZsYMOGDVy8eBFVVVUxlTKrUmuTkpKwsLDgyZMn4u+YXhBt1aoVY8eO5fXr11hbW9OyZUsOHDjA6NGj0dHR4eTJk9jb21OnTh1mz56dwfGSSqViLYI/RdyQ83u4ubmxY8eO7zrcsmtn4cKFBAQEsHz5cgoXLkx4eDjv37/n1atXvHjxgvj4ePbt20flypUzrVvQkUqlzJo1i/nz5xeItPvcQE9Pj+3bt9OrVy+8vLzy1LsntwgLC8tUB83Pzw99fX2xFlqvXr2wtLSkYsWKGe7f5ORkrKysWLZsWa4Olpo3b463tzfjxo3DwsKCbdu20bBhw1yzpyDw8OFDNDQ0qFKlSm6bIicL+PadERsby5YtW7h27RqjR49GV1f3pyapFBQUsLCwwMLCgmHDhiEIAm/fvuX27du4ubmxadMmPnz4gL29vSi02dnZ5dsC+3kJ2fhqzZo1GX5PqVTKx48fOXPmDPfu3aNNmzZoaGjk6qRjfHw8K1euZNmyZfTs2ZPn2VTOSgABAABJREFUz59TtGjRXLElJ7l79674/9/6ugoKCmIAgjyd+ueRCIK83Z2cgkl4eDiLFi3i48ePtG/fHicnJ5SVlfHw8ODs2bP/2OUP0gSeqKgoFBUVKVq0KJs3b+bvv//m7NmzOXQE/469vT0rVqzI9rz/YcOG4eXlRVJSEq1bt6Zx48ZYW1uLs09ZiVQq5fz582zdupUjR46INfPgf0LiqFGjAFi1ahUA79+/586dO9y7d49+/fqJs5my7ckH9nJ+hZSUFJKTkzM5199eS4MHD+bhw4esWbOGGjVqoKSkxI4dOzh//jyvX79m2bJl1K1bF8gYqfYnXJOHDx9m6dKl3L9//48RE7OLMWPG8O7dO44ePfrHnMvExESxmUD6KLSEhAQsLS3FKDRLS0vMzMx+KuprzZo1/P3331y+fDnPnMcLFy4waNAgWrRowV9//SWPXvtNJk6ciKqqKvPmzcttU+RkI1KpFEEQsjTCLDw8nDt37uDm5sbt27fx9vamatWqYo22WrVqySc1sgBBELhy5Qrr1q3Dzc2NypUrY2try5o1a3LVpv379zN16lRsbGxYvHgxRkZGuWZPTiKLUPueoJmcnMyxY8fYunUrd+7cwcfHh4oVK+aSpfkLubAmp0Czfft2GjZsSEJCAocPH8bCwoKWLVvSqVMn2rVrR58+fb47yA0ODubIkSP4+vqiqKhIeHg4Hz58QCKRUK5cOUqUKIGlpSWdOnXKUMw/p7GxsWHTpk3ZEqYrOy9HjhzhxIkTLFiwgPr169OwYUP2799Pr169WL16dbbM/L9+/ZqpU6cyduxY7OzsMtnk7e1NnTp1CA4O/uHM4p+Ucicna/netSO79qKjo3n16hXVqlUD0sQ1VVVVJk+eTMmSJQGIiYlBIpGgqamZ6fmSlJREeHi4uGxBJDU1FQsLC5YvX06zZs1y25x8T2JiIjVq1GDYsGEZUtoLAoIgEBQUlCkK7fXr11SsWFEUz2RCWunSpX/ruR4REYGpqSnXrl37YQfo3OLLly+MGzeOK1eusHXrVho1apTbJuUrBEGgYsWKnDhxQmwOI0fO7xIbG8v9+/fFqLb79+9TtmzZDOmjhoaGuW1mviI8PJyePXty48YNpk+fzqRJk1BWVsbJyYnu3bvTtWvXLJtwlPlvW7Zs4fjx40yePJnq1aujrq6eadmEhAQGDBjAkCFDqF279n/ed35Hdu5kZVAcHR1p2bKlfMLnF5CngsopkMge0P379ycmJobmzZtTp04djhw5gpeXFwsWLKB///507dr1u8LQyZMnOXbsGDNmzCA+Ph5FRUWqV6+OVCrl4sWLJCcnc/bsWT5//syYMWNy4QjTyM5UUNkL7siRIwwcOJDDhw8zYsQIxo0bh76+PomJiaiqqmaJgCV80/Xp/v37REVFZZohkYUmW1hY8Ndff313O7LQZbmoJud3+d61o6CgwIsXL+jTpw+VKlVCR0eHfv36sXr1avr27cu6desYNWoUJUqUEJ2Q9HX8ZHz58oUuXbrg5uaWI8eSGxw4cIAiRYqIqdxy/huqqqocOHBAHNSZmpoCEJssxTsygdD4VBJTpagqKlCikCKWumqoK+e9iMiYmBh8fHwyRKA9ffoUdXV1UThr0aIFkydPxsTEJEsnbWbNmkWnTp3ynKgGoK2tzdatW7l48SL9+/enefPm/PXXX2LHRDn/jIeHB4qKihki1eXI+V00NDRo0KABDRo0ANIi2D09PXFzc+Po0aOMHj2aQoUKic9jR0dHTE1NC3wU+n+hWLFiaGlpsXLlSoYMGQKkjV8KFy7Mhw8fgKwrJyMbR8TFxWFnZ8fBgwf58uULTk5OmZZVUVFh79698vHC//P582c2bNhAcHAwRYsWpXbt2mhpaf0RWRZZhVxYk1MgSf8AePz4MYqKiixYsABBEKhRowaJiYl06NBBdNyjoqIoXLiwuF6lSpWYMGECjRs3zrTtXr16oaCgQGpqKtHR0TlzQD8gJ5oXTJw4ESMjI27cuIEswPX58+c4OzsD/y0yzMvLi/Pnz+Pi4oK6ujoSiYSkpCSuXbuGqqoq48ePZ86cOZQpU0bcj2xfsv2nR147TU5Wkv7afvv2LaNHj2bp0qVIJBI6duxIXFwcw4YNY/369bRo0QIzMzO6d+8urv+9a1FbW5tnz57x8eNHDAwMcuxYcoqUlBTmzJnDpk2b5PdiFmJqasqCBQvo2bMnZ27dwz0kntfRSUiAlHR5B/5R4PYxjgqFVXDQK4SBRva+H75H+mYC6aPQQkJCqFKlihiF1qFDBywsLChevHi22uPj48Phw4fx8/PL1v38V5o2bYq3tzfjx4/HwsKCrVu3ftcHkZORo0eP0rFjR/nzRk62oKSkhK2tLba2towZMwZBEPD39xdTR5csWUJUVBSOjo5iVFu1atVQUVHJbdPzBLKUwz59+rBjxw46duxIVFQU9+7dQyKR0K9fvyzb17p163j//j0LFy4US8YsXLgQDw8PbG1tM2UKyMWi/yFLsQ4KCqJIkSL07t2bUqVKAfLz9CvIU0Hl/BHY29vTq1cvTExM2LdvH4GBgUyfPp2GDRty69Ytjh8/TqNGjWjVqtVPC0UpKSmkpKRkS52xn6VSpUqcP38+y2sCyGYnrl69ip6eHubm5vj6+jJt2jQSEhL4/Pkz9+7d+61tR0VFsX//frZv305oaCj9+vVj+vTpmQTC6Ohopk6dipmZGUOHDs3QsUaGPN1TTnaRfobOy8uLxMREpFIphQsXpkuXLowbNw4vLy/evXvHvHnzKFGiBMWKFfupbXfv3p26desyePDg7DyEXGHHjh3s2rWL69evy+/NLEYQBPbdeswn7XIZxLQfoSSBBqU0qFY8+wpxh4WFZRDPvL298fX1RV9fP0MdtO81E8gJBEGgSZMmtG7dGhcXlxzd93/h4sWLDBo0iKZNm7J06VJ59NoPEAQBY2NjDhw4gI2NTW6bI+cPJTg4WEwddXNz49WrV1SvXl2MarO3t5en0wHt2rVDKpWiqalJbGwsLi4uNGjQIMt8eQ8PD8aOHcuePXsoXbo0AA8ePGDXrl0ULlwYQ0NDBg4cmO0BCfkdeYTa7yMX1uQUaGRiTGBgIHPmzOHu3btit0htbW12797N8+fPKVasGI0aNcLBwQFI6yYaFhaGjo4OZcqUydU6av9E2bJluXnzJuXKlcuW7W/evBlfX19mzpyJrq4uL168EMWFUqVK/fTDVyqVcuPGDbZt28bZs2dp2rQp/fv3p1GjRj8caCUlJXH8+HFWr15NmzZt6Nq1K2XLls3qQ5Qj5x+5e/cuw4cP58yZM5QsWZL169cjlUoZMWIEe/bsYefOnQwfPpz27dsDfFcA/pbDhw+LTQ4KEklJSRgbG7N79255vZJs4ElYPNc+xP6UqCYjq8S19M0E0gtp8fHxmeqgmZub55lB5KlTp5g8eTJeXl75bjD15csXJkyYwMWLF9myZQtNmjTJbZPyHN7e3jg5OfH69Wu5kC8nz/Dlyxfc3d3FqLYnT55gYmIipo46Ojqip6eX22bmGDK/KCYmhsTERKKjo4mNjeXKlSscPHiQzp07U716derUqfNTPtS3203PwoULsbCwoFWrVuJnfn5+zJo1Cy8vL3x8fFBQUMjxSR45fwbyVFA5BRrZg7N8+fJUq1aNNm3a0K5dO+7evcuBAwdQUlKidu3aNGvWLMNA4MSJE2zbtg0lJSWWLFlCixYt8qSCn92poC1atODx48e0atUKV1dXseaEjH87H0FBQezcuZMdO3agqanJgAEDWL169U+1sVZRUaFr164EBQXx7t07eftzOTnOgQMHmDRpEmvXrhVTCBQVFZk9ezbVqlVj8+bNODs7i6Ka7Pt/o1mzZjg7OxMdHV2gIlF27tyJkZGRXFTLBj7GJoui2uKW1egwcwWVatT91/VSBLj2IRYDDSUM1P/9XSEIAu/fv8/UjTMgIICKFSuK4pmLi8t/aiaQEyQmJjJ27FjWrVuX70Q1SEsb37x5M5cuXWLgwIE0adKEpUuXoq2tndum5RmOHj1Khw4d8uw1KOfPRFtbm+bNm9O8eXMgrUj+o0ePuH37Ntu3b8fZ2ZkSJUpkaIhQoUKFAnsdy/wiLS0ttLS0uHv3LrNmzUIqlVK5cmVatGjBoEGDcHNz+yXBS7bsgwcPqFy5Mjo6OkyaNCnT2MTU1BQHBwcsLS3z5btATv5BHrEmp8CTPsQ4NDSUzZs3ExERwaNHj1BVVaVnz57Y29tTunRpNDU181V6YfHixXn27BklSpTIsm1+20gA4PTp07i7u9O5c2esra3/cf3ExEROnTrF9u3buX//Pl26dGHAgAHY2Nj80nnNT7+DnILBt7Of8fHxVKlSBUdHR/bs2SN+PmPGDJ49e0a9evXE9LJfvV6bN29Ov3796Ny5c9YdQC6SmJiIkZERhw8fxt7ePrfNKXAcex3Nyy9JwK8JazIqa6vQvkJGEVfWTODbVE41NbVMUWimpqbZ0gE6O/nrr7+4desWp0+fzm1T/jPR0dFMmDCB8+fPs2XLFnljkP/HzMyMbdu2yZ85cvIVqampPHv2TEwddXNzQxAEUWhzdHTE0tKywEZVTZkyhZo1a+Lk5ESNGjW4dOkS3bt3Z8mSJZiZmf1wvW/9rAcPHjB37lySk5PR09OjZcuWdOnSheTkZJSUlDIsu337dp49e0aXLl2ws7PL1uOT8+ciF9bk/FF4enqybds2ateuzcKFC+natSvJycm8ePECGxsbRo8eDaSlLkJaRFh4eDhfvnwBQE1NDUVFRQwMDPJEYVIdHR0CAwMpUqRIlm43NTWVqVOnEhISgoODAw8ePGDPnj0YGRkxZ84cOnTokGmd2NhYpk+fzr59+zA3N6d///60b9/+uy2u5cjJa6SPSN2wYQNlypTB1tYWRUVFTExMmDlzZoYaTTExMWKU6+9Es27atIlbt26xb9++rDuIXGTt2rWcP3+es2fP5rYpBY7YZCnrn0WS+v/emkxYK1KyDMfnjeXjy2dIJBKMatanzeTFFNLKHNGkgEDZQHeeeT4WhbRPnz5hamqaoQ5aTjQTyAlCQkIwMzPD3d2dypUr57Y5Wcbly5cZOHAgjRo1YsWKFXkm5TY38PX1pWnTprx9+zbPZRPIkfMrCILAmzdvxNRRNzc3Pn78SM2aNUWxzc7OLldrOmcldevWZcaMGTRq1IhJkyahoqLCiBEjKFGixA8nKL+X9jl+/HgaNGhAixYtaN26NX5+frx69QrIHCSQkJDAyJEj6dChA82aNcv2Y5TzZyIX1uT8cYSEhCCVShk1ahSHDx8WP4+KikJTUxMlJSXxQbx//35cXV3FttCCIBAcHMypU6ews7PL9agqDQ0NQkJC0NTUzJLtyV5c165d4+XLl3z58gUlJSWqVq1KQEAAKSkpXL16lVGjRlGnTh1xvdjYWCZMmICuri79+vWjYsWKWWKPHDk5TYsWLShTpgzKysoEBgZy6NAhXr16RaNGjVi2bBm9e/fOsPzvPgOCg4MxMzMjJCQkT4j0/4X4+HgqVarEqVOn5AXEs4F7IXHc/hgn1laTCWs6BoZ8Dn5H+Wo1SYiNYd/4fpQ0saT1hPmZtpGSmMCHm6co9vWjKKJVqlSpwEZEODs7o6Ojw9KlS3PblCwnOjqayZMnM2bMGCpWrJhJVMptvySnmDdvHhEREaxcuTK3TZEjJ8sJCwvjzp07YkTbs2fPsLKyElNHHRwcsnxSPbuRjTF27drF/v37uXjxIsnJyaSmpv6UaJiYmMiMGTPQ19dn7NixxMXF8fjxY1xcXOjRoweHDx+mQYMGLFq0iJSUFJSU0ipeCYKAp6cn7dq1Y8uWLfJuy3KyDbmwJueP5OrVqxw7doz169eTlJSEiooKX79+RVFRkUKFCokP/5o1a7J8+XJq1qwpriuVSpFIJHnCcVVRUSEmJibLUnRkDnnPnj0ZMWLEd9Mr5syZw5MnT/j777/Fz1JSUpBIJAV2kCbnz+D8+fM8ePCAWbNm0bBhQ+zt7Zk/P02kuHDhAsOGDcPHx4dChQplyf1vb2/PvHnz8r2Tt2LFCm7dusWJEydy25QCyak3Mfh+ThT//lEq6LPr57i6+S9cDlz/7nbMiqjSulzBj3B68uQJLVq04MWLFwW6Hln6geO3REVF4ePjg6OjYw5blXNUrVqVdevWFehjlCNHRmxsLPfu3ROj2u7fv0/58uXF1NHatWuLnTDzA1evXqVevXo/HDd8G6Hm6elJ165dad68OcOHD6dSpUpA2phEW1ub0aNHs3r1akaPHk1kZCQ6OjoZtufr68unT58y1YqWIycrkTcvkPNH8vLlSwwMDIA0cUoQBDQ1NcUUUNmg2cjIiPj4eFJTU5FKpSgrK+eZlANBELK0eYFMYATo2rUra9asySCs7dq1ix49elCrVq1MNRB+5NzLkZNXSUxM5N69e9StW5fw8HAiIyMpWrQo9+7do06dOjRu3JgZM2YAcPHiRZo1a8azZ8+ytIlG27Zt+fvvv/O1sBYbG8vixYu5dOlSbptSYElMlX7385iIUM78NY03HvdIjPuKIJVSqLDOL2+nICEIAqNGjWLevHkFWlSDH793/f39GT9+PI8fP2b48OFMnTo1hy3Lfvz9/QkLCxM7ucuRU9DR0NCgYcOGNGzYEEgrVePh4cHt27c5fPgwLi4uaGpqZmiIYGJikieCANIjK53RoEGD79omi/f5VnC7d+8eI0eOZNiwYURGRuLn50fFihUJCQlBW1ub+/fvExoaSpMmTXj79m0mYa1KlSpUqVIl245LjhyQR6zJkfNdZJFbs2bN4sSJEzRq1Ag9PT2UlJRITExkxIgRud7NLyUlBVVVVVJTU7Nke2vWrCE0NJTWrVtTqlQp1q5dS8uWLXn69CknT55EVVW1QBSBliMH0py79u3bo6qqysePH3FxccHR0ZHBgwdTvHhxtm7dCkDfvn2RSCRs27ZNdAKzylH18/OjcePGBAUF5Tnn92dZsmQJjx49ypBWLydr+VHEmteF4yQlxNNm8mLUtYvw7Po5Ti2ezJQLT7+7nT8hYu3QoUMsXLiQx48f/5ER1CEhIaxbt47w8HDWr19PSEgIenp6vHr1SozwKAgsXLiQDx8+sHbt2tw2RY6cPIEgCLx48UJMHb19+zbR0dEZGiJUq1Yt33TFfPz4MbNnz8bR0RF7e3vU1dUZNGgQJUqUoHLlyuzfv59Vq1ahpaXFjRs3OHr0KHPnzqVfv365bbqcPxh5mIkcOemQCWqyQW65cuUYOnQoSUlJJCQkkJiYyJcvX/JE1FpWRqtJpVKKFi1KREQEW7ZsQVtbm4sXL7J8+XJWrlzJhg0bxLpp/5R+IkdOfkAQBBQUFBgzZgwtW7akdevWdOzYEalUStu2bXF3d6dp06ZoaGigpKSUbaKRiYkJGhoaPH78GFtb22zZR3YSExPD0qVLuXHjRm6bUqApUUgR/yjEGmsyEuO+oqZZGDXNwnwJ/Yjb7h+LDEoSKF6oYAtNcXFxTJw4kd27d/9xopqHhwfKyso8ePAAT09Pse6Ynp4eT548Yd68ecydOxcLC4vcNTSLOHr0KMuWLcttM+TIyTNIJBJMTEwwMTFh4MCBAHz48EEU2Xbv3s3r16+pXr26GNFmb2+fZTWa/wvfNoC6evUqc+bMYfLkyUilUlq3bs379+85efIkampq6Onp0axZM44dO8b27dtp2bIlixcvFrNuvtfoQI6cnEAesSZHDv/8EE5OThbFtrz0oI6OjqZUqVLExMRk2TaDg4MJCAjAx8eHR48ekZycTPny5alZs6a8i46cAsezZ88ICAigf//+LFq0CGdnZyCtaLCnpyexsbG0bdsWyD5HbeLEiaiqqjJv3rws33Z24+rqip+fX4HpbJpX+VFXUK1iehyeMZzwt68oalge65adub1v43cj1hQlMNxMF3Xl3J8Uyi7mzp2Lt7c3R44cyW1TcpwNGzYwZswYXFxccHBwEJ9bgiAwfPhwzM3N6dy5M8WKFcv3g87Xr19Ts2ZNgoOD8/VxyJGT00RFReHu7i5GtXl4eFClShVRaKtVqxYlSpTIMXvSP4sEQcDb2xtLS0vWr19P1apV+fTpE0uXLsXe3p4lS5agrKxMZGQkFy5cYPXq1XTo0IGxY8cikUhQUFDI9882OfkfubAmR84/4OPjw9atW3n8+DEKCgpIJBKkUilXrlzJ9U5+ERERGBkZERkZ+Z+3lb6LWExMDFpaWsTExHDnzh1u3rxJiRIlGDNmzH/ejxw5OcWvdMa7ffs2LVu25MyZM9SqVYtly5YxYcIE8fvsdNbc3d0ZPHgw3t7e2bL97CIqKgojIyPu3LlD5cqVc9ucAs+x19G8/JL02+tX1lahfYXcLV+QnQQFBWFlZcXjx48pV65cbpuT40RERDBx4kTmzJkjFjCXRYGcOXMGW1tb9PT0xGfitxEi+Ym//vqLgIAANm7cmNumyJGTr0lISODhw4diVJu7uzv6+voZGiKUL18+20tV7N27l1u3bpGUlMSmTZtYtWoVkydPZujQoQwbNgwzMzOioqJQVVXlyJEjHD58mClTplCrVq1stUuOnF9Fns8l54/l7du3BAYGUq9ePR4+fEhoaCgtW7bMsMyAAQOYMGECFy9eZOfOnWzbto3ChQvnuqgmw8bG5rfWi42NRU1NDUVFRVGAiIyMZN26dXh4eKCgoECLFi3o0aMHzZo1IzY2Fvg1sUKOnNzg23Ru+L4wJvtMKpXi6OjIli1b6NGjB8WKFaN79+4Zls3OGdAaNWoQFhZGQECAmG6dH1ixYgWtWrWSi2o5hINeIQKjkzKlg/4MShKoqZ91TTfyIrJB2J8oqiUnJ3P9+nU+ffqU4bknE85atWoFQM+ePalSpQoTJkzIN3WWvsfRo0fFbs1y5Mj5fdTU1MRoNUjzi3x8fHBzc+PcuXNMmTIFiUSSoSGCubn5b/tEslge2XMqMjKSgQMHoqioiKmpKcePH+f48eMMHjyYtWvXMnDgQMzMzHjz5g1Tpkyhb9++9OzZk969e4vbk5X2kCMnLyCPWJPzx/L582caNGiAsbExZ8+e5fHjxxkGiSkpKdjZ2eF2/xH9py+kx1AXElOlXL98kd7tWmGpq5araTVSqZSvX7/+dBOF5ORkzp49y+7du9m1axdaWmlFrGUCw8KFCwkLC8PU1JRt27ahqamJn58fnTp1Euu1yJGT30gvqm3btg01NTXatm2LhoaGuIxMjPPx8SElJQUrK6sctXHgwIGYmpoyduzYHN3v7xIREUHlypV5+PAhFSpUyG1z/hgeh8ZyOSgGFH5+TlRJAg1KaVCteMEV1tzd3encuTPPnz/PE/WCcprY2Fg6duzIgAEDxFqRsoGmrBbr8ePHGTFiBO3bt+fp06csX75crOuYn9Kn3r59i62tLcHBwflaHJQjJz8gCAKBgYEZGiJ8+vQJBwcHMaqtevXqqKmp/et2pFKp+JwJDw+nWLFi+Pr6MnDgQO7cuQPA6tWrefv2LePGjePSpUvs3r0bXV1dXr9+Tffu3Rk/fry4zfz03JLz5yAX1uT80Tx58gRvb29atGhB8eLFM3wXGBHDugvu6JnZkJSYhGK6KDUlCQhAhcIqOOgVwkAj7zp4z58/Z/v27ezevRsjIyPmzJlD/fr1M0We2djYcPbsWSZPnkynTp1o0KABgwYNom7dujg7O8tfYnLyNT179kRVVRUNDQ3c3Nw4c+YMpUqVAr4fiZmT0Zlnz55l8eLF3Lp1K0f291+ZOnUq4eHhbN68ObdN+aNwdXXlZYISFh2d/4+9sw6Lonv/8L2U0i3Y2Ilii4GtKKjYhcFrF4iB3d2FjYqKnRjYjWKLig0qKiKgSPeyvz/47XzBREUWdO7req9XdmfOPLN7ds45n/PEDz3XZKmpqCor/fWiWmpqKrVq1cLR0ZGePXsq2hyFEBISQsOGDdm5cycWFhbCs0susEVHR1O9enXGjBlDr169CAoKIiAgADMzMwoVKiQsinNDeOjSpUt5+PChULVZREQkewkNDcXb2xtvb28+fPiAs7MzZcuWRV39x+PMx48f2bVrF7Nnz+by5ctoaWkxZMgQRo8ejaWlJU+fPqVv3750796dIUOGEBUVxeXLl2nYsKGwGSpGzojkZHL2CCoi8oepWrUqvXv3xtjYmNTUVOH1O2Hx7H+TiFG5qkhlZBDVIK06m1QGzyOT2P48kjth8dlteqZ48OCBIKJdvHiRy5cv07hx4y8GpejoaCZMmICuri7v37/HwMAAdXV1QkJChKIFOX3CLfLvIZVKM1W8Y82aNeTLl4+NGzcSERFBTEwMPXr04OPHjwBfnaRl58StSZMm3Lt3j7CwsGy75q8SFhbGunXrmDRpkqJN+afw8fFh5cqVzB5kT4/SupTWVUNZkrbJkx4VSVqhgte3LlPwne9fLaoBbNu2DWVlZXr06KFoUxSGiYkJp06dwt/fH/jfs0s+Zg8dOhQrKyuhOItMJmPZsmXs3LmTatWqceTIkQzHy1M/5ET27dtHhw4dFG2GiMg/S758+Wjfvj1Llixh69atWFhYfFVU+9xvx8PDg9q1a5OQkICenh7Tpk1DQ0ODMmXKCB5rZcqUQVdXlydPnvDkyRN0dHSwsbFBU1MTqVSaKVEtNjmVayFxHH4Vzd6ASA6/iuZaSBxxyanfPU9EJCsQPdZE/jiXL1+mX79+PH36VGE2mJmZ4ebmRtOmTb/6fvqH9Z2weM4Fxf5ULpvsCreZNm0a/v7+eHh4ZOr4uLg4VFVVvxkyId+hXr9+PVWrVqV69eps3bqVkydPYmpqysWLF7l165a4QySSI/H19aVMmTJfTOo+97xITk4mMTERZ2dnTE1NmTlzJpUqVSJfvnwcOnQoR4SPdezYERsbGxwcHBRtyncZPXo08fHxrFq1StGm/DNERkZiYWHB0qVLhWqPAHHJqdwPTyAsXkqiNJU8ykoYqytTySAvd65fJTo6Gmtr67/22R0dHU3ZsmU5cOAAtWrVUrQ5OZKjR4/St29fnj59ip6eHn5+fixatIhDhw7x6NEjYmNjcXJyYs2aNRQtWhQvLy9cXV3x8PDAwMBA0eZnICgoCHNzc96/f59j8tyKiIhk5FvrhcmTJ1O0aFH69evH+/fvqVKlCsePHyc1NZVZs2ahrq5OcHAwhQoVwt/fn/Xr11OxYsVMXzc4NpmrIfG8iEpCAhnWcLkpykgkdyMWLxD5Kt7e3ri4uPDw4UMhqeSyZcuoUaPGD8+VSCQ8f/6ckiVLAlC/fn2FimqZQT4IBMcm/1BUm29TlZjwMJSUlFBT16R03ca0GTuPc0GQX1OF/BqZf2A3bNgQe3t7YSc5q9HQ0Pju+0pKSiQnJ7Np0yYGDBgAQMuWLQkNDaVQoUIMHz4cIENuBBERRSMXzj7PhRYVFUV8fDwmJiY8ffqUa9euYWxsTKtWrVBVVSUpKUlI0tu0aVPevn2LikrOGAbt7OzYt29fjhbWgoOD2bRpU66rYJqbkclkDB48GGtr6wyiGoCGqhK1Tb7+jK9Xrx7Jycl/ragGMHfuXJo0aSKKat/B1taWM2fOoKenB6SlhoiLi2Pt2rW0b98ec3NzSpQogZqaGvHx8axZs4bWrVvniM2Gzzl48CCtW7cWRTURkRxK+k1NLy8vbt68SZcuXShbtizPnz+nRo0ayGQyTE1N6dGjB8OGDcPb25vVq1fj5eVFyZIlsbKyokOHDkil0kxf90cOEfLXn0cm8TIq6a9PkSCiOMTYLpEviIqKwtbWluHDhxMeHk5QUBBTp04lT548ijbtj3M1JD5Tnmq9l3kw/Uogw3acJejRPc67LSFFBj7vMxcSKk/k+TvIq+H8KvJB686dO5iYmODj40NiYiLGxsaMHj2arl27ChXWRFFNJKcgn7h93vejo6MZPXo0R48e5cqVK9ja2uLr60u/fv1YunQpAMWLF2fXrl00bNiQqKgodu3aRd68eX/rd5RVtGrVinPnzhEXF6doU77JvHnz6N27t5CbTuTPs3XrVu7du8fixYt/+tzvJXf/3fFH0bx48YJ169Yxd+5cRZuSY5E/18zNzYXX3r59S/ny5enatSunTp1CQ0ODt2/fYmhoyPz588mXLx/29vYZxKuc0lf27dtHx44dFW2GiIhIOuTPGXl1zhcvXrB9+3YWLFhASEgI48eP5/bt27Ro0YLp06dTt25dABo3bszdu3c5d+4cpqamdO3alYcPH2Jubk7ZsmWpVKmScI30z6Do6OgMc7afjTJKkcG5oNjfSuHj7u5OvXr1fvl8kb8XUVgT+YJnz54B0K1bN5SVlVFXV6d58+YZHnKbNm2iXLly6Ovr06JFCwIDAwGwsrICoHLlymhpabF7924uXLhAoUKFhHPnzZtHiRIl0NbWpnz58hw8eFB4b9q0adjb2wt/v3r1ColEQkpKCpD2MCtevDja2toUK1aM7du3AxAQEEDjxo0xNDTEyMiIHj16EBERkeG+fH19qVSpErq6unTp0oWEhAQgrTqora0tRsbG9LQogrtjdyJD3mXqs9LNl5/SdZrw3v8JAOe8r1Lbsg56enpUrlyZCxcuCMc2bNiQiRMnUrduXTQ0NOjZsyeXL19m2LBhaGlpMWzYMACcnJwoXLgwOjo6VKtWjcuXL2e4ZlJSEr169UJTU5MiRYpw69atTH228oFg9OjR6OvrU7JkSY4fP86VK1f4+PEjI0eOpESJEmhqamJmZsa6deuEc+Xf4eLFi8mXLx/58+dn8+bNmfqMRESyAvnESklJidTU1C88cbS1tWnSpAne3t7s2bOHSZMmsXTpUo4dO8b06dM5fvw448ePp2PHjjRp0gQ3N7dvtqUIDAwMqFGjBqdOnVK0KV/l7du3bNu2jbFjxyralH+G58+fM3r0aHbt2vVD7+OfJbfnzBwzZgwjR44URd7v8LXnWu3atTl06BAHDx5ER0eH5cuXc/DgQXx9fbl48SJDhw5FW1ubrVu3cuzYMYCvbmRkNyEhIdy7d49mzZop1A4REZH/IZVKheeM/P+9e/dm9erV7N27l9WrV9OqVSvmzp2Lg4MDEomEV69e4ejoyJYtW2jfvj0LFy4E0iJsUlJSOHHiBLNnzxbaS0pKQklJiZSUFG7fvs3KlSvx9PQEMhdl9DXk4lpwXHIWfRIiImnk7pmVyB+hdOnSKCsr07t3b44fP86nT58yvO/p6cmcOXM4cOAAYWFh1K9fn27dugEIVe3u3btHTEwMXbp0+aL9EiVKcPnyZSIjI5k6dSr29vYEBwf/0K7Y2FgcHR05fvw40dHRXL16VQgFk8lkjB8/nnfv3vH48WPevHnDtGnTMpy/Z88eTpw4wcuXL7l//z7u7u5A2oLdwcGBfTceM+G4L6p583J4/rhMfVYR74N4euUMBcqaExkazGbH7nQeNprw8HAWLVpEhw4dMiQk37ZtG+vXryc6Ohp3d3fq16+Pq6srMTExuLq6AlCjRg18fX0JDw+ne/fudOrUiZiYGIKCgggNDcXT0xNLS0uaNGlC3rx5GThwIB8/fiQlJeWHn+3169cpU6YMV69epXr16vTu3RtnZ2c2bNhA+fLlqVmzJvb29jRu3BhnZ2fu3LkjnPv+/XsiIyMJCgpi48aNDB069Iu+ISLyp5ALAVKpNIMokH4ns0uXLjRu3Bg/Pz+ePn1KREQEVapUYffu3bRv356LFy/SqlUrJk+e/NW2FI2dnZ0wYcxpzJ49m379+mFqaqpoU/4JkpKS6NatG1OnTs3gcfSryD0hk5KSePXqFZs2baJ3796CeJKbOH/+PLdv32bkyJGKNiXXUbt2bZYsWcKMGTMYM2YM79+/JzExkWnTptGlSxeqVq0KpCURX7VqFd27dycpKUk4X74hmd0cPHiQVq1aCRVMRUREFI88mmXVqlXs3bsXmUyGq6srwcHBJCeniVa2trYoKSmxd+9eBg8ejK6uLtra2qxZs4Y2bdrQvHlzob3hw4cLmyXy547cezYwMBBXV1ciIiIEZ43MRhl9jZ+JMhIRySw5Z0UhkmPQ0dHB29sbiURC//79MTY2pk2bNoSEhACwdu1axo8fT7ly5VBRUWHChAn4+voKXms/olOnThQoUAAlJSW6dOlCqVKluHHjRqbOVVJSws/Pj/j4ePLnz0+FChUAKFmyJM2aNSNPnjwYGxszcuRILl68mOFcR0dHChQogIGBAa1bt8bX1xcAQ0NDOnToQJQkDyoaWjTq68yL21e/a8e2kb2YblWCdf/ZUqxaHRr9NwJfr72UrtuUUnWboaSkRLNmzahevTpeXl7CeX369KFChQqoqKh8M0zH3t4eAwMDnj9/zuDBg0lMTGTjxo00a9aMHTt2oKqqysqVK2ncuDEtWrTg7t27lC1bFktLSwoWLPjdz7Zo0aL079+fsLAwSpYsSVhYGF5eXpQrV46NGzeybds2WrVqRYkSJWjevHkGbzlVVVWmTJmCqqoqrVq1QktLK8fnzhPJ3aQXzeTh0+nDktPn87h79y6pqan07NkTBwcH3r9/z61bt0hISKBFixYsX76ce/fuZWg/p4U4t2nThqNHjwoeujmFV69esWfPHlxcXBRtyj/DpEmTKFCgAEOHDs2S9uzt7dmxYwfHjh1j9uzZHDt2DDMzM1auXJkl7WcXKSkpjBgxgkWLFn21Ep3I95HJZEIIVpcuXTA1NWXSpEmYmZnRtWtXAgICWLZsGXfv3sXLywsdHR3Onj0reI8sXLiQ58+fZ7vdYhioiIhikEgkQsXh1NRU+vTpI1QF37hxI2pqauzatQsHBwc0NDS4efMmtWrVwtXVlY8fPzJgwACOHj1Knz59ePbsGYaGhgwdOpQRI0YwbNgw5s2b99UInTlz5tC5c2fs7e3R1tamTZs2tG7dmgULFlC9enXOe1/BsVVdptYzY7vLf+wY249Tq+Z88z7GVzXmw+sXwt97pw5j1dxpxCWn/jAq5+PHj7Rp0wYdHR1q1qxJQEBAhra/F2k0bdo0OnfuTK9evdDW1qZChQoZIo3u3LlDlSpV0NbWplOnTnTp0kWsup6LEYU1ka9Srlw53N3defv2LX5+frx7944RI0YAabsGTk5O6Onpoaenh4GBATKZjKCgoEy1LS/PLD/fz8+PDx8+/PA8TU1Ndu/ezdq1a8mfPz82NjY8eZIWghkSEkLXrl0pWLAgOjo62Nvbf9Fmek8LDQ0NYmJigLSd/IEDB9K3bnmm1S/G+n5tSIiOJPU7iTN7LtnK1EsBjPW6i934BajmVedT8Fv8zhyma6VCwr15e3tn8BgrXLjwd+8xNDSU1q1bkzdvXipXrkyBAgWIjIzk7t27DBs2jBEjRmBnZ8ejR49wdnZm9OjRyGQygoODadasGcuXL//uZyv/DKysrIQFm9ymo0eP0qRJExwcHFi4cCFeXl4ZvO0MDQ0zJHpP/xmKiGQ16b3J5FWmPvcuk/89atQoxowZg4uLCxs2bMDe3h4LCwv279/P1atXSUlJYcCAAYwePTrb7+NnKFq0KIULF+bq1e8L+9nNrFmzGDx4MEZGRoo25Z/g9OnT7Nixg02bNmVZmHJycjKHDx9m7969WFhYsGvXLqZPny6kU8gtuLm5oaenR4cOHRRtSq5EIpEIGxbVq1cnKiqKoKAgevXqRWJiIvb29rx8+ZLbt29jaWnJ1atX0dXV5cGDB7Rq1YrTp09TqlSpbLU5LCyMW7duYW1tna3XFRER+R+fV1sH8Pf3RyqV0qBBA2rVqkXlypVxdHRk5MiR7Nu3j969e6Ours7z58/ZsGEDHh4eQFqOzGrVqrF48WIePXqUIUJH7hWbmprK4cOH6d69OxEREbRp04YFCxYAad5srW1bU71NV6acf07lFu15dN6Ln0UC3A9Pu973onKGDh1K3rx5hQJOmzZtytDO1yKN0nv3Hj58mK5duwr3IU/9k5SURLt27ejTpw/h4eF069YtQwofkdyHKKyJ/JCyZcvSp08f/Pz8gDQhZt26dURERAj/xcfHU6dOnR+2FRgYSP/+/YWdjIiICCpWrCjk79DU1MyQvPv9+/cZzm/RogWnT58mODiYsmXL0r9/fwAmTJiARCLhwYMHREVF4eHhkemcIIsXL+bp06csOnSeaZdfMsDtMMBP5xTRMylAFZtO7Lr/VvhcYmNjGTfuf2Glny+SJBIJUqmUY8eO0b59e4oXL86ZM2dYu3Yt8fHxREREoKurS6tWrTLs1n7NtsaNG3Pw4MFvfrafI3el1tDQIDExkc6dOzN69GiCg4OJiIigVatWP3X/IiJZibKyMikpKSQmJn5XXJg+fTrh4eF4eXlx69Yttm/fzrJly3B0dERfX58DBw4QHR0tHK/oXEE/om3bthw6dEjRZgj4+/tz6NAhMewumwgLC6NPnz5s2bIlS4TMt2/fsnbtWvLmzcuyZctYt24dQ4cOFTymDQ0NOX/+PKGhob99rT/Np0+fmDp1KsuXL88ReRFzK+kXxzo6OuzYsYPatWvj7++PiooKy5cvF9JDmJubC3O7a9euER4ezuHDaXOkCxcuZMtzwdPTkxYtWogeiiIi2Uz6+ZKSkhJPnz7l/v37wtosLCyM1NRUzp49S/v27bl27RpaWlpIpVLq1KmDl5cXM2bMoGDBgnTv3p3evXsDaVWrnZycsLe3FzbtR40aRWJiohAJo6SkRL169WjVqhXKysr07NlTiDrYsWMHMqB21wEoq6pSsYkthSpW+en7S5VBWHyaE8W3onKkUin79+9nxowZaGpqUrFiReE+5HzvPuT3+7X7uHbtGikpKTg6OqKqqkr79u2pWbPmT9+HSM5BFNZEvuDJkycsXryYt2/fAvDmzRt27txJ7dq1ARg0aBBz587l4cOHAERGRrJ3717hfBMTE168ePFlw6TlSZNIJBgbGwOwefNmQbADsLCw4NKlS7x+/ZrIyMgMFb9CQkLw9PQkNjaWPHnyoKWlJUwQo6Oj0dLSQldXl6CgICEZZmaIjo5GXV0ds3wGJEV94uz6RZk+Nz0WrTrx+NJJAq6dQyqVkpCQwIULF4TP8XP8/f0JCQlh9OjRzJw5E2tra9zd3dHX16dly5ZIpVJmzJhBVFQUurq66OjoCCFiX1tUyENAv/XZfo+kpCShIqiamhrHjx/PsUnURf4NQkNDSUlJ+aIacfoS7DKZjFq1arF69WohN8fIkSPx9PRk69atzJw5k1GjRqGvry+ck9MX5HZ2dhw6dCjHCIAzZ85k+PDhGBgYKNqUvx6ZTIaDgwP29vY0adLkt9tLSEhgypQpnDx5EgcHB0xNTdHW1hbCqj9+/MiUKVNYs2ZNhnyaOZUZM2bQtm1bIbeqyO+T/jlTsGBBkpOTGTVqFOPHj+fJkyd07doVgGHDhjFnzhw2bdrEkydPBC+VMWPG/HEbxTBQEZHsJ31hp9TUVG7dukXfvn2Jjo7m5MmTQs7mPHny4OrqKkTBpKSk8PDhQ2bMmIFMJssQqVO0aNEM11i0aBHlypVDV1cXPT09IiMj+fDhg/BcMjExEY7V0NAgISGB0aNHs2HDBgxNC2SYz+ma/K+QzeZhXZhatyhT6xblrte+795nojTNg/dbUTlhYWGkpKT80n3I+TxiKiEhgZSUFN69e0fBggUz3MePIptEcjaisCbyBdra2ly/fp1atWqhqalJ7dq1qVixIosXLwagXbt2jB07lq5du6Kjo0PFihU5fvy4cP60adPo3bs3enp67NmzJ0Pb5cuXZ9SoUVhaWmJiYsKDBw+E0ssAzZo1o0uXLlSqVIlq1apha2srvJeamsqSJUuEPGkXL15kzZo1AEydOpU7d+6gq6uLjY0N7du3z/T9jhgxgvj4eGwqFGFlL2tKWzb+pc9Nz7QgfZZuY//qJRgbG1O4cGEWLlyYIU9UUlISW7dupWHDhtSpU4dKlSphaGjI06dP8fPzo127dlhbW1O6dGmKFi1K3rx5KVy4MPfv32f58uXcvn1b8L75fOH9o8/2e2hra7NixQo6d+6Mvr4+O3bsoE2bNr/0OYiI/CwymeyL/mxkZPRFomp5jrWIiAimT5/O69evadGiBSEhIcTGxrJ9+3ZatWqFRCLhypUrxMfHU6xYsey8ld+mUqVKyGSyTIvif5InT57g5eUlpAEQ+bO4uroSEhLCzJkzs6S9J0+ecP78eSHxuxx5WHVKSgoaGhq4uLhkyuNckTx58gQPDw9mzZqlaFP+KtIv6MzMzLh27Rr58+fnxo0b1K9fn9atWzNv3jyUlZXp378/NWvWxMXFBVtbW0aNGkX+/PmF86XfSZ/xq4SHh+Pj40PLli2zvG0REZGMyGQyoeiA3HFBVVWV//77j+3bt7Nw4UJKlChBkSJFOHXqFHnz5iVv3rwMGDCA9evXU7duXRISEtDR0aFgwYKoqKhkyL/9+vVr4d+XL19mwYIF7Nmzh0+fPgkROvLUHykpKcLzKSAggKioKCDNy7ZTp07EfPqYYd4YGfK/dEQOrruZfiWQ6VcCqdIqTZRXzatBcsL/ihVEf0jz0s6j/H0pxNjYGBUVFd68efPT9/Ej8ufPT1BQUIZj019HJPchkeWUbXERkRzA/hdRPI9M+vGB36C0rhrti+tkeE0mk3Hz5k02bdrEnj17sLS05L///qN169ZCtZsfcfbsWW7evImysjJ2dnZZkuMkISFBrLAlonCOHTtGnTp10NfX58GDBxgaGmJqavrNap1v3rzB1taW7t27M3bsWABevnyJhYUFR48e5fTp0wQGBrJq1Sq0tLSy81ayjBEjRmBoaChUL1UU3bp1w9zcnAkTJijUjn+B+/fv06RJE3x8fChZsmSWtTthwgSGDx9Ovnz5MhTrSElJybA7D//LZZgTadWqFU2aNGHUqFGKNuWvJX0OpTdv3hATE4OJiQlNmjTh0KFDgpeGu7s7a9as4cyZM2hra/PixQvy58//R0I13d3dOXLkCPv378/ytkVERP5HWFgY69ato1ixYvTo0QMfHx+OHz/O+vXradCgAY8ePaJ06dJ4eXnRu3dv4uPjKVGiBOvXr2fNmjXcvHmTunXrMnDgQNzc3GjatCldunRBIpGwadMmXr16RfPmzTEzM8Pb2xsvLy/69evHnTt3MDAwYN68eUyfPp2TJ09ibm5OrVq1KF68OFWrVuX9+/cEBQVx4cIFIiIiUFdXp0jxEtTuMYQaHR146n2aHS59seo9jOZDvz5fWevQCrMqtWk+dCLPr13AY3QfGvQczKxZs0h4fAN7e/sMEUZmZma/fR9NmzZl2rRp+Pv7C/nlXr16RbFixUhOTiY1NZWSJUsyZswYBg8ezLFjx+jUqRMuLi7iJlIuRfRYExFJRx0TdVR+cV2hIgFL0/9NLD98+MCyZcuoVKkS3bp1EzzPjh07RocOHTItqgE0adKEcePGMWbMGEqWLElcXBwJCQkkJSX9cgXB3bt3C5VeRUQUQVxcHNOmTWP//v0cOHCAqKgoDA0NvymqQVpZ944dOzJ27Fj8/PzYuHEjMpkMDw8PFi9ezN27d1m9ejVaWlo5JpzyZ7Gzs8PT01OhNvj5+XHu3DkcHR0Vase/QFxcHF27dmXx4sVZKqpBWmW1/PnzC6KaTCZDKpUKotrly5epXr069+/fz5DYPifh5eWFv78/w4cPV7QpfzXpn7uFCxemXLlyxMXFYWBggLe3N5DmQTZr1iwWLFhAVFQUI0eOZMiQIdSoUYPdu3dnuU1iGKiISPagpaWFqakpfn5+REdHc/v2bby8vBg1ahR+fn4EBARw/vx5rK2tMTIyolKlSnh7e5OSkkLr1q2ZMWMGLVq0AP7nverq6kpMTAympqb06dMHBwcH4XotWrT4aoQOpIWA5s+fH39/fwwMDBgwYAB2dnZAWi5uNTU19u7bz41D25nRoAR3vfZStn5zlNUypg5Jj+2YOTy+dIrpDUrge3w/5Ru2RAZUMvixg8Gv3sePUFNT48CBA2zcuBE9PT08PDywtbX9IgWKSO5B9FgTEfmMO2HxnAuKJeUnfhkqEmhcUJPKBmqcPn2ajRs3cvr0aVq3bk3fvn2xsrL6rljwI+SeBB8/fmTfvn3cuHGD5ORkVFRU+PDhA5MnT6ZGjRrf9TiIi4tDIpGgrq5OUlISmzdvZuDAgcL7N27coGrVql94MYiIZDXyxbuSkhLnzp1j+fLlrFq1SsgT+DWCgoIoUKAAXl5eTJgwgSJFimBqakpiYiL3798XFn5yLzWpVJrBQyc3kZKSgqmpKXfv3lVYvo2OHTtSu3btHF9J9W9g0KBBREdH4+HhkW0eY6GhoYwYMYIbN25gampKu3btcqQ3WHJyMubm5ixatChDagiR7OP8+fMsXryYo0eP0qFDB4oVK8akSZNYvHgxMTExjBs3jqioKJycnFizZk2G/ENfqySYWSIjIylSpAhv3rxBR0fnxyeIiIj8Fg8ePMDb25vOnTujpKTEqFGjqFu3Ll26dEFLS4thw4aRlJTE+vXr+fDhA97e3oLgBVnr9Sxf48TExKCtrQ18Oa9LH2W0qlcLanXoTfW23TN9ja9FGSmaWrVqMWjQoAzinUjuQVxBi4h8RlXjNK+zc0GxpKSmguT7k0K5qJY/JQIzs5qYmprSt29fNmzYgJ6eXpbYJPcwGDduHBEREXTr1g0NDQ2kUikxMTHC4vt7A9r69evR0NCgWrVqVKtWLYOo9vz5c86ePStWoxH543w+MWrcuDHVqlVDXV09wwJMPkFLTU0lNDQUBwcHevfujY2NDXPnzkVFRYXGjRuTnJxMz549hd1E+F8uttyKiooKNjY2eHp6CmXZsxNfX1+uXLnC1q1bs/3a/xoHDhzg9OnT3L1794+LavLf1JQpU9i9ezfNmjXj/PnzqKio4OjoyKFDhzIsknICq1atwszMDBsbG0Wb8k8ik8lo1KgRjRo14tmzZ9y5cwd3d3eCgoJ48+YNQ4cOxcTEBGVlZV6/fi140Pv5+VGxYkWUlJR+eZPjyJEjNGzYUBTVRESyCXNzc8zNzYW/GzduzOXLl6lSpQpVq1Zl2LBh2Nra8vr1a4oUKfLFeJGVY5i8crVcVJPJZBmeIxcvXqRk4eL4S1W55bWP988fUbpO5nNkfx5lpCguXrxImTJlMDIyYvv27dy/fx9ra2tFmyXyi4jCmojIZ0ilUqoaq7N15WKMajZFYlw47WGu/L+fi4oEZEAJHTUsTdXJr6HKy5fvOXbsGJUqVcpym9J7kQ0ePJjGjX+uwIJMJqNgwYJcvXqVBw8eUK5cOTQ0NIT3S5UqRf/+/YVjc2qeHZHcj7KyMpGRkUyfPh0LCwt69OiBrq7uF8fJ+6BEIsHU1JRevXqxb98+dHR0hLLljx49YsiQIdSqVStD1aXf8Q7NKdjZ2bF69WqFCGtTp05l3LhxGZ4RIlnPmzdvGDx4MJ6entkiHkgkEhITE7l37x6bNm3KUNxm3bp1vHz5MoOgrejfUVhYGLNnz+bixYvimKQgJBKJ0CdKly7N7du30dbW5tmzZwQHB1OjRg0Azp07R7NmzTAxMWHbtm0MGzaM6dOn899//6Gjo/NL/Wnfvn106NDhT9yWiMg/yeXLlylbtizGxsbfPU4uhnfp0oWrV69y5swZzMzMKFu2LFevXiVfvnzZZPH/kI8BsbGx5M2bl6dPnzK5c2eiY2LRLViUHgs2omNs+oNW0hAcIjRU/6TJmeLp06d07tyZ2NhYihcvzr59+zIUhRHJXYihoCIinyEfUJo0acKOHTsYNX4SNv2d0MpflMvXrlO+ZAnKFjbB3CAPmqrZ4xXz6tUrVFVV2blzJ9HR0XTr1k0o0ayiovJTRQhCQkKEEtaiiCaSHaT3WPD29sbR0ZEJEyZga2uLmpraNxdcx44d486dO0IS//Xr13P06FFGjRpFhQoVcHNzQ01NjZEjRwIZ+3Nu79uxsbHkz5+f169fZ5nna2a4efMm7dq1w9/fXyxu8geRSqU0adKE5s2bZ1txiK95Dvn5+XHs2DE8PT1p0qQJ6urqTJgwIUf8fgYPHoyamhrLly9XqB0iX+Lr68uQIUOYNWsWT5484caNG9StW5eWLVvStGlTbG1tKVOmDIsWLWLbtm2CN/zBgwdp0qTJD4Xk6OhoChUqxKtXr9DX18+OWxIR+at5+/YtQ4cOZejQoTRu3BgVFRUePnyIpqYmZmZmXxwvF8M9PT25fv06Y8aMEX6LihwfTp48ya5du9i8ebPw2s+k8JGLavLoJBGRrCT3b+uLiGQx8sFCX1+fxMREdNXViH5wlRYF8nBu0TjMJeHUNtFAQyX7fj4uLi6ULl0aV1dXVq1aRa1atShatCjFixdHQ0ODK1euAHwzWbs8p5VUKiUlJUUof63ohZPI301UVBSXLl0SFvMJCQmEhISwd+9e2rdvT968eb8pqsnDnN++fcu6desAGDBgAAULFmTw4ME8f/4cR0dHQVRLTU0V+nP6f+dWNDU1adiwIV5eXtl63SlTpjBhwgRRVPvDzJ07F4lEIlS2zQ4+F9Wio6OZNWsWN27c4P3798ycOZOjR49y9epVhRcyuH//Pvv372fq1KkKs0Hk21hYWDBr1iyWLFnC9evX6dGjB926dWPZsmXUqlWLRYsW0b9/f0qVKkVoaCgAPj4+eHh4cP/+/R+2r6amxogRI0RRTUTkN5DJZMJzvFChQjRv3pyzZ88SFRXFx48f8fT0FEIuP0c+N2vbti1z5szJ8FtU5PzK0tKSo0eP8uzZM+G1qsbq9CitS2ldNZQlfFGETkUCypK0nGo9SuuKoprIH0P0WBMR+QanTp2iQoUKKCkp0bNnT3x8fBg5ciTjx4/P1hCp390Zkp//4sUL/vvvPwoUKECBAgUwNTWlatWqWFhYYGBgkIUWi4ikkZSUxMCBAzEyMuLo0aNMnjyZrl27IpFIvujTX/M2i42N5dixY1y4cIFatWrRu3dvDh06xPLly9mwYcNXKygmJSWhoqKi8DC2rGDjxo2cPHmSPXv2ZMv1rl69Srdu3Xj27JlYleoP4uPjg52dHbdv36ZQoUIKs+PMmTOsWbOG/fv34+DgQMOGDUlOTubhw4csXbpUYXbJZDKaNGlCx44dGTJkiMLsEPk26Z/X8fHxqKurs2PHDpYuXcqpU6fQ19fn9OnT7N27lx49elC/fn1GjhxJhQoV6NKlS6ZCn5OTk7+56BcREfk+6cOvo6Ki0NHRISYmhmfPnlG1atXfak/RzJo1i2fPnn01D2xccir3wxMIi5eSKE0lj7ISxurKVDLIi4ZqzrBf5O9FFNZERL5DamoqSUlJCvfeSE1NRSaTIZPJ8PLy4uLFi8TExFCpUiW6d+/+3V1d+WA4ffp0oqKisLe3F0pnh4SEULRoUcaNG5eNdyPyNyMfUuSLLldXV0aNGsXMmTNxcXH55jmf53WS//vTp08cP34cd3d3NDU1efv2LVu3bqVcuXJftJOUlMSnT5+EUOfcTmhoKKVLlyYkJCRbhK6mTZvStWtX+vXr98ev9a8SGRmJhYUFS5cuVXihgBUrVnD//n3c3Nx4+fIltra21KxZk4EDB1K7dm0ALly4QMOGDbPVrgMHDjB16lTu3r0rVqnOwaQX15KTk+nevTutWrXCwcGBwMBAtmzZglQqxcnJCQMDA968eUOePHmE/Ew5IdxYRORvJjk5mUmTJnH+/HmcnJxo1qwZ+fLl++pvLzcJ2ZGRkZQsWZIrV65QunRpRZsjIiIgCmsiIkBKSgrKysoZBprXr19z9OhR3r17h1QqRVVVFalUyuTJk7NdaJMPgvPnz+f06dO0a9eOokWLsn37diQSCcuWLfthMtF+/foxcuRIypcvD6TlWrt58yb58uWjZs2aOWo3SiT3ExYWRp8+fRg8eDBXr17FxsYGS0vLL/qYvN/FxMQwatQoOnXqRNOmTTMIdAkJCQQGBuLl5UWHDh0oUqTIFxPD1NRUrl+/TunSpTE0NMzWe/2T1K9fnwkTJtCyZcs/ep2LFy/y33//8eTJk1wzuc5tyGQyunfvjp6eHmvWrFGoHfLfVZ06dVi2bBlWVlZs27YNf39/pk+fzv3791m8eDEBAQFs376dokWLZottCQkJlC9fng0bNtCkSZNsuaZI1rNq1SoCAgLo2LEjderUyTC/uHXrFtWrV1ewhSIifxefz+GPHTvG5s2bqVWrFiVKlODw4cPUqFGDoUOHZjgvNDSUzZs3Y2NjQ8WKFbPb7F9m5syZ+Pv7s2XLFkWbIiIiIG4FivzT3L17l02bNlG+fHkGDx4M/G9w6ty5M+XLl6dq1apIJBKkUinx8fEK2UGXL4QOHjzIjh07KF68OAC2trZYWVkRFhb2XWHt2bNnHDt2jJMnTzJx4kR69eqFiYkJtra2wjGiqCbyO6QXug4fPszq1atp0KABtra22NrakpCQ8NU+pqSkxP3793F1dcXPz4+goCD09fWpVq2akCstb968lClThjJlygBfT8Lu6+vLsWPHsLS0/PM3m420bduWQ4cO/VFhTSaTMXnyZKZMmSKKan+QrVu3cv/+fW7evKlQO+TjWd68eXF2dmb//v2ULFmSnj17kpKSwrJlyzh06BBNmzZl0qRJFC5cONtsW7p0KZUqVRJFtVyGfN4kk8mIjo4mOjoaMzMz6tSpA/xvfpGUlMTVq1eJi4vDysoqQxtRUVEEBQV91RtZRETk28hkMuE3lpKSgoqKCu/fv+fQoUNs2rQJHR0dEhISuHHjBj4+PlhaWgpztnz58uU6UQ3A0dGRkiVL4u/v/9W0ICIiikD0WBPJscQmp/IgPIHQdHHy+bIgTv7Tp0/s2LGDjRs38vHjRxwcHBg1ahTa2toZjqtbt65QFEDRyCettra29O/fn2bNmgmDYrNmzXB3d6dUqVLfbSMsLIwTJ07g5eXFkydPKFWqFJMmTaJSpUrZdBcifyPpd0nloQQvXrygfv36jB49Gmdn5++eHxISgpWVFXPmzMHIyIjLly9z69YtFi1aRMmSJTPlSSlfxD158uSHZeRzG8+fP8fKyoqgoKA/Jn6fOXOGoUOH8vDhQzH07g/x/Plz6tSpw7lz5zA3N1e0ORmQ5985ePAg69atw8TEhAEDBlC3bl3hmOzwaH737h2VKlXi2rVr4kLpLyAuLg4NDY2v9h15Xrb0eHl5cezYMRwcHESPNhGRHxAZGYmurq7wt4+PD6tWraJw4cL06tWLkiVL0qpVK6ytrRk1ahSvX79m8+bNSCQSXFxcyJs3b64Px54+fTovX77E3d1d0aaIiACisCaSAwmOTeZqSDwvopKQQIbyySoSkAHFddSoY6JOfs0vvStSUlI4e/YsLVq0EF5LTU3l/PnzbNy4ES8vL6ytrenbty+NGzf+wvPF29sbdXV1Fi9eTMeOHalbty7q6urkzZsXNTW1P3TXmePOnTv069cPMzMz8uXLx8mTJxk5ciSDBg36pqdJamoqr1+/Jioqiri4OAoVKkRUVBTr16+nbdu2NGrUKNcPriKKZ9euXRw4cIA2bdrQtGlTbt++jbm5OYULF87Qt+R9Tf5/eXXP48ePA2kl4SdNmiR4zhgZGf2wfzo6OpInTx4WLlz4x+9TEVSsWBE3Nzch71VWIpPJqFOnDo6OjnTr1i3L2xdJ89KpU6cOffr0YdiwYYo25wuSkpL477//+PjxI7169aJ169ZoaWnx6dMnNm/eLFTe/dP06dMHExMT5s+fny3XE/kzfOt5nZSU9M05lHzRr6+vj6OjI5CzkqWLiOQkjhw5gre3t/CsvHHjBiNGjMDJyYmbN2/y/v17evfujaamJkOGDOHy5ctoa2uze/duvL29GTZsmBABkJuJiIigZMmSXL9+nRIlSijaHBERUVgTyVncCYvnXFBsBjHtW6hIoHFBzQxlk2UyGfb29uzYsYNHjx6hqamJu7s7mzdvRldXl759+9K9e/dv5mBKSkqicePGwmTu/v37FChQABUVFaRSKZqamty4cSNL7vVXSU5O5sKFC0RHR1OnTh1MTU2/epw8XG7btm3s3LmTT58+YWVlhba2NkOHDhXL2ItkGbNmzcLHx4fRo0ezevVqSpQowbx584SQBDnpF0ohISFCkYG6devStm1bobjB3LlzuXHjBpUrV2by5MlfiN/pCQ4OpkKFCjx+/PivKVrwORMnTkQqlTJv3rwsb9vLy4sxY8Zw//79737OIr+Oi4sLT548wdPTM8duYHh5eVGqVCny58/P48ePSUxMxNzcHBcXF8qWLYuzs/NXQ7Czips3b9K2bVuePHmSqYqRIrmLV69e4ebmxqBBg75ZCffSpUtUqVKFpKQkYY72J/uciEhuQiqVAmT4PTx58oSyZcuyceNGTp06xe7du0lOTsbNzY3Q0FBGjBjByJEjUVVVZe3atcTExACgpaWlkHv4E0ybNo3Xr1+zadMmRZsiIiIKa4rA3d0dNzc3vL29f/rcV69eUaxYMZKTk/+6kJ2fEdXkfC6ujRkzhtWrV5OQkEChQoWIiYmha9eu9O3blypVqmRqUSOTyZBKpaSmpqKmpkZMTAxJSUkkJiaSkpKSrflmPic+Pp4DBw5w+/ZtkpOThaqgmpqa3zynfPnyHDx4kKJFi+Lr68uSJUvQ1tZm5cqVaGhoZKP1In8Dny904uPjmTVrFhMmTGD9+vXcu3cPNze3L55P6UW1WbNmcfToURo0aMCAAQMICwtj3rx51KhRg86dO+Pg4ECzZs348OEDK1eu/K49I0eORCaTsXTp0qy/2RzCzZs36dWrF48fP87SdmUyGTVq1GDcuHF07NgxS9sWSeP06dM4ODjg6+uLkZGRos35gs+9iyZNmsTWrVupWbMmBQoUYMWKFVSuXBlvb+8v0iVkpQ1169alX79+/Pfff3/kGiKKJzg4GDU1te8WlwkICGDYsGF06tSJDh06ZAh1ExH5l3Fzc6N69epYWFjw6NEjPn36RLt27Xj37h3nz5/nwIEDjBs3jqJFi3L8+HEWL17MqVOnuHv3LsuXL8fNzQ1VVdUMEQN/A58+faJUqVKi15pIjuCf9bE2MzNDXV0dLS0tTExM6NOnj6Dk/ywNGzbEzc0tiy1UPNOmTUNVVRUtLS20tLQoV64c+/fv/yPXCo5N/mlRDdLCRM8FxRIcl8zo0aNZtmwZcXFxpKamEhoairW1NRs2bMDKygodHR2qVavGxYsXv9umRCJBRUWFlJQU/P39efHiBW/fviU6OlohYQnp+6qenh7Ozs4UKlSIhg0bcvLkSfr27UtcXNxXzw0NDaV48eJoamqSN29eateuzZ49ewgICCAyMvKn7HB3d6devXpZcUsiuRCZTIZMJhNEtQsXLhAbG4u6ujpBQUEUKlSIvHnzsnnz5q+K/vLfjqurK/7+/qxdu5aQkBC2bdsGpIltly9fZvTo0UyaNIl27drx7NkzPn78yLf2f0JCQnB3d2fMmDF/6K5zBtWqVSMqKoonT55kabuHDx8mJSWF9u3bZ2m7ImnIK+Nu2bIlR4pqQIbF1aNHjzh27Bh+fn7s27ePu3fvMm3aNMqVK0dUVNQ3f4e/y86dO0lKSqJPnz5/pH2RnEH+/Pm/Kqql71cbNmwgLi6OBw8eULVqVY4ePSq8J/fYERH5V5DPuwCMjY2xsbHBysqKBQsWULduXczNzdmwYQNFixZFS0uL7du3A1CwYEEMDAyIi4ujWrVqbN26FTU1NeF5/7eIagD6+voMHTqUOXPmKNoUEZF/V1iDtBj1mJgY7ty5w61bt5g1a9ZPnS+TyUhNTf1D1uUMunTpQkxMDDExMSxbtgx7e3tCQkKy/DpXQ+J/WlSTkyKDq8FxrF69GqlUipqaGjo6OiQlJREaGoqLiwsxMTFERUUxePBg2rdv/8MJWkxMDIsWLaJRo0bUqFGD9u3bU7ZsWQYNGgSQ7d+7vK+WKlWKVq1acenSJTp06MC+fft4+PAhKSkpXz3P2NiY2rVr07dvX/bv38+VK1fw8vIiKiqK/PnzZ+s9iORuJBIJEomEJ0+eYGVlxaRJk7C3t2fbtm3Y2dkxbdo0Bg4c+N0J26VLl5g/fz5NmjTBwsKCyZMnExERwalTp9DT0+PEiRPs378ffX19OnbsyLBhwzA0NPxmm4sXL6ZHjx4UKFDgT912jkBJSYm2bdvi6emZZW2mpqYyZcoUpk+fLuYx+gPIZDIcHBzo2bNnrqlwWb58eYyNjTl27BghISGUKVOGffv20a5dO4yMjEhJSeH27dv4+vpm2TVjY2MZO3Ysy5YtE/vhP4r8+b5161Y+ffrEpEmTWLp0KaNHj+b27dtcu3aN8PBwMSRU5J9Bvr6Uz7sA1NXVUVFRoVy5ckKy/pkzZ7JixQoMDQ3p1KkTp0+fpmPHjrRu3ZoOHTpkCPn8m4XpESNG4OnpyYsXLxRtisg/jjiLIU3Zb9myJX5+fgBcu3aNOnXqoKenR+XKlblw4YJwbMOGDZk4cSJ169ZFQ0ODnj17cvnyZYYNG4aWlhbDhg3j1atXSCSSDGLH515tMpmMYcOGoaurS9myZTl79qzwnpmZGWfOnBH+njZtGvb29hls3r59O0WKFMHIyIjZs2cLr9+4cQNLS0v09PTInz8/w4YNIykpSXhfIpGwdu1aSpUqhZ6eHkOHDs30LnSLFi3Q1tYmICAASHO/tbW1xdjYGH19fWxtbXn79m2Ge548eTJ169ZFW1ub5s2b8+HDB+H9rVu3UrRoUQwNDVm/aC7zbarifz3Nm+yN3x1cezRlWv1izG5anqOLJ//v3l3+Y3az8kyzKs66vq0JCXjCi+hkPkTGkJKSQlBQEN7e3pw8eZJ8+fJluPfu3bsTHh4uiIMBAQE0btwYQ0NDjIyM6NGjBxERETx9+lSoCFqsWDE0NDTImzcv/v7+JCQkCAPdggULyJ8/PwUKFMDNzQ2JRIK/vz+QlrOmfPnyaGtrU7BgQRYtWpSpz+17lCxZklKlSuHn50dsbCz+/v4UKlSIqVOnUqRIEUxMTLC3txfC5y5evMjatWvR1tamV69edOjQATc3N9TU1NDT08PAwID69esLQuG8efMoUaIE2traQggpwOPHjxk0aBA+Pj6C55z8O07fr0Wvtr+bS5cu0bNnT5ycnPD29qZt27b4+vpibGzM0KFDv1gYfz6Rq1q1Kl27dmXdunW8evWKEiVK0L9/f/z9/dm9ezeJiYkkJCRw7do1tm/fTuvWrb9pS1hYGG5ubkJetr8dOzu7LBXWDhw4gJqaGm3atMmyNkX+x8qVKwkNDWXmzJmKNiVTyH+rrq6ubNy4kfLly6OqqsqSJUvo0qULZ8+epXz58gwYMCBLBbAFCxZQr149cdz4x/Hz8+PcuXNYWVlhaWkJwL1799i5cyeXLl3CysoqwzxZRORvRL4ek0gkKCkpERwczLJly7h16xbNmzcXig+Eh4eTnJxMnTp1sLCwYPbs2dSsWZPdu3fj6OjI48eP6dKlS4a2/2ZhWl9fn8GDB4teayIKRxTWgDdv3uDl5UWVKlUICgrCxsaGSZMmER4ezqJFi+jQoQNhYWHC8du2bWP9+vVER0fj7u5O/fr1cXV1JSYmBldX10xdUx4L/uHDB6ZPn0779u0JDw/PtM3e3t48ffqUs2fPMmPGDCH3jrKyMkuXLuXDhw/4+Phw9uxZVq9eneHco0ePcvPmTe7fv8+ePXs4efLkD68nk8k4duwYSUlJlC9fHkjzeHBwcCAwMJDXr1+jrq7+RcWzHTt2sHnzZkJDQ0lKShLEpUePHjFkyBC2b9/OId8AEmKiiAoLFs47snAidboNYNrll4w+fINKzdoK75Wp04TRh24w6cxjCpStxO6Jg5AA98MTUFJSwsjICHNzc5o2bZqhUqZUKmXr1q0UK1ZMSHIuk8kYP34879694/Hjx7x584Zp06aRkJCAnp4eMpmMd+/eceLECfbs2cObN29wd3dHJpNx4sQJlixZwpkzZ/D3988gwAL07duXdevWER0djZ+fH40bN8705/YtpFIpU6dOJSoqCltbWypVqsSDBw948uQJvr6++Pv78/79e65cucKVK1fo3bs3wcHBFCpUiOfPn/PkyRNKlCiBhYUFYWFhhISEMGfOHEEoLFGiBJcvXyYyMpKpU6dib29PcHAw5cqVY+3atVhaWhITE0NERESm7BXJvaQX3NN7aAYEBAjPQxsbG7p3706NGjW+mlNNWVmZDx8+MH78eFxcXHj16hWDBw/G1taW8ePHEx4eToUKFRgxYgT29vbkyZNH2KCoUaPGd+1bunQpnTt3VmjOw+ykYcOGPH78mPfv3/92W/LnyIwZM/6qkJCcwr1795g5cyY7duz4ZrXmnIaysjIymYzSpUvj5OQkeA01b96c2bNnC5Uau3btSqVKlbLkmoGBgbi6uopVQEXw8vLC0NCQevXqoaWlxcWLF/Hx8eHgwYO4uLjQtGnTL+ZYkP3RAyIif4o1a9awc+dOwSljyZIlNG/enPj4eFxcXNi9eze1a9emcuXKzJkzRxhbFi1ahLu7O2/fviVfvnxYWVmhoaHxV3uofQ1nZ2cOHjzIq1evFG2KyD/MPy2s2dnZoaenR7169WjQoAETJkzAw8ODVq1a0apVK5SUlGjWrBnVq1fHy8tLOK9Pnz5UqFABFRWVX54058uXjxEjRqCqqkqXLl0oU6YMx44dy/T5U6dORV1dncqVK1O5cmXu3bsHpOXiqV27NioqKpiZmTFw4MAvcoqNGzcOPT09ihQpQqNGjb4b1rFnzx709PTQ0tKiTZs2TJgwQfBWMjQ0pEOHDmhoaKCtrc3EiRO/uJaDgwOlS5dGXV2dzp07C9fat28frVu3pl69enySKtNk0Fgk/G+Bp6yiwsc3L4n99JE8GloUqVRdeK+6XQ/yaGqhopaHpoNcCH72kJioKMLivz6ILFq0SLiHESNGMHPmTGHnpmTJkjRr1ow8efJgbGzMyJEjuXjxIlpaWlSoUAElJSUsLCwYO3Ys27Zto2TJkvj6+iKTydizZw8ODg5UqFABDQ0Npk2bluG6qqqqPHr0iKioKPT19alatWqmP7fPkffVEydOYGBgwJo1a1i8eDGXLl0iPDycZcuWYWBggLa2NjNnzuT69evUrVuXzp07C6LoggUL8PX1RV1dneDgYAIDA1FVVaV+/frC4rpTp04UKFAAJSUlunTpQqlSpRReBVUk+0mf2Hb16tWMHz+eo0ePYmVlxezZszly5AghISEYGxtTqVIl1NTUvmhDSUmJ0NBQbGxs0NHRQSaTMWPGDG7evImdnR2FChWif//+pKSkULVqVUxMTIRF0o92VsPDw1m3bh3jxo3L+pvPoaipqWFtbc2RI0d+u63du3ejo6ODtbV1Flgmkp64uDi6devG4sWLKVmypKLN+SVat27N8OHDuX//PhYWFly+fJnTp09z7tw59u3bJ+Sjffjw4W9dZ+zYsQwfPpwiRYpkhdkiuRgXFxecnJwoWrQoUVFRzJkzBwcHB8qVK4e/vz/+/v7UqVMHSMsd++jRIyBtnBHFNZHcjHwTU01NjV27dhEaGkpqaip58+bl0qVL2NnZ8ezZM3bs2IGfnx/z58/H09OT9evXC/N3Pz+/L6rt/s0eal/DwMBA9FoTUTj/tLB26NAhIiIiCAwMZPXq1airqxMYGMjevXvR09MT/vP29iY4+H/eVFnhIVGwYMEMngJFixbl3bt3mT7f1NRU+LeGhoYw0X327Bm2traYmpqio6PDhAkTMoRffu/cr9G5c2ciIiKIjY0lICCArVu3sm7dOiBtATFw4ECKFi2Kjo4OVlZWREREZNgl+da13r17J3yOidJU1NQ10NDTF47tMHUZH14HsKRDHVztm/H40ikAUqVSTqyYwcI2NZhWvxgLbNLEqtiIj7x9EygUWkifV2D06NFEREQQFxfHrVu3GDNmDMePHwfSkp937dqVggULoqOjg729PR8+fKBcuXI4OzujpKSEk5MTlpaWdOvWjTZt2hATE4OysnKGe4Av+8X+/fvx8vKiaNGiNGjQAB8fn0x/bp8j76uxsbHMnz+fvn37IpVKUVFRIT4+nlq1agn91dramtDQUHr06IGNjQ358+fHy8sLFRUVunXrRtOmTSlZsiTNmzenePHizJs3T7jO1q1bsbCwENry8/P7ov+I/L2kD0OQyWScPn2aPXv2UK5cOXr06MHt27ext7enVKlSvHv3DqlU+sXmwufebfnz52f8+PEsXLiQNm3asG/fPvLly0f//v2pW7duhnMzG2K2bNky2rVrh5mZ2e/dcC6jbdu2HDp06LfaSElJYfr06cycOVP0VvsDjBw5kipVqtCzZ09Fm/LTpO8PiYmJrFmzhtGjR3PixAmKFStG4cKFuX79OnFxcYSFhbF8+fJfLmhw+fJlrl69+s+Ecot8G3kfkgusW7duJSoqihEjRgCwceNGatWqRaFChdi1a5ewQdqhQwfCw8PF3HwiuZLU1NQMm5h9+/ZFTU2NvXv3oqSkxJAhQzhw4AADBw5kw4YNGBoasmvXLgoXLoyrqysPHz6kX79+FChQgAIFCvyx4jK5CWdnZ/bv3y96rYkoDHE0+ozChQvTs2dPIiIihP9iY2MzeEZ8vhj5/G9NTU2ADJUaPw/fCQoKyvAQfP36tZCAW1NT87vnfo/BgwdTtmxZnj9/Luz6ZdXD1szMjJYtWwoeE4sXL+bp06dcv36dqKgoLl26BJCp6+XPn1/IK5ZHWYnkhHjiIj4J7xsVKUG3ueuZeOYxDfoMZ4fLfyTFx+J7fD+PLpyg75p9TL30Apdjd4RrFipcVCi08DWxUCKRULFiRerWrSt4B06YMAGJRMKDBw+IiorCw8MDmUyGj48Pa9euBUBXV5chQ4aQL18+7tz53/XS3wOkhRSnp0aNGnh6ehIaGoqdnR2dO3f+7c9twYIFTJo0iejoaFq3bk2vXr1QV1fn4cOHREREcPDgQUaPHk3btm0F0UEikVCqVCkWLVpEcHAwVlZWLF68mBcvXnD48GGWLFnC2bNnCQwMpH///ri6uvLx40ciIiKoWLFiBrHlc36nr4rkLKRSqfAd79mzh65du3L69GmWL19Onz59mDZtGsOHD0dFRYV58+ZRvnz5DDuiUVFRQJo4dv36daZOnYq2tjYAt27dAsDe3p6YmBguXbpE6dKlGTlyJCoqKj/1jIqIiBC86P41WrZsyeXLl4mOjv7lNrZv346pqWmuSaifmzhw4ACnT59mzZo1uV601NTUJCQkJMMz/ePHj/Ts2ZNy5cqhqqrK+vXrf+k+pVIpTk5OzJ8/Hw0Njaw0WyQX8nkfGjZsmFANdNu2bYSGhlKnTh2eP3/OwoUL+fjxIzt37qR27dpCpIB8DBG910RyC0pKSkgkEp49eyasLUaNGsWRI0eECuBnzpxhzJgxtGzZEiMjI44dO8aZM2do0aIFy5cvp3fv3kJ7uX3MyQoMDQ0ZOHAgc+fOVbQpIv8oorD2Gfb29hw5coSTJ08ilUpJSEjgwoUL300ub2JikqESibGxMQULFsTDwwOpVMqmTZuEhP9yQkNDWbFiBcnJyezdu5fHjx/TqlUrACwsLNi1axfJycncunWLffv2Zdr+6OhodHR00NLS4smTJ6xZs+YnP4Fv8/btW06cOEGFChWEa6mrq6Onp0d4eDjTp0/PdFsdO3bkyJEjXL16FX1lKefWL0TG/xbXd4/tJebTB5SUlFDX1gVAIlEiMS4GFTU1NHQNSE6I46RrWuEGFQkYq//Y7fnJkyd4e3tnuActLS10dXUJCgpi3rx5SKVSTpw4wd27dwEEj6379+8LRQ9SU1Pp3Lkzmzdv5vHjx8TFxWVIUp2UlMT27duJjIxEVVUVHR0dYVf1Vz+35ORk1q1bx+rVq5FIJJw9e5b79+/Tv39/nJ2dCQ0NpWrVqnz48AFPT088PT3ZvXu34AnXr18/VqxYwdGjR/H390cmk6Grq4uysjJKSkrExsYikUgwNjYGYPPmzUJBD0jr52/fvs1QDMPCwoIDBw4QFxeHv78/GzduzNS9iOQ8lJWVSU1NZd++fezZs4dq1apx+/ZtoXy7s7MzFSpUICEhgbx585InTx7hXF9fX6ZNm8bNmzcBuHPnDoaGhlSsWBEzMzMh5x9AQkICurq6Ga79MxPCFStWYGtrS4kSJX73lnMdurq61KlTJ1N5Mb9GcnIyM2bMEHOr/QHevHnD4MGD2b59Ozo6Ooo257eQjxmLFi0SvIiWLFlCzZo1MTIy4tGjR0JKiF/B3d0ddXV1unbtmhXmiuRykpKSBEFMLpDp6emRnJzMkydPqFSpEg0bNuTEiRN06dKFKlWqUL9+fUJCQjAyMgLS8g5/+vRJmGf9a/mlRHIH6ftlfHw8ffr0oXfv3mzfvp0JEyZQs2ZNypUrx/bt20lNTaVChQqsXLmSJUuWcOfOHZycnGjQoIHQhigkf8nIkSPZt28fgYGBijZF5B9EFNY+o3Dhwnh6ejJnzhyMjY0pXLgwCxcu/O7Dy8nJiX379qGvry8k+N2wYQMLFy7E0NCQhw8fCrkh5NSqVYvnz59jZGTExIkT2bdvH4aGhkBa+eSAgAD09fWZOnUq3bt3z7T9ixYtYseOHWhra9O/f/8vqsL8LLt37xZCK2vUqEHdunWZOnUqkFbeOD4+HiMjI2rXrv1T+Xrkg0XXrl2xsyiBmromWvpGKKum5Wp6dvUcyzrWZ2rdohxZOJGuc9ejmledqrad0ctfmLnW5iztUI8i5tUAkAGVDPJ+9VoLFixAS0sLTU1NmjdvjoODAwMHDgTSctXduXMHXV1dbGxsaNKkCTExMezatYsbN24QHBzMpEmTKFWqFK6urhm8wFq2bImjoyONGjWiZMmS1K5dG0AQHLZt24aZmRk6OjqsXbtWECh+5XNr3bo1+vr6vH//nrFjx7Ju3TqKFy9Oamoq8+fPF65fuHBhTp06RZcuXVi3bh1Pnz4lLCyM9u3b8/DhQ9q3b8/z589p2rQpWlpaWFpaMmTIEBo1akT58uUZNWoUlpaWmJiY8ODBgwyheo0bN6ZChQqYmpoKk1lnZ2fU1NQwMTGhd+/e9OjRI9N9QETxfL74WLRoEf3796dfv364uLgwcuRI4uLi2Lt3LwDr1q0TvNDSU7hwYaKjo4Vy5x8+fBDCsbt27SoIyPXr16dGjRo0atTol+yNiopi5cqVTJgw4ZfO/xv4nXDQLVu2UKxYsQwTc5HfRyqVCtVy5eNAbkZeyKB8+fI0bNiQcuXKcfLkSfbs2cOUKVOIiYnh8uXLwsLlZ0SMqKgoJk2axPLly0VxVwSpVEqzZs1ITk4G/rfJoqysjKqqKrNnz6Znz55IJBJevXqFg4MD8+bNY/LkyTx48AANDQ0SEhLo0KEDU6dO5cyZM8L5IiI5DXm/fPToEdeuXaNSpUr4+PigpqbGkSNH8Pf3Z/Dgwfj4+HD79m0cHR2pU6cOfn5+rFmzhj59+mRIvyGGQX+JkZERAwYMEL3WRBSCRCYGZYvkADwevKN3lSKMOnQdg4JFf/r80rpqtC/++14CqampKCkpsXbtWmQyGYMHDyYsLAxVVVW0tbW/O1l7/PgxFStWJDEx8YsKiVnF2bNnGTBgAI0bN6ZQoUKMHz9eSBwvz9UQGxtLTEwMxsbGKCkpERMTw4ULFyhSpAiVKlXKkNNB5N9F3tcBLl68KIgtLVu2pGrVqsyePZvw8HD27dtHZGQkI0eO/KL/p28jMDCQmTNnUr16dW7duoWJiQnjx48nNTWVmJgYtLS0ePv2bYaqwj87KZwzZw6PHj3Cw8Pjd28/1xIUFIS5uTkhISE/VTwnMTGR0qVLs3Pnzi82ekR+j1mzZnH27FnOnDnz1y3opVIpbm5uDBw4kGfPnrFlyxZevnxJUFAQysrK7Ny5Uyg8kpnfs4uLC2FhYWzevDkbrBfJ6Vy6dAknJychQiA9n89Vxo0bR0BAAKtWrSJfvnzC6wMGDMDf35/hw4ezZMkSSpYsiZub21/3WxTJfVy6dInChQtTrFgxIC0txoQJE+jevTthYWGcPHlSSB80b948ypUrB6QVdnn79q0w15H/DlJTU5FIJOIc/gd8+PCB0qVL4+vrKxbHEclWRGFNRGEcOXKEJk2apAlYjs6cveLDsB3nfnrAUJFAj9K65Nf4tQqt6ZEvDsaMGYOpqSmjRo367vEHDx6kVatWxMXF0bt3b5SUlH47ufi3iIyMpGrVqgwfPpyCBQtSuXJlSpQoIXgXSCQSPn36xJAhQwgJCUFJSYkGDRrQoEEDSpUqRf78+f+IXSK5Fz8/P5ycnMiXLx+mpqY0bNiQChUq0Lx5c/bv30+VKlV4/fo1pqamX1T+lEqlwsLl/fv3mJqaEhQUxPjx47l79y5aWlpUrFiRJ0+eIJPJcHNzo2zZssCXC6bMEB0dTYkSJbh48aIw+fxXqVmzJnPnzv2pPGmrV6/myJEjQuEWkazBx8cHOzs77ty5Q8GCBRVtTpaSXixLSkriv//+o3jx4rRp04bq1aszd+5c3rx5w+rVqzPV3vPnz7G0tOTBgwfieCQCgKOjIyYmJkycODFTx0+bNk3wnOzatSu7du1izpw5eHt7o6Ojw82bN1m3bh1r1qzJsPGQlJT01erVIiJ/AvnSukePHjRr1oxevXqxZcsWJkyYwLRp0xg0aBBXr15lwIABuLi40KtXLwCuXbuGoaEh+fLl49atWxnG+F/ZjPyXGTduHFFRUZken0REsgLxFyqiMDw9PYVqNkGvAli52QNVpZ8X1RoX1MwSUQ3+NxiampoSGhpKUFAQkZGRxMfHfzXcZd26deTLl08QuLIyp93nSKVSzMzMGDFiBJ06daJ06dKCsCEPVV61ahVly5Zl1KhRfPjwgdDQUDp16vRH7RLJPaTfR0lKSmL9+vXMnj2buXPncuHCBW7dukXJkiUZOHAgw4cPJzU1lSJFinx1QSLve926dWPAgAFYW1tz8+ZNli9fTsWKFbG3t2fKlCmcOnWKQ4cOCaIa/FqS3TVr1tC4ceN/XlQDsLOz+ykBPz4+njlz5jBjxow/Z9Q/SGRkJN27d2f9+vV/nagGGcOMbt26RWRkJDNmzKB69eoAqKiooK2tTUpKSqbaGz16NKNHjxZFNREgbd6yf/9+OnbsmKljIU1YO3ToEPXq1SMqKoqJEycye/ZsdHR0iI+Px9fXl7i4OJSVlYmMjMTLy4vg4GDU1NSIiYkRPSVF/iiBgYE0atSII0eOIJFI6NOnD5cvXyYkJIRevXoJ3r2QFrJoZ2eHl5cXQUFBjBo1iv/++4/Q0FB0dXW/2DgTRbWfY9SoUezevfuLwnIiIn8S0WNNJEdxJyyec0GxpGSiV8pFtarG6ll2ffmO0Lp165g8eTKmpqYULVoUJSUlQkNDmTNnDo0aNVJIOGV0dDSjRo2iVKlSdOvWjQIFCnwx0LZp04Zly5Yxb948bGxsaNu2LX369KF27doMGjRI3PH6R0nfX1NSUlBRUSE6OppevXphaWnJwYMH6dixI6NGjSIxMZE8efKwZcsWevTo8dWwZnl7gwcPRiqVsmTJEnx8fOjcuTOHDh1CT0+PSZMm0aNHjyxJUB4bG0uJEiU4c+YMFStW/O32cjuPHj3C2tqawMDATD2Hli9fzrlz5/D09MwG6/4NZDIZ3bt3R19f/5/YEX/x4gUtWrTg+fPnvHz5Eh8fH3bt2sXYsWMz5OL81th4+vRpBg0axMOHD8mb9+v5UEX+La5evcrAgQN58OBBps9J37/279/P4cOH2bJlC5BWYMrZ2ZklS5bw+vVrtm3bRmxsLIGBgWzevJmlS5eSmJjI/v37/8j9iIgcOnSIOXPmcOPGDeG1mzdvUr16dSQSCatXr2b37t1cvHgRSNv0mjBhAu/fv8fAwIBZs2ahr6+vKPP/OlxcXIiNjWXVqlWKNkXkH0EU1kRyHMFxyfi8jycgKgkJZBDZVCRphQpK6KhhaaqeZZ5qn/P+/XtiYmJITEwkISFByBNlbm4uJO7PbkJDQ7G0tERfX59Vq1ZRq1Yt4H9iYGpqKqdOnaJ+/fpMnDiRkiVLMmzYMOrWrcu6deuoWLGimF/tHyT9d+7h4cGZM2fo1asXjRs3ZsKECezYsYPTp09TqlQpIK3Yh6OjI3ny5PlmX5GH1YwbN44WLVoIxQj27dvHmjVrOHv2LF5eXtSuXRsDA4PfvoclS5Zw9erVn6qQ/Dcjk8koXbo0u3fvpmrVqt89Ni4ujhIlSnD8+HEsLCyyx8B/AHd3dxYuXMjNmzfR0NBQtDl/FPkYM3XqVG7dukVoaCiNGzemcuXKdO/enaNHjyKRSLCxsfnq5k1KSgoWFhbMnDmTdu3aKeguRHIaI0eORFdXVyiI9SvI+1tYWBgTJ07EzMyMgQMHUr9+fVasWEHTpk158OABw4YNIzk5GS8vL6Gibfp0BiIiWcHz588ZNGgQc+fOpWbNml99HtaqVYuhQ4cK4Z+AsKEJYr/MSkJDQylbtiz379+nUKFCijZH5B9AFNZEcixxyancD08gLF5KojSVPMpKGKsrU8kgLxqqf97rys/Pj7t372JtbY2xsTHh4eHo6enlOI+v1atXU6JECWrXro2uri4A169fZ+jQoSgpKVG0aFGhqqPIv8PnIuqRI0dYs2YNDRs2xMvLi9GjR6OiosK+ffsoUqQINjY2TJw4kfnz51OpUqWvimqpqalERkbSpk0b9u7di6urK/fu3ePIkSMAvHv3jvHjxwseBPJzfuc3Ex8fT4kSJfDy8hKFoXSMGTMGDQ0Npk+f/t3jFi5cyPXr10VRMgt5/vw5derU4dy5c5ibmyvanD9O+mfJ8+fPUVFRIW/evCQmJjJ37lw2bNhA06ZN2bt3L7q6ul/85letWsWBAwc4c+aMuLEjAqT1qaJFi3L8+HEqVKjw2+3du3cPJycnjh07xtq1a3n37h2LFy8GICAggFKlSnHgwAHs7OwIDAykaNG0IlmiiCGSlcTExLBu3TqePHnChg0bMrwn72sHDhxg2LBhvHz5UhDTIO03IZPJctwaI7czZswYEhISWLlypaJNEfkHEIU1EZF0yBcQ7u7uHDlyhGPHjnHp0iVq1qxJ69atmTp1KtWrV1eY59f9+/d5/vw5rVu3Rk1NjeTkZMaPH4+vry86Ojo0bNiQxo0bU7ZsWSIiIoiNjUVTUxMjIyMxDPQfIv13ffDgQU6cOEHevHnp168f5ubmLFu2jNevX9OpUycMDAxYuHAhycnJLFq0CGNj4wxtyft6+j4/dOhQzM3NGTRoENWrV6dcuXIMHDiQdevWIZFI2Lp1a5bdy8qVKzl79uwfKwqSW/H29mbo0KHcu3fvm8dER0dTsmRJzp07lyWLV5E0b806derQp08fhg0bpmhzFEJCQgKbNm1i06ZNWFhYMHr0aK5du8bFixe/yGElk8lwcHBg1KhR/4QIKZI5bty4Qe/evXn06FGWzaWioqLQ0dFhw4YNhISEMGnSJCAtD6hMJmPXrl0kJSUxYsQIPn78yJIlSyhYsKDoyS+SZchkMh48eMDMmTOZMmXKN595Z86coWnTptls3b9JSEgI5cqV48GDB39lLlSRnIW4yhYRSYdcZ16zZg3u7u60a9dOeC02NlahwpS/vz+TJk2iWbNmQjJ5VVVVFi1axIkTJ+jVqxd+fn4MHToUJycnLl++jLq6uhC6Kopqfz8vX74kICAAJSUlZDIZe/fuZfXq1VhYWODj48PChQuBNGFMKpVy4sQJ9PT0cHNzw93d/QtRDf5XaMDf3194zdramkePHgFw5coVDA0NOXz4MIaGhoKolhV7NgkJCcyfP5/Jkyf/dlt/G5aWlgQHB/Py5ctvHrNy5UqaNGkiimpZyKRJkyhQoABDhw5VtCkKY+rUqWzYsIElS5YI1X5r166Nj4/PF/1RIpGwcuVKUVQTycC+ffvo2LFjlglaMpkMbW1tAAwNDdm0aRMHDx7EycmJq1evCgWcAgMDmT9/Ph06dKBjx468efNGGKuWLVuW4W8RkZ9BLtCmpKTw4cOHr6YIkPctUVTLPkxMTPjvv/+YP3++ok0R+QcQV9oiIl/B2NiY0NDQDINjUlKS8O/s3N2UD8QRERFUrlxZmDwCQqVSFRUV6tWrx6pVq9i5cyclSpRg4cKFrF+/PtvsFFEs4eHheHh44OHhQVJSEu7u7ixZsoR27doxePBgdu3axePHjzlw4ACqqqrY29vz9OlToqOjgS/7tLxyVWpqKuHh4bRq1YqJEyeyZcsWWrRowblz5zh+/Dh58uRh6dKlLFiwgGXLlgFp/TIrfiObN2/GwsKCatWq/XZbfxvKysrcuHFDCGn6nMjISJYuXfpb+YtEMnL69Gl27NjBpk2b/kkPF/lYNH78eO7evYuVlRXx8fGcOHECV1dXevfuTbFixQB48+YN79+/B0BTU1NhNovkPGQymSCsZRUSiUT4TbZv3x5PT09u377NypUrhXHJxcUFR0dHmjdvTqlSpShYsCBv375FSUmJa9euMXLkSNTU1P7J37bIryN/Lsr7zY4dO1BSUmLatGnCHFw+nxL7lmIYM2YMHh4evHv3TtGmiPzliKGgIiJfYevWrbx48YKdO3cyceJETp06ha6uLvPmzcsgbGUnvr6+JCQkUKtWrQyheW/evGHOnDlIpVI+fPjAgQMHhHPkoRFiGOi/waVLlzh69CitWrXCwsKCsWPHYmJiwrBhw8iXLx+7d+9m7ty5nD59GmNjYyIjI4W8fOlJHxojz0fz8eNH/Pz8mDp1Ks2bN+fevXtYWFgwfvz4DHlqsiqsJikpiZIlS7J3716hUIdI5pk+fTovXrzIkO9O5NcJCwvDwsKCrVu30qRJE0Wbo1Dk48n169e5ceMGAQEBGBkZ0b59e8qXL8+mTZvw8vLCxsYGBwcHRZsrksO4c+cOnTt35vnz539EaEg/3zl16hTNmzfn1KlTLFu2jJ07dxIaGkqzZs3Q09PD19cXgEqVKtGqVSvmzZv3RRsiIl8jNTWVw4cPY21t/dVKx/v378fNzY3jx48LaVlEFMfIkSORSqUsX75c0aaI/MWIo4aIyFfo1asXjRo1olWrVhw/fhwrKyuWLl2qMFEN0iZ+NWrUECai8h2wlStXYmZmRqFChQT7zp8/z/nz59HR0QHEMNC/Hfn+SO3atWnfvj3Vq1dHT0+Prl278urVK65fv05KSgpdunShatWqnDp1CuCrolp6b7MBAwbQsWNH+vTpg7e3Nw0aNMDLywtDQ0MSEhLYvHnzF8mfs2qhtGXLFsqXLy+Kar9ASkoKvr6+TJkyRdGm/BXI84T17NnznxfV4H/jyenTp3n69Cl169bF0dGR8uXLEx0dzYsXL3BwcMDa2lrBlorkRLI6DPRz5KkQAJo3bw7A27dvCQkJQVdXFy0tLYoVK8bo0aMBcHR0JCgoiDx58tC5c2eCg4PFOZPId7l06RK1atVizpw5fPz48avHWFlZUbx4ccqWLcuQIUNISUnJZitF0uPi4sK2bdsIDg5WtCkifzGix5qIyFdISkrizZs3hISEoKKiQqlSpdDX11eoTSkpKSgrK38xGW3atClnzpyhT58+dOzYEVtbWwYMGECJEiUYO3asmJj3H2fZsmUEBATQs2dPatasSXJyMqqqqj88b8+ePVy7dg1nZ2e8vb1xc3Nj5MiR2NjYCLv5Xbt2pXnz5vz3339ZanNycjKlS5fGw8ODunXrZmnb/woJCQlf3UUX+XlWrFiBh4cHV65cydRv529HPqb4+/uTkpJC2bJlgf9VvYuNjUVFRSVDxTsREUjrO2XKlGHHjh1Ur149W689Y8YM7ty5w9WrV2nYsCF79uzh5s2bNGzYEG9vb6pUqUKnTp0oU6YMs2bNymCzOIcSAXj27Bljx47l7t27zJ07ly5dunxXhE1KSsLb25vGjRtno5Ui38LZ2RmApUuXKtgSkb8VcUtGROQrbN68mRYtWjB9+nQmTZpExYoV2b9/v0JskWvfCQkJX0zuEhIS6Nq1K507d+b69evY2toCaRW3unfvnu22ivx55DnRfoS83wwYMICIiAjCwsIAvikMpN9j2bhxI3369KFcuXIULlyYNm3a0KdPH5YtW8aVK1eEiWT+/PlJSEj4ndv5Kh4eHhQvXlwU1X4DUVTLGu7du8fMmTPZuXOnKKr9P/JxqGTJkoKoBgieq0pKSkL+T7lntYgIgJ+fH0lJSdmaN1M+tk2ZMoUlS5YwePBgJk6cCICDgwOzZ8+mSpUqREZGUrlyZQwNDQE4cuQIkZGRoqgmwocPH3B0dKROnTrUrl2bJ0+e0K1bt++KajKZDDU1NVFUy0G4uLiwZcsW0WtN5I8heqyJiHyGTCajcOHCBAYGCguF9+/fY2VlxbNnz7LdnoSEBKKjo9HT0xMWdnKPobVr11KzZk08PDzw8/NDQ0MDdXV1tLW1Wb9+vbjT+pfh7u7O+/fv6d+/vzD59/X1pXTp0l+tQCXvJ58+ffqux6X8uPReThMnTuTSpUtcvHgRJSUlYmJiWLVqFffu3cPd3Z3Q0FCmT5/O+PHjKV68eJbdo9wDZuPGjTRo0CDL2hUR+Vni4uKoVq0aEyZMoGfPnoo2J9dw6tQpAgMD6d+/v6JNEclhTJ06ldjYWBYtWpTt1/58PuTs7MzJkyeFCtcXL15ky5Yt9OnThwIFCjBixAj+++8/2rZti5KSEhKJRMy99o+RmJjIypUrmT9/Pl26dGHq1KlfrZ4uknsYMWIESkpKLFmyRNGmiPyFiMKaiMhnJCUl0apVK3bt2oWmpiaqqqrExsbSokULrl27lu32BAcH8/TpU+rUqYOamprwekxMDNbW1nh7exMdHc3NmzeJiopCT0+PunXroqqqKk4C/zIePHjAokWL6Ny5My1btuTixYs8fvyY/v37Z8qb5ntC69OnTxk5ciS1atVCW1sbZ2dnOnXqhIqKCjt37gTS+pyKioogvqWkpKCiopJ1Nwhs27YNNzc3Ll68mKXt/u2IInrWM2jQIGJiYvDw8FC0KTmekJAQTExMhL8fP35M2bJlxT4pkoEKFSqwceNGateurZDrp39OPn78GCMjI4yNjXny5AkeHh6oqqoydepUnJ2dMTExoUuXLhQrVoynT5+io6ND/vz5AThx4oSYQ/AvRiaTsWfPHsaPH0/FihVZsGBBBu9ckdzLu3fvqFixIo8ePcLU1FTR5oj8ZYjCmojIZyQkJODg4MCbN2/o0qULycnJHD16lEqVKtG4cWPy5MlDixYtFGaffGK4Z88eDhw4gIuLC1WrVlWYPSJ/ls+LA3h4eKCtrU3btm2z7BoBAQHY2dmxaNEi7ty5w65duzh58iSmpqY0bdoUY2NjQVz7mk1ZhVQqpXz58qxevVpMEp9JPs+Z96e+m3+NAwcOMGbMGO7evSsUgRH5NlOnTqVPnz4UK1ZM0aaI5FAePXpEixYtCAwMVPiGX/pNx8TERLZv387hw4eFTR153lp58RwDAwOmTp2Kk5MTu3btwsXFhadPn6Kurq7I2xD5A1y9epVRo0aRmJjI4sWLadSokaJNEsliHB0dUVVVZfHixYo2ReQvQ3RlERH5DKlUSuHChbG2tiYwMJDAwEDKlStHTEwMe/bs4ejRo9luU3r9W/7voKAgrl27Rv/+/XF1deXDhw/ZbpfIn0MmkyGTyQSR5PTp0wDY29t/VVRLTEwkNjY2022n//+HDx8YPHgwZcuWZffu3cydOxdTU1Pi4uLw9PT8ItTzTwk3e/bswcjISMxJkkmmTJnCvHnzWLt2LW/evAEgMjISEHNb/Q5v3rxh8ODBbN++XRTVfoD8GdK3b98MHtWfk5yczK1bt0hOTs4u00RyGPv376dDhw4KF9UgY6X0PHnyYG1tzeDBgzEyMuLevXuYm5tjbm4OwMiRIzE3N8fJyYmgoCCWLFnCypUrUVdXJzU1FdE/4e8gICCAzp0706VLFwYPHsytW7dEUe0vZezYsWzevJmQkBBFmyLylyF6rImIfIP4+HgkEgkqKiooKSmhpKREamoqUqlUYUms/fz8ePbsGVWqVBE8Aw4cOICbmxvPnj2jQoUK7NixA01NTYXYJ5L1vHr1ir59+xISEkLDhg2xs7OjadOmGUJagoODWb16NT179qR06dLfbe/znfo8efLw+PFjrK2tUVZW5siRI1SoUIG3b9+yYcMGJk6cKCyY/2S4YWpqKhUrVmTp0qUK9QjNLWzbto21a9fSp08fnj17RmRkJKampmzcuJEjR45QtWpVMTz0F5BKpTRp0oTmzZszYcIERZvzV/D27VvOnz/P7du3adKkCa1bt1a0SSIKoHLlyqxatYp69eop2pQMfP6c7Nu3L1paWixfvpwTJ07Qvn17nj59SuHChXFwcEBTUxNXV1eePHkihgf+BXz69IlZs2axZcsWnJ2dcXZ2/mrOWpG/i+HDh5M3b14WLlyoaFNE/iIUv20kIpLDiI+PZ8eOHfz333907NiRtm3b0qxZM4YOHYqSkpJCK8M9efKEvXv3Mnv2bO7duwdA+/bt8fLy4uLFi1hbW4ui2l/E0aNHsbe3x8XFhXv37mFiYsKpU6d49OgREolE2CnPnz8/gwYN+qGoBv/bqV++fDm9e/fm8OHD5MuXj6FDh1KzZk10dXV5+fIl7du3J0+ePBm8UP6kSLN//360tbVp3rz5H7vG30TTpk3x8vKif//+9OzZk0aNGqGqqkqZMmW4cOECHz58EEW1X2Du3LlIJBLGjh2raFP+GlJSUnj06BG1a9cWC5L8ozx79oywsDDq1KmjaFO+4PPn5KhRo3j16hWTJk2iVatWzJ49m8KFC7N9+3b8/PxYuHAh165dY9q0aZw5cybDufJquCI5n6SkJJYtW0aZMmWIiYnBz8+PiRMniqLaP8LYsWPZuHEjoaGhijZF5C9C9FgTEfl/5LuW165dw97eni1btqCtrU1ycjIJCQloampiYWGhaDNJSUnh6tWr1KxZk7x585KSkiJ41InkXtJ7ksn74qVLl2jUqBHHjh3D2toaX19f9uzZg56eHkOHDkVTUzPTBSrS597au3cvmzZtws7OjoMHD9KtWzdKlizJzZs32bZtG/r6+rRv354hQ4b80XuWk5qaioWFBXPnzsXGxiZbrpmbiYyMRFdXN8Nr8j7j4+PDli1bUFJSokePHtStW1dBVuY+fHx8sLOz486dOxQsWFDR5vwVyJ876Qud/ImiJyI5m7lz5xIUFISrq6uiTfku6cdTd3d3tm7dyrlz5wgODqZt27ZMnz4dKysr5s2bh0wmY8aMGSgpKXHz5k1q1KgBpKVW+PjxI2XKlFHkrYh8A5lMxsGDBxk7diylSpVi4cKFVKhQQdFmiSiAYcOGoaGhwYIFCxRtishfgiisiYh8xs2bN9m0aRNr1qxRtCkZ2LZtG1WrVhUmAJ+HL4hhX7mX9N/d9u3bCQ4OpmbNmlhZWbFkyRI8PT2FKpmHDx/m4MGDtG7dmvbt22eq/fSLhYMHD/LmzRtq1apFrVq1OHDgAIcPH8ba2prOnTuTlJREfHw8+vr6X5z7pzh48CCzZs3i1q1bYh/+ATKZjCpVqmBubs7cuXMpVKiQ0H9kMhkpKSk8fPiQM2fO8P79exYsWCCK7pkgMjISCwsLli1blqWFQf5VPt8oOHDgAIcOHWLFihXo6+uLFav/MapVq8bixYtp2LChok35IenHY7kw3KFDB3R0dNi8eTNbtmzh+vXrDB48GHNzc44fP86sWbPYsGED5cuX586dO5w/f55Ro0Yp+E7+LmKTU3kQnkBovJREaSp5lJXIp65MJYO8aKhm7lly48YNRo0aRWRkJIsXL6ZZs2Z/2GqRnMzbt2+pXLkyR48eZd26dYwcOZJKlSop2iyRXIw4qxER+YySJUtSqFAhHB0dOX36NOfOnePixYv4+/sr1K758+fz+vVr4e/PBQhRkMh9XL16FUj77lJTUxkzZgyrV68mJSWFCRMmsHr1arp160ahQoWEfE/NmjWja9eu2NraZvo68gVst27dWLhwITt27GD06NFAWihx06ZN2bVrF0eOHEFNTU0Q1WQy2R9f/Mp3/adMmSL24UwgkUi4fPky+fLlY/jw4cJr8v+rqqpiYWGBqakpefLkEcWLTCCTyRg0aBAtW7YURbUsQt7vfHx8GD58OJs3b+b9+/ds2rQpw/sifz8vXrzg7du31K9fX9GmZIr045C8n7Zu3Zply5YBaZtb1atXx9zcnJiYGDZv3kz37t0pX748GzduJDIyUhDV+vfvz8ePH8ViMr9BcGwy+19EsfphON7BcTz6lEhAVDKPPiXiHRzHqofh7H8RRXDstwujBAYG0r17d9q1a4eDgwN3794VRTURYmJi0NXVpX79+uzYsYMnT54o2iSRXI7oiy8i8v/IdykDAgI4e/YsiYmJBAYGkpyczIcPH2jdujWTJ0/OEFKXnbYNGDCAmTNncuTIEWxsbIRk83IUYZfIr5Oamsq2bduoUqUK6urqhIeH8+DBA06ePImWlhaVKlXiwoULvHjxgkmTJtGsWTOaNm1K48aNheT+3/NSlFfhs7S0JDk5mYULF6KlpcXVq1eJiYmhT58+9O3bl40bN2Jvb09ycjLVq1fPsODNDqHr6NGjyGQy2rRp88ev9TcglUrR1tZm8eLFpKSkCK+l/+3LZDLq1atHx44dFWVmrmLLli08ePCAmzdvKtqUXEVSUtI3K4GGh4ezePFigoKCMDc3p0OHDpQrVw4bGxtKliwpCpj/EPv376ddu3a5cn4iHwP79OkDpHm4BAYGCp53M2bMwMDAgF69ehESEsKkSZME8Xj27Nncv38fQ0NDRZj+V3AnLJ5zQbGkfCO2Sv7688gkXkYl0bigJlWN1YX3IyMjmTNnDm5ubgwfPpz169ejpaWVDZaL5HSCgoIoX748kDZnUldX/8EZIiI/RtwyFBH5f+RR0bdu3aJChQpcuXIFT09PvLy8uHHjBpMnTwZQyORQIpHg6OjIihUrKFOmDFu3bmX9+vWEhoby8OFDwS4fH59st03k50hMTATSdsLXrFnDunXrePbsGUZGRmhoaODp6QlAy5YtefnyJffv36dcuXKsW7cOc3PzDG19S/iSyWQcOXIEDw8PLl++TGxsLLdv3+bt27c8ffoULS0tXF1dCQoKon///gA4ODhQsGBBsjM7gNxbbfLkyaK3WiZRVlbG19eXzZs3C4myP38mSSQSzMzMyJs3ryJMzFU8e/aMMWPGsHPnTnFi/ROkpqYyfvx4kpO/7iUilUpJSEigU6dO2Nvb06hRI0xNTalevbqY4P0fY9++fX+NyF+oUCG6detG//79sbe3Z9u2bfTu3RttbW0GDBhAz549admyJZ8+feLQoUPs3r0bgAsXLtCiRQsCAgIUfAe5hx+Jap+TIoNzQbHcCYsnOTmZVatWUaZMGV69ekVCQgKTJ08WRTURgYIFC7Jt2zahWEVSUpI4DxX5bURhTUTkMwwNDUlMTOTFixeEhYURHR0tiCGKQi52VK9enX79+uHi4sKKFStwcXFh7NixdOvWDTs7O1auXKlQO0W+T3BwMKtWrQLScvmFhoby6tUrnJ2dSUlJoX79+gQEBHDnzh0kEgnFixcXRBMbGxuMjY0zJXxJJBJq1qxJxYoVOXXqFHp6ekyfPh1jY2POnTtHUFAQpqamLFu2TEgsLm83OycWJ0+eJD4+nnbt2mXbNf8GgoODWbhwIe3bt+fAgQOiUPGLJCUl0b17d6ZNm/aFaC3yfeLi4khMTPxqEQKZTIaxsTETJkygZcuWmJiYEBwcjJOTE5s2beLRo0fs2rULQAyR+8sJDAwkICDgr6oGO2rUKDZu3Ii+vj5OTk5YWlqyadMm3r17x8yZMwHQ19fn7NmzmJmZAdCwYUN0dHS4ceOGAi3PeZiZmWWorLpr1y709fU5cOLsT4lqclJkcOZNNA3bduTQoUOcPHmS3bt3Exsbmys9JkX+LD169OD27dsULVqUpKQkoqKiFG2SSC5HLF4gIvL/yJMpHzt2jGHDhqGpqUmJEiUACAsLY/DgwfTs2TNHJF1OSUmhWbNmLF++HIDr16/z4cMHOnToQOnSpRVqm8j36dChAwEBAaSmprJ27Vrq1KlD7dq1cXBwwMbGhi1btrB//35KlixJQEAABw8epEiRIr90rdevXyOVSilWrBgAFy9eZNOmTVhZWWFra4uJiYlwbHYXv5DJZNSpU4cRI0bQpUuXbLtubuVr34+bmxtr1qxBJpMxceJE7OzsxMXDT+Di4sKTJ0/w9PQUd6p/gcykH5A/57Zu3Urp0qWZPHky+fLlo06dOpw+fZoCBQpkk7UiimDp0qX4+fmxceNGRZuSZaR/FicmJvLp0yfKli3LwYMHadSokSAWp58n3rp1i+3bt1O7dm1xvEuHmZkZbm5uNG3alC1btjBy5EiOHDlCsGlFnkcm/VqjMhk6CZ8YbFlKfK6LZIr4+HhatWrFmDFjaNDM+reLZIj8u4jCmojIZ4SHh/Pp0ydSUlKIi4sjNTWV2NhYihcvTqFChRRmV/rKfxKJhIiICPT09BRmj0jm+HzxuWjRIubOncuKFSvo0aMHAPfu3aNDhw4cPHgQc3Nzrly5wsePH4W8Y78reqU///Dhw2zYsIHJkydTs2bN37iz3+PMmTMMHz4cPz8/UQz6CY4cOULRokUzVK6ytbXlw4cPXLt2TYGW5S5Onz6Ng4MDvr6+GBkZKdqcHMvvPnuuX7+Og4MDCxcuxMbGBkjbqLKxsWHGjBlYW1tnlakiOZB69eoxceJEWrZsqWhTspT0v4t3796xb98+HB0dv3rsx48f2bp1K69fv2by5MkYGBhkp6k5GrmwFhAQwMSJEzlx4gTlKldl5onb7J0xkuDnD5FIJJSybETbcfNR19YFYL5NVSw7/8fdY3v5+PYVlVvY0XzYRPZNHc4r3+sUqViNC0cOUDCfIa9evaJYsWIkJyd/1cNWRATSimRcDYnnRVQSEsjgLakiARlQXEeNOibq5NdUVZSZIjkcUVgTEfkKT58+5caNG9SpU4cSJUrw8eNHdHR0UFXN/oepXJi5c+cOlSpVIiUlRcidlN1eRiI/R3pR7erVq5QtWxY9PT2OHj3KpEmTOHnyJPnz5wdg7ty5bNq0iWfPnmX4TrOqKEX6vnLv3j0qV678223+ji1WVlYMGjRIEBdFfszr16+ZPn06RkZGlC1bllq1alG+fHkmTpyIpaUltra2gkdtcnKyQp5XuYGwsDAsLCzYunUrTZo0UbQ5uYKs8NROSkrC3d2d8+fPU7x4cWbPnp1F1onkROSFK96/f//NIhf/Av7+/jg7OzNq1CgaNmwoztvSYWZmRrVq1fD29ubUqVNUrlyZayFxHLruR1jQa4pVtSQhNprtox0oULYSrcekPTPm21RF2zAfPZduJVUqZWW3xuia5KfDlGUYFyvNFsduNGrQgA0LZ4nCmsgP+Zl8fioSviiSISIiR/RpFBH5f+Tu+4cOHWL69OkMGjRIqBLn7OwsJJVXVE6YFStWcOPGDby8vAgPDwcQcyvlUOLj44G0pPKhoaFYW1uzYMECevfuzalTp2jTpg2WlpaMHj0agAcPHjB+/HicnZ2RyWQZ8qj9SFT7+PFjhr/j4uK+modN7u0ICKKaovZVLl68SEhIiBgS85MUKVKEKVOmULZsWe7evYurqyudO3fG09MTW1tb4H/hRwkJCYo0Nccik8lwcHCgZ8+eoqj2HQ4fPoybmxvnz58H+GVRTf6MOX36NEOHDsXPz4+2bdsybNgwUlJSiI6O/uJYkb+DgwcP0rp1639aVIuPj+fWrVsZKomKolpGTp8+Te3atYU8l6HxUvQKF6dU7YaoqOVBS9+IevaDeXnnaobzLLv2Q9swH7r58mNWpRaFK1ajQNlKqObJS/lGrXhwz1cBdyPyJ/k8J9/P0LBhQ9zc3L54/XeKZGQlr169QiKRCNXeRXInorAmIvL/yCf1W7ZsYdy4cYwaNUp4wKWmpipMUJNPwgwNDYWFyLhx4zh//jyenp7frMomohjmz5/P/PnziYuLIzY2ljFjxtCtWzcOHTrE69evWbp0KT4+PqxatYoXL17QtWtXmjdvzoMHDxgyZAhKSkqZnngfPHiQ+fPn8/LlSyDNQ2DFihWEhIR89fjP21XUBH/GjBlMnDhR3D3OBHLx3N/fn2PHjnHp0iXs7e2ZNm0aLVu2pEOHDmzYsAH4n+ifmprKlClTFF50JSeycuVKQkNDhSTjIl9y8+ZNxowZQ2hoKMuXL2fEiBEEBwezfv16Lly48FMCmPwZEx0dTcGCBRkwYACmpqZs3LiROnXqMG/ePKHojiis/V38TdVAf4WQkBBcXV3ZtGkTampqnDp1KsP7ycnJYp8H1qxZw7Nnz+jXrx8ymYxEaSrRH0PZOa4/c1uYM61+MfZMGkxcRHiG87QNjYV/q+ZRR8sg/d95iYuNybZ7EMmdBMcmf1VUm29TFf/rF795nlxcC44T118iGRGFNRGR/0e+ADAwMCAyMpLXr18LpbljY2OFf2e3GCH3FHB2dqZUqVL07t0bCwsLFixYQHx8vDgxyyHIv4cmTZpw6dIlrl69iqamJvPnz8fKyoratWvTsmVLzM3NWbduHXFxcRw+fJgePXrw8OHDX6pKaGVlRWxsLHv27CE6OhoDAwPatGmDqalpVt9elnH58mVevXpF9+7dFW1KrkDusdi2bVuOHz/O0aNHqVWrFjt37sTa2pouXbpgaWkJ/O9ZoaSkxJ07dwRvI5E07t27x8yZM9m5c6cYJvsd8uTJw65du5gwYQITJ05EQ0ODpk2bMmXKFNTV1YUx8GfGnvbt2+Ps7IyBgQGurq74+PggkUiYPXs2bm5uBAYGoqSkJI5nfwkhISHcu3ePZs2aKdqUbOft27ccOXKEs2fP4u/vz/Tp07l16xbNmzfPcFxkZCTW1tYEBAQoyNKcgYmJCWfPnuXy5csMGTKEPMpKnHKdDRIJTnsuMe3ySzrPWvPTzwYl0TNQ5AdcDYn/6cqzclJk4PP+f15roqeZCIjCmoiIgHxRamdnx4ULF7h9+zYBAQEMGTIEXV1dIVm4orx8Xrx4IVx7yJAhHD9+nPz58xMaGqoQe0T+h1QqFb6b6tWrY2dnx6pVq3j16hWmpqZcunQJS0tL5s2bh52dHSdPnsTDwwM9PT1at26NgYHBT4X1yj2TDA0NGTNmDMWLF0dbWxt1dXXKly8vHCcPGc5JzJ49mwkTJojCRiaQLyRev35NgwYNcHV1Zffu3SxZsoSLFy9SrFgx7t+//9VzbW1thfB1kbQQ6a5du7JkyRKh2rPI16lUqRJVqlQBoEaNGsyePZubN28yevRoHB0d2b59O/DzY6Guri4nT57E1NSUY8eOYWRkxKlTp7Czs2Pfvn2/1KZIzuTgwYO0atVKyAf7L2FsbMz+/fs5f/48derUETY+PheG9PX1sba2platWqxYsUJhURE5gQIFCnD27FlOnDjBjrnjSYqLIY+GJnm1dIgMDebyVtefak8JEAs4/p34+vpSqVIldHV16dKli5D24tOnT9ja2mJsbIy+vj62tra8ffv2q20EBwdT0bwSm1Yu+eH1Prx+wfp+bZhmVZyZjcuwY2w/4b0OJXRZusKVUqVKUapUKQCcnJwoXLgwOjo6VKtWjcuXLwvHp6amMm/ePEqUKIGhoSGdO3fOkfN0kV9HfOyIiKRDJpPRunVrWrduTadOnXjw4AFly5Zl1apVCqsIKt8F2bp1Kxs2bCApKa0E+aVLl5g8eTKBgYGC7SLZj0wmE7yKTpw4QXJyMo6OjhgbG7N27VqkUinGxsYcPnyY27dvs3HjRrp27UrXrl0ziEuZLVAgTyIeERFBUFAQZmZmdOrUSbBFzrFjxzh06FCO2kVLSUlh27Zt9OnTR9Gm5ArkIsP9+/dJTk7m4sWLxMXF0bBhQ/bs2cOuXbsyVAdNT9u2bfH09PynF2vpcXZ2plq1avTs2VPRpuRYUlJSqF+/Pvv378/wukQiQUNDg6FDh+Lk5MTRo0fZtm3bL13j/v375MuXD4Dx48fj5ORESEgI7dq1E44Rx7Lcz78cBponTx7c3d1ZuXIlvXv3Br5eaEpZWRlnZ2euXr3K7t27adiwIf7+/oowOUdQpEgRzp07h7eXJ9KUFIIe32e6VXG2OHajQmPbn2pLBmipitXG/0b27NnDiRMnePnyJffv38fd3R1Imxs7ODgQGBjI69evUVdXZ9iwYV+c//LlSxo0aIBt7wE06v3l+59zes08StVuyJQL/ow7fo86XftleH/n/gNcv36dR48eAWmbUb6+voSHh9O9e3c6deokiH8rV67k0KFDXLx4kXfv3qGvr8/QoUN/8xMRyUmIVUFFRD4jOTmZ0NBQlJSU0NDQQEVFBSUlJdTVFV8Bxt7eniZNmmBqasrChQuZPXu2sBsqkr2knyjfunULZ2dnihUrhr6+PrVq1aJZs2Z07twZJycnmjRpwurVq/H09KR58+ZMmzbtizYyg1xUe/78OR06dMDQ0JAWLVrQpk2bDJ5qkObhaGJigqamZpbds0j2If+uPTw8WLJkCaqqqlStWpW6detSrVo1ihcvTp48eb7bRvny5XF3d6dmzZrZZHXO5MCBA4wZM4a7d++io6OjaHNyNPv27WPmzJk0bNiQ5cuXf/WYTZs2cebMGTw8PDJd1EDen1+9eoWtrS2HDx+mePHiLFu2DBUVFYYNG8aFCxeEBO8iuZewsDBKlSpFcHBwjpg35QakUikrVqxg9uzZTJ48meHDh/92Fd7czP4XUTyPTPrl80vrqtG+uPis/9swMzNj1qxZ2NvbA+Di4kJUVBRr16794lhfX18aNWrEp0+fgLTiBVWrVuXAgQPMnTsXTUtbHn36eh7a+TZV6TBlKSVrNWDP5KGoqOWhyYDR6JoUyHDc+KrGzNx+hEndvy386uvrc+HCBSpXrky5cuVwdXUVCicFBwdTpEgR4uPjefv2rVi99i/g331qi4h8g127dtGjRw+qV69OsWLFKFKkCJqamrx+/Voh9rx9+5YXL17w5s0blixZwvbt2xkwYACLFi3C0tJS3N1XAOkFsaioKPbt28fKlSuZPn06Fy5c4Pnz5xgbG9O/f3/WrVvH69evGTt2LEePHhVEtdTU1J8Oe1JSUsLX15clS5awcOFC5s+fT2RkJDt37uTFixeCbQDFixdHU1NT7B+5FPmiytvbm+3bt3P9+nUsLCw4d+4cS5Ys+SIR9tews7Pj0KFDf9jSnM2bN28YPHgwO3bsEEW1TNCxY0fu3buHo6Mj8PXK0zExMbRs2fKnFv5KSkqkpqZiZmZGz5498fDwIDo6mhEjRtC1a1eGDBlC06ZNOXjwIKC46tsiv4+npyctWrQQRbWfQO695uPjw969e2nQoAHPnz9XtFkKo46JOiq/GBWuIgFLU7Hv/a2kzyGsoaFBTExakYq4uDgGDhxI0aJF0dHRwcrKioiIiAxj2Pbt2ylYsCAdO3YkUZq5Maal0xRAxqqezVnasR63Dm3P8P7nYtuiRYsoV64curq66OnpERkZyYcPHwAIDAykXbt26OnpoaenR7ly5VBWVv5mwTGR3IcorImI/D9yAWLixImsWbOGoKAgwsPD+fjxI6mpqRQpUkQhdk2aNAkbGxvs7Oxo0aIFb9++xcDAgH79+pE/f37B/VgUULIPiURCSEgIvXr1ElzA9+3bR7du3ejfvz9Tp04lNjaW7t27U6hQIeE7MjAwQCaTIZPJMr0oTf+9ymQy3Nzc2LVrFw0bNqRmzZrY2NgglUrZtm0bb9++zTGVP0V+n5MnT7J37178/PwAGDhwIAsWLKB06dJCON33aNu27T8trEmlUnr27ImTkxO1atVStDk5GnnIeGRkJICQh+5rIepDhgz5pZBa+bNo7NixTJw4EW1tbWbPnk3NmjWJi4tj6dKlHD58mFevXglCnEju418OA/1dSpUqxcWLF+nYsSOWlpYsW7bsn/wd5NdUpXFBzZ8W11Qk0LigJvk1xByu/xqLFy/m6dOnXL9+naioKC5dugRknENPmzYNIyMjunfvjgqZWzNpG5nQfvJSJpzyo93ERXjOG8uH1y+E99WU/zeXv3z5MgsWLGDPnj18+vSJiIgIdHV1BRsKFy7M8ePHiYiIEP5LSEigYMGCWfERiOQARGFNROT/kU/627ZtC6R5IsXGxpKUlPRTieWzms2bN/PgwQOuXr3KlStXuHnzJhcuXODMmTPcvXuXMmXKZLBfJOuJjo5mz549BAcHA7Bz504WLlxI5cqVadasGRYWFuzevZudO3cKOR2WLFlCUFAQa9euFXKgQdr3lNnvKr1XW2xsLBKJhLlz51KjRg369+8PQL169WjQoAHBwcE8e/YsK287S/gXFwVZhYWFBT179mTMmDH07NkTPz8/jIyMGDNmTKaEoho1ahAREZEj+0V2MHfuXCQSCWPHjlW0KTkeFRUVEhIScHJyom7duqxevZqnT5+SnJyc4TiZTPbLYSrpn3sRERGUKFGCS5cusXfvXtzd3Rk+fDhaWlrMnDkT4J8OhcuthIeH4+PjQ8uWLRVtSq5FWVkZJycnfHx82L9//z/rvVbVWP2nxDW5qFbVWPRW+xeJjo5GXV0dPT09wsPDmT59+hfHqKqqsnfvXmJjY1nrMhAl2Y/npw9OexIZ8g4AdR09kEgyjE0Gef+3+RQdHY2KigrGxsakpKQwY8YMoqKihPcHDRrExIkThdzYYWFhYpGpvwxx1iIi8hldunShZ8+ezJo1iyVLlrB48WIWLFigMIHA19eXmzdv8uzZMwICAnjz5g0fPnwgKiqK5ORkUVDLBj58+MCuXbu4du0aAI8ePcLDw4N69eoB0LhxY+rVq8fatWu5fPkyVlZW+Pv7Y2JiInh8/KxHYXqvtv9j76zDokrfP3wPHRYggq2g2KICdoGda6FiotiIrWuL3YqFikHYhVjYrdiAGCiKYiAiiCAdM+f3h7+Zr6y6K0p77uvaa2XmxHPOnPPG531iyZIljBw5EgcHB4KDg3F1dSU6OprJkycD0KZNGyZNmoSVlVVmXfJvERISgo+PjyKvEoilyH8WeTuTnJxM0aJFWbx4MVeuXKFMmTIMGDCArl27EhcX91PtkZKSEp07d/4jB243btxg3bp17Ny586cLg/yp7Nu3D/gSwqKtrc3MmTPx8fFh3rx5HD58mDdv3ii2zaz+Rk9PDxcXF06fPo2ZmRmfPn3iwoULBAcH07hxY+Lj44Hvh6KK5F6OHj1Ky5YtKVCgQE6bkuf5p/fa6tWr/7j3oY6+Jn1NCmNSWA1lCd+IbCoSkKWlohz5hr4mhUVR7Q9m3LhxJCYmUrRoUerXr0/btm2/u52amhqenp6kRH9kv+OY/xxLvX3kj/OANsxpVBaP8f3pNGkhuqXKKb6vXPh/uW7btGlD27ZtMTExoWzZsmhoaFC6dGnF92PHjqVz5860bt2aggULUr9+fW7duvV7Fy6SqxCLF4iI/IMaNWrQpk0bSpYsiVQqJS0tDalUyowZM3LEnq5du/LkyROSk5N5/fo1Ojo6qKmpERERAcCzZ88oW7ZsjtiWn0lKSmLUqFHY29tjZmaGp6cnFy5cYMGCBRQpUoRWrVrRpEkTZs+eTVpaGm/fvmXlypXExMTQqlWrTKs+6OzszJ49e3BxcWHdunUUKFCA5s2bU7lyZbp3707Xrl2ZPXu2YvuMFkTIbE6fPo2joyMmJibEx8fTqVMnBg4cyIwZMxgxYoSiuq4oCP8748eP59OnTwiCwOzZszE2NiY4OJijR48yfvz4nz7OqVOnWLBgAdeuXctCa3MXMTEx1KpVCycnJ4UHssj3efr0KUuWLKFgwYIEBwezYcMGypUrB4C7uztubm60a9eOKVOmZNo5v26jZDIZ9+/f59atWwQGBhISEkK7du3Ys2cPly9fzrRzimQPHTt2pE+fPvTp0yenTclXPH/+nMGDByOVSnF1dcXExCSnTcp2ElJlBEQlEZEoJVkqQ11ZCX1NZZKCHzBkQF+ePn0qLqKIZAixSIZIZiMKayIiX5Gamkq7du04d+5cTpvyDVOmTKF58+a0b98egCdPnrBq1SoWLlyIvr5+DluX//Dw8GDVqlX4+/srPgsLC6N48eLAF6+1jh07smfPnnRheVKpVDG4+9pj62eZOnUqnTp1olGjRsCXVbg6deowYMAAYmJicHFx4f3796xcuZJ79+4RFxdHs2bNfvNqM4+NGzeir69P48aNOX/+PJ6enjx9+pTw8HDevn37n5Us/2Tkz8vKlSsJDg6mZcuWzJo1i0ePHhEdHU1qaqriXf9ZATU5ORkDAwOePn2KgYFBVl9CjiMIAn369EFHRwdnZ+ecNifXk5qair+/P/fu3ePw4cPIZDIcHR0V7c/Hjx9JTEykVKlSWSLaBwYGsnr1atTU1GjSpAk9evRAWVmZLl26YGNjQ69evTL1fCJZR0xMDKVLl+bt27dioZAsQCaTsWHDBubOncv06dMZO3asKCTxpc2vX78+06dPFxdSRDJEWHwqu57FkPYLSoiKBPqaFBbz+YmkQwwFFRH5irS0NPT09Fi9ejXPnz8nNDSU6OhoUlJ+fUXjd5HnuDlz5gwVK1YEvog3lStX5t69eyQlJeWYbfkR+VpD3bp1KViwIH5+fsCXQa1cVJNKpVStWpWxY8cycODAdM+HsrKy4hi/kiNoyJAhNGrUiDt37gBQrVo1Ll26xKtXryhcuDA2NjbcvXuXyMhIzMzMcpWoBjBy5Eh69OiBoaEh1tbW7NixgzNnzjBw4EBatWqFi4tLjr5PuRn58+Lv78+0adO4f/8+dnZ2wJe8fgsWLFBs+7MCh7q6Om3atOHYsWOZb3AuxN3dnQcPHrBy5cqcNiXXk5qaiqqqKqVKlaJly5Y4OjrSpEkTduzYwbJlywgMDERPT0+RWDkrvEyrVKlCvXr1mDJlCr169UJZWZk9e/bw8OFDheccIPZzeYDjx4/TvHlzUVTLIpSUlHBwcODWrVscOXKEpk2b8vTp05w2K8eRSCSMGzeO1atX57QpInkMsUiGSGYjCmsiIl+RnJyMn58fTk5ONGjQgNq1a1OyZEmqVq0K5EzlTVXVLw1348aNWbVqFdeuXePp06csW7YMXV1dMZdJJiOfPJYuXZoePXqwfPlykpOT04lk8n+PHTuWNm3aEB0d/d1j/CzykGOAMmXK8ODBAxo3bszt27exsrKiaNGibN68mZCQEI4fP06hQoXQ1Mx9uUSOHj2a7m81NTW0tLQoUaIEc+bMwd7enhs3brBnz54csjD38nWejy5dujBx4kQ8PT2ZMGECAHv37qV169ZAxtuhLl26/BF51oKCgpg8eTJ79uzJle9HbkNVVRVBEBg6dChv376lQYMGDBw4kLZt2/LhwwfWrl1LYmJiloVty59jOzs7ypQpw9OnT2nfvj3Lly9n5cqVCk/gtLQ0hg8fni4JtEjuQ6wGmj0YGxtz8eJFevfuTaNGjVi5cuUfl3vtn/To0YPg4GDFQqiIyM8iFskQyUzEUFARkTzEqlWrOHr0KImJidStW5c5c+ZQtGjRnDYr3/LixQsWLlyIpaUl/fr1S/fd1yGfv8upU6d48OABPXv2xM7OjrNnz+Ls7MzmzZvx8vIiLi6O3bt3ExAQAMDWrVspUaJEppw7s4iJiaFFixakpKQwadIkBgwY8N3tXF1duXnzJps3b85mC3MvX4cM79q1i/r167Nq1Sri4uIoX748b9++5d27d3h7e//S8aOjoylTpgzv3r3Lt0J8SkoKDRs2ZNCgQdjb2+e0ObmeAwcOKKoVb9++nbdv36bL1ejj40OBAgWoWbNmtuRt9PDwYNSoUYwfP54ZM2agoaGBr68vkydPZt26dWhoaGBkZJSlNoj8OrGxsZQqVYqQkBB0dHRy2pw/huDgYOzs7EhOTsbV1ZXKlSvntEk5xtKlS3n8+DHu7u45bYpIHiQsIZUb7xMJ/pyCBNKFh6pIQACMC6nRwFBT9FQT+SGisCYi8g/S0tKQSCSK/yD3JFqXizny1zY1NRU1NbUctip/M3DgQNq1a0fLli0pWrToL+VN+xmqVq1KeHg4S5cuZciQIQA4Ojpy5swZPD09MTQ0JCIiAl1dXZSVlTNV2MtMLl68SEhICIMGDfruhHzPnj00bNhQLLjxFfJQ3zdv3vD582eWLVtGUFAQp06dQiKRULp0aSwsLChZsuQvP3+tW7dmxIgRdOvWLQuuIOeZMmUKT58+xcvLK9e017mVqKgounbtilQqZeLEiTRo0ABXV1fs7Oy4efMmQUFBJCUlMXPmzGyz6ePHj4SHh1O1alWio6NZvnw5J06coEOHDixYsEDxm+Z0cRaR77Nv3z7c3d1/WfwX+XVkMhkbN25kzpw5TJ06lfHjx+fKsUFW8+nTJ4yNjXn06JEibYeISEb5UZGMmroaaKmKgX4i/44orImI5BGeP3+Os7Mzt2/fRiKRIJVKkUqlXL58GQ0NjZw2L98hCAJ37tyhT58+NG3alJiYGDZu3EixYsUyRVyTTxDlx5o7dy47d+5k3Lhx2NvbKz53cHDgzJkzBAQEKBL/Z5W496vcv3+fS5cuMWbMGHHS+wu8ffuWv//+G09PT4YMGcK6desU3wUGBlKuXLnfDm10dnbm5s2beHh4/K65uY6zZ88yaNAg/P39RQ/enyQtLY29e/eyfv16VFRU8PHxYcCAAaSmplK3bl1q1apFs2bNsl3I2r59O9u3b6dkyZIsWbKE8uXLc+rUKdTV1bG0tMx1bZ/IF6ytrWnXrh2DBw/OaVP+WF68eIGdnR1JSUl/rPeavb09urq6zJ8/P6dNERER+QMRhTURkVzI9yYzjRo1YuzYsTg6OuLm5sbevXsBWL58+R+5OplVpKWloaKiku6ziIgIFi9ejCAI2Nvbo6Kiki6xdkb5enL48OFDihYtiqGhIR8+fKBJkyaMGzeOkSNHKrzS9u7dS+/evb97rNTUVKKionK04uOnT5+IiYnh/v377Ny5k9GjR1O7du1vkliL3ibfIv+NXVxcCAwMJCoqivv379OrVy+mTZtGhw4dWLRoEaampr91nrdv32Jqasr79+8VeRvzAxEREdSqVQsPDw9atGiR0+bkelxcXNDQ0KBfv34oKSkRHR2Ni4sLO3bsoEaNGuzevRvIuXe1R48e2NjY0L17d4KCgjh06BDu7u6UL1+eLVu2UKpUqVzrrfunEh8fT4kSJXjx4gV6eno5bc4fzdfea3///TcTJkz4o96Vp0+f0qRJE169eiXm2RQREcl2RGFNRAR4/fo179+/p3Llyly4cIGiRYuiqamJlpYWampq6OrqZmvekM+fP38jStSuXRs/Pz9q1arFnTt3UFVVpXbt2ty9e/ePGjhlFZ8+fcLBwYHx48dTu3ZtlJSUFJNLqVTKkydPcHR0xM/Pjy1btmBpafnb51y8eDEnT56kUKFCNG3alAkTJvDgwQO6du3KxIkTWbt2LQsXLqRnz57At55qKSkprF69mjdv3tCtWzesrKxyVLz68OEDGzZs4Nq1axgbG9O7d29q164t5tz5AfLfKjExEQsLC7y9vSlTpgwXLlzAycmJwMBA6tWrx86dOzPlfBYWFixbtixTnt3cgCAIdOrUierVq7NkyZKcNidPkJycDMCECRNQUVFhyZIlaGpq8vTpUxYuXMiNGze4fv06xYoVy1a7vhbLYmNj8fT05Pbt26irq2Nubk5sbCxeXl6cPHkyW+0S+W8OHTrE5s2bOXPmTE6bIvL/yL3XEhMTcXV1pUqVKjltUrbRsWNHunTpokipISIiIpJdiP70IiLAmzdvePDgAW/evGHixIlMmjSJQYMGYW1tTf369RW5ZrKr8tI/q0wCFC1aFKlUirGxMfv27ePKlSskJCSIolomcP78eUxNTdHV1aVq1aoK8UouUCkrK1OtWjXc3Nzw8/P7ZWHixYsXimdo8+bN3Lp1iytXrqCvr4+rqysrV66kdu3a7Nu3j4iICKZNm6YQ1eB/1Ujl6yFqampMmjSJMmXKcO7cOWQyGRKJJFur18rPJZVKKVasGHPnzsXT05PKlSuzYMEC7O3tiYuLyzZ78iK7d++mSZMmlClTBgArKyuOHj3KpUuXFGGhmfGb/vXXX3h5ef32cXIL69at48OHD2LYz08ik8lQV1dHXV2d4cOH8/btW8zNzXF3d6dSpUp4eHhw5MgRihUrlu0VsL/ux44cOcKJEyeoU6cOjo6O9OnTh+HDhyOTyQgLCwNypkK3yPcRq4HmPoyMjDh//jwDBw6kSZMmLFu27I+pHDp+/HicnJzENkJERCT7EURERHIVHz9+FJ4+ffrN59euXRPi4uKEkJAQoV27dkKDBg2Es2fP5oCF+YeEhARh7NixQqlSpYTTp09n6bkuX74s1K1bV7hx44YgCIJw584d4f3798K8efOEjh07CseOHRNq1qwpLFmyRIiOjhakUqli33/+Ozo6Ot2xz549KwwcOFC4cOGC4jOZTJal1/P1OVJTU4Xp06cLtra2wqJFiwQfHx9BEATh8+fPwpEjR7LcjryMVCoVxo0bJ2hoaAgLFiwQEhISsuxcQUFBwsqVK7Pl2chq/P39haJFiwrPnz/PaVPyHNHR0cLnz58FQRCEkydPCmZmZkKjRo2E69ev56hd8ucyNjZWePTokeLzixcvCjVq1BBMTU2F/fv3f7O9SM6RkJAgFC5cWAgPD89pU0R+wIsXLwRLS0uhXr16wuPHj3PanCxHJpMJNWvWzPIxnYiIiMg/EUNBRUT4X4hdUFAQZ8+epWDBghQqVAgVFRUKFy6MkZERJUuWzBZb1q9fj7W19XdzZiUmJiKTydDW1gYQc838Br6+vvTr148aNWqwceNGdHV1s/ycixcv5vbt28yfP5/q1asTExPD4MGD2blzJ5qamnTq1AkDAwPmz5//r1WtBg4cyMiRI6lfvz7wJXT48OHDLF26lPj4eO7du5ctSdzlz9/s2bNJSEigcOHCuLi40LhxYwwMDPjrr7/yTdhhVhIREcHFixc5c+YMkZGRtG/fnoEDByqKVWQWgiCQkpKS6cfNbhISEjAzM2P69On0798/p83JE8jf1c2bN3P9+nUSExOpVq0ajo6OAMybN4/ixYszdOjQnDX0KyIjI5k8eTLXr1/H3t6exo0bc+jQIQRBUOS8FHM25ixHjhxhzZo1XLhwIadNEfkXZDIZmzdvZtasWUyePJmJEyd+k0s2P+Hq6sr+/fvF0HEREZFsRRTWRET4n7C2Y8cORo0aRZkyZShfvjyPHj0iKSkJTU1NmjZtyqpVq7JcgKlTpw7Xr1//JvGqv78/u3btIiIigrS0NARBQEtLiy1btmSpPfmNtLQ0li1bhpOTE05OTtjY2GTp5OyfedGmTZvGixcvWLRoEcbGxnTt2hUdHR3Mzc3x9PTExcUFIyOj79r98eNHDAwMiIuLo0CBAt9sM2LECMzMzBg6dGi2Vc+TyWRYWlpy4sQJxo4dS5MmTTA0NGTIkCFMmzYNe3v7LLchryEXBFJTU/H19SUpKQl9fX00NTXx9fVl165dmJmZMWPGjJw2NVcyfPhw4uPjMy333J/Cx48fadSoERcvXqRr166MGDECW1tbnj9/ToUKFRTb5RbBqmXLlhQtWhQnJycMDQ0BOHDgAE5OTpw5c0axwCSSc/Tv358GDRowatSonDZF5CcICQnBzs6O2NhY3NzcqFq1ak6blCUkJSVRrlw5Ll68+EfllxMREclZRGFNJMPEp8p4EJXEh0QpyVIZ6spKFNNUpqauBlqqeTNtn1yEWLduHUpKSunEgAULFmBsbMyTJ0/Q1tZmypQpWWaHn58fXbp0ISQkJN3EJjY2FjMzM0aMGKEYJMg9ENq1a5dl9uQ3goODGTBgABoaGri5uVG6dOksPZ/8ufrw4QO3bt3C1NSUMmXKMGHCBKKjo1m4cCFxcXE4OTnx5s0b1qxZQ/ny5b+Z2CYlJREcHEy9evV48uQJpUqV+kY4S0hIYNWqVcTGxrJ06dIsvS743+Q7PDycCxcu0Lp1a2xsbBQJrNu1a8fKlSupWrVqrpmo5xbkv92kSZNITEzEy8uLXr16sWrVKhISEoiOjkZLS4siRYqI9+4feHp6MnnyZPz8/L4p8CLy75w5c4a7d+/Sp08fbG1tuXTpEvClEufSpUsxNjbOWQP/H3nf9unTJ0Xhk3fv3nHnzh1OnTqFlZUV1tbWOWylSHJyMoaGhjx+/PhfPaxFcheCIODi4sLMmTMVOYXzo/eao6Mj79+/Z9OmTTltioiIyB9C3lRBRHKEsPhUDr34jPOjKK6FJfD4UzLBn1N5/CmZa2EJbHgUxaEXnwmLT81pUzOMXF+WVweFL0IFQFhYGEpKShQpUiTLwy63b9/OoEGDvplIq6urU7p0aSZMmEC7du1o164dHTt2FEW1n0QQBLZu3Ur9+vWxtrbm7NmzWS6qwZdiA35+fnTu3JmrV68yfvx4Nm/ezIoVK0hOTlZ4QG7YsAFPT0/Kly+PVCr9RlRLSEigWrVqNGnShMWLFwN884xoaWnRoEEDPn36hJ+fX5Zfm7xIwvr16+nevTsFCxakXLly6bwX5KvhojCUHiUlJaKiorh48SIbNmzA3Nycpk2bAnD48GEEQaBIkSKAeO++5s2bN4wcOZLdu3eLotovULlyZe7du0eDBg0YM2YMAM7OziQnJ+caUQ3+V8hAR0eH5ORkzp8/z86dOzly5AimpqZ0796de/fucfv2beCLUC2S/Zw7d44aNWqIoloeQyKRMHz4cO7cucO5c+do2LAhjx49ymmzMp2RI0eyf/9+Pn78mNOmiIiI/CGIwprIT+EbkciuZzE8i0lBKkDaP/wc0wSQCvAsJoVdz2LwjUjMGUN/EbnnT4sWLXj8+DGLFi3iypUrzJs3j6CgIKpWrUpkZCQFCxbMMhuSkpLYs2cPgwYNUnwmlUp59OgRgYGBVKpUCXd3dyIjI4mKiiI2Npbk5OQssye/EB4ezl9//YWzszOXLl1i3LhxWRoi+bUTcHJyMps3b2bz5s307NmTx48fU758eZSUlNi0aRO3b9/m+PHjAKioqCAIQjrxViqVcuHCBUXI05YtW9i3bx+hoaHpBBf5xFJTU5MPHz5keX41eXUxLy8v3r59i5qaGmpqaqxYsYK+ffuio6PDqlWrvrkfIv8jKCiIli1bsnPnTuLi4ujSpQsymYz58+eLVVS/g1QqpV+/fowbN4569erltDl5kjJlyjB06FCaNGnClStXGD16NNu2bWPRokVA9lW9zggymYyDBw8SFhbG1KlTGTx4MGvXrqVfv354eHgAX/pvsZ3Jfg4ePEj37t1z2gyRX6RcuXKcPXsWOzs7mjVrxuLFi0lLS8tpszINAwMDunTpwubNm3PaFBERkT8EMRRU5LuUK1eOrVu30rJlS3wjErkQGv+NmAYwp1FZxu67jG6pcuk+V5GAVUlt6uhrfrtTLkUecvXkyRN27drFq1evKFeuHIMHD6ZcuXLEx8ejoqKSZYm/9+3bx9atWzl79qzis4iICCpUqEDhwoWBLyGh8GUikZiYSJkyZXjy5Em25dPKaxw5coQRI0YwePBg5syZg5qaWpae7+vf4cKFC0gkEnbu3EmFChU4evQos2bNon379jx8+JCyZcsilUoVnkn/JCUlBQAzMzNOnDhBmTJlADAxMaFr164sXbr0m+IVI0eOpGzZskyZMiVbnoeGDRtSrlw5du/eneXnyo8sWbIEDw8Ppk+fTt++fVmxYgV+fn7s3r07y0NAt2/fzl9//YWenh4fP34kOjoaQRDQ0dFBT08vy877qyxYsIALFy5w9uxZsWBLBrlz5w7e3t4UK1YMGxsbAgIC+PDhA+/fv6dp06bUrFkzV/Yh8vYtLCyM4sWLc+HCBZYsWULp0qUpWrQo8fHxFClShAULFuRK+/MzqampGBoa4u/vny3e3yJZy6tXrxgyZAjR0dG4urpSvXr1nDYpU7h//z7t27fn5cuXWT7+ExERERGFNZHvIhfWqjVoxq5nMd8V1f4LFQn0NSlMcS3VzDcwC/nw4QOCIKCpqYmysjKCIHw3UXxm07p1awYNGoSNjU2Wnyu/Exsby7hx47h06RIeHh40atQoW89/8+ZNJk2axPHjx9mzZw/z5s3D1dWVtm3bEhYWRu/evZk5cyatWrUCvi1wIJPJWLJkCTY2NuzatYtnz54xfPhw3r59y8GDB7l37x5Pnz5V5EWR759dk0tBEJDJZGzbto3Vq1dTsGBBxo0bR58+fbL83HkZ+e8jD/d9+fIlc+fORUlJifDwcAoXLszixYu/m2fvd5EfT25Ds2bN0NTUpHPnzjx//pyHDx8SFhZGrVq12LFjR6adNzO4ceMGXbt25d69e9lWnTmv8/HjR5SUlNDR0aFy5crY2Nhw/Phx9PT0GDt2bJ5KI+Dr68umTZvw8fGhffv2TJkyRSGs1ahRgxs3bny3irZI1nHmzBnmzJnDjRs3ctoUkUxCnjJj+vTpjB8/nilTpuSL3GstWrRg8ODB9O3bN6dNERERyeeIy3si/4pPeOIviWrwJTz0xvu8ERIq15fPnz+Pg4MDLVu2xNzcHDMzMwwNDbl582a67TKbV69e4evrS9euXb/5TiqVIpPJCAkJYe7cuSxYsACAR48ecevWrSyxJy9z7do1TE1NUVJSwt/fP1tEtQsXLhAQEIAgCBw5cgQbGxt69+5NkSJFaN68OXZ2dixZsgQ3Nzc6duxI+/btFaIa8I0YlpKSgkQiwcnJienTp9OkSROmT5/O0aNHWbhwIbVr1yYwMPCb/bPLY0MikaCsrMyQIUO4ceMGEyZMwMPDg2rVqnHq1KlssSEvoqSkxLt375g6dSrVqlVj9+7djBkzhsmTJ7N582a2bNmSJaIa/C9X261bt1i3bh0mJiYMHz6cwMBAjI2NcXZ25t69e99UI85pYmJi6NOnD5s3bxZFtQywbds2Vq5cyfLly7GysmLOnDncuXOH3r174+joSMuWLfnw4UNOm/mfHDt2DFtbW0qWLEmTJk0YPny4ItT92rVr1K1bl2LFigFfFlTknr4iWcvBgwfp0aNHTpshkolIJBKGDh3KvXv3uHTpEvXr1+fhw4c5bdZvM378eM6cOSOGi4uIiGQ5oseayHcpV64c6ze5cDoSjiybQcTLIFQ1NKhm1ZEOE+ejovrFpXpaHX0met2iaBkjDswZjZqGFp/C3vDS9wbFjCrRd9EmZrc1y/XVQuUhJ/Xr12fEiBHY2toCkJaWRkpKChoaGlkqWsydO5eIiAjWr1//3e9DQ0MZN24cOjo6BAUFcenSJa5fv86sWbO4cOGCGAbDFzFqzpw5uLm5sXnzZjp37pwt5xUEAQcHB4oUKcLw4cMpXbo07dq1Q0NDg8OHDwMQHx/Pjh07FEUwevbsCaT3VJPJZMTHx1OgQAFFSHLfvn1Zu3YtjRo1Ij4+noSEBBYsWMDbt2/Zt29ftq8my98Tb29vzpw5Q2RkJFZWVgwePJj4+HgOHTpEjRo1qF27drbalRcICgrCxMSEwYMHo6enR79+/di9ezdXr16ld+/eimTyWUVaWhrLly/n6NGj1KxZkw4dOmTbO/KrCIJAnz590NHRwdnZOafNyVNcvXqVCxcuEBYWxsOHD1m4cCHNmjUDvrzHzs7OODg45LCV/01KSgoBAQGYm5szePBgdHR0GD9+PMuWLePYsWMsWLCAPn36EB8fz5s3b/Dx8cHOzi6nzc7XpKWlUaJECW7fvk25cuVy2hyRLOBr77Vx48YxZcoUVFXzVvSJHEEQSExMzPJxvIiIiIjYwoj8kBexKSgrKdNx4nxmXnjKSLeTBN++ys3923+4z/0zXrQYNpnZl56jV7o8p9YvIiAqKRut/jXknW2VKlWoVq0a8EXoUFFRQUtLK0s7Y5lMhqur679OBiIiIkhJSWHNmjWKz7KykEJe4+HDh9SrV4/Hjx9z//79bBMMjh8/jkQiYfHixURHR3Pnzh0ADhw4wOvXr5k1axYA2trajBgxgmHDhilENUEQFM+VVCpFSUmJESNG4OrqCkDZsmWZOnUqpUqVQhAEwsLC6Ny5M4aGhuzfvz9HQjTkua2mTJlC37598ff3V1zDu3fvGDBggCiqfYcbN24wb9483NzcSElJYdSoUZiamrJ06VLWr1/PuXPniIyMzFIbVFRU2L9/P0uXLv1GeP46YfWjR4+4evVqltrys7i7u/PgwQNWrlyZ06bkGWQyGR4eHtSrV48JEybQpk0batasydGjR9m2bRvPnj1DWVlZIarl9oqaampq1KlTB0DRztrY2PD582euXr1Kt27d2LFjB8uWLaNKlSr5JjdUbubKlSuULVtWFNXyMV97r125coX69evz4MGDnDbrl5BIJFk+jhcREREBUVgT+Reik2UYVjGlTE1zlFVU0ClRhrrdB/DS98c5NapZtqd09Tooq6hQq1133gU9JCIx91Ua+xFlypRh7ty5bN26lVOnTnH+/HkuXLiQpeElFy9eREdH518FCWVlZQoUKMCjR48UIV3BwcGKapF/KjKZjNWrV2NpaYmDgwNeXl6KsKCsZsSIEYqk/QULFmTx4sV069YNgAIFCnDkyBGOHDnCkiVLvru//HdMS0sjPDychIQERVEC+FLh09ramrJlyyKRSDA2NubGjRtMmzYtR5O3nzt3jsaNG2NhYYG2tjYDBw4EYPTo0Tx58iTH7MrNmJmZ0bp1ay5fvszr169xdHTk4cOHSKVS6tSpg5+fX7ZUY1uwYAHGxsZAekFFRUWFqKgoRo4cydKlS3OFsBYUFMTkyZPZs2dPrgtPzc1MmzaNK1euEBcXR8GCBenatSvLli2jRo0aPHjwgHXr1uHj46PYPi9MNuV5CQ0MDNi2bRvnz5/Hzc2Nly9fsnDhQq5cuYKhoSGCIIgVY7MBMQz0z6FMmTKcOnWKUaNGYWVlxYIFC0hNTc1pszIVMXBLREQks8j7WSlFsoxUmUDMq2BOrJxFaKA/qUmJyKRSSlau+cN9Cur9T9RQ09AiOSGeZGnuXhGH/4kccXFx6Ovrc/78eZKSkkhLS+Pjx48cP34cXV3dLDn3tm3bGDx48L9uU6FCBSwtLRk9ejQymYxhw4YREBDApk2bgLwxOcpsXr9+ja2tLSkpKdy6dQsjI6NsPb+hoSHJycmKv7W0tID/JfYvVaoU27dvZ9y4cYwePfqHBTDi4uKwtLRk8uTJDBky5BuBVZ5vKysrRGaE6tWrc/LkSRo0aECXLl2QSCR4e3sjlUqpXLlyTpuX67hz5w6+vr5YW1tjbW2Np6cnp06dYvv27YpKnAMGDMDQ0DDLQ7o7dOig+Pc/i2Xo6urSvXt3ihcvrshhlVOkpKTQp08fHB0dqVGjRo7akpcICQnhzJkznD9/Hl1dXSIjIzl06BChoaEMGzaMNm3a4OrqSqVKlXLa1Azz9WLC8+fPOXr0KMHBwYrFMD09PdauXUvPnj0pXrx4llfV/VORSqV4enpy7dq1nDZFJJuQSCTY2dnRunVrhg4dyuHDh3F1daVmzR/PBXIz/+xnxXZCREQksxCFNZEfoqok4ciiyZSoXAObxS6oaxfg2q5NPDx/LEPHUVfOO6JPdoccffr0CW9v7x/mVpOjqanJkCFDaNq0KVevXkVPT48VK1ZQqFChbLI09yAIArt27WLChAlMmDCByZMnZ4kHV3yqjAdRSXxIlJIslaGurEQxTWVq6KqjrarMgAEDcHR0ZPbs2cybN09hgzyxv1QqxdzcnEuXLn03bDMhIYFXr14xduxYBg8ezF9//QWAsbFxuklhbhr03blzh8KFC2NhYcG9e/fQ0tJi6tSp3Lx5k/HjxwPfDlr/dCIiInjz5g0LFy7EwcGBvn370rp1azw9PTl79iyqqqpMmzYNyF6BXC4AKysrK87bsmVLUlJSUFNTy1FhYsaMGZQsWZJRo0blyPnzKhcvXsTS0hJdXV0ePXqEs7Mz/v7+FC9enAoVKuDr68u0adOQSCR5WnhSVVXl6dOnDBkyhEaNGnHixAkOHDjAgwcPUFdXZ8SIEXn22nI7169fVzxPIn8WpUuX5uTJk2zfvp0WLVowZswYpk6dmidyr8nbu8DAQKpUqYIgCERFRfH27VtevXqFsbGxIg3Mv+2fmpqaJ65XREQkZxCFNZEfUkRdiZSEONS1C6Kmpc2Hl8+4ddANbR29nz6GBNDXzLmwtYzy6tUrvL29CQwMRFVVFalUirq6OosWLcoS8WbPnj20bdv2P73h4uPjCQwMRFNTk+bNm6OkpMTnz59RUVFBU1Pzj5lEREVFMWLECB49esTp06ezJJ9XWHwqPuGJvPicggTSVcUNioarYQkYFVKjfrFSjB49mpUrV+Lh4cGAAQOA/w3A5M/Lj56bsLAwtm/fjpOTE1WrVk33XW76PZOTk/H29qZOnTrMnj2bDRs20Lt3b9LS0ggLC0NXVxdHR0eaN28O/Jnek/9G+/btMTEx4fnz55QpUwYAfX19hg8fzl9//UVQUBAFChTIdqHj62c0MDCQI0eO4OXlhYGBAUeOHMkx4eXMmTPs2bMHf3//XPUe5AUsLCxYvXo1+/btY/ny5TRs2BAnJycsLCxYvnw5V65cUbQ1efneVqxYka1bt/LkyRMmT55MVFQUFhYWtGzZEk9PTzQ0NLC1tRVF/ixADAP9s/nae23YsGHUq1cPV1dXTE1Nc9q0f0Xe3k2cOBFjY2MsLS25desW9+7dIzk5meTkZG7fvv2f+y9ZsgQPDw/mzp1Lnz59ssV2ERGRvIMorIn8EKOCanQY78ihBRO54r6OEpVqULN1F4LvZCz/Tk1djSyyMPMZPXo0VapU4cCBA/z999/s27cPExOTLMtptW3bNhYvXvzD7+WTWz8/P5o2bYqmpiZ6enp8/vyZ+Ph49PT0sLKywsnJKdtyi+UUp0+fxs7Ojp49e+Lh4YGGRuY/V74RiVwIjU8npn2N/PNnMSm8/JyCVfka1KtXj8+fPxMREYG+vv433iA/msAaGxuzfPly4Mvv/HUxg9yEVColOjoaCwsL1NTUFKu1/fr1A+DJkydiCOh/UKFChXQeHvKcLoaGhhgaGgLZL3QkJCQwZswY7t27R2JiIrVq1cLY2JgSJUqQmJiYI3nNPnz4wKBBg9ixY0eOh6PmRapXr87kyZNxc3OjZcuWODo6oqb2pYL3xYsX6d69O0Ce9laTk5qaytKlSzExMaFbt25UrlwZHR0d9PT02LRpE7a2trmyPc3LyGQyDh06xIULF3LaFJEcpnTp0nh7eyvaGgcHB6ZNm5brvLnkbV1iYiLv379HX18fTU1NvLy8qFy5Mi4uLhgZGeHh4UFycjLq6uo/PNb169c5efIkO3fuVIy3t2/fTp06dahVq1Y2XZGIiEhuRiKIWRtF/oVDLz7zLOZXE/cLFEqIwtpEl6JFi+b6gXxqaip16tThwYMHmJqacv/+fQAaNmyYLtlzZuHv789ff/3FixcvfijcyQcFd+/eZc+ePcyZM0cR/rllyxZiYmLQ0dHh2rVrimqS+Y2EhASmTJnC0aNHcXNzw8rKKkvO81+i2te4ju5FzTZdqde5N757nHl17TQGBgb07t2bnj17/tBTIiUlBRUVlW/yW+WFCaCtrS0SiYQHDx5QsWJFFi9eTFRUFCtXrmTXrl05bZ7ILzBr1iysrKxo0qSJIlx54MCBdOzYEWtr62y1RRAEOnXqRI0aNf51sUHk55BKpSgrK5OSksKBAwfYsmULly5dymmzMgV5m/ny5UuUlJQoW7YsAG/evGHgwIG0bt2av//+O9ePOfIaPj4+DB8+PM9WhxTJGt6+fcuwYcMICwvDzc0tV3qvrV27Fh8fH7S0tHB2dkYikfyriCZH3tbExsbi7OxMeHg4q1atAr7kxx0yZAgDBw6kbdu2uS4froiISPaT+2dzIjlKQwNNVH6xj5ClpbJoaC9KlCiBtrY2lSpVYtKkSZlrYCaSnJxM8eLFSUpKomjRovj7+xMYGMinT5+y5Hzbt2/H1tb2X73h5Lr3y5cvefnyJYUKFVJUKNXW1ubVq1fUqFGDpKSkLLExp7lz5w516tQhOjqagICALBPVwuJTuRAaz8L2dXh+6/J/bj9o/T7MOvUmTYBavUaw4+gpevXqxcaNG9mwYQMzZsxIV9hAjpqa2jciWm4W1b6uHLlhwwa2bNnCoUOHMDExoWXLlvTt2zedF4xI3mL+/PlYWlqioqLC+/fvAZg+fTomJibZbsu6dev48OED8+bNy/Zz5yek0i9VuOX9yoMHD9i9ezfz589P931eRt5mli9fXiGqTZ8+nWrVqlGjRg2mTp0qTm6zADEMVOR7lCpVihMnTjBmzBhatmzJ3LlzFePU3ICzszN79+5lxIgRbNu2DQ0NDdTV1RXjG0EQOHXqFPb29kRFRaXbV0lJieTkZB4+fMidO3fo1KmT4rsbN26gq6tLzZo1kUgkKCkpKSIWRERE/kzEUFCRf6W4tipWJbV/2pNHjooEmpUuyMrwN6SlpZGWlsbLly+Jj4/POmN/E01NTYYOHYqSkhITJ06kffv2aGtrK5KyZyZJSUns3r2bu3fv/ut28slB6dKlSU1NZcuWLTRr1oyQkBC8vLxo3rw5sbGxP7XylpdIS0tj0aJFbNiwgXXr1tGzZ88sPZ9PeGKGnu+vkUmU8I8RaN++PSoqKuzcuZM2bdoofpN/hl3lFQ81+N8Edvr06URFRZGYmMi0adOYO3cu06ZNIygoSFEZTJzIpkf+O/9T6MiNhISEsGvXLq5evYpEIsHW1lYxWcgO7t+/z/z587l582auCyXKSwiC8M1zZmZmhru7uyK0Njc/hxlFnoDcyMiImjVr4uvrqwi5lnvsiWQOgiBw8OBBTp48mdOmiORCJBIJgwYNolWrVgwfPpy6devi5uaWK0IkCxcujImJiSIPrLxvlo9vtm3bxp07d6hZs+Y3C6IfP36kS5cuREVFMXHiRCwtLRXfWVpaUqZMGUqWLMmMGTOoUKECbdu2pXjx4opt5OJdXhnziYiI/B5iKKjIT5GRMDkVCViV1KaOviZ79uxh6NChxMfHo6yszNGjR2nfvn3WG5wBBEHg06dPqKmpoa2tTXJysiJ/V1pa2ncrOv4u+/fvx8XFhXPnzv30PgEBASxbtoynT5+ip6fH4MGD6dmzJ5GRkaSmpqbrzPMyQUFB9O/fnyJFirB9+3ZKliyZpeeLT5Xh/CgKqQBLO9Sh++zVxIS/447XTsrUMOeO1y40Cxbmr2lLqdSoJQAuQ/+idvseWHTtD4CyBOyr6aKlqkRSUtI3+d/ykpgmR27zzp07OXLkCLa2tjg4OBAYGEhycjIJCQmK/GAiP+bgwYNUqFAhV0wwvsf79+9p3rw5Xbp0wcvLC2dnZ3bs2EGnTp3o1q1blj+7CQkJmJmZMX36dPr3759l58mPfC3ap6SkfFMwRSaT/RGhST4+PjRs2BD4IqjJvUdEMo/bt28zYMAAAgMD8/3zJPJ7CIKAh4cHkydPZtSoUUyfPl2R6zGnCA8Pp1ixYt9d5IyIiCAtLY1ChQqhra2t+F7evt6/f581a9awZMmS7+YylkqllCtXDisrK168eEHv3r2xtbVFXV1dMX+QnysmJobChQt/18YfVaKvqauBlqrYnomI5AVEjzWRn6KOvibFtVW48T6R4O9US1SRgAAYF1KjgaEmxbW+eB306tWLefPm8fz5c9asWcOwYcOwtrZm0aJFOZIc+3tERUWxfPlydHR0ABSdoTxsz8DAIJ37d2awbds2Bg8enKF9qlevjqurq2LSlJqailQqzTdJvgVBYNOmTcyaNYu5c+cyatSobBnAP4hK4ntnefPQlzodezPrwlNue3pwaO44pp1+8F2bJEBAVBL1DbTSiWq5uSjBfyG3+dq1a8yaNYsbN27Qq1cv1NXVOXz4MKdOncLNzS1njcyFPH36FGVlZUqXLo26ujrJyclUqVJF8X14eDgGBgY5aGF6Dh48SM+ePZk3bx4FCxbk1atXdO7cmbNnz9KtW7csP//48eMxMzMTRbVfQN4Wubu74+rqirGxMVZWVpibm1OpUqU82e5kBPnEVy6qfW8hzN/fn5o1a6KkpJQvijbkFPIwUPH+ifwXEomEgQMH0rJly1zjvfbPPvdrj1Z9fX1evnxJSkoK2traiu/kYZ0ymYzw8HA+ffqUTliTtze7du2idOnSzJgxAxMTE3x9fdm8eTOHDh3C2NiYsWPHUqdOHQDGjRuHqakpY8aMUbTPP1uJvqGBJsW1RY9uEZHcTP4edYlkKsW1VOlmVAj7aro0Lq5FNR11KhRSpZqOOo2La2FfTZduRoUUohp8mZzv3r0bNzc3Ro0aRUBAAKGhoZibm+Pn55eDV/M/BEFQiCFSqZSoqCjevn3LkydPuHnzpqKIQWY5d75+/Zq7d+/StWvXn94nOjoaZ2dnOnXqhJWVFS1btqRu3bqsW7dOYXdeJiwsjA4dOrB9+3auXbuGvb19tg3gPyRKv+uJWcSwFHW79UdJWZk6HXsRGxlO3McP3z1GmgARid/+BnnZc0L+vDdv3hwnJydWrFjB33//DcCuXbto0aJFuu1EvtyL9evX4+TkxIkTJ5BKpfTt21cRFhwREcGOHTvS5a7LKeQ2GBgYKJKRt2nTBicnJx4/fkyVKlWy3FvN09OTc+fO4ezsnGXnyK/4+/tz9uxZ4Eshm+nTp1OzZk18fHzYsWMHBw8eJDw8PIetzFr+2Ud8LaqFhYUxbNgw6tSpo+gnRX4NQRC4dOmSmF9NJEOULFmSY8eOMX78eFq3bs2cOXNyTe41uah24cIFevXqRdWqVRX90Nd9nkQiITQ0FB0dHSpVqvTdY7i5uTF06FBMTEwIDw9n06ZNXL9+nevXr2NpaYm7uzsSiYTPnz/z+vVrLCwsFOe49yGeXc9ieBaTglTgm7FomgBS4Usl+l3PYvCNSMyqWyIiIpIJiB5rIhlGS1WJ+gZaP7197dq1qV27NgC6urrs27eP3bt306ZNG8aPH8+UKVNyNBdK0aJFcXR0BL5U+VFXV0dZWfmbCWVmCT3u7u707t37pzz25CtnO3bs4PDhw6xatUqxopaSkoK+vj6Qt/PmHDp0CHt7e4YPH87MmTOzJcfS154LydLvixwFi/5vZVJN88vznpwQT8EfHPNHx8mryO9P165duXnzJjVq1MDFxYVXr14RExOj8DASPRj+h0QiYeXKlRw4cEDhufbP7xs3bpwrvGfk7VvHjh2ZOXMmHz58wNzcnAoVKhAVFcWMGTMU28bHx6cLkckMPn36xMiRIzl69Kii0rHIzxMQEIC7uzvbtm2jSpUqtG7dmtatWxMUFISnpycHDhygSpUquco7MjtISUlh7969rF+/HgMDA0Uou5WVFTVq1MiTYfk5jUwm4+rVqzkezieS9/in95qFhQVubm6KOUF2I+93161bx969e4mNjaVLly7Mnz+fhw8f8vLlS8qXL59un5o1a+Li4gL8b0wuP86LFy94/fq1wrv74cOHvH37lsjISNq1a0e5cuWIiYkhPDyc8+fPo6+vT506dUhMTOTRZxmXwtLn9/XzPojv8X3YOR/4xvY0AS6ExtO4nD4PHwRgZGSUdTdKRETklxCFNZFsRyKR0LdvX5o0aYKtrS0nTpzAw8MjxzoJeQf5+PFjli1bhp6ensIFPDExkXr16jFw4MBMmQjLZDK2b9/OwYMHf2p7+fnU1NRo0aJFrs3T9CvExMQwZswYbty4wZEjR6hXr16Wn/NrF3+Aq1evEqNWHNSK/P7B01J//xi5APk9+vDhA3fu3KF69eo4Ojpy4MABYmNjqVWrFhMmTADyZu64rEQmk6Gmpkbfvn0Vn8k9+iQSCUWLFlWEbucGQVImk6GpqYmjoyOvXr2iWLFiHDp0CPgi3Bw7dozTp0/TsmVLZs+enann1tDQ4NatW5QrVy5Tj/snkJyczIABA6hevTrHjh1jzZo16OnpMW3aNExMTJg6dSr+/v5Uq1Ytp03NVuLi4ujWrRtxcXHMnDmTzp07AxAYGIiLiwvr1q0T26tfQFlZOU8v3onkPHLvNXlxpxEjRjBz5sxsF2vlY/uTJ08yatQoevfurXi2t2/fzo0bN74R1kqVKkW1atW4deuWYpwq778vXLiAubm5Im9adHQ0EomE27dv4+fnx4EDB0hNTaVYsWKcOnWKFi1aoKmpyUKnDWze7sqHl0EoKSlTrHxFOkxaSO32Pajd/seeoWkCzLsegqbh9/O0iYiI5CyisCaSY5QpU4Zz587h5OREvXr1WLp0KYMGDcr2Caf8fEWKFKFp06aoq6sjlUp5+vQpPj4+mJqaAt9Wd/wVLl26ROHChRX5Fn7WtooVK3Lz5k127dqVrmMvVqwYBQv+yIcq93Lp0iVsbW1p164dfn5+me4N80/kApB8ABUbG8vevXtZuXIlLYdOoGTTTkhUft1TTkUCpYr8vBdnbkZ+j2xsbChUqBBXr16lc+fOjB8/nho1aqTbVpyk/g+5IPnixQtCQkJITk7GyMgoXfhITnup/RP572djY4NUKuXSpUucPn0af39/pFIp1apVY8yYMT/dXmUETU1NypYtm+nHze8kJibSqlUrLl26RJ06dahTpw4NGjTg4MGDDBw4kK5duzJw4MB8tQjzsxQoUIBRo0bRpUsX4IsAuWPHDo4cOUKvXr2IiopCV1dXrBgqIpIDSCQS+vfvT4sWLRg+fDjm5ua4urpiZmaWbTbIx4Le3t6Kz96+fUtiYiLly5enSJEi3/TTSkpKLF68+LvHGzJkCAMGDFD8XaZMGQoVKkRsbGy6aJ3g4GDevHlDs2bN+Pz5M4tmT6fTtOXUbPUX0tQUXvrdREX130VGaVoayioqpAlw430i3YzEfGsiIrkNUVgTyVGUlJSYMGECrVu3pl+/fhw7dgwXFxdFiGN2IQgCJUqU+KagwKFDh7hy5Ypim99FXrTgZyfX8g7+2bNnXLx4kYcPHxIbG4uSkhKhoaEsWLAABweHPDNRSEpKYubMmezZs4ctW7ZkW4XYrxPxL1myhBIlSlCsWDGePHnCw6Bgjsbw3QIGP4sA1NTV+M/t8gqBgYEoKytz+PBhEhMTmT9/Pj169KBYsWIcPHjwjwsv+xnk79/QoUMBMDIyonDhwpiYmNCsWTMqVaqUq0S1r4mLi6N169bo6+tTq1YtxowZQ40aNShVqlSWnje33o/cjKamJl5eXrx48YKaNWvi4eFBz549adasGQcPHsTNzY2kpCSGDx+e06ZmK/K+Ui6qnT17Fi8vL5SVlencuTNGRkb07t2bY8eOKXIeioiIZD8lSpTg6NGj7Ny5k3bt2inSgGTHe/nPxcDExETc3d3x9vbm06dPdO/enWvXrv30uFoQhHRed3Xq1KFMmTI0atSIsmXLMmTIEP766y8OHDhA7dq1MTY2xmm9MzIBarX9Ej6qpKyJSQNLAO4d3cMdr52M2H4CgGl19On89xKu796MTCplyvF7TKujz5Qjt2hb2lysFioiksuQCGLmaZFcQnJyMrNnz2bHjh24uLjQsWPHbD3/u3fvCA4ORkdHBw0NDfT09Fi5ciUxMTGsW7fut8WrT58+Ub58eYKDg9HT0/vp/XKbl8uvEhAQQL9+/ahYsSKbN2/O1mqmnz59YtGiRTx//pyhQ4fi6enJy5cvOXLkCCoqKjj7BJFc2BDJL3pgmRRWo5tR/skT9ebNG1avXo2trS01a9YEvryfGzZsUISBivwPedvg5+eHs7MzW7Zs4f79+9y5c4fnz58TGRnJuHHjqF69ek6b+g3y9sXPz4+SJUuio6OjyHMYFBREeHg4TZo0yXD7d+bMGaKjo6levTpGRkbpquWK/BovXrzgxYsXtGzZEvhSRGTWrFkYGBjg4uJCjRo1CA0NRVdXF01NzXzTd2SUW7dusWDBAqysrGjSpAlVqlRBW1ubli1bYmNjg52dXU6bmKuRyWTIZLJvKqyKiGQ2YWFhDB8+nJcvX+Lm5pat3msAI0aM4NOnT1SvXp2QkBC2bt2KhYUFd+/e/a3jJicnc+jQIYyMjKhfvz4WFhbMmjWL+vXrM2LxOk67bqBSk1aYtulKmRrmaBYqAnxfWKtQrxk2S7agqq6BqoYm0+ro8/eRW3SpVz1D+a5FRESyHlHqFsk1qKurs3TpUvbu3YuDgwPDhw8nLi4uy88rr4537do1hg0bxogRI+jbty81atTAz8+PUaNGAb8f9rZ3717atGmTIVENvnh1JCcnc+DAAdq0acOJE186XC8vL548efJbNmUHUqmUZcuW0aJFCyZNmsTBgwezTFT7UbXF5ORknj17hiAItG/fnunTp6OkpMSTJ0/Q0NCgSalCyH4xR5qKBBoY/nchityO/N7Jc2rdvn2bFStWsGzZMh4+fIi6urpCVBPXY9IjF5wWLVpEWFgYAKampgwZMoRevXrRrFmzXCmqwf+8xmrXrs27d++ws7PDxsaGCxcuEBcXx9KlS4GMtX/h4eEMGjQINzc3li1bxvHjx4mMjAS+iEMiv4avry/W1tYMHjyYly9f0rdvX168eEGbNm2wtLSkU6dOFCtWTFEY508U1QDq1avHtGnT6Nu3L+bm5mhra3Pu3DlFugeRb/nw4QMXL17kw4cPKCkpoaKiwrt37xSVZ8U2XyQrKF68OEeOHGHq1Km0b9+emTNnkpycnC3n/vjxI69fv2bfvn3MmjWL+/fv4+PjQ+nSpfH19f2lYwqCgFQqRV1dnT59+lC/fn2Sk5Oxt7enSZMmpKWlYVipBsO3HUcikeA5fwILWlTGY1w/Yn9Qdb75oLFoFdZBVeN/48yvK9GL76aISO5BFNZEch1Nmzbl/v37pKSkUKtWLW7cuJGl55NPGHv27ElgYCDXrl3j+vXrvH37lhMnTlClShXg9ycp8jDQjCAXO9atW8edO3dQVVXl9evXAJw6dYrz58+n2y63ERISgqWlJSdOnODu3bsMGDAgSyZ78oHFPyf/8s8NDQ0ZNWoUcXFxvHv3DiMjI2rWrMmBAwdITk6mVvmS6H54hkQmzdB5VSRgVVKb4lp5P9eF/N6dPXsWR0dHvLy86Ny5MzExMSxbtoy1a9cqtv1TJ+z/hiAIdOnShZCQEMzNzdm5cyfwRbCSV1DNzQPg6Ohoxo8fT7ly5bC2tmbSpEnUqVOHd+/eERoamqHf3MDAgEuXLuHt7U2jRo04evQoS5YsoWrVqmzZsiULryJ/06NHD16/fq0oZjNr1izS0tJwdHQkODiYMmXKZEtV5bxAw4YNKVasGG/evGHUqFFMnjyZnj17UrFixZw2LVcybNgwFixYQMWKFalZsyaLFi1i+PDhHD9+HPhf0ncRkcxGXtDM39+fBw8eYGZm9tseYz+Dnp4eaWlpioI91tbWODg40Lhx41/OKyqRSBQLbfL3RV1dHVtbW3R0dChUqBDFSpSimJEJ1nPXM+1UAGP3X+VzxHuOr5j53WMWNizx3c/llejlfbNUmrHxq4iISOYjCmsiuZJChQrh6urKsmXL6Nq1K7NmzSI1NWuqLso7v8DAQA4fPsy5c+e4e/cuvr6+vH79msTExN8+x/379/nw4YMihCejtgUHB9O2bVuaN2+u+ExZWTnXTqIEQcDNzQ0LCws6d+7MhQsXsjRRuXxgcfDgQQYNGsTp06cVn8vvl6mpKdWqVcPNzQ34MpE4ceIEjx8/RlVVlZGdmtOqTCFUflI/kItqdfTzvreanLt37/LkyRN0dXUpWrQo3bt3Z9CgQbRq1Yq6desCuVscyknkkwMfHx9mz57Nrl27MDc359ixY+m2ya0UKVKEz58/M27cOLp160apUqXYuHEjJiYmvHnz5qePc+vWLQCFgDF06FDGjBmDpaUlVlZWlC1blqSkpCy5hvyM3BOiYMGCbNq0CS8vL+7du0ft2rXx8PCgcOHCbNiwARAnWHIuXrxIw4YN0dLS4saNG1SqVImZM2dy4MABrl27Boj3Cr7kWCxdujTnz58nJiaGOXPm4OvrS9u2bfHx8WH+/Pm8evUqV7dfInmf4sWL4+XlxbRp02jfvj0zZszIMu81+Xs/dOhQNm3aRFpaGqNGjaJ///7069cPmUzGnj17aN68OSdPnvylc8jfl6/HTAUKFKBOzfRFoIqVr0idTr0JD/5RBMr33zt1ZSUOHTrE8+fPAdKlakhISMiWiB8REZH0iAkURHI13bp1o0GDBtjZ2dGgQQN27txJ5cqVM/UcMpkMZWVldu/ezcKFCylZsiTa2tq8ffsW+CLyjR07lgkTJvyykOXq6oqtrW2Gc7TJO+aiRYvy8eNHXrx4oajMGBUVpSjxnZuIiIhg+PDhBAcHc/78eUWOrsxEnu9JXuHp6tWr+Pn5ceXKFerVq4eTkxOvX79m6NChSKVSVFRU0NfXp3379ixfvhxbW1sqVarE+vXrFVWbAGoX1aC4tgo33icS/DkFCV9c7uWoSL4UKjAupEYDQ8184an2NXKPvvHjxzN16lQGDBhAhQoVqFChgmIbcXL1P+TP4d27d9m1axcfPnygdu3a1K9fnwMHDuDu7p7TJv4U8uvo3bs3w4YNo2LFikilUtavX8+oUaOoX7/+Tx0nKioKFxcXDhw4wJgxYyhTpgwA5ubmwJfiJV5eXqirq9OtW7dc2X7lRuS/T0xMDPfu3UNXVxczMzO8vb05duwYo0ePxt/fn1WrVgHkiUI22YG5uTnu7u5YWVkxfvx4jh8/joGBAXXr1mX69OmcOXMGDQ2NPzYXnZwCBQqwZMkSxd/du3ene/fuwJcqh9u2bWPTpk2sXLmS3r1755SZIn8A8gWqFi1aMGLECMzMzHBzc1P0IZmFvI20trZGRUWFT58+oa+vz6BBgzh48CD79++nSJEi9OnT55tq6Bnl67ZFJpMR/+YZ1w8fp3rrLhQ2KEH0+1ACTntSpsbP55dTQkA9JZ7uNjZYW1sTFRVF165dGTRoEKqqquzatYsnT54wc+ZMdHR0fst+ERGRn0f0WBPJ9RQvXpwTJ04wZMgQGjduzPr16zPVa0be6eno6ODh4cGbN2948uQJERERjB49GldXVx4/fsyRI0d+6fjJycns2rULW1vbDO8rD88bN24cd+7c4fjx45w4cYLq1atTuXJlunbtmm67nObEiROYmppSsWJFbt++nemimjzkVT4okl/3vn37cHV1ZerUqUyePBl7e3tF6KKKigqCIKCkpESNGjWoW7cu7969A6B58+bpji+RSCiupUo3o0LYV9OlcXEtqumoU6GQKtV01GlcXAv7arp0MyqUb0S1r9+l5s2b4+vry9q1a9m4cSN169Zl9erV32wn8gX5c2hnZ0f16tVp2LAhysrKuLu78+7dO+zt7enUqVMOW/nffB0OHxsby9OnT2nbti179+7F3t6eU6dO8f79e+7cufNDDwKpVIquri4TJkxAR0eHM2fOfLNN9+7dsba2Zv/+/Vy9ejVLryk/IX/O+vbty6lTp2jZsiUDBgzg/PnzdOrUiVevXjF9+nQg96YFyG4EQaBgwYJYWVlx//593r9/z9WrVxEEATMzMzp06KDIH/gni2rwJe9hUlKSIipAKpUqnqNOnTrh5eXFqlWrOHr0aE6aKfIHYWhoyOHDh5kxYwYdOnRg+vTpWea91rVrV8LDw5k6dSpdunTh+vXrODg4sHXrVoYNG5ap1bGVlJQwL63P64f3cB7QhtkNy7JxYFsMjKvQfvy8nz6OALy+eY4qVaqwa9cuHBwc8PDwIC0tDfiSj9PCwgJtbe1Ms11EROS/EauCiuQpgoKC6N+/P0WKFMHV1ZUSJb6feyAjyL0BunfvTocOHRg8eDCpqamoqqrSpUsXpk+fzq5du2jatKliFTcjHDhwgE2bNinyof0Ojx8/Jjg4mJo1a2ZpaGVGiYuLY+LEiZw5cwZ3d/dMTxAt90yT4+7ujpeXFy1atGDo0KFERkbSp08fli5dirm5OSoqKrRo0YLevXszdOhQxe8p8mOWLVuGVColICAAJycnDAwMOHbsGBs3buTEiRN//OTzn8i9XHx9fZk2bZoi/PjTp0/MmzcPmUzG8uXLUVNTy2FLM8bNmzcVHmqvX7/m2LFjrF69mvDwcCZMmICDg8N3i49MnTqVbt26KUKGk5OTUVdXV9ynr72COnTogIWFBY6Ojtl2XXkV+X3z9PRk586deHp6UrNmTZo2bYqnpycdOnTA0dGRkiVL5rSpuZbVq1fz4sUL1q1bx65du9i9ezeVK1emfv36WFtb57R5OUpsbCzDhw+nQoUKmJub06lTp3RtvXx8dO7cOQRBoFWrVjlorcifyPv37xk5ciRBQUG4uroq+pjMIi0tjXbt2lGnTh26du1KnTp1UFNTIz4+nidPnmBmZpbhqtj/xaEXn3kWk/JrOwsyympK2D99GNbW1vTp04dbt26xceNGJk6ciJGREa6urlhZWVG1atUfHuaf42oREZHfR3yjRPIUJiYmXLt2jYYNG1K7dm0OHDjw28eUdyxt2rTh4sWL7Nq1C39/fzZu3EhMTAyGhoZ8/vz5l1d+fqVowT9JTU3l9u3bBAQEEBMTw/Xr19m6davC8yonuXHjBrVq1SIlJYX79+9nmqgWExOjyG8n/40SExMZPXo0586dY9asWVy9epVFixZRsmRJ6tevz5EjRxSrmqNHj2bWrFkA34hqolfHF+T3wcfHh5MnT1K5cmX8/f1JSUkhNjaWVq1a4e3tjUQiEe/ZP5BPPg0MDEhISGD58uV8+PABHR0dBg0axNOnT/OcqAZQv3599u3bh5mZGVZWVty+fZu2bdvSrl075s6di66u7nf3mzhxInXr1sXa2pp79+6hrq4O/O8Z+/oZ6tWrF7Nnz86eC8rjyJ+zBw8eMH78eJycnGjTpg3r16/HwcGBFy9e/PA3+dORrxs3aNAAHx8f4IvXn7a2Nn5+frRt21ZRrfZPpWDBgsyYMYMSJUoQERGBRCJJl3dOLia0aNEiwzliRUQyA0NDQzw9PZk5cyadOnVi2rRpmZanUyaToaKiwu7du1m8eDElSpTgypUrBAcHExERwd9//01qamqmh9c3NND86Xy+/0RFSYniSeG8fv1asTDw+PFj9PX10dHRQVtbm9GjR1O1alVevHhBVFTUN8eQSqW4u7uza9cucWwnIpKJiMKaSJ5DVVWVOXPmcOzYMWbOnEn//v2Jjo7+5ePJvSmGDfuy+nPs2DGmTJmCr68vW7dupUyZMsyZM+eXBKM3b95w584dunXr9ku2yTu8pUuXMmrUKI4fP86NGze4du0ap0+fVnSYOeF4mpqayqxZs+jatSvLli3D1dWVQoUKZcqxHzx4kM4L6MmTJ9jY2ABgb2/P+vXr8fb25ubNmzx48IDTp08zevRobt++zZMnXxLAdu3alW3btgHf3h9xle4L8vvg5ubG/PnzkUgk1K1bl9KlS/P06VOmTJnyzbYiMGfOHMVzVrJkSRYuXMi7d+9Yu3Yto0ePZsqUKYoBb14btIaEhODm5saqVavw9/fH3d2d5cuX8/79e0VI9ffQ19fnzZs31KtXjyFDhrBu3Trgy8RcPlGX7ztgwADxecogc+bMoVGjRiQnJ5OQkADAy5cvsbW1RVNTM889Z9mBvG+vX78+ZmZmiuq8e/bswcPDg5CQEBYvXqwouPGn3sNq1aoxYsQI7OzsAL6paghf7qXotSySU0gkEmxsbAgICODp06eYmZlx+/bt3z6uvB/S19fnzp079O7dmylTpmBvb48gCNSpU4c1a9YAmds+FNdWxaqkdobFNXnRrOO73KhYsSKqqqqEhoYSEhJCqVKlKFWqVDo7Hz9+jKenJ7GxsemO8/r1a968eUN8fDxKSkrIZLI/tv0TEclMxOIFInmWunXr4uvry+TJkzE1NcXd3f2bnFk/i3zA2Lx5c5o3b46qqioSiUSRr6BcuXK/dFx3d3d69eqFpuavVY6UD2xv377NmjVraNSo0Xe3y+4Bb2BgIP3798fAwAB/f38MDQ0z5bhyd3sjIyP09fV5/PgxHTt25Nq1a5iamqKpqYmxsTELFy4kNTUVPz8/HB0d8fb2pk2bNhgbG3Px4kVq1aqFsrIyHTp0AMQcOv9F27Zt8fLy4syZM5w7dw6A9evXY2RkBPDHJ/f+J+3ataNy5cp07NiRtm3bMmTIEDQ0NAgKCiI4OJjWrVvTuXNnIO8JkmXLllVUmyxQoAAAmpqaXLlyBUEQOH78OIIgKHLHycNJNm/ezNmzZzl48CD16tVj9uzZ3L59m7Vr14rJkzOI/H1LS0tDRUVFcY+VlJSwtrZm+vTptGrVivj4eFxcXIC895xlF/J7uWrVKm7evAl86b+uXbvGmTNnePz4Ma9evWLnzp1oaGj8keFRMpmMGzduEBoaCkDlypWpXLlynvS4FcnfGBgYcOjQIfbv30/nzp2xtbXF0dERDQ2N3z62p6cnFhYWrFmzhp07d7Jq1SoaN27MjRs3gMwfR8oryl8IjU9XJOtHfF2JvsLs2YoFFl9fX16/fq2Y/3xtZ8eOHb97rE+fPpGUlESrVq2+CXP9E9tAEZHMQsyxJpIvOHnyJEOGDMHGxoYFCxb8UicrDwN99eoVaWlpKCkpERUVxfHjx38pf41MJqNChQrs37//lysayScFzs7OKCsr0759ewoUKICysjLKysrZnphUJpOxYcMG5s2bx4IFCxg2bFimDDa+J9ycOnWK06dP07x5c65evYqNjQ1mZl+qJlWpUgV3d3fq1q3LwIEDCQ0NxdHRkdq1a4vJWn+BmJgYxo0bx4MHDxgwYAA6OjosXbo0XUifyBfkg1CpVMrFixdxdHQkLS2NSZMm0b1793TPcV4TJOXXduzYMYVwduHCBfbv34+npyd///033bp149OnT9SpU0exX1hYGAMHDmTz5s2UL18egMjISObMmcO5c+fw8fFBV1c3T92L3ILci0A+0REEgWfPngGgrq5O2bJlMz3/T35D/h7KZDL27t2Lv78/cXFxNG7cmD59+jB16lQ+f/6Ms7NzTpuarcgn0N7e3owfP55GjRpRtGhRUlJSKFiwIJUqVaJfv345baaIyHcJDw9n1KhRPH78GDc3N+rVq/dLx5G/B1euXGHRokV4enpy5coV+vXrR9WqVVm/fn2WVLeXE5aQ+luV6CMjI/Hx8aF58+YUKlTop8cd8fHxaGtrY2trS5UqVShTpgydOnVSLKgBYt8iIpJBRGFNJN8QGRnJ8OHDefbsGYcOHaJixYoZ2v/169f4+vpSp04dZDIZaWlpJCcnU6lSJVRUMu7cefHiRcaNG4e/v/9vTygfP35Mnz59+PDhA8WKFUNJSYmwsDBOnTqFqalptkzgQ0NDGTRoELGxsXh4eGT4/v4MTk5OhISEMHLkSIyNjZk3bx6+vr6EhoZy8OBBjI2NAZg9ezbnz59HQ0ODQoUKMWvWrHQT/bwmaGQ38oFkTEwM4eHhFC1alNDQUB4+fMiVK1fQ1dWlc+fO1KtXT1y9/IqUlBSFN2vr1q05ceIEqqqqeHp6snz5cqKioli6dCldunTJaVN/i8jISIYNG8bjx4/R1dWlTZs2tGzZ8oceszt27GD27Nl07dqVxYsXK8RYQRDw8/NL926K/DdOTk6sWLGCw4cPY2FhAXyZ4EgkEvFd/E3mzftSeW/QoEGULl0a+OKd++7dO2bNmqUIq/0T7rP8OmfPnk2tWrXo1q0bAQEBPH/+nIcPH1KmTBlsbW3F/lQk1yIIAvv372fs2LEMHDiQuXPn/pb3mr29PQcPHqR27dqYm5vTt29fqlSpwoEDB9DS0iIxMZEePXpk4hX8j4RUGQFRSUQkSkmWylBXVkJfU5mauhpoqWa8Pdq7dy9WVlYUK1bsm+/kgtm9e/eYNm0aRkZGJCcn4+fnx5kzZ3j//n2WiokiIvkVUVgTyVcIgsCuXbuwtLSkRIkS3wwG/22AKJPJMjWPSP/+/TE3N2fs2LG/fAx551e/fn1sbW3p168fUqkUqVRKamoqenp6vyT6ZZR9+/YxZswYHBwcmDp16m+dUxAERa4m+cB+586dhIeHc+/ePcqXL8/Dhw9ZsGABUqmUBQsWEBoaSpUqVQgNDcXe3p4GDRpw7do1ZDLZf1Zq/VMmSRlB/h4MGTIEPz8/ihcvTps2bahRowYNGzYUQ4B+wN27d1m+fDkpKSkoKytz8ODBdG3KsWPH0NHRoXHjxjls6e+RkJDAsmXL6NWrF2XLliUsLIwCBQqgra2Ntrb2N21kXFwcJ0+e5MyZMxgYGDBw4MBvhHdxcp4xlixZwooVK7C0tGTz5s2KAgViheNfQ94PxMXFKTwygoKCmDhxIpcvX6ZPnz5Ur16d0aNH57Cl2U/r1q3p0aMHw4YNU3yWkpJCcnIyBQsWFN9dkVzPhw8fGDVqFI8ePcLV1VVR2fpnkbcPAQEBHD58mJ49e1KlShWSkpJYv349ixYtolixYri5uWFhYZGrvLi+936mpaUp+mN5pMf39lm4cCFxcXHY29tTqlQpOnbsSIkSJfj8+TPBwcGsXr36m/HM117UYtsgIpIeUVgTyZf8l5jy4sULRf6ofyL3VAsNDUVJSYkKFSpk+PzR0dGUK1eO58+fU7Ro0QzvL0d+HTY2NowdO5b69esrwlSzI5nwp0+fsLe3x9fXl507d/5ySKucH/0utWrVolixYpw5cwaAZcuW8ezZM7Zs2YKjoyPq6uoKEWjXrl1MmjSJGjVqKPb/nrt6YmIi6urqoqj2A16+fMmAAQO4evUqZ86cwdvbm9jYWAwMDBg2bNgv5xXM71y8eJH+/fujoaHBzJkz6dGjBwUKFODs2bNIJJJ8VTkvNTWVDRs2EBoaSkBAACoqKgwfPpzOnTsr3rnU1FSuXbtG5cqVCQwM5MqVK7x+/ZquXbsqwklFfp69e/fSu3dv4EsbNmLECLy8vBgzZgzz58/PYevyPvLn1t7enrNnz9KjRw8mT56Mjo4Obdq0oWfPntjZ2f0xIVAfP35k5MiRnDt3jipVqtC2bVu6d+9O1apVc9o0EZEMc+DAARwcHBgwYABz5879pfzGKSkpREVFsWjRIo4fP079+vUpUqQIKSkpbN26Nd8s1sbExDB27Fg6d+5M586dSUpKwsLCgqVLl9K5c2cGDBhA3bp1GT16NMnJyTx//hwjI6N091Qmk/HgwQNMTU1z8EpERHIPeb9lEBH5Dj/q9CIjI5k0aRLNmjVjzpw5391GRUUFdXV1SpUqxaZNm36pUs7evXtp3br1b4lqX1OtWjWmT5/OsmXL2LNnD7t378bV1fWbSj+Zyblz5zA1NaVo0aL4+vr+tqgG/1vhWrhwIba2thw4cACAFStWEBoaSlpaGlKplL/++ovXr1/z8OFDLC0tefz4MSEhIbRu3Rp3d3eFqCZfF/h6ApSSkkJCQgJSqTRfDH4yG/k909bWpkmTJsAXjwUnJyd69+5NamoqxYsXz0kTcyVyT0tLS0v27t2Lh4cH7u7uNGrUiC1btjB58mRiYmJy2sxMIyEhgcmTJ3P69GlatmzJmjVrmDFjBjNmzAD+987JvaeOHDmClZUVAwYMoEqVKmLBgl8gMTGRgIAA4Esfoqmpibu7O9euXePSpUvo6elx6NChHLYyb6OsrEx8fDzh4eEcOHCARYsWoaOjw4cPHzAwMCAuLk6x3Z+Anp4e+/fvJyQkhEmTJvH27Vs6duzI5MmTc9o0EZEMY21tTUBAACEhIdSuXVtReCAjxMXFMWHCBFRUVDh69Ci7d+9m7dq1xMXFkZiYqMi/nFeRjwG9vb0BMDExQUVFhQsXLijSgKSmppKSkqKoMjpr1iy2b9+OhYUFDg4OREREAHDo0CEmTJhAZGRkjl2PiEhuQvRYE8n3fJ1Pas+ePZw4cYKtW7fy+vVrLCwsFAk8MxMLCwvmz59P27ZtM+V4K1as4O3btwiCQGpqKvDFK27NmjXo6+tnyjnkJCYmMm3aNA4ePMj27dtp3br1Lx/rn27iT58+ZcKECVSvXp1u3boxfPhwHB0d6dKlC02aNMHW1hY7OzuSkpJYtGgRz58/Z/fu3Tx9+pRKlSopjvO9FUNBEEhKSmL//v1069aNggUL/rLd+RX5fXv8+DETJkwgMDCQwoULM2nSJAYMGJBuW9HF//s8ffoUQRCoVKkSEomEM2fO4OXlRY0aNRg5cmROm5dpREVF8ddff3H16tV0nzdp0oRDhw6ly9siDxtTUVEhIiKCMmXKZLe5+YqXL1/SqFEjKlWqxOzZs7G0tAS+5LKTSCRiQvnf4J+eaJ8+feLu3bv4+PgQERHBsmXL0NLSwtvbmwMHDuDq6pqD1mY9r169IiAgAAsLC0V176ioKJKSkihRokS+8c4R+fOQe6/169eP+fPn/7T3WkJCAmZmZly7dg09PT3F54IgEBISQnh4OAcPHmTFihVZZXq2cO7cOV69ekX37t0pUqQIffr0oUaNGkybNk1RtKhnz54EBwczefJkbt26RcWKFfn777/p27cvtWrVws7ODlNTU0aNGqVIESPmAxX5kxGFNZF8z4sXL9DU1MTX1xcXFxfGjh2LlZUV8EWcGjduHBMmTMi0RJ0BAQF06NCBkJCQ3171/vTpE4IgUKhQoWzJpebr60u/fv2oWbMmzs7Oirw+GSEmJobt27dTp04dmjVrRlJSEqGhoRgbG5OamsrLly9RV1dn6dKlHDlyhHbt2rFkyRLu3LnDrFmzuHv3LgAhISF8+PCBunXr/uc5BUFg27ZtXL9+nU2bNomVLH+AXCxzcHCgWLFi6QTUqKgoRo4ciZ2dXU6bmeuQT8ZXr16Nj48Pz549o2LFirRt25aePXumE3HzgyApv4ZatWqxevVqSpQowevXr1m9ejXly5dn+fLlaGlpKbaX5/3asmULR44c4ejRo+KgOoN877nZunUrS5cupX79+sybN09RcVUkc3jy5AnHjh3j+fPnlCxZkuHDh6Ompsb8+fN5/PgxHTp0YODAgRQqVCinTc1U5GLZpUuX2LJlC48ePVJ4+LRs2RJra2tMTExy2kwRkd8mIiKC0aNH4+/vj6urKw0bNvzX7eV9/bhx47CxsaFevXqEhYWxYsUK9u3bh7m5OV5eXtljfDYSFRVFq1at8PT0pGzZssyePRsNDdPURxgAAQAASURBVA06d+7Mtm3biI6O5t27d8TExKCiokK/fv2wtrame/furFmzBlNTUxITE1FVVVXMVeR9WlY4L4iI5FbEka9Ivuf8+fMYGRlx/PhxWrdurRDVANatW0fJkiXTlZf+XVxdXbG1tc2UUJJNmzaxZMkS5syZw+fPn4H/uXFnJmlpaSxatIi2bdsya9Ys9u7d+0uiGnzxePv48SOnT59m48aN1KpVi/79+3Pw4EFUVVUxMDBgypQp1K1bl7t37xIUFMSJEydo164dCQkJnDhxAoBy5cr9lKj2+fNn6tevz4MHD9i2bZsoqv0LEomEDx8+kJCQQLVq1VBRUaF3796cOHGCMWPGKMKexfWW9CgrK5OWloabmxv79++nQoUKaGlp4eHhQZcuXRQhFUCeF9Xgf8mJFy9ejJOTE6NHj+bcuXO0atWKlStXphPV4Es4aHx8POvXr2fKlCmKkG+Rn0d+vw4cOMCqVatwdXWlefPmPHjwAB0dHczMzHj58mUOW5m/0NLSIjAwkP79+zN79mxOnTpFly5duHz5MpaWljg4OOQ7UQ3+96zt2rULGxsbJkyYwJAhQ+jXrx8LFizAyckpZw0UEckk9PX12bdvHwsXLqR79+5MmjSJxMTEH24vXxBycnKiSJEi9OzZk8aNG5OcnMyJEyfSiWp5vY/7Os2Nrq4ud+7coWzZsnz69InAwECMjIyoWLEiR48eZf369Zw+fZrNmzfTtWtX2rVrx4kTJzA2NqZatWrcvn2bpUuXYmZmxrBhw3j69KliLLRkyRJCQ0Pz/P0SEfkZRI81kXzP58+fGTt2LHPmzFEkZJdKpaSlpbFlyxYaNGiQrmrO73icJCcnU7p0aW7evPnD4ggZ4fz588THx6OkpESbNm2ypBpccHAw/fv3R1NTEzc3N0qXLp2h/QVBQCaTpRMST58+zcGDB/n8+TN79uzh8OHDbN68mQ0bNlCgQAEaN25McHAwAGZmZlSrVo21a9cilUrTud7/F0FBQbRp04bhw4fz999/5wtRI6v4/PkzhQoV4vz58yxcuJAiRYowePBgqlWrRtmyZUUPo//g6NGj3Lx5k6FDh2Jtbc3du3e5c+cOY8eOxd3d/ZsqmPmFhIQEPn/+TGRkJAYGBrx+/ZoLFy4wcuTIdAsSiYmJbNq0ifHjx+cLr73s5M2bN5QuXZo7d+5gZ2dH06ZN0dHRISwsjG7dutG+fft/Lbgj8uvIZDLu37/PqlWrSE5OxtramoIFC+Li4oKVlRWjR4/Ot+GQdevW5fjx4wwZMoT58+djamrK5MmT6dixI82aNftjCjiI/BlERETg4OCAn58f27dvp1GjRv+6/aBBgyhSpAh///03MpmMN2/ekJycjKGhYb7z6JS3cfL/f/r0CalUStGiRZk5cyZaWlqMGjWKIkWKKPbp3Lkztra2VKlShREjRmBpaYmjoyPLli1DU1OTYcOGsXz5cnbs2MHTp08V+4ntikh+Jutjy0REcpDU1FSuXLlCeHi4IjcZfPFAUVZWZvTo0QBs3LiRUqVK0alTJyQSyS9PDI8dO0b16tUzbQLUokWLTDnO9xAEga1btzJ9+nRmzpyJg4PDL00eJBIJysrKxMbG8vjxY+rVq0fDhg05ffo0z58/R0lJie7du3P06FHOnj1L3759sbS0pFOnTkRHR1OvXj3Gjx+frsP+GW7dusVff/3FkiVLsLW1zbDdfxIPHz5k69atNG3alC5dulChQgX279/Pvn37KFWqFDVr1qR169YZEjX/BMLCwvj8+TPGxsZ07tyZpk2b8uzZM4oXL44gCLx7946yZcvmW1FNzqNHj7h+/ToXL17Ez88PVVVVatSoQdu2bRUDcXV1dcaPH5/TpuY5YmJiGDZsGBYWFrx584aVK1fSqlUrQkNDOX36NJs2bcLc3FwU1bKItLQ0Jk2aRHx8PCtWrKB27dpoa2sjlUrZvXs39vb2+VJUEwSB3bt3U6BAASpWrMi2bdto1aoVhw4dUhR2Eie/IvkJfX199u7dy6FDh+jRowc2NjYsWLDgGw9seZ+2fft2JBIJz58/Z9u2bSQlJeHj40PRokWZPHkyzZs3zzcikbyNk///6+JDAwcOZP78+dSrV4/WrVsze/Zs3rx5g0wmo1atWty6dUtR/ODGjRsUK1YMJSUl+vXrx5kzZ4iOjqZ9+/asXbuWChUqoKysrJhjiYtwIvmN/DdaEBH5iuTkZDZv3oy1tTUVK1ZM54osF9oCAgKYOXMmhw4dYsqUKSQmJioa+ow6dG7fvp3Bgwdn3gVkEeHh4XTu3JmNGzdy+fJlxo4d+9OTh0+fPqWrfpicnMzkyZNp3LgxHh4eDBs2DGVlZbp164axsbEi+XmPHj24fPky7969w8nJiZYtW7JgwQKcnZ0zLEycPHmSTp06sW3bNlFU+wmUlJSoWrUqt2/f5saNG5QtW5bJkyczb948ihQpwokTJ36pLH1+Z/v27dja2rJx40ZevXpF4cKFqVWrFqVLl8bU1JTZs2djb28P5P2wkB+xYcMGhg4dSlRUFHPmzCEqKopDhw5x+PDhdNvJ2w9xoJwxVFVVmTx5MjKZDF9fX3bv3o1MJqNkyZIMHjyY+Ph4bt26ldNm5ktkMhlqamosWrSIKlWq0LhxY7S1tYmMjGT58uU0btw43z3LUqkUgFOnTqGlpYWWlhZjxoxBIpFw9uxZBgwYQIECBX6pGrqISF6ge/fuPHjwgLCwMGrVqsXjx4/TfS/vyyQSiSJHW0pKCoMHD+bWrVsMGzaMxYsXp9s2P1OxYkU8PDwICAjAzs4OfX19du3aRZkyZTAyMuLRo0eYm5tz5coV5s6dS6lSpahQoQI6Ojq8f/+emzdvMnz4cCpUqIClpSXXr19XtKtft6/5dQwl8ochiIjkY169eiVUqlRJCA0NFQRBEGQymSAIgiCVShXbWFhYCA4ODoKvr6+wYsUK4fjx40JiYqLi+7S0tJ8615s3bwQdHR0hPj4+E68g8zl8+LBgaGgoTJ8+XUhOTs7QvtHR0cLhw4eFQ4cOCWlpaYKvr69w9+5dYfny5UJycrJw8uRJoVSpUsLq1asFQRAER0dHYdasWYr9O3fuLHh5eX1z3K9/j//C3d1dMDAwEHx8fDJk+59OUlKS8PDhQ8W9/vqef/z4URCE/70fIv/j3LlzQs+ePYU2bdoIa9asEd68eSMkJCQIQUFBQkBAQE6bl2XIn4XvtWe+vr6CkZGRkJKSkt1m5UtkMpnw4cMHYc+ePULz5s0Fa2trYefOncKNGzeEevXqCampqTltYr7HwsJCmDZtmjBhwgTByMhImDNnzjfbZKSfyq3I3+tu3boJx44dS/edv79/TpgkIpJjHDp0SChRooQQHR393e9v3boldOzY8Zt9Bg0alG6ekJ+RSqXfzIPevXsnvHjxQhAEQfDy8hJGjBjxzXxi69atQsuWLRV/P3/+XNDS0lL8feXKFeHo0aPftKs/O+cSEcmNiDnWRPI9fn5+PH78mL59+yo+E/7fq2LSpEk8ffqUY8eOKb6bM2cOaWlpfPr0CWdnZ4CfyrHy6tUrnJ2dWbp0aZZcR1paWrrKoEIGPUNiY2MZN24cly5dwsPD4z/zS3x9HkEQFNe/adMm1q1bR0JCAsuXL6dVq1YIgsDEiRMJDg6ma9eurFu3joCAAG7fvs2GDRuwt7enefPmREdHpwv5zGjumhUrVrBu3TpOnTpFlSpVfno/ke+T0WfoT+KfIR63b9/GycmJ2NhYOnfuTPv27SlZsmQOWph9CIJAcHAwz5494+XLl1y/fp2aNWsybtw4sVjIbyB/xiIjI0lMTKR06dK8f/+eAwcOsHTpUpSUlHB2dqZjx475JuQotyG/r0FBQVy/fp3bt2/z999/8/79ezw8PKhatSoJCQlMmTIlp03NVG7dusXq1atxdXVFRUWFnTt3cuzYMXbu3PlNaJyISH4mMjKS48ePY21t/d3qleXLl+fw4cPExMTw8uVLduzYgYODA126dMl+Y3MhcXFxjBkzhnv37mFiYsKIESNo0aIFjRs3ZsyYMfTs2ROAcePGERkZyc6dO4mMjMTFxYXQ0FA2bNjAnTt3eP36Nd27d8/hqxER+T1EYU3kj8Xb25uBAwdy7949ypQpw+PHj3F2dsbLy4uXL18yf/58RaVJXV1dwsLCOHny5A9DPVNSUhS527KC3bt306dPH8Xfnp6edOvW7af2vXbtGgMGDKBFixasWrWKggULZvj8kZGRuLu7c+LECeLj4+ncuTMzZswA4N69e7i4uLB582ZkMhkVK1Zk4MCBzJ49m507d2JlZUWJEiUUx8qooCOTyZg8eTKnT5/m1KlTlCpVKsP2i4j8LPLnMzExkTNnzvDmzRuqVq2KlZUV3t7erFq1ikaNGjF37tycNjVb8Pb25sSJE6ipqaGhoUHlypXp3bs36urqJCcnExQURI0aNXLazDyF/Bl79uwZI0aMIDg4mNq1a7N48WIqV67MgwcPuH//Pv369ctpU/84XFxccHFx4ePHj6xZs4bDhw9jbm6Ovb19ni5k4ODgQGJiIv369aN58+bMmzePuLg4goKCKF68OB06dKBjx47igovIH8k/F6/lovu+ffvYs2cPycnJ1K5dGwsLC7p27Up4eDh6enqoqKiI7wwQHx/PyZMnMTU1pXTp0tjY2DBp0iTFIn7x4sU5evQoFhYW+Pv7s2HDBkaMGMGjR484ffq0YiwxdOhQHBwcvjl+Xm57Rf4cxOIFIn8sRkZGbN++nTJlygDw/PlzPn78iJmZGXZ2dgwYMICUlBQKFSoEwIwZM1BTU6NXr17fXdVSU1PLUnvfvn2b7u9Hjx79p7CWkpLCnDlzcHNzY/PmzXTu3Pk/zyPPraKkpIQgCKSlpXH06FF27txJvXr1OHjwILdu3eL48eMEBQVhYmKCr68v0dHRvHjxAm9vb1q0aEFoaCjAdyeGGRmApKSkMGjQIF6/fs3Vq1fTJVUVEckK5FVuFyxYwMePH0lISODatWtYWVnRvn172rdvT2xsLJC/vf7k12ZkZETlypWpW7culStXpnDhwgAsWrSIoKAg1NTUcHZ2TjcpEfl35M/M2rVr6dy5M2PHjmXy5Ml06NCB7t27M23aNIVYmZ+fsdyGvODRjh07OH78ONevX8fFxYUWLVowbNiwLKnMnV3079+fnTt3Mnr0aNTU1Hj//j0lS5Zky5Yt1KpVCxCfNZE/l38KZPJF8l69eikKm0VHRxMbG6uoBr5y5Urs7Oz+6PdGPmfQ1tamR48eis+6deuGjY0NM2bMoHXr1hgYGGBhYQF8mb+kpKRQtWpVjh07RuHChXF2diYoKEiRw/mfXtpyUU303hbJzYgeayJ/JN/rBIcMGUKVKlWYOHEiGzZsYNOmTZQsWRJPT0/27t3LkSNHWLVqFcbGxkD2rJ6kpaUhkUiQSCQZPtfDhw/p168f5cuXx83NTTEZ/lkiIiIoUqQIKioqzJ07l71793Ly5EnKly9PQEAAO3bsoGzZsowePZro6GjWrVuHp6cnZcuWxdnZ+bc81OTExsbSo0cPNDU12bNnj5hgP4N8fd/fvXuX7jcR+Xeio6Np2LAhjx8/pn379gwZMoRu3brh4uKCubk5derUyWkTcwx523f06FG0tbUpXbo0RkZGorD2k8gnBnFxcVy6dIm0tDRFWFFISAiDBg3CzMyMFStW5KyhfyB+fn5MmDCBixcvAtCoUSNKly6NoaEhTk5OOWtcJhIQEMClS5fYs2cPcXFx1KlTh61bt+Zp4VBEJCuQj6NOnTrF4cOHuXHjBiYmJtSuXZt3796xYcOGnDYxVxMdHU2hQoXo0aMHGhoaNGrUiD179tCjRw/GjRtHeHg4c+fOxdjYmIkTJ36z/5s3bzh16hRVq1b9JoWNKLKJ5DZEYU1E5P/ZvXs3ly9fZvPmzcAXD7HIyEgMDAwULs0dO3bk9evXCi+3rGzUBUFg9+7dqKqqUqBAAdq3b5+h/SZOnMjevXtp1qzZv4pa/xS93r9/z8KFC7l8+TLNmzfHysqKevXqMW3aNDp27EiPHj1IS0tj3759eHt7o6+vT7FixRg+fDgAenp6imP9jvj44cMHOnToQO3atUVvmF/g69/15MmTxMbGKnJdiPw379+/x9nZmVq1arFlyxZOnjyJIAhUrVoVT0/PPzLH3/fe5z95pf53ady4MUpKSiQmJtK3b1+6deum6FtSUlJQU1MTw19ygBYtWjBo0CD69evH2bNnWbx4MZs2bUJfXx+JREKRIkXy3O8it/f9+/ckJiaipKSEoaEh6urqPH78mJs3bzJ48OA8d10iItnBy5cvadWqFf3792fUqFHo6+sD0Lp1awYPHkzv3r3Fd+cf/HN+FBsbi4eHB9HR0Vy+fJl169ZRqFAhihcvDkDbtm2pW7cu8+bNS3ecixcv0qVLFzp27Mi9e/eYM2cOXbt2RUNDI1uvR0TkZxBnqiIi/0/37t05c+YMFhYWLFq0iBYtWlCqVCn69OlD8+bNadmyJcnJyaxZs4aIiAicnJzQ1dXNMnukUim3b99GSUmJIkWK/LSwlpCQQEJCAm/fvv1XMeqfRQnkHDhwgMqVK7Nu3TomTpzI+PHjCQwMpFKlSjx8+JDGjRtjaGjIX3/9RVJSEpcvX8be3l4hqH193F8dZLx8+ZI2bdrQu3dv5s6dK07cM8iLFy949uwZtWrVolixYpiYmChCmgGePHmCnp6eYnAo8gW5SBQVFaV494YMGYKdnR0ACxcuxNzcnCpVquRLQem/runr9zk5OZlz587RoUOHfHkvsgr5ZOPMmTOULVuWNWvWcP78ee7du8fSpUupUaMGQ4YMUUxIxIla9iH/bZYuXcqgQYOoUqUKrVq1omrVqkRHR7NgwQJ0dXWZMWNGnvtd5O/n0KFDef/+PSYmJpQsWRITExOqVavGoEGDAPF5ExH5HuXLl+f58+eKv9+/f8/du3dJS0tTvDPiu5Oer0U1QRAoWLAg9vb2ANjZ2WFoaMjgwYMJCwtj0qRJFCxYUFHcTC5SxsXFcfnyZdq2bcuuXbs4d+4cjo6OfP78maNHj9KpUyeGDx+ebvwhlUpRUlISxyQiOYIorIn80cgnhIIgoK6ujpubGwcOHCAiIgIlJSVWrVqFkpISI0aMQFVVlTdv3jB//nz2799Ply5d2LVrF6VKlUIikfDkyRMqVaqUaY25iooKa9asUfydkpKi6DDk/33PW05LS4shQ4b8lJeaRCIhKCiIx48f07JlS9TU1Lh79y6mpqa0bt0aNTU13Nzc0NDQoGnTpuzZs4eLFy9iY2NDgQIFsLOzU4gOcuTH/VX8/f3p0KEDM2bMYNSoUb98nD+ZW7ducejQISpXroydnZ0ifBm+/PY7duz4rsu9yBecnJwoWLAgjo6OlC5dmtu3b1O+fHm6du2arwsWSCQSUlNTfxgO9vDhQ6pXrw6Auro6gYGBNG7cOMNh5n8yysrKJCYmsnHjRpo1a0bRokXp2rUrVapU4dq1a7x580b0zs0hlJWVEQQBc3NzXF1dMTMz4/nz51y5coUbN25w7do1SpQoQenSpRkwYEBOm/vTJCQkoKWlxdu3b0lMTOTOnTv4+Phw9+5dAgICuHz5MjVr1vxu7lgREZH/CT3Pnj3j/v37BAYG8vHjR3r06KGIBIiMjOTjx49UqlQph63NfcjnBPL7aGhoCMD27dvx8vJi69atmJiYKOYTcpEyLCyMR48eMWLECODL/CAxMZF69eqhr6/P9u3b6dOnT7qFYzE0VCQnEUdvIiKgENckEgnW1tYAPHv2DFdXV9avX4+amhoDBw4kJiaGT58+4eTkRLFixUhLSyMhIYFz585x6NAhnJ2d0dLS+m1xSY7c+ys4OJhjx44RFRWFTCZTrP5Mnz79u9fyPeQdmvw6X716xdWrVxVV6LZs2cLWrVvR0dFhw4YNnD59mgoVKgBw//59GjVqxIULF9DQ0PjG5T2zXOAvXrxIr169cHZ2ViRBFck4NjY2NGvWjKNHj37jlSaRSHBwcEBXV1f0NPoHEomEDx8+EBkZqcih1rNnT/r27Ut8fHw6r8z8et+CgoIwNjb+bpiFt7c3mpqaCqF24MCB4mQ8g8hkMj5//sznz59Zv349hQsXZtCgQdSsWZPy5cuTnJys2E70gMh+5O+1ubk5Z8+exdvbm6SkJJo2bcrmzZt58+YNffv2xdramsTEREJCQnJ9vsV169Zx584dDA0NFQUxGjZsSMOGDUlLSyMoKEh8j0VE/gV5W+zl5UVwcDBly5alQ4cONGjQgISEBNauXcv9+/epVasWf//9dw5bm3v5uk+Tj6O6dOlCly5dFH2e/P9SqRR/f3/gS3g+wOXLl5k1axa1atXC0NCQgwcPEhoaytu3b7l58yY7duygVq1ajBs3jrJly6Y71/cidOTEp8p4EJXEh0QpyVIZ6spKFNNUpqauBlqqYj8s8vOIwprIH418EC33BPuaihUr4u7uTp06dThw4ADBwcGKVevOnTtTo0YNypQpw/79+xk2bBjjx4+nQIECAHz8+DFdrrHfsS86Oppu3bphaWlJ1apVEQSB1NRUxbl+hq87FPk1t2vXjhIlSnD+/HkMDQ2ZPn06Dg4OrFq1Ci8vL5KSkoiKimLNmjXcvHkTd3d3pk6d+l1vlsyYAB44cAB7e3v27duHpaXlbx/vT0UezlSiRAnFKp8c+UBGvlqYX8WhX0Hu1XH//n327NnD/fv3KVu2LLVr1wZIJzTl5/tWrVo1RZUvOfLnpm/fvmhpaSk+F0OJfx75PVRSUsLAwIDz589z5swZZsyYwe7du5k+fTqWlpYULFgQEMOKcgOamppoaWkxZswYypcvD0DZsmXZu3cvr1+/pmDBghw7dizXC2utWrVCV1eXoKAgdu7cyePHj/nrr79o2bIlJiYminFFfm7XRER+B/n70aZNGz5//kzDhg0VbbRMJqNQoUIMHTqUihUr5rCleYevvdi+LtAm/39ycjL37t2jcuXKAFy9ehVBEDAzMwO+VBaNi4ujSpUqtGnThrZt27J//37279+Pi4sLCxcuJDw8HF1dXVRVVRXn+7qtC4tPxSc8kRefU5AAaV9lnQ+KhqthCRgVUqOhgSbFtcXCLiL/jVi8QOSP5vXr13z+/JnixYv/qxC2bt06Dh8+zIULF7h9+zarV6+mffv29O/fn4EDBxIaGkpkZCTDhg1j1KhRbN68GTU1NUXekt8hLCwMGxsbLl26lKH95J2VvAN59OgRGzdupHXr1nTu3Jk9e/awcOFCrl+/rgjlKlmyJPfv3+fIkSPcuXMHX19fTE1NmT17NqVLl1YcO7MH4Rs2bGDx4sWcOHECU1PTTDvun4b8d0lISGDjxo1oamqiq6tLw4YNFUnRRb7l9u3bXL16lTZt2lC9enUSExNZuHAhu3fvpkmTJkyYMCHfPpdyIfbs2bPcuHGDkJAQNm3ahJqaWk6bli/ZtWsXoaGhVK9enaZNm1KgQAHWrl3LokWLuHnzJuXKlctpE0X+hWvXrrFkyRLevHnDrVu38kwC7ZSUFOLj43n//j1Xr17l3r17hIaGoqamxs6dO9MJ5iIiIv/NP72gRC/jrCEuLo4CBQpgb2+Pqqoqq1atIjExkWXLllGoUCFat25NgwYNsLCwoGjRorRv35558+YRGBjIli1bCAgIoESJEtStW5cOHToojut27ibvi5QD5f/2MVKRgFVJberoa2bhlYrkB8QWQOSP5ty5c2zdupVFixbx4sUL4Etn+U8cHByoV68erVu3plOnThgYGNCvXz927drF/fv3OXfuHJcvX2bAgAF4eXlhY2OjENV+V7tWVVWlZMmSXLlyhbi4OEUo6I+QfydP3hkREUFAQADz5s1DVVUVFxcX9u3bh42NDUpKSvj4+Cj2bdGiBSEhIdjZ2bFmzRpOnTrFli1bKF26dDovlswS1QRBYNasWaxZs4arV6/mW/Eiu5D/9osWLcLLy4vnz59z79491q1bh5ubm+IZF0mPsrIyL1++ZMWKFfj5+aGpqcmCBQvw8/NDT0+PDh06EBUVldNmZgnKysp8/PiRcePG0a1bNy5evMiNGzcyfJxPnz5x7949Hj9+nAVW5m2kUikABw8exMPDg0+fPjF06FCmTp2Kt7c3Y8aM4dWrV5QrV+63+wuRrCE0NJSJEycyatQorKysuH//vkJUy82/mbzfVlNT48OHDxgZGTFs2DA2b97MkiVLGDhwIFpaWrn6GkREchNyQe1rLys/P798JaqVK1eOc+fOZfpxZ86cSdGiRTE0NCQkJASJREJaWhrwJYrG3d0dADc3Nxo3bgygiM5ZtGgRkydPRklJiVevXnH58mXatGnD2bNnGT16NBcvXqRfv354e3vToEEDVFVVCQwM5MWLFxgbGzNv3jw2bdqEVCrFNyKRyKIVfkpUgy+ebBdC4/GNSFR81rx5c7Zu3ZqZtwfIuntfoEABcQ6QDYihoCJ/NA0aNKBixYrIZDJFWNM/RSN5B7p48WICAwM5fPgw7du3JyIigpkzZ7J582YAChcujI+PD3379uXWrVuKJN8SieSbstMZQSqVUr16dUxNTX+q0qbc/pCQECZPnkxQUBClS5fG1taWHj16sGXLFm7evImlpSWTJk1i3LhxREVF4evry9OnTxWJV9XU1FBXV1cMzDN70JCWlsbIkSPx9/fn+vXrYljZbyJfOU1OTiYyMpLz58+jpqbGrVu38PX1xcfHB4lEgpGRUU6bmquIj4/HzMwMMzMzReEO+PJ8Fi5cmFWrVjF//ny0tbXz7Yq0i4sLw4cPR0NDA0NDQ8zNzTPklfrx40dcXV0JCwtDX1+fqlWrZrHFeQt5279582Y2b96Mt7c3LVu2RE9Pj0GDBuHg4MC0adOA/B1mnFfx8fHBxsaGli1bcurUKUqUKAGgKPSRm38zeXs1YcIEIiIiOHLkCDVr1mTQoEH06tUr3ThFRETkv/neu3L58mVUVVUV75PIt7x+/ZqVK1fy6tUrihUrRkhISLrvT548+a/7Fy5cWBFdU7x4cXr37k316tVZu3YtZ8+eZfbs2fz111/89ddfAJw5cwZVVVWmTJlCmzZtUFFRYcuWLfzV347zb+OQkrE2Ty6uFddWobjWf4eFSiQSRc7twoUL06tXL5YvX54txRWaN29Ov379GDJkiOKzuLi4LD+viCisifzhVKlS5T+3+Touv0qVKop9PD09sbKyonXr1optR40axbp166hevToPHz7k+fPnNGrU6LdEIwMDA6ZOnfqfRQnkCILA7t27OXjwII0bN+bAgQNYWlpy6tQpevToQbNmzXjy5AnHjh3Dzs6OdevWceHCBUxNTRUlr7++7qwQEhITE+nduzdJSUlcvHgxQ/niRL6P/Pfav38/Hh4e1KlTh2HDhlGvXj3q1q3LrVu3MDExyWErcx9du3Zl//79FClSBHNzc8XnKioqCIKATCZTJPbOj6IagKWlJWfPnsXGxoaFCxeira1NcnIy6urq/7qf3MtFV1cXQ0NDmjVrRrVq1bLD5DxHeHg43bp1o3DhwuzevVvhKXzr1i1MTU0VFSlFgSP3Ia8SamVlBXxZ7JJIJD+snptbkD9Pjx8/5tatW5w+fZoHDx7Qq1cvJk2axNChQwkPDxcXtUREMsiFCxeQSCSKfMADBgwQ2+7/4PXr1+jp6VGsWLHfPpaOjo4ih7Cenh6qqqo0aNCAevXq0alTJzp16oS/vz96enqKnGxHjx6lVatW+IQnkiYTEAQZShkUudIEuPE+kW5GP9f2379/nwoVKvDkyROaN2+OiYnJN7mPRfIX+XOWICKSAX42BEJeOVROpUqVuHLlCgsWLABg5MiR1KhRA2tra1xdXenfvz/Hjh3D3Nyc06dP/5aN/9Zhyyf7Pj4+3Lp1C4lEgrGxMR8/flSIZJs2beLgwYMkJCRgYmJCpUqVuHr1KuHh4YwYMYK3b98yZswYihcv/k3i8swmKiqKVq1aKRI/i6Ja5tKhQwemT5/O+vXradu2LSdPnkQikVC/fn10dXVz2rxchZ+fH8+ePePVq1ff/V4ikeTL0u3y0ET5vytUqMDt27cpUKAANWrU4OrVqxw/fvyHbYH8c3kOR3k1ZSUlJZYvX463t3e2XEduR36fZDIZBgYGjBw5ElVVVYoWLcrSpUvx9vYmNjaWTp06AaLXUG5EEATU1NQUolpaWhrKysqKfjcsLAxfX1/FtrmRffv20alTJ3x8fKhevToODg64uLiwfPly9PX1c63dIiK5FWNjY2JjYxV/6+rqoqOjo/g7P71TgYGBlC9fnj179gCwZcsWKlSogK6uLp07d+bdu3eKbSUSCZs2baJixYoUKVIEe3t7BEHg3LlztGrVinfv3lGgQAFsbW2/Oc+/hVZOnjyZxo0bExMTA6S/v+rq6tStW5cTJ05QpUoV3N3dKVq0KDNnzmTLli28f/+eT58+cfr0afwfPGTGgG7MaVyeF3ev8eTqGdbaWOLYpDxL2plybtOydOd9HXCXjbbtmdvUmDW9mvPi7nWCP6eQkJqxeVLlypVp0qQJDx8+JDg4GCsrK/T09ChatCh9+/YlOjr6u/vJZDKWLFmCsbExenp69OzZU5GWJCkpiX79+qGnp0eRIkWwsLAgPDycGTNmcPXqVUaPHk2BAgUYPXq04rd5/vw5ALa2ttjb29OhQwcKFixIvXr1CA4OztA1iXwfUVgT+ePJyGTm622rVavGzZs3adOmDefOncPb25vly5dz5coVnj59yqxZs9i2bRubNm3i8OHDpKamKvaV52j4Ff452X379i29evVi6tSpnDlzhtatW1O/fn0sLS2Jjo7m48ePVKpUiWbNminKgFtaWtK9e3eKFSuGnZ3d/7F31mFZZG8DvukOA0WxMbCxFbsDV8VuBUXF7lVQwcJeAwNrTQxcGwOxOzFQUQEVUUK6+53vD37vfGLsGsCLOvd1eQnMzDnPmTjxnCe4f/8+t2/fBnLXKuft27c0bdqUhg0bsnPnTilAeg4hfydSU1PR0tJi6tSpeHp60qdPHxYsWICFhQVxcXEKljL/UatWLWbOnMmff/7JmDFjuHv3brbjua1kVgSCIKCiooJMJmPKlCnMnDmTK1eucOjQISpVqsT48ePZsGED6urqX+wL5H8PDw/nyJEjODk5MWTIEFavXk1aWtovqYz8HuTjxfr165k6dSpBQUHo6+szY8YMrl69ypIlS7C3tweyKzsl8g8fzw9UVbMcPeLi4rh27RqjRo3C2tqaJ0+eiGEf8gty2Xv16sXAgQMJCwtDVVWVsLAwPDw8JEWuhMR3Urp0aTp27PjF4/mtL/hevL29ad++PS4uLvTr14/z588zc+ZM3N3dCQkJoXTp0vTt2zfbNR4eHty5c4dHjx7h7u6Op6cnbdq04dSpUxQvXpyEhAS2b9/+VfXLZDJsbW159OgRZ86cEV1BP+y75GupEiVKUKVKFV69esXdu3fZtm0bLi4uFCpUiC1btqCvr8/JEydoaTMRp6uvKG3eAHUtHXrPW8ecSwEMWb2HW/9s58mFrI3B2PchbJ/Qn5bDJzH7oh+dJjmxe5o1CVERXAoI+ab7+PTpU65cuUKtWrUQBIGZM2cSHByMr68vQUFBODk5ffY6FxcXjhw5wqVLlwgODqZAgQKMGTMGgB07dhAbG0tQUBCRkZG4urqipaXFwoULadq0KWvXriUhIYG1a9d+tux9+/bh6OhIdHQ05cuXx8HB4ZvaJPF5JFdQCYn/kZiYKLp8Af8ZF00mk1GoUCEKFSrEP//8g7OzM8bGxty6dQtBEOjSpQsAPj4+xMfHo6amxtOnT0lOThZNk78n9trHi92XL1/SpUsXBgwYwKZNmzh37hyvX7+mYcOGnDlzhjt37tChQwfmzZtHmzZtWLVqFRUqVMiWFvzOnTuULl36m+T4Vnx9fenQoQPjxo1j6tSpuVrX74Q8tlpGRgb29vZcvnyZhg0bUqpUKQYNGkT37t25evUq+vr6kqvZZ7C2tqZu3bqcPn2av/76i7/++gtBEChWrJgYs+6/XCJ/JuTPf9q0afj7+zNo0CBWrlzJuXPnWLNmDerq6v8ZSy4sLAwnJyfev3+PkZER+vr6Ynbl8ePHU7Ro0bxqTr5F3rf7+vpy9OhRHj9+zNWrVxk5ciRWVlYcOXKE+Ph4DA0NASRl5E9CWloaL1++5OTJk1y6dAlDQ0Osra2xt7fn6NGj+eY5yvt6uaxTp06ld+/eeHt74+Dg8K+LOQkJif/mS67gKSkpPHnyhLt379KgQQPMzc2B75vvK5IrV66wdetWdu/eTYsWLYCsrNY2NjbUrl0bgEWLFlGgQAFev34tZrSeMWMGhoaGGBoa0rJlSx48eECHDh2+uf709HT69etHRkYGx48f/+JG/IfhelRVVYmPjyc0NFRM0AZZbqglS5akXB0LStdqCICyhibl6jYWyylWsSo12lvx6t51qrbsxIOTB6jUuA1mTdoCUKFhC0pUrsnTq2dJr1Ahm8Xil6hduzYqKioULFiQ4cOHY21tjbKyMuXLlwfAyMiIyZMnM3fu3M9e7+rqytq1aylRogQATk5OlCpVil27dqGmpkZkZCT+/v7UqFFDXFd+LVZWVtSvXx+AAQMGMHny5G+6XuLzSBZrEhL/w83NLZtV2cGDB8VsNR8iCAKZmZnZFp49e/ZkwIABQFYg7wYNGqCqqsr9+/dJSUmhTZs2JCUl0a9fP4YPH46rqyuAaDnyb3xs2Xb27FmGDh3KrVu3gKzdoZ07d2JhYcG1a9d49uwZZcqUoXnz5mhpaXH58mViYmKoWbMmQUFB2QZ2edm5rVS7ceMGLVu2ZMGCBZJSLYeRP8N58+YRGRnJnj17aN68OcHBwSxevBg9PT0xxbikVPsUNTU1zM3NmTJlCsrKyowYMYLRo0czadIkfH19sbW1JSkpSdFi5gjyHfTIyEhq1arF5s2b6dmzJ4cPHyYzM5PatWtz5swZlJWV/9WitmjRomJMsMGDBzN58mROnjxJcHCwaPn6K1r7fQvyfnby5MmMHTuWkJAQpk+fzpo1a+jUqRN37twRlWoSPw9Pnjxh0qRJ+Pj4MH36dHbs2MHEiROJiYnhwoULihZPRP79Hj16lJSUFJSVldHQ0GDChAn079+fEydOULZsWUAaFyQkcpLXr1/j7u6Ov78/QUFB4t9TU1O5c+eOAiX7NlxdXbGwsBCVagDBwcHZ1gu6uroUKlSId+/eiX8zNjYWf9bW1v7uoPn+/v4cPXoUR0fHr/JuUVJSonXr1owdO5YxY8ZQpEgRRowYQVxcHC4uLpQvX54CxibZrnnjc4/NI7qxoJUZTs3KcfvgDhJjslwto0Pe8vjsMeY2MxX/vX5wm/iIMHQMC4pZoSHLg0lXVxddXV2uXLki/t3b25vo6GgCAgJYsGABysrKhIWF0bdvX0xMTNDX12fgwIFERER8tk2BgYFYWVmJisrKlSujoqJCWFgYgwYNon379vTt25fixYszffr0bGvY/yKnnpNEdiTFmoTE/5DvdsgJCAj4rNVGWlrav5p4t2jRgunTp7Ny5UpGjhyJkpISFhYWrFu3DkEQcHNz49GjR3To0IHY2Fixoz169Ohny5NPemUyGc+fP8fNzY3ChQuzceNG9u/fz4ABA7hy5QonT55kx44dVKhQAQ8PD5KTk+nYsSNNmjRBX18fmUyGpqZmNtnzYkLt4eFB165d2b59O4MGDcr1+n435O9ocHAwgwYNokKFCvTs2ZM///yTFy9ecPPmTQVLmP+RLzo1NTVp27YtM2bMQFtbm1GjRlGqVCm0tbUVLeIPI5PJUFFRITAwkAEDBuDs7MyaNWt4+/YthQoVYsOGDYwdO1bsE77UN8gX7BoaGvTs2RMLCwtxgmZiYiK6HP+qiR6+hdDQUNLT08Xg8N27d+fs2bNkZmYyZMgQtm7dqmAJJb6VatWqMXDgQLZt20bjxo2Ji4tj9uzZvHr1ipiYGHFho2jFsvz7i4yM5Pnz5wQEBCCTyShTpgytWrVCS0tLofJJSPxqyMfGihUr0rNnTxwcHMT4mQCamppER0czatSor7J2UjSurq68efOGSZMmiX8rXrx4tpi0iYmJREZGYmJi8rkifojKlSuzbds2OnbsyPPnz7/6uvHjx3Pv3j2ePn3KixcvWLr0/+OmqSpnn9fsdxhF5WYd+PPUA5wuv6R+jyHwv+doWLQ4tSx74Xg5QPw373ogLawnYKCtiZqaGhEREQiCwJMnT0hISCAhIQELC4t/lc/e3h4lJSV8fHyIi4tj9+7dX9zILFmyJKdOnSImJkb8l5KSgomJCWpqajg6OvL06VOuX78uGlmAtFmiSKSZr8RvTWJiIsnJyaSlpYlKMDkzZ8784uLw33ZPevXqhZubGykpKUybNg0HBwciIiLYtWsXBw8epEqVKvTv3x9NTU0xXsDatWu5du3aF3ctIGuiXKpUKZydnVm+fDndunVj9+7d1KxZk4YNG7J06VIOHz7M8OHDmTdvHn5+fjRt2pROnTqhrKwstiUvTdG3bduGra0tHh4e32UKLvH1NGrUCAcHB86cOUNiYiLGxsaEhIT8Um6Muc3q1asZN24cDRo0YNasWZw5c+aLJvo/E3J3YYDFixfTvn17pk2bRlBQEG5ubjx48ICMjAxGjRpF27Zt/7MsgJo1a7J161bc3d1xdHSkcePG+Pv7S9/5BxgbG9OtWzcOHDhAYGAgycnJJCcnU7x4cdatW4ePj89nraIl8ieCIKCmpiZap+/fv5+ePXsSFhbGmDFjePv2LVOmTAHyx8JGvtALCQlh1apVuLi4cOLECTGAtYSExI8jzx4u/+aVlZXR0tLi6dOnAGIfr6ysTNu2bcUkQWfPnlWYzF+Dnp4ep0+f5vLly8yYMQOAfv36sW3bNh48eEBqair29vY0aNBAdAPNafr164ezszNt2rT5quD6d+7c4datW6Snp6Ojo4Ompma2NY+2qhKqH3TNqYkJaBkYoqahSdBjbx6ePiQeM+/UC9/Lnry4fh5ZZibpqSm8vHuN6LevIT6K169fs3//fpYvX05SUpKo/Pt4jfWxFVl8fDy6uroYGBjw7t07li1b9sX2jBo1CgcHB1GZGR4eLhphXLhwAR8fHzIzM9HX10dNTU2c5xUtWpSXL1/+5/2SyHmkGGsSvy2ZmZmsXbsWLS0tChUqJE6W/4v/UkzJZDIaNGhAgwYNgKwd4/HjxzNq1CgqVKhAWFgYV69epWbNmiQkJPD48WPev3+PnZ0dhQsX/teytbS0xLgOTZo0wcPDAzc3Nw4dOsShQ4dwc3OjXr16rF+/XqGJAQRBYPHixWzatIlLly5RsWJFhcnyq/LixQs2bNjAypUrARg2bBjJycmcPXuWtWvXoqqqSuXKlb857sLvRlpaGsrKyqiqqqKjoyPGJvqVLDo+DKJ/584dNmzYAGTtCO/atQsXFxeGDh1K06ZN/7Ms+cRtwIAB6OjosG7dOjp37oyzszONGzfOZvX7OyKPT5eZmUlKSgrDhg1jxowZTJkyhQIFCuDt7c2MGTNIS0vjyZMnv/39+pn4UFk2f/58PD09GT16NJaWlhgYGIgu1hMmTMDU1FSBkmahq6vLihUriIqKEhVqT548wdTUVExkJCEh8WPIM2NnZmZy5swZzp07h7e3N5UqVcLCwiJbH6+kpMTy5ctp06YNNjY2dOzYkWXLlqGvr6/AFnwZQ0NDvLy8aNmyJWpqasyfP5/58+fTo0cPoqOjsbCwYN++fbkqw5AhQ0hLS6NVq1ZcunTpX5V4cXFxTJo0iZcvX6KpqSluIsopoqXGh7ZhXWcu4eRfjhxbMoOytS2o3rYLKfFZVveGxiYM+msXp1fPZZ/9SJSVVShRrRZ/TF3I6R0uaGtrM3z4cMaMGYOPjw/Ozs48f/6cYcOGMWzYMCArYdvly5fp2bOn+IwdHR0ZPHgwBgYGlC9fXoxz+zkmTJiAIAi0a9eO4OBgihQpQp8+fejatSuhoaGMGjWKt2/foqurS58+fUSvoAkTJjBkyBA2bNjAoEGDWLNmzQ88AYlvQUn4lXICS0h8A+np6axatQqZTIaenh6jR4/+quu+JgD8h+c8e/aMFStWsHnzZgD27NnD7du3ad++vZhV6PXr1xQrVgwNDY3/DBz+YR1+fn4MHz6cY8eOYWhomK1eRQVKlclkTJo0iYsXL4pZgCRyFvlzfvHiBW/evGHs2LG4uLjQqFEjnj59SmxsLIIgULduXQoWLCglLfgC/v7+9OjRg9u3b/8Wln3nz59nyJAh1K5dm71796KtrU1cXByrV69myJAhlCpV6rvKPXnyJIcPH0ZZWZkWLVpgZmZGrVq1vrov+1WQtzcsLIxZs2bx/v17zM3NmT17Nv7+/gQHB6Ojo0O1atWoU6cO+/btEwNbS/wcyPvS8PBw4uLiRAVacHAwR48eJSAggGXLluWL/vb+/fts2rQJY2NjZs2aRWpqqmitVqNGjd/u+5SQyA0EQWDbtm3cuHEDmUxGlSpVSElJ4eXLlzRr1owhQ4Z8dj4eGxvLlClTOHv2LJs3b/5Pa3GJnOHgyzj8YtO+61pBJiPO/xF+B1z5559/xD40IiICPT094uPjsbOzY9WqVaiqqjJ+/HgqVqzI/PnzAThx4gTBwcHY2trmZJMk8hGSYk1C4n/ExMSQkZGBqqoqqqqqqKur57jV1/nz5zly5AhmZmafKPLevn2LTCb7psWtTCYjOTkZHR0dsYOXm6QrYmKfmprKkCFDCAkJ4ejRo1Jw7jxi7dq1bNy4ERMTEyZPnky7du0ULdJPwZAhQyhXrhyOjo6KFiXPeP/+PbNmzeLhw4csXLiQNm3aiMe+RQErP/f27dusWbOG8uXLc+jQIf7880/279/PsWPHcqsJ+Z4RI0aIiTBWrlzJ7du3mT59uriLHRsbi6enJ71791awpBI/SlxcHPfv32fPnj1ERUXh4OCQTVma15sa8vpev35Nv379mDhxIsOHDyc8PJyoqCz3pUaNGuULxZ+ExK/Chg0byMjIoFGjRpQrV46CBQty7tw5Jk2axKNHj/71Wk9PT2xtbenQoQPLly/Pt9Zrvwohiem4+cWS8R3aD1l6Gm30U3CeMZk5c+ZQu3ZttmzZgpeXF0+fPqVChQpERUWJ2VJbtGiBgYEBTZs2ZcuWLQwcOJBGjRqJVm63b9+mV69e0jP/hZC2qiR+e1JSUvDy8mLZsmUsXboUZ2dnHB0dc8V0tkiRIhgaGtK5c2cge8bPsLAwjhw5QnR09CfXBQYGfjYYsrKyMjo6OuLP8v8VMWmOi4vD0tKS9PR0PD09JaVaLhIWFgbApk2bmDZtGmPHjsXHx4du3boxbdo0KleuTHh4uIKlzN88e/aMkydPMnHiREWLkqcUKVKEjRs3Ymdnx+DBg3FzcxOPfUu/oaSkRGpqKhs3bqRhw4bMmDEDFRUVBgwYQGRkpBhf5ndB3pcHBgaira3N7NmzqV27Nrt27WLTpk2sWrWKCRMmAGBgYCAp1X4Rtm7dipOTE3Xr1uXAgQOi4vrgwYPExcWhpKSUp4kM5O/hkSNH6Nq1K5UqVaJt27Zoamri5+fHggULJKWahEQOIf/ehg4dyrBhw0QvgcDAQHbu3En//v3/M6FJ+/bt8fHxQRAEqlevzpkzZ/JM/t+RYjpqtDLRyRZr7WtQURKoRAz1zcrRokULPDw8AHBwcGDUqFH4+PhQpUoV9PT0KFGiBMHBwbRp04YDBw7QrFkz4uLiCAkJoUuXLgAYGRnh6enJsmXLvimbp0T+RlKsSfz2vH79mtGjR6Ovr0+NGjWoXLkypqamlCxZMsfrqlatGrNnz6ZUqVKf7GTXqVOHUaNGUaBAgWzXhIaGsmfPni/64OcHQkNDadGiBRUqVMDd3T1bGmqJnCU9PZ3du3fTunVrli1bRv/+/YEs199Ro0bx8OFDtmzZImYilPg8c+fOZdKkSWICkV8dmUwmTuyVlJQYOnQoXl5eWFlZfVd5giCgoaFBQEAATZs2RVNTEzMzMxYuXEiVKlVITk7OSfHzPfK+3NXVlePHj7N582ZiYmIQBIHmzZvj4+PDggULAP41q7TEz0XDhg1Zvnw53bt3F5MFZWRk8Pr1a6ZPnw7kbSID+QabfJG+Zs0abGxsgCyL+WrVqgHSOyghkRPIv20tLS20tbW5ffs2EyZMoFu3bvj6+pKWlsbMmTOznfs5DAwM2Lx5M5s2bcLW1hZbW1sxw7ZEzlPbSOublGuqStDaRJce9asA0LVrV1JSUoiJiaFq1arisw0KCsLMzIySJUvi5uZG/fr1qVGjBoMHD0ZfX58dO3aIa0tjY2PGjBnD3bt3xdjZEj8/kmJN4rcnKSmJRo0a8eeffzJw4ECsra0ZPXo0ffr0yZHyZTJZNsu0jzvQyMhI8eePXU8FQeDq1atERERgaWnJgwcP2LNnT47IlVP4+/vTuHFjrKysWL9+vULiuv1OqKmpMWXKFKpXr05CQgJTpkzhwIED4n0fOHAgFSpUULCU+ZvHjx9z/vx5xo8fr2hRcgX5ovn9+/dcuXIFHx8fMTPwh1koq1atira29hdTvf8bciVdu3btROveQYMGsWXLFmrUqEHt2rVzoCU/Bx/ev0WLFrFhwwauXbvGzJkzuXXrFgkJCUBWljXI28zMErlLo0aNqFOnDjt37qR58+bcvXuXu3fvMmXKFB48eMDdu3cVYiE2YcIE/v77b9zc3EhKSmLXrl2cPXuWIUOGAEix1SQkcphLly7RpUsXlJWVcXd35/bt28yZM4ezZ8/y4sWLr+oH2rdvz6NHj1BSUqJ69ep4enrmgeS/J7WNtBhQ0YCKBuqoKPGJkk1VCVSUoKKBOgMqGlDb6P8TWpUtWxZnZ2cMDQ0ZPXo006ZNo2PHjty/f5+GDRsSHh6Ov78/bdu2zWaNVqJEiWx17N+/n7p16wLSZsevghRjTeK3RW4x9vDhQ+zt7Rk0aBDVqlVDQ0MDLS0tDAwMxIXQ9yCP1xYYGIiGhgaFChX6RKkmCAIuLi6UKlWKbt26fbYcX19f3r59S/PmzRk7diwlS5Zk1KhR+cIiydvbm86dO+Pk5MSIESMULc5vgfy9DQ4ORkNDg/Pnz7NkyRLU1dVp1KgRN27c4Pr164oWM1/Ts2dPGjZsyNSpUxUtSo7zoSVsrVq1qFKlCidPnmT06NEsXLgQ+P++KSfqCQgIYN26dcybNw9dXV28vb2pUaMGb968oWTJkr/8Tqz8PgiCwPHjx3n9+jX16tWjZs2aLFmyhNOnT2NlZcWMGTMULapELpGUlISNjQ02Nja0a9eO/v37U6NGDUJDQ5k6deoni6ncQh4g/cKFCxQoUICKFSuyZcsWdu/eTZMmTejduzcNGzbME1kkJH5HEhIS0NXVBRBjDW/ZsoUNGzZQr169byrrzJkz2Nra0rZtW1asWPHbWNcrgqR0GY+iUghPziQ1U4aGijJGWirUKKiJttqnmxCfS/wSHBzM7du3qVy5MioqKjg5ObF48WJKlCjx2VibSUlJmJub4+HhQcWKFaUkY78IkmJN4rflQ8Xa4MGDSUtLo3DhwmRmZhIaGoqlpSUuLi7flF0zLCyM+Ph4ypcvL/7Nz8+PPn36MGPGDHr06PHZsoKCgj7revphR7tu3Tq8vb0ZMmQIzZo1++R4XnP27Fn69+/Ppk2bvqgUlMhZ5O/i69evCQkJQVdXFzMzM9TU1Dh06BBhYWG0adOGChUqSBnfvsCDBw/o2LEjAQEBaGtrK1qcHEf+3N3c3Lh+/Trr1q3j1atXWFlZoa+vj7u7O8bGxjleb1RUFGFhYTx//pyTJ09y69YtFi9eTMeOHRWWoTgvkN/vOXPmEBoaipaWFt7e3ly5cgWA58+fEx4eTpMmTaSJ8y+I/Jna29sTFxfH2rVrCQ4Opnr16nTp0oVt27bx/v17ihQpkmfPf8WKFezZs4e5c+eK8VzT09NRU1OT3kEJiVxCPs75+Phw5swZnjx5gra2Nl27dv3ujJ9xcXFMmzaNU6dOsWnTJjp06JDDUkv8KJ+b3yQmJjJt2jQOHjzI2bNnqV69unhM3gffu3cPa2vr/0xuIfFzISnWJCT+R1paGhEREWhqaqKuro6ysvI3L7xtbW0pV64cdnZ2oste3bp10dPTw9nZmYcPH2Y7/78mufJF25kzZ1iyZAk6OjpERUXRo0cPJk2a9F3tzAn27dvHhAkTxKCcEnlLmzZtKF26NOfPn6dDhw60bNmStm3bfhKfT+JTunbtSqtWrcRA8r8SsbGxGBgY8ODBA2bNmkXp0qVZvHixaHk7atQoDhw4QGBgIDo6Ojm2wL579y4bNmwgNjYWgEKFCqGiooKamhqrV6/+5ZW8ISEhdOrUifv372NlZUX37t0ZNGgQ7u7uNGjQgNKlSytaRIlcQj6Gp6enY2Fhwfjx4xk0aBBxcXFoaWlx/Phxrl69yoIFC9DW1s6zb+HatWusXLmSmjVrMn78eAwMDH5pBbeERH4gJSWFYcOGUbRoURo1akTx4sWpUaMGKSkpFChQAFVV1e9Sbnt5eWFra0vr1q1ZsWKFlBwsH/K55xodHY2hoWG2v8vPW7p0KatXr2bQoEE4OTlJsal/ESTFmsRvj0wm486dO1y8eJH4+HgKFy5M27ZtqVq16lddn5mZKWbiPHnyJIcOHWLMmDEALFu2TIyJ1qZNGzp27MiUKVOA/3bHkk/AQ0JCsLe3p1KlSvTr1w9tbW369evHrl27KFasWDZXpLzYiV69ejXLly/n5MmT2XZhJHIX+fuwc+dOLl68yJYtW6hcuTJDhgxh/fr1tG7dGmdnZ0xMTBQtar7lzp07WFlZ4e/v/8tNYs6ePUt4eDj9+vUjOTmZVatWcfPmTfr370/9+vUpW7YsAG/fvv2ia8K38qE76L59++jSpUu2PqFs2bI8e/YMDQ2NH6onvxMZGcm6deuoVq0aO3fu5MiRIwDUq1ePjRs3/lbx5n5H5Aqr69evo6enR5UqVfD09OTy5cvcvn0bmUxGu3btsLe3z5NxWj5W+Pv7M3bsWOrWrcv8+fMlSzUJiVxE/m37+PhgbGxMfHw8Bw8e5MiRI5iYmGBmZsa8efO+u/y4uDimT5/OiRMn2LRpEx07dsxB6SVyEnls7S9tZDx58oQWLVrg6+tLYmIiJUuW/KU3H38npKco8dsi1ylfvXoVJycnXr58SZUqVXjy5Anjxo3j2rVr2c77GHnwbhUVFXHC2qlTJ9TV1fH19eXQoUPZzLbt7e3Ztm0bGRkZXLhwAVVVVVJSUr6Yglveye7YsYOUlBQsLS0pXbo0hoaGPH/+nJiYGCDL0g6yMg7lZvBLQRCYOXMmrq6uXL16VVKq5THy98Hb25vRo0czb948bG1tsbe3x9bWFgMDA0mp9h/MmTMHe3v7X06pBlCyZEn69OmDq6sr+/btY+bMmdja2nLs2DH27dvH9evXycjIEN+RnFhky8swNTXFwcFB7BPkfdrFixfx9vbG1taWAwcO/HB9+RUDAwMiIiLo3bs3TZs2BcDJyYlKlSpJSrXfAPniycLCAiMjI+bPn8/Zs2cxMjJi5syZnD59moMHD3LhwgVxEyynkZc5Z84cFi1aRJcuXVi6dCmlS5dm2bJlNGzYkNu3b+d4vRISElnIx8Pq1aujrq5Oz549efv2LSEhISxZsoT379+zbds24PsC1evr6+Pq6sq2bduws7PDxsZGXAdI5C+UlZWzKdXk/XNkZCQpKSkcOnSIP/74g8KFC1O6dGlJqfYLIT1Jid8W+eLP09OTJk2asHHjRvr378/mzZtp164dJ0+ezHbex8g7wr/++ouOHTvi7u5OYmIivXv3ZteuXRw5cgQvLy+SkpIAaNWqFS1atKBdu3aMHj2agIAANDU1/7VDvXr1KqdOnUIQBHHROnfuXHr16kWxYsU4dOgQ8+fPZ+rUqaSlpeWam0d6ejo2NjZcuHCBK1euSK5Necz9+/cJCQkBwNnZmVq1aqGnp4efnx8At2/fpl27dsCXFcG/O9evX+fp06cMGzZM0aLkCpUqVSIlJQUVFRVu3LjB3LlzadSoEc7Ozvj7+3Py5MlsmwC5RVhYGF5eXmJm5YkTJ6Kurk6lSpVytV5FERYWRnJyMgsXLmTXrl2cPHmS+vXr8/z5cxYtWgR8eQyR+PVQUlIiMTERCwsLrK2tadu2LZqampibm/P69WvxnNyoVxAEDAwMKFu2LAsXLqR169YMGjSIiIgIxo0bx99//y1mqJWQkMg9rl+/TuPGjVm9ejWTJk3i77//pkePHpw6dQr4sczQbdq0wcfHBw0NDapXry6uVSTyL/I+/8KFC1hYWPD8+XPGjRsHSPODXw3JFVTit0XuLuHi4sKzl4H8MWoKcYIqMmUVnj56gB7pjOnaFg3lz5vzvn37llmzZlGgQAF69uzJxo0bqVSpEg4ODkycOJGEhATU1dV59+4d+vr6NG3alBEjRuDn50eFChU+keNLyBfJ3bt3JzY2lmPHjrFy5Ur+/vtvHj16hJOTE0+ePOHQoUMcOHCAIkWKADmX2CApKYnevXsjCALu7u7o6Oj8cJkSX09KSgp//PEHhoaG9OjRg9atW2NkZMTbt28ZPXo0YWFhFCtWTHQ/k/g8bdq0oW/fvgwfPlzRouQocjc0mUyGkpISSkpK3Llzh0OHDhEdHc3AgQOxsLDg/fv3GBsb55or2rt377C2tiYyMpJXr16RmprKtWvXMDc3z/G6FI28z96yZQsuLi5UrVqVGjVqUKdOHZo1a0ZUVBTFihXLdq7Er4/82woPDxezdj979owZM2bw+PFjzpw5Q7ly5XKtXsjqDyIjI8V5gJy4uDjq16/P48ePfzgjsISExOeRf4unTp1iyZIlXLx4kbi4OCwsLGjRogWtW7fGysoqx8aFc+fOMXz4cFq0aMHKlSul2Gs/AREREWRmZlK0aFFFiyKRC0iKNYnfFkEQCE3K4PzrKAKT/ucPr6YuHldBACUlyumrYVFUm2I6akBWgPBp06axYMEC4uLi0NPTw8nJiUuXLlGxYkVmzZpFSkoKu3btwtbWFlNTU27cuIGqqqpoVQSfzyTzJZ49e4a9vT0VKlSgb9++hIaGcvDgQWQyGaGhoezevZupU6cyefJkqlWrJl734WL7e4iMjKRz585UqlSJzZs3o6am9l3lSPwYgiCwfft23N3d0dLSwsrKio4dO6Kuri5aKRUqVEhaxH+BS5cuYWNjw7Nnz36pd1g+iZfJZPTp04eMjAwA1q5di5qaGu7u7pw/f55Zs2blukuiIAisX7+e/v37M2XKFAoWLMibN2/o1q0brVu3pmjRor/U+5mRkUHXrl2ZN28ekZGR3Lx5k5iYGExMTGjfvn22flji92T69Ol4eXnRpUsX7O3t0dDQ4N69e1SoUAF9ff0cq0f+XU2fPp33798THByMjo4OLVq0oH379piZmREbG8vDhw+lZEMSEnlEnTp1WLRoEe3atWPNmjUEBwezePFi8XhObXLFx8fz559/cuzYMTZt2kSnTp1+uEwJCYnvQ1KsSfy2eIcnc/5dInvnjMWgSHEqNGrJofmTmHL45ifnqipBKxMdBrWoy9y5c6lQoQI1a9YkNTWViRMnUr58eWxsbJg+fTpFihRh4cKFDBo0CEtLS/r27ftDcn64GJUPxAcOHODcuXO4urri5ubGnDlzUFVV5caNG7x8+ZIjR44wbNgwMWB5XFzcN0/k37x5Q/v27enatSuLFi2SAh8riI+VER4eHri6uqKjo0O7du3o0aOHtEv5LwiCQIsWLbCxsWHIkCGKFidXsLe35/379zg7O7Nw4ULOnDnD+vXradKkCTdv3hTjfuUm8vfU1dWVO3fusHXrVp4+fcrWrVvFzYdfAXk7w8LCWLVqFU5OTmhoaBAREYGXlxcXL15k5MiRUmy13xwvLy+WLFnCkiVLqFOnDpGRkaxcuVK0cuzVq1eO1hcXF0f16tU5fPgwmpqaPHjwgOvXr3P9+nX27NmDmZlZjtYnISHxeeSb5idOnCA8PJyhQ4cCkJqayq5du1iyZAkXLlygRIkSOVrv+fPnGTZsGM2bN2flypVSlngJCQUgKdYkfku8w5PxehOHoKzCAccsxVq7Mfb/eo1cuWYii0Mmk1GsWDHevn1L48aNCQwMBKBbt27ExMSwefNmChcunG1g+5bdqY/P/fj3U6dO4erqytGjRwGIiYnB29sbU1NTpk2bxs2bN6lWrRoWFhY4ODjwxx9/0KJFC6ZOnfrFOmUyGXFxcRgaGvL48WMxg+nEiRO/SmaJnEc+QfPx8cHPz4+nT5/Sq1cvKlWqhLu7O2vWrMHZ2VmyQvgXzp49y5gxY3jy5Mkv6QJ18eJF/v77b8aMGUODBg0A2LVrF7t378bT01M8Ly+yEQYEBDB8+HBcXV3FmGqpqanZXCN/ZuRKtWfPnjF+/Hj8/PzQ0NDgwIEDYgzMN2/eUKpUKQVLKpGfOHjwICtXrqRYsWIsXLiQihUrisd+9LuUjxEPHjzg+PHjzJ49G0EQkMlkJCUlER4eTrly5fIsa7iEhER2/Pz8WL58OV5eXhQpUoTSpUszYMAAunTpkuNW3AkJCfz5558cPXqUjRs3YmlpmWNlS0hI/De/hk+GhMQ3EJKYzvl3iQjKXx88NDMjgwwBzr9LJCg2mQsXLpCWlkaJEiUoX748ffv2xdzcnNKlS7NlyxYqVKggKtXkuutvmdTK3bvk1398bceOHSlVqhT169fn77//xtDQkEaNGnH+/Hmio6O5evUqW7ZsoXz58hw5coTo6GjGjx+f1ZYvZCNSVlZGQ0ODpKQkpk2bxtKlSyWlmoKRuwpPnTqVly9fcvz4cc6fPw9A7969uXr1qqRU+xcEQWD27Nk4OTn9cko1eb9y584dbt++zb59+4iIiABg0KBBREdH8/btW/H8vFhUFy1alDZt2lCpUiXu379PUlISSkpKeHt78+bNm2xy/4zIF0ArV67E0tKSV69e0bdvX5o1a0a/fv2Ii4uTlGoS2bh8+TKTJk1i/PjxHDhwgIoVK4rWjfDj36V8jFiyZAnr1q1j4cKFJCQkoKKigp6enhjTTVKqSUjkPTt27KB27dpoa2tz5swZbt68yapVq5gxYwaJiYk5HhpBV1eXdevWsWvXLsaNG8eQIUOIjo7O0TokJCS+jGSxJvHbsez4FVZNH0PEm5dUatwGJSUlCpUsS/kGzdk/y46Zpx8BsMSyNg17DeXByYOEB/oz91ogy7vWZ9S8FaQ/u8XQoUPJzMzE0NCQ+/fvo6OjQ9u2bXNMTvlO1s6dO+nTpw/KysqoqallU7R5eHjg7e3Nn3/+yd27d1mxYgW2trZ07NgRAB8fH4YOHcqyZcto1arVV9edkZHxyykiflZOnjzJ5s2bOXz4MDVq1ODy5csYGhri5OTEyJEjfwlLoNzi5MmTTJs2jUePHuVaxty85nM73A8ePGDGjBmULVuWsmXLEhwcTHx8PFu3bs1z+aKjo1mwYAE+Pj5Uq1aNgQMHcu3aNQIDA1m+fPk3xZbMjzx//pwpU6YwbNgwrKysgKy4m71796Z06dJs2rRJwRJK5Cd27NjByZMn2b9/PzKZjGvXrnHjxg0ePnzIpEmTqFu3bo7UExoayvnz59m1axfPnz+nZs2atGvXDltbW2ksl5DIY+Tz9KSkJFJTU8WN9vT0dGJjY8UQK7m5MZqQkMCMGTM4cuQIrq6udO7cOdfqkpCQyEJSrEn8VkQnplCufAUa9x9Joz7DeHrpFPvsR9J8yLjPKta09PQZvMoNHcOCqGlqscSyNr3mrKRU3Cv27dxOsWLF2LdvH4UKFRLryCnTbvkCtFu3bqxcuZLMzEzKlCmDqqrqJ1ZsMTExjB49mtKlS7No0SIgK6Cp3YRJGFSpR8O2lkTExBL1PpSOTRpgXlgbbTXlf5VVch3JH9y5c4fLly8TFxcHwNy5c7l06RITJ07k/v37CpYu/yIIAvXq1WPGjBn07NlT0eLkOAsXLsTPz49y5crRvXt3ypUrx7x58/Dw8KBbt27MmjULTU3NPE8Y4OnpyV9//cWOHTvYu3cvd+/eZdmyZVhZWXHr1q08kyO3OHbsGPv37yclJYUePXrQuHFjSpcuDWTP0PqrJGmQ+D7k42dmZiYtWrTAysqKkJAQZDIZGhoa/PHHHzRq1Ii0tDTU1dW/6535MHlJbGws/v7+1KtXj/fv33Pw4EHOnDnD4cOHc6mFEhISX0tGRgZRUVHcvXuXAwcOYGRkxOLFi/NknLh48SI2NjY0adKE1atXS7HXJCRyEWnmJ/FbsffMJWQZ6TQeMBIVNTWqt+lCiSq1vni+RV9bDI1NUNPUyvb3Fv1H4OHhgZeXl6hUk+uoc2qglCu1TExMePXqFdOnT+fp06di/JQPMTQ0ZOTIkTg6OgJZ7q6uN/0xG+FEsSaWvJNpkqpfFJ3yNbkWmsy6x5EcfBlHWHLmF11DJaWa4pA/Ex8fH2JiYrh//z4uLi6UK1eOpKQklixZwrhx4wA+eRcksjh27BgZGRl0795d0aLkOE+ePMHDw4P27dujrq7OypUrOXToEIsXL2bBggXcuHGDFStWEB0dnWcKHnn/V7FiRYKDgzE2NmbSpEk8e/aM27dvU7t2bRITE/NEltykS5cuODs706JFC27fvi1m601NTRX7TEmpJiFXqqmoqLB8+XI8PT0JDAykYcOGzJs3jyJFitCrVy+WLFkCfN87I//mZsyYwZIlS7C1teXYsWMUKVKEESNGiEo1af9cQkJxpKens3XrVsaMGcPq1atp3bo18+fPz7NxokWLFjx69AgDAwOqVavG8ePH86ReCYnfEWn2J/HbIJPJePk2GL0ixbIpjQyLfTkzj4GxyaflANEZSpQpUwb4fyVITiui5IPu9OnT2bdvH7dv38bR0ZF27doRGxv7yWS5efPmaGpq4h2ezK7n0aQXLI6qhiaqGprZzstEiUyUeBGdgptfLA+j0nJUbokfR+4qN3v2bHR0dNi1axeOjo6cOnWK5s2b06RJE2xsbABpEf85ZDIZc+bMYe7cub/M/ZH3MzKZjMDAQKytrenXrx+DBg2iffv23Lx5k7Fjx9KtWzecnZ0JCgrK051pef9XtmxZRowYQd++fZk8eTKhoaEsW7aMUaNGoaOjI7bhZ0Te55YuXZpx48bRp08fMjIyePLkCRoaGr/MuyaRM8j78QYNGjBs2DDWrl1L586dWbx4MTY2NkRHRxMREcGdO3eAL8c//RLKysokJydz7tw5xowZQ2ZmJrVqZW0UTp48maCgIEDaJJOQUCRqamoEBATQo0cPPD09KV68OGPGjGHevHns27cvT2TQ1dXFxcUFNzc3Jk6cyKBBg4iKisqTuiUkfiekWaDEb4OysjI6BY2Iex+STSkVG/r2i9d8aUKamimjTJkynD17NtfjBV24cIH58+dz4cIFRo4ciUwm4/jx45+VzTs8mfPvEpEpKaP8H8kZlJSVxYQM3uHJxMfH8+7du++SsWPHjuzYseO7rpX4PN7e3hQrVgxdXV2UlJQYOnQo+/bt4+zZs9jbZ2WwlSwRPs+hQ4dQV1enS5cuihYlx5D3MyNGjGDt2rU4OTkREBCAiYkJVlZWWFlZYWFhAWQt5F1dXfNcRrnCLDMzk/Pnz5OZmcmePXu4du0aZcqUYcGCBVy+fBkfH588ly0nUFJSQhAEMjIyAGjUqBH29vaMGjUK+HkVhhK5h7yP7t27N5cvX6ZTp068e/eONWvWsG/fPtq3b8/QoUMBvmsucePGDSwsLDAwMMDY2JiSJUsSHByMp6cnJUuWzMmmSEhIfCPyMWHp0qX07duXsWPHsnDhQho2bEiJEiWYM2cON2/eBPJmPie3XitQoADVq1fn2LFjuV6nhMTvhKRYk/glkclknyxyHj58iHaBwiirqnJ97yYy09N5fM6DoCffHqdKQyX3Px25/EeOHMHf358KFSrQoUMHzp07x5AhQz45X57tNOMbx+YMAc69S8Dj8k1OnDjBoUOHUFJSEl1UvoZTp059VqavQUlJCR0dHXR1dSlcuDD9+vUjJibmu8r6Glq0aMGWLVtyrfwfJS0ty4LwwoUL7Nq1CycnJ96+fYuBgQGA+D9IlgifIzMzE0dHR+bNm/fL3B+5Iueff/4hLCyM48eP06tXLzp37sy+fftQU1OjdevW9OvXD/h2y5ecQn6/R44cyfv371m9ejUlS5bk4MGDrF27lnnz5uHq6kpQUBDp6ekKkfFbkN/HtLQ00tPTef/+PUpKSqiqqpKZmeVGr6WlJSYQkSzWJD7mwz4oMTERGxsbnJ2dqVGjBoULF6Z69epUqFCB9+/ff1f5tWrVIjMzkzp16lC1alUA9uzZQ6NGjQDF9QUSEhJZY0JmZiapqal4eXmRkJCAm5sbw4cPx8bGBkdHR65duwbk3Uapjo4Oa9asYc+ePUyePJmBAwcSFRXFmzdvqFGjBiEhIXkih4TEr4g0C5T45RAEAWVlZZSVlQkJCeH+/ftkZGRgZmaGnlIGQ5Zv597xfcxrWYFHZ45QtaXlv5aX+b9FrRxlwEgr77LaNWrUiK1bt+Lp6cnz58/x9/cnICBAXGzLuR6W/M1KNTkZmQKe/mGEhoZy/PhxChYsyM6dO3NA+q/j4cOHJCQk8PLlS6Kjo3Fycvqucj6+Jz8bZ86cYd++fQQGBjJlyhSePn1KgQIFaNasGePGjcPPz0/RIuZ79u/fj76+Ph06dFC0KDmGqqoqkZGRuLm5UaNGDVRUVFi9ejWLFy9m1qxZWFtbZ5uUKyrrplyJoKWlJcYCXLx4MadPn0ZTU5OGDRtibm5O586dUVNTU4iM34L8PtrY2GBjY8OsWbNYt24dsbGxqKio/NTZTSXyDvm3OWTIEPr370+BAgVQUlLi7Nmz9O3bl4oVK2ZLgPQtFChQgKlTp2JpaUlUVBTlypUjICCACRMmANLmi4SEotm9ezcHDx4kKSmJ9+/fY2xsLB67dOkSy5cvJzw8PM83Zpo3b87Dhw8pVKgQVatWpX379jx58oRJkyZ98ZrEdBk3w5I49jqeAwGxHHsdz82wJJLSJWttCQmQFGsSvwhv375l6NChYpas1NRUDAwMqFq1Ks2aNUNHRwdra2salzGiRFVzxu+9QF/nTUQE+uN34wIBd66ipW+YLSNow15DObFiDo6NS5OZkYG3hzsIAm4zbDm98a9s9d++fZtGjRphaGhIsWLFGDt2rGh5BFmT2/Xr11OhQgX09PSYPXs2AQEBWFhYoK+vT+/evcXzo6Oj6dy5M0WLFqVAgQKsWbOG69evM23aNLp27UrNmjWpUaMGgYGBBAQE0KpVKwoVKsTgOmXZ5zCK5PhYsd4llrW5vHMdq3s3x6lZOfb8OZz01JRP7p+SsjIVGrXikMdJ3N3dWbduHX5+ftmy+KWkpDBw4EAKFSqEoaEh9erVIywsDMhuBfahTIULF2bAgAFfbYGmr69Ply5dePr0qfi3bdu2UblyZfT09ChXrhwbN24Uj128eJESJUqwZMkSjI2Nsba2Fu+fkZERBQoUoHPnzrx9m+Xu6+DgwJUrVxg7diy6urqMHTv2q+TKK+Lj47lw4QJLlizhzZs3lCpViq1bt3Lp0iXi4uLo37+/okXM12RkZDB37lzmz5//Sywovby8WLduHZDVtsqVK3Pu3DnWrl1LUlISXbt25ebNmzRr1gwlJaV81eZ169Zx5MgRhg0bhpOTE1OmTGHVqlUUKVIEyP9uzHJLnxs3bvDu3TvGjRtHq1atePHiBdOmTcPNzU3BEkr8LHz4XaqoqPDmzRsGDRrE2LFjGTp0KEuXLkVFRYUXL158VXlya/bY2FhevXoFQJ8+fRg7diyXLl1i3bp1mJubA5IVpYSEoqlSpQobNmygS5cuGBsbs3fvXubMmUOFChW4fPky06ZNw8DAQCFjoo6ODqtXr2bAgAE8f/4cmUzG0aNHuXfvXrbzQhLTOfgyjvVPorgaksTT6FQC4tJ5Gp3K1ZAk1j2J4uDLOEIS878luoREbiKNuBI/NZmZmchkMkqUKMG8efNEiyVfX1/S09MpXrw4d+7cYcCAAVy7do29O7ZRTl+d4GePODh3At0cVjD7wgvq9xjMzkmDyEhLFct+ePoQQ9bswfGSPxFvAji6aBq956/j7xvPiYuJEpU1kDVZXrlyJREREdy4cYNz586xfv36bLJ6enpy7949bt68ydKlSxkxYgS7d+8mKCiIx48fs3fvXiBr0mxtbc3Lly9F5ZuZmRmPHj3i2bNn1KtXj9WrV2NqaoogCMycOZPD9/2ZdvAasaHvOOu6NFu9Pl5HsV63n+nH7xHq9xTv458PlqqupoZl/6FoaWlRtmxZWrVqxe7du4GsRfCOHTuIjY0lKCiIyMhIXF1d0dLS+qQcuUzBwcH4+voSFBT01RZo0dHRHDlyhIYNG4p/K1KkCB4eHsTFxbFt2zYmTZqEt7e3eDw0NJSoqCgCAwPZtGmTeP8CAwN58+YNWlpaogJt4cKFNG3alLVr15KQkMDatWu/Sq68QCaT0aNHDzZv3kyvXr0oVaoUgiCQmZlJyZIl2bFjBxcvXhTPlfgUNzc3jI2Nad26taJFyRGqVq1K37592b17NyEhITg7OzN16lR8fX2ZP38+3t7eFC5cGGtrayB/vBdyGerXr0+FChVo0KABJiZZSWDMzMxEF7X8pAT8HHJrtK1btzJ27Fjq16+PpaUlNjY21KpViytXrpCcnKxgKSV+RubMmYOKigo+Pj4MHjwYQRCIj49nypQpnDp1Cvg6F05HR0d69OjBuHHj8PDw4NGjR7x48YLIyMjcboKEhMRXUq9ePbS0tFiyZAn+/v4MHz6cS5cu4erqysOHD5k8eTLq6uoKGxOjoqJYt26dqNhLSUmhV69e4u/e4cm4+cXiF5tGpsAnnjEZAmQK4BebhptfLN7h0rgo8fsiKdYkflqOHz/OtWvXUFZWFrPbVKtWDchS7sgVLGZmZvTu3Zvq1avj7u6ORVEt7hzaRf0egylVvQ7KKirU+aMvqurqvPG5K5Zv0dcWQ2MT1DS1eHz2OGZN21GhrgXNSht+kiq7Tp06NGzYEFVVVcqUKcPIkSO5dOlSNnmnT5+Ovr4+VatWpVq1arRr145y5cphYGBAx44duX8/K9ZboUKF6NGjB+fPn+f06dPUq1dPLOvjHa3y5cvTtm1bYmSqaBYoTJOBdrzyvp7tHIu+tugbGaNtUIDKzdoT/PzxZ+9nJkqc+Gc/ffr04eXLlzx+/JgdO3aQnp6OkpISampqREZG4u/vj4qKCnXq1EFfX/+TcuQyaWhoYGRkxOTJkz+5Fx9Tu3ZtDA0NKVy4MG/evGHkyJHiMUtLS0xNTVFSUqJ58+a0a9eOK1euiMeVlZWZO3cuGhoaaGlpifdPW1sbPT09HBwc/rN+RSOTyejXrx8ZGRmoqqrSsmVLIEv5oKKiIsYMlGdVlKwQPiU9PZ158+b9UrHVihcvTqFChQgICGDAgAFs3ryZnj17MmrUKGQyGRs3biQxMVE8Pz+8F3IZmjdvztChQzlw4ACTJk3i1q1bCILA4cOHOXnyJJA/FIGfIz4+HkEQCAoK4vjx49jZ2XHjxg309PSoWbMmvXr1Ys6cOWhpaeXbNkjkP+TvysaNG9m+fTu7du1i8ODBDBo0iOPHjzNv3jwcHR2Bf3fnVlZWRiaTcfnyZa5fv86KFSsoWbIkDx8+ZOXKlYSHh+dJeyRyDsnF7tdm+/btVKxYEQsLCx4+fMilS5fQ1dVl+fLlWFtbY2trK26c5mRcRHmStX9DR0eHlStXMmHCBNq1a0eJEiUICgri4cOHYkK0DCHLA8b/1r/PpT9MiCYh8TuiqmgBJCS+BZlMJro8FSlShD59+tCqVSsCAwM5d+4cBgYG7N+/H3NzczQ0NIiPjwfAwsKC8ePHExISQjEdNVSig7nqsZ8b+/4/iH1mRjpx4WHi7wbGJuLPceGhFDA2oZWJDsW01QC1bDFRXrx4weTJk7l79y5JSUlkZGRQp06dbLIXLVpU/FlLS+uT30NDQwFISkpi0qRJ7N+/n6SkJJSUlEhLSyMzM/OTyXZYWBgTJkzgzMXLJCXEI8hkaOkbZjtHr3AR8Wc1TS3iwkM/e29jQt/x6MZlBnazpH79+pw+fRpzc3M2bdrEmDFjsLKywtPTEysrK5KTkxk4cCALFy78JFaSXKYrV64QHx+PTCajQIECn61Tjre3N+XLlyc9PZ3169fTtGlTnj59iqamJqdOnWLu3Lm8ePECmUxGUlIS1atXF681MjJCU1NT/F1+/06fPk10dDSQtVD+3P3LL3h5eXH37l2Sk5PR09P75Hh+UJjkd3bs2EHZsmVp3ry5okX5YT5+Vx0dHWnVqhUODg5cvXqVtWvX4uDgQHBwMDo6OqILfH6jbNmy7N27lxIlSrBq1Srat2+Pubk5e/bsoVOnTvnSHfT8+fM8e/aM0aNHU7JkScLCwlixYgUdO3akbdu2rF+/HiMjI/F86duU+Frk74qamhoRERGcPn2aQYMGoa+vz8SJEzl16hQ1a9bEz8+PChUq/GtZiYmJdOnShfj4eMzMzDAzMwOy5iIVK1bM9bZI5AwhielcD0vmZVwaSmS3BnoRA1dCkiinr45FUS2K6eT/uJQSn6d48eJ0796d7t27A1khBlavXk2jRo1o3LgxysrKTJw4kQcPHuT5PFVDQ0PMbP0hIYnpuPnFfldCtPPvEimmo/q/9dK/8/r1a8qWLUt6ejqqqpJaQuLnRpoRSvwUyGQyMSmBfAGZkZFBRkYGMTExnDt3DoBp06axZs0aTExMUFVVxc3NjZs3b7J27VpKlChB8eLFAahQsjhNOvyB44XnOF7yw/FyAPOuv8G8Q3exzg8XqgZGRdGIC6O2UZbrY1JSUjZ3Czs7O8zMzPDz8yMuLg5nZ+fvXjSuWLGC58+f8+zZM9LS0sQ4Z58rz97eHiUlJVxO38Dpyit6L9jw3fXeP+GOIJPh6OiImZkZDRs2RBAE3N3dkclk+Pr6oqKiIsZ88/Dw+GyCA7lMPj4+xMXFsXv37q+WSU1NjeHDh/Pq1SseP35MamoqPXr0YOrUqYSFhRETE/PJgvxjhYL8/t26dYu4uDguX74M/P/9y48KiHbt2tGnTx969OjBnDlzeP36tXhMbn0p8WVSU1OZP38+8+fPV7QoOYJ8Yr18+XJsbGw4e/YsTZs25dixY+jr61OtWjXevn0rLqbz2zst/9YePHjAnTt3cHR0ZOrUqWzfvp0qVaqIMRTzo6K7adOm9O/fH1dXV6ytrfH19WXKlCmEhoZiaGhI0aJFef78uaLFlPiJUVZWpnDhwty9excdHR2aN2/OiBEjqFKlCqGhoZQoUUI8V77hJkduzXLw4EGuX7+Oqakpw4cPx8PDA5lMRsWKFaXx4idBcrH7/Xj16hVJSUmsXbuW+vXrM2LECDFDaOnSpbl9+zaQP6y55QnRPk7g9jVkCHAjVHpfJX4/JMWaRL5GbnEmV6g9ePAAZ2dn7t69i4WFBRs3biQoKAjIcgXr1asXaWlpnDp1Cj09PZKSkli6dKmYdbJPnz4ADBgwgDtnT/J0qzOJr3xJjY/l6YWTpCYmiHUrAypKUNFAHfth/bjmdYqrV6+SlpbGnDlzsg188fHx6Ovro6ury7Nnz9iwYcMPtVlLSwsfHx+ioqKYO3fuF88NCwtDR0eHMkUKkhgewpWd3x8zzPv4flp170tAQABBQUE0atSIcuXKcfPmTaKjo9m9ezdFixalQ4cOaGpqEhISIt77j+XX1dXFwMCAd+/esWzZsq+WITMzk23btqGlpUW5cuVIS0sjNTUVIyMjVFVVOXXqFGfOnPnXMuT3z9DQ8LP3r2jRorx8+fKrZcoLlJSUcHBwwMHBgZSUFBwdHYmNjSU8PBwlJSWUlZVJSfk06YREFlu3bqVq1api/K5fgeXLl+Ph4UG9evUYNmwY06dPx8DAABcXF1asWPGfFi2KRK7os7S0pGLFipw7dw4zMzMiIyOZM2cOU6ZMUbCEnyc9PR01NTU0NDTo1q0bmpqajBs3jhUrVpCUlMTmzZsJDAykUqVKihZV4idGrhzbunUrp06domPHjkyZMoXevXuzf/9+tLS0iI2NJT4+nvHjx38246+9vT1z587l7NmzlClThtWrV2NiYoK3t7dkRZmHDB06lFmzZv3neR8meAKyudh9DZKL3c9PZmYmdnZ2JCcnExkZScuWLdHS0hLnyT4+PuJGek5+w3fu3KFKlSoUKFAAa2trcS7p4eGBubk5hoaGWFhY8OjRI/Ga0mXKsHX1clb3bi4mcAN4++Q+K3s0Zm7z8vzjOE5Mhnbv2F5cbSyz1dvD1AAf36yELCdOnKBWrVro6+tTsmTJbHGXmzVrBoChoSG6urrcuHEjx9ouIZHXSKOvRL5l+PDhbNiwgfT0dBISEhg3bhx2dnYYGBgwbdo0PD09sbS0RFtbm40bN4ouiVOmTGHq1KkAjB07Fl9fXzw9PalcuTIODg4AtGnThgMHDuB79wYuw7qyvndj3l44gpmhOuX11VBXVqJaIQ3GVC1I93L6tKhrzrp16+jfvz/FihWjQIEC2XaVly9fzp49e9DT08PW1lZU4H0PEydOJDk5mfbt21OrVi0aN24MQFxcHJC1kyXPIOrv78/du3dpX7EYW8f1o2qrzt9V55tHd4kJfYvvvVtcvXqVM2fOEBwcjLOzM+XLl2fv3r00bNiQEydOoK+vT82aNSlZsiT+/v6flOXo6Ii3tzcGBgZYWlqKpu//Rs2aNdHV1aVAgQLs2LGDw4cPU7BgQfT09FizZg29e/emQIEC7Nmzhy5duvxrWfL7V7hwYRo2bEiHDh2yHZ8wYQL//PMPBQoUYPz48d92o3IRHR0dGjdujL29PfHx8djZ2TF8+HBmzJjB06dPGTx4MOnpUsalj0lJScHZ2flfFdA/C/IFd0ZGBqampri6umJnZ8e1a9d4+PAhTZo0wcfHh549e6KmppYvdrW/hCAIqKur06hRI/r27UutWrUwMDAQ40vmR+RjiHz82LBhA46Ojty8eZPBgweze/duSpYsqUgRJX4B5Mqxli1bEhgYyNOnT/Hy8sLV1RVtbW1cXV0xNzcnJSWFPXv2iIpq+ff++PFj2rRpQ6NGjahfvz6zZs3izJkznD59mlq1aimsXRJfR0hi+jcp1eTIlWshSdI84GdERUWFypUrs3//fhwdHZk3bx79+vVj2bJlPHv2DGtra0xNTcUkbDmFm5sbnp6eBAQE8OLFCxYsWMD9+/exsbFh48aNREZGMnLkSLp06UJqalYCt7RMgQcfJHBT+Z+L5oNTB7Fe5860Y3eIeBPAhS1//Wvdz2KzytPR0WHnzp3ExMRw4sQJNmzYwJEjRwBEr5KYmBgSEhJ+qQ1Sid8PJSE/BjmR+G2Ru3yqqKhw4sQJNm7cyPr16ylevDgnTpygY8eOnD9/nv79+9OhQwf++usv7t+/j729Pfv27ePAgQPY29uzcuVKVq1axZYtW2jbti2CIIiupB8SHx/PuXPn2L59O6ampqxYsYJz586RnJxM587fp6TKKWbOnMmVK1cwNDREWVkZQ0NDhgwZwq5du4iIiCAiIoLixYuzb98+1NXVOfgyDr/YtO+ur6KBOkWCH3Hjxg2WLVtG7969xayZ8phP8v9jYmIYP348LVq0wMbGBplMJu2S5yC9e/emWbNm1KxZk+PHj3Pr1i26devGpEmTFC1avmP16tWcP3+eo0ePKlqUH0IeJ00mk2FpaUlqaipaWlosX76cypUrA1lKY1NTU8aNG6dgaf8beV9x6tQpvL296devH+XKlVO0WF/k/fv3FCxYEFVVVcaOHUunTp3o1KmTeHznzp0kJCQwevRoBUop8asg/z7i4+PFuJpXrlzB2dmZtLQ0Zs6cSZs2bQgKCqJ48eLZ3KZXr17N7t27ad68OT179sTExOSTWKMSecPQoUMpUaIECxYs+NfzWrRowcCBAxk+fHiOzNW6l/s0cZRE/ufNmzcMGzYMmUxGiRIliIuLo23btjRv3pyqVasSFxf32aRg30uZMmWYMWOGGEPt5MmTjBs3jnbt2lG4cOFs4TMqVarEpk2baN68OUVLlKbpsMnU7TZAPL7EsjYtrMfToOdQAJ5d9eL4UnumHbvDvWN7uXNkN6P+PiGeP7O2Ea4X7jOyhfknck2cOBElJSVWrlwpxViT+KWQ3mCJfIF8UfmhcsbS0pLt27dz9OhRRo8eTadOnZg5cyavX79mz549bNy4kZMnTzJ06FBu3rzJiBEjMDU1JTU1lUmTJrF69Wpxp1ee8ODj+u7cucOePXuoW7euaAYdGRnJvn376Ny5s0ID3i9atIjw8HDCw8PJyMjg0aNHWFpasmHDBqpWrYqGhgbVqlUT5bMoqsWruLRv3gUFUFWChkW1KF6uCU2aNCE1NZXp06cDWcpOeR3y/11dXSlSpAgNGjQApADeOY2rqysFCxZEEATq1q2LsrLyJ0kiJLJiHS5evJhTp04pWpQfRt4//fXXX2Kg42vXruHi4kKHDh3o0qULq1atEs/PrwkL5Mj7io4dO9KxY0fx7xkZGSgrK+erPsPPz49169bRoUMHWrVqhampKW/fvgUgIiKC2NhYSpQoQatWrYD8f+8l8j/y70NHR4eEhATs7Ox4/PgxQ4cOZcKECYSHh7Nx40b69u2LiooKGRkZvH79mvLly9OtWzd0dXW5c+cOGzZsoFixYhQvXpwBAwZkS6okkfPcv3+fYcOG4efnR6dOncR+IDo6mkGDBnHr1i0yMjJo3Lgxrq6u2TwbICv754OAN2wZ3Ydalr0wqWLO8WX2THTPstrZateT5PhYxu72AmCjTWeaDBpN1ZaduLhtNXcO7yYhKpxypUrh7LwQKysrAAICArC1teXhw4coKSnRvn171q1bh6GhIZClYBk7diw7d+4kMDCQDh06sGPHjlxXxgqCQGZmpqQw+R+lSpXC1dWVt2/foqamRsmSJSlZsiQ7duzA1tYWIyMj6taty7hx4zA0NMyRseZDK+vSpUsTHBxMYGAgO3bswMXFRTyWlpZGcHAwAAJCtgRucgyKFhd/LlCs5BeToYllZmYtSG7dusWMGTN4/PixGOKlV69eP9QuCYn8SP6Z2Ur8lshdG+QDx9WrV7G1tWXv3r0kJyczfvx4PDw8ePfuHUlJSVy8eJFNmzbRrl07YmJi+Oeff3jx4gWzZs3i5MmTbNmyBQ0Njf+sV26oGRwcTPny5enZs6d4nSAI6Orq5lKLvx5BEDAyMqJKlSrUqFGDgQMHMnLkSKytralfvz41a9bMpvQrpqNGKxMdVL9xDFZVglYmOhTXURPvy6xZs1BXVyczM/OTBbCfnx9Pnz6lVatW+TrO089GZmamGPuiYMGCYgZcLS0tNDQ08pUiIr+wfv16GjdujLm5uaJFyRGOHDnCrVu3mDJlCpaWlvTr149q1arxzz//sHr1aiB/J+H4EpmZmaKbq6qqqvgu5xdXVj09PbS1tdm7dy+bNm1CRUWFffv20b17dwYNGsTMmTMJCQkRz/+Z7r1E/kZZWRldXV2KFi3KqVOnmDBhAidPnsTJyYno6Gh0dHQA8PHx4a+//mLhwoXExcUxbNgwXF1dmTp1KqVKlcLX11dUokjkDmlpaXTr1o1BgwYRFRVFr169OHjwIJDVl1lbWxMYGMibN2/Q0tJi7Nixn5Thef8ZrsO70qjPMJoNGUup6nWIfPOSxOhIMtPTCfV7Snx4KKmJCaSnJPPW9yFlajUEoFCJMozcepz5V14yYOIMBg4cKPZLgiAwc+ZMgoOD8fX1JSgoKFscKwB3d3dOnz7Nq1evePToEdu3b8+1e/Xh3F5VVZXLly+LY8Dv7ihlampK8+bNsbCwoGTJkvTq1Ytt27YRHx/PiBEj0NDQYOXKlUDOjJEfxkJ+8+YNxYsXp2TJkjg4OBATEyP+S0pKol+/fgAoofTZcS42LFj8OSb0LfpGxgCoa2mTnvL/8f/iI8Ky/q6SVUb//v3p0qULQUFBxMbGMmrUqJ9yLiMh8V9IWwgSCkFuCSZfYKWkpPDPP/+wZcsW7Ozs8PHx4dSpU+zcuRM9PT1OnDjBsGHDqFChAlOnTqVw4cKoq6tjbW1N2bJlUVFRQUtLSxyElJWVs2VW/Bh5R168eHFevHjB/v37yczMJDk5mfv371OqVKlcvwf/xecGG/ni+kvIs5Z+bfwOuVJNft3HdcoVd3JXz8TERGQyGcHBwZibm6Ourv41TZH4DwRBoFOnTqxevVrM9Cgp0v6d+Ph4li1bxvnz5xUtyg8j35VOT0/nxo0bLFmyhB07dlC9enWMjY0pWLCg6A76M05CP9wASEtLIzExkaCgIEJDQ/NFvDVjY2OcnZ25fv067u7uPHnyBJlMRt26denXrx9lypT5Ke+7RP5HPhdavnw53t7e/P333wQEBHD06FEOHjwojgOFCxemQ4cOBAUFZYu1Wb16dapXr64o8X8rbt68SXp6uujG1rNnT/76KyvGVKFChejRo4d4roODAy1btsx2/dOnT3Fzmk/rMQ5iBno1TS1KVDHnlfcN9I2MMa5YFS09fV4/uIWqugaFS5ZDx7AgANXbdgVABtRsb0WFCn9x+/ZtunbtSvny5SlfvjwARkZGTJ48+ZO4o+PHj6d48SyLoz/++IMHDx7k+D1KSUnh+PHj3Lt3Dy0tLdGNecSIEWJiKakv/X98fHzQ0NDg4sWLXLp0iQ0bNrBmzRosLS2ZO3dujnjMrFu3js6dO6Otrc3ChQvp06cP3bt3x8rKijZt2lC/fn3RcKFZs2bo6emhrPR5y5sb7n9j1rQdappaXNi6khr/eyeNK1YjLOA5wc99MCpTgbMbs5KWFdTMkj8+Pp6CBQuiqanJ7du32bNnjzj2GxkZoayszMuXL6lYseIPt1dCQpFIKzeJPCMjI4N79+6RmJgoDhYvXrygf//+nDt3jjt37rBjxw7U1NQ4dOiQaL48cOBAPD09CQkJYenSpairq6OsrMz+/fvp0aNHNhe5r3Uxkg/s9erVo0SJEpw8eZLw8HDat29PVFQUM2fOBFCYG+iPUNtIiwEVDahooI6KEp9YsKkq/X+20wEVDUSl2ueQ7yjJ7+nChQtZvnw5gYGBuLm5ZTs3IyPjt9+J/F68vLwICgqSLAC/ARcXF1q3bk3VqlUVLcp38/GOba9evXj8+DHh4eG0atWK58+fY2RkRJ8+fahZs6YiRf1h3r17x+3bt9mxYwd//vkn7dq1Y9q0aURERChaNBELCwtWrVrFggULKF++POHh4dy8eTPHg0lLSMj5cI5x9epVgoODMTMzo2jRotSrV08ce0uWLEmXLl0YNmxYNgtdQRDyjeXnr05wcDAmJibZFEOlS5cGssISjBw5ktKlS6Ovr0+zZs2IiYkRrbQgK4h8gaLFqN76j2zllq1jwct713jlfYNytRtRtk5jXnlf59W965St8/+B3L099rOmbwvmNjOlb40SPH78WOw/w8LC6Nu3LyYmJujr6zNw4MBP+lZjY2PxZ21tbRISEnLu5vyP8+fP4+TkRIUKFdDV1eXFixdcv36dAQMG0LdvX2bPns3Bgweld/Z/aGlp4evrC0Dz5s15+/YtixYtYsCAAf9x5dfTv39/2rVrR7ly5TA1NWXWrFnUrVuXzZs3M3bsWAoUKED58uWzWTDKLc0+xrxDd/4e3Ytlf9SlYIkytBw+GQCj0qa0tp3CVruerOjWgDLmWWFizAyyPIHWr1/PnDlz0NPTY968efTu3VssU1tbGwcHBxo3boyhoSE3b97MsbZLSOQ1UvICiVwnLS0NdXV1Ll++zNq1a5k9ezbVq1enf//+aGpq0qhRI2xtbenbty9nzpyhb9++jB07lipVqpCUlIS2tjYtW7akf//+2NraZos5IHeX+9YdMHkZR44cQUdHh7Zt23LlyhXKlSuHicmncQXyCxcvXhSz630NSekyHkWlEJ6cSWqmDA0VZYy0VKhRUBNtta/Xq1+5coU3b97g5eWFjY0NzZo1E5+NnKioKEaOHImLi0u2CZzEvyMIAk2aNGHMmDH0799f0eL8FMTGxlK+fHmuXr1KpUqVFC3OdyPvh5YuXcqrV69ISkrC0tKS3r174+joyJo1a9iyZUs2S4iflT///JO7d+9SqlQpGjRoQIMGDThw4ABly5bF1tZWIfEs5XXeuXOHtWvXUrhwYfT19enVqxcVK1Zk8+bNJCUlMWXKlDyVS+L3Qt4PJCYmoq6uzpYtWzA0NBTdsj48R0JxXLp0iX79+vHu3TvxWTRu3JiWLVuioaHBuXPn2LdvH8bGxjx48IBatWqJAdlbtGhBnz592H7IgyRU6LdoM8r/6+/8bl7k5F+OGBib0MJ6PFr6hhyaPxlVdXUa9rahepsuRAcHscKqIcNdD1KqRj2qF9ZmdremjB07luHDhzNs2DCSkpJYt24dBQsW5MiRI4wdO1aMFVmmTBm2bNlCmzZtAHBycsLf35/du3fn6D26cOECMTExWFlZERsbS0pKCkWLFiUjIwN3d3dCQ0Px8fGhW7dudO3aNUfr/tmQf9OdOnWiZ8+e2NjYsGXLFvbv38/WrVsV7jkjJdmQkPh2JIs1iVxDEASWL19Ohw4diIuLo1mzZhgbG3Pt2jUga6fv7t27dO/enczMTFq2bEm7du1Yv349VapU4cmTJ6xatYrMzEw2bdrE4MGDAcTMefIsn98z2ZTrk9+8eSPujjRt2jRfK9UAxo0bR2Bg4Fefr62mTMOi2vxRRo+epgb8UUaPhkW1v0mpJr//W7dupVu3buJu+cdBb/X09KhUqRLm5uYcPnz4q8v/3blw4QIRERH06dNH0aL8NKxatYpOnTr91Eo1yOrLzpw5w5EjRxg3bhxPnjwRrQjmzp3L0aNHf3o3L7llQr9+/dixYwfbtm1j1KhR1KpVC3Nzc86dOwcoxvVZrsibMmUKXbt2JTg4mKtXr+Lk5MS6devo06cPkydPztYOCYmcRj6H0dLSQk1NjStXrtC8efPPniOhOBo1aoSqqipr1qwhPT2dQ4cOcfv2bQDRzdHQ0JCoqKhP3DAB1NTUWP63GxnJSbjPGSP2KaVr1iM80J+3T+5TolptipqaERMSRNBjb8rWzrJYS0tJQklJCZ0ChVFVgltH3Hj8+LFYdnx8PLq6uhgYGPDu3TuWLVuWB3ckOzKZjJYtW4oJFQwMDChatCiCIKCqqkrfvn2ZPHkympqaPH/+PM/ly2/In//s2bMJCAjg/fv3DB8+HC8vL/bv34+Li4sY0uZDy8e8wqKo1jfHbJajqgSNjL/sDSMh8asiKdYkcg0lJSXOnz/P6NGjxfTR1tbWNGyYFYh1xowZJCQkEBYWhoqKCp07d0Ymk9G7d2/RbFxLSwtlZWUqVKiAhoZGNtfEH5loyssxMDDg3LlzODs7c/bsWa5du8atW7eIjo7+wdbnDunp6XmeHVJFRYVdu3Zx9OhRunXrJj7LjxfCampqLFiwgEOHDjF16lRsbGyIi4vLU1l/RubNm4eDg8NP6XasCKKionBxcWHOnDmKFiVHePHiBX/99Rd+fn6YmJhgY2NDfHw827dvp1mzZlSsWPGndrGW9xPm5uaUKFGC6Oho3N3dsbW1ZcqUKTRs2FC0PFYEXl5elC9fnu7du/P06VOWLVtG6dKl2bVrF56enqJcUsxDidxG/o69f/+eYsWKKVgaiY9RV1fn0KFDbN++nYIFC7J//366d8+KlTZx4kSSk5MpXLgwDRs2pEOHDp8to7axPoNWbCchMpyDc8cjk8lQ19KhuFkNippWQlUtK25tqRp1MTQugW5BIwCKlqtEk4F2bBjakbltqhD7+jmNGzcWy3V0dMTb2xsDAwMsLS1FufISZWVljh07li1Y/oeWlvL3e9SoUdJGIv+/sdOoUSMWLlwoJir5559/OH36NKqqqowcORJQzPjzownRimlLmewlfj8kV1CJXGX+/Pm8f/8+W0pn+P9g+FOnTiU8PJwdO3YAWbtuT548EVOa53Zw/O3bt7N//36UlJSIjo5GJpPx+vVr1q1bR8+ePUU58wumpqZ4enqKQWrzKwkJCUyaNIlz586xY8cOmjZtqmiR8iWXLl1i2LBhPHv2TEpH/5U4ODgQFhbGli1bFC1KjnD8+HFmzJhBWloaz549Q0VFhWnTpvH+/XuxX/xVGDVqFI8ePRLdQZOSksRwABYWFnnmDvrhYi8yMpKIiAieP3+Op6cn69at4/r162zfvh1XV1eUlZUlNzyJPEH+/iclJaGhofFJ8qCYmBhCQ0PFBDcSPye/qotdcnIynTp1IiQkBGNjYy5evKhokX4arl27xurVq3F3d+fQoUN4eXmxYcMG6tSpw549exRqne8dnvzdCdEkJH43JMWaRK5y9+5dFi5cyOzZs6ldu7a4QJFPFKOjo6lcuTKenp6fDc6dmZn5w9ZpnyMlJYXU1NSvjlWWXyhVqhRXrlwRA+bmd44dO8bIkSMZOnQoc+fOlbKIfkSbNm3o378/NjY2ihblpyAiIoJKlSrh7e3903wDH/Nh31egQAEgawPi7t27dO7cGV1dXZYsWcLFixcxNDTMd8r970HehkuXLqGlpUWlSpXEvvfvv//mwIEDnDp1Ks/bumzZMipVqkSXLl14+vQp3bp1Y9KkSezZs4f+/ftjZ2f3S9x/iZ+PjIyMTzZbDhw4gLq6uhibKiYmBiUlpZ9uHvO7E5KYjptf7FcpKj5GVQkGVDTI99ZA8fHx6OnpfXazRNqo+Dzm5ubcu3ePM2fOcP/+fezt7Vm2bBn169f/xDU8rwlJSudGaDIBcWkoQbZ3V1UJBMBUX51Gxlr5/t2UkMhNJBMJiVylUqVKmJmZcfHiRWrXrp3NJDwzM5MCBQowbNgwwsLCPrlWEIRcs144duwYfn5+aGho0KtXL0qXLk1GRoa4gMqvCylFuIL+CF26dKFhw4bY2tpSv3593NzcfuosjjnJtWvX8Pf3Z9CgQYoW5adh6dKl9OnT56dXqj19+pQJEyagr6/P4MGDad68Oebm5mzevJn69euzdu1aDA0NFRLQPzeQ96fyxYFMJiMqKoqgoCBevHhBvXr1sp2XV5QpU4Z//vmH2rVrU6VKFRYvXsz+/fvp1asXdnZ2CpFJ4vdGrnRQVVVFEASePXuGsbEx+vr6dOjQIZtC4p9//qFOnTrUqlVLgRJLfCtyF7uvtQKSo4JAKxPdfKu4kI9XsbGxorL34/FL2qj4FPl9s7S0ZOjQoXh7e9O6dWsApk2bpmDpsiimrUb3cmo5lhBNQuJXRbJYk8hVZDIZHTp0wM7Ojk6dOqGhoSEOrIrctTp+/DjPnz9HJpMxYMCAfJ+0QE7hwoXx9fXFyMhI0aJ8E4IgsHXrVmbMmIGDgwMTJkz47SdXHTp0oHv37owYMULRovwUhIaGUqVKFR49ekSJEiUULc4PYWVlRZcuXUhJSeHOnTtUq1aNTp06/RYuXvfu3ePevXt4e3vz9u1bKlasyOzZs0XrvbxAPvZkZGQwa9Ys3r59i6urK7q6utmOS4tACUUgf//u3bvHokWLqFmzJq1atcoWUwvg5s2b1KpVCw0NDQVJKvEjfIuLnbKQybn1i9g0e1K+3ViSK4jGjRvH5s2bMTc3p169etStW5fmzZtTpkwZRYuYL/kwK7C7uzv6+vq0aNGCQoUKAXDu3DmCg4MZNGjQL7PZJiHxqyIp1iRyFXd3d9auXUvFihUpUKDAFzMV5eUC5nMKvZ/FNN3AwIDAwEAMDQ0VLcp3ERAQwKBBg9DS0mL79u2ULFlS0SIphNu3b9OzZ0/RalLiv5k0aRIymYzVq1crWpQfIjo6mjVr1uDo6AjArVu32LlzJwkJCYwcORILCwsFS5g7yPvYBQsW8PDhQwYOHEiLFi3Q09MjPDycokWL5pksgYGBlCpVivj4ePT19ZkxYwYZGRksWbIEFRWVn2Y8kPj1uXfvHpcuXaJ27dq0aNFCWlj/YnyLi93+TevYunUr165dE5NI5Sfk/ea0adPQ09PDxMSEx48fs3//fpKTkzE2Nmbjxo00a9ZM0aLma+7evcu6deswMjKiWrVqNG/enO7du3Pv3j1FiyYhIfEfSIo1iTzh1q1bzJs3j02bNuUL67CtW7cycODAf1VqvHr1ChMTk3wVF0xbW5vw8HAxe9DPSEZGBkuXLmXVqlWsWrWK/v37K1qkPOePP/6gY8eOjB49WtGi/BS8e/eOGjVq8OTJE4yNjRUtzjcj3zjYv38/y5Yt4/3791StWpUDBw6gq6tLfHw869atw8bGhiJFiiha3FxBvuhKTk5GS0uLs2fPsnjxYnx9fenduzempqaMHTs21+qPiYkhIiKC8uXLY2dnx8OHD6lTpw5v3ryhRo0abNmyBWdnZ6ytrXNNBgmJnEKeSVdSAP8afI2LnSAIjB49mtevX3P8+PF8mfAoNTWVOnXq8PjxY/FvkZGR2Nvb06lTJ9zd3dm8eTPa2toKlDL/kpaWxqRJkzA1NaVYsWIsXryY8+fPM2bMGKysrKRsqhIS+RzJx0EiV5HrbStXrkydOnXo2rUrXbt2JSEhQaFyrVq1io0bN3Lz5k3u3LnDixcvgKwJQGhoKAB9+vThzZs3ihTzE362GGufQ1VVFXt7e06fPs2CBQvo168fUVFRihYrz/D29ub+/ftSwoJvwNnZGRsbm59aqZaQkICbmxurVq3izp07lClThlatWnHmzBn09PSYMWMGRYoU4Vfd65IrALS0tHj9+jUuLi5YW1tTqlQpbGxsOHfuHLdv3way7llOc/r0adasWYO7uzsbNmxgy5YtTJw4keHDh6Onp8fo0aNxcXHh1KlTOV63hMS3Iv8G3r9/z5kzZ9i+fTsHDx4UQ1jkRlInCcWhraZMw6La/FFGj56mBvxRRo+GRbWzxa1SUlJizZo1yGQy7O3tFSjtlwkNDaVgwYJ4eXmJfwsKCuLRo0d06NABHx8fSan2L6irq3P//n2KFClCv379mDZtGtOmTaNMmTJUrlxZ0eJJSEj8B5LFmkSekZmZibu7O+bm5gofIPbv38/hw4fF35WUlLC0tOT8+fO8f/+eyMhIihYtyo4dO/JNxi1BEFBWVhZ3qn8FkpOTmTFjBocOHeLvv/+mbdu2ihYp17GysqJly5aMHz9e0aL8FLx584ZatWrx7Nmzny62oNw6C7KUg1euXGH58uViAo/t27djb2/P6tWr6dWrlyJFzVPs7e0RBIFFixaJ8TfDw8N58uQJK1asyJXQAM+ePePevXsEBAQwZswYMX7Nh3h4eHDs2DE2bNggudtJKBS5y+fUqVO5evUqdevWRVtbm4yMDDQ1NWnVqhVt2rRRtJgSCiA+Pp6kpCQKFSqUL63Wzp49y5IlSzAyMqJEiRK8efOGmjVr0qFDByZOnMilS5cULWK+RP7NX716FQ8PDwwNDTl37hw3btzAxsaGZcuWSaFDJCTyOZJiTSJPyI8xa9LS0oiIiCAtLQ1/f386d+7Mxo0bqV27NioqKpiZmeWrwNXp6eloaWmRkZGhaFFyHC8vL2xsbOjevTuLFy8WlRG/Gg8fPqRjx44EBAT8sm3MaUaMGEHhwoVxdnZWtCjfhEwmY+nSpZQtW5Y+ffrg5ubGwYMHqV69Oj179qRq1aooKyvz7Nkzypcvny8XSDmNXGG2YcMG7t+/z6ZNmzh9+jRz586lefPmNG/enI4dO+Za/RkZGbx//57ixYuLY9KHY9P27dvx8vLCzc0t12SQkPgW+vXrx5o1ayhcuDD379/Hz8+PR48e0alTJxo3bpwv51YSuU9+f+7h4eFcv36doKAgWrZsSdWqVXn+/DmhoaFidmiJL1O3bl0eP37M6NGjcXR0zDcb/BISEv+OpFiT+C35nEXEggULmDVrloIk+m/kO5TJycmKFiVXiIqKYvTo0Tx69Ijdu3dTu3ZtRYuU4/Tq1YtGjRoxefJkRYvyU/Dy5Uvq16/PixcvKFiwoKLF+SYiIiI4ePAgL1++pGbNmvTv3x9vb282bdqElpYW7dq1o2HDhmI2zPy+UMoJ5G0MDg7G0tISDw8PTExMaNasGfXr12f58uUKlS88PBxNTU309PQUKofE7438O3n+/DkDBw5k8eLFtG7dWjyelJSEhoaGZFUpIfLh+JGcnMyjR4/w9/dnwIABeSrHkydPOHDgANHR0VSvXp3q1atTpEgRypYtm6dy/KxkZGSgqqrKs2fPsmUJz8zMRElJKV9t9ktISHyKpFiTkODnWNTGxsZSsmRJ4uLiFC1KriEIAnv37mXixIlMnDiR6dOn/zKWPI8fP6ZNmzYEBAT81Mkn8hJ5DK65c+cqWpTvIjExkevXr1OqVCkqVaoEICYquHHjBk5OTtSqVUvBUuYt8r52y5YttGnThjJlypCcnIxMJuP06dMYGxvTuHHjPM0ULSGRH3n06BETJ07kyZMnVK9enU6dOmFlZUXZsmV/ijmLRN7z5s0b3NzcOHLkCOHh4cyZM4ehQ4fmSd0ZGRlUrFiR0aNHo6enh5+fH4GBgbx79w5PT09pw+I7yMjIQFlZ+ZOxMCMjg8ePH2Nubq4YwSQkJD6LpFiTkPhJiIiIoFKlSkRGRipalFwnKCiIoUOHkpKSws6dOzE1NVW0SD9Mv379MDc3588//1S0KD8FL168oHHjxvj5+WFoaKhocXKED5VF165do3HjxgqWSHEIgsCjR4948OAB/v7+BAUFcf/+ffr27cvMmTMlxYGExP8IDg7m7t27HD16lJMnT7Ju3Tq6d+/+yXny/kVSSv8+CILAkydPWLp0Kd27d+fKlSsEBwezYsUKihcvTlRUVJ5Ze7948YKpU6dy7Ngx0tLSkMlkJCUlkZCQQKlSpfJEhl+duLg4PDw8cHNz48WLF1y/fv2niz0rIfErIynWJCR+EkJCQqhVq5aYtfRXRyaTsXr1apydnVm8eDE2NjY/7UL72bNnNGvWjICAAGnX9isZMGAAVapUwcHBQdGi5CgfL3p/VwVSeHg406dPp0iRIhQtWpRixYqhpqbGli1b2LVrV44vFu7evUuVKlWkjHQS+Rp5f5CYmMjZs2d58eIFmpqaVK9enRYtWhAWFoaBgQGampqf9B0hISEUK1YsWzkSvz7p6enUrFmT9u3bo6GhwbRp07IlZzl8+DA1a9akXLlyuVK/fEy7evUqbm5uDB06lAYNGuRKXb8rb9++5cSJE+zYsQNdXV369euHtbW1osWSkJD4iF/Dx0pC4jcgPT0dNTU1RYuRZygrKzNp0iTatm3LwIEDOXbsGJs3b6ZIkSKKFu2bWbhwIRMmTJCUal/J06dP8fLywtXVVdGi5DgfW5L8rotfIyMjBg0aRIkSJTA1NRXjRf3zzz94eHhgbW39Q8qB4OBgjIyMUFNTw9/fH09PT+rWrZuTTZCQyHFkMhkqKiqsXr2a+/fvY2BggJmZGVu3biU4OJj+/fuL5374bWRmZuLr68v69euZP3++IkSXUABJSUlcv36dmjVrMmPGDIoWLQr8v7LL1dWVixcvoqGhkWuKNfmY5u3tzaFDh/Dy8qJ06dLUqFGDKlWqYGVlReHChXOl7l+d169fM3fuXJ4+fcqdO3do0KAB7u7uohW/PJOohIRE/kCyFZeQ+ElIS0v7rRRrcqpVq8atW7eoUqUKNWvW5Pjx44oW6Zvw8/Pj1KlTjB07VtGi/DQ4OTkxderUn1IRKTcCf/v2rYIlyf+0atWKihUroqKiwqNHj5g5cya+vr5UrlwZ+H6l4/3795k9ezaenp6EhYVRvnx5Ro8eLR4PDg7Gz88vR9ogIZGTyBfJ7u7urF+/ni1btjBy5EisrKxwc3MjICDgi9fp6upy+fJlEhISfluF/e/GpUuXWLp0KfXr1xeVapCl7IqPj+ft27eMHDmSJk2a5Los48ePJywsjOvXrzN16lQMDAzYvHkzvr6+wP+PjRJfT3x8PGpqauzcuZPevXtjbW3NH3/8ga2tLV5eXqioqEj3VUIiHyG5gkpI/CT4+vrSrVs3nj9/rmhRFMaVK1cYPHgwbdu25a+//kJXV/eL5yamy/CJSuF9ciapmTI0VJQpoqVCjYKaaKvl3Z6CtbU1ZcqUwdHRMc/q/Jl5+PAhHTp0wN/f/6dJ8pCens7r168pW7Ysqqqq+Pj4cObMGSZPniwtcP+F+Ph4Jk+ezJMnTxAEgfr169OrV68fXgTKZDK2b9/OhQsXsLGxoWXLltmOL126lPbt21OzZs0fqkdCIjdISkpi7NixmJubM3jwYNE6pUqVKpw5c4YSJUp8ck1cXBzt27endOnSzJ49m6pVq+ax1BJ5TVJSEn379sXS0pKRI0cC/+8CLP9f7umQnJyMlpZWrlo4LVy4kKJFi1KmTBnKlSuXaxZyvyPbt2/n+vXrbNq0ieTkZK5fv8779+/p16+fokWTkJD4AMkVVELiJ+F3cwX9HE2bNuXhw4dMnDgRc3Nzdu7ciYWFRbZzQhLTuR6WzMu4NJSAjA+2Dl7EwJWQJMrpq2NRVItiOrl7P1++fMmxY8fw9/fP1Xp+JRwdHZk+ffpPo1QD2L17N5cvX2bQoEE0bNiQ0qVL07t3b1GpJrlrfB49PT2KFClCnz59qFGjBtHR0RQqVIikpCQ0NTVRVlb+ZndQQRBQVlbGxsaGP/74Q4zV9mFsu1GjRqGvr58rbZKQ+FG0tbWxtrZm5cqVJCcnEx8fT3R0NKamppQoUeKTbyI9PZ2rV69ibW2NqakpM2fOpG/fvvTv31+KtfYLo62tTcWKFYmIiACy93GZmZmoqqqipqbGnDlzOHfuHOfPn0dDQyNXxqO4uDji4+MJDw/n1q1bJCYmEh0dTZs2bZgyZUqO1vU7kZmZSXp6OocOHWL27NkAaGlpUahQIapUqSKeJ33nEhL5A8liTULiJ+HevXvY2tri7e2taFHyBYcPH8bOzo7hw4fj6OiImpoa3uHJnH+XmE2Z9iVUlaCViQ61jbRyTUZbW1uMjY2lmDdfyb179+jSpQv+/v5oaeXec8kNNm7cyL1793BycqJ48eLZju3cuZM2bdp88vffmY8XAtu2bePZs2cEBAQQERHBgAEDsLW1/e5FYHh4OMrKyqioqIgWP4IgiIo3CYn8xvTp01m8eDFJSUno6ury9OlTPD09SUpKQkdHhy5dulCuXLlPEqCkp6fj5+dH6dKl0dHRYfny5bx8+ZL169crsDUSeUV8fHy2sAkf9pmDBg3i9u3bNG7cGF1dXdasWZPj9X/8Pr57946nT5/i4uJCzZo1mT9/vpSp9gcZPXo0S5Yswd3dHRcXF4KDg5k+fToREREsXrxYur8SEvkE6SuUkMgnODs7M3z48C8e79y5M0lJSXko0fdTtWpVLl68mKt1WFlZ8eDBAx48eECjRo04/STwq5VqkGXJdv5dIt7hybkiX2BgIHv37sXLyytXys8rypQpw9mzZ3Ot/A/f+zlz5mBvb/9TKdVkMhkAI0eOFJVq8r8BREREIAiCpFT7CCUlJRISEpDJZMyfP5/t27dTv359HBwc2LBhA8uWLQP4JqWa/L4fPHiQdu3aMXHiRFatWoWnpyfp6ekoKSlJiw+JfElaWhqNGzcmJSWFkiVL0qtXL54/f86QIUNwcHBg4sSJomvdx++wsrIyVapUQUdHhzNnznDu3Dm6du2qiGZIKICPY5GqqKgQHR1N27ZtSUxMxMvLiw0bNlCoUCHOnz8vnpcTdhVyhY6vry+BgYEAmJiY0LZtW7p06YKGhsYP1/E7I39Gq1ev5uzZs2zcuBEbGxvMzMyYOnUq58+fJzQ0VLTulpCQUCzSDFNC4isoU6YMWlpa6OrqUrRoUYYOHUpCQkKO1mFvb8+WLVv+9RxV1Zz33h46dCjq6uro6uqip6dHnTp1uHTp0g+V+eTJE1q0aJEzAv4LxsbGHD9+nGGT/qRjtTKEBr78puvlyrWQpPQcl23x4sU0b978m57Z1atXsbCwwMDAgIIFC9K4cWPu3LnzXfUPHToUJSUljh49mu3vkyZNQklJie3bt39XuTmN/L2/ceMGPj4+/6pczm/IFxWPHj0iKCiI9PSs9+jDSW7hwoUZMmQIIAVv/pC4uDiGDh1KamoqJ06c4MCBA/To0YNatWpRuXJlzMzMePDgAfB19+1DS7R9+/axaNEihgwZgo6ODqdPn2bx4sWSta9EvkVdXZ2uXbuirKyMv78/jRs3ZtWqVTRs2JDWrVvj7u7+xWtVVFR4+fIlI0eOZMaMGQwfPpz27dtnO+dDZb/Er4sgCMTExFCjRg3MzMw4dOgQpUqVQkNDg4EDBxIdHc2tW7eAnMlILe9zd+7cibW1NVZWVtjZ2TFt2jS2bduGubn5D9fxOyN/Rqqqqnh6ejJr1izGjx+PtrY2169fp02bNpw4cSLbuRISEopDUqxJSHwlx48fJyEhAW9vb+7evcuCBQs+OScjIyPX6hcEIdfiNE2fPp2EhATi4uKws7Oje/fuZGZmfnM5udn+L6GkpESRRu3/+8QvkCHAjdCctVoLCgpi//79dOjQ4auviYuLo3PnzowbN46oqCjevXuHo6PjD+34VqxYkZ07d4q/Z2Rk4O7ujqmp6XeXmVs4Ojoya9asn2aHW65Ue/LkCVZWVowcOZI1a9awb98+UlNTPzvJlSa+/4++vj6RkZHExcWJyvygoCDOnj3L0KFD8fX1/abMnfJ7u2XLFpKTk+nQoQNt2rTB2tqaZs2aERERkeObIRISOY2lpSWxsbFMnDiRS5cuce3aNfr06SMe/5yCLCQkhNatW6Ojo8PNmzcxNjbG2tqa9evXi5afkqXm74GSkhKGhobs27cPFxcXAMLCwli+fDmtWrXi5cuX2Nvb8/fffwM5t9kzadIkXFxcsLOzo2HDhshkMoYNG0abNm0A6f37EWQyGUpKSmhqaoqbTXZ2dgwePJg3b97Qu3dv8dy0tDQFSSkhIQGSYk1C4psxMTGhY8eOPH78GMiayKxbt44KFSpQoUIFADw8PDA3N8fQ0BALCwsePXokXr9kyRJMTEzQ09OjUqVKnDt3DgAnJycGDhwonrdr1y5Kly5NoUKFWLhwIYIgiNZPMpmMxYsXY2pqSqFChejduzdRUVEAvH79GiUlJXbs2EGpUqUoXLgwCxcu/Kq2KSkp0b9/f6KioggLCwMgICCAVq1aUahQIQoXLsyAAQOIiYkRrylTpgxLliyhRo0a6OjokJGRkc198Pbt29StWxd9fX2KFi3K5MmTxWt79eqFsbExBgYGNGvWjCdPnojHhg4dypgxY7C0tERPT48GDRoQEBDwicyJ6TJexmWfTMhkMi5uW82yLvWY17Iie/4cRlJsNADRwW+YWduIu0f3sLhjTeY2L8/urZu4cuMWNWrUwNDQkLFjx4pl/Vf75c9BT0+PKlWqcPjwYZYuXcqwYcNEF42pU6dSoEABypYty6lTpz5771+8eAFAv379UFFRQUtLi3bt2lGjRg3xnL///pvKlStToEAB2rdvL7pefIk//viDq1evEh2d1fbTp09To0YNjI2Nv7p9H3L79m0aNWqEoaEhxYoVY+zYseJEbsyYMZ8EKe7SpQsrV64E/v29b9euHf7+/lhbW//rO5GfkC8UduzYwcqVK9myZQumpqbcuHGDtWvXis9T4su0bt2aOXPmkJGRwaZNm+jQoQOHDx+mcOHCTJ48Wcx++C0KSRUVFa5evUqnTp149eoVhQsXxsrKiilTptCsWbPcaoqExHcTFRXF4sWLmTJlChEREdmyKRoZGVGuXDl69eoFfF5BUaxYMY4fP85ff/3F0qVLmTRpEpcvX6ZKlSrcunWLvXv3AnzXZpnEz4Vc8dq4cWPxb0eOHOH27duUKVMGfX19jhw5wubNmz87n/qRepOTk0lPT8fc3JwVK1ZgY2ODpqZmjtXxu2NnZ8fBgwcJDg6ma9euWFlZMWzYMNEqe9iwYezZs0fRYkpI/NZIijUJiW8kKCiIkydPUqtWLfFvR44c4datWzx9+pT79+9jY2PDxo0biYyMZOTIkXTp0oXU1FSeP3/O2rVruXPnDvHx8Xh6elKmTJlP6nj69Cl2dnbs2rWL4OBgIiMjiYiIEBVrLi4uHDlyhEuXLhEcHEyBAgUYM2ZMtjKuXr3K8+fPOXfuHPPmzcPX1/c/25aZmcnOnTspW7YsRYsWBbJ2NGfOnElwcDC+vr4EBQXh5OSU7bq9e/dy4sQJYmJiPnF9nDBhAhMmTCAuLo6AgIBsu2sdO3bEz8+P9+/fU7t2bQYMGJDt2n379uHo6Eh0dDTly5fHwcHhE5l9olL4eNl9Y99mnl44xYjNR7H39EFLz5Cji//Mdk7QY2+mHLlFv8WbOb58FrPmLeDs2bM8efIEd3d30R32v9pvamrKlStXiI2NxdHRkQEDBrBr1y6mTp0KwK1bt6hUqRIRERFMnz6dYcOGfXaXuGLFiqioqDBkyBBOnTolKsPkHD16FGdnZw4dOkR4eDhNmzb9z1TrmpqadO3alX379gFZ7hqDBw/Ods7XPF85KioqrFy5koiICG7cuMG5c+fEANlDhgxh79694sQ+IiKCs2fP0r9//3997wVB4OHDh8yZMwc1NbX/fCfyE5s2bWL79u1UqFCB4sWLM3jwYFq0aMHLly85c+aMosXL94wfP546deqgo6ND27ZtmTt3LlOmTMHBwQE7Oztq1qxJcvK3WZNaW1vj6+tLgwYN6Nq1K/b29qSlpVGqVKlcaoWExI9haGhIy5YtuXz5Mu/evaN58+Y4OTnx6tUrLl26hJ2d3X8ql6tVq0ZUVBSPHj3i2LFj9OrVC29vb9auXYubmxvwbfEKJX5OPla8CoLApUuXGD9+PJcuXcLNzQ1HR0datmxJ8eLFUVJS4tatWyxatOib65Iraq9du8bw4cOxtrbm5MmTODo64ujo+NPEBM7vyENLVKpUCTs7O0JDQwGYO3cuysrKjBw5kv3791O5cmXKli2rYGklJH5zBAkJif+kdOnSgo6OjmBgYCCUKlVKsLOzE5KSkgRBEARAOHfunHjuqFGjhFmzZmW7vmLFisLFixcFPz8/wcjISPDy8hLS0tKynePo6CgMGDBAEARBmDt3rtCnTx/xWEJCgqCqqio0atRIEARBMDMzE86ePSseDw4OFlRVVYX09HTh1atXAiAEBQWJx+vVqyfs3bv3s20bMmSIoKGhIRgYGAiampqChoaGsHv37i/ei8OHDwvm5ubZ7s3WrVs/uV9eXl6CIAhC06ZNhTlz5gjh4eFfLFMQBCE6OloAhJiYGFGuYcOGicdPnDghVKpU6ZPrjr6KExZ5hwuAMOXILWGRd7hgVKaCMGzDQWGRd7iwyDtcmOnpIyirqgoLbocI0z3uCYAw4/Qj8biWQQFhmss2sczu3bsLK1eu/Kr2f4yRkZHwxx9/CIIgCNu2bRNMTU3FY4mJiQIghISEfPbap0+fCkOGDBFMTEwEFRUV4Y8//hBCQ0MFQRCEDh06CFu2bBHPzczMFLS0tITXr19/tqwhQ4YIDg4OwpUrV4SGDRsK0dHRQpEiRYSkpCShcePGwrZt2z573eeer/xZfszKlSuFbt26ib+bmZkJZ86cEQRBEFxcXISOHTsKgiD863s/ePBgQU9PT0hPT/+k/I/fifzGu3fvhG7dugmVKlUSLl68KP791q1bQnR0tCAIgiCTyRQk3c9FamqqkJCQIGzcuFE4fvy4kJCQIOzZs0dYu3atIAiCkJGR8dnr5H9/+fKlsGLFCmHWrFnC3r17hefPnwsPHz4U2rdvL4wfPz7P2iEh8b3cv39fCAwMFM6fPy8MHTpUMDU1FSwtLcW++kvfgJzz588Lbdu2FQRBEJKSkoQ6deoI3bp1ExwdHcVzoqKickt8iXxIRkaG0L17d/Edev36tdCkSRNhwoQJgiBkjfctW7YUFi5cKCQnJ39XHWPGjBHnS5GRkcLly5eFzp07CydPnsyBFkgIQvZ5RGBgoLBy5Uph4MCBQu3atYX169cLjx49ElJTUxUooYSEhCAIgmSxJiHxlRw5coSYmBgCAwNZv359tsyFJUuWFH8ODAxkxYoVGBoaiv+CgoIIDg6mfPnyrFq1CicnJ4oUKULfvn0JDg7+pK7g4OBsZero6KCrqytagwUGBmJlZSWWX7lyZVRUVET3TSCbu5+2tva/xheaOnUqMTExJCUlcffuXaZNmya6LIaFhdG3b19MTEzQ19dn4MCBREREZLv+Q1k/ZuvWrbx48QIzMzPq1auHh4cHkLXbOWPGDExNTdHX1xctmD4s+2vakJr5acyZmNC37J46hLnNTJnbzJSVPRqjrKxCQlS4eI5uQSPxZzUNTXQKFBZ/19LSEuv6r/bv3LlTdPs1MDAgPDyc5s2bf7ENwBefReXKldm+fTtv377l8ePHBAcHM3HiRCDrmU+YMEF85gULFkQQBN69e4ezszO6urro6uoyatSobGU2adKE8PBwFi5cSOfOnT/JuPk1z1fOixcv6Ny5M8bGxujr62Nvb5/t3CFDhrB7924Adu/ezaBBgwC++N4LgsD58+epXr06qqqqX/VOKBL5Dn1aWhqBgYEULFiQ3bt34+joyKRJk5g7dy4A9evXx9DQEJDiqn0tycnJWFtbs3XrVk6cOMHs2bNp166dmGTjSzF65FY4AwYMIDo6mpiYGHx9fXFzc6N8+fKcPn0aZ2dnQArgLpF/SUtLw8TEhNTUVKpXr862bdvw9/dnx44dYj/6XxZnLVu2JC4ujpMnT6KlpcWMGTNITU3Fzs6O9PR07ty5g729PQcPHsyLJkkoGOF/cXkdHR1Zvnw5p0+fpnTp0nh4eLBq1Sr27NnD6tWrsbW1ZeLEid/stnnkyBEuXrxISkoK+vr6ABQsWJCmTZtiYGAgjttSv/vjfDiPWLt2LW/fvqVq1aokJSVx7949Ll++zNu3bwHpfktIKBJJsSYhkQN8OOiVLFkSBwcHYmJixH9JSUmi217//v25evUqgYGBKCkp8eeff35SXrFixQgKChJ/T0pKIj4+XlSslSxZklOnTmWrIyUlBRMTkx9uR7Vq1WjcuLGYacje3h4lJSV8fHyIi4tj9+7dn7gy/pvyoEKFCuzdu5f379/z559/0rNnTxITE9mzZw9Hjx7l7NmzxMbG8vr1a+Dbg+lqqHzajRkULc5Ql304Xg4Q/82/+RaDIsW+WI7aZ8qBf29/YGAgtra2rF27VnT7LVy4MAYGBt/Uhs9hZmbG0KFDxVh+JUuWZOPGjdmeeXJyMhYWFtjb25OQkEBCQgKurq6flDVw4EBWrFjxiRvof7XvY+zs7DAzM8PPz4+4uDicnZ2znTtw4ECOHj3Kw4cP8fX1pVu3buKxz733np6epKSkiAq0nHoncgv5wnbEiBEMHz6crl274uLiQvXq1dm+fTseHh4sX75cwVL+nCQkJPDgwQNu3brFhg0bOHv2LOnp6aiqqhIcHPzZPkb+Xty6dQttbW3mz5+Pi4sLvXr14saNG2LwbrlCWwqgLZHfkCvr9+/fz5gxY+jSpQs2Njb8+eef/8feXYdFlb4NHP8O3aUoqCihKCY2Jli4YmF3rbk2axdgodjdiq1r7JrYrrG6xtqtGKiIiiDdzLx/8M75OSYqMKDP57r2WmfmzDn3mRlmzrnP89w3mzdvJjQ0NENTOJXrmTRpEt7e3ty7d4/WrVuzYcMGUlNTWbJkCVOnTkVXV5eaNWtm6T4JOYNMJkMul1O2bFlmzJjBmTNnePPmDaampvj4+LBu3TrGjx9Phw4dVJIxGf29PXPmDH/++SeQXmt2xIgRLF26lOHDhxMeHk7dunUB8b2bWZTv0ZAhQ/Dy8sLIyIjatWszbNgwnj17xqBBg3j8+LF4vQVBjcRfnyBkst69e7Ns2TLOnz+PQqEgLi6O/fv3ExMTw7179zh+/DhJSUno6emhr6//0R/B1q1bs2/fPv755x+Sk5Px9vZW6Qrar18/xo0bJxWvDwsLY/fu3ZkS/927d/nnn38oVaoUADExMRgZGWFqakpISIjUZSyjNm7cSFhYGBoaGtIoHg0NDWJiYtDV1SVPnjzEx8czduzYb4o3n74mWu+dc1dt1Z3Di/14+yI9ORn79g23T3y8aQCADDDT+fjX4ef2Py4uDplMhqWlJWFhYSxevPiD2mgZdffuXWbPni1ddXz27BlbtmzBxcUFSH/Pp02bJhXzj4qKYvv27Rla9+DBgzly5MhHi7d/zfsbExODiYkJRkZG3L17l6VLl6o8XqhQISpXrkyXLl1o1aqVNDruU597b29v3NzcpKRJZn0msoLyZGP58uW8fv2aI0eOMGjQINLS0tixYwdly5bl5MmTeHl5qTnS3EehUFCwYEEKFy7M5s2befz4MVZWVgwYMIAmTZpgZGT0wXPS0tKkz425uTnR0dHs3LmT1NRUSpcuzeTJk7l165bUUU0QciLl7/+SJUuYO3cuFStWxNHRkZCQEMaMGaNyge1zlMcG7u7uLF++nOLFixMREcGZM2cYM2YMFy5cYPTo0cybNw9zc3Np5LjwY1N+vjw8PPDx8cHQ0JDevXvz8OFD/P39qVevHmlpacyYMYMFCxYAGR9lPWzYMPr06UPfvn3x8/PD1NSUu3fvcuXKFWxtbb/7Qq+gSvleFixYEDMzM3bs2MHQoUNxcnJi+vTpzJs3T6qNLAiCeojEmiBkskqVKrFy5UoGDhyIubk5RYsWlaYzJSUlMXr0aPLmzYuVlRWvX7/+aNHYUqVKsXjxYjp27Ii1tTXm5uaYm5tLI9aGDBlCs2bNcHd3x9jYGBcXF86fP//NMc+YMQMjIyMMDQ1xd3enR48e9O3bFwAfHx8uX76MqakpjRs3pmXLll+17oMHD1KqVCmMjIwYMmQIW7duRV9fn65du1KkSBEKFixIyZIlpQTS1ypjoYfy+qrygLB6xz44uf7CmgFt8Klpy9JujXh249Jn12NvovPR+z+3/yVLlmTYsGFUq1YNe3t7ihYtqtKN62sYGxtz/vx5qlatiqGhIS4uLpQuXZrZs2cD0KJFC0aNGkX79u0xMTGhdOnSn+ww+j4LCwvq1av30QPmr3l/Z82axebNmzE2NqZ37960a9fug2W6devGjRs3pOlL8PHPfZ06dUhKSqJkyZLScpn1mcgKMpkMhUJBdHQ0lSpVAqBJkya0atWKvXv3cu7cOQwMDESB8G+gTFqOHDmSefPm0b9/f0xMTHB2dqZnz57SNKN3R1X06NGD169fA+mNPwYNGsTZs2dZsGABe/bsYfLkyZQvXx4NDQ0xNUbIsWQyGa9fv8bQ0BArKytu3LjBrFmz2LhxI05OTpQoUQL4ulG7FStW5MqVK0yePJlVq1bh5ubGpk2bcHBwYPv27WhpafHs2bNvvggk5E5aWlr88ccfxMbGMnnyZBwdHTly5AgXL15k6NChbN26VWp09KXPm0KhwMrKCjMzM16/fk2tWrUYP3488+fPZ8eOHVIzrZwy2vxH888//2BlZYWTk5N0X8GCBdmwYYNUakS89oKQ/WQK8ZcnCLnCkiVLuHHjxgejhATYcO05XZ1t8D4ZhL7x10/DdDTVoaW9yTdvPyIigmLFinH58mWKFCnyzev5EZw6dYrOnTtLUz4/Ri6XU7FiRXx8fFSmi+ZEu3bt4v79+4wcORKAixcvMmHCBPr164eHhwc6Ojo0bdqUgQMH0rBhQzVHmzspFApkMhnPnj1j7ty5eHp6Urp0aSwsLLh27RpHjx7Fzc2NsmXLoq2tDaR3TnZwcKBmzZpMmTIFV1dXdu/ezZ07d7h+/TrVq1eXOvMKQk729u1bLl26RKVKlejXrx9ubm44OjoybNgwrly58k3r3LlzJ8eOHcPHx4f8+fNz+PBhjh07xv3795k9ezZ2dnZiJOdPKiEhAQ0NDbp37869e/ewtLSkXbt2ODs7s3jxYhYtWvRBLdZP2b9/P/PnzwfSRxE7ODhgb29Py5YtcXR0zMrd+KnFxMRw7do19PT02LBhA4UKFaJTp06sWLECLS0txo8fT1pamrjQJwjZTEvdAQiCkDEpKSnSSaWg6tXpfeSxsf2mpJqWDKpZZewg8lPmzZtHy5Ytf/qkWkpKCvPnz6dXr16fPWnbtWsXGhoaNG/ePBuj+zaenp5ER0fj7++PnZ0dbdu2pWXLlpw6dYrly5dTuHBhYmJiRFLtOyg/KzY2NsyYMYP//vuPDRs2cPnyZdLS0qhbty7lypWTRuwqFApKliyJQqGgZ8+eeHl54eLigre3tzSKUjlKTS6Xi5ozQo60bds2nJyccHJyon79+kB6/capU6cik8mkUePfcoLcqlUrWrVqxY0bN1i/fj23b9+mTJkyjBgxAplMRkREBHny5BF/Hz8R5QUMfX19Dh48yLNnz7h8+TIAzs7OJCUlMXjwYCmptmPHDgoVKvTZkePu7u5Uq1aNqKgowsLCWLNmDTt27KBq1ao4OjqKz1cWMTY2plChQnTr1g1HR0eioqIYNWoUGzZsoGzZsowfP14k1QRBDURiTRByCZFY+7jq1asTGRmJ34JlRMsg9SvG4GrJoG5BQ6wNvv11jYyMZMmSJVy4cOGb1/EjuHPnDpUqVaJcuXIEBAR8cjm5XI6Pjw/Tp0/P8SMmlCcF2traFC9enOHDh3Pv3j28vLx49uwZV65cQUNDA39/f+DbToAFVRs2bCAwMBAnJycePnyIg4MD7dq1U+mIXKRIES5cuIC/vz87d+7k119/ZeTIkTRs2JB69eoxd+5cdHTSp3aLkzohJ4qIiGDt2rUkJSVRsGBBatasSbVq1ahevTp//fUXhoaG0vfjt36GX758ib+/P/ny5WPIkCGUK1eOvXv3smjRIjw8PBg6dKj4+/iJvPt7W7FiRaKiojh27Bj6+vqULl0aExMT+vTpw6VLl+jZsyfa2tocPXr0s8kxbW1tLCwssLCwoEiRIlSoUIFevXpJJR7E5yvrJCUl8ebNG06ePAlAhQoVmDRpEs7OzoSGhmJlZSWVsMjpx1qC8KMQU0EFIZeYNm0aUVFRTJ8+Xd2h5FiXwxI4HhKXgeSagpTERKqYKmhYsvB3bXPSpEk8evRIqqMnfN7WrVuZN28e//77b44+2Ltx4wbGxsYYGRlJI9SePXtG9+7d0dHRYe7cuVL9I0AcvH4n5esXHh7Oq1evuHXrFpMmTcLPz48GDRqgp6eHQqFg06ZNzJ07l7i4OKZMmULr1q2ldQQFBeHt7c3s2bOxtv50B2BByClCQkI4cuQIJ0+e5NGjR9jb21OpUiXq1KmjUn/yW124cIHKlStz5coVVq5cSWJiInFxcejq6tKyZUtatGghLgj8ZJTv9+XLlxk3bhwXLlygT58+DBkyBHNzc1q1asXNmzepUqUK27Zt++R6wsPDGT16NLVr16Z8+fI4ODjw5MkT6tatS2hoaDbu0c9H+XvZsWNHihcvjpmZGfv27SMtLQ0fHx9cXV0/WFYQhKwnLiUIQi4hRqx9WQVLfTo5muJoqoOmjA+6hWrJQFMGjqa6GN0+wYTurUlJSfnm7UVHR7Nw4cIc1b0yJ0tLS8PX15dJkybl+AO9+Ph4GjZsSKVKlaTkjY2NDceOHaNSpUpUqFCBU6dOScvn9P3J6ZSvX548eShWrBh//fUXS5cupWnTpujp6UnLdO7cmdatWxMbG8uKFStU3oPbt2+zbt06rK2tRcMCIUdLS0sD0guOt2rVipUrV7Jz507q1Kkj1UODby9ArnxelSpV8Pf3x9/fnxIlSjB48GD++OMPhg8fzsiRI5HL5SKp9pPR1NREoVBQoUIFpk+fzpgxY5g8eTKJiYnUrFmTwoUL8+TJE1JTUzl58iQKhYJhw4Z98J2akpKCpqYm27dvp02bNjg7OzN+/HhGjBgBIL6Ds5Dy73v48OHs37+fXbt20bx5cwICAnB1dSUsLIz58+dTsWJFAgMD1RytIPw8xIg1QcglJkyYgLa2Nt7e3uoOJVeIT5FzPSKRsIQ0ktLk6GpqYKmvSVkLPQy0NVAoFDRu3Jjy5cszderUb9qGn58ft2/fZuPGjZkc/Y9pw4YNUjIkNySiVqxYgb+/P6VKlWLhwoUqNfSCgoIoWrSoGqP7ce3YsYOdO3eyefNm4MOk5Zs3b6QOd7NmzaJWrVrUr1+fkSNH8vTpUzH9SMgVYmJimDJlCjExMYSEhLB7927pMeX0u8wYbXL69GmioqKoXLky+fPnB9K7Ci5cuJDly5djZmb2XesXcreEhARiYmJo1KgRbdu2ZeTIkdJn7tmzZzx+/BgnJyfy5s0r3f+xz2VkZCQJCQlitHA22717NzVq1CBv3rwEBQWxevVq/v33XxwcHPjll19o1qwZurq66g5TEH4KosaaIOQSKSkpGBgYqDuMXMNAWwOX/J9+vWQyGQEBATg7O+Pu7q4ydD4jYmJimDdvnlTfQvi8lJQUJk6cyKpVq3J0Uu3dE9qOHTvSp08fvL29qV+/Pv7+/pQsWZL27dvz999/qywvfD/la5kvXz6Vk7t3Hzt37hxv376lcOHC9O3bly5duuDt7c0///zD6tWr0dDQEFPbhBxN+fncsmULsbGxlChRgpCQEADOnDnDo0eP6NKlC5A5I2Fr1aqlsu3evXvz559/Mm/ePJFUE9DX1+fy5cvky5ePUaNGSfcfPHiQXr160b17d2rXri3dr/wuPnz4MKdOnUJXV5d8+fJha2uLiYkJRkZGGBsbq2NXfkrNmzfn2LFjbNu2jfv371OlShX8/PyoXr06AA8fPiQ+Pp4yZcqoOVJB+PGJxJog5BJiKmjmy58/P6tXr6ZLly5cu3YNc3PzDD936dKl1K1bFycnpyyM8Mexfv16ChcujJubm7pD+STlCUNCQgKLFi3CwMCAunXrMmnSJNzc3BgxYgT58uWja9eumJubo1AoRFItEylfy9q1a/P06VNSU1PR0tKS3pe7d+8yePBgnJ2d+fvvv2ndujWtWrVi1qxZKiMoRFJNyMmUn88DBw4wd+5ctmzZQr169QA4dOgQkZGRdOnSJdOS9sq/jcWLF+Pr60uDBg14/vw5Wlpa0t+Zvb29uEjwE6tRowaxsbEsW7aMfv36sXTpUmbNmsWiRYvw9PRUWVb5GVHWV9PS0uLevXtcvnyZmJgY5s6dKxJr2UihULBo0SKqVavGkCFDsLe3R1dXl5CQEHr37s1///3HhAkTRGJNELKBmAoqCLnE4MGDcXBwYMiQIeoO5YczePBgXr58yR9//JGhEQJxcXE4ODhw9OhRSpcunQ0R5m7Jyck4OjqyadMmatSooe5wvsjT05OKFSvyzz//EB0dTadOnejXrx8KhYKXL19iY2MDiKLAWUF5cp+cnCx19lTq3Lkzbdu2xczMjFGjRlG1alX27dvHr7/+yuDBgzEyMlJT1ILwdRQKBbt27eL06dPs3r2bCxcukCdPHmrVqoW/vz/Vq1fP9ETXkCFDqFevHs2aNWPx4sVcuXKF4OBgEhISGDBgAB06dMi0bQm5h3IEZUpKClpaWowYMYLjx4+zcuVKzM3NOX36NE2aNCFPnjzSsnK5nGbNmrFv3z5pPa9eveLJkydUrVpVjXvzc1F+R0RGRmJoaKhy8T0qKkrqmi0IQvYQI9YEIZcQI9ayzowZM6hcuTJr166lR48eX1x++fLl1KxZUyTVMmjNmjWUKFEiVyTVtm/fjr6+PhMmTKBOnTq4ubmxbds2zp8/z8CBA6WTBpFUyxrKRIIyqaYctRYeHk6BAgWoU6cOjRs3ZuvWrRQpUoRbt25hbm4ukmpCrqA8Ef7vv/8wMTFBT08PKysrBgwYQFRUFEWLFpWmcGVWUk2ZDJk/fz4AO3fu5OrVq3Tq1Al7e3uio6MZMmQINWrUYOXKlbRt21aMbvmJKJsZaGtrExERweXLl9m+fTvm5ubMmjULLS0tacrwhQsXGDVqFHZ2doSFhbF48WIaNWqEvb09+fPnJ3/+/OK3MRspvyM+NqXb1NRUSqpFRERw/vx5GjVqlJ3hCcJPRyTWBCGXEIm1rKOnp8eWLVuoU6cONWvWpFixYp9cNiEhgZkzZ3LgwIFsjDD3SkxMZOrUqezcuVPdoXxSYGAgpUqVokiRIpQsWZIKFSqwfPlySpUqxbRp01i9ejXr168nT5480nPEiUPWe/LkCevWraNTp04ULVqUadOmScWxz507h6mpKTExMbRp0wYQyU4h51N+PmfPns1vv/2Gn58fx48fJzk5mdTUVH755Rcgcz/LyqmnCoWCt2/fsmnTJlq1akWdOnWA9OS1XC7H29sbIyMj3r59mynbFXIPmUyGXC7HwsKCo0ePoqGhwdKlS7l//z6zZs2SPkO2trYMGzaMtLQ0bGxs2LJlC0eOHEEmk6GpqUm3bt1o2rSpmvdG+Jhhw4ZRuXJl8ubNq+5QBOGHJRJrgpBLiMRa1ipdujTe3t506tSJM2fOfPK1XrlyJVWrVsXZ2Tl7A8ylVq5cibOzM1WqVFF3KB9QKBTEx8ezaNEizM3NGTRoEC4uLkB617ygoCAALl68SNOmTUUX0Gwml8u5cuUKly5donnz5jRt2pR8+fLRpUsXfH19mTVrFi1atCBv3ryiPpSQK8hkMhITEylYsCDh4eEA1K1b96PLZcW2LSwsiIiIkKaznz17Fn9/f968eUP37t1xdXVV6X4s/DyU358ymYzk5GTu3LlDt27dsLW1lRK9VlZWNG/enP79+9O9e3d+//13QkJCCAsL4+bNm1JHUPF9nP3eTcYrX/+UlBSePn3KlStXqFSpEomJiWqOUhB+bKLGmiDkEh07dqRx48Z06tRJ3aH8sBQKBU2aNKFcuXL4+fl98HhiYiIODg7s2bOHihUrqiHC3CU+Pp6iRYuyf/9+ypcvr+5wPmvKlCkcP36cTp060aFDB9LS0hgwYAD37t3DwsJCGqEoRkVlv7/++os1a9ZgYGBAjx49qF27NnFxcbx9+xZHR0dAvC9C7nHu3Dnq16+PjY0NPXr0oGXLltmStFdOCT116hRTp04lOjoaExMTXF1dad++Pfb29ty6dYtSpUpleSxCzjdz5kyOHDnCjh07MDExAf73PdugQQO2bdum0vApMTERHR0dkVBTs3d/C1euXKlSK7Z169Zqjk4QfmwisSYIuUSbNm1o06YNbdu2VXcoP7TXr1/j7OzM5s2bP+hguWTJEgIDA1UK9gqfNnv2bM6ePZtjp4G+f1X93Llz+Pr6Urx4cQYPHoy1tTVv3rzBxMQEMzMz6cRUyHofS5StWbOGDRs2ULFiRdq1a0flypXVFJ0gfL3bt29jampKwYIFSU1NZf/+/WzYsIEbN26gqanJrFmz8PDwyJZYFi9ezLFjxxg4cCB169bl7du3DBs2jF27dnHu3DkpYS38fN797h07dixDhw4lX7580uOxsbHUrl2b8uXL061bN0qXLo2FhYW6whWAkydPcvnyZX777TeuXbvGhQsXePnyJYcOHWL06NG0aNFCHLsIQjYQiTVByCU8PT3p1q0bLVq0UHcoP7wDBw7Qt29frl69Kh0wJicnU7RoUbZv3y66XmVAbGwsRYsW5ciRIzmyELYySfb27Vs2btyIoaEh5cqVw9HRkeHDh/Pq1Su6d++Op6enukP96Sjfm/v37zN79mwMDAzQ0tJi5syZvHr1ikmTJqGpqcmCBQvUHaogZEhkZCR9+/alSJEiODs707FjR+kxhULB9u3bKVWqFKVKlcrSaXTvdt19+vQpRYsWZdmyZcyePZv69eszadIkLC0ts2TbQu7x7kWk9y9yhIeHM3/+fK5du4aOjo40vbBq1aqMHz9eLfH+7N68eUOTJk2oUKECz58/x8jIiEuXLjFixAh69eoFiOm5gpAdRGJNEHKJxo0b89tvv9GkSRN1h/JTGDJkCC9evGDbtm3IZDJWrlzJzp07OXjwoLpDyxWmT5/O1atX2bp1q7pD+awWLVpQunRpHj9+TGJiIjt27ABg0aJFREZGihMFNWrcuDGtW7fmwoULxMfHs27dOiIiIrCwsCAxMRE9PT1xsiDkeMrERHBwMGfOnEEmk9GhQwdSUlLQ0NBQ60iSI0eOMGPGDHx8fKhZsyaQXs81OjqaPHnyiCnWwidFRETw5s0bYmJiePjwIUZGRnh4eIjvZDV5+PAhd+7coVixYhQvXpwXL14QHR2No6OjeD8EIZuI5gWCkEuI5gXZy9/fnypVqhAQEECXLl3w8/Nj06ZN6g4rV4iOjmbOnDmcOnVK3aF81sGDBzE3N2fy5MlUrFiR2bNnA+nNCgYOHCgtJ04us19QUBDGxsb06NGDFStWsHTpUiA94VmvXj1q1KgBIE4YhBxPJpPx6NEj7O3tVRoDvPt7rq7vmMDAQGrUqEHNmjVJSUkhMjKSgwcPEhAQwMaNGylQoEC2xyTkTMrP6K1bt9i2bRvPnj1j/PjxVKxYkVKlSqGnpweI72R1cXBwwMHBAUh/r6ysrMTfryBkM/HtJwi5hEisZS89PT02b97MqFGjmD17Ng4ODlSvXl3dYeUK8+fPp2HDhpQoUULdoXwgLS0NgLdv32JkZISRkRFdunShRYsWuLm58fz5c7y9vQkNDZWeI5Jq2ePdAfRyuRwDAwNcXV2pXr06zs7OPHjwgI0bN1KyZEk1RikIX+f58+e0bNlS5T65XK5yO7u/Y5R/a40bN+b48eOcPHlSqsf0xx9/MHToUHFSLnwgLkXO7D+PYF67BZqVGvFvrC5nQ2PpP/R3goOD1R2ewP8SoO8mOMXkNEHIHmLEmiDkEiKxlv1Kly7N+PHjGTVqFPv371d3OLnC27dvmT9/PufOnVN3KB+Qy+Voamoil8vp168fkyZNIioqinPnzjFu3DgABg4ciIuLC9bW1mqO9ue1YMECTExM6NGjB2PHjiUpKYk///yTdevW0atXL8zNzUUjCSHXePXqFWlpaYwfP566devi6uqq8tlVJtmyc6SPTCZDoVBQv359wsPDGTZsGPr6+rRo0YLly5cTHh7OuHHjaNmyJRUrVhTT+35yoXEpnH2VwKPoZOzd25Coo4tDtUI8S4PQV4nY9xjHhWQDdOJSsDYUx6nq9G6SXqFQkJSURGJiImZmZuoLShB+EuJXUhByCZFYUw9zc3MMDQ05cuSIukPJFebMmUPz5s0pWrSoukP5gPLEcOfOnRQqVIjixYszatQounfvTosWLWjbti3a2tr4+PgA4ipvdlJeZY+Pj+f+/ftUrFiRWrVqMXHiROzs7Ni4cSMdOnRg5MiRwIdJCPFeCTlVxYoV2bRpk1Srs127dty8eZPw8HASEhLQ0NAgPDyce/fuqSU+Dw8PatSowZYtW+jfvz+bNm1i1KhR7Nmzh5UrV0q14N4fZSf8HC6HJbDpQRQPopJJU4CWjq7K46n/f9/D2DQ2PYjicliCmiIV3ieTyfjrr7/o0aOHukMRhJ+CaF4gCLlEhQoVWLlyJRUrVlR3KD+NtLQ0SpYsiZ+fH4MGDWLTpk3UqVNH3WHlWOHh4Tg6OvLff/9hZ2en7nBUhIeHkydPHq5fv46LiwstW7Zk48aNACQkJBAVFQWAhYUFOjo6YkSUmowbN461a9fi5+dHt27dPrrM+/WoFAoFycnJ6OrqfnR5QVAn5ec1NjaWly9fcujQIQ4ePIi1tTWhoaE4Ojpy9uxZunbtym+//Zatsb07Eu3w4cMcO3aM5ORkSpcuTZUqVdiyZQsREREsW7ZM1Jr8CV0OS+B4SBypX3GmqCWDugUNqWCpn3WBCRkWFxeHra0t586dk2qwCYKQNcSINUHIJcSItey3bds2LC0tadmyJWvWrKFr166Eh4erO6wca+bMmbRp0ybHJdW2b9/OnDlzuH37NmXLlmXLli1cvnyZ4cOHk5qair6+PlZWVlhZWaGjowMgkmrZ6MyZM5w9e5bExERGjx5Nu3btmDRpEtOmTSMpKemD5d8/uY+JiRGNRYQcS/l5NTIyomjRotStW5dbt27Ro0cPBg0ahLm5Oc2aNaNLly7ZHtu7Iz//++8/jI2Nad++PT179qRMmTJ07NiRly9fEh8fL5JqOZCtrS1Hjx6Vbm/duhVzc3NOnjz53esOjUv5qqTa2xdPGVPBkqSUVI6HxBEanyJGEucAhoaG9OrViwULFqg7FEH44YnEmiDkEiKxlr3kcjmTJ09mwoQJyGQyfvnlF1q1akWfPn3EweJHvHr1ihUrVki1ynISFxcXUlNTWbhwISEhITRv3pzjx49z8+ZN3N3duXLlirpD/GnJ5XKOHDnC8uXLWbNmDcbGxsyZM4cdO3Zw4sQJqlatytatWz/5/OTkZK5cuSIaGgi5gkKhwM7Ojv3791OtWjXq1avH+PHjGTNmDEZGRmqJSTnF87fffmPQoEFUrVoVgICAAOrVq0elSpXQ1xejj3K6devWMWDAAPbv34+rq+t3r+/sq4SvGqn2rlQF/PsyAZlMRmJiIkFBQd8dj/DtBgwYwIYNG6SR+YIgZA2RWBOEXEIk1rLXzp07MTY2xt3dXbpv+vTpBAUFsXr1ajVGljPNmDGDTp06YWNjo+5QJDExMZw6dQobGxv8/f3p1KkTBQsWRC6XY2VlxcGDB6latSqjRo1Sd6g/LQ0NDXx9fenTp480IkahUFC+fHkOHTrE0KFDefTo0Wef7+rqKiVPBSEnk8lk6Onp4eTkhEKhyBEjY5Wj1szNzTE1NeXChQs0a9aMLVu2sGXLFsaPH49MJpM6KotaaznP8uXLGTZsGIcOHaJ69epERUXRs2dPrK2tKViwIOPHj5fev7Vr11KzZk2GDx+Oubk5dnZ2HDhwAEgf3V2xYkXiUuQ8ik4G4PTGpaz3Sh9Neff0YRZ0qINvLTumNyrH0WUz/hdDr2YATHJ1wKdGEY6fPsOl67eoVKkSZcqUwdzcnE6dOhEZGSk9Z/r06Tg4OGBsbEzJkiX566+/pMc+F6fwdQoVKsQvv/wijl0FIYuJxJog5BIisZZ9lKPVvL29Vaa/6OnpsXnzZkaPHq22QtM50YsXL1i7di1jx45Vdygq+vXrp9J0ombNmgAqJ4nTpk1j165dgDhhzG5nzpzB398fgBo1aqjUl1K+P927d5c+V++/P+8mJm7evMnMmTOzI2xB+C7JyekJi5w4tTIxMZEBAwZQqVIlNm3aRGpqKnPnzqVNmzZ4e3uTlJSEhoaGGLWdgyxduhRvb2+OHTtGpUqVgPTvTS0tLYKCgrhy5QqHDx9m1apV0nPOnz9P8eLFefPmDSNHjqRnz54oFAqaNWvG48eP2XvuKspP55X926jQpC0AOvqGtJ20GO+TD+k2fzPnd6zl1t+BAPRdtQcA75MPmXgmGNtylfnv2RuKFy/OwoULuXv3Ls+ePcPX11eKw8HBgdOnTxMVFYWPjw+dO3cmNDT0i3EKX8/Ly4sFCxaIC1CCkIVEYk0QcgmRWMs+u3fvRltbGw8Pjw8eK1WqFBMnTqRTp07SCdLPbtq0aXTv3h1ra2t1h6KiePHiXLly5YNpKDKZDE1NTekAXTnN6f1Ok0LWWrNmjcpoNIVCIRVI/9hInvffH5lMJiUnnj9/zr59+7I2YEH4TqGhoSxYsCBHJgfS0tLQ09Pj0KFDeHt78/DhQw4cOEBycjKNGjUiPDycYcOGqTtM4T1HjhzBxcWFMmXKAOllGQIDA5k3bx6Ghobky5cPLy8vlSn1RYoUoXfv3mhqatKtWzdCQ0N59eoVurq6tGvXjp1bt5CqgFcP7/L2xTNK1EofuW9fqQZWxUqioaGBtWMpyjZsweNLZz8aV6oCrEs64+fnR69evcifPz9Dhw5Vqf/Wpk0bChQogIaGBu3ataNYsWJcuHDhi3EKX69y5coUKlRIupAoCELmE2cRgpBLiMRa9lAoFEyaNOmD0Wrv6t+/P9bW1kyYMCGbo8t5nj59yqZNm3LkdEovLy+qVq3KsGHD2LJli3S/8qRW+f7mxJEjP4O+ffvy4MEDBg4cyO3bt1USZRlx8+ZNBg8eTLly5ejevTu//fabGHUo5Gh//PEHd+7cyZHfOcpktoWFBdHR0UyaNIm8efPSu3dvfv31VxYuXMj9+/d5/vw5MpksRyYHf0ZLly7l/v379OrVC4VCQXBwMCkpKVhbW2NmZoaZmRl9+/bl9evX0nOsrKykfxsYGAAQGxsLQLdu3Ti5ZxsKhYIr+7dRtkFztHTSOy4/vXGJlX08mVK3BL617bmwcx1xkRGfjO3169eMHj0aIyMjjIyM6Nq1K2/evJEeX79+Pc7OzlKcN2/eVHn8c3EKX8/Ly4u5c+eqOwxB+GGJxJog5BLJyckisZYN9u3bJ02J+BSZTMaaNWvYuHEjx48fz8boch4/Pz/69OlDvnz51B3KB4yNjfntt9/o2LEje/bs4dmzZ6SlpUkntSIJo15VqlRh3rx5WFlZMXLkSA4dOqTyuHI66PsSExP577//8Pf3R0tLiyVLlvDy5Us6d+4sRh0KOdqOHTto3bq1usP4KIVCQUJCAvv37yclJYVXr14xbtw4LCwsCAkJwcvLi8ePH7N//34g/XdQjNpWv/z583Ps2DFOnz5N//79sbGxQVdXlzdv3hAZGUlkZCTR0dHcunUrQ+tzcXFBW1uHJ5f/5erBPynfuI302B/j+uFU+xdGHbiK76lHVGnVDaQE64fJ4g3TxnPz5k2KFStG586d8fPzQ6FQIJfLCQ4Opnfv3ixatIjw8HAiIyMpXbq0SNhmoebNmxMSEqIyKlAQhMyjpe4ABEHIGDFiLespR6spO4F+jqWlJQEBAXTr1o2rV6+SJ0+ebIoy53j8+DE7duzI0fXm8ubNS7t27QgICGDSpEmkpaXRvXt3ateuTWhoKAULFlR3iD+1smXLUqhQIaysrNi1axePHj1CX1+f7t27o1AoCA0N/WCKcVJSEitWrOD+/fts2LBBuj8tLS1HFIMXhI8JCQnh9u3b1KtXT92hfJRMJkNHR4dp06axefNmmjVrxqhRozAzM+Pt27ekpKTg6+tL48aNUSgU3L9/n4MHDzJkyBB1h64WcSlybkQk8johjaQ0ObqaGuTT16SshR4G2tmb4C9QoADHjh3D1dUVPT093N3dGTZsGJMnT8bIyIjHjx/z/PnzDHcLbdquE7tnjEFTSxvb8i7S/UlxseibmqGtq8ezm5e5dvBPirm4AWBongeZhgYRIcFYFnFAEwWhwY8wMzNjwoQJhIaGMnjwYPLmzYuGhgaxsbHIZDIsLS2B9C60N2/ezPTXRvgfLS0tBg8ezLx589i8ebO6wxGEH464tCsIuYRIrGW9gwcPkpCQQIsWLTK0vLu7O23atKF3794/5VXWyZMn079//xyfVFQW3K5duzbly5fH19eXLl260LJlSxISEtQd3k/PwsICExMT9u7dS2JiIkeOHKFZs2ZUrlyZwMDAD5Y3NTXF39+fXr16qdwvkmpCTvbXX3/RtGlTdHR01B3KJ2lqatKzZ0/69u1LkSJFePDgAX/++ScmJiYMGTKETp06cfr0aYYMGULx4sVxcXEhKSlJ3WFnq9C4FHY+imbJrQj+CY3n9tskHkancPttEv+ExrP4VgQ7H0UTGpeSrXEVLlyY48ePs2PHDuzt7UlOTqZkyZKYm5vTunVrlaYAXzK0V3deBd3BuZHq6MrmY/w5utQfn5q2HFsxizIN/jeyX0ffgDo9vVjeozETazvw+NpF2no2JTExkY4dO7J06VIcHBykkeKlSpXCy8uLatWqkT9/fm7cuEGNGjUy58UQPqlnz54cOnSI58+fqzsUQfjhyBQ/49mgIOQijRs35vnz51y/fh0XFxeKFSvG+vXr1R3WD0ehUFCtWjW8vLxo165dhp+XlJRE1apVGTBgAL17987CCHOWBw8eUK1aNR48eIC5ubm6w/miuLg4DA0NiY6O5saNG5w8eZJatWpRq1YtdYf201PWBdLQ0KBw4cIEBQVx7tw53r59y6BBgz5YVqFQiCmfQq7j5ubG77///tkyAznFvHnzuH79OuXKlaNChQrUqlWLoKAgVq5cSXh4OCVLlqRr167kzZtX3aFmq8thCRwPiSM1A2dOWjKoW9CQCpb6WR9YJktISMDCMh8DNh0lb2GHb1qHSXw4LR1MsbKyIiYmhgEDBnDgwAH69evHy5cvWblyZSZHLWTU0KFD0dPTY/r06eoORRB+KCKxJgg5XL169aQ6XjKZDDc3t5++rldWOHLkCIMHD+bmzZtfPfLl9u3b1K5dmzNnzlC8ePEsijBn6dKlC46Ojjm6gcO7UwOV3SaFH4tcLhdJNiHHe/XqFcWLF+fly5fo6empO5wvevf7MjExkcWLF3Pt2jWKFStG3bp1KVu2LMbGxh8s+yP7mqSa0seSa7a2tqxatYr69etnQZSZY86cOfy5ey+e87d/1f4qacmgvb0RhUz0ePr0KUOHDkVDQ4OAgACMjY1xdXWlX79+dOjQIfODF77o0aNHVKlSheDgYAwNDdUdjiD8MMTRqCDkcFOnTpW6Ienp6TFv3jz1BvQDUigUTJw4kfHjx3/TdLKSJUsyefJkOnbsSEpK9k7/UIc7d+5w6NChHF1b5/nz55w4cUIqgP/uiZ+4npRzfOq9yMh79Pz5cw4fPpzZIQlCpvvrr7/w8PDIFUk1UP2+fPHiBffu3aNly5b07NmTGjVqSEm195fNbZTdKo2MjNDQ0EBfX1+6vWnTJmm50LiUr06qAaQq4HhIHKHxuee4wNbWlvnz57Nw3hzqFNBH6yvfXmUysZCJHqGhofzyyy9YW1uzY8cOjI2NOX/+PLGxsRQqVChrdkD4Int7e2rVqsW6desIDg7mn3/+UXdIgvBDEM0LBCGHc3FxoUSJEly+fBl3d3fKli2r7pB+OCdOnOD169dfNQX0ff369aNw4cI/RdJm4sSJ/P7775iYmKg7lI9KS0ujY8eOeHh4fLRQeG4+EfzR7N27l8qVK2NlZaXyvmTkPXr06BFjxozhl19+ycoQBeG77dixg/79+6s7jG9ib2/PxIkTsbS0REsr/bQhNTUVLS0tIiMjiYiI4ObNm+TJkyfX1ciKjY2V/v25kWRnXyV808gtSE+u/fsygZb2uaNG7pMnT1Ruy2QyAh9GoKGtjYbG5y88vj9Cz9ramlmzZuHh4QHAzp07CQwM5Ndff6VWrVpSvTUNDQ0x+jgbKRQKGjRowIgRIxg8eDDFihXjzp076g5LEHI98Q0mCLmAcrrdzJkz1RzJj2nSpEmMGzdOOmn4FjKZDA8PjxxdmDoz3LhxgxMnTjBw4EB1h/JJU6dORVtbm5EjR6o7FOEzIiIi6N27N2FhYd+U7KxevTrPnz8nODg4C6IThMzx5s0bLl68mKsTwNbW1mhpaSGXy0lLS5N+K0+cOIGbmxtr1qzB0tJSGiGc2124cIFq1aphZmaGlbU1c8Z6kZqSLD0+poIl53esZVbzKkys7cDuaSOli2rhzx6zsk8LJtVxZHLd4mwd14+bIWHEp8il51+9epWyZctiampKu3btSExMzPZ9/JKEhATCw8OpYGlAz7L5cTDSQqaQo0hVHX2nJQNNGTia6tDJ0VRKqimTZsqk2pw5c9i7dy+1atWiQoUK7N+/n6FDhzJx4kTu3r2LhobGD/P5yek6dOiAl5cX8fHxpKWloa+f++oACkJOJBJrgpALeHp68s8//1CsWDF1h/LDOX36NE+fPqVjx47fva7PJQd+lJFsPj4+jBw5EiMjI3WH8lFnzpxhyZIlbNiwQVz9zuEmTpxIy5Ytv3kUrpaWFk2bNmX37t2ZHJkgZJ7du3fTsGFDqaRDbqahoYGmpiYvX75k6NChrFq1imLFimFqaoqjo+MP05lXU1OTuXPn8ubNG5buOU7QhdOc27ZGZZk7pw4zYOMRhvxxkhtH9nD/bHrtW4VCgduvQxh76AZeO88Q9TKEI8tmcD3if8mzbdu2cfDgQR4/fsz169dZu3Ztdu5ehvzzzz9cvXqVR48esWfDGgq9uUtryxTq2JhQylyXoibalDLXpaa1AQNKWdDS3gRrg/+Nynv39zclJYVXr17RoEEDPDw8uHDhAoMHDyY5OZkGDRrQo0cPYmNjf5jPT07Xv39/9PT0pGPWnDr7QBByG3HWIQg5WFyKnHOv4tnzJIYXVqXZ8ySGc6/iVa58Ct9n8uTJjB07Fm3trJ2mIZPJiI6OztJtZLXLly9z7tw5+vXrp+5QPioyMpJOnTqxcuVKChQooO5whM+4ffs2mzdvZtKkSd+1nubNm7Nr167MCUoQssCOHTto3bq1usPINIcOHaJ9+/bIZDLmzJnDX3/9xZMnT3j79q26Q8s0FStWxMXFBS0tLTQtC1G5ZVceX/5XZRm3HoPRNzbFzLoQ9pVrEHr/JgB5C9tTzMUNLR1djMzzUrPzbzy8dJawhP+Nxho8eDAFChTAwsKCpk2bcvXq1ezcvQxxdHSkcuXKPH36lKNHj7Js2TJ8x45m6/RxnFk2laIxj2lqa4xLfgMMtD99OqlQKNDW1sbX15fWrVtz9uxZNm3axJQpU7h27RpFihShevXqbN++PRv37udWu3Ztrly5go2NjbpDEYQfiqixJgg5UGhcCmdfJfAoOhkZqNT2uB8Jp0PjsTfRoXp+fawNc0fdjpzo33//5f79+3Tp0iXT163slJaUlMSjR4/YuHEjBw8epHHjxt+dTFAXHx8fxowZkyNHXigUCvr27UuTJk1o2rSpusMRPkOhUPD7778zbtw4LC0tv2tdDRo0oGvXrkRERGBhYZFJEQpC5nj79i1nz579YZIGycnJHDlyhN69e9OqVSsgvanS0aNHefPmjZqjyzz379/n999/57///iM6Np7UtFQKllAdWWuUJ5/0b209fZLj4wCICX/NvpnjeHLlHEnxsSjkcvRNzEhK+98FUSsrK+nfBgYGvHjxIov36OvFxMSQkJBAjRo1cHNzIzo6midPnnDv3j3+++8/aTrwl7rCKh9TTjeMjIyke/fudOjQAZlMxi+//ELlypWpVq2ayvN+lm6z6mJvb8+NGzeoWLEiycnJxKXIuRGRyOuENJLS5OhqapBPX5OyFnqfTZwKgvA/IrEm5AilSpVi8eLFuLm5qTsUtftcS/cVvZtT3qM1lVt04UFUMo+jkz9o5f4z8/Pz49GjR6xatSpDy0+ePJkxY8Zkal00uVyOQqHAy8tLKpz/559/oqenx/Lly9myZQsJCQm5rqbF+fPnuXr1ao49QQwICOD27dtcuHBB3aEIXxAYGMiTJ08YMGDAd6/LwMCAOnXqsH///ixJkAvC99izZw/16tXLsVPnv5aOjg5hYWEcOHCATp06ARAXF4dMJmP+/Pk4OTnRrVs30tLScvW0vt9++43y5cuzZcsW/g6HFYvmc/PY3gw99/CiqSCTMWTbKQxMzbn1dyB7/Eejq5l7khNyuZylS5eiq6uLnp4eefPmxcbGBltbW9zd3WnZsqX0/n5t8ktfX58lS5bQpUsX2rdvT0JCAvfu3aNEiRLMnDkTHR0dhgwZIpJq2cDExISTV29z9mU8S25FiAv5gvCdRGJNyBFu3bqVbdvq3r07hQoVYsqUKVmyfltbW169eoWmpiaGhoY0atSIRYsWZejA+nNJtY9RtnIHsjS5Zmtry4sXL3jx4gV58+aV7i9fvjxXr17l8ePH2Nraftc2MuN9GTt2bIaXvXjxIjdu3OCvv/765u19jLKuSEREBHPnzqVw4cK4uLhIHeEqVaqUqdvLLj4+PowbNw49PT11h/KBe/fuMWrUKE6cOJHrEpY/m+TkZLy8vJg/f36mTb/29PRk9+7dIrEm5Dg7duygQ4cO6g4jUyi7Ni5evJgTJ07w7NkzunTpgqWlJe7u7owaNYrq1avTqlWrXJ9IjImJwcTEBCMjI+KvXeXCjrUYmOfJ0HOT4mPRMzJBz8iEqNehnF6/CBlgqZ97Eo1paWn8+uuv3Lx5kz/++IMnT55Qr149du/eTWhoKLVr18bHx+eb1t2uXTuuXbtGmzZtWL58OT169ADA19eXxYsX07x5cw4dOkTDhg0zc5eEj/jSOYfyfnEhXxAyJvdcPhGEXGTv3r3ExsZy+fJl/vvvvwwli0LjUqQfOIVCIXVU+hJlci00PuXLC38HOzs7tmzZIt2+ceMG8fHxWbrNr5GamvpVy0+ePJnRo0ejq6ubqXEkJydz/vx5dHR0mD59OosWLZKSapD+3s6cOZPly5dn6naz0pkzZ7h37x6//vqrukP5QFJSEh07dmTSpEmUKlVK3eEIX7Bo0SKKFi1Ko0aNMm2dTZo04ciRIyQkJGTaOgXhe0VFRXHy5EmaNGmi7lAyhYaGBgqFAiMjIzw8PPD19aV48eLMmjWL5cuXExISQs2aNdm3b5+6Q/1us2bNYvPmzRgbG7Ng9GDKuntm+Ln1+owg5M51Jta2Z93gDpSqm/7+l7XIeRelPkVbW5uKFSuir69PkSJFuH37NqNGjaJHjx4YGRlJo8m+timT8rjWz8+Pnj17Ym5uTmpqKk2bNuXMmTNs2bKFdu3asWzZMmxsbDh69GiGj4W/xenTpylevHiWrT8n+5YL+f5LV+FctXqWxtWoUSPWrVuXpdsQhKwiEmtCjmBra8vRo0dVWpxbW1szcOBAkpPTW5z7+PgwaNAgIL3DkKGhISNGjADS24Lr6ekREREBQJs2bbCyssLU1JTatWtLI+JWrFjBpk2bmDFjBkZGRlItpunTp+Pg4ICxsTElS5ZUGcW0du1aatasyfDhwzE3N8fOzo4DBw5kaL8KFixIo0aNuHkzvajtuXPnqF69OmZmZpQrV44TJ05Iy9avV5f9C6eyrIcH3tULE/H8CQ/OnWBOy2r41rZn9/RR8M5BzLst3X3qFKddh05ERkaqvKazZs36aEv3t2/f0qRJEywtLTE3N6dJkyY8f/78s/vSpUsX1q9fL91et24dXbt2VVkmKSmJ4cOHU7hwYfLnz0+/fv2kk90TJ05QqFAhZs+eTb58+bC2tiYgIOCz78uLFy9o1aoVlpaW2NnZsWDBAmlbykK4nTt3xsTEhLVr1+Lr60vnzp2/+L5cuXKFS5cu0bNnzy8u+7VOnjzJhAkTsLKyonz58lK3pbS0NOLj4xkxYgQvXrzAwcHhq5OB6jJhwgQmTJiQqVNmM8u4ceMoXLhwjm2oIPzP69evmTZtGnPmzMnU9ebNmxdnZ2eOHTuWqesVhO+xb98+3NzcfqiOe8qEioaGBi9evOCXX36hSJEi9OrVS0qWu7u7qzPEb/bkyRPq168PpBd3v3v3LrGxsZz95zS/jfam35r90rLTLoeRt7C9dLvNxEW4D0gfMZ/foQSDNh9j4plgBm89Qa0u/Vlz9o5Up+rd7UD6sczGjRuzYxczLC0tvdHC7du3pdqVhQoVol69ejRq1IiUlPQLuV+bWNPQ0JASZW3atCEpKQknJyfy589PYGAg9evXp0GDBsyaNYvIyEiio6OztLt3rVq1uHfvnnRbeS7yo3v3Qv7XkCsgKjktSy/kHzhwgG7dumXZ+gUhK4nEmpCjvNvi/N9//+XYsWMsWbIEAFdXVykRdfHiRaysrDh16hSQXoS+ePHi0gFAo0aNePDgAa9fv6ZChQpSLZA+ffrQqVMnRo4cSWxsLHv3ptfMcHBw4PTp00RFReHj40Pnzp0JDQ2V4jp//jzFixfnzZs3jBw5kp49e2bogOLZs2cEBgZSvnx5QkJCaNy4MePHjyciIoJZs2bRqlUrwsLCiEuRk5gq50rgdlqMn4Pv6cfoGZuwcXh3GvQfw4Rj98hTyI7ga/+rH/V+S/fnz54xzlt1aP6nWrrL5XJ69OhBcHAwT58+RV9fn4EDB352X1xcXIiOjubOnTukpaWxdevWD5JYo0eP5v79+1y9epWgoCBCQkJUCvW/fPmSqKgoQkJCWL16NQMGDODt27cffV/kcjlNmzalXLlyhISEcOzYMebNm8ehQ4ek9e3evZvWrVtL3SAzavLkyYwYMSLTpzXK5XIWLVpEkyZN8PPzQ1dXV/qcaGpqYmBgQJMmTRgyZAiurq5S8d+c7O+//+bp06c5cprdoUOH+OOPP1i1apWox5ILTJgwgc6dO1OiRIlMX7dyOqgg5BQ/WjdQJWXSRTl1r0OHDly7do169erRrVs3LCwsuHv3rpqjzFzV8+uj9Y0/MVoyqGad8xr+fI6mpiZxKXLKNOtEpI0zPrvPsvDkTWbuOMTuA4eoXLnyN6/73USZjo4Offv2ZdWqVVJpgBcvXjBhwgQUCgVxcXHfvS/Ch86+SvjqpJqSAvj3pRgd/q1yywV14duIxJqQo7zb4tzW1pa+ffty8uRJAKpVq8aDBw8IDw/n1KlT9OzZk5CQEGJjYzl58iSurq7Sen799VeMjY3R1dXF19eXa9euERUV9cnttmnThgIFCqChoUG7du0oVqyYShH0IkWK0Lt3bzQ1NenWrRuhoaG8evXqk+vz9PTEzMyMmjVr4urqytixY9m4cSMeHh54eHigoaFBgwYNqFSpEoGBgdyISB9JVrFpe/I7lEBTS4v7Z46R374EZeo3Q1Nbmxqd+qp0oXq/pXvtLr9x5O8TKnF8qqV7njx5aNWqFQYGBhgbGzNu3Djpdf4c5ai1I0eO4OTkRMGCBaXHFAoFK1asYO7cuVhYWGBsbMzYsWPZunWrtIy2tjbe3t5oa2vj4eGBkZGRytXCd128eJGwsDC8vb3R0dHB3t6e3r17q6yvWrVqeHp6oqGhkeHaWtevX+fff/+lT58+GVr+a2hoaODl5SVdkZXL5VLCR/lj6ubmRoECBaRRjFk5zeF7KRQKvL298fHxybR6WJnl9evX9OjRg/Xr15MnT8Zq3wjqc/XqVXbt2oW3t3eWrL958+bs2bNHOukXBHWKiYnh+PHjP2SHYk1NTRQKBVWrVqVPnz48f/4cR0dH/Pz8qFSpEnPmzKF58+acPn0ayNm/cRllbahN3YKGX51c05KBk0Y0AfNm0rBhQzZs2JA1AWai0LgUdj6KZsmtCB5rmGNTtQ76hR2JM7EipXAZXL2XkVSyFqFxKd81miwtLQ1TU1OGDx8u3Xft2jV69eqFra0t5ubmUvfUO3fuqJQjWblyJUWLFsXCwoJmzZqpdFWVyWQsWbKEYsWKYWxszIQJE3j48CHVq1fHxMSEtm3bSjNhlDMpIP349unTpzRt2hQjIyNmzJjBkydPkMlkBAQEYGNjg7m5OcuWLePixYuULVsWMzOzDy5Kr1mzBicnJ8zNzWnYsCHBwcEqsS1btoxixYphZmbGgAEDPnmRXjkro127dhgbG1OhQgWuXbsmPf6lGR1t27ala9euGBsbU6pUKf777z8Apk6fgW/vjirb2jNjDHtnpo+4TIyJZufEIfi5l2JawzIcXuyH/L3f1YfRycSnyLl79y4NGjTAwsKC4sWLs23bNmmZwMBASpYsibGxMQULFmTWrFnAl2fMuLm5SQ3IHj58SN26dcmTJw958+alU6eMz8x535dmH0VFRdGzZ0+sra0pWLAg48ePl44n0tLSGDZsGHnz5sXOzo5FixYhk8mk4/qAgACcnJwwNjbG3t5epdSL8jPm7++PlZUVPXr0+KZZQ0LuIBJrQo5y//59mjRpgpWVFSYmJowdO1Zq4a6vr0+lSpU4efIkp06dwtXVlerVq3PmzBmVxFpaWhqjR4/GwcEBExMTqaj+51rBr1+/HmdnZ8zMzDAzM+PmzZsqy7/fGh0gNjb2k+vbtWsXkZGRBAcHs2TJEvT19QkODmb79u3SNszMzPjnn38IDQ3ldUIaCsA0//8SVdFhLzG1KiDdlslkKo/HhL9my+jeTGtYBt9admwZ9xtvw8NV4ng/bmXM8fHx9O3blyJFimBiYkLt2rWJjIz84klply5d2Lx5M2vXrv1gGmhYWBjx8fFUrFhR2r9ffvmFsLAwaZk8efKojNJ6N6b3BQcH8+LFC5XXy8/PTyWhaWNj89l4P2bKlCkMGzZMeh8zm5ubm3SgpTzoTEtLk/b7n3/+oUKFCgwePFia5pBTTzyOHDnCmzdv6Nix45cXzkZyuZzu3bvTvXt36tSpo+5whC9QKBQMHTqUiRMnYm5uniXbsLe3J3/+/Jw/fz5L1i8IXyMwMJAaNWpk2ec9p2jRogWzZ8/Gy8uLS5cu0bRpU86ePUvt2rXZsmULsbGxOfo37mtUsNTPcHJNIZdDWioGz26w2T89sdO+fXv27t2bo5P/l8MS2PQgigdRyaQp+GBUk4a2Dmho8jAmjU0Porgc9u0jl97vGpuWlsbatWtxdXVl2LBhaGpqIpPJmDJlCi4uLixcuJB27dqxf/9+xowZw7Zt2wgNDaVIkSK0b99eZV2HDh3i0qVLnDt3jhkzZtCnTx82btzIs2fPuHnzpkq9YKUNGzZQuHBhqUbyyJEjpcfOnz/PgwcP+OOPPxg6dChTp07l6NGj3Lp1i23btkkXpnfv3o2fnx9//vknYWFh1KpV64PmJfv27ePixYtcv36dbdu2qczCeN/u3btp06YNERERdOzYEU9PT1JSUjI0o2PPnj20b9+eyMhImjVrJh2XlvNoxb2zf5MQkz7YIC01leuHdlGhcTsAtvsOQkNTi+G7LzBoy3EenDvBxb9UpyrLgPPPwmnQoAEdO3bk9evXbN26lf79+3P79m0AevbsyfLly4mJieHmzZvUrVsX+LoZMwqFgjFjxvDixQvu3LnDs2fP8PX1VVnmUzNzPuZzs4+6d++OlpYWQUFBXLlyhcOHD0sJvpUrV3LgwAGuXr3K5cuX2bVrl8p68+XLx759+4iOjiYgIAAvLy8uX74sPf7y5UsiIiIIDg5mxYoV3zRrSMgdRGJNyFF+++03SpQowYMHD4iOjsbPz0/lao6rqyvHjx/nypUrVK5cGVdXVw4dOsSFCxeoXbs2AJs3b2b37t0cPXqUqKgonjx5AvyvFsT7U8aCg4Pp3bs3ixYtIjw8nMjISEqXLv3VtSO+xMbGhi5duhAZGSn9FxcXx+jRo0lKk/9/bP9b3jhvfqJe/u8qnEKhIOpViHT73Zbuvqcf03bKUuTyjMU8e/Zs7t27x/nz54mOjpam1H5pn4sUKYKdnR2BgYG0bNlS5bG8efOir6/PrVu3pP2Lior6bALyXe+/LzY2NtjZ2am8XjExMQQGBn7yOV9y69YtTp48meX1uN5NHioUCukAcsKECXTr1o0aNWpQp04devfuDZClNUS+lUKhYMKECfj6+n5wAKxuCxYsICIigokTJ6o7FCEDdu7cydu3b6XPe1bx9PT84IBXENRhx44dtGrVSt1hZBnlb6+2tjZOTk54enqycuVKevfuzfLly1m5ciUymYzff/8dyJm/cd+igqU+nRxNcTTVQVPGB0k2LRloyuDtvSs827mUi3+up2nTpqxevZoePXqwadOmbP09/ZqaYRktZn94sR+T6xZnYv2S7Lh4R2XkzruF55UjhDIqNjaWGzdu4O7uLo1CnzlzJr6+vgwfPpwmTZqQlJTE+PHjad++PRUqVEBXV5dp06bx77//Ssf6ACNHjsTExIRSpUpRunRp3N3dsbe3x9TUlEaNGnHlypUMxwXpx256enq4u7tjaGhIhw4dyJcvHwULFqRWrVrS+pYtW8aYMWNwcnJCS0uLsWPHcvXqVZVRa6NHj8bMzIzChQtTp04daSbJx1SsWJHWrVujra3N77//TmJiIufOncvQjI6aNWvi4eGBpqYmXbp0kUa7yU3yYVfBhRtH9gBw/+xxDM0sKFiyHDHhr7n3z1GaDJ+Cjr4hRhaW1OjUj+uH/1KJK1UBBwL3Y2trS48ePdDS0qJ8+fK0atWK7du3A+nfDbdv3yY6Ohpzc3MqVKgAfN2MmaJFi9KgQQN0dXWxtLTk999//2DZT83M+ZhPzT569eoVgYGBzJs3D0NDQ/Lly4eXl5f0em7bto0hQ4ZQqFAhzM3NGT16tMp6GzdujIODAzKZDFdXV9zd3aURu5D+/Tdx4kR0dXXR19f/5llDQs6X8wv8CD+Vd1uc3717l6VLl2JpaSk97urqSuvWralcuTI6Ojq4ubkxZswY7OzspOViYmLQ1dUlT548xMfHM3bsWJVt5M+fn0ePHkm34+LikMlk0vMDAgKkaXqZqXPnzlSuXJlDhw5Rv359UlJSOHfuHEWLFkVX0/SD5UvUbMAe/9HcPLYPJ9dfOLdtNbHhr6XHP9rSPYN5ppiYGPT19TEzM/vqBMXq1at5+/YthoaGKrUCNDQ06N27N15eXixatIh8+fIREhLCzZs3M9Q2/f33pUqVKhgbG+Pv78/gwYPR0dHhzp07JCQkfHN9j6lTp+Ll5YWRkdE3Pf9byGQyQkJCaNasGXnz5uXAgQM4OjoCsHTpUh49eoS9vf0X1pL9AgMDiY+Pp02bNuoORcWVK1eYOnUq58+fz3HTU4UPJSQkMGLECNasWZPlJ5TNmzenQ4cO+Pv7i5p7gtrExcVx+PBhli5dqu5QsoWxsTGenp7UrFkTW1tb6cKSg4NDjh6d9a2sDbRpaa9NfIqc6xGJhCWkkZQmR1dTA+3EGO4e38PDY0dYunQpZmZmKk1/tLW12bRpE1WqVKFYsWJq3AtVGS1mHxn6nNMblzJq/2WMLCx5++Jp+vPjU7Ax0cpwY6+PMTU1pVixYixfvpymTZsSHh7OsWPHqF+/PhMmTGDnzp2ULl0aExMTnJycpOcZGRmRJ08eQkJCpBkq+fPnlx7X19f/4PbLly+/KrYvrU95ATk4OJghQ4YwbNgw6XGFQkFISAhFihQBPj2T5GPenZWhoaFBoUKFePHiBTKZTJrRoZSWlkatWrWk2+9vJzExkdTUVJLS5FRo2p7z2wOo0rILVwO3U75x+nFeZOhz5Kkp+LmXfid+ucpsGaXQZ085f/68SgypqalSPd6dO3cyZcoURo8eTdmyZZk+fTrVqlUjPj4eLy8vDh48yNu3b4H0c5K0tLQPjhFevXrFkCFDOH36NDExMcjl8g9GAb+/n+9OC37fp2YfRUREkJKSgrW1tfS4XC6XXv8XL16ovBfvz5Y5cOAAEydO5P79+8jlcuLj4ylTpoz0uKWlpUpN5695DYTc5ce4hCT8MN5tcd67d2/atWun8nj16tVJSEiQRqeVLFkSPT096TZA165dKVKkCAULFqRkyZK4uLiorKNnz57cvn0bMzMzPD09KVmyJMOGDaNatWrkz5+fGzduUKNGjUzfNxsbG2mYuKWlJTY2NsycORO5XE4+fU3ePw00NM9DR//VHFw4mcl1HXnz9BFFylWRHn+/pXuZuk3QyOC55NChQ0lISCBv3ry4uLjwyy+/ZHg/HBwcqFSp0kcf8/f3p2jRori4uGBiYkL9+vU/WUPtfe+/L5qamuzbt4+rV69iZ2dH3rx56dWr12dr5X3O3bt3OXLkCAMGDPim53+PyMhI3N3dOXjwII6Ojrx584bHjx+TnJzM9evXefDgAUCOOQlR1labOHFijhppEBcXR4cOHZg3b16OTEYKH5ozZw4VKlTIlim7FSpUICEh4YcrnC5kv7gUOedexbPnSQzbH0ax50kM517FE5/y5SmNBw8epEqVKuTNmzcbIlUv5RTPbt26UbRoUbS0tHjx4gXdu3dnw4YN1KtXT80RZh0DbQ1c8hvQ1NaY1g6m1M+vxdbpYznz9zEGDRpEvnz50NHRQaFQSMmVIUOGcPToUZ4+faru8FVktJh95MvnGJiaY2RhqXL/ue8sZq/8HC1dupRatWrRrVs3aWqo8vhpwYIFXLt2jaJFixIcHCxd2I2LiyM8PFyl5u+3+t4LMjY2NixfvlxlpkVCQgLVq1f/pvU9e/ZM+rdcLuf58+cUKFAgQzM6PkVXU4OSbo0IfXCbl0F3uHv6MM6N0pusmOYvgKaOLuOP38Pn1EN8Tj3E9/RjvHb888F68hcshKurq0oMsbGx0gWFypUrs3v3bl6/fo2npydt27YFvm7GzNixY5HJZNy4cYPo6Gg2btyY6bOJIP1909XV5c2bN9K+REdHc+vWLQCsra1VaqC9+74kJSXRqlUrhg8fzqtXr4iMjMTDw0Mlzvc/V986a0jI+cSINSFHkMvl6OjoSC3O3/VuV0kjIyOpzTekf1m9fv1aZXkjI6MPusO9Ww+sWLFiHwwVnjp1KlOnTv1obMpaTu/63Jffu8PR31e1atWPDvfNkyKn36rdpL232uI16lG8xsdrBilbuitpyuAP//GfjOPdugQFChSQOqwq9e3b95Nxf2qftLS0VF4LPT09/Pz88PPz+2BZNze3D4pzvrvej70vBQoU+GgtDOCDOgufuk/Jz8+PIUOGYGxs/MllsoJcLqdUqVJMmzZNuu/o0aOsXr2a4OBg5s6dS5s2bbh06ZJUFFrdo2127dqFQqGgRYsWao3jfUOHDqVKlSpf1QFWUJ+QkBDmzJnDxYsXs2V7MpmM5s2bs2vXLpURDYKQUaFxKZx9lcCj6GRkqNaYuh8Jp0PjsTfRoXp+fawNPz5i9kftBvoxygsvyt+sKVOmsHHjRpo2bcqFCxcICwuTRthYW1v/0CMyHj9+zN9//60y7Q/+99oopybWrl1bGr2Une7cuYOHhwd+fn7Exsbi7+9PREQE1arXoPzgaRhZpo/mGVPBEs+xMzm9YQlxb8NxbtSKZqP9eXjhFOuGdiYtOQmfGkUoXb8p9fum1yFTFrP3aFCXzp0706tXrw+2P2LECP7991/279+PqanqLA1lHT5lA7HHjx+zatUqzMzMaNiwIfPmzaNp06Y0b96cs2fPsmbNGjp27IiTkxNjx46latWq0mi17/H+zImv1a9fPyZMmICzszOlSpUiKiqKw4cPf/PI/0uXLvHnn3/SrFkzFixYgK6uLi4uLmhoaHzzjI58+pro6+lRpn5T/hjbj0KlKmBmnd7AwcTSimIubgTO9abBb2PQMTDkbUgwUa9fYF/xfwMOtGTQqHFjts+eyIYNG6Qad1evXsXIyAgHBwe2b99OkyZNMDU1xcTERPqu+JoZMzExMZiammJqakpISAgzZ878ptfxS6ytrXF3d2fYsGFMnjwZIyMjHj9+zPPnz3F1daVt27bMnz+fxo0bY2hoiL+/v/Tc5ORkkpKSsLS0REsrfdTm4cOHKV269Ce39z2zhoScLecMRRB+WmFhYYSFhWXKj2JuZaitgb2JzpcX/AwHEx0MtMWf9McEBQURGBjIoEGDsn3b74/4On/+PD4+PrRu3RpTU1MaNmxIkyZNmDJlCvD9V0y/l1wux8fHh4kTJ6o9lnft2LGDv//+m8WLF6s7FCGDxowZQ9++fbN1dKGnp+cHF1YEISO+VLg9VQFpCngQlfzJwu2JiYkcOHAAT0/P7Ak6B0lKSuLUqVNs2LABb29vvLy8GDJkCIMGDaJNmzZER0dLF49+RCVLlpS6pL4/+jwlJQVjY2P69OlDsWLFMtzFPLNcvnyZhg0bsnDhQvLnz69S/N8gfyE2j1atf3nn1GEGbDzCkD9OcuPIHu6fPU7Rqq70WLgVY0srJp4Jps3ERdLyMuB6xMe7Mcrlcnr37s3169c5fPjwB0k1JeWxkoaGhtSRcf369dy+fZtjx45x5coV3N3diYuLo2vXrrRq1Qpra2sePnyoUlvse4wZM4YpU6ZgZmYmdbH8Gi1atGDUqFG0b98eExMTSpcu/V3TY5s3b84ff/yBubk5GzZs4M8//0RbW/u7ZnSUsdBDAVRo0o6XQbelaaBKbSctJi0lhbmtazDJrSibRv5KzBvVAQwKoFphSw4fPszWrVspUKAAVlZWjBo1iqSkJCC9GYStrS0mJiYsW7aMTZs2AV83Y8bHx4fLly9jampK48aNP6jtnJnWr19PcnIyJUuWxNzcnNatWxMaGgpA7969cXd3p2zZspQvXx4PDw+0tLTQ1NTE2NiYBQsW0LZtW8zNzdm8eTPNmjX77La+Z9aQkLPJFD/qL5yQK1y8eJEGDRrQp08fZsyYoe5w1Co0LoVND6IyNBz/fVoy6ORoirWBqDn1Mb/++iuFCxf+7Ii27LJ//34uXLjAxInpV/qWLFlC48aNKV26tHQydurUKZXpzdlp27ZtzJ49m3PnzuWYxNrTp0+pVKkS+/fv/+b6ekL2On/+PC1btuTu3bvZOko0JSWF/Pnzc/PmTQoUKPDlJwi5hp+fH48ePZI6tWWmjBZuf5eWDOoWNKSC5f+SJHv27GHu3Ln8/fff3xzLkydPsLOzIyUlRaURTk7g5uYmjUjatGkT69at4/Dhwx+MRPv9998xMzOjffv25M+fn/nz5yOXy/H19c0Ro7Kzi3IaqDJptHPnTgYPHkxAQADu7u7SKK2sYmtrS7du3Vi9ejUbN27Ezc2Nnj17kidPHumY+49boXR0LszwXecxL1CYMRUs6bt6L7bl08uobB7VkwIlyuLWYwiP/jvDH+N/Y8zB6wC8ffGUGU0qMuVCKGUtDZndvan0+Vi7di1Lly7F1taW1NRUtmzZolJv7lNWrlzJ8ePHyZs3LzExMcyYMYOkpCQWLVqEpqYmDRo0+Cm6gfv6+hIUFMTGjRu/vPBX2vkomot3HzGnVXXGHr6FntHX/UY7murQ0t4k0+PKLQ4cOEC/fv0+GJ0qCGJ4i6BWlStXJjIy8qdPqgFYG2pnuJX7u5QH9yKp9nGPHz9m9+7dDBkyRN2hAOkj0vbv3y8Vea1VqxYnT56kUqVKpKSkMHbsWLy8vLh+/Xq2x5aWloavry+TJk3KMSc+aWlpdO7cmWHDhomkWi4hl8sZMmQIfn5+2T71Wltbm0aNGrFnz55s3W5uZmtri46ODm/evFG5v3z58shkss+WN8io7t27M378+C8v+Bljx479ZFLtxIkTFCpU6JvWm9HC7e9LVcDxkDhC49PLU7i5udGmTRuaN2/+TXF8q+fPn9OqVSvy5s2LqakppUuXZu3atVm+3U6dOnH48GEAKamWlpbGxYsXuXPnDp07d8bOzk4amf3nn39y7969HPPbkh1kMhkaGhoEBQXRqFEjvL29qVSpEkePHiU5OTlbapguW7aM6tWr4+bmBqQXYn93KqqGngEGpuZEvQ6V7jPKk0/6t7aePsnxcV/cjrK7/buCgoLYvXs3Pj4+GUqqAXTo0AFXV1dcXV1ZsWIF+fLlY+MslcTDAADCLklEQVTGjaSlpdGiRQspqTZu3Djmzp0rPS8h4fvqvP1MXCx1ObNpKWXdPb86qaYlg2pW2TviUt0SEhIIDAwkNTWVkJAQJk6cmONKpQg5g0isCUIOUsFS/6uSax+7Yi6omjZtGr/99tsHnYTUxcPDg6pVq9KjRw+Sk5OZMWMGu3fvJikpiTZt2vDq1StWrVpFyZIlsz22rVu3Ym5ujru7e7Zv+1OmTp2KtrY2I0aMUHcoQgZt3rwZuVwudQfLbmI66Nezs7NTqWd548YN4uPj1RiRqnc7UGe2jBZuVygUUpF1pVQF/PsygSdPnnD69GlSUlK+OM0vs5vUdOnSBRsbG4KDgwkPD2fDhg0qXQuzy/379/H396dw4cI8fvwYe3t7tLW12bVrF+3atcPExCRHfaaymnJCkJeXF40aNaJChQqcP3+enTt3oq2tnSUjkT5m2bJlPH36FC8vLyC9du27I20USQnER73FNJ/1p1aRIbqaH55SOjk5ERAQQKNGjTLcyMrIyIh+/frRunVrdHR0CAkJYdeuXbRp00a6uDZw4EBOnjwpNQXYv38/jRs3JiYm5rv24WcQFxdHces8vLh0ml9+G/VVz/1ZL+QrFAp8fHwwNzenfPnyODk5qdT/FgQlkVgThBymgqU+nRxNcTTVQVPGB0k2LVl6owJHUx06OZqKpNpnPH36lB07dkgHlOqmPNBevHgxNWvWJDIyEoC1a9fy66+/UqtWLWbNmkX58uWzfQpQamoqEydOZPLkyTlmRMGZM2dYsmQJGzZsyFHdSYVPi42NZfTo0cyfP19t79kvv/zCmTNniI6OVsv2c6MuXbqwfv166fa6detUmv5Aeg2t4cOHU7hwYfLnz0+/fv2kUSLKEWOzZ88mX758WFtbExAQAMCKFSvYtGkTM2bMwMjIiKZNmwLpI2datWqFpaUldnZ2LFiwQNqWr68vrVu3pnPnzpiYmLB27Vp8fX3p3LnzV++bTCYjKChIuv3u6Lnnr8MZ3701U+qWYKJrUdYO7kjUqxfSsit6N+fQoqks6+GBd/XCRDx/8sH6H0YnszpgHcWLF8fa2pq9e/eqPN69e3d+++03PDw8MDQ05O+//2b//v2UL18eExMTbGxsPlqmYM2aNRQoUABra+vP1nu6ePEi3bt3x9DQEC0tLcqXL0+jRo2kx9u0aYOVlRWmpqbUrl1b6nQH6aPs3h0FuHbtWmrWrCndPnLkCCVKlMDU1JSBAweq1Ed7f1lHR0e2bt1KdHQ0I0aMoFu3bpQtW5bFixczefJkjhw5Qvny5aXlf/RKNMrf0aSkJLZv387UqVMxMjJCU1OT4cOHU7x4cRIT0+uSZWVHcGNjYw4ePMipU6cYPXo0HTp0ICAggKtXr5KUlMTOuZMoXLoC5gUKf/M2tGRgqf/xphQdOnTAz8+P+vXr8/Dhw69ed8GCBXF2dmb37t38999/uLu7ExISwpYtW6hatSpr1qxh/PjxtG/fPttHSGclX1/fLEm+GhoaEhsby8O7t2lZ0VFcyM8AAwMDLl68SExMDK9fvyYgIAATk593KqzwaeJMRRByIGsDbVramzCglAU1rQ0oZa5LURNtSpnrUtPagAGlLGhpb/LTXTX6Wv7+/vTp04c8efKoOxQg/UBbeTKh7MLarVs3Tpw4wfjx4/Hy8sLc3JyNGzdy5coVIGsPuN+1YcMGChYsSN26dbNle18SGRlJp06dWLlypaiVlYv4+/vj6upKtWrV1BaDsbExNWvW/K6i0T8bFxcXoqOjuXPnDmlpaWzduvWDJNbo0aO5f/8+V69eJSgoiJCQEJWr9i9fviQqKoqQkBBWr17NgAEDePv2LX369KFTp06MHDmS2NhY9u7di1wup2nTppQrV46QkBCOHTvGvHnzOHTokLS+3bt307p1a+m7ICvcCo+ncrMOjNx/mdGBV9HW02OP/2iVZa4EbqfF+Dn4nn6MubXNB+uQAQHr12NqakqHDh04dOgQr169Ullm8+bNjBs3jpiYGGrWrImhoSHr168nMjKS/fv3s3TpUnbt2qXynL///psHDx5w+PBh/P39OXr06Ef3wcXFhQEDBrB161aePn36weONGjXiwYMHvH79mgoVKmT4tXzz5g0tW7ZkypQpvHnzBgcHB86cOfPZ54wfP56+ffty/Phx7ty5Q82aNVm8eDGlSpXiwIEDdO3alblz5xIbG4tMJvtgBOCPRPnbvWTJEpydnQG4ffs2Pj4+uLi4sG/fPn799VeALO+UamZmxpEjRzhw4AAnT55k8uTJUvH/6BfBdJy+8rvWrwDKWuh98vFu3brh7e1N3bp1v2pqufLzsXz5cnR0dKhSpYpUyL9QoUJMnz6dpUuXMnfuXPr06QOk19kUMkZcyBeEzJWzqqIKgqDCQFsDl/wG6g4jV1Je0bx79666Q1Hx7miwkydPoqenh6+vL4UKFeLhw4eYmppiZWXFoEGD+Pvvv9HW1s7yYs8pKSlMnjyZdevWZdk2voZCoaBv3740adJEGt0i5HxPnjxhyZIlXLt2Td2hSNNB27Vrp+5Qcg3lqDVXV1ecnJwoWLCg9JhCoWDFihVcv34dCwsLIL3mWceOHZk2bRqQXt/O29sbLS0tPDw8MDIy4t69e7i4uHywrYsXLxIWFoa3tzcA9vb29O7dm61bt9KwYUMAqlWrJjV0yaouikn6ZpSs97/vmDo9vVjZR7V2TsWm7cnvUOKT6wi6fI6Xz58SFxnBtm3b2LdvH5s3b1YZKd28eXNq1KgBgJ6enlTvCqBs2bJ06NCBkydPqnQT9fHxwdDQkDJlytCjRw+2bNlC/fr1P9j+9u3b8ff3Z/Lkydy9e5cyZcqwcuVKadqcMnkD6aNgzM3NiYqK+mR3RqXAwEBKlSpF69atgfROdrNnz/7sc9q2bYujoyPnzp3Dz8+PIkWKSOUNYmNjKV68OEFBQfTt25dNmzb90COR30+WRUdHM3z4cAoUKEBiYiLTpk2jb9++LF68mAEDBnzQACIzvJvEsrCwUPlu7tevn/TvnY+ieRCVDMC0y2Eq63i3+6d9pRpS4wIA8wKFmXY5DEfT9K70J06ckB7r3r073bt3l2737t2b3r1Vu49+iYaGhtTgYezYsdjb29O1a1diY2MZN24c06dPZ9q0aejq6nL//n3y58//xc+1oCr9Qr428SlyrkckEpaQRlKaHF1NDSz1NSlroYeB9o/7dyoImUkk1gRB+CHNmDGDX3/9lXz58n15YTVp06YNbdq0ITIykq5du3Lnzh0sLS3Ztm0bTZo0YeLEiUyZMiXL4wgICKBo0aLUqlUry7eVEQEBAdy+fZsLFy6oOxThK4wcOZIhQ4Z8cxH5zNS0aVNGjRpFcnJyhotm/+y6dOlC7dq1efz48QfTQMPCwoiPj6dixYrSfQqFQmVEbZ48eVSmsBsYGBAbG/vRbQUHB/PixQvMzMyk+9LS0lS+g2xsPhwdltmiY2P5a8po7p89TkJMJABJcbHI09LQ+P8kh2n+gp9ZA1ze9wclqtTAWJ6EjY0NHTt2ZN26dSqJtff35fz584wePZqbN2+SnJws1dh817vPKVKkCDdu3Pjo9s3NzZk+fTrTp0/nzZs3DB8+HE9PT54/f45cLmfcuHFs376dsLAwKZH15s2bLyYgXrx4oRKDTCbL0Hvi7OwsjdBKTk5m8ODBGBgYMH36dKn2W5MmTXjy5Am2trZZ3hkzp3j06BHGxsasWrWKuXPn0q9fPzw8PNi3bx9JSUno6uqqLbbq+fUJikxAIfv6xF5WF7PX0NAgLS0NHR0dunbtyvPnz+ncuTN6eno4ODhgaGjIsmXLkMlkODk5MXz4cG7cuEFCQoJaR07nNuJCviB8P5FYEwThhxMaGsqGDRu4ffu2ukPJkI0bNxIXF8fVq1fZu3cvLVu2pHTp0hQvXhwgS0erJSUlMWXKFLZt25Zl2/ga9+7dY9SoUZw4cSLLRqkIme/kyZOcP38+W7oRZoS1tTUlSpTg5MmTNGjQQN3h5ApFihTBzs6OwMBAVq9erfJY3rx50dfX59atWyoj2TLq/e8wGxsb7OzsePDgQYaf860MDAxUiua/fPlSSv4eWLOYsOAg+q8/iHHe/Ly4d4OFHeqq1P/6XBgpiQlcP7IbeUoKejraWFlZkZSURGRkJNeuXaNcuXIf3ZeOHTsycOBADhw4gJ6eHkOHDv2gK+uzZ88oUSJ9pNzTp08zNCU+b968DB8+nHXr1hEREUFgYCC7d+/m6NGj2NraEhUVhbm5ubR/hoaGH7w2StbW1jx79ky6rVAoVG5/TkpKCqdOncLFxYUXL15w6tQpZDKZ1Fzh8ePHrFmzhkmTJv0USTVIH0WvfH29vLyoW7cujx8/ZsSIEVJSbeHChfTo0QMjI6Nsjc3aUJsCUU8JMS4EmhkvMZJdxezfHckXHh6Ok5MTS5cupU+fPnTp0oWBAwcC8PbtWy5cuMDmzZvR0dGhcuXKaGpq5pi6sYIg/Nh+jl8zQRB+KrNmzaJr165YWVmpO5QMsbe3lw64S5YsSVxcHMHBwbRp04Zr165x/Phxhg0b9tmT0G+1atUqypQp89HpWtktKSmJDh06MGnSJEqVKqXucIQMSktLY+jQocyYMQMDg5xzxbt58+Yf1K0SPm/16tUcP34cQ0NDlfs1NDTo3bs3Xl5evH79GkhPFLxbE+1z8ufPz6NHj6TbVapUwdjYGH9/fxISEkhLS+PmzZtcvHjxu+JPTExU+U+hUODs7MzmzZtJS0vj4MGDnDx5UlpelhSHjq4eesamxEe95diKTzcJ+JhbJw6goaFJkz5eHDt2jKtXr3Lnzh1q1aql0gzifTExMVhYWKCnpyclAt43efJk4uPjuXXrFgEBAZ+c1jxq1Chu3rxJamoqMTExLF26lKJFi5InTx5iYmLQ1dUlT548xMfHM3bsWJXnOjs78+effxIfH09QUJBKQrVx48bcunWLP//8k9TUVBYsWKCSePscbW1tFixYwPPnz/Hw8GDo0KFs2LCBzZs3c+3aNdq1a0e3bt2kGlo/cq01pcaNG2NsbCw1opg3bx7Nmzenfv36HD9+nBYtWnDgwIGP1snLDl3qVKZ2fj00yVhTCXUUs1coFJQrV46lS5eSmJhISEiIym+Oubk5169fp3DhwsyePRstLS3RxEYQhGwjEmuCIPxQlB17Ro4cqe5QMszDwwMPDw+qVatG/fr1qVevHrNnzyZ//vwsXryYhg0bUqxYMYoVK5apndQSEhLw8/PLMW3Dx44dS5EiRVRqvwg535o1azAyMqJt27bqDkWFss7aj959MDM5ODhQqVKljz7m7+9P0aJFcXFxwcTEhPr163Pv3r0Mrbdnz57cvn0bMzMzPD090dTUZN++fVy9ehU7Ozvy5s1Lr169iIqK+ubYQ0JC0NfXV/nv4cOHzJ8/n71792JmZsamTZtU6pj5jhxGSlIiU+oWZ0m3X3Cs9nXNWy7v3UqlZh2wN9KgcuXKWFlZYWVlxcCBA9m0aROpqakffd6SJUvw9vbG2NiYSZMmffRvx9XVlaJFi1KvXj2GDx+Ou7v7R9cVHx9PixYtMDMzw97enuDgYPbs2QNA165dKVKkCAULFqRkyZIfXEDx8vJCR0eH/Pnz061bN5XGBnnz5mX79u2MHj2aPHny8ODBA6lOXEZ06tSJ/v37U758eaKioli4cCHXrl2jffv2eHt78+TJE/r3709CQsIPP2pNOWV61qxZBAcHc+/ePcqWLUvHjh2ZMGECU6dOpX79+qxZswYnJye1xVndxpzOxc1ybDF75cgzuVzOmTNnSEpK+qCDeq9evRgxYgQAz58/x83NjfDw8GyNUxCEn5NMIY44BUH4gYwaNYq4uDgWLVr05YVzgHcbE0yZMgVDQ0OGDh1KZGQkv/76K2/evEFfX586deowZsyYTN323LlzOXXqFH/99VemrvdbHDp0iF69enH16tUc08VV+LKoqCiKFy9OYGAgFSpUUHc4HyhRogQbN278ZLJIEN4t3P4tiploY/H0MnXq1MnEqH4MEydO5PHjx1hbW2NnZ0fnzp15+/YtS5YsITQ0lFKlStG2bdtsqaenbsrfemU9tQ0bNhAQEEDJkiXp06cPZcuWlerN5YS6c7mhmP3Zs2epVKkS2tra0nHUu8dUcrmccuXKMX36dBo3bqzOUAVB+AmIxJogCD+MN2/e4OjoyLVr13LVgbryQFA5befff/+lV69euLu7s3jxYqKiopg1axY+Pj7cvHmT9evXM2fOnO/aZlxcHEWLFuXQoUOULVs2k/bk27x+/RpnZ2c2bdokTk5zmeHDhxMZGcmqVavUHcpHjRo1Cm1t7WxpAiLkTqFxKWx6EEXqNxwNa8mgk6MpRmmJGBsbZ35wuZyy02VsbCxGRkY8ffoUX19f7O3tqVWrFqVLl1a5kJIVnTFzoiFDhnD79m0GDx5Mw4YN0dHRISEhgQkTJtC3b1+KFSv207wW3yIjicebN2+ycOFCTExMmDp1qmhiIwhClhOJNUEQfhjjxo3jzZs3LF++XN2hfLP4+HgGDBhAkyZNaNWqFampqWhpaZGQkMD06dOZOHEit2/fpmTJkt+1nRkzZvDff/+pvWmBXC6nSZMmODs74+fnp9ZYhK9z//59qlevzq1bt6RufznNv//+S58+fT7ZUVEQAC6HJXA8JO6rkmvqqDGV27w7egjSpxO/fPmSYcOGSQ0k3r59y8SJE5k3b56aosw+ytcjKCgIXV1dTE1NOX78OC9fvsTNzY3bt2+zbNkyDh8+rLYYQ0NDyZMnT65LRL37Wbt79y7Tp0+nRIkSNG3aVNRsFQQhW+SMsbyCIAjfKSIigmXLlmX6dMnsZmBgQGxsLOfPnwdAS0uLw4cPU6VKFS5dukRycvJ3J9ViYmKYPXs2vr6+mRDx91mwYAERERFMnDhR3aEIX2nYsGGMGjUqxybVAKpWrcqbN28ICgpSdyhCDlbBUp+6BQ0/qCn1UQo5itQUoi8ewSA8OMtjy83e78b46tUroqOjpaTa9OnTKVq0KMHBwT9FAwPl61G0aFFsbGxYtGgRY8aM4eHDh3Tu3JmWLVuir6/PiRMnsj025TiLDRs2kJCQIN1/48YNkpKSsj2er5Gamiq9ts+fPyc4OBgTExM6dOggkmqCIGQbkVgTBOGHMH/+fDw9PbG1tVV3KN9MeWC7ZMkSHB0dARgxYgSDBg3Cx8eHffv2oaOjQ0pKCvDtndTmz59PgwYNvjtB972uXLnC1KlT2bx5M9ra2mqNRfg6hw4d4u7duwwePFjdoXyWhoYGTZs2Zffu3eoORcjhKljq08nRVCrcjvy9xgPyVOSpKSQ+vYf+zeNc2LmONWvWqCXW3Eb5W+Xv70+BAgXo0aMHBQsWZOvWrRw7doy//vqLZ8+eSQmcnyHJFh8fz759+1i3bh0zZ86kQYMG1KpVC7lcrlLKQtn4IKspE1POzs6YmppK92/duvWDBgE5jbJb7cKFCxkzZgy+vr5cvHiRIkWKqDkyQRB+JmIqqCAIuV5kZCRFixbl/PnzODg4qDuc76KsHZKSkkLt2rUxNTVl+fLlxMbGcvbsWY4cOYKtrS1DhgyhYMGCX12HJTIykmLFinHmzBkpeacOcXFxVKxYkQkTJqh0ohNyvpSUFKkgdLNmzdQdzhcFBgYyffp0Tp06pe5QhFxi685d/HXhJjUaNkXb0Ij8FuYUMNFTKdz+/PlzgoKCcHNzU2+wuYTyt23Pnj14enqyfPlyevfuTVpaGuvXr2fevHl06NCB0aNHqzvULKd8LWbPns3169fp0qULhw4d4ty5cwwYMIDWrVtz9uxZnjx5AqR3d80qL1684OrVqxQqVIjChQtjZmYmPaZQKHj06FGOPq6KiYmhWrVqFCxYkHz58tGmTRuqVq1KYGAgPXr0+GA6siAIQlYRiTVBEHK9yZMnExQUxLp169QdSqbau3cvTZs25c2bN8ycORNDQ0Py5s1LQkICf/75J2fOnPnqdfr4+PD06VMCAgKyIOKM6927N0lJSaxfv16tcQhfb+HChezZs4fDhw/nihOWxMRE8ufPT1BQEJaWluoOR8gFlBcxfHx81B3KD0lZO/Ts2bNs3ryZmzdvUrp0acLDwxkwYAA1a9b8oYv3v5vsGTx4MNu2baNZs2Z07tyZ2rVrc/78eVq1aoWhoSHHjh2jQIECWdYl9NChQ6xcuRJ9fX06duxIo0aNcl0y6vr161y7do2KFStSpEgRDA0NP1hGoVCQmJiIvr6oiSgIQtYQiTVBEHK16OhoHBwc+OeffyhevLi6w8kU7x/Uent7c+fOHWbMmIGdnR0AjRs3ZtasWTg5OWV4veHh4Tg6OnLx4kXs7e0zPe6M2rFjB6NHj+bKlSuik14uEx4ejpOTE8ePH6d06dLqDifD2rRpg4eHBz169FB3KEIukpaWhoaGxgdJhtOnT/Pw4UNCQkIYN26cmqLLvVJTU/Hz8+Ps2bNUqVKFIUOGkCdPHvbv38/8+fPVWrw/uyhHrYWEhEijqyC9/ENgYCAuLi48ffqUv//+O1viefr0KWZmZpiYmEj3KU8Rc1OS7WNSUlL4/fffef78OX/99Ze6wxEE4QclaqwJgpCrLV68GHd39x8mqQaqB7GJiYk8ffqUsWPHYmdnR0xMDLNmzSI+Pl4qAA0Zq0kze/ZsWrVqpdak2tOnT+nfvz9btmwRSbVcyMfHh7Zt2+aqpBqAp6cnu3btUncYQi6jqamp8n2sTDScPHmSJ0+eYGBgQGJiorrCy7W0tLQoWrQoY8aMYdKkSeTJk4cHDx6wfft27OzsSE1N5Ue/7q8cgVawYEF69uzJnTt3KFeuHKGhoRw+fJh169ZRtGhR7t69i0KhYMGCBVkSh/J1Lly4MG/fvuXmzZuEhYUB6cciuT2plpycTN26dVm0aBEnT5784T9XgiCojxixJghCrhUbG4u9vT0nTpxQeyH+rDRjxgyOHj3KwIEDuXjxIklJSbi7u1O/fn2OHTuGm5sbmpqan52+ERYWRokSJbhy5QqFCxfO5j1Il5qaSp06dWjSpAmjRo1SSwzCt7t58yZ169blzp075MmTR93hfJW3b99SpEgRQkNDPzpNSBA+RS6Xo1AoPhi5FhcXByA+T1/p/d+pxMREtm3bxoULFzA0NGTo0KFYW1urMUL1mDx5MlpaWowZM0ZlGuyhQ4dwc3Nj7dq1dOrUCSMjo0zfdmJiIoMHDyY+Ph5LS0s0NTUxMTHBwsKC/v37Z9k01KyWmJhImTJlpK7Qenp63Lx5M0fXjBMEIffKnd+UgiAIwNKlS6lTp84PnVQDGDlyJO7u7mzbtg0jIyPq1q1LmTJlGDBgAA0aNGDWrFnA56dr+Pv706FDB7Ul1QD8/PzQ0dFhxIgRaotB+DYKhYKhQ4cyYcKEXJdUAzA3N6dKlSocOXJE3aEIuYgyoaYcuZaYmMidO3fYtm0bQUFBGBoaihEwX+nd36nXr18zePBgzp07R4UKFWjfvj26urrs2rVLKtz/o7++yv0rUqQIf/zxB5A+UjIoKIjGjRvTrVs3bt68Sd++fTM9qabc9qVLl7h8+TKDBw+mfv36lCpVCm1tbRITE3NtUg3SmzVVr14dPT09tLW1SUpK4tChQ9LjcSlyzr2KZ8+TGLY/jGLPkxjOvYonPuXH70orCELmEyPWBEHIleLj47G3t+fIkSOUKVNG3eFkGeXV/Xev8l+5cgVvb28SEhIYOnQoe/fupXr16nTr1k2q2/KuuLg4bGxsuHHjBgULFlTHbnDmzBlatWrF5cuXKVCggFpiEL7d7t27GTt2LFevXkVbW1vd4XyThQsXcunSJdauXavuUIRc5ODBg4waNQqFQkFsbCw6Ojq8fPkSb29vfv/9949+5woZt3nzZooWLUqpUqXYvHkzGzZsQFtbm5iYGCZNmsQvv/zy07zGx44do169ekyYMIFNmzbRq1cvfv/9d/T09KRlMvO1UK5r7969PH78mMGDB0uPpaWlERsbi6mpaaZsS53evn3LqlWr8PHxoUyZMuw6/g9nXyXwKDoZGZD6zpmwlgwUgL2JDtXz62NtmDt/7wRByH4isSYIQq40d+5c/vnnH3bu3KnuULLV9OnT2bhxI507d2b06NEAbN++nTlz5nD48OGP1i1LTk5m9erV/Pbbb9kdLpB+1djZ2ZmFCxfStGlTtcQgfLukpCRKlSrFkiVLcHd3V3c43+zp06dUrFiR0NBQtLS01B2OkMMpkw7379/n/PnzODs7Y2dnR3x8PLt27eLBgwfMnDnzp0n6ZDbldEfl/zt27MirV6+YOnUqLi4uUp2xTZs2AR9OIf2RKPctOTmZGjVqYGZmxqJFizAwMOD69etcv34dW1tbOnTooLL891J+docNG8bKlSvp3LkzHh4eODs7SzVcf6TPd1paGpdex3PqVZJKMu1TtGRQt6AhFSxFJ1FBEL7sx/imFAThp5KQkMDMmTOZMGGCukPJdrq6uixZskRKqp09e5ajR4/Svn17Kal28OBB7ty5Iz1HR0eHvn37qiVehUJB3759adKkiUiq5VLz58+nZMmSuTqpBunFuQsXLsyZM2fUHYqQCyiTCY6OjnTp0oUyZcpgZGREvnz56N27N2/evFFZTvg6yhpi4eHhvHnzhpiYGI4dO4aLiwtpaWnExcURHR3Nnj17gNzfmfJzlKPSdXR0WLZsGUeOHCFv3rwsW7aMkydPcv/+ffbs2cOgQYMybZu2trYcP34cgB49euDn54eenh6rVq2iRYsW2NjYcPv27Y9+vm1tbTl69GimxZJdrkUkE7D/OJMbls3Q8qkKOB4Sx+WwhCyOLGcxMjLi0aNH6g5DEHIdcclWEIRcZ/Xq1VSuXBlnZ2d1h5JtlFeovby8AIiKiuLIkSP8999/5MuXj/r165OWlsacOXO4fPkybdq0wcnJSXq+uk7+AgICuH37NhcuXFDL9oXv8/LlS2bMmMG///6r7lAyRfPmzdm1axeurq7qDkXIZRQKBfv37yckJARzc3P69u37Q4+iyg5Pnz5l7ty5zJ49m6CgIC5evEiBAgV4+fIl27dvJy4ujvj4eABevHhBbGwsjo6Oao46ayg/RxUrVgRgypQphIWFMXbsWKmObPXq1Xn9+jX58uXL1G2XLl2aEiVKEBQUhImJCQYGBjx8+FBtr/XatWvp2bMn+vr6aGhoYG9vz5QpU2jSpMk3rzM0LoXjIXGkfeU8LWVyzdpQC2uDb58WunbtWnr06MHQoUOZO3eudP/u3bvx9PSkW7du312m4MmTJ9jZ2ZGSkvJdo7JjY2O/Kw5B+FmJxJogCLlKUlIS/v7+/PXXX+oOJVu9e/KWnJzMmjVrePDgAY6Ojnh6emJra0tSUhIAAwcOpGzZjF2RzUr37t1j1KhRnDhxAn19MZUiNxo3bhw9evSgWLFi6g4lU3h6euLp6cmcOXNEQkTIsIiICBo3bkyBAgU4f/48ZcqUoWLFihQqVEiaMid8vcKFCxMcHMzWrVuZPXs2S5cu5eHDhxgYGFC+fHkWLVqEiYkJd+7cwcbGhoCAAEaPHi2NdvtRRURE8OTJE9asWYO5uTlpaWmsWrUKQ0PDTJ/G/vbtWxYvXsyJEycoX748enp6eHp6Sgk+dalWrRr//PMPcrmcxYsX07ZtWympnVGpqanS63X2VUKGpn9+dD0K+PdlAi3tv6/emoODA9u2bWPmzJlSXOvWrcsxyeJ3Xy9BEL6eGL8uCEKuEhAQQNmyZalUqZK6Q1EbHR0dKlWqRI0aNRg4cCC2trakpaWhq6vLsGHDcHFx+WitteyUlJREhw4dmDRpEqVKlVJrLMK3uXTpEoGBgYwfP17doWSaMmXKoFAouHHjhrpDEXIBZRniRYsW0bRpU3bu3ImbmxuLFy8mOTlZmkonl4sugt9q/vz5LFiwgG3bthEXF4ejoyNTp07Fz8+PFy9eMGvWLHx8fEhJSWHgwIE/fFINwMLCAk1NTRYsWMDZs2dZuXIlN2/exMvLCwsLCxIS0qcmpqWlfdd2Ll68SMmSJZk8eTLa2to0aNAAuVzO4MGDmTt3Ls7OzpiZmVG9enWuX7/+0XXcuXMHOzs7tmzZAkCbNm2wsrLC1NSU2rVrc+vWLWnZwMBASpYsibGxMQULFpQ6mn+OhoYGv/76KwkJCTx8+JCkpCSGDx9O4cKFyZ8/P/369ZNejxMnTlCoUCH8/f2xsrKiR48eJCQk0LlrN7o4F2Zuqxo8v3VFZf0nAuYzs1llfGraMrdVDW4d3y89dmnPFpb92pjAuT50di6MrZ0dBw4ckB4PCAjAyckJY2Nj7O3tWb58+Wf3xcrKijJlykidSSMiIjh79izNmjVTWe7cuXNUr14dMzMzypUrx4kTJ6TH3NzcmDBhAjVq1MDY2Bh3d3dpWnrt2rUBMDMzw8jISBppvmbNGpycnDA3N6dhw4YEBwdL65PJZCxevJhixYpJF9BkMhlBQUFffG8EQVAlEmuCIOQaycnJTJs27aesrfa+WrVq0alTJ7S0tFAoFNLJRmpqKrdv3wb+d1KoDmPHjqVIkSL069dPbTEI306hUDBkyBAmT578Q3SFU5LJZHh6erJ79251hyLkAsqEWWRkJDo6OgAULFiQP//8ExMTE16+fKnO8H4INjY2bNmyhZEjRzJmzBhWrlyJjY0Na9asYdGiRbx584bffvsNc3Nz6bvoR+67pvzMrVixgtevXzNq1ChCQkKoVq0aNWvWZNWqVbRu3Zq4uDg0NTW/67XYtGkTtWvXZvHixURHR3Pq1CmmTp2Kg4MD3t7eLF++nLCwMPr27UuzZs2kUfFKly9fpmHDhixcuFBqrNCoUSMePHjA69evqVChAp06dZKW79mzJ8uXLycmJoabN29St27dL8aYmprKqlWrMDIyolixYowePZr79+9z9epVgoKCCAkJYdKkSdLyL1++JCIiguDgYFasWMHEiRO5eT+I0Xsv0mPxNi7v+0Nl/XkK2dJ39V58Tj2iXp/h/DG+P9Fh//u7fnbzMnmLFMX373u07TeUnj17Sq95vnz52LdvH9HR0QQEBODl5cXly5c/uz9du3Zl/fr1AGzdupXmzZujq6srPR4SEkLjxo0ZP348ERERzJo1i1atWhEWFiYts3nzZgICAnj9+jXJyclSgvLUqVNA+vdVbGws1apVY/fu3fj5+fHnn38SFhZGrVq1pPdKadeuXZw/f146dhQE4duIxJogCLnG+vXrKVGiBC4uLuoOJcdIS0tTmdIWExPDrl27iI6OVttUt4MHD7Jt2zZWrVolptvlUsrRIz169FB3KJnO09OTXbt2qTsMIRdQfn85OTlJJ8wuLi5s2LCBpKQk6tevr7Kc8G3s7Ox4+fIloaGhHD9+nBkzZnDmzBkaNWrElClTqFOnDmfPnmXv3r3Ajz1CUENDA7lcjoWFBbNnz+bvv/9m0qRJlC9fnsGDBzNmzBjkcjmrV6/+7m0NHDgQT09P1q9fj4uLC+vWrSM4OJgTJ07QtGlTqlatiqamJt26dUNXV5dz585Jzz19+jTNmjVj/fr1KrXPfv31V4yNjdHV1cXX15dr164RFRUFgLa2Nrdv3yY6Ohpzc3MqVKjwydjOnTuHmZkZVlZWbNmyhb/++gsTExNWrFjB3LlzsbCwwNjYmLFjx7J161aV12/ixIno6uqir6/Ptm3b8PxtGDom5phZFaR6+94q2ynToDkmllZoaGhQtmEL8ha2UxnVZmZViCotuyDX0KRKs/aEhoby6tUrABo3boyDgwMymQxXV1fc3d05ffr0Z1/zFi1acOLECaKioli/fj1du3ZVeXzjxo14eHjg4eGBhoYGDRo0oFKlSgQGBkrL9OjRA0dHR/T19Wnbti1Xr1795PaWLVvGmDFjcHJyQktLi7Fjx3L16lWVUWtjxozBwsJClOwQhO8kJlILgpArpKSk4Ofnx4YNG9QdSo6iHKmmLKKdJ08e+vbti4GBgbRMdhbYfvXqFb/++iubNm0iT5482bJNIXPFx8czcuRINmzY8ENOu6pRowbBwcE8ffqUwoULqzscIQdTfm82aNCAxMRE3r59i5ubG126dKFp06YUL15cZTnh22loaDBp0iQqV65MgQIFGDduHKGhofz11184OTlRtGhR+vTpQ6VKlbC2tkYul/+wHVmV+6Wnp0dCQgK+vr4cOHCA+vXrExoaipaWFhUrVqR8+fLUqlXrm18LGxsbPDw8SEpK4vjx44SGhtK6dWv09PTYtWsXZmZm0rLJycm8ePFCur1s2TJcXV1xc3OT7ktLS2PcuHFs376dsLAwKaY3b95gamrKzp07mTJlCqNHj6Zs2bJMnz6datWqfTQ2FxcX/vnnH5X7Xr9+TXx8vEr9N4VCoTIl1tLSEj09Pen2ixcvMMlXgOT/v21mrVoT8fK+P/hn41LevniWvp8JccRFhkuPG+d9p1GETvp6lcX9Dxw4wMSJE7l//z5yuZz4+HjKlCnz0f1R0tfXp3HjxkyZMoXw8HBq1KihMr00ODiY7du3S0lkSD/+rVOnjnTbyspK+reBgcFnmw0EBwczZMgQhg0bJt2nUCgICQmhSJEiQPrnQBCE7/dj/iIJgvDD2bRpE3Z2dtSoUUPdoeQ4Bw8eJCAgQLqdL18+lQK0ypO+rO70JJfL6d69Oz169FA5CBRyl1mzZlG1alWpXsuPRktLiyZNmrBnzx51hyLkcMrvTltbWwYNGoS5uTna2tq0bduWZ8+eMX36dPr160fz5s3VHGnu5+bmxrFjxxg7diyjR4/m5cuXtG7dmosXLzJo0CBCQ0Pp378/3t7egPo6XWe358+fc/bsWVatWoW/vz9aWlo8e/YMU1NTnjx5Anz9a/Hw4UMSExN58uQJ8fHxdO/enbp162Jtbc2JEyeoU6cO48aNIzIyUvovPj5eZQrhsmXLePr0qdSpHNKnKO7evZujR48SFRUlxaecOlm5cmV2797N69ev8fT0pG3btl8Vd968edHX1+fWrVtSXFFRUSrHNu8nua2trYl+/b+EYOTLEOnfb18848/Jv9Ns1HQm/H0fn1MPye9QAj4xvVZX83+vc1JSEq1atWL48OG8evWKyMhIPDw8MjQ1t2vXrsyePZvOnTt/8JiNjQ1dunRRee3j4uIYPXr0F9f7sQS/jY0Ny5cvV1lfQkIC1atX/+zzBEH4ej/Hr5IgCLlaamoqU6dOFbXV3hGXIufcq3j2PIkhplg1ZGXrcDY0lviUD6fIREZGsmvXLpYsWUJKSkqWxbRgwQLevn2Lr69vlm1DyFrPnj1j/vz5zJgxQ92hZKnmzZuL6aDCV+nTpw/169enadOm9OzZk9WrVxMWFkbZsmXp2rUrycnJX16J8FkGBgbSaJxDhw7RoEED5syZg7e3N7169UJLS4vSpUsD31+4PzdQKBQUK1aM48ePU758eaKjo9m9ezfLli2jatWqUmLm0qVLGWoEoPTq1Svkcjlz5szBzMyMmjVrMnbsWJydnbl27RpdunRh2bJlnD9/HoVCQVxcHPv37ycmJkZah7GxMQcPHuTUqVNS0icmJgZdXV3y5MlDfHw8Y8eOlZZPTk5m06ZNREVFoa2tjYmJyVcnBDU0NOjduzdeXl68fv0aSK9JpmwG8DFt27Zl97K5JMdEEvXqBf9uXfW/mBLjkclkGJrnBeC/3Zt59fDuR9ejJQNL/f+N4E5OTiYpKQlLS0u0tLQ4cOAAhw8fztB+uLq6cuTIEQYNGvTBY507d2bv3r0cOnSItLQ0EhMTOXHiBM+fP//iei0tLdHQ0ODRo0fSff369WPatGlSE4moqCi2b9+eoTgFQfg6YiqoIAg53tatW7G2tsbV1VXdoahdaFwKZ18l8Cg6GRn8r328pjHhrxI58yoRexMdqufXx9pQG4VCwdGjR/n3339xcHDIsqv8V65cYerUqZw/fx5t7e9rSS+oz+jRo+nfvz+2trbqDuX/2LvvsCbPLoDDv7D3EmQqS1HBiQP3pCoucA/cStWK27rQuqrW1Var1aq1LhxVq7j3XqhV3ANFcYAKsncg+f7gI5W6wAJhPPd19WqSd528xOTNyXmek69atmxJv379iI6OxtjYWNnhCIVY1lD6tm3boquri42NDaampmhpaaGnp6fs8IqdrPPdokULunbtysWLFylTpgz6+vrs3LmTJUuWABTLYer/9m4l0bVr1zh8+DBv3ryhdOnStGvXDolEwqpVqzh//jzOzs45fj+rX78+Ojo6DBo0iA0bNhAUFET58uWRSCR06dKFCRMmsHr1anx9fQkODkZbW5uGDRu+V8VsZGTE0aNHadasGerq6kycOJHDhw9jbW2NiYkJs2fPZsWKFYr1N27ciK+vLxkZGVSoUAF/f/9cn5P58+cza9Ys6tatS2RkJNbW1gwbNoxWrVp9cP3p06fz/OshzGtbEwMzC2p26Mn5LasAMHeoQMPew1jR3wOJigo12nbDtlqdD+5HDlQ1+WeIqb6+PkuXLqVbt26kpqbSvn3797p7fkzW6/tDypQpQ0BAABMmTKBnz56oqqpSp06dbOfxY3R0dPDz86NBgwZIpVIOHTpEx44dSUhIoEePHoSGhmJoaMhXX31F165dcxSrIAg5J5EX59Y6giAUeRkZGbi4uLBs2TLFRNEl1bWIZE68TPwnmfYvx1Yu4O3zJ3SfswI1CTS31sXVTJvg4GB++ukn0tLSqFWrFgYGBnh7eyOVSrMNGf1SiYmJ1KxZk2nTpmXrACYULRcuXKB79+7cv38fXV1dZYeT7zw9PenWrZt4zQo5Fh0dzcOHDzEzM8PBwYFr164hkUioUaNGgc5lWdxlzRmWVWkdExODk5MTc+bMwcjIiE6dOuHj40OPHj2UHWqBuXnzJr/99hutWrWiVatWik6SFy9eRCqVUrVq1WxzouVE69at+fXXX3FwcMiHiAuXnSFxBMd+eVWpk6EGnRwM8jAiQRCKGzEUVBCEQm379u2YmJh89Ne9kuJzSbV/S5fDiZeJXItIpnz58vj5+WFmZkZgYCBxcXF5Gtvo0aOpU6eOSFAUYTKZjFGjRjFv3rwSkVQDMRxUyJ1bt27h7e2Nt7c3Q4cO5eDBgzx58oQNGzYAxbtTZUHLSlB6eXlhYmLCmDFj2LRpE2XKlGHNmjW0aNEiW1KtJJz7qlWrMn36dDp06ICmpqbiOderV4/GjRvnOKkml8s5fvw4u3fv5sWLF1hbW2db7ubmxtu3bz+yddFV31wbtS/Me6tJoJ6F6JgpCMKniaGggiAUWjKZjNmzZ7N48eISXQkQnijNVVItS1Zy7cGV8+xev5q0tDQ6d+5Mw4YN8yy2HTt2cPLkSa5fv/75lYVCa8OGDaipqdGrVy9lh1Jg2rdvz9ixY0lJScnWRU4Q3pVViRYeHk5aWhqPHj3i9OnTrFq1ipUrV7J48WJATACelyQSCRkZGaiqqrJp0yZFZfXy5csJCQmhc+fOxMTEsH//fry9vUtMI4PSpTM7VMrlcsVzTk5OZv/+/XTp0iVH+0hLSyM8PJwFCxYQGhqKq6sr1tbWuLq6YmtrS0ZGRrHs6G2pq05za91cX0tlVf9b6ogpLgRB+LSS8UkkCEKR9Ndff6Grq/vRuTOKkz/++IP27dsr7pcvX14xB8aF18l837oaYQ9usXfhFH7wqMaMRvb80qsFT65d/OD+MqRStkz+mnVj+3MjVsaTJ094+vQpP/30k2IS3P86E8CzZ8/45ptv2LJlC/r6+v9pX4LyxMfH4+fnx5IlS0rMF1TInOi5SpUqnDhxQtmhCIVYVsKsQoUKqKmpIZPJMDIy4urVq2zcuBFHR0eg5HSpLChZc6ipqanx4sULunXrxuTJk4mMjCQiIoJevXrh6+ubo0ndi5t3k7ja2tq8fftW0YHzczQ1NenduzenT59my5YtnDlzhqFDhyKTybh9+zY//vgj8N+vDwojVzNtmlvr5rhy7d0pNQRBED5HVKwJglAoZVWrzZkzp0RUAjRp0oQxY8Ygk8l49eoVaWlpXLx4kUSpjCt3H5KalIhFeRdsnGvQ3Gc8WnoGXNiyis0TBzFh3zXUNf+puJGmJOM/YSC6xqXo/v0KVCVQv1FjIl+/onz58uzYsQP4bxUW6enpeHt7M27cOGrXrv2fn7+gPHPnzsXd3Z06dT48aXNx5uXlxe7du2nTpo2yQxEKOVtbWzQ1NbG0tKR58+YYGhpy/fp1Zs6cqZgTTMgf6urqPHv2jLZt2zJlyhTu3r1LdHQ0q1evxsbGRtnhFbjHjx+TmpqKs7MzAP3790dDQ0Ox/FPz/cnlcuRyOcbGxtSpU4fY2Fhq165Np06dsq1XXK+7XM20sdRV4+KrZB7/uwkUmck0OeBooEE9C21RqSYIQo6JxJogCIXSnj17UFVVpW3btsoOpUA4ODigr69PUFAQDx8+pFWrVgQFBbE38AZP/r6AfY26qKioUKPtP52cGvX5hhNrfiQy9BGWTpUBSE2M5w/f7lg4udD+27lIJBJUJZAg0SQtLY3Dhw8rLqD/yy/Sc+bMQVNTk2+//fa/PXFBqUJCQli9ejU3b95UdihK4enpScOGDVm5cqVIjAif5eXlhaenJ7a2ttjZ2eHo6MijR48Urx3RwCDvyWQyzM3N2bBhA05OTty4cYMNGzYwevRo2rRpw6xZs6hTpw6tW7dWdqgFJjU1lbNnzyoSa1mNDLJkvQbT09Pfa1CUNYx0zpw5XLp0iXv37uHm5kZKSgoODg6MHDmSMmXKFMwTURJLHXU6OaiTJJVxMyqFiOQMUjNkaKqqYKatSlUTLXTUxeeBIAi5IxJrgiAUOnK5nFmzZvHdd9+VqC8pTZo04dSpUzx69IgmTZpgZGTEmTNnePT3Rexr1gfgzIblXN3tT1zkKyRISE2MJzE6SrGPZ7f+JiNdSo95q/65uJZD+NsYbl68SO/evenSpUu2hFhuqy3OnTvHihUruHbtmkhGFHHjx49n7NixWFlZKTsUpShXrhympqYEBgZSr149ZYcjFHJ9+/ZFVVWV169fc//+fU6cOMGGDRt48+YNDRs2ZOrUqdjb2ys7zELnvyQcsz5jnJycCAsLY8yYMdjY2BAZGUn9+vWpU6cObm5uis+xkpDcdHZ2xtzc/KPL4+PjOX/+PNHR0fTs2TPbsqzzuXHjRm7cuEGNGjXo1q0bu3btIjAwsERN66CjrkJdcx1lhyEIQjEhvhEJglDo7N+/n4yMDDp06KDsUApUVmLt7NmzNGnShCZNmnDz0jme/D+x9uTaRc6sX0av+Wv47tQjpp95jKaeAZkDFzKVr9uUpgNGsWZIJ+LfvlE8/ubtWxwdHWnWrBlLly4FYMaMGaSmpqKiopLjrmoxMTH07t2b1atXl9hkTHFx4sQJrl+/ztixY5UdilJ5eXkREBCg7DCEIuDFixf07NmThg0bMmnSJC5fvoyuri7Ozs4sWbLkvQ6LQqa8SnRdv36da9euUbFiRfT09Ni+fTurVq2iXr16nDt3Lk+PVdh9rMGAVCpl165dnDp1iocPHxIbG/veOsHBwTg4OCCXy9HS0sLT05N169ahqqqa4+6igiAIQnaiYk0QhEIlq1pt2rRpJa4aqkmTJowdOxZzc3NsbGwwMDCgh3dvpOnpWFWowsOLJ1BRVUXXuBSyjHROrllKamL8+/vpP4J0aRq/D+2Mz6rd6BqXopy9PbHBcSxfvpz79+8DYGBggLe3Nzt27MjRuZbL5QwZMoR27dpla7QgFD3p6emMHj2aRYsWlfiOmJ6envTp04cffvhB2aEIhVRWFdTVq1dJTk4mODhYsezgwYP8/vvv6OnpFcsJ33Prxx9/xMvLi7JlyxIeHk5YWBhRUVE4Ojri5OT0n/bdtm1bYmJiAEhKSuLx48e8fPkSY2Njpk+fzvjx42nbtq2io2hJpK6ujq2tLWXKlKFJkyYf/GxPT0/H09OT+Ph4ypUrx4QJE9DS0lIMKRVzBgqCIOSeSKwJglCoHD58mMTExPcm0i0JnJyc0NPTo1GjRkBm4svG1h65njEqqqo41WuOU/3mLO5YFw1tHRr0Goqh+YcrJFr4jCMjLZXfh3Vm6Kpd6KqrcPflS/r370+LFi0YNWoU48aNo2vXrty9e1cxV8un/PHHH9y9e5fLly/n6fMWCt6aNWswMTEpkf/O/q1mzZokJCRw//59KlasqOxwhELgQ3NTAdjZ2ZGenp7tMQ8PD2rVqlVQoRU6WUnHrGTW3r172bNnD82aNePJkye8fPmSpKQkNDQ0OHnyZJ4cMz09nblz5xISEkJCQgLt27dn6dKl9O3bl7Zt25bYpFrW36BJkyY8efKE58+fY2trq/gbJSUlIZFIqFSpEpUqVQLg22+/ZeXKlWLOVEEQhP9IIhc/rwmCUEjI5XIaNGjAyJEj6dGjh7LDKRQSpTJ+vRNFxn94p1aVQD87DUZ/M5RRo0bh5ubGsGHDuHTpEpaWlmzcuFExrORj89M8ePCAhg0bcurUKVxcXL48GEHpoqOjqVixIocPH6Z69erKDqdQGD58OGXLlmXixInKDkUoBB49ekS5cuU+ujw6Opr79+9z69Ytbt++TXh4ONu3b6ddu3YEBASUyMTOqVOnuHTpEiEhIfTp04eTJ09SrVo1WrRogZ6eHgsWLGDs2LEfTFjm1r179xg5ciRLly5FU1OTnj178vvvv7Ny5Upmz56NsbFxHjyjouPdCrPExES2bNnCn3/+Sa1atZg7d65ivTFjxpCRkYGzszNOTk6ULVsWCwsLVFRU0NERc40JgiD8F6JiTRCEQuPEiRNERUXRtWvXz69cQuiqq+BgoEFwbNoX78PRQIPSRgbY2NiwatUq3NzcWLFiBUOHDqV9+/YYGxtz+fJl6tSpg0QieS+5JpfL+eabb5g1a5ZIqhUDs2bNwsvLSyTV3uHp6cn06dNFYk0A4OTJk9jb238wQebm5sarV6+wtrbG3t4eR0dHnJ2dkclkbNmypcQl1WQyGfPnzycgIIB69erRrVs3GjVqpKi8zjJhwoQ8O2alSpV49uwZERERNG7cmLp161K9enWGDBmCsbExqamp3L9/H5lMRo0aNfLsuIVVVlLt2LFj/Pnnn6ipqWFvb8+bN294+vQpdnZ2ALRu3ZobN25w6dIldu/eTXJyMiYmJjg4OGBhYcGIESNK/NQAgiAIX0pUrAmCUGg0adIEHx8fevfurexQCpXwRCn+wbGkf8G7tZoEvJ0MsdRRB6BRo0Z4eHgwZcoUEhISCA8PZ9u2bfz+++/4+fkxePDg9xJr6enp3L17lypVqpSYiaGLq/v379OoUSPu3r2LmZmZssMpNNLS0jA3N+fu3btYWloqOxxBiSIjI/H19WXLli3v/cAgkUh49uwZpUuXRiKREBUVhampKerq6kqMWPkaNWrEpEmTaNu2bbbHs4bUxsXFcebMGV6/fs2gQYP+07GyhjtevnyZVatWcfjwYWJjY1mzZg3dunUjPj6eQ4cOsWnTJqysrFi+fHmJmC9s0aJFBAYG0rhxY5o2bUqVKlWIiIhATU3toxV8cXFxXLlyhdu3b/P48WNFYyNBEAQh90RiTRCEQuH06dMMHjyYe/fu5clQkeLmWkQyJ14m5iq5piaB5ta6uJppK4aKhIaGEhsbi6OjI3fv3sXf358rV67Qvn17jhw5wvjx42nTpg2RkZGYmpoq9iUmMy4e2rRpg7u7e4nvBPohPXv2pFmzZnz99dfKDkVQot9//52bN2+yZMmS95ZlJddCQ0PZunUrkZGRvHr1irJlyzJs2DBsbGw+Opy+ODt79ixly5albNmyyOXybJ8VKSkpjBkzBjU1NcqXL8/w4cPzrKqvc+fOGBgYsGbNGlRVVTl9+jQzZ84kNjaWHj16lIg5w7Jeb3fv3uXp06fUrVsXExMTADZt2kS3bt3Q0NBQrPfmzRuuXr3Ktm3baNOmDd27d1fyMxAEQSgeRGJNEIRCoUWLFvTp04f+/fsrO5RCKzfJtXeTav+WkZHBhg0b2L9/P+XKlcPPzw99fX0OHTqEmpoaSUlJjB49mvXr1783nEcoug4cOMCYMWO4desWGhoayg6n0Nm2bRvr16/nwIEDyg5FUCIPDw8GDhz40SkJQkNDGTVqFIaGhnTv3p3ExETOnTuHqqoqP/74Y4nuSPmuRKmMG5FJRKbKeRsTh46GGua66tSxMkJH/b/9SJN1jrMq4l68eMHy5csJCgrC1NSUunXrcvDgQfbt25dHz6Zoefz4MfPmzUMmk7F8+XK0tbUV52z27NnExsby4MEDGjVqxIQJE1ixYgV2dnZ4eHgoO3RBEIQiS5SFCIKgdOfOnePJkyd4e3srO5RCzdVMG0tdNS6+SuZxXBoSyJZkU5OAnMw51epZaCuGf/6bqqoqL1++pF27dtkSmQ0aNGDp0qXs2LGDOXPmiKRaMZKWlsbYsWP58ccfRVLtIzw8PPDx8SE+Ph59fX1lhyMoQXR0NBcuXGD79u3vLcuq+Hn69ClSqZT169crltWpU0dR+VPSqtX+LSxRyoVXSTyJl77zGaUJqfA8TcaV6CgcDDSob66Npe6XDaHNSlyqqalx+/Zt+vXrR2JiIuvXr8fNzQ2AvXv3snXrVnr06FGiKq5/+eUXVqxYQbNmzfjpp58U7/dZr8vjx4+zdetW/Pz8KF++PABHjx5VdIguSedKEAQhL4nEmiAISjd79mymTJlS4uepyQlLHXU6OaiTJJVxMyqFiOQMUjNkaKqqYKatSlUTrU9WA2R9OfTz88v2BTAoKIhp06ahpqbGoUOHMDc3B/6ZI0co2pYvX469vT1t2rRRdiiFloGBAfXr1+fQoUOigUoJtWfPHkUXy3/Ler90cHDg9evXPH36lNTUVF68eMHq1atp3bo1QJFOSjx79gxnZ2diY2O/qOruU1XV1w/s4Nq+bQz6dTvBsWk8iUv7aFV1blSuXBkHBwf69euHm5sbiYmJXLx4kZSUFEXiSEVFhZiYGIyMjHK176ZNm9K7d28GDx78n2IsSKampixZsoSvvvoq2+NZr0sXFxciIiK4f/8+y5YtA+D58+eKhGRRfv0KgiAokxgKKgiCUl26dInu3bsTHBwsKmmUIC0tjRUrVrBhwwauX7+Or68v+vr61KhRg9q1a2Nra6vsEIX/KCIiAmdnZ86cOUOlSpWUHU6htnLlSs6dO8emTZuUHYqgBO3bt6dnz5706tXrk+stX76ctWvXUr9+fdTV1dHS0mLs2LHZ5qUsaf7rPKCfMmPGDB49evTev8us4Y0HDhxgzpw5TJgwgbdv3xIcHIypqSnjxo0DYPTo0Rw5coTr16+jqamZ4/iKUmLt33P7xcbGoq6ujo6OjuIxqVTK+fPnmTp1Ko8ePaJPnz6EhIRgamrKb7/9poywBUEQig1RhiAIglLNnj2bSZMmiaSakuzYsYPffvuNFStWsG7dOoYMGcKVK1e4desWb9++ZciQIcoOUfiPpk2bhre3t0iq5UCHDh2YMmUKUqlUVNCWMLGxsZw+fRp/f//Prjt8+HD69OnDrVu3UFdXp06dOgAldn618ERprpNqkDlM9MTLRCx11T46dcGnZJ3rNm3acOXKFXbu3Im1tTWWlpb07t2bjIwM+vXrR3p6OidOnMhVUq2oeTepFh0drZg7Df4Z3nn+/HkePXrEmTNn8Pf3JyEhAQcHBzG3rSAIQh4Q9b6CICjN1atXuXHjBgMHDlR2KCVWr169uHHjBubm5kRERFChQgX69+/PzJkz6dq1K48fPwYyfw0Xip4bN26wa9cupk+fruxQigQrKyvKly/P6dOnlR2KUMD27dtH06ZNMTAw+Oy6b9++JSgoiPDwcE6ePEmHDh0oV64cR44cATITGXnJzs6OefPm4ezsjLGxMQMGDCAlJSVb7NWrV8fIyIj69etz8+bNbNsuXLiQqlWroqury6BBg3j9+jUeHh7o6+vj7u5OdHQ0AE+fPkUikZCeng5kVmxNnTqV+vXro6enR/v27Xn79i3e3t4YGBhQu3Ztnj59yoXXyaTLYbKrGee3rGJB+1rMbl6BAz/NUJyLv/dsYeXAtoq4JruaEbhjHT90qEM5CzOGDx+eo88ZuVzOmDFjKF26NAYGBlSpUoXbt28zbdo0VFRUiIyM5NChQ5QpU0bxt3nw4AFOTk5YWVkxevTobPu7dOkS9evXx8jIiGrVqnHq1Kkv/TMVGsbGxgwbNkyRbMv6/6NHjwgPD0dFRYVu3boxZMgQhg0bhrb2fxuOKwiCIIjEmiAISjR79mwmTpxYrH9FLuxkMhnq6upcvHgRNzc31NTUSE5OViwfP348R44cQSKRkJGRocRIhdySy+WMHj2aGTNmYGxsrOxwigwvLy8CAgKUHYZQwHbs2EHnzp0/uU5Wkmjz5s3079+fTZs28erVK+rUqUPDhg159epVvsXn7+/P4cOHefz4MQ8fPuT7778H4Pr16wwcOJDffvtNUWXcoUMHUlNTFdvu3LmTo0eP8vDhQ/bu3YuHhwdz584lIiICmUzG0qVLP3rcrVu3snHjRl6+fMnjx4+pV68eAwYMICoqikqVKjFt+gxC4tIU6989eQDfTUcZ4X+cu6cP8nfAxysA7505wvBNRxm17RR//vknhw8f/ux5OHLkCGfOnOHhw4fExsby559/UqpUKUXy6M8//8TPz4+4uDgaNmyIqakpGzZsICYmhhkzZvDrr7+ye/duAF6+fEnbtm2ZOnUqUVFRLFq0iM6dOxMREfHZOAqrrOSkq6uroqIv63V77949zp49y8GDB9HQ0BDzqQmCIOQh8Y4qCIJSXL9+nStXrhSJuUuKs6wLaxUVFczNzbly5Qo+Pj5Mnz6dixcvMmvWLKZMmUJycnKJHOJUlO3atYvIyEh8fHyUHUqR4unpSUBAgKjSLEHi4+M5fvw4HTp0+OR6We+Xvr6+hISEsHv3bn766SemTp3KqFGj2L9/P5A/Fb6+vr6UKVMGExMT/Pz82LJlCwCrVq1iyJAhuLm5oaqqSr9+/dDU1OTSpUuKbUeMGIG5uTnW1tY0atQINzc3atSogZaWFh07duT69esfPe6AAQNwdHTE0NAQDw8PHB0dcXd3R01Nja5duxL49zXe7YPauN8IdAyNMbK0oUGvIdw4tOuj+246YCTa+oaYWNpQtV5jgoKCPnse1NXViY+P5/79+8jlcipVqoSlpaUisebp6UmDBg1QVVVFS0uL5s2bU6VKFcWwRwcHB0UCb9OmTbRp04Y2bdqgoqLCV199Ra1atThw4MBn4yisPtSVNuuzu3Tp0qirqzNmzBgqVKiAu7s7gwcPJi4urqDDFARBKHZEYk0QBKX4/vvv+fbbb8UQhEKiX79+dO/enYCAAKpXr46zszOTJk0iNTWVRo0ace3aNcW6IuFQ+KWkpDB+/Hh+/vln0dU1lypVqoSWltYnkw1C8XLgwAEaNGiQ48pOiURCUlISYWFh3Lx5kxMnTrB69WpFtW9+VAKVKVNGcdvW1pawsDAAQkNDWbx4MUZGRor/nj9/rlgOKLo8A2hra793PyEh4aPH/dy28QkJ2eZWM7KwVtw2trQhLuLjVXx6pUoDmXOtoaH1yTiyNG/eHF9fX4YPH07p0qX5+uuvsyWG3j1PMpmMwMBA6tWrh5WVFR4eHjx+/FhxnNDQULZv357t3J07d47w8PDPxlFUpKamcuPGDcLDw5k4cSL79+/n/v37nDx5El9fX8zMzHI0/FkQBEH4NHG1LQhCgbt16xbnz59n48aNyg5FeIeOjg6HDh1i0qRJdOnSBTMzM3r27EnFihWpXbs2aWlphIWFkZCQQOXKlZUdrvAJP/30E9WqVaNFixbKDqXIkUgkeHp6snv3blxdXZUdjlAAduzYQZcuXXK8fkZGBosWLSIlJYWUlBRkMhkmJiZMmjQJ+HDV0H/1/Plzxe1nz55hZWUFZCaS/Pz88PPzy/Nj5sS/f2eJefUSc8eKitsGZhY52k9GLn6wGTlyJCNHjuTNmzd069aNhQsXMnv2bOCfc581YX+PHj1QU1Ojffv2jB49mi1bthAZGQlknrs+ffqwevXqHB+7qMjqEnr79m2+//57rKysaNq0KV27dkUul2NtbY21tTVeXl7vdRQVBEEQck9UrAmCUOC+//57xo0bl60NvKBccrkcdXV1vvvuOwICApgzZw6///47RkZGzJgxg4yMDBo3boyfnx9Xr14lNjZW2SELHxEWFsbixYtZtGiRskMpsry8vBTzMAnFW1JSEkeOHMHT0zPH26iqqmJsbIytrS1NmzalZ8+ejBw5krJlywIQERGRbY6zvLB8+XJevHhBVFQUc+bMoXv37gD4+PiwcuVKAgMDkcvlJCYmsn//fuLj4/P0+B/z73zM2Q3LSY6LIebVSy5sWUXVll452o9qDhM7V65cITAwEKlUiq6uLlpaWooKwaSkJK5cuUJSUhIXL17Ex8eHsLAwNDU12bJlCxKJhM2bNyv21bt3b/bu3cvhw4fJyMggJSWFU6dO8eLFixzFUphlJcpq1qzJjz/+iJeXF05OTtnWkclkIqkmCIKQR0TFmiAIBeru3bucPHmS33//XdmhlHhZv+jDPxfhHTp0QCKR0KlTJwYOHMiZM2dISkpi165dyOVyAgMDWbVqFbq6usoMXfiEKVOmMHjwYBwdHZUdSpFVt25dXr9+TUhICA4ODsoOR8hHhw4dok6dOpiamuZquxEjRrz32ObNmzly5AjR0dFMnz49Tysee/XqRcuWLQkLC8PT05OpU6cCUKtWLVavXo2vry/BwcFoa2vTsGFDGjdunGfH/hRViQQ1CYrhoJWatuYXb3dSEuKo2b4Htby8P7sPNQloqeYsuRMXF8eYMWMICQlBS0uLVq1a8e233wKZVdd///03vr6+REZGUrlyZcaOHcvSpUvR19encePGdOvWjZiYGCCzYi0gIIAJEybQs2dPVFVVqVOnDitWrPiic1EYRUVFYW9vj729PUC2RJpoXiAIgpB3JHIxWY4gCAWgcePGqKmpoaGhQZMmTZg8ebKyQyrR5HI5U6ZMYebMmWhoaLy3PGto1PXr1/nzzz+Jjo6mW7du+Pv74+XlRfv27ZUQtfA5ly9fxsvLiwcPHqCvr6/scIq0wYMH4+LiwpgxY5QdipCPevXqRZMmTRgyZEiut826hJbJZKiqqnL37l3CwsIwNDSkXLlyedaN187OjjVr1uDu7p4n+8tLiVIZv96JIkMOk13NGLc7ENOyuUtGq0pguIsJOur/PdHz6tUrHj9+jLa2tiKxmZiYWGJ/DHJzcyMwMPC9x+VyueI/0ZhIEAThvxM/VQiCUCDCw8M5efIkhw8fZt++fWJicCX7448/2Ldv33tDQGQyGQBdunQhISGBFStWoK6uzrBhw2jevDmrV6/Gzs6O6OhoIHOuIaFwkMvljBo1ijlz5oikWh4Qw0GLv5SUFA4cOICXl9cXbS+RSJBIJIrEhLOzM+7u7tSuXTvPkmqFna66Cg4G7/84kxuOBhp5klQDsLCwoEGDBoqkmkwmy5ZUS09Pz5PjFAWxsbHI5XL69u3LL7/8wqlTpxTVehKJBBUVFVRVVTl//rxyAxUEQSgGRGJNEIQC8e6Qg8DAwGI11KKoefDgARMnTmTr1q2oq6tnW/bu3+nixYvs37+fWbNmUa1aNdLS0lBRUUFbW5sBAwaQmJiIqqqqIhknKNfmzZuRSqX069dP2aEUCy1atCAoKEgx0blQ/Bw5coQaNWpk63SZG1lJCsicVy1rioOSNhikvrk2al84TZeaBOpZ5F938Hc/05KSkrh9+3a+HauwMTQ0ZNeuXdSuXZvnz5+zcuVKFixYwNWrVxk9ejRLly5l+fLlDBs2TNmhCoIgFHlijjVBEApE1q/EWlpa+Pn5MWXKFCVHVDKlpqbSs2dPZs2ahYuLyyfX/eqrr+jQoQPnz59HKpWyb98+bG1t6dKlC02bNuXrr7/G399fzNNSCCQmJjJp0iS2bt0q/h55RFtbG3d3d/bt20f//v2VHY6QD3LbDfRdcXFx/Pjjj8yaNQsAAwMDRQVwXk8G//Tp0zzdX16z1FWnubUuC69HKOZaywkVuYzmNvqYa+XvUMT09HSeP39Onz590NbW5ujRo/l6vMLE2tqaYcOGER8fT3x8PN988w2nTp2iS5cuPHnyhIiICMUcdVlEQwNBEITcE3OsCYJQIExMTEhOTubw4cMFNqmy8L5x48YREhLCX3/99ckL56wLa6lUysuXL5kzZw6Ojo7Ex8dz7tw5/vrrLwYOHMjPP/+smBRZUJ7vvvuOR48eZet4J/x3Gzdu5K+//mLXrl3KDkXIY6mpqVhaWnL79m2srKy+aB9nz56lYcOGivfSpKSkEt3t+lpEMideJn4+uSaXI5HLiAk8zLxv+mZrpJNfEhMT6dGjB0lJSRw/fpxEqYxbUSm8Sc4gNUOGpqoKpbVVqWqilWfDUgujZcuWYW5uTteuXYmPjxfTBgiCIOQRkVgTBCHPfOpC9Y9VK/Dw8BAd9pTo0KFD+Pj4EBQURKlSpXK83YsXL2jZsiUnTpzAwsKCadOmcfbsWSpUqMBvv/0GwPPnz4mIiMjTLnhCzoSGhuLq6kpQUBBlypRRdjjFSlRUFHZ2drx69apEJ0yKowMHDjBv3jzOnj2r7FCKlfAkKRdfJfM4Lg0JZEuyqUkgQyYj9Oo5eBKEi405w4cPL9AKqfBEKRdeJxPykfjkgIOBBvXNtbHUVf/YbookUYkmCIKQf8RQUEEQ/rNPXag+jIGz4Uk4ePRB2zz/5lERPu3169cMHDgQf3//XCXVAGxsbHB3d2f+/Pm4urry/PlzkpOT6dOnDwA3btxgz5493Llzh+XLl2NiYiIu3gvQhAkTGDlypEiq5QMTExNq1arF0aNH8fT0VHY4Qh76L8NAhY+z1FGnk4M6SVIZN6NSiEjOID4llcBzZ4h69hi11yE8vncHDw8Pvv76ayDvh85+zOcq6rIeD45N40lcGs2tdXE1Kz7XLTk5z1KplOfPn+Pg4FAglYSCIAjFhahYEwThP8nx0A8yfw0ubheqRYFMJqNt27a4uroyZ86cXG377i/cixcvZsqUKXTv3p3ly5crhpBs2rSJO3fuMHLkSCwtLfM8fuHjzp49i7e3N/fv3xcVVflkyZIl3Lhxg7Vr1yo7FCGPSKVSLCws8q3KUy6Xk5CQgEwmw9DQMM/3X5RkfYb8/PPPXL16lZYtW1K3bl2cnJy4cuUKhw8fRl1dnfr169OoUaN8iyM31ypZivM1y4eq1zIyMkhJScHX15d58+ZhYWEhkmuCIAg5JN4pBUHIFRcXF06dOgXk/kI1XQ4nXiZyLSIZf39/WrZsqVimp6dHSEhIPkQsLFmyhOjoaGbMmJHrbSUSiaLDnZeXF8uWLWPDhg3o6+sjlUoB6NmzJ/PmzcPS0pIXL14wZsyYvAxf+IiMjAxGjRrFggULRFItH3l6erJ3714yMjKUHYqQR06ePEn58uXzJakWHR3NH3/8wYoVK9i5c2ee77+o6tmzJ6tXr6Zv377ExMQwZswYevXqxYwZM4iLi8vXaSLCE6W5TqrBP9cs4UnS/AlMiSQSCenp6aSkpJCRkYFUKiUmJgYNDQ3c3Nzw9fUFEEk1QRCEHBLvloIg5MqdO3do2rTpf75Qbd6xG0eOHFE8npCQIOZfywfXr19n7ty5bN68GXX1L5svRiKRIJPJcHR0xMfHB8hM6mTt791fvW1sbNiyZQunT5/+78ELn7Ru3Tp0dHTo3r27skPJd1nJXWUU2dvZ2WFjY8OFCxcK/NhC/sjrYaByuVyReFVTU+PNmzfo6+t/cVOE4iTr88Hc3Jy7d+9Sp04dfHx8UFFRISAggLp169K3b1+sra3zLYYLr5NJl4PsC5Lj6XK4+Co5H6JSrqtXr7JkyRLGjRtHvXr1uHPnDqVKlUJdXV1xLRYXF6fkKAVBEIoOkVgTBCFH0tPTs93PulD9on0V0wvVwiYxMZGePXvy888//+ek5b9/tVZVVX1v2e3btxk5ciTt27enfv36/+l4wqfFxcUxdepUlixZUiLms8t6ju8+14JMsnl6erJ79+4CO56Qf9LT09m1axedO3fOs31KJBLFe6K+vj6TJk2ib9++lC9fHplMlmfHKeo2bdpEjx49uHHjBosXL8bZ2Zl+/foRHh4O8MVVoffu3aNp06YYGRnh4uLCnj17AOjfvz+DhwzFr28nvqtvS8jVc9w/e4SlPZsxo5E9P3hU49jKBYr9RIc9Y7KrGX/v3coPbaozu3kFTq75kcdxaSRJZSQnJ9OvXz+MjY2pVKkSCxYswMbG5r+fGCWIjY3l5s2bNGzYEF1dXapXr45cLufQoUMsW7aMtm3bYmBgoOwwBUEQigyRWBOEEsDOzo558+bh7OyMsbExAwYMICUlBYB9+/ZRvXp1jIyMqF+/Pjdv3sy23fz586latSq6urqkp6djZ2fHvkNHCIlL4/nta/zaz4OZjR2Z29KFgB8mki5NU2w/2dWMwB3rWORZh5mNHQmYN0HxZXjbpvXUb9BQsa5EIuHRo0cA7N+/nxo1amBgYECZMmWyDWF8+vQpEomE9evXU7ZsWUxNTXM9b1hJMWrUKOrUqYO3t3e+7P/dxMaRI0dYuHAh5ubmfPPNN6irq4svlPno+++/x8PDg5o1ayo7lHwTHx/P+fPnWbduHXv37uXWrVsEBQVRtWpVoOAmPIfMYdC7d+9WSsWckLfOnDmDra0t9vb2ebK/ly9fcvnyZbZt28b8+fOZPHkyAwcOZOjQoYwbN06RNCrJshJmpqam3Lp1K9uyvn37oqmpSUZGxhc1CJFKpbRv356WLVvy5s0bfvnlF7y9vXnw4AEAW7dsocXgMcw49wTb6m5oaOvSbdZyvjv9mH5LNhO4Yx13Th7Its/QoEDG/XWRwSt3cnz1Yt6EPORmVAozZ87k6dOnhISEcPToUTZt2vSFZ0T5WrRowc8//0zPnj1p0KABLVq0oGHDhpw4cYKmTZvSvXt30tPTFVN/iM9zQRCETxNdQQWhhPD39+fw4cPo6urSvn17vv/+ezp37szAgQPZu3cvtWrVYtOmTXTo0IEHDx6gqakJwJYtW9i/fz+mpqaoqWW+ZYTEpyGxABUVVdqNm421c3Xi3oTxh28PLv25lobeQxXHvXfmCMM3HSU1IZ5l3u5UbNyKCg1aAJCY/uELNV1dXTZs2ICLiwu3b9/mq6++onr16nh5eSnWOXfuHA8ePODhw4fUqVOHTp06UalSpXw6e0XP9u3bOXXqFNevX8+3Y0gkEqKjo5HJZFy5coVq1arRsWNHxRdWMTdL/ggODmbt2rXcvn1b2aHkq9WrV7N+/Xrc3d2Ry+UkJyfz/PlzUlNT2bRpExUqVKBChQoFUlVRrVo1MjIyuHPnDpUrV8734wn5Z+fOnXkyDDRr8ve1a9cSEBBA+fLlqVKlCrdu3UJDQ4POnTtTuXJlRZOXkizrs2DMmDHI5XKSkpLYvn0727Zt4+HDh2hra3Pp0iXWr1+f631funSJhIQEJk2ahIqKCs2bN6ddu3Zs2bIFgNrubShTzS0zDk0tHGo1UGxr6eRC1VYdefL3BVyatVE83uLrb1HX0sbSqTKWTi68eHiHCNcq/Pnnn6xYsQJjY2OMjY0ZOXLkF81dWlgYGxsDmT/UHD9+HDU1NSQSiaIKLzIyksmTJ7N69WoqV65MRkZGtmp1QRAE4R8isSYIJYSvr69iomY/Pz9GjBjB27dvGTJkCG5umRed/fr1Y+7cuVy6dIkmTZoAMHLkyPcmeI5JlaEhB2vnaorHjK3KUqdzX55cu5gtsdZ0wEi09Q3R1jfEoXYDwh/epkKDFsjkIJV9uPqjadOmittVq1alZ8+enD59Oltibfr06Whra1OtWjWqVavGjRs3RGLt/549e8bw4cPZv39/vnypu337NhoaGiQnJ7N06VJUVFTYtGkTO3bsyLMqEOHjxo8fz7fffouFhYWyQ8lXqqqqLF26lCZNmnDr1i1evXpF9erVuXXrFjt27CAwMBBnZ2cGDBiAlpZWvsYikUgUw0FFYq3oysjI4K+//uLMmTP/eV9ZiTUnJyeqVKnC8OHDsba2ZvLkycyZM4fHjx/nW7VwUZNVXaqjo6NI1NSrV48ePXrg7u6umItOV1c31/sOCwujTJky2X7IsbW15eXLlwAYW2Sfu+3Zrb85/MtsXj+6T3p6GhlpaVR275BtHb1SpRW31bW0SUtKIDVDpjhWlvxoflHQsl7HLVpk/uD522+/MX/+fCQSCcOGDWPu3LkMHz6c06dPi6SaIAjCJ4jEmiCUEO9eANra2hIWFkZoaCjr16/nl19+USxLS0sjLCzsg9tlkcrkaAARoY/Zv3gaL+8FIU1JRpaRgXXFqtnWff8CNVFx/yN5NQIDA5k0aRK3b98mLS2N1NRUunbtmm2dd5MKOjo6JCQkfPoElBDp6el4e3szbtw4ateunS/HMDU1pVatWlStWpVWrVrRunVrmjZtqqhyFPLP0aNHuXPnDn/++aeyQ8l3o0aNUtyuUqUKVapUAaBJkyZYWVnx6tUrRowYQaVKlbIl4/OLl5cXEyZMYOrUqfl+LCF/XLhwAXNzc8qXL59n+4yKikJbW5tatWopHtPT0+POnTsAosrn/7LOQ7du3Rg2bBhly5YlMDCQixcvoqGhQaVKlShXrlyu92tlZcXz58+RyWSK5NqzZ89wcnLi6dOnqKlkHzK+zW8o9boNov8vW1HX1GLvQj+SYqI+exxNVRVF52tnZ2cAnj9/nut4C5t3h9QHBgayceNGZs+ejZmZGQMHDmTDhg3Y29sTERGBiYmJeC0LgiB8hEisCUIJ8e4F4LNnz7CysqJMmTL4+fnh5+f30e0+NI+R+v8vVAPmfotVxSr0nLcKTV09zvmv5PbxvTmOSeUjUyT16tULX19fDh48iJaWFqNHjyYyMjLH+y3J5syZg6amJt9++22+HcPCwoLz588jl8vR19enVKlSVKhQId+OJ2RKT09n9OjRLFq0qNgnMWNjY7l16xZubm6K+fqyvjSrqqpSsWJFKlasSO3atRVfcvNbo0aNCAkJ4fnz58WiUqUkystuoFmfjW5ubuzevZuRI0eiqalJUFAQr1+/Zvv27QAiEfF/WeehRo0aQGZX42vXrqGjo8OlS5fQ19dn0aJFVKhQQVFFlRNubm7o6OiwYMECxo0bx/nz59m7dy9Xrlzhhx9+QFddBTUJimZLqYkJaBsaoa6pxfPb17hx6C/K1236yWOoAGbamUnBefPmUbt2bZKSkli2bNmXno5CqXz58kRGRlKtWjVMTEywsLCgVq1aTJkyBTMzMwCio6O5ffs2jRo1UnK0giAIhYuYAEcQSojly5fz4sULoqKimDNnDt27d8fHx4eVK1cSGBiIXC4nMTGR/fv3Ex8f/8l9GWlmXqimJiWgqauPho4ub54EE7hjXY7jUZH8k6D7t/j4eExMTNDS0uLy5cts3rw5N0+1xDp37hwrVqxgw4YN+T6/ma2tLXZ2dpQqVeq9ZcnJyaKCMB+sXLkSS0vLL5rgu6h5+PAhkydPpkWLFqxYseK913NWE4G1a9dSunTpD+0iz6mpqdG2bVtFx0GhaJHJZHk2vxr8k1hzdXVl3rx5aGlp4eLiwvjx4zl37pz4seETTp48yZo1a2jSpAljx47l1KlT1K5dm99++w3IXcdfDQ0N9u7dy8GDBzE1NeWbb75hw4YNVKxYEQAzLTXe3Zvn5PkcWzGf6Q3tOL5qEVW+6vDhHb9DDlQ10eK7777DxsYGe3t73N3d6dKlS7H5kSMjIwMTExOmTp2Kt7c3FSpUQCqVsnLlSqZMmQLATz/9hJmZGdu2bVNytIIgCIWPqFgThBKiV69etGzZkrCwMDw9PZk6dSo6OjqsXr0aX19fgoOD0dbWpmHDhjRu3PiT+3LQ1+Ae0Gb0DHbNGceZ9b9gVaEKVVt68fjK2RzHpKv24eTPr7/+yrhx4/D19aVJkyZ069aNmJiYXDzbkic6OprevXuzevVqxXw1yhIbG0vt2rW5dOkS1tbWn99A+Ky3b98ya9Ysjh8/XqDdMJWldu3aHD16lLCwMEWli1QqRV1dHSjYjqDv8vLyYuXKlQwfPlwpxxe+XGBgIEZGRoqES15ydXXF2dmZZcuWcffuXZ4/f07nzp0Vk8MLmbIq0R48eEC1atXo3LlztuWGhoZA7hvfuLi4cPr06fceX7duHQA7Q+IIjs3sWF7FvQNV3D+cTDO2Ksu8axHZHvt6dQBOhhroqKuAui4bN25ULFuxYoViov+iLut9tnfv3gQFBVGmTBmmTp1K2bJluXv3Lt27dwcyf8CrW7euMkMVBEEolCRy0TteEIo9Ozs71qxZg7u7+3/eV9myZdm0aRMRNtUVF6pf4vmRbdw7vJMTJ07855hKOrlcTvfu3TE3N882X54yff/995w8eZKjR4+K7qB5YMSIEchkMpYvX67sUJROmXNWJSQkYGVlxbNnzzAyMlJKDMKXGTduHPr6+vnSxTE6Oppp06ZhaGjI2bNnqVChAjo6Ovj4+FC5cuVcDW0szrLOw5MnT+jatSsjRoxALpcTGhrKqVOnWL58eb4M7Q5PlOIfHKsYDpobahLwdjLEUked8PBwQkJCqFevHsHBwbRt2xZfX19Gjx6d5zErQ9Z7a3x8vKLx0YQJE1i0aBHNmjXj+PHjAIqh+eJ1LQiC8A/xbUcQhByLiIggIiICOzs76ptro/aF11NqEsh4+Uh0kMwja9eu5d69eyxcuFDZoShMnjwZqVTKokWLlB1KkXfnzh22bdvGzJkzlR1KgZLJZAQGBuLt7c3ixYs5d+4coNw5q/T09GjSpAkHDx5UWgxC7snl8jydX+3d/QL4+/uTkJDA999/T8WKFWndujVlypTh6NGjQOZrWcisNJXL5djb2/Pdd99x+PBh7ty5g4aGBmvWrMHZ2ZmUlBQgd8NBP8dSV53m1rq5vmZRk0Bza10sdTIrZdPS0hgyZAj6+vo0b94cT09PvvnmmzyLU9my3lv19fUJCgpCRUWFhw8f0rx5c1xcXGjRogUDBgxg1apVgPIqhwVBEAojMRRUEIQcuXLlCl999RUjRoygbNmyQOYF54mXibn6FVhNAvsmDyDiWYhicmfhy92/f5+JEydy+vRptLS0lB2OgqqqKps2baJWrVq0aNGCmjVrKjukIkkulzNmzBimTp2KqampssMpEFnVEAcOHGDz5s3Y2dkxY8YM9PX1efXqFV5eXvz1119Ki8/T05Pdu3fTs2dPpcUg5M7Vq1fR1tbGxcUlT/crk8lQVVUlJiYGW1tbJBIJTk5OvHz5EltbW65evQqIBMS7ss5Fhw4d6NChA0lJScTGxnLjxg2+/fZbtLS02Lx5c56fM1czbYAcX7NkJdWytoPMuUVv376dp3EVVi4uLgQEBNC+fXsGDRrEsGHDMDEx4dmzZ9y+fVt0uxUEQfgXkVgThBLg6dOn/3kftWvXfm+esy+9UB1/MOedQ4WPS01NpWfPnsyePTvPvzDmhbJly/LLL7/Qq1cvrl27hq6urrJDKnL27dvHixcvGDZsmLJDKTBZlSqnT5+mbdu2pKenM2fOHEaOHMmIESMUc2Slp6ejplbwlzHt27dn/PjxpKamFpuJy4u7rGq1vE7WZO2vWrVqLF26FICaNWvi4eFBq1atmDVrFpD7OcNKArlczp9//snJkyf5+++/AbCyskIqlfLy5Uusra3zfKihq5k2lrpqXHyVzOO4NCSQ7dpFTZLZqMDRQIN6FtqKSrWSRiaToa6uTvv27bl69SpJSUlUqlQJAHNzcypWrMitW7eoXr26cgMVBEEoRMQnvSAI/4mrmTbeToY4GWqgKuG9oRZqElCVgJOhBt5Ohtl+/RX+mylTpmBnZ8fQoUOVHcpHde/enXr16hWbOWgKUmpqKmPHjuWnn35STNpfEmRVQTx79gxXV1euX79OamoqAElJSdjZ2WVbr6CZm5tTuXJlTp48qZTjC7mTX8NA4Z+EWe3atWnVqhUpKSnUr1+f33//nVWrVlGtWrU8P2ZxIZFI2L9/P6ampqxfv54rV64QEBBArVq1WLNmDZA/Q2gtddTp5GDAcBcTGlrq4GKsSTkDdVyMNWloqcNwFxM6ORiU2KQaZE8EBwQEKOa9CwoKIikpCTU1NaZNm6boDpqRkaGUOAVBEAoT0bxAEIQ8kySVcTMqhYjkDFIzZGiqqmCmrUpVE63MjlpCnjl06BA+Pj4EBQVRqlQpZYfzSfHx8dSoUYP58+e/1wVO+LhFixZx6tQp9u3bp+xQlOL169fo6+sTGBjI+vXrKV++PL///jsnTpxQJNeUZeHChTx+/JiVK1cqNQ7h865fv06XLl149OhRvg7JlMvlvH37lqSkJKKjo4mJiSEsLIxu3bqJIXP/8qFKtJSUFJ4/f87cuXNRVVVlzZo1imHhQsHL+htFRERw7949goKCuHDhAgCNGzemVatWeHl5ERQUJF7fgiAIiMSaIAhCkZE1p8nr16+pUaMG/v7+NGvWTNlh5UhgYCAdOnTg6tWrlClTRtnhFHqvX7/GxcWFCxcu4OTkpOxwCkzWl7nExER0dHQUX763bdtGSEgIHh4ehWL4UXBwME2aNOHFixfii38h5+fnR3p6OvPnz8/X4/z000/cu3eP+Ph4YmNjCQwMxMDAgBMnTohGPZ9w8+ZNHj16REhICK9evUImkzF58mTMzMyUHZrwf7GxsQwdOpSBAwdiYWHB4MGD+eOPP1i5ciVjxowRr29BEATEHGuCIAhFQlJSEubm5owbN45Lly4xYMCAIpNUA3Bzc2PkyJH07duXY8eOiV+4P2Pq1Kn069evRCXV4J/J4BctWkR6ejqzZ89WTADfqVMnKlSokOfzLn2J8uXLY2RkxJUrV3Bzc1NqLMLHyeVytm/fjr+/f74dI6uqytTUlNq1a1OhQgXs7e0xNzdn7Nix/P3339jb24vqq484dOgQISEhWFpaUr16derUqYOZmRmPHz/mxIkTGBgY0L17d2WHWaIZGhpy/fp1kpOTqVKlCoMHD6Z69eq0atUKGxsbxXqF4b1ZEARBWUTFmiAIQhFw/fp1GjZsiFQqRUNDg9u3byt9OFxuZWRk0Lx5czw8PJg0aZKywyl0tm7dyo0bN2jXrh2dO3fm/v37GBkZKTusApWVfGjZsiUzZ87E3t4eX19fXrx4gZmZGQsXLlQ0L1C2KVOmIJfLmTdvnrJDET7i1q1btGvXjqdPnyrlC/+iRYswNDTEx8enwI9d2GX9W7916xZpaWlUqVIFDQ0NAMLDw/Hz88PU1JQ6derky/x4Qs5kVcr//fffbN26lbNnz3L9+nUmTJjA7NmzkcvlXL58WfzAIAhCiSd+OhMEQSgC7t69C4BUKlX8avzq1SslR5U7qqqqbNy4kR9//JErV64oO5xCZ9++fSxcuJCmTZvi4eGBgYGBskMqcFkVPcnJyZQrVw4/Pz/c3d25dOkSsbGxhaoawsvLi4CAAGWHIXzCzp0786Ub6MekpaXh7+9P7969GTZsGBUrVhRJtY/I+rdepUoVatasiYaGBnK5HLlcjqWlJRMnTmTcuHF06NBByZGWbFnV5TVr1iQ+Pp6EhARu3LjB7NmzefjwIV9//TXe3t7s3ZvZ7T0/Gk4IgiAUBSKxJgiCUAT8/fffJCUloaGhga6uLtOmTaN06dLKDivXypYty7Jly/D29iYhIUHZ4RQqb9++JSMjg/T0dDZv3kybNm2UHZJSyOVyfHx8aNiwISkpKQwYMIAXL14QFxdHhQoVlB2eQq1atYiJieHBgwfKDkX4iPzqBvohMpmMnTt3snv3btq3b0+9evVYu3Ytvr6+QObrWviwrK6SEolEkQR1cnLC3NxcUcUmKE9WsuyXX35RVMuvW7eOKVOmEBERwejRo1m9ejVRUVGoqKiI5JogCCWSGAoqCIJQCCRKZdyKSuHNOx1VS7/TUdXBwYHQ0FCmTJnCt99+W+SrmQYMGICKigq///67skMpNOrUqcOVK1dQV1dHX18ff39/WrdureywlEYqlSKTydDU1GTPnj2cOHGCn3/+uVDNVTVs2DDs7e2ZMGGCskMR/uXevXt89dVXPHv2rEBeL4mJibRr1465c+dSq1Yt1NXVgcwE0cOHD/P9+MXJkydP2L17N2PGjBHzdhUyr169YsyYMaSmptK9e3fF/HfDhg2jVKlSfP/994XqPVoQBKGgiHc9QRAEJQpPlLIzJI5f70RxLjyJu9GpPI6Tcjc6lXPhSSy/E8XOkDgWr9lAaGgos2fPLvJJNYClS5dy5swZduzYoexQCo0nT54gkUjo2rUrISEhJS6pllXlcPHiRUaPHk2NGjV49OgRMpmMdu3a8dNPPwEUqi9snp6eYjhoIbVz5046d+5cYK8XXV1dnj9/jpubG+rq6ooKNVtbW8LDwwskhqIqOTmZ9evXK+7b29vj4OBARkaGSKoVMhYWFjRv3pw1a9Yokmr79+/n5s2bODs7A5nv0SkpKcoMUxAEocAVnqtTQRCEEuZaRDL+wbEEx6aRIYf0f9UPp8shQw7BsWk8NqnIG81Sygk0H2RVZA0fPpznz58rO5wCkyiVcel1EnuexrP9cSx7nsZz6XUSSVIZtWrVYt26dfj7+2NoaKjsUAtcVgJk6NCh9O3bF5lMhr6+PmlpaYwbN443b94oOcL3NWvWjDt37vD69WtlhyL8S0EPAwVo3ry5Yv5IiUSCTCZj0qRJ6OjoFEgcRZW2tjYpKSlER0crHuvQoYPoHl3IZL3OfXx8MDEx4f79+8yePZsDBw7Qt29fevXqBYC/vz/NmjXj6dOnSoxWEAShYImhoIIgCAWgf//+2NjY8P333wOZSbUTLxNJl8P26b4Ylrai5fApn9yHmgSaW+viaqZdECEXiLlz53LkyBGOHz9erL9EhSdKufA6mZC4NCRkT6KqSUAOOBhoUN9cG0tddWWFqXT3799n1KhRBAQE0LBhQ65evQpApUqVuHfvnpKj+7AePXrg7u7O4MGDlR2K8H/BwcE0btyYFy9eFOj7yvPnz7G0tERNTU3xWGpqKmlpaejr6xdYHEVRQkICenp6yg5DyKErV66waNEiKlSogJubG/Xq1cPExITff/+dPXv2MGTIkBI7T6ggCCWTqFgTBEEoYOGJUkVSLTfS5XDiZSLhSdI8iWPdunU0bNgwT/b1pSZOnAjAggULlBpHfspNZaJ/cCzXIpKVE2ghkJqaSv369fnzzz+pW7cukDmkz9HRESicHec8PT3ZvXu3ssMQ3rFz5046depU4Mn60qVLs3z5cuzs7OjTpw+hoaGcOHGC5cuXA4Xz9VtYfC6plpGRgVSaN599wn9Xq1YtOnbsSM+ePWnbti0mJiYA9OrVi40bN9KmTRtSUlLE/IKCIJQYIrEmCIJQwC68Ts51Ui1Luhwuvio+iRdVVVU2btzITz/9pBhCVZy8W5mYE1nJ03eTazNmzKB37975FGHhIZfLqVatGqVLl2b06NH89ddfDB06lF27dtG5c2fFOoVNmzZtOHPmjOhyW4gU5DBQ+Od1efv2bbZt24a/vz9169Zl5syZuLq6cuDAgQKLpbiJjY3l5MmTrFq1ilOnTik7HAEUDSW6detGpUqVFI9B5rDerHlgk5OT8fHx4eXLl0qLVRAEoaCIxJogCMIn2NnZsXDhQqpWrYquri6DBg3i9evXeHh4oK+vj7u7u2JemK5du2JhYYGhoSGNGzfmzp077+0vUSrjbngUq7/2Ys+Cye8lCv7es4WVA9tme2yyqxmRz0KAzGGjiyaNpmXr1ujp6dGgQQNevXrF6NGjMTY2pmLFily/fl2x7Q8//ICjoyP6+vo4Ozuza9cuILNj3tChQ7l48SJ6enoYGRnl5WnLlTJlyrB8+XJ69epVZJITTZs2Zc2aNZ9cp7BUJhYVEomE+/fvM2zYMEJDQ1m3bh2Ojo7MmDGDAQMGABTK4cKGhobUrVuXw4cPKzsUgcwmIM+ePaNRo0YFdsysCfaNjY1JT0+nQYMGeHt7c+/ePdLS0hSVVoWp8UZRER8fz6FDh3j58mW2OdgE5cl6vb/7es567N1rGl1dXUqVKiXmWhMEoUQQn/CCIAifsXPnTo4ePcrDhw/Zu3cvHh4ezJ07l4iICGQyGUuXLgXAw8OD4OBg3rx5g6urK97e3u/t69yjl6we0gnbanXoMGHeF3U8u3E0gF5jpxEZGYmmpib16tXD1dWVyMhIunTpwtixYxXrOjo6cvbsWWJjY5k+fTq9e/cmPDycSpUqsXLlSurVq0dCQgIxMTFffH7yQteuXWnUqBEjR45Uahx56d+ViXK5/L2hYBnp6R/ctrhVJuZEXFwcCxYsYPTo0fj7+6OpqYmPjw9mZmZA4axWy+Ll5SWGgxYSO3fupGPHjtnmOSsoDg4O2NnZ0bNnTzZu3EhYWBjjxo2jR48epKWliaGgOSSXy0n//3ujjY0NgwYN4rvvvqNbt25Kjkz4FJlMprim2Xv4GEPn/0r5Tj48M62YrVGPIAhCcSQSa4IgCJ8xYsQIzM3Nsba2plGjRri5uVGjRg20tLTo2LGjokJs4MCB6Ovro6mpyYwZM7hx4waxsbGK/YSFhfG1Vysqu3f4bKOCT3Fp1oZSTlUVx9fS0qJv376oqqrSvXv3bBVrXbt2xcrKChUVFbp370758uW5fPnyl5+MfLR06VLOnj3L9u3blR1KjkVHR9OuXTvMzMwwNjamXbt2vHjxgkSpjJC4NFb5eHJ42RxWDmjDd/XLEvXiKZNdzbi47XcWedZhsZcbAHsXTuEHj2rMaGTPL71a8OTaRR7HpSm+hKSlpdG3b1/09fVxcXFRTOpfHGQlGy5dusSjR48IDg4mNDSUH3/8kR49evD777/z5MmTL0pCF5QOHTpw4MABMQdUIbBjxw7F0OGClJX4rVixItHR0Tx+/Jivv/6aYcOGMWrUKDQ0NETFWg5JJBJFYvTq1atoaWmhoaEBiHnqCiu5XI6KigrhiVJmBpznhlElnNr0wNilNqFJcu5Gp3IuPInld6LYGRJHeKJ4rxQEoXgRn/CCIAifYW5urritra393v2EhAQyMjKYNGkSjo6OGBgYYGdnB0BkZKRi3f3795OanIxbl/7/KR49EzNSM2SfjCfLhg0bqF69OkZGRhgZGXH79u1sMRUmenp6bN68meHDh/Ps2TNlh5MjMpmMAQMGEBoayrNnz9DW1sbX15dbUSlkpYGuH9hOx6k/MuPsE4wtywBw99RBvtlwmDE7zgFg41yDEVtOMu1kMNU9OrN54iDSU1O4GZUCwJ49e+jRowcxMTF06NABX19fZTzdfJGVkNi0aROjR49m//79TJw4kQkTJqCtrc3JkycZMWIEwcHBSo7042xsbHBwcODs2bPKDqVEe/bsGcHBwTRr1qzAj52V+B06dCgBAQH8/PPP+Pn54erqyoULF9i0aRNjxozh9OnTBR5bURMdHc3YsWMxMzOjb9++TJs2jXHjxgEoKtkE5cvIyGDv3r2kpKQgl8v5acch/rgTgbpNedQ0NEEle9WoaNQjCEJxJhJrgiAIeWDz5s0EBARw7NgxYmNjFXOKvDt8zcfHB9cm7qwb0YO05MQP7kdDWwdpyj8Xm/GRrz+4nqbq59++Q0ND8fHxYdmyZbx9+5aYmBgqV66siKkwVgDVrl2bMWPG0KdPHzIyMpQdzmeVKlWKzp07o6Ojg76+Pn5+fpw+fZo3yRmKYaA12/fA3LEiqmpqqKqrA9B0wCh0DI1R19IGoEbbrugamaCqpkajPt+QnpbGq6ePiEjOPAcNGzakTZs2qKqq0qdPH27cuKGU55sfVFVVSZTKMK3VnCd6Zdn+OJYzMaqolquBpr4hixcvpnTp0ty9e1fZoX6SGA6qfH/99Reenp6o///fmTKEh4czatQo+vTpQ6tWrfDw8ODnn3/mxIkTqKqqoqmpqbTYioqTJ08SHR3NuXPnuHv3LvPmzWPXrl28fftWUbkmKJ+qqiojRoygfv36DJ2zhESbyqio56wy80ONet7l4uIimlUIglCkiMSaIAhCHoiPj0dTU5NSpUqRlJTElCkfHuo5beHPlLYrx/pRvbMl0LJYOFXm9eMHhD24hTQ1hWO/LXxvHRUJmGl/fhL3xMREJBKJYo6qP/74g9u3byuWm5ub8+LFC9LS0nL6NAvEhAkTUFFRYf78+coO5bOSkpIYMmQItra2GBgY0LhxY2JiYkhO+2eYi6G59XvbGVpYZbt/ZsNyfuxUnxmNHZjZ2JHUhDgSo6MUlYkWFhaKdXV0dEhJSSkWlRvhiVJ2hsTx650ozBu2QWpmx+M4KXejUznxLJYqvnO5qVKaV8kZVK9eXdnhfpKnpycBAQGFei644q6gu4G+K2uIYkZGBjY2Nnz11VdIpVLKlSvHvHnzWLt2LbNnz6Zu3bpKia8oWbBgAd27d6dChQpkZGRgZWVF3759uX//PuvWrWPlypXKDrFYsrOzQ1tbGz09PczNzenfv/9nGwrZ2tpSs2FTHNr0RE1TK1fHy0qude/dl6lTp2ZbdufOHZo2bZrbpyAIgqA0IrEmCIKQB/r27YutrS3W1tY4Ozt/9MtT1VLadJr2I4bmVmwY2xdpakq25Wa2jrTwGcfvw7qw2MsNu+pu7+1DDlQ1+fwFrLOzM+PGjaNevXqYm5tz69YtGjRooFjevHlzXFxcsLCwwNTUNHdPOB+pqqqyceNGlixZwuXLlzl69Cg//vijssP6oMWLF/PgwQMCAwOJi4vjzJkzAGio/FMN+OHCwH8efHLtImfWL6PX/DV8d+oR0888RlPPAJDnqDKxqLoWkYx/cCzBsWlkyEEuyf5cVdTUUVXX4FG8lAaj5/JWp7SSIs0ZFxcX1NXVCQoKUnYoJVJYWBh3796lRYsWSjl+VpVOnTp1mDp1Km/fvkVTU5MqVaowbdo07ty5g7a2tlJiKyqyqpS/+uorHj16RGxsLBkZGUyePJmff/6ZNm3asGjRIrS0cpfAEXJu7969JCQkcO3aNa5evcr333//yfUlEglqlvbvDfvMqXQ5vEku/NXpgiAInyORi59WBUEQCtTOkDiCY7+8SszJUINODgZ5GFHhtHXrVoYMGUJKSgr6+vqFam64pk2b0rt3bx4+fMitW7fYtWsXSUlJDBo0iN27d3P2RSyXItL4dbAnNdp0oXbHPoptJ7uaMW53IKZlHQC4f+4of80aw4jNx9E2NOb0H0s5vmohg5f/ycCOHhxasYBHjx6xadMmAJ4+fYq9vT1SqVQpnQ9zasaMGdnifte1iGROvEzM1jX1c9Qk0NxaF1eznCUn5s6dS0hICGvWrMn5Qf6j8ePHo6enx4wZMwrsmEKm5cuXExgYyIYNG5QdCrt27WLVqlVs374dPT09Nm3axNatW9m7d2+hHIJfWMjlciQSCWFhYXTp0oWnT58SHx9PlSpV+Prrr+nSpQt6enrKDrPYsrOzY82aNbi7uwPw7bffcvfuXSQSCYGBgaSnp9OgQQNWrlyJjY0NAI2aNMW8iSe1/v8Zd3W3P2c2LCf+7RvKuNSg49QfMbYqg1wuZ//iaQQd3El6WgpGlmXoMfc3nt24QsD8iahKJGhoaNCsWTP27t2bLZYZM2Zw9+5dtLS02LVrF2XLlmX9+vXUqlVLaedKEATh34rvT+GCIAiFVH1zbdS+8LuVmgTqWRT/qofQ0FCmT59OYmIiaWlpxMbGkpj44XnplEUikTB69GiSk5MxNTWlbt26tG7dGoAqJlrkNGfkVK85TvWbs7hjXRa0rYGahqZi+GhOKhPzm52dHceOHcuz/YUnSnOdVIN/hg2FJ+Wsm9yUKVMKNKkGmcNBxTxryqHMYaD/tn79embPno2enh5yuZzevXszZcqUYjF8Oz9lJR2trKywtbVl3rx5vH79mgsXLtC/f38kEgnh4eGcPHmSZcuWKTna4u358+ccOHAABweHDzboyZIolSnqr++eOsjJtT/Te9E6ph6/j51rXbZOGQJA8MWTPLl2kXG7LzH9TAi9fliDjqExdTr3pYZHZ7yHjyEhIYG9e/d+MJ7i3MBHEITiofD+1C0IglBMWeqq09xa94srdix1lDcxd0EJDQ0lLCwMdXV1MjIyUFdX58GDB7i6uio7NADi4uIoVaoUVlZW702wPGRI5hcJBwMNvl4d8N62865FZLuvoqpKlxlL6TJjqeKxJv1H4GSogY66ynvVT3Z2dkV6Hq8Lr5NznVTLki6Hi6+S6eRQOP8N1K9fn7CwMJ48eYK9vb2ywykxXr9+zfXr12nZsqVS48iquJo3bx5qamqcOXMGFxcXjI2NKVWqFBEREVhZWSnWEz5uy5YtQOY8lgEBAezbt4/jx4/j6enJnDlzFBVTQt7y8vJCTU0NQ0ND2rZty4IFC7INYfbz88vWdVcqkyP7/+3AHetoOnAUpR2cAGg6cAyn1i4hOuw5qmrqpCYlEPEkGJvKrop1IHN6iySpjE/JauAD0KdPH37++ec8eb6CIAh5RVSsCYIgKIGrmTbNrXVzXLmW22FwRV3jxo0JCwtj3LhxaGlpkZyczLlz5xTLE6UyLr1OYs/TeLY/jmXP03guvU767MV5Xrhz5w737t2jRo0an1yvuFUmrlu3joYNGzJ+/HiMjY2xt7fn4MGDiuVPnjyhSZMm6Ovr89VXX703dHfPnj1Ucnahd7UyrPLx5E3IQ8Wy+W1dObNhOUu6NWFGYwc2Txycbf7Be2eOsLRHU2Y2dmRsR3cC/w76Z9v587G2tkZfX58KFSpw/PhxIHMoau/evRXrde3aFQsLCwwNDWncuDF37tzJ61OEqqoq7dq1Y8+ePXm+b+Hjdu/eTZs2bZQ+91ZWsiwlJYVp06bh5+fHoEGDuH79Otu3b8ff3x/4p9GB8HExMTF069aNRo0a8fPPP1O5cmWOHDnCTz/9hI6ODuXLl1d2iMXS7t27iYmJITQ0lF9//RW5XP7BBj1Z8+HJ3vmRJyb8BfsW+jGzsSMzGzsyu2l55HI5cRHhONZpRL3ugwj4YSJz3Cvx1+yxpCTEK7b93I8txbWBjyAIxYdIrAmCICiJq5k23k6GOBlqoCrhvSSMmgRUJZlzqnk7GZaYpFoWfX19vv/+e54/f07Lli0zhwG900XyXHgSd6NTFV0kz4UnsfxOFDtD4ghPzNlwwdyaOHEiLVu2ZP78+dja2n5y3azKxNwm1wpzZWJgYCAVKlQgMjKSCRMmMGjQIEX1XK9evahZsyaRkZFMmzaN9evXK7Z7+PAhPXv2ZNiMH5hx4j4VGrizfrQ36dJ/5hq8dTSAAcu3MWHv37wKvsu1vVsBCLt/k50zR+Hlt5hpJx9St3NfvLw8SU1N5cGDByxbtowrV64QHx/P4cOHsbOz+2DsHh4eBAcH8+bNG1xdXfH29s6Xc+Tl5SWGgxawwjIMNCvZcPbsWTIyMjh79izNmzdn7969tGzZkqtXryo5wqLDyMiIWrVqsW3bNk6ePEmdOnW4cOEC169fJzo6GhAJyoLwsQY9We/7Ku9UXhqaW+Hlt5jpZx4r/pt98Tm21eoA0KDn14zYfJwxO84R+ewxZzZkDueVIPniH6EEQRAKCzEUVBAEQYksddTp5KBOklTGzagUIpIzSM2Qoamqgpm2KlVNtNBRL9m/gZiamnL48GFFF8mP/bKd9XhwbBpP4tLypcJv/vz5zJ8/P8frZx0/p8N+C3tloq2tLT4+PgD069ePb775htevX5OWlsaVK1c4duwYmpqaNG7cmPbt2yu227ZtG23btsWuTlPuRqfSqO9wLmxZxbMbV3Coldmptn4PHwzMMqsSKjVuRdiD2wBc/msjdTr3pWyVmgBUb9+Dc+uWcOnSJaytrUlNTeXu3buYmZl9NKkGMHDgQMXtGTNmYGxsTGxsLIaGhnl6jr766iv69OnD27dvKVWqVJ7uW3hfZGQkly9fJiDg/WHXBS2rM6irqys3b95U3J4+fTply5bF2to623rCh2UNlZ0wYQJRUVEMHDiQ9PR0LCws2LlzJ8bGxqxbt07ZYZYI8fHxaGtrY2RkRFRUFDNnzsy2XF1FoqjScOvSn6MrfsCqQmXMHSuSEh9H8KWTVPnKk+d3riOXybCuWBUNbR3UNDSR/L8TtEEpM6LDQgv4mQmCIOQt8ckuCIJQCOioq1DXXIf2dvp0cTSkvZ0+dc11SnxSLUtWF8k5bVx5FHj6s+uny8G9WgVW7NhfANF9WnGqTPz3cByAhIQEwsLCMDY2RldXV7H83Yq+sLAwbG1tSc3IrDBRUVHB0Nya2DfhinX0TUsrbqtraZOWlNmsIjr8Oec2rVAML5rZ2JGI8JeEhYVRrlw5fv75Z2bMmEHp0qXp0aMHYWFh78WdkZHBpEmTcHR0xMDAQJGAy49Os9ra2jRv3pz9+5X/2isJAgICaNWqleL1qExZQ0ErVapEYGAglpaWTJs2jZCQEPbv38/XX3+dbT3hwyQSCVFRUaSlpTF9+nTMzc2ZO3cuU6dOZfv27dy/f5/Y2FiRoCwAH2vQk0VXXUXRqMeleVsa9xvBlslfM6ORPT93a8SD85lD81MT4tk1eyyzmpZjfltXdIxMaNxvOAC1vLx59fghRkZGeHl5FeCzEwRByDuiYk0QBEEo1L60i6QcuB6ZQniSVOnDKot7ZaKlpSXR0dEkJiYqkmvPnj3L1uXv1q1bNFLNfI5yuZzY1y8xLG352X0bmVvTbOBomg0eq3jMxViT9nb6QOYQ1F69ehEXF8eQIUOYOHEiGzduzLaPzZs3ExAQwLFjx7CzsyM2NhZjY+N8awKRNRy0b9+++bJ/4R87duxgwIAByg5DISMjA2NjY4YMGYKuri7lypWjXLlyWFp+/rUuZJJKpXz11VecP3+eS5cusWXLlmzNCqysrDh79izt2rVDJpOJBFseefr06XuPfapBD8DZ06fYGRJHcGzmsH7Xdt1wbdftvf2Uc2vMqD8//KNY3SqVWHAj6KOxFLcGPoIgFE/ik0gQBEEo1P5LF0kZmV0kC4viWploa2tLrVq1mD59OmlpaZw7d469e/cqlnfr1o39+/cTevUMknQpZzf+iqqGJmWr1f7svmt36kPgzvU8u/U3crkcWUoiD88dJT4+ngcPHnDixAlSU1PR0tJCW1v7g1+y4+Pj0dTUpFSpUiQlJTFlypQ8ff7/1rZtW44fP05ycuF57RVH0dHRXLhwQdEtsDBQVVVFRUUFX19fBgwYQKNGjUhOTmbPnj1MmTKFkJAQAJEY+AR1dXVsbW25du0agwcPZs+ePZw9e5bFixdTrVo1jh07Rnh4ZrWrSKopX3Fr1CMIgvAlxKeRIAiCkGt2dnYsXLiQqlWroqury6BBg3j9+jUeHh7o6+vj7u6umGD60qVL1K9fHyMjI6pVq5bt1++mTZsybdo0GjRogL6+Pi1btsw2PG/NH+sZ3MCFWc2cOLnmx2wxbJ/uy5HlcxX3Q66eZ17rqu/F+jgujWs372Bvb8+WLVsU3S3fJZFIePToEQD9+/dn+PDhtG3bFn19fdzc3Hj8+LFi3SNHjlChQgUMDQ355ptvaNKkCWvWrPnyk1lMbN68mcDAQExMTJg5c2a2aq0KFSqwadMmlk/7lhnNK3D/zGH6/bwJNXWNz+7Xxrk6Haf+yJ75k5jVpBw/dKjD+V2bAUhNTWXSpEmYmppiYWHBmzdvmDdv3nv76Nu3L7a2tlhbW+Ps7EzdunXz7ol/gKmpKTVq1ODYsWP5epySbs+ePbRo0QI9PT1lh6Jw8OBBevfunTmnoJ0dampquLq6MnToUJYsWcL9+/eVHWKR0LlzZ3755ReOHj3KypUr6dChA/fv3+fbb7/l5s2bDB48WNkhCv9XHBv1CIIg5JZELn4yEwRBEHLJzs4OCwsLAgICSE9Pp0aNGtjY2PD7779TqVIl2rRpQ5MmTRg8eDBVq1Zl48aNtG7dmuPHj9OjRw/u37+PmZkZTZs25fnz5xw8eJAyZcrg4eFB3bp1+eGHH7h79y61ateh/9LNWFWuyaFfvufi1tUMWLaNcm5N2D7dF8PSVrQcnll9FHL1PNumDmPyocxJw+e3daXzdz+hZ2jE1m/7s2blCtq1a8e6detYs2YN586dUzwfiURCcHAw5cqVo3///uzdu5eDBw/i6upKv379yMjIYOvWrURGRuLg4MC6devo0KEDy5cvZ/z48axYsUJ80cuhd4cNfQknQw06ORjkYUT54+eff+b27dsi6ZqP2rdvT48ePfKtw+uXWLVqFTdu3EBfX58XL17w7bffUq1aNQDWrl1LbGwsY8aMISMjA1VVVSVHW3jJ5XKuXbvG7t27adKkCeXLl8fGxkZxzoKDg0lNTaVy5cpiOGghkTUXanFo1CMIgpBb4lNIEARB+CIjRozA3Nwca2trGjVqhJubGzVq1EBLS4uOHTty/fp1Nm3aRJs2bWjTpg0qKip89dVX1KpViwMHDij2M2DAAJycnNDW1qZbt24EBQUBmXMn1WzeirKu9VHT0KTlN5MUXcRy6sm1S/wxug8jF/1Gu3btcrxdx44dqVOnDmpqanh7eytiOnDgAC4uLnTq1Ak1NTVGjhyZbUJ/4fNKyrAhT09P9uzZQ0ZGhrJDKZZiY2M5ffp0rv5dF4Svv/6a5cuX4+npiYGBAdWqVSMtLTORnJSUxPHjmZO5iwYGnyaRSKhZsyazZ8/G3d2d9PR0pk6dyoQJE7h16xZPnz79YHWqoDzFqVGPIAhCbonmBYIgCMIXMTc3V9zW1tZ+735CQgKhoaFs374923xbUqmUZs2aKe7/u9NkQkICkNlJ0tjCWrFMQ1sXHSPjXMV4eed67GvWw6l2g1xt96mYypQpo1gmkUiyTaotfF7WsKHcNqQoasOG7O3tsbS05OLFi+8NPRb+u3379tGkSRMMDQ2VHUo2WdVTurq6nD59mo0bN1KzZk3+/vtvAgMD8fHxAURiLTdev36Nr68vpUuXxtnZmeHDh3PmzBlGjRpFfHw8+vr6yg5R+L/i3qhHEAThY8Q7myAIgpBvypQpQ58+fYiJiVH8l5iYyKRJkz67raWlJdGvXirupyUnkRQTrbivoaVDWso/k8PHv33z3j68piwkJvwlG+f+M1m9rq4uSUlJivuvXr3K8fOxtLTkxYsXivtyuTzbfeHzZDIZrmbaJF47gTxdikwm++w2RXXYkKenJwEBAcoOo1jasWMHXbp0UXYY78kaklilShXWrl3L2bNnGTBgANu2baN169Z4enoCIrGWG+bm5jx79ox169YxceJE1NXV2bNnD1WqVOHhw4fKDk/4gOLaqEcQBOFjxLubIAiCkG969+7N3r17OXz4MBkZGaSkpHDq1KkcJaO6dOnC3ycO8zzoEunSNI6tnI9c/k8SxrJCFR6eP0ZSbDTxka857//be/vQ1NXD59dtPLh6QZHMq1atGnfu3CEoKIiUlBRmzJiR4+fTtm1bbt26xe7du0lPT2f58uW5SswJ/yQe9q1YSOtSGYRfv4AK8mI5bMjLy4tdu3aJDpB5LCEhgRMnTtChQwdlh/JREokENzc3li9fTmBgIPv27cPb25tDhw4xZ84cBg0aRFhYmLLDLPSyhlK3a9eOkSNHsmrVKjQ0NBg7dizVq1fH2dlZyREKgiAIgkisCYIgCPmoTJkyBAQEMHfuXMzMzChTpgwLFy7MUZWSi4sLPy/9hc1ThjK3ZWW09Q0xKG2lWF6jbVcsyruwoJ0ra7/pRtWWXh/cj5a+IceOHOXgwYNMmzYNJycnvvvuO9zd3SlfvnyuhumZmpqyfft2JkyYQKlSpTIbLNSqhaamZo73IcCLFy8wMTGhhoMNf69dgG/lUtQ31yLk3BEc9dVwMdakoaUOw11M6ORgUGSGf/5bjRo1SEtL4+7du8oOpVg5cOAA9evXx9g4d0PDlUFdPfO1K5VKgcx51gwMDGjcuLFimfBxWZV9/fr14+rVq/j7+9OwYUP++OMPJk+ejLZ20Uu4C4IgCMWP6AoqCIIgFGqFuYukTCbDxsYGf3//bPPGCZ+WmprKzz//zJ9//omamhq7d+/myJEjbNq0iaNHjxarLn8jRozA0tKSKVOmfH5lIUe6detGq1atGDRokLJDybXU1FQyMjLQ1tYWw0Fz6ciRIzRo0EAxnP/AgQP8+uuvjB49mg4dOhSr9w1BEAShaBHNCwRBEIRCrb65Nk/i0nI10X2W/OgiefjwYdzc3NDW1mbhwoXI5XLq1q2bp8co7jQ1NRk6dChaWlrcv3+f7t274+DgwC+//KLs0PKcl5cXkydPFom1PJKUlMThw4f59ddflR1KjiRKZdyKSuHN/ydx11ABcx11LGMjeHzvNs2bN1d2iEVG06ZN2bhxI/v27SMoKAhzc3M0NDSIjY0FxLx1giAIgvKIxJogCIJQqBW2LpIXL16kV69epKWl4ezszO7du8VwpFxKSUkhLS2NZs2a4erqSpUqVTA0NFR8MS5OVSeNGzfm0aNHvHz5Emtr689vIHzSoUOHqFOnDqampsoO5aPS0tK4/fw1TySGhMSlIYFs713BsVJkcgkvQhOplCjFUlcMCc2Jy5cvs3LlSgYPHszKlSsxNzcnPDycbt260bVrV7S0tJQdoiAIglBCiaGggiAIQpFwLSI5x8m1otpFsiSIiorC29ubt2/fUrNmTczNzSldujRly5albdu2xbLqpHfv3jRo0IBhw4YpO5Qir1evXjRp0oQhQ4YoO5SPOvMkknMRaaioa3x6RZkMNVUV8V71haRSKWlpadSrV4+ffvqJFi1aKDskQRAEoYQqPj8JC4IgCMWaq5k23k6GOBlqoCqhWHaRLM6yGlYcPXoUQ0NDzp8/T8eOHTE2NubGjRucO3euWCbVIHM46O7du5UdRpGXkpLCgQMH8PLyUmocLi4unDp1CoAZM2bQu3dvxbJrEclcjuXzSTUAFRXS5XDiZSLXIpL/c1wSiYRHjx7laN0VK1Zgbm6Onp4eb9++/c/HLihyuZyEhARCQkLYt28f48aNo02bNjRt2lTZoQmCIAglmBgKKgiCIBQZljrqdHJQJ0kq42ZUChH/n7dIU1UFM21VqppooaMufjMqjLKGdxobG+Pm5oa6ujotW7akZcuWACQnZyYW5HJ5sUuwtWrVioEDBxIbG4uhoaGywymyjhw5Qo0aNTA3N1dqHHfu3Png4+GJ0lwPWQcUyTVLXTXF0PWnT59ib2+PVCpFTS1vL9elUiljx47l0qVLVKtWLU/3nd/evHnDhAkTCA8PR0dHh1atWtGtWzcePnxIWloacrmcypUr5/k5EwRBEIRPEZ86giAIQpGjo65CXXMdZYch5EJWwkxNTY3ffvuNdevW0bp1axo3bkzdunUpVapUse3qp6+vT6NGjTh48CA9evRQdjhF1o4dO+jSpYuyw/ioC6+Tv6jJCmQm1y6+SqaTQ/7Pt/b69WtSUlJwcXHJ92PlNXNzc6pVq8Z3332Ho6Mjr1694u+//2b06NGYmZkxaNAgrK2tMTMzU3aogiAIQglS/K5eBUEQBEEodCQSCTExMXTv3p3Fixfj5+dHRkYGS5Yswc3NjdjY2GKZVMvi6elJQECAssMoUuzs7Jg3bx7Ozs4YGxuzbds2PDw8AIiOjqZdu3aYmZlhbGxMu3btePHihWLbpk2bMm3aNBo0aIC+vj4tW7YkMjJSsXzDhg3Y2tpSqlQpZs+ejZ2dHceOHQMyqyf79euHsbExlSpVYsGCBdjY2GSLK2vdLIlSGSFxafhPGMicr5yZ0diB3wa15/Xj+4p1tk/3JWDeBNaN7Mn0hnYs79uKt8+fKJY/jksjSZo5ZLpx48YAGBkZoaenx8WLFwFYu3YtlSpVwtjYmFatWhEaGvrBc5eamsr48eMpW7Ys5ubmDB06lOTkZB4+fEiFChUU+87qSnrhwgVq166NoaEhtWvX5sKFCzk+lwVt7NixyGQyvvvuO2bNmsWZM2eoXr06qampeHp6iqpQQRAEocAV3ytYQRAEQRAKhaw+SQ8fPsTDw4O2bdvSrVs3Fi1axJEjR7hw4UKx/zLcoUMHDh48SGpqqrJDKVL8/f05fPgwK1euRFNTk7Vr1wKZc/YNGDCA0NBQnj17hra2Nr6+vtm23bx5M3/88Qdv3rwhLS2NRYsWAXD37l2++eYb/P39CQ8PJzY2lpcvXyq2mzlzJk+fPiUkJISjR4+yadOmz8Z5KyoFCVChfgvG777M1GP3sKpYlW1+Q7Otd+PIblp8/S3fnXpEqTL2HFk+V7FMAtyMSgHgzJkzAMTExJCQkEC9evUICAhg7ty5/PXXX0RERNCoUSN69uz5wXgmTZrEw4cPCQoKUnSlnTVrFk5OToqhrDExMZw4cYKoqCjatm3LyJEjefv2LWPHjqVt27bZ5l772LlUhvDwcCZPnoyBgQHNmzenX79+/PLLL9jY2GBoaIiGhgaiN5sgCIJQkERiTRAEQRCEfJXVuODJkyc8e/aM+fPnc+PGDV68eEFCQgKlS5dWcoT5z8LCAmdnZ8Wk90LO+Pr6UqZMGQ4fPkyPHj3YsmULAKVKlaJz587o6Oigr6+Pn58fp0+fzrbtgAEDcHJyQltbm27duhEUFARkDilt3749DRs2RENDg1mzZmWb1+/PP/9kypQpGBsbY2Njw8iRIz8b55vkDNLlUMvLG01dPdQ0NHEfOoHwh3dIiY9TrOfSrA1lKruiqqZGdY/OhD+8rViWLoeI5IyPHmPlypVMnjyZSpUqoaamxpQpUwgKCnqvak0ul7Nq1Sp++uknTExM0NfXZ8qUKWzduvWD+92/fz/ly5enT58+qKmp0bNnTypWrMjevXs/ey6VwcLCgtOnT9OnTx+6dOlC+fLlKVWqFBMnTiQyMpIrV65kq7gTBEEQhPwm5lgTBEEQBCFfqaqqAqCnp0e1atW4ffs2Dx48wMzMDAMDA/r165dtqF1xlTUctFWrVsoOpcgoU6YMUqmUgIAAtm3bxsaNGwFISkpizJgxHDp0iOjoaADi4+PJyMhQvN4sLCwU+9HR0SEhIQGAsLAwypQpk21ZqVKlFPf/vfzd2x+TmiFDlpHBkeVzuHVsL4nRkUgkmb9fJ8a8RUvfAAD9Uv8kkTW0dEhNSnxvPx8TGhrKqFGjGDdunOIxuVzOy5cvsbW1VTwWERFBUlISNWvWzLZeRsaHk3ZhYWHZtgewtbXNVsX3sXNZ0LL+vkuXLkUulxMTE8P169e5f/8+t27dolGjRjRt2pSrV68qJT5BEAShZBKJNUEQBEEQ8k1KSgorV66kfPny1K1bl7Zt2xIXF8e9e/e4ffs2169fR1dXV9lhFggvLy+aN2/OsmXLivV8cnnp+fPnnDx5kvLlyyOVSrGysgJg8eLFPHjwgMDAQCwsLAgKCqJGjRo5GgJoaWnJgwcPFPeTk5OzDXu0tLTkxYsXODs7K2L4HE1VFYIO7uTuqUMMWrEDY6uypCTEMatJuVwNS9RUzXxdfKgzbpkyZfDz88Pb2/uT+zA1NUVbW5s7d+5gbW392WNaWVm9V/X27NkzWrduneO4C0pW0rRZs2asXbuWuLg44uPj0dPTo0qVKlSqVAlLS0vat2+fLckqCIIgCPlJXNUJgiAIgpBv3r59S1xcHBcvXmTXrl0AGBgY4ObmxqBBg1i2bBnGxsZKjrJgVKhQAX19ff7++29lh1JkLF++nPXr19OmTRvmzJlD9+7dgczqNG1tbYyMjIiKimLmzJk53meXLl3Yu3cvFy5cIC0tjRkzZmRLfnXr1o158+YRHR3Ny5cvWbZs2Wf3WVpblfTkBNQ0NNAxNEGaksThZXNy9VzVJGCmnZkIMjMzQ0VFhZCQEMXyoUOHMm/ePMUcabGxsWzfvv29/aioqODj48OYMWN48+YNAC9fvuTw4cMfPG6bNm14+PAhmzdvJj09nW3btnH37l3atWuXq/gLkrm5OYmJidjb29N38BA6jJyKtYc3l9ONmPnXabpPmsP+o8eVHaYgCIJQQojEmiAIgiAI+cba2poRI0bQp08fxRBImUyGXC5XzL1Wknh5ebF7925lh1Fk9OjRg23btrFo0SIcHR2ZOnUqAKNHjyY5ORlTU1Pq1q2bq+oqFxcXfvnlF3r06IGlpSV6enqULl0aTU1NAL777jtsbGywt7fH3d2dLl26KJZ9TBUTLVzbdcPIsgzzWlfhp84NKVul5ie3+Tc5UNVEC8gcbunn50eDBg0wMjLi0qVLdOzYkYkTJ9KjRw8MDAyoXLkyBw8e/OC+5s+fT7ly5ahbty4GBga4u7tnq9J7V6lSpdi3bx+LFy+mVKlSLFiwgH379mFqapqr+AuSRCLhm8nTKeXeg1NyC86EJ3I3Oo0oVT007V1w7ebDA3NXdobEEZ4oVXa4giAIQjEnkYu2OYIgCIIgCAXi0qVLDBo0SFF1JHycnZ0dvr6+bN26NV/nzEpISMDIyIjg4GDs7e3fW75ixQq2bt36XnOEf9sZEkdwbNoXx+FkqEEnB4Mv3r4kuRaRzPEXCUhl8s8Oq1aTQHNrXVzNtAsoOkEQBKGkERVrgiAIgiAIBaROnTpERUURHBys7FCKhDNnztClS5c83+/evXtJSkoiMTGR8ePHU6VKFezs7AAIDw/n/PnzyGQyHjx4wOLFi+nYseNn91nfXBu196dGyxE1CdSzKJqJn2fPnqGnp/fR5gj/hb+/Py1btsz22LWIZE68TCQDSY7mKkyXw4mXiVyLSM7xcdetW0fDhg1zHa8gCIJQMonEmiAIgiAIQgFRUVGhQ4cOBAQEKDuUIuHcuXN07tw5z/cbEBCAlZUVVlZWBAcHs3XrVkXDgLS0NIYMGYK+vj7NmzfH09OTb7755rP7tNRVp7m1bq6Ta1kVVZY66l/yVJSubNmyJCQk5EujAG9vb44cOaK4H54o5cTLRNJzOd4mK7kWniSGhQqCIAh5TwwFFQRBEAQh3yUmJqKhoYG6etFMHuSlgwcPMnfuXM6ePavsUAqlV69e8fbtW96+fcvIkSMJCgpSdki5klVRlZPkjximmDsFNdx23bp1rFmzhnPnzn3xsQRBEISSQ1SsCYIgCIKQb7IaFOzevZunT58qHo+OjkYqLZnVI82bN+fWrVuKjo1CdqtXr6ZKlSq0adMGAwMDbty4oeyQcsXVTBtvJ0OcDDVQlfBeBZuaBFQlmUkebyfDfEuq2dnZMW/ePJydnTE2NmbAgAGkpKQolu/bt4/q1atjZGRE/fr1uXnzZrZtFy5cSNWqVdHV1WXQoEG8fv0aDw8P9PX1cXd3Jzo6GoCnT58ikUhIT08HoGnTpkydOpX69eujp6dH+/btefv2Ld7e3hgYGFC7du1s7wUSiYSlS5fi4OCAqakp3377reJ9490hmYlSGV0cDQncsY5FnnWY2diRgHkT+FiNgDQlmT+/G87MJuX4sVN9Tq/7hYH1KpIkzdz3Dz/8gKOjI/r6+jg7Oyu6FmeRy+X4+vpiaGhIxYoVOX78ny6jdnZ2HDt2THF/xowZ9O7dO9v5WL9+PWXLlsXU1JQ5c/7pEJucnEy/fv0wNjamUqVKLFiwABsbG8XyT8WVdT7Gjx+PsbEx9vb2H21gIQiCIBQckVgTBEEQBCHfZM2BpKamhoWFheJxf39/oqKilBWWUmlqatKyZUv27t2r7FAKJUdHR3R1dUlMTOTChQtUr16dJ0+eKDusXLHUUaeTgwHDXUxoaKmDi7Em5QzUcTHWpKGlDsNdTOjkYJDvwz/9/f05fPgwjx8/5uHDh3z//fcAXL9+nYEDB/Lbb7/x9u1bhgwZQocOHUhNTVVsu3PnTo4ePcrDhw/Zu3cvHh4ezJ07l4iICGQyGUuXLv3ocbdu3crGjRt5+fIljx8/pl69egwYMICoqCgqVarEzJkzs62/a9curl69yrVr1wgICGDt2rXv7fNWVGZS8N6ZIwzfdJRR205z6+geHl448cEYjq9aSEzYcybsvcqgFTsIOrADgJv/34+joyNnz54lNjaW6dOn07t3b8LDwxXbBwYG4ujoSGRkJDNnzqRTp065es86d+4cDx484Pjx48yaNYt79+4BMHPmTJ4+fUpISAhHjx5l06ZN2bbLSVwVKlQgMjKSCRMmMGjQoI8mFwVBEISCIRJrgiAIgiDkucjISA4dOsT1O/c4FRqFtlsbDr2RsedpPBdfJWJexg5zc3Nlh6k0np6eYp61j3ByclIkCjQ0NFi2bNkHu3UWBTrqKtQ116G9nT5dHA1pb6dPXXMddNQL5hLc19eXMmXKYGJigp+fH1u2bAFg1apVDBkyBDc3N1RVVenXrx+amppcunRJse2IESMwNzfH2tqaRo0a4ebmRo0aNdDS0qJjx45cv379o8cdMGAAjo6OGBoa4uHhgaOjI+7u7qipqdG1a9f3tp04cSImJiaULVuW0aNHK+J815vkzOYITQeMRFvfECNLGxxqNyD84e0PxnDz6B6aDhyNtoERhuZW1O85GDkQ8f/9dO3aFSsrK1RUVOjevTvly5fn8uXLiu1Lly7N6NGjUVdXp3v37lSoUIH9+/fn7MQD06dPR1tbm2rVqlGtWjVF5eWff/7JlClTMDY2xsbGhpEjR2bb7nNx2dra4uPjo/i7hYeH8/r16xzHJQiCIOQ9kVgTBEEQBCHPBd4P4US0CgeTjAiMlHI3OpXHcZn/P/8qmae2ddkZEkd4YskcDtqmTRtOnTpFYmKiskMpdMqXL09SUhLq6ur88MMPDB8+XNkhFVllypRR3La1tSUsLAyA0NBQFi9ejJGRkeK/58+fK5YD2RLf2tra791PSEj46HFzu+3H4nxXakbmEE69UqUVj6lraZOW9OF/Q/ERrzC0sFbcNzS3zrafDRs2KIbCGhkZcfv2bSIjIxXrW1tbKxpafCquj3m3QldHR0fxnMPCwrI933dv5ySuf+8X+OTfQhAEQch/IrEmCIIgCEKeuhaRzD09B0wquqKipo5cJXu3wHQ5ZMghODYN/+BYrkUkKylS5TE2NsbNzS1bx0Mhk6GhIWpqaowdO/a9ah4hd54/f664/ezZM6ysrIDMZI6fnx8xMTGK/5KSkujZs2ehivNdmqq5+9qib2pO7Ot/EmGxr18q9hMaGoqPjw/Lli3j7du3xMTEULly5WxDKl++fJnt/rtx6erqkpSUpFj26tWrHMdlaWnJixcvFPfffe45iUsQBEEofERiTRAEQRCEbPr378/UqVM5e/YsFSpUyNW2WR0RI14+Y7KrGRn/n9D8Y9LlsDrgCOZW1p9crzjy9PRk9+7dyg5D6RKlMi69TmLP03i2P45lz9N4jj1+w3ez5yo7tCJv+fLlvHjxgqioKObMmUP37t0B8PHxYeXKlQQGBiKXy0lMTGT//v3Ex8crJc6FCxcSHR3N8+fPWbJkiSLOd5XWVv3Alh9X5StPTv+xhOS4GGLfhHNx2+9IADNtVRITE5FIJJiZmQHwxx9/cPt29iGlb968YenSpUilUrZv3869e/do06YNANWrV2fr1q1IpVKuXr3Kjh07chxXt27dmDdvHtHR0bx8+ZJly5YpluUkLkEQBKHwEYk1QRAEQRA+qFGjRjx48CDH64cnSjnxMpH0XBZXZMghNUNOeFLJGhbq6enJ/v37Fd0US5rwRCk7Q+L49U4U58KTsg0Xvvw2neV3okr0cOG80KtXL1q2bImDgwOOjo5MnToVgFq1arF69Wp8fX0xNjamXLlyrFu3Tmlxenp6UrNmTapXr07btm0ZNGjQe+tUMdHK1T5bfD0Og9JWLGhXk9+Hdqaye3tUNTSpaqKFs7Mz48aNo169epibm3Pr1i0aNGiQbXs3NzeCg4MxNTXFz8+PHTt2UKpUKQBmz57N48ePMTY2Zvr06fTq1SvHcX333XfY2Nhgb2+Pu7s7Xbp0QVNTEyBHcQmCIAiFj0QuaosFQRAEQXhH//79sbGxUXQQzKmdIXEEx6YBEB32jAXtavL95XBU1dQ+uV3I1fNsmzqMPy7ep5ODwRfHXRTVrFmTxYsX07RpU2WHUqCyKhtzkoRVk0Bza11czbTzP7BixM7OjjVr1uDu7q7sUD5JIpEQHBxMuXLlPrvuu+8xuXVp+x8EHw/gTuC5L9o+v6xYsYKtW7dy+vRpZYciCIIgfCFRsSYIgiAIJdz169dxdXVFX1+f7t27k5KSAsCpU6ewsbFRrPfDDz/g6OiIvr4+zs7O7Nq1S7EsLkXK0hmTmd28Agva1+L+2aPZjnE1YDM/dqrP9IZ2LGhfi8Ad69+L43FcGklSGUuXLsXZ2ZkXL15w+fJl6tWrh5GREZaWlvj6+pKW9s8Xa4lEwsqVKylfvjxGRkYMHz68SM1H5OXlVeSGg7q4uHDq1Kkv3j43STXIHC584mViiZyLT8iuvrk2apLPrwcQF/GKp0GByGQyIp4+4tymX+neuWP+BpgD4eHhnD9/HplMxoMHD1i8eDEdOyo/LkEQBOHLicSaIAiCIJRgaWlpeHl50adPH6KioujatSs7d+784LqOjo6cPXuW2NhYpk+fTu/evQkPDwfg+6UruHf2CCM2n8B301FuH9+bbVs9E1P6LfFnxtkndJmxhP0/TuPlvRvZ1pEAo/yms27dOk6fPo2NjQ2qqqr89NNPREZGcvHiRY4fP86vv/6abbt9+/Zx5coVbt68yZ9//snhw4fz7gTls6zEWlFKBt65c+eLK+xyO1x4+3Rfjiyfq0iulbThwkJ2lrrqNLfWzVFyLUMqZfec8cxoaM+aIR1p3bYDk0aPyP8gPyMtLY0hQ4agr69P8+bN8fT05JtvvlF2WIIgCMJ/8OmxGYIgCIIgFGuXLl1CKpUyevRoJBIJXbp04ccff/zgul27dlXc7t69O/PmzePy5ct4enpyaPdOGvQagpFFZhOCJgNGEXL1vGL9io1aKm471GxA+bpNeXr9EtaVqmU+KJeze9E0oh4EcfHkSQwNDYHMoZJZ7OzsGDJkCKdPn2b06NGKxydNmoSRkRFGRkY0a9aMoKAgWrdu/Z/PTUGoXLkyKioq3Lx5k2rVqik7nHx34XVyrufgy5Iuh4uvkunkoJ63QRVTT58+VXYIOZLbpHLWkODPJWiNrcowattpVCXgXkYfVzPtQpHAtrW1FQ0JBEEQihlRsSYIgiAIJVhYWBjW1tZIJP+UgNja2n5w3Q0bNlC9enVFEuv27dtERkYC8PZ1OIbmVop1jS3LZNv2wflj/Nq3NbOalmdmY0cenDtGYkyUYnlyfBxX/tqA55AxiqQawMOHD2nXrh0WFhYYGBgwZcoUxTGzWFhYKG7r6OiQkJDwBWdCOSQSSZEbDmpnZ8exY8c+OUx3+vTpjBiRWR0klUrR1dVl9LjxhMSlIU1JZlpdG5JiowHwnzCQOV85M6OxA78Nas/rx/cBuLxzA0EHd3Jm/TKmN7Bl/ShvHselMWvOvI8OSV63bh0NGzZk/PjxGBsbY29vz8GDBwv4DAn5zdVMG28nQ5wMNVCV8F4FmyxdCrIMrNXT6VPRGI3Xj/H392f27NlkZGQoJ2hBEASh2BKJNUEQBEEowSwtLXn58mW2So5nz569t15oaCg+Pj4sW7aMt2/fEhMTQ+XKlRXbmZS2IPZ1mGL9mFcvFLfT01Lx/3Ygjfp+g9/Ru0w/85gKDd3hnWNqGxjSb8lmVkwazvnz/1S6DRs2jIoVKxIcHExcXBxz584tFFUnecnT05OAgABlh5Frnxqm26RJE8U8bFeuXMHCwoKjJ08jAZ7dvIqpbTl0DI0BqFC/BeN3X2bqsXtYVazKNr+hANTp3JfqHp1p3M+XmedD6bfEHwmgWrrMR4ckAwQGBlKhQgUiIyOZMGECgwYNKnavGQEsddTp5GDAcBcTGlrq4GKsiYOeGmXVUmle1pCRVc3oW9UKSx11Ll68yKNHjzA0NCQqKurzOxcEQRCEXBCJNUEQBEEowerVq4eamhpLly5FKpXy119/cfny5ffWS0xMRCKRYGZmBsAff/yRbTiTh1dnLmxZTezrMJLjYjj9x1LFsgyplPS0VHSNS6GipsaD88cIvnTqvWM41W7ArF/X0qlTJ0UM8fHxGBgYoKenx/3791mxYkUenwHla9CgAc+ePSM0NFTZoeRKzZo1qVu3LmpqatmG6ULm6yo4OJi3b99y5swZBg0axOvwMBITEwj5+wL2Nesp9lPLyxtNXT3UNDRxHzqB8Id3SImP++Ax0+VQ9StPrKysUFFRoXv37pQvXz7ba9bW1hYfHx9UVVXp168f4eHhvH79On9PhqA0Ouoq1DXXob2dPt3KG9GrijV1zXXQUkVRnda2bVv69OnDN998o3gPEwRBEIS8IhJrgiAIglCCaWho8Ndff7Fu3TpMTEzYtm0bnTp1em89Z2dnxo0bR7169TA3N+fWrVs0aNBAsdxv5DCc6jdjSY+m/NKrBS7N2yqWaerq0f7buWyZ6MOsJuUIOvgXlRq3eu8YcmBwx7asXbuW9u3bc+3aNRYtWsTmzZvR19fHx8eH7t2758t5UCY1NTXat29f5KrWPjVMV1tbm1q1anH69GnOnDlDkyZNKF+jDqFBl3ly7QIONesDIMvI4NDSWSzsUJsZjexZ0NYVgMSY/7V3r0FW1ocdx3/n7AUXFlZQA17YWREhyCgRJVMIsGiUJkhMk9YmEZNMZUqN1ih2wNhkaorxbpMYTGudZLRqQiYh1pjRGEfQJKOQUqShQqCMBqN46VZAKyywu2f7grJKwMg+EBbh83l19pxnd/9nd2DOfs//8urbft9H5n/nbZckJ7suDU7yrloezL5RLpdTVVWVJDnmmGMyZMiQVFdXZ968eWlvb+/h0QFwMHF4AQAc4k4//fQsW7Zst4+98MKbSzqvvfbaXHvttbu9rqGuNpfPuSlr/uYrXfeN/cT0nW6/9eO3GnL6B3LVw8tzQr/a9K4p55xzztlphtGqVat2un7OnDldt393id9dd9212+9xoPvoRz+auXPn5vOf/3xPD2WPfe5zn8upp56aefPmpW/fvvn617+e+fPndz3e3NychQsXZtmyZRkzZkxO+aPxWbHosbywYlmaRm+fsfYfP/lhVj7+cKb/0/z0P6YxW954PXOah3b9XkvZefOsDS8+n9u/eFkeW7AgY8eOTVVVVd73vvdZ6snb6uzs7NpDslQq5dvf/nb69u2bqVOn9vDIADhYmLEGAOwT4wbW7bKJ+J6qLiVjB9Xt2wG9i0yePDlLlix5V+3/9E7LdJubm3P33XfnpJNOSm1tbZqbm/Pv99+b/sc0pr7/kUmSrZvfSHVtbXo3DEjbls356W07h9v6I47K+nVvLpGtbNmc8u9ZksyhraOjo+sAjR1KpVJeeeWVfOMb38i4cePy8ssvv+0BLQBQhLAGAOwTd956cyYO7JWqdG/2UHUpOfPYPjm6d80faGQHvt69e2fSpEmZO3duZs+enUceeaSnh/SO3mmZ7rhx49La2pqJEycmSc4de2qqaw/L8aPf3F9t9NQ/z+FHD871Hzo5X/vT8Wk8+bSdvsbpfzIt//3s6vz9xBNyzxWfyXtOGJ7LLr/ibZckc2hbvHhxHnzwwSTbZ9s+8sgj+cUvfpGzzjorP/vZz3LxxRdn+fLlOfnkk3t4pAAcTEqd5s4DAHupUqnkzjvvzPTp03PuJVfm/Z++NFU1tSmVf/97eDui2uijDt3Zahs2bMjMmTPz/e9/P21tbens7Mwtt9ySyy+/vKeHtluNjY259957u4JZd/zw2dez5rVt73zh2xjWUJuPD+lX+PM5uK1YsSKf/OQnUyqVsmnTppx88sn59a9/neuvv36nvSMrlUrK7/B/EwDsKXusAQB7rVwuZ/r06dmyZUse+OaN+cXyVVn08qa0H350KpVKqnsd1nVtdWn7QQUn9KvN2EF1h/RMtSRpa2vLj3/847S2tiZJ+vXrl5EjR/bwqHavpaUlLS0taWpqKvT54wbW5Tevb0t7gbd1D/XlwryzkSNH5vbbb89rr72WiRMnpr6+PpdddlmOOOKIJG/utyaqAbAvmbEGAOy1HTNAbr755mzdujVf+tKXkiSb2ypZvn5LXvrfrekoldOrqpSj6qpzyoDD0rvGH7c7rFq1KmPHjs3GjRtTW1ubZ599Nscee2xPD2snS5Ysydlnn50ZM2bkpptuKvx1nmppzcJ1m7oV18xspKjNmzenV69eKZfLXYcYAMC+JKwBAHttwYIFWb16de65557MmjVrp2VXs2fPzvjx43Puuef24AgPfMuXL8/73//+tLe3p62t7aCOAN2Ja6Ia3fXWk0AB4A/NW8UAwF4bNGhQli9fnjVr1uSKK67I+PHjM3PmzDz00ENZsGBBjjvuuCTb/+Bl90455ZTcd999GTdu3EEfBUYfVZdpwxoyrKE2VaXscppsdSmpKm3fU23asAZRjW452P/9AHBgMWMNANhnHnrooUyePDkLFy7MY489lpUrV+acc87JjBkzenpo7xqb2ir5z/Vb8t+tHdnaUUmvqnLeU1d10C6f3bFcuOUtz/eog/j5AgAHF2ENANgrO5Zdbdu2LS0tLXnttdfSu3fvwhvcH6pe2tSWJ19pzbOvb0sp2WmZ5I4DH4b0q824gXU5us+hfeADAMCBQlgDAPZKR0dHqqqqct1112Xp0qVZunRpxowZk5qamjQ1NeWCCy7ISSed1NPDPCCMHDky3/zmNzNp0qSd7n/rnmMbXvxtbpp6Wr7yby+lqnrXA9ztOQYAcOAwvx4A2CtVVVVJkrvvvjs/+MEPMnDgwHzsYx9LfX19Hn744fTu3buHR3jgWLFixe+NanuivTNZuG5Tnmpp3fcDBACgW4Q1AGCvrV27Nk1NTWlra8uWLVty/vnn54477khDQ0MaGxt7engHrJc2tXUrqu2wI669tLntDzMwAAD2iLAGAOy1crmcCy+8MJs2bcqIESMya9as3HjjjV2PVSqVHh7hgaGpqSmPPvpoKpVKbrjhhpx60rD83aRh+e6V07P5tQ27/Zx//9F389WPj8vV45ty00dOzy/n/0uS7XHtpytfyNSpU3P44YdnwIABmTBhgp81AMB+tOvGHQAAe+iFF17IYYcdlsbGxq6ZabNnz85tt92W9evXZ9asWUm2H3DAm+bOnZv7/vX+TL/jR6k7/Ij8+Ka/zY9uuDKfuv6OXa6tH3BkPnvrdzLguKb85qknc9eln8pxI9+XY0eMyt3/+PUMOebYtLS0JEkWL16cUqm0v58OAMAhS1gDAAq76KKL0qtXr7z3ve/NqFGjMmLEiAwdOjS33XbbTnur7diHje1uv/32XHzNLdk68Ji0dyYf/KtZufGcU9PR3r7Lte+dMLnr9pDTPpAT/2hS1i5bnGNHjEpVdU3+67fP5LnnnsvQoUMzYcKE/fk0AAAOecIaAFDYt771rTz11FN58sknM3/+/Lzyyivp379/hg0bluHDh+fCCy80g2o3nnvuuVz5F59KZ9782ZTLVXljfcsu165+4tEs+Odb8j+/fSadlUratrRm4NARSZLxn/nrLLvrHzJ58vb4NmPGjHzhC1/YP08CAABhDQAobtCgQZkyZUqmTJmSJGltbc3ixYvz5JNPZtWqVSmVSuns7BTXfsfgwYNzwTXfSM2Jp+3y2IYXf9t1u33b1nxn1oU5b85tOan5w6mqqck9V3wm+f+ltb361OeCq76SP/vW3Dz99NM588wzM2bMmHzwgx/cb88FAOBQ5vACAKCQHZvk//znP8/cuXPzoQ99KMuWLcsZZ5yRL37xi7n55puTRFTbjYsuuijf/+pXsuHF55Mkb2z4n6x8/Ce7XNfR1pb2bVvTp/8RKVdXZ/UTj2bN4se7Hv/1zx/Jq8//Jp2dnWloaEhVVVXKZS/vAAD2F6+8AIBCdgSza665Jg0NDXn55ZdTXb19MvyXv/zlPPPMMz05vAPaZZddljM/fE7uvOS8XD2+Kf/02Q/n+f9cust1vfrU5yOzrsu8K/8yc5qH5j9+cl9GTPzjrsc3PP9svvzpj6a+vj5jx47NxRdfnDPOOGN/PhUAgENaqdMxXQBAQRs3bkxzc3N+9atf5bTTTsuiRYtSW1ub4cOH54knnsiRRx7Z00M8oDQ2Nubee+/NxIkTs6mtkn9csT4de/FKrKqUXDJyQHrXeK8UAKAneBUGABS2ZcuWTJkyJXfeeWf69++f2trarF69Og0NDTnyyCPj/bs3tbS0pKWlJU1NTUmSPjXlDOlXu1df84R+taIaAEAPcngBAFBYa2trhg8fnq997Wvp27dvpk2blq1bt2batGlJtu/DVlVV1cOj7HlLlizJ2WefnUsvvTSNjY1d948bWJffvL4t7QX6Y3UpGTuobh+OEgCA7rIUFADotkqlknK5nPPPPz9z585NW1tbHnvssbS1teX444/PhAkTenqI7xpPtbRm4bpN3Ypr1aXkzGP7ZPRRwhoAQE8yYw0A6LZyuZyOjo7U19dn48aNOeGEE/KJT3zCiZQF7IhjexrXRDUAgAOHsAYAFLJ06dI88MADefXVV3PllVdm1KhR6dWrV08P611p9FF1ObpPdRa93JpnXt+WUrJTZKsuJZ3Zvqfa2EF1Obp3TU8NFQCAt7AUFADolo6OjpTL5bzxxhv53ve+l0WLFmXx4sWpVCoZMGBAZs6cmfPOO6+nh/mutbmtkuXrt6SltSNbOyrpVVXOUXVVOWXAYQ4qAAA4wAhrAEC33Hfffenbt2+GDh2a448/vuv+F198MQ8++GBOPPHETJo0KR0dHQ4uAADgoCasAQB7rFKp5KqrrkpLS0v69OmTW265JTU1NSmVSimVSj09PAAA2K+ENQCg2yqVStatW5fBgwfvcr8DDAAAOFR45QsA7LEFCxZk7dq1KZfLGTx4cNrb23d6XFQDAOBQ4tUvALDHrr766vzyl7/s+ri6+s0DxiuVSjo6OnpiWAAA0CMsBQUA9titt96aefPmpbGxMcOHD09zc3POOuusna554oknMnr06NTV1fXQKAEAYP8Q1gCAblm9enWefvrprFy5MmvXrs3UqVPzwAMPZMCAARk6dGhuuOGGrFq1SlgDAOCgJ6wBAN3W3t6ecrmc2bNn5/7778+cOXOybt26rFmzJqNGjcoll1zS00MEAIA/OGENACjs8ccfz+bNmzNlypS0traapQYAwCFFWAMACuns7EypVOrpYQAAQI9xKigAUIioBgDAoU5YAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKEBYAwAAAIAChDUAAAAAKOD/AA+SftHxWZGLAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1224x1224 with 1 Axes>"
       ]
diff --git a/docs/speech-toolkit.rst b/docs/speech-toolkit.rst
index 28a64adb..6051d94a 100644
--- a/docs/speech-toolkit.rst
+++ b/docs/speech-toolkit.rst
@@ -58,6 +58,7 @@ Features
 -  **Speaker overlap**, detect overlap speakers using Finetuned Speaker Vector.
 -  **Speaker Vector**, calculate similarity between speakers using Pretrained Speaker Vector.
 -  **Speech Enhancement**, enhance voice activities using Waveform UNET.
+-  **SpeechSplit Conversion**, detailed speaking style conversion by disentangling speech into content, timbre, rhythm and pitch using PyWorld and PySPTK.
 -  **Speech-to-Text**, End-to-End Speech to Text for Malay and Mixed (Malay and Singlish) using RNN-Transducer and Wav2Vec2 CTC.
 -  **Super Resolution**, Super Resolution 4x for Waveform.
 -  **Text-to-Speech**, Text to Speech for Malay and Singlish using Tacotron2 and FastSpeech2.
@@ -93,6 +94,10 @@ Malaya-Speech also released pretrained models, simply check at `malaya-speech/pr
 -  **FastVC**, Faster and Accurate Voice Conversion using Transformer, no paper produced.
 -  **FastSep**, Faster and Accurate Speech Separation using Transformer, no paper produced.
 -  **wav2vec 2.0**, A Framework for Self-Supervised Learning of Speech Representations, https://arxiv.org/abs/2006.11477
+-  **FastSpeechSplit**, Unsupervised Speech Decomposition Via Triple Information Bottleneck using Transformer, no paper produced.
+-  **Sepformer**, Attention is All You Need in Speech Separation, https://arxiv.org/abs/2010.13154
+-  **FastSpeechSplit**, Faster and Accurate Speech Split Conversion using Transformer, no paper produced.
+-  **HuBERT**, Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units, https://arxiv.org/pdf/2106.07447v1.pdf
 
