diff --git a/15_processing_sequences_using_rnns_and_cnns.ipynb b/15_processing_sequences_using_rnns_and_cnns.ipynb index f882ab7d5..05566ef41 100644 --- a/15_processing_sequences_using_rnns_and_cnns.ipynb +++ b/15_processing_sequences_using_rnns_and_cnns.ipynb @@ -179,7 +179,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -256,7 +256,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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TF5vyAXuIjTPHxOjaOGpAcosqWfLkJk42O9hYeNyrt4XXrIHHH4cbbjjA4493PZCgKxpwVNA6VtVA9u5Srj1nLHdeOMkvyz7PSh5O/pEqGpodPn9tNThsLDxOc4sTAKfTeO22cPs+m29/+0CXAwm6owFHBa1/fXIIp4GrMsb6rQ6zxg6n2WHIP1rttzqowJaZGk/roDRv3RbubIBAd6PXuqIBRwUlYwyv5BzkC+PjGJcQ6bd6tK7GqP04qr9mjR1ORGgIZ50R45Xbwt2NRutr0NGAo4LSlgOVHDhez9Wz/Xd1A5AUHcHomAhdAVT1296yWuqbHNwwN8Urt4W3bOl+NFpr0NFMA0p14aUtB4kKt3PpmZ5LAdJfM5OHa8YB1W85B1w/VmZ7aTj0j37U82i0BQtcx/VEA44KOjUNzfzns6N8ZcYohob5P53grLGxHKo8SVlNo7+rogJQTlEF8ZFhjPNxpoz+0ICjgs47249ystnBVX6+ndZqZrJrAqhe5aj+yC2qJCMl1md5AAdCA44KOi/lHGTiiChmuWf6+9v00TGECDy5ttCnqXVU4CuraaToeD2zx/l+SH9/aMBRQWVPSQ2fFp/g6tljLfOLMP9oNU5g0/4Kljy5UYOO6rXcogoAMlK803/jaRpwVFB5JfcQdpuweNYZ/q5Km42Fx10LowPNLb7N56YCW86BSsLsNqaf4Zt1nAZKA44KGs0OJ699cogL00eQOCzc39Vpk5kaT6g7qZvNJj7N56YCW05RJTPHDCfcHuLvqvSKZQOOiMSJyOsiUiciRSJyXRfH3SgiuSJSLSKHROQREbG3258tIg3uZalrRWS3796FspIPdpVSXtvENywyWKBVRkosz99yDpFhIcxOifVLih0VeE42OdhxuIqMAOm/AQsHHOAxoAlIApYAj4vItE6OGwr8N5AAnANcxOlLTt9pjIlyP9K8WGflQblFlTy2Zq/H+jSe+KiQyLAQoof4fyh0R7PHxXH5rDPYdkjzqqne2XboBC1Ow+wA+oFiyYAjIpHAFcD9xphaY8w64E3gho7HGmMeN8asNcY0GWMOAyuAeb6tsfK03KJKrlm2gT+s3D3gjvTaxhZ++MpWcooqqW9y8M2nN1uyY37h1CTqmxx8vK/c31VRAaD1MxxIV8TW+6nnMhlwGGMK2m3bBszvxbkXAHkdtj0sIr8FdgM/NcZkdzxJRJYCSwESExPJzj7tkKBTW1vrsXbYW+lgV4WD9LgQJsb2fL/59zknaXa4etIbm528sHoLNRPC+vSaLU5D9sEW3tjXRE2Ta5sBmvpYnifboTvNTkNECDyzeiu2Y9bpYwLftYHVWakd3vukgdGRwtbNH/v8tfvbDlYNOFFAVYdtVcCw7k4SkW8Bs4Fb2m2+F8jHdXvuGuAtEZlpjNnX/lxjzDJgGUBaWprJysoaSP0HhezsbDzRDrlFlfxu1QYcTkOY3dFjgsFlH+1jR/kubALuxTi5Iuts5k5I6NVrbSwsx+GE1z45xIHjTZwzPo6vnX0GD7yZR3OLk1C7jWsvntPrX4aeaofeuPjYJ2wsPM75F8wnxGaNYdvg2zawMqu0g9Np+K/slVx25hiyss7y+ev3tx2sGnBqgY7j/KKBmq5OEJHFwG+Bi40xbfckjDGb2h32rIhcC1wGPOq56qruvPPZ0VOuVtbvLe/yj/2Tawv5zX928aWzRnHj3BReyTnEK7mHeHv70R4DTm5RJdc+sZEm99ogybFD+cdNc8hKS0REmDRiGBsLj5OZGm/Z2xALp43k7e1H+bS40mu5sVTg21tWS3VDi2U/x12xasApAOwiMskYs8e9bQan3yoDQES+CDwBfMkY81kPZRvAOj8dg0BReV3bvw2uK49F00aSNvLUC9an1u3noXd28qUzR/GXq2diD7HxhfHxxEWG8fePCjlvYgKXnjmqy9dZvuFAW7AR4BtzxrAgfUTb/owAGAGWlZZIaIiwMr9EA47qkrcTdnqLJQcNGGPqgNeAB0UkUkTmAZcDyzseKyIX4hoocIUxZnOHfcNFZJGIRIiIXUSW4Orjec/770IBlFQ3sHZPOYumJfHDRWncd2k6tY0tfOX/rePJtYU43ffM/rF+P796O59Lp4/kz9e4gk2ruxemMWNMDPf+azuHKus7fZ1nPz7Av7ceQQRCBMJDbb26BWc10RGhzJ2QwMq8Yxhj/F0dZVGBlLCzPate4QDcDjwNlALHgduMMXkikoyrT2aqMaYYuB+IAf7TLlXJWmPMpUAo8BCQDjiAXcBiY4zOxfGRp9fvp8Xp5CeXTSEl3rXQ2ZUZY/jxa5/x0Ds7ef3TQ8QODWfdXldQ+uu1swgNOfV3UJjdxqPXns1lf13LXS9u5aWlmW0Byek0/O69Xfz9w0IWTk3ipnPH8enBE5a+bdaThVOT+Nm/d7C3tJZJSd12W6oglVtUyexxgZGwsz3LBhxjTAWwuJPtxbgGFbQ+73KlBmNMGTDHKxVUPapuaOb5jcVceuaotmADkBAVzrIbMvj9yt08tmYfUINN4Nvzxp8WbFolxw/l11+bzl0vbuXPq/dwz6I0mlqc/OjVbfx76xFuyEzhga9OI8QmnDsx8K5s2rvEHXBW5pdowFGnKa1poOh4Pdefk+LvqvSZZQOOCnzPbyqmprGF2+ZPOG2fiDA0zN42Ek1wpek4p5u0LpfPPIP1e8t5LHsvQ8NDeGnzQYoq6vnhojRuz5oQcL/2upIUHcGMscNZmXeMOxZM9Hd1lMXkuvtvAinDQCtL9uGowNfY4uDpdfs5b2IC08+I6fSYzNR4wuw2QgRC7bZe5RB74KvTGB0TwSPv7qaoop7QEFfuscESbFotnJrEtkNVHK066e+qKIvJKaok3G5j+ujOv1dWpgFHecW/Pz1MaU0j35mf2uUxGSmxrLglkx8sTOtxbk6roWF2Lpn6+bLQTqcZlNmVF01LAmB1fomfa6KsJqeokhljhhNmD7w/34FXY2V5Tqfh7x8VMm10NOf10J+SkRLLHQsm9qmD/yszRhMR2rcro0AzITGK1IRIVmrAUe2cbHKQF2AJO9vTPhzlcat2llBYVsdfr53llVtdrVdGVp/EORAiwiXTknhq7X6qTjYTMyTU31VSFhCICTvb8/gVjog8KiJvdbI9WkQeEJEp7bZ9X0S2i4heaQ0Sxhj+9uE+xsYN4bLpI3s+oZ/6c2UUaBZOHenKB7e71N9VURbx5rYjANgtlPaoLzz6h15EJgDfAX7Zye7ZwC9wzY1p9TdgBHCjJ+uh/GfLgUo+LT7BreennjJ5U/XdrLHDSYgK19tqCnDNvXlxczEA3/lnriUznvfE038R/hvYZozJ6WTfLKAR16RNAIwxJ4HnOH39GhWAcosq+fFr2xkWYeeqDGstchaIbDbhkqlJvJ9fwl/fLwjIPzDKc9bvLWtLZhuoS5H3KuCIyEQRaRaRX3bY/riI1IjIbBEJB64Hnu/k/J3A74FwoFlEjIi86t79IjBVRM4d0DtRfpVbVMl1T2xkX1kdJ5sc5B+t9neVBoXUxEgaWpz8adWeAa8LpAJbZLiry90WwINlehVwjDF7gSeB74tIAoCI/Bz4NvA19xVNJjAcWNtJEd8ECoG3gLnux93ufVuBauCL/X8byt82Fh5vS5xpzOAcquwPrat/GqApQH/VKs/YX15HWIiNuy6a1OtpBFbTl1tqvwRCgHtF5GZc/TE3GGNWu/dn4vpebO/k3G3AGOADY8xG96MIwBjjdJ+T2c/3oCyg/a+tQP31ZUXnTkggwj3fwmkgdmjfFqFTg4MxhtX5pSxIT+SuiycHZLCBPgQcY8wx4M/A94C/A/9ljHm53SGjgWpjTFMnp08DwoBPuii+zH2+ClBnDB+CAeZPTgzYX19WlJESy4pbM7k9awIjo8P53bu7KCjpclkoNUjtOFzNseqGUyY9B6K+DhrYg6sfZoMx5rEO+yJwDQrozNm4rn62drH/JDCkj3VRFrJur2vNu3u/mK7BxsMyUmL50RfTeeW75xJut/HNpzZ3uUyDGpxW7SzBJnBhu/WdAlGvA4573Zm/AxuAeSIyo8Mhx4Gu/tLMAvYZY7rqSY4DyrvYpwLAuj1lJESFkT5Ssxt7y9i4oTx38xeob2rhm09t5nhtV7/v1GCzKr+E2SlxxEUG9i3V3o5SOxv4N66BA1lAMfCbDoftAkJFZEwnRUyl3XDoTowHdI2aAOV0GtbtLee8iQnYAnRCWqBIHxnN0zfN4fCJk1z1tw38aZUOlx7sDlbUs/NoNZdMTfJ3VQasx4AjIhOB/wNWAt9z99H8ErhMRC5od+hH7v9+oZNiTgAz3KtvZopIW4+yiAwHJrc7XwWYXcdqKK9t4rxJif6uSlCYPS6OuxdOprC8jr+8v4er/76Bp9YVUtPQ3HZMblElj63Zq8FoEHh/p2vi78WDIOB0m0tNREbiCjQ7gSXuEWXgmqz5I+C3wLkAxpgDIrIZ+Aqu5aHb+znwFK6rpAjgfGCde9+XgCbg9YG+GeUfa/eUAfSYqFN5TrPDILg6Rluchl+9vZNfv7OTKaOiSYkfyqr8EhxOg91m4+dfnsqU0cMQEfaU1JB3pJqZY4dz1pgY7DYbITYhNMRG3pEq8o5UM29iwin9cLlFlby9r4lh4yu1f84PVu0sYeKIKMYnRPZ8sMV1G3DcI9NOyy9vjHEAU04/g8eBv4jIHcaY+nbH7wDO6eJlrgdeMcacMsFAROJwBamFuPp3fmyMOW1SqfvY7wP34hp48C9cy1E39rUc1T/r9pYzOSmKkTER/q5K0MhMjSc81EZzi5PQEBv3XZpOZX0zOUUVrMovodnhmpLe5HDyszd2nHb+cxuKuiz7j6sKSIkfyviESEJE+LCgDIfT8PaBjToC0ceqTjazqbCCWy/oepmPQOLpbNHLcV353I4rs0C3RGQmsACY3snux3Bd+SQBM4F3RGSbMSavQxmLgPuAC4EjuK6Ufune1utyVP80NDvYtL8iIJe7DWTdZczevP841z+1mRaHE7vNxo8vTWfCiCje2HaY13IPY3DNVr/szFFcmD6CFqdhdX4Jq/JLMLhWX7XbhOO1TRSW1dLizqfSOvFUA47vZO8upcVpuHhK4N9OAw8HHGOMQ0S+jWsYdG+MBL7lzmTQRkQigSuA6caYWmCdiLwJ3MDngaTVjcBTrQFERH4FrADu62M5qh+2HKigqcXJ+ZP1dpqvZaTEdvrH/wvj43nh1tODUWS4nXe2H3VdFdltfGve+LZ9ExKj+GhPWdu+R66cQUZKLLlFlSx5YiMN7iwSOqHXt1bll5AQFcasscP9XRWP8Ph6OMaYjcDGXh77bhe7JgMOY0xBu23bgPmdHDsNeKPDcUnugQnJvS1HRJYCSwGGDh066JYs9pbhWd8iOuOrXHRmMqZZh+laXdjodCKSz6Sh+DNmP7SrV/vCRqcTe+HNhCdN5AtpY3E21vm62sHJZmfsf62gbtc6QkIW+rs2HmHV/PFRQFWHbVVAZ5M8Oh7b+u9hfSnHGLPMGDPbGDN7zJgxGGOC/rFmzZoej8lc/C3mTk7C2dTg9/r6sx0C5dF4eCdVG16m8fDOXu9rPLyT3145C7GH8kx2nt/fQ7B8Fj7adQRbeCQv/eGnfn/fvW2Hnlg14NQC0R22RQOd5fToeGzrv2v6WI7qo7KaRnYereZ8HQ496KVE20gfOYxXcw76uypBY1V+CRGhNs6bNHhuV1s14BQAdhGZ1G7bDKCzjv489772x5UY16i3vpSj+mi9O53N+YPoC6E6JyJcNXss2w5VaS43HzDGNZDj/EmJRISG+Ls6HmPJgGOMqcM1l+dBEYkUkXnA5bhGwXX0HHCziEwVkVjgZ8Az/ShH9dHaPeXEDg1l2ugYf1dF+cDimaOx24RX9CrH6/KOVHOkqmFQZBdoz5IBx+12XPNqSoEXcM2tyRORZBGpFZFkaBt48AiwBihyP37RUzm+exuDkzGGtXvKOHdiAiGaziYoxEeFc2H6CF7/9DDNDmfPJ6h+W72zBBG4KMCTdXbk8VFqnmKMqQAWd7K9GNdggPbb/gj8sS/lqIHZU1pLaU0j52t2gaBy1eyxrMwvIXt32aD79W0lq/JLyBmiNDsAAB83SURBVEiOJT4q3N9V8SgrX+EoC/uowJ3ORvtvgkpWWiIJUWF6W82L3ss7Rt6RaqaM6jjeKfBpwFH9sm5vOakJkYyJHervqigfCg2x8bVZZ/DBrlJdHsELcosquWOFa53Kl3MODrrkqxpwVJ81tjjYVFiho9OC1FWzx9LiNPx76xF/V2XQ2Vh4vC2VUIvDlUpoMNGAo/ost6iSk80OXY4gSE1OGsaMMTG8knOwV5P9VO+d6R7xKUCo3TboUglpwFF9tm5POSE2ITM1zt9VUX5yZcYYdh1zLXWgPOd4ves25ZLMlEGZmVsDjuqzd3ccY2R0OAUltf6uivKTr844gzC7TQcPeNjKvBKSosN58KvTBl2wAQ04qo8+3F1KYXkdR040sOTJjYOuU1P1TszQUBZOTeKNbUdobHH4uzoe091Kqa0L0XnrM9/Q7ODDAtdw88G6VLtl5+Eoa3pjm6uj2ADNuj5KULtq9lje3n6UH76ynRvPHRfwn4PcokqWPLmRphYnITbhrosmMT7BNeWvsKyWv36whxaH9xaiW7ennPomB4umjfRouVaiAUf1idM9wTxEBmenpuq9oWGuHF9vbjvCyvxjAd/nsLHwOI3NTgzgdBh+v7Kg0+O8tRDdyvxjDIuwc874wfud0oCj+uRQZT2Tk6K4fOYZp600qYLL5v0Vbf8eDFe7o2IiaB1zF2638duvn8XU0dGIQP6Ran706naa3Cl9zhnv2QEzDqdh9c5SLkwfQZh98PZ0aMBRvdbQ7GD7oSpumjeOOxZM9Hd1lJ9lpsYTFiI0OQwhtsC+2m1qcfLE2v3EDLFzfWYKF6YnnRI8JycNY2zcUB5+bRM5JQ5yiiqZPc5zQSe3qJKKuiYWTh28t9NABw2oPthxuIomhzOgf8Uqz8lIieWZb32BEJuwcGpSQH8u/vJ+ATuPVvP7q2byw0Xpnb6XjJRY7pgZzmVnjuQPK3fz2aGOazv233t5xwiz25ifNrjntmnAUb225YBrdM7sAP7Dojzr3IkJnDshnp3HAnc+zifFlTyevY+rMsb0mJBURPjN184kPjKcu176lPqmlgG/vjGGlfnHOG9iAlHhg/umkwYc1Ws5BypITYwcdBls1cBkpY1gX1kdByvq/V2VPqtvauHul7cxKmYIP//K1F6dM3xoGH+8egb7y+t46J2dA67DrmM1HKw4ycIgyL6tAUf1itNpyCmqZE6KZhdQp8py3wbKdmcQDyS/+79d7C+v43+uOothEaG9Pu/cCQksvSCV5zcVszLv2IDqsDLPvfbNFA04SgGwt6yWqpPNzB6nt9PUqVITIhkbN4QPd5f6uyp9sn5vOc9uKOKmc8dx7oS+J6K9+5I0pp8Rzb3/2k5pdUO/6/Fe3jEykmNJHDb47xxowFG9kuPuv5njwZE5anAQEbImj+DjfccDJuvA2j1lfHd5LqNjIrj3i+n9KiPMbuPPV8/iZLODW5/L4f99sKfPWQgOVtSTf7SahdMG/9UNWDDgiEiciLwuInUiUiQi13Vz7I0ikisi1SJySEQeERF7u/3ZItLgXpK6VkR2++ZdDD45BypIiAojJV7Xv1Gnmz85kfomR9sPEyvLLarkpqe3UNPYQnldE/lH+z/gYeKIKG6cO45th6r4/coCrnuib+meVuWXAAz64dCtLBdwgMeAJiAJWAI8LiLTujh2KPDfQAJwDnARcE+HY+40xkS5H2leqvOgt6WogtkpcYgMzhxPamDOnRhPWIiN7AC4rbZ2TxkO97IKDg+sORM9xE7rt6Kxxck/1u/v9bINK/OPkZY0jHEJkQOqQ6CwVMARkUjgCuB+Y0ytMWYd8CZwQ2fHG2MeN8asNcY0GWMOAyuAeb6rcXA4VtXAwYqT2n+jujQ0zM4XxseRvdv6AwdaZ/LbPJSeKTM1gfBQGzYBEXh7+1FufS63x36diromNu+vCJrbaQBipQWURGQW8LExZki7bfcA840xX+nF+f8Gdhlj7nM/zwam4VrPaDfwU2NMdhfnLgWWAiQmJma8/PLLA3szvbC30sGuCgfpcSFMjA3x+uv1VW1tLVFRUWw+2sL/bmvk53MjSI2xXj29rbUdgllv2uDd/c28uLuJP8wfQvwQS/2WPcWTnzWy+VgLXx4fytT4vn33umqH1u/y5Dgb+04YXtvTRKgNLk62ExoiTOnkO772UDNP7WjigbkRjAuw71VX7bBgwYJcY8zsrs6z2iyjKKDj9N0qYFhPJ4rIt4DZwC3tNt8L5OO6RXcN8JaIzDTG7Ot4vjFmGbAMIC0tzWRlZfWn/qfZvP84b2w9wqiYCIZFhFJa00BpdSN7y2rZerAODISHOiyZ+DA7O5usrCyy38xjSOhBbvjyAkJDrPuHxFta2yGY9aYNxkyt4cXdH9EYN5Gsc5J9U7E+cjgNP1i7mi9OH80fr53V5/O7aoeOW75TVsvtKz7hzcIaAGzSzJfOHMW0M2JIjAonYVg4Wz7bzbBwJ2fOmEVGgA3G6e93wqcBx33FMb+L3euB7wHRHbZHAzU9lLsY+C1wsTGmvHW7MWZTu8OeFZFrgcuAR/tW8/7ZsK+cJU9uwtnuIjLEJiREhbnr59rW2OxkY2G55QJOq5yiCmYlDw/KYKN6b0JiFGcMH0L27lKus2jA+aTYlbOsp4wCA5WaGMWXzxrF7mM1ruzTBv5vxzHe2n70tGOXPLXJkj84vcGnAccYk9Xdfncfjl1EJhlj9rg3zwDyujnni8ATwJeMMZ/1VAXAJ73eDqfhF2/mtQUbm8BtWRO4+5I0bDZpW3ujwZ0OvaCkFmOM5TrlaxtbyD9SzZ0XTvJ3VZTFiQjz0xJ549PDNLU4LZn1eFV+CaEh0jZZ1ZvmTkggPHQvzS1OQu02Vtx8DmmjoimvaWTZR4W8sLk46NaVstQnwhhTB7wGPCgikSIyD7gcWN7Z8SJyIa6BAlcYYzZ32DdcRBaJSISI2EVkCXAB8J5334UrN9Kv3s6noKQWu00IEVdH5YXpn6/kl5ESy4pbMrln4WQunT6SN7Ye4UevbqfFnf7cKj4trsRpNH+a6p2syYnUNTnIKaro+WAfM8awKr+EzNT4PmUV6K/W7/gPFqa5rmDGxREVbmdcQiRXZIwhPNQWdOtKWa0PB+B24GmgFDgO3GaMyQMQkWRcfTJTjTHFwP1ADPCfdlcGa40xlwKhwENAOuAAdgGLjTFen4vzxNpCnvn4ADefN57LzhzFxsLjna4dk5ESS0ZKLMYY/rx6D395fw8nTjbz6LWziAi1RifilgOV2ARmJQ/3d1VUADh3YgKhIcKHBWX9mr3vTfvKatlfXse3543z2Wu2fsc7277ilswu/zYMVpYLOMaYCmBxF/uKcQ0saH2+oJtyyoA5Hq9gD97Yepjf/GcXXzprFD+9bAo2m/T4YRIRvn/JZOIiw/jFm3l8/X/Xc8nUJC6YPMLvH8ScAxVMGRXtk1+EKvBFhduZMy6OD3eX8eNLp/i7OqdY6Z5kaZWcZV0Fo8HMUrfUAt36veXc88o2MlPj+OM3ZrTdPuutG88dx39fPIn8ozX85f29LHmyb7OWPa3Fadh68ISms1F9Mn9yIruO1XC06qS/q3KK1fklTD8jmtHDh/R8sPIKDTge8q/cQ3zrH1sYFT2Ev98wm3B7/26JhYbY2kY1tK6d7i8Ha5zUNzl0wqfqk6y0EQB8aKFJoGU1jXx68ASXTAmOFDJWpQHHA1bvLOHuV7bR5HBSUtPA3tLafpeVmRpPeKjtlOf+UlDpGsAwW5ckUH0wOSmKUTERfGih5Qre31mCMXh9OLTqngacAXI6zSmLMLUMMDdTa2fi+RMTcBqIHeq/vpM9lQ7GxA5hZEyE3+qgAo+Ia9jxh7vLeLQfGZS9YVV+CWcMH8KUUT3OIVdepAFngJ7dcIAD5XVtw589McQxIyWWP1w9A7tNWLGp2DMV7SNjDAWVTu2/Uf0yNnYo9c0O/rSqwO99kfVNLazbW84lU5MsN88t2GjA6URVo+nVF2T3sRoe/r9dXJg+gpeWthtv74GRJyOGRbBo+khezT1EQ7Pv1xgprqinuslo/43ql2b3fDKn+Xxio7+s3VNOY4tTb6dZgAacTlQ2Gq7tYV2LhmYHd734KdERdh658iwyxsVxx4KJHh3meP05KVSdbOatbUc8VmZvvZJ7CIDIMMuNnFcB4LxJidjdozTtIf6d2Lgqv4ToCFc2a+VfGnC60NTi5Mm1hV3uf+Td3ew6VsP/XDWDhCjvLA2bmRrHxBFR/NPHt9Vyiyp5fI0rv+l9r223xD14FVgyUmL52w0ZhIhwYbr/5pM5nIYPdpWyIH2E5gK0AP0/0AWbuJLt/fyNHTS1nJpu5sOCMp5ev5+bzh3HAvcQUG8QEZack8y2gyfYcbhjEm3v2bCvvG2BKn/fDlGB6+IpSXz97DPI3l3Gifomv9Qht8g3yTpV72jA6URsuPDi0kyWXpDKcxuKuO6JjW2LKR2vbeSeV7YxOSmK+y7t31roffH1s8cwJDSEf24s8vprtWrNKiAEV54n5Xm3nJ/KyWaH3wa/rN7pStY5f7L3k3WqnmnA6URMuPCF8fH85LIpPHrtLPKOVPPlR9fx/KYirl62kcq6Jv5yjW/yncUMCeWrM0bzxtYjVDc0e/31APaX1xEaIlw+ITRo0qYr70gbOYzzJyXwzMcHaGzx7eCX3AMVvJJzkKmamskyNOD04CszRvP6HediE/jJ6zvaJnXWN/nuy3N9Zgonmx285u7I9yan0/DujmMsSBvB4klhGmzUgN16fiplNY28te30tWC8Jbeokuue3ERlfTN5R6q1H9IiNOD0QvrIaL4xe2zbc2OMT/s1zhwTw4wxMfxzUzHeXhJ8++EqjlU38MXpmgJEecb5kxJISxrGk2sLvf75bbWx8Hhb36vTx99X1TUNOL00P20EEX5cv2JJZgp7S2vZtN+764y8u+MYdptwUbp2sirPEBFuPn88u47VsH6vb/7wZ6bGty21GKb9kJahAaeXTltMyce3mr5y1miiI+xeHTxgjOHdHUeZOyGeGD+m1FGDz+UzR5MQFc4T3Uw18KTUhEgwMDc1TvshLUQDTh9kpMR6fHJnbw0JC+HKjLH8346jPPLuLq/ck95dUsOB4/VcOn2Ux8tWwS3cHsKNc1P4sKCMgpIar79edkEpBrj30ikabCxEA04AmZkcg8MJj2fv80p+qnd3HENEM+oq71iSmUJEqI2n1u73+mu9v7OUhKhwzjojxuuvpXrPcgFHROJE5HURqRORIhG5rptjbxIRh4jUtntk9aesQHCwwrWglcE7EzLf3XGMOSlxJA7zTuYEFdziIsO44uwx/OuTQ167SgdXHrcPC8q4MD2xz4sgKu+yXMABHgOagCRgCfC4iEzr5vgNxpiodo/sAZRlaZmp8YTb3f/LRDzaEXqgvI5dx2pYpKPTlBdlpsbR4jReu0oH2HKggpqGFsssJa0+Z6mAIyKRwBXA/caYWmPMOuBN4AZ/lmUVGSmxPH9rJmeNiQFjiIsM81jZ7+UdA2DRNP2SKu8p9vJVOsAHO0sJC7Fx3sQEj5etBsZqqYAnAw5jTEG7bduA+d2cM0tEyoEKYDnwsDGmpa9lichSYClAYmIi2dnZ/X4T3vbtSU7uPQo/XL6WO2d5ZnG0lzacZFy0jb3bNrPXva22ttbS7eAr2g6ea4PwEw7sAi3GNWo5/EQR2dmendD81if1TI61sWXDOo+WC/pZaNXfdrBawIkCOmaprAK6WqbvI2A6UARMA14CWoCH+1qWMWYZsAwgLS3NZGVl9b32PlRs38MfVxUQNe4sZg9wkbSjVScpfPcDfrgojaysiW3bs7OzsXo7+IK2g+faIAuYMauCb/1jC5OSorjla/MGXGZ7hWW1lLz7IXdckk7W3HEeLRv0s9Cqv+3g01tqIpItIqaLxzqgFojucFo00Ok4SmNMoTFmvzHGaYz5DHgQuNK9u09lBZpbzh9PUnQ4D72zc8Czt1fmlQBodgHlE3PGxfGteePYdvAER06c9GjZ7+8sBeDCdO9lcVf959OAY4zJMsZIF4/zgALALiKT2p02A8jr7UvQNr94wGVZ2tAwO3cvTGPrwRO889nAclS9u+MYk0ZEMSExykO1U6p7V2WMxWngVQ/nB3x/VwnpI4cxJnaoR8tVnmGpQQPGmDrgNeBBEYkUkXnA5bj6Zk4jIpeKSJL73+nA/cAb/SkrEF1x9hjSRw7jd+/u6ncm3oq6JjbtP65XN8qnkuOHMm9iPC/nHMTp9Ex+taqTzWw5UKlXNxZmqYDjdjswBCgFXgBuM8bkAYhIsnuuTbL72IuA7SJSB/wHV4D5TW/KGgxCbMJPvzSFgxUnWb6hfylvVuUfw2lg0TQNOMq3rp6TzKHKk3y8zzMj1T4sKMPhNFw0RQOOVVlt0ADGmApgcRf7inENBmh9fg9wT3/KGizOn5TI/MmJ/PX9PVyZMYbhQ/s2VPqlLQeJGWKnsdm3a5UotXBqEjFDQnlxSzHnTRr4EOYPdpYQFxnGzLGaysaqrHiFo/roJ5dNoaahhVuezenTRLo3tx7mk+ITVJ9sYclTm3TNEOVTEaEhfG3WGazMK6GybmBLULc4nKzZXUZWWiIhml3AsjTgDAK1jS3YRMgpquQbf9/AKveos66U1TTys39/xl0vbQW8OwlPqe5cPWcsTQ4nr396eEDlfFJ8gqqTzbqshsVpwBkENhYex+DqeHU4Dd/5Zw4/eGkr+UeqTznuZJODR9/fQ9b/rOGFzQdZNHUk4Xb/rfGj1JRR0cwYE8NLWw4OaHj/+7tKsNuECyZrdgErs1wfjuq7zNR4wuw2mluc2ENsXDxlBO/mHeO1Tw8zb2I8WWkjyD1Qyab9x6msb2bRtCR+9MV0JiRGkVtUycbC42Smxmsad+UXV89J5ievf8a2Q1XMHDu8X2W8v7OUc1LjGBah6zhZmQacQaB1cbj2gaPqZDMvbC5m2YeFbassisBDi6dzfWbKKedqoFH+9JUZo/jV2/m8tKW4XwGn6Hgde0true4LyT0frPxKb6kNEh0Xh4sZEsp350/gpnnj2mbC2nDNVVDKSoZFhPKls0bx5tYj1DW29Pn8Zz4+AEBStC6rYXUacAa5eRMTCA/VfhplbVfPGUtdk6PPWTNyiyp51h1w7n5lm460tDi9pTbIdXa7TSmrmZ0SS2piJE+t209ZTWOvP6vLNxygNVFB60hL/YxblwacIKD9NMrqRIR5ExJYvrGIP5TsJsxuY8Utmd1+bnccruI/n7mWRbehV/CBQAOOUsoShkW4/hw5DTT1cLVSWt3Arc/lkBAVxoOLp7P7WI1ewQcADThKKUu4aEoST67bT1OLE6eBxKjO0zQ1NDu4dXkuJ+qbefW2uUwbHcPFupx0QNBBA0opS8hIieWFWzO5+bzxJA4L44G38lmzu/SUY4wx/PDV7Ww/dII/XzOTaaNj/FRb1R8acJRSlpGREsv9X57KO987n3HxkdzybA6v5Bxs2//oB3t5a9sRfrQoXTOcByC9paaUspwR0RG89J1MbvvnJ/zw1e18evAEtQ0tvLntCF8/+wy+Oz/V31VU/aABRyllScMiQnn6pjnc/OwWnt9UDLiyZXxj9hhENCN0INJbakopywqz2zhnfNwp2TJyi074s0pqADTgKKUsbe4EzZYxWFgu4IhInIi8LiJ1IlIkItd1c+zf3EtOtz4aRaSm3f5sEWlot3+3b96FUspTWrNl/GBhWo+TQZW1WbEP5zGgCUgCZgLviMg2Y0xexwONMd8Fvtv6XESeAZwdDrvTGPOk96qrlPI2zZYxOFjqCkdEIoErgPuNMbXGmHXAm8ANfTj3We/WUimlVH9YKuAAkwGHMaag3bZtwLRenHsFUAZ81GH7wyJSLiLrRSTLM9VUSinVV1a7pRYFVHXYVgUM68W5NwLPmVPXqb0XyMd1i+4a4C0RmWmM2dfxZBFZCiwFSExMJDs7u++1H2Rqa2u1HdB2AG2DVtoOLv1uB2OMzx5ANmC6eKwDZgH1Hc65G3irh3LHAi1Aag/HvQt8r6d6Tp482Shj1qxZ4+8qWIK2g7ZBK20Hl67aAcgx3fxt9ekVjjEmq7v97n4Yu4hMMsbscW+eAZw2YKCDbwIfG2MKe6oCoDPGlFLKDyzVh2OMqQNeAx4UkUgRmQdcDizv4dRvAs+03yAiw0VkkYhEiIhdRJYAFwDveaHqSimlemCpgON2OzAEKAVeAG4z7iHRIpLsnk+T3HqwiMwFxgCvdCgnFHgI10CCcuB7wGJjjM7FUUopP7DaoAGMMRXA4i72FeMaWNB+2wYgspNjy4A53qijUkqpvrPiFY5SSqlBSAOOUkopn9CAo5RSyic04CillPIJDThKKaV8QgOOUkopn9CAo5RSyic04CillPIJDThKKaV8QgOOUkopn9CAo5RSyic04CillPIJDThKKaV8QgOOUkopn9CAo5RSyic04CillPIJDThKKaV8QgOOUkopn7BcwBGRO0UkR0QaReSZXhz/fRE5JiJVIvK0iIS32xcnIq+LSJ2IFInIdV6tvFJKqS5ZLuAAR4CHgKd7OlBEFgH3ARcB44BU4JftDnkMaAKSgCXA4yIyzcP1VUop1QuWCzjGmNeMMf8Gjvfi8BuBp4wxecaYSuBXwE0AIhIJXAHcb4ypNcasA94EbvBOzZVSSnXH7u8KDNA04I12z7cBSSISDyQDDmNMQYf98zsrSESWAkvdTxtFZIcX6htoEoByf1fCArQdtA1aaTu4dNUOKd2dFOgBJwqoave89d/DOtnXun9YZwUZY5YBywBEJMcYM9uzVQ082g4u2g7aBq20HVz62w4+vaUmItkiYrp4rOtHkbVAdLvnrf+u6WRf6/6afryOUkqpAfJpwDHGZBljpIvHef0oMg+Y0e75DKDEGHMcKADsIjKpw/68/r8DpZRS/WW5QQMiYheRCCAECBGRCBHp6tbfc8DNIjJVRGKBnwHPABhj6oDXgAdFJFJE5gGXA8t7UY1lA30fg4S2g4u2g7ZBK20Hl361gxhjPF2RARGRB4BfdNj8S2PMAyKSDOQDU40xxe7jfwDcCwwB/gV81xjT6N4Xh2t49SW4Rr3dZ4x53idvRCml1CksF3CUUkoNTpa7paaUUmpw0oCjlFLKJzTgtBOsude6y18nIheJyC4RqReRNSLS7cSuQCUi4SLylPv/e42IfCoil7bbHxTtACAi/xSRoyJSLSIFInJLu31B0w4AIjJJRBpE5J/ttgVNG7insjSISK37sbvdvj63gwacUwVr7rVO89eJSAKukX73A3FADvCSz2vnG3bgIK5MFDG43vPLIjIuyNoB4GFgnDEmGvgq8JCIZARhO4Drb8KW1idB2gZ3GmOi3I806H876KABN3futUpgems6HBFZDhw2xtzn18r5iIg8BIwxxtzkfr4UuMkYc677eSSudBazjDG7/FZRHxGR7biSwcYTpO0gImlANnAXMJwgagcRuQb4Oq6RsRONMdcH23dCRLKBfxpjnuywvV/toFc4n5tM57nXguEKpyvTcLUB0Da3aR9B0CYikoTrM5FHELaDiPyviNQDu4CjwH8IonYQkWjgQeDuDruCpg3aeVhEykVkvYhkubf1qx004HyuT7nXgkRQtomIhAIrgGfdv9aCrh2MMbfjen/n47p10khwtcOvcGWiP9hhezC1AbjmOKYCZ+Ca7PmWiEygn+2gAedzmnvtdEHXJiJiw5WNogm407056NoBwBjjcC/rMQa4jSBpBxGZCVwM/KmT3UHRBq2MMZuMMTXGmEZjzLPAeuAy+tkOGnA+p7nXTndKrjr3fdoJDNI2EREBnsI1aOQKY0yze1dQtUMn7Hz+foOhHbJwLehYLCLHgHuAK0TkE4KnDbpiAKG/7WCM0Yf7AbwIvABEAvNwXSJO83e9fPC+7UAErtFJy93/tgOJ7ja4wr3td8BGf9fXi+3wN2AjENVhe9C0AzACuAbXLZMQYBFQhysPYVC0AzAUGNnu8XvgVff7D4o2cLfDcPf//9a/B0vcn4W0/raD39+UlR64hvf9292oxcB1/q6Tj973A+5fLu0fD7j3XYyr4/gkrtFK4/xdXy+1QYr7fTfgul3Q+lgSZO2QCHwInACqgc+AW9vtD4p26NAmD+AaqRVUbeD+LGzBdZvshPvH2CUDaQcdFq2UUsontA9HKaWUT2jAUUop5RMacJRSSvmEBhyllFI+oQFHKaWUT2jAUUop5RMacJRSSvmEBhylAoSIRIvIAyIyxd91Uao/NOAoFThmA78AQv1dEaX6QwOOUoFjFq5lAvL9XRGl+kNT2ygVAERkJ5DeYfO/jDFX+qM+SvWHBhylAoCIzMGVzTwP+I1781FjTJH/aqVU39j9XQGlVK9sw7UQ2qPGmI3+roxS/aF9OEoFhmlAGPCJvyuiVH9pwFEqMJyNa72erf6uiFL9pQFHqcAwC9hnjKn2d0WU6i8NOEoFhqnocGgV4HTQgFKB4QRwtogswrWW/B5jzHE/10mpPtFh0UoFABGZDjwFnAVEAOcbY9b5t1ZK9Y0GHKWUUj6hfThKKaV8QgOOUkopn9CAo5RSyic04CillPIJDThKKaV8QgOOUkopn9CAo5RSyic04CillPKJ/w9G8k1cJ0wOdAAAAABJRU5ErkJggg==\n", 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" ] @@ -288,47 +288,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 1s 86us/sample - loss: 0.1004 - val_loss: 0.0559\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.1001 - val_loss: 0.0545\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0386 - val_loss: 0.0269\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0379 - val_loss: 0.0266\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0205 - val_loss: 0.0162\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0202 - val_loss: 0.0157\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0133 - val_loss: 0.0118\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0131 - val_loss: 0.0116\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0104 - val_loss: 0.0098\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0103 - val_loss: 0.0098\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0089 - val_loss: 0.0087\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0089 - val_loss: 0.0087\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0080 - val_loss: 0.0078\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0080 - val_loss: 0.0079\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0073 - val_loss: 0.0071\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0073 - val_loss: 0.0071\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0066 - val_loss: 0.0065\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0066 - val_loss: 0.0066\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0061 - val_loss: 0.0061\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0061 - val_loss: 0.0062\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0058 - val_loss: 0.0058\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0057 - val_loss: 0.0057\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0054 - val_loss: 0.0055\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0054 - val_loss: 0.0055\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0052 - val_loss: 0.0052\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0052 - val_loss: 0.0052\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0050 - val_loss: 0.0049\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0049 - val_loss: 0.0049\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0048 - val_loss: 0.0047\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0048 - val_loss: 0.0048\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 0s 39us/sample - loss: 0.0046 - val_loss: 0.0046\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0046 - val_loss: 0.0048\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0045 - val_loss: 0.0045\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0045 - val_loss: 0.0045\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0044 - val_loss: 0.0046\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0044 - val_loss: 0.0044\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0043 - val_loss: 0.0043\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0043 - val_loss: 0.0043\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0042 - val_loss: 0.0041\n" + "219/219 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.0042\n" ] } ], @@ -355,13 +354,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2000/2000 [==============================] - 0s 19us/sample - loss: 0.0041\n" + "63/63 [==============================] - 0s 618us/step - loss: 0.0042\n" ] }, { "data": { "text/plain": [ - "0.004145485937595368" + "0.004168085753917694" ] }, "execution_count": 9, @@ -380,7 +379,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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u4uyzz6Zt27Y1ek9PPvkkTz31FC+++CJ9+vRh9uzZXHXVVaxZs4YzzjiDf/3rXzz99NPMmzeP7t27k52dzVdffQU4RfyKK67gzjvvZO7cuRQXF7Ny5cry34G/WA8GCAmBnbHOUGU22nEYc5I6dAjcbgTA7Xae+9itt97K9u3b+eijj8rnTZ8+nQsuuIB27dqVz3vwwQcZOHAgHTp04JJLLmHChAm1+ov/6aef5pZbbuG2224jJSWFBx98kD59+lRqM2zYMK6//nq6du1K165dmTZtGiEhIXz44YcAJCYmAs69WVq2bFn+vKopU6Zw0003MWbMGFJSUhg3bhzXXnstjz/+eKV2N910E9dddx1dunTh0UcfBfjZXS2rM2XKFO677z5+85vfcNppp/Hoo48yYMAApkyZAsC2bdto3bo1559/PsnJyfTr148xY8YAcODAAQ4fPszll19O586dOf3007n++us57bTTarz9huDXHoyINAWmAxcAe4H7VXXuMdqOB+4DooE3gd+qamGVNi7gW+ANVb2+Ptn2JrjgCJCZCXXcL2yM39SiJ1EuPR3S0tCiIiQiAl57DV/fO9nlcnHOOecwY8YMLrjgAnbt2sWHH37I66+/Xqnd66+/ztSpU9m8eTO5ubmUlJTU6q/t9evXM3bs2ErzUlNTWbBgQfnz7OxsHnjgAZYtW0Z2djalpaXk5eWxffv2Wr2n9evXl3+Rlxk8eDAPPvhgpXk9evQofxwREUFiYiK7d++u0Tb279/P7t27GTRo0M+2UzYw4pprruHZZ5+lY8eODB8+nAsvvJDLL7+ciIgIWrRowfXXX8+wYcNIS0sjLS2NX/3qVzXuPTUUf/dgpgFFQBIwEnhBRH52UwURGQ5MANKADkAn4OFjrO+rhgiWn9iOopBI68GYk1dqKixeTNHEibB4sc+LS5nRo0fz1ltvsX//fmbOnEnTpk25/PLLy5cvX76ckSNHcvHFF/Puu++yevVqHnnkEYqKiho0x/XXX8/q1at5+umnWbFiBWvWrKF169Z12o632yVXnRceHv6z5W63u0brV8+x4Oq20759ezIzM3n++edp1KgR48ePp1+/fuXHnubMmUN6ejqDBw9m4cKFpKSkVOpJ+oPfCoyIxAJXAw+oaq6qLgfeAW7w0nwUMF1VM1T1ADAZuKnK+q4FDgKLGyJfk6Yh7IzsbAXGnNxSUym6+26/FReAESNGEBUVxT//+U9mzJjBjTfeWOnL97PPPqN9+/b85S9/oV+/frhcLq8jz6rTtWtXPv/880rzqj5fvnw5d911FxdffDHdu3cnNjaWrKys8uWhoaGEhoZSWlp63G1V3dW1fPlyunXrVqvM1WnWrBktWrQ47naio6O57LLLePrpp/n8889Zu3Ztpffdq1cvJkyYwCeffMKgQYOYPXt2g2WsCX/uIksBSlW14ljgb4AhXtp2B96u0i5JRJqp6j4RiQMewenh3FrdRkXkduB2gKSkJJYtW+a1XUlJdzLpRIs1a/jqGG0AcnNzj7mOQAimPJbFu4bIEh8fT05OToPkKS0tbbB11dSIESN46KGHOHjwINdee2359ktLS2nXrh3bt29nxowZ9O3bl0WLFpXvQitrV1BQUO3z22+/nbFjx9K9e3dSU1NZuHAhq1atIjExsbxNly5dePXVV+natSs5OTk88MADREZGUlhYSE5ODm63m3bt2vHBBx/Qs2dPIiIiSEhI+Nm2xo4dy6233krXrl0ZOnQoH374IfPnz+f1118nJyeHkpISAPLz8yv9nFWVgoKCY/7sCwsLcbvd5OTkUFpayl133cXjjz9O69at6dGjB/PmzePzzz/nySefJCcnhzlz5gDQt29fYmJiWLBgAeHh4bRs2ZI1a9YwZ84cLrroIlq3bs3mzZv59ttvGTx48DG3X1BQ0PD/Z+pyn+W6TMDZQFaVebcBy7y03QxcWOF5OKBAB8/zZ4D7PI8nAf+sSYa+ffse857Tt96qOi32XtWICNWSkmO2C6Z7vasGVx7L4l1DZKnufum1FYj7va9atUoBHThwoNcs9957ryYmJmqjRo306quv1ueee05DQ0PL2/3jH//Q+Pj4Yz5XVZ08ebI2b95cY2NjdeTIkTpx4kTt3Llz+fKvv/5a+/Xrp5GRkdq5c2d97bXX9LTTTtPJkyeXZ1m4cKF26dJFw8LCyl/rbVvTpk3TTp06aXh4uHbp0kVfeeWV8mXFxcUK6MKFCyu9pk2bNvp///d/x/wZ/eUvf9GePXuWZyktLdVJkyZpmzZtNDw8XM8880x95513ytu/+eab2r9/f42Pj9fY2Fjt16+fvvfee6qqumvXLr3yyiu1VatWGhERocnJyTphwgQtLi4+5var+4wBK7Uu3/t1eVGdNgS9gbwq8+4G3vXS9hvg1xWeN/MUmGZALyADiNAGLDD33KM6Jvxl50fyww/HbBdMX1yqwZXHsnhnBebYLIt3gcjiiwLjz4P8mUCYZ+RXmZ6eYlFVhmdZxXbZqroPGIpz4H+7iGQB9wBXi8jX9QmXkADrim2osjHGNBS/FRhVPQL8G3hERGJFZBBwBTDHS/PZwK0i0k1EEoCJwEzPspeBzjg9mV7Ai8B7wPD65GvSxDmbH3CGKhtjjKkXfw9THoNzXstuYB7OuS0ZIpIsIrkikgygqv8FngCWAts800OeZXmqmlU2AblAgarW625hCQnwE61wx8RaD8YYYxqAX0+0VNX9wJVe5m8HGlWZ9xTwVA3WOakhsiUkAAj5bboQawXGGGPqzS4V41F2LbuMohTyv7UCY4KP2oVYjY/46rNlBcaj7Lyuj7a5CN+xhc8/LQ5oHmMqCg8PJz8/P9AxzEkqPz//Z1ceaAhWYDy+/975NxMXYZSy5q2tAc1jTEUtWrRg586d5OXlWU/GNBhVJS8vj507d/rknjd2uX6PCy6Ahx+GjeqMJBvSeiPgqv5FxvhJXFwcALt27aK4uH6964KCAqKiohoiVr1ZFu/8mSU8PJykpKTyz1hDsgLjkZoKI0bA8oUpUAJdw+w4jAkucXFxDfIlsGzZMnr37t0AierPsngXTFnqw3aRVTBiBPxUkkhJo3g7F8YYY+rJCkwFAwcCCPsSXHYujDHG1JMVmAratoXkZNgoKVZgjDGmnqzAVDFwIHx5wAXbtoHnMt3GGGNqzwpMFYMGwaocF6jCli2BjmOMMScsKzBVDBxY4aKXtpvMGGPqzApMFT16wK4YKzDGGFNfVmCqCAuD0wYkcCAs0YYqG2NMPViB8WLQINhQ4qJkg/VgjDGmrqzAeDFwoHNNspL1VmCMMaaurMB4MWAAbCSFqL074ciRQMcxxpgTkhUYL5o0gYJ2ngP9mzYFNowxxpygrMAcQ8IvnALj/t52kxljTF1YgTmGjhc4BWbPCiswxhhTF1ZgjqF/WiN20YqDX9pQZWOMqQsrMMfQqRNsDXchm60HY4wxdWEF5hhE4EhrF033WYExxpi6sAJTjYjuKSSW7mbPpkOBjmKMMSccKzDVSBrsHOjPeMt6McYYU1tWYKrRabhTYHZ9bAXGGGNqywpMNSK6dgYg7xsrMMYYU1tWYKoTHc2BuGSid26ksDDQYYwx5sRiBeY4iju46OLOZNWqQCcxxpgTixWY42jU24WLjaxYEegkxhhzYrECcxwxPVNoygHWLt0X6CjGGHNCsQJzPC5nJNm+9ExUA5zFGGNOIFZgjsdTYJod2MjmzQHOYowxJxArMMfTsSMaEmLHYYwxppb8WmBEpKmILBSRIyKyTUSuq6bteBHJEpFDIjJDRCIrLPuniPwkIodFJFNERvssdEQEdOxI9/CNfPaZz7ZijDEnHX/3YKYBRUASMBJ4QUS6V20kIsOBCUAa0AHoBDxcocnfgA6qGgdcDvxVRPr6KrS4XPSIzrQejDHG1ILfCoyIxAJXAw+oaq6qLgfeAW7w0nwUMF1VM1T1ADAZuKlsoWd+2amP6pk6+yy8y0W7go1krFNyc8N8thljjDmZ+PPbMgUoVdWKd/D6BhjipW134O0q7ZJEpJmq7gMQkedxik40sBp439tGReR24HaApKQkli1bVuvgbQBXUS4tyGbVqnAaNar9OnwlNze3Tu/JFyyLd8GUBYIrj2XxLpiy1Iuq+mUCzgayqsy7DVjmpe1m4MIKz8NxeikdqrQLBQYDE4Hw42Xo27ev1sl//6sKOiTkE73hhh/qtg4fWbp0aaAjlLMs3gVTFtXgymNZvAumLKqqwEqtw/e+P4/B5AJxVebFATk1aFv2uFJbVS1VZ1dbW+C3DZTz5zxDldPaZbJuXbzPNmOMMScTfxaYTCBMRFwV5vUEMry0zfAsq9guWz27x7wIw5fHYJKTITycAc02sn59HCUlPtuSMcacNPxWYFT1CPBv4BERiRWRQcAVwBwvzWcDt4pINxFJwNkFNhNARFqIyLUi0khEQj0jzn4DLPFZ+LAw6NyZ08M2UlAQytq1PtuSMcacNPw9THkMzkH53cA84LeqmiEiySKSKyLJAKr6X+AJYCmwzTM95FmH4uwO+xE4AEwBxqnq2/iSy0XSYee+MDZc2Rhjjs+vY25VdT9wpZf524FGVeY9BTzlpe0evI888y2Xi4hFi2iRmMdnn8UwdqzfExhjzAnFLhVTUy4XFBQw1PW99WCMMaYGrMDUVEoKAIOTvmX7dvjxxwDnMcaYIGcFpqY8Q5V7xqwH7DiMMcYcT70KjIhEi8gwEWnfUIGCVps2EBVF+8ItxMRgF740xpjjqFWBEZGZIjLG8zgC+BL4H/C9iFzkg3zBIyQEunQh9qcf6d/fejDGGHM8te3BDAc+9zy+HGgMtAQmeaaTW0oK0T/+yMCBsHo1HDkS6EDGGBO8altgEnDOYQG4EHhTVXcD84FuDRksKMXEEP3jj1za5FNKS+GrrwIdyBhjgldtC0wWcIaIhOL0Zj7yzG8EFDdksKCTng6vv06I203/iRcwgHQ7DmOMMdWobYGZAbwOrANKgcWe+f2BDQ2YK/gsWwalpQBIcRHXtFhmx2GMMaYatTqTX1UfEZEMIBlYoKpFnkUlwOMNHS6oDB0KkZFofj6iSsGAoaz4BNxu5/i/McaYymr91aiqb6rq/6nqjxXmzfL5tcACLTUVFi9mX2oqqNLtrBgOHoQNJ3e/zRhj6qy2w5R/LSIXVHj+oIj8KCIfikirho8XZFJT2XDffRAeztlbZgE2XNkYY46ltj2YSWUPRKQP8GdgKs4dJ59suFjBqyQ+Hi69lCbvv0arxGI70G+MMcdQ2wLTHvje8/gq4C1VfQL4I5DWkMGC2qhRyO7djOn8ofVgjDHmGGpbYApwTq4Ep6CUDVM+VGH+ye/ii6F5c36VN4vMTNizJ9CBjDEm+NS2wHwKPCkiDwBnAe975qcAOxoyWFALD4frrsO14R0S2E96eqADGWNM8KltgRkLFAEjgDtVdZdn/kXAhw0ZLOiNGkVIcRHXh8634zDGGONFbc+D+RG4zMv8cQ2W6ETRqxeceSZ3/DCLO1eMCXQaY4wJOnU6RVBEzhORsSLyOxE5t6FDnRBEYNQouud+yaEvNlBYGOhAxhgTXGp7HkwbEfkSWATcB0wAPhKRL0SktS8CBrWRI3GHhPKb4lmsXh3oMMYYE1xq24OZinMNsi6q2k5V2wEuz7ypDR0u6LVsSfF5F3IDc1jxaWmg0xhjTFCpbYE5H/idqv5QNkNVtwB3eZadciJvH0VbdpLz9pJARzHGmKDSUJdpdDfQek48l13GkYgmnLFqJqqBDmOMMcGjtgVmMTBVRNqVzRCRZOAZ4NT8Ez4qiq0DruWigoVsXXs40GmMMSZo1LbA3AXEAFtEZJuIbAU2A9HA7xs42wkj8rZRxJBP1rMLAh3FGGOCRq0KjKruUNU+wMXAFOApnJMsR3gen5I6/aY/mSGnkfj+rEBHMcaYoFGnYzCqukhVn1XVqar6ERAPXN2w0U4cIaHCZ51H4frpU9i8OdBxjDEmKNi9GBvIoctvwI1Q8PLsQEcxxpigYAWmgZx5UVsWk4Z71mznPsrGGHOKswLTQPr3hzkyipjsrfDpp4GOY4wxAVeji12KyDvHaRLXAFlOaI0awaYzr48SmuMAACAASURBVCIvoxExs2bBkCGBjmSMMQFV0x7MvuNMPwCn/MGHvufE8ob8Gl2wAI4cCXQcY4wJqBr1YFT1Zl8HORkMHAgvPDeKG3NnwMKFcP31gY5kjDEBY8dgGtCgQbCcwRxq1hFmzgx0HGOMCSi/FhgRaSoiC0XkiOdKANdV03a8iGSJyCERmSEikZ75kSIy3fP6HBFZLSIX+e9dHFu7dtAsMYR5YTeiS5bAjlPnLtLGGFOVv3sw03BuuZwEjAReEJHuVRuJyHCce82kAR2ATsDDnsVhwA5gCM4Jng8A/xKRDr6Nfnyffw4HDsAT2Tciqmx/dE6gIxljTMD4rcCISCzO2f4PqGquqi4H3gFu8NJ8FDBdVTNU9QAwGbgJQFWPqOokVd2qqm5V/Q/OIIO+fnkj1Vi2zDkF5gc68QlnE/vmLOwSy8aYU5Won74ARaQ3sEJVoyvMuwcYoqqXVWn7DfCYqr7ueZ4I7AESVXVflbZJwDagl6pu8LLd24HbAZKSkvrOnz+/Xu8jNzeXRo0aeV2WkRHH3Xf3pLAwhJuZwQxG8/W0aRzu1q1e26xrHn+zLN4FUxYIrjyWxbtgygJw7rnnrlLVs2r9QlX1ywScDWRVmXcbsMxL283AhRWehwMKdKjSLhz4CHipJhn69u2r9bV06dJql69YoXrLLaqNOaRF4dGqd95Z723WJ48/WRbvgimLanDlsSzeBVMWVVVgpdbhe9+fx2By+fkJmXFATg3alj0ubysiIcAcnGM6YxsuZv2kpsL06TDsqjjedP8S97z5UFAQ6FjGGON3/iwwmUCYiLgqzOsJZHhpm+FZVrFdtnp2j4mIANNxBgtcrarFvolcd1OmwJyQUYQcOgjvHO9CCMYYc/LxW4FR1SPAv4FHRCRWRAYBV+D0QqqaDdwqIt1EJAGYCMyssPwFoCtwmarm+zZ53XTqBL3vPo8facOBZ+w+McaYU4+/hymPwbn75W5gHvBbVc0QkWQRyfXcfhlV/S/wBLAU5wD+NuAhABFpD9wB9AKyPK/LFZGRfn4vxzXhL6EsjL2Bxis+xL0rK9BxjDHGr/xaYFR1v6peqaqxqpqsqnM987eraiNV3V6h7VOqmqSqcap6s6oWeuZvU1VR1SjPa8qm1/z5XmqiUSNIfmAUYZTy9d1BF88YY3zKLhXjY5fdezrrYvsT+8ZMcg7bOTHGmFOHFRgfCwmB2DGj6FqyjlnjVgc6jjHG+I0VGD/oOOEaikMiYNYstmwJdBpjjPEPKzD+0LQpJRddzrXuudw3vijQaYwxxi+swPhJ9J2jSGQvRe98wEcfBTqNMcb4nhUYfxk+HE1KYkzMLMaNg5KSQAcyxhjfsgLjL+HhyMiRnF/0H7Iy9vLSS4EOZIwxvmUFxp9GjSKkpJiHXPN44AHYt+/4LzHGmBOVFRh/6tEDevXi1vBZHDoEkyYFOpAxxviOFRh/GzWKmO9WseiMcax+Pp116wIdyBhjfMMKjL+5nItJn/vtVP7nTuOlm9PtppfGmJOSFRh/W7sWAFElSgpptHKZXc3fGHNSsgLjb0OHQlQUAKJuolvG88c/QmFhYGMZY0xDswLjb6mpsGQJ/PnPSIcO/Pngn2i55TOefjrQwYwxpmFZgQmE1FR49FFITycsuQ2Lwi7ig4e/5KefAh3MGGMajhWYQGrZEpYsIax1C97Kv4CX7vg60ImMMabBWIEJtDZtiPh0Ce64Jvz+3fNZN3dtoBMZY0yDsAITDJKTCf9kCQUhMbQZlYZmfBfoRMYYU29WYIJE456d+PzRJeSXhFMwKA0yMwMdyRhj6sUKTBC56k8u7uq2mNwcN+5zz4PNmwMdyRhj6swKTBAJCYE//qMr57k/Iv9AAZx3HmzbFuhYxhhTJ1ZggszAgdDjujM5t3gRedmHKRh4Lvz4Y6BjGWNMrVmBCUK//jV8VdKbIYX/o3DXPvIHnoedJGOMOdFYgQlC333n7C5bST8u4gN0109oWhrs3h3oaMYYU2NWYILQ0KEQGQmhofBl6EAuLH2Pgu+3UTRkGOzdG+h4xhhTI1ZgglBqKixeDJMnwyefwK0zz+HXke9QumEj+/uej+4/EOiIxhhzXGGBDmC8S011JnAO/J9zThoTL3uLxzIuZ3OX4TRdtYimHeMDG9IYY6phPZgTRMeO8MQ3w3nnxjdJPrCGLaddxFd//4jk116D9PRAxzPGmJ+xHswJJDQUfjXrUrb0eJ1e94xA7h8OgL72GrJ48dEujzHGBAHrwZyAOt19Ffzq14TgJhQ3ml9A1otvBTqWMcZUYgXmBBU2/i4kMhIFBCVx9hQyel5H6RcrAx3NGGMAKzAnrtRUWLqUH0aPJnf2v/kgZTxt175H6IB+5PcfAu+8A253oFMaY05hVmBOZKmpbB85ksY3XMWlG6bw35d38Oeop9jz1Va44gr09NPhhRcgLy/QSY0xpyC/FhgRaSoiC0XkiIhsE5Hrqmk7XkSyROSQiMwQkcgKy8aKyEoRKRSRmX4JH+RE4Jrb4rjz+/HcfPZmrmE+G7KawJgxFLdqBxMnQlZWoGMaY04h/u7BTAOKgCRgJPCCiHSv2khEhgMTgDSgA9AJeLhCk13AX4EZPs57wklOhkVLw2j+u2volvMFg/mU93LOQR97DNq3h1tugXXrAh3TGHMK8FuBEZFY4GrgAVXNVdXlwDvADV6ajwKmq2qGqh4AJgM3lS1U1X+r6lvAPt8nP/GEhECbNhASInzGYK7ShaTo98xvPJrif86HM8+E4cPhf/+DFSvgb3+zc2mMMQ1OVNU/GxLpDaxQ1egK8+4BhqjqZVXafgM8pqqve54nAnuARFXdV6HdX4G2qnpTNdu9HbgdICkpqe/8+fPr9T5yc3Np1KhRvdbRkI6VJyMjjrvv7klxsRAWplx++S7Wr4/jp4wS7uAlxodNpXlJNioCgDs8nG+eeorD3X/Woax3lkCwLMcWTHksi3fBlAXg3HPPXaWqZ9X6harqlwk4G8iqMu82YJmXtpuBCys8DwcU6FCl3V+BmTXN0LdvX62vpUuX1nsdDam6PCtWqD72mPNvmS1bnHl9uhfovxihblD1TKXNW6j+4Q+q776revhwg2bxN8tybMGUx7J4F0xZVFWBlVqH731/HoPJBeKqzIsDcmrQtuyxt7bmGFJT4f77K5/g37GjM2/Vukj6/vOPFIdFU0IoRYSzck8HCp99CS67DG3aFM4+Gx55xNmNVlISuDdijDkh+bPAZAJhIuKqMK8nkOGlbYZnWcV22Vph95ipv04jU4n4ZDGhj05m4z8+Zu4fvuC05gc4j8U8JfewZX0BOmkSDBpEQaNm7D/nSnjuOfj+e6fPY4wx1fDbtchU9YiI/Bt4RERGA72AK4CBXprPBmaKyGvAT8BEYGbZQhEJw8keCoSKSBRQoqr2Z3ZtpaYiqal0B54GnnwyimXLzmPevPP465t/I0T3cS5LuaBwEWmffkTTT992XteuHQwbdnTavNm58GZkpF0TzRgD+H+Y8hggGtgNzAN+q6oZIpIsIrkikgygqv8FngCWAts800MV1jMRyMcZyny95/FEv72Lk1hoKKSlwSuvOKfNXHx9M95kBHfwEl3YTCc2c3ejl/istD/589+CkSMhKQkGDaLjK6/AkCEwfbqd3GmM8W+BUdX9qnqlqsaqarKqzvXM366qjVR1e4W2T6lqkqrGqerNqlpYYdkkVZUq0yR/vpdTQWQkjBkD0dFO4YmKgl/9qRN7f3k7o6IX0Ch/D2fxFf+T4agqAlBcDKNHQ+PG0LUrXHutMwz6gw9g1y7btWbMKcQu12+qVXZ3zWXLnFs5V9z7lZUVyooVZ7FhwUOc869PCHUXUUI4D/MQbRMKGJj/Da7FX9D49dfLX5MXk0hR9140Oacn9OwJvXrB6adDeLjTID3d+8aMMSccKzDmuCreXbOili3hl78EfpkKdy0m8+VXOdLvZuIPpfLfz+DBFXDgAMRzkEGN1tL5yDf0yltDz5Xf0GvNc4QWezqlERHQrRu0bg2LFkFpqdN9snvcGHNCswJjGkZqKrsKCxk6NJXenlluN2zYAMuXN+HFF8/h/dXnOAsUQotL6BX1PRe1/oaBsWvolv8NbZZ8TFhxsdMmPx8uucQ5INS799GpZcuAvD1jTO1ZgTE+ExLidEy6dXOuTpOWBkVFEBYG48eHkZfXnc++7c6L317H3r0wgHSWcB4RFKESyg8xPUn6+Gvi3nijfJ1FzVqyo1lvIlP70PYyT9Hp2NG52qcxJqhYgTF+Ud2xHFXIzoZ161J5+90lhH22jPePDGX+tlTy8yGOQ/RmDUPiV9Nl/9f02rea9pn/g1mlzgri451jOX36OAVHhOSlS23ItDEBZgXG+M2xjuWIOHu+WrYEhqUCqYwA/lEKW7bAt9/G8+23Q1iwYAgZntNyo8jnDNbRm9WcXbqas9aspvPyF4goLQCgI6CvvooMGAD9+kGXLuByOf+2b390UIExxmeswJigFRrq1ASXyxlMcMEFR3ezER7NlRP7odqPDzfAMxtg4/oSJuXdw108Syhu3KrsXbmVRl99S3RJbvl6NTSUwpYd2BXTheieLloN7uIUni5dnN1tERFOQxvRZky9WIExJ4zqdrMBqIax551r4JqXKS0qojQ0gkd6vsnCXQMo3rUbFxvpwiZcpZvovHOT83hjOrxx+Og6QkIgORlp3hxWr3ZGtIWFOefynHuuc1JpixbWAzKmBqzAmBPKsXazgbOrrcUVqbB0MVtmzKDTLbfwbGoqzwI5OUlkZiaxYcNgpk+HiUvLXqU0Y59TbNhIF/cmTt+xiXN+/ISWJSVHTx69557KG0tMhJYtORjdkh+Lk2jatSWt+3j28yUlHd3nl5lpl9AxpywrMObkk5rK9sJCOlX4Qm/cGPr2daZOnY7uaouIEOa+lUhSUiJbtw7ghx8gfSt8/XU6k5anEabOyaNjeZa9NKclWbSPyKKzO5uWu7KI2J9FEitosiYL5uV7jdMRYMYMp9vVq5dz29F27Zx/k5OheXMbBWdOSlZgzCnnWLvaevas1ArSnUb5fYYytkUqW7dSPn2xFb74ArLL2yuNySGJbFqSRSuyuCViNhcUvUcIirrdHFmxlshP0wkvrlyI3BGRHGzcjtD27Yg/M7lyASr799tv7XiQOeFYgTGnpOp2tVVtFAf0xhkBXVF6+tGeUHi4MOWZOJo3jyMry0V2Nnyzpi3nvruYUHcRxRLBpe53+Lh4AM3YRzt2kMx259+i7STv2067fTvo/O1iWpTsIkTdlbZVfgU3CcHd7xeEdu4ITZpUmjJ3N+HrzfF0H9SEM8+usCwyslJo22Vn/MUKjDF1dLxBB2W9oLLjQUsHpHL4MGRnJ5KVlUhWVm/mzIHn3zt6DdD2rSE2soSw3btofHAH7djOjcxmOB8SguJWN7u+3EHBV/tIkIM0cR8gDOcuFSme6eiNLRzuyCg0vgkSFYn8uIOObje8+ir8+tfQo4dzPMkzrd6RyMcZifS/MIHUwaHe37iNrjM1ZAXGmHo4bk+owvEgwTknND4eUlKcxe3aOUXKOR4E8+ZBamoYkExpaTIHDgwid1EH9OaPcRcX4Q6NYOmtC1gbm8revbB3j3I4O5+9mw7CoYM0wctUeJAmuw8ygHTOwE0IoKWllMx/g/B58yrF7e2Z3I8K+TFN0cREtKlTfEKTEgnTQsIWvO7c4TQ8HJ56CgYPdnpKCQnOwS6RmtUg602d9KzAGBNA1fWCQkM9HYvfpEIHp1HI0KHcWOnLWIAY0tNjSEtr7dldB7NnO6f0HDjgTAcPwjdr0jn9H2mElDhDuO/ru5hVJT1h3z7CD+0l7OBemrGXxLIpby+J2z0TP5DIVySRBXh23xUVwdixld6PW0I4Et6EFkVNOJ8EDkkT1p2WQEzrJmhCAiEJTQhJTCAmdw+JLz9Gx+IidM4ceOUVpH9/554QFab0r8JY9rEct1DVq0fldsOnn5I8Z44VuwZmBcaYAKvN8aDqFle/uw4gFW44usvu6fJGMUA7Vqxwbk5aVqSmToWmHeBQDuw4DDk5ELduBb+ZMYzQ0iJKJYxXu09hl7YmLPcgEUcOEJl/kMi8AzTmIAkcoIkeJGTDLqI2OM+jKfh5rMJCuOEGr+/rF4TQk0gKiGJfZBQaGUVJWBTu8CjcEVGEuwtovusbRN0gIeQkd0ejogh1lxDqLibEXUxxXgnF+cVEhxUTGVIMJSVIcbEz/LykxCkwVBjt16GDU52Tko5OLVqw/kASX/yQxJnDkuh7UQunCFbVELsPT6KenRUYY04SNS1UVYdwlxk4sCZFaiDc5DQKHTqUO7w0qjz4AebMgdNOg+1HIP9AAUW7DxL71VJSX7oZKSnGHRrG4kEPkR3VHndeAe78Asgv4MBPBeQdKCCSQqIoIM5dQHRBAWGlBYSXFhBFAS5+QnAjgFvd7N6Wx0baUEIYxYRTTLjXx6USjoaFoRHh9HN/zuCiJc4xLoStWTEc2V9I0+KvSCjKJqbUuQpEV8/E8877LIiMIz8uicL4JAoTkggLcdNq5buIuxQNDWPfr8dA586ER4USHh1GRHQoYZGhbPohlO++D6XrGaGkdA11uqplU2Ym3H8/HYuKnB/cCy84lzqKjHQKWmQkREby+epIlq6IZOi54rueXQOwAmOMKef73lQU0BJG/QZGdijvTQ0/TqGKiKh8eyBVZ37Rx+noFWlQXAThEZQ8P4fGLuciqZoPC+bB66877UNC4PzznUJaUOB0nAoLoXBbOv0/WOH0ykIimNrjFdZEp1JY6LST/DxKf9pN1OFsksimBbtJIpukwmyS9jhTC76jPVsJ8Qy4kJJims99xuvPx+WZ+Nexf8QCTrhbbvG6fIBnKiKcgvBI3BFRlIZF4g53phB3CXG7N4OnZ7fvzCEUJbWD6BiIiUFiYyA2hl0HYtixL4bu/WJw9XSW/WzKyKAN1Ok+GVZgjDENrr69qbJ1HKtQiXj+mL8gFZY4jWToUE6vsq7mzeHtt48WqYceOv5ov6d/1iCG9PQOpKV1YJVnPYsWORfvzstzbl2Unw+70tPpdHsaUlyEhkXw2b0L+allHwrzSsunFZ+W8tXnpYRQShilnNG1lM4dSikpLKW0qJR2+9cwZv1dhGoxpRLOc0mPsD2kIyHFhYQUFxJaUojmFxBaWkgknqnYM3F0OoN1NC4b0KFuStdmUMgPxJBXPoXiphXQF+D96n9VLaHNcX6bXlmBMcYErfr2qGp2bIo6F7vo6AqNXKngOtroHC/r6lulV/bK9KqZfgHpZ5YXuz/WoGf3wQdOsSsuduYVF0PIF+lwQxrqKXZ7nn2LA6enli8vLlLemFvEf/6VR5Tm0Tgkj3t/l8etv8lzqmbZtGAB/PvfR8fR15IVGGPMSa1GRaqh1tMQgzHq0bMr1y4V2hzt2Z3xs0ZCYvNIFrwTyaGiBA5EQLffAFWbJSfD+++j+fl1qjBWYIwxxo8aouD5s9ixeDHZAwfuqktOKzDGGHOKqmmh2glZdVl/SF1eZIwxxhyPFRhjjDE+YQXGGGOMT1iBMcYY4xNWYIwxxviEFRhjjDE+YQXGGGOMT1iBMcYY4xNWYIwxxviEFRhjjDE+YQXGGGOMT/i1wIhIUxFZKCJHRGSbiFxXTdvxIpIlIodEZIaIRNZlPcYYYwLD3z2YaUARkASMBF4Qke5VG4nIcGACkAZ0ADoBD9d2PcYYYwLHbwVGRGKBq4EHVDVXVZcD7wA3eGk+CpiuqhmqegCYDNxUh/UYY4wJEH9erj8FKFXVzArzvgGGeGnbHXi7SrskEWkGJNdiPYjI7cDtnqe5IvJ9HfOXSQT21nMdDSmY8lgW74IpCwRXHsviXTBlATitLi/yZ4FpBByqMu8Q0LgGbcseN67lelDVl4GXaxv2WERkpaqe1VDrq69gymNZvAumLBBceSyLd8GUBZw8dXmdP4/B5AJxVebFATk1aFv2OKeW6zHGGBMg/iwwmUCYiLgqzOsJZHhpm+FZVrFdtqruq+V6jDHGBIjfCoyqHgH+DTwiIrEiMgi4Apjjpfls4FYR6SYiCcBEYGYd1uMLDba7rYEEUx7L4l0wZYHgymNZvAumLFDHPKKqDR3k2BsTaQrMAM4H9gETVHWuiCQD3wHdVHW7p+0fgfuAaOBN4E5VLaxuPX57I8YYY47LrwXGGGPMqcMuFWOMMcYnrMAYY4zxCSswNSQikSIy3XPtsxwRWS0iFwU4k0tECkTkn4HM4clyrYis91wfbrOInB2gHB1E5H0ROeC5lt1zIuKX871EZKyIrBSRQhGZWWVZmohsEJE8EVkqIu0DkUVEBojIIhHZLyJ7RGSBiLTyZZbq8lRp85CIqIgMC1QWEYkRkedFZK/nOoifBDDLrz3/p3JE5DsRudLHWar9jqvLZ9gKTM2FATtwrhgQDzwA/EtEOgQw0zTgqwBuHwAROR94HLgZ54TXc4AtAYrzPLAbaAX0wvl9jfHTtncBf8UZgFJORBJxRj4+ADQFVgKvByILkIAzIqgD0B7n/LFXfZylujwAiEhnYATwU4CzvIzzO+rq+Xd8ILKISBvgn8Afcc7zuxeYKyItfJjlmN9xdf0M+/NM/hOaZ3j0pAqz/iMiPwB9ga3+ziMi1wIHgRVAF39vv4qHgUdU9XPP850BzNIReE5VC4AsEfkvzqWHfE5V/w0gImcBbSss+iWQoaoLPMsnAXtF5HRV3eDPLKr6QcV2IvIc8LEvMtQkTwXP4YwafT5QWUTkNOByoK2qHvbMXhWILJ7HByv8vt4TkSNAZ5w/oHyRpbrvuGbU4TNsPZg6EpEknOur+f0ETxGJAx4B7vb3tr1kCQXOApqLyCYR+dGzWyo6QJGeAa717OpoA1wE/DdAWcp0x7leHlD+H3kzfip8x3EOAT5JWUR+BRSp6vuBzAH0B7YBD3t2kX0rIlcHKMtKYL2IXC4ioZ7dY4XAWn8FqPIdV6fPsBWYOhCRcOA1YJav/gI9jsk4V5veEYBtV5UEhOPs3jgbZ7dUb5yTYwPhY5wP/WHgR5z/qG8FKEuZWl0/z19EpAfwIM7ul0BlaAQ8BowLVIYK2gJn4PxuWgNjgVki0tXfQVS1FOeE87k4hWUucIfni93nvHzH1ekzbAWmlkQkBOeqAUU4H0B/b78XMAz4P39v+xjyPf8+q6o/qepe4CngYn8H8fxuPsTZVxyLc0XaBJzjQ4EUdNfPE5EuwAfAH1T100DlwNm9OkdVfwhghjL5QDHwV1UtUtWPgaXABf4O4hno8AQwFIjAOS7yiuf/v6+37e07rk6fYSswtSAiAkzH+av9alUtDkCMoTgHaLeLSBZwD3C1iHwdgCx47tfzIxAMZ+w2BdrhHIMp9Fy77lUCUOyqqHRtPXHuadSZAO2a8oz++QiYrKr+usTSsaQBd3lG/GXh/P7+JSL3BSCL33Y/1UAv4BNVXamqblX9CvgC549Ln6nmO65On2ErMLXzAs7okstUNf94jX3kZZxfbC/P9CLwHjA8QHnA+RL/vYi0EOfaceOA//g7hKf39APwWxEJE5EmODev+6b6VzYMzzajgFAgVESixBkivRA4Q0Su9ix/EFjry92rx8riOS61BJimqi/6avs1zYNTYM7g6Od5F3AHzghJf2f5BNgO3O9pMwjnD7oPA5DlK+Dssh6LiPTG2QXt6yJ4rO+4un2GVdWmGkw4QzoVKMDpLpZNIwOcaxLwzwBnCMcZ/XMQyAKmAlEBytILWAYcwLlh0wKghR9/F1plmuRZNgzYgLMbZhnQIRBZgIc8jyt+hnMD+bOp0m4rMCyAv6fuQDpwBOf6iFcFMMtYYBPObqgtwN0+zlLtd1xdPsN2LTJjjDE+YbvIjDHG+IQVGGOMMT5hBcYYY4xPWIExxhjjE1ZgjDHG+IQVGGOMMT5hBcaYE4g490sZEegcxtSEFRhjakhEZnq+4KtOnx//1caceux+MMbUzkfADVXmFQUiiDHBznowxtROoapmVZn2Q/nuq7Ei8p7ntrLbROT6ii8WkTNF5CMRyRfntsUzRSS+SptRnnuRFIpItvz8FsNNxbnV8RER2eJlGw96tl3ouYjkbF/8IIw5HiswxjSsh4F3cK6J9jIw23O3QkQkBufmZ7nAL4CrgIFUuF2uiNwBvIRzAdEeOFeCrnrF2geBt3Gubvs6MMNzhWQ8N8i6B+c20S7gUuBLH7xPY47LrkVmTA15ehLX41wMsKJpqnqfiCjwiqreVuE1HwFZqnq9iNwGTMG5JW+OZ/lQnHuOuFR1k8j/t3fHIFVFcRzHv39QAjeXCGwPQ2iKosaChjY1Wm1xlWooF2kJapDa2qMmxUFQaGhpSCRac0hwSUJehDiIRvVz+J/kcUl8V9/Nht8HHpx77nuH84bLn3PPn/OPL+ThpQ8PmIOAJ5Imy3UPWVxtXNKriLhHnkY8pJMpJ2G2z3swZvW8A8YrfZtt7aXKvSXgZmkPkkectxdpeg/8Bs5HxBYwALw9ZA77R7ZL+hkRLeB06ZoBJoC1iHhDrpjmJe0eMqZZ1/kVmVk925JWK59vHf42OLgwm8r9TlRXJqI8y8oy2ufIVcwWMA18LAWizP4pBxiz7rr8l+uV0v4EXIiI9jrmV8jncEXSBrBOFuE6Mkk7khYk3QUukjVOrh5nTLOj8Csys3pORcSZSt8vSa3SHo6ID2RBplEyWFwq916TSQAvI2IK6Cc39OckrZbvPAaeRcQGWam0D7gmabqTyUXEGPlcL5PJBLfJFc/nmv/T7NgcYMzquQ58rfStA2dL+xEwQlb1bAF3lPXUkbQdETeA52Rm1w6ZDTbxZyBJLyLiB3AfeAp8BxZrzG8TeEAmE/SSq6ZhSWs1xjDrCmeRmXVJyfC6JWn2pOdi9j/wHoyZmTXCAcbMzBrhV2RmZtYIp3y1DwAAACNJREFUr2DMzKwRDjBmZtYIBxgzM2uEA4yZmTXCAcbMzBqxB8NnTLh+rzk2AAAAAElFTkSuQmCC\n", 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" ] @@ -413,7 +412,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -439,28 +438,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 3s 436us/sample - loss: 0.0972 - val_loss: 0.0491\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0967 - val_loss: 0.0489\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 3s 363us/sample - loss: 0.0372 - val_loss: 0.0298\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0369 - val_loss: 0.0296\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 3s 364us/sample - loss: 0.0255 - val_loss: 0.0220\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0253 - val_loss: 0.0218\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 3s 363us/sample - loss: 0.0200 - val_loss: 0.0178\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0198 - val_loss: 0.0177\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 3s 365us/sample - loss: 0.0167 - val_loss: 0.0152\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0166 - val_loss: 0.0151\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 3s 367us/sample - loss: 0.0147 - val_loss: 0.0135\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0146 - val_loss: 0.0134\n", "Epoch 7/20\n", - "5472/7000 [======================>.......] - ETA: 0s - loss: 0.0135" + "219/219 [==============================] - 3s 13ms/step - loss: 0.0132 - val_loss: 0.0123\n", + "Epoch 8/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0124 - val_loss: 0.0116\n", + "Epoch 9/20\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0118 - val_loss: 0.0112\n", + "Epoch 10/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0116 - val_loss: 0.0110\n", + "Epoch 11/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 12/20\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 13/20\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 14/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 15/20\n", + "219/219 [==============================] - 3s 14ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 16/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 17/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 18/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 19/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n", + "Epoch 20/20\n", + "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n" ] } ], @@ -480,18 +504,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/63 [==============================] - 0s 3ms/step - loss: 0.0109\n" + ] + }, + { + "data": { + "text/plain": [ + "0.010881561785936356" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.evaluate(X_valid, y_valid)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" @@ -499,9 +554,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "y_pred = model.predict(X_valid)\n", "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", @@ -524,35 +592,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0038 - val_loss: 0.0042\n", + "Epoch 1/20\n", + "219/219 [==============================] - 10s 44ms/step - loss: 0.0492 - val_loss: 0.0090\n", + "Epoch 2/20\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0070 - val_loss: 0.0065\n", + "Epoch 3/20\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0053 - val_loss: 0.0045\n", + "Epoch 4/20\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0045 - val_loss: 0.0040\n", + "Epoch 5/20\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0042 - val_loss: 0.0040\n", + "Epoch 6/20\n", + "219/219 [==============================] - 10s 44ms/step - loss: 0.0038 - val_loss: 0.0036\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0037 - val_loss: 0.0033\n", + "219/219 [==============================] - 10s 44ms/step - loss: 0.0038 - val_loss: 0.0040\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0036 - val_loss: 0.0035\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0037 - val_loss: 0.0033\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0034 - val_loss: 0.0038\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0036 - val_loss: 0.0032\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0035 - val_loss: 0.0034\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0035 - val_loss: 0.0031\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0034 - val_loss: 0.0031\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0034 - val_loss: 0.0030\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0034 - val_loss: 0.0030\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0033 - val_loss: 0.0031\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0034 - val_loss: 0.0031\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0034 - val_loss: 0.0031\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0033 - val_loss: 0.0039\n", + "219/219 [==============================] - 10s 44ms/step - loss: 0.0033 - val_loss: 0.0032\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0033 - val_loss: 0.0030\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0034 - val_loss: 0.0033\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0033 - val_loss: 0.0033\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0035 - val_loss: 0.0030\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0034 - val_loss: 0.0033\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0033 - val_loss: 0.0029\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0032 - val_loss: 0.0030\n", + "219/219 [==============================] - 9s 43ms/step - loss: 0.0033 - val_loss: 0.0030\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0032 - val_loss: 0.0032\n", + "219/219 [==============================] - 10s 43ms/step - loss: 0.0032 - val_loss: 0.0029\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0032 - val_loss: 0.0029\n" + "219/219 [==============================] - 10s 43ms/step - loss: 0.0032 - val_loss: 0.0029\n" ] } ], @@ -580,13 +659,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2000/2000 [==============================] - 1s 274us/sample - loss: 0.0029\n" + "63/63 [==============================] - 1s 9ms/step - loss: 0.0029\n" ] }, { "data": { "text/plain": [ - "0.0029341068249195816" + "0.0029105597641319036" ] }, "execution_count": 17, @@ -605,7 +684,7 @@ "outputs": [ { "data": { - "image/png": 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ss2zcuJHi4mI8Hk/ljvFw9u7dy9atW8mtsh1RUVGcdNJJ/PTTT5Vl33//PXfccQeffvop+fn5eL1evF4vP/74Y8DbUlBQwJYtWzj11FOrlffr14+5c+dWK+vWrVvl46ysLADy8vICWs/69evxeDzV1hMdHU1ubm7lwGsjR47kjDPOoEOHDgwZMoRf//rXnH322URFRdG9e3cGDx5M165dGTJkCIMHD2b48OE0b9484G0NhlCfg5kClAGZwGXAEyLSxU+90cAFQHegGzAUuKZqBRFJA24HVrsZsDFHE+9JJ8G8eXDvvc5fl5PLfqNGjeK1115j165dTJs2jfT0dM4//3wAZs+ezZgxYxg5ciTvvPMOq1at4vrrr6esrCyoMZx77rnk5+fz1FNP8emnn/LZZ58RExMTtPXU7MI/Njb2F/MOdV6ptuvp1asXGzZs4IEHHsDr9TJixAjOOOMMvF4v0dHRvPvuu7z77rt069aN5557jvbt2/P555/Xe/21EbIEIyJJwDDgDlUtUtXFwBvAFX6qjwAmq+omVd0MTAZG1qjzAPB3nJaQMSZQublw220hSy4Aw4cPJyEhgenTpzN16lSuvPLKyh3w4sWLOemkk7jhhhvo1asXxx13HN9//33Ay05NTeWYY46pdo5BVVm6dGnl8507d/L1119z++23M3jwYI4//ngKCwspLy+vrLP/nEvNiwOqSklJISsri48++qha+eLFi+ncuXPAMR9OmzZtiIuLq7aeiooKlixZUm09jRs3Zvjw4TzxxBO89dZbfPDBB3z33XeAk4hyc3O56667WLZsGVlZWcyePTtoMQYilIfIOgDlqrquStnnwAA/dbv45lWtV9nSEZETgT7A9cBFh1qpiIzGaRGRmZnJggUL6hJ7paKionovI1giKRaIrHiOxlhSU1MpLCys1zIqKirqvYy6Gj58OHfddRd79uzh4osvprCwkIqKClq1asW0adN45ZVXaNu2La+88goffvghTZo0qYy1tLQUr9d70OfXXnstDz74IDk5OXTp0oVnnnmGrVu3kpGRQWFhITExMTRt2pTHH3+ctLQ0tm7dyl/+8hdiYmIoKSmhsLCQ9PR0EhMTeeONN2jWrBnx8fGkpqb+Yl1/+MMfuP/++8nOzqZHjx7Mnj2bRYsWsWjRIgoLCykqKgKc8yg13+vi4uKDvv8ej4fy8nIKCwtJSEhg1KhRjBs3jkaNGtG6dWumTJnC9u3bufLKKyksLOSxxx4jMzOTbt26ERMTw7Rp00hJSSE1NZV58+axYMECBg0aREZGBl988QU//fQTbdq08bv+kpISd/5f6jrWcm0noD+wrUbZ1cACP3UrgE5VnrcHFBAgGlgOnOybtwD4fSAx9O7dO6DxqQ/laBzrPVgiKZ6jMZZDjZseqFCP9V7VihUrFNBTTjmlWjylpaV61VVXaZMmTTQ1NVWvuuoqveeee/TYY4+trHfXXXdply5dDvrc4/HomDFjNDU1VVNTU/WGG27Qa6+9VgcMGFBZZ968edqlSxeNj4/XLl266Ntvv61JSUn6/PPPV8byzDPPaE5OjkZFRVW+tua6KioqdMKECZqdna2xsbHatWtXnTNnTuX89evXK6DLli2rtv2A/utf/zro+zNixAg955xzKmMpKSnRm266STMyMjQuLk5POukkXbRoUWX9p59+Wnv27KnJycnauHFjPe200/Sjjz5SVee7ctZZZ1W+tl27djpx4sSDrvtQ3y1gudZ1v1/XF9Z6RdAT+LlG2S3Am37q7gVOrPK8N1Doe/wHYGqVeZZgIkQkxXM0xnKkJxh/IimehhyLWwkmlCf51wExItK+Sll3/J+kX+2b56/eIOA3IrJNRLYBpwCTReQxF2I2xhhTRyE7B6Oq+0TkVWCCiPwe6AGcj5MganoRuFlE5uIcGrsF+Idv3kggoUrdV4F/41z+bIwxJkKE+kbL64GpQB6wE7hOVVeLSH/gv6q6/xbXp4C2wJe+58/6ylDVPVUXKCJlQIGq7g1B/MYYYwIU0gSjqrtw7m+pWb4ISK7yXIFxvulwyxwYxBCNMcYEiXV2acwRRKt3ZmFMvbn5nbIEY8wRIjY2luLi4nCHYY4yHo+HmBh3DmZZgjHmCJGRkcHmzZv5+eefrSVjgsLr9bJ9+3ZSXerw1HpTNuYIkZKSAhzoKbcuSkpKSEhIOHzFEImkeBpqLElJSZU9SQebJRhjjiApKSmViaYuFixYQM+ePYMYUf1EUjwWS/DZITJjjDGusARjjDHGFZZgjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNcYQnGGGOMKyzBGGOMcYUlGGOMMa6wBGOMMcYVlmCMMca4whKMMcYYV1iCMcYY4wpLMMYYY1xhCcYYY4wrLMEYY4xxhSUYY4wxrrAEY4wxxhWWYIwxxrjCEowxxhhXWIIxxhjjCkswxhhjXGEJxhhjjCsswRhjjHGFJRhjjDGusARjjDHGFZZgjDHGuMISjDHGGFeENMGISLqIzBGRfSKyUUQuPUg9EZGJIrLTN00UEfHNayYiH/nK94jIEhE5NZTbYYwx5vBiQry+KUAZkAn0AN4Skc9VdXWNeqOBC4DugALvAeuBJ4Ei4CrgW9+884E3RSRDVctDshXGGGMOK2QtGBFJAoYBd6hqkaouBt4ArvBTfQQwWVU3qepmYDIwEkBVS1T1G1X1AgJUAGlAegg2wxhjTIBEVUOzIpGewEeq2qhK2VhggKoOrVF3LzBEVT/1Pe8DzFfVxlXqfAF0AmKBZ1X16oOsdzROi4jMzMzes2bNqtd2FBUVkZycXK9lBEskxQKRFY/F4l8kxQKRFY/F4t/pp5++QlX71OnFqhqSCegPbKtRdjWwwE/dCqBTleftcQ6HSY16CcAlwIhAYujdu7fW1/z58+u9jGCJpFhUIysei8W/SIpFNbLisVj8A5ZrHff7oTwHUwSk1ChLAQoDqJsCFPk2tpKqlgAzRWStiKxS1c+DGbAxxpi6C+VVZOuAGBFpX6WsO1DzBD++su4B1NsvFmhb7wiNMcYETcgSjKruA14FJohIku/S4vOBl/xUfxG4WURaikgWcAswDUBEThaRfiISJyKJIjIe56q0T0OyIcYYYwIS6suUrwemAnnATuA6VV0tIv2B/6rq/rNaT+G0SL70PX/WVwYQD/zdN9/jq3OOqm4JzSYYY4wJREgTjKruwrm/pWb5IiC5ynMFxvmmmnU/pPrhM2OMMRHIuooxxhjjCkswxhhjXGEJxhhjjCvqnWBEJDYYgRhjjDm61CrBiMiNIjKsyvPngGIR+UZEOgY9OmOMMUes2rZgbgTyAUTkNOAi4FJgFU6HlMYYYwxQ+8uUW+J0mw8wFPiXqr4sIl8Ci4IamTHGmCNabVswBUCG7/EZwDzfYw9Ox5PGGGMMUPsWzLvAMyKyEjgO+K+vvAsHWjbGGGNMrVsw/wt8BDQHhvvuzAfoBcwMZmDGGGOObLVqwahqAfAHP+V3BS0iY4wxR4XaXqbcuerlyCJyhohMF5HbRCQ6+OEZY4w5UtX2ENlUoCeAiOQArwPpOIfO7gtuaMEXotGhjTHGUPsE0wlY6Xs8HPhUVX8NXIEzdHFE83jCHYExxjQctU0w0UCZ7/EgYK7v8fc4g35FtLKyw9cxxhgTHLVNMF8B1/kGCBsEvO0rbwnsCGZgbrAEY4wxoVPbBDMeuBpYAMxU1f0jTp4HLA1iXK6wQ2TGGBM6tb1MeaGINAdSVHV3lVlPAT8HNTIXWAvGGGNCp9ZDJqtqhYgUi0hXQIHvVXVD0CNzgSUYY4wJndreBxMjIg8Bu4HPgS+B3SLy4JEwLowlGGOMCZ3atmAexLkc+Vpgsa+sP/AATrIaG7zQgs/OwRhjTOjUNsFcClylqnOrlH0vIvnAsxwBCcbjgdiIb2sZY8yRr7ZXkaXi3PNS0/dAk/qH475t28IdgTHGNAy1TTCf44xqWdNNvnkR76efwh2BMcY0DLU9RDYOmCsig4FPfGUnA1nA2cEMzC2bNoU7AmOMaRhq1YJR1YVAB+DfQLJv+hdwJv5bNhHHEowxxoRGXe6D2QL8uWqZiHQHhgUrKLdERVmCMcaYUKntOZgjWmysJRhjjAmVBpVg4uLsJL8xxoRKg0sw1oIxxpjQCOgcjIi8cZgqKUGIxXWxsbB1K5SXQ0ytzz4ZY4ypjUB3szsDmL++nrG4Li4OKipg+3Zo2TLc0RhjzNEtoASjqr9zO5BQiItz/m7aZAnGGGPc1uDOwYCd6DfGmFAIaYIRkXQRmSMi+0Rko4hcepB6IiITRWSnb5ooIuKb10FEXheRfBHZJSLviEjHQNa/v5NLO9FvjDHuC3ULZgpQBmQClwFPiEgXP/VGAxcA3YFuwFDgGt+8JsAbQEffcpYCrwey8pgYSEy0BGOMMaEQsgQjIkk4d/vfoapFqroYJ1Fc4af6CGCyqm5S1c3AZGAkgKouVdXnVHWXqnqAh4GOItI0kDiysy3BGGNMKIiqhmZFIj2Bj1S1UZWyscAAVR1ao+5eYIiqfup73geYr6qN/Sz3AuAJVT3mIOsdjdMiIjMzs3dW1lo8nij+8Y/P6rQdRUVFJCcn1+m1wRZJsUBkxWOx+BdJsUBkxWOx+Hf66aevUNU+dXqxqoZkwhn5cluNsquBBX7qVgCdqjxvDyi+hFilPBvYDFwSSAy9e/fWK65QbdVK62z+/Pl1f3GQRVIsqpEVj8XiXyTFohpZ8Vgs/gHLtY77/VCegynilzdkpgCFAdRNAYp8GwuAiDQH3gUeV9WZgQaRnQ1btjj3wxhjjHFPKBPMOiBGRNpXKesOrPZTd7Vvnt96IpKGk1zeUNX/q00QOTnOnfx5ebV5lTHGmNoKWYJR1X3Aq8AEEUkSkVOB84GX/FR/EbhZRFqKSBZwCzANQERSgHdwzuf8qbZxZGc7f+1EvzHGuCvUlylfDyQCecBM4DpVXS0i/UWkqEq9p4A3gS+Br4C3fGUAvwH6Ar8TkaIqU6tAArAEY4wxoRHSLh9VdRfO/S01yxfhjI65/7niDM88zk/dF4AX6hrD/gRjd/MbY4y7GlRXMQDNmkF8vLVgjDHGbQ0uwYjYzZbGGBMKDS7BgCUYY4wJBUswxhhjXNGgE4zXG+5IjDHm6NUgE0xODng8kJ8f7kiMMebo1SATjN0LY4wx7rMEY4wxxhWWYIwxxriiQSaY5s2d4ZPtbn5jjHFPg0wwUVF2qbIxxritQSYYsARjjDFuswRjjDHGFQ0+wRwYI9MYY0wwNdgEk5MDpaWwY0e4IzHGmKNTg00wdqmyMca4yxKMJRhjjHGFJRhLMMYY44oGm2AyMiAmxm62NMYYtzTYBBMdDS1bWgvGGGPc0mATDNi9MMYY4yZLMJZgjDHGFZZg7GZLY4xxRYNPMMXFsGtXuCMxxpijT4NOMDk5zl87TGaMMcHXoBOM3QtjjDHusQSDJRhjjHFDg04wLVo498NYgjHGmOBr0AkmOhqysuxufmOMcUODTjBg98IYY4xbLMFYgjHGGFdYgrGbLY0xxhWWYLJh3z7YuzfckRhjzNElpAlGRNJFZI6I7BORjSJy6UHqiYhMFJGdvmmiiEiV+U+LyDci4hWRkfWJaf/Nlnai3xhjgivULZgpQBmQCVwGPCEiXfzUGw1cAHQHugFDgWuqzP8cuB5YWd+A7F4YY4xxR8gSjIgkAcOAO1S1SFUXA28AV/ipPgKYrKqbVHUzMBkYuX+mqk5R1XlASX3jsgRjjDHuEA3R2W0R6Ql8pKqNqpSNBQao6tAadfcCQ1T1U9/zPsB8VW1co95i4FlVnXaI9Y7GaRGRmZnZe9asWdXml5cLZ555GpdfvpHf/W7DYbejqKiI5OTkw9YLhUiKBSIrHovFv0iKBSIrHovFv9NPP32Fqvap04tVNSQT0B/YVqPsamCBn7oVQKcqz9sDii8hVilfDIwMNIbevXurP1lZqldd5XfWL8yfPz+wiiEQSbGoRlY8Fot/kRSLamTFY7H4ByzXOu73Q3kOpghIqVGWAhQGUDcFKPJtbNDl5NhJfmOMCbZQJph1QIyItK9S1h1Y7afuat+8w9ULCrvZ0hhjgi9kCUZV9wGvAtvi3egAABjmSURBVBNEJElETgXOB17yU/1F4GYRaSkiWcAtwLT9M0UkTkQSAAFiRSRBROq8LZZgjDEm+EJ9mfL1QCKQB8wErlPV1SLSX0SKqtR7CngT+BL4CnjLV7bfu0AxcArwtO/xaXUNKjsbCguhoKCuSzDGGFNTTChXpqq7cO5vqVm+CEiu8lyBcb7J33IGBjOuqiNbdu4czCUbY0zD1eC7ioED98LYiX5jjAkeSzDYzZbGGOMGSzDAMceAiCUYY4wJpoaVYLZtgyVLflEcFweZmZZgjDEmmBpWgtm8GQYN8ptkcnIswRhjTDA1rAQDUFwM06f/ojg7207yG2NMMDW8BAPw+OMwfDisXVtZFB0N333nt3FjjDGmDhpWgmnZEt57D+66C955B7p2hauuYsWrG3n9dSgthV/9ypKMMcYEQ8NKMC1awODBcPfd8MMPMGYM/POfdL+oA5M8N9GcPEpK4MornaNo+/aFO2BjjDlyNawEU1Xz5jB5Mnz7LTvPGcH1TOEH2vJ/8hekYC9XXOFcWXbllU6jp6Ii3AEbY8yRpeEmmP1ycsh8/Wm+nLWGH084l9v1//jG04Yfrn2QkRf9zBtvwJAh0KoV3HorfPddUrgjNsaYI4IlGJ+e/9OBzl/MgpUrkdxc2jw5nsfePo78CU/w75ke+vSBRx6Bq6/uS7du8NBDzlXPxhhj/LMEU1PPnvDWW7BwIbRrR+xN1zPsz514/aIZ5L+2mDdyb+HEiiWMG+fcO3PGGfDii1BUdPhFG2NMQxLS3pSPKP37O0nm7bfh9tvh8stpIsK5wND4x7lz5jyeW3sKL70EI0bAddfBb34DvXvDzz87V6Pl5oZ7I4wxJnwswRyKCJx9Npx5Jlx2GcyahQCUlNDq90O4Z8AA7r6iL2uT+jDtqz48MacFM2YceOmJJ0KvXtC2LbRr5/xt2xYaNw7nRhljTGhYgglEVBTceCO8/jpaWopER8PAgbBxI/L223T2enkQ+HNKNvPpwzL6sFz7snljb2ata8ru3dUX17z5gWRTNfG0awdZWc7qliyBBQuc1VhLyBhzJLIEE6jcXJg3j/VTp9L2qqsO7PX37YPPPoPlyyn77zI6v7ecC/Q1Z942oG1bygb0Ie/YPnzXpC+fSS/KPltNs68WMPfHgTzwcm61S6Dj4pzbdTZtAvUq8TEVPHBPGecMLqVVizLipQzKymDpUtq9+qrzooEDQ/hGGGNMYCzB1EZuLj+WltK2apMiKQn69YN+/Wg+Br5bAn97ey9nNltBl+LlsGwZccuXkv3ay2QDA8E5fqbKKBH02NZ4iKGiuAxviZM8dFMZMd4y4igjqlzhzzhTDTmA/vvfeDJbEtWnNzEndHaG5Dz+eOjUCZKTf/miYKuocHqpfvddWs+fD/Hx1uQyxgCWYIIuNxdyc1OBX/kmn/x8WLHCubnz/fedMlUkqRFxJ5zgNF1805ad8fzzlThKKuIoj47j7PPiiEqIY/ueeLbujKPNunc4ffcrROPFi/D19jSi3vqeDm/9lzg8lavc0+RYNjV2kk7T/s7f6K7Hk5CZSkICxMaCfHKIY3EeD2zZwldvb+K7BZvo0WwTrWM2Oc2r/dPWrZV3oR4LThcIF1zg9PXWr59zA9GRyo5TumvJElrNmGE/So5ilmBCpXlzOOssSE2Fjz5yDnPFxcEzz/zinysLONW3bxs0EE6q+b+3pDMM+g/e0lIkNp68Pz/N0uhc1q/zUPzV9yT8sIYWu9dw/J61dN6zhk4/zSfxnZLKl2+iJZ9yPLtJ4wJeI5oKvETzXtL5xEVXkFX+E5meTaR5thOF0hXo6nttSUwSBak5FKVm83OLwRR3zab5tq849vM3EPWiqnj/8xbRc+YA4Dkmh7IT+7E+qx8LtT9d/6cL/QdEIVK3t9G1fX5xsfMjYP+0ZAk88ICTPGNj4dlnnYs9mjZ1TpKZ2quogHXrnEPKb70Fs2bRxuuFF16AqVPhkkucXmfNUcMSTKj5zuUcbi/ptIQOvYwNvvNBg3NzGQxALNAJ6MQ991zIlRPA64UYqeD6X2/g3LZrSP5xDSmb1tBl2xoytn5IjNdp8UTjZXDxf9iafBx5jbJZEtuDrVHZrC3MZnVBNptwphJJoWKPULET+MEJ52SWMI93iKUMD3Gc4XmXfSTTj8X027qY/q9/SFdm0hXY82QqczmVJdH9WBbXjy8T+qLxCZUNuPj4ao25alNhIXz4obNN0dHO/qh9e2jUyJmSkpy/WRs+JuH9mXz3ST5RrXJo9HM+jYryiS/IJ3bvDqJ25rN7XT7FP+WTXpFPQkH+oTueKy2FK64AQKOj0eYZeJtnsju+BT+VZZLeKZOWvVsQnZVJVFYLp4+hzExIT4dPPz3sr/RgJc1gLCdYsXzyYSlr/rWaASmf0W7vSiepfP65cw0/OB+g1+tclenxOO/vtdc61/n37Qt9+7Iy5kTe+aY1A08X99+XAFpTgS7HWr0HiKqGO4aQ6dOnjy5fvrxey1iwYAEDI+Sk+qFiWbLEGVttf0Np3jw/3/ePPnI6//R4DlrpYMspL3d+9BcXQ0kJ6MdLyHt5OolnX07+cbmV5cXF8Mq/lVWvbeBUFtOfxQxJWkzrfWsA8ETFsbF5H75p3o89Mc1I2pfPuuSebIo/jtiSQuJKCogtLSS+tBDv3kKi9hXQmEIaU0gKhTTmwPPGFJLGLhpTxMEaSMUkkE/zymkHzdgT05wd4jzPU2fKqviJqfyOWDyUE8O9/IUCUmnBNjLZTibbqz2Op+wX6yonmigqEMBLFB8lncn6Rl0oSMhgb0IGRYkZbPVmMn91Btu1OeVR8fTr5zSS9rfw9v/tuGsJXfIXsDpjIN81y602TwR27oQPPnAaCdHRMGCAk99UnYSs6kz5+TtIT29W+bzqvF27YOVKpywqyrkVLCvL2edWnY7LX8JxmxawteNAdnbIJUmLOCbvczI2r6TZT5+R+PVnpG1ZXXm4tiw+mcJ2Pdndpie7j+3JzmN7EVW0l19NPBPxlKHRsawacitx+/aQsWEpzTavIqa8FIAdNGW59MXb+0RKu/dlR+u+lKRmEh3tbGdUVPW/Ncu+/dbpPL28HGJi4M47oU0bKC/zIkWFxBTsImPtAgbMuo6o8jK80bF8OPhedqR1wFuheCuUCo+XnTuUZZ96Ua8SHaUMPM3LMS2U2BglLsZLbKyStuNbOr05CakoR2Ni+eGelygdMIS4ZikkJAoJCU6OXbLEyT/9+nHQlvzh9jPBSnaBLEdEVqhqn4MGcwiWYGrpSEkwENov4aHi8ZukOuyEjz+GxYth0SJYujSgHkUriKKAFIqkMU1bNyYhIwVvo8aUJzbGk9CYqO/X0ejzTxAUlSg2nHYFX532v+yJacbOqObsLU/ig/nCokXOTlXE2aaTTz6wU9q/g2q1eQltf1zAj20HsrV1brWd1wcfwNy5vmWgXPirPZzeeTsJe7eTsGcbiQXb6frtq7TfstCJBdgX24TYihLivSV+t20PqeyMzmBvfAY7YzLZFZ3BrpgMEiqKuGz3Y0RrORUSw6TmE1kXdwJeiaZcoyknht0F0ewqiKYCZ0pNiyYlLRqNciavRNOpZBXH7/2QdWmn8W2j7sRIBTGUE0M5sVLOrvxydmwrJxqnvHmTclIaleMtK8frqUA95bQrXcPtFROIwYMSxWayyGETUTj7kTya8xk9WUkvPqMnn9GT72mH+uk05GSWMJAFLGAgn3DgixVLGV35ihNZSl+W0ZdldGE10XgB2EgrX2lf9tGIlmzmC7rxE61IZxdp7CadXdUe1yxLY3fl8txWShw7aEYeGdV+3OTTnDwy2CnN2RnlTLtjmtNJ19KvYj6fxA/km7hupEghKVJIqjg/phI8hVTsKSSZQlIpoEVyIalSSJLXmZK1gIzyLXQsX42geIni47iBbIg+jr3ShL3ShD1RaeyqaMLmn5uwV9L4Oa4Jz76SRt8zmjj/pPstWUL2Kads3qSaXZdttwRTS0dSggm1w7WoDpmkJkyAe+458PP58svhqqucu1JTUpy/jRuzZFUiCz6Ugy/Hl828paVExcfXqlVWGwEtw18sJ5/sHI7bvh3y8vh6YR7/uCOP9PI8jonazkUD82jmzYM837Rjh5PFIpACFR068/N5/8PPHXtR1L4n+1KzWL5C+N//dRrGsbEwZQr06HGghbF/iomBZcs+4dRTT65WFh0Ny5fDeecdeH/fenkffaNXErViGdErlxH92TJiNnx/6PhEKG2UxuZ9aewind2SToeT0khtmw5N0pwmXno6Mbu2k/zgXajHg8TFUvHYk0T17E5UtDjfRRFWfRHFVaOEMo8QHRvFc1OFzl2EUk8UpWVCaZkgn39G9rjLkHIPGh3NdxeOpyQ+lehd+cTsyWfPt/l4t+eTQR7NySeFwoO+r/sFcqqygihKYhtXm5JLd5JR+D3iW15hfDM0KppGpbuJ9f6ytV1NYiKkpTlv/I8/0sfrZblqnc6aWoKppUjaqUdSLFDPeIKx16+yrB9q3q/kZ3UhOV9R31jKy53B8YYPd/bYMTFOr6vHH++0+KpMX6+uYPUXFXQ9voKO7cqrz3/zTXjtNSdZRUU5/Rqdd56zvCrT2m9j+PyraE7oFUOXbjG/mM+XX8LVVx/ysGpt3t96/Si54w64//4DP0pGjIDrr69MHKSkQFRUaD6nACrV/Ip/MLeEk4/bceDCkrw8mDkT/vvfA83rs86C88+v/IFFSgpfrG/MZdc2ZpenMaVxjXlzXiNyT5FfxnGw/6fiYtizh1UL9vDHkbtJLt9D0+g93Hnjbtqm7YE9e2D3bvjkE1i9mj5Q5wSDqjaYqXfv3lpf8+fPr/cygiWSYlENQjwff6x6//3O33DHEkRBiaW+783HH6smJmpFVJRqYmL93uNI+Zx826TR0fXfpvrGEqDDvnUBfk4BfQQBVDpkFV8svcCrddzn2lVkJnIc8tK5Bq6+702NKw/rvaxI+JwCvCIzkhz2rQvwcwroIwigUiBXq24/5ZQth1nTQVmCMaah8NcTxZEuUpJdMEXS55Sby2an06s6sTvGjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNcYQnGGGOMKyzBGGOMcUVIE4yIpIvIHBHZJyIbReTSg9QTEZkoIjt900SRA93CiUgPEVkhIj/7/vYI3VYYY4wJRKhbMFOAMiATuAx4QkS6+Kk3GrgA6A50A4YC1wCISBzwOjAdSANeAF73lRtjjIkQIUswIpIEDAPuUNUiVV0MvAFc4af6CGCyqm5S1c3AZGCkb95AnBtEH1HVUlX9O06fcL/ysxxjjDFhEso7+TsA5aq6rkrZ58AAP3W7+OZVrdelyrwvVKv10vmFr/ztmgsSkdE4LSKAIhH5pm7hV2oG7KjnMoIlkmKByIrHYvEvkmKByIrHYvGvY11fGMoEkwwU1CjbCzQ+SN29Neol+87D1Jx3qOWgqk8DT9clYH9EZLnWcfCdYIukWCCy4rFY/IukWCCy4rFY/BOROndBH8pzMEVASo2yFPA7KELNuilAka/VUpvlGGOMCZNQJph1QIyItK9S1h1Y7afuat88f/VWA92qXlWGcyGAv+UYY4wJk5AlGFXdB7wKTBCRJBE5FTgfeMlP9ReBm0WkpYhkAbcA03zzFgAVwI0iEi8iN/jKP3Az/iqCdrgtCCIpFoiseCwW/yIpFoiseCwW/+ocS0hHtBSRdGAqcAawE/iTqv5TRPoD/1XVZF89ASYCv/e99Flg/P4T+yLS01fWGVgLjFLVz0K2IcYYYw6rQQ2ZbIwxJnSsqxhjjDGusARjjDHGFZZgAuC7mOA5X/9phSKySkTOjoC42otIiYhMj4BYLhaRtb5+5r73nVcLRxytRWSuiOwWkW0i8piIhOR+LxG5QUSWi0ipiEyrMW+QiHzt6z9vvogcG45YRORkEXlPRHaJSL6I/EtEjglHLDXq3CkiKiKD3YzlcPGISCMReVxEdojIXhFZGMZYLvL9TxWKyBoRucDlWA65n6vLd9gSTGBigJ9weh1IBf4CvCwircMYEzh9uy0LcwyIyBk4F2X8DueG19OAH8IUzuNAHnAM0APnM7s+ROveAtyHcyFLJRFphnMF5R1AOrAcmB2OWHD673saaA0ci3P/2PNhigUAEWkH/BbY6nIcgcTzNM5ndLzv7x/DEYuItMTpb/FmnPv8bgX+KSIZLsZy0P1cXb/DobyT/4jlu8T67ipF/xGR9UBvYEM4YhKRi4E9wMfAceGIoYp7gAmq+onv+eYwxtIGeExVS4BtIvI2B7oZcpWqvgogIn2A7CqzLgRWq+q/fPPvBnaISCdV/TqUsajqf6vWE5HHgA/diOFwsVQxBRiP8+PAdQeLR0Q6AecB2aq6v9eRFeGIxfd4T5XP6y0R2Qe0w/kB5UYsh9rPNaUO32FrwdSBiGTi9K0Wlps7RSQFmIDz6yasRCQa6AM0F5HvRGST77BUYphCegS42HeooyVwNn76qAuxan3r+f6RvydEie8wTiOMNymLyG+BUlWdG64YqjgR2Ajc4ztE9qWIDAtTLMuBtSJynohE+w6PleL0uxgSNfZzdfoOW4KpJRGJBWYAL7j16zMA9wLPqeqmMK2/qkwgFhgO9Mc5LNUTp3kdDgtxvvQFwCacf9TXwhTLfrXqPy9URKQbcCfO4ZdwrL8xcD9wUzjW70c20BXns8kCbgBeEJHjQx2Iqlbg3HD+T5zE8k/gGt+O3XV+9nN1+g5bgqkFEYnC6XmgDOfLF44YegCDgYfDsX4/in1//6GqW1V1B/A34NehDsT3+byNc6w4CadH2jSc80PhFHH954nIccB/gZtUdVGYwrgbeElVN4Rp/TUVAx7gPlUtU9UPgfnAkFAH4rvY4UGc4UnicM6LPCshGFzxIPu5On2HLcEESEQEeA7nF/swVfWEKZSBOCdofxSRbcBYYJiIrAxHMKq6G6elUPWO3XDdvZsOtMI5B1OqqjtxTmCHPNnVUK1vPXHGRmpH+A6xHgu8D9yrqv66agqVQThdPm3zfZdzcE4qjw9TPP4OP4Xru9wDWKiqy1XVq6rLgE9xfly65hD7uTp9hy3BBO4JnCtLhqpq8eEqu+hpnA+2h296EngLODOMMT0P/EFEMkQkDefKm/+EOghf62k9cJ2IxIhIE5zB60Jy3Nq3zgQgGogWkQRxLpGeA3QVkWG++XfijGnk2iHWg8XiOy/1AU4SftKt9QcSC06C6cqB7/IWnJFrp4QpnoXAj8BtvjqnAqcD74QhlmVA//0tFnG6x+qP+9/lg+3n6vYdVlWbDjPhXM6pQAlOU3H/dFkExHY3MD3MMcTiXAG0B9gG/B1ICFMsPXA6RN2NM2DTy0BmCD8LrTHd7Zs3GPga5zDMAqB1OGIB7vI9rvo9LgrX+1Kj3gZgcJg/py7AEmAfsAb4TRhjuQH4Ducw1A/ALS7Hcsj9XF2+w9YXmTHGGFfYITJjjDGusARjjDHGFZZgjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNcYQnGmCOEOOOlDA93HMYEyhKMMQEQkWm+HXzN6ZPDv9qYhsnGgzEmcO8DV9QoKwtHIMYcCawFY0zgSlV1W41pF1QevrpBRN7yDSm7UUQur/piETlBRN4XkWJxhiyeJiKpNeqM8I1DUioi20XkhRoxpIszzPE+EfnBzzru9K271NeJ5IuuvBPGBMASjDHBcw/wBk5/aE8DL/pGKtzf++w7OH07nQj8BjiFKkPlisg1wFM4nYd2w+kF+qsa67gTeB2nZ9vZwFQRaeV7/TCc3rWvB9oD5wJLXdhOYwJifZEZEwARmQZcjtMRYFVTVHW8iCjwrKpeXeU17wPbVPVyEbkamIQzHG+hb/5AnPFG2qvqdyKyCafj0j8dJAYF/qqqt/mex+AMrDZaVaeLyM04vRF31fANJ2FMJTsHY0zgFgKja5TtqfJ4SY15S4BzfI+Px+nevOoATR8DXqCziBQALYF5h4mhsrt2VS0XkXwgw1f0L5zRIdeLyDs4g6+9oaqlh1mmMa6wQ2TGBO5nVf2uxrQjCMutzWGEmi0Txfd/rKo/AR1xWjEFwGRghe/wnDEhZwnGmOA52c/ztb7Ha4ETfOPQ73cKzv/gWlXNAzbjDMJVZ6paoqpvqeofgb4445ucWp9lGlNXdojMmMDFi0iLGmUVqprve3yhiCzDGYxpOE6yOMk3bwbORQAvisidQBrOCf1XVfU7X53/Ax4Wke04o5Q2Agap6uRAghORkTj/05/iXEzwPzgtnm9ruZ3GBIUlGGMCNxjYWqNsM5Dte3w3MAxnRM984HfqjKWOqv4sImcCj+Bc2VWCczXYTfsXpKpPiEgZcAswEdgFzK1FfHuA8TgXE8TijMh4oaqur8UyjAkau4rMmCDwXeH1W1X9d7hjMSZS2DkYY4wxrrAEY4wxxhV2iMwYY4wrrAVjjDHGFZZgjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNc8f8kSOgwYERMewAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -661,47 +740,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 6s 847us/sample - loss: 0.0221 - val_loss: 0.0053\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0232 - val_loss: 0.0052\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 5s 720us/sample - loss: 0.0043 - val_loss: 0.0037\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0043 - val_loss: 0.0036\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 5s 715us/sample - loss: 0.0035 - val_loss: 0.0032\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0035 - val_loss: 0.0031\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0033 - val_loss: 0.0032\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0033 - val_loss: 0.0033\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 5s 720us/sample - loss: 0.0032 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0033 - val_loss: 0.0034\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 5s 714us/sample - loss: 0.0031 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0031 - val_loss: 0.0029\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 5s 718us/sample - loss: 0.0031 - val_loss: 0.0028\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0031 - val_loss: 0.0034\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 5s 721us/sample - loss: 0.0031 - val_loss: 0.0028\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0032 - val_loss: 0.0028\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 5s 721us/sample - loss: 0.0030 - val_loss: 0.0029\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0031 - val_loss: 0.0028\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0031 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0029\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 5s 717us/sample - loss: 0.0030 - val_loss: 0.0029\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0029 - val_loss: 0.0027\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 5s 718us/sample - loss: 0.0029 - val_loss: 0.0027\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0031\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 5s 722us/sample - loss: 0.0030 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0031\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 5s 718us/sample - loss: 0.0030 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0030\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 5s 718us/sample - loss: 0.0029 - val_loss: 0.0027\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0030\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 5s 719us/sample - loss: 0.0030 - val_loss: 0.0028\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0027\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0029 - val_loss: 0.0031\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0029 - val_loss: 0.0028\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 5s 719us/sample - loss: 0.0030 - val_loss: 0.0027\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0030 - val_loss: 0.0027\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 5s 722us/sample - loss: 0.0029 - val_loss: 0.0030\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0029 - val_loss: 0.0028\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 5s 718us/sample - loss: 0.0029 - val_loss: 0.0026\n" + "219/219 [==============================] - 6s 29ms/step - loss: 0.0029 - val_loss: 0.0026\n" ] } ], @@ -729,13 +807,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2000/2000 [==============================] - 0s 200us/sample - loss: 0.0026\n" + "63/63 [==============================] - 0s 6ms/step - loss: 0.0026\n" ] }, { "data": { "text/plain": [ - "0.0025768048670142887" + "0.002623623702675104" ] }, "execution_count": 21, @@ -754,7 +832,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -777,7 +855,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -950,7 +1028,7 @@ { "data": { "text/plain": [ - "0.027277293" + "0.027510807" ] }, "execution_count": 30, @@ -999,47 +1077,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 0s 61us/sample - loss: 0.1348 - val_loss: 0.0610\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.1343 - val_loss: 0.0606\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0501 - val_loss: 0.0427\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0496 - val_loss: 0.0425\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0388 - val_loss: 0.0356\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0385 - val_loss: 0.0353\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0334 - val_loss: 0.0314\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0331 - val_loss: 0.0311\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0299 - val_loss: 0.0286\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0298 - val_loss: 0.0283\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0275 - val_loss: 0.0264\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0273 - val_loss: 0.0264\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0258 - val_loss: 0.0251\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0256 - val_loss: 0.0249\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0245 - val_loss: 0.0237\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0244 - val_loss: 0.0237\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0235 - val_loss: 0.0229\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0234 - val_loss: 0.0229\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0227 - val_loss: 0.0222\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0227 - val_loss: 0.0222\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0221 - val_loss: 0.0220\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0220 - val_loss: 0.0216\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0216 - val_loss: 0.0212\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0215 - val_loss: 0.0212\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0211 - val_loss: 0.0207\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0210 - val_loss: 0.0208\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0207 - val_loss: 0.0204\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0207 - val_loss: 0.0207\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0203 - val_loss: 0.0203\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0203 - val_loss: 0.0202\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 0s 37us/sample - loss: 0.0200 - val_loss: 0.0198\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0200 - val_loss: 0.0199\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0197 - val_loss: 0.0194\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0197 - val_loss: 0.0195\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0194 - val_loss: 0.0192\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0193 - val_loss: 0.0192\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0191 - val_loss: 0.0190\n", + "219/219 [==============================] - 0s 1ms/step - loss: 0.0191 - val_loss: 0.0189\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 0s 38us/sample - loss: 0.0189 - val_loss: 0.0188\n" + "219/219 [==============================] - 0s 1ms/step - loss: 0.0188 - val_loss: 0.0187\n" ] } ], @@ -1073,47 +1150,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 6s 855us/sample - loss: 0.0665 - val_loss: 0.0317\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0669 - val_loss: 0.0317\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0268 - val_loss: 0.0221\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0265 - val_loss: 0.0200\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0188 - val_loss: 0.0166\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0183 - val_loss: 0.0160\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 5s 733us/sample - loss: 0.0159 - val_loss: 0.0137\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0155 - val_loss: 0.0144\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 5s 725us/sample - loss: 0.0138 - val_loss: 0.0127\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0139 - val_loss: 0.0118\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 5s 725us/sample - loss: 0.0132 - val_loss: 0.0117\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0128 - val_loss: 0.0112\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 5s 731us/sample - loss: 0.0119 - val_loss: 0.0117\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0122 - val_loss: 0.0110\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 5s 722us/sample - loss: 0.0115 - val_loss: 0.0119\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0115 - val_loss: 0.0103\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 5s 725us/sample - loss: 0.0110 - val_loss: 0.0113\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0111 - val_loss: 0.0112\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0111 - val_loss: 0.0097\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0110 - val_loss: 0.0100\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0104 - val_loss: 0.0098\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0108 - val_loss: 0.0103\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0100 - val_loss: 0.0092\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0102 - val_loss: 0.0096\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 5s 726us/sample - loss: 0.0101 - val_loss: 0.0101\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0104 - val_loss: 0.0100\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 5s 731us/sample - loss: 0.0100 - val_loss: 0.0103\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0098 - val_loss: 0.0103\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0097 - val_loss: 0.0092\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0095 - val_loss: 0.0107\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 5s 720us/sample - loss: 0.0094 - val_loss: 0.0088\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0092 - val_loss: 0.0089\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 5s 729us/sample - loss: 0.0091 - val_loss: 0.0088\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0094 - val_loss: 0.0111\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 5s 727us/sample - loss: 0.0091 - val_loss: 0.0081\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0095 - val_loss: 0.0094\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0089 - val_loss: 0.0079\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0093 - val_loss: 0.0083\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0088 - val_loss: 0.0081\n" + "219/219 [==============================] - 6s 28ms/step - loss: 0.0094 - val_loss: 0.0085\n" ] } ], @@ -1152,7 +1228,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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9x5u+XpKmucrraw4iAjOv6OWyMmsW6Hv5ZRgyBJ56Cgqr3+bJg39D4p6nWT4vt/5s3z7GlMFIKVUCfAA8IyJhIjIWmIjRH3QGEblWRDpXf38R8CTwUVPL0VxjQ0YeIYEWBndv57HXrLkL23BQD/HWvONkUTnvbDrE5OE96NqulUvLrglIWVlw8iTMmVM92GHlVCNFUP4HLn09bzFlMKp2P9AKOAEsA+5TSu0SkajquUQ1eTAmADtEpAT4DCP4/P185XjqIi40mzLzuCQqnKAAz721urVvRY/wVrVZHzTN0976PoMKu4N7xsW6pfyEBPjgA2Pe0T/+ATffDMvfs5LQy+Y3Q7xNG4yUUnlKqUlKqTClVJRSamn19ixlzCXKqn7+sFKqc/VxsUqpPyulKs9XjuZ6hWWV/His0KP9RTVG94pkY2ZezSAVTfOYwrJKFq+3cd3grsR2bO2210lIgOnToarK6CNKuFLgF7+Ar7+G4mK3va6nmDYYab4nLTMfpTwzv6iu0b0iyCupYP8J3/+j1HzL4vU2isqruG9cb7e+TnIyrFwJXbvCV1/B6tUY65WXl8OXX7r1tT1BByPNZTZk5BFoFYb39PySDrX9RrqpTvOgdftzmLt6P8Oj2nOxG/tJnSfEPvUU2O0wZQok268wFkFatcptr+0pOhhpLrN6TzYdWwfz4zHPj2qLjgylc9tg3W+keUyaLZ8Zb26ktNLOriOFzU6Kej51MzNMnQphYTBmDCROtRo56xYvNpKn+vBy5DoYaS6xbn8O6dnFHCsoa1ZyyJYSEUb1imRDRq7uN9I8IvVgDlXVc9vsjqbloGus+lIEtWkDt90G334Lb85IJnHXn0l2XGGkBvLh5ch1MNJcYskGY8XJ5iSHdJVRvSLILizn5GkdjDT3i2wdDBiTGpuSg64pGlqgb9YsY9XxQ29/w3IS2YTTrFcfXY7crLnpNB+z70QRAliamBzSleKr+42W762g96B8vRy55lZbbacIDrBw77hYrujXyS3vt7oL79WIi4Phw+G1rTezjWf9YjlyfWektdjOwwWkZxdzx9gYfvfz/iy5O94rgaDwtDGif3O23StNhdqFo6isklXbj/LL4d357c/6e/z9LgL33AM7GHrmXVENH1yOXAcjrcUWrc8kNMjKQz/rxwMJfbx2R5LqNHjBW02F2oVh5bajnK60M3WUd/7pz5ljpKcLC65kvvX+M3dWL0eenGwc5yt0MNJaJL+kgo+qPyG2DQn0al3iYyMJtBppCa0W7zQVav5PKcXSDVkM7NqWIT08l/bK2ciRcMcdMG5CIMsCplHYY6CxIywM5s8nuVuSzyVQ1cFIa5F3Nx+iosrBjDEx3q4KcdHhLLxjFFaBhIs66j4jzS12HC5g97FCpo6OQsS963U1pCZf3bp1UFoewNIndhkTjyIiSO5621kj8HyBDkZas9kdisXrbcTHRtC/SxtvVweAS/t0IK6zlU2Z+VTppcg1N1i2MYtWgVYmDuvm1XokJMD774PVCs8/D2rCVSQf6k3iTXafC0Sgg5HWAsl7TnDk1GlT3BU5G901gLySCtYd0H1GmmvVDFz4xdBuXm+WBmP18V//Gg4cgJnJt5HIcpbP+MTnAhHoYKS1wML1mXRpG8LPBnb2dlXOMLiDlTbBAXy8/ai3q6L5mVXbj1JaYWfqaPOMVnv6aQgIgDdWtOW+dstIsC30dpWaRQcjrVkOnCzmu305JI2OItBqrrdRkFX4+aAufL7rOOVVdm9XR/MjyzZmMaBrW4Z6aeBCfbZsMZrqgoNhXtkdJH9VZaT29jHm+i+i+YzF620EWoVbvTS09XxuHNqVorIq1qTneLsqmp/YebiAH44Uctuonl4buFBXTbqgv/3NSN59/w02EkveJPm1dG9Xrcl0MNKa7Pv9OSzdkEV8bCQd2wR7uzr1GtunA+GhgbqpTnOZV77ZR4BFiOkQ5u2qAGfmrfvtb415R2mF/VjOLSQ+Ek1ysrdr2DQ6GGlNkmbL5/Y3N1Jhd7AhI8+0WQ4CrRauHdyVr37MprTC95osNHP5cMsRvtqdTZVDMXPRZq+/7+smULVajYX3Pl8dxMDBVpb3eYLERHwqIJk2GIlIhIh8KCIlImITkdsaOO5XIpImIoUiclhE5ohIgNP+FBEpq16qvFhE9nruKvxP6sEcKmsyFdvNneXgxiHdOF1pZ/WeE96uiubDDueX8uRHO2ufmyG7R30JVKdPN9Y5WtbxNyTs/g/LF55m0ybv1bGpTBuMgLlABdAZSALmicigeo4LBR4COgCjgQnAw3WO+XX1UuWtlVL93VhnvxcSaAXcm6nYVUb1iqBTm2DdVKc1W15JBTPe3IhDQXCABasXEwE7e+SRs+cRDRwII0bAQtt4qKwkgeQGE62akSmzdotIGDAFuFgpVQysFZFVwHTgUedjlVLznJ4eEZElgA+OsvcN36bn0D40kDvH9mJsnw6mznJgtQjXD+nKkg1ZFJZVmmJeiOY7SiuquPPtTRzOP83iO0cRYLWQejCX+NhI077vf/UrePDBNuwIjGPI11/Dddd5u0qNJmZciExEhgPrlFKtnLY9DIxTSt14nnNXAnuUUo9WP08BBmF8mN8LPKGUSqnnvFnALICOHTvGLV++3DUXY0LFxcW0bt26yecdKXbwxNrTTO4byC96B7mhZq7hfH37T9n5W2oZMwcHMba7fwSj5v7+fIUZrq/KoXhlazk7T9r59fBg4jq75nO7u6+toCCQm24aw72R/2V26FNsfvNNt71WfRISEtKUUiOadbJSynQP4HLgeJ1tM4GU85x3B3AY6OC0bTTQBggGfgUUAb3PVU6/fv2UP0tOTm7WeY++v0P1feIzlVNU5toKuZjz9TkcDnXp7G/Ur97c4L0KuVhzf3++orHX983u4+qVb9LV5sw8l77+5oxcdd3La1T0Hz9RS1JtLi3bE7+7SZOU6tKmSFViVerYMbe/njNgs2rm/32z9hkVA23rbGuLEUjqJSKTgOeAa5VStZNLlFIblFJFSqlypdRC4HvAd+5dTSKvpIIPthxm8vDutStc+gIR4cah3fgu/STPf7HX66OgNNf4+sds7nx7My98me6ytauyC8t49rPd3PzaenYdLSTAIqbJudgUM2bA8aLWfM1V0LUrxMT4xDLkpuwzAtKBABHpq5TaV71tKLCrvoNF5BrgdeB6pdTO+o5xojCa7LQmWLrBRnmVgzsv6+XtqjRZ745h2BXMTd7PgrUHvbb4n+Y6c77YU/t9RfXotub8Tj/dcZTlmw9zrOA06dnFZ+xTSjW7XG+YM8dYMuK6gmVE8HMWMYNr+AJsNmOdciC5WxKbNjW8gqw3mfLOSClVAnwAPCMiYSIyFpgILK57rIhcCSwBpiilNtbZ115ErhaREBEJEJEk4ArgC/dfhf+oqHKwaL2Ny/t2oF9n3/ukeKKoDDA+hZhhWK7WMt/sziY9u5gAi/GZ0qFgcLemp+f5T8p+Hli6lW/TT7LvRDFTR/bkpVuGERJonlFzTTFypDH3aN2jq5jKMj7klxTUNDCVlpL8+09MvcaRKYNRtfuBVsAJYBlwn1Jql4hEVc8XqslD8yTQDvjMaS7R/6r3BQJ/A04COcCDwCSllJ5r1ASf7jzKiaJy7vLBuyKA+NgOVP/f8rl/MNqZSiuq+PNHu+jbqTVL7h7NjDHRCPC/XcebVM4PRwp48cufUuZYgB4RoUwa3p0ld8fzu5/397k76Jo1jhKzX2EguyijFe9xEwDJjCcx+xVTLy1h1mY6lFJ5wKR6tmcBrZ2eN/ijVUqdhPoWiNcaSynFG2sz6N0xjCv6dvR2dZolLjqcO8f2YsHaDP6ZOMyn/sFoZ3r5630cOXWaFfeOYWRMBKNjIwmyWliwNoPJl3RnZEzEecs4UVjGzEWbadcqkOLyKqrsjjM+pMRFh/vseyQhAZZ3/g2J2a/QkywWMYNYDhpLS3T+DQkJy7xdxQaZ+c5IM4GNGXn8cKSQOy/rhcXiu11tU+J6AFBaobN4+6ofjxayYG0Gt47seUbQ+e3P+tG9fSse/2AnFVXnXlCxrNLOzMVpnCqtZNFdo1g60zfvgs4l4YUbWB48g1wiWMM4pvA+y4NnkPDCDd6u2jnpYKQ1KM2WzxMrf6B1sJXJw3t4uzot0r9zG9q1CmRDhu4v8kUOh+LxD3fSvlUgj1570Rn7woIDeGbiIPadKGb+mgMNlqGU4uEV29lx+BQv3TqMQd3aERcdzgMJffwmEAGQlETCG9O4p41xF9SLDBLemAZJSV6u2LmZtplO8640Wz63vZ5KeZWDAIvw47FCn/6DtViEkTERbMzI83ZVtCZKs+Uz79sDbDt0in/eMpT2oWdPuJ4woDPXDe7CS9/s49TpSq69uOsZ79c0Wz4vfLmXdQdy+eM1F3H1oC6evASPS+6WxOJgGN7uBFsOX8IHxT2Z7O1KnYcORlq9Ug/mUl7d5OFrQ1wbEh8bwde7s8kuLKNz2xBvV0drBOcPRRaBqPDQBo+dPLwHn+08zoLvMli4LpM7x/aiW/tWZOQUszg1C7tDYRVhVIxvv4/Pxzmjd9fCfAZM6sT030QQ3s+8gxdAN9NpDegVaazZ4gsJURtrdC/jGjbouyOfsWLzodoPRQCp5/jd7c0uqp1AWGlXvLbmIH9ZtYu319mwO2rSnqlzluHr6i4tcdGNfflFwGcEqArTLynh8mAkIv8SkY/r2d5WRJ4SkQFO234rIjtERAdFk/k2/SQBVuHecb39pnN3QNc2tA4OYIOeZ2R65VV2Zv9vN+9sOoQAFoGg83woio+NJLh6jlBwgIXXp49g85+uYtEdowgxUcZtd6kbiACwWHh41BoKK0NJSsLUAcmlzXQi0hu4B7i0nt0jgL9gTGat8SrwR4yccW+5si5a82XllvLelsNMj4/mj3U6i31ZgNXCiJhwfWdkYmm2fJbsLucvm9Zgyy1l6qgobhjSlW2HTp03W3ZcdDhL7o4/K7P2Ff07smTm2dv9TX1rHAFcNqkDo9Zt4NNVcSxbFsCmTeZsrnN1n9FDwHal1OZ69g0HyoEfazYopU6LyCKM9Yd0MDKJfyfvw2oR7hvf29tVcblRvSJI2buXnOJyOvhQjr0LQZotn1teW0+VQwFVPHbtRdwzzngPju3ToVFlNDRHyJfnDjVWQyl+ZNwV/IE53JzxHkVF5kwFBI1sphORPiJSKSJP19k+T0SKRGSEiAQD04Cl9Zy/G3geI3N2pYgoEXmvevc7wEARqe9uSqsjzZbP3OT9bkv4acst4f0tR7htVJRfdvLX9Btt0ndHprNy65HqQARWofZ7rWXmfBNH++DT9Gqbw//9X/3HJCcbue28qVHBSCm1H1gA/FZEOgCIyJ+BO4FfVt8JxQPtge/qKWIGcBD4GBhT/fh99b5tQCFwTfMv48KwKTOPqfNTeeHLvc3OVJxmy+eTAxUNnvvv1fsJsAj3++FdEcDg7u0ICbTopjoTysorAYx/Sv7ct+NpI+OtTHUs4YbAL1i/HtatO3N/TV+Tt3PWNWXgwNOAFfijiNyF0f8zXSn1dfX+eIxclDvqOXc70ANYrZRKrX7YAJRSjupz4pt5DReMf6/eT4XdgUM1L+FnzTDZ9/ZVctvrZwezzJwSPth6hKTR0XTyw7siMDrB46J1v5HZ5BaXs/5AHtde3IXJfQP9ZtCMGSQkwPJpH7M092ratHbw/PM/7at30IOXNDoYKaWOAy9hJBt9DfiNUsp5OdRuQKFSqqKe0wcBQcCWBoo/WX2+1oCMnBLWHchxWvtCGN3r/Hm4nK07kFM7TLa8ysEr3+yjrPKn9Dj/Tjbuiu4dF+uaSpvUqJhI9hwvpKC00ttV0aq9s+kQFXYHv/95P27oHaQDkYsl3B7NCm7GXungww8hPd1cgQiaPrR7H0a/z3ql1Nw6+0IwBijU5xKMu6ZtDew/jZGhW6uHw6H44/s7CAm08vqMEVzWpwN2pfhkx7Ga1Wwb5WSR8eupGSr7bfpJrnw+hffTDvPx9iO8v+UwPxvY2W/vimqMjo1AKaPZU/O+KruD/6bauKxPB/p08r0lSnzCqFEkBK1j0dXGInuJieYKRNCEYFS9btBrwNZ3/aAAACAASURBVHpgrIgMrXNILtDQx5nhwAGlVGED+yMwlnjQ6rFkYxYbM/J48vqBXDWwM4vvGsXdl/Xi7XWZ/POr9PMXgDFce/nmQ4yIDmdy30BW3HspS2eOpkObYH6/YjsPLtuGUvDVj9l+vxrqsJ7tCbJa2KiDkSl89WM2xwrKmDEm2ttV8V8hITB6NFOO/4cxY2D7drjqKvMEImj8aLpLgJUYgxjGA1nA3+sctgcIFJH6MmoOxGlIdz16AXqNoXocOXWa5z7bzWV9OnDzCONHKyI8cf0AbhnRk1dW7+cvH/1wzhF2SimeWLkTqwivTB3OjdXNIJf27sDK+8dy45CutcdW2f1/8bmQQCvDerbXk19N4u11mfQIb8WEAZ29XRX/dsUVJG9uw750RadO8O678MEH5z/NU84bjESkD/A/4Evgweo+oaeB60TkCqdD11R/HVVPMaeAodWrrsaLSO0wGRFpD/RzOl+rppTiiQ934lAwe/JgRJx6jET4++TBXBobycL1Np7/ouERdiu3HeG7fTk8cs1FdGt/ZmuoxSLcPraXz65u2VyjYyP44WghxeVV3q7KBW3P8UI2ZOQxPT4aqw8vUeILkttOJNGxjOV/TOPrryEgAKZOhdWrvV0zwzmDkYh0wQhCu4Gk6pFvAIsw7oSeqzlWKZUJbARurKeoPwPZGHdX64EBTvuuByqAD5t1BX5s5bYjpOw9ySPX9KdnxNkJIq0WIb63ETgUUFbp4Jvd2Wcck1dSwV8/2c2wnu2ZFl9/M0jNzHV/W9flXEb1isDuUH7fJGl2C9fZCA6wkDiip7er4teSkyFxThzLLVNJKPyIwYPh6quhogImTmw4RZAn5x+dMxgppY4rpWKVUuOVUuVO2+1KqQFKqboTVecBk0UktE45PyilRiulWimlRCm11mn3NGCFUuqMNhMRiRCRD0WkRERsInJbQ/WsznF3XEQKROTN6gm4TS7HTL7Znc1jH+ykf5c2zBgT0+BxY/t0ICTQUjvKbuG6TN5PO1w7sOFvn/5I4elKnpsy+JyfPP1yXZdziIsOxyowL8V9E4i1cysorWTl1iNMGtad8LCzl4XQXKN21NwKCwlxhbDGaIR66CEIDITycpgy5eyA5On5R65OULoYOALc35iDRWQYkIDR7FfXXIw7ps5AEjBPRAbVU8bVwKPABCAGiK1TXqPKMZNNmXnMXLSZskoHmTklbDt0qsFja+5qHr66P/+aOoyB3dry+xXbuePtTTz/5V4+2HKEicO6cVGXth68AvPbfawIB5B6MK/ZE4i1llmRdojTlXZmXKoHLrjTGTnrLr8cNmyAsjImTID//heqqqB1a0hN/ekcbwz7dmkwUkrZMbIylDbylC7AHdUZHmqJSBgwBXhSKVVcfSe1CpheTxm/At5QSu1SSuUDfwVub0Y5puBwKJ75eBc1mVAaM6Cg5q7mxqHdeXfWGJ66cSDr9ufy79XGj/XTHcf0P9s6Ug/mGm2bGM2bH2w57N0KXWDsDsWi9TZGxoQzqFs7b1fHrz3yiFNAUcq4FQoNhZgYEiuX8Oc/w6FDsHu3cYi35h9JU+apeIqIDAfWKaVaOW17GBinlLqxzrHbgb8rpd6tft4BYxJtByCqCeXMAmYBhIaGxpWWNjaeulb4hJm0HTERZTc61pXDTvY7T1BxdE+Tymk//g7ajvolIhaUvYpTa5dQmLrCHVX2SUHdLqLzrc8i1kAQQUQ4nbGFU2uXAEJI1GDKsnY2+eeuNU6bEZOImHA3+WsWUbh++flP0FpsKsZwaOc+lBJgJrCMzzAysi0BrgYSgZTmvEyaUmpEc04060qvrYGCOtsKgPpmxNU9tub7Nk0pRyk1H5gP0L9/f7V3r+dHmr/4VTqvfLOPO8bGcMPgrqRm5Bkp71/Y3eSy0mz5JC1IpbLKQWBIEB+88xpx0cYffUpKCuPHj3dx7c2jsdeXZssn9WAuw3u2Z+eRAl5bE0SrXpdQ07UWFGAx5YAOX//9pdnyufnVdTgUdJ1wO6uXvXrGz9jXr+9cvHptMTFgs52xKQxYGh3NazuvpU8fOHFiGnfcAW++2bxFj5xH/DaVWYNRMVC3k6MtUNSIY2u+L2piOV71+pqDvPLNPhJH9ODJ6wdisQhxMU1L9+OsobVdtJ84LytwaZ8OTIuP5v4lW/g2/STwU/4//bNzreQ9J2qbofXP2IOyshrcvnmz0XfUqhW89RZcdhnceWf9hycnG/1Qrl6KwqwrrKYDASLS12nbUGBXPcfuqt7nfFx29ei8ppTjFWm2fO5ZvJlnP9vN9YO7MnvyECwumm9xoY2Qa6mw4AB+M6EvAdU/f6v1wphz5WlVdmOGiOUCmtdmClFR9W5O7nQLiYnw3nuwYAGIwN13w5Il9RzrxhF2pgxGSqkSjBVhnxGRMBEZC0zEGK1X1yLgLhEZKCLhwJ+At5tRjsel2fKZOj+VL3ZlYxGYMUZP/PO2uOhw3vzVSAKtwpjYCB3I3WDb4VN0ax/C73/ez5TNoH7r2WeNgQtOkoOvIbH0rdrBCrfdZgQkpWD6dHj/fadj3TywwZTBqNr9GMlTTwDLgPuUUrtEJEpEikUkCkAp9TkwB0gGbNWPv5yvHM9dRsNSD+ZSUf0pUYDNesSbKVzRvyNJo6NZdyCX7MIyb1fHrxwvKGNDRh63jIjigYS+OhB5UlISzJ8P0cZQ+mTGk2h9n+UfhZwRXO68E/79byMgJSbCqlWeGWFn2mCklMpTSk1SSoUppaKUUkurt2cppVorpbKcjn1RKdVZKdVWKXVHnQm69ZZjBvGxkbWTVXVzhbncdVkv7A7F2+syvV0Vv/LJjqMoBb8YpleM8YqkJMjMhJwcNllGs/yGRfUGlwceMDIvOBwwY4ZnhnqbNhhdCGI7hKGAy/t00M0VJtMzIpRrLu7CklQbJTp/nct8tO0oQ3q0o1eHMG9X5cIWGckjN+wmYe1fwW6v95A//MHIzFBQABERxnxZd9LByItqVhv9f1fp5gozuvvyWArLqlix+ZBLykuz5Z8zu7q/O3iymJ1HCvjFUH1XZArTpsHRo5CSUu/u5GT49lu45hpjMb7LLzea7hpipBPq1uzU6zoYeVHqwVxCAi0M6dHe21XR6nFJlDH0+83vM7E7WjY5vGbJ9xe+bDi7ur9btf0oInDDEB2MTOGGG6BtWyMnUB3OfUT/+x9MmGCkC7rmmvqLqjkeSpqdLUAHIy9KPZjLiOgIggL0r8Gs7r6sF1l5pXy563iLyvl27wnKqxw4lJF+6J1NWU1apdfXKaVYte0oo3tF0KWdf68k7DNatYKbbjKGzDllnKlvsMJXX8GIEfDll3DzzWcW43w8FDR7Dqf+L+gleSUV7DleRHxs8ye2au7380FdiIoI5fXvDja7DLtDsWafsZBxzYCVFZsPc/Or61m7L4e0zDy/b77bdbSQgzklTBzW3dtV0ZxNmwZFRfDxx0DDo+ZEYN06uOgiYz5SzYRYV46yM2sGBr+3McNIfqpH0Jmb1SLcOTaGpz7+kTRbfrP69p7/ci/bDp3i3itiadMqkLjo9uw/UcLc5P1Me2MDIkaQMmv6IVf4aNsRAq3CtRd38XZVNGfjxkGPHkZT3S23nJnhu47AQNi8GQYMMLI0KAWffOK6UXb6zshLUg/m0SrQqvuLfMDNI3oSGmTlsQ92NPnu5dMdx5iXcoCpo3ry6HUDeCChD/GxRuqhlD+M56qBnVEKHAoqqvxzyXeHQ/Hx9mOM69eR9qF63SJTsViMma6ffw4nT56Z4bseYWGQlgYdOsDbb8N997luuLcORl6SejCXETHhur/IB+w5XkRFlYP07GJuenUdD72zjW/TT1JSXnXOEXJ7jhfy8IrtXBLVnqd+cfYSWsEBVu4b15vg6veAQ1GbisifbMzM43hhGTfqUXTmNG2akZiuf38jOMXE1J8LqNoPPxhfn3wS5s1reJXYptLNdF5Q01+k/zh9Q+rBXBzVgw2UglXbj7By2xGsAg4ABQFW4dVpcUwYYIxsXbP3JL95dyvBgRbmTYsjOMBab9lx0eEsnRlP8p4TfP7DMeZ8sZeIsCBu9qNluN9Ym0GARejYOvj8B2uet2OH0SmUX/2BymaDWbOM75OSzji0bh9RQoLuM/JpP/UX6cELviA+NpKgAIuxHEeAhTd/NRK7Uvx79f7auWKVdsVdCzfTM6IV0RFhfH8gB6WMfqDD+afp3LbhEWQ12cPvG9+be/+bxh/e28GOwwV0aRfi8xnXNxzM5asfswG4c+Emv+0T82lPPHH2BKLSUmO7UzCqb7BCQoLx/KfRdM2ng5EX1PQXDe6u+4t8QUPLcYQGBdSuGWW1Wpg6sicnisr5bt/J2r9tu73xSySEBQew4FcjuPOtTSxOtSFAcKBvD2p432kFXb1chEk1tLSEzWY02WVlGZm9S986K48dnBmQoF19a841ig5GXqD7i3yP89pHztvqC1JptnySXk+l0u5ocs7B4AArY3pH8v2BXBRQ7uP/wKV6MLtVLxdhXlFRZy26V6t6+6bsniwP/iUJR6cBSWcdVhOQrrwyLPSsnY2kg5GH5RaX6/4iP9JgkJrZ/IUNx/TuQEjgfsoqHSgFvSJ9N4/b0YLTREeEkjiyp883OfqtZ581+ohKG06e8Aj/B+XAE7vP6keqYdwxHc1ubjX0R3MP21jdx6A/Ifq3lixsWHPHdc8VsYQGWVmw9mDtgnS+xO5QbMs6xWV9O+hFHs3MeWkJkdolJurVUJOeC+hg5GGpB3Or5xe183ZVNBOLiw7nsesG8NyUIWzJOsW/Vu/3dpWabN+JIorKq3QQ8gU1S0s4HMbXhgJSA6vFuoIORh6WejCPETHhBFr1j147v18M7cbk4d351+p9pNnyvF2dJtmcaQwV1sHIB9WzKiyBgcZ2N9H/ET0ot7icvdlFuolOa5KnJw6ie3gr/t872ygqq/R2dRptiy2fDq2DiYpodp+25i11m+5CQ43h3/HxbntJ0wUjEYkQkQ9FpEREbCJy2zmO/ZWIpIlIoYgcFpE5IhLgtD9FRMqqlykvFpG9nrmK+un+Iq052oQE8tItwzlWUMYDS7fwyYEKn0iqmpaVT1x0e0T8L6vEBcG56W7fPiPL9wMPnHtRoxYwXTAC5gIVQGeMMYTzROTsXCqGUOAhoAMwGpgAPFznmF9XL1PeWinV3011bpRV248SYBHsDt/rjNa8Ky46nCmXdGdNeg7v76s0/ZpIJ4vKseWWMiJaT+z2C926GU10X3zR8tmtDTBVMBKRMGAK8KRSqlgptRZYBUyv73il1Dyl1HdKqQql1BFgCTDWczVuvDRbPp//cJwqh2LGmxtN/Y9EM6ee1c1dip8mkJpVzfv7Et1f5D/uvx/i4oxh4D17NiqPXVOYbZ5RP8CulEp32rYdGNfI868AdtXZNltEngP2Ak8opVLqO1FEZgGzADp27EhKA0vxNtfCXeXU3NxWVDpY9vUminp7J4NxcXGxy6/PTPz1+loV2LEK2JXRjB98ykZKyuHzn+gFK/dUEGCBvAPbSMloWjOdv/7+wPevLWrIEHqlpSGFhcYGmw37XXexd/duTlx1VYvKFjOtNikilwMrlFJdnLbNBJKUUuPPc+4dwF+BYUqpnOpto4EfMZr9bgX+Xb3/wLnK6t+/v9q717XdS7e8to4NGfm1M9G9meIlJSWF8ePHe+W1PcGfry957wnufGsTvxjWjZdvHe7t6jRo8n++xyLCe/dd2uRz/fn35/PXFhNTf7aG6GjIzERE0pRSI5pTtEeb6aoHFKgGHmuBYqBtndPaAudcylZEJgHPAdfWBCIApdQGpVSRUqpcKbUQ+B64zrVXdX77TxSzMTOfm+K687uf9/fpXGOadyX078SwTlbWHcg17UTYsko7Pxwp1O9xf9TQpFcXTIb1aDNdI+5uwoAAEemrlNpXvXkoZze9OZ9zDfA6cL1Sauf5qsBPKz97zPw1BwgOsPDYtQOI1Gn0tRYa2y2Af28r5/sDuYzr19Hb1TnLrqMFVNgdur/IHzWUx84Fk2FNNYBBKVUCfAA8IyJhIjIWmAgsru94EbkSY9DCFKXUxjr72ovI1SISIiIBIpKE0af0hXuv4kzHCk7z4dYjJI7oqQOR5hJDO1lp1yqQ99PM2V+kJ7v6sfomw4aGumQyrKmCUbX7gVbACWAZcJ9SaheAiERVzxeqCcNPAu2Az5zmEv2vel8g8DfgJJADPAhMUkp5dK7Rm2szcCiYeXmsJ19W82OBFuHGoV35YtdxU06CTbPlExMZSgf94cv/1JfHbv78BpOnNoXZRtOhlMoDJjWwLwto7fS8wbUFlVIngZEur2ATnCqtYOmGLG4c0rV2WK6mucLkS3rw39Qs/rfzOIkjzbMqrFKKLVn5jOvXydtV0dwlKcklwacuM94Z+Y3F622UVNi5d3xvb1dF8zPDe7anV4ewMxavMwNbbik5xRW6iU5rMh2M3OR0hZ2312WS0L8jF3WpO0BQ01pGRJg8vDsbMvI4lNfwOjSeVjPZVQcjral0MHKTFWmHyC2p4L7xfbxdFc1PTRreHYCVW494uSY/2WzLp01IAH07tT7/wZrmRAcjN9iYkcvzX+ylf5c2jIzRnxA19+gZEUp8bAQfbD2CWSavb7Hlc0lUOBaLTo6qNY0ORi6WZssnacEGCsuqOHiymC1Zp7xdJc2PTb6kBxk5JWw95P33WcHpStJPFOkmOq1ZdDBysdSDuVTajU+pDocydTJLzfdde3EXgqzCMx/v8nry3a1Z+SgFI3Qw0ppBByMX69epDWCkeQgMsOi1izS3Ss8upsqh2HaowOvLSny64ygiYI4GQ83X6GDkYpm5JQDcdXkvnYNOczvnO+8KLy4rkWbL5/0tR1AK7lq4yet3aZrv0cHIxVZuO8LQHu340/UDdSDS3C4+NpKgAMsZz73h4+1HcVTfEpl9rSXNnHQwcqH9J4rYdbSQicO6e7sq2gUiLjqcJXfHc2nvSBwKOrXxTgqefdlGYv2aJVJ087TWVDoYudDKrUexCNwwtKu3q6JdQOKiw3n+5qFYBN7ddMjjr3/gZDHrDubyy+F6iRSt+UyXm85XKaX4aPsRxvbpQKc2Id6ujnaB6da+FeP6dWRF2iEeuqovAVbPfc78T7KxRMoT1w/QyVG1ZtN3Ri6yJSufQ3mnmaSb6DQvuWVkFNmF5aTsPemx1zyUV8rKbUe4bVS0DkRai+hg5CIrtx4lOMDC1Rd3Of/BmuYGEwZ0okPrYN7xYFPdvG8PYBXhnnF6iRStZXQwcoFKu4NPdx7jqoGdaR2sWz417wi0WrgprgfJe0+QXVjm9tc7VnCa9zYfJnFkDzq31U3TWsvoYOQC3+07SV5JhW6i07zulpE9sTsUKza7/+7otW8P4lCKe67QS6RoLaeDkQus3HqU9qGBjOvX0dtV0S5wvTqEER8bwbubD+FwuC8XwsmicpZtzOKXw7vrhSM1lzBdMBKRCBH5UERKRMQmIred49jbRcTutOR4sYiMb05ZzVVSXsVXP2Zz3eCuZ0w+1DRvmToqikN5p1l3wD0TT9Ns+TywNI2KKgf3J+glUjTXMGMHx1ygAugMDAM+FZHtSqldDRy/Xil1mYvKarKvfszmdKVdN9FppnH1oC60axXIO5uyuKxvB5eWnWbL57bXUymvcmARyCupoFeHMJe+hnZhMtVHeREJA6YATyqlipVSa4FVwHRvlnUuC9dn0jYkAL18i2YWIYFWfjm8O1/uyiavpMKlZacezKWiynHGc01zBbPdGfUD7EqpdKdt24Fx5zhnuIjkAHnAYmC2UqqqqWWJyCxgFkDHjh1JSUk5b2W3ZleyNcv4Y79t/noeGRlCn3Drec/ztuLi4kZdn6/S1we9xUGF3cHt81bzi96BLntfBp+y134fIBB8ykZKymGXlF3Dn39//nxtLWW2YNQaKKizrQBo08Dxa4CLARswCHgXqAJmN7UspdR8YD5A//791fjx489b2Zf/8z1GKyDYFZS3j2a8DywznpKSQmOuz1fp6zOa02TdOnbk2EkvUC5L0RNfaecfm79gcPd2POGmZMD+/Pvz52trKY8GIxFJoeG7nO+BB4G2dba3BYrqO0EpddDp6U4ReQb4A0YwKm5KWU115NRpdh4uwGoRUEonh9RMJfVgbu3CQjVLS7gicGzKzKPSrnjwyr4tLs/hcJCTk8OpU6ew23+642rXrh27d+9uaVVNyZevzWq10r59ezp06IDF4voeHo8GI6XU+HPtr+7nCRCRvkqpfdWbhwKNHXCgMNa1A0hvYVnnNDd5PyIw97ZL2H+imPjYSJ0cUjON+NhIggMslFU5EBGXfVD6bl8OQVYLo2MjWlzW4cOHERFiYmIIDAxExPjTLSoqok2bhhpDfJuvXptSisrKSrKzszl8+DBRUVEufw1TDWBQSpUAHwDPiEiYiIwFJmL0BZ1FRK4Vkc7V318EPAl81JyymuJQXinLNx3i1pFRXD2oCw8k9NGBSDOVuOhwlsyMZ2DXNgQHWBjUrW4jQfOsST/JiJhwQoNa/jm2pKSE7t27ExQUVBuINHMSEYKCgujevTslJSVueQ1TBaNq9wOtgBPAMuC+mqHYIhJVPZeoJixPAHaISAnwGUbw+XtjymqJf63eh8UiPKDnWGgmFhcdzp+uH0hphZ0vdh1vcXknCsvYc7yIy/u6bnK3O5p7NPdx5+/LbAMYUErlAZMa2JeFMTCh5vnDwMPNKau5MnNKeH/LEWaMiaZLO52PSzO3+NhIeoS3YsXmwy1e9HHNvhwArujn2rlLmgbmvDMytVdW7yPQKtw3Xufj0szPYhGmXNKD7w/kcOTU6RaV9d2+k3RoHcSALq5p8tM0ZzoYNcGBk8Ws3HqE6fHRegE9zWfcFNcDpeD9tObPB3I4FN/ty+Hyvh2x6BneppWSkoKIkJOT4+2qNJkORk3wyjf7CAm0cu84fVek+Y6eEaFc2juS99IONzt56q6jheSVVHC5i9ML+bKtW7ditVoZO3Zsk8576qmnuPjii91UK9+lg1Ejrdx6hI+2HeXqQV2I1Ctaaj7m5hE9yMorZUNGXrPOX7PPWD3WlYMXXGLJEoiJAYvF+Lpkicde+vXXX+f+++/nhx9+8Nm5Q2aig1EjpNny+d3ybQB8tvMYabZ8L9dI05rmmkFdaRMcwIq05q1ztCb9JAO7tqVjGxN9EFuyBGbNApsNlDK+zprlkYB0+vRpli5dysyZM7npppt44403zth/9OhRkpKSiIyMJDQ0lGHDhpGcnMySJUt4+umn2bVrFyKCiPD2228DxvDp995774xyYmJieP7552ufv/jiiwwZMoSwsDC6d+/O3XffzalTp9x+vZ5gutF0ZvT5D8eoad2osrtuNrumeUqrICs3DO3Kyq1HeWZiVZNWJC4ur2JLVj53XtbLjTUEHnoItm2jld0O1kbk0ktNhfLyM7eVlsJdd8HrrzfuNYcNg5deanJV33vvPaKjoxkyZAjTp08nMTGR2bNnExgYSElJCePGjaNTp058+OGHdO/ene3btwMwefJk9u/fzyeffFKbo65du3aNfl2LxcJLL71EbGwsNpuNBx98kAcffJDFi1s8fdLrdDBqhMzcUgCsgk77o/msm0f0ZNnGQ3y64yi3jGz8DPrUA7lU2hXjzNZEVzcQnW+7Cy1YsIDp040FAMaNG0doaCirVq1iypQpLF26lOPHj7N+/Xo6dDD62Hr3NvqZi4qKaN26NQEBAXTp0qXJr/vQQw/Vfh8TE8OcOXOYOHEiCxcu9Pk5WzoYnceJojK+TT/JzwZ0ZlhUe532R/NZw3u2p3fHMN76PpOc4opGv5e/23eSVoFW4mLc/L6vvkM53diUOTExRtNcXdHR4MbM2Pv37+f7779n2bJlgNG8lpSUxIIFC5gyZQpbt25lyJAhtYHIlVavXs3s2bPZvXs3BQUF2O12KioqOH78ON26dXP563mSDkbn8fb3mVTaHTx+/QC9iJjm00SES3t3YHGqjfTsvQQFWBqVzXvNvhziYyMIDjDZ8ijPPmv0EZWW/rQtNNTY7kYLFizAbrefkZ9NKaMd/9ChQ7XfN5WInHVuZWVl7fc2m43rr7+emTNn8swzzxAZGcmWLVuYOnUqFRWuXbfKG3z7vs7NisoqWZxq49qLu+hApPmFNiHG50+HgsrqbN7nciivlIycEvONogNISoL58407IRHj6/z5xnY3qaqqYuHChcyePZtt27bVPrZv386QIUN46623uOSSS9ixY0eDc32CgoLOyFJeo2PHjhw7dqz2eXZ29hnPN2/eTEVFBf/85z8ZM2YM/fr14+jRo66/SC/Rd0bnsGxjFkVlVXpekeY3JgzozPw1B6lyKCyW82fzrhnSfUU/EwYjMAKPG4NPXZ9++ik5OTnMnDmTyMgzf3a33nor8+bNY9euXTz33HNMmjSJ2bNn06NHD3bu3EmbNm0YMWIEMTEx2Gw2tmzZQlRUFG3atCE4OJgrr7ySuXPncumll2K1Wnn88ccJCflpcn3fvn1xOBy89NJLTJ48mdTUVF5qxuALs9J3Rg0or7LzxtoMLu0dyZAe7b1dHU1zibjocJbNiqdbuxCCAyxERYSe8/hV247SJjiAglLfbwZyhTfeeIOEhISzAhHAzTffjM1m4/vvv+fbb7+le/fu3HjjjQwaNIi//OUvtZnJp0yZwnXXXceECRPo2LFjbd/TCy+8QGxsLOPHj+emm27i7rvvplOnTrXlDxkyhJdffpkXX3yRgQMHsmDBgjOGffs6aW77pj/r37+/+ut/v+aR93ew6M5R5v1U2Ez+vtqkvr7z23+imOte+Y6rBnTiP0lx9R6zbGMWj32wE4CQwMb1LzXF7t27GTBgwFnbfXXNn8bwh2tr6PcGICJpSqkRzSlX3xk14NU1CWrXnQAAEF9JREFUBxjUra1Of6L5pT6dWvPQVX35bOdxPtt57Kz9q/dk8+TKH2qfN6Z/SdNaQgejepRWKQ6eLOHecb31ol+a35p1eSyDu7fjzx/9QF7JT81wyzcfYuaiNKIiWhEcYNHz6zSP0MGoHrmnFZ3bBHPtxU2flKZpviLAauH/bh5CwelKnvl4F0op/vXNPh55bweX9o5k1YOXs3RmPL/7eX+XN9FpWl16NF097ApySyrYfrhA/wFqfu2iLm15IKEPL329j22HTpGZW8qkYd2Yc9NQggIsxEWH678BzSNMd2ckIhEi8qGIlIiITURuO8exr1YvQ17zKBeRIqf9KSJS5rR/b2ProZTSbeTaBWFMbCSCkfbKahGmxUcTFGC6fw2anzPjO24uUAF0BpKAeSIyqL4DlVL3KqVa1zyAZcCKOof92umY/o2thG4j1y4Um2351HaNKtXsZSY0rSVMFYxEJAyYAjyplCpWSq0FVgHTm3DuwpbWIzxYdBu5dsGIj40kSA9U0LzMbH1G/QC7Uirdadt2YFwjzp0CnATW1Nk+W0SeA/YCTyilUs5XULtg0YFIu2DERYez5O54Ug/m6kTAmteYLRi1BgrqbCsAGjNL7FfAInXmLN4/Aj9iNPvdCnwsIsOUUgfqniwis4BZYOSISnFj1l9vKy4u1tfnw9x1fYMEijIOk5Lh8qLr1a5dO4qKis7abrfb693uD/zh2srKytzz96WU8tgDSAFUA4+1wHCgtM45vwc+Pk+5PYEqIPY8x30OPHi+evbr10/5s+TkZG9Xwa309fmGH3/8sd7thYWF5zzvH/9QavXqc5e9erVxnNmc79p8QUO/N6WUAjarZsYHj/YZKaXGK6WkgcdlQDoQICJ9nU4bCuw6T9EzgHVKqYPnqwKgZ7Fqmg8bORISEyE5uf79ycnG/pEj3fP6t99+e+2S4c6Pbdu2uecFPSAlJQURaTDTuCeYagCDUqoE+AB4RkTCRGQsMBE435q6M4C3nTeISHsRuVpEQkQkQESSgCuAL9xQdU3TPCQhAZb///buPTiqMk/j+PdnLiQC2RFNIpORQAh3SyAiSrGwbIHCjlW7WJlKTQW3CBUWBVmK9QKUrBAu7hhd2cJZBopCSsT1koLZ2bW4/SOxkkEFRQbkopGVwCC46iqCNIEi7/5xOk0nJBBDmpNzeD5Vp8w5b3fzPt3IL33Oe963ovmC1FCIKiq8xyXKuHHjOHHiRKPtzjvvbNNrxa9ZdCPrUMUoagaQDvwv3lDt6c65/QBm1iN6v1BsVSszGwH8gsuHdKcAS/EGNXwD/CMw0TnX6nuNRKRjaq4gXa9CBNCpUyduv/32RltycjJ1dXXMnj2b7Oxs0tLSuO+++6iuro49r6qqCjNj8+bNDB8+nNTUVLZt834/fvvtt7n77rtJS0ujV69ezJ8/v9GieefPn+fpp58mNzeXTp06kZeXx0svvQR416JKS0vp1asX6enp9OnTh+eff576+vrY8/ft28fYsWPJyMiga9euDB48mO3bt3PkyBH+OvqGZWZmYmaUlJQk9g1sRkcbwIBz7v+AiS20HcUb5BB/7D3gspXvnHNfAwn6oi4i7W32bNizBy5eTCeplYvK/vznMH48dO8OJ07AgAGwaJG3tcaQIbHVztvFnDlzqKioYO3ateTl5bFs2TImTJhATU0N3bt3jz1u7ty5vPjii+Tn59O1a1e2bdvGpEmTWL58OaNHj+bo0aM8+uij1NXVxZaJmDx5MlVVVSxfvpyhQ4dSW1vLsWPHAKivrycnJ4eKigoyMzPZuXMn06ZN49Zbb6W0tBSA4uJiBg8ezM6dO0lOTmbfvn2kpaVxxx13sHHjRgoLC9m/fz/dunUjPT29/d6UVupwxUhEpLVuucUrREePQo8e3v71sHXrVrp0ufR78ahRo9iwYQMrV65kzZo1PPjggwCsWrWKd955hxUrVrB06dLY48vKynjggQdi+88++yxPPfUUU6ZMAaB3796Ul5fz8MMP88ILL/D555/z5ptvsmXLFiZMmABAXl5e7PkpKSksXrw4tt+zZ092797NG2+8EStGtbW1PPnkk/Tv3x+A/Pz82OO7desGQFZWFrfd5s9KBSpGItIhNHxDOX060uo1fxpOzT3zDKxcCQsXJv4UHcDo0aNZvXp1bD89PZ3Dhw9z4cIFRo4cGTuelJTEiBEjOHDgQKPnDxvWeMmfjz76iJ07d1JeXh47Vl9fTyQS4eTJk3z88cfcdNNNsdNpzVm1ahVr1qyhtraWSCTChQsXyM3NjbU//vjjTJ06lXXr1jF27FgKCwtjhakj6IjXjERErir+GtHixS0PakiEm2++mfz8/NiWk5PTcPtIs8vOND3WuXPjKwv19fUsXLiQPXv2xLa9e/dSU1NDZmZm7LVb8tZbbzF79mxKSkrYtm0be/bsYcaMGY2uOZWVlXHgwAEmTpzIjh07uOuuu1i7dm1b34J2p2IkIoHT3GCFK42yux7y8/NJTU1tNGDh4sWLvPfeewwcOPCKzy0oKODQoUONClzDlpycTEFBAfX19WxvIVh1dTX33nsvM2fOpKCggPz8fA4fvuzefvr06cOsWbPYtGkTpaWlrFmzBoDU1NRYf/2iYiQigXKlUXN+FqTOnTszffp05s2bx+bNmzl48CDTp0/nq6++YsaMGVd87oIFC3j99ddZsGABn3zyCYcOHWLDhg3MmTMH8IpIUVERU6dOZePGjXzxxRdUVVWxfr1310vfvn3ZvXs3W7ZsoaamhiVLlvDuu+/GXj8SifDYY49RWVnJkSNH+OCDD6iuro4VydzcXMyMTZs28fXXX3PmzJkEvUstUzESkUDZtevKw7cbCtKuXde3XwDl5eUUFRUxZcoUhgwZwt69e9m6dWujkXTNGT9+PJs2bWL79u0MHz6c4cOH89xzz9GjR+wuFl599VWKi4uZNWsW/fv3p6SkhFOnvNnTHnnkEYqKiiguLuaee+7hyJEjPPHEE7HnJiUl8d133zF58mT69evHQw89xIgRI1i2bBkAOTk5LFq0iPnz55Odnc3MmTMT8O5cmV3tXOSNqF+/fu7TT8N7O1JlZSVjxozxuxsJo3zBcPDgQQYMGHDZ8dOnT7d6AEPQhCFbS58bgJl95Jwb1mzjVeibkYiI+E7FSEREfKdiJCIivlMxEhER36kYiYhvNIAqWBL5eakYiYgvUlJSiEQifndDfoJIJEJKSkpCXlvFSER8kZWVxfHjxzl79qy+IXVwzjnOnj3L8ePHycrKSsifoYlSRcQXGRkZAHz55ZeNFpg7d+4caWlpfnUroYKcLSUlhezs7Njn1t5UjETENxkZGZf941ZZWcnQoUN96lFihTnbtdJpOhER8Z2KkYiI+K7DFSMzm2lmH5pZnZm90orH/5OZnTSzU2a21sw6xbV1M7P/NLMfzazWzIoT2nkREWmTDleMgC+BpcBVV30ys/HAPGAs0BPIAxbFPWQFcB7IBiYBK81sUDv3V0RErlGHK0bOud875/4AfNuKh08GXnbO7XfOfQcsAUoAzKwzUAg845w745yrBv4b+PvE9FxERNoq6KPpBgH/Fbf/JyDbzG4FegAXnXOfNWn/q+ZeyMymAdOiu3Vm9kkC+ttR3AZ843cnEkj5gi3M+cKcDaBfW58Y9GLUBTgVt9/wc9dm2hram11MxDm3GlgNYGYftnVNjiBQvmBTvuAKczbw8rX1udf1NJ2ZVZqZa2GrvvorXOYMEH+TQsPPp5tpa2g/3YY/R0REEui6FiPn3BjnnLWw/WUbXnI/MDhufzDwlXPuW+AzINnM+jRp39/2BCIikggdbgCDmSWbWRqQBCSZWZqZtXQ68VWg1MwGmtktwD8DrwA4534Efg8sNrPOZjYS+DtgfSu6sfpac3RwyhdsyhdcYc4G15DPOtoEhWZWBixscniRc67MzHoAB4CBzrmj0cc/DswF0oGNwKPOubpoWze8IeL3443Om+ece/26BBERkVbrcMVIRERuPB3uNJ2IiNx4VIxERMR3KkZxwjaX3ZXm+TOzsWZ2yMzOmtl2M8v1qZttYmadzOzl6Od02sw+NrO/iWsPdD4AM3vNzE6Y2Q9m9pmZTY1rC3y+BmbWx8zOmdlrcccCny96K8s5MzsT3T6Nawt8PgAz+7WZHYz+m3nYzEZFj//kfCpGjYVtLrtm5/kzs9vwRho+A3QDPgTeuu69uzbJwDG8GTX+Ai9LhZn1DEk+gN8APZ1zGcDfAkvN7O4Q5WuwAtjVsBOyfDOdc12iWz8ITz4zux8oB6bgTSYwGvifNudzzmnzBnF0xitEfeOOrQee87tv7ZBtKfBK3P40YEeT7BGgv999vcace/HmIwxdPrxpVk4ARWHKB/waqADKgNeix0KRD6gEpjZzPCz5dgCl7ZVP34wu6Uvzc9kF+ZtRSwbhZQNi92QdJsBZzSwb7zPcT4jymdnvzOwscAivGG0mJPnMLANYDDzRpCkU+aJ+Y2bfmNkfzWxM9Fjg85lZEjAMyDSzz83sz2b272aWThvzqRhd8pPmsgu4UGU1sxTgP4B1zrlDhCifc24GXr9H4Z36qCM8+Zbgzbp/rMnxsOSbi7esTQ7ezaBvm1lvwpEvG0gBfoX3d3MIMBRv4oE25VMxuuRGmssuNFnN7Ca806nngZnRw6HJB+Ccu+i8JVB+AUwnBPnMbAgwDvi3ZpoDnw/AOfeBc+60c67OObcO+CPwS8KRLxL972+dcyecc98Ay7iGfCpGl9xIc9k1mtMvuvZTbwKW1cwMeBnvt7RC59yFaFMo8jUjmUs5gp5vDN6CmEfN7CTwJFBoZrsJR77mOMAIQT7nrR/3Z7xMTbUtn98XwTrSBrwJvIF3wW0k3lfLQX736xryJANpeKOy1kd/TgYyo9kKo8fKgff97m8b8q0C3ge6NDke+HxAFt7F/S548zSOB37Em18xDPluBm6P2/4V2BDNFoZ8P4t+Zg3/z02Kfn79wpAvmnEx3ijILOAWoArv1Gub8vkeqCNteMMQ/xD9S3MUKPa7T9eYpwzvN5f4rSzaNg7vongEb9RPT7/7+xOz5UbznMM7LdCwTQpJvkzgXeB74AdgH/APce2BztdM3jKio+nCkC/6+e3COzX1Pd4vTfeHJV80Qwrwu2i+k8BLQFpb82luOhER8Z2uGYmIiO9UjERExHcqRiIi4jsVIxER8Z2KkYiI+E7FSEREfKdiJCIivlMxEgkIM8swszIzG+B3X0Tam4qRSHAMAxbi3fkuEioqRiLBMRRvCYkDfndEpL1pOiCRADCzg0D/Joc3Oud+5Ud/RNqbipFIAJjZPXizyu8H/iV6+IRzrta/Xom0n2S/OyAirfInvMX1fuuce9/vzoi0N10zEgmGQUAqsNvvjogkgoqRSDAU4K3ftMfvjogkgoqRSDAMBQ47537wuyMiiaBiJBIMA9GQbgkxDWAQCYbvgQIzGw+cAmqcc9/63CeRdqOh3SIBYGZ3Ai8DdwFpwCjnXLW/vRJpPypGIiLiO10zEhER36kYiYiI71SMRETEdypGIiLiOxUjERHxnYqRiIj4TsVIRER8p2IkIiK++3/T47iHG3ndkAAAAABJRU5ErkJggg==\n", 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" ] @@ -1225,47 +1301,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 6s 863us/sample - loss: 0.0498 - last_time_step_mse: 0.0388 - val_loss: 0.0416 - val_last_time_step_mse: 0.0321\n", + "219/219 [==============================] - 7s 30ms/step - loss: 0.0508 - last_time_step_mse: 0.0400 - val_loss: 0.0429 - val_last_time_step_mse: 0.0324\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 5s 726us/sample - loss: 0.0385 - last_time_step_mse: 0.0277 - val_loss: 0.0330 - val_last_time_step_mse: 0.0219\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0395 - last_time_step_mse: 0.0283 - val_loss: 0.0351 - val_last_time_step_mse: 0.0243\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 5s 730us/sample - loss: 0.0320 - last_time_step_mse: 0.0208 - val_loss: 0.0319 - val_last_time_step_mse: 0.0225\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0323 - last_time_step_mse: 0.0211 - val_loss: 0.0302 - val_last_time_step_mse: 0.0188\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 5s 730us/sample - loss: 0.0292 - last_time_step_mse: 0.0181 - val_loss: 0.0284 - val_last_time_step_mse: 0.0179\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0298 - last_time_step_mse: 0.0187 - val_loss: 0.0274 - val_last_time_step_mse: 0.0152\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 5s 725us/sample - loss: 0.0272 - last_time_step_mse: 0.0155 - val_loss: 0.0267 - val_last_time_step_mse: 0.0145\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0273 - last_time_step_mse: 0.0153 - val_loss: 0.0259 - val_last_time_step_mse: 0.0139\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0247 - last_time_step_mse: 0.0122 - val_loss: 0.0241 - val_last_time_step_mse: 0.0117\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0251 - last_time_step_mse: 0.0125 - val_loss: 0.0227 - val_last_time_step_mse: 0.0091\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 5s 730us/sample - loss: 0.0224 - last_time_step_mse: 0.0097 - val_loss: 0.0236 - val_last_time_step_mse: 0.0116\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0231 - last_time_step_mse: 0.0103 - val_loss: 0.0235 - val_last_time_step_mse: 0.0105\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 5s 731us/sample - loss: 0.0214 - last_time_step_mse: 0.0089 - val_loss: 0.0197 - val_last_time_step_mse: 0.0071\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0216 - last_time_step_mse: 0.0086 - val_loss: 0.0220 - val_last_time_step_mse: 0.0100\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 5s 727us/sample - loss: 0.0203 - last_time_step_mse: 0.0080 - val_loss: 0.0196 - val_last_time_step_mse: 0.0075\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0212 - last_time_step_mse: 0.0084 - val_loss: 0.0206 - val_last_time_step_mse: 0.0082\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 5s 729us/sample - loss: 0.0197 - last_time_step_mse: 0.0074 - val_loss: 0.0183 - val_last_time_step_mse: 0.0061\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0211 - last_time_step_mse: 0.0085 - val_loss: 0.0202 - val_last_time_step_mse: 0.0079\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0192 - last_time_step_mse: 0.0071 - val_loss: 0.0193 - val_last_time_step_mse: 0.0077\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0205 - last_time_step_mse: 0.0082 - val_loss: 0.0199 - val_last_time_step_mse: 0.0075\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 5s 731us/sample - loss: 0.0191 - last_time_step_mse: 0.0069 - val_loss: 0.0190 - val_last_time_step_mse: 0.0078\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0200 - last_time_step_mse: 0.0077 - val_loss: 0.0188 - val_last_time_step_mse: 0.0065\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 5s 727us/sample - loss: 0.0190 - last_time_step_mse: 0.0071 - val_loss: 0.0190 - val_last_time_step_mse: 0.0066\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0197 - last_time_step_mse: 0.0075 - val_loss: 0.0206 - val_last_time_step_mse: 0.0082\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 5s 724us/sample - loss: 0.0188 - last_time_step_mse: 0.0070 - val_loss: 0.0185 - val_last_time_step_mse: 0.0070\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0193 - last_time_step_mse: 0.0072 - val_loss: 0.0195 - val_last_time_step_mse: 0.0074\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 5s 726us/sample - loss: 0.0190 - last_time_step_mse: 0.0070 - val_loss: 0.0185 - val_last_time_step_mse: 0.0063\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0192 - last_time_step_mse: 0.0071 - val_loss: 0.0186 - val_last_time_step_mse: 0.0074\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 5s 723us/sample - loss: 0.0186 - last_time_step_mse: 0.0067 - val_loss: 0.0185 - val_last_time_step_mse: 0.0069\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0188 - last_time_step_mse: 0.0067 - val_loss: 0.0201 - val_last_time_step_mse: 0.0103\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0186 - last_time_step_mse: 0.0069 - val_loss: 0.0177 - val_last_time_step_mse: 0.0064\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0186 - last_time_step_mse: 0.0067 - val_loss: 0.0184 - val_last_time_step_mse: 0.0071\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0183 - last_time_step_mse: 0.0065 - val_loss: 0.0176 - val_last_time_step_mse: 0.0061\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0187 - last_time_step_mse: 0.0071 - val_loss: 0.0178 - val_last_time_step_mse: 0.0063\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 5s 728us/sample - loss: 0.0182 - last_time_step_mse: 0.0065 - val_loss: 0.0179 - val_last_time_step_mse: 0.0063\n", + "219/219 [==============================] - 6s 28ms/step - loss: 0.0185 - last_time_step_mse: 0.0069 - val_loss: 0.0172 - val_last_time_step_mse: 0.0056\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 5s 725us/sample - loss: 0.0182 - last_time_step_mse: 0.0065 - val_loss: 0.0176 - val_last_time_step_mse: 0.0063\n" + "219/219 [==============================] - 6s 28ms/step - loss: 0.0183 - last_time_step_mse: 0.0068 - val_loss: 0.0206 - val_last_time_step_mse: 0.0099\n" ] } ], @@ -1307,7 +1382,7 @@ "outputs": [ { "data": { - "image/png": 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1e+CfwPi61mO4x5p0a5RQQ80fqqp1eBM6tww1AxYMr1u4+SBZuYU8GHuRW0eLVpzsOmaMNcl1+4FmvOp4nPjHo2vdtWdndg1EOwF/EelWYdsAYEstjlWs50C4yveXyq+K/rWsx6iH1ek5BAf40ad91fjfcIZ3bsHq9FycTrM0hOEdTqfyZtIeurcJ44pebdxWb9WMC2Bl5nY4YFfUFSS2fKhOz5nsypaBSFULgLnAcyISKiIjgbHAR1XLishYEYkQyzDgt1gj5QCSgVLgtyISJCIPu7YvbvCLuECtSs9lcHQEAX6ee2kN69yCE6dL2H7I3Oga3rF4+xF2HD7JA7EX4XC4726ouowL7dtbCVM/On49l+/9iMR/7PX5pYpsGYhcHgSCgSPALOABVd0iIqNEJL9CuVuB3VjdbR8CL6vqBwCqWgSMA+4EjgH3AONc2w03O15YzI7DJxt82HZVZedbnW6ycBuep6pMSd5Nx4hgru/f3q1115Rx4a67ICsvnKWMJu7IHB5/3K2n9TjbBiJVzVXVcaoaqqpRqjrTtX2ZqoZVKHebqkaqNX+op1pDtCvWs15VB6tqsKoOUtX1nr6WC8XazFxUG37+UFUdI0Lo0DyY1RnmOZHheSlpuazPOsb9l1+Ev4d6AsaOhaZN4cOIR6yV9HycbQOR4XtWp+cS6OdgYKfmHj932XMiNUuIGx72ZvJuWoYFccvgjh47Z0gI3HILfFJwDYUr1sPRox47d0Mwgchwm1XpuQzo1IwmAX4eP/ewzi3Izi9iz9ECj5/buHB9smYvy3Zlc03fth5/3d95J+QXBfG53mAt6erDTCAy3GLF7mw27TtGp4iGy7Z9NsNdi+OZYdyGp6Rm5vHE3E0AfLJ2r0eXrJ882VowLzpa+cD/PnjwwZ8sIe5Ly0OYQGSct9TMPH713hqcCl9uOuDRP8gyMZEhtGoaxCozYMHwkO+2HaZsxkBxqWeXrB86FG69FUZ1SGdRyeXsPxVRaQnxpKcW+dTyECYQGectJS2HIleut1KnevQPsoyIMKxzC1almedEhmecPF0CgEMgwN+zS9aX5apbmNICJ37M5MfFCZIKhxH/0sU+lZnbBCLjvPV1TV4VPP8HWdGIzi04dOI0iTuKvHJXZlw4nE5l2a6j9GrXlEev6sGM+0Z4fMn6uDj4zHkT/hTzJg+iQBKxxJNIovMWnwlCYAKR4QY7D1vTuu68JMYrf5BlwoICAPg6o4SE6SkmGBkNJiUth4ycQu4ffREPxXX12ms+LjqNh3iDDDpzP29bQYh44qLTvNKe+jKByDgvTqfyUUomQ2MieHZsH6/9QQLsP1YIWDmeiks822dvXFhmrM6iWXAAP+/b1rsNeeEF/hL8/xCcvMMkHmAqcSGr4YUXvNuuOjKByDgvS3YeJSu3kDsvifF2U7jkopb4udKrBPh5r4vQaNyy88/w7ZZD3Dyoo1emKlSSkMDm3/8Hf0qJ5ChT5UGSHpkPCQnebVcdmUBknJcPVmbQumkQV/fx8idDYHB0BH+9rjcAD3qxu8Ro3D5L3UdxqXLbsE7eboqVFHXaFfz6YT9yaMUrl8wlftoVPpcE1QQio94ysgtI3nGU24dHEehvj5fSHSOiaR4kbDlw3NtNMRohVWXW6iyGxkTQzQMrEJ9Nxczcjz9h/f0d2JxD4hz1uYzc9nj3MHzSxymZ+DuE24dFebsp5fwcwtC2fiTtOMqJ08Xebo7RyKx0DVK4fbh3X/NVl4fo2BGGdznC3JNXEtd2W52XIfc2E4iMeiksKiFx7V5+3rctrcObeLs5lQxv509RiZP/bTns7aYYjcys1XtpFhzANX3bebUd1S0PcVN8AKkMIXNOSvk8I19ZHsIEIqNe5m84wInTJdx1aYy3m/ITFzVz0KF5MF9sOuDtphiNSE7+Gb754RA3Derg9UEK1S0PceM91jPRzz+1JtrGxeEzy0OYQGTUmarywfcZ9GoXzhAbDggQEa4f0J7lu7LJLTBLTxnuMXfdfopKndxmo67oirp1g74t9jN3e28o9q1uaROIjDqbsSqL7YdOcnn3llRehd0+rh/QjhKn8vUPh7zdFKMRSM3I5c3k3fRs25TuXh6kcDY3/ewEy5yXcvirdd5uSp2YQGTUSWpmHn+d/wMA76/IsG32gt7twunSKpQvNpruOeP8pGbmces7KeQVFrPnaL5tX/MAN/2mA4qDBe/41vNR2wYiEWkhIvNEpEBEMkXk9hrKPSYiP4jISRFJF5HHquzPEJFTIpLv+vrWM1fQOC3a6r2Mw3UhIlzfvz0p6TkcOXHa280xfNis1ZkUl1oveqeXkvrWVv/LwukctJ95y1t5uyl1YttABEwBioA2QAIwVUT6VFNOgDuBCODnwMMicmuVMte7lhIPU9WrGrLRjd0h15u6NzIO19X1A9qhCgs3H/R2UwwftT4rjwUbDiKAnw+85kXgpoHpLDo2mONZvjOXzpaBSERCgZuBp1U1X1WXAwuACVXLqupkVV2nqiWqugOYD4z0bIsvDIVFJSzefoThnVt4LeNwXXRt3ZRe7cJN95xRL3uO5nPP+2to0yyId+4awh9s/pqfPNmaN3TTHSEUE8jCf+7+SRm7Lpbn7+0G1KA7UKKqOyts2whcfraDxHpyPgp4u8quGSLiANYDj6nqxmqOnQRMAmjVqhXJycn1b73N5efn1+v6FmcVc/xUMT9rXUh32cfJ9H0kp7u/feer4vX1aVrEpzuL+eS/i2kVYsvPXXVW39+fL7DLteWddvJ8ymlKnMrDffzxP7yNPsJ5v+Yb8voCAppz4429+eufSmnLQWbOPkP763881/r1zXn22d787W9bSU4+1iBtqDdVtd0XVjA5VGXbRCD5HMc9ixWwgipsGwkEAyHAn4BDQPOz1dO9e3dtzJKSkup8TGmpU+NeTdLr/rVMnU6n+xvlRhWvLyunQKOf+FKnJu/2XoPcrD6/P19Rl2v79odD+sbinbo2I9etbViy44gO+b9vtcdT/9VNe4+5te6G/t0tXqzasqXqDa2+1xAp0MLCytsXL27Q0yuwVuvxnm/XO6J8ILzKtnDgZE0HiMjDWM+KRqnqmbLtqrqiQrEXReQurED3hfua2/gt2XmUtKMFvP7LgbYdsl2dTi1C6NY6jP8sT2doTAvbdqsYdTNt6R7+/t/tCBAUsPu8u8xUlZ2H83lvRTpz1uxFgUA/R/nKw76iLKPCjVcPoFBD+DZkHOFtgokvfI/E+U1su1ieXQPRTsBfRLqp6i7XtgHAluoKi8g9wJPAaFXdd466FWuAg1EH7y5Pp014ENf2825qk7pKzcwjPbuAEqdy+zspzJxo3z5+o3YKi0p4Y7H1/EOBItfaU3X9vaZm5DJ3/X7yz5SwYe8xMnMKK+0vddavXm+LOzCDT5jB1XzJX3mWA4fbkxh0I3EH7sAa92U/tuw0V9UCYC7wnIiEishIYCzwUdWyIpIA/B24UlXTquyLEpGRIhIoIk1cQ7tbAiuq1mPUbPuhEyzfnc2dl8TYJst2baWk5eC0umjL37AM3/b6ol2cOF1CgN+PnydHdG5RpzpSM/OIn5bCjFVZzN9wgObBATw/ri/T7xxCkwCHT4yQq9FTT3Fl8Vf0YzObGMAkphF35mt46ilvt6xGdn5XeRDr2c4RYBbwgKpuEZFRIpJfodzzQCSwpsJcobdc+5oCU4E8YD/W8O5rVNW8G9XBe8szaBLgsFWW7doa0SWyPHg6RHzzjcUot/XACd5dns5twzoxe9IljOnZGqfCvmOn6lTPtKV7KHVNiPMTuKpPW+4YEc0Vvdsw474Rth8hd1ZZWSQRSwYxALzBwyQRC1lZXm3W2dg2EKlqrqqOU9VQVY1S1Zmu7ctUNaxCuc6qGqA/zhMKU9Vfu/ZtUdX+rjoiVfVnqrrWW9fki7LzzzBvw35uGtSRiNBAbzenzgZHRzDjvhF0bxNGRGiAb76xGACUOpU/z9tM8+AAnvh5TwZHR/DOnUMY0LEZ//flVo4V1i6v4Pd7slm09TAOqX5u0ODoCB7y4YUVk1r/kngS+YTxRJDLMFYRTyJJrX/p7abVyLaByLCHGSlZFJU4uWdkZ283pd4GR0dw27AosvOL2JdXeO4DDFuauSqTDXuP8fR1vWkeYn0o8nMIf7+pH3mFxbz89fZz1pGeXcADH6+jc6swPrhnmG/f+VQjKQlrYELQnVzFIm5kHqsYwceB9xJf+J5t1ycygcioUUpaNm8v3cPFUc3p2jrs3AfY2PDO1ife1em5Xm6JUR9HTpxm8tc7uKxrS8YObF9pX5/2zbhnZAyzVu896+/3eGEx976/BofAu3cNYVS3Vj5951NV+WJ585sQ9+4dEBXFL5nDScIpfOBREuc3se1ieSYQGdVKzcxjwrurKSwqZcv+E7ZO9FgbPdo2JbyJvwlEPig1M48J/1nNqZJSnh/Xt9rpA7+/sjsdmgfz+8QN/Ou7nT95va5Oz+GGN5aTmVvAW3cMJjoy1FPN95hKi+UlJEBmJmNubE5LRw5zDl1u68Xy7Dp82/Cy7/dklyd69NVhrBX5OYRhnVuwygQin5Kamcdt01ZSVKr4O4ScgiJiWv40iIQE+nP3pTG88N9tvPa/Xfx78W5+NbIz7Zs1ISOngA9XZuJUCPAT/P0a5+fv6hbB84+9jJvnfcJHCyZRUOAgLu6nC+rZQeP8jRjnrcQ1kc8XkpvW1vDOkaRnF5hs3D5CVZm6ZA9Frg9EqmfPfF1x8mlxqTJtaRrPfLGV97/PLM8Yb/fs2W43ejS/ZA6FpxwsXOjtxtTM7YFIRP4lIl9Wsz1cRJ4RkV4Vtj0iIptdeeAMmygpdfLFxoNEtwjhD1d2bzQPc4e55pqYuyL7O3LyNPe8v+aso9uqGtElkiYBDhwCQf4Opt85hNS/XMFH9w6jib+Pzw2qr379GB2+kbbBx5kzx9uNqZlbu+ZE5CLg18Cl1eweAvwNa6JqmbexMiLcBbznzrYY9bdg4wHSsgt4645B/Lyvb2VSOJs+7cMJDfRjVXoO1w9of+4DDI9Kzczjyz1FbGU305elU3CmhGdv6EPf9uGkpOcyokvkWT8QlQ3VT0nLqVR2VLdWzJj40+0XBD8//EZdyvgVXzD9v3dw8iQ0teECs+5+RvQIsLGGuToXA2eArWUbVPWUiHwI/BETiGyhpNTJvxfvple7cK7q3dbbzXErfz8Hg2NamAELNpSamUfCOymcLnHCrh10aRlC4v0j6NraetccHFO7zAmDoyOqDTQ1bb8gjB7NLxe+xRvcwYIF1jgGu6lVl5iIdBWRYhF5rsr2qa6VUYeISBBwBzCzmuO3Aa8CQUCxiKiIfObaPRvoLSLV3UUZFaRm5jElaXeDjmCbv+EA6dkF/O5n3XA4Gl9KvuGdW7DzcD65BbWb/Gh4RkpaDmdKrGc8Aoy7uGN5EDLO0+jRXMr3dGhRaNvuuVoFIlXdDUwHHhGRSAAR+StwD3Cj6w5oBNAcWFZNFXcCaVgZry9xff3BtW8DVlbtn9f/Mhq/sk+M//h2BwnTU+odjMq6P6o73rob2kXvduFc3afN+TbZloa7nhOZuyJ7GVYhV1xQgIORXVt6sTWNy+TFQ1gSdDXxUav4+ms4Vs1SRN5eMK8ugwSeA/yAJ0XkPqznPRNUdZFr/wisZLibqjl2I9ARWKyqKa6vTABVdbr2j6jnNVwQUtKyOV3ixKlwprh+yTutobApfLaruNpg9vmGA2TkFPLIFd18aqmHuujfsTlB/g5WpV9AI6d8wPHCYhQY0c6v0QyOsYuhl/gT75xF16PfU1wMn39eeX/ZRNihQ73TPqhDIFLVg8DrwG+At4DfqmpihSLtgROqWl2fRx8gEFhXQ/VHXccbNSgq0fLvFSg4U1znOr7dcoiiUicKnC528tHKjLLFA8vvhvq0D+fK3o3zbggg0N/BoKgIc0dkMx+szKBteBPu6xdkgpCbxcVBYsIC/rZ/Em3blFbqnivPxpDo3flFdR02vQvrOc9KVZ1SZV8TrMEI1RmE9f65oYb9p7AybRvVyM4/w4crM+jeJozfX9GNgZ2a89aSNBZuOljrOlSVta47IHF9fb7hADdP/Z41Gbm8/t0uMhcuEa0AACAASURBVHMKuX5A+0Z7N1RmeJcWbD14guOn6h7MDffbfSSfZbuyuWNEFP6N8LmkHcTdFUUi8RzPg2+/hZwc+wQhqEMgEpGfYQ23XgmMFJH+VYrkYD0jqs7FwB5VPVHD/hZAdm3bcqH524ItFJwpZcrtg/jdFd2Z5Vrc7ZE560nacaRWdXy56SCpmXncfWk0N3cLYM79I3j55n7sP3aKW95aWb7Q2OuLfpoepbEZ1rkFqpCaae6K7OCjlRkE+jm41QeXGfEZw4cTF7CC12M/x+mEO++0TxCC2o+aGwTMwxqwEAtkAS9WKbYdCBSRjtVU0ZsKw7ar0RnYUZu2XGi+2XKIhZsO8psxXenWxhpFFBzox7t3D6V7m6Y88HEqH6/MPOtouuOFxTz7xRb6dWjGX37Rm+suCmRY50h+OTSK5D/GMarbjw+Giy+AxeMGRUUQ6OdgVZoJRN528nQxn6bu47r+7WgZFuTt5jRewcEwbBgTj79Kmzbw3//Cr39tjyAEtQhEItIV+Ar4FviN6xnQs8C1IjK6QtGlrn+HVVPNMWCAiFwtIiPKRt656m8OdK9wvOFy/FQxT3/+A73ahfPr2Isq7QtvEsCH9wwjMjSQv8z/4ayj6f7+323kFRbz4k39fpJnKzjQj0eu6O77q1LWQZMAPwZ0amYyLNjA3HX7KSgq5a5LY7zdlMZv9GiS14RSUGA9F/73v+2TifusgUhE2mIFoG1AgmuEG8CHWHdAL5WVVdUMYDVwfTVV/RU4DHyO1bXXq8K+XwBFWHdcRgV/X7iNnIIiXhnfn4BqEjVGhgWVZwgoG023YvfRSmVW7slhztq93HdZZ/p2aFbtecpmpDe2tVnOZljnFmzef5yCMyXebsoFy+lUPliZwYBOzRnQqaZefcNdkpqNI945i0+f2kDLltC3L7ZZFuKsgUhVD6lqF1WNVdUzFbaXqmovVa06CXUqcJOIhFSp5wdVHa6qwaoqqrq8wu47gE+qLt8tIi1EZJ6IFIhIpojcXl0bxfKyiOS4vl6WCk/bRWSgiKSKSKHr34Fn/y+xh/eWpzNn7V5uGNC+xgACcGXvtgS5lsJWYM7afazPsu6KTheX8tS8zXRqEcwjV3Q/6/l8fVXKuhreOZJSp/LMF1sa/TMxu1qxJ5u0owXcfWm0t5vS6CUlQfwrQ0iUW7n61OdMmgQrV8I//2mPYOTuZKMfAweAB2tT2BUUxmB19VU1BetOqQ2QAEwVkT7VlJsEjAMGAP2x7sjud9UfCMx3tSsC+ACY79puW4u3H+a5L61Hal9tPnjWN8rB0RHMnDiCx67uwdPX9UKdys1Tv+e3s9Yx4d1VpGUX8Pcb+xEc6Oep5vuEsqwRn67dd14ThI36++D7DFqGBXJtv8aTz9COykfHfeIg7uJjsHQpDzwAIrB+vTVgwdvByK2BSFVLgF8BtV2PuS1wtytzQzkRCQVuBp5W1XzXHdQCYEI1ddwF/ENV96nqfuAfwN2ufbFY+fReV9UzqvovrJHLY+p0YR50/FQxT362mbJZQ8Wl5x48UHY3c+9lXfjm96O5olcbFmw8yJqMPPxECAk0y05VtXGvNb1csbo0v9lyyLsNusBk5RTy3fYj3DYsiiB/8yGpIVVaMK91a0hOpmOUg5uDvmT6m2cYNsz7C+ZJ2YRGOxGRi4EVqhpSYdsfgctV9foqZY8DV6nqKtfPQ4AkVW0qIr937bumQvkvXfv/UaWeSVh3V4SEhAwuLKxtLHUfCQiidfz/EdSuO6gTxIE6Szk8+ymKDmyvdT3hI26h+ag7EIcf6izl2LKPOZHySQO23PcEtu9Jm1tfQPwDAAFnKfmb/8fx7xPxaxpJk6h+nM7aXKf/d6P2Iq97lNBel3N07vOc2rPa2825INwG/AdrwifA91zCSL5nCL9mLW+76zSpqjqkrgfZ9aNyGFB1ztFxoLosiGGufRXLhbmeE1XdV2M9qjoNmAbQo0cP3bHDs6PJz5SUct8Ha1mxO5sptw+idXiTH9PW/2NbnepKzcwjYXoKxSVOAoICmDv7bQZH/5gEIzk5mdjYWDdfgX3U9vpSM/NIScuhW+swlu3KZk5gIM0HXQMIqkqgv8OWgzd8/fe3Ki2HX05LASDqtmcq/R/7+rWdi1evLyYGMjPLf7yElQxmLQUBj+I88xbumMde38nwdg1E+UB4lW3hWMlRz1U2HMhXVRWRutTjNSWlTn4zcz3LdmXzyvj+XOPqM6/vG2BN67IYlVVcGuCqPm15IPYiHpyRyoa91meXsjlV5v/Pveas3Vv+vfk/9qCsrPJvJ/MYQ1nDb/kXdxV/yHffwRVXVC6elGR111W3BLm72XVl1J2Av4h0q7BtALClmrJbXPuqK7cF6C+Vw3T/GurxirUZuYx9YwXfbj3MM9f35pYhndxS74U2Cs4d2jcP5unr+lCWZeZCmFPlDUdOWANwL5R5a7YR9WPmiqGsIZ5E2nCI1o6j/POflYt6OhGqLQORqhZgreT6nIiEishIYCzwUTXFPwT+ICIdRKQ98CjwvmtfMlAK/FZEgkTkYdf2xQ3Z/tpKzczj1mkpbDl4An+H0K+jmUvhbYOjI3j6F70BuPeyziaQu1lhUQmpmXlc2bvNBTVvzRZeeAFCrMfucSSTSDx3MIOrLz3BwoWw2zVkzBs56GwZiFwexEqEegSYBTygqltEZJSry63M21jrHG0GfgAWurbhygIxDms9pGNY6yeNqyFDuMelpOVQ4rQGi6hqo0+t4yvuHhlDz7ZNWbT1CHYczOPL/rf1MKeKS7nvss7mjt3TEhJg2jSItuZtxTmWkvjkOhZuvQgRmDLFe4lQbRuIVDVXVcepaqiqRqnqTNf2ZaoaVqGcqurjqtrC9fW4Vnj3UNX1qjrYNZl2kKqu98b1VKdPe+vxlWC6KOxERJg4qgs7Dp9k6S6Ti9edvth4gHbNmjC0lkt/G26WkAAZGbBgATidxF1WwqefQkAAvPkmjB/vnUSotg1EF4LTxVbGpNuGRZkuCpu5fkB72oQHMX1Zmreb0mgcKyxiyc6jXD+gfaNcht6nXH01REbCxx8TFwf33gtFRdCsGVx+ueebYwKRF6Wk5dAkwMEzN/QxQchmAv0d3HVpDMt2ZbPtYE2rl9RNambeWbOkN3Zf/XCI4lLlhgFmDUyvCwyEX/4SPv+cpC8LSEyEa6+F9HT43e883xwTiLwoJS2HIdEtCPQ3vwY7ShgWTXCAH9OXpZ93XamZedz2TspZs6Q3dvM37KdLq9DyLmnDy+64g6TTI4i/3Y/ERPjySxg+HN54A957z7NNMe+AXpJbUMT2QycZ0cX0ldtVs5AA4od0ZMHG/Rw+cfq86vp8/X6KSpw41eqSXbi59qvrNgaHjp9mVXouN1wAKwD7iqRTI4h3fEpit78QF2flnrvySggNhYkT4ZtvajguCSZPdm9bTCDyktXp1gg5M0DB3u65rDMlTuWD7zPqXcfRk2dYuOlg+RLtYGVXf2T2ehZs3H9BdNd9uekAqphuOZtISoL4XwqJCQuIW/8aHDgAwJgx4OcHpaVw440/TYTaUPOL7JpZodFLScslOMCP/mbukK1FR4Zyde+2zFiVxcNjutY5gWxRiZOHZqyjsLiEV27pz+ETZ+jdrimr0vN4b0U6n2+w3gCC/B3MnNh4B6zM33CAfh2a0aVV2LkLGw2uPBFqh5HwkcKsWfDoo8TFweefW8+LTp2C2bN/HEHXkEO7zR2Rl6Sk5TAkJsI8H/IBE0d35vipYn4za32d71yeX7iV1Rm5vHxzf8YP7sRDcV2J69mGJ6/pyX2jOpffIZ0pcbJo22H3N94G0o7ms3n/ccYONHdDdvH4465g0r07DBsGH39cvi8uDj77zLozmjsX8vIafn6ReRf0gh+fD5luOd8giMB3245wy1vf89S8zazPyqOk1HnWkXCJa/fy4cpM7rusM2MHdvjJ/jE92xAU4ChPKfRp6l7Sswsa+mI8bsHGA4jAdf1NILKlHj1gwwZwOKzEqDNmcO211lLi2dnWprFjzx6Ezve5kema84Ifnw+ZgQq+ICUtB8Fau8ipMGNVFjNWZREc4MeZklJUIcDPwXu/GsrIri1Jzczj8/X7mbUmi0sviuTJa3pWW2/F5LStwgJ56esdjJ/6PR/cM+ysq/L6ktSMXD5YmUHvdk1p26zJOcsbHjZjBnz6qfW9qpWde9IkAB54IIHvvrPujsAKNtUFoop3S/VlApEXlD0f6tfBPB/yBSO6RBLo77CW1fB38GbCIE4VOXln2Z7yTN1FpU7umL6KqMgQ9uWdotSpCDBxdBf8/WrueKiYAXxITAsmvLuaW6el8MerulNQVOrT2dNTM/O4bfoqikqc5J+2csz56rU0Wk89ZT0MqqiwEJ56iqT2CSxZAg8/bGVd+L//g7Vr4ZNPrJF14L4uO9M15wXm+ZBvKbtzKUvSOaZnG37Rvx1PX9eHJgEO/AQC/YTxQzoiQKkrf6AIbD1Q+8mwXVqF8dkDlxIREsAzX2zl1W98e85RSloOxSVW9hCn0+RStKUKS0NUlJTZhfixp0n0u41/T3Hwdcs7CPQv5auvoFcva4lxdz43MndEHlb2fOh6M4zVp1S8c6m4req6T5UWJaxH/sC2zZow7uIO/HvxbhRr1J2vrtczokskIlaPj8mlaFNRUZUWywNIIpZ45pBYeD1xJxcBcOWRGXwdlMP1jgXs2xfAkCHQtCnMm+eewQvmI7mHrUozz4cak6rrPlW9e6pPAInt0Zog192yKgzxwSAE0L9jM/wdDgZFNTe5FO2qwtIQUBaEEkn0TyCudFGlonFnvuaLiDtp0gScTnjoIfeNoDOByMNS0nLM/KFG7nwXJRwcHcHMiSO4cWB7FFi8/Yh7G+gh2w+epKjUya9GmnWdbKvi0hAirGl+FYl/3viTIFTu8GECA+FnP7MOqzrhtb5M15yHpaTlMiQmgoCzPMA2jLKuwOAgf95emsbo7q0Y2bWlt5tVJ6mZuUD9l7w3PCQhwfoCylcFn1FDl53jk/LuOHc+IzLvhh6Uk3+GHYfN/CGj9v7yi150aRXKo4kbySuwxXqOtbY2M492zZrQvnmwt5ti1FVNXXZPri8POnFxVhCKjz//OyMTiDxodbr1CdEEIqO2QgL9+detF5NTcIY/z9vsUyvGrjPDtX1XhS67JOKsINTyIeL+NrpSMXcFI9sFIhFpISLzRKRARDJF5PazlH1MRH4QkZMiki4ij1XZnyEip0Qk3/X1bcNfQc1+fD7UOCYrGp7Rt0Mz/nhVD7764RCvfLOdL/cU2X5I94Fjpzhw/LQJRL7MtZrrmpcXk/jCbuKyP4HXXvtJsbJgtGZN/U9lu0AETAGKgDZAAjBVRPrUUFaAO4EI4OfAwyJya5Uy16tqmOvrqoZqdG0kbT9Km/AgNu077s1mGD5o4qgu9OsQzpvJaXy2q9j284vK2jYk2owO9XWPPw5xf74EbroJnnvOWj2virg4q1x92SoQiUgocDPwtKrmq+pyYAEwobryqjpZVdepaomq7gDmAyM91+LaS9p+mKy8QjJzCm3/JmLYj8Mh5YMVFCh2zS+yq9TMPIID/OjZrqm3m2K4yz//aY3b7t27Ul46d7DbqLnuQImq7qywbSNwzlXUxVptaxTwdpVdM0TEAawHHlPVjTUcPwmYBNCqVSuSk5Pr3vqzeG2tlUZDgaJiJ7MWreHkRYFuPUdt5efnu/367KSxXl/L06WunHeKQ4SgY5kkJ+/zdrOqteSHU0Q3hRXLltbpuMb6uyvjy9fXetEiepaW4igpsTZkZlJ6773s2LaNI1dccV512y0QhQFVc6IcB2rzseoZrDu8iovcJgDrsLrwfgd8IyI9VfVY1YNVdRowDaBHjx4aGxtb17bX6HRxKXuXfodDnAjWLPPbrhjqtf7z5ORk3Hl9dtNYry8WKGmxh5e+2s5jP+/JfaMv8naTqlVYVELWt9/ywOUXERvbo07HNtbfXRmfvr6774ayIOTid+YMvT/+mN7PP39eVXu0a05EkkVEa/haDuQDVRe0DwdOnqPeh7GeFf1CVc+UbVfVFap6SlULVfVF4BjWXZNHzVu/n2OFxfz1ut7nNePeMO4Z2ZmmAbDRxs8ZN+49TqlTzWu8sakhL12N2+vAo3dEqhp7tv2uZ0T+ItJNVXe5Ng8AtpzlmHuAJ4HRqnqufgrlx9WaPaLUqby9ZA/9OzbjrktjsHoQDaN+Av0dDG/nz/+2Hub4qWKaBQd4u0k/sS7Lev55cZTJHtKoVJOXrnz7ebLVYAVVLQDmAs+JSKiIjATGAh9VV15EEoC/A1eqalqVfVEiMlJEAkWkiWtod0tgRcNeRWVf/3CIjJxCfn35RSYIGW4xsoM/RSVOFm466O2mVGttRi7dWofRPMQ7z0CNBlJlkitg/fzCC+ddta0CkcuDQDBwBJgFPKCqWwBEZJSI5Fco+zwQCaypMFfoLde+psBUIA/YjzW8+xpV9dhQI1XlrSV76NwylKv7tPXUaY1GLibcQdfWYcxdZ7+BCk6nsi7rmOmWa4yq5KUjOtr62ZUe6HzYbbACqpoLjKth3zKsAQ1lP3c+Sz1bgP5ub2AdrNidw+b9x3nppn74OczdkOEeIsJNgzow+esdZOYUEB0Z6u0mlUvLzuf4qWITiBqrCnnp3MmOd0SNxtQlu2ndNIgbB3XwdlOMRmbcwA6IwNx1+73dlErWZljPh0wgMurCBKIGsmnfMVbszuHeyzoT5O/n7eYYjUz75sFcelEkc9fvs1X+udTMPFqEBtK5pX3u0gz7M4Gogby1ZA9Nm/hz+/DzH1FiGNW56eKO7M09xVobZelIzcxjUFSEGZhj1IkJRA3gi437+e/mQ1zVuw1Nm9hveK3ROPy8b1tCAv1sM2ght6CItOwC0y1n1JkJRG6WmpnHI3OsLEJfbjpocsoZDSY0yJ+f923Ll5sOcrq41NvNYV2meT5k1I8JRG62fNdRSp1Wn31Jqb0TUxq+7+ZBHTl5uoTHPt3o9Q89azPzCPATs8yJUWcmELmbq2/cIVZOObMIntGQAv2tP+EvNh70elb3JTuO0DIsiC0HqqaLNIyzM4HIzTbtPUZkaCB/uLK7ySlnNLjV6bnlOau8uTTEyj3ZbDt0kkPHT3s9IBq+xwQiN8otKGLJzqOMH9yRh8d0M0HIaHAjukSW3xWJiNfuwKcvtxZL84W1kgz7MYHIjRZuPkiJUxk70ExgNTxjcHQEMyeOoFNEMC1CA7i4k+cTjRaVONmQdQwR8DNd0kY92C7Fjy+bv34/3duE0cusSml40ODoCP54dQ9+N3sD3+/J4bJuLT16/rnr9pFTUMTT1/XidLGTEV0iTW+AUScmELnJ3txC1mbm8djVPcxkPsPjru7TlmbBAcxek+XRQFRS6uTNZGuZk3tGdjavfaNeTNecmyzYeACAsQPbe7klxoWoSYAfN17cgW+3HCa3oMhj5/1i0wGycgt5OK6rCUJGvZlA5Aaqyufr9zM0JoKOESHnPsAwGsCtwzpRVOr0WKYFp1N5Y/FuerZtyhW92njknEbjZAKRG2w9eIJdR/K5wQxSMLyoZ9twBnZqzuw1ez2SCPWrHw6x52gBD4/pisMsc2KcBxOI3GD+hgP4O4Rf9Gvn7aYYF7jbhnVi95H8Bp/Ho6r8e/EuurQK5Zq+5nVvnB8TiM5TqVNZsOEAl3dvRYtQszSy4V3X9W9PaKAfs9fsbdDzfLftCNsPneSh2K5m0UfjvNkuEIlICxGZJyIFIpIpIrefpewzIlJcYZnwfBHpUmH/QBFJFZFC178D3d3eVek5HDpxmrEXm245w/tCg/y5YWB7vtx0gBOnixvkHKkZufxl/g+0bhrIDWZwjuEGtgtEwBSgCGgDJABTRaTPWcrPUdWwCl9pACISCMwHPgYigA+A+a7tbrNgwwFCA/240jysNWzil0OjOF3sZMGGA26vOzUzj9veWcWh46fJKyxm077jbj+HceGxVSASkVDgZuBpVc1X1eXAAmBCPaqLxZon9bqqnlHVfwECjHFXe1PSspm3fj9DYiIIDjSrsBr2MKBjM3q2bcrsNVlurzslLYeiUidgjZozqXwMd7DbhNbuQImq7qywbSNw+VmOuV5EcoGDwBuqOtW1vQ+wSSsPH9rk2v511UpEZBIwCaBVq1YkJyeftaG780p5afVpShRW7Mpm+rzv6BrhG8EoPz//nNfny8z1weCIYmZsK+LeN7/hknb+bntt+ueVlH/vJxB0LJPkZPcNFze/uwuT3QJRGFA1h/xxoKacOYnANOAwMBz4TESOqeosV11V+w1qrEtVp7nqokePHhobG3vWhv6weBclrnipwJnm0cTGdj3rMXaRnJzMua7Pl5nrA8eOo8zYtprvskpYcdDptkzwwWk5sDqFcQPbM+GSGLen8jG/uwuTRwORiCRT893NCuA3QHiV7eHAyeoOUNWtFX78XkT+CYwHZgH5damrrlxr35l1hwxb2nzgx89gZdmw3RE0lu46ip9DeG5cX8KbBJxXXU6nk+zsbI4dO0ZpqbXCbLNmzdi2bdt5t9OufPn6/Pz8aN68OS1btsThcO9THY8GIlWNPdt+1zMifxHppqq7XJsHAFtqewooX55lC/CoiEiF7rn+WIMhzkupU1mw8QAdI4K5dWgnLrmopUnyaNjKiC6RBPo5KCp14nC4b3mIZbuyubhT8/MOQgD79u1DRIiJiSEgIAAR4eTJkzRt2niTBvvq9akqxcXFHD58mH379hEVFeXW+m01WEFVC4C5wHMiEioiI4GxwEfVlReRsSISIZZhwG+xRsoBJAOlwG9FJEhEHnZtX3y+7fxy0wF2H8nnT9f0MusOGbY0ODqCGROH0zTIn97twt3yGs0tKGLz/uOM7t7KDS2EgoICOnToQGBgoMlTZ3MiQmBgIB06dKCgoMDt9dsqELk8CAQDR7C62B5Q1S0AIjJKRPIrlL0V2I3V3fYh8LKqfgCgqkXAOOBO4BhwDzDOtb3eSkqdvL5oFz3bNuWavm3PpyrDaFBDY1rwq5ExbNp/nAPHTp13fct3Z6MKo9yY3dvdXTxGw2qo35fdBiugqrlYAaS6fcuwBiGU/XzbOepaDwx2Z/s+33CA9OwC3p4w2OTXMmxv/OBO/Gvxbuau28fDY7qdV11Ldx6lWXAA/Tt6fvE9o3EzH0fqoLjUyb++20Wf9uFc1dtMYDXsLyoyhOGdW/Bp6r7zSoSqqizbdZTLurY0KX0MtzOBqA7mrttHVm4hf7iyu+nTNnzGLUM6kZFTyJqM+idC3Xk4n8MnzjC6u2dXfzXq5plnnqFv377ebkadmUBUS0UlTv713W4GdGrOmJ6tvd0cw6i1a/u1JTTQj0/W1j8R6tKdRwEY1c09AxUag3Xr1uHn58fIkSPrdFxsbCwPP/zwuQteQEwgqqVXv93B/mOnuGFAO3M3ZPiUkEB/ruvfnoWbD1JwpuTcB1Rj6a6jdG0dRvvmwW5unRvMmAExMeBwWP/OmOGR006fPp0HH3yQH374wWfnBtmFCUS1sCoth2lL0wB45ZsdDb7Wi2G42y1DOlJYVMrCzQfrfOzp4lJWp+cy2o53QzNmwKRJkJkJqta/kyY1eDA6deoUM2fOZNKkSYwfP55333230v6UlBTGjBlDaGgozZo1Y8yYMRw4cIBf//rXLFmyhClTpiAiiAgZGRkkJycjImRnZ5fXkZGRgYiwdu1aAEpLS7n33nvp3LkzwcHBdOvWjcmTJ+N0Ohv0Wj3BdqPm7Oj97zPKv3fnLHXD8JTB0RF0aRnKp2v3ET+kU52OXZ2ey5kSJ6M88Hwo6IknYOvWcxcsk5ICZ85U3lZYCPfeC++8U7s6Bg6E11+v/TmBTz/9lOjoaPr168eECROIj4/nxRdfJCAggI0bNxIXF8eECRN47bXXCAoKYunSpZSUlPDyyy+Tnp5Oz549+fvf/w5YuS0zMjLOeU6n00mHDh1ITEykVatWrF69mkmTJhEZGcm9995bp/bbjQlE5+B0Kpv2HUMw6XwM3yUijB/Skclf7yAju4CYlqG1PnbpzqME+jkY0dmGr/uqQehc293k3XffZcIEa1GAyy+/nJCQEObPn8/48eOZPHkyAwcOZNq0aeXle/XqBViZFQIDAwkJCaFt27rNQwwICOC5554r/zkmJoZ169Yxa9YsE4gau0XbDrP/2GkeuaIbAX5WEDJ3Q4YvunlQR175egdPzt3EY1f3rPXreNmubIZ29sxSJ2defpnAuqTAiYmxuuOqio6GBspyvXv3bpYvX87MmTMBK8gnJCTw7rvvMn78eNavX8+NN97YIOd+6623mD59OpmZmZw6dYri4mKio6Mb5FyeZALRWagqby3ZQ6cWwTwc1xV/P/NIzfBd+/JOIQIpabkkTE+pVUbuQ8dPs+PwSW4a1NNDrayjF16wngkVFv64LSTE2t5Apk+fTmlpaaV8a2VztPburd/IxLKMBRXnehUXV15hd86cOTzyyCO8+uqrXHrppYSHhzNlyhTmzZtXr3PaiQlEZ7EmI491Wcd4bmwfE4QMn5eSlkPZ+9yZ4to961y2y+bDthMSrH+fegqysiAqygpCZdvdrKSkhA8++IAXX3yR6667rtK+CRMm8N5773HxxRezeHHNKS0DAwPLs42XadXK+v89ePBg+fcbNmyoVGb58uUMHz680tDvPXv2nNf12IUJRGfx1pI9tAgN5JbBdXu4axh2NKJLJEEBDk4XO1EgOjLknMcs3ZVNy7AgerWzccbohIQGCzxVLVy4kOzsbCZOnEhkZOVnZrfeeitvvfUWc+fO5ZJLLmHSpEk89NBDNGnShGXLlnHVVVcRERFBTEwMq1evJiMjg7CwMFq0aEHXrl3p1KkTzzzzDC+99BIZGRk8//zzlerv3r0777//Pl990qS+MgAAEfZJREFU9RVdu3Zl9uzZLFmyhIgI339UYD7m12DHoZMs3n6Euy+NMcuAG43C4OgIZtw3ggdiLyIk0I8Pv8/E6aw57Y/TqSRvP0ybpkGsyzrmwZba17vvvktcXNxPghDALbfcQkZGBkePHmXRokVs376dESNGMHz4cGbPnk1AgLV0xh//+EcCAwPp3bs3rVq1Iisri4CAAGbPnk1aWhoDBgzgb3/7W/moujL3338/8fHx3H777QwdOpSMjAweffRRj1x3Q5PzyT/VWPXo0UN/8dwcvt5yiO+fHEPzkEBvN8mtGvsqkeb6zu2TtXt57NNNPDe2D3deElNtmRcWbuWdZekIEBTgcNsqr2W2bdtWPpqsjK+u11NbjeH6qvu9lRGRVFUdUtc6zR1RNUqcMH/jAW4bFtXogpBhAIwf3JHR3Vvx0lfb2ZtbWGmfqvL6op28syzd+pkf588ZRkMwgagaJ4oUAe69rLO3m2IYDUJEePGmfjhEeHLupvLRWiWlTv48bzOvL9pFbPdWNAlw4GfmzxkNzAxWqMaJIuUXXVvaM6+WYbhJh+bB/Onanjw17wfmrNnL2IEd+M2s9SzadpiH4i7ij1f1YF3WMVLScsz8OaNBmUBUg5VpOaRm5pk/PqNRu21oFF9uPMhzX2zln9/t4uDx05WeGw2OjjB/A0aDs13XnIi0EJF5IlIgIpkicvtZyn4lIvkVvopEZHOF/RkicqrC/m9r246SUtMnbjR+Dodwx4goCotLOXj8NAF+Qp/2zbzdLOMCY8c7oilAEdAGGAgsFJGNqrqlakFVvabizyL/v727D66qTg84/n0uCTcQQOQlYZN1iQHCBpCXqKjlRRxxgEZLW3xDy0oVY8PLiNYdnUEBxQFxeIndihVkJcIOW8eltGqxM0JSXioOL2JtiKLIxhqSdrUQEgKEhad/nJOQXAIkN/fm3HN5PjNnyP39zjk8T+69ee4953d+R4qB0CvJ7lHVj1sbhB0TN1eL3/9YS0DgvDpDtm1SX9PeYuobkYgkA1OAF1S1RlV3Av8CTGvBthnAGOCdtsZxbVAiPlTVmFh1a2ZPOibYoATjnVj7RpQF/FFVDzVq+xy4vQXb/gLYoaq/D2n/jYgEgM+AX6rq51fa0TVBsSJkrhr1F7raoATjlVgrRF2AEyFtVUBLrgD7BfBySNvDwH5AgCeBfxORn6vqRZeJi0gekAfOvE/FUZq5NxbU1NRYfj4WrfwGC1Qf+Z7iIxHfdbOuueYaqqurm7SdO3fuorZ4Eg/5nT59OvKvP1VttwUoxrk+rrllJzACqA3Z5m+B96+w39FADdDlCut9iXPO6LJxZmVlaTwrKiryOoSosvz84eDBgxe1nThxokXbLl2qum3b5dfZts1ZL5a0NL9Y1tzzVg/Yq2HUhnY9R6Sq41RVLrGMBg4BCSIyoNFmw4CLBiqEeATYpKo1VwoB59uRMcbHbr4Z7r8fioqa7y8qcvpvvjk6///06dMbbvXdeAmdMdtPRIT33nvPk/87pgYrqOpJYBPwkogki8goYDKw/lLbiEgn4H5gXUj7z0RklIh0FJEkEfkl0AvYFbUEjDHt4o474N13my9G9UXo3Xed9aJl/PjxVFRUNFmGDBnS6v3U1dVFITp/ialC5JoJdAL+F9gI5Ks7dFtExohI6LeePweOA6GfjboCbwDHgHJgIjBJVe3iIGPiQHPFqL2KEEAwGKRPnz5NloSEBLZv384tt9xCUlISqampPPXUU02Kzbhx48jPz+eZZ56hd+/ejBo1CoCDBw+Sm5tL165dSUlJYerUqVRWVjb5PwsLC7nhhhsIBoOkpqbyyCOPNPStWLGCoUOHkpycTHp6OjNmzOD48Qunw6uqqpg2bRopKSkkJSWRmZlJQUEB4Nx2HJwZxEWk4XF7ibXBCqjq/+EUl+b6duAMaGjcthGnYIWuWwIMjUaMxpjoePbZIAcPtm6btDSYMAF+8hOoqIDsbHjxRWdpieHDwf173Gbl5eVMmjSJadOmsW7dOg4fPsyMGTMIBAIsX768Yb0NGzaQl5fHjh07UFUqKioYO3Ysjz32GMuWLePs2bPMmzePyZMn88knnxAIBHjzzTd58sknWbx4Mbm5udTU1DS5AV8gEKCgoIDMzEzKysqYM2cOc+bMYf1654DS888/zxdffMEHH3xAamoqR44c4Q9/cG58uGfPHlJSUlizZg133303HTq0761vYq4QGWNMa1x7rVOE6m/Q2l73ifvoo4/o0uXC5+IxY8aQk5NDWloaq1atIhAIkJ2dzSuvvMITTzzBokWLGta9/vrrmxSm+fPnM2zYMJYuXdrQ9s4779CjRw/27t3LyJEjWbRoEXPnzuXpp59uWOfGG29s+Hnu3LkNP2dkZPDqq68yefJkCgsLCQQClJWVkZOTw8iRIwHo27dvw/r1d4Xt3r07ffr0icSvp1WsEBljYsbSpWfo2rV1t16pPxz3wgvwxhuwYEH0D8sBjB07ltWrVzc87tSpE3PmzOHWW28lELhw1mP06NHU1dXxzTffcP31zoz+jQsIwL59+9i+fXuTwlbv8OHDZGRkUF5ezp133nnJeLZt28aSJUsoLS2lqqqKc+fOUVdXR2VlJWlpaeTn53Pvvfeyb98+7rrrLu655x5uv70ll2hGnxUiY4xvhZ4TuuOO9jtH1LlzZ/r379/i9UUuDNhNTk5u0nf+/Hlyc3NZtmzZRdulpqZy6tSpy+67rKyM3NxcHn/8cV566SV69uzJ/v37mTp1asP5qUmTJlFWVsaWLVvYunUrubm53Hfffbz99tstziFaYnGwgjHGXFFzAxMuN5quPWRnZ7N7927Onz/f0LZz5046duxIv379LrldTk4OJSUl9O3bl/79+zdZ6gcvpKens3Xr1ma337t3L3V1daxcuZLbbruNrKwsjh49etF6vXr1ajh/tXbtWgoLCzlz5gwAiYmJnDt3ro2/gfBYITLG+M7lRsd5WYxmzpzJ0aNHmTlzJqWlpXz44Yc899xzzJ49m86dO19yu1mzZlFVVcUDDzzAp59+yrfffsvHH39MXl5ew0wM8+bNo6CggJUrV3Lo0CEOHDjQcJ5pwIABnD9/noKCAo4cOcLGjRsbRsTVmz9/Pps3b+brr7+mtLSUTZs2kZmZSTAYBJzzSlu3bqWyspJjx45F6TfUPCtExhjf2bPn8off6ovRnj3tG1d6ejpbtmzhs88+Y/jw4Tz66KNMnTqVxYsXX3a7tLQ0du3aRSAQYOLEiQwePJhZs2YRDAYbCkV+fj6vv/46a9asYciQIUycOJGSEuda/6FDh/Laa6+xYsUKBg0axFtvvXXRYb5gMMi8efMYNmwYo0aNorq6mvfff7+hf/ny5RQVFXHdddcxYsSICP9mLk/UvUWwuWDgwIH61VdfeR1G1BQXFzNu3Divw4gay88fSktLyc7ObtJWXV1N164tmVrSn+Ihv+aet3oisk9Vb2rtPu0bkTHGGE9ZITLGGOMpK0TGGGM8ZYXIGGOMp6wQGWM8Y4Ol/CVaz5cVImOMJxITE684Y4CJLadOnSIxMTHi+7VCZIzxREpKCuXl5dTW1to3oxinqtTW1lJeXk5KSkrE929zzRljPNGtWzcAjh49ytmzZwE4ffo0SUlJXoYVVX7OLzExkdTU1IbnLZKsEBljPNOtW7cmf9iKi4vb/ar+9hTv+YXLDs0ZY4zxlBUiY4wxnoq5QiQis0Vkr4icEZF1LVj/KRGpFJETIvJrEQk26ssQkSIRqRWRL0VkfFSDN8YY02oxV4iAo8DLwK+vtKKITACeA+4E+gKZQOM71W8EPgN6AvOA90Skd6QDNsYYE76YK0SquklVNwM/tmD1R4C1qlqiqseARcB0ABHJAnKABap6SlV/B3wBTIlO5MYYY8Lh91Fzg4F/bvT4cyBVRHq6fd+qanVI/+DmdiQieUCe+/CMiPxXFOKNFb2AH7wOIoosP/+K59wg/vMbGM5Gfi9EXYCqRo/rf+7aTF99f3pzO1LV1cBqABHZG849NfzC8vO3eM4vnnODqyO/cLZr10NzIlIsInqJZWcYu6wBGl9dVf9zdTN99f3VGGOMiRntWohUdZyqyiWW0WHssgQY1ujxMOB/VPVHty9TRLqG9JeEn4ExxphIi7nBCiKSICJJQAegg4gkicilDiG+AzwmIoNEpDvwPLAOQFUPAQeABe4+/gIYCvyuBWGsbmseMc7y87d4zi+ecwPLr1kSa5MNishCYEFI84uqulBEfgYcBAap6nfu+k8DzwKdcIrM36jqGbcvA6cw3QJ8B8xS1Y+jn4UxxpiWirlCZIwx5uoSc4fmjDHGXF2sEBljjPGUFaJGRKSHiPyTiJwUkTIRecjrmNricvP2icid7vx7te58fH09CjMsIhIUkbXu81QtIgdEZFKjfl/nByAiG0Skwp1H8ZCIzGjU5/v8AERkgIicFpENjdoecp/XkyKyWUR6eBljuNzLVU6LSI27fNWoz/c5isiDIlLq5nBYRMa47a1+bVohaup1oA5IBR4G3hCRZmdi8Ilm5+0TkV7AJuAFoAewF/jHdo+ubRKA/wZuB67BGTH5rjvRbTzkB7AEyFDVbsCfAS+LyI1xlB8477k99Q/c99ubwDSc92EtsMqb0CJitqp2cZeBEB85ishdwFLgr3EmEBgLfBvua9MGK7hEJBk4Bgxxh34jIuuBclV9ztPg2khEXgZ+qqrT3cd5wHRV/RP3cTLOtCMjVPVLzwJtIxH5T5xJb3sSZ/mJyECgGHgS6E4c5CciDwJ/iTMStr+q/pWILMYpvg+56/QDSoGeIdN1xTwRKQY2qOpbIe2+z1FE/gNnns+1Ie1h/W2xb0QXZAF/rC9CrkvOTedzg3FyA0BVTwKH8XGuIpKK8xyWEEf5icgqEakFvgQqgH8lDvITkW7AS8DTIV2huR3GOUqR1X7RRdQSEflBRHaJyDi3zdc5ikgH4Cagt4h8IyLfi8jfi0gnwnxtWiG6oAtwIqStCudrZ7y51Dx8vsxVRBKB3wCF7qeuuMlPVWfixD0G55DHGeIjv0U4n6i/D2mPh9zqPYtza5p0nAs933e//fg9x1QgEbgX53U5HBiBc3g8rNysEF1wNc1NFze5ikgAWI/ziXK22xw3+QGo6jlV3Qn8FMjH5/mJyHBgPLCymW5f59aYqn6qqtWqekZVC4FdwJ/i/xxPuf/+SlUrVPUHYAVtyM0K0QWHgAQRGdCoLV7npmsyR597HLcfPstVRARYi/MJbYqqnnW74iK/ZiRwIQ8/5zcOyAC+E5FK4Blgiojs5+LcMoEgzvvT7xQQfJ6je++373HyaWh2/w3vtamqtrgL8Fucu7omA6NwvlIO9jquNuSTACThjL5a7/6cAPR2c5viti0Fdnsdbxj5/QOwG+gS0u77/IAU4EGcQx0dgAnASZzRc77OD+gM9Gm0LAPec/MajHOIfIz7PtwA/NbrmMPIsbv7nNW/5x52n7+seMgR5/zeHvd1ei2wA+dwa1ivTc8TiqUFZ7jhZvcF8x3wkNcxtTGfhTifVBovC92+8TgnwE/hjMbK8DreVubW183nNM7hgPrl4TjJrzfw78Bx94/WF8Djjfp9nV9IrgtxRpfVP37Iff+dxLnxZQ+vYwzz+duDc0jqOM4HprviJUecc0Sr3Nwqgb8Dkty+Vr82bfi2McYYT9k5ImOMMZ6yQmSMMcZTVoiMMcZ4ygqRMcYYT1khMsYY4ykrRMYYYzxlhcgYY4ynrBAZ4xMi0k1EFopIttexGBNJVoiM8Y+bgAU4V7UbEzesEBnjHyNwbgNx0OtAjIkkm+LHGB8QkVLg5yHNm1R1ihfxGBNJVoiM8QERuRlndvgSYLHbXKGqZd5FZUxkJHgdgDGmRT7HuTHer1R1t9fBGBNJdo7IGH8YDHQE9nsdiDGRZoXIGH/Iwbn/0gGvAzEm0qwQGeMPI4DDqnrC60CMiTQrRMb4wyBs2LaJUzZYwRh/OA7kiMgEoAr4WlV/9DgmYyLChm8b4wMiMgRYCwwFkoAxqrrT26iMiQwrRMYYYzxl54iMMcZ4ygqRMcYYT1khMsYY4ykrRMYYYzxlhcgYY4ynrBAZY4zxlBUiY4wxnrJCZIwxxlP/D6lVsKWY7IbJAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -1339,47 +1414,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 7s 989us/sample - loss: 0.1936 - last_time_step_mse: 0.1913 - val_loss: 0.0901 - val_last_time_step_mse: 0.0863\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.1929 - last_time_step_mse: 0.1902 - val_loss: 0.0877 - val_last_time_step_mse: 0.0832\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0531 - last_time_step_mse: 0.0441 - val_loss: 0.0559 - val_last_time_step_mse: 0.0469\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0537 - last_time_step_mse: 0.0449 - val_loss: 0.0549 - val_last_time_step_mse: 0.0462\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 6s 823us/sample - loss: 0.0470 - last_time_step_mse: 0.0374 - val_loss: 0.0453 - val_last_time_step_mse: 0.0354\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0471 - last_time_step_mse: 0.0375 - val_loss: 0.0451 - val_last_time_step_mse: 0.0358\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0437 - last_time_step_mse: 0.0337 - val_loss: 0.0423 - val_last_time_step_mse: 0.0320\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0437 - last_time_step_mse: 0.0337 - val_loss: 0.0418 - val_last_time_step_mse: 0.0314\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 6s 820us/sample - loss: 0.0414 - last_time_step_mse: 0.0310 - val_loss: 0.0402 - val_last_time_step_mse: 0.0301\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0409 - last_time_step_mse: 0.0306 - val_loss: 0.0391 - val_last_time_step_mse: 0.0287\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 6s 818us/sample - loss: 0.0390 - last_time_step_mse: 0.0283 - val_loss: 0.0377 - val_last_time_step_mse: 0.0261\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0385 - last_time_step_mse: 0.0275 - val_loss: 0.0379 - val_last_time_step_mse: 0.0273\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 6s 822us/sample - loss: 0.0368 - last_time_step_mse: 0.0255 - val_loss: 0.0369 - val_last_time_step_mse: 0.0255\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0366 - last_time_step_mse: 0.0254 - val_loss: 0.0367 - val_last_time_step_mse: 0.0248\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0354 - last_time_step_mse: 0.0240 - val_loss: 0.0369 - val_last_time_step_mse: 0.0244\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0349 - last_time_step_mse: 0.0235 - val_loss: 0.0363 - val_last_time_step_mse: 0.0249\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 6s 824us/sample - loss: 0.0342 - last_time_step_mse: 0.0227 - val_loss: 0.0361 - val_last_time_step_mse: 0.0233\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0338 - last_time_step_mse: 0.0221 - val_loss: 0.0332 - val_last_time_step_mse: 0.0208\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 6s 817us/sample - loss: 0.0332 - last_time_step_mse: 0.0216 - val_loss: 0.0340 - val_last_time_step_mse: 0.0223\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0329 - last_time_step_mse: 0.0214 - val_loss: 0.0335 - val_last_time_step_mse: 0.0214\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 6s 819us/sample - loss: 0.0324 - last_time_step_mse: 0.0210 - val_loss: 0.0338 - val_last_time_step_mse: 0.0229\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0322 - last_time_step_mse: 0.0206 - val_loss: 0.0323 - val_last_time_step_mse: 0.0203\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 6s 824us/sample - loss: 0.0316 - last_time_step_mse: 0.0202 - val_loss: 0.0332 - val_last_time_step_mse: 0.0214\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0316 - last_time_step_mse: 0.0198 - val_loss: 0.0333 - val_last_time_step_mse: 0.0210\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0309 - last_time_step_mse: 0.0192 - val_loss: 0.0307 - val_last_time_step_mse: 0.0187\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0310 - last_time_step_mse: 0.0191 - val_loss: 0.0310 - val_last_time_step_mse: 0.0187\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 6s 819us/sample - loss: 0.0305 - last_time_step_mse: 0.0189 - val_loss: 0.0322 - val_last_time_step_mse: 0.0198\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0305 - last_time_step_mse: 0.0186 - val_loss: 0.0310 - val_last_time_step_mse: 0.0189\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 6s 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[==============================] - 6s 821us/sample - loss: 0.0288 - last_time_step_mse: 0.0169 - val_loss: 0.0284 - val_last_time_step_mse: 0.0159\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0289 - last_time_step_mse: 0.0168 - val_loss: 0.0295 - val_last_time_step_mse: 0.0174\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 6s 818us/sample - loss: 0.0286 - last_time_step_mse: 0.0166 - val_loss: 0.0284 - val_last_time_step_mse: 0.0156\n", + "219/219 [==============================] - 6s 29ms/step - loss: 0.0286 - last_time_step_mse: 0.0168 - val_loss: 0.0290 - val_last_time_step_mse: 0.0163\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 6s 818us/sample - loss: 0.0282 - last_time_step_mse: 0.0162 - val_loss: 0.0323 - val_last_time_step_mse: 0.0214\n" + "219/219 [==============================] - 6s 29ms/step - loss: 0.0281 - last_time_step_mse: 0.0161 - val_loss: 0.0288 - val_last_time_step_mse: 0.0164\n" ] } ], @@ -1451,47 +1525,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.1567 - last_time_step_mse: 0.1508 - val_loss: 0.0714 - val_last_time_step_mse: 0.0660\n", + "219/219 [==============================] - 16s 72ms/step - loss: 0.1580 - last_time_step_mse: 0.1509 - val_loss: 0.0712 - val_last_time_step_mse: 0.0636\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0629 - last_time_step_mse: 0.0561 - val_loss: 0.0564 - val_last_time_step_mse: 0.0495\n", + "219/219 [==============================] - 15s 70ms/step - loss: 0.0621 - last_time_step_mse: 0.0495 - val_loss: 0.0561 - val_last_time_step_mse: 0.0422\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0539 - last_time_step_mse: 0.0466 - val_loss: 0.0505 - val_last_time_step_mse: 0.0433\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0502 - last_time_step_mse: 0.0352 - val_loss: 0.0465 - val_last_time_step_mse: 0.0300\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0495 - last_time_step_mse: 0.0421 - val_loss: 0.0472 - val_last_time_step_mse: 0.0401\n", + "219/219 [==============================] - 15s 70ms/step - loss: 0.0456 - last_time_step_mse: 0.0299 - val_loss: 0.0424 - val_last_time_step_mse: 0.0263\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0448 - last_time_step_mse: 0.0369 - val_loss: 0.0410 - val_last_time_step_mse: 0.0311\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0413 - last_time_step_mse: 0.0247 - val_loss: 0.0398 - val_last_time_step_mse: 0.0219\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0391 - last_time_step_mse: 0.0287 - val_loss: 0.0391 - val_last_time_step_mse: 0.0278\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0382 - last_time_step_mse: 0.0219 - val_loss: 0.0360 - val_last_time_step_mse: 0.0194\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0365 - last_time_step_mse: 0.0254 - val_loss: 0.0346 - val_last_time_step_mse: 0.0235\n", + "219/219 [==============================] - 16s 71ms/step - loss: 0.0353 - last_time_step_mse: 0.0195 - val_loss: 0.0336 - val_last_time_step_mse: 0.0181\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0343 - last_time_step_mse: 0.0227 - val_loss: 0.0335 - val_last_time_step_mse: 0.0227\n", + "219/219 [==============================] - 15s 70ms/step - loss: 0.0328 - last_time_step_mse: 0.0174 - val_loss: 0.0337 - val_last_time_step_mse: 0.0186\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0331 - last_time_step_mse: 0.0214 - val_loss: 0.0319 - val_last_time_step_mse: 0.0204\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0323 - last_time_step_mse: 0.0166 - val_loss: 0.0327 - val_last_time_step_mse: 0.0167\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0316 - last_time_step_mse: 0.0195 - val_loss: 0.0302 - val_last_time_step_mse: 0.0181\n", + "219/219 [==============================] - 15s 70ms/step - loss: 0.0315 - last_time_step_mse: 0.0157 - val_loss: 0.0299 - val_last_time_step_mse: 0.0137\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0309 - last_time_step_mse: 0.0190 - val_loss: 0.0298 - val_last_time_step_mse: 0.0180\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0295 - last_time_step_mse: 0.0144 - val_loss: 0.0290 - val_last_time_step_mse: 0.0140\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0300 - last_time_step_mse: 0.0177 - val_loss: 0.0301 - val_last_time_step_mse: 0.0179\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0289 - last_time_step_mse: 0.0141 - val_loss: 0.0295 - val_last_time_step_mse: 0.0161\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0294 - last_time_step_mse: 0.0171 - val_loss: 0.0286 - val_last_time_step_mse: 0.0165\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0279 - last_time_step_mse: 0.0131 - val_loss: 0.0267 - val_last_time_step_mse: 0.0120\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0291 - last_time_step_mse: 0.0168 - val_loss: 0.0281 - val_last_time_step_mse: 0.0154\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0272 - last_time_step_mse: 0.0126 - val_loss: 0.0267 - val_last_time_step_mse: 0.0127\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0284 - last_time_step_mse: 0.0161 - val_loss: 0.0316 - val_last_time_step_mse: 0.0197\n", + "219/219 [==============================] - 15s 70ms/step - loss: 0.0268 - last_time_step_mse: 0.0124 - val_loss: 0.0261 - val_last_time_step_mse: 0.0121\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0283 - last_time_step_mse: 0.0159 - val_loss: 0.0282 - val_last_time_step_mse: 0.0165\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0263 - last_time_step_mse: 0.0121 - val_loss: 0.0261 - val_last_time_step_mse: 0.0116\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0279 - last_time_step_mse: 0.0155 - val_loss: 0.0274 - val_last_time_step_mse: 0.0153\n", + "219/219 [==============================] - 16s 71ms/step - loss: 0.0258 - last_time_step_mse: 0.0113 - val_loss: 0.0267 - val_last_time_step_mse: 0.0124\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0277 - last_time_step_mse: 0.0156 - val_loss: 0.0271 - val_last_time_step_mse: 0.0144\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0269 - last_time_step_mse: 0.0126 - val_loss: 0.0264 - val_last_time_step_mse: 0.0124\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0276 - last_time_step_mse: 0.0151 - val_loss: 0.0267 - val_last_time_step_mse: 0.0142\n", + "219/219 [==============================] - 15s 71ms/step - loss: 0.0248 - last_time_step_mse: 0.0103 - val_loss: 0.0243 - val_last_time_step_mse: 0.0098\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0273 - last_time_step_mse: 0.0149 - val_loss: 0.0267 - val_last_time_step_mse: 0.0141\n" + "219/219 [==============================] - 16s 71ms/step - loss: 0.0239 - last_time_step_mse: 0.0095 - val_loss: 0.0237 - val_last_time_step_mse: 0.0098\n" ] } ], @@ -1559,47 +1632,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.2113 - last_time_step_mse: 0.2018 - val_loss: 0.0844 - val_last_time_step_mse: 0.0756\n", + "219/219 [==============================] - 14s 66ms/step - loss: 0.1988 - last_time_step_mse: 0.1942 - val_loss: 0.0773 - val_last_time_step_mse: 0.0724\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0710 - last_time_step_mse: 0.0599 - val_loss: 0.0621 - val_last_time_step_mse: 0.0490\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0694 - last_time_step_mse: 0.0627 - val_loss: 0.0632 - val_last_time_step_mse: 0.0566\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0573 - last_time_step_mse: 0.0442 - val_loss: 0.0533 - val_last_time_step_mse: 0.0404\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0587 - last_time_step_mse: 0.0501 - val_loss: 0.0537 - val_last_time_step_mse: 0.0426\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0504 - last_time_step_mse: 0.0364 - val_loss: 0.0477 - val_last_time_step_mse: 0.0329\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0505 - last_time_step_mse: 0.0387 - val_loss: 0.0473 - val_last_time_step_mse: 0.0351\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0458 - last_time_step_mse: 0.0316 - val_loss: 0.0438 - val_last_time_step_mse: 0.0296\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0454 - last_time_step_mse: 0.0333 - val_loss: 0.0432 - val_last_time_step_mse: 0.0314\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0424 - last_time_step_mse: 0.0284 - val_loss: 0.0409 - val_last_time_step_mse: 0.0273\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0420 - last_time_step_mse: 0.0296 - val_loss: 0.0396 - val_last_time_step_mse: 0.0263\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0398 - last_time_step_mse: 0.0261 - val_loss: 0.0388 - val_last_time_step_mse: 0.0244\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0381 - last_time_step_mse: 0.0246 - val_loss: 0.0367 - val_last_time_step_mse: 0.0231\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0381 - last_time_step_mse: 0.0243 - val_loss: 0.0367 - val_last_time_step_mse: 0.0236\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0358 - last_time_step_mse: 0.0220 - val_loss: 0.0355 - val_last_time_step_mse: 0.0228\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0360 - last_time_step_mse: 0.0223 - val_loss: 0.0348 - val_last_time_step_mse: 0.0212\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0341 - last_time_step_mse: 0.0204 - val_loss: 0.0341 - val_last_time_step_mse: 0.0204\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0342 - last_time_step_mse: 0.0203 - val_loss: 0.0342 - val_last_time_step_mse: 0.0195\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0330 - last_time_step_mse: 0.0194 - val_loss: 0.0320 - val_last_time_step_mse: 0.0182\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0331 - last_time_step_mse: 0.0192 - val_loss: 0.0320 - val_last_time_step_mse: 0.0185\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0319 - last_time_step_mse: 0.0184 - val_loss: 0.0310 - val_last_time_step_mse: 0.0175\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0321 - last_time_step_mse: 0.0184 - val_loss: 0.0313 - val_last_time_step_mse: 0.0173\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0312 - last_time_step_mse: 0.0180 - val_loss: 0.0313 - val_last_time_step_mse: 0.0191\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0310 - last_time_step_mse: 0.0176 - val_loss: 0.0304 - val_last_time_step_mse: 0.0169\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0301 - last_time_step_mse: 0.0170 - val_loss: 0.0297 - val_last_time_step_mse: 0.0169\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0304 - last_time_step_mse: 0.0170 - val_loss: 0.0298 - val_last_time_step_mse: 0.0165\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0292 - last_time_step_mse: 0.0162 - val_loss: 0.0287 - val_last_time_step_mse: 0.0157\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0296 - last_time_step_mse: 0.0166 - val_loss: 0.0291 - val_last_time_step_mse: 0.0160\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0286 - last_time_step_mse: 0.0157 - val_loss: 0.0284 - val_last_time_step_mse: 0.0155\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0293 - last_time_step_mse: 0.0162 - val_loss: 0.0288 - val_last_time_step_mse: 0.0163\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0281 - last_time_step_mse: 0.0154 - val_loss: 0.0275 - val_last_time_step_mse: 0.0146\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0291 - last_time_step_mse: 0.0161 - val_loss: 0.0285 - val_last_time_step_mse: 0.0163\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0275 - last_time_step_mse: 0.0149 - val_loss: 0.0272 - val_last_time_step_mse: 0.0142\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0284 - last_time_step_mse: 0.0158 - val_loss: 0.0282 - val_last_time_step_mse: 0.0153\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0270 - last_time_step_mse: 0.0145 - val_loss: 0.0266 - val_last_time_step_mse: 0.0142\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0281 - last_time_step_mse: 0.0154 - val_loss: 0.0274 - val_last_time_step_mse: 0.0144\n", + "219/219 [==============================] - 14s 65ms/step - loss: 0.0266 - last_time_step_mse: 0.0143 - val_loss: 0.0263 - val_last_time_step_mse: 0.0137\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0287 - last_time_step_mse: 0.0164 - val_loss: 0.0282 - val_last_time_step_mse: 0.0153\n" + "219/219 [==============================] - 14s 66ms/step - loss: 0.0261 - last_time_step_mse: 0.0138 - val_loss: 0.0264 - val_last_time_step_mse: 0.0145\n" ] } ], @@ -1637,47 +1709,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 3s 412us/sample - loss: 0.0760 - last_time_step_mse: 0.0615 - val_loss: 0.0554 - val_last_time_step_mse: 0.0372\n", + "219/219 [==============================] - 1s 7ms/step - loss: 0.0760 - last_time_step_mse: 0.0615 - val_loss: 0.0554 - val_last_time_step_mse: 0.0364\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0480 - last_time_step_mse: 0.0281 - val_loss: 0.0423 - val_last_time_step_mse: 0.0211\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0480 - last_time_step_mse: 0.0283 - val_loss: 0.0427 - val_last_time_step_mse: 0.0222\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0390 - last_time_step_mse: 0.0182 - val_loss: 0.0371 - val_last_time_step_mse: 0.0164\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0391 - last_time_step_mse: 0.0181 - val_loss: 0.0367 - val_last_time_step_mse: 0.0157\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0350 - last_time_step_mse: 0.0151 - val_loss: 0.0335 - val_last_time_step_mse: 0.0143\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0350 - last_time_step_mse: 0.0151 - val_loss: 0.0334 - val_last_time_step_mse: 0.0132\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0325 - last_time_step_mse: 0.0136 - val_loss: 0.0314 - val_last_time_step_mse: 0.0124\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0325 - last_time_step_mse: 0.0133 - val_loss: 0.0314 - val_last_time_step_mse: 0.0121\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0308 - last_time_step_mse: 0.0123 - val_loss: 0.0297 - val_last_time_step_mse: 0.0108\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0308 - last_time_step_mse: 0.0122 - val_loss: 0.0298 - val_last_time_step_mse: 0.0112\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0297 - last_time_step_mse: 0.0117 - val_loss: 0.0289 - val_last_time_step_mse: 0.0106\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0297 - last_time_step_mse: 0.0118 - val_loss: 0.0291 - val_last_time_step_mse: 0.0120\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0286 - last_time_step_mse: 0.0108 - val_loss: 0.0279 - val_last_time_step_mse: 0.0103\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0286 - last_time_step_mse: 0.0109 - val_loss: 0.0278 - val_last_time_step_mse: 0.0099\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0280 - last_time_step_mse: 0.0106 - val_loss: 0.0279 - val_last_time_step_mse: 0.0111\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0280 - last_time_step_mse: 0.0108 - val_loss: 0.0278 - val_last_time_step_mse: 0.0113\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0274 - last_time_step_mse: 0.0105 - val_loss: 0.0270 - val_last_time_step_mse: 0.0103\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0273 - last_time_step_mse: 0.0105 - val_loss: 0.0268 - val_last_time_step_mse: 0.0101\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0268 - last_time_step_mse: 0.0100 - val_loss: 0.0264 - val_last_time_step_mse: 0.0097\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0269 - last_time_step_mse: 0.0102 - val_loss: 0.0263 - val_last_time_step_mse: 0.0096\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0264 - last_time_step_mse: 0.0099 - val_loss: 0.0263 - val_last_time_step_mse: 0.0108\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0264 - last_time_step_mse: 0.0101 - val_loss: 0.0263 - val_last_time_step_mse: 0.0105\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0259 - last_time_step_mse: 0.0096 - val_loss: 0.0257 - val_last_time_step_mse: 0.0094\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0259 - last_time_step_mse: 0.0097 - val_loss: 0.0257 - val_last_time_step_mse: 0.0100\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0257 - last_time_step_mse: 0.0096 - val_loss: 0.0264 - val_last_time_step_mse: 0.0110\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0257 - last_time_step_mse: 0.0096 - val_loss: 0.0252 - val_last_time_step_mse: 0.0091\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0253 - last_time_step_mse: 0.0095 - val_loss: 0.0253 - val_last_time_step_mse: 0.0096\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0253 - last_time_step_mse: 0.0095 - val_loss: 0.0251 - val_last_time_step_mse: 0.0092\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0250 - last_time_step_mse: 0.0092 - val_loss: 0.0248 - val_last_time_step_mse: 0.0091\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0251 - last_time_step_mse: 0.0095 - val_loss: 0.0248 - val_last_time_step_mse: 0.0089\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0247 - last_time_step_mse: 0.0092 - val_loss: 0.0245 - val_last_time_step_mse: 0.0087\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0248 - last_time_step_mse: 0.0094 - val_loss: 0.0248 - val_last_time_step_mse: 0.0098\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0245 - last_time_step_mse: 0.0092 - val_loss: 0.0242 - val_last_time_step_mse: 0.0092\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0245 - last_time_step_mse: 0.0093 - val_loss: 0.0246 - val_last_time_step_mse: 0.0091\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 1s 153us/sample - loss: 0.0242 - last_time_step_mse: 0.0089 - val_loss: 0.0237 - val_last_time_step_mse: 0.0084\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0242 - last_time_step_mse: 0.0091 - val_loss: 0.0238 - val_last_time_step_mse: 0.0085\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 1s 152us/sample - loss: 0.0239 - last_time_step_mse: 0.0089 - val_loss: 0.0240 - val_last_time_step_mse: 0.0086\n" + "219/219 [==============================] - 1s 5ms/step - loss: 0.0239 - last_time_step_mse: 0.0089 - val_loss: 0.0238 - val_last_time_step_mse: 0.0086\n" ] } ], @@ -1705,13 +1776,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2000/2000 [==============================] - 0s 67us/sample - loss: 0.0240 - last_time_step_mse: 0.0086\n" + "63/63 [==============================] - 0s 2ms/step - loss: 0.0238 - last_time_step_mse: 0.0086\n" ] }, { "data": { "text/plain": [ - "[0.02401665359735489, 0.008551412]" + "[0.023788686841726303, 0.008560806512832642]" ] }, "execution_count": 48, @@ -1730,7 +1801,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1768,7 +1839,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1802,47 +1873,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 3s 400us/sample - loss: 0.0742 - last_time_step_mse: 0.0663 - val_loss: 0.0523 - val_last_time_step_mse: 0.0421\n", + "219/219 [==============================] - 1s 6ms/step - loss: 0.0738 - last_time_step_mse: 0.0655 - val_loss: 0.0538 - val_last_time_step_mse: 0.0450\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0476 - last_time_step_mse: 0.0367 - val_loss: 0.0441 - val_last_time_step_mse: 0.0327\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0476 - last_time_step_mse: 0.0367 - val_loss: 0.0441 - val_last_time_step_mse: 0.0326\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 1s 148us/sample - loss: 0.0418 - last_time_step_mse: 0.0305 - val_loss: 0.0391 - val_last_time_step_mse: 0.0271\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0417 - last_time_step_mse: 0.0301 - val_loss: 0.0390 - val_last_time_step_mse: 0.0275\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0373 - last_time_step_mse: 0.0249 - val_loss: 0.0343 - val_last_time_step_mse: 0.0205\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0368 - last_time_step_mse: 0.0243 - val_loss: 0.0339 - val_last_time_step_mse: 0.0202\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0327 - last_time_step_mse: 0.0179 - val_loss: 0.0313 - val_last_time_step_mse: 0.0158\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0326 - last_time_step_mse: 0.0180 - val_loss: 0.0312 - val_last_time_step_mse: 0.0164\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0308 - last_time_step_mse: 0.0155 - val_loss: 0.0297 - val_last_time_step_mse: 0.0143\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0306 - last_time_step_mse: 0.0155 - val_loss: 0.0294 - val_last_time_step_mse: 0.0143\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0296 - last_time_step_mse: 0.0146 - val_loss: 0.0290 - val_last_time_step_mse: 0.0140\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0295 - last_time_step_mse: 0.0145 - val_loss: 0.0300 - val_last_time_step_mse: 0.0162\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0284 - last_time_step_mse: 0.0134 - val_loss: 0.0278 - val_last_time_step_mse: 0.0128\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0283 - last_time_step_mse: 0.0135 - val_loss: 0.0278 - val_last_time_step_mse: 0.0130\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 1s 148us/sample - loss: 0.0278 - last_time_step_mse: 0.0131 - val_loss: 0.0278 - val_last_time_step_mse: 0.0133\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0276 - last_time_step_mse: 0.0130 - val_loss: 0.0273 - val_last_time_step_mse: 0.0127\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0272 - last_time_step_mse: 0.0126 - val_loss: 0.0272 - val_last_time_step_mse: 0.0139\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0269 - last_time_step_mse: 0.0125 - val_loss: 0.0264 - val_last_time_step_mse: 0.0121\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0267 - last_time_step_mse: 0.0122 - val_loss: 0.0269 - val_last_time_step_mse: 0.0123\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0265 - last_time_step_mse: 0.0121 - val_loss: 0.0268 - val_last_time_step_mse: 0.0135\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0264 - last_time_step_mse: 0.0121 - val_loss: 0.0267 - val_last_time_step_mse: 0.0132\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0263 - last_time_step_mse: 0.0123 - val_loss: 0.0261 - val_last_time_step_mse: 0.0123\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0260 - last_time_step_mse: 0.0117 - val_loss: 0.0259 - val_last_time_step_mse: 0.0120\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0258 - last_time_step_mse: 0.0116 - val_loss: 0.0254 - val_last_time_step_mse: 0.0116\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0257 - last_time_step_mse: 0.0116 - val_loss: 0.0265 - val_last_time_step_mse: 0.0132\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0256 - last_time_step_mse: 0.0117 - val_loss: 0.0254 - val_last_time_step_mse: 0.0116\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0255 - last_time_step_mse: 0.0116 - val_loss: 0.0256 - val_last_time_step_mse: 0.0119\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0253 - last_time_step_mse: 0.0114 - val_loss: 0.0250 - val_last_time_step_mse: 0.0112\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0252 - last_time_step_mse: 0.0112 - val_loss: 0.0249 - val_last_time_step_mse: 0.0110\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0251 - last_time_step_mse: 0.0114 - val_loss: 0.0250 - val_last_time_step_mse: 0.0114\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0249 - last_time_step_mse: 0.0111 - val_loss: 0.0246 - val_last_time_step_mse: 0.0107\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0248 - last_time_step_mse: 0.0112 - val_loss: 0.0249 - val_last_time_step_mse: 0.0118\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0248 - last_time_step_mse: 0.0112 - val_loss: 0.0251 - val_last_time_step_mse: 0.0124\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0245 - last_time_step_mse: 0.0110 - val_loss: 0.0244 - val_last_time_step_mse: 0.0108\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0244 - last_time_step_mse: 0.0108 - val_loss: 0.0241 - val_last_time_step_mse: 0.0104\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0243 - last_time_step_mse: 0.0108 - val_loss: 0.0240 - val_last_time_step_mse: 0.0105\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 1s 149us/sample - loss: 0.0242 - last_time_step_mse: 0.0106 - val_loss: 0.0241 - val_last_time_step_mse: 0.0103\n" + "219/219 [==============================] - 1s 5ms/step - loss: 0.0240 - last_time_step_mse: 0.0106 - val_loss: 0.0238 - val_last_time_step_mse: 0.0103\n" ] } ], @@ -1870,13 +1940,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2000/2000 [==============================] - 0s 67us/sample - loss: 0.0241 - last_time_step_mse: 0.0103\n" + "63/63 [==============================] - 0s 2ms/step - loss: 0.0238 - last_time_step_mse: 0.0103\n" ] }, { "data": { "text/plain": [ - "[0.024071006283164026, 0.010298316]" + "[0.02378549799323082, 0.010262805968523026]" ] }, "execution_count": 53, @@ -1895,7 +1965,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1933,7 +2003,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1986,47 +2056,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 3s 383us/sample - loss: 0.0683 - last_time_step_mse: 0.0605 - val_loss: 0.0482 - val_last_time_step_mse: 0.0405\n", + "219/219 [==============================] - 1s 7ms/step - loss: 0.0681 - last_time_step_mse: 0.0601 - val_loss: 0.0477 - val_last_time_step_mse: 0.0396\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0416 - last_time_step_mse: 0.0342 - val_loss: 0.0368 - val_last_time_step_mse: 0.0283\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0414 - last_time_step_mse: 0.0340 - val_loss: 0.0367 - val_last_time_step_mse: 0.0285\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0334 - last_time_step_mse: 0.0251 - val_loss: 0.0307 - val_last_time_step_mse: 0.0220\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0338 - last_time_step_mse: 0.0257 - val_loss: 0.0307 - val_last_time_step_mse: 0.0218\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0273 - last_time_step_mse: 0.0172 - val_loss: 0.0251 - val_last_time_step_mse: 0.0141\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0282 - last_time_step_mse: 0.0184 - val_loss: 0.0259 - val_last_time_step_mse: 0.0152\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0243 - last_time_step_mse: 0.0134 - val_loss: 0.0238 - val_last_time_step_mse: 0.0128\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0249 - last_time_step_mse: 0.0143 - val_loss: 0.0246 - val_last_time_step_mse: 0.0141\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0230 - last_time_step_mse: 0.0121 - val_loss: 0.0226 - val_last_time_step_mse: 0.0116\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0234 - last_time_step_mse: 0.0125 - val_loss: 0.0227 - val_last_time_step_mse: 0.0115\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0224 - last_time_step_mse: 0.0116 - val_loss: 0.0220 - val_last_time_step_mse: 0.0110\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0226 - last_time_step_mse: 0.0117 - val_loss: 0.0225 - val_last_time_step_mse: 0.0116\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 1s 127us/sample - loss: 0.0218 - last_time_step_mse: 0.0111 - val_loss: 0.0216 - val_last_time_step_mse: 0.0107\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0220 - last_time_step_mse: 0.0111 - val_loss: 0.0216 - val_last_time_step_mse: 0.0105\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0214 - last_time_step_mse: 0.0107 - val_loss: 0.0211 - val_last_time_step_mse: 0.0103\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0216 - last_time_step_mse: 0.0108 - val_loss: 0.0217 - val_last_time_step_mse: 0.0109\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 1s 127us/sample - loss: 0.0211 - last_time_step_mse: 0.0105 - val_loss: 0.0209 - val_last_time_step_mse: 0.0102\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0213 - last_time_step_mse: 0.0106 - val_loss: 0.0210 - val_last_time_step_mse: 0.0102\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0207 - last_time_step_mse: 0.0103 - val_loss: 0.0206 - val_last_time_step_mse: 0.0099\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0210 - last_time_step_mse: 0.0102 - val_loss: 0.0208 - val_last_time_step_mse: 0.0100\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 1s 129us/sample - loss: 0.0206 - last_time_step_mse: 0.0102 - val_loss: 0.0203 - val_last_time_step_mse: 0.0097\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0208 - last_time_step_mse: 0.0102 - val_loss: 0.0208 - val_last_time_step_mse: 0.0102\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0202 - last_time_step_mse: 0.0098 - val_loss: 0.0200 - val_last_time_step_mse: 0.0095\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0205 - last_time_step_mse: 0.0098 - val_loss: 0.0206 - val_last_time_step_mse: 0.0101\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 1s 127us/sample - loss: 0.0200 - last_time_step_mse: 0.0096 - val_loss: 0.0207 - val_last_time_step_mse: 0.0105\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0204 - last_time_step_mse: 0.0099 - val_loss: 0.0204 - val_last_time_step_mse: 0.0099\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0197 - last_time_step_mse: 0.0095 - val_loss: 0.0198 - val_last_time_step_mse: 0.0093\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0202 - last_time_step_mse: 0.0097 - val_loss: 0.0199 - val_last_time_step_mse: 0.0093\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0194 - last_time_step_mse: 0.0091 - val_loss: 0.0191 - val_last_time_step_mse: 0.0087\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0200 - last_time_step_mse: 0.0097 - val_loss: 0.0201 - val_last_time_step_mse: 0.0095\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0191 - last_time_step_mse: 0.0089 - val_loss: 0.0188 - val_last_time_step_mse: 0.0085\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0196 - last_time_step_mse: 0.0093 - val_loss: 0.0197 - val_last_time_step_mse: 0.0091\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0187 - last_time_step_mse: 0.0085 - val_loss: 0.0183 - val_last_time_step_mse: 0.0077\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0194 - last_time_step_mse: 0.0090 - val_loss: 0.0192 - val_last_time_step_mse: 0.0086\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 1s 127us/sample - loss: 0.0181 - last_time_step_mse: 0.0078 - val_loss: 0.0178 - val_last_time_step_mse: 0.0074\n", + "219/219 [==============================] - 1s 4ms/step - loss: 0.0190 - last_time_step_mse: 0.0088 - val_loss: 0.0188 - val_last_time_step_mse: 0.0084\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 1s 128us/sample - loss: 0.0175 - last_time_step_mse: 0.0072 - val_loss: 0.0173 - val_last_time_step_mse: 0.0067\n" + "219/219 [==============================] - 1s 4ms/step - loss: 0.0186 - last_time_step_mse: 0.0083 - val_loss: 0.0184 - val_last_time_step_mse: 0.0080\n" ] } ], @@ -2078,47 +2147,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/20\n", - "7000/7000 [==============================] - 2s 276us/sample - loss: 0.0684 - last_time_step_mse: 0.0550 - val_loss: 0.0385 - val_last_time_step_mse: 0.0249\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.0668 - last_time_step_mse: 0.0543 - val_loss: 0.0365 - val_last_time_step_mse: 0.0230\n", "Epoch 2/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0341 - last_time_step_mse: 0.0215 - val_loss: 0.0306 - val_last_time_step_mse: 0.0183\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0323 - last_time_step_mse: 0.0192 - val_loss: 0.0294 - val_last_time_step_mse: 0.0166\n", "Epoch 3/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0292 - last_time_step_mse: 0.0173 - val_loss: 0.0274 - val_last_time_step_mse: 0.0156\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0283 - last_time_step_mse: 0.0156 - val_loss: 0.0269 - val_last_time_step_mse: 0.0144\n", "Epoch 4/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0270 - last_time_step_mse: 0.0153 - val_loss: 0.0263 - val_last_time_step_mse: 0.0148\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0261 - last_time_step_mse: 0.0136 - val_loss: 0.0254 - val_last_time_step_mse: 0.0130\n", "Epoch 5/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0255 - last_time_step_mse: 0.0138 - val_loss: 0.0248 - val_last_time_step_mse: 0.0131\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0248 - last_time_step_mse: 0.0124 - val_loss: 0.0245 - val_last_time_step_mse: 0.0122\n", "Epoch 6/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0246 - last_time_step_mse: 0.0128 - val_loss: 0.0239 - val_last_time_step_mse: 0.0121\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0240 - last_time_step_mse: 0.0117 - val_loss: 0.0233 - val_last_time_step_mse: 0.0108\n", "Epoch 7/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0238 - last_time_step_mse: 0.0119 - val_loss: 0.0232 - val_last_time_step_mse: 0.0114\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0233 - last_time_step_mse: 0.0111 - val_loss: 0.0229 - val_last_time_step_mse: 0.0107\n", "Epoch 8/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0233 - last_time_step_mse: 0.0115 - val_loss: 0.0231 - val_last_time_step_mse: 0.0113\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0226 - last_time_step_mse: 0.0104 - val_loss: 0.0229 - val_last_time_step_mse: 0.0105\n", "Epoch 9/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0229 - last_time_step_mse: 0.0111 - val_loss: 0.0233 - val_last_time_step_mse: 0.0121\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0221 - last_time_step_mse: 0.0099 - val_loss: 0.0219 - val_last_time_step_mse: 0.0098\n", "Epoch 10/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0226 - last_time_step_mse: 0.0109 - val_loss: 0.0222 - val_last_time_step_mse: 0.0105\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0219 - last_time_step_mse: 0.0098 - val_loss: 0.0213 - val_last_time_step_mse: 0.0090\n", "Epoch 11/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0220 - last_time_step_mse: 0.0103 - val_loss: 0.0217 - val_last_time_step_mse: 0.0097\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0212 - last_time_step_mse: 0.0091 - val_loss: 0.0209 - val_last_time_step_mse: 0.0088\n", "Epoch 12/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0219 - last_time_step_mse: 0.0102 - val_loss: 0.0221 - val_last_time_step_mse: 0.0107\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0210 - last_time_step_mse: 0.0089 - val_loss: 0.0212 - val_last_time_step_mse: 0.0096\n", "Epoch 13/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0216 - last_time_step_mse: 0.0100 - val_loss: 0.0215 - val_last_time_step_mse: 0.0099\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0206 - last_time_step_mse: 0.0086 - val_loss: 0.0203 - val_last_time_step_mse: 0.0081\n", "Epoch 14/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0215 - last_time_step_mse: 0.0099 - val_loss: 0.0212 - val_last_time_step_mse: 0.0096\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0203 - last_time_step_mse: 0.0082 - val_loss: 0.0202 - val_last_time_step_mse: 0.0082\n", "Epoch 15/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0213 - last_time_step_mse: 0.0098 - val_loss: 0.0210 - val_last_time_step_mse: 0.0093\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0201 - last_time_step_mse: 0.0080 - val_loss: 0.0197 - val_last_time_step_mse: 0.0078\n", "Epoch 16/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0211 - last_time_step_mse: 0.0095 - val_loss: 0.0208 - val_last_time_step_mse: 0.0094\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0199 - last_time_step_mse: 0.0080 - val_loss: 0.0197 - val_last_time_step_mse: 0.0079\n", "Epoch 17/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0209 - last_time_step_mse: 0.0094 - val_loss: 0.0207 - val_last_time_step_mse: 0.0092\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0196 - last_time_step_mse: 0.0076 - val_loss: 0.0193 - val_last_time_step_mse: 0.0074\n", "Epoch 18/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0207 - last_time_step_mse: 0.0092 - val_loss: 0.0205 - val_last_time_step_mse: 0.0092\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0192 - last_time_step_mse: 0.0073 - val_loss: 0.0192 - val_last_time_step_mse: 0.0074\n", "Epoch 19/20\n", - "7000/7000 [==============================] - 1s 108us/sample - loss: 0.0207 - last_time_step_mse: 0.0092 - val_loss: 0.0205 - val_last_time_step_mse: 0.0090\n", + "219/219 [==============================] - 1s 3ms/step - loss: 0.0191 - last_time_step_mse: 0.0072 - val_loss: 0.0187 - val_last_time_step_mse: 0.0069\n", "Epoch 20/20\n", - "7000/7000 [==============================] - 1s 109us/sample - loss: 0.0204 - last_time_step_mse: 0.0089 - val_loss: 0.0205 - val_last_time_step_mse: 0.0087\n" + "219/219 [==============================] - 1s 3ms/step - loss: 0.0190 - last_time_step_mse: 0.0070 - val_loss: 0.0186 - val_last_time_step_mse: 0.0068\n" ] } ], @@ -2212,11 +2280,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train on 7000 samples, validate on 2000 samples\n", "Epoch 1/2\n", - "7000/7000 [==============================] - 2s 267us/sample - loss: 0.1299 - last_time_step_mse: 0.1258 - val_loss: 0.1229 - val_last_time_step_mse: 0.1199\n", + "219/219 [==============================] - 1s 5ms/step - loss: 0.1300 - last_time_step_mse: 0.1260 - val_loss: 0.1229 - val_last_time_step_mse: 0.1199\n", "Epoch 2/2\n", - "7000/7000 [==============================] - 1s 121us/sample - loss: 0.1222 - last_time_step_mse: 0.1178 - val_loss: 0.1218 - val_last_time_step_mse: 0.1190\n" + "219/219 [==============================] - 1s 4ms/step - loss: 0.1222 - last_time_step_mse: 0.1178 - val_loss: 0.1217 - val_last_time_step_mse: 0.1189\n" ] } ], @@ -2279,7 +2346,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -2293,7 +2360,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2304,7 +2371,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2322,7 +2389,7 @@ " '/home/haesun/.keras/datasets/quickdraw/training.tfrecord-00009-of-00010']" ] }, - "execution_count": 107, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -2333,7 +2400,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -2351,7 +2418,7 @@ " '/home/haesun/.keras/datasets/quickdraw/eval.tfrecord-00009-of-00010']" ] }, - "execution_count": 108, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -2362,7 +2429,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2375,7 +2442,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -2385,7 +2452,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -2738,7 +2805,7 @@ " 'zigzag']" ] }, - "execution_count": 111, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -2749,7 +2816,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -2769,7 +2836,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -2788,7 +2855,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -2799,7 +2866,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -2807,61 +2874,61 @@ "output_type": "stream", "text": [ "sketches = tf.Tensor(\n", - "[[[-0.02352941 -0.27500004 0. ]\n", - " [ 0.25882354 -0.08499998 0. ]\n", - " [ 0.03529412 -0.36 0. ]\n", + "[[[-0.08627451 0.11764706 0. ]\n", + " [-0.01176471 0.16806725 0. ]\n", + " [ 0.02352941 0.07563025 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", - " [[-0.21960786 -0.29437226 0. ]\n", - " [-0.18039215 -0.15584415 0. ]\n", - " [-0.14509805 -0.05194805 0. ]\n", + " [[-0.04705882 -0.06696428 0. ]\n", + " [-0.09019607 -0.07142857 0. ]\n", + " [-0.0862745 -0.04464286 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", - " [[ 0.05555558 0.22352943 0. ]\n", - " [ 0. 0.1372549 0. ]\n", - " [-0.02222225 0.03921568 0. ]\n", - " ...\n", - " [ 0. 0. 0. ]\n", + " [[ 0. 0. 1. ]\n", " [ 0. 0. 0. ]\n", - " [ 0. 0. 0. ]]\n", + " [ 0.00784314 0.11320752 0. ]\n", + " ...\n", + " [ 0.11764708 0.01886791 0. ]\n", + " [-0.03529412 0.12264156 0. ]\n", + " [-0.19215688 0.33962262 1. ]]\n", "\n", " ...\n", "\n", - " [[-0.00392157 0.3333333 0. ]\n", - " [-0.07058823 0.5652174 0. ]\n", - " [-0.03921568 -0.2753623 0. ]\n", + " [[-0.21276593 -0.01960784 0. ]\n", + " [-0.31382978 0.00784314 0. ]\n", + " [-0.37234044 0.13725491 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", - " [[-0.10526317 0.00395256 0. ]\n", - " [-0.05263157 0.07905138 0. ]\n", - " [-0.07894738 0.37944666 0. ]\n", + " [[ 0. 0.4677419 0. ]\n", + " [-0.01176471 0.15053767 0. ]\n", + " [ 0.16470589 0.05376345 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", - " [[-0.06857143 -0.2235294 0. ]\n", - " [-0.02285714 -0.16862744 0. ]\n", - " [-0.00571429 -0.25490198 0. ]\n", + " [[-0.04819274 0.01568627 0. ]\n", + " [-0.07228917 -0.01176471 0. ]\n", + " [-0.05622491 -0.03921568 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", - " [ 0. 0. 0. ]]], shape=(32, 133, 3), dtype=float32)\n", + " [ 0. 0. 0. ]]], shape=(32, 104, 3), dtype=float32)\n", "lengths = tf.Tensor(\n", - "[ 58 24 42 65 104 16 42 31 37 65 32 58 20 105 35 25 55 48\n", - " 58 27 36 27 26 41 11 41 133 33 60 19 20 76], shape=(32,), dtype=int64)\n", + "[ 29 48 104 34 29 35 28 40 95 26 23 41 47 17 37 47 12 13\n", + " 17 41 36 23 8 15 60 32 54 38 68 30 89 36], shape=(32,), dtype=int64)\n", "labels = tf.Tensor(\n", - "[ 36 292 86 229 290 237 217 30 226 14 213 19 220 159 17 46 235 131\n", - " 102 245 149 226 73 282 245 84 277 155 59 193 137 266], shape=(32,), dtype=int64)\n" + "[ 95 190 163 12 77 213 216 278 25 202 310 33 327 204 260 181 337 233\n", + " 299 186 61 157 274 150 7 34 47 319 213 292 312 282], shape=(32,), dtype=int64)\n" ] } ], @@ -2874,12 +2941,12 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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PDkjZsv65Wa9uCN0nED1wh7WDGM3Wi9N0VXM0ilZNuaOXA8BwAKmBlxmAPcbEPW6DpmHn5kNjXgbwq81iqj5CO2GLULUxcDXABH+klXkBej8nruS10Vmrv1w4Yzn/wqOAuUuu0H1pu/Brt6gbB+BWAIkIg01cv+hGtUbhyv6+7CxyeA3xQ1I39s6MqxjyQ9mU/R5Jo+9t2DUkVVczbEP1Kb9KTBGfqS8fnKC2993bOHAXQAl6ZXMPIpbs9CZ4AdL8r2XmA+hem8X0SCj1cjjhxGi26gBMB9gdAOWnaqsb6lyZswG8B2AMAmPz+QVv/LK+atJ1lc4eswEMM2jqPT0T9r+8pfbkf9ksplYZ3wInjBjN1snpusp/KQRfS5Wzhx3AYI2idYxb1P0e5kxQN3r1SueB6pbczwFsO6OH1atVtqxYNOvViqO0GfIc1sDMQN8RGeuucfu0Z2yvH64AMAx+5xYAKwdofd/kbXV58Qc3f1d67qsATgpWR7TAHdZjMGfJlQmVjh7mn6snngNghP9ZBoB2AFgzIOW35pz4km3flkx722YxOWSU+jttDfyktF98W2pP/juA6QCS0nVVzYdas+4A8Hq06O2OGM1WGpS6aeP2uhHD03VVt/583/UL5dbUEYxm66kE6ZtAyoIPwKmxOvBxuhdGszVhaNrPi/fZ+//V6U1UAVjby7D7haFpv/z3WDfxRrOVBqT8Nt3pjf9PaXPvBAA1OqVj0ek9vnh50axXyyL3DjjhYu6SK2ifvf+0rbWjZgA4r81LDQB+7Z+8RalXObZsqhn/JoBtNoupQR6lx8ZotmoL8774W50rY9KW2lFxAMYCKAi8LAEggAXSBSkmc7S5w3oEjGZrfwAz1YL7Ro+k0ROkRgYhEYAQi5Glofcu1w9N/2XRrzWjCx1eQz7AmganbtqZHV9230uzF38pt77uhtFs/SeAJwenbvzGevv8KXLr6QyBG6LHAZyiUzqn7VhwiVVuTRzO0bjlxav7fGm78FqfpL4ZQFJvw846BuH6A/Z+n9gspg5f/IxmKwGYBGAegLN1SidjjJ5wifrHbBbToXDp54QPo9maBuDKJE3dPY3u1FSAeQLpegQwEaD5sXSdPxK3vHh1P5u976lb60ZeBrAzArOuAGg1gAtsFlOd3Bo7A3dYAxjNVm2PhP0zFSQ9YGvqYwDg0yhav5iYu3JNcenU1SJTrkAQeSjRRGDQHZegbryr1Rt3no+pAOBbteBafFHf1z+xTP+QrzoPMycXvfxwrSvLDGAV/HYUcws6jGarTq907FYIvuxJuSsGLpr1Ks+R7kbEwsI7o9maY1DX3+8SdTe4RR0AfCiQ+Mj+R/7y84m2PeP5mZfvqBt6R5mj11AArl6Ju78blLr5/udmvbr+hIVzworRbBVGZKy7ttWnv29n/UlZAKl1Sue2UZk/bthUPe4Npy/hQ8T4df5I/C9dkAVWg5FAkBxjsn5YkxlXcfnTM16Pyqhxe7q9w3rjC9dP3VQz9srqltypAFKTtYfcWfqKt3bUD7uzbQ5qLAzSneGWF68Z9KXtggt9knomgJ4GTb3oFVVvt/gSbAA+6wrvMdo4/eGnZh1o6vuC/xG1IoYHxFkv3HDpioN/eVNJ3s89kva8zkSrONFPIEcuDUDe6T0+v7C0uZd6b+NAMVBmZzwABpALUWbDNy2eftqvNWPurHD2nAwwRYFh17aCpJ33vDh7yaeh7stotg5QCp67JSZcCZAkMcWrAB61WUy7Q90XJzgOX7d7JuyrbPIkDWx0p14CwKhXOliSpv7DCmfPIpvFtKX98egi1/m2tH1vAJqz40rernT2HEQQKxkU9wBYZrOYRDk1Ho9u6bCOuG+ZtsGddhHAZgJ0mkAik5jifQCLAXwbi1GvYDGarYpx2d/dsb+x/601rTkZgadj2pmKVgru+qhYZMrTAmVKfABiKrWkPUaz9XYAjwG4zmYxLZVbD+ePHO3iO3fJFaoKR8+Cn6snGgDkjc764eK61vS4/fYBTgB5yZraMXZPskpiivY7IYoAFH98in1wfsGbVy6csVzWhUhGs3UIADNBukIgERJTLGYQHrNZTPvD3fdNi689ZVXZ1Buc3oS/A0wzIGVLWYLaPvPdWy083UpGAvb/XSCqGKieg28BvDS552efvXzT882yCowCzv33Q3/bXjf8XwDGGtT1tcMz1j+3qmzq/dEagOhWDqvRbB3QL3nrwrJm4+QWX7wCwP78xL2fn5S28YVnZy7bJrc+OTGarXcBeBCAAmAMoLtj2ZmKNgLpGKUAsuFPgI/5KSej2apQkLdYIDZuSv4nZy2atfQ7uTVx/AQu1t8CUBMkpOuqbDWtOTUA8ghiHmvndyrIJ4lMeQBAWUHSjlSV4C3ZWT/0CwBlp+Z9pVEJ3q0rS6adD7CHDpfNA4gBoBTtIZ9O2VJU7sj/t81i6lA931Bx+dP/d/GhlqyFexoH5wJwJmlq35yQ++2i52Yu2xRJHQBgNFsz+yRtX1TWbLzQJeoJwOcp2kNPbiy6dmWktXR3bnnxmpNWlU75tNmbnO9/hrEkTf3Lv95/9Qx5lUUfRrOVDOr6y1UKz9La1iwVgB8A3GGzmNbJra09Xd5hnbPkSkOjO+Wh1eWTh0tMOQGQxAEpWyubPYY55Y78j7tTNPVYtMlx0QGAVtlyxc4Fl7whs6wuw98X3nbZ2srT3xTI94jElM3oIlNOsxdfN7a4dOpaleDZ2+RJHmSzmHxya+L8fgO6AP6FotApnZ5WX/wPAMoGpPyWFK9qrvmlesInAMom5HzTkKarKTleybsBd7/7F5eo/zhwQ+sDcPOEnJVJ+xr731nVkpcKoLRHwv5XRmT89MTTM14PW/Rq7pIr6JuSaVOc3sTbAZypVzpYhr7yv7amvv8XDYtIzrQ8nra3ceBMAP8EkJYXf6CuzNHrSgBfRWvkqqtgNFvVAP6hFDwPS5KglqDwBSKrMR8gCDeBUosz3KJuHoDM/slbKgyahkveufXRNXJrO0yXdVj7zXt/oEfSzhBIvF5iikSt0nnI5Yt7AsCrR6qPyvE7rXql45IWX9xMgL6zWUzT5NbUVTjv8fs37aw/afhU44d5z8z8b7ncekLJkPlvXOvwGpYCmG+zmBbIrYdzxKLjJ3yxNpqtZwH4CsBLAF453F5g9uAsgN0H0Ph4ld3l8BpuB/CSzWIK2UJOo9lKAvmm5cSVvl7m6JUIoArAE2Oyflj6zq0W2R3V9gy9d7l+cOqmZZtqxk52ifpkgG0al138Waa+8sHusjNRJLl+0Wzz92VTZnolTS8F+b46y/jxk18cuLgZXTQnNVwYzdb4wakbl+1pHHShR9RIAL2kEtz373n4okq5tcnusIYyydlotmqV5P1bbsLBpw829UkC4APYh6fmfb0mWVP37MIZy6M6oThaMJqttwH4t1bR8rZL1C/kP/QTw3/XzyoS1Y0bfnvgyrPl1hMOjGbrcoBdOiX/0+uXzF68TG49nNAvIOlt/uQOCYpHAaTaLKb69q/PXXIFObyJt62tOO3KFl/CUIBVjMtetSZDXznrRFYhz11yhaqkufe8TTXjLgIwNE7Z3Dg0/ZfP1laePiOUDnG4CET9rlALriKPpO2pVzqqWnzx8wAsj3QKRVfEaLb2Fsi3UGLKaQZNfbPdnfJ3m8XEy+2dIMPvW5bd6E67G2CzVIJXGJS6ecXmQ6MvsVlMTXJpktVh9Q+obBX8UQAXQGcEM7De+ML15+5pHHjLvsaBYwGkJKgb7X2Tdny7sWb8bB5N7TxGs3USwL73P4rt1ezRgNFs/QuAjwGcZ7OYPpNbTzg40/J4Wm1rRhWR5G5wpWfYLCan3Jo4oeXcfz+0r6w5P++3B67UHOu4QMS1MElT92yjO3WQkrz1PqZaAGCxzWJq6Wh/AUfvqjhV06NOb2Kqkrz7fEx1P4C3bBZTzEUo5y65QtXoTrF8X3bWZAZhmEpwHxqdtWbVppoxi1t98aPBo4CdYs6SK9P3NfZ/dVvdiMkA+XLjDy4ZlfnjveFMR+mOTHn0sWEKEj/dWT+0B4BDgPS6gsRGkalWRNpe5XZY7wok8VNnC/IbzdbTAMwG0B/AcIF8IOADkSkXAfiO56YGzx+/l9hfzS43hQ8/tb3cYezhldQpsXih7SiX/OfO6eurJr0M0Is2i+lGufVwQsuQ+W+WJqjtjrXzbxzY0XOuePqfN6ypOPPvAM5QC67mkZnrVm2tHXHZ1gcvP+oNzcznZ6Q2exNfXF85aYLIVJkK8m4+NW/FV4nqxnldYZYs4NCfnR1X+kqls0d2YAGbBMANHhw4LoHP71KNonWRW9QlJ2tqv2xwp11vs5iOuDUqJzQYzdbRAJ4HMMpvs5HfMUsZqY6OQjH8pVKU8OdZFXfkpMBUV6CQP4Ne6Vha2OOrJxbNWtqtV/qHkGKAOv29cP7MiKJXk5rcvQYMSdu05eN/3d9lnVUAeOfWR5cazdaBAG4/57GHN31xx7zFcmvihAaj2aoBErMc3sR/d+a85XOeegnAS0azdWJ2fNl76yoLpwGwGc3Wx3sm7Hv++7vn/D69OODud5Ncov5m4Px/ApSaqS8vqW7JPVtkqhVLb36uyyy2CCy8+hJATv+731vmFnVXw784TgNQIQDusB6FG1+4/pI41Zl3O72JQ92idtPpPT6/fenNz70st67ugM1i+tlotr4PsBEABewVVyOC9tq+zl5E8Xvm9BYAH0CdubMsBJgAAASJuXz6vdxZDR02i2mtAPETgLWA3/GfEA2u9ItFpqI9DYNmy60lQsxP1dbUlTYbn7/22Vv6yS2GExrSdZXD4L+B3RzM+TaLafWqeXOzDJr6QgC/ALDUuTIaTnlw0edGs/XfhQ8/tV6CcAjAAoB+Gpiy+YKf7p2Zb7OYvu7KK+vdou4FAK2BiJWQoatQySwpKjGaralG82eLvrJd8DZjwiBAmgnQaO6sRpxigNwARH9ZOzb9ymf+eUekOpfVYQWAJE0tCSS2dNIpKgbIAzAfg+CSoPiuuJgMxcWUGC6d3Y2T0jf0jVM167izeqKwKwHsafHFd4vP0WYxuU/OWjPd6Y33FZdNfTIwfceJcQambLkeACb3/PRPi606w+b7r1pls5jOGZ25+sIkTV1jhTP/HAC32Zr6js7Sl9el66rG2Swm0xd3zPs4JMKjnMD4OlmvdDypUzira1pz7jKarZPl1hUtzHv1L+rLFt6+DGC7AZqZpK17fXJPa1+b5bwl0b4rU1fksL0CmA9gWqr2kP3HitMfHffg4lsi0b/sDmtefMmYRHVjXGfO+d+HRvcCNPnVqdPWAXgTwHfFxSR3mkOXoKYla79H1HR4gQTnz8xefN1YgAr7JO34qStHidqz+MaXPmVQ3AaQSUne7hJZ7tJsrx+qE8jni1c1fx+K9t795yMflTuMj8OfEgaAxJLmgoU/33f9T6FoP5awWUxrty+47F+tYvwQAHsAZr1+0ex/ya1Lboxm66kf7Llq27rKwquTtbVVAEZsKrr2qmdmvmaTW1t3xmYxrbVZTI/YLKbPT8n5dmS8qmlzlTPvGaPZemu4+5bdYT3Q1HdPizeu03Up23xoawsLGQPwJID/FBYyXrg8BFQ6e5Z5JU3Ul4yJZjZWj/8bAPRL3vaK3Fpk4NkElX09kfTs7MXXTZVbDOfEqG3NypaY8teFM5a7Q9hsMQAP/As7PejmufI2i6kWwOR0XZX3+7Ip/x50z9tnya1JDm5efO2Y0fe/VAxglUvUq0dlrpl/au6KITaLaYvc2jh/5JmZ/y1v8iSPA/A+gKcuevKeH+YuuSJss2rRUIf1UwC5NotpZKjaLC6mUwHoCwsZ38s5SPrc9dELPqb8m80yLVVuLbFIYCp8K4BGm8U0QW49cnDzi9eMWHlw2k8iU+zySpqRXblCQldm7pIr6NP9l9YyRh8dsJx3fSjbDnWt2K7ALS9ePeAr2wWfeCVNHoBpNovpW7k1RQKj2aoF8C+l4LmfAAVjtMDHVI90phQaRx6MZqtiUOqmDdvrRgxTCe5lXklzQzh2PZQ9whqnas7WKZ0h88iLi4kA3AfgseJiUhzveM6RGZy26ZQ4VXOy3DpilaFpv5wBYBCA11uiyYoAACAASURBVOXWIhfPzVy2ySXqL/NKmiHw/yY5MYiPKYdKTJEyPqfYEOq2286UhbrtWOXZma/t9EqaiQD2CSR+ecPzs/5Pbk3hZO6SK2j6czc/RBB3AligErxfT8n/ZNLeRy6Yz53V2MBmMYl9k3aM0CsdT3glzTUAe2f24utCvqZIdoc1WVPbPyeuNC9U7QXSAy4AYCosZGJxMSmKi0n29xlr1LRk7/OIWj5YBIlC8FkU5MWEnJXdescVm8X0gYJ8rxGkeTcsmsXzWWOQdRWnFQBAq0//gdxaugs2i6mmT9KOc9J1VVJxyTmPGc3WQrk1hQOj2TrgS9uFxd+VnjsvQd2kB3DmjgWXnLto1tLVcmvjdI6FM5az7QsuvQ3AXIAu3FE39OBlC29PD2Ufsjty1S05lVUtOb+Gss3CQtZcWMhKAw8fB/A6j7Z2jkpnj3KvpOY5rEFgNFsVmw+dbEzTHdq4fM6TJXLrkZtze79nTtHWij9WnPGQ0WzllTxijDpXZh8A2FQz7gu5tXQnvjHfVjYs/edhPqbcBcDaVZxWo9k6fsDd71rG3P/i9wC2uEXdsP7JW54+vccX+TaLaaXc+jgnhs1ievrUvK8WHWwqSFxXedpXRrM1M1Rty+6weiWN0ulNrApH24H0gGoA1YWFTP4SGEWG8Sgy3IUiw/hob1ctuFUEiZckCo4zJKZMq27J4buDAXhmxn8rlYL3nBZfvAHA03Lr4XQOg6a+UCCxwmYxNcitpbvx4uwluwA6nSCVKAXPNzcsmjVHbk0nQiBnudgl6u6sac2ZpBZcKwH0/epO89yFM5a3yq0vKunM9T1cPkYnee2Wp29mEKYBNEAg39rTH37yRqPZelfg+w8a2Rdd9Zn3YUO8qnnFr/dffUm4+iguJiosZKy4mIwAqLCQHQhXX3+iyKA8lOa9UOUls8GuGE4ggYExj5qJag/ZCCR5lSzRp2SpWhfZCMS8SmbwKVlKm8dJPiVL1rroAIHgVUrJPiUMOpdgA6BlYD0DvbkJdDqK7CecD3b+E/f9trdxwJBtD/5d9puaWGPCgkU/VDryhktQpNssJh6lDmA0Wx8AMH9c9ncPvjX38Xvl1sPpGGMfeNGVqLbbV5hvD1mkhNM5blo8ffAv1adsqG3NhMQUZ9ssplVyawoGo9n6IoAZ/kdMAugevu33MfA7nt8CUMFfSWMyiuxrbUu1ZDyoUQOIq0n35rg1Ul6PMk0WgBcYmBKAh0CTQ+ELnAi9zZ+MVwreHzySRgH/ZgMenMBmRLLXLFWQmNTLsKcgnH0EnFWCfwFManExDQlnxLXmOf0wSWC3px9SJStAE9JrVQaGP94YSAQHI2wmBq9PybJdWqmXxq34lRhEn5LlurRSvtal2ARA8qqkPLeG9dS6FBsBwKtiPd0alqtzCRvhX9jTk0AAoAGwvOE/cY/bDeIS43RX0Kuyq5y5e9yitlfQH0I35YIn7jUcah02cXDar7s+ve1e7qz+kQdz4kpu/e3Q6LuH3vv6kt8euLL0+Kdw5MS/JWuOUkFit87FlptFs5ZuG33/S70lpvgGwOf9737P7BZ18Yih6gr/WHJVtk5puqLVF8/8W39T99j2u8hwmlchXSww+lkh0U57oq+nI146KaNGuVflE9TN8WLfFr00PK1WuVkhkdqlFscqRRqtFIX2u57pAPwo3Z/oymdq7eEnMw798TACgYHpGNgSKjI87YgTV8Xf7tgVcH4LARRHypHdb/nL2pFFr75R79JcBZACfse7EEFu5yqrw+ov/aOV9tv7hT3BOuC0Xg8gvbA4UUKxYSKASQjBl1f7jD7FrWG3ptUq+2g8wskZUPUFAJ+C1QN416dg33nUklvfqvgvABWBvDo3nXu4X13g7zDtH+sDf0d8XGQYT6CVDExFIMbAdMmNyufinIIFRYZ7AbwCYDA6aajVLbmVAEJZc7Fb8OuhsSYAcInaeXJriTZsFpP3hkU3XvBNybTPAHrZaLZOtVlMkty6OMdkEECKCmdPXiJQZn6+74YKo9l6hpLcG92i9ulAhNJtNFtjYvvsnypPXeLyxekHpGx5dGf9UDtiyNkOGr+T+LVKFNSHnzI0KWFo+t8hCQ4FEhwKMLCzAZDWc+zlNgIjbXO8uDfOKbwmMGqyJ/qSnXFSalalqlQAPcDAVABAoDQAi+OdCrgfSnBpICgBEAAPigwRi77Wu9KfB3AZ/M6qDydwkyJ3hFUDkGB3p4Qlh7U9hYVsF4Bdvh8S/6oQ8S4AiUDuzn55Nc/pBbdGujy5QTk+3qkYkgrlKQRSSsR8AL5iYM+W5Xl2iwp8ZZzuYkoEPugiQwVCfYdTZF+LIsNkAhUCKC7P9W4QJDyUUaM6E8BTDOwh+P1fiUAdNlSNolXjETU8HaDzXAWgZE/D4I/kFhKNvHTTC98GdkRZnB1X8gCAe+TWxDk6vQ07z9lvH4B4lX2r3Fo4gM1iqhoy/413HF71XIAEnGDEKlIYzVYTkGPSKlr+8+Udd5nl1hNBCgEoAICBSQR63REnftCY5EtKrVP9pnMJDfZEn7cpUfSoPcKi9BrlRYL/ewUDEwk0H34Hr1AitkFgNJGB3RrvEPo0JYqXGpqUFxn+z7n793pzRYYfDvsCANYB6F+T7r0vuUFxOoDMQLtaAIUUIZuxWUxr+817/zKPpH2/b9K2DSvMdwTdr6wO65C0DSlba0chU1+uiWS/HrV0nb5VAfgXnR37Bx8IozfHi5U+JStIblT2yYBqCoDDBfU3EeiJqkzPPpeWvWWc7momAD2O2JZ97VH7ORHatBuoD3Z7QPson5K9o/IJveH/0XR4cBuQsmXs3sYBSSHX2oW55cVrBhP+OjVdX/Xy+ntn8Mjh0VmSHVcy51Br1t3XL7px3cs3vfCZ3II4RyZR0/hXteDG5J7WPcDlcsvhAHB4DW8DOLz4Kuqn1cc+sPivAmUtk5hyv0vUdydnFfjfbm4q8qdAvBB/u2NtfJsDDEWGAkOT8g0Apzr0voa4FoWOQEo6nDIRuL4HokdfNyyMe0FUsPfTapUjAGxnRYlfeVXMpvYKrx/Bx9iZAfz9cC4sA9MSiDwqaYa6yPAOiuz7wv8RALsfvviDcQ8ubjjUmnVE16ijyBpBy0/YnwcAfZO350eyX12rsB3w38HgWD94/5f8PYCHExyKpcmNynvgd/is9cm+ew/2dA9GkX0kiuzmrNmtS4zTXc0ReQMdpci+QeUTrgk8ktCJwc3WVFDb6tN7T3RVX3diV/2QGQwCRmas+1huLdGMzWJiozLXXSKQ5FhZMu0hf54kJxrZemhUs0Di9oUzlvNdyqIE/zQ6qyJIW3ACC1gigdFsHV/TkvOOxBR6gOUCCNmOljGB34GcDOBeBBZMHX7JtlSrqHpe9y4D2wpgmETs+tp0MZVAZxzp+MOkzHVWpN/SMp5A+QDeA3Cuyks3AVh51OoA/nbOADCvMdH3kcpLqQC2tFoSHrAt1bbPlQ0LVc68xxrdqT2NZmvP4x99ZGSNsK4un9wCANvrhq+JZL8EUgNoIdACHHt6vhD/C+czt4a9UZXlvco43cVSAKRERu6JsjHw79cAHuhIOoDfSU2dBL99rIyVHCm52dM4aCLANr1w48ufyq0l2nl25rLtRrP1MgCfCRAfAnCb3Jo4f8S/xkA12CeqPpRbC+ePpOuqU1N1Nb4v77gr2sflKQxE/tRJKBAD6Qsh50gzq0WG3j1J/ZrAaIIjTrTFOxWThPuayoz+Vzs2E1tkr0aRYTOASwOLro8zW2xfS8DaJH//eQxssc4lzM+oUd6EIsMcAPkI74KsDwA8olM6L4G/Pn6nkTXCavekqAGg3pVeE8l+W7XS6V4lq0SR/ZHjfDnFAFwAfARyad3Cc8bpLnnrgHWSslx3EgDUpfiqOmGIhQAL2AY7/CPgHIMR9y0bCmAUQP+VW0usYLOYrInqhjcZ6F83LLqxS28/GYtMyv16EIDUVG2NTW4tnD/S4tPX1rZmlsmtowOsAKgV/sU2UZ++EG5sS7WK2mf0LzKwLQKjk5x68fbaNF9vFNmD/S6LA6kDQGc+3yJ72cF8z7SKbM+z2lZBA2A5gAdxrCjtCWKzmHan6aqbMvSVQa9bkNVh7Z+8pScA5CfujUhI+jDEMMARLx4/unyMcH7sQGoAYNSpgrvFBBbIwWQSuvkg0xEKknY9R5CQF3/gfbm1xBITc1fOSdbWtRSXnX2n0WxNllsP53/UtmbMAYB0fSX/XqIMpzexprY1s05uHccjMDP3+zW0W8/UFRl6Z1eqNqfVqWZ41GwngCFxdzgeP6EgmN8nsQAAA7u6Mz6KcbqL5cxq/YcAWhgou9l2nUtYyNBXfnuwqSAx2N2vZHVYcxMODgaAfsnbIje7XmTQatykSGhWvNWx4+1rOxCJjVryyv3bq6bVqdZ39BybxbQ2Q195JwDEKR0PdetBpgMYzVZhS+3I/jnxJbbV99zS7bdi7QyLZi2trXeln+aT1CkAnvdPQ3Pkxmi2jt9RP+w6gGFn/dCbeC57dEEQRQX5tMc/Un5sFtNam8X0SHe9jtiWahUtj8bPB/Cb2kM9DqV5n6nM9p6MIntI6lBXZnlqAaA815MTZBNWAIyBgYGFNQq+vW54EUAE/1jf6TFFVof1p4rT9gHA5poxv0aw2z4EIqVImyPYp5wcjl53atFEdUvuMgBw+hKbjncsBxPcoi693GGcL7eQYDGareNDsXVeMNgspl8I0v0ALp2U+/Uzke6fc0QKAVL6cw+JpwVFGXkJB/v1TNg/Sm4dnONQZOiVfkhZom9VPCARW0egIem3tMwJZWohMWwBgHiHQn28Y4+s0b62OV56iEBghMfCHJzTA4wB7EL418d06nojq8Pq9CWoAKCmNTtie1TXpnqnAUBTglgZqT7lpDzHkwcAh9K8Azp5aj1BcqRoD40Og6wuRYr20K0AawEQk7VXA4PGdwBbgCAGkVDwl4K3Hu2ZsK9pXeVpM41ma0SrhnD+TJ+kbYHq5axT1UU4kaHJk1Ra70qPSP1yTuexLdUqpPsTbwawRd8iGCqzPK+V9PRMCVVUtS1Z1eoNAJBkV54RbP5pokNxH4AygVG4b4IKARxehNfpG2FZHdZeibv7AEBB0g5fpPpkhPEAUJ/i2xWpPuVEkPwRVkadi7DaLCaWHVemSNHWTgmPsq7B7MXXJbb6dBcOTPmt0mYxOeTWEyTXAEzjL0TOdACui/TU/MIZy70J6qaJXkntAvCa0Ww99nYvnDBD4wX4oFM6n0B3zz2MQuzulHK7J9kutw7On3H8O75/Sr3ykMDoWQA/EmhQ9o2t14RxwfbgwHT+VAS7aKrILvkU7H0Gds7BV7R9Qi/xd4oBSPDnzHb6RlhWhzUrrnw4AJyUtjFiDmt6raqBgVUYp7u6RYQ1u0pdDwAZh1SbOntuiy9u/cGm3rHqhEWEr21/OavVF096lSOoMh1yc9PiawsA8WIAzD9VAwC4gSCum/7czUVzl1wRMcfVevs9WwD6B4BTB6duWhqpfjl/xGi2KvY2DhrOIHy6Y8Gld3BnNfpQkJcpyKc7/pGciFFkIBQZbox3Kn6JdwjxVZme1wCcjSJ7WNc1+BTsfAAgf25o0Ok7ldmetQRSaF1C2LYV948ltAWg/QjiRlhWh3VDzfjNAFDWnF8dwW77EWh3BPuTm8PVEDpd+LvRnbreK2lyjGYr36L1KIhMdTmA6g3VE16SW0tnMZqttKv+pO8VxNI0itZZAN0N/7Z+N2kU7j7flZ573xe2i3YZzdZLIxjxfK1v0vYDO+tPuuqMR548LUJ9ctpAEE8FkMMgvC63Fs6RKUjaPTRdX9Vfbh0cP6Uvacc1x4vVAJ4HsE5g1Ddrdus1KLKHvQwmI9ZCILATTN8RFXhHFNi+jBplOCOsUAnuZK2iZVswN8KyOiIeUasC4Hnvn49EbBcVUWAjnXqxNVL9yU1Ftqc/AFRneDtthInqhioAmlGZa/jAeARuWnxtL4L0F73S8aHNYorYLEEImbvPPiBneMb613Y99LclgZW8q2wW0/NnGz/KG525+gmPqBUBvBWnbK6Y+tjDnxjN1rvDmeNqs5hY3+RtUwDU7Lf3f95otvIoUoQZnPbrcyrB7UtUN/Itc6OUuta0PXZ3UtSXtery+KOqs/LK1N/oW4S02lTvswDOQpH9YKQkqHxCMgAPgYpwAuU3jdNdTCHRUgJNQpEh6N2ojodS8Pbon7I1qHUKsjqsyZpD4wSIiNQij6rndfkKiTSOeMkdif6iAYVIh79jV2fPHZO1Wg0AKdraM0MqqotQ3ZJzJ4OgmJS34ge5tXSWMQ8smQTgMQAfb6g+5dr2ry+csbz13X8+chuAIQTprxpFa/zO+mHnAQj7wqxFs17dJzLllQAGGtT1L4SrH86fMZqtml31JxmNiXt3/vbAFS1y6+EcmTpXZlmrL77bBF6ikZKXtae0aqUdAF4g0LpWnTQw7R8t/4hEVLUtosBMDOwHFNkfPNEV/l4lexsADqV5nwiNuj9iNFvVrb54qnbmfB/M+bI5rEazdXyjO/U0CYIaEVqZnFWtzgWAjBplzE3fBktmjaoi8O/2zp5b6cz7AgB+rDjdE2pdXYEN1RMGK8i7V6tofVNuLZ3hHy9e1cMnqb7VKZ1OANfZLKajDrA2i0k8YDnv/Xp3+gL8bzMJLcAKw6nRZjGtyE/Y94ndk3L1tc/eYg5nX5w/cI5XUuv2NA6+XW4hnKOjFtwkkBgTdVi7HP6o6sy8MvV3ag/1b9VKZgBT4m93RHwh98FXtIMUEvWtyfCFZJZadU/TXkec2JzQrAhXOlYaAFS15G0N5mQ5I6yFbfYYjlSdv/4AQKBuUSEgQFB1WAFgW92I7QBEpzcxL7SSYh+j2doLwESRqV5ZOGN5zGzXazRbyXrgr/9pcKUqJuWuuNNmMdV37EwqBsjtd1qJBqZsPiWsQgEMy1h/daK6oaa4bOqtRrM1Ldz9cQC90jETYIcAfCO3Fs7R6ZeydUSCyh5soXhOkBx8RTvOp2A/AFhMDGvKcz2TdObmRyMdVT1MToV6FAAQw4uhajPOKdyjdQvpKDIMDlWbhylI2lEAAMmaWmcw58vpsK4NOKsRq/PXkOS7NJCYbAt3X9FCVaZnKABUZnk6nZNis5i8KsFdk6arCrtzEmuMzFj7DADEq+wd2zEtapBmSUx5EYNgfnH2kg4Pcoe3WCSw+Rm6ioZdDSdNDjjtYePpGa/bmzzJZwGUTJBeimTFgu7IP168Ktcrqc4ZkrrpYIzmZHcbalqyd7T44nkFl0hRZBjPihI/yCtVryGG8QBuJtCZPa93rZZTlspHEwHYMw6pPg1VmwR6G4DEwK4IVZuHMSbumwAAJ2etSQjmfNkc1lNyVg4FALXQ+hYiVOdP6aN8l5a5UGTvNoOxQiQGAMQQVD5aXsJBjU7ZOia0qmIbo9lKexoHjuuRsL9+64OXH5BbT0eZ9cINf1OQuEijaC0G0OkyXDaLae0By3kP17TmjJCYwosI1Eu1WUybE9SNCxiE8+tdaa+Es6/uzoqS887xShokaev+I7cWzrGpackp9UpqSW4d3QJ/XdPvCXShwEDOOPFmFNkXocgu++fvU7DzRYH9FFKfpshe3aIT97s17DbbUm1IgwQbqsfXA8DBpj7rgjlfNoe1wZV2npK8mNb7vbmRqvOX4FBIOpewIhJ9RQvptaqDAJBVrd4XzPm1rRkryprzu80itQ5ycrMnKbW02Xin3EI6itFsjS8unfpknMohTsn/ZLbNYgp6sLVZTAcJ0s0AJo7OXP1hCGUekTN6fP5wz4R9h9ZUTL7MaLYWhLu/7orLF/c3AAdWl095Q24tnGOjUzoVBCm4rTg5Hca2VEstWvFdBMpDEkhMbFYmyywLgH/Rl1KkzEPp3uZQt92cIH2udQuqrCpVSHNZG92pcQCwu2FwUIEe2RzWHfXD0kSmWPXkDW/VRqTDIoMCQF8AycFuXxajBJ3DCgDNnqRfGYRUo9kaVAi/K6Ig750ARECIWOmSEyGwa9XzblGX0+rTn/XszNd2nmibfyl4a/mAlN/KNtSMn9b7rk+Gh0DmUVk4Y7lY0lwwSmIKF4DXjWar8rgncTrFTYunDwbYFLXgfvdYi/A40cHAlM0jFSTq5dbRpSkyUM8S9eN6lyKX+TdV8SGKtinuUaruDwAqLz0d6rYza1TzAbi0buHiULaboa8YAkg+AEHt0iaLw2o0W9MBDGcQvo5Un03xvhsAqBjYJAS7fVkMUp3hHQMA5Tme9GDOz44rrQOAkzPXjA2lrlill/nT8SJTXgwwBYCPI1WS7USYkLPyeQBXArh/z8MXfReKNhfOWM4MmoZTJCZUSUzxutFsDeuKZZvFVApgNoBxIzN+5PVBQ0xda/p8gOiMnlZZc/I4HaPC2XOLjylZpLdQ7i7YlmpJIvaYwOj/GNhiABMA3IsTqHMaagh0CQBHar0q9HXsi+xNAD5lYJfblmo1oWo2Q1850aBpFIK9KZbFYZ2Q882dANAnacfPkepT6/LfKZzo9mWxhtLnj6wKUnA5rEPTf2kBgAS1fXIodcUqAomX+BcLRrS6RdAU3PXxwPVVk24wJu5pOC3vy4dD2fbbcx8rBWg6gMEFSTveD2XbR8JmMb01KOXXA5tqxp7db977UX+jEEv8VHWqUaNo3f3CjS+HbPEGJ3xUOfPK4L+WRWoHuu5DkYHinMJqgdFtErElBLqJiprWosj+SLQ4q+L9iRMZ2FQGFocwBeCqMj3bCJSSUaP8JFTt72kYVOLy6fcEe74sDmtpc6/RWkWLODh1U3Gk+lT7BAFA1IX1w01qvWofAGRXqSuDOd9m77MCAFaVnc13VQEgMtXhfCERUW5HRrNVJzLlO15JVZ+fuH/CslueCfliQ5vF9NWQ1I0b9jUOPHfqY49cFur225Omr57IIJR4JO1rRrM1Ptz9dQf8ecE01i3quk196lgnXtWkAoC8eBvPYw0lRQYCsCC9VnVKXYpvW0lPz+xoWFzVHklgTxAorAG41DrlagYGXaswBSFyit2iLtEtaoNOpYu4w2o0W6mkucDoEvUfL5yxPCJbstY8p1cxsFEArIiysH4EOKEc1q/uNNcAaJSYIqwljGIFvdKRDzAnAnYUqQWDwZCfuNcKYAhAVy675ekd4eqnd9Kus1WC++DO+qGPGc3WpHD1AwCv3fJ0BUBXA6ygZ8K+T8LZV3dhaPrPjwOAUvDEWIm27suQtI3DAGBo+s9h/b11J2xLteRWS/8BMA/AS6n1yqHG6S5Rbl1/oshwrkoUxjAwEWEMwKl8wsRQO8UaRWt+oroh6I2IIu6wKsjbB0BPRLAwtUctXU+gpNpU74/RFNaPBDXp3gkAUJrnDnpP9jhVU126rmpS6FTFLomaxkt6Gfa02Cymh6PZWT3r0UdvO9jU5/Sh6T9/a7OYwpor/vSM1+u8kuZvAHLiVM2vhrMvALBZTKuGpv2yqqS54HTTvxfcEu7+ujJGs5VKmnoX5ifute99+MJSufVwOkZJU+/NALC7YUjURf9ilXiH8LnGI8zxqKT3AMyKxshqycvaiRKxtwFsJtBkhDEA59SJCgAIOMahcoozC5J2Zgd7csQd1nHZ398PAMPTf1ofqT5T6pUjGRhzxEvdrlyLykstAKAQKeh9wXsk2DQssEtYd8ZotiZWOXM1ChKjujSa0Wzts7thyL16pWPLoJTNpkj0abOYfjYm7lnm9Cacf9Uzc8OyD3Vbehn2TtMqWvZsqxtxn9FszQp3f12Y4Y3u1KQKR495cgvhdJwKZ88KANjbOFBuKV2DIsN9aXWqqY0G3+aKHO9l0eisosigST+k+lASEF+X4r0ORfZV4QzAOeOlUxkYRAUeQwic4iHz34hzizrY7H2/CraNiDusuxqGGBPUjc78xH0bI9WnvlUxikBrjNNdMVGGKJQkNyr3A0BOpTroWm0H7H3fq23NpHAXiY8BxgBEexsHLpNbyNGYs+TKBLXg+hiAr8UXP80y/UNXpPoel73q5lRtTfkP5VOuM5qtueHsa+GM150uUX8BgHitouVNvgtWcBCkywF4vZLmbbm1cDqOQVN/OIc1Tm4tsU7TE3FLARQBWJZkV46KyjQAP//RuYS0xiTfv1LntITdf0qrVTJRgd3K+U3zQuEUO7yGdABocKftDbaNiDqsRrNVUduaOajZk/RWpPZfr3lOXwBgJIDPI9FfFHK4ZmXQ+cJuUbcdIBWAsDoh0U6GvuIv8Nfj+0luLUfjgL3fSo+kHWRM3HObzWIqiWTflukfuupcGWcApAbY0r89dVdYxxebxbTdmLjnPy5RX1jTkv16OPvqitz333OUCWr7rdlxpbtsFhNfVBlDDE37ZQgADE3/JU9uLbGM/cm4pYnNymudevF7ANejyB6Vzmrd03FFAG4E8FjaP1qeDHuHRQalwGi0UqSgo6HtGZK6cRAA5MYfjI0c1tz4g6cCMACI2JSqqGBmACjP8ZxwsfRYpDbVeyoDw4n8EHsbdjUCwOisH84InbLYI0Ft/3umvsJjs5iCKnocboxm6wVbakeN7pu0bUXxvFtl2cbUZjHtjlM13Q3QFJUi/It4hqX/PC87rmTP2srCi4xm64Bw99eV+PLAhWc3eZKVvQ27QnZR4kSG/fZ+vwHA1toRUTkWRT1FhvEoMnxpaFJe69SLaw6l+86MYmd1UlKj4j6nXqwAcHck+qzK9JgAxLk00q+hajNDXzkWAAanblId79ijEWmH9S4AGJ/zbcQiVGm1qhyfgjV5VeyjSPUZTai81MQIJ5SPMyBlSxUAKEgcFxpVsYfRbBUONPbTIoI3W53h/CfuGwKwpQB+2dM4eJqcWs7s+dnCXoZdlesqTrsg3E7kwhnLWaWz52kAOQG2/K9P3RXWDQy6EtUtuecDcK6pOPM+W7ERCwAAIABJREFUubVwOke5w1gNACXNBSEvVdflKTKMZ2A/ADgbgBjXorjTON0VkYpFnabIkJhar3yJGBpq03xTUWSPyPdNjC4HgOpM72+hanN1xeQyANheNyzo9IKIOqzb6kakGjT1FW/OecIWkQ6LDEqVj05RivS+cbqrW243aGhSHhAYtZ5IGzUtWesA5ltXWdidpw0HSFDEV7fkhr1AfmcZcPe7px2w992gJG88gMtsFlPQUy6hYOGM5eyAvf8oCYom+LdSDfqOuiPYLKZKteC6EaCRAK0MZ19dhRtfuD6eIF0C4EObxeSUWw+nc6Rqa9QAkBNXkii3llhDFNg0/G/DBQbgVBnlHBXbUi35FOxNAAUCowvyr3NtiVTfGTVKQSJWmn+d65dQtekRtSkAUOboVRZsGxFzWI1ma5zTm3CS3Z2yPFJ91qZ6LwCQJBHrzlNeKvhrtQXNe/98xAvQQQC9QyMp9uiTtP1SAFArXFFVyspoto53ibpvmjzJapEpASBDbk2A34kEMBPAqOHpP1nD3d/uhy9+b3Dqph2/VJ8y3mi28hJsx8Etau9iEAwTclaGbMqPEzmGpf/cFwCGpG3sJ7eWWEMhESNQqMs1hRxdq/COUqRznXrxKRTZv49Yx0UGItAEgVFI+8yNt40SyOc+kRvkiDmsfZK2XwRARZAiVn+VEeYyMJT28KyLVJ/RRlO8OFUUWMKJ7lKRqq1pTdXWdFtHQKtw/T1O1czOMX6wW24t7SgESAkAzP9zLpRTTFtsFtMHg1M37tt8aPSUIfPfnBju/rbVjRgD0H4A/zWarYZw9xfLrK0oPEktuJxpuupFcmvhdJ7dDYO2AUBx6dRTjWYr36a4c4wDUE6g+YjWTYSKDBMyapQXNMeLOw+l++6IZNdlue4JALKdejGk634MmsaTkzT1J1RpKGIOa4LaPkdJXpzT64OQhZiPR1qtMsGnZFvyr+t+5awAAEWG8QkOoY8gQcAJbq2WE19CrT59t613ub1uGCNIqyNV3aKjpOsqAw40kxCF0YK8hIOTGOigw5u4NNxbqdosJgeAKwGW1y952+pw9hXLGM3WBJeon+KRtK8unLH8hNKFOPJQ5uiVBQAeSXs1gJXcaf1/9s47vK3y+uPf996rbUvee1xn7wUZzkIQEgJiU9pCwiZAGQkU2iqUIUZBgV8poewAoUBaCmUjCE0gIoMMQgbZiZPIjh3voWHte9/fH3bATUNJbEn3yr6f5+GJsaX3fJXIV+e+7znne3JUvqYdS0FnBDXiB3I1EWp+Rl9KQd8hIJWpPrY80eWMmhBzEQA0Z0Z3xXLdvc0jD3pC6T0qa0hYwrqj6bSUFLVnx/M3L21JSECbKY+AjFZFmb5sN2gmIISAAD20VtvRdNob/mgK2xd3rnirI1MEO8gXMX0utZbjGZG1dSwAZOvqPoUMrWJfuuWV2mNWqkUph+Ne/+uyWzaMz1u7dn/r8BHjbK9fF+94ycjIrM33ANByJPIPqbUodA8d55vV+SWLOHnJ90aMHvZmAoKGnMgnUms5Ea6lWpUqwmyhBLkALoPN3ZZoDdlNKiMFbRMZxNT6WgRbEqUqXU9urhKSsPJWR25UVA9pC2Um7ALZlBlZAAARTpRlV3eCcFJQoKOwvIe7b+Rg5xdlPRWVbIzLWX8lAGTp6rdKreV4VlfP0gA0enreumvklqwew2W3rB6RtcVZ7SubddGfH7w13vHSNc2zGQjftgSz/8xbHcqcyuPwRVJvydA2Ri393v1Gai0K3aMk9XBWx1c0bl7yvZH0Nm48Bf2u5IZgXO2qu0tppfpBo5fNqM+NvA6be7tEMqYSkG9iaaDQf+FHUwA6CKCD0YMTgYQkrMMzt94AADqufVUi4gEAFyWXh1WiWFMYSVgJgtxwlYaqCAjxa4U96GGtzojMLW0AMCFvtaQjkyTiMoYImFzw1XdSCzkegXLlAPn2hZuXJvxO/FToZ9p/kY5rP7S9ccIDvNWRHc9YL/9mSVAEOweg6gxtw/IFS+b0dYe2H+CtjtzD7kGZLIkulVt5i8LJc8Rbtp9BtB0gD0CGJytyRHjYOAzAOAIiS5MRajNaCMgfAbyaf0vgRik0uJZq+wMY5k0RjsRyXQ0bfBQgAAhBD04EElQSQK/Rcz46q/SjxHzg20ycyc1mMCJ5t6+OswKAjBZuEAC0p4iv97RWp8y0fz8AhARtnzOv3tIwSWQgfv/MvLcapdbSlflL5poYCJPSNU0xrTWKB8/Me8sbiBouBpCuYQNvxNtK1WW3HDg9d90bLcGc4RVtQ1/4qcfxVkc5b3Us7EM1gJcDhG0M5C+WWohC9/FHU7JFcPtddsvjSrJ6crRkRF+ioAhoxXel1nI8la9ppwosPhIYuhfAHVLpSPUyswHAbRJ2xmpN3uoY7I+mTO7os+jZiUDcE1be6iC7msfqGCKsXDxvWaKG804gIOmcQN5PUDxZYvSyhQCQ3aT6tKdr/fWmN48AaN7eOMHTY2FJBG91cACZEKWqxI0VOUncofTLRLDsmJxNTVJrORlcdsuOohTXMyFBN7s5mL0k3vEKU6puS9M0f7Oreew1vNUx8vifdyap3wD4E/pI40qO/ugf9ZzP5bJbZH+To/DT6Lj24QaVNzH9IL0Bm4mkt3JD2g1itc7qrZFazn9gM2kLa9QvAyA1heFbYHNL1giZ2aIqBhApqlG/Gov1FiyZo9Fz3g8BtAPk4p6eCCRih3UgQIp9EVPCksfmjOh9FJRGWdqX61cRZenozllzB3/2wSfHIfSxWaxnFC2/AIChKOXwPqm1HM/X1bOzAKApkJs0o4lOy/3GmmeorlpbM+PXvNUR13roxfOW0bZQ5sUAWlkS/ef8JXOPbxg0d/7Zo2OqZGHG438e3uAvyBuZ/d0eqbUodJ8FS+YQkTIlA9N2Z0qtJYko5wSSldLOJsTa9BRZzAlkKCPi0pIbgl9LrGUqgO9ilTRXevq/7Y+mDhmdvekvLrvlk56eCMQ9YR2V9e2dAGBUt34V71jH0PuZ030pooe739OaqJhyxJci/DqopQJs7pg4HxWluIhJ3TI5FmslC55w2nkAMCp78xaptZyAqQD2f3LPAzGtN4oni+ctE+rai6YDjADgjXMW2eNaX+qyWxrzDVV3CpQbWuXpd/wNrLOjH5HGoClR/hx0D7kYACpah0h25KjQc5a7LkkPCTrU+Epl2ekuR8Iq8VYKGgDwgdRaulL7ou5FADdR0EXMg56PpNTiWqo1iYROdhujMTmx462O0dsaJ1hy9TXrP7r7oUdisWbcE1ZvxDg7Q9sYPbP48wPxjgUAsJnydEEmO8XHPJmQeDImxccKlGB3rNbL1DV4vRGT4YzHFnOxWlPubG2YpANorYqJyKpO7Il/TGM0bGBmtq4uVrvnCcNlt1SqmeACdCTca+JdQ7r+/t+8PSh957ptjRPH81bHjC461mtZP83R19ajlzeudNQM0zkA1nxnuz7p3jMKPxISdKUA0BjIk6qLPKlwLdXqCMWVbpNwFDa3V2o9P2AzjcytV93oTRHaKkvDD0otJ62Nnc5QQvx6sce9RvOXzE0lEN8CSEu9v/CCWOgD4pyw8lYHd9g9ONMbNi5LYEfqOQBAQD5LUDx5YjOxnEDy9AEmZmUR2xsnLBMpSyo9A/rSqKDJAPlGbh3VWxomTQkJOu2QjB11UmvpDueWvf+34pRD9ftaR5QDeBRxriHd3zpiFoB9AP42/qFXso59PyqqfSJlV/fmZBUAgoLulwAZOih9pzLKKskZnL5jEgDkGaobpNaSDJRUqW9XRRnCCJBPTmAzmQC8RygaQhrxNP66YEhqSWlubigA5Nepn+vpWjW+kuUUzIh0TdMdLruluefqOoj3DuvpAIwRUZOwN0qbKfpAlKUBAH3aI9ttjA4BoKGgsay9PNT5Z5+oY7395WtGAigrM+2XV5E+gA215sEAsLVhwv9JraU7LJ63jB7xlS3pKB8FA1CdSd3yJW/99F7e6jiNtzpiem1y2S1+AHMIxPxMXeOWY1MKolQVbgrkymr6QzzY2jDxXAYCHZC2JybNFArSkalrnAEAp+eu6/Xv2x5jM5UzlDwGAEYfd2NPLcpjAbUZy8MqcR8F7UdAfpV1h//Qzz8r/lDQaQD2webu0fuKtzrKv6ufPGlw+o7tWx+6JqYTGeKasI7J3rgQoEhVtyVm/qrNxKV62QJfinAANresdsQSjS9FvAwAagrDqliteXruuiYAmJC3+lexWlPOVHnLZgLAgLS9PbKTixNTADT5IqYkbqAhnwEIABAIqEiBEED+BGCzlvW3n23/v9281XEtb3UUxCKay275bnze2n/vbRlV/OmhX84FAAJBzTHhSb15QgBvdTAN/oIZIljH8ze/npjSLIW4saluag2BGGSJILtGUBlipqDH6uSlb6y0mcwA1qgjTG7nd6ISqvkB8SHjFJGBxWfomVcAb3UYAPwNINX7WkdOj426H4lrwnq0vXhMnqHau+PhOYm6E5zAikSb5ub+lKB4siWjheusMyXLY7VmYUrlXoZEaWswM/fnH538fN84Ph9AeEXlhW9KreV40jTNv8jV1xxx2S1Je2PWeQw/A8D9FMzU7x++Kh1AHoCreFNFVbW3tATAUgA1pz/0qmfWokXLeatjFm916Lobc1Pd9PMBrBUo92r/hR+upGBSo6LqNPTisVYD0nZfBKAIwN+l1qLQc6KiupCCqZRbmZJMcQLH7B6ppI2VLYsNEwD8g4AcS6AppE6gAcBmKicUX7IiIYZ2pn9PdqFHZm3+GsBAANe67JaYj8CMW8LKWx0pDf6C/Lr24p8c2h1rBIZeCEAE0KfHWQGALsjkAmgqmhdwxWrNxfOWRUTKVRxoGy55vU2CKAfwnctukdXrnbVoUUlbKDOlxHioSmotPcVlt6zvOurEZbfUu+yWt5b/fuHgoKBPBTBGwwb/mKr2RA60Dp0B4AuAtpz1+FONMxc98RxvdYzgrY6TNiFw2S1CqbFiFQCVQFWdDVgEkMPuS5zQcf4/qZkgxmRvlE8Nn0K3MapbxxnVbX1qHna3sbnXh9X0ZQAQCa7oqYFOd2l+Rn+/0cNuFEFTAIQARAmIXCaTmAmIGgBIhxOVuTuLDPrjezN3NJ1+2pjsjdtcdktcTtXj2e09DR0fAivjGOM/CGrFOwA0G37v69PjrAAgytLTGREH43BHcghA/9gvKy/mL5mbypFfTC41VXwGWKSW8x/sbx1xGgB8WzdtkdRa4knn7vH2zv8e460OPYAz0jQtv/RFUq9s8BfcCuBWhgj1licf8fqjKc8ddg96y2W3nHAsC291FOYZjnxQ1z5g/Al+LEAeHx4xhbc61AwZW5hnqF7/4d0Pu6XWo9BzKGVKedMBWd1EyxlNmFkF4BaWksRPx7CZtAD+kgnVLe16ob4pK3p+aZXm2M2xU6oEuiutpuiMdDdH0LHZF0Y3roO81WECtK8CdL9e1W6OscQfiFvCOjp700M7m8aKKSrv2njF6IrwsDHXILL6lvToJ4ZEBJQ5IkPHeIzigYwYrzswbbeh2lc6JsbLyo69LSOnRqmK5Olr5Dp/NQRAjtriRmfj1Oed/1034N4PiqOielaGtvHqw+5B0/zRlL8AeGrofe/sH5a5ranGW/pYnb/IyxDhDC0bKAZSrqxrL+JOz1332daGCRsFytk6va1FAEt76aSAc0TKGo/6Sh+VWohCz+kohzExh9yD+rSL46kQ0IohXZCBJzVaZgRiZjn6c1S9qjVna1X/0gWZTABPGPzsfYbrfcfcPmVxrREeNp6VJrIzvIZoU2o79xcAq7qTRJcaKz6r9PQvBMiUv8//c9xujONWEnDIPagwz1BzdPvDVyXEZowVyTkAkNHKPZGIeHIm+ogxXR1hGFWEfB7rtfUqX2UgamDPWWTPifXacmJ/64ghALDu6NkJK2k5WQoMVdfn6I82yq1UIdFUPHbJEZfd8urmB284Y2bpxxoAEwE8oOd86q0Nk6bU+YscAFaLlHnEH025CaA7ADL8X3c9ZhGoagVAguhoeggBeEPK1xIv8vTVCxkIbVDKpHoLxQDQHjHulVpIstCUFUkBAF+KODFhQW2mS4uq1Q4uSjKaMiPzYXP/ATZ3oqzpTw6bqYgVyT8A7G/NEEbD5n6sO8nqrEWL5lV6Bkwem7Nxjctu2RAHpT8Ql4SVtzryvOG0ghof3+N5XidLWCX+moLWo4+PswIATiCDASDVx8bcXWx744T3AGBf68hePotVnAzA5bJbaqVW0pVRDyzT17UXGnP0tcoHVhcWz1sWcdktm1x2y6Pf2a7vNyFvTQ6AtwDQjhpVKgL4xGW3HAL+o+GrR97WcuaXT1szm4PZ5SOyt9S47BZ5fVgqdIsx2RunAgBvPNAmtZZkwehhtwJAZjN3NN6xXEu1hoA9dRmA9xhKdralRcdk3eH/a7zjniqupdrUoEb8hoLqCcjFJTcEu/V3w1sdWftbRzyiYkL7S1IPx712Li4Ja4rKfV7nlwm5q3ct1WoIJbNb0wUPbG4xETHljDdFOAMAIhzdH4flj82M67V1rAuWzCFGtfuSfqa97VJrOR5POG28CJbZ2XTaYqm1yJm3FzzZCOB5AJ27qCQEEGfXxxzf8NXb2FQ37dyIqEFUUN0vtRaF2KBXtU8BgGGZ2/t8n8bJYvJ0JKqaMKONayCbqSS3XnVQF2SujLL0ZQDTsm/3fx/XmN0kq4l7XxtiiutzI0/C5u7WaMQFS+YQg8r7JoCMiKi5fPG8t+L+eRmXhLXUeHChjvMJg9N3JGS3s6haPUEVJQSAMhQbQFArXiwSiprCsCvWa0/IW1Pd8efqa2K9tlzYWDu9nyeczqaqPbJzBeJI5NhsO9lpkxt9YRf1Z7gSQOXuljGSepQrxI5vjp51FKDiEW/ZJqm1JBEeCir6dcLgeAWgNqMFwFZtkOiP5of/j7vfczNs7nC84vUIm+mmlHb27IBWXJL3m8DD3V2mOZj9VHskdXY/097XXXZLQhLzmCesvNVBDrQOz8jUNu344g/Wnk2hPUk4gcwCIGa0ci8nIp7cyWjh2ihBZTzs3t65096sZf3Bo76SmBkSyI06f9F4ANjeOOFFqbUcT6mp4o4sXb3PZbe0SK0lGejtu6g/xe0vXzOMQJydpatb4bJb+vypUy+iBCA1n9zzgDyTITlic4sCCwKKi2LudGUzcY3P6tcRkE8BVBGQcQU3B34X0xgxpOZl3TUU9FkAX+iCzG+6uw5vdRSsrTn7mkxtw5GRWVvuiKHE/0k8dlgHh0VNRrWPT1izSlglzhEYugU2t3JMAoAVCc+KJG4d5EFBv73ax8fb1lcydFz7WQD1A5DVcQ5vdTCVnv4Go7qtz9dpK/xvKtqGzKNgyGm565XZq72ILF39FJOmxS+1jqTCZipnBUAXZHIAfBmzpNVmKgDwVXaTanJzRmRPS3p0CmzuipisHQeEh4252Y3ckpCG0ihLr4TN3a0NxQ5ba/oKQLTNwZyzF89blrDm35gnHXmG6mO2nQmZv1r1qnaYOsKUNWdGlWQVwJFXtFoKOjCkFuNZYH4IoL22hjVD2zSnJPVQwGW3yMI2rwvDoqJaf8g9eInUQhTkzd6WURMAuuOlW175QGotCrFDENniAkOVRmodSYaZgBwbih8Tg5Cal3W/FwndCWAcBZ2bOd8/LGNBu3xvJGwmFSuSd1URIjRlRa/g7vd0+4Ruf+twB0DO1bL+11x2Szz6ZH6SmCesqSr3LRnaxuixbtx4U3JEczoAcFHyZCLiyR1KMJ2AsC0ZUXW8YozK/jaLIWLZgiVzum2RKVd4q0N/1FesUbFhp9RajidD23hu55frJBWiIGuG/PHdfgAmA0SxYu1F8FYH2xrKYva1jvin1FqSDCcFDQMA7XBpdXZ7JZuJFR8yPlpwVLUorKYcgNOJzbMsJirjiF8nLAMwjYDcUDQv0O0ZvrzVcfWellHnAhRBQXd9ou2sY5qw8lYHV9E2xKjl/AlztwJwLoD6jFbuywTGlC2FNWo9ABja2XfjFYMAW0XKYnfz6N64y3o6BcMebBv6utRCjifPUPObVHWbiB8nNSgo/Bejsje/AAClxgpld7V3kQ+AEyl3WGohSUXHbNEzw5wohNXUC6Bbs0JrXtYNpqBfMJT8MaKiH9XlRfrD5pb9eMHGZ/V2fYC93JMqOGBzd/smduT9fx8K4PmOMYEEAEm4nXWsd1jHU7D6o77S12K87glxLdWqBIZeHFKLm5RxVh2wIhkEAEYv+228YmxvHP8ZABxoG54frxhSka2rvaDzy7gOQO4OFa1DtQbO912nZamCwn/BWx1kZ9PY4XmG6vqv712wT2o9CrFjUr7zDAAYnL6zTxuGdAdi86wHwR80YSYdwKRTfX7r04ZfZDdyuwFMB3C9+o/ei/nrgo0xFxprbKaxWU3cgqBG3N+SEb28u8vc9tK1k1gm+j2DqIgfRgUiggTbWcc0YS01VtwAUAog5gPrT4QuwFzGikTbnBlVdpw6adcLM0VC22Bzx3Ow9CEA0LDBQXGMIQlpmparsnV14Z/yo5cK3uooCIua/Dp/0T+k1qIga0b6o6mFde1FD0ktRCHmjAKAMtP+ZqmFJCPqCPMSOkZczT/pJ9lMDGymhWlt7D8BuKuLwlfA5l4aN5ExpOpVbX8K+iEBadaGmGn8dcFuuY7yVkfO565L3wpE9WQm/8lNAM6CRKMCY73D+ovClKqAy25JyC9UboNqGAUVdQHGnoh4yQAlmNhuEEk8Y4zPXVvDkQiGZ265Kp5xEg1vdZCD7sEahghfS63leMpM+y8BAIYISv2qwk+Srm28HaBRAHErCVKQhg215lYAWFszIyEbQr0Om9vnTRG+pgS/rnxNO/rnHl75mnaAN0WoBPAYAXlHHWFKi28MvpcApT3HZmLT2rj1lKA4ytLLYXM3dGeZq5+dXwDQL0TKFkQEtfmlW155W8pRgTFLWHmrI6XS098QFtRvx2rNk+BcArIh/c72ugTGlDWGdiaiDTIfxzPGu3c9Lmo5f2OlZ0BvmwU4QKRcWr2/UHYf9iZ1641qJoQL+v1zh9RaFOTJgiVzWEqZ6/uZ9jfI7YRAISaUAmje+ciVsnPgSxZa06NPEwrk1quu+58PtJnmFVeptxh8TFFTZuQFAFfC5vYmRmVMeNToZbMbs6OvcPd7upVYXvvs7Wku94A9DBFHAbj0kP3CtTHWeMrEcof1DIBwjYH8hBxZ+p5MuRLA6QGN0K07h16JzZRFQDJUUbI13qF8EdPm5mBOSrzjJJIBabt/AQBa1r9Rai3Hs6t5LKvl/DsWz1vWrWMdhd7PZ4cvm9oWymSN6jbF8a8XUpBSOcOkaQlKrSOZKbkh+BUB+UwbYubCZrrvhDNZbabzAbzMgKQSIJTVrHoTNndy9A3YTOXCQ8Z3AFgBvJx7q/+m7izDWx1qZ/Xsf1R5y1KnF/77aZfdsjy2QrtHzBLWfqZ9txEIEQDxz8JtpnJDO/M3ANCGmPNi7l6RpDRkR2YCgM8gJGDHmR4CxAEdQ4R7B3rOP1fL+nEO/+EuqbV0hbc6UiKiepgnnK5YbCr8JBFRcwUA/7bGiU9IrUUh9oSi2txsXX1C3CN7OSsAZFLQh3BiI4GRtKMXBwSERYI74buNzVROQVcxFJd36u/WuK0FS+ao1EzwXYDMBpibXr/92btjrLTbxCxh9YaNZ/CmCq/LbknEHaCZgDAA0PmnOQExZQ8ldAYANGdGa+Ida1L+14UAkypStizesRLFzuYx4JjIxsXzlsnqQ2F09qbLALAGlUfxD1c4IQuWzDWomPBcNRNc7rJbfFLrUYgtvNVBmoO5TEXbUGVUWQ+hoHoKCgLCUFA1/jt/cFIC2pn0JbwTvlvYTFoAjwLQEBAAEAFMOdVleKuDHGgbtjUsai/Uc977XHbLKzFW2iNikrDyVkd+YyBf3+TPezYW650ETgDHkorkeEMlgNwGdSsFDaki5Jt4x2qPpK4CgG2NE0riHSsR8FaHUaTcUF/E9LnUWo5HzYauJhBxVvFniiWrwgnZ0zzyzxFRbeiftldx/OudpANIAVAltZBkh4CsAhA6lrQC2PwfD7C514fVdAaAPwKY0TnHVbbUvKy7OagRGwGcRUAEAFECEsYp5kW81UEAPLm7eczwMTkbVu1+9Nd/ioPcHhGrHdabAcAbMSVmvJTNvd5rELaLoCKS4A2VQIYQkP0FNwfibim6o+m0VQBwxFuWG+9YiWBczje/AkAytQ1xr/89VTbXTRZVbPjAX29684jUWhTkB291lO9vG3EjQLGnZfSViXafUYg/ZxQtnwYAIzK3SC0l+bG51xOQMwnIKwDEkFq0u5Zq/8O1UbvQ6yQ2z+Oyzi1spjTYTC8VHlW/CEDbnBG5EcA0dI6cOlXtefrqvwC4G8Bz2xomzYi94J7T44SVtzrKCcT70WF59kKiLpYiSyMCh4is31AJJqwSpwS0ojtB4Q4DgEnT8rPjQZIBQugvCURMLvjqO6m1dIW3OlgKdmJY0CbSPU4huTADYKVyn1GIP+2RlEEAkGuoUSbixAKbez1s7nltpujDmjAzLr2VXQmb6Yd+DKeTDHA6ydNOJ+knpcyf4uhLukUCQ10AbgTwlMcoZGfO97/a+bpOOdG+5Kn7l9T5ixZk6+pWApgvV3OaWOywmikI03GxRMIuliYPV6WKkspExEoGmp/R61QRku5LERLyRnPZLb5UdZtYknr40kTEizff1U8WWCLs/etNb8a9/vdUmFq44jwAqdm6WmWclcIJydA2bO/YMEiimjuFU2Jz/dQQAHxZdcEKqbX0JtLuan/YbYy+Z/JwkwH8tsuPTADmAZBXwmozFVKb8YOCWvXvQxqREwmdBJv77pzb/N02CuKtjrlbGybdWJji2jO54KsLXXaLbF16omk3AAAgAElEQVRDY5GwOgFC0XHFTOTF0gjAk6BYsiezRVVGQJDZzL2cqJgEtOKQe1DSN3jwVgcDkElRqlottZbjCQuaywFgfN7a7VJrUZAnwzK35wAEufqaVZDAfUYhIZQCCABQ5uvGGJOH+yWAdynokw3P6f/Q+e2tAFLMZiqLky3XUi1b94JuGQXdQ0BmRzjR1pATzWYe9PTIgv3yp62/A+jrAFbV+Phxch+bGIuEdRs6tldXIoEXy3a9MK5dL/SK+skYMQQAGEr2JCqgJ5z+bXskNStR8eKFufjzWQBMBYaqvVJrOZ5NddNVBGKtiokoSYjCCVlbM9MEABwjXK8kq72TMtN+i0ndEpTrUW1SY3OLAK4J6MTGzGbO7n7KcKbZTEWzmcrj79pmGlZ8RL0xr159ZUAnNgAYobrP+1B3rVaPMcP+f5dtbZj4RLauvhHARQma8NQjepywnln82VQAGJqxbXUiL5asQFiRgazvBhJJS3r0cgAIaMUDiYqpYYNVAC2++cUb9YmKGQ98YeOFADA651s5HrtPoWBWL563TB4XTwU5chqAunX33aqUSPVSvGGjyahpUxyu4oXNHWjMjp4FoN7k4ZbBZip2Osl8p5PYpJLkWqpNdT9leBPANlYkZW2m6P0NOdGBsLkP9nRt3uqYcLBt6OsqJlw1Pm/NVJfdkhQuXj1OWH1h41AAyE9wMbg2xPhSfeyGRMaUM6yAoWGVGNVZvQkrk5iQtzoLIAxHokndlby5fooOoE1qJvSl1Fq6ctML88YDKC5KPdzjC5RC7yVN02zJ0tUflVqHQvxoCuQxR7z9ZDdyrzdRen1wFyuSswEYBELXDtynvTmjmTtHEjE205SCo6qDJg83N8KJnwMYmnZX+6P8dcEeb1zc/OKNFzFE+DeAhkA0pfz5m19Pms+XHies39ZPFQDgqyMWR8/lnBJKDWsXTB4upI4wCa3BbArkLgeA9bVnpCYybhwoB8h6ue1iNgZyzweA4ZnbZOW8pSAfzn3iMaM7lJ7BGw8kxQ6JwqnDWx06ADlQZrDGH5t7Z1glPsFQlBTUqoaN2qEfB5tpaqLCu5ZqiyOPpr4BYK0qQoI1BeH7VPd5L4LNHRML+v4LP+y3ruasdw0qr8Gg8p7jsluS6kY3FjWsZQCCABK2w+paqiUioRkt6dEBiYopazrGcQwGkNAazD0tozcCQEswJz+RcWPJrS9dOxDA4FJjhexmnG5tKM8EqG9vy8h3pNaiIE/2tIweScHgu/rJT0mtRSE+zCr9cDIAjM3ZkNSlV8mCOsKIAMROxyg1Bf3o6Eu6/3Mt1cbPhtxmKqc245uF1arDXJRcRUGfJiDDCm8KxGx4P291FAhUtbI9kto+uWDVpbse+XVFrNZOFFxPF+CNBy5sDWb6tz98VcJ2p1J8jIGhBAAUVxcAPr1wRYqfNbXrhExDYkPXEoihLF3DRAAvJDZ0bDjqK5kNAAPT9siu4QrAVIBs+PreBXE3glBIWk4DAApGVvODFWJHvb+gFABSVZ7DUmvpIzgJSIiCqikBiaiotqBWfbdI6EzYTL+Fzd290jGbSdeUGRkV1NLp+bUqNyuSAr9OmEyBiXowqQSEcAJom0l4JP2u9gdi+YJuf/nqASmq2at9EWMqBTPjpVteSUqb7x4nrN6wKc2kaU3oaKOsZpUBADJaubWJjCtLbKZyA5jXAUAfYC6FzVSeKDMFl90iTnh4CUnTtMxMRLx4sK1xYh4AYWXVBa9KraUrd7x8VSHBL0fzpoo3AYvUchRkysC03Tcc8fL+oKBPqqM9hZNne+MEAgCra2YpM1gTgc29HjbTjLCaXrhnaOAKj1FYMGKn/rSMVu4aACv9i1L3hdTi2+luLgTA2fl4BkBOmyk6yq8XZ2U1cW51hMkMqcWxUY6ervczQQKSkdWs6hqJakJMS5SlmmPfICBCupuLaTM5b3WkZmjPWhUUdPmjs7+96qO7H0rKZBWIQcLaHMxRNwdz3o2FmFPA2PmnUsMKXEc63G1AQAg6jBsSNq0hENVvbg1mZiQqXhyYDGCby27xSy2kK4fdg2ZSMOCNB5T5qwo/SWMgpyjPUNPivPdOWdVfK8SUUgAiAFmZmvRqbO71GmD9GGAhAGAGPoLN9FiUpQvUYfK4PsA9CIBSUBr+U6qoBqEERJXm5pD2o9ekj4uS+qBWbA+r6SpNmGwPaMW25swIm+plV5o83EH2AU+YtZnKAXyJDuOlmM6y560OLYCPWoJZ+aOyNs//6O6H3orV2lLQo4SVtzrS0eEIkdCjipqC8LjCo2rU5YaL8xIZWGbUvKx7IB+q6wlACYgACVxuvOG07wBcy1sdJNlmBC5YMkejYi6bXpRauUJuu5g7m8f1AyCuOmJJmBGEQnLBWx0GICutLZT1nNRaFOLHkIzvf1HjKwnveHhORGotfRqbO8gBi6KPGNMo6B86N4iIyMAT0Ikf6QPsprBKrK/LixB1mKzLq1fXsQ94qKnLEjoARf+97nrYTDPQsdnkjNUJ6YIlc3QlqRO/r/L26w+Qqz6+x7YsFutKSY8S1hkln878sup8jMtZzyXyA18TIhoAIJTEpHMuKbGZLi+A6kG/XgwSimv0AXYgYvhmP1nSNM31baHM1CkFK0sAS1LNgdzXOmJKRNQwBYYjspy/CmB7sszHU5CE0ehonFXqV3sxTYEctZ5rb5ZaR1/E6SS/BzDWbKZXHPseJ5CPASwAoCIgEV2InHfsc1cNoKQ7gTqeH7PPbt7qYNI05/yrLZQ5YELe6r+9c+eipE9WgR5OCWjw55UAgFHTlrBh9QCQ1axyA0Bug2prIuPKhdBjqbcDeJuAfBNR0RL9H3z/gs39eKKTVQAYm7NBAwAGlW9GomP3lL0to4YBwLqjM56VWktXFiyZo1MxoTMHpO1WmgoVfpKJ+V/fAQCjsr+VY8OgQoxoCuQx9f7Cr6XW0Udh0ZGH/kjH5+wMAA8AmCHF5+7/grc6CIBn2kKZ56Vrmha9c+eia6XWFCt6tMO6o+l0AIDzyLmJHrh+bO5nctawdtSsmHGqO6I2U7lfK7ygD7Ojoyxdwwlkdtpd7ZLWXlb7+M8A3L+m5uwk7GSn5QA5CpnNN9zRdFp5RNSQbF190hbHK8Sfam9pgZ7zhcqMB/ZLrUUhPvBWB4uOTbtK3uooV6x3E4vZTB8/4Q9ivCMaS8blfOPc0jB5OoD/aw1lLZRaTyzpadNVGYA2l93SFgsxJ0t9TmRWboMKR4pCbHEiA58qHfNRs+tyw+ezAjktu0kVBjCJgk4CAAIC2ExNYZWoElhQXZDZDaDdZxCKBJZGTR5uPYD21rToADYKnRGcWR9kOQpKKaEPweaRvFHoQOvw7wEgEE2R9T/FiUjXNF9u0rQelFvDyiH34JEAsL72TKU2UeEnqfHxGQC+lJvhhULsKEk9eHWVtz8L0DMA8iVvdcxQklaFn2Lgve9bI+Lk6SMyt+zZ2Tzu98nWV/Jz9ChhLTPuv9gXMYqxEnOyaEIkAgCUoD7RsU+Ea6lWxUUxqahGYwIwxJsiXMiIGG4ACwAZefU/nCj4AbQBnckqQAEcDmqphlDoAAQApHJRkqcOExU63E0MaW1sSufj0flcQRUlE9DRWSgpLrvFP+DeD1oytY3TpNZyKpz+0GtFraFcVYnx0LdSazkehkSni5SrdNkt1VJrUZAn5Y+8YACKhwHkA6m1KMSPen9+58hAQtDRRW6GTHf2eiNOJ8kD8DGAJ8xm+i+p9fwveKvjRkDzOEsi7/dP2/vrT393f69KVoEeJqxNwRx1uqY5YQ5Xx0hzcxwAseSIZigS+ctrMxkADGpNi86KqOh52Y1cIwEZXELUQxhKfvi71PsZr18vihT0HQKyq80UbXGbhIrSKs1GABMJSNcRFguMd/v+4zVojwtbuVRLiqrVZ3EC+QRxGH3RUzI1jZqQoD0jmY6smgK5EwBge+OE56XW0pUFS+aQFNW5F+fqa/fJbXKBgnwYkbX1ytr2EmZC3up25X3SewkJerbzyyhkdt3vI7QCcKPDzVO2XPPsHU8B59xFQL8QqOqKxfOW9cqJEt1OWHmrgwHSUrzhtL/FUtDP0lH/eSUFZQCsJjbTEwC21BSEyzQhsierWbVPYKj7aEG4vfjG4M8fmR9fT2ozkYbsSGlYTS/IrecYVZThQ2pxGiUYowXDAkB6GwcKCkpQSSh2hDR0c1tahKa3cm9oQ8z37AOeltQuIdI6/+vklEdY8NcFKYAv4zH6oqfwVkc5UKBDRwNf0hxZadjAmSFBGwaIrBr31tTMHOAJpzO8qeIrqbUoyJd9LSN4AMjQNq2UWIpCHGFIdLxIuW8AfArAmQzX1t6E2UxDAGRtjMNbHbMJZs0vST3kGZOzac4z894KS60pXvRkhzUPHZuBibaLM1NQtvOInANwLwAUHv2xkY8VCYqrNRAfMoYZSpopqKfdIBZyUXJAG2L2CQz1t2REh6V4mWod2IsoKAcA4sPGChYkK6dRld4lnp+LkiqPUThCCT7UBZl1fp1wuC4vcqDfNSEP0DFbTXcqr6C7BdvyLPSeCZBj0yaS5sgqR197jUiZ9nX33RaSWktXWoLZkwDg+8bxyvxVhZ+kytu/AEDDctelW6TWohAfbnvp2hkivbxsfN6ate/eaT9x849Cn6b/wg+nAar3KZjvtVxw5jPz3urV48+6nbCeXfrxBSsrL0R5/iptgo+knAQkSEFVACIE5MoIRyvrc8NTUr1s0OThQlGWZrdkRM9N8TEN+gAbEBlkiwyyWQGpAE5jRGRkNnOZDO2oCyUgoKBAh4PWOxGOHmrIiXDqMPk4u0m1m33AI3bNYPUA+iXyFcuYAWm7h1e0DQNABYAkxZEVb3VoCHhdv7R9n0ut5XhSVO7ZvojRA5BdUmtRkC8MhPEi2O96W1OFwo+sPTpjAgCY1G3KzauEOJ1kIYA5ZjMdIbWWrtzy4g1XcMy5bxERVVGqmv3vP/y+VyerQA8S1kp3/3QAUDGRbbGTcxJ0ukIQEDM6j8ZVAIqAH452OQA5wFPH/p/Fj16uAEDQURdaUqU+g6HkcwAcAYmwIi45tl5hQl5M8uMNm8qN6paoJ5z+AJLnyGocBcMdbBv6utRCjseg8l2Wo6/1fLXwbkFqLQryZP6Suekgvxw+JmtjnVK/2ntxhzKmAdj/yq0vrpVaSx/nMID1TidhzWYqi+syb3UMZshFz6aq3dGpBV/+6rmbX+8TJkrdTlgPtA1XAcDqmlnfxE7OSRKDo/HOulAnbKazILO60GSBtzoGAoXFKib0gMt+ftIcWWXras9rDOQDMitd6LA6LtT0T9vjkFqLgnzZVDdtrEhZMET4QmotCvFh/pK56QS/msEQQdldlRizmb4N4G2pdRzjzMf/MhAYtEKkbNQXNo587ubX+8wc5m4nrARiPwqm1mW3BGIpKOHIsy40SRDvAxgaETWJ3WXvIRnapusEykW32K6tlVrLcZQDwMG2oYltZFRIKurai4YBwJaGybL5EFWILb5I6u0UjNpc/FktcLHUchQAOJ2EmM1U0hKcW168YZgvPHkrRyJClKrKDz5+UZ9JVoEeWLOWGg9eXJjiOn4Ck0IfYeh9/7wTIFcDlAD4Z8e0APnDWx2kom2IRsWEE38y8DPk6msuA2gUgOJwpfCTGFSeMwDaCECZ09tLWV09K59ACBlUXsU8RGKcTmJwOslRAHdLqYO3OtK+cF38j9ZgJnNmyWe/ddkt26XUIwXdTljr/AUiIbRPZfcKwDmLFl3GWx2rAtGUv3R8hwA/TgdIBkoEqsqq9xe+I7WQ49FygcsKUypDLrtFcgczBfmSqvKc39+0D0rDVe8lKqpnUbArn5n3lltqLX0ds5m2A/gAgGSNsPOXzM0mED6jYIZGqer8Jb95+UWptEhJt0oCeKtDBRjSqr1lK2ItSEGe8FaHOVdf83q9f0QpgVhPwTwNkJshQyOD/8WAtD2/qGgbCg0b2Ci1lq7wVoca6KfNMxxVnIsUfhLe6tABhepS48F/S61FIT5c99xt04Hz+pvULS9JrUWhA7OZ3iZV7AkPL1GnqMbuAkg2Q4RfHnr8wj5bu96tHVY1E7yk87nd3qFVkD+81UFueP6WOwfc+8E6AKuaArn6yQVfvT+77P0hLrvlLgAzADwAICnMAgAgReW5Ss0EMZv/YIfUWo5jHMBo6tqLZG3/pyA5owHCVHoGvCe1EIX40B5JuQUAphatlNVNdV/H6STqn39UbOGtDrbBX7DskHtI9pTCL1879PiF7yZag5w45R1W3uooJ1C/BQAE4u94q+PTZElWFE4O3uogAM5UMaHHvqy6YKKW9fuiUM8XKPfK3+f/+Ycmu85/96T6t9/RNA46LrB18bxlsjIM6J+2Z+7BtqFQMaF1UmtRkC9FKa5zq308OCb8ndRaFOLDprrpGSyJHHrupr+tllqLQgdOJ/kDgEecTmIwm2lCbE/LrJ+Uq5jISxFRMxLAb9+64+m/JCKunOlOSYCZgrAAQDscjsxIsqRF4cTwVkd5tu7ofUa1drQnnFEYETVHh2dufb5/2t57e0MtFW91nAmoRvoiqjel1nI8LBEuydTWR7+zXV8ntRYF+ZKmabmiLZRBZ5R8egS4RGo5CjGGtzr0AMwCVT0vtRaF/+AbAI8CUKOjBC6udGwMYm1E1DAdjbhkQ7xjJgPdSVidAIkCUCeLs5HCieGtDjKt8N9TRMpes+7oWfkAOa8xUEAACpZEFgtUZXX87r6g1DpjQccUA7q8w0aWXsFbHS/J5WSgY0d7uCpV7f5Uai0K8mZv6wjBwLV/t3jeMqXhqhcytXDl7WtrztYUpFQqZgEywmymawCsSUQs3urgACymYLqWXJqhbAyeesLqslvW91/44V8FqrqbQPzlYfsFff4vMVngrQ52dPamMw0q383f1k1RAZqJa2pm5XX8VAx2dvwDQFSgqnqX3dIrktVOzPjx/c5BXheAAQDJ9obTZGcVqyAfOhqu1APdYfUiqbUoxIe69oLz1EwQp+d+swK4VWo5Cl3orGHVms3UE68Y85fMNeUbpq6qbS8eCyDa8V1lY/AY3ZoSMCFvbdn62jNxfr93dwEXxFqTQozgrQ5NmXH/jGx93fydTeP0QMqo7Y0TTACgYkKNAFaYNC1byvOdB1ZUXtgsUGYFAFUv/QVxouMoRwOZTTUYnf3tjdsbxyNTW680WSj8JKOzN83c3jiBNapbJRuvoxA/Ok5ahhUTiJ8+M+8tr9R6FH7E6SQEQAuAFwHcE48YvNVhzNCevbElmD14QNrupyvahr2DThdOuZwGSk23EtYaX0k1ANT781tiK0ehJ/BWR6pR3XrmoPTdv9vXMsIEmAYd9gzSHPYMgkHlrQPwTzUT/Obs0k8OPH/z610G51917Pkz0Et/QVx2y3re6rgbwLMAuUtOr681mHGpignRtlBmitRaFORLisp7JQBMKfzqMDBXajkKsedyAP0oGGUChMwwmyl1Osm9AOIyXYa3OnIBfN4SzOo/Mmvzg5/c8+DDnT+SzeeUHOhWwlrl7d8MAJvqpvtiK0fhVOCtjhwAU0dnb1p42D0wB0gv8oTTmS0Nk5CmaTmKCJ4HsGY2//6uF295tYvJw2UnXC8Zu/5PhQFpu+sr2oZhQNruOsAitRwAx2pr+/cHKACygrc6kmZEmEJi2VB7RpAhUbeaCSkNGL2MjiYb8R8UDAB6B291fKBcB+SF2Uyfice6t7503bR0jXl5ayiTAOSCT+55cHk84vQGupWwatigMSRooi77+dFYC1I4MZ2jpngA0wan71hQ215UBqSnA8CuprFitr6u1hNO/xOANaOyNn/34d0Pd9n9lkdyJjW88WBxRdsw8MaKYqm1dOEKAOQ4xzDlg0rhvxAoNwbARqXhqldyY+fUHQBEbjX2CgCcTqICUArgoNlMY/I7yFsdI1ly0ftqNqQ9q/izG1+77XklWf0fdCthHZa5zby7eXS3nqtwclz+l4XMt/VThwGYVmqsuClVlT3UGzFpAOCwe2CoMLWq0RNOXwRgjUnT+t36+2/pMldUSVBPxMa6absAYEOtebfUWo5Rknrw/CpvP3RO3pBVba2CfJi/ZK6J4Fcj07XNShLTy+i/8MP+gOpygFAAApTrgFy5GcBfAeQD6PH4wTMff+p8YPCbAlW1a1jPuNdue357jxX2crqVdB71FVeJlB0SazF9mQVL5ug+Ofir0SLYabn6mjne8JiR6HQSq2svdJeZDjQcaB32pEC5r8OidueqhXeJEktOOrzhtAgA+CLGhAx+/jkG/fG9bEEsLSkzHjh42DPoVfTC2mGF2LC7edRdFAyTq69JlVqLQuyYv+Sq3Ezd9O3NgRxRoNzlAAZBuQ7IlRUArgfg7+lCv3r69/dUeyc9qWX9NUFBP2XbQ1dX9lxe76dbCWu9v7AVQFuMtfQp5i+Zm72i8oJxgWhKuUnTckkgcukoESwAoC2UUTMwfXdFjbf0mdZQ1vKQoDu0/PcLlWPAHjIwfVfWgdbhGJy+M0sOu9BhQTsPAFvl7Xehy26Rza6vgrzoqHMefi8A7GkZfRlvdTynJDTJD291EC174ashQWs4s+Szha/d+oLSbCVjzGa6D8C+nq7DWx1XA9Pt2br6xon5X5/97E1vKMnqSdKthFXNBE1RURWOtZjezK0vXdt/Y+30ic3B3HFa1j87Il4+XKAcAFB/JGXv8Myt29pCGS+7PAM/2PenXyhuR3GgNPVQ0YHW4ShOPVQotZYFS+boGPKrO0TKrjj4+EVKsqrwvzADnXezgDZbV/tyZ3Neg4SaFHrOH4KC3sJA+MNrt77whNRiFH4ep5OUAmDMZnq4O8+/8M+2N4DxVwHky8ZA3iXP3vSGMr7sFOhWwlpmOjCpLZSRFWsxvYnfvHTd+J1Np5Uf8ZYNZUj0TJFePrjzR+GwoNk8NnfD+rCgXbqj6bR3Djx2iVuxWYw/ziPnCJ1/Sr5b3RbKeEykbN5puesWyWG3V0HWOAGEAKohoExjIH8EgGre+umH04v+vX5dzYxNAuWmQzlKThpueP439wGWRwjoP0WwT0qtR+Gk+RrAWpziXDne6iC5+prX6v3jrypKcW2r9vEWl90S+vlnKnSF0G40u4158I0NUVGVvfORK/rHQVPSwVsd5JzSD2cfdA+aUtE2rBSg0wBS2vljL4BvTstdF1UxkXc21Jrf6WUOUklBx7EqVqHDOCAE4EwpP9wH3vvBt2o22O/skk9zFs9bJkilQyE56Hz/mgE4dVy7JxA13MCQ6PUi5UydI9FEgEYHpe96RMsGXv74Hpuy+ypTeKtjgIoJ7cjSNaA831n81I1vN0mtSeHkcDqJBUCd2Uy/O9nndFqtvgTg+jzDkU8m5q25bPG8ZbLoo0g2upWw8lbHSgA6l90yJfaS5A9vdXBnFn92Wb0/f8bu5rFZAKYCyO78cQOANaOyv23L1DYuX3XkvA9ddosy/ktieKtjIYBHALAApQD5o8tueVwiLacD+BbAXS675WkpNCgkP/OXzDVtqp36eZ2/qLyLrfIx2vScr7ko1aU/4i37OBA17ClJPegZmrHDu6N57Mpv7rtV6UGQAN7qSEXHuKq8AWm7p620/m6P1JoU4sf8JXNNu5vHfF/RNrQEwMMAbC67RfITvmSlWyUBKiacSjt2qfoEvNWhK89fdYU/arhoe+MEHYDyVUfO63QloocB8vnQjO3VvKni688PX7ai4w2pHPPKDCeAMEB1AAhDotukElKSeuiZI17eT8EslUqDQvLzzLy33J3ubV8CUAFUGJqx7b2KtuHfR0R1sV7VXu4OpRcFovqrAOirvP1R5e04FOOtjtZUlduTn3JEdaB16DsU7OFB6TsD/Uz7m5e7Ll3hsluU2roY88Q/pjG88bLNLs+AAQCZpSSryYfTSdIAjAGwwWym//OklLc6TCrm0s8joqZkQt7qv79z56IHE6Oy99KtHdYpjz7fruP87Sut9+TEQZPk8FZH2ujsTVeyRJi7pWESBch4ACoCEQB2UTBfl5n27R+W8f3a525+/aSPBhSkhbc6ylVM6MqIqLkdwEMuu8WWaA3nPfGn0v2tw13Ds7Z+/9HdD41OdHyF3kfXcoETlbl0mo5kjs9bM0XH+aeurj6nAQCfq685Q6RMv8ZAPgWgP+5pLWma5kCWrj5a0TbsPQCHR2V9K+Yajh5ZUXnRKpfdorgcngK81VGuYf1PhAT91En5zg/eXvDkpVJrUjh1nE5yOYB3AIw1m+lPbnpcsfie/utrzf8CyAiDyjNv1yNXvJ4wkb2YbiWsQ+97x6Xl/Ee22q6dFgdNCaPLhX7HgLQ9/Q0q7w07m8ayAlUNBUBYEgXHRLeHBO0XeYYj2yfmrVm/eN6ybnUHKsgH3ur4CKBTzyz+fODS255r+flnxDT2/QAeHpezvvz93z6qWGwqSE5nQps9peDLMwTKjtlQa3YD4AtTKmcGovrclmC2CoD2uKc1Z2rroyZNm/uQe/DHAFyn567lUtWefauOnLfGZbe0J/yFyJQy6yflFMQJEDVABQI67bD9AqU5LglxOkkOgFEANprN9ISnEFcsvmf8/tZh69pCmRAod4HLbvkisSp7L92tYT0A4FuX3XJl7CXFj1tfui5nW8OEIUfbS0p1nO/CYFR/GQU5ZosJDRuAhg1t8YTTPjKqWzeai5dvfmbeW80Sy1aIMbMWPXHB/tbhH08u+Opff5//58sTFXfCw0vUDf6CSgDbXHbLuYmKq6DQEzoT2pwzipaf7Q2bBmxpKA8C4ItTD1k84bRUdyhDh45mxq405uprGA0bPFrl7b8cgGtKwZcGhohb1tTMXO+yW3o8fF3u8FZHloYNzGNJ9BF/NPXYWLIogAekqp9XiC+81TEKoF9o2FCauWj5bS/9ZslrUmvqTXQrYe1n/ZVCbzgAACAASURBVKiREFojUNVv5DhGhbc6snVc+4hxORuur2gboq73F6YRiMMpmC7zN6kIgOlMVkVAfPGi/m/fs3jesoBEshUSyISHl1S3BrPYiKgucdktCenYvPrZ+X9dXX3O7YUprl+vu++2fyYipoJCvOGtDgZAzoyST2fXtxfk72weRwGU8cYDFzUFclhfxJSK/05oGwoMVVoQuu+or3QVAJe5+PP0YFS3ZkOtebPLbknK6zBvdZCzih237msdfnWNjx8DQJ2maWp0hzLSaYdxYQTADDl+biqcHE4nmYiOWaz/8W94/XO33vLVkfMWAcQD4BzFDCb2nHLC2nGMTr/pfHoAEv3y8VYHObfsvQmVnv7DdjePSQPo0OLUw5fXtxeow6L2h3oslkSiAlV9D9A94/PWZoUE7YrvG8d/BiALwBcAVFAuIn0O3uq4AMDHAOa67JZliYg56oG3DnBMtGBa4QqjMspKoa/AWx2MignlmouXn3/YPdBU0TZMzUAo400Vl9S1F0b90ZR0AOr/fBatL0pxGcKiZluDv2CdmgkemVa00tQazPxqS0P5drkltFf99c7iNTUzL0OH3/wQLddOw4L2eZGyL7jsll0/V2eskDw4nWQjAI/ZTGce+9442+u/9IZN/zSoPJ62UNZIl91SJaHEXkt3EtaFAH3s2DF6isq9d0rhV49+4brk7/EY1zD5kRdUR9tL+gMYOjZnw9XNgeyCKm9/FsAQAIYuD20uMFQJWs5fccg95F8A9pxV7GhKVbu3/lRyoFxE+i681cEwEHYZ1N7Us4o/K148b1lcR43wVscNAF4B8GeX3XJPPGMpKCQTvNXB5OprikZlbz5vb8tIzRFvvxQd1z40z1B97lFfSSgk6LLQsbHwAwwRGgtTqnTtkZSNLcHsTUZ1a8P4vHXa2vaiFbubx+xKxKzrzlKJyXmG6kebAjnmqKgGgA1Zuvq/Ty746u9KOVnvxOkko9GRsB4GAN7quBbAK3rOd+CsEsdFz970xn5JBfZiurnDii8BqAEKFgIjgCMAdgN4Y3zemk/evdN+ylvhvNVhmFb471nBqH7it/VTowCGpmuaznKH000iZX8YMpiqbgt6w2mrAewZnb2JManbdqyumfWhy25pPNWYCn2bq/664JU1NbNumJTvXPD2gief6claC5bMIctdl6SHBF3OyKzvRmdoGyetqZl5WKRsTqa2fmZzMGdCxyOlO5VQUEhGeKuDHZS+s6zUeHDmtoaJpDGQl2FSt4zJ0DWdWe3lAxFRnYvjRjRyJNJUkHJE0xLM/NoXMX2foz/aNjJrC3OwbfDnLs/AfT1xGZrzzG/zKcgzm+snjw0L2v6A6BudvdmlYYP3vHPnIqXBpg/xq8W//2Rj7RnnA1gJ4FJlHFx86W7T1Q87kxf0e/volobyK2t8pRYAUwhEZOnq9zUG8v8E4H10dNSZ0bmLedMLN5X9u/KiQgBD+5n2XixSdqzLMzAMoLRLCAHAwTx9tT8vpdqzrWHSKwD2DEjbU7HSeo8y8FohJixYMtfw6aFfHBSoqgbAv3DcTntnbV7mkIzvBxamVJVvrJ3W4ouY0vINR8ZkaBvL97eOqIqI6nQ1GywTRDZdoKoThRFYEokIlNN2nkooTRcKCjGEtzrY0dmbBuXo687aUDs97A2n5WXp6iamqLyTq7xlXpFyhQDYLk+haibUmmeoVtX7C78ICdp9xamH/IPSd0W21Jd/3BrKOuyyW8Jd1i8HqDlV7fZ5w2njAPorgOgytQ21zcGcBwC8rYz56js4nSR3+eGLbn1v/zXnRKh64qD0na79rSOGKFar8adbCetPcclT94/jmOhTW+snDohSVSEgBgmgoSAACOVIKBSlGt2xxzMkGsnV10br/QUfi5TdNSBtd+uAtL1VWxomLd/0wLzw/wiloBATeKtjMYD5HfaWEACyWcv6M9VsqJ8nnEYBwh7/HAIxatS0kUDEsC8sairTNU3Rfqb92RVtQx3ucPqhUmNFaGjG98LOprEbq31l9QAm4ofh7kq9tIJCIuGtDm5i3tdDUzXuM74+Mrs9IqpL8g1HpmvY4DiXZ0AbQIpxXEKrZf3uHH0tU+UtWw8wMwDKdd5wBgC8OTp703sf3f3QvyV5QQqSctML825bWXXBsyJlAVBBxUTMBx67ZK3UuvoC3XK6+ik++O0jWwCYO2t7pqaovEt9EWP/Y/WuLCMcPj3HeXBfy4i/tYaytoiUq1x//y3ijyso7lAKCael04sdAGUBFBJCD5QYDwl17UXOpkDu7ixdffuY7I2pB91D1hx2DzpMwbRuf+iqU7nTW89bHTOg1EsrKCScDmtsyw4AO070c97q+H/27js8qip94Pj3nZZeCCV0goIKOFYsWK+gawHXitjjrquyi/WHuri22LGwtsVFXVcjNixrAzt4FRR7G2lKCZ1Aeps+5/fHvcEhUhKYJBM4n+fhYe7MvXfOJJM775zznve4juz1wT5uR3jYrJUj64GCrunrjlXInuA4BLCDVaWAe0smjrpNf1btumatPGlQTDnsLVHhmOdIQAesbSChPaxN2UMpds+S6J4lLeno96imaVsSN2dDj45owMb3xCysDj/9nmhDrRqwgp6JryU//R7VNG1L9PVBa0q/J9pHqwesmqZpmqZpmrYjHNveRdM0TdM0TdPajw5YNU3TNE3TtKSmA1ZN0zRN0zQtqemAVdM0TdM0TUtqOmDVNE3TNE3TkpoOWDVN0zRN07SkpgNWTdM0TdM0LanpgFXTNE3TNE1Lajpg1TRN0zRN05KaDlg1TdM0TdO0pKYDVk3TNE3TNC2p6YBV0zRN0zRNS2o6YNU0TdM0TdOSmg5YNU3TNE3TtKSmA1ZN0zRN0zQtqemAVdM0TdM0TUtqOmDVNE3TNE3TkpoOWDVN0zRN07SkpgNWTdM0TdM0LanpgFXTNE3TNE1Lajpg1TRN0zRN05KaDlgTREQMEVnV3u3QNBEpEZFjReQfIvKf9m6Ppmmatm0iMkVEbrZv65iiCVd7N0DTtNahlLq7vdug7ZpEpAT4i1Lqo/Zuy7aIiAk8p5TSX+60dqWUGtvebUhmuodV0zRNazMiojtKNE1rsV0iYLWHSK8TkZ9EpF5EnhKRfBF5V0RqReQjEelk7/tHEZknIlUiYorIoCbnudY+T7WITBOR1C0855UiMl9Eetvbo0TkB/u8n4vIPvb914nIa02OfVREHmq9n4i2KxCRIhF5zr5dICJKRApFZIWIlInIjXH7OkRkgogsEZFyEXlZRPLar/VaRyUiU4G+wNsiUici19vvvYtFZAUwa3PDnY2pLPbtIhF5RUSes6/RPhHZQ0RuEJH1IrJSRP4Qd6wpIveIyFf2tfnN+PeviBxqX3erRORHETHs++8CjgT+Zbf1X63/E9J2BnHXy1r7s/40+/6LRGSOiDwgIpUiskxETrQfO1tEvmlynmtE5C379jMicmfbv5qOYZcIWG1nAMcBewAnA+8C/wC6YP0crhSRPYAXgauBrsA7WBddT9x5zgJOAPoD+wAXNX0iOwflIuBopdQqETkA+C9wGdAZeBx4S0RSgOeAE0Qk1z7WBYwBpibwtWtaoyOAPYERwC1xX8iuBE4FjgZ6ApXA5HZpodahKaUuAFYAJyulMoGX7YeOBgYBxzfzVCdjXQc7Ad8D72Ndq3sBt2NdR+NdCPwZ6/0bAR4BEJFewAzgTiAPuBZ4TUS6KqVuBGYDlyulMpVSl7f4BWu7qiVYX3ZygNuA50Skh/3YIcAirPjiPuApERHgLWBPERkYd55zgRfarNUd2K4UsD6qlCpVSq3GukB9qZT6XikVBF4H9scKFGcopT5USoWBB4A04LC48zyilFqjlKoA3gb2i3tMROSfWBfkY5RSG+z7LwEeV0p9qZSKKqWKgSBwqFJqLfApMNre9wSgTCn1bSv8DDTtNqWUXyn1I/AjsK99/2XAjUqpVfbfRBFwph6+1RKoSClVr5TyN3P/2Uqp95VSEeAVrE6Eifa1+SWgoPGLvm2qUupnpVQ9cDNwlog4gfOBd5RS7yilYkqpD4FvgJMS9sq0XY5S6hU7FogppaYBvwIH2w8vV0o9qZSKAsVADyBfKdUAvAmcA2AHrnthBbLaNuxKAWtp3G3/ZrYzsb6ZL2+8UykVA1ZifaNvtC7udoN9XKNc4FLgHqVUddz9/YDx9nBUlYhUAX3s5wPrDX2+fft8dO+q1nq29P7tB7we9/5cAESB/DZun7bzWtnC/Zteo8vsAKBxGza9/saffzngxurh6geMbnL9PQIriNC07SIiF8al+VUBe2O93yDuOmsHqfDbe/UF7IAVq3f1jbh9tK3YlQLW5liDdXEDrO5SrMBydTOPrwRGAU+LyOFx968E7lJK5cb9S1dKvWg//gawj4jsbR///I6+EE1roZXAiU3eo6n2iISmtZTaxn31QHrjht0T2nUHn7NP3O2+QBgow3pvT23y3s5QSk3cSls1bYtEpB/wJHA50FkplQv8DEgzDv8A6CIi+2EFrjodoJl0wLqpl4GRIjJCRNzAeKyh+8+bewKllAmch9VbdYh995PAWBE5RCwZIjJSRLLsYwLAq1hv3K+UUisS95I0rVmmAHfZF2JEpKuInNLObdI6rlJgt608/guQal8H3cBNQMoOPuf5IjJYRNKxclxftXtknwNOFpHjRcQpIqn2pK/ezWyrpjWVgfVFZwOAiPwJq4d1m+wUl1eB+7Fyqj9spTbudHTAGkcptQhrSP5RrG/mJ2NNHAi18DwfAn/Cmlh1oFLqG6w81n9h9cIu5veTtYoBLzodQGsfD2PlUX0gIrXAF1gTBzRte9wD3GQPlZ7Z9EE7ZepvwH+wRrDqgR0tkj4VeAZrODYVayIhSqmVwClYk2w3YPW4Xsdvn38PY+VrV4rIIzvYBm0XoJSaD0wC5mJ94fECn7XgFC8AxwKv2AGs1gyilB4NSQYi0hdYCHRXStW0d3s0TdM6CtHF/zVtp6d7WJOAiDiA/wNe0sGqpmmapmnapnTJmnYmIhlYQwrLsUpaaZqmaZqmaXF0SoCmaZqmaZqW1HRKgKZpmqZpmpbUdMCqaZqmaZqmJTUdsGqapmmapmlJTQesmqZpmqZpWlLTAaumaZqmaZqW1HTAqmmapmmapiW1pKjDOmnMqGGAAZjjp02f287N0XZR3mLvxvehr9Cn34ea1kGtmjB7499y74lH6r9lTbMVTJix8W+jZOLIDvW30eZ1WOODU+BrYAzwFFbwHAJG6KBVa2t2sGoCTuz3oQ5aNa3jsYPVWYAb+29ZB62atjFY/RiUGwiDHFsyceSc9m5Xc7VpD6sdrM4ElQoIEAGJb4MbK5jVFxetrZ0EymO9LZUHxEC/DzWtzUweO+to4DDAHDdl+Hb97f305F+65jjPmOKMZqYCKJRH9N+ypjUyQKVYn3OkAGbBhOm/DsxdkBOOuT8vqRn4roPoopN2e7XMKdFFD1/yfFKtLNXWKQHHAGl2UIArLfSrOGJLwvVpo0ApkChWL5emtbV3gOutYBUnUNPO7dG0XcbksbOOBT60PwcCk8fOGtHSoHXVhNlHZ3tGTXdGMzMVKgo4QTmj7ppvWqfVmtbhmIDfClqJgrzocYTyakI5x61v6HEKcEYMJ9OXjsHlCDW8OWHGTx5HcNn+3b7IKw90fWtx1eBPgMUlE0cG2yO1oE0D1rSuVcf5N+QCxICgwxWdFqpLHd6WbdC0zfEV+uZ6i71GH0/D/9VHnWdWRj0P/eGFgY4Pzv310fZum6btAq60/hNpyQjHqgmzh8Wc/lHEXIMduE91hnKrGvK+fTC94sBXQukr7nI39DEc4ezrVk2YPRSdz6rtwkxTTn7mBM6/cc7kP66u63cQTQLNggkznEC/Hhkrhxbk/HrOgvJ9A1XBzvkx5Tjuy3VHdwGOt/ZUsX1umaqgk1h/rwQLJswY3hZBa5vlsP7rkmOGB2vSZzpc0WAs4vrR4YocEIu4XEAQ8LAxRYBbxk+bfk+bNErTmjBNSamIuO+6b+3ux9bHXPsOSq2dtiCQdY6v0JdUQyOatrN4+dFCqfj1j3XRUG66fVcU5Mht9bCumjB7mEJ9jDW0iSAxhXKAUoLjdaA05gie64il5GB3kqDzWbWdSEt6OU1T/gZcBBxhGCoU/9joB29wfF16hCfNVZ8you+MXstrdov5yoZGUp0Naf2yl5y2pq6PNxBN+zQcSynvnrHyqHDUPaY8kJ/bOFqel7qhNs3lv3h1Xb/XSiaOjLXSy239gHXSmFEOrMj8nbi7GzzZ9d9m9y6bU7ag7/soeRcrfzXMZiZd6SoCWlu77o38rEWBzC+XBdMHAY8Dl/sKfZH2bpem7Wz+e+Pk0/3lg15Lz//2rYbSA7sBBwGDx00Z/svWjls1YfYNwJ2AQ6GUWHl5olAIUg80xCSUK8rtth+LALf0nnik7hDROrSCCTPSgEuBB0C5QMXAMR2o3qPTvMNrQjk16+p7rwM8vTOXHVAXzq6tCnauApXSKaV8N38k3R+IpodBpTglmhFVzRpsDwLHlEwcOdcOlGcCHiFGitOvAtEMF7C4X/bit/bp8u2jj176bEmiX/cOpwRsLpicNGbUMFdqcGxKTv1BSKcclPS09la4MwJ3h+vT3nClhLPEoYZ4Mv1dQrXpdzjc4cMzula5/JXZIyaNGTU8Jae+ILVT3YF1a3OrwHM0ICChSWNGHaODVq21jcxdHzxRrV/zdFmf+h8aci7r7g4cde0b+Uc/cGrphvZum6btTPzlg84GVUnMfS6QCbGlKbnLXoHh+27jUBPrQ9Qt1vwHAKcgYeC43hOPnGtXDJjJbx0iZuu8Ck1rPQUTZggwCDi+X/bia51SkB9VLqf1qIBVU98AKsr9XfKjypWB9X4PZrjrMt2OcENVsPMSkGD3jNXUhrJXrqrrvxAkuG/Xrw+uDOYtXVa950JQwcN6zjpkg7/7L79WDvkF1EnAGHvo32k/x1w7aB0BGAqHmeGu/y4QzTgd1LjlNQP+b3Vd36sLJsx4BygBXkhUusB29bBOGjPqKOB8Z0pwUDToOQJQIAFgBNbwzCzsn2IrmDJ+2vS/ttK5NW0j05R7gUWPrCsYvDiYMT7DEV1aF3Md5iv0lbZ32zRtZ/BM0b27168buhDk4XFThl8LMPWuWz+uWXm0kd71pxP/dMfV723t+Ph6qzW93tnNEckYk1Iz8MH+t475eHP76HQAraMomDCjE3Ds7rkLxq+v77lvbTgnFSDbU1nWN3vpkp/LDpwG3EXc6HTTwNA0RYA5QAZwgGGoFg3Xx/WkbvE5mrpsyl9Gz11jXF4T6nSUfVcASEiOa4sDVrtH9TNAoPFYAStH6CZ7487fHrfiVneG/+6sXuX969fnfhWsylydklPvyO6zvmfd2s4/+SsyN7jTg0NiYddJsajjOBV15tsnXoAjGibm3MfaVACPj+k/4QuF2kOQt/UFSGsLo6cVjFsYyLofWOeR2EnfXjhvYXu3SdM6uqdvevDrhrJ9hzpTKgrHPnzmswDTHr6ke9mCMT5wzAOOGTdleLM+pExTxgL/BnoYhlrXis3WtIQrmDDDBRw0IHf+Ff5I+kmr6/plgThcEvbvnruwYnHVXndElfu9kokjl8cds80cVtOULKCbYagl29muFlcDKJgw4wZQd9mTKKMgN5dMHLnDqTjbE7DeAOpuOxCNYgWqLkA8WfXfhWozvgL+BMoJOEHqgCHjp01f2eQ8AgwVZ3SMOz14Zag23Q0oUD5gH+u8EnSlBhZEAqkHgFLiiLHf0LS39thwzR8VSkDFanq++77H32t9WuV+j+vgVWsNpinHA1dOWLnXXQ0x51upEss9KLPqb5NPX/NEe7dN0zqqyWNnHQFqtr0ZTuvy80R/mfcxoBQYBzzqzlg3+tJJ5746eeysjR+aW5qMZZqSitWTVNnSniRNaw8FE2b02S1n0Z/djtBffqkckqVw5IBSvTNL6mtDOU9Wh/JeBb4qmTiyxfMnTFN2A5Ybhopuc+cEs4Jc9alVZ18FQNqzh1V9bh/uB64CurozGi4O16fvZtfRC6Xk1EaC1VkZ7kz/hVc+NXOqaUr6ojcPDdat6Xx4dp/199WXdhoUDbmzgWhqXs3q1JyGd6uWdX/Q4Q5/GAu7+9hPFwUc4orMUBHn5/2P/WHIPhtuq02p2eMSQZxqYw8vSpCAIjpiyQnH/Al4wTCUWVRUdDhwFGAWFRXpYFbbLqYppwO3Aie9UZk/8Lv6nHcro24HyHm+Qt+r7d0+TeuIJo+ddRNwx2YeCjpTqkLRYG6WtRl7CigEcYAEgRbXaNW0ZFAwYUZar8ySP3ZNK71+fvm+eaFYagFATkpFzOMIvbvB3/1Z4KOSiSMrduR57F7VhcC7hqH+suMtb7mCCTOeAS4AjiyZOPLzRJxze3NYfUAqcOHGiVZnj7wRxR12cm4UlLPLoJWULeh7pDuz4e/pXapH1a3tHI4GPW4kFs3pu8GZmlv/v9Ifd7tk/LTpFa8/3f2Bko/2GxOLuHpbQS924WcpAwaMnza9BjbmI32srKXFFFaSPUAkkrLhgZJjzvgTcMNnn127KhxOfw9rslYAGKGDVm17maa4DENFAA58dkiXkHK8BRzaz9Pw8PRzllzTzs3TtA7H7jW18+NULKXTr1OClXv8CvRLy1twsr9i0J6bOSwC3DJuyvDfDS+apuwLHAtMMQxV36qN13Ypzenhb6pxKN0loVVDu39+5sIKb5+qYOfBQIrLESI3pWJ+mb/7fx0Sef/k3V6el8hVpezc1QuAeYahvk3UeVuiYML0J0FGlUwc2SNR59zegPU9IG/8tOkHx93XuOyqG8QJsVVpnWsn+ctzHsTOZ/VkNSwK1Wbc6kwJvbPfnz+cgjV0c/nksUf/NVSb9lgs4gKYB1yV1Xv9R7WrupHdZ/1Vlzzw1SOmKcXAFwPe+/QHRewzOzciDLjsINkPsRFLTjjmC5/v7Izq6j7LIpG0LnGpC98A1+igVdtepike4Hbg8SuW770u1xn+oCrqPqKfp+Gd5aH0k32FPj0MqWktsLVAYPLYWQ7gK2A3iGVYnysSYgs9rDqHVdsR8bmap+z+wlfVwU5Zn6080XlhbcqfOsXkXgAF4a9TIuOD/Wd6FJL51bqjSoC0/bp+OSyqXKm+sgN/AdJ7Zy77w6q6fnuDw9F4/tyU8sqqYOdi4P3Des6a+8KVk6pb43WYpohhtFGB/a047t771lQHO+V8dcslGYk653YFrFOuPHxRuD6lzxVPzUqPv3/SmJHD3Bn+98P1aemu1NDfIwH37eCw91ERkE0WBZg0ZpQbuA+4GsCZEkJFHQNjEZcDic3L7FGx/rIHv+gFYJoyE5i5+/uz+ohyjQVQxGIxR6hclCunbNBDjpq+bz1gmrfeDupN4Di7Npmy8mkFiKnU1OpTDz30kbexcp3qk+EXq3UMpin9gB+BWw1DPXz9G/nuXwIZ7y0JZgwHXgMu8BX6/O3bSk3beUweO2sE8FFqpwXvBSoHfcrWc1g9WFVq6nUOq9YSdrA6C2vkGLeCwSEn+4Zc5Ecd2LV9iaL4LDXCl6lbTSn1uyTkiSi3s3FCeorT//Ciu878v9Z+HaYpufbr+IdhqK1W2Ght+9367K9uRyj69a1/2StR59yuOqwqJqWRoLtX0/sdruhB4fr0rMyeZZ/Wrel8P79VCohi1cYzweqNdaaGxniyw38K1WRk22eNdtp97bXu9OCStd8MnIVy1Net6TK08dyGoUYArHpv9md27moUJOKMpXZWKEfXBVeHow1dK9LT1y9oaOjad8CA95eCenHx4hOHAscBDhAJBHKnfv75/93Xq9dXd7pcgfuB601TugLFwAOGoWaZpuQApwMzDUOtME1xASl6mGnXZhhquWnKXo29N/edWhoGRniLvdcA/+zmCh4+/vX8IyedVrq4fVuqaTuHcVOGz/zvjf8qD9b0PW7gyAs+8mStyfutOs2m7NV7Qpt9UEta2zPc3goMUJ5uEQf7hpwMCTljbhyOUGpFuCpvXXlO+cDuKuaKCYRTFP93SPdP1qe5G4LmyhMXAP4je33oyPRU17677MzqkokjVdNyUMFo2itt9Do6YdUmLmuj59uiqmBngO8Tec7tClgbNuR+BQyNv2/SmFF7g+teUO/Wren8JXBU3HD8R8BtPYb+svJflx19F2RdHw24XdGAG3uSlgCOsvn9KjN7ls0AMZye8INXT31/bfxzLHio6NQsRhwmyBRgRcwRGEws5XxBiCiRz0oLrmpQ4V5du85/+Pzzv7gaoKioaJjVFuWx2iINoVDWncuWDQe4oqio6HXDYB3QFWuJWIDdgf8CpwErgP2Br0xTTjYMNd00ZW/gXuAGw1A/mab0xqpB+45hqA2mKSmAwzCU7m3byTQGq6YpA4F+hqE+8hX6Hrzglb7ZPzdkFZm1nT/2FnuP9hX6lrZzUzVtp+D01JwTC2d+UP7rqTf2OOCxN4EZm9vPNOUA4ATgEcNQdW3aSG27/Pemh6+DIfdZsYLEHr/mpZ9Tc5equrWHvgWUp3f7ISsla6VULjn5I6A8q9ccf2ru0vVnXfFMMFFtmDx2VvpolycjRSE9ok7CKBpEvZPnrp7Ya/e3P+/mfdaxcs4tp9WsOnqggPnsw8fPtRbvjDdyk634wvq0oBzUjjIMtcw05bDkGDlWXUESutDO9uawPgFcAhw5ftr0OS/ct2ensoW9F4cbUqMo2Rsr4Jttl7aKgnwM5IE6YNP1BFQMa1JUk0UGFIAfZJNlWn+d+Ng6T11B13DGyi4DbriscskdxfM99f0HrZGK6Bz3QketBEjP2DD++usnPxh/NjtoNbB6eI8FdTtxS/UVFRVtksBvmuIG+gAbDEPV2gHpecA0w1AlpimHAI8BfzYM9aNpyqnA68CB7W8c0wAAIABJREFUhqG+M005A3gV2M9+fARwDXCZYajVpil7AQcDrxmGqjdNSQOUYahAi38ZWruwU1T6AXs1TsYa9eLuo5aH0p8Fwt1cwdEzz/vl03ZtpKbtJCaPnfU8qNMye36xX+EtN252yVadw9qxTB476ygk+hHK4W6MC8QZaBBHODUWzhK2vvhQFVDhSl+f6kqpJlA58BOgIr3rj/nOlOpw7aqj3gUqsvuY6a7UyvKKX0/7CagaN2V4xH7uYeIInpfaafEf/BV79kS5MsISW/5lWkOXH1ysW3DfHwe08stPKHtU+Brg3mToKLvyyfOz3lpyTs3Q/DkfvHrNPU2j++3W4h5Wu6zVn633kvpo0phRx6Tk9Lg2XJ+W13XvkhsvvPnn9cD6SWefdB/KcQPgBHUsyLfp3aqfc7giFXVrulwCuNm4nJ5yAhGQOcAIO4B1Yy8DBrBqwuwD0/Dm+zv9+OzAv19euWrCbCOF3QYtS1nyxSxZdoiyl25taOj6RdM22xOt5tq3Bbjd7tnd7FJ9hqHCwNK47VVYPaqN218CB8Yd8h4wAFhlb88H/gE0FvjNAHrx23DVH4CHgXeBemAs8E/TlM6GoSpMU84BzgHOMgwVME05CNgTq1xXzDQlEwgbhkrYt0ytxS4GYo3BKsD0c5ZM9xZ7D3MRm1UZdX9y8au9b3jqzFUT27GNmrZTcKZWFEWD2edEArkfTB4763E2M3z8/ZMn+FRMblaK/vaomZakHr/mpULo9gTKuQZUPlYsElbR1GP/NvmkuZPHznICObn9391LJNa/cunICiAvs/tXRyKxPnVrD10C5Dld9YfEwmm5wEFAXsMGb561SinnANSsNDZ53sfGzYg4nOEwZKeqWIr4ywfjzlr5a7i278Vu5Zgz1+MsAm4qmDAj65kTRgWAD4CHDEO92UY/mu01CrgReBtol6oA8eaX79sNoC6c/WMiz7s9KQFG3G0P8NdgdebpTk/43xfe/PPdGx9RjlqrB1UcWD2Zr/310Tn3AEwaM+olfuvxBCTuNofTZN1nu5TVU0BtWuW+V5Tc/NZxTnJfFGT1J6x4V8GhTdbT3Vr3e6m97+vAA4moGmD3jC6J214ALIjbfgt4K+6Q/2AFq+X29hysN1uVvZ0J9MTKRQEYA/wNeN7evgOrhzsTwDTlamCoYajz7e1jgDzDUK/Z29lAwM7x0hLAMFRJ423TlIMMQ30N4Cv0Lbzm9e5/+LEh+9Ov6nPv8hZ7q3yFvint1lBN2wlEA3mL3ZkrVwQq9uoH6k6Q4OSxszZWC7A6UpyzsD47/jFpzKhNRuc6lKKc30YEi6o75mvYimfvLHo+4j/iXHEGvlXR1D+A7EmTHNZxU4ZHgQoY/jkQV8Nz+PObOeVG//vPKJe/fFCPqmUnpgB5OQXvHxELZ+TVrj6iFMhL77zguEBNQQHW5ytAJFzb9+lxU4bPBnh/UlHJTxsOcgzvM2Ms8BLWJL42L7zfUoahnjdN+dww1LL2bgvA4qrBWQALK/b5XQfijtiegNW0S4ukAgKxMeD4JRpyN61FadpFnt1NezLtC0n8H+LG25PGjNqY9zF+2vS5qybMHqaImYLDo1BKkI+c4dyhgohChfpEuzQsc63HDo4322MaLzNz3fC6uu6IRO699dY7v9qO17/DDEM1AL/GbX8NfB23/STwZNwhtwGPxeWlvA3EvzEzgNy47b9irRb2mr39NLAXMATANOVuIM0w1DX29h+BUOOsQtOUTkCd3dOsbYVpyrnA86Ypww1DfQzw4GnrfvYWe/sC04B/n/LSbidkOyOnTx29Qs9c1rQWevGf4y70ZB18e6i2Tz97Eq+DJiNw1m3ltssduu1OkI4X7BXl/AGYjtX5EqIoZ8TOErTaZcruh6POTes8vySz+7fHnXXF05VYv6eEvMbT/zI9AsStqjm8yWf88Ns2rf+7aczQL3vJe74NB7K0eo+9DUOtBA6za5omJTsVoJthqF+TJVgFSHfV9mqIZAGsT+R5HdveZVN2sDkC69sHIB5Q/YADtrDfLUCzv+2OnzZ97vhp0++J29+wlveyngwYbP+PII6YxE6wH3qNZiwO4HQG/gox3O6G3K3tl0wMQ9UahopPUZhlGOqRuO27DEONijvkUqy0g0bFWOXDGmUCWXHb/wDiS258QFyPsGnKf0xT/hG3fa5pypGN20/dMOX4yWM/LLIvBLuaV7GWkdwkX9VX6KsDTunhDnywNJhxyupQ6ixvsdez2TNomvY7k8fOOmPy2Fk/VfxyRnE0mN3X6al5HPBjjdg17Zww+a18wDY7LpKVQh2FFYw7+S0o7/BefvSiLE/WijlYnzP/8pcPHmAHq23O7sXdGJvEp5Y8esnUtQrHdytr+/ez55aQHBOYtugO4DvTlM7t3ZB4B3X/bCTACQX/S1gNVtjOSVcAk8aMugHUXXa+aQTYpMZqotjpABvLQ2AtBfsw4C6VqujbKd94sPJX/WwjYC0qKjof1LPN3X9XYZqSB6Qahlpjb58HNMx7aWYEOK3TwNeGezLWdy9fNPqJiL+LJ7v3p5dFgtn4KwYtVtGUPFB51pmsn+muumyivRxeMD714vo38uWXQMbTS4IZhcDHwOm+Ql/VFk+iaVpjqaM5gANUxJ1eetql/zx3+tZKIE0aM6oY1PnAEeOnzeiQ1yBVlD0Mewhc7OtpR+9hnTx21nEOd90LsXBml9TcxY8EqgZcPW7K8KQNAgsmzHjYKeG/Pn7cmTUuR7QgmStOmKb0BEYYhpra3m2Jd8Rd/5q4qrb/34/s9WHfqVc8tHLbRzTPdpW1spn2kqeb5JsmWu+JR85dNWH2xjQBe/tnwPjc/Utf4DJ7Aphnc8NAVoWA2IicnJWjoO/QuIoETYeUdlmGoSqabD9vfTCoz0Ck8tcz7EfUOKC8bt3Q5SnZy7s5XA0ro9GUSiDP7vTeZX+m9kS4b7Byk69uvP++U0sVcJG32DsT1H/zXOGV17ze/bgHT1uX0NweTdvJGKAcdrkjwg3dvcD0IWePKAWOxqpC0/SYapCaDpu7CkhRzdzA3ZkxQapSQjJqJwhWhwHvxcIZDohFAlUDXkrmYBVgaP6cim9Kj3AvKN/3hytGf5uUwWrjUuF2J1NSBasAq2r7x4DI7NXHrU7kebc7YB0/bfrcpvmmCWtVE70nHrlJjkvjdnnRzGHARUCqHTCVFRUVZQJ98vN/+ENdXf7B0OMsEFd1dT88nqrVoVBuHq0cZO8kjN9uKuVw1z/QeY/XbjjriuJNEtCtC5LE94CbbdjGpGEYqs405Tngk8097iv0Tb30tV6dvqvPeejjmi5veou9x/sKfT+0cTM1raMwrf9+V82lAmvCTHbTA9LyarzB2rSkzTdsrlAKpSlBoh09WLUZ8V886AAdGl3S1r8BFD324/WuSd/OGNZWNVRb6DF7RO+8ZFzVLcXp7x2MppaXTByV0La1OIc13mbyTduUNZwvVwL28qvqCaAWmF9aut9D9fU9zgVcdg9sLBTKnUxc7opOB9gqEwhYHxhILJy5tGmwClvPB9rVGIa6wzDUpwCmKb/723rijNWPpDmiQ6PWpMVPT31pt3PbvJGa1gFY1xGpBPmKuOuKYagqw1D7GIZ6p+kxzpRwgSstlNbmjU2w1IB87A5LTnu3I0FM6z8FHaRDIzel4hRQyh/JPAqYaa9alTTsSWBLgcXJGKwC9Mteelz39NUJfw/vSEpAsugC2KtlKbBqok7t0mWBCgRysurqej5E3GzA+Jqs2paNmzJ8rr2O9/HAnyF215Qr39gzGsp+uWlQam/rn6nNNOVS4EK7csAmpcRmn7/oO2+xd1iaRGcvC6Y/f/bLBfu8dFbJhHZqqqYlMwV83dwvwXVrO88HVb7tPZNXwYQZo+/POnTYaPVVVsnTqT0K/hRYu+2jkte4KcPnPjbu3XqHM7AmGsopTPYODdOU/HBsdBG/LVqQdGlu9iSwpK7vvaaud6XbEU748rA71MOaJEyseqURe9LP7UVFRS9cfvm0F6+99okn0D2q223clOFzx00ZXiTOwM3gyIuGsq4GZu6i1QBaYgNWjd3UzT3oK/StOiq7/Kiu7uDyef6sv3uLvbd4i70dfihT0xLJ4WrIy+r5+e+uNaYpR5umLLVXDIyXAZKUOYfNYfXkqZc+qT+4P8BrS84d0d5tSgQVTamJhnJ+N0kuGRmGKl1Ru9sYQZGMvcKmKTeZphze3u3Ylrpwjrsy2OXnRJ+3wwesdhC6xaC0qKhoblFR0T06WN1+KuoZat3aZGKVtgWGoV4HTjUMVbOlfR44tXRlaTh1D+BZ4Lb+KfVfXvdGfocfztS0RIlFPeGwv0vJZh5aA3xHkxFCd0ZgQEp2fYcpV7gZBuBYrboCEFbuY9u1NQmjQCIdJtZ45eqJr2S6q5ekOAIVwIhkyWG1FwC6GDi1vduyLUKsu9sRSnjZsp0hJQA9zN/aHI211DZX/1DbDMNQyjSlC/Av4IbNFXX2FfpC3mLvRQNS6jMXBzNOLwunfOwt9h7nK/TVtn2LNS3JKBeByj1+aXq3YahfgTN/t7uiaywmGZPGjBrWQSsFfAKwSnVRgByTObfDBHlb40ytzE/rtPjwTUuDJx/TlJOwgsHra8PTfwU6J0uwCmAYqsY0xUuSr7x15ZPnZynOydy/2xcD4bSEnnun+IPQWs/ksbOG4QieAZHF6IlVLZWF1WtywJZ28BX61OtnLz2jr6ehqDbmGgp8cvDUwT23tL+m7QqsiSXKBSqylX02ptxMGjNqWKQh1R2uS88BZlpLtXY4a0AoI+fNiMBuWYubpjx0SLFwWm2ornvSrMK0FQOxSqbVCVElxJKmQ8805VjTFKdhqDrDUP72bs/W/LD+kC4AZf78hJdu1AGrtkV2LdaPiaVkg6M/mynWrW2Z3au6u2Go17a174xzltwGnCyovTwSK7nyfz1Gtn4LNS05rfcVukAku88nm83XM025BKixl5EGOI7faid1yLSlg/Ln/BHg9IHPfRdKjZJd49xiSlFHoqJpDaHavqvaux3bYhjqYWCwYajIgNyF+3fPWD2ovdsEYJpyAPAh1oqKSW9F7W5HASyt3rM+0efWAau2NQbWxZ+4GnpaCxiGqgcwTRlumjJqa/v6Cn3vHp1VfklYCWZt5+e9xd6j26aVmpZc/BV7uADC9fmLt7DLt8D92GltGd3Le1t3qygdNG0pqhxHAOyZ5/sh6mSdKyLd2rtNiRETcYSd7d2KrTFNyQAwDBUFqAx2LqkJ5SR8lvt2+h44C3iyvRuyLfbEwSesLVWU6JJgOmBNZkU5wyjKuYGinOb90lu6/7aZIEEgilU71EzQeXcpdt28u4B/2Le36NHT1z4fjDn2UMhaUB+MebngprZppaYlj7q1hzoA/BWDNhuwGob6zjDUjYahNgCoqHMPcUaUONTtwIiOmMP63frD6kCtu/S0+W+7w/I50L+925QIztSqbhn53x/W3u3YEtMUF7DINOW2xvvK/N1X1Yez230ugWlKhmEoZRjqlWRPBbAZbOzkwkWCO7mSJkdDi1OUIwFP7PYU5CYAQZQqyi7xp8V6usNS4Y44KhVK/Gmx3dxhR7k7IlUKlQH0A2KCBCnK2eE1qBtrsaZ38V3SacB0Tr3oww73IZAM7AlYZwLVdg29rfqhcF6Jt9h7eK4zMne+P+uOUS/u3mN5KP1yX6EvqZc01LREcbjrXLFwJkh0ixNM7MU5+n7775HLIXcAqP+Nnzbj9jZsZqINAZkPUJMdzey2wZ257JmULv0vCiZLT992sXJYeyxt73ZsRQpW7+XnjXc4JUJMiXvLh7Q+05ThwEumKccbhvq+PdvSAiYQsxZyIuGdXLqHNck03Js5GJiRGnI0BquND9WHPGpDxEUJMF8JC6xttQyYD9Tb+zsADwn6ZjNuyvC5/Y+9ekVuwUfHm6akJ+KcuyLDUKvt5Vtd20oNAPAV+ioOzaw8uIc78O3yUPrfgEe9xd6kHlbTtETpvMf/ugHk9N1qzed/Aj6nJ3wA0AtkRps0rhVc9eR54naEDuiVWVJvmuIs7xw5FKB/SWrX9m7bjrJyWPskdE35RDIMVW8Y6jbDUB823rdHp3kH5aWW9WvPdgHrgFnAonZuR7NZVRVkif0v4SXBdA9rsijKSa/MjUzLCThHKVStII8IcglW93pYkEtzr6nf+Mt3APEFB6UoZ5hCzcIqVu+MONSqBP5y7wPuaMzv0XbIOOAh05QDtvWt+f5TS6u9xd6DgXuBa/t6Gk667o38g+4/tbRDr+bTGrzF3mFYX9JMX6FPjwR0cP6KPSIAobqeW8phBXgR+C677/r7Kxf3IqNb5Udt07rEK23oOSQc87j6ZS+pMQwV/fkV96fAKKAAWNC+rdtRMYc4w+3aW7klpik9gd2BOfGjX2X+bovrw1nt2kFjGGo+cHZ7tqGl/vr4n3LhjAGpTv+DC+8anfDrsO5hbWclT6dK9PbsM4EFnapco6pyo8vW9AwPo6j6KuIWRNjm8H5R9VxBhgMPREXVxpzq6RVPpQ5PRBsNQzXoYDVh/g2c3NwhHl+hL+Yr9F23T1rN0ytDaf0/run8sbfY2+F7XRLpwGeHHAHMBu4EZtrBq9aB1a09NAzgLx+yZEv7GIb60jDUs7WrO/dJya7fMPbRz1a2XQsT64u1Ri+Az9eMeAKg3/KUKwAqcyMdfrUrZ2pV14xuPx7a3u3Ygkuw6t/2jr9zg7/HqkA0vV1yRk1TjjFN+adpSodbSCYSc58N4hjW0wy0xvl1D+tWFEyYcThWr82s1iggHLw7a0heivMTZ0w6Az7gqLyr6mdv3MEKUpv/vPb+q59Knd1zjeeVPis9L1CUUwy8saP5rKYpxwF3A8cahqrekXPtygxDhYDpAKYpfYFSw1DBbR33/FnL/zz8+T3MDZGUx4G5o17c/Yzp5yz5sZWbm/RMU87r4yl4YkkwszFdIunW/tZazump9kRDOYgjtNW87cf+duS5EX/27hG//Ket2tZKhtj/zwco7xxZmVHvIOpUHf7LVyycXhOq7bW1nvL29ADwpWGoTb7suB1BRyTmbq9e4cOBkUCHm3D7yarjswFCUc8zrXF+3cO6BXY5hpmg7hRin/ef8FZxwYQZI4fe9t+u8fsUTJhxQ4tKNxTlDFNF2bdSlPNsSsjxfUa9M6u0W/gV4ACKqmdv8/hm6Htx4C1XVC4VJB+4HpiZgMoBtUAA6LHDDdQwTekG/ADc0dxjZp33y7PAcCexbmURz/d/ebX3Oa3WwCRmmuJu7H34uKbzH5YEM9JBxdArse00cnd7twAgp+CjA7e0z6Qxo4b5K7KeAxHgwg66WAAAA3PnnZPqbPCXTBxZBlDSP/ifkEcFu5S7O2yvcSMVTY2E6nrlW3W9k4udv/pe0/v3yvMdlOWpzm+nNt0JHGAYqqE9nn9HhKKpBwLLn7/ywVb5gqID1i0zABcICkHhPB+YXubPX39g0X8bCibMeBvrg/EOYGazglYrz/QToAi4APjQGZO++X9rOIui6i2u6LKdeio25uTscCFtw1BfGIY60jDUwh1umYZhqPVY74MpLTnOV+iba2SXn+5E1X9Zn/tfb7E36deVTiR74p8PuMlb7C14szL/tK6uUEOOMzIKO31G57B2fIEKq+h4sKbv1j74DNTGSalOOnCd6OpQp15d00vjyyjNjTnUKqzKLx2WvfhMF1BDgZnJFLSaptxlmnLK5h5b39BzkT+SnvDC99toz0GmKXvAb/W7O5pUZ8Oxme7qVsu51gHrlplYZRkiIH7gWMDYr+uX/0t1+ZcDx2DNxneCau6s/KsEcQuCsnqE5lBUXdo6zccEFIBCJazXyTTFY5rtW+5jZ2EY6hHDUEthYyDWLA+dtu6jmph7d5AfQf3v3Jf7PdV6rUwOdvoEdq/Dq5UR1zfA/6I4YhUR975zzl/4rq/Qd48OVncO9ev3bwDwl+29fCu7mSABOnjPesGEGbK+oWf6ytr+rzbeZxjqCeDXiFN527FpiWBY/4mASpoVyOxlfc8Ehm7u8dKGnqvCsZRwG7ZHsOY3vLqtWt3JauyUiwcHoul53i7fhVrrOXTAugV2zurGSU8lE0d+XDJx5CdvjL/9jM9uGjcIOA5U2I4JHVjDu1tU9WDGicBpQAyIiFWQ32y1F2BNwnoMQJCTdzSHFcA0ZQiwHiu/RksQ05R7AdM0xdPcY3yFvvXA8O7u4M8+f/afj3l+j2e9xd6d8u/ZNOVK4JfGoPWdqm43P1fe+25gf+D8HwrnJWt+nLadnJ7qVACHa8ujovbiABuv0R1xsQBbTyAHO3+1UV1mzOWKSsbqJ9Ky2qdZCWGCsgMYsbfbn2GoADAIa17G76Q4/S4h1mZzfOwKBScDFzSnVncyeq/k9AEAa+r7PN5az7HLTrrqP+HtYamuhtMUYgYiGV8UZP/q8nb5LtNXdkBdSc3A/YAjgPdLJo68Z3PHl0wcObdgwoyjM9w119SHs08B7iuYMEOAfQEzfpLW6ifSDuza4Joedqkqd0TOw/qgNRMRRG5DY9f8zwk63y9YpWRWJOh8muULrKt5iy5UvkJfw/Vv5B/4fQOvrgunXgCkeIu9hb5CX6vM0GxLdrkZMQy1GngdyADKAD6u6Xx5QDn32i+9+tOpo1dMb892aq0jt/8Hg8sXjSan4EMvjHptS/vZQWpHDVQBOLr3e2M+WXUC+3f7oraxL8A05YL8zu5jum1w02uNpwfWHIIOx158xgAmA3uLI9xaI4rNZi84gWGoGLDZSgCD8n46aGGFN7uN2tPZMFS5Yai1wNq2eM5WcggQXV4zwGytJ9gpe2S2pWDCjMMUMscfybwuEMmYAZSX1AwsfXvpmCUlNQNLgfeBm4Xox1vLTS2ZOHLuvDvOOQs4EWIDQc0AtWlOa1FOZs817qdcEQmv7REqpKj6PYqq72mDYJWKTpGeAGu7h/ok4nyGocKGof5qGOq7RJxPsxiGet0w1PWGocItHQ6679TS8Lpw6qlYk+vO6un2rx7/ev5urdPStmFPqPoRmAhgGGqlYah7DEM1eIu9hwSUc5Kg3k93RDt8yR9t8/wVe24ACFb332JZq53Fuvpe/QF6Za74Nu7uBcDL9u2Ctm5TIo2bMnwucDISIS1v4cft3R6sbwWLTVMGbmmHdfW9FoRiKdus3rKjTFMygK9MUya19nO1tm5pa0dnuGqXl0wc2WqTxXbKgNVb7B3mLfbesJV6jLdjrQiFPbt4el7qhpsP6znrtRSn/yOsYXsE3Fmeqm0Of5dMHDmra1qpPdNQnNiTnEqeTnXGRE0TxOtQckrfiwNt2hsU8sTcADEHnZt7zIK9Bg1bsNegGxbsNWiLgfoPp/f548+HDbh3a/toLWeasicwxzSlRQGnr9CnfIW++w/JqPxnaTglb2ZN1/e9xd6C1mll6zBNSWlcAcxeM/tvwK3x+4x/PX9wmiP6EajVCjn38TNWJ3qiopYkGjbsU2f/v6a929LaFlV6PUD59KVnbUwJMAz1Tfd17iKA8rzwSe3VtkQZN2X46uzecz5pKPP2/fflb7f3F81q4FugZEs7rGvovTqm2mRhwSDwH+DNtniy1jL6wRscNaGcAbvnLmzV2rU7RUqAHZgO7+YKluc6w0dDxpl24BjwFns3mTV87MT7p8DgEUAUUCBh4O7vii6aCxvLWR0Oyq2QaG0odwZYwwj2EMJmbfD3uBfUSKyfaRgwM+odcxxKDg27Yn9331T7fqv9ALage6lnDnB9rzWeZq1FbQegHyuUB4Gfhw780lnn+jWWGk0LFwQOcK3zLHJUuxweMo63B6+vWLDXoBGDFi7o0ENySSQEdAa6Ay1ee/s/Z64af+jUQe/Wx1yvgJp7/it9L35u9Ip3Et7K1nEFcL9pyt6GoeYZhnol/kFvsdeV4+z0SlhJ5lFZFX+efPqainZqp9YGnKkV6dFAHk7PLlHyeQgwv2TiyE1Sgqpzostyqp0Ag9ulVQnWsMH7R2BBLJIxafLYWQeOmzK8XRajMQz1KfDp1vZJd9V5GiIZrR6xGoaKAJtNO+xIvi49Yi9AllTv+WhrPk+H72EdNnWvM0HNAe5cH0n59y/BzLNBXICASiFuVuKhtz9+7uKqQZfZqYIRrG82m6x3+9tkK7lF4TimZOLIuaYpZ2JNisnbUjvsNXT/DJDlqXy2JPXcPbuWuQ+t6BT5bnWv8P2t8dqbofHbTnNnoBuAWxBBIeJ37A0cIWHHIa5ST19Hg3N/UXIwCsRKoE+aWZ87A8NQy4AhhqE+395zfHHBgo+Aw1Mk5pnXkDVj2NRBb3qLvSMue61X0n05NU0ZYZrSWGfzceB4mkw8iXNPddQ9OMcZuXLy6Wte2cI+2k4ip8+n+wHkFHy0e3u3pTVd9eR5kuaqHzYwd94mf5+mKd1K+gfrYw7KOle4N7RX+xLpsodG+7FSl/bN7P5Vu3wmmqYcYJqSua39Bnf+4UBBtdo10zQlwzTlHdOUZF0BrKUOAagPZ2/1i8COSroPsZa47o38LKHT82wMvFUs0xGdWRdzHgGkAQ4XsY3F+Nc19D4z7nAnsGJzK1jZ98XfL1i9plvNzSiZOHLqwbc/+djBsuAiovwZmJlX6Tox76r6dpn1t6FLuFPXMjfru4b36WYtXbktJlYvXyoQcUQdf2jae7pgr0HDBJmJFax22FIyycowVNTOY70UWG8Y6vWWnsNX6Jt//AsDrl4TTi2ui7n+CPzx87o89iveOxZFqoD6XGc414HyV0Q9C4D6fp6G3RVUrQilfwPU751WMzisHKWLAplfA/UHZ1QOCMQca37y58wD6o/KKs8NxBylX9V3WgPU+wp9LRqet8vKTAU+A0YbhqoFPtjcvhe/2vse6HQt8G/zvEWt+g1eSw7+8j1XAwSqdt+pJ3j+VDa0jz+S4ciwHcQjAAAgAElEQVRw1zWtXVkJTFKijgMpaPOGtZ5XUnMXT/FX7HnVUzdMufPie8a22UiJaYoTa+j9W2Cr9atX1RXMUziOLJgwQ5r2fCdIX2BPIKUVzt3mBuTO/8vymgGBcMyzqDWfp8MGrN5ir0DXh0A8gopY7ygJ18VctwJ0dQXv2xBJOSLNEdkTmGMfloXVvRqlBcGWYahXTFNeNQyl7A/afQ1DfbnZfTO++GxCzXvH+53ODWmx6GiKqtuslltTIY+KAihRnZqz/6CFC+Yu2GvQ8GhG5FOVEQ16P136u2De3mcEVs+qqdMBWoULuBhYhjVDvsXWhNN6Y+ViO4FYnjO0qJMrvGxJMKMEyMhxhg8LxBwxrC9j+TVRV++gcvQE9gAyfvZnN5bYuhjgq/pN30Kf1m6aFr1/8RBE8IeVowyo7+4O9AnGHGsqo57FoOr3TK3fL6xkTboj6pzvz/rogIzeBwAf+hqyPokWDzlzWFblHuvDKQsXBTKXpjmi/iMyK9LLIp7VPzZkHQ85f+/sDNaLcPX2/Cy0jsdfMagGoGH9fuvbuy2taVn1HgMAfthwyAvx9xuGCgM31H2deSiog3eWwtfjpgxXz9/77sWBqt1fi4Zy/gFc24ZPHwPOwhpd3ap19b0bc6edzdm/pQxDLTBN2cv+PXd4VcG8Qb0yS+rMf1yzxbTJROiwAasTdUXUGoK/UyHvYAdQjfmq3mLv0cD3tTH39d5ib/Hw0D7RNNcph7sk9E1tuNPrNCk9tS1xtdGKgGtMUwY0XX+Yopzsu3DsXk8aY2NXfFRcdFvljr/S7ddrjWceQP56T7PzIQctXDD3p+P6P+NamXLxgr0G9Rq0cMHqze1DBy8lk8zsagEnYvWybC8Tq7fcDYQrop6LPzl/UbN/Z9e9kZ9SGk7J+b4hxwVkHJ1Vtm95xMPP/uwaIPOQjMoRZRFP3ZJgxnogY1Ba3YnlEU/5mnDqBlAZaY5YZ3/MGQO6ARnrwin9gzFH/4gSZww5/Jv63ManuhBgdlwA7I85+bCma1xrFOVRjwvkQPT7bpfgSi3PjAQ640ot75A1KZtLiO2trAHC36XCmKY4BqenqYx6R3r5I+lpna9saNUJLW3lvL/f//rksbOeBq58/JqXn7rswbNabWWkePZneLOuH5nu6pS6cA57dvI5YWTCAlZ7gZjzgad2lmC1YMKMdOieXebPn9jaz9UhA9Zx/+t5VYy8h1IlOjOgnLf6Cn0xmrwRfYW+mLfYeyvw+uDU2tvWVvb+wB/JSDuy12fzpl7x8I4kOU8Evm8arK56Ms3TC/dLbmIFN0b+9O0XeE8o/NcVruLLH23PmcyNKQzNXkUJwL0ydRLwF+AM4JFEN0rbNsNQ5QCmKZ2AUwxDPdOS432FvrneYu/GnvCWrgB1/6mlQaxFIhr92mSX/zXZvr3pOewV0a4BlhiGes0ekutx39rd1w1Oq81fFkxP+SWQ6cxyhHMOyKgeujyYXl0SSg9kOCJ5Q9Jqj/mpIbtPQDmPsCt6NC69qQPWXUBWz7kHVS4dRU7BRz1h9Pft3Z7W4u3y7aW/VA6JBqLp6zbz8Gc12dGMbhvcdK5w9wJ2pgUybhRH8AKHp2b25LGzJgGmXf6qVZim9AYuAx61l8XeqiFdfjjgy7VHM6TL9xlYM/kT5TysfP0fgc2O0nZABwJOkFZ/PR0uYPUWe/s5yCvq5Aw3HJJZedF9p67fWhf0m/nuQP3acMq1K0oPLgWYV77/Zle2aC7DUFXANADTlP2AK4d9nlmc53C+IEjPmKi/1fWo2TO4Ku1AhfwVaLecu3X5Iele6iGQEr00tShnXnNrvw5auGDh/H32XBzLjF6NDljb2xXATaYpnzYu49pcdpDangFeFDgb+Ap4zTBUFFhlWI817bn/psn2U3b1D50vvQuqXXuwAqguGdGlvdvSmlbU7ubM9NSsWXjr6M31JP/bEWMA4MWqxbrTBKzjpgxf99+bHvrSX7bPEaDuAqJP3zLpwbS8X28/++opda3wlAZwA/Df5uy8oma3n4ETfy47INGVDP4D/GgY6qsEn7fdHJj/+eXflh5Gv+zFrV6fvUNVCRg2dVA68EYMkYqoZ//7Tl2/amv7+wp9qqsr9PfKqMeT1veZvzvTl677ruiipj1FO+LQnCrniZ6QfJgecPZUqJhDyQ/p7roiIVr76arjD0rgc7VYlw3uYwBSgo6hwEyKcppdNzXorVvurHD3//7cXnu1WgO15pgIHNLSYLW9mKYcYZryrmlKml0GzjAMNXZ7zmUH3BuX3mxpL7HWMU3+64fDIv6ufwOIBLpMmTx21k5b77kq2LlLmb/7e5t7zDDUs9k1zlcB1ncNn9K2LWt9/rJ93scqLSkgrob1+19XvvDM8sljZ5lP/N+Lj7446Yrrpt59c+42T9QMhqGeA3ralVi2aW19n3UAv1TunYinxzQl3TSlm2EotTMFqwCl9T0H5qRUBD75x1W/Sx9MtA4TsF7/Rr70cAfmgdoXONdX6PulOcf97M/+TimUM62kZ3qfp/K3sphAixmGmrLPT+nFgrgBBIkBxr8ve7pK4XwJOKNgwoxtltBoFUU5HldM7lYoBJH/Z+/Mw6sqzj/+mXP3mz0khJ2wmgBRxBUXHIkLGq1arVVpi6271rW/1qhVU9dUq1atK3XBulWte1RUYBQQBFQkQABZwh4gC9lz1/n9cc7FiIGE7IH7eR4ecs6dMzP35uacd9553+/LPkpQGTW2XAD3t/GndNAMo7QAKbVfSv0dgFLiCKVEt8wqbVShyw6MwKrOI6Wuaku/hVMK5xVOKbw/aqweGCglRPKId54HHUn6s7OfSucdd8+/+gIp7EHKTSlhWzPMV6bROAJiUOfOrlOYATQAQdAN3rRvXsIs4RoTqEu9pvyHcx6o2iB3PHHlzPlT//LCy6/+4/q/PXPDm3uUltwTkXtTS0IBIiQ4y90A/WPXO5tr20LuApbuTRqzp7KpJr1XpS+5Uwof9BiD9fOqlFt+8MWmj/NWflo4pbDFYuih+v6/A4QQoEVYWMfthi0s3sOMcQnSaNsy2b3jdcB7dF/VJaLAOxOC/wP6CYR/97m1hEPe27AIWIKZVRmli1FKDAO+AnK7ei6NUUo4lBL/xdxuQ0qtgAwpdackUkTZP7Dim5FSa1f8xgJEOAB6n+9bPYmDkpeeDzBhwPQ9CdTfXO8NbwI2JO20t2nh1x2xYlatHRQx8fd/+/OUa54+6aZrnp54RMqoVwcmDvnkTyAeAAL+6v4XVqw+645gQ3LpE1fOXPzcLU8XvPL3P+c/ceXMtBYM9ZpS4h/7MrfRKYvHAhzae36Lq0Q2wwvA/VLq/argSXpuQR9Mia5OicftETGsWdOyJoFxD+g30hz+C/blWn/FeNx93gERAm3DX9HOu0t5lfPISzgRK7klEid6fP/PZs3ZfJJ/XeWI0zHjEDuNyodjfplQZTujPCm4JLnCfuXuc2spwb6+OfatrqsX3ph6+BGP7Ng9xjBKJyKlXqOU+APwflfPBUAp4bS8vwGlRAAzXhXYVb0lSg/H2o2SgDotYft8gI8rewNwWoLprGrNceTnyPEjg5YNt8F7M2aJq2/YMOaL01K2/3mQFv8DpCasNk6aPP8v75qVSpq6vqccf1yZejQIiZUEuaL84EQAmwh+TtN8ClQDvwIG76FNj8YyWn/2XPr1dc9vBh6OHL/x+O+TfJWDzqjadEI6MMFXOXhSQ8XI04Gbn7hy5kpPr2XlDu/2uVUbT3z8mqcnbrDCSCQipEb/mh3APhmK6ypHLAF+tazs0L1qrzeHUkJYYQDLgGVt6as7Igd8/Ee16TTG9f5qEzRbxb7NCK27t2rIDe/0OWl2da+PglqsDCOOLpxSWLsv16fnFox3Js+a7Uqbbqvfcq4/WHmE3Bc5q7Yw9Jb388LadgeQXpyf0zkC2HkJScCSsNDBDYP8R6b/vqHVVVIWXdt7UsxnvT72D6375yEfrb+xHWcZpQ1Y3qgkKXWLSu52wPjnYG7djZNSl0Ruyl0xlyhtJ2taljgpfseoLQH3Icvr4xKAUal23+QdQWeSKdMbpR3RmNvg2dVF+X8CzgBO3NszqS4/dpnTL0baw8aEfXU67K+88fjFnrrSMSfWbhs3GvQEw16XEw7GRL6sJRBOM7+7ogHI3lcFgvTcgssxs/kHFOfntDo2UynxIKbCyZ/2x3vkhHsfe2VTdfqFZwx7o9djl73c4TKe3drDmjUtK9tGr7fsImybGF9x1SPnlOyTsQpm1arxj743vwaOTXSV3beok4xVgLC2TQPutIng74B7Onq84hfcop/D8aozYPQxtBjfFmMV4PDHt3+yPCNjiXOtt0uTx6L8jNeBdKXEMZ2l5aeUsANeKya1ELNClQN+olEcpRuTNS3LAAYCmVmeqt9UhexD1vu9Gsj8vCq1cWxddW3Y1jhcLJxi962JtwUXrfXFrAQ42FN1THXYtmOdL+YHgEM8lcdVhhwlxX7vaoCx3srjK4KOzev93rXW8QnlQcf6DX5vsUCLE+PLrnSKcNL0ytT7BYhDvFXHbw84124OeDbaCduzvNXHbgu41mwJuDc5Rdg52lM9viTg+mFrwL3FLUKuTE/N0VsDrlUlAfdWjxHyZLhrjtzsd6/YHnRtizGC3pHu2iM2+d3LdwRdO2KNYOwId+1hG/3uZaVBV2m8LRA/zFV36Hqfp7A85CxPtAUShrjqxhb7PEsqQs6KJJs/Kd1Vf/Ban3dxZchRmWzzJw921WetafB+WxV2VKfYfSkDnQ2jf2iI+aYmbK9Jtft6D3A2ZK5qiFlYG7bXpdl9af2cDRkrGmIX1Idt9X0cDX3DWpy8PegcgpVPYIam6XMAA8SM9NyC7N2NVqWEbex33kkJDbYMwNDoGSIvITtqtML5175YD3xk/XvwjccvdlRvHn9kw84Rh0HoSjD6mAardlpe7X36zHq5t3vLGnozILbY3do5WrGzLsC+v94jN1QP6wN81xnGKnRjg9XajpoewrCFtPB9XpXaanmJFHepswYY1Xt+p3qkivNz1h1519T1vqDrjvTcD0Mg9qlYwb6SsNN40xkwJtV6Qu/H3FzTLlv4AvEGcE/hccPSs+asKW6PPqO0mZeBZDqgAktTWMbqN5jyVJdJqVdjblNG6YZkTcuyHx1TfmwQ4/hFtYkhYFSyzX+yQ9hTA9owAArr4/EYoSBmXPSbB7lrdLLdv3JeTfJbwOa6sP1oGkmKlQZdU2ZNXtVu9y6lxArgxpzE7U9Jqbe1V7/dlaxpWeMFepZGO0AErNA0w/JgRxJid/98M8uTgx8mVNq02Hu7A57zr30xgLmInvvElTMXgp4B2iVsPluvkW9Xw8R96m9Ur8VjZm8+hYNTF/UF1rRmTpaRel2jhNT9iin/utYOpx4B4uXOGrPbGqy97P77yoJOKxhdtEk0vNyfsB03hESgut0m2ELinRXfrK4bPRj03YC/qZV0u5CXcGMi9nM1Gm+9cTJ5CePbYyXuy6j9xLUi5p7A0PpHgHPaYaZR2oiUeldGplLC3lExo1Y1t9VS6qBS4iV+XjwgSheTNS3rN6Bzetv9veJswZFrfN4aECPm1yY3zm7eBJSOdNduWlYfNxUoGuqqXfPeBWv3ttXZpsITu2Mtev4MFEupX8PUsn7T0ubd7ymcUjjvvg+Sn64O26/+rjb+7BWVh1eDvhK0BrGnxLINQbv+B3AD5rN6v01Aa0+ueXrivCeunJltc1XkpB38XHzSsI+fMeVPW84PO0cVAiwrG9sqz6FS4gpglpR61f7qXfXY604HEXd42pz6zohfhW5qsGZNy4r1COMYM9yH4F7+oFvEjobkclc8LK6P/YkUVp9Zi3clFZScOLZDVq2rd46yxhQ2wEk7r5DXP+8emlBp+zARe6b4Md6s3VbiY9/d8M3S8cNLXctjDmprX1HaF6XE8cALSolT2lunVSlxMfCcUuJQKfUSKfVD7dl/lLZj7UK9BEJsDzrxaYEd/UkQCgY6G8qGuWq3f18X/86Xv1lZ2Zr+27nwRBj4BWaFn9esh/gBYaxGOCau4hbgT/eftT3ERZCeW7Ad2Ahc25QTwwq/+XPVQzGj4qvtp9e7Qhd5bqmJeldbwI/JXOe26vqS2gGlAOurhu9zyJVSIgEzBPC/wB9bNYEewNLScRkAcc6qFqs2tZVuabACf63Xdmdve0Pu9qDboI0r/NTYdYdXAYa98iBgPuwyVmdhGnf+PrMWT2xPozU9t8AAchKcFddU+pMxjW9hA9ovLCEv4dyBwvkskFzrCc2IqbcdQwdUBbJVOB4B7i3KyMwH3stcURS9aXYPNmFWjNqTLM4+oZTohxmnuhp4D0ijldthUToFiXVjEaBrw/Y7v5uy7G6lxE3A74GjH//l1tquSoqzygrnAndLqWuUEidLqTuiilGPQEpd3/hYELbZjcDiH+775R7vp0qJfpkeT218NZT3Cn3Tv+OnuV+glPglUCWl3pMCw15J9Wz17qjvy8C4tfusoy6lrlRKHAzs19/1TTXpw4DKWRtPn9VZY3Y7g/XCNwYfBXE3gXhxxuQf/t7W/rKmZY3HI8xqTUI/mzUta5Vl/EorIFsAbhv+L4+e9VppMZnTgG/H8cWa7znGHcIxgX3wwL47PemExduPeq+391zX9rp+7ppAXK31TAHTo9DmUoPrn3dn9d7ueNaDcbShxbdlycHzel1XO8uqZCVphYRVM/wAoNF/EYjrijIys6NGa9djVW05oT36spQHvsIUMT9dSl0BtPnvL0qHojA1oB0aEQhqEXk4bwAWSakjSapPKSVGSKmzwfxdd9JWfAZwE+b36r0D2ViNoJS4FoiTUt/nsjX0ykguPAx+ubdLZvtc4XKA/lucB/zntw/cBpQBrTJYR/danKE29WV0r8WDgBaXHFVKZEmpC6XUW1szbk/CJgLHhbR9QXH+GeHOGrNbGax/eTdNVIdiC1wibIQRN7dTt9IKLQDTEyWVEnoit2+exVkBwKkRwX6s/6GGhBTMG6zjW07Auk4DDX1mLc5uymi1AqqPXbRtfPW0Zdec3RB87tpA2JUwIHadPiip8JaVFVlzQH8JWmCK+KtWv5O8BAH8doDhfFZoXAG7vssRFHf3uq7WjGE0jdSOMCSHN1ExK2qwdhOUEk5M43K2lPrtfbzuHOANKXVIKXEl0TjVHkPhlMIm40yl1G8BbzVq+j0/3dn5TClRLKX+A7RvHLRSYghwpJT6v1LqeUqJIVLqvZbQPsAYDyQC+EKu4Pa6vkubaf9/8VW2E4DDO3xm+xfHAqmtvXhFRVYhQGHpuJKWXqOUOBRYpJS4Skr9bGvH7glcN/U3qWF9/qhxvedvN5XZOoduZbB+Upn6S43odYi38qWXf7WhxWXUmkFpbCF02C4EEYPx/svF3WmL9AknO/HleKn5fMGJ538G0GfWYhcwug/rnylh0GGYrlE3uxlpSglDSh3++4J7rghp21PFVcPxhzyAnmkTwQcPS5s3/dHLXtEA6bkFH1rXn9rahKsNz7mP6m8477OFxUShWbBpgP9vgy5p6KzYEQWg0Vq0MZ44SodxDFAHtNhgBX4DPIcZVjBHSt1kTfMo3ZeWxJlKqZ/a7dQsYAfsWnCvVUr8W0p9l3XO0Qa5tDzgdKXEh1Lq2qix+jMmS6l1em6BAJt9S+2gvda2l1K/s2Op92yATf19cQOgU+SDejpS6gbM+OBWUVI7oAJgc026bx8uK8R0eL3e2nG7M40Libi23BCvsaER+/K8aTPdxmDNmpYVA+IRYMn3dQmXtFe/hVMK553wyI3ry7VvML1mZxdOKZynlPgVMGjVidnfKiXmABuU+vUbUuqbSk4c6wO+7TOL64AZoN2AGMaychgbucF/BGy57KnLNhSVn51nOmG1Hpm09K5Pb745zxz5rEaz0AtBnDkyaemSfc6my0swQoa+pj/ORzETF64ztHhy0CUNnZawkLmiaF5RRuYWYBtwTTQcoHshpfYrJU6wbtJ7RSkxEdBS6lnAfzC3j+d29ByjdB+k1Hc3OnQDrwHfAVi1zjdaXqKXlBIGpo6kf0/9KSWOBDZLqTdjKgH8tVE4QpRGRGKJD0oqdK6syMJt3/vHpJRIPcju7gVghEWnbb32VJQS/YE3gRuk1Ata20+fmI0xJbUDGRi3Nr6F49qsXYpHWztmd8Y0VvUXgA2Er9K58Tnq+/Dd9qM71Tg3mm/SOYz2VH2AKWp9TeGUwjZvT2VNyxqfNS3rlqxpWePDdcMqjZ3jaxptmZVKqSNxKUOA/sBWAKVEolLi89fFoQLIjqf8SRt+ktl+j3WtxlxJ/SreWXkH6LDphDVCqyrGNLkaO77/Z30BhiWuPL7FbyAvYXzgnvgngEW2sHgsaNdLNg3wS/IqHyevstOza7WhQ2FXaHXUWO2eRIxVpcQIK+nmZ1hxqk8At1jXBKTUn++vsitRmkdKXS+lvllK/YF1ygk8i3mPAzgCKFdKnABmGIklUYV1nATMxPSsIqXeLqVutWdrf0cp4VBKvH7FIf+4DmBs6sJDm7nk1tqY8EkA/bY6o4uA5ukPxLGPpVh3Z3Sv74cBZCYvGdZcW6XEUUVlWWuOvuuZh9NzC9q59nvX0khDVgIOEAbgcHs3TBKE1xfn57SpONG+0i0M1sNeGn1QUX2cPMhds7ZwSuGctvZnua5nY0pLzNiiG2oq/cl7uolWAjv5cfsgPancNvzg773Xbf1iAs+Kk/55Km82LESmHDHrzbfPm3XvBVLqvwDpVf7EK0H4MAXc97hVXu1P+AJgaem45ldreQkO8hKu0ujZ9iBXa/ShwH0uv3Ho4D80tPmzaS3hhGDfwJCGw7pq/O5Cem7Br9JzC27pxjemS4FblRKpAEqJQUqJfyglnFaizVmY8kJRovwMKXWJlPpGKfV31qlK4AWgyDq+AChTSkT0gnyYWUN/6tyZ9kysUIv+vqA7DmBzzaAlzVwyLanC9p+On9n+geVVPdhSOmk1S0sPXQ7w/Y4jmi2p/krRZeMf+uZvg0vq+l8PzBx569unXT91co8vFqCUOAsoVErEYto22tQNJmDUjhiSkVzY6e+xy0MCsqZlCTAeBV0t0Ce1U7dX8KPUj8PuXdMnVJPZpBahlLpUKZEaSTqQKv4kjR4EDAbOTitxvHhI2lfuj5jMRoafs5XBZ42cNWPLqhP1l1IyNT23YClWXMee4lMX7zjqK4CN1UMSm5xtXkJq0KbPrI4L5SUIW4qhhQdAINDoEFBDXmWrvGBFGZm74k7a4h0VtbYy+zbnquZb7r+k5xZMBv0yoEE0dFgRiLZxO/C4lDqy8h2DqQX4DjBXSn1A/w6j7BtS6hXAtY1OrbT+f0spcQxmxsUlwCAApUQ8UB312u8ZKfXx6bkF/YHbN1YPXd9M28U7lnrdABsH+OIHRmNY94i1SK9ojwTCbXX9K63/f7ZresXTl6YFw45L5mzOTvKFPEfDmcdYvj8DcPvDro8K1p3HB7e8vzGsbRuT3TuCg+LWJqwoz/pfQ8i7fnhikW94YlHd/K0nzNnp67WzOD+n2/ytKCUygTIp9XbMEMAtQIqV4FkKbAjVDvlbddXB7/dKWPm/zp5flxusoz3VNy6rjzsVxI1v/rp4rwHoLeHP76Z53CL5Fw3aZlYSRgSSQ8mpRuz6po1FYNcXPC8hG3gQdhmL9owV7g2/Sfu/R0FcCxghbCKbt/+n1Em9pdTaMliaM1q2gA7GOSvHWOMI4JAdKYH74qptmW6MwfaQELE1RqAmNvRtfLX9XYG4A3C0JsmpKCNzSigmeI12hEfbcHit9+Irysg8sbVGq+E36vAbner+74YMMv8TArSrNTWqO4EgcIFSohIzseZjIF1K3eJs1yhRdscKA0iVUn+tlOgLTAEWAvFAQ6MY1+cx/06OtK5LxjQius1DuTvQx7sprqRuALGOyr16qZQScSMd7hQAW0gcUIUWWsGzwGClxGFt/b4NiF0Xs6lmCC5b/WVjbn818Yg+c4/+dtvRvSv9yWPh7ENB2ARhneQq3Vgf9LznC3nO1AgBIpTm2fx2/7j1QxZvP2oN0Mcfco1eUZ6V1hDyHgKwemcmq3dmRoaqH/XX/9ameLY5NlQPnQ5iy9CEFc7+sRv07M2nvA1syUhesv2gpKUVkSTujkIp0QczFCgfMw59PnBK5HUDnZhq93+2esMVowDWVh5U1HRPHUeXGqyHvTQ6W+B9MMHmr6sMOf/VHn0W+zwPNWh70lhv5Ts/NMQsqw3bP/LXDvuvwxbYY7afUiIG+HB0iqcytdShBUJoNBowELM2MxTgckxJp1AslU9G/iCUEgXAZ1Lqf+5xTvk5oWPznuYsr7qi8uHLj0jAPhDon1rqoM4TqgD+BhQ4gsa3jj9Vm4H1eQlf0ApN1aKMzEuBqUatDcvJbAmLC1coITBz2diRZ4xevGpGS/uLoG3aoZ1h775et5+hgHozEU8YdJOQmsZIqcNKiVMwY7lGAddLqR/r4mlF6fm8B6QpJY6SUtcBEeWB6da/CG8CCY2OvwCWAhcCKCV6W96bAxalxJhrxw1ceNucpzgsbd4ouGhvzQ+rjQlPgmgMawt4HujVHoujWGfV8QC+kPu3vpDnt7M2no4hQkHgK7sIPnDCwOmVZw9/ZUScs/pU4OKLP/lwNNbz+us7L2/yeZ2eWxAD9D0sbe5RHnvduDmbT94M9E127zheI4aCGAvkrK3MiFlbmQFwDcCK8oNZu3Okfi+3YDWwdUDsOm+Su9xXWHrYO8CWw9Lm2pPdZZs/W/+LBUAWu+34Xj91siitT3PO3ZLtBFwnD36/f5U/wfH11hNqfz/6sXMS3WUnfbHx1H9/u/3DHaemv/OQ1kbCPxbddfvS0sM2A64j+3wpdwZifOHewlFVcbiE8IVm3o54ND23YGln7gMXLb4AACAASURBVDJ2mcFqxpkanwCGL2TYMYP72/TGs6ZlJUPcr90iNL+vw3fuf361ISIrVYpZFahJpNS1SomKnYmhZamljlM02gkQcOr/c91aNe91JY6ZrU9//XluWV9P7KcPnvjyPAClhAuoB/zWsQP46+ilnsLUUseImphQWW1MWPbebk+epe02Z13IHrTZjtLodwTiw3p3+FPvzTVN1/NupaZq2Bu6W9QZiB8LFYQBoYUWot7Qht/2eVFG5pe+MTXPBgY3vHrEQzta9Mcdjg/2Cab5m0sQ2K8pzs+Zl55bkA2cDPqXBuG7LnnyKv9zVz/1YFfOy8rSfhA4R0pdDpwtpa5TSpwGfNqVc4vSc7EW8nWWAfAsZkLWXjPVpdT/bXS9AP4FlFjHTmCdUuJhKfXt1ut9DgSR9d0orQvErAbGrK8a1pwO65JeZfapwGWdMK8eTaPEwTazuXqws1HBn3CKe9t7x/Sf+fvHLnvZCi08G6Wejnx/q4olzT6vi/NzaoHVkLMaeGVP7a5+5uI+9cGYjFkbTwfoNyblm1PDYdvg5eVjS4C+1f6Eg0rq+sdgas3yzbZjd+vBfKSn5xbUAHbBBW7dyLfy2fofUxheWHZd5McTAaYXn/Oz+SwomYCwVxHbGypqhvU1dxgFdIEme1d6WOWP4wuDdnjjDhG+O6CNxAZtu+KBs7dppcRvgSB86MZMDtjzZKQ2y43kJXwkzK1e5bq1KjKfE44XH+Ucz0dDpNR1h7806hSfth0GY1ThlMLzGnVzaHyl7baUUnsYcMTW2oittREWuvjT0OHL3gyd0PdvI+4enP77hloAT1vebBMUThh6rb3O1UcLHUYTxkwEux5IEVoo4bd9B1ymhb7NtTT2ZdtW591FGZmXYMa37tVwNWptpfYS54p2nnKPIxIC8ocnrn5hedkhq77YeOq96bkFs4rzcxZ19lwiWsCYi6a+QDpQbnnAkFJ/bLWLAxKimphRWopVAOBLzIpBL0mp39vXPixD95lGp+yYpVoXWsdDgDVKiYul1NOUEm4gfn/3wEqpSy7OLbgUmF9cNaJph8WPbcu3L/PGQDSGdU9Y0muXAG9ZFfraTHUg8TXMBFYHEChtSHswYqxue9JzQVKFfYwMxBe0c0VJAJ684sUSrEWeSc6ru7cxdXyJB/od3/+zE2sCcf2/2370OOBUK2RNA0uAuaNTvhuFFrVLy8YtPDhl4YhxafMv31A19D8zN+Z8FOOo1mNTvx4cCDvLF5RMKAJ8Ewd92Lc+EFM+b+uJmwDfpPS3XYudK/rXwlxb7KrbAzuPvo0OKAHfErrSYFXWhypoBzH6G97pMymkU67u62iY/ulFP0QyLy8F/Imu0qG9vSU0p4GqlBBIlkupf/IllFLfr5R4Ukpdd/C0MadqjE+s1Vcwa1rWq8DThVMK50mpF/jvjcvHvCmj0WG/Uz/vurX6sj/d9tY9vrDntr/VXcILzbwXpYQHEJaXzIkZR7JCSr1aKZEI3AG8LaWeo5QYAHzk/bjXK3HbU+/VaNAEMbdHXmoiZvXxhTelvmQrc0x1L4yfAMzUzvDCb65Me93zRdI8oU1jvfF1C27t5Y31pwqb39G3KCNzfFTaCp6/5smN5z1y6yEldf2nAx+n5xYcW5yf0ykJTdYN+iXM6kU3SKkLlRIZlvG6e1uBWZ5QKyXGR2MJo+yNRmVb1wOfAe32nbYWUo83OlWNqS7wpXV8IvCRUuI4KfVc615nWLsG+xWpnq2JO+r7kuQqbTbTOmjXyQD2oGiXamT7Icdi7gBUAm+0R4eNdtMkjbbXdV78+N44XgPQ6JtEXkJ2RxitLZifxny/lZBTBGAp15yAKT8VAP7vxUlnfA2cDmyRUn+r1BmpmM67B56/5knLgXHBbr3vbiflcOEbg49dWh9PRsrCHxZv+t3PPpfOosti8AqnFM5LsfnrE2yBOiA7opHaWmZX97rJJnQgy1PdWF7lbOAUX8gT9IdcpXu6thELML/4P0NKXQlgoK3a7QJMg/93wAxLSgtnwCgA/BodFAify288r5SYcPLgDw4FcNvrxysl8pQSOWAaH0qJuVZZTCwvQx1wgzW0E/gAs4RmZODLMOMTAWqBta7CmBGYsaoI02O9YU+G5REP76gcN23L+SIs0oE/arse5VXJD6GZA9yjhZ5TeNyw5UUZmXOLMjI3xb6dWisQacBYYIalPHDA89aN960CcYogLOKdFYuveebiDpX9shYyWIbpDhppDTZlrFrnNWaM9F+ixmqUvaGU+DWwWCkRK6UOS6n/YCVedAhS6h1S6oel1JFk2yJMjeCIpNbFQKmVDIJSoq+lQtDjOWf4qzcDjO83a0Rzbeu84RMB+pY46zp6Xj0RKfVs4DDMOOt2ozg/Z15xfs79uxll51nPWAQisiXeLbDmmY3p0Ioo2LgwZen+CLv+5q7b1922eFswC2CQs17s4XPpFLo0aaQs5Py+KuSY11ZjNWtaVrZfGycHtHH7Q+eULIucl1JXSKl1fTDGV1w14vsWdPUcsFephhDGB6b2auNnv3Yf7t15FwB5lfMWj62r3to38B0QWX39b0jCD4kAC0uOjcH88pxgzTGMaXjUW8cNwM2YYtxgGqRHAS82ek9xkVrF1vHZzrXe5zBXTnvVhG1M5oqihswVRU/UyYr+objgHBFJJNIYRrVtGGZs7uehpGCR3uUNp1v9kXY1xfk5P5yS/t51vpDb/fmGM95Mzy1I7ohxFt6UekNMQa/S7yb3Px/A0sq8qyXXSqk/klJ/CVB4/LCJRRmZt0QXHVGaYCNmrH9cVwwupS6WUudHQlowPbw3NlK4uANYbxXAQCkxxIqz7XGsqxo+H2Cnb4/64LtIKbVP7fgZ9Wyk1N9KqfeljGqrEIhI4luILtgSb47i/Jx5L046o/bFSWfcqZQQUup6zJ2LK9rS71c1yWsAFtQmft0e82wtXaoSoBGan1p++8xf3k1zJNiSXq0O2TeGET8pi2bd2P5uiPdiwtrWbNlKKfXTzbUx9cjG/I9GqZ0CrTM8NbskuSoTQ/dVJobW9buiPmKIn7mucoQNmFNa3ycVUx5m1/uWUp+52zweaPSzxvT87hWrfOouV/2+bNsf8fCOyqKMzL8AM7BiUwyfLTvSh2Xc7HqNbvZH2tU8c+W/Xx19+2tltYH494EPDr7jlZOX3DW5zd4QS0ooIe3KjJEx9PoHpuzDfwuPGv5Pe6VjFUCwt38IYUL2UucG89g3lLAI2EudGwGCab5hIih8tjLnplBMaJCtzjFEo7VANBRlZGZHwzsOXKzQkgcw457vk1J/BZzWxdPahZR6GbCs0akXgXlWyAKYDoY4zITdiIbkOtmCEsVdzWfrz/oG4Kst2T8019bv1L0ANgz0xQ+KxrD+BKXENZia6bl72mVqT2q9oSM8dUadgbgXmNUV4QBNoZQ4DPjekuhswAy38QK1UurmEvtaQjJAbdjekp3qDqNLPawxRnCQW4SyItvprWFlQ8ydlSFH76NjK94snFL4kxuVlDqkNadpjWts6tfjWtKfUiJJKXH43luJf4Gox1plaYwrbz6j4vJG4z4qpX6/0fH8D9ee/zXoUIqnpM0acXsic0XRvMwVRfe3xgixrskG7hCInxgyjV8DokZOEyy7+8LpwGTQ41O9W9dcP3Vym3LqrNjT2ZixyFIgDEv5AREWuzxg2hH24NDuXcdO7cHe6Nihvdp63ag37GgQiKinPEpkd6c/ZsJet0dK/bWU+qVGp+7BDHdpHKv9fORFpcShVg5AtyPFU5IKMCblG3dzbcOG+ffrCERjWJtgBGZlqw43VgGERlbHh+rIq7yvGxmrE4FFmFUMkVI/K6X+lZS63WTQBjvrjhRmEaMulVbrUlkrgW2AZbnNyJqWtc9xrFnTsuIh5jI74QUJtuD/NdXmtRWXHqax1dcFY7a0sNvngMOVEoP3ZFhaVR92eTNbMu/i/JzgoXnT6O0tObWF8+h0LEO0yfeyt9eimBTn57w1+bGbXpu7Jfui9VXDnnsvt+A3xfk5+3QjVUqMllIvk1JrpcRUzHCRbZirZodABGzV9lNas2iIesqjWPGgfwdus+LYftPIY9mjkFLPbHQoMLWyywGshK1FwN1AnrXbdjjwTXtUQmorGcmFR8zZ3IffjXpqOtyRtLe2fqceCNEY1qaQUt8QCRHpcPISEjwYLiOsn2q+ccdhvd/rgG1S6lcxtY6voAMlDD1G6KAYIyTm/XbvakIdTZfKWmkIW5JWLlohaxVjBO+tDdt7BzFyHjh7W5Mf5Kfrz7YDrKoY05IYVoD7aMHnYhmp+zTfQMi5dM3Og3rkwyFKy3jluocnD8l9f2Uw7PybILwFaHIh1RRKiXOAt5USE6XUs6TUprdoBbQ23KMxbQkbibLf4AXOxExQ2dRTjdXdsTxsBY1O+YDzgOXW8VhgPmYo12uWQTsEcxu1U7xzjVlVMXoNQH3Q22xRj15l9k8xje0ojVBKxEmpqzvxO3yUQAi3T3zUSeP9hMbv10qQXAm8ar3/JpPF24sVDXGloEuzpmWNb2vOUVvoypAAZSYvoUEbfRwNe9Wj250b3+lzfEPY+GN/R/28wimFe9TA/MWw1/oDHJK6oNlsTAAp9SIp9fyO2LavCcQv9oU8ae3db5TuhcZ2t00En9QYf7rg0T9/uLe2Vubzwdbhx5gG7s9iltsS7tER/UTpOSglxigl/gwgpV4LDJJSv93F0+pQpNT1Uup3pNQrrVOrMfV7PrOOTwe+xTRkUUoMtD6nZmWm2oPtdf3qAO786rE9VkiMUBsTHgZmDGtHz6unYJUHLrG01juFnQnBS6zk42ZzStob6+93tVIiUm3yZCn1lM4Y2wrZnACiN40UkbqCLpW1ArIN9CM2tBboh7KmZd3S0g9jZlWvu8OIkGhmZSHQiQAjEouOaenclBKZVjB3uxLvrCgF3e+Kpy890Euc7tcU5+foM4a+cV1G8vfr52+VOem5Bb9vqp31cPwU+LeV0dkgpX6oPWOPokQBJgN/Vkr0ApBS13TxfDodKXWllPq/UupI0shnmJ9LZOftMuvneAClxCilxEEdZcD2cm9PA5ic+bRTKdF7b22FJgDgd+puGY/bRUSKUnSY7Nru2EJiQp037COvsqqjx1JKOJUSkxt9N+Zihis6AKTU1R09h0ZI0BFbsUtzH7pUJSCyrX76a8OyNvq9JwP3gQ6d+9/0d2tC9r9Mv2j1WsuAlYByidC2LG/1w8vrYw8OYx8ChDcFPE9mTctauSc39YbqISGAovKsvcpV7cYZQL5S4i0p9ba2vcsfOSR1YeLszacImwgdBcxqr36jdD8eveyV0JF3TR2JqaE7NeO2NytX3Purt60kkAuBl62tnauBrVGN1CjtiVLiVKBESv09ZhznQ1Lqsi6eVrdBSr0DaFxB6Fng24jeNnAnpiD9QAClxFHA9kaasW1iaOLKI8tKepM96KOVwGvAVXtqGzFY67yh3kCzqgIHApbU2U2dNmBegi0Omzdk6D2WVG1n0oGXgRuBf1oKHl910ti7o8z/NO1R5KktdKlKQISNfu8X/Fij2raqIe7cLQHPmqxpWZtBzwF9LzDXp21rFtUmnqU1faz2Bs1Y/N9tH18PsKxs3LI9tWmC5zBrBLebsQqwsXpIAcAXm051tWe/UbonC+64zA+c67LVLw9q+1uXPnnF1cAkTHme08EUvZZSr+7CaUbZz7C2DV/Eqrgnpa5r5FmM0gRS6k1S6ncbnboV+F2jheQzwL8jLyolspUS/Vs73uqKzCLQYUOEr8c0TPZIfJVtFUC/Lc41rR1vf0IpMVopcUgnDzsKiLeFxRcdNYBS4mGlxMMAUupVmPrrzcY4dzSmM1DMAbGVdijy1Ba6hcGKKZIfEb2v7++o/wtmpadywLBq4wJ8NNxVe3C9tmfTcpF8N8BVh+Q/opQ4tiWTkVKXWyvwdqW4asRigNpAXKtvdFF6FsX5OTW5R97ySIpnm5ix4Yz8P0x/txizaMReY1ujRNkXrIp5v7BCS+owyzlf3MXT6rFIqdfspkJwEWZBF5QSDuBd4LbIi0qJs5USqS3tv8KX0gCiQUo9TUo9d69tk0KHAtR5w1FHh8kdwOedKVm2IyVwFUBNTOib9upTKSGUEmMbnXLQaNdbSr2gKxIC90AJsLMrjVXoJgZrJJ4VuANE9icXrX6wcErho5gyJfWggyAagHveuWBt4U/b793iP2nw+4cDeO11yZgGbotQSoxVSryilEhow1vbnU2gw/1ji1scTxulZ6OUEEMSVv/hhnF3L9aIqrC2T7/v6/yyaAhAlHbml5iZ/xHPfWFnVP45UJBSL5dSR5J7Q8DxwD8BlBKDgHewiskoJbxKiXMtJYImSXSV9jFEMKSUsFvPmpQ9tXX6RRlAeXJwWDu9nZ7OVcAvpdT+zhrQ5RNHBuzhUGlKsKgdu70e+FYpMRxASn2tlPq6duy/3ejjaOgXYwS7XK+5S2NYG9OUTNTe9E5bKiu1tWZALcDnG874w7W/+mZfyop5gZOBg2inrMDi/JzAoXdO0wmuCtke/UXpniglBmBux94qpa5SSkzuE7NlB4ghDsO3qLwh9btLn7zi4H9f/cyKrp5rlJ6LUsINDJVSLwfeBs4BukRy50DC8notbnRqM3AkZmlbMGNf38KsGvaJFTowDpiRdmXGIYCUp5943MzYY2KA4cB3mN7waU2Nl1BpWw3Q4A5P5MeS3QcsUupyzKIqnUZ8tb23Rq9MX+86mlbqkSsl4jHjbj+SUi8AXgd28uP3ptsSbwsm1obsXV4GudsYrHuiNXqnjVlWNq43wPc7jvQqJVyAv4XerflA3/bWeKsJxK4qKjs4Jj23YHxxfk5UWmj/pB9wCWbC1XQp9QaAYsnyy566/PqZG05/8vMNZ76anlsgi/NzOjzjNMp+y2vAYUqJEZY39d3mLojS/ljPiIWRY9sWpxJhcZJ3ZlLFojfTLvImJZ0YSgheGjM9+X9a6HPQiD988mV43XHDKzCTqC7EFH9vEkOLEED/zc6pHf1eujNWKeFngReaC6NoTwL3xF3gwBgoEGFgBnkJ2ftS5Uop4ZFSRypj/hGoBxZYiWMvdsik25lVDbFfAHtVs+gMur3B2hbScwvGg74fBILw0z9UZDw9ImnFCGB9c9d2ROyIOR/3SMya8DPScwuyo0Zrz8eSvrkBcEmp86XUC5QSA5rKyp561bNT03MLNgHvGyL4wXVTf/OLxy57ufJnnUaJ0gRWnGSVZaDeD8RHt/47joV/SnW5FscmOza7E4J9fIP8I+smOItiyuw7nLZQYmBYsL9POordm4xauyPsCg/UrnB6r6ohIYFwNNHduZEfbOGQcUjJOm0Zu6/vbQ7begeOS9vuIGzQKRqx3Zh0IAezWl+nGKxKCXGULWaqPagRZpGjSJJ3i57bSonngJHA8VLqWqXEcCn1zo6bcYfhwyzw1KXs1wYr5hfLAaARti82TZo7ImlFiw1RpcRITOmTm6XUM9pnPtowqwju2xc/SvfFKqN6OOCxkl703iSEivNzPh556/8u9YfdL64ozypMzy0YUpyfs19UHIrSMRRlZE4IxQVzHJd5rgyMrP8HcLe1rRilBRRlZIpgb3+M79DqMY41Hrdztdce9ob6+g6uyXEUu8vsJa5A2BXuF+zvk/ZtznKj1mbXQqfG6pRdYv32Ehf2kh+f2cZOe9AREgZB4QE2YOg1wQENAXuJa46t3LEqlBzw+0bX9HYUe+Y6NrrXhZ3hYcIv/isQjpBhM5b2GeQDUEqkYcbEvtPUjp7LJzYDbEsLnDS4kVLBgYaUeq0VL9xpTPgi7ixDi1iNDmFmfu81yVspEYNZYe0/ltPrS2CVUsImpQ71UGOVwc66QRv9nmhIQAejAD/myiA0Z/NJN7987SP7Ei+yCaiBdlvZKoE29Q66WM8sStuwsjufBn4tpV4P/GFfPF2r7jt32gWP/vnM+VvlucAT6bkFVxXn50QTsaL8jKKMzPHADFu13Z788GA0+q7lZNwmEAFtD9u0O+wSNbatAhEIe0Mx4ZhQvH2HcwUQCCUGksNxoWTHRvciIBDs7e8bjgsmO9d4vwICgf4Ng8LxoURXUcxsIOAfUj9ExwbjXYVxXwJ+/8jaoeGYkNf9XfxcIOAbUzM07Ak5PAsTFgCBhrHVg7Q7LDzzE5YAgfojK/tohw555yb+AATqx+9M1A4d8H6ZtAUI1B+z065tusE7O6kWCGauaL42ufX+JVY54e/PHGxz/uBNBnrVyYoJ9q1Om3NlTING9/KNrTnHXuKstpe4ajU6JZTmP9LY6fAZGG77dqfLPr3Xrn6NOhue+btyaquFX1QY9UaMdoQ3g60Iu65qyKrOsJU65zk2uAvDMcGq+qOqEu3bnEtcy2KLBaJ61MIf9uVvdnmkNPK/Tp305229YpKs86cBLwCZwM/i2hMr7esAAg59NQeowWopMwSl1IFOGzQvIdZAPAYsFYirMBcVqplwgBzMbf6NwEwpdZNxyT2NeFswPoww/vJumnjg7G1d9pzarw3W4vyceem5BWcCn4J45sVJZ8xXikGRmMLmUOrOQ4DpQK2U7TOf4be883FQ2yeAOKWzwwFmzBy268afPXFN1LPbCiIrZaAMiAP6A+tbsy37+vUPnpeeW3AfcEuSq9SHmTUaJcruSCxFF43Wod6B9bZSx5uEsQd7+4eHk4LDnMti5gOOcFxwWDguNIgdzvWAA5uOtdbIMYBDNBhpRtieCBwGOGzljl62nQ4PMAhwONa7PWhswEQA56pdTpWLAFxLY38yMffiuJ8cexb8VFTFM++nifKer356vHxUhsbQYRE0aoBAOCYYD/iNWvs2IBCMDwy0YfcKhAD0soNHaqffu0vdxquSdvUlELiWxKIdug5YLxBl4ZhQmbbrH4zN7oVAWf34nZnGTsd3rqKY77Shy+pOKheExdrD/7WtrtnfQjtglUSe98Vtb50p6rXbOv0hcDiwdm/X9t/sPHdvr+/n/Am4UClxTGdVAixPCr6XXGEfCFxIXuVcYM7ubSyP6lTgcyn185jJj8eyn+2cFtbHzwJO+riyt/MBMzygS9ivDVaA4vycz9JzP9zpsjV4MKuX3KaUiLeCoHexKXf2LmNuQP7x8/Ly8saD/hLzQeF74IFrzjzyyCfnSqkb2jKfoHaWAmVdY6zqWYATRGDGzGHXQu1cgX/1xInlvh/bRA3aPaGUmIqpHjFZSr1RKTGmHeSpbkt27xhV3pB63XmP3JL01o33/64dphpl/0JhPiQcAhGwb3deZBk+HUZRRqYNcNQdtzNOO8PemJnJDYCjfvzOvtqhY7xfJu0AHPVHVQ7D0F7PvMQNgKPh8KoswOVeFL8acDQcWjWOsLC7v49bCTgaDq4+UgSF4VoeWwQ4/Bm144XfCDlXe1cBjmA//9HCZ/iMWvtawCEEaZjGNgDh2NDmYErDAueqmC+BsvpjdvYlINZ4FiZ8D5TVnlZWdcRDO7r9ToUv5AkDFQBWUYc9FnYoSfNP6LPNiYhszh2YrAHmdpax6rsvbmxiwDaxPCm4Mvn62p/EyyolbMAIKfUKoA7oCyQASKmDdF1Fqo4kYqS6iBqsHUvfmI1Or6P2F8CpmNv8P9Gf3ZQ7e7xGfwHYBSK0/q8fF2R4OXiFH7sZDaA9A71Vn3pKjzC+f+53t8RunThrZ/pbwxoSlxxn8yc/OWDB48tWT5rgAMTwT74cRyPDd/e5pHhK0msDcZ0meDxj5jABHAc8AyISgOU0j2PQeJkxc9gWoAL0KECD8M2YOSw7arSCUiKuUd3mdfw0TrXND5Di/Bx9/dTJv/5u+1HfL9p23G/Tcws+Ls7Pea2t/UbZf8hcUTQvspWMtS3eCWOGMLOad1+gb9rtePFux5/vdtym73IkHAIz5j9gL3f+OvOrnn9f8tprUgwR2hXnrpQYDwyXUv9n97aeemMDwNa+/kvWKXF3Z+qPdhek1G8Cb3bKYHkJx7gwXtHoygZ3+NQmWjwLnKGUGCSl9iklJu7vutoj3TWDVzXEckxseQrQZco2B4TBGtL2ZVtqBvWVUn+HqXm3OxK0Q5h2rN0IenNGVB9uX+E0daJt2Bi5+ddG2qYEMDNzSVt6c+TaqzRaD/3sExEWATRaCzPktWFT7uzs3Y3WZHfpCLsIJtHBzJg5zAZ1N4D9TnDGAZVmAQaEFT97K9QOA2MAeCowjVphVhXDjRlX1eMfDG1BKXE88IFSYpKUer6U+r6OGOfRy17xpecWjAU+AT3tnIdvj33nprsPaAmbKD8lspXc1fPobLrCWO8M3La6IULofo3kDacAv1ZKvLy78ZNQZS8GCNr1X4GngC2dPuEuRClxDLCoUwz1vITxGv2FQNgFwt9vq7OfUiKAGZJwn5VM+wxQgFWIaH83VgHijKAbwCHC7VlIaZ85IAzW7XX9ZgNXpecWiBcnndELSG9UtQRAgfZrwnaB4RMY2ZvSPl3IzmSfTYt5g+3Op5x9P/YHtp1Q7qjva/fFrRrckLDq8JgdR622+ZJFIGbz0IaEopPdFWO8tmB8b0y3bJMqAGt2ZiwF9ljVpK3MnJmcpIm7FOyXg3c4BAJQ/xB47gBxCHvY8rfCAWaAdlulcK+dOTM1SRP3Kogmr9kfUUrYgVQp9VZM79F7mPGqHUpxfk5Dem7BWYmuspXLyw559uyH7ih59093fdDR40aJ0t3Z34x1U94w1YH5jJiRnluQ/eIk8jDVaH5m/Gg0AsHAja4h64b6tnb2fLsSpURvTI3aB4FbO3SwvARnyND/MMKmXaTRhjCffR8A12Jm/L9nqXMcUAod39QlzgEu+6I6pUt1ww8Ig9UQoeKwtnmGJKwaCNwLnKSU6Be5OQzIP37eptzZkkZb+W88/GIfSDaw17//u9vvfEUp0a8s48lEeefVsAAAIABJREFUs6rM8QDP5uXljceNJMSLedfdc4UVB7tr+4omVABC2m4H2j0OZ8bMYSmg/wjxt4PNwBSyPh+Cb2dP3BKRStnjjT974pp5M2YOi3gyNkPwPk38H0FfjSnn4T9AwgQ+AuKs4P5qTM9Hp1Ccn1P5x2ennDxjQ87MxTuOeiE9t+C44vycaDWsKFH2L6QlPLPLsSGl3uN9dVtaYEKfbWYU2YHgzduNMuBMYGVHDlL1UMy4eOzP2sLiMI0Oa7ShBQiNklIvVUr03ZtU4QFAJDTIvddWHcwBYbCeMGB6r1kbT2dEYtHJwCOYWX27UErEMYnvG980EhI2jK2qGkhq6vLB1qlZmDGMkwDMpCy+NHVVCeXl5V2Tl583dVPu7F3bV03FsCY4K/oLEW5T4lYE0ysa/D2EjwDnQSA8EF4iqJmqSXgie+Kafbq5WcboPICZM5Nf08TNAPvx1sv7rW6sUuJQYLH1MHi6K+fyr8unFabnFhwNzDVEaMY1z1x81hNXvLio2QujRInSU1BmeJZwgA6BUABKiSuBUin1W40be+qN9QBb+/ovX6fE61LqJZ0+4y7CUmT5pMMGyEsQZcnBfyXW2q4OGbrOFha/FIiSkjT/PdVx4XkjfuObZ83jQDZWGeOp6r+0Pp7DY3YOA5Z11TwOCIO10pc4E7hzSelhzmfls9820eQS4B/WKmoHwNatYzMAKiqGbLfaXA2UNLrmTNiVlGWAfiIvL29pXn7eXrevYp1VAzz2ujZ7WK0t/C/BZjcLZ4U+A9sN2RM3LG9r3wATJ5YHZswcdjPo2ZgDBCI31v2CvITxgFw/yFfNUB4HfgW8JaV+u4tnRnF+zpqDbnsrB1jwdcnxXx5021sDV9573gF9w4wSZX+hOD9n3sn5D9z+w87R+SMTl330ae7NkefFFcBq4CcGa0KVfT1A0K5vATYAB4TBuuiatMttO+1n24vdD2bNXTOr3QfIS+gDPN+r3H5aWXKQlQfVe/0uPU9KXdIHsvu0+4A9l1gjZANY3RAzOWta1o7CKYVd4rgymm/S8/l2+zHfAJTUDkgEUEocqZQ4sVGT2cDtEWM1Ly9vfDAY83cAny/plry8vPFS6hlS6sYriw/MVbLG2t4xMD2Qe2VLzcB16yqHz2+HtyXZZTATAtus7Ilr2sVYjWB6XOunmWM0TNtfwgGCd8dLbRridw/a4Hyg7xbHQ3TkKr4VrLz3vG+O6z/jxtL6NJsv5PkwPbegy6uMRIkSpX1IdFU8KAiVrdo5pqbR6ROA83dvGxbaABiyzj0IM+Fnv6coI3O8d2bSU65v4k6zlTkKLLWIdmP98+6pAXt4q0ZPBP64bHRdX79LHyKlLmn24gOQ+bVJFQA7Q47zgBlZ07La9ffRUg4Ig7U4P6dWEC6Lc+48xDr1APD3yOtS6m+k1Pc3ukRiehUB7bSOUUqMV0pMBsjLy5s3fHiBHyIxRcJPCypXaQxnSDvaIYY1aMU26nBLx24NAt9NoOvBbeuI/rsCnzP8sEDYAJtAOA5a5SmTUtc0e2En89zVTz0G4gLgyBhH1cKht7x3u5mwESVKlJ7MmzfeH9bYZgIT03MLBICUuqqpGNXtvQPHAQTt2nYAxbBKoYUhEAhEJBytTfjvjZvguy/uYfISPhi8wXVpg1uzfFT9PeRVPjEhW5ccSKEWraA3pnfOAO2iHX4freGAMFgB+sducKV4tp1sHV4N/ALMShVKidGWGHAEBfjN3w9CiEDEI3oFZuiAATBgwDdnGEZwObAeyM7Ly2vWA+m21aWkebe0WSVAUJts/hRQQIclQ02cWF4J4gMQZ5lSWT2cvASnp94YqNFamzJf3bpEbnF+zju9PVteqA3EZ4a17S7MrOKo0RolSg+nX8yGJUD/Uwa/eyKAUiJFKfGYJae3C2+dbR1ASR//BUqJi7pgql2Bsv4P0w73aJ0Xf5w9KL5w+sWNwBkaPW1770Dc6PMD97RtmgcMCmiwbCLDLYJdEqJ2wBis9UHPos3V6XUAUurljVz/E4ClWKn/YHpPgWy7vf51EGjtGGq9dCswUkodtvqZZbc3jHQ6K39an3AvOGz+hN7eLf3b+n40cUHzp/CfO36rPvgJ0FtQdXHHjtPBmHGrrxmIFIH4P4G4A8hupjZ0l7O9vt8a05MvADxuW91fr5862dHV84oSJUrrGdt7wUKA7XV9z7FO1QG/AzIat4uvtm0ECNo5H7igM+fYVdTJim8AQnHB74Hs1ujvKiUyvn/b/rbOi39YID42tOmvBUICsXLY73zdbletu2LFrGZ7jdCjBrq0QdvvzJqW1WY7Zl85IJKuAMoa0r4Gjk3PLbC9OOmMMOaNYSvwLaZ00U8ysfPy8ubl5eXNB44Efc/f//7HuPr6O9cBAwoLL7ywvj6ZhoaElHA4zgEkATPy8vKa9bJW+xOqlpeNbYcAcmMoEAZ3h2fsCWqmaxLQeG6bMXPY8h4Zy2oKQs8APIAWiHnd3VBthALRADhBi4aQ9/Q5m08qS88tmAx8WJyfc6BsE0aJst/gMPyfgt6yeMdRqQBS6jqlRHLEIRIhZGibLSzILPKclnx97baumW0nI3RvgMDIujVjXtnc4vu0UsI4an5sH0+DccHhMTEPxNbabNZO2lyNHg8Ywiycozpm4vsvltE6L2ta1vPAXKcIzR/30uhpAW0UtCQJy4p7lVM+C5XmLNIptKIQyAFjsMY4qrfUBuIcWSmLRkipVyglbgcWSKkvAl7aw2VHgx4IwlFfn/JI5GR5+XDs9oYqu73B5fc7sKpDtVD2SbhD2t4eMay/AKMOjHHNj9k2NImDzVhZxxBgRg/VY5WASyCwSnJLeohEV3F+zrz03IJsQAq0OmHAJ+fO2yovAN4HPfuSJ696/rmrn3qxi6cZJUqUfeDRy17R7+UWzAROSc8tEMX5OXp3YxVgR2rg2D7bnFTHhTzJXTDPrsA7K9kAcH8TP71FF+QlnFDvDuUd6vBKd4MIA4bLZ6zc3M+/2lNvXJF8fe1mYSnDAKoHOSs6DaWEw6iw22I+7mUTARET7O/ra1TZHUatzUFIeMOJgX5GlT087f/Zu+/wOKqrgcO/M1vVJcuWu73YYEsGbGNswNRBosskhI/g0FsA00MgILqoVkLoJTY9lEAggVBECxJLcezQDAEs47ruclffOnO/P3ZlhHGRJdmrct/n4UG7O3PnrHd25uydM/fa4n23X8p/5jm9RzktbnDFrOtv/t3IDZnOWK5qcK5yxMCNSnEbdrY0OtY7LQwUKZdYDm9OvVJ7LkUSk2EEq/ILdqj3vMckrAf0/yi1Yukk+qWtOAKYS7wEYI3fL78E/tM8QsBmTDaVTSgb5C/77vvYovT06stE1P4//DDpmFWrxk0n/u+43V9t8eJ65RmSsbhve95LfEgrx9jmh7sggTQTl6MBmm9C62pfeD8QViivxH9g5CU5nh2SmL4x8W9+/ExfSfl1wG/dRqSsYumkp0ff/PyZdZGciwJlxTt1gG1N0zrO7tlzFiyoGXX60b7XJkHxm36/HATcBJzdXLaW1uhYCLC2T/SAxX65yDTVtdtqs5toLrPb/sxKpVlHAe+lhBx4Q4raTOu77Drnb1w31lX95Jp1PEntNOctv18M95w0b8on2U4rL5IjISPVTrNynSs8USs32t9odBh2mpXnXO1usHpFhxiNDkt57D7GRleDnRkbbAQdMeWyexm1zgY7zRooYcPGUBlGkyOoPHYeUUOJwishI6ZcKltiIlg4JWrYCKlYOMQSISYitrjyrJGGWCLbjxziNSybflsJGLnx1ID+zU/aYhBzkBtzQNQJEYfCE0EMRXNpxg6P7d5jEtbVjQM+AKZ+Xn1w8/y/q/x+GQ78C7iAzSYTSPAn7sB3Eb+M8EJGxqrPgftMUynT5NnS0tL5JH61ba8c4Kih//K8v+QEiVjug1rMId0WZvx/bfvQ28CfSNgNUF1zPNbS2plSmlUEHA4crVBXrJqeEhpwYfC6ZIfWFoGy4ijwl8sfP+PVQO3wx/63bnwh8P3+tz329vh+M2565IK/fpPsGDVN27YROXMqFtSMKl1cu0cR8SlAAfoR/0FdDZDR4FgBEHOqvYCL/H650TRVNCkB7yKh8XV7eb/IJLxXQ04rFt+XePZkALHsOudLlNZWNb+YGBLLVKL8jcet/9oV8HqiQ0K9nWvdLqtXNM+51BuODQwPcWxwRq2c2CDXUu/62IDwCMcGV6OVHfO5lnlWxPpH9nRsdNZaWbHdnCs8S6y8yJ5GjWujnRHzOas9S6zcaL5R56hRqfYgx1rXcjsrNsxocNTZHjvPUetaZ6dY/YygI6gMlWEEjaAySMuLjlSiWpcgtoYylI1TKSVElNt2iqJJORUogmB7sKQepworw26yU+wGiUkthooihGyv7RRLaoEm5VAx5bYRS2qxpV65bRunihGTGolKrZ1myee9PAPf7Zd6TdSBI+rEmlATvujkWbFKo8bVEB5br+wMq3HCPWvDzbElygEqRixX7tuesxyAkjbcTCeqh4yS4Ssp9wBB4NZAWfGtfr9kALcAG4AnTVNtsTYoMaOVyRYSUr9f9gL6mab6oJUxFAEfJJK/MFDUlqT1x0kDcCbe006/RF9ROdAP3sPAOqKoMFCxM7e1s0XuzEiNuNWq1CYjQwmHOG6um5HsmNrLV1LeV7BuFlEXG2JbMds9Fbg7UFac1LmfNU3bNl9J+WJgdqCs+MQtvR67PfNqpyV3N6XYvVOvre8RE4h8dfaAK1JmZd3fdNjGC/adXr2lzqQfxS/1t5wSfdONtFX5BRco1Kaxa4WOyQ+VKDCIKbftkJjU22lWmsSk1k61U41GY6WVE+sjIWO1yrAyjRrnIqtPZKA0OJbaWVaOY4Pzh1i/yBBHrXORlRXLcmxwzY/1Dw9ybHQttLNiqcZG55JY/3Afxzr3Qjsr5jbqnNWx/uEMZ7U7YGdYttFk1MT6RwzXgpRVGATtjFgIIbjP31b8rJxkZ2quSQX8ra1hzQjZRz1xn3Uz4Bfkxh2tYe0xCSvAsJLXN7gckdVhK/XcZ46Z9DmwFrjLNNXdO9KO3y+FwKPEZyUZb5qqVZNi+ErKX+THuzxjwM2BsuKp21hlqyoqfdeCowyCjxYVrrykLW3s2PaG3w1cUlS4MHVnb2tXWPKUN3/IUvfbgmQCEymtnZ/smDrCRdPPKfxk+VFXN0QzjwV73cT+H1XmpVaf+8D5z3dA3bSmaR3NV1L+JKhfTeg7o/crV079WdJR/ZeUf/Zb7T5xyZDwbkPPDQWSEOIuV5Vf8H/EZ/waUzC3avvjo26lPrUqv+BuhbpKEFEoZeVFFiq3eis2MDzYqHUG7F7RLMcqz5zYoFA/R41ridUrmupc6VkQHRLKdmxwrrCzY+Jc4VkZGR50Ote419ppVtAVSKkb/f7i2M56793ZF5fmnZL2Qe7fmg6q+fO+T676w46u32NKAnwl5RMFIydspeQAFWe/+1bRM8dMuhqY3YbmqomPvToduKG1K/VJWXXE2mA/QNo9/qdQ96giqwxkn7a2sYNygJpdtK2dbui5obmUZh2tUDNjTjVr5VPeg4aeG5qb7Lja6y8XPl0JVPpKyifketc9N3PV4Sc7jciBr5eU/wF4OVBWvEt/hWuatm2je3+x/H/rxufkpqyZDLzo98vvgBNMU5kAaY2OBQD1GVYfv1+uAqZtNutid5SZ+H99q5been3qq4JcArgEiTrXeM5syxBZWsfwfJ3RB0AseaMt6/eYcVgBUyF2ou7T45BYIfAwsMMDMSfGcT3aNNWbpqlaVSs44dYn9lwb7N97RM53/wVupo3lAM0KCzfUg/wLvEMqKod3WB3M1oUPgJh7529nFyqtnb8mL3qpYUuvftWudynN6ha9xwCBsuLPDx74QcG4vJmXxGz3RuDFLPeGxec9OuXqZMemadqPBmUEXgf4fv3YfRNP1QNr/X5xAWQ0OFYC2AYu4DRgaDLi3JXCezZMBAhOqGtXT2YiOS0icc7VyWpyOde5hwKhlFlZ/2nT+h0cT2fmT9SNegFDxP6c+EgB9/v9sp9pqs92uEG/7A3sDbxsmmqbX6y1wf5nAdbi2hG/CpQVr9rx8LdE3gBOAMYAX3dMm1vdVhbY3a5+pO/FwZdq7kvLzap1PAS8QGnWSZTWWsmOqyM8cP4LCnjUV1I+3WVEzhRRj1UsPf5uX0n5EUBJoKx4J+8zmqZtz6MXPv2Vr6R83rL6YfkApqmeBJ5sfj3mUB6nJYz8wTvXc319zxjZSugDYOVFNrS3qUSSqhPVTsBOsSZK2Jg7as7cNp1je0wPa6I3s0iwHwHF3r2/vJP4Zf3+tGGIo8QNV/8DXuDHyxdblH/DK4eDugT4dP5dJ3ZQsgrxGagU0NimOtgd424E9/rEDV/dSvaVjY8IciVwwsbs2EeBp727oMd61wmUFVvz7/rV04cOer93uqv2JuKTYXx13J/uXHj2I5dMSHZ8mqZRCerQ8x6dsukqlt8fv4N8Xe/o/gDV/aLbPM90J57v0ucB4Qn3rNW1992JU+0fGdmU1tbVe0zCCvGkdXHZ8ZeNyv3m+/+tnTD+i+oDTzdNNdw01VttaG4u8BzxkoKt3ontKymfmBtO/ff+IVfqgJgc2JHzwBcVLlkF0Q3gGdVRbW5JIkkdkfivojsmrZTWPrC+V2xmTo3zoL6rXaXJDmdnePD852u/u/3UO4Dhu2dXvTG/ZtQw/7JjP/WVlN/jKynPTXZ8mtZTHdDfvx4kwyHW6QB+v7xJ/PxCeoNjHkBGvSPo98uf/X45K4mh7ioZtLZ+VesS5owemWfUOw2jyWhLvgX0sIS12Zz1Y49wSMx+f8kvrmlrG6apYqapzjRN9eK2ygHyI45LT27wOg4OOZnc4HGNDznOb+s2t8x9HziHVFQOb9dkBNsWvYx48W/LGb26nfoM65CYQ72TEjJuojTrpGTHs7MEyoo3flDyhxN8mfNHgDwP/M5thFaedN91b/hKyrtNHa+mdRW9vOteAPhqzcT8xFP/JTFdeHqjoxqg10ZnhPixd68khLhLRYcGD7DTu9k9Ez2cRIy9AVxLUt5ucxs9aVirlnwlb5WATD2tYPqTRw59c3/gF6apFu9IG36/9AcuBN40TfXl5q+f/tCVExwLjq6cEHalbzb+20p35pJ6d1r1soZV+z8BfA12bzAOBfyXTCtsdb1NReXwscBssC8oKly87fHq2qiicshb4CwGrMRECl1xatbWKc1KAT5QqAkrBkYuH3R+aFqyQ9rZxt7y7NjeKavfXVAzqi+wArhlfN9Pn/3HlVO79eDkmtaZ+ErKvwVWBcqKj2r5fPSOzGtdMSnrSeOw/u/I3eZL0Oiz96cLs5Mdi9Yx/le4W5lrpfda5bb7jvrfD2va0kaP7GEFOHhgxcO9vGtilUuPO9OyjeVAW+oq9iM++cCjm7/wq3tv3Pv7dWNn9rckFUChlELFiI9MUKksd/+G6vFFwEvAXJBPQd0JVDwypXJHLrl/A1YIwqVtiL+VXENBZoPcRHdOVgFKa4M1WbFfh7yKftXuByjNGpnskHa2r2898+sPSv7QzyFRE1gOPLGsflj9sX+666L4dMKapu1sgv2hYB8yZdp56RCfutPvF+/63Oh+AGv7RDO23UL34VrmXe5c5/422XFoHcfOjp1oZ8Tstiar0EMTVr9fTvvt3veX9k1d9YdVjYNd573/xmumqb5rQ1MfANcAZ7R80ldSPmT2momvD27MlsExl+FOq35VkBsEOfSSaYWXXTKt8IwL7jkty5WyNgUYR3yAZIjPcb9Dl9yLChcqsH4Ab15F5fBD2/AetqmictBo4pegnikqXDi1WyerCdlXNq7cmBMzHRa1wDuUZu3EcovOY+HUEz4CJu7X7+NborbTqtow5lHg431LnzaTHJqmdXuHDXqvXmF4LeU8y++XbKAWuCi9wfEDbKphPdfvl78kN9JdIoNt3BuidT3uealBFF+1p40embACo4Giqg1jHgA+BnXHGQ9d0arZqloyTdVomupu01Tzmp+7aPo54x0S+zjdJvfIhrRGV9qqFRkDZp1yybTCqZtf6r/g3lPDl0wrnA3cmxhyC+JzIvtbG0P8Bih3AYgTeL+jb4gSomXxvxq79HSsO2rAhcH/CDJJofqFPPbswNPePsmOaVcIlBWrl3/3x9s2hPKygYtA7bE+lPdh4dR7F/pKyrt9b7OmJYvXGXwMlJqxonCwaaoa4lfjvkxvdKyGTTWsQ4FdNVlM0lhZ0T2ig0MDkh2H1jGq8gscEjNGGA3Oj9vTTo9JWO+ZPGniPZMnXXfP5EkTTVNdC4wLlBWrbM/6awTVJ81Vv8rvl5TWtFWVXzCxKr/g+m8PGV7o98vZfr9c6ffLsXtc/+qAz1Yd+olTooPPqvfMMTCc0cb+h59y1UORbbWXSGQLQdVjRHZ0NikTVGI8XeWkg2+IUqT4ILqqqLC6u8+s8nOltZ+t7hu9yxOW/v2qXW9SmuVIdki7SqCsOBooK562b9//FOzX75OKQN3wAcD3vpK3pl88/ZzRyY5P07qbaVOeXALyVVMs/QAA01TXmab6OOKyUwGaUmzDNNUtpqkOSG6kO59EDY/y2nr6024iNK5uf8BrZUUXtaedHpGw3jN50kRQn4K6C6hIJK0K4Otbz/zvuL6zVm2cNQL7hv3vrsov2GYPZeL1j4A7HWtd5a5F3qeBe9c29b0+ans+3BDqbV+U9eX3qco4wHA13HjJtMLWzlGvQKVhu/JAfbgDdax+oHkQ3h3qnd2eisrhg8BRAK6HO6rNrqbfRcE7gin2zd6wsT/wIKVZPaqm859X3rXx5d+VHWEr51DgUUGd98GSSd/sd9vj030l5T1mXEhN2xW8zsaZgn3Q5Y+fnuv3i/j9MmBDr9gB0LNqWI0mR8w9P7VdvXFa52GnWScChMbXb2xPOz0iYUXskwAjPiqTSvFkNbz90HmFVzX3uJ7z4syrb5n1V/qtr70EqNhO0moCzT1tng0vTHjh5hkPvHDjjIfygYF7h13nuAKHj/Dm/LAud8Q/H9iBKM0fR46i1T2l8ZrS2B3xR7FbOrbGNDwl8cerHddm15N6bcPtwN3AxWv6RP+Z7HiSIVBWvCZQVnz5sbu9euSgjCVVa5oGXAAsPPiOh+/8w1O/Tk92fJrWHRw44MM1CsPZGE0/FzgOWOGwpB4gq9bR5PfLeL9fXvf7ZXhyI915qvILHMRvgtbjsHYTKf/JDimULaG2j8EKPSVhVcaQxB8WCOHa9OxIQ8qfQd0OqiLLuf4hSKSz4GHbyaIfCCsUIPLCwGMnL60fdlrYSund27329qOD7ktRznC4drdxJ1/21x2ZfswPkrgEIlF2qKfU9Wni/zN2YHutoC6GaLiocOHcjm23SyqpyYoF8ta6fhW9I/OM7S/ePT164dMfVl73+1HABMH+dnnDbte/F/hlta+k/BRfSXnPOJ5o2k4iMB2UVbm0OAf4HLgioz4+cUB2rTNK/KZcH/GbkrqlxiM29AMI79kwNNmxaB1DLNlTkHn7PrmqXTfSdfsTzFM37DMY1HEg5SAf/PiKAOJA4alxpvbCUMoSIeJwGvfsM3nI8OteP6hw6r27+UrKD86/4ZVzf31/SeWEW59499gT/vzHaw6+sPGfux9GxHBw1NIvnSgApY53Vd8KHAJcfvGjxyzbkTgvmVY40+Fd/wKAGJFf7chYrKASA0mrvXdkm9tSUTm8N3gywf5HR7XZpZXW2jXZ1lhb1CxXTJ6gNKvDR2ToSgJlxV/8YvhLRUVD3rohbHlXAn8T7C/PfeTiq5Mdm6Z1VU9e/Jc1IJ8pjMNNU60xTfWgKGIADWmWwzTVTNNUY0xTfZ3sWHcWo9HIAVAupadl7SbsFOtgKzO2vL3tdPuEFSVPgaSm99twB3ArkLgbX4FSlqGUkdlnA7Vnr3zx06F7flRy0BQ+GDp+iqUcny6qHbkI+CRkpT75efUhhzdEMycC8m3v3d/+pqiX/9Pd9/xsYvX3HLLya5VnK6v3yn08qb2/XQU825ZQ0/p8Nwygd8FLrb4jPzEqwJ8TD+/rwFECjgdxgOe+Dmqvy/OdE6o1lBQr1GLLUO8tfdJbnOyYkumB819QT178l7vCVko+cKbXGRxauaz47oIb/z7DV1Le7e9k1rSdIce7drZg73/Z42f09/sla3Xf6K8A1ufGekTpTcrMbBvA+3XGJ8mORWu/Ly7p28cIOnpH92hqd1vODoin07pn8qTDkH4HuTKall34wH9mJZ473OENv21FHFnZ9ZHQgI2Ndp86Rqy5rL6mbMPZV4I6ND4eqk2OZ/1MjyN073l73/+bLE/NvScft+o/P7Y+iarpBc7GbMfqc5Z+1uv7zH0iKGeDN2eBec4dV7Rp+rDGNWMWIdb4ky97ZkdmGDL5sabWSDzugDrW6BXgrAZp17hp3U5p7YbqaSln5K53zuq/yv0CpVn5lNZWJzusZAqUFdvAc5c/fvobi2pH3Pfdun1/CXx1wG3TPx7Xd9aNj174tD7xaForjcubtbhi6fGyIdjndCAn7FHjYVMNqwN4HfiHaapnkhnnTtRc7qBrWLuBtIpewwFci1Oebm9b3baHNT4yAO+jjJRofWpe4jFX/f2tmd7shoewnVKTmpJWNbC3551Re+xmmipEvI40BCoGEtwY7n1V2aFTlu+RM7cwL7XaAPD7ZazfL9P9fhlcMLcqtmrc+AfnjrwUw0pJRamM1A+zDmtrzFY424Fy7OjUe342TfsqETpglIDKyr654BgDTdXxiQm0lvpPCX5en2FNcsZwAW9RmtUjej6258Hzn6996+qbzwWGOyV69/pQ3qHvBk7w+0rK7/OVlPdOdnya1hVEbfdjoCIzVhblAc/lrndOg3gNq2kqC8gCWjUEY1cUnFizH0Bw/1pLeIsQAAAgAElEQVR9zOge9gZwbHC1uyOt2yasxMcndSX+dtDiRqr0HzxjUApEUCLNvZIEyopnAkUgN4MUBcqKZ5qmmgX0AZp7V/cAJpMoLagflv0LJYmEUSmH7XBMf/a0p6ft4PSqABiuujFINHVH1hVq6wED1Ad00LSpivSi+K7hKG1vW91V7uVN7wgyWaH2qU+3vgk87fUkO6bOIlBWXLNg6gnXHDH0jQleR/Bl4HLBXjT5/mvevfzx03OTHZ+mdWbPXvpgHchMoNA0VVVao7EC4jWsAKapDjFN1W1nu5ImRyoATtWuIZC0ziE6NHiScqgwsKS9bXXnhNUf/58C2HTXfVV+QWFWY+gXiICK17HSolcyUFY8M1BWPDWRvAJgmsoyTWUn/n4FyDVNtQYg2/fvcSIxsC1E2azsd4jUpw+5EPjokSmVVz972x37v3TfRanbC/aRKZWn2tGMMShnb6Ci9UmrcQOAUPtgBw5p9VugEbzrOqi97qm09q01ebFnMxocw/pVu57raWO0bs9fLnz6izl3/OYUYHS/tBWB/1YfdvRbi07+3ldS/ltfSXm3LkfStPYYkL5kLqhxVzx+2m5r8qKToOfUsHq/yUgDSJmR7U52LFoHsGRcrH84UjC3ym5vU932pHHV39+aed/pR39pW8ZwbEfxVX9/ayZAJCd2yYpemaSEowzaUGenRK2nj5z52Q4leonLMgCk9p6TM2htxemu+uhDIU8OKwccEi+BjQ8/cnf9ygNBLB6ZUrkA+CG197cup3fjkrrlh74ojlCuK3XNtdFg716QOize4qZ1TVpRi6pIzwN7Bah2jW/WrKJyyHHgPBJQIP+uqBzeIb223VXfi5vOidyZUe8NG5cBXwFlyY6pswmUFX8PjD7+z7f++tt1438PPJ7p3jj1rIcvm/bR8mNuDpQV67ITTWthz9xvvl3ZMJSl9cNOdKSRDzBwubsACPj9cjswxDTVWcmNsuNV5RdMVKgSiZ8Hn6vKL1hZMLdKn3+6qKr8AnHh9ShRP1TlF0xs72fZnXtYsaOudGxjbcvn5uf16tvodTNq5Vp7jzU14UEb69t0R38z01Q1v/jnnx8euvzdhj41Xy4EFY7XwKog8NvMIZX3pfb+7l/AbGBQcMPII+uWH3o+UKks7yuR+iHjVSxlGPAKSBCI0aJHeFsqKocdBWKCUVFYuKGDTvrGTYkhv4QfE2dtG9xR43fA34CpjX9M/w+lWR01UkO38ubVt7wCHJjmrD/F7QhnfrT8mBuBT3wl5QclOzZN60y+Wn3AE6DCaetSz+q3ypMO4LTln4lji82Psxt2Kwp1OPHzDujzT3dwPJAhSsax/UmZtqvb9rAmpmMdCQioGfdMnnS5Ky0YiLlzJ2Q0hcmrC94FvN3ejN/vl2OAYX3t/GDu2gXv5415agnIXeFa37mnX3fnS1D4k+VffugsR3DjiBENKydeBZxD/EdDjHhCex/xL6h/e+OwVlQOPQYcb8ffH5MrKodPa29PaHzsVcfeiQkWFK1MnHu80lpblWZOB05NDRoTFWoGpZlfbcyxMpwx/ptZ76xUqMCywRHLNvjCd04omOyQkyXRm/rSFY+f9tp7gRPOD1mpNwCfHvnHP63K9a455aUr/vxRsmPUtGRbH8obB7hGy6K9lZLE4TiewJmmuiW50e08yqnEiBkolJIdnkBH62zstNjJRqMT4nlOq68cb023TViJ/8PY8bFElQAPRRu9YAiNXhfvjB729lUvl3fEpYYTgUnKUE6VZmX2GfXi88Tv4pwBd/5s4cTsV1WPTKl8EjgVcDXPbJVIUrcbU0Xl8AHgeJIfhwdovqmsne8neg84U0DOAIYCfl0O0DqCHKRQliAOAIXKS2s0BrojsgdwhiAMWeZBoRSlWStsUctrs6y+rqh8nN7omBF1qpWr+kcsw2bmoPNDtUl+OzvdA+e/EAYe9pWUPz0qd/ZTC2vyfz1/46hKX0n5ky4jXDr/rhNXJjtGTUsiE5TMskcRwYVHRWxDuncHQlV+gWFgnAwsFWQ68KEuB+jaYn0jVe5FThTK7ogfIKJU9ywfSwxjVUE8q48CM0AdEe9wVSBy/VV/f2tqe7fj90sKYPW5evdQ1BeaM/Yfy/Zq7bqJG6tMWtGj2qyysvevFJkPgeSAcoIYxN9fu2pNKytzxysyP4fwN0WFK8e2tZ0eK36pruX+VkRp7UxKszzA4KDXzq/Jjp2YXeMMpoSM9JhD5cecapwnLIbEP8OWVluGWl2XaeV4Q8YHKSHjy5DHXrO2TzTqCRuf5F3StKNDn3V6+9/2WL/VTQOvA3WR04g69sz9+sNv1u73f4Gy4m6fvGva5nwl5RNBVQLecTLPutX17ON7G4ufpbR2pt8vxwO3AceYplqd5FA7zOzTBt7o/TLzduD0grlVLyQ7Hq39qvIL9gDmAS8D97f3B0i3TVhhU9Jq8mNWX4FSKQYKW4wDm2/Eao+KyuHFYB/V617Xb91LWWBEHFNWT5s7CxgHLDLNjhmaIz6DlX058BtQjeA4CEgl8f7aXw4w7CngDKH2sMLC9f/Z7graz8WTVhPwU1rbus+jNMsJDKhPt0Y3pFuTem1w1noiRq+Iyx5rORjjDYkSZPO7ZTfGHGpdQ7qVntpkvO2OGt81pFl1G3Nioaxa5weZ9Y61lNZ2yS+2edf9o1KcTW9XbRgzFFgP6o5jfK89MW3Kkw3Jjk3TdqV40sq9wL7AkEBZcTWA3y+FwJXAFNNUK5IYYoepyi8wYr0jtYAntH9d2oR71u7I5DlaJ1WVXzAAWAFMKZhbNb297XXrhHVz90yeNHH46o2v5TSG5h72+Zdme9uLJ5HqYxAnCpxLwDPfiEX3aHg7ttu6X0DsvMMPV0+1o30DGoeDcTZ4rwFxxruHI+cUFS7/a3vjb7GdAuA74IGiwoW/76h2tQ5QmmUAfTfkxPYNee0j8ta41jst6R/y2PvZhtorJWjEBEnbbK2GqFNtbEq1POkNjtcdtiyoy4gFa7Osxn7V7ndcManu7Amtr6R8X+CPQFEv79pYpnvjNYG6EQ8kZtXStB7BV1K+B6gf+qcte3LmTRedn+x4dpaq/IL/A/4R2S34hzHvBP683RW0LmH2yYP6eP+XsSY8svGJsa8vbff+26MSVoCq/IL1wEsFc6suaW9bFZXDrwN1O4gDG4xasDNoURms1oN8Do1O4B1Ie4l4bahJvNf3SwiNFMJHKlJj4OoFsTEQOwzcDWD0BTbvXYsBNxcVLmx3OcOP72PgAvAMABlaVLhw7fbX0DqN+Nivvdb2ju4XdSmz/ypXtSBDm1KsQ5UwMrXJCAuSs9la4ajTrgumKDLrHa8DgY3ZMZpS7Q0DV7rfAlZSWmu1qce4A/lKyuXQge9fWbVh75vWBvtnA7MHpgfunHHjJf/c1bFoWrIUlf15aXXjwEFFQ8pzHzz/+W43mP7sUwcanq8yvhbEA4wqmFvVLUdA6Im+nNLPnerPCYdH15ePfXn5pPa216MS1s9KcjMz/pVXGy5ofHnsa0snt7e9eA8rFdgqhZiQ+4DDci2TSO2ZjX8OjgfwDAK1H7Dnj/dHKcWmB9Li7/iLoNZCJBUcs8D5FcTWQWQkpJwev4Gs/fWqP30PQw4B18dCQ2Vh4eqijmhT62RKszKr+0YmKmF8/2p3DeBrSLOOEMXQtCZHFMjbbI1YzGGHHZakCWITn9WtKBlJK4CvpNwATgF1B4hvUHpg2fIG3wmBsuKvkhGPpu1Kpzxw9bkzVx3+JNgXBsqOf8zvl1zgQ+BPpqmeT3Z87fXl+f3uTv0k5+pYv/Ble/sXPZzseLSOVZVfEAIeKJhbdW172+pRCevs0waO9n6Z+U0sNzLTud59VUfcgVjx7+ET09813nEtZXXKt85nAH/B3KqZfr+4gf8DFth2ziJF2jhwXw6qmE3TbEkF2C8L9RkK50eQ9m1R4cLIFrcTT45NOvDO/YrK4QJ8Diof7BOLCgPvd0S7WhdTmpWyYkBkojMme/Zd4woDQ8Nu+1x3RPolBvAm7Lbf8Fxf/8tkhjll2nnpteGc5z6rPqTIUs4MQ6yXjvG99uijFz79STLj0rSdyVdSLsAXQAqw1zPHTHIBLwFPmmbHTBiTLFX5BYadYi1SXrt38MDaHF272v1U5ResA/7eEVe1e1TCWpVfcAbwLPGBl8NAUXuT1qr8gvHA58DZBXOrNtWV+v3iAtYCz5mmugxa9Mi2uJM8GcNGVVQO3x1C14DjTHB5aPHvoYex0oCWox64FcoQRJpSrNmpQccJlNYuTWZovpLyLOBaQ2JXC8qV7q5/pjbc65pAWbEuZ9G6pVE3/v2cplj6U4cOeu+6Zy99sNvMpleVX3AS8ApwWsHcqr8lOx6t430/bkSd1TsyZ/T7gQPa21a3nulqCwYDCjAUykMHzKIRHRh6QMUv869q+bxpqigwHrii+blEMlgE3MwuTg4rKvvtU1nZ952Kyt3mAPPBe368GkEpfjqor6aRuPxfBNxkOTh8TZ/oGylBYyRQFb4r447A097Nb/TaZQJlxbWBsuLrj/W9NtGXtWBWbbjXmcDCw+58cNrF08/pnay4NG1nOXLomy9neTZY8zeOuiDZsXSUOaPyD1JO+3GFWgL8PdnxaDuHSrEMHPTqiLZ6Wg/rRIWqIH5pBYTjRlXNfacd7ZkK9WG8KQnSAT22HamyMvcQReovwW0SHxoFiAXA+SDwD2AQnaDHV+siSrOGKtT9gpwQ9NpN3pAcJ6V1SZ+ZyldSPsohsT9ayjkp1dkQbIqlXwE8HSgrjiU7Nk3rKAU3vnxLMJZWCox55phJNwJR01SnJTmsNqnKL5ioRH0sSpxKVEyUHNqZzp1ax6nKL5hDfHKjs/U4rDuoKr9gopUdvdSocZ4kyAzgqIK5VTt8YqvKL+hHvBRgUOKpGHBzwdyqn9y97/fLdUCdaapH2ht7a1RUDhwJ3uNBnQwyIfH0Z8DLQn1lYeGa2T9dvuNrY7XubcVjKbf2q3ZNcdiSp1DPrhgYvXPQ+cF5yY7rt49eeNEnK448P2yl7GOINf+wQe/9M9Ndc/0D57/Qsw5yWrfkKynvBSxzSOwfTx59wjzAMk3VJcsDqvILrlOoOwWRxBXKCkFu3tlJa2Iue5PEvSY7c1s91ezfDOrr/TpjMLB7dGjwIucS76GJeyHa3anX4xLWZs31rNEBoX+Orlx80o6s+8WleSd4P8t6yahzqMQsRZtmm9r8w/D75T1gtWmqMzss+M1UVA4fApwE0WvB1XzH9xcQ/EoIP19YuF7flKJ1rNKsVOAGhbrWcmAEU+zbMxoct1Jam9RxUhM3qPwyw13zeH0ku7fbCH8ZsT1XBMqKZyQzLk3rCIfd9cC7K+qHHn3Mbq+NfviCv36b7HjaKpE4VgBeFZ86HQBBGpWokJ0Zy5aQsc4IOzYoh23H+kWGOmqdAaPBWW27bYkNCRU41rm+d9S4Vtqplis6PLiXc6V7tmO9e4WVGfNERzTt6Vzi/cK51l1t5UY8kfymEe5v0jY6Gly/J97bF6GTXRHtKqryCwToDeweGlP/C4nJXp7v02uB3W2vtY8RcvxkKE6FIpGwbrFTb0f02IQV4Jvjhv7bvSj1iOig4NOu5SnzacWvrqr8gqOVqH/a6ZY3PK7+1NSPcpaxjV9sfr84TVN1+KXJisrhgyB4PThOBXdW/FlrkRCcp0i5rKgwsKCjt6lpm1v6pPfY3PXOx9OaHAOBz4Je+3cpJfVJPwlc8fhpnvk1o26fs37s6UD/vqkrvhmXN+uGv0x5qjzZsWlaW100/dwj3ll84r+z3Bvv/+a2M65MdjztkUhab1GoIwUxFEoJMtP2WvOiw4IHOVd5Ao6NrlrbY2dZ/cPjHOtcy4wGZ1C57EwrJ7a7UeNcZ0QMSzlUqnLbuRIyIqLEQYuR0LdGoVAee5YRdvwO+EKP/fpTiaS0f2hs/WHKY5sp/81aB+xuZUcPlqDRzwg7Nt3/pEQhSpYAC6KDQraVF2nwfpX5LLBAOe08iRlv0aLsUPewttHnV/VJSfFnzzEaHT5AIdhWbnSac537DTsttiF4SG2UiPww/tHVoar8gol2qnWH0eQ4DPjOyon+cq+ZC5bsqljjl+6tkyE8BrzpYCQu90eCII+Aa3pR4UKdpGq7XnzyglMU6l6gb022NSOnxllMaW1tskPzlZSneR1N1wA3hS0vCuNJoDRQVtwtprTUeh5fSfnrLiNc+HDRKWGPIzLANNUWh0LsClr0tHZIQgPw5W/7O+3MWIbnuzRxLU1xRn3BzMjIpiEpn2QPNpoc0xXKTfyOY1sQQznt+sjuwbWupd7bjCbHmwVzqza0/511flX5BUZofN2ednrsyJQZ2UGJGrvFekcOxaHGOFa7bUFSm5dVKEuQxVZOtD42KOxwz0l7RiyZFxnRuCaye3DuhHvX1m9jOx1WhtGjE1aAqvyCO4AbtrNYnUJlJOptbEGOKphbVdHabfj9chsw1jTVL7a2THxM1Fi20HCwwpsGXjfYQyB4EngUOEeDMuLfM2s1OB4C9UpR4aKk1w5qGsDyx72DvCGjPHe9c29BVtuirlo6JPKi75xQ0g8ylz525ogPlhx/ZchKPU+wrXF9Z87um7ry5EcvfGZ5smPTtB3hKyk/DPD/ZuQTy47Z7V8Fpqkakx1Te+zKutKW2wLmAUdFh4R+71jrGmsEHU7AtnKii6O7Bed5v8q8nvgN2rsktp3h86v6eCQqB3r/m5nhqHUNjvWJHGRnxgpdgZQGsWQQ4GmxeMT2WNWxQWGPc5XnX0aT49vooNC6yKjGNc7lnhljX12W9B9GOmFN/MJTKBcQU6nWb40mZyDWJ7J7dHjwaPe81NWODa4DFGr/RMIaSxSHt7oO48MPjauUyjxO0WsRhLJA1oE3BAyA8NHgtMCRSvPoBT9hK6ARjDRAQFkgN3Xk1Kya1qFKs/YFpgHjG1Osem/IeNqh5CW/Wbea+J3NywD8fhkOhE1TLU883h0ImqZakXi8B9Bommpl4vEIoN401arE45FArWmq6sTjfGCjaarViccFwHrTVGv8fhEgf/o3V6WubBgyfUn98H0NidW5jfBLISttGVARKCvucickrefxlZTLgLSlDU3RVGdNpPetwId6322fqvwCB/FhKItjfSKXOte6N01nrYjnSILMA2pi/cPDiUq9c517ERCLDg2OkrCxwVntmQ/EIns07SNNxmrXCu8PQCy8Z8N+Rr1jhWtpylwgFtqn/kDHRmfAFUiZB0SD+9Ue6FjvWuRemLpAibJCE2v3c6xxL3QvSF2snLYdnFg71rnavcg9L22Z7bFUaGLtSOdKz1L3vLRVdppFcGLNYO8Xmb0dNa4D7bRYJLpbaH/XopQ6o8nRV6F2k/gMmfH3Iipk5UUwGh0zjQbnF1av6Jrw6HrbucJb7p6fuqCzl0b0+IQVtv8Lr72XLSoqh/8aeHmzpxuBlRDOBgLg+RhYKdQPUMjXkD4LWFVUuLCxs0w4oGmtVprlqMmKvpVV6zym+Q7Rr8c0Lq/JseaYpjoBwO+XOcB3pqlOTjxeAMwyTXV64vEy4H3TVOclHq8GXjVNdVHicQ3wjGmq3yUeNwEPm6a6JvE4BpSZprrR7xcHiaJ/01S3/+rem46pCeW8s7huJMTHZg4BRfrEr3UFE2+fNm1V4+ALaTHpi953O8635rBBzmrPfcRnq5TEjUNVQCA6OLSPBI0m5zr3SsAZ6xceJUGjyVHrWg84rZyoT8JGyGhyNAJOO9XqLRGJSsywiJ/DXTszdtttgYtFRqPjC9trLQ+Pq892rHO95Z6XNguoLphb1WWTvu0WJ/cEieRzq1/2grlVM6vyC4po+6WBfOIHFiPeQ6r+BMYNRYULW7XjFBUunFlROXzT9nWyqnV6pbVWdmnWx8CRxO/KdfkCnk+/zml6qsVSVwEt61yvANa3eHwJUN3i8YVAy0v45wKLWzw+E5jf4vGpQFXibwX8BvgO4Ipxd3xw9+e3vQhqMvGRPponztDfLa3TW9U4eDmbzil63+1oe/sXLa/KL7gXKAZcgkSB8zqqLGD2qQMN71eZTisj5gweXJPrrHYr7+zMqJ1iuYKH1PicKzxNnu/Tm2yv5Q4eXFPgWuqtcc9Lq7NTLW9o/9p9nEu91e6FqTV2mpVqp8Uuc6xxH5y4cS0mEePmUf+b2y2vwOoe1l1A95BqPdKP07tu2u8TM2h1Cr6S8p/Fp3uptK5A77u7RlcYt3Vn3LjWWemEdRfRA/RrPVI8aTUBf2dKVpslTvwm4NcnfK0r0fuu1qwrJNYdQSesmqZpmqZpWqdmbH8RTdM0TdM0TUsenbBqmqZpmqZpnZpOWDVN0zRN07ROTSesmqZpmqZpWqemE1ZN0zRN0zStU9MJq6ZpmqZpmtap6YRV0zRN0zRN69R0wqppmqZpmqZ1ajph1TRN0zRN0zo1nbBqmqZpmqZpnZpOWDVN0zRN07ROTSesmqZpmqZpWqemE1ZN0zRN0zStU9MJq6ZpmqZpmtap6YRV0zRN0zRN69R0wqppmqZpmqZ1ajph1TRN0zRN0zo1nbBqmqZpmqZpnZpOWDVN0zRN07ROTSesmqZpmqZpWqfWrRJWETlERH5oxXJni8inuyKmnam7vA+tY4hIQESO2AntmiKyvKPb1bTtEZFnROSOrbz2k+N9y/1fREpF5PldFaem7Wrb+m50V90qYVVKfaKUGpnsOJrpE72madrO0dmO95q2JSLiF5HftrMN3TlFN0tYuyIRcSY7Bk3bWfT+re0Mer/Segq9r/+oyyWsics+14nIHBHZKCJPi4g38dpPejRFZLCIvCoia0VkvYg8vJU27xaRT0Uka/NLSSLiExHVvNMkfi1NFZHPRKRWRF4XkV5baDMNeAcYICINif8GJNr/h4g8LyJ1wNmbd+13xPvY4X9YrVPZ0mcuIsNFpDLxeJ2IvCAi2VtZ3xCREhFZmFj+5eb9tMU+fZaILE20dUOLdVMS++RGEZkDTNis7eZ26xPfw1+1eO1sEZkhIveJyAagdKf8A2ndkojsIyJfJfatvwM/ObaLyLUiUg08ra9gae2V2J9WJPa3H0SkKPH8Vo+fiddfEZHqRA7wsYjsuZX27wQOAR5O5AAPb55TJJbb1Au7hWPo34FpwMREGzVb2dYkEflaRGpE5D8iMrrD/qE6iS6XsCacBhwNDAdGADduvoCIOIC3gCWADxgIvLTZMoaIPA6MBo5SStW2cvtnAucCA4AY8ODmCyilGoFjgZVKqfTEfysTL/8S+AeQDbywrQ3t5PehdULb+MwFmEp8vysABrP1hPBy4ATgsMTyG4FHNlvmYGAkUATcLCIFiedvIf7dGk78e3bWZustJH4QzgJuBZ4Xkf4tXt8fWATkAXe26k1rPZ6IuIF/Ac8BvYBXgP9rsUi/xPNDgQt2eYBatyIiI4FLgQlKqQzix7pA4uXtHT/fAfYgfoz7iq2cx5VSNwCfAJcmcoBLWxley2Po6cAUYGaijZ91UojIOOAp4EIgF5gOvCEinlZur0voqgnrw0qpZUqpDcRPiKdsYZn9iO9of1BKNSqlQkqpljUgLuBF4gfA45VSTTuw/eeUUt8lktKbgJMTSUZrzVRK/UspZSulgttZdme+D61z2uJnrpRaoJT6t1IqrJRaC9xL/IC6JRcCNyilliulwsQT25Pkp5eXblVKBZVS3wDfAGMSz58M3KmU2qCUWsZmP8iUUq8opVYm9t+/A/MTMTdbqZR6SCkVa8X+rWnNDiB+PLtfKRVVSv0D+LzF6zZwS2L/1/uV1l4W4AFGiYhLKRVQSi1MvLbN46dS6imlVH2L18Z08JXNHT2Gng9MV0r9VyllKaX+CoSJf6e6ja5aG7Gsxd9LiJ/cNzcYWKKUim2ljd2Jn6D3U0pF2rl9F9AbWN2G9bdnZ74PrXPa4mcuInnEk8dDgAziPzg3bqWNocBrImK3eM4C+rZ4XN3i7yYgPfH3AH6+j7eM40zg98R7f0ms17vFIjuyf2taswHACqWUavFcy31vrVIqtItj0roppdQCEfkd8YRzTxF5D/h94kroVo+fiZKUO4FfA32I/5CC+DGwo65u7ugxdChwlohc1uI5N1vOjbqsrtrDOrjF30OAlVtYZhkwRLZesFwFnAO8k7g00KwRSG3xuF8rth8F1m1hObWF57b0/La22db3oXVdW/vMpxLfd0YrpTKJXyqSbbRxrFIqu8V/XqXUilZsfxU/38cBEJGhwOPEL6XlJi5PfbdZHFvb7zVtW1YBA0Wk5b40pMXfer/SOpRS6m9KqYOJJ3wK+GPipW0dP08lXtZ3BPGyKF9ina0di7d0vodt5xmbr7O9fX8Z8atiLeNNVUq9uJ31upSumrBeIiKDEkXQ1xMvSt7cZ8QPgGUikiYiXhE5qOUCiQ/zeuADERmeePpr4FARGZLo4r9uC22fLiKjRCQVuA34h1LK2sJyq4HcVlwq+Bo4TkR6iUg/4Hcd8D60rmtrn3kG0ADUiMhA4A/baGMacGciwURE+ojIL1u5/ZeB60QkR0QGAS1/tacRP3iuTbR7DrDXDrw3TduamcTvCbhcRJwiciI/LTXRtA4jIiNFpDBR5xkCgsR7UWHbx88M4pfb1xNPOu/azqZWA8OaHyTKuVYQzyMcInIu8fsFttfGoESd95Y8DkwRkf0lLk1EikUkYzvtdildNWH9G/A+8aLkRcDPBs9NJJDHE79kvhRYDkzewnJ/JZ50VoqITyn1b+IJ8P+AL4nf/LK554BniF9S9RIv0P4ZpdRc4vWlixJ37m2te/454jWEgcT72pSAt/V9bGU7Whewjc/8VmAc8ctO5cCr22jmAeAN4H0RqQdmES/kb41biV+KXUx8f3yuRWxzgHuIJxergb2BGa1sV9O2KlHSdCJwNvFSl8lsex/XtGzGy1YAACAASURBVPbwAGXEr45WE7/B6frEa9s6fj5L/Pi4ApiTeG1bHiBe/7pRRJrvBzifeIfDemBP4D/baaMS+B6oFpGfXc1VSn2RaPNh4t+dBcS/R92K/LRcqPMTkQDwW6XUB0navh94Xin1RDK2r2mapmma1tN01R5WTdM0TdM0rYfQCaumaZqmaZrWqXW5kgBN0zRN0zStZ9E9rJqmaZqmaVqnphNWTdM0TdM0rVPTCaumaZqmaZrWqemEVdM0TdM0TevUdMKqaZqmaZqmdWo6YdU