 References
 -----------
diff --git a/example/dependency/load-dependency.ipynb b/example/dependency/load-dependency.ipynb
index fdf5d097..d1a09942 100644
--- a/example/dependency/load-dependency.ipynb
+++ b/example/dependency/load-dependency.ipynb
@@ -38,8 +38,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.07 s, sys: 951 ms, total: 6.02 s\n",
-      "Wall time: 6.92 s\n"
+      "CPU times: user 5.15 s, sys: 925 ms, total: 6.07 s\n",
+      "Wall time: 6.8 s\n"
      ]
     }
    ],
@@ -264,12 +264,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### List available transformer Dependency models"
+    "### List available transformer Dependency models\n",
+    "\n",
+    "```python\n",
+    "def available_transformer(version: str = 'v2'):\n",
+    "    \"\"\"\n",
+    "    List available transformer dependency parsing models.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    \"\"\"\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -310,19 +326,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>bert</th>\n",
-       "      <td>426.0</td>\n",
-       "      <td>112.00</td>\n",
-       "      <td>0.855000</td>\n",
-       "      <td>0.84800</td>\n",
-       "      <td>0.920</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>0.820450</td>\n",
+       "      <td>0.79970</td>\n",
+       "      <td>0.98936</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tiny-bert</th>\n",
-       "      <td>59.5</td>\n",
-       "      <td>15.70</td>\n",
-       "      <td>0.718000</td>\n",
-       "      <td>0.69400</td>\n",
-       "      <td>0.886</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>17.50</td>\n",
+       "      <td>0.795252</td>\n",
+       "      <td>0.72470</td>\n",
+       "      <td>0.98939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>albert</th>\n",
@@ -330,7 +346,7 @@
        "      <td>15.30</td>\n",
        "      <td>0.821895</td>\n",
        "      <td>0.79752</td>\n",
-       "      <td>1.000</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tiny-albert</th>\n",
@@ -338,23 +354,23 @@
        "      <td>8.51</td>\n",
        "      <td>0.786500</td>\n",
        "      <td>0.75870</td>\n",
-       "      <td>0.817</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>xlnet</th>\n",
-       "      <td>450.2</td>\n",
-       "      <td>119.00</td>\n",
-       "      <td>0.931000</td>\n",
-       "      <td>0.92500</td>\n",
-       "      <td>0.947</td>\n",
+       "      <td>480.2</td>\n",
+       "      <td>121.00</td>\n",
+       "      <td>0.848110</td>\n",
+       "      <td>0.82741</td>\n",
+       "      <td>0.92101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alxlnet</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>14.30</td>\n",
-       "      <td>0.894000</td>\n",
-       "      <td>0.88600</td>\n",
-       "      <td>0.942</td>\n",
+       "      <td>61.2</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>0.849290</td>\n",
+       "      <td>0.82810</td>\n",
+       "      <td>0.92099</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -362,23 +378,23 @@
       ],
       "text/plain": [
        "             Size (MB)  Quantized Size (MB)  Arc Accuracy  Types Accuracy  \\\n",
-       "bert             426.0               112.00      0.855000         0.84800   \n",
-       "tiny-bert         59.5                15.70      0.718000         0.69400   \n",
+       "bert             455.0               114.00      0.820450         0.79970   \n",
+       "tiny-bert         69.7                17.50      0.795252         0.72470   \n",
        "albert            60.8                15.30      0.821895         0.79752   \n",
        "tiny-albert       33.4                 8.51      0.786500         0.75870   \n",
-       "xlnet            450.2               119.00      0.931000         0.92500   \n",
-       "alxlnet           50.0                14.30      0.894000         0.88600   \n",
+       "xlnet            480.2               121.00      0.848110         0.82741   \n",
+       "alxlnet           61.2                16.40      0.849290         0.82810   \n",
        "\n",
        "             Root Accuracy  \n",
-       "bert                 0.920  \n",
-       "tiny-bert            0.886  \n",
-       "albert               1.000  \n",
-       "tiny-albert          0.817  \n",
-       "xlnet                0.947  \n",
-       "alxlnet              0.942  "
+       "bert               0.98936  \n",
+       "tiny-bert          0.98939  \n",
+       "albert             1.00000  \n",
+       "tiny-albert        1.00000  \n",
+       "xlnet              0.92101  \n",
+       "alxlnet            0.92099  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,13 +410,19 @@
     "### Load xlnet dependency model\n",
     "\n",
     "```python\n",
-    "def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
+    "def transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
     "    \"\"\"\n",
     "    Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    model : str, optional (default='bert')\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    model : str, optional (default='xlnet')\n",
     "        Model architecture supported. Allowed values:\n",
     "\n",
     "        * ``'bert'`` - Google BERT BASE parameters.\n",
@@ -430,46 +452,10 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen model to /Users/huseinzolkepli/Malaya/dependency-v2/albert/model.pb\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 58.0/57.9 [00:09<00:00, 6.36MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen vocab to /Users/huseinzolkepli/Malaya/dependency-v2/albert/sp10m.cased.v10.vocab\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "184%|██████████| 1.00/0.54 [00:00<00:00, 1.08MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen tokenizer to /Users/huseinzolkepli/Malaya/dependency-v2/albert/sp10m.cased.v10.model\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "135%|██████████| 1.00/0.74 [00:01<00:00, 1.12s/MB]\n",
       "INFO:root:running dependency-v2/albert using device /device:CPU:0\n"
      ]
     }
@@ -498,49 +484,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:root:Load quantized model will cause accuracy drop.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen model to /Users/huseinzolkepli/Malaya/dependency-v2/albert-quantized/model.pb\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "103%|██████████| 15.0/14.6 [00:03<00:00, 4.24MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen vocab to /Users/huseinzolkepli/Malaya/dependency-v2/albert-quantized/sp10m.cased.v10.vocab\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "184%|██████████| 1.00/0.54 [00:00<00:00, 1.25MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading frozen tokenizer to /Users/huseinzolkepli/Malaya/dependency-v2/albert-quantized/sp10m.cased.v10.model\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "135%|██████████| 1.00/0.74 [00:01<00:00, 1.14s/MB]\n",
+      "WARNING:root:Load quantized model will cause accuracy drop.\n",
       "INFO:root:running dependency-v2/albert-quantized using device /device:CPU:0\n"
      ]
     }
@@ -573,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -788,10 +732,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x14fd008d0>"
+       "<graphviz.files.Source at 0x14fdb9f90>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1009,10 +953,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x14fd00490>"
+       "<graphviz.files.Source at 0x1471f5cd0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1031,14 +975,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:running dependency-v2/tiny-albert using device /device:CPU:0\n"
+      "INFO:root:running dependency-v2/alxlnet using device /device:CPU:0\n"
      ]
     },
     {
@@ -1244,17 +1188,17 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x150457810>"
+       "<graphviz.files.Source at 0x1769cdc10>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "alxlnet = malaya.dependency.transformer(model = 'tiny-albert')\n",
-    "tagging, indexing = malaya.stack.voting_stack([model, alxlnet, model], string)\n",
+    "alxlnet = malaya.dependency.transformer(model = 'alxlnet')\n",
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], string)\n",
     "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
    ]
   },
@@ -2374,7 +2318,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x14ff526d0>"
+       "<graphviz.files.Source at 0x168e76690>"
       ]
      },
      "execution_count": 14,
@@ -2389,7 +2333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -3483,16 +3427,16 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x167bb5f50>"
+       "<graphviz.files.Source at 0x16ab2e590>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tagging, indexing = malaya.stack.voting_stack([model, alxlnet, model], s)\n",
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], s)\n",
     "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
    ]
   },
@@ -3507,16 +3451,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<malaya.function.parse_dependency.DependencyGraph at 0x164f0f290>"
+       "<malaya.function.parse_dependency.DependencyGraph at 0x16ab39c10>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3535,8 +3479,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -4629,10 +4575,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x1692e0690>"
+       "<graphviz.files.Source at 0x17ae39450>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4650,7 +4596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -5502,7 +5448,7 @@
        "              'rel': 'punct'}})"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5673,7 +5619,7 @@
     {
      "data": {
       "text/plain": [
-       "<networkx.classes.multidigraph.MultiDiGraph at 0x1667816d0>"
+       "<networkx.classes.multidigraph.MultiDiGraph at 0x16ab39d50>"
       ]
      },
      "execution_count": 22,
@@ -5693,7 +5639,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5865,7 +5811,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x360 with 1 Axes>"
       ]
@@ -5906,142 +5852,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "r = quantized_model.vectorize('Kakak saya mempunyai anjing. Dia menyayanginya')"
+    "r = quantized_model.vectorize(s)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
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-      ],
-      "text/plain": [
-       "<graphviz.files.Source at 0x16aa85e90>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "string = 'Husein Zolkepli suka makan ayam. Dia pun suka makan daging'\n",
-    "d_object, tagging, indexing = model.predict(string)\n",
-    "d_object.to_graphvis()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6051,16 +5871,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(7, 2)"
+       "(89, 2)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6075,12 +5895,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -6110,13 +5930,6 @@
     "        textcoords = 'offset points',\n",
     "    )"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/example/knowledge-graph-from-dependency/load-knowledge-graph-from-dependency.ipynb b/example/knowledge-graph-from-dependency/load-knowledge-graph-from-dependency.ipynb
index 6a08cf06..7621253d 100644
--- a/example/knowledge-graph-from-dependency/load-knowledge-graph-from-dependency.ipynb
+++ b/example/knowledge-graph-from-dependency/load-knowledge-graph-from-dependency.ipynb
@@ -44,8 +44,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.11 s, sys: 927 ms, total: 6.04 s\n",
-      "Wall time: 6.12 s\n"
+      "CPU times: user 5.14 s, sys: 883 ms, total: 6.03 s\n",
+      "Wall time: 6.61 s\n"
      ]
     }
    ],
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "infrared-discipline",
    "metadata": {},
    "outputs": [
@@ -82,8 +82,8 @@
     }
    ],
    "source": [
-    "quantized_model = malaya.dependency.transformer(model = 'xlnet', quantized = True)\n",
-    "alxlnet = malaya.dependency.transformer(model = 'alxlnet')"
+    "quantized_model = malaya.dependency.transformer(version = 'v1', model = 'xlnet', quantized = True)\n",
+    "alxlnet = malaya.dependency.transformer(version = 'v1', model = 'alxlnet')"
    ]
   },
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"422.82,-481.59 420.87,-471.18 415.9,-480.53 422.82,-481.59\"/>\n",
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        "<title>17</title>\n",
-       "<text text-anchor=\"middle\" x=\"692\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (melanggar)</text>\n",
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        "<!-- 14&#45;&gt;17 -->\n",
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-       "<text text-anchor=\"middle\" x=\"640\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"504.71,-479.67 511.04,-471.18 500.79,-473.87 504.71,-479.67\"/>\n",
+       "<text text-anchor=\"middle\" x=\"500.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
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        "<title>11</title>\n",
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+       "<text text-anchor=\"middle\" x=\"227.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (Langkawi)</text>\n",
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        "<!-- 10&#45;&gt;11 -->\n",
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-       "<text text-anchor=\"middle\" x=\"317\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
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+       "<text text-anchor=\"middle\" x=\"236.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
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        "<!-- 16 -->\n",
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-       "<text text-anchor=\"middle\" x=\"657\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (selepas)</text>\n",
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        "<!-- 17&#45;&gt;16 -->\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M517.68,-434.8C503.94,-422.24 485.13,-405.05 469.89,-391.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"472.03,-388.34 462.29,-384.18 467.31,-393.51 472.03,-388.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"508.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
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        "<title>18</title>\n",
-       "<text text-anchor=\"middle\" x=\"750\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (SOP)</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;18 -->\n",
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        "<title>17&#45;&gt;18</title>\n",
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+       "<path fill=\"none\" stroke=\"black\" d=\"M536.5,-434.8C536.5,-423.16 536.5,-407.55 536.5,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"540,-394.18 536.5,-384.18 533,-394.18 540,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"545.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
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        "<!-- 21 -->\n",
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        "<title>21</title>\n",
-       "<text text-anchor=\"middle\" x=\"679\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
+       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (mengambil)</text>\n",
        "</g>\n",
-       "<!-- 18&#45;&gt;21 -->\n",
+       "<!-- 17&#45;&gt;21 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>18&#45;&gt;21</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M735.63,-521.8C725.33,-509.47 711.31,-492.68 699.79,-478.89\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"727\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "<title>17&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M557.75,-434.8C573.4,-422.13 594.89,-404.73 612.18,-390.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"614.71,-393.19 620.28,-384.18 610.31,-387.75 614.71,-393.19\"/>\n",
+       "<text text-anchor=\"middle\" x=\"603.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
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        "<title>23</title>\n",
-       "<text text-anchor=\"middle\" x=\"787\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
+       "<text text-anchor=\"middle\" x=\"536.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (badan)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;23 -->\n",
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        "<title>18&#45;&gt;23</title>\n",
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-       "<text text-anchor=\"middle\" x=\"800\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M536.5,-347.8C536.5,-336.16 536.5,-320.55 536.5,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"540,-307.18 536.5,-297.18 533,-307.18 540,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"565.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
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        "<title>20</title>\n",
-       "<text text-anchor=\"middle\" x=\"645\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
+       "<text text-anchor=\"middle\" x=\"634.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (tidak)</text>\n",
        "</g>\n",
        "<!-- 21&#45;&gt;20 -->\n",
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        "<title>21&#45;&gt;20</title>\n",
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-       "<text text-anchor=\"middle\" x=\"680.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M640.08,-347.8C639.13,-336.16 637.84,-320.55 636.74,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"640.22,-306.85 635.91,-297.18 633.25,-307.43 640.22,-306.85\"/>\n",
+       "<text text-anchor=\"middle\" x=\"661\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
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        "<title>25</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
+       "<text text-anchor=\"middle\" x=\"727.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (masuk)</text>\n",
        "</g>\n",
        "<!-- 21&#45;&gt;25 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
        "<title>21&#45;&gt;25</title>\n",
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-       "<text text-anchor=\"middle\" x=\"728\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.29,-347.89C673.43,-342.51 681.04,-336.31 687.5,-330 695.26,-322.43 702.91,-313.37 709.41,-305.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"712.21,-307.15 715.48,-297.08 706.64,-302.91 712.21,-307.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"716.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
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        "<title>19</title>\n",
-       "<text text-anchor=\"middle\" x=\"643\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
+       "<text text-anchor=\"middle\" x=\"545.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;19 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>25&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M690.67,-260.8C661.96,-247.39 621.94,-228.7 591.19,-214.34\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"656\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
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        "<!-- 22 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
        "<title>22</title>\n",
-       "<text text-anchor=\"middle\" x=\"646\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"637.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (suhu)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;22 -->\n",
-       "<g id=\"edge25\" class=\"edge\">\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
        "<title>25&#45;&gt;22</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M719.38,-347.8C705.79,-335.24 687.18,-318.05 672.11,-304.12\"/>\n",
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        "<!-- 24 -->\n",
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        "<title>24</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
+       "<text text-anchor=\"middle\" x=\"727.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (ketika)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;24 -->\n",
-       "<g id=\"edge26\" class=\"edge\">\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
        "<title>25&#45;&gt;24</title>\n",
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-       "<text text-anchor=\"middle\" x=\"752.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"731,-220.18 727.5,-210.18 724,-220.18 731,-220.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"742\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
        "</g>\n",
        "<!-- 28 -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
+       "<g id=\"node27\" class=\"node\">\n",
        "<title>28</title>\n",
-       "<text text-anchor=\"middle\" x=\"878\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (surau)</text>\n",
+       "<text text-anchor=\"middle\" x=\"819.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (surau)</text>\n",
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        "<!-- 25&#45;&gt;28 -->\n",
-       "<g id=\"edge27\" class=\"edge\">\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
        "<title>25&#45;&gt;28</title>\n",
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-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>22&#45;&gt;19</title>\n",
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        "<!-- 32 -->\n",
-       "<g id=\"node27\" class=\"node\">\n",
+       "<g id=\"node28\" class=\"node\">\n",
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-       "<text text-anchor=\"middle\" x=\"738\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
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-       "<title>24&#45;&gt;32</title>\n",
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+       "<text text-anchor=\"middle\" x=\"937.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (Sabtu)</text>\n",
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-       "<title>31</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pada)</text>\n",
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-       "<g id=\"edge33\" class=\"edge\">\n",
-       "<title>32&#45;&gt;31</title>\n",
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-       "<text text-anchor=\"middle\" x=\"750\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "<!-- 25&#45;&gt;32 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;32</title>\n",
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+       "<text text-anchor=\"middle\" x=\"854.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
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        "<title>26</title>\n",
-       "<text text-anchor=\"middle\" x=\"821\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
+       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (ke)</text>\n",
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        "<title>28&#45;&gt;26</title>\n",
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        "</g>\n",
        "<!-- 28&#45;&gt;27 -->\n",
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        "<title>28&#45;&gt;27</title>\n",
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        "</g>\n",
        "<!-- 30 -->\n",
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        "<title>30</title>\n",
-       "<text text-anchor=\"middle\" x=\"1014\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
+       "<text text-anchor=\"middle\" x=\"912.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (Langkawi)</text>\n",
        "</g>\n",
        "<!-- 28&#45;&gt;30 -->\n",
        "<g id=\"edge30\" class=\"edge\">\n",
        "<title>28&#45;&gt;30</title>\n",
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+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"1013.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 32&#45;&gt;31 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>32&#45;&gt;31</title>\n",
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        "<!-- 29 -->\n",
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        "<title>29</title>\n",
-       "<text text-anchor=\"middle\" x=\"1014\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
+       "<text text-anchor=\"middle\" x=\"912.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (di)</text>\n",
        "</g>\n",
        "<!-- 30&#45;&gt;29 -->\n",
        "<g id=\"edge31\" class=\"edge\">\n",
        "<title>30&#45;&gt;29</title>\n",
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        "</g>\n",
        "<!-- 33 -->\n",
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        "<title>33</title>\n",
-       "<text text-anchor=\"middle\" x=\"738\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
+       "<text text-anchor=\"middle\" x=\"1013.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (lalu)</text>\n",
        "</g>\n",
        "<!-- 31&#45;&gt;33 -->\n",
        "<g id=\"edge32\" class=\"edge\">\n",
        "<title>31&#45;&gt;33</title>\n",
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-       "<text text-anchor=\"middle\" x=\"753.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
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+       "<text text-anchor=\"middle\" x=\"1036\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.files.Source at 0x1401cd950>"
+       "<graphviz.files.Source at 0x14f0e3310>"
       ]
      },
      "execution_count": 4,
@@ -551,13 +551,14 @@
     "### Parse knowledge graph from dependency\n",
     "\n",
     "```python\n",
-    "def parse_from_dependency(tagging, indexing,\n",
-    "                          subjects=[['flat', 'subj', 'nsubj', 'csubj']],\n",
-    "                          relations=[['acl', 'xcomp', 'ccomp', 'obj', 'conj', 'advcl'], ['obj']],\n",
-    "                          objects=[['obj', 'compound', 'flat', 'nmod', 'obl']],\n",
-    "                          get_networkx=True):\n",
+    "def parse_from_dependency(tagging: List[Tuple[str, str]],\n",
+    "                          indexing: List[Tuple[str, str]],\n",
+    "                          subjects: List[List[str]] = [['flat', 'subj', 'nsubj', 'csubj']],\n",
+    "                          relations: List[List[str]] = [['acl', 'xcomp', 'ccomp', 'obj', 'conj', 'advcl'], ['obj']],\n",
+    "                          objects: List[List[str]] = [['obj', 'compound', 'flat', 'nmod', 'obl']],\n",
+    "                          get_networkx: bool = True):\n",
     "    \"\"\"\n",
-    "    Generate knowledge graphs from dependency parsing.\n",
+    "    Generate knowledge graphs from dependency parsing, we suggest use dependency parsing v1.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -600,13 +601,16 @@
     {
      "data": {
       "text/plain": [
-       "{'result': [{'subject': 'Najib Ahli Parlimen Pekan',\n",
-       "   'relation': 'memuji sikap mengaku melanggar SOP mengambil masuk',\n",
+       "{'result': [{'subject': 'Najib',\n",
+       "   'relation': 'memuji sikap mengaku melanggar SOP',\n",
+       "   'object': 'badan'},\n",
+       "  {'subject': 'Najib',\n",
+       "   'relation': 'memuji sikap mengaku melanggar mengambil masuk',\n",
        "   'object': 'suhu'},\n",
-       "  {'subject': 'Najib Ahli Parlimen Pekan',\n",
+       "  {'subject': 'Najib',\n",
        "   'relation': 'memuji sikap',\n",
        "   'object': 'Ahli Parlimen Langkawi'}],\n",
-       " 'G': <networkx.classes.multidigraph.MultiDiGraph at 0x140338c10>}"
+       " 'G': <networkx.classes.multidigraph.MultiDiGraph at 0x14a51a850>}"
       ]
      },
      "execution_count": 6,
@@ -626,7 +630,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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OMGTIEFq0aPEeqxMRkbeh7Qyv8KLQMxqNREREEBQUxK1bt+jbty8rVqzgt99+4+rVqwCsXbuWa9euvedqRUTkdSj43tCVK1fw8/PDw8ODTp06sWHDBnLmzMnPP/9M1qxZCQwMJG/evNrnJyKSSCn43lBISAjdunVj3759lC5dmh07dhAREUG2bNmYOXMmnTt3xsvLi2rVqrFo0SLOnTtn6ZJFRCQOBd8bKly4ML/++isdOnRg5syZRERE4OjoyJw5c9i7dy+NGjUiderUzJ07l969exMYGGjpkkVEJA4tbnlDsQtezp07R2RkJHnz5mXnzp0sXLiQRo0a0axZM+bOnct3333HrFmzqFSpklZ6iogkIgq+dzR//nxWrlzJJ598QpMmTdi4cSPdunVj+/bt5MmTh5iYGGxtbS1dpoiI/D9dueUdffDBBzg7O9OkSRMWLVrEgAEDiIqKYuHChZw6dYqYmBi+/PJLypYta+lSRUQEdXzvJO4Q5oIFCxg0aBCTJ09m9erV9OzZExcXF0JDQ3FzcyNv3rzY29tbuGIREVHwxZODBw8SERFB/vz56dixI2vXrjUfCw8P54svvqB8+fI0a9bMglWKiIiGOuOByWSidOnSAFy7dg2j0QjA3bt3sbW1JSgoiC5duvDJJ59gNBp1hRcREQvSdoZ4EHfFZnR0NI6Ojly6dIlp06bRvXt3Pv30U06dOsXy5cs5ePAgMTExFqxWRMS6aagzAfj7+2NjY0OfPn3o06cPxYoVo06dOty8eZMKFSowY8YMS5coImK11PElgLRp0/Lo0SP27NlDREQEDg4O+Pj4kD59eiZNmgTA2LFj2bFjh/kx+vwhIvJ+aI4vgeTKlYslS5Ywb948WrZsiZeXF+vXr8fZ2ZmBAwfy008/0aBBAyIjI6lTp442uIuIvCcKvgRiMpmoUKECLi4uBAYG8uOPP5I5c2aGDh3Ktm3b2Lt3L97e3tSpU4dUqVJRpkwZS5csImIVFHwJxGAwYDQaKVGiBIsXL8bBwYEvv/yS//znP/z5559ky5YNgIIFCxIVFWXhakVErIeCLwHZ2DydQnVwcGDcuHEsX76cnj17ki1bNkJCQrhw4QJOTk6kSpXKwpWKiFgPBd97MHHiRGbOnMkvv/zCxIkTmT17NiEhIRw5coTcuXNTqFAhAF3XU0TkPdB2hvfgzp07REZGkiVLFnbs2MHkyZMpVqwYWbNmpV27dgBERUWZL2l27tw58uXLZ8GKRUSSLwXfexQbbrH38IsVGRmJg4MDAN988w1bt25lxowZ5k5QRETij4LPAuJe3Do6Oho7u6cjzl9++SWHDh0if/78XL16lRYtWtCmTRtLlioikuxoA7sFxN2zFxt6Q4cO5a+//mLChAlMnjyZHj16MG3aNCIjI83n6jOKiMi7U/AlAl999RWHDx9m+PDhFC5cmCdPnnDp0iVCQkKYNWsWq1atAtAmdxGReKChzkTg7NmzREdHU7hwYUJDQ9mwYQMLFiygWrVq1K1bl3r16jF//nxKlSpl6VJFRJI8bWewMJPJRP78+QEIDQ1l1apVrFixgtatW9OyZUsAvLy8uH37tiXLFBFJNjTUaWFxhy9DQ0NZunQpjRs3Nodely5d8PLyomHDhgDPXOVFzbqIyJvTUGcic//+fby8vADo3r07MTEx9O7dG5PJxI4dOwgJCaFAgQLmIBQRkTejoc5EJk2aNMDT0Ltz5w5jx47l6NGjrFixgkuXLrFixQpatGiBwWCgQYMGFq5WRCTpUceXSJ0/fx5bW1uyZctG586dyZ8/P6tWreKbb74BYM2aNfz8888YDAbs7Oye2RsoIiIvp+BLhOKG2K1bt2jZsiV+fn48fvyYGjVqYDQaad++PV27duXJkye4ublZuGIRkaRDi1sSobidm4ODA5GRkezYsQN3d3c2btxIxYoVqVGjBpcuXSJLliz88ccf5vP1OUZE5NXU8SUBu3fvpnv37gwbNsw8v3flyhVKly5Njhw58PX1pXjx4jRt2tTSpYqIJHrq+BI5k8mEr68vM2fOJCIiAoPBwIULFyhVqhT9+vVj//79NG/enH79+nH+/HlLlysikuhpVWciZzAYMJlMlC1blrJly/L3339TqFAhRo4cyaBBgzAajYSEhODr60u6dOksXa6ISKKnji8JiDvn9+DBA7799lsGDRoEwPHjx1m3bh3Zs2cnRYoUREZGEhwcbKlSRUQSPc3xJSFGoxEbm/99Vjl48CBr1qwhKiqKH3/8ke+//57Tp09z9epVunTpQtu2bQG01UFEJA4NdSYhcUMvJCSEdevW8eDBA6ZNm8aQIUNYu3YtS5cu5cGDB/z6668ULVqUQoUKKfREROJQ8CVRrq6udOjQATc3N/7880+2bt3K0aNHcXFxAWDr1q2ULFmSQ4cOUaRIEYWfiMj/0xxfEpYjRw7Spk1LQEAA5cuXN4feo0ePmD9/Pt26daNo0aLPhJ5GtkXE2mmOLwkLCgri8uXLeHl5Ua5cOX799VfCw8MZMGAANWrUYOrUqQCMHj2a7Nmzm+/4ICJizdTxJWE2Njb06tWLEydOsGXLFnbs2EGbNm1o1aoVU6dOxWg0cv36dRo2bMjPP//M/PnzLV2yiIjFqeNL4q5cuULz5s0pW7Yshw8fpnr16owYMYLp06ezZ88ebt++Tfv27albty59+vRh5syZODk5ARAYGEiqVKks/A5ERN4vBV8ycPfuXW7cuIHJZKJUqVKYTCbat29PjRo1qFKlCu3atSM6OhpHR0dWr16Ng4MDCxYsYNKkSezcuRN3d3ctfhERq6GhzmTAx8eHDz74gFKlSgFw/fp1/Pz8SJEiBT4+PlSqVIkrV67w/fff4+DgwOLFi/npp5/4+uuvSZEihUJPRKyKOr5kJnaz+t69e5k1axbnzp3jypUrLFu2jEqVKrFs2TJGjRrFuHHjqFmz5jOPERGxBur4kpnYa3uWL1+e/v37Y29vz6JFi6hUqRLr169n4MCBFCpUCBcXF/bs2cOmTZsUeiJiVdTxJVOxXVzsjWrnzp3LpEmTuH//PjVr1iRdunQ4OjoSGRlJr1698PLysnTJIiLvhYLPCjx48ICqVavy66+/cvz4cTJnzkzdunUBuHjxIqNHj2bAgAEUKFDAwpWKiCQ8DXVagTRp0rBz504+/PBD9u3bh9FoBODq1atMnDgRLy8vwsLCAMzH9HlIRJIrBZ+V8PDwAJ5ezsxgMBAUFMT48eNJkyYNH3/8MSVLlgTg7NmzAJr3E5FkS8FnJezsnl6PfPTo0WTJkoWdO3diMBho0KABRYsWBeCLL76gQoUKhIWFqeMTkWRLd2ewIiaTicKFCwOwcuVKTCYTxYsXB2DYsGEsXbqUEydO4OzsbMkyRUQSlDo+KxJ3+LJ69eocOnSIdevWma/t+ccff5AlSxaio6Ofe6w6QBFJLrSq0wrF3sn90qVLtG/fnuPHj3Pu3DkyZcpEdHS0eVgUns4JpkyZ0nLFiojEM3V8Vij2Tu45cuTg559/5sKFCy8MvYMHD1K1alV27Nhh/pk+J4lIUqeOz4rFdn7Ac6F39OhRmjVrRo4cOciQIQM1atSgdevWlipVRCTeqOOzYrGhBzwTesePH6d79+5069aNHTt28MUXX/DTTz9x/fp18zn6vCQiSZWCT55x7NgxWrVqRfPmzRk0aBAAGzduJDQ0lJMnT7Jr1y5A+/xEJOlS8Mkz7O3t6dixIwMHDgTgxx9/5O+//6Zt27akTZuWjz/+mE2bNlm4ShGRt6d9fGIWu88vdq/fqFGjuHr1Kg0bNqRixYq4u7vTvn17oqKiLFypiMjbU8cnZnGHL+/cucO1a9eoU6cOlStXxt3dne3bt7Nz504yZ8783GM15yciSYVWdcpLPXr0CAcHB1xcXNi+fTv9+/dn/PjxFCtWjJUrVxIcHEy+fPlo0KCBpUsVEXlt6vjkpVKmTImLiws7d+6kU6dOfP/992TLlo3q1auzbt06ypQpw7hx45g7d66lSxUReW0KPvlXXl5eDB8+nDp16vD111/TvXt3AG7dusX69es5cuQIMTExANy+fZvbt29bslwRkVfSUKe8UtxN7pGRkbRp04YpU6ZgY2NDkyZN8Pf3p2TJkixYsIDbt28zZcoULl26xLx583BwcNC2BxFJdNTxySvF3eQeFBREYGAgx44dI02aNKxevRpfX1+GDRvG48ePmTFjBhcvXmTEiBE4Ojoq9EQkUVLHJ29k7969dO3alc6dO9O7d29MJhO3bt1i9uzZnDp1ih9++IGsWbOazzeZTApAEUlU1PHJazMajZQvX54ZM2bg7++P0WjE39/fHHpjx44la9asrFixgpUrVwJPt0jos5WIJCbq+OSNxJ3zCw4OZtSoUdy4cYOJEyfi7e3No0ePCA4OpkmTJvTq1YtPPvnEwhWLiDxLV26RNxJ3zi84OJhr164xYcIE/Pz8WL16NSEhITRu3Jjly5fTrl07ateujaenp4Y7RSTRUMcn7yQiIgJHR0c+++wz6tSpQ7Zs2Rg0aBCFChUiPDycKVOmAGBra2vhSkVEntIcn7wTBwcHgoODOXnyJOHh4ZQoUYIJEyawePFicufOja2tLWvXruXkyZPmx+izlohYkjo+iRd79uxh6tSpuLu74+fnR6NGjRgzZgw7d+5kyJAheHh40Lt3bz766CNLlyoiVk5zfPLOjEYjlSpVImfOnFSrVo0mTZowatQotm7dyurVqylRogTt2rWja9eumEwm6tevb+mSRcSKqeOTeHXz5k0yZszI+vXr2bx5M9mzZ6d///7Y2NgwZMgQDAYD3333HQaDQQteRMQiNMcn8SpjxowEBQWxZcsW0qVLx4ABA7CxsWHNmjVs2rSJ2rVrY2Nj80zo6bOXiLxP6vgkQdy9e5e0adNiMBhYv349M2bMoG3bttStW5ejR4/y119/kSVLFmrWrGnpUkXEyqjjkwTh4+ODwWBgw4YNjB8/npYtW5I7d25mz55N//79uX79Oj179mTZsmWWLlVErIyCTxJUrly56Ny5My1btuTs2bP8+eefTJo0ie+++47FixezatUqjh49aukyRcSKKPgkwRiNRvLkyUPr1q2Jiopi6tSp1K9fn7JlyxIREcHvv//OmTNnWLt2LdHR0RiNRkBzfiKSsBR8kmBiL2/25MkTDAYDrq6uuLq6ArBt2zauX79Op06dGDFiBHZ2djx48ABAqz1FJEFpcYskKJPJxJgxY8iRIweVKlWiefPmpE+fnps3b1K/fn0GDRrEoEGDuH79OpcvX6ZLly506NDB/FiFoIjEN21glwRlMBj47LPPqFmzJqGhoRQpUoTw8HAaNWrEgAED6Ny5MydOnGD58uXcvXuX//znPxQrVozixYsr9EQkQWioUxKcj48PmzZt4tChQ6RJk4YGDRrQrVs3du/ezZkzZzh8+DBZsmThww8/JFWqVJQsWZLTp0+b5/xEROKTOj55LzJkyMAvv/yCnd3//sgFBwdTokQJ8/c3btxg0aJFDB8+nEKFCj3zeA17ikh80RyfvHf379/n4sWLZMuWjXLlyjFhwgQCAwMZPHgw7du3Z/To0RiNRr755hty5szJp59+aumSRSQZ0VCnvHfOzs7079+fffv2sWnTJnbt2kW3bt0YNGgQo0ePJiQkhMuXL/Ppp58yffp0ZsyYYemSRSQZUccnFnHr1i0aN25MyZIlOXbsGM2aNWPAgAFMnDiR/fv34+/vT5s2bWjatCn9+/dn2rRpODo6WrpsEUkGFHxiMQ8fPuTu3bvExMSY79jesWNHmjdvTtWqVWnRogX37t3Dx8eH1atX6y7uIhIvNNQpFpM6dWry589vXsji7+/P/v37AXB1daVw4cKEhoYyadIkbG1tmT17Nhs3bjQ/Xp/ZRORtqOOTRCF21ebhw4eZPn06Bw8eJCgoiDVr1lCiRAkmTZpE3759qV27Np999hlNmjSxdMkikkRpO4MkCgaDAaPRSKlSpXB3d6dv377Mnj2bEiVKMHPmTKZOncry5cvJlSsXvXr1okyZMqRPn97SZYtIEqSOTxKV2M4vLCwMZ2dnpkyZwuzZs5k0aRLlypUDMB/T3j4ReRsKPkm0AgICqF27NuPHj8fX1xej0YjBYHgu7DZs2EB4eDg5cuSgWLFiFqpWRJIKLW6RRMvT05Pff//dHHo2NjbPhd7EiRNp1aoV58+fp1WrVvj5+VmoWhFJKjTHJ4la7G2MYm9xFMtkMhEcHMzatWtZvnw5NWvWpHDhwnTu3JlVq1aRL18+S5QrIkmAgk8StZfN4RkMBjw8PMiXLx8TJ06kTJkyfPTRR0RHR/Pw4UNA1/cUkRfTUKckOQcOHODu3bsAdOvWDXd3dw4ePIjJZKJOnTrmRTAKPRF5EQWfJCmPHj1i6NChbNu2DYBChQoRExPD4cOHzQtflixZwsiRIxk7diwHDx60cMUiktgo+CRJSZkyJaNGjeKnn35iyZIlmEwm7t69S8qUKYmMjKRs2bJs3ryZhw8f4u7uTuvWrfnjjz8sXbaIJCLaziBJSuzqzsOHD9OnTx8yZsxIWFgYGzZswNfXl4wZM7Jw4ULz+evWrWP06NFs2bKFlClTWq5wEUk0FHyS5MSGX2BgIAaDgZQpU7JlyxbmzZvH4sWLAYiOjsbOzo7w8HDq1KnDkiVL8Pb2tnDlIpIYaKhTkpzYrQ2pUqUyd3FHjx7Fy8sL+F/oAWzevBlnZ2dz6N27d+/9FywiiYqCT5IFe3t7smXLBmAOvQsXLuDn50f58uUB+P7776latSp79+61WJ0iYnkKPkkWqlevzpQpU1i+fDnh4eGsXLmSadOmERAQQL9+/Zg5cyb79u2jTZs29OrVS1d4EbFimuOTJC92zu/QoUP07dsXV1dXHB0dqVSpEj169GDTpk3MmjWLoUOHUq5cOebPn8+CBQtYu3YtLi4uli5fRN4zXblFkjwbGxuMRiMffvghW7duJTIyEk9PTwBmzpzJhAkT+PDDD80b2318fMiWLRtOTk6WLFtELETBJ8lC7IKXFClSmH82cuRIjh49yoQJExg7dix9+vQhb968zJ07l4YNGz53/U8RsQ76my/Jkr+/P8eOHaNXr17UqFGDjRs34ujoSHBwMO3bt2fIkCGWLlFELERzfJJshYSE4Ozs/NLOLjIyEgcHB/P3uqi1iHVQxyfJlqur60tDLzo62hx6sdfzNBgM6HOgSPKn4BOrExMTY97r161bNwYMGMDhw4cBhZ+INdBQp1iVmJgYbG1tAejYsSN3796lRIkSHDx4kIYNG9KtWzcLVygiCU3BJ1apa9euREZGMmzYMLJmzcrmzZv55ZdfWLt2rbkb1JyfSPKkoU6xOt27d+fhw4fm0Hv48CEHDx7k5s2bfP/998yePRvQjWxFkit1fGJ1rl27BkCWLFl49OgRS5YsYePGjXTs2JEKFSpQsWJFZs2axYcffmjhSkUkIWgDu1gVk8lElixZAAgKCmLOnDns2bOHAQMG4OvrC4CtrS2PHz+2YJUikpA01ClWJe7wZVRUFHv37qV79+74+voSHBxMixYtqFChAtWqVQMgLCzMfL4GR0SSBw11ilV78uQJbm5uBAUF0aVLF7Jly0bbtm25f/8+W7duJTo6mrx58/LZZ59ZulQRiSca6hSr5uLigslkomfPnri4uNCtWze2bt3KypUrCQgIYMeOHdSuXRtXV1datGhh6XJFJB6o4xMBbt68ib29Pe7u7rRp04a6deuyePFiOnbsiIODAwcPHmTcuHHmfYDa6iCSdKnjE6tnMpnImDEjAFeuXCEkJIQOHTrwySefUL16dYKCgujXrx/w9OLX6dOnV+iJJGFa3CJWL26Iubq6cv/+fVatWoW9vT2bNm2iRYsW1K9fn127dpExY0Z+//138/kaMBFJejTUKfL/Yocvjx49Stu2bRk0aBCtW7fGwcGB33//nbp161K/fn0yZsxI3rx56dixo6VLFpG3oOATicNoNGJjY8OZM2e4du0adevWZdu2bTRt2pS5c+fSuHFjTp06RcOGDdm+fTs5cuSwdMki8oY0xycSh42NDUajkYIFC1KwYEF27txJrVq1WL58OY0bNyY0NJSbN29SuXJlMmTI8MxjteBFJGnQHJ/IP8S9h5+XlxcrV66kSZMmREVFceTIETZu3EjRokVxcnIiODiY27dvA7q2p0hSoaFOkZeIHfaEp1d52bNnD2vWrCFjxowMHjyYLl26EBQUxF9//UWXLl3MtzRS5yeSuKnjE3mJuJ1fZGQkmzdvxtXVlcGDB9OyZUsuXbrEL7/8woIFC/jzzz+fuZO7iCRemuMTeQ2urq4MHDiQNGnSsHPnTu7du8euXbsA8PT0BKBs2bKcOXOGvHnzPhOaIpK4KPhEXlO6dOkAePDgAfnz5zf//L///S8rV65k0qRJ5M+fn+joaHPwadhTJPHRx1KR13T//n3++OMPKleuzPbt21mxYgU///wzFStWZMiQIfTs2ZPIyEgGDx7MjBkzAA17iiRGWtwi8ppCQ0OpUqUKffr0oXjx4kyaNIk5c+bwn//8h3bt2nH//n3u37+Pq6srbdq04eOPP6Z79+6WLltE/kFDnSKvycXFhXXr1tGwYUMKFy7MiRMnmDRpEu3atWPMmDEcPXqUe/fu8fHHH7Nu3ToGDx5MZGQkDg4Oli5dROJQxyfyhh4/fkxAQAAxMTHkzJnTfC+/zp07U6VKFRo2bMj58+cpUKAAK1aseOaxmvMTsTzN8Ym8IXd3d7Jly0bOnDkBCAoK4siRIwQFBQGQOXNmUqRIwbRp0wCYMGECq1atAp7O+emzpohlqeMTeQexHdzJkyf5z3/+w7Zt27Czs2PDhg3kyZOH7777jmHDhlG/fn0+/vhj6jVuxumH4dwLiyEixoijrQ3ezrYUTu2Ei70+h4q8D5rjE3kHBoMBo9FIkSJFGDhwIAEBAYwcOZI8efIwYcIEli9fzu+//076fEX4afVOrp4OwNbGhug4HzcvPAK/O6FkT+FA2bTOpHO1t9j7EbEG6vhE4kHs5c1iF7OMHj2a1atXM2PGDIwZ8rDrVgiRMcZ/3dhuZ4AqGVwp7uX8nioXsT4aW5FEY/To0eZ73F2/fh03NzdiYmIA8PX1ZebMmfH2WgaDgYsXL77w2Ny5cylfvrz5ezc3Ny5fvvzK54sNNAcHBwICAti4cSNTp07FmCEPO28+IdrEv4be8m96sumX0ey6FcKs9TvIkyfPG76rxOPq1asYDAaio6MtXYrIcxR8Em+yZs2Kt7c3ISEh5p/NnDkTX1/f13r8l19+aQ63zJkz8+TJE2xtbd+6nitXrmBjY2O+ePTbevLkCdmzZ3/hMV9fX5ycnHBzc8PT05PGjRsTFRXFzp07yZCvCDtvPiGGN1vFGW2CwExF2f3nmXeqOz7s3r0bGxsb3NzccHd3J0+ePMyZM8fSZYm8EwWfxKuYmBh+/vlnS5cBwPz580mVKhVLly4lIiIiwV7nl19+4cmTJ1y4cIFHjx7Rr18/7O3t2XcnhGjj680kGP+/s40VbYIDd8MSotw3lj59ep48eUJwcDDff/89nTp14uzZs5YuS+StKfgkXn3++ef88MMPPHr06IXH+/TpQ6ZMmUiRIgUlSpTAz8/PfGz48OG0adMGePFQ2aVLlyhVqhQpUqSgQYMGPHz48KV1mEwm5s+fz3fffYe9vT3r169/7pwdO3aQK1cuUqZMSY8ePV66zeBVw6JxpU6dmiZNmnDmzBnCjQZGdPuE0TULMrxidn7t8BH+l/4yn7v8m56sGf05c3q1ZFjZLFw+uveZ57p8dB+flclLaJQReNpNjx8/nsKFC+Pq6kqHDh3w9/endu3auLu7U61aNQIDA82PP3jwIGXLliVlypQUKVKE3bt3m4/5+voydOhQypUrh7u7OzVq1CAgIOBf35/BYKBhw4akSpWKs2fPYjQaGTt2LDly5CBNmjQ0b978pf9PVq5cSdasWTlz5gyXLl2iSpUqpEmTBk9PT1q3bv3Mn5esWbPyww8/ULhwYTw8PGjRogXh4eH/Wp/I61LwSbwqWbIkvr6+/PDDDy88/sEHH3DixAkePnxIq1ataNas2Wv/ozZ//nxmz57NnTt3sLOzo3fv3i89d+/evdy8eZOWLVvSvHlz5s2b99w5GzZs4MiRI5w6dYply5axdevW13uTLxEQEMDKlSspVqwYpx+Gk7dcVQauOczXO86RPm9hln7V9ZnzT2xZSeUO/Ri+9wpZin74wuc89fB/vzcrV65k+/btXLhwgfXr11O7dm1Gjx7N/fv3MRqNTJo0CYBbt25Rt25dvv76ax4+fMgPP/xAkyZNuH//vvm5Fi1axJw5c7h37x6RkZEv/f8Vl9FoZPXq1Tx69IhChQoxefJk1qxZw549e7h9+zapUqWiR48ezz1uzpw5fPHFF+zYsYOCBQtiMpkYMmQIt2/f5ty5c9y4cYPhw4c/85hly5axZcsWrly5wqlTp5g7d+6/1ifyuhR8Eu9GjBjB5MmTn/mHNlabNm1IkyYNdnZ2DBgwgIiICM6fP/9az9u2bVsKFiyIq6srI0eOZNmyZebFL/80b948ateuTapUqWjVqhVbtmzh3r17z5wzePBgUqZMSebMmalcuTInTpx44/cK0Lt3b3NnlS5dOn766SfuhcVQvEFrHF3dsHNwpFrXQdy58F/CHwebH5e/Um2yFv0QGxsb7B2dnnteE3A/7H/vr1evXqRNm5YMGTJQoUIFPvzwQ4oVK4aTkxONGjXizz//BGDhwoXUqVOHOnXqYGNjQ/Xq1SlZsiSbNm0yP1f79u3JnTs3zs7ONG/e/JXv/fbt26RMmRJPT0++/fZbFixYQJ48eZg2bRqjRo0iY8aMODo6Mnz4cFasWPFMlz5x4kTGjx/P7t27zRv+c+bMSfXq1XF0dMTLy4v+/fuzZ8+e535P06dPT+rUqfnoo4/e+v+NyItoH5/Eu4IFC1KvXj3Gjh1Lvnz5njn2ww8/MGvWLG7fvo3BYCA4OPi1htkAMmXKZP46S5YsREVFERAQQNq0aZ85LywsjOXLl5sXypQpU4bMmTOzaNEi+vbtaz7Px8fH/LWLiwtPnjx507cKwKRJk8yrUc01BD5ky6QRnN6xnpDAAAyGp58xQx49wMk9BQAeadP/63NHxBjNX8d9n87Ozs99H1v/tWvXWL58+TPDu1FRUVSuXNn8/Zu89/Tp03Pz5s3nfn7t2jUaNWr0zGpVW1tb/P39zd+PHz+eYcOGkTFjRvPP/P396dOnD35+fjx+/Bij0UiqVKmeee5/1nf79u2X1ifyptTxSYL49ttvmTFjBrdu3TL/zM/Pj3HjxrFs2TICAwN59OgRHh4er30Jrxs3bpi/vn79Ovb29uabwMa1evVqgoOD6d69Oz4+Pvj4+HDr1q0XDncmlIMbVnB29xY6TF3BN39cZtDG4wDPvNfXuWanw1v8Dc2UKRNt27bl0aNH5l8hISEMHjz4zZ/sX15n8+bNz7xOeHg4GTJkMJ+zbds2vvvuO1auXGn+2ZdffonBYOD06dMEBwezcOFCXcZN3isFnySInDlz0qJFC/O8Ezy9uLOdnR1eXl5ER0czYsQIgoODX/Esz1q4cCFnz54lNDSUYcOG0bRp0xdud5g3bx6fffYZp0+f5sSJE5w4cYJ9+/Zx8uRJTp8+HS/v79/YRYZi5+CAi0dqosJD2frLqDd+DgNwaOcWzp0790aPa9OmDevXr2fr1q3ExMQQHh7O7t27X9i1vYuuXbvy1Vdfce3aNeDp/QrXrl37zDkFChRgy5Yt9OjRg3Xr1gFP/xy4ubnh4eHBrVu3GD9+fLzWJfJvFHySYIYNG/bMnr6aNWtSq1YtcufOTZYsWXBycnpm+PLftG3blnbt2uHj40N4ePgzoRrr1q1b7Ny5k759+5q7PR8fH0qUKEGtWrXeW9c3sMtnpEqXiTG1CjGhSXkyFyrxxs9hAi79sfm5odx/kylTJtauXcvo0aPx8vIiU6ZMjB8/HqPR+O8PfgN9+vShfv361KhRA3d3d0qXLs2hQ4eeO69IkSJs2LCBTp06sXnzZr755huOHz+Oh4cHdevWpXHjxvFal8i/0SXLJNEYNmwYN2/eZPbs2ZYuJV6svBzM30GRb/VYk9HI40unGVitGKlTpzZfEk1E3p3+JkmiYDKZOHv2LNmyZbN0KfGmbFpn7N7y1nummGg+rVCU1KlTExMTo9ATiUf62ySJQvHixbl58yadOnWydCnxJp2rPVUyuL5x+BmjIqmY1oE86dIQExPz3DymBmlE3o2GOkUS2PH7Yey6FfLMrYhexGQ0YoyOopKPI+WzeD4TeteuXSM0NNS8PUR3chd5e+r4RBJYcS9nWuf2ILeHA7YGnusA7Qxga4DMTkaaZXF+LvTu3r3LkiVL6N+/Pxs2bABebyuEiLyYOj6R9yg0ysiph+Hcj3MHdi9nWwqldsTV/mnQxQ29GzdusHbtWlauXMkHH3zA0aNHady4MT179rTk2xBJ0nTlFpH3yMXehtJpXV55TmzoXb16lU2bNnHq1CkGDhxI3bp1OXny5NNLot27h7e3N6BhT5E3pY5PJBGKjo5m9uzZ7N69m7Zt21K7dm0A6tWrx+XLl2nVqhXZsmWjdevWFq5UJOlRxyeSCNnZ2VG3bl2yZMlCzZo1AWjSpAk2NjYsW7YMDw8PypYtS9q0aalWrZqFqxVJWhR8IomQyWQiQ4YM5uteVq9eHRcXF5YvX46joyM3btygYsWKpEuX7rnHadhT5NW0qlMkEYobXtevXzffSd7R0ZHz58+zbNkyPD09SZ06Nffv3zffkshgMGifn8i/0ByfSCL2zw7u7NmzbNq0ifPnz9OjRw+KFi3KtWvXGDBgABUrVnzlzXlF5CkNdYokYnFDLzIykh07dnDs2DEmTJjAjRs3mD59Ot7e3ixfvpyKFSuSPn16mjZtasGKRRI/dXwiScjt27e5f/8+Dg4OFCxYkPHjx7NmzRpq1aqFjY0NmTNnplWrVpYuUyRRU/CJJBFxhz3nz5/PkSNHmDx5Mo8fP6ZmzZq4ubkxevRoSpYsSUREBI6Ojs89TkQ01CmSZMQNr1KlSvH1119ToEABHjx4QL58+ShSpAglS5akZ8+emEwm8ufPT48ePRR6Iv+gVZ0iSYzRaCRv3rxs27aNy5cvs2TJEvLmzUuPHj04cOAAx48f5/PPP2fhwoVMmTLF0uWKJDrq+ESSGBsbG3P49enTh0yZMtGrVy8AsmfPTqpUqYiMjGTGjBlMmTLlhbc2ErFmmuMTSaLizt1Nnz6dx48fM2DAACZOnIibmxsdO3YE4K+//iIoKIgPP/zQkuWKJBrq+ESSqLhzd+XKlaN169aUKFGCGzducPr0aTp27MjRo0dZsWIFgYGB5MuXDzc3N2xsbLTgRayaOj6RJM5oNGJjY8OJEycYMmQIbm5u9OvXD09PTxYsWEBISAjdunUjV65chIaG4uLiouFPsWoKPpFkIDb8goKCcHZ2xsHBgeHDhxMYGEjPnj3JlSsXN2/epFu3bgwePJhy5coBcPjwYUqVKmXh6kXeLw11iiQDNjZPF2h7eHgATy9ttmHDBtauXUuGDBm4ffs2X3zxBbly5aJAgQIAFChQgNy5c7N69WqL1S1iCQo+kWTIw8ODyMhILl68SGhoKIMHDyZnzpx8+eWXeHh4UKZMGfLkycOqVauA/3WMItZAf9JFkpnYWxotXryYKVOmUKpUKdzc3BgyZAgeHh5Ur179mdADFHpiVTTHJ5IMxXZw165dY/HixfTq1QtXV1fq1avHpk2bGDFiBFevXsXFxYVSpUrRpk0b4Om1QNOnT2/h6kUSloJPJJmKDb/Y/+bOnZsCBQqQKlUqateujbOzM87Ozjx+/Jg6deowc+ZMVqxYwbp163BxcVEXKMmW5vhEkqnY4LKxseHKlSsUL16cJUuW8Mknn5ApUyZKly5tPnfDhg0sW7bMvPk9LCwMZ2dnS5UukqD0kU4kmTMajWTLlo0lS5YAEBISYl79GTvgU6ZMGWbOnEnRokWJioqiVatWbN682WI1iyQkBZ9IMhfb+UVHRxMcHEx4eDghISHA06u/GI1G0qRJQ86cOQGwt7cnc+bM2uYgyZaGOkWshJ2dHSlSpGD27NkYjUbzz/85l7dhwwaio6OpV6/e+y5R5L1Q8IlYmbRp07702I4dO9i1axe5cuXSFV0k2dJQp4gV++ei7hMnTuDs7EzTpk3x8fGxUFUiCUvBJ2KFYoc6Y+/QsGnTJn744QcmT55MlixZyJgxoyXLE0lQGuoUsUI2NjbcvXuXDRs2kC5dOrZv307KlCnp2bOneeGLSHKlDewiVurJkyfUrFmTTJkyUbZsWZo0aUKGDBksXZZIglPwiVixiIgIwsPDcXR0xMnJ6bUfd/bsWVKmTKnLm0mSpOATkdfm7+/PvHnzWLBgAaGhoezdu5d06dJZuiyRN6I5PhF5pfv37+Pg4EB4eDgLFy5k//79bNu2jXv37uHp6Wnp8kTemFZ1isgrHTp0iKxZszJ06FD279/PyJEjSZcuHUWKFMHe3p4FCxZw7949S5cp8trU8YnIKxUvXpwaNWrQs2dP0qRJ88wCmOnTp7N7926yZ8+Ot7e3BasUeX0KPhF5qYCAAH777Teio6PJnDkzKVOmBJ5ufH/w4AEXLlygU6dOFC1a1KJ1irwJLW4RkZeaPXs2v/32Gz///DMFCxZ87vjjx49xd3fn/v37eHl5YTKZzJviRRIrzfGJyAtFR0eza9cuSpcuTcGCBZ+5vFnslV/c3d2ZO3cuRYsW5dy5cxgMhucugyaS2Cj4ROSF7OzsGDx4MHZ2dkRFRZk7OZPJZL6jw4wZMxg5ciQVK1Zk2LBh7Ny5Ux2fJHoa6hSRV3rZ8OWsWbMYO3YsM2bMoGLFipw7d45jx47RvHlznJycNOwpiZY6PhF5pReF17Rp0/j666+ZO3cuvr6+hIWF4e3tzcWLF/n111/ZunWrhj0l0dKqThF5Y3ny5GH58uWUK1cOgD179jBhwgQCAwNZtGgRzZo1w87OjqpVq6rzk0RHHZ+IvBGTyUTlypUpX7488HShy5QpUyhTpgwODg7cvXuXb775hrVr15pD7/Dhw9y6dcvClYs8peATkTfyz+7t2rVrREVFMWLECJYvX07//v2ZOXMm+fPnx2AwsG/fPrp06cLOnTstVLHIsxR8IvJO7O3tCQgIYN++fWTIkIFVq1aRMWNGqlSpwvHjx/n666/p1q0bjRo1snSpIoBWdYpIPFi/fj0DBw5kwoQJ1KlTB4BTp07Rp08fmjdvTrdu3TAajdjY2Jj/K2IpWtwiIu/so48+wsnJidu3bwNw4MABvvzyS5o2bUr79u2JiYlh9OjRlCtXjipVqmjBi1iUOj4RiVeHDh2id+/edO7cmXbt2hEREUFkZCQXL16kQ4cOjBkzhjp16ij8xGI03iAi8cpoNNKmTRtatWrFtm3bqFmzJm3btuXBgwesWLGCuXPnEhwcbA69yMhIC1cs1kYdn4gkiMePH1O/fn3atm1LvXr1qFu3Lm5ubhQrVoyffvqJiIgIFixYgKurKy1atNC8n7w3muMTkQRx7949AgICKF++PN7e3tSuXZu9e/fStm1boqOjqVatGhcvXuTKlSsKPXmv9KdNRBJEjhw5GDFiBN26daNUqVLs2bOHXr16UaxYMSZMmMDt27dJnTo1hw4dAiAmJoabN29auGqxBgo+EUkwjRo1YuzYsRQoUIDevXvTqFEjxowZw4IFCzh+/DjTpk3j+PHjABw/fpyZM2dauGKxBprjE5EEd+fOHby8vFiwYAETJ05k6dKl5M2bF3h637/69evz66+/kilTJgtXKtZAHZ+IJLh06dIBcObMGfr27ftM6NWoUQNXV9dnQk+fxyUhKfhE5L2IvVvDV199RVBQEFFRUVStWpX8+fOzZMkSwsLCOHDgAJcuXdItjSRBaahTRN6rs2fPki9fPsqWLUvx4sX5/vvvWblyJRs2bCAwMJD79+8zatQo6tWrZ+lSJZlSxyci70XsZ+z8+fNz5MgRMmfOzJQpUzh9+jSnTp2iYcOG7Nixg3nz5jF16lQCAwMtXLEkVwo+EXkv4l6erFSpUixduhSAhQsXEhMTQ8uWLQHw9/cnODgYO7v/bTPWwJTEJwWfiFhMeHg4MTExfPTRR9ja2nLy5En2799P8+bNcXd3Z9OmTRw9elTX9JR4peATEYtxcnKiTp06dOrUiS+//JIBAwYAULlyZdavX89HH33EsGHDOHLkCKDOT+KHFreIiMXt3buX//73v+TKlYtixYpx/Phx+vTpQ4cOHahSpQoff/wx06dPp3z58pYuVZIBBZ+IWMyLbk20evVqvvvuOwYNGkSLFi0AaNy4MTVr1qRLly4ALFiwgNq1a+Pp6fnea5akT0OdImIx/wy9u3fvMmTIEPr160eLFi2Iiopi0aJFpEqVinLlygHwn//8hy1btvD3339bomRJBnR3BhFJNHx8fNi1axfp06cnOjqalStXcuTIEUqWLEnOnDl58OABV65coXPnzhQrVszS5UoSpaFOEUlUjEYjNjY2/Pbbb+zdu5fChQvz6aef4uLiAjy9z5+joyMODg7mc0XehP7EiEiiEhtkRYoUIW/evHzyySe4uLhgNBoBcHd358KFC8+cK/Im1PGJSKIV29H9s7NbsGABKVKkoEGDBhasTpIqBZ+IJEkPHz4kderUz/zMaDQSGRmJk5OThaqSpEDjBCKSJBw5coTLly+bv/9n6MXExLBx40YmTpzI48eP33d5koQo+EQkSbCxsWHv3r1ERka+8Lifnx8HDhzA1tYWBweH91ydJCUa6hSRJCMsLAxnZ+cXHrty5Qr79++nWbNmODg4EBMTg62t7XuuUJICBZ+IJHlxQ27RokUULlyYggULvvDKMCIa6hSRJM1oNJpDb+7cufzyyy9MnTqVQ4cO6U7u8kIKPhFJsuJuc5g6dSq7d++mSpUq+Pr60rhxYw4cOKCOT56jS5aJSJIVG3qTJk3i1KlTNG7cmEqVKuHh4cHvv//OgwcPePDgAWnSpNGwp5ip4xORJO3q1ascPnyYWrVqUaVKFTw8PPDz82P+/Pls376dNm3asGPHDoWemGlxi4gkeXfv3sXV1RV3d3f27t1Lnz59aNeuHb169WLnzp18++23rFmz5rm9f2KdNNQpIkmej48PAH/88QedOnWid+/e9OjRA4CtW7eSLVs2hZ6YaahTRJINo9FIt27dzKE3evRoQkND6dOnD/C0MwwICADQak8rpqFOEUmWvv32W/z9/albty7u7u4sXrwYo9HIhQsXGDx4MDVr1tSCFyuljk9Ekp0LFy5w5coV6tevT6FChVi3bh3Lly+nXLlyLF26lK+//prTp08r9KyU5vhEJNnJnTs348aNI3Xq1EyePJnz58/z+++/06VLFyIiIqhduzYxMTHm89X5WRd1fCKSLHl7e2NnZ4ezszONGzemUKFCzJgxg1mzZrF//34yZ87MlStXOHLkiELPyqjjE5FkLWvWrAwcOJA8efJQtmxZVqxYwYMHDzCZTNSqVYucOXPy008/kSdPHnV+VkIdn4gka7Vq1WL48OH06tWLHTt2kDFjRlKkSEHlypX54IMPaN26NR9//DG7du1S6FkJreoUkWQrbgd39uxZcufOzc2bN2nYsCFNmjRh6NChAAwYMAA3Nze++eYbbGxs1Pklcwo+EbEaYWFhFClShGbNmjFq1Cjg6ab30aNH4+npiaurK40aNaJWrVoWrlQSkoJPRKzKX3/9Rd68eYGnd23fuHEjRqORgQMHcvnyZXr37s2mTZvw9PS0cKWSUDTHJyJWIfYzfmzo7d69mxUrVmBjY0O/fv3w9vbG1taWqKgo810f4j5Okg8Fn4hYhX/O2Xl6emJnZ0fPnj1Jly4dBw4coGXLlvTu3ZvUqVOzZcsW/vvf/2quLxnSUKeIWK2IiAgcHR3Zt28fHTp04PPPP6dDhw5s2bKF8ePH06RJE7p3727pMiWeKfhExGrFxMQQHBxMoUKF+Pzzz+nTpw9bt25l6tSp1KxZk27dugHg7+9P2rRpLVytxBcFn4hYvcuXL5M9e3auXbtGs2bN+PTTT813eJg+fTpHjhxh1KhReHt7s23bNgoVKkS6dOksXLW8Lc3xiYjViv3cny1bNgDWrl1Lrly5zKE3b948jh07RunSpfH29iY0NJQUKVJw/vx5i9Us707BJyJWK3bhSux/CxUqxJ9//smZM2cYMWIEfn5+lC9fng4dOrBs2TLq1q1LoUKF8PX15cyZM9y8edOS5ctbUvCJiPy/ypUrM378eAYMGMBvv/1GlSpVaNu2LWvWrGH69Ol07twZV1dXIiIiuHfvHsePH7d0yfIWNMcnIsKzlzc7fvw4p06dol27dqxatYpJkybRu3dvGjdujJ+fH5999hnHjh0jRYoUFq5a3oaCT0Tk//3zGp2rV69m0qRJ9OrVi8aNG3Pw4EHatWtHr169zPOAkvQo+ERE/mHQoEEEBgZy/PhxvvrqKxo3bsyhQ4do1aoVX375JR06dLB0ifIOFHwiIv9w6tQpFi1aRMuWLSlatCj79++ncePGjBkzhvbt27/0cbqrQ9Kg4BMR+Rdbtmzh2rVrdOnSxfyz2JC7ceMGu3btolChQhQvXlzhlwQo+ERE3pLJZKJRo0Z4enpy8OBBJk6cSLVq1SxdlvwLbWcQEXlDRqORyMhI7t27x99//823337L7NmzmTRpEnfu3AFg/fr1BAQEWLhSeRF1fCIib+jixYsEBARQunRpJk6ciKOjI926dTPf6y8kJIS//vqLR48eUbVqVUuXK/+gjk9E5A3dvXuXTz/9lHPnzlGyZEm2bdsGPL3X34wZM+jSpQt58uShatWqrFy50twFSuKg4BMReUPly5dn5MiRNG3alOXLlxMSEgLA7Nmz2bdvH7Vq1cLNzY05c+bQsWNHrl+/buGKJS4NdYqIvIG4qzYPHz6M0WikSJEi7Nq1i99++41GjRrRrFkz5s6dy3fffcfs2bOpWLGiVnsmIgo+EZE39M8Qmzt3LmvXrqVNmzY0adKEjRs30qNHD7Zt20bu3LkxGo3Y2Ngo/BIJO0sXICKS1PwzvPLnz4+TkxNNmjRh8eLF9OvXD6PRyKJFi/jrr7+IiIhg5MiRFCxY0EIVS1zq+ERE4smcOXMYNmwYY8aMYePGjfTu3RsbGxuCgoLw8fGhQIEC2NraqvOzMHV8IiLxJG3atCxYsIAcOXKwcuVKypQpYz4WFhZGv379aNSoEZUrV1b4WZBWdYqIxJM6derg6+tLSEiI+e7u9+7dIygoiAcPHtC8eXP69evH77//rtCzIAWfiEg8i4iIwNbWlps3b/Kf//yHzp0706JFC6Kjo5k5cyZbtmxBs0yWo6FOEZF4VqRIEX766ScMBgMXL16kX79+ZMyYkebNmxMWFkalSpWe6fhiV33K+6HfaRGRBJAlSxbu3r3Lzp078fDwIGPGjGTIkIGUKVPy/fffAzBhwgT+/PNP81YHeT+0qlNEJAGtWrWKDRs2sGPHDtKkScO6devIlCkTAwYMYMKECTRt2pSBAwdSqlQpLXh5TzTUKSKSAGJDrHHjxqRIkYI7d+4wbdo0MmXKxLBhw9i6dSu7du3CycmJNm3asGbNGvLnz2/psq2Cgk9EJAHE7dyqVatGuXLlcHZ25osvvmDWrFkcO3aMLFmyAJA7d24ePXoEwJMnTxg0aBBjxozBw8PDEqUne5rjExF5D5ydnfnhhx9Yvnw5/fv3J0uWLISEhHDkyBE8PDzw8fEhJCSE9u3bY2tri6urq6VLTrbU8YmIvAdjx45l3rx5jBs3jjlz5rBu3Tr8/f3x8/Mjd+7cZM+enfPnz1OkSBH69++PnZ0dBw8eJHv27Hh7e1u6/GRFi1tERN6Dc+fO4ezsTNasWVm0aBHz58+nWLFi5MiRg44dO5rPi7vAZcmSJUycOJEdO3bg5uZmqdKTHQWfiMh7FB0djZ2dHU+ePHkmzGJiYrC1tX3m3CtXrvD555/zyy+/4OPj875LTbY0xyci8h7Z2T2dYfrnHF5s6BmNRgAuXbpEhw4dyJUrl0IvnqnjExFJJGKHOUNCQqhQoQLVq1c3b3bXHr/4o45PRMRC/tl3GAwGwsLCmDdvHtmzZzeHntFoVOjFI63qFBGxkNgwCwgIwNPTk8uXL1O/fn3SpEnDo0ePzIGn63jGLwWfiIgFBQUF8fnnn5MnTx62b99OnTp1GDduHEeOHCEsLEz7+RKA5vhERCzs+PHjbNy4ER8fHzp16vTc8aioKOzs7DTcGU8UfCIiiUDcWxPF3dpgMpm4evUq48aN45dffnluy4O8OQ0ci4gkAnHn8eKGm8FgwNvbm/Pnz9OhQwdiYmIsUV6youATEUnkXF1dWb9+PdevX1f4xQMFn4hIEhAbfteuXaNjx44Kv3eg4BMRSSJcXV3ZsGEDV69eVfi9AwWfiEgSovB7dwo+EZEkRuH3bhR8IiJJkMLv7Sn4RESSKIXf21HwiYgkYXHDr1OnTubbGsnLKfhERJK42PC7fPkyHTt2VPj9CwWfiEgy4OrqysaNGxV+r0HBJyKSTCj8Xo+CT0QkGVH4/TsFn4hIMqPwezUFn4hIMqTwezkFn4hIMhUbfpcuXVL4xaHgExFJxlxdXdm0aZPCLw4Fn4hIMqfwe5aCT0TECij8/kfBJyJiJRR+Tyn4RESsSNwFL9Z6bU8Fn4iIlXFzc2Pjxo1cvHjRKsNPwSciYoWsOfwUfCIiVspaw0/BJyJixawx/BR8IiJWLjb8/v77b6sIPwWfiIjg5ubGpk2b+Pvvv+ncuTNGo5Ho6Gj+/PNPS5cW7wwmk8lk6SJERCRxePLkCXXq1CFnzpw8ePCATZs2ERAQgIeHh6VLizcKPhERecajR4/IkSMHQUFBODk5MXXqVNq2bWvpsuKNgk9ERJ7RokUL1qxZQ2RkJAAVK1Zkz549z5wTEmXk9MNw7oXFEBFjxNHWBm9nWwqndsLFPnHPotlZugAREUlcSpcuzaFDh7h37x7h4eH4+fkRGBhIqlSpuBMSxX7/MC4HR2IAouO0Thcegd+dULKncKBsWmfSudpb6i28kjo+ERF5ob/++ot58+YxZcoUFi5cSMYy1dl1K+SZsHsZOwNUyeBKcS/nhC/0DSXuflREJIlp164dX3/99UuPGwwGLl68CEDXrl0ZOXJkgte0e/duMmbMaP6+QIEC7N69+18flzdvXsaMGUNwcPAbhR487QR33Qrh+P2wt6w64Sj4RETegq+vL6lSpSIiIuKtn2PatGkMHTr0hcfmzp2Lra0tbm5upEiRgqJFi7Jhw4a3fq24/vvf/+Lr6/va598JiXqj0IsVG353QqNeeDxr1qzs2LHjzZ40Hij4RETe0NWrV/Hz88NgMLBu3boEe50yZcrw5MkTHj16RIcOHWjevDmBgYFv9BzR0dHvXMd+/7A3Dj3z65vgwN3E1fUp+ERE3tD8+fMpXbo07dq1Y968ec8dDwwMpG7duri7u/Phhx9y6dKlFz7Pvw2LxrKxseGzzz4jLCyMS5cuMWfOHPLly4e7uzvZs2fn119/NZ8bO6z5/fff4+PjQ/v27Z97vrid1vDhw2nWrBlt2rTB3d2dQoUKceHCBcaMGYO3tzcZM2Viy9at5seGPw5m5bd9GF2jAGNqFmLblNEYY2IAOLZuMdM+q8umCd/wbaWcjKtXgvP7dnApOJLQqNe/GkxgYCD16tXDy8uLVKlSUa9ePW7evGk+7uvry9ChQylXrhzu7u7UqFGDgIAA8/H58+e/+vfztSsRERHg6T+srVu3pnXr1mzduhV/f/9nji9ZsoRvvvmGwMBAcubMyVdfffVOrxcdHc3MmTNxc3MjV65ceHt7s2HDBoKDg5kzZw79+vXj+PHj5vPv3r3Lw4cPuXbtGtOnT//X51+/fj1t27YlMDCQYsWKUbNmTYxGI7du3aJt38Gs/G6g+dzlw3thY2vHwLWH6bV4F38f3M2R1QvNx2+cOY5nlpwM3XWeip/2ZOW3fcFk4tTD8Nd+v0ajkfbt23Pt2jWuX7+Os7MzPXv2fOacRYsWMWfOHO7du0dkZCQ//PADAGfPnqV79+6vfH4Fn4jIG9i7dy/Xrl2jefPmlChRghw5crBo0aJnzmnUqBGlSpXCzs6O1q1bc+LEibd6rYMHD5IyZUp8fHxYvHgxq1evxsPDg7p165IjRw4MBgOVKlWiRo0a+Pn5mR9nY2PDt99+i6OjI87O/76qskKFCtSsWRM7OzuaNWvG/fv3GTx4MPb29hSp2YjA29cJexzE4wf3OL93B/UGfoeDsytuqb0o17orp7atNj9XSp+MlGrcFhtbW4rXa8HjAH8eBdzjfljMa7/vNGnS0KRJE1xcXHB3d+err756bh9h+/btyZ07N87OzjRv3tz8e7xixQo++uijVz6/9vGJiLyBefPmUaNGDTw9PQFo1aoV8+bNo1+/fuZzfHx8zF+7uLjw5MmTt3qt0qVLs3fv3ud+vnnzZr799lsuXLiA0WgkNDSUQoUKmY97eXnh5OT02q+TNm1a89fOzs54enpia2v79Af2jgBEhoYQfP8uxugoRtcoaD7fZDLikTaD+Xt3T2/z1w7OLgBEhIYQEfP6Q52hoaH069ePLVu2mOc0Hz9+TExMjLmul/0e3759m0yZMr3y+RV8IiKvKSwsjGXLlhETE2P+hzciIoJHjx5x8uRJihQpkuA1RERE0KRJE+bPn0+DBg2wt7enYcOGxN2SbTAY4u31HG3/NzDokTY9tg6OfL3rPLZ2bxYfcZ/n3/z444+cP3+eQ4cO4ePjw4kTJyhWrBivs+08Xbp0nD9//pXnaKhTROQ1rVmzBltbW86ePcuJEyc4ceIE586do0KFCv+6oCK+REZGEhERgZeXF3Z2dmzevJlt27Yl2Ot5Oduav07h5UOu0r5smjCM8CePMRqNPLhxhcvH9r3yOewMzz5PXFFRUYSHh5t/RUdH8/jxY5ydnUmZMiUPHz7k22+/fe16mzZtyvr16195joJPROQ1zZs3j/bt25M5c2Z8fHzMv3r27Mlvv/0WL1sH/o27uzuTJk2iefPmpEqVikWLFlG/fv0Ee71CqZ8dMm0+YgoxUVFMaFqOEb45+W3QZzwOuPfK5zABhVO/eOi1Tp06ODs7m38NHz6cvn37EhYWhqenJ6VLl6ZWrVqvXW+BAgWYPHnyK8/RJctEROSVVl4O5u+gyLd+fG4PBxpnTxGPFb0bdXwiIvJKZdM6Y/eW04Z2Bijj836v16mhThEReSfpXO2pksH1jcMv9kLV6Vze710a1q5d+8rjGuoUEZHXcvx+WLK4O4OCT0REXtud0CgO3A3j0gvux2dneLqQJUcKB8r4OL/3Tu91KfhEROSNhUYZOfUwnPtx7sDulUTuwK7gExERq5K4Y1lERCSeKfhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSqKPhERMSq/B+6LS0xDATmLgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -779,7 +783,7 @@
     {
      "data": {
       "text/plain": [
-       "(35, 58)"
+       "(34, 58)"
       ]
      },
      "execution_count": 9,
@@ -821,7 +825,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1224x1224 with 1 Axes>"
       ]
diff --git a/load-dependency.ipynb b/load-dependency.ipynb
new file mode 100644
index 00000000..d1a09942
--- /dev/null
+++ b/load-dependency.ipynb
@@ -0,0 +1,5956 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dependency Parsing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "This tutorial is available as an IPython notebook at [Malaya/example/dependency](https://github.com/huseinzol05/Malaya/tree/master/example/dependency).\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "This module only trained on standard language structure, so it is not save to use it for local language structure.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.15 s, sys: 925 ms, total: 6.07 s\n",
+      "Wall time: 6.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import malaya"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Describe supported dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:you can read more from https://universaldependencies.org/treebanks/id_pud/index.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tag</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>acl</td>\n",
+       "      <td>clausal modifier of noun</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>advcl</td>\n",
+       "      <td>adverbial clause modifier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>advmod</td>\n",
+       "      <td>adverbial modifier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>amod</td>\n",
+       "      <td>adjectival modifier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>appos</td>\n",
+       "      <td>appositional modifier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>aux</td>\n",
+       "      <td>auxiliary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>case</td>\n",
+       "      <td>case marking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>ccomp</td>\n",
+       "      <td>clausal complement</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>compound</td>\n",
+       "      <td>compound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>compound:plur</td>\n",
+       "      <td>plural compound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>conj</td>\n",
+       "      <td>conjunct</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>cop</td>\n",
+       "      <td>cop</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>csubj</td>\n",
+       "      <td>clausal subject</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>dep</td>\n",
+       "      <td>dependent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>det</td>\n",
+       "      <td>determiner</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>fixed</td>\n",
+       "      <td>multi-word expression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>flat</td>\n",
+       "      <td>name</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>iobj</td>\n",
+       "      <td>indirect object</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>mark</td>\n",
+       "      <td>marker</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>nmod</td>\n",
+       "      <td>nominal modifier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>nsubj</td>\n",
+       "      <td>nominal subject</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>obj</td>\n",
+       "      <td>direct object</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>parataxis</td>\n",
+       "      <td>parataxis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>root</td>\n",
+       "      <td>root</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>xcomp</td>\n",
+       "      <td>open clausal complement</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Tag                Description\n",
+       "0             acl   clausal modifier of noun\n",
+       "1           advcl  adverbial clause modifier\n",
+       "2          advmod         adverbial modifier\n",
+       "3            amod        adjectival modifier\n",
+       "4           appos      appositional modifier\n",
+       "5             aux                  auxiliary\n",
+       "6            case               case marking\n",
+       "7           ccomp         clausal complement\n",
+       "8        compound                   compound\n",
+       "9   compound:plur            plural compound\n",
+       "10           conj                   conjunct\n",
+       "11            cop                        cop\n",
+       "12          csubj            clausal subject\n",
+       "13            dep                  dependent\n",
+       "14            det                 determiner\n",
+       "15          fixed      multi-word expression\n",
+       "16           flat                       name\n",
+       "17           iobj            indirect object\n",
+       "18           mark                     marker\n",
+       "19           nmod           nominal modifier\n",
+       "20          nsubj            nominal subject\n",
+       "21            obj              direct object\n",
+       "22      parataxis                  parataxis\n",
+       "23           root                       root\n",
+       "24          xcomp    open clausal complement"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "malaya.dependency.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### List available transformer Dependency models\n",
+    "\n",
+    "```python\n",
+    "def available_transformer(version: str = 'v2'):\n",
+    "    \"\"\"\n",
+    "    List available transformer dependency parsing models.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    \"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:tested on 20% test set.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Size (MB)</th>\n",
+       "      <th>Quantized Size (MB)</th>\n",
+       "      <th>Arc Accuracy</th>\n",
+       "      <th>Types Accuracy</th>\n",
+       "      <th>Root Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bert</th>\n",
+       "      <td>455.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>0.820450</td>\n",
+       "      <td>0.79970</td>\n",
+       "      <td>0.98936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tiny-bert</th>\n",
+       "      <td>69.7</td>\n",
+       "      <td>17.50</td>\n",
+       "      <td>0.795252</td>\n",
+       "      <td>0.72470</td>\n",
+       "      <td>0.98939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>albert</th>\n",
+       "      <td>60.8</td>\n",
+       "      <td>15.30</td>\n",
+       "      <td>0.821895</td>\n",
+       "      <td>0.79752</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tiny-albert</th>\n",
+       "      <td>33.4</td>\n",
+       "      <td>8.51</td>\n",
+       "      <td>0.786500</td>\n",
+       "      <td>0.75870</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xlnet</th>\n",
+       "      <td>480.2</td>\n",
+       "      <td>121.00</td>\n",
+       "      <td>0.848110</td>\n",
+       "      <td>0.82741</td>\n",
+       "      <td>0.92101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alxlnet</th>\n",
+       "      <td>61.2</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>0.849290</td>\n",
+       "      <td>0.82810</td>\n",
+       "      <td>0.92099</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Size (MB)  Quantized Size (MB)  Arc Accuracy  Types Accuracy  \\\n",
+       "bert             455.0               114.00      0.820450         0.79970   \n",
+       "tiny-bert         69.7                17.50      0.795252         0.72470   \n",
+       "albert            60.8                15.30      0.821895         0.79752   \n",
+       "tiny-albert       33.4                 8.51      0.786500         0.75870   \n",
+       "xlnet            480.2               121.00      0.848110         0.82741   \n",
+       "alxlnet           61.2                16.40      0.849290         0.82810   \n",
+       "\n",
+       "             Root Accuracy  \n",
+       "bert               0.98936  \n",
+       "tiny-bert          0.98939  \n",
+       "albert             1.00000  \n",
+       "tiny-albert        1.00000  \n",
+       "xlnet              0.92101  \n",
+       "alxlnet            0.92099  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "malaya.dependency.available_transformer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load xlnet dependency model\n",
+    "\n",
+    "```python\n",
+    "def transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
+    "    \"\"\"\n",
+    "    Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    version : str, optional (default='v2')\n",
+    "        Version supported. Allowed values:\n",
+    "\n",
+    "        * ``'v1'`` - version 1, maintain for knowledge graph.\n",
+    "        * ``'v2'`` - Trained on bigger dataset, better version.\n",
+    "\n",
+    "    model : str, optional (default='xlnet')\n",
+    "        Model architecture supported. Allowed values:\n",
+    "\n",
+    "        * ``'bert'`` - Google BERT BASE parameters.\n",
+    "        * ``'tiny-bert'`` - Google BERT TINY parameters.\n",
+    "        * ``'albert'`` - Google ALBERT BASE parameters.\n",
+    "        * ``'tiny-albert'`` - Google ALBERT TINY parameters.\n",
+    "        * ``'xlnet'`` - Google XLNET BASE parameters.\n",
+    "        * ``'alxlnet'`` - Malaya ALXLNET BASE parameters.\n",
+    "\n",
+    "    quantized : bool, optional (default=False)\n",
+    "        if True, will load 8-bit quantized model.\n",
+    "        Quantized model not necessary faster, totally depends on the machine.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    result: model\n",
+    "        List of model classes:\n",
+    "\n",
+    "        * if `bert` in model, will return `malaya.model.bert.DependencyBERT`.\n",
+    "        * if `xlnet` in model, will return `malaya.model.xlnet.DependencyXLNET`.\n",
+    "    \"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:running dependency-v2/albert using device /device:CPU:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = malaya.dependency.transformer(model = 'albert')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Quantized model\n",
+    "\n",
+    "To load 8-bit quantized model, simply pass `quantized = True`, default is `False`.\n",
+    "\n",
+    "We can expect slightly accuracy drop from quantized model, and not necessary faster than normal 32-bit float model, totally depends on machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Load quantized model will cause accuracy drop.\n",
+      "INFO:root:running dependency-v2/albert-quantized using device /device:CPU:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "quantized_model = malaya.dependency.transformer(model = 'albert', quantized = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predict\n",
+    "\n",
+    "```python\n",
+    "def predict(self, string: str):\n",
+    "    \"\"\"\n",
+    "    Tag a string.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    string: str\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    result: Tuple\n",
+    "    \"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
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+       "<title>8</title>\n",
+       "<text text-anchor=\"middle\" x=\"223\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (dan)</text>\n",
+       "</g>\n",
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+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"321\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (mengantuk)</text>\n",
+       "</g>\n",
+       "<!-- 9&#45;&gt;12 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>9&#45;&gt;12</title>\n",
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+       "<!-- 14 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"440\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (memandu)</text>\n",
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+       "<!-- 9&#45;&gt;14 -->\n",
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+       "<title>9&#45;&gt;14</title>\n",
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+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"219\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (sebentar)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;10 -->\n",
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+       "<title>12&#45;&gt;10</title>\n",
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+       "</g>\n",
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+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"329\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (sekiranya)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;11 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>12&#45;&gt;11</title>\n",
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+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"440\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;13 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>14&#45;&gt;13</title>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x14fdb9f90>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d_object, tagging, indexing = model.predict(string)\n",
+    "d_object.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<title>15</title>\n",
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+       "<title>3&#45;&gt;15</title>\n",
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+       "<title>2</title>\n",
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+       "<text text-anchor=\"middle\" x=\"303\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
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+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d_object, tagging, indexing = quantized_model.predict(string)\n",
+    "d_object.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Voting stack model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:running dependency-v2/alxlnet using device /device:CPU:0\n"
+     ]
+    },
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+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "alxlnet = malaya.dependency.transformer(model = 'alxlnet')\n",
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], string)\n",
+    "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Harder example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://www.astroawani.com/berita-malaysia/terbaik-tun-kita-geng-najib-razak-puji-tun-m-297884\n",
+    "\n",
+    "s = \"\"\"\n",
+    "KUALA LUMPUR: Dalam hal politik, jarang sekali untuk melihat dua figura ini - bekas Perdana Menteri, Datuk Seri Najib Razak dan Tun Dr Mahathir Mohamad mempunyai 'pandangan yang sama' atau sekapal. Namun, situasi itu berbeza apabila melibatkan isu ketidakpatuhan terhadap prosedur operasi standard (SOP). Najib, yang juga Ahli Parlimen Pekan memuji sikap Ahli Parlimen Langkawi itu yang mengaku bersalah selepas melanggar SOP kerana tidak mengambil suhu badan ketika masuk ke sebuah surau di Langkawi pada Sabtu lalu.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
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+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-962.98C421.28,-956.25 388.21,-947.36 359,-939 320.79,-928.07 277.84,-915.03 245.33,-905.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"246.19,-901.61 235.6,-902 244.12,-908.29 246.19,-901.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"374\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 8 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>8</title>\n",
+       "<text text-anchor=\"middle\" x=\"358\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (jarang)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;8 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>11&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-958.12C434.76,-952.81 421.98,-946.34 411,-939 400.15,-931.75 389.44,-922.23 380.52,-913.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383,-910.97 373.49,-906.3 378.01,-915.88 383,-910.97\"/>\n",
+       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 9 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>9</title>\n",
+       "<text text-anchor=\"middle\" x=\"445\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (sekali)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;9 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>11&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M477.52,-956.85C473.97,-951.26 470.16,-944.96 467,-939 463.06,-931.58 459.21,-923.31 455.87,-915.66\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"459.07,-914.24 451.92,-906.42 452.63,-916.99 459.07,-914.24\"/>\n",
+       "<text text-anchor=\"middle\" x=\"489.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"534\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;10 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>11&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M501.05,-956.91C504.77,-951.32 508.74,-945 512,-939 516.08,-931.5 520.01,-923.1 523.39,-915.35\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"526.67,-916.57 527.35,-906 520.22,-913.84 526.67,-916.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"532\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"643\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (mempunyai)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;29 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M520.16,-956.8C544.14,-943.56 577.45,-925.18 603.33,-910.9\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"605.08,-913.93 612.15,-906.03 601.7,-907.8 605.08,-913.93\"/>\n",
+       "<text text-anchor=\"middle\" x=\"591\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">42 (berbeza)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;42 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>11&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M531.23,-967.04C624.77,-951.63 849.29,-914.65 952.04,-897.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"952.89,-901.13 962.19,-896.05 951.75,-894.22 952.89,-901.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>2</title>\n",
+       "<text text-anchor=\"middle\" x=\"58\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (LUMPUR)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;2 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>1&#45;&gt;2</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.48,-869.8C145.65,-856.78 116.85,-838.78 94.16,-824.6\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"95.82,-821.51 85.48,-819.18 92.11,-827.44 95.82,-821.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 5 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"150\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (hal)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;5 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>1&#45;&gt;5</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M185.1,-869.8C178.89,-857.82 170.51,-841.62 163.49,-828.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"166.6,-826.45 158.89,-819.18 160.38,-829.67 166.6,-826.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"184\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 7 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"222\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (,)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;7 -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>1&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M199.67,-869.8C203.54,-858.05 208.75,-842.24 213.16,-828.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"216.54,-829.77 216.34,-819.18 209.89,-827.58 216.54,-829.77\"/>\n",
+       "<text text-anchor=\"middle\" x=\"225.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 3 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>3</title>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (:)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;3 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>5&#45;&gt;3</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M122.98,-784.15C113.38,-778.33 102.59,-771.55 93,-765 80.89,-756.73 67.96,-747.11 56.77,-738.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"58.66,-735.56 48.61,-732.21 54.38,-741.1 58.66,-735.56\"/>\n",
+       "<text text-anchor=\"middle\" x=\"108.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 4 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>5&#45;&gt;4</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M141.7,-782.8C135.98,-770.93 128.26,-754.93 121.76,-741.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"124.78,-739.66 117.28,-732.18 118.48,-742.7 124.78,-739.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>6</title>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;6 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>5&#45;&gt;6</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M159.92,-782.8C166.82,-770.82 176.16,-754.62 183.98,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"187.14,-742.59 189.1,-732.18 181.07,-739.09 187.14,-742.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M601.31,-869.8C567.25,-855.74 519.11,-835.88 483.74,-821.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"724.56,-828.29 729.95,-819.17 720.05,-822.94 724.56,-828.29\"/>\n",
+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"874.4,-821.02 864.02,-818.86 870.79,-827.02 874.4,-821.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"938.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M988,-869.8C975.7,-857.36 958.91,-840.36 945.22,-826.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"947.49,-823.83 937.97,-819.18 942.51,-828.75 947.49,-823.83\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1062.27,-829.31 1065.27,-819.15 1056.58,-825.22 1062.27,-829.31\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;12 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;15 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;16 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"442.14,-742.38 439.73,-732.06 435.18,-741.63 442.14,-742.38\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;17 -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"538.2,-742.72 537.45,-732.15 531.45,-740.88 538.2,-742.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"541\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;14 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;20 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;25 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;24 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>25&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 33 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>33</title>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;33 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;33</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1219.27,-611.28C1244.62,-602.95 1272.28,-593.67 1278,-591 1294.65,-583.24 1312.15,-572.91 1326.69,-563.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1328.83,-566.42 1335.32,-558.05 1325.03,-560.55 1328.83,-566.42\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1336.9,-479.43 1330.13,-471.28 1330.34,-481.87 1336.9,-479.43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1340.6,-695.8C1328.01,-683.36 1310.82,-666.36 1296.79,-652.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1383.86,-695.88C1391.53,-690.44 1399.8,-684.22 1407,-678 1415.92,-670.29 1425.04,-661.15 1432.91,-652.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1435.51,-655.16 1439.74,-645.44 1430.37,-650.4 1435.51,-655.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1432\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 63 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1501.56,-695.8C1511.18,-683.59 1524.27,-666.99 1535.08,-653.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1538.02,-655.2 1541.46,-645.18 1532.52,-650.86 1538.02,-655.2\"/>\n",
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+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1455,-608.8C1455,-597.16 1455,-581.55 1455,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.5,-568.18 1455,-558.18 1451.5,-568.18 1458.5,-568.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1464\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1553.99,-608.8C1553.3,-597.16 1552.39,-581.55 1551.6,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1555.09,-567.95 1551.01,-558.18 1548.1,-568.36 1555.09,-567.95\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1562\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1573.82,-608.8C1587.56,-596.24 1606.37,-579.05 1621.61,-565.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.19,-567.51 1629.21,-558.18 1619.47,-562.34 1624.19,-567.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1616\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549.8,-521.8C1549.66,-510.16 1549.48,-494.55 1549.32,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.82,-481.13 1549.2,-471.18 1545.82,-481.22 1552.82,-481.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1648,-521.8C1648,-510.16 1648,-494.55 1648,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.5,-481.18 1648,-471.18 1644.5,-481.18 1651.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1667.63,-521.8C1681.96,-509.24 1701.58,-492.05 1717.47,-478.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1720.19,-480.4 1725.4,-471.18 1715.57,-475.13 1720.19,-480.4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1726.18,-347.8C1712.44,-335.24 1693.63,-318.05 1678.39,-304.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1680.53,-301.34 1670.79,-297.18 1675.81,-306.51 1680.53,-301.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
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+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
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+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x168e76690>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d_object, tagging, indexing = model.predict(s)\n",
+    "d_object.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</g>\n",
+       "<!-- 5 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"150\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (hal)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;5 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>1&#45;&gt;5</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M185.1,-869.8C178.89,-857.82 170.51,-841.62 163.49,-828.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"166.6,-826.45 158.89,-819.18 160.38,-829.67 166.6,-826.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"184\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 7 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"222\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (,)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;7 -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>1&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M199.67,-869.8C203.54,-858.05 208.75,-842.24 213.16,-828.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"216.54,-829.77 216.34,-819.18 209.89,-827.58 216.54,-829.77\"/>\n",
+       "<text text-anchor=\"middle\" x=\"225.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 3 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>3</title>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (:)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;3 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>5&#45;&gt;3</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M122.98,-784.15C113.38,-778.33 102.59,-771.55 93,-765 80.89,-756.73 67.96,-747.11 56.77,-738.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"58.66,-735.56 48.61,-732.21 54.38,-741.1 58.66,-735.56\"/>\n",
+       "<text text-anchor=\"middle\" x=\"108.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 4 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>5&#45;&gt;4</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M141.7,-782.8C135.98,-770.93 128.26,-754.93 121.76,-741.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"124.78,-739.66 117.28,-732.18 118.48,-742.7 124.78,-739.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>6</title>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;6 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>5&#45;&gt;6</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M159.92,-782.8C166.82,-770.82 176.16,-754.62 183.98,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"187.14,-742.59 189.1,-732.18 181.07,-739.09 187.14,-742.59\"/>\n",
+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M601.31,-869.8C567.25,-855.74 519.11,-835.88 483.74,-821.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"724.56,-828.29 729.95,-819.17 720.05,-822.94 724.56,-828.29\"/>\n",
+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"874.4,-821.02 864.02,-818.86 870.79,-827.02 874.4,-821.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"938.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M988,-869.8C975.7,-857.36 958.91,-840.36 945.22,-826.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"947.49,-823.83 937.97,-819.18 942.51,-828.75 947.49,-823.83\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1062.27,-829.31 1065.27,-819.15 1056.58,-825.22 1062.27,-829.31\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;12 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;15 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;16 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"442.14,-742.38 439.73,-732.06 435.18,-741.63 442.14,-742.38\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;17 -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"538.2,-742.72 537.45,-732.15 531.45,-740.88 538.2,-742.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"541\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;14 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;20 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;25 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;24 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>25&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 33 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>33</title>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;33 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;33</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1383.86,-695.88C1391.53,-690.44 1399.8,-684.22 1407,-678 1415.92,-670.29 1425.04,-661.15 1432.91,-652.81\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1432\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 63 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1501.56,-695.8C1511.18,-683.59 1524.27,-666.99 1535.08,-653.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1538.02,-655.2 1541.46,-645.18 1532.52,-650.86 1538.02,-655.2\"/>\n",
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+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1455,-608.8C1455,-597.16 1455,-581.55 1455,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.5,-568.18 1455,-558.18 1451.5,-568.18 1458.5,-568.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1464\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1553.99,-608.8C1553.3,-597.16 1552.39,-581.55 1551.6,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1555.09,-567.95 1551.01,-558.18 1548.1,-568.36 1555.09,-567.95\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1562\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1573.82,-608.8C1587.56,-596.24 1606.37,-579.05 1621.61,-565.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1624.19,-567.51 1629.21,-558.18 1619.47,-562.34 1624.19,-567.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1616\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549.8,-521.8C1549.66,-510.16 1549.48,-494.55 1549.32,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.82,-481.13 1549.2,-471.18 1545.82,-481.22 1552.82,-481.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1648,-521.8C1648,-510.16 1648,-494.55 1648,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.5,-481.18 1648,-471.18 1644.5,-481.18 1651.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1667.63,-521.8C1681.96,-509.24 1701.58,-492.05 1717.47,-478.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1720.19,-480.4 1725.4,-471.18 1715.57,-475.13 1720.19,-480.4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1726.18,-347.8C1712.44,-335.24 1693.63,-318.05 1678.39,-304.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1680.53,-301.34 1670.79,-297.18 1675.81,-306.51 1680.53,-301.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
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+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1766.25,-347.8C1781.9,-335.13 1803.39,-317.73 1820.68,-303.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1823.21,-306.19 1828.78,-297.18 1818.81,-300.75 1823.21,-306.19\"/>\n",
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+       "</g>\n",
+       "<!-- 74 -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1719\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">74 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;74 -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>76&#45;&gt;74</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1815.12,-260.9C1804.89,-255.52 1793.82,-249.32 1784,-243 1771.6,-235.02 1758.59,-225.31 1747.49,-216.58\"/>\n",
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+       "</g>\n",
+       "<!-- 75 -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1812\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">75 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;75 -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>76&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1833.68,-260.94C1829.3,-255.56 1824.96,-249.35 1822,-243 1818.71,-235.94 1816.5,-227.82 1815.02,-220.21\"/>\n",
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+       "</g>\n",
+       "<!-- 77 -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">77 (suhu)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;77 -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>76&#45;&gt;77</title>\n",
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+       "<!-- 80 -->\n",
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+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">80 (masuk)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;80 -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>76&#45;&gt;80</title>\n",
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+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"1852\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">78 (badan)</text>\n",
+       "</g>\n",
+       "<!-- 77&#45;&gt;78 -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
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+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"1946\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">79 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;79 -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>80&#45;&gt;79</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2012.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node83\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">83 (surau)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;83 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>80&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2038,-173.8C2038,-162.16 2038,-146.55 2038,-133.24\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 85 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>85</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">85 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;85 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;85</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2105\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 87 -->\n",
+       "<g id=\"node85\" class=\"node\">\n",
+       "<title>87</title>\n",
+       "<text text-anchor=\"middle\" x=\"2245\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">87 (Sabtu)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;87 -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>80&#45;&gt;87</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2163\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 81 -->\n",
+       "<g id=\"node86\" class=\"node\">\n",
+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"1961\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">81 (ke)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;81 -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>83&#45;&gt;81</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2022.42,-86.8C2011.25,-74.47 1996.04,-57.68 1983.55,-43.89\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"2017\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node87\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">82 (sebuah)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;82 -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>83&#45;&gt;82</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2052.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 84 -->\n",
+       "<g id=\"node88\" class=\"node\">\n",
+       "<title>84</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">84 (di)</text>\n",
+       "</g>\n",
+       "<!-- 85&#45;&gt;84 -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>85&#45;&gt;84</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"2144.5,-46.18 2141,-36.18 2137.5,-46.18 2144.5,-46.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2153\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 86 -->\n",
+       "<g id=\"node89\" class=\"node\">\n",
+       "<title>86</title>\n",
+       "<text text-anchor=\"middle\" x=\"2232\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">86 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;86 -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>87&#45;&gt;86</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"2239.6,-45.53 2234.63,-36.18 2232.68,-46.59 2239.6,-45.53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2252\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 88 -->\n",
+       "<g id=\"node90\" class=\"node\">\n",
+       "<title>88</title>\n",
+       "<text text-anchor=\"middle\" x=\"2316\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">88 (lalu)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;88 -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>87&#45;&gt;88</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2259.37,-86.8C2269.67,-74.47 2283.69,-57.68 2295.21,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2297.93,-46.09 2301.65,-36.18 2292.56,-41.61 2297.93,-46.09\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2299.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x16ab2e590>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], s)\n",
+    "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dependency graph object\n",
+    "\n",
+    "To initiate a dependency graph from dependency models, you need to call `malaya.dependency.dependency_graph`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<malaya.function.parse_dependency.DependencyGraph at 0x16ab39c10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph = malaya.dependency.dependency_graph(tagging, indexing)\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### generate graphvis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.44.1 (20200629.0846)\n",
+       " -->\n",
+       "<!-- Title: G Pages: 1 -->\n",
+       "<svg width=\"2356pt\" height=\"1088pt\"\n",
+       " viewBox=\"0.00 0.00 2355.50 1088.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 1084)\">\n",
+       "<title>G</title>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-1084 2351.5,-1084 2351.5,4 -4,4\"/>\n",
+       "<!-- 0 -->\n",
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+       "<title>0</title>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\">0 (None)</text>\n",
+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"489\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\">11 (melihat)</text>\n",
+       "</g>\n",
+       "<!-- 0&#45;&gt;11 -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>0&#45;&gt;11</title>\n",
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+       "<text text-anchor=\"middle\" x=\"500\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\">root</text>\n",
+       "</g>\n",
+       "<!-- 1 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>1</title>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">1 (KUALA)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;1 -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>11&#45;&gt;1</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-962.98C421.28,-956.25 388.21,-947.36 359,-939 320.79,-928.07 277.84,-915.03 245.33,-905.01\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"374\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 8 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>8</title>\n",
+       "<text text-anchor=\"middle\" x=\"358\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">8 (jarang)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;8 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>11&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M446.75,-958.12C434.76,-952.81 421.98,-946.34 411,-939 400.15,-931.75 389.44,-922.23 380.52,-913.44\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 9 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>9</title>\n",
+       "<text text-anchor=\"middle\" x=\"445\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">9 (sekali)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;9 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>11&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M477.52,-956.85C473.97,-951.26 470.16,-944.96 467,-939 463.06,-931.58 459.21,-923.31 455.87,-915.66\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"489.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"534\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">10 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;10 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>11&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M501.05,-956.91C504.77,-951.32 508.74,-945 512,-939 516.08,-931.5 520.01,-923.1 523.39,-915.35\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"532\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"643\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">29 (mempunyai)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;29 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;29</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"605.08,-913.93 612.15,-906.03 601.7,-907.8 605.08,-913.93\"/>\n",
+       "<text text-anchor=\"middle\" x=\"591\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\">42 (berbeza)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;42 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>11&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M531.23,-967.04C624.77,-951.63 849.29,-914.65 952.04,-897.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"952.89,-901.13 962.19,-896.05 951.75,-894.22 952.89,-901.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>2</title>\n",
+       "<text text-anchor=\"middle\" x=\"58\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">2 (LUMPUR)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;2 -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>1&#45;&gt;2</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.48,-869.8C145.65,-856.78 116.85,-838.78 94.16,-824.6\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"95.82,-821.51 85.48,-819.18 92.11,-827.44 95.82,-821.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 5 -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"150\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">5 (hal)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;5 -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>1&#45;&gt;5</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M185.1,-869.8C178.89,-857.82 170.51,-841.62 163.49,-828.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"166.6,-826.45 158.89,-819.18 160.38,-829.67 166.6,-826.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"184\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obl</text>\n",
+       "</g>\n",
+       "<!-- 7 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"222\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">7 (,)</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;7 -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>1&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M199.67,-869.8C203.54,-858.05 208.75,-842.24 213.16,-828.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"216.54,-829.77 216.34,-819.18 209.89,-827.58 216.54,-829.77\"/>\n",
+       "<text text-anchor=\"middle\" x=\"225.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 3 -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>3</title>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">3 (:)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;3 -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>5&#45;&gt;3</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M122.98,-784.15C113.38,-778.33 102.59,-771.55 93,-765 80.89,-756.73 67.96,-747.11 56.77,-738.53\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"108.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 4 -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>4</title>\n",
+       "<text text-anchor=\"middle\" x=\"109\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">4 (Dalam)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;4 -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>5&#45;&gt;4</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"124.78,-739.66 117.28,-732.18 118.48,-742.7 124.78,-739.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>6</title>\n",
+       "<text text-anchor=\"middle\" x=\"199\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">6 (politik)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;6 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>5&#45;&gt;6</title>\n",
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+       "<text text-anchor=\"middle\" x=\"207\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 13 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>13</title>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">13 (figura)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;13 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>29&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M601.31,-869.8C567.25,-855.74 519.11,-835.88 483.74,-821.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"484.71,-817.9 474.13,-817.32 482.04,-824.37 484.71,-817.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"567\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">31 (pandangan)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;31 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>29&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.62,-869.8C616.6,-857.47 601.58,-840.68 589.25,-826.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"591.63,-824.3 582.36,-819.18 586.41,-828.96 591.63,-824.3\"/>\n",
+       "<text text-anchor=\"middle\" x=\"619\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 37 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>37</title>\n",
+       "<text text-anchor=\"middle\" x=\"663\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">37 (.)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;37 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>29&#45;&gt;37</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M647.05,-869.8C649.79,-858.16 653.46,-842.55 656.59,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"660.08,-829.71 658.96,-819.18 653.26,-828.11 660.08,-829.71\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"750\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">38 (Namun)</text>\n",
+       "</g>\n",
+       "<!-- 29&#45;&gt;38 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>29&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M666.62,-869.8C674.16,-864.21 682.49,-857.92 690,-852 700.67,-843.59 712.18,-834.1 722.24,-825.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"724.56,-828.29 729.95,-819.17 720.05,-822.94 724.56,-828.29\"/>\n",
+       "<text text-anchor=\"middle\" x=\"723.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 39 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>39</title>\n",
+       "<text text-anchor=\"middle\" x=\"837\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">39 (,)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;39 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>42&#45;&gt;39</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M962.41,-870C949.59,-864.57 935.59,-858.32 923,-852 906.1,-843.52 887.92,-833.14 872.65,-824.05\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"874.4,-821.02 864.02,-818.86 870.79,-827.02 874.4,-821.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"938.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">40 (situasi)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>42&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M988,-869.8C975.7,-857.36 958.91,-840.36 945.22,-826.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"947.49,-823.83 937.97,-819.18 942.51,-828.75 947.49,-823.83\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1005\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">54 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;54 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>42&#45;&gt;54</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1005,-869.8C1005,-858.16 1005,-842.55 1005,-829.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-829.18 1005,-819.18 1001.5,-829.18 1008.5,-829.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1020.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 89 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>89</title>\n",
+       "<text text-anchor=\"middle\" x=\"1077\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">89 (.)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;89 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>42&#45;&gt;89</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1023.33,-869.69C1028.87,-864.19 1034.84,-857.99 1040,-852 1046.63,-844.31 1053.37,-835.53 1059.26,-827.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1062.27,-829.31 1065.27,-819.15 1056.58,-825.22 1062.27,-829.31\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1067.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">44 (melibatkan)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;44 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>42&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.57,-869.96C1060.39,-864.52 1074.39,-858.29 1087,-852 1103.94,-843.55 1122.18,-833.27 1137.56,-824.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1139.43,-827.18 1146.25,-819.07 1135.86,-821.16 1139.43,-827.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1313\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\">55 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 42&#45;&gt;55 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>42&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1047.7,-877.19C1076.21,-870.48 1114.47,-861.18 1148,-852 1188.29,-840.97 1233.69,-827.11 1266.65,-816.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1267.98,-820.03 1276.47,-813.69 1265.88,-813.35 1267.98,-820.03\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1209\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\">dep</text>\n",
+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"284\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">12 (dua)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;12 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>13&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M399.78,-791.27C379.93,-785.61 355.62,-777.02 336,-765 324.82,-758.15 314.09,-748.54 305.31,-739.58\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"307.83,-737.15 298.42,-732.27 302.73,-741.95 307.83,-737.15\"/>\n",
+       "<text text-anchor=\"middle\" x=\"361\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nummod</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"360\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">15 (&#45;)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;15 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>13&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M419.84,-782.75C414.38,-777.16 408.37,-770.88 403,-765 395.7,-757.01 387.93,-748.11 381.03,-740.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"383.62,-737.7 374.47,-732.35 378.29,-742.23 383.62,-737.7\"/>\n",
+       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"442\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">16 (bekas)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;16 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>13&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.95,-782.86C437.02,-773.2 437.27,-760.92 438,-750 438.17,-747.44 438.39,-744.77 438.65,-742.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"442.14,-742.38 439.73,-732.06 435.18,-741.63 442.14,-742.38\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound:plur</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"540\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">17 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 13&#45;&gt;17 -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>13&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M474.41,-791.92C490.88,-786.68 509.32,-778.27 522,-765 528.07,-758.65 532.12,-750.1 534.81,-741.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"538.2,-742.72 537.45,-732.15 531.45,-740.88 538.2,-742.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"541\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"326\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">14 (ini)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;14 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>17&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M496.71,-696.99C460.93,-683.59 408.9,-663.7 364,-645 363.9,-644.96 363.81,-644.92 363.71,-644.88\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"365.14,-641.68 354.56,-640.97 362.38,-648.12 365.14,-641.68\"/>\n",
+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"416\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">18 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M514.91,-695.8C496.08,-682.9 470.12,-665.1 449.52,-650.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"451.28,-647.94 441.06,-645.18 447.33,-653.72 451.28,-647.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"495\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"504\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">19 (,)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;19 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>17&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M528.43,-695.65C525.09,-690.16 521.63,-683.96 519,-678 515.78,-670.7 513,-662.52 510.74,-654.91\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"514.04,-653.73 507.98,-645.04 507.3,-655.61 514.04,-653.73\"/>\n",
+       "<text text-anchor=\"middle\" x=\"534.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"587\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">20 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;20 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>17&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M549.51,-695.8C556.14,-683.82 565.09,-667.62 572.59,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"575.73,-655.62 577.5,-645.18 569.6,-652.23 575.73,-655.62\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">25 (Tun)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;25 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>17&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.06,-695.94C582.08,-690.41 593.1,-684.12 603,-678 616.69,-669.53 631.41,-659.62 644.02,-650.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"646.19,-653.61 652.37,-645.01 642.17,-647.88 646.19,-653.61\"/>\n",
+       "<text text-anchor=\"middle\" x=\"638\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">21 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.71,-608.8C574.69,-596.93 567.91,-580.93 562.2,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"565.4,-566.02 558.27,-558.18 558.95,-568.75 565.4,-566.02\"/>\n",
+       "<text text-anchor=\"middle\" x=\"581\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"633\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">24 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;24 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>25&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M667.3,-608.8C661.3,-596.93 653.2,-580.93 646.38,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"649.33,-565.52 641.69,-558.18 643.08,-568.68 649.33,-565.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"664.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">26 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M683.08,-608.8C687.97,-596.93 694.56,-580.93 700.11,-567.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"703.36,-568.75 703.93,-558.18 696.88,-566.09 703.36,-568.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"705\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">22 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-521.8C551,-510.16 551,-494.55 551,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-481.18 551,-471.18 547.5,-481.18 554.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"551\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">23 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 22&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>22&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M551,-434.8C551,-423.16 551,-407.55 551,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"554.5,-394.18 551,-384.18 547.5,-394.18 554.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"560\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">27 (Mahathir)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-521.8C711,-510.16 711,-494.55 711,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-481.18 711,-471.18 707.5,-481.18 714.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"711\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">28 (Mohamad)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M711,-434.8C711,-423.16 711,-407.55 711,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"714.5,-394.18 711,-384.18 707.5,-394.18 714.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"720\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"628\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">30 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;30 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M579.34,-782.8C588.03,-770.7 599.79,-754.3 609.58,-740.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"612.69,-742.34 615.67,-732.18 607,-738.26 612.69,-742.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"617.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 33 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>33</title>\n",
+       "<text text-anchor=\"middle\" x=\"788\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">33 (sama)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;33 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;33</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M611.46,-782.9C649.69,-768.2 704.57,-747.09 742.97,-732.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"744.28,-735.57 752.35,-728.71 741.77,-729.03 744.28,-735.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"706.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"838\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">36 (sekapal)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;36 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M798.12,-695.8C805.17,-683.82 814.7,-667.62 822.67,-654.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"825.84,-655.57 827.9,-645.18 819.81,-652.02 825.84,-655.57\"/>\n",
+       "<text text-anchor=\"middle\" x=\"828\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">conj</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">32 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;32 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>36&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M828.72,-608.91C825.62,-603.22 822.16,-596.84 819,-591 814.79,-583.23 810.22,-574.77 806.06,-567.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"809.07,-565.27 801.24,-558.13 802.91,-568.59 809.07,-565.27\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"872\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">34 (&#39;)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;34 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>36&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M844.88,-608.8C849.58,-597.05 855.9,-581.24 861.26,-567.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"864.67,-568.76 865.13,-558.18 858.17,-566.16 864.67,-568.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"950\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">35 (atau)</text>\n",
+       "</g>\n",
+       "<!-- 36&#45;&gt;35 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>36&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M866.82,-608.82C875.43,-603.39 884.76,-597.18 893,-591 903.61,-583.04 914.7,-573.6 924.25,-565.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"926.8,-567.49 931.86,-558.19 922.1,-562.3 926.8,-567.49\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">cc</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"921\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">41 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 40&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>40&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M921,-782.8C921,-771.16 921,-755.55 921,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"924.5,-742.18 921,-732.18 917.5,-742.18 924.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"929.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1085\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">43 (apabila)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>44&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1155.99,-782.8C1142.84,-770.24 1124.84,-753.05 1110.26,-739.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1112.63,-736.55 1102.98,-732.18 1107.8,-741.61 1112.63,-736.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1149.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">mark</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">45 (isu)</text>\n",
+       "</g>\n",
+       "<!-- 44&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>44&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-782.8C1174,-771.16 1174,-755.55 1174,-742.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-742.18 1174,-732.18 1170.5,-742.18 1177.5,-742.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1183\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">obj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1279\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">56 (,)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;56 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>55&#45;&gt;56</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1301.29,-782.71C1297.94,-777.22 1294.52,-771.01 1292,-765 1288.95,-757.72 1286.45,-749.54 1284.5,-741.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1287.87,-740.99 1282.17,-732.06 1281.06,-742.6 1287.87,-740.99\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1307.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 59 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">59 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;59 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>55&#45;&gt;59</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1322.11,-782.8C1328.45,-770.82 1337.03,-754.62 1344.21,-741.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1347.32,-742.65 1348.91,-732.18 1341.13,-739.38 1347.32,-742.65\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1354\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\">62 (memuji)</text>\n",
+       "</g>\n",
+       "<!-- 55&#45;&gt;62 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>55&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1348.41,-782.8C1375.9,-769.45 1414.17,-750.86 1443.69,-736.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1445.47,-739.55 1452.94,-732.03 1442.41,-733.25 1445.47,-739.55\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1419\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\">acl</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1048\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">46 (ketidakpatuhan)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;46 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>45&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1144.44,-697.49C1133.99,-691.7 1122.28,-684.86 1112,-678 1099.85,-669.89 1087.05,-660.16 1076.12,-651.44\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.15,-648.58 1068.17,-645.02 1073.75,-654.02 1078.15,-648.58\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1141\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1174\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">48 (prosedur)</text>\n",
+       "</g>\n",
+       "<!-- 45&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>45&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1174,-695.8C1174,-684.16 1174,-668.55 1174,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1177.5,-655.18 1174,-645.18 1170.5,-655.18 1177.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1190\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1055\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">47 (terhadap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;47 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1149.92,-608.8C1131.93,-595.95 1107.16,-578.26 1087.43,-564.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1089.22,-561.14 1079.05,-558.18 1085.15,-566.84 1089.22,-561.14\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1134\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1159\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">49 (operasi)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.96,-608.8C1168.91,-597.16 1166.16,-581.55 1163.81,-568.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1167.22,-567.41 1162.03,-558.18 1160.32,-568.63 1167.22,-567.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">compound</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1263\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">50 (standard)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1206.53,-608.94C1214.71,-603.8 1223.11,-597.72 1230,-591 1237.22,-583.96 1243.7,-575.08 1248.97,-566.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1252.04,-568.45 1254.19,-558.08 1246.04,-564.85 1252.04,-568.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1258.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1360\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">52 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1219.27,-611.28C1244.62,-602.95 1272.28,-593.67 1278,-591 1294.65,-583.24 1312.15,-572.91 1326.69,-563.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1328.83,-566.42 1335.32,-558.05 1325.03,-560.55 1328.83,-566.42\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1322\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\">appos</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1324\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">51 (()</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;51 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1351.22,-521.69C1348.48,-516.1 1345.52,-509.83 1343,-504 1339.77,-496.53 1336.53,-488.32 1333.66,-480.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1336.9,-479.43 1330.13,-471.28 1330.34,-481.87 1336.9,-479.43\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1358.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1396\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">53 ())</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1367.29,-521.8C1372.31,-509.93 1379.09,-493.93 1384.8,-480.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1388.05,-481.75 1388.73,-471.18 1381.6,-479.02 1388.05,-481.75\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1396.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">punct</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1272\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">57 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;57 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1340.6,-695.8C1328.01,-683.36 1310.82,-666.36 1296.79,-652.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1298.95,-649.72 1289.38,-645.18 1294.03,-654.69 1298.95,-649.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1336\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 58 -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1358\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">58 (juga)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1358,-695.8C1358,-684.16 1358,-668.55 1358,-655.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1361.5,-655.18 1358,-645.18 1354.5,-655.18 1361.5,-655.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">60 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
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+       "</g>\n",
+       "<!-- 63 -->\n",
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+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1555\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\">63 (sikap)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
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+       "</g>\n",
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+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1455\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">61 (Pekan)</text>\n",
+       "</g>\n",
+       "<!-- 60&#45;&gt;61 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>60&#45;&gt;61</title>\n",
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+       "</g>\n",
+       "<!-- 64 -->\n",
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+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1550\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">64 (Ahli)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;64 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>63&#45;&gt;64</title>\n",
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+       "</g>\n",
+       "<!-- 69 -->\n",
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+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\">69 (mengaku)</text>\n",
+       "</g>\n",
+       "<!-- 63&#45;&gt;69 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>63&#45;&gt;69</title>\n",
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+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">65 (Parlimen)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.82,-481.13 1549.2,-471.18 1545.82,-481.22 1552.82,-481.13\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1559\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">68 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;68 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>69&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1648,-521.8C1648,-510.16 1648,-494.55 1648,-481.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.5,-481.18 1648,-471.18 1644.5,-481.18 1651.5,-481.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1663\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\">70 (bersalah)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1667.63,-521.8C1681.96,-509.24 1701.58,-492.05 1717.47,-478.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1720.19,-480.4 1725.4,-471.18 1715.57,-475.13 1720.19,-480.4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1722\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">66 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 65&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>65&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-434.8C1549,-423.16 1549,-407.55 1549,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-394.18 1549,-384.18 1545.5,-394.18 1552.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1558\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">flat</text>\n",
+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">67 (itu)</text>\n",
+       "</g>\n",
+       "<!-- 66&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>66&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1549,-347.8C1549,-336.16 1549,-320.55 1549,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1552.5,-307.18 1549,-297.18 1545.5,-307.18 1552.5,-307.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1557.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">det</text>\n",
+       "</g>\n",
+       "<!-- 72 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\">72 (melanggar)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;72 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>70&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-434.8C1745,-423.16 1745,-407.55 1745,-394.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-394.18 1745,-384.18 1741.5,-394.18 1748.5,-394.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1764\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\">xcomp</text>\n",
+       "</g>\n",
+       "<!-- 71 -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1652\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">71 (selepas)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;71 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>72&#45;&gt;71</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1717\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1745\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">73 (SOP)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;73 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>72&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1745,-347.8C1745,-336.16 1745,-320.55 1745,-307.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.5,-307.18 1745,-297.18 1741.5,-307.18 1748.5,-307.18\"/>\n",
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+       "</g>\n",
+       "<!-- 76 -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"1850\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\">76 (mengambil)</text>\n",
+       "</g>\n",
+       "<!-- 72&#45;&gt;76 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>72&#45;&gt;76</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1766.25,-347.8C1781.9,-335.13 1803.39,-317.73 1820.68,-303.74\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1823.21,-306.19 1828.78,-297.18 1818.81,-300.75 1823.21,-306.19\"/>\n",
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+       "</g>\n",
+       "<!-- 74 -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1719\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">74 (kerana)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;74 -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>76&#45;&gt;74</title>\n",
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+       "</g>\n",
+       "<!-- 75 -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1812\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">75 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;75 -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>76&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1833.68,-260.94C1829.3,-255.56 1824.96,-249.35 1822,-243 1818.71,-235.94 1816.5,-227.82 1815.02,-220.21\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1844.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 77 -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">77 (suhu)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;77 -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>76&#45;&gt;77</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1859.92,-260.8C1866.82,-248.82 1876.16,-232.62 1883.98,-219.06\"/>\n",
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+       "</g>\n",
+       "<!-- 80 -->\n",
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+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\">80 (masuk)</text>\n",
+       "</g>\n",
+       "<!-- 76&#45;&gt;80 -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>76&#45;&gt;80</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1971\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 78 -->\n",
+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"1852\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">78 (badan)</text>\n",
+       "</g>\n",
+       "<!-- 77&#45;&gt;78 -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
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+       "</g>\n",
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+       "<g id=\"node82\" class=\"node\">\n",
+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"1946\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">79 (ketika)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;79 -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>80&#45;&gt;79</title>\n",
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+       "</g>\n",
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+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"2038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">83 (surau)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;83 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>80&#45;&gt;83</title>\n",
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+       "</g>\n",
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+       "<title>85</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">85 (Langkawi)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;85 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;85</title>\n",
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+       "</g>\n",
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+       "<title>87</title>\n",
+       "<text text-anchor=\"middle\" x=\"2245\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">87 (Sabtu)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;87 -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>80&#45;&gt;87</title>\n",
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+       "</g>\n",
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+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"1961\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">81 (ke)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;81 -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>83&#45;&gt;81</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2017\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
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+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2047\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">82 (sebuah)</text>\n",
+       "</g>\n",
+       "<!-- 83&#45;&gt;82 -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>83&#45;&gt;82</title>\n",
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+       "<g id=\"node88\" class=\"node\">\n",
+       "<title>84</title>\n",
+       "<text text-anchor=\"middle\" x=\"2141\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">84 (di)</text>\n",
+       "</g>\n",
+       "<!-- 85&#45;&gt;84 -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>85&#45;&gt;84</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2141,-86.8C2141,-75.16 2141,-59.55 2141,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2144.5,-46.18 2141,-36.18 2137.5,-46.18 2144.5,-46.18\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2153\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 86 -->\n",
+       "<g id=\"node89\" class=\"node\">\n",
+       "<title>86</title>\n",
+       "<text text-anchor=\"middle\" x=\"2232\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">86 (pada)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;86 -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>87&#45;&gt;86</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2242.37,-86.8C2240.59,-75.16 2238.2,-59.55 2236.17,-46.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2239.6,-45.53 2234.63,-36.18 2232.68,-46.59 2239.6,-45.53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2252\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">case</text>\n",
+       "</g>\n",
+       "<!-- 88 -->\n",
+       "<g id=\"node90\" class=\"node\">\n",
+       "<title>88</title>\n",
+       "<text text-anchor=\"middle\" x=\"2316\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">88 (lalu)</text>\n",
+       "</g>\n",
+       "<!-- 87&#45;&gt;88 -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>87&#45;&gt;88</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2259.37,-86.8C2269.67,-74.47 2283.69,-57.68 2295.21,-43.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2297.93,-46.09 2301.65,-36.18 2292.56,-41.61 2297.93,-46.09\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2299.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x17ae39450>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(<function malaya.function.parse_dependency.DependencyGraph.__init__.<locals>.<lambda>()>,\n",
+       "            {0: {'address': 0,\n",
+       "              'word': None,\n",
+       "              'lemma': None,\n",
+       "              'ctag': 'TOP',\n",
+       "              'tag': 'TOP',\n",
+       "              'feats': None,\n",
+       "              'head': None,\n",
+       "              'deps': defaultdict(list, {'root': [11]}),\n",
+       "              'rel': None},\n",
+       "             1: {'address': 1,\n",
+       "              'word': 'KUALA',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'flat': [2], 'obl': [5], 'punct': [7]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             11: {'address': 11,\n",
+       "              'word': 'melihat',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 0,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nsubj': [1],\n",
+       "                           'advmod': [8, 9],\n",
+       "                           'case': [10],\n",
+       "                           'advcl': [29],\n",
+       "                           'dep': [42]}),\n",
+       "              'rel': 'root'},\n",
+       "             2: {'address': 2,\n",
+       "              'word': 'LUMPUR',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             3: {'address': 3,\n",
+       "              'word': ':',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             5: {'address': 5,\n",
+       "              'word': 'hal',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [3], 'case': [4], 'compound': [6]}),\n",
+       "              'rel': 'obl'},\n",
+       "             4: {'address': 4,\n",
+       "              'word': 'Dalam',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             6: {'address': 6,\n",
+       "              'word': 'politik',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 5,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             7: {'address': 7,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 1,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             8: {'address': 8,\n",
+       "              'word': 'jarang',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             9: {'address': 9,\n",
+       "              'word': 'sekali',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             10: {'address': 10,\n",
+       "              'word': 'untuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             12: {'address': 12,\n",
+       "              'word': 'dua',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'nummod'},\n",
+       "             13: {'address': 13,\n",
+       "              'word': 'figura',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nummod': [12],\n",
+       "                           'punct': [15],\n",
+       "                           'compound:plur': [16],\n",
+       "                           'flat': [17]}),\n",
+       "              'rel': 'obj'},\n",
+       "             29: {'address': 29,\n",
+       "              'word': 'mempunyai',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'obj': [13, 31], 'punct': [37], 'mark': [38]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             14: {'address': 14,\n",
+       "              'word': 'ini',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             17: {'address': 17,\n",
+       "              'word': 'Perdana',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'det': [14],\n",
+       "                           'flat': [18],\n",
+       "                           'punct': [19],\n",
+       "                           'appos': [20],\n",
+       "                           'conj': [25]}),\n",
+       "              'rel': 'flat'},\n",
+       "             15: {'address': 15,\n",
+       "              'word': '-',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             16: {'address': 16,\n",
+       "              'word': 'bekas',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 13,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound:plur'},\n",
+       "             18: {'address': 18,\n",
+       "              'word': 'Menteri',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             19: {'address': 19,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             20: {'address': 20,\n",
+       "              'word': 'Datuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {'flat': [21]}),\n",
+       "              'rel': 'appos'},\n",
+       "             21: {'address': 21,\n",
+       "              'word': 'Seri',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 20,\n",
+       "              'deps': defaultdict(list, {'flat': [22]}),\n",
+       "              'rel': 'flat'},\n",
+       "             22: {'address': 22,\n",
+       "              'word': 'Najib',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 21,\n",
+       "              'deps': defaultdict(list, {'flat': [23]}),\n",
+       "              'rel': 'flat'},\n",
+       "             23: {'address': 23,\n",
+       "              'word': 'Razak',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 22,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             24: {'address': 24,\n",
+       "              'word': 'dan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 25,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'cc'},\n",
+       "             25: {'address': 25,\n",
+       "              'word': 'Tun',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 17,\n",
+       "              'deps': defaultdict(list, {'cc': [24], 'flat': [26]}),\n",
+       "              'rel': 'conj'},\n",
+       "             26: {'address': 26,\n",
+       "              'word': 'Dr',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 25,\n",
+       "              'deps': defaultdict(list, {'flat': [27]}),\n",
+       "              'rel': 'flat'},\n",
+       "             27: {'address': 27,\n",
+       "              'word': 'Mahathir',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 26,\n",
+       "              'deps': defaultdict(list, {'flat': [28]}),\n",
+       "              'rel': 'flat'},\n",
+       "             28: {'address': 28,\n",
+       "              'word': 'Mohamad',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 27,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             30: {'address': 30,\n",
+       "              'word': \"'\",\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 31,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             31: {'address': 31,\n",
+       "              'word': 'pandangan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {'punct': [30], 'amod': [33]}),\n",
+       "              'rel': 'obj'},\n",
+       "             32: {'address': 32,\n",
+       "              'word': 'yang',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             36: {'address': 36,\n",
+       "              'word': 'sekapal',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 33,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nsubj': [32], 'punct': [34], 'cc': [35]}),\n",
+       "              'rel': 'conj'},\n",
+       "             33: {'address': 33,\n",
+       "              'word': 'sama',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 31,\n",
+       "              'deps': defaultdict(list, {'conj': [36]}),\n",
+       "              'rel': 'amod'},\n",
+       "             34: {'address': 34,\n",
+       "              'word': \"'\",\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             35: {'address': 35,\n",
+       "              'word': 'atau',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 36,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'cc'},\n",
+       "             37: {'address': 37,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             38: {'address': 38,\n",
+       "              'word': 'Namun',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 29,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             39: {'address': 39,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             42: {'address': 42,\n",
+       "              'word': 'berbeza',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 11,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [39, 54, 89],\n",
+       "                           'nsubj': [40],\n",
+       "                           'advcl': [44],\n",
+       "                           'dep': [55]}),\n",
+       "              'rel': 'dep'},\n",
+       "             40: {'address': 40,\n",
+       "              'word': 'situasi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {'det': [41]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             41: {'address': 41,\n",
+       "              'word': 'itu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 40,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             43: {'address': 43,\n",
+       "              'word': 'apabila',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 44,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             44: {'address': 44,\n",
+       "              'word': 'melibatkan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {'mark': [43], 'obj': [45]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             45: {'address': 45,\n",
+       "              'word': 'isu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 44,\n",
+       "              'deps': defaultdict(list, {'compound': [46], 'nmod': [48]}),\n",
+       "              'rel': 'obj'},\n",
+       "             46: {'address': 46,\n",
+       "              'word': 'ketidakpatuhan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 45,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             47: {'address': 47,\n",
+       "              'word': 'terhadap',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             48: {'address': 48,\n",
+       "              'word': 'prosedur',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 45,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'case': [47],\n",
+       "                           'compound': [49],\n",
+       "                           'amod': [50],\n",
+       "                           'appos': [52]}),\n",
+       "              'rel': 'nmod'},\n",
+       "             49: {'address': 49,\n",
+       "              'word': 'operasi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             50: {'address': 50,\n",
+       "              'word': 'standard',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'amod'},\n",
+       "             51: {'address': 51,\n",
+       "              'word': '(',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 52,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             52: {'address': 52,\n",
+       "              'word': 'SOP',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 48,\n",
+       "              'deps': defaultdict(list, {'punct': [51, 53]}),\n",
+       "              'rel': 'appos'},\n",
+       "             53: {'address': 53,\n",
+       "              'word': ')',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 52,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             54: {'address': 54,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             55: {'address': 55,\n",
+       "              'word': 'Najib',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'punct': [56], 'nsubj': [59], 'acl': [62]}),\n",
+       "              'rel': 'dep'},\n",
+       "             56: {'address': 56,\n",
+       "              'word': ',',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 55,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'},\n",
+       "             57: {'address': 57,\n",
+       "              'word': 'yang',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 59,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             59: {'address': 59,\n",
+       "              'word': 'Ahli',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 55,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'nsubj': [57], 'advmod': [58], 'flat': [60]}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             58: {'address': 58,\n",
+       "              'word': 'juga',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 59,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             60: {'address': 60,\n",
+       "              'word': 'Parlimen',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 59,\n",
+       "              'deps': defaultdict(list, {'flat': [61]}),\n",
+       "              'rel': 'flat'},\n",
+       "             61: {'address': 61,\n",
+       "              'word': 'Pekan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 60,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'flat'},\n",
+       "             62: {'address': 62,\n",
+       "              'word': 'memuji',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 55,\n",
+       "              'deps': defaultdict(list, {'obj': [63]}),\n",
+       "              'rel': 'acl'},\n",
+       "             63: {'address': 63,\n",
+       "              'word': 'sikap',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 62,\n",
+       "              'deps': defaultdict(list, {'flat': [64], 'acl': [69]}),\n",
+       "              'rel': 'obj'},\n",
+       "             64: {'address': 64,\n",
+       "              'word': 'Ahli',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 63,\n",
+       "              'deps': defaultdict(list, {'flat': [65]}),\n",
+       "              'rel': 'flat'},\n",
+       "             65: {'address': 65,\n",
+       "              'word': 'Parlimen',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 64,\n",
+       "              'deps': defaultdict(list, {'flat': [66]}),\n",
+       "              'rel': 'flat'},\n",
+       "             66: {'address': 66,\n",
+       "              'word': 'Langkawi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 65,\n",
+       "              'deps': defaultdict(list, {'det': [67]}),\n",
+       "              'rel': 'flat'},\n",
+       "             67: {'address': 67,\n",
+       "              'word': 'itu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 66,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             68: {'address': 68,\n",
+       "              'word': 'yang',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 69,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'nsubj'},\n",
+       "             69: {'address': 69,\n",
+       "              'word': 'mengaku',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 63,\n",
+       "              'deps': defaultdict(list, {'nsubj': [68], 'xcomp': [70]}),\n",
+       "              'rel': 'acl'},\n",
+       "             70: {'address': 70,\n",
+       "              'word': 'bersalah',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 69,\n",
+       "              'deps': defaultdict(list, {'xcomp': [72]}),\n",
+       "              'rel': 'xcomp'},\n",
+       "             71: {'address': 71,\n",
+       "              'word': 'selepas',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 72,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             72: {'address': 72,\n",
+       "              'word': 'melanggar',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 70,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'case': [71], 'obj': [73], 'advcl': [76]}),\n",
+       "              'rel': 'xcomp'},\n",
+       "             73: {'address': 73,\n",
+       "              'word': 'SOP',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 72,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'obj'},\n",
+       "             74: {'address': 74,\n",
+       "              'word': 'kerana',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             76: {'address': 76,\n",
+       "              'word': 'mengambil',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 72,\n",
+       "              'deps': defaultdict(list,\n",
+       "                          {'mark': [74],\n",
+       "                           'advmod': [75],\n",
+       "                           'obj': [77],\n",
+       "                           'advcl': [80]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             75: {'address': 75,\n",
+       "              'word': 'tidak',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'advmod'},\n",
+       "             77: {'address': 77,\n",
+       "              'word': 'suhu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {'compound': [78]}),\n",
+       "              'rel': 'obj'},\n",
+       "             78: {'address': 78,\n",
+       "              'word': 'badan',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 77,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'compound'},\n",
+       "             79: {'address': 79,\n",
+       "              'word': 'ketika',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'mark'},\n",
+       "             80: {'address': 80,\n",
+       "              'word': 'masuk',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 76,\n",
+       "              'deps': defaultdict(list, {'mark': [79], 'obl': [83, 85, 87]}),\n",
+       "              'rel': 'advcl'},\n",
+       "             81: {'address': 81,\n",
+       "              'word': 'ke',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 83,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             83: {'address': 83,\n",
+       "              'word': 'surau',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [81], 'det': [82]}),\n",
+       "              'rel': 'obl'},\n",
+       "             82: {'address': 82,\n",
+       "              'word': 'sebuah',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 83,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'det'},\n",
+       "             84: {'address': 84,\n",
+       "              'word': 'di',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 85,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             85: {'address': 85,\n",
+       "              'word': 'Langkawi',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [84]}),\n",
+       "              'rel': 'obl'},\n",
+       "             86: {'address': 86,\n",
+       "              'word': 'pada',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 87,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'case'},\n",
+       "             87: {'address': 87,\n",
+       "              'word': 'Sabtu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 80,\n",
+       "              'deps': defaultdict(list, {'case': [86], 'amod': [88]}),\n",
+       "              'rel': 'obl'},\n",
+       "             88: {'address': 88,\n",
+       "              'word': 'lalu',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 87,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'amod'},\n",
+       "             89: {'address': 89,\n",
+       "              'word': '.',\n",
+       "              'lemma': '_',\n",
+       "              'ctag': '_',\n",
+       "              'tag': '_',\n",
+       "              'feats': '_',\n",
+       "              'head': 42,\n",
+       "              'deps': defaultdict(list, {}),\n",
+       "              'rel': 'punct'}})"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Flat the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(('melihat', '_'), 'nsubj', ('KUALA', '_')),\n",
+       " (('KUALA', '_'), 'flat', ('LUMPUR', '_')),\n",
+       " (('KUALA', '_'), 'obl', ('hal', '_')),\n",
+       " (('hal', '_'), 'punct', (':', '_')),\n",
+       " (('hal', '_'), 'case', ('Dalam', '_')),\n",
+       " (('hal', '_'), 'compound', ('politik', '_')),\n",
+       " (('KUALA', '_'), 'punct', (',', '_')),\n",
+       " (('melihat', '_'), 'advmod', ('jarang', '_')),\n",
+       " (('melihat', '_'), 'advmod', ('sekali', '_')),\n",
+       " (('melihat', '_'), 'case', ('untuk', '_')),\n",
+       " (('melihat', '_'), 'advcl', ('mempunyai', '_')),\n",
+       " (('mempunyai', '_'), 'obj', ('figura', '_')),\n",
+       " (('figura', '_'), 'nummod', ('dua', '_')),\n",
+       " (('figura', '_'), 'punct', ('-', '_')),\n",
+       " (('figura', '_'), 'compound:plur', ('bekas', '_')),\n",
+       " (('figura', '_'), 'flat', ('Perdana', '_')),\n",
+       " (('Perdana', '_'), 'det', ('ini', '_')),\n",
+       " (('Perdana', '_'), 'flat', ('Menteri', '_')),\n",
+       " (('Perdana', '_'), 'punct', (',', '_')),\n",
+       " (('Perdana', '_'), 'appos', ('Datuk', '_')),\n",
+       " (('Datuk', '_'), 'flat', ('Seri', '_')),\n",
+       " (('Seri', '_'), 'flat', ('Najib', '_')),\n",
+       " (('Najib', '_'), 'flat', ('Razak', '_')),\n",
+       " (('Perdana', '_'), 'conj', ('Tun', '_')),\n",
+       " (('Tun', '_'), 'cc', ('dan', '_')),\n",
+       " (('Tun', '_'), 'flat', ('Dr', '_')),\n",
+       " (('Dr', '_'), 'flat', ('Mahathir', '_')),\n",
+       " (('Mahathir', '_'), 'flat', ('Mohamad', '_')),\n",
+       " (('mempunyai', '_'), 'obj', ('pandangan', '_')),\n",
+       " (('pandangan', '_'), 'punct', (\"'\", '_')),\n",
+       " (('pandangan', '_'), 'amod', ('sama', '_')),\n",
+       " (('sama', '_'), 'conj', ('sekapal', '_')),\n",
+       " (('sekapal', '_'), 'nsubj', ('yang', '_')),\n",
+       " (('sekapal', '_'), 'punct', (\"'\", '_')),\n",
+       " (('sekapal', '_'), 'cc', ('atau', '_')),\n",
+       " (('mempunyai', '_'), 'punct', ('.', '_')),\n",
+       " (('mempunyai', '_'), 'mark', ('Namun', '_')),\n",
+       " (('melihat', '_'), 'dep', ('berbeza', '_')),\n",
+       " (('berbeza', '_'), 'punct', (',', '_')),\n",
+       " (('berbeza', '_'), 'nsubj', ('situasi', '_')),\n",
+       " (('situasi', '_'), 'det', ('itu', '_')),\n",
+       " (('berbeza', '_'), 'advcl', ('melibatkan', '_')),\n",
+       " (('melibatkan', '_'), 'mark', ('apabila', '_')),\n",
+       " (('melibatkan', '_'), 'obj', ('isu', '_')),\n",
+       " (('isu', '_'), 'compound', ('ketidakpatuhan', '_')),\n",
+       " (('isu', '_'), 'nmod', ('prosedur', '_')),\n",
+       " (('prosedur', '_'), 'case', ('terhadap', '_')),\n",
+       " (('prosedur', '_'), 'compound', ('operasi', '_')),\n",
+       " (('prosedur', '_'), 'amod', ('standard', '_')),\n",
+       " (('prosedur', '_'), 'appos', ('SOP', '_')),\n",
+       " (('SOP', '_'), 'punct', ('(', '_')),\n",
+       " (('SOP', '_'), 'punct', (')', '_')),\n",
+       " (('berbeza', '_'), 'punct', ('.', '_')),\n",
+       " (('berbeza', '_'), 'dep', ('Najib', '_')),\n",
+       " (('Najib', '_'), 'punct', (',', '_')),\n",
+       " (('Najib', '_'), 'nsubj', ('Ahli', '_')),\n",
+       " (('Ahli', '_'), 'nsubj', ('yang', '_')),\n",
+       " (('Ahli', '_'), 'advmod', ('juga', '_')),\n",
+       " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n",
+       " (('Parlimen', '_'), 'flat', ('Pekan', '_')),\n",
+       " (('Najib', '_'), 'acl', ('memuji', '_')),\n",
+       " (('memuji', '_'), 'obj', ('sikap', '_')),\n",
+       " (('sikap', '_'), 'flat', ('Ahli', '_')),\n",
+       " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n",
+       " (('Parlimen', '_'), 'flat', ('Langkawi', '_')),\n",
+       " (('Langkawi', '_'), 'det', ('itu', '_')),\n",
+       " (('sikap', '_'), 'acl', ('mengaku', '_')),\n",
+       " (('mengaku', '_'), 'nsubj', ('yang', '_')),\n",
+       " (('mengaku', '_'), 'xcomp', ('bersalah', '_')),\n",
+       " (('bersalah', '_'), 'xcomp', ('melanggar', '_')),\n",
+       " (('melanggar', '_'), 'case', ('selepas', '_')),\n",
+       " (('melanggar', '_'), 'obj', ('SOP', '_')),\n",
+       " (('melanggar', '_'), 'advcl', ('mengambil', '_')),\n",
+       " (('mengambil', '_'), 'mark', ('kerana', '_')),\n",
+       " (('mengambil', '_'), 'advmod', ('tidak', '_')),\n",
+       " (('mengambil', '_'), 'obj', ('suhu', '_')),\n",
+       " (('suhu', '_'), 'compound', ('badan', '_')),\n",
+       " (('mengambil', '_'), 'advcl', ('masuk', '_')),\n",
+       " (('masuk', '_'), 'mark', ('ketika', '_')),\n",
+       " (('masuk', '_'), 'obl', ('surau', '_')),\n",
+       " (('surau', '_'), 'case', ('ke', '_')),\n",
+       " (('surau', '_'), 'det', ('sebuah', '_')),\n",
+       " (('masuk', '_'), 'obl', ('Langkawi', '_')),\n",
+       " (('Langkawi', '_'), 'case', ('di', '_')),\n",
+       " (('masuk', '_'), 'obl', ('Sabtu', '_')),\n",
+       " (('Sabtu', '_'), 'case', ('pada', '_')),\n",
+       " (('Sabtu', '_'), 'amod', ('lalu', '_')),\n",
+       " (('berbeza', '_'), 'punct', ('.', '_'))]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(graph.triples())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Check the graph contains cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.contains_cycle()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate networkx\n",
+    "\n",
+    "Make sure you already installed networkx, \n",
+    "\n",
+    "```bash\n",
+    "pip install networkx\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<networkx.classes.multidigraph.MultiDiGraph at 0x16ab39d50>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digraph = graph.to_networkx()\n",
+    "digraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "nx.draw_networkx(digraph)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OutMultiEdgeDataView([(1, 11), (2, 1), (3, 5), (4, 5), (5, 1), (6, 5), (7, 1), (8, 11), (9, 11), (10, 11), (12, 13), (13, 29), (14, 17), (15, 13), (16, 13), (17, 13), (18, 17), (19, 17), (20, 17), (21, 20), (22, 21), (23, 22), (24, 25), (25, 17), (26, 25), (27, 26), (28, 27), (29, 11), (30, 31), (31, 29), (32, 36), (33, 31), (34, 36), (35, 36), (36, 33), (37, 29), (38, 29), (39, 42), (40, 42), (41, 40), (42, 11), (43, 44), (44, 42), (45, 44), (46, 45), (47, 48), (48, 45), (49, 48), (50, 48), (51, 52), (52, 48), (53, 52), (54, 42), (55, 42), (56, 55), (57, 59), (58, 59), (59, 55), (60, 59), (61, 60), (62, 55), (63, 62), (64, 63), (65, 64), (66, 65), (67, 66), (68, 69), (69, 63), (70, 69), (71, 72), (72, 70), (73, 72), (74, 76), (75, 76), (76, 72), (77, 76), (78, 77), (79, 80), (80, 76), (81, 83), (82, 83), (83, 80), (84, 85), (85, 80), (86, 87), (87, 80), (88, 87), (89, 42)])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digraph.edges()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digraph.nodes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'KUALA',\n",
+       " 2: 'LUMPUR',\n",
+       " 3: ':',\n",
+       " 4: 'Dalam',\n",
+       " 5: 'hal',\n",
+       " 6: 'politik',\n",
+       " 7: ',',\n",
+       " 8: 'jarang',\n",
+       " 9: 'sekali',\n",
+       " 10: 'untuk',\n",
+       " 11: 'melihat',\n",
+       " 12: 'dua',\n",
+       " 13: 'figura',\n",
+       " 14: 'ini',\n",
+       " 15: '-',\n",
+       " 16: 'bekas',\n",
+       " 17: 'Perdana',\n",
+       " 18: 'Menteri',\n",
+       " 19: ',',\n",
+       " 20: 'Datuk',\n",
+       " 21: 'Seri',\n",
+       " 22: 'Najib',\n",
+       " 23: 'Razak',\n",
+       " 24: 'dan',\n",
+       " 25: 'Tun',\n",
+       " 26: 'Dr',\n",
+       " 27: 'Mahathir',\n",
+       " 28: 'Mohamad',\n",
+       " 29: 'mempunyai',\n",
+       " 30: \"'\",\n",
+       " 31: 'pandangan',\n",
+       " 32: 'yang',\n",
+       " 33: 'sama',\n",
+       " 34: \"'\",\n",
+       " 35: 'atau',\n",
+       " 36: 'sekapal',\n",
+       " 37: '.',\n",
+       " 38: 'Namun',\n",
+       " 39: ',',\n",
+       " 40: 'situasi',\n",
+       " 41: 'itu',\n",
+       " 42: 'berbeza',\n",
+       " 43: 'apabila',\n",
+       " 44: 'melibatkan',\n",
+       " 45: 'isu',\n",
+       " 46: 'ketidakpatuhan',\n",
+       " 47: 'terhadap',\n",
+       " 48: 'prosedur',\n",
+       " 49: 'operasi',\n",
+       " 50: 'standard',\n",
+       " 51: '(',\n",
+       " 52: 'SOP',\n",
+       " 53: ')',\n",
+       " 54: '.',\n",
+       " 55: 'Najib',\n",
+       " 56: ',',\n",
+       " 57: 'yang',\n",
+       " 58: 'juga',\n",
+       " 59: 'Ahli',\n",
+       " 60: 'Parlimen',\n",
+       " 61: 'Pekan',\n",
+       " 62: 'memuji',\n",
+       " 63: 'sikap',\n",
+       " 64: 'Ahli',\n",
+       " 65: 'Parlimen',\n",
+       " 66: 'Langkawi',\n",
+       " 67: 'itu',\n",
+       " 68: 'yang',\n",
+       " 69: 'mengaku',\n",
+       " 70: 'bersalah',\n",
+       " 71: 'selepas',\n",
+       " 72: 'melanggar',\n",
+       " 73: 'SOP',\n",
+       " 74: 'kerana',\n",
+       " 75: 'tidak',\n",
+       " 76: 'mengambil',\n",
+       " 77: 'suhu',\n",
+       " 78: 'badan',\n",
+       " 79: 'ketika',\n",
+       " 80: 'masuk',\n",
+       " 81: 'ke',\n",
+       " 82: 'sebuah',\n",
+       " 83: 'surau',\n",
+       " 84: 'di',\n",
+       " 85: 'Langkawi',\n",
+       " 86: 'pada',\n",
+       " 87: 'Sabtu',\n",
+       " 88: 'lalu',\n",
+       " 89: '.'}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels = {i:graph.get_by_address(i)['word'] for i in digraph.nodes()}\n",
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,5))\n",
+    "nx.draw_networkx(digraph,labels=labels)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vectorize\n",
+    "\n",
+    "Let say you want to visualize word level in lower dimension, you can use `model.vectorize`,\n",
+    "\n",
+    "```python\n",
+    "def vectorize(self, string: str):\n",
+    "    \"\"\"\n",
+    "    vectorize a string.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    string: List[str]\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    result: np.array\n",
+    "    \"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = quantized_model.vectorize(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = [i[0] for i in r]\n",
+    "y = [i[1] for i in r]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89, 2)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "tsne = TSNE().fit_transform(y)\n",
+    "tsne.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (7, 7))\n",
+    "plt.scatter(tsne[:, 0], tsne[:, 1])\n",
+    "labels = x\n",
+    "for label, x, y in zip(\n",
+    "    labels, tsne[:, 0], tsne[:, 1]\n",
+    "):\n",
+    "    label = (\n",
+    "        '%s, %.3f' % (label[0], label[1])\n",
+    "        if isinstance(label, list)\n",
+    "        else label\n",
+    "    )\n",
+    "    plt.annotate(\n",
+    "        label,\n",
+    "        xy = (x, y),\n",
+    "        xytext = (0, 0),\n",
+    "        textcoords = 'offset points',\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/malaya/dependency.py b/malaya/dependency.py
index c4497a92..3c4a362e 100644
--- a/malaya/dependency.py
+++ b/malaya/dependency.py
@@ -50,7 +50,7 @@
     'csubj:pass': 32,
 }
 