0TdM0TdM6NZ2wapqmaZqmaZ2aM9kBdCW+kvKJgAn4A2XFM5McjraT6M9Z03oWX0n5RIdEj83xrv9cKePj9aG88cB+6GOAtpPcc8pxB2EbhwL+q/7+lt7HWkFPHNBKiSTmY8AJKgJi6gNZ95P4nD8FBIgATwPP6s9a07onX0n5L4DX+PkVRwWEgCL9/dc60v1nHH2UFXG+F38kQaBIJ63bp0sCWskh0SNBJXqkxZ3p3viGr6R8SHKj0jqaU6JHE/9eCCgPqAuBikQiq2laN3HF46fJmQ9f/qhgv8Kmc6FSYM+P56oIKDfxqy2a1iHumTxJrIirLN4nIgAu9D7WKjphbaUROd8Piu9cyhZsqy6SlQnM2eP6V6+54vHTPMmOT+sYvqz59fG/mq88iKAPKJrWrfhKyvPeXDT51Y+XH31RL+/aRuI9qTGQEBh3A+EfjwH4kxWn1v0Yrtj5wD4tnoqi97FW0QlrKy2qHZlqEIs4jejtCuMQMEYC/qjt+eNn1YdsHHvLswcnO0at/VY1Dl4I4DIi5SARIIY+oGhat3HKg1fdDOo7WzmO652y+q6DB1b0BQqBm4lf/n8c5HDgCxALWJTUgLVu47k7R/1K2TLd6Q1/BfbDAA5P5De6HKB1dA1rK/hKyp3AKuDfgbLiU1s8L4cNerfss+pDLgrG0tKBR/unLbtp5k1TNiYtWK1dfCXllwEPAv2AYYbECvfr9+nCl664+6Ukh6ZpWjv4Ssqzc71rXlofyjs6012zpC6SXRwoK/5+G8vvAWpuv9QVz8+6+cKzdmWsWvdzz+RJ6WLYXxuu2KA+ey0Zb4VcJ6793ndr3t6LLzzjxu8fS3Z8XYHuYW2F8X0/PQPo7XEEX2/5fKCsWP310oeuDcbSBgEPg7q4MZq+9sR7b7zBV1IuyYlWa4/+acsOFqwQsCZQVjzzqaNPGD5lzJ8f8PvFlezYNE1rmwNv/8v/Ad+tD/U5oqDXN68e43t11LaSVYBAWfH8ETnfL9wYzj3z4uln77aLQtW6oXsmTxLgL8o2drPC7mNPu+aH72Ih93sANYG+3iSH12XoYa1aIWx5L3cbYY72/asSTvrZ64Gy4jrg8rMevuzTHzbu+cRXaybeARzoKyl/AshHD43SZaS76g7NSzXkvzdf0HzpYTrwJi0K2jRN6xp8JeXpI3K+e29l414HGhKbZyvnAe9cc/0XrV1/cMbi38/buNebby8+6SygdOdFumMSHSImcAD6/NLp5Y5c9sT6HwafLoZ1++9ffOdDgI0LB3wFxCL1qf2SHF6XoUsCtsNXUm6AWpbuqqv67vZTj2jF8k7gUuBOUKnxZyUG3HhAf/9rL11x97ydGrDWLsOue/17ryNUPeeOyUXJjkXTtLYbVvLGwTaOZ0ANG9vnsy+GZC46+sHzn9/hci1fSflrxJPDoYnOiaTwlZRnAIWDMxZNqQnnHFEfyXECFvHh9/TQW53UPZMn7SWG/VVKbl191tA1/U79w7xo82v3nnLcQoc7uvCKv/77qGTG2FXoHtbt2x9kQEM065rWLBwoK44B9w+/7l8DLOX8Q2LYCidQNmuVWbbnTS81NEYz3gFmHTbo3ZrZa/ZfXhfJ2Rf9Kznp4r0WzqFNsfT3Wz7v90smcAbwhmmqZcmJTtO01tjnlr96fVnzP7Y5YAKwGMT811W3fdzW9gyx7rSV44RxeTP/CsW/6sBQtynRizp6YPqSs11G5GzYPR3Euax+WGIJBYiDH0cx0eePTuaeyZPSgVeUbWyIhdz7tkxWAVLzatJjIfchSQqvy9E1rNtR0Oub2wTbAsp3ZD1LuV5LDAgcA4IOiV44sf+Hr6U4m74EJgD3fLT8mCfrItnvAXegx/pMukMGvj8MSMtLXbl5L0wO8BDwi10flaZpreUrKd93Y7j3l7PXTJywd+8vv09z1Y0OlBW3OVkFWDT1F18Mz5q77rv1Y4/zlZTfvDOP076S8tx9S5/6bfHdd8x3GpH1wNcrGob+LmSleAemL3m5X9ryPzklSqqzfk2L84sexaQT+tvdIySt74YZoEYCp172ROWKzZeJBd0V4Zo0uWfyJJ2LtYLuYd0GX0m5ZLqHTByW/cOGipKra3Zk3UBZ8UxfSXkRiSk+F049YSacsOlOwCPL7h5aF8l8ZnXTQJP4Dwf9KznJUpxBEyDXu2aIr6T8OhK93qaplvj9MsI01YLkRqhp2pZc8fhpKSsbhrwFBx0KssYhsePevPqWdzqq/eUNQ26NWCkPAbcAJb6S8g65BO8rKXcYWBP27Ttz6g8b9xwMOcPWh/pKUyzN7pu68qsVDb5HgPdn3XThSl9J+W6gZqY6G2oPH/LOUeWLTk5FTyHdaa3/YdDlkfrU0dnDVlWeN/XLyi0tE65N/xg4BRgI6Kt326ET1m0bWxfJSQvGUn/flpUTB5EtHkj+XfKHJb6S8utBfQrKANG/kpPsg6WTQgBVG8acBcopKOUrKb8beB7emhMwkxufpmk/5yspH+WQX//NUq4xw7J++GZR7cjDF079ZYcOLRi2UjOIX4M34jPgiUkbOxd8JeUDDujvv2FZvW9/8A2zceR8vvpA8lKrV9dFcm4F3h2SsfiL964tsZrXufSxM3cXfv22wnA3xTImPnLBX6seib+kE9VO6J7Jk/aC1KliWJ+k9Ko/ZmvLOVPCgVjQQ3r/DRPRCet26YR1204E7KjteW1nNB4oK555+F33LlvROGRIxErRRfNJZitHYugaMQBUvAD5GuAatxGKFt99uyvbs+HLGSuPuGF0789nv3F16ZrkRatpPdsVj5/mWlBT8BiMPcVSrvqB6YHzK6/7/RM7aXN+4rNfeQHj/9k77/imyu+Pf557s5M23XuE3QJlT1lXEBTLV8GFigu1DFERZ0DUOqkoKihDEAEFFccPRSOgjECByh4FUpCRQumiK22zk/v8/kiKiIykTSd5v159pUnv8zwnaXJz7nnO+RwBsQV6OlCl1ogHxGwaX2ENuedoafdwAF3+KuAQIDTYAawCsD4x8LR264ypRf+MSr34W9fXv5YGivvsY4gzAHAOOTVrtM5Hz8lPPbByVnIEK0r4w2kTVFKeve/yvNVLCe2QV1p0sA1EAaY7AXzfgGY2S/wO6zUIkxY+T0BP73njyQv1tYaDCv+wOaVpAPbX1xp+PCNWkTu0yBhd5aAiAQChO+r9CABpu2Ddc5W2oB7HSrv1ALA+u6Qnur+xorTcGvZ/AHZycetyleJy7dy0VX7ZDT9+6hmVWtM6SDxiXYU1rH2QuHRHhTX07h0zpxRdf2TtcKd4DZWw5jt4SkbZeKlapdZU39nmm/eu9JkfP39Kf6tT8szO/JsVABm6I3+YnCV2MMS5jafsK4mBJzO7he/+63rnC5VawwIhqwy24AAubv2Hy5/+LLO+nqOfujNn7CgiVkZqnTZBtDTMMOup+dsLr3U8I3DuB6FWgz7S0FA2Nmf8slZXod/bi7oVGuMP9I3eumr11NkP1WqSdGV/uHOMkG64YvRUpdaMB/AlgDb6jFR/C8BGZMh7c8spJczZqja34Qq5YVotIY+t/03BEkffnpFZ6pMVScFllvDWAIIAQMRYzDZespmAzxqW8JtRJjSumpe2st4udvz4udGYumQc2ZE/7MUSc+QbAHX0jcpcFiXPe74hLxRVao0IrnP2uNaBx8/oq9p+GSCq2Nsz4q9+O/KHxlidUg5AOwAQMLbzDl70S4Qsf/uAmM1bPn7y22s6MJcydck4sjXv1hUV1tCHATynz0idWy9PyI/PmDN21AQAnwOUAsQCYNj12q7OGTvqEICzL6z+7X8NYmQzxh9hvQqFxvhUANhVMOSVWk2QruxPQbfB9RqbSbpy2JWcVqnAWGB2yBGn0HeEv2d1o5Jb2aZKyNh2Xi33mOMo1XOo0mrJQQAjOI7yKrWGiVXkdm2lPPHs0ZLuQTarpAMFk7rx7B0A+Dlr1ZqDYta8v2/0NqvFIf1id+HgQ/qMVP9Voh8/XqJSa2ITAvr+VWKOjGOIcxNP2fGrn3u/wfP+9BmpNpVa80jboKNdTlZ0SgHo2wZrCDafSwVDnDYAf8oE1YuHxG84KGKsm/5xptO8WievWrW6whp6b4Qsf+Xu19P8zmrzwB3cIgQeFlKzYlsB5UnP+jasJeB3WK8CQxz38VSQpc9I/Y8UhYfcBkBAQEBdeU8cruQExa/HujN3o3XQ8dsB/FZ7i/3UBVfUhMTZeXHOtY7TaslgABsBjIArAssDOABgfM0xD8x7IU4mMI3fXTAouMqu7GLnRQ9ty7tVAmAKgMJOr32bnRK235pfHf/Z2ao2W/UZqZZ6fGp+/DRrpi4ZR349NfYhgJ13rqqVZEDMxu/CpMUPNmb6jT4jlVep8S1AO7ly3ikfITu/oV/0tgfmpa10b++OrfX8KrXmfuCmexMDT2Z3C9/9mLfOblNHl5R8cfcxOUfXImo3vvmgPWElib2dFhEFiBMeyo0FxJQGVORGRn8zu730wZdPmOvd0GaM32G9ApMXPT6Yp3d36Ra+a9Wlye/eQEED3c4qJa6rrSv2onbwgl0AkF3So9E6qPgBBsX+0Sfz/AiSGHjSeJ1D9wD4BEDu1Q749tk5eQDerrk/dck4scUpHb35bGq4nRf1pZS59a8CLhzAKAC2zq99c7pd8DHjifJOHxntgVp9Rmq+L56THz/NnZGz34vjacouHmwMgB0UzGOrnv24qcjLaQFihTvfvdgU+/Y/zmrtueujmQ8D/b4AyLbcyra3bp0x1Xn9Uc2H7JvajGKJ8GdXVSux6ZKSh7UEp9WQG9HdaRFLFNGl66sLQrcB0F4vHQAAzGUBP4Bnbio61DoeQIN2wnRrCnNoJtJofof1CmzNG/EIAFRYg2uV4K5fJiGREuEkxkkLxXZmHoABANKK58uEEVNM4y89dsnkxeUqtaas3BLucdWpH99DCO0HAO2Dj1Zf6ziOo2a4lAM8Zm7aKiuA1e67nwHAhIVpyX/kju4AoL9EYL7vSEnPDnZetAoAkmd+X5EYeKo8pyzlE4DsBHBIn5F61UpTP35aIiq1ZjTQZTFDnKF9orat3V04+C59RmqTcd4u19r2xRf+mI9mDskp6/KVUlxearCGjG6Juy8E+JJQwrrvthj9cdOFoMcBoLogdOELq39b6+k4q0GxGwB4h6AtGsBhVak1Af2jt0w+V6W6H2jVFS65NpuvtIXrE3/R1WW4rzi2wyXmb0YtejRbZgWMlFiZ3wsjbYuiJpsnI10pqAxw/h1QxagcApohdDCVuKQQq92MNcfErKX0yNsP+Fu0NRIqtWYygAVKUXn8obceyrve8Vot6QIghOOo1hfr3/p+hvh4eUpXAP1VgX+nlZojWlfZlVIAEBC7M1KeX3K+OnEZgKyUsH0Hfn3xdb9mn58WyTOLH078u6LjhpyyLh0AHGCI45HTs+480th21TcqtSYWwF8i1hIwInHtyM8mrGjSzoO3uNMAngHwQM3OIwW1ERCuuUdY54wd9R+/wZPoKgDMe2JYpL1aWiiPLJ87ad6O53xtm0qtEfSL1t5l54WP7yu6SQaQ/gAEDHGApyzc7eMdAF7XZ6TO8vX6vsQfYf0PlAPAuP+Jtbr6k1iZxwCURxWJXA0H0g2OsmWSHnKjaIfQwagBOAHY4C7ESgw8GVJtD7xiyoCfBqM1AKvBFnzt7Xi38kNwF/ZhSsAgXbkC11CB8JQNr6itAHa7f+YCgEqtiQfQPyn08HP51QnxAF4AIMwu6Ymur39tMNhCfgGwc3Dchrxgcekfc9NW+aOwfpo1KrVmBEvu+YqCRCaFHP4pp6zLA6dn3dni39fPLH44XkDu3uCgQqXNKRn82YQVBxvbJl/idlY3A5AA4AnI05TlZwCIMQ4rkzaudT6BczcAArz0G8KSzhUXHWxN7WbR/XPGjlrtqaN7Le6f+1Jvljim7SocrABEg/8q4JQEPISM7YidF38YLT+XVW0LMFXZlX8AlDSXxkV+h/UyomTnTxSa4uCWpfD6n3h+sbRDDIR3EZBPkW64mECtGm8pR7ryewr6BgFhccmbutwSmlluDRvhw6fhx0tiFfoRBmtIxZG3H+CvdgxNDxwE9/uhy2GZAwALV67qxYsPX9qkz0g9B+AckPo9AKjUGikDZ8/eUdvVpwwdwuAq7HtkW96tEDA22y9qzTaA7hwa/7tFIjCtWjBx+Vlf2uPHT33x7JKHIw8U91kBtL7VSQW6tkG6cetfnr6pse1qCFRqjSgxsNdeHiQiQFQxKvutcS3KWXXDUVDxJXUdQaZbyu+Wbg3aKd0WNE+XlNw9OUfXnC9MtAD42nStLNjbvh9AYauURQDYNGfsKI+jszXc98krsTKBcdq+4v6xVbagvgDXCgBEjOUCgB8DReWZg+P+yPpswlf/SjkYNuvDE2cq23UQMbbUnHfvbfJRbr/DehkU5DwAyIVVvxrtgRnepgMI7WQeAREURtrWRP33zxsAvE5BCbnkTV1qidwP4B6VWiPXZ6Rer+jHTz1g50WqSFn+NSs0eQYPMTwY4rqKFl3ypwbJw9JnpJoBbAfuGAUAKrWGxCpyO7QJynn2aEn3sFJLRDsAMzefS2UA+q5KrTkmYiz7+kZnOox2+dL9xTdl+SW1/DQ1VGrNQIXw9rVGuyJYKSr7wmALeXaj+sUbolpapdYQAEtzK9tG3BSz6bNvnv1I09g21RNaAKCgICA2ANpec4t3H0tOeoBQ8j2ANwDMbEwD68ILq3/LmjN21Pdwdcf01uHkAOJu++vZd8mt72eI4wPOTMwu6ZlSZIrtCgzqCRBGwNisANYLiP3j4aq1x0WM9c9/1DT+Kydvcije56ngS4tTYPXC3kbD77BeRpEpNgIAjPbAd/QZqXu8GpyuZMMh6GQR87qoyeb/FmylG7KqP1CsDTCyd/KgDzDplVkAECQuKa2whqFz6P7OQOouXzwPP57j+tKIQTGif7jWcSxPllPQRymokFxs30oBwEFAtA1g6r9wO585AJ6qeezBec+HyQSmR3YXDoyotAWnOKngrszzwxUAHgdQ1nHm6uzOYfttxabohfrKdn/qM1KvWWTmx0998eySh5Sbz94+C1BOMtoVuUMTfp+59KmFCxrbroYkSp63tNAY9xCAV7959qP3GtueeuSAO0izB8BLNTmrHXU5PxxLSloGYMa+SZGFPRcVfdaoVtaNbAD3A/A2Qq4F4FabuLIUlkqtIbeq1ow6WZE07FRFcjug85Dj5SlyAp4CyALIW1zcutwo+fnvMsavcRfqjb7uwgXG+JoLpIEAdnppd4Pjd1gvI1J2vmuRKRZyYWVtilqeIyCxEiu56kk3wMimA7iTAbmoCtAraqdoY+4diJTncwD8DmvDEwIgECDXbtyQbsgi6cqbAXBVckfXAKNgrHuLiwdwa/7n0tMxE8311h7SE7559qMSAB/V3J+6ZBxrdUpu3XJuZJzVKe1DCB25u3BwDIDhAPiOM1efaRd8zHza0P6TKlvQZgB6fxTWT33TevraHqESLrParpQB9HMK5sWlTy28oS6exnz02sJCY7/xMYrcdfnViU262KWu8GLnEMbKigC8d3mBlWlE2QzJ3sCHJHuU7+mSklcm5+gqGsnMOiGPKutjLAyBNMxwP4Blno5zR2cvqk3URGefWfJw53OVre47eKFvIoBbNujHxLiHnADI8h4RO8/EKs5+/+mEr92+ivcSnPqM1OLkmd/nB4lLHwUw2+sJGhi/SsBl3PXRq38dvtC7b2rrHwRz01Z5LqHi6my1AwAhIGYAV85pTFcyFLTEJqJbxTOqxgDAbbNndc4p65KtEFZOOfL2AzdUhKEp8PCnU+/JPD/ih+SQQ1PWvTzDs9c/XfkkgCV2AR0ldJDHANxjE/IojLIfTjgn7ot0Q5OVo3nq88cSfz9zb0cA/SOkBfcbbEFtrU4pAQAxa65KCDxd+Xd5x7kA2RksLtl34M1Hm+xz8dO8mLAwTfJH7p2vAGSmkLFWDo778+OlTy18p7HtamhUas0dAF0TLc871icqs5db+q7FcmBs3C5xtqKPI94S2GWDvuryvx9KTRwhPCX9nYD8AODB5Bxds3JMXCoBdCtAhAC1AuRmb/NQn13yUGi5JeyxzPPDo+EKKHRx/YWWAmRju+CjxzsEH13va/WIUR++lXOqIqlt59ADoh+mzbpqDUdTwB9hvYzj5SkXWMZR4JWz6oKDy1kFrpWHkm7gDR/LTRIL8z+tlhCOozSnrMsJALTaHhheN+v91AaLU5oCAAmBpwu8GBYDAEIH+RPpBs3ZpZLHFNXssoRz4i4ATpjeV8zNTjF/2/d2Z5NrArBg4vJcuBofrAPw+q3vZ7DHy1M6A7gpVnE2rdgU3R4gswHAYAvm+7+9qKLAGL8CQFa38F2Hfn7hrQYVt/bTMrjro5mDikzd1wNEBmCVnRc/s/SpheWNbVdDc/N7H70BtH8VIDkFxvh+Ld1ZBQDR37JIZ7gt90rOKgB01eT+oUtKfgPAO5ZuVbkA1A1rYZ3h4E4TA4ggqHXBTK2WfMxxdOPVBkxdMo41O2T3bzo7KtFJBcMY3DeEB8sC1AaQ7dHycx93Cd+7T8Kav3P5I7VrYnQ9TlYkfWhxyJfsKRr4kUqtWd2UtVgbxWHNU2de7K4QlzGoSb04RntACFx5gd6iBVBTAXnNKkFC8a3Eyrw4eGuAChzO6DNSbW2n/1weLCnpXSuj/dSJPYWDzACwQT/mT0/HVCmcg2Umxsi+XmkDgIQnLMsBLEe6ciiAWTIz+2HnI9IPTdmKx2VmdjnSDU02YrDhFbUTwCHXT+pCAFCpNREA+ncO3f9cgTGuFYDJAKYdvNAXnV/7xlhtV/4GYOeAmI35YdJizdy0VTdEkYwf71GpNSyA54B+70oFJjI49o+Mr56ZO72x7WoMXDrf7dPdd1vBFUVrUt+BvkaXlBzFgE1kTOz1/ucZ9ljLNJFO9vLhW1qt6LLxjK5BDPQNWgA2AEKA2iNS9N0B3AlXG29otUTIcdQ+5fPH+mWev6VXpS14oIDcO9JBhTWpgYcVosove0ft0Dt5dsHypz9rsLQIi0Ne053tGQATmnIDgQZ3WN3O6hYAIgrqODH74/+jrGWF/MLAHXEZgxq9PamQsbYTMratXg905Tca4Uq4fulaEkfKSsEyAC8ylAwFsBQAIuXnxWLW0q+WZvupG60BXPCmAIlnaAermGdll/8h3bAZ6cp+Z1SWryKLhGNlZvZLAJNPfi3elxdve5nj6BUjDE0NfUZqMYBfgNRfAJf0DkOcXftEZb5yxtAuqtquvAnA2B35t4Aldscvak0WQHdycettEoF55aJJS/1RWD946vPxQ2IVvb8/X50YAZBfzA75xK+emduoed6NDOe6qb3Od3PDEWG7R1AsAoA/rnVcco7OufeZiP/JNoZsEOZJluqSkgcn5+gcDWNl3fh3HirRBsSU7Zu5/dMhJX/+MCtWkRscF/D0I09tXG0wOe6tEQ8qIITXDI7dYGAIP3f5lM/cQbL/VvLXN3Jh1RCjPQDwQqWgsWhQh/XUO8uVRCr+SWiOFRMQEBChrKzXWABjAeCcWltiCzgtZhyKHUJz9A6HqPxsVcwfDtYesLHjM7OK69u+qUvGiR382PCu4XsiXcV+3kFBxUY5n6t4qfp6/2wdT2ipRULHydwOa7kldIfVKfU3D2gEYhS5t1odUq+25ZSVgkIKeuXuO+kG2gp4GOnKxwA87GTo+21PSXqHlgomQKvsh3SDd+oTTQB9RqoNwB7gjntqHuv/9qJWHYKPPH20tFvUBXN0GwDPa/NGCgG8plJrTgoY296+UduoySH/8kBx/y1Nqa2mn/rFLdc0gWDMx2LWKu0cuv/tI6U93vAX9EELECsAMVwtMbWNak0DwEucal7opMTBSK53bK9Pi7N0SckTAHzrDLJ/CMDnnZ/qC3fOahYAqNSaNIAuAsCcMiRDX9nWyRCa3Td66/ak4Owtg+I2am8Zaj/mUsFqXMKlBedcDivlm3oDgQYruspTZ44G8BkFjQUoJWCcFNRuCTr8FmsLLRCZ4iIdotLeDknJLeKq1nZChZfncxbzrDHfEnQsQGiM/1loidptk53Nq4r5U9/1yeX57jXqlGrQ+bVvVNV25ZkY+dnpO1+bnOHNWP0yCVHlivnicPu2iCmmIdc7vvwTea7MxMQVRNsFqvEWqlJrPgHwBIBA/0m9Yeny+srqSFl+3p/ql5M8HpSuPA9gPdINT1zvUP0ySQDrxKbY86I2DCUhZgmflZNkzjUEOcdzHG0xBU2PfPZsoIQ1j9tdOCi6whraWUDsnIMKg91/rpKwpuzOYfsdF8xRi3Ir267TZ6Q2y2pgP9dmyueP9dyZf/OScmtYdwAbu4bvfvaXF95sTtu79YpKrekvILbvWMYecatqbcS8tJXNYtelNuiSkv9TjOxJG9bsm1tvZQtFg82DK57s+Xnh0vq3tO6o1BrCxa+bfrSkW9oFc7Tqn79QHiBv6DNS3wEArZasAdAfQDzH0UZvlqBSa26GqwvZEgDLmmo6ANAAEdbsRVN6SSo6r5GicxyAw5Sx3sfwEgqAIyDaduqnr/ji5Kkz5aaQvT3t8rxRisKhJtYeGMez1j6iqtatBLbQFwBAZEpA6MkncG66cApbmAAAIABJREFUtoRQNp+CdgIAAmLLU2cO89ZprbYr4wAg35hwwNvnGX5BIACAsBLBVZOsL0VoJ4vFNuYdVa64PYDj4dIC4wVztGJI3PpYIPW6vez9+AaVWiMEgiWVtuAfPR2jXyYRJkIUXa3gRQGerDHeUgWgD9KVAQCmCe3kzW4HZf0dAhBolS8j3dAiOlJ99fS8SgALa+5PXTKO2HnR4C3nRrYyO+S9BYz99n1F/VUU7GAASHr1h7Ptgo+ZcyvbfFppC94E4IQ+I7VJV6n6uTruqOo4Efu/paAQyQRVz5scAZ/88sKb/gvwS9BnpGY9sWDywk1nR83KKevyPIA3G9umeoQjIMT9u8fbzZZuVQ/KtgUfkW4Nek2XlPxDco6u0dMFr8YTCyaJNp393z0AXtGeG9klSFzqFBDbpw4qehKA0B21vLRrWxqAjhxH7VotIQC+AvADx9G1jWA+AAS5bxfoM1KbdJe1enNY89SZLICng3Dfu5SxyU2he5bLSntPSHhveM0VxTXftHEZg4zAoG0Atl1hbjmANqaQfUOdoopbFUVcAQXl3C1PgVrmYbRWHu992tAB4dICr3Os5CZWDAAMJR5FzBRGdjWAdwDcDOB4x9BD0q150ZAKTH0B+B3WhiMerhar19ZgvQShnbQlIMQodwo8cVgvkm6oAvDW+S8l3wVVsHOVlYLRFHRM3hcSUWGUfUbvUc4WpcXo7rCy1f2zHHC14Fx76v5OAPorxeX3n6xI6mh2KD4DACFjMw/L+LDqdEX7zyjYHXJh5e6jbz9wQ2lzNleeXvxIkogZ/YGNF4/ieXb3cNUv0xdOXLa5se1qqiiEle8T8A+eKO80VqXWvN2CL9S0FNQJ1znW7mmDld4fXzivS0q+HUAmJXQBGiO58zqo1BppK+WJ1wzWvi/B5UsdkwsrJwyJ27Bybtoqs0qt+RbuHd9Lo5YcR0vwj18TCqAbgEwA0GqJAICS42hpQz2P7hF/3XSguB96RW6n9aVE4CvqxWHNXvT0gwHioe8LrOFxBMx6S+DJF9q/9PwxX83vcmZxGBh0GMAngCsdgILuBHDdKv2rES4rHH7a0AH9YrYWAU96NbYowh4cWSxEZYAjMvD6hwPAKSehFU6WzhSlKw8dZ2ZrAEzbmPs/m7d2+6k9g+M2pG7LuxU9IrLsnn5YY/NFUgCIKhKtrs2aiY9bTgAYiXRlvFVM58flif4Xky96F3uVYgCZFLQvAdlyrcK95sq8tK+L5gFFcG1BvXvvx9OZPUUD2wO4KSHgdFqFJbQjBfsWAJjsCtrnrSVVxaaYlXBJah1JDDx16J9Wg36aAh1nrr6XISO/c1AWAF52UOFHCycu8+crX4O5aavoL2pNBoBVIZIL9wD4vrFtqg+Sc3RZB+9I2CE+IR9sj7E83mXzGY/Pack5uqyjvdp9xFQLXtr3RHRFz6UFT9enrZ4ydcm4xJ35Nz8LRD98xtA+PFahr+gduf2dPUUDPz769gM88AAAVyQd1wmacRwt0WpJF7gcesCV1LpcqyX9OI4ertcn8g9RABAXkNvkJeZ86rDmqTMVAN4Kwr3POUUV1CEuHi+wRqxop55c718wcRmDsvLUmafgkpZ4ojY5rAeL++QzxFn5adrX3uhxAgCsYj4UAEwyPspDh7UfQ6FkHCSIgm6abf/6/kcwHQ4qjPV2bT+1p8wcngAAEbKC414Mq+k44vX75F+kG85JgDvOLpWMjMsTpQF4w93qFRTUQtKVQ1ui03opbqHqHNdP6pcAoFJrggH07RK+77kiY3RbAI8AeOrghb44Ud7JqlJr1gHY2T96S2G4rPC3eWkrm/yJtiVyx4fpEYdLen8AKB5RCA36EYlrpy2atPTnxrarGfFDgKhisUJYuQQt1GEFAOIqLB7MmNgyb8eahlS8JjkQ8KQ0Szlel5T8YXKOTu97Cz1DpdbEAHSamB3zvNUpZQD8DiDjfLVq+46ZU2rt43AcpQBq1BAOApgH4AgAaLVkFIBqAFvdx/mcA8X9TgOgP58cl/dJfSzgQ+rssNYUOplC9raTkq4jCRVGgji+qIz77d0uE5bk+sBGj6GgJwE+PD6Dq9WXvNUpjQCgr83YhHPiCgCILBJ6ug3GwRUNJgCEA5ijnQGeTww8PQjAotrY4Md7jpT2cACwr9fftc/TMSWh9mFhpUKUhjgMoT6wIeEJyzoA65Cu/AzAFHe7VwEATqslJwCU1dfJqimiz0gtB7AeSF0PXNTx7HRTzOaXzhjaxZkcis4ARmcV3AwGTn6tWrMXwM5BsX86RKxl1dKnFjXpPKyWwF0fvZp2tqrDQlehO3mr2q58Z9GkpY1eQNKc0Gek2u/+eMb/7Ssa8HDb6T8PODlr9I7Gtqk+EJ2QbwEAtkLY2tuxvedcsOqSknvCpRP9sy4p+QcAmz0p3PIVkxY9MVxf2XY2kNIRIIJAUcXWHpHrF34+6YsffL0Wx9ET+HfThJkAnBxHBwBATbMhHy8bBMDQHNJS6uSwup3VTRRUIivrRRyi0iqBLXRA/KxhWfEY5iMTPcccukclqmrTrrbj5cLKLgxobQtgxABA4FkOKwAtAXHCpX1mZwm/JVhcRuXCKr8Wa8PSGkCuN5JLhKItAFQEOc76wmG9hLUEZAoF5QmInYJqAWwA8Ddq9pluQNz/m8NA6sM1j/V5a0l0p9CDU46Vdo0tMsW2BuikzPPDJQBeVKk151ji2N0nehsx2+XLD17ou8Ety+WnjqjUGgWA2cBNk0MlxdUjEtdOXjx58crGtqu5sq9owGQAqQ4qfBFAi3RYAeRRQk3OUDuHWgRjknN0Z3RJyR/BtQPVhYC8qktK9khtoLa4GjzQcQBJBsbcLGRsJFhS8m25JfzVPW88ecbblME6cDPcW/ZaLZEDyNJqyescR322k9FKeXzABVMUe/0jG5+6Rlg5AGICQigoZe3KDxqzcxXjUOxnbSHt89SZbFzGIK9zqChlEloHH6uVxMiFMHtceIkQ5UGO8ODrHw6kG7JM7yt2SMzMYAZkGNINWVXTf95XXhrq7xjUgETJ8kawjNOrLeXQMmE+gOI2j1hNPjbHAQAEZDmAL7ZyVbsBfA6gFAC0WsIC6MNxtEWnCXjC7tfTCuCKPgBw9agHcO++opsSSy0RnRnCc1n5QyPhygmzdHj1p+xOofv5Mkv4En1lu1/djRHcX0z/LYzw81+eXDBpklI04AODLVgOkI9KLREzF09e7D9f1QF9RqqxlfrX+RRkZtrCiSOWTP78muL6zZHkHB09PLQ1hYgfXodprBQUbl9DTEA41JO4fdc3vrqNIPh3CoYAAAH/1chWa2bPS/v6aH2sdy04jpoBnHHfDYcr/78YALRaEgYgiOPoybqswfMCpUxgavLRVaDuDquWgjqJq4eulVDPJJ3qC4mhYyZckagIeJlf6IocKEhuZZtfarO2VcxHAYBJxod45LACMEtpkNAOwsx05Sk6qPA0AH971gak3BoqTQw85a1GZDTqmr96BcqCHQ+HlAtgEfMzJNOrijjXw0suOeQ+AN9otWQox9Etvl6/ObN48hILgK8vfWzK54/13HLutg4mR0AvEWtJPXShd3snFfYFgPav/pQfLT8HoG24uwe4rSm3JGxMVGqNBMBbwKgXQyQXnJ3D9t//24uvt9icy4ZmZKv/+/bP3P+9lleV8AGu0w2qucKYGC1TKOpVhym0AMwUVEJAGArqYamId6jUmigg5Au4W5EBcFCwOY3hrF4Ox1E9gEud/mcBTNdqSTzH0cLazptb1aYAzUSZiKnL4LiMQVnmkAOZAGCT6x9tzOgqAFDw+QBglxR6nSsDl7wRKqyhtVIziDsv1gNAbL7I422d0DLBYYEDF/N8o+R5DoY4VVOXjCPXGufHN6jUmiCrUyo+Ud55jTfjzBK+r0nqFPraHtaJflYR75RMr7qarNovcDWX2AoAWi25Tasl3X1tR0th/sTl+469c/83+ozU57PfGtchtfWPQQycAwG8FCIuKSyojo8CiBCuCt0aKTw/lzBx0ZMPSgXGHAAvAVjcLWJ3tN9Z9S0LJi7TBYoqftSVdU1SqTXRjW1PfcBWCLcTnkTqkpKVtRmfnKPLIiDDCMjrFHQXAPX+x6MX+9LGCQsn3AbwfwEkFK7ibQeAptz5aSGA8TXOqlZLXtJqyd21mCcIQLNo4lLnoiuhOeoAgKGWkEMaH9hTJwyJPwYF5d4HU/iu24B7vcoHipaffajAmIAAYXlkLZcXu2+9afGpICAXdSZbK09ICo1xrNUp6QBX5bSf+qWmFa7HGqwAIHBAWa2gRTIfG6OsFBAK+vvV/s5x1ATgS8CVfA9gNoBKAAN9bEqLZF7aSsM8V57gDgAfdnj1h6cAzAfgRNP+YmpwXA016AyG3PGGQljltBHH7adm3bkOGNXYprVISi2R0+FKX5mKfxfdtAicwXY9Wy6ENaV6CIBaCeS7c1az9j4b8ZnohOyUZKfySV1S8ubkHN13dbXvprcXjiq3DlsrFZgtZod8kPtClkMTThXiOFoAYCVwMV3sIbjSJH5yPyZ1pxRcE4XQ0CZMWmRq6hqsQB0jrAAgNMc4ANg6TZlj9IE9dYK1Be0BAGlZF6/6wqvUmv6FxrhXAKDKHjTLndfmFaUh9nbu26DrHVuDWcJ3tIp4ec39g8V9vgeA9fq75Fcf5cdXDI7bMBYABsRs9Ly6OV3JCh2MMLhC4Fv5nnSlAkB7AuKRWoG7UnQQgMcAQKslCq2WLNZqSW12F25IuobvHQMAQsb6AQB/OoCbiYueHEXA7wJIuoQ1/zIkbkM7l7Pqp77QZ6SeDJEUZ4pZ84vPLH64xUkbWnpXlgAAH+C4s65z9ZpXXEEZJBKQTAArj/Zof39d5lOpNQ/lGxP+j4AW3By/boQ+I3WvPiM1S5+ROqu5nBM4jjoBdIdrJwRaLekAoFCrJbddb6zNKRbLhKZmUZRa5wirQ1QWy9qVje6sAkBAwYgTAHiRsZXIy6Ec/ad9HItadMmyiWgMAJhkvMzTynEnS6PsQjhqQrMmh6Im0pcAwGOZJT+143x1QgAAhEhKvGnFGwHXhZ5Pc1gLomz3RBeKiCHQcd7TPTOOowYABvfd3gDGAfgCXkaMb1R0ZV3CgsUlRQfefHR6Y9vSFFCpNWywuOS1KnvqGyLWarY6pfcce2fsT8DYxjbthqBXZNaXf+TeOWRv0YBnAbzS2Pb4EmJjtlFQm2R3oOH6R1+frr/rq3VJyaN4Eb+NWJlv96VFJfVcUpjuzRxTl4wj56sTNgADhwPYYnIE3LVg4rJmsTV+JTiO8gBqisYdAH4GsB8AtFrSGUDA5QW7KrVGAEjYY6XdNqEZUOcIqy3g9ACHuMSrDpX1RVzGIAcFX+wUVLX1cqgWLokpoJZdsqILRUcBID5P7HF3CoWRPRdYxWbW3E8MPFkIAMkhhxpeE+wG5FRFMgFQ9umEr895OiY/2nYTAJSE2sXXO9YbRDbSHwAqgpzeOM8XcRdhxXIc3Q0AWi1Jf2ttiCZlRecPUlakeL1j0NJRqTXSKltQcrk1bFVj29IUGDZrTicAW8qtYW+ESi4cGJ74a299RupPjW3XjcTiyYu/AuiWAmP84yq1ZmZtdvqaKj0XFdoIyHHiYNr7as7kHF2VaVjZHc4wW4U0M2iGLil5qi4pebouKfm6r5tKrRGuO3P3V3uLBg5vH3xEB+A2fUZqs3VWL4fj6CmOo49yHC12PzQdwG9aLZFcdmhN8VqzeO51dljFlW1zGYe8QRsEXAubIjfQLs/1Sj5Dn5GaFSYp1ACAkLHdVsttgNrksAbA1cUCANAtfHc+S+woMUeMakknq6YLbQ0vo5FCO0kEAKuYXvClJaFlQhGA4sSz4v21nYPj6MWTzl6jsu2P5TEjAfIigE1+p/XfqAL/vh2uz+wN3e9epdb0756+fEduVasjBHx3AI8WmWJ7fjZhRaNXRd+IRMvPVQIIA/AmgE0t6XvAGego5KXOvr6cs/fHF84JisRtCIiegn5CQd+loJuv5bSOnz8lGqC/23jxQwFCw5zkkMOdbgCd5okAUjmOWgBAqyXfabXkoceY8w/0tQhwm+R0XRQcGow6O6ysPYiyDoXPJX5qC+OQHREaVZXejmsddEIOALe3+nFPbdYtC3Z0BIDicM8jbw6WRlYpnBeLvH459WA/JxXQC+boBLSwk1VTJExafHObIJ3HOccAEF4irAaA2HzRVh+b0wPAfqQbfNLFZEVJ/NFLJvJXwF9GqLR4GkOcuCXh1xs29abt9DXjAGwvt4Tf5OSFSAg4NVWfkfqVPiP1humq1pTokb58aJEp5k5X9zAwaGGfW2eENYSYmbAjvduN8OW8yTm6Mrhb27o7R0oAfHq0awduzwvhFxV3VGpN/6RXf/j4UHEvPQEdCmB89tsPvjg3bVWLf79zHK3mOPoXAGi1JBBATMH+yUPDytp8NtAiQKfC5LHzJ21u8v5GnR1WnjVFOgVGx/WPbBiElsgDrEPhtUbb/uJ+2wFgT+HAWj0Xu5DGAoBF4rn+LsNDYhXzikse4uD6wBG0sJNVU0Ol1rCl5jABQ3hvNVhj4PpGuZr0lNfol0mUFLSLIdBRq6YVV0ELgLq//PwV8JdxpKSHIkBoOPXFU4tqrV/YXFGpNUkj3p99wkGFK+H+DqBgHLlV7WqrkOKnjqjUmk5llvAfCWghAAuavqSSV+iSkvsLT8pSCAjYKsFaT7btvUQDl04rD5fqRzJjZbZI/wq0Hund7p1HH3rnfwA2W5zS58qs4aKkkMML9Bmpy31sQ7OA42glx9HBZSfuOgsADAgIiADNwN+os8PqFFW0swYebzKVyTxrugAgTDf3Na9Uhxy8yAkA+caEWq0bWSw8AAAJ58TnPRqQrhQwlJCwUuH6Sx7VAtTvZDQMsRQs83d5p9+8GVShdKTaBdSKdIPP+qYHVDEDCQhMMv5vX82Z/Wh2VoTAZgxkHRbA1Sc5ZUXKdH9qAKBSawKtTmlngy2kznI4zYnx86dwKvVvKwEcPVXRIbFd0NEjADWjhTlHzY0pnz/WR8RYtgKwOqmwP0CGAngdLUu5giMgLABQUDEl1Kd1GpfotM4kIIMARFh6VM6hYlrKVglefXnvql/ezPpCMuB8NgS806kr65bvy/WbJ8wGCthcHcRAAL60sS26HnVWCRBYwg2EF9RqG70+qI7SxgWevx0OyYVuAHZ6Oi4x8GRCbmVbDIjZxNRSj8zbHNaayOrFHFZ9RmpWnzcXV1qdUqHBFnJLCzpZNTkkrKmdxSkDvM9hDXAIqNmXXQNCy4RxABBdKPrCh9PigkN0isHFDihaACKAOkZ/1/rN9hLju7NHF7X4rbAr0Sn0wP1HS7uzItaibWxbGgKVWtM2XFqwoMR863AC3kLBznFSwQd/ql+54G9N27io1JqQYPHN6xnCB3cIzh6y4RW1HoAe9dR2tBHRwuUciQkIY21nHA7gHV8uUKPTeslDLwJ4UZeU3HZXh7br2uUWtJ255ysYRHLWzrA3H9n4XBvGxC7teCynpb3WHjFl0dCsp9766Neq0k5397GRYoYKZs+ftNkK1y6idsqiofXyurhTD7jarFFnh5VQoVRgjWgyRVdCU6wWwGPyokFSb8ZFyvJb5Va2RZi0qFZR5wqlo0uQQYAKpcPmSVJkQZQtPrpQhPIgR+SlrVyLzbEHAZfzWhs7/HhG76gd4zLPD8ctCb+avblAkZtYI3z/ZdIDripNvS8npSCBTpBjADiACtzdBgWnrPK3z1hlE9atSPkxSmj5s7uscuPs0UU+ixg3dUSsdYKAseE21Zp9QG0awzQPnvp8/JCsgiHPA+GpF8xRtq7he7ISA0+nXdpm0n2e8Z9rGgGVWiMFsLbcGirvF731ye+mfrCtsW2qL5JzdFm6pORhADhHhHWa6JSsty4pOSo5R1fvKTkjR3+YL2BsQUFdy8qHnji8/u6j29uFWgzDARZg8KguKXlw0aKcvwAIOY629OKrf5ErNVUclTrQLlCfGnoh6VcAywHwALHOn7R5mC+c1vmTNrMAIoJbazhzWbtBQPsn4ZIP9XqNOjmsRz97SaDEHXKH2KcF03VCWt71IACITAnB1zv2UvYWDcgEMLy2OawOAY2koAiaZnR6cjzPIBIALBLe5y0+/VyfUxUdQMBTubDK26r8GLi17XyFUea8H0CF/OVqn0Y8hYSPDGbt8mKHWMuCUh4ABSytRMbfz9jkYgBTCu2SaVurBM6UFZ2XA2QNgI3Zj2Z71XijuXHoQm+RTGDcPy9tZXlj21IfqNQaFUBnMmTMEwx4HsA8gLz/ywtv3nD5uk2VpFd/GKIQ2r6vtivDAXLfd1M/+LGxbapvaiKguqTkHwEcAZABd+OTeuY5By8KK7FFDZr93RPbdUnJ0ynQnYCwlKcErmhfNYDtWi25i+Nos9Ak9QVHS7szAOiXdpVILTb84rQqJwKEASAFnO+u/mTCGyU59ztwlYjostc+Zk0XukYCiAuM2zqcUrZ91fmBxQDiRYq8QU67PBgIFgIQlJ/+T2Copk6nYRxWp6g8GgDMIQfaurrKNT5OYUUBaw+CTZbXGYDHJwGesjxQ+xzWsFLhAQD9PD0+Nl9UAQDRhaIt/5pHWhhXKwP8eEW+MUEMQD83bdV1W9fVoF8mESdCFFUe7AwP8ZUh6UqhlDByg9K515ftzVJWpBAWkEcLLRGbxp3IevWX8NfPWGW9s82Bs9c+cDrLfUxAb3n5jHybZLiJF9wH4Akh4Z2jvm1zItcmewvA79mPZnutuNGUUak1oQCbUm0PfK2xbfE1kz9/vK/e0HYh0CUFIM4wadHqvlHbMj6d8PXBxrbNjwt3VPVNQPoinDJCwDspiGd1Dy2E5Bzd38eSkuYQkOn7JkXu6LmoaEl9rfX04keSBOSu13kwv56edcd298NaAmIDICT/6K7b4FIaOAoAWi0ZA+BZAA+6W6C2ONzpQA/DtfW2cRdVTO0FmAEqdj3EcCU5928DKO+6z/NLZ8zXWcqSjwGIZ0SVnXl7p4sF7pV5Q2p+tQDIo5QxiwPPGkwXgn8GkCcNPZZgLmv3CqgAAJy10byvk8MaUDBcAABiQ9L26x3bUJhDDpTIiwfCpjh9K4B0T8e1CjyhOlPZvi45rCJ4r8EKXJLDCgBSgcnTRkd+6gBLHO2dlPUqf1VuZBIJCJws9WWkqiNDCRtcIfBp/ioAiRMMjpoDNADw7p0X3rv8gOxHs6vgEpSenrIiRUxAh7YVGz88ZZUnAPgWoLbUb9uUKBjn6mOWgPezH832mTJCY9EtfFfawQt9oRSVNZlzVl1RqTVxAGYQjEljiVMQIS38sdgc/dzu19POA2mNbZ4fuC6Uekdu/07E9upvc0rk7vQcUDAUteis2NwxjSj7SLIr8GXREUWGLin5y+QcnUc7k95yvLzzAh6M+DbVmkXAHQD+nZ4AQJuco8tKdh1+6YeFhcs/ugAAWi0ZD6AdgJnujlItAe6SVDHhFpk9rJdNcPF1CW3/k6Myv99Ke3Wsu9kDw1jK26UAVAKQPIG4Yr844pCgKm/ANwBzThG1xyQJOZFXcmzc31MWDf3XbuH8SZsjzKUdM0Gc1bKw7AxTSRceDZ3DKjTHSAFAZEo4W5d5fEnStNf5szP+LJaWd/HKqQiXFSaeqWxf6xzWygBnF5mJEXv6ghaH27tHXBDiQphdEX7J4+eqWvsFuxsAmcDYI1F58jgwyuMx4SXCQPft776ygye0J0MJ4OM0A7g7mDjAlGu1hIVLjLyY4+gV0w7caQDrAKxLWZHCAuinYJwPVzkFT5y1yaYBeK7ris67esgNxQzoO0vvyWsyhZbe4KCCsWLWDC5+/S5XcKH5ctPbC1XhssK1BD2TKVhKQb64JfHXpYsmLd3b2Lb5cXHPx9O77i0a+ASAJ/YUDZS1UebknzIkvQjgI7i2RG9IdYZe84pLDo1UTROdkc6Dy1Fc5Os1VGpNG6DTwEBRxXcLJi771zn7CgVa/4Lj6I/49w5tNwC9a5xVrZY8DuA8x9ENvra7AdG6bihqop1uB9L9ugzF/EmbHwOwCa6CXRsoM2zKomFXed2GXvHR1fMej2eEozfzdkUsKDt8/DvP7aitwXWStbIoj8YDgE12tkldcTC85BRrD/KqXezuwsFbAeCXUw/WKoeVZ2iok6UeXwBQgggAsEj4Fp0v2BTp9Nq3iiq7kjE7ZN5+cGLctz6TRCkLcUx1MpQvDrf7TNIKALrLDK0BoJXYqADQGkAhPPDQ5owdNeCx3xNffuz3RD7rYd2kfooKkZg4uwN4U8zwkXuNQXfsNgbvTlmRcmjAyqSPpq6Juu/lnyPJ9eZtKhwp6SEFsNmbVJCmBvfeJ4kqtWZuvjE+J7ukZ+ekkCPZANrpM0ZN9jurTQOVWtP9prcX7txf3P8gQCcB+F4urOq6afoLsfqM1EVwSc21NOkqrxCdkX4GYAsFfXdfWlSir+dnif19APZKW9DzdZ2L4+hUAIMAQKslBK6dqYvnU62W3KXVkui6rtPAnHdFV8laXOV96HZghwF4DSDDpiy6xav36vxJm6WV54b8xTvEbSXBfz8xZdHQWjurQB0jrBbl8W4SQyeYwneFAePqMpVPcQqqDYSyHRtyzSCD4Aj+kba6LpHFwlMAEJ8nPnbp4+HSgtol0frxGKM9sBUAnKpI3ujNuKII+5jIYiHyo23mmOsf7hEBVazYJqLnI6aYfHrRF8A62gBAnNCiAFAOVz7WNWXe5owd1R8uLWABAPOcsaOGzV5dlAXgoPvnzWlrooZsqgzrRUFGVzoF0zZXhoOAzl63IuWnWKF5YxdZVZNVHFCpNbEA6WB1SustZ66+cOebjYqR5w67YE7sC1AnQJZLBcZZ616ecaqx7fMDTF0yjlTbA17ccX7YnYBsQIExrrp7xK49SnHw8KAGAAAgAElEQVRZ2rIpCw5deqxfnQFIztHRI/3bvsAYBPvZMuHvADr5au6Ji5581EnH3J0QcGrFtlef9UkOKsdRp/uWarWkI4AgANBqSQRc0dg3ALzt3tHqBCD7ajtaTQExax5sdUoB4E19RupVd/j+HXX1nPmTNgsB/GCriosOjN/69sOvvvVt7a11USeHVVE0+DwASMu6HairIb7EEnwoWlrWzSvHr7XyeKvThg51zWH1RhLjijmsYtaiuMKxfnxImLQwpcQcBYY4vcphlViImILCJqInfWJIupIVg4kD8KVP5ruEbVWhBQDwlzF484K780sAfOrBMA6grOuqm4pxhdy6j8cUbgWwFcCcF9ZEdSqyiyYdMitbA3j6vF36fHmV0JGyImU5gDXBrG3TtoeON5kdhN5RmVP3FA5CjPxss3IU3M6qFoAo35gAVeCJc9Hy/Lu/nfphs0zLaGmo1BoBgHsYMlbNU7arVGA0AVBTMJ//3/PvVDS2fU2ZzlknDxy6M2Gr6Kh8sC4puWdyjq7OrZJVag0Rs7c9JREYLT0i/5rhulb3LRxH7XDnt7pvU+AKDACu4uvtcGnm/Z9WSyQA+KYmmZUUkv3isdKuNEZx7rCv5/7+00dZcdCAHdaKtr0BMvnhV9/yScpHnRxWgTXMVXRV1a5JFWMIjQkbGae8a+5rvykS3x5Vff0RQJi0OP60oUOtc1iNMmdH1kmCJB4eXxbs6B9SLkB+tM14abQur7rVsasOqgNdVnT+n5jwAy2U/Tn70exm9YXta9oF6e4rMUfh9lY/ltck4nuCslJgBFCoGm/xiRNmlDk7y02sHL7PXwUAJQDYKVPu3qpycBy9nv6c1nXjWae1OWMKjwJ4BgBSVqQE9pWXv5Zrkw4x8YKxAJ408gJn6rdtdGdtsncA/A6gM9wJ/Y3xHiy3hN0sE1TT3lHbdwGTG3r5usABVOjevnPqKzss1M543u+sNjIqtUbeO3L7ggBhypgquzKAp+zxpJDD77YPPvr+vLSVvmyz3KIRHZffCeAEgM90SckDknN0dd1tut3qlPYB8PQnT35T7x2t3FHUS2tPdACehCv3EwAeAPCJVku6cBzN1WoJaQqR11MVSQEhktIzW2dMrVUa5NWYP2kzEQcO+dFaqeotj9rz3WPpr/gsP7lODqtVcbqtuLo1LIHHTe70jiaByBR/GABYuzIagEe5gbsLB20DMOKXUw865tZiTUoQ4GSpN5qqwU6G0piJZp++WS7lpZ8jlX9VB99b4RROBEgvC2UB0JdSVqT8LmMcf/SVVxSJGf7nD0YXNZkoWENwvLyzUUDsFpY4vd1KjYEP81cNSmea3MTifIytNNZXk7rpIKnucdyiQJKkygmX3uFQAPHXGhPd68RfBfvbEHGA+bzVoLj3hdW/eexUuuWvXgKAlBUpYiHhh7cRGz84aZUnAvgOoHYCCFx9h4k1ZUXKsIZ0WlVqDQGSIwj4n+emraqXiuR6REtAKXV5rDaAaBvboBuZSYueaL9eP+ZBgDy9p2hgaHzA6SoK3FdtV/60/uXpTaqeozmQnKMzHOvUQU2czJfmfoYFACbVdq52M9YMFLPO72xOUT4Fu9iHZnoMx9EyAEsveeio+35NcfpbWi3pB+C2mjSDhkal1siBwMRqe2BGPUyfbq1UjRYFnFslCz3m08rWOjmsNsWZPuLq1qiO2mJ0XVA0DezS/HKhOQbm4EMpwCCfFrNcDYWRPQ3A4zdfSLngBIAulz8eIcuvc/J5yoqUFBb8RBEJfcpMWQKgHKAUIDXFMZyJF6RuqQoDAbWsX5GyS8naczpLqyrKHMLF34/Ve7VV3twos4QHAciZm7bKq6tck9Q52Mmi1KtqvmsQXC5Q8IQ67ULqVS6tJ8gZR3sAiBNZjACWANBcb0xxtioaPAtG4Fz+wurfsrRa8gqACI6jL3i6bp46s/86LOAAaOMyBiW7FQduEhH+IxtlernfgxK48m4aMsraGkACBTO7Adf0CfqM1Kwur6/8ttIWPI4ljtRTs+68oXdIGguVWtM6QpY/u8KSerdbCmitiLF+mPnqM5mNbVtzx3hb2VfiQ4q54oOKR3RJya8k5+gM3s6hUmv6Ewi2UIgEBLwEQC80gTxhjqO7Aey+5KECAKdrnFWtlnwIoIDj6JyGsilGfvbmfGMCCy/a13vC1+++/h3AjQWw1FYVn3bfMyt8Gkmuk8MqK+mTQ8EP6fb4Ko+23RuK6sitfLD+Adjk524G8H+ejGkTpGtzqiK5rjms3nzIAnBZ/ioAiBibrDaLp6xICegpq/jwjFWWCohinWBsIQLzwUSx+eud1SG7ALIRgNAtXzG8l7zCIST8o3uqgxgHSK9Kp+DJHdUhLIBXUlaknAtibafaSkyVh4yBG+xgggFsbimpBELGmkRBvJYPE9kYYpLx53xlh9TCJADYrxpv8XnF+n5T0BEA9x63KM5FTgqOAdBeh+T+bjmXK+K0ilQAYC5V1pzEogFEa7WE9SQSkKfOHAAgE66cAmueOnNYdkZ2FoDMlBUpzwJ0E1yFiQwLftqkn2Jli+4+X+cKXk/oF62d/lcBhzZBul21/Hw3KpW24CwA45xUcKSxbbnRmLjoyXG7CwaNB8JuLjZFOzuGHDoWrTj3/NKnFjVnSaMmRe85F5zZXOuRjIXNhEs/fVotpuEoGBYAqOuCgkMTcFgvh+Pogprf3YoDbeHyH2oeexfAOo6j9aYVHRegfybfmIBhCb8d9dX5cNGzayY6bdxYadgRvbmk88TLtVh9QZ0cVtYRIIB3TlqDIDLG7waAgPO3dshTZ/aPyxh03TdtsLg0GkCtc1gtYr61k6UlnnYrqlI4ewntJODynNe8apXO0zVTVqT0D2JtUyucIhmAoftMQfIwgdXCgL7Ag3y1/sGTJZcce1EQ+BLH82Ie3Es/RyqNPHt3ZlVoIIB+NsqM3GsMCsTFJE9qTVmRcnNzd1qnLhnHAne3TgnbXwqM8XxgulIoAJEFVrE+adunXyYhiRD1AvBtPWlCKQEY57wpHEFBfwMAAjJDl5Q87GpOqzTU0MdcqoRAaq1xyl/kOOpxyopDVPagwBZC4Ao//avtXvaj2Vn/vAfp2TChbeGO6pBpKSs6dwNIFoDf6vO9da5KlaIQGmydQw/UuaijMQiTFjEl5ki0DdKFA6lNpxd2C8WVQoLhAH0ZGDNMzJqdAD4AyLzfX3613vMib0RStKd36JKSP6egz+x/InpDj6UF672cQuu+pbXpotQYuHNZR7sdV2i1JBTAU3AVcm3XaokYwKMA1nhQg+Axx0q7SRXCyqKlTy3M9cV88ydtHg0o5xPWsl0Ruf/2x995tl5SHerksNolhW1Zu7LecjBri/zCwLYAQKhgOAU/JE+dyV3Pad1bNHA7gNtqm8MKQFKjw7ppc5t7ACQD2Dhs6KkrrksoZE629onXKStSBgB0W4VTxLiFf38NZO0f9pYbMmePLvrPvG5n4KqvwQejiwy4rFq911cdP7RSdhpcer3iENa2MmVFSufsR7ObrYal9tzIaDsvRrkl9A9vxvGERjKUEPgohzWkjJ1EQALKguwJPmvzegmxQnPPEoeIB9AVACHubia4RtRBEmQcZS5VIrRD3jkAqHFW3UVbd3EcnX+19bRaQmLl80wCWwgoKE+u8IVx6XvwpZ8jf9xcKVxgo+zjAL0ZwLT6ymt1OR8qFYAfvE0DaSp0Dtuv0p4bibZBuvYA6qUw0w+gUmuEvSK3vxkhaz2l2BQTCJD8OIV+TveIvz7+dMLXN1QL1cbAGWp7nZjYCYJzkm90ScmhyTk6jz+vt6rWSDboxxCZoGqzyREwsznp29YUYXEcLXXLZNXUwwwE8DmAPAC/a7UkHK5aisO1LdxSqTUMENgJwJq6Ww58O+fp50HunA3K7qVOycj7nllebzvudWoc4JAWpzgkRUG+MsaHcAB415c0EfGMeUGeOrNeW55KrExxYJVgz6bNsbfB1ZP4LYBmbtocd8WkY4WRzZeb2P/IgUXK8lWRsnzVtdZKWZEiA7AYIDX/PweArB0P5Wy7krNaW6yU/QnA/7N35oFRVdcf/9w3+ySTyb5AgLAnaABXjIo+waWKS9XWpS5of1WpWHE3iMu4tOJutVFaWxWX1qV1qaZqK/EpYtwVAgZZw07Ivs0+7/7+mAkGTMgkmZDFfP4J8+a++84M894779xzvscHMihA1obMY4Bv8xfn958Kuy7S4E/KAahoHN+l5ZZtw/0zAXZm+NM6G9spLmeBo8nwKEBSvXEGLmdBj+fcC4uij4g3hEyEncbWh8og+4g6NGxOqxJKqObC+eV7S/FcATygaWJfUnHTG4e/ex2AQDwMzNzXQ+IDP6/0+aVh3Q99qrESPm9jjknxHQCkAyW9Mf/+YG3dpC8Avqo8ckgmqRe4+qmLMiYu+OcNwNovK4+eL8CS7dh4EzD641vn3jDkrO4fDly2viowzv2EabM1CTinK/tuaxp1GsCxI/77wEByVvdGVWVAVaU78rKEsLpK67XrPMKa2GMANE0ka5ow/3iWjpmS9vnhQLJBBD7tqa1Fc0oOrVt/6kJz3I5gQvZHv5i7aEavpof2yGG1NORuMHoy+2NOlQb4JDIIUld022SgfO3CJ69a8dSv212BHZf43TggksPaLRw+k3601et9I1zgBIABzIuXlIx9fknJ2EuXlIydv6RkbKtz0m4Oq1HxW42Kv0N1rGtfz5yYavStB5lHWHoo2FvLH5Fo10wQt0vEUcAJhHNtPjrvlVErzn9lVG8EB3uV4fEVhwHYjc0VXdnP0WSwRf4Zi9+7KhBGABF+6FBjMOcebPDFrasJmpfnrS4vFYizCYfh92m7HjBlSt3wfTtv3QtMUVW5rxbM6+KqjvhEInXglmjScIicpxEZrdbXMeewzGV3AExO/WLASkFtax61DaDKk2noa1sGCzmFxQU5hcX35C549R/vbzp1hy9kewDYLNBPOyJLi/t4wVUPVCyc1a+0M38KWMoc1xCW+nto1SETotYlX1lz8HCQ28yKb9DkFquqlKoqV6mq9EY2vQycr6qyVeHmLmBTpFkBrX/3RYK54XKAE0f9u0cPYUVzSvKAd6Vu2haXvvyQi251be3JfNHQI4dV0c3Jim51bC1cGvMIUU+I3CxnCsTtAuVoYJoU/hpb/YGPx1Wq5VsLl/6oEj/RUpcB3cxhdTkLJDLdHBAjJ62WRmSrI4kP9LeBswgvt98DLFlSMrbAb9JHNzpCP5IZ2tacs3pbc87q9g6Tvzh/3NKm5A/qQ6bMKfbG+cCxRNr79Vb+X9nsstKy2WX3Rv6+D+TnWZu+WOVx5K/0OL7JX5x/Ym8ct7cYFr/1DIHO8aPe6tLJmthgFACZlebPOxsbBZpEShml3mk3cQKNkX9XRY53qESWlOfmtXu+KsbggUarv3rv7aoqfaoq1wJomshvb19VlduVQEJWyFLjz144PapOV+HfrJgJ4u2IekCvdMj6vvaAbIe5vunfN7jKemP+/cHw+AoJMNKxfqgTXg/JKSwWJ9638C6QHwMLvCH7eQ5Tw+aZI9/+v4qFs47ZuPC0tweg9NmgIW91eUga9KuB4f7x7qicz/AyN8eBKBmoaT/RoKpyl6rKl9pseg24p01R7FuaJv7Wzq67+WLnUWajCDRXezK67dj/7ZYnLhcG73LQBYjjf3XTQ10uYu4O3XZYtxYuLZDIiRI5EVjSH53W7IXT7438/bJuzIuHNmX99w1z86gRwHebbn3nlhVP/Xp3K9UvK49aBvDm+l91KSdX08TVOzL9DwFSIEhqDMm8Nc3PQPO7QtTdMnNGxenAA4BO+Ps2AaoSEiazX6RHuxx87AsTjwFKfdJgHmX2nPbCLzff19aZ7IrNPaFsdlnzK+dWHG4S8hgQHuC9n/193MbDn5t0f/7i/H71G2iPVdVTa4xKoKqrwt4SOYzw/+GuHhvhaihtite3hwxSB2biaoj5/59DCY7LMPpa03VUAPHD0vsN5bl5e5z7Lz0y1q6HDKnxw2o6TPHRNPEzYIWmidP22j5b08Qh5pYRQjd4utQGMfLbvQBkc6IhcFdX9o2GnMJiQ403I7fJn/hKrOfen0xO+8oOkONcd2hf2zJQufDxa4/OKSy+E1izpi7/Nn64/4UqPcP//Lcrn4x5x7khusekVd8v8+W2rLV8G39EeW7eQx09ZLdyUs7rZwBpecnLN+4nE/sFqipL9qot+JRwygCaJoSmif9qmri49c2iOSUFhzYnnDDCb1n+6rX3dlkz+C/X/X1s0ZySh721ExfJkMUExBNOt9ov9CTCqhIu5mhbEdxvmXzZ0768eXefKVDygCWGYPzv43fOqN284N2eOllZVWnBEOAlHFUNZFR5XzAou3IVUT8hMuY9kP7W90ds9VQbdWGy+pSRErmkrdOaYd82OsO+bXTbA/zmn9m3NenGD43oXuDIN87b8HYPbe4xX1+8aikwNdXoW7YtYMvxSOVGYEl/d1rdwfiMgG7pctFKXVLol36TLnE1xCTyYvWJNSC29IazCiAhIdEYaBWt0ATCJ5EhgdCBs0JJgTVf/i59d5uvHV+OH4YUeGocb+xj2vcJNwfYrRsbyZ9aCMw1+tLizO4RXc4TLZtd1phnbV7dGDKcfMTzeY/G8jeUHV9xNOGe3wM2fxVgdW3+OoDluw7bL7rSg4WcwuLMnMLia6bc8fymj7cdvxTkbcDmBHPt4/xwzfYzACrKf2qYv7dfG0mZuhZYsi+ndW3dpOkAY5xr3tlf9vVHVFXepaqytQ23k3CBgALw1E3PzEQEPp7mM6af1WKeVjSnpNPrbNGcElE0p2RK0ZySO566cXFjwJ25Drg2vCImAKVXUto6oicqARoQkkhDexXB/ZXshdM3by1cekbDiDcXOrafcIUSsi/bWrj0T4cmfpfxZf2kqHRYNU2cBaxXVbkcuK02JRgSiCOIyEYZbm8sRRN5hJ8+MCgbaqS0WHSZ9E+wPzJhQ4vKDxFXW2V64M4MODE8NrhHt6z8xflXQqIr3eivm2pvOOmhMyvXxPgr6TbHOmpyPmxKzomoFEAnVej9AZPiyzMb/F2WprK7FbcUdCl6uC/MAUUAvVbI0awb/d97498FyFtdXlqemzdTIFTgw5AjeJDwKY/b309+vTw37z7gHn3K2NEAvob4DtvERlQDHgTQNGEEQqoq/ZomJsZVTk8FLgW61XSiWTcs1lEObdHF74DLY6UYkO2ouGlrcw7HZL/31UDUX21lY8OEGoAGf3KP0rh+CuQUFieMdGy4wqgEboYJSSAUdyD+uyOytH9LKQpfvub+8si4fxC5Zg/kIp3BipBiMpH7pETaZHzoYjq4t2xomDgOWFd0xbM9LiQaLKiqrCdcdwKAYvCfLaVBUcL3aiOESp6+5Ql3wJv8ZtCdXmKK2/FdyJfg0INxRwO19vRvzzFYRh0W8iXFAVLqps0J2R/+t3HrsS8Diwnf7/er79dthzV74fTSrYVLXwAuAk6MssiiX5C9cLrMZvrNWwuX/h64RyKvuqfpQPE8Rn6z47SbthYufb+jz6NpIh4oIhxlumi3TqXKHrJRke2tFb01QvhuMYidi1VV7ti80To1e6tZFxIpBdJn0V+PzG3b2fLmel0aQje9kWEo98S/AvazQLy9K2g576EzK1t671vpGr94efTBuwKOzxQkOooPMLCff7xdZfLtL5wY0JOSRzvXjO589J5YfYoC/EjVobsEDTILqOyRrlwH5C/ONwJx/JDDSkR7tfX3+ckX16QtiXs/+VaCYr40yItGJG3dvKUum4L1W2aV5+b599VgICK7Ugw8DjynqrKxbNGVKkDjsPcSu9OmeYvfHlHxELvTZojBg8/K6oOTHeb6Xc9d9Vh7xWQDhlEJ61o2NY4jM27LmL62pT/yi0fmWx3mxtu+3XV4AaQWbG4aY022VgXGOL9/ZUND7l1r/3Bm+d66yxEndcDct36CaISLp62AEM2GS8tz89YDj+atLt+duveLR+ab4Khjj9peVlKee8N8QNvX9eunirdu/PM68jKBNAoImKx1X+shy9SgO+1XwKWBlizCwadwAMq9Kz9kSy2v8Qbirpe6+Y3LH/pVZetcRXNKthJ52Ju7aMZ++657dL/0xW9cY2kerTQOe7epOzepviZ74fRG4OpVRdd/pm+f+fRvsZqllHcC87cWLt1DlkfTxCSgXFVls6aJ44Co+9CrqqwhvGwKwIaxPkNNSrB+8gr7YwZdvL9hrK9ugyYSgN+NdX4/bVMwtPrDxuR1bmnMyTR5i3cGrGeWzS7rN3q3+Yvzp0H822ah+49x1M7VmlK/58dNCfoVOYXFBZD4JsCaugOn5BQWF3QlqiKRwwTis1jZEzTKcR6bbk+K1YRtmB5fk7W0OYVxlpYOJbgOe7RqNXBheW7eX/X44Jum7+OOFKkSZ7PvOglX7avBAFADbAFO0zQxGzg3u+7JVAAhlW4VpVlF6AuvNBCRuYrJg09OYbEZEiazl77wQOTDW+YFx93yOsPitgzlsEaY99QFhu/rDvz56trJJ8JRvwSRZDW2+IC/Ai/WetM+/dp1yaAtwBnstF0Zkga9XISUS4AHQsmBG789dVSRZZ09AGjpVxUcULC9LOGWL54/TSJPB3ydXL9+ksxdNKNUvf6d5SOCSuYRPtMvr3j0nFKAojklRmCiwVrzSMibfEJktTQEhrt/fc/Vd3Y0F33wsNcjh9WT/I1uaR6Nbmw+ClgeI5v2O1UHPPzy5u3HzlWRBcqeObmlAJompgHLgN8Az6qqbLeKP1pUVf5J08RfDLc3hiVTNFFK+P/it82GunvMmW/nu6URkKGdAcvv+5OzOudfw12QdDOIbX6pFDx+1o51kbf6+8VBhVa9ut1SUlHZXPGM1ZaDJW1XWmBcrLLLzX7RAt1z7jpDR2QCJBkDnZ7feavLtS+uS8tuXJNdYQsEkhWEQSJtIUfw9fLcvNuAd/NWl+/RjjZSkXqWpokLgIuBGmvDAQ4Ax44TPuyOzccm1Gx7ryGddKNv1a6g9YpYPPhMTCo7+fu6fLtAH9D5q63o0tBQXjt5v1Tj9mdyCosnAxfEm2Zd1Rxw2kG6QbwxKeUbbXxi+Qt/vOzFAdvYZIg9absyVJ6b92ZghPdypcb4hHmd7U5ASiRXPlEqDHopgAEEUkibbgmdRP+/J+1X5j11gWWL+ZzJDfH1Lz9/5+zd383cRTOCwKqiOSV3EG5U0LrU36XmOvuDHjmscVVHvA3cm7j5F42dDu7f3FiZtaTAv/VsLMhga05umx7qXwCFxKgzBICqyrb6fvMA5yXvvm0yp5RIM1IASCkQov/khE57Pu93Hj3pjjSjv7YqaDmybHZZzyvm9x8ayAAIC8gQCC3aHZNrDcMBTAERGx1Pl1MoiHirT0Qdpe8Ky5qT/QBftCT+L5rxhz1c1fTJL06pdLq9iRIJAiE8ig34C0DZ9LFeaQu9a9psKwKW5q0u90V2/TvhVJCLdcU7UeiWZoH4kSxWNHzRnOgFCErl6VhF6Z2W+nkCnZNH/+tbOK3zHfo5ujQ0eILxP0kd1pzC4pETkla66n3J58IwOxAyKsGvjsl+74uKhnG3fLTg6saBnKM8ROdEul79edXUCaOBmwiHAmVFUqZ3lXN08+kbSh0SaUKRUviV9yBcKd/djlCDjUZ/4mkhaTRNTvuyNtztdU/mLppRWjSnZHcL9/251B8tPXJYTZ5hrTfcnJ6b0qf8ucJcf8083Ol/Epa7TNL4/rqfHTMMKNM0cZSqyjoixSa9garKcKTt3eL5QfcYaZYmIQkCQgdd663jRkv+4nwB3AnG2+xK8OOD4xp+8eDPKweSs0rFwlmlP7vv3odW102+JS95+TPv3LQg6pMxocmYDJBUb+xSd6yOqEoNONKqTUafWdctnQ/vDq1d3Rqi3SFkliP9ZvmRQPwXiSaC4lMgL5jiP1/Gha40brLOAn4uhfQsP2VUk54UfC+tbMLdVY+tuRAI+RLWHmTwOw1jbr+gWzeH2pDZFPlb2dnYaPl21+G2OFPzhieueHZQSN1YDW5sppacvrajtwmn76CmWCtX5yaXnf35zunjwXL4mroDGeHY0DQsbrNre8vIJ7698+KqcIB/iJ8SitfwJnA1YAIRKJp0tqE8ZfSLp28ofVkgVEJCy1tdXqppIgV4T9PEdaoqP+pjs/ucD7ackgmwrXlUhy22+2qpP1p65LBmL5zu2XzL/xoDcVtnwPR7YmXU/iLSl7dGVWXtJe8WPwahe46T7oUVC2cF1mkcSjhPr8OuU71hku7J8bk3/8ZstK/XhbFl7vfXLurTH8+Nb2TYcq225au9jvHA39y6cc6DP6/sNykKXaHGm/4f4JadLdldUgnwm/Qx5oCCLuT2WJRou+36aIC6pGBOZgzm25up9oZDvnU7mWJvsEcz/sX7J2bJ0Pg447h6T97fy+9t89Z3wG3AbeW5eXGAGsrw/5/SZDzTvMF+EXBR2rwJOwMTWqrNY4aZQraWbhc2jbW0ZK73xZFl8sbEh88pLLaD5WC/bnksFvP1B1Jsu1KsBm9U/6cDlfG3vH40mJaAMNV408Wy7ccTZ2qsDOiW24C/L13wu26pUAwxeGjNbQXU59TpU8oTR587IanMvldhKUAG4RWguj4xtP9xJLBtXX3egJXG63GRcsC+TZEi2G73m/6MpgkrYW3Gr4BLWrcvOv7sq8D7iKrKL4GT9qdNFQtnleYUFs/UPaNUv2dUn0ut5C/OjxekvSoR46faGz741u28rGx22YBdXqnyZLYA1PlSu+Rw1yYHf55ZaWbLCH/gRy3SukH6LpMEcDQZYhKx3RsBmQCJhoC7s7EAO78anwbQtD3l5Y7G5K0ubyGsDFCsacJs+Tb+d46XMkzSol9gXh13gDLKIYKrvk5cecS45bB0HxoAACAASURBVP4J7k3WLxPmC118F1nG65Rssyd3vS+OcdaWmPjwU9M+O+/bqmlmu7F50ERWqjyZZbpU2m0tPRjIXfDqFKvR937An9Saa64nWmr+fmz2excP5u5FQ3SdvNXlpTmFxYC8CyRr6g68KKew+Jm290xVld9pmji4NSVA08Q9QK2qyof7yu6+JN7UeKrF4Pn2K9evB+y51GOH1dwy6r9Iw4BzWAEfsAhYAzA8vmL6tuYc1tVP+p2miaK9ckz3G/1FauXEv4//FVgekIhMA/qc53+5+c99bVNPGZtYbllfn0dOwtqsruyX0GhokEhdCnpUbNeKzavYAeLchopYzLc337idmwA+bEpdGeUuYwCCbmtUBT2qKv2oPMQ1ACxcM/+u8UKa1/iGb4btcrTtc+dkwkmjW1ecMHp9MNu7zFaaeH/e6vIOUxS+cTu3AJR7OtaB7QoGEfqNQQQ5ftTbn8O5sZiyz/GHrNXA8L62I9aceN99cWvqDlwA9huDujEg0IMyrHUeqPelPDHkrA7RASqI1pxuI+0U0rZxVhVgEhCzlKOBxC8fmZ/bHDjakZ/6VU1f29ITeuywCmlcD5y6tXCpkr1wepdbffUFmiYsqip9QJGmicmnPXjnGdubDz4e4MEv78oEcUiF2vdOY1+QvzjfDNIFlvmRTf4Qyoq+tClWTEj6Lml9fR6jEtbndWU/u8dgBHbmXOqNSa/7pvjQaEezgRZ7aHcrqhiTEPkbVQ5r/LCaE5u3p2BNatrc2VhNE5cQFup7rvVm4B9Zc5x9E/imul896NH15y0/feQE85q4Y4CfGXaZzzRtsR4L3Fyem/eJ78DmXaGkwD/sS5N2AMcS0UxsDJkUgOqgpbbrH/fHfL3rCKPV6Pn2scueHzQ3qCRLtc0bsvVGFkmfcfYjCy6t8Yz9M+HK5GeD0nwDMIEhQf8hOkcDGelas28pPFWVuqaJs4n4PJomJhLuoDU/UqMyqPmi8ugDAb7ZNe3+vralJ/TYYfUmrA5aG3PN9SNfy8tmer+XXNE0cSTwkqaJMwhHV0vS7Tu2S1qX2kS/79bUG9z4RoZjq996HyTMAjGyTfeqLklA9We+2HlUBcDXlQVdcsBDihyj6OyM1VpskyM0zdFsoCYlaOsNh3W8pXn6Ol+clAhvNOMVY+hwxRQkaeyOqiiG/wrQVVUubt1grz7UDGCtP6AIoO66LdWqKv8K/PWL69NsllVxM00VtiOkkKdYVsZPB86WSARCAt7y3LyZI+4IjNzitzHK7O5xmnBOYbETlEM8wbjf93Su/oTTUjPO3TIis6sawv2RnMLiFOABOPLSJEu195js965/7qrHWltK9otVpiH6NxULZ5VOvf053aAEm2q8GSd3dk5EHrBbgw5HA2cDrl42s79wJOD1huxf9bUhPaHHNwdvYrkfQOjGg6Pdx+VyFbhcrvkul6sv+s43AJuBjaoqW4DzKhrH3Uw4RSBIP+/WFGvyF+fb8hfnX/1BY8qWlZ6E35pFqA64DoSHQfZ9VHsyPQBNAWeoK/v5LPoRDc7QsFjZkVpt3ATgbDB8F6s529KsG0YYwpGHI6IZ37g5faceMKz41Y1roll6PQk4v+0Gc0tOEoBj54wvNU2cDmzWNDEFoOW06mBt4SZv5aLVz08qX31w3bzNv/AeslsFb7fm8XCTdwrAeGuLLRqb98VhmUsvA5Q0285B4/TkFBYXVDROyPaFbAJYEqmkH3DkFBaLCx677hGbsWUbyIuBhXW+1OQ2zuoQQ0RFTmGxaPQnBk1K4N2uPsCpqvwbMFZV5U4ATRN3aOF26oOSDPu2C1Osu7ZVLJzVJ6mOsaLHDmvC1pP/BeDcenpUy6V3333zb4ClwD3Akv3ltGqaaA2QmYA8Iq25VFW+v6TwhneAmcDtwMyBHr2Ihsv/NTzlkldH/NuAvhX4Y0AqK49x1Nw0M6HmoLLZZY/Q5vvor92rusr4pFUGgFEJ67K7sp/Vq/hMAfFlrOwwBxQTgLPRGPPl6vzF+QU7ArZJQRQFWJK/OD+a82s0iH1WX2uaEJomTKoq5d5LaCFTw2SJvjN74XQP4QYba4gUfgEW4D/ApQD+PPdrIWfwcmCPB6IVHscqgBXuhB5XgXsCcWeaFB+HZi5b1tO5+hEqCBFZ9WhdBRpQ5BQW5wD/WbZ95jUp1qrgpOTlMyoWzppfYf3VVFzO+bicA9IJH6LPcOoYrDvd2d26NquqbATQNJFNWAv99Fga11+YcvvztipPRuoIx8YdfW1LT+lxSoCiWysi/+ywgDrilJ5ut1eeFQqlT/jhHWkCobJ/ln8WapoIAXcB/wb2kODpL8VOvc3hz01K8EjDXEi6HkRKpsm7aWfAeuby2Sv3qKaOOKmD6vuYmLTKurbuALLjN02KeieX06wgnHFuQ2yaBgBuW2iCzaPoAtESqzlbsSnBKz26sbUQodP0lr8/MEEIZdwES2JLZ+k8M4DFmiZOVlVZ1vYNf9yW0xEhMxyLqsoaTROJwG+B9yKtjFVgBUSW5VSeKs/NWxmxTctbXV7qXpx/BMCuoKWpyx96L1bWHOwwiMCHT17x9EBvaNIW7YfGFzqgaH1sT9TMe+oCS6V72KsK00/UMQRAXn1IxieL/njZiwFczgKJ/BAwCIQPl3MmroZBdd0ZoncYm1g+cX19HvGmxmhSmTpEVeVWTRO5ROSvNE0cBSSoqnwnFnb2NQ3+5EMA8W3VtAf62pae0mOHNXvh9KbNt/zP43esPwWm37f3+xFn9QPA4nanY7dXbnS7M7LDuaKSrnQc6i6aJgoId8Z4WlWll0i056dE/uL8pEnWpsU69pMBI4h3Rpndj719/vp3+9q2/cVnO6bvAvh617SoK9EDRv0MU1AhJGTMWgx5bPo0U0ARplsbYlr9fOMbGRaLSDzHEz6vokrnCHrMuVJXjPbUTuuzmgg7vms1TWQAB6qqXAJg9KaZPcnftB17HuDWNPEGcK+qyk/2nmxvzcQsk3fsjoCVUWZ3VHm3HZFTWJwO5IekaUFP5ulvVCycVTp2/pszFRFamhm3dfPSBVcPCKcup7D4YDj/KRAHj3au2bSxYcL0ioWnboFTAQga5EmGECaxZ+R4QHy2IfqWnIT1x62vz6NgmBa/V5ZSl1FV2bYRzo3AZE0TuX2lFhRjjoz8HfDnVSx00AnaKkNIU07770oVZEQMXITc7synhAjOUJSAX1H8q1wu1/74ElOAjUC/rpArmlNSUDSn5JaiOSUxWxq79vXM8VMWH3gvUPGd13FattlbnW32HFc2u+yUn5KzClDlyfIBeILx0eWwupwFxqB4AUCRXBOrJUtng/F7Q4htsZirLe82pP22PmQ2j7W4nyHKdI6qVTmJALVrhz3X0RhNEw4gETgn8sA3H3hb04R1a+FSi9GbLm11U3bnIEY6t20BcoFx0dieYfJNFkgm2Zp9nY/umIKsD+YB5CSsjVlEvL+w/t4zloF4e0vTWGNOYXG/1mO9+qkL00578M6lIL8AMSzZWjV7cuqXoysWztrSdpwxJN4DdImEQZQvP0Tv8/WuaW6AnS3DlsZ46nOBk1RV+jVNGDVNXKxposfBvb5ibGL5VU5zbWPFwlk9ikT3B2LisJpbRv3P2jih3eVNp7MiMdLyVwJ+QLvjjt9/rOumB3Xdmu9yuWJWzNIWTRPjNU2cr2nieaAMyFNV2e1OPL1NxEktAX4PfNBTpzV/cX7GMS9M/OtHTSlrdLgZeA+Y+sZ5G7LeOX+d1nOLBx4TklbqACMcG0ZGuYvKD6sQrWoJPcYYElZFiphePPIX52eBuAt4d70v7v/KZpfdG2Xu8RgApNLaZhlNE2ZNE9M1TbSKGCwm/PsZG3ldRLjK1u9JWj5JIIQSiN9DeUFVZQOQr6ryxWjs/87jWC7Bc//PK3sUda50Z023GDxyStoXg6ZhQFsCuvk9YCSt/2/9kJzC4pPe2XjWirLqQ4/Ojt/0PyDva9clz+2tp6ppYgSuhlKBeFEgQsCJQ+kAQ0RLnTfNCVBWfWhM7+uqKn2qKlu7QZ1O+Pq3X5sIxYqcwmKxtSknJd2+c1C0p46JwwpsAkZtLVz6o6f+hoacMRDyKErgD8DMNhHVZwAlPn7HrTGyYW8eAv4OnAmMi+iu9mOkCrK1DayBbjpH172eedDxL45/FdhYFzJdOtzkXXWco+a0stll55TNLlseI2MHJHnJK3SArLit0VaDagKhAwiEnxhFf/wmfXTAqMc0HWCspeUDgbQBV3elG5ljePVpAJPO/TBe00RrodRRwEfAcZF81DOB1YSjpqiqXKuq8itVlbo/fsMpAE3D3zXtPbeqygCApomZkbywDvFLxQAiqs5c+2JDQ26mL2Qt/uNlL/bz8717DI+vWAZwWMbHN/S1LW3JKSwuOOC2fzwy5fbnvgTeDeiW+kMzll3w8a1zf1axcFb93uMjv4cNmibOaIoPLQcMOzL9+7MN9hADnGRr1WRFBOsrFs7qzXP9dcL5+/+BsCxm5Jo4UBjrC9nsa+snPdHXhsSCmIS5PUnLDba6Kfa6nJfGZjN9Xet2l8s1CsRZYHjw9tvv3sMxdblc6x588PL6YND+m2eeUedeeqnW4xu4polUIBCJ7swBrgQaW6sB+zNxmV/kt+w8nEhBRZeWxiKV4GcA4wyknCkRikAulog//Pv89Wt6x+IBiRdgedVh0VWVuhpKA3c71hhDIkUgzoxV9CdolGP9Zlkbq6tewfN5JzbrcROn2hs+ev6Xm6PqE61pYiRgkHrBBKPNJ23JzZ8DNxB+0CslrFG4VFVlg6aJQwi3NPzRjSFu13QfgMGXXNLBcUyEO8qtA07uyJ5Uo29sY+hHPm+XyCksHgGMB/Fkjybqxxya8cnyRn9icJcns99U1EcktkpaAg4rgFH4Fwel+Yp/XvuHfTkSK4AHgSX1iUGbo9mAoqMC7/e+xUMMBlKsVcdYDF5Lbx4jot36Aexu5/4a4evjmb153FhhUvzTA7oZ4Ed1BAORmERY/XGbGwCMvpQD225PSlr/dLiwiqL29jMafY94vYmmTZvURT2Vt9I0YQe+Bv4YeXqfrqpy60BwVovmlNjdVfnHG+07g6b4HY8CM+cumtGhc3TTGxni8OcmZecvzj91+gsT/yWQy0DeDJxtVnRtRkK1umL2ykvKZpcNOattiCxJhnwhW9Qd2aRCbl1SqCmWS5U2j1IT12KISd5V/uL8o5t14/Mgd8hwO9R20TSRHKmERdOEgbDDML95R3JD0GMpBc4B/gGgqtKrqvK1yIMfqiq/VlVZ0d68Rl9qBuBz7JzZrqMcibLOAs7a1+dIMgRyHYZgwr7GdMbRw9+fDzAp5ZtvOhs7UPnjZS/KJr/zpU2N47L6UR6rCpgjkluhoDR/v6+ol6YJRVVlk6rK+aoqm5PqjP+WSD1jlzm4vwweYuBT0Ti2psmfsN+E8CP5+6cAt0A4t1/TxPj9dfzukJe84gaLwaMflvFxr2h+729i4rA6t572GoBjxwm7C6ldLldcY2P2MU7npu0ul6vdlo/19aNbb9qX0U1N1kiPYFRVuglruz4CXAPcE3kiGggUypAtLejOnHn5gxdd29ZZvemNDNO817JOOfaFiZfnL86/f9rzeaUfNSWHPNKwBXirPmQ+S4Zb0wGEPLrx/UfO3Plhn32Sfo5CSA6Pr8iJZmzoroRMc0BRzH7xr1jaIBDxpqDosSZeOLIulwDpIFKWu50HtL6nacKmaeKANsPfA/4MoKoyBFxMOJo6mnATjVdVVW5vO38kB/yBfZ1HQXPNobri37avtsyqKteoqvRomjB1dIHf4LOvrAuaevSAtaF+wiSbsSUwPrE81kUY/QxRAqQDB3Q2cj+hAZFCxn2nzmiaOB/QIqthAMTf2OwWYR3gAzvab4gh9iagWzKbA07z/myiEXl4L4+8XACsaJNK1e9Y3zDRkWytXv/qtfdGHaTpz8Qqh7Ui8jenzbaLQiGL0etNumQf+x0RicDu7njTlYNqmhgHfKVp4jAAVZV/UVW5nHD7yJmRJ6J+zd9u+dNcPbj1Vr25RDfVPXnH0S/kTv+/f2a/cOwLE1/LX5z/6TsNaY0lTanFtSHzn4F5Xt1gG2n2rB1pdt9HuPDl+DZdqWKWZzlYMRn8xnT7zonRjDXo4W5N8S2GmKkpbP+zzQgkuG0hcwymUwmfN4A0ZBi957Z57y/AkjYNM+YDhbt3VOW/t38+fjPIUXHpdR0pdh1IOH+rw2iZbmqa5ktY44jS3qeBD9oUc+0mhGLSEd3WYM0pLBbbW0aO9QTj3vzjZS92qZPZQGOsc/UnANMyP7ytr22BsOTW6ITvPwKINzX8vJPGKzrQAuyR1+qx6vU+sz6jF80cYhAx6daXZwBJIA+j7zq/PQpc2aZbVoda9H1BTmGxsyXgyN7RMuKFvrYlVsRKqqFeF/5gyFJ31dbCpZ/8b+Jtn8Kx80B87fM5281ti6BF1AMEEOiGJms14ZtpHICmifOAtyItV9uN6nbGQ+eeKgBj6gGbHAIZV7Uqxw+YU/I2jxRgry4fWQWYkydszUUKW+3a4ZsBc9LY7QdLXZjqN2atBSzOnJ3TZMggGrekrQHMCSN2HRkKGPWWnclrAXN8Vk1BwJNi8jcNz5Wh7YAUfowzTvlo1owvJ9TgMNeHDAb/V25TwzP5SZuVALw2WXvSbQnZpwNa2yhs/uL8mURE2AdLV6rewheyNpZVHfJZNGOb4kOnOpoNBA1yeaxOlIBJDgdocoSG2bs5R1hBQh53yrgjxX/SlukgDQroF6Ruu1rTxF2qKmuBxwkv8StASFXlj3IDfU32fBBYElvaFWFVVblA04Qrksf1I7YWLhUmRoSEtWpJlKY/CrwZOT/3IMEQGMEPfb67TKKlJrfel5INRGvLgGXJ/Ou/n3rHc+6KxnH9ZjnSbmrZDlAwTPsiHC9oH1WVL2uaeGXv35TPrBudjYYk/c6EY5U7GodWiIbYJyFpOD6i49422LVf730RR/UZCKsSASs1TVyrqrJfFDhl2LfNrHQPFwyS/FXopsP60LmnFgCqKc7zyeSLS7aO46N0IU1Gozc9B1jiqJu6GESu01lx67XXPrv7wvT3ByYYalZnG/3NdrMzpzJhfFKTd4P79L+HFPsFdv+WW7PiSkc/csFnih40GB3Dq0daE5vHVn03cgVSMcdn1YyzJLjHNe1IXpOQXa16ah0bTfaDx9RvyPwfiJPXvDXtbqvzgKPdNQlffvXkqeXWpKaJBnMgs6UyuQwwmx3usYoxlOStc2wEzEabb5gQMi7gttYCZmEIJSCFMRJ0FtWr9nxYqinfUwmpds2e3T3r1u+pztVQsecqQePWVCkUqQOHAz5vfXyiHjQKqdcSiTIDIeJ8acxcNwvCSgGHA4cLxS+F4r9AD9nthJ17b9Gckt15roOxK1XvIfxBaYoq9y9olKf6zHrIcktTXeejo2P4NrMCkFRn/F939o/InWmAedSGMxltrlyz0bk+4SB7/TWjLR6FSDQ0ooW6T2rXZCdE/r6093uaJlJUVda0Vvp3QJLAEG/yZkaleaqq8ivgq8j8hkhqAgB2JTTSpujd7vx1QMo3C5ZtP55pWR+uCKfMDm7qfSkvAufmFBYbKxbO6vPcz1U1B68E+N+mM9qNxmua+C2wU1Xl6z96AHI5CxIxTgIQkndxOWcMyVsNsS98Idtb4boNqYDoD/q9W4C7CRdloWnCCTS3vcbtb0YmbLx6lzuTY0f8t2ywXBO77LBGnNWlIA0Bt5XyV48iGPqsYqLzcIQQ6FK3balK+o0w+hDLt9/90Lmzbox0tbLAhN1Ljw0VGTRUZGBybCeUPQ62Nj60y/eDtGDTtlSatu1Oc6J5RwrNO1IAqP5ujxXFmYCveUey8FQnEPSahwGpAY8lXuqKnXBPcz+gKwbdD2wH/Eabz2O0BuIDbusywG9PbRxtsARsjZvTlwJ+x/DqsQZLwFS/IWsZ4HeOqhxjMAdF7drhXwC+pLHbRyjGULDm+xGrAH9K7pYUYdB91atGbQL8qZM2mYUiPVUrc+qBwPUv/edHUaqiOSWX68Htf/Y3/ZNwCpgBW8uq/yVM3fSwr2FUcvPOwwGy4jO/PKel6sB0dqdcSBtDHWG6hdngNadYq3KiGZtYb/AEjXwcy+MbQyIBwBxQupvDqhLuVIbUzRy1/vxxhvEvrHp29lsvd2Ou0ZG/G9pujOS+fqNp4hxVlW90tLM75fPD7DWHE7DurOzKQTVNzAIe1jRxtKrKKoDKgGWTAdntHNby2inD7Mbmlkz7tp/KOVECXBZvajicfh5BiRT5XQTsICwTtDcqP9yLhrpdDdEpFQtnleYUFl9IWLrypU7SUHqdSPrhPW02PQskapqY0dEKVW/zXc1km8PcuO3ZuX/q0vW5P9OdCKsKGECAlHgb4+oqDZuqxzsPGSmkUOpFi95gkUZLU/WauLT6YMvOpE9CflODxdmSZE9tHN24JW1pyG9qtKU0JttSG0fWNNirgGutY0JvOpVNK2vXDPtUD5jc8cNqHPbUhsTq8pErbMlNhtS8zRO99fFrqleP2JxdUJ7SsCl9qy25yZd+YIXJaAuMjeSuDijisz6b1rxjGjbTYRsU/9ZhxmDtB7957flTfjxyxkORyNqSsFarEIqpKXe/GzwIsBtb4lKsVZ0vpbqcVoGYYAoS04Kr2qTgxOQ6I42OkNLNkniNcBTVBFKagnbT8SuvzS+aU/IqsGDuohlRO30JI6p+2bg1RcZn1e3ddase+BPs21kP2CpPAmjO1Lzwy658hs3AVsDWukEirMFu5rDmFBYrkHYg8Nre4vSDlalpn332bdU0Dkj59jb4VYdyYfuLg9I/PfSbXUdwyuh/WmHWHnq6qipDmiZUoKPiPU0idYFQJDIg9kO77iEGPhULZ/3joDsWX9kSiL84d8GrO70h+1t97bgCROoGXgGcrc6qpgnL/tSCzyksNkBCHjBo8lehew6rBngAE4iA7jfNuuDlx0u3Fi4tANQPE5aegt9yuM8x7JjLXH/p1LN/+umZuc2budaQpnhnzy1rt4mApol/AwcDYy+cX+4DKtu89yBwqaaJ0a3RmoFCyO84wmirDl2xaMHYzsbOXTSjtGhOSThXVfGdqAccFxfNKXl77qIZr+4HUwcNDb6kLY2+xE6Xy3elBU5IrzIZ3LbQxu7mmraHx6ZPpg4anEFLdxzWtr+Delvl5//Kf/j9n236+VfDK488GeRZi++893uDpeHsCwsXlnc2V8BjTjfZ/J4rHindY9lKVeU24LrO9nfsmFkLYPSma135DKoqywivjOzGLPRUpyGQ3JV5WhmftKpgbd0BaYSjjj8J3rj+ro1T73iuenVdfkZf2wJQ7UmPB/hw64mHALvTXTRNnAW8o6rSQ3il68e4GkqbHor7IKHJeILXIn9pm9/Y507HEAODNPuOD+rq8o8GWQhck1NYPLOvndaIk/qP1teRh7UXNE2coqpyRYc7xpADUr4+elXNwQ6DCA6qc6nLKgHXv/x2KeGbze3AzMhrshdOL31r1MPP1gWVo5OSNmx2uVxRhaGFCH0P0lNVdeAeUZ6IBE5rJfUCwn3M23tCuQv49UBzVgE8NZMsQU/ym9GOn7toRuncRTPvRbecDPITROjFfzx85a9708bBhkQJ6Bg6/d3rijwDoDo12K3ivY7I2mFaB5Babfq6u3OEfwcz7n1p6r2ZAYOPt8a8/AYw1p624uPmnYfkNVSc8FXRnJJ7n7zqrX32JvBUO/2RlJjdaJo4Q9PE5GjsUEL2EUB13jV31HTnc0R0DB/RNDFMQToyTL5utWlOte66AWDGiOKV3dl/oFLvS3mxwZecm1NY3Kvi6Z2RU1hcsKVpzPEALYGEN1srtiO6v/8kLDO4TxKajJ8A2HzKf3rT1iEGF2vq8j0g9b2Kr/obDcDnhJun7Jbi7E0SLbWXA5w46s1NvX2s/Um3vrjrX3679PqX37631VltpbJyyvm6bsZsbrk22rnCHa7EJmB3lZOmCRuwDLgTwhEZVZV75GlpmhihaUKoqqxSVflKdz5HX1I0pyQFGAtKp9G+vZm7aIY3YeQHF5hs1aJ+w8l/KppTkhN7CwcncabG+HT79pzOxmVUmlok0q0r4S4nsUKRwgFg8yo9esCKaLA+HX4lb11UMG/MpXdfe6w5fkceiNfCBQmi6rm773qlaE6JrYNpRgO7e0xHLqSPAn+IxoagpeqIkLGpJ58ji7AG8wleqQRXeRxNka5tXWJ51aFOu7G5+um5T0TXwWzwUALYRjrW93Wfc5VwkSggzZHXqKpcTTi48UgUc8QDLbgaBoVe5BD7DQ2Ej7CsY38ovvoRqiq/UVV5lqpKdySf+yNNE3N785hf7yowmxRfg9ngG1Sa1DHz9F0ulwH4HbD0t7997e2u7Gux1PstloYjWl9Hlo80IhXFe6NpIoXwE8uD3Ta4j0kc/e4lAPa0bzd2MrRdLrrlngpbyncz9KDdD7xdNKfEGUv7BisOU2NSoqU2p7NxAjFVIJbnXOqNaZVnoyM0GaDFHmpXSqoLqOzWYMUYec1lD8xePXfRjAvtqStVi7Oirmnb0b8E1hT99r+/eeXxS3ZH4l68f2IWkOrIrtpdwaiqUiecevO7aAyQIjTJl7DmR5qqUX8AVa4Bxvxu04HrQRgl4ihgSVec1pzCYqM76DjEHYx/rbt2DFSOyX7vS0GIDPuOTtM3ehkNpB8kAikUQpqmiREAqio/iEYPuyEhWBA0yF6PPA0xuIgs/+9e8e3rdIAoiCdc+F3dmwfxBOMOCuiWksGW0x+zC0RW1td3Ajlmc+NTXd03Lq7KFAqZsjRNvKhpYgyAqsqbVFX+s4NdaoH7iWigDUT8LZmHgI49beWyzke3z0UL7l4K4myQEy0JFV+3dUiGaJ+d7uHfrak7DMCDJAAAIABJREFUYJ/t/CqesYqQIqd5rPrOWB/fb9anhhRJ3E3N3dYcjaDxg25pkL0iC5feM++jX//+qnRE6DhgG9L4VMOmmY1/vflvvy6aUyI8NY48AJPNv8eSkarKOlWVnT5EbS1cajR6MzA3j+lQRSAaVFXuSjb4b4i8VOjist6UtM9/BiQYFb/WEzsGIs9d9dh2p6W+YmXNQX36sBp2EsSMeFNjnVEJ1P31pJ/nAt9rWrjxRjQIiSNk6Jtq6iEGNhULZ5VWLJx17wBwVlFV2aCq8hxVlS8DaJq4WNPEc+01U+kul/zpqgnA2DhTU7fTzvorMXNY6+pGn2c2Nwazsr7p8vJ8be24F4NBuxIKmY4HDt3XWE0TcaoqparKR1RVDticNfeuqQ5Qvjv36qf3rtDuEnMXzVjiHFnyjK8xZ0ztulPfKZpTMj+iKDBEu4ggiH0WG5r94mCDLiwNzqAn1kdPqTEuV3R67AiHtXfF/PArcWVHDSPmPnmCBhQk5rx3lx4ye30No/8GrHBX/+J2gNq1w98AePqWg//w7B2T1//luiNOi9KEbIEwGv1JZT39LGYlNC4SnZN0cVnPavDMBThx1L/3SzFDf6Pel/KSJxh3YE5hcXxf2lGxcFZpc8B5fUC3pDz85Z2VhNMAov5tJDQZN1r8So9a8w4xxAAkHRhBuJA9JuxyZy0AGOHY2KfXhN4gJg6ry+Wa6vUmjQ0E4hZceukHUUs3RPqdXxjJYeWbb379s33lo2qaOIfwk3u/6fDSHYrmlAjCTQG6nL/aHhfe8vvLFVPT6yFf8nGEteCWDDmt7ZNoqUlOse7aZwu9YTvMwwGSa41/jvXxBcIpEPWdj+ycJIP/e4B0o2+fmq5zF82QFxTed0fQk5YC/AHkATJkPBbAkvCbGx69aP7suvVZhTWrR4xp2pbyckRreZ+0pH18DIAvfn2PlrbyF+ebdgZsWQlK4Bu7Evo9MLMr3dq+qjzSajO2VDxxxTOremLHQMUggh8AxtzkFef0tS2Jlpo3QPor3VkXqKpcEEkxiZZhQCIu59B1a4ifDKoqHyTcRl6PFKH+M6KD3S1yCosLvqudcgFIVtfmX91HLWt7jR47rC6XqwBCzwJeKQ1dTQe4Anh+1KiPhgHY7dVHdjJ+DfAhMKAr31Lz/lEApMdlfBkTxwVADzi+JNwyS2lb+DDEnjgtdWnx5sasToZNBaQ5oMR8ScVr0fP8Jj0mnV4n2xtHAOTamqOq6p+7aEYQaAZ0qdcDZlAcv0DYnw1X2QpARPXbCZmajgNwp33aI4dVII8Hkht1s+vTi8pve3zUyk8jXWI6Jaew2BKUpmmeYNy/e2LDQGbWmFe/MooAdmPLnL625dHjZs87OP1Tsz9kPW/eUxeYOt8jgstZIJGHSmQOsGTIaR3ip0SbB7s8YDrg6P5s+q9BGCLX8v6qmtBteuSwhp1VWQLKlIiT1KmYvaYJRdNEq8NQBEyXUvkIQNeVdp8sWmUgVFV+q6ryAlWV7ev5DRDc1ZOmAVgSNsckwhrhA2htoykF/bBasj+wqXHsl5sax+0zGtccFzonYJTVuBqaY338oFHmeK36PuWmomWjz74a4HtP3NYu7KaB8Eu9AWFwIISYpZg2RBQ4BGA0mJ0/n/v8728vedZ137SOJnFs/9l2iQya3MM/68FHYLy15VGz0P3Ae5FNi4F3ItW0++SwzKXnAjanubZfd3rqTR677IUam6llxfKqQ/tD/rp7avrnFQ3+JOXf6889ugv7nQAgENB/pYmGGKJXibTTzlFV+SmApomrNE38PNr95z11YZzTXHdxpNV7v1VN6Ak9jbCqgDl8o0MS3YXmOaBE04RVVWVAVeXHmzcf/SUQqq6eVLv34EjXiH9omljQQ1v7De6qKcMAf+3as2LWRWnuohmlIFRhdO8gvOT8TazmHlyIID9U17eLKSBGt8SFOq1s7g5xLcq2+GZDTKSyNvvt4bamQWvUD3Dh3wkz9WBlpdQ9H81dNONdk31trsHq8yKsfzDaj33QEh/nadxyzHEtOw/7tGhOyfKnbnz2jy//8bI9hP4FYoxAbM699rZuF4/lL863bvDac0aa3avLZpe1phL9B3iZyFV3X+jScIkgxDHZ//20uzYMBpr8if8MSVN+TmFxtxovxApVlff/c83sA4BmieH8LuyaJhBIZIhBeJMdonfIKSwuyCksnj+Ylr0jCkmt7YwvBqJO9SnZfMrNDf4Uc17y8qcYOKoJXaKnDqsWcQAAsc8LTcTxhHBl/wOE20sC4HK5gkAVcEo4arsHJsBLR11SBiTycOCbuYtmxPQzzV00o1QG7ReBSAYujOXcg4U0244Mp6W2Y4F6l9Nh8St2Z4PhL71xfIFwKlLUxWKuNKNPRv6md2U/b93DnyLdDqT7q4fOPTXdvcuZZE9p+t/1L726YN4z1954+cPnj3eOKskD5gHN/qaRV1eXn/9+0ZwlXxXNKbnp6Vsfywtaqo4MWqq71Uq1DacEUczrfPGtKgGoqnxJVeUfo8l//KqywGRUgisev/z5AZ0i1FMc5vqlgJia9tn/9cXxNU3cqWmiAGDFXRe4bcbm/5kU34Xznrqw08rnimesaRL5K+AzgbgNmImrYVDdZIeIPTmFxaeAXAby98AHg8lphXA7Y+Ao4LcQ1p2/7c3Uv0xZfODC9mT/cgqLRzUFnDcAr79z04LLB4pqQlfpaUpAqRCBh8OvQhe6XK4ffUGaJqyaJp4D5gKoqlyiqvLp1h67kXkKQGaAPAhY0tZpjSz/X8IA1lxtyyuPX2IRBt8x9rRve8sBL1FMzWuMtuqHX3l8dqfLqj81Eq216TaDO2UfQyYDCMS3vXF8XcjU5rhQd7qy/oip9kY7wBiL+7Cu7Jd58LpJgD1x9M4k4BcgRNO21Fvajrlw/h9Wz10047G5i2YclZL70hH21LInIw+n93mqD/xO9yWNrMOdWTSnZER37beI0CWEH1R/FHHWNKFqmnhD00S70fCcwuI4EEcEdMs73T3+YGHGiHe+sBg86FK5YH8fW9NEEjAbOLV1W0HWhysCusVW6c7qVBzd5lH+JhDJVamBh3A13DvkrA4RJedG8u4FSAvhaOSgIrIC3QCwrCnp5jfqMy/T4Sba0arOitvyVrjjF/P6wtb9RY+LrtLTV40EGDnyk3Vtt7tcrgKXyzVf0247CHCy70RiFYgUfYRzmDRNDNM08bqmieERGatBodFXu/b0C2XIigyZeyUqNHfRDJkw4sN3g55UR/OOw/sk4tKfWVt3wCc73dkd6oxWpQZ+DVCXGIy5xM62v9gcihQGt12PydLtBp99O8Bab9zaruzXUpmYAyAUfYXR6r8CZPn1L7/doUTcedf85bNL75l35dxFM6YZrTXjk0d89F8zRnbVD88ANv/15r/WPXv7g38qmlPSWTHbbq59PTNTR5w2wdq8sWx2WbCdIcmEixCGt7d/QdYH/wcYs+K2fBHtMQcrf7zshRaB/HBF9f7PY1VVWQdMAVyt20wG//0Cvf7THerUfe7sctrTdxkLPFa9PO0q96u9a+kQgwx/FFlDg4aXaodvA2R7LWhPvv8Pv93RMiJ/WuZHJRULZ23pKxv3Bz12WFtaMrcCeL3O3S0aXa47CkD/GOTdoCz58MMF96mqvHcf02ggJOFfYGtqwSTgCCAmBSr9gaI5JQUhf+ITAJ7aSb/oLekpxeC/EWSFpzbvkt6Yf4CzzxxWi0+MDxpkqMEZ6pITGA3Dt5vtAGlVxphUta/3xTUB1IbMUUvJATRtS3UC+JttO4Je0+SksTv2KYvVlise/eW66U0zbwTIMol5prjtj0jdZG3ZdfBcYNsTc98pfXHhzf/4+wPXjdvXPKXNSScHpEKSIdCudJiqyteAfFWVFe293+BPOtUgAhyU/tmH0do+mPGG7G+DyM0pLO443SWGRIpnz9U0oUTE0HfnMv95zl/dEuVl4IxwJLxD5ghEqs2rXNb7Fg8xyHiaNk5rkqVqZ2RFZr/8/vsA7Yf0S7m7UUxOYbG9vHbKzUbFvy4zbtu5fWbdfqLHDmtzc2YdwK5dk3fn5ZnNzTeCooTlFTBJGdZ87IhIKkETYV3SmS6Xq1RV5fvAGFWVg0lfUWW3syR3t9OMNedf/7gfxENAwd/mLzq5N44xUBkWtzk7ztSU2tH7CU1GszEkluZc6u2Nx3cnQKx0WDNNXi9AssEfdWQTACHHALTsShwLAsUYurMruzdnfHgSgC115arLH7rwussemG1DBCcBdyoG36j6ipPOq1s/6/uiOSXv/+WG52985fFLfqSb3KIbfw00fNaStLqj46iq9GuaMEWqZa1t3/uuZmqSLg0fP3HFM73a4nCgkG7fvgzg0IyPo2qrGwNOAV4C2q1iDi9RYj96+Pt3tfd+xTPWlKBB/j5okKW4Grrd7W+InyaR7mpqgrn+SZBr63yptxdvOPvkoG5I62vbeoOy2WWlSQb/HIDxlpaPWrWq402NfwBGBXXz//3xshdj3uimv9Fjh9Vg8LVqJu6O8vj9jmHhJx8ZlbTCM8/MyAASnM6KclW9c7imiTPhh4q5QYQGemsxSYherIY1WOqfUUzNQakbnh/qfvUDidaaVKMSaL8DiMtpIJzD2iv5q1WpgUkAdYnBmLThO8jeGBRIRlo8XcphTciuvsho9QWR4jTg20vuWv5RFw99CIAn+dvdqRVznzyxfO6iGXcmj399eNLYt04F8QdgZKB5+P1V312wpmjO++8VzSm59M/XvJp8+j/GXAPyaJAJwPvtFRG0oQB4HDirdUNOYXEScLBEWdJFuwctBVna53Zjs2zwJZ20nw5ZDJwMvN7em4dnLn03wVwXWFc/8fj23k+uNdxmDAnrjiz/C71p5BCDl4qFs0pX3HXhlaAcpAj9jVfXXGp+4It7XskpLD52gWvui2W3T31yMGn6HhFf/0y8EmypCf4/e/cd31T1/gH889zs7klLSyHsUcLeKAZwFxT9OnFU/ImiqLi+EBQwqEjdoKIIKlZFBZxfjYthBAEVVCTsWaBA90ibZt57fn/kFsooXQkt7Xm/Xn2R5ubecxJObp977nPOUYcDwKUZLz3u8IZOidPmbMnKSKvrOfyC1OCANTr6wCVEPpjNZgkAZs+e2RPAYIAWAjQLco/puY7hdEZ1AwCttrQIwAMAHquce7U5mbxw1EZN5MH5AKDU5U+TpxgKiknzr3eoQo9tdJd2jAXwLPjqVwCAHYV915W6YwrPtu1IG/doALqiaF9FMMr2quSezVAxIIPhXhyXywC49rtCdtZlP2dRmJdJggfAYKXO/W1dyw3LvSSfgZWCxDNygW96KJON/+9rlskLR80E0DWm85c3h8Tu+BYQOgN43+uOLOi+dcprnfMHQOXTnpGPdTqjka0FMMBoZJ9UPndx8spHAAidonYG5cLiQjR/4lLRJeq+2VuSGqs3WajmPerHaiWN1UoJ8riCH6sbWzB/4lLR7ol6I8eR0v2M6bbMkSERZcpbJGLWlHtcbwWrrlzLkJWR5hjbYdkNgxLXrd5dbOjSj/b8MoMtG9+DsiZJjH5pLkHri+NyWbmo/KTIp+mtN32zdF9J95cYBBS4Ero1t1kSqtPgFXccjlaHAPSt/D0qKuvzkpJ2ImPKGWaz+ayBweny8nppASA3t/eX3bt//SSA8Dou63fBcJd2/AjAYz5nq7pM9l6/skq6rIV/5QwFTgYGLX0Urg/VtPuwckUvAHDqpKDMYZt0XH0YANoc1fwWqGMykLNMUtVpxgmvQxcKIAQAYrtmr6xHsR0IdLDXxPfPmTYxeeEoBoxaDmD5zPeMil32pPej7B3u7FzYV2pbbBB85EV21A5FZMSxjgsmrQmbvHDUWRdqMBrZXwBgtVIygJLs8lcHqwQPS43dUp+6N1sSU6yE/xZ9BwD7g1TMbAB3W63Uw2hkNaRj0KcAHlMLrpsALKx81q2WHtN4hASB0Y1BqiPXwsyfuJQBuHTMjNd+mEqfXamFF0SAyEjzozjQVPHMZd+Nk/6MF4BfLsSZKPQmSycAt4ZHX3MTEv8XIuiyb5Gc+srNlemFF9z7qqsGB6xOZ2wJADsAmM3mRKB9p+jog/9OmfJRrYJVP9YWIISHZ+cajcyNKukFzVA2AAgqR4fzUJYFYE/BP7qQT8gNoF3EvrbHy1POOmNFdIkyGoAv+Zi6zr2OtVQ5gDBgS/IqIAkKYiMNmYahlXlN57L4iUFqIL4tQCBB+vv26TvX1bVMn6ZwqKSo2FfzK/0MmYaodiEV2w4pC5OFmK0f7Y3fNKlr3uDb4txx9+orUvqhuPf/ARj/1gM//hTV/qcDSm3J8zc9tOSU84fVSjEAtgL48GBplzYAVs6f+LGjrnVvzvQRe3/PsnfG4Na/TgPS7g1SMR8CKKo5WAUA/BWpKSqL0RQ8DzlgzVqijU5iqtmOEPFw6NTyOrc9jjuDOZIAjGBgU/6npCsZAyQQwAAvlLCKva55TlhyDQC4oRSfmfXQYrc+x5YcdnjJozf90WTTDvUmS2KbsINTAHoQ0IcBgK+i0xYl0CdV//Fu284Zevg7olrM3/YA5bBKlT08DwCCUFzc8Za6HCMxccujgIQ+fT4c0dD6NHXxqZmFJLgR2mpLrVewqK/JC0dtVOoKvMqQ3DIAo4OZgnChiNIUxRIxdTWb+wDYCXNpUC6YiqN8gwCgMMYbkJQDQ6ZhqAiK9DAhFWeZm+9sdLFlQwF/ug2ThDrnD+567RlB4YmI8oYeFmtZx1QAfx7y6BIHhRYvvyIyP936wE8V75ifWTxn7sMD4zt9qxZU5SMBvA9iI4v3j30sf/v4Iwsmrfnkncc+G7/8jQmRAGA0siIAM9YcvvozAKkA1tS17s1d7/hN/4SrSr3Hytv2C/SxqyyPvcNoZC/WZp+sjDSWEpb160F756jOT37ZGgBSjqjvVnsFoSRKzAh0HbkWxhwZkrcg5C2XRioEYCXQJY4wcfnckGvum+a7d+EOpl+oJe+wKeovFihIgkCAApIiUnRP+nzPXQsWbX3coTdZvrvouTdffemziz5e8k3nc83PfV5c/sKLsXe88ch7hllLNwM4ml3e3iQyhdA5asc7ANrunD2hb5jgy8mGPUaT8O0UNNMVrarT4B7W8PBjw12uqITnn3/8CiD0v4Cwzmw212lKoPLyxDK1ulxUKLxLGlqfpu6mhzLZ2w9/U+Aq7tTQVYJqhUmq7UwMAQ9W/f7NH7QWVVJYqvKopMtdWml7QGb1PwtRwdozMJSFSyUBOjMa/f8QAGjDBN8TU79OuEHObT2rsqOxJ2ZIUOrcdV4aOCx3ZBoACs0fXmPv2KQvkl8WED1ZApUAZHzvhuwzUiFueiizcvChdfkbdz3qc0dOLt43thuA//gqWt1auPt6vPXAj58ySb0UWP3eoZ5fvQQAfVv9vh1Iq2v1m7X5E5cyvcmyrMwbebneZKGsjLSAzHQhr1L4sdVKh41GZqrLvtsK+00FMMYraW6COXKRAvQEgF+S73W+HYi6cS3P4fe0w1OOqMcR6P9a5auiHSGiy62WHtZ4hHfDnyh3zjjxSv91VWtz5K0AwMBEJUmeQRF/PVDUKv/B346OLs4ub985u7x92oIt06ESPDfP3mhZbYjbXHpV+68GlLij+z85/pcSeeq2oKUoPv7ezVG2gn537CnuaQR6pO0pTtVEqIvtAOYA+HTjzEknxij4OyWUcQCU6pj1r6tj1o+qzZ215qLBAWtFRexhUdSoRVFlAQQFwIbIiwbU+CHKOWkx5eVPOwFsaC6LA9RE8oTbJE94db18ASW6o3YBqNMo8mburDmsjhfDWoV6FcqycNEWrILjClU7AQzQT3DVqneyFqwAOQFoAEblkvL6jeXRxwyZhocBfGlLt51RTkV+VAwAKHVu55QPVh6uS2HZpnVDAVRO8P5wtmndV20yLj7je27INCgIbA5DzONJKldpnk/d/587tx+r6fg3PfSBF8A8AFgwac1DUe1/nOy2p4x3FqZeCeBWUrjc7Q4Poq5w447Y9Q8smLTGAMDKL8ZOsQbA7QL5UgFUuxhEHSkAFENO/aqLrIy0nXrTd//qlBUT7WG+SyPKlYkM7JmgjQrjmif/wKkJXoU0MEVU9wEgAfjSp2AL8uN9v1Y7DaE5UgngGgD/EGgFAKtx2l8bjcAHlS8Z8uw7Kf1a/T5nU87w0Hxn6662ggFX2AoGQCBf3iKTZd2I5IejVh0a02/57rvf9Eia1ZN6v5gboS4p/OXw1ftWPDq3ToGsPDjKSJDWMQg6AOPViptu94haJYBcgBb2itu0qn3kXoucl3s6I+QeCoBpIwTfXWgBuauVGhywejwR5QC8AKsc+Syg9gnArwO4CGAVALWcufhIPIYgzcF6OoW6tEz0hjXXyZTrrFPUjvb7Snoopiy+jaqeEEIrFD0BILZI9WEQi49CAPNXbem2jYZMw2gARh1Jf/QKsd+x2RFpBLBcAXZg4udtfo1Seh96aVzuiVxPUojXM1EBn1NTp54ymREnF104a6L/418ldBIQ/44EGqUhcUmvEPuUl8bl1vluwuSFo7zAqHkA5i2YtEYN4FJd7M5XovN7d7uGCTi+8amrAHYFQO4Fk9bwdBdZauw/f2wv7IshrdeagWtvCMQxjUbmAzBZ7mmts0GJvx2m3Nix4eXKVAYGAr0Cc+SWC3HwC9cIzJFDGdhaAimVIsEeLv5jjxTvTbnHtVkJQH+OXY+19sxJOq5u59JIT2mnly0922t+n3nfEeC+E0u7jntlVtsobdHkv3OHtLJ7ovuuPXpZbxy9HAAeBPDgwn+nnthXb7I4NQonharKtGWeyENeSVMUoixXJIYeTc5xJP9T4QvLj9YUaNtGHOiwuyh1LxB6LcCUDCe+S/ZIdfHKXvGbf8ku07/60zSTWMOdIyv8CyZoAAjlkuJmQ6bhTVu6LWgdLU1JgwNWQfBGSpJQ7r8Ih1THwT0PiqKqNyD9oNGUu2vbM3uhC09ery87Oix5+Rt3qeRepaAJTfyrjf3wKN2y1yck3PzwktxglnUhiFCXRAKA0xeiAeCqfF4i1l9gBAD/BqtsR4jYT+kjTSDXz5RvB1V+Z9YYMg0KANeHKXzzf3dETyCwK3/MNLwA4F1bus3BRMWV8mtrPWiqCiv8AyLPSPQ3ZBqGKiDdqRNiJsLfA/B/m+/c8X793tWpJi8c5QHwvfH5rfuORLh3jy9Tb0qUFAPgvzjms19UYfnvjB29Z31Uvqe4R7uGHstqJQX8Pd6vG41sb33vgEVqip4ZpMga619Y8uTy2+D/Z1wtVOjEd0KcCiUAEMgXWaZcEfm4Y3Nt9o0pUt7g0kienETvcn0ty/v68WcOA5hW+XvXpz7PcIva/8q5/1KcLmfLwIT1ERuPG78rccf6hievfsIjavB37tBtXknDksIOj2JMCPFK6jaKkL3dnGG7YvdUdNa6xJCeABT+0yODVlHxhUsMuX3T0/e4gHtqVbeTnRRk1JB4zM0UcwCsvfKTTuN/HL/vh1q+xQtWgwdd6XSFBqXS3QUAiHxfoRbzrlqt1MVqJTIa2fE//ngwAlDA7Y7oAmC12Wxu9vOJ+Vwxv4EpYc++OOg9n+5S/WoAKDl4ZbNZ4rYh/s4btg4Afj407pTeopIo8T6PSvLBXJoXrLIZId6rqnbAV0DY0m2iLd22YlhYcXJPnf0+BtoLYJ6axIL756duqPLSz1+5eUydvmvy7f/RkBP9K9MBLvq421UAs4oQJpVLCkUHjWOOLd0WkGC1qpTwrGkiAb7Y/Uvhv9io1cIkLU2pJ+bTAmdiF73J0tAOiS4AboN/8YZ6W3z/os2/Sr23uqFi4P9nXF2YI+8OcSoMDIyxWi5EVGXfgVq30EHjpmn6Ca56dwy5Rd03ALnhb7vuAmfig29Per/zltl3PpqVkfbfO3ssDLvHMD9px3M3j/ngyjHjZgyZumPm0CdmaLvOujuk3buJqtjfdMqUJR5Nwnf3A3D6j0NOlxjySlZGmuvcpZ/Jlm7baEu3zd18545MAMPVJJbn+jTfT/g8ZWqNO1/gGhywulxRuT5fiAIA2rT5461aBKsdAPwDYDoAeDwRl/m3UI2TiDcXzoKevwGAu7R9QrDLcpd22AYAPmd8tcuRtjDyesyn3l0ILxOUPiWrT69jrYU5FMfDHIrzkvry4rhc9ulNhxbZ0m2XqEgakax2l3qPxp4IPBiYGvX4rr0+5IbfXx5846Zbe97Tx5BpeGvAhz322EXl9wDJgTj59rnDgjJVjK2gX6JG4ayoaLvudVQJnHk6wBlWA4hopTs+rCEHMRrZTviD1o8aWqG/hA4fjPc8RbtUrT4BMJqnA3DnZI4c6n0u/FsA7wL4kUBGkhciqm3b8SnYNABlBGrQxbM8Av/E+eb0EflGI3MYjey4/Fg0GtlAo5HNgf/8Ksgppxp1zPpu5zpOfdjSbYeM4YWXRSm8BZsdkc8bMg131rzXhavBKQGiqJWvXKQyQfD+UotdsgDMgH8dagBiiJxO0GKuvElwH2WSBqrQY90B/BnMslShx0u8jtbQRu9JBUa1nDzhanSP+bfDzqLeuKTNj5FAmj+30hypVkFIUvnwapCLjwRQGuQyzvD3ndvXAUic+92VmwAMYGAQBabYMvBYmiHTsMyWbjtQ3b7XfNYxKkXtvHunM6xtvk/TkRA/jIEqVy4q8zH6q72mwnHAHdJDvmUWlO+wf/Wm2D4Avp0/cSnDRFRNheCqGJS4dtOfOSPQPnLPdAB1XrLRaqUQAFcYjeyrWs63WqORKT/++MPB6169wzftonxX64VZgTgo1zz5c1atSh+pGRgj0Aswl65FHdpy2cth6aGi8J/yEHFz2NTyOg8WPJ0cXNb1fGP198wyNUAKAHe16/nYJ7/evntuQ+tT1SvX5e4yZBo6wL9McuZmqwoIAAAgAElEQVRNy/Sjlt+cdVcgy2gqGtzDSiTKk7ALf0yYYK02x8lqJbJaKdJoZJLRyF6rvCLR6Yqv12iKCyBfdbSEHNbYbp+XAUBI/PZrg11WZNtfKgBA8mkn86VZgXC1PRQAVIInpPI5R4jYF/7e/aAu9SkKLMkeLrYKZhnV+eSlLqT2KgcAAMXb1+4ZdujAtljvQAB7hn/czTLus/bTDJmG6b0ye958z+dtFl36SWeLIdPw70G3rnBtWewr+T7NFAAdGfDNoNDiDwaFFl8OIHpL+vaR39xyoC9ARsjf4WBMs9IrftMgAEmAxOdfrcHyR144EKkuyttR1Lu+s6c9AOBLq5V6BqpO3x+8IYpBkPKdiR0ArG4pS0ly9WIEoCJ/z6SEuqakmCOHhpUL7xKA0ArB0FhLs8rnwdEAzQRwqxKS5JQUm+7/Iml8EMoqA5DWWuX6Z6crPP3SpZ0zDZmGZjcZRyAGXSWJogIaTUm1vTSyJ+AfaTq0Mlh99llTiijGaWNi9n4+ffr8gF51NGVEvv0g0evI6x2wEePVKdz9n1YA4ClLMQBY3dJHVP+Zc/EGAHeuOnzNiWVA7RHi3aEVCmQnu0vaBKncvAUhQrykVHvUUoO/c/UhulUnsvqTOhwf+/hD++yGTEMSgMccouIRuxh2NeAfw/2HIxpqkrwAfgHoq4GhJSXRCs/Xr1yXm1Xd8U8b/BVw4Sr7FAC4Qv/NXmBssIppNko9MZ8CuFdvsmiyMtLquhDGPAD/Go0sUNNiAYARJ0dG80FX3LlYCcQAgEAe1PGODQO7h0CV51kFGrGtVT0vPvZV4rE/yqN++K085h1DpuGwLd0WsCW65bLcU79OGMwq8FWOV3snAJ8h03CfLd3mq3HnC0SDe1hFUUsAEBOzr6ZudyuArwHkVNn3cgAoKur8QkPrcSG56aFMBqbI8lW0Cqn51Q3DJM31/kctJ0e4BpXJ9ycCx+hipUYi5vMpUZuUlnPSmyxD9SbLzNN7kFrlq8IIhLhClbsxrvjzd7Tt43/EDt76mP+7aku3HbOl255QEXsNYEzOtZJCBN+i0REFOlu67Qpbus38/g1H5p0rWD0f/s4bEqlROIu0Cqe1MetxAVkDQNcpasfVtd3BaqUYq5VCjUbmMxrZygDXx+pvYwxoIalfXP1ktXP/zcB8ErFNqGO+c/Zi7USJMIGBNbkBfq9el7PWLqm6AnQUYD/fvqLtxECX8eK4XG+OVzsWwDMA7tarK/Y/8lVisxlw3eCAtZLDkbD4bM/LU6PAaGSbjEb2SNWpUZTKipsAdhTA9kDV40JBgjtPUFZ0DXY56vDDkf5HTEQT+vI2ll5xm9sDwPCk1a0rn9O6hVSBUa7+kKbeS1pe/dJzna54IeM3gK0H8AzAfrvihblr71owuXLRhkvlf68EsPp8Bq2v3DxmOBOFOwBAUIlnXNW7mOJLgCpH3bsrJOUHL47LDdTiBg2mN1kEpy90sFvUfVvNZNrcaa7Qf/UXQUS0puiROuz2PoCNVisF/C5AVkbaRn3E3sMahUtCC1pKkqs7hYhxBFLnJHo31Wlwnjmyb/JR9SteNXM7QqU7IKcoNaUBfrZ0WzaASyIEX+m2ivBF13/W/r4glMFs6ban+4WULD3k0bW12mN/M2QazLVZurupa1DAumSJ8USOhN2ecka+lNVKagBWq5WmnLnvSB1jwuXR0QdKzWZzi/sjFBJvay2oHIZgl6MKzRskqMoYgJngI6oRoirXAoBG6QwFAGaOGMrABjCwJNQxkHxo0R3JxufnmfQmy6odhX327C42DMeJVUgg7C7udbH1yNV/6k2WzVsUKfMqn8d57On2T13F1gAUDgCh8cVn5NCezLUKXg5qQwxMXHc5gFid0lHnAUQt1TuT3j0ari7b/2/BgNA67DYPwKvyQgEBl2Xvssot6nJ4sMqdS9IxdRwAqLxU66Xa7a+E9QfwM4GK1R7qGvbf8qUwl85tSsFqJVu6LXdIWPHwUIV4aK879E1DpuHmYJSTeeOR29UkzREhpALsaQJbN3pp51uCUdb50qCA9ejRwSOr/Hq2OVRVALIBnLEsY3b24CGiqIUgiLVulM2Jt6KV1eeMowWT1ihqfnX9OXL6KSRRkwUQX8ISwO/HjZsAYM3hMbkAwMjf80n+lAkNgFHn2n/Ys2+r9CZLmt5k+fSHrOsPZ9k7zwWgB+iZWG3e3fJSqT6AnB0idz8gkG8aAF+2OymFMcDHCF6mYF9h8NFgvs8qjDi5OhUqCqLOWm7l3H5NLVgFAAWJkwBgVMr3Wxu7LhcSuydqhUfU9tabLGHnep3VSgIAGI3MajSyD4JYpVYAwviAK+5cFBJdCaAivkBVqzmrsxfrhmvc9KcoMDWAS4Wn7XVacroxvHJd7gG7qOoF0EaAfXL35ykLg1GOmykc8C/oBAZS5Pk0nxgyDeuHftR91qNfJY6s8QBNTIMCVp9Pc0mVX8/oNZLnJ7vVaGQrcBo5f9VXWNjlrKkEzZ2nrO0meRqgoM3FumDSmhsAoQMktR7+AVf8D8VpOawCo1UA3HLOk8DA7s5erDvlNqreZCG9yTLo0oyXV5V6oisAfAfgsnhd7neXtfvmXgCdszLSzH+ZJyxBlZ7KNdMfe/vA3GtfzMpIG3Kp6q9dOUJE4XtiWtbNnpnKR11TMgfMfs+pN333rN5k6eOftikorFXeM0SPKjNI5QTNnzkXq9WC68iC+z6o1eo2nJ9WUWEFoDTEba62V8VqJRWAX61WujeYdfEHqdIYgEWAzxLAVcc/pVUaAwtBbe54mSOTk4+qPlL6yHssyZMOc+ne81PRhrOl2+wArkxWuY5tckTdN/Sj7k8EoRgrADfAfADzENgHAELKJeXsVfb4NX0yU3cZMg3PjFzaZejUrxOa/KwCDcxhFf6o8suJ/EirlXpZrWSxWqnaYEyprLiFyLfZbDaf93kpmwKFtigfAHSxOwI2dUxVy99IJ6W24H15kAMfcCXrn7BBDwBDk9Z0AACYSzcSaCSBngJg8ikR0+ao+jXfsxHfLXr56rE3zTOtEiDuAfDH/pKuI1LCD+YkhmTfDiBp48xJ1y6+f9HirIy0EyktWRlpG7My0uaectvTHBmuZb4urZn97fueW9r+b9YleVDi2g8Fko4C9CSAfyLVxaVXv/TcSr3JMjCQwevjy77bCJwSjAR1KeBA05ssKokpLvZIWktj1+VCc7n+m81K8kIleM8VjIYAKAIQkPlWz2Gk/wL9lKVZOe50RgAkT2l1zsVNDr+n7cbAVhMoTiHRRSn3uL4+P1UMHFu6rcIQUtYjRPCtKZeULxkyDQFdrarK1FqzADJuTd9+ty3d1tcYXnBRb13ppyKEXABPFfg0GzaUR7sNmT3nGTINl8hLfDc5DUquDws7vre8/MTYlapzqHYE0BUn8/lO8d57l/Xy+YbrW7Wy7WpI+ReyKP0qReGum6CN2n8ZgJ8Dffyi/WNvF10x4YAkwj9FSIsfcAUAWoVTAACV4D05Q4M/z2kjALy1aNRKQ0H5e8Pd+6+6vWxzWqkvGYc0uTtz3Un3MAhf/Dh1ep2nIjua5JmUfEwtFMR6j8QByMpIOw6kpQOA3mSJV5D3ulhd7nM7C3uPAvAnwA5f+4o5J1RV9vqGY6M+zcpIkxr2rtmRyvWrAVr9ys1jRvsD2aZveNLq/6w/NjosVpsb1AU2mqPXJ35cuHrGZ3//kzdIVd1rjEZWarXSuKqDYYOkTG6DEkD8XMRVxwr/HS8N+e9AdoY5cujpuajS7IjLE5Wq7xigAJiRzPYL9u7LS+NyywyZhisBfAjghTtWtE1rrXIbXxyXG5Dv5NmmHHzj+uPrAawHAEOmoVX/kJLZR73aS0pF1SQAU7Qkuq/9rMP2A+5QM4CVtnRbnZeQDYYGBazR0Qf1lQFr1Qn/jUb2ldVK3xmN7Ky9OUeODOsPAKKofrkh5V/IvOWJGwCg5ODlvRZMWjM0kPmlCyatiQBiMgD2D0h8GEy4GADPYQWw/tjorQCwNvuKQ5XP6U0WLYAxCvKlAw+PEZkK7Shn71vq18T/qpZ3m6L6rLWahHwApfVZqCrCrujGwFAeJv1w+vq4WRlp+QAWAVikN1miAVwTonSkby/oM9LHVB8DeLHLU19YRiT/vCNUVb5g/sSl9eghpcHytFVVe9oD3hbk27xGANZADaxx+kLGA8Dg1us2AHcH4pAtisMX/jWA2XqTJSYrI62o8nmrlSIAvAhgptHI8oNfEzYAoHKAXgSwig+84s7KXLqRzJEjAUxlYOMATABwJ5kjv/cpWLk9QuwXVibkqplwscoLAuAl0AU/z6gt3eY1ZBpu76Yt67ulInLEVrDXfsg0PGpLtwV9QLot3ZYH4H4AMGQawgnsyhS184WD7pBUAP8DmOOazzrkhQnixzZnxKu2dFvQ54+vToMC1sLCzqfMvWq10kMA9huN7PvqglU/ugrA8cLCri121Rp79ogUAGCS5jIAFwdyQn9VWPb33vLk1gDNnPz2Fb8BCOgExRe4ynaZrjdZDN1itt6rFrr08EhatciUOV2it/3cLuLA4pWHrvkidfY2lr1Y90TSMdU9AL4RBWY9+p72hbb/5/qxLgWGlytSAGzVT3AdOdfrsjLSigFkAsh8aNEdyasOjxnt9IVd65NU6asOX6NWkG/mNybL57HaPMtFyatWzp+41FnLKljlaatUCGBPu95kURji/kpKDjvU99cjV3QEQjPgn6jbozdZAjJ10d95Q0MV5Nvx1n1Ldje8xi1PjDZ/fZErnvonbLgXSMuosmkogDvgb29BDVgfXnxHgloYd0dC6LFf1j310LPBLItrBsylG2GO/BPANQQSGJgCwCWCBGeoQ4hTipSCk2kDhGayCIUt3SZO/Tohda8r9HURwhQFWNjUrxPuO59TDMorZq0AsMKQaVADGBkiiLcXeDXjD0rKmQBMfTJ7rhsQWpKrJmnOW/85dl6nJG1QwFpREV8Z/bvl5P10AHsAfF/dPkuWjNQIwrCxSqX7hyeffLnFTWdVhVG+PSYATCsvbdmgL92CSWt6A+JLQPJw+ak3F0xas5P3rJ7UNnyf8XBZJwDsHoCwp6iHr2vM9oO7igyTGYQ1P0+bdsrJoc1E58swR84HMBnAi22y1T+Iz0S8ppDoaZhLy2oqL29BiCoeyqEE+rAu9Xzj3o+Own+L6MOHF98eX+KOeWJt9uXtAIwvdLW6d+Whsazj9G8+EplyOfw9VtWuZvT4su82vnLzmNGQez+rSwfQmyyCPmJvXPeYrf23FfalI2UddLHavE4donZfvq+4e06xO06pUzrah6tLexU6452AKtJW0J9sBf1POxLTKsl3ORrYnuWe7+EiU77VkOO0ZBcnr9z4Y9Z1cPu0NwA4EbAajewnq5XaGY0s2Lmr2JQz/BaPpKWu0dvPGHzLcdWwEsgN/xKtXgBXCk/bN2oAyAOxViPAF+BNwYvjcsUfMg0PCmB2EWTa7w4ZefWnnbp+f+u+896LbEu3eQD8BOCnqV8n3GW1xw51MsU1SpJu/8MRPQrArYZMw++RCu+qgaEla1fZ48sh/40J1mwzDQpYQ0Nz4x2OBADMZTQyr9VKF6OGgVwuV9SNkqTSxsZuPdiQspsBK/yj93QACJDqNRhmwaQ1FN3pf/dX5PV6DNB3BBSe83H790JV7I6Lk3M5AUCSoHz2h6lPPnPOncylXgDzjizRrmyVp3w1xKl4FMCtBW+EvF8eJs3QT3BVe+Hl1kj/IVBYXrzXfsYEqLX0+sSP8wFMAwC9yaIblrTm8RxH8vUHSrteC+BOleD2Xv7Ci/v3FKdOB1AMYBj87ev3aG1+5JDWawfs6ztCs7ckdadW6ej/x7xpsw/ZOxbnViSLKsHTJlaXO7DQ2coFaMKz7J0VWfbOJ8oudLVCYU4rKMnrAHBUlJSlUZqiQokpfi9wJmyPVBdV9Gn1Z+K/+QNyS9xxTwNMAxD5mGKS3mTZDWBZ1UFpdXFx8s93rzt6uaZt+IF/6vnRtXjzJy51dn7yy9XbCvslA4DVSskAOstTWAU9WAWA446UsQAOrDo89p3zUR7XDPh7WU9cZJ+Sw3qubc2AnAYwPX1FSv+/K6IuA7DMkGm4VQ4gG4Xcy/sbgN+mfp0wzc2Esb/YY3sz0LWlomrGKns85LhDAuAxZBqCMp83sQbk2i9adLXp2LFBc1Wqcgwf/orGaGQ1fqBms/k5gE2Pjd2T/NBDn+bU9PrmbMGkNUNB4uVgilsBJAEYMnnhqB212Xf5G+mK/O13XAMI0wAMVmhKJDCaK3oi18K/BG7l1WeLXyygKjnPsurVed1vXZsjB/kUbIlSpB5utbRf4xGehf//74yTp/v58Hc1HuH/ysJ894Q/4XgvQG8DAKA3WdQARveI3fLyvuJu7T2SVifPCiEjL6rMwVqVUvC4fJL6KEEs6BS1q125N2LLcUfK32rBXTwkyarPq2j9+66iXrYQZVn+Ze2+La5N+kFlDmuMNk9R5Gp1I4Be8brjOTHago92FxuKUcfc1itfnPvVnuLUcWntV7SRe5y5etCbLE8AeAnA3Af7zEkdkLhxJIB2RiMrDnbZA2e/e1u+M/EjgN7LykgL+FKUHNecGTINjwB4TUfiuksiCq99aVxu0L+zdfXIV4kjrPbYn0QIWvkpH4BZtnTb3ECX1aCA9bnnps7y+UJmC4IbKSm/PzJhwi/za9rHbDb/BcBpNpsvqnfBzcyCSWtSQOJfClVZKGPKNyVv2NfVBZkLJq3RaKP2PSp6Q2Z7HUlqAAcEpWN+bNcvP7rpoSXF8mtODH7hweqZAjE4KGuJVqF10VsJuaobCRTN5ECRQIUAvD4FC2NgOpUoVE4P4kQQlwnUmywqAG8D7G5/7zpjAK0F8O1FySs7lHki//03f9BmAHlXtf/C/vZ979trOGRD66NICDn6ULkn/BWHL0KAP5J2oQ4XCHqTZT3AFFkZY4YEs67NXc+Zn9xb7o18x98m4LpK/+Xjb096/+1gl6s3WYYSpHUMggJgboBG8sFWHFc3Qz/q/rBDUsxPVLlzjnu1XeQ80ybBkGlQdtQ4du13h3YEmFe+s+tFkFZMrHfAKq9q9SsAlXyL1Q1gZNXZAk730kv3j3c4EpZqNCVfTJ8+74Z6FdxMLZn1SkZFXp9p/t9IUurybCGxuwrs2SPeBbBHFXq8k+gJmyx5w3sAiFOHZdu10fvn2Y9c8uzkhaMu+FGSFyxz5GwAM+BfdAAE+hPAlrIwsaPSR6k6l5AAf/6BD8AsmEsDftVZKSC9xwHW+ckvn/VK6qfkIFoCaEZWRlqNn4HeZAmHf37Ql7Iy0p4Mfk2br/amb59koDlyGowIYGZt/g8awjDzk+5l3sgVAFLlp3wAZgW7XI5rjv7v8zbz/3RETQZoE4CrGnOkfiV5rtYPANzeN6T0f/9URGYgyDmsDVk4wAj/iGDUZjJos9k81OFolQkAbnfk2LMs49qiVeT1LYW8hBoAEj3h3ezZF40G8CmAv7yO1sskb/gIgMUAmOIpbxN1x1Ozn+bBaqP7EYAbgI/8y7I+AnPpfeFPlF+qcwnXw9+r6MN5GBwgB6cnVtpq7GAVALyS5nv/DAWMASR0jbaNqM1+xpQfHgKg7B7z73kdhdocMQj2KlNiKxDERQL0Jkvc1S/OWe/whW0HWHv42/15af8c11y9d0P2FIBuBNA/XPDZHv8qoVtj1mfq1wmKaIXnGwC3A3jqwxsPX3s+lvduSMBqBeA9mTPHRJz7hHQ1QJWDvATwlU5OZ5V7qX0AXEzUjYxo81sYAAOApfLtPMhJzaGTF45qyTMsNB3+W/wngsQzBgdUty1IzrrSViOqDKIFkmYlhmQf313c8wq9yVLj3ZWDpZ0HKMjH2kfu/eE8VLO5C6/yWARw+nTADfbw4tsjO5j+Nw3A/h1FvYf0jP1nZ99WvxsAXIImdAHFcRcqW7rtq86a8nudTGizoTxmrSHTkNQY9TBkGoRdzrB1xaI6LUHpfseWbnv+fJXdoBxWs9k8VBDcUyRJc7NOl2+ZNm3BmOpeO2fO4+u93rBhAIkAPDh1ZSwO1eeeys+fcquX56ZyFxq9yRICYBWA/p2jto9faZr6xTle+zeA0qyMtJHnrYLNlJwqskH+1Q0gYLmkepNFSAo79IDTFzqv2BWnAGABMDUrI61Wg0c5jqubO1e0Tf+nIuJNgPLgzxXNOl9lGzINBOBNAA900ZZ/11FTcU2gVuSqjQYFrJXkgVQOs9l81tt9Cxdem56T0/eDsLBjm8vLk74EYOXBat3wgVRcc6A3WWLCVPbtPkmZOKj1ums+fPD1b09/zYDZ78cXOFvlAvR0VkYan2g+AAbOXpyX70yKj9bk7/xn9l09AnHMS56fP/aQvdMsAAPidLmFhri/nlsyecG8QByb47jqGTINQwD2Y5ggagaHFV8777qcgC/vfrqpXyfQLlfoTwfdoZfBP+vItPOxEldVgQpYnwfYVI3GHjd9+mslp21TAexvQfC2Tkn5PXXChDW5DS6Q47gL1uR37hr0y5GrVlX4Qh0ADcvKSDtlTua73nzwBWv2VVMHJKy/5fNHn1/WWPVsTvQmy4cA7hBIxICE9d2WP/JCvVcO05ssXVPCD3xzpKxDV4J0lEGYDmBpVkaaFLgacxx3LpO/TPrPZkfkcrcklIoQRtjSbduCVZYh00AJSvfHuT7N+A4ax+oD7tDLznewCjQsh/WEmJg9NoAUMTF7TWdulR4BqKckqe/hwSrHcQvu++DPCl/YUIA0BGnlg4vuPGUAwY6i3h2VgscXq8s7o/eVq7dSAJCYAn/mjLixPgd4cNGd3To/+eU7ALZnl7VrMzjx15/HdlxmyMpI+4gHqxx3fi24/tgXWpL6ihCcANb3/7DHu4ZMQ8AHs8tpAM/l+jTj45XuL7tqHY0SrAIBCljDw4//TxC8vpKS9qfcanr//UsHCYLvBbW6bAOAbwJRFsdxF76sjLTtAsQxChLb/5M3ZFPqzE/DKrflVST18EnqVe9MereiMevYnCSFHkoFJABsM4B79SaLoqZ9KulNFp1h5iezVx0au9MnqSYCWMSg6LDskReveH3ix01uInOOayl+vX33VgAPAyzcwxT/B7D1hkzDq4ZMQ2Kgyuils/8E4EkAi/J9mhvPZ87q6QISsE6Y8ItDklQWpzO2l9lsPjF/yrFj/ecwJrDExC3TzGYzH9XOcdwJBzKu2XBxm5Vzjpa3DXF4I77QmyzqsS/P7gCgO4A1jV2/5kJvsgw97kgZIZ/uewNISQg5+pM8GKtaUxbfprjihYzpAHaVeSNnxWgLtl3V/ssxWRlpD2RlpOWdj7pzHFejLvBfjQL++eseBdjRMZ92LDQu7TrFkGmIrM9BDZmGoX0yU9dsdUZc1kVbvgPA/bZ0W6PeSVHW/JJa+xHAtRER2X0B/G02m9MA3aUAnrz77tW/BbAcjuOaiSWTF8zSmyyHALwbri75IlpbeAQALmnz40EgrZFr12wY2cm+CQFgyK1IGg3A2GPGMtONXT+YN/uOH06Zz1lvshhjtJd9UuSKb60kr83HVCM3zLzfCtx/vuvOcdy5WQHyAFDJy3H/X0dNxQ2FPvXVJaJqHoAXLvm469/ddeXrtznDZ/52+y5XdQcyZBqS2msqbhUlPAjo2okQCGDiAVfIfY0drAIBGnQFAAsXjhuYk9PnT52uYLvbHf4uIDwvSco8gLqYzWZPQArhOK5ZGvzMoldzK5IfVQluj8gUpCBp1N7nr+MXugFw2gpoDGAKgE5EsFGaQp/dE/WyxBRbYrT5V5V5Irp5Jc1ggcRjFyWv+iZaU/jQ/IlLxUZ7AxzHnZOcu2pElVWm5NzTgQDGhwi++yskpRqAHcCXqTr7ur2usH0eJlwVLnhVbdSuMfvcoSFeJqQAgBISfCDIC474AMyypdsafZW6gAWs/pWr2IaTzxDktWUv4VNYcRx3LlMW30abc4f9fbRc3wf+1Uhc4JPNB4wctBrhX+VqPuQ5nbtE2b4o80YNOO5I6QxAcXIhGHobwONZGWnORqkwx3EBY/5frPpvR9R1Bz0hVwDsPwBF+L/r/gxONUQWrhT/KfSpP1FC+hWA1gfhZ1SZ+z2YK1jVViBTAozyKkxV8mKJ/M+j0d8ox3FN1/yJS5neZFkBoBf855DKpZ75uSMA5MB/IwDoTZZtkHtjfjaZKp+bAzCTv+eViQCO8GCV45oH8zWFHgDLACy75/M2Dx9wh3yX71OPgD9iFT0QnrbetmNO1X0MmYbROK3XtrEFMmC1wr+Kihr+9apF8PWjOY6rvV/gP4dUXtVbG7U2zVTV4LWK7wB6FCfz4KznvWIcxwXduzdklxsyDdNxyuqZdMYgVzlIbRKBaqWApQQAlWkBJ247xYGvaMVxXB1UuXVt5ekA5xf/7Dmu5Thb3mtTF9CAleM4juM4juMCLSDzsHIcx3Ecx3FcsPCAleM4juM4jmvSeMDKcRzHcRzHNWk8YOU4juM4juOaNB6wchzHcRzHcU0aD1g5juM4juO4Jo0HrBzHcRzHcVyTxgNWjuM4juM4rknjASvHcRzHcRzXpPGAleM4juM4jmvSeMDKcRzHcRzHNWk8YOU4juM4juOaNB6wchzHcRzHcU0aD1g5juM4juO4Jo0HrBzHcRzHcVyTxgNWjuM4juM4rknjASvHcRzHcRzXpPGAleM4juM4jmvSeMDKcRzHcRzHNWk8YOU4juM4juOatBYfsBIRI6JO8uMPiOi5eh7HSkT3BLZ2XHNHRFlEdOlZnjcSUXawjl/TNo5rSohoOxEZ5cdmIvq4kavEcSCiH4govbHr0VK0+ICV4ziOO//qcsHEGEtljFnrUQbvSOCChjF2FWMsszav5W2x4XjAynFcgxCRsjbPcRzHcVx9NZuAlYhSiOhLIsonovXdiA0AACAASURBVEIierPKtruJaCcRFRPRT0TUrh7Hv4uI1hPRG0RUSkS7iGj0aS9rJ7+mjIh+JqK4KvtfI9/WKpGvtLpX2ZZFRE8Q0Vb52MuISFtl+xgi2iLvu4GIetW1/lyTNpCIdsjtc0nV//tKRGQiov1y29pBRNedtn2i3MYrt/c7yzG6EdFBIrqlNmWfq93JbXYaEW0F4CAi5Vme+y8RfXFaHd4gonkN+Ky4ZoCIPgLQFsC3RFRORFNrcY6sLrVliNw+S4jo3yqpA3MAXAzgTbmMN8+2P9eyyW1r+tnOg0QUTUTfyXFFsfy4TZV9T/SayjHCb0T0svzag0R0lbytVm2RiC6q0paPENFd8vNpRPQPEdnl581V9tGTP7UxnYgOE1EBET0VrM+rUTHGLvgfAAoA/wJ4DUAoAC2Ai+Rt4wDsA9AdgBLADAAbquzLAHSSH38A4LlqyrgLgA/AowBUAG4GUAogRt5uBbAfQBcAOvn3DHlbFwAOAJfJ+06V66SWt2cB+BNAEoAYADsBTJK39QOQB2Cw/D7T5ddrGvtz5z8BabtZALYBSJH/79cDeA6AEUB2ldfdKLcPQW57DgCtq2w7CmAgAALQCUC7Kse/VG5HhwGMqans2rQ7+fEWeV/d2Z4D0FquZ5S8XSkfs39jf+78p/F/Ktum/Lg258jK15oBfCw/TgZQCOBq+btxmfx7vLzdCuCexn6v/Kfp/tRwHowF8B8AIQDCAawA8HWVfU+0L/hjBC+AifI5834AxwDQ6a+tph5tAZQBuFX+DsQC6CNvMwIwyG28F4BcAOPkbXr445jF8nm3NwA3gO6N/dkG+qe59LAOgv+P+X8ZYw7GmIsx9pu87T4AcxljOxljPgDPA+hTn15W+P/YzmOMeRljywDsBpBWZfsSxtgexpgTwHIAfeTnbwZgYYytZIx5AbwMf8MaVmXf1xljxxhjRQC+rbLvRADvMMb+YIyJzJ8v4wYwpB7155qmNxljR+T/+znwn7BOwRhbIbcPSW57e+Fv9wBwD4AXGWObmN8+xtihKrtfDOB/ANIZY9/VsuzatLvX5X2dZ3uOMXYcwFr4A2oAuBJAAWPsr7p9PFwLUJtz5NncDuB7xtj38ndjJYDN8AewHFdbZz0PMsYKGWNfMMYqGGNl8rZLznGcQ4yxxYwxEUAm/BftCbWsw20AVjHGPpVjjELG2Ba5HlbGmE1u41sBfHqWesyWz7v/wt+B17uW5V4wmkvAmgJ/Q/GdZVs7APPlLvYSAEXw90Il16Oco0y+pJEdgj9QrpRT5XEFgDD5cZL8WgAAY0wCcOS0OlS3bzsAj1fWX34PKaeVy13YjlR5fHqbAgAQ0Z1Vbs+XAOgJoDLlJAX+3v3qTIL/rsIvdSi7Nu2u6r7VPZcJf1AB+d+PzlFPruWqzTnybNoBuPG0dnoR/IECx9XWWc+DRBRCRO8Q0SEissN/AR5FRIpqjnPi7zhjrEJ+GFbNa09X7XmciAYT0S9yakIp/Of0uNNeVl0M0Ww0l4D1CIC2dPaBHkcA3McYi6ryo2OMbahHOclERFV+bwt/l39NjsF/YgUAyMdIgf82bk2OAJhzWv1DGGOf1qXiXJOWUuXxGW1KvhuwGMCDAGIZY1Hw38KqbItHAHQ8x/Enwf/9eK0OZdem3VW9eKvuua8B9CKingDGAFh6jnpyLUvVtlLfc+QRAB+d1k5DGWMZZymD46pT3XnwcQBdAQxmjEUAGCE/XzUOqK2a2uK5zuOfwH+XLIUxFglgYT3rcEFrLgHrnwCOA8ggolAi0hLRcHnbQgDTiSgVAIgokohurO5ANWgF4GEiUsnH6A7g+1rstxxAGhGNJiIV/F8CN4DaBM2LAUySr7BIfn9pRBRez/fANT2TiagNEcUAeBLAstO2h8J/sssHACKaAH8Pa6V3ATxBRP3lNtLptJSXMvhvx48gogycqrqyA9LuGGMuAJ/Df8L9kzF2uC77c81aLoAO8uP6niM/BjCWiK4gIoV87jdWGRhTtQyOq05158FwAE4AJfK2pxtQRk1tcSmAS4noJvIPYo0losrUwHAARYwxFxENAjC+AfW4YDWLgFXOFxkL/2CTwwCy4c+JAmPsKwAvAPhM7tLfBuCqehb1B4DOAArgz2W5gTFWWIv67Yb/dugb8r5jAYxljHlqse9m+PMJ3wRQDP9AhLvqWX+uafoEwM8ADsg/pyxewRjbAeAVABvhP+kZ4B8YULl9Bfzt8RP4g9Ov4R88UPUYJfAPSLmKiJ6tqewAt7tMuc48HYCrai6AGfJt/LGoxzmSMXYEwLXwBxn58PdS/Rcn/7bNB3CDPGr79aC8C645qO4cPA/+XOoCAL8D+LEBZZyzLcoX81fDf7FWBP8A1so81AcAPENEZQBmwX+B1+JUjl7jaiBPL3EPY+yixq4Lx11IiKgtgF0AEhlj9sauD8dxXCUiyoL/b/uqxq4Ld27NooeV47imiYgEAI8B+IwHqxzHcVx98dVoOI4LCiIKhT+F4RD8ObQcx3EcVy88JYDjOI7jOI5r0nhKAMdxHMdxHNek8YCV4ziO4ziOa9J4wMpxHMdxHMc1aTxg5TiO4ziO45o0HrByHMdxHMdxTRoPWDmO4ziO47gmjQesHMdxHMdxXJPGFw5oAfQmy1AARgDWrIy0jY1cHa6ZMWQaTrQvW7ot6O1rZ7fuJ8rrvmsnb89co+HnVq4laCrtvFksHHC+Pswpi2+jo+VtlZtzL1LE646rhrT+NeZAaVff9sK+Yow2XzOk9a9t95d0LdtdbHDH6XJC+ids7LynuEf+wdKuFXG6nPA+8ZtSdxenZh8p6+CI1+VEpsb902d3Uer+44625XG6nOjuMbb+u4tTd+VVJDnidDlxnaJ29d9TnLqtyBXviNXmtmofubfv3uIetlJPjDNWm5fQJjyr976Sblsd3ghXtKYgoXVYduqB0s42ly/UG6kpSozX5XbJKu2Y7WPq3gAjAvMpBd+ovc9f91uwPiOuZTFkGkYD7CcACgBOgEYHM2jd2a37rQA+ZGAKArkAjOZBK9cY9KZv7wOEBQATCIxFaws+LnK1+ipKU3h8WNIvJSrBs2f+xKXM/9qm8Qef487mbO1Tb7JEA+jXJuzgY0fL213F/DfkXQBGN1YbvmACVr3Jorq6/eddchxJsX/nDQOA2GFJa8YcLOkce7wiJQ2AgiAhVpu3tcCVuB2AsnPU9qF2T1RhbkXycQBKfcTe/nZPVGGRK74AYMqk0CM9yzyRxWXeSDvAlDHaAn2FN7TcJYa4AKbUKR2xHlHjEZmKAUwFkKIxPwOCCIC8DIJXIB90CqfOKeqKJaZ0qQS3MkJdEl3qic72SeoKndKhi9EWJB4vTy6VoGx98iiSHRDmKcj7r8hUXcFPoFw9/PfrBM2G8uiH7KLqaQBh/meZBNAMW7ptbiDL2tmte6i7Z/lMoUh1r+qYJrryeQYmEmhm9107A1oex51N5R/1EGWZPTH02OwDpV1jT25lAOiU1xMkD4OQpRacHq+kSWUgBpAbjfgHn+Oqenjx7fGWAzfMEplyMvwNmEWqi/crBV+7Qlcr1clXnmjfPgCzsjLSGuWce95SAqpG8AD+BRB3SZsf+1f4QuM35VxcCiC2d/yfVzq84bp9Jd1zAcQmhmQPLveGK8q9kQAQ8f3BG0455oZjo075nYFQ6onuCiAUgK/UEx3rFrUK+D9kn8gUAkGSAJQD5AtVlZf6mPJYmTfyIABfYmi2UOyKO3DcEXJAgCR1jd4+pMCZsC+7XH9AKXhZn/g/h+RWJO0+UtYhS6Nwol+r3wcdc6TsPGTvdDhEWS70T9jQ70hZ+51Z9s5HI9Qlyn4JG3scsnfcdbC0S260pkDZP2FDxwOlXfcfKO1aEK87ruyX8HvS3uIehw6Udi1JDMkW+idsjNpT0iNvb3FqeXLYIdav1Ub1wdIu9m2F/dwDE37zrXh0rlTPz321P+BmDBB2AmymyJQkBxhuvcnCT6BcjU7e+me+GGXUbLuo0gHYArDuANQACQCOBaIsq5UobEWr8brfIi8ToLhesy0s3Bfn8YgR3o8Eu/ImAJrKlwaiPI47F/k8ugaApsIXTtnlbT0do3au3l/SfRgAFQCvkrw3+pgqp13Evr5JoUeu2lowINfhDY9jEC5hIAKIAKjh/zvIz7fceac3WYaqBPeYtuEHYg+UdrmF4dbI015CpZ6YTgCgFly7tUqnucwTWcQgfA25naMRz7nnpYdVb7LcAWAJwOQeSqr2tWqFy+cRtUcAFLYJOxinErwlB+1d1gIo7NdqY1sidvSv3GHrABSOSrEIf+UNiS91x36Jkx8mD75Oc3p3f/cZyxY5fWET5c2NesXEXRjkYHUNwLQAQUe+kv6hpW+Pi8597fuSVtevK49p72XC3WqS3CMjCga9PC73eH3K2dmtexSA8UwpPUA+IZUJzEcSfcKIveu4uvC3ga/kMzmHNRNABIDk7rt2igF8qxx3hm5Prchwibpp8t8uCcAzWRlps2tzq9//GrYGIC0AJIRk/zSk9dq0+ROX8nbLBZXeZAlPCj18cbuI/fduyR8Y5/SFDj89/lKQt0xiilDm72AV4Q9OLwLQWiV44JMUXzIo/vbHb3QcQBwa6c5s0AJWvclCFyevvPdwWfsZh+yd2pzcwhhAPwH4ol+rjdFhqrKStUcv3wCgwBD3V/G3T8zy1KMsnh9UB3qT5WIAa+E/8fJbVFyNDJmG6QCeAyAADIkq94KV4/c+aLXSTQCWATC8lduu2y5X2Ir+IaW4Le5o50tHSvusVroUwL0AJhuNLN9qpZ4ABgH4zGhkFVYrRcFDulaPduns7VSxWLUvpAOJpASwxdWnbLOvtfu5sB/iknDaIKud3brfDOAzT3vnLb1/yFrWCB8J10LoTRYKUZZtrPCFDQYg1ee2vvw36tIIdfEVdk/08MSQ7C05FW1GZGWklQWt4lyL0nPmJ6Hl3si+GoVzSMeo3Q8cLO2kdvrCkiBHqEryuHxMpZXv/EMleD7e+/z1dwBnxlB6k0UYmfL9PUfK2t+yr6R7dwCJCvhECYLAQBJAHjRC3NCggPVsgWLnJ7+6yCcp7mdQ9AHQI1xVKgkkLi/1xFwL3gvaZOhNFgeALQCe4P8XXE3kHtbVAFQA8yrALt2Svn2D1UqtAPQGsN5oZBXpK1JW/V0RNVoJ6RMfhNi+ISWH7o7PHgFguNHIiqxWelx1QPty1Osps8UY7y53n/IXtX+FpyhzNWBKyescYodrSOlBbydnqtHIfJueiHs89Pu4FyCBSA4Uuu/auXHrZe11inyVw9vOdajPN4fbN+ZnwzVvepNlIoBFAvkWSUyZhQZ0jExZfBsdLuvw7j95g9MB2q0g77X7547bF9AKc83ew4tvj91f0u2i7YV92wJsQLwu58YCZ4KWQSAAiFCX+EJVZTvDVPatV7X/6iqXqL1+0dYnPIC0BiBVXQJOvcmiIIgXxenyPst3JiY2Zi5rvQPWHjM+u7PCF5aJKlecAAiQ1svTuzKC+PLYjsvnvj7x42LeC9q0pM78rCROl7v31yenDGzsunAXhtpMX2XINIQArNB/+/NEov69tnTbYgDYNqTTJUKJ8mf4L16JQPDFeg4rClVPE2hF7sJdYwHcYDSyG3Z2667yxXn2KwvUKfLhfQBmVQ6y2nq5fqXyiHYEMWrVfdfO0qC+ea5FmrTw/8b9fOja5RITrABdmZWRVudxBGejN1lGAWyFVuGMGJb0y8z3J7+VEYjjcs2P3mTRGdv8cEOBM2HQtsJ+YQAGEKSe7OQ0+jltw/c7wtWl2w7ZO334SP/Z7TpF7f7ZaGTbrVbqAuD5/2fvvAOjqLo2/tyZ2b7JpveyoSbA0gQhUhzAggZfxd4Qy4cNFV+xxAbYo75YsaJiFHsXYqWMIIJ0SSB0EtJIQtr2MjP3+2M3MYTUTaPs75/szty598xmZ/bMvec8B8BDPE8PdMYP8x27GoAKoDRYWTt1x5PX/9Z1Z9o2fiddqTnnOXYxCPB6pwp4PwQ0io+QKNia12YtrQEA34cTcFRPEIKUdaCURPW2HQG6gAWGfxMaF9T12DVmyjYFAxiWorRfauA8Nxa5NQyg0P2rptFwL7gMwGJBIKyhX9zDqs1BCgJCKCiVQj0fm9YdmFnfMA34HMDnm+dEnamK1i/nylWRFFQk3r6OCfhXHNY8AmAjgKsAvNv9ZxzgdMKYmaMLUZ39gU5hYc+IWn/Xh3ct6hJnFQAKsjJW3fLm7dN2Vo1YuarowmeMmTkigIUFWRknh2xPgG7BmJmjBjB0eOTfN9pF/di9NYNZAIOF4gvqFYoqAGzuF5qfH60tzf2z5NwPLu77aen0/p8G8zytEwQSAqAMQCiAeTxP9wJoyFbvjB/mCxWYpGSc97tl5aXh6sp3jJk5/QuyMsROnXQH8NthdUmqNQBm+DLNG/2QEAdOgGyyAK1zxJawEUDTDMEAJxteZ3UVvNecGwsMU7Cgbn1XO7H/Jl1BCVBMWDqQ8SY8A4fcWgSJHiQqnWBB3zNLrMJJuZmNZli/8XUTYj+7ZpRqa5AEGSAgHrZG8XbTsfJT087XsmFfUo4Gu0yWx1W5QSvRfKGAzZSl+2S9+BACDmuArue1WldYyKCwf2748K5Fe7u68/fvfHv96CfeiwbIBwBeDFdXnDd03tL/md2hZyCwEnnK8q88mvVvu6g3AxiVFHTwSrekPBOIVwGE2145BjqFWQbobwBZNjA0t7R/6K5Nyw9etdn7UJPR0J8gTPvZ9/ICnqe1gkBMAA50h+2+7+Rll7/8yIeby8fNBPCOMTPn/3rqQctvh9XmCd7qfUU+AfBWI7HZKQgs/Z8MVAHo09tGBOg0F1FQNQEBBVUQEB4LDID3YZFFYye2E8QqHIvKPGp1/awpS6g9w1CuNUvc/M32kPeeTdxzBADleW+MkSnbdA1ASgE8+3py3jZBIITnaZUAMhRAMppxQDfNjVSotwS9xUF1M5FInqef/ebhXxVv9u0+zv603fl064y4PZpNhmlbb4w7e+SHpX905hwDBKhn+kuPPQmk3wyQZ3568NGl3TXOpvn/ZzFm5lwZrKx5tsoZkQmQcwFIANwBucFTizmLr1P9eODqWwHmJYBydlHXsK/UmmhNCj7g1HD2dx2i7s94feGOUdHrDtQXnqh3UBcBEAQyBsDVAO7z3W+/bjwOz9Nuj4n++r/P3mjMzCkC8Fhy8H4NgGu7e0ygEw5rvL4guMRqRLS2JOfvebc2XFQfTp2WAWA0z9OATNIJTErw3vBSW2Ji2y0DnKgULFGTRKKczFCAggIAUx0iDtDbmCeUHqZe9Lk+XKdDP3yCQBgAX8oU6+ceHqQUoRlJQEF92r1VouqcqSGVG+od1GYQAXz9enLeHgCbAMwAsJTnaSl2obSpPfmpaTHq6KANXLkqWdZKXzJ29qZhPxy2t2mokt5PQS/QbDBcACDgsAboNJOfWzisxDr8sXh94dESa/KC7h7PNzv18JlPLh5dYY+dAu+Dpl/XbYATC2NmjhLA5HB1xW0uadrF9UlRvkx9qmId37kkzVyRKgpXPTy3yb30zoZXgkASAVTyPHUCGALgBgCvACjkefp+T5xLM8xLCjpwVqG53zUXvPBs7c8PPnJn24d0DqbtJs0zKHxHPwAYHLGtX5Nd5QAKOmFTgB7AoKqhbkmlPP/5rF6t3hXAf1Qu8h5LyRiXii6lBFUEhAmr5W6kAKGgIrxOY7tDcwSBvCMI5B0A4HkqSxTsGxXG6SKYZ1VE+lHDSOcA5DEAU3Jn5q5vxVmFgshak8Y8EcCfAO7Gv2EBx7ErLfVsANvYcmWUY2zd4sFb916Vtju/bWcVwMj3y/YQkJ8B3JCfmtZjhVACnJoYM3MmHqxL/cktKW3DIzdm9GR8XoU97nGASN5QGioiEFJ3UmLMzFEPn599ZcaLT+9niacawM9VzshzEoMOHY7TF7wOwAFABIjTJWn+V5CVUdDakrogkBEACgFM921aCiCe52lht59MKxRkZdARUX9PjdKWbs6vHnaHMTPnmu4e0+8bfN7REUUAsLvalN94O8/T1ztrVIDuZ3vlmJ8AnLenxhQK4Ghv2xOggywwJMVAcSUAiBy9TO1iNPW7jsR6RiUfVmnRTAyrIBCG56nse/00gPE8T3nf7hp4fy3xf18n6KvElFH7XboEAC+7KHt/7g25MrzSVq0iCOQigkGwymylb6xFzbXLT01jnGeYl6sQdAEF3UtAzhv5YWluRz8KMcr9DVehnGYfX/sAgMDKTgC/aCTwz1Kw7pxDV7Bv9OD4BVkZ6wc//tmtNk/w+wNC83J/e+ihwOzqSYIxM0c7Mf63x/fWDpoAJAytdUUEOUWtlBhUsLXA3P8pgPz+84OPOH1tP0MrYZO+1a0sAAU8T9+EtzJoJoB1AMDz1NVT59UWr876xOPTdf8FoNk3LLon7qO7XlvYXeP57bCW2RKdAFBqTa7tOnMC9CBVvr/hCDisJxUFS9SsEapCX+Y8FB6iKI/yLA6yMEvUTuav8KPsrwBeBSAIvHkz7ztOEMhdAB4WBJLM81QEcBjAznonludpJgCYsk2hQMj3AEkYpav9ccnlRfd10MQyGZArPaqilhrkp6aFA8hWbwm+wD3AdlgKE8eN/LDUr++hc7T5K+3q0PeVBzSXIuCwBvATBeOa6pGV9StODHphSX7nU9d8MOGZRY8drBswwJiZoy7IynD25PgB2s/di2fEFluMt26rGGsCcMGakvO0Ws4qAvgQwNfBqtrVwiP3HlcIqblMfUEgOgBDeZ6u53kq+2ZVFYB3tQvAC919Pv5SkJXhNGbmXBypOVK4oezsF0c98cHGzfNvXtsdY/kdEpCgLwgFgHh9obrxdkEgMwWBHBAEEtRZ4wJ0HyOiNgQBwPj430f1ti0B2skCQ7o9K2i5sVDVsEx5NNzzZlmsJyT6Tvut2oes66166Xe9nYsG8DQFFYLrWLMv/gkA9gL4HoAeAHievsvzdHb9jCsA3PddzJks6AaAjCWg1yy5vOji9pgmCGSIIJBZvn43i5Q4rTJrba7t9ouSnqGMfAjA+RT0Lnd/h9FfZxUARi+stBE7s4grUw3NT00L87efAKc3Stbl9sUWyuhFlZsiS8r/ibLSAG/cd4ATiFlv3ZpkzMy5wZiZ82POwcuLtlWMXQDQcQA+7GvYfcW5yT/qC7IyZhVkZfy6cd6sjlTtfBnArz7HFQCm8jz9b5efQDdRkJVRNzpm3USZMmVHHdHfGTNzBnbHOH7PsA4Iy0stthoxKHx7UpNdRwBsQCec4QDdT4iq2gwALkkd3du2BGgdQSBMYqHy4T5QPaF1Mg0xx5URHv3OIQ4JAGv0tjvTGKqcFGzhAO/1x8aWKTabDRIBAJ6nvwFoUeh51EeDhilJyDqWUCJTnLtjZt7qDph5H4DzBIF8xvPUqiRUm6q2nt200c4RA+5WOrSPAAAFdRGQraMXVnZaEoWALAFwj6yS65MRAgToEDZPcBwAB0CeA7CiFzP0VxPI29Sc/Yk5i6/74NVZn0i9ZEcAAMbMnAgAl4SpK++sc2WM8G0uMihrl46KWfe3W1Itzr7rdbGx1FRbCAIZC+BtABf7YlFfAfARADsA8Dw96f7nb962ZIcxM+dsAOuUjHPt7HduvOCN2z7c0pVj+O2w5laesR8Adh4dcYw+Hc/TXwH82km7AnQzq4su3AAAm45MqGqrbYBeRxlRxc0nPkF+nyLA8zuHOILgDca/D8AbAPJtevkbCnoVAEpA3LFHlPfH3u443NYApmwTD7DfS5TUTgyqvv3VS8vadFZ9ItVqnqdHAMwBoOV5agUAkRLxgEurNmWb0uurYuWnpukZsAsoKHzhDCy6aNk1bXf+9ryz+lVRlfwMAg5rgA4yZ/F1hCVXTJco92tB1rSnetOWgqwMesOie9asKT5/TpUz6hEAvWrP6YgxM+cCFWu/N0x1NB1I1AGEqXWFFg6P3LRRouxT/1SembPtiZkUmNl2Z2iIS50MoIzn6U54xf3dAKLgzfTf1W0n04MUZGXsn/zcS1eWWhOF9WX878bMnL4FWRk1XdW/37OglY5YbwyrLcnSVcYE6FGqfX/De9WKAM0iCGSqIJBPBYGQtF2a7wxmTtF4PwE5AK8ix7MA/gYAnqeWwVd6rnaoZbtLSR0A2qW/ets38S8T0N8BlIpgznj10rIWM/ob2cfCqwDwcf3YPE/LAW+RARmEs8ncEAArfUUHIEa6l1LQUOIt5dwhBYP24El05nBlKu2utNRX81PT0ruq3wCnPm5JNV2iitiz4ladEA/woaqqhxSMq/zPkinn9bYtpxN3vnPTwKHzli4H8JNL0p5XZk8MitUVCQBGypRL+fa+p8f8MPeJ5e0VyhcEUj8pqAHwLbyKKeB5Wsjz9Eyep5u6wm5TtindlG16uP5e25usevi+P0ZGb7i92hmhA7DMmJmj7aq+/Z5hTdAfiii2psAnGtuAIJAz4RWyvYbn6brOGhig26gjkDE4Yts5QMZLvW1MgAYnsH45KArA8DEb9JkaJzPVoZKr1S4SSkAIABlAhE9W6omm/Sg9zM+shNHtcVaHZg+5lyL03jiFs65KVI7ffMOu6tba+woAUJ6nkiCQ+QBKmrZhIV8keZ+FCXx6kltviButqTRc7O5vX6Hcp52H5itXdQr1P0GfA7iBUHI3gFn5qWlTurL/AKcufxSfPxwA9ArLCTE7/+qsT1w/ZOY8B+AVY2bO2IKsjA29bdOpjDEzJwzAXCVzUaZbVjAApQAhABHLbEkrCrIytnW0T0EgbwAYCOAcnqc2QSDnANjRlXbf+FVi0BZ7yDUAFgGUIwA1ZZteBrAjTW0JDeM8WGcNWwfAPFxbJ8UoXLW/1EVV587MbXC4fU4uD0CoXw3rLJ/es/AdY2ZOtyUohgAAIABJREFUNUC/iNUVb5qz+LqRr876pNPqBn47rP1Cdw8utqYgLWxHZJNd1fBK39R1yrIA3UpBVoac9tiXHqeoDSTHnQAIAokFsALA8/DGMn2S/pdeo3IzbwNYpnExL8Abf6ogx5RCPh5OIrkALsUCgwoL6pq9SQzLHjKOAlkUZDwHedlQrWXmi5eUt7p0IwgkDMBngkDe4Hn6I8/TZmdi+6js6n0uPerLNqcdplvUG4OXyFqp1N3ffvmwZYV16IbsaylIvIb1xu82OMrdMU6AUw+7qD8HwOZ371ic19u2NOJ9jnE/mxx08BMgo29vG3Mqcve7M5KLrcbFwJixANHrFJZVSZqKn/fXDnoKHSwxLwikD7wVn571JbLmAqhupMKysTO2Pvh9tMIhM+cLlohIAGMMrOcii2SI+7cFAfXe++YCQL7z2J/27faGSuySKdtk1jEixxLKAAoNAAJQz7DsIZP/mZnXJRONBVkZX12ycN6U7ZVjbttQdvaPxsycqZ0t4eq3w7qjctQuAPinclRB4+2+smA3dcaoAD2DQ9Qd3F+bVtbbdpyu+GZUU3zXzBF4n74rAWDgbvUTSjd51MPRTQqRXIkFdU4sMDSUPW5t9tSqk2r0Npa4Ofl15QLDkqZtTdmmdAKspSAEoKII5vm2nFUfNngVBgytNaqTFFUAoGGkRQ6Z+3zB5+LdBCSa2Nmxo1+q7LYHWSnEM4mxsAAgtuXUBwhQz+1v3zIAmD5WxTpPKEm0gqwM62UvP7JtS/lZ40zzPhmY++R1e3rbplMFY2ZOMIB7WHJ5pkQ5XZi6cm21M3L2tidm5vr2/4V2lJgXBKIFIPsqUI0FsADAjwB28Dx9uzM23vJ1wmAFoTM22kIUHsqMYBFxlgRG5dttlinZb9KaD+U79D+7KfsYAA6gHoBcCmDvaF1NGkdo3HprWBmA4MEaSzoBjcpzBO8DEBzBudNrRIURgBYAAYiSI9JqU7bpGQAf5s7M7XRhgu/nPnn72KfewRFbwm0Aso2ZOflo4zNtDb8d1mpnpBMAymxJNn/7CNDrVAMIyAD1Hu/Bm1nf13fD81YKWWA4NwaKBxwa+Wh5tOc/KTe6vFqMXsezzQvdo5CjARYKkdwC4HosMDSJZaXXep3VBibCJ0rdFEEgCgCzAbzF89QlCGRCYxms5qgQVTEALA6Zu/fDPyuziBR6jRTuXjRk3YHNbdnuL/mpadEKqKPkEHENW6v4BV0cbhDg1MXmCboPIIRP/GUXcHlvm3MM+VVDrwTIIYs7ZA4a1+oM4BcTn3ktOFZX/AXHpE8QZaVOouwPkxNzPvpg9pvfNm7XnFZqUwSBpADYDuBeAEvgjVFd5UtC7RCmbFNwstJ+TgTnnrXTEcQ4KTsYCI337qUigO1aRv5qsLbWXulRLT7g0m39a0a+3Oj4VQB4gDRe1j/YZJhPmxk3HcBKgCoIAI7QQ26K+QCdf+Fn/SpDWM+buY7g53Nn5vqtB3zElnAHSzzxElXM8K26uYyZOVP8cVo7EcNaEFVsNaKPYbeqsZyDT0dsP4BneJ42W+EmwIlBYtChCKeobhrSEaCTeCvmHP907ptRvQLATzxPzQDeAfAzvNmiAICq17TTw8AtJSB7tA6WT7nR2mpMaXOE1HKRAEBAGDS7NE7O8L0Q0fZM5Hh4NQLLAHzRlrMKACoineGmzO4vnpOiKBNyqxjprnaeaX6wo+fRQW4jIEq2VjErbXf+3rabBwjgZW3JlHCWeI6qWcfnvW1LU3Y9fXWpMTPnY4DeNHPR3f/Lvuv1pk5IgHYwdN4nWrM75E6g70OHLX0jUoL3Fh8yD+ALsqZtBqa1ux9BIHcC8PA8XQxvCfp34YtL9U06tOmsmrJNCgBDglnP5DiF87ZDLq0SYJMK3VpS6NZCTaRyACs1RNo+Rl9TzRL61SvTj5hb69PnpHbYAcydmbvelG2aAhCeAsLfM/LXm7JNyRGc+36LxN1R5NYsADBn1EeDvjlLX7PitUvLvujoGAVZGXT0E+95Kh2xQIu/Se3Db4c1xbDXVGw1IjUsL7jJLgeAZQD2+dt3gJ5ByTot1c7wuLZbBmgv/5Z3hBqA05iZM7mR02oC8BmAuwC8wfN0A7yaxQCA8je1F4TXcd+KHK1SiOQ8LKjrsLMKAASk/uZ2XCb+bd/E3wyEpQP0DYCUoIVAe0EgiTxPi3ierhYEMoLn6fb2jq8kdEyK0noA0LxDZKLhKpUjRy+sdPhzLu1h09xInVYb+jAY+tfgzfsCzmqAdmPMzFECzHkSZb44UfVOh0RseS/v6Bm32EXde/BKIwVoJ8bMHPWQiC2vybTfzfDK6P0Wqq58dvUj//2jPcf75KgG8Dzd7dv0HwAuAIt9Sa8PtHb8g99Hk50Ofepht3YYAR0Tp3DezECtkUEUZkkBkRI5iBV3ukR2AQHdeG5w5f6F08v3d+KUO0TThKv690dF1acXGCru/a0ucrIEcpOHMjeutkT834iPBs8TKfMmgE9zZ+a2W67K5tFvATAdoFI7JklapDMxrP8AwNbysaWNt/tmYG71t98APceB2jQBQGpv23GKwQNQ+yrmqPsYdj8qCNN+43n6Gs/T7YJAzoZXDupYFhhSo8BlSyzqymI9FyXd4uzwslI95iBprM7KOFhKnkKjeFdTtonEKrTP6RlRHqWrnf/6pWXNSvgIArkbwHOCQIbzPN3fEWd11EeDwlyUY6Z4xVoupgx9cNCu3d2qMajco72LsbNqO1/zU3eOE+DUY1zcilvWlZ4THK4u70iRjB5l+f3zNqQ/9fbOLeVnDTdm5mgLsjLsvW3Tic49i68PWlF40SxAPzfv6BlxKcF7a/qF7L79+7lPftnBrp4GcK8gkDiep7UALq/Xmm6Oud9F9ylyaybnO4PiAJypZcKm2mXOp59NnCKI1aiybzjo0i0BsNEuc4f+npHf6cIprWHKNpFzgiuTyj0qY64jWA0gYajGfEWJWxUOqEYAYAEKU7ap/hAKwPlzXdSU3Jm5vwP4fe530Y+Ve1SP/uMwjACwiAF9dfrnKYX7XbpXABIEYHVrCgN2McinBU5eBvBtj8ew1rnDXABwxJ4QqHV88lINQBuoWd0xWlry9+Hx/vHKohRbjOetLLzwvJsyl+kpmNUFWXTNMa0XGNJdCvkGJcjlBETmJJyZdIuzU7OEnIhBNr3sCJ5rbZpEMrnMo46KVziyWnJWfXwLIALeogQdwkXZtPA6inG/qVM8ic4611Brt8oE5aemESW0l1HQPbJOyurOsQKcetQ4I67iGDfGxa9aAdzc2+a0SJkt8U4Af8CrVP9WL5tzwmLMzFGqWfssNXfeq3ZRzwJYC+C61Y/8V2jP8YJABgD4H4D7eZ7uBbAUwC4ATgBo7Kyask3qdH31ZWZJwe90BGkAjAGi+vl2UwD5EZx7cxhny91uN7wFIHfFtfs8XXGepmzTJADnA1gOoOAsffXZVolL2uEItgGIT1HZM6wSq68UVR4A8SvMkcfIj+5wNF0YJ03fHLNsv3B6+UEAt/jGHjFAbX13v1M3FCCv+Y5xmLJNU1pyWmO0xf2P2BMQqSl7ftP8//O7DHdnYlhji61G9A/ZxTUtSSYIZBOAPJ6nAbWAE5gRURvit1WMxVTjtwOBjH96256TAWNmzoUAvoN3ecndOHjcFw5Q7zRJAOYrGHfmx/l3BMFbrebYYPMFhnQKulLpIRoAoKA3kAXmzi1pLzBwWrAaimMzVB/8PpoAkc8ApKjEo1nQ9DBBILfAm+V6K8/TEgDz/Rk+jnHyN/+kAOMhbokiffTCyi65QbeEGOnmuUrlaAIye/TCyhNySTfAicuu6uFxLBF/f23W0sretqUN1nKMe7uCuLNSMpeFUTCrerF07AnHnMXXafbXps4DRl7tlLRGrcJ6eHJizuurijIWtiWlJAikPwCG5+keABYAwwH0AbDXV4FqlynbxCDblDpYY54hUua8PU49AAxbbw3zFXShpQD526i0r05QOnb9aQ3/IHdmbqtxp21x5zdxEWut4eEAEoxK+3WVovIcm8wlNmn2EAD8ZT0md9p9xK0UGQIZ3pCzLXpGHEEBo03mlMceTgEQCsBjYNyv18mKOwHSqpxX7szcbQBGm7JNj8OrisAQUDWASWghLjUhqGBiuT0OZ8WtrgP+rwOfwrH47bAmBR8cXGw1YmBYrqqZ3d8CKG1me4ATCI7xlAPAUUdURG/bciLSeCZ1XNzKYeX2uLlAah9f4DgAqgAIj38v0skAWF84AAFAKZh3ANzfQrA5T0DUAEBBZQKS0AVm9wegJiDHCF1bJHY+QMbEKJwP/37tvua0WeMAGOGNvfUr3jQ/NS19zgC6IKWAQmbo3KErDuX7009HkMM8S2QzJ4GjH3X3WAFOLYyZOQMA9Jcod0IUC2iNgqwMeuELT+fvqh4xHKBPAnjU30zrUwljZg4H4Bq9YtqrVk9wKMe4/xFl5QXVzqhfP5j9ZptL7T4VlA3walxfw/O0TBCI8e7CIVHINv0nUem4igGdCmhYgBh2OoKhIhIY0DUyyEvJSvvBAWrblpemH9nSXptN2SZmnL66H0foGX9YwutnRCcpiDxkr1NfAlAjQPo1LkJZ4G61WNSawRrzV2Gcp/SQU7u32KOZ4KDcq6BUAdBzAAKrzIEFlWIVzooyj/p9AH8NVFu37XHqk+D7jftzxp71pmzTN2h/EYEVAB4GqIoFJQlKZ4vFFfKOjrAA1P3DgWtHvdoJbWy/HdZ/KkdtA3DFxmZq0fM8PaH07AI0z6YjE9YCwOby8YGZqSb4nNWVAFQAyLrSKUTHWaiWs+bYRd0FAMMSUFAQodFhqwDiAKCoDyy3i3oAmOPdBhHHPrXuRoN3S1zwMxC9MeVRnhnRFQrUGsSCEN82U7aJUZOQ6w2sxz5ca361vq2vKp3I83QrvCVen2mPCkBz5KempVPQVca9HCcTil9GMNLgzp5M22PGK4g2wTPAvmbYD4dbjCsLEKA5RkX/+fTm8vGI1pb81tu2tIf86mE7fTNincq0PhXo/8h348PV5S+wJCZZooo4h6jJm5yYsyhIWTf/1VmftDWj+iCASTxPL+B56vn6d+Xsr6rj6N3ZpvtDWM80ioHj4V1BQ5FbLccoXC4dI31nk7mVkZxr+yhdXe4Ll5Q3u3JkyjapAcSnqi0jwzjPxI3WkGIRJC6EFSd5KEm1yd4S2+uOnRHFIVeDQ5rWZHm+DSgAot3pCD5Hy4i8XeYaaWQT336aA5AnJJB/frt2n7vx0aZsU1Lj9x1RG/hXYQC8mpHXFbi1HlO26WE0cXYnPvPaTU6pz4W+7+3Kzjxo+e2w2jzBbgCosMc1+4+rL+Hob/8BeoT6LPSAFuvx8ADV+C56SiB/PSV5+a2vzVpa0/+R78YzRPzWJWm0aFSatCArY70xM6dB3L/+ohw+/6OHal3hL/Ux7P5w1cNzGy7U6lBxQWgNKxOQFwH80J5Sqm2hdJNhMqGoDZG2hPy7+TInZfs6JXbGi5eUO4CGGtefwhunOsVXDrYz8ABR1deNNevIG6ZsU15XlfprgTsIJYxyj+6WbhwjwCnKYUufkVHaUtvf827tsazszkDBrILXAwE6UIHpVMOYmTMdUHxzxJ5IfB9HpkQVL7Q0oyoIJAReOcEl/y0clJ6mSbw+iJGC52QPeZ8jdLyHDhhAfU6iReKK+qhsJVpG+rzMo/4RINt+v3af3ZQ9JF1NpKtqJS5iuz34urOXDhwsgQyrk7hIwFtSuzG7m1SZqpUUXf0x+CAAMArAKLtc787RQgCJABgAEkC4YNbzbCTnHjzqo0G7XZSRghkxTkFkI6BU+0IC3K3FoDZm9rdxSg8laeutYUEAkgdrrCPdMvnvPpcu3Demc/B7Ey6w7c+MZyDdJCNlSiMnvFMPWn47rPH6gvgSqxFpYf+gmRjWDwGMBtDdkywBOsHEhF/pmuLzMTZWuBDI+LbtI04fCORDtOEJlTgp2Jdem7W0BgD2PTv9T2NmzhgAOxgifjxn8XWT6yVxmhOcnpjw24fLD17xkppzNjzNyk8EJ4ZSdkhNqLQrbI4ts6vsDq3lGAD/GG9y2gBvOT8NCX/FQZldAPnMV161luepKAhkOoDiLhpaACihIBBZIC+ZMOjGGaBN90UadFzYPZDIskH5uw91xxgBTl2MmTkhQFwygBd725b2UpCVsT7t0S+qGUZ22TzBl5+O4QCTnnu5DzDgPfxb+EQEwDSNUxUEQgBwPE898IZqvftNdYxOBHkp12GoP/ZmN61/SWkk57ZZZW7/Ppe+Us+IwxKUjqvqRIVtWPZgPcAkOanXLy3zHOefnkBQESARBNTJQtZIIC4KMsQus4oDLl0UgFgAMMsK/PvsA6CRI/nf72LC3JQZvcYSDgDJfVS28wgw+IBLVwkgiSAsqXHhmZ2OeuecwlvhFWqp9oyVABgZbEG8rnBViS1pnM+x79SDFuPvgQn6wkEAMDi8WcWbX+Ct/BDgBEavsBQBQLUzwu/vwamIMTOHUDC3A8TCEikLwHFLGAVZGYciNWXzZcpN/Ltswg5fCEGzvDZraY1M2a27qoY3BLwzlDxEQOSQWrb9qtXtYxiAhgS6co/yeQdl49L1NcteT86LBbAT3sos4Hmay/O03Vp6rWGfVB1DQPD3AOCpaxhpbwJxoxtngNijiiwiMkGOs2u6bYwApy4s8UyFd8JmeW/b0hEitBVchLpScTo6q7e/fcsgqzson4Goh1cL9ZjCJ6ZsU7op2/Rwxmd9zwewF8As36HLqkRFumAJj/m3t6aTsYRUiiq9Q2YnAbjSKnPnF7s1yRaZGySDSYLfUBGARUFks5qIfoVb/WsvpQB1A7g3RWW7ary+6h4tI04GMG6IxnzxBH3V9QAZB+DcGM6ZJYIRKRgNQONFykQd3x/QKGyAA+hMU7apcoU5smqNJfwXeP24dw65tJceFZX9ADoIQHKTKomlaln1Q5wr6QiVFZRSAko5xHNyCUs8kwD0Xff4nVMAMgnAPDTzW9oR/NdhPTpqM4DzN5WPO04TjufpCVcxJMDxvHnbkmpjZo5zb82Q1iSOTjuGRW58+p/KM88GcOuB5y5e3FK7o47o9YBMj9gTBqHN2ByyEcA1xswc5s++l5niobyVgHzIzDd3ul5zPYUfqAclQxVbaxBtIaivqGK4RE2kfSGs+Ci8q/VLAfzeVWPWU3ZUf1NfAL+cK/62N1gtoH1B+36Rn5pG1AhOl1XSIVknn/AJMwFOPBKCCv5XYk2WRFnZkYDBXqfI0mcdgOjetqOnMWbmRACXfMkxIjslOefu3wsv3o5GoVcPfh/9XwaRz8sAd9itoVmlfWsJwYvzlg582SYPYtyUaeLrdChO1AlvMioAUIDUMKA6GaS5hPMmEA5AkIcy8Pg/PwifvQSAEsArh1w6HHLpGvbmNZGpKhM1TY9trr8GVESSJMoUiyBCrMIpGlV2st1u+M4hs3HUWxnxFoDUZ4Ft66d0lVZXTCkpLMsYaAG5+ChkqIN25iFkcz6jrHjr8eGvFwOvn8PzVADaV+q2PfjtsDpEnQgAheZ+zca++WLkpEAc6wlPNQIxrA3wz74SVelIzYzXF9SWWI3vt9aWguHhdQJZeJOzeLRwUQ4MzS3fU2MyhKiq3rOYo8YBtYrKCM9bXVkXV29lxgCAXSvv9sav0psAknJZWNlfY/W14TxPK9BGZRZ/ePD7aDLhaNjEogiIu4LV03Nn5na3sPkEAjKMuNhZoxdWBu4vATqEMTNnHNDPV6cdv59k2fZ1AAb0thE9yd3vzkgmuPIHCqavKCvPW3zHu6sAQBDIfp6nlQBgl5n/o95kVwAgJR51CAvKEECmgMiAWtWMpHPLjEsCkQmoO4iVgmwSWyGCsSuIrDCwnpg6SVHkoYxNTSRNKOeJrxKV+9yUsamIFKVi5AS7xOwTwR7RMZIhQuEylrjV292UtRlYT3gk50opdmssLsqkUBBNa+fUfmgZA1TJDeGVRASQNUBtdYWxnsgNttC/AEhpaktCKOcJ+csatgmAFKtwTCzzqB/0OczHoGc8+60yl4IGRRviclFuki+JylDmUU9lQOdSitvhXYV3M6D5KTSoPO/QLdUqSR+/U+Yy3LIaAPapWMeT5yQt//mN2z5sqNooCOQ1Qy17vefpoFiFyPzcFfkZQCcc1jh9YWKpNYkWZE077gdDEMj98MYG6QHYOmFfgG4mUnMkKFhVc27TOOTTlQJz/6cBSsfErJ257rHZbS3hCADc3uQsEByrGHAMLkltBQCli9yYYraTQlXIVuNdh7d2meEAwqsV4QAQV6b85IHvow1BTOgiJUOLx+hq+wEYAmBVV45Xj1zLPWcsh2FPX/nPHnBWIca4PmUrFG4iMydFskyAE44ruioJpKdJDDoYXmGPjW+75anBqCc+CFazY3IJkfWguPBQ1kX1zupdABYKAkngeVpZ7NY8QIGvfVKDHoBM2T4zr9v+p6Zsk9bi4oakqS13l3nU6bWSom9d+xKrHAzokWiFK8oqsXsssuKgjhGZOIVzaJWoLK+WFE4WdLAExhe+QGJlX9ypNyQAMoCfv7nq0HHnZso2EQBpRqX9fofMXFXvrCohlQ7WWvcVujT3V0vKLetn7Ka+Eqw3+A793ai0Xz7ts77LAU0wQLhyj8YZS1T2wqoxy+xVE216dc30HbaEYQCBAxTJwfv3JgYVPPlnybmf7nnmcgpcfowt6X/pv1G6yW0E5HEAD2CBYXJXOK1+O6yxuuLUclt8S/Pqf8ErPB6Y/TjBIUQ+WusMD/yfAAyb9zEPhM0CyP8+mP3mj221r1cFiNGWfHLEHpdyXvIPtpYc/wJz/zgAeJT7hCjhwY/2Sbvv6VrzAa/gdbHAm20VlcHZFlmhmKirepkQvMXz1C9t1bYwZZvYa9YHzeZkoK/d06763J1hV2rqrSyU9T/YP+Wnpk1J251/UjgbAU4Y6vUiJZxk2fYRmgptqTVJY8zMIW0J4p/sGDNztED0MoDqMlK+fueKgdkZgoBinqd58EoOPglv0hW+v/rgT/USS/AjHMnnwB1zrCnbRFRECjWw4nkMoTdVelSjJZDQxsflN1EDaAcaGSSlzKMGgJEARtpkDvtcegDoBwBSi+EKhMC7msfD94BlyjaRAWrr+RoizeMQFCuCMRa4tYhXOCzRnOuFclH17paZuw407sWYmaMcMFB/tYtxXFsjsRJl3bcXuLVgPSHwWAa7PWYTJEeSusYbwnANgBqzbzrVZ4dYaO7/4R+P3PtJSyepcjNn4V//ssseCv12WHMrz9goUXZkc/t4nv4Fr9Ma4ASnwh63FcCg3rajt5mz+DqNhpv4s0dWmu2i/on2HleQlbH+mlfvn3nEnrDmt8JLpgDY0ULT3LNILv7DrgcFMJv94TIsMCzqqqUSAHCo5UsklpZaJPaxfU79lUoibXjj0tKXuqr/5rhwozzvP2uInoJCVaK8Lz81Lae7HMj81LSBBORlCgrivamfVLNjAU4Mzjd+5/q1YDqitcVryu0Jj55E4QDYVjE2B8B4dKLAx8nAPYuvN+gVF66weoJHAuTaKwZmrwBwEMBmeKto5gN4pvExHdEQbYwp25ROQAVvSAGoKdvUEGzqoiwqxO5TBSCgpQywN0bhdIZznvIDLu3XNpnbnaBwVDwUd8DC87R+NnQlvPZ5WNC1s7+NuzfPHjQGUI7f69QnMKAwsJ6dNZLyTipzP8TVnmtwS6qR+0vOHWvMzLmmj2HPRS5JnVBiTRbZoLyEMsbqPUdWguwJBlc1ya4Xo47WWvrmADh4RvRfyiBl3W6h6IJVBVkZtY10yVutgtUIgYKKBETpK4rTVvt24bfD6pbVMrxPqMfhk5RQA/DwPBX9HSNAj8ACSDRm5qSfTDfurubnQ5fOcctq9aTEn15aMvuNDonQfzbnf2uNmTlbAFwL4OXm2oyP/105tbwQhNQvRtJjnpQ7i+vZILXaTbQ1ofL+9yuTYi1eTb65XdF3SzzwfXSQwRP2AEPR7Q7k5nsjB2uCQtYTC+smXgFqDifZ7FiAE4NyW1w1AIRrjn7z97zbTrZ7Xp3vrwGnqMNqzMxRJgeP/sfmCUq+Lu3dfc/M/PELgEIQSExnV4oazaRuIaCeJKXjonCWvaFKUtUruPidhBfCul1uymy0y9w/oazbOUBtiyxwaX4vF9X7YhTOmuFa89EXLynvsCpL7szc9ekfp/1HBu6wy5xSAr5fYwkPZ0Gp7AnZKFoG/RHniU8gkq6fpS51NoD/rQKOKY1Vak20RmgqPAByFEF5caCY5L1pUzAKM+SYH7R1ANeXcw3so7Jjgy30aQDJQWl/3GHKzhQKsnKb1RhvkQV1620v6Ofp7WyWTSu/qn/Q2rsxrLG6oqQKe0xLaW9T4M1Enghgrb9jBOhejJk56QTyf2gXVKA4mTFm5iQB6scB/Lhk9huP+tNHmLpyebUzcv7w+R+9X+sKf6/p5+gSNRobvHH4FFQijeRYuoI9A50fDc3Vkmqn8vtSj/rtZKW9bPk1B7p1leOwW/OSJ5nVTCeiRChhKaino0/SxZlrxwOYAEBIyJrQ7HcvPzVNp9GFfEfcTJCDr71BK4Tuh+/GGQgHCNBRtleOqQSAXVXDu0qDuMcYHL4tdGfVCIyLWzkQyDjS2/Z0CQsM6QD4/X2dioJ4eRjwHQrN/ZIv7vvp5nOTl91Z36wLnNXxABXgSzaiIChstuQppQSojuDceqfM5FtkxS41keoGayzGclH1R7FbsyOEddeM1tWJIiW7Xru0rMvj9kd+OGKimnI317nCZMqIQ5RK5WiJyKCyUhKtAz2ieShE60ACqhwDYMxh4nFFasspgbyZgvktxbDH3cewzyEUTf1SolzB7meuaPjsTNmZEwB6FrxJaqKGiO8rGcrXSVxqhaiKqxBVkwHc4Y2ZJRTEln/2AAAgAElEQVSAy5RtmlKQ1bEZbL2d/QVAlt7ObmizcTvx22GN0pYNqHOFqlvYvRfAIwAO+9t/gB6Bpw1zfvS0XV5NCjr4W5HFSCgYv8NK9QpzQbUzArWusJvgla86xvnfVD6+7GJuDyQKO0vI0wCErgwHiC7nqgBgdkT0UIfMkjil866u6rs5TNmmICDoYnWitNGT4tiiOqi9w93f/vLwZYfbfU7FmWunAvjJt8QvFmeunZ2QNeEYGbH81DQOwGeMjesrRrivPePtI5/5dp1239MAXQNDJMiUBUEnZDF7iQhNOQEAlhFPjcQrr7O6BgCXckAj/q9gFueb5HzMIyveX5J3l3TjLzlBAHBhyle6WleY+FfpFBcAXNT3c32VI9LT2vtyW7x745EJbgAIT+Uy3ET0TbJRGsl6yjWEySlwGv5miScsTOEcK8vqH0sKbvpO5YmQBifn6IvMKa7SyjEelaacBMWt1tWaU1wW33tP3GrdgdqBKmNmDhuhKSfp3vfOXVUjxAT9IWZE9N/apu/zq4Y59temSRzjHqNiXReKMrfbJWlq43SH08M1FefvrjYdldUlfTSJNN5NnGA0pQAAkRKwdiNc5sH7RE/Y7vCgvSpNzC8x1S7D9yLj3pQenjfMSlmNzRF0FEBohNpyfqnEQav7cySAUP6T+0bYJRYOyjIAtI0mklkH5e50HLNWTu0qIqtdlGHgbeivb+Dy/W2H/Ff78Nth3Vk1fKMkc3HN7eN5ehjAc35bFaCnEHzCy0qASDgNl1dHzM9+usbVZ+DA0B1//frQw35roh629I31PZG2dIHrUpkiWFVcpeGRqk5fG4JAGHgLABTxPP2KlcjYoxyRS1nuBgDfvHtZSbdWLmMhPyCBiXRSdpoc7Q7DQe0dRCYVHexmAgUlBATU+8D0RnHm2rz6mdZNcyOJIk2zUZWvGwFgtunPA5+13l2AAG1zbvKPA38tmA4+8ZczgYt+6G17OsIfxVMFAFhTfP7RXjalq+ApKEtAQCnh+jjrIx7w9E+Hrni6ccOfDl1xzIHLDlzdofc1hbOgTXrPqwxFFaSg4M4Y2ZF8C4BbAMDsbXYJgA/sTY4/6oju0PtiawqKrSktvhdlJUS5oY4MSm1JMLsNlGPEXXJQbhUhUhy8N0ZQoI4lcrCsLSAKbUGqAki1A7ADAFc1ggWw0d6oEDeAAy6dpCSyE8ABADU6RizXMWLlYbf2LwA1w7V1fUVKCvMcwX8DqJkYVKUTKSn8yxp2OHdmnrtp3Cz88A2K411hCSUqHIl28zHeMuCdxm+HVZSVFL4Mvab4Ylj1AMTuyk4O0Hl8We4zAXzGEs8LB5675LSatTJm5kwEIh4BKPbUmEZ0Mo5XAIgTLVzgZ0SuO3uguQhmnewwdM7selgAVwLYA+CrIAurejgqhAFBEEC71Vl94PvoNJaEPxbKuv5efd3ejfnPpQUDgPKAtqNP0ssBPEBBFT6n9ZhyrtrVoY8zdnaEc5hlw4gvit/s0pMIcNpSZkuo8v096UIC8G8Ma0irrU4eBPjWnT3gsEFOA7zSTb+Mil5ns3n0tfnVw/YAwOjoP9MtnuCq3dVD9wLAmTFrz6p1hVbsrRmyHwDGxPwxvtoZUbavdvABABgT+8eEKkdU8f7atEMAcGbIobMP157lrOaqdbKt38EzQw4llytdhw7VDTjMMW52VPRf48tsCQcKzf2KlYyTGxm9YVypNXHfYUvfUjVrVw6P2pheYk3aW2TpU6blLOqhkVvGFFmMe0qsxiN6RZ1mSMS2Mw+b++SX2pIqgpU1ukHh/4wqMPfbecSWcNSgqtanhe0442DdgLwKe9wwgF7gndygMkBe1ynMj+U9da0VAEzZT/3rLBJ4CHDB+YbKjRUeZdIWe0gIgHsBOsM3OSID+GiUrvYXFZGPrLOG7QJQu/WGnZ7O/FN8mqx+qy4AgMLDVAOA2sl0STVFoBMOa4y2OKnGFd5SSEAIvIL09wJ41d8xAnQ/kxJ/WiYUTcXI6A3jvQ+XpxVXw6ufCnQyYahe4gotBKXHeGzBQcQBUqt8qzMGCwIZD2Abz1ObIJDzAZixwED+0miTflfqfa3Ie6Zs06HuqjS11hI210MZDNOanwCAtN355vzUtBrK0D4d6Scha8L64sy1synomxSUA0Cr+n3g2i9MJNG3p17NgH2CcvLXYoLr6jY7CxCgneyoHH0UAHZXDy3tbVs6yuiYtfKmIxMwLHJjOpDxZW/b0wVsBiBXM5p9tzoeNG6lA+rrzT/99X+fbXL/aioZmLGwc++bMv2FY99f1mT/FU3eN70tXdvk/fXNjuLNuCeT4I0h9QD4YudT1zQk+rbiLB4CAFO26W2AXIF/J0feXXJ5UZff6/1VXagnukJRAQAhdVyXXWd+O6zhmop+LkndkgiZBcD9CCRcnfAsmf2GbdBjX5gL6pqvWHYqkxq2o9/u6qEAqIQuSIJqrfxcvMXtgQJQy+JGf/sXBGIEsBrA8wAe43laBwC2jfr+v2q1ukYlnrstHtmUbTIC3AwA778y/cjP9dvFULcKINfnp6Zl1ydCFWeubdA2bCmhKiFrwuLizLV5AJbKjFNrZT5fGPJawixKaH9QrCEic/3ohZWn3XczQPehYNzEIyvBEU9namX2CjG6khoAYIgc09u2dBEpBIQJlx3PbaED96K9WegnMW1NbgCtO4tdMfvZQ5w4Maz51cM2UkqaveB9UlZtPM0EOFGwi/qtdlHfXLrkKU2dKxRKxul2y6oF6Oab5ED1vjMgAY4gz66OSk0LAlHxPHXxPC0QBHI1gF8b768NkWYanA0JJCK6Ue4pTuFcWupRUYA0aNXmp6als1D4qn1hZX5q2pSgS94FvJW1VACcxZlrp7TitK4venjlK4ysfi387bGSbC5LBQApwvOK6c8DruaOCRDAXyYn5Qz4tWA6JiT8Pgq45LvetqcjLDtw9REA8raKsadElTeLXhobZGXh4ej+rqo3fzLQ2XPt7OxnT1CQ7BKNhSpURHrOiQJeaPuItvH7CVOmLKFgWtRYFQQSKggkUKP+pIAeIJD797YVPU2ZLTHSLatXF2RNe667n+gT2ErjYRqBoPut5pbavHH7qvQ3bl/18Bu3r0qv3+YLATgoCMQEADxPv+F5eoxObMRRdqBECJSQZADzAEzpjqfuq780TizzqMaZNJZ/cmfmNo7/4wkI9cWhqqVQz/XWqD8ECqqG14lVwjsb0CzbL01UHrXMnUKpDEXsGaxP01Vyp9ofEgTS4VIyAQK0RokluRIASq2JRb1tS0fxVbcy4xSJYbVr5csAoDTOXdbbtgToWgpSXG4KSjUOprKr+vTbYY3SlibpFJbWfky2AnjF3/4D9ByjY/6Mo2Ai7148I7a3bekpLnzxGS1Ah8D7Pe12+riPiiJYySfjchw+J3U1gKcBuvqtu5adDQAHV7waXLb1DkvRn/MGNtvxAkO6ys1OL+I49HWJJPfQ4Q4vES28alr6wqumPbzwqmnN2lbPTkfQ/QAs0YrjYkoFeJd/ZDAc0fSdcae+4mylrzKzCMCNZmZ888b1Dc5PTfuvcpduv3qF62LRvB9c3BneZE4C0Tm2bigAU0fOJUCAtsirGlkNAHtqTCelk6RX1LEphr2tXqsnC2HVnEUm1JJ8s/Ngb9sSoGvheUoJiDPIynZZDKvfDmuoqsqoV5hDW2nyOIBsf/sP0HM4Ra0AANsrzjT2riU9R4K+4AqAcOmxq93dPtgCAx9BrUojylkAK5tzWllV9Q0AVQFgAKKSJZXw5p0/7XMcHfJD9d7L+5uLz/6o8cxrPSJLL6KgbImCQ4IoAa3MZDaHz0ldBeApACtbclpHfzRoIoCLKEjWy9OPHGq8L213/nr7+Jo7XBPVO3RTnypRJUyGa/+vsjP384WU0nkAjgkHyE9Ni/nnP0k/EhtbA+AlAnLQk+C8ngvuP4c1JBA2atBrhJJJnn6OZF+ZZwgCUXTkvAIEaAkV62AAQMk6u6/mZjei4eyyTJkuEhvpXRQiiWcoyettOwJ0Gy7q/V3rEvx2WPfWDN5SYY/d09J+nqdLeZ6u9Lf/AD1H7tEzVgBAkaXPaTPDurNqeCIAaBXWX9tq21lcSnkGIQDjzYmqT4g6BlZl9kk0UAlElIMT/wClrAbe7HmmpeMsenGsDIISjkOCR+ywli5h5LvgLaPM+hzm48Z48PtoEs65f1QTyYYWVD/I2aOvDo1+ajijMoTJ1oob3Hnf/CYeEOZW775+Zu3Km4PyU9Me/ueC5Dm7TAOXAChU7NVOExNdZe7+9gvTdufzQ1cc+oSynm8BQDH56nODLnkXPE8rAcCnhrBLEEjfjpxbgADNwSf+0h8AxsWtOqO3bfGHSkfsP4XmfiW9bUdXIDF0qIejJ6O8WIB24FbIwdVh0jld1Z/fDisFw7QRwxomCCTB3/4D9CgHACBYWTO0tw3pKUqsxiQANSsPX9RlZeNawsPRzQDgS3U/LiHqjdtXZbjNxhhNRN5agDxOiDwh8axnY0AVV3iLEdBmj8MCAxdSx/Ut0DDlIiEoD5GWd6R61pLHh79FZVzbaAyGU7uOWyZdZQ7/T4lHYxiitXybOzPXBni1lgWB3PDHSvUFxZlrF0TufOB8mbNaao1f/Sdp0WUfA5jmGmxdrtijHchY2F8o6DOKQ5pXIJIZAJYQkIHDlhUmDFtW2KA0wMjKRAoZnDPaBGClT2UAACoBHARwqoilB+hFiiwpFQBQbEk+WSsx1uEUiGEtWKKOZWUSVh0mBve2LQG6B0YmZrWTdFkJYb8d1ghNeaJBWd1aUtW7AH7xt/8APUdBVkadTmGmxuADTYXmTlk44hkD0K2+JIZuRW9nVwPAepJaC2BKY6cy+8ln9AB9TaEttxknPZA4+OopC+98c+pfPE/LZ789eT2nrvnGpxN73uy3Jzd1Ri8jIEk/BWmzAeCIluS016aFV02bW7034XZthOWIQu+cptC63gUjW0Sn6omFV01rqGBnyjaxLso+BeDAZlvILY264FhXyIsx257+CcB8AvKxwhEfY7rtrRUAkLY7Xxr+TdFFVEE/JSCEeDW3KNVKr6ftzr89bXf+vmbM4uFL3kKjGWWep1t5np7P87ROEAgnCOS0ebAK0PXsqhpeAwD7agd32Q9pT2IM3hcTrKzt19t2dJaEYmUCAOhszDe9bUuA7oGTSKXOzpZ3VX9+O6zBytr4YFVdRCtN3oQ3jjXASQBLpIMH6wbYe9uOnmDO4ut1AIaOiPq7R6W81rhH7mjsrAoCIQzrWg6QPvrYTQ8zrGcmz9NjYmpFZ/ifvpe7Gm8vWKImDrW8SGJo8Xva0P0AsNUesqItGz59cQB5846JnwL4H4Av7UcNxnveX/nTPUtW3AaZ5QGEMZy4+pMXBsYDwFCN+Sl4E58eez05TyMI5FFBIMp+v6wZkbzmM05bNQL2sK3PA7gxIWvCcd8fxsMsAuAAIBIQJ2PjWhM7FwhIvT5XS9Jc8wD87dOkDRCgw+gVZg4AGCJd4RVxP7nQcHbRLSmVbbc8seEkb4iP3sZ2+ypXgN6Bgrp8ajFdgt8O68G6gTuKLMbtLe3nebqK5+lJpXF3OmN2h260eoJbewA5ZVhXOskkUgVEmVvWE+OVxLkTAECjMjeoaggCUTtrU1ZYSs6aqI3I3X/tAy+9zvN0TdNjFdpyEQC0EblxjbeH1rCXaJxMRGWk5w+GoQMAKgJoVaZn4VXTxlfuTD7gqA6+RmWw/QDg2rlfLG/QOZ37xfKtqhDrTbLEDKjeF7fthWsy5luOhN4frXA6hmvrvgSQrq4Z/FTSmo83AljHSDqLPWJTxoAH52QmZE1odqbaV0RgCnxyW/VFBZojIWvCepGrc0mcuRhNErUa8TqAOTxPC1o71wABWiJOXzgVAGTKXAtg5cnmtOZXD1vllLSsMTOHtN36xMWil6ZQUArglNCUDXA8TpXcz6WSz2lJHaejdKbSBwuQFivQCAIJEQSSJgjkpL6oThcUjKsQoEkzF92j6W1bupujjpghAJB7dNRXPTEeK8ENAFpNbUNSW0XuDa6SjQ/0p5S47UdNfEvH6mP/jgMAddieY+SdDGbuJgp6lFByRzTnnKEmEgFwZkv9+LL/V4oOVQpAJVed9vm5Xyw/7vq96x3ha11U7Rdusy6SkTF/3Po4xcRqrPj4isNyv1/WmOP/XiQr7cnDKCgD4NaBczN/auv803bnr0/bnf9ca84qAOx9cWEEJxq09ogteS0VGeB5Wsnz9F0AEATSXxDIrLbGDxCgMcWWFIM3ZpsQtJDMeIJTC+9vt/7/2TvvwKrK+/+/nnN39iIEEuCytyxXmIdEcQRXHThrVbQodZW236BSI60ardTSilKttbaOYqu/qsRNchAFByAKqAwhQCbZ+87z/P64NxiRrEvCJcl98UfuufcZnxxyz/mc5/l83p/2Gp7MeIzyLKdF6mTVNAXblhDdQFZ0qtWpWCxOJYJW1HE6S+CyVtay5Hjr4cQ2mtyGbxuzyyQNQnQfqQPWJ4BQwk21M4NtS3cTY6lIA1mHL5Gn27GWxlQA4DLv1zSRqWki2d2YuMxROXoQ0njP4tVprWb8NlWM/RSgrnBGTfN7tSvCpwIXCMQTZ4UPn1DotiU6pMEArJv4/MTWLgoqvpszICQItbU5G0rjvpRIBEIoumB4o5jll5WaC0qzFJAOdGmWdVjF6RMAIkvmHlOJ4BjcBTykaSK+K+0I0btp9ES8ii/0RNKNVeG6i8n9Pk0BOHvIGz06jjWm2lBl9IhQOEDvRRX4/tFFD4YBO6zhxoZ+UZbqfm00eR24kiPJ0SFOZioc/d4G+KxkVq+vLBRhqrvQHrXXm5+dobff+vjZrQ+yAShuQwlwX8Wui++rOTjnPqPtsCtu1KtPttXXUT3yIIC7YcBVzTqsbhMv6kJKh0VfjS9RCdq/KGi++3MrigPAigXz01YsyLgvZljxIACJBIG7rjDhGlWVbkD4Lz66QHTHjb7ZAe5oMYc7gVRVlRXgiwnuYntC9ELyszM2CTwvgiTeenhBT6tbbzE66gDqXZE9t5JkVrQQiOFGr2g1rDBEj0fDVzgGieyS+4Ux0I4F9fZdQEVrn6uq3AnsDHT8ECeWnRVTNgGUNyX1ai1We2aOSTDYNDDiUIcz6o+XiiHV6RyCw8RM+PqDvz44onzEA4DJ02RzVe6+bApt1IRWTHVjdHckIK8Gcelrtz9x2SXxhuE10d5PYu5uKOX5iRq+SlIWkDoI7VjjLFmzdtMfrz7XbbS5yt31YZctWbN200uPjgqvLeh3VkNp7BSD1XkjmAcBsupAoqgOc2PSBR9OqdAPxzqrpj9zoyHKfNEdBlf0IYFhNZDX2rZ9oDTFfLXQ3GBvGPK7jMMdaa+q0oM//k3TxK1AqqaJm/zOdYgQrZI++O3PPjh4wXUT+22ZCTeckFj2ruLT4jkfAWwqnttjt9KLBrhGDiw2RzXavGUnNPM1xIkjq2ZT1ePhz8bWGBdVx3hWx97VeNz3i+OJYTXi956PhaaJCE0TkzRNhB/HHCFOHCUCvSneeviMYBvSzYyVKObC+iGvn4jJNE1ccsgQkw0w0XNohKgPXy59xQAAodDONonBXHdFi7amSMPhJQJhjKkx/gxg+/XbN5mFfo4RXQ4yNZW2VpZ1xYL5/aTXaPY6TRuBmY9fe86HpV8Oq28ojf0fyGXSY/CvrAshvQpeo0QgOBzrNACqpXbML42uuMTalLfeSMme9VBXO6sApsaURLetsEPO6jGIBXpF9Z8Q3U+4qe5ZRXiaPipM74mJptX+nz1Wi1VXOBugKtYberjsxTSG638GsDoMtV0x3nHIWlUl9Q8rTG6jyUxgGzAp0DlCnDjyszNkYlixiLOWzQu2Ld3J0Kjd8wFsxoZu24patSg3ddWidUv9W/hvVR0adRhgmrdKnC+2GwHR1tZ8SzxN8d/4XkmvQPeOD39nlkR+TFbN7uY2W366c32s0b2h0G0Ln/j8xCPlJl/6wyjx3LLJM1csmH8ryK8BdLfpMuBR3WNMjB9TQMr0r9dOvC53pe2cLyM9ikRHIgXe6ki31+Y0IHTcZt20Prws9Vop3Pu8lvK7u/Rk+SnI3BBpdMXFWmvH/iOQ/qoqHwJ+oqrSrWkiUtNEyHkN0Sorb36xSZfG9zy6Ob2nZdvPGLjOCnDmAG1OsG0JlJRCsxMgqtbwWrBtCdF9JN/S9A1QYnMo9q4YL+CQAKuxKSbKUt3qCivwBXAZsLuNNiFOIhwe2+eVjoSB7bfsuURZqhdYDE3MG/L6Lriiy8f3OalSA8wg2fnvdYWRSl3cc1xEox4HSARgsFYWeh3xlx+jGMAPkLrFC+gJxv3Pzo76600DTLsNEs4gKzq1paZrmcfyZL8q879nfRu9ZsVb878GBhutQxZ4HBa/Bp7vnjzw9G89xVtGDpl6yzsWgJzqxMObqoc8WiONStJppUWzD9k+CSuKeay/0XmrIiOum1Yjfv7A4UcGAKcIafrpKTf/vbtWRCb7jdwS6ACqKr3+ONb/AjGaJqarqgzF0Ic4JlHmqg21rtiL1JS3VcjIC7Y9HcVqbCoHaHBHdJm+ZRAYBTgj6w3HKiASohehC7ldCqYb2m/aLgGvsB5uHHhgT9X4VjP8VFWWqqp8VVVlqJxiD6HGFfeZW7ck2zNzjidU5KRmZ/kUl1HxfLny5he7y5FRAaPfQZQSWV5idAub0pzkrwACryN+Y3vOKoApvGiWMDhLFyQsGTXAvLv5Oy84KpTginXJxvM3JRFVFXYpvoIdF0cMqDQMnr0da1zdRFtCzTuK0eOVumKeess7FcDGfKftb9sao0pqvKbbgL+XxLvGLHv8o0uXrFm7aWh4/R6AORWK22Mr+afXVFMFvNyZE7Eud3jqutzh96zLHd6unEndgPfvAKhP/HBXZ+Y4GlWVEvgTsDLkrIZoi5nJ6zYDNHrCbwm2LZ1h3cH5+wG2l59aFGxbAqXR5j3bbZRlZNWEvqO9nIp4T7iQjMx/znrcCd3dGcNq0jRxqqaJpOOYI8QJJNpcWQJYT+v/0ahg29Id2DNzZnikaVKDO/LbbpxGA+EEPCAcBclb3vtnmM1gSvikxOdnHtHXb1e/FEAxOk5PCNs5AJ+D6sFXMepIKIGmCSNAmNN4b3MfiZQVUc6Xh5+79ZR+4w8mLn5q/Y6m8uh+ui7KizePOvP2AxNcT5UO+e+KkmHTyz1mZkZUPrr9+h0Lt1+/va55jPKvh7wBEF183q3mBntYQ79PXknJntXWjsoP8Dmpcj3IB0Fq63KHp2qaiNI0cYWmiR8l9llqR/XzGmubxvzy3uMWEVdV+baqypcANE3M1DQx5njHDNH7ePLnz61X8BR/VjJrSg8rHtCEL5yox8awCinGNIR7e+3CSIjvMbnFfxUpGFhkGnu8YwUua2WqS0iOODC4jSYxwOf4wgJC9ABOTdpoBIixVpwdbFu6Gt8NSa4DTCB/0l03KP+q6ZHKTjnuZItA16sYvgUg1rzPARCe+EX7sVtZ0Sa9doCpv6eUijj3emB287hk1WzSNHEOUPavB8ctB8ZKgdSReBUpPhlfeePtBybEqqos8xUNkNPQlf4Iff3kOln+tSPyFyA+9Ehl9FOXFv7fMWZPAhgeOWV2vbta5m7a9UJHz8G63OECuA8w+Veajfgc7tHAGuBUAE0Tp2uaKNc0oZobhiQIafhE08QDmiYG+z8P1zTRX9NEQNcpvzP/LPB0SPIqxNHYM3NSdQz9QIymB1W8ys/OkDZjPWPjvkwPti0BkRVttDqEIbLO8O9gmxKi+4mpMa4FMLuVie21bY+AHVaj4g6LMle3JdhdDVwI9CjJkL5MUf2gtwE+LkzvjZmbKkeE8zHQjdVtFq9O27R4ddrDi1enbapzxcyUKBudJWf0jzIUl8YrhRajtcLxs+VL2syarH48PLzBG/uGW9pMbsXwQfwdjaqm1uZrau00Ta1trnDzje5R1tYcTLzPHNHk2Dih4l9fjKrm3TNKKYt1mq3C+8bE5ycubjJ7L/at6wp0KUyyNCp2cljNi8B526/ffsxtRaPNeVGSbRhxlgF8U/OJ7tSbZnXkd8/NjQsHxyfA+f5KeB7/irMGbAcmAs0laGuA/1irJlZJ5Fhn5P4SfOEMzdeVc4ES4BQATRNzNU28qmlioP94iKaJNE0TxyxO4pe9ygCu9ocKhAhxhPHxW28XRxQ7elbFK5PidjS4I3vqQ9gQgTAZdLEj2IaEOCF8J5ENTVb9vOMdKOCkqxpnXFmNM25ja5/7tRBDzmoP4pvKSV8D3kZPxKBg29INaPj0Sq34tta17p7wxlW3jYaMqSm6dwWwxBZx4JWqhkFXxBuLnEcnTR0hKzrVq8gLzBbxi1pvfCRAxeDy5hCGSmAC0B9AVeXBFQvmfwFcGzeycNneQZ6PgctBmgUIlxQO4IncaYf1cz7tj6KDrki8Brn85csPZrVlu9Hm1CdYZtDgria/7isXHThf63KHJ0CUBobx4HgZrE8AcwAtPe275t/1yE1KVeUu4NYdT91xs0AoHlvxDsDM93ET24Bf8H1FslhgDL7/R4BLgMeBBMCpaWIRvupXp6mqrNM0MROYCjzpX2FdBeSqqvzvkdOdlZWKz1HRsrKyepSAfIjjo19Y6buGSu8VHikk3VMIo9uodcXuqXXFBioBF1QO93NnJJaZqInylIWkPPoAWTV6/WMRXuC4d24DdlhpJ4YVQNPEqUC9qsrujBkM0UXkZ2e4R97zWmm0pWpGsG3pavKzMzbZM3MuBt4B8eSJqG7jkca7ARbEfnYWtWkMU7588zPPDVdMNOdES+SG2hXheyMaDB8YdOFqCPMO9Bjk5CgMIxQdg61J4YvwlGIqGRA1eP08AFWVTk0TY5tXC/949Xn9wXA/8PYNv//isRuAic9PTAehSmI9SmsAACAASURBVNC+un7HponPTxzfFN/wr3fPKJ2SVGGlJN7pLYt1OtuzPdk9OzE+eiB7HB+u15FLl6xZ2+b5WpdrnwSG/weGgdC0ND2tKNv/UasPtc3YKqYNAvCaq97xr4ri/32/w+dkNh+/BrQMpXgZ+BKfIw9QhM/JrfcfXwjcDvwFsAG3ArdqmlBUVcoXXjjjQTgn018pzJmVlZUeclq7F/+2uwpowaowpWnCoqrSqR06bzfwjP/tf/awilfV9FDdYSE5HaA6xru3R/4CITqNyS3esjjFBWRFK2TVBFxhMmCH1WJoikoKLxzWTrPXgHXADYHOE+LEMihyv8XptZ0WbDu6iQ9AykGR+09IssLHhWkxRuGutpSNCye8qD7Osm+OFzNxxgIAQ0S9YbQiSZZIbE2KUfpWfxEIJNJbe/i0nSCTyndeM5VLfGO23NqOGFCp1RUmRCDFEW1Uf+GATS2Od058fuLi+ljXurJYl4kOaL8WZG4QIyKnnOPwNLhGWmefM3fNvW06uLm5/TIh4iGQ5SBmp6cVfdaZ82SpH5YCHI45cMUXnemnqrIUKG1x/AbwRosmmUC2/5w1apqYD4xUVSkfeeSONK93xkJQmsOimreEe5LTclJiz8wxAAPPSFo/Vwg59ZNitQoYGmspOwcSkpol1kYsfc1tVLyKwxtWD2BWHGFCSMXptfmODY4wwffHFoMjHCROr63Bd9wUDvzgWCKky2tt9B9HSITe8hiEFLyiuN953Q0GfyiJ0IGhF67IchuFZ9vWw6lvAHvyszOaBfpPOoZG7R5c747qkQnN/cpNtRJZLQXHpQgSoudgdSrvA1cCIzgOqdOAHVZF6OZIc017N/6rgZCsVQ+irCnp7XpX1Pxg29Ed5GdneMcve5lYS8Wp3T2X76ZtOivaK953N0ZdCvx+MwuGAnil4gVcBinSNbV2BvAw0F/VokZLZK5EmgTCddg90g2i4Lr7shqOHn/FleefhkwYE20v/WzhI5vbvPBvv377Jt/Kq29lq7VqWM1UDfrvr2IPXRa1y/NWfnr2w606q365qnsh8jxw1wsazk9Lq9zc3rk5GincZyCNWwdlz+7SOFNVlTrfr76iqjIH4LHHbpnncCS+I6WhOQbQSwcc+RA+/EL7/acmbpwRbqqfs6FwXgkwtJ+teJZXGoZCPwHC9GlJs669lCCKmjxh5hbDyHBTQ35K5IGynRVTPgcYHLVvnNngCvu6YvJmgCFR300wCo/5m8pJW33He09RhK58W3nKNgB71N5JALuqJn4JMDR6zxRdKp7dVRO2AwyL3j3Voxtde6rH7wAYHvPtqR7d5Io2Vw/4umKSp8ETMcHvPAuQZ2wvmxYjUebjSxhk/LJ/ey2GpoJKR+J6YM+s5PdinV5r7mclsz/Mz86oI7hUNLjD+wfZhkAZJRC77Dc4QnHlfQSnWd9hcSlUxnouioM/BDpOwA5rkye8YUf5tI/baqOq8qNAxw8RHOpcMduAa+2ZOXH52RmV7XboYTR5wvbvrJjS7U/2MwZ+cOnHRWfFz/U6nWBVgL2lDVOXAmyoX2iojDI+NnvZmk1oogZwAB6yajaJrOg0/I6lw8JLRllRdfTYKxbMV0D5M1BaXxTfobigo1de28JWMvO2Rk8dhdbc91prsy536EwQ630lY4UXzBelpR3qtLP61TM3Rcdy3biGxA3lPhGE7qe+PulUEMLvrHiBD4AHQuEAPvwOafzEhM2nxlvLzt5YNLfcpVsHxVkPpxqFdxwk6SCsWw9Pb9mt1Om11SVHHCipccau8UrT3lGxO+qHx+w6/FnxrA1bsm5w+sMB/EoduGtccdd/+evrWpzzjKMs6bpjfwyz0Z9bwdG2gDj/JyP/9c1X5dPG7Kkan2RSXGOGRe+6/mDdMC8+1Y+fbiicB/BLX/+1hwdH7rO6dMunJQ0peRZD0/7BkfuG7a8dafXo5re7O7xgf+2oTcC47pyju3Ab9TNdZrklVLO971A8wP3N4INmvAZ5CcFwWOlYDOspQISqynbj2EKcHAwIP1Re3DCIqYkbZ0BGr0ua06WxhBOgX9jkCb9BoDM+7NCFuu5u9DgSUvB/33RplLsbzoqbDaiq/Br4+khHXyLWJgB5x/8G2+K/+ZFzHW0vebAmP+lM4Ia7/vVul9RobqYgc8McK0n2rTUfUHao38rWW3qWg7l5O10CZwKdrhYUfeDSq4U0YGoc+FVABgeEkgc0gTSDcNGLndXWYkbtmTnRo2J3npIcceD8LaWpDbWu2H7R5sppNlPjNMFAXaKEbS//wUZEZZMnrGJw5P6yRk/4G/XuqJ2DIvdVj4/fVltQZ9fW/nrZMVYcf+hA+uPIj6z0n+CY0V8CF2uaOF9VZd2xbckAaFEM55LmOGzOe/ShqMGR++ftqJgSW1g/JCHM2DAZOLe8KfFU4Gyn18ae6vHNzX9lz8xJ7+bfrwaItGfmGPKzM3qM+H7Vn8KjYj3G8KpYd1PIYe072G9w1HmXR32bUG48rp2JgB1Wg/BY7NF7Rvz4qfYHLAeGApMCnSfEiWVSv8/rihsGEWmuPYteqPIQaylXPLqp2wsjbD2cGh2B+ytX+dhxYf125HkcCRq+lVQzGFzO6uEvttV/1aLcGIhSGssm/kD/9C8L06J0T+yvrHF1dY7KyH92td1eU80Kr9PSuK/uSx3aijEzOfzJ/J5AM6wLMjekGohcCWCpH7GwIHPDSynZs7rdicnKytqUlZWVDkKlF6sD2DNzUgW6JhFGEProe/+zJd5afkpp40APGCN3V41nd9URJ6uuyRNenBheXO3yWt6vdPTb2s9WXDat/0ZnWdMA7dW7H+yS0C6/ExeM830I2A8cCa/pjC1v/+aeWnwlf3+EPTMnymJoeszptS70rdx3fzz0tP4bR20pnc4Fw/6dAhkHumueria22jgUILHM9Pdg2xLixGLQhU/uMCtakFUTUDhIwA6rBEOEqS6qnWb3Hs8cIU48JQ3J6wDWF5zTIyVT2iM54mB8Qf2Qgd05hz0zZx5w5jiHbT0YjI1lk7IWr07btGpR7pEVnQ6UZR0OoHvCvmn5pqsu7AnAaApvemTJmrUBZ1sei92PrHw0zD112gHPR5hiqorvWr3+mCs363KHG0CcCryPb1W1pWxVZ1Al0ih+WFzghDgzfie1VzqqAPbMHLPF0PSE02trjhtVnF7rcJupoT7BVvpRaWPyxnBTbdHM5HVKtTNu/afFcwp2P/STY9xEFp5Qu7saTRNGVZUeVZWvAK90xxz52Rm19syc54BrORJi0L3x0G7dVABQ3DCovXvwyUbzYkEo4aqP0WjzHgxrMiQWJDvHpcDOQMYI2JnUpVF+WXZ6ezGsARkVInj8b8nyGntmTjGI4cG2pTvYUz3ubafX0m21w/2rWm9JFDHAK+Z4kZUGxCdwpApWh5ykyOSPz68rnIEtfmcppAGwYsH8WcB1gGwqj7l3xYL5ue3JTXWUgswNqTam/FIiGWw8g/KwXQdbayuou0YS2Q+8L6Sn5Qe8yuuM2HfAXD9USCSih+lgnsyk/m71KBj0L6fXNhWk/6FDuEC5cF3mr476e7nqxBt4gtA0MQh4X9PEL1RVftCdc53ocIevyk77BGBz6YyF9sycf/cUSa6KOPeV8ZUmaiO9+3qapx3i+KiM85SFFRoweMUFBOiwBlTpyp6Zo+DLWGgzdkbTxAhNExcGMkeI4BFprq6Ksx6eGWw7ugOn11oEwmrPzAnrpimukAiDUYLdbRBV0YXuxavTAlkJPQ0gvN/2IxIgxjDHz/wvW247dhWqQBgEAkUoDPamfddaQ4nhZpAIatcdz4Tm+qGDBQKvuepZIP1EhAP0dq5c+evMamfctwJ9HHA5iFkglgHdHVN5MqIDxfiqpXU7+dkZm/KzMx4+MedZpvhfLKYHlZW1NinjPIrujaozTAi2LSFOLGGNvlK8A0rMhkDHCGiFdcbAdaaPi9IZE/fVyHZiWK8ClmuaMLUUBA9xcjM4cp/lUN1Qe7Dt6A6GRO01HagdwRlJ64dDxvauHNuemWMEzgGB3aPoZoTiMDc+F8hYdYUzKkAWLbjr6SOxg6YwZ4On0QpIvasr80h0zafuA7rU8eqeY8brrcsdLsDWH+SHaWmVhYHOV5C5QRGIm4H19uUX9ex955MAe2aOCXgY1CVJYQW1c1LeuWj1or9r/o/7lKOqaUIBpKrKQk0Tab2xLG9SWOFVJY0p4Csz3TM0hLOiU8MxjML3wL2OrOj0Y1b7C9EribuzoZys6H3A5EDHCGiF1WpsMgPYjA3trVL9HV9pxC6NtQvRveytHvtyrSvWYM/MsQXblq5mWPTucIC91WOXdvWqxMSELS8AY4FlZzhM23RkXUrZqN8GONwIEHtbvtFUHl3geyV+B6R3VTgAQNWwF+sFgnLDDj6seEH/6PBr/2ul6RgQI0FZczzz1aSsvRsY5jVVB+TQh/iexX/92Znx1tJ9wBLgyZLGlP4tnNW+yJ+Ap5srmgXbmO7AKw3+ghnSQ8/REFbx+RzdsUMUogfgsOjFLpN+VqD9A1phXXdwvg7wxeHUNm+YqioLgYBXYUIEB6fX1pzoM4wAY01OVjYUnHUQoMKRuAC4uKvkZ85Y/vS08qZTFoyK3XHwCmPRClftuUuFyf3WrX+50B3IeIqpfprJVn60HNxooHTJmrVZx2vv0cTkXxEPsLtsZ3mtYX/pkjV5rdzoHat8BbnkcX2vrTWjb/KaamTN4P+9Bhccz1B9GntmToZJufAVg/CGDY3efWfe0rv/HGybgolfb7UWcPsLR/RKypoGVAAVIFYQxDK3nUSTPmWRUMx6H6U2ytuYWGaKObwqLDFxcWOnE7sDWmHle0e3vRjWAZomLtc0ERvgPCGCwPDob8sBpvXfeE6wbelqPNLcz/9SoYue8u2ZOabSxuS/6tJQMSx69zk2r2mh1C1h0YPz9rbf+8e88PA9cbo7wmaJ3v+DKlPW2Lr5lqiGNsukBoqiW0YAVDVWxzlrwl89VhtfZSvLXJ+clXjZX+nqCPbMnFR7Zk67K9cFmRsGWupGjhK6eeUpNz8b7IpBPZIx9/5nzqh7Xv0UWOvWzbtTB2pzQs6qEKoqparK+4BfBdue7kSgDwd2nbiY2S4gq2aTLvA6LXI/EAoH6IPEVxj/DJBYZhodSP+AHFY15e1ogAnxW9vTs5yKT0qk23UvQ3Qd4xO+2A8g0E8Pti3dwPu+GFAJyC55yu8fVvgoME2i3Lp60bPfVu87fwzoTYqpIaCKHjUHzhoMUHto7nMASXnb1KS8bUv3hY2OMYU7uqXUsTNyz9kSr7fRU6sAn7XSTPXt5glo4ezbM3NSJ9///GaQHwO/o50kEIl3IWBQvLZVXfgr9BmGZr6Z6vRaNZduPd2nAiDuem7xqg+DbVcw0TQxCdiqaWIUQG8NBWgmxlo5c3Ts9oRg29EpsqIVgxSK1am8EHJW+yYGXWz1v5wSSP+AHFaj4jEDWI1NlnaabgAmACewik2I40Ug94Ks2Vw6s1uco2CSn52xaWzcl/8BQWJY0e+Od3XipicXXVTelHjXoMj9X+RnZ/xn1aJcBbgElLeuvOvpQEvbjvD/3JuW98yjIPOQ8vdrzl1o3pU8sVTTxLDjsflY6MbG2U3GEoNEkjR17/5Wmmn44tEl/rg5n2MqP6x2Jkzzi6a3TAL5EV89c6PJa6la5rYV7krJnhXQCnRfR6LcKI9cuoUEprfVvo9gwfe32dBew56OPTPHVOWIU3Rp2BZsWzpJOL6n3S6tzheiR1HsUWSTw6z/hqzoTueQBOSwfnDwgnqAzaUzPm2rnarKWlWVO1VVNgUyT4jgsPLmFyWI7/jecepVlDUNuAWk43BjctLxjGPPzDGtOzj/foPirZ/U77MrAWJHvH49MMAceSg30HEjBnxyuQRW/CTigm+Y9msAhFC8BgMNw63nAlcDaJowa5p4TtPEcUuQWasmVjU0OssVk6fymv/79utjtfEVB3AfBG8jkO4vFqCC8F9HJCCPOLPHGiOy6JyfGJ0Jxqa4L/7f8drch2n+XvakhJtuwR+ziqrKz4BT/XkTvZ3BoIg91ePeCbYhnaEg2WkHKI9324NrSYggcqZBx2pxiWR8ShGdcloDjWFt1tFqL4Y1XNPENc3bNCF6Dv3DCp2R5uozgm1Hd7D5/htrQbwD8tLLH18a6HcAgX4PiCkur/X6J275524AZ/WwyxBuogatD6isbVLettQ37BNnP59uk40m83ITzk2AA6lLg9eL4pTLgebM+mTgHCAFQNPEcE0TBzVNzPMfh/vfa/N3LMjcYBAoI4RuiUwwDKtesWB+GxcRrwOkbFHZSgOc+JwnksIKRIKtJKO1lWtT08Dbgbqowvk5HTsjIVqy+K8/O1Wgz7EaG14Dfkvf1FcFjshXvaJpYjH0/jCAZiLN1WP8L/cF1ZBOYnIrMQC6Elph7cOoAt8/AsghCehmPSv5vf4Ak/t92p4jGgm8AKQHMk+I4JEccVBvcEfGzHloZa8srTs27svPQSTHWCsC0gC95ambrxBCZkWaq9/Kz854DWDVolzRWD5xNNLw7oI7/n6os2Mm5W1LBZn38aCUpEPxVgH8yYVtBoi00/Zt2HFFzt/lrEPvPtK8iqSqcr+qyoF8X3LSgC8Mp8h/PBvYC8wC0DQxUtPEzZomYlrO6wrPvx4wxVsGWGb1v2xYvCU5rzWndUvp6Xte33uB3hyj6neW0oHfJthKvQMiCvjYdJdOVvTSo5+e9zz89BKJnAFEAO8VZG7oEWLnJxN7qsdlAiJt0Nt/6lEJN92DBTDz/QJKn2BSv8+vBJg35H+NwbalM/Q/bHIAJJaZjlY/CdF30Phe5rTTu0MBOayK0M0AZoOrvQtFGTAGn9Maogex9XDqP3Rp4EDtiEHBtqU7GB23458G4dG/KjtV7Wxfe2aOeX3BOb+3GRvcc1LevfPIB8IzERgOyjGz7DuACph9oaBI4HDJ3MmyZO7kTer77+9JLinYffVvdv8ovKZZvkdV5W5Vldeoqtzh/+grfMXgv/AfzwOeBsIANE1crGniZaGbUwGEUFCEQqJ10DGffNMfXnHlU1/eOP+NfedE0SKxqrnCT3lT0oaI8rBtZpd4D3iQFls+h5Zqp1rqhj3afKYI6TB2Gntmjnl31fiZivC+8+TPn9sQbHuCiV8RoAm4BPhLsO05keypGucReL02Y2NPi2FtrsYaWmHtq2TVbGqyegs8Br2BAJQiAnJY1xecWwHwWcmsLW21U1XpVVW5S1VlSLqm59GcEDM8qFZ0E39a+FKBVxpzShuT0zoixXQU9zi9tpGN7ojLn7jln0cSh6KSN/4JdEwRRYFud+9DIpASgy4lP3z6nAzobW/X/xBVlYWqKp9VVdl8g3gK3/9nsf+4HzDpwOGSjVJKpJToUuew46Ab0DRNjLty5a+vHJr55r03rLqt6FD9kJclQvguG/JHDqeCzm2G1yeAMNPCKS3I3DBHSEOu0I3lfB8+0KdjLwPBpDivAPp7pWllsG0JJpomzgbe1jQRo6pS7yuhAM2UNiaHSQz7V978YkAaz8GipL9rJkBxkiumvbYhei+6guK0yMZAlCK6NYYVQNPEVV2RFBLixDIu/otigKmJG38SbFu6kW1Af5C/p4P1uOc9kr0M9N+C/t7+7Ateb/lZQ9kpoyxRB0tveezaotb6t8VIx3ePGLww82sH1+XVymVrfCIDf7z6vJkgh4EcA6zrjNPaEv/NfV/zDV5V5TNFn4+cuKX8vVsrnIUur3A1fGV6ekuFs2jutFtzvt1bNWbnltLpL0vE7/MOZQyINldiFB4U39f+Bw7npUv/ePYzphVqquFbo1cqSIkXcBdEzh4uhecDiSwQGKYKxFz8sZcp2bP68nZ2p0kKL1wVaa6uBt4Lti1Bph8QTx+toGhUXGMEemtKHictJrcwAegKFcG2JUTwCG80FIc3Gra23/LHBBSfOCv5/SEbCs/m1P4fj4SM9pqvAN4CPgpkrhDBYWTMN3t3V06QjZ6IXqkUAGBUXIpHN+HLcpdmECpt1OO2Z+bMgwnL/Yez7Zk5qc0xhKsW5Y6AmGSvM+buQGyZlPdGaql1+JCZOxuZu8PR/HazPef5tU9bbqV3ibPnqrP9A8RpLlvhR0aZMnPAKUW3XPjgh1s1TUTtrJiM23d+AKlP6//JwYkJu/iueqJ96+FTLsmavvh2TeO84XstXzxti305yuvkt+7r2aHbvT8zvvfBbH3sRll20f2uiIMOV8T+c8fedX9z5buQo9pJhi19/XpdDosaEfP1uu3Lr+mTjlozqipf0jSxRlVluwsmvRGjcE+Ms5Untbz+9ATiK00lAMlF5t3BtiVEUIkEDgbSMcAVVmkCMAhPR7ZizsBX4zpED2LlzS96PdK069vKU+qDbUt34dHNOYDTX7nJANS01vb0B/52KfAf35EAhJEWW+IGS9W1/pcByTWVMvg+RZd1qbsczc6IC/8KpvQa3vW/p9OFW+krFsxPqNyTfKE1rq44bNChtQBScWrgk6TbUT7lgN9Z9YJwvpd/8dVj4or+kzHsfZaducIGOIfus4xKLjT/26Y4TFe57uWf3nlslaNdBzx37KzzXpIlDa7NTXFbh4+96/6D4KuWtS53+NKjq2SFaB17Zk6qLo1/A9hbPW56J8NXegWaJmyaJt7SNJEGvnCzYNsUDIZmvnm5wxsuDjcN6EcHd4VOIiL9P0Mhgn0Yl0m310Z6BwfSNyCHdUPhvCKAT0vmtFsQQFXlIVWVrToCIU5qvqOXxrBCc4a7mAsiG9iv4F15w6rFD7dsY8/MiZr3yCOfH24a8F/wVoE4Zgym0Va5xBx5qGbx6rQDnbUjPe/pi4HzYz21T1ndHsVoK/sWSF+8Oq159WQDPq9aA9KXrFnbJasqpvCmZ6QUEY7q8LMiSuc8DdBv56/3F2RuSLVn5lywp3r8kKFRu7aDWAakPzPvbkDcDSB0/d9TtkT3H3LQskCR4vPKOM/4rXKEK9xY//XymD1bL5GRv2zst0nkz7ls7YTb/lSiacKamxv5HsgN+KthhZzWDqOCbNa67avJarH45Nuig21IMJEoWb5XosclLlbEuc/WhZT44thD9FEMXmHyGmRA8deBShZ1JoY1AwhXVflKe21DnFwMjd5lLKizj7/zmWuEr5hA78O/pbbptAf+tsJidOzTDp2bac980wtKA+h1oPx6d9X4lGmJGz9Jjjxw7hvfXTUO301CaxEOkAzDI8L7b325s/MvzlskGlCfj6DavXB9YRkMIDJ54++v/tXKI07pkjVr5R+vPo+olHLbwkc/Py5nVdNEFOA89NG4n7obhl6cOGmfe9D0b9zGd5aOAaTRkTixDD3XAE4vbNtfO/rM/OwMJ8C63DuWRte6lfgKN/GVLktUgzzPY5BPG73i9ocNl07WMZp/YvR60qqnzWiM27rv8Pg/XK6b60pyc+OGSFIywXiW/0YLXRza0MvRBFKXvtAVHYQWbINONKoqizRNTFVV6Qm2LcHixlW33QkZ43wlpYWghyUumtyKV1dwK8tqeuW9JEQHyIoWBoQSW21cH0j3gBzWmckfjPqo8CzOHKAN60AM6yJ8Auchh7WHEW8tO7y/ZrShoN5uB3pckH9n+Pz+heV3/+2qEYV1g98Ew72+BU0F4CCIma/+8sFNAH/2OVhHO1kXATSUTv1dZ+d9jZvPkRiiTiX3GXPl5FlAQdXei146up0QOF311qrOjt8STRMjga1et+GOqv1JvzeFOfSwhJrRqir3F7yz4TKJRIJ4kEarQJpAXN3srAKM/K6+PLnIoSj+283Bgcb9g2+p+DnA7of+NBLgDMfAU4DVYZVTF7vM0ZHoEfeA+DUoAsgDmeoPp+hRN9tgkp+dsWn67578ZVHDkD9HW6pWfvnAT/uMk69p4mfAVODuvuys2jNzDHHW0++JtZR7vdIwv9YVO40WD809gag6QzHfa0SH6JtY8d1YAwoLCSgkwKsbmvt15AJyPTA3kHlCBJfNpTP/DbCldPqAYNtyInh84cuHJYY3feVFBT6nlWfbuykYLFULwbt78eq0bzozX1LeNkVieAjIn/FtwwMgzwVeW7w67UcrELrHUNlUEd3pi72/0tx1/sO9wKpd/0sd5a63JRrM7p9fekth84OIBjjX4GIzOpdGHvwsPzvjB7/PoMKm/or/zADSZTYPzc1NfK4gc4NpujPpUYB4S9k/95x1+fJd8658GtgPht+A+xtB9YL0tO/SQKThVwloUS0rRDsUNQx5FqDGGd/X4v/GAGMJXNGmt/DTSke/RIPiufWr5de+10OLRkQR0mDt0xxKcQ6GwMvzBnQR2FQ89yDAJ8Xqt+21VVVZGYph7bF85//Za5UCjsE6wOFPNGoC3m+r8Zo/3zTC64qaEpmysbyzE83mjQeBKSacy4fUV/0KhDl22NqdrTR3ALaOjKtpIrbF4fXAz8BXuvLQx2P/1VQetRh4z1Ed+Wxzo5TsWZuexrnoKZx6KgbH4rrxI/c88sSpLcetifbafW681AFHdbSsljLi+poBH39c6YwbGCY8zsY5S7/RFdt+sN4E8lPg1PS0gvFpaRWvAKSnfbcpPe27h0POaufIz85oFOiFYca6ycG25USiqjITyFDVwGLeegN3PHNdf4GeDXxS3pT0t2DbEyh1Ed4zG8K8fWLxI8SxMblFc3negJK5T4QO63RNE78McJ4QQWRY9LcHQGdyv0+vDrYtJwp/IlZ6c6JRe6sY5d9cOQtpwH9D6TBJeduMX5G6KImDzgv5xws1B9KGCoOjzmirfO5Y7c1RDQMiBlTMbm9c/3ftoKaJ5uSUq4Czmz9vqoh6Qygy0hZf86sla9YeWcm945lr+/3PVLZCR5akY5qFwWkxNQ78ZMdTd05pbhNdY0jWkVXAibxDPQAAIABJREFU/QLSdce06abGJHfJuOdO+yZqDykx+y3Aw8AngpqfpaftOy897bs2i4uE6DhDor4Lj7eVnd1+y56NpokETRNvapoYDqCq0hVsm4JJUX3KfyRK4siYncvzszN6bPynyS08QGWw7QgRPJJKzU6AxDLTx4H0D8hhnT4wdzzAjIHr7B1oPg9YoWmir2/p9Dhyly5xRJprGysc/WLbb917aC412qEtN2m4EaipLZjT2RXW66rpF+PGdNO4NZcbdE9YuvRaX77i9n8ccyVJegwVrgbrjy72mibiNE0s0zQxzP9WHvAY/p17VZUVzaVbVyyYP6euMGFYxIDK1297csP2luNsLU19ud4dGT930NvZC7PTN9cMeuMWxR3ljT5w6YsFmRviyYoeKhDnKIg/i6za3xc41sYkbb/r4367rjBL4aGoMZGB4SUAt6Sn7VfT0sqf7+T5CNEODq9tY1H9oL6gwWoHpgCJQbYj6NgzcwZtLp1++pCovV+8n/mbt4Ntz/FgdSr14Y2GL4NtR4ig0ixtduJWWN26yV/sXHREnuJRIAJ/QGCInkWdK+bTQ3XD+sJNstM89Ys354CcCTIKWLdqUW6HZJpuy7s1UqAvBzZXMOClyOSPfw5ECIPjf631cTdaD7hqwyvAV0fdn+0PvjCB3wLpAKoqv1BV+YCqyuqW/VcsmG8CngTy6woTfrBibs/MubSg3p4eY6lc/dziVX8BmLjoiX8rumWeQAyTwvt2eRz/lEgqw4b8d9/yF18B3lJ0a6wzstBb7YzSGz1hDIwo1YGEjpyDEJ2npCFlvVeaou2ZOb26tKWqys3ACFWVobAReBgUeaB2xCXBNqQLCMWw9nFK+rtS/T/jA+kfkMP6ecmsfQAbi9K+a6+tqspGVZUNfa3ecy9iL71Yi/V4UIzOK/1FBDqliVhG8nMSJWUsm/9UMney1D3W2xRTvUwY88qHbXRrAmnVNCHwqRSsBlBVWQikqKp8pq05Y0cUvQqMEwbvXUvWrG1sfn/2gytvFOgvAt9UORPuaNknJXvWeincVyM9p8VURsxsFKNddTV/2GxuHHy59D9/hleOpah+oAQYEF4SyvzvRqzGhv0Aw6J3TQ22Ld2Bpol7NE3cBKCq0tFe+97Oz1cvvAG4xmZseDI/O6PT+s4nG15FJlXGesYG244QwcPoERYAXSEgxZsTEcM6WtPEfZom+vz2Tk9kYsLmCKDf7U9flxxsW042PI6Ybb5X0ksHZZqS8rZFfMy56gAO5I9j80urFuWaGkqn9lMMzpwrbv9Hw7H6aJqYVDMp/KzPp6eedqX84kzgRWBt8+eqKkvbmnPFgvkp1fuSzg1Pqiz55Utvv978vj0zJ/VQ3bC/SYQF5FDg1KP7Dno47TVz5N+3GKmnyXmVRfHaTM7wfS8JX0Kax1Yz0rWneNqXAALnTaFkqu5DTXnXCzAocv9Fwbalq9E0YQBmAbP8D2V9GntmjvisZOZ9UeZq/awhbz4WbHuOl+rHww0GXQhFpzDYtoQIHgkVpiKAgcXmTinqNBOQw5o6IG8KwOyUdzvixIzGV9lmUCBzhQguNmPjLoD9NaPGBNuWkw/Fv70l/sIPK1O1xR90jPHFDHlo1dzVEpgDItbjiH+2ZSNNE2EP580z3Zj3q589JJ945Zkz/w9tYoYJWHel/GKzqsofabW2weNSV7xNFVEzjnr/XIkQxyo125I419ZYj+yHQ5+KQOiWhmE78IUg/BZI/7By5B6T4mrYUz2gMzaF6CReacgD2Fg0d3IPK8nZLv5SqxcAt4R24wC4vMrRb5hbN/3iLzf/qzjYxhwvMTXGCP/PT4NtS4igclzleQMqHODw2jwAXt3Q1IHmbwHmvixL0pP5rGT2G0DWjoqpfSrxqiOEJWyf01g+EVNYadYtf7yqXem2cXnvngeJiwAJYmVS3rYdDyXsyGqqGONCGt9NytuWGkbdJUP5ekgMD138KelNHszRoPvUT4UApBmESgcrRD23bPIdkHIZsOzuF97Zd9THuj83q9UV4oPPWs8aLCzDatxXe8Egm9ulZM86UkChJrP2EeCLgKqhZUWn4q8cRlZNaHW2Dd4/cNEYQLp18yx8deTbVbE42fErAWQBi1VVhuIbgTueuTbaIC77o1eavmzyhD8dbHu6iONyVEL0Dsrj3ekJFSYKkl0NKQH0D8hh/eLwmd8BfFx0VkF7bftydZJegj9OWYbiWI9CMTWcrhgb6YizCqDgvdPvIArAJHQ511U7+LSwhJ2Hl6bNnQx81EiEspMzMNPkCqfuo5F8taUI+/YSfdAa3WBUhJRI0bHSnCsWzLeYI+IfMUc2upD84ejPB0YcuLi0YWCNV5oeBfKO5fwklZgul0i9LsJ0AzWk8L2zCsCdz1wjFHHFJKPwvtYRm1ri+V3U+QZfaIMUCCdZ0ekhp7VNVN8PIUDa8MmV9fTzdSo+JZlEQgk5AHxbOeEtrzQlDww/8PeNy25rN+yuJ3AoxTlpUIGFkv6uUUnBNiZE0LA1KrE6UqYUmqcRwLXrRMSwxmmaeEDTRK9MFOjt5Gdn1IYZ6z0TErb+NNi2nGzUF59xQPdY2y2e0Uw5Axt9YhnSA7jP2O2o8LqizI6qEffgc0b82/NSd2F7YNfc9Plr5959/zWrny288o1nhcHjZuiBXfx69X0dnfLXrnqbNaxfzZ23P5v7A0WP9OzHIsoakyZNSPjiUH52xkPHXKnLip5tdivXCsSHKUv//K+U7FkPt3RWAbzSOFaXxqhpSR9H/ah/OwjJuQIhBEKhE0lrfRgNpMP3NyQAcZM9M+f0INt0XKiqXINPEWBvsG05GbBn5ly1u2rCdJAUNQz5VW8J/QhrNAgAo0fkB9mUEMEiKzo1vMlwhuLbKlzn313rFAE5rKcnfXg6QNqgnH4daB6GL9ZtSnsNQ5ychJnqS4vqB/X5RIgfI1JA6VD2blLeNgFMA/EhiN8C6Wd/2TQacOme8Nfxbcc7AA8+ubi8Ft3V5NKDckjhPmqi4w10wLF76hczx4K8F/jvTQ9vWX30599Vjz3brVtEeVPib485QFZ0qkR+gO/7m9raxWVDwVkpANWO+FYluVrDoIsd/pcdTlrry7QoanEP6LcDAuTGyx5fqt35zDUdqoJ2MuCXZXtM08QMAFWVoW1i4Nq/3P07kP/wP4xAL3qIi680OgESKkwhHda+i8r3i50B/W0H5LA2ecIcAB5p7EgMayFgUFX5bLstQ5yUlDclaZWOxLBg23GyoRgbx5ojD3VIo/Z8XpgPDB7M7vdL5k5++M87Hv/EaK24zRxRsGvx6rTakrmTN9Eikcl/3IwGwplcckBWxCZSnxBub28+YdDzhEE3Ane30uQSoLKw3r62lc9VfBcVJLJVJ7nGFXc2wDeVk6qP9XlbOCx6tX/8p4FQOEAH+L6oxQVPAKeMiPl2++bSmXPW7rtikz0zp6dIBsUCF9KiAltfxp6ZY5hy//OrPio8674oc1UNRx5ce9VDXCiGNYQmkcIviRjQ33ZADuv28lP3AnxYcM7h9tqqqpTNlXZC9Fi+Azk47eEV1mAbcrLw0mN3heuesHBrzL4OnZMDjL4QYBIb3wWoK5w+2+OIt9gSvt7Y3KZk7uRNJXMnP3yUs8qSNWs3AemJFKwFKIlMvuVfD467vrW5Viw4/9cNJXH9wxJqNi5Zs/ZHceY/X70wzGJouioxrGhzfnZGa8mQGiAlEoE45sXFt10p7/bXBFnT2e3LqljPOQCHBrleDjmrnSc/O6P6g8xfTUkKK7jRK40pILdeuCLrb3c+c42h/d7BQ1VlJXAasDzYtgSbSff/Kw5YW+VMuC0pvOAdddC7w4E0/A+uPT2prpniJNe5AIdSnBHBtiVEcGiy6jsEAqdFbifABYqAkq7oRAwrgKaJLOBLVZX/L8D5QgSRMwdoMZ8Uq2J03E4VeCfY9pwMVO29MBGgrnB6h6ScdnLacJDbn5n7p88BHNUj5wHemvx593ak/5I1azeNy3v3CqSsLUwZIg5/ZH9sxYL5I4Ec4BOgf8zQkvM9DtM1EDcXoKE07rQVC+an+h3eIxyoHX6p02szjo/P+6yNKbcKhBf4GFh6rIuLQE+XCMNR25cdvghF1xjKAExusaujfUL8mE9++/Pn7Jk5b8daKl7/quy0mw7VDj3dnpmTkZ+dcSjYtrVE08TpwMXAfaoqO5So2Ju5YdXiCwzitP+CVED8/JNlP38afs6ffd+hXuGoNhPWqNQBGLyi3UTtEL0Tm0MZBWB1KlmBLlAEtMJ6WtKGVIB5Q/4X2V5bPwuB2YHMFSL46FL5BGBX5fhQ8YfvGQQgdVO7MaxJeduigFkg3gZ45S/XC4T3ckBbvDqtoqMTfj33HAdCfL7TPmU/UknwxajKjSBrgOLq/UnP1hfHp+H3IGklTujbylNmAA1bS898qLW5yhLcaYDRadafa+3iIlFoThIjgC2esCafTNaAEnNZZ/qF+DH52Rkls1PeO3Na/41PVDnjhwLbJyx76TF75tqlJ1HizoXAlUCvLi3bHvbMnFR7Zs5LeYfOe8XlNYuzBr+5KD87o7fIVx2T6FpjJcDAYnOP15QNERi6kBP8LwMqGgABOqz1rqgGAKfX2pEYVlRVpqiqbC2WLsRJzmcls/MA9tWMiQ62LScLEQM3ngUQ3n9ruytFabx6D2CcyKYtALrXOh9pGBk5cGOnM6NH8NXhJlvYSLfB0KyhijDo+4A7rbF1l9jiaq8FmmglBu76J243grwYeOvL5de1+v31GOVVAKX93QfbMOd8oADEMgLYvnQb5RCJrCKrJiQU3wWsvPlF+erdD94OYpLAe6jeHb0EeAhYb8/MyQi2fcAy4DR/SECf5PTlT18i0D8CrgJhbnBH/uRvt/211+d3SGQU4CSrxhVsW0IEh4p4zy26kJT0dx2tB95hAgoJ+KZy0l6A9QXnhnTz+galIBvMBmdPSerodoT/adEWv7PdFdZvmTrJQpNnFF+9CVD13XlXArga+m9su+ePSWHfF3uVUy4uSUzxDCo+oIBwS6/h1pbb/isWzN+HX4z/6HAAs8G5EET/CQlbtkHrPkz/UpNDIut15dirprc8dctP4aLUBFvpbzfff2OrK7Vt0RDuVc0uER3K5uta8rMz9g3NfPPfIH/nrzZhAvnm3Icer7IYHU99W3nKivzsjIBqeXcWTRNGYAWwQlXlQaDDOwq9Cf8q92KF/pdJn4wbgC5RJtKizHJvpTLOkxFVazCZgm1IiKARWWcweIyyPOnWJmf7rY/NiYphvQ2wqKp8PMD5QgSR/OwMeebyvyoR5tqfALcF256TgbrCGUUgqxfc8VxpW+18clZDTwH+t2ruU02rFuWmQtRVAM6a4atXLcrd08GSrgBoXPwk8EDe9PP//tNXnzrIMZxS//Exx/yy7LQZivDoQ6P2PN/WPIoUk4HP7Tc4jrn6uaN8ykKLoUmmDsj7O9zYUfN/QHiD4bAUsjygziHaRKLkAvfiCwvxxFrK3691RZ9bUTvqXuA3Y+9b8+HpSR99rQg9K+/Q+aPxP+B0Q5LPaOBnwFagzb+53oQ9M8cA2IGxI2K+/jWMnQlC0b+vFifoXSoAbWJrUspBdFpJJETvwepUYoH1xzNGQA7rtP4bZ2wpnc55Q181tLVK04KzgHAg5LD2UCTi68L6IaEiJd8zCES7SS3h1NzYQPRAYDeAKawk092YdHSMaYedhJK5k8uT8rZ9U9pv4OAla9be2hmD7Zk5AgbOBN75yy3/KmytXf5zVtsQzFOcFvnPY0kg2DNzkmDwmQL9ibbGaQ+TR5hAdLjwQoiOk5+dscmemZOO3xH94oGfbbrzmWvEm/sWnKpLw1USsXB9wbnpIG/GtwKrCKTHnpnzU+D1/OyMDoV7tYeqyp2aJkaqqmxXUaYnYs/MMaYPfnNOrTP29M9LZyrAuARbyXkGER/tlSYFYG/1OPxKGuBLZHwWOEj3PCCclIQ1GWqAkyoJMMSJo+LPYbY4jCME4pXjGScgh7XWFV0L4NUNHYpHUVX5k0DmCXHyUNqYnAfcbs/MUfKzM/q8TJkprPRMoXjaW11NFUT4kynkL4e+s/Wd//Oa5vlvXh5akYtqj9F8UXuQkectzltkWDV3dYdLN46N26Z+UznZDvy+rXZGjzhPIIyVce7Ggcf4PMJU+8t6d5RRovylk6b/AF3IJF1hS6DbPCHaxu8MHXGIVt78olwJnwOfX//E7b9xeGzXfVoyZxFwOggkwgS8DDD2vlcaYyyVTcUNg94D9k9I2GKJtVQc3FA47y3gYH52RpvXfk0TFwDRqipf6A3O6lUrfxW2qXjuMGDcmLivLvfopkl7q8c6gdHrDl7Qcqf7gFF4SsbFf7lze/mpzwFf24wNMU2e8NfwPaC6gX/2FUe1GYmMBmpD1Wf6Jo1h+jnxlUIp6e+KOJ5Vr4DuFXuqxn8H8N6Bi0MB1H0G/TtQLEOi9g4D+nwZRa87LNYWv6tNhxVQJYo/Xk0Yh5a67vE6462W6H2POWuGVeJTCej0jSuG8o92MeWMz0mbBrQlTfUDwk31WQIvM5Jzc9vaGUkpNMcARNYZ/nb0Z3c8c22kEOctGRS5f9+Ge3+xp7O2N5P/nFUMxhxXFesZ3pFyeSG6lud/8RcP8Jw9M+dbYB1IM+AF8TDgTY44cGGDO7I/cCZwxY7yac1hYCsBfcKylxwR5rrqkoaUD4D8af03RlgMjq83FqV9MDp2e8HSM7hlZ/mk4TcufX2ILo25PcVBq75/wJVVBttla8MnyBVlt+wCMS7CVDOz0TP7yJ/pt5UTibcedoN8G8TakbE764dG7T3w3oGL/pefnVF/rHFbrnb3lHPRlTSG6ad5jLI+lLXbN4mtMvb//+zdeXxU1fn48c9z7+yTZLJCEragKCCiKG7gNkCxWtzaurR1q7tWW2ztgnXp1KXS/qqtWitq69JarfqtrVaqrQLBqqh1QVFxA8KSkH2ffe49vz9mwIhAQhKcLOf9eqG5mbs8M7lz55lzn3MOgCie68t++lLDqnra0rb2AfdPvTHjpLJa1w+jHvvVupFJpz9sWCWNTjvqsdnZcmsgJa35liO/1UzltzlUb5b9YXOmI8VMQZbqAcp7Z/bYf+Ut3XA8e+W/fzzw22zHk013XrLUA7nOcO1B3d3eqARJ3wtUKnXIx7Eg8OQFv7zgR305/qvM/T1w5Qb2PpBdSFjfbji4vNDT9NFD3/3tum5WnQ505Haaq7Z94Ln1J54aTfmNaSWv3blrUX9WSYMj31BCfquZIhSYod+X2fFp6YAE+UwyNe+GLetULFjsOGbcPw5ujI6c8Gb9DAdQUZ6z8ZTmWLGP9CD3o96om7m18ezDlqmpy5c8ZHcm85xg3ACEZt/869WCrFrTNuldIDqzfOn05ljxJx8077caiM0e+/Sk+kjZ2ncbp1cZkoodM+7J4sZoac3rdYc3BlzN0b0L3o8//v2be31nJ9Ppaetz/N69Z41csmHe+HAyd+KonPVzvY7wl4rbbPthV6Qsz4pwQfsKlknQelNN/NhpJD84cMSKD1Y373dfOJn3VsDV8tEbofO6lEx0Xxa3bWv3cONKGB2i7GHf0DFc5YTNUkCNrHct63blnehVwjqt5NUj32mc3rPW/VBgxnjcN0i6Z+QL3phBxXr31oe7W85vc5Df5ujTMkBmOjBlXZ+3oTPH8nqjRqUraayOeK1YS4Hlz281n/NHzE+AekJtqR6+FMNGOJm7FGBV43R9BxdGZ/6/05qsQ3j+ndeYA7DsorfemlraXF6EwRX9cPx1oDa7ic0BFvVkg4oFi08D94Sm2Ihu68hjbvsEw2aD6+qOzyQI6RpY/3eA1f+tPqZPX1r8EfNEAGfKOAJYQiigp2bNku6SqaqF81Kw7Trzfrblp4oFi93HVjxx+PTWurnjjdqvLW6f8/oL5sTjOpNSkFnFUdW+11RbOaZu2eblmtmfOcbSDcdv/dlWDp6t+vrW5bZEIf+rO4I9FjyVsjE7BTte4GksiqV8dZFUTp1DkqkxeesmtMYKP26JF290m1G1d8F7+9ZFyt+pj5RXuc3IaMHzTYUIiNrzqr+3WeobW2KjpnNMcqSvxjrb+ex6EcoE8JCwHnHf+HN3qPkGtD5zpgRnynwv23Fo2ZEy1UGmRZWE2vtUG9+r5KMtUdBMD0cI4LMDl9u2qGWNxSnLFzE+zgmbNSlTuZsLU4f5w8aH/ohZm3TY3pYC65CcTmO1L2rWJ5y2rzXfOjin03jfFzUbEk47pzXfmp7bYb7rjRlNcZed1xawDsjtMN/xxoyWmNvOb8+z9s9rN9/2xI3WlKmONS2OkPTwLigwHCnJdSZlNnCqL2qKL2oCXJN+XKnkTbnKkZL3DCUboh7b6si1AgUtjiecKVnXlpeKtOZbzYXNjlW5P+wcNontq5uPXgmk6iKjCrMdS7blV/x7dmvVl8kpe8VONzBtXwk1s0DkmPrXIiM/nlCSM+rFp8++9rqqvh6/dtY0dfSy+2MNlH+1J+tnplB9KDPI/6UVCxY/vqPbkh2/znH4ksboloLUyuJtHjti1HNnvlg9d7pgfWfdwhP7NHZqwmkvcCaFzBfZXe58pg0cVQvnxQl9K6pQPwaMOY6Pxt2fOu6713P2bWTqNm3lmAO8Crg8ZsQ7e+y/xm3qGKfeaTw45TajvqNG/+egDe17tH/YMrXdbcZyDi19IbihY3xtVfteLW4zmju1+I051Z3jNm4Oj2lxmYn8Un/NEZvDozZGUjkdInZ+ynY445anGAikbGdOVfuE8kgyZxzgiFs+V5dwBaT60NLlrzdES/9vbdvESoWx9pXrLk4R+vEMhXpJgQiScGM9/8W/mkOTQuUCnbqGdXhKuOwvJZ2qva8lIb1KWNe17b0OiPVw9UpB4mQuXIaSa0dcFtn6weQAuk6f5Nxm2dXNsnubZU/m39b9hwLLgCVbju+w5XTHjzvTxw8FnPUlyT3jbvvQ8hpXh2nLiM4c+7CESx1W2GxWA6NcCdmzpMGRK8jRAIF2B4F2BwplEwrUJ5x2POq1vXnt5pOC1LTlpZydObY1qsb1FFDTkWPVD4XEtmrhvNQeC57a6HVGpmU7lmyzkr49ANx5G3c6a8tbHFkAUPbuhAOBjzuqjzilv2Jwkni2mZGXjl/24th1s47Y2eD+kE4Gt7zXd5oc5naakwApaHHcvu1jdeHyn/kcnepL4/75f+lJi3qn+Ta/L882J5LufWYxjIb3GcKCWxoFAPM8x7PF16fO3l7dZiz979RtxoE95X+fXf76I9s83uuWzvEL/nm4Qp4DnCBJSzkvevSKX33+/A+1rUjdkPtnp2WcDRyvW/z7R9X9HncFbl99SXKanipxGAoFHF4M0zbsyr7uqi81rD1rYQ21rSAU2Hrh+sIvAjs7fqgtOQI+IP0PgNxtbrGawMY/eDxjNrmLgPKmwuTMpFNNL61zVQFlKYea4UxKkSAnAiMC7Q5Jj6HAtQA5nYZK3JRruZLGO8DmjhzLH3fbieIm5xNATUNxUkW99saxG93vDfRShPGBj3OjKd/R2Y4j2zqqjwwANH146k7/XjWMHwlQ0ibloL582aLZvR4weVvvcugfgUuj5PyydNnK22tnTdvZ+6qS9MxXW3opV+5k3dMATFs+84W0YsHi0bBPRYm39k+3X/hQn6ZSLWxxfBWQiMf6mS9mpuPRycFgVwnYpC+ZST5NUrP+d1238ISXetrpyWkZzwBnA3q64H5S0GLmAnhi8rmaeG1YGC+IMydsPtvXHfUqYZ1a/PpRn7ROyunxBukPo+xduPp4/DEXxGJANVBdlB4WZqvPzNITCjiryxOTRLFP+WZXDChrzbeOEcU4V5tRC4zyxIxJOZ2GBzgGoKRx64goNqFAfcxtm0mn6sztNJ8HahqLkkWWyaaR9c4lZLnFNpzMebE+Wjq3YsFiqVo4b1hOp5ke+J8LMkNT/evOS5bO2VFP/9Ft7Sc1eXLI935Uf8HtF/+nn0PxZmI4HeSk0mUr5+woac10rLkG+CXIJTv8wA4FZijU1QCC3EcosL5LIvkdEGmIloX6IfYLgbW+mHkjobZhP0TakBBqW9H5q5ylORFzbtJhn+C8piPriWpXPU2ewz6rzh8xaS5IHVUIOsHqB4F2hw8gr8OhX89hqNNvzcwJm1iG+sDsfvWd6lXC2hovbLCV2dOSgOEj1JYclb7IbX1jFmzTYusEmm73u4qaHSVAed2I5FzToqy4ydkClCWdaq4zKTmk77mOKG5yfqbsJ6fTIHljXtSZktXA5pb8VBmwtqDV8TxQU1OW8Cnh3VE1rg/7u8W2NjL6v8DJQCHDdIpFIAjKyNSDetjB7fU7L1k6yxHMOaIwZRFvm5B35yVLZ/RmCKsdKaDuvBbSI4XQgxrQcv+G92vCY9kz8EHHTno1BwFDELru83v3nlngdZz4I68jsuLN0Ler+hL3hj96jhmL++iI1/617ycdOlkdQsI59ic5EXNuU1GqfbDOMNJUlKrxR0wSLvtLQJ9GwtDS4i670J0wsEV1Gt2vrg0xYb/9jZywSfWoxMaxfdxXrxLWjR17rAf26+Oxh62i74UTZFpsR27TYpvbdSEUcG4YE5/qjRqjShqdBlDWVJQ6xR03DGenGQVG+cPGVGdSDgROASjfvLV/gU0oUB/2WTmiWOeLmq8ANbUjE3s4k8arRc2OVy1D1Wwck2iqODfWo1vVPkfnhkgqhwn5qw+EeX0aT20QqwSJg/KSThZrtl3h0dsu3NNwznuyOSfAuIYUIA76uVNROwWZv5myejIBwX4lrwdqwmMZm7f2QOCJHaxWKemZeLbe1gV4q/7QS6Ipv2Nm+dLH07Ns9l5Bi+MiWxQNJck/jevTnrSBZmS98+/ApaV1Ll+3Kw9QtsFHChUeWefUszL1k4aS5MHgtI9ZAAAgAElEQVSjq91sLktOGJXtYLQvXGGzGbEM1Tz2/Nimvu5r99ewar0XakuOTc/B/eaWXxVv02LrAsK/ynH6I+YIhSqvKU+enNthkNfhsBSqPOVQJ3qjRgA4UaFGlNa5BDgLwLSFcetdWNfntZu2fGKLqm8psCrccXk5J2y+ljJV/eayRI4nZrxa0ugc8XdfyYlXdVxGTs6GE6FvAwAPVpctmr3izkuWzgE5FvgO8IM7L1n6yGWLZicA7rxk6SjDecKyuHLntvtNCtbFbXZDpyIL1zTgA5A/AZXd1LDyftP+bwO8UTezaocrpeu9nyY99MGXCbWtSA9ltcc3QL2T4+z4XEesXRIKuHIxj1Kov487L6ZvDw49Wzr/jclqFH1QcW5MEQqsBcZnO5ahIq/d0QngjRp6WKthyJkyxtIlh+mLXiWs+xStPLKmc4zu8DdA+H/cmQSqBapHdWmxFaDrMBLNt/tdnTnW1IIWR35eh5mTcNrjW/Otr+Z0GhFPzAAYm9thTHImZRJwnsMSxmxKj4mrUOydaOAvrl/ws/ozhvWEJZlb+yvuvOT5N8B4Mnf0f5+C2ccumv/EZMh/xk7m5FcdsPYaGHmjbVpPALf2ZznABct+MBXOPsxP+01rZh11c0+22dCxZz1AeyLftbP12nOtQm/UMJ3XtK8AmJC/+quftE7eD+SC2y78S1+HsjrdlTRKBLm3L/vRBqbmglRNYYuDxqLk14vhoWzH01txl90uioN2+kbReiyvw0wCFLY41mQ7Fu2LVXW/R8bh2tcy+XN/DODeq5KSllhhXcJybXcKOm1gqKwUI/N/d2WlfK2yUiYVfS+cWLdHfP2b08Pfqwy2J11Xd/z2/SnRC147NHzsC0d3PGz8rH3Ky4d3TnrhqA7emhb+XqffmlU1Lv6H5vxUG6DSxZIpptobp2T32Q0Mly360lP+EW+t79g045hF8//2H2U5ViGpPCDYsHdVDkDe5Cef7s9kFaCZkh8AzOLvPZ7lqsDd2A7gc3SckZn1Z7ssU+UoUVuvLYZYv/M5Oq2Aq/nhvsRMKDDDFrUoadpxoL87oGkDQOH8cEfKVElXYuuEAYNSR67lNS0prbrfo4cN7QcJpz0SIGWqjmzHon2xRHGoIJ7G4qS/P/bXq4R1c3jspkgqt7E/AtB2XWWlTK+slL0yP0tlpdxeWSmnZJZdlZXSDizIrG4CfwO2DDIfBSr4tFy2GrgSeCOzvEYZFLflW3fm/KizsuLc2IWFrY7jBIkBKQvDXib7DusW1q5ibePmgylWvGCunfKb3sIPf37Zotlv/o9ZCYAovn4vnXiZY8scJNa7if6zp9u0xgsPAEUklTMTWLKjpLWg1fG6K2k0AVQsWFzxUcuUkWNy1y1++/qzej9DSXr0gaWehOlzWOIADun1vrQBzWHJe3kdjnC24+iL/FbHw6YtVKx367uI/aClIDUHoHpUQve5GmbKa1zFAJ6Y8X/9sb/enkC6hrUfVVZKSWWljO6yfGllpZzWZXl5ZaX8ussmzwLfBwgGlSLd7XtKZjkB/J5MaUAwqCLAAcBdmeVwMKj2DwbVo1seDwbVrcGgej+znAoGVVMwqD7twZ0e2mgOcN3PvF+rfl320vVdGVa8cB+QLa9VKto0xQfQTlE50PjIrOs/1ymrL0qXrQyAzE7heuzOWYt6fIteYQTTP31mBIDuXA6iPmyZetmuxrmNoCDu9NFF9fDY2uC0kUFcwwrgsOTDzI8hQoEd3o3Qeia3w6wGMOzPd1DVhjZnKt2wlt/meLE/9tersoK9C96d2RovLOqPAAa7qQ9OnUH6A7gRKAYq7xj37iuAPxhUnQCVlXIMkBMMqicyy7cBZjCoLs/s5t+ke5tvmVD7ImAt8Fhm+Q3g4y6HPY10yygAwaDas2tMwaBasM3yyr49S7aOZbv0+ntHticK5lcsWOyvWjhvULek9JNKIDOT26e99XNpOTiBu8+9Ird1OM/89CWOc45k479hlyYdq0yPboA7PX6rVG5vpfZca7I3agTm333uCIdx4mWCeurjX3ytT8+jNZAyAm2mALb0YEQDbfBqDaQCOZ3mPv1Rr5YtCac9wpU0UKiLBDmHUGCOntii93xRMwZEM+OZa8NIzGV93ZUwYgYygX6YjKNX15WWWFFtzPIOq/qeqQ9OPRo4Ffhkirc9zylqz5WRQBy4gEyzFSgFEns3kvvqvr6OIj4d+utyYCyfDieUIj0rzBY/A7omf4cFg2rrUFPBoPpB11iCQbWs357YLqqPlL8CzAf2QA+s3WXUgPQsOlvqVU1S++7NB1Uws1+PV03Fsbk020ewuBJO6PF2mckDZnnM8D0xy7vvMeOe3AM+P4GAYVNs2Mo9rjV2d8p2eeaOe+pZ+FqfYnak5GLLRAG/dlj8Q3/4D12WqTY7LDGbb/MXFM4Pt3S/xcBjWjJOoRDEoAdjHGs7l3SokY4U4WGVMGgQCsx2YxyeWVrSH1/8epWwNkTLatmapA198x7Z82LwLtrylN+L5u1gTRFQ3seay/xr477l331w6lVA5R3juIh0KxwAwaC6sutWwaD65zbL/TaFZ38r9tZuaoyWMrFg1ZEwb9gnrPDpqAFblkuXrfRBiekm9nR/Hqd02cqjYfIksP5556xFu1ySU7Vw3orv3nPW8Us2zPtw+aYvX1WxYPFfqxbO+3Q/ocCMHMy9lMKcH3nu5LeN/ap9js6+9egPBY7JwRwT9lk3+3/c+dM+7Usb8IqanU8Cpxe2OMqAwZmw2vKsQv2U9Oejjb4j0CedOdZRnpgR8GY7EO2L9iuAbSei6csOe1nDqsz0gOVD29QHp86Y+uDU321MeLqOP2l5xLrr2ED9PsCRpDsxZV4LpQBaLNfBz7WXXA7qRmDJd9fvOz4YVIPy4r2tGWXLawAKPE3HZjuWAWwCQB1j+q1VpnTZyhmg/g24wDw2vbzr7rjoz+sjqdxz45Z3CnDxNg8HlcIQAQcpDpC1pU+u+dZhvY256n6PU6H+H7DOHzF/3tv9aIPKlgH3+zqpTfaE2lYIEgTWKZS1YcyAbT8YFPxhY5MjJXoihmGk7Vb/d4HpgqRI31Hul1KwXiWsewQ+OnhMbtXkvh58IMvUpi4FLlMYTiBB+oVPxJT55/93ct3qVeesepF0Z6RrgYtArgaZCdyaqRPsektpSLjjoj+tNbA6Xq+bWZvtWAaqybwRBBjJxn67SJsk5wLurYt9O6f+aoi11GEkbv3uPWdtnbGuWhWtSOBAqfSF4VV7En05jjsudwqyX0eOdSuhNv2pPwxsLk10AtSOTJya7Vj6JNT2UkeO9RXbwFPY7HiSUED3cO8lV9KwnDphHTZq7vZO84fN26IeezMwC7gO6Jc68N7WsNYpxNnXgw9wQVCuzJzxNsh9pGdyqVx1zqqtL3zm58/8IaY+OBWB+QpMUKkddXAZrGzMj2zb1DNr7kA+DccBzOTZ9btSZ7ozNsbELudin76tVi2cpy69+7ybn1t/wnMraoKPVCxY/FCus9XVkbzj3APlIznXfIYTHK8y1Vhnv25N6t1xQgHvSHGeFPXYDU1Fqbtyu99CGwLibrVaofBFzEF/Bzj3h50fNN7hu7W4yXklcAVwa7ZjGowsUWNFkTJCgRm6fn2ICwXMMpy/UUKioSR55tjzYy8C/TJCAPR24oB4cWNrvGhzfwUxQFXKpx2jLOBPq85ZdXPXZHVHVp2zaoVCvg4okCE3HV3A1dzidXQekO04Bqr/MbvWINV816w7q7tfu3uly1bOVZjfAv4Bcg0wp7upWLtz18X3Pe80Eo80xkr3AfWLjmR+CGzrTbX3UVekLp+5xi77+BrHQ8kqz7d62zLyPUPJCG/MOLXi3NiQLx/S0irOjcUFqc7rMBPZjqU/FDc5fwQ8pVC/aP2Nv9flMcNWKDDDUFRIukxqiR4mbGizRf1IkKCh5NKx58eW9vf+e5WwCrZDsIf0h1Am6bwmvSQX9CRR3UZ9uqZVTQe1JFNiMCRMKFjtjVvekvn3njHoW1F2hxSuPWwcq/tjX5csu3xSDq1PO4mvBc6onTXt5r4mq1tEUzmZGAVQyhD7gaqF8/67ZuGJK/Y0Nh9riG2251ov7Op+w7/KOc1G3WijXibUtrw/YtUGlQ0M8rFYtwq1KeBCy0Q5k7K06n5Pv8zYM4wEYWvHG48tqm9DjmgD1sY/eM4Abk447eeBB3bHMXqVsI7OXb/f+MDH+3W/5qD3Ueb/7/Ri21mAsYsDtQ8Ka1snPmorkyfXfOv6nU3zOVy5iexfSF1zX/dTumylLOHr98bwOY/h0R/XzpoW6Y/4uniedKfBFEjMVo7ntz4SaltbPyK1NK/DHB+7OfeMHu8xFLjIFzH+KuAQOEi3qAw/bXmpopjbPiLbcfSbUFt93cjkrf6I6R233nVVtsMZZCoFiQK2QgmKH7b81v8MoUB+tgPT+lEo4C+vcd2Ycqj45rLkxZkvev2uVwlrW7ygvi1eMOSLqEc6YkcBlDjiR/Vi8323tFz1teZwoGmJF6cyP/6AnUzzORxdtGz+iDi+wETecne/dre+20HBEQr5/h9n3fq3ftjfZ1QtnLd1BjNgTmZ5q6jX/rpCveOJGwsJBXZahlp1v2dUdGHu48AiQATZMo5lsL/j1ga8T1wJMQkFhszQh6Muil4N3C/IVfpL2C74dJbEa6Je+5zWAmtNfqv5ZWBN6oa8n1Td79Hl7UPDLaYt41xJ47hx58XW7q6D9CphbU/ktzXFRvRLfd5ANfXBqTPqU+7LARpSrl/vyi39k/+6x0Lgm8Dj6ZEDmNOLkoKBrDQ9CgJDbhSEvnqKc48FeJuZr/ZlP+ct++FpgnULqKctnLd3v0XvVC2ct6Jq4bybt01WASrOjUUFuUihRnX6rT/vcCehwAmjqp0fe2Jyio36W6ZFpd+GMtEGl0C749+GEhMoyXYs/ewKy1D1Mbe9ZN0D7hHZDmbQCLWtINR2s+8nnX8qnB+eIMiBwBsOSxaW1jqbkzfmnqNHYRi8qu/x3gRcbIu6hVDbbp3UqJc1rJbTkNRuafIdQIIKybQQiMsl9q9++I+RI7vb6FuPjbtoXdz3k5GO2Grgmz3tqDXIPCvp+lzQSclWmbFR7wWIkPfj3o6VWrpspf9lvrwojxbjyzw6v3bWtOy910Jtr7YUWP/zh42T6u/0ndz1ofX3eabaP8/7G/CUYcumTaMT5xuh9lPp0mqrewUPSxsBbFGDdyzW7Qm1tW8uS9zsjou3vMb122yHM2iF2lYSajumujyxQBSNzpTxAPBq823+b2Q7NG0XhQJlpbXO74d9VmTD2ERodx9OlNr1z8JDr78nlu9ubvr3TxaM2g0xDQiZFtUlgCvzK9NnpFIR25wP8g7pSQMqV52zasWP/zFSLMX4yvaiExIYv/SI1Xp0XvMBvz65bsiOpDD5mkd/Ek3lLASurlo47xfZjmcgKF228ipQN4CYmVKQq2tnTbu5F/u5B9QFk3njjGWzLnhkd8S6K6ru94wZu8G10lCyATgYsOIu+3LTkttMC1uQnwH/j1DbkOgZrvVN9T3e00bVuB4Ne63l/qh51VD70qJCebcI8gPgK4Tansl2PINaKGACZ9iibjaUlId91nv+iHk6obYhN7rOkBMKzCTduWpse641I+/Kzrd29yF71cLakQg0N8VKdludwkCQaRXdMinAkbNyG8834UOQO0G9AOoXoF6a+uDU2mfaShL/aR+xJoH5WxB3TDkC/24bUZHdZ7B7RVM5vwY2gRo6nSv6rhJIgELSrc+Vu7qDE5f9+hrgQpBfDoRkFaDi3NhGQ8lFwDRb1H+B19wJ4/aUQ63dNDpxPKG2m3Syqm1R1OjIB/BFjaMYgkMZCXK1Qr1rGerR9fd5JmQ7nkEt1GYRavvThrGJfetGJJ/xRYyxwDvJG/P+sv4+jx46cYCK/DLnEoV6EdgLkLwO0/NFHLdXCWsklRNujJZu6u9gBppV56xaseWW/u1f23xfh+2YCjwISKb3vwDVbrF/N8IRfz89qDuQnpAhmJ2ovxhVC+dZFXkfLxfUcZfd/e2Dsh3PQJAebkrmGNj/Uxgx4I1d2f7wZQ/t9S6HXl/OuhbSt9QHkhqFsg0lhynUQcCvPHFj7zEXxJ7NdmDawOJJGN8GkHRJ1dCrcQ+1xWrKkz8TRe6IesdiQgHdEauPKs6NtYz8TuQrguwB3O5I8Y3Rm1xv2j/P+wWhwBz9Gmdf1f0eZ/hXOWcQCjzni5p3kUmCSOeRwS8ihl7XsDqM4degsuqcVQq4m62dSiQKXP762e9/vz7lvgAkzjDqbLJP0dv3KgzeqJtxZrZjGShqZ01bYWPeCHiBw3u6XemylY417HtfFF9kOsu/XjtrWnL3RdkrQTJFy6Qn0mgl1GbveHVtOKq63zNeoaYrlMUQvhaOuij6RNKh/uqNmXsDNzIEW5KzItTWSKjt+xvHJGbH3fZLhpKrgOcV6iaFWqpf4ywIBQKEAleUbXa2+yPmQ8Ak4C4gxhf8Hu9Vwprvbhk9seDdg/s7mMGgS6nAdXTp/b+j3w9lv7/4/uXAstrI6BMqFizWvTwz/LQtM7Ds/Xmpx62k+TT+EjhCYV5876zf7taelr1UKUgCSGX+X5nleLQBKL/F/LMgrqjHvpsh3vHOnTS2jNOtR0vpZ2PPjy33/aTzCOD3CrWltd4DPGZdn/eLTfd6Lqq639MfQwdqO7DxD565Lb/1v6JQ1cBvgPU1ZYlbgT0ItX1HkNl8we/xXnW62vvqvzXnu5tfee26C7+yG2LSBpGKBYvPBP7sc3Q8HEnl/m57wyMNR9OWPdncSpErRs7c7mamOmvZVZc8zyl3lbKx8q1ZJ8/6omLcZenWjSBQOVSTEK13Gu/wTStucl6nUF8FyAxtNmSTVYDYzbkneuLGkwqlBIkxxJ9vVoQCMxRqCeAGbEFWK9QUQQxbVKeh5Jm4y/5vbWny5XHnxXapBEv7vKr7PebYDa5jDSWXA8faokg51D9cSeMGQm1vZju+XiWsFQsW1wJPVS2cd1H/hzS0ZAbVDwKVQzGZm/DTv89K2c7MnMHpD6mh+Dx3RWY4q+WkW11iwOwdJa2ly1YeA+oxLx3GsTw68a5Zdw7ZkSW0ISgUmNLpt+7xh42ZgJWZLEJI3yq8jlDbLo+SMVhU3+MdNarGtSnmtt7wxM3v6mR1N9nmi/LGP3jGmZbML611FhpK5gLlALao1YaSpzv91suNxaklFefGOrIZ9qASCuRGPfZ84DpvzHACmy1D3bNpdOKxcefF3s92eFs4ereZ7TXEPqBiweIZ/ZGczL/3DFnZcIi5vn2C44hRz3mLvfW5//jkjDDgOLR0eWGRt77gX+tOrQMcB418qbTA01T03PoT1wOOA0esGJvvbi5eunHeh4DjgBGvTPA7O4perJ77HuDYv+S1yV5HpPCVzcG3AcfU4jemus1Y3ut1h68EHPsUvTXNaSRz3m44ZCXgmFiw6gBDLM/q5mmrAMeE/NUHgHJ+0rrPe4BjfOCjabYyZH37hI8Bx5jcdfumbIfaHB6zHnCU+jZNTNpOqyk2cjOQB2qP9LMUVbFg8Ttjc9cWWcpoqe6seBsI71v8xr4Jy1P7UcuU14HwYWWVUyJJ/6Z3Gg9+G4jMGvOv0eFkTvVrtUetBSJfGf84TiPZdNuFfxkQNY4p2zUzXdr4mSloh/uFO8jWchvlGUH1j2DaZ+bQvmzZJfI+06+Hg34KYkTJi/+dCyvuAp2wagNbKDAj6rG+70oYY03kUH/YiDYVpd7wRozf+KPmvaSvA0OydrWrUTWuMIAnbtZmO5YhLf1FYOtnypgLYutJz7IIoYDUlCW+4kzKuSWNzgLgipyw+SNv1FAqlPe0IM+25aWWB34Qfk/fIfq8DX/0zC5qclzmx5zrjRm5YZ/VWF+SvHdEgzNkXteeGJftALexyy2s6RZD9XJmc9vn6PikxFc3srpj3Ecp5VR+Z3txrqu9vD5Stt5Wpngd4XyPI1LUGiuqUxim04jnOIyUP5ryhUEcgu1UGAOl/jFlStIwxVIJ29MBpHyOzhxTLDqSgTogle9uKjHEtptjJeuB1Ehf9RggVRcZ9TGQGpO7di9bGbHqzooPgD1BTckkcwpYV+bfWBBJ+VNt8cJOwO82YyVxyy2fdrjrsRioSL67OZCynY2dybxNoCJ7Bj6c3JHMW1MfKf/QECu2f8lrBzZFR7y7oWPP1U4jnjis7IWpteFR73zcus8nPkdn4sjRz42u7hz70buN06tznO3R2WMX24Jqvu3Cv1jdBZBpPb4VOIx0R5wEuoV1SwvrElAuUKaJZVs4rwHwEP4ohn9vUGeDTOqS7KeA63ozbqumfWFCgSMVqjLTkkrKUG0CPzBtebM916rP7TDGCRLk02Q1/fNATxBCAakaF3c5k+IYVeNSgLlhTDzgSgilda44YG4cHS8rbnDu440bh9miWjpyrK8GOhz7AjYQR5cEZF8okFM7MjHfGzW+FGh3jAPGAyQcdtSVMpyAoVB22G/9ISfsWAJYm0sT+7kSUlfU7FwHWNXliX3ccaktbnJuAqyNo+MTvFGjvrjJWQtYG8bER3ujRlNJo7PJFmVtHJMo9EWMtpJGZ0fKVNam0Qm3N2pERtY74/UlydT7U6IqGOzFrezd4dOkPQwco1DzlIAoHhLkDkJtr2U3wJ3rTcJ6VXoM0nQS5jRi9aNyNrqrO8euStrucL67qWCEb/PYdW17vZ603ZFib23xSF9NxUctU15K2u5omX/jyJG+mop3mw5YnrJdsTG5a8tL/TVj36w7bKmlHMnxgQ9HlfpqRq/YHFwCktq74L1RI3w1pS9Wz10OpPYpWlk+wru5uHLTcS8Dqf2KXy8r8tbnLdv4lTeB1IEjXi4p9DR5nt9wwmogdWjZ8vyAq8X4z/qTNwCpmeVLPDnOTvWf9Sc1A6lZY/5Fnqs18eSab6WqFs7r15Mqk9At4dMWh+0mcxULFm8pKPfNG//4XjXhMeZb9YfFAd+sMf86cnN4VPiD5v1bwPYdVvbCVzaHRzesb5/QKNj+fYpWHtMQKauvj5a1ClZOec7G6a3xorZwMjcBdo7LSBQn7F4NkRY1sGIBT0teNOmrjlm+epcZs8bmrtujITrynbZ4YRzUlwETsEHuAf403JPVLTJJa3AUa9fWMO5ahTnl0072AvDf0XyybhN7ngbiIHN+dFfvqmlZFQpcA1wPSKYzzGcetkUhinWCtCvUVMAQJAU80Z5r5accqriwxfEGYHbkWJMsU+XntzlWAman35pkGyonr8PxLmCGfdZEJXhywuaHgBnxWhOU4PJHzHWAEfXY4wHTGzOqATPuskcD4k4YDYCZcNojRYlypqQdMFOmyheFMm2JA6YtyiNq6/Bbu8xGIbDlNRjyJRCDTiggwIS6EckbC5scRzotKctWKAplC5JSKMsy8Ro2EUNJVKHshEsVOVLSbNrSaYsi7lblzqTUOixptQzliHrtCnfcWO9MSXPKVK6Iz97LG5WPnSmjKelQ3rDfmuiLGO+7kkZTwmn7w357sj9svO1KGs1xl53XmWNNzu0w33AlDbdCnQKYmXO2OeK1/9pQkrxn3Hmxt7P12uyKXrawdp+EaWkDoYY104PfU+hpyD207IUJH7Xsk1rTOtnOdzcVHjjilRmftE6u29CxRzTP1TJin6K3j17btndVfaQ8kuNsHzkub83MjR0VVe2JgoTHjJQUeRv2aYyOaIhb3gCo/HTypSyQa6sWztMX6+0oXbbyWuDngIBSgrpl86wDf5R5bOv5oZNVbcBLt9AsAZwKlez025flhs3VwKiW/NQsy1T7Fzc5NwAzFGp8l4Q2ZhkK21BOZ8poAKykQ+XZhnK5E8Ym0rOnFdkGLm/MWAtYMbddbhvK6YuaHwJWxGuNsw3MnLD5PmB1+q09lSC5nea7gNWea00C7LwO823AastL7QskA+2OtwGrJT+1vyhi+W3p5eaC1AGGTWd+m+MdwG4sSk43LWkraHWsAqz6kuTBjpQ0FbY43gesiMc63xszpmcSXEuh7hPkTLp8FuoW1gHq0/PWpVCpqNe+whc1XwTMTaPiUzwxo7O4ydkAmNXliQM9MaOlqNlRl1me4YsY9QWtjs0K5dhcljzSFzGq89scm21R7voRySN9EXNDXoe52TKUp7E4dZQvYqzL7TRrLUP5mopSR/jDxlp/xKxPmcrXmp863BcxPvFFzcaUqfxtAeswf9j42BM3WlKmyunItab7IsYad8JoS5kqN+y39vNGjbWupNGZMlVu1GtP9EaNDQ5LopahcmIee7wnZtSYtiQsQ/mTTlXmSkijocSyRXlTDlXoTEq7IG6F8giyJYH+GaG2G7P6d9lFve10lfUkTMsu/cWl5z4tEfj0tdLJqTZo9aQWsEtiy1BJ5rb3nNKCDIayh+FuuNewDoH3ZK8SVk0D/cVlV+iWVG3YGYoJwlB8TtrwMcjPX52wapqmaZqmaQPaQOmdr2mapmmapmnbpRNWTdM0TdM0bUDTCaumaZqmaZo2oOmEVdM0TdM0TRvQdMKqaZqmaZqmDWg6YdU0TdM0TdMGNJ2wapqmaZqmaQOaTlg1TdM0TdO0AU0nrJqmaZqmadqAphNWTdM0TdM0bUDTCaumaZqmaZo2oOmEVdM0TdM0TRvQdMKqaZqmaZqmDWg6YdU0TdM0TdMGNJ2wapqmaZqmaQOaTlg1TdM0TdO0AU0nrJqmaZqmadqAphNWTdM0TdM0bUDTCaumaZqmaZo2oOmEVdM0TdM0TRvQBnTCKiIVIqJExPEFHOs9EQlmfhYRuV9EWkTktczvLhWROhHpFJGibvb1gIjcuLtj1rSuRORIEfmwj/u4UUQaRaQ2s/xVEdmYOe8P6J9INU3TtL4YjnnGgEpYRaRKRL6UjWMrpaYopSozi0cAc63mLRQAACAASURBVIHRSqlDRMQJ3Aoco5TKUUo19fY4IvJtEXmx7xFr2mcppf6rlJrY2+1FZAxwJbCPUqo08+tfA5dnzvu3+iNOTdO04UJEKkXkgj7uQ+cNDLCEdQAZB1QppcKZ5ZGAB3gveyH1ny+ixVobWHr4Nx8HNCml6rf53ZA47zVN0wYb/Xn9qQGTsIrIn4GxwD8ztx9/3OXhM0RkQ+ZW5dVdtjFEZIGIrBGRJhF5TEQKd3KM40VkpYi0isjLIrJfl8eqRORLInI+8AdgRiaOR4Att1lbRWRpZv1JIvKciDSLyIcicloPnuNkYFGXfbdmfh8QkT+JSIOIrBeRa0TEyDy2XkSmZ34+M1MisU9m+QIR+Ud3r0WX0orzRWQDsLS7WLWBS0QOFJG3RKRDRB4XkUczt/KDIrKpy3pVIvITEXkHCIuIo8s50iEi74vIVzPrfgl4Dijfct6LSCdgAm+LyJrMeuUi8rfMubpORL6XhZdAG6Iy52t15vz8UETmbHvrcwfn+Q9F5B0Racu8HzzZeQbaYLK98y3z+53mFpnrbm3mfHtBRKbsYP83AUcCv8tcV38n2yl17NoKm2lNfUlEfiMizcCjbCdv2M6xdpjfDBUDJmFVSp0FbABOyNx+/FWXh48AJgJzgOsyiR/A94CTgaOBcqAFuHN7+xeRA4H7gIuBIuBu4CkRcW8Txx+BS4AVmTi+CWw5GfOVUrNFxE/6w/1hYATwTeD3Ozppu+x79Tb7zs88dAcQAPbIPJezgXMzjy0HgpmfjwLWZtbZsrx8F16Lo4HJwJd3Fqc2cImIC/g78ABQCDwCfHUnm3wTmEf63E0Ba0hfQAPAz4GHRKRMKfU8cBxQs+W8V0rlZPaxv1Jqz8yXqH8CbwOjSL8frxARfT5pfSYiE4HLgYOVUrmkr1NVPdz8NOBYYDywH/Dt3RCiNoR0c75193n6DLAX6c//N4G/bO8YSqmrgf/yaVnV5T0M71DSn/UjgDPZft7Q9bn0KL8Z7AZMwtqNnyulokqpt0l/WO6f+f3FwNVKqU1KqTgQAk6R7TehXwjcrZR6VSllKaUeBOLAYb2I53jSJQP3K6VSSqk3gb8Bp+zqjkTEBE4HrlJKdSilqoBbgLMyqyzn0wT1SODmLstH82nC2pPXIqSUCiulorsapzZgHAY4gNuVUkml1BPAaztZ/3al1MYtf3Ol1ONKqRqllK2UehT4GDikh8c+GChRSl2vlEoopdYC9wLf6P3T0bStLMAN7CMiTqVUlVJqTQ+3vT1zXjeT/lI1bbdFqQ0VOzvfdvp5qpS6L/N5veWx/UUk0I+x1Sil7sjkFz35vO7P/GbAGiwJa22XnyPAlpafccDfM03grcBq0ifhyO3sYxxw5ZZ1M+uPIf3taVeNAw7dZl9nAKXdbLc9xYALWN/ld+tJt2BBOiE9UkRKSd+efRQ4XEQqSLeSrewSU3evxcZexKcNLOVAtVJKdfndzv6un3lMRM7uctuoFdiX9DnYE+NIlwx0Pe9/yvbfb5q2S5RSnwBXkE4A6kXkryLS0+vzjj4jNG27ujnfdvh5KiKmiCzMlAu082mrbE+voz2xq5/V/ZnfDFgDLWFV3a/yGRuB45RS+V3+eZRS1TtY96Zt1vUppR7pRZwbgeXb7CtHKXVpD7bd9jk2AknSJ9wWY4Fq2PqmipC+RfGCUqqD9MX5IuBFpZTdJabuXotdfX21gWczMEpEpMvvxuxk/a1/cxEZR7pF9HKgKHNr6V1AdrDttjYC67Y5x3KVUl/ZtaegadunlHpYKXUE6euhAn4JhAFfl9V60zCgaZ+zg/MNdv55+i3gJOBLpBuNKjLb7Og6uu3n7pbO3Ds7p7fdprvP7v7MbwasgZaw1pGu4+ypRcBNmQ9iRKRERE7awbr3ApeIyKGS5heReSKS24s4nwb2FpGzRMSZ+Xdwl9ranakDRmdqEVFKWcBjmeeRm3kuPwAe6rLNctJJxpbb/5XbLMOuvRba4LWC9Df9yyXdieoken5L30/6wtcAICLnkm5h7anXgPZMRwVvpqVhXxE5eBf2oWnbJSITRWR2pu4uBkRJn+srga+ISGHmTtMV2YxTGxp2cr7Bzj9Pc0nfbm8inXT+optDfSavUUo1kG6QOjNzDT0P2LMH+9iaN2xHf+Y3A9ZAS1hvBq7JNGn/sAfr3wY8BfxHRDqAV0gXK3+OUup10nUevyNdQP0JvSzMz7RyHkO6dq+GdIvnL0nXw3RnKelhgmpFpDHzu++S/ta1FniRdGeu+7pss5z0m+SFHSzDLrwW2uCllEoAXwPOB1pJF+Q/TfoC2t2275Ouj15B+gI4FXhpF45tASeQrg9cR/ruwB9ItzJoWl+5gYWkz6ta0h1Ofgr8mXTfhSrgP6TLojStr3Z0vsHOP0//RLpsrxp4P/PYztxGuv61RURuz/zuQuBHpJPeKcDL3exje3nDVv2Z3wxk8tlSOE3TBhsReRVYpJS6P9uxaJqmadruMNBaWDVN64aIHC0ipZmSgHNID+PzbLbj0jRN07TdRc+goGmDz0TSdc85pMdVPUUptTm7IWmapmna7qNLAjRN0zRN07QBTZcEaJqmaZqmaQOaTlg1TdM0TdO0AU0nrJqmaZqmadqAphNWTdM0TdM0bUDTCaumaZqmaZo2oOmEVdM0TdM0TRvQdMKqaZqmaZqmDWh64oAeuvOSpTOAIFB52aLZK7IcjqYNarecfvzW99OVjz6t309an1UsWLz1nKpaOE+fU9qgos/f7umJA7oTCsxYHZl99tL2y85XiANIChLUSaum9U4mWV0KygXEQebopFXri4oFiw8HlpG+a5gA5ugPfW0wqFiw2ADOEuw/KERA9Pm7A7qFdWdCgRnAkrBd4AFEEGyUq860zwb0yaRpu+iPV033QNlNgAcEUF5Qv7vl9OMXABHgKHSrq7aLRvo23VQXGe3MLDpJt1Tpc0gbcCoWLBZg7/1LXpvfkQjMhYmFQKHaWqGp3CDHoM/fz9EJ684FFcoz2vWuvIbCRmEBb7lS2Y5L0wad+3564Nxoc96TgBewQEn6EZkI/AcUpP8Tu+X043Wrq9ZjnYm8isz5kwJJApVZDUjTuqhYsHjMPkVvXYCSM2B/D8iotxsOocDdaDkk+XBKOdeCugpwghigvnfqbxdUlPs3fue2C/8SzXb8A4VOWHci6rFfccWFYucaDJJEzDhPeN3xGof6U7Zj07TB4pbTj3cAP4Sy6013UhVN2nB90wdjnwUJkk4s3kLs+1HyDRDJlAoE0S0MWg9ULFh8KOSNA34L1KNrALUsq1iwuHjPwAff9Ds7Ln6v6QAfOMa/33QAOc525Xd2PBtO5l1f4G584ajR//nwtgv/ojLb/Jv0da8x19X23f/VHvltjxk5tmLB4iuAx6sWzrOz+ZwGAl3DuhOVlTKmsMnxl873jil9q+bKvWbm/0bdYRxy/NnHXFcF7B0Mqn9kO0ZNG6huOf34GU5/9AJQX02GfQXA/wGXXvno043bWxdYQrr11Xb69/+B0zej1E75ntL14trOTL3uL890JAIzQUZXLZzXke14tOEl01nqmFxnqzWh4IOTVzftlxezfHsBuM0oXkf4tdZ48V99js7KueOeeue2C/9idbfP+feeIS2x4p+8UD33WyBTfY6OT2aUV97zx+8s+n+7/QkNYDph7YEfX/6f/41KyUGXjvxmzCXJt144sr3NNpkITAwGVTLb8WlaNm1vBI1MAlqZaS3Fkx+5p2jSHota1nxV7JSvyFe86kCnv25GavWkTkcyPjFR6MhNOOrHRBrXikpVe125Z2E4iiHdKWuWTlq17fnO3d+e+WzV117ap2jl8qd/eF0w2/Fow0s6WVWVIC4AkxQF3qaPGqMj/2xgLTlhz0dX9uWWfsWCxSZwRsDVfE9botAN6jmQBVUL573ZX89hMNEJ6w5UVsooILb4ne+0l3xwUqLFGe+8ueDMc4HHWwOpj9/ZL3LkUXNUXbbj1LRsyiSry0A5QSkxY48qy9euIv88OR7/uDS9luDwHI7De8jnthfbwpnswJnsVIzoaI/ECl+MNv91nuHcG5f/OEApkKsvWzT75i/0iWmDwp5XPflbSxnfnTf+bzPuvPiB17Idjza8HHL9Pf+qj5Qfl+5AimVI6oa1N5/08/4+zqV3n5f3eu3hP2yIln4HKNq74N1Pxgc+vuDuS/6wvL+PNZDpGtYd+znwVbtjzMN+20Fn4btv8bO2/2v4ne+fJY3OEw563X9dpSmXA2XBoKrJdrCaliXfANzpC7agLO83xbbaSHryAVDpR8o71xHYvCbpinQY7mSn6Ux04Ep24EhF02MFoARThcUyNr60/14ftSU+3Ft5Dldi5gmgExHtcyoWLM4Dx3nAozpZ1b5oFQsWHyCUzs509rNAErZy/Gd3HOuui+9rB66rWLD4ljG5a+9Y17bXWR+1TFlSsWDxIuCGqoXzhkXjmU5Yd+w3wJJ408Rf2ShG5K6/GCDst08KtNn/54ua3xlX5RqzviJxcGWlTA4GVWuW49W0L5zDV7dXKjICwAKSYiSOOfqV+ae9PaLs8oZcH+MbWimIRmvzyt5OmO2uV8xW5xqrIGknpoVHG+/6q6XDeaVCOREEW5qAbx74/rrAssljoeWvYucfz4S25Y+/9Y2zP3B96LvbiJpvAh8C08mUIUz+YLUuFxiGppW8+ruVDYfmGmLdmu1YtOFl32sf2QPy/qUw6wX7MoXsyxfQ2a9q4bw24Owz77jiFy9Wz50PXGJK6sJTf3PViveapp34/o2nt+/O42ebLgnYiQt+f/Heo9899UMDNl33+y+N2fpAKOAGnlOoGe/uG32hqTj15WBQ7XCsq9u+PfcUO2nOtVOOB/RQPdpQ8dgd50hr1ZfjYsSiiY6KhUDl7MrL1trCG89PqRg1si2s9t/YEAPm7CipXD1p8tb618kfrF6xetJkeeugvLM2J4sfAARMct2zOPz1uzHs9ChYCmWTHiBeCbLT/WtDU8WCxY48V0t7gacpsfyn8/OzHY82fFx+z9kTXtl89LstsaKUpZyHVC2c9362YqlYsHjvvQree/bjlinjgUbghiJP/d1vhM6NZyum3cnofpXhpbJSpLJSrqmslL1VR/nvS2wDh7fx6c+sFGqLd/qt0+JupSZ94D0iWJk3ekf7u+X042ekoq7H7JR5EbAk0xlF0wa9hvfO2S8ZLnWarvDtly2affOhrm+sUw77uSa/tyhlmvjjyYfoJpmc/MHqFZM/WH3zlnUmf7BabU6WjEo/KoBtRyVO5ZG337d64plnAmfY+amP0o+KAF4rN/Xw65ePPHX1pMkzVk+afFUmCdaGtq+1Jwq8sZT38mwHog0fFQsW+55Z9/WHW2JFztljn/lJNpNVgKqF8z567ic/3sPrCM8AVgG32croOOmWn/08M4PWkDLknlA/2Au41lZyZKJ+/8MAyny1v9t2pZwfddY2FaWOc6SIpky15MXnjGcrK8W9nf0FAcnM6rNlBhZNGwpOAexo05Q7Vk+a7HJ94HsLW6au29+1SQw79VFZ0SW9bPmszAz+DpAynKNBHOduLptx79LgnevMVud5gkQBS6FSRtSo8D9f+BjwInAjsEQnrUPX/HvPEFBXAp/URUY9ku14tOHh/N9f4gL1iKUcB9nKPPXeS+++M9sxbbH6xtNeAeZMH/HypW5HNPl2wyHXAW9OufaRE9Lvl6FBJ6zbCAbVR8DoH1Q+sLY85vcn3G3hc2+44r3trTvqougSQU4xLSomr/Z82ZmQCV0fr6wUc/TM9z2k57YGJIWegUUbAh674xwx3S2XGs6ON2ZXXtYI3Ofc4C2NTm9f1JLMG+kraau98tGnI73Zd6Zs5iYA033wE4ajDNLf+JxAMJMEzwGuFeSo8Nzm8bbTXkL6emYolBv9xXDIiqT8F4Icskfgw8erFs7rdkxLTeur+feeIQ2RsrdATjTEmr9u4QlPZDumbVUtnKf+9oObFtWGx+QCZwB54WTeUysbDmnc46qnPj9EyyCkE9btCAZVQ6mj/VejLQPieXfsdOVQ2/ONxam7ipqd7P+2b9uhd+aN3H/ddQV7VW/Zxy91Das2WFQsWDyjYsHiqzIDY3/GS0njciteUBQZtbwlNSL+AHDGJ4FRi36cf1XCirtyw3UFoS3rTn1w6oypD069auqDU3vc6lkwodoP4Cu2ajJ3J2xg65SbXUsJDv5NQ5WRNK5VqKhK99g1lKhX+/DUtQHs1c1HfcNtRpP7Fr/5m2zHog0PT6751tXvNB60z/QRL69Ye/OJO88Jsqxq4Ty7auG8h4FJM8qWPbY5PNptK/PVigWLl0y59q93be96PljoUQK6qKyU7wNH/XHV9y51t37pIAPBlZ6dZ6dKLo9cHl2YW5ATNr9Vf6fvwfenRM8NBpUN/BOYHW0MvIio7+eUNR2625+EpvWDvX76xFngvg9wgM3kqx9tilo5dU5XrTe3ZFnF/uFysbFpWv/yMY56N89O86p7Z9mXTFv3NAoHjx/d/OsHH5j20zxHdDQ4nSAKiE99cOqcVees6vZLm50y4wDJSFl5+jfyMPD7HU0gkOmwNccKJL9vtjlPRTEPWNpfr4c2MFQsWLwnFASBhbdf+FBDtuPRhr6JV//fheC9AfjzG/Uzz8l2PD1VtXBeAuadXrFgcS7wG1Dnh5M5swX7/IoFi48ejNMX64T1s2zA+m/1MSedlHCBu9Umnt+jGSW8MePsmNveq6TBcXZ5tTOnslJOzSSty4JB+N1FwagVd03ardFrWh9ULFjsEuyTx+Su+33S3rMIwPBW4Qy8iWF2ePye2lGGqyWQBPZY+xVS9iec9UIb748xue/wcVE74fGNrQ9Tlwtt1vh8Oshvcdcqw9UgCKIULqyc44FuL5RtVf+fvfMOj6Lq/vj3zvaSbHojgaUnQECaEOoIYiG8iqKioCIqiqKi8lODBaOoxC4oSlEpiopdIaK8AhdRwIIoLZQAG0ivu0m278z9/bEb3ogQUjbZlPk8Dw/s7txzz7C7M2fPPfd7YjwA4HGyG+TeyvApAN6ua4yvVGBXVmLScgAPHurXW0c83BpJQaD9EKc79VK+NcEDkH/tK5CQ8Dczl977lFu44lmN3PqL3aO7w5SR2uZklUwZqVXGtMzj8GrFypi3tGo66nEdbm1IJQG14Hm2mOfZdQqGWV3dHFNri7bOWTaufh/QdIsgEzDOqhPR7YT6Gn0Vl0Mp+U/Ny06L/it7WbD0A0Gi1TFn+W1DUl9e+C2AHAZufak9iusVcmAHp8l2arssgzL0N5CgLB2nrMhOVFe/N94R+36YPQZ9s/eCAUg8LThv2hT5oDznKkekVcRJxRBBLJgyw5E3/Q65tceeWlPJHCUTHh/49BpP8oJ1m41pmY8MfebdKx5YeUt0zQFLZ29NWTLzzRUAeRoAPLYfONGTD/jqV+t5Sl8wMA4ecjcDkzZgtRNmvXN3fJkj8to+YX+bTBmpUrMWiWbFmJY5lJ6+cn6EtshyaZcN15syUttyG3YKEBcAj3e1i027afH/DQ20Uw1FClh9UEq6A8Bd78y6obOHG6wAIY6K3i81xIbiyarqsnDPME5ERfJ+bVRUkby2iO9+APGvTp0U6k+/JSQaw9yV00nXtA3jjWmZX3x3csqvh8ou+o9KZj8IYKLNExSxOe2xMZpOn/5EfPtLCYEHwBefTT15Z3LOZBmYiMjSvSAAOEB2zYkdEZcVb30LAATIbj76wpS1pozU98WwXV96qwEAAGKI4cAvMbq8vGpXcE8AL5XYYzdtOH59Yff532RNeHjTBhHiDsGTOwtgvmuTCNF9+h/1q/VgMLwarQCghLQBq13w35yrpjkFDSK0RfMC7YtE++aSF15PBpDJwBUW2+IS35z1QUGgfWoKvuX/8QAWAJimkjm12eakn6cufjThAkNbFVLGDwClJA7AEUrJo/tKlo24yEPAIFoJuAb36e1yu+O3/OWaOdFFio+75Ki+MK1SdzfOdFiC4kvKq3IjEZF06noAK/x/FhISdeMrtr8iRFXaRcFdchMDpwRQppI5lozr/N2Xb9+9akfNsclrklM4BfrB23dQQK2A0VnaY7ih8jhUrkoRIGeCyQTx1Esi5Ee/eueJT2pNu8/7F2OEEKdTe+yRTfd8ucvnT8SgqF03ECJes6dohDtC5MaJrpMy0eUV5WDeP8wSQg6UicKz3+hcu+fU71Qp8WYTVERS5mgXGNMyFQAeALBl7X1LNgTaH4n2y+xldyRWukbuUXBOl1tUjTJlpBYG2id/4AtadwHArHfuDt+Sk/pGiT3mY2Na5gRTRqo9wO7VCylg9VIJ4IHj5l7biqydnurtYdBH/V1227PzXA01RCm5Ab3xY2WwcCzxiKanWIatSDcM0UbE/VWVGwlXlSa5GfyXkKgTb7DKKECUZmcEIjSFVQOjdr+6t3j4s4efv94BXH/mWO9ufu+xAPMA5F0Aa/fP2L9rxbyP+jMupndUyV5GQK4DkAiA/jlTzGV/yUYZuhTvPGvqZO8uf7wO4PPaG65MGamlQOrbr06dtGqM5vcb5GrVCIf1fwMZgENBvbFNk9wfcH0OoGz4wmUlMdp8018lw94E8LspI/VfG2+SDmftOpSU+Bhh5A3GsYf6HDrc5mq1JP7JiLgtL+7MH99Jr7BIjQIkmg1jWqYemPyhjAjc+M7fzV1xz4qjgfapOVh5z/KlxrTMEgDrVTL7p3NXTp+8eNa6Vi8RJwWsAHieVQN4+/GF79wfK8hClIIKTJQtaagdSkk8gI8AZCTe5OpleU33nqFSfjuAp4r+6r4QQEVlbqTCz+5LSFyQaG3ukiJbvNL3UCi1xyz64+k7zpZhq4GHt2YU8Eabp2oCTV3I/mXm6iiEVR34Ielw1lc1A3649bJbmMhBpvS8U/Pco19Hy/RcWJqHkT2/33roH8u4r06dxEX2M91XlR9+NRA0xGNXBYtuucApejDRfUQEGDjAleg6cmVltFKzp2hkFwAXe0TF9X+VXJwI4AoAGLDgA2uouuy4qbLnagBWjnjiRCb/4eteth6qIzq4e9i6Nvk/TyKgGNMySai6/42RmgLniLhtG4BpgXZJoh0yd+V0DUdu+EJk8oEC4yavuGdFu87kmzJSPx3w9AdJFmdYuqmy504ArV7FqMMHrJSSEQBiAHyjlL3+bG/RKTAoRVtp/3cbaovnWS6lZDCAUwCwP9k+t+9BzYhQs/yZW8ZsVXzw07j9AKQMq0SLYkzLnAXEDyEQwUA8vi5StI4hFCACALmvUP/MsY68vhcZLCeAocc+qj1AUcXdDLloHfKl8ySe8z6X49RMrxblhhR9+e6a495/fNDYiuNxVwCYXnLAmMDJPSLA1so18q8Uuts/4+SebdbCwwvgDZrpY2s31M6OLgdwx5Bn3g8utUcPBNiwaF3+faerunYG8BoAiEyGK0/ufFJ5REcAQHFUOzcrMekrSSmgTTOmwhEZq+Qc97aFLJBE28OYlkn6hif+JTJ5LyXnuPfoC1PadbBaAx///bPHzElX/F0ydLgxLXOuKSN1caB9qgvCWJtTafArlJK1AMbfv/XD8VWukKy5NregBvntniVXj2igHRnPM+Gs53ScgL/779N2D66UYV1Z0r7SstB+0RedkE975GjH/o+XaBGGL1yeVmiNXwRgE4AXAYwAQC+kwZe8Jvl7AEMBTKrJrq6c+WUflyrkoPH0hpzUzNeNNcfuHZw49qeuXWm0xYr+p4uZEO52cFXy44V6Ls6hRIhKJXeflke67CqZxq5QyQljCLXZq+LMVeUxTouHBHliCpXjqo8Zp0Unn3y9IsR9UCkvUh4lAvGIek+oECzEyguVR4lIPKLeEyYECzHyQuVhIhJRCPKEi0FCdLk5utKsDIpVe5xIqC4Gga8QwStV92TS4azzZZMlWjk95n+d6WGKiwF0biu1dhJtC2Na5kIATw6O/mXrFw+9MD7Q/rQkxrRMGYDPAEzuEpx9z/bH5y4PtE/nQ1IJAGYC4KtcITPCBIhKV7AsKG7XqYYYoJRwALZTStJqP8/zzCrK0N9iEHozgvy+nLmn6JFz1QVhffx5AhIS52LKa08sK7TGLwpWVuwGcI0pI3W7KSN1UX0Eo6MVjgSDzO2oXXMa7Dn6KgAEV+QuqH1sdoLhRY9MBr3DV/ItEtGtYFVFISTUrlKV/23orCrXa4PscqU83llmG2U/bL74VN4fnUurD8kIK6swdHeZ4v4TonSabaHmrGymFm0grBxAKZMzs+9xKYBSpmAWphZtAMoAlELufawS3KeqlFpRxsQzwSoDIALstYFTO7fl7i4dmfEZr8z1MNnEOF3O31KwKtEcDHj6g3kAngTw3p6ikZcG2p+WxtfeeLpBVX4it6rzsoFPr/60tV4vO3yGFfDWrnxvurZwpFVbONih6qWL+b33bemP1bvYmlKiA7AYwHaeZx+c65ic99WpssMRn67PuUgbElx52x0rf1rjL/8lJGrju9g8BuDq+KCT2YOidg9aMuvDqobYuOyjnsUcGPl+WnYkAPw+L5IcLH9ZlAkuDPntdVnS4SwRAF6dOikFjP0CgHCMYdjxAmeozXHJiwurH99jC5l08+7wH+Xl+pqbgAfAgnnrN57Jdi6dvTUFYNsAooJXbWDs+bpZ1ee8J+T8fuuDez+dCTCFSDjurQHX4AdjigDABWB8W+zu0lHxbRT8Cd7SNQdAxknvn4Q/ufWtB57ZkXvpAr2yameVK2SsKSPVE2ifAkXKwnemFlgTfAovxI5WeL3ssDWslBIlgB8BvFxif7i/U1AH9xM8lYBqT0OCVcCbSQVwZ51zdcNKg6osDzno2VVX/gbSDeuQbumwXw6J5sF7kxd/Ajg5ACG3qusdPz9xX4OCVQAocKvz4KvFBoDKrAmzrJ07I07x+cma1XDO+AAAIABJREFUYBUAwNglAAgIgQiIBztFrFpyXeFutT1oaIzCUax2yEQPGODtsvKP2tmls7fKQTxPgslUPmMEIDwa2YHFe3FN3ZWVuH4tAP71UVeP+zF8+KUAZPhf44FWdQGWqBMeIDX3KDmk90/CjxjTMkcAlz0apS0oHh67ffKSWR926PtxgbVzN3glDGUAVGiF37eOXBIQB+9NzL0zf9xwA9zlKrcmXmU48VtDjFBKLqeU9KjrGJ5nLgA3Wjo5Rmm0dtis2hAArzbedQmJc6PgnBNr3eQZgJGNNGWAV+4NAFCmHvgEABTZUx+tfVCvwjJv8MoYQIizUqtaC2Csg8mii53qRz02TV+A/AyQpwCMn7d+466ls7emLJv75ToAh8DkE71+sn8FtI0l6XDWrqTDWYt+DB+5ACBOn6Ir5w/bEi2HjHh+8r13DA1rHCEhUSfXv5E2CmAbAJJbbIvrt2TWh/+Sx+uAUAAu7/eNcV2Cslud/myHDVh5npl4nqXM2vzlfgATUwTXYYCDPuaPH+trg1IiA/AOLtDj3DffTzzPiqF2H8l3BtkAPFDxhu61xp+BhMS/cYuKIF8FZ0O7Q/0DFRHiEpT2aAD4+va5c0siB3YGYxDkmrXeZXxvmUCsovxxEAJVSPVB+AJSDRGeB5hj5EldLIBOCn3szpjBiV10MTOnLJ29dRPAfhGcIdMA1gPAfICMrAloG1sOcC58y1mXRGoKcwDCDYza3aa6unR0LjN+owMIYnS5f6IVLk9KtE2mLZnXO9ucRNUyuxrA5efScu6I1HTDClaaV3AQmNWjvzbQPp1NhywJoJSEAbDzPLMnR+xZ9mdxCterOswJsJOc3P7VBQ344Hkm+GSxguo5L1Hohiory/WaLHdwTqLZ8lDZEt3B8Aes7zX2XCQkajCmZaoB7noAfwLkc9RDDeBcJK9JJgSc0iZy3ZPXJKc8gWkvgZz5bXtmaV29J2hSrtgzCAAUuotyVIZBY597+IMHRqvvHBHsiIC+9NAi4DA4+ZRHzSeU8GXKLDizgR8CAOILUpslGDFlpO56YOXNQ787OWXf3uLhLxrTMnsB2CIFP62f3fljOwFAQpDpyd1PzZbeL4kmY0zLDAYuWS8jHuHSLhvvXj773ROB9qk1UdMNy5i2wV5qj3nAmJbZx5SReijQftXQUTOsTwE4seqbnpqjFX1Hd9efKFcxMgIg39xw/5p67ULzZVfB86yQ59mx+ozZ807q8MrcyM4AR77L7h9ncgQXhpfLX0O6oW8TzkVCAgAwNGbHWwDiFJwrrb5qAOfhEgZCyjwqY4RZtrWK9VZ6Y03mQa2sbTHGpB/tdgkAwG29dKKrKmGRwRZzfagtFlZFBTyew3AGqyxyleOa8N6fXhme+FkwgIm+gv766MH6hSWzPizxiMqXABgB9iyALa11F6zE/6hwRiQCcP9eOHproH2RaPvMXXmzTsG5NgKkr8AUVy+f/e6Hgfap9cI9DzBriKqsVUlcddSA9UsAi57Z9caIanewYbCb7AKISh/7W0PqV5dQSj6llJALH3oGHuzM4YrNuYkfArCJhH2f875akrqSaDTGtEzF4fLkmxKCTlZP7Pp5vctazsN039/cDb+PVrlUIQgvO/AeQBbAt2z/15SES80hvQYxoQwgaoBoRE5RteTdYY88s37gC/gr6kNRLnhQqCnJmL1k8tc3PrTs+xsfXFbty6aOB3DGVhN9rS9qbz0kIQCryRJLNAfphhSkG55AuqFJPwr0CsslcuI+bMpIbXCLbAmJ2hjTMrkjFX33ukXlaL3Ccp8pI/X7QPvUmjFlpJZeFPkbNTvDR81cOuf2QPtTQ4csCeB5tgPADvnmrz7ziMqKblVhKia3uTRhh79sgJnTACp5vkG6YBTeomYVQDhEVo91KcTr5B6yI7xM/jPSDXFItzgaci4SEj6mV7lCtBqZ/cbFs9Y1Sauus8I2+JRbg4uPaAROfbkspOwABhxYNg3A+JqOUcRD3pPBClEoA5GFgRDOKbqDPglV2m8p8yjEoTnaE4yw+C5F2n91TmnOEoA6oATMzUAU8DYToC08f/sm3RBi0wj/ccvZ08GQdSPeqo8nkG4Yj3RLg9/ruSunE4ZJQxLD9x0FJvvfX4mOxtrD5f179gg59POPaY+0qqxha6Vz8Ik7DpZddGzb6StvM6ZlrjJlpAZcA7XDZVgpJXdSSmLvW3FrDzByXU9D1l+CLWaQ6NF+csP9q531tcPzLIPn2fyGzD1v/UZfdok8qQqpPmUtCh3yfnFyeXGU+0W9VRYK4D2kGxqSsZWQwNyV0xUE4hMA/iq2x37aVHtmUREdLnOVX3ZkoiDIVOh5/Cvgf7WryEpMmqI4ok1w6ZVlTCwDAfkdvmxpiMx9dQTnFiIr1N2D48oK5q3f2CrE3k0ZqbsYuCsBiAD5RKphbSLphlEsPXil+TXd367ng44CKNPaZWuDqmXd4a1RJgxMWa0TbmqM+cyT18Vb3cGkymX4zK9+S3Q4uqZtmAvvqhHLNvcZLJUD1Y8lsz4scYuq+QAZDSA10P4AHSxg9clPrQRww47cCTd4mAJjBOfvAMIAfFNPGxdRSsY21od56zfumrd+4yK52nUpGHGDYXPMPfb58HbamGbVCm811rZEx8TiDH2FgevRK/TA+039FZy8Jjm6UlDEXLq9189lEaOUsfm/iDpb4Zna1azEpBQAnxAQ4nLrQ8GcED15a2uW9nOcGld0XtAxwaUgjJHn/XB6fsOUkboFwA4ZcQ8MtC9tmnTDJQC2E5A7DZWy/owgFMBzAMYwgCe+GmVGQI71dFxLKWnwSp5HVA4CgJzKHt/513mJjkawqmKRtxwIBLV+eEvUi3fVMltBpKbwo7krpysC7UyHClh5nmUD6GV161ZbXGE3Afi9CxMnEM6FoPgd2+ppZj6ATyklmqb4MnvxzmOh3Qt+tBaFxa9eMOAJAC9YtcJ2nU12b/FS7cKm2JboOBjTMrmd+ZdMDFGVVSSF7bugvNqFkEOcoHEwdKu69D+c6BYjy/ZfDV+9qa8cgGdgcgCwKjkOAPQx5UUAkLwmWeUBl9DlZIgAoKI6P/ycXd8CyaCoXWaBKZLvXT6zX6B9aYuYVqlDXQrxa+bVtQUAj8rFvYZ0y9NIt+yQPV25Hb4a5aogYZIlRLiK55mHUkIoJdr6zpMQdOJqnzTbvuY4D4mOQdKT68dYnOEaAibA22lP0vNtAKaMVPew2J8+KbHHBO0q4O8JtD8dKmAFAJ5nx9YevHcCgH6Eie9WFwyNUWiL99/65NMV9TRxG4AreJ41eanTVho8Wa5xlFblhz+3oZe8d0mk5z8OlXgiskT+CNINw5tqX6JD8B+XqO5hdobfv3jWOqGpxnqorQtu/bkbKkL6k7CI/x4atfOLjUmHsxbV1K7Cd7EXCIFD5gYAEPkEOQD00VSlyAQii6xS9FXoHNvmrd/Y6jbL6JWVqwFgVz4/OsCutD3SDapOecoNCjcJhvfm7yHnUnpIt+xCumWR4WHrJp5nf/qevQ/AXkpJdH2mUsvsE6O0BR5TRqrVj2cg0cGwe/RpAEoZuAnw/fCWyoEahs2t/z+A/VFsi5vnlU4MHB0mYKWU3EYpWU4pUVs9+oVKzoG7UHacicpYV3X8knqM5yglHM8zO8+zvf7wac6y7W6l3nGHq0qL/F97P2ec6ahSO7lhBCSPgX1b/bL+dqQb5jd1t61E+2TuyukkRFW2hINwEsD6ptpLXpNMFEe1MUHitVAIFgfizKPOPsYXuFYVxQ49wcRygGjgto55b+nsrSkhMveU+GINiCDjQroWtsqbwk+5l38LoLjCGfGvc5Oog3SDAsB6hYeMdCnZXAIyBr4AoJ6bqvYB2A6guD7TZZuTBLtHu7nxDkt0dG5+88GrAFwJ4HVTRuq2Jkr9dVg+e2iRCJA0AJ27Go5kBNKXDhOwAogH0PfeHz9WHCgdlBCiqvg+ijin+sTMN9Rj/G0A/qCURPnTqXve+vnboE6lx6zFIVPefXToMKRbSkXCJgkcwnRW7j0GthDAFilolTib/OrO95id4Z1HdNq63ZSR2uQ+2N0s7oGT/pwYVB3UGQJTvXLD/assZx+TlZhECIimKOJic41CALw77/k/rAZ71wItY2DFcrXrX+oArQFTRqrIEc9/ZcQzacZb93dIlZSGYlqlVpkNnmwAVwO4T/V41ZKaLGp9FQB4nm3neXYXzzNGKQmnlCymlJyz4YoxLTOKgYurcoVQP56GRAej2Bb7plpmw7CY7e8H2pe2jikjdUuX4OzCYmvcA7e8OTcmUH50mICV59lzAMbYPEE3AERXbI9dyETFdFXIieo5y8YV1cNEOYBsAH5v46aNtFwDMLvgktOtP8riuacrs1xKcTMAEBAZpEJxibMwpmWS34tGTZMRT0G4uvQ+f9i8bl+37yuirgTAIMrV82pasNbGNrrCAECuJdkVTCgDJwsHaurCnIqkhGItCMin0x456vaHT83BqE5bLAKTB6tkzmmB9qXVk26QdT6lXBVikXcuinJ/i3TLUj9YHQdgFoAe53pxYNTuyQAQrc074oe5JDogxrTMpKMVfROidfkfrX/wpcJA+9Me6G44cp/VE0R25F3ml/tNY+gQASulJBQAeJ6JkZqChWqZ7dRDZvVpZ6VRC4ZV9bHB8+xrnmc3NFB3tV7cPD/roCrYvqi6IFyd/d3QaQCgdcgWEhAPADBvwTj197wSbRcCYQyAkQKTP7941odNrvPLSkzqoy1JiQQh8HVOPeePJKYTuwCAKnJPF8AFtaGyAj5Jq96lygkykRBddHmrXspVy+xvAIzR05d3D7QvrRnTKrWMgS3nGLlJJGxB9L22q/1hl+fZZwC61pRW+ZRXzsj5Vbv1MwBAZCTgu5Il2iyPAcSRU9njwUA70l54f87bXwD4GMDDxrTM2ED40O4DVkpJOIBcSsl9l7344ogSe2zswKhf/5KDXAUATkv3OndWU0rCKCUzalqxNhcOs/4FhdaRay8PWrTupd6dfEttlwmEWV1KRkxdnK2mn69EYDGmZaYYVBWZSs5hA9Dk5a6sxCROBN71yADmlX85725azfYQNwBY8iL/AACPM+idOcvG7Xrk62hDVH6QStC6EJxQ2qqliFbcs/IYQPa4RPX4QPvSakk3EH019ycBuUMk7AXu6Uq/KpfwPCsCAErJEAB7ANwFeD/bxyr6DgMYSuxx6yTNTImGcs/y20cSiLeGqMo+NWWk+n1FtIPzFIGo6hf+56ZATN7uA1Z4BdheBkCPVvS7HmBuszNsjlKf+wCR2U/PWTbuQstOtwFYBaBXczo5b/1GIbRH/lMeu5JzlAdlUkpCkG7ZVhDnfkbl4pSd8pTpzTm/RNvAewNn28zOCJ1bVKoAXNRUm47Blas4ICUnNhIumT0fdbRN5ewyPQBUgEUDgMehYACwPzuhf1yJBmaNZ/O0R442Wa2gudErLD8TiCn3r7ilS6B9aXV4m5e8HFGm6F8a7v79VGfXk804214ADwJY533IeIBwdWX5JSTq4u/ioY9xRCQj47ZKmuZ+xpSRerx/5B8HDpZd1L/XE1/0bun5233AyvOsnOdZ+rO7XskG2K0A+fomh+h0WaN76WP25NfDxBsAUnieZTW3r7c8cWi1Uu/43GyKGlBysPOdABA/y/4ygFUKD7kf6QZJ8FyCB4gSABg4hibe0A/16x2v2q+fnttDZCrRCEP03tI5y8YtOlewCgCu3tbeFVoVbErnJQAgOPc+8urUSSm9ijWzZIygVCu81xR/WooRcfQYA8eZneF3B9qX1oZbLr4CYB6AtyLKFMOMMx3N1pKR55nA8+xNnmfVlBL5w4PTryYQiU/oXdLMlGgQxrTM2Hxr58tExr239O7VfwTan/ZItjnxcgbO5hLULa4X364DVkpJf0rJKEoJSQgyZQAkrGfIoW+q8keMB1PAVRX/1AXGy3meiTzPfm0pn13VmrsIgaXo764Pf/Ryr5oyhHkMrMyhEr83rVI3qWGBRJuH+m7mDE28oWclJhHi4ZbCRVxfjjF65EwJmaCqs8TAE+UaUKav/RFkCgB8tAO3VGrdONCt8uvG+tOSaOTWVQRi9Y68CZGB9qU1UfKWdqPCwz3sloufAJiLdEtL9g/X94/cU9095DCClGY3JM1MiQZiUJU/AzAFAxdQ+aX2zMGFNxUCeBXA9Tct/r9rW3Ludh2wAvg/eFuuqvYWDxsTrDTb+oT/9QmAyQCKnZXGrecbSCkZAOAkpeTiFvIVADBv/caKsF55nzst+lh7edBrlBI10i0VBbHu1WonFxVWLn+1Jf2RaF1M6vZpNUBIpKbgEJp4Q3f2qZ4H4KpqNcmwepK83auKhnxU1xjNbsNRjdMN35It4N0Y+LeqKBgl8ZXYee/eVtcs4FwsnrXOzsBtBnC5MS2TXHBARyDdMC+yVJFqNngO5XVy34p0i9iS0/M8MwO4LNuclFnlCtm3+opJekqJ1JFMol7cv+LWbg6PZlY3w5GDpozU7ED7056J0+e8rldUivnWhJUtef1s7wHrPQAm3vb9xthKV+jASlfIiyMdCjWR2acog07/OWfZuAvV2u0HcKwF/PwHSr39bk7p3luVH/6Aw6J9CgDi7rY/5pGx74KrZLcj3dCs9bQSrZed+fwIAEgM2/9UU4LVg4N6TVRka17yhLirZ98v88RW9iAOhaVkzrJxdW5SIG5Ob1UrAQYmU10MuWZsBgAjAUFWpPNE8prkNrNJJkaX+zuAhBBV2fsdenNPuiHF9VzQNgCvAPgsxCIfYJzpCIgsGc8z8X8/hvAGgHdqKwhISJyPTSevnekUNOgRcrg5a64lAOx88l5z5+ATy3Iqe4QBuKyl5m3XASvPMyvPs1+jtXlpvgYBq2wl/aYwQSPXRf/52wXG/s3zbCLPs/q2bPUb0x45KoguxQzBoRCPfzf0zKYauUDuAGD3yNjHplXqZlUtkGidlDuihgMo35F32TeNtZGVmJTC2WTfEhdHOItc1bUAC2OquuFk2P7wCwWcrgRH/8IQPdShjlKFdhR0UeFVHp3zkQq9C6UGtxHAlrYStKpltnIAMDvDZgDY0iGD1nRDCgP7SenheAYmAliCdEuTm1A0hUhNYSeD0hwJYDyAW3zNBlSUEl0g/ZJovRjTMoM8TDEHwDcr7ln5baD96QgcKrvoYQAnAWQY0zJbJJZslwErpURGKVlPKRk3d+V0lUPQ3NnNcLTIlJF62lo8aBgAW1XuqHPWuPhasN5DKdG3sNv/YN76jfv1seVbHWbdxLUL+82glBCkWworQjxvyQUySGMnqwPpn0TLM3fldCLnXKkcEbaZMlIbvVzLwMYB4AgIwIis/+k4TimokRd0HLjAJi5zmHxYtVoJdbhrNwCoDLsvlVtVxpOxNsB7PWkzO7tNlb3CvaXAhKAN+e1neAA1P35FAKMD54oXg6o8TqesjOB5VsjzzOR7+lUAv1FKtAF0TaKVMiDyt+UAQjkivBBoXzoKpoxUZ5DS/DyAi8Z02twinQ3bZcAKIAHAYABhG07cMMHiDOMMqorXP31zBgHY1QB+uPuN6+3nGTsCwNvw1rkGFH1c+a2cQqisPB2xRBS4OQBgMQgL7GrxSFSxYjLSDZ0D7aNEy+EWlZd5RGXkiLit5/vsXhBKCTHfl5vKOOYB4GEEQnmot8KkIPiECxfYxFVQHusEY3CUp6QDwKkcBANATmw1Qx36ra0U6lt+FtG2/PYnlIA44HvvKoOEPwPtULa5z+/51V3OVmX5CsA6nme2QPgk0XoxpmVqss2J13U1HCk/seiqOldOJfzLuITvVsdoc21/FI2YZkzLVDb3fO0yYPX9Ku8F4CuRye8AULy3ePgbokd3M0A66WL+OFDH2J8BDIG3o0NAyYhWGvd2qd7otOiDc7b1HwUAxpkOpnFwVxIQjoG9Y1qlluq7OghbT01MBAACNKU9ptbVz3qqamrR6wAWvDGZy1SQHqJTYam2qszj9s/Yf8662OQ1ySnJq/vNLw3WJIbYHFbg8lwAYI6iYTalR+iZG3QffPqt57PR2lh9xaTdgMiCleUH0VF3pHsblIwHsOBAP/svfw62Pkdp6+swxfNsC8+zFwCAUpJEKVkulQhI+JhpdQcrGOPuCLQjHY3Fs9YJJfaY62wefRiAZpcIlDf3BC2Nb8nIwfNMvG/FjGQC8SqN3Pb2oeemulY+UjoKEJkm7Mja84xV8Dxz8zzbAwDJa5IJgNQgzj0tXO42mVzaLQAUF2ktfWRg+j22kCMAFP01lQMAptlnNxwCoOirqRoqMsizHEGHACh6q6sv9jBCjjt1WQAU3VXWoW5G2CmX9hgARWelbZCLce5Ct/oUAHmswtGvWpABUETs7WlhkWaViJPR16/LSHx+etrh/Ui3nHS+EPSCysU9p3SRNwEErLevRMvhELRjAZh25E1otMwazzMrgBspCPl8gFL9tyXcPP2PrlxI+JHCuoJVgNHQKoXSKVcgzlyh/Db2sX2DcmZB4/QQBpms74ng1/qdMFwyb/3GNhP0fXVsmhLgSPeQI6e/evi5NuO33/EGrbvKKDkEIBLebGvAiNQUxLtEVVgdh4wBcBWAZwA0uS2xRNvlrndmqYGrHwXITlNlz0bX9Us0HoHJvwewjSOeZ+5fecvnb876oKC55mp3ASuAxwFMpZT0P1X19BMMHDcmfvNGYCpcVQkpAH6a+sB7/5K8eObb8D7RCt2W1Z8lbC4XlF3sIpcIqDUAgqtEBapcCgCYDwB/2Qz/GLvPHvyPxwftQf94fMyhY5x3Q8PFANwFblUQ8d4UwgC4qwV5BAOcACwA8+g5T1SVIDcDICDgdvctE67ZESerzI1YTSl5kufZpoJY90uRJfL/iylU3Ip0wzNIt0gt6Noxc1dOV8jJ9VcoZc6vDj03tVHamJSSiQD28TzL5XnG3lzf9UaNLU6pcAfBaTHWVYPEA1AOOqoDGEPXIhu5/0tl1I4BpwB4P6QioCxRRtwKoM0Eft8cnyYHgL3FKdsD7UtrgOfZmRs+pYTz7thveYJV5mi7Wxd0vtd5ni2nlHzM86zSpyAwhueZ9B52QOwe7VsA6dLVcHTBtvkPtaRmsIQPU0Yqu+GNtDd/Kxz9ZX51wofwrtg0C+0xYP0VAG77fqNDpj0+UhO18fQvmt8q1zyzaAAwLNkls6fd8lnnySoiTvrVGlIJkH5qIqQ4WFzNJqtb5RAd0QqnC2CHATIEAAcwESDvAlg9WGuOUnMi+aU67BgA9zBdhUHFie6fqsILALhH6MtlGk5wbqmMtO6fsb9BbSopJX0B/PVLVeiaT8o73QVAUakT3Gad5xNSEjLTfDJ6EXhsMs50uJFuGAVva8PFAKb56z9QovWRVdb/KQ9TaHoFH2jUd5ZSooK3xfAOANcBwFGHfmxfS08bAK3HHlFXb+i/wQjiSrUIr7ZD7RHgZqJIZLEy4C+IYBCIjGwPH3mnMS3zDwD/TYndVvrx3Fdae71hTc1Vm9CObSkoJaMBLKeUXM7z7HRLz3/cnLQHQExdx/A8q/T982oAX1FKruZ5Ju0O70AY0zJlKtnoyyI0ReX9I/74IND+dGQ+fTDjq2HPrvh1T9GI4ca0zOcAZDZHiVW7C1h5nm0AsEH555x7lKE740E8DGA7/tCaZIkYhs/6v5xRdSZDyhwADmo54ee+qipPlkO/wibK93rA5X0/LZv55Hm2AFAAxA1gdXPV51FKEnieneZ5dpBSMnBkUMXBT8o7rYc3u0VDq5R/EU681ET7x3xU0ks37dGjVqRbDrL04OcIyDP5yzVZcXfbW7xVmkTz45Vb6psGAIfKL7rKmJaZ0tCLAc8zJ6VkOHxtsq5b31UL6K7pXdXLwcntbtGjOXG+saEyF0iFDjJRiRizWQTAcYy5OHmMBgCOB8fiL10KCtUxcgDvAsCugkvQ+4nPnU5BcxxAQc/Qg50Y405km5O2AijsEXKoz3FzopWB2xao2tEx8T9E/ZR7OfqG7+0CpAbChdZKIQAzgLbQVW8DgNsBbAS8CjE8zxqUJJBos1zrFDQJTrtm6uJZ66TsaoApsnV6DcB6eFe5HzamZfp9X0C7ClgpJf8BsI3nWXWEquzZSuIBISAAZKElA2HVFriq1GXPRsmdpwZoK/Osouyn5VPyPJSSWwDQs7MJ+2fs35W8Jnk8fEFjMwarUwB8TCkZw/NsN8+zAwCw37u86p1zBrAmvf+y0qzOz1tLQlZSSubzPMvJjXe9El4mfzyyRP440g2LkW6prGMqibYJDxDfd5XIvY/rv/Reqzb7ZM1zEXLXgiNMHxxZnSCoww7nznx23nkv+D3U1plCQRRARIRpzF8Csus+HjxqbwzICAAwKZOEQnU0AOYByBMALIOjf5lSYoshp6q6WwHEltqjulU6Q3sAmAgA2eY+8MXO9ua4sNUHOfEEAYBeWalq6blbMzzPjlFKRvI8C0gQEKXNT3AJddawnsEXnK4CAEpJCICfKCXP8jz7vDl9lAgsc1dOJ3pF6itWd9BRBu6LQPsjAQDo/j+ZQKYACA8/l4i1m4CVUtINwLcAHjemZS5TaEeGqnVZDBBEnVvDRVX2AAH30v4Z+58/a1wogHcAvA/ggbPt+oLU5r6Z/hfAa/B21jovM9L3vbD4tgmXVeWF3+C0aC+mlPTh72S2siW6KRo79y2AF+Ht7iXRvqC+xhfEl+mn9R7orfHLpJQc4nn2YM3zB+1BF0e6Ql3EGaa0lQS/VJeNbLsO4wq0jJOJP+/p0eMEn3cS2TLjiFifyMioIJp5GIm7AdD/BZ6p755tx9fCLxhgCwA87JOUUqGBAbi/2Ho6tRIAfi0Y+0tLz93a8Yn1KwE8D2Azz7P/ttTcQYrKSBvRnreGtQ5UAIoA5PnZJYlWRok9Zl6129B5VKf/rvzw/jftIKEnAAAgAElEQVSkjHrrgBIwD/MmVUQ0g0xge5K1OglgJLxLktPctm4yl3nITABP3eLocYyAIyDCv3YR+jpZ9QXwbEs6Sym5klLymW8Jq5LnWZpvB3edeOyqGUzk3Mcyh1p4nrkAIPwBayYBeQPA7JK3tNOb3XmJFsWUkborWptnC1GVO9Bw+SUZgD0AzuhaJq9J1psFxXBjUcrPAAAmr3PDirxM3z/IpiB5Bs/Pa1RTnQDQubLQp7cPaE6yjaaM1EUX8suUkcpMGakWgHwOEDu8P8e5aG1eoPrFSzWsdSOHNyM+piUnPW5J/LPA2vlIQ8fxPCsCcBnPs10AQCmZQSlJ8ruDEgHFmJZJduaPu0bOuYrC1SVzA+2PhBdTRuouhcx1mbfUkmxpjlWzdhOw8jxjPM92fnVsWqmMuO8F8OeRB99bs3/GgUXaomF6ubpMiOzz4R5KycWUEhkA+DII4HmWw/OstIVdjgbQE0BEQwbNW78xRxNWtcJp0Q9a+2y/9FovLXAqRbu+Wraq9E1tQLt0SfifIlun09XuoP82onbVw/NsPs+z5TXPxSvttwPQDHBFdSFyuwO1gtmzSV6TnJJQpOklEgY6sPjBiq67ThdrQtC1sgC1Lh8NWqnxncN4AE+FqkuOFttip131SvpTDbHhD1LitnYHgEFRu+Jbeu62gE+kfxjPs5Z+b0IAxDSmVW5NGYNPo/UF+JRdJNoVYwCM8IjKhYtnrWt0ExUJ/3P0+SnbAPIRwEanLHzH7zrJ7SJgpZRcQylZSClROwTNdIEp+gyM2v0bACydvVVjK0kOITLX11HJa3sD+AXAo76l0s2Uktdb0M+ulJKxvodrAFzsywo0iBBj4eMKncNcnh33wJbN8k2UEg7pFmtZuCdN4+AUEWWKJ/3ruUTgIWEeUdkgfTtKyXWUkiFnP68k4vxgzi2oKrtGqA0nC+csG3d++SIG3lioQ0G4A04lUyjDfonINUSgpzUHDIQBgD62rGdDz8aUkbrLlJH6/OhOPw4PVZcd21c6ZIExLfPyhtppCjIiBAOATlHdLq6DzQHPs2oAoJR0p5TMaO75vEEqGwGwTgC2NCZoBc7oDQ8FcD8AUErCpbau7YMuwdmfKGWOKnjL+CRaGb1D928GiL5X6KHn/G27vVyoh8Er1eP8KXfCpTLiccfrTRm+18YDROu2xq7geXYYwCx4OwXJAezEBepG/cz7AN6jlMh9GeFGLUVOe/So1W1VXys4laF5vyYmAQgFgLi77UvgLYn4P/GZ4H8FKhJtk7krpxMCMTJOn6Ou7xhKCQfgOQD/UI5IXpMcesKpjYhzhf8oOEMM9rI+S+qyM+hISEGwTYGTsVYGX/vSE0GdWKylAhwnEABQ6ByNbhG8ZNaHFeWOyKEAOQiwL29feu+tjbXVUH7Om3AaAHbkTQh4O9I2QBqAVyglhgse2TR4718EAFP+73EjDPEsn+eZxZec+AjANt/3QqKNMmDBByNyKnvEDIz89SdTRqqUXW2FJIbt/1KnqHTsKxnSqB+bddEuvrw8z9IADLrt+41au0d/jcDkH7951wc5AKAOO/wYiMcGXwEwz7PVtTT8CgE0q34bpUTt08AEvMHyBJ5nTe4kM2/9xm1EJnxWcrBzQu7u3oNqvfSIwLFqh1rcblqlbguyNBIXQGRcPAPHdQk6EVLfMT7R94vh/czVZjJA5N2PTtvhe/xTXXb6nwi6VASDWy5mARhflZWx92RQHJGJDExUOAGgIrvT7gaczr/w1rXiihBVOdldMHbVwKfX9G+KvQZQ8710ttB8bZl5AIbwPLM08zwUZ94P4peNG74ygdcALAlUMwQJ/2BxhT0CoGJ/6aCbAu2LxLlZPGud2+oOXlrhjBhsTMsM96ftNh+w1qpDtfeP+ONxAMEEwrsAsHT2Vpm7Om64LnJ/6Zxl41y1xlwB4El4BfevaEbfNPDufn7R52N2bWmhphI94MRznFwg5hMxb27bSmZTSgjSLebCGPc7WrtM2ylPKdVvtQM2nLhRCQB/FqfUSxidUhJMKSG+zXy5tV8Lk7kelkE8laipupHIHAKAv89n5/WbLx8LkOkEwJi/Invf9l0X8PGbOpuCvZruhKFG9kjWmPOqjSkjtXB4LJ3oFNTmCmdEpjEtM6GpNi/E0Jgd/QFgWOz2BtWRd0R8n6UcAKCUjPdlLf2Ot76ZjANwGmAn/bVxg+fZDzzP1gEApeRySskK3/VZoo1w4+JHLgUwGcCSQ8/dWBVofyTqZB0AeYwud7Y/jbbpgJVSogdwmlIyCwAqnGH3RmoK3Vd1X/+z75BhgitY7qxKeLbWGAKvVMtl8GYMNjSXfzzP7PBKbTWLJMz0x47sIxx7zGnR9644EbsUQDIAdLrLPh/AZwoPeQzphteQbvB7al6iRQkHAKegrm+983oAX5/95KzP47ubBUW/ZG3VaUd573ClPj9nzrJx5832ix5ZmrfxKgHHIAPAyzhPwumgKIgE4Di3CgB0MeWJjTinf7Fs9vtUYPJxAILVMtsvD6y8ucG1sQ2Bg6gDAK3c2uQVj44CpeRSAD8CaLYMlykjdVev0ANfAqTXPctn/qcZprgI3tUHiTZEpStkhVLmQGLYvuUXPloiwPwVqSmwKznno/402qYDVgBaAF8A2GdMy0w8XdUtRClzLqnV9eJqAB6PPeKMsLBveYgHcDPPsz0AQClJpJS8X2vpvtFQSnSUkncoJb188z3N8yyzqXbPh+BUvg6wvTnb+lvyfutVUuul1QxMycAeYmBbpKC17TIg8rfBAJAYtu+Cy5m+H2Rfwdf5pza7raGXiyDQlfV9SXAZYp2Wbv/SSq3h1amTYsG4MQBEEQyMQABAt5z6T5VbpoDdIHMQ0SukqtA44xp7bmdjykj92xh87GYPkyfsLhi7w5iW2WyKF78Wjj0GANtOT8xurjnaIVsA3ALg0+acpHfYgXcJRPxZNMLvASvPsxcBDOd5ZqeUKCklUpuzVo4xLbP7obIBXeL1pm+/f3R+gzafSrQ8poxUFqYu/fxUVfdgY1pmF3/ZbdMBK8+zYp5n9/I8+xXAHQA8edVdXq55Xa4pvlehLTo8Z9k4M+Dtp+5bKq3ieXa8lqnhAK4E4I8bbyiAKWjCZoGGMG/9Rk9wQukC0SMLtZcGf0UpqenBPQAAI/8UZ5dog+gVlX0BwBicfcFaS99mvhU8z1ae4+UbARwcePj2GrmR89avaqPMG73dSnDj3z0tjp8uKv1i3vqNu+CVHILDodtFRE4EAPPJ2F8beEp1Qh9/cMPgqN3zi22xkQC+MKZlKi84qHFINawNxPf5+pDnmYdSoqeUBDfHPG/O+uAAA7e5yBZ3qa/hhF/heebw/fNuABspJYP9PYeEX3kMIO4TlkS/LjFLNB9HKpIX+P45zV8222zASinpRinpDQBzV96s08itD4SoSneaMlKLAGDZ3C9meOxReoWuoPavsecBUEqJorYtnmerAfSuqS+t0WltgC+EUnKJz1YugF48z1Y0+uQayKxXft2oiajcZsmJGmorCzpFKRkD72YFBwODL2ilLeWPhH/5Jf9SDwBszrmqzs8lpWQapWTKueoLH/4qZgiA0RFy548KXWE6iAeEc55TP/Xte0dfaSsOGRTSrfC3ees3fravp9lV3KmyEgAGRu0eCgB2PXeSYwIHr7SV3zvmrX/wxQyAzAJwWULQyZ1zV05vcp3s2RiDj6YCQKSmYLi/bbd3fNfQXQCac3n2IwBdE8P2NWcG9B0AV9dabZPa9LYy7ltx6xCOCHfo5FXrTRmpUna1jWDKSDXJOdfvOkXlfXNXTvfLj842G7ACeALAH5QSzaHy/jPtHp1yUPTuHwFg6eytKYIz9F0AsJVcNHrp7K01y+EHAezieeY+21iNcgCl5EEAPzRQs28KgK2Ukok+W+bGn1bjsJcargdQceKHQcWCW5aFdMsuAjKegGwGwInkjDKCRBvCp0M5BwBEJv/6fLqUviB1FoB7z/X6SafmTgAYl3fFcXtZn55gMjBRtanWdwMA8OrUSXJ7mSEDYHmqYNtkAFATMaibyjYEAJScKwEAPL2sDjARICC66Ipm6SZkykh9Pznij3Wnq7oO/r1w1Of+zLQZ0zJTcip7XA0AJfaY1Y3V++yo+K6hS9GMAevg6J2ZCs4FlczxQnPN4Wus8S0AUEp6ADju25Qr0Uo4UDroaQDc2IQfzrVqJNGKyEpMSslKTJqflZiUAgDDYnZkW93BcWZn2FX+sN+WA9bHAdzA88x+rKJvKsByBVG+CAA4ZeVNAKvJ+sjhWw7nebbKJ4FVF+UASlGPdo21MrVfAZgB4PuGn4Z/mLd+Y5lc43raadF3Ov1z3+cBAOmWXQLHpouECRWhwvpA+SbRJHiA+bKLTIHzlHb4arMnALixpttPbbKd+kEA2xN2YnJPb09Vb/np2fZUIdVPAOgPkAdvnp9VCgAuxtnyXepDAPBr4ZhsAPCo8R1hIggIZCp3tD9O9Fx0Mxy9JVqbty7f2nkyvMoe/oJn4HzXP3LmGtGROPXE5jGn529dkJu2o7Hi/Mt4nlEAoJT4Pcv+xUPPl0Zoin7bXzoo3piWqbjwiCbjAPAn6uj8JtGyGNMyI02VPcdzRPjo7btX/XzhERKBIisxaQQD28HAnmdgW7ISk1KUMucTAPNsz71ilD/maLMBK8+zIp5nmy5Z9HpPgF0OkPfX3PemBwCUuoJrvUcxAYA7pOsPpygl19dHioXn2VoAN/lqtIIoJUHnOo5SchuAvyglwTzPBJ5nawOt8RfZN2eZJqzSXJEdO21zpjqTUtJPtqCy1GIQDoZWyHoj3eBXTTSJFoES7+cYAM6pS0kpiaKUqHzZopKzXx+8tk8PAEMB8olcUzwYXjUqD3yNAGqO+3BRUpLHpkpXh1SZ4N3MCAAQQNzlgrKmdbEBgGj7O+F7whiDSJjnWMhpv5zpOVg8ax0rsnW6FcBaAM/euPiR1f6wKyOeHQAj8P5n/OP/ob2Tm7Yj5dTjmz8ngmo7mPwZAFsaG7QCAKVkJoA9PtUWv1JgTXhBZPJQAJf62/bZ8DzL5Xl2VS35ridqNs9KtDzGtMwUGXF/AUDtEZULLzhAItCMBSAjIAS+ZMiMvm+bAPI9gJuMaZlNLutqcwGrr150MaXkYgCI0BQtJ2Ckm+HIxwCwdPbWgY6KXnGq4JM7AfIUgPGdhr00Et6lq3p1aeF5xnzB7QYAG84T6GYDOAI/aFD6i2mPHBXsFXpe9Mg0+b/2Hg0gCQBCzfKbOUbk+LeIvEQrx5SRukvOuSd7H5HV59GlfBfA7vN18emnqVoKAGOd0T8LrqAR6tCjpwAsADB+zrJxZ+wV/d3tUcElFw3G4nvnrd94JktLwGQ6zqMHgB4hWSlKzuHuX3biYgKREBASUWW/tmYJqDkwZaSKAO7sZjiS92vBmBn9nvpoelNtXtL5u4sAAoOqfBOA8f7S+2zt+ALTbURUT6mRLAOgRtMyzCbfn+bIgn5PIFTG6U61qKY0pSQOwENoRvkuifPja9G7VWDy0d7f6d5ujhKtGgpA8O2bcQvBnp0A/pjQ5ZsiAJ34hE3nLFdrCG0uYAXQBcB0AH2MaZmyv4sv7herP31y6/yHDy+dvZUAeBkg5c7Kbqlzlo1b5Lsh3w9gdENqS33Lqm8BeKtmiZVSMoJScrvv9Z95nl3L86zC72fYBOZ98t3fAFlSdiRef2pHH+8GgnTLfgBbRMIekrpftT2OvXBtJoC9AM6nd/oWgDfOl+E/7NAnRcsdhUl/Pj6YCRoog/Ln1vpuAABenTopBYzcBpBXb56ftan2eDUR9V1VtkEAQMCitQorAPBgovcZ4tVobep51oUpI9XdL+LPAXLO/Ue12/C+MS1zfFPsHSgdNIQjAhvTafNdHSVYBQBG3JczMBX5X7DKABCn/niju4vxPNvG8+zq5rgWmjJSnf0i9uaXOaJGT3n98WZRJDgXPM/yAfSHd6MuKCVGSkm9WyNLNBn+eo4q58q+xCByrEaKUqIVk3Q4axcBWUxAwAi7sfSl7BMAyi83fr1JJbMzVe7FD75976atS2dvbXTirM0FrDzPTADiAXwM4FKXqIrMr+7yGACE9vj2cQDjOUV1xpxl48yUEs63VCrwPDvYiLk+53n2OQBQSoYBeAbAIzXdtVor4b1PL5JrXKzydNSSLf+VX04piS+Mdv2XYyRK6SKLAu2fRMPpEpydzRHP6HuX3xZz9ms8zzbzPFtzrnHJa5L72kR5QpFb8xyAewDsueXxhf9oKvDRy70UqmDrd0QmlAH419Kbk3HWPJf6IAAcM/c5YXaGHwZAa2pYGdcyKhRLZn1Y5hZVlwE4IiPuzLvemXVLY20VWuMHi0y27c27Psjzo4utHnvovpEEBMxbZuIEsNylyS1QWhNuPPry61c2xTalJJxS8gmlxC+NJGpwCaoFTkGNPUUjJ/rT7oXgeZbvKw1TANiEWmUyEs3LQvn75S8pVnJz5V9gnXIRt0C+pvTCoyQCjWNQ5U8AYB9lHsPz7DTPswnXXVn8xZBq/a/JFZ26iaLyEga2orFBa5sKWGvkpnieOXieOcPUJY8DrAzAt0tnb5VXnh59v0Jb6A7v9eU7viHXA8iilHRtwpxRlJJuAL6DdyPWcJ5nF9yQFUhue/bvYnVI9aNOiy607HD8BgBpDjV7xS1nxTGFihGB9k+i4XQ3HNktMjk5bk48I6ROKbmFUvJIXTJsITL3bICJd5WN1ALoK9eUfHD2MeVHO81zVupCwnvlrZu3fuO/Wh6KIJ4KQVnme5gAIPjKya9AlCkrAYJSnea7pMNZLZKlNGWkVgyJ/uU6g8os2557+QpjWmaDawwnvbwwCUA/AM3W0KM1kpu2Q6E290t2q4rKADwF4JL4jNH3WGO2j2ZELNKWDXk/N23HwibUs6oBjAIw0G9OAzhSkfwFgDz4Uc+xIfgUEf4PvhbbEs3P9brNkwkBOAKoiAuTYjdeE2ifJC6MJ9b1I5MxUZmt/Udr7US3PBRAzaoObITd0Rj7bSpgBfB/lJJfKSXa2cvuSLQ4Q8YMjPr1pCkj1QngNsEZGi249TNuuH+11Xd8AbxagacaM5kvk7obwCsApgK4g+eZxR8n0txUF4S/BuDH07/0ceXuSnzHONPx/+ydd3gUVffHv3dmezbZ9ErI0hMggDThlXIBRV8DKhbwZ6EoCBZARXFBgVgJInYUREVUVBQVlCgWYAA1NAEJEDoBEkhPNtm+M3N/f+xuDLyUlE3TfJ7HRyZ75+yZZHfm3HvP+R5JKZIXOEb6INVwdWP710LN2Hgm5R0AjsOlyV2r/HgogJHwJnldyMw1UYQDmxyvchRpC3rewSmsLDhhw8qqYxaNGRHuLA+YCbDNygDHoxezw4HxgZwY5M0ruwpgbQBscBNeBAgcSkWDyritfuylI4mhmYOdkqYCwGajKX1+TWSpDOrSOQAwuNX63fXmZNPkTk5WRymdUePj0wbNb5U2MAMAkicvOc7JGhOAaAb2NGpZhEUpywXQgVL2uT+dzk5LkSO0eb9ykEY88t7Y9v60XV0oZemUsi0AIAhkqiCQdy7U827Bf0gc68HAwMBEgLk0DvImAAgCiR/04ps3GE3ps1qk6JoefRYVWolEtinOqVtV/flJXZkTAJin4BeHlOLZ2thvbgFrDoB9lDLb+uxRKRJTIlBlnvXl2/dGgogvAsiQ3fovfIMpZVsoZXdTWlllXS18RVbeldTZAFIpZb9SyvK8RV8zBYFE+PPC/M2MVeuYQuOaDkBnKwr6ThCI8kwr55cyYbYKvdTSi7mZkZ2W4gCwBWDX+X5GKZsA4MaLyVgBwC/m8F4lkkoZ6Qhb7TS36yFL6nfGPPreeVtrmmDLCoAFAeSRu548clE7aiLrE9S2HqjMI/NIYskqpwHgEBBm7uGPa6wJn01b9AdAngRYNMBMgLy5ug+wvQV9YwMUFSXB6pLN9e1nU2Hfsvt4SWlewCAdwMVXlmMYGCMghF1GPu1KUMrsACAIZLAgEL9oLwJAz6iM9TJ4cqws6QF/2awDUfCkpYmN7cg/klRDjM7ORYgK9i0BmcuB0KAZ1vUAsDVn2OrTFW1/BNiLADa2BK1NkgwG1mvP/8VV5nwfDs7f87PWjWyFxH7Wul0bdOLCyxm4FM0qYKWUraSUTfIIiJOJAP74+JE3f3Wa234GpojUx2xb+PCSocwrR/VobZLkBYFEAtggCORa73t+QSnbV2VIJwCpAMb645rqk+krfj4YFF/0S0VORFvzqYjM4+2dz5eEilkBVq67ZaG+bWP710LN6BK2OxcgSS+uHDbCN2GilFkuNV4ENwaAu/uemWUAlGCKxVVfX/5Mj1GOsoAbg+KLts1YtW7/pew4GWfNcWkzAQgA8U3+3AqFgycg4IhcLfWNeiAWgOwJoDklgGVGU3q7y51gNKVrrWJgX6sYuPKNSSsvGqD/E1FaW5t4tyGmvNUPm1qlDbzYdQv4Wz5NRB1ykr0T/hcBPFMdKcHqoOHtqwCWdaikW6N3JaOUPQNglFdNJkwQSMt2tX8ZR0B4pcg9hVTzfKSaK9ON1hy7y6uRSwgATaCy7Mcxb8x8vh7bN7dQQ+x9zTYCopZCxTG+n+VajO59aomt1ruX7VNLtLaFrs0mYBUE0s8nTj0k/of7ACQGqUpXLp6yMbr8zKD+qqBTu8fNm/2td/goAK/Bk6dWU6wA9LiEjAal7BCAHgBerYXtBqf8dOQoANnZm7qHiE7Fz7xExhMGWW/lJza2by3UjOiA3LUAYHXr3wKw53LFfzPXRPFqIt2nAjbpePdMlT7n1MNLhlYKoi8aM4IrOdrKRHi5VBdZNuZSdgBPDmuZpCz13mQ+hqeyfDjsoUcBAke+4bB/rrDGCABxAsynKduWg3T41lef2XFV6kfBFzuhc9jeuwBoCeR/Tf5qjmkrCcwbdrPMOXMlddETFxvTKm1gBgE3DQAIyFO+dIHa4F3xHw1g2KVW/2uKZ3JBPgMwsOucz4z+sFkXquzazQTwhSCQ+MuNb6F6ZC/XEKdKnuNUyVlINR+98PViR9RSgNgBiAATQaDffm7wMwBy2s76btGDS+8b3vBe/zsxmtL7Xyw140xk6CYAyD6ePNRoSv+/q59buhpgE72617UulAWaScAqCKQVgN8BzAKA7PJ2D2h4G2j8T18DeBYgKld5QuVD1yv+35VStqua9lXevCQlpcwKT2HVV5caTyk74p1dxwoC+d6r2dckmbFqnQ1g00S7OvKU0O2ukEet+wnIWgb2QMkbARdtitBC02TD6ZHfAcj/7viYAwAev1zxX4WkGO9kfOgNZd2YaI9UaMMPnp9XSOT7APRlEj/t7pmHL1spz4HxQbzbAABdwnaXACDDE9Y4GOFcAAHHWKP0X/cG0MMAMhce0er2iWGZR3YX9OtT6og4YjSlTzGa0s/rwKTkXNNVnAMj263a1hg+NxLDAPThZPVz3SZ96LzMuO8BQOZc/9O6uqZ4K+wrBIEo/NXqtJ3h0LcAoFdWrG9CW8Fz4AnMzwCAIJAGk976JxJWrBipdnG6klBxy8Ve//s7j7kAGTQ0/getVmEdCWArY2T6jydv+6nLnM/3GE3pdxlN6S0yZLXkwharF+L9/m0C8CLAfuv//LvlbWetPWI0pZunKxZsLNIYUCBGjgXwWb6t1W2esy7eXbEm+L2dXj2RB+A2ALuNpnQD0KmrknMu/49V17cY8gO8uvzDKW/cegwABIFoKWX2GspYXQvgTQBnAKypQceq1gB6wlM5Xask4oZgxqr075dO/0+e+VTkTd+vDHuiQye7M/GwNsyplhejGaQ2tOAhOy2FGU3pv1rdgTd8e/Sumym99NjfLKE9AOaMPnwPByDfnD18nu+1lQs6teVVCUsIx7JEu3rlpa14UBNZ31pl7w4AoZriAgAodYa1UwQURcLCQRNiafAcVh/eB1iV1cCUzp2eXt3bKWlfAfBupO7sq6Nff+qJLx9d8I7RlE449DIEqcsy3pz0abMonqwrXz3zwSSVyv12rBRhj5Ei/0f6LDU1tT88DxDhzihue0DBANjCt48Hhr174dha8giA1wSBXEUp21sXQ8fNiUEAQ54trhOADUZTeqM3fPBOGn8DAEEgFMA3gkBSKGX/Gm1ffxJo4W8HUB5WrHj8UmPO/86nAMA6AOseWjqha6E9Km1n3oAkACsVnMt6y6tzjpwo6zR+33P37DvPSKqh8nNfNeWgBeCvm1rfrCS61WDgCIgzKzFpWFUVmGnL7gmL0g36Kt8Wq/YGocTq1qs0vOO4TdSvj9KddTmCMap/QWYQgEEKzhUvyso18ASrdeoq2CwCVkqZCGANAJD1301h4HVuWf1u+Wn6LqdwsJB26QuAWyEIpBOAPwSB/B+l7Ocr2RUEEk0py6OU/SAIpE91V2Sr+LVNEEg7SpnDa09NKbvcCkbjQdgwJnG7zu7oNDXwlowjCafUpRGFysFINRCkmv81uXzNGUEgYyd0GT5i+YFpBoekvR3AqouNS16RrAAwOsoe9Rsnq64F2EsPLxlWuRpbeCBhnuRU8pHdT867d/bBK/7tnYyznnZp9wHA1tzrfgSwYGfeQHaj+mQgAQGTL95hq7E4/OLtu4ym9CE9IzNmZpe3f25H3qDFRlP6jYC8VQbfuswZ9sWVrTR/vnrmg0kH+Zz3GBj2KPKgUHD7+PnfMFnm3W63vhhAEMC6e/MBHV+Y5WETFJYidUX7Oq+wVmEJgOy6BqteKEBkeHYG1Z5jNKVg4yQ8BW2Zje1IcyT/HV10JBS3E5AVqqcrbDU9/53Jy/cDGGE0pXMAhrYOPPnWvsLePWSm+MtoSv+9c+jeX9qHZL39Zu73HeEJmngALqQahv3bglZBIGoAkldn+GoATwJ4mFKWL2vkcWBQkPNXRDMAwO/zRKcAACAASURBVGhKpwSjP2Tg4whkmYHIAHGXu0LPmzzuPjqvgybPcNNqx3iu9+uFP3sbvVAAQl0mmU3qQXMxBIEMFQTyiPcXjJiA3IXB6uL8GWUavdsW1UuWlLPHTPvgiHe4G8CvAK54cxQE8iiAQ4JAEgCgpsGqjyrB6mgAB5pqLtPk1zMOAuR5S15o62Pre32jdXCPKSTSGp7tlRaaB227hu8pAoDt5wZdMj+7v77kEQCR1xdf3QZEJqEdv61sFLBozIirJKfqHoC8de/sg5dMe6mKDCKWS0qfdNUZ7//jHeYOOwEO7lLdmUud21hkp6Wwbx5/YQEBC4LnZkwBLs3zKpvWhLaU6w07sd/BwLyLIAAHLkKtrmitUlkjvUOC4XkGVPb+5t0hO5X2GJ2/fPBqZq8BAEEgcb46hFoiAHACjAGMSwrd28EvTvoJStkpStm9lDKLNxVisSCQyxYBtvA3jLBXCYi2KMy9ri52stNS5Oy0lF83zno8KVhdGgPgSYBFHSzpkbr+5Khzm6RuyxiDCgBfF0WM5oIgkGBBIHf5YhNBIEPgqdXxFTBq4enqFg0AnJV/H39LJboBCFPfuzd+WNorvwPYxMDLsQGnRzJwA+Bt8X1hEEpE8hkAaLYZkgHPqnh2Wsr8uu6INPmAFcDNAJ4CIHWb+2nvs9bW+k7B+zcqFLZ3AZwGU77uG0gpO0EpG0MpK6iG3bUA3oVHq9UfnIBnZl3iJ3v1wSKV3ma3Fxne2BimE2TCypwq+YXGdqqF6kEpSw3TFiYCOFDmDLtktfRZl2a0loFpzwwNUmiKt//f42/vAoDPFnbkVXr7twArgedGUy04MIWBd/uKEM08cUthmvypdo4LlzklZF7TZHOh/0yd4MxOS3mFQH4D8C0mEwX+4Q8pANAy7Vc8OBAG8ODQQYp5qmfPDyL79FliTE1NpfC0uPYWsHgeTAzsOANrv2/ZfX6p7vfhzfPPBPB0bW348hcVnPuFcG2+40hpl7FGU3ofvznpXxLhaXTQ6KoGzYXwImUPp0ousOjlH/xlc3fq+PzstJRXbm73ecdrW383PURZurE9OduFARBBIBJesT9S25Sf2dVCEIjOlz/tbXa0RhCIr3NdFICV8Oh2A0AWgPnwpFqCUiZQyjpSyv4CgO7pp34gIEsAQFbJd/z3lldCfz51874TZR3/E6k7+wmAbn/MeXDd5YJQzZ6g7wCIfJmyuz+vs8kHrJSy6QB6UsrEclfIOADOYXaNRRYDOgXGbV358JKhDq826uOCQKIuZ8s7y1jktXuSUjbLX12rKGW7KGWjKGVWbxFXa3/Y9SczVq1zBbYqnumyaJWniyO+P93aFax2cVef/kDTssrahBEEEi8IpDPgSY/hibiBQB48bdk9/6NkkbwiWXXKpUvqeI7+TsCFi/bIZ32vWc6FznFZtAlhnXLWzFi1rtpi/2oiB8Sr7Mnew34SU/BqS3SCElwS41RwqyNjF0/Z2KRXLBm4dX9XF9ctj6q5cMcL9y+7nrVxd0ekLVGKe+COF+5fRimz+LRSU1NTMwDiLWDBsNTU1AxLzIZYAhJIZJVfZe8oZWcBLITnwVlrstNSMo69NGpup5D9yRJTnAGwzmhKr3Unw/qCUrYfQHtK2UoAEATS3bdL2MJFSDV0V0gkSe3iXjBOcPg9Re2NSSvZ+w8tfTODe3xHPFeEdG3XrGXccIxxzMXI08sWJc9d+dnkJRMbpZNaTfG2nB8rCGSg9zgAQAU8+eIAUA6gHQCf3OAxAMkAvgAAbxrkHErZsUu9h72veScALO937VsA1jklTc7gVj/fvWPupLHZaSlXTNdIOpRlZzzLlAPE62t5mRelSQesVQT8C6ctuyeEQB6rZPjWfXrw9by69JQm+MQc79AuAF4GcOsVTHYD0Lc2+qw15F14cmmb3MrT+Gf/ehvA58VZ8Z2OWEJnMDAx/ozqlsb2q4XL8hyAbb4Z9OD49UUMnNLm1k++cCABGw4guE/JVYmEd+QB+AkAFo0ZEVyRG/4gpxD3qgLtU2ry5k7GWU45tb40GwoQFi/y3p1mzte9hNby2hqE86uL/3cL65/I5l+1Y2PcccrOAaJwxwv3L7vYmNTU1IzU1NT5nuAV4NwBWwBAV9TH7xNuStl830PycpJs1WHltNeOAeRGQFaHqIv2TH1vrNEvTvoRSlkx4NmSBbARwDuN61HTxRIgPcvAnKjjhOaypBqSOUZmAfh0pOn3zodilNw+tBnIwK22ufWjf8oetbLtrO/2GE3pkzs/s6pR1R68WvKtqhx/JggkFQC8ReELAYz3HlvhSXva4D12UMqSKWW+AFWilO2vbn2N0ZROpkU8qxAJh8A8uQ2B9BxAen30yNuf1eQaXJ0tPHFzV+2cEeG3iVqTDVi9s9EDgkDuAYAyZ+gLDFzQvXKZCiCtJWfIhNFTV0hA5Ww2CcD7F7HTxbc6BeAZAEN8eaf1yGsAnqWU/U9f9ibCDMLJ7PiRNibG8BmACUg1XFS3soUmwZMA/o9SVg4AGt7xLoEsbskZHn3hwPZq6+tR9ki3osIYHhj3x56HlwyVAYBXuRcAiJBFxf13PXmkRp3fZBCpQlaWew8FAO4zCtmb5ETgTXcSandpDYe/8qiaC5qy5HjClNCWdP+muucEFPXfAADqio6RVxpbWwSBLASwThDqVqyXnZaSNTT+x6crXAbDpjM3rDGa0pvkCialrAzAfQBeAv5eiGnBw7klWr3ayd1UFizlI9VcL9vz2cs1aptW2iQTVgHgMcCz6nps/i2/ZaeljH/66ifvuCdpCQICD8SpwjYtEVV5JTe8PH+P0ZTe19OoqH4RBHKLIJD7qv4IwAdVjt0Aqt63ewOoXHiglL1KKdteVz8eWjqhB4G8NkcZuywnMMKekvv7/pNpN83LTkup8U40sfKfEBcH3a+hXerql48mG7DCI9x/EN4c0z/ODukezleUhduCb+ZV5t8eXjJ0E+DJ3QAAStlRStl51a3eBP/vAbztHSN6FQfqFe9sZpnXh2RBIH77g/mDGavWnQtpl5duKwiJWF6Q3PucLSggP9L9UWP71cL5VNlhKKKUVQrdvzN5eREDt88lq0dXLR5KXpGsO+HUxV99jpYBzCU5Qh4AgE/nJ90qufkHtGHlP89YtW53Tf0gYCo1kQYlr0jun52WkqHiHCPPKdz4M/zkGcZcIO5SyVHaLPpo/KuI/mvuXwBAwP2PAPtlOAkAoqq0W7045eEwPPmsfF0NffjwO4v1qvJJFrehO4APGiK4qA2UsrWUsuPew7cFgbzeErh6iMlTjVSKhMgcm3Pl0bUjskAxT2fnw/Ki3R8j1Vx04ettg4+uIcEZ8xC3MkgV8RPUrT/kjjv0XQFsB9ie0a8/9fHU9+5NqMl7Jq9I7p+8InlW8ork/oJADIJAKvM5BYGkCgKpqmR0F4AZVY6fBbDId0ApG0cpe77K8ZkL4526YDSlk77PLZux6cx/9xAi/xfAk/HlBR/orK52tV0hVZ3QfQ0AnJPr6y8/m2zA6s2zuJ1StsFoSu8gyqprhls1p5mk5oPbrn8Z8GiuAjgoCKTqHxqCQAIEgRBvcHonPB+GBsd7Q/oEwCdN7eZUcjTuNQCsrCS485dnkmHPCbkeqYY6P0Ba8Ct3CwL5QRDIebmqniCVdQMQA7Cq/bRvJJJGFZ3fTweQ1eOfm5GzaMwIUrCvzRMcLzsMCfkP1dSB5BXJ/RmgczKuB4ANySuS+x956bafY/Q5x07ZHDomnYPMuXkAGxaNGdGk81j/bVgjtt0LAGUJq4ure06rtIFWUVUqO4OO3FZfflHK3qeUzfDXA3fvs2Pfh6eY6+6ekRmb/GGzvvA+B9wA3P7qAvYP4H4AJ8NKlJ/Wi/VUQ3udnX9UJizdpWKPXWwIpYytKYtxA1ASAoBIsrrVihcBTFHzDvWOvEH3pp+844jRlP5R+1lrrmlj+v6iXZ589FjRZSQB+w1gzwPY8Kc1aAk8aYK+mKsAwJkqccEkeFIWff58Vx1pTn/Q77mliQB+KbDFvqJXlp+83rjm+uy0lFdc/UvdBERLHFxt7wXZjGelYqTrdn/52iR1WAWBRAOQfdX+HUMOvFJY1EWOdQR2BvDenY8u/d47VAmPPuvOKufGAtgMT+vUdyllOxrW+7/xdsMa7ft3Y/lxCQZ65GEIkWROzi8P1hjDLTcB+PaKZ7bQUKjg+YxfKHJP8ffqVKVOnppIEzoW9bZz4AO0YQe+8BaF3sVkrj+TuQfufurwiVr4QD3/O1+T76wlYVEv2+53/668P1+vr4XGRRAICVdOGy3zdiYrKg7W7Gz5uMbcyVo/nv2NIJBEeFaRxvryPevA/K7hf967u6D/4E5Pr55y+MXbl/jBRb/jfQ486gtUBIEkAejkk/76t3H6A83A1lAPc6jlVzWzKqrbsKfaHPhSybVXadaoXMTNMTL5CgVdAkHlHc1FeOdP3vShpZOXTLx7w+mUITLjR4tMOc6TBsUYQNx9n1s2/6rI7Qd+yh71G4Ci7LQUt5aT/2uRFb7gVLneHFmQc+7GeWuP3zlLXp++MTuNnZfPTClr0EYm3kB7SELg0f4ljtYpgGwFuCmF9phl705eLgMAU7DPADym2R1Yq3z2pENZ7K+UBCdnVgz0l99NMmAFMBvAeEEgMbO3vuOqcLW6cRSzEAKNEyCVHXu8OX2PXnBuHjwB6/4G9PeSUMp8GrE+7dctlLIab8vWAwI83zoOgBijKy+16qR3dwrkxwbI8W2hGlDKPhQEsvwikx0Bnmr3ys4hT6yJipPk8BuvKujvVuryy/TRu9a9NXHoSMKrlzOGY5D5Dy60X00EgIjw3CtE/J2r+l2uJvZdMPh0PiU0gzzWfxEatTmxkHFOW7dJH9ZosqxwhW0H4LeHzGUIANAdnormOgWs2WkpbPqyz3oeLO7xnVPSvm00pZ/MTkv5yS9e1gNVvtOzAFwvCGRDE655qDcMZn4qA0N+lHtVjfbbq0lkgfINtYvrUhDhfjfyYdtlW1BnjsvM6P9J4garzA9h4IZljsusnHwvnfL+SgArjab0R0M1BetLHBHXeBtuqApssfN+yh5Vaafj7K/tesNoHtHfeEJacJzj3Mhh3xYO6wiAB+R516a9/Pmxsi4/AEDPyIxEJedSbc8bvA8AekX9nsQTid+RN2g/APSO+q0zIYzszBt4AAD6Rm/pKjNe3pV/zUEAuDp6czeRKV1/5v/nEAD0ixG6OyWNfU9BvyMA0D9mUw+7qLPuLbz6KAB0CD4wAkgaA3DKUxUdEBtwqrhr+J4b3ntw2Xla9LqtIX8COMeXKpNRS/gyxSd8serJrMSksKRDWXWdlIKwJrfwVznz7kMp+8RoSr85VuTW3G1RIzD2j/Sxc58Z4R0zHsCflLJMQSChAF4EMJtSVtqIrl8Sb4V3JoB0SlmNt2brg7cfoA85zfrFCo1zwR3/+a1P7DnV0KxE+/ykO12zG9u3fzPe6tAulLJLPnCNpvQ0ePSJb8pOS/m+3ydJEwPM7Zbduv9xAHjYUfrqHoBtha+bCwidsWpdrVY/k1d0vR0gX8WrbBt/+L/jlRJo17ywuPyOPVt0ksLK8+oeKx79+IXxtbHfQv1wZtamk2D8jvi0QWNqct7p2b+8SGS1qbzVOn2XR16215d/gP+7AxpN6UEA+03JuROHtk6/e+mU96vVHKOx8BYXt6eUHfCuukZUU0e8+eNJQctmYAdIavkN9WA/joEddCtZ9tlYd4/qyGUlr0j+GkBS5rjMzpca412d3ADPgoEYH3hifuvAE4rfz157FkBkW8PhQYTICflRn7aVnNEOFA5X2K2d6qSKUY/IAJ7JTkuZf7EXD3ZJXAuO9emceTi2NsazEpMogE0Abkw6lPVj7d300CRzWCllhyhlnwAAGCYOtSvchHeWakKOTwAqc1fnw1vtB88M/V4A1zSGv9XBuxrcD8A0oGlUijrN+o8AQHSoLaKCTZA4JrU7ru7UyG614Plcr/WmxlyU1oHHbQDQNWz3QQCwyopR3fIG2QFmBfApgOEA4T3Ln4RDHWSnMsftXx2vspvLJeXVySuSKz+3WoXtyzLJk/esi6xoFhqG/xb2LbtPCxCjNWpLjR80lpgNEQSEkxWW/9SDa+dBKXN6dbQnCgLpXVd72Wkp5cMT1o7XKS3c5jPXLzOa0ltd+azGg1LmpJQd8B7eB+CwN03gH4/EsRsAtCIgF5VcqwvZyzVEJmwZAVGq3Nyt1dV2VRK5LQ85/7K2z5fIG7r16anPrZz22tzstJQl2Wkpz22c9fi1G0xPdFDy7rxwffaWrDmPq3kiDoKnSYcEMGdC0LHpADoD6HxN7K9DafyP1/mOB8T9Mmxwq/XX+o4Hxf187aC4nyuPB7daf+2AuF+G+Y5p/I/XXRP761Df8ZD4H4b/J3bjEN/x0Nbrru8Xs4n6jo1BRx6DR0JMBODEZXbGHD3LCXFzMbsejajtKusuBsZc7Wzjann+eTS5lABBILMArKGUZT28dHyvTu7RI2IkHgz8k6OnLi8EAEqZXRBIV3gDVErZTkEgRkrZ/1T/NSUoZecAQBCIAcBqQSDzKGV/NJY/M1atsy2688Z8hcbdq/X9jheQaniXl8lky0L9Cp2NW8rNK2803/7JeGfoFJfuqzwbnu9A3qVstA462eF0RTskGI4FPfZtdDuNu/X1bUu68froXZnjUp8qf+2eBdGyWwl4turrLJRfKipnWmTFUni2ircAwLGyzq925/PuDwTgKAnmF0/ZSB5eMrTpbdn8C3EEH5hLGA8iaTbX9FyFI2I9gEn6/MENpUUZAGAOgF8ATKyrsfceXLZ7yPzXBp00d1wPIN1oSh+YnZZSfsUTG59NAJYDOHKlgf8EKgKlpXoL71RI5Psrj64ZKhd5jWPkv1adlBYw03L8ymd40HFScrTSmX2lcd779mV3rCIVriAlkfsAwPH5N281mtKHwaNjLWyePb3KuSlZ559Z/8dGU/p2XP4ZBADg7PxyACO1m0OS4NkhrhFJh7IsmbStk7jJkJqeezGaVMAqCCQBwDx4ujZkZRX2fHCoQwWozIVwGT7yjgnwCuXeByBNEEg3StmBph6sXoAeQAyAsMZ2RBtWocHfLdu2AngkwMqNBXAnUg0UqeaWIho/YTSl8wBuAtjnAJSc9pTYeckNH/La3I8zx2VmeFfdee8W6dbL2dp+bkABAPx0clT84NANt3YsvJrnZBUU2qLZny3sqOZV8RN5lbvIbdW9CkCobTqAD4usWAngFYDdB2/ACiBL5PkipcSFS247x6vQER7JohYaGW1xbw4AAoqurnGbS11x320AoLTHXnKF359QyiyCQAYByPGXzU2zHvvDaEq/HWA/RGrPHms7a+1bMlP82pQ1eCllJwA8DlQ2G1gK4ClKWXZj+lUvpBoiDeBjSkOkraHTrX7pNlnFdng0Ud5r00qFZ2Pd8zpU87TkFckcoICWk373hxtlkuJPl8xF+I6rE+Q2FNX1Rb1f/yMAF2fnewP4sjbvxZUqvyBOcmtWYhKXdCirToV1TSpgpZSdEgQSB8BpNKVzVzs73BYicwhpLbx11xNv+ERz1wgCKQbwAIB8eLRamxWUslxBID18mrCCQPSUMktj+CI5lb+7rRqfTlo7BgYCAgbm20ZuEl+w5oLRlD6AJ+5bO4bs15ldoXlnLQkGNW/valCVDSKI4jqDU/TkXcjU78exmK9VgDyFMW4Kff0hS5A471jb4CPR87enpSUEHS8psMVs3lt49ZnstBRWxX5/Ankcg3oiAMia3K9O5/Qz35AzXGZgB+5+auG61+7JvEt2KxShnXLenvDcxovmJtWUzHGZ1us/65BRKKrGPrEm6ulXbsnPzU5LYS8+8Jk54Jwr3KwqAeHtw9ASsDYJ9HlDrAAg8/aaaLD6yGOQnaKmoC88XfvqHUrZKaBy96kvpeyXutrMTkv5+YaXX/rxUEn3EQB7DsAzRlP68Oy0lBqvOjcCXeDZdo4BkN24rtQL9xIQLrRU8WA92H6NYyRQZ+d7drjHUpNgOBIgfJ5b4xdlIZusOA6gvT9sNRZJh7IcB7t1Oiir5REAZtbGBufkBHi6ciWijvFak8thpZQVU8osXdTn7urjUAWXEzlToS57AQAEgZgAXAtgM6WsnFL2cROUi6oWVYLVgQCyBYE0Sv6tq0L3B5O58EVjRgTAs20seYNWt8izrU0h17a5YDSlpwDYwjRnHztOyibnwzKP12dNUYVv6KOMXq0aFfWj+DZ0mCiF4ZXya5BkTwAhACEyikO26o8H/tb9x/KQ6GNO7es/n075eG/h1acAWDo+/fXJa9NeKUh6ZtVPALYwkMkA4TntKbQJ/kU18vDkCI0UwAEs6e3Jv/aX3YrpAI6VHG71gj+vr4PG8oWbcWSv1XCP72dx6pztQTYrmFwCbdjBJ/35fi3UHlfg8TskhQVlxi9qvPPUKm2g7NblQtTmDa8P367AqwC+uVB7uLYcKun+B8Bkr5SFSsG5Nv735Zc2GE3p1V14axQoZb8DMFLKMgBAEAitazvbpkL2cg2ROPYIA8tAqjnrymdUn9z3tHMA3CMT9jJSzTVSCmqtsnUEAB0nFvrDlxDeJRKw6Jlropr1M9SZaJU4O5e0c0ZEQG3Od8c59gKAo0fFpLr60mQCVkEgDwoC+V4QSAAAdHVxH2kYwBF54uipK3xBaR6AjwF81Fh+1gNHAPyKRlqZUmicZwAgsFVhT6SaMwjITAICu1p68beBFYsB1JuA+D+Jqe+NNWp46ypOe4LoEpZCG/4LeoZvxb26XM08a1fD+6en4/H8/9MooQAPDkqmYN2tnUSAiQBzBXLuzzhN/mZV2BZRl/A+DIlzmL7dwsPa6G//DA3a77S6dQF2UTcIgMLz8GVQG3awa07fBJ5V9nvgOMVvjwPoC8Jen7FqXY1asF6JzRXhHwE4kC+qKzVcupw9syrA6QaYC478hPDFUzY265vzPwFBIByRlV1FTaG1ppJWPjhR96fanNgYMkuzAAz3o9qLABBvgQlzxgacKTpUkjwYwJE2pu9/Hff21LTpy+5uqi1dLQAgCKQNPPm99dYJqiHhJUziZWLMj3L7V3oy1RAUWaB8wqaVnKdbuxbU9PTWKse1ANA3oEznD3faaWzRDISXgVrpmDYV+FLl+0TkEPBjWK06djp7WPbLWolxFXydF+WaTMAKr14vpcw699lFg2NLE/iS4FPs6tHXhwoCuUEQyA3wdIcYTymrV6mVhoRSlk8pu5NSViQIhBMEUp8tEf+H0I65DADUQbYU74/eY2CiS816ATgFoN4FxJs7O0ybh7TNHr1zhBQY8ICqHGmnp+LrI6/iteyZmFhwK/pYupwIlPQfOgKPfMK81ZkciMMg6R8CyNwOauttL8Uf3vJWwv7rBuqLYwboi5/VcOJyoiqGImT7QGv06k6OhMXWgHYv/6oI2uvi9ZlSUPx78s3n+pAoqxEyJMieNtNuJm8bwKtdLLb3kVX+vs7McZkMwIcArr7ry4RBAKA/oFYGOD0Ni0TJqddZ8z5ZPGVjS8erxkWtLu9QQWTlrisPvTgKV9guTtbE5Zi2NugEhFJWUGVVsc7bqedXdJMhW56eFsXAxQOYo+Rc3Tfn3PBU+snbc42m9FSjKT2uru9XH1DKTgK4HcArQGXL8WZLXK7qPzJhDofG761Y5ytFEkgYud44wVHjydYBu74CAEpElV/S4E46dT8BwJaKMI0/7DUWyjOa7wGAyKRW9/U+iwolYud+Uh3XaevqS5P54FPKlgBYAgCsoPsiBiAvoOReAC8BKIOn0jkGwHpUaa/zD2MmgGe9hWQNsuIq2tUbAKD0eIwnME01Wyyv6K0KkQynlOkbwofmzD7T5kGRIBtul4MIAYFkHoCT6lysD/6dHdQdcwdqSu5bMGndSs/ogcgxbX3XqT95Z2GXVyYkh2QWZ1K2zJvq8hyAX9657ewJAKk++5NWt+rNgGk7rcF6TlU2VBv3hSrAGYwbD01GiD0KWZF/rMwLPFnRNW8QQooLN4p2fpU6yLns/544Wi9FiN115q8zbUGL4Nm67Q2gY4DTkyYmS6Ww6brcBeDWxVM2Dnt4ydCW/OdGIPzgowrObdATWVnr37+oKslXuEL1FdEbEoCB2X50r1oIAhkAYNMzayNS15ZFywCEqkLuNeHCApPstJRzAF6YvuzuhWXO0Nmbc67vC2AugTxn+IIFebkVCZOsYuD67LQUv3deqi2UsrUAIAiEB/CDIJDt49ev+wHVqPRuUqQaAjmQ2wGsNE5wXFY+qiacW6K9PwaqhwC8pjVV1CpHuVRSBQGQ99mDqq0qcDmKRdVxALDLfDiacW5/0qGs3IPJnQqlMPddAN6ojQ0CkgEgNSsxKSjpUFatFTuaRMDqnUkfp5Sxt6f82isaXK+9alf+sjlPrBSEJzfD0wVFAhBLKWsyN5F64D0A5WhAWZMJL+w5u2jMiBLJqapcXVC5yDKVi8xAqiFcoOUl8CgyHKKU/dZQfjUHOsz+dsBMnt9wgxTsCVYhY3PI1oMLo1dNhPdBcuFDtlXawAxBGLQbHimfvd4fLwCwzlslfB7Lbs/ZBWAsACSvSFZ0Km+/ru+RsdcrZBXSE5fIucFHDmSOy5wPAIvGjFgMELezPGDehXb8xad3nD41bGXHPw/YA9skr0hWfQkIWpfoJAxqJpUAhBC0tGltVBjvoASElMdsDAJqp8dujdqsMZwZBUldNBSeVfWGZtvWipBP15ZFPes9diWvSB5W26D1YrwxaaUTHlUaGE3pbbuE7V123NxpgF0MSAdw7LoFL//SKWT/q28/8PExf72nH+ABHFm+/+FY/K0k4jKa0oc1h6C1MNz9fESRMkDi2HL+ysOrhfOlQG0IUbztUMtuTsac2ib6Gnh31wpJUfTXuP2iouVSjAAAIABJREFUP/yKUTos59watFVbuwPwi/JAY+FuY7fz+aqetT3f1cF2UnVUR2wDyiaglkEv0ARSAgSBhAM4AGD2l2+NI1zosZ02whDbfWmgd0gRADelzPWPlPeoAqWshFL2DqWMCQJpLQgk5cpn1R1OKZ5VaB0+pQCoXdzXxNN2bigADbytchvCl+aC0ZTeXykrhV6SQQEAEiTIRGQDzf0eyByXmZE5LnP+pR6uXrHwiQBOCgIJhEfK6or5XLMKh6XSgw8OlYnE1nR9XcwNPlIp+rxyQaf2hJemqAJtG2asWndJ/VZ/UCCq58ogoQBGJB3KyljbduBUIivBpBIAAANjqKPuawu1R1KWjQMA3mX4ubY2lNaEHwAgMPe/fs2Dri7fl0a2Wl0S621+AR5/T4Lqhey0lBPrnpwzzC4GBAG4h0AuOFra5cEfT9522GhKX2E0pfczmtIbPT/7xW0LuqfteOmmzTn/vQ+e30u9/278ic7G3W3XyM4z8S6/aXyrXdxcjZPTmA3SVNXTFbVOYQvh3QOilE6/7Sp21VY4ACBC4Wr2KVJ8nnopb1YqshKTatWIw9XetgUAFPmqPnXxo9EDVgA2AFMBrC45essoVtKR7NDZ0Crk0LXe12cD2OcrxvoXkQZguTegqVcCIssCCceqdrLYJRNmNweJj1LKbAAGRT7U6YOsxKTZWYlJ9d79pjlAgHFP8YwPA/BW1Of4Ivi3IjvnGtzmpWtrMpO+FZ4V9d1XUmNYPGXDtNJjI57WBJ5xZrT5+qZSXd5cAJUrTmUnYqYziedC2ua9V+uLqj4/c2AFBs71cvKK5FkfDozreTQwjhHnGRDJjVzi4BcG28cbTelzvU0SWmhAdEX9SgFAXzBoy5XGXtJGSc+/AIAXg+L95Vd1SV6RHLPbZsgEWAzAXPB05Klz84vqkJ2W4sxOS1l5Mm3kNTcYv7ldryxfBc/3NCMm4Izl+gVp842m9AZPler33NJEoyl9xdGyzttOlHWMMwYdyYCnc1KD/W7qTKqhc4CND+dkzK9u56krYVmoHwDgSQAfRD1kW1oXW7luTalFUtQ67/tCzro1BwFgt83glxSDxoQvV/zq/Wft8lhfKzwFIEt1VBdcFz8aPSWAUmYTBPIrk/nlkqiNL+FktlvBffTVqCzf6tQOAEpvs4B/E1MAtKGU+bVSNysxiXN2tYSLMc5Ezc4gN1+mDOC6R5W6oUlYT3stT8izFYhh4Ym6YLeWs/L9zr6XuDtC2dEIGT6ZGUdWYtLQpENZTX77qT65W5s3Zpi9I5ZHrMUPIb/DXdY79SnTM5cV+6/K4ikb+6uD3h8c0+dV6MKzNtgKunGLp2yUI7qsAAAUHvB0siO8fSCT1AsArh/h3OlB8b+NWzv182IA63y2Fo0ZoQH0dwDsp3tmH1zj50v9HzLHZYo3f9E274RT1w1gz2tivhbNZwbLbpvIx+VvA4vuz2kl1wN2HgwgpuayXflPQWmPCWdELIufP6TWlfat0gbaT8/eUCRqChpUbi95RXJbAL8Uiyp+YGCJaUtF2FZcIr2mvlky5YOvAXxtNKUHdgg+OLvCHfTo4dJkE4CHus39dG2k7ty+Y2WdlajHHFKjKb1Vh+ADXxbYO/UHmAMgrxKCBcLsx4qq0TGvqXE/ALfaxb3jD2PZyzXaCKb4WeSZQyGRJ+pqz824cDfj6qz/6+OL0dn25BXJZjfjQv1lsxHZyzgmutvaJwH4qpY2MgDcnJWYRJIOZdVqwtKoAasgkD4A2gLYXnriv4Mgq7BF54ReU7LCN4ZStg5VHs7/Fihl5QD+AgBBIOMAiJSylQCwc0YEAWFx6n2BAcrTGp0Y4WrlSrQOUh0OKFAUqIhkcBvFVs7BylOaM5xFwclqqRULkDtwZQo7AdGr9+uJer9nkaBUp0a5HABwBFkRUeODys+6g81yhYtB0nLgOQUrkzlWAFTqIirwL89P/PStGfMm2EcG79Zl4auwn0HAJFXIzmq3sfRW0W92lrdRZm94CwAe9f5XGaj6YJKvsJKJTFbNHz11efGF9rRh5kftxYYogCyq7TXVlLMuzVYA3QDCE8JYQPiBQ1yxrosYueM0uIGtRxWexmfRMQRgKoBQ/Is/Lw2NpCodKiusF909Wzxl4xAA/wGw8UpFce6AbBUjrMFWyKd/G3OLioQudzFOZiBDFt96drv3pUb97GSnpVQAKbOMpvTZ8KwwPVjhCry73BVyr3cIS567siBcm+8+ae70C4CCxNB9ISHqYnvGuSHrART0ixGcrfSnzrxy/5fVKjh55L1xXdZn3zIFUE06VpbEd4vYlalV2G7/YvrCyvqGptQ56UpkL9cExClU00QF26U1VfhF57T1adUMjhHt2RjXS7GT7WV1sfXA13FBQGhIEO/2awtfNZFsgbzYw582G4OkQ1mufde1sXIV/FW1teHsbClRH9SHWa8rHgpgQ21sNFrAKghEB08isvncn1O7mE9TuUht4cyGfAeNFbYIwtiO8DQJWEYpczeWn3UhKzGpcgZ84YrkzhkRRHVYF6Q6pjPIWinc0c88SJGttatOat2yRop2JdluVOSoC/hCpTOkfesRxE30B6Z3XMg5eVUAwoKJJ7cLAKAoVEFR+HeqOWdWiEqJ8HBzQQByQFAgRrq0CoYMvlR5TAp2i67O1tbKk9otB/Vx1zAOkwAQmSNSRvu4eTO+TJ+PVIOvzeaXWV/E/gXPB0yJ5rL95A9SDf0B3APgU1+L2hzTVn0X3PSoBSIWxn0ERhgD4ELNficUlek4jCl1+Vkg8obA2G0jRHv4CUnUlVvzev8FYADAhsHTXgAABuGC5P3PFnYkshiXqg6yWpzlAb+igXAwfiU8xXhKAiYGqcq+BHTPlqnMzwSYs99towwOCLWbUaIN4uDJQ2+hARAEwic4VwfZlIX5i6dsnAVA0IbtH+i2xtwhOsKCANbRO9R+JSUHhSNyA+cO7Hup1/1F8ork/gDuVSB0opaTuAiFs9/6u475bWvWX3g7zv0B4I/Oz3xRYBP1j3m+mwBjRGlxBakBXA8g4lBJN6X3tOkAsO0cBQCsNqWXAyiM1J3VaRU226ny9psAFHaL2Bmt5pwFO/MHnFNyzjGM3dxfYgoZwHIG7vm1M5491bBX619izimfUYpEYdFLf9VZ2wgAUg2JHMgcAF/FTrY/XVdzAZzUCwA6aywRVxpbE8IVLj1PWFd/2mwsFLnq94hMHs1KTNIkHcpy1PR8Kcq1FQfxhPKEti+aU8CalZjU39AjzmQbXqx0t3U8UnL0locBcD/pbLBVBM2bsmi3puAVfjJ49phmm+GXrClJJfb+ZUFMI2t1m0LLAPD2/mWRTMFUuq0hRQA4ez9zK/BMqf09+BwAzn61uQ0IFNpthjPe1ztBAtHuNGR7X+9CRMI0fwadAMDZ+5p7EBcnafYGHgXA2/uYe3F23qnerz8KgHf0Lr+aWHmrOivAc9yz/D+chS9XHfEeX1UxgCvnS1THdccB8K62thFKaLsREI6ByfuGtC3nbFwpb1aKDCwkgAuLILInbZGz89Bt+nvXgHPwUP+lB3hmISDnlCc1R90JDqMcKB0hTu4AVMxi716epChQbVee0h6QA0SLvb/ZoDinPqA+oM8hINbOu45Wa8n95zEjjgLsfniS90UQInhfOupWsHK7Vnoi6VBW+6zEpGG4RPD9jyTV0J+BbSEgCgY2kaQaKFLNGVbi/iiMKYJfCtp1sExR0ZkD+0gGWVbD7UoBniBXCRC32xY9scudw8oBjADwNKVsO+BbiSXX4DIThXN/drgejKgNxrw3Hlm2qcHk3jLHZWYkr0ge1kVb8fj1hoLbwriAnUczY2ErDg4PJOIsa2Dom5OP/4D5XQYTEPKG0ZS+v5lsWzZrwrOmBikdkThbFhIBsBcBQuzFvuelrysgAcA0AOYtnrLx2UsFrbw7+C8At+SYtqpbpQ101oe/3mB1EwC1CMIMvDjlxyYYrF6ITQxcDeBBeL+bFrdhxP7n78oAAKMpndxg/KaV1a1P2Jo7nAGI6BX1x3CHqAk7UNwzD0CEknMNrHAZDPB858P3FfapfBa7ZTUAmbUzHHpsw6wn32z4q/MzqYb+anAzASCkTDEWqYYVvgWA2nD4M5WilU61WWvnHBwjU/3h4nZLCA8AZ1yan/xhz0eBqN4qMpLgT5uNBZHJH/DkC/eEZ+JWI3SbQtMBVKiO62pVuAU0QsCalZj0AICl6r16qPfq4VQZVnJXO/nw4v344OCHgEfiZ0HkE5Wd844AgDbj/Fzd/zneZjj/eLuhZq/vuOB45/nHml3n7/hqdl9wvOe82iimPKmtLKIhIDxXweuYVs4DsI+AlDqSK7pyFv6A6rhuJ4BSGy1J4EqV+zV/BR6RdVKJbUippc+iwvMCEEEgagDfAFhLKXsKfmDGqnUZr951wztMUkzlFNJtj61c77mRpJqZ5fWA00HlfEekGvikQ+Zms/3kDxjYQ/j7+6EAQA++ZZoQxFJu+xAOnG79iUory2xwUPHDC2/Jr1Eji4eXDM1YPGVj5QTAEzAwCALp6GvZe+lxf7NozIj+AHkLQJE5O7rBu+BkjsvM8Kar/IZ4+88AK+GUUnezvv1EwsrfDNIZMeBsJn6L69aSFtBAKG2xPQDAIpGLpAQwJovnJNmdo+CUrQiniLsOwKBLrbS6Ak4Xq6ytSUX0ht7AwHqR5QngxNFWmVd7gmgi5bi1YfXxPv4mOy0lw2hKr/xuVp2MeVZiU84AOPP3GSmXzC03mtK5G9t8lSCcuWGmTQycBIAHOOm4ufM/otCYgVEAHPHsEtVZ8i7+jGq2zs5Hno1xLY2dbPeLlmuFrIgAgFy39oA/7PlwMy4XQC9/2mwsxBjnTsU5NRzdK6ajFgFr0qEsKSsxaQcD61dbHxpjhbU9A2MEhDAwHOs60sk4XpvF5ZG8XknuYX8eTGUaWSG2drSWAySDZk/Q7wAkZ5KlI9PJOs2fQdsBSM5kS0emllWaXUF/ApAcPSo6MpXMa3cY9gKQHL3K2zOeQbvDsB+AbO9rbgsCSbvdcBiAbO9flgBG3NpthhMAZNuAsjgiEod2myEXgGQbXBpFXMShzQguACDZhpSEECfn0P4RXAZAsl5bHEAcvEP3W7AVgGxJKSIARH16uJh0KIt50wEqt9F5i2JY0q46r0x6enICftWiZZLiHADIouK8ZfqQMsWLAD6H5wu3w5/v2ZQ5877mzliiuotjYAxMBuCqEG87GZj737l/EQd+N+zKOyshREOwpqbBqg9vgHDe54FSJnrVAh4AUE4p+/xi4wBfsMoEgKg87V1Jr4uNq2+8KhJvAMDBLwdqCMduefDtoeNffeaNbSUkud+kP9PQriyXqSV3EdAgKm3/aty63FsBwCpLzLddDUACJF6WcuGq+NrzMwcPVeAdHKeIUV5qMmEL3+5SWVtDUpmHop50JJWQr/Fs7jAJIM0q3chfOaSeBgUpJ42m9I8BjMM/LPXKopfPBlp4eJ/7dbuuVEOCDvxMmbBfXCr2oL98jFPae+e6tQjm3Wf9ZRMAohQOPl9UR85cE8W/fEt+o0jE+YvkTSdy9/dr7+CLlEm1teFMsrhUhwJ6Hkzu9Dxxcz/UdLe2MQLWbwFMZYSpLQGxYkFQP21+8Jn8j0OGRncKyVySPOnbTADLAQyhlGXWox8X3oD3XHB8oXh/zgXHJRe16i17STqUleHvbXRKmUMQyEhKPVt7gkDCKWV1zg/UhFZ0dJTombfndlU2AAADG07+LQFrqiEmllO9JSqZ5FayCXor39otR/9hFie87QQT5zI3nNqj8wnIGw7Gf14PHnAA7oanDfHl7I8C4EtcJmjkQjhBIEG68G468+lICQDURcmjGJPPlkX1JWOOfq0gwBtZiUn7/xXpJI0IV9J5CAAowROFptBBOPcjoiMsS204OdZWJIQCuMMzUoLL8j14dRKnCzul/fj5aZNlN383pxJXjH3mwAeCQEic+e2fAMBwelTvHNPW/q3SBvr1b5e8Irk3oOrNQ14lgfsLjaAE0JS43Kptc4YRWADAqWJrNC6ysLbpANnLNSRarVyjdhLGMTLJX9JYABCqcNNCUY3++lKzv2wCQGu1w5AvaojE0A4N2BCovuDLlF/zZcobshKTZqEWcQ2x88cJI2BuzAYwIysxaVhNbDR4wOoN5IaCge7tNv0mGaTLl3JkJDjgcGnyxA8yp+24P/nNtQAONbRv/sb7h/DrTadKsNoBwDZBIE9Ryt6vi011oK2Hq0JDHvv0p/NvAKnmQntaYLGoYI8HAi/U5T2aBakGLYA1vEy0TEI/9eyK3YJA+kbsf2C7IQd4Ftb8YrD9vaI33XbMESDJIOv97QKlTBIEMhLw3OQvxqIxI6IB3OU5YhJAalr05XcoZeXLf71qo2hXD100ZkTgjFXr8t69d0VuXnS/Vu1OfA9edrV0v6pHvnjtQV1geSch2dazCwNDfz3vYlzEtQnzh/gm5n8sGpPeH8AIT/4qRzglB8mxCxW5mFOR602zI+zqdx4cdLrzGLiLOy5dF7fjbRCQkQCuyzFtHeavoHXmmiiiJmHvOxlfKIGbnDku06+BQnOlOVX+V5egCj4SADQubipSzbm1tRNg5RZonFyPwnD38ohHbH4tQjvk0J9mDGEv35Lv1zqA/bbA7wDc9nN5pL8aezU2eQDCADwPwFXTgFOVrbUAAAHhUIv0kAZvHCAIJDh/yaG/do+/qcKtCuy3X1eR7eAq/VBuzR0eTym7v7kqAzQg2QA+Qy2r7apSnhO+TxYVF+2O5NDIu/UWzoBUwz8in+pSsNSg/g61nMvA+gC4WzGnfDcAhB2eMs2QcxMORmxxbYUc1Tnsz6/OujRXtVbZizLHZfpVI9cHpczsDVyDBYGYBOHvfMSVCzolqAJtWQALBcgkeCplh81Yta7RH3Ilx2LeBQBtuPkqQSB8cdDR46JChwNJ41AW1FbCP2SLs6mxeMrG/sWHb9+hLOnRhxBPd1yOEI5nikFVx3k/I8MA8rQqcLRFFTDpzQ4jt9GQ9r4YggCMKFSB9skANgUUDMyDJwWJMMZUpywHUz2pKHWnVFS+6WR8986aio9agtV/NhLHujEwC4Dab7