-_transformer_availability = {
+_transformer_availability_v1 = {
     'bert': {
         'Size (MB)': 426,
         'Quantized Size (MB)': 112.0,
@@ -66,18 +66,18 @@
         'Root Accuracy': 0.886,
     },
     'albert': {
-        'Size (MB)': 60.8,
-        'Quantized Size (MB)': 15.3,
-        'Arc Accuracy': 0.821895,
-        'Types Accuracy': 0.79752,
-        'Root Accuracy': 1.0,
+        'Size (MB)': 50,
+        'Quantized Size (MB)': 13.2,
+        'Arc Accuracy': 0.811,
+        'Types Accuracy': 0.793,
+        'Root Accuracy': 0.879,
     },
     'tiny-albert': {
-        'Size (MB)': 33.4,
-        'Quantized Size (MB)': 8.51,
-        'Arc Accuracy': 0.7865,
-        'Types Accuracy': 0.7587,
-        'Root Accuracy': 1.0,
+        'Size (MB)': 24.8,
+        'Quantized Size (MB)': 6.6,
+        'Arc Accuracy': 0.708,
+        'Types Accuracy': 0.673,
+        'Root Accuracy': 0.817,
     },
     'xlnet': {
         'Size (MB)': 450.2,
@@ -95,6 +95,60 @@
     },
 }
 