enGqLCixQTXUo5yxogT/Kfdx7cjAsXwZ30t107431frkh/224MZK3kKx6qbYc1366tjFqkvTRGp6uZTOZO24o7pym0BeJfQUVGzz2RiTwR0TrwxJ+N4FOzg1LmppRNpZSdBABBIANqa4tJvBqMXFR/LqRM8Yp3NjSwtvabPB75qs0aJxcCz4exAAByTFs7BJ/8v5sZ5IzHijsdClSay/IivtlkkRWB2S7t3Abw7DZ4ZrI9AWDRmBG6gv3GNW6rJji8y+nUGavWvT9j1br5TSFYBYCobtkiAIQnnXkHwNaCWJ0IxlAU3h27r3oMG+niRvbwn8V7T3xy03uPr8wB2O8ACSIK29ME5LLdj2asWpehCXk8jVPEaQFYgloV28ITc/YCsHtVBDhwrKTkaOwLObmWvYxJsswkSEzkj5bvug7/z959x1dRpQ0c/53b0wuE0AnSwSjYQdQBdNUNdlesi2vF7i7qRl0VXZVY0LWtKBZYy8prWQtRdxUYFY1SdtXQVEqooaS32+e8f8wELjE9NySB8/XjJ9x7p5ybzJ37zJnnPAcWtjVozZyXGf9tdfJ5ifbglgFu711t2ZbS+fnc4YuCTukCWj3YxhDyeYGIdQVt52T8wRf1XFC3CA9LsgdbNR6hMYe4q30ACbbgn62KGF2a8Nlelpj/0YqAs/qU4qUA4fTAeqBFvbPQMTmsH+7433UZgcp+F21J3jxnh6/X1emxW19PdpeGfzd03u8Hp6z1mTO1Ks2l6+Ic4D0rv7XFkyy44r2Dw0F7Q8fCVxLpDzk4ywlRvwXeSWgCUfv+DUD7cc6VKxM8p33t8PUI/RX/49VGzLsYMTckiFAWCAniQ4CI2WbKgWRgcRRzz14BvtY0uXbWlMnjgTlGwDnM7gn8fuq9+a9FaR9RY3OEF4OUFZvTtvQ4dFPAtXuUW2L2+GH+fjUOsNudHeW5aYvGQe/3MbtFwyAuP2XWbxdtzf5qMVYOZEO379NGvZa4e9Vl9vhe3w7+/pVTBof9rpXAJmes9+RgTUxc+caeV5dv7AkUs9PzppHuyajZ6S2ILfYXCqIwyhvIBtGrIuw8vqsPRFGaMCNpbCz2RMw8+4XMSJrU0hzWbS/GPNJHus6piQn/LfbPVT9Fu4l3vJ8uwrJ72gCXt3u0t+017IMAKg3HaYCWOS9zUlfO0x65Zm3emuEj5gPnA2e0NOCULnPGUumSX7dmPMN+72Hd9OWDK0t+OecEiVz2lkw7FNiys6bvVf/+c/blg1PWDtI02er5rw9iHwJXAR+3ZmVHTGCwO8FbfzmZGeXeqnijPOg0Lm998zo9HQgD1I5ijS88+WWnr2daRb+PntmauuwVl93nBeamO/1/7uYIbM+fmr8jIzt3rMD4CuTDwHOYeb4LrSC2zTRNSitYHQfyS2A4EAr7XOuisf1ou/iOn8pBbCrflF4KnL1OuH/YW9BCHjCjnjuJa/dOKGGTwLEAfXNOyOubc8LMxnJN/ZV9UgB8ZT8eG/Y738KcHOMsI2yzO+P21gPvNmzLbz2HL05aXZZ3crG/MCrz1mf9c9B1AuMutwgvyZ+a3+LSOEqXowmEtEpauWnpLeQZSSm9Cp1XeD1Gya4eoXbpjV9Y0b1bCBsFgZioT2tdGPQcZf5LRF7sdXXPAvZQ98Cglq549KzdYSDo3OKpNwWxKfs1YNV1cYXDU/IZiL7b+n27BsHY7jE7np172mQ3QO3tbaVlNE2GNU2+rGnS0HWRquvieqs8UrPUFCWu95bEN1gFIOSQn8R67Z7wA4k9o9PiTmZGeV5FfLggbDMMYNJW34Kerpq+vws7Kmf/oXjYltUlo5OO6fllTK9Rt6XtDHriezt91pSRUpMIuzULFZi9XS6ifFIS9vAU9u6ktiJApyTs4Y02Z2iMpknp6/U/QmUrAUj3Lnq+qalAleYTdu8RmOkrLQ4iKzZPFNKopKqwKrKAdyjsdz9w8yufC+BmALsn+FjPI9YvOvK63G3AJOBe2pAvnTkvc+zmQMyzEiEC0nbMte/22TMPsa6LRGv2Q+XAogN+iZQSaStNCrV0LMRjNilSYny2UzL+4Iv6LXuAgLT1BqgIO9tjFH9tndgwB06psm+M+FDISAm1Ki1OIv3SJls14dl+SwnQddEv5Et+qXzzROlJXVuwwDdwXJK7xH/7UfcsALbqurhG0+Rb+6s9B7BrgfuAz2luGQ0pYqW0r2/o5ZQyxzPAVLshJgFvRKORnY1NssOwkbY6/SJ7ckHoH0I6lp8b4s3iUO9FIFmy7RRfjGPDTY7YAvK9idZUgHtmBTNA1k61ao94vlER6QS1y//q3wU5WXkybH8L5DWYn9dOfdJL7FuUUrm92/A3HxsqTig6fPyoHfH8nHI4GT9+ef2a4SPeVmWt2u6tJ6edK8MXHBqTumaRt2TE59QzqURjpOFLDFR/DHLPl2jd4+rZ+N7Fd+z6MWNUYt+i/KT+u3dNn79gs66Lb2urlLTS+VA7gFA6422hR4F51muvA/2AMQC6Lv4JxGiaPNt6/ChgaJrMth7fCNRomnzFejwZqNQ0+YX1eKT1eIv12AmEmmp/Y9NpK60wozyPGUmTQnbOkUL+MbHSfi0zkv7GjPImyzFunRNze19cVxpCPm67zxwE2x6GeyqPWetLoK/LW+84jrYY6qka/rMvHoH8q0T8pyunA9QasXaN8eOpGV8418WMXzN8RNyItWuqW7K+jDPiggO9k1qz7/3Ww6ppcsu6T156XYY88l/eQ3Iqg0mDy/3JN6THFRZjBkHtUpj6IPQIcIymyWZfLdqcoZ7uxOqERhb5XiJLfW7jwrY3r3OKr7YXOoLuwridJ70nbf746u55VxUjxwMOaxYeh8NRcR7wU/7U/LWwpwRNObAUxDXxzorF1keqX1P7s4LVxcBDIJeA/MZKLVgC8iuQe9ILzB4tMZE29nDtD4FqT64RdLDrx4G9e68Il/itiYuq4vrV5rAqbVTyy9lnIELB+F7Lrrlh9sSZLe25dsTO/UCGtuFJGbS4vuNq+vwFMjat/DikWL8u95jUFc9nJei6cABf6br4Qxua/g7gA0IS/AFpi5yd7WXg8YjHy9m39nMyEDn94BTgrIjHM4FbIh6/BzwW8fgnYG7tA10XX+i6mBHx+OXv7k39K3s+kyy0glelrWaU5znvqbjDJsXxdkMkAG8yI6nxMk8zkib0KnTe73cZwc39Aw+3Z/OS7KHxAMM9VQ2WEmwtQ4rD420h48epK+8/EILVWs5NMQ8JKdzAaS1eOSQq7LucdevaN8t+62F968lpp4b9518s4cX1cRXTCcVuANs/rPJVUZux4mCnadIAfgTQdXE6MAH4c2M9C0LIJE9KVa8GNzqjPFz2t7iq2Brb6QWvekQ0CzZ3Ip6y0DWpLn/ftOruednDbrvjB7JzY62R08JhrxQ2V3HGcE+VHrlSvLPckR633b0w+7Y5Gdm5rwDLQT5++bM3/mfujc+WNbI/DaQ1JWXt7f49/7ZZk5rtmYXICiY6/QnPW5SkA3eHA84ZBekjHN74kwBYM2KqvbzwkKJWT5GiAPDctEXdwXUh8MoFN81t8K5IQ56+8vYbg1XeATbXCOC0CZ4Ucf8NsyfOrLvcZXet3jZryuQLQOY5Y31Lf3j15DcyJv4gkgbsLgfQdSFa2tuaPzU/L3Ne5kRAA6E/c27hnuNZ0+QHkctqmpxV5/E1dR7XrVpS94vzBiCy7NzfgMjanT+xb5mlQ10bYvpi5hkKiXQJNZ1wVDnuqVjKjKQbgTnlCaFvk2YkvQfoewZhzUhy7EoLjnYExVUp2K92RpzsAAAgAElEQVSyG8JuC+DP2OQeTjv+HZZWJxcAFIVcUe/FXeePKzM7Iw44X0khi43U4A3Auy1Z0ea3ldh2uVs14dF+6WHVdXFhoKr3pzaH1/7ziI9s3lDckGN7fpk797TJt1kF8JX2MQEz96zBvKFZUyaLcMARrtja/T+NbchmMM8dsNn7b3YNj3YjO9rW7K/GbjVu+211+PQe2zDeG3bbHY+A2YPazbPLm+Iuqjzx0AdDBoIqw65HruuyB0Ihw+m2lg93j9mRDaJfdSihqQR+XSClmYaI35plLAQEgKBV/hI68e3/egkj3vyHvDIhnP+bUNhM4TJsjvDWvhOjPgr3YBPXc9lzgAd4uqXrzpoyOSVYvf6vwpaIM3Yi1u15raHlp89f8N/Y9NL/BGtiMkI+113rPj5mzIrZpxdaL1+t6+IDXReN3Zn5lfyp+Xn5U/NnRru3SdPkNk2T2yIeL9Q0uTTi8dORQbGmyWs0Tb4Y8fhYz/LEGVJIs2qBwKCrffa6ghnlL1XEhzcnVTqOksiHJPKrqkfjSoz7EzcA3h67nctSyxzXCoQdwPqptWeTJKIXsOu13232Nblwyw0G0SkHybbFiLVrQoGR1UWiyjFh2Z/SkppeYy8pZEDaZavquu+XgLVk3RknVG4bjz2m6NkPC08+LsZRvXPK8FeeAe4GztwfbThI/Rk4SdNkla4Le2QB+gguEA4ZtjfWG0hSheMVAJsUJ7dHQzvK1uyvxhpIXQZOsksMcvBmRY7yL/alr61wFP+4LGBzAGwOxO5TT6/El/bzporBm2sfL7/vyn8fkrT2p+U7xo3NyM7t39B+C3Ky8vomFDwBAhvGXzAvLu41f4qTQCzFjFgb/bt0NkJw2J5/YYhQaKtZJFp0/GxcXd1z0xY5faVDzo7ptrr4htkTV7dk3VlTssbanMH/IgMJzrjfSiHckmbkQ9fsTPnGungyRzlLm2a95MAsHl4FZo9ry95N5zNi7Zq8cLfgRQBGXPh1lcPaPuJqbHMlEmEeU3ZX0BYfcsj1wGM+t3FLSXLwcYn0EYWqFM3RzRE4NtYWqon2dm97Pz0N6NXfVXNgToIkedTmtxH3SbcTm154r3B6YEAww9uqdJt2D1ifm/b52MLlt5wJcvczIv0bEId7Q3G3n3ta0S/AIMxyQEo7sMoiVVmB6kvAC3W/WFKHbk0DiE0rS65vG3vMKN9oCLnF6zngyltpAlxW2RUOxf6r0iOO+F+6sXeUft3Xq6nTg72hfPhvpFlqaEFjJa62VB5yF1BmYM8syMnKK8jJmmn9zAMmO0TQPyBxnX7LnEu6UDAgl1s/DYkILUrsSY3NeBSYpKoEtNn5YX+yywi5b27JSlah/y+NoDMDpAApnLE7vqVZfxOhg6itlboneNA0+XfgDE2TUtdFErBU17v+xWzmkvXvArvsVQ6jyYWVVrEb4tPaCS4EwusK2k5y3V15CjPK7/LcWfl06q01t4uI3OqW1m1tKQdySLrTH/UKFSUh51EAfVy+dpkRsaO5V8e/DpQJKc5vyXqi2r7NXuwsbHrJX2vXgPW5aYvGmgNIRF8guZ+zdI7HXrP1vrG3fgygaXKnpsn26IZX9iWBbcCWunlnzlh/H4CY1Mq0pjZSmhKqdAbFETuej3G3TzM7xFIAiYEkzPeE9rmiT3YX9UwMpLmA2lv2+7zeN2Fj37SYwlF1ttnHrBZAJsjFDQWtBTlZgRhH9acOEbzg5jmXJtR5bfcxvb76eFPF4B6Lt5w+JQrvc7+Qht0q7C3ezR8w7J8r43rw/fBP56pgtW2sc+kjwGZ/+aCWVlPRMHtDASGM0GYjKePzi5vzN5k+f0GeM65mubCFA9QZ8BdxLumBeUFXCl2/t1UKuV3a5W/VoKt2Ygage8qk1RuQzijPY0b5zPYOVgF2hlzVW/wxLZ5wpynLqlNiAfKqUg/Iyjoj1q4JGAkhXTqNKcumpzX7Fr+90rHdXuZs1XTM7Rqw2pzVp4LNDiDBnlDdPW5sb/2jgUnrllplSpT9wPpiuUfT5AMAui566bqZI7Tz+0GVACXrejeawwrg9tv+5ggLeu50HdWuDd6PKnsuOk8gsNsXyp/sb5KPcW7kTFUehy8hwUhxE3GCjcy/c4qgL2C464541dj72Wq0WPS43otXhaTTU+zt8avp3QJh18UgV1YEUp7IyM69J1oTErSnuJ4lAwDcSVUfJg7eNRgg3lnh7NhWdW1msGroIPqB7I01SUAL6CCsHkM7wtZjyZSbXylo7squBK9bGjaXsBn1juzWNPkLcLSmydpptf+q6+LZBlKQOjUrSD2cML0kUlUKaC/7MSBtTOa8TBeItBC2TU0v3WKDrZ8HXA5rLd+YynwRtLkdO1yXN3cdaZOhTpnD6ozZtQRrYEkYxHZ7eOPY3ovvwJxy8rP23Leyr9reEOv23bfsHbQRDyDD9iZr0MVX29/B7K09pZ2aud+5K4aeE4jd6k9wPe8vEbvpn7B+Q+TrO6r7/rKhfNj/GhossrFi6NJyf2rdOnQ6Zk8smL2yekP77+bZ9YTAKP96+6RfDaB/548zgyBmA71AziCKs2i1l9juFaMBEvsVdSv1ddsI4LQHGi9hozRFA+Hc93HzTZ+/IM/mCBXbnKLIlfA7YXdlPNPcdWdNmTy2ekfqKBBIw/aZlV7w6wbue+fGDXisiiVdjYYEK0XoQJmZSGnAcXGlhwL0c3mjXvnmEHf1uW4R9uZPzY96fddOQ/CYRFZ5ViSObu4qwQzvqHCPQN27ks3SrgHrVY9e+TnYlgaFUTY/PiC2OGX2NeesrtI0+ZCmSRWwdgBNk+WYJV5eAkgasHM4QEKfoqZnnphRXhp0GL94PcYV7drI/WRr9lcjXTV9ezr83WY4pYEfJyO7/TC0hZtJAFIjA0mzh1Zcbj4SD0X22Nb16BXv1khs7wJnZWTneupZJNEa9GKjC3yBlq7vtQOganvqsmU7xucBLN58+u6ObVWXp+/NIxUtHoTy5mND7VKKHp6UlO4OTzJpo95Y2ILVNawR2zTz+NM0eTtwNYCuiwxdF3m6LjJb0uYOpAvrdy1a8btWuha7kIcDZLiiPuaKqrCjd4ojGP0NdyJHvrCjUiA+As5eM3xEs8qk2suc62zljuLW7K/dAlZdFyN1XWiebqsHlzkCyYGE7ZUv/easBF0XWnvtU2keTZNPapr8H0BC36LzAWK6VezJS501ZfLYWVMm31lfb0pZcni3xyf6FT8d223/tbh9SIwrgJAtHPOKA0P4cZJfdMQ+V8MJrrIB8c6Ko+vr2czIzh0rMM4D6QG5T+/nkORVKwAGJa1t8jPWM27rJ0BCWkzhR/XsRwfCVvmr/foFmpGdOzYjO/fOum1q6HmAkNftBuhx+MZ+p2a8fylATSjhQKzbu99YuaYfADW0YvDajv8O6ivDdgJVh/vt7oqPL7jp1dLmry11cxY3CWbJNb05a0X0uPbBLPjfgn12nBFr1+SFY8LLpZBlwCRVKeDA9nVVahXA0urkj6K97V0ht9gR9ORGe7udTbCf7wuge82JpTc2Z3l7iXOTrcbub82+2rOH9Rbgw5KaXv6g4WZI8ponHLbwbcC0dtyn0gK6LkaF/Y7JALt+HBiC2hHFcgnwILCwbtCaVG7/q0DQrcQ5bv+3OHp+nHNFjOGsvDUQV7By3Wkn7rZhuEeJAq7kP8fqurhN10XfjOzcsZWBpNSqYEI69d+O1yTCKvIv9ul9Gp66MgzQI257kz1LZb7U3SDZ7e15ct39WL21d1lFCm5rrLc2mqw2LAIeFBhfaQ8/+XVGdu6MjOzclwTGVyAfqhukA8SkVgwDiOtR1i3VU3QcwOi079Qc8W0U13N5hs1Z6W7N4DVp2M8EkBJ3sLrnky1Z15NS1QME7qTq72jFLGuaJr8GRmma3ArmjFK6Lqa3ZBv7m9EtOCrU3ydVsHpQ6A3gl/Yt0dxo5rzMWMyLtQM2f7WWf0zlu9JpSOeGmFObs7y0G6kS2aM1+eHtGbDeWhWMP60omJjskNTkFU54ADgC+NXgEqVjrHg+K7Fw+dDakjVzrWD1LOv2c723oF1Bm47Z09Ol81jjC0853x5MsntTfnz3xC8SxtuA42yrmRr64q+J5fbHgGMw37uk4Xy2/1mvGdTp/fyx6MhCgOU7jv++qbb4wrHjrKC3of3MBhkekLjupBa+zbaYgFmg3iax2Qsqho4D7gOulNjsVl1Od922upOrjwXYvWrAS+vLh78J0CdhU/x+bPcBKexPKJaGo8X1HK3P9BMAYe8X+Eqf9rZkfV9pwmVAsRGya62dEjgif96JOcVqiyYb2N8chW6frdKhpgo/CAx010yyIcNASTS3e2JC8USAkZ7KrpjH3SJHP1pUJIK295xbPaPXDB/RaEy5ZviIsRjiN0AirZj+uN0CVk2T3jdWX3tJGGITMArnnjYZTZNeTZMqn63z0KzAA8CTfEjhE8K25/MVpr5b0DPK/V6Psd7n7tr1WF01fS8ACpO2npljk+IkCdImQCDth66MeUjTpDlt4N4ZqH71u0j17K5NHH8OmBTZ+7mpYrAPMIKGuzklwGq3W28x94KcrIqMxPVVgbD7jJa+z9YalvLDeKtJBuAFxhXkZAlgnPUYa+T5Pm2t2NJ9Lcia39+zMvBt4YlLAP5TcNau/dXuA5WvdNgvMhxTd3Bfc2jsOc8bQKjZNVxfufuIESDPBl6++ZWFbS4/qGkyqGnyPKC2Wsnxui4+0XXRu63bjpY1w0e4RFh0t5c4l3d0W5T2J5BDE+1BI39qflTTlopDzmEAqY5Ai6dP7qLeBXoa8aGmOlXaNKgx6gGrrguh6+KfuZ/FZ60sHnOZx1FDavLPg4DFXb0+3wFIBwJWUEJVYepxDk/gCjBqgDk0cAuwMiG83uO3JZQ/ETe47mtdwapn7xglkb8F5vbNOSEELJTgD0kbBrawK2jLhdrb8XvLWdW9HZ/oKrs71bPLC9xS97WCnCxpF8FAn/iCX43+r6sgJytPEDKSXcVr69sPgDcU+1Jhdb+YjOzcnq1+482UkZ2bvqF8+ISesVsqgb9Etmnv70SusxHaWretRtDpFnajRtfFdz1idqQCBI0DqWxvl6ODLWT13kvggmeumvj5G48M69fUioEq9xuA3RlfsyqaDYqoHtAH6Ad0mlHU/kOrRgAinBhq1VznSteywR9XWBZ2fhft7a7yJtoAllR1+3e0t90ZhVODH0u7IQOHeGc1sahuzXDWrJn26mqPHtZewBGvr7n2nMpAckKqIXcEqnv5Sn4547u6ReuVjmUFo5NA/AXEH0JeZ2mwJiYNRCwwtaH1eux23geQVOEYv7/aGk22UMzjAmGr6JP7MQAzyvO+Dh96wROh3/FA8PezImsDRs5AFbmNjOzcXgUVg5N7xO74uCAnq97jOs5Z5Ur1FDUZsJ7/5J1OicOWkbRuQ0M5qjtretcWn/5Nc99nGzwRNNy2qmDicQU5k3/13gtysvKOTM/7RWLLuGXOJYMiX3PGeYc63EEBeBPdZUkAR/b4plU195S94nsuHWtzVqa0dL3p8xfkOePPWWn3HFME4kS7K/hwoMozqWhN/1XW4Mo9AyxnTZmcOO/+zLufv3H8O7OmTF5VvSN1DECwKnZ2Q+Ws2kLT5P8Bh9XOxqfr4n1dF7+L9n5aItw9MBHAf0SFuso6OPQBsa0dtjsYKM6fmt8lBhu21aHfrCsP9Qz84lob13/N8BENdkz6xlR4ACTyX7RiUGPUA1ZNk9tzN5x/5LfbTzp1QNC22hVI6BH2p3gKV9x6o1kAW+lMps9fkDd9/oKZ0+cvmAu2F60SSoD02ByhhvJUVwK7JLLL5bFuzf5KxO/UhoXcu34eeVPOktrnpzsv/+bv4bOYz/hjmlnr9HwQYm3JYfc2tEBlIGnzqqIxTeawLt853gPw/e5j9UYW+8Fp85f3T1h/SzPa1moXP/2nm4CLgZyVf724wfnqHSL0psTGV9tO2aeIvTPWP9IRE3BpmtS6eXb1BugZv1UFrG0U8ifvloajxSNr5z911VF2Z/8jEnp3/3D6/I+W3Prav+/ukVlwbcjnLAO+AvmlOYCOJUBp0eoBD9YUJZ4LxIFoLH87KiJ6W1OBdKDp8nrtyLMs0Qtg3+luSekvpQu64/10YccY2NfpjfrFSR+n96zuDn+Lc867Muc2z8O2gK0b8GxDuanSJW8GqDm15LHWDGqMasCq6yJG14V9e1Xf2RJ735NkzWqxZwr2zl9DUuEDEF6QhrAbxPUs/V+9S80oNyrjw1tCDjml4FVPV0vzGC+kY6DD3+PhyCerg/GjAfzhmAk0o0B/gqvsepsIryrIyWowqJPYwgb2w5sRANeOom+wZl9BTpYxJGV1cZE3/fCM7Nx2KcQ/5r55nrUlmY91j9kZSHEXzWxs2e92nPg24CvxpR0d+by3OHGLvyJ2BcA32yeoHNYo8ZUOrZLhGNnSi/6ygkl/AhtChPd8li+7e/WLSFsmyJUgHGYeuxSAntCn6OKeR6xPAy4CfDSQvx1tmiaLgOOB1wF0XVxkVRTYrxc7tmpHDwD3mri1+3O/yv5XFnb0CmNz9HC2qsJSo0rCrth4W3hn1DfcuRVKJBJ5vUR+UV/Q6lmREGu4w9uOemrXt63ZQbR7WP8YDDvXf1d40indY3Zs7VGT9AQNzMGudD6RKQJ2V0i7ZtZ3H1k5yb8qaF8Ta+Q5QzZ7t2JHS6eJ7DBbs78aG3KWvCYJ+4B3Il+rDiYcY/UuC5q4uJo2+8rjKgPJw49OX9LgrSQzSJWHgBxJEwHwhH4fZwAcnra0f2Pt31xxyIM1oXg7ZrWNqCv1d7+rxNfDPTDp55v+d//URgfZFORk+W0itMxjr/lt5PPCZqQl9d81TtfFoRK7WTxW5bC2iRWkngUyFuTC5gatz01bNDbkTb8QwFsy8tHI9abPX1AO4jqQfpAhED7gL9c88e0/L7njp+K95wIzf7u1FQJaQtOkEdHj2g8Yzp4BfvtHqHvgGOkwikesXRP9KEbpVPKqUpMB/luTPD+a282clxnjNewJBYHYf0Vzu13AkYAhEAizzON7q0YPvXzZ9LQYgDXDRzhFyHa8zW//oLU7iHbAumL+T1f8HJLOHkXenn+4cfbJ+5z0WlNDUNm/alMEbpn72ZfWUw8Ai+r2dKTvcj4CkFBl7xL1WLdmfzVWIhfagykDQLiBw/ZdQuh7ZxNq/OLq04JzTgBwO3wNpgNgBrzCSq9oNAAOGq4kgBhHTaO3kKqCSQswo+rTGluuNbSH/3YEkA28/vatOS82Z50je3wb9oc9Q2968bIBe54UpAi7UQmsG5K8aoC5nMphbSMNpN26Pe8BJjZnJUfM7odgzy2uXx2DZhAqJoCoNyiNSBfa7+dtTZOPAidqmjR0XcTquvhU18Xx7b1fGRceG0oPqCusg0Mf62e0c1gHWj8P+BqsdegC4ZfIkEQGAJ/NZ3815uukylVHDbkr2Mv/ABBnuFvf8xzVgPW9Xy759uvtE8c6bIGlwEIwZ2m5YfbEmSpY7bL+C6ygbk/HjPKtwFpDyNM7olGtoAmE20xREQZ1vrwLcrLynDb/3QCHJK19ufEC/eJCYNk/bny6sdGlOubdBcxcwIYD4CXbTi4D+LZQa/Q2SUFO1u4kd8mWXnFbmjWjSHPdMucSAWKR2+6VQLOLuvvCnpckNj7bdOYYgDcfGyqMkM1TvTO5esXzWWN6xBb2B5XDGgU64AMprTsAk5+btiixsRVevefJkeFAgmaWsmr4DldHBqVN0TRZewE5ABgEtEsqTCTHFk+5vczxZdNLKl3dYTEVpwKMiqmsiuZ2x8WXXAhwTFxpIJrb7eysnNRJAnGvQGjAIO+4sr9Il9xhq3I85Cx0Z0skwm+7szWTBkAUA1ZdF1kFFYMf94bi4rW+/36roZHTSteiafJfmiZvsno6eui6SK19rSQlVC4FJxe86unUhcAtOlZ6ikDUO8Xkbwe++3Scs1LaRMODya6dfdVE4Ihkd3GjU+5ZM1RNALaC3HbWoDcbC0abzGGtNSjpp407qnunHfvAi6lNLdtcCzZccHlBxeCkI9Pz3ijIyWp2vml+0VHvAUFfOPZPGdm5YwtXDDkJacNfHtcPWFixtuc2gNwNF6gc1jYwL/bFJBB3Azkgj3bG7tw4/6mrj6lv+eemLbLX7D78eWk4vTGpP02ji9/h0jS5BhihafJLAGsmugd0XUS1w2XN8BE2YYjetmpHVMt4KZ1TUIoBAP1c3oJobvdnb9xwgI3+2HBTyx5oRqxdkzdi7ZqZ1k/jiFcKH8r8cn1fYLZESitdwEErxzNF5QOv66KPP+z+6OeSURc6bf5vXrp+doum/1M6P+vLIRf4sLaebsgh37Ubgp47nJ2+vFXfnBP2SU+xHu/jqavf8Eop5q4rGz4gIzu33h6srZUDpgEc33tRk3NEF+Rk5WUk/vIsiIzqYMIfG1pubK/FRwIc10vv1tQ2/7tr7F8kdrGzps+EppZtjozs3O5h6XgM+Pqb7ROvauHqo81b1fIEYOFuZ/cLAaM2D7i/d8vQaLRR2edO1Z2J/b64MRyITy3++ZxPnpu2aEzdZYWjejZwItI+64qHb3jhQLjDpWkyFPFwGDAyIt81KrzHlA8C3KG0QKepC6u0nzW+hF1AyWNn74xa6anMeZlji8LucwB2h9yvZ87LVJWRTP8QZp58m8YzResKdfus5ffP9oVj44OG+84obVPpRKwvh3uA+2rr6fbY7XwBCHv8tk4fsIIZtPbNOWFmfcFqrZpQ/IsgPMB59b2+qviII8HYlLvxd87m7POwtOWzYxzVwW8LT7wsIzv3zrqDrzKyc8W2qv6jAAqr+zRZzB34FigHGZU81iEpqz4DmQxMK8jJamkAoAG22tJHPyQeJokYZLmjezc3QIpnd5c4PrqKy+5+YHZMt5/Gy7CnGvh69s3vffr8Tf965Llpi575+/W5G2Uo9ioz1ZnbDsRSgpomr8asYoCui166LhbouhjS1u0a8eETAAIjq11t3ZbSJfQh+vmrGkiH9W9VGclSmy6A1WHUmpJWEKWA9b1fLum+var/tT1iCn8pyMlS+T8HKE2Tn2qaXAig6+I3ulbhkcilIbs8q6PbFkXfxToqi/slbHi47gtj7pv7EHAI2PrRjNJXAE9f/Xq5NxT3VlUwaTTIh8H4OiM7d0VGdm7+yL+85ROEw5srB/0BYFPFkIeb2mZBTlaoX8KGLUnu0svN3NPWO/r+l079pXTU6CN65C0ryMla2YpN6EDYCo6CaxKG/wPrpPR9YuYtK+xH/Qag1Nf9H82sbas00+X33/Y1cD1ITziQfKoRTLoDuFLuqcjQvvVTO5qmydoBiiOAMVjJum0R+2VyDYBzXcyCtm5L6fy6OQLjejj8UR1gl+7wWVOxylbN5HQgi0wXaO022hyw6rr4bWUg6YOqYKLtsLTlj7d1e0rnp+siGZgPPLY7LeS3hxm16RVPRgc3KyoKcrLkiG4//rClcmDPQ7I/fKQ20Lr8uRvPrggk32UFZzZaFgxsN38IrNvlqcAvPeO2L+vm2b2OvV+2zcrt6Rm3bUm5P9WRu+H8p1sbCGZk57p2e3s9CXJTz7jtWa3ZhpWn+wWIXVjTt9YO4vmq2/jeYLMGybQ+Z0lpVCZ7jh0ZBh4E28VmLeWDo5SgpslFwEBNk+sBdF08quviytZsS4RsfQGc2zw/RbGJSidVFbYlBaVIiOZt+xC2VBAk2UNvAZPyp+Z36VSczqbNAWuZP2X8d4UnHAvy3y9dP7tZ5XCUrk3TZBlwOnCzkMwWCPpscx3X0e2KlhU7j58NAgPb7QLjy0PveeNpfctpc20i7KN1xdQ/wKyyELKCiYsLcrLOXXTnn04o8vWcSgtrFS/bccK/AULSeQPIZvX01tUvYcNTwAgQ1/392ldLWrp+rWR3kc3jqPbVrargsvlqS7uEOQgCpw6iYw4gDFk/F1u5qgdVKUFNkwEAXRdO4CjM+q0tFuzjO1HaZc2ItWvKo9k+pfPJnJc51i8drtKwqxfIRdEKWotDrhOB3eVh56UqWI2+NgesL/4wvX9NKMEG4r5oNEjpGjRNfqtpstznMT4wkEFhcAczkg6U276DAQlCSGyOqmDyTSCSgoZHgLgZKxhovPTVXtZye4KIyPUae60RI2rbB8TEOqqubsmbO/7B567ZVtV/Wq+4zZsKcrI+acm6daXHFg5OcpWnRz53/QuXD7IJ4/fdPDvXY+Y9N/t3pTRfQ8HpwVpK0EoTmATcDaDrYrSui9d1XaQ1Z33pMY4Odw9EfbpypVPS2Htny5NqD/w9c16mo5Hlm3TH++lOGzIL5Cf5U/OjOiBQMbXpD3ThU7enbaw4+qLByWt2fZ59W2M1KZUDlCsgzhbgFJIxwEJmJE1iRnlX/6LUMeteOs0pK4XNCg7tQPeCnKxGpy2tjxWw1ft7aey1JtrnAURNKO7yjOzcgMD4l8R2BKAX5GTl3TLnkphvtk/sv9vbs0/vuM1H9onffMaPu48w/MaAEwEKq/v1zMjOHduWYLKgYvD3IcMxLPK5hZsn3+gPe4TW79PbZk97+f3WbltpmhWUdvXPW9RYA0Jr61+OBk7E7OFvknN9zC6c8of2apvSqeiAH6RLgL0k7BoNLM2cl3lN/tT85a3ZYI1hu9JAJI6NLymKakuVPVp9NanrwtMjdsd6byjOluQubVXOkNL1pe9yDcSajo0DZJDH3l5PcS/YriMK5TiiKaJ9d4M4BcRTIK+SiE9BPgQsOST7g6IP10+p2e3tuRZYuL26/6PLd449ISSdR5rBtwCEnTb+vfzhGF9YOvcEBBnZub394ZhpIF5TwarSkTRNzgWGaJoss6aYflnXxakNLS8Q/UXQdrDNTnRQsm7XTwJxj0SMA34H9AS59KL/G/C/U2gQ2vQAACAASURBVN8cktTSbX5XlTIYpJFoDz0d9QYrQBt6WD/deHafTzee7Upyl3z97h8fUqMqD166QPgxg9VOEdBFQ2SvZ0Z27krMwE7vLLe26/TKfj7qnn/GVwcTrrJ6ggGx9aj0vJXFvrTPNpQP+zreWbF5Uv8FhR+sv3g05ix0Ufl7pXh2p/lCsXsmjhicvOaf68qGO0HMaMt2FSUaNE1as82RBozDnLXvV5bfmN4rjtTkYG+fv77XlQOPFbTuOZ9nzsv8bISn6ouV3oTRIFZmzsu8MX9qfrPmvTdzYO0XAz8+fvbOTe3V5oNdqwPW9WXDXwoYHncs1X+KZoOULmZGeR4zkiZhBXQHQDrAr7Tilv1+Vx1MfAW4BCsQNbBf9/YfZ9Zp80U8BXkZ2bl7/l5tDcDjHRUjAmF3akZ27th+CRvKtlUNOfGwtOWrPpw+Y0Nbtqso0aRpcpeui8Ow8hZ1XUwGTgBmaJr0ijBHAwQP8akc1oNU/tT8cmD06HmjxoYRLwDvn/nWIVsHur1nP3VOYb0XOgCZ8zLHgdRBOEGmZc7LHKsGXLUPIWXLZ1B98I1JmfNW3bC8X8LGbYvunH5IO7RLUZQWsqoFaOynnmBzf/IrwG5VP9BBTjihz2dHvnbTU6vbe/+K0lq6Lh4AzgGO0DQZXDN8xGTgIyMudMKoFb8s6eDmKR0sc16mc6inas5Gf+zUkBTVEnEX8Fz+1PwwwO3vpydVh+235FWl9A9huwCw7jLJMIh78qfmt3icg9K0VgWspz/60Oo1JaNHJLjKrsl/4JI57dAuRVE6uYzs3DuBhwBh1aeVIN4syMm6tGNbpihN03URq2myRteFK/GlXt/HLE8aAZwxYu0aleKmAHDGPweNKgjEzgJOjbeFdoUl+V7psAvksRIRY0P6DEQeyOMxL9wDqPqr7abFAWtGdu5EMGc7snpVVMkaRTkIWT26C0G62TuA0wtCnROULuO/f+h1iee7pNcxQFjfaW2ZjUc5sGTOyxQxIvygV9rusmaQkyA/GB9fsiLeHn7msbN3llt1XDVAV8Fq+2lNDuuxIMKYJX5qR4WrP5CiHGQKcrKsfFhxH8hTzPJf6pygdC0xecn9JTIszKoZ6vhV9pE/NV9mzsussmaTswNhEEufP2/7zIhlOv04hwNBaxLMdcw6d52mzI+iKB3D6km9H7NShDonKF2RLvbMGKaOX6VeOuoY6XCtymHd34M7FEXp3NQ5QenK1gwfsef4VekASn3Ubf+O16qAVVEURVEURVH2F1VzTlEURVEURenUVMCqKIqiKIqidGoqYFUURVEURVE6NRWwKoqiKIqiKJ2aClgVRVEURVGUTk0FrIqiKIqiKEqnpgJWRVEURVEUpVNTAauiKIqiKIrSqamAVVEURVEURenUVMCqKIqiKIqidGoqYFUURVEURVE6NRWwKoqiKIqiKJ2aClgVRVEURVGUTk0FrIqiKIqiKEqnpgJWRVEURVEUpVNTAauiKIqiKIrSqamAVVEURVEURenUVMCqKIqiKIqidGoqYFUURVEURVE6NRWwKoqiKIqiKJ2aClgjCCEKhBAnd3Q7FKWthBCXCyGWtHEbmhBia7TapCiNidb5VwgxVwjxYDTapCj7gxDiOiHETiFElRCimxBCCiEGd3S7OhsVsCqKoiiKonQAIYQTeAL4jZQyXkpZ3NFt6qxUwBplwqR+r4qiKIqiNCUd8ACrOrohnZ0KrH5ttBDiRyFEuRBivhDCI4RIEUIsEELsFkKUWv/uW7uCEEIXQjwkhPgaqAEOsZ6bKYRYam3rAyFEase9LeVAJYToJ4R4zzo+i4UQz9azzDghxDLrWFwmhBgX8VqqEOJVIcR26/h+v4H93CyEWB157CtKlB1tHWOl1jHpARBCTBZCfC+EKBNCfCOEOKx2BSHEGCHEf4UQlUKI+Zhf/oqyR33nSCHEICHEIutxkRDiDSFEcsQ6BUKI2614oFoI8bIQIl0I8Yl1rH0uhEiJWP5MIcQq6xjVhRAjIl7b5xZ/bdqKEGIo8JP1dJkQYlE9bc8SQvxPCFEhhNgihJhR5/XfCyE2We/jnsjUGiGETQiRLYRYb73+f105DlEB669dAJwGDAQOAy7H/D29CgwA+gNeoG5QcBlwDZAAbLKe+z1wBdAbCAFPt2/TlYONEMIOLMA85jKAPsBbdZZJBXIxj79umLefcoUQ3axFXgNigVFAD+DJevZzD+Zn4SQppcprVdrLJcCpwCBgKPAXIcQRwCvAtZjH7wvAh0IItxDCBbyPeQynAm8D53VEw5XOqZFzpABmYn4/jwD6ATPqrH4ecArmsXgG8AlwF9AdMy642drHUOCfwK1AGvAx8JF1fDZISvkz5nkXIFlKObGexaoxY4lkIAu4TghxtrXfkcDfMT83vYAk6/3Vuhk4GzjJep+lwHONtakzUwHrrz0tpdwupSwBPgJGSymLpZTvSilrpJSVwEOYB0CkuVLKVVLKkJQyaD33mpRypZSyGrgHuMD68ChKtByDeSK6XUpZLaX0SSnrDrbKAn6RUr5mHZ//BNYCZwghegGnA9OklKVSyqCU8ouIdYUQ4gnMIGKClHL3fnhPysHrWSnlFuv8+xBwEXA18IKU8jspZVhKOQ/wA8dZ/zuBv1nH7jvAso5qvNIp1XuOlFKuk1J+JqX0W+e1J/j19/ozUsqdUsptwFfAd1LK/0kp/cC/gDHWclOAXGt7QeBxIAYYRxtJKXUpZb6U0pBS/ogZGNe283zgI+v9BIB7ARmx+rXA3VLKrVabZwDnCyEcbW1XR+iSjW5nOyL+XQP0FkLEYvY6nQbU3gJIEELYpZRh6/GWerYV+dwmzBNrd2BndJusHMT6AZuklKFGlunN3l7/Wpswr8T7ASVSytIG1k3GvHMwRUpZ3tbGKkoT6p4ze2Pe2ZoqhLgp4jWX9ZoEtkkpZZ31FKVWvedIIUQPzLtOJ2DeGbVh9kBGivyu9tbzON769z7nWCmlIYTYwr69na0ihDgWyAEOxTzu3Zh3Emr3u+czI6WsEUJEDtoaAPxLCGFEPBfGzJvd1ta27W+qh7V5pgPDgGOllInAidbzImIZ+au1zA9Krf5AEChqlxYqB6stQP8mrpi3Y564IvXHPGFtAVIjc7fqKAUmA68KIY5va2MVpQl1z5nbMY/Rh6SUyRH/x1p3CgqBPkIIUWc9RanV0DlyJub39mHW9/ql7Pud3hL7nGOt47Efe4PCGsy0q1o9W7DtN4EPgX5SyiRgdkQ7C4HI8TQxmGkztbYAp9f57HisHuMuRwWszZOAeTVVZuUD3tfM9S4VQoy0emgfAN6J6JFVlGhYinnSyhFCxAlzkGDdwPJjYKgQ4mIhhEMIMQUYCSyQUhZi5mX9XZiDC51CiBMjV5ZS6pg5Uv+yrvYVpb3cIIToa51n7wLmA3OAaUKIY4UpzhqIkgDkYY4PuNk6ts/FvAWsKLUaOkcmAFWY3+t9gNvbsI//A7KEEJOEWaZqOmbayjfW698DFwsh7EKI0/h16kFjEjDvgvmEEMcAF0e89g5matc4K1/2fvYNumcDDwkhBgAIIdKEEGe15g12BipgbZ6/YeajFAHfAp82c73XgLmYaQYerARtRYkW6wLoDGAwsBnYiplPFblMMWYv6XSgGLgDmCylrO3tvwyz938tsAtz4EDd/XwG/AFzsMuR7fJmFMXsTfoPsMH6/0Ep5XLMPNZnMXv812EOAMTK2zvXelyKeey/t78brXRejZwj7weOAMoxB6W2+riRUv6E2UP7DGaccAZwhnV8AtxiPVeGefFfbyWWBlwPPCCEqMTMUf2/iP2uAm7CHERWCFRinsP91iJPYfbO/sda/1ugy3Y6iH1Tf5RoEULowOtSypc6ui2KoiiKohzYhBDxmEHxECnlxo5uT7SpHlZFURRFUZQuSAhxhhAiVggRh1mdIB8o6NhWtQ8VsCqKoiiKonRNZ2EO+toODAEulAforXOVEqAoiqIoiqJ0aqqHVVEURVEURenUVMCqKIqiKIqidGoqYFUURVEURVE6NRWwKoqiKIqiKJ2aClgVRVEURVGUTk0FrIqiKIqiKEqnpgJWRVEURVEUpVNzdHQDOkJGdu7JNhE+1ZD29wpysvI6uj2KUisjO/d44EyHCCzrl7Cx+/bq/sP94Zh3CnKylnR02xRFib7hd799YchwjgxJ5yfq+6jzmzVl8tUI4xKk7Y3p8xfMae56PRd/PxY4Gfh8x4TR6u/cCgfdxAEZ2bljAR1wgQR4E8RLwNcFOVmBjmybcnA79ZGZD/xUmvkXEKLOSz5govoyU5QDS9ZjD56xuvjwDyUCEF5gkvqcd16zpkyeCTIbzFO03RV4ORxwXTd9/oJgY+uZwar8EoQD8AKTVNDacgdjD6sG2Pc+FFOAi102X0h7+G8bCiqGPAp8UpCTtb1jmqccbDKycxNtIjzTkIdev/fZ2itJITA/pxqgTnCK0sXpunADozRN/ndV8eijzGcFgBP1Oe+0Zk2ZPBb4c22wChAOuK4Ernzi4tMLEnqXBANVnrd9pQn/sTmDPxhB5wjMv6ceO+2OP9WQWBtvqb9zKx2MAasOBAAniCBwhsCIH5a66qFfSkf0AV4COO6BFyoR8vsd1X1DAnnPxpwzvu7ANisHoFvmXCJ21vS6H7QrDWnvleAq/awykHwC4AARthazA0HM47bLse5oaICueo6UA1lGdu544GK7CK6a0P8TsbUyo3BtyWG7YxzV9uP7LBy1pWLg9p9KM4uuH338PUmuspOffuLuS2DcoebaUgIGiLIx98075cie3wxbXzZs88byYWXdY3bEjO6xdMgvpSM3baoYXB7rqDwixuE9qtjX4zn1mdp/XInVdwYq4qxotfbOtPlQhu0ZFVvSAO4C7jKCztpljIr4pEDYsLvNEUNSWnGHHu32WSkHGqAfqL23B11KADT8JZqRnSuAQxNcZb8zDNufq0OJrtrXhiSvqqwMJL6xo6ZfEfCxOlEobZGRnZuR7C6aX+bvfky8s2JjVTBxSkFO1rLIY9NaVKOLBHsZ2bn2o9OXDEn2FJ+wZNvJNd5Q3HEgr8MMusMgXusdt3nnoOS1qT8WHfVZuT9125Dk1b5hqfk1O6r7rH/njzN/dVtNBbxKZ5aRnSuSXCUndYvZ+fKG8uGHRPa+tT/pBzFBfS7a36wpky8E+TrmucwAcCdXvX7jC19Mfe3BUddX7Uh5rmZ3MiA3212ht6SUk42gc6Qh7Lx15pVs6zWgdlN/B16PdkBppRzoVsqBnwM05eCgDFib8ofnbpj5xdbfTDekw2k+Y+C0BQgaHmqvmgYlrfnjwjtvf7oDm6l0QbfMuSTmux0nzthR3fdGkHJMj+/+1T9hw1VPXf2Gv6Pb1pSM7NykwclrRvVP2HDyf3cdV13m79YtxV00JtFdNnZr5YCqsHSm86u7NhLzS1wCwqDxyiTlMY7qQKpnd2xhdb9vDGmXICdZ6/hBqDxepcNEXjydnvFu8trSzEs3lg89DjjEJkLSkHZhHevhHjGF/06NKXptbclhu45K/3rAxP65rxR7e7z38spbn4tzVjjH9vpi1MbyIYXry4e/Ge8srzmu15f3bCwfsnl9+fCSFHeR58j0vOHryoZvKqgYUtrNs8szpsd3w38pG1GwqWLw6SCvNFOFZBjEPQU5WTM79BdzAHvzsaGi5Oc+D/kr4u4EvrI5QjlGyHE4oE+fvyAPQNdFf2nw/sbPx3xRur7XhSB6Wj2p4stjTua7IzQcoYARsjt3IUSfHRNGG9FuZ7/F370YxHW1dcEUAu7dMWH0AXdcqIA1QkZ2rgt4BLi1e8wOb5E3XVhXLEEw5oG4GoTd/PKVOG3B98PScf+GmWd+37EtVzo768vusiRXyaXlgdQEuwjlhqXj+oKcrM2doF0a8BWwNT122/BhqSvPXFN8WM1ub6/4GEfViCRX2bG7vL1ChrTH1Vk95BDBXX0SNsWV+VKXlAdSf+jm2Vl2eNrymI0VQ/69sXxoAvAhZs5WEDj5+N4LNya7S4b9e9NZNSHDlTIsJX90t5jdR+Vt11ZJbKm94raMjnVUD19fPqwAxCCQqREBr+6y+y74+aHzdu+3X5CisOdzshBwYx6QQmAgEZ+BeMMuQtvC0rHnWD9r0Jt3nzPkTbumyccBdF0cCyzTNGlEbPME4EubCF+wYeaZb7egHYutdgQATV3EtY9ZUybb43sX61Xbu423uwMfh/2u86bPX+Crb1ldNwfKbs0bnla1I3l79Y5u9o19B/PO5MsZueG/gXV9R7pGFv236pQPP06ePn9BuL5ttNb1i69PWcBlawLEpIM0QKge1gPddbOvGP/Z5jOeCRmu0Q5b4O+/Hfhu9ofrLzqUfW/PLgTpFEijV9zWvO3V/caASBzZ7Xtv77gtt750/ewXO+wNKJ2W9SXzJeAAycjUH/5xxzF/uVzTOu7Dl5GdmwT8BeSf2NPr+avbmUV2Edo+KHltWrk/ZcnOmj5L453lO8f11l27velf/m/XcRsKcrIaPfm25Zb+3iBBuqyg1eawBeXQlFVfry4ec6EaGKnsL0fMePWfJb60C2s/IzGOKn1i/49vfe6aeT/ULhN5rM89bfJlwETgME2T9VafycjOfRS4FehekJNV0dy2DLrz/XPD0vlurKPiydUPXvSn1r8rpSGzpkyOAd4Ezk7st2tJXHqZdvHtPzcr0Hzh1rGTd5QP+Gju724kzlvFxNB7i/6vx/UTz/34NQZt/ukd4NLp8xdE7Y7aaYuf+fZ7TjjWRnCDgfMlVA7rgW3Y3e+cbxPGfEPaDH84ZkpBTtZ79S1X98t32uwr+wUN1ydLC08YVRlMwkZ4Sdagt/Mm9P303nNP313vlZhy8DnugRd+2lHTd6j1MATc2xG38TKyc4VDBMcOS105d21JZkZYOpwRt+wliPectsCLpwz4UAQN59I5171Yur/bWE+b93zm+ids8CW4yt5eVTx6INhCAuPV0wb+67Xnr31FDYhU2kVGdq4duB3kg+zJXzR7sPZcgM1IGu9zGZf+NNx7ZGlq+PeaJtfoukgEvJomGyx3dNT9L9fYRXjbd/deM6Qlbbr1pYt7v7/ukm3H9dIXvHXLY2e0/t0p9XnjkeFDi3/u81mwytMPxK3T5y9oUepfz8Xf23vt3lyyO7ln4vgVn1/iP87+8DK0vtNeffyuGL/3kbj00opgtfuSQFVsJhGpBa3Rc/H3J4FcbFWTuXrHhNEvtXZbXcFBHbCe+kiO+6fSzEeAW2IdlT9r/f59zd+vffWLlm7n5jmXpi3anHVFwHDdHgh7ug1IXEeMo+aWBGfFs2//cWbU81WUrmNg9kdXSGwvCwwpEWFrhOh+rbV4/QuXD9xUMfj2VcVjTgRGOW3+0ODktflrSg5/GnMQQO0t+y5RAzIjO3cg8GeBcaVNGI5EV9lHpf7urwODUIOzlCi54YXLj/m28KR/FPt6DAPecYjAyyHpGkPkMTYjaSzwhUQ6pUBu7h+YnvEH35NNbfvYB14csbOmz+qj0pe8+c4fZ17SknbdMucS8cH6C0PdPLtfXDHjiuta896U+s2aMvl8YQ/NlVLEJfQqvfWaJ759qqXbOHPx44uXcrJ2+qJ3OV5+PPn+Sc+9KrEt3TrhmMmvPTjqjV35Ay4GIa2c/gAwqTVBa9biv6Wu4KQVIDJiqJJD+DHpPxOur2zpdrqSg7GsFQCXPvPHo6uCgxcDccDTNaGEO/5+7aut6qZ/+urXdwOPHPPAnCdHpy2dv2LnuNM3VQx+KsFVds89/8jyxzsrRv75oq+afctHOTAMzP7oTIltDsj/OO2BhwNhzzj2Q0Bl9kpKDUQpcLxNnHORIR12mwivMKT96qDhfuuTO+6qspb9iS42Cr8gJ2sjMO26F/7w0uaKQU+tKh79G+AMzNG7/ozs3C4ReCudk9Wrf6NDnHm+3SZd3WN23FLk7fnMupnnSODTyGUNIc+wSeEUCJAynLHJ7WnOPnbW9PkNwPKd4+9tafueuvoN+UF2bnGxL72lqyqNmDVl8m+Bt2XYDhCo3NZ9aUu30WvxiomSiVpmMG/zoT//r/+qUUdfHMSTpvH+TjiGfuNX31NTlDi6qjB1JGAH6UoZVHivrospmiZbFCOEcSwBkQHQi025B3qwCgdpwJqRnXsOTHrVbfe5T+r7ac68G5+5MxrbXXrv1QG4+hzrNtJ5wKzXVl/fN8lVsuv57NzpT2q/L07xlHyrabIgGvtTOq+rn7/2BrvttGeA70OG67yfHzqvCmhx731LWcGqDtSWZKuOc1a9Oa734twXpr00v+7yVmDXJYO75699dTlwfEZ27oMg7wZhA+kEodFF35PSsQZmfzQWxJcgHCHpMvrH/3zTojv/9GxDy1fFGZcmVNkAwqIF9TXtInh2WDrXFORkrW9NO+0iWOlxeAc0vaTSXM5YX06wpnZMHTZaWNy/5/+zd+YBUZVfH/88dxb2VVBUlHHXCvdKNOuilSamtv/KSlvcK9sbKwvbxMpKKzOt1N5sL1uk1LK57qVmGGZqLqMC4sYOA8PMfd4/ZjAyQBETRD7/yNx57vOcO87ce+6553yPLaUJGBaC3KaY9DhgxfbQznEgpT8FL2qaMAHfRnXbdXTngUbFgBEhZeNYu4rXF9M04a+qsugk1hoIl3RScOs6BmU35z9U7QM+C6lKYqbeMXHurQGDXnp+NfAliL9curHD6XJWy2NPSnDbkxI+zXeGtuzfcnGiRNiBN55aO/PD5N3X7bl3zm3NATRNnFOf/7mCxZocu3xfwrRQn+ySQa2+uNmelFBwBpdX8TQewCtG/mLqM8Nvr8hZrUckC6TL+7fOWdpkoYHap4l/+jteZRhA6LtzOwRVNlYmBscFFxha5AW5UwViMtCfxNwTOjje8796fqNNmadqZ7PAtMaN/Q9cfKr7N/BP3hxzWYDbaTwfT1K/i2o2a5lgG2dqzu6fQYaCuHFp/IQcv0a529OjWrYy6K4N78W/vN2bzzw1JOawFU9B3lNI5bLAptkWVZVZ3qk+0zTxeVVrRdlSwoB3QW43UVLsS+HqzPiuO07luM82zokIq/cRzzVmZdiNTt0vxhL81/eHm3ycpBgLb4pdMFlLHZH6n0Rj7EkJEhKmWKzJzwB9TUrpzM923NHFIEp3rEmcN+/puJChmiaGq6pc+V+s38CZp8vT/9cGwpfq0pCTUxzeZ+aoD/aeYRM0b1FIWSe3H87w+mcce1LCulbWb1VgMYidDekADZwKFmtyf4jueFy+uVbh4MQQg0C8AWSE5Bv7kJh70o9j12T07++WJkJ9shaeqq25JaFbnG6f0FPdv4F/UpwTNBJQfEIKJpXkBgqqWQz1M5e/kk5ry6V8O//T+MmpAIWdfA8fjIym/9EvwzXtIlVVpaaq8gMAVQWOi956pbGW4RH+L3s9AfhcVeWxm5sO/LZ6O12aNMf+fTqt77qcz1ZCnxoc/dlDvXdY/35EKsxO3Y+m/vtfO9r83U8V5GpAAeGIXRDb/79yWqHMcWUl0HX0W6OG/7B3yLVZxZHjH1n5rugWuf6dF36ZNujxi63+QBTwY3mtvgbOHu6Zc3tHk3LZZoMoLXVLU6+dU4edaWcVe1LCOs+FF1XBre1OGnJOOG97kq5eY7EmvwQ8P/jlZy5a/PBT1c4/a+DcpeMTn7UC/09AbJOI+4CLqCKv+3BE6VuRR0zdgZur46wCZBVH9gOy12RcvuBU7c1zhu3kXPFS/mPefiDOAI0eBH4uyQ2c9tAni6tViR5lS7kU2oz3o+DrSNLvLNu+rmP/QIBu+9a2JYK7OEHE1itzWL7I6wLgdaAYeEfThDJBfj/8KN3O68Uy289cGSNwp4eQ9Ux17D2bqfcOa2O/9JcOOZqX5fO5DxS1OBTkeWyqeHNVTFQzV6UmzBk3dyGw0GJN7tQyaPdHGzL7dNFRtk7fmLh7eKc5raICMkIBh6YJpcFxPXuwWJODFHH9hwq6sV/L7ybOHTdnS23ZYk9KWKdpYiHQCOQ54bACtA/b8sGunI7PG5XS2UD32rangbOD++be2ijUt2/q4SKTcEvTMHtSwl94mgRUiPuZ4IgwYbwzL8idkxXu+sRSjbUmzh1uMogbhurS8P2epKtdJ96jYgJMeSUOV0Dkqe7fwN8Iob8PtDb6lXw8cf4P1XJWx9ru6Wjiti9L8dnlIPC2N+NnSwBNE8FrjZsigwpyillv3kd3RlbXLlWVqZom2gPpADvlBaOK8X/DREnqVno+CPwmMTz5Zvxbdb5L4umi3uZQWqzJcRZr8rxDjma9QJd4ctyceO5yNED3tlmtVq7K6cKelPDnj9ZHuob5Hm0HYk7qkR6WSaveMj+99rWfLdbkLsCPmiZeONN2NVB9Js69NQD0Rbo0dHZJ09C54+bMrm2b8IhenzPOKsCyxx7b1yxw36+/H76wncWafHxXrgYa+BcWa7JYsufatzMLmwVc1mJpktdZrRKDLqYa3MhiX/1ayx3F1XJwilyBI9zSFNo3+ocjp241nBf+extdGgInzh1uqsk85zrTbxocl58ecTNIXA7zA9NvGhx3svtG2VKU3+j7PdCoK6vHZcZ3zQfQNBFUJAN+UXBf2TJv504hlfbpP3fodir2qar8S1Vl0QTbWPE+Dz5ZjJ8ikLe0JfVdA6XcyvQlJ56l/lAvHda/0wDkSE/lsDIRxFNA//kDB//+esyW53yFvh3EPuA/TQc4Eb8m3rHTnpRwT88ma87v3viXn/fnt2oLpExZ+0q7jZlxnQE0TRg0TYzWNNGQs1THuOHVScqO7PO2gtJfwT3KnpSwuLZtAlBV+aSqyi9q244zzf781g/o0hAI/K+2bWngrOAhp+5zHYjH3xv/1rMnGpz9WsBAibxLIGY2nlBkq+5iK/Zf2R6kHmjKm35q5npI8dKQSwAAIABJREFUK4hZBbAmo3/DNeEUibKlxC3uf8OU9CYthbfbbtnT1pPl4b10sLRn86tL4u85FpFXVZm/gX5GHaMhRu74GuDwHzHPVscZPp7dnP/MDrpFd2P1505896TS67yL+ZHB4oMafY/ONuqlw4rnS1fWztENBNqTEqZ685FigGYhhlJzgOIqqk1ntTyfPzB11xcPPh+nS0M0MDmtICbsjZQnEjo+8dn6eVvuuV9K3gYuh2MO7L/6aDZwZrFYk8WGg5fM+DOrS8uLm674bnfSkHm1bVN5NE341LYNtcBqg3BtCzZnPz1x7vCG30gDFWKxJsdd9Mzc70F/0SBKF4FIOtE+GW/7Gc1O8bnLiBOYcirrOnXfASBWzRozv0b57QcKW+wAOOJo8pi3qLiBahBlS4kDVvzZtvPlnw6+g/QmLXSq8bR1qO2lG0G+AHz+Bxc9BKBp4lZNE60AlnJTlj/5ervlO34ApNtpGgAsPxWnNcqWMuQ3+j7sS8HuGHbcCtxSio9vEUHXARO9awdqmlijaaJ/dec/m6ivDqtWTuYG4NjjF1WVW4Hz/RQ9NMRQGn3mTasae1JCtj0p4bmrWn3Zqkvk+vnFbv9mK9IGvvzoyneOvP/HuIst1mQFGA1s1DQRXtv2nqtYrMlxBlG6HLgHeOWXA5cNrm2byqNp4lGg6FxzWu1JCbJXUy0lzxnW4rAj6obatqeBuofXwfvpUFHTgYAI8cl+x1sYWyVND5juDCgyBBxtVDqLxNxqN4K5a9bYC4HOAaa8SvNjTxZ/Y75Xg1U+ACxvcFqrRyiHR4A0IYRwGwzsad/mACfZcepq2yvNdtDlwxCOFgN3Z8Z3lZomGgEzgUeibCnG3ZzfxkHAR+YiV2/vboK/62VOGo9jLb8EfIsJbPYlo3qacTwI/J7CJYtUVW72Dm0O+AJFADunzk6wP/3lnDTrqnr1vaiXDqs9KWGdRHnS+9Io0N++7IXXZk7/pHc7TRNGVZV6eqlv6sFSn+21amgVzBz1weGvH5pyB9BG4B7tloagn/YnPAxseXvzQ51K3aYdQDaApokhmia61KrB5xAWa3KcQF/llqZ4bwT/i5O54J1h1gBPcw4UVh5PiE/2AwK9cG1Gv0G1bUsDdRKVY0/ghDuruPGJz52JIY0EYiqwMuqg+ZRE2gtKg+8FuCx62YZT2b88weYc782YUDgFR+hcx8KfN+PpTqYb3G6aO+0/nIyzGmVLERvo91YOEbIPS8Z9LLoVAaiqPAr0xRPxjAMaSZSvAM1TK3Nq9TIRpN8HwuB9aYwk/REnfh17seznzPiux645qiq3Az2Bn9Osq+J88jp8YyyJHCWRWn1yWuulw+pFAXQQSITYm9fu3lkpj21//udpWbfMfOhL3e3j70YprW0jT4Q9KaFkT9KQuX2b/xAS7nv4bsC17kD8vY+unHt90voXpl/y3Jv+eO7sjklbaNqxL3gD/w2Xy79PIhK4rDaNqQhVlWtUVT6nqrKwtm0508waMz9TorwP/M9iTW5U2/Y0UOfQQApPYw1RVohbJdmhrmUSGQrcQ2LuKd2c/nLg0gijKN1rUpxLT2X/8hwsaubw2C+rLXJ/rhNlS4ncQq+AGLaVhOUemXHj4nm0+sV+UsVLfhQ8CgyRKA/fLqYvBTZomhgJoKryD1WVpT3QXjJQqkeSvuyhTxavMwU6NgtFd3CSEdzy5BHewevwuoHSI0QFGXEWN8X+r5a+qiqlVxpLRRrK0qHKOnbVC+qzw6rhEeB1AQ6zUvxAdJD9qyOOJu61Gf2uKSmyXKQXN+0R+9TC9+6aNfaJcW/fGVy75lbNjFELSzYljnwX6NIlcsM9geb8om1ZnR9IK7DsnrTqrc+3Z50/DUDTRASwX9PEtbVrcb3G20mKqsXFaxlNEz6aJs7Javkgc85cwOfCqFXv1LYtDdQtBrf+xAVCNPbP2Ab0P1GjidLngi8OzTF0P9rItZnE3NRTWdOjWiH6uaRp0YxRC2v0NMZiTQ6UGLqB+LSsmLihWcbJI9DvdWE25BB5290fz/i/5gf3g0frtErutD080olPUhiHVuMJEmUDu4B/dCzbTtfW0exOT41PyAMoLfBfJXVDcXWd1ShbyiVO/LqBeBNPJ7XrdUyXujDPeSt+1sEqdl0vEAKQ1WkXfDZQbx8XlhdQB6HteOG6dcBrAOPfvqNvqjn9+0KXn29uaeCty/ddbQL5hMWavLx54N4N3Rr/svyN0QvW1OoBVIK3e9abFmvyLOBS4PEDhS0eem3TUw9O2/D1C7d1GvhFfMslq4AdAJom2uLRpPxKVaWzFk2vN4T5HLk5pyQ0Q2J8gyrExWsTb+6qA3gKeK6WzTnjpD4z/Le+z7+evfVol8ss1mTFnpTQoGncAADa/qv6AlwQ8ds9740fXfVvNzFEMSFmSORBYMCprtm72U/3r83o5xPln/bTqc5RxoVNVt+74eAlPsHm7Hm/P3NrjaO19QFvmlY/ifLT8edjb36vCmhXDUje7cfNT7gxHMml0ZeRsXtGHk5tRUSnfe2rmj/KlhKq8L9nAskrfIKxH0SLPQGqKguA644b1wFCIwsInfD3VpkPBH/4UntxyyM7TupmZYJtrIhg6PyjNDkkUR7NjO9adKXtrQ9/J84MvFXVvvlRtmFBmfFI4ZojpHFBdFLfOnd9OlXqrcMKHqcVWKdpIkbTuB94R1Vlwawx81Z1W3D+emkuDEpo/dmVRx2Rd67JuDwGSEgviBmcXhAzZbF18VYQ33UM37yxQ9jWxTNGfVCnHq16cyZXACtGvzXqts2HL5x4sKj5Ewu23vPAzwfUtW1D//Txtn8bATwKRAOHNU0YVFW6a83ws5zbXr+/f3bJFe16NdUWfzzxpam1bU9lqKos0TTxGJ5c1nOStHzLeInyEXAlcE7pFTZQOQWlwQOBbe+Nf+uEzmN2qOuZsBzjxQIxIuLeosOnuubBoqaDfQ1F8uKmq36CMac6DQB5zpD/+RkLiW/x/Uq4tUZzne1YrMmBwCSQkySKAInFmrw32Jyd3yxwf8jOnI6/gjkBz9Nk56/5lywtCgpS+vD9hi/iJ7nnfhdQDFDqMGdUtkZT26Y4UObqGJu1J+WGaLHnUzzX08nHjw0k56YCQgG+LdvWIiY9fv/eaMMVBdmXc5Ktsvdw3t1HaNqmL99++ln85KIoW4ohhHbXtuaP7LXxw7dVta9PfptrSv0ynPnNlo7rPOq9ulZbUSPqtcNajmHAS8DnQAGAy5O/6po56oNsYDqAxZo8cYBl0RX781tdt/Vo19bAxG1ZXUy7czq4v7Ymfw0kd2v886rfDvWKwHvHVheia3PGzf0/4P8s1uTz/I0FU/7K6XT9juxOv1isyfP8jR+9NOvym79QVVl2sv1U00SBqsoRtWnz2cqq9CuuBukMNOU/UNu2nAhVlS/Vtg21iUT5EjhkVorvp8FhbQAY//bIloLr+hmV0hknGnt0pn/T4ALD44X+7qMBRYb/O9U1PcounSwC95c1DXxYrMkGiG3qY3B8OWPUQkdN5jqbufrlKZcL5GuCHhaJUj7tSQJ5BuE2H3U0jnLppoF4itKQQvqm+bW/3ECpO5L0+wDy9jXOAsi1R+2saJ0oW0qcgFWAAWTpRvplAlfh2fYvIsh8KJDcvJT4ofsBSAyJa1cc1Ws/4OfWv896zX+Rv8MwybdE2VVZLnSULUXApWMMlKY1JqOs1WtCLhE+geSOr+pzSbOuamWmZXPd4Eisb84qnCMOq6rKGZomvlVVmVa2LUBxWZy6Eh67IDauTIvV+7h9GbAMPHdvaovvrDuyLuiRUdjyIuDa3w71opnIls0M+exzhzkt1uT4uuC0AtiTErYCN4x/+45Lftw7eIRTN9xW5Aq8c9r659Omb3xmxEM9n14BpOB5VIxXy3Us8LWqykrvMBvwYLEm+wG3g/jynfGzKzzB1SW8aQGNVVXur21bagN7UoLzqhef37AtKzZh/Nsje88aM39tbdvUQO1SUBr8iEQxxLdYshWqTvNvlGV6UiLlwSau0QGPFtTk4t8diJIYvq7BHGVcBESWuP0+Pw1znVXcN/e2Jhsze4/MKGx5HfS80KQ4iQ6yr9mf3/p9EK8BJm9NwZjfpoxYB39LmIH0dTcNELrRGCgKXUtnD36j7Pzt6/23shzWoeUKbBVAVVVZ4ZO1KFtKJHQIOo+NX5Ztc/jq15oNngeaTt1gCM8xXQ9cD2QWTQtMLwjUMxofNr0E/Ap0AdSE8xNFckR8DzemkW/Gv+W9wZETQKSn0+b9qj4jibxNIKTi9qtTmuCni3rvsGqaEN7qud1l22IXxMYJDO29Z6DlsQtiK+x2ZU9KKICEJ8EjEh8VkNYzXBZ/3sOd09IgJB0Nh3w2KaHzJ869tXtdShmYNWbeamC1xZo8uUPYlvd25XYY6HT72kYuWby4eeDeGWueHP+jd2h7YBaeCqJZXnUBqaqyId+vAvo0Wz5tTUb/sAi/zFOOtpxhpgF3aZoI9laPnnO0CvnrhT+zOg/S9l91PdDgsJ7jrEq7IsooSrP8jIXzqxrnfia4uwExViDejB7l+LKqsSeie+O1UzcdiqOR7+Ea55vGRmx8dsuR7rqPobjePzEol3uaATLObBg2yun2VYA/gPs6hG35dPEjkw96x6ZSwVNPby1LPymI9+/ofj4PMG3OGnDFlhf3tgndfkOvTvvij/7ZksaddzepxIwLAARuALfEoFVh8iAQYisXHmupvrt1cXPTEbcAcLqNxfkBhZODCo2FQJzBLYY1PmzqAVztmVsqbhSxw68VjdyHc44aIj8AGGW7/3IYeWVLts9ZH3+Tq8KVgd/n3imCfAc9jnDtbPX0//ZVYedZS31WCUDThAlI0TRx53Fv3eS5egs4SQ07e1KC/HnymA2tXSXfKR5NNRQkBqd/+8W7r//LYk0ebbEmm0+n/TXFnpSQufQx66BQn6xI4CmQvdMLYn7o89ysDIs1+YqRSxbvANoCZQ7YUOAvTROta83oOsz27AsSGvkeKrmx/fyz5WLxMXAv3i/6uYgnqqp8W+QKHG6xJp9TTRQa+CcWa7KvRBnokqbPZoxaWGkevz4luI/LINe4hSzGU7RYI3blduzYLGD/kV8T7zhU07n257fq2jJ4d9a252/IrulcdRlvEdVKbzep+SDuaOR7eMUVMV+PAWLtSQmvlzmr4HFMy3Wz/Af2pIR1ziub/ZlniqCNTNU7Gn/7P3te25ZL7cO0NUfU8wDezR43+PjmC8/Yrn4S5CAQH4J4QmJQM+O7Vvo0NZTDdwj0DOC3sm2HG7tG7IuUiwC+y2qzcc7G+DUk5r5FYu7tPo/nB2eHuloAQ/CkGIhFja/grwALE9Lf354Z39UNsJ5+dyi4ZDdWz6rqMws8cMXlpuImPo6w1GUn/IDPUuq1wwqE4qmWPyY78chXTcIEcpj3Gl5tDbvzm65bKYREIhFQEhVkn+6Wpn3A237GgoO3zHxofl1zXNc/NeqoPSnh2fZhf7SOa2r7+lBRlA+wDOT6eVvuvXPRX7cUeYdm43k0sRdA08RATROX1pbddQmLNbnTEUeT1lnFEU89evOqsyICraryZ1WV88/1iLlAnwU07hr5i7W2bWmg9ohrapsABIZXFelMDIkTEptPqeKrSHyATjVZ02JNbp5bEh6dUdjylZrM452rTU5Jo0Z789o8X9O56jIWa3IIMFeieOUDpQ4krZs8tt/ccXPmVLdJS5QtRUgUK4C/KHhq2WOP3d4mdPt5EmXJAb15f4CDoskEynUM0zTRcw0Dnw0gT/Thu0cOxHd/oSpndbxtfIiDgMu6sib7OEH/0j93tloFkJ0X3Jvj2rOG3V+YRmLut8DjpcLkeMlyB53ztzNmzzed0ub6jo6ypfgfpOUgHeOnb8e/vvnfK/+NuSj6RqAwOGNgvT3P1WuHVVXlYVWVN6iq/K5s2z6nnyYRMf6Ky4rn7rnCdIDK6NTpK7uilLoFMkMI7p1rff5hIM4gXIPCfY8oazP6jQD+sliTRw+YllSnIjrLHnss96OJLw8r1X2aAaN9DY4WK9IGPL7UPmy3xZo8ZeSSxX1GLln8ajkVgafxPFYGjkWsz0l8DI4JQKlEmV/btpwsmiYMmiZae9sGnrMMafPxj418D7qOFDeeWNu2NFB77M+PucsonJS4fapqq6oKPOc5gfCIsNcAP2PhMO+f31Y58CQQ6EO8f9V4rrrKmNl3X2s2FP8BdARKPY0RRAk1KJocwMf3ARcJ9MOpxL0MsOTRSX/akxKuDXNlbwRwC6MC0oz3//tmuTF0M31oyV9zvoh//IT1Hd8w8pIS/JEwA0DTRBNNE5v2v+M7ppHTPdwzqoquZIm56yZ0m5i8z68Zl+RsSnKZdWOzDPPbtx7+fD6ewFuV0dXUt8eFSvSbgC+ik/oWnNQHcxqIsqXERdlSJnlayP731FuHVdNEW00TTctvi10QO2SrI6hze98C2y+3/TktdUTq1Oo4qwAbNoy9QtfNBlCigBmJiYlx9qQEuWvq0O97NlkbGmTKHQocAN4+WNS0YOCLU5PqWsTVnpRQYk9KmDvA8lVMn2bLXyl2+7nwpAw8K9DXWqzfzrBYk2NzS0L7A7cBaJrwA3Zrmhhbq8bXAvfNvTXEKFzjY4J3brMnJdT4sd4ZpDEeYeubatuQ2mTGqIVuH2Pxa2n5rcIs1uTY2rangTOPxZrcO62gdSeXNMnC0uBvj3/8W4bD153r/VPnNHSQig7c+2SIObsYT95ljYgJ3vVEmM+RbHtSwq6azlUXaWX99rbl+xI+8zEUNzEpJf2By05HY4St9JgIEM7BTzPju5aUf++CkBQ3gMtTV6WM7zL1ysW24BYS5UVg75/0PKmbXBfmq4GCFPq+T2KIf6vdPr1b2s1to9PMs6L88rp7Rnm6VVHBd6q9bbn/iqBLhlnYljurxc2PZ0aVdnEpbPzdN/YGi9Ne+lLak6urWl8pDZoiUILymi05YwXgHidVLsej8738TDit9bnoajrQQ9NEjKpK96UfdGgJ5veA33YUB151KhPOm9evn8PRK9GjnPGPu6V1AN4OJt9YrMnf9miyZlxmYfOp27I6Pwbc3D1x/sy+zX+cXZeKs2aMWlgCPGSxJh8FngWheHN77wPue2zlHEeLoD37Ri5JfjGu2YObxnR+JRnYCqBpIgoYetEvgX/5O5SLAY3E3DqhlnC6se2/6vpCV5DoGbz2bOuadBC4g3NYi7WMjIKYJDz5vOOAKqVhGqiXDPX8IwTHnbfLkx+kX+NXbKDUqM80uZRPa3JOs1iT/RTRIbx1yPa1m5+5tUZFjx2f+CysxN0m/PyI33488eizi3Fv3xm81D70ZYlxlIJcd1n0sjFvjF5Q1lGsRteUKFtKN2jXSsFFb5bO9yhS/U1QUeE2ByEX6yjvgLh7V25HNTOq7UcgukWQMXZL/KC/1QMSQ8qKwFYCaUV+7gtyQ9zXBeWYi8O7f3Vnl4JtxQv+fLQIFCVm398PV3VpwCRctA7M+nF7fuMpFXW8yqPReMDYgZRxP8f/TxJfvLPTMu3+bFPo6qQdr5iuLrLtTZvr2zl6VHGFuctBBy6PdRsLclx+B+bW5POqJirg5/lTmkGo1PD/60TUZ4f1IaC9qkr3o181MQUZAlPy3KZAN+Lm1BGpJSfc+zgSExNDzOaunwshBZ6Wrwa8d0v7J9mG6gbHUIMr6L3opL6rvfJYsyzW5LfwdEdJzCqOfHlF2pVTW1m/uU9ieM+elFCXuk7Z8BxTmSzIzUBYy+Dd1n15rVsA767L6MefR7sURvgdjB25JPnV5y9pYenkznjJzyHK8iNLSAzpXx+d1nxn6O3AzhVpA96obVuqgzd3dX5t21EXsCclHO2ROG95vjNk9L1zbnv+9dH/l17bNjVw5ogO3JOZVtAKTz5k5e0qGx01RjpN+i7zE6dFZzlelwbzzpzzkmo6UbHbfyAgthzp8fRpsKvOEPfs7PaK6LlJl8YAYJpT93nyjdELKq2Ery6+FD5dTAACFs2Nf22jpol2QIGqygMAQtGDEdJpn3b1qKtfntJr6b6hFyhtg3tGGfbm3cek9zUt4SPgU1ULzpTIlXh9JoHA32HA32Hgt8D2MssUJi7PWZ1VEKgf0BX31tAc4yKh46dIXsty+vlG+BaREL1t72C2/8vG823fh0DTScCSb+Mf/Khse7YpdAxS5sfJZatCcg2DQnINy0gMGUpi7j9SFNKsq5oq+F0GJHUe9d6ZbArklQmVgHByBlrA1tuUAFWVO8tyV3/IjXh0n9M/7MKAnA9SR6T++xtzAubNi/dRFOdXTmdQcEzMildBxOPNf727uD9I8aXBFXSHRC5Ls646Fha3JyVIe1LCEiCuX4vkJ6UU+yWGt4C/rn3lyVkT595aJ/q8ex+39Md7TPakhK/tSQnzlz32WMdit38gcIFBuB6M8DtYsCe3XQ/gsydWz3rx982DdOGJNCu6xCdVb3V7bR7Hf8GIN+7tB1wKcu7Z2N5T00RzTRPdatuOukCPJms/c+o+htQj3RsirOcYlpCdBQCRfpnfU9kj5sSQpgZddDGXKqdFwzLU5+jtIAvxdCSsEb7GwhtBHgbW19yyuoHFmnzVgcIW6w4VNTV0DN/8f4B+QaNNgzRNtCwbo2lC1TTRp9zrMZombij3+lVNE/eWe71M08SzAKNs9/crwXcogBtjWaOIjcBjZeN9Ah3XCEU3f/hyu4ctITuvNLcWFBlCfDJLov/XWvzpBHoATfBEExXhKdaWwCJdyCv3tShRh3Z7IwmkvqRVx4t+71LUbUus45LVffMvNzyd97ZA9DtS4u8KNzsA7gaWeyO1x2jHlq+B8GCyEsu2jbXd017BfYufKPy0w9jMBCkYJhCdJHJD2lzff6R4FYX/NgWPL1elRuvpJMqWInxw3AcCM8U/Af2rKko7XdS7CKumiUjgeeAFVZX22AWxF4GSCHz6c2HYXacyZ35+s1W6br7QxyfnkdtvX/uyd/M6gDTrqkmgCABvsr7KcWFxb8T1eYs1+QXgSl9D0cubDsWN23q0y00Wa/LjwLzajriWtbGtYLvEk3/1B/CqxZpsAi4MMWcPcwjTgwBuKXBiUhJLb7/jV2vy+3WlkcLpILckLMkgSrmq1aLFMLi2zTkVpgKXATG1bUht42csWgDyXnte+6EWa/KT1a02buDsZXX6FYcADjuaPmVPSthU0ZjsUNd9YTlG3IpcbKhoQDWYOHe4gAHXtwvbevCHxx6tTJT+ZOfyEwwd1jH89z+WPPr4Wd9We+Lc4aa0gphl0PsyEKmluvnZbVldPgYM27Iv0H8/3P1X1dMgATwdKg8Dg7yvxwN7gM+8r8/Dk29cxh48qVAs48bbJAai2SnTaLvS+/6dwG6A6TcNjkM0BQkHNrR/Nqid3FMQ36RYOVzsG/b7nvEjXYu7AiPsSQnr0ELihKf4yyQ8EfqXlKfz1rUEnLaUGcDqz+In79O0pwTwP+AIwIL9nVs43GajQbgRFaQRRtlSIgyoF7cnZc/K+JG/lB2EnY6JOgZDPxZ9DZegPJ33NYkhfVxGuabpAfPHpc8GdTS5FaeUBs1oeHN4SdDOgjZP3FHtYNyp0pslL65lYE+BzlV89PDb8TN/O/FeNac+RlgvAoYDfg9/1aR5kFL6k4I8CIxJHZFa7QtUYmLimKysdhdGRaVkxMXN+LCCIZq3mlRygiR9b8R16QDLV537t1z8RInbdxcw26SU7Bs+84EPxsy+27+69p1p7EkJpfakhLW5zvDEpkrWfoDXXcMY7nycX2UHAzWsqq1LWKzJvimHL2od4pOjvTF6wdbatucUeQ0YWdtG1AU8OeZiFnB+dOCeQSfcoYH6RKj335zKBig6I4p9dPf+Fs7fa7rYd3uu65JT0shgUkrn13Sun/YP6utwBRBgKjhbGpZUisWaHPHdnuuX/Xqwj9oudOtmIA5oh9cXcekm+fXOW8rLN92KJ++8jAuBa8peqKocoKryoXKvx6iqfCPKlhJVgt/NAC3Y+V6Z1JSqyi8Au6aJ14TBfTkS3Stx6fNbx7iXpWLwjc7YuqfQFTQY5HOUSV15Ut2OPYUsS30bbZvYG+jSjD3rvPNLVZXLVVVunn7T4LiswqCPALbkRJFeFFRR0dVjbkzmHXQ9Fg2JsqUYUrgkzkzxunfjp/+tCJGYuzmjWWmcRO4zuZVE4LlS2eonc1G0P1I5Y224o2wpQalcfB+AQNfOlLMK9dBhVVWZDESpqvzzl4Kwdwp0Y0DfoKPPpY5IrfREVRnTp4+6DnhDCPcPHTp8EwE8evyY9J4PbZaUSrcpdwvQPzqp7wmjizNGLZTvjn/rBYlyMTAwzPeoWJNx+fCl9mF/WqzJY+qaqsDx3Pb6xIdAbgkoxpIuw4tfc19fvEm2r7am7VnAtSAaZRVHnrW6h6oqN6mqtNW2HXWFYHPOR2al2F3s8vugskrxBuofnSM29gHo2WRNxU+yEkP8g/MMocAnljuKaxx5L9V9BgNsPdr19ZrOle8MvQoo+fVgnzdrOldtYbEmx3V+6oMPQG4p1c1xkX4HHj6v0ebu9qSEIkAT6G4AgXTvyu34Xtl+qiq3q6rcW+6182S69nXi1zkgfIDcdQy8/7i3LwbGNO68J8srmaXnBoWJzVG9YiJlxpLcw8Efe4YdJ0OVmLuOxNyp5es09tDpZoAerPhR08T/NE1MKpN/tEQdeEGXQgHQEfpv2c1+pJyzO8p2f3eB+z6QH2TGdz0WEBHoVwEWJ76vHn9cMXcWpxp15R2JlIBS6I73Abfbp6D1mayvmJxPmBlAx7jgDK5bvxxWTRP+AKoq82MXxN6a4zYNNAn53BvXHphd3bnmzBkU73CEf2YyFR6R0nC9EDIW+Jf4s+IKuFNgMuRYPi0+GWe1PGUR17imWpQl+K/F96UwAAAgAElEQVRbQBwAZgebs3Ove/Xx2XXJcbVYk+Ms1uTnLpj84c+r0q98ua9xU3C8klLUXGTZQPTj7/zXepMO0DRg/4s+Bkcm8FNt23KqaJoI8OaBNa5tW+oCec7QLk7dLI4UNwmlnFB4A/Ub6ZF4o3ng3sOVDOkvEH6+JadHZznYnD3SpJT8bk9KyDzx6MqZOHe48DE4/mcQrhWeVuFnH57fmFyZ5wwdjicfdOyGp++e7lXVwZ6UsK5Z4L5xAEHmnHdreg3pblsUZqfD1Z5Xcl5mfNcCAE0TYQCqKpcArW+1bnsTT9T0yWXq1fsUqYvhP7/pKnH7fQuiLPWiyiBMKr06gNw2N/61H/GkXl0DuEgMiWgl8r25t1KCKNme13hKeWfXTsd3FHTzAD75h3/Smj/m+lFQAHxVybI/AsW6VHC4LxPSvO/36KS+WdX5jE6Vq2wze4F8AMBAqe5PXo3aFleXeuOwappQgPWaJl5+cFGUqiDnAKudUplS3bkSExPDMjIunK3rhuKmTTfdlJiYmKeqcoeqyn/15w3bfUsxgN/Rnqfcwm/GqIVSe/z+j4C4mOCdNwebc1y/HuwzBvir7eOLxtV2cZb3hLMCeKKgNPji6wKWbHrfOD3QKHR/ibzC7nsLlbXFO1vpP3V69wOFLZp3jVyfcjYWW5UjBo8KRL/aNqSOoHpOewLgmFB4A/Wb1CM9dwMFXim/f5Eb7LLqnlasNS6QunvWmI75zpA23Rqvr8w5PmlK3L6DS9x+Ub2aakdqOlctogIG72/ODTQ9fkB6geVdIDfPGV7jNtIZtBrtIBCAgXz0DYCmiWHAHk0TXQDKVAIe+mTxuj/Gdthrb96h5UV2zWHcrFx17563/H0NRZ+ClAGmvKsru65F2VJCPMcmvvHOOQ6IV1Upi3Xl7ZSj0SaDwZ0PYgrQv7ycVZQtpXUqF8c2Jv2rBfFJx7bH2Na23cUFURfwyy+Z8V1LKzzAxNx1AtE/X+m9TycUZ9iaNWnWVXFp1lWTyhd9n24m2MaKPMKTzZQoIGVrtu7aHX9pVU04Tjv1xmHFE7r/1KErP/9WFPyJj9B9ewbkTEgdkVotiYx58+L9hHB/CaKVrpuvbN36p72aJmZpmqiwaMU3r2MUgH9Wt1U1PQB7UoJc8fjEj3s0WRcs0AcCB1y6edbKtCtye05599Hairg29k+fjOfzBYRbde447O0KAp6zkFobdv2X7MrteAtI1yFH0zG1bUsN2Q1cjueuvAHQQHovBNJN/UphaaByQqksfzUxRPEvUnrmhriPkJhb4+LXH/cNiZMoFJUGTK3pXMv2DokFMCml0040tg6jgSgGXJXJH9mTEqRA32xWimvkcPWyfeRXFgEM49Cv8+NfLEuH+hn4ArCXHx9lSxErGfKAH/mlFmVbDxDbgEU35nx6flTxQdG3+Y+VBov68cXTgOk8NqzVNBEOoKqy8Mjr/o/+fDjm2mynP+Gd0p556JPFFWivykQQpQew/EOxpAT/MSDcG+hfteJOYu66AsfEVIUcmmQdjJbomkQ+Cyz/r5zWLxl9zS4uCA/h6BYQ4i+6PPxfrFMV9cZhVVVZoqrymUf3n9f9iMuncQsfx8Pzrt9freT5xMREkZ/fbL2UBtVgKJ6QmJi4GugGjKCSz6oo/NcxblNOTnRS39PWEGDGqIVyT9LVS4G4S6OXPmIyOI8ecURNA3b2m/rKs2cy4mqxJkcdLmrS2/NKugBnrNhdIBBCInVxhvTXziQ3z3jYH+RIEF/ZJj3wr6j62YSqymJvEcDZHKE5bdiTEtY1DUi7FaBjWOrP9empQAOV0zJoV69Qn6O+lbzdw+RSzEH5htOlcToYSN9ytHuNU4l0aUwANs2/540aF4LVFhXIJlb4m+sSuSEQRJeJc4efcgvwGP56B0QTAAV9mqaJ+zVNCFWVmaoq71JVmXvcLgMKCOnpIOjhV+56908gEWRQyJGiztcc+Jqs36M7V7bWVnp29ye/9DHuVYB9miZ65Lwa0GzvnuZJv2Y1B+RXt0/e8vLx+91te2CIQN7WmLRPMuO7Hijb3sH245Wg3wOsyIzvWmk72P2Tfrpsz5RPlkn8EnRCySmdOEygmAXCAPhJUfrmH288Mv2PNx5peboir1G2FH+J8iqQWkDIfpBZ1KBd7qlSLxxWTRPtNU1cettnLe8EaQXe+eKmPf/KNz0J7snKandBo0bbV02enDQXQFXlV0Ckqso9FazrY3CGRzvCtoT+a6bTgD0pQb5/z8yXDxU1iwIGCvQDu3M7PLl836BDZ6I4y2JNNgALJUYziOEgnnojcsr/xSgHrwV+FIgnKZdEXl8wG0qSQDTqGP7717Vty+lA00QPTRPxtW1HXeGiqFVfGEWp42BRs7TatqWBM4NLGoP9jYWVpfZcDehGt6jx7/2+ubcGGRXn1WG+h9fVVDZt/Nt3dAIZF2zOPuuLJu1JCetOlDZW7Pb7xqn7sDLtyk6nskaULcX4C/37A/jgyHyNIRHAy3iCTv9igm2cKYjsuSB3A2V5pO3AoxxgRKdz1pYH5jzY65rj942ypZgyielSRODCgh0RhoyNbdf8Ovuq0R9v6mXX0jt40xrEgOk3Df6Xo7iaQQ+aKXH3Zsnz5eaLyyM8GRRf0C+prM1pmnVVHNLwk8nR7IqybX7KGjyptjogdaQ4LyRtyIPBaVfbgdUS+ZxE1ijy2p0VXwEtwzn4jBtjwvms35MZ3/WMS3HWFx3WBwvchrt2l/gbwgylhdlu8/FVgSfkhRceHAbBrwFfHz3a4VoA752ZVFVZVNE+4X/daTYXxLgVt/m9it4/XXhPfEsnzh2+LN8ZYl2bEf8/YLZAnzx85oO2CL9DY/+Llq8XNlm9dMPBS/opuO/anTTkQ31KcAe9iC1F/nqxkFzj/1jBWVkEcCJ+PRjXN9icU9AhbMtHJx59VpAItAC61rIddYIZoxbKr63Jf2aXRDSqbVsaODNkFMQcAirsbubw1ScKyR7fSflHa75Oi+Eu3WzqFrl+a03V5I44Go8FIeKaaZs86k71m21ZnRcBidklERcApxJRvq6YgCYAPdE2mETpbGC1qsrUigbvo21SPmHR8Sya/lH8lDLnS/MqB5jcCKNBuILz0yO+fGO0urskN/BFhIwIaJLtvjxqQIsfew0NHfbTwhvtO7qOBFDQHSYpTd7OT6Q3aWHeFBv39Eu2lCllovpRtpQLIfIy4OnZ8W/sLGeOKj0RUrzqBCoVtzlVvbnASCRBhs/TQ00LmpfoHSnRY3UDWXN2xYTer7iCRoTsve5GiewvEEikn0S/opI5qyTatr61pHf/81m/dw8dQ5z4EsmBGqtfnAr1IsKa5zY++EJG25/z3UZXF/+8u1NHpFbLeXvnnSsHu90+XxqNjr3ArYmJiWV34kmaJr71FnT9i/BdIy0CxWByND8jVeQzRi2U702YNbXY7d8VGBjik+1ck9H/1sW7r995uiOurazfqBsP9u53XqOUPVe3+WQeiSGhihTfGNxkZ4e5Lq2vzqrFmty+oDSka54z9IUZoxae9SLdXh4Grq9tI+oSJqVkn0lxnlfbdjRwxqgwh7VoWmAbv2IlOC/YXaFTU102HrzkPJDFuc7Q6TWda33mpS0E7gO+Bkd9uXE+EX+CdEb4ZV5x4qH/ZIJtrPAn73kAEyXyfDY84Q02Vfj/GmVL8dtA/5v8yP8znIOPlG335pr2B576pW23vCWd+peGt0v/uiTXvxEwG8lzhZnhU7P9I8Yb3KWcX7IxKzJ2z4+tW9tvGH/BKp9+LXZkAo70Ji3cn1x9p2Fbm9jLgeVlEdMwDr0N8igefezyrAQhAFlV62A8uu9O8EiA+Rp+bi6R0kfZ5go2flYSYFz+fudR75VcMG7GHIGYDDi8ElgIlOvTrKsiqvvZujC/6sboEOiXFxF8A2DXGHbGumqVp144rM9ntB2Tr5sukYjHXr/2wCfV2TcxMTEiLa3X60LoJc2a/XpLYmJieUcsE9jr7cn+L7LazH8GoDByzYGK3v+vKJPDuix6aZueTVaPd0uTHZjtZyw8dMvMB9+vqeNqsSY3lhg+lCg7XLqx2wPGL4xFfu7VEtlaIK6LHlW88fQcSd0jKiDtKW+u7mlpz1gX8GoZ7jzxyHOHzpEbG+tSaTFx7nCf2ralgf8eX0NRdNvQrc2P3+7vMAwEaHzYZK3pGhZrsgAGg/jxiwdeqFH1tMWa7AsMkBi+KpN/qu/YkxJKmwfucwWZ84ZUd99cGj1cRHAbgM6s3d9PLKowml6GgusBoLmDoHFvxs/+x+f70CeL1z30yeKpOwwdvtxb1No9xfXcNaC8gjdVQCLl9vYXFLUw7LTHDlrRpOUlW5cN8dt3s9ktMLY5MgFE/7U94te6DUYQwoBXy/Vm29Ojs2ncrRc/aJnxXf/x/biQn5oBCNyfUEWb0+ikvutcxtxfQcogw3uKj7INwAW8w3HpedFJfdcJRH+BeEIgngDaS1G6MfXtcSetGHOVbeY4YAiIZyzs8AF5hRHnx2WNGM40Z7XDqmki8INlfgdLpfJKiKF0EzDjhDuV4513BviD/AKUpi6Xn3rnnT/+XP59VZWvqqq8p9IJpGGYFC5Kgv86ow7c9JsGx02/afCklj/m9vr8galvAb0NwnVVI9/DYm1G/9uAnRZr8pgB05KqfTGeOHe4oXmg/ReQ4cCNyx57LLfRUeNCf4fh/CMRrndJzK2xGkJdZezsuwILnMG3tA/bmllT/cS6hKaJJpombm3QYv2bLEfk925pxLb/qpYnHt3A2cwNr05SSty+Jj9j0b8krSRyCLCDxNwat7UcYFk0GGjVKmRHjTv/XBq9dCLg3yJod70931ZEvjPoN3tuW//q6CNrmgjYiHqsqj6XRrepqqxUl3Ss7Z72ZkqeiyBjQ2Z810plzPKdoRtABALNgB+AEsB1JLxJSYFviL+dTlOBtr3XBGYYdHGtQDzV4u7iLx/6ZPE6e8v29mMBU6+Wq41ht5soOdoM+6jj1xLIiQouhvGetTJnFWD/JFsfgyu4DyAK3LcJp94J4Vlon6bmBXlb0x8jOqnvuuikvlOjk/q+IHH31w3FzYP3D162f9Ly3pWtUcYltvd999N2RgQHHMBrOzlvJgilNX/UWgHvWe2wZjh9Oi44Eh1hFrr74oCc21NHpJ60Xua8eaooLIxMAXGpEO47ExMTfyn/vqaJVpomqtSEC9k3zCaFa1fnUe+e9vzRyph+U0JvkCvxSlhMv2lwnD0pQe6aOnRJjyZrQ4PMOUPw5GrNPljUrHDgi1OnWazJfS3W5EkncxL45cClL6UXWCx9mi3/1J6U8DuJIbcEFRhucPjqX0beUzT2Pz/AWmSpfVhCQWmwCDLn1KnOVrELYvvELoidFLsg9lST5tsB/0clxQfnInvy2q8GyHOGWWrZlAb+YzYcvCRQopB6pOc/pN3s83ybSsGV2aGuvZXtWx3+yu6UAHBe+OZvajrX/vxWA8xKsezW+Jfvam7Z2YHFmhyX5wy/SKL4Aj+drNO6Rg54PYfICwCi2HtgdfztK6sa/xPXPlaCn+jFD89WNa5Z4N50gG6N111ZPlVgXf/LytqlLm6128df0cW7upCpwDSACbZxAQZKE0DmePNNrUAYiD6l+Dw5K35W9vFrbUAN8qdw81vxb1b5XZTmPeMEAk+k10CJHqsDpYX+7g3AJ0ClqSgtktQ1edHJg4Tue0BI8/I066p/FZOVZyedHzhCM9P5bHgc6LGNHipIdtDt2cqKwv5rzmqHdeqBtiMzS32VQt2YMP2azD+qs+/evZc9lJ3dtl1k5NYVTz/97Ifl3/N2zPodSKpqDoMryKLovhuqb/mp4xeePxOEEf5+1FD23oxRC2XqM8O/BXr3aLJmXKApv3BbVudHgRUgXxDoa1pZv5llsSb/76oXXxgy4e2RF132woxjhXcWa/KYzKLoiUalZGWE36ERaXN9b5bI94CVfsXKzWfyOGsDiTIasP96sM+c2raljNgFsYOAVWV9rU/Raf0V6Eg9kx+rCX7Gwl0AzQPtvWrblgb+c7wqLnqf8k5QswzTBEUKhM6m07HI7tyO5wO/vTlmfo2euFmsyWJPbvv2Tt3nq5mjPjhehqk+o3LMJ5G+nEDfuyyg9D23HFNAySTmwar2ibKltMkj/DaJMved+Fe/rWpst8a/7APwNRRfAX+nCjga+V3Vhi18LLo93eSgabai45Pe3JlIYq4LIIeIZ9yYwvvw/XQgy4hTjWLvx34UHAL+VaAdZUtpLjF0LiCk6lzlxJA+jeS8az0vpASlxEDWHKB/wKMFPwIDgAeqmiJ27Js/KLq5O7BZIr/YOvPJLyoaF237pQXwJLDok/inXgNu9hSDCTjO7ziTnLUO60OLmrwJYoJZuN9IHZG6tDr7Tpky+VEQLwI/HT58XkX5HDpwH547lgpZ+22by4FWRY3WG6pleA2YftNgH0dWYHPPYwbpopK2cfakBPnFAy/MTiuwhHLsByKQCCExjAM++jOry9fJe274ZW9e22KLNXlv7FMfpIOcDSgu3edCx562VzU+ZFrgNEul1KjfeDoEtesyY2ff1R/oF2TKXVjHOlvFgRSek8WJT+IVoarS4c1jrbDLz7nIlTFfZxgVJ038D1xd27Y08N/SPNA+0POXuAZYYbEmDyqaFtTPXKpYAULzjPeRGFKjiFHvZ99qDLI3sLim9irC3Q1oXtZB6RxCA5zeVqaE+lT+5FnTxPXAkj62D3rspLPFs1UeBKpsFdqc3R96G4cknsgYo3ClgCxYd0A9ph4RZUuJ2ke7gI78tjfGbg7zLVEuk4IpLe4uPrauxpDuvhQejWL/tPZs3uTCfE0mMUFxLPuyIimo7qx8CCCYrEr9GOfzQWOAn/zE5jSJLnWDYz0o8QHPfTmuLG9VVeV6VZVHNU0omiYWaJq4saK5opP6HnaZs/oVh/2eEZwx4Nr91hUfp1lXPV5e9qodW1YpuMzAgwDR/JXgTW+o1O84WaJsKXFRtpRJpxKlPSsd1tgFsc3XFISPjzY76Bd89PHq7Dt9+t1jpVSmeZwA4oCLjx/jFVufp6qy0jtvo6PJowAu3yNbqmt/DRgHShSIiSCe4rh2b8fjlcN6ByjrMuLA0/XoggubrL7vwqhVc/BEkVfousG/bD8zpabJxg9mmUqF+3Bk6S2mJ/MP/reHVfvsyW33sCLcXNpi6We1bctxfAc4vFIpArDELoitdvtCTRNDNU00OGdeZoxa6BbIzM2He4ZWJ1+ugbOHslSo9AJLtzInCDCB/s038uLlEsqCDTWOGLUN3ZYIQuka+UuNn7h1b7xuOuhEB+6pViDmbKeswYBRKX0G9AM5JRHDLdbkyqQ3DUBAGm2ORRQvZvnWqrRBE2yvXZ1O64suYvmGqoT5y/AUu4k/QXQsPw0I0WdP+qwYu8/VwDaDLp4rezPKltLWjUktJuC1N+PfKo1he5m+KwdoWWE9TB6h14RxyH0Fn27+15uJIYajM/1XmEuV2bqQ6xQheunGIuEI3+QTndS3smt/ANAGqDQ/3/LM0CJH+KZWEtfnAuWm8lqtMba1/f+kR8yF2FZlxne1d7Atb5FB61bt+H0H3uYPVeXZVoXXSf0JeI5y6gkny1mnwxq7INYA8n2Hbii5LvjAo48k5OSf7L6JiYlCiCivg/uP0PaxD1/TRARwJfBVZfqrAE1+n7wIuCI4fdDCUzmO6rJwWocYxdRqWkCTbNpfvb5QVeXMk9nPnpSwzmJN7o/nOLW/hZsT/pFC4b1oLwfd9KzxPdFCORID4qboUcWfn94jqXtYrMk+ENvTpDgXvzl6wb9PGrVI6ojUddd83PqGDr4F32wqCrYfKPUb3dLssDz6VZPBLw47WHGv6Yp5FE/RQJWPwc4VPN93n0ggClhusSZX2oGngbMPz/+vXAEITwteUQrSAKI00JS/6quCy68YatiMiVJA6OnmoE0V9t4+SbYc7dbBz1joiAneVeOc0+3ZF7SK8s84uPrJe86o+kxdwPsbXGexJqcAi6IC9k8GngbQNOELnK+q8ldVlZ/cYHvujxJ8vUEl6W5EZqX661G2FAHqwwrurObsOen0tki/zJyC0sBjQa1odj6Yq0fmjdz3XZIAIZGtBOJCvD7EeWycsZXuOijvAaTRJrZs3z/p+a8nd1G2FB+IjfCl8IPj1QpIDAkCFjbKMl16NLw0NT9Iv8pyR7FDPL78kG9O7F+V2ayqMt/bKMYFnnocYL+qyn+0qe886r3SNOuqTRJ5rUAoEunrUOhXgv/NgH07XRMAcmk0AZBZNB6cGd+10nVPBoHez5ujDBX4XyfirIuwdvbL+wxEPx0x4ZGEnJNy2spxt5TGFoCTykPb1wALgdZVTaS4AtoDRcC/OmD9F+Tta/yiXmowN+v51+/AjursezJdRsrubqcFvr7pJuMKQ26w6zMScz+tqd1nAwJ9KBBRqpvfrG1bKmLR/3YnDww9fMWIiPTYaJPj831Ovys3FoasjF0QWx0ViOvxdPRpwIMKsuz8V2s5WQ38Z8QrfnsxN9JQ/PYbBLqpacB+2Sk85ZP+Lb+7JjLmj2YvuG9+ZbZ7yMEbnU+Zr3K89H2Xp/9vrsWa3Ka6C1msyZdkFUf2drgCltdUu9liTY7Od4bGZBZFv1qTeeoBXzcL2Lc3tyRs8vi3R7bzbpsJLNc0EQ7gIGA+CG8bV/HFe/EvV9pswJfCYcClOobJb8W/edI3Aq1CdkiHKzB4xBv3Rt5gey7kEM3PG1iwNFiAEAi87VBVgC62b837aHtlR1IOZsZ3zbjK9vr/s3fegVFUax9+zmxP74UECJ0Ai1hAQcEJIKigguXaReGqIIoKlnhta/vEXlHsxop6RSzYIYOooAhEIkVqgIQkhJC+fed8f+yGGzCVIs3nH3Z3zpw5Mxl233nL77WVkn5X/VwD+ebcRg5xCogINxG75ZJuec16is8oVwJnAtfHT3H2zbjK7QJQdPMOgy+62SibqkqfqkqpaSIa+Al4oYmhGuCRwXC/eGBA0UCgN3DTmqzTXFfk3pEA8lrg45VZZ+yTsQqQwLYpwVf/U09oy/6HlYf1pLczB7n0yLHHhlVxflxxm8SUX399+AAhTnpOSiUXlDsJeRwdDseeRtxrwApVlU2G+jVNtGsX/8RNBk9CbZe7rjjg+Y5PXDi6PUSfIwyBWaMv3nnAip8KrJdYpF8e77LK5RWxgYujD9SBDjGSw4peKncneXy6pdXe+r8bVZXzAS6FC8bO6vzyek/41cCP9hz7XOCb/HH5zT6lqqo86rw1LaAJZChO3KxQ9z8cZthz7Eq7zqntq82lAnTM0oih9JzfK2t7ZRbXdRi3ZmffcyTKh6nhWz8ZkLLwtjD35vGptfqtG6p6XAlMON7xRl5SWPHS1TuPKQKWjug4x13mSjEv336SE4g8Je37YWXOZM+fFfYyIFOgXylRBDAiI3vuwH3x1MdadlxU4UmAozwSUjB9lJw4c8KUbwrGfPLVpvOmApOA+4EvVVXuTMnNixMM2dX4I5yqmU3NNTl3kiWaM9834iuvJeaVtqzj97L+rwEjFhSe3sHdO60dwMnlK5YKxPFAgAZGVyntxwDGGmJuBljO4AmA4SS+uWcZQ+53EvEgsJth2ofF96zihICO8X/NhxzR16YJ8/NSYHBZ9bG27JrdWgbrhjpdKt5WyfGpqqzSNHEPsLix7enTBy8qzF44DLhyh1lc82Nkp1Fdyd++HvtnAC7CZ4KIsbPo7X1tlNgx9+d4D+n10op3ArltTS04bAxWe449Eoxv2ZRA1UXxRdFhih5L0MPZIg6HQ9hsPXOE0C3p6b/cM2HCd4towg0dahLwS2PbGnCCuaYznqj1f4tWp8HifTLgMRPXbdtjB+oYha9YB6Vh/lgg1trcQs24yn2kdHlqls7Zn47VaR8TzHHju0M5NKxp4gzgXzemMP6GzX2KQd4DnADcZs+xD2vOaNU00QM4F5ipqvIvsipHGwXTRy3qddesWU5/5CXAiEP1b/4PreP62e2iA5Kbf6uL6QKG4TWW4hQRapEp8Qf01I8/HOFd93Sps92kxcXq8cBlxXXtr1lQONJX6Yl7AsS5wE7gGpcv7M7VO4/ZJQH37eYxux3rx6LhDd9KSb38oVRAqOxF+8t64mxltwkhPTvdiav3do4jhZkTX/ts8EPPfVlYk3Ft5zs+nbnxYfk7UBjaPFlisAl0mcIWzyC+1uDURuf5mouvriPKMpSPn34v64G2pFHhCdjqHVe3CpffJq2G6jO3/9TZr+iFRl15AdD+J9YvrwGxuYjO/03JzQsnWGW/YDEjH0xmy8m/M2hwSm7evcC39YZaIV36dmJN+U9Zl9Vtft06oF2R6X4TykhFAhKP0a1s33NN3ojN7YRubnVzIFWVr9a/1jRxPbCgYQewUC7sops+WDLKizFNrcuf8uPoy2VKbp7ByGknJVFY+F3WpH1+gPJg2wrQjk3PLMsa+397M8dhkxKQbPR8DrKTW1dGhSl6N1WVzXay2IMrXK6EnmZz7b0TJnz3Y1ODNE1cq2nizpb0V7t+/cMiozeesPLjX2rDGvaKd/4v8/yAx3R+ZNqOhRlZK37VNHHL/j6Ga3pkZNxO4/cBA9G6kOfgqNqnLi2HEzrGUJNuITj0Q8PtgZOBRIKFdPWFJBZaXnd34P9oIdXlaMLpj8wLvcxrduA/HJJM+yS5+9B3u99oz7HPXlATV/xjbfy9PqlcACw0oD9AqNhUCLyA9szV77pm3fjYkwXTR10KJJ/c7vtnLQbPOhC3Avnx1u1/9Ev8pYdbtz0Du7ob6om24h+OT/5pGpAFnDC0wxdDT03/phcQSTCk6yJY1LpPnvqM7LkRGyt7xMZayueHCmaPem487oG14aYakRax5fsbX7lUAFyXe12iGVc26FUSRSe6zYMAACAASURBVOwkaepf8j9DpOTmhdcRdSewaD7n3bEXS4gHiUT+C4Wzu7tW1Ua59dgdif6FOKoerjdWr869aSiIYR358+uSrH6BE/luFpBsxn13SVY/WUqH2SDCCBYtzQtVynepJDHG54qbIR1RM9tvNf9iCIgRgNwz3aAhJmfaUlNd+zZHzELpAdnAdXtus+fOHfpjkintsgIvl62NvSr08Rg/lrTtpN/Y1mPtydjcR0aCsAEsyxrbZK5xSxwWHtaLPux4X6k/6tRMa81nH15Y8FNb9n3ttRHHwEnPgPKj2x33YAvDBwJpqiqbFY7XFffFim5FSGObntb2htIVna4RBt0V36NwKkHhYm2/HsARrdhQ3pBIy7Z2vv+kXePa5zyVwwmD8MUEpEmyR3jnEOV14FVVlTo5dg1wB6WuhAKsamHfb4EIVZV/W5OLQ53ksKKwUmca9oSlsTCqtuU9/uFgc8Z7XfsU+mwjBXIMJJ4igw9sRSDeHBSxc1OUwT/zsTGlNQD2HPtXhFK/9ow+FEwfVQujboRgK2qLwXWZzVh3X17ZiRcBMhhxkRLwlrlSs5fc++8G+49qONXPjRe17hWnSRTjhqqeByySdjigacK8ecFDZ9dt79cP+dbHx/X6ssMPhSPP9wSs44HXNpL5oBdbmAlPmQ9LpYewJvva9+bX11YyIAU4f2/aiRqEb3hAmpDRJiEtRkasXxQmke7wOsMNDcf9hjpOIUA/fnopJbdHjJlThnfj99KFWePqO5XFE0zcVAg5Ruzu/Ph8q51Z+bfcJRA4bYG5AUXOiao1Phsa0+jvkcEXXURQV7tNhNIDBgDlAJomLKoqPZNzJ5nMnPZFGNW1lxaV54U7Tz65MHthQtxI52OVxG/WMX7awtQtsoiRXwNksnTCvqQWHPIGqz3H3hmibo5Q/KuvTdrcWdPE5aoq327Nvg6HQ0RFtZ+rKIFom23HpFtvfaHZfFNVlVdqmmjW1b41WxsohOEZAImcXpi98Ndm5CX2iScuHD0clNNkgFvOu7YwH8hvcac24jHrj1i8ynkCMS3tGteT+3v+Q5kbX7lUhJvOUCPNlVuKaju9xL7/4BxQ6qs8NU0YnuvI7zds7jOMYJHgDZGK/9bb5iR/9uiY0ka/lEMarP/osDagW+zqTqXONNIjC9oBWw/2ev7hr9hz7ApB6cGzYw3e6ysCtggAiVjRzVo7J8XkmbWwJv7D/HH5f7nvQ0Zqi/+fC6aP2g48CTzZ486Pu3sC1ouNwnudX1qSQDeBuKX3XbM+Gtbxi68aE/Kvr2zf13NNsJVevcOVWAVKk1HAI50ZE+cPsUS//LGnqnNC8BOR3X/lWXf+GOXpbq5Of/CxyfPa54895Wyzwb0tgLFdV/Ln/ph1eaMP4cfnfpy2gz7/6smybVrW+DY5uuoJSNM6gECSVQpdF5NKPg0TiNzIW2obarOaIeMMYM6njLcCs73YrFvpenKDqTRF6l4JJoHUX151X/+34kePzbAWErupLJcUrg6/rXYLAI7olYQegP6XbvA//JbtJoM3Nm5vzkdV5TbY1RxpvqaJLz5j8U4fFpvKnKdjncNeBv74Pq3ujZ2kdBrMF//9KOuufUoPTMnN26UHm5s14S+NE9rCIW2w3jon2WYg4b8BFL1vWPXlBsHdQFkbpriourp9WlzcupwpU95tVi9V04SiqlJXVdmsQL5uqj1T8UUBIBBG2ijL0Free6y7wRyZPsvntBTLgGGGpomBQJ6qStf+OkbxTNt9qV7zLS6r/p3NrRx1Vak/FI6wV3tjlU7R62b/dNf1Dx/s9bQGTRPhBB9ccvLH5d8HLLrgg4z4Ne7I8Tv8pkcIylc1te9NQKGqyiNeqqw1rCo/5mdg3M/bsv7xrh5C2HPsNmB4R7MzO0wx93fqRhMQELDu+LDKFUudMbfnj8svOBDH/vOh89YC9934yqX3u/zhl8/bcmb/gFT+VeePPPe7zWfT664P3nf6I14BFuzPBiM3vnKpye0fdXpmXP6Wr277zwGP3B2KzJg4XwXme6q6iJDuNIBBkYbpU2uResAs/uhovidgEMLsM6z3mgz0ZNmTcHmj8xXR5T8g9WS27kuhshPAnGqoOa4qLzI+UG2WyCzhiB5Yb0zGUfLQTlISgc0g5wNWEAE34btUXEoWnMqvUb3FouhjlEFVeeYOG7aePemcuzj7h+8p0RLUEkjLdBAyWKuafQByx6zqGF46OHzFK+NF36tf39vUER+wYoPsVeDD8gywQGPM1PTp/eQmx0fffJvYYbRB+utiRdnkvZwfgMm5EwVMrG/A1Gav8J4c0jms27zWbwMox6aY3Le+dF7RUlWVY1RVft2afR955IZU4Dngl507u01obqymCROwTtPExJbmNfiiFwLIYI7TAQshVxYk3+CtCYuP6170yfGT5oYDPwPT9tsBHNF9UkpMU11WvaQ02XcBjqqjLmeqwpMwGOD3sgHPH+y1tJZQSH8W8Gv9Z50sromRiq9gSV3MlfYce3wzu18DnH2g13i4sNOduAOgyhNnOthrOdqx59gTz53V6f5zZnUuArkD+Gyr13ZcmsldEq74xwOJCy77M/PNC7ZeeKCM1YY8c/W78uVJL7+14eExNwBpA1Nzb0yPLFjp9IefDcy3Gpw7xzx598+d7/j0mIzsuW1u5FGPPcc+0J5jv+PLivSH3LYNojgsb9tetl8+EhhISO8oZKzqgMsUvu1hk6W6RgCLMm0ivtqPH2MKiC9fzXpqfmMT9cv91A5cC+LlD7Lu/WFvF3RM4q8X6jYDLlt41MjyH+v/zgqh3NKU3LyBFSTdElr2dYAluHYJu+efjuhf/Yf5xq3v0WPVBvlF+SmKz2Rm4B/LAcz+eO9nS6YmprZmTdbK3gsEBqK2nmPb2/MKyV5d8zbTrgMZfTHPvDlLHBsD8N8ehW9qSWZGljq3vJz1zF+KvtrCOvrOAzDjqizJ6vfnvswFh7CH1Z5jHwqRJ3ey1C2ZmrIpV9NEoqrKVntXLZaaX9zumFgpDeMdDkdLLu0ogobnxpbmdUev6mut6gUwE3jnQKQDPHHhaAtE34SQv5vD3VMI/p3OpI36q01R9LKtYztMnwpEjc0t+mdc5T6a+lXvIsJUPabWF7kFxN+ipbu/UFW5W3e3R8eU+uw59nOApQL5FHBFE7v2aymCcDTRIXKDdUtNF7rH/pEKo/4pvPqbuejDjgOsQr873xWVAspx6zwRSpTBF4g1+D6uCJhf0xHa7Is2HfT7tWD6KD+MehZ4NiN7bjhwdkp40fQV2/ufpGPIA1ae+dhDeRlR6174ctMFkhbyWTVNJAERN2zukwzkAhZL0pcIAX7JQKTItefYs1qSqjsC0QimLZkI1hS8Abx1zROXLZoxcf7n61KNP5VFG0XXIi/lUcYIoElHQww7vionWQq4b18W5PSFdzKlSrzAiPKfdUARuxfXqcE8agG7HIASEN76MVtftVrTMZ/hcymULo2RtYU28fMVJ3isHrfFvn6NXwqpGMpNCeFfxi9Z/WXmp8A7mWtWN/m3N3oSiwCM3rgoWqmU1BjH5s4ZVEK/gQOYt+oc8eZTBLtgXjY/xXirkJLJa0S3wuyFndOnD27RLmqMYFOEQVkAQ/iiV/B5ZN84JA3W4e91SwLr2yDWbvKEZwEfAr01TXQOyU41y3333X2+lBntExNXfj158kctFaOgqrIcaNYLW4/fWvqY352EOyb/hp4333NANFgtMbX3eiojOiLFhEtuXRsg+J/3q/0x9/YZYaZIt2GZFMQg5cnCUd0WtYUjhhtfudQg5VnD+sQvX//FrXcfdt5lTRORwMj68H7+uPwVg9/p8VJlwDz5utnt1rxw7ra/yIb8Y6zuTvfYlUlbarrQPnJTD/bT/69/aJprP04zmoU+Pt8VdWK53zwIonoCRCj+rV6pPGhE/+zkiIplTeVhHwoUTB9VB7wPvG+/593EGm/MBQqBy1eV97t0VXm/SwEdpBBIebzj9TfL3cm5Z3d5Pys9YrP9+y2j711b0Wfr9MHtPrIaXR5FGj4JEDDtEsWSQa0SKWWbOwAdCUyeOXTRjInzdxWwTZ45dFHDbf3mLKqJrg1EbQ2D6ECZv6ch75vGCnja5S4dpHN82gnM/+qLrKn71FZ8XWVvp6dnfBWwtaN7mwBhBsY1yC39sYE32AfCCnwGTC/J6reo4A2rsKwNW1+4NCqtdrsZAopujvW9vOgk+zWd6/4sNvv9z4HQdEvgBMVjeAa4TiLHr+6ZObQpo9UbXmA012VQl/BLGgzeK2nNlNw8BTIeB1kWx/aRQCdgbUpuXoyRE/v21ldsTPJ0TvWGFb5CsNh7L5DzQBDFzu/fyXpov+iAH3IG621zkkW4Ep4nkMkSMTp/XH6dpolpQJfWGKsOhyMeDDOAZWVlvVsMf4ZyAhNUVW5uaWxh9kJLmOEkvTYl19nrhocPiLH67iM9MvzOjGxbfHXBdS/8MC+0xiuAhaoq99kTmFRmehSIK07xvpo60dWomPDRwHebz+7n9EcIHaVVBXyHIFcDT2iayFRVuQbgpIjKO3+pjblycW3sFHuO/dn8cfm75WZqmjgZuBSYqqrSfRDWfEixfPuJywF+KR5ScJCXcsRy65zk+LXu8PM2esJPhNjRIJJEsPp+HvDCKRHli148b9tvB3ude0P+/ZeWAS9omli2tiLz4seXPNDPq1uHBLVfhSh3J48Hxn+2YVcK5ZcA2QtfBkCxbT4mrMOrSPwIIZEydGXAB1L720/oECBkpP7FUBubO/3SkujTo/ptdJPX2UrkphpDRiDPQBa7/Q4HW7AaHgNKfmPoBfuyFk0Tp5ksH/dzR5migM/cVnmrxxIojLm5btf6upNXupZ+KPg/T2ZreDEZQ0FcU5LVrxQg5ofoN4sXx6YhBRIpfZ1cN59121sLaomelBHx5weZa1Y/DLC6Z6YqkXpIyqpeprBRg9UVtzzZXJeBN6KgJ7B0b86tI39O20yPgSCuejPr0UJ4NKhvm7v8Nj9m83GGb55zJh5zfljZwKErn799RO/rH/m2LfP/O3dqb7jiZIBq4s7YmzU2xiGXw/ptVeJ1Gz3hqceFVX2ePy5/OYCqyjWqKue2Zv/IyML5IOOA8Q6HozXJ6+cDBZom7C2OhKFKwKZEFZ35r5aH7h0ly7tODniNRKbtmAagaSIFyAHO29e5/Q9EXQ3cBDydOtF19b7Odzjj9EecCrCqvN8+VS0eRN4ATgJ25QU9Nqa0qiJgPsMnlWTggUb26QpcCCQ1su2oo9ydXAlQ64s2HOy1HElc8EGnDvYc+3h7jv3T76sStm/0hL8EnA9ifm9b9b0joss65Y/LPy1/XP5zh4OxqmkiMdSbvf79dZomljfQ676oe+zqf/t1UzbgAl1XhN8fZqw5F+jeLnzLyad1/Oz69IhNFwEXJNhKpwxqN/8jkzfxB+eWCVL3xaD7w/AUXvyb7m43Uwh5NKYDNMsOUp+I0KukzyBQdF2v2WgV2tbT/1IQdApz7wYGAfeUZPXbJwk/py9ssjsuJhohxCC+Xm72Cp8hIHbLw+zCylMBhjF7qQ/rkC6srKw3VgP3R93sLjdfgQzeJgIRMG8Ki6gl+nSAZQx5osFUmkB461uk0kxtTNiOE38GiCo6fa/yS6/JvTF9J4nTU9hSCuySBJuQOy3MiG9aV1ZUny5mRXgiN1wJ0hVdOPrJwuyFbYrnf8HlfwB0ZcULJVn9/HuzzsY4pDys9hx7HxCPA98sdcacG8r1uReYrqqyRdmZRx+dfKnTmd43MXHlgsmTP/q9lYedT9CIa1ZFAMATuW6auaazS2BoNNF7X3niwtEdQdwAvHX5natmhz4uBboA+1TJvPVV64Q03fyyzyh/NfnFrfu61sOdWGvZRTWemI3rHx5zWKZEhLpV/aUjW/64/IV9c/rMlHDj9bNTVzx/bvEbDTa/paoy5+9b5aFN15jVyvrKTDpFr+20h7bmP7QRe469J3B2vNF7/U5/ePvQx1sTTd65XSx1C3+sjX8mf1z+IZuSEupPX62q0q9pYghwAzBBVWU1wZzwxzVNxKuq3EmwI9ZawAq4gIeA+zZOP7siqMmqqLpUtFUPXlRvdK4jWDTbkOcysucONCjeXIO5wuIuOcvnq+07Zd1ddxw1huqMifMH0kj4f09ScvP6wjHJncXKz/9M73VW19K6gi1+WVhF3L0Z2XNjga8Lpo9a1GfOUpOMOv725ECJf/wnhnYzPpg/sLl5W+KNP6Y8H0i2nmML1OqDK/LmGHRhi6w1LGk4ZhlDfADLGVyzg1Szfc2f/109KdNhO6OsW8doLqlzGrwSaQb89bmvEVS9UEdEfnHW8fVdu8hcs3rR6p6Zw0SwicVYmpEgNLnalQIYfNF7VXT1GVdOBcQpfDntjazHd3monYQ/5sec1I6C24HH4jZecVKRKDcXK5W92+vxuWQvzGpNzU5Kbt6AUJoEx/Dz9U2XVLSdQ8ZgHfR2zxFWIT71SINHIsblj8vXQ1JO44FnW9rf4XCcBokvgL4pLGxHq399QobwMy2NW/PU/YrV23dYXfIPlBx7r57O/k+zikjZ+UVtaayCFHc3WJ+kFcVgzeKITkvH/LDXLJ0lKb6LO45377cnnsORa1682ur0jezfKz5vJYxpeYdDFE0TBoL9tTeqqnyt/vMR0WUPLqqNvTrPGf2YPcf+br2hELqX/iFEj9g/TOsrM2kXvqU10ZV/IFjVTtDI+AEQaSbXFLc0nAPBVpHOgGF9v7Dq3K1e6507/JbF316y7pC75zRNdAAuI/gAV6hp4nzgI+AYYAUQG3qdBFQT7P++DKgDUFU5i6BSB6H3u4qB26LJGpl5OzaE8OhGr78286hqDzxj4vxTgPkgjSDli9d//rnuDy8yhZWmmSML7a7ynit1f7hutJan2k+M7bcmIV5PLGg/aGNXwakrAp3jqswxHkSMAXGPUXLP8xPn+ft3sRq/OiGFCxfVYNS99wC3f/DshNuT+76eDDykqtKlacICePf8LmxwX2v54/IXaZow/Vo+N07vbcXl1T+7bt3cKFAEwYcVADRNdO3EI6eV0p4dpB6ryIB+02tvXwMS9zcJlJ1aXuMptNr8yd7fTKWW2YD27ItDC52E9+uP9jMcv9s1CRmt/5bI0XpkYCJBVZe/4I5eFbBW9cIdvao7DG7TdU/JzesNYgrwyhtZj7/b4HNh4OxTIqko7bm07k2t9t4rllp+uL8GnwEgny2Wk/09rkhv3b1d70g5vakOZHvLIWGwBm8W45cgDAJpINg+slRV5aeaJlJUVTZbxe5wOAaC/AowgGLZvPnUvrTiwmqaGEnw5s1taWxEaVZ/AGf80kJVlftdJ++5CcMGeGtj+8R0Kl08Yfpvu7zJmiYeAL5QVfkXb1prqHk8whaJ4ROBsFm8YkDH8e59M36PAL7ffFY/HQM1vugXD/Za9gVVlQFNE4OBiIafPz6mtOiM97pOKPTZ3gRuJ5QeEAphPgf8GPrRPapZuv2kzQBLSwetONhrORyw59hPCRZSEGquIijyWf0dza4qi0l/YZvP+uqvV6zaclAXyS5R9FOB1aoqCzRN9CZYVHetqsqvgBSCXtE8gr3plxCUDNwBoKryU2BXdx9VlQVAwf5cY8hA0lxgFoovENH1UW9TeqJHGh88e1UyXPwmGE0hT5zQ/bYzgWq/OwYgWuomAdSUh5vjVibFm4/ZUrN9ZVpEZIftPlIqA2w3BkpLFCXOD+hCohhl8ffHxMalVngC3bZ5I0Pd/0ze2tRRwCkEW6JCMGI7UdNEgqpKfeZXEZfPrUy6AsKHglQAz5TZqVPOjePehPTy0iJTO2xlFStKk339Omy1sLqn66lSTbynqnI7MDKJoktAD0RQfUGn0i2VkbV1cSCQuqRci88XiJNMpZZ/Za5ZvQlgbu7ii3SMyCacZJlrVu9ccXpGsbLTNGHJtMTJ/Z8o+4ut4YxfolureuEN33JcW6775NyJogND522jk8uP6c6G29J3br+oMC6p74DN+bVKrbUEELX4gikVAnSps9JQyNAWjjE696nHgh2MoSSr3zdtWV9rOCQMVuAsgsnGyGBPd1XTxHJVle6WjFUAk6lmnM8XYagXGqb1FZZ3E5TQOLEVY88BAkmrbjmmFWPbjLfW9gDInTRw+WmaSCb4RVpKI+Hflih4wyqiTIY/gM4SOUY4qlfutwUfxugYhgJsqur+4cFey35gWGMPUF9dsj7HnmM/A+Rd136c9vNL5xXNU1UpNU0MJfTDfLRTUte+DsAdCPsnh7UZpn6ScswyZ/R4MF8ZqpIm1LZ0FohJX1y84W+VxQs9eCUDUlVlqaaJKOBt4B1VlR8B0QQLnG4gKH1UDCwAKkJTLAMiVVXWAoQKbv/uLn8j6q+lRNRLYR3xHtYXJn+ZbAofsh5EBEhv0LAUPhDDGgvfD8j98HOBP708o+T5Wrrf37XY9zhwyxaD8mSuzfcsSDMIfF0j2wdMCr22578n6DyWUGvT6i1D72PQQ0vqC7bnViZtL/FZluc5o+9p9163q4p9GR3kLt1UAUhLqc/80tLSExaWWjMGE9BhVd0d39tG7hyPRsAgtwH1ofhZRXS6F5TEWmJs/Z0fLhEwJKTB6pGK7C8N8tPeK/7cVSztxToSqFjCsNk0QSDW/7qpwOYImxerAt/tuT1sx4BNAJHFw+MLsxcObK205g+cdVUZaclD+Pz9D7Pu3uFwOC6zWCr/I6Uh3dD9tEir10OP4kJnbPzWX8rLe9wuBWZFioVSSoMET7lS02QLXICU3DwDZN0CMJqc4fvSgrUpDgmDNVzx96vTDYAIAD4D+gJgqaaJT/fUnGwMny88JfhKBthdI60lRgBprRnot5RNRCorM+4fs7Pl0W3j5aknjYOEESBunjD9t10yHKEv41j28u/UcbN5qkB03p7om5c02bnP/YCPFBJtxVfUeKM3rHnogrZ0TTskqTdWNU1EqqqsabgtTPHfpEtxwWaP7WN7jj0uf1y+rqqy18FZ6aFHj9h8158VdtpHbup2sNdyqGHPsQuCP76TFRIu0IPOgF+AY4OeKOEDnssfl3/AjFVNE8YG7YjvAv4MGaQKsIWgkZlNML8/nf9FGkoIetVWAoTyTne5L0NzHtTuZklG9+jtfgv1v3kcoAY0hxIzJs7vDtavvTXpptiuX9xXsf6cb2gmhzUlNy8Jug9XCLxuxj0lgir3wDVyBnBLf58Mz7UxDIQaSLb9oXcO/9i6vcL0U0HKJWVhno9Pd5r/sCC+Cc4bjEqf8FavS7wy8alQjHpoud9YNyKqDK9U3sitSbgIpEWAssodxR8+6/GBNBOGikpMMQtMS/zm4eP9IAW/A9cDt6qqLL8+96sdQKJR9/tv2TinaynR6O1dZbrTsM5Ybh7k7eTcpe88OXeiwcy484z4tY1Zg5tMy7Muj3wMuEVxGy6kEYPVWp3ZB0BI42nA4MLshcNaMlpTcvMiIO0+kHmDNuXd7Vhw75cgzvB4oqmwhbM5PoUTvbm/3HPHoyc13O+n/8z+fodSPWC5sWCUw+Fo9hgGfM8FMGHEW/Rq1lPzmhu7txx0g3XqJynHeGT8Gakm99riYAhTe7rjqjxgDtBiBanD4egAypkE84yWAlpLF7YeVZVOggnxzbL2sSeODfMMiC3r+UxsgTa2n6rK/SYy/todxyu+usgXTWFut89p/UuIeq97wDuiRwrEowSvywFTNTjcOOcJh63Sc0yP3gnLlsM+qZ4cMmiaOBd4R9NEH1WVu1I+frl8dcnlH3V4Os8ZPRWYCLxw0BZ5CPLN7dmBztmf4g2YR2Rkzx14NOUQNoU9xx7V11b9RKzB+q+KgDkKREWqyfVxD1vdi8+MLZ4XDGULlVCu3/46rqaJ0wCTqsovQ+9/JfjdfGloyOUEpbA+CqXCTCBUKBvynu1KCAzlJ+5V7/i/gzGzOp9V5g87oaPZ9edmb1gO+/laHmrMmDh/oCm8eBoi8Qyk0Yk0DLnklmfqO/U1ed5RlN9RTbxFImavp+/4fiz8LKGm92aDucpnjV0/peC+W7sAi1Jy814GSLEWDawwWK9ca7Zds95Sd/Kp6d8Y4X+GsIBTZP0rCEiUx0bHbt8I5ObWJLwCQh0dU6J/ueHCE73x5WOl1Yqt9h3MiT/wM/L0vGIz0lhXSSiXGcCg691R4MwdPxiNpf52AO6O7iVhP8aeIZFY1odlr+6Z+V3mmtWLqog/34st8mQ+KWwu9zRzzWrnqj49PkcXl67q3WOrCCjf7qHJqkokIhiNbpVmbwarXy4gM33E1tz/1hV3rm9AJEGIFenddSF13WSuu2jP/VKtrvZpgei6c1qwqSblXpcc4JpJAH7MfZobuy8cdIN1XnXCVTrombbaSd9esr5h9f2dTe7UgJiYgg8rKzsooEx1OBytyp/SNKEQFH9+TVVli/piYeUDhgI4E36bR/Dpfb9RuTH1UsAW22Xbw+P/b9kuwzQU9ppFMMz1eVvm3PKadXg7g/kLRedPRYorkyY7D4hm7OHI72X9rwRYX5E55yAvZX+yGHgd+EsVdp4z+hbADjzS/61eXzzWYXV/4BLg/KO9CCsje+5AMFDqbNcemJeRPXfY0Wq0Tpmdeu6CmviRIC5d4YoKTzG5ndH4bqgKmF77+pL1rvpxIcOqzddI00Q7IEVV5bLQ+0eBdqoqLwsN+Q9B/ckvQ+8/ABrK9vSu97YCqKo8LPWT7Tl2C4Q/akAW9bTVnvrFxRv2Sdj+UCeoBCDn++pSrABC8Y6/7oXTf21pv+tzr23n57IbO7FqzSZ6DQd8eQy+9uuZ/eSrt72+zrWzeyzAhNypo+HyfysEZiw+57LFnMPijOy5b0abK+bmbj3zzm7/mW336ZaJBdNHFbul4W3gSkLpAl6pfKuqchFAPhRqmtj62YZ/ba3cSuZzvQAAIABJREFUfga2ZP82pN7O7Foe0siVht+sVoZXGO87dox/l7RVqZJmABizfT6uMrPEIIWx0JIMCBE0jHcZlN9zQQ+Q0ofp0ZbO39vFVWX5M9wqgzm3t6/umTmsgdGqgdRlMNLgFwitubnOzZ1+0lY57OIeletrOm+svslg9ewICyt7uqoq406XyWRam9zeGOF2ff7fM+8q2HNfqQRS/MbyFu2HeZy/CKA96xYvybqgsqXxe8tB1WG159hTdMS1IN5+ZmzJfABNEz1C6gAt8tBDt3Soqmo/IC5uw9rWGqsh2hFMsGiu73pDxgC/nzy6YLiqyv1msD5x4Wgr8CCwtGJDu7v22BwP9CaYq9V6HNFRaUXmV6VAKUz3Xouj6qCGvg4lggZKUHGi1hedHXp/2KOqcpuqyutVVRbuuS1/XL4ErjUgLalm9y+6JJ6gTFr4377QQ49hQW/Lbp6KowZ7jt1sz7FfPODtXnm5NQkfS7gK+MiIfuJ3l6wL//GyNc/nj8t3tThRCE0TMQ1e/0vTREOP/nQaFDIBNUDDdIIraaAtpqryiYZGaUNj9XAmyuB7BOgZQPz78TGlR7SxGkIN5uqGvJq6uXNrdvqMKy93Eim6kf+iAd/VAv3jkqx+JQCe6oxZui8ydcbE+dF/cOKTNurEaN7e1aa1YPqoxUPSv01vH7nxBZ9uGQmsPuvx+58c6u37K8GuTfcAwxp6tTtlf37ylV9/8frsdVfQKz5vU1iqPypFrncaZFUAJAbQT3C7oUHx3aTcyan1r7N2LsZZZkHYAmvNBWE3AG7Az+7pHqNA/PLfrL8ahntiXhdW1IgXFYD06YMXBcw7JwkEAvF8fTqAw+EY6HA47ggWoYcO+MYsscHdb5YSgAFr1pqB293uuNSbb37zIWDY6p4JmwMGA+G28ntoBEttp+3W6h5aY9vqScnNy6gmrhPAAOYPb+nc9oWD6mHtbq19f6073ALioQYf3wr8S9NEu/qk+Kbw+SJuARnw+y1tEtUPSZn0pBUG+4qXr+kZy6WnuGNWvd9WCYmWiOlc/G7lxtQOQtGvmvr+l7s9xaiq3AH0aSBO3TKO6EHAawZdpIMc0WGCe+F+XfDhjxqUUdn9yfdgLmh/ommiFxCjqnI3zcf8cfmbrvpv+7m/1cWMuXVrr7qlV6zc/9nwhyEpYVu3lTjbA1JvY+77Yc1Jb2dmdLXWvWMSkX19Uol06cqGY8OqZicaPbc/MbZ0fWvm0DTRFxgNPBIK0d8L3KVpIiyUV92dYPGsQVVlAHiaYBQAAFWVuzW2aE2nwcOdKbNTx9YG4m9MNbl//vaSdV8f7PX8TWgEW4srILy04v9YsAe9bQow30n4CQFM4afzXi7sKopfArDgeN91m+nRLYnCx1/OemY3Qf9nrn7XBUzOyJ77dLip5v38HcffXFjbYXSN+xJHpLmyU5KtuGNG9tyhQK8oc8UoSUx0aNdAvmngzT4RNydRFD7WzVIXWOeJyM7we6p1hCnjKveuKOg8eX4qAoy6H4PH/5Sr0nSz55jasmPfL1y0umfmUEL5uZlrVi+6JvfGTLhyQApbn21NMZLQxTzgPoI/Vn/5bjJ6E14BphBsHoPD4RgOzA3+vuFxOBzDgJKo5PazS609Ow4pXOTuHLvktEmTPvmxfo6Zp45ZbsKdmMHqgsVZFzellBLL/4oVm6K+qGzajKwX96lZQ0scNA/riW9npmx0hw3pYa3blD8uv2Ee6U3AqJaM1Rkzzu8B8hoQOVOnvr66tcfVNGHQNKGoqpShL9JmCdtx4hiBwo7Mpy/WNHFda4/TEk9cODquekvi6PDkirKp73/ZZCOCVodtHdEDJfIHoCegG3Th3E9LPZLQgs0PJRxhhQ4NUkieaGz7b3Ux5wNLvFJ5yp5jb21k4YimV/yKKICksOKvgSM6HeC2OcmGCz7IGG/PsX9apxs3rHBGnZxqcu8ATgfR/a0LtpzX0FjVNBGnaeJMTRORoffnaJrYommivkh1AEFpqPr33xKUUDMBqKp8UFVlr/rvWFWVy1T16Gw3CmDPsRu0mvj/GIV09rbVXHmw1/N3MXnm0EWW2HW/gg7ow1sj5N+feY8A7QSBR35k1HFh1GwIp/qV+u1x3easkcCf7azZQNF20u9taq6C6aPWDe/wef+BqbkvVbgTUoB3a7wxV2+oyryWYHTzZL9ucvE/XXWpx5qvBCgj/c11nvDPQMr1RnPs1SmJ4SE5MgCOq/v9LYBBVcu/2zA7da7QBcYS87MQ1FTNXLP64fowfiFdLgPBcSxoMR2ifv9AhH+BVHS/VOSwPXJYSZ8+WLrD1/+wXVSd8sLTV5aA/hVgDsl52RTFM98vWP9bxx79In3VVUUxSdENjdUQl/mwRhWQOb6xNax4ZbyQwp/ojlod19Q6R+S+0DDv9anWnNu+cNAMVqdunOZH4NWV8xt+rqqyVlVli55BXTe9D9ISHl7S6A90M4wBNmma6NKawdbq7idJ9EJP1LrHCIpK7y/+o/uNRm+ddeSeGzRNKJomlmmaaPRGaoKhBCW9IPhUpu6HNR5RFEwftUiiLARRzBFmoIQebMYBZze2PX9cfgD4t5AybphevWPDW5Y9U1COOuZvHVUNIKW4+Ui6Fxpiz7HH2XPsUxfWxO1Y4458DeRA4JFYg6/rrakbT36u4x8/h5q0ZGqa+FTTRL1s3wBgLkEBfYBtBB/w6qNy7wMRqiq3AKiqXKSq8slQIes//JUpEnGCVyr/fmpsSYuFvkcSBoNbKianb/LM4Xt2+/oLKbl5ho30+ncaG5yAF0QfJ5EPNxSgv3jaswU/2WWgNCw6qlNV2a8lWf2aveeeufpd+f6Nj08E8RxByxmQukFxPnTycZOGy9QPXrKm/lcabJsk4ItMrRtkxFMLRAcLDIVECPxC/O931REdpUWovQGKoiL/QzD8qhuLLY16zpcxpAvIEht1rda/9vaqqxa6YnIO37mbDOFDD906/MEHb5v/XmDTWZ9ZfqO8qkOyzbZzVfB6SQkCXbdYf2/fQ6mxRXBK9YK3fzrrvN3qGx7OHaHYqL0HZB5NOG4MnrhoIY2K31Zqbmx7Sm6eWMGg9wFGMOuOkqx+B7wm4qCkBFz5UftOED0ZxHufXbxhOQRleYAPAUdLIvkOhyMFuvWOjNy2ZNq0V1vtXQ1RRrCCtKClgauevSs6khEjBMrrapZ+WxuP0yQ599n7Q4cbQORMeW3e8kaGRBN0s7cledkgEEhkQBxF4c29wAlsPRINFFWVjd1Lu8jftCX82dgowysxMZxf7HqgiyO6JzADR9URdy1aSQagl7lSN7U08HBhdc/MgYC6YJjXN29I4BqI6ADCokP+xXFF7l62mkvHDPfND4nprydYgPc+wXy7zkC9N2URQWmoPABVlUto0GNRVeUBDf0dSdz8ScpgI/GPG4XU3NJw1DXscO6wF9H6xgvnlJMansGa6zqzeuZmutf5Mb/fcMCZryyetCwzzoCUbA1PGHv2zMVXfzbxpFfsOfaBZqGP8UolHyg+NqxqmEcqiatckSVAUudehjNK6tKEMDgRxjpFGFx3rnBF32mMCD4/GKPz/LXGa1+oNA2cFjrUvNqYS2+MqHxHIsGIlN5QgdNOY9SD9evpa/hhqz/Je7lSZ9jce+na6j1PKCU3zwiMBPHJjKyZLUZ16zGvDXsHOCf82/hEQmpGwfxU27fBrltS9vC329bf36X8HblwIugLQ/n4VFtsclmH7qLz9kIy1njKOX/3uf9gQLaLiPan8umnH2Td26ihGV14TgRARKk6t7HtEVROqyWYtv5W1vTprT2vfeGgGKw+qcwSYEs1uR9v8HFXIJNdT0DNcgsoxpqa9EtbHro7qip/INhWsEWkwXWrQNiq2n+yeb02ROyvqmpPVfgHwqCbwxKq/6+JNVYArc7LLXjDKjpiPksgtgjES0DuUWyENEuirbibLpXW5wUfZoTyWB8CrlNVWbzHZvWayir9u7Bw5YH4eD4pKr40TMpzcUQPOxrvl/SIgqE7XEmVax66YL93rvs7qW8reflCn3OU4AmkMJy0wExuGhzXrap4mTPmjEfar9lAsMipNzAf2EBQWH8JgKrKdQTVJAi9r+IQloY6XLDn2EWEEvuUIqQYHLnz9ifHlhyNyhxxQHlLgybnThRwbTaI9VXEfVNF/AvH8cOSL7Ju3s2DuinZNC1klxFQJHVWcZ49x/6HQP7olWJX1Hi5sz4tFR0o9yluY0J44Y4drpQSAnJtZvh2ZbvPkrYzYD4BUITQ8SckZoVqHABMrqjTE3rUvVpQ4TF3mV628/us20oWbZ8RNvLH2JGT6wfdcN38bghjJ19Hd6Oe85HMuvobLorpwfJlbRHTN1Safg+97A7Ue6fH1hulQMAjfEutmM8KUwKPOXVDfYQ1sKhLH0UgGbhxJSaTc8iec8/jvKFm3OUJlGQ3s4TY0L9/0Z6/LndSZC3XPhZ627PVJ7WP/O0pAfYce2K+K7Jve7Pr928uWVf/B6n3DnUJPck3yeuvD+slRGCKEP4PHQ5Hm0Irmia6h7qitIrw0sGZuuLx7uj5/OXsXuG61zxx4ehja4oSOoUnVc2Z+OxPjRY4aJpo1AXfFEY//xaIE6qi/B/gqPq/o9H4aC0mgzcuwlwT0fLIwxYfMJDgw9+eaFYpPPft2Mk2k5HnYqORSDNHafqIEHpmcvi2w7rLVchYnQfyoTpFeVpIYVAAkx/9po1VS69KLJyQPy7/91BNQJiqyucAQl0En1dV2aoiq3/Ya8bV6sbj/VK54cmxJa3KXzzSMFh3nGiwVHQLSlw1TR3RU0H078ianAqSrtIxyC10261f7YTcqedWRIjOQpcIXWLQIdwtPwbulcGuWRAs8nqlX1jVqcOjyroD5vxx+UkLL1sT98MVvyetuvabviuv1s7/6MKCc3cGzDcR1Dn3Az7dkFBfYLSrwr/IZ9uY4fMHhridm7bPCFPCnMpHn6QMVQAsXs9mIcX3QleEaZMtIxTh2I21HHOqgl/24re22hCbpJC6t7OzYZpXZugc/SB8XlPFG0XKDpy6YVCweFT6t8QmBjYlponjK5eui/ZVfuz1RmU5HI5douMpuXnHgBjmxfpYc0VSNanfHwdQl/jzXxRlNMa8AxBD2c6SrH5/7rn9QHEwclinSYRlizdsV7KupomUUCFUi+7y6ur2T0ipmNLTf8nZi2O/CuS2ZmBh9kKD0Rs/WNEts6XB9wTw5l4cbzeeuHC0AB4DymuL465sbIymCRNQrGmi1SkISdtN5wUU6auIDbQ1n/eoY1ttx02bq7s2+1B0OBPylKWrqvxrIV/wQWZYX58rcGF1De9GRbLCYlY4yK1al0xLDF95fPeU1T0zL1rdM/O5xr70DwRbazrXba3u9MXfcawDiApYQYgaW6i5JFJXwBPzY/QNqiq/2jUw2ITkH/4mpn2S0tuA/jzIH3XEX5rCHA3MmDh/YMAdHxXwxMQD85ozWn9m5EURVHn6sOS1YEE1c1dkjd5lDKXk5hkWMfKpCKq5aGG1fvK6qm3HbfBcszPiys7ASIKGqp+gHvUbb1+w5YenxpasC+XvN0pI2mqX1FWs0T0wlpKN9e9LsvotAtm+nd9vKI/390wqM13l90RH5sYGG0KlVpYYCEWqRbC9vLrnMTaR2UvHuODFrBlb23LtMtes9gfifX5EUB7htddOywJ9lKL4ZtevT2m3kO/MK4iWtgBSjPQajPd92/vEYgO+zZ3ifz1B1y0XA4uFCOS89tppQwF6sPxtA34P8HLzK5BpAD5bsbvhpym5eYk7ST4bYAhfnNTYngeKvzUlYNonyd0NJN4M8r9541augV3VzZ8S/NEc1dz+DocjATIGG42uzyZM+G5vZEFuI5gf2iLesC23mZ0dEiX6SlWVe2Mc/4XEPgV3lP2RMcxg9WbflPNtUy0NrcBMWtHlCwBHdAczynDgqYyr3EeDrt++YmFvOocdRqiq9EtH1KkBA2cbA+K/u3ncHVWLKp8LM0yqLkcLs+FIiBcfFJU8Y3ZE/9GUZ74+L5KQREtjY/4Y1NVi2GmK9XZxtvd1dB9rXRa501BpsvraeXoEUj2DzKvC1ykugzUQ7eumRwV6G7dZikVAREpFJkboCXtGFCbsIZS938nInmsC0nQMG1scfGijBQstEIlV+CVSwSgfxi/mHsjr9w8ts8Yd/hEQPiii4t6Xzis6Wpu3qA1D7DQhJZiSm3c8xJ0A3O4k4i4QSXYWz94jhH7DTpI79Cktf7hribijawmTfxp64RWbXFFjIxT/3FrdOJ1g8VObOofVN8M4MfeDzM2khg3km18/yTr9YYDb5iQLIwldU/wBIlzKn8Cj76ecudYvTN0B4q1FQiJ9AmGQSLmniP9xuZ90hk52gnKdbcaw0/StcYe5k8PhGGgy9X1DCF2kpy++f/z4eb87HI6ztm07/kOD4tPPcA4yRmA99+pjpclvMHYERj+d9Vo1WfDss5dMqK5u/0dZWa+PTvz8s/5bI+x9juWnFXOzbmxWriqy+LRtADFbztstMhBJRX5NMFvgzZeznvlbCwj/VoN1kyfsKR3MQ6PK9zQAn6KRLj17IkRgmpSGML/fdsfeHF9V5eLWjCvMXjjQRPqDMih1cde66S8s65Z93Zct7dccT1w42mC0pU42Rbh8CT22Pt/UuFA/+FZ1+QKoDQ88Gl6nCIFocs5/+B+R5sq0aHPlkV0w4ogeCGiGAEjkJLFHjmp8ufERgbj97h07uT4liYfiYy1ORbn36xz7fXt+0a/umTlQCrkAiVEgAiv7df/Zn+bpYSyybFPcBrM06skIEgw+EwDmDWGYN4Tt2t+0zYJpmwUp5ElAufAoXkRASpO+RQQMRXqsT/FluNuZf4/wK35lJMGozwHXyD01/etjFxSernSJXuNr4Tn5kCZ/XP6iQW/31E1CVo9cppQKxPbMP9Yc9QoQBxt7jv0cCMtsZ3K9+NJ5RU3KFh4FaARD6yaQfproypRA8ZM7SKkCMXMxw3+MZkdVd/Leqt8+PveWkwWX/J9E+fLC39frXrrxfr8Hb6pyRZ2aYXau7GBxnTvj3G1eYE/pplazmR4qwCJG7pLJqvCb0v0oxlR/ALNXuUwiLU91HPcnUAAyfX1U9zkovC+l/NGX7sk75rtNu31ndSP/vm10YgDzfm1L/mo9QhdrdiTEj0DK+T5fpBWkf8uWwWGh5gBzdN2kSISvVrgVp9k66c/wCDp7izf+PPKMXYVSU6a8t+q55y66uby859NGp/KRHmGUW+lyYSsOX7/grsBGgP65H51QQ7f6ZkaT2nxC+8jflhJgz7HHr/NEDLEq+tynx5bsupghPdRZqipnN7f/668P7awo/tttth2LHQ7HqrYcW9OE0DQxtbVSVsBoEEqotZrZmfjLJ6F2rvvCZX6XpZ3fZb7yktvWNmkwaZro3NpjFc+0xVrdyr+qogNFOKqOeOHt/YFA2iLM1Ud6lycVkKH718IeYSopWAMwxOXiRKdLzo6MUL4ODzsNmNdQZ3B1z8xB0qB/IKQwhTquGIVH6afUGqIwSIC10iLne3rVLfEnep8CJgfivdc6T624y5fuHg708Cd5U+uG7rT1Wr0mPHPN6g6989Z27fvtptjeeWtHZq5ZPb7PTxuuPPbdohGKX3mABrlkHGCVC10ajgfoFL3usH54sefYTTW6UbSvDsy3eenhb+f5veW9/uFAcuPs1DSQLwIrtvlsNx7s9RxMJs8cusgUVnodQFhS3o+N6bCOy71dLSdpyPFoK4BuLiLtVSTcW19Rn5KbJ1Zz3Mdm3NYu/HGT21JxhctYQ5W17FQgUOANuzZkrO4TgsAZBNV5dnkNF9fFxgGk+P0YA8L2R7uYtTXGsJOi2fEbCGMVCWt6rVrzk5Bim3mr1bRnOtPvDOwUSUVNe9btVRMfb1end3tikplg5JWQt1oFfTRIBQRSYihRKuRz3a34FEH2Ku+8Pee54YZZz7jNyoytsUnHJbm2r2qYatEYhdkLB0r0GyQSiZxTmL1wIMBWui0BGMRXH5dk9XM3N8eB4G/zsCrIW3REuEs33F7/maaJzgTzR95WVdnsyRcXH3dDIGAR8fFLW+zD2whdCeaO1hCsjm0Wiewv/pe87ZeK5/9UVe51SOe9R3vECqXLI1JXlshA07ImmiYswEqC7UNvb2pcPakl5osA4TXLFsf+Q5Bqb2xV9c7YvX4KP0zQBMINWAVCkUh3Q1kEp02/JsIZrDXq4/Hov9isBoTY5dn8bXJyD2texM0GzH1FQKmQQgaQSIHwCSlOty/YuN89n5lrVi9a3TNzGC2kHuwvFhad5gbI3Xr6nAN5nL+BjiAMg5cJA4CnV22rokj/cOAo8Vm+FZAaZ/T+S7v0z8NagWJ/4HMmv2a07nzcW90htrHt33DRxQLpa8/GScVkPL2NDCeIhlHYyzfRK/kYfnrxm6zJ6x6d81FEecTW+kwDCQxhHxUtJudOCjdz+VkZrPlpQdb4hkoO7QFSAgF0ZEV259tyJUrP7vy+YQnDSGODc3XPiwcCKQRbvs+rT2cKduxKPBZ4s6GObGvQNCFUVUppkNak7dsxBAIEFAWECCCEFhNTcHtlZWdABkD4y6KTxVftTOYJGzwcVxr3XWNzbjjJkOwVZk5Zmd/ngaXznw8ErO86HI6mvmcvbOC0MwHqibkfdIUeAKSyuTUe2v3O3+JhnfZJclej0G9PMnoW54/LX9lg00XA80BME7sC4HA4Yny+iPHAx//+97dt/oGpL0QhqDfYLH+8cPOdAnFawFD7PnC3QMnqd9Ws+9t6zIa4q8LflrqSHJ1R8ty0D75ozvBVgGuBD1qc1BGtEGzN9ltSmemo0/bbB474HNb64irgXr8ii/xG+djm163B8I4jelC4U+kHIBCoLjeGoFfT36lE+l9+xn9m+Ly4N4TL0Ecq8g6gvZBisEDcAxzQvNI9u8McYDKA/2fvvMOjqtI//jl3enpPSAKEJgkwEEFEFPQGUFRA14odZS0olt+K68Y+ukVcZdeGDVvsru6KBTvkKiooqIEgoRN6Qkiv0+75/TETDBCSmRAMwfk8jw/Jueec+2ZMZt77nvd9v7pXmrb/Bvc6bPSz1J8EMKjQECGRZYYKU7vvcSEOH/Y8+ymrmyIHDbDWa9qla4/2B+OAmPnMOOlpipvnaYofOnfGon1qSFLyC3qAuFKiPF9NXF0ZqROy+XZzSU52FcDo/DfS8KUMfreCk26cO2ORLcwZG10Wvg2fs9Y5pzFLOC3HSRhJ7Nwn9e9UWT0doIfHg4DwDfqwE0H+EkN5E8BQX9aSKpHNvlRzOhN9+eUcIAwIKp1Q08QU4AdNE2HmtWHWhPJy1HwN+6pV+vE//PCSw+FY0tgYH2swOJuAezwWfeJjxxqaot2e+is3uWp1Q6Nj5bw/RrbcMyW/wLBUTBjdw72tJqm6Bq/XOhNY6E8t2IftuYutwGR/X3cP4K4zOb/ZwsDmFI0zgukn25n8JhHWJXVxN7mkQQwJq9w/Ovog8F9VlSVtrY+K2vZkTU3PKOCvbc1ri1Z6Uh7A9tzFYVGG029yW3c11qZ9dl3FgBfNQOYGDWt7EeCDMWfq5HjoMdYSVb/i6oeWv9qOjY3AK23NaWZnD9ftqbvMmU6zfq3lztrfY2+/DmEQ7og+0Rv6dOe8xYDwOa1Ldj1v/Tlth/n99O3mV1x/jyw3IU7xH+9Tv8fkTSs1yX9Y6v+rVBvO6L3eFAUM8UZ5ZjeOqXp85L/Kmv9mlnAY80m7goyo9WeUNvSoK/rb1EM+SuxK4oyuSZuawoirZbhALDj2jd9tcU+XY8+z24B5wOZ1TRGTu9qeI4z3gdsUY13e3BmLHmpODcjmmzcLONEEyiP5nDMVIILqW5sX6SjLgTiB/sSunOH6K9/ef0bt9rFKWcQ2QPwD+CSYAquDsZM+YwDPYiY/0XLc4BaDjSZJvFdnhyVJqTZFDgPuWcQ5ewBWMeorWJCEwIvEIJGe5sKrWPb8xYSTk/nw2/byV/3F5wZVlR6gEl9NT5JAbAFIKC/3JpSXu4BXHA6HBaKH+79+8Pj8//y1hISoCbzziIjstVapHjTPUp35Ob72hs2c5caSlrqj/nUQF+MLjjW3NNzn9WuIX/5xWPlx/YDbBMIMaGeNU/Z+YJbkZHek4L1TOOwRVnuePa5WN14FvPvYObv2RkebQ96qKtvMpfjrX3NPr61NuSQ8vGSPw+EIOj9L08Q4TRNvaJpICWD6fYo3LNnYlHTG0GteqAVOxZfEPTDY+7bgLhARzprwdkUONE1M0DSRGMimMVWGK1wm3burh/uNQ7Dtd0VG7gKhS4Mh3FRr7Wpbfit6Xt30kZDMNujCbnYrKv6DtIY9Ju/WRQlKWWGUMWO5ZWrPLUqkq2/jG0DGkB/W39HCWT0qcenmngm23Z6utuNQWdkQVd5/l3Qruoh1DawPVvUvRCdit9V8DgwQyGsLpxV269zozkeXINE94Wfjb2+Vkl8Qs5rjThrM8q3AFuAG4Kt3c+76HCAl/6fbtpCZAiBRXkrJLxitGBunAlSFb60qnFZ4b2c4qwAmnGeD/KYkJ7u25fjPStjmJI9XKuCZnzReAPRl1fteTCmA3MaArVlripYIKW4DEIhbm0+IVnJCbAx7Vrye87c2FSs1TVjxRYnvAlBV+Q0wRlVlsdNe1xtAmvR/4z/hiojY+QcgAvgwJb8gcSvH3GjAnR9N+e3977ju+caYVQvD95xwwvbcxVOa7xFH6T8VvFuGbd/wtK/4DXx9W/eNTm++/63JtvLhOfUJS1elzx47J3322AePmxi5qoa4uwAm8Wqr0t+/FYfdYc0wNzwJRAJ7j9X9uZo/apq46KAL8cmQeb3WD6Q0ifr65KjWwtcB0BOfLnabvzSrnr7pPIl+m0S+2HO2+pV/+AsR2u34AAAgAElEQVRgChBUkVczrz2YNRah/5/R5nx31tsf/dLWXE0T4cAnwK1tzQPAET0orNGQZfSIBzKuagq9MQaOUaKwouz4gJTOjhYEoo59FeT0+lKLjo4QCARCF17x4LCPiy/NWlN0sHZrRxU763q7ttX2bVVysDvhlIb0Uev1GonE1a/xva625/fKsLwhx65qjDwp01q7fuW0VV92tT1HHsrJgKRFTiRwvQurcSsDzs/hvXuA3vGUvAiQkl8QBcrDLTYwCvSc6uIJ4R6lid7e8E4T/Lg+f2a2G0vmCXxRt/+1UqPRVqcoK4B7n+t17u5ktpZ/l3NZYRobR5toqinJyW7OUX7X/6/w2z/AjaV3GWnzDnZfTRPR4BPxAFbhc9rxj/lOTXUypJDUn1Zxd7MjbLHU3akobpKSCr+KZ9dzICO8mGY258naqoZMBgok8qU1//r7wPPz/35BBcn9T+KT5f+45+5vIyJKrgeIitq2oGUO69Y7F55hakx7DeQOZ/Tacc3jvVnb/HlZ8ELOnA+DeW07m8PqsF7wdkbyLrflon6W+u2F0woLW1yKB0poRfJrP1R8v9yAUOiAIo+/h+qAto70t+cuNoSVjX5KN9WI6l7vPdBibbmqyo9UVXYocb5yY4/7hSJJyNwWiM5uEz7t7ufbm+hV5G2AU5G/z2bUh4DF/+/RncN6IBr+CnyBcALPeZ3KTHxfewCn8CrdvYF+wPh7sKYTuL75EYtNeIdnb8AoEMtH/qssFGHtAux5dqOOeF4idjfqhpO62p4jFM3/fgNAbVz1coH+J+DT9Tk5y1cz4qIY9nhV3n8HoAfFP/66VHqNuMT1u18eLxFnGnQLx6+cFdaeclagLOS8UQBWGp7d/1qE4hkRaXW6Uk756t3dxuQepfR6ECCcumOT2GFqMXWHNOlu58D6qwAGsOJ6ACOuT/bfE0DTxJXAVk0TqQCqKmeqqnx5/3mWXyJ2CSlKRs4pcwI4HA5RUdE3wWyuLViadczZFST/YRDLvyrJyd77t58+e2wTcJFUXNGmuozlZTUT7zXqekMCO2cCjCufsgCkjG3snbo9d/Ej23MXLyi+e0G90E0fA9ECQ0Lcxmn9Afrkf5OxhYHN+QxnBvGyHhYOq8O6pinyBqc0iASj69qW46oqd6qqPFNV5edtrY+M3FHj+0rqdCC52h9q//Vp5eDMMDf0THKH7bh9yA3/3uJfKzRNXK5poncw92xmztTJI1y1YTlSFw9emrvm5/bmq6r0qqr8XlVlm10Mtrxo7Q9cVRPp/RFHdVlHbPu9ovb8JB5gcPzPfbvalt+UX4uw7gVycFRfn/LBtnlAjn/ssBZTHWmM7/XhcYCSFbeiK5T+Oo3b5yebTA2kppfJ6K2JFLa/IsTh4Bhr3fPAcODGjy7eGHpPbgV/zuo4hKcIPBQNaHpCoiQO49uXU/ILMkvpNbCW6Afm5jzdmJ3//rW7yOjvX3oxiHtu4J53ehWljRUIIRAoUhF0kqR0DXEqUKLxh31OXC5/p5fSoBvCoo2e+jhKrvMPvw2wjqE7S0nf26oqa02R9Ma5Kw3lpgQAHeWKZLa5tuccv1eYRNNEhKaJeP+3XwOv0X7/+R7AzhbfD5bSmFrvjH/mcy682YSzeiAF0/ZflD577NqmmFUfleu9I9ZHGoZcvMVt+/PXJ6/dnru4LElGl9ikRVgaexwPzATShJDl+DouAOxV7GrC9hXAYH4oLMnJ7vI0scP2hj3ylUExwC3Ae8+fv33vU4amiX6aJuIC2cNgcNoBzOa6F4DxbbRgOBj/0zTxTlsTVj+RO1giHwS+sFYPbilt2gtfAdQZQd6TNx4+RhisrudA7kEqDwWyRtPEtZomhrY3L2276Q6DLtCF/CBYu37vGIQ3EiDMVNet9eM7hKN6CY7qB1sKCPzGVflHDLWu6CyAlPAdXSpJe6h8Wp04OWuLQJGC5ycql7bsoRvit2Hym/2GbXKGTetjqd8G/Ler7TmSmfnMuCW2+NV5WxOsxq96DByYUO2Rk945ZquZxv8D3F5Mz56T/9DAEno/CxBL6cSSnOy3SnKyH4x595ab63cPQ6Kj44VO6g4wM/96kxHXZBNNi0pysvcJbBU0RCfrCPFLY+S7Jlw39mJdeUlOtr+riEj1YCluOd9Yaplv3GOOOubDxREbGRIpW3T78adBrgIeAVBVuckfVW3zPcgb5z7ek+xsPhkkMnLnjQBvjxwfDmKkC9uNT+fM3dHa2rCKET+82duEkDB1i0soXotVCu/7wCzFXFNdErWytDH254j02WOzDe7oqf5WiHv7YE/Kf/RsiaEXQG/WjQ/g5TzsHDaHNdNW918g2ia8f9/v0lzge39VXJtUVfUdAHL1nXfOubYDzirA50CbKiOmhrTPpeKO8JgrbkyfPbblL+w2IJMOvAm5Gyx3eJvMw2P6lH4w6+2Patqbr2kiEt/rck5b81x/i5xj1JXpgIypMd7nVzQKESALt06uB1hWMva7rrYlRNfxQ8nJEiB/25nd+qFPIrKzN0nqLbAujQ6lTIXoOPY8u9jiCnvUK0VtP0vDWYXTCkPdWtphScRxGa+pkTRZFCojDGJlX8MtBrzX9mH1SmDwEiauAejN2jeKcibuPYGVXusz6GZDRa+PS5b1+pjGlO8vb02EIFgaCbvMgzliLAtacxx7ATgjJhhK6WWJYc+bAFfn/ykCSOzJhv3n/wLEJVRVnAfCXEbqy5omRgKoqnQC/wCeC8Y+0aRE6TEeN/hqepqaIq8kvM7lsekPhVO9Enj9YGsLYpTl76ebOa3ETYqTRqMrIafng+rV6bPH/kux7qlx6QbrgL/c7AVInz225Snc+Acnvr70R1R/kbx+64s5jxwRJweHpa2VPc8eYyTyxD6W+u0fXLTpx/0u/xno3d4xvcPhiAZOBjGnrXltoary0baub89dfJaN7NT6xG//N3BW7rr91upAmx0MWmPO1MkG6HWxwewutcXV3BygnbX+LgatP0A4oiOqozzfRHuMwyQSf1uiwy5feRTye81hDbEvGfiK0Lp1D1bFKz8dtkneW5gBuqIcdnWwEPsSbXD/X7XXpErEtf8+p6Sgq+3pDiwbYMXr/5TTBaxPNV/YSISw0KCBXOirWZJsYeBeqfGX7p3zLmSfC0LGbJuYVG1/lJGDPv05CAXzg/IpF/cGKY24/73/tePCqyYvr4/BE33qFEBfyYmfABQyaipAFOU9W85vGl5TZ/0pipSwTf/YQlrjPNTBwOOaJrJVVa5QVRmUs1qUmWVSMJjMa8Pf9xecL3S7IyyLew+VjTJCnCle/8cLOXMO6kddPSp8GMDJuz1PAG/6nVIAamvTPwL2af7vv74E4MN8Y4s0A2WfVl9dyeGKsN7sQbFucYYd0AJBVWWhqsp2Czx69PjxPsAYE9MxVR1NE2M0TRzUId+eu7g5qrkqvOykA7oVaJqYpmliQgdufQWIIV6X6ca2JFj3x1/gte9TjCN6NI7ox4BVUTWGoVXRnp+ARn4j+cqjjTFpX/QDGJ70XYfykkMcHfSPWX12uKm2sXj2pG7dgzVvQbUhoRZW9BWECc+5ndXiJ0T7/Om9lGynrsyJM7jWE0ChbAgfJXFGX59xKRFIT0mUheRKj76G42Y1S1fhU5hU585YJJ6aueD+ht3HnucfF0IqSnLZ8M2qKg8Ib3YEHeNpIJa9kjO7eP9rtV5jsss8gEalx6kgBfBuSn7BNVvInAvwC8dPSMkv2HvK6U1wL5HAurDBqcewojJM1L8CXIMv8toRkvG9KDvxBacse8KjWZ3aV/SoKl/6Qs6cgwoMzcy/3mbAMwvIv+pPJ97c0ln1swOIczgctv3XpuQXWF3YXgY4hffvLsnJPmLa/3V6hPW2+ck9FRJv0xEfrJi2am+xkT+CeAfwkKrKnQffwUdVVe9jDYYmd3T01qBUIvz3GggsxpdD+3hrc+oTli4M2zMqTSAuTJ89trUuAA8AXwEBtyh5/Z8D443WXk9LXSnyukwBpxJomnio/zpLVdJu0ySvQTo/M0ek/WKy9DzTYrJlO11CIKRA3Bzzp/on/WkAKqC1zEcM0T7bazOOBahxxaR3tS0huo5aV3RstLmy27eD08O8twMU9FVokMLU3vwQnYM9zy4UEh4AvCeGV17/2Dm7QqkAgbMbIQijJj9J7FhSHJ51Z3X4r54qUuoCPOGepm9BPie9tqsVY8PXusc2UiJNuuI11sSsX+dPKbwWmK+qsrQjhlyff2MGTD8hhvLHWru+tikiqik+R/fLVjefap7Hr52LmouTlgDUnVu2Yeemofoea5LiQZmtqrKaQ3iYcWU0XmkutuFOa8oE3tkVFetdlHmcYvK4GbFl7X2+NvGtU0niw16MKaP44qHWRAtiYzdaKyv7kZq6bAS+XvN7Gcp376/kRAC+4ux/dNT+w0GnOqz2PPvoRGP0KzoicmR45Qv7XR6DT3b0yVaW7oPD4TBCwlDgrauu0jrSUqoYOJeDHJdvvXPhyDD9+JENiUt/GTjr9oM5fQPx9Y8NmMoNPe7wNFksSfbNcy+/+5eA3sQ0TUREVxmuTttpjhUIUWAw44hPwAu8FxnB8yW7GeZ0egXCZ4tfwSgYu0JARu6C0TDgboANVYOuzshd8Hrx7Emh1/F3SGlDmsSX396tsayIjPWGe3eWRxlj8X16dWmPxN8R5+mIKUDuY+fsWtjVxnQzJgI0EHWdl9LFLS9MW1jD1kSD0nu3x5BRt+czL7FW4G+6J+xe4ARn1KYrPun1/ozeKT/vwZfW8yi+KGSHpNPL6HE1KOJEPiva3/nzFTDKCzzGVAVf9qIXIdzAf0HmgDCD9Caz7XtNO/ZaYJ6qSvdlyx+rABKqSCjuiE3NFGVmjTZhvRfAuMMys8xjLvowe4xRFwYU6ZUfZo+pPdjalPwCYWDK2Egqd6ezsdXjfKu1ugLA47H2o4XDmpJfkAQnnub/dsT+hWhdTaelBPgrVBeWecz9Qcpl9bH7HG+rqnwX6KGqcn17e1mtFeOAODr4Bqyq0qmq8r3WJF+35y42Krr5WYFSonjCTmljj6YDjujbYM7UyQnO6ohrQH5w+d2/zA3C1rrsgrBH8Dd2X2a1SC+AEHiEYJnVKgUidPx/6KjsfUCTe/WeQ/y+yMhdYOQo6MFalJkVrriU4w31hjdjDa51MQb3FV1t0++BWe8lZ9gU72smoRcBHa6v+L2SxqbrLTSWAGnbGJDsG5Vy8vd1z/Ta49bHFDnpWe41ep0xVtC9wMcznxknZz4zbsnLgx/7oDSymC1O26uqKjfjEwTqsFz7N0zqIdCrbNTn7X8t1uCa6TGlK15LPyx1+bqlYemXwPiSnOx5g1j+FMAglj/9mDi7D/AscHxKfsHohcPHJiAlSPl2y3SBDqAKfCmNAqGUR8eer6MIAB1Fp+3Pr5O9mIbWEnvP3JxnvK1N2LVr+GcAu3fb90uL0l8E6MGWipKc7J8Owf7DQmfmsKqAxRc5F/u8oP6WDqiqrAxko4iI0geE8BIXt77NCv/W0DQxTNPEDX7lqANoiPvxKeBY4OYBf7mx/CB7nKNp4i+BdDJoxhpT9zTICBC5wdosEJpAuABPf5fLjRAIKTFKSYbbPR8YHzr+P2Q0wOVrNScVga51rTkhuoLTM/53PGAYlvjDAblb3QnnkLrLAbNu8X7R19Kwpcprij7/7T79210Y4pBYVh/zuFNXLCdFVNxfOK3wiMnt6w7k5L9g2UPK4CyWl+Pr/9mMd/kAK35hAb8inwAUyb6O2WCAUo91Geyth5GaJuI1TdiDsSUlv0AAp0uUL+bmPH1AEW6DbujZGHEa6C4iqt91RpU/dX9JTvYSTRMim29LAFYz8jMgDxipqvJ7gT5BIvDVRGPmEIIiUkhN+tqiSsC9LTllC0Lgj/Z6aCOAlc6Gpw24K4FX27hFcyustOaBi/PvOw2USQApbB3bUdsPJ53psGog/d683PuCappQgJ81TTgC3aiysm8Pq7Vqw803v96qQ9kOZ+PrdXbAz7b+oSczrVWDr2mKXr2TX6XUWmMi8McABAcAyLvffpKzJuz8yLTyFbPe/igotRlNE//T1JoB+FtKLEoxfQIwqqlp+QW1ddeeenvJuSFn9dDxH/+PT7Lt+hEUYqwd+dUK0d0pqU/rAxBlri7uYlMODcEMadZpGF+59OeG6HsB1jZFhFSWDiP2PPv4Sq95ikA+8sS5uw5a8BKidYoYMdpJmNhJxhaQ5+BzTj0g3P5irPH42j41K/Dtc7KYamqaYhJ6VeG0wv0VMv8H/FfTRMD9tc/g9bOA1F6s27+LEfY8e3qjiDzRFXGSHKJ/V39eVPkthdMKlyxc1G+6V+9zxyBW3uifalVV6VFVuRxAomxHCISuo0jZplPZHg0TKmIEQnhj3N/WR5gnVve0XBbuqak9fvNqTt713X0lOdmt+gQ5+S+M2E7/rJHkryzJyW5s4xY1iuLxxMRsmtI8sIycBQAK3hc+ybmpQ3L0h5tOc1gLpxUuOcZS/zlAL3PjTS0qVq34jvbbVXsCcDgc/bxeS6/Gxvh2c11bQ1XlA8BAVZX75Hhsz10sbJXDHhG6ubExduV5+/Vc3X+PGUC7Tfyb2bO6101SCmd4UtVVwdiqaSIMSAAimxu7f2MMjwtTPE1ZPctG/eXmLQfVIQ4RPMWzJy3Z3Zg6AWiqbEq8rKvtCfHbU1A2ygSweMeprUomdhfMq8LDpUn/duS/yqp1RCGwG+hIV5MQAfDn+cnxFuF9HeR6L8q9XW1PN2UiwG56npnArhJ8iaP34jtqXzLzmXFLZj4z7npaKPC17LXapIvjwhSPuRWBjFuBi1RVtnr83Rrb6H82wLF8c0BRdU9z46O2aNWgC4u41DA//JRI95MLF/UuBF4A8VeLDE8CGMp3g/dbagS4YOECbnvz6byDOZWBYP4lfCxA0/E1d1z58IOD95BqO6P+vTXDt63n1IaP6g62rogRV4N0mnBOb2t/h8MhTaaGJo/HFgvQI//H0+uIMQJYaJjVUbsPN51adLXZFbYQmFTnNc5vHlNV2QD8JdA9wsNLr66vT4ZDKCBQVblt/zGPZc/lRmfCJIEyyz5j7tIA9mgK5F5zpk4eCWIqkgcuzV2zIkg7G4CTm7+359mNYLEDb946qVoPZq8QgVE8e1JVRu6C9wTey26ed9mdj1/zWnVX2xTiN2UMvmO2VKBNGeQjlaLMrH4C0V/UGx8HKJxWqE98Y8CWaq/xotvnJ1/xzz+UHlGFEkcDm5xh7zilIXlkeOVdL56/va3IVYiDEEPZ9VUkYqVuzxg+Vp/JeXIDrQj7+J3UfZw9n5NqDsP3t7vQnmcf3xwUU1W5N0qqaSJJVeXu9mxZxaheIAufzXl8nwjrqFeyTndK23n1SacySBbSi234CqyMQ/xTFDNOE0AyO4csXNR3NAgV0IaI269bx1D31V++KvS0pkNShjLttB4LrJ18zudLgZeAFQmrbPcCn5SUDGu1TdaQ/I+TIPVKEK++k3PPptbmtMTpjFrmdEZZUvILjGD4BCCbb177NOfGI/YzsVP7sLqlYgWo8JprADRNZGqaOC4ogxTPdTZbudPhcLT7gu+PponHNU0c0E145XNX90QqL3kse7ZzkDZXLfYYqWniRU0T7bY+euPhY4QtvuZjYfDW4JdcC9LefXJkk4zOcUAMEHQrrxCBc1Lql99JDFFVzrgOVZeG6J5k5C4YLdCn+3sqfubrHNH9cA6puxVAj/B80TyWam5aXq8bjV/WJMwNSbR2LvY8+/R1TeFqlOL+9sXzt+/f/SZEAPTN/3pSFYnRIGkiImI+VycGuYUKQvoSRFsvmtU0cRGwuT2J85T8gghgLIhPW47b8+wnN0jjRzHRI6hVEpgoP8LnH4MveVTqIDHhFgBpsmIq8B3IvwML95DaM4md5cLiXW/aag24/mV/frwuJVIiTwE+G8Sye4D+wH14zG4Ar9faaiS5P4VvANZktgZ6Or0DSDuJj/cWnRUw5o8dtfu3oFMd1jRT4yDf/1SaK8/uABYerABqfxwOR3RtbWqkweB+L9h7+52/BHzdBfYhpviiewyuGFHbY2Fu+uyx7SXKZwCTgXYjrHuKep7XWB6VENtv1yez3v7ooG0m2rC3QNPEn5vHepib7lOQDLHVfBXMXiGCI8G2+2mroaFiyc6cYV1tS4jfFFUihL9BebftFKHUGiZ549yehlOq9irxLa+P+RzALZXr8EWgQk5rJ3DSa5kTQT4PQtTopuGh17VjNBB1LCD9f3tGgv/b00C6AITPb9FambMQX9/TNoNdE3jnNsA8mB/2qpPd/L8eUy3CuyDD7DHoURNJlKUcy4/47X3G13NVjAFxZxVxkwGqifSf5Aqho9jKZXL8dtn3bWOZ+VOl3tCjKDMr4JzafRDcIBC2ypOadpXQ885UNtUBH8TEbE4CiInZfICzn5JfYP2RU0b2oWjripyzAjrpjY4ujnMbRa9vOfMSABPOC0tyso9oMZVOdVhjje4hVqErLTSVbwbOUlUZaJPuiSCMdXUpAbeFakZVpVRVeQlwW8vx7bmLRyu65WqB8ph9xpMH1d1tsc87QLKqyta0hfcyZ+pko7vedj+wvqEs+vJg7cWX2/sDsLV5YHVDRC+L0CtXNUaFqn0PI49d87q3yRv2pFs3n5yRu6BXV9sT4jdjhf8DSKebKsUVZWaZTVtsCUqd4eWRc8r2Hv0b0E/wf6nQjZ3xIwl7nt0oJP/hVwmm0OvacRbiCwJ1SKXRd/wvcqIUd43ic1wPOBZXVVmmqvIWVZV1/mLvVvmZk8YreOQWjim159mN9jz7XVpt/BsGZFhmdC99nchiIh+joAOuZePHbbx+/LiNcvy4jUvGj9v44Kviki8BtovUVSAaAe8ueki3MBuu5JnzG+3e3a4+uq30AeechYv6Bf2AY/k5sr9UpPfa855zVpBi7MnGu0pysmV4eFkaQHj47h6tLLvEjTVqM1lt5q62xGhsKv0+wy4ADLjlWbzUViH6EUGnOqy/NEb+6JTKrubvVVVWq6oMOFoYFlZ2oxDeajrQGF/TRIT/nnvfxFfOm25zW0sXSOEuBe4JdK9AugNEpu25HxgE5M585qugxQ1UVTaqqrxGVeXbAPY8+zluDOmN0hBNKELyW/AyIDLjVs7uakNC/DbEWMr7+b+cC4zvpsIRJwHhwqV80HKwn7U+0/eV9NJNnfEjCXueXQBzq3VTlPBJhYbksA8BfwHSeFoUWQW7R+G0wiU1ummCF8UK3HiweZom4gBN08QBvYlT8gtGl5N6ko6ROhm5QFoHrgH+Brx7U3LD1M3W0xWLbORkFiEEbjDfsv8eg1jmEnjRkRb/z3TPd4ycD3AM62MrZ3j+tudWD954bgYWBuu0GqqN41yKubjaGHM78P33nPoEwK5d2SsASkqy93HWZ+bPEDbq7gW5glZygg/GWvOg9avS+gKQwK4Jc3OeOeJz3zvVYZWIMImo0zQRrWniQ00TIwJd+8gj1xpdrogxMTHF5Q6HI+BqPwB/vmmZpomLW46Hl6qzTU3JsTXpHz+fPnvsQSvrWuwTrmnia00TZ7Q1b95toyIbKyL+Yo2rrQKCTl/w3ytqH1sV9z/9+TKhCMlvQPHsSZv7RK2rLK1PPTcjd0Gn/h2EODJJCtt1R7S5wgXc0k2dVZyD6u6WitT1cG9+y/FSt9VmxtuE3yFo0aUlRAeIN7oexSf9+aBEjCX0uh4yJTnZS0pysh88lOr5wmmFy4CPFORfbpufnHaQaTX+/w4IJAn0s/A3iwcsbvOAPsDFK6etmuo19T5+KWM4GY1wGnQQL4wft/EAW+fmPCMlhoYfUX9qjrr+JEZ5DLh1Ew2DkRRhAIRolnNVA/35Vo7vcx3Q95MTT+nrNplSzvz+y3ea1aZ03awDeL2WffyjauJzG4nofTIfLQ5Gmer7vkP+AhDTULPnnK9+6haFhJ36QR1vdA0MVzxmYACtCdi2QV1d6gkej024XOEPd+DWOvAUsKx5YHvu4n6Wuj7XSuH+YPDMRwKNriYCBn7NtG6Vmm2JN3karYbwxKr/m/X2R0E/lfjzV9drmngE4LJ3etkbdWN/4btv6En+N8KpWxyVzgQLLTo1hDg6ychdYN1QlRUXbyv7tnj2pCM+knAwjDstQz1pzsrBP67b+wBuz7Mbqr2mES4M/ymctuofIafq0Lj8nV5zyz3mm+ONrs+AuwqnFS4pnFb4YOh1PTIYHVHxio6I+qk+utWTSFWVHmCKqso3979m93yaBfgPIrx6oi1yeuG0wrcAPmbyBI8wcRoLwNcL9pU2zGjCl9YHwFaOifNi+vnycUuLUcS16DR7EQF/lhdlZpmVKuMcp9HI66f/QQzZuIZb814wN1+PidmUDBAbuymp5bovOW+cmaaKRHYGLFqUkl8wepuxbzTAGYVL44GFDofjiD/V7VSHNVzx9Ik1uGP9jXT7tGw3EQBTAE99fcoBv2Ttoapyp6rKWaoqNwCsnDddeMwVb0ukW0jTDUHsU6yq8iRVlZ8ebM6cqZMTgVxg/pUPrDhA0i1AzMA/gc8AVjRE36iDu4ep6SpCT/K/GTvrej+P70k84LyfEN2WHF0aLJuqB3bkgfiIoCgzK9VQZUowbrP8s+V4f0vdqUA8cND3rRCBYc+zTyloiJqRbGpaPzK86pwW9RghjhCW1MVtB6mXecwDga+G5g05IODQnNanaeLshz+KedmeZ7/j5NcGPrW1tuJsgLCmFZ+DYcw3p9+ZB5CSX2ApYOSxw+RPpLJjLjC+tehqMzZqzX0oGga+I3mQx+LvNT9+3MYlke8pK/1T32trn/140FBvDP9w7ATvnth4pn30rtMgf3V2w8P3pAKEhZWlNI+l5BcMBWWCC+vDc3OeDqhWyG/vZwCZu4qJbmoIOhLcVXRqH6h0JfIAACAASURBVNatLts6s5BNmiaE/yknYMzm2mukFIV33fVIUD3ANE30BGJVVTb/gmBqSHvE6IobUZ/47bMDZ+XuaGN50ET12v1ZzdbECKHIOzq6h6pKJ34d6uy8wRmgTAfx7GeXbOioAxyiAxTPntQw6oF5WnlTwqU3PXf5HU9c+2qn/q6EOHJItJVML2tMrgeR3/7sIxOJnCgQiP3a8UQavH8WSE6JLP+hq2w7Gpj+bvpFEPMiiJ9K3dach/+wvlsck/4OUX0trgAwWYS+6Mw3+83b5grbCmiF0wqX2PPsViBxSnTC3Z9UJx0HUlZ6zcJj6d2oSHfjpjOnT2y54am88/QX4gJOl08yftzmg+bHNmOj3mihMQnAjXkUiLjj+VI0HyybyhsbbN9baRylX7hoUfzccePKv2trv4Lzet5sIeLWmkjr8/POvej81LKSiuPW/HJZ1pqivc7uzp0jCvz/FjaPpbP+3R309UgMPwX0ygGVJD4KIhJg1KbV4MvR7hanup2duxfR11I/DJ9UWsA8/vhl2S5XZGxc3KbNHbjnjcBPmibiAbbnLo6L2HXq5R5zxUZn1LqbgtlI08Snmib+erDr/7r4zP412xKHRfUq++XWNz9e0wFbm+8zRNOEFWCAtf5jBakY0R/s6H4hOs6wxB/e9+hmZdG2M1/urn05Q7TNLfMuFW7ddM4xsb/UFM+eFJAgyJGIe0DjfXqYtxEobDn+S2NkbITi3fzEubu6pRDCkcC5b/U57pfGyDciFa8bmFQ4rbDdmocQXYaGr3WmB6TLKHR9mytsBvAPkN8dmzdIAo3A1g+rU47zoODr34reaM6q0IVpH9XNlPwCsV4eOy1VbsfO8v8EYkAFKRvXMHwVwEpOzAKw0ri34EmpNcyP+tAIOkZRHdZmIOrni9KHmjbZHvXGu/Y8/I8Lkl1GS0x64i/3tnRWAaQ0SN+/RgmQnv/DlO30HyBRjMD8lPyCdj+/UvILbIs472aAY8qKP7d5XAAPAuMdDscRf6rbqQ6rTeipAqppO/fjACoq+p8CUF3d84Cm/wHwMHCOqspyAIn+iEDEGl1x5w695sWAq/f9eaVb8UkctorUlX8gRWPtjvhTO2Bn830MwDfAY9l5g/usbYoYmGmtW/HztF9C0b0u4IstZxWBlPXuqAnAwpDTevTx4aYLh1Y54w34dMq7JUWZWQZTsTXJ08O5JmtN0d5januePc4llWG1uvG1rrSvO2PPs/dY74x41yWV2hMiKs8pnFbYrlJSiK7Dny7n7zgg1DrdeD++OhYAGWv07DSg3wtcm2B0/tPXv1V6BXgNhvC0bBZHt9zvNPnxNcWiv+JrZdUYqLy6ARickl8wupjM3oD8minvN180bwzbZagUhC80SD3S3P/7Pw+Y1tomRZlZZmtB5DzhUho3nBx9+bfG00cnsqPog5xZB7TgjI3d2MP374ZkAA/m6b/WjwV8pH9r8xdnl87vbzQ2uB0Oxz3dwVmFTk4JUISMVgQ7VVUGWzk/BSjKzX0s6Kilv1/qhwCrnrrl5hjOv8ptLZnXx3HBynaW7r+PxFcV2iov3zvsIuh5AeC49Y1PSoK1swUCuBzY6UW5C3Cvboqccgj7hTgEJIqKLz1egLT4Zfa6xR9viMDQpXEKwLrKIU93tS2HwFXCrdjMG8P+23JwoLXuj2ubIhST0D/vKsO6M7fNT04ziYQv3VJJ8EhF/dc5Jcu72qYQ7eN3WpcA2PPsAHcBJhDuMo/l/JY1IPY8+/z+lvoZY2O54p/Cho5Ra762cFG/0VXMejpM1jEGDUnkMNp5//dFMuVAQIBYaMS10oNpbUnOsS1zSHtJJJGfGUTDSTqNw/V7gH0irUWZWaOlQf+38CrHC6+4YMZpLw4FEspIO7u1+0opRvn+NZwAvAsyyZ8aEdCR/oz8mwbDH/8GYKV+urM2/h6TqWF7W2uONDotwqppon+jbvCsbQz/ov3Zv/LSS+N6CuEdHxGxc1UH7nm9pokzAbbnLrZEbZ88y2PZ01ib9mnQ+aVtNRqeM3WyaNgT9ZTR6tQjepQ/GuzeLVFV6VFV+eE7FT2sIKcBzxVOKwxFV7sODXA2txSLNFUF9aAT4sgnxlL+R4uhsbB49qTSrralIxRlZo0GnpZIJPJu//cACOTVNuGV46P2LGtjixCtYM+zm1c1RC73SpEZa3BdVjitMOSsdkP2jbgeWLBcOK1wyXsXbZr2pfn/bgJYyei9KYvbSZ21nFFKDgux+sQtD+jd2goqPmcVwGTENXIQy837zVmIwK00CiI+UaQ3mX6LFiXsreIvyswaLYX8WniVURLp3TA4qclK/V/DqF1ckpN9QL6rw+EYXVXV5xqAqqqMG2//972nG3GPTmHzUgLsbbuGY98GEOh1qWVVeS5XZGJjY8KCAH7eI4ZOc1g9UlymI8xWRQ+qiX5lZZ9zpDQQHb0t4Ia3sNfB/D/gQv/QHYpu7WV0Jpwz9JoXyoPZy8/jmiYO9oY1ubE8OtYWX/vv6x5dElRR2P5ompisaaJvqdvyvBFpPC686qlD2S/EoeHvxzk+3lr2FqDXumMmd7VNITqPK568eWCVMz5jWOKybums+lElUhG+D0gz/qM/e55drGmKiPNINn5anTS8Kw3sbtjz7Arw8g63LeXYsOrHv75s7fyutilExwmk9dgmBqUA5HKDR9NECsB8LuwvgVP5JJjbafjaTwLS00SEomP4suWErDVFS4QUp0ij/lP41wrC5W2SRF61cFE/pSgzSwB/E1I0n3DL/46ZfE8T4eZT+OCFg9xThb3zzZtiev/Rg1kMYfl/Aultm5JfYF/D8MEAw1l8w1mbPs8AImhFMexIptMc1ncqeswFiDe6bMGsq6npORIo37Fj1Lxg1qmq1IHBwK2Fz9xwhsR7t8T7VvrssZ8Fs08LfgA+3n9wzkWTxuDTJ95SuyOhw50BADRNmIG3tzht961tCh+QbHJ+8NL52zpcvBWicyiePWnJj46rLgbxODCj/x3zT+pqm0J0Dl9vnzgGoNoZe9Biym6AJhBOidTx9YfU/OPngUhwY+hLSB0vYOx59tFhiqcAuBi44+ULth2gZhTi6COdDZfEUapniyUfA4+l5BeEfc+JfY9jGYmUQfu9V4Fm1S7h9wXEqwBrGP7O/vOy1hQtER5lrOJR1ke9bXKCcgwuOd2d3vQNMA6/glq9Ldz9uX1cloJnwUs5Dx+sQEsDXCB1EKIkNnoEUPMlF7Qb8PK1sdJfBohl96YFObe8Ghe34VKAlJSfglbp7Eo6xWHVNCGsip4EkGxyxge67qWXVBPIM4GPg1W3At/xev9Pv64M3z32ad3YoFT1eetvwe7RYq9XVFXe23JsztTJo/H1QUsC2QM4rqP7+3ED2U/v7h0Gwr3DbZtxiPuF6FzusRgaK+JsZV/cMu+y8K42JkSnMAXYtrbSvrirDeko/mrh8QJxt0CML31mzQ+aJo6xCO8E/5SQOl6A+Jx6ubhBN9p9H/4ELB0eonuzkSFeBe9K4ErgTyPIf0gXhsiJNJ+Ki5xAe6YOYMW7ADZqL/IP/dzavKw1RQ3OzPpbbD8o0YYS6VYaxDxjieVEd+/GF4GxwL133Pan93WDIVLHeFCBI39R1HiQ95rM1TU7bWkZiu5dVJKT7WrP1lpi7wZlOEAlSVMA9uw5ZiBAVVXvqkB+3iOFQ3ZY/e2ZlmeYG04B+KE+NuDYusdjvRpEXEJC0fog7xmvaeIHTRMqcJW5Ib2311z5F/t1z3QovK1pwqppwtLKpRx81YDge63UjuzfjKpK+X5lcnqDbjgvyuB+q3Ba4a5D2S9E51I8e1Ld6FTtyd0NqbaFWycFrBoS4sjk5nmXRRsV1+RE267vu7O6Ffic1qw1RQ/6nddHgCXHWOv8qU9Sp5v0UexKRr4yKBp4GkTze7pOyMn/XZCSX2BoJKLnHlK/VFW54CL5866tHDOtJ+vdmawGfA3/A91vcc604igqnI1ERsay21uSk33QNMTs+Vs/9Sa6P415y2TSY6D8Oo9sGGlamrWmaMnjT49/c2NqxkUZFK0pyclu1eltxuFwLHE4Hvh7cf+ofzaYbWLMjh96B/Bzm77ggvsAIqn4X0lO9mqHwzHa5Yq+CKCpKf7V7qBw1UxnRFgTgcpyj7k5tByQ2gLAnj2ZI0DXw8PLXg7ynj0AaSsfniDR5wCLzQ295gS5R0vOB2o1TfTfb9zpT6z2gjjkDwRNEzdtdoY9akAyKrzqkIq3QhweXp755H3Au3XuqD9n5C4Y0NX2hOg4W2r6XeLRzQZ74o8FXW1LJzMXuGOjM/wdAAFvE1LHa5Pr/5t6rVnRS0EOwefcezrjPT1E92AI32cDFjNNRf6hcWWkRZ7J6wbRwT1riPsUwICnXUVPU4nlG+FGooNrkBT1p+hPLVzUb/SHXHFtIxEMYnnALT2/STwpASnps7Ui+/nnJ17SzvRrJIoBYBzvOQCE8JwGsvmhrVudzByyw6qqcpuqygnFLlsVQKa1Nrq9Nc04nTEngpJ/1VWLtgV5z1WqKkfFbrz8IYQ3xhmx+Zb02WP19lcelFX4ohbFLQcNZvdZIPcA9wHjZ739UYc/EDRNmHa7zX/f5AyzexGP/euckhWHYG+Iw8stIF1JYTs/uWXepR19PwvRxawoO34oyPqS+vR/d7UtnYmqyg2qKp9r0I3NaVT/DTmrrWPPs5vsefa/flMX97QBqYwKr7oMOIWQBPbvimS2XQgwgXf9gTV5iwFP5fEsur95zsJF/YKNNP4CsIfUNlWs/CxyDvSrfwpAQSmhx5kezDcB77yYMydgsaVKksYapefHKFEl9+zJfMThcLTqx12bf0svfA+3GHA/8GzOE4UAkZE7VJ8R3e9k5pAcVk0TfTVNRAOEK95eAMkm1/7tHVrlySenjgCy8PdQDeKeEZomzNtzF58aVjGib2Psyvf73X1Fm6H09lBVWaCq8s6WcrKvz85UvS7jydG9dy+b9fZHfz8UZ9V/D/fjpX2+ktAkEQ8dyl4hDi/FsyftPDE1/8PdDan9iiqG3tnV9oQInozcBQKYDOLzj/98V0NX23M4OC1qdxbAH2JLQrnwrfCn91JyogzuNcDdIF4VkPD8+dvfCqSiPMTRxTJyBEgpER9fl3/zOIGcksrm960ysWV3ooXBOK39KewJ0JP1Ge3NzVpTtESPkDOR/u4CQsq3xAWngQwH7m979a9clO8YBIzwKob/mUwN/9fUFNsDuLq1uRuw7y1k92J6GMDhcCTU1PQ61mSqWwfibrqJwlUzhxphfRZYqmlCfFcXtwngq9r47wNZaDC47wRIS1sasAaun/8TXssOif4ssC6sYsRF7a5oA00TQtPEgP37sJau7HM2oNviag+aCB0M//deyoQar3HyMdb67wunFR6K8ECI34BEW8kVZqXp53WVQ/6UkbsgoavtCREcA2JW3QWkp4Rt70iLu26BF+EBaNKVsq625UjCnmcX9jz7lVpN/GduXekTbXBPL5xWeOVXl62t6WrbQnQNNcT3BrHppZyHy79j4h8Fuszm28fw1ak0E9TxeCI7ewAYcYcFMv+Ev62fh0GeDLqsIGJVAaOOG8IPW0pysgOuvZGIXIBJvLa6oSHxSeArkA+98MJpx7Scl5Jf0Hc1x50GMJJFT5TkZPulhuV9ICLc7ohzHA7Hg93JWYVDd1j/AvzZrxLVXFUdUA5rWVlmktHYuOuaaz4Ntnr369iNVyQIlD5SuK9Pnz32ULXB+wLrgL2SbHOmTrZJr+EKEO9dmrum3fyUQNjqtD1hQOp9LA03dcZ+IQ4vj13zutelW68Aoi2Ghv9k5C64IyTb2j3IyF0wen3VYAdIShrSLjta/799Uxu3EeDLmsT5AJomfvedLWa9l5xhEd75wEs64ocTIytGf3PZmpe62q4QXYuV+lEWGjam5BdElZE2Rcf45rycRwuAL32iMRKCPB7/mTG7ADaTFfCD0Phxm5aAsuJtpke5sSCQF7a/6le+4/RECw1lFhredzgcMixs961C6DF1dckftZxnwP0YgBFXfS/WzwJ44YVTzwB5o9lc8x+Hw7E6mPseKRySw6qq8idVlR8BZFprjwUYaqtutxrX4XBE67r5BI/H9mqw9+z/6deVsZsu0Rvil63v+eC4oMQGDkIl8EdgYfNATJ9d9wNxQtE7Rcpx9KtZWRuc4ZkDbXU/PPKH0qAVvUJ0DcWzJ61KDtv+ldMblgPyb8DCo9X5OcqY7KsEF/ibbatdbM9hwSkNOoBbKl5NE3cCyzRNRHWxWV3G8FcG5yytj13nlsoU4E4dccqj55QEdOIX4uhlZv714W4svYfxXXhv1twBRAKPwd7OAMuBHcD4YDoFNBE+yP/lyGDsKSVp11JOzADx2hc5MwJWqEvJL7C5sZziJOztuTnPSIDbb3/qp+jorZ9UVvYb4HA4xgBcmX/79V5MkwE8mC+cm/O0G6C0dNg9BoNbT0lZ8UAw9h5JdMhh1TSRqmniYU0Tyc1jFkXvCdDD7Gxsb31c3PoZgNFgcB7QqL8tvlpoztEV14sCQ4W1anCnOA6qKitUVb6oqrK4eaypMvKPlqh6V8rwDZ3hEJNgdL0JuLa7bOd2xn4hfjsqmhK/9T19C4UWKkMhjlxiLWUj/RGTgDS2uyuDrLVxAP0t9WnAd0A+cKgnTt2OC9/OMNvz7P9wS2WhU1fKToksv8qfoxp0b+8QRx8LuLSfFyNlpL7XQOSsdDZUl+Rk//DrDNEAYkMwzurM/OttAu8w//reM/JvzAx07aecNkHHILL4Kahi0JEsvAawRVC1jzhSVVWfC4BtwNPTH3/KvIhzcwHCqV4NPgkvh8NxqssVMdrrNedOn76w24oVdchhjXw15Y6I/yT9Keax9MuX35J04qrjB6SVbo/aZGuSjP9WuFae1sfolx9rFV03XGM0Nsr09KVLA7lfUWbW6NVZmXfGrDh7kaKbjwNu7fX3iZ2Sm6Zp4viWjvecqZNHNFVFxEn4+yV/XnfIvRvHvX6MfYvLNsxuq13xzWVrQn1Xuxlu3fIpCL8TIA39YlYPantFiK5k+lPXZ9W7o07tGbmpGLgHGO+X3z3qSDH7RFqSTc6eqio1VZUzVVW6NG1vn9Gjnqve7Tm6ymuqAO4AXnRKw8DHz911MLWgEL9DmogYBLCZLEsZaaZktv9r3xmeNPAE3N3It6ftTInBOISlKwB+ZkxA0sgp+QVpC8Vk41g07pYP/DGYIi8ntukWGpjMq1+3HHc4HPWK4voTMMQV61zhwtoLoCcb3irJyZYvvaSajMbG50FuBvFEED/mEYex/Sn7UpSZNTqMmBv93z5sKYoA4PFnmx9mY3c3z12dlemVJmkQLlElEG7d4jV7w2R4fc4FSsrukrqR72/fUPhq3xjjTstKwOWNdyV5o7xx5s22pYDb08OZLo16qglbmiF5qBK56zqctg1ui6v/a4f4cwO+givgI/9/0/3D1wMNrprwxzrjHmUey+1GdO/kmNIv258d4kijePakJRm5C8YBE2Mte67bWDXosuMfmLf5h3uvubfdxSF+cxZtnXwtSH1Q/MorF99181GtYrS4Nm4DwNK6mL3RIk0TPYCPNE38VVXl/C4z7jBjz7MLYDpEP24Runl0RMXs587bcUjS2SGOTnqx7vStDNBBnA7s+BH1wX1n6KmgB1WQ9wmXRgLUEnMr8OUWMvsFuPROXRrkH3hXgOEG4I8LF/VrNxUhJb9AwOj4MOo+fzTnhQNs7dnzu//tqrBXfZn+B1+kV0rWyex7x731bvGZ5f3P93hsvaKitrxy660vdesTmI5EWFV8CiFIpO5Jdv4oTfqNn5/iXffGePTG42o+kQbpAP7qSXP+x5VVvxx4FfivHudZVtIvrtRtNiuJpXtWS6teJs16A9AIKOgiXGlUwvEVQg1RagyZht3mdCW2rzHs+BsUFBPmpl4mZ9H7s4oyswJqnxUA5+HPZ3n9oYEZwuCdbomuy5/19kfVh7rxhW9nHA/ykuHh1Zt6mJ2lh2xpiC6hePakJcWzJznGpn+ZGWct+2Z3Q+o9GbkLZvtbJ4U4QsjIXZAKXA8i79kZzx/VziqAWyr+vAflbJ/kKODLyd/DUZwaMP71Y86LUty7gedBfO+Vok/IWQ1xMMKoO82IWwHGRlD9UklOtnvfGaYtYAmkl+pedAzZQMMWMr8CudZM05j21lybf/MJAu91Q/l5XRK7IThJ5WEg0huIfKu1i1ddpcn8Xif82i1ECCTCGFHvfKWuLv0skNTU9LqgO6latUbQEVZ8+WBOwCQQbmOp5aasNUVL7nyr74wyj+L562U7zmxr8Zf/vvJjqvUeP48YPvFsxwftOoVFmVmjjb0zv0IIkxACKQ2gex8G7lgxpfdaT5JrXti3MS9nrSkK+vje391gb5eCqs0p10mvQUT33v1KsHu1hhfxlklIsa4p4mRVlaFWVt2cx695rTojd4EKPAn8ZVB8wR9umffGsMeued3ZxaaFAIYk/Dj/lz3HmiTK37ralt+CeKNzTLnHAsiLQZxrz7OPL5wml2iaON3/3oamCdH8dXdn1KtZ9gbdOAvM03wj0gPi7p+n/bK9ay0LcSSzlmGNEgMmnEzgnVd9uhEtEWYgqF7N8ZSc2YSteGPOKd7x+c8ZttFvwsz8GaK5GKp1O479twGv4Qqe9M+RAautDePbe1dwImZar/tJyS9IIWHwAABF9yIRCKQnrqE2Hxjvr8Fodo67bYpU0BFWv5b1ePxKIf7v2egMX1PjNW1oa63D4RhdV5c8zmYrr3c4HAFFMLPWFC0RloiZSOmWUnoRSiOK8TZp0jVTsXV02LcxLwIbV2dl/n35TUlnBPOzaJoYpWliDMCcqZOVpsrI84DvLr9r9X+C2ac17Hn2zHVN4b37WBq+XHzZmpCzepRQPHuSF7hheNJ3n68uzx748ebzP8jIXRBQH74Qh4/+d76XXlQ+bMSQhJ/XF8+etKmr7fktqPGa0loUBO6N1LRwVi8B/tudc1rtefa0YXlDbpvw+jH1DbpxJXAFCOnvAAEHeh8hQuwlJb/AJjH0Af6fvfuOj6JoAzj+myvplYQQeqS3QEAQEYW7xIIGRV9LfEVFrCAqKq8a9AWxEvUFBUVRbKCo2AuxgOQOC9jASJAiBEIPJSGk59q8f+wGQwyQyqXM9/MBruzOzh17u8/OPjODAc/b860v/PXPpTwR4Kj2WNtjbM8Yignq1oN1EkAivsknwvAtV3Y4QT1O20zc4CDy344mpwRcRSCmU82RCbLpNLwjWwt2Ws+s8k6twLMRIMJxaO2Iv/6YH1GUP99jMI6Iycl+GK2B0UUz6IBaq05XvTdtXN1708aZ5cGqLogTjMGqN0Wnud3+viUlkX41aZru8sEjC4TBOFIIMU0IkdD1s2dm9cnYfHnRebnt3MGuCcAWJMmByyO+/HNQjy0be/W+Z2Ov3m2rUfRU4FWAwOjcJKA78GJ163US00GUXB+xu53dLp6upzKVRiArJVF+fO8TFwSYCu92enzOA7n8phcndvZ2vVoyl8dnqluaPDsLTkv0dl1OFac0pKKNhABVn4yCgFb6v01G7MLY0HEfdJwXv7hHJrDLg3jGKUX+gIAji4GL0dIdmsUJWGlYw/gmHv3Kpgz/Z6peSoaA87jBZmU/c95pJQSJ7fRaALCBIe8AHCHi9OOtY8A1HYTbIf2ngrkzmJYkxGfOrE6wGm1Lj95Px6g9nPa/qt4fa/vvDIkhDCDHJ3LYexNvnLj+EsvEbGvcan1igKMNjE1tooDKapMSUKVIU1kfwHWCRSxowwKhtwhYqEHTdIeUc1ZXXn7I7IP70GbbennNxDZ9RZHxMb9fQzoDsyVy1rpRMbmGHPNDxgLTO703bSyootgJQDSAEHKW0dfhNppdH1a3Tscz+eO2F0HE1WYhnw01uVzAH3UtU2l8NjyeNCcmOXW3QXiWrD80aPPlzz448KN7ntzo7Xq1NDe8cEccjLoZxGt/PHJdprfrc6pkjMtYHbsw9jbgVZBzMsatP+b4aLHIV+x28ZrFIhv98E73fdomONdlvveXovB+wMVri8N8w40Oh0A+LhFvr7x289GWsdiFsQlo5w+7ml5VORE/iscAdCBz72/Wy9dVvZShAAJqMoTlQIA8Wv+oP08H6Y4g+zyI+0dHx7Nti+6Q9Lshgr32uUxoBeYI4MfKy53ARQAeTJ9VfiPalm6CKx4G6M1vF9isNzsqL6MHqc3id1JvASsQHGBwFx7/bWnnaIuucFDPV8anv7T/T+BfABt79e7l6Fn8lGmf73nGAtN84NmMkV1+d/QoXh7wffgTaZZ5VuCKsC73/tj+jNm/z0oa3QlaRbcvPbx9wObDg6jjf+6WssCZPkIKS/Chly0WWcUtCKW5yEpJ/OjmF2/7z8rdo57aX9z2m5jk1AuyUhJV0HoK5ZZFLjYKt0+P8D+fgxbTwFru9WCD88lAo/t2tDtGx7BYpFufAetFYLbFIhvNxfP9n7YxfpsfOcIpDf82ETnWhSHAgMz1IF5pZXR8ODQo7/unL93/j5xAPUhtFidgpWH9wIUhACHkLDv+UsIXreN3tfRi7djNxHkkhvUA2da4kkG2T5wBFCYBkyouG21LPwti5wDk0PbM3xg0czAZCApOmD5ZUXf+eHgvMQVFhFYVcK8GCCJvi8168wk+Y/NQ16lZjzrk8j240xFw3J52AQGHWoHA3z/nJxq4abr3po2b4j7bOcaYbwoEhgNvGvJNQwO+D384L7TrAaTnK+CWvG0Xvpnz8Z1/tC898hMS0X1bfhdgxcZevWvdky52YWyfXQ7/WF/hfvHi8AP5+tBZSjP26u0vP+f0+AwF4SPwrLr5xdtu8nadWoqY5NTO6w4O7t4peFvaV/c/2GQHxK6tjHEZsptfsS3b6Rcy/K1eY3TMRgAAIABJREFUxxsLMhiw7M8Yd+O8CWkz5k1I80pP4diFscNiF8ZOjV0YO3bE2z1fXlUYXuaUhjRgrEHw5TlBOdPPDz3YIWNcxl0rr938XVXBqqJUV7QtfZgTvysANnBGUrQt/R/7fVpaKwH4gvvc6o6JWobf4Gh2OrKtcUdH4pAI+zZ6m7XhpzSTbBMEyFnaHWVt1r0t9O8LHqckyFll4f/8DH7b6d32NDZtzrbGHfN7GGz78BJgMEAhYX2rU15TV58trIGcIIe1uLj1VUB+SUlkwowZM2rUI6+29JEDVgGrfp3S+j7fP4Iu3+93+hSQ/QGQgn1lF1Pmv69tgNMMpghwHqhTTzoDcoYHUVTgMT8M/Ap8B4yrlw+kNFpZKYnpMcmpZ4X6Hl63cvcFr/ab9k7u+seu+cTb9WrO9Gly54Bge36PFvsb+704dBLwr3yP+fnYhbH/Kb9Nfv+nbQRg+upIv8PjN9xxm8+Rbl/qM4Dd/+D9L1914IzHdvoIj9/3hRG5gGlo4OEOBiH9Vxe22gsYhwQe7g74/FoUngWYTg/I6yvBuLY4LBMwDQo4MsAlhVhXErIFMA4IODLEKQ3uDSXBmwFTf//8M0uloeyv0qC/gM4CeaMEAwgOu83uzj4lBzr6lCxYXxLy9Jrr/zzuuUNRaslS4XGV53VJ6HDtkWEgsKI6Y6Jup4/JgPuY1MF9xHwKjAKeiralf5JtjVu9i27fgzhTHw0AId3uwfzaGQyyutsCLC58zOsZ+nDFF+Nsn12WTbePAYbz5ayPrA9WKwBu6uotYPURnqh25tKOVb23YMGF0XDGlSAWnapgtbIhsw4WAYu+n5C2BSlXIN3+Bo+byF1vsTO0BFfIefw0tBvhhzeYfDhsSJuQZpw0P75GuV+TP2l7iSTiyvbmkneS22XmAo8CasiVFiIrJXHb7S+PH5q286KPCp2hH8Qkp96YlZJYL0OkKcfSglVpA3wBN4iOtNzfWg/AAPIsYFXswlgHYISoo6MDpIdu4owj3RAIPLj9d4dt+uL3ovBjCvm50vNfKz1fUxx2zPO1xcdODvRHpefrSkKOea41DwkAD4iUpf/O/G81P5+i1IYdcKAFq8fpoGc4B6QEIajGsE/RtvRoINqDcc0xpeB0eDAD8j8g7oi2pb8O5w6P44eNf3DWTf6y6IL/8Ph93fmL6m4LwEzZZU58i4GjObZaS3EnPWCWrGLURycqozmpt4DVJYUpwOA+JsVAHwnAEhbW+hwQ/lFR672eYzFpfvzqeRPSEoIL9lzffcsH4ze2LvXxLzMxcNsH9+/tO2T8wcgh3dzuPo8DN7/+0PNfBbZJn51012vVyjf5qTDsNqOQrr7+BQ9bLNIDvNGwn0ZpbF687Y0/Y5JThwCfAAuT5tx/5ZLJT1/s7Xo1QzcCvloAJKGJjy9YRxbtH4F28uUn4IcuvkVdAw3ukIySkB+KzQXtgElSm/PF6TCWPNvHr8DoZ3D7rC0O+xVw9ffP7+pj8Bh/Kwr7HXANCsg7zSSk/KUofAPgGhJ4uIMB3D8XhW8FXMOCcttIKcp+KgrfA7jPDsoJEeD4vjAiB3BZgw+ZPQjHyoKIYmAIiG/5O3hIPaXfkNLiZFvjVkfb0o920Mu2xlV1fLCDKAP8QAoQ9hOV2Y11U7fSn0j2+EHc0dclhnb6EHMCpC+IScC7Zfhd97a80gOuh8EUwN8d0086wsUk2wQRzGU3hZKzY7X1mgoTgcgH9I7rgHBLhIUWcuwTsh7GlI5dGOuDNtbXfzPGZTwBfw9jBfiCFEZjSW6HDr9Ejh9v93pekt0uQoAHixb0y93k6vxUaHHpCzd/8e2dAPMmpJmBMeC5EwwjhMHplh7zQuCFSfPjfz9embELY/sB64CZGeMyHrLbxRAg02KRuafiMymNS0xyqm/P8IxNmw/Hxgg870kMGYCtuc5rf6pMXjDW/5fsc17YV9RxvPaK9OidOBNa6nerz3K1gr+DwYSqes/Pm/hNidFckul2hNwyaX78Kf+u9HpaUL37lUZEy111vQHGnoIjo+Pjc6q8mIq2pQ8TeL6XGIwgS0HElwfBWquntOuTEOBD8W8OAs7KtsY5V6R1nQ48Au7HwPgV+m/gZOkAPWxpA/NptXYwtrlLrfdMBrjZds/FSxn3ubaElCBKgYTjBOPNTn21sJaP81dxlAALSB/tikPgdgesbgzBqq47cO+BLsZCsdXjPhLgN6P8jUnz453Ah8CH7z074V9HdsYnuUoirwZufDV5Qb7JL/epouwh/5s0P/6Y4SPamMpe2+/yKQIxW+9o9TnaFdS/T9mnUhqNrJTEsskL3um2Na/XB25pvhrk1QLpiElOtbTUwKquYpJTY6ICLKsOFLdr62cqerfUFfgqiKGAvSV/p/rwVicf6kmahdthXuqNYBVU736lcUqIz1ydltbqUknoH5KQuzh+679FYtA7VYljbunrrbnTgKcAepF+3zLr7c60tNbPQchkYBEYH06Iz5RU8zeQT6sLAX7DmgJaByz0YDWCvdtyaPsqx285bpbqZZSA4UG5nQD6+BVUzGG18/eg1gDz6mNb9cFikWu2pA5pe3hr26CgtrlbpixZmlPVclffM//j2569Kglo7x+x8VmPM9C/KHvIE8DOl+/+cPZ7z906COC6DzpZ97t8zxgQkP97xriMHLR7c1cAasKAFmzOLYvdbmn+FS1nD4nBx4DrtZjk1G7erltT0+3BT/4N/HGwODpoRIdvntv0+FXXZKUkpmWlJM5sycFquYxxGaszxmXMPF6w+tYTDxsAX2EsbfRjsirKqRYfn7sJDDPAcP6KtK7Hmz3NjnYHA7Rb+/byN862vXUl8BiQJXDLIoI/XJbWfagk+E4oOwKlt+nBarX5U3g1eNZkW+P2AfhS8kv5ezm0651tHTizJQWrUE8BqxvCAfwN7qMHQ23YKrkAwGAoc8yYMeOr+thWfcnfGXW19BjNBXsirz/ZspPmx+fd+MSke/1bbfYDLgR+c5WG35Oz+co18yYu++jIrnOmCymLWpsc4wAsFumxWOSPFos8bgqB0mLYgTKtp6jH7cEUA2y0znz2mzteGdfHu1Vr/PpNeycw8ZnHNrk8Pu+A3CAxDFh0x9x7vF2vpsY3ZGc4QHD7VQO9XRdFaaSeA5kNznfT0lr9YzrjbGvc6mh2/AegG+veKg8Wk2yPjt1F1/eDOHwAOP1Mvn03k9iInxn2NbBDUDI4IX5PaeXyTuRW2+TepQTEDmXFEYCLbbPml+Efq78dlW2N+8cEAS1BvQSsPxW2igFYUxx2sPw1LYfVcDOAx+MjajIVa0Oz2cRzJv+yacBvU5Ys/bW661330COeSfPjv540P350RK/3rf6tNi/1SMN5F/x1k2XcHw+KQRtvfnDehLTAD17899RXH3htrrfGO1QaD731L0GbN9pwDtDF31S4eEd+1/O/3n7Z7zHJqf+NSU4N9HY9G6OY5NSBhc7QNX/mDOwxuM0PP8ZGrh2ZlZK43dv1aooKsweZARwFHTJOtuy8CWnD5k1Im6qOX0pLkhCfWSIo+ALMbSV+91a1zJksXwaQR2RctC19WLQt/bqVXPyqEVdBAh9flG2Ny71SvvVAB7nD9Q7jQrfQ44r4+JxqTxJQbhlXDZcYMOF8LtqW3u1XEm4DuJDFY7OtcQdPtn5zVedOV3oivT68jHSCGJkxLmP1jBkzpqI1kRvRms+nz5gxY2ada1xHdrswFuxt5frrs2GEnZY9+6aU36bUpbzLF/faZd57drT1wNkuV0EnP5CFCHcQ0iDBUAokeCtnTGm8Jr5846hvd1w8yenxGW0UroNntrV/Hel/4JY5tywu83bdvG3ygrHiQHHbj1bvGzkaDAeBa7NSEm3erldTNm9CWnu0Yb9unTQ/fsEJlhsGMk3vPFKGOn4pLUhaWiuzJGwtiCCgV0J85jHHY33ygVXaiAASfUIAgFIgfrG8/Bdw/ryZfqc/Kp5kAD+u+MY66dya1iPalv4hMKw922L20MUBEMbBRzZZz5tRt0/YtNVHC6sFpFl7KMwg34tdGHtnXsC+9WhjoLmoxhAOp4rFIt1bU4d8JozuEt/QoifqUtbARX3j/nKZOxg7L/suvJMtwOiTbwGxDWkELTm7PDFbUY7x0m2vf73lycsuBoZH+B9w/Lj33Os+z7x6XUxy6piY5NQWOztaTHJq66Xbrvp69T7rZTEhmbuBASpYrTtzYHYIgMFc6DrJorcBfmjnBnX8UlqU+PhcJ4h7gBhwJVexiEX752igWs6kv/c0mE/vScb0/qzeup6h8d1tthqlfk2yTQw04hztT+GKA7QvATBTtqOlB6tQPwGrXRvHTLq1P8IBzE1r8+Pn6zssz9/rv/fzPJ+8SxtyKtaamJU0erTHZbpYuo2fXjt1Y52GnHJJwzTgyLqS0CuuunOhnDD30pXABH2oiUYVqCuNU1ZK4qphbe0du4Zuuk1qM6B8Gu578MDgR177WJ/JqcW4Zu6U20Guc0vTyAi//Q8NaP1r16yUxEPerldzENLhu94AoZ2qPnm+//w4seixxz4EeT0IidZhVh2/lBYnIT7zW3D8BeLhFWkxT1eastUOlAAuLe4RZejn+v/Ihy8C7gU+SIjf9Vgohy5wY8wvIPy5ilO2nkwekRPcmH2NuM514msEuIQ3Tqu/T9h01dc4rMeMrzdkUZ9+vfwLX9xUEtSzVBqjQDpifEoOBBldC9aXhMzKGJfhlWn4ZiWNHobw/Ig0CPQm/ClLltYqkL7lww6X/FQU/hnwSMa4jBkV39NzvyyAXd1OU6orJjnVFGTOm1PoDL1dv3qXRuH8Znj7tEzg+UV3zN3s5So2iJjkVHPbwF2vZhe1vz7AXLivyBkyKislcZ2369WcvP7QC+eW5PRZHhCZMX7845PfrPjevAlp/ia/3I9cpa0u9A3LXFuW13UKMAx1/FJaqBVp7ZLBbyZwdKzT8nFT9bQAC/rFXCt5aMxNzD87jrX6NK+ipHz5aFv6HcDzbdk+Zx+n7acaw1BF236fA+Ku8ucj+Lz7+9bpNc6DbY7qJWA9ntiFsQIYHGp0TpSScfkeswEoMgvPN0MDD/+5pijMViKNZ3KKBpKelTR6Ksgn9WDABUyfsmRprfJqx7zXZddeh1/7Pv4F7RZeuSu7XiuqtFgxyalTQT4GwghIgdspMfrob68P8Tn8y+A2q3aVuv1eeOeu2U269TEmOXWYwH2FxDgK6NMldPP3/SLXXjb3lrerHGZOqb15E9JGop1gEybNjz86zeMr/3k7xlnY7gPgdL+wLfOD26+adNWdCxvLeNmK4hUr0rpOBZ4AhD573LSE+G3HpBCmpbXqIvF9E/zPAGECadCnXXUB0xPiM2dG29JNYezPzSMqGG0KaQcnGOhfa4mVBSDKO+L+K9sa90mDfdAmpt6mZq1KxrgMCfwK/Hr/p21uW54feY5LGq6Skmt/KIz4F8hp2pJSnru4e/5Bl+8PHkRRa1NZZGuTI3pjadC3ElHS0ackKspUFr2mOPRrEKVdfYui2pjLwlYVtloJlPTxKwhrbXb4riyISAdKBwXkmSJNDuey/KhsoDRjXIZHr5Jdv/r5x9zCNZmFJXZhbBwEdujkU7xQBatKPbPrBzUz4DQZ3OfHd/o8ZsWO0Z1c0mwtdIZcn7Yr0QTyoZjk1J+jA3Zn9G+9JmNfUYdXv/jP9EY/1Ml9r18Z9Fnm1f0cbr8rQd4tMRj0zgsPpk291+udMpsrU8CBVq7iKIy+h4/msL7zzL3XeZzD3wB3GRjH3JRy2xdaCquitHh2oBSknxaElt2xIq3rnIT4zMIVaV39gTshbBoQBK5lYJ4PYjGVYotsa5yrn+3LZcDleiPEMRMOVGbh00vsXBoI0IHM33+zXq6C1QoatIX1eO77tI3/r0Whn+S4fM9Hu4Ih2OByFHhMG0D4+wl3lEnI0EKPsRCEH+BzsjJPxID0mIWHMmk82CbHV3Y6bA7Ja10kt4TKdUBpuMHR5bDH3Km85bWNqfTR2ICCxc9ell3lEDqxC2M/AaxATMa4jLy61E1RKtNzVy1UMYPTXQuuDT1SFn79yt2j2gLnghyizyFfBGJl19BNW4RwZ2/N6+sHfJuVkvjDqf8EmskLxgqnx2fEj3vjux4pa9U/0FxwXpnLr4+rvI/m0V620g1iWlZKogpYG8ib059ZVHTg9OsCWv8xbfxj9zw+b0LaDSBfNvkfcod2sl199T3zP/d2HRWlMdFzVy1QbAX/eCATnCVg6gKGYJCpgoKX4uMPph67/LHTruopBHbAByS9WDvVbr0ppfL22trWDpMYVpU/v5yXfeZZX3JWXq4l80rACtWf/xrg/k/bGCWEfX0kSgL+gwLy2oUaXdG2gsh9gF+sf37PIKOr3erCVpsA/15+Bf39DZ62vxeHrgP8uvgWDfIRntabSoPXAf7tzSX9jUKG7nQEbAX8/IVrYIk0BlXq9QewF1jf1beIEKNr4+/FoW/F+BSNyXIETgsQrvd+vn6jmnZV8ao7X7k+Zm9Rx6Q1+8/qBJwL9Kj4vsAlzUaXy+H2OwQUR/pnRwhkycGSthuB4i6hm7p7pPFwVn73tUDxgNa/9HO4ffdvzB3wG1B8Vru0XiWugL2/HzhzPVCc0GlpdKEzOPvnfSN3AkWXdH3XJZCFn2Vec6ZROC/qEf5n+KGSNs6DJW27GYT7LI80ttKrUmIyONb1j1xjOOIIW5SZ13sX8C4Vfv9qxqqGoQ9VtVIfxcVp8MnP9jhCO6Idf6+aND++Tp1PFaW5W5HW9W7gWe2ZBMTtCfGZL1V3/Whb+jCBe2wAhROc+ODA/74gjnTpxOaQDQzZAyIe5NAKMYgDsLS0maxOxmsBK9TsNvwpqEd58Ozq6lv4ugDfrWVBZiDWiCfOfXQOYdB2WEpBxHuz3opSWf/pb8/Ld4RNAGEAPBF++7eF++Xs2JrXZzsQcFroX2eWuPw92UUds4GAVn4Hepa5/T1FzmAHEAD413yrHqkN41beYgrAJoHnl7PapZmcHt/Fv2SfszwrJfGY1oITtSQr9WfehLSKY2LrpBuEZdL8eK+1wCtKU6HntOq/IekCMT0hPrPGd4Rusd09OJVrl3gwdfn7VekGcQBk2wrHTw/w32xrnLrrVEGD5rCejB7sef1ElTEuY3XswtgE9JPnp1dvP6ZO933axjezNCB2S1nQ3cC/9WCgfNw1r9dfUcrlO8LfBsajt1zmlLa5fs2MGyvso4knXH/ygrHG3YWdA9fsH+4DBFwQ80mf/UXtRPrBoYVAwMgOX488UNy2cGPugINAwBnR343akNM/utAZNkDL9ZIeP2PJM5ueuFIfw/Di425LD1LV76fh2dFabHz1jiHoQ1edA6iAVVFOzo72GzKDqPVwbwusz/0WbUtfBEwHDCA9RlyPuTEvA6FPwASoIeWq5NUW1qamJmkMiuItp7rlUt/eMb8L1WLauOhD7V2PdjFjRP9/UsNWKUr1HC9Htab0nNZjjpfZ1rjV+uvX64stUukA/6QC1hpqLGkMitKYqNv7TYMaI1pRvK/iWK4qMK0+FbAqiqIoiqIojVp9TM2qKIqiKIqiKA1GBayKoiiKoihKo6YCVkVRFEVRFKVRUwGroiiKoiiK0qipgFVRFEVRFEVp1FTAqiiKoiiKojRqKmBVFEVRFEVRGjUVsCqKoiiKoiiNmgpYFUVRFEVRlEZNBayKoiiKoihKo6YCVkVRFEVRFKVRUwGroiiKoiiK0qipgFVRFEVRFEVp1FTAqiiKoiiKojRqKmBVFEVRFEVRGjUVsCqKoiiKoiiNmgpYFUVRFEVRlEZNBayKoiiKoihKo6YCVkVRFEVRFKVRUwGroiiKoiiK0qipgPU4hBAzhBBv649jhBBSCGHydr0Upa6EEOcIITbXc5lHfy+KcqoIId4UQjzu7XooSn2qHHMIIb4SQozTH98ghPjBuzX0DhWAKUoLI6X8Hujp7XooiqIoJyelvNDbdWgMVAuroihHVXUXQd1ZUBojIYSxHstS+7hyUmo/8a4WH7AKIdoJIT4SQhwUQmwXQtx1gsVvFELsFULsE0JMqVCGrxDiOf29vfpjX/29lUKIy/XHZ+vN/Bfpz88VQqQ36AdUmjwhxANCiD1CiAIhxGYhRIIQwl+/HXpYCLFBCHGfEGJ3hXWkEKJbhedHb50KISyVls3St7EOKBJCmI7zWk1+K4oCHN2/7hNCrBNCFAkhXhNCtNFvcxYIIb4VQoTry34ghMgWQhwRQnwnhOhboZw3hRAvCSG+FEIUAdZK2wkWQtiEEHOFxlcI8T8hxE4hxH4hxHwhhL++rEUIsVvfx7OBN07ld6I0PkKIjkKIj/XjW44Q4gX99vuPQohnhRC5wAwhhEEI8V8hxA4hxAEhxCIhRGiFcs4WQqwSQuQJIXYJIW7QXw/Vlz2or/tfIYRBf8+o76uHhBDbgMRKdbMLIW4+Tr17CSGWCyFy9fPDVQ32JXlZiw5Y9Z3lC+APoD2QANwthLjgOKtYge7A+UCyEOJc/fWHgDOBOGAAcAbwX/29lYBFfzwC2AaMrPB8ZT19HKUZEkL0BO4Ahkgpg4ELgCzgYaCr/ucCYFwdN/VvtINkmJTSVfk1wEPNfiuKUtHlwHlAD+Bi4CvgQSAS7TxUfvHzFdoxNgpYCyyuVM41wBNAMHA0j08IEQGsAH6UUt4lpZTAU/r24oBuaPvt9AplRQOtgM7ArfX0OZUmSGit9UuBHUAM2r7ynv72ULTzdhTavneD/scKdAGCgBf0cjqh7cPPA63R9r3yRqnngVB9nZHA9cB4/b1bgNHAQGAwcEU16x0ILAfe0ev3b+DFihd6zUmLDliBIUBrKeWjUkqHlHIbsAC4+jjLPyKlLJJSZqBdkf9bf30s8KiU8oCU8iDwCHCd/t5Kjg1QZ1Z4PhIVsCon5gZ8gT5CCLOUMktKmQlcBTwhpcyVUu4C5tZxO3OllLuklCXHea2mvxVFqeh5KeV+KeUe4HvgZynl71LKMuATtBM1UsrXpZQF+uszgAEVW6+Az6SUP0opPVLKUv21dmjH0Q+klP8FEEIItCDgHv03UgA8ybH7qwd4WEpZVmm/V1qeM9D2o/v0c3yplLL8gmivlPJ5KaVL30/GArOllNuklIXAVOBqoaULjAW+lVK+K6V0SilzpJTpekCcBEzV9+8sYBZ/xwlXAc/px9tctDihOkYDWVLKN/T6rQU+opoBb1PT0vMxOgPthBB5FV4zoh1Qd1Sx/K4Kj3cAsfrjdpWW36G/BrAa6CGEaIN2tXUJ8IgQIhLtR/JdXT+E0nxJKbcKIe5GO3n3FUJ8A9yLtn9V3h/rYtdJXjvRb0VRTmZ/hcclVTwP0k/qTwBXorVOefT3I4Ej+uOq9tNEoBCYX+G11kAAsEaLXQEQaPtsuYMVgl6lZesI7Khwd6miyvtcVed7E9BGLyezijIiAZ8q1mtfoczaHM87A0MrHZdNwFvVXL9JaektrLuA7VLKsAp/gqWUFx1n+Y4VHncC9uqP96LtOP94T0pZDKwBJgPrpZQOYBVa0JEppTxUfx9HaY6klO9IKc9G28fKb3Xu45/7Y0XFaCfsctEn28xJXqvpb0VRauoaYAxwLtqt0xj9dVFhmar20wXA18CX+i1SgENogXDfCvtrqJQy6CRlKS3TLqCTqLpTVeX9pKrzvQvtImwXWppWZYcAZxXr7dEfn+x4fqJ6r6x0XA6SUk6s5vpNSksPWH8B8vXEe3898bmfEGLIcZafJoQI0PNDxgNL9NffBf4rhGitt5xOByqOSbkSLQ+x/Pa/vdJzRamSEKKnECJeaJ34StFOwm7gfWCqECJcCNEBuLPSqunANfo+PYq/01Bqq6a/FUWpqWCgDMhBu9h6sgbr3gFsBpYKIfyllB60QPZZIUQUgBCivcq5Vo7jF7SgMUUIESiE8BNCDD/Osu8C9wghThNCBKHtp0v01tnFwLlCiKuE1lE1QggRJ6UsP2Y/oXcO7IzWaFUeJ7wP3CWE6CC0DojJ1az3UrQ7uNcJIcz6nyFCiN61+hYauRYdsOo70cVot+q3o10FvYp2dV+VlcBWtOT+/0kpl+mvPw78BqwDMtA6Czxeab1g/r79X/m5ohyPL5CCtm9moyXWP4iWJ70Dbb9dxj9vAU1G27fz0PKqPq1LJWrxW1GUmlqEtk/vATYAP1V3Rb2T1a1oLU6fCSH8gAfQjtc/CSHygW9R4w8rVahwfOsG7AR2o+WcVuV1tOPtd2jHwlL0BgMp5U7gImAKkIvWcDBAX+9OoAitA9cPaB2lXtffWwB8g9apdS3wcTXrXYDWCfxqtJbfbLQ7cL7VWb+pEdrvXFGUpkwIYQHellJ28HZdFEVRFKW+tegWVkVRFEVRFKXxUwGroiiKoiiK0qiplABFURRFURSlUVMtrIqiKIqiKEqjpgJWRVEURVEUpVFTAauiKIqiKIrSqKmAVVEURVEURWnUVMCqKIqiKIqiNGoqYFUURVEURVEaNRWwKoqiKIqiKI2aydsVUBqXmOTUYYAFsGelJK72cnWUZkLfr+KBNLVfKc3BrKTRR4+VU5YsVfu00qRE29KP7r/Z1rgmsf+qiQOUo/SgYiVgBunpEf7n2h35XeeUuf03RPrv33qopE1fVDDb4tT1IkZfPw3wA8oAq9p/lKbsxYkj7ijJDZ6rPROlQIIKWpWTibalDwf+1ZrdmYOxH1nOVdtc+JhiWd2xLTs6LeeqDInB3Jefu0axN8bGZb8B5t6s6RnOgU6ruPAXwNyD9N6h5Lb/lfhfAHNX1vcNoKBNBsPWAOZO/NXbj+KIv4jLAMxtyerhQ1nwDnpuAcyhHOpxhFYRYJCA04fSf+20npnqvW+melTAqhw19NFXNuwvbt9beyYBUWkJqf+lHaBV0NG8xSSnhnQO3rpoR0EjAD+cAAAgAElEQVSXMSAkCAe1CDZjklOngnwChAAI9ck5VOIKfNnh8UtV+5DSlMxKGh0MTAM5BUR5Sp0bmDZlydKZXqya0oiNtf03YSfdZm9hQGz5cbCeOAGnmVKjD2WmIkIPAK4g8oL9KPY9RLtMwBnJvig/ivx20y0DcAZzOK6AsNZ/n+M9dGTbnl10ewL4ONsat78e61hvVMCqANDroQ+mlboDHhV4pMTgBpyB5vwxRc6QbKBHuO+hhw6XRQzUd3AXMD0rJVEdoJuR8pbUrmEbi3cVnNbb4fYbCwRVvHjxNxXuHdFh+YMvT3h1YXXLveLZqVesOzjkA4fH7NHLMaAufJQmZFbSaBHRc9fTeVltbneX+QQAX4A8D4QfIIXJNerexV8v83Y9lcbjVtvkzmlcdlkhYUnAmQI3EgPaMVB6otmx8jBRs8sIKO3Hz+Ed2Rq+nCvSXfg6BvFdcAcyA75k7BYXPs7B2MwdyDR8ys0HAOdQlrs6kOmaZ51fqwBOTwdYgXY31dWT3zftpmt0EaHRID2d+SsnjEPv/8HwJ7Ktcfvq71upGxWwKsQkpz4ApPgaSz6RiOccbr/hVLr9qwcz+g6OExVoNCv6/68NpK92QHW7wbgYPHYwzEM7sElfY4mxzB1gAL4GHstKSVxVnfKvfC75yvQDZ/RyenzbgbxNb2VQFz5KozcraXRv4AUg3i+8oMRgdl868fkflmk5rHIccGtw+5wdBXsiu0xZslSdUFuwaFu6ETjPl+IJHoxjnPgCrDfiWtSebXt30mMBFc6h3swdrZzDGm1LF0DfMA7e6EfJ7dl08gWkEedPZ/HNXjNlM96xPrbeW/UFFbC2eEnP3f/lz9kjLwT5HojrslISXcdbVnXIar5iklNfACZpz6QMNBfM+fOxf9+jv3f0//3iru9lfb/7vEl5ZRG3Aq1jQrbkRAXsu+f9u596q6py7XYRYLHI4grbGQZ8h9bhswR14aM0Uose7RfpLPZLzdve5nQQBQjPQ21P37rgmvv+clZc7vUHBy0/nNnuXIS8Ycp7qdW+86A0H9G29N6nY39lA4MHlBAUDOT25ZeN7dk+dxlJH2Rb46S+XJPp6NTJ9lNfB36XB5J/UxEhnfSXV7Ui+5uz+fLrV6xzfznVdVIBawsWk7z0ARApvVv9sbNd0M7ur90+3+HtOineMeKJOX/uLOjaB4SLarSgxySnBvYIX//MvqIOtxU4wgzAyhCfwykFjtAjEoMFsL85anQ6sAF4yWKRT5evGz/zfx9tO9L7X51Dtt688sHJrzXoB1OUGtJ7/98J8lwQrYPa5qQX7os4f8qSpQePs7wRrVPh6cDAKUuWbjmV9VW8Y6LtjpgddL9/LSMHAUMFbtmVDXu2EjsZSM22xpV5u4715UbblFFfcu1gEFcAAwAMuH71YFoSyd5P11svyjwV9VABawvV48GPHnJ4/B4H+d6Yru/eMOeWxc3mx6XUTExyqq+vsSS/TcDeQzsLur5ADVrQz3/qqcC/Dve7CXgAaCfwIBFuEI7ogN0Xp4yYkAh8ZrHIleXrXPlc8l2/Zp8zp1/E2jFL75v2ecN8KkWpmVlJo9v5tTryQmluyGV6rrU0mJ2T7nn7m5eqsW5HhFzvG1zsatV9T6dr7v+rqOFrrJxqHW0/n+3E5yYQHQ24LB5MRiOujW5MC3wofWen9cxG2VmpPo233Zewl5hb/mB4D2AgQDu25+3ltCeBD4FoGqgVWQWsLdBVzz2w9JfsEYlmQ9nHTo9v0onSAJTmLyY59XLgQ6NwXZg5c8zXtSzDLzpg90/Zxe0HnKxjXkxyaiywDrgqKyXxg7rUXVHqYtFj/bp4nMbHcre2O026jWcCokInQxcwvbq9/xc+Ejvz0IbOyT5BJa/f+dqKmxqw2ooXDLUtuXcn3WdJfb4lP4q+SOCjd/0ofq+2nZ+aumhbetczWTZ7M3EDDxPVUXvVgz6qTCn1nKerJg5oYbQOViMSe4ZnbOvVKuOaObcsVsFqC2fAPd6DcZ9bmpbXtoyslMTSmOTUiegd8wzC7RnebsUuSKxq8d3aP7JDbbenKLW1+KmeHXO3tL/UURBwEXQ+D4TR6OvY53YbZ4DcDOIN/u4YY69uueMezpg6+5oLIxyF/rfMShr9/pQlS79poI+gnELRtnQ/YBr0SK7wsquUwNWvWWe/6616NQbZ1rhMiBsDEG1LjzHgfNmD6Xy9U60/cA1QbwGrmpq1BTnj0QVPAynAe5sPx/ZUaQDKHa+M64OQid3DNqzJSkl016UsPY0gwddY8tgDQx4svCl27tnHWTTPbCiTA6N+vqYu21OU6poz7rzAWUmjk2ZdnfjZ/vSuOx0FAXOBniCebt0v69Ko2Kz2U5YsfXTKktQlQAIwnVpMBiDdxskg/xRG9/uLn+rVtyE+i3LqjLMlTwzjYDbwIIiv0TqKluf5271Zt8Ym2xqX5cE8A0QJ2tjEEjwTL7U9vWK87T6/+tiGSgloIcbMevi9Pw6ekRTmm7MsrywiUaUBKACDZrwxLbc06tELYj655OUJr35RX+Xa7SIcMFgsMqeq92OnL84JMBVt+nn6rcPra5uKUk7vOJUAOALbHB5ffCiku3QbjcC+wOjcjICI/MUH/4x5qyGGoXpjWtx5hzPbLvMNKd5aeji455QlSz31vQ3lxDb26n20N37vTRtr3MIXbUsPQWvcmRhBtqsV+8d9bx33TlPq5e8tFb6jNT1ZO3czg3oKPL9JDNdmW+M216VsFbA2c/owQtOBUaeF/rW+f+Rvg1XLqlIuJjl1LeDJSkkcXB/l2e0iFMi3WE58YIlJTk0DfLNSElXAqtSrWUmJw4AfAAMIMLhLQ9rnbM3f3foOpPhhypKldbqTUB0v3Tn88eID4Q8BU6YsWTq7oben/G1D715nCylWAEbAASTUJGi91vbQjJVcPMmJXwTIOZfw5sxXrHMONFiFm7letuXj8mg9G2TAmSz/qD3bxs2zzq/Vb1DlsDZjWrAqV4IwA+7tR3pMtE29RwWrCgDDHnvpTug0EKiXE6rdLgTwFZAJXHeSxXeDHFkf21WUisyBpc87i/z/njbVY3z8lv/98sSprEPxgfBpQF+QKW8+PGDHDY/88dGp3H5LtLFX71HuEOfjwmQYKJzG8v9/M1pr30kD1mhbepTAM1dyZVJr9hQepP2wbOvAn/WO8Aq1G0d2k/W8hdG29GVt2L3sJ84f60fhoI9s6Z8Dn9W0lVoFrM2Yv6nw8hJXoFl/KoFz0FoelBYuJjl1mKDDc9puwe0xyakf1sMA/gbgPeDQyRaMi/qpc8bBwZ0mLxhrnHPL4gZv8VJahllJo68A/9NBegAPCCfaGKmn1JQlS+Wz1466yWBy7yvYE/HOi5POaX37vO/zT3U9WoqNvXqfBXxpzDcLiUQi3YARgUFIYT/RupNsE8QBOsyBxGskhpBW7J89gqXT51lfUkOT6aJt6cY27LpJ0H6eRBhBuHvavv2mK3922sSgP4sJJpwDnVpxoNdOum9z4mvypzDKj+I2eUQWgMF/Px19AUoJ6g30Bu6KtqXXaBQBFbA2YyWuwO7aI+nWD9x2b9ZHaVQsEmHQh+8xUc1WiBMWaJFuYG51li1z+a11S9OIDTlxHYAdddmuogAsfCT2Wui0AMRqEFOBswB7TTtO1Zd73v46d+Gjsfcc+rPT8xQEzAHGe6MeLYE0yCThEQJAaJOfvOqKKrvIdMC3kyu6rH1V6+ithZf6M/bCEoJjwzmw/TBRIzdYL/gTLjiV1W9Uom3pgUD/dmxPiGLPNRsZ5IKAbvvp6F9hMdMRWo3aTJyhDL8o4Egp/kYXZrMBdymQ60/RoQ5kFpbh/30xwTnt2B4swbqPmB4gDNSg9bucymFtpmKSU88AfgLeBdbTkNOpzgg9epuAGUdUInoToOc2/4AWsZZSxylS7XZxNVAIpJ4sf1Xf/iXAZ8A8YLGanlWpi+dviu/mcZk2C4OnzFns1/l4s1J5w6yk0Y8A080Bpbfc9ca3r3q7Ps1R+piOX/luDholkW6BcAAJhRcd2hD4dcSvwiNCgZeAb8pzWdva1g6TiO9AmAA68dfHQ7BdPc/6kvMEm2l2brPdGfs75/TZSY/T/CkcFkzeqAO0N+vDUhFIvjThyDhC5Aofiksd+E3Rgk3hpBZjrOoXCSv4e9i4GpWhAtZmaPKCscZfss85dKgkCqfHt3NWSmKD3YqSM0KGAd8J7YdfAiSooLVp6PnghxluaWztkj6X1TFYFWjBbylwbjUD1nHAm9qtW1FGHQNmpeWalTQ6DPgR4enYZsD2q6+duvFLb9epollJo03mgNI/3U5Tj6h+WdaxyZvs3q5Tc7P+rG5figJDvHAaHhGIoyMDbOzV+yaJfBWQQh/I3vrSuxlhHNyaR+s2+upuYFq2Na5aE0Q0RdG2dKMRZ7eRfD5xC/0jdtG9NciBIKL+Xkpu78Xvvi7My7cS+7EJR/oYXt9VcVKE+hgloS5lqJSAZuj7PefdkVvaOmxEh2/mL7pjboPmTWVHO5+KzjaX70c1buJXvKdTyLbIQmdwyOppE+uaCiDtdmEBIqoTrAKE+x5MOFzWmtreGlIUgGevvWCkMBoWS7cxGmk479qpG23erlNlU5Ysdb2d0mv8wYwYW3Z6l//NSho9bMqSpS2qJa+hGXPN3YGve2/aWDnojEILVgVg3hsZ9S/glTwiWws8bqnNyNQs0uXKA0Ejzp/cmIuBuFh+um0vMa0gurUbc0Aal2PA5QHWgfhyKMt9jLiWr+LCT7OtA/P+2cHsjGOe6QFmnY7TdSlDBazNTExyaji0fgjkj+G+Obc39PbKfOTHwDkSKUUz+eG3FPuKOv5e5vbrVZcy7HYRBhRZLNIJZFd3PSHkeu2R9DSXE4ZyamljrZrStIse6dSngmyUrk3etGpW0uixwAcgHwEe9Hadmov0Kzp29iWoG/BKFW/b9ZZVs9sgeOaGmyeDLAAxSiIKaSZjqkbb0uOBb0Ca3BXCur/o74xk3wG07yb9HJYeiGLPd393KIvzRnVrTQWszUzH4G2LdhWcFgHijjm3LG6QfA+7XRiAfhaLXCdghUBQ5O/+M7DEeGtzSAd49YEFYx2F7XtLt1/qpPnxTf7zHE+hM+Qg0KeOxcwF+tvtYrDFIqs9GUVuadRG7ZF4CZXDqtSOhaOzNQpBI2+ln7Jk6YfPXjvqLY/TOHXRY/1yr5+2/n/erlNTFLsw9ugt5YxxGavdrZy3AhSPOHy48rK9N21c/Ue/2HO/G3jGjE8s55+b18Vc2p/VI5dZb9cvmBvv/lIdk2wTjLvpOhvOHa/l4wpASmAxMK2MgB1rrJdXiAOaVoBamQpYm5Ernn3w3N0FZ47uF/H72qX3TUtvwE39B3jMbhdxI3cGtwWQBh5sisHqvAlp5Qe/1UAHg0/eIx5H1y76cE8PzJu47KZJL52/yJt1bChhvjn+hc7gkDoW8wHwW02CVYBAU0GbIlcwRuF8NnPmpZl1rIPSMm3UR7nw0ESmyozqv/2e3C3trjy4oeN0Pfc21VujGDQFFYPTQQF5Jflu0xwIPBuEBByxC2MT3v0jOFIK6fYEuT+ovP5E26SO3zz/4qPFBCeAfPtiFk5eYH0u91R/joYQbUu3BHH164WEneZH4Y5SgnwAo37H6sVsa1yWl6tY7wwnX0RpCmKSU8Vv+4dPF0IeOS10y5UNvLkFwBRgk0D0BwgqMq5q4G3WOy1YlTaQT2r/8pbHEdJKC1b14Z6kaeGryQvyX7z9q/HzJqT5n7jEpiUmZGt3s8EZXpcyLBb5hcUiqzWUVUUDon4dBXBBzKd12bzSggV3OJigPZLPAQlNIfAbe//mHEeB/214jMFoaQEr9GlklUq0YFWuAPkE8OPa4rDft5YFjUCLW4yAD2AxHjH1FVL8MmT2wSMV14+2pff+jtGbSvFPMOK8G8T1zSFYvcV297m9bN+uBGyFhBpH8MXsRN4+DbCizWpZ4977TYVqYW0mzIaysU6P7zkeabrt+VsXbavv8vU0gOuBtywWeRh4AaDw16Ax/iWGXOP0/CrnjG/kLICPfhsFYAEY3gS+RR92wz9i/Q+OonaDpcf3dWDW6w+98HNA64y5OZuS8vT17U01bWDbkR6rS1wBfWuzrt0uEtDSCebr+as1knWk20EAs8G5uzbbVxR3mfkqn5Aix50LbFO8XZcakYb2eu62AaQvCAtN/NZ0A7EAflq6h5QgvmpvLvlsj9P/OZB+gLGXqyRbCvMQjPKliitG29L/BSzMI7L0fN5/4E3r0y944wPUp2hb+vkgpwuuPcsHpwT5IIjn3rdOKwGYp+1DzXo/Ui2szcDtL98Q7W8qfjPE53AW8FoDbeZC4A3g0oovGjwMKgxyN9WZitL1g6EHRAnwxqT58auABPQr1RufuOt8d2mrCMCKcK0oye0+KmdT0pdowzg9BqzQ0wqanHxH+AEQ5pjk1NocBy4H7kJviq6pvUWdwoGyzzKvGVSb9ZWWbVbS6NDig6HhHqepoY53DckOlOkXyQaaQCqDl3wPCP2OVynw2NfXbH0ZiA83Oj8yIum+zjxHSOGT1cfZE2CSbaL5EtusH4GPgD/dmAc0h2A1xvbjBcDXIIZLjJ427Lo+2zpwZrY1rsTbdTuVVMDaDNh3XfhgviPceGbb7x7PSklskODRYpGpaLccPj764oxQs3+JwRxYZFzcENtsaP6tNlq0R+IFIKG8pXTS/PjVk+bHz6zwXE6aH2+f9NL5V0b2fr+vMJTaOHpbSvqgtQQ0OVEBewMAhrZdGViL1ScBwy0W6ajpitqkBfIKrXWJFfokBopSExeCMLtKfN/2dkVqSktdEAkGs8umX+/V6qKvBYjWvhrxFpCQMS5jNUDGuIzV3127+Yr49Z5X/r1cBAO0/9N31CO3nDX5I2794hcSzurPqg2R7B2ZbY1rFndwQsl5C6S+nwi5kx6dvFsj71ApAU1cTHJqLwiaALzxysRX6rW1QU8DeAJ41WKRmRaLtFdapKdAmE1ufq3P7Z4qHrfv7T7BO0tveeaGydVdJ+mu1zbMm5D2kJ7z6gvSCKLJ5e8CdA3b1PVAcTsi/A5EAgXVWcduFwGAj8Ui84ADtdmuv6nwqhJXoFE/T6sxWJUaC4jKe6gkN7hQuow/e7sutTFlydLVs5JGXwzsBHk/le5cKRBgcE0r9hi3gRifMS7jHw0xfbaKzgaP1gHe6IEyv3ZPgRAmyiYus94+/5RXuIG0ta05X9KptcDjkYgm08GwIagW1iZs8oKxIjpg9+cCTykwtQE20Qm4heMcTA9GOpMA8oPdTa6X97wJaT3LjnQJMpiLF9R0Xa3lVVjNQXtsem/V2+dNSGtyv6WNOQN+BPh538ia9PCfAmy120Xr2m63xBWkj/0q3bTgg69SO7OSRptLc4N6BbfN3TtlydKmmo7ElCVLi/zCCz4CMebtmb0v9HZ9GpNL3+syvdhj6t/FpzirqmAVIC+Qj5wmcAtwGeHXPsN8gUm7rUObTbAabUsPkRhfBTZJDPE0805VJ6NaWJuwTbmxE7OLO3Qf1i7t43fvmrW/vsq124WwWKS0WGSW3S76AVWWbfCIszxCktvKta6uYyN5wQOALM3ttbw2K+vpAvHzJqTdBzxtDtyXAzT4RA31Ka8sIgcgpzTKXIPVPgc8Fous1VztXZI/HwbGUSAWApsBuxqDVamhER6XyVSwJyLZ2xWpq7DT9s/enx9w0+Gt7SYCX3m7Po2BNjpAwHSQbHMEnBW7MHZYeTpARf95e8OCK56Zem3X3dtGrO11Bhm94t1ArS+kG6PerPl2I4PagxiebY37CVjp7Tp5U5NrFVI0Mcmp/psPx95vEs7NnYO3ja2vcu12YQRes9vFbQAWi8w+3nSbEbmmMiH5I2Z8aZNK/J43cdlwkOP1nKAldew09T+f4F1fO4vaTnzryWmz66uOp0K476G+2r/lwwOdnMUi/7BY5BO12d7kBWONbQL3fONjLM0D7shKSZypglWl5uQYoER6DN94uyZ1NfaBTZuk2/iSo9B/1Kyk0e28XZ9GwgJCTxkSJo7TR+Am25SkHwdfPmThpVNlRq8EFwgHzehuTX/b0ks2cvqQIdh+04PVFk8FrE1UiM/hx4HOLmmekDL+k/qcktCINv9ym2osO0Ag1tXjtk8Jk1/erfrBEP7OoayVSfPjZWgn2xU+Qbsz83da7pg3IW1EPVWzQcUkpw7LK2t1D8DhsogXTtbxyW4Xvex2MUufirVW7LtGTdxX1Cn4zLYr38lKSSysbTlKy/XOMz2E0dcxwehXlg8M8HZ96slskEa/sMIUb1ekkbBr6UISbcrdfwah0bb0rnYuWRRGrtmHkitoZrfKo23pIQfo8LzAsyWanRd4uz6NhQpYm6CJ828cUewKvLdD0PbfslIS7fVRpt0ujHa78Nd7fV9qschHT7T8ztf8ugPt8oPdR060XGPkKonM0B7VTw7lVXe+WeQo7DAExDaQn7zzzL1n17mSDc8iEeXTWh63FaOCeOBGtAC/xmKSU0OPOFpNA1aF++bcUZsyFOXAupgb3WW+ZnepTxTNZND9KUuWbgtqm7vdUeR73dspvTp4uz7eljEuY3UP36IPQdDJp+SZyukA0bb0aGBZEcGFZ7J89E7rsI+zrXEzm0uwCtCWrMUgO0oM4xZYn8vzdn0aCxWwNkHLdoy5T0rhHND6t4n1WOxrwBd2uzBVZ5pNv1LD2QAFwe499ViHU0XvES/+R4XhrOpi0vz4w8Bog6k4qDD79BVvTHs2qq5lNjA7iDLtoRDAdyda2GKRLwJdapO7GpOcOizE5/BqkK2Bu+bcsrjKFBNFORm3w6ct4NH32TrdHWlM/MIL7/c4zez/ves13q5LYxBkdN0N0r3TEXBMP5uJtkkdwzi4FmQbMFz4hvWZJp8WcowZocNSX7pgZbsjhaM7s3lNcwrC64MKWJuYmOTUUW5pGu2W5unzbnvzt3osegWQVt054aMOmkMA2u/1ebMe63BKBLRelwAS35Ad0+pzlqpJ8+O3hp227B5nURtj8cEB782bkOZTX2XXNz13NMFkcLwPiBHtl11R1XJ2uzDY7SIGQJ/hrNpGPjnHFJOcehPI7/IdYb2FNlB6o/1OlMbNbhemkI4HMoAywEUzGmFi3MMZHwPLQdw9K2m0r7fr420Lr9yVDSID5Phhb/UeARBtS/f7kQu/KyCs7TCW3f+eGPir3S5Os9tFKzh6rOpnt4so/bnRbhdD7XbRTn/uY7eLeLtddNSf+9nt4pLy45vdLoLsdnGN3S666M9D7XZxi90uuunPI+12cbfdLrrrz9va7eIhu1300J93ttvFkxXe7263i7kVnve1/5+9846Pqkr/8HPu1NRJI6EIBmwUY8WC9QDqqmB3ZRcLoIui/KysGvtYYXWxo1hWBdtiXyV25FixFyIgqBCQnkB6MvWe3x/3BiMS0iaZJOTx4ye5d055J8zc+95z3vf7KvGfeuPtr5T4b918vz3uPT8Knx634fMjXlp4Bdnlobye87/v9DsIsaTbYe1ETJp5XnKaZ9MLLiO0CrinteMpJZxKiT0ApNRPS6nvaEb3vYBiGlAQ6NAIcw+Hpyz6jzsnNLukaGP8fcoDD4ExARhuuCtmv/DAuA4rCl40bdSCUf1fGtsjYV3gu+IDx2yr4lW/IveN/Va6f1nzaMK5jY2Xm18gcvMLdsvNL7jwkNse+n5Dde8g8LgVciCwNQQlft8w/L5r8Pu6L8bdNIejdxv91as5+/w6BTtm0RLh7xoYrsh0oFf6rmvui7ct8cZSCtB7guhVYzo+GDL7gMOA54vpnXsm93796vCrH7KbLgfqdLSdQCGWFCNAAvA5UJeUnIK1MHOKfZwO/A841j7OAp4FjrSPs4FHgYPs455Y99262OlewG3AwHrH/wT61+t/tt0PIBP4iz0vQCqwL5AEkFrhOFBY9XpxmyGGlf3QZXYQYkW3rFUnYmHx0FvKgpkpI/rN/dcTFz0cjMGQdwAXKCUGSqnXNadjTYJ5EujSxKurOt32bs3GfdZB8ys0NZXJM0c8/eiU54aFq3teWLVh394zJn3wFqBiuZobK+6b+Gx0wDWvTzS142lgVm5+wUNbMvf9vkP647nRbvogft8S/OV/eA+5+QVZwIheSb9NSPckHFUazHIClNTkbNwjY9HPv5QOfHGIufLKEx2fu0yEPsn1yTla6zsAUyCC+H0jtx6zm24aoBC4fqdhPz1x1jVLYnH961Dk7L383dJfe9VUrs0cM33M6IumzJlrxtumOCKxs2JNICllzzmboPchvD3nePHcvfAsUmqtlBgP/GD3iQB/BRbZx7XA8VjyeQAVWM5onW54CTAUWGkfr8FyPtfbx0VAX2CzffwTkAFU28ffAx57XqTUn1NvB0lK/Sm/O6dIqT8Cdqp3PB/Yo+7YFTaeMw1xrjC1MND8nNgvQhfZQYgVQm9bsaibDkZufkEfYKkhImr51JNGx2JMe2tklJS6eULLfp/DFDpUlhb9POPS6kNjYUt7MmPSB4XAr5Nnjmiz6jJP336TESjr/3Oost8AIAqEiFG8bKzJzS84DPgItBBorTGmA4+t8Iy9TAjq4qQjwI25gefuAQ7NStjwN7cRHLu2um8CCCGIVu6WvqSyOpzy6JqqnZ8DfimaNkrj9w3T8FndMrNGVwqscop1Y+Ivn9qub7ibbjoo955zzNnRoHs2cMqUOXNfi7c98cJaYWUe4NI4KMu5zrm3Z828guGXHRVv29oKpURu+mbHy56VuxQPqCg+ZlOyu8rhrUjLnlyzIz+4/IHukIBOQoa3+CnQTlM7L2nNOHYYwFi7OMBvzXZWLXY1tDAySp3NrhLVERCO2t08ab+2aSzl2dfdbIYq+822Dx107ASRw7ESWdBWMss/gaUFDGVVJ+kAACAASURBVJ2kAVNjBrXTuMCYdK1BtAJ4v6Q2e7zLEQr3T/35SWCYxpHx7tVX9/n0+otuLpo26ueiaaM0gEZL2PJUHBGIZ7H1auhCMYjdtC1KiUOUEkcoJTpsiE0siAbdzwMrEOa1z921e5d+r9vDVgYYGUw44J2ynOucHk/O07n8dHS87WpLpNRFe58a2X/g5UuP3Zxd+3ivytqU7GLXFfG2qyPRHRLQCRj34MWTNgeOPWrPrG/mzv3njctbOdzZwBPAahrJDN8Oe9k/O50G6zN3XJ+ioyM8npTV7THdu6BvwHJYIyBUe0zaAhTWCrALRBg4B8zMfR2/zCyM5PJ29CDjc3MQ3+rdknslrv5iXU3fW0F8+OG1lzaqpSqs9xywxiYMzAb21uj+AnFqdzhAN03kWqzt2t3ibUhbMmXO3Mgjlw97tWpt5hXRoOsiYEa8bYoXe/Y79ez5nDIKeDkCE2YMn9kltoNtzWtJvSp/SonDgYVS6nKAnhvcF2DF1N5h3pz6nnFTxQ8Njbcj0b3C2sHJzS9wfrj6L5M9jtqSAb5l42Mw5CzgGDuepkWUZIYnaTSVydElMbCnXSlfNSIHoOK3I+e09VyTZ45YkJD143UgcKcWvd0RwwHgd8UA7ESWommjXirynvVhH7OMRUbf9x+KnhT9Vu8OiMi6mr7/K5o2qqCpwv9KVizU6C1j4y9fsCkjUgH0LNo5uLjN3lQ3XY0xwCkNVd3rSiT32nyH4YoESn7a6bR42xIves3/9vgPOXHSABaV7srCs9YP3ycab5tiQW5+wRHAx6BvO0p8rX68ca+HA1NTJFby18NbGvrLNXB+1NBVYZf+InpL6s3dSardDmsnQF8EYs9gNOH8+yc+s6klIyglXEqJO5US2VJqU0r9XmssSqwxMoMeXZ7yz6pOVZLVpo/9s130Y8+97dJ/IaILQhX9h8yY9EGH/b4VTRu1YKtSqScDfB7KewBr9bWlMkKzP5SVd+Mvn1q3mhp16I8FgsxNzv1jZH43XRwpdbWUujDedrQHZ161dJMZdt5hhl3Dp48ZvWe87Wlves7/fpjGeMlEfD+Ib4d8MvycWFZyjDdHAI79xTLjUfc97iFi5SRv0Hh796Xey4E/Fuvxl5dUpEbneEKGxzC5AZi3ozutHfYG2g3836PjBnsdtfekuMu+AloTgD8EmAwcFwu7EmsdPm/QeCsWY7U3KTt9dAxAcu8FNe02qXY8DOwmHIGR7TZnK6n1mpeEnebP990x9XX+uPra3FXiV7BW9beQXex6DiClyrFrTIztpsuilBBKiceUEjG5dnUiHgJdm5BR8XDjTbsO/5h/+YkeapTAXAvGsf8ZPr1Z6jWdgHlA7UHGYjR2+QvwZG5y3t5/uce7deP0MucqjdaiixXKaCndDmsH5uPVR98aMj3isN7zbqpLYmkJUurvgd2l1LMabdwIkVtTfUAunTB+FUBH3bkA3rRfVzbSNJa8ZDhrQgmZP/2nHedsMWX3JO2SEDB6bc6IroBtrr42GSn1s9tI7CvS6LKIQ+/QqwXdNIlMYDi/a1vuEEyZM3eTb+eNS2tLkw/7T/7QwfG2pz3oOf/7nd/ljCe91DqO55nx64fvszHeNsWauvCr9c7UuSGcRBCYoF0h0afvb+7P8fvy8fvqO64KWzZLo3f4JNVuh7WDkptfcFB5KP1UUxv/fnjSE81ezbSrevxXKXEygJQ6Jlvg63qFxgJsyA53Sh3EqnUHFwNVYy55ot1Kyk6eOaI2IWvxFzUb9+ozY9IHPRvvEV/Syp3HAPjKHVe2ZhylRA+lRNqfXvCX66pkMxrwmn9tzfjddH2k1CVYiVadUpGkNbgSg/9Ai2jZip7/iLctbU3P+d/3AN4NkWAkUTHsP8Pv/iTeNrUVRdNGLbj75rtPmGUec/E94dP1pY7zSn/q7TpKaN4BpkYNvXzNowm3Fz3pFfjLF5T5ol8BhNz6lB09SbXbYe2AXPrYma5Ud+kc0OtA3NrCYTzAzljCxzHDV+7sAxBymy1O2oozO2EpJLQr1euHTgTDACa099wt4GRgmTdotDZmcAqwXinxJwkxLfg0qdpw4/e5WjlHN10Uu9SmkFJrKXXMq9J1dMb5F34D4nng/OljRqc32qGTcuH8yb16s+IXgZkLnPDt8FO+irdN7cGkW597cEb0lHPfqB6ZcVXFpQ9c1ueEk4Fjwi6d3Get+9qdV7rfxe8b7IqILwEqUqM7tLMK3Q5rh6SoYtc7K0LpOx/ae96LRdNGVTanr72y6pRSVwJHSKkfiKVtqZWOwUCg72pPp3Q03MmrD3Mnr2532yfPHLEU9HzhCF78yuOjO6yc3Kr/ePuZQh9TmRxdaGeqtoYXgclS/rmqWGqlY45AuPi9rGE3XYnYlN89CliulBgUK7M6G05v8B4gKX2Xtc/H25a2oOf874/4H+M/W0e/1KN46V/rh+/TZVdW66OUyFRK5BZNG/XU/jmffvpjyf6D3vj1jAdzA88NHVF9zwk1CdGbBWKoRi/0BMR4gB4lrv3ia3X86XZYOxi5+QUZPxQfeHais+qHrISNlzWnr1LCwHISZtsrE7FdlfD7hmn0yYCXTpqxGA0npzgTNsdF3cCX++7XOurpFSwfkB+P+ZtCWpnzVEMLyn2Rd1o7lpT6Gyl1Q3G73wHUJESHt3aebtoZyxm9Dr/v+NWPeUeVPJA4Fr/vNPy+C4ofTHyzZlryJ8DHwO207jpRg1X+ckWsTO9sXDrrvW8Te5StK1+Zfej0MaMT4m1PLOk5//thwAcmrlyNEXqPMzplIm8LuRpYopTosVPyyiPB/MDEeRFw69pIr7cGl855B9gt6DYXOKMiTaMB3uyM99xY0u2wdjBcRmgqkF4TSR5338Rnm7XCJaU2gU+BT9tCrzDgMS8SbKk00+kyFmdM+sCIBtPcNcV7vRGP+d1JG24WRrBi8y8nHBiP+ZtCaqXjEGD9Tms8T7RmHDt+9QClxDZXs4t2Dv4SNTQ1iWZnCJHopg7rofVj4DagYKc1nrlZm1zPAi8BM3uUuI7zBo2DsYplCI1OCLrN8S2ZSkr9iZT6FCl1V5I1ajY1xWljzYgzGRgXb1tijKz7RWjT7a3+9Ga7JOuOwP3AJCl18X0Tn40K+No+/3tVRH95iTfkeBMwBQI64T031nQ7rB2I8x+eeEbEdJ7fO2nV60XTRjW5soVSwqOUyAWQUt8ppW6T6iiusEjVaDQ6Sucsq3kcVnW3Ni3L2hBnXPxUtTY9D6Edo2ZM+mCneNiwPdbNTEjW6OOA/+Evb2396hOBL2kguzt3QiAcdeif08qc3XWyOxG13uhlAuGwD82Ax5y/ITs8Cdgb6LOmdyjZ0OJwoFajTQB3SPytuStDSokBSomU2FrfafkQ+Eo4otc9d9funTIUqwEUEEKbptYRvJXvjQA9b0dwWqXUq+ur9jiNyBu/V6z+w71VCUSQlutgdym6HdYOQm5+gVC/HXelxxEI7p+z4PJmdn8E+KRNL/B+X4LDFEcIxHsCcQN21aI2my/GzJj0wTAwX7GOzEus47jwGGAk9fz6/jjN3yCmwRUCkbyxRzgWAu1vAKcDPzfUwB023nVGxW74fd3XoU6CO2j0qPfQGvQGjetyLqp5BH/5Qvzla/ucX1ttXxdGCsT1pmCsQBQD86v/lfxAM+JaZwKd5vrSlkyZM1en77p2ro46dgrXeKbF255YsX74PgvQ0ZEJlW+vSNs4DVfoV4eABNASwN6h2SfOZsaU4+68/YRzH7rwu5On33hK/fM/33HKJ04jtN4hokupr3dtf5eoVymw3Y3uQHTY5I8dkDNDpmcoMPGB82cXNbPvXcCHdqJVm7ApI3xt5mZXmkZPFf6K+W01TxsiQdifd+Gwjtv/hjh55ojlT1z34KZA6a7Hz5j0gXPyzBGR9rahIbJKnHlRQ4dqEs3ZrR1LSr0ReHl7bSIOXeiMipSSzPD+WRDzzODpY0Zvqdk9Zc7cHfpCHxP8vr4OxOHA80AhoBq8gVrnFzisfu+FHeZXSbWO/7NF0AP4fY3dfP1YtdS7AbxpVVMdntDkkiV9h08fM1pMmTO305eoveq1HNeRwYRHFwdSdgGioHEK7TgjY22dss1tQDawL4BS4iKgVErdqRLQ7BVjGVg/uiRSesiMnzbnuTTGc7n5BSPqa1tHTM+3QJ8/6V3b36X2tbpj0r2y0QG4+NGz+yQ4q2c6RPg7oEmxg0oJbz2N1UVS6ifb0kZP0Jhc6zXDK3cOqbacpw1RIOxsdREhjlsrkUD65dFgmgc4Pl42/Am/z+EJGUc4TPFq7oRAqx58lBJJSokzlRLZ22u3rleoHCDk1me2Zr5tYTmr+kOsm94823ntphWUp0Ye0mgDuKZ+qd1G8ZeXOEzxlEZjx8C7aSQWT0r9mZT69VYb3UUYe+WycDTovgFt7ItVSKFTkzcrz/1OeY/nFgdS9sxLqPgMOFzAjeOzfnv74OSyZXazScD5ufkFR+fmF8yc+cMU/3cbDzyvbgylxMtKiSn1jhPb+W00yj6z9jwSO+ZbGMEZGuHSVni3k62+A6nu0mJDRPa49LEzxbbG6qbbYe0QLCwZen9tJClpZL+C+4umjWpqTN/lwCtKibavguL3DUqudqSbhr4/d0KgUz7ZT545YgFwln34L/s4LoSre/0XWAt6Urxs2JrN6ZETsFYzWlMCuI6hwDP2zwaJOnhDoyPZG9skLE9iJSkYdCcrtJraaSmpSdWO48vSomvwlze7SlzQYy61f9VAiO08MColLlZKDGiZpV2a2cIwS73plZ26iMLlr/bMFui5JuJ0lzCvfe6MlYcWjitcsHDcj3fslVh5vJT6Xrvpbxe898KhoN8FLvh8nexx37c3HJabXzBMKSGo578oJRzABqXEjfaxUEocpZSIq37tbt6qa+0dPcOZvNQAbdqxqlG2+g4MyfouxdROb1Q7d1gZt8bodljjTG5+waCVFbue6HXUPPvohY891Yyu04FjpdSL28i0+vwDCCfVOO5sh7najLQBBd8ApOz0Ub942jF55oiwN/3n90AfN+e+iYfF05Y6og59rSk0q/oGY1EQ4lOsJJztjpU7IVAtEAud0TZ56FLWjz8lMXTTAhICxnhnVBha0KKHrKBH36VBa/TdbCcWTynRH7iPjrT70EGYMmduIK3/+q8CpSkDZl58aIe4bjSX457fNWdxbcqvWBq75357zqKp9V+vU7dRSuy1qGTvomA04R6oW3AUAG63I3C0XUziFCn1dPtFDzCV3685fYD3gLH2eD6lxI1Kid3a9A1uRYYjbEuy6agjcVXIlfrdFIeI6H4py8u23vpfVbHLKwDvrxyd0Z42dia6HdY4culjZwqvo+ZxoDoQTbyisfZKiQSlxDSlRLKUOiSlfretbVw+y5MaceiLAx7zM/zlnbq2syth0yYAM5wc90zb5J5fPwRQ8dsR58TbFvw+kbnJmR1y6+/6nRdY29rhpNQRKfVCKXVVY23DTvPnqKEPLnrSG+ttsC+AKAgSs0uv6o5hbTnan3ooVkzpwoxLq99s9gB+36i0cudOmzMjrwt/xT+3F0ogpV6BVZ3v6Zba25XRWowHXVW9Mf3CeNvSHPJm5Q3Lm5U3dXUo4fP1YY/3kOTSfxWOK2wwjC0Y9Wx8dsn5Kb87q1sy6MXBvVQAwF5BnauU6COlrgGeBDYpJZxACVay0v/sAQZjfYYH2H33Vkq8pZQYYh877VXbmBLC+Aog2Yi8DIz8+dob7909fdGbqyoHZOfmFwyp33ZN1c7fAgSiibmxtqOr0O2wxpGyYMa0QDTxkIEZC2cVTRvVFGdwGHAF7RjDlLnJeZ4zKlwlWZFOL+p8xsVPVQKh6g37/RZvW8Zc+tiXYLwRqupz2oxJH1wXR9UCgMGGFjt7g8YjsRhMKXG1UmLfprTdlBnBYYoUoTkgFnPX4U6pybS34nB6Iju0jmersDL6FZCu0QObLVzu9yUBM4DFWZtcZzSli5R6jZS6vLmm7gicN/WbdSAeBT3m4YsP7RRV4qykI/0B6Hwg10RcOvO0Ndc01D43v2DY9Z88OG9t9c6pWHJOUSCS4ir/1iDKj8X7j8nNL7jmjV/P2B+r1Ha13fXvwEIg1dbuzQLuVEp4pdQLsKrqLbcd03SgN1ZxCoC/ApvrQlGUEn2UEnsoJYyWVG1TSuyvlDhJa6s0+v5J5f8tHFe4AGDJ5n3GgagGbtiq2wrQeufUX0Y0dZ4djW6HNU7k5hfID1f/5YIkZ8WmPdJ/vKopfaTUHwC7SanbTfjeV+E8QaNXuEPirvaas42pBDqKvuOHQAboW4B58XJay3yRus9fq5Nc7ESrO4AjmtI+odaYBdD3N3fv1s5dn/QB6w6u+73itx5JsRx7B0NiiZmDdb+Qzem8KSP8NrAzMAl/+Z9K9NZHKXGkUuIZpUTPFti5w+BKqr0foR0C/fr0MaOv6QQJhRLwWKulOgr4GmqYm18wDPSHxbW9BgtMDUw+sOdH71059PqfHxh51pGGMJ/dHOyxH3Dryz+fc9P4t+deKKUus7u/ApwBlNrH2UAeELSPL8SqnIaUWgEPABfbr63Auh5X2MfnAUsO/CJZAvM1+jaNVtqfav2tG3diLwMeXlSbsgngw8qsL+peKJo2apPbCM4EfcakmeedUO98INlVYVaHk06y/g7dbE23wxoH7A/juyB81ZHU5P/9OrbBGsFKiUSlxCtKicMApNTNTnhoKRXTk/YGhgvE49mTa7qEwLvTuykhMavwyHjbYaE91jaXiGtikCcgTg+6zBCQ29qxbDkrH9b2XKP4KpwfAaahY6u3WLU+o35xiHaNW+tiKGGpaiAQzYoFjt6SunfGZuehm9MjS/GXf9yELrnAYUD36up2uOSJeSu9vupl1RvTdwNupeOrYCisVVJoJOEOS37QBaARUSDzon3unDEk64cFQDCit0Rz/V4Rqq6j1EVS6hfr4mCl1A9KqfPqVX18CphQ73gP4CC77edYq7mf2K89C3wW9JgPAR5hXaPdtV79uvan3qLRH2v0HRo9D79v4jac13OBkQHtqFsc2VT/TR6T+9pjHkdALNm8161153LzC4ZVhVNFSW3PDGBet9P6Z7od1rigJWz55tVpgjZECtZWRm7b2vRngh79iEZTmRx9tr3nbiuEEamJhpPjbYaNmA+YcU0M8vtOTAg6Et1h4aJ1dd+3IKWuklJXNN4S8JdXRxz6t1qveWJr561P9YZ0F4BwRCOJPcr+GsuxdyiseFO74AZXN13Kymc4TPEIUKKFblIIk135Z4CUurZFtu5ABMpSVtvxnX9y3Doa1la4mAtUgRhZtzXeAMr+aWI/IEmp50qpz5NShwdmLMyz41mbXflJSv2DlPqFesdXSqkPrdfkIeB6+7Vfs4qdJFcae9iFMqyqbWGRJRA3CIRDIBCIBOBh7AeHmn+ljFBKuKXUYSn1kl091cOdmJHCcYXBevPw4Pmzl7mM0P0rK3bdKze/oC7pVFqLF91lWBui22GNAzmJa8P29oimgS+dXW5VSKk3APtIqZ9pVyP9PnfmJuce1UnmkpR/VrXbqm5bE67J+SlY3r9DJI9Z0lriOvsCNSVOUlt/B+r0MVt9kbQzccc0p09lStQ0TPZqzbxb43CH+wG4k2uXBysS455k11nIzS8YlptfcM2W1R2/b5hGnw6g0Xc29YGmNC3yb+Aggbgi85KadY21V0p4AKTUXWInpy2xNYal/aAL1uqliptBTcAjoj4DvbwRZ5WiaaMWOERYZyasL6J+xSdAKZGc5KraK8NTDIgbt369tUipP5BSvwSA3zdsz0WJB7qiBkBEIB5dPiB4w6eHVp6CJY8YrtfVYf/vKvdFbgO+V0p4AaKIzAQjGmUbVIV9twIBQfS/ufkFw9K9xfZKSsN+wY5Od6WrOLChpk9/0CGnEb4rYroLtv7S2R/2N4FvgX9Kqbcb+9VGnGhokZZc7Yi5qHucqcCKbeooPAz6Vo9v+aEw4uH2nrwqMdovqcbA3vZt1UXSTmYYA3wAzGlqv6Rq40l32LgFvy87VkoUKX1KTioryiFYnjQHxHXTx4z2TJkzN9h4zx2XwdfPGS5InKcRAkQkN7/g6Rkpe+93fPgHB4DWwv21u9/V99w/5ZnP1o5YBFQdvfPrOtFZVXzfxGeDtpMrT/Z+sPTfDsdllcnRDSlVjqbuzryqlKiQUv+tzd5gF2D6mNFehPlv9JaVOBN4sqOrYKQ5IsMSHZEmhXoIMDM8mxd/c9N5W7+n6mWlQ8o8jsCiommjpm6zc+yQ2P6RsP7Oq3Y5Jzh1F4ARgN+3HBiv0RMFQthFMaJBj34BSLCTvlgRTFyznTl2A+3WGHmgPykNZNWpFESBS2PpjHcVuldY25n9/U96gL+DePmXO065voEPZRArOPy79rWungFu81qNXg28Ey8b2oKEzMUDXIkb2r7YQhOZPHNEuTf952qtnafGxQDBLrVes5YY1Kq2tRGHYClZNBl32KiLG5sWi5AEgKr16VoIHQRhAgboaR08zi/uhE3XBI0hbEfICUz4T9Upewe0i4g2COI2plade9Jna0e8CCwGVr238sTf/vfr2EBufkEQ+BT07UdHC18wokakLC1yJv7yphYamUcjur07Oo9fPXQU8A3aOASrlGkE617R6lLKbU1JxBWqjjqbVH45ol2hn8sGL9n6/B1fTEutDPl8JbU93wFLWzXWdtZDabTWDYVrWdfJonpnTODJ3AmBe6XU9Z3pLLaKX62HtH4IAAPqZLVEXb9utqLbYW1n9sz69jYgfYBv6dytX7NLWubYN/4rpNRxiR1d/Zj3EE/I2Le4R+Rn/OXb3M7otGixqePEsFroqPvJUMXOCTMmfdCrXSf2+0RytcObEDCeblapzUaQUocbb/U7Gu20f44nBnG008eMHhap9R6sTYcH9LX26cvo+MkpcSVseur+3SJALXDo0UOf3Oe7hD5nrtY97n4levj41D6/XrRPjy/+DyuUZOIBPT9+cde0xc8An4PmSGOhGOX4wvHfyPDFh//y8gdNnVtKPV1K/VDs31XnZ/qY0e6HLz7sufKV2XOFI5oNHAvi8Lpt8Y6+upo3K88TxUjdGPF82ZpxclN/OQ2EOKzPew6lxHCgSClxaKMdW4K/fEF1YrQ85NIhGn6QV8JKDAOILN29NmhrwG4h1REe0sdVm9nALMouFx4BEcRKSGv1TldXpjskoJ35dsPBo9xGIPJbZf9V23h5DpCrlNi3uTf9WNJnjft4jTZDbv3PeNnQVtRuHvQFENOM9NYSrMidheVQ/QUrk7W92AXwCUSTVj4aQylxLZYGYn5z+gnE0Hp15uviaFt8E3Z4QmdEgy7H70kpGvt3L9aGXoe+wccLr6NmSCDqXQbGU4AqmjZqgVKjXw9lklsEe58pl+k/xweNehys2NdDxI/z73M+6FltZuKPjNs7wVn9yYUzz73t4UlPbFfD2XY6PpdSd62H4xhwz1nH7gvOJ2s2pu2d3GtTYXKvzSecefXSupyCTvE57u+u2WVFKBGviBY3pb1DhL0Z3k2n5+YXvFp/B/LL9YcNBVhRvttqLL3VucCyNjEaSAg4vhWazAYf5P3lC/D75gEHLhpS+1lJj8ih1AssBgiahrenK7rNUKSiaaMW5OYXjMS63in7tMT+7sXmXXQturTDetVrOa6KqDPp06oMAXgOTd7cd3kwcei6sLc38GZjAeCxJje/QELaIKwP9bu5+QVbB41PA/rE01nF73MKxLnAWztNrP02bna0HRVA0oxJHzgmzxzRUW6QPwgjtNmdsub/aEeHdUN2eELORhdlvsgvabEZsh+WIHdzUfx+oW/V6sL0MaMNd3Lkb9GgC0vz0ZJkwnKEDWdi7dnP3bnHQ2OvWlq6nWF2OC597KwkU5960N49vv7+f1Nurr+l+QCQWU8KaJsUecdigtMAUqmOnO746P6X9SGXvLvypDcH5L9+l4njlqJpo/5U+cyuNPQJcBFWtnU3wPQxo12+3PWvmZHs40BvAHHiBfcuaDf97VjSz1Nz5IpQIgcklzVactSKg3aK4tqcXCxpp5G2YzcMMicCrKnKvX/823MXFU0bdTaAUsIA0qXUDW29twiHKUL8MbnqTwTc0b2cUUMMWZRw+4eyckn9h668WXkOcDiXBZIbrEhp3//r+wDdjup2aBeH1ap0wRk5zsDaPRKqqz6qzPwF8AzyVg5McUT6flmd/gPg2c1TlZfoiPb5ocb3LeDJddfs4zHMHksDyT8A3t6uwJ5OYaatCiUuBbyZztCuAp1YEvGsBzyJRiTH1MId0I4g4IHsP7y/T6v+8H257uhnd/s811P7yufV6Q8Xjiuspu05zv65ZSVJKfEjcLCU+j0p9ScNd20f1vUMXddrvbtX0G0+7Ym3MW1Ayk4fD6xcfThZg5/pDSPiXvEKYPLMEfop/7821W4avO9/rpnpPG/qpEjjvVqPJyj2M4WmLC36VSwcVil1i+rM4y9fIPy+L7Hqf5/RytCEa0JViT09aZWzg2UpP1Fv5cKdUr17qDJx/KZlfb6aPmb0/lPmzO3W+7SZt2rUviHTg6n/mCQlpX6vsb5KCXG4kXK0wxR1wXjc4fpPSUXvTfssWHvknZsCOVeCHjvm3qtm9Uxac/19E5+t7/z+ilVl6MNYvp/Oih2ycgZwbHlRz4EpfUpWulNqjhx/88JOq9TyY21qGKAk7G6KFq8EUbcl4gJk//zXXRneTa9vDvSou59vvQszAzhCKXGglDpm9/Gg2+xhmKRtS2Kk6q7knsnVjrM8GL0ABGKeVKkjkX9wONPt9xFTR3pHps0dVttZVYB7Q8TLhkrvlteWBP5YcOjn4JbYwhOB8OqQF5ehDaAXEKyMOnxeQ7iAVCDoQFd5jWgIKAQCPZyhAR7DdCwLJH8KQU4XZAAAIABJREFUBHf3Vu3mFqb+sTb1MyCY7giNLY26DrOCmzUbIu5h6yPeYcAdebPyPstLqKhMNKL//aI6fU7huMKYOw17pBfWLC3NA/QWjTngFuBCpcQAKXWr67i3ltQKx9ig24yu6xV+LTfexrQBOupeAxCq6Jsab1vqE6zYeaoZTnoiULr7gcBn7TFnWrnTBXydOyHQHg9r26U6MVrlCRppzhsqWuysPnHdfudAr1tAPB8sSxk/Zc7c+o7RAoDHrx66qrwo51pg/vQxo4+dMmduh5A4izdVYauCT2HJ/k/DFsWHScArtrTe9pj24541o/damBgUluh7GFAPnj9rETAqN79gWJKr6pkv1h95rc+zeXRufsHfi6aNWgxgZ1O/1GZvrBNhy1V9DNSFs1x7/t2ft3U2fJuzKeJOBVgSSClsQnMFBEF7sf4OF2ocd1SGfCGBNrWVRLn1LswLWAlQNVsP1hrCTnMXV9hIMm9OPbQkK2JkF7v2AA4JeMy/JgcdW5wVgUCjXQIhqbdCenjypr0+rspkz4SK7gSqGNEeK6wStNPWHTWznKF3SiKeO4DgngkVyTmuoGteRY+lQHC/xDKyXaHg2+XZ5YXjCmOux5c3K+8HrGxUF4hwiiNyis8RTPstlLCfgf7Lj7UpR2rEaOChvWbt+dHQpPKghhlfV6epwnGFTc12bRBDmEUAqe6yFypC6fdbMWIUAm92BGcVv69fEo5do4a+M3dCoEtKAFWtO+grgIrVR7b63zOWRGqyXwMex1qFb3uH1e8TwH7Ai7EYTikxGTgFOL4lMmw1iaYrscZI3jgj0ZU9uabZITGPXnHwTrWbM55wJQVrwtXeC7ZyVrfwj399fdP0MaM/B/2ywxtacd/4o2dHaj2zO3riSluT5tl0cmXIt+LXqSfVOad7YwmpB2i8atknpRlRB/AydfF49VbJi6aNWnDpY2cOXFPV75GvNxx6CvDDATf/57lL9rv1v31TVvYDZncXCwDQF/C7s9ouuyztQZojvGd51FmrEY3uaNjb/5cAj9gLSzuBmBo2PbdhfSYlW8V4SqnnA/MBlBI5QEmr46H9vmFJOHxYGwafZBdvWWfdZBp6RXFWeEWPEtfbwD0a7dxWFbhq05ENkGhEy+gmJrSHw6oERDUYAqIlEc+tjcWOtlXR+sJxhQvyZuVtCXL+9Kyf6uyYA1w95dWc3b+uTjt0c9R9kFPo0V9Vp/UBTgPWHTB78Gf7JFZsDmrj3tl/XbW4JfMv2bx3GCA7cd2/7x9x9klK8bWUugpodNutPTCF/oehhbAr1HRV6iowdagV1skzR5Q+ctmLvwhHcBJwQ1vPt6pv8PB+v3nSS9MiZS0JOt0GQaCqpZrBWSXO5wXiiOxiVzawPe3CPzF9zOhDIOsp0Dp7rxXnnX3d4srttZ8yZ+5bMy855O7qDenXYa0ijps+ZnSHz7ZuKy597ExHOHrCsEEZC3+BkwCQUn+vlBgIrN5WH3sFtr+UermU+g3gDVukZ5t/w/smPhsGzs3NL7haEJ1WXNvz3Olf33LOX3d/qnbPrO+eboO31amYPmZ0Hoi/WqLxOkozy+B2ZHo4gyMdQjvVmUubukiQhSUT5cDSJK0smjaqBuuz1eB3VCmRAXwN/Be4snVWI239VTRaVydEv0kIGGcZWixLvLpKJ9qNhN/3Pdt4SAP4tiatGuDL6vRuubYY0SKH9ccDd51kVDv7iqiYO+inJdu9yBeOK1xwxDN7nFMadT9voL+JIrbXvM2xneVt2jz9lA3LsLIOnwS44tWeh31cmZEX0I4joohRn1enpwAT82blLU1zhL/dM6Fi5eao+545ZxQ1aVvRENEsUzsYO+ixfYFrsDROP43JG2slRU96Xb1crmuiDnNZ4tVVRfG2p61I3+WN3qW/nkD6rq8fDiM+j7c99UnsUbiy4je5+xPXPbDTubdfvE1HIVYkVzkOBKhOin4fC4dVSv041gpxixCIOie1D81wWO/++/GHgPgQhBNEeOPCAU2KS67ekFFth+YYgJtWKhN0ZuYuP2NgVDtFVTjlDyupUuql2+l2ITBdKXGAlPrHps5VNG1UMXDexIfP/+Tj1Udf+VjhlEEC862C5ROnv7vy5GJ2wCzpZ6cNzHN4+n0aDbrKQPwN2BNQXeUBqiiYEABRnDcrb1gTE52VLffkao7jLqXerJR4GChoubX1bSAIuAQinFTruAR/+Zbvg1LiRiATyeVS6obeU52cVXcMa4xotg7rkoGDjjIqnA8T5VqNnrdk4KBGdQ1Lo+5VAFHEQQb6wzNf2Pm0lhjb3tx9yvpPvjpn8cOF4wr/flRqSfqIlJLTQU8BlldGHad/UpWZv7g2eV3erLwvjnlut6cmv9L7isOeGehtaLxd05aMB5j6xb+WAntIqTuEswrQZ7XrSk/IcAbdukM5cbHGjCSsBghV9u4oCgFbqFh9+DUAtZuGNKn2emvIKHVmAeGd1nheabRxI9irba1ic3okALCxR3hEc/ql7bLOH0zr4Qxm9SaakGTQ9NKyyr4ZYguwq+bM25WIaufhAEUVu70IoJQYo5R4SCmRuJ1uLwJ3YBUQaDaPXfjok4FoYh4wyWmEh7638oT/2fGbt2Jlh+8QernTx4xOLPmp72s6aqT0GLLykilz5r4xZc7cqV3FWc2blTcsjGO3sJWcNM/Oadku9sPKSOxiJs15eJFS3yGl/gFAKZHbIqOhrjDAFhu2kQiaDmRsr5TwXgkVxwHsn1j2J3WMblpGSwoHHABogUAgPJGc4BlKicaU2I8ETLuSgyuirVrjSgnX1kK7HZU7T94Qve/UdS8Xjvvx7sJxhccf7StJPyCpdAKI24Dw+rDnnI8qM6eXR12lebPy3j35vwMen/xK779Z0haWXMey0iF7gSZsut8Z//bcnDi/pd/x+4a5osatAOnlzjNiVW2oI1K+8qilANUbhnY8WSPt+A7YyO9qEm3J/kAh/vJYxCqfrJT4TSmxW0sHqEqOrgUQmt2b0684tO/7oZ47E8rqRc3OAx1VuYP6+/3+pMb6TZkzd0FSzuZbAFL6lLzYVRyEltAr6bcznSK8GStjHyx93mFYxQP+gFLiGKWEIaUullLfur0b9vZQSjz61LGjryiaNuqRv+S+tneKu7wIxJaa7DT9waPTMn3MaAOYFan19Pf4asafc+OPL8fbpjZA2op1wk6kkk3pVDRt1IKiaaOmtnSlXSnxV2BZqwoL+MsXNFRQRUp9OTBue903ht29BFp/U+PbtcU2dPMHWuKwKoEIYH0KjcD+ledhBdtvtw/W8nrEhNrVYe8M+/yZWNUq+rbAjrhy18kbqp84ffVTheMKbyocV3jYMb7ivoO9lZOBmUCvX4NJ531Umfk8sDFvVt5LPXIfesydXWAYCSuhg12Qg27zUn7/LHQo29qAuvjGlO22igOTZ44wvWm/LDdclWe88MD4bampxISiJ70i4tAjylMjsUqsK8Yqq9limbB+v3mWAOEeJa7GMtJRSuQoJW5VShhBkdkf2FLVUCckTQQ2Tp8+sfDhh0+9we/3N7jjUZkx6NdgZk/Kq3cevCNXwKqNJB68a/qSmqJpozRYq1TA0K21V5USh2OFMU1ozXy2bmYWdhz5g+fP/qU6nFIXc7itLPAuSWq/je8BpwNTLnroo1nxtqeNUEDgd6eV9lLleAcrHebrWA6qlDhYKbEHwPYe1vJm5Q1bH/EeohECRJNWlrtpnGY7rHbM6kjgRtMbXZH0fkaK98vUzwCUEk6lxJ+SWey4FXt5XYz89Kyf6jT3lgNvYgf2KyVOU0qMbOF7iSv/PnnDmjljih4qHFd4eeG4wryjU4v3ynXX/BP4H+jDAwmrhrgzPhGJ/f6DkVAUpQNdkCMOfYxGo62t0S59s8jc48VagNS+H46Kty3bwpO24hMznOKoXDvsyLaaI2OzYw9nVBght14Yi/Gk1J9Iqc+0JYpahr/cBNZhxbA2xknAFcBgd+lGKxRVa9AmztKNrxlG+Nna2vQhGzbsdQuwwe+/6alHHx19zZNPDk/YMp3fP6xa7zQ71KMP1TkD94smJM3fEZ3W3PyCfmXBTGdxTc4jAEqJBIAGsqw/Af4GtMq5klKbUupTsbZbARje762eAFkJ69+imdvAnZH7Jxw1uWJV9ojUvsULgXvjbU9bYd37xUgneipWnPptebPydmrreaXUFVLq66TUQaVEglKiX2vHtEOfHgBeajwMSo8A7bAPuvoiULvRkhVWBv20ZMGgn5bcVivLTtFOHUh9qtf4JQMHpQP/BH5USmRv3adwXOGCwnGFU+sHXUupP5JSn1/vSf564Oq615US7pbY1xG4+5T1hW/8/dfpheMKzwXuBW3Jaouw6c1548kOc0H2+w5JqnWk13rNl4VdmzpWNeU7In+7/OEaRCQaqurVIUXjy4uOngboQOlubVMjG0itdA4B6FHi+k9rx1JKGEqJmKxWBzymrk6MHrGduZwAUupHgUFS6h9d5ZtmJ65airt4DYmrlgUT1q+688Ybbz+/T5+vEjye8pOAl4XQp61dO/SOVasO3eD3+2feemv+SDAvBNzWAoggkpiyo95UDgfYFMgpsP++Pysl/qBSoZQ4WynRT0qtpdRzpNStklyq+7zUX8H9tWwPD8Du6Yvv7DDXxjZi+pjRw8M13nuFI6qSsksPbkiCratQOK5wwXfjFl0L/AV0cpYz+P2Vr+VkNtoxdjwPvK+UaFUtHPvzOho4u7HKb7t7qnrUSXnSxReB2pMWOax1DL134w9GjWOEMEUfjX5OBIyPgf9KqTdCi5IxhgH/sPv6gNVKifGtsbFjIBSIIBARQgeHZf3YlIof7YUfKE4MOMY1FK/T5dDOzYHS3Ttk5ubkmSM2AV8iIqPbag5T6AOwdB6bIuTdGEOAMqXESa0dKOgxo46o2Gb5RqXECGBRXSKFlHoVWLGo7mgZ6Y4lJDiLj6uLRZ0wYX7wmmvued3v95/br9/HvXv3/up24G3g7GjU+z6IswF7ZVbjrKncIW8qA3xLz3eIcC1WbXYvVmngLYmXSolM4H4gPxbz2VJZxUqJE+qfX1G+hxPgs7UjumI5aMAqDHD/uSOfwIi+CXqZjjpOHnvVsh1Gf7ZwXOGiQ5NLp2+KuDM/q0x/Nm9WXntJBk0FrpVStzgEqs7ZlVJvkFJ/31j7DRFvXwdmxCX0HcDI9i4D31VplcMK1mqrduhLBOLY1Fm97pRSXwWglOgFLLTjnpqElDpQdyMCEoBXsW+qSolcpcSEui2rzkT9kIiLslcuPjtrzU1KCUdj/dqa1Y95JwFHBzzmI/jL417tqB2pBN3hYljrSO71eTHaGDrnvoktTmLaHlXJ0X/UJJg1+MtbvoX/O2VYmd3ftXYgX4XzDW/QMOyiBluzAStk4E86r9Gwa3Vtie+Zix//YP62xp0wYX7l+ecXXH/TTbedAWR7POXvA3Vxr9qorf7CUVs9fEdMvKoIpe2Xm/prddG0UVEpdZWU+vr65Vjt+uyHAJfHaMparDj/r+qfdBqhXNBlRdNGdcmMajvcZF642jsB0/B6fNV37oilgWeetsbvNUx/hen6C3Bte8wppf5CSv0SgFJisFKiJfkBs5QS/23KIlzerDxPedQ1Iorx/LfnLLqh21mNHa12WAEGL/ppZnBI1RLvdymHLBk46HT7dBrWxanRJIptIaVeL6W+QEr9jX3qDOAxIAN+3x7sLNSFRAxKqLoEOKfVlThiQI9i14VhpxlZ3zN8f7xtaU/cKb/lJGQt6rCx0s6Ezc+CQfkqGXt5K79PJFU7vBGnjsXqKlLq36TU/noPmq1hDZBEvaIOSomh9jyLgOHbrAinRU1CZsXeSolnG5vA7/dXRyqdd2Jq0FoDATMp5fId0VnNzS/ILKnNSV5V2f8BpUSmnVAiAJQS5yglxgFIqZe0ZnWqPlLqlVLqy6TU6+ufH+D7+eScxLUNJsh1ASTgtatYRYPlyb3ja078qDUdtwDPALdd8HKfdis9q5ToDXwJ3N7MfgL4BvimsVAAgF081ROw/J/nWmJnNw0TE4cVwLnOsy+W8PZTSwYO2lNKvQQ4SEq9DEApcYtS4uRWTHEXsLeUuk5UfJZSIiZlJdsTKfVnUuovYEu2bHzw+w7zhIy9nBGRnzshUBw3O+JANJRUGijr75gx6YMOmWRTtvz4F4FN4epeh7XB8P0cpkhKrXQ06tw1BaXEwFg9PG7KiHgA1vQOHWyPfQbwlVLiKPhjzGN9HJ6Q150cyAPGNmWeXtH5dySu+gkjWPMQQoz0+/07nLNqcxhA2PTMx5LoWQDsbt+gxwJnxvIapZTYVSkxZFuvra3aqcbUxs+xmquj4fFVbQAtAG2L4qs4mxQ3CscV6j28VRf0cgWqv6xOu3LvWXvu1R7z2g+7lwH3NLOfllLfJaVuUhHOgCn8LhHVTsyaltjZTcPE7GK052e/BIHTtUPXRtPCX351eY/cuhuMUsILjAIaTKhoDPtDs6jeqe+ALfFOSol/KCWakmHcIVBKTAHmxctpDTvNezR6g0A8HI/548WMSR8MiwYzeulIUg9gXkd0WifPHBEF811EZPQLD4yLaehIwGPWheh8s92GTcDWX16ElSzZaoIesxjAFRb97VOvAf+HXSe8IdxJgUwzYgDMbyzUZvqY0X8rL8rZPzlp7Yobp931fzuws8rgzO8uMUQ0irU9/xhWGepl9nX7FODkluqsNsDVwOfbCuuqCvuSimt7ddmiJZ6U2mswTIQR/Teww5YBruOlMStqdvVUHxTRYoOJeD1vVt6fErXbAin141LqdUoJoZQ4qLH2SomxzVEuypuVd/SacEJOWBtEMN7ulrOKLTF1lgb9tGRtzZGl1xuVzoRElT57ycBBDrBiU4GDsWNWlBJDlBJntKZCjpT631LqqfZ4ucCjwN/tYyMW1XfamGKsLdDtVZNpE1Y/ljDZFTGGlmRF3sdfvqM9BUq2fO51h80MTxvwdhnamW5GEv8Wy3ErU6KTNJq1vUI/xWA4Ezgbq+pRq+m9zv0+QG2CeaJSIklKHZJSz2gsfKa2NPm7it96fCilHrG9tnYc4bMgRG1JWs8dUcaqPsU1PQf3TlpVUTRtVEBKXYm1X/0/pYRXSl0rpY71teE64DQp9R8SjU6a7k8AcgRmm5YjjhfTx4zevWJ11oCkHuVvX/H8W1ft6M5qHQ+dtnYRiJNA56Q6wj/sM2vIje3o4F0ALFBKHNhQA3sx6Z/2/41y+as9s0A/an2NrCJJdND7S2cl5qt7Qx/a8AhCX2TUOg4Hbq47L6UO19NpvAyYQb1YtdYgpS4CduP3WuZ/AX5qTeWdtkZKPVtKfZaUut2TDPqscZ0eNXRFdZJ5aXvP3QFQYNatGnXYkpyGs+Y+gNJfR+0Ry3F95Q5H2KXX9r6gtqK1Y0mpa6TUz22189Ea1gKYhj4Gqzpe09BGDQiPUuIdpUTD231CH8uOUyBju+TmFyQV1/bKWl3V/5Hb3siYftqc/s98WJGxP1aOQJsUrZBSb5RSv7v1+Z1SVhwAcFifeTu3xbwdgJtABKo3ZIyPtyEdjcJxhV8P8NS8VBF19Ywi/DSxfGsMmAVMYqvkv/rYuwuHAOMbG+yY53ZLXBZIWgrkgg5h3Vt2SOWRtqRNtqMH/7j0YY1+DLjum4k979xGkwuBI6XU5fbS/MjWrohKqX+VUpfZhwFgKbASQCkhlRIHtGb8tkIp0U8p0aQnuJjg9x0pENJhiptyJwQ6pLRTWzJ55ogFHt+K6QBO7+YbJs8c0SFXO/5+xUNLga/NSGLsksP8PuEOG7u4w8Y7sRhOKXGIUiImIuBKCSf+8tqIQ4dSKxw/SanfbGpfd0pNhjMh2Ac4ButheJs4E4KfWb9pTffN5CDA6c56r/ilzb0vXxZIOvOl0l6XX7Fq8NX2amtMUUpcVReLvDWFxfunAJQF0zuS3F9MeOaOQSeAHutwhx+fMmduixKQuzrLg0mLrUpY7bcqae8gPCql1kqJnkqJwfVfV0r0Vko4beWi7f675c3K86wLe19aFUpIH5Zc+ggIiVUUo1vOKsa0WfxkzdGbrwj3CVQlLPBdvmTgoD8E2kupI1LqxfbhKOB9rJipmCClni+lPlFKXSeBcxvwWL0M2I4ULjAWuLlOX7KtqU6MPmcKXQo80h7zdUSC5bs8ChAJZJbG25btYbgq54MeNue+iTFZedqUEdkD6KHRsdK6nANs64G0Wdg3iyVKiYMjTl3jCRrNui65U2p7OlyRHKzqVw821K7H4FXvAxiu6AJ28DjC/bIXXA0m3oxP0qMg7Ox1b1gbz+XNyrvxopd7n3nVazkxiZ+2NSwvxXqg+BOrKnfxARSWDO1yMazlq7JvMpxRsoasbPBz2Q3KTkQDaNcqkLYv8ALwal3yqB0H/wZWDP12ufK1nASviM4FjgNxwaOnrZm0rSJJ3cSGNnNYhz6wsSqaHdqfiCgBXlsycFBaA03fwoqD+x9Y+q1t4FAeD5xpP025gcVKifNiPEdLuRsYbIc1tClVdyWfkFTj6L0xOzwff/kOI1i9DVaCDjvcFXvH25Dtkdb/3V9AiEjQd0ksxgt4zXEAq3cKtVp/1f6OngJMa+1YQAlWeeZKb9D4PLna0azYyap16R8GKxLXSKnvAS5pSGdx7JXLokDQDDs/2ZGdVYBVlf37ZSVsLMMReBOotSvyRIBq0P6PqzKfeb8iqyxvVt7TQ2cPPmfKqzm7tnQuWxIrF2vh4E8kOKrqVl67lNTT9DGj9wqUpuyP4M6z8n/qsgoIrcV27I4zMKM7uWrXt6ejZycY/h9W9aq6Cm4mVrGB7S7q5M3KO2xBZfpvibU9jjp049C3T1tx2o9tbO4OT5tqme77/JplSwYOOl2j50d6B7/6akqPwQdMLw7Xb2MnSTwDoJRIAj4D3gQmx8oOKXUFVjYzgA9LYWC1PWcm1hbE61Lq8DYHaEPsVeC60IVdpNS/ttVcydWOKRq9TuiuUD2s5UyeOSLy6JRntCux+FQgJs5gW+BwVT+JiN5Vtvy4bVZ/ai49ip1ejTbDLv1Ka8eyL/Rft2YMO0zna7synqU5q3xrgH2aNZA2agCvUqIHVijQrTQkXSN0yOkN5bTc6s5Pbn6BC3r2TXWXvnz/zoueenlzz8tUZVYmoArHFS4496W+uU5hXvVdta9nGI4LasdZ71X04IDZgxcFtONFnyM8rzLq1CbiiLo+jc1pX1v/dH3NzS8YJkgcb28Jz83NLxjZVUqzGs7INDPiLDfDzlg81HVpCscVfnDanP6LfwkkDs6blecH3mkvx1VKvbDud1sR4LO6QgPb4qrXcsT7FZlXgWOqoyZHHLnuSATiL8CRfr9/R5bJa3PaXFJp0E9LPg0cWPG8a613V+/XKTMaaV4LTMeq/YtSwhHr1VYpdbGUeqyUui6O72/AS8BAe864hAsoJc4Bliol9muL8cvuSforcKRATMu5qCbmMWqdD/FjoGzXP1VN6kiccfFTYbTjLRDHzpj0Qas/l+6wsbtALB4wLri5tWMpJUYoJVpc2MAuCPAFVjz7FkrTIpka3bPoSW+TK9p5MyqyhSOahrVSm461OrJNnN5QUmJWeaNyNl2ZBGf1/kBS35SixcDa0zLWv1l/C/OJ038revS0NRd9dc7iU4GcfRPLT90nsWJ+UBvVwE3lUdfHJnwC+nYaSZJRSuyulPhaKbF/A02O14i6kIQukwj39O2DzzQjzuOSe20umDJnbocOPeooLAsk32diOIAbaL/kqy0oJQZghScua6jNvrOGHFRYm7IprB3TANEj0ANh/detCtAOtIsGaLRHeLyZGJ3j3OCZuGTgoFMbaielNqXUD0qpP7FPXQW8p5RoS+mnmVgJYHWVf+5SSjwfB8f1deAmYEmsBy560iscUfFYyGWGsOS/dnjC1b0+NMNJvWZM+iB+xRuagDt5zQKgZ/qur7em6AZFT3qFKfSwiEPHatvqBuBfrej/DZaz+lT9kxGnXisQJNYYA5o6kNdX0xtIsFd9z2c7ZUSjYefGmhLfDr09u0+PL68BSHDWvCqlHi6lblBOqnBcYXT2X1e9Ovuvq0YsHPfjQUB2miNk6+IKASQkiMh1V72W09D1MhNri3VdA6/76qo/0YUS4YoX7TxOOKOB5N6broq3LZ2IOi1Wg/g4f3XV+t7a+oVzXux3YN6svBciGJ9vDHvcu3qq3gdqy11lUYFAdydytgvtcrM+YHqxNmoc44AvtKGf+fri7BOa2HUjsKoN9AC3IKWOSqk/qndqM1BSr+jBiUqJmGzJNmJHmZT69q01CmPBzivdw1OqHL6K1OjsGNWP7/QII/gr4E3M/r7FsXntQWrfDxVAuDq7VXqsQrOfoUV6SVY4VmFAp2DFnjcZpYRTKXG7UqKPXQjkka2/2z1KXG8CZBe7Upo6btmKnB919P/ZO+/4Kqr0Dz9nbktvhNAhgEKiBmygWMdgh7XhimVXLD80iohlVWxsLAusii7LRsGOHXWtYCccRY1lRSFCQg8dAqQnt8/5/XEnGCAJKTchwDx++JiZOXPOmeTemXfe877f16amjR45TNfVc8C7Zqz6XqiArSTgdnVoz3pbs7z0qOR4Z0n1LcdMabbuaf6Y/B1lQef9INxAEJRyK/uIRdXxazJmZ+wlwabrKk/X1dD6SupOeO4qW4yj4i92zVdA6AXooAgHmDZ65IkqaDtLBWwPX3X38k37PsPCRIZCQxTsB+PPjGHVgPFSClfugsS7P/iy/6M3vdv/u8U1cT8K1EjgYZ/Sur1/+dqzTo4puWmAy5sPIBAvAFY4QBvTbt6l9MICr+/wmqtUVNAV8VPcG40kYe1C19ULuq6ug10yEx+abvs2Q9fVZF1X480xOwPvEfL0tgtSiiOkFAvDphqQHS8E4iFgU/JOx/iw9HmTDMApAAAgAElEQVQQkNj/k0iAiPi1I/f3XBpj9ITnF4OxpGrLiV1b00+PTc4+AC6v9mo45mW+YC1v5mn9gPE0rghS+4BvUtW6kPi/+AshN9381x8bOIHQi269Y2j2gLC5fO1SVacjkjpxnijxdB7QO27NdiC/JStJZujAcOBBO+q0odGlbxUHnJ2AJce/csRjd33QJRFASpHYUAIcwMbK1LFV/rjEk7vnflk0dcSUg8FYBXAlVL2GMEqBGft7LgcS+WPy8+wYnmgtuJJ2loSSUgyRUsTpulLj1x117Gdlvb4PGIn/jNa4/4IETjojtmr1mXE7Tsgfk//3/DH5lQCXd9rc/QRHMMPs4l7LWG172nU5dPDH61Z706vHaOX2COC1grT05ox/BDCUNk4Uq4uuq+3AscC/IRR3J6X4ZU/NtjBTBXQBeoWjs83dfLcBpwQ19bjlXf2DgDf+c4DKTcPCWvq0bdDmASfnZOXGt7QHe1AMBozEMvtXrZ2NlOJ0KcW4hryY9bR3AOi6WkFIEaNBiZ+tXXxlADURwUlkxzclhk0HZf4NlXPH0t7J5v576mvsiq/u4YqrOaop8z4Y6RO36lgg2VC2t4AHa1eSmkutdM+vY5Z++8KlG69QiMOBOV5lu+uHqoRtQ1854kJCChIrGiqX+0vxSecJjO0JrtL7WnxBHYxZtw+72FsW0z+x/5Zv75wzt92LwhzoBLAVVxv2vHY2Vl2EVIpeDcXNqgVeI/JYDYEmwCEI/inB9sKTF2/Nr3uerqspZWWpWxyOqmB2dvaO9prvoUy7x+8d++rm1wRiAjDC16/m9aaep+vqKyDVfOghpZggpQhrFaAGxl1SZzkrltBSxSZzDoOlFGlhHm89kKbrqvUi2tnxIqHMfqfXaQQ39PK92PrZHTxUrM9cCnj8NV07fMZ4ZFJBHmBL6PvpXS3tw+MyRgQ1VRSmUrwXE8rE36eqhpSiK/CLlOJygMbiJQG6bHN0UygiPVoGML8JRqusO4+Ax/UJcBkNrIr4qiILPWUxG/Y174OVXrFFEwC6RG/6QtfVG+HqN39M/rb8MflXnx6783a/0krcyvbBgxsHHLnWGzmnvnK5h937QR9gpEJ7bvrY16rDNY/9TdXmTreC2uaM8Vyzv+dygOIGmpxwGQ5M2bWLCIWl6CCcq7w2AipUYkQIJUDJ2vZmMng3gIqKXj6bzR+OMtcWTWB/JZw84+tX8z/nmqjLf7uo9y1NPcn8YNVKUT0I/F9bTbCB8RfoujpR11W5uWsK8IVZczic4xhmBbCrpRT7DJ1ohDOj3FoP4L7Uaz2WMkAdxs3MNITmX29zlXXICmh1ien281fC5jEqN594RU5WbosyZzWDY8rj97IbWsrtwMAmeufKCMm2FTelY4HQm5N1G9JTFWeAKgaxyNRXnWeOuxdBr3Ob4bcb9R07FFiy/biEGEe5cfnAF9pEPu8/l2z+V7Vh7w1MLA86jpuxre89Z75++IcZszPur5v1Pajzz68KDC3GUf58I90dUDz1l3NuJ2TwvPGXewtarcRxKJJi9/bs66o+tb3Gqw2J0XX1kylvJQGKfDb19HaXt8owfgGhgee++bn9752f238YcBWw+vPPY48JBCJ7ejyJTa7KZ9E69ovBml5YoHxpNWca0cHlrsLoKQVp6enNOV/X1U7gKEJZ9Ugp0qQURzZ+VptwLXBFHQPzKynFdWHqOw14CRjbkpPNrPBHgQ0unzY9THM6qIjsVBCjOWpO3N/z2Bfbl159tApGqKA3sR8wv9lGa3Z8N6df0yLd2ivhmI+ZMLW9sTZSiqOlFBFmacM/6brKbWL30vx/k7NuTaP1FeDoaaNHxhAyqH+WUuz1e7JF+JyaPdDmSZQdlWp/7DEDk37XnDb/iLYaI39Mvm9Gn9+9D3RfubKrw1O5LRBxASGP/PyM2RnDUifOc/6+89hj+savXP/7I1eubat5tCfTRo8cZvjt08yEoaxQbLVFc/Eprbgs4GgXY980VudJKXaFD50eu7M3IJwi+Nlan+2MOFv5CRDYChEjMT/DQaNLFfDksmWXOgEn/KHjatG27DdJnyFPbi/Xqm1nAjXKbsz7+W/JvZtzvq6rrXUyjJ8CPmtqTF240HW1TdfVd+ZmPOAlVC0GKUWElOKslnpfdV0VACcR0qVtNg6/uEdTYmh5XOA1ssu9LenjYCfgTvrSX9VV5GTldvQ4Vv2PH1UE+/A61sNVAJEebWtrJyKlOFZKMb12SayBNl2Ab2lJFazs8ryAzQi6I4wtwHCyy5sUyxbTteQnwJGcvn4i8I65e6+wAGdszVEI1fNQNCj+/K+JRwSVvefGytRHgSaHYzVGnXLX3aQUn0kpRpqrQk+lOHwZLqHeIyRrVddjfokvGBGzpnzgDeGYQwdBB2EcbHqy7U1Z0LG2NOisd3WkDYggpN1cUbtjgy/iLxoKPXbnraE4WjEEbJh/VxvggOiBuq4eiIracRVAz555h3LVyHZlv2pQphcWbAyk+K7GEH1dv8d818wkrLqMAS7XdeUzPZ39wznPpmBmTY/QdVXrxRoFfAG0eHlD19WPpvc2rlnGeHa86LrVcbnfblSXJgYbFFE/1PFV9fwWNAfQrJel/YAEfLURVRFJBU3/fGfHD1OoWsPxqSYmMjVGOnA9jcSv6rraRmhlYHLLhhBev0MVNtVYBYjtsWOuZg8Eq7YlDDXj3DOBO+u2mTZ65DD39viuht+uAfMPNaM10l6TBXB0yk+FZvW/ZmHKktUqALikFMuAiebhnUAyIT3cMiAD6LvKGz2LP17k/YB02dx/A1YDX7b2mjoQEvBR5zr352QOYNyg2iWGVdeVW9fV1bqungHImJ0h1nijByn4bNrF21bNz+17riL2O1CdCd1/AyEPes1SgJ07B9jACNps/s/aY74W+9lgBcj4ZvXn3kFVLziKIntiLvE3F11XxXU8nVcAhVKK/b3U+y4ho/UbACnFHVKKl6QUzVI5kFIkA8uA5iTcTLAZYrAjoOVYsasNY4/YuQ4gKjn/pP09l8YYNzMzJCMkgn/XHJWbPCVpl+Zk5fZp4uk6Ic8AhMHzo+vqdSBe19VuWbHmi+JdtZXadF29aZZcbTb2oKiKq7Q1WG2mPq68e4XbCNg/8JTEHTFt9EhBKDFyt9+RI9p9N2A7VL1gmyr7DHLZ3JzcPfeLprSXUnTZI9SqELPkrZlP8DWwytz26bo6XtfVO+b277quiupIYE0Chp/iHtLNG4w87piUvP8VTR1x0MQSm7HTu67T3LZoJv1d1YOS7b42D++TUlwupdjt/iBQJ6Y6g73HJnur5uf2fxa0T0CsE1ScB+jgeV0TmwM2bWsRgM8X2we0Zddeu+CgSRrs6LSbRFRjRPwWO5aQ8Txp8XmpGwd/WvRcK7r7nFCsyc8QWprXddXuck7mDb1uzfYYINEUJ0ZKcTLwm66rRj/suq52SCleJlQybp/4H43T7fAkgECMJzv+g+Z4qg4lEvp+Xryj4EocMVsuJExLpG2FabTm5WTlvgH8hgi89faMa067bPzL+8rUl0BQoWwCERbPT31Z34RCYm4BegKLWjtGC5kHjEIzZpQVpaR3je+cufofLz4dKE1558uKJ272V8dcFPJSY3AIesGK3V0P7xO3uqp33Nqd9R2XUmQA/XRdfWjueoeQYV/riX6MP3Ry0XV1E3tgrm7dCjxeqwhhGq15AIff995zGsFAj5j1D4TpsjoMppFq3WtbQZVhW1sVtEe35RhSiljgaeADYFfOyZlxVS+eFWvDIdSl5kvtZ6Bdmpm5s/YZnSeluF3XVSmApvmHYDqkLNqH/e5hhVASFnBzMMG/0b7Z+ewvY7sOb2lfuq526rp6WNdVUEoRQ0gcu8lKBG2FrquHMcXMzS/MZ5iGZRPOfUDX1Y+NNsqOjyQ7/hZ7gA8FQohD1IvUHITmX4IIuis3nnLAZPSOm5m52hG9eSLKfqK3ovdeJQT3Irs8TyAWCkQxzYgJrQ+zeMendVcvpBQOKYUwl4FPpJGyqE3Fbzc6lSQGmr1CYo/whtQ7DDGuLPfCU7r/+B+cFYfd7NKicmM8A/4c023nAnuU9zzMqkqHkhcsa+b1fSp98d02VfWZoevKKEhLvyRf7/f6D1PiZ9VpNgF4sU4xgQep8/fUdfWsrqt5+xjqeBpIFE2dOC/Ob7guN7C9/p8bXlnVmuuxODjZ5o8o9ChboC3H0HVVCQwG7q3dlzE7w2bH2dcuFEIIzBfbb4Znrq4GkFL0Ns8tBXjxxcxUw3CkJCcXtqlxbbE7HcJgBUgvLPB4TqgYhRJVkQsTcgrS0lsskl4HG7AQ+DUMfbWaOjJAVcB5mMtrUopUKcV808NRL6Zh8JCU4i+7HciOjyvOifogYFPbgRkCUaRQfoWyYqn2wWXjZyuUbbkRiOroMay7kZD65dORnZYurdhwup6TlbvPcAaPy0jxOYzyMHjauxCqQGUASCmiCK1oPAig62qLrqtWL/MKJbwOv2i2EHfA4xoYijETpET0dgqlIYRAE5oYGD/04xv/lZc54aWvPr9zztwph5KxCqAQN4MQw7otKC5ISx8GvGvb6rwy7o2uN+Sf0e9Ms9mjwODa+5Suq691Xf3QnHF0Xc0BOtentzu4809TgRhNBJ9u5eVYHLSoNo1hlVLEAei62mDG2tdySoHH7gJhlm0WQcxnp5RiMLBWSnFlbeMtW47rB+D1xr6DRbvRIUICajl+evFPBWnpI4D5SlOfLktLmysQC9ILC1r0cDH1Une5/E35imRgYgPLmu2C+UD4ts6uPoQSf8oApBR9gYCuq7oC50FgeOdiezrZ8b28TqPA5dOOAW5N2e5IqIgN7oip0kZqSnwtECcS8qxKKxygcWyusm0q6Dh2f8+jOVw2frbKyco9GfgNeP2Z8R8dfdOMC8obah+0qT4+p3K3VkJD19WvwKA6u9yEkmfCKk1kD4qq2CpbSzxw0qxx7yz2rAeUTSkwlGJzzapDtqa7lEIz1NibNBFQDpvvZeAmAIFA+QnYt7iGAF/puipq5TjClDzbK8wpdeI80Tmy9197xKyrOb7Ldz/DBa0ZyuIgZVBkxdDf3XGxbdG3lCICWCSlmKPr6v66x6K1wPVFPpt7Z0CM7OxQ/wbVRVBW+7JWRCjMcNeKlt8fnQ5QXp7apHhwi/DQYTystaQXFnxjRARnCUMMI/TGP9/0CISDHkDv/Wms1oeuq6+BAXUM1EeAxXWVAXRdGcO+j7n/yGVRf1Kofzh94n1CAf4yYFPD4u6s6qz9vUKSXa7ILs8ju3yKZazum+jOS6KNYESXt2dc067VVVrLuJmZ5cCVYPSOTFzZ6ApCVI22OabKJsM1tpTiMClFimmcjNV19Wq4+m4NdRJfHkx0dn28dr8AEpwp+yuutiPgXLTtxNIoe3XhMze+VAZIhVIKhajjSQoD06UUb9cJKajLKdvdXWOCyjZp+tjXW1QO1uLgpyzoKDQQnP/mYW3lTHsd2E0T+q4PuriAv/R11Wy+/JxVuYKqhSCSQfwZQo4vXVfZteEAABERJWcLESwHNmPRbnQ4gxVAeLRN5s1UAC7CFIep6+pW4ErYFY/3TylFh4hB2aNq0APAGF1XPgApxctSijEun3YS4DR/L3gdxktkl19sf7CiWct2Fn/gLjvsHZSN7cuu6LW/59Jcxs3MzIvr9c2C6uJj+j5986djGmonEHGaEqUNHW8KZjnCxVKKsYQSAGe3pr/GMGNYW/SSeuecuXl3zpk75bjksyoQwgwJsAUHxg9JDvc8DxTeW3lVZIknuYfT5vtjVUeYHlZUfcZlS9kKbGygAtrNQNnW6p7PhHE8i4OM9b6oAoANvsiwOxDMIiZ/13U1v+5+WdEps9qwi3hbwCxfHvgPgCL2GCnFM1KK4/fuTZ3pcFS7CMXtW7QTHdNgRUgRWtpTgBZM8B//852dw3JjreNdPZ9QVnP3cPQbTnRdFem6+hhAShEJ9CMUPyirtzv825dFG6Uro3yb56Z4w+h9PiTxV3X/HwDKcfh+nkqLCHgSzwEWKsM1Iycrt159VgOV7LMbJ7RSgzUeWEmoxOoNhBJ02gShhMfhFy2SxKpD7T0kIITwcYjGckspEuJdpZMVNtvRnX/aYu7WhRIKQCCchsMIS1KqrqvJuq7u2HP/zbOuPcomAqO7RW/4smjqiJr6zrWwANAIKfpEa4GocPYrpXhESnFCfcc8yjYKqPqtJv4pgMzMnUuBn0CcC1xCqOrkLrKzszM9nqQony/WBczPzs62nsHtRIc0WM2Y1eHAA8GYwHe2MsclzhVR8wrS0sPmDdB19TzQX9fVStilyxYTrv7DhSlufBrwRMFb3Vm3INmxY0mctu2XBKen1HkD4Q2ZOOSwRxWvBohM/v3s/T2XlnDtI7cHgb+AoRwxm77dM7TBeCjuZAE2R0BkAPNbYbSeB7yq6+pDXVdfmOL8bYI9KKpjq2ytqnXfc+qpu+limtuHIldU+2OzAHZ4Umaa+yQhkfugQinNr125+PzU/IK09M4tHURK0beBUAB+Kx56Q1DZxeDOP7/Q0v4tDg2GRJcdCzAspjRsxX9MLfPrgLP2PHbXB12ibRiX2zHm5Y/Jr1OxyvgUxGBDxZ8PvLHHaRNCgUaibvU2i3agQxqsEDJa0wsLJrtPLzvd179moXNl1HnAv1tRDWsvdF1thV3aga8De3kHOgpm9vWlGGjmlwVCfz8n1hemxST2/bRYc1Qpm6Mqc3/PpaWMm5m5PqHfp8/7q3p0LV+vz6h7TFPiNKA2vKZFN1cphY2QtuaNDRklHZGeU0/N6zn11CmHsLEK8PLnRResALVtyfYhqbCbQ+BBFR08y3NsxdeONRFpQMGyI9P+2tzVLLP6VSFw/57HUifOs2+u7n0R8NXMrBc+b/XVWBzUbA84lwGs80buS1+6yZhFTtKAJ/Y8Vhm03xFEiz4xpnR53f1ClJSDEErFzgwa/XZ5Zl96SXfYbDVnmrJXlhJPO9NhDdZahkzbHnSujjqd0IftFn+q+/uf7+zsCOcYuq5WAyebY9R6C8IhqxVuMgAUylAoCCkHHLLLneHgsvGzlRFwrazackJETlbuAeupvuruaXdq9po3fJW9r8vJyj29ziEpELW6hs26uUopNCmFwwyjSabh+MSw4nMYyTuTAie39TiHAllfvj202h83AEgB5qdOnDcMdjkEphz5y8r5x7yxSReIo4GVIihecS6LLl5yZt8BzRjGR+iF5r09D6TGrRwD9CIk1G5h0ShrvNGrAFZ6Y8KixSqlSJdSaLquKusrIPRdVeLhGqoyxhZ8rM458Shfdigi0XEcMH9+bv9hAGVlfa8LBqOiEhLWvoW5epOdnX0ovxC3Kx3eYIVdhQXu9g2o/q+jKPKEyLz4zwrS0sNttP6g66rG9CC9CeR2JG/S/8Z1uRQ4C8ErAvGAQNyAKYDeUtkvC8jJyh2GcvQHcRgw/0A2Wo1A1I3AKjTfB8+M/+ixnKzcYWSX53lcxiMAZXGB55qqHGF+9l8CXpZSaISWxb5t/KzwYAsKt8srrOzbViKlGNc9ZsPTTVm+TC8sWAqc4h5SPsexwZXg2BixqCAtfUJBWrqtvvZ10XVVretqlq6rZXses4ng1Dhnqb9P3KqPW3k5FocADmF4AaK0QKulraQUXYCfgMn1Hc+YnREB4iID8c7jF23bJcVmGImDFSmO+r435eV9rgS1Ljp6xzXZ2dlTLGO1fTkgDFYIGa2DP1p/qb+nZ7qtxJEJvFOQlu4K9zimB+lW4H5dV8qskR4X7nGai3N51BQjMqiqz905Kb2wYEp6YcFz5v+tL0zr0NmVKa0iOIDDK8bNzKxyxq6bieFIMPzRdwEyJyv3lK1d/c8YQiGgb1P7Mr8HBeY/pevqQV1Xr7TV3OtiM4QvptqW0MoksUMa84XjinJvQvfQ8uW+C4mkFxYEj3118+UioPUnVHLyX4Fu3p2Lz039W0Fa+r31xcpLKXpJKf5salzuRurEeYetLk9L7h6z4b2v75vQptWLLA4OamNXj4suHxKG7rYD44Hn6zt4fHTZ3UBsgs1fW4qY+bn9L1TEfwVaBCgfdZb9H3vs5lOB00D8e+zYT3xhmJ9FM+lQhQOawqCv1t5WkJa+Cpjh7+1e+/MdnTOGPLm93trYLUXX1U91Nv8C/FNKcaoZOtDuFKSlD3MQcViwk+/xIU9tX1f32LTRI4dhFgmISLyD2p/N2vMW+0YCXrO6iiDMIvjtja+yjytkoAgBOBGBL7/Z8EDhcO/c37obBd32db6UojPQSddVoa6rqea+BClFRTiqWO2T7PhhCpUoEEMJJYm1qpzsoYquKzXskZkTd3q6LASmA9sAWTR1xD5/l+mFBesL0tJHeI6ufMhZGHW/zaM9Tqi6mbcgLX3PFZ3LgccIFT9Zv0dXWUCgsGRQh80NsOhYbPO7VgGs90a2uly2eb96uaHjxX7nn6O1gDoxpvTz3NykaEX8ZNBuBWODoOpqRYIX83k6PHN1Xv6Sq9d4PAmGEIaVPLifOOAMVoD0woL/LBrTfVDET3Fj7VtccwvS0s9OLyyobKPhlgJzCVW72FXNpY3GqhelqSnCEMW2nc6H6u43jVVJ6O/oNwKbNc3eXQN8OVm5wy2jdd+Mm5mZl5OVOxwRvABlywLuycnKfX/czEzv/p5bC5EgPISWsQxH1LbNVVuHDP7AOEkk2dfhvvPFD3yBhPVBb8KbDXw+3gG6SymO0HVV6xV7gZC02jFtPfmATV1gDwoIvTzULsVZn+NmIqUQSREPv7zDnWLYtcDfCx69rMFKaPVhhmFNWnZEmk0g7gU0hXIJhM7uf48nga91Xe1mrN763F8SHdolNwvUJysmj7LCOyyaxHJPzEaAdb6oqpb2YSaJfgjMqpWH3JOM2RnRENXPjvHCuXFRIxXG66C5BJU7hNjx/hlnGNJsmgeQnZ3dA/r2TkgoWnjbbbOb9V2yCB8HTEjAnhw7e/MNKtK4Rvi1IQr1xe/HHz6yoWWr1qDrapGuqxt0XQWlFLFAnpTinHCO0Ri/ZHX9mzDE6f7e7pfTCwv2LHmog3JiqgUY/o0OwIYltdEsxs3MzBv3zFn3AlcDR0d3+eWT/T2nlmIaobVyTvoN0/7a3xm7PqVX7HfPoxTu6tQLg9748aByG4jXvRW4to6xCiEFjRn1tA07QU1th1BiIVYGbouQUvQs9yYUFZYMSu0Xv+KH5hqrdRGGmAt4zCRPzdfHvZtesa6r4B4rUgBsqurzgN9wRZ7W84sFLR3b4tAjUgvWxrAmtKKbLoT01RsLGRwJRJ0bH/gVtHfA5gIVUERfJIS6t572t4AQZWV9r23FvCxayQFrsAIcuWjFbOBSYIhWZftYoR6hbXVJOxEyCNvKm7sbBWnpwyIXJvzNiAh4vEdVP1pPEwkEQYHQhOboSSizUVkP+hYwbmbmxzHdfsyv3nZc5nN/e+XC/T2fljJuZmbeuJmZU2o9qP/3z7E7zot/7K3DIxcSEpYQUOelRkqhSynGA+i6WqLr6ru6/em6ek/X1YvtMXeXXwsCCMRUwAoHaBlx89Zc6g0qu215acb/taajXRJYDvVoMD6wxbEu4uqCtPQLAaQUd0spbqvvvF+2nXSqXfhXRTuqprdmfItDi5NjSmIAjoqsPH1fbRtC19VmYAjw34ba9HVVPxYhgtWZsbWGcW1+tXaarqvdilu89NIZnYUIjAfjw+zs7AM6ZOxA54A2WAHSCws+CHb2zwcQCBsQoWzGBW0xlq6rImCorqvvAaQU46UUl7fFWAVp6cMUSoqg6KJ57FrMJ8lH7dnmzjlz82wu/4MgcEQPCTpiHX4QuBLWfGqFA7SMyKQVZyACK31VPZ/OycrttL/nEy4cAW1oD+fvyk4AQQBB0Og57NEjpBQ3EhLVvrG+xBkpRTcpxT5jX8OF365OUaitZJffbxmrLeP9lVeulRvO7SQw5hdNHVHQ2v7SCwvyjshf/qC93DFQIP6nUG8vGtP9NkJSgHuVpkydOO94YEhAOaZPH/t6u4ZPWRzYbPZHbAHY4Ivc0JLzpRQXSCkiTc9/vZ+9jNkZceu8UT1SXTW/a4IFoIKmo8dGPY6emppOTyhlj+7R42dLR3g/c8AbrAD27c5swAMohRII7lk8ss9/C9LSW7OsUC+1XwJT6ufPwMXhHqMgLd0W6Ox9kVBRAAj9nfT62nY+at3zCIXhD9piUhZPE3b3z96y/ifmZOXuZXxY7JvLxr+0E2UfDaqz5izNzcmaf++BLHVVB9nNucJzQdIkhsa8qbq78v/lLhl4Yvn6084ExgKn1qdTSKgE6zopRdgVOerD7zBGVsQFD4r70v5AStGjyh/3T58RkaT3+uz7cPZt5gmcZyQFqiIWxT6V9Ejqs8Bf92x3RNJvs23CHwBeDef4Fgc/y9yxZQBb/BEVzT1XStEP+AC4ex9NLzQQtkJP7B3DM1fngefJkIe1Oje0/QfZ2dna9u1HDrPZPAV2u/vZ5s7JIrwcFA+G9MKCPIHIBO4PdPfe7O/n3uBcFXUJsHbpcQMe+vmOzi0uOdgQZgZiJvB/EHpQSCn+2lrt1oK09H7A1/btrjQESu1Djuaqu5dvF1riZiOwXnU7fvrElKNm/wR0F5q3VUuBhzLjZmb+6kpY/YXhSxwE/IMDXJ8VwPRWDo+PK/i2p3Op2OwddNfO5aMO2/j9pBFL35p/LBBs4LP7GqGY1rZPQsuOj4x0a06hxFdtPtbBy+Siiv5ZTs2zNc5Z9ki4O08vLCj1HF9xirKpjY5NEW91yUo7tu7x1InzEpeXHjUwLSm/oGjqCCs5xaK5eACitEBSc0/UdbWG0DP5qcba2YXxV0KKFj+YuyaBqobYwnqajwBxeDAY8fC110prtWA/c1AYrPBH5ZZBuWufGfzR+j6EMpq/1X6S5DAAACAASURBVKptk6K+Sdi6LGPg7eHWbdV1FdB1VRvPOg6YBfRobj8FaenDlqWl3fvbhb0/Vpr6HThKCfUX4GSBmEQjxQFysnKFI6pnFxUsFp4y+1Ux3X663RG1rcjmqnzq7RnXRLf02g51vGX9vg8tEwkBRGqOqtvfnjGmwxSSaBHZ5XnFXQKnrfYft02hYX79HZqj6hxC2bB7GTi6rn7XdfV6O83wCoHQ4ipt+e003kHH28vHfL66LN3hMyKemD729bCVt6xL1SXbt+yctNZpuII1SlNfLh7Z54E6Ca/XBJXdtnTnsVe3xdgWBzf5Y/KVXRgc5qppVgyrlMIBoOtK6rpq0Dt75/td+inFWQMjqlblj8k3pBTX2rQttwH5wJ9rK1rVEh297VmbzVdKI/GwFu3HASlr1RTSCwt+A/70v5u73BDxW+zftCrtSeC23y7t9aZjRdQnmk87GZDhEN4vSEsXcSd2e6hmeMkXrkWxEUtvH9CjclTxMNeSmE3BFH/PKJmwXgQ0pze9OjXY2dc/6pvEJYDLN6B6oFIMdRF9JmB3LY8WgWRvqX2H6+gjCgprZWIanZ8touQoo+pIG/zO9vzUtGseXux/c/W4l0tWjMouWfWn62in7O6DD20B4AacoDTDH/Pnig2nH5eTlXvRuJmZB6xBlXqtR629feI9wMshaU2CKuj8AogFdvNsmpVi0oAfGwgXCB8h/dVZAoFC/V1kx39txbDuTU5W7imEVCC+qI1TNz3jcYB3ddnUu2wi4Lth0LRcKUfG67oqN2OTjwbW6LoqllLEAGcAv+q62iilSAIuAnJ1XRWZMctXA//VdbVKSpEK3AI8q+tqBTDESAqklN+w+Y64F7s97lgV+YhCBUH4Bu9YUbo4ecD3RVNH/Na+vxmLgwWlqN7oi2hycpOUIgFYJKW4X9fVm421/aEqcUQQjWS7r3Z5/zSlIgcBgwEbqNz5uf0zh2euzps6dcIJHk+Xrikp+fNuvvm/bfLyZ9E8DlqDtZbjn972bEFa+nPAmcphTHP9HnOPQt0DGAplLB7Rp8C+1ZmnVdsDwSR/z0CKL8OxNmKp5rUZwbhAdyPBf5h9s2u1CGjCiAx2VlHBrlqJo1go4VA2IxaNSBAq8od4EflD/K5x41/bO0/FVRANBX84PZ0r/vjZfFAbth3OJ9ILC/YU4G6QoCfpfGELImyGcpfGnCSlSO56DFdUbDhtQ8Dd+facrNyZ42ZmWl+2ZrJLnxV0zVH1Q2z3H8eXrz9jOPDb0+M+eaXTgPeeHD3h+QPScD08Sm5d5j6bPs7/cVzMu/ZuzhUKXd1Ze1xKcRKhkoZ/Ap4DBgAr23JOhlC3CrXrfmTH0l/dCzMsJReUQ2j+7PdfOH/Gxdd/cish9ZLt5d6E1avLB/Q7OuWndSd0W7iIkHD/LKArod/ldYTK7XYHPiJUFOV1c/sF4DJCetPdgamEqpytIiQTdBPwGbCCkErKOt+R1T9gVy8D1wuETYFrYMmG7vYjd8yGEe3xK7E4CAmilZcEnc3RYY0AfgXqW9LfjQrD8Sdg9XdVSW8D6Lq6dn5u30n8oTG9697j8STeDFSXlfXZK07bYv9w0BussEsE+8uf7+w8OOLn2AW2YufpgCYQmmNNZAbQH3BrFTbsyhkvgkIBlSIgXCgh0HAD5cplVAWT/YaosucJrygPdgokBrp5U53Lo74WHluFv4enU6Cnt5drSczX3mMqB9t2OlYFUnyJjrWR+fYtrm3+w2pi/T29cVHfJPwm/JrHm1Hl1Ertx9g3up4RCIdA+IFm6RYKm/tyghGLVFArrilOTAV2CsH8mG4/bylbc/4jMd1+fAgy7wvzr/SQwPRgmUbThQtysnKTgAdV0HFrycqLrpl56wePBn1xk8fNzHTvz3k2F49DuxJgYOQ3dHOusAGvkh1/JtnlRVKK/sDXwD+mbu7/fYRmvLzGG5W8pC0N1uz4TA0xSqGUQgXN74Fss/EOXHRAA4EynOwovOymt568+eWux7IMeGrhpjNvDxhOimu6Xgak80eM3jbgfGCJub0eOJ4/qrqtAFIJlbIE+A2IBrwAuq5+NLeRUnQCNui6SgUoKE8HuBJwBDSbVti5pyc1ZtGUtrh4i0MDDeV3CiO5qe11XW0FRu2r3fj3ug2ETsMF/PPffZZ2MosAbQftS+BBwI5573nmmYsHwqArQMy6774nS1t+NRbhRKj2Ldq03zHjrOYT0qH000h8aDiQUtgJPThW6bqqVwLLnJNOM0MU5ky//sgdBVf8Htvzuy+25//8JfA40PPOOXM35WTlCmfcuioVcNlsroqY66dkWbW8w8RbT2WdVbn5xMd8lb2PBjZEd1k0Kyo5f+pl42cH9/fcmsL8+8bdX1gy6tELEx8werqWBhQqYGg4dyQH3u5S7Pir1CtGP7rpsOJtgYh5hF5qfcDw/DH54f2eZMcPc0cE73B5tAs0xArgTuA4QFrhAHtjeljNe5ch0AI2DHs1aPdt6v/Fc3NKhq0GVqyecqHeVnOQUtwHPAz00HW1DUL3rx0R8RdMHvrXuwuSUh8vmjpiYluNb3Hwk/n6AHesLVD64eVrujfWzgyFmQi8ZBqtjXL9uz3f+Kk68YozYneMuiRp69mENNxTdV1Vzc/tvxjoC9y58Ju//h4ZueMNt7tTakLC2mNuu+0VK7ylg3DQJF01lV1C2KFKQG1qrJoEgZnAWxD6kpmSWLvNKb2wYEpz51Ky6gIdNGyOqhlxvYp/A+iUtuF+gCMvHy6S+s/9zl/T1eUpHTA6LFdiAcDlt8/8cuzj1xwD6ELzlVdvO/bR0rXnrTPjCzs8hSWj1gEYOP4F6GUJweOqo4PlXYodVwK5uozzDy4PTiRUKcYGKqyV04peihDGQ3EXATLCo10qwOmzG9lkl39BdvkUy1itn92rmGmnRsStPwK0hcD0rlsHlyX7Hd3SOy1+r42nMQe4qdZYhdD965ErLu5fkNRHEApBsLBoMdWGbV2x39UUHdajgIeAJumu/1yd0NMlghsiNON94F/A3aaxOgzUIFCxSjEjLq54gdvdKRVQZWX9Ilt8IRZh55DzsO5vpBRXE9K9vEjX1c7W9JWTlfsxkAH0TeyfpW1b3M/tiPYsvGWWHC6l0JQScuXHr2X4a7puBY4aNzPzgPAAHki8PWOMze9Ozilbe+4FKFs3zV79aeJhHz9++W3PdtiSlM/cMvc+IxD1DyB+3MzMCggZkX3WOa8TiOkKFb0gMpIJXVMQKAT4DITeEg/rymH9z1SJgaz4Hu7tXXp4A0FNDTE0hjoCf0homWEAD5Jdbi0lN5OcrFyRdPj7OZvXjLjJGXQgUDME2ofAUEC2RwGRoQ8/53T7o6u7RG8q+WriXV3aejyLg5uM2RkS0PLH5J+2r7am9uo6XVeNPtsyZmf8CfgQeCF/TP7Y2v3zc/t3Ab4k9BxFKYyioqPFxg0ZgpCz6cHs7GzrvtRBOCRiWDsYPqDU/Ndi3p5xbWfElecIzffCzTkjFKwIThs98iNvWczQaaNHijvnKENKMRKhRgEvJvaf+wRk3h6WK7DYhRkGkJWTlXsHcKcyHJN2Lh913tM3fzZdGc6Hxs3M7HDxT9Fdfr2kautxJKfNqQzJFobUA4AXyI4/DJjYLRi6/59fWc1lVVW2NOH+VGXHfSQQy3Ym+Wtqooz8Xhtd3xAyjHTgG2CHQg3e3jkwLrbSFmGsj+gRLO/UQ5U6KF0XQaxeUhPZ2bu4LCFYGOEWv0a7baMAmxWz2nLGzcxUk994pHj2tnO5uEb80CvguAXUeMAA4c3Jyh0eLqNVSnEZofjV3forrul+EWBPYfO+BNstLPaJUwRRiMTG2kgpuuq62mpqrzZKxuyMYaDeByEE6uqn5sUnHR1V+X9KdfonxF4OIsIsZy5Ao7ysix2UAuHDui91KA65kID9ja6rt3RdXaDrypBSxEop3pZSpDW3n4An8Q6UzZHY77O6yTDzgV6uhKojzLEqktPffNUZs8lbufmEq3Oycq2/dxsxbmZmzbiZmY90Gvju8c7YzV8ow3krsGr2Q1NmvT3jmg61rOQp7V8uNP+Oy8bPrm955SOBcO+0aQbAyW7Pa30jar4L2PH6HWo0MKVTiWN6r42uXKAG+E6hJivUt0ChQMzpvN1+mmZwWOWGiBJlAAhUUBjr5nf6h8iuOKnTrdVHRN9TdRUhaaVJwHArDKDlzFtz6UleTXneigmMADUztFdo4QzlMOMFpwJ31HP4ZmDt6rL018IxlsWhTS+n5/AEm//who5LKQYCRVKKMQAbJy4cu3Hiws82Tlw4toFTzgBsAArspYHIMw2V+l9F3Fjwu4FBIE4PBu0PL1t6arnbHR202byPA8Ozs7Ot+1IHwjJg9i9HEHqgNLuqR+nqEd0QwUqbs/KZ2n1Jh29aAhDTpWyXKsAl/zc3kDTgvRUBd+ckYfPsM5PSonWMnvD84rGPXXsOcIzmqFxVteWEG0pWXlSUk5V7QU5WbocoPOCv6Zpg+GOr663eZVbE+jnG+RVATo/oxxJvqz590XHVF3x/ctXaJRk1x2/q7rumJDEwGfgRQpJshKosvAsct7NTINZ1X2WnzQnBycqhCP2HAWLBXmNZMautYuJLF6cV13Q7KyN50bqiqSNKQLwa8hbtKnohwzGOWZJ6EHsYrGdNfewu4PQE1468oqkjrJAji1azI+DMLws6Suo7Nm30yGG/vnD2FSUru78NfLNyas4/FOpZ4Bzg2QaMVhG6SxnGGTF+dWpMtBPEEPDfL6juPjxzdeHwzNV53393ZXRJSZ9OmmZc8uCDU++xjNWOh2Ww7kdMuZhUXVffA0gpxkgpjtrXeTlZuXYQI1G2jy4b//IuSSVXfPX3Nqe/smJD8m4isAmpX14tNF8Rimk5WfPvPeDLjB4AjJuZubjTgPdOTOz/0d+DvphS4EPNUfnTW0/dVK9SRHuRc9MXJ4M6BuhNQyVns8vzFqcIH8BRUZUlsOuzeuSgUf5fetzgnr1kcM1TwD2ESikGBMIDPEl2+aLk8TVVAMe+uvktXz/3BUZs0BNM9vnaIcHxkEJKITzByG/8hosoR9WtUJuYpZ0O4kvA5oor0sM1nq6rKl1Xu5JhUifOG7ay7IgpoCjzdrokdeI8675i0WrKg46NfrW3aTJt9MhhoBYaPvuktV8dc+mWRf0vMzTvfeYLcy27OWXu/qCL7XBX4Po/J3orHurmrr4wMWDThH8ZcOTwzPWTMzNLggDPPjtiDKi7gOfuu2/aR214eRatwDJY9zO6rmoApBSRwKPAvfs6J6Hvp9cAnRxRW+fX3X/lXStU0Od4318TMWja6JG7/rbDz676LSJx5Q/KiOgF/IOGDBWLsHLZ+Nnqyrv+9TBoGcAtoA3euXzUmzlZX72Uk5XbqGRLW+GI3jrN9LwJQtJuen3tFtfErQTl02Bz7b7axAYpxVhgudQrdlJHcaOup1RKcSzA0R+u/zjY2TfXtsMZUZCWHtdW13UosnDjcOfnRRfa4pylv8+Z8NgXtfvNmNVzHTGbCnxV3Se/8cRtF7dmHClFnJRirpTihD0OXQbCFvooiVrBdQuLVhGtBWwaKmbP/cIWPBOwmfcvR/GSvrHK5tlTlWK3EqrxmvPVrM6+vidFB+Li7MRC1aeCquOHZ67eVZwnOzs7cufOw59xOiuNqKhiKw67A2MZrB0EXVdu4FhgAoCUooeU4uj62vprulwhND/xqV9+Vs/h+UCnmO47d8uwdJekLauzTBhWmSKLxhk3M9M/bmZmTtLh7x/ujNn8CmhXglo5++F/zJ11+5zzcrJy28XrnZOVO9xf1SMjlFCgAoR0iGV9bQNonUBsfeyibfXFuX4FvAasqW9ZX0pxLvCLlOIiAOeaqKcFQgOaVR/conFWlaXf7w7EJDlt3if2PDZuZqaR0Cd3lND8laWr/vRYTlZua14W+hMKX9rlyprw3FUi2l5xfuieQqOfJQuL5tDfVXOEhordc3/SYZvNl2cFEITzbEvXnnBsnSY+4PfajYzZGZogYqRNhNKtAAOiF2Zmlux5T3vE602ITEhYf8vddz9dFubLsQgjlkpAByJUdWMXk4GLpBS9dF1V1O4MxUEe2xvh/3L0rS9t2bOPlEFrlhQv6Ycz2nMHdR8gyvYVIe9tbcEEuee5Fm3L6FtfXAeMycnKfcgetf2Fqs3DRoAaARiggi9Nevyjmu1Hv4+yqaiU33o7Yzb3KFtzfh5AdMqiPo7obd3K1p73A0B0l1/6OqK2p5StPfdHgOiuP/dzRJR0Kis652eAmK4/9bdFlCWWF539PwBX/KozoP+1IY8YPuBF4JWGMsgTbb5BXqX56jum62otf7xYxQFH67r6pk6TBebxeeZ2nkJ5jE7+y4GPW/4btKhFShFTVPGvmxJcOwMnd899C67fq81l418qyMnKHQEsQAReenvGmEsbSLRrFF1Xv5oV0HZR7O76QHUgbkD/+IJPV5enLwRk0dQRVsiHRatZ443cEEAbmjE746T8Mfnf1+7fubyXWWJcKcCp2TtPjLVpKKVqDVKN3Us6X5RXbY8dGu0NuWkQXvZ47j355HUjodcdIJ65+eZ3LQ3hDo5lsHZcbgfeqDVWpRR9dF2tI1Ry8TCU48n6Tvrr/ct++9fVZ28tK0rZzaMybmZmXk5W7nDMilrtoc9oUT/jZmauAc549o433/XXpIwiVG5Tqyk+bhRmDFZN8dHUFB8NcAtAdfEuR8J4gOptx+3WZ/XWIbttV20dutu2t/ywupsasL6xz4BLMwYkaIHqJlzOP4GrpRSpuq62Syk0XVde4N+1DdILCzxLzk2tETW2UcBVTejTYh+8/PvNj6+rOCw5JWrz+9PHvu5tqN24mZkLZ9327pSAJ+kBX3W3N4FmxVBLKZyA30y6AiB14rwIOOPaKHvVhqOSf714/r1/a3B8C4vmEJKgctQWAvgqY3bG8LenBI4KxvpvSOrce0dJbJQRUsAAw7+RHYGeBAFNKaWJP+Tx7v6giy1WS/r3Bp+2VohqBTH9gKuHZ67edc+bPPnOcwyj8wc2m8cdDEZaoQAHAFZIQAdF11WJrqvPAaQUmcBqKcX5kZ1+nwUgNO9e3tVagl7n+4bfcfy00SOddfePm5mZN25m5hTLWO0Y+Gu6TAPhBoKgvJGdlt6G8A8EBsb2+O7ETmlzzgFC2z0Xnthp4Ntn127H9fp6WKeB75z1x/aCk5IGvHtm7XZ87/knJw14b3jttjN23Y2gvPsKBahlq99VuskX8UVjbUzuBS4xjdXTgV9NMe/dMZhjL3a6CtLSLWH5VpI6cd4wufH8LIDimm7n7ivZKbH/x9kRics3lhedeUlOVu7xzRzudmCllGLXEq3A+BuIvjWBmGsbM5YtLFqADsoW+lE5r/s8eJdCPatV2o8fsLXkXKFUAJSBwNAcPSkNGnxfFWCrVrYAGN5z6ql5AFv8rr9VGvYe5yVs36iJnfGgDPBeVDtIdnb2MJ8vZm4gEGkLBiMcmIUDLDo2lof1wGAR8M/lH75VHXAnnwwKZbjeaEgUXLMHvjYC9pviU7deCLzT/tO1aAq7e72FvO4f4+v8LTP3aN3s7RV77FiRk5WbTxM87BmzMwSIFJ8SGxu/AtB1VQZ8bm6eSUj6aK+63o71kS8BN5kTfXNf/Vo0jF3zPRQwat9FRW08eoN/z8vGzw7mZOUeDfwKfJiTNf95EJ818cV1KTBX11UlwC3PXj3Erl30cLSj8pvfHhozfx/nWlg0F0nohdolgJMKVHcIiVIl1ng4bu2WX//Xr/uHKKRm73GU0LxXBrvnLa85+ZHbeurKA7X3r/jRdoz1ASXOEiIIyrsB7JfPz+1XACJXiCuylbKb9o8Q7OM7ZNExsAzWAwDTKLh/6Vu5k/gj8aHBB1XKoLU/b110GJrNuAnLYO3QmEZDu9womzrWKTE7u39b1cnR3eG2NXOI/sAywKjn2CKlqcpgiu9aLIO1xZwx+cmbA8bAs0JbyqCJVcLGzczcmZOV+wghzcpJoO7Jyco9Y19Gq66rucDc2u2vN5z7d6U0dVJ3eT+Mac2lWFjsRf6Y/LyM2RlnaBhfAL/GuXkVQuoUAkFKpfuFO+fMfc5sngc8B+cBD+/qQ0NdYCCO6eNyTxqRUOwLGv2A4CKwnQM8ZBjioZiYUltlZbIBwsDK6ThgsEICDiy+BNzsIyv3qnuWr9EcgRWla7o66ztuYdEYDqGOBOjjckc3pb2U4hgpxY3ANYCu68ojpdDM6kgApBcWBP393FXCq53RJpM+BEidOG/Y2ooBM0JbSoF4FhjejGSnZEIFHABcwD2NFbOQUhwnpdglL5Q6cd6plf74EQHlePTpG1/6tqXXYWHRGPlj8vMMtPcMtPTL7rU/D9xAaBXnhvTCgufqtpVSnCWlSK3dvvuDLqKz3ftanM1fNb7L2oeVcj4C3rWmsaoIVbyydeu+bLum+XdV2rOKBBwYWAbrAYTpDdmle9mYd8TwOz5EaUOnjR7ZJKPDwqKWBZXJboDFNXFz99XW5DrgISDKjGUVwAzgqbpGq1Zhm2UrddgL0tL3jnG1aAp6bcIJoYfv+mZm5kszUzpgepYujEhcvnbO9LHpezWUwk5I5eEFgAnPXeWIc5a+CcZGQol2FhZtRoLN/wuQPCymZHh6YcFz6YUF59ZjrNoIqY7cUrvv28rES7YFImKOjKx8V6jEWYbqMR4cfcD3lmEIwzAEIILJyZsunjRp8jfZ2dlTLGP1wMEyWA8wmpo45Yh2fw84EvtvvrqdpmZx8JACUGPYNzWx/a3AiWboSi1+898u7MWut80fh7d6hocmsvYHgQrSzGXMPV54T41OWZTjLe/bZ+fyS7/Nyco9d4/mQeDPwGMAa8oHPlbhS+xxWs8v3y6aOqKmFddgYbFPhkSX/Q7gM7TGnl8KOAmYCaHY1UrDMTHdFdh6WYJjmCLxRhCrUZ5TFv92dtSSxefYiov7rABOO/usFd+1/VVYhBsrhvUgpVPaxu+2/dofI2C7Anhmf8/H4sChq90zamsggmgt0A9Y3FA7KUVPoMo0VItq9+u6UlKK2+v83BXY2YW0QuUwSgMpvizguXo7tWiM0tofEl07vCXelB+a28HuccyZ37+c/c+3q7cOeRr49Pm7X/pvXK9vrr9s/EvlpozVQoDUifOS4Li/Rtqrf0l07fxbWK7EwqIR7EIt0FDbfqlJaDCsTdeVQSghGYBBkRXjaoyo42/o7EUIrSuogFJiwrcLb7wUuCA2dtMCnzd41llnrgy2xzVYhB/Lw3qQctXdy7cj1I/l61Ki9vdcLA4cMmZnDNsWcI0GqDZsr4d0ERvkJeB7c2luN3RdKdNYjQS+AV5ILyxQvv7uMlupY1BBWrp172k+twDe7tHrnyzxpsQAf2pth9dk3/MNMMTmrHjRW9FnVOmac1c9c+v717025f4vXv77E38CcGqeyUCiOxB93fSxrze78ICFRXN57KJtykDkAqeFsv73RkoxVErxJymFyJidIYoDzsnpEQEDUCBQSrB9e89XgduA6ZWVPYZfe620jNUDGOuhcRCjgrZPQRw7bfTIpP09F4sDBl1RG3cq9lXC9x7gPl1XDT4EzJLD0zC9/I4NEf/Uamx24KgwzfeQYMhDz1+oicDNsY7SRZure98DrAb10ITnrmowaaqpjJuZ6c7690XXJ/b/aFLA3clm+OJeKF93xlnV245+58GHpk0MKMeNPWLWfVI0dcSSMFyKhUWT6OrwLAW6nR67Q2+gyY3Ac+ZqwG1b/RGxZQH1uRDCoxQBpTSxedNRfRIS1n4F3J6dnW29bB3gWAbrQUxkUsVPgEg6fNMt+2xsYRFCEirdCiF5KrlXAzORStfVIl1XH+yrQ11Xs3Rd5QGUj9lSKzQ/uSAtvVHBe4sQqRPnDdvu7vquoeyi0h9/HDCkW/SGf4M4utyXMCVc41x5178eAe0ZEAo0FMK2sTQ9y6H5vMek/HB7uMaxsGgKR0ZW/gZQGnBe1ECTO4Hhx8w+8hRQTwAsrI7Uv65w3rZ1y4DFSxafbfP77bPi49edbRmrBweWwXoQk9B36zeaPYivMtJKcrFoEvlj8vOATBuGt4vds9Xc3oVprM6RUkxqbt9Siq4qOjhLoVCoEQo13zJam4QOtWEXmgboQ7sunJUUsb36xy2nj06dOC+c9/G5gAcIKDCW20UfbzDyjv/c8MqqMI5hYbFPnEJ9Aqp4iTuu3hVCXVdl49cdtSzB7n+nVj1DKBy+1SdOWbXqhOMqKzv/feLE6VnXXistY/UgwTJYD2KuvHtFtRHQPqvampSyv+diceCQPyb/+wER1StLgs7up7yWFrHHYQdQTUgPuFnoutoa+3bKy4ASf2iB6q2c7qGAJJS1D6b+8vSxr3tLPJ1vcAeiU4FR4RqoVkkgaPNM/SChNLDN4V0GPBuu/i0smspjF21TIH4HRu4ZSy+lSJJSTOhk92XvCLi6CpQhlAgMLT5BRFT2Serceemn2dnZDzfQtcUBimWwHvSI+UDatNEje+zvmVgcOBR6Yv7mV5q9POg4s+5+XVc+XVfXAk+0pF/HusiXAY9CKUAzIoKnWl7WximaOiKvc+TmdZoIGMCEOtqrc0AVRNqrnpzw3FWOcI03bmZm3vs9lvdaTWTUGb0+nVU0dUQgXH1bWDSVkJGqTgMSBOqbPYzWjCU1sf/aGXA8CLwXZzjOytycuaJnTU+bpvkmjhv3zvn7adoWbYhlsB7kxHQr+R9A0oCNE/f3XCwOHBRiAVAuULu8d1KKW2urypiJDs0mvbAgTyCGGzGB9wGERzsPsEIDGsGMYe1rKJsGTE+dOG8YQNHUEcFTe3w1zx2I6bnd3fUfYRyvz9qKAaOdNs+Hz98869/h6tfCopnomGXZFMIOPJoxO0MDeK+ka+/ZO3oRIYyCUhlC6AAAIABJREFUE7ydb7ik9Jg3EnwJRwC3Tpo02SpscZBiGawHObE9dizUnP5AzY64wft7LhYHDvlj8n09ne5VkVrw6rs+6OIytVQfBca2tu/0woI82/+zd9/xTZX7A8c/36w2nbRQ9gh7RtyKM4i7qDhREREV11X0OuuO40odqLgV/WG94N6S68bjxL2qsqHsDd3ZeX5/JMWChe4mLc/7vu7LnJMzvilPzvnmOc8ot/4IRGJNA2objWB354peqv/5t8pO3phnt1QUfbNm5ImOPM8/hhdriBRL+ZOACoSTJzfF8TStgQyQAKgQ0SYxR2SbA98e9N9Bk4yy9k9aJBJwWRjffc2hM0tLe3bq1OnXF91u96NxjllrRjphbePOvm5hOBKwvuPbkuGYOnZ0o4fA0XYfXa2+9ysjFtNPFZlHulxqHTCUaNLaFAzZNk1otF1mEx23LTKAGv9W0ybNCntDqdeCDARerKp9bagLnrjkqspQ2nEDs/54qyg/d0VjjqVpjRHr8DkK5Dbg0Gxz4OatYet+ZRHrMwpSAyGb2JaO/j+QY4ALLr307XFxDllrZqIa9mRPa0Wmjh19KfAEqAdBXr/mldl67mStVs4CZyqwxSrhP4PK/K8dRwxorFgzABdgDJ4/T5fJXYgloi7AqNaGteq9g0B9RbQKNgBy3X6dv1yfZi2r/Gzl8X8BFYd1+yiYlbypeNqkWTsdM9eR5zkE1OtJZm/KMY53ej0yaebWnW2rafFw+MyBb28JW0/K8XZkn017kxJKwWwKnX/bbffMiHdsWvPTCetu4NFJIy8IlKY+C0qB+IBROmnVahPr9PAV0bGUAhHE1dRJq9Z4jjzPjaDurhrapxaVQEWGrTjFYgoGtvhyFgLlneyrB673du1GdNKIAODaMTHWtHhzFjhHdKzs+Nkh6w9Jiq5RYcF0qNvt1mV1N6CbBOwGAqUpHUERuxnp9oJaXbkAASECZnS5SVQGiB9UmOgYqhcf2MW4cv/OX9wKTAAuG57z/UtD2v/yNvAE8EbHlLVLUq1la4ESwF4WzOhArJEs0fuCq8U/habVonBC4dw9N+/5JoAgCCaFLqu7DUu8A9BaghhEx820otsLanVnxGrkrSC63CSoovzcuY48zygQF9uaDOTusFXukzssb7cUa3LwKfoaoSW49FD6o8AYdFnd7egmAbuJqWNHb2sDp5sDaHUVG/vQBRi6OUDbtqt2spqWSNxu97ayqpsD7D50wqppmqZpmqYlNN2GVdM0TdM0TUtoOmHVNE3TNE3TEppOWDVN0zRN07SEphNWTdM0TdM0LaHphFXTNE3TNE1LaDph1TRN0zRN0xKaTlg1TdM0TdO0hKYTVk3TNE3TNC2h6YRV0zRN0zRNS2g6YdU0TdM0TdMSmk5YNU3TNE3TtISmE1ZN0zRN0zQtoemEVdM0TdM0TUtoOmHVNE3TNE3TEppOWDVN0zRN07SEphNWTdM0TdM0LaHphFXTNE3TNE1LaDph1TRN0zRN0xKaTlg1TdM0TdO0hKYTVk3TNE3TNC2h6YRV01o5ETlPRL5qoXM9LyJ3t8S5tNZFRIpE5MgG7LetTImIS0RW1WVbTYsHEXlfRCbEXjfLtVdE7CLynoiUiMhrTX381soS7wB2VyLiBvoppc6JdyyapmmaptVOKXVcC5zmNKAT0F4pFWqB87UKuoa1kSTKVNs6TWsNRET/iNW0GP190OKkF7BQJ6vb262TqtgjrBtF5C8R2SoiM0QkWUSyRGS2iGyMrZ8tIt2r7WeIyH9E5GugEuizk3VdReRdEdkiIotFZFJs/2OBm4CxIlIuIr/F4/NrrY+I9BCRN2Nlc7OIPFbtvQdi5XWZiBxXbf12j2pFxC0iM2OvHSKiROQCEVkBzImtP0REvhGRYhFZKSLnVQsjS0Q8IlImIt+JSN9m/+Baa7FfDdfTfzw2jZW5frUdTET2EpGfY2XtFSB5h/cnxa6tW2LX2q6x9dfHrq1V/w+KyPOx9zJF5DkRWSsiq0XkbhExx947T0S+FpGHRGQL4G6aP4sWbyJyQ+zfu0xEFojIqNh6t4i8JiIzY+8VisiAWG6wIXb9O7racQwRuXAn5xgkIh/HyuMCETkjtn4/EVlf/QeQiJwqIr/WcIw7gNv4Oz+4QET6isic2DV/k4jMEpF21fbZ1X3hfBGZF/tOfigivZri7xkPu3XCGjMOOAboCwwAbiH6d5lB9FdOT8ALPLbDfuOBi4B0YPlO1r0ErAK6Eq3iv0dERimlPgDuAV5RSqUppYY326fT2ozYTXU20bLlALoBL8fePgBYAHQA7gOeExGpx+EPBwYDx4hIT+B94FEgB9gTqH5hPQu4A8gCFgP/adgn0tqgmq6nDSIiNuBt4L9ANvAacGq1948ApgBnAF2Ifi9eBlBK3Re7tqYRLdcbgVdjuxYAIaAfsBdwNFA9ATkAWAp0RJftNkFEBgKXA/sppdKJltGiapucQLScZQG/AB8SzQO6AXcCT9fhHKnAx8CLRMvOWcATIjJUKfUDsBk4qtou58TOuR2l1O1snx88BwjRst6VaHnuQezH1K7uCyIyhmjl2ClEr+VfEs1LWiWdsMJjSqmVSqktRC9OZymlNiul3lBKVSqlymLrD99hv+eVUn8qpUJKqeCO64DOwCHADUopn1LqV+BZokmtpjXE/kQvWNcppSpi5aqq5mq5Umq6UipM9IbchWgbqLpyx47pJZp0fKKUekkpFYx9H6onrG8qpb6PlfNZRBNaTYMarqeNONaBgBV4OFYOXwd+qPb+OOD/lFI/K6X8wI3ACBFxVG0gInaiSe80pdT/RKQTcBxwVay8bwAeAs6sdtw1SqlHY9d2byPi1xJHGEgChoiIVSlVpJRaUu39L5VSH8auaa8RTe7yY/f2lwFH9RrNnRgNFCmlZsTKzs/AG0QrqyB6XT4HQESyiSbNL9YleKXUYqXUx0opv1JqI/Agf+cku7ovXAxMUUrNi322e4A9W2stq05YYWW118uBriKSIiJPi8hyESkFvgDaVT02qmG/mtZ1BbbEEt7qx+/WVIFru50eRBPTmto1rat6oZSqjL1Mq8exq5fdHsCSnW1Y/VxEm7/U5zxa2/aP62kjjtUVWK2UUjscs/r725aVUuVEa7GqX2OfAxYope6NLfcimgSvjTV3KSZae9ZxJ59BawOUUouBq4jWSm4QkZermo/ErK/22gtsiv34r1qG2q9zvYADqspVrGyNI1p5BTATOEFE0og+FfhSKbW2LvGLSMdYzKtjOclMok/TYNf3hV7AtGrxbCFaW9sq8xCdsEb/sav0BNYA1wADgQOUUhnAYbH3qz9irX4RrWndGiBbRNJ3OP7qXeyvabuyEugp9e8IUgGkVFvuXMM21cvjSqKPdDWtvmq6nm5X/kSkpvJXk7VAtx2atvSs9noN0Rty1XFTgfbErrEikkf0On5BtX1WAn6gg1KqXez/GUqpodW20dfmNkgp9aJS6hCiZUYB99ayS32tBD6vVq7axR7pXxo7/2pgLnAy0Set/2gOsAtTYjHvEctJzuHvfGRX94WVwMU7xGRXSn3TsI8YXzphhX+JSPdYFf1NwCtE26B6geLY+tvre1Cl1ErgG2BKrOPBHkQvnLNim6wn+phB/xtodfU90Zt4voikxsrVwXXY71fgTBGxisi+/P2IamdmAUeKyBkiYhGR9iKiH/trdVHT9fQ3YKiI7CkiydS9I9Ncom1NJ8fK4SlEH39WeRGYGDtuEtHHnd8ppYok2ulwMjCm+mP9WI3WR8BUEckQEVOsQ8uOTb60NkREBorIEbFy4iN6fw/Xslt9zQYGiMj42LXWGutsNbjaNi8A1wNO4K16HDsdKCeak3QDrqv23q7uC08BN4rIUNjW4fD0hn28+NPJUvSi9xHRRvZLgbuBhwE7sAn4Fviggcc+i2gj6DVEC+ftSqmPY+9VDQa8WUR+buDxtd1I7BHVCUQ7i6wg2qFvbB12vZVojelWop2ldtluSim1Ajie6JOGLUQTXt0xUKuLf1xPlVILiXZc+QRYBNRpoHWlVIBoZ5HziJbdscCb1d7/lGjZfoPoDbsvf7dFHUu0HeI8+XukgKdi750L2IC/Ysd9nWibb63tSgLyid7T1xFtAnJTU54g1vzvaKJlcE3sPPfGzl3lLaI1vG8ppSrqcfg7gL2BEsDD9t+Dnd4XlFJvxWJ4OdaU4A+ibbhbJdm+edDuRUSKgAuVUp/EOxZN0zRN09o2EVlC9DG9zjvqSdewapqmaZqmNTMROZVoW9Q58Y6lNdKzeGiapmmapjUjETGAIcB4pVQkzuG0Srt1kwBN0zRN0zQt8ekmAZqmaZqmaVpC0wmrpmmapmmaltB0wqppmqZpmqYlNJ2wapqmaZqmaQlNJ6yapmmapmlaQtMJq6ZpmqZpmpbQdMKqaZqmaZqmJTQ9cUADOfI8IwAXYBTl585tq+fUauYscG77tyicUKj/LbQ2z+12byvzbrdbl3ltt6fvyS1LTxzQANFCGjFAbCAh4L+OjEW29vYNPX5af/AXAL0zF/TNStrc9ecNB30J0Ddzfv/MpK0df94w4muAfu3mDUi3lXT4ZcOB3wD0b/fnoFRrebtfNx7wLcCArD8G2y2V6b9t3P97gG6pRYeuqeh5qIpWivuBUfoLEh+xZNUAzEAAGKWTVq0tc7vdx4N6GzCD+IFROmnVdleOPE8yMA7Uk0Qr/oIgRxTl534d59DaNJ2w1pMjzzMSeA5Ub5Cq1QoiCEoU5giAEBFAFKYmWYaIgEjVOTNtWxYd0OWLW565dPqrLfLBtW2cBcMeAbkithgCbiucUDglnjFptdO14vXz2GNnZFit3v9s2jRon2Aw9QC2NSFTIZDb3G63LvNam+bI84wwSejIrKTNWzb7OgVA7dclddW4dRXdkhWmHZtU+oTIZ/t3/lJCyjrjp/UHvVOUn+uPS+BtlE5Y6+GIKVP/tbRk4KPRxFEBhEECtEBtZ+zRw6eADRRmwuZwtEXHb8CMw7t/8F7B5Y8ubc4YtChnwbBHQS6PlgHxomtYE14sWZ0DWNG14js1Y4bLunVr31NKS3scD+oUkDSLpbI8FEp5E9TZIBZQQZDDdQ2r1lY58jw24BbgZlCmapVTW7unLStPt5X8Pm/Lnl8CblBWIALisZr8ewQjSX1i2/qsJv+vw3N+CJYEsp5etHXou0X5uWUt/2naDp2w1pEjz2NLs5aUlgczkmKFNwx8AtwRjzasJ/R5Zf03a1xnbvZ1OhnY1yxBspM3f7fR2/ke4P2i/NxgS8S0Oxr9Ut/1ywMpHU2ooghytk58Et++Lwx50q9Ml8S+u7pWvBq32y3AcOAci6Xy8lAoJQkoAV7Lzl44Jz19zasTJxrhWBvW/wIdAIfb7S6OY9ia1uQceZ7sPXJ+eHpJ8cATKoIZSX+/oyIgDwLXF+Xnqmrb/6MN6+Tp5wz8bOVxe5YF2h2Qai0b7Q2l9I8oM0A42Vy5aHD73yvXlnd/ZF1l9/8V5edu1O1g604nrHXkyPPcC1wvRIIKkwBBEqQd6WkP3TgqrCxTft2wf2+FqYNZglv3yPlxaUUwffJHN9zwTbzja0ucBU6bGVUeRqzAisIJhb3iHZNWuxNe6rOwKJDaP3bj8aNrWHG73T3atVt2l8+XeZrPl50KBJOSin/Ozl781ebNA2656aYHfTXssxeonzIzV7z/73/PyI1D2DWaOnb0tpv+Na/M3q3/XbX6u+zpiSM/KBpzWkRZzgNS+mQu2LyhsvPU8mDmrUSfyjT4fn/F9PFd/rf0NGdYWQ7JTt44tiyQMSAYiebCNnPlpmA4ub1CVNV1KRFyikSlE9Y6OPjux09dXe54HXgKeIEE/TXkyPNYgWO7pS2/e21Ftz0iygLwU8/0JR/s1fG7GdMmzVoS5xBbPWeB8zDgcwuRP0PIkGMzN2bdP2Z9Sbzj0nbOWeC0m1AbIkhahim4ujRiPX13S1arevgnJRX/kZW17OT16539lbIcDEhq6rqycNiW7/NlP+12uzfXdqxp08YvLi7u1Tc1dcOga699ZkHzR79rU8eOHoFSBmAD/IiM1EmrVptYzeYEu6V8H18oZV8grDDNNEnowaVTTvq92jYumvB+f9a0a1Pmrh25J3BomrXkmvJgRk71Jz9F+bn6yc9O6GGtanH5M+cOKgsc/WqGrXhDaaDdNUX5uZVAQl4MY80A3gPeO+6+e7rP2zL8FGDiirK+N68u73nTO3me14HngY+K8nND8Yy1tUoxhUdXRkwRZ0rZD79UZg4NKDmM6N9cS1ynRpA0q0RCKeaw6evx8xPy+9tcosmq+gzE5vdnyrp1e2GxeNeHQhY3MOu6656q1w9Zu33L6Vu39vmmvLzrbcC45oi5PjK8vnNLk5Ns0a4FKimj0n8uCXqN1hLDqClTr4CB00DEG0qld+bCH4e0/+2ixy8q+KX6drEktUnL0ktXPlAJfAN848jzfAHqU/6uxTWa8lxtja5h3QVHnkeEiEdEHX1Mr7fPePKS/3sz3jE1xEVPTjrjj817nbGmvNfhQId0a0mwa9rKjxdsHXZtUX7uvHjH15oc+2K/0hASsaBOWR20f2qRyCm/nPvnW/GOS9u5Q2YOmlcWtiRFkG+BEYUTCnvHO6aW8tBD55kDgfQir7d999gqlZRU8mrnzr+cNXGi0eCLv9vt/g9wk92+6bAbbnjsy6aJtmE+OXDfWb/36Hi2MpkwRSIMX7HhrSO++/GUeMakJS5HnucoswQ/CCuLKRFqNnUb1rrTCesuOPJmXwnyMHBFUX7uY/GOp7EceR5b55RVZ6XbSvIXFQ/pCGIySejHEV2MhQu3Dn1to7fLYPSXZqecBc4MUFv7JFW+s9SfeiGwGbi2cELh1HjHptXskje6jfy6PHvOPinF7/9U2e434FoguXBCYTjesTU3t9vdDngJOBYiCiQMEqQJxlB1u90ZZrNvvd2+xd++/cKsxiS/jTVv0OB3VmalnVjYsxMD1myk38ZSPzBy8Px5+jqmbXPl9HHy3tKxl0eU+SEToVURzJ1BzCRQfxRt1/TUrDtx8VMXjjVL+CG7pWIO8Hi842kKRfm5gW9vu7jg47zru4B0Ba6xW7ydvl5z5NkbvZ3fAv4DfBr7xaf902EgpqX+1McKJxRuMRMp72L1jYp3UNrOfV2efTyoUJo5fHX/5HIrYDk8fdMe8Y6ruT333NFHiYR+Ao6EyKVgOhjkNppowH+3213avv2iV8vLu2auWHHISY2PuP4MQ6xfvpj0rTKpE9v7ypcBWKKjVluI1lhpGgD73zndtrRkwJ8RZX4E1P8iWJwgI4Hb0Mlqq6HbsNbAkedJTTIfe1+KtTx0ePcPL33sohfaXDV0UX7ueuDBK6ePe+jLVUfN2uLvcCYgoOydU1ZNc+R5Dqg+fIcG2ebA2C1hqw/kG4CO1oBYJXJAvOPSauYscNqACSDvPnbKmvkXvdFt0yKgPGwZCPxSy+6t1p133nSsyN4eszkUDoUso9zuO7+IvdWkN+UNG5wXAvsrZcl3u90et9vd0kPpDUj9JPsATApLju9OUyQyw2uzoFBKEKOFY9ESlCPP0wG6vr6hsuvgfTp9MzfNWnpKweWPhoh+H3Si2oroGtaaPewP23uUB9KPfeyiFxbGO5jmNG3SLLXFn/MoiA9UWFCsq+yxH/CFI8/T5mui6sMi6rReNm9l4YRCH8CWkPXDFQG7Hgg6QR2QuvVOICfLHPgvwNzy7PcAfqpsJ7vcsZVyu93idruvikRsHpClXbr8eITb7f6i9j0bfL4gRK4HBnboMO/J5jrPznS6ZNDg5G8yI0TkjeEfFT0PrKpMMYfDmSFVnrvp25aOR0s8Fz914UlmCf4CHChExr/x7/8cFEtWtVZIJ6w7mPDYFfcDF4K6d1n+iXPiHU9LiD0OGQVyq0kih6Rayy4DBoH6+aSpt/90xTPje8Q7xnhzFjg7bQglJSvYNhWuX5kLQXo6C5zJ8YxNq9lSf8opmeZg6MC04qpRHFbE/tszXjE1lxkzRqZnZS1eADwEvBMOJ+11wQWffNXc5+3V64vZaWlriouLHRPuuisvs7nPB2AYkvn9zdm3KdTLgpgkIrnzBg0eoSwsKUu3bbWUWK2pH2UPb4lYtMTV/6Y3TzJWHveW3eLtCBy+LP+EmfGOqTaOPM8IR57nRt0sr2Y6Ya3Gkefp9e3aw6/snlZUcVLfl+6MdzwtqSg/d25Rfu6UJVNO+vrPu858EhjYO3Phl79v3Hdvz9LTf3LkecY58jxtsmaqjo4AWBFI+b+qFV2t3mJA9kvdemDcotJq5Cxw9tkYSupfFrbcfd+Y9WGAwgmFZTYJB/onlZ8c7/iaktvt7rx8+WEfbt3ar3+HDn99BpzmdrvLW+LcEycaymarPDMUslvC4eQbWuKcwKWETLdvyh5iWtx7DCUZva2AK73r5m7BZLKBkARNZ7ZQLFqCuXL6OHHkea4PRpLeArXg8O4fHlyUn/tdvOOqzbBbXzpUiHwB6h7gC+ets27VlUXb0wlrjCPPYwFm+cPJvo4paw+dNmmWN94xxVNRfu6Wz268emT/rL+OimAuAmZ2sK9bfPFTF8alg0W8dbT4J5pQZcDPVesGJFeUAphFHR63wLQaWYhcDEQiyHPV16ebw5VlEUu7OIXV5KZPP24sqB9BhouExl5++atHuN3uSEvGMHnyzA+BWaD+/eijZw1tgVPev6H02Pv/GHaprOh5JL8Mn2xe2O/UTUFv0hdBbxJhW+SLSHL4wh+uydmdf2Dvli59+vyMRcWDFwP3Aq/5w/Z9Hr/4+R/jHVdd9EhfdhFgiQ21ZSkLtrtz9tLTV/TOe/dnR57ngSOmPDjximfG79YzK+pOV0Sr4dslbXq62N/BCXLOm1ff3fo6ZLgzBTiE6EDeCngBd0mjG5R/dMMNnzjyPHPaJW2+0htMnfpR0UlvOfI8DwJ3FOXn7hbtN50FTsk0m0b2Tqrc8PaZS7cNhzTPlzYb4Nvy7N3i79BaXPd2pyS7KfuaNHNg0UdnL1pV/b3NIdsXQJ84hdak8vOvvCAQ2PtZi8VbHgqlHHz77Xf/Gq9YrNbKW0Mh29mRiOVdoG9znMMwpDNQ6XKp0sdfnlOCKIWIREzW8KruR3Twbf51LnD+5r2C6zp+l3SEqdR8LlDQHLFoiceR5+kCp7wF0me/Tl998sP6Q85M9I7DhiF9AIfLpeYs2DLsCeB0wAwEe2cueCTdWur8fdN+qcAVS0sG2paV9Oe9PM/PwOfD2v+8pHfmIs+jF71QFMeP0KJ2+4Q12lZEGcX+9jYhohSmpfGOqYHGU+3irFAXFz+c8lZmieU6k5JluEsa/MUtys+NAA9d/sy5ng+WnZIXUqZrzBI899zHJr+UlbT5qmmTZiX0RaEJ9C0JWy3JpvDT1VeuDyZvBIqBfvEJS6vJ12VZY8oiFvNQe9mLNby9glY+5JHb7TYBd0DWLVZr+fyuXX86beLEz/6MZ0w333zfsmnTxn+6dWvfI91u995ut/vn2veqt/8CnQxD9oJPDUQigBmRAGCYkwLZYb+NZSkd3s+h9LSUL7P2Riesu4VJT150pnDCAwpTlknCp7/27ymvxzumOnoO6GEYMnBpvprryPOMJDaJwGc3Xr2twsmR57Ef3v2Diesquh22YKuzE3DZH5v3Tvpj856PvZfn+RUw9u301bouqatfe/SiF1prDlOr3X7iAEee52bg7uiSCoG0yrl8lTvjQUGuIvY8YUfepPBCJQRSfOZ3gXLAaGgNrCPPc0COfZ1no7dze5OEP48o81NAb9ropAPOAufFwFPAoMIJhdvNnX7QfwcvSTaFw3PGLRwQn+i0HTkLnLOBvYBehRMKt+sRfPorjufn+9InHJO5wfHAmPXL4xNhw82YcUSXrVsdP5SW9uxG9GZ3mdvtDsQ7LgC3250JLAEKgSPcbneT3lwMQ0YA3Vwu9TrAszdMXx/y5mSFAxmH/+upI+bOnDL4uPW/9v1f+0ErnjjglWBn4CCg++D589r8JBG7sz1vf2FCRTDt+SSzv6I8mHFIUX5u3J401MYwRICTgY9cLlVuGDIQKHe51Or6HMeR50ke1fO9CxYXD957eWk/B6iDQJKjD1flN+DzgVmF6UWl/db5w/b32sp9ebdvw2ox+TvGXlbNAmPEMZwGMQwZ9dvwylMUKqjY9r+w4u9fI3a/eUCKzzwMuIlogv4p7swG9UQsys/97qCuc7p0TytyR5R5b1AvgWqzkw50sfoutkpkM/CPIc662HzmgDLt1u2KEsm/3uy6J6jjQM3YMVkFSDGFVwKsDSS3ulpxt9t9yvLlh/5aWtq9W8eOf7wETEqUZBXA7XaXiITvBFxduvx8S1Md1zDECuByqblVySpAJJSyQcy+tf966oi5AOGg5TuA0pU5JaEOgfeAzpWHFF/WVHFoicWR5zE58jx3FPvbP59k9s139fjggEROVmOGAW8AFwG4XGpBfZNVgKL8XN9zlz31+Oc3XXlBUX7uqOE5P2Yf1eudyzqnrn4U2ATqkgVbnRP94eQbgc9jNbet3m6dsF717Nk9k8yBKzJsW1YAt9J6Z7wIFWeF54dNXAX8GDKrdYKYBdlZpwMTYKURj0anTZoV/OqWf90BPBJdI9LYYyYiZ4HTVBy27tE3qbK4cELhP2qMFvlSXygJWy3OAmdSPOLTtlcWtkwFMbnSN79f0/s/V7abDfC7NyOlZSNrHLfbfSjwOkhHEP+GDcMebeoazKbQs+dX05OTtwY3b+53tdvtbnSTM8OQFOBHw5BLd3wvWNFla8jbcUnV8oTbCrcAW4IV9na+g0rejSRFlHVV0tjGxqAlFkeeZ8SAm964u3va0jVEZ6p6vjyYuedjFxXEtVnMzhiGZBqGHAvgcqlC4Ci23TebxjvXuL1hELSMAAAgAElEQVTTL33myW9vvXhyUX7ukSYJ/wdUJPbA1SqEPWdNu/btC5+4uEWGnmsuu3Ub1g+WnXy1L2yXYx1v5T11yXMvxTuehnK51Oe4MwNEa4dt1vD2eWp5Svij1ErTYYJYiTboDhOdP9logtN7gDxQ5tZaQ12L4d6I2bwqkHxvTW8qZBHRHwC9gfktGlkLchY4BchNNYXO7GL1rV7sT3sPwGkv7drOHGz3ZXn7vwD2sJd2yzAHM7+KLg81ExkWRl4snPBHs/8QdBY4zUJG/0xz8NdHT1n79U42a5VjsZrN/hPC4aTYF1vMRH8YJtyP64kTP/Ped9+/Jvl8Wc8DFxJtStMYVmAesLiG91KBNdVXiDm83mwN7bHffZu2/DV70EvWIvtx8wYNtg2ePy9haqK1hos9wZsTiCQlryrvTf92fxqLioeen+Cdq+4FxhuGdHe51FaXS33S3CeMKMvHQB7R7084K3lTydy1I08CNc+R5/kP8GxRfq6/ueNoarttwurI8zgg5RLghdaarBqG2IBzgJkuMlxEk1EUSgWt6gnbzWWXA6QBscf/LmAT0IFGtGGtrig/d64jzzOb6Dilx7TSGupdORKgPGLx1PRmv6SKLYv9qeydUjyaNpCwXv92J/Er07A5pR06AkO7Wb0nKtgHks0g6RURC4v9aQDXAxR6M7bb//cdlsOYAHW5s2DYv0Gm1VRL3YSOUkiPkrD1mp1tsGdKyfrCygw12F52FvB4M8bSpJKSSv6srOwIKJXoPwwrK3NeAC4Adeezzx7zzoUXfri2ocdyuVQJUOOYqta0NcOs9o3ZsSGSAUjNKckKem29ASQiLwJnK1FHA7MbGoOWOITwkQpzcrTmUIUXFQ/9KBGTVcOQfYF1LpdaBdwFPONyqa0tdf7YfXkUsQ5cP7vPnztyyoMnLysZeDXwWLqt+L4zHs574ft1h04uys9t6SmVG2y3TVh7pi95Y0VZb8B0c7xjaYQTiXa8WEn0BhYArIIEbUGZtd2W0eS0WZLJZHNlaSCS5F065cS2lqzS1eq7rCRsWfft+HlranrfkVS5cLE/lYAytappbJ0FzhHA6ByLP+BI8jp/qcgoDmEaaKHD3iFM2x6Xrw8mVXS2+sMmWBiJdmQyxR41zQBeHmov7Z5lDrX/qjz7NwCnvbRnujmU+U159lBgItuaHclDwDnnvNbzta5W/wNVg/k3pW5W79R1waSSMKZ3drbNf09fETnov4PLNoVs1qY+f3OqrOz4TfSVvAY87Ha7E/a75na71QMPXHRHeXnXT3y+zFeBQ+t7DMOQHKKzdl3ncqkaE96wL7PSZPYVVV8XqEg2AuX20bHFjyNJkUDI4Z2GTljbhD7tFpy1pHgIoMIgVU8VE4phSBbwOTATuDjWRrXe7VQbK1Z5tO068dmNV7/lyPO83S1t+amCev77dYdeAhzVJ+/du07o+8qL0ybNSvjEdbdMWI/Mf+CoFWWD996309dfvf7ve1bVvkfCeoPo2Kvf4C5RuDO3/aJqitrTuuqf9dd+y0v7dmip87WUw2YOTCoL23oMsFcU7mybT0pzFgMlf3gzWsVYrNe93Sl1qS/lZUjNBZGNoSQ2hpIwoUqB31PN4dkDkkvNy/32FzaEkr4PYVr//tmLVSzB/RSwxmr4niucULjTMhbb/uxq2z9kJjL+t8rM/CWm0FXOAueTQAj4bFfHqatjX+zXY20wecgwe9kPs85YvsvHv2URy49lEYu9sedsYVVtpF9L5GS1yrXXPvPpAw9c9OumTYP3c7vd3d1ud32vs/sCxxN9nFpjwhoJpUb8JX22a7cYKEv5GThz6tjRGdfMn1f624k9/7AuThk2b9DglMHz51U26MNoCSFaYzhkcO+MhUuWlQ54jgQalcYwxAIc4XKpj1wutdUw5GTg23jHtaNYbfTrV04f98aaiu6jI8pyZwTz83PXjHzmkLsfv2pVueM34HAS6G9b3W6XsEanFx18mxDZ2DFlzenxjqehDEPMLpcKA3+31WvGWtRdWVvRY4E3lNKppc/b3LaGbQcC5nnetDt2tk3hhELlLHAuAvq3XGT15yxw2oHzzeTcFEa6Roc/AVDhVFP48cPSt1x135j1O320VjihcK6zwLntB1FtSWZN21/3dqc7S8KWqd+WZ40G7og+3sbnLHCOamzSujpoHwdQHLZcXIfNVwBHN+Z8La1jx9/7bdiwB+3bz+8W71jqqry868nAAqKjkpxXn31dLvW+YUgvl0vt4oegSgGpqL7GmurdEqywk9FjoxP42rYw9VpgDpALvFa/T6AlCkeeJ5vomLoLNvs67l2Un5toPz4mA1MNQ4a7XOp3l0t9FO+AdmXapFlqGrznyPN4Duo6x71gy7ArV5U7ngBU7Lrsd+R5Eq4T+m43SkAH+7rxwCEK061PXPz8unjH0xCxGV+WGIbkxjsWgE3eThuCkSRfvONoBqOAiEKMXW3U2eoLp5lCCTmcl7PAmTbhtR5vJ0l4A/BYGFnW3eqdCniBEEigImJ5eVfJapXCCYVzCycUTqlrcrnj9vePWe9/5tTVlyvkmVhbTAHsQKOGXBn/Wk8TqAuBLzxnLal1lroh9rIcQXW97u1OqY05b0symcKZADZbZau5Zrvd7iKTKfAkqAnPPntMnXrrG4YMMAw5GmBXyeqrj55nBUnO6PH5vtXXZ/VZZwZIyqioatj6hUKtiySHL2jYp9Di7crp46RP5oK5oDoD436/c1xCJKuGIT0MQwbFFp8BxhAdg7jVKMrPjbw4eeptm30ds4GXQQmIieh1OeF+1Leai19TuHL6Oakgz7ZL2ryFaNvP1ioF+IsaxgWNB7ulwm6SsDnecTS1zlbf5dnmwLrCCYXFu9ouxxIor4iY005+uU/CDG014bUenZ0FzluB5T9Xtjupk9XvE5QLOPT9sxdfCzKK6JAwja7dbIDPQHyxpBXgEGeBs8HlJ8Mcugqkr8NWWWPHuB2ZUPMUwlJ/SrNMIdoc1q3baxXA2rV7/xTvWOqje/dvH7JYfGrTpoF3ud3unQ2zV91twIuGIem72si7ZVAqQLCyw/zt16d/BrBlcdcSgMHz54UDgyuLJCTH/PDvnB4N+xRaPH2z5ojLl5YMHHBA5y8+LsrPTYjybxhiBj4DngRwuVS5y6Xecbla50xMRfm5YaJDbXn5e/z2iY48z7A4hvUPu1XC6ll26sWbvJ2se3b8fmpRfu4/BhVvLVwutdTlUse7XGpRvGMB6Ndu3r5p1tI21YZ1/xeGZKwPJrXrbPX/Vdu2hd6MFxTCYn+qowVC2yVngTPn+Jf6vfSnN30tcCfwtd0UPthz1pKc3yf88XlVL/361pY2pdg5R4HcTLRm4rhMc3D2dW93alDCX1iZfnyShEOD7OXT67L9H96MDwAW+tKyG3K+OEmO/bdVPck4//w5y0Hl+XzZ/YHj6rDLJODoXTcFgPK1+ycBeDcP3a4Na9nqDkuBcNhvq5oQhki74KMSMpHyedax9f8EWjw58jx9Nno732OW0NedU1efGO94DEMOMwyRWHO8SUQ7lrYJ0cf/ErsuyxVAsklCP49/9Mrnrpw+ri4/NpvdbpOwOvI8WaGI7WbgE2Plca1u6tUqhiHnGoYk1I12WUm/DeXBdH9bmuXKq8yHKUT+8qXXpaxUjREZt9mTrn6r8z4Hzxz0f8DylYHksT1svhUHpG4dXTih8MTvx//1Tbzi2plqCfPF7S2BB0rC1mP/8qb96Cxw1qtdvbPAeezWsM3lV+a37h+zvq7DxqwAEJSjvnHHS07OX8MBOnYsTKjvfl2EQikPA4tFQg/PmDGyxh8lhiF9DUNsLpfyulzq59qOabaVpkVfRbZrw3rNK7PDYg5vsqVX7l+1zj633UtAkanSfEpjPofWsq6cPi4p07blA1DhsLKMi3cvdsOQ44n2/j8ZwOVSn7lcqiieMTW1ovzcuUX5uVOK8nMfS7WW7dUzfVnZl6uPPv+dJWc978jzxL0J1W6TsA7O/u11UFnAtYk4bltdGIY4gBnAv3a1nSPPM8KR57mxuRJIR56nvSPPc+pR+fd9vsdtM8vLg+0OiyiLnTY0NauZyDFEa7NqTfYGJ5etBBhuLzmruePakbPA2XuPgmFPzylt/2NZ2HIe8CrIkLfOXNrr2dNW1ekRebwZ4xZcN8xe+uqKQMowYFZdk9boSATqXaLjD58QG5mgVgekbl0LsFdK6fgGB93iVAaA1epN+KFnduR2uwNZWUsfU8rS3+/P+EctuGFIMvAJ0WGA6qRd7w+cAFl9Pf8YTi45q9xuSQ5uS1gHz5+nlCXyukId/ePkjgMb9im0lraoeMjMkkB2//06f/VkUX7u8njEYBiSZhhSVcY+JFqjulsMkfbnXWeuHZ7zQ2ebyX8PyHizhH655KkL4lrLvVuMErC3+/kBJf4hI4e1/2Xx7Otu/S3e8TSUy6WKDEP2BIp2tk0sYfwclAUIHXffPZ+XBtp9vLq81+r2yRvMe+T82Gdx8aDClWV91ndOWWXas+P3HeZt2WPJ8tJ+W7ulLQ8Nz/netLrcsfm3jfv5ivJzVex4xwLljoyFh/tCKS7ongqwtGRgIMO2NRgbl7P6dK8J1bOwIbItwQutEtn84dmLa30E60jyrlriTw2XhK1dWiI2gCvf7HLsUn/KPZCyh0LCOVb//4bZy/IfOnndly0VQ1N66YzlY50Fzh+A+7tYff2ve7vTwfePWe+tZTcX0TIH0WuZizqUvWdPW1W+7wtDKlYFkltN8rdx49AFAKtX71/TjE8JLyNjxSNeb9a/160bPtrtdt8OfFQ1PJfLpXyGIdeww6xVu+LdOsAHECjv+o/JOgJldiMcsOxZfV2lq/j71E+yTaYSyy1AK/qhsnty5HkOhOEnd7Cv//S1q/JvjGMorwJDDEP6u1wqCDwfx1ha3LRJs4LT4GZHnmeOzezzfLoi953+N71xbzCSXEIchr7aLRLWLb6cu0B5U61lJ8U7loYyDLG4XCoUm4t4V44BZa2aQ3jeluFHEputabOvI5+tPH7bhusqu/NBUfdty6vLe7G6vNe2ZUfeeyGQWBkRikr7BntnLCnPtG15tCSQ/V5YWX7Y6s/Zl23jczbZdK9xtUfBsFxFUnI3a2Wd5qa+b8x65Sxw/loUSGn2dtHOAueewE3Q/jSbKEk3BZ8ri1hv//jsRS0+MHVTK5xQ+MCpr/QesNCXNilQYZrjLHAeVjihcFdJpRH9T/1nf/Ir858bQubW9ISpqg1rq5tOEWDiREO53e67gGeB24Eb3G73KJfrjh9dLhV0udSb9Tle5YY9/QAV6/f5R8fTsN9WDHSbOnb0Qde8MvsbAJUUeT1iixQlf5fRqqbk3R1d9vR5nU2c8lIE8+pN3k6ntfT5DUOGAYtdLuUD3IAllqzutorycz/91zMTDpiz4vjXvaG0G4AIcRj6qjVdsBvkmHvzc4EzQB545ar75sU7noaI9Uj81jDkhjps/iHgrxpn00Tor6ykTYcAg7qnFR14VK93zuuSuvI44OiuqSvGHd79gwc62NddClzYKWX1bSO6fPZupm1LPnBnstn3a/SQAhAG812f3fTv7N/uHH9jUX7uN0X5ucFYYd3W4zzRxm2rL2eBc4RC3gRYHUw5vK6PmYm2Y222NqyHzhx45aEzB5YBvwDHWFD3u9I3Df5m/PwLCycUtvpktcobY5dd1NNW+eDmkO1A4BNngfOWXfwbbIqWTXmH+o92sIJW1IY1O3vR/gBduvzcmq/ZHWPXJQGsSUklJwGLDEPq0hlrOyZreQaAmALb1cJPHTt6BKizADOoT6PLsN/UjcoUMM0Q5NB5gwZ3r+GQWoJYUdr3M4U4cuxrLyrKz93lCC1NzTCkP/Ar0XFVcbnU9y6XSrg+APHw+EUFv3lDaTOIfolNoGxEn2q1mDZdw3rl9HFSGRrxcqq1NFARzLg/3vE0gh34CVha24axOYRHEh3bsksE8yRf2P7ZId0+njrziodvBL7bYZcXd3YsR57nA7avPf1kZ+ekDTQDiHFFv4gC0baRLurw2follScv8af2ue7tTqn3j1lfUdv29eEscN4P1msBBBVWyGm/TPjz45q2jTXhcJGgM5XUheesJdc4C5wAV4M6DLippskFeti8p6wM2DGhbv1twh9/1Occw+yl3ef70vpf/3YnqcsYtAnADmCxeBNiDMoGMkTCEaVMJjAFU1M3fAsMpwHD82X2NFxbl4wme8BbnaItlrZxEf3eAlTdUOcCKFvkFQmY7vDtUTYF3SwgITnyPCfD3oOGtv/5Q891t37YEueMVQgNcblUoculFhmGXEp0Fkntnz4D5QeSQZmoZYzyptaaf63X6t0lZ562sqx32tD2vz5blJ9bHu94Gio2xtvFLpeq00wtsZ5+9xTl516xX6evj85K3hT8avVReY48z4uxGUPqpK3VntZFF6tvRfSVUtSjiUO6ObxUIazw24c0VSzXv91JTn+l97vAtSAKBIUootNW/oMjz3MG8BVwF62/A9ymauO0JlHDL/k0U+g8u4TVMZkb6/3kxBcx/xBSJv7wpndtfKjNb8uW/vMBVq48uDTesTSU2+2em5xcfLTJFL4NGDV58qy3XS51nMulltT3WN6t/TYABMq67dgZx2DbEyZRVPv+Dvl9wYJQF3+lZW3SMQ3+EFqzGX7bzO5Em4z89OfmvZu/c487cwTuzFtyNlheAL4xDOkI4HKp6S6X2tLs528GzgLnCGeB88Z6PBmsl9jQV0ekWUu/ApMAOc1xnp1pswmrI8+TpDDdCxR+v+6wyfGOp6EMQ0YbhjS4Z+tr/57yRe+MxdlEk87TrSb/4vMfv8xd1/2rDXPR5pNVgA1BW2cQ0k2h/6Mej5l/qcx8A+AvX3qTfIGdBU7Ld+Xt3pzvSzshx+L7neiIBSF2kUR3sK87g+h32szfHeBaKwPwxRIPoYbPvNCXaraaIj/cN2Z9uL4HX+xP/RRgZcDeYh3lGqk3EAYOiHcgjXHDDY99emp2YMXpHSqmLb3zpSNq36Nmvi2DSgHKVh+ytvr6a16ZXTWW5OexVdt1UjNvtt5l2WjLmTdocNyGoNP+6crp48zZ9g2/miWUAYwrys8NNOf5ih9MvUGhvgTuGvKX/ZTuK233Axub85zNKZaoPkN02K27gE+bM2ktD2YeAfwJatqFT1yc2RznqUmbTVj37fR1AdBbiFwbm8Wh1TEMsQCPAw825jizJj/oL8rPvcsk4f2zkjfb5qzMvd2RN/s5R54no2kibTvCmMYBP8XahtYnSa+axKF/Y2NwFjjtwOtbwrYxXay+5/dNLd2TajXdO4vLLKHYtKQqQivvABf9jDIK5HVAciz+btXfdxY4s8OY+peGre828BQrANJNoYQf5sjtdo8AdQ4oM/BpdLl1+uuRm2/LWH388xmrcvezVnabvSrvywZ9FpOlIhvAnFT8jyYS17wye641zftvwJzVd812104JmF4EUKg6TRWrNT9HnmfEu0vGvrusZGD7A7t8/mpRfu6CZjuZO3Of8J0Zs9uVWvKJNR0RxNJvSXKwpWepMgwRw5AkwxBbbNlqGDLcMCQntpxqGHKGYUif2HJ7w5BbDEOcseUeH39meuXsV3u9DOpLUJOIVlQ0e4VFUX5usIN93fUgjpJA1lvNdZ4dtcmE1ZHnaV+4ae9T+mbO37Qs/4SP4h1PQ7lcKgTsD1zRFMdbOuXEXw7s8nmXNGvJIyDnCZE/zn/8squb4thtwWVvdM0F9rKbwq80YPeNNomEByeXnduYGK5/u1PvrlbvalAnAld8dPaiifeNWa/qMjPV+sru30ZfybO0gSYcsc86LtUU8tlN4WeqvzckuWwsQJop9ENDjn1kxsZigIH28tMbHWjzcwGmqpE/aMU152Z/9mmiLAhmBGnwZ0nr8uMogOx+79Q47Nzk5z79JSmzvLJifdbI6usHz5+3IpwdXBbuELypIefVmlas2dIchfl4IPz1mlGPNcd5Vk1PvrDivrS/gB/NETm4wh7+mJqeWrkzRyh3xl24M0cAGIZ0r2oqEFseaRgytNrypYYhh1VbfsQw5KTYa7NhyEeGIefGllMMQ5YbhlSNo94uFsOlseVsoh2+qq5JWcArQNWTiEzgrohiL2eBc/+bVw68+7ZVA88o9GaMBczR68O2nLvZKyx+vP2C//XNnD//x3UHH+zI8/RuznNVaZMJK/C4P5xs2eTtGM/x2xolVruKy6XWu1yq1s5WdfXIpJllf9x19pXAQWm20pQ5K3OnOm+bVZAIs1jE28aQ7WoTikPTNtd7wP3CCYUq1RTavCGU1KDpRQGcBc5uH5Xk/G99MKndganFUwsnFNbr4i1EqsYkfb61J6tVCicUBvokVb61IpCSNbxg2H5V660SOceE4pD0LQ2aW9wqqsiMCizzp7SGTkxGbNguaOU15/atw5+OvQzTiM/iK+6zEgkFz7iiYKdPz/wlqbcHyu3dpo4dPaD6+mAf79eWTbaUwsP67tOQc2tNygWq6pqpaMofY+5MwZ3pwp35affVSdNtARkUMis30Cv1hvKjBTmC2FMr3CVzcWeOiDUTuEWhPo0lrd8B/6l21FeBy6otTwGqz6B2KuCMvY4Aqfw9VrQf+Iy/x1EvB27i78lptsb2fz+2vP6/m7qNu37l4B7OAueIa1cMZuraPs/fuWbAdOC70ohlbCerf/mApLIZgBdUCAiDorvV+6+WmHZ7ScmgoxSmEI18ClxXbS5h3ds94xxQY0EoCWQ/0oo7njxtGPKqYUizzOFblJ/73cge7w/smb7knbJAu/HAb8fcmx/3uZrjxVngNM/3pQ+0SmTO1JPX/9WQY2wN24zNIVty7Vv+08TXehwK6psw0s0m6tjpp626rr7HcPX44BCAYxxv9WlIDImq0JtxCVASQbb9TX73ZoQtEvnj/jHrNzfkmPeNWa/CyOKG/nu1pNgA+1U1gpOrBtxvjcq6fhgACNrX/g8Y1T3/0AZ9lkBZj1KUpWTXW8mLgBJzeLunHsk/ZlwDRCwbbHqq1jgzS+gbQGI1g43/MRbtSHVj5b1pd5elhYuJJoiDgxZ10+puwU6WW0vvwF0S7bjoLpmLu2QK7pKqMugCRLbv6HkV8H/VznA8UH3Eob5AXtWCy6W6uVzq7thr5XKpg10u9VxsOexyqfNcLuWJLQddLjXF5VI/xJYDLpd60+VSywCuWD5s3+8r2j3rjZhuBfW1X5mXFAVSzksxhb0OW+Xt7m4Lb7mq87JuV3Re/nCs+dRtvWzeySDYTJHDaAFF+bmrTITvBsac9/jlebXu0EhtLmG1mgJ3x14Krfvx2QJgXnO2q3lk0szNX9w8eQww0iLBpIVbh75zZP79sx15noS/iTcDF9DNr8xP17bhLiwG5Tjqxf7W2jf929mv9jr5L1/a5zZRWcDh35/7V4Oasayt6L4BYENll7W1bduaFE4oLDWhngZ12uQ3u4x0FjhtCtkvoMw1DrNWVybUKguRRrc5bgk5OX/8BNCp02+D4h1LYwRTVvcE8LUr/F9Dk1UAk6WyPRLe5Sx017wye01yu7LlVnvg+hfvH7Dth//g+fM2AJ8qkxr3wzU5zVIhoNVNWFnagWA3V75CY5sxRWtE5wD3pHjNNyf5JGVzdvBxoI/1ltIpjom+2jpVGYBfoZREO3p+43Kp11wutS0ml0v94HKpomrLm2MTDDSHowF7rNOpAB92t3oHXN9lyf7vnbXkzvaW4IPAni6X+r2qydjss5Y8AeqLpf7UQ50FzhYp26P7vvpYB/u64C/rD8hz5Hka/ISxLtpUwurI81g2VnZuFxuSaJc9qhOdy6Xuc7nU7S1xrqL83M+P6/PG/kPa//rb4uIhucDP/W56a/9ad2xD+iZVTLMQ8QPvNfQYe6WUpIGYh9rLDq/rPs4C5/GF3vRZAsWHpW8+oXBC4S8NPf/8LXtsAPhlw4FtKmEFGJWx6UUzSlYHk+/bP3XryUByjsVf26xvuzQoubx7kikytPYt4y85ubgQwOdr16LDyDQ1ZQ6kAFgrejaqR3Zy9sKDbanrOta2nb1D2TuBcrt10189t6tx8u1V9qdEpJf4TBMaE4fWaGcBm73h1PGNbcakUCMVqqqyJWINyR3tJ1dejrukbgmlu2SuIKMk2gdAytLCoxsTT2McOat/KnBirF1qBPACd9zQdcl9wIeGIUkul4q4XKqG2RjlOaBvsoRdLRHrI5NmltktleeWBLIzidZIN5s2lbACuREsmWC6nlY6dqhhyD6GISNr37JpPTrpv2s91926J3AsqIxwxPLtKQ/eMseR57G1dCwtzVngTF3utw/sl1yxpHBCYW3z1++UmeijnWX+lG61bQtw/us9Hgf1LshfFRHLoIdOXvd57XvtnEWCSQBmCTbrkDDx8ODJ635LM4ffXOhLHbY1ZD0XYM+U0ga1X61SGrF8URGxyIH/HZzw7bcvuOCTTcD6kpJerXqKyA7zL3sHILl0UKNmMAqU9lgdDqStrG27rYu73gZ4g5XJZ1RfH+rsf1iZVNj+bcZBjYlDa7jJ08d3skjwtBz72i+K8nMbXa4r7ZGhgqCiFVZ+QT6t90GizQQuKk8Nb0jyy9Urnkuu87jlTeW6tztlJpsiy0HtBdwucKtV1FGxNqn5wL9cLrXTKZpTTaE3rBIJ90ryPrOzbZralzdf8TLwDqhbJz5+2fDmOk+bSlg72NfdLoTXAQ+34rFDbwRmGobE5bF8UX7uh3t0+HHvIe1/W/rzhhEjge8ceZ494hFLCxoTwmRZ4Eu7tPZNd+7HynYfAyz1p+5yXDpngVMOnjnorh8q2l3WxepfDYwsnFC4oTHnBjiwq+ECONbxdquuhduZkrD1JpCkRf7UowW1/MGT1/3WmOOtCti/AqiIWHo0TYTNbhnR8VhbrdLu/0BeBwcAACAASURBVBsEUNL93WGNOU7I194bDmTWOiXxNa/MLgX1DhI558X7Bmz7YbLfQxuXS0RmmyosufMGDTbv6hha81iwZeh5IWU179nxh/onljtyZ/ZJ9ZpPCpnU78DNVHWkaqCy9PCNtqDJ1GOlbWKjY6sHZ4Ez6cOSnJeXB+ztD0wtfvbRXn888EivP0c92POvwwBcLvWdy6X+t6tjfDt+XkVPm/eXRb7Uns4CZ3rLRA6dU1feYJFQ6pLigR848jw3Nkf/oTaTsJ732OV7bPbm7LVPp28XF+XnhuIdTyOcAxy3q3YxjjzPQY682bf1vfGdg5sjgHevdW/wXHdLPyEyBugKkZ/GPnz9h1dOH9es7VPixYSaCCxXyFeNPNQGUOVmIjsd29NZ4DQBD5WGrbdkmIMfDk8pHVI4obCskecFYE15z9UA6yq6bWqK4yWawgmFC7LMgW9BLEBJYwfGtkl4NUBnq89Z27aJID19VZrNVtos3/mWErYVtwNQZl+jfpCLyZ8lZn+drvMdBq/8A2XKCPqSths1RpnUK0BX315lZzcmFq1hFmx1HgpqVbEv+8nGHKdoRrI5ZFYvAmFLREaLu3RKY5JVgC6XeP8P+ESQG3BntsgTmMlvdkkxod5QyLHApOmnrbqIaFOAZcC6+hxriT91cgSxAWfUunET+fbWSxZ0SVvxxYqyfp1pptkWLU15sHgyVh03BiDZ4m2V44rG5jNWsUT1951t58jzjAI+BpGwstzRO+/dsMJcJkS8/8/eeYfHUV19+L2zVaveXSRb7pJtGVwopg42oUTUhHx0DAnFiWmhihKiUA0EEuBzYjoOECDko4tuM1RjjI3BRXKXm6rVVtq+O/f7Y1dGGNuSdldara33eZLdmZ0591jszpy595zfyUxoyHL5E2odvpRqg/B7R6SuL2x2Z1Q2unOrzAa3f0LmioNqHUO/r3Hkb0w0tukH5yyZuL294Lst9tFVaZZGOS33q2GbWses2dRaWJOVUOc7ecTn276tO+I4m9Hx1pLaY08Q6J+/WVr+MMFZnrjtVd+Z614fdJAka+YYS/ubr52zWY/E1spZK+XMF8diUwK/Aebs/vltb2YnjbEkrVzvSSoA/m4PmK5/8Iy6iMbszKbWcXUAy+qPiGi5tT+TbfR+3hwwT5dB6ZiFxQuKu92NbHeOTm5yLbRno0tuKV5QvL0vZGAiwWDwVXm9SRP++tfLjTfc8ERcPpSLgNkbfI1MTsxkayg0JjTuhJO7caz7YWEI3NxYkfeTtsmOkxo/Tvw4A+EVNwPPR+LPAD3jiLv+mQP5J4L4+6t/vC+ia2CCSzxvDIjDWlP8ZanXObpME+kuupBlihRfNGT5ns/+qXRV1PnlS6ONZpGwVkfkJSu+m+7NXztN08S7qiqrgUvDMPk1yMoEoV8DPB1ld/fKtrZRHwPH8NPmBVG7ru4XM6wFpeUG4HfARy9c9bewhMT7Ab8Flnd0udgT1zx5viHRZH8eZKj6T0qb0fkD8LxR8X2YnVCzw2LwbANahNBNLr8t0elPKgCO9gdMp1Y0TppS4xg6C7jb4U++98vq48/bYh/9IPBqiyfzvx9vPfXhTa2F7wPf73Tlrnl381nL6p1DVlfZx4wCkCiHgHyJ/aNXPQDfONLOlAiGWVz/iIY9RcjKWp/lZxfg4gXFye+05L653pNUMMXW8g5w3cpZK6MWrAKYFG8CgM3Yvt/lsHawzpPUEixCEBGrgHzelmkBSb3fcjC92MowWrS0jHgTFNHePiQv1r6ES3rVucsAUrefEpZ0XAc+V1aLz5m7qjvHnnfTOpcMGJ6TuuGXD519yq50nUMebmjQbYGPzasTB1UUFvVI2WOAyBiZtu4hEMaijBXvR2SoLHVkTr3pdIctsLY5PXBnlNwDQPmz/Ut7sn9nerPxl5Sl9trSevGCYsM2b8K/NnoS86Yltrxxb/7atwgWo3W7eHd3Vs5aKSfb7Gtd0lB87euDToqet13yMchAUKZM+oly0ft+McOq5r13o7b95GFZCbVlsfYlAuqB1cBel3N/aJj2lMOXMligByRCgvA5/MlzejLTWVBaLgBTQcr6pAlZ3+VXNk7yb2wtFLm2HZnFWcsOqmg6qH5H+3BfTkJN7tiM1dMrmyZu3Oka5MmwNgw1Kd4z6pxDhtBLT0+xoDVgOhP45u9n1kalI1qtz/oRcGPxgmLTylkrfQDXvD64EDJf1BEHmYT+uwW/2fZMF2bCYkrO4uOW1B7L8cPfNsB+23lSA+Eh+P2LSAXEK5WO5fXOwW9//j6HGojoI/lRfDyuaM1/Y3zqtjNozX+zOI+jw85dlAGr8DkGbe7u8YrR/2/db7wqtaD2DuD6jv2GFtNjwJvA8fwo2D5AL/N9w7RJaZbG1rHpaxaFa6N+nk3JxviMQPgTnYbjEy9pj7oEpMWj/MoYEJ8BVxJsEhBVDlow8Ugj8nE/yoRx1vbnnj1r2yUAmiYKVFU2R2I73ei7DeSpmj1zJhDZg0E3qZpbsvik+++7v7J50q0FKeve0G79Y1Svp/tFwLquecLZSSa7flz++68GJ1rjD1WVbxK8cO6RgtLy02DsxemWne+2eDLuJnhz7fGyfNXcEgl4gabQ/zqze5X6T2YdC0rLXwQWEoVgoT9w8av5J0DaJJBXRtHsesCYa3SPAdac+tKoCfZAyncGpBJAnL78otU97qLVXaod+VsBHL4kR2+NEWtWzlq5uHhB8UxC3/8Il/G1kGC5DHWS0iJ2sBcZPHhZe03NVAYN+uFCgnqTcYvfsjM3Mgt6smJydHuFIvfgTUsa1+b5vG22C+gUsAIfSIN0+PPcdzIQsPYJBaXlQyGtGPjLI5e9GHaQqSvyZYE41meUfzDdbt8eRRd3Ybm17XPKUt+VyJu2PW1dMOx37upo2S5eUDxdwKd+FINA6iVpdRdrmvirqsrVkQarAI+cWbO6eEFxeQBxSfGC4hZgUV+kPY3LWHX7uuYJ51XZx0a9+DfuUwIKSstzqx3DJjp9iX978Levtsfan