+_transformer_availability_v2 = {
+    'bert': {
+        'Size (MB)': 455,
+        'Quantized Size (MB)': 114.0,
+        'Arc Accuracy': 0.82045,
+        'Types Accuracy': 0.79970,
+        'Root Accuracy': 0.98936,
+    },
+    'tiny-bert': {
+        'Size (MB)': 69.7,
+        'Quantized Size (MB)': 17.5,
+        'Arc Accuracy': 0.795252,
+        'Types Accuracy': 0.72470,
+        'Root Accuracy': 0.98939,
+    },
+    'albert': {
+        'Size (MB)': 60.8,
+        'Quantized Size (MB)': 15.3,
+        'Arc Accuracy': 0.821895,
+        'Types Accuracy': 0.79752,
+        'Root Accuracy': 1.0,
+    },
+    'tiny-albert': {
+        'Size (MB)': 33.4,
+        'Quantized Size (MB)': 8.51,
+        'Arc Accuracy': 0.7865,
+        'Types Accuracy': 0.7587,
+        'Root Accuracy': 1.0,
+    },
+    'xlnet': {
+        'Size (MB)': 480.2,
+        'Quantized Size (MB)': 121.0,
+        'Arc Accuracy': 0.84811,
+        'Types Accuracy': 0.82741,
+        'Root Accuracy': 0.92101,
+    },
+    'alxlnet': {
+        'Size (MB)': 61.2,
+        'Quantized Size (MB)': 16.4,
+        'Arc Accuracy': 0.84929,
+        'Types Accuracy': 0.8281,
+        'Root Accuracy': 0.92099,
+    },
+}
+
+_transformer_availability = {'v1': _transformer_availability_v1, 'v2': _transformer_availability_v2}
+
+
+def _validate_version(version):
+    version = version.lower()
+    if version not in _transformer_availability:
+        raise ValueError('version not supported, only supported `v1` or `v2`.')
+    return version
+
 
 def describe():
     """
@@ -151,25 +205,40 @@ def dependency_graph(tagging, indexing):
     return DependencyGraph('\n'.join(result), top_relation_label='root')
 
 
-def available_transformer():
+def available_transformer(version: str = 'v2'):
     """
     List available transformer dependency parsing models.
+
+    Parameters
+    ----------
+    version : str, optional (default='v2')
+        Version supported. Allowed values:
+
+        * ``'v1'`` - version 1, maintain for knowledge graph.
+        * ``'v2'`` - Trained on bigger dataset, better version.
+
     """
     from malaya.function import describe_availability
 
     return describe_availability(
-        _transformer_availability, text='tested on 20% test set.'
+        _transformer_availability[_validate_version(version)], text='tested on 20% test set.'
     )
 
 
 @check_type
-def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):
+def transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs):
     """
     Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.
 
     Parameters
     ----------
-    model : str, optional (default='bert')
+    version : str, optional (default='v2')
+        Version supported. Allowed values:
+
+        * ``'v1'`` - version 1, maintain for knowledge graph.
+        * ``'v2'`` - Trained on bigger dataset, better version.
+
+    model : str, optional (default='xlnet')
         Model architecture supported. Allowed values:
 
         * ``'bert'`` - Google BERT BASE parameters.
@@ -192,15 +261,22 @@ def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):
         * if `xlnet` in model, will return `malaya.model.xlnet.DependencyXLNET`.
     """
 
+    version = _validate_version(version)
     model = model.lower()
-    if model not in _transformer_availability:
+    if model not in _transformer_availability[version]:
         raise ValueError(
-            'model not supported, please check supported models from `malaya.dependency.available_transformer()`.'
+            "model not supported, please check supported models from `malaya.dependency.available_transformer(version='{version}')`."
         )
 
+    module = 'dependency'
+    minus = 1
+    if version != 'v1':
+        module = f'{module}-{version}'
+        minus = 2
+
     path = check_file(
         file=model,
-        module='dependency-v2',
+        module=module,
         keys={
             'model': 'model.pb',
             'vocab': MODEL_VOCAB[model],
@@ -240,4 +316,5 @@ def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):
         sess=generate_session(graph=g, **kwargs),
         tokenizer=tokenizer,
         settings=label,
+        minus=minus
     )
diff --git a/malaya/knowledge_graph.py b/malaya/knowledge_graph.py
index 6c76e019..c1f78ef9 100644
--- a/malaya/knowledge_graph.py
+++ b/malaya/knowledge_graph.py
@@ -74,7 +74,7 @@ def parse_from_dependency(tagging: List[Tuple[str, str]],
                           objects: List[List[str]] = [['obj', 'compound', 'flat', 'nmod', 'obl']],
                           get_networkx: bool = True):
     """
-    Generate knowledge graphs from dependency parsing.
+    Generate knowledge graphs from dependency parsing, we suggest use dependency parsing v1.
 
     Parameters
     ----------
@@ -147,14 +147,6 @@ def parse_from_dependency(tagging: List[Tuple[str, str]],
                         obj = obj[1:]
                     results.append({'subject': subject, 'relation': relation, 'object': obj})
 
-        if d_object.nodes[i]['rel'] == 'appos':
-            subjects_, relations_ = [], []
-            for s in subjects:
-                s_ = d_object.traverse_ancestor(i, s, initial_label=[d_object.nodes[i]['rel']])
-                s_ = _combined(s_)
-                s_ = [c[1:] for c in s_]
-                subjects_.extend(s_)
-
     post_results = []
     for r in results:
         r = _postprocess(r)
diff --git a/malaya/model/bert.py b/malaya/model/bert.py
index 1f69b2b7..5df928de 100644
--- a/malaya/model/bert.py
+++ b/malaya/model/bert.py
@@ -923,7 +923,7 @@ def predict(self, string: str):
 
 
 class DependencyBERT(Base):
-    def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings):
+    def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings, minus):
         Base.__init__(
             self,
             input_nodes=input_nodes,
@@ -935,6 +935,7 @@ def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings):
 
         self._tag2idx = settings
         self._idx2tag = {int(v): k for k, v in self._tag2idx.items()}
+        self._minus = minus
 
     @check_type
     def vectorize(self, string: str):
@@ -989,7 +990,7 @@ def predict(self, string: str):
         )
         tagging, depend = r['logits'], r['heads_seq']
         tagging = [self._idx2tag[i] for i in tagging[0]]
-        depend = depend[0] - 2
+        depend = depend[0] - self._minus
 
         for i in range(len(depend)):
             if depend[i] == 0 and tagging[i] != 'root':
diff --git a/malaya/model/xlnet.py b/malaya/model/xlnet.py
index 841be6e3..b1cccb67 100644
--- a/malaya/model/xlnet.py
+++ b/malaya/model/xlnet.py
@@ -928,7 +928,7 @@ def predict(self, string: str):
 
 
 class DependencyXLNET(Base):
-    def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings):
+    def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings, minus):
         Base.__init__(
             self,
             input_nodes=input_nodes,
@@ -940,6 +940,7 @@ def __init__(self, input_nodes, output_nodes, sess, tokenizer, settings):
 
         self._tag2idx = settings
         self._idx2tag = {int(v): k for k, v in self._tag2idx.items()}
+        self._minus = minus
 
     @check_type
     def vectorize(self, string: str):
@@ -997,7 +998,7 @@ def predict(self, string: str):
         )
         tagging, depend = r['logits'], r['heads_seq']
         tagging = [self._idx2tag[i] for i in tagging[0]]
-        depend = depend[0] - 2
+        depend = depend[0] - self._minus
 
         for i in range(len(depend)):
             if depend[i] == 0 and tagging[i] != 'root':
diff --git a/malaya/train/__init__.py b/malaya/train/__init__.py
deleted file mode 100644
index 437954b6..00000000
--- a/malaya/train/__init__.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import tensorflow as tf
-from tensorflow.python.distribute.cross_device_ops import (
-    AllReduceCrossDeviceOps,
-)
-from tensorflow.python.estimator.run_config import RunConfig
-from herpetologist import check_type
-from typing import List, Dict
-import numpy as np
-import collections
-import re
-
-
-@check_type
-def run_training(
-    train_fn,
-    model_fn,
-    model_dir: str,
-    num_gpus: int = 1,
-    gpu_mem_fraction: float = 0.95,
-    log_step: int = 100,
-    summary_step: int = 100,
-    save_checkpoint_step: int = 1000,
-    max_steps: int = 10000,
-    eval_step: int = 10,
-    eval_throttle: int = 120,
-    train_hooks=None,
-    eval_fn=None,
-):
-    tf.logging.set_verbosity(tf.logging.INFO)
-
-    if num_gpus > 1 and not use_tpu:
-        dist_strategy = tf.contrib.distribute.MirroredStrategy(
-            num_gpus=num_gpus,
-            auto_shard_dataset=True,
-            cross_device_ops=AllReduceCrossDeviceOps(
-                'nccl', num_packs=num_gpus
-            ),
-        )
-    else:
-        dist_strategy = None
-
-    gpu_options = tf.GPUOptions(
-        per_process_gpu_memory_fraction=gpu_mem_fraction
-    )
-    config = tf.ConfigProto(
-        allow_soft_placement=True, gpu_options=gpu_options
-    )
-    run_config = RunConfig(
-        train_distribute=dist_strategy,
-        eval_distribute=dist_strategy,
-        log_step_count_steps=log_step,
-        model_dir=model_dir,
-        save_checkpoints_steps=save_checkpoint_step,
-        save_summary_steps=summary_step,
-        session_config=config,
-    )
-
-    estimator = tf.estimator.Estimator(
-        model_fn=model_fn, params={}, config=run_config
-    )
-
-    if eval_fn:
-        train_spec = tf.estimator.TrainSpec(
-            input_fn=train_fn, max_steps=max_steps, hooks=train_hooks
-        )
-
-        eval_spec = tf.estimator.EvalSpec(
-            input_fn=eval_fn, steps=eval_step, throttle_secs=eval_throttle
-        )
-        tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-
-    else:
-        estimator.train(
-            input_fn=train_fn, max_steps=max_steps, hooks=train_hooks
-        )
-
-
-@check_type
-def prepare_dataset(
-    generator,
-    data_dir: str,
-    shards: List[Dict],
-    prefix: str = 'dataset',
-    shuffle: bool = True,
-    already_shuffled: bool = False,
-):
-    prepare_data.check_shard(shards)
-    filepath_fns = {
-        'train': prepare_data.training_filepaths,
-        'dev': prepare_data.dev_filepaths,
-        'test': prepare_data.test_filepaths,
-    }
-
-    split_paths = [
-        (
-            split['split'],
-            filepath_fns[split['split']](
-                prefix, data_dir, split['shards'], shuffled=already_shuffled
-            ),
-        )
-        for split in shards
-    ]
-    all_paths = []
-    for _, paths in split_paths:
-        all_paths.extend(paths)
-
-    prepare_data.generate_files(generator, all_paths)
-
-    if shuffle:
-        prepare_data.shuffle_dataset(all_paths)
-
-
-def get_assignment_map_from_checkpoint(tvars, init_checkpoint, logging=True):
-    """Compute the union of the current variables and checkpoint variables."""
-    assignment_map = {}
-    initialized_variable_names = {}
-
-    name_to_variable = collections.OrderedDict()
-    for var in tvars:
-        name = var.name
-        m = re.match('^(.*):\\d+$', name)
-        if m is not None:
-            name = m.group(1)
-        name_to_variable[name] = var
-
-    init_vars = tf.train.list_variables(init_checkpoint)
-
-    assignment_map = collections.OrderedDict()
-    for x in init_vars:
-        (name, var) = (x[0], x[1])
-        if name not in name_to_variable:
-            continue
-
-        assignment_map[name] = name
-        assignment_map[name] = name_to_variable[name]
-        initialized_variable_names[name] = 1
-        initialized_variable_names[name + ':0'] = 1
-
-    if logging:
-        tf.logging.info('**** Trainable Variables ****')
-        for var in tvars:
-            init_string = ''
-            if var.name in initialized_variable_names:
-                init_string = ', *INIT_FROM_CKPT*'
-            tf.logging.info(
-                '  name = %s, shape = %s%s', var.name, var.shape, init_string
-            )
-
-    return (assignment_map, initialized_variable_names)
-
-
-def calculate_parameters(variables):
-    return np.sum(
-        [np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]
-    )
diff --git a/malaya/train/model/__init__.py b/malaya/train/model/__init__.py
deleted file mode 100644
index fb00b5ac..00000000
--- a/malaya/train/model/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from . import alxlnet
-from . import bigbird
-from . import pegasus
diff --git a/malaya/train/model/alxlnet/__init__.py b/malaya/train/model/alxlnet/__init__.py
deleted file mode 100644
index 792d6005..00000000
--- a/malaya/train/model/alxlnet/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
diff --git a/malaya/train/model/bigbird/__init__.py b/malaya/train/model/bigbird/__init__.py
deleted file mode 100644
index 792d6005..00000000
--- a/malaya/train/model/bigbird/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
diff --git a/malaya/train/model/bigbird/attention.py b/malaya/train/model/bigbird/attention.py
deleted file mode 100644
index 48e78be8..00000000
--- a/malaya/train/model/bigbird/attention.py
+++ /dev/null
@@ -1,1279 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""BigBird Attention Layers."""
-
-from absl import logging
-from . import utils
-import numpy as np
-import tensorflow as tf
-
-
-MAX_SEQ_LEN = 4096
-
-
-def get_single_block_row_attention(
-    block_id,
-    to_start_block_id,
-    to_end_block_id,
-    num_rand_blocks,
-    window_block_left=1,
-    window_block_right=1,
-    global_block_left=1,
-    global_block_right=1,
-):
-    """For a single row block get random row attention.
-
-  Args:
-    block_id: int. block id of row.
-    to_start_block_id: int. random attention coloum start id.
-    to_end_block_id: int. random attention coloum end id.
-    num_rand_blocks: int. number of random blocks to be selected.
-    window_block_left: int. number of blocks of window to left of a block.
-    window_block_right: int. number of blocks of window to right of a block.
-    global_block_left: int. Number of blocks globally used to the left.
-    global_block_right: int. Number of blocks globally used to the right.
-
-  Returns:
-    row containing the random attention vector of size num_rand_blocks.
-  """
-
-    # list of to_blocks from which to choose random attention
-    to_block_list = np.arange(
-        to_start_block_id, to_end_block_id, dtype=np.int32
-    )
-    # permute the blocks
-    perm_block = np.random.permutation(to_block_list)
-    # print(perm_block)
-
-    # illegal blocks for the current block id, using window
-    illegal_blocks = list(
-        range(block_id - window_block_left, block_id + window_block_right + 1)
-    )
-
-    # Add blocks at the start and at the end
-    illegal_blocks.extend(list(range(global_block_left)))
-    illegal_blocks.extend(
-        list(range(to_end_block_id - global_block_right, to_end_block_id))
-    )
-
-    # The second from_block cannot choose random attention on second last to_block
-    if block_id == 1:
-        illegal_blocks.append(to_end_block_id - 2)
-
-    # The second last from_block cannot choose random attention on second to_block
-    if block_id == to_end_block_id - 2:
-        illegal_blocks.append(1)
-
-    selected_random_blokcs = []
-
-    for i in range(to_end_block_id - to_start_block_id):
-        if perm_block[i] not in illegal_blocks:
-            selected_random_blokcs.append(perm_block[i])
-        if len(selected_random_blokcs) == num_rand_blocks:
-            break
-    return np.array(selected_random_blokcs, dtype=np.int32)
-
-
-def bigbird_block_rand_mask_with_head(
-    from_seq_length,
-    to_seq_length,
-    from_block_size,
-    to_block_size,
-    num_heads,
-    plan_from_length,
-    plan_num_rand_blocks,
-    window_block_left=1,
-    window_block_right=1,
-    global_block_top=1,
-    global_block_bottom=1,
-    global_block_left=1,
-    global_block_right=1,
-):
-    """Create adjacency list of random attention.
-
-  Args:
-    from_seq_length: int. length of from sequence.
-    to_seq_length: int. length of to sequence.
-    from_block_size: int. size of block in from sequence.
-    to_block_size: int. size of block in to sequence.
-    num_heads: int. total number of heads.
-    plan_from_length: list. plan from lenght where num_rand are choosen from.
-    plan_num_rand_blocks: list. number of rand blocks within the plan.
-    window_block_left: int. number of blocks of window to left of a block.
-    window_block_right: int. number of blocks of window to right of a block.
-    global_block_top: int. number of blocks at the top.
-    global_block_bottom: int. number of blocks at the bottom.
-    global_block_left: int. Number of blocks globally used to the left.
-    global_block_right: int. Number of blocks globally used to the right.
-
-  Returns:
-    adjacency list of size num_head where each element is of size
-    from_seq_length//from_block_size-2 by num_rand_blocks
-  """
-    assert (
-        from_seq_length // from_block_size == to_seq_length // to_block_size
-    ), 'Error the number of blocks needs to be same!'
-
-    assert (
-        from_seq_length in plan_from_length
-    ), 'Error from sequence length not in plan!'
-
-    # Total number of blocks in the mmask
-    num_blocks = from_seq_length // from_block_size
-    # Number of blocks per plan
-    plan_block_length = np.array(plan_from_length) // from_block_size
-    # till when to follow plan
-    max_plan_idx = plan_from_length.index(from_seq_length)
-    # Random Attention adjajency list
-    rand_attn = [
-        np.zeros(
-            (num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])),
-            dtype=np.int32,
-        )
-        for i in range(num_heads)
-    ]
-
-    # We will go iteratively over the plan blocks and pick random number of
-    # Attention blocks from the legally allowed blocks
-    for plan_idx in range(max_plan_idx + 1):
-        rnd_r_cnt = 0
-        if plan_idx > 0:
-            # set the row for all from_blocks starting from 0 to
-            # plan_block_length[plan_idx-1]
-            # column indx start fromm plan_block_length[plan_idx-1] and ends at
-            # plan_block_length[plan_idx]
-            if plan_num_rand_blocks[plan_idx] > 0:
-                rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
-                curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
-                for blk_rw_idx in range(
-                    global_block_top, plan_block_length[plan_idx - 1]
-                ):
-                    for h in range(num_heads):
-                        # print("head", h, "blk_rw_idx", blk_rw_idx)
-                        rand_attn[h][
-                            blk_rw_idx, rnd_r_cnt:curr_r_cnt
-                        ] = get_single_block_row_attention(
-                            block_id=blk_rw_idx,
-                            to_start_block_id=plan_block_length[plan_idx - 1],
-                            to_end_block_id=plan_block_length[plan_idx],
-                            num_rand_blocks=plan_num_rand_blocks[plan_idx],
-                            window_block_left=window_block_left,
-                            window_block_right=window_block_right,
-                            global_block_left=global_block_left,
-                            global_block_right=global_block_right,
-                        )
-
-            for pl_id in range(plan_idx):
-                if plan_num_rand_blocks[pl_id] == 0:
-                    continue
-                for blk_rw_idx in range(
-                    plan_block_length[plan_idx - 1], plan_block_length[plan_idx]
-                ):
-                    rnd_r_cnt = 0
-                    to_start_block_id = 0
-                    if pl_id > 0:
-                        rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
-                        to_start_block_id = plan_block_length[pl_id - 1]
-                    curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
-                    for h in range(num_heads):
-                        # print("head", h, "blk_rw_idx", blk_rw_idx)
-                        rand_attn[h][
-                            blk_rw_idx, rnd_r_cnt:curr_r_cnt
-                        ] = get_single_block_row_attention(
-                            block_id=blk_rw_idx,
-                            to_start_block_id=to_start_block_id,
-                            to_end_block_id=plan_block_length[pl_id],
-                            num_rand_blocks=plan_num_rand_blocks[pl_id],
-                            window_block_left=window_block_left,
-                            window_block_right=window_block_right,
-                            global_block_left=global_block_left,
-                            global_block_right=global_block_right,
-                        )
-
-        if plan_num_rand_blocks[plan_idx] == 0:
-            continue
-        # print("Start from here")
-        curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
-        from_start_block_id = global_block_top
-        to_start_block_id = 0
-        if plan_idx > 0:
-            rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
-            from_start_block_id = plan_block_length[plan_idx - 1]
-            to_start_block_id = plan_block_length[plan_idx - 1]
-
-        for blk_rw_idx in range(
-            from_start_block_id, plan_block_length[plan_idx]
-        ):
-            for h in range(num_heads):
-                # print("head", h, "blk_rw_idx", blk_rw_idx)
-                rand_attn[h][
-                    blk_rw_idx, rnd_r_cnt:curr_r_cnt
-                ] = get_single_block_row_attention(
-                    block_id=blk_rw_idx,
-                    to_start_block_id=to_start_block_id,
-                    to_end_block_id=plan_block_length[plan_idx],
-                    num_rand_blocks=plan_num_rand_blocks[plan_idx],
-                    window_block_left=window_block_left,
-                    window_block_right=window_block_right,
-                    global_block_left=global_block_left,
-                    global_block_right=global_block_right,
-                )
-
-    for nh in range(num_heads):
-        rand_attn[nh] = rand_attn[nh][
-            global_block_top: num_blocks - global_block_bottom, :
-        ]
-    return rand_attn
-
-
-def get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
-    """Gives the plan of where to put random attention.
-
-  Args:
-    from_seq_length: int. length of from sequence.
-    from_block_size: int. size of block in from sequence.
-    num_rand_blocks: int. Number of random chunks per row.
-
-  Returns:
-    plan_from_length: ending location of from block
-    plan_num_rand_blocks: number of random ending location for each block
-  """
-    # general plan
-    plan_from_length = []
-    plan_num_rand_blocks = []
-    if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
-        plan_from_length.append(
-            int((2 * num_rand_blocks + 5) * from_block_size)
-        )
-        plan_num_rand_blocks.append(num_rand_blocks)
-        plan_from_length.append(from_seq_length)
-        plan_num_rand_blocks.append(0)
-    elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
-        plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
-        plan_num_rand_blocks.append(num_rand_blocks // 2)
-        plan_from_length.append(from_seq_length)
-        plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
-    else:
-        plan_from_length.append(from_seq_length)
-        plan_num_rand_blocks.append(num_rand_blocks)
-
-    return plan_from_length, plan_num_rand_blocks
-
-
-def bigbird_block_rand_mask(
-    from_seq_length,
-    to_seq_length,
-    from_block_size,
-    to_block_size,
-    num_rand_blocks,
-    last_idx=-1,
-):
-    """Create adjacency list of random attention.
-
-  Args:
-    from_seq_length: int. length of from sequence.
-    to_seq_length: int. length of to sequence.
-    from_block_size: int. size of block in from sequence.
-    to_block_size: int. size of block in to sequence.
-    num_rand_blocks: int. Number of random chunks per row.
-    last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
-      if positive then num_rand_blocks blocks choosen only upto last_idx.
-
-  Returns:
-    adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
-  """
-    assert (
-        from_seq_length // from_block_size == to_seq_length // to_block_size
-    ), 'Error the number of blocks needs to be same!'
-
-    rand_attn = np.zeros(
-        (from_seq_length // from_block_size - 2, num_rand_blocks),
-        dtype=np.int32,
-    )
-    middle_seq = np.arange(
-        1, to_seq_length // to_block_size - 1, dtype=np.int32
-    )
-    last = to_seq_length // to_block_size - 1
-    if last_idx > (2 * to_block_size):
-        last = (last_idx // to_block_size) - 1
-
-    r = num_rand_blocks  # shorthand
-    for i in range(1, from_seq_length // from_block_size - 1):
-        start = i - 2
-        end = i
-        if i == 1:
-            rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
-        elif i == 2:
-            rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
-        elif i == from_seq_length // from_block_size - 3:
-            rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
-            # Missing -3: should have been sliced till last-3
-        elif i == from_seq_length // from_block_size - 2:
-            rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
-            # Missing -4: should have been sliced till last-4
-        else:
-            if start > last:
-                start = last
-                rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[
-                    :r
-                ]
-            elif (end + 1) == last:
-                rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[
-                    :r
-                ]
-            else:
-                rand_attn[i - 1, :] = np.random.permutation(
-                    np.concatenate(
-                        (middle_seq[:start], middle_seq[end + 1: last])
-                    )
-                )[:r]
-    return rand_attn
-
-
-def full_bigbird_mask(
-    from_seq_length,
-    to_seq_length,
-    from_block_size,
-    to_block_size,
-    num_rand_blocks,
-    rand_attn=None,
-    focus=1024,
-):
-    """Calculate BigBird attention pattern as a full dense matrix.
-
-  Args:
-    from_seq_length: int. length of from sequence.
-    to_seq_length: int. length of to sequence.
-    from_block_size: int. size of block in from sequence.
-    to_block_size: int. size of block in to sequence.
-    num_rand_blocks: int. Number of random chunks per row.
-    rand_attn: adjajency matrix for random attention.
-    focus: pick random mask within focus
-
-  Returns:
-    attention mask matrix of shape [from_seq_length, to_seq_length]
-  """
-    if rand_attn is None:
-        rand_attn = bigbird_block_rand_mask(
-            MAX_SEQ_LEN,
-            MAX_SEQ_LEN,
-            from_block_size,
-            to_block_size,
-            num_rand_blocks,
-            focus,
-        )
-
-    attn_mask = np.zeros((MAX_SEQ_LEN, MAX_SEQ_LEN), dtype=np.int32)
-    for i in range(1, (MAX_SEQ_LEN // from_block_size) - 1):
-        attn_mask[
-            (i) * from_block_size: (i + 1) * from_block_size,
-            (i - 1) * to_block_size: (i + 2) * to_block_size,
-        ] = 1
-        for j in rand_attn[i - 1, :]:
-            attn_mask[
-                i * from_block_size: (i + 1) * from_block_size,
-                j * to_block_size: (j + 1) * to_block_size,
-            ] = 1
-
-    attn_mask[:from_block_size, :] = 1
-    attn_mask[:, :to_block_size] = 1
-    attn_mask[:, -to_block_size:] = 1
-    attn_mask[-from_block_size:, :] = 1
-    clipped_attn_mask = attn_mask[:from_seq_length, :to_seq_length]
-    return np.array(clipped_attn_mask, dtype=bool)
-
-
-def create_rand_mask_from_inputs(
-    from_blocked_mask,
-    to_blocked_mask,
-    rand_attn,
-    num_attention_heads,
-    num_rand_blocks,
-    batch_size,
-    from_seq_length,
-    from_block_size,
-):
-    """Create 3D attention mask from a 2D tensor mask.
-
-  Args:
-    from_blocked_mask: 2D Tensor of shape [batch_size,
-      from_seq_length//from_block_size, from_block_size].
-    to_blocked_mask: int32 Tensor of shape [batch_size,
-      to_seq_length//to_block_size, to_block_size].
-    rand_attn: [batch_size, num_attention_heads,
-      from_seq_length//from_block_size-2, num_rand_blocks]
-    num_attention_heads: int. Number of attention heads.
-    num_rand_blocks: int. Number of random chunks per row.
-    batch_size: int. Batch size for computation.
-    from_seq_length: int. length of from sequence.
-    from_block_size: int. size of block in from sequence.
-
-  Returns:
-    float Tensor of shape [batch_size, num_attention_heads,
-                           from_seq_length//from_block_size-2,
-                           from_block_size, num_rand_blocks*to_block_size].
-  """
-    num_windows = from_seq_length // from_block_size - 2
-    rand_mask = tf.reshape(
-        tf.gather(to_blocked_mask, rand_attn, batch_dims=1),
-        [
-            batch_size,
-            num_attention_heads,
-            num_windows,
-            num_rand_blocks * from_block_size,
-        ],
-    )
-    rand_mask = tf.einsum(
-        'BLQ,BHLK->BHLQK', from_blocked_mask[:, 1:-1], rand_mask
-    )
-    return rand_mask
-
-
-def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
-    """Create 3D attention mask from a 2D tensor mask.
-
-  Args:
-    from_blocked_mask: 2D Tensor of shape [batch_size,
-      from_seq_length//from_block_size, from_block_size].
-    to_blocked_mask: int32 Tensor of shape [batch_size,
-      to_seq_length//to_block_size, to_block_size].
-
-  Returns:
-    float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4,
-                           from_block_size,  3*to_block_size].
-  """
-    exp_blocked_to_pad = tf.concat(
-        [
-            to_blocked_mask[:, 1:-3],
-            to_blocked_mask[:, 2:-2],
-            to_blocked_mask[:, 3:-1],
-        ],
-        2,
-    )
-    band_mask = tf.einsum(
-        'BLQ,BLK->BLQK',
-        tf.cast(from_blocked_mask[:, 2:-2], tf.float32),
-        tf.cast(exp_blocked_to_pad, tf.float32),
-    )
-    band_mask = tf.expand_dims(band_mask, 1)
-    return band_mask
-
-
-def create_attention_mask_from_input_mask(from_mask, to_mask):
-    """Create attention mask from a 2D tensor mask.
-
-  Args:
-    from_mask: int32 Tensor of shape [batch_size, from_seq_length].
-    to_mask: int32 Tensor of shape [batch_size, to_seq_length].
-
-  Returns:
-    int32 Tensor of shape [batch_size, 1, from_seq_length, to_seq_length].
-  """
-    mask = tf.einsum('BF,BT->BFT', from_mask, to_mask)
-
-    # expand to create a slot for heads.
-    mask = tf.expand_dims(mask, 1)
-
-    return mask
-
-
-def original_full_attention(
-    query_layer,
-    key_layer,
-    value_layer,
-    attention_mask,
-    size_per_head,
-    attention_probs_dropout_prob,
-):
-    """Full quadratic attention calculation.
-
-  Args:
-    query_layer: float Tensor of shape [batch_size, num_attention_heads,
-      from_seq_length, size_per_head]
-    key_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    value_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    attention_mask: (optional) int32 Tensor of shape [batch_size,
-      from_seq_length, to_seq_length]. The values should be 1 or 0. The
-      attention scores will effectively be set to -infinity for any positions in
-      the mask that are 0, and will be unchanged for positions that are 1.
-    size_per_head: (optional) int. Size of each attention head.
-    attention_probs_dropout_prob: (optional) float. Dropout probability of the
-      attention probabilities.
-
-  Returns:
-    float Tensor of shape [batch_size, from_seq_length, num_attention_heads,
-      size_per_head].
-  """
-
-    # Directly take n^2 dot product between "query" and "key".
-    attention_scores = tf.einsum('BNFH,BNTH->BNFT', query_layer, key_layer)
-    attention_scores = tf.multiply(
-        attention_scores, 1.0 / np.sqrt(float(size_per_head))
-    )
-
-    if attention_mask is not None:
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
-
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        attention_scores += adder
-
-    # Normalize the attention scores to probabilities.
-    # `attention_probs` = [B, N, F, T]
-    attention_probs = tf.nn.softmax(attention_scores)
-
-    # This is actually dropping out entire tokens to attend to, which might
-    # seem a bit unusual, but is taken from the original Transformer paper.
-    attention_probs = utils.dropout(
-        attention_probs, attention_probs_dropout_prob
-    )
-
-    # `context_layer` = [B, F, N, H]
-    context_layer = tf.einsum('BNFT,BNTH->BFNH', attention_probs, value_layer)
-    return context_layer
-
-
-def bigbird_simulated_attention(
-    query_layer,
-    key_layer,
-    value_layer,
-    attention_mask,
-    num_attention_heads,
-    num_rand_blocks,
-    size_per_head,
-    from_seq_length,
-    to_seq_length,
-    from_block_size,
-    to_block_size,
-    seed=None,
-):
-    """BigBird attention calculation using masks in quadratic time.
-
-  Args:
-    query_layer: float Tensor of shape [batch_size, num_attention_heads,
-      from_seq_length, size_per_head]
-    key_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    value_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    attention_mask: int32 Tensor of shape [batch_size,
-      from_seq_length, to_seq_length]. The values should be 1 or 0. The
-      attention scores will effectively be set to -infinity for any positions in
-      the mask that are 0, and will be unchanged for positions that are 1.
-    num_attention_heads: int. Number of attention heads.
-    num_rand_blocks: int. Number of random chunks per row.
-    size_per_head: int. Size of each attention head.
-    from_seq_length: int. length of from sequence.
-    to_seq_length: int. length of to sequence.
-    from_block_size: int. size of block in from sequence.
-    to_block_size: int. size of block in to sequence.
-    seed: (Optional) int. Reandom seed for generating random mask.
-
-  Returns:
-    float Tensor of shape [batch_size, from_seq_length, num_attention_heads,
-      size_per_head].
-  """
-
-    if seed:
-        np.random.seed(seed)
-
-    plan_from_length, plan_num_rand_blocks = get_rand_attn_plan(
-        from_seq_length, from_block_size, num_rand_blocks
-    )
-
-    rand_attn = bigbird_block_rand_mask_with_head(
-        from_seq_length=from_seq_length,
-        to_seq_length=to_seq_length,
-        from_block_size=from_block_size,
-        to_block_size=to_block_size,
-        num_heads=num_attention_heads,
-        plan_from_length=plan_from_length,
-        plan_num_rand_blocks=plan_num_rand_blocks,
-    )
-    temp_mask = [
-        full_bigbird_mask(  # pylint: disable=g-complex-comprehension
-            from_seq_length,
-            to_seq_length,
-            from_block_size,
-            to_block_size,
-            num_rand_blocks,
-            rand_attn=rand_attn[i],
-            focus=1024,
-        )
-        for i in range(num_attention_heads)
-    ]
-    temp_mask = np.stack(temp_mask, axis=0)
-    temp_mask = np.array(temp_mask, dtype=bool)
-
-    rand_block_mask = tf.constant(temp_mask, dtype=tf.bool)  # [N, F, T]
-    rand_block_mask = tf.cast(rand_block_mask, tf.int32)
-    rand_block_mask = tf.expand_dims(rand_block_mask, 0)  # [1, N, F, T]
-    if attention_mask is not None:
-        attention_mask = tf.minimum(attention_mask, rand_block_mask)
-    else:
-        attention_mask = rand_block_mask
-    return original_full_attention(
-        query_layer,
-        key_layer,
-        value_layer,
-        attention_mask,
-        size_per_head,
-        attention_probs_dropout_prob=0.0,
-    )
-
-
-def bigbird_block_sparse_attention(
-    query_layer,
-    key_layer,
-    value_layer,
-    band_mask,
-    from_mask,
-    to_mask,
-    from_blocked_mask,
-    to_blocked_mask,
-    num_attention_heads,
-    num_rand_blocks,
-    size_per_head,
-    batch_size,
-    from_seq_length,
-    to_seq_length,
-    from_block_size,
-    to_block_size,
-    seed=None,
-    plan_from_length=None,
-    plan_num_rand_blocks=None,
-):
-    """BigBird attention sparse calculation using blocks in linear time.
-
-  Assumes from_seq_length//from_block_size == to_seq_length//to_block_size.
-
-
-  Args:
-    query_layer: float Tensor of shape [batch_size, num_attention_heads,
-      from_seq_length, size_per_head]
-    key_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    value_layer: float Tensor of shape [batch_size, num_attention_heads,
-      to_seq_length, size_per_head]
-    band_mask: (optional) int32 Tensor of shape [batch_size, 1,
-      from_seq_length//from_block_size-4, from_block_size, 3*to_block_size].
-      The values should be 1 or 0. The attention scores will effectively be
-      set to -infinity for any positions in the mask that are 0, and will be
-      unchanged for positions that are 1.
-    from_mask: (optional) int32 Tensor of shape [batch_size, 1,
-      from_seq_length, 1]. The values should be 1 or 0. The
-      attention scores will effectively be set to -infinity for any positions in
-      the mask that are 0, and will be unchanged for positions that are 1.
-    to_mask: (optional) int32 Tensor of shape [batch_size, 1, 1,
-      to_seq_length]. The values should be 1 or 0. The
-      attention scores will effectively be set to -infinity for any positions in
-      the mask that are 0, and will be unchanged for positions that are 1.
-    from_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-      from_seq_length//from_block_size, from_block_size].
-      Same as from_mask, just reshaped.
-    to_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-      to_seq_length//to_block_size, to_block_size].
-      Same as to_mask, just reshaped.
-    num_attention_heads: int. Number of attention heads.
-    num_rand_blocks: int. Number of random chunks per row.
-    size_per_head: int. Size of each attention head.
-    batch_size: int. Batch size for computation.
-    from_seq_length: int. length of from sequence.
-    to_seq_length: int. length of to sequence.
-    from_block_size: int. size of block in from sequence.
-    to_block_size: int. size of block in to sequence.
-    seed: (Optional) int. Reandom seed for generating random mask.
-    plan_from_length: (Optional) list. Plan of where to put random attn. It
-      divides the block matrix into chuncks, where each chunck will have
-      some randomm attn.
-    plan_num_rand_blocks: (Optional) list. Number of random per block given by
-      plan_from_length.
-
-  Returns:
-    float Tensor of shape [batch_size, from_seq_length, num_attention_heads,
-      size_per_head].
-  """
-    assert from_seq_length // from_block_size == to_seq_length // to_block_size
-
-    # cast masks to float
-    from_mask = tf.cast(from_mask, tf.float32)
-    to_mask = tf.cast(to_mask, tf.float32)
-    band_mask = tf.cast(band_mask, tf.float32)
-    from_blocked_mask = tf.cast(from_blocked_mask, tf.float32)
-    to_blocked_mask = tf.cast(to_blocked_mask, tf.float32)
-
-    # generate random attention and corresponding masks
-    np.random.seed(seed)
-    if from_seq_length in [1024, 3072, 4096]:  # old plans used in paper
-        rand_attn = [
-            bigbird_block_rand_mask(  # pylint: disable=g-complex-comprehension
-                MAX_SEQ_LEN,
-                MAX_SEQ_LEN,
-                from_block_size,
-                to_block_size,
-                num_rand_blocks,
-                last_idx=1024,
-            )[: (from_seq_length // from_block_size - 2)]
-            for _ in range(num_attention_heads)
-        ]
-    else:
-        if plan_from_length is None:
-            plan_from_length, plan_num_rand_blocks = get_rand_attn_plan(
-                from_seq_length, from_block_size, num_rand_blocks
-            )
-
-        rand_attn = bigbird_block_rand_mask_with_head(
-            from_seq_length=from_seq_length,
-            to_seq_length=to_seq_length,
-            from_block_size=from_block_size,
-            to_block_size=to_block_size,
-            num_heads=num_attention_heads,
-            plan_from_length=plan_from_length,
-            plan_num_rand_blocks=plan_num_rand_blocks,
-        )
-    rand_attn = np.stack(rand_attn, axis=0)
-    rand_attn = tf.constant(rand_attn, dtype=tf.int32)
-    rand_attn = tf.expand_dims(rand_attn, 0)
-    rand_attn = tf.repeat(rand_attn, batch_size, 0)
-
-    rand_mask = create_rand_mask_from_inputs(
-        from_blocked_mask,
-        to_blocked_mask,
-        rand_attn,
-        num_attention_heads,
-        num_rand_blocks,
-        batch_size,
-        from_seq_length,
-        from_block_size,
-    )
-
-    # Define shorthands
-    h = num_attention_heads
-    r = num_rand_blocks
-    d = size_per_head
-    b = batch_size
-    m = from_seq_length
-    n = to_seq_length
-    wm = from_block_size
-    wn = to_block_size
-
-    blocked_query_matrix = tf.reshape(query_layer, (b, h, m // wm, wm, -1))
-    blocked_key_matrix = tf.reshape(key_layer, (b, h, n // wn, wn, -1))
-    blocked_value_matrix = tf.reshape(value_layer, (b, h, n // wn, wn, -1))
-    gathered_key = tf.reshape(
-        tf.gather(
-            blocked_key_matrix, rand_attn, batch_dims=2, name='gather_key'
-        ),
-        (b, h, m // wm - 2, r * wn, -1),
-    )  # [b, h, n//wn-2, r, wn, -1]
-    gathered_value = tf.reshape(
-        tf.gather(
-            blocked_value_matrix,
-            rand_attn,
-            batch_dims=2,
-            name='gather_value',
-        ),
-        (b, h, m // wm - 2, r * wn, -1),
-    )  # [b, h, n//wn-2, r, wn, -1]
-
-    first_product = tf.einsum(
-        'BHQD,BHKD->BHQK', blocked_query_matrix[:, :, 0], key_layer
-    )  # [b, h, wm, -1] x [b, h, n, -1] ==> [b, h, wm, n]
-    first_product = tf.multiply(first_product, 1.0 / np.sqrt(d))
-    first_product += (1.0 - to_mask) * -10000.0
-    first_attn_weights = tf.nn.softmax(first_product)  # [b, h, wm, n]
-    first_context_layer = tf.einsum(
-        'BHQK,BHKD->BHQD', first_attn_weights, value_layer
-    )  # [b, h, wm, n] x [b, h, n, -1] ==> [b, h, wm, -1]
-    first_context_layer = tf.expand_dims(first_context_layer, 2)
-
-    second_key_mat = tf.concat(
-        [
-            blocked_key_matrix[:, :, 0],
-            blocked_key_matrix[:, :, 1],
-            blocked_key_matrix[:, :, 2],
-            blocked_key_matrix[:, :, -1],
-            gathered_key[:, :, 0],
-        ],
-        2,
-    )  # [b, h, (4+r)*wn, -1]
-    second_value_mat = tf.concat(
-        [
-            blocked_value_matrix[:, :, 0],
-            blocked_value_matrix[:, :, 1],
-            blocked_value_matrix[:, :, 2],
-            blocked_value_matrix[:, :, -1],
-            gathered_value[:, :, 0],
-        ],
-        2,
-    )  # [b, h, (4+r)*wn, -1]
-    second_product = tf.einsum(
-        'BHQD,BHKD->BHQK', blocked_query_matrix[:, :, 1], second_key_mat
-    )  # [b, h, wm, -1] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, (4+r)*wn]
-    second_seq_pad = tf.concat(
-        [
-            to_mask[:, :, :, : 3 * wn],
-            to_mask[:, :, :, -wn:],
-            tf.ones([b, 1, 1, r * wn], dtype=tf.float32),
-        ],
-        3,
-    )
-    second_rand_pad = tf.concat(
-        [tf.ones([b, h, wm, 4 * wn], dtype=tf.float32), rand_mask[:, :, 0]], 3
-    )
-    second_product = tf.multiply(second_product, 1.0 / np.sqrt(d))
-    second_product += (
-        1.0 - tf.minimum(second_seq_pad, second_rand_pad)
-    ) * -10000.0
-    second_attn_weights = tf.nn.softmax(second_product)  # [b , h, wm, (4+r)*wn]
-    second_context_layer = tf.einsum(
-        'BHQK,BHKD->BHQD', second_attn_weights, second_value_mat
-    )  # [b, h, wm, (4+r)*wn] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, -1]
-    second_context_layer = tf.expand_dims(second_context_layer, 2)
-
-    exp_blocked_key_matrix = tf.concat(
-        [
-            blocked_key_matrix[:, :, 1:-3],
-            blocked_key_matrix[:, :, 2:-2],
-            blocked_key_matrix[:, :, 3:-1],
-        ],
-        3,
-    )  # [b, h, m//wm-4, 3*wn, -1]
-    exp_blocked_value_matrix = tf.concat(
-        [
-            blocked_value_matrix[:, :, 1:-3],
-            blocked_value_matrix[:, :, 2:-2],
-            blocked_value_matrix[:, :, 3:-1],
-        ],
-        3,
-    )  # [b, h, m//wm-4, 3*wn, -1]
-    middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
-    inner_band_product = tf.einsum(
-        'BHLQD,BHLKD->BHLQK', middle_query_matrix, exp_blocked_key_matrix
-    )  # [b, h, m//wm-4, wm, -1] x [b, h, m//wm-4, 3*wn, -1]
-    #     ==> [b, h, m//wm-4, wm, 3*wn]
-    inner_band_product = tf.multiply(inner_band_product, 1.0 / np.sqrt(d))
-    rand_band_product = tf.einsum(
-        'BHLQD,BHLKD->BHLQK', middle_query_matrix, gathered_key[:, :, 1:-1]
-    )  # [b, h, m//wm-4, wm, -1] x [b, h, m//wm-4, r*wn, -1]
-    #     ==> [b, h, m//wm-4, wm, r*wn]
-    rand_band_product = tf.multiply(rand_band_product, 1.0 / np.sqrt(d))
-    first_band_product = tf.einsum(
-        'BHLQD,BHKD->BHLQK', middle_query_matrix, blocked_key_matrix[:, :, 0]
-    )  # [b, h, m//wm-4, wm, -1] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, wn]
-    first_band_product = tf.multiply(first_band_product, 1.0 / np.sqrt(d))
-    last_band_product = tf.einsum(
-        'BHLQD,BHKD->BHLQK', middle_query_matrix, blocked_key_matrix[:, :, -1]
-    )  # [b, h, m//wm-4, wm, -1] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, wn]
-    last_band_product = tf.multiply(last_band_product, 1.0 / np.sqrt(d))
-    inner_band_product += (1.0 - band_mask) * -10000.0
-    first_band_product += (
-        1.0 - tf.expand_dims(to_mask[:, :, :, :wn], 3)
-    ) * -10000.0
-    last_band_product += (
-        1.0 - tf.expand_dims(to_mask[:, :, :, -wn:], 3)
-    ) * -10000.0
-    rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * -10000.0
-    band_product = tf.concat(
-        [
-            first_band_product,
-            inner_band_product,
-            rand_band_product,
-            last_band_product,
-        ],
-        -1,
-    )  # [b, h, m//wm-4, wm, (5+r)*wn]
-    attn_weights = tf.nn.softmax(band_product)  # [b, h, m//wm-4, wm, (5+r)*wn]
-    context_layer = tf.einsum(
-        'BHLQK,BHLKD->BHLQD',
-        attn_weights[:, :, :, :, wn: 4 * wn],
-        exp_blocked_value_matrix,
-    )  # [b, h, m//wm-4, wm, 3*wn] x [b, h, m//wm-4, 3*wn, -1]
-    #     ==> [b, h, m//wm-4, wm, -1]
-    context_layer += tf.einsum(
-        'BHLQK,BHLKD->BHLQD',
-        attn_weights[:, :, :, :, 4 * wn: -wn],
-        gathered_value[:, :, 1:-1],
-    )  # [b, h, m//wm-4, wm, r*wn] x [b, h, m//wm-4, r*wn, -1]
-    #     ==> [b, h, m//wm-4, wm, -1]
-    context_layer += tf.einsum(
-        'BHLQK,BHKD->BHLQD',
-        attn_weights[:, :, :, :, :wn],
-        blocked_value_matrix[:, :, 0],
-    )  # [b, h, m//wm-4, wm, wn] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, -1]
-    context_layer += tf.einsum(
-        'BHLQK,BHKD->BHLQD',
-        attn_weights[:, :, :, :, -wn:],
-        blocked_value_matrix[:, :, -1],
-    )  # [b, h, m//wm-4, wm, wn] x [b, h, wn, -1] ==> [b, h, m//wm-4, wm, -1]
-
-    second_last_key_mat = tf.concat(
-        [
-            blocked_key_matrix[:, :, 0],
-            blocked_key_matrix[:, :, -3],
-            blocked_key_matrix[:, :, -2],
-            blocked_key_matrix[:, :, -1],
-            gathered_key[:, :, -1],
-        ],
-        2,
-    )  # [b, h, (4+r)*wn, -1]
-    second_last_value_mat = tf.concat(
-        [
-            blocked_value_matrix[:, :, 0],
-            blocked_value_matrix[:, :, -3],
-            blocked_value_matrix[:, :, -2],
-            blocked_value_matrix[:, :, -1],
-            gathered_value[:, :, -1],
-        ],
-        2,
-    )  # [b, h, (4+r)*wn, -1]
-    second_last_product = tf.einsum(
-        'BHQD,BHKD->BHQK', blocked_query_matrix[:, :, -2], second_last_key_mat
-    )  # [b, h, wm, -1] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, (4+r)*wn]
-    second_last_seq_pad = tf.concat(
-        [
-            to_mask[:, :, :, :wn],
-            to_mask[:, :, :, -3 * wn:],
-            tf.ones([b, 1, 1, r * wn], dtype=tf.float32),
-        ],
-        3,
-    )
-    second_last_rand_pad = tf.concat(
-        [tf.ones([b, h, wm, 4 * wn], dtype=tf.float32), rand_mask[:, :, -1]],
-        3,
-    )
-    second_last_product = tf.multiply(second_last_product, 1.0 / np.sqrt(d))
-    second_last_product += (
-        1.0 - tf.minimum(second_last_seq_pad, second_last_rand_pad)
-    ) * -10000.0
-    second_last_attn_weights = tf.nn.softmax(
-        second_last_product
-    )  # [b, h, wm, (4+r)*wn]
-    second_last_context_layer = tf.einsum(
-        'BHQK,BHKD->BHQD', second_last_attn_weights, second_last_value_mat
-    )  # [b, h, wm, (4+r)*wn] x [b, h, (4+r)*wn, -1] ==> [b, h, wm, -1]
-    second_last_context_layer = tf.expand_dims(second_last_context_layer, 2)
-
-    last_product = tf.einsum(
-        'BHQD,BHKD->BHQK', blocked_query_matrix[:, :, -1], key_layer
-    )  # [b, h, wm, -1] x [b, h, n, -1] ==> [b, h, wm, n]
-    last_product = tf.multiply(last_product, 1.0 / np.sqrt(d))
-    last_product += (1.0 - to_mask) * -10000.0
-    last_attn_weights = tf.nn.softmax(last_product)  # [b, h, wm, n]
-    last_context_layer = tf.einsum(
-        'BHQK,BHKD->BHQD', last_attn_weights, value_layer
-    )  # [b, h, wm, n] x [b, h, n, -1] ==> [b, h, wm, -1]
-    last_context_layer = tf.expand_dims(last_context_layer, 2)
-
-    context_layer = tf.concat(
-        [
-            first_context_layer,
-            second_context_layer,
-            context_layer,
-            second_last_context_layer,
-            last_context_layer,
-        ],
-        2,
-    )
-    context_layer = tf.reshape(context_layer, (b, h, m, -1)) * from_mask
-    context_layer = tf.transpose(context_layer, (0, 2, 1, 3))
-    return context_layer
-
-
-class MultiHeadedAttentionLayer(tf.compat.v1.layers.Layer):
-    """A multi-headed attention layer.
-
-  It implements following types of multi-headed attention:
-  - original_full attention from "Attention is all you Need".
-  - simulated_sparse attention from BigBird with full quadratic implemention.
-  - block_sparse attention from BigBird with memory efficient linear impl.
-  """
-
-    def __init__(
-        self,
-        attention_type,
-        num_attention_heads=1,
-        num_rand_blocks=3,
-        size_per_head=512,
-        initializer_range=0.02,
-        from_block_size=64,
-        to_block_size=64,
-        attention_probs_dropout_prob=0.0,
-        use_bias=True,
-        seed=None,
-        query_act=None,
-        key_act=None,
-        value_act=None,
-        name=None,
-        **kwargs
-    ):
-        """Constructor for a multi-headed attention layer.
-
-    Args:
-      attention_type: Type of attention, needs to be one of ['original_full',
-        'simulated_sparse', 'block_sparse'].
-      num_attention_heads: (optional) int. Number of attention heads.
-      num_rand_blocks: (optional) int. Number of random chunks per row.
-      size_per_head: (optional) int. Size of each attention head.
-      initializer_range: (optional) float. Range of the weight initializer.
-      from_block_size: (optional) int. size of block in from sequence.
-      to_block_size: (optional) int. size of block in to sequence.
-      attention_probs_dropout_prob: (optional) float. Dropout probability of the
-        attention probabilities.
-      use_bias: Whether the layer uses a bias vector.
-      seed: (Optional) int. Reandom seed for generating random mask.
-      query_act: (optional) Activation function for the query transform.
-      key_act: (optional) Activation function for the key transform.
-      value_act: (optional) Activation function for the value transform.
-      name: The name scope of this layer.
-      **kwargs: others
-    """
-        super(MultiHeadedAttentionLayer, self).__init__(name=name, **kwargs)
-        self.query_layer = utils.Dense3dLayer(
-            num_attention_heads,
-            size_per_head,
-            utils.create_initializer(initializer_range),
-            query_act,
-            'query',
-            head_first=True,
-            use_bias=use_bias,
-        )
-
-        self.key_layer = utils.Dense3dLayer(
-            num_attention_heads,
-            size_per_head,
-            utils.create_initializer(initializer_range),
-            key_act,
-            'key',
-            head_first=True,
-            use_bias=use_bias,
-        )
-
-        self.value_layer = utils.Dense3dLayer(
-            num_attention_heads,
-            size_per_head,
-            utils.create_initializer(initializer_range),
-            value_act,
-            'value',
-            head_first=True,
-            use_bias=use_bias,
-        )
-
-        def attn_impl(
-            query,
-            key,
-            value,
-            attention_mask,
-            band_mask,
-            from_mask,
-            to_mask,
-            from_blocked_mask,
-            to_blocked_mask,
-            batch_size,
-            from_seq_length,
-            to_seq_length,
-            training,
-        ):
-            if attention_type == 'original_full':
-                logging.info('**** Using original full attention ****')
-                attn_fn = original_full_attention(
-                    query,
-                    key,
-                    value,
-                    attention_mask,
-                    size_per_head,
-                    attention_probs_dropout_prob if training else 0.0,
-                )
-            elif attention_type == 'simulated_sparse':
-                logging.info('**** Using simulated sparse attention ****')
-                attn_fn = bigbird_simulated_attention(
-                    query,
-                    key,
-                    value,
-                    attention_mask,
-                    num_attention_heads,
-                    num_rand_blocks,
-                    size_per_head,
-                    from_seq_length,
-                    to_seq_length,
-                    from_block_size,
-                    to_block_size,
-                    seed,
-                )
-            elif attention_type == 'block_sparse':
-                logging.info('**** Using block sparse attention ****')
-                attn_fn = bigbird_block_sparse_attention(
-                    query,
-                    key,
-                    value,
-                    band_mask,
-                    from_mask,
-                    to_mask,
-                    from_blocked_mask,
-                    to_blocked_mask,
-                    num_attention_heads,
-                    num_rand_blocks,
-                    size_per_head,
-                    batch_size,
-                    from_seq_length,
-                    to_seq_length,
-                    from_block_size,
-                    to_block_size,
-                    seed,
-                )
-            else:
-                raise NotImplementedError(
-                    'Attention type {} is not implemented'.format(
-                        attention_type
-                    )
-                )
-            return attn_fn
-
-        self.attn_impl = attn_impl
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.query_layer.trainable_weights
-            + self.key_layer.trainable_weights
-            + self.value_layer.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        from_tensor,
-        to_tensor,
-        attention_mask=None,
-        band_mask=None,
-        from_mask=None,
-        to_mask=None,
-        from_blocked_mask=None,
-        to_blocked_mask=None,
-        cache=None,
-        decode_i=None,
-        training=None,
-    ):
-        """Implements a multi-headed attention layer from from_tensor to to_tensor.
-
-    Args:
-      from_tensor: float Tensor of shape [batch_size, from_seq_length,
-        from_width]
-      to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
-      attention_mask: (optional) int32 Tensor of shape [batch_size,
-        from_seq_length, to_seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      band_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        from_seq_length//from_block_size-4, from_block_size, 3*to_block_size].
-        The values should be 1 or 0. The attention scores will effectively be
-        set to -infinity for any positions in the mask that are 0, and will be
-        unchanged for positions that are 1.
-      from_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        from_seq_length, 1]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      to_mask: (optional) int32 Tensor of shape [batch_size, 1, 1,
-        to_seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      from_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-        from_seq_length//from_block_size, from_block_size].
-        Same as from_mask, just reshaped.
-      to_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-        to_seq_length//to_block_size, to_block_size].
-        Same as to_mask, just reshaped.
-      cache: (Used during prediction) A dictionary with tensors containing
-        results of previous attentions. The dictionary must have the items:
-            {"k": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head],
-             "v": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head]}
-      decode_i: (Used during prediction) current location of decoding
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      float Tensor of shape [batch_size, from_seq_length, num_attention_heads,
-        size_per_head].
-
-    Raises:
-      ValueError: Any of the arguments or tensor shapes are invalid.
-      NotImplementedError: For unknown attention type.
-    """
-        from_shape = utils.get_shape_list(from_tensor, expected_rank=3)
-        to_shape = utils.get_shape_list(to_tensor, expected_rank=3)
-
-        if len(from_shape) != len(to_shape):
-            raise ValueError(
-                'The rank of `from_tensor` must match the rank of `to_tensor`.'
-            )
-
-        if len(from_shape) == 3:
-            batch_size = from_shape[0]
-            from_seq_length = from_shape[1]
-            to_seq_length = to_shape[1]
-        else:
-            raise ValueError('Need rank 3 tensors to attention_layer.')
-
-        # Scalar dimensions referenced here:
-        #   b = batch size (number of sequences)
-        #   m = `from_tensor` sequence length
-        #   n = `to_tensor` sequence length
-        #   h = `num_attention_heads`
-        #   d = `size_per_head`
-
-        # `query` = [b, h, m, d]
-        query = self.query_layer(from_tensor)
-
-        # `key` = [b, h, n, d]
-        key = self.key_layer(to_tensor)
-
-        # `value_layer` = [b, h, n, d]
-        value = self.value_layer(to_tensor)
-
-        if cache is not None and decode_i is not None:
-            max_len = utils.get_shape_list(cache['k'])[2]
-            indices_select = tf.reshape(
-                tf.one_hot(decode_i, max_len, dtype=to_tensor.dtype),
-                [1, 1, max_len, 1],
-            )
-            key = cache['k'] + key * indices_select
-            value = cache['v'] + value * indices_select
-            cache['k'] = key
-            cache['v'] = value
-
-        contextual_output = self.attn_impl(
-            query,
-            key,
-            value,
-            attention_mask,
-            band_mask,
-            from_mask,
-            to_mask,
-            from_blocked_mask,
-            to_blocked_mask,
-            batch_size,
-            from_seq_length,
-            to_seq_length,
-            training,
-        )
-
-        return contextual_output
diff --git a/malaya/train/model/bigbird/beam_search.py b/malaya/train/model/bigbird/beam_search.py
deleted file mode 100644
index ee1d50a6..00000000
--- a/malaya/train/model/bigbird/beam_search.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Beam search branched from Pegasus.
-
-Original source:
-https://github.com/google-research/pegasus/blob/master/pegasus/layers/beam_search.py
-
-This beam search implementation is designed for TPU usage only and prefers
-flexibility over efficiency. Transformer attention caching is not enabled yet.
-
-Mostly follows implementation in T2T. Several difference to pure beamsearch:
-1. has finished and alive seqs, use 2 * beam_size to grow alive seqs,
-   which makes beam_size=1 doesn't equal greedy.
-2. prefers finished seq over alive seqs.
-3. prefers lower indices when equal probability (though unlikely).
-4. with custom length normalization and constraint.
-
-Notations:
-  B: batch_size, M: beam_size, T: max_decode_len, V: vocab_size, U: undefined
-"""
-# pylint: disable=invalid-name
-
-import tensorflow as tf
-
-
-def length_normalization(start, alpha, min_len, max_len, out_of_range_penalty):
-    r"""Create length normalization function.
-
-  Combines length penalty from https://arxiv.org/abs/1609.08144,
-  and length constraint from https://www.aclweb.org/anthology/W18-2706.pdf.
-
-  scores = \sum_j log(P_j) / ((start + lengths)/(1 + start))**alpha
-          + out_of_range_penalty * (length > max_len or length < min_len)
-
-  Args:
-    start: int, length normalization start offset.
-    alpha: float, [0, 1.0],  length normalization power.
-    min_len: int, minimum decode length.
-    max_len: int, maximum decode lengths.
-    out_of_range_penalty: float, penalty for lengths outside min len and max
-      len. Use a negative number that penalize out of range decodes, does hard
-      constraint if set to -inf.
-
-  Returns:
-    fn(log_probs_BxM, length)->scores_BxM: a function to normalize sum log
-    probabilities of sequence with current decoding lengths.
-  """
-
-    def length_norm_fn(log_probs_BxM, length_int):
-        """Normalize sum log probabilities given a sequence length."""
-        dtype = log_probs_BxM.dtype
-        norm_flt = tf.pow(
-            ((start + tf.cast(length_int, dtype)) / (1.0 + start)), alpha
-        )
-        log_probs_BxM /= norm_flt
-        too_short_bool = tf.less(length_int, min_len)
-        too_long_bool = tf.logical_and(
-            tf.greater(length_int, max_len), max_len > 0
-        )
-        out_of_range_bool = tf.logical_or(too_long_bool, too_short_bool)
-        log_probs_BxM += out_of_range_penalty * tf.cast(
-            out_of_range_bool, dtype
-        )
-        return log_probs_BxM
-
-    return length_norm_fn
-
-
-def beam_search(
-    symbols_to_logits_fn,
-    init_seq_BxT,
-    initial_cache_BxU,
-    vocab_size,
-    beam_size,
-    length_norm_fn,
-    eos_id=1,
-):
-    """Beam search.
-
-  Args:
-    symbols_to_logits_fn: fn(seq_BxT, cache_BxU, i) -> (logits_BxV, cache_BxU)
-    init_seq_BxT: initial sequence ids.
-    initial_cache_BxU: dictionary of tensors with shape BxU.
-    vocab_size: vocabulary size.
-    beam_size: beam size.
-    length_norm_fn: length normalization function.
-    eos_id: end of sequence.
-
-  Returns:
-    Tuple of (beams_BxMxT, scores_BxM). Beam searched sequences and scores.
-  """
-    B, T = init_seq_BxT.shape
-    M, V = beam_size, vocab_size
-    dtype = tf.float32
-    int_dtype = init_seq_BxT.dtype
-
-    def _loop_body(
-        i,
-        alive_seq_BxMxT,
-        alive_log_probs_BxM,
-        alive_cache_BxMxU,
-        finished_seq_BxMxT,
-        finished_scores_BxM,
-    ):
-        """Beam search loop body."""
-        # Decode one step with beam
-        logits_BMxV, cache_BMxU = symbols_to_logits_fn(
-            _flatten_beam_dim(alive_seq_BxMxT),
-            tf.nest.map_structure(_flatten_beam_dim, alive_cache_BxMxU),
-            i,
-        )
-        logits_BxMxV = _unflatten_beam_dim(logits_BMxV, M)
-        new_cache_BxMxU = tf.nest.map_structure(
-            lambda t: _unflatten_beam_dim(t, M), cache_BMxU
-        )
-
-        # select top 2 * beam_size and fill alive and finished.
-        log_probs_BxMxV = logits_BxMxV - tf.reduce_logsumexp(
-            logits_BxMxV, axis=2, keepdims=True
-        )
-        log_probs_BxMxV += tf.expand_dims(alive_log_probs_BxM, axis=2)
-        log_probs_BxMV = tf.reshape(log_probs_BxMxV, [B, -1])
-        new_log_probs_Bx2M, topk_indices_Bx2M = tf.nn.top_k(
-            log_probs_BxMV, k=2 * M
-        )
-        topk_beam_Bx2M = topk_indices_Bx2M // V
-        topk_seq_Bx2MxT, new_cache_Bx2MxU = _gather_nested(
-            [alive_seq_BxMxT, new_cache_BxMxU], topk_beam_Bx2M
-        )
-        topk_ids_Bx2M = topk_indices_Bx2M % V
-        new_seq_Bx2MxT = _update_i(topk_seq_Bx2MxT, topk_ids_Bx2M, i)
-        new_finished_flags_Bx2M = tf.cast(
-            tf.reduce_any(tf.equal(new_seq_Bx2MxT, eos_id), axis=-1), dtype
-        )
-
-        # get new alive
-        _, topk_alive_indices_BxM = tf.nn.top_k(
-            new_log_probs_Bx2M + new_finished_flags_Bx2M * dtype.min, k=M
-        )
-        (
-            alive_seq_BxMxT,
-            alive_log_probs_BxM,
-            alive_cache_BxMxU,
-        ) = _gather_nested(
-            [new_seq_Bx2MxT, new_log_probs_Bx2M, new_cache_Bx2MxU],
-            topk_alive_indices_BxM,
-        )
-
-        # get new finished
-        new_scores_Bx2M = length_norm_fn(new_log_probs_Bx2M, i + 1)
-        new_scores_Bx2M += (1 - new_finished_flags_Bx2M) * dtype.min
-        finished_seq_Bx3MxT = tf.concat(
-            [finished_seq_BxMxT, new_seq_Bx2MxT], axis=1
-        )
-        finished_scores_Bx3M = tf.concat(
-            [finished_scores_BxM, new_scores_Bx2M], axis=1
-        )
-        _, topk_finished_indices_BxM = tf.nn.top_k(finished_scores_Bx3M, k=M)
-        (finished_seq_BxMxT, finished_scores_BxM) = _gather_nested(
-            [finished_seq_Bx3MxT, finished_scores_Bx3M],
-            topk_finished_indices_BxM,
-        )
-
-        return [
-            i + 1,
-            alive_seq_BxMxT,
-            alive_log_probs_BxM,
-            alive_cache_BxMxU,
-            finished_seq_BxMxT,
-            finished_scores_BxM,
-        ]
-
-    # initialize.
-    init_i = tf.constant(0, dtype=int_dtype)
-    init_alive_seq_BxMxT = _expand_to_beam_size(init_seq_BxT, M)
-    log_probs_1xM = tf.constant([[0.0] + [dtype.min] * (M - 1)], dtype=dtype)
-    init_alive_log_probs_BxM = tf.tile(log_probs_1xM, [B, 1])
-    init_alive_cache_BxMxU = tf.nest.map_structure(
-        lambda t: _expand_to_beam_size(t, M), initial_cache_BxU
-    )
-    init_finished_seq_BxMxT = tf.zeros(
-        tf.shape(init_alive_seq_BxMxT), int_dtype
-    )
-    init_finished_scores_BxM = tf.zeros([B, M], dtype=dtype) + dtype.min
-
-    # run loop.
-    (
-        _,
-        final_alive_seq_BxMxT,
-        final_alive_scores_BxM,
-        _,
-        final_finished_seq_BxMxT,
-        final_finished_scores_BxM,
-    ) = tf.while_loop(
-        lambda *args: True,  # Always do T iterations
-        _loop_body,
-        loop_vars=[
-            init_i,
-            init_alive_seq_BxMxT,
-            init_alive_log_probs_BxM,
-            init_alive_cache_BxMxU,
-            init_finished_seq_BxMxT,
-            init_finished_scores_BxM,
-        ],
-        parallel_iterations=1,
-        back_prop=False,
-        maximum_iterations=T,
-    )
-
-    # process finished.
-    final_finished_flag_BxMx1 = tf.reduce_any(
-        tf.equal(final_finished_seq_BxMxT, eos_id), axis=-1, keepdims=True
-    )
-    final_seq_BxMxT = tf.where(
-        tf.tile(final_finished_flag_BxMx1, [1, 1, T]),
-        final_finished_seq_BxMxT,
-        final_alive_seq_BxMxT,
-    )
-    final_scores_BxM = tf.where(
-        tf.squeeze(final_finished_flag_BxMx1, axis=-1),
-        final_finished_scores_BxM,
-        final_alive_scores_BxM,
-    )
-    return final_seq_BxMxT, final_scores_BxM
-
-
-def _update_i(tensor_BxNxT, updates_BxN, i):
-    B, N, T = tensor_BxNxT.shape
-    tensor_BNxT = tf.reshape(tensor_BxNxT, [-1, T])
-    updates_BN = tf.reshape(updates_BxN, [-1])
-    batch_BN = tf.range(B * N, dtype=tf.int32)
-    i_BN = tf.fill([B * N], i)
-    ind_BNx2 = tf.stack([batch_BN, i_BN], axis=-1)
-    tensor_BNxT = tf.tensor_scatter_nd_update(tensor_BNxT, ind_BNx2, updates_BN)
-    return tf.reshape(tensor_BNxT, [B, N, T])
-
-
-def _expand_to_beam_size(tensor_BxU, beam_size):
-    tensor_Bx1xU = tf.expand_dims(tensor_BxU, axis=1)
-    tile_dims = [1] * tensor_Bx1xU.shape.ndims
-    tile_dims[1] = beam_size
-    tensor_BxMxU = tf.tile(tensor_Bx1xU, tile_dims)
-    return tensor_BxMxU
-
-
-def _flatten_beam_dim(tensor_BxMxU):
-    shape = tensor_BxMxU.shape.as_list()
-    tensor_BMxU = tf.reshape(tensor_BxMxU, [shape[0] * shape[1]] + shape[2:])
-    return tensor_BMxU
-
-
-def _unflatten_beam_dim(tensor_BMxU, M):
-    shape = tensor_BMxU.shape.as_list()
-    tensor_BxMxU = tf.reshape(tensor_BMxU, [shape[0] // M, M] + shape[1:])
-    return tensor_BxMxU
-
-
-def _gather_nested(nested_BxMxU, indices_BxN):
-    def _gather_beam(tensor_BxMxU):
-        tensor_BxNxU = tf.gather(
-            tensor_BxMxU, indices_BxN, batch_dims=1, axis=1
-        )
-        return tensor_BxNxU
-
-    return tf.nest.map_structure(_gather_beam, nested_BxMxU)
diff --git a/malaya/train/model/bigbird/decoder.py b/malaya/train/model/bigbird/decoder.py
deleted file mode 100644
index a15dd559..00000000
--- a/malaya/train/model/bigbird/decoder.py
+++ /dev/null
@@ -1,681 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""BigBird Decoder Layers."""
-
-from . import attention
-from . import beam_search
-from . import utils
-import tensorflow as tf
-
-
-class PrenormDecoderLayer(tf.compat.v1.layers.Layer):
-    """Decoder layer of a transformer in Pegasus style.
-
-  The layer_norm is taken before self-attention.
-  """
-
-    def __init__(
-        self,
-        hidden_size=768,
-        intermediate_size=3072,
-        intermediate_act_fn=utils.gelu,
-        attention_probs_dropout_prob=0.0,
-        hidden_dropout_prob=0.1,
-        initializer_range=0.02,
-        num_attention_heads=12,
-        use_bias=True,
-        name=None,
-    ):
-        """Constructor of a decoder layer of a transformer in Pegasus style.
-
-    Args:
-      hidden_size: (optional) int. Size of hidden dimension.
-      intermediate_size: (optional) int. Size of intermediate dimension.
-      intermediate_act_fn: optional) Activation function for intermediate layer.
-      attention_probs_dropout_prob: (optional) float. Dropout probability of the
-        attention probabilities.
-      hidden_dropout_prob: (optional) float. Dropout probability of the
-        attention.
-      initializer_range: (optional) float. Range of the weight initializer.
-      num_attention_heads: (optional) int. Number of attention heads.
-      use_bias: (optional) bool. Whether key/query/value uses a bias vector.
-      name: The name scope of this layer.
-    """
-        super(PrenormDecoderLayer, self).__init__(name=name)
-        self.hidden_dropout_prob = hidden_dropout_prob
-
-        # Attention layers
-        attention_head_size = hidden_size // num_attention_heads
-        self.self_attn_layer = attention.MultiHeadedAttentionLayer(
-            'original_full',
-            use_bias=use_bias,
-            name='self',
-            num_attention_heads=num_attention_heads,
-            size_per_head=attention_head_size,
-            initializer_range=initializer_range,
-            attention_probs_dropout_prob=attention_probs_dropout_prob,
-        )
-        self.cross_attn_layer = attention.MultiHeadedAttentionLayer(
-            'original_full',
-            use_bias=use_bias,
-            name='encdec',
-            num_attention_heads=num_attention_heads,
-            size_per_head=attention_head_size,
-            initializer_range=initializer_range,
-            attention_probs_dropout_prob=attention_probs_dropout_prob,
-        )
-
-        # Dense layers
-        self.self_proj_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.cross_proj_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.expand_layer = utils.Dense2dLayer(
-            intermediate_size,
-            utils.create_initializer(initializer_range),
-            intermediate_act_fn,
-            'dense',
-        )
-        self.contract_layer = utils.Dense2dLayer(
-            hidden_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-        )
-
-        # Normalization layer
-        self.first_layer_norm = utils.NormLayer()
-        self.second_layer_norm = utils.NormLayer()
-        self.third_layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.self_attn_layer.trainable_weights
-            + self.cross_attn_layer.trainable_weights
-            + self.self_proj_layer.trainable_weights
-            + self.cross_proj_layer.trainable_weights
-            + self.expand_layer.trainable_weights
-            + self.contract_layer.trainable_weights
-            + self.first_layer_norm.trainable_weights
-            + self.second_layer_norm.trainable_weights
-            + self.third_layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        layer_input,
-        encoder_outputs,
-        self_attention_mask,
-        attention_mask,
-        cache=None,
-        decode_i=None,
-        training=None,
-    ):
-        """Implements a decoder layer of a transformer in Pegasus style.
-
-    The layer_norm is taken after self-attention.
-
-    Args:
-      layer_input: float Tensor of shape [batch_size, seq_length, hidden_size].
-      encoder_outputs: tensors with shape [batch_size, input_length,
-          num_hidden_layers, hidden_size]
-      self_attention_mask: bias for decoder self-attention layer. [1, 1,
-        target_length, target_length]
-      attention_mask: bias for encoder-decoder attention layer. [batch_size, 1,
-        1, input_length]
-      cache: (Used during prediction) A dictionary with tensors containing
-        results of previous attentions. The dictionary must have the items:
-            {"k": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head],
-             "v": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head]}
-      decode_i: (Used during prediction) current location of decoding
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      float Tensor of shape [batch_size, seq_length, hidden_size].
-
-    Raises:
-      ValueError: Any of the arguments or tensor shapes are invalid.
-      NotImplementedError: For unknown attention type.
-    """
-        with tf.compat.v1.variable_scope('attention'):
-            with tf.compat.v1.variable_scope('self') as sc:
-                normalized_layer_input = self.first_layer_norm(layer_input)
-                self_attention_output = self.self_attn_layer(
-                    normalized_layer_input,
-                    normalized_layer_input,
-                    self_attention_mask,
-                    cache=cache,
-                    decode_i=decode_i,
-                    training=training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('output'):
-                self_attention_output = self.self_proj_layer(
-                    self_attention_output
-                )
-                self_attention_output = utils.dropout(
-                    self_attention_output, self.hidden_dropout_prob, training
-                )
-                self_attention_output = self_attention_output + layer_input
-
-            with tf.compat.v1.variable_scope('encdec') as sc:
-                normalized_self_attention_output = self.second_layer_norm(
-                    self_attention_output
-                )
-                attention_output = self.cross_attn_layer(
-                    normalized_self_attention_output,
-                    encoder_outputs,
-                    attention_mask,
-                    training=training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('encdec_output'):
-                attention_output = self.cross_proj_layer(attention_output)
-                attention_output = utils.dropout(
-                    attention_output, self.hidden_dropout_prob, training
-                )
-                attention_output = attention_output + self_attention_output
-
-        # The activation is only applied to the "intermediate" hidden layer.
-        with tf.compat.v1.variable_scope('intermediate'):
-            normalized_attention_output = self.third_layer_norm(
-                attention_output
-            )
-            intermediate_output = self.expand_layer(normalized_attention_output)
-
-        # Down-project back to `hidden_size` then add the residual.
-        with tf.compat.v1.variable_scope('output'):
-            layer_output = self.contract_layer(intermediate_output)
-            layer_output = utils.dropout(
-                layer_output, self.hidden_dropout_prob, training
-            )
-            layer_output = layer_output + attention_output
-        return layer_output
-
-
-class PostnormDecoderLayer(tf.compat.v1.layers.Layer):
-    """Decoder layer of a transformer in BERT style.
-
-  The layer_norm is taken before self-attention.
-  """
-
-    def __init__(
-        self,
-        hidden_size=768,
-        intermediate_size=3072,
-        intermediate_act_fn=utils.gelu,
-        attention_probs_dropout_prob=0.0,
-        hidden_dropout_prob=0.1,
-        initializer_range=0.02,
-        num_attention_heads=12,
-        use_bias=True,
-        name=None,
-    ):
-        """Constructor of a decoder layer of a transformer in BERT style.
-
-    Args:
-      hidden_size: (optional) int. Size of hidden dimension.
-      intermediate_size: (optional) int. Size of intermediate dimension.
-      intermediate_act_fn: optional) Activation function for intermediate layer.
-      attention_probs_dropout_prob: (optional) float. Dropout probability of the
-        attention probabilities.
-      hidden_dropout_prob: (optional) float. Dropout probability of the
-        attention.
-      initializer_range: (optional) float. Range of the weight initializer.
-      num_attention_heads: (optional) int. Number of attention heads.
-      use_bias: (optional) bool. Whether key/query/value uses a bias vector.
-      name: The name scope of this layer.
-    """
-        super(PostnormDecoderLayer, self).__init__(name=name)
-        self.hidden_dropout_prob = hidden_dropout_prob
-
-        # Attention layers
-        attention_head_size = hidden_size // num_attention_heads
-        self.self_attn_layer = attention.MultiHeadedAttentionLayer(
-            'original_full',
-            use_bias=use_bias,
-            name='self',
-            num_attention_heads=num_attention_heads,
-            size_per_head=attention_head_size,
-            initializer_range=initializer_range,
-            attention_probs_dropout_prob=attention_probs_dropout_prob,
-        )
-        self.cross_attn_layer = attention.MultiHeadedAttentionLayer(
-            'original_full',
-            use_bias=use_bias,
-            name='encdec',
-            num_attention_heads=num_attention_heads,
-            size_per_head=attention_head_size,
-            initializer_range=initializer_range,
-            attention_probs_dropout_prob=attention_probs_dropout_prob,
-        )
-
-        # Dense layers
-        self.self_proj_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.cross_proj_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.expand_layer = utils.Dense2dLayer(
-            intermediate_size,
-            utils.create_initializer(initializer_range),
-            intermediate_act_fn,
-            'dense',
-        )
-        self.contract_layer = utils.Dense2dLayer(
-            hidden_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-        )
-
-        # Normalization layer
-        self.first_layer_norm = utils.NormLayer()
-        self.second_layer_norm = utils.NormLayer()
-        self.third_layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.self_attn_layer.trainable_weights
-            + self.cross_attn_layer.trainable_weights
-            + self.self_proj_layer.trainable_weights
-            + self.cross_proj_layer.trainable_weights
-            + self.expand_layer.trainable_weights
-            + self.contract_layer.trainable_weights
-            + self.first_layer_norm.trainable_weights
-            + self.second_layer_norm.trainable_weights
-            + self.third_layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        layer_input,
-        encoder_outputs,
-        self_attention_mask,
-        attention_mask,
-        cache=None,
-        decode_i=None,
-        training=None,
-    ):
-        """Implements a decoder layer of a transformer in BERT style.
-
-    The layer_norm is taken after self-attention.
-
-    Args:
-      layer_input: float Tensor of shape [batch_size, seq_length, hidden_size].
-      encoder_outputs: tensors with shape [batch_size, input_length,
-          num_hidden_layers, hidden_size]
-      self_attention_mask: bias for decoder self-attention layer. [1, 1,
-        target_length, target_length]
-      attention_mask: bias for encoder-decoder attention layer. [batch_size, 1,
-        1, input_length]
-      cache: (Used during prediction) A dictionary with tensors containing
-        results of previous attentions. The dictionary must have the items:
-            {"k": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head],
-             "v": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head]}
-      decode_i: (Used during prediction) current location of decoding
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      float Tensor of shape [batch_size, seq_length, hidden_size].
-
-    Raises:
-      ValueError: Any of the arguments or tensor shapes are invalid.
-      NotImplementedError: For unknown attention type.
-    """
-        with tf.compat.v1.variable_scope('attention'):
-            with tf.compat.v1.variable_scope('self') as sc:
-                self_attention_output = self.self_attn_layer(
-                    layer_input,
-                    layer_input,
-                    self_attention_mask,
-                    cache=cache,
-                    decode_i=decode_i,
-                    training=training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('output'):
-                self_attention_output = self.self_proj_layer(
-                    self_attention_output
-                )
-                self_attention_output = utils.dropout(
-                    self_attention_output, self.hidden_dropout_prob, training
-                )
-                self_attention_output = self.first_layer_norm(
-                    self_attention_output + layer_input
-                )
-
-            with tf.compat.v1.variable_scope('encdec') as sc:
-                attention_output = self.cross_attn_layer(
-                    self_attention_output,
-                    encoder_outputs,
-                    attention_mask,
-                    training=training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('encdec_output'):
-                attention_output = self.cross_proj_layer(attention_output)
-                attention_output = utils.dropout(
-                    attention_output, self.hidden_dropout_prob, training
-                )
-                attention_output = self.second_layer_norm(
-                    attention_output + self_attention_output
-                )
-
-        # The activation is only applied to the "intermediate" hidden layer.
-        with tf.compat.v1.variable_scope('intermediate'):
-            intermediate_output = self.expand_layer(attention_output)
-
-        # Down-project back to `hidden_size` then add the residual.
-        with tf.compat.v1.variable_scope('output'):
-            layer_output = self.contract_layer(intermediate_output)
-            layer_output = utils.dropout(
-                layer_output, self.hidden_dropout_prob, training
-            )
-            layer_output = self.third_layer_norm(
-                layer_output + attention_output
-            )
-        return layer_output
-
-
-class DecoderStack(tf.compat.v1.layers.Layer):
-    """Transformer decoder stack."""
-
-    def __init__(self, params):
-        if params['couple_encoder_decoder']:
-            name = 'encoder'
-            with tf.compat.v1.variable_scope(
-                name, reuse=tf.compat.v1.AUTO_REUSE
-            ) as scope:
-                super(DecoderStack, self).__init__(name=name, _scope=scope)
-        else:
-            name = 'decoder'
-            super(DecoderStack, self).__init__(name=name)
-
-        self.params = params
-
-        if params['norm_type'] == 'prenorm':
-            decoder_class = PrenormDecoderLayer
-        elif params['norm_type'] == 'postnorm':
-            decoder_class = PostnormDecoderLayer
-        else:
-            raise NotImplementedError(
-                'Norm type {} is not implemented'.format(params['norm_type'])
-            )
-
-        if self.params.get('num_decoder_layers', None) is not None:
-            num_hidden_layers = self.params['num_decoder_layers']
-        else:
-            num_hidden_layers = self.params['num_hidden_layers']
-
-        # Decoder layers
-        self.decoder_layers = [
-            decoder_class(  # pylint: disable=g-complex-comprehension
-                self.params['hidden_size'],
-                self.params['intermediate_size'],
-                utils.get_activation(self.params['hidden_act']),
-                self.params['attention_probs_dropout_prob'],
-                self.params['hidden_dropout_prob'],
-                self.params['initializer_range'],
-                self.params['num_attention_heads'],
-                self.params['use_bias'],
-                name='layer_%d' % layer_idx,
-            )
-            for layer_idx in range(num_hidden_layers)
-        ]
-
-        # Normalization layer
-        self.layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            sum([layer.trainable_weights for layer in self.decoder_layers], [])
-            + self.layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        decoder_inputs,
-        self_attention_mask,
-        encoder_outputs,
-        encoder_mask,
-        cache=None,
-        decode_i=None,
-        training=None,
-    ):
-        """Return the output of the decoder layer stacks.
-
-    Args:
-      decoder_inputs: tensor with shape
-        [batch_size, target_length, hidden_size]
-      self_attention_mask: bias for decoder self-attention layer. [1, 1,
-        target_length, target_length]
-      encoder_outputs: tensors with shape [batch_size, input_length,
-        hidden_size]
-      encoder_mask: bias for encoder-decoder attention layer. [batch_size,
-        input_length]
-      cache: (Used during prediction) A dictionary with tensors containing
-        results of previous attentions. The dictionary must have the items:
-            {"k": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head],
-             "v": tensor with shape
-                  [batch_size, max_len, num_attention_heads, size_per_head]}
-      decode_i: (Used during prediction) current location of decoding.
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      Output of decoder layer stack. A float32 tensor with shape [batch_size,
-        target_length, hidden_size]
-    """
-        # Expand encoder mask to broadcast over num heads and from_seq axis
-        attention_mask = tf.expand_dims(tf.expand_dims(encoder_mask, 1), 1)
-
-        # if self.params["use_gradient_checkpointing"]::
-        #   decoder_layer = recompute_gradient(decoder_layer)
-
-        if self.params['norm_type'] == 'postnorm':
-            decoder_inputs = self.layer_norm(decoder_inputs)
-
-        layer_output = decoder_inputs
-        for layer in self.decoder_layers:
-            layer_cache = cache[layer.name] if cache is not None else None
-            layer_output = layer(
-                layer_output,
-                encoder_outputs,
-                self_attention_mask,
-                attention_mask,
-                layer_cache,
-                decode_i,
-                training,
-            )
-
-        if self.params['norm_type'] == 'prenorm':
-            layer_output = self.layer_norm(layer_output)
-
-        return layer_output
-
-
-def create_self_attention_mask(length):
-    with tf.name_scope('decoder_self_attention_mask'):
-        valid_locs = tf.linalg.band_part(tf.ones([length, length]), -1, 0)
-        valid_locs = tf.reshape(valid_locs, [1, 1, length, length])
-    return valid_locs
-
-
-def inplace_update_i(inp_tensor, updates, i):
-    """Inplace update a tensor. B: batch_size, L: tensor length."""
-    batch_size = tf.shape(inp_tensor)[0]
-    indices = tf.stack(
-        [
-            tf.range(batch_size, dtype=tf.int32),
-            tf.fill([batch_size], tf.cast(i, tf.int32)),
-        ],
-        axis=-1,
-    )
-    return tf.tensor_scatter_nd_update(inp_tensor, indices, updates)
-
-
-# pylint: disable=invalid-name
-def left2right_decode(
-    symbols_to_logits_fn,
-    start_symbols,
-    context_BxU_dict,
-    batch_size,
-    max_decode_len,
-    vocab_size,
-    beam_size=1,
-    beam_start=5,
-    beam_alpha=0.6,
-    beam_min=0,
-    beam_max=-1,
-    eos_id=1,
-):
-    """left to right decode.
-
-  Notations:
-    B: batch_size, V: vocab_size, T: decode_len, U: undefined dimensions
-
-  Args:
-    symbols_to_logits_fn: logits = fn(decodes, context, i). Shoud take
-      [batch_size, decoded_ids] and return [batch_size, vocab_size].
-    start_symbols: starting ids [batch_size]
-    context_BxU_dict: dict of Tensors.
-    batch_size: int, decode batch size.
-    max_decode_len: int, maximum number of steps to decode.
-    vocab_size: int, output vocab size.
-    beam_size: Number of beams to decode.
-    beam_start: start length for scaling, default to 5.
-    beam_alpha: Length penalty for decoding. Should be between 0 (shorter) and 1
-      (longer), default to 0.6.
-    beam_min: Minimum beam search lengths.
-    beam_max: Maximum beam search lengths. Set -1 to use unlimited.
-    eos_id: end of token id, default to 1.
-
-  Returns:
-    decodes: Tensor[batch, decode_len]
-  """
-    dtype = tf.int32
-    start_symbols = tf.expand_dims(start_symbols, 1)
-    # When beam_size=1, beam_search does not behave exactly like greedy.
-    # This is due to using 2 * beam_size in grow_topk, and keep the top beam_size
-    # ones that haven't reached EOS into alive.
-    # In this case, alpha value for length penalty will take effect.
-    if beam_size == 1:
-
-        def decode_loop(i, decodes_BxT, cache_BxU_dict):
-            logits_BxV = symbols_to_logits_fn(decodes_BxT, cache_BxU_dict, i)
-            decodes_BxT = inplace_update_i(
-                decodes_BxT,
-                tf.argmax(logits_BxV, -1, output_type=tf.int32),
-                i,
-            )
-            return i + 1, decodes_BxT, cache_BxU_dict
-
-        def loop_cond(i, decodes_BxT, unused_cache_BxU_dict):
-            finished_B = tf.reduce_any(tf.equal(decodes_BxT, eos_id), axis=1)
-            return tf.logical_and(
-                i < max_decode_len, tf.logical_not(tf.reduce_all(finished_B))
-            )
-
-        init_dec_BxT = tf.concat(
-            [
-                tf.cast(start_symbols, dtype=dtype),
-                tf.zeros([batch_size, max_decode_len - 1], dtype=dtype),
-            ],
-            axis=1,
-        )
-        _, decodes, _ = tf.while_loop(
-            loop_cond,
-            decode_loop,
-            [tf.constant(0, dtype=dtype), init_dec_BxT, context_BxU_dict],
-        )
-        return decodes
-
-    else:
-
-        def symbols_to_logits_fn_with_sampling(decodes_BxT, states_BxU_dict, i):
-            logits_BxV = symbols_to_logits_fn(decodes_BxT, states_BxU_dict, i)
-            return logits_BxV, states_BxU_dict
-
-        length_norm_fn = beam_search.length_normalization(
-            beam_start, beam_alpha, beam_min, beam_max, -1e3
-        )
-
-        init_dec_BxT = tf.concat(
-            [
-                tf.cast(start_symbols, dtype=tf.int32),
-                tf.zeros([batch_size, max_decode_len - 1], dtype=tf.int32),
-            ],
-            axis=1,
-        )
-
-        beams, _ = beam_search.beam_search(
-            symbols_to_logits_fn_with_sampling,
-            init_dec_BxT,
-            context_BxU_dict,
-            vocab_size,
-            beam_size,
-            length_norm_fn,
-            eos_id,
-        )
-        return beams[:, 0, :]
diff --git a/malaya/train/model/bigbird/encoder.py b/malaya/train/model/bigbird/encoder.py
deleted file mode 100644
index 5a7f5934..00000000
--- a/malaya/train/model/bigbird/encoder.py
+++ /dev/null
@@ -1,515 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""BigBird Encoder Layers."""
-
-from . import attention
-from . import utils
-import tensorflow as tf
-
-
-class PrenormEncoderLayer(tf.compat.v1.layers.Layer):
-    """Encoder layer of a transformer in Pegasus style.
-
-  The layer_norm is taken before self-attention.
-  """
-
-    def __init__(
-        self,
-        attention_type,
-        hidden_size=768,
-        intermediate_size=3072,
-        intermediate_act_fn=utils.gelu,
-        attention_probs_dropout_prob=0.0,
-        hidden_dropout_prob=0.1,
-        initializer_range=0.02,
-        num_attention_heads=12,
-        num_rand_blocks=3,
-        block_size=64,
-        use_bias=True,
-        seed=None,
-        name=None,
-    ):
-        """Constructor of an encoder layer of a transformer in Pegasus style.
-
-    Args:
-      attention_type: Type of attention, needs to be one of ['original_full',
-        'simulated_sparse', 'block_sparse'].
-      hidden_size: (optional) int. Size of hidden dimension.
-      intermediate_size: (optional) int. Size of intermediate dimension.
-      intermediate_act_fn: optional) Activation function for intermediate layer.
-      attention_probs_dropout_prob: (optional) float. Dropout probability of the
-        attention probabilities.
-      hidden_dropout_prob: (optional) float. Dropout probability of the
-        attention.
-      initializer_range: (optional) float. Range of the weight initializer.
-      num_attention_heads: (optional) int. Number of attention heads.
-      num_rand_blocks: (optional) int. Number of random chunks per row.
-      block_size: (optional) int. size of block in sequence.
-      use_bias: (optional) bool. Whether key/query/value uses a bias vector.
-      seed: (Optional) int. Reandom seed for generating random mask.
-      name: The name scope of this layer.
-    """
-        super(PrenormEncoderLayer, self).__init__(name=name)
-        self.hidden_dropout_prob = hidden_dropout_prob
-
-        # Attention layer
-        attention_head_size = hidden_size // num_attention_heads
-        self.attn_layer = attention.MultiHeadedAttentionLayer(
-            attention_type,
-            num_attention_heads,
-            num_rand_blocks,
-            attention_head_size,
-            initializer_range,
-            block_size,
-            block_size,
-            attention_probs_dropout_prob,
-            use_bias,
-            seed,
-            name='self',
-        )
-
-        # Dense layers
-        self.projection_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.expand_layer = utils.Dense2dLayer(
-            intermediate_size,
-            utils.create_initializer(initializer_range),
-            intermediate_act_fn,
-            'dense',
-        )
-        self.contract_layer = utils.Dense2dLayer(
-            hidden_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-        )
-
-        # Normalization layer
-        self.first_layer_norm = utils.NormLayer()
-        self.second_layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.attn_layer.trainable_weights
-            + self.projection_layer.trainable_weights
-            + self.expand_layer.trainable_weights
-            + self.contract_layer.trainable_weights
-            + self.first_layer_norm.trainable_weights
-            + self.second_layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        layer_input,
-        attention_mask=None,
-        band_mask=None,
-        from_mask=None,
-        to_mask=None,
-        input_blocked_mask=None,
-        training=None,
-    ):
-        """Implements a encoder layer of a transformer in Pegasus style.
-
-    Args:
-      layer_input: float Tensor of shape [batch_size, seq_length, hidden_size].
-      attention_mask: (optional) int32 Tensor of shape [batch_size,
-        seq_length, seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      band_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        seq_length//block_size-4, block_size, 3*block_size].
-        The values should be 1 or 0. The attention scores will effectively be
-        set to -infinity for any positions in the mask that are 0, and will be
-        unchanged for positions that are 1.
-      from_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        seq_length, 1]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      to_mask: (optional) int32 Tensor of shape [batch_size, 1, 1,
-        seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      input_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-        seq_length//block_size, block_size]. Same as from/to_mask, just
-        reshaped.
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      float Tensor of shape [batch_size, seq_length, hidden_size].
-
-    Raises:
-      ValueError: Any of the arguments or tensor shapes are invalid.
-      NotImplementedError: For unknown attention type.
-    """
-
-        with tf.compat.v1.variable_scope('attention'):
-            with tf.compat.v1.variable_scope('self') as sc:
-                normalized_layer_input = self.first_layer_norm(layer_input)
-                attention_output = self.attn_layer(
-                    normalized_layer_input,
-                    normalized_layer_input,
-                    attention_mask,
-                    band_mask,
-                    from_mask,
-                    to_mask,
-                    input_blocked_mask,
-                    input_blocked_mask,
-                    training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('output'):
-                attention_output = self.projection_layer(attention_output)
-                attention_output = utils.dropout(
-                    attention_output, self.hidden_dropout_prob, training
-                )
-                attention_output = attention_output + layer_input
-
-        # The activation is only applied to the "intermediate" hidden layer.
-        with tf.compat.v1.variable_scope('intermediate'):
-            normalized_attention_output = self.second_layer_norm(
-                attention_output
-            )
-            intermediate_output = self.expand_layer(normalized_attention_output)
-
-        # Down-project back to `hidden_size` then add the residual.
-        with tf.compat.v1.variable_scope('output'):
-            layer_output = self.contract_layer(intermediate_output)
-            layer_output = utils.dropout(
-                layer_output, self.hidden_dropout_prob, training
-            )
-            layer_output = layer_output + attention_output
-        return layer_output
-
-
-class PostnormEncoderLayer(tf.compat.v1.layers.Layer):
-    """Encoder layer of a transformer in BERT style.
-
-  The layer_norm is taken after self-attention.
-  """
-
-    def __init__(
-        self,
-        attention_type,
-        hidden_size=768,
-        intermediate_size=3072,
-        intermediate_act_fn=utils.gelu,
-        attention_probs_dropout_prob=0.0,
-        hidden_dropout_prob=0.1,
-        initializer_range=0.02,
-        num_attention_heads=12,
-        num_rand_blocks=3,
-        block_size=64,
-        use_bias=True,
-        seed=None,
-        name=None,
-    ):
-        """Constructor of an encoder layer of a transformer in BERT style.
-
-    Args:
-      attention_type: Type of attention, needs to be one of ['original_full',
-        'simulated_sparse', 'block_sparse'].
-      hidden_size: (optional) int. Size of hidden dimension.
-      intermediate_size: (optional) int. Size of intermediate dimension.
-      intermediate_act_fn: optional) Activation function for intermediate layer.
-      attention_probs_dropout_prob: (optional) float. Dropout probability of the
-        attention probabilities.
-      hidden_dropout_prob: (optional) float. Dropout probability of the
-        attention.
-      initializer_range: (optional) float. Range of the weight initializer.
-      num_attention_heads: (optional) int. Number of attention heads.
-      num_rand_blocks: (optional) int. Number of random chunks per row.
-      block_size: (optional) int. size of block in sequence.
-      use_bias: (optional) bool. Whether key/query/value uses a bias vector.
-      seed: (Optional) int. Reandom seed for generating random mask.
-      name: The name scope of this layer.
-    """
-        super(PostnormEncoderLayer, self).__init__(name=name)
-        self.hidden_dropout_prob = hidden_dropout_prob
-
-        # Attention layer
-        attention_head_size = hidden_size // num_attention_heads
-        self.attn_layer = attention.MultiHeadedAttentionLayer(
-            attention_type,
-            num_attention_heads,
-            num_rand_blocks,
-            attention_head_size,
-            initializer_range,
-            block_size,
-            block_size,
-            attention_probs_dropout_prob,
-            use_bias,
-            seed,
-            name='self',
-        )
-
-        # Dense layers
-        self.projection_layer = utils.Dense3dProjLayer(
-            num_attention_heads,
-            attention_head_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-            use_bias,
-        )
-        self.expand_layer = utils.Dense2dLayer(
-            intermediate_size,
-            utils.create_initializer(initializer_range),
-            intermediate_act_fn,
-            'dense',
-        )
-        self.contract_layer = utils.Dense2dLayer(
-            hidden_size,
-            utils.create_initializer(initializer_range),
-            None,
-            'dense',
-        )
-
-        # Normalization layer
-        self.first_layer_norm = utils.NormLayer()
-        self.second_layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.attn_layer.trainable_weights
-            + self.projection_layer.trainable_weights
-            + self.expand_layer.trainable_weights
-            + self.contract_layer.trainable_weights
-            + self.first_layer_norm.trainable_weights
-            + self.second_layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(
-        self,
-        layer_input,
-        attention_mask=None,
-        band_mask=None,
-        from_mask=None,
-        to_mask=None,
-        input_blocked_mask=None,
-        training=None,
-    ):
-        """Implements a encoder layer of a transformer in BERT style.
-
-    Args:
-      layer_input: float Tensor of shape [batch_size, seq_length, hidden_size].
-      attention_mask: (optional) int32 Tensor of shape [batch_size,
-        seq_length, seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      band_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        seq_length//block_size-4, block_size, 3*block_size].
-        The values should be 1 or 0. The attention scores will effectively be
-        set to -infinity for any positions in the mask that are 0, and will be
-        unchanged for positions that are 1.
-      from_mask: (optional) int32 Tensor of shape [batch_size, 1,
-        seq_length, 1]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      to_mask: (optional) int32 Tensor of shape [batch_size, 1, 1,
-        seq_length]. The values should be 1 or 0. The
-        attention scores will effectively be set to -infinity for any positions
-        in the mask that are 0, and will be unchanged for positions that are 1.
-      input_blocked_mask: (optional) int32 Tensor of shape [batch_size,
-        seq_length//block_size, block_size]. Same as from/to_mask, just
-        reshaped.
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      float Tensor of shape [batch_size, seq_length, hidden_size].
-
-    Raises:
-      ValueError: Any of the arguments or tensor shapes are invalid.
-      NotImplementedError: For unknown attention type.
-    """
-
-        with tf.compat.v1.variable_scope('attention'):
-            with tf.compat.v1.variable_scope('self') as sc:
-                attention_output = self.attn_layer(
-                    layer_input,
-                    layer_input,
-                    attention_mask,
-                    band_mask,
-                    from_mask,
-                    to_mask,
-                    input_blocked_mask,
-                    input_blocked_mask,
-                    training,
-                    scope=sc,
-                )
-
-            # Run a linear projection of `hidden_size` then add a residual
-            # with `layer_input`.
-            with tf.compat.v1.variable_scope('output'):
-                attention_output = self.projection_layer(attention_output)
-                attention_output = utils.dropout(
-                    attention_output, self.hidden_dropout_prob, training
-                )
-                attention_output = self.first_layer_norm(
-                    attention_output + layer_input
-                )
-
-        # The activation is only applied to the "intermediate" hidden layer.
-        with tf.compat.v1.variable_scope('intermediate'):
-            intermediate_output = self.expand_layer(attention_output)
-
-        # Down-project back to `hidden_size` then add the residual.
-        with tf.compat.v1.variable_scope('output'):
-            layer_output = self.contract_layer(intermediate_output)
-            layer_output = utils.dropout(
-                layer_output, self.hidden_dropout_prob, training
-            )
-            layer_output = self.second_layer_norm(
-                layer_output + attention_output
-            )
-        return layer_output
-
-
-class EncoderStack(tf.compat.v1.layers.Layer):
-    """Transformer encoder stack."""
-
-    def __init__(self, params):
-        name = 'encoder'
-        super(EncoderStack, self).__init__(name=name)
-        self.params = params
-
-        if params['norm_type'] == 'prenorm':
-            encoder_class = PrenormEncoderLayer
-        elif params['norm_type'] == 'postnorm':
-            encoder_class = PostnormEncoderLayer
-        else:
-            raise NotImplementedError(
-                'Norm type {} is not implemented'.format(params['norm_type'])
-            )
-
-        # Encoder layers
-        self.encoder_layers = [
-            encoder_class(  # pylint: disable=g-complex-comprehension
-                self.params['attention_type'],
-                self.params['hidden_size'],
-                self.params['intermediate_size'],
-                utils.get_activation(self.params['hidden_act']),
-                self.params['attention_probs_dropout_prob'],
-                self.params['hidden_dropout_prob'],
-                self.params['initializer_range'],
-                self.params['num_attention_heads'],
-                self.params['num_rand_blocks'],
-                self.params['block_size'],
-                self.params['use_bias'],
-                seed=layer_idx,
-                name='layer_%d' % layer_idx,
-            )
-            for layer_idx in range(self.params['num_hidden_layers'])
-        ]
-
-        # Normalization layer
-        self.layer_norm = utils.NormLayer()
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            sum([layer.trainable_weights for layer in self.encoder_layers], [])
-            + self.layer_norm.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(self, encoder_inputs, encoder_inputs_mask, training=None):
-        """Return the output of the decoder layer stacks.
-
-    Args:
-      encoder_inputs: tensor with shape
-        [batch_size, input_length, hidden_size]
-      encoder_inputs_mask: Mask for enccoder input. [batch_size, input_length]
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      Finaly layer encoder output. float tensor with shape
-        [batch_size, input_length, hidden_size]
-    """
-        encoder_shape = utils.get_shape_list(encoder_inputs, expected_rank=3)
-        batch_size = encoder_shape[0]
-        encoder_length = encoder_shape[1]
-
-        if self.params['attention_type'] == 'block_sparse':
-            # reshape and cast for blocking
-            encoder_block_size = self.params['block_size']
-            blocked_encoder_mask = tf.reshape(
-                encoder_inputs_mask,
-                (
-                    batch_size,
-                    encoder_length // encoder_block_size,
-                    encoder_block_size,
-                ),
-            )
-            encoder_from_mask = tf.reshape(
-                encoder_inputs_mask, (batch_size, 1, encoder_length, 1)
-            )
-            encoder_to_mask = tf.reshape(
-                encoder_inputs_mask, (batch_size, 1, 1, encoder_length)
-            )
-
-            # create band padding
-            attention_mask = None
-            band_mask = attention.create_band_mask_from_inputs(
-                blocked_encoder_mask, blocked_encoder_mask
-            )
-
-        else:
-            blocked_encoder_mask = None
-            encoder_to_mask = None
-            encoder_from_mask = None
-
-            attention_mask = attention.create_attention_mask_from_input_mask(
-                encoder_inputs_mask, encoder_inputs_mask
-            )
-            band_mask = None
-
-        # if self.params["use_gradient_checkpointing"]:
-        #   encoder_layer = recompute_gradient(encoder_layer)
-
-        if self.params['norm_type'] == 'postnorm':
-            encoder_inputs = self.layer_norm(encoder_inputs)
-
-        layer_output = encoder_inputs
-        for layer in self.encoder_layers:
-            layer_output = layer(
-                layer_output,
-                attention_mask,
-                band_mask,
-                encoder_from_mask,
-                encoder_to_mask,
-                blocked_encoder_mask,
-                training,
-            )
-
-        if self.params['norm_type'] == 'prenorm':
-            layer_output = self.layer_norm(layer_output)
-
-        return layer_output
diff --git a/malaya/train/model/bigbird/modeling.py b/malaya/train/model/bigbird/modeling.py
deleted file mode 100644
index 64d11ed9..00000000
--- a/malaya/train/model/bigbird/modeling.py
+++ /dev/null
@@ -1,516 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""The main BigBird model and related functions."""
-
-import copy
-
-from . import decoder
-from . import encoder
-from . import utils
-import tensorflow as tf
-
-
-class BertModel(tf.compat.v1.layers.Layer):
-    """BERT model ("Bidirectional Encoder Representations from Transformers").
-
-  Example usage:
-
-  ```python
-  # Already been converted into SentencePiece token ids
-  input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
-  token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
-
-  params = utils.BigBirdConfig(vocab_size=32000, hidden_size=512,
-    num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
-
-  model = modeling.BertModel(params, train=True)
-
-  _, pooled_output = model(input_ids=input_ids, token_type_ids=token_type_ids)
-
-  label_embeddings = tf.get_variable(...)
-  logits = tf.matmul(pooled_output, label_embeddings)
-  ...
-  ```
-  """
-
-    def __init__(self, params):
-        """Constructor for BertModel.
-
-    Args:
-      params: `BigBirdConfig` dictionary.
-    """
-        self.params = copy.deepcopy(params)
-        self.scope = params['scope']
-
-        with tf.compat.v1.variable_scope(
-            self.scope, reuse=tf.compat.v1.AUTO_REUSE
-        ) as vs:
-            self.embeder = utils.EmbeddingLayer(
-                vocab_size=self.params['vocab_size'],
-                emb_dim=self.params['hidden_size'],
-                initializer=utils.create_initializer(
-                    self.params['initializer_range']
-                ),
-                scale_emb=self.params['rescale_embedding'],
-                use_token_type=True,
-                num_token_types=self.params['type_vocab_size'],
-                use_position_embeddings=True,
-                max_position_embeddings=self.params[
-                    'max_position_embeddings'
-                ],
-                dropout_prob=self.params['hidden_dropout_prob'],
-            )
-            self.encoder = encoder.EncoderStack(self.params)
-            self.pooler = tf.compat.v1.layers.Dense(
-                units=self.params['hidden_size'],
-                activation=tf.tanh,
-                kernel_initializer=utils.create_initializer(
-                    self.params['initializer_range']
-                ),
-                name='pooler/dense',
-            )
-            super(BertModel, self).__init__(name=self.scope, _scope=vs)
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.embeder.trainable_weights
-            + self.encoder.trainable_weights
-            + self.pooler.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def call(self, input_ids, token_type_ids=None, training=None):
-        """Constructor for BertModel.
-
-    Args:
-      input_ids: int32 Tensor of shape [batch_size, seq_length].
-      token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      sequence_output: Tensor of shape [batch_size, seq_length, hidden_size]
-      pooled_output: Tensor of shape [batch_size, hidden_size]
-
-    Raises:
-      ValueError: The config is invalid or one of the input tensor shapes
-        is invalid.
-    """
-        if token_type_ids is None:
-            token_type_ids = tf.zeros_like(input_ids, dtype=tf.int32)
-
-        # Perform embedding lookup on the word ids.
-        embedding_output = self.embeder(
-            input_ids,
-            self.params['max_encoder_length'],
-            token_type_ids=token_type_ids,
-            training=training,
-        )
-
-        # Generate mask.
-        input_mask = tf.where(
-            input_ids > 0, tf.ones_like(input_ids), tf.zeros_like(input_ids)
-        )
-
-        # Run the stacked transformer.
-        sequence_output = self.encoder(embedding_output, input_mask, training)
-
-        # The "pooler" converts the encoded sequence tensor of shape
-        # [batch_size, seq_length, hidden_size] to a tensor of shape
-        # [batch_size, hidden_size]. This is necessary for segment-level
-        # (or segment-pair-level) classification tasks where we need a fixed
-        # dimensional representation of the segment.
-        first_token_tensor = sequence_output[:, 0, :]
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token. We assume that this has been pre-trained
-        pooled_output = self.pooler(first_token_tensor)
-
-        return sequence_output, pooled_output
-
-
-class TransformerModel(tf.compat.v1.layers.Layer):
-    """Encoder-Decoder transformer model.
-
-  Example usage:
-
-  ```python
-  # Already been converted into SentencePiece token ids
-  input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
-  target_ids = tf.constant([[43, 76, 38], [56, 8, 0]])
-
-  params = utils.BigBirdConfig(vocab_size=32000, hidden_size=512,
-    num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
-
-  model = modeling.TransformerModel(params, train=True)
-
-  predictions, _ = model(input_ids=input_ids, target_ids=target_ids)
-
-  log_probs, logits, pred_ids = predictions
-  ...
-  ```
-  """
-
-    def __init__(self, params):
-        """Constructor for TransformerModel.
-
-    Args:
-      params: `BigBirdConfig` dictionary.
-    """
-        self.params = copy.deepcopy(params)
-        self.scope = params['scope']
-
-        with tf.compat.v1.variable_scope(
-            self.scope, reuse=tf.compat.v1.AUTO_REUSE
-        ) as vs:
-            self.embeder = utils.EmbeddingLayer(
-                vocab_size=self.params['vocab_size'],
-                emb_dim=self.params['hidden_size'],
-                initializer=utils.create_initializer(
-                    self.params['initializer_range']
-                ),
-                scale_emb=self.params['rescale_embedding'],
-                use_token_type=False,
-                num_token_types=None,
-                use_position_embeddings=True,
-                max_position_embeddings=self.params[
-                    'max_position_embeddings'
-                ],
-                dropout_prob=self.params['hidden_dropout_prob'],
-            )
-            self.encoder = encoder.EncoderStack(self.params)
-            self.decoder = decoder.DecoderStack(self.params)
-            super(TransformerModel, self).__init__(
-                name=self.scope, _scope=vs
-            )
-
-    @property
-    def trainable_weights(self):
-        tvar_list = (
-            self.embeder.trainable_weights
-            + self.encoder.trainable_weights
-            + self.decoder.trainable_weights
-        )
-        self._trainable_weights = list({v.name: v for v in tvar_list}.values())
-        return self._trainable_weights
-
-    def _encode(self, input_ids, training=None):
-        """Generate continuous representation for ids.
-
-    Args:
-      input_ids: Int tensor with shape [batch_size, input_length].
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      A float tensors of shape
-          [batch_size, input_length, hidden_size].
-    """
-        # Perform embedding lookup on the word ids.
-        input_embs = self.embeder(
-            input_ids, self.params['max_encoder_length'], training=training
-        )
-
-        # Generate mask.
-        input_mask = tf.where(
-            input_ids > 0, tf.ones_like(input_ids), tf.zeros_like(input_ids)
-        )
-
-        # Run the stacked transformer.
-        encoder_output = self.encoder(input_embs, input_mask, training)
-
-        return encoder_output, input_mask
-
-    def _get_start_token_ids(self, tensor_for_shape):
-        start_token_id = 2
-        batch_size = utils.get_shape_list(tensor_for_shape)[0]
-        return tf.ones([batch_size], dtype=tf.int32) * start_token_id
-
-    def get_inputs_from_targets(self, targets, start_token_ids):
-        """Converts target ids to input ids, i.e. adds <s> and removes last."""
-        length = tf.math.count_nonzero(targets, axis=1, dtype=tf.int32)
-        # Add start token ids.
-        inputs = tf.concat(
-            [tf.expand_dims(start_token_ids, axis=1), targets], 1
-        )
-        # Remove </s> from the input.
-        mask = tf.sequence_mask(
-            length, self.params['max_decoder_length'] + 1, dtype=tf.int32
-        )
-        inputs = (mask * inputs)[:, :-1]
-        return inputs
-
-    def _decode(
-        self,
-        target_ids,
-        target_mask,
-        start_token_ids,
-        encoder_output,
-        encoder_mask,
-        training,
-    ):
-        """Compute likelihood of target tokens under the model.
-
-    Args:
-      target_ids: tensor with shape [batch_size, target_length, hidden_size]
-      target_mask: self-attention bias for decoder attention layer. [batch_size,
-        input_length]
-      start_token_ids: int32 tensor of shape [batch_size] for first decoder
-        input.
-      encoder_output: Continuous representation of input sequence. Float tensor
-        with shape [batch_size, input_length, hidden_size].
-      encoder_mask: Float tensor with shape [batch_size, input_length].
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      A dict containing the output ids, the output log-probs, the output logits.
-    """
-
-        # Prepare inputs to decoder layers by shifting targets, embedding ids,
-        # adding positional encoding and applying dropout.
-        input_ids = self.get_inputs_from_targets(target_ids, start_token_ids)
-
-        input_embs = self.embeder(
-            input_ids, self.params['max_decoder_length'], training=training
-        )
-
-        outputs = self.decoder(
-            input_embs,
-            target_mask,
-            encoder_output,
-            encoder_mask,
-            training=training,
-        )
-
-        logits = self.embeder.linear(outputs)
-        output_ids = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
-
-        log_probs = -tf.nn.sparse_softmax_cross_entropy_with_logits(
-            labels=target_ids, logits=logits
-        )
-        log_probs = tf.where(
-            target_ids > 0, log_probs, tf.zeros_like(log_probs, tf.float32)
-        )
-
-        return (
-            tf.identity(log_probs, name='log_probs'),
-            tf.identity(logits, name='logits'),
-            tf.cast(output_ids, tf.int32, name='pred_ids'),
-        )
-
-    def _init_cache(self, batch_size):
-        """Initialize cache for decoding."""
-
-        max_decode_len = self.params['max_decoder_length']
-        num_heads = self.params['num_attention_heads']
-        head_size = int(self.params['hidden_size'] / num_heads)
-
-        cache = {}
-        for layer in range(self.params['num_hidden_layers']):
-            cache['layer_%d' % layer] = {
-                'k': tf.zeros(
-                    [batch_size, num_heads, max_decode_len, head_size]
-                ),
-                'v': tf.zeros(
-                    [batch_size, num_heads, max_decode_len, head_size]
-                ),
-            }
-        return cache
-
-    def _get_symbols_to_logits_fn(self, decoder_self_attention_mask):
-        """Returns a decoding function that calculates logits of the next tokens."""
-
-        max_decode_len = self.params['max_decoder_length']
-
-        def _symbols_to_logits_fn(target_ids, cache, i):
-            """Generate logits for next candidate IDs.
-
-      Args:
-        target_ids: Current decoded sequences. int tensor with shape
-          [batch_size, i + 1]
-        cache: dictionary of values storing the encoder output, encoder-decoder
-          attention bias, and previous decoder attention values.
-        i: Loop index
-
-      Returns:
-        Tuple of
-          (logits with shape [batch_size * beam_size, vocab_size],
-           updated cache values)
-      """
-            decoder_input = tf.slice(
-                target_ids,
-                [0, tf.maximum(tf.cast(0, i.dtype), i - 1)],
-                [tf.shape(target_ids)[0], 1],
-            )
-            self_attention_mask = tf.slice(
-                decoder_self_attention_mask,
-                [0, 0, i, 0],
-                [1, 1, 1, max_decode_len],
-            )
-
-            # Preprocess decoder input by getting embeddings and adding timing signal.
-            decoder_input = self.embeder(
-                decoder_input, 1, start_pos=i, training=False
-            )
-
-            decoder_output = self.decoder(
-                decoder_input,
-                self_attention_mask,
-                cache.get('encoder_output'),
-                cache.get('encoder_mask'),
-                cache=cache,
-                decode_i=i,
-                training=False,
-            )
-
-            logits = self.embeder.linear(decoder_output)
-            logits = tf.squeeze(logits, axis=[1])
-
-            return logits
-
-        return _symbols_to_logits_fn
-
-    def _predict(
-        self,
-        target_ids,
-        target_mask,
-        start_token_ids,
-        encoder_output,
-        encoder_mask,
-    ):
-        """Beam decode output tokens and probabilities.
-
-    Args:
-      target_ids: tensor with shape [batch_size, target_length, hidden_size]
-      target_mask: self-attention bias for decoder attention layer. [batch_size,
-        input_length]
-      start_token_ids: int32 tensor of shape [batch_size] for first decoder
-        input.
-      encoder_output: Continuous representation of input sequence. Float
-        tensor with shape [batch_size, target_length, num_hidden_layers,
-        hidden_size]
-      encoder_mask: bias for encoder-decoder attention layer. [batch_size,
-        input_length]
-
-    Returns:
-      A tuple of:
-        `log_probs`: Log-probs of output tokens.
-        `logits`: Logits of output tokens.
-        `pred_ids`: Predicted output sequence.
-    """
-        batch_size = utils.get_shape_list(start_token_ids)[0]
-        end_token_id = 1
-
-        # One step logit function.
-        symbols_to_logits_fn = self._get_symbols_to_logits_fn(target_mask)
-
-        # Create cache storing decoder attention values for each layer.
-        cache = self._init_cache(batch_size)
-
-        if encoder_output is not None:
-            # Add encoder output and attention bias to the cache.
-            cache['encoder_output'] = encoder_output
-            cache['encoder_mask'] = encoder_mask
-
-        decoded_ids = decoder.left2right_decode(
-            symbols_to_logits_fn,
-            start_token_ids,
-            cache,
-            batch_size,
-            self.params['max_decoder_length'],
-            vocab_size=self.params['vocab_size'],
-            beam_size=self.params['beam_size'],
-            beam_start=5,
-            beam_alpha=self.params['alpha'],
-            beam_min=0,
-            beam_max=-1,
-            eos_id=end_token_id,
-        )
-
-        # Get the top sequence for each batch element
-        output_ids = tf.cast(decoded_ids, tf.int32, name='pred_ids')
-
-        # Calculate log probs for given sequence if available.
-        calc_ids = output_ids if target_ids is None else target_ids
-        output_log_probs, output_logits, _ = self._decode(
-            calc_ids,
-            target_mask,
-            start_token_ids,
-            encoder_output,
-            encoder_mask,
-            training=False,
-        )
-
-        return (output_log_probs, output_logits, output_ids)
-
-    def _decode_and_predict(
-        self, target_ids, encoder_output, encoder_mask, training
-    ):
-        """Decodes a sequence given the input and the encoder.
-
-    Args:
-      target_ids: tensor with shape [batch_size, target_length, hidden_size]
-      encoder_output: Continuous representation of input sequence. Float
-        tensor with shape [batch_size, target_length, num_hidden_layers,
-        hidden_size]
-      encoder_mask: bias for encoder-decoder attention layer. [batch_size,
-        input_length]
-      training: Boolean indicating whether the call is training or inference.
-
-    Returns:
-      A tuple of:
-        `log_probs`: Log-probs of output tokens.
-        `logits`: Logits of output tokens.
-        `pred_ids`: Predicted output sequence.
-    """
-        # Create initial set of IDs that will be passed into symbols_to_logits_fn.
-        start_token_ids = self._get_start_token_ids(encoder_output)
-
-        # Create causal self-attention mask for decoder.
-        target_mask = decoder.create_self_attention_mask(
-            self.params['max_decoder_length']
-        )
-
-        predictions = {}
-        if training:
-            predictions = self._decode(
-                target_ids,
-                target_mask,
-                start_token_ids,
-                encoder_output,
-                encoder_mask,
-                training=True,
-            )
-        else:
-            predictions = self._predict(
-                target_ids,
-                target_mask,
-                start_token_ids,
-                encoder_output,
-                encoder_mask,
-            )
-
-        return predictions
-
-    def call(self, input_ids, target_ids=None, training=None):
-        # Run the inputs through the encoder layer to map the symbol
-        # representations to continuous representations.
-        encoder_output, encoder_mask = self._encode(input_ids, training)
-
-        # Decode.
-        predictions = self._decode_and_predict(
-            target_ids, encoder_output, encoder_mask, training
-        )
-
-        return predictions, encoder_output
diff --git a/malaya/train/model/bigbird/optimization.py b/malaya/train/model/bigbird/optimization.py
deleted file mode 100644
index c48b222c..00000000
--- a/malaya/train/model/bigbird/optimization.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Functions and classes related to optimization (weight updates)."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import re
-import tensorflow as tf
-
-
-def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):
-    """Creates an optimizer training op."""
-    global_step = tf.train.get_or_create_global_step()
-
-    learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)
-
-    # Implements linear decay of the learning rate.
-    learning_rate = tf.train.polynomial_decay(
-        learning_rate,
-        global_step,
-        num_train_steps,
-        end_learning_rate=0.0,
-        power=1.0,
-        cycle=False,
-    )
-
-    # Implements linear warmup. I.e., if global_step < num_warmup_steps, the
-    # learning rate will be `global_step/num_warmup_steps * init_lr`.
-    if num_warmup_steps:
-        global_steps_int = tf.cast(global_step, tf.int32)
-        warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
-
-        global_steps_float = tf.cast(global_steps_int, tf.float32)
-        warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
-
-        warmup_percent_done = global_steps_float / warmup_steps_float
-        warmup_learning_rate = init_lr * warmup_percent_done
-
-        is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
-        learning_rate = (
-            1.0 - is_warmup
-        ) * learning_rate + is_warmup * warmup_learning_rate
-
-    # It is recommended that you use this optimizer for fine tuning, since this
-    # is how the model was trained (note that the Adam m/v variables are NOT
-    # loaded from init_checkpoint.)
-    optimizer = AdamWeightDecayOptimizer(
-        learning_rate=learning_rate,
-        weight_decay_rate=0.01,
-        beta_1=0.9,
-        beta_2=0.999,
-        epsilon=1e-6,
-        exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'],
-    )
-
-    if use_tpu:
-        optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
-
-    tvars = tf.trainable_variables()
-    grads = tf.gradients(loss, tvars)
-
-    # This is how the model was pre-trained.
-    (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
-
-    train_op = optimizer.apply_gradients(
-        zip(grads, tvars), global_step=global_step
-    )
-
-    # Normally the global step update is done inside of `apply_gradients`.
-    # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
-    # a different optimizer, you should probably take this line out.
-    new_global_step = global_step + 1
-    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
-    return train_op
-
-
-class AdamWeightDecayOptimizer(tf.train.Optimizer):
-    """A basic Adam optimizer that includes "correct" L2 weight decay."""
-
-    def __init__(
-        self,
-        learning_rate,
-        weight_decay_rate=0.0,
-        beta_1=0.9,
-        beta_2=0.999,
-        epsilon=1e-6,
-        exclude_from_weight_decay=None,
-        name='AdamWeightDecayOptimizer',
-    ):
-        """Constructs a AdamWeightDecayOptimizer."""
-        super(AdamWeightDecayOptimizer, self).__init__(False, name)
-
-        self.learning_rate = learning_rate
-        self.weight_decay_rate = weight_decay_rate
-        self.beta_1 = beta_1
-        self.beta_2 = beta_2
-        self.epsilon = epsilon
-        self.exclude_from_weight_decay = exclude_from_weight_decay
-
-    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
-        """See base class."""
-        assignments = []
-        for (grad, param) in grads_and_vars:
-            if grad is None or param is None:
-                continue
-
-            param_name = self._get_variable_name(param.name)
-
-            m = tf.get_variable(
-                name=param_name + '/adam_m',
-                shape=param.shape.as_list(),
-                dtype=tf.float32,
-                trainable=False,
-                initializer=tf.zeros_initializer(),
-            )
-            v = tf.get_variable(
-                name=param_name + '/adam_v',
-                shape=param.shape.as_list(),
-                dtype=tf.float32,
-                trainable=False,
-                initializer=tf.zeros_initializer(),
-            )
-
-            # Standard Adam update.
-            next_m = tf.multiply(self.beta_1, m) + tf.multiply(
-                1.0 - self.beta_1, grad
-            )
-            next_v = tf.multiply(self.beta_2, v) + tf.multiply(
-                1.0 - self.beta_2, tf.square(grad)
-            )
-
-            update = next_m / (tf.sqrt(next_v) + self.epsilon)
-
-            # Just adding the square of the weights to the loss function is *not*
-            # the correct way of using L2 regularization/weight decay with Adam,
-            # since that will interact with the m and v parameters in strange ways.
-            #
-            # Instead we want ot decay the weights in a manner that doesn't interact
-            # with the m/v parameters. This is equivalent to adding the square
-            # of the weights to the loss with plain (non-momentum) SGD.
-            if self._do_use_weight_decay(param_name):
-                update += self.weight_decay_rate * param
-
-            update_with_lr = self.learning_rate * update
-
-            next_param = param - update_with_lr
-
-            assignments.extend(
-                [param.assign(next_param), m.assign(next_m), v.assign(next_v)]
-            )
-        return tf.group(*assignments, name=name)
-
-    def _do_use_weight_decay(self, param_name):
-        """Whether to use L2 weight decay for `param_name`."""
-        if not self.weight_decay_rate:
-            return False
-        if self.exclude_from_weight_decay:
-            for r in self.exclude_from_weight_decay:
-                if re.search(r, param_name) is not None:
-                    return False
-        return True
-
-    def _get_variable_name(self, param_name):
-        """Get the variable name from the tensor name."""
-        m = re.match('^(.*):\\d+$', param_name)
-        if m is not None:
-            param_name = m.group(1)
-        return param_name
diff --git a/malaya/train/model/bigbird/utils.py b/malaya/train/model/bigbird/utils.py
deleted file mode 100644
index c41d7c97..00000000
--- a/malaya/train/model/bigbird/utils.py
+++ /dev/null
@@ -1,806 +0,0 @@
-# Copyright 2020 The BigBird Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Helper and utility functions."""
-
-import re
-
-from absl import logging
-import numpy as np
-import tensorflow as tf
-
-
-############################### SHAPE UTILS ####################################
-
-
-def get_shape_list(tensor, expected_rank=None, name=None):
-    """Returns a list of the shape of tensor, preferring static dimensions.
-
-  Args:
-    tensor: A tf.Tensor object to find the shape of.
-    expected_rank: (optional) int. The expected rank of `tensor`. If this is
-      specified and the `tensor` has a different rank, and exception will be
-      thrown.
-    name: Optional name of the tensor for the error message.
-
-  Returns:
-    A list of dimensions of the shape of tensor. All static dimensions will
-    be returned as python integers, and dynamic dimensions will be returned
-    as tf.Tensor scalars.
-  """
-    if not tf.executing_eagerly() and name is None:
-        name = tensor.name
-
-    if expected_rank is not None:
-        assert_rank(tensor, expected_rank, name)
-
-    shape = tensor.shape.as_list()
-
-    non_static_indexes = []
-    for (index, dim) in enumerate(shape):
-        if dim is None:
-            non_static_indexes.append(index)
-
-    if not non_static_indexes:
-        return shape
-
-    # assert False, 'Static shape not available for {}'.format(tensor)
-
-    dyn_shape = tf.shape(tensor)
-    for index in non_static_indexes:
-        shape[index] = dyn_shape[index]
-    return shape
-
-
-def reshape_to_matrix(input_tensor):
-    """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
-    ndims = input_tensor.shape.ndims
-    if ndims < 2:
-        raise ValueError(
-            'Input tensor must have at least rank 2. Shape = %s'
-            % (input_tensor.shape)
-        )
-    if ndims == 2:
-        return input_tensor
-
-    width = input_tensor.shape[-1]
-    output_tensor = tf.reshape(input_tensor, [-1, width])
-    return output_tensor
-
-
-def reshape_from_matrix(output_tensor, orig_shape_list):
-    """Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
-    if len(orig_shape_list) == 2:
-        return output_tensor
-
-    output_shape = get_shape_list(output_tensor)
-
-    orig_dims = orig_shape_list[0:-1]
-    width = output_shape[-1]
-
-    return tf.reshape(output_tensor, orig_dims + [width])
-
-
-def assert_rank(tensor, expected_rank, name=None):
-    """Raises an exception if the tensor rank is not of the expected rank.
-
-  Args:
-    tensor: A tf.Tensor to check the rank of.
-    expected_rank: Python integer or list of integers, expected rank.
-    name: Optional name of the tensor for the error message.
-
-  Raises:
-    ValueError: If the expected shape doesn't match the actual shape.
-  """
-    if not tf.executing_eagerly() and name is None:
-        name = tensor.name
-
-    expected_rank_dict = {}
-    if isinstance(expected_rank, int):
-        expected_rank_dict[expected_rank] = True
-    else:
-        for x in expected_rank:
-            expected_rank_dict[x] = True
-
-    actual_rank = tensor.shape.ndims
-    if actual_rank not in expected_rank_dict:
-        scope_name = tf.compat.v1.get_variable_scope().name
-        raise ValueError(
-            'For the tensor `{}` in scope `{}`, the actual rank '
-            '`{}` (shape = {}) is not equal to the expected rank `{}`'.format(
-                name,
-                scope_name,
-                actual_rank,
-                str(tensor.shape),
-                str(expected_rank),
-            )
-        )
-
-
-############################### DENSE LAYERS ###################################
-
-
-def create_initializer(initializer_range=0.02):
-    """Creates a `truncated_normal_initializer` with the given range."""
-    return tf.compat.v1.truncated_normal_initializer(stddev=initializer_range)
-
-
-class Dense3dLayer(tf.compat.v1.layers.Layer):
-    """A dense layer with 3D kernel."""
-
-    def __init__(
-        self,
-        num_attention_heads,
-        size_per_head,
-        initializer,
-        activation,
-        name=None,
-        head_first=False,
-        use_bias=True,
-    ):
-        """Constructor for dense layer with 3D kernel.
-
-    Args:
-      num_attention_heads: The size of output dimension.
-      size_per_head: The size per attention head.
-      initializer: Kernel initializer.
-      activation: Actication function.
-      name: The name scope of this layer.
-      head_first: Whether to output head dimension before or after sequence dim.
-      use_bias: Whether the layer uses a bias vector.
-    """
-        super(Dense3dLayer, self).__init__(name=name)
-        self.num_attention_heads = num_attention_heads
-        self.size_per_head = size_per_head
-        self.initializer = initializer
-        self.activation = activation
-        self.head_first = head_first
-        self.use_bias = use_bias
-
-        self.w = None
-        self.b = None
-
-    def call(self, input_tensor):
-        """Constructor for dense layer with 3D kernel.
-
-    Args:
-      input_tensor: float Tensor of shape [batch, seq_length, hidden_size].
-
-    Returns:
-      float logits Tensor.
-    """
-        last_dim = get_shape_list(input_tensor)[-1]
-        if self.w is None:
-            self.w = tf.compat.v1.get_variable(
-                name='kernel',
-                shape=[
-                    last_dim,
-                    self.num_attention_heads * self.size_per_head,
-                ],
-                initializer=self.initializer,
-            )
-            self.initializer = None
-            self._trainable_weights.append(self.w)
-        reshape_w = tf.reshape(
-            self.w, [last_dim, self.num_attention_heads, self.size_per_head]
-        )
-        if self.head_first:
-            ret = tf.einsum('abc,cde->adbe', input_tensor, reshape_w)
-        else:
-            ret = tf.einsum('abc,cde->abde', input_tensor, reshape_w)
-
-        if self.use_bias:
-            if self.b is None:
-                self.b = tf.compat.v1.get_variable(
-                    name='bias',
-                    shape=[self.num_attention_heads * self.size_per_head],
-                    initializer=tf.zeros_initializer,
-                )
-                self._trainable_weights.append(self.b)
-            if self.head_first:
-                reshape_b = tf.reshape(
-                    self.b, [1, self.num_attention_heads, 1, self.size_per_head]
-                )
-            else:
-                reshape_b = tf.reshape(
-                    self.b, [self.num_attention_heads, self.size_per_head]
-                )
-            ret += reshape_b
-
-        if self.activation is not None:
-            return self.activation(ret)
-        else:
-            return ret
-
-
-class Dense3dProjLayer(tf.compat.v1.layers.Layer):
-    """A dense layer with 3D kernel for projection."""
-
-    def __init__(
-        self,
-        num_attention_heads,
-        size_per_head,
-        initializer,
-        activation,
-        name=None,
-        use_bias=True,
-    ):
-        """Constructor for dense layer with 3D kernel for projection.
-
-    Args:
-      num_attention_heads: The size of output dimension.
-      size_per_head: The size per attention head.
-      initializer: Kernel initializer.
-      activation: Actication function.
-      name: The name scope of this layer.
-      use_bias: Whether the layer uses a bias vector.
-    """
-        super(Dense3dProjLayer, self).__init__(name=name)
-        self.num_attention_heads = num_attention_heads
-        self.size_per_head = size_per_head
-        self.initializer = initializer
-        self.activation = activation
-        self.use_bias = use_bias
-
-        self.w = None
-        self.b = None
-
-    def call(self, input_tensor):
-        """Constructor for dense layer with 3D kernel for projection.
-
-    Args:
-      input_tensor: float Tensor of shape [batch,from_seq_length,
-        num_attention_heads, size_per_head].
-
-    Returns:
-      float logits Tensor.
-    """
-        hidden_size = self.num_attention_heads * self.size_per_head
-        if self.w is None:
-            self.w = tf.compat.v1.get_variable(
-                name='kernel',
-                shape=[hidden_size, hidden_size],
-                initializer=self.initializer,
-            )
-            self.initializer = None
-            self._trainable_weights.append(self.w)
-        reshape_w = tf.reshape(
-            self.w, [self.num_attention_heads, self.size_per_head, hidden_size]
-        )
-        ret = tf.einsum('BFNH,NHD->BFD', input_tensor, reshape_w)
-
-        if self.use_bias:
-            if self.b is None:
-                self.b = tf.compat.v1.get_variable(
-                    name='bias',
-                    shape=[hidden_size],
-                    initializer=tf.zeros_initializer,
-                )
-                self._trainable_weights.append(self.b)
-            ret += self.b
-
-        if self.activation is not None:
-            return self.activation(ret)
-        else:
-            return ret
-
-
-class Dense2dLayer(tf.compat.v1.layers.Layer):
-    """A dense layer with 2D kernel."""
-
-    def __init__(
-        self, output_size, initializer, activation, name=None, use_bias=True
-    ):
-        """Constructor for dense layer with 2D kernel.
-
-    Args:
-      output_size: The size of output dimension.
-      initializer: Kernel initializer.
-      activation: Actication function.
-      name: The name scope of this layer.
-      use_bias: Whether the layer uses a bias vector.
-    """
-        super(Dense2dLayer, self).__init__(name=name)
-        self.output_size = output_size
-        self.initializer = initializer
-        self.activation = activation
-        self.use_bias = use_bias
-
-        self.w = None
-        self.b = None
-
-    def call(self, input_tensor):
-        """Forward pass for dense layer with 2D kernel.
-
-    Args:
-      input_tensor: Float tensor with rank 3.
-
-    Returns:
-      float logits Tensor.
-    """
-        if self.w is None:
-            last_dim = get_shape_list(input_tensor)[-1]
-            self.w = tf.compat.v1.get_variable(
-                name='kernel',
-                shape=[last_dim, self.output_size],
-                initializer=self.initializer,
-            )
-            self.initializer = None
-            self._trainable_weights.append(self.w)
-        ret = tf.einsum('abc,cd->abd', input_tensor, self.w)
-
-        if self.use_bias:
-            if self.b is None:
-                self.b = tf.compat.v1.get_variable(
-                    name='bias',
-                    shape=[self.output_size],
-                    initializer=tf.zeros_initializer,
-                )
-                self._trainable_weights.append(self.b)
-            ret += self.b
-
-        if self.activation is not None:
-            return self.activation(ret)
-        else:
-            return ret
-
-
-def gelu(x):
-    """Gaussian Error Linear Unit.
-
-  This is a smoother version of the RELU.
-  Original paper: https://arxiv.org/abs/1606.08415
-  Args:
-    x: float Tensor to perform activation.
-
-  Returns:
-    `x` with the GELU activation applied.
-  """
-    cdf = 0.5 * (
-        1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))
-    )
-    return x * cdf
-
-
-def get_activation(activation_string):
-    """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.
-
-  Args:
-    activation_string: String name of the activation function.
-
-  Returns:
-    A Python function corresponding to the activation function. If
-    `activation_string` is None, empty, or "linear", this will return None.
-    If `activation_string` is not a string, it will return `activation_string`.
-
-  Raises:
-    ValueError: The `activation_string` does not correspond to a known
-      activation.
-  """
-
-    # We assume that anything that"s not a string is already an activation
-    # function, so we just return it.
-    if not isinstance(activation_string, str):
-        return activation_string
-
-    if not activation_string:
-        return None
-
-    act = activation_string.lower()
-    if act == 'linear':
-        return None
-    elif act == 'relu':
-        return tf.nn.relu
-    elif act == 'gelu':
-        return gelu
-    elif act == 'tanh':
-        return tf.tanh
-    else:
-        raise ValueError('Unsupported activation: %s' % act)
-
-
-########################## NORM & DROPOUT LAYERS ###############################
-
-
-def dropout(input_tensor, dropout_prob, training=True):
-    """Perform dropout.
-
-  Args:
-    input_tensor: float Tensor.
-    dropout_prob: Python float. The probability of dropping out a value (NOT of
-      *keeping* a dimension as in `tf.nn.dropout`).
-    training: Boolean indicating whether the call is training or inference.
-
-  Returns:
-    A version of `input_tensor` with dropout applied.
-  """
-
-    if not training or dropout_prob is None or dropout_prob == 0.0:
-        return input_tensor
-
-    output = tf.nn.dropout(input_tensor, rate=dropout_prob)
-    return output
-
-
-class NormLayer(tf.compat.v1.layers.Layer):
-    """Replacement for contrib_layers.layer_norm."""
-
-    def __init__(self, name='LayerNorm'):
-        super(NormLayer, self).__init__(name=name)
-        self.beta = None
-        self.gamma = None
-
-    def call(self, input_tensor):
-        inputs = tf.convert_to_tensor(input_tensor)
-        inputs_shape = get_shape_list(inputs)
-        inputs_rank = len(inputs_shape)
-        dtype = inputs.dtype.base_dtype
-        norm_axis = inputs_rank - 1
-        params_shape = [inputs_shape[norm_axis]]
-
-        # Allocate parameters for the beta and gamma of the normalization.
-        if self.beta is None:
-            self.beta = tf.compat.v1.get_variable(
-                'beta',
-                shape=params_shape,
-                dtype=dtype,
-                initializer=tf.zeros_initializer(),
-                trainable=True,
-            )
-            self._trainable_weights.append(self.beta)
-        if self.gamma is None:
-            self.gamma = tf.compat.v1.get_variable(
-                'gamma',
-                shape=params_shape,
-                dtype=dtype,
-                initializer=tf.ones_initializer(),
-                trainable=True,
-            )
-            self._trainable_weights.append(self.gamma)
-        # Compute norm along last axis
-        mean, variance = tf.nn.moments(inputs, [norm_axis], keepdims=True)
-        # Compute layer normalization using the batch_normalization function.
-        # Note that epsilon must be increased for float16 due to the limited
-        # representable range.
-        variance_epsilon = 1e-12 if dtype != tf.float16 else 1e-3
-        outputs = tf.nn.batch_normalization(
-            inputs,
-            mean,
-            variance,
-            offset=self.beta,
-            scale=self.gamma,
-            variance_epsilon=variance_epsilon,
-        )
-        tf.reshape(outputs, inputs_shape)
-        # outputs.set_shape(inputs_shape)
-        return outputs
-
-
-############################# EMBEDDING LAYER ##################################
-
-
-class EmbeddingLayer(tf.compat.v1.layers.Layer):
-    """An embedding layer."""
-
-    def __init__(
-        self,
-        vocab_size,
-        emb_dim,
-        initializer,
-        scale_emb=False,
-        use_token_type=False,
-        num_token_types=16,
-        use_position_embeddings=True,
-        max_position_embeddings=4096,
-        dropout_prob=0.0,
-        name='embeddings',
-    ):
-        super(EmbeddingLayer, self).__init__(name=name)
-        self.vocab_size = vocab_size
-        self.emb_dim = emb_dim
-        self.scale_emb = scale_emb
-        self.num_token_types = num_token_types
-        self.max_position_embeddings = max_position_embeddings
-        self.dropout_prob = dropout_prob
-
-        with tf.compat.v1.variable_scope(name):
-            self.word_embeddings = tf.compat.v1.get_variable(
-                'word_embeddings',
-                [vocab_size, emb_dim],
-                dtype=tf.float32,
-                initializer=initializer,
-            )
-            self._trainable_weights.append(self.word_embeddings)
-
-            if use_token_type:
-                self.token_type_table = tf.compat.v1.get_variable(
-                    'token_type_embeddings',
-                    [num_token_types, emb_dim],
-                    dtype=tf.float32,
-                    initializer=initializer,
-                )
-                self._trainable_weights.append(self.token_type_table)
-            else:
-                self.token_type_table = None
-
-            if use_position_embeddings:
-                self.position_embeddings = tf.compat.v1.get_variable(
-                    'position_embeddings',
-                    [max_position_embeddings, emb_dim],
-                    dtype=tf.float32,
-                    initializer=initializer,
-                )
-                self._trainable_weights.append(self.position_embeddings)
-            else:
-                self.position_embeddings = None
-
-    def call(
-        self,
-        input_ids,
-        seq_length,
-        start_pos=0,
-        token_type_ids=None,
-        training=None,
-    ):
-        if input_ids is None:
-            return None
-
-        # subtoken embedding
-        output = tf.nn.embedding_lookup(
-            params=self.word_embeddings, ids=input_ids
-        )
-
-        if self.scale_emb:
-            output = output * self.emb_dim ** 0.5
-
-        if self.token_type_table is not None:
-            # This vocab will be small so we always do one-hot here, since it is
-            # always faster for a small vocabulary.
-            one_hot_ids = tf.one_hot(
-                token_type_ids, depth=self.num_token_types
-            )
-            token_type_embeddings = tf.tensordot(
-                one_hot_ids, self.token_type_table, 1
-            )
-            output += token_type_embeddings
-
-        if self.position_embeddings is not None:
-            # assert_op = tf.compat.v1.assert_less_equal(
-            #     start_pos + seq_length, self.max_position_embeddings)
-            # with tf.control_dependencies([assert_op]):
-            # So `position_embeddings` is effectively an embedding table for
-            # position [0, 1, 2, ..., max_position_embeddings-1], and the current
-            # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
-            # perform a slice.
-            position_embeddings = tf.slice(
-                self.position_embeddings,
-                [start_pos, 0],
-                [seq_length, self.emb_dim],
-            )
-            output += tf.expand_dims(position_embeddings, axis=0)
-
-        if training and self.dropout_prob > 0:
-            output = tf.nn.dropout(output, rate=self.dropout_prob)
-        return output
-
-    def linear(self, x):
-        """Computes logits by running x through a linear layer.
-
-    Args:
-      x: A float32 tensor with shape [..., hidden_size]
-    Returns:
-      float32 tensor with shape [..., vocab_size].
-    """
-        with tf.compat.v1.name_scope('presoftmax_linear'):
-            logits = tf.tensordot(x, self.word_embeddings, [[-1], [1]])
-        return logits
-
-
-########################## TPU/CHECKPOINT UTILS ################################
-
-
-def get_estimator(config, model_fn, keep_checkpoint_max=10):
-    """Create TPUEstimator object for given config and model_fn."""
-    tpu_cluster_resolver = None
-    if config['use_tpu'] and config['tpu_name']:
-        tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
-            config['tpu_name'],
-            zone=config['tpu_zone'],
-            project=config['gcp_project'],
-        )
-
-    # Batch size book-keeping
-    # Estimators handle batch sizes differently among GPUs and TPUs
-    # GPU: Estimator needs per core batch size
-    # TPU: Estimator needs total batch size, i.e. num_cores * per core batch size
-    config_train_batch_size = config['train_batch_size']  # For estimator
-    config_eval_batch_size = config['eval_batch_size']  # For estimator
-    effective_train_batch_size = config['train_batch_size']  # For human
-    effective_eval_batch_size = config['eval_batch_size']  # For human
-    if config['use_tpu']:
-        sliced_eval_mode = tf.compat.v1.estimator.tpu.InputPipelineConfig.SLICED
-        distribute_strategy = None
-        config_train_batch_size *= config['num_tpu_cores']
-        config_eval_batch_size *= config['num_tpu_cores']
-        effective_train_batch_size = config_train_batch_size
-        effective_eval_batch_size = config_eval_batch_size
-    else:
-        sliced_eval_mode = (
-            tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V1
-        )
-        distribute_strategy = tf.distribute.MirroredStrategy(devices=None)
-        effective_train_batch_size *= distribute_strategy.num_replicas_in_sync
-        # effective_eval_batch_size *= distribute_strategy.num_replicas_in_sync
-
-    is_per_host = tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V2
-    run_config = tf.compat.v1.estimator.tpu.RunConfig(
-        cluster=tpu_cluster_resolver,
-        master=config['master'],
-        model_dir=config['output_dir'],
-        save_checkpoints_steps=config['save_checkpoints_steps'],
-        keep_checkpoint_max=keep_checkpoint_max,
-        train_distribute=distribute_strategy,
-        tpu_config=tf.compat.v1.estimator.tpu.TPUConfig(
-            tpu_job_name=config['tpu_job_name'],
-            iterations_per_loop=config['iterations_per_loop'],
-            num_shards=config['num_tpu_cores'],
-            per_host_input_for_training=is_per_host,
-            eval_training_input_configuration=sliced_eval_mode,
-        ),
-    )
-
-    if config['init_checkpoint']:
-        ckpt_var_list = tf.compat.v1.train.list_variables(
-            config['init_checkpoint']
-        )
-        ckpt_var_list = {
-            name: shape
-            for name, shape in ckpt_var_list
-            if not re.findall('(Adam|Adafactor|global_step)', name)
-        }
-        vars_to_warm_start = '({})'.format('|'.join(ckpt_var_list.keys()))
-        warm_start_settings = tf.estimator.WarmStartSettings(
-            ckpt_to_initialize_from=config['init_checkpoint'],
-            vars_to_warm_start=vars_to_warm_start,
-        )
-    else:
-        ckpt_var_list = {}
-        warm_start_settings = None
-    config['ckpt_var_list'] = ckpt_var_list
-
-    # If no TPU, this will fall back to normal Estimator on CPU or GPU.
-    estimator = tf.compat.v1.estimator.tpu.TPUEstimator(
-        use_tpu=config['use_tpu'],
-        model_fn=model_fn,
-        config=run_config,
-        train_batch_size=config_train_batch_size,
-        eval_batch_size=config_eval_batch_size,
-        warm_start_from=warm_start_settings,
-    )
-
-    # assign batch sizes
-    estimator.train_batch_size = effective_train_batch_size
-    estimator.eval_batch_size = effective_eval_batch_size
-
-    return estimator
-
-
-def log_variables(variables, ckpt_var_list):
-    """Log trainable variables."""
-    logging.info('**** Trainable Variables ****')
-
-    model_var_list = {var.name: var.get_shape().as_list() for var in variables}
-    num_params = sum(np.prod(shape) for shape in model_var_list.values())
-    length = max(len(name) for name in model_var_list) + 2
-    line = '{{:<{}}}{{:<13}}{{}}'.format(length)
-
-    logging.info(
-        'The model has {} trainable variables '
-        '({:,} parameters):\n'.format(len(model_var_list), num_params)
-    )
-    logging.info(line.format('Name', 'Initialized', 'Shape'))
-    logging.info(line.format('----', '-----------', '-----'))
-
-    ckpt_var_list = ckpt_var_list.copy()
-    for name, shape in model_var_list.items():
-        name = name.split(':')[0]
-        if name in ckpt_var_list:
-            warm_started = 'from ckpt'
-            del ckpt_var_list[name]
-        else:
-            warm_started = 'random'
-        logging.info(line.format(name, warm_started, shape))
-
-    if ckpt_var_list:
-        logging.warning(
-            'The warm start checkpoint contained %d variables that were not used '
-            'for the model:\n',
-            len(ckpt_var_list),
-        )
-        for name, shape in ckpt_var_list.items():
-            logging.warning(line.format(name, 'not used', shape))
-
-
-def add_scalars_to_summary(summary_dir, scalar_tensors_dict):
-    """Creates a host_call function that writes summaries on TPU."""
-
-    #  All tensors outfed from TPU should preserve batch size dimension.
-    scalar_tensors_dict = {
-        k: tf.reshape(v, [1]) for k, v in scalar_tensors_dict.items()
-    }
-
-    def host_call_fn(**kwargs):
-        writer = tf.summary.create_file_writer(summary_dir, max_queue=1000)
-        always_record = tf.summary.record_if(True)
-        with writer.as_default(), always_record:
-            for name, scalar in kwargs.items():
-                tf.summary.scalar(
-                    name,
-                    tf.reduce_mean(scalar),
-                    tf.compat.v1.train.get_or_create_global_step(),
-                )
-            return tf.compat.v1.summary.all_v2_summary_ops()
-
-    return host_call_fn, scalar_tensors_dict
-
-
-########################## DEFAULT CONFIG UTILS ################################
-
-
-def get_default_config():
-    """Default values for BigBird."""
-
-    default_config = {
-        # transformer basic configs
-        'attention_probs_dropout_prob': 0.1,
-        'hidden_act': 'gelu',
-        'hidden_dropout_prob': 0.1,
-        'hidden_size': 768,
-        'initializer_range': 0.02,
-        'intermediate_size': 3072,
-        'max_position_embeddings': 4096,
-        'num_attention_heads': 12,
-        'num_hidden_layers': 12,
-        'type_vocab_size': 2,
-        'use_bias': True,
-        'rescale_embedding': False,
-        'scope': 'bert',
-        # sparse mask configs
-        'attention_type': 'block_sparse',
-        'norm_type': 'postnorm',
-        'block_size': 16,
-        'num_rand_blocks': 3,
-        # common bert configs
-        'max_encoder_length': 1024,
-        'max_decoder_length': 64,
-        'couple_encoder_decoder': False,
-        'beam_size': 5,
-        'alpha': 0.7,
-        'label_smoothing': 0.1,
-        'weight_decay_rate': 0.01,
-        'optimizer_beta1': 0.9,
-        'optimizer_beta2': 0.999,
-        'optimizer_epsilon': 1e-6,
-        # TPU settings
-        'use_tpu': True,
-        'tpu_name': None,
-        'tpu_zone': None,
-        'tpu_job_name': None,
-        'gcp_project': None,
-        'master': None,
-        'num_tpu_cores': 8,
-        'iterations_per_loop': '1000',
-    }
-
-    return default_config
diff --git a/malaya/train/model/pegasus/__init__.py b/malaya/train/model/pegasus/__init__.py
deleted file mode 100644
index 792d6005..00000000
--- a/malaya/train/model/pegasus/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
diff --git a/malaya/train/model/pegasus/base.py b/malaya/train/model/pegasus/base.py
deleted file mode 100644
index a1c6a2a3..00000000
--- a/malaya/train/model/pegasus/base.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Base model definition."""
-
-import abc
-
-
-class BaseModel(object):  # pytype: disable=ignored-metaclass
-    """Base Abstract Class of All Models."""
-
-    __metaclass__ = abc.ABCMeta
-
-    @abc.abstractmethod
-    def __init__(self, *args):
-        """Construct model class with parameters."""
-        pass
-
-    @abc.abstractmethod
-    def __call__(self, features, training):
-        """Build the class graph in training/evaluation mode.
-
-        Args:
-          features: dictionary of tensors.
-          training: python boolean indicate of whether model is training
-
-        Returns:
-          tuple of loss and outputs. loss is a scalar tensor and outputs is a
-          dictionary of tensors.
-        """
-        loss = 0
-        outputs = {}
-        return loss, outputs
-
-    def predict(self, features, *args, **kwargs):
-        """Build the class graph in prediction model.
-
-        Args:
-          features: dictionary of tensors.
-          *args: additional args.
-          **kwargs: additional keyword args.
-
-        Returns:
-          dictionary of tensors.
-        """
-        del args, kwargs
-        _, outputs = self.__call__(features, False)
-        return outputs
diff --git a/malaya/train/model/pegasus/layers/__init__.py b/malaya/train/model/pegasus/layers/__init__.py
deleted file mode 100644
index 779c85e7..00000000
--- a/malaya/train/model/pegasus/layers/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/malaya/train/model/pegasus/layers/attention.py b/malaya/train/model/pegasus/layers/attention.py
deleted file mode 100644
index 79aaca3f..00000000
--- a/malaya/train/model/pegasus/layers/attention.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Attention layers.
-
-Notations:
-  B: batch_size, I: max_input_len, M: max_memory_len, D: hidden_size,
-  H: number of heads, Dh: hidden_size per head, Di: input dimension.
-"""
-#
-# pylint: disable=invalid-name
-
-import tensorflow as tf
-
-
-def split_heads(tensor_BxIxD, num_heads):
-    B = tf.shape(tensor_BxIxD)[0]
-    I = tf.shape(tensor_BxIxD)[1]
-    D = tf.shape(tensor_BxIxD)[2]
-
-    tensor_BxIxHxD = tf.reshape(tensor_BxIxD, [B, I, num_heads, D // num_heads])
-    tensor_BxHxIxD = tf.transpose(tensor_BxIxHxD, [0, 2, 1, 3])
-    return tensor_BxHxIxD
-
-
-class Attention(object):
-    """Multihead scaled dot product attention."""
-
-    def __init__(self, hidden_size, num_heads, attention_dropout):
-        if hidden_size % num_heads != 0:
-            raise ValueError(
-                'Number of attention heads must divide hidden size'
-            )
-
-        self._q_layer = tf.layers.Dense(
-            hidden_size, use_bias=False, name='query'
-        )
-        self._k_layer = tf.layers.Dense(
-            hidden_size, use_bias=False, name='key'
-        )
-        self._v_layer = tf.layers.Dense(
-            hidden_size, use_bias=False, name='value'
-        )
-        self._output_layer = tf.layers.Dense(
-            hidden_size, use_bias=False, name='output/dense'
-        )
-        self._num_heads = num_heads
-        self._hidden_size = hidden_size
-        self._attention_dropout = attention_dropout
-
-    def __call__(
-        self,
-        input_BxIxDi,
-        memory_BxMxDi,
-        bias_BxIxM,
-        training,
-        cache=None,
-        decode_i=None,
-    ):
-
-        B = tf.shape(input_BxIxDi)[0]
-        I = tf.shape(input_BxIxDi)[1]
-
-        M = tf.shape(memory_BxMxDi)[1]
-        H, D = self._num_heads, self._hidden_size
-        dtype = memory_BxMxDi.dtype
-
-        q_BxHxIxDh = split_heads(self._q_layer(input_BxIxDi), H)
-        q_BxHxIxDh *= (D // H) ** -0.5
-        k_BxHxMxDh = split_heads(self._k_layer(memory_BxMxDi), H)
-        v_BxHxMxDh = split_heads(self._v_layer(memory_BxMxDi), H)
-
-        # cache saves previous activations before time decode_i during TPU decoding.
-        if cache is not None and decode_i is not None:
-            M = tf.shape(cache['k'])[2]
-            indices_1x1xMx1 = tf.reshape(
-                tf.one_hot(decode_i, M, dtype=dtype), [1, 1, M, 1]
-            )
-            k_BxHxMxDh = cache['k'] + k_BxHxMxDh * indices_1x1xMx1
-            v_BxHxMxDh = cache['v'] + v_BxHxMxDh * indices_1x1xMx1
-            cache['k'] = k_BxHxMxDh
-            cache['v'] = v_BxHxMxDh
-        bias_BxHxIxM = tf.expand_dims(bias_BxIxM, axis=1)
-        logits_BxHxIxM = (
-            tf.matmul(q_BxHxIxDh, k_BxHxMxDh, transpose_b=True) + bias_BxHxIxM
-        )
-        alignment_BxHxIxM = tf.nn.softmax(logits_BxHxIxM)
-        if training:
-            alignment_BxHxIxM = tf.compat.v2.nn.dropout(
-                alignment_BxHxIxM,
-                rate=self._attention_dropout,
-                noise_shape=[1, 1, I, M],
-            )
-        outputs_BxHxIxDh = tf.matmul(alignment_BxHxIxM, v_BxHxMxDh)
-        outputs_BxIxD = tf.reshape(
-            tf.transpose(outputs_BxHxIxDh, [0, 2, 1, 3]), [B, I, D]
-        )
-        outputs_BxIxD = self._output_layer(outputs_BxIxD)
-        return outputs_BxIxD
-
-
-class SelfAttention(Attention):
-    """Multihead scaled dot product self-attention."""
-
-    def __call__(self, x, bias, training, cache=None, decode_i=None):
-        return super(SelfAttention, self).__call__(
-            x, x, bias, training, cache=cache, decode_i=decode_i
-        )
-
-
-def ids_to_bias(ids_BxI, dtype=tf.float32, padding_id=0):
-    """Convert ids to attention bias for attention."""
-    pad_BxI = tf.cast(tf.equal(ids_BxI, padding_id), dtype)
-    bias_Bx1xI = tf.expand_dims(pad_BxI * dtype.min, axis=1)
-    return bias_Bx1xI
-
-
-def upper_triangle_bias(D, dtype=tf.float32):
-    """Create a upper triangle matrix for decoding bias."""
-    upper_triangle_DxD = 1 - tf.matrix_band_part(
-        tf.ones([D, D], dtype=dtype), -1, 0
-    )
-    tensor_1xDxD = tf.expand_dims(upper_triangle_DxD * dtype.min, axis=0)
-    return tensor_1xDxD
diff --git a/malaya/train/model/pegasus/layers/beam_search.py b/malaya/train/model/pegasus/layers/beam_search.py
deleted file mode 100644
index b502bcdf..00000000
--- a/malaya/train/model/pegasus/layers/beam_search.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Beam search.
-
-This beam search implementation is designed for TPU usage only and prefers
-flexibility over efficiency. Transformer attention caching is not enabled yet.
-
-Mostly follows implementation in T2T. Several difference to pure beamsearch:
-1. has finished and alive seqs, use 2 * beam_size to grow alive seqs,
-   which makes beam_size=1 doesn't equal greedy.
-2. prefers finished seq over alive seqs.
-3. prefers lower indices when equal probability (though unlikely).
-4. with custom length normalization and constraint.
-
-Notations:
-  B: batch_size, M: beam_size, T: max_decode_len, V: vocab_size, U: undefined
-"""
-#
-# pylint: disable=invalid-name
-
-import tensorflow as tf
-
-
-def length_normalization(start, alpha, min_len, max_len, out_of_range_penalty):
-    r"""Create length normalization function.
-
-  Combines length penalty from https://arxiv.org/abs/1609.08144,
-  and length constraint from https://www.aclweb.org/anthology/W18-2706.pdf.
-
-  scores = \sum_j log(P_j) / ((start + lengths)/(1 + start))**alpha
-          + out_of_range_penalty * (length > max_len or length < min_len)
-
-  Args:
-    start: int, length normalization start offset.
-    alpha: float, [0, 1.0],  length normalization power.
-    min_len: int, minimum decode length.
-    max_len: int, maximum decode lengths.
-    out_of_range_penalty: float, penalty for lengths outside min len and max
-      len. Use a negative number that penalize out of range decodes, does hard
-      constraint if set to -inf.
-
-  Returns:
-    fn(log_probs_BxM, length)->scores_BxM: a function to normalize sum log
-    probabilities of sequence with current decoding lengths.
-  """
-
-    def length_norm_fn(log_probs_BxM, length_int):
-        """Normalize sum log probabilities given a sequence length."""
-        dtype = log_probs_BxM.dtype
-        norm_flt = tf.pow(
-            ((start + tf.cast(length_int, dtype)) / (1.0 + start)), alpha
-        )
-        log_probs_BxM /= norm_flt
-        too_short_bool = tf.less(length_int, min_len)
-        too_long_bool = tf.logical_and(
-            tf.greater(length_int, max_len), max_len > 0
-        )
-        out_of_range_bool = tf.logical_or(too_long_bool, too_short_bool)
-        log_probs_BxM += out_of_range_penalty * tf.cast(
-            out_of_range_bool, dtype
-        )
-        return log_probs_BxM
-
-    return length_norm_fn
-
-
-def beam_search(
-    symbols_to_logits_fn,
-    init_seq_BxT,
-    initial_cache_BxU,
-    vocab_size,
-    beam_size,
-    length_norm_fn,
-    eos_id=1,
-):
-    """Beam search.
-
-  Args:
-    symbols_to_logits_fn: fn(seq_BxT, cache_BxU, i) -> (logits_BxV, cache_BxU)
-    init_seq_BxT: initial sequence ids.
-    initial_cache_BxU: dictionary of tensors with shape BxU.
-    vocab_size: vocabulary size.
-    beam_size: beam size.
-    length_norm_fn: length normalization function.
-    eos_id: end of sequence.
-
-  Returns:
-    Tuple of (beams_BxMxT, scores_BxM). Beam searched sequences and scores.
-  """
-    B = tf.shape(init_seq_BxT)[0]
-    T = tf.shape(init_seq_BxT)[1]
-    M, V = beam_size, vocab_size
-    dtype = tf.float32
-    int_dtype = init_seq_BxT.dtype
-
-    def _loop_body(
-        i,
-        alive_seq_BxMxT,
-        alive_log_probs_BxM,
-        alive_cache_BxMxU,
-        finished_seq_BxMxT,
-        finished_scores_BxM,
-    ):
-        print('i', i)
-        """Beam search loop body."""
-        # Decode one step with beam
-        logits_BMxV, cache_BMxU = symbols_to_logits_fn(
-            _flatten_beam_dim(alive_seq_BxMxT),
-            tf.nest.map_structure(_flatten_beam_dim, alive_cache_BxMxU),
-            i,
-        )
-        logits_BxMxV = _unflatten_beam_dim(logits_BMxV, M)
-        new_cache_BxMxU = tf.nest.map_structure(
-            lambda t: _unflatten_beam_dim(t, M), cache_BMxU
-        )
-
-        # select top 2 * beam_size and fill alive and finished.
-        log_probs_BxMxV = logits_BxMxV - tf.reduce_logsumexp(
-            logits_BxMxV, axis=2, keepdims=True
-        )
-        log_probs_BxMxV += tf.expand_dims(alive_log_probs_BxM, axis=2)
-        log_probs_BxMV = tf.reshape(log_probs_BxMxV, [B, -1])
-        new_log_probs_Bx2M, topk_indices_Bx2M = tf.nn.top_k(
-            log_probs_BxMV, k=2 * M
-        )
-        topk_beam_Bx2M = topk_indices_Bx2M // V
-        topk_seq_Bx2MxT, new_cache_Bx2MxU = _gather_nested(
-            [alive_seq_BxMxT, new_cache_BxMxU], topk_beam_Bx2M
-        )
-        topk_ids_Bx2M = topk_indices_Bx2M % V
-        new_seq_Bx2MxT = _update_i(topk_seq_Bx2MxT, topk_ids_Bx2M, i)
-        new_finished_flags_Bx2M = tf.cast(
-            tf.reduce_any(tf.equal(new_seq_Bx2MxT, eos_id), axis=-1), dtype
-        )
-
-        # get new alive
-        _, topk_alive_indices_BxM = tf.nn.top_k(
-            new_log_probs_Bx2M + new_finished_flags_Bx2M * dtype.min, k=M
-        )
-        (
-            alive_seq_BxMxT,
-            alive_log_probs_BxM,
-            alive_cache_BxMxU,
-        ) = _gather_nested(
-            [new_seq_Bx2MxT, new_log_probs_Bx2M, new_cache_Bx2MxU],
-            topk_alive_indices_BxM,
-        )
-
-        # get new finished
-        new_scores_Bx2M = length_norm_fn(new_log_probs_Bx2M, i + 1)
-        new_scores_Bx2M += (1 - new_finished_flags_Bx2M) * dtype.min
-        finished_seq_Bx3MxT = tf.concat(
-            [finished_seq_BxMxT, new_seq_Bx2MxT], axis=1
-        )
-        finished_scores_Bx3M = tf.concat(
-            [finished_scores_BxM, new_scores_Bx2M], axis=1
-        )
-        _, topk_finished_indices_BxM = tf.nn.top_k(finished_scores_Bx3M, k=M)
-        (finished_seq_BxMxT, finished_scores_BxM) = _gather_nested(
-            [finished_seq_Bx3MxT, finished_scores_Bx3M],
-            topk_finished_indices_BxM,
-        )
-
-        return [
-            i + 1,
-            alive_seq_BxMxT,
-            alive_log_probs_BxM,
-            alive_cache_BxMxU,
-            finished_seq_BxMxT,
-            finished_scores_BxM,
-        ]
-
-    # initialize.
-    init_i = tf.constant(0, dtype=int_dtype)
-    init_alive_seq_BxMxT = _expand_to_beam_size(init_seq_BxT, M)
-    log_probs_1xM = tf.constant([[0.0] + [dtype.min] * (M - 1)], dtype=dtype)
-    init_alive_log_probs_BxM = tf.tile(log_probs_1xM, [B, 1])
-    init_alive_cache_BxMxU = tf.nest.map_structure(
-        lambda t: _expand_to_beam_size(t, M), initial_cache_BxU
-    )
-    init_finished_seq_BxMxT = tf.zeros(
-        tf.shape(init_alive_seq_BxMxT), int_dtype
-    )
-    init_finished_scores_BxM = tf.zeros([B, M], dtype=dtype) + dtype.min
-
-    T_shape = init_seq_BxT.shape[1]
-
-    # run loop.
-    (
-        _,
-        final_alive_seq_BxMxT,
-        final_alive_scores_BxM,
-        _,
-        final_finished_seq_BxMxT,
-        final_finished_scores_BxM,
-    ) = tf.while_loop(
-        lambda *args: True,  # Always do T iterations
-        _loop_body,
-        loop_vars=[
-            init_i,
-            init_alive_seq_BxMxT,
-            init_alive_log_probs_BxM,
-            init_alive_cache_BxMxU,
-            init_finished_seq_BxMxT,
-            init_finished_scores_BxM,
-        ],
-        parallel_iterations=1,
-        back_prop=False,
-        maximum_iterations=T_shape,
-    )
-
-    # process finished.
-    final_finished_flag_BxMx1 = tf.reduce_any(
-        tf.equal(final_finished_seq_BxMxT, eos_id), axis=-1, keepdims=True
-    )
-    final_seq_BxMxT = tf.where(
-        tf.tile(final_finished_flag_BxMx1, [1, 1, T]),
-        final_finished_seq_BxMxT,
-        final_alive_seq_BxMxT,
-    )
-    final_scores_BxM = tf.where(
-        tf.squeeze(final_finished_flag_BxMx1, axis=-1),
-        final_finished_scores_BxM,
-        final_alive_scores_BxM,
-    )
-    return final_seq_BxMxT, final_scores_BxM
-
-
-def _update_i(tensor_BxNxT, updates_BxN, i):
-    B = tf.shape(tensor_BxNxT)[0]
-    N = tf.shape(tensor_BxNxT)[1]
-    T = tf.shape(tensor_BxNxT)[2]
-    B = tf.cast(B, tf.int64)
-    N = tf.cast(N, tf.int64)
-    T = tf.cast(T, tf.int64)
-    tensor_BNxT = tf.reshape(tensor_BxNxT, [-1, T])
-    updates_BN = tf.reshape(updates_BxN, [-1])
-    batch_BN = tf.range(B * N, dtype=tf.int64)
-    i_BN = tf.fill([B * N], tf.cast(i, tf.int64))
-    ind_BNx2 = tf.stack([batch_BN, i_BN], axis=-1)
-    tensor_BNxT = tf.tensor_scatter_nd_update(tensor_BNxT, ind_BNx2, updates_BN)
-    return tf.reshape(tensor_BNxT, [B, N, T])
-
-
-def _expand_to_beam_size(tensor_BxU, beam_size):
-    tensor_Bx1xU = tf.expand_dims(tensor_BxU, axis=1)
-    tile_dims = [1] * tensor_Bx1xU.shape.ndims
-    tile_dims[1] = beam_size
-    tensor_BxMxU = tf.tile(tensor_Bx1xU, tile_dims)
-    return tensor_BxMxU
-
-
-def _shape_list(tensor):
-    """Return a list of the tensor's shape, and ensure no None values in list."""
-    # Get statically known shape (may contain None's for unknown dimensions)
-    shape = tensor.get_shape().as_list()
-
-    # Ensure that the shape values are not None
-    dynamic_shape = tf.shape(tensor)
-    for i in range(len(shape)):  # pylint: disable=consider-using-enumerate
-        if shape[i] is None:
-            shape[i] = dynamic_shape[i]
-    return shape
-
-
-def _flatten_beam_dim(tensor_BxMxU):
-    # shape = tensor_BxMxU.shape.as_list()
-
-    shape = _shape_list(tensor_BxMxU)
-
-    tensor_BMxU = tf.reshape(tensor_BxMxU, [shape[0] * shape[1]] + shape[2:])
-    return tensor_BMxU
-
-
-def _unflatten_beam_dim(tensor_BMxU, M):
-    # shape = tensor_BMxU.shape.as_list()
-    shape = _shape_list(tensor_BMxU)
-    tensor_BxMxU = tf.reshape(tensor_BMxU, [shape[0] // M, M] + shape[1:])
-    return tensor_BxMxU
-
-
-def _gather_nested(nested_BxMxU, indices_BxN):
-    def _gather_beam(tensor_BxMxU):
-        tensor_BxNxU = tf.gather(
-            tensor_BxMxU, indices_BxN, batch_dims=1, axis=1
-        )
-        return tensor_BxNxU
-
-    return tf.nest.map_structure(_gather_beam, nested_BxMxU)
diff --git a/malaya/train/model/pegasus/layers/decoding.py b/malaya/train/model/pegasus/layers/decoding.py
deleted file mode 100644
index 89533cc1..00000000
--- a/malaya/train/model/pegasus/layers/decoding.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Library for generative model decoding."""
-#
-# pylint: disable=invalid-name
-
-import tensorflow as tf
-
-from . import beam_search
-
-EOS_ID = 1
-
-
-def process_logits(logits_BxN, top_k=0, top_p=0.0, temperature=0.0):
-    """Process logits using gumbel noise and mask top_k or top_p.
-
-  The downstream task can perform probability sampling using gumbel-max trick
-  (taking the argmax of processed logits) (Statistical theory of extreme values
-  and some practical applications: a series of lectures. 1954).
-  Use cases:
-    greedy: top_k=0, top_p=0.0, temperature=0.0
-    random sampling: top_k=0, top_p=0.0, temperature=1.0
-    topk sampling: top_k=k, top_p=0.0, temperature=1.0
-    nucleus sampling: top_k=0, top_p=p, temperature=1.0
-    random sampling biased toward greedy: top_k=0, top_p=0.0, temperature=0.5
-  Notations:
-    B: batch_size, N: number of logits, K: topk value.
-  Args:
-    logits_BxN: tensor of [batch_size vocab_size]
-    top_k: k in top_k sampling.
-    top_p: probability in necleus sampling.
-    temperature: gumbel noise sampling temperature.
-
-  Returns:
-    logits: processed logits which is original logits add gumbel noise and
-    values outside top_k and top_p set to -inf.
-  """
-    if top_k > 0 and top_p > 0:
-        raise ValueError(
-            'Only one of the top_k and nucleus sampling should be specified.'
-        )
-
-    if top_k > 0:
-        top_values_BxK, _ = tf.math.top_k(logits_BxN, k=top_k, sorted=False)
-        min_value_Bx1 = tf.reduce_min(
-            top_values_BxK, axis=-1, keepdims=True
-        )
-        mask_BxN = tf.cast(tf.less(logits_BxN, min_value_Bx1), logits_BxN.dtype)
-        logits_BxN -= mask_BxN * logits_BxN.dtype.max
-
-    if top_p > 0:
-        sort_indices_BxN = tf.argsort(
-            logits_BxN, axis=-1, direction='DESCENDING'
-        )
-        probs_BxN = tf.gather(
-            tf.nn.softmax(logits_BxN), sort_indices_BxN, batch_dims=1
-        )
-        cumprobs_BxN = tf.cumsum(probs_BxN, axis=-1, exclusive=True)
-        # The top 1 candidate always will not be masked.
-        # This way ensures at least 1 indices will be selected.
-        sort_mask_BxN = tf.cast(
-            tf.greater(cumprobs_BxN, top_p), logits_BxN.dtype
-        )
-        batch_indices_BxN = tf.tile(
-            tf.expand_dims(tf.range(tf.shape(logits_BxN)[0]), axis=-1),
-            [1, tf.shape(logits_BxN)[1]],
-        )
-        top_p_mask_BxN = tf.scatter_nd(
-            tf.stack([batch_indices_BxN, sort_indices_BxN], axis=-1),
-            sort_mask_BxN,
-            tf.shape(logits_BxN),
-        )
-        logits_BxN -= top_p_mask_BxN * logits_BxN.dtype.max
-
-    if temperature > 0:
-        logits_shape = tf.shape(logits_BxN)
-        uniform_noise_BxN = tf.random_uniform(logits_shape)
-        logits_BxN += -tf.log(-tf.log(uniform_noise_BxN)) * temperature
-    return logits_BxN
-
-
-def inplace_update_i(tensor_BxL, updates_B, i):
-    """Inplace update a tensor. B: batch_size, L: tensor length."""
-    batch_size = tf.shape(tensor_BxL)[0]
-    batch_size = tf.cast(batch_size, tf.int64)
-
-    indices_Bx2 = tf.stack(
-        [
-            tf.range(batch_size, dtype=tf.int64),
-            tf.fill([batch_size], tf.cast(i, tf.int64)),
-        ],
-        axis=-1,
-    )
-    return tf.tensor_scatter_nd_update(tensor_BxL, indices_Bx2, updates_B)
-
-
-def left2right_decode(
-    symbols_to_logits_fn,
-    context_BxU_dict,
-    batch_size,
-    max_decode_len,
-    vocab_size,
-    beam_size=1,
-    beam_start=5,
-    beam_alpha=0.6,
-    beam_min=0,
-    beam_max=-1,
-    temperature=0.0,
-    top_k=0,
-    top_p=0.0,
-    eos_id=EOS_ID,
-):
-    """left to right decode.
-
-  Notations:
-    B: batch_size, V: vocab_size, T: decode_len, U: undefined dimensions
-
-  Args:
-    symbols_to_logits_fn: logits = fn(decodes, context, i). Shoud take
-      [batch_size, decoded_ids] and return [batch_size, vocab_size].
-    context_BxU_dict: dict of Tensors.
-    batch_size: int, decode batch size.
-    max_decode_len: int, maximum number of steps to decode.
-    vocab_size: int, output vocab size.
-    beam_size: Number of beams to decode.
-    beam_start: start length for scaling, default to 5.
-    beam_alpha: Length penalty for decoding. Should be between 0 (shorter) and 1
-      (longer), default to 0.6.
-    beam_min: Minimum beam search lengths.
-    beam_max: Maximum beam search lengths. Set -1 to use unlimited.
-    temperature: Sampling temp for next token (0 for argmax), default to 0.0.
-    top_k: Number of top symbols to consider at each time step, default to 0
-      (consider all symbols).
-    top_p: Nucleus sampling probability.
-    eos_id: end of token id, default to 1.
-
-  Returns:
-    decodes: Tensor[batch, decode_len]
-  """
-    batch_size = tf.cast(batch_size, tf.int64)
-    max_decode_len = tf.cast(max_decode_len, tf.int64)
-    dtype = tf.int64
-    # When beam_size=1, beam_search does not behave exactly like greedy.
-    # This is due to using 2 * beam_size in grow_topk, and keep the top beam_size
-    # ones that haven't reached EOS into alive.
-    # In this case, alpha value for length penalty will take effect.
-    if beam_size == 1:
-
-        def decode_loop(i, decodes_BxT, cache_BxU_dict):
-            logits_BxV = symbols_to_logits_fn(decodes_BxT, cache_BxU_dict, i)
-            logits_BxV = process_logits(logits_BxV, top_k, top_p, temperature)
-            decodes_BxT = inplace_update_i(
-                decodes_BxT, tf.argmax(logits_BxV, -1), i
-            )
-            return i + 1, decodes_BxT, cache_BxU_dict
-
-        def loop_cond(i, decodes_BxT, unused_cache_BxU_dict):
-            finished_B = tf.reduce_any(tf.equal(decodes_BxT, EOS_ID), axis=1)
-            return tf.logical_and(
-                i < max_decode_len, tf.logical_not(tf.reduce_all(finished_B))
-            )
-
-        zeros_dims = tf.stack([batch_size, max_decode_len])
-        y = tf.fill(zeros_dims, 0)
-        init_dec_BxT = tf.cast(y, dtype)
-
-        # init_dec_BxT = tf.zeros([batch_size, max_decode_len], dtype = dtype)
-        _, decodes, _ = tf.while_loop(
-            loop_cond,
-            decode_loop,
-            [tf.constant(0, dtype=dtype), init_dec_BxT, context_BxU_dict],
-        )
-        return decodes
-
-    else:
-
-        raise Exception('beam decoder not supported.')
-
-        def symbols_to_logits_fn_with_sampling(decodes_BxT, states_BxU_dict, i):
-            logits_BxV = symbols_to_logits_fn(decodes_BxT, states_BxU_dict, i)
-            logits_BxV = process_logits(logits_BxV, top_k, top_p, temperature)
-            return logits_BxV, states_BxU_dict
-
-        length_norm_fn = beam_search.length_normalization(
-            beam_start, beam_alpha, beam_min, beam_max, -1e3
-        )
-        beams, _ = beam_search.beam_search(
-            symbols_to_logits_fn_with_sampling,
-            tf.zeros([batch_size, max_decode_len], dtype=tf.int32),
-            context_BxU_dict,
-            vocab_size,
-            beam_size,
-            length_norm_fn,
-            eos_id,
-        )
-        return tf.cast(beams[:, 0, :], dtype)
diff --git a/malaya/train/model/pegasus/layers/embedding.py b/malaya/train/model/pegasus/layers/embedding.py
deleted file mode 100644
index 9fdf961c..00000000
--- a/malaya/train/model/pegasus/layers/embedding.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Embedding layers.
-
-Notations:
-  B: batch_size, I: max_input_len, D: hidden_size, V: vocab_size
-"""
-#
-# pylint: disable=invalid-name
-
-import tensorflow as tf
-
-
-class Embedding(object):
-    """Embedding layer supporting shared input/output weights."""
-
-    def __init__(self, vocab_size, hidden_size, name, dtype):
-        self._vocab_size = vocab_size
-        self._hidden_size = hidden_size
-        self._name = name
-        self._dtype = dtype
-
-    def __call__(self, tensor, is_input_layer):
-        if is_input_layer:
-            return self._ids_to_weights(tensor)
-        else:
-            return self._weights_to_logits(tensor)
-
-    def _ids_to_weights(self, ids_BxI):
-        """Maps IDs to embedding weights."""
-        weights_BxIxD = tf.nn.embedding_lookup(self.weights_VxD, ids_BxI)
-        weights_BxIxD *= self._hidden_size ** 0.5
-        return weights_BxIxD
-
-    def _weights_to_logits(self, states_BxIxD):
-        B = tf.shape(states_BxIxD)[0]
-        I = tf.shape(states_BxIxD)[1]
-        D = tf.shape(states_BxIxD)[2]
-        states_BIxD = tf.reshape(states_BxIxD, [-1, D])
-        states_BIxV = tf.matmul(
-            states_BIxD, self.weights_VxD, transpose_b=True
-        )
-        states_BxIxV = tf.reshape(states_BIxV, [B, I, self._vocab_size])
-        return states_BxIxV
-
-    @property
-    def weights_VxD(self):
-        """Gets embedding weights."""
-        with tf.variable_scope('embeddings', reuse=tf.AUTO_REUSE):
-            # Initialization is important here, and a normal distribution with stdev
-            # equal to rsqrt hidden_size is significantly better than the default
-            # initialization used for other layers (fan in / out avg).
-            embeddings_VxD = tf.get_variable(
-                self._name,
-                [self._vocab_size, self._hidden_size],
-                initializer=tf.random_normal_initializer(
-                    stddev=self._hidden_size ** -0.5, dtype=self._dtype
-                ),
-                dtype=self._dtype,
-            )
-        return embeddings_VxD
diff --git a/malaya/train/model/pegasus/layers/timing.py b/malaya/train/model/pegasus/layers/timing.py
deleted file mode 100644
index 986a9448..00000000
--- a/malaya/train/model/pegasus/layers/timing.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Timing layers.
-
-Notations:
-B: batch_size, I: input_length, D: hidden_size, N: num_timescales
-"""
-#
-# pylint: disable=invalid-name
-
-import math
-
-import tensorflow as tf
-
-_MIN_TIMESCALE = 1.0
-_MAX_TIMESCALE = 1.0e4
-
-
-def add_time_signal(inputs_BxIxD, start_index=None):
-    """Adds a transformer-style timing signal to inputs.
-
-  Using periodic signals as in https://arxiv.org/abs/1706.03762.
-  Generalized to allow each example in a batch to begin at a different index.
-
-  Args:
-    inputs_BxIxD: input representation.
-    start_index: tensor of starting pos. [batch_size]
-
-  Returns:
-    output: representation with time signal added, same shape as input.
-  """
-
-    dtype = inputs_BxIxD.dtype
-    B = tf.shape(inputs_BxIxD)[0]
-    I = tf.shape(inputs_BxIxD)[1]
-    D = tf.shape(inputs_BxIxD)[2]
-    start_Bx1 = (
-        tf.zeros((B, 1), tf.int32) if start_index is None else start_index
-    )
-
-    pos_1xI = tf.expand_dims(tf.range(I), 0)
-    pos_BxI = tf.tile(pos_1xI, [B, 1]) + tf.cast(start_Bx1, tf.int32)
-    pos_BxI = tf.cast(pos_BxI, dtype)
-    N = D // 2
-    log_time_incr = math.log(_MAX_TIMESCALE / _MIN_TIMESCALE) / tf.maximum(
-        tf.cast(N, dtype) - 1, 1
-    )
-    inv_scale_N = _MIN_TIMESCALE * tf.exp(
-        tf.cast(tf.range(N), dtype) * -log_time_incr
-    )
-    time_BxIxN = tf.expand_dims(pos_BxI, 2) * tf.reshape(
-        inv_scale_N, [1, 1, -1]
-    )
-    signal_BxIxD = tf.concat([tf.sin(time_BxIxN), tf.cos(time_BxIxN)], axis=2)
-    signal_BxIxD = tf.reshape(signal_BxIxD, [B, I, D])
-    return inputs_BxIxD + signal_BxIxD
diff --git a/malaya/train/model/pegasus/layers/transformer_block.py b/malaya/train/model/pegasus/layers/transformer_block.py
deleted file mode 100644
index 52765186..00000000
--- a/malaya/train/model/pegasus/layers/transformer_block.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Transformer block.
-
-From "Attention Is All You Need", https://arxiv.org/abs/1706.03762.
-
-Notations:
-  B: batch_size, I: max_input_len, M: max_memory_len, D: hidden_size
-"""
-#
-# pylint: disable=invalid-name
-# pylint: disable=g-long-lambda
-
-from . import attention
-import tensorflow as tf
-from tensorflow.contrib import layers as contrib_layers
-
-
-class TransformerBlock(object):
-    """Transformer block.
-
-  Attention block of self-attention, attention over external memory, and
-  feedforward network.
-  Initialize the block with
-    block = TransformerBlock(hidden_size, filter_size, num_heads, dropout)
-  To create an encoder self attention layer, use
-    x = block(x, x_bias, None, None)
-  To create a decoder attention layer, use
-    y = block(y, upper_triangle_bias, x, x_bias)
-  """
-
-    def __init__(self, hidden_size, filter_size, num_heads, dropout):
-        self._self_attn_layer = attention.SelfAttention(
-            hidden_size, num_heads, dropout
-        )
-        self._attn_layer = attention.Attention(hidden_size, num_heads, dropout)
-        self._relu_layer = tf.layers.Dense(filter_size, activation=tf.nn.relu)
-        self._output_layer = tf.layers.Dense(hidden_size)
-        self._dropout_fn = (
-            lambda x, training: tf.compat.v2.nn.dropout(
-                x,
-                rate=dropout,
-                noise_shape=[tf.shape(x)[0], 1, tf.shape(x)[2]],
-            )
-            if training
-            else x
-        )
-
-    def __call__(
-        self,
-        training,
-        inputs_BxIxD,
-        bias_BxIxI,
-        memory_BxMxD,
-        bias_BxIxM,
-        cache=None,
-        decode_i=None,
-    ):
-        s_BxIxD = inputs_BxIxD
-        with tf.variable_scope('attention/self'):
-            y_BxIxD = contrib_layers.layer_norm(s_BxIxD, begin_norm_axis=2)
-            y_BxIxD = self._self_attn_layer(
-                y_BxIxD,
-                bias_BxIxI,
-                training,
-                cache=cache,
-                decode_i=decode_i,
-            )
-            s_BxIxD += self._dropout_fn(y_BxIxD, training)
-        if memory_BxMxD is not None:
-            with tf.variable_scope('memory_attention'):
-                y_BxIxD = contrib_layers.layer_norm(
-                    s_BxIxD, begin_norm_axis=2
-                )
-                y_BxIxD = self._attn_layer(
-                    y_BxIxD, memory_BxMxD, bias_BxIxM, training
-                )
-                s_BxIxD += self._dropout_fn(y_BxIxD, training)
-        with tf.variable_scope('ffn'):
-            y_BxIxD = contrib_layers.layer_norm(s_BxIxD, begin_norm_axis=2)
-            y_BxIxD = self._dropout_fn(self._relu_layer(y_BxIxD), training)
-            s_BxIxD += self._dropout_fn(self._output_layer(y_BxIxD), training)
-        return s_BxIxD
-
-
-def stack(
-    layers,
-    training,
-    inputs_BxIxD,
-    bias_BxIxI,
-    memory_BxMxD,
-    bias_BxIxM,
-    cache=None,
-    decode_i=None,
-):
-    """Stack AttentionBlock layers."""
-    if (memory_BxMxD is None) != (bias_BxIxM is None):
-        raise ValueError('memory and memory_bias need to be provided together.')
-    s_BxIxD = inputs_BxIxD
-    for i, layer in enumerate(layers):
-        with tf.variable_scope('layer_%d' % i):
-            s_BxIxD = layer(
-                training,
-                s_BxIxD,
-                bias_BxIxI,
-                memory_BxMxD,
-                bias_BxIxM,
-                cache=cache[str(i)] if cache is not None else None,
-                decode_i=decode_i,
-            )
-    return s_BxIxD
diff --git a/malaya/train/model/pegasus/transformer.py b/malaya/train/model/pegasus/transformer.py
deleted file mode 100644
index 4032e85d..00000000
--- a/malaya/train/model/pegasus/transformer.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# Copyright 2020 The PEGASUS Authors..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Standard Transformer models.
-
-Models contain embedding, encoding, and loss functions, and expect text ids as
-inputs. All models have same format as below:
-  model = TransformerModel(...)
-  loss, output = model(features, training)
-Features and outputs are dictionary of tensors. Features usually inlucdes inputs
-and targets ids.
-"""
-#
-# pylint: disable=invalid-name
-# pylint: disable=g-long-lambda
-
-from .layers import attention
-from .layers import decoding
-from .layers import embedding
-from .layers import timing
-from .layers import transformer_block
-from . import base
-import tensorflow as tf
-from tensorflow.contrib import layers as contrib_layers
-
-
-class TransformerEncoderDecoderModel(base.BaseModel):
-    """Transformer encoder+decoder.
-
-  Notations:
-    B: batch_size, I: max_input_len, T: max_target/decode_len, D: hidden_size
-    V: vocab_size
-  """
-
-    def __init__(
-        self,
-        vocab_size,
-        hidden_size,
-        filter_size,
-        num_heads,
-        num_encoder_layers,
-        num_decoder_layers,
-        label_smoothing,
-        dropout,
-    ):
-        self._dtype = tf.float32
-        self._embedding_layer = embedding.Embedding(
-            vocab_size, hidden_size, 'weights', self._dtype
-        )
-
-        def block_fn(): return transformer_block.TransformerBlock(
-            hidden_size, filter_size, num_heads, dropout
-        )
-        self._encoder_layers = [block_fn() for _ in range(num_encoder_layers)]
-        self._decoder_layers = [block_fn() for _ in range(num_decoder_layers)]
-        self._dropout_fn = (
-            lambda x, training: tf.compat.v2.nn.dropout(
-                x,
-                rate=dropout,
-                noise_shape=[tf.shape(x)[0], 1, tf.shape(x)[2]],
-            )
-            if training
-            else x
-        )
-        self._vocab_size = vocab_size
-        self._num_heads = num_heads
-        self._label_smoothing = label_smoothing
-        self._decoder_scope_name = 'decoder'
-
-    def _encode(self, features, training):
-        inputs_BxI = features['inputs']
-        inputs_bias_Bx1xI = attention.ids_to_bias(inputs_BxI, self._dtype)
-        states_BxIxD = self._embedding_layer(inputs_BxI, True)
-        states_BxIxD = self._dropout_fn(
-            timing.add_time_signal(states_BxIxD), training
-        )
-        with tf.variable_scope('encoder', reuse=tf.AUTO_REUSE):
-            states_BxIxD = transformer_block.stack(
-                self._encoder_layers,
-                training,
-                states_BxIxD,
-                inputs_bias_Bx1xI,
-                None,
-                None,
-            )
-            states_BxIxD = contrib_layers.layer_norm(
-                states_BxIxD, begin_norm_axis=2
-            )
-        return {'memory': states_BxIxD, 'memory_bias': inputs_bias_Bx1xI}
-
-    def __call__(self, features, training):
-        """Create model.
-
-    Args:
-      features: dictionary of tensors including "inputs" [batch, input_len] and
-        "targets" [batch, output_len]
-      training: bool of whether the mode is training.
-
-    Returns:
-     Tuple of (loss, outputs): Loss is a scalar. Output is a dictionary of
-       tensors, containing model's output logits.
-    """
-        if 'inputs' not in features or 'targets' not in features:
-            raise ValueError('Require inputs and targets keys in features.')
-
-        context = self._encode(features, training)
-        self._context = context
-        targets_BxT = features['targets']
-        bias_1xTxT = attention.upper_triangle_bias(
-            tf.shape(targets_BxT)[1], self._dtype
-        )
-        states_BxTxD = self._embedding_layer(targets_BxT, True)
-        states_BxTxD = tf.pad(states_BxTxD, [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
-        states_BxTxD = timing.add_time_signal(states_BxTxD)
-        states_BxTxD = self._dropout_fn(states_BxTxD, training)
-        with tf.variable_scope(self._decoder_scope_name, reuse=tf.AUTO_REUSE):
-            states_BxTxD = transformer_block.stack(
-                self._decoder_layers,
-                training,
-                states_BxTxD,
-                bias_1xTxT,
-                context['memory'],
-                context['memory_bias'],
-            )
-            states_BxTxD = contrib_layers.layer_norm(
-                states_BxTxD, begin_norm_axis=2
-            )
-        logits_BxTxV = self._embedding_layer(states_BxTxD, False)
-        targets_mask_BxT = tf.cast(tf.greater(targets_BxT, 0), self._dtype)
-        loss = tf.losses.softmax_cross_entropy(
-            tf.one_hot(targets_BxT, self._vocab_size),
-            logits_BxTxV,
-            label_smoothing=self._label_smoothing,
-            weights=targets_mask_BxT,
-        )
-        return loss, {'logits': logits_BxTxV}
-
-    def predict(self, features, max_decode_len, beam_size, **beam_kwargs):
-        """Predict."""
-        cache = self._encode(features, False)
-        B = tf.shape(cache['memory'])[0]
-        D = tf.shape(cache['memory'])[2]
-        T, V, H = max_decode_len, self._vocab_size, self._num_heads
-
-        bias_1xTxT = attention.upper_triangle_bias(T, self._dtype)
-        for i in range(len(self._decoder_layers)):
-            cache[str(i)] = {
-                'k': tf.zeros([B, H, T, D // H], self._dtype),
-                'v': tf.zeros([B, H, T, D // H], self._dtype),
-            }
-
-        def symbols_to_logits_fn(dec_BxT, context, i):
-            """Decode loop."""
-            dec_Bx1 = tf.slice(
-                dec_BxT,
-                [0, tf.maximum(tf.cast(0, i.dtype), i - 1)],
-                [tf.shape(dec_BxT)[0], 1],
-            )
-
-            bias_1x1xT = tf.slice(bias_1xTxT, [0, i, 0], [1, 1, T])
-            dec_Bx1xD = self._embedding_layer(dec_Bx1, True)
-            dec_Bx1xD *= tf.cast(tf.greater(i, 0), self._dtype)
-            dec_Bx1xD = timing.add_time_signal(dec_Bx1xD, start_index=i)
-            with tf.variable_scope(
-                self._decoder_scope_name, reuse=tf.AUTO_REUSE
-            ):
-                dec_Bx1xD = transformer_block.stack(
-                    self._decoder_layers,
-                    False,
-                    dec_Bx1xD,
-                    bias_1x1xT,
-                    context['memory'],
-                    context['memory_bias'],
-                    context,
-                    i,
-                )
-                dec_Bx1xD = contrib_layers.layer_norm(
-                    dec_Bx1xD, begin_norm_axis=2
-                )
-            logits_Bx1xV = self._embedding_layer(dec_Bx1xD, False)
-            logits_BxV = tf.squeeze(logits_Bx1xV, axis=1)
-            return logits_BxV
-
-        decodes_BxT = decoding.left2right_decode(
-            symbols_to_logits_fn, cache, B, T, V, beam_size, **beam_kwargs
-        )
-        return {'outputs': decodes_BxT}
diff --git a/malaya/train/model/product_key_memory/layer.py b/malaya/train/model/product_key_memory/layer.py
deleted file mode 100644
index fc4d49d7..00000000
--- a/malaya/train/model/product_key_memory/layer.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import tensorflow as tf
-from tensorflow.python.ops.init_ops import Initializer
-
-
-class Scaling(Initializer):
-    def __init__(self, seed=None, dtype=tf.float32):
-        self.seed = seed
-        self.dtype = dtype
-
-    def __call__(self, shape, dtype=None, partition_info=None):
-        stdv = 1.0 / (shape[0] * shape[1])
-        w = tf.random.uniform(
-            shape,
-            minval=-stdv,
-            maxval=stdv,
-            dtype=self.dtype,
-            seed=self.seed,
-        )
-        std = tf.math.reduce_std(w)
-        scale = (std / self.reference) ** 0.5
-        w = w / scale
-        return w
diff --git a/malaya/train/model/product_key_memory/model.py b/malaya/train/model/product_key_memory/model.py
deleted file mode 100644
index 28613362..00000000
--- a/malaya/train/model/product_key_memory/model.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import tensorflow as tf
-import math
-
-
-def init_(t, dim=None):
-    dim = dim if dim is not None else t.shape[-1]
-    std = 1.0 / math.sqrt(dim)
-    return nn.init.normal_(t, mean=0, std=std)
-
-
-class Model(tf.keras.Model):
-    def __init__(
-        self,
-        dim,
-        heads=4,
-        num_keys=128,
-        topk=32,
-        dim_head=256,
-        input_dropout=0.0,
-        query_dropout=0.0,
-        value_dropout=0.0,
-        **kwargs
-    ):
-        super().__init__(self, **kwargs)
-        assert (
-            dim % heads == 0
-        ), 'dimension must be divisible by number of heads'
-        self.topk = topk
-        self.heads = heads
-        self.num_keys = num_keys
-        dim_query = dim_head * heads
-        self.to_queries = tf.keras.layers.Dense(dim_query, use_bias=False)
-        self.keys = tf.zeros(shape=(heads, num_keys, 2, dim_head // 2))
diff --git a/pretrained-model/fnet/README.md b/pretrained-model/fnet/README.md
index b8e6e0a9..d24fac36 100644
--- a/pretrained-model/fnet/README.md
+++ b/pretrained-model/fnet/README.md
@@ -64,10 +64,10 @@ python3 run_pretraining_large_tpu.py \
 
 ## Downloads
 
-1. **BASE**, last update 2nd June 2021, [fnet-base.tar.gz](https://f000.backblazeb2.com/file/malaya-model/pretrained/fnet-base.tar.gz)
+1. **BASE**, last update 28th June 2021, [fnet-base.tar.gz](https://f000.backblazeb2.com/file/malaya-model/pretrained/fnet-base.tar.gz)
 
   - Vocab size 32k.
-  - 500k steps, V3-8 TPU.
+  - 475k steps, 1 Tesla V100 32GB VRAM.
 
 ## Citation
 
diff --git a/pretrained-model/fnet/sentiment-base.ipynb b/pretrained-model/fnet/sentiment-base.ipynb
new file mode 100644
index 00000000..551e0c69
--- /dev/null
+++ b/pretrained-model/fnet/sentiment-base.ipynb
@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = ''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import model as modeling\n",
+    "import tensorflow as tf\n",
+    "import tokenization\n",
+    "import optimization\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = tokenization.FullTokenizer(\n",
+    "    vocab_file = 'BERT.wordpiece', do_lower_case = False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rules import normalized_chars\n",
+    "import random\n",
+    "\n",
+    "laughing = {\n",
+    "    'huhu',\n",
+    "    'haha',\n",
+    "    'gagaga',\n",
+    "    'hihi',\n",
+    "    'wkawka',\n",
+    "    'wkwk',\n",
+    "    'kiki',\n",
+    "    'keke',\n",
+    "    'huehue',\n",
+    "    'hshs',\n",
+    "    'hoho',\n",
+    "    'hewhew',\n",
+    "    'uwu',\n",
+    "    'sksk',\n",
+    "    'ksks',\n",
+    "    'gituu',\n",
+    "    'gitu',\n",
+    "    'mmeeooww',\n",
+    "    'meow',\n",
+    "    'alhamdulillah',\n",
+    "    'muah',\n",
+    "    'mmuahh',\n",
+    "    'hehe',\n",
+    "    'salamramadhan',\n",
+    "    'happywomensday',\n",
+    "    'jahagaha',\n",
+    "    'ahakss',\n",
+    "    'ahksk'\n",
+    "}\n",
+    "\n",
+    "def make_cleaning(s, c_dict):\n",
+    "    s = s.translate(c_dict)\n",
+    "    return s\n",
+    "\n",
+    "def cleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    \n",
+    "    string = ' '.join(\n",
+    "        [make_cleaning(w, normalized_chars) for w in string.split()]\n",
+    "    )\n",
+    "    string = re.sub('\\(dot\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    \n",
+    "    chars = '.,/'\n",
+    "    for c in chars:\n",
+    "        string = string.replace(c, f' {c} ')\n",
+    "        \n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    x = []\n",
+    "    for word in string:\n",
+    "        word = word.lower()\n",
+    "        if any([laugh in word for laugh in laughing]):\n",
+    "            if random.random() >= 0.5:\n",
+    "                x.append(word)\n",
+    "        else:\n",
+    "            x.append(word)\n",
+    "    string = [w.title() if w[0].isupper() else w for w in x]\n",
+    "    return ' '.join(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !wget https://raw.githubusercontent.com/huseinzol05/malay-dataset/master/sentiment/news-sentiment/sentiment-data-v2.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from unidecode import unidecode\n",
+    "import re\n",
+    "\n",
+    "df = pd.read_csv('sentiment-data-v2.csv')\n",
+    "Y = LabelEncoder().fit_transform(df.label)\n",
+    "\n",
+    "texts = df.iloc[:,1].tolist()\n",
+    "labels = Y.tolist()\n",
+    "\n",
+    "assert len(labels) == len(texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import json\n",
+    "\n",
+    "# with open('/home/husein/sentiment/strong-positives.json') as fopen:\n",
+    "#     positives = json.load(fopen)\n",
+    "#     positives = random.sample(positives, 500000)\n",
+    "    \n",
+    "# len(positives)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with open('/home/husein/sentiment/strong-negatives.json') as fopen:\n",
+    "#     negatives = json.load(fopen)\n",
+    "#     negatives = random.sample(negatives, 500000)\n",
+    "    \n",
+    "# len(negatives)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# texts += negatives\n",
+    "# labels += [0] * len(negatives)\n",
+    "# texts += positives\n",
+    "# labels += [1] * len(positives)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "for i in tqdm(range(len(texts))):\n",
+    "    texts[i] = cleaning(texts[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actual_t, actual_l = [], []\n",
+    "\n",
+    "for i in tqdm(range(len(texts))):\n",
+    "    if len(texts[i]) > 2:\n",
+    "        actual_t.append(texts[i])\n",
+    "        actual_l.append(labels[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "input_ids, input_masks = [], []\n",
+    "\n",
+    "for text in tqdm(actual_t):\n",
+    "    tokens_a = tokenizer.tokenize(text)\n",
+    "    tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\n",
+    "    input_id = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "    input_mask = [1] * len(input_id)\n",
+    "    \n",
+    "    input_ids.append(input_id)\n",
+    "    input_masks.append(input_mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epoch = 2\n",
+    "batch_size = 60\n",
+    "warmup_proportion = 0.1\n",
+    "num_train_steps = int(len(texts) / batch_size * epoch)\n",
+    "num_warmup_steps = int(num_train_steps * warmup_proportion)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_initializer(initializer_range=0.02):\n",
+    "    return tf.truncated_normal_initializer(stddev=initializer_range)\n",
+    "\n",
+    "class Model:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        dimension_output,\n",
+    "        learning_rate = 2e-5,\n",
+    "        training = True,\n",
+    "    ):\n",
+    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
+    "        self.MASK = tf.placeholder(tf.int32, [None, None])\n",
+    "        self.Y = tf.placeholder(tf.int32, [None])\n",
+    "        \n",
+    "        model = modeling.Model(\n",
+    "            dim = 512, vocab_size = 32000, depth = 12, mlp_dim = 3072\n",
+    "        )\n",
+    "        sequence_output = model(\n",
+    "            self.X, input_mask = self.MASK, training = training\n",
+    "        )\n",
+    "        \n",
+    "        output_layer = sequence_output\n",
+    "        output_layer = tf.layers.dense(\n",
+    "            output_layer,\n",
+    "            model.hidden_size,\n",
+    "            activation=tf.tanh,\n",
+    "            kernel_initializer=create_initializer())\n",
+    "        self.logits_seq = tf.layers.dense(output_layer, dimension_output,\n",
+    "                                         kernel_initializer=create_initializer())\n",
+    "        self.logits_seq = tf.identity(self.logits_seq, name = 'logits_seq')\n",
+    "        self.logits = self.logits_seq[:, 0]\n",
+    "        self.logits = tf.identity(self.logits, name = 'logits')\n",
+    "        \n",
+    "        self.cost = tf.reduce_mean(\n",
+    "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
+    "                logits = self.logits, labels = self.Y\n",
+    "            )\n",
+    "        )\n",
+    "        \n",
+    "        self.optimizer = optimization.create_optimizer(self.cost, learning_rate, \n",
+    "                                                       num_train_steps, num_warmup_steps, False)\n",
+    "        correct_pred = tf.equal(\n",
+    "            tf.argmax(self.logits, 1, output_type = tf.int32), self.Y\n",
+    "        )\n",
+    "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INIT_CHKPNT = 'fnet-base/model.ckpt-475000'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dimension_output = 2\n",
+    "learning_rate = 2e-5\n",
+    "\n",
+    "tf.reset_default_graph()\n",
+    "sess = tf.InteractiveSession()\n",
+    "model = Model(\n",
+    "    dimension_output,\n",
+    "    learning_rate\n",
+    ")\n",
+    "\n",
+    "sess.run(tf.global_variables_initializer())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "import re\n",
+    "\n",
+    "def get_assignment_map_from_checkpoint(tvars, init_checkpoint):\n",
+    "    \"\"\"Compute the union of the current variables and checkpoint variables.\"\"\"\n",
+    "    assignment_map = {}\n",
+    "    initialized_variable_names = {}\n",
+    "\n",
+    "    name_to_variable = collections.OrderedDict()\n",
+    "    for var in tvars:\n",
+    "        name = var.name\n",
+    "        m = re.match('^(.*):\\\\d+$', name)\n",
+    "        if m is not None:\n",
+    "            name = m.group(1)\n",
+    "        name_to_variable[name] = var\n",
+    "\n",
+    "    init_vars = tf.train.list_variables(init_checkpoint)\n",
+    "\n",
+    "    assignment_map = collections.OrderedDict()\n",
+    "    for x in init_vars:\n",
+    "        (name, var) = (x[0], x[1])\n",
+    "        if name not in name_to_variable:\n",
+    "            continue\n",
+    "        assignment_map[name] = name_to_variable[name]\n",
+    "        initialized_variable_names[name] = 1\n",
+    "        initialized_variable_names[name + ':0'] = 1\n",
+    "\n",
+    "    return (assignment_map, initialized_variable_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tvars = tf.trainable_variables()\n",
+    "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
+    "                                                                                INIT_CHKPNT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saver = tf.train.Saver(var_list = assignment_map)\n",
+    "saver.restore(sess, INIT_CHKPNT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_input_ids, test_input_ids, train_Y, test_Y, train_mask, test_mask = train_test_split(\n",
+    "    input_ids, actual_l[:len(input_ids)], input_masks, test_size = 0.2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pad_sequences = tf.keras.preprocessing.sequence.pad_sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "for EPOCH in range(10):\n",
+    "\n",
+    "    train_acc, train_loss, test_acc, test_loss = [], [], [], []\n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_input_ids), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_input_ids))\n",
+    "        batch_x = train_input_ids[i: index]\n",
+    "        batch_x = pad_sequences(batch_x, padding='post')\n",
+    "        batch_mask = train_mask[i: index]\n",
+    "        batch_mask = pad_sequences(batch_mask, padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        acc, cost, _ = sess.run(\n",
+    "            [model.accuracy, model.cost, model.optimizer],\n",
+    "            feed_dict = {\n",
+    "                model.Y: batch_y,\n",
+    "                model.X: batch_x,\n",
+    "                model.MASK: batch_mask\n",
+    "            },\n",
+    "        )\n",
+    "        train_loss.append(cost)\n",
+    "        train_acc.append(acc)\n",
+    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
+    "        \n",
+    "    pbar = tqdm(range(0, len(test_input_ids), batch_size), desc = 'test minibatch loop')\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_input_ids))\n",
+    "        batch_x = test_input_ids[i: index]\n",
+    "        batch_x = pad_sequences(batch_x, padding='post')\n",
+    "        batch_mask = test_mask[i: index]\n",
+    "        batch_mask = pad_sequences(batch_mask, padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        acc, cost = sess.run(\n",
+    "            [model.accuracy, model.cost],\n",
+    "            feed_dict = {\n",
+    "                model.Y: batch_y,\n",
+    "                model.X: batch_x,\n",
+    "                model.MASK: batch_mask\n",
+    "            },\n",
+    "        )\n",
+    "        test_loss.append(cost)\n",
+    "        test_acc.append(acc)\n",
+    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
+    "        \n",
+    "    train_loss = np.mean(train_loss)\n",
+    "    train_acc = np.mean(train_acc)\n",
+    "    test_loss = np.mean(test_loss)\n",
+    "    test_acc = np.mean(test_acc)\n",
+    "    \n",
+    "    print(\n",
+    "        'epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\\n'\n",
+    "        % (EPOCH, train_loss, train_acc, test_loss, test_acc)\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pretrained-model/fnet/test-fnet.ipynb b/pretrained-model/fnet/test-fnet.ipynb
new file mode 100644
index 00000000..a370dc4e
--- /dev/null
+++ b/pretrained-model/fnet/test-fnet.ipynb
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tensorflow as tf\n",
+    "import malaya\n",
+    "tf.compat.v1.set_random_seed(1234)\n",
+    "import numpy as np\n",
+    "import logging\n",
+    "logging.basicConfig(level = logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip3 install einops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def get_shape_list(tensor, expected_rank = None, name = None):\n",
+    "    \"\"\"Returns a list of the shape of tensor, preferring static dimensions.\n",
+    "  Args:\n",
+    "    tensor: A tf.Tensor object to find the shape of.\n",
+    "    expected_rank: (optional) int. The expected rank of `tensor`. If this is\n",
+    "      specified and the `tensor` has a different rank, and exception will be\n",
+    "      thrown.\n",
+    "    name: Optional name of the tensor for the error message.\n",
+    "  Returns:\n",
+    "    A list of dimensions of the shape of tensor. All static dimensions will\n",
+    "    be returned as python integers, and dynamic dimensions will be returned\n",
+    "    as tf.Tensor scalars.\n",
+    "  \"\"\"\n",
+    "    if name is None:\n",
+    "        name = tensor.name\n",
+    "\n",
+    "    shape = tensor.shape.as_list()\n",
+    "\n",
+    "    non_static_indexes = []\n",
+    "    for (index, dim) in enumerate(shape):\n",
+    "        if dim is None:\n",
+    "            non_static_indexes.append(index)\n",
+    "\n",
+    "    if not non_static_indexes:\n",
+    "        return shape\n",
+    "\n",
+    "    dyn_shape = tf.shape(tensor)\n",
+    "    for index in non_static_indexes:\n",
+    "        shape[index] = dyn_shape[index]\n",
+    "    return shape\n",
+    "\n",
+    "\n",
+    "def create_initializer(initializer_range = 0.02):\n",
+    "    \"\"\"Creates a `truncated_normal_initializer` with the given range.\"\"\"\n",
+    "    return tf.truncated_normal_initializer(stddev = initializer_range)\n",
+    "\n",
+    "\n",
+    "def layer_norm(input_tensor, name = None):\n",
+    "    return tf.contrib.layers.layer_norm(\n",
+    "        inputs = input_tensor,\n",
+    "        begin_norm_axis = -1,\n",
+    "        begin_params_axis = -1,\n",
+    "        scope = name,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def embedding_lookup(\n",
+    "    input_ids,\n",
+    "    vocab_size,\n",
+    "    embedding_size = 128,\n",
+    "    initializer_range = 0.02,\n",
+    "    word_embedding_name = 'word_embeddings',\n",
+    "    use_one_hot_embeddings = False,\n",
+    "):\n",
+    "    \"\"\"Looks up words embeddings for id tensor.\n",
+    "  Args:\n",
+    "    input_ids: int32 Tensor of shape [batch_size, seq_length] containing word\n",
+    "      ids.\n",
+    "    vocab_size: int. Size of the embedding vocabulary.\n",
+    "    embedding_size: int. Width of the word embeddings.\n",
+    "    initializer_range: float. Embedding initialization range.\n",
+    "    word_embedding_name: string. Name of the embedding table.\n",
+    "    use_one_hot_embeddings: bool. If True, use one-hot method for word\n",
+    "      embeddings. If False, use `tf.gather()`.\n",
+    "  Returns:\n",
+    "    float Tensor of shape [batch_size, seq_length, embedding_size].\n",
+    "  \"\"\"\n",
+    "    # This function assumes that the input is of shape [batch_size, seq_length,\n",
+    "    # num_inputs].\n",
+    "    #\n",
+    "    # If the input is a 2D tensor of shape [batch_size, seq_length], we\n",
+    "    # reshape to [batch_size, seq_length, 1].\n",
+    "    if input_ids.shape.ndims == 2:\n",
+    "        input_ids = tf.expand_dims(input_ids, axis = [-1])\n",
+    "\n",
+    "    embedding_table = tf.get_variable(\n",
+    "        name = word_embedding_name,\n",
+    "        shape = [vocab_size, embedding_size],\n",
+    "        initializer = create_initializer(initializer_range),\n",
+    "    )\n",
+    "\n",
+    "    flat_input_ids = tf.reshape(input_ids, [-1])\n",
+    "    if use_one_hot_embeddings:\n",
+    "        one_hot_input_ids = tf.one_hot(flat_input_ids, depth = vocab_size)\n",
+    "        output = tf.matmul(one_hot_input_ids, embedding_table)\n",
+    "    else:\n",
+    "        output = tf.gather(embedding_table, flat_input_ids)\n",
+    "\n",
+    "    input_shape = get_shape_list(input_ids)\n",
+    "\n",
+    "    output = tf.reshape(\n",
+    "        output, input_shape[0:-1] + [input_shape[-1] * embedding_size]\n",
+    "    )\n",
+    "    return (output, embedding_table)\n",
+    "\n",
+    "\n",
+    "def embedding_postprocessor(\n",
+    "    input_tensor,\n",
+    "    use_token_type = False,\n",
+    "    token_type_ids = None,\n",
+    "    token_type_vocab_size = 2,\n",
+    "    token_type_embedding_name = 'token_type_embeddings',\n",
+    "    use_position_embeddings = True,\n",
+    "    position_embedding_name = 'position_embeddings',\n",
+    "    initializer_range = 0.02,\n",
+    "    max_position_embeddings = 512,\n",
+    "):\n",
+    "    \"\"\"Performs various post-processing on a word embedding tensor.\n",
+    "  Args:\n",
+    "    input_tensor: float Tensor of shape [batch_size, seq_length,\n",
+    "      embedding_size].\n",
+    "    use_token_type: bool. Whether to add embeddings for `token_type_ids`.\n",
+    "    token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].\n",
+    "      Must be specified if `use_token_type` is True.\n",
+    "    token_type_vocab_size: int. The vocabulary size of `token_type_ids`.\n",
+    "    token_type_embedding_name: string. The name of the embedding table variable\n",
+    "      for token type ids.\n",
+    "    use_position_embeddings: bool. Whether to add position embeddings for the\n",
+    "      position of each token in the sequence.\n",
+    "    position_embedding_name: string. The name of the embedding table variable\n",
+    "      for positional embeddings.\n",
+    "    initializer_range: float. Range of the weight initialization.\n",
+    "    max_position_embeddings: int. Maximum sequence length that might ever be\n",
+    "      used with this model. This can be longer than the sequence length of\n",
+    "      input_tensor, but cannot be shorter.\n",
+    "    dropout_prob: float. Dropout probability applied to the final output tensor.\n",
+    "  Returns:\n",
+    "    float tensor with same shape as `input_tensor`.\n",
+    "  Raises:\n",
+    "    ValueError: One of the tensor shapes or input values is invalid.\n",
+    "  \"\"\"\n",
+    "    input_shape = get_shape_list(input_tensor, expected_rank = 3)\n",
+    "    batch_size = input_shape[0]\n",
+    "    seq_length = input_shape[1]\n",
+    "    width = input_shape[2]\n",
+    "\n",
+    "    output = input_tensor\n",
+    "\n",
+    "    if use_token_type:\n",
+    "        if token_type_ids is None:\n",
+    "            raise ValueError(\n",
+    "                '`token_type_ids` must be specified if'\n",
+    "                '`use_token_type` is True.'\n",
+    "            )\n",
+    "        token_type_table = tf.get_variable(\n",
+    "            name = token_type_embedding_name,\n",
+    "            shape = [token_type_vocab_size, width],\n",
+    "            initializer = create_initializer(initializer_range),\n",
+    "        )\n",
+    "        flat_token_type_ids = tf.reshape(token_type_ids, [-1])\n",
+    "        one_hot_ids = tf.one_hot(\n",
+    "            flat_token_type_ids, depth = token_type_vocab_size\n",
+    "        )\n",
+    "        token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)\n",
+    "        token_type_embeddings = tf.reshape(\n",
+    "            token_type_embeddings, [batch_size, seq_length, width]\n",
+    "        )\n",
+    "        output += token_type_embeddings\n",
+    "\n",
+    "    if use_position_embeddings:\n",
+    "        assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)\n",
+    "        with tf.control_dependencies([assert_op]):\n",
+    "            full_position_embeddings = tf.get_variable(\n",
+    "                name = position_embedding_name,\n",
+    "                shape = [max_position_embeddings, width],\n",
+    "                initializer = create_initializer(initializer_range),\n",
+    "            )\n",
+    "            position_embeddings = tf.slice(\n",
+    "                full_position_embeddings, [0, 0], [seq_length, -1]\n",
+    "            )\n",
+    "            num_dims = len(output.shape.as_list())\n",
+    "            position_broadcast_shape = []\n",
+    "            for _ in range(num_dims - 2):\n",
+    "                position_broadcast_shape.append(1)\n",
+    "            position_broadcast_shape.extend([seq_length, width])\n",
+    "            position_embeddings = tf.reshape(\n",
+    "                position_embeddings, position_broadcast_shape\n",
+    "            )\n",
+    "            output += position_embeddings\n",
+    "\n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "def gelu(x):\n",
+    "    cdf = 0.5 * (\n",
+    "        1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044_715 * tf.pow(x, 3))))\n",
+    "    )\n",
+    "    return x * cdf\n",
+    "\n",
+    "\n",
+    "class Forward(tf.keras.layers.Layer):\n",
+    "    def __init__(self, dim, mlp_dim, dropout, **kwargs):\n",
+    "        super(Forward, self).__init__(**kwargs)\n",
+    "        self.rate = dropout\n",
+    "        self.dense1 = tf.keras.layers.Dense(mlp_dim, activation = gelu)\n",
+    "        self.dense2 = tf.keras.layers.Dense(dim)\n",
+    "        self.dropout = tf.keras.layers.Dropout(self.rate)\n",
+    "\n",
+    "    def call(self, inputs, training = True):\n",
+    "        X = self.dense1(inputs)\n",
+    "        X = self.dropout(X, training = training)\n",
+    "        X = self.dense2(X)\n",
+    "        X = self.dropout(X, training = training)\n",
+    "        return X\n",
+    "\n",
+    "\n",
+    "class FNetBlock(tf.keras.layers.Layer):\n",
+    "    def __init__(self, dim, mlp_dim, dropout = 0.1, **kwargs):\n",
+    "        super(FNetBlock, self).__init__(name = 'FNetBlock', **kwargs)\n",
+    "        self.norm_fourier = tf.keras.layers.LayerNormalization()\n",
+    "        self.norm_ffn = tf.keras.layers.LayerNormalization()\n",
+    "        self.ffn = Forward(dim, mlp_dim, dropout = dropout)\n",
+    "\n",
+    "    def call(self, inputs, training = True):\n",
+    "        X_complex = tf.cast(inputs, tf.complex64)\n",
+    "        X_fft = tf.math.real(tf.signal.fft2d(X_complex))\n",
+    "        X_norm1 = self.norm_fourier(X_fft + inputs, training = training)\n",
+    "        X_dense = self.ffn(X_norm1, training = training)\n",
+    "        X_norm2 = self.norm_ffn(X_dense + X_norm1, training = training)\n",
+    "        return X_norm2\n",
+    "\n",
+    "\n",
+    "class Model(tf.keras.Model):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        dim,\n",
+    "        vocab_size,\n",
+    "        depth,\n",
+    "        mlp_dim,\n",
+    "        dropout = 0.1,\n",
+    "        dropout_embedding = 0.1,\n",
+    "        max_position_embeddings = 1024,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        super(Model, self).__init__(name = 'Model', **kwargs)\n",
+    "        self.dim = dim\n",
+    "        self.hidden_size = dim\n",
+    "        self.vocab_size = vocab_size\n",
+    "        self.dropout_embedding = dropout_embedding\n",
+    "        self.max_position_embeddings = max_position_embeddings\n",
+    "        self.attn = []\n",
+    "        for _ in range(depth):\n",
+    "            self.attn.append(\n",
+    "                FNetBlock(dim = dim, mlp_dim = mlp_dim, dropout = dropout)\n",
+    "            )\n",
+    "        self.layernorm_dropout = tf.keras.Sequential()\n",
+    "        self.layernorm_dropout.add(tf.keras.layers.LayerNormalization())\n",
+    "        self.layernorm_dropout.add(tf.keras.layers.Dropout(dropout_embedding))\n",
+    "\n",
+    "    def call(\n",
+    "        self, x, input_mask = None, token_type_ids = None, training = True\n",
+    "    ):\n",
+    "\n",
+    "        if input_mask is None:\n",
+    "            input_mask = tf.ones(\n",
+    "                shape = [tf.shape(x)[0], tf.shape(x)[1]], dtype = tf.int32\n",
+    "            )\n",
+    "\n",
+    "        input_mask = tf.expand_dims(tf.cast(input_mask, tf.float32), -1)\n",
+    "\n",
+    "        if token_type_ids is None:\n",
+    "            token_type_ids = tf.zeros(\n",
+    "                shape = [tf.shape(x)[0], tf.shape(x)[1]], dtype = tf.int32\n",
+    "            )\n",
+    "        (self.embedding_output, self.embedding_table) = embedding_lookup(\n",
+    "            input_ids = x,\n",
+    "            vocab_size = self.vocab_size,\n",
+    "            embedding_size = self.dim,\n",
+    "            initializer_range = 0.02,\n",
+    "            word_embedding_name = 'word_embeddings',\n",
+    "            use_one_hot_embeddings = False,\n",
+    "        )\n",
+    "        self.embedding_output = embedding_postprocessor(\n",
+    "            input_tensor = self.embedding_output,\n",
+    "            use_token_type = True,\n",
+    "            token_type_ids = token_type_ids,\n",
+    "            token_type_vocab_size = 2,\n",
+    "            token_type_embedding_name = 'token_type_embeddings',\n",
+    "            use_position_embeddings = True,\n",
+    "            position_embedding_name = 'position_embeddings',\n",
+    "            initializer_range = 0.02,\n",
+    "            max_position_embeddings = self.max_position_embeddings,\n",
+    "        )\n",
+    "        x = self.layernorm_dropout(self.embedding_output, training = training)\n",
+    "        for no, attn in enumerate(self.attn):\n",
+    "            x = attn(x, training = training)\n",
+    "            x = x * input_mask\n",
+    "\n",
+    "        with tf.variable_scope('pooler'):\n",
+    "            first_token_tensor = tf.squeeze(x[:, 0:1, :], axis = 1)\n",
+    "            self.pooled_output = tf.layers.dense(\n",
+    "                first_token_tensor,\n",
+    "                self.hidden_size,\n",
+    "                activation = tf.tanh,\n",
+    "                kernel_initializer = create_initializer(0.02),\n",
+    "            )\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "# x = tf.placeholder(tf.int32, (None, None))\n",
+    "# model = Model(768, 32000, 12, 768)\n",
+    "# o = model(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = tf.placeholder(tf.int32, (None, None))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(768, 32000, 12, 768)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-3-2cce56a796df>:295: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Dense instead.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-3-2cce56a796df>:295: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Dense instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.__call__` method instead.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/huseinzolkepli/Documents/tf-1.15/env/lib/python3.7/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.__call__` method instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'Model/mul_11:0' shape=(?, ?, 768) dtype=float32>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "o = model(x)\n",
+    "o"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sess = tf.Session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sess.run(tf.global_variables_initializer())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 433 ms, sys: 23.6 ms, total: 456 ms\n",
+      "Wall time: 492 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[[-0.14676172,  0.12887064, -0.6372858 , ...,  0.37827602,\n",
+       "          0.5740778 , -0.84948176],\n",
+       "        [ 0.4179115 , -0.3492962 ,  0.99186915, ..., -0.82482225,\n",
+       "          0.7381474 , -0.86820143],\n",
+       "        [ 0.7566453 , -0.1262211 ,  0.04796262, ...,  1.421154  ,\n",
+       "          0.7783395 , -0.01940406],\n",
+       "        [ 0.12364741,  0.39592683,  2.253871  , ..., -0.11123513,\n",
+       "          0.922476  , -0.94825745]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "sess.run(o, feed_dict = {x: [[1,2,3,4]]})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pretrained-model/performer/fast_attention.py b/pretrained-model/performer/fast_attention.py
new file mode 100644
index 00000000..1be24cb6
--- /dev/null
+++ b/pretrained-model/performer/fast_attention.py
@@ -0,0 +1,529 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Implementation of multiheaded FAVOR-attention & FAVOR-self-attention layers.
+
+Prefix Sum Tensorflow implementation by Valerii Likhosherstov.
+"""
+import math
+import numpy as np
+import tensorflow as tf
+import util
+
+BIG_CONSTANT = 1e8
+
+
+def create_projection_matrix(m, d, seed=0, scaling=0, struct_mode=False):
+    r"""Constructs the matrix of random projections.
+
+    Constructs a matrix of random orthogonal projections. Each projection vector
+    has direction chosen uniformly at random and either deterministic length
+    \sqrt{d} or length taken from the \chi(d) distribution (in the latter case
+    marginal distributions of the projections are d-dimensional Gaussian vectors
+    with associated identity covariance matrix).
+
+    Args:
+      m: number of random projections.
+      d: dimensionality of each random projection.
+      seed: random seed used to construct projections.
+      scaling: 1 if all the random projections need to be renormalized to have
+        length \sqrt{d}, 0 if the lengths of random projections should follow
+        \chi(d) distribution.
+      struct_mode: if True then products of Givens rotations will be used to
+        construct random orthogonal matrix. This bypasses Gram-Schmidt
+        orthogonalization.
+
+    Returns:
+      The matrix of random projections of the shape [m, d].
+    """
+    nb_full_blocks = int(m / d)
+    block_list = []
+    current_seed = seed
+    for _ in range(nb_full_blocks):
+        if struct_mode:
+            q = create_products_of_givens_rotations(d, seed)
+        else:
+            unstructured_block = tf.random.normal((d, d), seed=current_seed)
+            q, _ = tf.linalg.qr(unstructured_block)
+            q = tf.transpose(q)
+        block_list.append(q)
+        current_seed += 1
+    remaining_rows = m - nb_full_blocks * d
+    if remaining_rows > 0:
+        if struct_mode:
+            q = create_products_of_givens_rotations(d, seed)
+        else:
+            unstructured_block = tf.random.normal((d, d), seed=current_seed)
+            q, _ = tf.linalg.qr(unstructured_block)
+            q = tf.transpose(q)
+        block_list.append(q[0:remaining_rows])
+    final_matrix = tf.experimental.numpy.vstack(block_list)
+    current_seed += 1
+
+    if scaling == 0:
+        multiplier = tf.norm(tf.random.normal((m, d), seed=current_seed), axis=1)
+    elif scaling == 1:
+        multiplier = tf.math.sqrt(float(d)) * tf.ones((m))
+    else:
+        raise ValueError("Scaling must be one of {0, 1}. Was %s" % scaling)
+
+    return tf.linalg.matmul(tf.linalg.diag(multiplier), final_matrix)
+
+
+def create_products_of_givens_rotations(dim, seed):
+    r"""Constructs a 2D-tensor which is a product of Givens random rotations.
+
+    Constructs a 2D-tensor of the form G_1 * ... * G_k, where G_i is a Givens
+    random rotation. The resulting tensor mimics a matrix taken uniformly at
+    random form the orthogonal group.
+
+    Args:
+      dim: number of rows/columns of the resulting 2D-tensor.
+      seed: random seed.
+
+    Returns:
+      The product of Givens random rotations.
+    """
+    nb_givens_rotations = dim * int(math.ceil(math.log(float(dim))))
+    q = np.eye(dim, dim)
+    np.random.seed(seed)
+    for _ in range(nb_givens_rotations):
+        random_angle = math.pi * np.random.uniform()
+        random_indices = np.random.choice(dim, 2)
+        index_i = min(random_indices[0], random_indices[1])
+        index_j = max(random_indices[0], random_indices[1])
+        slice_i = q[index_i]
+        slice_j = q[index_j]
+        new_slice_i = math.cos(random_angle) * slice_i + math.sin(
+            random_angle) * slice_j
+        new_slice_j = -math.sin(random_angle) * slice_i + math.cos(
+            random_angle) * slice_j
+        q[index_i] = new_slice_i
+        q[index_j] = new_slice_j
+    return tf.cast(tf.constant(q), dtype=tf.float32)
+
+
+def relu_kernel_transformation(data,
+                               is_query,
+                               projection_matrix=None,
+                               numerical_stabilizer=0.001):
+    """Computes features for the ReLU-kernel.
+
+    Computes random features for the ReLU kernel from
+    https://arxiv.org/pdf/2009.14794.pdf.
+
+    Args:
+      data: input data tensor of the shape [B, L, H, D], where: B - batch
+        dimension, L - attention dimensions, H - heads, D - features.
+      is_query: indicates whether input data is a query oor key tensor.
+      projection_matrix: random Gaussian matrix of shape [M, D], where M stands
+        for the number of random features and each D x D sub-block has pairwise
+        orthogonal rows.
+      numerical_stabilizer: small positive constant for numerical stability.
+
+    Returns:
+      Corresponding kernel feature map.
+    """
+    del is_query
+    if projection_matrix is None:
+        return tf.nn.relu(data) + numerical_stabilizer
+    else:
+        ratio = 1.0 / tf.math.sqrt(
+            tf.dtypes.cast(projection_matrix.shape[0], tf.float32))
+        data_dash = ratio * tf.einsum("blhd,md->blhm", data, projection_matrix)
+        return tf.nn.relu(data_dash) + numerical_stabilizer
+
+
+def softmax_kernel_transformation(data,
+                                  is_query,
+                                  projection_matrix=None,
+                                  numerical_stabilizer=0.000001):
+    """Computes random features for the softmax kernel using FAVOR+ mechanism.
+
+    Computes random features for the softmax kernel using FAVOR+ mechanism from
+    https://arxiv.org/pdf/2009.14794.pdf.
+
+    Args:
+      data: input data tensor of the shape [B, L, H, D], where: B - batch
+        dimension, L - attention dimensions, H - heads, D - features.
+      is_query: indicates whether input data is a query oor key tensor.
+      projection_matrix: random Gaussian matrix of shape [M, D], where M stands
+        for the number of random features and each D x D sub-block has pairwise
+        orthogonal rows.
+      numerical_stabilizer: small positive constant for numerical stability.
+
+    Returns:
+      Corresponding kernel feature map.
+    """
+    data_normalizer = 1.0 / (
+        tf.math.sqrt(tf.math.sqrt(tf.dtypes.cast(data.shape[-1], tf.float32))))
+    data = data_normalizer * data
+    ratio = 1.0 / tf.math.sqrt(
+        tf.dtypes.cast(projection_matrix.shape[0], tf.float32))
+    data_dash = tf.einsum("blhd,md->blhm", data, projection_matrix)
+    diag_data = tf.math.square(data)
+    diag_data = tf.math.reduce_sum(
+        diag_data, axis=tf.keras.backend.ndim(data) - 1)
+    diag_data = diag_data / 2.0
+    diag_data = tf.expand_dims(diag_data, axis=tf.keras.backend.ndim(data) - 1)
+    last_dims_t = (len(data_dash.shape) - 1,)
+    attention_dims_t = (len(data_dash.shape) - 3,)
+    if is_query:
+        data_dash = ratio * (
+            tf.math.exp(data_dash - diag_data - tf.math.reduce_max(
+                data_dash, axis=last_dims_t, keepdims=True)) + numerical_stabilizer)
+    else:
+        data_dash = ratio * (
+            tf.math.exp(data_dash - diag_data - tf.math.reduce_max(
+                data_dash, axis=last_dims_t + attention_dims_t, keepdims=True)) +
+            numerical_stabilizer)
+
+    return data_dash
+
+
+def noncausal_numerator(qs, ks, vs):
+    """Computes not-normalized FAVOR noncausal attention AV.
+
+    Args:
+      qs: query_prime tensor of the shape [L,B,H,M].
+      ks: key_prime tensor of the shape [L,B,H,M].
+      vs: value tensor of the shape [L,B,H,D].
+
+    Returns:
+      Not-normalized FAVOR noncausal attention AV.
+    """
+    kvs = tf.einsum("lbhm,lbhd->bhmd", ks, vs)
+    return tf.einsum("lbhm,bhmd->lbhd", qs, kvs)
+
+
+def noncausal_denominator(qs, ks):
+    """Computes FAVOR normalizer in noncausal attention.
+
+    Args:
+      qs: query_prime tensor of the shape [L,B,H,M].
+      ks: key_prime tensor of the shape [L,B,H,M].
+
+    Returns:
+      FAVOR normalizer in noncausal attention.
+    """
+    all_ones = tf.ones([ks.shape[0]])
+    ks_sum = tf.einsum("lbhm,l->bhm", ks, all_ones)
+    return tf.einsum("lbhm,bhm->lbh", qs, ks_sum)
+
+
+@tf.custom_gradient
+def causal_numerator(qs, ks, vs):
+    """Computes not-normalized FAVOR causal attention A_{masked}V.
+
+    Args:
+      qs: query_prime tensor of the shape [L,B,H,M].
+      ks: key_prime tensor of the shape [L,B,H,M].
+      vs: value tensor of the shape [L,B,H,D].
+
+    Returns:
+      Not-normalized FAVOR causal attention A_{masked}V.
+    """
+
+    result = []
+    sums = tf.zeros_like(tf.einsum("ijk,ijl->ijkl", ks[0], vs[0]))
+
+    for index in range(qs.shape[0]):
+        sums = sums + tf.einsum("ijk,ijl->ijkl", ks[index], vs[index])
+        result.append(tf.einsum("ijkl,ijk->ijl", sums, qs[index])[None, Ellipsis])
+
+    result = tf.concat(result, axis=0)
+
+    def grad(res_grad):
+
+        grads = tf.zeros_like(tf.einsum("ijk,ijl->ijkl", ks[0], vs[0]))
+
+        gr_sums = sums
+
+        q_grads = []
+        k_grads = []
+        v_grads = []
+
+        for index in range(qs.shape[0] - 1, -1, -1):
+
+            q_grads.append(
+                tf.einsum("ijkl,ijl->ijk", gr_sums, res_grad[index])[None, Ellipsis])
+            grads = grads + tf.einsum("ijk,ijl->ijkl", qs[index], res_grad[index])
+            k_grads.append(tf.einsum("ijkl,ijl->ijk", grads, vs[index])[None, Ellipsis])
+            v_grads.append(tf.einsum("ijkl,ijk->ijl", grads, ks[index])[None, Ellipsis])
+            gr_sums = gr_sums - tf.einsum("ijk,ijl->ijkl", ks[index], vs[index])
+
+        q_grads = tf.concat(q_grads[::-1], axis=0)
+        k_grads = tf.concat(k_grads[::-1], axis=0)
+        v_grads = tf.concat(v_grads[::-1], axis=0)
+
+        return q_grads, k_grads, v_grads
+
+    return result, grad
+
+
+@tf.custom_gradient
+def causal_denominator(qs, ks):
+    """Computes FAVOR normalizer in causal attention.
+
+    Args:
+      qs: query_prime tensor of the shape [L,B,H,M].
+      ks: key_prime tensor of the shape [L,B,H,M].
+
+    Returns:
+      FAVOR normalizer in causal attention.
+    """
+
+    result = []
+    sums = tf.zeros_like(ks[0])
+
+    for index in range(qs.shape[0]):
+        sums = sums + ks[index]
+        result.append(tf.reduce_sum(qs[index] * sums, axis=2)[None, Ellipsis])
+
+    result = tf.concat(result, axis=0)
+
+    def grad(res_grad):
+
+        k_grad = tf.zeros_like(ks[0])
+
+        gr_sums = sums
+
+        q_grads = []
+        k_grads = []
+
+        for index in range(qs.shape[0] - 1, -1, -1):
+
+            q_grads.append(
+                tf.einsum("ijk,ij->ijk", gr_sums, res_grad[index])[None, Ellipsis])
+            k_grad = k_grad + tf.einsum("ijk,ij->ijk", qs[index], res_grad[index])
+            k_grads.append(k_grad[None, Ellipsis])
+            gr_sums = gr_sums - ks[index]
+
+        q_grads = tf.concat(q_grads[::-1], axis=0)
+        k_grads = tf.concat(k_grads[::-1], axis=0)
+
+        return q_grads, k_grads
+
+    return result, grad
+
+
+def favor_attention(query,
+                    key,
+                    value,
+                    kernel_transformation,
+                    causal,
+                    projection_matrix=None):
+    """Computes FAVOR normalized attention.
+
+    Args:
+      query: query tensor.
+      key: key tensor.
+      value: value tensor.
+      kernel_transformation: transformation used to get finite kernel features.
+      causal: whether attention is causal or not.
+      projection_matrix: projection matrix to be used.
+
+    Returns:
+      FAVOR normalized attention.
+    """
+    query_prime = kernel_transformation(query, True,
+                                        projection_matrix)  # [B,L,H,M]
+    key_prime = kernel_transformation(key, False, projection_matrix)  # [B,L,H,M]
+    query_prime = tf.transpose(query_prime, [1, 0, 2, 3])  # [L,B,H,M]
+    key_prime = tf.transpose(key_prime, [1, 0, 2, 3])  # [L,B,H,M]
+    value = tf.transpose(value, [1, 0, 2, 3])  # [L,B,H,D]
+
+    if causal:
+        av_attention = causal_numerator(query_prime, key_prime, value)
+        attention_normalizer = causal_denominator(query_prime, key_prime)
+    else:
+        av_attention = noncausal_numerator(query_prime, key_prime, value)
+        attention_normalizer = noncausal_denominator(query_prime, key_prime)
+    # TODO(kchoro): Add more comments.
+    av_attention = tf.transpose(av_attention, [1, 0, 2, 3])
+    attention_normalizer = tf.transpose(attention_normalizer, [1, 0, 2])
+    attention_normalizer = tf.expand_dims(attention_normalizer,
+                                          len(attention_normalizer.shape))
+    return av_attention / attention_normalizer
+
+
+class Attention(tf.keras.layers.Layer):
+    """Multi-headed attention layer."""
+
+    def __init__(self,
+                 hidden_size,
+                 num_heads,
+                 attention_dropout,
+                 kernel_transformation=relu_kernel_transformation,
+                 numerical_stabilizer=0.001,
+                 causal=False,
+                 projection_matrix_type=None,
+                 nb_random_features=0):
+        """Initialize Attention.
+
+        Args:
+          hidden_size: int, output dim of hidden layer.
+          num_heads: int, number of heads to repeat the same attention structure.
+          attention_dropout: float, dropout rate inside attention for training.
+          kernel_transformation: transformation used to produce kernel features for
+            attention.
+          numerical_stabilizer: used to bound away from zero kernel values.
+          causal: whether attention is causal or not.
+          projection_matrix_type: None if Identity should be used, otherwise random
+            projection matrix will be applied.
+          nb_random_features: number of random features to be used (relevant only if
+            projection_matrix is not None).
+        """
+        if hidden_size % num_heads:
+            raise ValueError(
+                "Hidden size ({}) must be divisible by the number of heads ({})."
+                .format(hidden_size, num_heads))
+
+        super(Attention, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.attention_dropout = attention_dropout
+        self.kernel_transformation = kernel_transformation
+        self.numerical_stabilizer = numerical_stabilizer
+        self.causal = causal
+        self.projection_matrix_type = projection_matrix_type
+        self.nb_random_features = nb_random_features
+
+    def build(self, input_shape):
+        """Builds the layer."""
+        # Layers for linearly projecting the queries, keys, and values.
+        size_per_head = self.hidden_size // self.num_heads
+
+        def _glorot_initializer(fan_in, fan_out):
+            limit = math.sqrt(6.0 / (fan_in + fan_out))
+            return tf.keras.initializers.RandomUniform(minval=-limit, maxval=limit)
+
+        attention_initializer = _glorot_initializer(input_shape.as_list()[-1],
+                                                    self.hidden_size)
+        self.query_dense_layer = util.DenseEinsum(
+            output_shape=(self.num_heads, size_per_head),
+            kernel_initializer=attention_initializer,
+            use_bias=False,
+            name="query")
+        self.key_dense_layer = util.DenseEinsum(
+            output_shape=(self.num_heads, size_per_head),
+            kernel_initializer=attention_initializer,
+            use_bias=False,
+            name="key")
+        self.value_dense_layer = util.DenseEinsum(
+            output_shape=(self.num_heads, size_per_head),
+            kernel_initializer=attention_initializer,
+            use_bias=False,
+            name="value")
+
+        output_initializer = _glorot_initializer(self.hidden_size, self.hidden_size)
+        self.output_dense_layer = util.DenseEinsum(
+            output_shape=self.hidden_size,
+            num_summed_dimensions=2,
+            kernel_initializer=output_initializer,
+            use_bias=False,
+            name="output_transform")
+        super(Attention, self).build(input_shape)
+
+    def get_config(self):
+        return {
+            "hidden_size": self.hidden_size,
+            "num_heads": self.num_heads,
+            "attention_dropout": self.attention_dropout,
+        }
+
+    def call(self,
+             query_input,
+             source_input,
+             bias,
+             training,
+             cache=None,
+             decode_loop_step=None):
+        """Apply attention mechanism to query_input and source_input.
+
+        Args:
+          query_input: A tensor with shape [batch_size, length_query, hidden_size].
+          source_input: A tensor with shape [batch_size, length_source,
+            hidden_size].
+          bias: A tensor with shape [batch_size, 1, length_query, length_source],
+            the attention bias that will be added to the result of the dot product.
+          training: A bool, whether in training mode or not.
+          cache: (Used during prediction) A dictionary with tensors containing
+            results of previous attentions. The dictionary must have the items:
+                {"k": tensor with shape [batch_size, i, heads, dim_per_head],
+                 "v": tensor with shape [batch_size, i, heads, dim_per_head]} where
+                   i is the current decoded length for non-padded decode, or max
+                   sequence length for padded decode.
+          decode_loop_step: An integer, step number of the decoding loop. Used only
+            for autoregressive inference on TPU.
+
+        Returns:
+          Attention layer output with shape [batch_size, length_query, hidden_size]
+        """
+        # Linearly project the query, key and value using different learned
+        # projections. Splitting heads is automatically done during the linear
+        # projections --> [batch_size, length, num_heads, dim_per_head].
+        query = self.query_dense_layer(query_input)
+        key = self.key_dense_layer(source_input)
+        value = self.value_dense_layer(source_input)
+
+        if self.projection_matrix_type is None:
+            projection_matrix = None
+        else:
+            dim = query.shape[-1]
+            seed = tf.math.ceil(tf.math.abs(tf.math.reduce_sum(query) * BIG_CONSTANT))
+            seed = tf.dtypes.cast(seed, tf.int32)
+            projection_matrix = create_projection_matrix(
+                self.nb_random_features, dim, seed=seed)
+
+        if cache is not None:
+            # Combine cached keys and values with new keys and values.
+            if decode_loop_step is not None:
+                cache_k_shape = cache["k"].shape.as_list()
+                indices = tf.reshape(
+                    tf.one_hot(decode_loop_step, cache_k_shape[1], dtype=key.dtype),
+                    [1, cache_k_shape[1], 1, 1])
+                key = cache["k"] + key * indices
+                cache_v_shape = cache["v"].shape.as_list()
+                indices = tf.reshape(
+                    tf.one_hot(decode_loop_step, cache_v_shape[1], dtype=value.dtype),
+                    [1, cache_v_shape[1], 1, 1])
+                value = cache["v"] + value * indices
+            else:
+                key = tf.concat([tf.cast(cache["k"], key.dtype), key], axis=1)
+                value = tf.concat([tf.cast(cache["v"], value.dtype), value], axis=1)
+
+            # Update cache
+            cache["k"] = key
+            cache["v"] = value
+
+        attention_output = favor_attention(query, key, value,
+                                           self.kernel_transformation, self.causal,
+                                           projection_matrix)
+        attention_output = self.output_dense_layer(attention_output)
+        return attention_output
+
+
+class SelfAttention(Attention):
+    """Multiheaded self-attention layer."""
+
+    def call(self,
+             query_input,
+             bias,
+             training,
+             cache=None,
+             decode_loop_step=None):
+        return super(SelfAttention, self).call(query_input, query_input, bias,
+                                               training, cache, decode_loop_step)
diff --git a/pretrained-model/performer/model.py b/pretrained-model/performer/model.py
new file mode 100644
index 00000000..ef61db37
--- /dev/null
+++ b/pretrained-model/performer/model.py
@@ -0,0 +1,230 @@
+import tensorflow as tf
+import numpy as np
+
+
+def gelu(x):
+    cdf = 0.5 * (
+        1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044_715 * tf.pow(x, 3))))
+    )
+    return x * cdf
+
+
+def get_shape_list(tensor, expected_rank=None, name=None):
+    """Returns a list of the shape of tensor, preferring static dimensions.
+  Args:
+    tensor: A tf.Tensor object to find the shape of.
+    expected_rank: (optional) int. The expected rank of `tensor`. If this is
+      specified and the `tensor` has a different rank, and exception will be
+      thrown.
+    name: Optional name of the tensor for the error message.
+  Returns:
+    A list of dimensions of the shape of tensor. All static dimensions will
+    be returned as python integers, and dynamic dimensions will be returned
+    as tf.Tensor scalars.
+  """
+    if name is None:
+        name = tensor.name
+
+    shape = tensor.shape.as_list()
+
+    non_static_indexes = []
+    for (index, dim) in enumerate(shape):
+        if dim is None:
+            non_static_indexes.append(index)
+
+    if not non_static_indexes:
+        return shape
+
+    dyn_shape = tf.shape(tensor)
+    for index in non_static_indexes:
+        shape[index] = dyn_shape[index]
+    return shape
+
+
+def embedding_lookup(
+    input_ids,
+    vocab_size,
+    embedding_size=128,
+    initializer_range=0.02,
+    word_embedding_name='word_embeddings',
+    use_one_hot_embeddings=False,
+):
+    """Looks up words embeddings for id tensor.
+  Args:
+    input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
+      ids.
+    vocab_size: int. Size of the embedding vocabulary.
+    embedding_size: int. Width of the word embeddings.
+    initializer_range: float. Embedding initialization range.
+    word_embedding_name: string. Name of the embedding table.
+    use_one_hot_embeddings: bool. If True, use one-hot method for word
+      embeddings. If False, use `tf.gather()`.
+  Returns:
+    float Tensor of shape [batch_size, seq_length, embedding_size].
+  """
+    # This function assumes that the input is of shape [batch_size, seq_length,
+    # num_inputs].
+    #
+    # If the input is a 2D tensor of shape [batch_size, seq_length], we
+    # reshape to [batch_size, seq_length, 1].
+    if input_ids.shape.ndims == 2:
+        input_ids = tf.expand_dims(input_ids, axis=[-1])
+
+    embedding_table = tf.get_variable(
+        name=word_embedding_name,
+        shape=[vocab_size, embedding_size],
+        initializer=create_initializer(initializer_range),
+    )
+
+    flat_input_ids = tf.reshape(input_ids, [-1])
+    if use_one_hot_embeddings:
+        one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
+        output = tf.matmul(one_hot_input_ids, embedding_table)
+    else:
+        output = tf.gather(embedding_table, flat_input_ids)
+
+    input_shape = get_shape_list(input_ids)
+
+    output = tf.reshape(
+        output, input_shape[0:-1] + [input_shape[-1] * embedding_size]
+    )
+    return (output, embedding_table)
+
+
+def embedding_postprocessor(
+    input_tensor,
+    use_token_type=False,
+    token_type_ids=None,
+    token_type_vocab_size=2,
+    token_type_embedding_name='token_type_embeddings',
+    use_position_embeddings=True,
+    position_embedding_name='position_embeddings',
+    initializer_range=0.02,
+    max_position_embeddings=512,
+):
+    """Performs various post-processing on a word embedding tensor.
+  Args:
+    input_tensor: float Tensor of shape [batch_size, seq_length,
+      embedding_size].
+    use_token_type: bool. Whether to add embeddings for `token_type_ids`.
+    token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
+      Must be specified if `use_token_type` is True.
+    token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
+    token_type_embedding_name: string. The name of the embedding table variable
+      for token type ids.
+    use_position_embeddings: bool. Whether to add position embeddings for the
+      position of each token in the sequence.
+    position_embedding_name: string. The name of the embedding table variable
+      for positional embeddings.
+    initializer_range: float. Range of the weight initialization.
+    max_position_embeddings: int. Maximum sequence length that might ever be
+      used with this model. This can be longer than the sequence length of
+      input_tensor, but cannot be shorter.
+    dropout_prob: float. Dropout probability applied to the final output tensor.
+  Returns:
+    float tensor with same shape as `input_tensor`.
+  Raises:
+    ValueError: One of the tensor shapes or input values is invalid.
+  """
+    input_shape = get_shape_list(input_tensor, expected_rank=3)
+    batch_size = input_shape[0]
+    seq_length = input_shape[1]
+    width = input_shape[2]
+
+    output = input_tensor
+
+    if use_token_type:
+        if token_type_ids is None:
+            raise ValueError(
+                '`token_type_ids` must be specified if'
+                '`use_token_type` is True.'
+            )
+        token_type_table = tf.get_variable(
+            name=token_type_embedding_name,
+            shape=[token_type_vocab_size, width],
+            initializer=create_initializer(initializer_range),
+        )
+        flat_token_type_ids = tf.reshape(token_type_ids, [-1])
+        one_hot_ids = tf.one_hot(
+            flat_token_type_ids, depth=token_type_vocab_size
+        )
+        token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
+        token_type_embeddings = tf.reshape(
+            token_type_embeddings, [batch_size, seq_length, width]
+        )
+        output += token_type_embeddings
+
+    if use_position_embeddings:
+        assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
+        with tf.control_dependencies([assert_op]):
+            full_position_embeddings = tf.get_variable(
+                name=position_embedding_name,
+                shape=[max_position_embeddings, width],
+                initializer=create_initializer(initializer_range),
+            )
+            position_embeddings = tf.slice(
+                full_position_embeddings, [0, 0], [seq_length, -1]
+            )
+            num_dims = len(output.shape.as_list())
+            position_broadcast_shape = []
+            for _ in range(num_dims - 2):
+                position_broadcast_shape.append(1)
+            position_broadcast_shape.extend([seq_length, width])
+            position_embeddings = tf.reshape(
+                position_embeddings, position_broadcast_shape
+            )
+            output += position_embeddings
+
+    return output
+
+
+class Forward(tf.keras.layers.Layer):
+    def __init__(self, dim, mlp_dim, dropout, **kwargs):
+        super(Forward, self).__init__(**kwargs)
+        self.rate = dropout
+        self.dense1 = tf.keras.layers.Dense(mlp_dim, activation=gelu)
+        self.dense2 = tf.keras.layers.Dense(dim)
+        self.dropout = tf.keras.layers.Dropout(self.rate)
+
+    def call(self, inputs, training=True):
+        X = self.dense1(inputs)
+        X = self.dropout(X, training=training)
+        X = self.dense2(X)
+        X = self.dropout(X, training=training)
+        return X
+
+
+class FNetBlock(tf.keras.layers.Layer):
+    def __init__(self, dim, mlp_dim, dropout=0.1, **kwargs):
+        super(FNetBlock, self).__init__(name='FNetBlock', **kwargs)
+        self.norm_fourier = tf.keras.layers.LayerNormalization()
+        self.norm_ffn = tf.keras.layers.LayerNormalization()
+        self.ffn = Forward(dim, mlp_dim, dropout=dropout)
+
+    def call(self, inputs, training=True):
+        X_complex = tf.cast(inputs, tf.complex64)
+        X_fft = tf.math.real(tf.signal.fft2d(X_complex))
+        X_norm1 = self.norm_fourier(X_fft + inputs, training=training)
+        X_dense = self.ffn(X_norm1, training=training)
+        X_norm2 = self.norm_ffn(X_dense + X_norm1, training=training)
+        return X_norm2
+
+
+class Model(tf.keras.Model):
+    def __init__(
+        self,
+        hidden_size,
+        vocab_size,
+        nlayer,
+        head_size,
+        intermediate_size,
+        dropout=0.1,
+        dropout_embedding=0.1,
+        max_position_embeddings=512,
+        **kwargs,
+    ):
+        super(Model, self).__init__(name='Model', **kwargs)
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.dropout_embedding = dropout_embedding
+        self.max_position_embeddings = max_position_embeddings
diff --git a/pretrained-model/performer/test-performer.ipynb b/pretrained-model/performer/test-performer.ipynb
new file mode 100644
index 00000000..1091d43d
--- /dev/null
+++ b/pretrained-model/performer/test-performer.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "black-italian",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pretrained-model/performer/util.py b/pretrained-model/performer/util.py
new file mode 100644
index 00000000..f461ef79
--- /dev/null
+++ b/pretrained-model/performer/util.py
@@ -0,0 +1,195 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Keras-based einsum layer.
+
+Copied from
+https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/dense_einsum.py.
+"""
+# pylint: disable=g-classes-have-attributes
+
+import tensorflow as tf
+
+_CHR_IDX = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m"]
+
+
+@tf.keras.utils.register_keras_serializable(package="Text")
+class DenseEinsum(tf.keras.layers.Layer):
+    """A densely connected layer that uses tf.einsum as the backing computation.
+
+    This layer can perform einsum calculations of arbitrary dimensionality.
+
+    Arguments:
+      output_shape: Positive integer or tuple, dimensionality of the output space.
+      num_summed_dimensions: The number of dimensions to sum over. Standard 2D
+        matmul should use 1, 3D matmul should use 2, and so forth.
+      activation: Activation function to use. If you don't specify anything, no
+        activation is applied
+        (ie. "linear" activation: `a(x) = x`).
+      use_bias: Boolean, whether the layer uses a bias vector.
+      kernel_initializer: Initializer for the `kernel` weights matrix.
+      bias_initializer: Initializer for the bias vector.
+      kernel_regularizer: Regularizer function applied to the `kernel` weights
+        matrix.
+      bias_regularizer: Regularizer function applied to the bias vector.
+      activity_regularizer: Regularizer function applied to the output of the
+        layer (its "activation")..
+      kernel_constraint: Constraint function applied to the `kernel` weights
+        matrix.
+      bias_constraint: Constraint function applied to the bias vector.
+    Input shape:
+      N-D tensor with shape: `(batch_size, ..., input_dim)`. The most common
+        situation would be a 2D input with shape `(batch_size, input_dim)`.
+    Output shape:
+      N-D tensor with shape: `(batch_size, ..., units)`. For instance, for a 2D
+        input with shape `(batch_size, input_dim)`, the output would have shape
+        `(batch_size, units)`.
+    """
+
+    def __init__(self,
+                 output_shape,
+                 num_summed_dimensions=1,
+                 activation=None,
+                 use_bias=True,
+                 kernel_initializer="glorot_uniform",
+                 bias_initializer="zeros",
+                 kernel_regularizer=None,
+                 bias_regularizer=None,
+                 activity_regularizer=None,
+                 kernel_constraint=None,
+                 bias_constraint=None,
+                 **kwargs):
+        super(DenseEinsum, self).__init__(**kwargs)
+        self._output_shape = output_shape if isinstance(
+            output_shape, (list, tuple)) else (output_shape,)
+        self._activation = tf.keras.activations.get(activation)
+        self._use_bias = use_bias
+        self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
+        self._bias_initializer = tf.keras.initializers.get(bias_initializer)
+        self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
+        self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
+        self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
+        self._bias_constraint = tf.keras.constraints.get(bias_constraint)
+        self._num_summed_dimensions = num_summed_dimensions
+        self._einsum_string = None
+
+    def _build_einsum_string(self, free_input_dims, bound_dims, output_dims):
+        input_str = ""
+        kernel_str = ""
+        output_str = ""
+        letter_offset = 0
+        for i in range(free_input_dims):
+            char = _CHR_IDX[i + letter_offset]
+            input_str += char
+            output_str += char
+
+        letter_offset += free_input_dims
+        for i in range(bound_dims):
+            char = _CHR_IDX[i + letter_offset]
+            input_str += char
+            kernel_str += char
+
+        letter_offset += bound_dims
+        for i in range(output_dims):
+            char = _CHR_IDX[i + letter_offset]
+            kernel_str += char
+            output_str += char
+
+        return input_str + "," + kernel_str + "->" + output_str
+
+    def build(self, input_shape):
+        input_shape = tf.TensorShape(input_shape)
+        input_rank = input_shape.rank
+        free_input_dims = input_rank - self._num_summed_dimensions
+        output_dims = len(self._output_shape)
+
+        self._einsum_string = self._build_einsum_string(free_input_dims,
+                                                        self._num_summed_dimensions,
+                                                        output_dims)
+
+        # This is only saved for testing purposes.
+        self._kernel_shape = (
+            input_shape[free_input_dims:].concatenate(self._output_shape))
+
+        self._kernel = self.add_weight(
+            "kernel",
+            shape=self._kernel_shape,
+            initializer=self._kernel_initializer,
+            regularizer=self._kernel_regularizer,
+            constraint=self._kernel_constraint,
+            dtype=self.dtype,
+            trainable=True)
+        if self._use_bias:
+            self._bias = self.add_weight(
+                "bias",
+                shape=self._output_shape,
+                initializer=self._bias_initializer,
+                regularizer=self._bias_regularizer,
+                constraint=self._bias_constraint,
+                dtype=self.dtype,
+                trainable=True)
+        else:
+            self._bias = None
+        super(DenseEinsum, self).build(input_shape)
+
+    def get_config(self):
+        config = {
+            "output_shape":
+                self._output_shape,
+            "num_summed_dimensions":
+                self._num_summed_dimensions,
+            "activation":
+                tf.keras.activations.serialize(self._activation),
+            "use_bias":
+                self._use_bias,
+            "kernel_initializer":
+                tf.keras.initializers.serialize(self._kernel_initializer),
+            "bias_initializer":
+                tf.keras.initializers.serialize(self._bias_initializer),
+            "kernel_regularizer":
+                tf.keras.regularizers.serialize(self._kernel_regularizer),
+            "bias_regularizer":
+                tf.keras.regularizers.serialize(self._bias_regularizer),
+            "activity_regularizer":
+                tf.keras.regularizers.serialize(self._activity_regularizer),
+            "kernel_constraint":
+                tf.keras.constraints.serialize(self._kernel_constraint),
+            "bias_constraint":
+                tf.keras.constraints.serialize(self._bias_constraint)
+        }
+        base_config = super(DenseEinsum, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs):
+        ret = tf.einsum(self._einsum_string, inputs, self._kernel)
+        if self._use_bias:
+            ret += self._bias
+        if self._activation is not None:
+            ret = self._activation(ret)
+        return ret
diff --git a/session/dependency/albert-base.ipynb b/session/dependency/albert-base.ipynb
index e2309c4e..c03eaced 100644
--- a/session/dependency/albert-base.ipynb
+++ b/session/dependency/albert-base.ipynb
@@ -7,29 +7,13 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = '3'"
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -45,12 +29,13 @@
     "from albert import optimization\n",
     "from albert import tokenization\n",
     "import tensorflow as tf\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import json"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -66,235 +51,82 @@
    "source": [
     "tokenizer = tokenization.FullTokenizer(\n",
     "      vocab_file='albert-base-2020-04-10/sp10m.cased.v10.vocab', do_lower_case=False,\n",
-    "      spm_model_file='albert-base-2020-04-10/sp10m.cased.v10.model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
-    "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = ['[CLS]']\n",
-    "                labels_ = [0]\n",
-    "                depends_ = [0]\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenizer.tokenize(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.append('[SEP]')\n",
-    "                labels_.append(0)\n",
-    "                depends_.append(0)\n",
-    "                words.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
-     ]
-    }
-   ],
-   "source": [
-    "sentences, words, depends, labels, _, _ = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = ['[CLS]']\n",
-    "        labels_ = [0]\n",
-    "        depends_ = [0]\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenizer.tokenize(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.append('[SEP]')\n",
-    "        labels_.append(0)\n",
-    "        depends_.append(0)\n",
-    "        outside.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "    return outside, sentences, outside_depends, outside_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}"
+    "      spm_model_file='albert-base-2020-04-10/sp10m.cased.v10.model')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
+    "import pickle\n",
     "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test \\\n",
-    "= train_test_split(words, depends, labels, test_size = 0.2)"
+    "with open('train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(40289, 10073)"
+       "{'PAD': 0,\n",
+       " 'X': 1,\n",
+       " 'nsubj': 2,\n",
+       " 'cop': 3,\n",
+       " 'det': 4,\n",
+       " 'root': 5,\n",
+       " 'nsubj:pass': 6,\n",
+       " 'acl': 7,\n",
+       " 'case': 8,\n",
+       " 'obl': 9,\n",
+       " 'flat': 10,\n",
+       " 'punct': 11,\n",
+       " 'appos': 12,\n",
+       " 'amod': 13,\n",
+       " 'compound': 14,\n",
+       " 'advmod': 15,\n",
+       " 'cc': 16,\n",
+       " 'obj': 17,\n",
+       " 'conj': 18,\n",
+       " 'mark': 19,\n",
+       " 'advcl': 20,\n",
+       " 'nmod': 21,\n",
+       " 'nummod': 22,\n",
+       " 'dep': 23,\n",
+       " 'xcomp': 24,\n",
+       " 'ccomp': 25,\n",
+       " 'parataxis': 26,\n",
+       " 'compound:plur': 27,\n",
+       " 'fixed': 28,\n",
+       " 'aux': 29,\n",
+       " 'csubj': 30,\n",
+       " 'iobj': 31,\n",
+       " 'csubj:pass': 32}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(words_train), len(words_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
+    "tag2idx"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -308,10 +140,10 @@
     {
      "data": {
       "text/plain": [
-       "<albert.modeling.AlbertConfig at 0x7f6599d24eb8>"
+       "<albert.modeling.AlbertConfig at 0x7fd3b80e0cc0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,11 +164,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "epoch = 30\n",
+    "epoch = 3\n",
     "batch_size = 32\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
@@ -345,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,8 +210,15 @@
     "            e = tf.expand_dims(tf.expand_dims(mask_e, 1), 2)\n",
     "            output = output * d * e\n",
     "            \n",
-    "        return output\n",
-    "    \n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "class BiLinear:\n",
     "    def __init__(self, left_features, right_features, out_features):\n",
     "        self.left_features = left_features\n",
@@ -404,8 +243,17 @@
     "        output = output + tf.matmul(input_left, tf.transpose(self.W_l))\\\n",
     "        + tf.matmul(input_right, tf.transpose(self.W_r))\n",
     "        \n",
-    "        return tf.reshape(output, output_shape)\n",
-    "    \n",
+    "        return tf.reshape(output, output_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_NEG_INF = -1e9\n",
+    "\n",
     "class Model:\n",
     "    def __init__(\n",
     "        self,\n",
@@ -437,14 +285,20 @@
     "            config=albert_config,\n",
     "            is_training=training,\n",
     "            input_ids=self.words,\n",
+    "            input_mask=self.mask,\n",
     "            use_one_hot_embeddings=False)\n",
+    "        \n",
     "        output_layer = model.get_sequence_output()\n",
     "        \n",
     "        arc_h = tf.nn.elu(self.arc_h(output_layer))\n",
     "        arc_c = tf.nn.elu(self.arc_c(output_layer))\n",
+    "        self._arc_h = arc_h\n",
+    "        self._arc_c = arc_c\n",
     "        \n",
     "        type_h = tf.nn.elu(self.type_h(output_layer))\n",
     "        type_c = tf.nn.elu(self.type_c(output_layer))\n",
+    "        self._type_h = type_h\n",
+    "        self._type_c = type_c\n",
     "        \n",
     "        out_arc = tf.squeeze(self.attention.forward(arc_h, arc_c, mask_d=self.mask, \n",
     "                                                    mask_e=self.mask), axis = 1)\n",
@@ -463,6 +317,11 @@
     "        self.heads_seq = tf.argmax(decode_arc, axis = 1)\n",
     "        self.heads_seq = tf.identity(self.heads_seq, name = 'heads_seq')\n",
     "        \n",
+    "#         self.decode_arc_t = tf.transpose(decode_arc, (0, 2, 1))\n",
+    "#         sequence_loss_depends = tf.contrib.seq2seq.sequence_loss(logits = self.decode_arc_t,\n",
+    "#                                                      targets = self.heads,\n",
+    "#                                                      weights = mask)\n",
+    "        \n",
     "        t = tf.cast(tf.transpose(self.heads_seq), tf.int32)\n",
     "        broadcasted = tf.broadcast_to(batch_index, tf.shape(t))\n",
     "        concatenated = tf.transpose(tf.concat([tf.expand_dims(broadcasted, axis = 0), \n",
@@ -537,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -567,20 +426,20 @@
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From <ipython-input-19-9e67073b97d5>:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-4486bc1f4ea9>:61: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-19-9e67073b97d5>:145: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-4486bc1f4ea9>:101: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "dim is deprecated, use axis instead\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:36: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
       "\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:41: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
       "\n",
-      "INFO:tensorflow:++++++ warmup starts at step 0, for 3777 steps ++++++\n",
+      "INFO:tensorflow:++++++ warmup starts at step 0, for 29336 steps ++++++\n",
       "INFO:tensorflow:using adamw\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:101: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
       "\n"
@@ -592,7 +451,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -600,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -619,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,16 +494,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.03448276, 0.00862069, 35.837]"
+       "[0.015625, 0.15625, 35.86845]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -659,16 +518,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.03448276, 0.00862069, 334.12787]"
+       "[0.015625, 0.15625, 156.66893]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -683,531 +542,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:06<00:00,  2.08it/s, accuracy=0.484, accuracy_depends=0.516, cost=2.22]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:21<00:00,  3.89it/s, accuracy=0.566, accuracy_depends=0.537, cost=1.86]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 3.945502, training acc: 0.359588, training depends: 0.484499, valid loss: 1.935619, valid acc: 0.549020, valid depends: 0.557201\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:53<00:00,  2.12it/s, accuracy=0.613, accuracy_depends=0.516, cost=1.89]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  3.99it/s, accuracy=0.519, accuracy_depends=0.638, cost=1.4] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 1.645403, training acc: 0.568282, training depends: 0.596351, valid loss: 1.478896, valid acc: 0.505224, valid depends: 0.625250\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:52<00:00,  2.13it/s, accuracy=0.71, accuracy_depends=0.774, cost=1.4]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  3.99it/s, accuracy=0.552, accuracy_depends=0.661, cost=1.28]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 1.304036, training acc: 0.532805, training depends: 0.659042, valid loss: 1.332415, valid acc: 0.525559, valid depends: 0.640700\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:51<00:00,  2.13it/s, accuracy=0.774, accuracy_depends=0.806, cost=0.961]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.98it/s, accuracy=0.719, accuracy_depends=0.689, cost=1.12] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 1.085138, training acc: 0.674575, training depends: 0.705453, valid loss: 1.183981, valid acc: 0.711554, valid depends: 0.665849\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:52<00:00,  2.13it/s, accuracy=0.871, accuracy_depends=0.839, cost=0.629]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  3.99it/s, accuracy=0.875, accuracy_depends=0.743, cost=0.957]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 0.942497, training acc: 0.844188, training depends: 0.737978, valid loss: 1.045847, valid acc: 0.858686, valid depends: 0.705353\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:51<00:00,  2.13it/s, accuracy=0.903, accuracy_depends=0.871, cost=0.478]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.00it/s, accuracy=0.89, accuracy_depends=0.79, cost=0.834]  \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 97788/97788 [8:20:21<00:00,  3.26it/s, accuracy=0.899, accuracy_depends=0.858, cost=0.255]   \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:50<00:00,  6.20it/s, accuracy=0.907, accuracy_depends=0.86, cost=0.237] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 5, training loss: 0.829288, training acc: 0.877085, training depends: 0.763971, valid loss: 0.920929, valid acc: 0.882823, valid depends: 0.741860\n",
+      "epoch: 0, training loss: 0.371303, training acc: 0.896068, training depends: 0.824540, valid loss: 0.368443, valid acc: 0.895804, valid depends: 0.822702\n",
       "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "epoch = 2\n",
+    "\n",
+    "for e in range(epoch):\n",
+    "    train_acc, train_loss = [], []\n",
+    "    test_acc, test_loss = [], []\n",
+    "    train_acc_depends, test_acc_depends = [], []\n",
+    "    \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_X))\n",
+    "        batch_x = train_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = train_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_X))\n",
+    "        batch_x = test_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = test_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "    \n",
+    "    \n",
+    "    print(\n",
+    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
+    "    % (e, np.mean(train_loss), \n",
+    "       np.mean(train_acc), \n",
+    "       np.mean(train_acc_depends), \n",
+    "       np.mean(test_loss), \n",
+    "       np.mean(test_acc), \n",
+    "       np.mean(test_acc_depends)\n",
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'albert-base-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:51<00:00,  2.13it/s, accuracy=0.935, accuracy_depends=0.871, cost=0.361]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  3.99it/s, accuracy=0.901, accuracy_depends=0.822, cost=0.718]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 6, training loss: 0.739655, training acc: 0.891333, training depends: 0.785187, valid loss: 0.820224, valid acc: 0.892312, valid depends: 0.772881\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:50<00:00,  2.13it/s, accuracy=0.935, accuracy_depends=0.871, cost=0.25] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.00it/s, accuracy=0.904, accuracy_depends=0.818, cost=0.701]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 0.667338, training acc: 0.898980, training depends: 0.800897, valid loss: 0.782918, valid acc: 0.897009, valid depends: 0.786254\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:51<00:00,  2.13it/s, accuracy=0.935, accuracy_depends=0.871, cost=0.302]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.98it/s, accuracy=0.906, accuracy_depends=0.763, cost=0.833]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 8, training loss: 0.605908, training acc: 0.904081, training depends: 0.814617, valid loss: 0.948492, valid acc: 0.899839, valid depends: 0.728617\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [09:52<00:00,  2.13it/s, accuracy=0.935, accuracy_depends=0.903, cost=0.106]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.00it/s, accuracy=0.913, accuracy_depends=0.835, cost=0.636]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 9, training loss: 0.551869, training acc: 0.910046, training depends: 0.827293, valid loss: 0.711028, valid acc: 0.904523, valid depends: 0.806755\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=0.935, accuracy_depends=0.903, cost=0.0999]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.97it/s, accuracy=0.916, accuracy_depends=0.83, cost=0.653] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 10, training loss: 0.503403, training acc: 0.916149, training depends: 0.838755, valid loss: 0.736089, valid acc: 0.907483, valid depends: 0.790575\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=0.968, accuracy_depends=0.903, cost=0.0375]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.95it/s, accuracy=0.922, accuracy_depends=0.823, cost=0.679]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 11, training loss: 0.458036, training acc: 0.920184, training depends: 0.849114, valid loss: 0.787972, valid acc: 0.909890, valid depends: 0.784483\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=0.968, accuracy_depends=0.903, cost=0.0167]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.94it/s, accuracy=0.92, accuracy_depends=0.86, cost=0.524]  \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 12, training loss: 0.425709, training acc: 0.923783, training depends: 0.855288, valid loss: 0.631989, valid acc: 0.913192, valid depends: 0.825509\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.014]    \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.94it/s, accuracy=0.923, accuracy_depends=0.844, cost=0.603]\n",
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 13, training loss: 0.394036, training acc: 0.927609, training depends: 0.862290, valid loss: 0.703918, valid acc: 0.916367, valid depends: 0.809119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=1, accuracy_depends=0.903, cost=0.0114]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.95it/s, accuracy=0.927, accuracy_depends=0.846, cost=0.586]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 14, training loss: 0.365359, training acc: 0.931189, training depends: 0.868609, valid loss: 0.716191, valid acc: 0.917518, valid depends: 0.811041\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=1, accuracy_depends=0.903, cost=0.00699]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.97it/s, accuracy=0.921, accuracy_depends=0.816, cost=0.706]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 15, training loss: 0.335326, training acc: 0.933748, training depends: 0.875376, valid loss: 0.836861, valid acc: 0.919010, valid depends: 0.783978\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:03<00:00,  2.09it/s, accuracy=0.968, accuracy_depends=0.903, cost=0.077]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.95it/s, accuracy=0.929, accuracy_depends=0.86, cost=0.546] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 16, training loss: 0.313728, training acc: 0.937244, training depends: 0.880229, valid loss: 0.682732, valid acc: 0.921244, valid depends: 0.826276\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=1, accuracy_depends=0.903, cost=0.00441]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.96it/s, accuracy=0.93, accuracy_depends=0.874, cost=0.527] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.290307, training acc: 0.941347, training depends: 0.884853, valid loss: 0.619366, valid acc: 0.923550, valid depends: 0.838670\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=1, accuracy_depends=0.903, cost=0.00124]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.95it/s, accuracy=0.934, accuracy_depends=0.87, cost=0.52]  \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.267072, training acc: 0.942742, training depends: 0.890352, valid loss: 0.622666, valid acc: 0.925755, valid depends: 0.841910\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.08it/s, accuracy=1, accuracy_depends=0.903, cost=0.000763]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.94it/s, accuracy=0.936, accuracy_depends=0.864, cost=0.564]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.246293, training acc: 0.944917, training depends: 0.895192, valid loss: 0.661275, valid acc: 0.927806, valid depends: 0.838913\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 20\n",
-    "\n",
-    "for e in range(epoch):\n",
-    "    train_acc, train_loss = [], []\n",
-    "    test_acc, test_loss = [], []\n",
-    "    train_acc_depends, test_acc_depends = [], []\n",
-    "    \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(train_X))\n",
-    "        batch_x = train_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = train_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = train_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "        \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(test_X))\n",
-    "        batch_x = test_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = test_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = test_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "    \n",
-    "    \n",
-    "    print(\n",
-    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
-    "    % (e, np.mean(train_loss), \n",
-    "       np.mean(train_acc), \n",
-    "       np.mean(train_acc_depends), \n",
-    "       np.mean(test_loss), \n",
-    "       np.mean(test_acc), \n",
-    "       np.mean(test_acc_depends)\n",
-    "    ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:03<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.293]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.98it/s, accuracy=0.95, accuracy_depends=0.849, cost=7.78] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 4.323974, training acc: 0.968458, training depends: 0.890752, valid loss: 8.057157, valid acc: 0.951016, valid depends: 0.826663\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.0636]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.97it/s, accuracy=0.952, accuracy_depends=0.844, cost=7.6] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 3.255801, training acc: 0.977711, training depends: 0.880561, valid loss: 7.695939, valid acc: 0.954823, valid depends: 0.820113\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:03<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.0211]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.96it/s, accuracy=0.958, accuracy_depends=0.849, cost=7.53]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 2.608050, training acc: 0.983265, training depends: 0.873710, valid loss: 7.513161, valid acc: 0.957639, valid depends: 0.815794\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:04<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.00828]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:20<00:00,  3.93it/s, accuracy=0.958, accuracy_depends=0.84, cost=7.2]  \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 2.133166, training acc: 0.987352, training depends: 0.869883, valid loss: 7.389467, valid acc: 0.959708, valid depends: 0.813561\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:03<00:00,  2.09it/s, accuracy=1, accuracy_depends=0.903, cost=0.00396]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.04it/s, accuracy=0.956, accuracy_depends=0.835, cost=7.27]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 1.773008, training acc: 0.990335, training depends: 0.867795, valid loss: 7.350123, valid acc: 0.961060, valid depends: 0.811831\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "train minibatch loop:  69%|██████▉   | 67520/97788 [5:41:46<2:14:14,  3.76it/s, accuracy=0.933, accuracy_depends=0.802, cost=4.35] "
      ]
     }
    ],
    "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 5\n",
+    "epoch = 1\n",
     "\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
@@ -1226,19 +674,20 @@
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1252,19 +701,20 @@
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1275,12 +725,1272 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'albert-base-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['[CLS]', '[SEP]']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in rejected\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
+    "\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
+    "\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    bert_tokens = ['[CLS]']\n",
+    "    for no, orig_token in enumerate(left):\n",
+    "        t = tokenizer.tokenize(orig_token)\n",
+    "        bert_tokens.extend(t)\n",
+    "    bert_tokens.append(\"[SEP]\")\n",
+    "    t = tokenizer.convert_tokens_to_ids(bert_tokens)\n",
+    "    return t, bert_tokens, [1] * len(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
+    "        )\n",
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
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+   ],
+   "source": [
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
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+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"329\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">12 (Jan)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;12 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>11&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M329,-434.9735C329,-423.1918 329,-407.5607 329,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"332.5001,-394.0033 329,-384.0034 325.5001,-394.0034 332.5001,-394.0033\"/>\n",
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+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"465\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">34 (sekatan)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;34 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M470.5496,-782.9735C469.6016,-771.1918 468.344,-755.5607 467.2656,-742.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"470.7394,-741.6904 466.4485,-732.0034 463.7619,-742.2519 470.7394,-741.6904\"/>\n",
+       "<text text-anchor=\"middle\" x=\"480\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"583\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">38 (Beliau)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;38 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>39&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M707.5833,-782.9607C694.9632,-777.5064 681.309,-771.2876 669,-765 652.742,-756.6952 635.3275,-746.6064 620.4507,-737.601\"/>\n",
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+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"693\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">41 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>39&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M735.7765,-782.812C731.1157,-777.255 726.1699,-771.0188 722,-765 716.8303,-757.538 711.7452,-749.0903 707.3125,-741.2476\"/>\n",
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+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"811\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">46 (memetik)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;46 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>39&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M764.2248,-782.9735C772.5312,-770.7252 783.6589,-754.3165 792.9768,-740.5766\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"805\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"912\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;61 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>39&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M792.2244,-782.8938C803.8883,-777.3896 816.5597,-771.1612 828,-765 844.0466,-756.358 861.3283,-746.0476 876.02,-736.9661\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"874.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1149\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">64 (berkata)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;64 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>39&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M804.3091,-789.5368C877.1531,-773.5735 1008.9367,-744.694 1086.7469,-727.6424\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"988.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"1549\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">15 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"1502\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">16 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;16 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>15&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1517.8627,-956.8612C1511.8966,-951.8691 1506.4435,-945.8951 1503,-939 1499.5725,-932.1369 1498.35,-924.0789 1498.2329,-916.4206\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1515\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"1714\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">20 (Ketua)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;20 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>15&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1583.1881,-956.9735C1608.6301,-943.5587 1643.5368,-925.1533 1670.8926,-910.7294\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1657\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;15 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>16&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1515.7074,-906.2288C1519.5967,-911.7869 1523.674,-918.0136 1527,-924 1531.0831,-931.3493 1534.9369,-939.6081 1538.2438,-947.3046\"/>\n",
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+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"1375\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;17 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>16&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1475.6855,-869.9735C1456.5286,-856.8503 1430.4,-838.9512 1409.561,-824.6756\"/>\n",
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+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"1502\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;24 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>16&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1502,-869.9735C1502,-858.1918 1502,-842.5607 1502,-829.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1505.5001,-829.0033 1502,-819.0034 1498.5001,-829.0034 1505.5001,-829.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1523.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"1604\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1691.2079,-869.9735C1674.8365,-857.0253 1652.5867,-839.4276 1634.6591,-825.2485\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1674\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"1714\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1714,-869.9735C1714,-858.1918 1714,-842.5607 1714,-829.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1717.5001,-829.0033 1714,-819.0034 1710.5001,-829.0034 1717.5001,-829.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1726\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"1820\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;25 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>20&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1735.9633,-869.9735C1751.5972,-857.1419 1772.7942,-839.7443 1789.9879,-825.6326\"/>\n",
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+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"1899\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">23 (,)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;23 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>20&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1759.5793,-870.3217C1773.3303,-864.7296 1788.3853,-858.3462 1802,-852 1822.412,-842.4854 1844.6855,-830.8662 1862.7353,-821.1227\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1852.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"1365\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1372.928,-782.9735C1371.5738,-771.1918 1369.7771,-755.5607 1368.2366,-742.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1383\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"1512\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">30 (mempertikaikan)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;30 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>24&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1504.072,-782.9735C1505.4262,-771.1918 1507.2229,-755.5607 1508.7634,-742.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1518.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">acl</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"1712\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1713.5856,-782.9735C1713.3148,-771.1918 1712.9554,-755.5607 1712.6473,-742.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1726\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"1824\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">26 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1820.8288,-782.9735C1821.3705,-771.1918 1822.0892,-755.5607 1822.7054,-742.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"1454\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1495.7196,-695.8511C1491.0572,-690.2952 1486.1248,-684.0497 1482,-678 1476.9287,-670.562 1471.9947,-662.1215 1467.7172,-654.2782\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1502\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"1570\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1524.0176,-695.9735C1532.1832,-683.7252 1543.1223,-667.3165 1552.2823,-653.5766\"/>\n",
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+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"1824\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">27 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1824,-695.9735C1824,-684.1918 1824,-668.5607 1824,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1827.5001,-655.0033 1824,-645.0034 1820.5001,-655.0034 1827.5001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1836\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"1824\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1824,-608.9735C1824,-597.1918 1824,-581.5607 1824,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1827.5001,-568.0033 1824,-558.0034 1820.5001,-568.0034 1827.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1836\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"1570\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;32 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1570,-608.9735C1570,-597.1918 1570,-581.5607 1570,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1573.5001,-568.0033 1570,-558.0034 1566.5001,-568.0034 1573.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1607\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"465\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M465,-695.9735C465,-684.1918 465,-668.5607 465,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"468.5001,-655.0033 465,-645.0034 461.5001,-655.0034 468.5001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"502\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"465\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 35&#45;&gt;36 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>35&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M465,-608.9735C465,-597.1918 465,-581.5607 465,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"468.5001,-568.0033 465,-558.0034 461.5001,-568.0034 468.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"485\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"600\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>41&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M669.1225,-695.9222C662.1442,-690.3684 654.6177,-684.1062 648,-678 639.3794,-670.0458 630.4398,-660.8443 622.6457,-652.465\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"625.1914,-650.0626 615.8519,-645.0603 620.0335,-654.7951 625.1914,-650.0626\"/>\n",
+       "<text text-anchor=\"middle\" x=\"668.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"693\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;43 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>41&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M693,-695.9735C693,-684.1918 693,-668.5607 693,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"696.5001,-655.0033 693,-645.0034 689.5001,-655.0034 696.5001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"708\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"811\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M811,-695.9735C811,-684.1918 811,-668.5607 811,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"814.5001,-655.0033 811,-645.0034 807.5001,-655.0034 814.5001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"839.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"943\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M842.9906,-695.8475C852.4395,-690.2916 862.7307,-684.0469 872,-678 884.888,-669.5924 898.7043,-659.8447 910.6921,-651.1372\"/>\n",
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+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1064\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">62 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;62 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>64&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1124.9239,-695.8061C1118.2253,-690.3493 1111.1151,-684.1679 1105,-678 1097.4414,-670.3761 1089.8763,-661.4313 1083.3395,-653.1796\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1125\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1149\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">65 (,)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1149,-695.9735C1149,-684.1918 1149,-668.5607 1149,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1152.5001,-655.0033 1149,-645.0034 1145.5001,-655.0034 1152.5001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1169.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"1221\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">83 (.)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;83 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>64&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1175.3871,-695.8513C1181.9537,-690.575 1188.6409,-684.4737 1194,-678 1199.8774,-670.9001 1205.0175,-662.3166 1209.1912,-654.2328\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1224.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1330\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">68 (menjangka)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;68 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1201.0094,-697.6766C1216.7226,-692.1098 1233.8249,-685.4005 1249,-678 1265.0799,-670.1583 1281.9601,-659.8927 1296.124,-650.6287\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1294\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"642\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M682.4328,-608.9735C675.3211,-596.8418 665.8168,-580.6287 657.8099,-566.9698\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"660.6304,-564.8603 652.5537,-558.0034 654.5915,-568.4004 660.6304,-564.8603\"/>\n",
+       "<text text-anchor=\"middle\" x=\"679\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"743\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">44 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;44 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>43&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M703.36,-608.9735C710.3323,-596.8418 719.6502,-580.6287 727.5001,-566.9698\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"730.7049,-568.4175 732.6532,-558.0034 724.6358,-564.9295 730.7049,-568.4175\"/>\n",
+       "<text text-anchor=\"middle\" x=\"733\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"943\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">48 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>47&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M943,-608.9735C943,-597.1918 943,-581.5607 943,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"946.5001,-568.0033 943,-558.0034 939.5001,-568.0034 946.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"955\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"761\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">49 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M898.1337,-521.8225C885.0913,-516.3167 870.8903,-510.1054 858,-504 839.7197,-495.3417 819.8938,-485.0792 802.9586,-476.0382\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"870\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"860\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M917.7325,-521.6446C911.0188,-516.2809 903.9786,-510.1878 898,-504 890.8252,-496.5742 883.8215,-487.7681 877.8213,-479.5811\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"918.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"943\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;51 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M943,-521.9735C943,-510.1918 943,-494.5607 943,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"946.5001,-481.0033 943,-471.0034 939.5001,-481.0034 946.5001,-481.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"964.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1051\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;57 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M967.0634,-521.8816C974.4712,-516.2203 982.6176,-509.909 990,-504 1000.5446,-495.56 1011.9396,-486.1216 1021.9947,-477.6808\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1027\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 52 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"852\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">52 (Sri)</text>\n",
+       "</g>\n",
+       "<!-- 51&#45;&gt;52 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>51&#45;&gt;52</title>\n",
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+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"948\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">55 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 57&#45;&gt;55 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>57&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1028.5195,-434.8023C1021.5864,-429.1378 1013.9493,-422.8446 1007,-417 996.9619,-408.5576 986.0721,-399.2015 976.4128,-390.8322\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1027\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 56 -->\n",
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+       "<title>56</title>\n",
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+       "<title>67</title>\n",
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+       "<title>69</title>\n",
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+       "<title>73</title>\n",
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+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1576\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">75 (tulisan)</text>\n",
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+       "<title>71</title>\n",
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+       "<title>72</title>\n",
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+       "<title>73&#45;&gt;72</title>\n",
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+       "<title>74</title>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7fd0245bd160>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1289,7 +1999,7 @@
        "'albert-base-dependency/model.ckpt'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1301,22 +2011,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:++++++ warmup starts at step 0, for 3777 steps ++++++\n",
+      "INFO:tensorflow:++++++ warmup starts at step 0, for 29336 steps ++++++\n",
       "INFO:tensorflow:using adamw\n",
       "INFO:tensorflow:Restoring parameters from albert-base-dependency/model.ckpt\n"
      ]
@@ -1327,7 +2029,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word, training = False)\n",
     "\n",
@@ -1338,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +2056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,14 +2101,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 315/315 [01:27<00:00,  3.61it/s]\n"
+      "100%|██████████| 313/313 [00:51<00:00,  6.09it/s]\n"
      ]
     }
    ],
@@ -1443,7 +2145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1458,7 +2160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -1467,43 +2169,41 @@
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    1.00000   1.00000   1.00000    905035\n",
-      "            X    0.99997   0.99998   0.99998    159607\n",
-      "          acl    0.89111   0.88994   0.89052      6051\n",
-      "        advcl    0.75213   0.78003   0.76583      2373\n",
-      "       advmod    0.89975   0.92642   0.91289      9378\n",
-      "         amod    0.86607   0.87808   0.87204      8145\n",
-      "        appos    0.87914   0.89496   0.88698      4779\n",
-      "          aux    1.00000   0.37500   0.54545         8\n",
-      "         case    0.96890   0.97142   0.97016     21521\n",
-      "           cc    0.96049   0.96393   0.96221      6405\n",
-      "        ccomp    0.70574   0.67583   0.69046       873\n",
-      "     compound    0.88800   0.89660   0.89228     13530\n",
-      "compound:plur    0.93381   0.93981   0.93680      1246\n",
-      "         conj    0.94147   0.93436   0.93790      8608\n",
-      "          cop    0.94652   0.96651   0.95641      1941\n",
-      "        csubj    0.75000   0.39623   0.51852        53\n",
-      "   csubj:pass    0.77778   0.77778   0.77778         9\n",
-      "          dep    0.81778   0.72871   0.77068      1010\n",
-      "          det    0.91665   0.90606   0.91132      8314\n",
-      "        fixed    0.87862   0.80565   0.84055      1168\n",
-      "         flat    0.96177   0.93608   0.94875     20400\n",
-      "         iobj    0.71429   0.42857   0.53571        35\n",
-      "         mark    0.88640   0.88577   0.88608      2854\n",
-      "         nmod    0.86857   0.90150   0.88473      8020\n",
-      "        nsubj    0.89466   0.93382   0.91382     12633\n",
-      "   nsubj:pass    0.91977   0.81904   0.86648      4045\n",
-      "       nummod    0.95316   0.95864   0.95589      8003\n",
-      "          obj    0.90795   0.92092   0.91439     10357\n",
-      "          obl    0.93016   0.90607   0.91796     11466\n",
-      "    parataxis    0.72669   0.62953   0.67463       718\n",
-      "        punct    0.99482   0.99724   0.99603     33312\n",
-      "         root    0.93869   0.94093   0.93981     10073\n",
-      "        xcomp    0.85300   0.80468   0.82813      2524\n",
+      "          PAD    1.00000   1.00000   1.00000    627830\n",
+      "            X    1.00000   1.00000   1.00000     60741\n",
+      "          acl    0.83673   0.81078   0.82355      3192\n",
+      "        advcl    0.65863   0.62082   0.63917      1585\n",
+      "       advmod    0.94333   0.93545   0.93938      6460\n",
+      "         amod    0.89656   0.89594   0.89625      4363\n",
+      "        appos    0.80183   0.74289   0.77124      3061\n",
+      "         case    0.98074   0.98002   0.98038     10862\n",
+      "           cc    0.98250   0.97005   0.97624      3473\n",
+      "        ccomp    0.51095   0.40698   0.45307       344\n",
+      "     compound    0.88936   0.93235   0.91035     11027\n",
+      "compound:plur    0.61538   0.55172   0.58182        29\n",
+      "         conj    0.89128   0.87559   0.88336      5112\n",
+      "          cop    0.97607   0.96453   0.97026       592\n",
+      "        csubj    0.33333   0.12500   0.18182         8\n",
+      "          dep    0.63277   0.63456   0.63366       353\n",
+      "          det    0.94329   0.91823   0.93059      3840\n",
+      "        fixed    0.90071   0.73837   0.81150       172\n",
+      "         flat    0.95078   0.96312   0.95691     18492\n",
+      "         iobj    0.00000   0.00000   0.00000         1\n",
+      "         mark    0.91609   0.92954   0.92276      1703\n",
+      "         nmod    0.84561   0.82701   0.83621      4457\n",
+      "        nsubj    0.84280   0.84806   0.84542      6891\n",
+      "   nsubj:pass    0.81498   0.77772   0.79591      1903\n",
+      "       nummod    0.95994   0.95256   0.95624      4427\n",
+      "          obj    0.89881   0.91319   0.90594      6128\n",
+      "          obl    0.86886   0.84068   0.85454      4965\n",
+      "    parataxis    0.51012   0.35097   0.41584       359\n",
+      "        punct    0.99626   0.99768   0.99697     20300\n",
+      "         root    0.88585   0.91420   0.89980     10000\n",
+      "        xcomp    0.71181   0.72865   0.72013      1522\n",
       "\n",
-      "     accuracy                        0.98785   1284494\n",
-      "    macro avg    0.88860   0.84152   0.85761   1284494\n",
-      " weighted avg    0.98786   0.98785   0.98782   1284494\n",
+      "     accuracy                        0.98614    824192\n",
+      "    macro avg    0.80630   0.77893   0.78998    824192\n",
+      " weighted avg    0.98598   0.98614   0.98603    824192\n",
       "\n"
      ]
     }
@@ -1515,16 +2215,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.8118309576064845\n",
-      "types accuracy: 0.7931625589721538\n",
-      "root accuracy: 0.879281746031746\n"
+      "arc accuracy: 0.821895729831751\n",
+      "types accuracy: 0.797527251552637\n",
+      "root accuracy: 1.0\n"
      ]
     }
    ],
@@ -1536,63 +2236,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 79,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'bert/embeddings/word_embeddings',\n",
-       " 'bert/embeddings/token_type_embeddings',\n",
-       " 'bert/embeddings/position_embeddings',\n",
-       " 'bert/embeddings/LayerNorm/gamma',\n",
-       " 'bert/encoder/embedding_hidden_mapping_in/kernel',\n",
-       " 'bert/encoder/embedding_hidden_mapping_in/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm/gamma',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/gamma',\n",
-       " 'bert/pooler/dense/kernel',\n",
-       " 'bert/pooler/dense/bias',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -1610,13 +2256,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1651,7 +2296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1659,7 +2304,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from albert-base-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-36-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-80-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -1667,7 +2312,7 @@
       "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
       "INFO:tensorflow:Froze 40 variables.\n",
       "INFO:tensorflow:Converted 40 variables to const ops.\n",
-      "3746 ops in the final graph.\n"
+      "3731 ops in the final graph.\n"
      ]
     }
    ],
@@ -1677,165 +2322,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
-    "string = 'husein makan ayam'\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "def entities_textcleaning(string, lowering = False):\n",
-    "    \"\"\"\n",
-    "    use by entities recognition, pos recognition and dependency parsing\n",
-    "    \"\"\"\n",
-    "    string = re.sub('[^A-Za-z0-9\\-\\/() ]+', ' ', string)\n",
-    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
-    "    original_string = string.split()\n",
-    "    if lowering:\n",
-    "        string = string.lower()\n",
-    "    string = [\n",
-    "        (original_string[no], word.title() if word.isupper() else word)\n",
-    "        for no, word in enumerate(string.split())\n",
-    "        if len(word)\n",
-    "    ]\n",
-    "    return [s[0] for s in string], [s[1] for s in string]\n",
-    "\n",
-    "def parse_X(left):\n",
-    "    bert_tokens = ['[CLS]']\n",
-    "    for no, orig_token in enumerate(left):\n",
-    "        t = tokenizer.tokenize(orig_token)\n",
-    "        bert_tokens.extend(t)\n",
-    "    bert_tokens.append(\"[SEP]\")\n",
-    "    return tokenizer.convert_tokens_to_ids(bert_tokens), bert_tokens\n",
-    "\n",
-    "sequence = entities_textcleaning(string)[1]\n",
-    "parsed_sequence, bert_sequence = parse_X(sequence)"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def merge_sentencepiece_tokens_tagging(x, y):\n",
-    "    new_paired_tokens = []\n",
-    "    n_tokens = len(x)\n",
-    "    rejected = ['[CLS]', '[SEP]']\n",
-    "\n",
-    "    i = 0\n",
-    "\n",
-    "    while i < n_tokens:\n",
-    "\n",
-    "        current_token, current_label = x[i], y[i]\n",
-    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
-    "            previous_token, previous_label = new_paired_tokens.pop()\n",
-    "            merged_token = previous_token\n",
-    "            merged_label = [previous_label]\n",
-    "            while (\n",
-    "                not current_token.startswith('▁')\n",
-    "                and current_token not in rejected\n",
-    "            ):\n",
-    "                merged_token = merged_token + current_token.replace('▁', '')\n",
-    "                merged_label.append(current_label)\n",
-    "                i = i + 1\n",
-    "                current_token, current_label = x[i], y[i]\n",
-    "            merged_label = merged_label[0]\n",
-    "            new_paired_tokens.append((merged_token, merged_label))\n",
-    "\n",
-    "        else:\n",
-    "            new_paired_tokens.append((current_token, current_label))\n",
-    "            i = i + 1\n",
-    "\n",
-    "    words = [\n",
-    "        i[0].replace('▁', '')\n",
-    "        for i in new_paired_tokens\n",
-    "        if i[0] not in rejected\n",
-    "    ]\n",
-    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
-    "    return words, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_graph(frozen_graph_filename):\n",
-    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
-    "        graph_def = tf.GraphDef()\n",
-    "        graph_def.ParseFromString(f.read())\n",
-    "    with tf.Graph().as_default() as graph:\n",
-    "        tf.import_graph_def(graph_def)\n",
-    "    return graph\n",
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
     "\n",
-    "g = load_graph('albert-base-dependency/frozen_model.pb')\n",
-    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
-    "heads_seq = g.get_tensor_by_name('import/heads_seq:0')\n",
-    "tags_seq = g.get_tensor_by_name('import/logits:0')\n",
-    "test_sess = tf.InteractiveSession(graph = g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h, t = test_sess.run([heads_seq, tags_seq],\n",
-    "        feed_dict = {\n",
-    "            x: [parsed_sequence],\n",
-    "        },\n",
-    ")\n",
-    "h = h[0] - 1\n",
-    "t = [idx2tag[d] for d in t[0]]\n",
-    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
-    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('husein', 2), ('makan', 0), ('ayam', 0)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(zip(merged_h[0], merged_h[1])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3\n",
+    "pb = 'albert-base-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
     "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'albert-base-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/albert-base-dependency.pb\"\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
     "\n",
-    "s3 = boto3.client('s3')\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
     "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
    ]
   }
  ],
@@ -1855,7 +2381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/session/dependency/albert-tiny.ipynb b/session/dependency/albert-tiny.ipynb
index 2feac1fc..4761c1a2 100644
--- a/session/dependency/albert-tiny.ipynb
+++ b/session/dependency/albert-tiny.ipynb
@@ -2,17 +2,17 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -29,12 +29,13 @@
     "from albert import optimization\n",
     "from albert import tokenization\n",
     "import tensorflow as tf\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import json"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -49,8 +50,26 @@
    ],
    "source": [
     "tokenizer = tokenization.FullTokenizer(\n",
-    "      vocab_file='albert-tiny-2020-04-17/sp10m.cased.v10.vocab', do_lower_case=False,\n",
-    "      spm_model_file='albert-tiny-2020-04-17/sp10m.cased.v10.model')"
+    "      vocab_file='albert-base-2020-04-10/sp10m.cased.v10.vocab', do_lower_case=False,\n",
+    "      spm_model_file='albert-base-2020-04-10/sp10m.cased.v10.model')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
@@ -69,7 +88,7 @@
     {
      "data": {
       "text/plain": [
-       "<albert.modeling.AlbertConfig at 0x7f5f8410c438>"
+       "<albert.modeling.AlbertConfig at 0x7f2901d82d68>"
       ]
      },
      "execution_count": 5,
@@ -87,236 +106,17 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
-    "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = ['[CLS]']\n",
-    "                labels_ = [0]\n",
-    "                depends_ = [0]\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenizer.tokenize(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.append('[SEP]')\n",
-    "                labels_.append(0)\n",
-    "                depends_.append(0)\n",
-    "                words.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
-     ]
-    }
-   ],
-   "source": [
-    "sentences, words, depends, labels, _, _ = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = ['[CLS]']\n",
-    "        labels_ = [0]\n",
-    "        depends_ = [0]\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenizer.tokenize(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.append('[SEP]')\n",
-    "        labels_.append(0)\n",
-    "        depends_.append(0)\n",
-    "        outside.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "    return outside, sentences, outside_depends, outside_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test \\\n",
-    "= train_test_split(words, depends, labels, test_size = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "BERT_INIT_CHKPNT = 'albert-tiny-2020-04-17/model.ckpt-1000000'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "epoch = 30\n",
+    "epoch = 3\n",
     "batch_size = 32\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
@@ -325,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,8 +158,15 @@
     "            e = tf.expand_dims(tf.expand_dims(mask_e, 1), 2)\n",
     "            output = output * d * e\n",
     "            \n",
-    "        return output\n",
-    "    \n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "class BiLinear:\n",
     "    def __init__(self, left_features, right_features, out_features):\n",
     "        self.left_features = left_features\n",
@@ -384,8 +191,17 @@
     "        output = output + tf.matmul(input_left, tf.transpose(self.W_l))\\\n",
     "        + tf.matmul(input_right, tf.transpose(self.W_r))\n",
     "        \n",
-    "        return tf.reshape(output, output_shape)\n",
-    "    \n",
+    "        return tf.reshape(output, output_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_NEG_INF = -1e9\n",
+    "\n",
     "class Model:\n",
     "    def __init__(\n",
     "        self,\n",
@@ -417,14 +233,20 @@
     "            config=albert_config,\n",
     "            is_training=training,\n",
     "            input_ids=self.words,\n",
+    "            input_mask=self.mask,\n",
     "            use_one_hot_embeddings=False)\n",
+    "        \n",
     "        output_layer = model.get_sequence_output()\n",
     "        \n",
     "        arc_h = tf.nn.elu(self.arc_h(output_layer))\n",
     "        arc_c = tf.nn.elu(self.arc_c(output_layer))\n",
+    "        self._arc_h = arc_h\n",
+    "        self._arc_c = arc_c\n",
     "        \n",
     "        type_h = tf.nn.elu(self.type_h(output_layer))\n",
     "        type_c = tf.nn.elu(self.type_c(output_layer))\n",
+    "        self._type_h = type_h\n",
+    "        self._type_c = type_c\n",
     "        \n",
     "        out_arc = tf.squeeze(self.attention.forward(arc_h, arc_c, mask_d=self.mask, \n",
     "                                                    mask_e=self.mask), axis = 1)\n",
@@ -443,6 +265,11 @@
     "        self.heads_seq = tf.argmax(decode_arc, axis = 1)\n",
     "        self.heads_seq = tf.identity(self.heads_seq, name = 'heads_seq')\n",
     "        \n",
+    "#         self.decode_arc_t = tf.transpose(decode_arc, (0, 2, 1))\n",
+    "#         sequence_loss_depends = tf.contrib.seq2seq.sequence_loss(logits = self.decode_arc_t,\n",
+    "#                                                      targets = self.heads,\n",
+    "#                                                      weights = mask)\n",
+    "        \n",
     "        t = tf.cast(tf.transpose(self.heads_seq), tf.int32)\n",
     "        broadcasted = tf.broadcast_to(batch_index, tf.shape(t))\n",
     "        concatenated = tf.transpose(tf.concat([tf.expand_dims(broadcasted, axis = 0), \n",
@@ -517,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -547,20 +374,20 @@
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From <ipython-input-19-9e67073b97d5>:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-4486bc1f4ea9>:61: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-19-9e67073b97d5>:145: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-4486bc1f4ea9>:101: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "dim is deprecated, use axis instead\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:36: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
       "\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:41: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
       "\n",
-      "INFO:tensorflow:++++++ warmup starts at step 0, for 3777 steps ++++++\n",
+      "INFO:tensorflow:++++++ warmup starts at step 0, for 29336 steps ++++++\n",
       "INFO:tensorflow:using adamw\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:101: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
       "\n"
@@ -572,7 +399,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -580,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -599,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,74 +442,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:52<00:00,  4.31it/s, accuracy=0.438, accuracy_depends=0.75, cost=1.91]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:54<00:00,  5.83it/s, accuracy=0.257, accuracy_depends=0.502, cost=2.65]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 4.816708, training acc: 0.169616, training depends: 0.458381, valid loss: 2.643911, valid acc: 0.272960, valid depends: 0.511708\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:57<00:00,  4.24it/s, accuracy=0.688, accuracy_depends=0.812, cost=1.48]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:57<00:00,  5.51it/s, accuracy=0.52, accuracy_depends=0.534, cost=2.12] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 2.339175, training acc: 0.435387, training depends: 0.530496, valid loss: 2.102147, valid acc: 0.550210, valid depends: 0.540273\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [05:07<00:00,  4.10it/s, accuracy=0.688, accuracy_depends=0.75, cost=1.01] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:56<00:00,  5.59it/s, accuracy=0.576, accuracy_depends=0.593, cost=1.77]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "[0.0, 0.09375, 29.16653]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sess.run([model.accuracy, model.accuracy_depends, model.cost],\n",
+    "        feed_dict = {model.words: batch_x,\n",
+    "                model.types: batch_y,\n",
+    "                model.heads: batch_depends,\n",
+    "                model.switch: False})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 1.898127, training acc: 0.583338, training depends: 0.571907, valid loss: 1.739949, valid acc: 0.603071, valid depends: 0.593208\n",
-      "\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "[0.0, 0.09375, 120.98015]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sess.run([model.accuracy, model.accuracy_depends, model.cost],\n",
+    "        feed_dict = {model.words: batch_x,\n",
+    "                model.types: batch_y,\n",
+    "                model.heads: batch_depends,\n",
+    "                model.switch: True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [05:05<00:00,  4.12it/s, accuracy=0.812, accuracy_depends=0.812, cost=0.676]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:56<00:00,  5.59it/s, accuracy=0.621, accuracy_depends=0.618, cost=1.63]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop:  66%|██████▌   | 64243/97788 [2:44:46<1:39:56,  5.59it/s, accuracy=0.867, accuracy_depends=0.726, cost=0.871] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  85%|████████▌ | 83568/97788 [3:34:59<34:25,  6.88it/s, accuracy=0.87, accuracy_depends=0.753, cost=0.777]   IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:12:02<00:00,  6.47it/s, accuracy=0.887, accuracy_depends=0.825, cost=0.534]  \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:33<00:00,  9.35it/s, accuracy=0.881, accuracy_depends=0.833, cost=0.496]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 3, training loss: 1.638650, training acc: 0.631884, training depends: 0.612456, valid loss: 1.568366, valid acc: 0.638094, valid depends: 0.624893\n",
+      "epoch: 0, training loss: 1.188455, training acc: 0.822894, training depends: 0.697532, valid loss: 0.690450, valid acc: 0.879138, valid depends: 0.771131\n",
       "\n"
      ]
     },
@@ -690,277 +533,105 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [05:05<00:00,  4.12it/s, accuracy=0.875, accuracy_depends=0.938, cost=0.35]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:56<00:00,  5.60it/s, accuracy=0.697, accuracy_depends=0.616, cost=1.58]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop:  29%|██▉       | 28730/97788 [1:20:49<2:43:21,  7.05it/s, accuracy=0.878, accuracy_depends=0.798, cost=0.597]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  58%|█████▊    | 56970/97788 [2:40:22<1:41:24,  6.71it/s, accuracy=0.89, accuracy_depends=0.816, cost=0.497]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  62%|██████▏   | 60199/97788 [2:49:11<1:24:55,  7.38it/s, accuracy=0.878, accuracy_depends=0.786, cost=0.672]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  65%|██████▌   | 63584/97788 [2:58:16<1:39:38,  5.72it/s, accuracy=0.868, accuracy_depends=0.822, cost=0.511]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  68%|██████▊   | 66826/97788 [3:06:56<1:39:53,  5.17it/s, accuracy=0.873, accuracy_depends=0.804, cost=0.545]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  72%|███████▏  | 70225/97788 [3:15:54<1:01:21,  7.49it/s, accuracy=0.881, accuracy_depends=0.768, cost=0.641]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  75%|███████▌  | 73415/97788 [3:24:27<55:26,  7.33it/s, accuracy=0.907, accuracy_depends=0.738, cost=0.964]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  79%|███████▊  | 76845/97788 [3:33:36<1:00:03,  5.81it/s, accuracy=0.889, accuracy_depends=0.799, cost=0.525]IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  82%|████████▏ | 80161/97788 [3:43:31<44:58,  6.53it/s, accuracy=0.863, accuracy_depends=0.777, cost=0.801]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:30:08<00:00,  6.03it/s, accuracy=0.895, accuracy_depends=0.831, cost=0.433]  \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:35<00:00,  8.72it/s, accuracy=0.892, accuracy_depends=0.857, cost=0.382]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 4, training loss: 1.489354, training acc: 0.722123, training depends: 0.640292, valid loss: 1.474930, valid acc: 0.717230, valid depends: 0.640443\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:55<00:00,  4.27it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.221]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.87it/s, accuracy=0.767, accuracy_depends=0.642, cost=1.51]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 5, training loss: 1.384911, training acc: 0.771040, training depends: 0.661793, valid loss: 1.381199, valid acc: 0.780014, valid depends: 0.664027\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:46<00:00,  4.40it/s, accuracy=0.75, accuracy_depends=0.938, cost=0.15] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:51<00:00,  6.10it/s, accuracy=0.771, accuracy_depends=0.652, cost=1.47]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 6, training loss: 1.304746, training acc: 0.789611, training depends: 0.678879, valid loss: 1.339804, valid acc: 0.782671, valid depends: 0.670663\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:44<00:00,  4.43it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.12] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:52<00:00,  6.01it/s, accuracy=0.789, accuracy_depends=0.684, cost=1.42]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 1.238359, training acc: 0.802682, training depends: 0.693803, valid loss: 1.275019, valid acc: 0.802976, valid depends: 0.688489\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:46<00:00,  4.40it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.0916]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 8, training loss: 1.188670, training acc: 0.814486, training depends: 0.703934, valid loss: 1.232425, valid acc: 0.812130, valid depends: 0.698801\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.30it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.0607]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 9, training loss: 1.142564, training acc: 0.827337, training depends: 0.714674, valid loss: 1.207361, valid acc: 0.825732, valid depends: 0.705253\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:59<00:00,  4.21it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.0462]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 10, training loss: 1.102756, training acc: 0.834300, training depends: 0.723572, valid loss: 1.174121, valid acc: 0.831028, valid depends: 0.713229\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:59<00:00,  4.21it/s, accuracy=0.875, accuracy_depends=0.938, cost=0.033]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 11, training loss: 1.068813, training acc: 0.838724, training depends: 0.731223, valid loss: 1.150037, valid acc: 0.836768, valid depends: 0.718662\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:59<00:00,  4.20it/s, accuracy=0.812, accuracy_depends=0.938, cost=0.0252]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 12, training loss: 1.035727, training acc: 0.843621, training depends: 0.739361, valid loss: 1.132964, valid acc: 0.839806, valid depends: 0.722642\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:58<00:00,  4.22it/s, accuracy=0.875, accuracy_depends=0.938, cost=0.0201]\n",
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-     ]
-    },
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-     "output_type": "stream",
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-      "epoch: 13, training loss: 1.005898, training acc: 0.847731, training depends: 0.746342, valid loss: 1.114029, valid acc: 0.844609, valid depends: 0.727709\n",
-      "\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
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-     ]
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-      "epoch: 14, training loss: 0.981429, training acc: 0.850139, training depends: 0.751663, valid loss: 1.097263, valid acc: 0.847585, valid depends: 0.731702\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     ]
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-      "epoch: 15, training loss: 0.958836, training acc: 0.854603, training depends: 0.757034, valid loss: 1.085392, valid acc: 0.850718, valid depends: 0.734842\n",
-      "\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
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-     ]
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-      "epoch: 16, training loss: 0.937661, training acc: 0.856612, training depends: 0.762016, valid loss: 1.090674, valid acc: 0.853433, valid depends: 0.732561\n",
-      "\n"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.919759, training acc: 0.859255, training depends: 0.765594, valid loss: 1.063900, valid acc: 0.855051, valid depends: 0.740344\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:45<00:00,  4.41it/s, accuracy=0.938, accuracy_depends=0.938, cost=0.00658]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:52<00:00,  6.00it/s, accuracy=0.843, accuracy_depends=0.726, cost=1.25] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.901599, training acc: 0.861192, training depends: 0.770272, valid loss: 1.058046, valid acc: 0.856491, valid depends: 0.741880\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:46<00:00,  4.41it/s, accuracy=0.938, accuracy_depends=0.938, cost=0.00555]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:52<00:00,  5.99it/s, accuracy=0.848, accuracy_depends=0.723, cost=1.26] "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.886771, training acc: 0.863076, training depends: 0.773270, valid loss: 1.073614, valid acc: 0.858486, valid depends: 0.735672\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "epoch: 1, training loss: 0.631874, training acc: 0.882788, training depends: 0.782693, valid loss: 0.586480, valid acc: 0.886249, valid depends: 0.789142\n",
       "\n"
      ]
     }
@@ -968,7 +639,7 @@
    "source": [
     "from tqdm import tqdm\n",
     "\n",
-    "epoch = 20\n",
+    "epoch = 2\n",
     "\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
@@ -987,19 +658,20 @@
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1013,19 +685,20 @@
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1036,110 +709,36 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'albert-base-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:46<00:00,  4.40it/s, accuracy=1, accuracy_depends=0.938, cost=0.195]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:52<00:00,  6.00it/s, accuracy=0.884, accuracy_depends=0.713, cost=19.2]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:21:31<00:00,  6.23it/s, accuracy=0.934, accuracy_depends=0.823, cost=3.76]   \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:36<00:00,  8.57it/s, accuracy=0.958, accuracy_depends=0.844, cost=3.73]\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 14.238445, training acc: 0.899972, training depends: 0.760470, valid loss: 15.305143, valid acc: 0.895161, valid depends: 0.726821\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:42<00:00,  4.46it/s, accuracy=1, accuracy_depends=0.938, cost=0.176]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:52<00:00,  6.05it/s, accuracy=0.888, accuracy_depends=0.705, cost=18.6]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 13.270262, training acc: 0.907359, training depends: 0.749328, valid loss: 14.915302, valid acc: 0.898323, valid depends: 0.718794\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:54<00:00,  4.28it/s, accuracy=1, accuracy_depends=0.938, cost=0.16]    \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:50<00:00,  6.21it/s, accuracy=0.889, accuracy_depends=0.701, cost=18.3]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 12.749146, training acc: 0.911169, training depends: 0.742163, valid loss: 14.635544, valid acc: 0.900516, valid depends: 0.714716\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:36<00:00,  4.55it/s, accuracy=1, accuracy_depends=0.938, cost=0.145]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:50<00:00,  6.25it/s, accuracy=0.891, accuracy_depends=0.695, cost=18.1]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 12.350799, training acc: 0.914132, training depends: 0.737238, valid loss: 14.427675, valid acc: 0.902087, valid depends: 0.711193\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:36<00:00,  4.57it/s, accuracy=1, accuracy_depends=0.938, cost=0.128]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:50<00:00,  6.22it/s, accuracy=0.891, accuracy_depends=0.693, cost=17.9]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 12.030086, training acc: 0.916556, training depends: 0.733690, valid loss: 14.261791, valid acc: 0.903387, valid depends: 0.708722\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
+      "epoch: 0, training loss: 5.254430, training acc: 0.932902, training depends: 0.787293, valid loss: 4.992470, valid acc: 0.935338, valid depends: 0.786505\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 5\n",
+    "epoch = 1\n",
     "\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
@@ -1158,19 +757,20 @@
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1184,19 +784,20 @@
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1207,12 +808,1272 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'albert-tiny-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['[CLS]', '[SEP]']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in rejected\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
+    "\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
+    "\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    bert_tokens = ['[CLS]']\n",
+    "    for no, orig_token in enumerate(left):\n",
+    "        t = tokenizer.tokenize(orig_token)\n",
+    "        bert_tokens.extend(t)\n",
+    "    bert_tokens.append(\"[SEP]\")\n",
+    "    t = tokenizer.convert_tokens_to_ids(bert_tokens)\n",
+    "    return t, bert_tokens, [1] * len(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
+    "        )\n",
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 214)\">\n",
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+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f29e298e208>"
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+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
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+       "<text text-anchor=\"middle\" x=\"249\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;61 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>12&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M249,-260.9735C249,-249.1918 249,-233.5607 249,-220.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"252.5001,-220.0033 249,-210.0034 245.5001,-220.0034 252.5001,-220.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"269.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"346\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">64 (berkata)</text>\n",
+       "</g>\n",
+       "<!-- 12&#45;&gt;64 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>12&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M272.7251,-260.7538C279.6992,-255.1952 287.2615,-248.9726 294,-243 302.9839,-235.0372 312.456,-225.9664 320.8186,-217.7045\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"324.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"436\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">15 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;15 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>14&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M504.9735,-608.9735C492.3752,-596.3752 475.3755,-579.3755 461.4055,-565.4055\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"501\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">case</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"558\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">20 (Ketua)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;20 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>14&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M530.252,-608.9735C535.0857,-596.9585 541.5298,-580.9401 546.9913,-567.3646\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"558\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"715\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">38 (Beliau)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;38 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>39&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M724.5136,-608.9735C722.8885,-597.1918 720.7325,-581.5607 718.8839,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"722.3169,-567.4313 717.4832,-558.0034 715.3825,-568.3878 722.3169,-567.4313\"/>\n",
+       "<text text-anchor=\"middle\" x=\"742\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"808\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;40 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>39&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M747.7433,-608.8228C753.7926,-603.2662 760.3039,-597.0273 766,-591 773.3268,-583.2471 780.8731,-574.3965 787.5125,-566.269\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"800.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"983\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;43 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>46&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1076.5503,-608.9735C1058.9092,-595.9669 1034.9051,-578.269 1015.6292,-564.0571\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1065\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1101\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1101,-608.9735C1101,-597.1918 1101,-581.5607 1101,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1104.5001,-568.0033 1101,-558.0034 1097.5001,-568.0034 1104.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1129.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1337\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1149.8994,-608.9735C1187.3176,-595.1795 1239.0491,-576.109 1278.6342,-561.5162\"/>\n",
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+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1561\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;51 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>46&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1157.405,-616.3321C1248.9283,-599.0223 1428.1298,-565.1298 1513.5396,-548.9762\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"352\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">16 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;16 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>15&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M418.5951,-521.9735C406.4312,-509.3752 390.0177,-492.3755 376.5295,-478.4055\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"378.8464,-475.7663 369.3826,-471.0034 373.8106,-480.6285 378.8464,-475.7663\"/>\n",
+       "<text text-anchor=\"middle\" x=\"412\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"448\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M535.2079,-521.9735C518.8365,-509.0253 496.5867,-491.4276 478.6591,-477.2485\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"480.7774,-474.4616 470.7629,-471.0034 476.435,-479.952 480.7774,-474.4616\"/>\n",
+       "<text text-anchor=\"middle\" x=\"518\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"558\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M558,-521.9735C558,-510.1918 558,-494.5607 558,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"561.5001,-481.0033 558,-471.0034 554.5001,-481.0034 561.5001,-481.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"570\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"676\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;22 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>20&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M582.4497,-521.9735C600.0908,-508.9669 624.0949,-491.269 643.3708,-477.0571\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"645.6098,-479.7548 651.5816,-471.0034 641.4558,-474.1206 645.6098,-479.7548\"/>\n",
+       "<text text-anchor=\"middle\" x=\"637\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"786\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;24 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>20&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M603.918,-522.6412C619.5029,-516.7384 637.0137,-510.094 653,-504 678.3868,-494.3225 706.479,-483.5585 730.2788,-474.4228\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"711.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"351\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;17 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>16&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M351.7928,-434.9735C351.6574,-423.1918 351.4777,-407.5607 351.3237,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"354.8217,-393.9624 351.2069,-384.0034 347.8222,-394.043 354.8217,-393.9624\"/>\n",
+       "<text text-anchor=\"middle\" x=\"364\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"459\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;18 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>16&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M374.1705,-434.9735C389.9519,-422.1419 411.3489,-404.7443 428.7047,-390.6326\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"424\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"703\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;25 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>24&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M768.8023,-434.9735C756.7833,-422.3752 740.5651,-405.3755 727.2375,-391.4055\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"762\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"858\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">30 (mempertikaikan)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;30 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>24&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M800.9184,-434.9735C811.2481,-422.4919 825.1532,-405.6899 836.6529,-391.7944\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"837.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">acl</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"664\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">26 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M694.9192,-347.9735C689.5331,-335.9585 682.3525,-319.9401 676.2669,-306.3646\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"698\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"772\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M840.1807,-347.9735C827.7272,-335.3752 810.9229,-318.3755 797.1135,-304.4055\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"841\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"888\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M864.216,-347.9735C868.3189,-336.0751 873.7756,-320.2508 878.4274,-306.7606\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"886\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
+       "</g>\n",
+       "<!-- 33 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>33</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">33 (melaksanakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;33 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>30&#45;&gt;33</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M895.2961,-347.9735C923.1717,-334.5003 961.4624,-315.9932 991.3625,-301.5415\"/>\n",
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+       "</g>\n",
+       "<!-- 27 -->\n",
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+       "<title>27</title>\n",
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+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"664\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
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+       "<g id=\"edge34\" class=\"edge\">\n",
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+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"888\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
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+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">34 (sekatan)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;34 -->\n",
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+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
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+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
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+       "</g>\n",
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+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 35&#45;&gt;36 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>35&#45;&gt;36</title>\n",
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+       "</g>\n",
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+       "<title>41</title>\n",
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+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"888\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
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+       "<title>44</title>\n",
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+       "<title>43&#45;&gt;44</title>\n",
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+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>45&#45;&gt;41</title>\n",
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+       "<title>48</title>\n",
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+       "<title>49</title>\n",
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+       "<!-- 47&#45;&gt;49 -->\n",
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+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1436\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
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+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>47&#45;&gt;50</title>\n",
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+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1534\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
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+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>47&#45;&gt;57</title>\n",
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+       "<g id=\"edge55\" class=\"edge\">\n",
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+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>57&#45;&gt;55</title>\n",
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+       "<title>56</title>\n",
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+       "<g id=\"edge59\" class=\"edge\">\n",
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f29091327f0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1221,7 +2082,7 @@
        "'albert-tiny-dependency/model.ckpt'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1233,22 +2094,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:++++++ warmup starts at step 0, for 3777 steps ++++++\n",
+      "INFO:tensorflow:++++++ warmup starts at step 0, for 29336 steps ++++++\n",
       "INFO:tensorflow:using adamw\n",
       "INFO:tensorflow:Restoring parameters from albert-tiny-dependency/model.ckpt\n"
      ]
@@ -1259,7 +2112,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word, training = False)\n",
     "\n",
@@ -1270,7 +2123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1286,7 +2139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1331,14 +2184,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 315/315 [00:54<00:00,  5.80it/s]\n"
+      "100%|██████████| 313/313 [00:37<00:00,  8.27it/s]\n"
      ]
     }
    ],
@@ -1375,7 +2228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1390,60 +2243,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    1.00000   1.00000   1.00000    901404\n",
-      "            X    0.99997   0.99998   0.99997    158217\n",
-      "          acl    0.74523   0.72259   0.73374      6056\n",
-      "        advcl    0.44763   0.44416   0.44589      2319\n",
-      "       advmod    0.80839   0.80245   0.80541      9537\n",
-      "         amod    0.74481   0.69167   0.71726      8144\n",
-      "        appos    0.71137   0.68084   0.69577      4963\n",
-      "          aux    0.00000   0.00000   0.00000         9\n",
-      "         case    0.90625   0.93745   0.92159     21056\n",
-      "           cc    0.92435   0.90888   0.91655      6453\n",
-      "        ccomp    0.32162   0.13918   0.19429       855\n",
-      "     compound    0.76535   0.75323   0.75924     13008\n",
-      "compound:plur    0.76103   0.77066   0.76581      1186\n",
-      "         conj    0.79454   0.78507   0.78978      8640\n",
-      "          cop    0.87581   0.90736   0.89130      1943\n",
-      "        csubj    0.66667   0.04082   0.07692        49\n",
-      "   csubj:pass    0.00000   0.00000   0.00000        18\n",
-      "          dep    0.41637   0.38321   0.39910       929\n",
-      "          det    0.81424   0.77924   0.79636      7909\n",
-      "        fixed    0.63932   0.41054   0.50000      1101\n",
-      "         flat    0.85963   0.91321   0.88561     20856\n",
-      "         iobj    1.00000   0.03333   0.06452        30\n",
-      "         mark    0.69997   0.72039   0.71003      2879\n",
-      "         nmod    0.71129   0.68985   0.70041      7964\n",
-      "        nsubj    0.74144   0.81233   0.77527     12719\n",
-      "   nsubj:pass    0.68649   0.56466   0.61964      3905\n",
-      "       nummod    0.84427   0.87244   0.85813      7581\n",
-      "          obj    0.79591   0.78073   0.78825     10380\n",
-      "          obl    0.75820   0.78392   0.77085     11144\n",
-      "    parataxis    0.25150   0.06231   0.09988       674\n",
-      "        punct    0.98207   0.98323   0.98265     33034\n",
-      "         root    0.84186   0.87362   0.85745     10073\n",
-      "        xcomp    0.62652   0.63961   0.63300      2489\n",
+      "          PAD    1.00000   1.00000   1.00000    627830\n",
+      "            X    1.00000   1.00000   1.00000     60741\n",
+      "          acl    0.80688   0.76441   0.78507      3192\n",
+      "        advcl    0.60052   0.58044   0.59031      1585\n",
+      "       advmod    0.92613   0.92570   0.92591      6460\n",
+      "         amod    0.86782   0.85927   0.86353      4363\n",
+      "        appos    0.76876   0.70271   0.73425      3061\n",
+      "         case    0.97508   0.97247   0.97377     10862\n",
+      "           cc    0.98150   0.96228   0.97179      3473\n",
+      "        ccomp    0.52577   0.29651   0.37918       344\n",
+      "     compound    0.85386   0.91240   0.88216     11027\n",
+      "compound:plur    0.47826   0.37931   0.42308        29\n",
+      "         conj    0.87337   0.85133   0.86221      5112\n",
+      "          cop    0.98953   0.95777   0.97339       592\n",
+      "        csubj    0.00000   0.00000   0.00000         8\n",
+      "          dep    0.59312   0.58640   0.58974       353\n",
+      "          det    0.93306   0.90026   0.91637      3840\n",
+      "        fixed    0.91270   0.66860   0.77181       172\n",
+      "         flat    0.93104   0.95138   0.94110     18492\n",
+      "         iobj    0.00000   0.00000   0.00000         1\n",
+      "         mark    0.89771   0.92249   0.90993      1703\n",
+      "         nmod    0.81300   0.79987   0.80638      4457\n",
+      "        nsubj    0.80429   0.82122   0.81267      6891\n",
+      "   nsubj:pass    0.77960   0.69890   0.73705      1903\n",
+      "       nummod    0.93708   0.93517   0.93612      4427\n",
+      "          obj    0.87808   0.88969   0.88385      6128\n",
+      "          obl    0.84346   0.79980   0.82105      4965\n",
+      "    parataxis    0.43750   0.21448   0.28785       359\n",
+      "        punct    0.99444   0.99635   0.99540     20300\n",
+      "         root    0.86422   0.89430   0.87901     10000\n",
+      "        xcomp    0.67055   0.68068   0.67558      1522\n",
       "\n",
-      "     accuracy                        0.96997   1277524\n",
-      "    macro avg    0.70128   0.63294   0.64105   1277524\n",
-      " weighted avg    0.96929   0.96997   0.96946   1277524\n",
+      "     accuracy                        0.98296    824192\n",
+      "    macro avg    0.77217   0.73949   0.75253    824192\n",
+      " weighted avg    0.98272   0.98296   0.98277    824192\n",
       "\n"
      ]
     }
@@ -1455,16 +2298,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.7087220659183397\n",
-      "types accuracy: 0.6735055899028873\n",
-      "root accuracy: 0.8178452380952382\n"
+      "arc accuracy: 0.7865049894214071\n",
+      "types accuracy: 0.7587000482179278\n",
+      "root accuracy: 1.0\n"
      ]
     }
    ],
@@ -1476,63 +2319,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'bert/embeddings/word_embeddings',\n",
-       " 'bert/embeddings/token_type_embeddings',\n",
-       " 'bert/embeddings/position_embeddings',\n",
-       " 'bert/embeddings/LayerNorm/gamma',\n",
-       " 'bert/encoder/embedding_hidden_mapping_in/kernel',\n",
-       " 'bert/encoder/embedding_hidden_mapping_in/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm/gamma',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/kernel',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/bias',\n",
-       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/gamma',\n",
-       " 'bert/pooler/dense/kernel',\n",
-       " 'bert/pooler/dense/bias',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -1550,13 +2339,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1591,7 +2379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1599,7 +2387,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from albert-tiny-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-34-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-32-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -1607,7 +2395,7 @@
       "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
       "INFO:tensorflow:Froze 40 variables.\n",
       "INFO:tensorflow:Converted 40 variables to const ops.\n",
-      "1730 ops in the final graph.\n"
+      "1723 ops in the final graph.\n"
      ]
     }
    ],
@@ -1617,101 +2405,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "string = 'husein makan ayam'\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "def entities_textcleaning(string, lowering = False):\n",
-    "    \"\"\"\n",
-    "    use by entities recognition, pos recognition and dependency parsing\n",
-    "    \"\"\"\n",
-    "    string = re.sub('[^A-Za-z0-9\\-\\/() ]+', ' ', string)\n",
-    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
-    "    original_string = string.split()\n",
-    "    if lowering:\n",
-    "        string = string.lower()\n",
-    "    string = [\n",
-    "        (original_string[no], word.title() if word.isupper() else word)\n",
-    "        for no, word in enumerate(string.split())\n",
-    "        if len(word)\n",
-    "    ]\n",
-    "    return [s[0] for s in string], [s[1] for s in string]\n",
-    "\n",
-    "def parse_X(left):\n",
-    "    bert_tokens = ['[CLS]']\n",
-    "    for no, orig_token in enumerate(left):\n",
-    "        t = tokenizer.tokenize(orig_token)\n",
-    "        bert_tokens.extend(t)\n",
-    "    bert_tokens.append(\"[SEP]\")\n",
-    "    return tokenizer.convert_tokens_to_ids(bert_tokens), bert_tokens\n",
-    "\n",
-    "sequence = entities_textcleaning(string)[1]\n",
-    "parsed_sequence, bert_sequence = parse_X(sequence)"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-35-e8b29a78c927>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.gfile.GFile.\n"
+     ]
+    }
+   ],
    "source": [
-    "def merge_sentencepiece_tokens_tagging(x, y):\n",
-    "    new_paired_tokens = []\n",
-    "    n_tokens = len(x)\n",
-    "    rejected = ['[CLS]', '[SEP]']\n",
-    "\n",
-    "    i = 0\n",
-    "\n",
-    "    while i < n_tokens:\n",
-    "\n",
-    "        current_token, current_label = x[i], y[i]\n",
-    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
-    "            previous_token, previous_label = new_paired_tokens.pop()\n",
-    "            merged_token = previous_token\n",
-    "            merged_label = [previous_label]\n",
-    "            while (\n",
-    "                not current_token.startswith('▁')\n",
-    "                and current_token not in rejected\n",
-    "            ):\n",
-    "                merged_token = merged_token + current_token.replace('▁', '')\n",
-    "                merged_label.append(current_label)\n",
-    "                i = i + 1\n",
-    "                current_token, current_label = x[i], y[i]\n",
-    "            merged_label = merged_label[0]\n",
-    "            new_paired_tokens.append((merged_token, merged_label))\n",
-    "\n",
-    "        else:\n",
-    "            new_paired_tokens.append((current_token, current_label))\n",
-    "            i = i + 1\n",
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
     "\n",
-    "    words = [\n",
-    "        i[0].replace('▁', '')\n",
-    "        for i in new_paired_tokens\n",
-    "        if i[0] not in rejected\n",
-    "    ]\n",
-    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
-    "    return words, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3\n",
+    "pb = 'albert-tiny-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
     "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'albert-tiny-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/albert-tiny-dependency.pb\"\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
     "\n",
-    "s3 = boto3.client('s3')\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
     "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
    ]
   }
  ],
@@ -1731,7 +2474,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/session/dependency/alxlnet-base.ipynb b/session/dependency/alxlnet-base.ipynb
index 09268dd2..6e4c45e2 100644
--- a/session/dependency/alxlnet-base.ipynb
+++ b/session/dependency/alxlnet-base.ipynb
@@ -7,35 +7,19 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/husein/alxnet/model_utils.py:334: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/model_utils.py:334: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n"
      ]
     }
@@ -53,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +45,7 @@
     "from prepro_utils import preprocess_text, encode_ids\n",
     "\n",
     "sp_model = spm.SentencePieceProcessor()\n",
-    "sp_model.Load('alxlnet-base/sp10m.cased.v9.model')\n",
+    "sp_model.Load('sp10m.cased.v9.model')\n",
     "\n",
     "def tokenize_fn(text):\n",
     "    text = preprocess_text(text, lower= False)\n",
@@ -70,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,274 +86,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
+    "import pickle\n",
     "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    segments, masks = [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = []\n",
-    "                labels_ = []\n",
-    "                depends_ = []\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenize_fn(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.extend([4, 3])\n",
-    "                labels_.extend([0, 0])\n",
-    "                depends_.extend([0, 0])\n",
-    "                segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
-    "                input_mask = [0] * len(segment)\n",
-    "                words.append(bert_tokens)\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                segments.append(segment)\n",
-    "                masks.append(input_mask)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1], segments[:-1], masks[:-1]"
+    "with open('/home/husein/xlnet/train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends, train_segments, train_masks = pickle.load(fopen)\n",
+    "    \n",
+    "with open('/home/husein/xlnet/test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends, test_segments, test_masks = pickle.load(fopen)\n",
+    "    \n",
+    "with open('/home/husein/xlnet/tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
+      "WARNING:tensorflow:From /home/husein/alxlnet/xlnet.py:70: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "sentences, words, depends, labels, _, _, segments, masks = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    segments, masks = [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = []\n",
-    "        labels_ = []\n",
-    "        depends_ = []\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenize_fn(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.extend([4, 3])\n",
-    "        labels_.extend([0, 0])\n",
-    "        depends_.extend([0, 0])\n",
-    "        segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
-    "        input_mask = [0] * len(segment)\n",
-    "        outside.append(bert_tokens)\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "        segments.append(segment)\n",
-    "        masks.append(input_mask)\n",
-    "    return outside, sentences, outside_depends, outside_labels, segments, masks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels, outside_segments, outside_masks = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)\n",
-    "segments.extend(outside_segments)\n",
-    "masks.extend(outside_masks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'PAD',\n",
-       " 1: 'X',\n",
-       " 2: 'nsubj',\n",
-       " 3: 'cop',\n",
-       " 4: 'det',\n",
-       " 5: 'root',\n",
-       " 6: 'nsubj:pass',\n",
-       " 7: 'acl',\n",
-       " 8: 'case',\n",
-       " 9: 'obl',\n",
-       " 10: 'flat',\n",
-       " 11: 'punct',\n",
-       " 12: 'appos',\n",
-       " 13: 'amod',\n",
-       " 14: 'compound',\n",
-       " 15: 'advmod',\n",
-       " 16: 'cc',\n",
-       " 17: 'obj',\n",
-       " 18: 'conj',\n",
-       " 19: 'mark',\n",
-       " 20: 'advcl',\n",
-       " 21: 'nmod',\n",
-       " 22: 'nummod',\n",
-       " 23: 'dep',\n",
-       " 24: 'xcomp',\n",
-       " 25: 'ccomp',\n",
-       " 26: 'parataxis',\n",
-       " 27: 'compound:plur',\n",
-       " 28: 'fixed',\n",
-       " 29: 'aux',\n",
-       " 30: 'csubj',\n",
-       " 31: 'iobj',\n",
-       " 32: 'csubj:pass'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}\n",
-    "idx2tag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test, \\\n",
-    "segments_train, segments_test, masks_train, masks_test \\\n",
-    "= train_test_split(words, depends, labels, segments, masks, test_size = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xlnet\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "\n",
     "kwargs = dict(\n",
     "      is_training=True,\n",
     "      use_tpu=False,\n",
@@ -382,25 +129,27 @@
     "      clamp_len=-1)\n",
     "\n",
     "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
-    "xlnet_config = xlnet.XLNetConfig(json_path='alxlnet-base/config.json')"
+    "xlnet_config = xlnet.XLNetConfig(\n",
+    "    json_path = 'alxlnet-base-2020-04-10/config.json'\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "37770 3777\n"
+      "1173456 117345\n"
      ]
     }
    ],
    "source": [
-    "epoch = 15\n",
-    "batch_size = 16\n",
+    "epoch = 3\n",
+    "batch_size = 8\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
     "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
@@ -429,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -674,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -692,30 +441,30 @@
       "  * https://github.com/tensorflow/io (for I/O related ops)\n",
       "If you depend on functionality not listed there, please file an issue.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/alxnet/xlnet.py:253: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/xlnet.py:253: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/alxnet/xlnet.py:253: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/xlnet.py:253: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:696: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/custom_modeling.py:697: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
       "\n",
       "INFO:tensorflow:memory input None\n",
       "INFO:tensorflow:Use float type <dtype: 'float32'>\n",
-      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:808: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/custom_modeling.py:809: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use keras.layers.dropout instead.\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:271: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:109: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/alxlnet/custom_modeling.py:109: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use keras.layers.Dense instead.\n",
-      "WARNING:tensorflow:From <ipython-input-24-117b6be1eb3b>:138: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-9-117b6be1eb3b>:138: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-24-117b6be1eb3b>:172: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-9-117b6be1eb3b>:172: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "dim is deprecated, use axis instead\n"
      ]
@@ -726,7 +475,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -734,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -770,26 +519,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "tvars = tf.trainable_variables()\n",
-    "checkpoint = 'alxlnet-base/model.ckpt'\n",
+    "checkpoint = 'alxlnet-base-2020-04-10/model.ckpt-300000'\n",
     "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
     "                                                                                checkpoint)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from alxlnet-base/model.ckpt\n"
+      "INFO:tensorflow:Restoring parameters from alxlnet-base-2020-04-10/model.ckpt-300000\n"
      ]
     }
    ],
@@ -800,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -812,24 +561,24 @@
     "batch_y = pad_sequences(batch_y,padding='post')\n",
     "batch_depends = train_depends[:5]\n",
     "batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "batch_segments = segments_train[:5]\n",
+    "batch_segments = train_segments[:5]\n",
     "batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "batch_masks = masks_train[:5]\n",
+    "batch_masks = train_masks[:5]\n",
     "batch_masks = pad_sequences(batch_masks, padding='post', value = 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.028846154, 0.014423077, 44.482525]"
+       "[0.08235294, 0.105882354, 40.901375]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -846,99 +595,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 2,  4, 22,  7, 26, 12, 22,  3, 28,  8, 23, 14, 13, 28, 29,  8, 22,\n",
-       "        17, 29, 22, 19, 22, 28, 16,  8, 28,  3,  1, 16,  3, 30, 23, 22, 16,\n",
-       "        16,  8,  8, 22, 32, 32, 12, 16, 16, 16, 22, 23, 22,  7, 23,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0], dtype=int32),\n",
-       " array([11, 22, 11, 19, 34, 35, 47, 19, 39, 10, 34, 24, 34, 43, 10, 10, 11,\n",
-       "        18, 34, 22, 23, 22, 43, 10, 37, 41, 19, 34, 10, 19, 10, 34, 11,  2,\n",
-       "        27, 10, 37, 11,  7, 11, 19, 25, 31, 25, 42, 34, 22, 34, 34,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0]),\n",
-       " array([ 9,  0,  0,  2,  0,  3,  0,  0,  0,  3,  0,  9,  9,  7,  1, 11,  9,\n",
-       "        11, 12,  0, 11,  0,  0, 11, 15,  0,  0, 16, 15,  0, 15, 19, 20,  0,\n",
-       "         0,  0,  0,  0, 19,  0, 19,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0], dtype=int32))"
+       "[0.03529412, 0.07058824, 185.67154]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tags_seq, heads = sess.run(\n",
-    "    [model.logits, model.heads_seq],\n",
-    "    feed_dict = {\n",
-    "        model.words: batch_x,\n",
-    "        model.segment_ids: batch_segments,\n",
-    "        model.input_masks: batch_masks\n",
-    "    },\n",
-    ")\n",
-    "tags_seq[0], heads[0], batch_depends[0]"
+    "sess.run([model.accuracy, model.accuracy_depends, model.cost],\n",
+    "        feed_dict = {model.words: batch_x,\n",
+    "                model.types: batch_y,\n",
+    "                model.heads: batch_depends,\n",
+    "                model.segment_ids: batch_segments,\n",
+    "                model.input_masks: batch_masks,\n",
+    "                model.switch: True})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:07<00:00,  2.97it/s, accuracy=0.875, accuracy_depends=0.75, cost=0.65]  \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:55<00:00,  5.45it/s, accuracy=0.844, accuracy_depends=0.543, cost=2]   \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 3.291615, training acc: 0.697776, training depends: 0.453986, valid loss: 1.953125, valid acc: 0.845485, valid depends: 0.548856\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:09<00:00,  2.96it/s, accuracy=1, accuracy_depends=1, cost=0.113]        \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:57<00:00,  5.37it/s, accuracy=0.87, accuracy_depends=0.656, cost=1.52]  \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 1.659280, training acc: 0.863677, training depends: 0.603499, valid loss: 1.461601, valid acc: 0.876069, valid depends: 0.648819\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:15<00:00,  2.94it/s, accuracy=1, accuracy_depends=1, cost=0.00758]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:58<00:00,  5.31it/s, accuracy=0.905, accuracy_depends=0.711, cost=1.27] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 391153/391153 [19:08:06<00:00,  5.68it/s, accuracy=0.933, accuracy_depends=0.667, cost=0.248]    \n",
+      "test minibatch loop: 100%|██████████| 1250/1250 [02:10<00:00,  9.56it/s, accuracy=0.871, accuracy_depends=0.834, cost=0.396] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2, training loss: 1.285719, training acc: 0.884337, training depends: 0.684235, valid loss: 1.193979, valid acc: 0.889046, valid depends: 0.707937\n",
+      "epoch: 0, training loss: 0.607109, training acc: 0.893016, training depends: 0.811151, valid loss: 0.446882, valid acc: 0.895227, valid depends: 0.837708\n",
       "\n"
      ]
     },
@@ -946,293 +644,229 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:28<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.0161]       \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:58<00:00,  5.30it/s, accuracy=0.902, accuracy_depends=0.717, cost=1.12] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
+      "train minibatch loop:  15%|█▌        | 59495/391153 [2:52:23<16:05:17,  5.73it/s, accuracy=0.918, accuracy_depends=0.826, cost=0.41]   IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  16%|█▌        | 62533/391153 [3:01:16<17:05:51,  5.34it/s, accuracy=0.89, accuracy_depends=0.884, cost=0.321]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  16%|█▋        | 64313/391153 [3:06:25<15:51:45,  5.72it/s, accuracy=0.918, accuracy_depends=0.874, cost=0.302]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  17%|█▋        | 67375/391153 [3:15:15<15:32:33,  5.79it/s, accuracy=0.902, accuracy_depends=0.853, cost=0.331] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  18%|█▊        | 70411/391153 [3:24:09<16:20:56,  5.45it/s, accuracy=0.912, accuracy_depends=0.856, cost=0.559] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  19%|█▊        | 73247/391153 [3:32:21<16:13:38,  5.44it/s, accuracy=0.908, accuracy_depends=0.853, cost=0.419] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  20%|█▉        | 76413/391153 [3:42:30<14:28:51,  6.04it/s, accuracy=0.871, accuracy_depends=0.857, cost=0.57]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  20%|██        | 79403/391153 [3:51:15<16:15:16,  5.33it/s, accuracy=0.894, accuracy_depends=0.867, cost=0.412] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop:  21%|██        | 82460/391153 [4:00:07<15:33:00,  5.51it/s, accuracy=0.919, accuracy_depends=0.823, cost=0.5]   IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop: 100%|██████████| 391153/391153 [19:10:06<00:00,  5.67it/s, accuracy=0.933, accuracy_depends=0.733, cost=0.234]    \n",
+      "test minibatch loop: 100%|██████████| 1250/1250 [02:10<00:00,  9.58it/s, accuracy=0.902, accuracy_depends=0.834, cost=0.235] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 3, training loss: 1.071584, training acc: 0.895355, training depends: 0.731697, valid loss: 1.047562, valid acc: 0.898181, valid depends: 0.740972\n",
+      "epoch: 1, training loss: 0.429685, training acc: 0.896941, training depends: 0.841408, valid loss: 0.403095, valid acc: 0.901645, valid depends: 0.845275\n",
       "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "epoch = 2\n",
+    "for e in range(epoch):\n",
+    "    train_acc, train_loss = [], []\n",
+    "    test_acc, test_loss = [], []\n",
+    "    train_acc_depends, test_acc_depends = [], []\n",
+    "    \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_X))\n",
+    "        batch_x = train_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = train_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        batch_segments = train_segments[i: index]\n",
+    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
+    "        batch_masks = train_masks[i: index]\n",
+    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_X))\n",
+    "        batch_x = test_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = test_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        batch_segments = test_segments[i: index]\n",
+    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
+    "        batch_masks = test_masks[i: index]\n",
+    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "    \n",
+    "    \n",
+    "    print(\n",
+    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
+    "    % (e, np.mean(train_loss), \n",
+    "       np.mean(train_acc), \n",
+    "       np.mean(train_acc_depends), \n",
+    "       np.mean(test_loss), \n",
+    "       np.mean(test_acc), \n",
+    "       np.mean(test_acc_depends)\n",
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'alxlnet-base-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:22<00:00,  2.92it/s, accuracy=1, accuracy_depends=1, cost=0.00245]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.24it/s, accuracy=0.905, accuracy_depends=0.737, cost=1]    \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
+      "train minibatch loop:   1%|▏         | 5291/391153 [15:18<18:27:31,  5.81it/s, accuracy=0.952, accuracy_depends=0.85, cost=5.07]  IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop: 100%|██████████| 391153/391153 [18:50:23<00:00,  5.77it/s, accuracy=1, accuracy_depends=0.733, cost=0.427]       \n",
+      "test minibatch loop: 100%|██████████| 1250/1250 [02:11<00:00,  9.52it/s, accuracy=0.969, accuracy_depends=0.853, cost=2.11] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 4, training loss: 0.929319, training acc: 0.904813, training depends: 0.762597, valid loss: 0.926313, valid acc: 0.906362, valid depends: 0.768899\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.18it/s, accuracy=0.899, accuracy_depends=0.766, cost=0.924]08]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 5, training loss: 0.823146, training acc: 0.912222, training depends: 0.785236, valid loss: 0.848220, valid acc: 0.910573, valid depends: 0.788035\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:33<00:00,  2.88it/s, accuracy=1, accuracy_depends=1, cost=0.00253]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.23it/s, accuracy=0.91, accuracy_depends=0.789, cost=0.822] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 6, training loss: 0.740302, training acc: 0.920387, training depends: 0.802050, valid loss: 0.783450, valid acc: 0.919229, valid depends: 0.800596\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:30<00:00,  2.89it/s, accuracy=1, accuracy_depends=0.938, cost=0.156]    \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.21it/s, accuracy=0.899, accuracy_depends=0.789, cost=0.807]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 0.669474, training acc: 0.926937, training depends: 0.817003, valid loss: 0.746666, valid acc: 0.922263, valid depends: 0.809512\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:29<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.0024]       \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.23it/s, accuracy=0.925, accuracy_depends=0.789, cost=0.775]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 8, training loss: 0.612354, training acc: 0.932819, training depends: 0.828322, valid loss: 0.719806, valid acc: 0.927458, valid depends: 0.808264\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:36<00:00,  2.88it/s, accuracy=1, accuracy_depends=1, cost=0.00618]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.21it/s, accuracy=0.919, accuracy_depends=0.78, cost=0.781] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 9, training loss: 0.558708, training acc: 0.938466, training depends: 0.838968, valid loss: 0.658846, valid acc: 0.931531, valid depends: 0.827488\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:29<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.00506]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.22it/s, accuracy=0.928, accuracy_depends=0.815, cost=0.78] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 10, training loss: 0.515627, training acc: 0.943391, training depends: 0.846884, valid loss: 0.629950, valid acc: 0.936099, valid depends: 0.833015\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:29<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.00351]      \n",
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-     ]
-    },
-    {
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-      "epoch: 11, training loss: 0.475926, training acc: 0.948360, training depends: 0.854740, valid loss: 0.598152, valid acc: 0.938958, valid depends: 0.838362\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:28<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.00395]      \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 12, training loss: 0.439909, training acc: 0.952905, training depends: 0.861413, valid loss: 0.560686, valid acc: 0.939924, valid depends: 0.851370\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:33<00:00,  2.89it/s, accuracy=1, accuracy_depends=1, cost=0.00072]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.19it/s, accuracy=0.931, accuracy_depends=0.861, cost=0.543]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 13, training loss: 0.407941, training acc: 0.956605, training depends: 0.867787, valid loss: 0.543810, valid acc: 0.944994, valid depends: 0.855204\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:28<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.00081]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.22it/s, accuracy=0.945, accuracy_depends=0.847, cost=0.578]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 14, training loss: 0.379358, training acc: 0.959959, training depends: 0.873444, valid loss: 0.514084, valid acc: 0.948265, valid depends: 0.860735\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:28<00:00,  2.90it/s, accuracy=1, accuracy_depends=1, cost=0.000883]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.19it/s, accuracy=0.948, accuracy_depends=0.87, cost=0.487] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 15, training loss: 0.352141, training acc: 0.963349, training depends: 0.878968, valid loss: 0.509567, valid acc: 0.950678, valid depends: 0.861329\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:30<00:00,  2.89it/s, accuracy=1, accuracy_depends=1, cost=0.000291]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:57<00:00,  5.36it/s, accuracy=0.951, accuracy_depends=0.844, cost=0.497]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 16, training loss: 0.331996, training acc: 0.967374, training depends: 0.882138, valid loss: 0.480076, valid acc: 0.952791, valid depends: 0.865909\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:11<00:00,  2.96it/s, accuracy=1, accuracy_depends=1, cost=0.000665]      \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.310162, training acc: 0.969735, training depends: 0.886819, valid loss: 0.514947, valid acc: 0.955326, valid depends: 0.852492\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:15<00:00,  2.95it/s, accuracy=1, accuracy_depends=1, cost=6.9e-5]        \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.15it/s, accuracy=0.945, accuracy_depends=0.853, cost=0.601]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.293612, training acc: 0.972500, training depends: 0.889618, valid loss: 0.454646, valid acc: 0.955980, valid depends: 0.875600\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:38<00:00,  2.87it/s, accuracy=1, accuracy_depends=1, cost=0.000245]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.16it/s, accuracy=0.951, accuracy_depends=0.896, cost=0.451]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.273301, training acc: 0.974312, training depends: 0.894809, valid loss: 0.463847, valid acc: 0.958968, valid depends: 0.871426\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "epoch: 0, training loss: 3.915045, training acc: 0.947627, training depends: 0.841807, valid loss: 3.780288, valid acc: 0.949707, valid depends: 0.841579\n",
       "\n"
      ]
     }
@@ -1240,7 +874,7 @@
    "source": [
     "from tqdm import tqdm\n",
     "\n",
-    "epoch = 20\n",
+    "epoch = 1\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
     "    test_acc, test_loss = [], []\n",
@@ -1257,26 +891,28 @@
     "        batch_y = pad_sequences(batch_y,padding='post')\n",
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_train[i: index]\n",
+    "        batch_segments = train_segments[i: index]\n",
     "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_train[i: index]\n",
+    "        batch_masks = train_masks[i: index]\n",
     "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1289,26 +925,28 @@
     "        batch_y = pad_sequences(batch_y,padding='post')\n",
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_test[i: index]\n",
+    "        batch_segments = test_segments[i: index]\n",
     "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_test[i: index]\n",
+    "        batch_masks = test_masks[i: index]\n",
     "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1319,194 +957,1274 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'alxlnet-base-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['<cls>', '<sep>']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in ['<cls>', '<sep>']\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in ['<cls>', '<sep>']]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
+    "\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
+    "\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    left = ' '.join(left)\n",
+    "    bert_tokens = tokenize_fn(left)\n",
+    "    bert_tokens.extend([3, 4])\n",
+    "    segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
+    "    input_mask = [0] * len(segment)\n",
+    "    s_tokens = [sp_model.IdToPiece(i) for i in bert_tokens]\n",
+    "    return bert_tokens, segment, input_mask, s_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
+    "        )\n",
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:37<00:00,  2.87it/s, accuracy=1, accuracy_depends=1, cost=0.00701]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.15it/s, accuracy=0.962, accuracy_depends=0.853, cost=4.4] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 3.009300, training acc: 0.978004, training depends: 0.877064, valid loss: 5.160714, valid acc: 0.968942, valid depends: 0.854963\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:37<00:00,  2.87it/s, accuracy=1, accuracy_depends=1, cost=0.00202]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.14it/s, accuracy=0.98, accuracy_depends=0.887, cost=3.67] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 1.917773, training acc: 0.986471, training depends: 0.877437, valid loss: 4.625864, valid acc: 0.972671, valid depends: 0.856262\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:41<00:00,  2.86it/s, accuracy=1, accuracy_depends=1, cost=0.00113]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.15it/s, accuracy=0.974, accuracy_depends=0.85, cost=5.15]  \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 1.547230, training acc: 0.989546, training depends: 0.875326, valid loss: 4.631369, valid acc: 0.974594, valid depends: 0.853452\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:37<00:00,  2.87it/s, accuracy=1, accuracy_depends=1, cost=0.000275]     \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.17it/s, accuracy=0.983, accuracy_depends=0.876, cost=3.34] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 1.307442, training acc: 0.991479, training depends: 0.874090, valid loss: 4.400067, valid acc: 0.975908, valid depends: 0.854123\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:39<00:00,  2.86it/s, accuracy=1, accuracy_depends=1, cost=0.000533]     \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:01<00:00,  5.17it/s, accuracy=0.986, accuracy_depends=0.855, cost=3.28] "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 1.156469, training acc: 0.992754, training depends: 0.873411, valid loss: 4.205251, valid acc: 0.977972, valid depends: 0.853291\n",
-      "\n"
-     ]
-    },
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: G Pages: 1 -->\n",
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+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "            model.segment_ids: [segment_sequence],\n",
+    "            model.input_masks: [mask_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(xlnet_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(xlnet_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
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+       "<!-- 33&#45;&gt;39 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>33&#45;&gt;39</title>\n",
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+       "<text text-anchor=\"middle\" x=\"748\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 14 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>14</title>\n",
+       "<text text-anchor=\"middle\" x=\"1339.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">14 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"1348.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">16 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;16 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>14&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1341.3648,-608.9735C1342.5836,-597.1918 1344.2006,-581.5607 1345.5871,-568.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1356.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"937.5\" y=\"-1145.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">15 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;15 -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>15&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M983.5242,-1156.6827C993.9063,-1155.9906 1001.5,-1153.4297 1001.5,-1149 1001.5,-1146.2314 998.5337,-1144.1929 993.7743,-1142.8843\"/>\n",
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+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"829.5\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;17 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>15&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M915.1223,-1130.9735C899.1934,-1118.1419 877.5964,-1100.7443 860.0784,-1086.6326\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"902.5\" y=\"-1101.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"937.5\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">20 (Ketua)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;20 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>15&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M937.5,-1130.9735C937.5,-1119.1918 937.5,-1103.5607 937.5,-1090.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"941.0001,-1090.0033 937.5,-1080.0034 934.0001,-1090.0034 941.0001,-1090.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"952.5\" y=\"-1101.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"1155.5\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;24 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>15&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M982.6697,-1130.9735C1017.0148,-1117.267 1064.4143,-1098.3507 1100.8887,-1083.7944\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1080.5\" y=\"-1101.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M906.2127,-1043.9735C883.1319,-1030.6753 851.5394,-1012.473 826.6045,-998.1065\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"879.5\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"888.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;22 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>20&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M927.3472,-1043.9735C920.5144,-1031.8418 911.3828,-1015.6287 903.6899,-1001.9698\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"928.5\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"986.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;25 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M947.6528,-1043.9735C954.4856,-1031.8418 963.6172,-1015.6287 971.3101,-1001.9698\"/>\n",
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+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"1092.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;21 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>24&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1142.4464,-1043.9735C1133.4924,-1031.6085 1121.4681,-1015.0036 1111.4613,-1001.1847\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"1191.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">23 (,)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;23 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>24&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1162.9592,-1043.9735C1167.931,-1031.9585 1174.5593,-1015.9401 1180.1767,-1002.3646\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1197\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"1319.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">30 (mempertikaikan)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;30 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>24&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1189.4809,-1043.9735C1214.7687,-1030.5587 1249.4639,-1012.1533 1276.6538,-997.7294\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1258\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">acl</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"1092.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;18 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>21&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1092.5,-956.9735C1092.5,-945.1918 1092.5,-929.5607 1092.5,-916.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1096.0001,-916.0033 1092.5,-906.0034 1089.0001,-916.0034 1096.0001,-916.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1104.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"1274.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1310.176,-956.9735C1303.9009,-944.8418 1295.5148,-928.6287 1288.4499,-914.9698\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1320.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"1390.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1334.2112,-956.9735C1344.3974,-944.4919 1358.1094,-927.6899 1369.4494,-913.7944\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1372.1965,-915.9638 1375.8076,-906.0034 1366.7733,-911.5379 1372.1965,-915.9638\"/>\n",
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+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-1145.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">26 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;26 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>26&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1518.1902,-1156.6424C1528.5134,-1155.8951 1536,-1153.3477 1536,-1149 1536,-1146.2827 1533.0756,-1144.2686 1528.3619,-1142.9578\"/>\n",
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+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-1058.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">27 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1470.5,-1130.9735C1470.5,-1119.1918 1470.5,-1103.5607 1470.5,-1090.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1474.0001,-1090.0033 1470.5,-1080.0034 1467.0001,-1090.0034 1474.0001,-1090.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1482.5\" y=\"-1101.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1470.5,-1043.9735C1470.5,-1032.1918 1470.5,-1016.5607 1470.5,-1003.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1482.5\" y=\"-1014.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"1390.5\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;32 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>31&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1390.5,-869.9735C1390.5,-858.1918 1390.5,-842.5607 1390.5,-829.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1394.0001,-829.0033 1390.5,-819.0034 1387.0001,-829.0034 1394.0001,-829.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1427.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"532.5\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M534.8784,-869.9735C534.4721,-858.1918 533.9331,-842.5607 533.471,-829.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"571.5\" y=\"-840.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"908.5\" y=\"-797.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">46 (memetik)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;46 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>39&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M893.4368,-869.9735C896.0098,-858.1918 899.4235,-842.5607 902.3505,-829.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"521.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 35&#45;&gt;36 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>35&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M530.2208,-782.9735C528.7311,-771.1918 526.7548,-755.5607 525.0602,-742.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"548.5\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"634.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">38 (Beliau)</text>\n",
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+       "<!-- 46&#45;&gt;38 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>46&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M852.3063,-792.1031C819.7417,-786.1375 778.2417,-777.1375 742.5,-765 720.1262,-757.4021 696.2717,-746.4632 676.6879,-736.663\"/>\n",
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+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"727.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;40 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>46&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M854.9613,-782.8844C840.931,-777.6079 825.9661,-771.4982 812.5,-765 795.9425,-757.0101 778.4022,-746.8075 763.5825,-737.6303\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"833\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"816.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">41 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;41 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>46&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M889.4375,-782.9735C875.9918,-770.2586 857.8051,-753.0603 842.9561,-739.0183\"/>\n",
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+       "</g>\n",
+       "<!-- 45 -->\n",
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+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"930.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M913.0584,-782.9735C916.0672,-771.0751 920.0688,-755.2508 923.4801,-741.7606\"/>\n",
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+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1027.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;61 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>46&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M952.8254,-782.9686C963.0311,-777.8773 973.517,-771.8332 982.5,-765 991.7586,-757.9572 1000.5554,-748.7823 1007.8747,-740.1591\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1019\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1134.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M964.6972,-789.3444C989.3424,-783.3891 1018.3287,-775.2209 1043.5,-765 1062.0471,-757.4689 1081.5425,-746.9337 1097.7097,-737.3925\"/>\n",
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+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1507.5\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">64 (berkata)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;64 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M964.5833,-792.8544C1076.6622,-776.5758 1327.5956,-740.1297 1444.8644,-723.0973\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1258\" y=\"-753.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"816.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;43 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>41&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M816.5,-695.9735C816.5,-684.1918 816.5,-668.5607 816.5,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"820.0001,-655.0033 816.5,-645.0034 813.0001,-655.0034 820.0001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"831.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"720.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M796.6087,-608.9735C782.5784,-596.2586 763.601,-579.0603 748.1064,-565.0183\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"782.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"821.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">44 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;44 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>43&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M817.536,-608.9735C818.2131,-597.1918 819.1115,-581.5607 819.8817,-568.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"831.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1129.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">48 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;48 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>47&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1133.464,-695.9735C1132.7869,-684.1918 1131.8885,-668.5607 1131.1183,-655.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 64&#45;&gt;14 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>64&#45;&gt;14</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1472.6903,-695.9735C1446.7857,-682.5587 1411.2443,-664.1533 1383.3912,-649.7294\"/>\n",
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+       "<!-- 62 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1459.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">62 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;62 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>64&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1491.6037,-695.6612C1487.3319,-690.2021 1482.9463,-684.0558 1479.5,-678 1475.3325,-670.677 1471.6542,-662.3145 1468.6256,-654.5091\"/>\n",
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+       "<!-- 65 -->\n",
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+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1544.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">65 (,)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1515.1664,-695.9735C1520.2763,-683.9585 1527.0887,-667.9401 1532.8622,-654.3646\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1548\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"1616.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">83 (.)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;83 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>64&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1545.3878,-695.9598C1554.6715,-690.7617 1564.2907,-684.6708 1572.5,-678 1581.3032,-670.8466 1589.7523,-661.7921 1596.8518,-653.3041\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1607\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1725.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">68 (menjangka)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;68 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1559.6276,-701.5362C1582.1151,-695.4802 1608.4843,-687.4562 1631.5,-678 1650.3374,-670.2605 1670.2892,-659.7905 1686.9423,-650.3507\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1681.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advcl</text>\n",
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+       "<!-- 49 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"947.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">49 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1084.6337,-608.8225C1071.5913,-603.3167 1057.3903,-597.1054 1044.5,-591 1026.2197,-582.3417 1006.3938,-572.0792 989.4586,-563.0382\"/>\n",
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+       "<!-- 50 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1046.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
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+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1104.2325,-608.6446C1097.5188,-603.2809 1090.4786,-597.1878 1084.5,-591 1077.3252,-583.5742 1070.3215,-574.7681 1064.3213,-566.5811\"/>\n",
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+       "<!-- 51 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1129.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;51 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1129.5,-608.9735C1129.5,-597.1918 1129.5,-581.5607 1129.5,-568.1581\"/>\n",
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+       "<!-- 57 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1237.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
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+       "<!-- 48&#45;&gt;57 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1153.5634,-608.8816C1160.9712,-603.2203 1169.1176,-596.909 1176.5,-591 1187.0446,-582.56 1198.4396,-573.1216 1208.4947,-564.6808\"/>\n",
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+       "<!-- 52 -->\n",
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+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1061.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">52 (Sri)</text>\n",
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+       "<!-- 51&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>51&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1115.4104,-521.9735C1105.7457,-509.6085 1092.7672,-493.0036 1081.9662,-479.1847\"/>\n",
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+       "<!-- 55 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1157.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">55 (yang)</text>\n",
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+       "<!-- 57&#45;&gt;55 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>57&#45;&gt;55</title>\n",
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+       "</g>\n",
+       "<!-- 56 -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>56</title>\n",
+       "<text text-anchor=\"middle\" x=\"1260.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">56 (tidak)</text>\n",
+       "</g>\n",
+       "<!-- 57&#45;&gt;56 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>57&#45;&gt;56</title>\n",
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+       "</g>\n",
+       "<!-- 59 -->\n",
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+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1395.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">59 (mengelirukan)</text>\n",
+       "</g>\n",
+       "<!-- 57&#45;&gt;59 -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>57&#45;&gt;59</title>\n",
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+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1061.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">53 (Muhyiddin)</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
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+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1061.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">54 (Yassin)</text>\n",
+       "</g>\n",
+       "<!-- 53&#45;&gt;54 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>53&#45;&gt;54</title>\n",
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+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1339.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">58 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
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+       "</g>\n",
+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1450.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">60 (rakyat)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1406.896,-434.9735C1414.6393,-422.7252 1425.0126,-406.3165 1433.6987,-392.5766\"/>\n",
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+       "<!-- 63 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1459.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">63 (Saiful)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1459.5,-608.9735C1459.5,-597.1918 1459.5,-581.5607 1459.5,-568.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 66 -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1614.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">66 (beliau)</text>\n",
+       "</g>\n",
+       "<!-- 68&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>68&#45;&gt;66</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1702.5007,-608.9735C1685.9805,-596.0253 1663.5284,-578.4276 1645.4378,-564.2485\"/>\n",
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+       "</g>\n",
+       "<!-- 67 -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1725.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">67 (sudah)</text>\n",
+       "</g>\n",
+       "<!-- 68&#45;&gt;67 -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>68&#45;&gt;67</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1725.5,-608.9735C1725.5,-597.1918 1725.5,-581.5607 1725.5,-568.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1754\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 69 -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1827.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">69 (ada)</text>\n",
+       "</g>\n",
+       "<!-- 68&#45;&gt;69 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>68&#45;&gt;69</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1761.1666,-608.8601C1769.8768,-603.6639 1778.8755,-597.5989 1786.5,-591 1794.7465,-583.8628 1802.5738,-574.9066 1809.1365,-566.4994\"/>\n",
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+       "</g>\n",
+       "<!-- 70 -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1703.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">70 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;70 -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1801.8071,-521.9735C1783.1858,-508.9086 1757.818,-491.1102 1737.5161,-476.8662\"/>\n",
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+       "</g>\n",
+       "<!-- 73 -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1827.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">73 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;73 -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>69&#45;&gt;73</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1827.5,-521.9735C1827.5,-510.1918 1827.5,-494.5607 1827.5,-481.1581\"/>\n",
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+       "</g>\n",
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+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1958.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">75 (tulisan)</text>\n",
+       "</g>\n",
+       "<!-- 69&#45;&gt;75 -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>69&#45;&gt;75</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1854.6433,-521.9735C1874.4036,-508.8503 1901.3551,-490.9512 1922.8505,-476.6756\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1911.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obl</text>\n",
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+       "<!-- 71 -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1703.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">71 (balas)</text>\n",
+       "</g>\n",
+       "<!-- 70&#45;&gt;71 -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>70&#45;&gt;71</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1703.5,-434.9735C1703.5,-423.1918 1703.5,-407.5607 1703.5,-394.1581\"/>\n",
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+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>72</title>\n",
+       "<text text-anchor=\"middle\" x=\"1824.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">72 (daripada)</text>\n",
+       "</g>\n",
+       "<!-- 73&#45;&gt;72 -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>73&#45;&gt;72</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1826.8784,-434.9735C1826.4721,-423.1918 1825.9331,-407.5607 1825.471,-394.1581\"/>\n",
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+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"1958.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">74 (mengenai)</text>\n",
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+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>75&#45;&gt;74</title>\n",
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+       "<title>77</title>\n",
+       "<text text-anchor=\"middle\" x=\"2100.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">77 (berhubung)</text>\n",
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+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>75&#45;&gt;77</title>\n",
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+       "<title>76</title>\n",
+       "<text text-anchor=\"middle\" x=\"2056.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">76 (beliau)</text>\n",
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+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>77&#45;&gt;76</title>\n",
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+       "<title>78</title>\n",
+       "<text text-anchor=\"middle\" x=\"2166.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">78 (kesan)</text>\n",
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+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>77&#45;&gt;78</title>\n",
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+       "<title>79</title>\n",
+       "<text text-anchor=\"middle\" x=\"2121.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">79 (positif)</text>\n",
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+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>78&#45;&gt;79</title>\n",
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+       "<title>80</title>\n",
+       "<text text-anchor=\"middle\" x=\"2239.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">80 (sekatan)</text>\n",
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+       "<title>78&#45;&gt;80</title>\n",
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+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"2239.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">81 (pergerakan)</text>\n",
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+       "<title>80&#45;&gt;81</title>\n",
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+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2239.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">82 (penuh)</text>\n",
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+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>81&#45;&gt;82</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M2239.5,-86.9735C2239.5,-75.1918 2239.5,-59.5607 2239.5,-46.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2243.0001,-46.0033 2239.5,-36.0034 2236.0001,-46.0034 2243.0001,-46.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2259.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f56a89d7940>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 5\n",
-    "for e in range(epoch):\n",
-    "    train_acc, train_loss = [], []\n",
-    "    test_acc, test_loss = [], []\n",
-    "    train_acc_depends, test_acc_depends = [], []\n",
-    "    \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(train_X))\n",
-    "        batch_x = train_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = train_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = train_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_train[i: index]\n",
-    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_train[i: index]\n",
-    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
-    "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "        \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(test_X))\n",
-    "        batch_x = test_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = test_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = test_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_test[i: index]\n",
-    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_test[i: index]\n",
-    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
-    "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "    \n",
-    "    \n",
-    "    print(\n",
-    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
-    "    % (e, np.mean(train_loss), \n",
-    "       np.mean(train_acc), \n",
-    "       np.mean(train_acc_depends), \n",
-    "       np.mean(test_loss), \n",
-    "       np.mean(test_acc), \n",
-    "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "            model.segment_ids: [segment_sequence],\n",
+    "            model.input_masks: [mask_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(xlnet_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(xlnet_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1515,7 +2233,7 @@
        "'alxlnet-base-dependency/model.ckpt'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1527,7 +2245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,12 +2261,14 @@
     "      clamp_len=-1)\n",
     "\n",
     "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
-    "xlnet_config = xlnet.XLNetConfig(json_path='alxlnet-base/config.json')"
+    "xlnet_config = xlnet.XLNetConfig(\n",
+    "    json_path = 'alxlnet-base-2020-04-10/config.json'\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1558,19 +2278,11 @@
       "INFO:tensorflow:memory input None\n",
       "INFO:tensorflow:Use float type <dtype: 'float32'>\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
     }
    ],
    "source": [
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "tf.reset_default_graph()\n",
     "sess = tf.InteractiveSession()\n",
@@ -1580,7 +2292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1598,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1614,7 +2326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1659,14 +2371,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 630/630 [01:59<00:00,  5.27it/s]\n"
+      "100%|██████████| 1250/1250 [02:00<00:00, 10.39it/s]\n"
      ]
     }
    ],
@@ -1682,9 +2394,9 @@
     "    batch_y = pad_sequences(batch_y,padding='post')\n",
     "    batch_depends = test_depends[i: index]\n",
     "    batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "    batch_segments = segments_test[i: index]\n",
+    "    batch_segments = test_segments[i: index]\n",
     "    batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "    batch_masks = masks_test[i: index]\n",
+    "    batch_masks = test_masks[i: index]\n",
     "    batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "    \n",
     "    tags_seq, heads = sess.run(\n",
@@ -1709,7 +2421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1719,12 +2431,12 @@
     "    \n",
     "temp_predict_Y = []\n",
     "for r in predict_Y:\n",
-    "    temp_predict_Y.extend(r)"
+    "    temp_predict_Y.extend(r)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1733,43 +2445,42 @@
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    0.99999   1.00000   0.99999    644667\n",
-      "            X    0.99998   0.99999   0.99998    144988\n",
-      "          acl    0.95995   0.96137   0.96066      6058\n",
-      "        advcl    0.91687   0.93839   0.92751      2386\n",
-      "       advmod    0.97160   0.97620   0.97389      9496\n",
-      "         amod    0.95264   0.94761   0.95012      8342\n",
-      "        appos    0.97560   0.97638   0.97599      4995\n",
-      "          aux    1.00000   1.00000   1.00000         6\n",
-      "         case    0.99147   0.98685   0.98916     21680\n",
-      "           cc    0.97523   0.99377   0.98441      6418\n",
-      "        ccomp    0.95249   0.90112   0.92610       890\n",
-      "     compound    0.95478   0.95656   0.95567     13399\n",
-      "compound:plur    0.97575   0.98067   0.97821      1190\n",
-      "         conj    0.96575   0.98929   0.97738      8494\n",
-      "          cop    0.98201   0.98708   0.98454      1935\n",
-      "        csubj    1.00000   0.90476   0.95000        42\n",
-      "   csubj:pass    0.91667   0.91667   0.91667        12\n",
-      "          dep    0.96490   0.94781   0.95628      1073\n",
-      "          det    0.96461   0.97375   0.96916      8230\n",
-      "        fixed    0.95762   0.92188   0.93941      1152\n",
-      "         flat    0.98208   0.98030   0.98119     20967\n",
-      "         iobj    1.00000   0.82927   0.90667        41\n",
-      "         mark    0.96463   0.95609   0.96034      2824\n",
-      "         nmod    0.96933   0.95492   0.96207      8207\n",
-      "        nsubj    0.97533   0.97086   0.97309     12867\n",
-      "   nsubj:pass    0.95811   0.94145   0.94970      3911\n",
-      "       nummod    0.98952   0.98590   0.98770      7659\n",
-      "          obj    0.97249   0.96839   0.97044     10440\n",
-      "          obl    0.97129   0.97222   0.97175     11483\n",
-      "    parataxis    0.95691   0.91348   0.93469       705\n",
-      "        punct    0.99883   0.99955   0.99919     33252\n",
-      "         root    0.98284   0.98372   0.98328     10073\n",
-      "        xcomp    0.92520   0.94988   0.93738      2474\n",
+      "          PAD    0.99976   1.00000   0.99988    339805\n",
+      "            X    1.00000   0.99936   0.99968     62631\n",
+      "          acl    0.83797   0.82854   0.83323      3202\n",
+      "        advcl    0.63223   0.66865   0.64993      1684\n",
+      "       advmod    0.95967   0.93403   0.94668      6700\n",
+      "         amod    0.90195   0.90054   0.90124      4464\n",
+      "        appos    0.85624   0.74838   0.79869      3088\n",
+      "         case    0.98172   0.98093   0.98133     11117\n",
+      "           cc    0.98209   0.97993   0.98101      3637\n",
+      "        ccomp    0.50143   0.49157   0.49645       356\n",
+      "     compound    0.91041   0.92329   0.91681     11381\n",
+      "compound:plur    0.55000   0.66667   0.60274        33\n",
+      "         conj    0.89766   0.88735   0.89248      5140\n",
+      "          cop    0.96975   0.97302   0.97138       593\n",
+      "        csubj    0.25000   0.16667   0.20000         6\n",
+      "   csubj:pass    0.00000   0.00000   0.00000         1\n",
+      "          dep    0.65012   0.72576   0.68586       361\n",
+      "          det    0.94992   0.91641   0.93286      3912\n",
+      "        fixed    0.87597   0.77397   0.82182       146\n",
+      "         flat    0.95129   0.97231   0.96169     18638\n",
+      "         iobj    1.00000   0.25000   0.40000         4\n",
+      "         mark    0.91882   0.92439   0.92160      1812\n",
+      "         nmod    0.85028   0.84376   0.84701      4429\n",
+      "        nsubj    0.83595   0.87600   0.85551      6992\n",
+      "   nsubj:pass    0.82935   0.78216   0.80506      1951\n",
+      "       nummod    0.97242   0.95072   0.96145      4302\n",
+      "          obj    0.90211   0.91702   0.90950      6351\n",
+      "          obl    0.87606   0.85241   0.86408      5075\n",
+      "    parataxis    0.46886   0.31762   0.37870       403\n",
+      "        punct    0.99703   0.99631   0.99667     20881\n",
+      "         root    0.89346   0.91070   0.90200     10000\n",
+      "        xcomp    0.75430   0.74016   0.74716      1601\n",
       "\n",
-      "     accuracy                        0.99475   1010356\n",
-      "    macro avg    0.97044   0.95958   0.96462   1010356\n",
-      " weighted avg    0.99476   0.99475   0.99475   1010356\n",
+      "     accuracy                        0.97964    540696\n",
+      "    macro avg    0.81115   0.77808   0.78633    540696\n",
+      " weighted avg    0.97957   0.97964   0.97954    540696\n",
       "\n"
      ]
     }
@@ -1781,16 +2492,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.8943757029483008\n",
-      "types accuracy: 0.88690168487317\n",
-      "root accuracy: 0.9425595238095238\n"
+      "arc accuracy: 0.8492916621693761\n",
+      "types accuracy: 0.8281206797454291\n",
+      "root accuracy: 0.9209962611210157\n"
      ]
     }
    ],
@@ -1802,60 +2513,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'Placeholder_4',\n",
-       " 'Placeholder_5',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'model/transformer/r_w_bias',\n",
-       " 'model/transformer/r_r_bias',\n",
-       " 'model/transformer/word_embedding/lookup_table',\n",
-       " 'model/transformer/word_embedding/lookup_table_2',\n",
-       " 'model/transformer/r_s_bias',\n",
-       " 'model/transformer/seg_embed',\n",
-       " 'model/transformer/layer_shared/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_shared/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_shared/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_shared/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_shared/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_shared/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_shared/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_shared/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_shared/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_shared/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_shared/ff/LayerNorm/gamma',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -1873,13 +2533,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1914,7 +2573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1922,7 +2581,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from alxlnet-base-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-46-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-35-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -1940,27 +2599,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import boto3\n",
-    "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'alxlnet-base-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/alxlnet-base-dependency.pb\"\n",
-    "\n",
-    "s3 = boto3.client('s3')\n",
-    "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-38-2caa363a97f9>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.gfile.GFile.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
+    "\n",
+    "pb = 'alxlnet-base-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
+    "\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
+    "\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
+    "\n",
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
+   ]
   }
  ],
  "metadata": {
@@ -1979,7 +2668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/session/dependency/bert-base.ipynb b/session/dependency/bert-base.ipynb
index 23afbdf6..36167fa2 100644
--- a/session/dependency/bert-base.ipynb
+++ b/session/dependency/bert-base.ipynb
@@ -7,132 +7,49 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = '3'"
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n"
      ]
     }
    ],
    "source": [
     "import bert\n",
-    "from bert import run_classifier\n",
     "from bert import optimization\n",
     "from bert import tokenization\n",
     "from bert import modeling\n",
+    "import numpy as np\n",
+    "import json\n",
     "import tensorflow as tf\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import unicodedata\n",
-    "import six\n",
-    "from functools import partial\n",
-    "\n",
-    "SPIECE_UNDERLINE = '▁'\n",
-    "\n",
-    "def preprocess_text(inputs, lower=False, remove_space=True, keep_accents=False):\n",
-    "  if remove_space:\n",
-    "    outputs = ' '.join(inputs.strip().split())\n",
-    "  else:\n",
-    "    outputs = inputs\n",
-    "  outputs = outputs.replace(\"``\", '\"').replace(\"''\", '\"')\n",
-    "\n",
-    "  if six.PY2 and isinstance(outputs, str):\n",
-    "    outputs = outputs.decode('utf-8')\n",
-    "\n",
-    "  if not keep_accents:\n",
-    "    outputs = unicodedata.normalize('NFKD', outputs)\n",
-    "    outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])\n",
-    "  if lower:\n",
-    "    outputs = outputs.lower()\n",
-    "\n",
-    "  return outputs\n",
-    "\n",
-    "\n",
-    "def encode_pieces(sp_model, text, return_unicode=True, sample=False):\n",
-    "  # return_unicode is used only for py2\n",
-    "\n",
-    "  # note(zhiliny): in some systems, sentencepiece only accepts str for py2\n",
-    "  if six.PY2 and isinstance(text, unicode):\n",
-    "    text = text.encode('utf-8')\n",
-    "\n",
-    "  if not sample:\n",
-    "    pieces = sp_model.EncodeAsPieces(text)\n",
-    "  else:\n",
-    "    pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)\n",
-    "  new_pieces = []\n",
-    "  for piece in pieces:\n",
-    "    if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():\n",
-    "      cur_pieces = sp_model.EncodeAsPieces(\n",
-    "          piece[:-1].replace(SPIECE_UNDERLINE, ''))\n",
-    "      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\n",
-    "        if len(cur_pieces[0]) == 1:\n",
-    "          cur_pieces = cur_pieces[1:]\n",
-    "        else:\n",
-    "          cur_pieces[0] = cur_pieces[0][1:]\n",
-    "      cur_pieces.append(piece[-1])\n",
-    "      new_pieces.extend(cur_pieces)\n",
-    "    else:\n",
-    "      new_pieces.append(piece)\n",
-    "\n",
-    "  # note(zhiliny): convert back to unicode for py2\n",
-    "  if six.PY2 and return_unicode:\n",
-    "    ret_pieces = []\n",
-    "    for piece in new_pieces:\n",
-    "      if isinstance(piece, str):\n",
-    "        piece = piece.decode('utf-8')\n",
-    "      ret_pieces.append(piece)\n",
-    "    new_pieces = ret_pieces\n",
-    "\n",
-    "  return new_pieces\n",
-    "\n",
-    "\n",
-    "def encode_ids(sp_model, text, sample=False):\n",
-    "  pieces = encode_pieces(sp_model, text, return_unicode=False, sample=sample)\n",
-    "  ids = [sp_model.PieceToId(piece) for piece in pieces]\n",
-    "  return ids"
+    "import itertools\n",
+    "import collections\n",
+    "import re\n",
+    "import random\n",
+    "import sentencepiece as spm\n",
+    "from unidecode import unidecode\n",
+    "from sklearn.utils import shuffle\n",
+    "from tqdm import tqdm\n",
+    "from prepro_utils import preprocess_text, encode_ids, encode_pieces\n",
+    "from malaya.text.function import transformer_textcleaning as cleaning"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sentencepiece as spm\n",
-    "\n",
     "sp_model = spm.SentencePieceProcessor()\n",
     "sp_model.Load('sp10m.cased.bert.model')\n",
     "\n",
@@ -141,282 +58,90 @@
     "v = [i.split('\\t') for i in v]\n",
     "v = {i[0]: i[1] for i in v}\n",
     "\n",
+    "\n",
     "class Tokenizer:\n",
-    "    def __init__(self, v):\n",
+    "    def __init__(self, v, sp_model):\n",
     "        self.vocab = v\n",
-    "        pass\n",
-    "    \n",
+    "        self.sp_model = sp_model\n",
+    "\n",
     "    def tokenize(self, string):\n",
-    "        return encode_pieces(sp_model, string, return_unicode=False, sample=False)\n",
-    "    \n",
+    "        return encode_pieces(\n",
+    "            self.sp_model, string, return_unicode = False, sample = False\n",
+    "        )\n",
+    "\n",
     "    def convert_tokens_to_ids(self, tokens):\n",
-    "        return [sp_model.PieceToId(piece) for piece in tokens]\n",
-    "    \n",
+    "        return [self.sp_model.PieceToId(piece) for piece in tokens]\n",
+    "\n",
     "    def convert_ids_to_tokens(self, ids):\n",
-    "        return [sp_model.IdToPiece(i) for i in ids]\n",
-    "    \n",
-    "tokenizer = Tokenizer(v)"
+    "        return [self.sp_model.IdToPiece(i) for i in ids]\n",
+    "\n",
+    "\n",
+    "tokenizer = Tokenizer(v, sp_model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
+    "import pickle\n",
     "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = ['[CLS]']\n",
-    "                labels_ = [0]\n",
-    "                depends_ = [0]\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenizer.tokenize(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.append('[SEP]')\n",
-    "                labels_.append(0)\n",
-    "                depends_.append(0)\n",
-    "                words.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1]"
+    "with open('train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "sentences, words, depends, labels, _, _ = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = ['[CLS]']\n",
-    "        labels_ = [0]\n",
-    "        depends_ = [0]\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenizer.tokenize(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.append('[SEP]')\n",
-    "        labels_.append(0)\n",
-    "        depends_.append(0)\n",
-    "        outside.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "    return outside, sentences, outside_depends, outside_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test \\\n",
-    "= train_test_split(words, depends, labels, test_size = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(40289, 10073)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(words_train), len(words_test)"
+    "bert_config = modeling.BertConfig.from_json_file(\n",
+    "    'bert-base-2020-03-19/bert_config.json'\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
+    "BERT_INIT_CHKPNT = 'bert-base-2020-03-19/model.ckpt-2000002'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "BERT_INIT_CHKPNT = 'bert-base-v3/model.ckpt'\n",
-    "BERT_CONFIG = 'bert-base-v3/config.json'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "epoch = 30\n",
-    "batch_size = 32\n",
+    "epoch = 3\n",
+    "batch_size = 16\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
-    "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
-    "bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)"
+    "num_warmup_steps = int(num_train_steps * warmup_proportion)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,8 +174,15 @@
     "            e = tf.expand_dims(tf.expand_dims(mask_e, 1), 2)\n",
     "            output = output * d * e\n",
     "            \n",
-    "        return output\n",
-    "    \n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "class BiLinear:\n",
     "    def __init__(self, left_features, right_features, out_features):\n",
     "        self.left_features = left_features\n",
@@ -475,8 +207,17 @@
     "        output = output + tf.matmul(input_left, tf.transpose(self.W_l))\\\n",
     "        + tf.matmul(input_right, tf.transpose(self.W_r))\n",
     "        \n",
-    "        return tf.reshape(output, output_shape)\n",
-    "    \n",
+    "        return tf.reshape(output, output_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_NEG_INF = -1e9\n",
+    "\n",
     "class Model:\n",
     "    def __init__(\n",
     "        self,\n",
@@ -508,14 +249,20 @@
     "            config=bert_config,\n",
     "            is_training=training,\n",
     "            input_ids=self.words,\n",
+    "            input_mask=self.mask,\n",
     "            use_one_hot_embeddings=False)\n",
+    "        \n",
     "        output_layer = model.get_sequence_output()\n",
     "        \n",
     "        arc_h = tf.nn.elu(self.arc_h(output_layer))\n",
     "        arc_c = tf.nn.elu(self.arc_c(output_layer))\n",
+    "        self._arc_h = arc_h\n",
+    "        self._arc_c = arc_c\n",
     "        \n",
     "        type_h = tf.nn.elu(self.type_h(output_layer))\n",
     "        type_c = tf.nn.elu(self.type_c(output_layer))\n",
+    "        self._type_h = type_h\n",
+    "        self._type_c = type_c\n",
     "        \n",
     "        out_arc = tf.squeeze(self.attention.forward(arc_h, arc_c, mask_d=self.mask, \n",
     "                                                    mask_e=self.mask), axis = 1)\n",
@@ -534,6 +281,11 @@
     "        self.heads_seq = tf.argmax(decode_arc, axis = 1)\n",
     "        self.heads_seq = tf.identity(self.heads_seq, name = 'heads_seq')\n",
     "        \n",
+    "#         self.decode_arc_t = tf.transpose(decode_arc, (0, 2, 1))\n",
+    "#         sequence_loss_depends = tf.contrib.seq2seq.sequence_loss(logits = self.decode_arc_t,\n",
+    "#                                                      targets = self.heads,\n",
+    "#                                                      weights = mask)\n",
+    "        \n",
     "        t = tf.cast(tf.transpose(self.heads_seq), tf.int32)\n",
     "        broadcasted = tf.broadcast_to(batch_index, tf.shape(t))\n",
     "        concatenated = tf.transpose(tf.concat([tf.expand_dims(broadcasted, axis = 0), \n",
@@ -608,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -626,33 +378,33 @@
       "  * https://github.com/tensorflow/io (for I/O related ops)\n",
       "If you depend on functionality not listed there, please file an issue.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use keras.layers.Dense instead.\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From <ipython-input-19-492cebd31fa1>:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-66c1ff637216>:61: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-19-492cebd31fa1>:145: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-66c1ff637216>:101: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "dim is deprecated, use axis instead\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
       "\n"
      ]
     }
@@ -662,7 +414,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -670,14 +422,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from bert-base-v3/model.ckpt\n"
+      "INFO:tensorflow:Restoring parameters from bert-base-2020-03-19/model.ckpt-2000002\n"
      ]
     }
    ],
@@ -689,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -705,16 +457,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.017699115, 0.026548672, 27.245255]"
+       "[0.01369863, 0.06849315, 32.680145]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -729,16 +481,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.026548672, 0.03539823, 172.58093]"
+       "[0.02739726, 0.02739726, 152.1837]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,106 +505,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 5,  5, 11,  5,  5,  5,  5,  5,  5,  5,  5, 17,  5,  5, 17,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
-       "       dtype=int32),\n",
-       " array([ 8,  8,  0, 12,  8,  3, 12, 12, 12,  3, 12,  9, 14, 12,  7,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]),\n",
-       " array([0, 1, 2, 0, 2, 4, 0, 4, 0, 0, 4, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32))"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tags_seq, heads = sess.run(\n",
-    "    [model.logits, model.heads_seq],\n",
-    "    feed_dict = {\n",
-    "        model.words: batch_x,\n",
-    "    },\n",
-    ")\n",
-    "tags_seq[0], heads[0], batch_depends[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:31<00:00,  2.00it/s, accuracy=0.971, accuracy_depends=0.6, cost=1.25]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:23<00:00,  3.75it/s, accuracy=0.838, accuracy_depends=0.556, cost=1.77]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 3.836751, training acc: 0.643685, training depends: 0.463537, valid loss: 1.824992, valid acc: 0.822402, valid depends: 0.554086\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:32<00:00,  1.99it/s, accuracy=0.914, accuracy_depends=0.686, cost=0.757]\n",
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-     ]
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-     "name": "stdout",
-     "output_type": "stream",
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-      "epoch: 1, training loss: 1.535756, training acc: 0.847021, training depends: 0.603705, valid loss: 1.364804, valid acc: 0.861101, valid depends: 0.638994\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:31<00:00,  1.99it/s, accuracy=0.914, accuracy_depends=0.771, cost=0.626]\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 1.208463, training acc: 0.870229, training depends: 0.667346, valid loss: 1.156371, valid acc: 0.878010, valid depends: 0.672618\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:32<00:00,  1.99it/s, accuracy=0.943, accuracy_depends=0.857, cost=0.364]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:23<00:00,  3.76it/s, accuracy=0.908, accuracy_depends=0.72, cost=0.918] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 195576/195576 [12:15:03<00:00,  4.43it/s, accuracy=0.911, accuracy_depends=0.808, cost=0.425]   \n",
+      "test minibatch loop: 100%|██████████| 625/625 [01:13<00:00,  8.45it/s, accuracy=0.883, accuracy_depends=0.8, cost=0.549]  \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 3, training loss: 0.991278, training acc: 0.886089, training depends: 0.709585, valid loss: 0.976100, valid acc: 0.888830, valid depends: 0.715051\n",
+      "epoch: 0, training loss: 0.824410, training acc: 0.875569, training depends: 0.747707, valid loss: 0.470517, valid acc: 0.887186, valid depends: 0.798389\n",
       "\n"
      ]
     },
@@ -860,277 +528,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:31<00:00,  1.99it/s, accuracy=0.943, accuracy_depends=0.943, cost=0.134]\n",
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+      "train minibatch loop: 100%|██████████| 195576/195576 [12:20:26<00:00,  4.40it/s, accuracy=0.92, accuracy_depends=0.827, cost=0.29]     \n",
+      "test minibatch loop: 100%|██████████| 625/625 [01:13<00:00,  8.51it/s, accuracy=0.895, accuracy_depends=0.833, cost=0.458]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 4, training loss: 0.835998, training acc: 0.900016, training depends: 0.740500, valid loss: 0.869583, valid acc: 0.899467, valid depends: 0.735284\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:30<00:00,  2.00it/s, accuracy=0.943, accuracy_depends=0.914, cost=0.16] \n",
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-      "epoch: 5, training loss: 0.719168, training acc: 0.911870, training depends: 0.763465, valid loss: 0.822546, valid acc: 0.907530, valid depends: 0.742367\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:30<00:00,  2.00it/s, accuracy=0.914, accuracy_depends=0.971, cost=0.0434]\n",
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-     ]
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-     "name": "stdout",
-     "output_type": "stream",
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-      "epoch: 6, training loss: 0.626348, training acc: 0.921981, training depends: 0.782014, valid loss: 0.737583, valid acc: 0.916234, valid depends: 0.764498\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:30<00:00,  2.00it/s, accuracy=0.914, accuracy_depends=0.971, cost=0.0123]\n",
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 0.552959, training acc: 0.930948, training depends: 0.796875, valid loss: 0.685112, valid acc: 0.922712, valid depends: 0.774410\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:26<00:00,  2.01it/s, accuracy=0.943, accuracy_depends=0.943, cost=0.046]\n",
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-     "output_type": "stream",
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-      "epoch: 8, training loss: 0.490956, training acc: 0.938122, training depends: 0.810010, valid loss: 0.662742, valid acc: 0.928302, valid depends: 0.777865\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:19<00:00,  2.03it/s, accuracy=0.914, accuracy_depends=0.971, cost=0.00199]\n",
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-      "epoch: 9, training loss: 0.438395, training acc: 0.945358, training depends: 0.821848, valid loss: 0.621119, valid acc: 0.933866, valid depends: 0.790374\n",
-      "\n"
-     ]
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-    {
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-     "output_type": "stream",
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-      "train minibatch loop: 100%|██████████| 1260/1260 [10:19<00:00,  2.03it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.00255]\n",
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-     "text": [
-      "epoch: 10, training loss: 0.395347, training acc: 0.950774, training depends: 0.831319, valid loss: 0.612359, valid acc: 0.935495, valid depends: 0.792212\n",
-      "\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:19<00:00,  2.03it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.00537]\n",
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-      "epoch: 11, training loss: 0.362436, training acc: 0.955936, training depends: 0.838433, valid loss: 0.618489, valid acc: 0.940789, valid depends: 0.789790\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "train minibatch loop: 100%|██████████| 1260/1260 [10:18<00:00,  2.04it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.0152]\n",
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-      "\n"
-     ]
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-      "epoch: 13, training loss: 0.305042, training acc: 0.962461, training depends: 0.852766, valid loss: 0.610651, valid acc: 0.946821, valid depends: 0.791881\n",
-      "\n"
-     ]
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.00246]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.05it/s, accuracy=0.959, accuracy_depends=0.814, cost=0.45] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 14, training loss: 0.281669, training acc: 0.965676, training depends: 0.858502, valid loss: 0.554830, valid acc: 0.947843, valid depends: 0.808811\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.000368]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.04it/s, accuracy=0.963, accuracy_depends=0.825, cost=0.433]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 15, training loss: 0.260893, training acc: 0.966931, training depends: 0.863944, valid loss: 0.503035, valid acc: 0.949811, valid depends: 0.825009\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:08<00:00,  2.07it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.00177]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.03it/s, accuracy=0.965, accuracy_depends=0.829, cost=0.428]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 16, training loss: 0.242291, training acc: 0.969192, training depends: 0.869187, valid loss: 0.508218, valid acc: 0.949675, valid depends: 0.826038\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:06<00:00,  2.08it/s, accuracy=0.943, accuracy_depends=0.971, cost=0.000353]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.97it/s, accuracy=0.969, accuracy_depends=0.846, cost=0.387]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.228146, training acc: 0.970846, training depends: 0.872674, valid loss: 0.488844, valid acc: 0.953624, valid depends: 0.829054\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:12<00:00,  2.06it/s, accuracy=0.971, accuracy_depends=0.971, cost=0.00017]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:19<00:00,  3.98it/s, accuracy=0.97, accuracy_depends=0.815, cost=0.472] \n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.212680, training acc: 0.972107, training depends: 0.877212, valid loss: 0.521204, valid acc: 0.953701, valid depends: 0.822856\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:08<00:00,  2.07it/s, accuracy=0.971, accuracy_depends=0.971, cost=0.000746]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.02it/s, accuracy=0.966, accuracy_depends=0.838, cost=0.415]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.200057, training acc: 0.973270, training depends: 0.881180, valid loss: 0.477151, valid acc: 0.954184, valid depends: 0.836681\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "epoch: 1, training loss: 0.415683, training acc: 0.891427, training depends: 0.806351, valid loss: 0.393516, valid acc: 0.892850, valid depends: 0.813033\n",
       "\n"
      ]
     }
@@ -1138,7 +544,7 @@
    "source": [
     "from tqdm import tqdm\n",
     "\n",
-    "epoch = 20\n",
+    "epoch = 2\n",
     "\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
@@ -1157,19 +563,20 @@
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1183,19 +590,20 @@
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1206,183 +614,1371 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'bert-base-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:08<00:00,  2.07it/s, accuracy=1, accuracy_depends=0.971, cost=0.595]    \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.02it/s, accuracy=0.984, accuracy_depends=0.847, cost=2.97]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 1.472636, training acc: 0.989410, training depends: 0.881004, valid loss: 4.534208, valid acc: 0.972969, valid depends: 0.827531\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:09<00:00,  2.07it/s, accuracy=1, accuracy_depends=0.971, cost=0.0106]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.06it/s, accuracy=0.983, accuracy_depends=0.835, cost=3.12]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 1.061602, training acc: 0.992888, training depends: 0.879195, valid loss: 4.405046, valid acc: 0.975273, valid depends: 0.824042\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=1, accuracy_depends=0.971, cost=0.00832]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.05it/s, accuracy=0.983, accuracy_depends=0.831, cost=3.13]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 0.873258, training acc: 0.994493, training depends: 0.878462, valid loss: 4.285817, valid acc: 0.976536, valid depends: 0.823561\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=1, accuracy_depends=0.971, cost=0.0052]   \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:18<00:00,  4.04it/s, accuracy=0.985, accuracy_depends=0.828, cost=3.16]\n",
-      "train minibatch loop:   0%|          | 0/1260 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 195576/195576 [11:52:11<00:00,  4.58it/s, accuracy=0.971, accuracy_depends=0.834, cost=2.2]    \n",
+      "test minibatch loop: 100%|██████████| 625/625 [01:09<00:00,  9.00it/s, accuracy=0.945, accuracy_depends=0.828, cost=4.11] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 3, training loss: 0.737532, training acc: 0.995564, training depends: 0.878754, valid loss: 4.118284, valid acc: 0.977577, valid depends: 0.824038\n",
+      "epoch: 0, training loss: 3.542283, training acc: 0.951459, training depends: 0.815969, valid loss: 3.709175, valid acc: 0.950416, valid depends: 0.814605\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [10:07<00:00,  2.07it/s, accuracy=1, accuracy_depends=0.971, cost=0.00105]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [01:17<00:00,  4.05it/s, accuracy=0.983, accuracy_depends=0.822, cost=3.11]"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "epoch = 1\n",
+    "\n",
+    "for e in range(epoch):\n",
+    "    train_acc, train_loss = [], []\n",
+    "    test_acc, test_loss = [], []\n",
+    "    train_acc_depends, test_acc_depends = [], []\n",
+    "    \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_X))\n",
+    "        batch_x = train_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = train_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_X))\n",
+    "        batch_x = test_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = test_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "    \n",
+    "    \n",
+    "    print(\n",
+    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
+    "    % (e, np.mean(train_loss), \n",
+    "       np.mean(train_acc), \n",
+    "       np.mean(train_acc_depends), \n",
+    "       np.mean(test_loss), \n",
+    "       np.mean(test_acc), \n",
+    "       np.mean(test_acc_depends)\n",
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'bert-base-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['[CLS]', '[SEP]']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in rejected\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
+    "\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
+    "\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    bert_tokens = ['[CLS]']\n",
+    "    for no, orig_token in enumerate(left):\n",
+    "        t = tokenizer.tokenize(orig_token)\n",
+    "        bert_tokens.extend(t)\n",
+    "    bert_tokens.append(\"[SEP]\")\n",
+    "    t = tokenizer.convert_tokens_to_ids(bert_tokens)\n",
+    "    return t, bert_tokens, [1] * len(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
+    "        )\n",
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 0.638962, training acc: 0.996465, training depends: 0.879061, valid loss: 4.045756, valid acc: 0.978286, valid depends: 0.823873\n",
-      "\n"
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+   ],
+   "source": [
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
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+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"165.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">11 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 10&#45;&gt;11 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>10&#45;&gt;11</title>\n",
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+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"165.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">12 (Jan)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;12 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;12</title>\n",
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+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"363.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">15 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;15 -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>14&#45;&gt;15</title>\n",
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+       "<!-- 20 -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"473.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">20 (Ketua)</text>\n",
+       "</g>\n",
+       "<!-- 14&#45;&gt;20 -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>14&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M423.5536,-608.9735C432.5076,-596.6085 444.5319,-580.0036 454.5387,-566.1847\"/>\n",
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+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"623.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">34 (sekatan)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;34 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M577.5176,-608.9735C585.6832,-596.7252 596.6223,-580.3165 605.7823,-566.5766\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"608.8629,-568.2653 611.4978,-558.0034 603.0385,-564.3824 608.8629,-568.2653\"/>\n",
+       "<text text-anchor=\"middle\" x=\"609.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
+       "</g>\n",
+       "<!-- 38 -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"839.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">38 (Beliau)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;38 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>39&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M986.3077,-610.2093C970.1513,-604.5785 952.4192,-597.9507 936.5,-591 917.6686,-582.7778 897.4623,-572.4162 880.3927,-563.1836\"/>\n",
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+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"949.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">41 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;41 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>39&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1015.0603,-608.9941C1008.2344,-603.4422 1000.8971,-597.1631 994.5,-591 986.3803,-583.1773 978.0574,-574.0951 970.8124,-565.7812\"/>\n",
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+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;40 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>39&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1038.5,-608.9735C1038.5,-597.1918 1038.5,-581.5607 1038.5,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1042.0001,-568.0033 1038.5,-558.0034 1035.0001,-568.0034 1042.0001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1059\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;61 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>39&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1064.8871,-608.8513C1071.4537,-603.575 1078.1409,-597.4737 1083.5,-591 1089.3774,-583.9001 1094.5175,-575.3166 1098.6912,-567.2328\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1114\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1211.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">46 (memetik)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;46 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>39&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1090.7342,-611.124C1106.4559,-605.574 1123.5064,-598.7617 1138.5,-591 1153.0019,-583.4929 1167.9642,-573.4985 1180.5382,-564.3473\"/>\n",
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+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1539.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">64 (berkata)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;64 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>39&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1090.698,-617.9357C1183.7415,-601.7784 1377.8188,-568.0764 1477.4921,-550.7678\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1333\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 16 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"267.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">16 (Seri)</text>\n",
+       "</g>\n",
+       "<!-- 15&#45;&gt;16 -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>15&#45;&gt;16</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M339.1403,-521.8946C332.0083,-516.34 324.3031,-510.0842 317.5,-504 308.5839,-496.026 299.2874,-486.8197 291.1633,-478.442\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"363.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M450.7079,-521.9735C434.3365,-509.0253 412.0867,-491.4276 394.1591,-477.2485\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"473.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M473.5,-521.9735C473.5,-510.1918 473.5,-494.5607 473.5,-481.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"485.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"591.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;24 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>20&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M497.9497,-521.9735C515.5908,-508.9669 539.5949,-491.269 558.8708,-477.0571\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"561.1098,-479.7548 567.0816,-471.0034 556.9558,-474.1206 561.1098,-479.7548\"/>\n",
+       "<text text-anchor=\"middle\" x=\"562\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"273.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;17 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>16&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M268.7432,-434.9735C269.5557,-423.1918 270.6337,-407.5607 271.5581,-394.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"283.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
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+       "<!-- 18 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"273.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;18 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>17&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M273.5,-347.9735C273.5,-336.1918 273.5,-320.5607 273.5,-307.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"277.0001,-307.0033 273.5,-297.0034 270.0001,-307.0034 277.0001,-307.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"285.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"402.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;22 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>21&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M458.7888,-434.9735C448.6026,-422.4919 434.8906,-405.6899 423.5506,-391.7944\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"454.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
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+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"493.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">23 (,)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;23 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>24&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M560.3833,-434.8807C552.2639,-429.5618 543.7612,-423.4322 536.5,-417 528.091,-409.551 519.8809,-400.4249 512.9263,-391.962\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"557\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"572.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;25 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>24&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M587.5632,-434.9735C584.9902,-423.1918 581.5765,-407.5607 578.6495,-394.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"707.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">30 (mempertikaikan)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;30 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>24&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M615.5353,-434.9735C632.8774,-421.9669 656.4746,-404.269 675.4238,-390.0571\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"667\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">acl</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"572.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">26 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 25&#45;&gt;26 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>25&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M572.5,-347.9735C572.5,-336.1918 572.5,-320.5607 572.5,-307.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"584.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"693.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M704.5992,-347.9735C702.7033,-336.1918 700.1879,-320.5607 698.0312,-307.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"809.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M728.6345,-347.9735C743.6784,-335.1419 764.0756,-317.7443 780.6204,-303.6326\"/>\n",
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+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"572.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">27 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M572.5,-260.9735C572.5,-249.1918 572.5,-233.5607 572.5,-220.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"572.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M572.5,-173.9735C572.5,-162.1918 572.5,-146.5607 572.5,-133.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"809.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;32 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M809.5,-260.9735C809.5,-249.1918 809.5,-233.5607 809.5,-220.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"813.0001,-220.0033 809.5,-210.0034 806.0001,-220.0034 813.0001,-220.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"846.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"773.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M654.5801,-521.9735C677.5081,-508.6753 708.8913,-490.473 733.6611,-476.1065\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"735.5654,-479.0482 742.4597,-471.0034 732.0534,-472.993 735.5654,-479.0482\"/>\n",
+       "<text text-anchor=\"middle\" x=\"744.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"855.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 35&#45;&gt;36 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>35&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M790.4905,-434.9735C802.3647,-422.3752 818.3875,-405.3755 831.5546,-391.4055\"/>\n",
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+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;43 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>41&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M951.3648,-521.9735C952.5836,-510.1918 954.2006,-494.5607 955.5871,-481.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"970.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 63 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1086.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">63 (Saiful)</text>\n",
+       "</g>\n",
+       "<!-- 61&#45;&gt;63 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>61&#45;&gt;63</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1105.5272,-521.9735C1102.2449,-510.0751 1097.8795,-494.2508 1094.1581,-480.7606\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1112.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1202.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1209.6352,-521.9735C1208.4164,-510.1918 1206.7994,-494.5607 1205.4129,-481.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1237\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1334.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1242.0097,-521.9319C1250.9965,-516.3783 1260.7598,-510.1138 1269.5,-504 1281.3013,-495.745 1293.8443,-486.12 1304.7319,-477.4697\"/>\n",
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+       "<!-- 62 -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1454.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">62 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;62 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>64&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1515.4239,-521.8061C1508.7253,-516.3493 1501.6151,-510.1679 1495.5,-504 1487.9414,-496.3761 1480.3763,-487.4313 1473.8395,-479.1796\"/>\n",
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+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1539.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">65 (,)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1539.5,-521.9735C1539.5,-510.1918 1539.5,-494.5607 1539.5,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1543.0001,-481.0033 1539.5,-471.0034 1536.0001,-481.0034 1543.0001,-481.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1560\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"1611.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">83 (.)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;83 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>64&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1565.8871,-521.8513C1572.4537,-516.575 1579.1409,-510.4737 1584.5,-504 1590.3774,-496.9001 1595.5175,-488.3166 1599.6912,-480.2328\"/>\n",
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+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1720.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">68 (menjangka)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;68 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1591.5094,-523.6766C1607.2226,-518.1098 1624.3249,-511.4005 1639.5,-504 1655.5799,-496.1583 1672.4601,-485.8927 1686.624,-476.6287\"/>\n",
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+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M958.5,-434.9735C958.5,-423.1918 958.5,-407.5607 958.5,-394.1581\"/>\n",
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+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1059.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">44 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;44 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>43&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M979.4273,-434.9735C994.3238,-422.1419 1014.5209,-404.7443 1030.9035,-390.6326\"/>\n",
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+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1334.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">48 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>47&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1334.5,-434.9735C1334.5,-423.1918 1334.5,-407.5607 1334.5,-394.1581\"/>\n",
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+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1157.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">49 (Menteri)</text>\n",
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+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
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+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1256.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
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+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
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+       "<!-- 51 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1339.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;51 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1335.536,-347.9735C1336.2131,-336.1918 1337.1115,-320.5607 1337.8817,-307.1581\"/>\n",
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+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1449.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
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+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;57</title>\n",
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+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1250.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">52 (Sri)</text>\n",
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+       "<title>57&#45;&gt;55</title>\n",
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+       "<title>57&#45;&gt;56</title>\n",
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+       "<title>59</title>\n",
+       "<text text-anchor=\"middle\" x=\"1584.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">59 (mengelirukan)</text>\n",
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+       "<!-- 57&#45;&gt;59 -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>57&#45;&gt;59</title>\n",
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+       "<title>52&#45;&gt;53</title>\n",
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+       "<title>54</title>\n",
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+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>54&#45;&gt;54</title>\n",
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+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1528.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">58 (untuk)</text>\n",
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+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
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+       "<title>60</title>\n",
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+       "<title>59&#45;&gt;60</title>\n",
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+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1720.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">66 (beliau)</text>\n",
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+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>68&#45;&gt;66</title>\n",
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+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1822.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">69 (ada)</text>\n",
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+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>68&#45;&gt;69</title>\n",
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+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"661.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">67 (sudah)</text>\n",
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+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>67&#45;&gt;67</title>\n",
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+       "<title>70</title>\n",
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+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
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+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1950.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">75 (tulisan)</text>\n",
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+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>69&#45;&gt;75</title>\n",
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+       "<title>71</title>\n",
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+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>70&#45;&gt;71</title>\n",
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+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"1827.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">73 (Najib)</text>\n",
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+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>70&#45;&gt;73</title>\n",
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+       "<title>74</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2092.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">77 (berhubung)</text>\n",
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+       "<title>73&#45;&gt;72</title>\n",
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+       "<title>77&#45;&gt;78</title>\n",
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+       "<title>80&#45;&gt;81</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M2215.5,-86.9735C2215.5,-75.1918 2215.5,-59.5607 2215.5,-46.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2219.0001,-46.0033 2215.5,-36.0034 2212.0001,-46.0034 2219.0001,-46.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2252.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2346.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">82 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;82 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;82</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M2261.4389,-86.8926C2272.3359,-81.7694 2283.6477,-75.73 2293.5,-69 2304.2091,-61.6847 2314.7628,-52.1467 2323.5989,-43.301\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2326.135,-45.7134 2330.583,-36.0975 2321.1092,-40.8408 2326.135,-45.7134\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2330.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f59ed53e518>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 5\n",
-    "\n",
-    "for e in range(epoch):\n",
-    "    train_acc, train_loss = [], []\n",
-    "    test_acc, test_loss = [], []\n",
-    "    train_acc_depends, test_acc_depends = [], []\n",
-    "    \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(train_X))\n",
-    "        batch_x = train_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = train_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = train_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "        \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(test_X))\n",
-    "        batch_x = test_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = test_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = test_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "    \n",
-    "    \n",
-    "    print(\n",
-    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
-    "    % (e, np.mean(train_loss), \n",
-    "       np.mean(train_acc), \n",
-    "       np.mean(train_acc_depends), \n",
-    "       np.mean(test_loss), \n",
-    "       np.mean(test_acc), \n",
-    "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1391,7 +1987,7 @@
        "'bert-base-dependency/model.ckpt'"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1403,17 +1999,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1427,7 +2015,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word, training = False)\n",
     "\n",
@@ -1438,7 +2026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1454,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1499,14 +2087,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 315/315 [01:18<00:00,  4.01it/s]\n"
+      "100%|██████████| 625/625 [01:08<00:00,  9.15it/s]\n"
      ]
     }
    ],
@@ -1543,7 +2131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1558,7 +2146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1567,43 +2155,42 @@
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    0.99996   1.00000   0.99998    877864\n",
-      "            X    1.00000   0.99986   0.99993    145204\n",
-      "          acl    0.96111   0.96190   0.96150      6037\n",
-      "        advcl    0.94287   0.93895   0.94091      2408\n",
-      "       advmod    0.97171   0.96904   0.97037      9464\n",
-      "         amod    0.96283   0.94008   0.95132      8128\n",
-      "        appos    0.97426   0.95940   0.96677      4852\n",
-      "          aux    1.00000   0.50000   0.66667         4\n",
-      "         case    0.98907   0.98834   0.98870     21519\n",
-      "           cc    0.98089   0.98708   0.98397      6500\n",
-      "        ccomp    0.95515   0.92164   0.93810       855\n",
-      "     compound    0.95432   0.96565   0.95995     13479\n",
-      "compound:plur    0.96507   0.97778   0.97138      1215\n",
-      "         conj    0.96943   0.98036   0.97486      8604\n",
-      "          cop    0.96407   0.98531   0.97457      1906\n",
-      "        csubj    0.92157   0.85455   0.88679        55\n",
-      "   csubj:pass    0.93750   0.78947   0.85714        19\n",
-      "          dep    0.95199   0.93574   0.94380       996\n",
-      "          det    0.97043   0.96678   0.96860      8248\n",
-      "        fixed    0.94176   0.93672   0.93923      1122\n",
-      "         flat    0.98010   0.98217   0.98113     20755\n",
-      "         iobj    0.87500   0.80000   0.83582        35\n",
-      "         mark    0.94507   0.97448   0.95955      2860\n",
-      "         nmod    0.96363   0.95912   0.96137      8121\n",
-      "        nsubj    0.97076   0.97091   0.97083     12788\n",
-      "   nsubj:pass    0.95192   0.96362   0.95774      3986\n",
-      "       nummod    0.98563   0.97942   0.98251      7773\n",
-      "          obj    0.96915   0.97071   0.96993     10551\n",
-      "          obl    0.97549   0.97164   0.97356     11389\n",
-      "    parataxis    0.95038   0.90415   0.92669       699\n",
-      "        punct    0.99752   0.99773   0.99762     33438\n",
-      "         root    0.98046   0.98124   0.98085     10073\n",
-      "        xcomp    0.95153   0.94749   0.94951      2590\n",
+      "          PAD    0.99944   1.00000   0.99972    481710\n",
+      "            X    1.00000   0.99690   0.99845     61945\n",
+      "          acl    0.84049   0.83081   0.83562      3298\n",
+      "        advcl    0.66176   0.62500   0.64286      1656\n",
+      "       advmod    0.95277   0.94836   0.95056      6700\n",
+      "         amod    0.91014   0.90522   0.90767      4621\n",
+      "        appos    0.81969   0.77457   0.79650      3052\n",
+      "         case    0.98062   0.98199   0.98130     11492\n",
+      "           cc    0.98549   0.97787   0.98166      3750\n",
+      "        ccomp    0.56966   0.48042   0.52125       383\n",
+      "     compound    0.90827   0.93299   0.92047     11133\n",
+      "compound:plur    0.55882   0.63333   0.59375        30\n",
+      "         conj    0.89626   0.88883   0.89253      5424\n",
+      "          cop    0.96823   0.96661   0.96742       599\n",
+      "        csubj    0.66667   0.40000   0.50000        10\n",
+      "   csubj:pass    0.00000   0.00000   0.00000         1\n",
+      "          dep    0.65768   0.67218   0.66485       363\n",
+      "          det    0.94642   0.92567   0.93593      3969\n",
+      "        fixed    0.93103   0.78947   0.85443       171\n",
+      "         flat    0.96042   0.96700   0.96370     18393\n",
+      "         iobj    0.00000   0.00000   0.00000         4\n",
+      "         mark    0.91283   0.93645   0.92449      1778\n",
+      "         nmod    0.86204   0.85472   0.85836      4591\n",
+      "        nsubj    0.86476   0.86536   0.86506      7145\n",
+      "   nsubj:pass    0.81694   0.79646   0.80657      2034\n",
+      "       nummod    0.96463   0.95920   0.96191      4436\n",
+      "          obj    0.90765   0.91968   0.91363      6412\n",
+      "          obl    0.88052   0.86034   0.87031      5191\n",
+      "    parataxis    0.43636   0.38710   0.41026       372\n",
+      "        punct    0.99723   0.99574   0.99649     20643\n",
+      "         root    0.89938   0.91530   0.90727     10000\n",
+      "        xcomp    0.74105   0.77125   0.75585      1718\n",
       "\n",
-      "     accuracy                        0.99562   1243537\n",
-      "    macro avg    0.96396   0.93822   0.94823   1243537\n",
-      " weighted avg    0.99562   0.99562   0.99562   1243537\n",
+      "     accuracy                        0.98402    683024\n",
+      "    macro avg    0.79679   0.77996   0.78684    683024\n",
+      " weighted avg    0.98390   0.98402   0.98394    683024\n",
       "\n"
      ]
     }
@@ -1615,16 +2202,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.8554239102233114\n",
-      "types accuracy: 0.8481064607232274\n",
-      "root accuracy: 0.9203253968253969\n"
+      "arc accuracy: 0.8204592062639473\n",
+      "types accuracy: 0.7997014021904779\n",
+      "root accuracy: 0.9893670748261997\n"
      ]
     }
    ],
@@ -1636,227 +2223,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'bert/embeddings/word_embeddings',\n",
-       " 'bert/embeddings/token_type_embeddings',\n",
-       " 'bert/embeddings/position_embeddings',\n",
-       " 'bert/embeddings/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_0/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_0/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_0/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_0/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_0/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_0/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_0/output/dense/kernel',\n",
-       " 'bert/encoder/layer_0/output/dense/bias',\n",
-       " 'bert/encoder/layer_0/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_1/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_1/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_1/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_1/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_1/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_1/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_1/output/dense/kernel',\n",
-       " 'bert/encoder/layer_1/output/dense/bias',\n",
-       " 'bert/encoder/layer_1/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_2/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_2/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_2/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_2/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_2/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_2/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_2/output/dense/kernel',\n",
-       " 'bert/encoder/layer_2/output/dense/bias',\n",
-       " 'bert/encoder/layer_2/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_3/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_3/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_3/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_3/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_3/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_3/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_3/output/dense/kernel',\n",
-       " 'bert/encoder/layer_3/output/dense/bias',\n",
-       " 'bert/encoder/layer_3/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_4/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_4/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_4/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_4/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_4/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_4/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_4/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_4/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_4/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_4/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_4/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_4/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_4/output/dense/kernel',\n",
-       " 'bert/encoder/layer_4/output/dense/bias',\n",
-       " 'bert/encoder/layer_4/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_5/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_5/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_5/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_5/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_5/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_5/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_5/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_5/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_5/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_5/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_5/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_5/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_5/output/dense/kernel',\n",
-       " 'bert/encoder/layer_5/output/dense/bias',\n",
-       " 'bert/encoder/layer_5/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_6/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_6/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_6/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_6/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_6/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_6/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_6/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_6/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_6/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_6/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_6/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_6/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_6/output/dense/kernel',\n",
-       " 'bert/encoder/layer_6/output/dense/bias',\n",
-       " 'bert/encoder/layer_6/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_7/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_7/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_7/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_7/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_7/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_7/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_7/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_7/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_7/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_7/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_7/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_7/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_7/output/dense/kernel',\n",
-       " 'bert/encoder/layer_7/output/dense/bias',\n",
-       " 'bert/encoder/layer_7/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_8/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_8/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_8/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_8/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_8/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_8/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_8/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_8/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_8/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_8/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_8/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_8/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_8/output/dense/kernel',\n",
-       " 'bert/encoder/layer_8/output/dense/bias',\n",
-       " 'bert/encoder/layer_8/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_9/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_9/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_9/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_9/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_9/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_9/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_9/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_9/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_9/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_9/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_9/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_9/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_9/output/dense/kernel',\n",
-       " 'bert/encoder/layer_9/output/dense/bias',\n",
-       " 'bert/encoder/layer_9/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_10/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_10/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_10/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_10/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_10/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_10/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_10/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_10/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_10/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_10/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_10/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_10/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_10/output/dense/kernel',\n",
-       " 'bert/encoder/layer_10/output/dense/bias',\n",
-       " 'bert/encoder/layer_10/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_11/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_11/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_11/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_11/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_11/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_11/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_11/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_11/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_11/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_11/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_11/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_11/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_11/output/dense/kernel',\n",
-       " 'bert/encoder/layer_11/output/dense/bias',\n",
-       " 'bert/encoder/layer_11/output/LayerNorm/gamma',\n",
-       " 'bert/pooler/dense/kernel',\n",
-       " 'bert/pooler/dense/bias',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -1874,13 +2243,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1915,7 +2283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1923,7 +2291,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from bert-base-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-37-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-32-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -1931,7 +2299,7 @@
       "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
       "INFO:tensorflow:Froze 214 variables.\n",
       "INFO:tensorflow:Converted 214 variables to const ops.\n",
-      "2771 ops in the final graph.\n"
+      "2768 ops in the final graph.\n"
      ]
     }
    ],
@@ -1941,182 +2309,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string = 'husein makan ayam'\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "def entities_textcleaning(string, lowering = False):\n",
-    "    \"\"\"\n",
-    "    use by entities recognition, pos recognition and dependency parsing\n",
-    "    \"\"\"\n",
-    "    string = re.sub('[^A-Za-z0-9\\-\\/() ]+', ' ', string)\n",
-    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
-    "    original_string = string.split()\n",
-    "    if lowering:\n",
-    "        string = string.lower()\n",
-    "    string = [\n",
-    "        (original_string[no], word.title() if word.isupper() else word)\n",
-    "        for no, word in enumerate(string.split())\n",
-    "        if len(word)\n",
-    "    ]\n",
-    "    return [s[0] for s in string], [s[1] for s in string]\n",
-    "\n",
-    "def parse_X(left):\n",
-    "    bert_tokens = ['[CLS]']\n",
-    "    for no, orig_token in enumerate(left):\n",
-    "        t = tokenizer.tokenize(orig_token)\n",
-    "        bert_tokens.extend(t)\n",
-    "    bert_tokens.append(\"[SEP]\")\n",
-    "    return tokenizer.convert_tokens_to_ids(bert_tokens), bert_tokens\n",
-    "\n",
-    "sequence = entities_textcleaning(string)[1]\n",
-    "parsed_sequence, bert_sequence = parse_X(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_sentencepiece_tokens_tagging(x, y):\n",
-    "    new_paired_tokens = []\n",
-    "    n_tokens = len(x)\n",
-    "    rejected = ['[CLS]', '[SEP]']\n",
-    "\n",
-    "    i = 0\n",
-    "\n",
-    "    while i < n_tokens:\n",
-    "\n",
-    "        current_token, current_label = x[i], y[i]\n",
-    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
-    "            previous_token, previous_label = new_paired_tokens.pop()\n",
-    "            merged_token = previous_token\n",
-    "            merged_label = [previous_label]\n",
-    "            while (\n",
-    "                not current_token.startswith('▁')\n",
-    "                and current_token not in rejected\n",
-    "            ):\n",
-    "                merged_token = merged_token + current_token.replace('▁', '')\n",
-    "                merged_label.append(current_label)\n",
-    "                i = i + 1\n",
-    "                current_token, current_label = x[i], y[i]\n",
-    "            merged_label = merged_label[0]\n",
-    "            new_paired_tokens.append((merged_token, merged_label))\n",
-    "\n",
-    "        else:\n",
-    "            new_paired_tokens.append((current_token, current_label))\n",
-    "            i = i + 1\n",
-    "\n",
-    "    words = [\n",
-    "        i[0].replace('▁', '')\n",
-    "        for i in new_paired_tokens\n",
-    "        if i[0] not in rejected\n",
-    "    ]\n",
-    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
-    "    return words, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_graph(frozen_graph_filename):\n",
-    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
-    "        graph_def = tf.GraphDef()\n",
-    "        graph_def.ParseFromString(f.read())\n",
-    "    with tf.Graph().as_default() as graph:\n",
-    "        tf.import_graph_def(graph_def)\n",
-    "    return graph\n",
-    "\n",
-    "g = load_graph('bert-base-dependency/frozen_model.pb')\n",
-    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
-    "heads_seq = g.get_tensor_by_name('import/heads_seq:0')\n",
-    "tags_seq = g.get_tensor_by_name('import/logits:0')\n",
-    "test_sess = tf.InteractiveSession(graph = g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "h, t = test_sess.run([heads_seq, tags_seq],\n",
-    "        feed_dict = {\n",
-    "            x: [parsed_sequence],\n",
-    "        },\n",
-    ")\n",
-    "h = h[0] - 1\n",
-    "t = [idx2tag[d] for d in t[0]]\n",
-    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
-    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('husein', 2), ('makan', 0), ('ayam', 2)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(zip(merged_h[0], merged_h[1])))"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[('husein', 'amod'), ('makan', 'root'), ('ayam', 'obj')]\n"
+      "WARNING:tensorflow:From <ipython-input-35-96a697e0d332>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.gfile.GFile.\n"
      ]
     }
    ],
    "source": [
-    "print(list(zip(merged_t[0], merged_t[1])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3\n",
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
+    "\n",
+    "pb = 'bert-base-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
     "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'bert-base-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/bert-base-dependency.pb\"\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
     "\n",
-    "s3 = boto3.client('s3')\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
     "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
    ]
   }
  ],
@@ -2136,7 +2378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/session/dependency/tiny-bert.ipynb b/session/dependency/tiny-bert.ipynb
index 2e5db223..57226da6 100644
--- a/session/dependency/tiny-bert.ipynb
+++ b/session/dependency/tiny-bert.ipynb
@@ -14,389 +14,134 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
       "\n"
      ]
     }
    ],
    "source": [
     "import bert\n",
-    "from bert import run_classifier\n",
     "from bert import optimization\n",
     "from bert import tokenization\n",
     "from bert import modeling\n",
+    "import numpy as np\n",
+    "import json\n",
     "import tensorflow as tf\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import unicodedata\n",
-    "import six\n",
-    "from functools import partial\n",
-    "\n",
-    "SPIECE_UNDERLINE = '▁'\n",
-    "\n",
-    "def preprocess_text(inputs, lower=False, remove_space=True, keep_accents=False):\n",
-    "  if remove_space:\n",
-    "    outputs = ' '.join(inputs.strip().split())\n",
-    "  else:\n",
-    "    outputs = inputs\n",
-    "  outputs = outputs.replace(\"``\", '\"').replace(\"''\", '\"')\n",
-    "\n",
-    "  if six.PY2 and isinstance(outputs, str):\n",
-    "    outputs = outputs.decode('utf-8')\n",
-    "\n",
-    "  if not keep_accents:\n",
-    "    outputs = unicodedata.normalize('NFKD', outputs)\n",
-    "    outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])\n",
-    "  if lower:\n",
-    "    outputs = outputs.lower()\n",
-    "\n",
-    "  return outputs\n",
-    "\n",
-    "\n",
-    "def encode_pieces(sp_model, text, return_unicode=True, sample=False):\n",
-    "  # return_unicode is used only for py2\n",
-    "\n",
-    "  # note(zhiliny): in some systems, sentencepiece only accepts str for py2\n",
-    "  if six.PY2 and isinstance(text, unicode):\n",
-    "    text = text.encode('utf-8')\n",
-    "\n",
-    "  if not sample:\n",
-    "    pieces = sp_model.EncodeAsPieces(text)\n",
-    "  else:\n",
-    "    pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)\n",
-    "  new_pieces = []\n",
-    "  for piece in pieces:\n",
-    "    if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():\n",
-    "      cur_pieces = sp_model.EncodeAsPieces(\n",
-    "          piece[:-1].replace(SPIECE_UNDERLINE, ''))\n",
-    "      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\n",
-    "        if len(cur_pieces[0]) == 1:\n",
-    "          cur_pieces = cur_pieces[1:]\n",
-    "        else:\n",
-    "          cur_pieces[0] = cur_pieces[0][1:]\n",
-    "      cur_pieces.append(piece[-1])\n",
-    "      new_pieces.extend(cur_pieces)\n",
-    "    else:\n",
-    "      new_pieces.append(piece)\n",
-    "\n",
-    "  # note(zhiliny): convert back to unicode for py2\n",
-    "  if six.PY2 and return_unicode:\n",
-    "    ret_pieces = []\n",
-    "    for piece in new_pieces:\n",
-    "      if isinstance(piece, str):\n",
-    "        piece = piece.decode('utf-8')\n",
-    "      ret_pieces.append(piece)\n",
-    "    new_pieces = ret_pieces\n",
-    "\n",
-    "  return new_pieces\n",
-    "\n",
-    "\n",
-    "def encode_ids(sp_model, text, sample=False):\n",
-    "  pieces = encode_pieces(sp_model, text, return_unicode=False, sample=sample)\n",
-    "  ids = [sp_model.PieceToId(piece) for piece in pieces]\n",
-    "  return ids"
+    "import itertools\n",
+    "import collections\n",
+    "import re\n",
+    "import random\n",
+    "import sentencepiece as spm\n",
+    "from unidecode import unidecode\n",
+    "from sklearn.utils import shuffle\n",
+    "from tqdm import tqdm\n",
+    "from prepro_utils import preprocess_text, encode_ids, encode_pieces\n",
+    "from malaya.text.function import transformer_textcleaning as cleaning"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sentencepiece as spm\n",
-    "\n",
     "sp_model = spm.SentencePieceProcessor()\n",
-    "sp_model.Load('tiny-bert-v1/sp10m.cased.bert.model')\n",
+    "sp_model.Load('sp10m.cased.bert.model')\n",
     "\n",
-    "with open('tiny-bert-v1/sp10m.cased.bert.vocab') as fopen:\n",
+    "with open('sp10m.cased.bert.vocab') as fopen:\n",
     "    v = fopen.read().split('\\n')[:-1]\n",
     "v = [i.split('\\t') for i in v]\n",
     "v = {i[0]: i[1] for i in v}\n",
     "\n",
+    "\n",
     "class Tokenizer:\n",
-    "    def __init__(self, v):\n",
+    "    def __init__(self, v, sp_model):\n",
     "        self.vocab = v\n",
-    "        pass\n",
-    "    \n",
+    "        self.sp_model = sp_model\n",
+    "\n",
     "    def tokenize(self, string):\n",
-    "        return encode_pieces(sp_model, string, return_unicode=False, sample=False)\n",
-    "    \n",
+    "        return encode_pieces(\n",
+    "            self.sp_model, string, return_unicode = False, sample = False\n",
+    "        )\n",
+    "\n",
     "    def convert_tokens_to_ids(self, tokens):\n",
-    "        return [sp_model.PieceToId(piece) for piece in tokens]\n",
-    "    \n",
+    "        return [self.sp_model.PieceToId(piece) for piece in tokens]\n",
+    "\n",
     "    def convert_ids_to_tokens(self, ids):\n",
-    "        return [sp_model.IdToPiece(i) for i in ids]\n",
-    "    \n",
-    "tokenizer = Tokenizer(v)"
+    "        return [self.sp_model.IdToPiece(i) for i in ids]\n",
+    "\n",
+    "\n",
+    "tokenizer = Tokenizer(v, sp_model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
+    "import pickle\n",
     "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = ['[CLS]']\n",
-    "                labels_ = [0]\n",
-    "                depends_ = [0]\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenizer.tokenize(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.append('[SEP]')\n",
-    "                labels_.append(0)\n",
-    "                depends_.append(0)\n",
-    "                words.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1]"
+    "with open('train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends = pickle.load(fopen)\n",
+    "    \n",
+    "with open('tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "sentences, words, depends, labels, _, _ = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = ['[CLS]']\n",
-    "        labels_ = [0]\n",
-    "        depends_ = [0]\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenizer.tokenize(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.append('[SEP]')\n",
-    "        labels_.append(0)\n",
-    "        depends_.append(0)\n",
-    "        outside.append(tokenizer.convert_tokens_to_ids(bert_tokens))\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "    return outside, sentences, outside_depends, outside_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test \\\n",
-    "= train_test_split(words, depends, labels, test_size = 0.2)"
+    "bert_config = modeling.BertConfig.from_json_file(\n",
+    "    'tiny-bert-v1/config.json'\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
+    "BERT_INIT_CHKPNT = 'tiny-bert-v1/model.ckpt'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "BERT_INIT_CHKPNT = 'tiny-bert-v1/model.ckpt'\n",
-    "BERT_CONFIG = 'tiny-bert-v1/config.json'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "epoch = 30\n",
+    "epoch = 3\n",
     "batch_size = 32\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
-    "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
-    "bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)"
+    "num_warmup_steps = int(num_train_steps * warmup_proportion)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,8 +174,15 @@
     "            e = tf.expand_dims(tf.expand_dims(mask_e, 1), 2)\n",
     "            output = output * d * e\n",
     "            \n",
-    "        return output\n",
-    "    \n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "class BiLinear:\n",
     "    def __init__(self, left_features, right_features, out_features):\n",
     "        self.left_features = left_features\n",
@@ -455,8 +207,17 @@
     "        output = output + tf.matmul(input_left, tf.transpose(self.W_l))\\\n",
     "        + tf.matmul(input_right, tf.transpose(self.W_r))\n",
     "        \n",
-    "        return tf.reshape(output, output_shape)\n",
-    "    \n",
+    "        return tf.reshape(output, output_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_NEG_INF = -1e9\n",
+    "\n",
     "class Model:\n",
     "    def __init__(\n",
     "        self,\n",
@@ -488,14 +249,20 @@
     "            config=bert_config,\n",
     "            is_training=training,\n",
     "            input_ids=self.words,\n",
+    "            input_mask=self.mask,\n",
     "            use_one_hot_embeddings=False)\n",
+    "        \n",
     "        output_layer = model.get_sequence_output()\n",
     "        \n",
     "        arc_h = tf.nn.elu(self.arc_h(output_layer))\n",
     "        arc_c = tf.nn.elu(self.arc_c(output_layer))\n",
+    "        self._arc_h = arc_h\n",
+    "        self._arc_c = arc_c\n",
     "        \n",
     "        type_h = tf.nn.elu(self.type_h(output_layer))\n",
     "        type_c = tf.nn.elu(self.type_c(output_layer))\n",
+    "        self._type_h = type_h\n",
+    "        self._type_c = type_c\n",
     "        \n",
     "        out_arc = tf.squeeze(self.attention.forward(arc_h, arc_c, mask_d=self.mask, \n",
     "                                                    mask_e=self.mask), axis = 1)\n",
@@ -514,6 +281,11 @@
     "        self.heads_seq = tf.argmax(decode_arc, axis = 1)\n",
     "        self.heads_seq = tf.identity(self.heads_seq, name = 'heads_seq')\n",
     "        \n",
+    "#         self.decode_arc_t = tf.transpose(decode_arc, (0, 2, 1))\n",
+    "#         sequence_loss_depends = tf.contrib.seq2seq.sequence_loss(logits = self.decode_arc_t,\n",
+    "#                                                      targets = self.heads,\n",
+    "#                                                      weights = mask)\n",
+    "        \n",
     "        t = tf.cast(tf.transpose(self.heads_seq), tf.int32)\n",
     "        broadcasted = tf.broadcast_to(batch_index, tf.shape(t))\n",
     "        concatenated = tf.transpose(tf.concat([tf.expand_dims(broadcasted, axis = 0), \n",
@@ -588,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -606,33 +378,33 @@
       "  * https://github.com/tensorflow/io (for I/O related ops)\n",
       "If you depend on functionality not listed there, please file an issue.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use keras.layers.Dense instead.\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From <ipython-input-18-492cebd31fa1>:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-66c1ff637216>:61: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-18-492cebd31fa1>:145: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-10-66c1ff637216>:101: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "dim is deprecated, use axis instead\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
       "\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
+      "WARNING:tensorflow:From /home/husein/bert-standard/bert/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
       "\n"
      ]
     }
@@ -642,7 +414,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -650,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -669,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,16 +457,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.026415095, 0.00754717, 39.253395]"
+       "[0.01369863, 0.09589041, 30.746227]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,16 +481,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.01509434, 0.01509434, 528.8345]"
+       "[0.01369863, 0.09589041, 157.3611]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -733,63 +505,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([29, 19, 19, 13, 19, 13, 12, 13, 19, 19, 12, 19, 19, 12, 12, 19, 12,\n",
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-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tags_seq, heads = sess.run(\n",
-    "    [model.logits, model.heads_seq],\n",
-    "    feed_dict = {\n",
-    "        model.words: batch_x,\n",
-    "    },\n",
-    ")\n",
-    "tags_seq[0], heads[0], batch_depends[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:54<00:00,  4.27it/s, accuracy=0.659, accuracy_depends=0.549, cost=3.14]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.38it/s, accuracy=0.725, accuracy_depends=0.499, cost=2.45]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:33,  5.89it/s, accuracy=0.716, accuracy_depends=0.475, cost=2.77]"
+      "train minibatch loop:   8%|▊         | 8228/97788 [21:47<3:47:18,  6.57it/s, accuracy=0.815, accuracy_depends=0.494, cost=1.85] IOPub message rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "NotebookApp.rate_limit_window=3.0 (secs)\n",
+      "\n",
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:20:01<00:00,  6.27it/s, accuracy=0.884, accuracy_depends=0.755, cost=0.702]   \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:36<00:00,  8.65it/s, accuracy=0.89, accuracy_depends=0.741, cost=0.799] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 0, training loss: 5.789861, training acc: 0.452741, training depends: 0.414746, valid loss: 2.697248, valid acc: 0.692594, valid depends: 0.480903\n",
+      "epoch: 0, training loss: 1.250804, training acc: 0.847605, training depends: 0.668640, valid loss: 0.735416, valid acc: 0.884954, valid depends: 0.744215\n",
       "\n"
      ]
     },
@@ -797,33 +538,116 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.805, accuracy_depends=0.549, cost=2.5] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.37it/s, accuracy=0.816, accuracy_depends=0.559, cost=1.86]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:44,  5.60it/s, accuracy=0.816, accuracy_depends=0.503, cost=2.11]"
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:17:28<00:00,  6.33it/s, accuracy=0.886, accuracy_depends=0.771, cost=0.613]  \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:35<00:00,  8.70it/s, accuracy=0.89, accuracy_depends=0.743, cost=0.689] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1, training loss: 2.288419, training acc: 0.767721, training depends: 0.503435, valid loss: 2.034172, valid acc: 0.806141, valid depends: 0.522205\n",
+      "epoch: 1, training loss: 0.664987, training acc: 0.888118, training depends: 0.757840, valid loss: 0.641807, valid acc: 0.889845, valid depends: 0.765465\n",
       "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "epoch = 2\n",
+    "\n",
+    "for e in range(epoch):\n",
+    "    train_acc, train_loss = [], []\n",
+    "    test_acc, test_loss = [], []\n",
+    "    train_acc_depends, test_acc_depends = [], []\n",
+    "    \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_X))\n",
+    "        batch_x = train_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = train_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_X))\n",
+    "        batch_x = test_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = test_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "    \n",
+    "    \n",
+    "    print(\n",
+    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
+    "    % (e, np.mean(train_loss), \n",
+    "       np.mean(train_acc), \n",
+    "       np.mean(train_acc_depends), \n",
+    "       np.mean(test_loss), \n",
+    "       np.mean(test_acc), \n",
+    "       np.mean(test_acc_depends)\n",
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'tiny-bert-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.854, accuracy_depends=0.646, cost=2.1] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.38it/s, accuracy=0.846, accuracy_depends=0.585, cost=1.6] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:26,  6.09it/s, accuracy=0.852, accuracy_depends=0.539, cost=1.78]"
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:22:12<00:00,  6.22it/s, accuracy=0.886, accuracy_depends=0.788, cost=0.576]   \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:36<00:00,  8.61it/s, accuracy=0.9, accuracy_depends=0.778, cost=0.629]  \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2, training loss: 1.842319, training acc: 0.825247, training depends: 0.546368, valid loss: 1.727046, valid acc: 0.836843, valid depends: 0.564716\n",
+      "epoch: 0, training loss: 0.602583, training acc: 0.890296, training depends: 0.772322, valid loss: 0.611718, valid acc: 0.891331, valid depends: 0.773028\n",
       "\n"
      ]
     },
@@ -831,294 +655,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.854, accuracy_depends=0.561, cost=2.07]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.36it/s, accuracy=0.867, accuracy_depends=0.595, cost=1.45]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:37,  5.79it/s, accuracy=0.849, accuracy_depends=0.562, cost=1.62]"
+      "train minibatch loop: 100%|██████████| 97788/97788 [4:07:31<00:00,  6.58it/s, accuracy=0.891, accuracy_depends=0.783, cost=0.613]  \n",
+      "test minibatch loop: 100%|██████████| 313/313 [00:34<00:00,  9.09it/s, accuracy=0.885, accuracy_depends=0.788, cost=0.621]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 3, training loss: 1.606321, training acc: 0.843694, training depends: 0.584820, valid loss: 1.566533, valid acc: 0.847156, valid depends: 0.592860\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:54<00:00,  4.28it/s, accuracy=0.841, accuracy_depends=0.585, cost=1.95]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.37it/s, accuracy=0.864, accuracy_depends=0.632, cost=1.38]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:33,  5.89it/s, accuracy=0.861, accuracy_depends=0.587, cost=1.53]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 1.464989, training acc: 0.853639, training depends: 0.611301, valid loss: 1.459764, valid acc: 0.855135, valid depends: 0.613328\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.878, accuracy_depends=0.622, cost=1.7] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.36it/s, accuracy=0.87, accuracy_depends=0.634, cost=1.33] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:32,  5.92it/s, accuracy=0.868, accuracy_depends=0.596, cost=1.45]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 5, training loss: 1.367900, training acc: 0.860362, training depends: 0.629246, valid loss: 1.385906, valid acc: 0.860289, valid depends: 0.627439\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.866, accuracy_depends=0.61, cost=1.68]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.37it/s, accuracy=0.868, accuracy_depends=0.653, cost=1.27]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:40,  5.72it/s, accuracy=0.878, accuracy_depends=0.616, cost=1.34]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 6, training loss: 1.294124, training acc: 0.865166, training depends: 0.643810, valid loss: 1.331457, valid acc: 0.863615, valid depends: 0.637117\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.29it/s, accuracy=0.866, accuracy_depends=0.585, cost=1.66] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.38it/s, accuracy=0.874, accuracy_depends=0.666, cost=1.2] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:35,  5.83it/s, accuracy=0.879, accuracy_depends=0.63, cost=1.31]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 1.232699, training acc: 0.868785, training depends: 0.655266, valid loss: 1.282819, valid acc: 0.866522, valid depends: 0.646933\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:53<00:00,  4.30it/s, accuracy=0.866, accuracy_depends=0.72, cost=1.47]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.37it/s, accuracy=0.885, accuracy_depends=0.676, cost=1.12] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:33,  5.91it/s, accuracy=0.876, accuracy_depends=0.63, cost=1.3]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 8, training loss: 1.182210, training acc: 0.872922, training depends: 0.665300, valid loss: 1.252104, valid acc: 0.869890, valid depends: 0.652689\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:49<00:00,  4.36it/s, accuracy=0.878, accuracy_depends=0.61, cost=1.43]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.42it/s, accuracy=0.882, accuracy_depends=0.66, cost=1.15] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:35,  5.84it/s, accuracy=0.874, accuracy_depends=0.63, cost=1.28]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 9, training loss: 1.138188, training acc: 0.875656, training depends: 0.674430, valid loss: 1.213842, valid acc: 0.872358, valid depends: 0.660089\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:50<00:00,  4.33it/s, accuracy=0.89, accuracy_depends=0.671, cost=1.43]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:57<00:00,  5.44it/s, accuracy=0.887, accuracy_depends=0.681, cost=1.06] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 10, training loss: 1.100374, training acc: 0.878828, training depends: 0.682079, valid loss: 1.187184, valid acc: 0.874205, valid depends: 0.667161\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:50<00:00,  4.34it/s, accuracy=0.878, accuracy_depends=0.659, cost=1.42] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:57<00:00,  5.46it/s, accuracy=0.878, accuracy_depends=0.686, cost=1.08] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 11, training loss: 1.066078, training acc: 0.881652, training depends: 0.688804, valid loss: 1.156008, valid acc: 0.875828, valid depends: 0.674163\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:50<00:00,  4.34it/s, accuracy=0.89, accuracy_depends=0.659, cost=1.42]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:57<00:00,  5.45it/s, accuracy=0.89, accuracy_depends=0.693, cost=1.07]  \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 12, training loss: 1.036247, training acc: 0.883852, training depends: 0.695145, valid loss: 1.138222, valid acc: 0.877338, valid depends: 0.678166\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:51<00:00,  4.33it/s, accuracy=0.915, accuracy_depends=0.659, cost=1.32] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:57<00:00,  5.44it/s, accuracy=0.892, accuracy_depends=0.709, cost=1.03] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:48,  5.51it/s, accuracy=0.884, accuracy_depends=0.666, cost=1.12]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 13, training loss: 1.007638, training acc: 0.886176, training depends: 0.701007, valid loss: 1.117662, valid acc: 0.879050, valid depends: 0.680728\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:50<00:00,  4.33it/s, accuracy=0.866, accuracy_depends=0.695, cost=1.23] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.41it/s, accuracy=0.9, accuracy_depends=0.7, cost=0.974]    \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:44,  5.61it/s, accuracy=0.889, accuracy_depends=0.67, cost=1.13]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 14, training loss: 0.983746, training acc: 0.887991, training depends: 0.706029, valid loss: 1.111054, valid acc: 0.881190, valid depends: 0.681359\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:51<00:00,  4.33it/s, accuracy=0.915, accuracy_depends=0.695, cost=1.31] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:58<00:00,  5.42it/s, accuracy=0.889, accuracy_depends=0.7, cost=1.02]   \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:29,  6.02it/s, accuracy=0.892, accuracy_depends=0.673, cost=1.07]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 15, training loss: 0.960521, training acc: 0.889907, training depends: 0.710786, valid loss: 1.088889, valid acc: 0.882030, valid depends: 0.688486\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:39<00:00,  4.51it/s, accuracy=0.902, accuracy_depends=0.695, cost=1.3]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:54<00:00,  5.83it/s, accuracy=0.908, accuracy_depends=0.71, cost=0.902] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:26,  6.09it/s, accuracy=0.893, accuracy_depends=0.674, cost=1.07]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 16, training loss: 0.941127, training acc: 0.891492, training depends: 0.714844, valid loss: 1.078972, valid acc: 0.882973, valid depends: 0.689849\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:41<00:00,  4.48it/s, accuracy=0.89, accuracy_depends=0.659, cost=1.21]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:56<00:00,  5.56it/s, accuracy=0.897, accuracy_depends=0.704, cost=0.948]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:41,  5.69it/s, accuracy=0.886, accuracy_depends=0.692, cost=1.02]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.922439, training acc: 0.892776, training depends: 0.718796, valid loss: 1.060917, valid acc: 0.884413, valid depends: 0.695307\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:34<00:00,  4.60it/s, accuracy=0.915, accuracy_depends=0.646, cost=1.28] \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.84it/s, accuracy=0.892, accuracy_depends=0.718, cost=0.977]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:17,  6.37it/s, accuracy=0.896, accuracy_depends=0.667, cost=1.05]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.906757, training acc: 0.894139, training depends: 0.722021, valid loss: 1.048187, valid acc: 0.885442, valid depends: 0.698038\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:38<00:00,  4.53it/s, accuracy=0.89, accuracy_depends=0.659, cost=1.21]  \n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.85it/s, accuracy=0.895, accuracy_depends=0.722, cost=0.961]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.891351, training acc: 0.895233, training depends: 0.725029, valid loss: 1.037661, valid acc: 0.885347, valid depends: 0.699040\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "epoch: 1, training loss: 0.587275, training acc: 0.891074, training depends: 0.775791, valid loss: 0.609368, valid acc: 0.890627, valid depends: 0.773412\n",
       "\n"
      ]
     }
@@ -1126,7 +671,7 @@
    "source": [
     "from tqdm import tqdm\n",
     "\n",
-    "epoch = 20\n",
+    "epoch = 1\n",
     "\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
@@ -1145,19 +690,20 @@
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1171,19 +717,20 @@
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1194,219 +741,1298 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'tiny-bert-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:38<00:00,  4.52it/s, accuracy=0.915, accuracy_depends=0.659, cost=22.5]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.84it/s, accuracy=0.926, accuracy_depends=0.716, cost=9.6] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:09,  6.63it/s, accuracy=0.932, accuracy_depends=0.671, cost=11.2]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 10.320434, training acc: 0.924881, training depends: 0.717967, valid loss: 12.149561, valid acc: 0.915361, valid depends: 0.686787\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:37<00:00,  4.54it/s, accuracy=0.927, accuracy_depends=0.646, cost=17.4]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:54<00:00,  5.83it/s, accuracy=0.925, accuracy_depends=0.691, cost=10.2]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:20,  6.27it/s, accuracy=0.924, accuracy_depends=0.648, cost=11.3]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 9.671070, training acc: 0.929382, training depends: 0.712335, valid loss: 11.713265, valid acc: 0.917885, valid depends: 0.683404\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:38<00:00,  4.53it/s, accuracy=0.951, accuracy_depends=0.646, cost=16.5]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.86it/s, accuracy=0.938, accuracy_depends=0.68, cost=8.52] \n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:27,  6.07it/s, accuracy=0.929, accuracy_depends=0.646, cost=10.6]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 9.260620, training acc: 0.932450, training depends: 0.708046, valid loss: 11.539114, valid acc: 0.919409, valid depends: 0.678840\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:37<00:00,  4.54it/s, accuracy=0.963, accuracy_depends=0.646, cost=14.3]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:54<00:00,  5.83it/s, accuracy=0.937, accuracy_depends=0.691, cost=8.97]\n",
-      "train minibatch loop:   0%|          | 1/1260 [00:00<03:22,  6.22it/s, accuracy=0.929, accuracy_depends=0.654, cost=10.7]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 8.946695, training acc: 0.934782, training depends: 0.705428, valid loss: 11.285476, valid acc: 0.921092, valid depends: 0.676844\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 1260/1260 [04:38<00:00,  4.52it/s, accuracy=0.939, accuracy_depends=0.659, cost=21.7]\n",
-      "test minibatch loop: 100%|██████████| 315/315 [00:53<00:00,  5.87it/s, accuracy=0.941, accuracy_depends=0.691, cost=8.72]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 8.691786, training acc: 0.936757, training depends: 0.703728, valid loss: 11.110484, valid acc: 0.922271, valid depends: 0.675406\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['[CLS]', '[SEP]']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in rejected\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from tqdm import tqdm\n",
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
     "\n",
-    "epoch = 5\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
     "\n",
-    "for e in range(epoch):\n",
-    "    train_acc, train_loss = [], []\n",
-    "    test_acc, test_loss = [], []\n",
-    "    train_acc_depends, test_acc_depends = [], []\n",
-    "    \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
     "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(train_X))\n",
-    "        batch_x = train_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = train_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = train_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "        \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
     "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(test_X))\n",
-    "        batch_x = test_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = test_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = test_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.switch: True\n",
-    "            },\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    bert_tokens = ['[CLS]']\n",
+    "    for no, orig_token in enumerate(left):\n",
+    "        t = tokenizer.tokenize(orig_token)\n",
+    "        bert_tokens.extend(t)\n",
+    "    bert_tokens.append(\"[SEP]\")\n",
+    "    t = tokenizer.convert_tokens_to_ids(bert_tokens)\n",
+    "    return t, bert_tokens, [1] * len(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
     "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "    \n",
-    "    \n",
-    "    print(\n",
-    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
-    "    % (e, np.mean(train_loss), \n",
-    "       np.mean(train_acc), \n",
-    "       np.mean(train_acc_depends), \n",
-    "       np.mean(test_loss), \n",
-    "       np.mean(test_acc), \n",
-    "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
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-       "'bert-base-dependency/model.ckpt'"
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    "source": [
-    "saver = tf.train.Saver(tf.trainable_variables())\n",
-    "saver.save(sess, 'bert-base-dependency/model.ckpt')"
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
    ]
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   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
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-     "output_type": "stream",
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-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
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+       "<title>9</title>\n",
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+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"321\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">10 (Saiful)</text>\n",
+       "</g>\n",
+       "<!-- 9&#45;&gt;10 -->\n",
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+       "<title>9&#45;&gt;10</title>\n",
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+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"321\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">11 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 10&#45;&gt;11 -->\n",
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+       "<title>10&#45;&gt;11</title>\n",
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+       "<!-- 12 -->\n",
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+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"321\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">12 (Jan)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;12 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>11&#45;&gt;12</title>\n",
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+       "<!-- 15 -->\n",
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+       "<title>15</title>\n",
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+       "<title>14&#45;&gt;15</title>\n",
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+       "<!-- 19 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"492\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
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+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"602\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;21 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>20&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M680.4871,-521.9735C665.8856,-509.1419 646.0884,-491.7443 630.0302,-477.6326\"/>\n",
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+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"701\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">23 (,)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>20&#45;&gt;23</title>\n",
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+       "<text text-anchor=\"middle\" x=\"721.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"792\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;24 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>20&#45;&gt;24</title>\n",
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+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"912\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;32 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>33&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M885.8376,-521.9735C890.3951,-509.9585 896.471,-493.9401 901.6203,-480.3646\"/>\n",
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+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"1038\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">34 (sekatan)</text>\n",
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+       "<!-- 33&#45;&gt;34 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M911.9449,-521.9735C936.3552,-508.617 969.8068,-490.3133 996.1159,-475.9177\"/>\n",
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+       "<title>38</title>\n",
+       "<text text-anchor=\"middle\" x=\"1246\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">38 (Beliau)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;38 -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>39&#45;&gt;38</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1333.2079,-521.9735C1316.8365,-509.0253 1294.5867,-491.4276 1276.6591,-477.2485\"/>\n",
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+       "</g>\n",
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+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"1356\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">41 (Najib)</text>\n",
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+       "<!-- 39&#45;&gt;41 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>39&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1356,-521.9735C1356,-510.1918 1356,-494.5607 1356,-481.1581\"/>\n",
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+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"1445\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;40 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>39&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1379.4397,-521.9941C1386.2656,-516.4422 1393.6029,-510.1631 1400,-504 1408.1197,-496.1773 1416.4426,-487.0951 1423.6876,-478.7812\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1435.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 64 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>64</title>\n",
+       "<text text-anchor=\"middle\" x=\"1547\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">64 (berkata)</text>\n",
+       "</g>\n",
+       "<!-- 39&#45;&gt;64 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>39&#45;&gt;64</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1408.1022,-524.2056C1424.942,-518.511 1443.5142,-511.5953 1460,-504 1477.1476,-496.0998 1495.286,-485.8059 1510.5379,-476.5368\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1501.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
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+       "<!-- 16 -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>16</title>\n",
+       "<text text-anchor=\"middle\" x=\"430\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">16 (Seri)</text>\n",
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+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>15&#45;&gt;16</title>\n",
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+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"431\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
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+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>16&#45;&gt;17</title>\n",
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+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"431\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
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+       "<g id=\"edge22\" class=\"edge\">\n",
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+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"602\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
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+       "<g id=\"edge27\" class=\"edge\">\n",
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+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"790\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
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+       "<!-- 24&#45;&gt;25 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>24&#45;&gt;25</title>\n",
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+       "<g id=\"edge29\" class=\"edge\">\n",
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+       "<title>30</title>\n",
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+       "<path fill=\"none\" stroke=\"#000000\" d=\"M796.216,-347.9735C800.3189,-336.0751 805.7756,-320.2508 810.4274,-306.7606\"/>\n",
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+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"671\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">27 (Wajdi)</text>\n",
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+       "<g id=\"edge31\" class=\"edge\">\n",
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+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"786\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M812.9552,-260.9735C808.2596,-248.9585 801.9996,-232.9401 796.6942,-219.3646\"/>\n",
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+       "</g>\n",
+       "<!-- 31 -->\n",
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+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"902\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M836.9905,-260.9735C848.8647,-248.3752 864.8875,-231.3755 878.0546,-217.4055\"/>\n",
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+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"671\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M671,-173.9735C671,-162.1918 671,-146.5607 671,-133.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"674.5001,-133.0033 671,-123.0034 667.5001,-133.0034 674.5001,-133.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"683\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"908\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M992.6583,-434.8259C981.9008,-429.7047 970.7318,-423.683 961,-417 950.309,-409.6583 939.759,-400.1149 930.9208,-391.2722\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"998\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"1039\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;36 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>34&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1038.2072,-434.9735C1038.3426,-423.1918 1038.5223,-407.5607 1038.6763,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1042.1778,-394.043 1038.7931,-384.0034 1035.1783,-393.9624 1042.1778,-394.043\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1059\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 46 -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>46</title>\n",
+       "<text text-anchor=\"middle\" x=\"1160\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">46 (memetik)</text>\n",
+       "</g>\n",
+       "<!-- 38&#45;&gt;46 -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>38&#45;&gt;46</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1228.1807,-434.9735C1215.7272,-422.3752 1198.9229,-405.3755 1185.1135,-391.4055\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1222.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">dep</text>\n",
+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1261\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 38&#45;&gt;61 -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>38&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1249.108,-434.9735C1251.1393,-423.1918 1253.8344,-407.5607 1256.1452,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1259.6459,-394.4527 1257.896,-384.0034 1252.7477,-393.2633 1259.6459,-394.4527\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1275.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1017\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1124.8624,-347.9535C1114.4841,-342.4005 1103.1807,-336.1309 1093,-330 1079.0645,-321.6079 1064.1206,-311.7718 1051.2034,-302.9888\"/>\n",
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+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1149\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1157.7208,-347.9735C1156.2311,-336.1918 1154.2548,-320.5607 1152.5602,-307.1581\"/>\n",
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+       "</g>\n",
+       "<!-- 50 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1256\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;50 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>46&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1179.8913,-347.9735C1193.9216,-335.2586 1212.899,-318.0603 1228.3936,-304.0183\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1234.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1356\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;43 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>41&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1356,-434.9735C1356,-423.1918 1356,-407.5607 1356,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1359.5001,-394.0033 1356,-384.0034 1352.5001,-394.0034 1359.5001,-394.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1371\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1547\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">62 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;62 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>64&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1547,-434.9735C1547,-423.1918 1547,-407.5607 1547,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1550.5001,-394.0033 1547,-384.0034 1543.5001,-394.0034 1550.5001,-394.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1567\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"1632\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">83 (.)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;83 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>64&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1571.0761,-434.8061C1577.7747,-429.3493 1584.8849,-423.1679 1591,-417 1598.5586,-409.3761 1606.1237,-400.4313 1612.6605,-392.1796\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1625.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1339\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1352.4776,-347.9735C1350.1754,-336.1918 1347.121,-320.5607 1344.5022,-307.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1347.8708,-306.1465 1342.5179,-297.0034 1341.0007,-307.489 1347.8708,-306.1465\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1357\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1440\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">44 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;44 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>43&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1373.4049,-347.9735C1385.5688,-335.3752 1401.9823,-318.3755 1415.4705,-304.4055\"/>\n",
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+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1051\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">48 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;48 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>47&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1128.6943,-260.9735C1114.3717,-248.2586 1094.9989,-231.0603 1079.1815,-217.0183\"/>\n",
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+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1161\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;51 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>47&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1151.4864,-260.9735C1153.1115,-249.1918 1155.2675,-233.5607 1157.1161,-220.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1177.5\" y=\"-231.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1067\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">49 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 49&#45;&gt;49 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>49&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1120.6322,-721.5246C1131.0804,-720.6508 1138.5,-718.1426 1138.5,-714 1138.5,-711.3462 1135.455,-709.3631 1130.5195,-708.0507\"/>\n",
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+       "<!-- 52 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>52</title>\n",
+       "<text text-anchor=\"middle\" x=\"1468\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">52 (Sri)</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;52 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>52&#45;&gt;52</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1503.7532,-721.8361C1513.7932,-721.4929 1521.5,-718.8809 1521.5,-714 1521.5,-710.9495 1518.4895,-708.7852 1513.7805,-707.5072\"/>\n",
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+       "<!-- 57 -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1468\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;57 -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>52&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1468,-695.9735C1468,-684.1918 1468,-668.5607 1468,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1471.5001,-655.0033 1468,-645.0034 1464.5001,-655.0034 1471.5001,-655.0033\"/>\n",
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+       "</g>\n",
+       "<!-- 55 -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>55</title>\n",
+       "<text text-anchor=\"middle\" x=\"1468\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">55 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 57&#45;&gt;55 -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>57&#45;&gt;55</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1468,-608.9735C1468,-597.1918 1468,-581.5607 1468,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1471.5001,-568.0033 1468,-558.0034 1464.5001,-568.0034 1471.5001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1488\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 53 -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>53</title>\n",
+       "<text text-anchor=\"middle\" x=\"1627\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">53 (Muhyiddin)</text>\n",
+       "</g>\n",
+       "<!-- 53&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>53&#45;&gt;53</title>\n",
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+       "</g>\n",
+       "<!-- 54 -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1798\" y=\"-710.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">54 (Yassin)</text>\n",
+       "</g>\n",
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+       "<title>65</title>\n",
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+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>65&#45;&gt;65</title>\n",
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+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"2212\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">68 (menjangka)</text>\n",
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+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"2332\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">69 (ada)</text>\n",
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+       "<title>66</title>\n",
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+       "<title>67</title>\n",
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+       "<title>70</title>\n",
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+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"2420\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">73 (Najib)</text>\n",
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+       "<g id=\"edge73\" class=\"edge\">\n",
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+       "<title>75</title>\n",
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+       "<title>72</title>\n",
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+       "<title>74</title>\n",
+       "<text text-anchor=\"middle\" x=\"2420\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">74 (mengenai)</text>\n",
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+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2691\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">82 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 79&#45;&gt;82 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>79&#45;&gt;82</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M2691,-521.9735C2691,-510.1918 2691,-494.5607 2691,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2694.5001,-481.0033 2691,-471.0034 2687.5001,-481.0034 2694.5001,-481.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2711\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f32744c0cf8>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, bert_sequence, mask = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'tiny-bert-dependency/model.ckpt'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "saver = tf.train.Saver(tf.trainable_variables())\n",
+    "saver.save(sess, 'tiny-bert-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from bert-base-dependency/model.ckpt\n"
+      "INFO:tensorflow:Restoring parameters from tiny-bert-dependency/model.ckpt\n"
      ]
     }
    ],
@@ -1415,18 +2041,18 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word, training = False)\n",
     "\n",
     "sess.run(tf.global_variables_initializer())\n",
     "saver = tf.train.Saver(tf.trainable_variables())\n",
-    "saver.restore(sess, 'bert-base-dependency/model.ckpt')"
+    "saver.restore(sess, 'tiny-bert-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1442,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1487,14 +2113,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 315/315 [00:57<00:00,  5.52it/s]\n"
+      "100%|██████████| 313/313 [00:38<00:00,  8.12it/s]\n"
      ]
     }
    ],
@@ -1531,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1546,60 +2172,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    0.99996   1.00000   0.99998    943088\n",
-      "            X    0.99999   0.99981   0.99990    145797\n",
-      "          acl    0.85006   0.80040   0.82448      6042\n",
-      "        advcl    0.61783   0.60566   0.61169      2437\n",
-      "       advmod    0.86865   0.86755   0.86810      9513\n",
-      "         amod    0.82596   0.78837   0.80672      8217\n",
-      "        appos    0.84113   0.79100   0.81530      5000\n",
-      "          aux    0.80000   0.50000   0.61538         8\n",
-      "         case    0.94714   0.95046   0.94879     21376\n",
-      "           cc    0.92151   0.94487   0.93304      6349\n",
-      "        ccomp    0.59326   0.26201   0.36349       874\n",
-      "     compound    0.85764   0.83530   0.84632     13667\n",
-      "compound:plur    0.83743   0.91349   0.87381      1156\n",
-      "         conj    0.87306   0.90624   0.88934      8500\n",
-      "          cop    0.90592   0.93670   0.92105      1943\n",
-      "        csubj    0.75000   0.05263   0.09836        57\n",
-      "   csubj:pass    0.00000   0.00000   0.00000        16\n",
-      "          dep    0.66704   0.55176   0.60395      1082\n",
-      "          det    0.89147   0.84818   0.86929      7970\n",
-      "        fixed    0.80819   0.61696   0.69975      1120\n",
-      "         flat    0.90396   0.93947   0.92137     21129\n",
-      "         iobj    0.00000   0.00000   0.00000        25\n",
-      "         mark    0.74718   0.83845   0.79019      2767\n",
-      "         nmod    0.86083   0.78159   0.81930      8017\n",
-      "        nsubj    0.85174   0.89750   0.87402     12712\n",
-      "   nsubj:pass    0.78514   0.82246   0.80337      4061\n",
-      "       nummod    0.88943   0.93509   0.91169      8026\n",
-      "          obj    0.89982   0.84423   0.87114     10618\n",
-      "          obl    0.84081   0.88283   0.86131     11385\n",
-      "    parataxis    0.48635   0.26667   0.34446       735\n",
-      "        punct    0.98350   0.99126   0.98736     33736\n",
-      "         root    0.91085   0.93726   0.92387     10073\n",
-      "        xcomp    0.69305   0.76415   0.72686      2544\n",
+      "          PAD    0.99958   1.00000   0.99979    656062\n",
+      "            X    0.99998   0.99232   0.99613     61945\n",
+      "          acl    0.64541   0.81625   0.72085      3298\n",
+      "        advcl    0.27776   0.56039   0.37142      1656\n",
+      "       advmod    0.91310   0.92373   0.91839      6700\n",
+      "         amod    0.82278   0.86302   0.84242      4621\n",
+      "        appos    0.76714   0.69299   0.72818      3052\n",
+      "         case    0.96439   0.97337   0.96886     11492\n",
+      "           cc    0.98187   0.96747   0.97461      3750\n",
+      "        ccomp    0.45118   0.34987   0.39412       383\n",
+      "     compound    0.83453   0.89652   0.86442     11133\n",
+      "compound:plur    0.43478   0.33333   0.37736        30\n",
+      "         conj    0.84660   0.85878   0.85265      5424\n",
+      "          cop    0.97119   0.95659   0.96384       599\n",
+      "        csubj    0.25000   0.10000   0.14286        10\n",
+      "   csubj:pass    0.00000   0.00000   0.00000         1\n",
+      "          dep    0.45412   0.53168   0.48985       363\n",
+      "          det    0.92230   0.89720   0.90958      3969\n",
+      "        fixed    0.88889   0.70175   0.78431       171\n",
+      "         flat    0.91953   0.94618   0.93266     18393\n",
+      "         iobj    0.00000   0.00000   0.00000         4\n",
+      "         mark    0.88054   0.91620   0.89802      1778\n",
+      "         nmod    0.77113   0.77499   0.77306      4591\n",
+      "        nsubj    0.59707   0.82001   0.69100      7145\n",
+      "   nsubj:pass    0.74934   0.69813   0.72283      2034\n",
+      "       nummod    0.89096   0.94680   0.91803      4436\n",
+      "          obj    0.88715   0.87539   0.88123      6412\n",
+      "          obl    0.83020   0.74976   0.78793      5191\n",
+      "    parataxis    0.09302   0.19355   0.12565       372\n",
+      "        punct    0.99385   0.99501   0.99443     20643\n",
+      "         root    0.45797   0.14820   0.22393     10000\n",
+      "        xcomp    0.56806   0.68743   0.62207      1718\n",
       "\n",
-      "     accuracy                        0.98102   1310040\n",
-      "    macro avg    0.77906   0.72946   0.74011   1310040\n",
-      " weighted avg    0.98076   0.98102   0.98073   1310040\n",
+      "     accuracy                        0.97313    857376\n",
+      "    macro avg    0.68951   0.69272   0.68345    857376\n",
+      " weighted avg    0.97255   0.97313   0.97156    857376\n",
       "\n"
      ]
     }
@@ -1611,16 +2228,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.7189048051328787\n",
-      "types accuracy: 0.6942783162846734\n",
-      "root accuracy: 0.8860992063492065\n"
+      "arc accuracy: 0.795252499643322\n",
+      "types accuracy: 0.7247015428088897\n",
+      "root accuracy: 0.9893913291696776\n"
      ]
     }
    ],
@@ -1632,107 +2249,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'bert/embeddings/word_embeddings',\n",
-       " 'bert/embeddings/token_type_embeddings',\n",
-       " 'bert/embeddings/position_embeddings',\n",
-       " 'bert/embeddings/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_0/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_0/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_0/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_0/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_0/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_0/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_0/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_0/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_0/output/dense/kernel',\n",
-       " 'bert/encoder/layer_0/output/dense/bias',\n",
-       " 'bert/encoder/layer_0/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_1/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_1/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_1/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_1/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_1/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_1/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_1/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_1/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_1/output/dense/kernel',\n",
-       " 'bert/encoder/layer_1/output/dense/bias',\n",
-       " 'bert/encoder/layer_1/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_2/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_2/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_2/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_2/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_2/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_2/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_2/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_2/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_2/output/dense/kernel',\n",
-       " 'bert/encoder/layer_2/output/dense/bias',\n",
-       " 'bert/encoder/layer_2/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_3/attention/self/query/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/query/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/key/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/key/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/value/kernel',\n",
-       " 'bert/encoder/layer_3/attention/self/value/bias',\n",
-       " 'bert/encoder/layer_3/attention/self/Softmax',\n",
-       " 'bert/encoder/layer_3/attention/output/dense/kernel',\n",
-       " 'bert/encoder/layer_3/attention/output/dense/bias',\n",
-       " 'bert/encoder/layer_3/attention/output/LayerNorm/gamma',\n",
-       " 'bert/encoder/layer_3/intermediate/dense/kernel',\n",
-       " 'bert/encoder/layer_3/intermediate/dense/bias',\n",
-       " 'bert/encoder/layer_3/output/dense/kernel',\n",
-       " 'bert/encoder/layer_3/output/dense/bias',\n",
-       " 'bert/encoder/layer_3/output/LayerNorm/gamma',\n",
-       " 'bert/pooler/dense/kernel',\n",
-       " 'bert/pooler/dense/bias',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -1750,13 +2269,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1791,15 +2309,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from bert-base-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-36-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "INFO:tensorflow:Restoring parameters from tiny-bert-dependency/model.ckpt\n",
+      "WARNING:tensorflow:From <ipython-input-32-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -1807,158 +2325,66 @@
       "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
       "INFO:tensorflow:Froze 86 variables.\n",
       "INFO:tensorflow:Converted 86 variables to const ops.\n",
-      "1403 ops in the final graph.\n"
+      "1400 ops in the final graph.\n"
      ]
     }
    ],
    "source": [
-    "freeze_graph('bert-base-dependency', strings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string = 'husein makan ayam'\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "def entities_textcleaning(string, lowering = False):\n",
-    "    \"\"\"\n",
-    "    use by entities recognition, pos recognition and dependency parsing\n",
-    "    \"\"\"\n",
-    "    string = re.sub('[^A-Za-z0-9\\-\\/() ]+', ' ', string)\n",
-    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
-    "    original_string = string.split()\n",
-    "    if lowering:\n",
-    "        string = string.lower()\n",
-    "    string = [\n",
-    "        (original_string[no], word.title() if word.isupper() else word)\n",
-    "        for no, word in enumerate(string.split())\n",
-    "        if len(word)\n",
-    "    ]\n",
-    "    return [s[0] for s in string], [s[1] for s in string]\n",
-    "\n",
-    "def parse_X(left):\n",
-    "    bert_tokens = ['[CLS]']\n",
-    "    for no, orig_token in enumerate(left):\n",
-    "        t = tokenizer.tokenize(orig_token)\n",
-    "        bert_tokens.extend(t)\n",
-    "    bert_tokens.append(\"[SEP]\")\n",
-    "    return tokenizer.convert_tokens_to_ids(bert_tokens), bert_tokens\n",
-    "\n",
-    "sequence = entities_textcleaning(string)[1]\n",
-    "parsed_sequence, bert_sequence = parse_X(sequence)"
+    "freeze_graph('tiny-bert-dependency', strings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def merge_sentencepiece_tokens_tagging(x, y):\n",
-    "    new_paired_tokens = []\n",
-    "    n_tokens = len(x)\n",
-    "    rejected = ['[CLS]', '[SEP]']\n",
-    "\n",
-    "    i = 0\n",
-    "\n",
-    "    while i < n_tokens:\n",
-    "\n",
-    "        current_token, current_label = x[i], y[i]\n",
-    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
-    "            previous_token, previous_label = new_paired_tokens.pop()\n",
-    "            merged_token = previous_token\n",
-    "            merged_label = [previous_label]\n",
-    "            while (\n",
-    "                not current_token.startswith('▁')\n",
-    "                and current_token not in rejected\n",
-    "            ):\n",
-    "                merged_token = merged_token + current_token.replace('▁', '')\n",
-    "                merged_label.append(current_label)\n",
-    "                i = i + 1\n",
-    "                current_token, current_label = x[i], y[i]\n",
-    "            merged_label = merged_label[0]\n",
-    "            new_paired_tokens.append((merged_token, merged_label))\n",
-    "\n",
-    "        else:\n",
-    "            new_paired_tokens.append((current_token, current_label))\n",
-    "            i = i + 1\n",
-    "\n",
-    "    words = [\n",
-    "        i[0].replace('▁', '')\n",
-    "        for i in new_paired_tokens\n",
-    "        if i[0] not in rejected\n",
-    "    ]\n",
-    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
-    "    return words, labels"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
+      "WARNING:tensorflow:From <ipython-input-35-b945beb65a73>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.gfile.GFile.\n"
      ]
     }
    ],
    "source": [
-    "def load_graph(frozen_graph_filename):\n",
-    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
-    "        graph_def = tf.GraphDef()\n",
-    "        graph_def.ParseFromString(f.read())\n",
-    "    with tf.Graph().as_default() as graph:\n",
-    "        tf.import_graph_def(graph_def)\n",
-    "    return graph\n",
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
     "\n",
-    "g = load_graph('bert-base-dependency/frozen_model.pb')\n",
-    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
-    "heads_seq = g.get_tensor_by_name('import/heads_seq:0')\n",
-    "tags_seq = g.get_tensor_by_name('import/logits:0')\n",
-    "test_sess = tf.InteractiveSession(graph = g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h, t = test_sess.run([heads_seq, tags_seq],\n",
-    "        feed_dict = {\n",
-    "            x: [parsed_sequence],\n",
-    "        },\n",
-    ")\n",
-    "h = h[0] - 1\n",
-    "t = [idx2tag[d] for d in t[0]]\n",
-    "merged_h = merge_sentencepiece_tokens_tagging(bert_sequence, h)\n",
-    "merged_t = merge_sentencepiece_tokens_tagging(bert_sequence, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3\n",
+    "pb = 'tiny-bert-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
     "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'bert-base-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/tiny-bert-dependency.pb\"\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
     "\n",
-    "s3 = boto3.client('s3')\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
     "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
    ]
   }
  ],
@@ -1978,7 +2404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/session/dependency/xlnet-base.ipynb b/session/dependency/xlnet-base.ipynb
index 17c6084a..65217d98 100644
--- a/session/dependency/xlnet-base.ipynb
+++ b/session/dependency/xlnet-base.ipynb
@@ -14,22 +14,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../Malaya-Dataset/dependency/gsd-ud-train.conllu.txt') as fopen:\n",
-    "    corpus = fopen.read().split('\\n')\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-test.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))\n",
-    "    \n",
-    "with open('../Malaya-Dataset/dependency/gsd-ud-dev.conllu.txt') as fopen:\n",
-    "    corpus.extend(fopen.read().split('\\n'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -53,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +45,7 @@
     "from prepro_utils import preprocess_text, encode_ids\n",
     "\n",
     "sp_model = spm.SentencePieceProcessor()\n",
-    "sp_model.Load('xlnet-base/sp10m.cased.v9.model')\n",
+    "sp_model.Load('xlnet-base-29-03-2020/sp10m.cased.v9.model')\n",
     "\n",
     "def tokenize_fn(text):\n",
     "    text = preprocess_text(text, lower= False)\n",
@@ -70,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,318 +86,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tag2idx = {'PAD': 0, 'X': 1}\n",
-    "tag_idx = 2\n",
-    "\n",
-    "def process_corpus(corpus, until = None):\n",
-    "    global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx\n",
-    "    sentences, words, depends, labels, pos, sequences = [], [], [], [], [], []\n",
-    "    temp_sentence, temp_word, temp_depend, temp_label, temp_pos = [], [], [], [], []\n",
-    "    segments, masks = [], []\n",
-    "    first_time = True\n",
-    "    for sentence in corpus:\n",
-    "        try:\n",
-    "            if len(sentence):\n",
-    "                if sentence[0] == '#':\n",
-    "                    continue\n",
-    "                if first_time:\n",
-    "                    print(sentence)\n",
-    "                    first_time = False\n",
-    "                sentence = sentence.split('\\t')\n",
-    "                if sentence[7] not in tag2idx:\n",
-    "                    tag2idx[sentence[7]] = tag_idx\n",
-    "                    tag_idx += 1\n",
-    "                temp_word.append(sentence[1])\n",
-    "                temp_depend.append(int(sentence[6]) + 1)\n",
-    "                temp_label.append(tag2idx[sentence[7]])\n",
-    "                temp_sentence.append(sentence[1])\n",
-    "                temp_pos.append(sentence[3])\n",
-    "            else:\n",
-    "                if len(temp_sentence) < 2 or len(temp_word) != len(temp_label):\n",
-    "                    temp_word = []\n",
-    "                    temp_depend = []\n",
-    "                    temp_label = []\n",
-    "                    temp_sentence = []\n",
-    "                    temp_pos = []\n",
-    "                    continue\n",
-    "                bert_tokens = []\n",
-    "                labels_ = []\n",
-    "                depends_ = []\n",
-    "                seq_ = []\n",
-    "                for no, orig_token in enumerate(temp_word):\n",
-    "                    labels_.append(temp_label[no])\n",
-    "                    depends_.append(temp_depend[no])\n",
-    "                    t = tokenize_fn(orig_token)\n",
-    "                    bert_tokens.extend(t)\n",
-    "                    labels_.extend([1] * (len(t) - 1))\n",
-    "                    depends_.extend([0] * (len(t) - 1))\n",
-    "                    seq_.append(no + 1)\n",
-    "                bert_tokens.extend([4, 3])\n",
-    "                labels_.extend([0, 0])\n",
-    "                depends_.extend([0, 0])\n",
-    "                segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
-    "                input_mask = [0] * len(segment)\n",
-    "                words.append(bert_tokens)\n",
-    "                depends.append(depends_)\n",
-    "                labels.append(labels_)\n",
-    "                sentences.append(bert_tokens)\n",
-    "                pos.append(temp_pos)\n",
-    "                sequences.append(seq_)\n",
-    "                segments.append(segment)\n",
-    "                masks.append(input_mask)\n",
-    "                temp_word = []\n",
-    "                temp_depend = []\n",
-    "                temp_label = []\n",
-    "                temp_sentence = []\n",
-    "                temp_pos = []\n",
-    "        except Exception as e:\n",
-    "            print(e, sentence)\n",
-    "    return sentences[:-1], words[:-1], depends[:-1], labels[:-1], pos[:-1], sequences[:-1], segments[:-1], masks[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\tSembungan\tsembungan\tPROPN\tX--\t_\t4\tnsubj\t_\tMorphInd=^sembungan<x>_X--$\n"
-     ]
-    }
-   ],
-   "source": [
-    "sentences, words, depends, labels, _, _, segments, masks = process_corpus(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(26, 26, 26)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(words[0]), len(depends[0]), len(labels[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('../Malaya-Dataset/dependency/augmented-dependency.json') as fopen:\n",
-    "    augmented = json.load(fopen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_augmented, depends_augmented, labels_augmented = [], [], []\n",
-    "\n",
-    "for a in augmented:\n",
-    "    text_augmented.extend(a[0])\n",
-    "    depends_augmented.extend(a[1])\n",
-    "    labels_augmented.extend((np.array(a[2]) + 1).tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_XY(texts, depends, labels):\n",
-    "    outside, sentences, outside_depends, outside_labels = [], [], [], []\n",
-    "    segments, masks = [], []\n",
-    "    for no, text in enumerate(texts):\n",
-    "        temp_depend = depends[no]\n",
-    "        temp_label = labels[no]\n",
-    "        s = text.split()\n",
-    "        sentences.append(s)\n",
-    "        bert_tokens = []\n",
-    "        labels_ = []\n",
-    "        depends_ = []\n",
-    "        for no, orig_token in enumerate(s):\n",
-    "            labels_.append(temp_label[no])\n",
-    "            depends_.append(temp_depend[no])\n",
-    "            t = tokenize_fn(orig_token)\n",
-    "            bert_tokens.extend(t)\n",
-    "            labels_.extend([1] * (len(t) - 1))\n",
-    "            depends_.extend([0] * (len(t) - 1))\n",
-    "        bert_tokens.extend([4, 3])\n",
-    "        labels_.extend([0, 0])\n",
-    "        depends_.extend([0, 0])\n",
-    "        segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
-    "        input_mask = [0] * len(segment)\n",
-    "        outside.append(bert_tokens)\n",
-    "        outside_depends.append(depends_)\n",
-    "        outside_labels.append(labels_)\n",
-    "        segments.append(segment)\n",
-    "        masks.append(input_mask)\n",
-    "    return outside, sentences, outside_depends, outside_labels, segments, masks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outside, _, outside_depends, outside_labels, outside_segments, outside_masks = parse_XY(text_augmented, \n",
-    "                                                       depends_augmented, \n",
-    "                                                       labels_augmented)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "words.extend(outside)\n",
-    "depends.extend(outside_depends)\n",
-    "labels.extend(outside_labels)\n",
-    "segments.extend(outside_segments)\n",
-    "masks.extend(outside_masks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'PAD',\n",
-       " 1: 'X',\n",
-       " 2: 'nsubj',\n",
-       " 3: 'cop',\n",
-       " 4: 'det',\n",
-       " 5: 'root',\n",
-       " 6: 'nsubj:pass',\n",
-       " 7: 'acl',\n",
-       " 8: 'case',\n",
-       " 9: 'obl',\n",
-       " 10: 'flat',\n",
-       " 11: 'punct',\n",
-       " 12: 'appos',\n",
-       " 13: 'amod',\n",
-       " 14: 'compound',\n",
-       " 15: 'advmod',\n",
-       " 16: 'cc',\n",
-       " 17: 'obj',\n",
-       " 18: 'conj',\n",
-       " 19: 'mark',\n",
-       " 20: 'advcl',\n",
-       " 21: 'nmod',\n",
-       " 22: 'nummod',\n",
-       " 23: 'dep',\n",
-       " 24: 'xcomp',\n",
-       " 25: 'ccomp',\n",
-       " 26: 'parataxis',\n",
-       " 27: 'compound:plur',\n",
-       " 28: 'fixed',\n",
-       " 29: 'aux',\n",
-       " 30: 'csubj',\n",
-       " 31: 'iobj',\n",
-       " 32: 'csubj:pass'}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx2tag = {v:k for k, v in tag2idx.items()}\n",
-    "idx2tag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
+    "import pickle\n",
     "\n",
-    "words_train, words_test, depends_train, depends_test, labels_train, labels_test, \\\n",
-    "segments_train, segments_test, masks_train, masks_test \\\n",
-    "= train_test_split(words, depends, labels, segments, masks, test_size = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(40289, 10073)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(words_train), len(words_test)"
+    "with open('train_X.pkl', 'rb') as fopen:\n",
+    "    train_X, train_Y, train_depends, train_segments, train_masks = pickle.load(fopen)\n",
+    "    \n",
+    "with open('test_X.pkl', 'rb') as fopen:\n",
+    "    test_X, test_Y, test_depends, test_segments, test_masks = pickle.load(fopen)\n",
+    "    \n",
+    "with open('tags.pkl', 'rb') as fopen:\n",
+    "    idx2tag, tag2idx = pickle.load(fopen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "train_X = words_train\n",
-    "train_Y = labels_train\n",
-    "train_depends = depends_train\n",
-    "\n",
-    "test_X = words_test\n",
-    "test_Y = labels_test\n",
-    "test_depends = depends_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:63: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
-      "\n"
-     ]
-    }
-   ],
    "source": [
     "import xlnet\n",
     "import model_utils\n",
@@ -432,25 +125,25 @@
     "      clamp_len=-1)\n",
     "\n",
     "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
-    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base/config.json')"
+    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base-29-03-2020/config.json')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "37770 3777\n"
+      "1173456 117345\n"
      ]
     }
    ],
    "source": [
-    "epoch = 15\n",
-    "batch_size = 16\n",
+    "epoch = 3\n",
+    "batch_size = 8\n",
     "warmup_proportion = 0.1\n",
     "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
     "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
@@ -479,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -724,50 +417,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py:507: calling count_nonzero (from tensorflow.python.ops.math_ops) with axis is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "reduction_indices is deprecated, use axis instead\n",
-      "WARNING:tensorflow:\n",
-      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n",
-      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:220: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:220: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:453: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
-      "\n",
       "INFO:tensorflow:memory input None\n",
-      "INFO:tensorflow:Use float type <dtype: 'float32'>\n",
-      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:535: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use keras.layers.dropout instead.\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:271: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `layer.__call__` method instead.\n",
-      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:67: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use keras.layers.Dense instead.\n",
-      "WARNING:tensorflow:From <ipython-input-21-117b6be1eb3b>:138: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
-      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From <ipython-input-21-117b6be1eb3b>:172: calling log_softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "dim is deprecated, use axis instead\n"
+      "INFO:tensorflow:Use float type <dtype: 'float32'>\n"
      ]
     }
    ],
@@ -776,7 +434,7 @@
     "sess = tf.InteractiveSession()\n",
     "\n",
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "model = Model(learning_rate, hidden_size_word)\n",
     "sess.run(tf.global_variables_initializer())"
@@ -784,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -820,26 +478,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "tvars = tf.trainable_variables()\n",
-    "checkpoint = 'xlnet-base/model.ckpt'\n",
+    "checkpoint = 'xlnet-base-29-03-2020/model.ckpt-300000'\n",
     "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
     "                                                                                checkpoint)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from xlnet-base/model.ckpt\n"
+      "INFO:tensorflow:Restoring parameters from xlnet-base-29-03-2020/model.ckpt-300000\n"
      ]
     }
    ],
@@ -850,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -862,24 +520,24 @@
     "batch_y = pad_sequences(batch_y,padding='post')\n",
     "batch_depends = train_depends[:5]\n",
     "batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "batch_segments = segments_train[:5]\n",
+    "batch_segments = train_segments[:5]\n",
     "batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "batch_masks = masks_train[:5]\n",
+    "batch_masks = train_masks[:5]\n",
     "batch_masks = pad_sequences(batch_masks, padding='post', value = 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.04255319, 0.014184397, 128.52495]"
+       "[0.03529412, 0.07058824, 140.91621]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -896,16 +554,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.0070921984, 0.04964539, 544.50476]"
+       "[0.047058824, 0.023529412, 352.61438]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -922,391 +580,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([26,  6,  6, 28, 26, 18, 19, 18, 28,  6, 26, 32, 27, 28, 27,  6, 28,\n",
-       "        19, 19, 28,  6, 28,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0], dtype=int32),\n",
-       " array([21,  3, 15, 18, 10, 21, 10, 21,  7, 10,  1, 21,  1,  7,  3,  8,  7,\n",
-       "         9, 10, 18,  8, 17,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0]),\n",
-       " array([ 3,  1,  3,  3,  3,  6,  3,  0,  3,  0,  9, 13, 11,  9, 15, 13, 15,\n",
-       "         3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "         0,  0], dtype=int32))"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tags_seq, heads = sess.run(\n",
-    "    [model.logits, model.heads_seq],\n",
-    "    feed_dict = {\n",
-    "        model.words: batch_x,\n",
-    "        model.segment_ids: batch_segments,\n",
-    "        model.input_masks: batch_masks\n",
-    "    },\n",
-    ")\n",
-    "tags_seq[0], heads[0], batch_depends[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:04<00:00,  2.78it/s, accuracy=0.8, accuracy_depends=0.5, cost=1.95]     \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:03<00:00,  5.12it/s, accuracy=0.843, accuracy_depends=0.545, cost=2.06]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 391153/391153 [20:11:00<00:00,  5.38it/s, accuracy=0.867, accuracy_depends=0.733, cost=0.328]    \n",
+      "test minibatch loop: 100%|██████████| 1250/1250 [02:16<00:00,  9.19it/s, accuracy=0.945, accuracy_depends=0.853, cost=0.234] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 0, training loss: 3.876894, training acc: 0.650480, training depends: 0.419876, valid loss: 2.197435, valid acc: 0.826289, valid depends: 0.512468\n",
+      "epoch: 0, training loss: 0.401834, training acc: 0.901875, training depends: 0.845306, valid loss: 0.386272, valid acc: 0.902461, valid depends: 0.848041\n",
       "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "epoch = 1\n",
+    "for e in range(epoch):\n",
+    "    train_acc, train_loss = [], []\n",
+    "    test_acc, test_loss = [], []\n",
+    "    train_acc_depends, test_acc_depends = [], []\n",
+    "    \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(train_X))\n",
+    "        batch_x = train_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = train_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = train_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        batch_segments = train_segments[i: index]\n",
+    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
+    "        batch_masks = train_masks[i: index]\n",
+    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        \n",
+    "    pbar = tqdm(\n",
+    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
+    "    )\n",
+    "    for i in pbar:\n",
+    "        index = min(i + batch_size, len(test_X))\n",
+    "        batch_x = test_X[i: index]\n",
+    "        batch_x = pad_sequences(batch_x,padding='post')\n",
+    "        batch_y = test_Y[i: index]\n",
+    "        batch_y = pad_sequences(batch_y,padding='post')\n",
+    "        batch_depends = test_depends[i: index]\n",
+    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
+    "        batch_segments = test_segments[i: index]\n",
+    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
+    "        batch_masks = test_masks[i: index]\n",
+    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
+    "        \n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: False\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "    \n",
+    "    \n",
+    "    print(\n",
+    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
+    "    % (e, np.mean(train_loss), \n",
+    "       np.mean(train_acc), \n",
+    "       np.mean(train_acc_depends), \n",
+    "       np.mean(test_loss), \n",
+    "       np.mean(test_acc), \n",
+    "       np.mean(test_acc_depends)\n",
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'xlnet-base-dependency/model.ckpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:22<00:00,  2.73it/s, accuracy=0.9, accuracy_depends=0.55, cost=1.35]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:05<00:00,  5.03it/s, accuracy=0.886, accuracy_depends=0.609, cost=1.59]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
+      "train minibatch loop: 100%|██████████| 391153/391153 [19:55:40<00:00,  5.45it/s, accuracy=1, accuracy_depends=0.733, cost=0.617]       \n",
+      "test minibatch loop: 100%|██████████| 1250/1250 [02:17<00:00,  9.12it/s, accuracy=0.969, accuracy_depends=0.859, cost=1.82] \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1, training loss: 1.890330, training acc: 0.857705, training depends: 0.550963, valid loss: 1.625166, valid acc: 0.878244, valid depends: 0.601878\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:23<00:00,  2.73it/s, accuracy=0.85, accuracy_depends=0.7, cost=0.788]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:06<00:00,  4.99it/s, accuracy=0.902, accuracy_depends=0.636, cost=1.37] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 1.410951, training acc: 0.887465, training depends: 0.646661, valid loss: 1.243608, valid acc: 0.898173, valid depends: 0.682270\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:23<00:00,  2.73it/s, accuracy=0.75, accuracy_depends=0.75, cost=0.889]  \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:05<00:00,  5.02it/s, accuracy=0.911, accuracy_depends=0.718, cost=1.1]  \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 1.064192, training acc: 0.905402, training depends: 0.724641, valid loss: 0.975716, valid acc: 0.909459, valid depends: 0.749798\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:25<00:00,  2.72it/s, accuracy=0.95, accuracy_depends=0.75, cost=0.542]  \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:05<00:00,  5.02it/s, accuracy=0.941, accuracy_depends=0.748, cost=0.93] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 0.837911, training acc: 0.922502, training depends: 0.774991, valid loss: 0.811241, valid acc: 0.923954, valid depends: 0.790458\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:24<00:00,  2.72it/s, accuracy=0.9, accuracy_depends=0.8, cost=0.952]    \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:05<00:00,  5.03it/s, accuracy=0.957, accuracy_depends=0.768, cost=0.798]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 5, training loss: 0.686040, training acc: 0.935296, training depends: 0.807902, valid loss: 0.716174, valid acc: 0.933297, valid depends: 0.809396\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:23<00:00,  2.73it/s, accuracy=0.9, accuracy_depends=0.85, cost=0.566]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:04<00:00,  5.07it/s, accuracy=0.948, accuracy_depends=0.839, cost=0.688]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 6, training loss: 0.574526, training acc: 0.945672, training depends: 0.831804, valid loss: 0.622911, valid acc: 0.940280, valid depends: 0.835143\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:23<00:00,  2.73it/s, accuracy=0.95, accuracy_depends=0.8, cost=0.212]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [01:59<00:00,  5.28it/s, accuracy=0.95, accuracy_depends=0.841, cost=0.594] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 7, training loss: 0.491905, training acc: 0.953639, training depends: 0.848994, valid loss: 0.572177, valid acc: 0.944793, valid depends: 0.846656\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:28<00:00,  2.71it/s, accuracy=1, accuracy_depends=0.8, cost=0.237]      \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:06<00:00,  4.97it/s, accuracy=0.961, accuracy_depends=0.852, cost=0.484]\n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 8, training loss: 0.424984, training acc: 0.960825, training depends: 0.862493, valid loss: 0.532216, valid acc: 0.949322, valid depends: 0.856430\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:29<00:00,  2.71it/s, accuracy=1, accuracy_depends=0.85, cost=0.081]     \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:07<00:00,  4.96it/s, accuracy=0.957, accuracy_depends=0.843, cost=0.57] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 9, training loss: 0.372532, training acc: 0.966455, training depends: 0.872990, valid loss: 0.523298, valid acc: 0.953737, valid depends: 0.851046\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "train minibatch loop: 100%|██████████| 2519/2519 [15:27<00:00,  2.72it/s, accuracy=1, accuracy_depends=0.85, cost=0.0546]     \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 10, training loss: 0.329999, training acc: 0.971050, training depends: 0.882039, valid loss: 0.489395, valid acc: 0.958892, valid depends: 0.860195\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "train minibatch loop: 100%|██████████| 2519/2519 [15:26<00:00,  2.72it/s, accuracy=1, accuracy_depends=0.8, cost=0.303]       \n",
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-     ]
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-     "name": "stdout",
-     "output_type": "stream",
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-      "epoch: 11, training loss: 0.296270, training acc: 0.975037, training depends: 0.888372, valid loss: 0.414677, valid acc: 0.960939, valid depends: 0.883841\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "train minibatch loop: 100%|██████████| 2519/2519 [15:27<00:00,  2.72it/s, accuracy=0.95, accuracy_depends=0.85, cost=0.0516]  \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 12, training loss: 0.263290, training acc: 0.978965, training depends: 0.895745, valid loss: 0.402342, valid acc: 0.963851, valid depends: 0.887353\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:27<00:00,  2.71it/s, accuracy=1, accuracy_depends=0.85, cost=0.00413]    \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:07<00:00,  4.95it/s, accuracy=0.975, accuracy_depends=0.902, cost=0.358] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 13, training loss: 0.238861, training acc: 0.982122, training depends: 0.901446, valid loss: 0.393664, valid acc: 0.968449, valid depends: 0.882857\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:33<00:00,  2.70it/s, accuracy=1, accuracy_depends=0.85, cost=0.00173]    \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:06<00:00,  4.96it/s, accuracy=0.97, accuracy_depends=0.914, cost=0.349]  \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 14, training loss: 0.222116, training acc: 0.984062, training depends: 0.904335, valid loss: 0.413641, valid acc: 0.969395, valid depends: 0.878797\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:24<00:00,  2.73it/s, accuracy=1, accuracy_depends=0.85, cost=0.0861]     \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.16it/s, accuracy=0.977, accuracy_depends=0.907, cost=0.311] \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 15, training loss: 0.206292, training acc: 0.985854, training depends: 0.908097, valid loss: 0.333951, valid acc: 0.970574, valid depends: 0.901331\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:01<00:00,  2.80it/s, accuracy=0.95, accuracy_depends=0.85, cost=0.169]   \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 16, training loss: 0.190376, training acc: 0.986826, training depends: 0.911747, valid loss: 0.326896, valid acc: 0.971972, valid depends: 0.903429\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:00<00:00,  2.80it/s, accuracy=1, accuracy_depends=0.85, cost=0.00131]    \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 17, training loss: 0.177621, training acc: 0.987951, training depends: 0.915344, valid loss: 0.332637, valid acc: 0.971900, valid depends: 0.902321\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:00<00:00,  2.80it/s, accuracy=1, accuracy_depends=0.85, cost=0.000426]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.15it/s, accuracy=0.966, accuracy_depends=0.93, cost=0.245]  \n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 18, training loss: 0.166710, training acc: 0.989098, training depends: 0.917499, valid loss: 0.322491, valid acc: 0.974446, valid depends: 0.907440\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:01<00:00,  2.79it/s, accuracy=1, accuracy_depends=0.85, cost=0.00151]    \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:03<00:00,  5.12it/s, accuracy=0.973, accuracy_depends=0.909, cost=0.285] "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 19, training loss: 0.155884, training acc: 0.989576, training depends: 0.920570, valid loss: 0.300692, valid acc: 0.976090, valid depends: 0.909638\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "epoch: 0, training loss: 3.694703, training acc: 0.950186, training depends: 0.846899, valid loss: 3.673369, valid acc: 0.951140, valid depends: 0.844435\n",
       "\n"
      ]
     }
@@ -1314,7 +719,7 @@
    "source": [
     "from tqdm import tqdm\n",
     "\n",
-    "epoch = 20\n",
+    "epoch = 1\n",
     "for e in range(epoch):\n",
     "    train_acc, train_loss = [], []\n",
     "    test_acc, test_loss = [], []\n",
@@ -1331,26 +736,28 @@
     "        batch_y = pad_sequences(batch_y,padding='post')\n",
     "        batch_depends = train_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_train[i: index]\n",
+    "        batch_segments = train_segments[i: index]\n",
     "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_train[i: index]\n",
+    "        batch_masks = train_masks[i: index]\n",
     "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost, _ = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            train_loss.append(cost)\n",
+    "            train_acc.append(acc)\n",
+    "            train_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "        \n",
     "    pbar = tqdm(\n",
     "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
@@ -1363,26 +770,28 @@
     "        batch_y = pad_sequences(batch_y,padding='post')\n",
     "        batch_depends = test_depends[i: index]\n",
     "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_test[i: index]\n",
+    "        batch_segments = test_segments[i: index]\n",
     "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_test[i: index]\n",
+    "        batch_masks = test_masks[i: index]\n",
     "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: False\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
+    "        if batch_x.shape == batch_y.shape:\n",
+    "        \n",
+    "            acc_depends, acc, cost = sess.run(\n",
+    "                [model.accuracy_depends, model.accuracy, model.cost],\n",
+    "                feed_dict = {\n",
+    "                    model.words: batch_x,\n",
+    "                    model.types: batch_y,\n",
+    "                    model.heads: batch_depends,\n",
+    "                    model.segment_ids: batch_segments,\n",
+    "                    model.input_masks: batch_masks,\n",
+    "                    model.switch: True\n",
+    "                },\n",
+    "            )\n",
+    "            test_loss.append(cost)\n",
+    "            test_acc.append(acc)\n",
+    "            test_acc_depends.append(acc_depends)\n",
+    "            pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
     "    \n",
     "    \n",
     "    print(\n",
@@ -1393,194 +802,1296 @@
     "       np.mean(test_loss), \n",
     "       np.mean(test_acc), \n",
     "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "    ))\n",
+    "    \n",
+    "    saver = tf.train.Saver(tf.trainable_variables())\n",
+    "    saver.save(sess, 'xlnet-base-dependency/model.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:03<00:00,  2.79it/s, accuracy=0.95, accuracy_depends=0.85, cost=0.854]  \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:02<00:00,  5.15it/s, accuracy=0.989, accuracy_depends=0.895, cost=2.23] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, training loss: 1.406487, training acc: 0.990304, training depends: 0.911989, valid loss: 3.639354, valid acc: 0.980902, valid depends: 0.892664\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:55<00:00,  2.81it/s, accuracy=1, accuracy_depends=0.85, cost=0.000302]  \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.24it/s, accuracy=0.991, accuracy_depends=0.936, cost=2.01] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, training loss: 0.790412, training acc: 0.994914, training depends: 0.917277, valid loss: 3.557691, valid acc: 0.982399, valid depends: 0.902226\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [15:08<00:00,  2.77it/s, accuracy=1, accuracy_depends=0.85, cost=0.00217]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:05<00:00,  5.02it/s, accuracy=0.993, accuracy_depends=0.918, cost=2.68] \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, training loss: 0.662908, training acc: 0.995871, training depends: 0.917625, valid loss: 3.628110, valid acc: 0.982800, valid depends: 0.901144\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:50<00:00,  2.83it/s, accuracy=1, accuracy_depends=0.85, cost=0.00313]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.23it/s, accuracy=0.989, accuracy_depends=0.93, cost=3.5]   \n",
-      "train minibatch loop:   0%|          | 0/2519 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 3, training loss: 0.599805, training acc: 0.996370, training depends: 0.917015, valid loss: 3.422855, valid acc: 0.983998, valid depends: 0.900396\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "train minibatch loop: 100%|██████████| 2519/2519 [14:50<00:00,  2.83it/s, accuracy=1, accuracy_depends=0.85, cost=0.00308]   \n",
-      "test minibatch loop: 100%|██████████| 630/630 [02:00<00:00,  5.24it/s, accuracy=0.995, accuracy_depends=0.9, cost=0.604]  "
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "((8, 52), (8, 52))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_x.shape, batch_y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_sentencepiece_tokens_tagging(x, y):\n",
+    "    new_paired_tokens = []\n",
+    "    n_tokens = len(x)\n",
+    "    rejected = ['<cls>', '<sep>']\n",
+    "\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < n_tokens:\n",
+    "\n",
+    "        current_token, current_label = x[i], y[i]\n",
+    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
+    "            previous_token, previous_label = new_paired_tokens.pop()\n",
+    "            merged_token = previous_token\n",
+    "            merged_label = [previous_label]\n",
+    "            while (\n",
+    "                not current_token.startswith('▁')\n",
+    "                and current_token not in rejected\n",
+    "            ):\n",
+    "                merged_token = merged_token + current_token.replace('▁', '')\n",
+    "                merged_label.append(current_label)\n",
+    "                i = i + 1\n",
+    "                current_token, current_label = x[i], y[i]\n",
+    "            merged_label = merged_label[0]\n",
+    "            new_paired_tokens.append((merged_token, merged_label))\n",
+    "\n",
+    "        else:\n",
+    "            new_paired_tokens.append((current_token, current_label))\n",
+    "            i = i + 1\n",
+    "\n",
+    "    words = [\n",
+    "        i[0].replace('▁', '')\n",
+    "        for i in new_paired_tokens\n",
+    "        if i[0] not in ['<cls>', '<sep>']\n",
+    "    ]\n",
+    "    labels = [i[1] for i in new_paired_tokens if i[0] not in ['<cls>', '<sep>']]\n",
+    "    return words, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from unidecode import unidecode\n",
+    "from malaya.function.parse_dependency import DependencyGraph\n",
+    "\n",
+    "PUNCTUATION = '!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'\n",
+    "\n",
+    "def transformer_textcleaning(string):\n",
+    "    \"\"\"\n",
+    "    use by any transformer model before tokenization\n",
+    "    \"\"\"\n",
+    "    string = unidecode(string)\n",
+    "    string = re.sub('\\\\(dot\\\\)', '.', string)\n",
+    "    string = (\n",
+    "        re.sub(re.findall(r'\\<a(.*?)\\>', string)[0], '', string)\n",
+    "        if (len(re.findall(r'\\<a (.*?)\\>', string)) > 0)\n",
+    "        and ('href' in re.findall(r'\\<a (.*?)\\>', string)[0])\n",
+    "        else string\n",
+    "    )\n",
+    "    string = re.sub(\n",
+    "        r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', string\n",
+    "    )\n",
+    "    string = re.sub(r'[ ]+', ' ', string).strip().split()\n",
+    "    string = [w for w in string if w[0] != '@']\n",
+    "    string = ' '.join(string)\n",
+    "    string = re.sub(f'([{PUNCTUATION}])', r' \\1 ', string)\n",
+    "    string = re.sub('\\s{2,}', ' ', string)\n",
+    "    original_string = string.split()\n",
+    "    string = [\n",
+    "        (original_string[no], word.title() if word.isupper() else word)\n",
+    "        for no, word in enumerate(string.split())\n",
+    "        if len(word)\n",
+    "    ]\n",
+    "    return [s[0] for s in string], [s[1] for s in string]\n",
+    "\n",
+    "def parse_X(left):\n",
+    "    left = ' '.join(left)\n",
+    "    bert_tokens = tokenize_fn(left)\n",
+    "    bert_tokens.extend([3, 4])\n",
+    "    segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
+    "    input_mask = [0] * len(segment)\n",
+    "    s_tokens = [sp_model.IdToPiece(i) for i in bert_tokens]\n",
+    "    return bert_tokens, segment, input_mask, s_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dependency_graph(tagging, indexing):\n",
+    "    \"\"\"\n",
+    "    Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    for i in range(len(tagging)):\n",
+    "        result.append(\n",
+    "            '%d\\t%s\\t_\\t_\\t_\\t_\\t%d\\t%s\\t_\\t_'\n",
+    "            % (i + 1, tagging[i][0], int(indexing[i][1]), tagging[i][1])\n",
+    "        )\n",
+    "    return DependencyGraph('\\n'.join(result), top_relation_label='root')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 4, training loss: 0.545647, training acc: 0.996865, training depends: 0.917185, valid loss: 3.373970, valid acc: 0.984332, valid depends: 0.899482\n",
-      "\n"
-     ]
-    },
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+   "source": [
+    "string = 'husein makan ayam'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "            model.segment_ids: [segment_sequence],\n",
+    "            model.input_masks: [mask_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(xlnet_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(xlnet_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
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+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>5</title>\n",
+       "<text text-anchor=\"middle\" x=\"62.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">5 (Penerangan)</text>\n",
+       "</g>\n",
+       "<!-- 4&#45;&gt;5 -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>4&#45;&gt;5</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M179.8343,-695.9735C157.212,-682.6753 126.2472,-664.473 101.8077,-650.1065\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"157.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 8 -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>8</title>\n",
+       "<text text-anchor=\"middle\" x=\"210.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">8 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 4&#45;&gt;8 -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>4&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M210.5,-695.9735C210.5,-684.1918 210.5,-668.5607 210.5,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"214.0001,-655.0033 210.5,-645.0034 207.0001,-655.0034 214.0001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"232\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">appos</text>\n",
+       "</g>\n",
+       "<!-- 6 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>6</title>\n",
+       "<text text-anchor=\"middle\" x=\"52.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">6 (Bersatu)</text>\n",
+       "</g>\n",
+       "<!-- 5&#45;&gt;6 -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>5&#45;&gt;6</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M60.428,-608.9735C59.0738,-597.1918 57.2771,-581.5607 55.7366,-568.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"70.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 7 -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>7</title>\n",
+       "<text text-anchor=\"middle\" x=\"146.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">7 (,)</text>\n",
+       "</g>\n",
+       "<!-- 8&#45;&gt;7 -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>8&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M189.7202,-608.9921C184.0789,-603.5383 178.229,-597.3119 173.5,-591 168.0599,-583.7389 163.0898,-575.2327 158.938,-567.2711\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"194\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 9 -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>9</title>\n",
+       "<text text-anchor=\"middle\" x=\"227.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">9 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 8&#45;&gt;9 -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>8&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M214.0224,-608.9735C216.3246,-597.1918 219.379,-581.5607 221.9978,-568.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"232.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>10</title>\n",
+       "<text text-anchor=\"middle\" x=\"119.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">10 (Saiful)</text>\n",
+       "</g>\n",
+       "<!-- 9&#45;&gt;10 -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>9&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M205.1223,-521.9735C189.1934,-509.1419 167.5964,-491.7443 150.0784,-477.6326\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"192.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 17 -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>17</title>\n",
+       "<text text-anchor=\"middle\" x=\"227.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">17 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 9&#45;&gt;17 -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>9&#45;&gt;17</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M227.5,-521.9735C227.5,-510.1918 227.5,-494.5607 227.5,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"231.0001,-481.0033 227.5,-471.0034 224.0001,-481.0034 231.0001,-481.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"239.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 22 -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>22</title>\n",
+       "<text text-anchor=\"middle\" x=\"227.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">22 (Umno)</text>\n",
+       "</g>\n",
+       "<!-- 17&#45;&gt;22 -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>17&#45;&gt;22</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M227.5,-434.9735C227.5,-423.1918 227.5,-407.5607 227.5,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"231.0001,-394.0033 227.5,-384.0034 224.0001,-394.0034 231.0001,-394.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"239.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>11</title>\n",
+       "<text text-anchor=\"middle\" x=\"820.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">11 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;11 -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>11&#45;&gt;11</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M860.6186,-982.7808C870.8323,-982.2694 878.5,-979.6758 878.5,-975 878.5,-972.0776 875.5048,-969.9686 870.7648,-968.673\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"890.5\" y=\"-971.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>12</title>\n",
+       "<text text-anchor=\"middle\" x=\"794.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">12 (Jan)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;12 -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>11&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M815.1128,-956.9735C811.5569,-945.0751 806.8278,-929.2508 802.7963,-915.7606\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"821.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 18 -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>18</title>\n",
+       "<text text-anchor=\"middle\" x=\"894.5\" y=\"-884.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">18 (Razak)</text>\n",
+       "</g>\n",
+       "<!-- 11&#45;&gt;18 -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>11&#45;&gt;18</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M835.8328,-956.9735C846.4494,-944.4919 860.7408,-927.6899 872.56,-913.7944\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"873.5\" y=\"-927.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24 -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>24</title>\n",
+       "<text text-anchor=\"middle\" x=\"490.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">24 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;24 -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>33&#45;&gt;24</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M562.6447,-695.9735C549.3452,-683.2586 531.3562,-666.0603 516.6685,-652.0183\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"518.9779,-649.384 509.3311,-645.0034 514.1406,-654.4437 518.9779,-649.384\"/>\n",
+       "<text text-anchor=\"middle\" x=\"556.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 34 -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>34</title>\n",
+       "<text text-anchor=\"middle\" x=\"621.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">34 (sekatan)</text>\n",
+       "</g>\n",
+       "<!-- 33&#45;&gt;34 -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>33&#45;&gt;34</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M589.788,-695.9735C595.3122,-683.9585 602.677,-667.9401 608.9186,-654.3646\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"612.2252,-655.5512 613.2226,-645.0034 605.8652,-652.627 612.2252,-655.5512\"/>\n",
+       "<text text-anchor=\"middle\" x=\"615.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">obj</text>\n",
+       "</g>\n",
+       "<!-- 15 -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>15</title>\n",
+       "<text text-anchor=\"middle\" x=\"752.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">15 (Datuk)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;15 -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>16&#45;&gt;15</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M802.4464,-695.9735C793.4924,-683.6085 781.4681,-667.0036 771.4613,-653.1847\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"774.2369,-651.05 765.5369,-645.0034 768.5673,-655.1556 774.2369,-651.05\"/>\n",
+       "<text text-anchor=\"middle\" x=\"800.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 20 -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>20</title>\n",
+       "<text text-anchor=\"middle\" x=\"862.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">20 (Ketua)</text>\n",
+       "</g>\n",
+       "<!-- 16&#45;&gt;20 -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>16&#45;&gt;20</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M825.2384,-695.9735C831.7923,-683.8418 840.5512,-667.6287 847.9301,-653.9698\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"851.1003,-655.4652 852.774,-645.0034 844.9416,-652.138 851.1003,-655.4652\"/>\n",
+       "<text text-anchor=\"middle\" x=\"857.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 19 -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>19</title>\n",
+       "<text text-anchor=\"middle\" x=\"779.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">19 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;19 -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>20&#45;&gt;19</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M845.3023,-608.9735C833.2833,-596.3752 817.0651,-579.3755 803.7375,-565.4055\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"806.1108,-562.8228 796.6756,-558.0034 801.046,-567.6548 806.1108,-562.8228\"/>\n",
+       "<text text-anchor=\"middle\" x=\"834.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 23 -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>23</title>\n",
+       "<text text-anchor=\"middle\" x=\"862.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">23 (,)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;23 -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>20&#45;&gt;23</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M862.5,-608.9735C862.5,-597.1918 862.5,-581.5607 862.5,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"866.0001,-568.0033 862.5,-558.0034 859.0001,-568.0034 866.0001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"883\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 25 -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>25</title>\n",
+       "<text text-anchor=\"middle\" x=\"941.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">25 (Dr)</text>\n",
+       "</g>\n",
+       "<!-- 20&#45;&gt;25 -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>20&#45;&gt;25</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M887.9844,-608.8488C894.6991,-603.4859 901.6854,-597.3421 907.5,-591 914.1542,-583.7421 920.44,-575.068 925.7545,-566.9496\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"930.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 21 -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>21</title>\n",
+       "<text text-anchor=\"middle\" x=\"335.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">21 (Pemuda)</text>\n",
+       "</g>\n",
+       "<!-- 26 -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>26</title>\n",
+       "<text text-anchor=\"middle\" x=\"337.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">26 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 21&#45;&gt;26 -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>21&#45;&gt;26</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M335.9144,-521.9735C336.1852,-510.1918 336.5446,-494.5607 336.8527,-481.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"340.3553,-481.0812 337.0861,-471.0034 333.3571,-480.9202 340.3553,-481.0812\"/>\n",
+       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 27 -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>27</title>\n",
+       "<text text-anchor=\"middle\" x=\"337.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">27 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 26&#45;&gt;27 -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>26&#45;&gt;27</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M337.5,-434.9735C337.5,-423.1918 337.5,-407.5607 337.5,-394.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;21 -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>24&#45;&gt;21</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M458.3839,-608.9735C434.5877,-595.617 401.9777,-577.3133 376.3304,-562.9177\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"434.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 30 -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>30</title>\n",
+       "<text text-anchor=\"middle\" x=\"490.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">30 (mempertikaikan)</text>\n",
+       "</g>\n",
+       "<!-- 24&#45;&gt;30 -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>24&#45;&gt;30</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M490.5,-608.9735C490.5,-597.1918 490.5,-581.5607 490.5,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"494.0001,-568.0033 490.5,-558.0034 487.0001,-568.0034 494.0001,-568.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"501\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">acl</text>\n",
+       "</g>\n",
+       "<!-- 29 -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>29</title>\n",
+       "<text text-anchor=\"middle\" x=\"460.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">29 (yang)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;29 -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>30&#45;&gt;29</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M484.284,-521.9735C480.1811,-510.0751 474.7244,-494.2508 470.0726,-480.7606\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"497.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nsubj</text>\n",
+       "</g>\n",
+       "<!-- 31 -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>31</title>\n",
+       "<text text-anchor=\"middle\" x=\"576.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">31 (tindakan)</text>\n",
+       "</g>\n",
+       "<!-- 30&#45;&gt;31 -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>30&#45;&gt;31</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M508.3193,-521.9735C520.7728,-509.3752 537.5771,-492.3755 551.3865,-478.4055\"/>\n",
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+       "</g>\n",
+       "<!-- 28 -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>28</title>\n",
+       "<text text-anchor=\"middle\" x=\"337.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">28 (Dusuki)</text>\n",
+       "</g>\n",
+       "<!-- 27&#45;&gt;28 -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>27&#45;&gt;28</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M337.5,-347.9735C337.5,-336.1918 337.5,-320.5607 337.5,-307.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"341.0001,-307.0033 337.5,-297.0034 334.0001,-307.0034 341.0001,-307.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-318.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 32 -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>32</title>\n",
+       "<text text-anchor=\"middle\" x=\"469.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32 (kerajaan)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;32 -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>31&#45;&gt;32</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M532.3438,-434.821C522.9329,-429.831 513.4469,-423.868 505.5,-417 497.5884,-410.1625 490.5267,-401.2346 484.7981,-392.7632\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"542.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 36 -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>36</title>\n",
+       "<text text-anchor=\"middle\" x=\"590.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">36 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 31&#45;&gt;36 -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>31&#45;&gt;36</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M579.4008,-434.9735C581.2967,-423.1918 583.8121,-407.5607 585.9688,-394.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"589.4696,-394.4324 587.6029,-384.0034 582.5585,-393.3203 589.4696,-394.4324\"/>\n",
+       "<text text-anchor=\"middle\" x=\"604.5\" y=\"-405.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 35 -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>35</title>\n",
+       "<text text-anchor=\"middle\" x=\"657.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">35 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 34&#45;&gt;35 -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>34&#45;&gt;35</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M628.9592,-608.9735C633.931,-596.9585 640.5593,-580.9401 646.1767,-567.3646\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"649.4608,-568.5818 650.0503,-558.0034 642.9927,-565.9053 649.4608,-568.5818\"/>\n",
+       "<text text-anchor=\"middle\" x=\"679.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 40 -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>40</title>\n",
+       "<text text-anchor=\"middle\" x=\"968.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">40 (,)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;40 -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>46&#45;&gt;40</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1115.3343,-697.5642C1098.14,-691.9713 1079.3293,-685.2756 1062.5,-678 1042.944,-669.5456 1022.0331,-658.4235 1004.9111,-648.7239\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1083\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 41 -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>41</title>\n",
+       "<text text-anchor=\"middle\" x=\"1057.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">41 (Najib)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;41 -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>46&#45;&gt;41</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1147.8791,-695.9735C1130.9124,-683.0253 1107.8535,-665.4276 1089.274,-651.2485\"/>\n",
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+       "</g>\n",
+       "<!-- 45 -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>45</title>\n",
+       "<text text-anchor=\"middle\" x=\"1171.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">45 (sengaja)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;45 -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>46&#45;&gt;45</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1171.5,-695.9735C1171.5,-684.1918 1171.5,-668.5607 1171.5,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1175.0001,-655.0033 1171.5,-645.0034 1168.0001,-655.0034 1175.0001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1200\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advmod</text>\n",
+       "</g>\n",
+       "<!-- 61 -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>61</title>\n",
+       "<text text-anchor=\"middle\" x=\"1268.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">61 (.)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;61 -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>46&#45;&gt;61</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1207.7759,-695.9949C1216.3794,-690.8331 1225.1757,-684.7504 1232.5,-678 1240.0709,-671.0223 1246.9804,-662.2002 1252.6767,-653.8634\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1265\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 47 -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>47</title>\n",
+       "<text text-anchor=\"middle\" x=\"1375.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">47 (kenyataan)</text>\n",
+       "</g>\n",
+       "<!-- 46&#45;&gt;47 -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>46&#45;&gt;47</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1227.7223,-699.8233C1247.6142,-694.0344 1269.9058,-686.6492 1289.5,-678 1306.89,-670.3237 1325.1389,-659.8826 1340.3298,-650.4467\"/>\n",
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+       "</g>\n",
+       "<!-- 62 -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>62</title>\n",
+       "<text text-anchor=\"middle\" x=\"1542.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">62 (Wan)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;62 -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>64&#45;&gt;62</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1603.4239,-695.8061C1596.7253,-690.3493 1589.6151,-684.1679 1583.5,-678 1575.9414,-670.3761 1568.3763,-661.4313 1561.8395,-653.1796\"/>\n",
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+       "</g>\n",
+       "<!-- 65 -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>65</title>\n",
+       "<text text-anchor=\"middle\" x=\"1627.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">65 (,)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;65 -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>64&#45;&gt;65</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1627.5,-695.9735C1627.5,-684.1918 1627.5,-668.5607 1627.5,-655.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1631.0001,-655.0033 1627.5,-645.0034 1624.0001,-655.0034 1631.0001,-655.0033\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1648\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 83 -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>83</title>\n",
+       "<text text-anchor=\"middle\" x=\"1699.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">83 (.)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;83 -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>64&#45;&gt;83</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1653.8871,-695.8513C1660.4537,-690.575 1667.1409,-684.4737 1672.5,-678 1678.3774,-670.9001 1683.5175,-662.3166 1687.6912,-654.2328\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1702\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 68 -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>68</title>\n",
+       "<text text-anchor=\"middle\" x=\"1813.5\" y=\"-623.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">68 (menjangka)</text>\n",
+       "</g>\n",
+       "<!-- 64&#45;&gt;68 -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>64&#45;&gt;68</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1679.606,-697.0539C1694.9463,-691.5319 1711.6045,-685.0033 1726.5,-678 1743.5858,-669.9669 1761.7144,-659.6521 1776.9757,-650.4032\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1772.5\" y=\"-666.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">advcl</text>\n",
+       "</g>\n",
+       "<!-- 43 -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>43</title>\n",
+       "<text text-anchor=\"middle\" x=\"1041.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">43 (Asyraf)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;43 -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>41&#45;&gt;43</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1054.1848,-608.9735C1052.018,-597.1918 1049.1433,-581.5607 1046.6785,-568.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1050.0621,-567.2053 1044.811,-558.0034 1043.1775,-568.4715 1050.0621,-567.2053\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1064.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">conj</text>\n",
+       "</g>\n",
+       "<!-- 44 -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>44</title>\n",
+       "<text text-anchor=\"middle\" x=\"1151.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">44 (Wajdi)</text>\n",
+       "</g>\n",
+       "<!-- 41&#45;&gt;44 -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>41&#45;&gt;44</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1076.9769,-608.9735C1090.7149,-596.2586 1109.2969,-579.0603 1124.4688,-565.0183\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1121.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">flat</text>\n",
+       "</g>\n",
+       "<!-- 42 -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>42</title>\n",
+       "<text text-anchor=\"middle\" x=\"1024.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">42 (dan)</text>\n",
+       "</g>\n",
+       "<!-- 43&#45;&gt;42 -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>43&#45;&gt;42</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1037.9776,-521.9735C1035.6754,-510.1918 1032.621,-494.5607 1030.0022,-481.1581\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1041.5\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">cc</text>\n",
+       "</g>\n",
+       "<!-- 48 -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>48</title>\n",
+       "<text text-anchor=\"middle\" x=\"1296.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">48 (Perdana)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;48 -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>47&#45;&gt;48</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1359.1311,-608.9735C1347.7972,-596.4919 1332.5403,-579.6899 1319.9225,-565.7944\"/>\n",
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+       "</g>\n",
+       "<!-- 57 -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>57</title>\n",
+       "<text text-anchor=\"middle\" x=\"1424.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">57 (lengkap)</text>\n",
+       "</g>\n",
+       "<!-- 47&#45;&gt;57 -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>47&#45;&gt;57</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1385.6528,-608.9735C1392.4856,-596.8418 1401.6172,-580.6287 1409.3101,-566.9698\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1422.5\" y=\"-579.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "<!-- 49 -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>49</title>\n",
+       "<text text-anchor=\"middle\" x=\"1134.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">49 (Menteri)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;49 -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>48&#45;&gt;49</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1257.2676,-521.9203C1245.6529,-516.3664 1232.9762,-510.1046 1221.5,-504 1205.3713,-495.4206 1187.937,-485.3623 1172.9409,-476.4559\"/>\n",
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+       "<!-- 50 -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>50</title>\n",
+       "<text text-anchor=\"middle\" x=\"1233.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">50 (,)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;50 -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>48&#45;&gt;50</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1275.2721,-521.666C1269.7501,-516.3024 1264.0738,-510.204 1259.5,-504 1254.2172,-496.8342 1249.448,-488.4388 1245.4798,-480.5517\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1280\" y=\"-492.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">punct</text>\n",
+       "</g>\n",
+       "<!-- 51 -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>51</title>\n",
+       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">51 (Tan)</text>\n",
+       "</g>\n",
+       "<!-- 48&#45;&gt;51 -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>48&#45;&gt;51</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M1300.644,-521.9735C1303.3793,-510.0751 1307.0171,-494.2508 1310.1182,-480.7606\"/>\n",
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+       "<!-- 55 -->\n",
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+       "<title>55</title>\n",
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+       "<title>57&#45;&gt;55</title>\n",
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+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>57&#45;&gt;56</title>\n",
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+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>57&#45;&gt;59</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">52 (Sri)</text>\n",
+       "</g>\n",
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+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>51&#45;&gt;52</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">53 (Muhyiddin)</text>\n",
+       "</g>\n",
+       "<!-- 52&#45;&gt;53 -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>52&#45;&gt;53</title>\n",
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+       "<title>54</title>\n",
+       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">54 (Yassin)</text>\n",
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+       "<!-- 53&#45;&gt;54 -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>53&#45;&gt;54</title>\n",
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+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>58</title>\n",
+       "<text text-anchor=\"middle\" x=\"1596.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">58 (untuk)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;58 -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>59&#45;&gt;58</title>\n",
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+       "<!-- 60 -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>60</title>\n",
+       "<text text-anchor=\"middle\" x=\"1707.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">60 (rakyat)</text>\n",
+       "</g>\n",
+       "<!-- 59&#45;&gt;60 -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>59&#45;&gt;60</title>\n",
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+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>63</title>\n",
+       "<text text-anchor=\"middle\" x=\"1542.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">63 (Saiful)</text>\n",
+       "</g>\n",
+       "<!-- 62&#45;&gt;63 -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>62&#45;&gt;63</title>\n",
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+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>66</title>\n",
+       "<text text-anchor=\"middle\" x=\"1705.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">66 (beliau)</text>\n",
+       "</g>\n",
+       "<!-- 68&#45;&gt;66 -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>68&#45;&gt;66</title>\n",
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+       "</g>\n",
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+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>67</title>\n",
+       "<text text-anchor=\"middle\" x=\"1816.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">67 (sudah)</text>\n",
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+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>68&#45;&gt;67</title>\n",
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+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>69</title>\n",
+       "<text text-anchor=\"middle\" x=\"1919.5\" y=\"-536.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">69 (ada)</text>\n",
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+       "<!-- 68&#45;&gt;69 -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>68&#45;&gt;69</title>\n",
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+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>70</title>\n",
+       "<text text-anchor=\"middle\" x=\"1805.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">70 (kenyataan)</text>\n",
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+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>69&#45;&gt;70</title>\n",
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+       "<title>71</title>\n",
+       "<text text-anchor=\"middle\" x=\"1928.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">71 (balas)</text>\n",
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+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>69&#45;&gt;71</title>\n",
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+       "<title>73</title>\n",
+       "<text text-anchor=\"middle\" x=\"2034.5\" y=\"-449.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">73 (Najib)</text>\n",
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+       "<g id=\"edge73\" class=\"edge\">\n",
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+       "<title>75</title>\n",
+       "<text text-anchor=\"middle\" x=\"1928.5\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">75 (tulisan)</text>\n",
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+       "<title>71&#45;&gt;75</title>\n",
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+       "<text text-anchor=\"middle\" x=\"1878.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">74 (mengenai)</text>\n",
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+       "<text text-anchor=\"middle\" x=\"2020.5\" y=\"-275.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">77 (berhubung)</text>\n",
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+       "<text text-anchor=\"middle\" x=\"2137.5\" y=\"-188.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">79 (positif)</text>\n",
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+       "<title>78&#45;&gt;80</title>\n",
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+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2013.0621,-132.2053 2007.811,-123.0034 2006.1775,-133.4715 2013.0621,-132.2053\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2049.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 81 -->\n",
+       "<g id=\"node83\" class=\"node\">\n",
+       "<title>81</title>\n",
+       "<text text-anchor=\"middle\" x=\"2140.5\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">81 (pergerakan)</text>\n",
+       "</g>\n",
+       "<!-- 79&#45;&gt;81 -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>79&#45;&gt;81</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M2138.1216,-173.9735C2138.5279,-162.1918 2139.0669,-146.5607 2139.529,-133.1581\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2143.0324,-133.1181 2139.8792,-123.0034 2136.0366,-132.8768 2143.0324,-133.1181\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2175.5\" y=\"-144.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">compound</text>\n",
+       "</g>\n",
+       "<!-- 82 -->\n",
+       "<g id=\"node84\" class=\"node\">\n",
+       "<title>82</title>\n",
+       "<text text-anchor=\"middle\" x=\"2004.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">82 (penuh)</text>\n",
+       "</g>\n",
+       "<!-- 80&#45;&gt;82 -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>80&#45;&gt;82</title>\n",
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+       "<text text-anchor=\"middle\" x=\"2024.5\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amod</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f091170fa58>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "epoch = 5\n",
-    "for e in range(epoch):\n",
-    "    train_acc, train_loss = [], []\n",
-    "    test_acc, test_loss = [], []\n",
-    "    train_acc_depends, test_acc_depends = [], []\n",
-    "    \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(train_X))\n",
-    "        batch_x = train_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = train_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = train_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_train[i: index]\n",
-    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_train[i: index]\n",
-    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
-    "        \n",
-    "        acc_depends, acc, cost, _ = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost, model.optimizer],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        train_loss.append(cost)\n",
-    "        train_acc.append(acc)\n",
-    "        train_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "        \n",
-    "    pbar = tqdm(\n",
-    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
-    "    )\n",
-    "    for i in pbar:\n",
-    "        index = min(i + batch_size, len(test_X))\n",
-    "        batch_x = test_X[i: index]\n",
-    "        batch_x = pad_sequences(batch_x,padding='post')\n",
-    "        batch_y = test_Y[i: index]\n",
-    "        batch_y = pad_sequences(batch_y,padding='post')\n",
-    "        batch_depends = test_depends[i: index]\n",
-    "        batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "        batch_segments = segments_test[i: index]\n",
-    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "        batch_masks = masks_test[i: index]\n",
-    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
-    "        \n",
-    "        acc_depends, acc, cost = sess.run(\n",
-    "            [model.accuracy_depends, model.accuracy, model.cost],\n",
-    "            feed_dict = {\n",
-    "                model.words: batch_x,\n",
-    "                model.types: batch_y,\n",
-    "                model.heads: batch_depends,\n",
-    "                model.segment_ids: batch_segments,\n",
-    "                model.input_masks: batch_masks,\n",
-    "                model.switch: True\n",
-    "            },\n",
-    "        )\n",
-    "        test_loss.append(cost)\n",
-    "        test_acc.append(acc)\n",
-    "        test_acc_depends.append(acc_depends)\n",
-    "        pbar.set_postfix(cost = cost, accuracy = acc, accuracy_depends = acc_depends)\n",
-    "    \n",
-    "    \n",
-    "    print(\n",
-    "    'epoch: %d, training loss: %f, training acc: %f, training depends: %f, valid loss: %f, valid acc: %f, valid depends: %f\\n'\n",
-    "    % (e, np.mean(train_loss), \n",
-    "       np.mean(train_acc), \n",
-    "       np.mean(train_acc_depends), \n",
-    "       np.mean(test_loss), \n",
-    "       np.mean(test_acc), \n",
-    "       np.mean(test_acc_depends)\n",
-    "    ))"
+    "string = 'KUALA LUMPUR: Ketua Penerangan BERSATU, Datuk Wan Saiful Wan Jan membidas kenyataan Datuk Seri Najib Razak dan Ketua Pemuda UMNO, Datuk Dr Asyraf Wajdi Dusuki yang mempertikaikan tindakan kerajaan melaksanakan sekatan pergerakan penuh. Beliau berkata, Najib dan Asyraf Wajdi sengaja memetik kenyataan Perdana Menteri, Tan Sri Muhyiddin Yassin yang tidak lengkap untuk mengelirukan rakyat. Wan Saiful berkata, beliau sudah menjangka ada kenyataan balas daripada Najib mengenai tulisan beliau berhubung kesan positif sekatan pergerakan penuh.'\n",
+    "sequence = transformer_textcleaning(string)[1]\n",
+    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)\n",
+    "h, t = sess.run([model.heads_seq, model.tags_seq],\n",
+    "        feed_dict = {\n",
+    "            model.words: [parsed_sequence],\n",
+    "            model.segment_ids: [segment_sequence],\n",
+    "            model.input_masks: [mask_sequence],\n",
+    "        },\n",
+    ")\n",
+    "h = h[0] - 2\n",
+    "t = [idx2tag[d] for d in t[0]]\n",
+    "merged_h = merge_sentencepiece_tokens_tagging(xlnet_sequence, h)\n",
+    "merged_t = merge_sentencepiece_tokens_tagging(xlnet_sequence, t)\n",
+    "tagging = list(zip(merged_t[0], merged_t[1]))\n",
+    "indexing = list(zip(merged_h[0], merged_h[1]))\n",
+    "dep = dependency_graph(tagging, indexing)\n",
+    "dep.to_graphvis()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1589,7 +2100,7 @@
        "'xlnet-base-dependency/model.ckpt'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1601,7 +2112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1617,12 +2128,12 @@
     "      clamp_len=-1)\n",
     "\n",
     "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
-    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base/config.json')"
+    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base-29-03-2020/config.json')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -1632,19 +2143,11 @@
       "INFO:tensorflow:memory input None\n",
       "INFO:tensorflow:Use float type <dtype: 'float32'>\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
-     ]
     }
    ],
    "source": [
     "learning_rate = 2e-5\n",
-    "hidden_size_word = 128\n",
+    "hidden_size_word = 256\n",
     "\n",
     "tf.reset_default_graph()\n",
     "sess = tf.InteractiveSession()\n",
@@ -1654,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1672,7 +2175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1688,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1733,14 +2236,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 630/630 [01:56<00:00,  5.39it/s]\n"
+      "100%|██████████| 1250/1250 [02:03<00:00, 10.15it/s]\n"
      ]
     }
    ],
@@ -1756,9 +2259,9 @@
     "    batch_y = pad_sequences(batch_y,padding='post')\n",
     "    batch_depends = test_depends[i: index]\n",
     "    batch_depends = pad_sequences(batch_depends,padding='post')\n",
-    "    batch_segments = segments_test[i: index]\n",
+    "    batch_segments = test_segments[i: index]\n",
     "    batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
-    "    batch_masks = masks_test[i: index]\n",
+    "    batch_masks = test_masks[i: index]\n",
     "    batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
     "    \n",
     "    tags_seq, heads = sess.run(\n",
@@ -1783,7 +2286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1793,12 +2296,12 @@
     "    \n",
     "temp_predict_Y = []\n",
     "for r in predict_Y:\n",
-    "    temp_predict_Y.extend(r)"
+    "    temp_predict_Y.extend(r)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1807,43 +2310,42 @@
      "text": [
       "               precision    recall  f1-score   support\n",
       "\n",
-      "          PAD    0.99998   1.00000   0.99999    632972\n",
-      "            X    1.00000   0.99997   0.99999    143586\n",
-      "          acl    0.98091   0.98226   0.98158      5806\n",
-      "        advcl    0.97098   0.95161   0.96120      2356\n",
-      "       advmod    0.98802   0.97806   0.98302      9527\n",
-      "         amod    0.95966   0.97100   0.96530      8208\n",
-      "        appos    0.98846   0.98947   0.98896      4936\n",
-      "          aux    1.00000   1.00000   1.00000        10\n",
-      "         case    0.99454   0.99110   0.99282     21128\n",
-      "           cc    0.98704   0.99518   0.99109      6429\n",
-      "        ccomp    0.89091   0.97313   0.93021       856\n",
-      "     compound    0.98091   0.96643   0.97362     13079\n",
-      "compound:plur    0.99068   0.98401   0.98733      1188\n",
-      "         conj    0.98303   0.99214   0.98756      8524\n",
-      "          cop    0.98664   0.99071   0.98867      1938\n",
-      "        csubj    0.96000   0.96000   0.96000        50\n",
-      "   csubj:pass    0.95652   0.91667   0.93617        24\n",
-      "          dep    0.98182   0.96716   0.97444      1005\n",
-      "          det    0.98698   0.97756   0.98225      8065\n",
-      "        fixed    0.96071   0.97162   0.96613      1057\n",
-      "         flat    0.98389   0.99064   0.98726     20411\n",
-      "         iobj    0.96154   0.80645   0.87719        31\n",
-      "         mark    0.96611   0.98539   0.97565      2806\n",
-      "         nmod    0.97956   0.97285   0.97619      8030\n",
-      "        nsubj    0.98317   0.98402   0.98359     12701\n",
-      "   nsubj:pass    0.96930   0.97858   0.97392      3969\n",
-      "       nummod    0.99113   0.99327   0.99220      7879\n",
-      "          obj    0.98266   0.98076   0.98171     10342\n",
-      "          obl    0.98468   0.98256   0.98362     11183\n",
-      "    parataxis    0.95595   0.95455   0.95525       682\n",
-      "        punct    0.99952   0.99949   0.99950     33107\n",
-      "         root    0.98888   0.98888   0.98888     10073\n",
-      "        xcomp    0.95951   0.96027   0.95989      2517\n",
+      "          PAD    0.99976   1.00000   0.99988    339805\n",
+      "            X    1.00000   0.99938   0.99969     62631\n",
+      "          acl    0.84425   0.83292   0.83855      3202\n",
+      "        advcl    0.64532   0.68824   0.66609      1684\n",
+      "       advmod    0.95594   0.94239   0.94912      6700\n",
+      "         amod    0.90791   0.90995   0.90893      4464\n",
+      "        appos    0.84555   0.77299   0.80765      3088\n",
+      "         case    0.98213   0.98372   0.98292     11117\n",
+      "           cc    0.97966   0.97993   0.97979      3637\n",
+      "        ccomp    0.48588   0.48315   0.48451       356\n",
+      "     compound    0.91807   0.92646   0.92224     11381\n",
+      "compound:plur    0.51163   0.66667   0.57895        33\n",
+      "         conj    0.90245   0.89455   0.89849      5140\n",
+      "          cop    0.97639   0.97639   0.97639       593\n",
+      "        csubj    0.33333   0.16667   0.22222         6\n",
+      "   csubj:pass    0.00000   0.00000   0.00000         1\n",
+      "          dep    0.66500   0.73684   0.69908       361\n",
+      "          det    0.94574   0.92229   0.93387      3912\n",
+      "        fixed    0.82857   0.79452   0.81119       146\n",
+      "         flat    0.95545   0.97113   0.96323     18638\n",
+      "         iobj    0.00000   0.00000   0.00000         4\n",
+      "         mark    0.91407   0.92163   0.91783      1812\n",
+      "         nmod    0.87013   0.83202   0.85065      4429\n",
+      "        nsubj    0.84932   0.87142   0.86023      6992\n",
+      "   nsubj:pass    0.80982   0.80318   0.80648      1951\n",
+      "       nummod    0.97935   0.94793   0.96338      4302\n",
+      "          obj    0.90603   0.92001   0.91297      6351\n",
+      "          obl    0.87106   0.87054   0.87080      5075\n",
+      "    parataxis    0.46512   0.34739   0.39773       403\n",
+      "        punct    0.99665   0.99622   0.99643     20881\n",
+      "         root    0.90154   0.90740   0.90446     10000\n",
+      "        xcomp    0.74568   0.75453   0.75008      1601\n",
       "\n",
-      "     accuracy                        0.99678    994475\n",
-      "    macro avg    0.97738   0.97381   0.97531    994475\n",
-      " weighted avg    0.99679   0.99678   0.99678    994475\n",
+      "     accuracy                        0.98035    540696\n",
+      "    macro avg    0.78099   0.77564   0.77668    540696\n",
+      " weighted avg    0.98030   0.98035   0.98029    540696\n",
       "\n"
      ]
     }
@@ -1855,16 +2357,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "arc accuracy: 0.9310084738376598\n",
-      "types accuracy: 0.9258795751889828\n",
-      "root accuracy: 0.9474206349206349\n"
+      "arc accuracy: 0.8481110435316473\n",
+      "types accuracy: 0.8274148274750857\n",
+      "root accuracy: 0.9210116457364005\n"
      ]
     }
    ],
@@ -1876,180 +2378,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Placeholder',\n",
-       " 'Placeholder_1',\n",
-       " 'Placeholder_2',\n",
-       " 'Placeholder_3',\n",
-       " 'Placeholder_4',\n",
-       " 'Placeholder_5',\n",
-       " 'W_d',\n",
-       " 'W_e',\n",
-       " 'U',\n",
-       " 'U-bi',\n",
-       " 'Wl',\n",
-       " 'Wr',\n",
-       " 'model/transformer/r_w_bias',\n",
-       " 'model/transformer/r_r_bias',\n",
-       " 'model/transformer/word_embedding/lookup_table',\n",
-       " 'model/transformer/r_s_bias',\n",
-       " 'model/transformer/seg_embed',\n",
-       " 'model/transformer/layer_0/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_0/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_0/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_0/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_0/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_0/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_0/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_0/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_0/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_0/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_0/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_1/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_1/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_1/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_1/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_1/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_1/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_1/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_1/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_1/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_1/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_1/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_2/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_2/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_2/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_2/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_2/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_2/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_2/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_2/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_2/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_2/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_2/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_3/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_3/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_3/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_3/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_3/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_3/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_3/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_3/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_3/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_3/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_3/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_4/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_4/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_4/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_4/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_4/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_4/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_4/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_4/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_4/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_4/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_4/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_5/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_5/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_5/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_5/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_5/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_5/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_5/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_5/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_5/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_5/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_5/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_6/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_6/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_6/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_6/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_6/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_6/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_6/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_6/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_6/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_6/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_6/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_7/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_7/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_7/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_7/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_7/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_7/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_7/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_7/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_7/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_7/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_7/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_8/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_8/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_8/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_8/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_8/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_8/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_8/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_8/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_8/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_8/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_8/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_9/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_9/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_9/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_9/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_9/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_9/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_9/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_9/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_9/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_9/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_9/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_10/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_10/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_10/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_10/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_10/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_10/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_10/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_10/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_10/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_10/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_10/ff/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_11/rel_attn/q/kernel',\n",
-       " 'model/transformer/layer_11/rel_attn/k/kernel',\n",
-       " 'model/transformer/layer_11/rel_attn/v/kernel',\n",
-       " 'model/transformer/layer_11/rel_attn/r/kernel',\n",
-       " 'model/transformer/layer_11/rel_attn/o/kernel',\n",
-       " 'model/transformer/layer_11/rel_attn/LayerNorm/gamma',\n",
-       " 'model/transformer/layer_11/ff/layer_1/kernel',\n",
-       " 'model/transformer/layer_11/ff/layer_1/bias',\n",
-       " 'model/transformer/layer_11/ff/layer_2/kernel',\n",
-       " 'model/transformer/layer_11/ff/layer_2/bias',\n",
-       " 'model/transformer/layer_11/ff/LayerNorm/gamma',\n",
-       " 'dense/kernel',\n",
-       " 'dense/bias',\n",
-       " 'dense_1/kernel',\n",
-       " 'dense_1/bias',\n",
-       " 'dense_2/kernel',\n",
-       " 'dense_2/bias',\n",
-       " 'dense_3/kernel',\n",
-       " 'dense_3/bias',\n",
-       " 'heads_seq',\n",
-       " 'tags_seq',\n",
-       " 'transitions',\n",
-       " 'logits']"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "strings = ','.join(\n",
     "    [\n",
@@ -2067,13 +2398,12 @@
     "        and 'adam' not in n.name\n",
     "        and 'gradients/bert' not in n.name\n",
     "    ]\n",
-    ")\n",
-    "strings.split(',')"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2108,7 +2438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -2116,7 +2446,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from xlnet-base-dependency/model.ckpt\n",
-      "WARNING:tensorflow:From <ipython-input-46-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-84-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
       "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
@@ -2134,215 +2464,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def merge_sentencepiece_tokens_tagging(x, y):\n",
-    "    new_paired_tokens = []\n",
-    "    n_tokens = len(x)\n",
-    "    rejected = ['<cls>', '<sep>']\n",
-    "\n",
-    "    i = 0\n",
-    "\n",
-    "    while i < n_tokens:\n",
-    "\n",
-    "        current_token, current_label = x[i], y[i]\n",
-    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
-    "            previous_token, previous_label = new_paired_tokens.pop()\n",
-    "            merged_token = previous_token\n",
-    "            merged_label = [previous_label]\n",
-    "            while (\n",
-    "                not current_token.startswith('▁')\n",
-    "                and current_token not in rejected\n",
-    "            ):\n",
-    "                merged_token = merged_token + current_token.replace('▁', '')\n",
-    "                merged_label.append(current_label)\n",
-    "                i = i + 1\n",
-    "                current_token, current_label = x[i], y[i]\n",
-    "            merged_label = merged_label[0]\n",
-    "            new_paired_tokens.append((merged_token, merged_label))\n",
-    "\n",
-    "        else:\n",
-    "            new_paired_tokens.append((current_token, current_label))\n",
-    "            i = i + 1\n",
-    "\n",
-    "    words = [\n",
-    "        i[0].replace('▁', '')\n",
-    "        for i in new_paired_tokens\n",
-    "        if i[0] not in ['<cls>', '<sep>']\n",
-    "    ]\n",
-    "    labels = [i[1] for i in new_paired_tokens if i[0] not in ['<cls>', '<sep>']]\n",
-    "    return words, labels"
+    "transforms = ['add_default_attributes',\n",
+    "             'remove_nodes(op=Identity, op=CheckNumerics, op=Dropout)',\n",
+    "             'fold_batch_norms',\n",
+    "             'fold_old_batch_norms',\n",
+    "             'quantize_weights(fallback_min=-10, fallback_max=10)',\n",
+    "             'strip_unused_nodes',\n",
+    "             'sort_by_execution_order']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['Kuala', 'Lumpur:', 'Sempena', 'sambutan', 'Aidilfitri', 'minggu', 'depan,', 'Perdana', 'Menteri', 'Tun', 'Dr', 'Mahathir', 'Mohamad', 'dan', 'Menteri', 'Pengangkutan', 'Anthony', 'Loke', 'Siew', 'Fook', 'menitipkan', 'pesanan', 'khas', 'kepada', 'orang', 'ramai', 'yang', 'mahu', 'pulang', 'ke', 'kampung', 'halaman', 'masing-masing.', 'Dalam', 'video', 'pendek', 'terbitan', 'Jabatan', 'Keselamatan', 'Jalan', 'Raya', '(Jkjr)', 'itu,', 'Dr', 'Mahathir', 'menasihati', 'mereka', 'supaya', 'berhenti', 'berehat', 'dan', 'tidur', 'sebentar', 'sekiranya', 'mengantuk', 'ketika', 'memandu.'] 57\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "73"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "string = 'KUALA LUMPUR: Sempena sambutan Aidilfitri minggu depan, Perdana Menteri Tun Dr Mahathir Mohamad dan Menteri Pengangkutan Anthony Loke Siew Fook menitipkan pesanan khas kepada orang ramai yang mahu pulang ke kampung halaman masing-masing. Dalam video pendek terbitan Jabatan Keselamatan Jalan Raya (JKJR) itu, Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar  sekiranya mengantuk ketika memandu.'\n",
-    "\n",
-    "import re\n",
-    "\n",
-    "def entities_textcleaning(string, lowering = False):\n",
-    "    \"\"\"\n",
-    "    use by entities recognition, pos recognition and dependency parsing\n",
-    "    \"\"\"\n",
-    "    string = re.sub('[^A-Za-z0-9\\-\\/():,. ]+', ' ', string)\n",
-    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
-    "    original_string = string.split()\n",
-    "    if lowering:\n",
-    "        string = string.lower()\n",
-    "    string = [\n",
-    "        (original_string[no], word.title() if word.isupper() else word)\n",
-    "        for no, word in enumerate(string.split())\n",
-    "        if len(word)\n",
-    "    ]\n",
-    "    return [s[0] for s in string], [s[1] for s in string]\n",
-    "\n",
-    "def parse_X(left):\n",
-    "    left = ' '.join(left)\n",
-    "    bert_tokens = tokenize_fn(left)\n",
-    "    bert_tokens.extend([4, 3])\n",
-    "    segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
-    "    input_mask = [0] * len(segment)\n",
-    "    s_tokens = [sp_model.IdToPiece(i) for i in bert_tokens]\n",
-    "    return bert_tokens, segment, input_mask, s_tokens\n",
-    "\n",
-    "sequence = entities_textcleaning(string)[1]\n",
-    "print(sequence, len(sequence))\n",
-    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)\n",
-    "len(parsed_sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
-      "  warnings.warn('An interactive session is already active. This can '\n"
+      "WARNING:tensorflow:From <ipython-input-87-5eb1154ea798>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.gfile.GFile.\n"
      ]
     }
    ],
    "source": [
-    "def load_graph(frozen_graph_filename):\n",
-    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
-    "        graph_def = tf.GraphDef()\n",
-    "        graph_def.ParseFromString(f.read())\n",
-    "    with tf.Graph().as_default() as graph:\n",
-    "        tf.import_graph_def(graph_def)\n",
-    "    return graph\n",
+    "from tensorflow.tools.graph_transforms import TransformGraph\n",
+    "tf.set_random_seed(0)\n",
     "\n",
-    "g = load_graph('xlnet-base-dependency/frozen_model.pb')\n",
-    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
-    "seg = g.get_tensor_by_name('import/Placeholder_1:0')\n",
-    "m = g.get_tensor_by_name('import/Placeholder_2:0')\n",
-    "heads_seq = g.get_tensor_by_name('import/heads_seq:0')\n",
-    "tags_seq = g.get_tensor_by_name('import/logits:0')\n",
-    "test_sess = tf.InteractiveSession(graph = g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h, t = test_sess.run([heads_seq, tags_seq],\n",
-    "        feed_dict = {\n",
-    "            x: [parsed_sequence],\n",
-    "            seg: [segment_sequence],\n",
-    "            m: [mask_sequence],\n",
-    "        },\n",
-    ")\n",
-    "h = h[0] - 1\n",
-    "t = [idx2tag[d] for d in t[0]]\n",
-    "merged_h = merge_sentencepiece_tokens_tagging(xlnet_sequence, h)\n",
-    "merged_t = merge_sentencepiece_tokens_tagging(xlnet_sequence, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('Kuala', 23), ('Lumpur:', 1), ('Sempena', 5), ('sambutan', 23), ('Aidilfitri', 5), ('minggu', 6), ('depan,', 7), ('Perdana', 23), ('Menteri', 10), ('Tun', 11), ('Dr', 12), ('Mahathir', 21), ('Mohamad', 21), ('dan', 16), ('Menteri', 10), ('Pengangkutan', 17), ('Anthony', 24), ('Loke', 19), ('Siew', 21), ('Fook', 21), ('menitipkan', 0), ('pesanan', 23), ('khas', 24), ('kepada', 27), ('orang', 24), ('ramai', 27), ('yang', 31), ('mahu', 31), ('pulang', 27), ('ke', 33), ('kampung', 31), ('halaman', 33), ('masing-masing.', 33), ('Dalam', 38), ('video', 50), ('pendek', 38), ('terbitan', 38), ('Jabatan', 39), ('Keselamatan', 40), ('Jalan', 41), ('Raya', 41), ('(Jkjr)', 50), ('itu,', 38), ('Dr', 50), ('Mahathir', 50), ('menasihati', 23), ('mereka', 50), ('supaya', 52), ('berhenti', 50), ('berehat', 52), ('dan', 54), ('tidur', 52), ('sebentar', 56), ('sekiranya', 58), ('mengantuk', 54), ('ketika', 59), ('memandu.', 58)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(zip(merged_h[0], merged_h[1])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('Kuala', 'nsubj'), ('Lumpur:', 'flat'), ('Sempena', 'case'), ('sambutan', 'obl'), ('Aidilfitri', 'compound'), ('minggu', 'compound'), ('depan,', 'compound'), ('Perdana', 'nsubj'), ('Menteri', 'flat'), ('Tun', 'flat'), ('Dr', 'flat'), ('Mahathir', 'flat'), ('Mohamad', 'flat'), ('dan', 'cc'), ('Menteri', 'conj'), ('Pengangkutan', 'flat'), ('Anthony', 'flat'), ('Loke', 'flat'), ('Siew', 'flat'), ('Fook', 'flat'), ('menitipkan', 'root'), ('pesanan', 'obj'), ('khas', 'amod'), ('kepada', 'case'), ('orang', 'nmod'), ('ramai', 'compound'), ('yang', 'nsubj'), ('mahu', 'advmod'), ('pulang', 'acl'), ('ke', 'case'), ('kampung', 'obl'), ('halaman', 'compound'), ('masing-masing.', 'det'), ('Dalam', 'case'), ('video', 'obl'), ('pendek', 'amod'), ('terbitan', 'compound'), ('Jabatan', 'flat'), ('Keselamatan', 'flat'), ('Jalan', 'flat'), ('Raya', 'flat'), ('(Jkjr)', 'punct'), ('itu,', 'det'), ('Dr', 'nsubj'), ('Mahathir', 'flat'), ('menasihati', 'parataxis'), ('mereka', 'obj'), ('supaya', 'case'), ('berhenti', 'xcomp'), ('berehat', 'xcomp'), ('dan', 'cc'), ('tidur', 'conj'), ('sebentar', 'advmod'), ('sekiranya', 'mark'), ('mengantuk', 'ccomp'), ('ketika', 'mark'), ('memandu.', 'ccomp')]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(zip(merged_t[0], merged_t[1])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3\n",
+    "pb = 'xlnet-base-dependency/frozen_model.pb'\n",
+    "input_graph_def = tf.GraphDef()\n",
+    "with tf.gfile.FastGFile(pb, 'rb') as f:\n",
+    "    input_graph_def.ParseFromString(f.read())\n",
     "\n",
-    "bucketName = 'huseinhouse-storage'\n",
-    "Key = 'xlnet-base-dependency/frozen_model.pb'\n",
-    "outPutname = \"v34/dependency/xlnet-base-dependency.pb\"\n",
+    "if 'bert' in pb:\n",
+    "    inputs = ['Placeholder']\n",
+    "    a = ['dense/BiasAdd']\n",
+    "if 'xlnet' in pb:\n",
+    "    inputs = ['Placeholder', 'Placeholder_1', 'Placeholder_2']\n",
+    "    a = ['transpose_3']\n",
     "\n",
-    "s3 = boto3.client('s3')\n",
+    "transformed_graph_def = TransformGraph(input_graph_def, \n",
+    "                                       inputs,\n",
+    "                                       ['logits', 'heads_seq'] + a, transforms)\n",
     "\n",
-    "s3.upload_file(Key,bucketName,outPutname)"
+    "with tf.gfile.GFile(f'{pb}.quantized', 'wb') as f:\n",
+    "    f.write(transformed_graph_def.SerializeToString())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2361,7 +2533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,