56iaSJL08QloRzWPXL83AevA/1VoKLZk3XW5rmnLo6FCkJovJnEqWTY7vikcreCZEZK4wfRsnl0cqMVYKzVcWbxguLJVV7bQrtu9B2d3HTFylkrIw9WQ51cOnpdd2Zb28g6QC7ceuq+JWL2YSMe6BDJjvTie3Jq/dcgyDZ6q4Cwc2H7CrO5/XvQZXt7btxONJicHQWBIuxA5T+PzRJAQtKg5d1WGjjvxnVSBpRHXY0pOQ+dfcqwjv1FlRUe7zhHtaHGMmXVoWMSwvVpgO4zKHHblQRXNV4K20hZ6qicetPp9uRA3Y6h3vlRc24PNKX7HxWINKtbWRBdy3KGDK5WIkF/o3nQM3vWVQ0fm/D/AGSCvJM+Snt65LIXpY7hX8CMgtLyIdG0HbcXvg7MBvel3oDVqGN4Kta+hMks4AGCS5GVu384ovTt6ZLCh0ICwiNAHEwMly1DQWq/vrF3h+IFxQZBcn6awff1I2fWbIiWXV2KTwCWO1Nng7wNxE6/VKY99quaioiNBwPMzwCjRMr2BxP1BLdhuTEgnB6znv1hSs74JneWPFyp/MRzr57jStCLElzKGpNPNHnNMttt1ccZfXgTMRhDnVzclKVGJP8Sz6x0JXdUqr/e34NVgEsu+cRTVla2pb19cNxONNgaD6kHyNzw27DTAezbj04ABU/r8B7p8Pqc1n8QnF09F7i/Y7+xxnK94lXewqucDLwWrl8DdA+D0K8anLjNsfhPs9eFZaAs9QjgBUUKX1K7Mi3lenevdYMEyLjG8YHjgaTV2Q3GQylLTaOsNSpFrcckN531WVsmoZx83yZPYtRjmARD4JdOvxEQCn2Y9pSdUPtqg2vQHYcO+uxRKDkrWnbj9sIHcM2T54sUc8ufc207tlfNLflZsBcnPAwcpqpyj/5LREnwP5MgJOej9qFv+zMzJWJQc8D812ga/bI9Ix0kDt2YByQAV66ctTLyYDWIKpEdD5nC4lEcUmDQhTQYAsIlpUAghc8okxVduBRdtLsS9PF+o0wxBIRT0YXd6Ffgxy9UPLcujpjt3gQLQIPf0qWeZz9ic/DBNT5pGfbaRIDW/DfC1nb2tI60AXjsBd3OYQW4/pV3Npls7vXmZOdtnVu1GppN7xGsITg3XJ8G6B4FpeVjdrQPT0wxt7wYloGy1OkS+RlBpRqTIkWfaCgnOg0XCkQKcE20bG7y2EZYhN9BaMWyNx6aG/0Wdyw6fy798+9WDUrc3rquecJh0bQb1wHrom2//MVO1yDTiNT1/421L+GgacKkqlKqqvx270eJjaE3AfaDvNH+Qr7ZdY8B2Q68E2XTaqflzgAQUetPTRNmTROJAA5bYKmugI5EIFwt6YHffXlU25TPjm172vgn+7TfK3/48BL9er48qu120+32qcunOvJWTHae/tWR7TNMt9unpFzfnmr1KqpAuIjz1sXRYLjZmQyQavDFzUpTWtqmbJPJEdWbQF9icuU0AXgTt+1Vq7grrGnr0wGM1sYez6wl5+1c6m2zJbdU5e5aGi2qrPD7hrqXSqP+66XXZQ8O168BusWNgFzbXBxuepRKaBmdYPyiRsGnrilr/S6gyPcDQt7mvyvl/nDSqTRNJGiaeE7TxKzz/jP8l9u9CanFtvaFK2etuqc3gtXiBcUJwGQQ/6EXg+K9UevI+0uLJzOvoLS8MFo24zpgbfOmzQJavq5Rb421Lz1F08RkoErTxD5vPuMzVqQCmBTPU+wHeaP9gZP/PTql1meZOs7avm3lrJV7bXEXJhrgBvwgvHQjINQ0oYRehaaJGzRNnBTatgAO4DqApYc6Pv/+IKezMcv/FjBzzQTXm8DvgS8BNrWO2+z0J9WrqnwPQFVlq6rKt1RVNu4aLLj8vysP+UBNBwAYY3XkAxRZ26N2Qe1tpFRW+HxJyj333GCLtS/hkNhwVLBoRQTClpFKHLR8LEDykK/H9PTclk2Drgb8rp2pZ3be7xvrfFv4FWFZmbTfymvEmhGlb08HeVlIlvHlMGURvwI62q/26QN3W1Lgc4MUJkOAG4GFYQStXmAYMHilK+VqkN6NbluvpTIenth8FWAdYnK9Fo2c/zB4GdCtBmfUuoXFbcD6h8cvHiHQzwL5QtXckrD7v8eQAPAdsHZfB6VamlWAXwx/68GBYDU6bPclnOGTimgOmP4Ybduhi8KugHD3i4SmiTM1Tfyy0/YPwHwAVZWSYI7dKaFtD3A78EnHtj01kJR9pfN0yloXq6r0qaqcr6pyPYBZcScK9ECXTgb7ZUfcDSbeWeVKbgfY7E3oUS5kLGltLXgPwOdLKoixKxGRXXHtv8M9t736UDuAq3nM8p6e+8cX328E3gV5/r8fHLsraLZ9kvEksM20zXp8uH4NsG+Sza2zQqltEH46khAIBOJf9PEDd5rdKGVwdavb6VShFbKbNU0kq6oMAL+4asvE70GeCJiaA+ZXeqsQqtZnOdEkdDnJ1ha1ouKeUDW3pCYvqao60dR+3TVPni+6PqNr4jZgbXZnPShRzCcMf/OTWPsSDqoqf1BVeYqqyn0mcH9dc+wWkE6T4tvUV74dAFwIbKrxWaOivdoZTRPTHhu+KrnjiVbTxAuaJl7pdMgtwLWdtl8hFJCGGK2qP8psqaq8X1Vl55axVk0TH2qa+Fn3k9HpFUekWxujLiWyv1LrC9Zc1fmsPWp7GGM2A5hMzrCX1GNJy7D/mxh8ff2gcG147CMEgLt5bFhSRqnD674CMdjvNu/6HRZVVujAyxJ54nfnDx0arm8D7B27NzWU3ibDTm9rSvdfJZHSb5DXxuCBWxPBdK+ezO4eBNxLqFNWKGg9JPiRECAt9EJaQ/GCYlHltRX4pHj3wTPqWqNtv7sMStz+aqM7x7hw6ylRaSkdlwFrQWm5+Lrm2MIkU+u6J37/ZFxVdWqaUDRNzNE00a3OGRJlGIiqSPTqBviR61/PnQby+KEml7Zy1soe/001TeRpmji60/Ztmibe6HTILcAjnbZXAys7bZ8BnN6xoaryHlWVL3Xa3qeGqqpKF8EL5s80KOscQ6tcfltDd/8tBzpDTa4sgGyjJ25yWPPyFtsBMjLWx2WBkMk1pBHAm7wu7Fklc8qWXABTYnXXqwl7wJZlny8MAW/zxsE/uYm6p9g1gTBKsx51gfgBAJSQKoe4izDT28xecbjTpruMf7L3efvpquGe5ToSj1lfwz5md0OpXZMBVFUuBSaoquwsifUR4A5exoUCTCxeULxXWcswGQOMBPFulO32iG/rjioDvO2+lAei0RkzLgNW4DCJMqHdlxrVCu8+Yjrwv3QKWvZFirl5Wpqlcb8Vgu9rVrmSLwBBUUL7HpckNU0kapqY1Gn7HE0Tb2ua6FjSuAF4r9O2C+is/3sznRqcq6q8T1Xl3Z22q0NBZ9ioqjxRVeXPumU1unNaXP7E3ZtBDLAXRlud4wGKEtr7pNI4GhgMnjVCBHx2e15ctt9NbJheAyCVgLWrY3dn3uxF0+fNXnSLybrzDICUvC8zwvHhvJvWtsqA4Xm/yzLzobNP2ZUL7B/sfU9P8jdaViTH5ex1f2dw4rbTrAZnbdXckr+Eld5Wlnp0ksOQbXMqvdZ8ZV/YnMpBCkK0pPk/72J292ZgiaaJsQC7KwAF08TEDBC3A/8HnDfM7Fx7wxu5UVsdm2pruQdglMWxezOgvmYCSAPI6SAjbucelwHrqLSKRxQRcBNM6o0rVFV+CUyhm6LJft04dFjyprgssOhvFC8onl7ts56joK85Pb3uUwBNE0dqmviXpomU0GGzge81TaSHtpOAbKDjv8F84ERCiViqKh9WVXlBxxiqKjeoqqzqg3/OzzAr7iSD8A/MxHeT1a6kbQAb3IlRFevuTS65RJNSGta7XJlxfU3IXXnr33ty/LzZi04GPgfucdRP+Q2As7FofQQuvAAkJQ1u/EPHjkMeapBKu3Ge4jIcUlFYNKAWEGV8url4eMrGbncn+wnBAqePCOaOnhGLpic5DaYsgNx68wt7+lzThDn09klgDsGuh3sk1PzknpWzVp41IcH+9DZvwijNnvlp8YLiqAjtr3MnTrYKv2ejJzGl66N7FRVQQrdLMxGmP8RdwDq17NnUrfaR0yZkrthSNbekLdb+9ISOL7Sqyu9CuSz7pKC0PMnpTxbrmif8p/e9278JJbYvApErYPwLO4d2VAPnAMcBHa0i3wb+B/AAqKp8SlXl4R1L9aoqK1VVfqmqMrwLbxTQNHGoponNmiYO77w/P7lqyqDE7XGr0dnX7PRbdIBqnzXO0ij0KiH8cTkL2DL8v5MAWob93+TuHP+fx2aJFx+4/m+g/5egnJEIveLaOXFnuH6kjaz5zGRzB6Su3LTbRy8DwjvCdf2ezhsgPApKy207XTnmLW0jw+0WpfJjoyOTz6j/LIe/D+j4zf2sUFrTxP8Cr2uaEKoqG1VVPhkqou2Sl/9ny6VDTO6LPNIwDPhu6r/Gzw+3EOvGN3IzJy2Y8FKbbhrllkYzfdTdah9oBPN9ISilqEViLO4C1kZ3zjk+3aLsdOXMjrUvPSE0g7dO00RPJB6GA7gDtkhmEgYIohJ8OieA0CvcSRMAVFW+rqoyv6PSXlXlOlWVr6qqdMbO1S6pBpYQCqo7qHcN2m73pm2NjUvxx2CTe0joNSoVrH1FZuaGPEXxj3/2WTWu/AYwOfLqAdxpq0q6Onbe7EWDdq79n/KWTSXXGqzNLpAdNz4FwJJSFXZqze/uW6YbE7yvOOrSMh46+5Scjv1FlRUV/lyPS3jFFeHaHmCPTAQh3P7EpWGerwFeGSzYwugXZ1OWekzUvOsGbYmBywJCBgjmh+7OGmAFP2rE9oj3z9vwPDAHZI5XGq4A+VXxguIfDnu+6I0LXx2mHf/imD8WLyg+6pgXxuXf9EbuT8YoXlA8vXhB8S2HPl9045L2tFqJcg6hBFli3BwmmPohLgtuifBSQToRN8UGPyIvA/FDjSM/1rkZPcUKfEGwCKdbTM75Wv2u/nBGp1XYocvr+wD7RiOog2cC4bMHTG/H2J+wUVW5HThn9/1t3rQ2oM+LEeKVAovzoBqflaKE9lgvm/UIn8/2aSBgnVRXNykTCHuWMRYk7jw8qMgg9D3msM6bvWg6SNVoqzsMBv1C+hOM1vT1jyUPWXxjw+qLpgD/BlEAkJL/mQt+G7YvrsaUe4HzgLOBxzr2S5N8xrTdOqeisGhEUWVFj7ppDbBnDs5ecu6KhsMYnrIhzHasrYspS50pECrBe+j9EvlhzeMJ9wy5wnVXNH3d8/ipZyWhFIW2FvruTjnhy6PaTgA+U1X5sarKf0RhlCEE5S4NBAPORL9Upn/vTMmRiGMBmgNmPrJnMXnBhI1+lIokxWcWGGdKUFy6UUjht4+xtD+93pM0m2CwGvPmMFaD8213wMaQxK2pkdqKqxnWK+Zfeg6IqaNSKz+omlsSV7l6qirrVVVeoKrym+6eYzZ4DgUYl74qnmR3+iVd6aPGI51ypgAwKZ4kk+KJq990LFnjSl4HsM6duCXWvvQEuz3vEwC3O2N4rH3pKZJgJlTu93fcvvtn82Yvugz4HMS9fueg0wEbSKO7eczWhtUXdTxU7OoK+D9XPRdR04/rX3lntWLyrzMnuW7uvN+03dpRzDvQRCBKeAOWQy0GFwdnfxN+C/Uf9aPfAo52JUjHoFrTnd57kqMmTL87Vc9aLS1/S3wKeCmk/wpgUnRmAL8BfhHF4bRQsxk/CDdwwbKLVueemlaXnKAECoGThpud9xVZ25dKxGog36EbZkiEISiRhe6Wxr++ds7m6+hH97oTC95osRqc5NhqTorUVlzNsC6rO+LXRuHTx2d+H42nmT5D08SFBJ/EenRjXFJzbDVI/4aWwhW95NoBReiHG/eBKoCmieuAuzVNpIcaDJBjqx2XYHS0d3HqACFaAyY/wHZvQsx0CsNBUXybdd1EQkLjQUDcND0AaB3++qS0LWfROuyNyfmoCzv2B2dW+Qc/W1IVCvBg8H8SQIYKOJg3e9H0OfNnRPR7TiuoW9u0fuipT5dOPex3c5ctASiqrKhaPXnsKmnRrwXmRmJ/gCBrmg72gVz8yGUvhiVF9jPKWnfWP2udlLfd/KbZpzxNWWoR0AxoUdNnLUudMthsesviVYb6Ff1bgy4mEiwc8hl08RHwiKrKqF07Vs5aubh4QfFMgkv4Wkegec/pDe33BPNm1wIfALs6exYvmDgd5CIQRoKzqR932KKf3OseuexFOe62/26qbCqui9RW3MzGBJO2c0/wS9O/H7v8X1Wx9qe7aJpII3ghviGM04eD2PbBzaXR+ZEPsD+xFHgIsHTsaHJn1e105Ya35HYAkmP05AOMsDh9XR3bn8jPX9wAkJxc/atY+9JTzO3DawFcmct2nxVTg/I3EAxMOxbQOi+kCUIzSR0sDAW6YeP3mK4DKVs2D/5l5/2eSe1rDS2m3O/OyTsqEvsDBHXTgYNA/BBVu5e4dxgD4miCralvlMh7JfJTylJnRmK36llrpvee5MeBpWavMNQM8t5nvKPtkG353us2j/AoG0Z7Hg51Goz6g25IPaDbbVRXzloVksjqH7Ope8MTSFjjDthyuz5y38RPwJqy/koghaBkRNwQ6mQ1Hrizp+dmJdQdmWFtiHav+wH2A1RVfq6q8k+qKu0d+1z+RE+LJ7Mmln7FE3lm12SAArMzrjRNL7lk0Q4h/I6WloK4UkkBsDUeUh98J827faSFeswD6KCvAdkM4ghbzoq95ShG3CXosge/2QBiIXDBQ2efsisYVlqNN0mkbl2RHM0l3wOSkwpemwqkjUqriH5+fVmrC3hfImVoyd4EvOd4IGlx9eMJpZSl9mgVWZalHD+4xrTD7FMul8inBaJobaH7UYBhv3P/c+tw70k78rx3RP3fEQE9DXJjQYq5pdkgfKMjtRM3AasulduyEmrdBLX44oJOMlbbVFX2WDrHG7AMGpy4PdodMAbYT9A0YdA0savC2SB8NqvBGVdpPrFkrTtpJUhpVfSIGjnEAimNlV5vcnrXR/YvdMWtAOR+X/aH3T76+sfbkbxFGHxFINKBl5z1B/9pD43dCAW4WqQ+2bJaPwRG5hRvvqxj38FvbN0kEBpwbkVhUdypMfQnGly5RwKMSNlQ1UtDLBLBnE8/4JHIty0e5dAhNeb7gG2UpT7U9tfEWwN3pvydstRfee5NHr31aevRzX9PnEhZ6gj3fcljWh9OvFaWpXwrEB8ZAmLn9qHeP4oy++Waar8EqNQ0kQ+gqvKDWEoaxivjM1dkBKQp8dqnzsuLxE5cBKwFpeXjt7aNSslKqH8+XoqtQp2Q3tc08Wg45xeUlpvt3jTT6saDu9VgYIADkneBtzo2ks32oaPSKotj6E9c4dCNOgj3A2fUxcU1pTOK4q1WFF9R10f2L+z570wEsOe9c1DHvpAywK5ucAj9XhmwdgSJqxKyVv0pa/y/RxBscVxlSdn0RehAkT76rYgrj1OG178gDAFp3551Wuf9vmGuT4ExjuMbz4t0jAOZZXVH2gA+3npq79zLgjmrHUVGx4ky+6+353nTHLbAb4GvJfLq5HbjPQZdXAP8n8WrrB+2zfJZeotxJbDJ6lHWpdqNfwOmAn5jQJy/YYy7o732G8A8oL5XfD9A2GIf+RbAh1WnZUViJy4CVuBSwFfZNOm2WDvSAwwEc2vCzdvJAwSIuKpgHqBP+Sewq2NQuy/ZXuPIWxlDf+KKTKM330B8zpZkZGzMlVIMe/ZZNa5WYMztBbUAzqylf9A0kTFv9qIjQf8cxK5CEsXoDEn/BV6YM3/GKb+9++q7z776mSpr+vrtwuDOT8n/XBWK7wuA5g2nRnxPOP+mtTUyYHjZ3Zw8/aGzT9mVquCZ4HhBKhJzReJp+zp/gC6ZQbAganyvjfCjc5JguAAAIABJREFUgsBigIJL3G2JN7U/S1nrmQEDD0t2ifjrupCv1wzyPtmWFLgZuFgX8o2OlAKAuhzfbQSDVFRVblZVeVtHYesA4VHjGLYCwOlPjqgNdr8PWK9+8oJks+K+wmpwflA1tyRuOtKoqvSHcgyfCuf8wwdrMwDGZ64Y+KEMsEdUVb6hqnJXe2K/btab3DkDOazdJMfomWRTAgmx9iMc3O6096Q0Ul09LSqtHPsKW+O0egAhhZ9gDuqVoHQKuiW6LzkkzK78RLPaklq1XQasBnfLqKlSN4WCSHHUP64svz/S4iuCrVozLCmOszp2HPK3hk1CF+WmHdbpFYVF/f5e2R8J9o6Xx4NMByLuJR8OxoB4o3PKgCLFg4Nnuy5PvqH9AcpaFyhSPNDpc19jpq8WSNY0MZBeFSXSLDu3AYxIXXtkJHb6/Y9wR/uwG7261Xbk0EVfxtqX7qJp4sTd22b2FL9uGg8wLHnTQAAywB7RNCE0TeRrmsgDEAQsSSa7KdZ+xQubPLZVTt0Ql7+v9vbBiwF8vqS40mLVDU4DQO4Pt1+5+uWF9cB0QLJrBmxXz3HSRr73kzbDrVW/+AdA246jRsyZP6PZlLR9EYD0W28icsWAjwwWr9+U5L5/t/0vAfn+XM/xEdg+kFGDL0H9UmLRdemnKQMzd5e90lT71+tHu/7usAX+Acysz/VfrKryQlWV/j73dT/l2LwPa02Kh3RL04xI7PT7gHVZ3ZFHKiKwLclkfzDWvnSHUO7qncBjofdh8W3dka0A71f9Km4C9XijeEGxqXhB8XnFC4r/EuN+y+GiAJWEJNOMij9pbPrqg/Z9ygAdeKRBBhBxqVtrMjm2AhiNrlvLysri5rtrz3t3AoB96PuTbDnLXwaGC4PrORCNwSPkrv9z7hy/e97gGoLd6qYA+NoHLwrujjwYuv6Vd3zW9PZP22sych86+5S0jv2+oe53pFGXgRzfX/d1/gB7RQPRoVMWu65Lu6UM7Ebqjjzfb5ce6jCF5KriMk2oP/PIZS9KKZXNPzRM2x6JnX4dsBaUlo8CZujS8ETUBId7GVWVkuDT3Lmh9+EyHKitmlsykBLQe5wEvAjcDiyMt6BVVWUAuBh4CsCnm/VtbSMGmkx0k1SDLy9BBOJyRlpKMQQkfr/1JGBhvASt5rZR1QCNxoYbnPWTzwKJDFjPA5n1Y/vz4H3Jay+4ofOs6Zz5M7wmW22LNW39RcE9hkU/zsxKnQiDIUdtxi1IYQJ+3bFv0sLNrf5B3h/MFbb8isKigSXiHhLqHb8RqABmRtpLPppomjhU04QISU8eAVwZa5/2Z/zStMEvTfuvSsDknK+fEegywdj+XKx96Q6aJsyhH0C7qsoNkdgakrh1Roa1Pq4EzeOQjnwahVgtV0WIqspXVVWuKigtV0AoDa5BcbnEHQvSDL5x6UZfTtdH9j/8ftuhwXdCEEffXVvT5J0AjoZiY6gRAAT973hPqLsVIa1WtfP5psT6bZ72vLR5sxeJOfNnLDZaG68FQPHJSLteAd8i5AZjguean4y53Xqn8CtpwHER2j8gybFVJ+clVSX2s2D1OGAJcC6AqspNAzOrvUuGtb7drLgLI7HRbwPWgtJyU0XjpCkjUtftqLj77IimkfuQO4EvNE1YujyyCxz+pMwM686B2dVeJFHxh2YjpU4sl6siQNOETdPEjNJDS4cApFkarbH2KV7Y4bWua/CbV8XajzDR+sVSaw8JmOxGgFGth3+dbhAEf3vCx4/trUIraZLddVbnzV403dkwabv0J1iBfIAr/v4/QdlA3WyZN/vjf0aSx3r9K+/I9JE1W/wuS/EL944/tNNH70pkmz/L+8dwbR/IGETA79eN/ULNQtNEx/XxU+D3wGsxdOeAYnRaZbJXtyZf/eQFYetH99uAFShxB2xJm1oLdxeY7s+sARZHKoFRUFqutHoyzBtaxr8eJb8G2APTk5prAHKMnmX047Z2XTAWWJidUHsaQGHGyokx9idu8KMoPqnYuz6y/1FWVrZYUbxViuLdBswsKyuLi++uJ3lDCYBNUU4+Mkkh29bqAY4D8QXQYE6puhFAKF4/KAJoApg3+6NjQP8M5KlBS/q5wf2dA1QxG/gkkqBVMflvA6j7fsSu2dSiygq3t9BZr7QbTl56XXZyuLYPVGoc+StrnXkxX/nRNHEpsFrTRLqqSl1V5XxVle5Y+9UXVBQWTa8oLLqlorAoZqlD65onvArw1sZzw17V6rcBq9ngvhKoBt6LtS/dRVXlv1RV3hAFU7kEK2UHNFh7kWWO1AYAtzQ8FafBKgQfkk78Ysfx7wNsah27PMb+xA02xZ+bavAlxdqPcElMbEi12RrT4iVYBbC0jcoFEAhFCEGGTBZz5s9YbEnbuBURyE7N/+wJg6V5J7Ad8ADXApiTqu8CxRhMF5AYrU13vPbkaQ8Ig/v4UI0W0Si+uvgvPywBvgRxYedWrRjl/YrbQOJ7mRFVOR+guIH+IB/3PbCYH78wBwShIFUD7gIWxipobfFkVoTeFoRro18GrHMev/hQX8A0c0Lm8hVVc0v6vbSEpokiTRNna5qIyt/z2Lz3ZwIcnL2kX/732V9oDpgDAPaAKe56snegqtKrqvLD1zdc0A5Q7xxSG2uf4gWbEhiSY/REJGQdS1yujGVOZ3bMZ656gsGX+g6ARAdkoN5rtM6bvajMYG6xIg3C58idHvCkPyJ1S4FidC5F8V32n8cuGedtH2oMpQkEAPzuTFvtitk3SqnUdyq8AmTE6REmm/t1YEL2xM2/6thnWZX0HNAopDgnEtsHIiNT145OszSO6PrI6KNp4mJNEzcAqKpcqqryglCR1QFDIM13KcEJMAMxzHcflryxFqAw44ewc8H7ZUD0ybaTz5QYGJa8+b5Y+9JNZgNPAGldHdgdWj3pBQCZCfVV0bA3wJ4ZbXGkAuSZXZmx9iUSNE2MvO2wGy4HyEqo6w8zGXFBo99cu8Vri5vZyd3x+xOqdd0YVznLusH5MYBUfB9+79T/2RyQgLzdWT+lBKCl6sQMgtqnCIM7H91kaN504lOgTAfxHojPOoq1ZMDiRzdnKOb2RQCWtI2AOG7O/BmLNU3M0TQxKxwfMwu3vSkUHVdTyhUd+4oqK3x6kv99aZC/XnpddnZkf4UDC4F0+nRz2BKPEXI8cJKmiX6RQxsLlHZDukQigw97Mct3n5yzZKtB+LEZ248I10a/C1gLSssNTn/SecBH/5z9zBex9qebXAccqaqyKRrGVjQc5gVYuPXUT6Nhb4A9k292DQYYZnbF5Ok/ipyQZmm+C6Ao4/u46y8fKyTC7JVKa6z9CBeLpdUohD+uHrZaCl5pkQRwZi+27aBjoksYQBgJTpFOAHJAJ+BJHw4SvyvnKECAnGEwN08LregGQsVamu5NqQNQFP/rnZQCzgFO7RhX08T9miZ2yVXti/NvXrsBKG+vzpz40Nmn7Ap03NPsS0RAmIw15qsi/DMcUGxsLfzW4Ut29dV4miZO6GimAlwOnBiSADzgqCgsShF+5XiBeE8g/gTMLKqsiMlD+iOXvejRpbJlef0RW8O10e8C1kNyv5gNDFNEIKyWpn1JqNOQRVVlQFVlNKuNhwOtVXNL4rIgJF5Y5kjdBrDSmbIs1r5EyKufbj/hZID1zeO/i7Uz8YIRPSXb6Am7YjXWJCfvGC6lkvTss2qsZq96zKTLnglIxd+mG9sPyZn0tA/0DikhqZja2qzp634DqEEFhF2yVx2YA97UZFv2imeBPwEz58yfsdhgtg8BcDUV7pISVFV5NHARQKjF5pmEGg5omlA0Tfxb08Qv9uan1JXngMF0krLSE/V/SGSj9buUs2JZvBKHuOijHFZNE5nA/xH8fqCq0nmgBqsAngntfwWSpSL/XFRZcV+sgtUOJEoV+1MOa4sn46YkU6v89Zjn3421L93gDGCtponR0TQ6PGVDSYa1vt/n7sY7dt2kA7TpRmesfYkEVZWN72wKSr/VOvN27w40wF4QAtsgkyeuWpt2xm7P00ChunpaXKWBKLplm8mZ15gxunwtKEcTFJVXpG6yeduGjgB2EuztHiDY2SqEEIrROe+Su67/7Zz5M+7rmE01JdYcDCAUz0+E/VVVOkOvflWVY4Gy0EeDgENDr2iayNU08ZamiUM6nf6OMAa8yUN3zuvYkVSedahApAJFEhmz4pV4Y2LWsvEC3XbNk+f32oNVx4yqqspG4ARCxXoHMhWFRYqx2nKBL8/dNn5N5dJY+wOQY6v2JRjbi8M9v18FrAWl5bnrW4qGJBid/3rwt6/GQ8vEeoJVh1XRNNrqSbclmdqao2lzgJ8z2uLIBBhudmbE2pdIuWj8vBKAQYnb4yp4iRXFC4qFTwq51p34Sax9CRevN2UHgM+XlBJrX3qCrnhbLfZx1aoqH58zf8ZXwOGKsc0uA1aj7k+0Ao8B1xCcJbsS9A5t1oDuT3pxd3vetvxqAJOt/qx9SVqpqvSFXqtVVY4GXgh9lA8UErofapo4fOrvy/+bkrezuq06fcRDZ59yx0NnnzKdYLFKMOgSWImTZg2xRpeKXaLQ4snslXzrUBOATZomTgZQVblYVWWfpSD0Y04yNJsSgNJYO9LB8ORNJrfflnL5Py8L67vQrwJW4GIQxgbX4LgotlJV+aWqynNVVUZ1NrTFk2nZ2jbq/WjaHODn5JtdeaHXYbH2JVKGJm29AkBKcVFBafnAzE/XmEAIrzTEbdqNzVZvBEhP3xBRu8O+xpO6Zpjf0jipY3vO/Bl2xeR+48cjpEUx2cfNmT/jPiDrx9uUkOwhSNT9tmwAb3teHrCwuzqsHa2zVVV+q6pyrKrKJaGPMoFRjrrUeUiDCfgzsHDNkMydgFcidQQBqUitR//wMLnmyQsSp5Y9c1ZBafmd8fjbXtM4+RuAT7efGNU2yJomOmZsvwL+DnwTTfvxjkReDVSbtlufjLUvHSyvP+wFicKHW84YHM75/aY38jVPni/SLCfeHpCG1SvvPH9trP3ZF5omEgkmc8+P9pNcQWl5GpACjCooLZ/en9rZ7W9840jbAPCdMzXu8z6fXnn148DcOueQ44CFBaXl/apvd39jsq01+TtnKkNNrridXU9L25LtdOaQmNhQBHwba3+6i9Gd/Y3iTzpD08QjqiqvAfC7sp8EzgMsIDBY2i6cN3vRzQQrmr0E5Xj2VuEcqtr/SZvasL/7qirLgfJl/zzlVkAn1Lq5Kjsta3x140yBUNHRupsPeM2T51s8AevQ96t+JYC0qblfTbYYXCO+qp65GUgbl77qCIPiH7qm8eANQFp2Qs0kXRpSG905bUAanGvrZO6GOPxtd4jzJwBReUDUNHEWcJWmiRNCjXpuiobd/YVvr8o5OZHME/1DPPOKF23qNy3eA9K0OfS2ANi8j0P3SL8JWFc1Tv5Viycz6Zi8D76A82PtTlecBjxMsBfxV9E0PCq14tKNrUWAPAmEGocXp7jBoRtl6DXuu53Uu4aG8qiFQhRu2vs72UZPNsAQsztudVibmkZ/BVBbe1Bc5S2bXENXSeSZ6MadHfvmzJ+xeN7sRccJxXuP1M3H+dqH5gB/mTN/xu3zZi+aSfD7rHVSAQDgP4/NErBLvSrasj2fEGxeYNIh8GH2zAmPnfH76olZy7ZkJdSdXv/gPacEdEOGN2BJSrG0TPy+4dCVQFqGtb7IpPiH1jmHNAJpcN5PmlMsq/upqs+65vEywejUgSygRQjpSrM0ehvdOZ8ALWmWxpktnoyDQgG5mTj7bY9KrVA3thYxLHnjaUC0ZvvcBNMz0oC6KNncb7CsSL5UGqR0H9w2r+uj+47CjB8aK5smcXD2kpOgpMfpWP0mYN3YUvRrkC2bWsZdF2tfukJV5UuaJlapqlwZbdv1zsGh1prRmS0YYO+MtjiyN3gSGWlxxG2leCf+BVxA8DcdN73lY8Uie5YD4HtnyqJY+xIubnd6HYDfb4urvOWAqdVh8KUydOnDT9Opb9Sc+TMW/+exWTPb66ZudO2cOAK47bmy++vmzL/5MfZyDbTvOOLR4DsJCB24ZvegNlyuf+WdxddddNlZbcm2+StNxfm11sHnA+ev2jl1tyOlTDA6AJkKokVKxZtqaWqucw7+CERLQcr6tMGJ25MX1xz3NtBcnPWtdUjSNv8HVWd+C7RIlPY1d5+91+5LoTSAhSATQApQtGj8+/qCoO/jzgDY2jbysYLS8lXhTsBompgJZKmqfEVV5TuaJso70joG+JGKwqI0I+YTgQWHPNxQ0eUJfcjY9NUbK5smIRETwjm/XwSsp/z1ziEw5dcgnvji9jn9umJb04RVVaW7N4JVgDZf2mcEpwxiKvJ7IDDU7M7f4Ekkz+weEmtfIqVqbsnnBaXlMwjNRA3Myu8bP4oFwCsN8VDcuUdSUrb77PY8UlK2F8bal57QnvtZXur2U3GnVkwm2H57F/9z1QI5b/aiScByYIyj9pBHn7/njm8uvO3OJXuy5WkZc2Xwnej4v6xo+HjVExcVLNr2y2sdQ864DGTnApFAgtHx0jF5H76Vam5panJnVny89bTairvP1vdq7GeU9MiXqrkliwtKy2emWRoXCqT+3V8ujqfftgpKKAlZhDUBU1FYNF0iVfNVib/yTnBITROvqqrUB4LVPeMf7LnZWGNJJFi82K949LIX2t4qfaf6+4ZDw1oV6hdFV8nm1sdAmMdnrHg11r7sC00TI4BtmiZ6dsXpGR35uwuAgXSAXuTr9rRKgKWOtB9i7Us0qJpbsrhqbsl9A9+ZrpmYYB8KMMLiiOMc1s0BgISEximx9qUnWNpGfgHgylhx754+nzN/RjtwUse2fdux78+bvehn7Rwf/+Mrl3fa9BOFB/yC0vKkMbe+dsfHW0/Z5PAlXQO8CeIcEK7QGF6XP/Efj89+6tUHfvvfhU/94fHq5046RWqa6NVZ7qq5JYudvqS/NXuyEwpKy+NJFUJjlzSZlPTwv09FYdFMifwSuCftsbyJWbeOuklVZQ8eDg4sll6fbRIe5QbfEHdjUWXF8lj7s2dEI3BsOAWEMQ9YC0rLxXd1h0/JTqitffem2z6LtT9dIIFFwIreGkDNe28SwDF5HywbCDx6F480SACXbvDE2pcB+pYsozcfYKjJHZUZuVjQ3DyiCqChoWh1jF3pEdbWCWsBzI7h2/Z2zJz5MzYl5iz/3+CWSAMWzZv9sZw3+2N93uxF/nmzF/n9ruzHg59LP/AUoUYC4fh09ZMXpP7ywbv/CWz06Za/pJhbvj+p4PWzquaWnFc1t+RVYCZwB7tNIoSaEnwGPBjOuD3Bq1sXEbxnh93asq8J/a1mpll27kw2tRLGPe0kQIhgipzV0GS6qqKwKKpqA/sTie9mnmRoMhkDud6/x9qXPREMUuUEkCMJFgf3KGjtDykBh7sDtgK3y3ZZrB3ZG6E/qgrvaFVzS87u+rjwl2Tt3lQvgMuXGLctI+OF0RbHkA2eRMZY2uN2lm2A8Pi0LXMzwBJH+pex9iVcrr/+KXdZWZlX181xlcPqs9Y1mdy5pGw7ZZ9/e0f9lGqQeqiQENClwdq6OuDO/BY4hGAbV0LpAFvDCVYLSsuPAv5oNpxxojeQkGgxuL7yBBJOX3LH5V8HhWCChK7nP7OvqtKvaWIhYVQ895REk32J05ckJ2SuuAZK4kb2sGpuyeLT/lr22g87D7n84D//a+SKv1y0qQenvwbMkUhLaPtXukXfuvx3g9+UgiunPlUz0GCnE0KKq4Ht1u9S5sbal72gAkroN9vjFJGYB6w5tuo/1zsHO0C8Emtf9kSnhHerQAZm3PfQk5taC98HOGzQp0UAS2qPrQDGgX5PqC+2J9zq/uX1R+wAWFp3VE9+1AOEwSCTe1gohzU71r4M0LdIhBXAJ5W4FhhXFJ/Ham0ZH2s/ekLb0PeaMzZejCdl/XHAvjS3NRAekCaQBjCIgDtzIsiOwtSO4wKEkQow/vZXjoPEhSCEN2DVizK+f/S9m269pqd2VFWW9fSccFh917ntR9z1z7adrpyJfTFeNPlh5yH/AC5v8WQeCXT73lZUWbG4orBol0oEkKUn+59P+DLtCok8pqKwqAzYDhwL3Zca2x/5dk7uqYlkHK9bA2UTVqzrr4G8FtJThjBSeGKaEnDF/EsHt7gzTpyYtXxb1dyStlj6sg/UYNK9EBLFuKm18PfAm8CbS2qPnbuk9ti5oe0HQDEFZwNkx5NDOHQsd/Qb7bT9lSWO9O8BFrenx9WS6gCRU2htGw0wztqeGGtfIsFkciSYzW1xFcBMuuzpNl1x+wOW5pn7Oi40YzoTxB2gPNPpRte52EYHng1ndjXF3Py/ITUWQOgVTQfV9tRGB5omhKaJczRNHB2uje5Q7Rj2VK0zL7ugtNzS9dH9ipUgW5JM9h7XfxRVViwuqqy4L/T6tvswe6Y/13OpCH4fXpHILyXybuCAbpdrqrLeIk06TrX5pVj7sg9WE3zS/JgwanRiGrB+UHXm6V7dSqKx/bZY+rEvcmw7KgERTBiX3oKU9aXAVGDqUUM/OvOooR+dGdq+GPRQkCl0wkz+P2roR4cAzMgvj/vuS/0dn1QEgFsaBh4ODjAyjb6RAENNbltXx/ZnvN7kytbW4f1KuqZbCL3R3DZyi6aJfd6D5syfsTjU8eppgtqbfhDe4MwrfoI6qf/q6fAFpeWZdc7BBaBLolOwZQHmAldEYKM7fB4aa1ovjxNVquaW6KPTKl0JRuevIrV1yEMNgeJPNz0NTAok+t8DEMG0EQsHaLvcisKidPNG28HSIP99yN8b1sXan71xXH75uQATMpe/Gs4KdMxSAkJL7bcDG5bUHvt6rPzoimHJm6+vdw4lxdz8L7s3/XHt1ms7/ZFLOlfhLS8oLV8HvA6yuWruKWEtTbR60h0ATn9iSyR+D9A1oy2OvA2eRAqtbamx9mWAvuXL9ozvAT5ty+wVebq+QkpDExCPs8QNlraRzu5WfIcaC3ReGoa9NBPoJn+SGK3AhcAwQnUH4dYhqKp0h3rabw3Dl24zImXdN5vtY5mW+8XNUHJab44VbRQReLPBNWj2mFtfG7z+3l/VRGqvqLIiUFFYdJdEHkcwWFUCyb5ff3t1zrPTHq0Pe7Y8HpFCXiqkSFDchvtj7cu+qHHkFwOMTF23R5m6rohJwBq8KOgaKGaQPhCH0w/F8Y+559G8Wue0Iw7K/qbpzev/cnFXx1fNLVl87D2PPrOlbdQtV8y/9PzHZz/1Yk/HXLlz2jaAr2vUHWG4PEAPyDV5hm/wJDLE5BkoujrwsAIEgjN1cYvF0mrVdeOgWPvRUzzJG3MMvuTknpwTCkw73yfCumfMnv+7XwjOuEoRgWc33nfGrmt0qIL5c4JFIe6e1iGoqtwMQa1uQES7bTfAJ7f+sXpK2XNt29pGFETbdm+zrnniM8Bsn245GvhPNGyGclxnAMfp1sBYpd04y/p1akVFYdExRZUVcf0w2l1WTxp3NGZ5n26WWycuXd+vJRormyYlAzWPXf58WH7GKiVABWHu5IMaIz/2SY0j71ZfwMzU3MU3dvecCVnL5xsVr7687vC9qgl0QcdDxMAydS/zdXvacoDP2zPWdnXsAPsXoy2OIoCJCfaYS/tFQkJCY46i+AbH2o8w2CECtgRNE8P7euBVO6f8zWzwKCePeP1vu32kAobdugz2CE0TqUAFcHOkfu6NJnf2y3XOofkFpeXx9t39TqA7shNqfhNNo6Hc1nsnrFh3sevYlmuVNoMHWLJ60rhLozlOf6SisGi68IqPFY/BoLQbhsRBDu8Ugk1BwiJWX3gZzLuVejAfqf91c/rD4xcfoUvD7FRL82t3XPDhM9097x9XPLfVr5sXNLgGzwhH4PmIIYuOBjhh+BuZPT13gJ4RCH39fVLprxWVA/QSqYZgDmu+2RWItS+RYLfnf+zxpMZd+pDBl7LG6EkPiIClT2e4C0rLj93eXjDBZnTc/7+XL9i92FIDZKimK6ycVlWVrcAL4ZzbAz4H0qwG56ReHCPqVM0t8Y9MXecyiMCpvTXG1Pm1jwgpDpbIrxWv8uSKM4ZVVhQWxXWe+r6QyD+I0OSfkAL66eQfwB8evyRDoE8szPjBG66NPg9Yr3riwuEG4b8OWBes/uyf3Zy22Ec9LYQujhyy6IEwTn8cSBySuPXqnp7Y5M5qBrB7U/urasJ+w2hL+3CACQn2eOocM0AUWOZM+xbgvdbcsFoE9hd03dQEIu6+vyJg2SqkyZCz6sawb1495ZonzzcYhP9RYFuzJ+svu38evA+JVSA2EsF9SVXln1RVahG6u1em5n61EmBK7uJbemuM3sITsLxS68yzFJSW91rDjqLKilpHSeOJ7in2z8yVtrHAkorConG9NV6sWHbp4IcQnC+ROlHq9NabbGwZd5hEIcO6M+zGS30esG5sHfe2LkX2+MwVV1bNLbmnPwarB93x/MQ1jQcXTs1dvG7eFc99E4aJb3JtO9ol4tZrnjxfdH34j1Q2TdoC8HXNcY1hjDtAD8g2eQsABpk8cXfDHyBiOvrDx3UOa1JSbQpgeeKJkrgqvHJlLssF8FsbJvTVmOuax/83II2TchKq366aW7LH/FKD8LcZhK8m0vuSpgmrpolbNE0cHImdPZGXVPW9zdjmWNc8ISfatnub7e0jOiSXjurNcQ55qME3+d87jhWIk4FBUsjvll06+JHeHLMvqSgsujDhi9Rr/UM9rYFM3xmEurD1Zx3atc3FBQBfVc94LlwbfRqwFpSWq2saJxePTF3/9rs33vZRX469NwpKy6cXlJbf0rlFmBDyEYloz0vaEpYER9XcEpljq3m2xpGf8Pams3sqP9KhwzqwTN3LLG7P+BpAs2dujLUvA/Qtw83OCQoysHLWStn10f2XxMS6LACDwTMk1r70BCn0LQC2+uk3bS/9vNfz7gpKy9UVge8JAAAgAElEQVSKpoNOB0m9a/Ale2sJmZ+8uSgveUs0ltqtwPXA6VGw9RMeuexF6fQnv73TNWjC7veuOOBbge4dlrzp4r4YrKiy4gNgcmCQ12n7Iu3qNeML51cUFlm7PLEfs/KYUTcBCwTiE0OLMa/4y41vd+jUxtq3fSHQpwKNwF5bMndFnwWsZzx0RxLIJ4BNG1sLz+mrcfdFQWn50QT7QN9FqK/tnMcvvqjFkzljUva3Xzz0u1fCFpRfuXPa7YBDl4Ye6fIdOugzFeC0US+b933kAFFAAQigdEtaZ4D9h0QlMMwk9HgrWvkZDQ3j3wPYtu2ouMrFTWia5AEwO0b8EljYB0Hr6cFiKgGIvRZUtXgytja5syKWXFJV2QJMUFX5s9SDKLEDyAa5697VS+NElaq5JZ7hKRudft34i74as6iyYrt7Slt+INk/X+jiCon8ctnvc3t1hre3+O7svGeM9eb79ST/YuDUCd+ud8Tap+4yOGnbOfnJm9xVc0vCniToswu2QQQ+BDEmwdj+h6q5Jc6+GndfJJtaziFYlW8gVBVa0TTpgUSTneHJGy/f99n7pmpuid1icL1mEP6Lrnriwvzuntfozm4AcPiS+sXfaH9mtMUxCqA4wZ4Ua18G6FvWuJOXe6ShIdZ+RIqum5pDb+MqrcXsHJ4JuwTfI+kM2F3+A9IFui4I7LWxS4sns7bNm9YajQFVVdYBaJrI1TRh6ur4npBlrRsTfCd23buiab83sXvTFlQ78q0FpeV9pn99yEMNrolL1/8eOB2THG9dnPr5D78YEVcqAhWFRTdZv0++xDfMtc55TMtJRZUVcdNWuqC03Fzbnme1GR1h569CHwWsBaXlBy+vP/ywoowVayruPvuDvhizO6RamquD76QO+EBv3tQ6Lndi5oq3H7v8+bCnrTs4Nu+DjwLSaNrRPuyO7p6zsaVoK8DCrafEzZcxXskwevMBBpk88dbmcIDIsRDsnBTXpKVttgJkZlYWxtqXHvI64KKPikWq5pYsthpcJ5wy8tX63x/04Bt7y1E1KV5hEL6oLRlrmhgFbCDKHbAUJRCqrei4d/XfYpvdaXJnvxVsYc6RfT12UWXFW87jmn8hrfp20zbrkxWFRQ9XFBb169XMpddni+9PHf4ycD/wsmlrwsRDHm6It6Ls8ToGw9rm4h53petMrwesBaXlRuApidJQ0XRwr/ZZ7inb20dUBt+JeSCvSTbb/2oQ3rYltcdEJWXhvKKnXjApntXL6484tKC0vLvFVx06rHG1xBePfONIXwzwkT27Ota+DNC3DDa5xycq/riXu0lKqjUDmM3tY2LtS0/Im3v0YmAmoWKR0HavUnnPb744a+zzY2869/O9amQXpKyflGOrHRvFYTcBDwEfRtEm9c4hXwTfiWfop0o7++BrgR4Ym77q97EYfNqj9V8YWkyjgceAP/oHe+q+vSbn8Fj40hUVhUXTE7S078zrbWf78txfABcUVVbEnUZ7kqn1iNDbsDVYoQ8C1ik5i18EpgJXVc0taert8XpIh+D2+8C8Nm9aoi6NNuCgaBhXVSl9uuVRYBJwWHfOmZKzeIYi/ESS5zFAtzGEXgdyWA8wLELPtSmBuA9Yq6unfgNQUzNtQ6x96Sl5c49enDf36Pv6IljtQFVlG4CmicGaJn6WqtXoztnY6kmPmtSZqkqpqrJMVWVU+7tbDC5D8NX9cpwFq1TNLXHmJVc527wpR3R9dO9QVFnhKaqsuNo1zX6X0mRKtX2Y8V5FYdEvY+XPnlhTVHgM8JnBYTxIInVjtaW0qLIiLieycmzV1xmFVwr0iJQtejVgHXfbf0ev3Dn1rDFpq2uB//bmWOEwKevbMxQRkCAPJvS3kAhJdPOBXjIKn7cw44enu3Nwozu7VgwoBPQJoyyO0QBTbC1xXTU6QM+p8trWNPgtYRdV9hd03dyRbxlXOayxRNOEGfgW+PvunzW5s2uc/qSoF7JomsjWNDFf08TIaNg7euhHE0OvcdU8oINax9AnahzDUgpKy2MqxzblhR136Cn+iUKKLUD5DycXvLr0+uyYpohVFBaJ787Ju05a9EWEVlwFQhe6OCaWfoXL/7d35+FNVekfwL/n3uxpmm50g0JKC20pYVFcyqIR1EGj4ijigk5xFJBxlHFcJrhWB6Wjo874G1FhRqfKOLgMAhpBZQmIFFlkKdCyhxZoS/e02XPv+f2RFCqylDZN0vZ8nocnJL25922a5c2557yvzmTOO9yUPdBHpYSCW9WZBYJdlrDqTGbiFpTvekWpPUldeV0kjhhWOVISJcTrA0hNoOOWDyBBnQ9kLTQ2Z8WV7DvQkJOlM5ljLrT9UVvmMYFK2PzVEIjlvX0BIEHikVxoW6bHUaAHzGHVao/aA5eXhjuW7sJgoB4AD+Ms7VPlvIvnia8rvsDKANyFIM3bPNaiOwEAFc2648HYX6h5Rfkq+JOxsFc30H9/aC+APCHWu0R6RDlZWazdXZqdE5YycXtzs3MArFTs0LwuykWBgnrRDZoCXMDNpyt0dG6BYJclrINj9zwHYAJAnlr0yJu7u+o4HaUzmfNOOvrmekSFFMDfAcxGYD5VsE+x7Km7ZJpApTyA+y68NZWAjbCGxFZHzCYA+NaWyJo09DJxvCcjQeLudoXXzxQTc8QGUEilzsxwx9KdGAx0qcFADwKAxUJOrS/IiCkbESOvb3dVl4s43nEA/Q0G+lEw9ldWP6wGAPY16LtlpQuVpGUjgUj1CVsfC3csAJBTVuocWnzwdtco2+tcvSQVwPY9Iwf/KlTHL83Oidlxe9pmiNgD//TBx6AUowjI1egGTQHOJ1F54ppAu2MBnUy8uyRhnfrWY5nHmgcUpKgrqgEs6IpjdJaCd0wGaOvvLwWQYC00zuuK+UDWQuNPALZyEB7Xmb46b6HnYX22jtdIm0JW7qOXa/37d8t5QUzHSQiNUXNCty9ndv/9FgqQltrabHlBQUHYR6u6E4uFSCwW8k8Ap1qcVttT9zV7oxvOc7cOMxioLXDcrLZJckeoJM0S/2VLtzw7tHfunbZk9bGWk44UfbhjaWvkouNPEJDLKKF1xMmt3H5nvxWl2Tn8he/ZMTtvHsCXZufMAHBAtkc9yjPEXibEerNyykr/prcc9uaUlRZ3h6YA5zLp9ReG1rn6XJkefeAIgOfQyQHBLklYfzhx7ctOn0oY0Wfz/dZCY0QuaOkbdfQm//9op7P+9higOXBIBD8AwFycp9BzrSPpuFeUtnRlLIxfhtw+GABGqRu75Zs+03EnffJjRz2qbvkh0FYgSVXDf2p1NUta289goD74H7tTUwDqXIknPIKiy9r1WixkDIC9AKZ0Zj+jU9fmBi6HBiOucKi0py2sdvRN0pnMz0VS44OcstK99hvqrvZkOw4pdmomAlhRmp0T9LMxO6b0u5Nr5psBvAegFBwuHfG/iiFDiw9WB/tY4bKz5vLZApV4MmPL7gjGgGDQE9bMOUtvBTCFgit456H3VwR7/8GgM5nTDjdlpaeoKvYApNNZf3tU2tMO+ofFz18o+4S9/wmXoOqSb/jMz2l4XwoAJEg8ETe/mulyCgBdlpiEkAGB9k3oZgXkI8Q9BgM9VSdbKbHLOCJ05aKbTQCeBtCp1uQVzQOPA0B5c/qxYAQVHqQagAygBYiwbl2XvVFTIy9TDwLwIIBxlBcPlozNWFaandPpGPcOzUorzc5ZLN+lWUwcPO8aZXsFwNVD9pZt73TgESRjztIMAPcD5L2Fs97bFox9BnVk6XfvTeunkl77mU+UHnX4ol4L5r6D7FkKDpWO/kZrofFoKA7oERXfAXgG/hJK5xzRJRBlFITNYQ2BHQ7tJgC3rGxKdETyk5UJPiUnJMUHGkd0cxb4F49J0L0XZoSFwUApAFgsZAQA+aCYF0Yebhqc0IXHE+AvAN8p+xqG1gLA/sBlN6U6yyBOxJz1yCkrpQD+tXdItg8C+TdfK70FgLE0O2d6TlnpBxe7v9LsHKV7WPNnMqhvoKAeAvISKP4yctHxHtnVMitu98p99UOpQKXzgrXPoI6wrquY+LLNo5WM7buqwFpo9ARz38Hy0Lu/NRCID8o41/uhSlYBIE1zuBIAOAjLcZ4R3ay43WMTlVU94YO0O2B1WHsrCnlUD2gcUFBQUAxgPAILMwLXmYtgsRAOwCcAXjthT9vt9Km6fEqWxUIGWyxkicVC+nTk/lFSmxQA1IHLbuob4p+Sh8DUPEs4gzkXIpJUAALxr3LnAby/63pd3fa7+k0vzc654FzkLY/3IXtzsu8AUCbfpTF6BzpPeHLto3LKSl8YuuVAj0xWM+Ysyy6tG54xNGH7FmuhsTJY+w3aCKvOZB4LaH4D4G8LZi38d7D2G2yl9cP/IuG83HW65e8At4fsuDlxu0ZXNA+EIW3lrvcfnn/OD5U6Z+Jxp0/ZZZO8mdMGyu3Zh91q3KA9yRLWXsZJeXeZS7Mu3HEEQyBJZYlqBxkMVLRYyBQA5bXO5BcQWNLcxVpbkw4FsPZi73xlimXIqvJbkJeyLhe4O6hdtELFWmgs1pnM4wF8AZBGAFvCHdM5WIi/7KUUgNeb6P6aa5TeKi1XLgAwa8/IwSupTEzgm6T/zSkrPfW3LM3OyRMVwjRFtOZuQokGwC4A+cOXl1vC82uEjkAlBQAcO2sunxTM/QZlhPXRhfdqNbLGpQRiOYBng7HPrqAzmYcctWVeJuM989+eUbQzlMcuqb3UBwAnHSkbz7ddjTO5psWrDVqnFebc1JzQOpGeJay9T0+Zw8oEgcFAdxoMtCFKalMQiF0+amkw0DL4y1xddLIKAFZbZoX/MqMiqIGFmLXQuB7ADAADL03a+Fa44zmbwAr9U22Eh60/PNnX36WBf35rHOfk5/BN0ukA1pRm59j3jBpUXzJmYCOADcTFzeBPyjSegY6vAFyaU1ZqCdsvEiLT3v79zQDuBPA3a6ExqGXXgpKwltReOr/ZExN/TdqKd62FxqB3CQkWCed5GYDd7tW8EOpjV9rTVACwu+6S83bX4YggIxDZHNYQKHFG/wiAvnprNVt01Ys8vCRVBoDPlNt14Y6FiRwWCyHj+n03U8J5laE4nsFA3RYLIRYLaVfb7rYONg6pD1x2+xrSkzI+XtJfc8hWVj90+pBnP4nIjm1nlpca8XmFM6es9F8AFlDQ1gEPEcB2Ic53lHPzBABHQEBAfLLDqo05ZaU9/nNdZzLn7Tw56iMZ76RXpljmB3v/nU5YdSaz/kjT4CkqSfOS9x+eH7TJtcE2890H7/KJslvTtfuWWQuNIZ+oHiuvzQr897yjpwOiD17WT2PNOt82TNBwYDVYex0OVAMAKs4nC3csTOQwGChtcsfuFqiEdrZO6kW4G8Ami4UYLuZOWnm9DAB44r0vklbXd8Tfp/+Hxilq77F7oyUOX5Qp3PFcpLUExA1/sx83gCeHfXtkJNcimQjAie7fpardAs/DtQ3uPlqPoBA3VRoGBPsYnUpYZy+cKpXxrv8AaHD4NDOCFFOXsFRMfFDGu9xD47c/E47j948+fL1a0kwvtBitwZVQ2ezRdst2e91Nutw+hDvdPILpJSzN/kXgu5za78McChNhNp4Yv1Sk/KnqASGwBP5T4j8AwKUf5l41ZlH2gpFFuXfoi/QD7/hEN/ym/2bMGlY09DV9kf5UW9dUdfktACBQST4irCRURyx9/CUzgEUA/ePNf30xN9zxtNeZ0wVaR2DPdXtPppba7gJooCQcoeiCEnudWnRV5ej7sUdQ6LPjdplWPjUnYk9N6EzmKwHlBABP/9+Mj0JWGaCtQ41ZVSK4C57uaHTHNwCIyAoLPY2SiHEcaKhGUpjI0Vpn0xXWKJiIo5XVq5o8cWTVGumtEs6nNhjof7ryeAYDdQFYCAD6In0eAbF4BCkBMB0Aylyatps/rC/STyjJLyk+1Jil898UmSWhOkIpsT/jFWT3+ESpGYAu3PG0VyAZ/cVjf67be6KrXn6rn5RoZwWudlkzpg6PLulMZt2PlVfdmKw+tjcrdverwQwq2BJVJz7iINQC+L9wxdDi1aqcPvXBC23HEZ+CIz42pzIE9ro0W3zgemRZEebc8qL8veJzlbagn7JiuresuN3DAIACfwAwK1RTAywWMu4aTe1H1N8AAgAVEyTu4iSJawtwarT3VGMIj6isCtzWY045l86dUj4kfsd3pfXDB/gHmZjuQGcy9ylvzlhp88bS7LhdryMILVjPpUMJ6+yFUwlA3wOIWGXvd8Pfp/8nYhOsm/760j0nHamZV6Su22AtNIat5amUcw9QSuxNF9ouRX18yEDt/ojqr9yDcWAVAnodAkQDgJIT2XQQ5meONA3aAQAHG4bkA7gmhFMDfDG8Vx3IV0UA7nvijw+YFFvdBBAXzkhMlZKWQYDoAPBPhKBTY6jsqr1sMoAqAG/oTGZ29ivCPbLgNwNlnOt7ABki5W9Y+dTTTwSjBeu5dOgNe3/DkP8B5Ho57/i3tdBYHuyggkVnMpPdtZc+zEGo7KOsnhbOWDgi9s2OK7ngiE6jO662wZVwJBQx9XY6mSNXSsSubMPIRKCNLXG1ALDVHrMp3LEwkaXGmVIDAH/Z8kqtwUC9FguRWSykyxu5GAy0eLdTsx2glIDOBciEQQrHOL2q+Ua0mQtZkl9SrDOZ73H61FcDRAUgv6tjCyVrobGFQHgOQN6Y1FWF4Y6HOTedyRyzuWrsNoHyWVpZwxRroXFNVx/zohNWncmcV1o//NcAhVtQPhDJk73VUtvNAEaL4F96a/qiC45udpXc5/4b5RaUqLL3vWDNPbtX01LnSuzWtfW6CzknanlQ1qSh91EELtkcVuZnYhU1KgAYqN3XWkFiJYAlgW5YXarGJ7suQeIhM/qUX12SX1JsMNDDBgP1luSXFJfkl8wb7xm2SWf68gEA//bfgwBtpgn0FLdkfFKUoq5wldRe+rDOZFZc+B5MqOlM5mgAK6odqeqxfdc8u/Ole78MxXE78iI0+F8oBACJ2BfL7IVTiVbW+N8oaVMTgPfDGYvdG50MAJX2tF0X2pZA0BII2ZH8RaCn2OeK2uaifEO442BCa5S6MRcALlE1poQ7FiayZMfu1gNAbvyO2MBNbwB4wWCgXTZ1yGIhcn2R/uZGQSap9cnwfm3a1dM+S7ur7TY5z356/Q8nxtcB3D8B7ARIjy2Z9Pfp//HKede9Nk+sGsDscMfD/NyjC+9LipXXbgXoKIDcUfT7t14O1bE7UiXAErikiOAXyzfWW+9wCSrV6NTV73z86BthXXWfoKi6rdaVDKWkJflc2+hMZo5ANFFwfQH0BbBGZzKP7ylzkyIUDzaHtfehUAOAnBO94Q6FiSwHG7O3A7htd+1IFwAYDPSrrjyexULuBPCahvN+3CxKARB4KYRtDm367xf8ZnCtM/G3myqvyQVUNzl9KhCIIgX3B/jftwwALD3xM8Ly9B/+pzOZvwTo89nPfJbgElRLeuLv2d3oTGZVH6VhQ6M7NnNI/M4nvn7ymWWhPP5FJ6zWQmPx4Gc+F7WyphM1zuQpkfgk0pnMPKB6HkDZxhMTHgljHFEA5gJJswEKp0/9ks5k/j4zpvTAwcacHADDcuO3zzzRkqYB4pMouLYdVhQ88S56YP5D70RJm1+P5IVt3dUAmSP3pFceFe44mNDa6og5CgDFLXHn7TrH9D41zpQ6ADhiG/yzL7IWC3kYQD+Dgc4J8iFLAGxOkHrWN7slTwCEB+XhKL//vq8cmS8DhACiC+AoAEJBRABXWQuN89DDSyapJC2fOnzqm12C8gkAD+tM5h6zuKw7CkzPWFbjTMrQJ2x7/ssnXng91DF0rA4r5bxRUlvJlhceiMgnz6ikH17aWj0mF8Cd1kJjyDoZBU7jG9SS5tKc+F2P8+TyEQKVRp2qVALI5Lxz/cHGnFOP+6HGLK+cd7UAROkftD61rcATIXV1+c2v8cSXv8xkLgTwKYBR6MHfrEPJQ0miAMj0Rfq8kvwS9lj2HmwOK3NWfZSV6hpnCnTRB9Rn/CgbQIbFQniDgXbqM8ViIZMB5AJ46ZN905I3HLuuuNmrnSlV7xXk/Rbxgj2TixLiuXEDP7WJlHvyu6OTSr2i7Fv456tG7FnNYHP4otL8Jb0Igf81ew16eJIeqR5deK8mTXPFrorm9AEAuf/LJ14oCkccHZpI7hHlziO2wQeCHUww6Exm6eGmwX9MUVe4Lkva8HkIj5sHYDVAX7b7or7YWj1mrD9Z/bloWeOBSxKLlwIwAkgb129VXpMntg6AA/7Wbq1Fd2dNTP8iOTf+p78IlFMCWKTgHY0E4vcA/TN6QHeTcNIX6fMqvcoMD+UlAFbri/TssewlhquaRgFAXlS95kLbMr1LVtzuHAAYEr8j6YwfPQ7AaLBE56FAOwcF2g69Xzyy4L60NeU3/OHdnU/87sFvl5SvODJ5dbNX+1cAeuIcuEgran+SRO33PnzlrLmTB3+0dUpW0fIDr/x6A9pUCuhFAxWWQEkvEQCJljXc8ejCe9lrNsR0JrNsbfmNX1U0D9Rdnvz9QmuhMSzJKtDJTlcR6jf1rj6KnLidD3322LxQzk80AJD7vw1SAOIhgFs2KHZPAyilBxqHEgCra5wpqHGmGAA0DIrZE7Ph+IQtACE88c0VqISPkjZd10dZ1edoc+bk5YfufkTKeTIRmCrgElSqNqOwPaK7SRgZcPoLG3ssexGBEgUAyAhlTSOYn9lXr98BADtrLq9ve7vBQD3eudHXUNDVAEBAXCjQTkBB03nfM2YvnCrdUzfS4BWld1bbU0d6xDsuESkPgLYA2JSXsvZ7hcQ5f23FjT/sf+V2qi8qyATo/v81pEyYozx4XWsd2ECS2qven6yFxmKdyTwBgCFGXpvd6E74zeaqcT/pTOaR4ayp3pvoTGYJgI+bvdqr4hUnn/70D3+ZF854OpSwKiX26EExpaP9g4SRY9LrBUpg1PMA2VxaP3xBiA9vAeAGqBQgXoDc53+TOf0Y6UzmawD6HQAeIDjQeLplskAlzwJAizeaEkIpRwSbSPnDMfK6fenaA5q9dSM+b/FGKwC8Bv/frdecGuoiFoAKAJEA1AcQS7gDYkJjtzP6AACsa44/Ee5YmMhS40yuB4DjLQN+MdjhlYqvS31865ytc37JvfG1uVl760aOBXC9UjLpdqdPzROISFRV1icoT66IV9R8sf749UXWQqMHuOln9y3JLzk4afHA41Veef7n9cl/hIUAwFiDgYakbFCkaZuoT35zTt3W6jGzAXynM5mN1kJj/fnvzXTG7IVTpVmx+n37GvTpAP64reD+N8MdU4cSVp8o8TZ5YqqDHUxn6EzmvGR18nsA6Z+kOj77x+dnhHSR0ulvg8SAc88vHY9Asno2Sold4IiwqdkT81OcoqZRn7BNY7VlmjdXXbUHQDqAqwHyKICE8xyDaYeS/JLiWxenLzzkjpqVKnW9/M09B9lj2XsMDlwOB/vSx7SRoq7QVNrTkK7dr/nZgEyBVq4E15f626QKBOTUgMHtbz4dva16zBgA18fIa6c1ukfGAAAHoTpBeXJjf82hzVekfP/NsD7big0GesGRwTje+8xht7poXXPCXZPjqnIBPGCxkH4GA63rgl+52/j8sXl/1JnM6wH6SZyi9sDDC/KvfXtG0fZwx9UT6UxmXsJN/tAnytKvSFn39SezXw17sgp0MGH1inLnUVvmoWAH01Gt80er7P2UgIiTjpSwJNPtOG3zNYAnASr3J62ttf2IoJE2fJ+krhxqtWUmwD+tQbvu2ETA39Ma/qkAAAAPQAwsWe28Q+6otwDMOuFVWsMdCxMa+iJ9HgGdSf2vv6/1RfoJbMEd02pw7J5BlfY0ZMXuTgOwrc2PHiAgiQBmU0D9iXD1sT95Z47t8+K/PmhwXZ4V2MZFgLK8lLXlyerji2/JWPwBIZhvMNDFFxPDVkfMRwB+D2DO0xVZi4aqmmcsuP14r05WW1kLjUvveevxR7dV57276ujNK3Qm8xhroTFicpGeYPbCqTzBXQt8ouwuCecp+GT2qy+GO6ZWHe3ewQMYFSmLfgjE6wAo/EkgJ1BwhrAGdG4+gHgIaGB1MuH8/0CavbGrVpmeTDr4yq+zrYXGmHF9v02ZqFtyu1LSciuAZQBoYGRWBuA9nclcECmPfzdmDVwODGcQTEgZ6Kn3vchtfMKEx966ETsAYPvJK6pab7N+oNC6JXijikRXXI7Xbxouvj33T96ZHwJ41ebRSof12bo1RV0xGUDcm9dMu+S/s/86aVLm4s8Iwd/QgRH8kvwSKiPCSgBpzaLEVNwS956+SJ9nsRDWlQ/Ax4++/l6/qKPXuQWlBMCG4c9/dEm4Y+opZi+cSo7YBu2k4H5LIL588JVfR0yyCgCE0os7c+5PkujGwN2dCPOqxUcW3peypXLMvipHPw38Nerc4Y7pbKa/M+Oh1UeNfxchkf38J1QAiAfniblNBQJpoMQHHyj34Trf/ZgLu+KjHHuK1H1o6V2Hh4U7FqbrBapBbIA/aXUi0J89vFExkUJnMj8J4FUAMwAcjJHXTZnGr5z5B9FM7vE8jWIxpz47rqTZJajeO9I0uMhaaDw1D9piIUYAzwG4tj2n/s9neNHQZ0Xgz4FBCpqrtK17KLGcB2Doyq5b3YnOZM7hidci4z198lLXTn//d+/8K9wxdWeBPOM1AGMuSdz4Y5rGmhdp9d872JoVaDPaZwhWMBdLZzLHfXVoyrJqR2pUTtzOzwE8iwhM4HQm8zVrym/8R9tkNVpW/yOBOBogz+ECMQd+NgEgzyt4xxL/rYSgB/aRDjUN57M1CRJtuONgQmYTABuArWDJKtNGYDBmrv8aXQBgjcsd9eC94jocJQnVW+mgURR8nxVPPaNbO+exeW2T1YDWJDUWnSSCrAbgDEwbI2XOqNFfNiSOfrx8yLOsBJ+ftXOsq3oAAA81SURBVNBYekP6F7cpJA7nmnLjP3Qm88Rwx9RdZcxZNgag6wGMAeDbfvLKxyItWQU63JqVuOAv5EsIxA3BDal9fvfe/bkEty2m4AYTiL9e8VRoW4S1V7rpy4kc6HKBStuezhFsnrhlF1OqpHXbdNPyl/y3UAFtJv4zHVPtU3wD4Npwx8GExnXRNSO+s/WJGaJo/umTO60sWWXaMsA/3Q3wLxpYtD52ZnWC0/NEAm25e/+827b9bOsCbZ5NI0yrTvLUDrrX84zBQNdZLCSvtRRVZ5TklxTri/QTAjEdjOa9735rS4wDaAGAOfoi/Xj2ZQv4x4yiH3QmczqAbwC6fMzct/95vEVXAbYoud10JnOaRtrydbM35lQ+GJhWGXGP30WPsAaeBOMB+jEAblifrW8HP6zzu+rltwZtrhq7ned8OQCMRwpvjshk9YZXX/4AoF+L4KUp6nKb/1ZKAXjQwUSTgh8AoBEgva2IdFc5BKCvvkivuOCWTLd32K0aDgDRvG9LuGNhIo4lMD3LBxBXofydrXFu1+MOhVCPgqa1P9vS3zhgvaaZmzHwkGKOd270WAAIRrLaqiS/pLgkv2ReSX7JZw2C7HX/aCsh8A80GYJ1nO7OWmg8CcAQJbVVHm8ZMAugL4M11mmXdNOXdwLY1eyNlhOIgr/EY+SWzOzQoitrobHYWnjT1JGJxVt31lyuHzhn+b3BDuxcdCZzZnlzxqpGV7xvfNrXD1sLjatCdeyLoTOZx5XWD59GwRGAeqrs/SYCsAFkEzqYaM5eOJXIONdkKeeuBrCWJaudN0zZpACA8ZpaQ5hDYULgkFudDACb7LEh64LHdA+np17h+eclH86+k6z/q0TkiNLFac7S2coAQEJAwFEIUh8Z17XRkbUAcQOI6IQiXKyFxia3oAh0YCIEgFIttb37+wX5+rAGFqEMr/wt7sbX5h6i4BYD2AdwQym4cZE+ENbRKgEAAJ4IowGsFym/QGcyd/kT46F3H7iVJ75iACoflY5ZMGvhe119zE4YCxDBP9eXcBScQUK8XB9lZUVHnwzfHJ1U6BEVKq8oGwz2DTIo1LxQBgAnvPL+4Y6FCQU6EsDRkvySxnBHwkQe/2CMcd5vJSsTiL+KBIg/ATKcsamFgoKCUhKCqVmB0/+n2rOy6QC/5BXlKwILwQWACnZv1LAVR27boTOZ5wemDTAAdCbzWKtt0E+ldcPTRyVtWAtgnLXQeLD1uR+pySrQyYT188fmeQHcxUFoiZHX/fDIgvvSghTXLwycs/yydcd+9blK2qKVca6rrYXGSC8YbIH/1P+pb8Ry3qVOjarIvtgd3fHmHE5nMv/Z5VM/FWjNyhZcBUlxS9y3AFDm0sgutC3T/cVLvJP6yxxslTVzXhT0VEKKs41oFjQV+3gqOhViDYALtmgNhjZTBCI2oQinNiPkzwFk3A3pSyZEyWz/A/AgQA/c8OrLB/L/8ch1YQ4zbGYvnKq87Y1n1wN0HQCBgI75/LF5462FRm+4Y2uvTiWsAGAtNFZOGGB+0eaJ0awuNy7Vmcxzgj3ylzFn6cMi5b93++S14/quum7/K7fvDeb+u0Lb00sIDLE7fOqm/Q25my9mPzPemR5v88SUw18B4avAN0h2Wih4TgJwgNVi7fHGLMpW1vmkMjkRfwp3LExkK+/vsREQOJTiXpwjIeUosRGQjaFIVpn2aTtK+M7MD9bsfPG+KQAGpmmsyw41ZmeuOzbxW53JvCTn2U+vDHesoaQzmad8eejOwz+dzBs3ULt/K4ARhwtv6XbP24uuw3oumXOWzPdR+SyAUgLqi5LZ7ix5aeoXF7uf3y/Izy23pY/dVXtZAoBLoqRNN7V4ta2jXy4A4yN5yPp8dCZzHYCPrYXGR9q5fX+ALiOgI65IWb90U6XhNgBXwj+yylZBBsnVi7JsWonXtvyuw/3CHQvTdfRF+ssAbAZwe0l+yZJwx8NELvHF6AkcJata1MLkqCdb/nfWjQq0lQCWo6BpZmijYzpi6luPDfjhxLW/BfAogJh07f7aclv6FIFKLdZCY8SVcOosncmcNqLPjy8caxkwqdaZnOC/lXbrTpkdas16Nj4qr4C/GxOhINJmT8wSnclco5E2VWTElpLDjVn/tHliN2tl9XuaPHEj4E+69l6ZYsm1ebSX760byQO4BJiS2ma3Bzgi1AE0+YzVkd3zweY8sjhFbXJ7tn1g/qzfAjfOAzgFRwTj4tmvfR34UbtLYTHtI+fEY7VeeUy44zgffZGeSCCOlxB6PQFd56SS4gSJm7tE1aQ57FY7DrrV3lSpix+qtEVd6Po+V5T9qEflGyBzSLIULeozr+92Rrec8CqETLldOlBuV515/SeHtrnWJxezFc2y/jKn8kLXN9ljbTZBSocqbfJUqUtx5vV1zfFNbspjhKpJkShxy8+8/q0tsQnAL66PUjUq4yQeWdvrMRKvdJWtjw0ArlA3qDS8T9J6XUV8DzioBAjCmSWmZ+MouREAouy8+1zbUFA5JbiMK9DmsVHWyPefR988CuAFncn8+mXJ339YUnPptQKVrgGw6ddvPLu4v+bIW5FYe7S9dCYzGdf3u/EuQfHUlqqxCQC5ZEfNFVBLbb5AoyES6KxpQDfNIYI2wtqmG5OM+Isdz6fgNFp5/QSHVz3AK8oDW1IKgAQaD/iDgAgKsgcgPw2O3d2UprGWri6/aZG10Gg7vV9I4T8NHrEr2C4k99nFYmZsacmyx18cfq5tdCZznpRz/0kU+UlqWYvN5om50lpoLA1lnL2Nvkj/JoDpADQl+SUhfcN6YmlSn/0udfIRt1qj4nz9hyqbbznqVtqqfQrIiNg/QeIeV+uT2T2UjwEgv+AOmQugAOAECFu4wpxdgTaPgm4AwJFAN8dfJKT+bTYCwDm3YSKazmRWALifJ76nBSrpFy1rqLJ5Yt8hEOUAvjpSeHPE/z1nL5zKO33qaRuOTxjm8EVNBDAYAJQS+x6nT/2RStKyzOGLikUPyaGCNsJqLTQW60zmCQAxUJCfna7O/8cjknXHJqYDGBYjr3uh0R3fWlFAVPD2D6/XLX/iremL6vw3Gc+x3+5/GtzhUzeV1et/PNvPZr77YN9yW/qLwPBpXlHOA5Sqpc0P7HppKktWu95hAGo150sEUN3Znd26eKD6kFudCCBpdFT9zZVeOY641W6AJmfIHZNqfDK7TZBy/jMHierW+zlECTbbYxH4UlfrpaRGSqgnmveV1vp4HqBj/N+QqQiQr9Wc74chypbLKjyKsiqv4qSG86qzlfZLj7qVe0/65LVa3qsZrLCPtLqVu2t88vpY3hOdqXCMOOxW7arzyRrjeE9MhsIx7JBLtbNekDUlSNyx6XKn/oBLvaNRkNr6SNzxOrkzd59L/ZNNkLYkSdwJ/eXOIWVO9bZmUWpPlroS02Su7L3OqC12UeJMlbqS+8pcg3c7NT86Rd7dV+pMSZW5B+1yaDa5Ke/pJ3P2TZG6M3Y4ojd6KefrL3P0S5J6Bm63R//gAycMkDn6J0o9um127fciCNXJHAP6SD0Dtthj1gPAQLk9PU7i7bfVHvM9AGTI7RkxvDd5myPmBwDIlNszo3lv4k+OmI0AMEjeMjiKF+K2O7SbAFwLUOMZCxa77XsJ06UM8CerAKAE8CgKtFtQ0ORrs81dABDYhj2fuiFrodEF4J3ZC6f+u8Ed/8aGY9feCOBFCg4AfTpzzhdHfFS2OUFZ1TAkbpdkX0Pup9WOvjsB1IRjCkFg8M6gkrRs0siaYqsdfa/lyJTbRSpJJBBFAKtlvOsf1w1YvuHtGUXbz7hvj8ihgjbC2l49acT0YulM5iYAewA8fv2Apft9onRGceXVSU5f1GiAjvJ/mAYGoP0Lq563FhrnhTPm3uDBz/uZfrTHzovn3UvqBPlfzzbyFmgskDQ2qm5soyCN3+2MdgJIylY0T2oUpFyVV+EAkCQjQqaH8uQXB/Gri5d4VAAq6nyybQCqRqqaBjtEfts+V1QxgKpfaU9CxQllL91S6zrj+L943bARwvZjjx/Tbv7R09UA5IGSVsQtEz12tfh+XIPkCQp6CQFZAiABpxfAshHWbk5nMj8N4M8AOICCQDxEwYNATKfgTk0j4onPlaQ+Tk/aU1f4qLRMF32geVDs3qaNJ675wu6Nrm6bzLYmmWhnoqgzmSXXD1iW1uyJTiiuvMYHIGZM6upx5TZdRkVLxt0AleD0GWoHQFeMTl1TESOv/9v8mf8+GuSHJOKEPGEFLv6P2BME+lRv9F8jYmBOCU8gChTcJpWkpXhA9CFZaf2w6fDX/+tVyXw4Tfw485HjXuVb/gWDEJOk7p1VXsVBAMkxvHekQ+QVHspJz3ZfORF8ck5ssQnSHQCqsxQt/QBY97miVgOoHhtVrwLovg0t8WUl+SWdKh8SSLoMACws2bp47PFj2s3fKMAAYF2j1jdM4iOvRtl5DYBmChpFQAgF9RKQfwH4kCWr3d+5BtNmL5yqdAuK0d9Yb5VRcIMGRB/8FU+EUYebBtsAko7T7XwBoFkra2hKVh8T9zXo1wK4G6BSAipQcAsB2AbF7B0rUF57uCmrAoA2VlEzxCvI5S3eaBGA+sy4fokiQVn9Xa0zeZK10OgM9uMQycKSsPZGOpN5DkBfOT13l64fn/b1SoXE+a/5Mz842Wa7XpfMh5u+SP8cgJdar/MQRQHcQQBV/aTOWCUn2A64o1YAqBqhaopWcsKx4pa4TQBOluSXnHNRBsMwPUiBdjSAhRR0CGlzFgwFTewsWA/RgRFR6VX9vrlcEPm8H05c6waQmaoun+j0qfo1uBMU+OUCT6+CdwhKiUNscCfsBdCUpjmcpJQ4vfsbctcCaNInbE1TSJzNW6rGrQfQOCZ1VfRRW0bSsZb0v6MXnpluiyWsIRIYYV0DQBroV90rn3CRKDDyFvjbUA9bkMMwzFn5pwusgb8tK5sKwJyTzmQeC+A7gAaSTDIRwPqOzn9lg1ksYQ0p9oSLXOx0McMw7XJ6uoCFJavM+bDP/OBiCSvDMAzDMAwT0VgBbYZhGIZhGCaisYSVYRiGYRiGiWgsYWUYhmEYhmEiGktYGYZhGIZhmIjGElaGYRiGYRgmorGElWEYhmEYholoLGFlGIZhGIZhIhpLWBmGYRiGYZiIxhJWhmEYhmEYJqKxhJVhGIZhGIaJaCxhZRiGYRiGYSLa/wOF3nqFBKvAFAAAAABJRU5ErkJggg==\n", 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" ] @@ -2927,12 +2994,12 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2954,7 +3021,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -2968,7 +3035,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -2976,9 +3043,9 @@ "output_type": "stream", "text": [ "Epoch 1/2\n", - "107813/107813 [==============================] - 617s 6ms/step - loss: 3.8689 - accuracy: 0.2050 - sparse_top_k_categorical_accuracy: 0.4180 - val_loss: 2.6451 - val_accuracy: 0.3895 - val_sparse_top_k_categorical_accuracy: 0.6816\n", + "107813/107813 [==============================] - 537s 5ms/step - loss: 4.0875 - accuracy: 0.1695 - sparse_top_k_categorical_accuracy: 0.3734 - val_loss: 3.0641 - val_accuracy: 0.3146 - val_sparse_top_k_categorical_accuracy: 0.5977\n", "Epoch 2/2\n", - "107813/107813 [==============================] - 514s 5ms/step - loss: 2.3302 - accuracy: 0.4522 - sparse_top_k_categorical_accuracy: 0.7382 - val_loss: 2.1406 - val_accuracy: 0.4888 - val_sparse_top_k_categorical_accuracy: 0.7707\n" + "107813/107813 [==============================] - 530s 5ms/step - loss: 2.7211 - accuracy: 0.3765 - sparse_top_k_categorical_accuracy: 0.6655 - val_loss: 2.4447 - val_accuracy: 0.4287 - val_sparse_top_k_categorical_accuracy: 0.7174\n" ] } ], @@ -3004,7 +3071,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 78, "metadata": { "scrolled": true }, @@ -3016,16 +3083,16 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6822151" + "0.6090606" ] }, - "execution_count": 121, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -3036,12 +3103,12 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3056,17 +3123,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. compass 33.168%\n", - " 2. pond 14.609%\n", - " 3. bracelet 11.321%\n", - " 4. blueberry 6.964%\n", - " 5. clock 5.258%\n", - "Answer: compass\n" + " 1. oven 10.271%\n", + " 2. cell phone 6.902%\n", + " 3. cooler 6.363%\n", + " 4. laptop 4.605%\n", + " 5. keyboard 4.434%\n", + "Answer: picture frame\n" ] }, { "data": { - "image/png": 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a9cvLHNuitufXR/JOAaixVIjeOs7hf6TlVWfKc0MfrYpbrgCor8/ty3gPlkouWDpoQlDjX94cdfQDsFsg7faT8usWLZxRnfYYlYwv59KXNJ3VR4g9sUcxOwG2BaAFhXzsmQ9mf9/jZ7dErziKwP7JQEf1N4V/3Bm3/crn9m3T3xsLYA2AS3xu36LMWpoe9JvLuwEc10eIxTWGS+pV85J03j9lXNTNzFtexn8Vyj9/d9z6ewBHOjklfpA5vGhDxPkHn9sXyrR9B2Le8jJTnWJ+fnXQdSoDqTzYjVNctf/3pr9UE0irLhZiVW+f/8PhmbYzXYhekY7J23vrurDz942qqQjAqiGW4N+Wnbvl1XS032NE3YzoFWmYtWl2WOMf3h6zuQDUWkn9+/H5e57qaPBOphC94iAkRjym9jeFAwAkF68880PUMXKwJZT30jlbe/SPM9mIXlEA4CawOxiob19TZMPuuPV2ABVI4XK8HifqluhrGm8EcIqNVAZgYZjxf/O5fXszbFqbiF6RjnbU//2rkOvSGOOIh7ZKBXc8gKcBPNGbYiiSxfXLy/rUxs1Lvgy5JgFkT6xYQgSglMTF9GhRN3P1K+UXbIzkza+KW0cDaBpkDr0zzBq85b6Zu32Ztq0tRK9YCuBBAOcmjrCUBjj1BkSveDvAbkzExTAA+IvPvS7pcTG9QtTNiF5xtADtZhV0NgeoKuhRAAsB9EcPWGHeGpMWD1/RoAqn6pF0KQtw6g20CBqz6d/H4z6378pkt9OrRN3Mtcv6nvhJU9FlYY0/E2BEAM8ADaAYelhP2CLlgQmgDgUeZTO6sE8AMAnAKRZSr1p90fqkrhTqlaJuRvSKA5xcfGVAE4bqv3wVwM09rSfsKblKehKiVzRZSV0ZZdykY/Lq73jsjJ03J6vuXi1q4Kee8F2ArADQV4i8NNbReHZvmXrPZa5bXlb2VdC1oUYx2/Wbxo+TUW+vFzXwU084xcHFZwU102H9TJF3d8Wt04xk5z0f0SuWAPgIQOkgc2jq6+dt/ry7dWaFqJuZt7yM3xy1v7Ixknc6gA8AnOFz++oybZfBgRG9YoWZ1LVWTsvL45Q5u+K2vuiGq5ZVom5G9IrnAWyRk1O0I/P85z84s2p5pm0yODBzXyk/4/1A0fMqSABIA9Dlm/6s3MbZ5/Y9P8HR4AbI/F5jn+dEr3hapm0yODAPzqp6WQW3WF/bySOxTE3qSl1ZKWoAePLMHf/K5+NDGWg9gFcnLT70edE7+gbd/zbomfwjMSnDGBLrLuWuVJKV7kdLRK9ot5D6TpTxE/V0AmHk+FhxT+bkJUPrGzXBEdKE47v6P8ranroZn9sXijLuNX2aGkhsyyxl1iqDtmhQTY+GNF4AsKGrdWS9qBOQDFBzbDZDFy9rBqknwvh3db/66K7WkROi1i9jJ5qg+giMOzm/NiOJKw3ax0bq5wBjw62BLq/rzAlRAwlhS/l7ryGAWxd23pBpewxa5/OL1jeWCrGwXzWN7WodOSNqALhv5m6ZAYur4tbpolcszrQ9Bq1To5i9VXFrob7Ur9PklKgBgIHufO4uxfbCXUqNb/gIYw+VNCJ6xYmiV+zAsCp9AsAJPXl+Z8n6Ib398Q0fofCJwX0AgAqo4ncbhEzalM3o26WUAJgBsP8DwOshwlceZvP7Sk2xxncaSypb7mXzmxcGj94SdfiKheiXdYrl950d2ss5UX87fIRG+u01SzzYqO825NwVK5k0C3eM3X+imbRffR4s3AtgiIuPT4poXF897Vx7hG2kMiunsnrVVAnQqK7OK+RcD6UBGp9YVPDT64wa1EsQvSJNcNQPtnDqyR8HimIKuMElQlTiiY0FLApA+WtDLr00UwHayoFVD7UG69aHnU9pIB5gdwEQAFIA3Dza1ljEg5V/HXZtAuAqNUWPBVBYr5r1fNkE/Dxdboi6LcTvNgi+4SMUDuA0QDNcj58RvaMn2jn1bEWjTTHwTaVCdJKLj0/ZGrU3ANygT4OF+S2Kq35VqOlnioZspC0PM953kDlcd6i1qWZXzPrB0nMqfxH2K3rFT9GBxRKtpCmWO/M5cs79MGgdfbHFJ/vuSsDUIj6uRRn3RVAT1hTyserR9gBti9r+/WPMvj6Vezp2Z7WQ0UsZNCMh4YpxACCQFj/M1njvmlDBTZlIsq4L2Yj9MOgWsh5KoAAsbudUWhMqmA9gw+He0XOuX17Wa5JcGu6HwU+0vORPc9WsfqexeFaccfMAHOHk4pqV0xbWKpb/9bl9PXrHXUPUBgdEzzh19Y6Y9YadcVs5gIa+psiyw2yN99w7s3p9pu1rDUPUBh1G9IpHAuzPAM7gwTQV3KMA7vW5fVszbVtLDFEbdJprl/U95cug68oG1XwqwLihluDWUlPsj4+dsfO1TNsGGKI26AaiV+xfJkQW1qvm82KMA4A3zKQu/PKi9XIm7TJEbdBtfv9K+SA5UHwhgLkAivubwo3VccudCjgOGchKZYi6t+Nx/bwtnMef0XWXole0j7X7H98QdsyKMMGOxLh3l/Z57w7GOHVvJiHojwHcCWCl/jpj+Ny+0DNnbbswwoQ7kYhG4gCW9jWhhqh7NxJ+ntfucp6MFPBuYgtrBkrYJ6ezcUPUvRu5xfMu58lINglXg05wcsrWRIpl/GI7vlRiiLo3s68PPTfTPnVLfG7fqoBmOkafev9bOts2RN2b2deHfjDTPvX++Ny+3Q5OeRjArKtfKf9duto1RA2gYv6KiRXzV9xQMX9FjxJFB5Can7AM3JB1hEnOvfc4ubiyJui6TvSKadFbzou6Yv6KcwB8COB2ACt7mbBlhp/GZNN+Q9YRFs6o3hNm/OUBzVQB4Lx0tJnzouZIOQGJhbjdyrSZETz+VWGbFgQAAnFIbJTU41AY5wWwhod27/XLywpT3V7Oi3pYwfo9iWca0INGEDqKoFBlXGA/MLC6mEl7onKRldo/K7343D7tcLv/QRVc2e64ZUmq28t5UWvgPgSA4/q9i2Lb7umVC6b3mBGEjmCOc/UmhXbUFSsrzHFujDlG8zNtU2ssPmvbM8VC9Mu1ofxJolcsS2VbOS/qjfWj6wFgw97Dzlx966XvZdqezqJyzKJyzBJ0aL9TeFZdXmU6Hx5XlzIbpZo6xXK+vuHUbalsJ+dFXWDZYwaAuGZK+zq8ZBCya4cGHdqIiosjQUGlawg0GsBFmbarNXxu38Z8Pr6EwC6fu6x8RqrayXlRH9X3w6EAcNrgpefJMj0vy2TKtE2dwRqhH60RqtRfvhgX2Pcxk/Zo5SJrym/IusIER/0dFtK01UHXjalqI+dFvcU/bCcAhFX7HgDHAhiUWYs6h0nh/OY41wAA8PhZdVnsEXOcs5RVm3qkb33vzOpNUcbNa1RN40WvODUVbeS8qDc1jGwAgH9vPmcFgEGSxDZl2KROoXLMqnLM1vz6oDmRhxjY67YIdwU8rj6ZtK0tGOjvANtiJfWJ65eXWZJdf86LushaawGAPFPALEm9L7g8ZNeGBR3aoS2PEWg+A3MG7WpS9/xOFj63Lzre4V8SYfzAmrj54WTXn/OiHl/28RAAOKJs1WhZpmNkmdbLMg3LtF0dxRbiahxNXHifuA+P/9uGAnW9Lcydtetx21EZNK9NSoTYrXmcsmFNyPUb0SsmNadIzot6c8PwHQCwpeHQLQB26o+8jBrVUTyuiYJGh/CMyrHfIoGAU72YGKL9qsxzM2hhm9w9o5o1acJvASoF2KvJ3ArQELV/uB8AKhuH7pUk9qMksZMlia3JtF0dgYGdwMA4JOI+9pniH3hpZDWB7gNwPjyucRkysT14gGkATgDYymQJO+dF3cdabUn8rbE3H5NlssoyJf0GJtn4XSoRCKztzTT/pnIsGMhT3+iJ0+f4aeXOPil7u03Oi3pc2aohAHB46ecjAUCW6VAAewDMzKRdHSG/kQ8DgMqzhwFM/sUiAY/fX1sSf8vZxJeWV5l64lbWsp6rGgAlLe4m50W9sX7UdgDYVD9is35oE4DHAHyfMaM6CMdoEoAtws2BNle9uPzCeQC2WGLc7T1t+lxfYf60/vLkZK04z3lRVzYObQSAbYFD6gFAkpgqSexPksS+yqxlB6ZykZVXOTY1ZtK+PFA52/xADMCNAA7bW6jckh7rOs5wa6APAExz1XyerDpzXtRl9p02ACi27ba3PC7LdIgs04DMWNU+pjidzmtk29NH2duB4kuDdjXgCHI37n3QYW+/ePpIhaOf86I+vOSLwQBwWMnqkc3HZJkcSOyNfU2m7GqP/rvMAwHAHOMeaLewx6815Wl/tMQ4vqheeKknrWXcEHGuBYA3/aVJ23sn50X9ff3obQCwce/oH5qPSRILIrH06P8yZVcHkABs7nNN8LuOFC6rMX2LxEqIaQws44lvUknOi/rHxiEBANjRVOFveVyS2MuSxCozYlQ76P70tLBV60x+aAmJrEkAYGNgUtIN6wIjrIGxADDNVZM0Leb8ni/lju32quAAlNp37TOLKMtEAE4BEJMktjIz1rWOLcxN5TWyNBTEd9jaL96MDCDGwKwEIg3M1hMGrlkKtvzL+Z5aLP7yYAAYXbxmeMvjenDTQgDXZcKuA1FWYxoGAAUNwsIOn5QY8ptMoJsAfMOBroHHlfEb4e8izq8Bw6dOKuv3jKkEgO/2HLaxlbdnoGdOwkgANtvmBzqXwd/jXwWP/04AsxiYKWhXP65cZO1RY9fJIOdFvaOpIggAu4IDG/d/T5LYJklikfRb1TaVi6y8wrNTg3a161tSePybd/eNv+wI8QNKaoWMjl2PtAXGAcn1qXNe1P0c2+wAUO7Y3mr4oyzTJbJM89JrVdvkN/LHCyqZGvPV1q4sHSZqYW6N2H8dIX4ePK7h7Z+RGhRGv9gZt7vkvKhHF685GABG9Pn60DaKHA9gevosOjBF9cJhAFC+27ygO/VUXBxhHCM3AwvGBe21ykXWTtxzJo+NkbwGAHjTX5q0uO+cF/W6uiO2AsC3dWPbGu+dI0ns+DSa1B4SgM3w+Ld3uyaPf3ddsfKASeGGOAP8892ur5PoW0dfor982wg9TRK7ggNDAFAd6t/U2vuSxJJ+eewquj99WqNT3Z2sOkuuDt0Rsqly0V7+1/C4xier3o7g5OIXIpHuDTBCT5PHAOeWPADon/ejq60yskxXyzL9N31WtU6fPcJEQSUubNM+S2a99jA/k0C7GdiSdKZWCGucvnKfqUhiyrecF/WIom8GAcChRb6hBygWBdAky5QRv7MZZxM/HgDKakz3J7Vij78hYtGuJNCwvCbu3aTW3QaiV6xQwE8B6GWAbkYSNzvK+RlFX924rQCwru6INmMoJIk9CeDJtBnVBhqxkzhGm+Dx70h23dYbAq/XP+D4pHiP6Rh4XCfC40+puAeaQ0u2xWwaQNf63L6kfp6c76mrggNCAFAT6teqT92STPbUux63CRqHUxtcSso2uy9sEE5GYnHE08rt+QWpauf0FwaLO2K2Y0bbAuuTLWjAEDUG5W9yAsAA55YD/hNlma4AUCfLlJ8Ww/ajsF4YL6hEcRN7M2WNePwhABcxsP4Bp5q0oP392Rp1/EkDIk5eOT8V9ee8qA8tXDcQAIYWrh/STtHVAB4GkJEFubYINxEASupMqU1Q4/F/XluifFjYIAxVb8s/K9nVS0sOHQXgQoAeeeKMnRuSXT9g+NT4qubozQDwTe34A4ZxShL7EsABl06lkrjAfi0o2ESexqRfrveHV2kKA/uE1+hReFwfwuNP2hBiHyH2ml8RGBHuTlad+5PzPXVtuDwCAHXhvr9ub78XWSaSZTpclimtQUCVi6wmYjihvlCNp6O9PtcEYwS6kIE5gnb1vWSlVxC94oiNEUfFMGvwozUXfVudjDpbI+dF3cdaLSWesYvQ/kZGMwGsBTAh5Ya1oP9O0xhBJQKQvlk/j39DTamywhHihxftFZKVdvdWgILrI84zk1Rfq+S8qAVOuTjxjDi0P6u1EsClSKxfTBsmhTsOAIrqhX+ms92wTTtH5diq/AD/Z3hcg7tT1+9e6XcawM4G2EM+t68uWTa2Rs6LujrUL4hEhiMF7cxqSRLzSxL7pySxjqzgThpRs3amRmwrPP6d6Wy34guaRCoAAAtuSURBVOKIymt0LgAtLrCXKxdZu5yQflvU9oCFNDoxf0/Kx/tzWtQV81fkASQCtATALQAmt7eRkSyTQ5bpbFmm/umwsXKR1cRpNLG+UImmo71f4PFv21uo3G9SaIwjyHXJ/RG94pjKmH1wAR9/4sGZVZXJNfCX5LSojy5//0oAplL7rhcrF0y/q4M7c5UD+BcSq2JSzsBtZtGkEAkK/SMd7bVGY77615BNXVNcJ5wGj0vsfA3MA8BfrVj/nGzbWiOnRe2PFswwcVFMKH///Y6eo+80cBQSqclSDsdIAgBXo/BCOtprjYqLI8we5k8hUAMDe7ZykdXR0XOveaX8AoB+UypEn/W5fQ2ptLOZnBb1d3sPL1YZ/9+H5iz2t1/6ZySJfSFJLC27eYWt2myVY9vT7U//Ao+/NmbSriLQ4Y4g1+GIxdXBgqsspCpjHf5uLWroDDkr6or5KwYBOFRjQqennXW/+mZZphNTYNpPNNzv4E1xGtNQoAZT2U5HMd8YeLm+QPmmuE6YAI/rmPbKi17x6IAmHBNl/C33zKhO248yZ0V9dPn7NwLAQOfmj7pwehSJlGSTkmrUfhT4hcMFlcgW5h5MZTudobBBmESgbQCegcd1wB0XXHz8EYDVIRFekDZyVtQ7AoPG55vr42NLP+v01LckMQVAhSSxv6bAtH2aAgB7mHstxe10HI+/EYCbgQ3eW6h82Faxy1/u/1u/ajpirL3xI5/b124EZDLJSVFXzF8h7GyqOLgxVvjsg3OWdGlHLj3fXkoJ2tXLFJ7tzrg/vT8e/wd1xcrqonphTPyO/NNbK7KqqXC2AK2+nykyJ93m5aSoOajjARQAeKurdehxIP+U5dRscN9wv4O3RLlhfpealhGDzhIzs8kMbJ1Jocf2369R9IrHAzRZAXfbghk1KZ09bI2cFPW4slV3ABqGFGz4oKt16GnJ8gCkJN+z7k/zBQ38namov7v0vzwc0IOeioN29T/NQU/zlpdRP1PkJTNpfgCPZ8K2nBT1xoaRA0tt1bXvzL+uWyGVksTOliSWqgxHEgDwGvWo5JT74PGvrStW3nKE+PEuPz8PAD5tKpi2K24tHmP3v+1z+8KZMCvnRF0xf0WBP1o0uCZcnrReRJapyzERbRHIU6+OmbQ6ePy7kl13Mgk6tDMVnn1T2CDc+PrCcm8wbnoKQM33kbwLM2VTzom6j7VmOhK5JpKS8kCW6TkAyR2d8Lh4e4g7qClP69GCBoCKiyNRQaXb11rMzltK+lwU46gvMVbkV01jM2VTzom6f962v1j4MBOLv0xW7oyPkAhJTSZjeI1MRfVC2mbhusnQL6wW1mIFA4ckJabpCjm1nKti/goyc6P79LHV+l677pakZF6SJPZIMuppCQOTKLHFT4djUjKMPD4SjVkYs8QBsCQmpukKudZTD4lp1rKq4ICk3pXLMgmyTO0t3O0wjfnqn6JmrbGn+9M/4fGvGhuNnXDTnvrHDo3FH2NEJyQrMU1XyKme+qC8rRfuaDoYSJI/3YLHAJwuy1Te7UAnj4t3El/UmK9+3eP3kW6Jx79qBrAqLfG47ZBTPbVNCM3pY61RKhdM35Tkqp8C8Dsk5/scwzGyFPiF9reSM2iVnOmpK+avMBNG5Pd17PxPsuuWJJa0S63CsymC2qv86R5HLvXUExg4e1VwQEoWr8oylckydTv5S9Chzo1YtHCv8ad7IDkj6or8H67QU8amKvHhbABLZZkGdrkGj4t3Bvj8mJmlLOVXLpAzoo5p5t8MdG4JVi6Y3qlVLp1gMYAxALqT4X8sx8iWH+DTslQsW8kJn7pi/opiYKC92Fb9UKrakCRWDaBbWYfCVm2WLcIBhj/dLXJC1AAmA0R14b4pzXCkj1XPAXC7JLFOB8bHzNqlAGK2+YGqpBuXQ+SE+1GRv3GuQPEgEplLU8kAAH8A0Pm4B49LyG/kHSrP0pLJP5vJelFXzF9BeyKl44YUbqivXDA91SvAPwRQLEmszWVOB2AMgRx5Qd6bbKNyjVxwP0YEYgXmbY3CXaluSF+7+IudcztCIE+d7WziAcOf7jZZ31MDmAoAISXv9XQ0Jst0iCzTclmmTm3fpnHsvLBVi8HjN/zpbpL1oh7o3HJtnslfU7lg+rY0NVkPQEQiPVnHSPjTNgBp+eFlO1ntfoy91WsNxAb0H9ln7dp0talnRD2kk6eNIZDTFqEXU2FTrpHVPXV9tPg4hZn4b+qOvDUT7csydej7bXAplwOAyjHDn04CWS1qgE1BBgLWZZmKZJk2ALiyI+U5DaeFrVqMv6XR8KeTQFa7H33tO38Hwq5Pb74i3bno6gF8DqD9TYc8LsEJ3qEI+FfKrcoRslbUo25+oTwYP8g+puSzVE+4/AI9J4i7g8XHEshpUrAilTblElnrfgTjzpMAYG3t0f+bKRtkmayyTKUHKrOnSLkGAOIC63JiHYN9yVpRW/jI6QCrBfB1JtrXbxI3AzhghiVzjI4LW7WY6SbDn04WWSnquU9ewJu56Kxhhev3Vi6YrmXCBkliGhL7yCxus5DHJTib+D7WCD2dLrtygaz0qd/bPm1sIO7iDhG+W5ZJOySJPdVOkbEAnAQygpiSSFb21I2xwskAsLb26JTFT3cUfdq81Q1Ha4vj8wAgaFc/Tq9V2U1W9tQOU+OZoXjeuq0LTusJfurTSGRH/UU4qj3EjYpYtJBjXlPK9xvPJbKup77mydklUcU2fmzpZxnJuNkK1wI44xdHPS7BEeIPska5Z9JvUnaTdT31W5UzJijMBIUJj2baFgCQJNbq9hsM7AgCOZHB9FzZStb11FHVNhlA5JvaI9O3OX07yDL9Spbpdy2PNbjU+wAgJmhJyeln8DNZJ2qHKXC2lQ99XrlgeiTTtrRgFoC//pTH2uOaWODnj2FgMCvcEnhcrd5IGnSNrBL1nEevGBeMO8vHln7W0/ZJuQ3AQZL0U7ZbiUCkZzY1IYNpb7ORrPKp3/7x9DEAEIjn351pW1qix1i3RAbQvCtYRtPeZiNZ1VMDmAJg17q6cZ9k2pD9kWU6VZbpRVkmDh7/KoVjLGzRdgCYDI8/Y2lvs5GsEfXcJy8wmbnIbwotdV9WLpjepb0RU0wfACMB9IXHxfEauKhV+9QQdPLJGlFXh/qdHtOslsNKVm/NtC1tsFiS2ChJYrsA5BMIBX7BEHQKyBpRf1olTQEY+6zqV29n2pbW0GOsIctESGxMCgA97YY2K8gKUVfMXzERwGUAUUS1L9Vf9zhkmaYAqNx0SOTXAFDVNzY4wyZlJVkhaoBJAGv+LD15iGw7gDWWKNkAQFAo2TsaGCBLhvQcpsDqYDwfAGMA9dghMkliGwDMhOyaCQAldaaMLGDIdrKipw7G879LPKNlACZXLpjeo2/Aomatn/7U8KlTQFaIGkCx/ndxTxe0LNP47QNifweAbQOiGVmVk+1khaiP6ffuRAAYV/axPdO2dIBv8v38bgaG/jvMXd9Kw6BNskLUMdVSBgAFlr31mbalPSQ5f1xpnamYQOAZ/dcIZko+WSHq1dXH1gDAym2ntRq73MOQ8PP33pNHanotWSFqJKagAWD/wKGeiAwgCkCBEcyUErJC1MOLvj7JzEWUygXT4+2XzjCJWI/JSKRPMIKZUgAx1hNjfzrH0bc9/n1DtKgiqtqlnj76YZB6er2o9Snxj5C46oTRC8apDVJLNrgfEpBYQgIwC4Bbe2rsh0F6yAZRywAi+kISDmAnAVhpCDt36fWi1l2NyWYu+kXiCPEwhspyml4vaiAh7JhmnQtQGMZQWc7T628UW6K7HBIA2bhZzF2yStQGBkCWuB8GBi0xRG2QdRiiNsg6DFEbZB2GqA2yDkPUBlmHIWqDrMMQtUHWYYjaIOswRG2QdRiiNsg6DFEbZB3/D55IletaYgwQAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -3081,17 +3148,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. palm tree 87.323%\n", - " 2. tree 5.223%\n", - " 3. windmill 2.620%\n", - " 4. broccoli 0.631%\n", - " 5. flower 0.566%\n", - "Answer: palm tree\n" + " 1. trumpet 19.415%\n", + " 2. moustache 8.475%\n", + " 3. rifle 8.052%\n", + " 4. bowtie 5.834%\n", + " 5. dumbbell 4.446%\n", + "Answer: boomerang\n" ] }, { "data": { - "image/png": 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JGqDbGZqixcj0dTPWNRR+sL6x4MH8kjK9srS4rqdssUfqKJBfUjYUmCcwr3KpAUyp/jpkuu6rLC3u1ghaUVA4TQq5XEjRvnv8/4DFhWsqesUsw97kl5RNBlakOBvfnjG87NQHZv+tR8Rli7qL5JeUTUtSW88dmrrpzK8aCseCEF5n019PGvby/D9d+0TU6qmUTx1zv6PBORegcE1Fr09bMKP0vgXrGwp/NDVP//Pf5957TU/YYIu6C0RW0t4C6QbI9VSvqvHlnVVZWlwZ7b4qCgqLgSXQN0Q9d9EsdcV27YudbXnDQBxZWVq8Jt422FF6XeNUwN2ewb/Gl/d0LAQN0HjVtgvb70cuHHs1D8z+m7GzbbAGohXkM9ctvDrloCdFGVvUXWB0RsV51j1pdDaD/6FSUVA4raKg8GbnOk/HnNFv9gVhV5YWb/c4Wq8FMWlH22A93v3b7kcnGX3zf8ZJRMXw1PVVlU3jFhLlYJ6Pbsj1Jr+WVayElCck0iX42iaBMHB74ZqKXh0l187M+37z8apdkycDZ1SWFsdtpsae0uskhnT8AWit8eVNrSwtPqSiQe18dEOuwGRE0qepAx3V7oFGdrAoNDRwjmu9p1ZpcWRI1RzrNbOzhbR0LBBIJJEpPZNOLp33NKt2TT4B+BB4Or+k7BFgSTyCnuyRuhNceP8vf/ph9bf/CPyisrR4/t6vf/q9IU4jNzTZtSY5x7nJk256jHGhUb7zHVuSWtQmh1cKmS+k8HY8RzpMpENuV/zqF2aSsTM4oTXPscHzhaPeebVEulCliSFCMsn8UvGr1/XW6bz9kV9SNgvkU9Yj4SMO0Xz2SH2IfHj4ESdMHHvEvYPTX+eS2rLmioJfXCeFHBEc13axo9oVVBudHjepQwRi93WK4lNxViZJqbAF+ASFt3yTG4erDY63XF9635VOc3PrqXU7p8yv+cbIUlFQ+JT/mKbfBiY1t6b+Y+Dp4QHB1EkvbO5Tgo4w3PojAOmMRzSfLepD4ItvFVyXYopHvru6/btw/B+AkCLs2OEycMgqQBeITb5jGocIn/J+0srUZcCWCf9b16UFmMiIPB1g9V/Hr3Zu9Oyzem0fQMeKt3aDMImD+2SL+iB8clXefW6Z/vP2qzWJNM308HNqo/MXQPVh738Vkwi4jgH9SkCpAjJi0U+sqSwtXpFfUjYDeBnkR5WlM2P+a2NP6e2HyJTay573Mm6oyUoVQcWBBEMgAmqj88HCNRVbC9dUxETQEe7SdfGJrgvVSA0bptscFsO+YkplafG7I9K+et+lBE/6yaJLc2Pdny3qfVBRUDhNIpcDp0sw7yu4lKdOm/a5sOKVZ8TpYq0CWKZp0gjn+4eiykFx6DNmjEpfuyRousUnO6bGPDTVFvU+kEiNSAkKE6FMrN3Q/Lz7nBMK11TcHa/ZB02TT2ma/CmAY3PSUtGmUFFQGKOyGLHnrS3FDwM1VS0jp8e6L1vU+0AgdLC2T4cUB62OpPsrS4ub49W/rguvrovdMdRqo2NjZBEmPV42RJvK0uIw8G+QZ13z8A9i+j5sUe+DwjUVKySisio1Rz5wylltW49T5sXZhOuBBl0XaQDhQQEJEChqHhlnO6LKsYOXfg7Ca0hHSSz7sUW9H7am5CpNzhTRNIKL//Gzu804d/8u8HtNk00AwXFtWQDh3NDEONsRVXI91Y+7FH/bh9UnFMayH3tFcR8cecdfr/zBOy//pbB5o/z2hx/2+D9++QmjT3LUuJaaScbZEz778qWetqc75JeUPQZcCORWlhYHYtFHj39hvY3RN79wcV0g+y8NrhSSfGGxr8SJsUTXRZaui5Eds506alxbARS/mhZPW2KBwHweSD0856OrY9WHLeoO5JeUXWBI52IQNLpTSA35cIeDMb9a34vzgQ3AqPYnQkP9TQChEb4+7X4AnD362eUeR4s0pPrzWPVhixqYu2iW59z5t/8PeA5YC8Lf5Eo2AabVfr4qzua8DlyLJWwAQmN8dQBGdujoONsSdR6Y/VRrsqP1ldW1R+Tkl5S5YtFHvxd1fknZkJfWX7T0s5pjjjws+5MPgKOA6UM8W94DuD75kbjODWua3KhpclHHvNSTF1YHpZCN7pUpX8TTllhR6x/4MIh0ICYVCvp17MesB3/2I5gxz0RNTnU1XL7kxtueiLy04rPvXrcAUo53rfPEbV+grotsYAr7yOUhpKglLPrsPPVevK6KcNvwtA23QfHL0W68X47U+SVlytQ7F97/3raTFqQ6myQwpfw3s57oeIz7i5SVAM7NnngW8TkZeBn4xpSX6Q2bRlboW3G0JWZUlhYHxmet2lHdOvjoY+98JOqfb78bqQtu/UcmJD9R3Tps5pCUTe9PHrji3AdnP/mNsE7pMmtEUMFMDY/YVzsxYgmWsD/b+4XwgFCGCIi8ONoSU6pbh9zqC6c87QunaPklZS1EMc91vxqpr3n4uos8jrZqkKcBP97aMuLYfQkaoPWUugYpJMGxbWfGyz5Nk62aJt/UNBne+zVnlVt3bHNXx8uWWFPnz30BaAF+BCwH7gLejMYUar8YqfNLygRwNcxckO6qFycNe3n243MWLD7QOVPm14S+eG18i7PSszMeNkaWxK/ESvBYtffrIqTsALLiYUs8qCwt9h3zm0c/q/PnnBUyXSpRyskN/WCk/smiS7OHpmzUgUUg3jakOvRggm5HhJQtap0zXp/RUcD9WOl3v0F4cMAtkRkf3ZDbpwp1HoiCrFXvhUx3RNBSEqWNxQkr6kgptXvf2DRzTVXLyG8PTN66EDhj1Z3fO+SRV6pmg3TFJzhf0+RSrP187+zr9dBQf1YkUm94POyJB29XnfZrpxKIxNWI5URpU25CinpPWjB+0RZOyRmTXvHQB7df+8NINvxDJjTKP9xIC8dtFU/T5BZNk/59veb+PKUMIKUsJ2G+s7+ePtM3MHlbOYDA/Dhau8wT5gPaCw1kZLVKGF81FnYqP0c7SoPjQ7XeuU+RRRNdFy5dFw/pupi8X1sCavtFYsL41ZomZban5h8AHkfr7GjF2SSqqHWBxArzp8tpwRw1rnJhiNQ47DgZBVwB7DdeOjimLRJT3TIpxrbEDV0Xx+1syysHaAunphKl2Y+EFHVlafEKRYSfxUrT1WU/zUw2mgERHNs2OKoG7oWmyTVAJvDi/o4J5fvCAGZKuCiWtsQLXRcjgHeGplZe1+FpF9bsR7dISFEDGNJVAcKBlfaqS/iPbB4KEBruPzJqhu0HTZNhTZPB/b3ueT/9MwDPioyo5b7uYaqBszc1jn7LeigBqaoi1NjdhhN2nnp46vrMzc2jmZb3Vjp0rVSDY7vrPWCuuzwlZqkQInHT/wSe0DT5wv6OU1oc7e8hIXxqTZMB4KWdr5QdBphEMlsZ0nFXfklZM1aU4rfpwipjwo7UQ1MrBwNkJtUO7GobrvXJFQCOna7kaNm1D7KBfCz3Y78UrqkwpMP0h4b7j4mhLXFB18VgXRfX67rIYncGJxkGGQRRAzwB8h2QXVplTFhRf7zjuDcAXqs8p8vl14yM0C6AcG5wfLTs2htNk7s0TR6pafLxgx1rphqqdJtjYmVLHDkNeAjIiYzCM0DcHsmzV+hSfP+0DhMKe1YZD5mEdT+CRlITQFg6uzzK+o5rrEspyyE8zD8DuDNqxnURpd7xmVrvrO1pO7qLpsnHdV0s1zT5FVgX9nRYGs8vKZsPfBdrqdGgk7NXCTtSj8tclQYwKfeD/K62MWV+TUCqstn1ZfLmqBm2F7oudF0Xh7S1SSDqsNyVPk+7oPeP3D0n21kSVtTDUjdmAOQm7xh1sGMPhDDENqXFkRQdq76OrgsXsB04pCv+8MBAiukN92n3Q9fFpbouHoy8932S6d41G0ubAitTltaZPhLW/VhedeoKgNc3nX2QEeHAmG6zFVXGREiRKbxLDvV4IzfkVZocfX33y1jguANNX7aFksdZ96QBotNBTgk7UgdNd0vkbrdmLsLD/INMrxGTHSe6Ljo1qLhXpbyo+FTRl3PqaZq8A2vL2j7JLymbFDCTjwPxVxC30YXFs4QVdVHOx06Ab2V/1q1sQGqN81211hmrAvbv6Lp4tBPHp2H9JJ8cI3tiiq5bc9GaJveb8Wpg8tYnBGYj8LPK0uK7u7IanLCiHpG2HoBMd+3Y7rSjNjrXClOkVxQUxuKzKgPeO5QDI+mF5wBI5H/6Qum5fbBE18WC/b147h9uO29H25CiowctX1FZWtzQ1U4S1qd+fdPZGwDe3Tbj0+60Y2SFgmqdU/FPbB4CbImKcRE0TXZmmlBjz/flIAo7ROJJZOX0M6zl8W9gLbBMvVdgNAz0brusO30l7EgdMDztLsOZ3Yn8CnyrdQCAMSB4eFQMi6DrYoCui874xrpAmLKbkYc9haZJqWnyFk2TD+79mvX9yLeAkRLV++L6S7p1YZ6Qos4vKUtOdTXcaD2Sp9KNkEbXes9SgKT306OdzPBp4O1DPdhMCa/EKs+xQiDiVc0gaui6GN0xP+BeaIA7cl/QzUi9hHE/xt/6/OjJA1eUVtQePgpyJjQHM9zW3L0QdGNDp2O7ewOA2uyIdiGhR4hUKzgU/Ec03568PNMVHNt2/8SXNvU1QWcDa7HKi+yrWq9u/ZF0ZQpvb/qsqPNLylzasP/+aFPjmOkbm8aNA8/497ZNJ8Nd2wg8DGwEfo8l6C5/UOEBwXrHThehYf7DgeejZb+myX925njXGu8UM8kIBce1/SdaNsSRAPBD9rP/EngfRCuwErihu9u6+pSo5zx6+RHbW4Zd+cnOaUOBU/QtZ6Y4REgCrwELDsv+5J0lN962+8Iwv6TsY7qZJMU/pak2pSwHY0DwuM6cV1FQeBZwVjg7uDF4eEtr0or0VYpfNYLjW3MDE1qPdlV4P3ZXeHcECloHGHmBPM+yzJXCEDLwrZZBxsDgwOS3sj4HCExoGWTkhAYm12ROAVamlOUcyfy+c4EIoGmyBVh0gEOyAC/wfDT2KfbqpOtzF81ytoZSr3hj88x8EGcCkwAEZpVEWTIqfc2Hh+V8uuTB2U/VxNKOLwoLGqVLPjvh87XXHui4ioJCxX9E89XqDufvHNvcOZGa4tHExBr1+oxPresiAzgFKNM0uc/5/tmPXDvr9U3nPHX0oGW3PPfT3+/LPekUvW6kvv7Ry8a9uXnmsb6w9xSHcv45YdPltZZLeSc7acfdUwa9+6Fb9f/ngdl/k1AcF5uEFI0EOKaioHDavsT00c9yR3jezZil4rg86dPUcabLaA/GERJpGLmhV5QW9QHFp4aD41oHBCa2nKA0Oqq8r2V/EBjfOtDIC+R5lmd+LgwhA4Wtg4yBgUHJetZnAIEJLYPVHa5LHLtcp2P54FFJ+BJHZgJPAlOBD/Z1QFVz/kCAJIcvKmmTe1zUcxfNEsu3nnJ0nT/3lCS17fyAccFEaU3K7FSFseT4YS9vcyqhPyz64f9FshZdFVf7IoseQ4BhUsjlFQWF3y5cU/Fe5LWJ0mH+xKtkXSWCClhC+z4q2wRiCeAUiJCjxvXbvf4Znu2kDRuwSjp36/qgh3gG2MwBttVV1E10AiyrOu2QZ4MORI+4H9/61bNpbeGUk1UROsvrbLmsKZipABLkR0cNfLfFqQb/tGLb9P9UlhbHu4DQN6goKLxZIu8U7J5Trg5nBTcRFiMcTc5BgC80wvdJcLTvT0c9vOPvHc6bRsSf7yhoXReTsHzItzVNHvI2sf21lwjklyxZCOL8ytLinGi0F5eROpLLrgA4c3jq+pv8Rn4OoBjS2Zjj2bl2XObq/3684/h7Kktn7rR+rXoVukAEJdIpEBIY5KhzDZJIpGL+SZjK7Ye/Wlm/90kR4e1LfNdjBcB36gs8QHu9Fl0XpwFHAn/cX5IegJHp687zhZOjtmYSM1Hnl5R5AG1c5qpb0lxDjmgKZnoBmoIZOw/L+WT5ypoptwErlt78829k+OxNFK6pWFFRUDhDWFuNhgOzsXzbsDCVbYVrKr4h6INwA7DwQEE9CcR0rNDa3x/ooJ2teWR5dlVGq9Oouh/5JWX5E7I//Zk/7LlgfeP4DBAeVYSDo9LX1m1oHPcbQzqXVJYWRzV+Ip5EXIA32ePb9plZiJ5C1zC/PjgAABTcSURBVEVKZEpvn+SXlKmAD/hDZWlxVIqGdmukzi8pOz7F2ThnkHfrxA2N41RwjFtdewQ5nh2hVFfjk83BjGcN6Vj2eslNMU/dFQ/aR2266NvqupiCld108f6mtxKNAwkaIMXZOKwllO6kQ+Gm7tJlUbcnYWwJpTu+akjD62he2xpO+5lb9f/3vhOvXtexEE8i0U3f9izgJuAv0bOod6LrYhFQp2nylwc6bmre2+e+sflsvj3kteRoTdF2Z6TW2BO7EG4Npy2uLC2+33qYkHqOBndg+dMxqfTayzAitwPy6c5jxgN81VBwwBG9M3TZp46EC0YC3IWPKOUWtuk/RH7tl2ENrlHTUHemUd53iJA5wFNdFS1jEhldF5N1Xdyv62JQT9sSayKlPg6KIsIng2z3FjqdtGa/7Xbj3LSwdCk7fXkP2II+JA4DrsGK3UhYdF0kA1W6Lm482LEj07/UIqUxTKK4UtodUQ+J/P1G0R2bb6JpcjGQoWmys/PafQ0ncC9Wxa39kl9SNnx9Q+Exaa76CuBXRPHXvssXilPz9Onvb9eYmPuhGa/Aor7OvkrJJRqaJhs5hBRtAvNBiSKagplnVJYWb4qmDV0eqYOGaxTAYO+WhKntFyt0XRTpunhZ18WEnrYllui6SNJ1ccLB9l5eueBHv5Eo5+R6qv8UbUFDN0T9yc5j6wBerjyvy0nN+xHZwDCsYpiJzGlYsxnT93dAfkmZ952tJ1+b49nRfOzgpfNiYUR35qmHArWVpcUJsVoYSzRN6lgXionOUqxYD/0Ax9wWMt0Dd/kGfvuB2X/zxcKILot6WOqGk1uCaTHLsG/Td4jMN2uwRK8sLf77/o67buHV5wrOvRF4fGPpWQe8kOwOXV58mXj7k/UAjcGsM+0pvf2j62IkVhDUtZom3+hpe77BvPTdcdrMa+z09zj2ln8fHzIdS0E4BNIc5K16dmxmRfWyqtP+C9QenvuRLz/tq50vrr+kMNnR/F9TKmknj1hS8Kdrn4hZ7ZouiTryn/ke1nq4H3vxZb/ouijEKiZ/m6bJL3ranq8xL32aRC4DHAIhQ6q5K5Ak3Smt6kdAW2uykRtwy4ysesebgK8xLTw44JapA2qcr/ukM/Av59GnrwgcfnaDkaEOp5rhSg2vGUfxiRx3gE5lCMSJsdRLV90PLfJXgHRGyhrYot4HmiYrgPN62o5vMC9dBJ3mo66QskcDgrBpTR0kAVnOkBjmCIt0IBdITm9yeCJHnuURIWaF32WW+u7uCCBTwmxHWfBFb9GtP6st+RjILsr5+Ljq1iGn1fjyCgERycOiEUO9dFXUulV4xvrJkQg9ijYlFLoukg6066NHmJeuAo+4Qsph0lrNMwUi5Awr5zlv2OOCtGdF992d8otAIDXrzuCluf81j/mum3DWCM+mzUepax+4MvfvlebWwZcOFTXnKgIBUvlO60rnd0qLdevs4n9Gftk7xqHrsXx7XfapJ8/7y8z6QPZ/kh2ty1fd+T0tumYlBpHMRNuAH2qa7BXhppWPJ6Vm1akfpzU7xgG/xcq8qrGXT63rwqlpMrTqroIfTAhvX4iEEA5+GJr7zo5Mce+SG2/bU8jU8su/tnlib/98z8Vk13OwHCrd2vmSX1K2GDgXyKssLe4XQe+dQdfFAOBnwHOaJruVfTUqzEtPCTnk686wmFqTE/pb7vVtl+7rMF0X/9rWMmzMLe88/N4c9YVrfu54XlWFpROJrKnPMF7ObFDXCMSef4RuXnBGk+6KWgPeGpW+9oalN//8D1Gzyibq+EpTczx+5SXg6DaPcUPyL1vub39N18XZwNlXvLLkz8BFI9O+/F5l0+hcEKHTlI/KHnY+cLoicABmWDFDDlPxSqQUCD/7GJV7mm5t55oy8J1lXzUUhIBbAFvUexGZztvU05tsN/85aWKOcLwvkQ6BOO/DY1qXoovLHl91/ctvV50++sSh11/cFvZeAvIqEGJj01hGpa8pL8wu//6Caxd/zrwHdo/CqilmRFJGdCvxZizp9sbbGXfPf2p94/jvgRjRlzfVRhtdF0lYVbfu0zR5a0/YkF9SNm2asvq7j7vu+b6qhAY+mT75nd9sv2F5pnvXdFUJH7PLtye0WxVhaUhVWKGghIHbK0uLv5kCzHIzdMAlkQGBOKm3jdTdzrWwvrHg9sg0zfejYE8iIYAfEMVMqZ1hZMlL0wpE5bIHnX/6RZt0DzzPdye/2X7D8cCN9YHslAHJ1VUTcz/8N3AOMNqQjuMjO5jCHGiGwhLwKYaQodZko623CRqilCJh/K3Pf+BUQqNnDF+Sa+W4s+lJ8kvKkoq9b/ruDj9DC0lcFrzZdGbWbDt+yNJHFpX//L7K0uJ9lnvrzAzFroeS/5tT6zzDFLJAuaNpbQzeRpeJSjKbowctL1++9dSjq9uGXATsd+2/P6Hr4jhgnabJnXHq72hg2hWvLHn+p+rz784J/4edMp0LgncY28gNUjfkwpdvunXFgfygvcspH4icWueVwGZFip8Ac6LwFqJGVFI9eRxtN4Ns/WD7ifsNOexPREqrlWEtj8eqD6Hr4qgOJSfO/qq+4P7znW9unOv41wgHBnmiPnCs+sUiYhHGMK9xB/C0RF4TvjPt9xFfu1cQFVE/+sNFNSCeBy7KLynrVjHOBOJ04IFoNhgRcvt3dgnwETAlv6Rs2jWv/avotx/83rhY1Z3ti9GKkOp9zv/bHKvFjqbU8BqBcKkGNwJv9hZhRy2X3mDv5ue3tQ6//IQhr98NxXOj1W5fJDKF93402yx7PXWYLzxg6br6wpV3/7505ebmZ5OzkmpWhA2nDtITNl0IDPMwKptApoIwiPGSdFqzQ5FIetv0XtREPWXQOy+/XXVa4POayWfll5RVE4fl0N6KrotzgO2aJju1Kyi/pCx5xvCXtDp/7rhPdx5DQdbK811q8NhNjWPqG4PPeEG4gTFYWVPNrS3ezWmu+t37HqeIL2USRhqIebSXpYvt7IQeqZYg6UV5s6OdIPIx4OpI5v8giH4ZkqrrohL4QNPkRR1nFID3ZwxfMsEf9kx+d9sMCYwalb727LaQN6+6bSjA13KCuBS/Mci7VVWEsbayadyLwIZjBr2tel3NK5dunvl+ZWlxqGOw0GOOe5Tpjs9MBU5lXuNb8Xivcl5awFD43GGKub1lei/aor4HaK9fCIh1wLOTcj+oHZyyZenDP3h8ZdQ660HmLrrU+0rluUkBw5N25ID3RmV5do15Y9PZm4HUwqzPJ+Z4dhy5unZSRZ1/wEAwLwahYOW2DrKnXiCAmeJsas1M2uXb0jzqJW3oy5MKsssnVzaOvuqVyvNe/MOJV7R+94yag0b45ZeUTZulvH7lXa7HZ2O5Az7isHy9+jmnMuGLZGNnbujtAXPatFj21RmiLeppwFKQbpASlPXAKPbk3KsCPpqU+4HhcbS9uWL7Sc9UlhY3Rs2A/TB30SyxrOpUd30gJ21C9qd5Q1MrC1Zs02qbgpnukelrxwxJ2XLUx9XHfuE3kt153i2FOZ6dEyvqilaFTVdyuqtuZLKzNb+6dUi1RElRMDJN1EOtfxiKxJtjfR7inTEZFe8OT93QtHRL8fMLZlx0jNfZ+hdgrKbJTbouxgKqpsk1nX6T89IfAH4SeRQGbmdeY7eLAh2IlntTklNa1Va/27w76ebmW2LZV2eIenmMvSfw80vKkk8e8eL3NzSMH7+hcfwgkFNAdCjTK9cWZq1UgZcr6iY+jTWSHYv1c/35yPQvcwqyysetrDkqsLVlhDrIWzV4XObq476onbh5l2+QmZW0c8Tw1I3HrW8oWNccShcpzqahWUm7JmxvHVIdMt0uVYSyQKQa8tAuHxQR9qc6m1wtobSNhnTUp7vqnQO92wZubByzLGS6dw32bk4dmlo56POaKa8EDE/dmIwK74i09SnLq05eHjSTGm45+qZz05PqAg9/VvLApqYxU+gQknlY9icX/2LK7VcC92uafDsSG3It8JCmyW3d+uDnpf8U+CNWUsYg8Qg0mpeeDewC5jKv8RvlmXuKHqn58uNHLxv1ec1RR21uHj3WpQROSHK0nRKp+8LXM6YeSsk2KT2ONhEynTVh07UrSW0L56VsGVLTlvdpSyitKsNd6xidsWbkxsZxy+r8uVWDvFWOwqyVg1bXTlqxs23w9vy0dcaE7M/E2vrD1n/VUNjU3Tozui50IKBp8rRJiyccm2Nm3OZrPDK4tfr00r+ePvNz4FOsrV3Pdaefvam/33tXZoPjVkOR96umeC4e/m39/d6xmQ2OL9s8xk3Jv2y5N9b9HSq9oo7i3EWzxNItZw5rDmb8FuQsa5ZVShCvpjobXz9i4PujNzeN+l9l09iNmUk1/ql5y1I3N41ct7r2yJ1AW2VpcVTfRNHiot2/NuWXlx+yOIoWF01TkTOmpdSlftKaHmyT6i8FOBWkYaCcUH55+QpdFyIWubt3Lkh+dUCN89TKEYGM/Cv9MXfpADb/OenU4Vvcr24fFHw47zpfr1lV7BWibmcf237iPnsy/W/jzt8Vdj0nrf10hgPjpSO8zWZNyPVyZTD5fWBj+eXlrbBH/AKpS+tXRQfp+uYvjAyDuL388vLY+bjz0hdI5MViXlN2zPrYi8DvUo9yB5WPfEnmZZ6S5ifj1e/B6PE6ih2J+OC7y0/EW9BFi4vOB9dfsQQNoIZRij9qzXBizQ0DMPXJQhk0xUZQR4BUVCQC+ZcQihq5KMQjjFd9Uj3NOqP7ReQPgcEC0T2/vJO4g4oTwONXYlpxuLP0KlFD54JqokHR4qJpXiV8cZIwZoJ7FIg1IEdizdiEgBmnpu2s/6A1M6fRcA4d6PBPzXKETlvrT0myjhGEQXqF4Q5JJQg4QYR80vHrLDUwpdFwphmIuZ1xY7qC321OBuqTYtnJXjSmhYemNzloTjEcqXHs92D0OlHHk4j7oLeaqqsVlTQl9HST6bwcxBR2+9Sr9hbj3zucG3GVRKhVOh4GHmbPQgt1hjsN6zN+oGhx0apYCltI8lq9ZiCeom5JMQ9Lb4KGjHC2LeregwZEfGBpNJnOVeWXl4exfikOKMDyy8tXFC0u2u0qdRDsCoCixUU3s2d+PrZxEfPSFTeKcAXFP2LS/n7IrXGsA8iudZTHs9+D0d9FrUemECUIK1aiE0SEvD+h6numJ2PuU+cCqkBsjWEf38AVUlSAZJ/aEM9+D0bUSuf2RcovL1+hIMl1BLcAM6LpHpRfXr7CLYxAhhraEe2296Z6YPAogIb0+OZ0b04xRgI0poV7VaLQfi3qbz813m2iYCL+EW3RFS0u8gakI6nBcD0Y64vEJJ9yBoAUcnAs+9mbtmRzEkB9ptGrSlL3a1HXG64UgNqwa3MMmm8PBYhZdk8A5qVPy2hyzAbIbHD+PJ6B+jk1jlqA4Ztd+fHq81Do16Ie424dCJClBg9lPb5THO2tvxjgiOTGpmi3vRcakQtSiUzyu83rY9wfAG2lqecokisAFCle7S27XqCfXygOc/nGfxXwUuBpGR7ttmvDrsEAA5yBWKcb04GgRLoA1RkUFzEv/XOJdJoKy9uSzfdSW1R25oZEW7KpZNc6jO4+Tm5TZrmtUtTtg2Kv2fUC/VzUn7Wl1QNsCCR/EO221we8AFX3nbsjtqtt8xpXMC99hkBozSnGNm+Lcgvwe4FANSG1xZpVHFDj/Npp3X3cIbgi5tvGOku/FnW94XIBVIeSYjEVNo5Y+9PtWBF5K1IB5qUPB+YBikRKU7BMleKNNo8xoCXFLMysVz90hhVfa7IxqNVrjs+qc3zgMIS/xWvktSWb47LqHCschgi2eI0hbcnmmOxax3uqKULNKcZQn8ccnVvjQCCmC4SCJeg3gF/3ll0v0M9FPdTlG1EV9JDn9Ed9SsqjGFPynP6Pot3uIfAGcDPgFIiQKrmZeY0rkoGO2/y9kVs7KZHb/h6nRm4R3/k49gSd9SpBQz+/UBzq9E8BmOBpdh/s2M5wxjNjBvpMVfUoZnxG6o5YApsB3E4sNgrEuv0o0K9H6tW+1HUAa/0pX0Wz3aqgZ2Sk/bguW+8m4o702fa7Sb8eqZtNhwqwJeipi27Luyv5xH+ktunfoh7gCIzBqnkS1ZosE5ObrhFIjvbWR71Esc3B6d+idgYmJwlTKb+8PKrbf7YGkxSvYjT8+fyqQDTbtTk0+rWo1/m9FSGpVEe73V1hd26L6dgRibm2iTP9WtQBqToMRFTDJicuPuy4iE89DnjTFnb86deiTlNDIz2KEdW4jyw1+Fhkf2PHpIk2caRfT+mlKuFxqiBqCy9Fi4uKwTU+cvFpxmnDrc1e9GtRV4eStjiE2e0l8qLFRdM8wvg+KN8H8Rnwc2AancwbYhMd+rWoDYTHkGq3Ao4sn1ku9Umlfc/rneWXl+vYI3SP0a99aqcwc7LUYJcrH1z2j+HZwF0gkiJ+tAkURMs+m67Rr0dqIG2oyz+0sycVLS5y5rva7twV9t6INTBE/PLOb961iT79VtRFi4tUUFgX8C491HNuemGg+LA14zJw3VoZTB472OnfNTG56afvtmRtoAu592xiQ78VNZHISp+pHlJJuKLFRScMdKa9UBt2ZYH8AsRZ20JJZa9+b137aqQt5l5Cv/WpizxNeQAeET79QAsk1/9r8OlFiw/7D7CsLuwSx3jr/zopuWlS+eXlS6K9vG4THfrtSN1sOE4H8En1ZOD4osVFX8vNUbS4aHCWGny43sg6RwWfAbeEpPLAY+dXtfWY0TaHRL8V9aagJ9+6t7tc2mVFi4u0bEdwlYo8G9yz6gynY3xS67sj3W1X33vujl5Vqthm//RbUUvEB7vvWlNxVwLO2rBTAYFA/l0ibn3+oo0bes5Km67Qb0WdqQZT6g0XIIVVPQuVyGRzlhr8x9uXrr2kRw206TL9VtStppofKYsHSIXd6f+Fr85w/bHnLLPpLv129iMo1SWAP1JbRumwIhjzBOk2saXfitoSrpgO4nX25GYxgZweNMsmCvRbUcPu/NLzsPYohu1Q0cSgV1Xn6im6WmLOpndii9om4ejX7odNYmKL2ibhsEVtk3DYorZJOGxR2yQctqhtEg5b1DYJhy1qm4TDFrVNwmGL2ibhsEVtk3DYorZJOP4f5hguvMDdvyYAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -3106,17 +3173,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. police car 86.970%\n", - " 2. car 6.125%\n", - " 3. ambulance 1.263%\n", - " 4. firetruck 0.660%\n", - " 5. van 0.635%\n", - "Answer: police car\n" + " 1. hexagon 31.216%\n", + " 2. wine bottle 11.910%\n", + " 3. octagon 10.838%\n", + " 4. triangle 1.982%\n", + " 5. crayon 1.962%\n", + "Answer: square\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3131,17 +3198,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. beach 39.056%\n", - " 2. river 19.501%\n", - " 3. ocean 17.474%\n", - " 4. the great wall of china 6.477%\n", - " 5. speedboat 3.810%\n", - "Answer: ocean\n" + " 1. moon 26.485%\n", + " 2. frying pan 14.230%\n", + " 3. ear 13.880%\n", + " 4. spoon 12.745%\n", + " 5. boomerang 8.637%\n", + "Answer: ear\n" ] }, { "data": { - "image/png": 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\n", 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YMeOuux7dZb5rw9i2CmYYai3wnybXH9Mk56FhqKdMUyaybdPNv7DzNzbG1F4HfGMYKuRcFwHXGYb6g3P9O+xvx2wA05QHgOMMQ53uXF8MdDUM9bRz3R+IGsZO04XsMXRol55o0buUpQ8TPbpORfUeXQ8tv+PKexb+ubl7ifG+5xV3TT9JLM8sAEEiloQ/FaWf5PimG4DCV7NfnxIMprqjUd8TgNmaUdfv9x8OfA1c6vf7p8Sir3sCpiluw1Bh5/fDgVzDUO8416OBQw1D3ehc/xPIMwx1mHP9H6CfYajDnetnAbdhqKud66uAgGGoKc51LlCzu7YktHuiOHbK1DmPXXnGY9GgNkxF9R5ApGJh7uwd3UuMT4WL5ToVbAkDLlH6cY6gAVyLtJVX1ddn90lLW/Pd2LF/f6i19Wpaw2WWlWRX3YloFLTz+zxgXpPrJ7e795fOYlgjD2ObNo2sx55zNDIae37QOAi8A6zAztmIacrb2J6fe5zrG4EfDEN95FznAhsNQ9U2VvjkDaVbB8LREwvbpJ0OHRKYMHzYnaAecBZWItimRasF1BFWFc8azFZ7mzD2pqrHAfd62RL9r2euhuAGFQIxWjlKDwbrf44dHgBJLJ+3AtMUDfAZhp1SxDRlGFBvGKrUuZ60eenZDWu++L81gHnIpUX/UZb+1prPxz6ke2sOTuv1yZTA5gNm1645foHuqT7MiviOql13dA5ojXv0i9oi7I4uCZuObd0oLLOD9bWaPuNPnrOqeNZWe9u5XggYn7iX9EO4zr5TGvd+tDRB7ANMdv6Q4EyAWyqXAAxDWWyz5zGMn+au+fb1kucB0946ofj29Y+qQEaBXA9Q+d3FYLs7zwZAIlaTFeg2/x86JGrnhMvOJnJxxTkIMGf760p/yWBgJCifve9bM3dUh9/vH+zzbZwgknGUUm4L+8Mp7OIPaSfHAFxbLTqJfo9yvefN/MHty/4utXrlqe9a4dS1yV2/qU3pNr+i4ttfHcxPv4XNtjS228OOxQu/3z/Y46l6JxRKyQT9/uTkiiWRSNLyUCi9R0rKumN8vs2nV1bu36CU+2RAA4XLVX9FJJLyI86HNGF6xAbHPi4BlQSIL+fbW6/54y1PtKKMwa62qfd0/H7/XcAfG497NUUkilJUg55uP6MiIPf6/f5dMifY13jyhtLBunfz5VbUc5Omh1ZHg1kHjJ5YGJfV0s4eylfHXvgBlNL1hreBYz2emt79+s1ygX4WEAAizS3uJIgdoycWzrnh8YvGuJK2XBYNZvUBXnnyhtI7nRE5pnT2kXp7D8nPvBnOPQYJc2OX8eSNH7yOcg93zrcGaaN3oyU6taghIdo9kUm3v/hZqKbfsc5lBLh39MTCmJl9nf6UhyPkhJj3IEI1vRucFf64mH2dfqROsGfx5A2lydjh5t7F3rXYZu9GS3T6kTrBnkV63xljqlcO8bmSKl+4/rFLpsWjjc7u/UiwhxGs7jdKtCBW1FPX8t3tI2F+JNhlPHlD6YnAbOekf5v3dLSWhPnRGfFnbPX44K9qvWjaW671nIp9+FonjntrEqLubNjCLAU8ChXZ/FjK274G7SNfg1ZTlxzNqsqI5mVvcn2TFNTqalOi2dXp0YOyN7nmJwW13gp1L6ALEsKfURQHYc8AFQZLs4/u7XhPTkdImB+diPIXknK6bXDN9QW0/tKB7eAKFRXkHvxVMd8yMOn2yUUZ/Wa8IXrwkUtueu3BWNcPCVF3HvwZ5yrUk4L0VSgliKVQkar06N3uiJSm1OsN1WnR5C2ZkZ5dNrpWJQf0YHVaJGVLZrRH1wr3Sl+Dlq9Qk7HNAkuQ0/FXlcajq6Yp0vT4WqxJiHovZ8Xfk07K3uR6LrVOPwhYVJsS/V1qnV5J+23qm4DLLFGzVvQLnZ97dUPc4pSYpmjOXuyYkhD13oo/wwOMtUSNU4I76LX+nBzQf4+/Ktxi2RaI3p9+lW7JC1XpkTUZ1a7++KtiGnbBOSr2KfCvJoeBY0ZC1DEgt3jaVq9B+fihcV+SXzUp6baeaz236pb0U6h/reoTGt/31w1fxLKNdU/7Xu+x3jMcOyDPbGBGLCeOpilPArMNQ70WqzobSYi6gziCNkG5BRVVaBeXjx/677g05s/ooVATBBkR9FgBb0i7GH/Vuy0XbHd7E4HrHRu9AYiHRyTmJFx6HcfAPqokCnEB7+QWT1s5IH1JdYZ386fzKo57Gjv02DG0czSPjks/Kei1nvChHSiIu8Fr/W1dj/C9uVc3bI71m9mO5QqFk50h5n5l0xQP0C3W8UQSou44JnY8bjf2wsJToHpvauhy4bLqAw8BrgXVIKgkhVggwdziaUWtFrY/41wN/p3coItCKeDSpDtr/pkbt7fzE0zAUihN4nOIYib2gd2iWFaa2PvRQRxxFoHcCzKkfPzQ28rHDxs+//5fuXskr9ofuEyXyDcKwTkh7aG16an9GUnAk7I1dBpRYGDs38WO2q+aI8hLYueqjIfp8SdgQozrTIzUscAR9s/+4Z/ee/1SYGlu8bTloEqBJEDXJLJm+3t/VucLSZKZoc/NrHL1BUJAvEbLlvge+zzc3JZubCuGod6OdZ2QGKl3Sm7xtMG5xdPudCaDbX69EVv0Ugg8ohHZlKQ3/P36p0ddvLMyPde678usch28oWt4OvbIfi+7YaK2oWt4EMDyfsEdBdjvEKYpfU1TzollnQnvxw449cHHbl9evf+fGnMMpHm2WA2R5Iqw5W1waSGPz1XXvSaU6eSStKMItcZOvumZq4fMXl30fnUoPQj6C8DrPyvnzzgbmBbV1L9X9g1dmHt1w277J216POXR7M2u31Z0CR/QdUz9D7Gu3zTlKWAkkBOrWNyJkboZjAcf67ahvtdDNImrkKQHq5LddV8AM5P0wOdJerBxpU1AeTxa8LTW1P3U9S/MqA5lXA56KnALUNJ0pF/x96SzLVFvK9R83ZIrdqegAbI3u+YBdN3ojlc/HgGOjGVw+X3Opi6YXLB1oWTByAU/G1lzi6dpcMALYCGokEI0kHBFoMewpiOqI8QSUG63FnLdMOjhE+HCVvZCO8A5Sa3bAV7EAObgz8jq4Xb/I+JSno1dItf2uj4Qt430bSDgPPraWrAsL3+wQhkIZt05lZ+mTutC7dCNW19PndaFlKFdlqW8m3P8IpV3pyBm/uKyDptXnVrUh08+9KQ0PXyNUlRUWZ6gC+t0kOOdlxsKJhcUbi9sn173UiCacg5of1bwNjvwLZePHzont3haEYhx3sAp1pHdP3urDV0zQUJ2WDTn2p/hAl5zhyVtc1b04l7XB75q37uOLWt6hvJ6rfWwsk/wyL6wsLXlyvLyBwMfC+JG2QKGbY+NNF4r+yBuoCwvv6ijwu60oi6YXHAKMGNL1NNoYlkRxBkmBMDLdosJp49/+HeB6MGXOyczxgBvl48fusPtl9u8HkNpjGJrmvJ7YIZhNKa5br6c/YHgXpCzgIrNmZEPsra4CgUZlX1r3b/a+75jjSckKwCS6/VgG4saOOF+FQorNTpXr3X9V+lKDw6qOUnf4PnRvSpppRJ1Kgojlgs8nVbUwCPbImeqKMg4kOnYwW2SQEmOK7S8aYGVNQPOZlsuxjb/gZ0sXCOxEy7tUNSwVdijgBV3J73wdNYWV+GmrMgX2bfWPdfa9nYFXSrdSwByNrnaulvPVKiQIEkIUb3WdXNzI7Azorc7GGRzdMqJ4rVv9nkRe1k6Yv9ICJjumBpFLqzHBOjhDv6uabmGaHJjFrFWp9NoimGoGuB4YCzYIjdN2eFu/fLxQ1edk/zx+ispPS2iqzmbsiOntKW9XUS989gmmzp/cdkcQQotX6TBSo6u3pFJ4TzvfGvRYdMDOqFLb9DkQ48DPunnCWxaGfJdEEVOpplJ4bDXBs5eHvIdA7LfgpELVt866XLvf5ZeulEh00E+p4M77kxTUrFH62mGoe5s7p7lzycd3WVV6v8qItmecVx27gv3j5va3H27k5XPJZ3Wd5X3o7U9Qk/1vCEwuq3ly/Ly7wX8QO/8xWVrY97BZuhU5kfB5ILuIG+BWt7b02D897KlK7C3Tf6M5aHkXwFLsJP63BKIJP9SoaWe0vvD8pdufjwWx5jqgfexv1p/jj/D1y3J/Z5bQp7rwr8NLlH9zgb2OFF7g9o6gOR6rb35W94ExkUzwr9iWzaxuNJpzI/b3+nu6+IKfgYqG+TCiRetXrGz+xeMXLDMI9brGuqm297ucdSctcYAAI+rISY2rWEoyzDUHYahpgOYppxtmtINAH+GAH9PapCczVmRsUtU37c1iVxx66QrUmLRdizpVuFeC5BR7TrcORnTJvIXly2K5oTqrcxIcex71zydRtRf16dP3hjx9j8mZcukBSMXzGu5BJycVjkJ0Jc0pDxUE8o8FvjuuRuf+TbWfTNNyQBeBf6MP2Nw0GN9AVwmyO+7jql/9NQ+H3xrKVd6dSijWTNlN1MAoFBDgZL2CDuaHXnftTwpsywvv2vMe9cMnULUBZMLRqwPJ12SpoUnP3/xqltbW+6xC9bNdIv1yvJg8qlJ3aaekZT+dVySZTr5yE876suUVxTK9ITkKGcbqQmQ6d00wa2FqmeuOv3geLTfEQJJ1lgAsT1JjR6hNuH5Pvl+x2V3fmx71zx7vahv+VfPiwX1PDCrxnKPamv5oNLfBjyu7Nlud683znZWHGOOYai5abX60YJ4nPAFURyBPD7qlUDY8kyOKvc5ucXTMuPRfrvwZ2QkNcixCoVCtcsj5PCNEvVjND1yfWw72Dx7tagLJhdkz63PeClVi7i6u4IjFoxc0OZDp9FA79MUjUmmlR4N9L4yDl1tZAZO5oLtt5F69IZXAO/hXT+7L47ttwmF+psgOYL8WpB27xLMX1ymgofXbtBq9aO++G3X/eLQ1Z+w14q6YHKBDrxaHXW5DvHVjpx++ZJ2HQkKbR4MiG0MKJdzHSdsQWz1yTYVyNm5//q8q29taGOg24j4daD1rHnG94ggV4Tc1mP4q/6Ov+qhDm17FfWgWELKezlx98XvtaLu6wlMBs4EuXnSxateaW89kaqjX4rWHqiU5aV+xbXBSNXRL8Wwmz/HXzWnOYE8PuoVFbK8j62qHdAtt3ha/7j2oSX8GT17rHNfV5ccrVrTK3xPLKpM+ip9KrBckJ3uI48Fe6WoL/tn/7ErQ77L+3vq5wDPdqSu8vFD5/g0KyiWByuQO2RXhDjYEVXB7InOr7tvtHbcjZoSV0q9flzu1Q2BFsu0gvzFZSqaFvlAiTr7i9927RuLOnfEXifqgskFly4MpP3RJ5HV+b7asxeMXNDhJVERS+lisTsFDVA+fuiyTG/lkpykDXfdOuny3ZIbfX238IvA2Qp1O/6q72JZd8Ox1V+IEs213tPmlcm2sFeJ+oSX884A9SqIN6BcOe9XdYuJCyxsuYLK9kbsdg7OmfdpZUO31PeWXTixpWNisUb50y/pWuG6os4XqVreP/RUzOtPsl5QotYmfZV+UKzrbspeJeokzXqabSerXbTDZ9ocltJkTxH1J2uKXgEIWUmj2O5UTDxR/vTzBZkiCi05oHtzl3uPb7lU2zhmQkVUlLwBnF2Wl58W6/ob2WtEXTC5oH9FxNPH3uvcvl10O8ItVqom1p6yD+Yo51FoSziF9uLPkPVPJT+rhLcUSgRB7KA8cWk33LvhA8AbGLwlbq7LPeUf2RoesQN1MxzIZwfHsdpDVOmWbgej+Rk9Zszbevxr3ZDD42pzv/1+jm//zOKTftiS7zwjOnYmq/jgz+gHTOq+wX1GvS9a4wtoHuyN/XELxRA8vPYjvcoVcZf7To1H/bCXiHr0v3r9BnIu9kj0gblXLnoHeCeW9Ue1BoVEogWTCwY3/aD0nDH3FNA+di4jPWbM+0Mflr59HB8tenLIxJiYK6YpsrqmX/JLi268MhS9+4ll1Qe6tuVSV1GQLi3V0VbKX0iSpAZ5sZu4f6kpiQKja1OtickB/Tjimx6DYyZUhBfl5U1y1bqudralfhSLPdRN2eP3UxdMLnClaZFVuqic41M3d/vz+etjGj+uYHLBYKXkE0EhooKHJ1f/6qVLVrwB0GPG1+OAexrXGxsPl/uopYHkjxXalwcwfxdBakYAACAASURBVA0Q/Z5BKcCMtozmdzx/UXKKu3bVOz+MYHOwS1aWd2OoT1r5pws2HjUYxI0deqEwpl4Zf0Y/hZokyBk1qdGNKXXasdp91ctiVn8rKMvLHwP8FbCAIDE6HNDI3jBS31BjubqnaJFLYy1oBwNUo169Oa7QI8AbANlsKN1E99sBt5OZ9foj+fiwEEmnLuQ4HzDmewZ5m9QV6DFjXtHOhG2ackZtKM0YU/ra2gzP+X+uCmV7U9w1K4Exm4Nd/vn1uJGR3OJptwCPg3Vb+fhzY/LPdkbn57rjvkQQLeS2bq/Mifwl7f8advkEOZoaydNrXWDP6WIeeHKPFvXYt7sfKHT9o0Km11muf8apGRPlQhFRCMF6S78X7MDgTwlvzFGn//c7Dj/EQ8PTzw554mU4fGvBHjPmubNY//xmul3ueGV+dpjXqetQ4NvfzXzm/KykB18vrxroAjSFLC7sO21Kmqdq3OOjXtn6lTmk77vfzFh5Dqf0+WgQnNvxd+jP6NdH97zqisqJgSRrqa9BzvD8vubH3I7X3C5UkjWHWkYrlBWPUGp7tKhXhnxvCaQflFRz7xvDy+NiJy0YuWDOfuOeinqyPp3pzvj6989ctLpRkEnApMHy0czBfPQ44DbNv74K3G8YajHAuiGHh3vMmPcUcJEdvwOtBysWNxW+acoFwL+eXzjmt+vre/1lfX1v7Jgf3PDN/Vc8A1f8rE/ZSRvnASyvHtgh71T5C0mStVkfl4HrN66o6FXpkUc2Z0WLc6/e9aNzU1wbPTMBBHkHeCTWNvUe69IrmFxwRFlD6iF9PYF33xheHjevw7VP3eCxAv31LtXHfdd0kmgYqsow1O8NQ31gGCoPWACcgZMp3jTlANOUE16XIz4Firqw9nUQurDuatOUN0xTLnOqUsDUuetOzMK2IQGxgOwd9WnCtVO2AOuXV+/f/lVFf0a/bhtcizOqXfdEdLUYKMi4re723S3o7Xg31oKGPXSkvuOd7gLd/gqycXko+fJY13/Cy3k3BJU+IKS0d4a4B/0A0CetfKf7EQxDlZim9DYM1Rj/4hbg10C31+WITcCrV6nZqQs5trBSdSvLkQ2ZpikacB+wuS6S9j7g2OetcpktF6zctr63yB/ST9UsbtOQ03wBjfXdwq8FfNav9iQx1xVt6p5Skk3gmKpjgL/Huv49UtRVUdcTwIlpWuTmT35VtiWWdR/70sFDAsr1tHN5c4lVcQHA1xuOm7GjMqYpPuAL7CNZD+LPGHyypKVFXNZX3rB+qGlU/wEoupsbpt/NS75xPNc9pJIWLR5yuuWYH2vLxw8NOgFsDFpxUv3ArG+7bW7IOaI176l6Qmq39Br9dGCkDqcLgmOv/rL7TfVtiRzVLE5sDgOISVgwrVpvANBqXTH93zayx4m6YHJBqkcyrujubqgx0iontlyibUTgziZxH93oNacCBKO+mh2VMQwVME35GPgef8ZghTI1hccT1gDMvLKkqxbnN8xZysFuUIUb6NPXTcgcPePG3z45RD3WWM+O4lg3R9hyza9s6Nont3iaVj5+6M/TsvkzDgLOrfdFb0kLaI3fMrVN7rCAA1vT1vY0FbGVEkkW9GnOKmMoFmHBfF9kVAF4y1LicnxujxM1cGdI6ZkbwtoJd5+7KaapzgomF2SBfpwzUbNAwhnRnMV1wH4Z32XZ4cOaxzCUvbPMzLgTaDyShUK5eqz35Pa4MXDPpTPm3emELCOKzjLyTwIeM01JwbbH321ivuyUZVUHTQd+gR3taR3+DDdwUk1q9FZ3WM5OQvMAuMPa8o1dIrMzt+h3uiOaJXYUKnd7vQpNIiZ5FQqp17Qm2XO9ymUNIw75xGPJHjVRHPbawJtA/Q5475uRC2P+h+vjCUwCldbFFbr19PSNFRdkrR1foLpvAuibtqzFoOKmKUmbMyMLgZAT0BAF1vzD6n7pCNd0okFFLPTAIo5qHKWHAf/Cjt6EaYrPsbd3xvJ0ankx4767Nz+WUq5QG4HS1FrtnKDXqq73Re8Fct13V+d2HVN/svvumtn4qz5hBydr2oCh7MCVmiCalWytds4nKkBTmrrzmzNznynLy09tR90A1J1W2R0gcGzVce2tY2fsMSO1feDV9zfseM9Dtl+y7ihXvtHviLWhtIsOTKoru7l7+dPYI+dX18w5bzXAnLXGzFZU8/X8w+vLDDPdAK4EWLp/sGJzdrS/Yai6dTBn+Ixxo2YxNNfCVVo+5MTG/r/ltDfLuR4LjDJNOdgwVJ0TdsAAzJrUaH19svWHr9JSD0sPhXEFrdEhS7MCPmt6ckB/RpCPMm6r26Gp5Ai53X+3UP9A2LPcR2OaOb3OdYnzkhHKDYjAaHe57zrgwoUnDpwUOK56wjGPVrQp0I1W5bJt6mpXewPk7JQ9RtTA6c1sK42ZqL+uz7hDUA39PIErDENFgfMArPftrZ2haFL9TiuwuR+oaCqcA5wfsON73Cg8dSP+lwxDzdkWC/tQc8HIBR81qWcukNIoaAs1U0AXhLRaXdJqdeo9wcqJ0V+w3p38yrW9Xhy5K7wXZXn5g90kXa5QIeWzHpeA/nYT+7nx8cGyvPzjgPv1Ss+dyR9n3r4oL+92QZ7JX1zWqlMyW23qxSkxz0wAe5ao/+c8WsR4l9gRkw85HrRLFfKHv1ywbmvcZ9MUfVDXe/ebX3Es+dnz03dmUwO0IjtrDXAV8L0taGViu/BCBZMLbga6AOaCkeo97EyyKJQh4HLsVgX8Z0PX8D3dRtd/80jxtM1E2PKHq2OzeaopToTW/YElhqHqvh7R+xKvlvYqFi5BItsJ+ifkLy77DDhz7o3db/TOTb9BQ/6iULd/PaL3h5EeoVuOebRix98kP+Wcsrz8RZ128WWQr2o+QIoWmQMUxcr0uOOd7tLNHXzPI9Fa4M+Nz5umnAhsOSTn66EAfdOWpbemPtOUAaYpRnOvOaHG3jYMtRD7m8Ztf/uIF9QzwANASdPYIoKYggSw94g3AOO7ja7/xn7NWuHRG5o9JWKa4jJNyTVNSXeuu5im/No0Jde5PtA0ZYppyiDn+iTTlErTlBOcKk4GvsKJwKTV6uc6gt76dlr6Wxz19PqnD/38+0HAEJViVSZ9lX5V8vTsZWV5+deV5eWfVJaXf6cz8eTLW7v1+erKXmeW5eWfZ/ki4wEU6gKgpPGeWLHHjNQ9PEHv/ADk+2orX7h4Zcw+uR/XZA+vt1yZx6ZsfvnvF69qOoIsAV6cX3Hsd8BlM1ae09rI/X8BjjBNyTWMnW5xNEEaQHkAzTGttsa9Nk35FEjHYIFhphdZoorKc4PRFf1Dmw3ANCXz8G53H1ZR392uzJQ+dp3cZRjqn9ijbBlwObb/vAcwCfglUO60cziQ5fRnDfAasMm5/hK4wPk74FrtnQRcrFBt9pzkLy4zvxjbdZBWrY/1zcq8GHimcSItiCrLy69NIWfrSRct4CKemXT3GFHPrM6pAfi6Ln1WS/e2loLJBR5w/QFY9Hld1jVNXzMMVQHcfNX704YDhC1Pa09N3w0EWhA0C0YumFMwueBWJwOus3UVNFAWYmKfatkC3I2/6o8zTfkG289sAYsBKydpA99WHh7JLZ4mL55FDfA5UOE0sQq4FvjUuV4C9AM2OO/vW2DrKG8Y6kdgTJPrDTTZl37I3O9nleXlb10caqtJcMyECgU8UpaXPwF4ARjZZNT/NrR//YJo19Bm35zMt4AugrxJDAOtN2WPEXVA6QBE0X6+0NBODkqqffy7htT9gXMWjFzQrM/72B4zB3++7hQO6/KFpyWbGsAxLVpLF+xRGsDq56mvODql6tlx522cA2Ca8lu2ZRyoBwYBK512qq96f9r/YWevyjQMtZkmoRMMQ9UCzze5DjWWbS+OkDs0YuYvLlNlefnPCPJLton2tkFTl/+k3o58gFpijxH1IF+VZ34gg1QtclHB5II5HbWpz3p1/5xNUd91/Tz1lStCye83d49pynUH55x56+frTqF/+o96a+s2TRkMnGwYqqV4yyb2Jng3EF4RSr5g2mVLm26aeqzJ7wr4pmlhjxZcFbK89EtbejDbJtJ7PPmLy+a0JNpYfIB2xB4zUVwV9g0GqLX0wWw3mWoPq8O+3wUsTXq6g1fvJDbIjCWbD3kL4MPl55W3ofrTgHscD8IOaUzHgbMY0tYPqtH3vSSAgZnfxTQj7K4gf3HZnPzFZQ/FYxdeS+wxI3VlxH2oc2Sqw5OHE/+RNxDct4JMfu7iVf/d0X2Gob6/6v1pnwEXBaO+tmSfegL4c2sSWjpCbtf7qA5lzgRYsPGomCXO3BfYY0SNPXkKAl5H3GZ7a+rhDpbURXVdoMU4cHnZ849cvGkQ/dN/aLWonXjTcefTtUY5ENgY6L7nhPfdC9hjzA9nRBuSokV+FHAVpm3s1Z56Bk0+9NglDan9D/bVzvx65LctRkLdP3PxpZpEGXfCb9rUjmmKYZoyzTQlqT39bA3l44cqXcJrU91Vh8Wrjc7IHiNqsIV9Yuqm0z1i1c+oyflDweSCu9piWxdMLhhsIVOAzQsC6b9oTZk5a4wXUTTQZD9qK/EB+2G70eJG//SlGRneLSe0fGeCRvYoUQNMuGD9j0GlP6KQfFB/FNT/jnrpYL+deat5CiYX9D32pfynQX0C5AKpwKGtaa+yoXudhV5nGKqtAdvfNwyVbxhqSRvLtYnqUOasdXW925ptdp9mD7Kpf0ID9n5nTaEkpPT7gPuOfzl/fT9PoKKsIfUNoOt+3vqjK8Ke7uAeEFCNeT3BXuxwkti3QL+0pYdUNnRNNk0pNAxV2toONi6+NCb/bGkxpr1sDPT4Ajg/t3hacvn4oa3ZdLXPs8eN1A4m9qQxgp1O4tfA7zL1cP33DSn5TkrmMT8GU47XRWUBxcBwtu6haP0yb7K7rleyq84HPN3izdt30vZXlwNxs3kzPJs2AORnzy+IVxudjT1ypHaWmB3nvTSNmfdwweSC3wPjAB1UdEvU8/CCkQv+BFAwuWAljsO/tT7hxZsO+8alhXTgrHZ09Ufga+I4OBzXc5bnw+W/oGfKqtOAz+LVTmdijxQ17NS/Wwr8nm1Rk8xWlNkZ3ojlqTYM1ebQW4ah1hPnNGpr63qXAnyx/sS6eLbTmdhTzY8d0tFVuu3plrzmoOykimzTlItNU9ozWmOakuEc54o5CzYe/T0QqQlldotH/Z2RvU7UYAt7wcgFD8Viz7VSdItaepdFlYc9BLQ5bYNpyn7YO+cu7WhfmqN8/NCoLuEtSXr9Jbs6s8Deyh4f9TSeOCKZjX0uMpjsqjt90QPDZ7elDsf7cQ/wtmGoBfHoo2B9ohCciXDR7s5Ns6ezx9rUuwiDrd9W4qqPpJ6MLfLWV2C78u6PdceaNqEQFYs9MfsKe6X5EUNMO06HAlR4VMGjA0xTitpciSlimjLINOWAlu9uM6YTew/iGOG/M7FPi7p8/NA5AzKWLNIkqjSJnnZi79JLgJPaUZUHe7/zbbHtod3HFHf1ty4JbSJherSKfd38YFnVQc8CjwPfA70MQ7U5GaZp3ndkevqKKZoWnWYYse4h9Ehe0ysY9Xpm3z1mjxB0SenArXFKigqX7hF9aso+L2rsFUGAXMNQX+zsxlXFs7b+M/uMP3kOgN/vHwJ8WF3dT0CNmDTp7DGjRr0X00ieK2oGfC+oVp/MiSclpQOLgPdAuYBoSenAYuAjoWoTWKsLCzep3S36fV7Ug7p+XjO/4liO6/nxuaY5bAN2GIMnthe4I+gZCuUBxYo7p7+mKe/CPsne81dZQefvqPTkaOpzCyaOqUtfdc7Xq48bc1PEW9k9c/lFlx826vmoacrRQLCpl6S5D8r2hC0vQHUc3n6rKS3NLlAk/x68v8Be+AJbP48AKDIAFS4pHbgaVD9s07ahpHRg4a4W9j4v6t6py7+fX3Es9eHUAUAdUIgd9277UdvADrwoChDluRTQBjTsxypPGSjQcZG3fCRZKuM1gH7/m9xYdvjK4pn1A1xT3Za7xv3jrFcC7rq+X4d9q/d306sbiCVIcFXxrKLmhJ3irunmknDF9s/vCkpKB+4P/B9kjsI+DD8D5ATsb44IyC1g1Qh1IxTeEHjy2HrYWCUBt5SUDvy0qHDpLvMd7/Oifur6F1flFk/bvGDjUdWGoTYCvXdwq4lziNaJi1EEfPVD1v9GUZf91zRd/SfX5ZqVmr6woiqp4qD0led9K2ipgcwFg6LeyvzU9cZcpYf6RpI2HobS0jz0C2iRtGTsZJw7TeiT6q7uk+nd5IvLH2AHlJZ2O0/h+isk9QGJgPVPoebvhYWV023zQgx+al68Do32tpQ48U4EuBSivUtLc+4oLKz8dAfNxZR9XtQO5dj7sHfID2ed8qWv8qgVWT9e9nVy5bFPNI6oa/0lVQA1loy96K67mokNd/IO63RMjxJaiH9REei+Zkswe14r3keHKC3NFkXameC6DdJOB0tB6E3w3lpUWL628T5HyM2aFEWFS+fYdrcYwExQx4BMUKT/r6R0v7+AbAJmxNMkSYga6JNa7q6PJJ8EYJpyCradeKFhqKbHwbIDOXOXBnLmvmoYaus/xOOpyQ2F0tD1ho1O+euBAYahipu24ff7T8JWuOn3++cA9Bl/8pxVxbO2hhLYkU1tKZcrGHXF7VxkSelAHdQFkPI8uNKA9UCxUP9CYeH6DW2tbzvR/6+0NOdLRdrToI3FXhRoKCkdWBQvYSdEDWR4N29eV98r34mEVIsdWCYLOwoSsHVH3s+i3aSnrzxz48Y8+vT5tFF0hwAFpinSeHDA7/efA2oq9tdxxO/33+T3+yeBLWxaWCF0a8GMTO+mmB++LSnNLQJrHLj7gfQFvVKomaxIu6GocGnMTrAXFlbOLikd+CqoPzqJVr2tPcTRHhKiBr6tPOJN7FG0i2Gor2gmOGJTkTZly5b+IRHVsHy5cTz2P+k3hqG2jzJ1KiDO5MkFPOn3+xc2jtgt4dIi6b1TV+S26U21gG0iaNPtVORKgdwN+vjCwg3xChlsAkFn8qiI48roPr2i2IRy5zG38YnGY1pN+MQ0ZULTJ/x+/+BIJOUUpfQkoMTv9w9uRtAA79h7v7emgtZoRVTRRgKR5IbFmwpaExS+LQxvctY4ClhFhUvjFgPbNjWkEFgGsjyeNnVC1MBxPT9uADix1/QLYatdvMo0xe1cC/ZGp0XbFTXs85DgzPYN5/4xpilfbzu/OG5u//7mV9v2mRCilSNVbvE0AfE2RJNj7adudBFG23L8rSM4wn4M2K+kdGA89skACVEDkJO0YRHAxkD3HOepH4GpQBrYO/EMQ91uGGr7lUITCDkbooRtwlgPLGwsD0QGDPi4S1bWj6LrQQsoaq3pcWKvEg8gvVPLc1q8uU3UH+t8yO4D4jZp+zmh9wCE2pbiELabhKixfdXA5u82F4QADEN9ZBjqesNQmwBMU5KbMUewhSmFuh763tnr/I1T/g3DUL8yDFXtXFuGofavru7zhaZFy1oraIAcX0UmQN+08hjHF9FzIFpfVPjjH3flil9R4crvIVylSIrbYeWEqLdRzna+6iZHtJ7DztPyM/x+/5xo1Hs1SAp2APSm5beman7hhSEp0ainVzicYvn9/lafYPmg/BcRgHkbjjVbW6Z1eBW4YhYLvG24HwXXgJLSgTuM5dIREt4Ph9z071PrwqlDGq9NU57FtpEPBP7DzkPpfqLrwTUi1hN+/31dQQK9en15Xyh0SfrHH989TylXDzilhzMx64U9qWyVCRKM+rwADdHk1uZRaZGS0oECDGT3HTZ4BxgH0YuAp2JdeWKkdvDqweWbG7q47YkZANOAiY4r73XDUE/upPjx0ai7eyTi84I8AExYt+6wtPr6rohQBUzLzFyGM0lseoKlRQZ1/bwLwH4Z38XQpg5dAmRAsH/s6mwTCyASBOtPzo6+mJIQtcN3mw/9b0S53UAOgGGofxuGehTINk3J2nlpjO3cY/dblke/444n5d57HzD8fv+vt2zZ74RtwXZaf4KlZ8qqXtC65KWtwRaR+2X7ynNmPETVCo4H3Q2uVKAk1n1IiHob5c5jbuMTpinJwIPAxhYCrJuNmW6x3XXv+/1+ZZqim6acaJric0yNraEdWjtZLFkxdB3AzFWn58ToNLmBnWcc7I1URgzqbE8fnMWo1n9rtZaETe1wSp8PojNXnYnR571LYeiXztOzsHftjTEMtUOb1u/3z/H7/Vv3cDQRbCHwIXCuacqRhkEXw1C3tKVfYct7JIBCH44dU6+jR7pM7N2GPmcl0exAXR3og7IAPR4+8oSoHXyu+nkAX64ffEJu8bTBjnDuB+oNQ32089KN7r2fTbxmAsOdx0K2pX9rNV2S1l25saEH2N+qHT5Nvm0XXeQjsLSiwtil92tbH/pUg54Krpj7yPfpuB9Ncb7aP8HZRQYUvXjWsEXYadvmOdmvdjlHjfv7mspA955sC3wZk8O3JaU93wTfhaB6FBUua/NOvI633/87UOGiwhWtCrncFhI29TYM51FANY6IRdhBGdsdfck0JdU05RrTlAPbWja3eNqAykCPni4JvwhyLzE9TZ78kG3Xau0KtdZxXBFwl8Wl5nhUupdiggqBeAQlyrY1Gzf9FwIvtbNeH/bizVLTlO8NQ7U605ZgXa3QVER57i0fP7RDORKb4WtQ68AaQfvfWwdQ2UBc9ognRmoHewQUI9lVs04hDcAPTlbcM+lABCanjjzgUaDV+WFunXS5O92z5c7uyasXxUHQFBUutSBQBXJmaWm2N9b1t4zqAfVxWSpPiLoJ5eOHzqmPpA0BSQLLb5oyAljopEBuN4ahlhiGetowVGFry8xZa5xfFcp2HZS9cIcp82LAc6ChyNilOWVKSvt5bOklkVh82QWUjx+6OM2z5VUNddOa2j6vABeZplxomrJ/e+s0TfGapow3TbmotWU21Pe6CaiduerMd9vbbsskTwIiu96udjuh3fSjSCy+7BqG9H3vIY8eVE/Ou3Mxtp/5LaDVgmyGEPA74E3TlBY38Zz6x8ev7xUR47gGV0qviPZRvEL4FhUurQLrc4iOaPnuWBK9xvmlqZsyZiRE3QxPjPpHWSjqvWt1bf+8q96f2hs4CnispXI7wjkGNhSYTguT89ziaX2SN/f/22W1Xk5ucMmltR7vudHq8U/f/J/09ra/M4S6StD7lJbmHBuP+psnfLSzB71NWwZaS8JPvQOmfph+02/Nyfe4tHDtGf3/ffDjo15pa0q6NpNbPK0HSn386xrvAZmWJvLz1I5LvenLqzxpK9bVrD75KWAedh5HAzBHTyxss7uvpLTHiZAyG7ipqHBpm5M5tb29gRqo9RBdA67XiUNossRI3QymKXqqp+ZPV+RPDAciKfvXhVNfMk0pNk3p0KYi05QXTVP+0txrY5698sA0z5avB4Vc/bMsXUBZyv4JJWV99zhwN/B1NJR6UM3qk8/BPpmzys4dqR4ASp68obQdZkrKJ8AyoNWuxg5yHEgXcI0vKlz6UDwOKCRE3QyGoaJAj+N6zjwq3bNlqbny7NOCEe9DwNHtrdM05VBgJM0kLc0tnpY1e/VpM9zB5B6nBVwKlClinSLI3YIY1z50429GTyz84+iJhZdc/9glqb7ssmzgZFD/sWuQdtumdjiwyCegzi4pHTAu/rv2gmMdsyNuE+CE+dECucXTTgRmp7hrxn/7h0vvbG89TkD2xwG/YajPm9SfBnyEUkf8JlpV5a7tkQxSMHpiYYvZwuyRWc10dt01AIXtM0F6PgPJ19mbjCRInM4s2ocTwg0QrSkqXN0l1vU3khipm8E0ZZRpynUA5eOH/i/Ds2lGOOq5Y/SzIwe1t07DUN8bhjrHMNTnpimpALdMuqJrr5QVS0AdNUIq/+uu7dk1o1/pW60RNIAtYHE8F5EX2yNom2Qnk277R/xWkgduD2iPx6l+ICHqrfzl8rNOmTB82J0Thg8bDFzo/ABwUu+SB6JK0yrqe5SYpnjaUm9ZXv7Qsrz8cWV5+YMBTFNeAmbmFk/zTV8+bOrauj49zsj68rneW/qeqblrPvekrbqmhSp/wuiJhW/ovo0bNE/dr5+8YXp7TYfGbw6L+KbgcHJOep6PU/1AQtQAPHH1aedaEf1jUA8CpXOfHno/TZJ+Pnn9i6X90pdNTf8sKSd4//GTGgXaEmV5+acp1FSFukehSv43MWUJkB2I+P4B6s36SNoxWd7K0UesPKYAwAqnDf/lzZPbFFDmyRtKB0cD2TlWKMMFUtq+yWLdavsx8iFxDZcQvgWiS4oKl66OT/02CVED4fqkm+xTGAKQBOqWNV8cEGx6z/1vvPbubV//kz5rtlypoKSVwj4GQOzMWp6k+anh+nDyy4/O9d8Gcg5ww22+xcdbEd+Jvuyyx0ZPLCxvR/cN0Bzfn7TLdBDCafZj/cJ4CbqktNdAcPcQAm3OLNxW9vldehOGD/NgezUsQIHSQC6t/L7nmX+5/KxnrYjr36f0/mxhTiBnHAiafaNXWrdZ3wQCCuUD9Hdrzv7v8yUX/l6h9z47963PDp13xYdVW05/1JO2cmlqz8/vbUduUqcNFQHx0E7TQZG5zH5M32l6kI7hO8NuI+mO+LVhs8+P1DkHrZwAdNHc4buASY3Ph6rSsqyIfgdQUlOTvl6UdAUViiKENZdMOHJ4/+2Xrw+557XU3OJpBaf88YmrL3v8//59ySX3Xn/v4F/P+1/fQxsEobo+53cKvUCIWkd0++wIJPoCiBWq6Vv0y5snt8sNNXpi4ZzkrvO/AHD5Ki5q/2QRgPPj6NI7H1gCrpgnUN2efXqknjB8mLiTu17oTa9ryD5w9cNrvzywmK1RHBUgglKeuu+66fUnLyN5VpYxu/eg+94ZeNKZi7P7Xw/q2tziaaVePdDdo4UK6sIZGsCKmoGsqBmILpH6L7vnf/dt735mmjx/EemLiQAADINJREFU1uWLP2RW70GsS8lWyxdcs6mX0g3NXXPzjX/9xfKOvI9oOHUZWCdk7ffue/bpsbYSOtXOmqeGg5wf69jRpaU5/SDjDAi9WVS4Ku4+5H19pL4xXJ/UK1jte2zE7Uuc8LLSxJZWUU0pLbOuYVV4v8CA/MVlcx4ffMGni7P7sy2RjzomJ6ki66Dshev7pf34JDA8xV1z/Ln7TRkYVa7U8vFDjyx74Jdnf54/4OqopjNm/lscGBKr59rDunrSlm/OOfBfO4sn0iqCW/avBm1Te0d70E6xH+Pj0lMkXWinAWn4IJb17oh9dqS2XXfqr7Y4tVsnDB/2n7FTps6ZMHxYke4NjYkGPSPSpEbP3Gyp9Zkp4y6+s6oc+P/2zjw4yvKO459n3z2TkAMSTjGQCGZBC4UqUqb2dVfqBTpFtOMx006namakVRtaB6pWPNqpFVqPVMSp12gHaL2xFuy+vGgHxKMjQt14AInhJifJZjfZ4+kf77shYMix7AZM3s8/ZDfvPs8u+e5vf/t7fgfhWPZ6kHeaJV8d6hn/uuMn51Q+A1yjqvIlXRfjMMzli4/d/HynyFY88sSzL825ZHppJHbbVW0uBwg6Ws/IqQtel6yNTB0RH460NaW+gN1sM9xZB6mf1PP5Gu7ZwEFJXkZDeUmGrKUWSvwSjr7+TutUsWbdltuf33CDWwk3tCRyqB2RK2oK8x8149fJChkfiLtB+J9d9PhzQBFHj33nAMvN+9B1MV3XxS26LnKm1H9VsffM2c1Gg1QBUjqcw/akXP+YxDO8yu/KrTmZvJTzQbyfrINMr+tRlA3ycuA1o9om8wxZS604Y1/Ewi7Mo+FjrFOwzHtFVsmYvMiwziy5Y1oTmMWvnX94VZX1XX5eq+tiM5CMxV6JkYz0wsGVVc/mb3w97/C+80gIGzaZwFMdDVWWa0tIMcsOoP3IGWGkIivLtdn9XUPTRlwG+WdD5CG/b+/vU9m/Z+QvQeRA6470r909Q9ZSx8Iu88uZeA7wV6xZtwUgWOad3aGIN5o9LsVo3yzj9DNUpqpyT5dRGvcDpaoqQ8C23PadTN/2Z0qq11Gy6/V4U/6kO41ZKGysLNfmVZZrX8s37YlVFS8sSETzxidiOUWkkKkn8cwDEIT/3p/H9X39HBVkHDzHN6zPGEPYUkcnxDscACsq1qzrtCIJV+Kq3QWFIm5XOLfmQLwuN/vf+wuGLUuKvr+Y4q41f37400Vnbya2+538I9VK1VnXSkRn+y0X8IZQwrFVFS80RkNjNwCf5Y7X7TZH24dNuy4P3LrS12aKVgWaHNn7FkbbRnate0yh2Y1rJvCBz9fwYa+X9pOAVjoHFLOTrPJGJidydWXIitpT2HxT675CXHmtkzC6/gPQaM+q2V2Ux5jGFjm+KdQxvim07PoNm9L2h5iy47PN2+ZO+NS+3zXVUbT3PqG03yfjzgTYokBlVtGO73e0jMsBLgRuOFKrdj62slw7AIlRyT500dCYkHtE8ItIvfdMEAr9/EQJaBOuBmUWsDJdr+9YYsvAnvzkOenuUn1lSKaempGPdzFGU0VA+JKW+MmF/g0h4Z47+8u9q/PD7Y96q4Jp+yPourgfuLbgoeKvHHtcFx569HP3Z6+uvTAWGfFduvGpV972cnb+hLfntdV7x4TrzsmFxAIQ00zLngCW3brSd18X691nv9w4ZJHvGm8G2Q7ionRa0YA23gG2kNHd9Gh3KctSZw7VsHYAuO3u9o2rKmatiLU7msNK3tzRTSE8E+v/5H259v2eFkmB7UCOc5fHBnzHmCXDJmBTdxeXP7IgBAvWJG9Xlmtvc+yE3LchmYLabwu4FONNjfmvmsIaPeD8MeCA9hXgqiMDZVsnYqiKWsecMw4SYUuIlj1FZgGA5FBuFu1ZxaOnp3lTVZVrgbVBvJ35xLouxgLPAY+oqlzX0+NvXenbUlmudXZXTSVaom3MtUs57BVwzwMRB2S6Y9MBrdgN9nuAreBabFTXDBxDUtTJQxYMi60XTql9b/+Hk58AbgYhEgIaw3lTMcZipJ12b+h8x253st/1YYwpXn3q3pSiVQYgoJV6IH8TOM6DjlfAuRzDd0+rFRWEX5QMGw+xcr+vZsD92yHpU3eHebgSQEq3TUoSNtucVCMeJ0LT8h5A2BbnvpBfk/Wes1TEbd9Lp8/eEwGttAh4DeQFEH7K79t/S4b2yYbEfohG/L49IzOxR28M2Tj18ZgC9pe01jXNrKvdn25BB7TS2ZIRv5aJAlfzdbbJzQvjSqQsvunjeZN+AMYAUl0Xfe6117+9x/0c4ntBzgSxMFOCNvkF2IaB66oM7tEjlqU+jh2zz2qJD4/WTXuzZmI61w1opUswDmKUo9OcAYlEyM8ErWWI9rCUI1QQ2/y+ne0nXKwbNG14scQ9ETx5IEug7UZwdoCjAOTZ5mUdINRMfWEznkP+DhCb/L6d8zKxR18Ykj51TyiNjpjS6OjxC1uK6EAHUrqIClvBKkUiiLVcmXgmNp4xEk8ZcpgHox92NKAVH4KOg5D1ONAO8ZkQrwVnLVAMbdeB3Q5Om3G7IO/oVgLwSJB1QAOI5NsoOeMlI6KWOJ8FkSNoeTIT6/cVS9RdCJZ5PUA+cCDdayfHUrg/EctyNtjnKvXxvypHHE/PeOnzLQCaNlyAY6okZzJwPiRuxJgKa2a2KRyNwAG445BowsjwewdCRcB2yF4P1Aia63y+Bmkm/XcNA+rpfm0AAa10JGSdB9H/+HyHMtmptVcs96MLH5WPnpWlF7wXz43qyhHH0kx8iQuWeTVglLcqOPX43+m6yAUKk62DjRZdPAzcBtjM5KtK4LdAU19DZaawVTIYKw5oJatBXAtc7/ftXJ2JPfqKJeoubJtXfJfzy6z7JVIKo/G6P53C/uim0cWed/N3An+YUlX1m+N/r+tiB1CrqvKy5H3dWNoBOZXrCwGttBC4GmJ3gzIOI+Yd4RQ/R8v96IJjl8f43mZWf5Nm/1PExVKBUNouaqg6wSVLgPqudxydppVZS9tXAlppHkQWAbeDq8A4ZlfMAgXRdZqvJerTAZEQAWBpsvo7PrLjROJLCffW3KlSSYQ9mwq6nUygqrJbX9QU8ikTiaaNLAB5ryT7XFDmgNsJsRhEV4PzYRAeBsBv7yuW+3EcwTLv7HhB9A5bs/2aeGH0E/sh57e9VcGTrtj434zJf7S1KYvpMtKuO9dG18VUYGJvR+aZRtOG50myrgHXxSDng8iCRAhsTwFrBI1bfb6GTvEMhN/eVyxRn4DtvpJl9n2ue4C7vVXBB1JdJ1jmFe3ntL7m2pEz33RtwBj3fI+3Kvi1ShNdF3/DmAY2VlXlgJQ/JTEy65wXAz8yoi82BagD/gGtmwXR1T5fQ8b7dJ8slvtxAuz7XPcCJRJ5339/OiY84+n9y/u7RrDM6wCedO3ImR89I1xt3+MeRe8f0XcDFQMl6IBWqgAXQuR+cMzC0EQzxDYJwhskecv9vp2xgXgu6cKy1D0QLPNmxYo6DtmaFY+I2lYIKV7uazTkgzuKxnnez92g1DunYIysuxe4APMjeqByPrrDCBW2Xg1iKWSNATHKyKmOHADHnWB/tb8nmqcTlqh7YdulxYsc1Z7HBAKJjHVManvG9UX2KuBgx8Tw4WlvVUe6Xm/22LsynhX7mS2iFMbGR+761vrqB/uzp66L7wOXqqpMuR92V8yCABUiDnCOBuUK4EyjziDxDtgfB970+3a2pWO/U40l6l4IlnmXAA9wguSvhCuesLUrXwIH4yM6RtkaHGcJKWwSSbQ4snza+urF/d1T10UFRhL/WaoqG/vzWPPApggYKzgyX+IuBef1IO3GSblMgHgL5FpBU8Dna8hoB9JTgeVT946OWVAgkfHYhMjvHNWej4FR7d7QxSLOOOfn2XuBUaJVORNpiF8g4s4aT30P6/bEX+KJCVvBVh7QSnUzVi0EjWPBUSLJcQJjIfRDUHLA3WLcjs0wyqeMqh5J14FeApASEiv8vupfpfqf8U3AstR9wHQpVHrxhc3rjjn9S8V3NsNjOkgnxildLTACyP761YkY2L4A9kKkEOQ+8LwF7BO0OCVOF7ie4DQ8kcwUlqjTTF/fAD1hpqk+yNFGlduBALTlCxKHJTn/BPZC6JDfd6DXofWnUwx5ILBEfRpyOud7fBOwRH2aMtSsazqxRG0x6LBqFC0GHZaoLQYdlqgtBh2WqC0GHZaoLQYdlqgtBh2WqC0GHZaoLQYdlqgtBh2WqC0GHZaoLQYdlqgtBh3/Bwn+ayYPMgJsAAAAAElFTkSuQmCC\n", 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" ] @@ -3156,17 +3223,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. cruise ship 72.567%\n", - " 2. hot dog 7.100%\n", - " 3. bathtub 5.121%\n", - " 4. aircraft carrier 2.007%\n", - " 5. lipstick 1.999%\n", - "Answer: cruise ship\n" + " 1. monkey 21.633%\n", + " 2. mermaid 8.163%\n", + " 3. lion 5.269%\n", + " 4. panda 4.989%\n", + " 5. bear 4.380%\n", + "Answer: monkey\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3181,17 +3248,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. tiger 26.381%\n", - " 2. bee 23.236%\n", - " 3. crab 14.726%\n", - " 4. cat 4.107%\n", - " 5. pig 3.669%\n", - "Answer: bee\n" + " 1. chair 21.944%\n", + " 2. fork 18.342%\n", + " 3. rain 7.399%\n", + " 4. rake 6.514%\n", + " 5. see saw 4.658%\n", + "Answer: line\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3206,17 +3273,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. pear 46.334%\n", - " 2. nose 26.864%\n", - " 3. wine bottle 9.419%\n", - " 4. triangle 3.053%\n", - " 5. sock 1.686%\n", - "Answer: nose\n" + " 1. candle 16.676%\n", + " 2. yoga 8.688%\n", + " 3. matches 6.009%\n", + " 4. see saw 4.360%\n", + " 5. syringe 4.331%\n", + "Answer: trumpet\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3231,17 +3298,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. ladder 88.018%\n", - " 2. bench 2.438%\n", - " 3. bed 2.012%\n", - " 4. skyscraper 0.932%\n", - " 5. spreadsheet 0.795%\n", - "Answer: ladder\n" + " 1. shovel 17.438%\n", + " 2. lipstick 12.475%\n", + " 3. yoga 7.909%\n", + " 4. screwdriver 7.077%\n", + " 5. syringe 6.195%\n", + "Answer: anvil\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3256,17 +3323,17 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. tree 66.494%\n", - " 2. broccoli 24.757%\n", - " 3. bush 3.839%\n", - " 4. cloud 0.666%\n", - " 5. toe 0.582%\n", - "Answer: tree\n" + " 1. headphones 15.297%\n", + " 2. motorbike 10.071%\n", + " 3. submarine 8.707%\n", + " 4. tractor 6.187%\n", + " 5. mouse 5.668%\n", + "Answer: pickup truck\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3281,12 +3348,12 @@ "output_type": "stream", "text": [ "Top-5 predictions:\n", - " 1. the great wall of china 12.385%\n", - " 2. diving board 7.888%\n", - " 3. piano 6.098%\n", - " 4. fireplace 4.797%\n", - " 5. hospital 4.436%\n", - "Answer: stitches\n" + " 1. ant 14.048%\n", + " 2. radio 9.584%\n", + " 3. stereo 7.772%\n", + " 4. mailbox 4.642%\n", + " 5. power outlet 4.436%\n", + "Answer: calendar\n" ] } ], @@ -3308,13 +3375,16 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:From /home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: 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", "INFO:tensorflow:Assets written to: my_sketchrnn/assets\n" ] } @@ -3334,7 +3404,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -3348,7 +3418,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -3360,7 +3430,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -3376,7 +3446,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -3576,7 +3646,7 @@ " [70, 65, 62, 46]]" ] }, - "execution_count": 127, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -3594,7 +3664,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -3621,7 +3691,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -3674,7 +3744,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -3756,7 +3826,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -3800,7 +3870,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -3823,38 +3893,38 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_4\"\n", + "Model: \"sequential_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "embedding_1 (Embedding) (None, None, 5) 235 \n", + "embedding (Embedding) (None, None, 5) 235 \n", "_________________________________________________________________\n", - "conv1d_22 (Conv1D) (None, None, 32) 352 \n", + "conv1d_12 (Conv1D) (None, None, 32) 352 \n", "_________________________________________________________________\n", - "batch_normalization_13 (Batc (None, None, 32) 128 \n", + "batch_normalization_3 (Batch (None, None, 32) 128 \n", "_________________________________________________________________\n", - "conv1d_23 (Conv1D) (None, None, 48) 3120 \n", + "conv1d_13 (Conv1D) (None, None, 48) 3120 \n", "_________________________________________________________________\n", - "batch_normalization_14 (Batc (None, None, 48) 192 \n", + "batch_normalization_4 (Batch (None, None, 48) 192 \n", "_________________________________________________________________\n", - "conv1d_24 (Conv1D) (None, None, 64) 6208 \n", + "conv1d_14 (Conv1D) (None, None, 64) 6208 \n", "_________________________________________________________________\n", - "batch_normalization_15 (Batc (None, None, 64) 256 \n", + "batch_normalization_5 (Batch (None, None, 64) 256 \n", "_________________________________________________________________\n", - "conv1d_25 (Conv1D) (None, None, 96) 12384 \n", + "conv1d_15 (Conv1D) (None, None, 96) 12384 \n", "_________________________________________________________________\n", - "batch_normalization_16 (Batc (None, None, 96) 384 \n", + "batch_normalization_6 (Batch (None, None, 96) 384 \n", "_________________________________________________________________\n", - "lstm_7 (LSTM) (None, None, 256) 361472 \n", + "lstm_2 (LSTM) (None, None, 256) 361472 \n", "_________________________________________________________________\n", - "dense_4 (Dense) (None, None, 47) 12079 \n", + "dense_1 (Dense) (None, None, 47) 12079 \n", "=================================================================\n", "Total params: 396,810\n", "Trainable params: 396,330\n", @@ -3893,7 +3963,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3901,54 +3971,54 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - "98/98 [==============================] - 5s 50ms/step - loss: 1.8384 - accuracy: 0.5482 - val_loss: 3.6627 - val_accuracy: 0.0244\n", + "98/98 [==============================] - 2s 25ms/step - loss: 1.8693 - accuracy: 0.5302 - val_loss: 3.7022 - val_accuracy: 0.0947\n", "Epoch 2/20\n", - "98/98 [==============================] - 2s 22ms/step - loss: 0.8671 - accuracy: 0.7679 - val_loss: 4.6268 - val_accuracy: 0.0382\n", + "98/98 [==============================] - 2s 24ms/step - loss: 0.9026 - accuracy: 0.7640 - val_loss: 3.4733 - val_accuracy: 0.0956\n", "Epoch 3/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.7277 - accuracy: 0.7951 - val_loss: 4.9519 - val_accuracy: 0.0355\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.7513 - accuracy: 0.7918 - val_loss: 3.2685 - val_accuracy: 0.1969\n", "Epoch 4/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.6526 - accuracy: 0.8111 - val_loss: 3.4128 - val_accuracy: 0.1545\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.6747 - accuracy: 0.8075 - val_loss: 2.4068 - val_accuracy: 0.3199\n", "Epoch 5/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.5957 - accuracy: 0.8243 - val_loss: 2.6260 - val_accuracy: 0.3164\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.6180 - accuracy: 0.8198 - val_loss: 1.7314 - val_accuracy: 0.4766\n", "Epoch 6/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.5528 - accuracy: 0.8348 - val_loss: 1.2522 - val_accuracy: 0.6334\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.5774 - accuracy: 0.8285 - val_loss: 1.0197 - val_accuracy: 0.6877\n", "Epoch 7/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.5177 - accuracy: 0.8430 - val_loss: 0.7217 - val_accuracy: 0.7916\n", + "98/98 [==============================] - 2s 24ms/step - loss: 0.5395 - accuracy: 0.8376 - val_loss: 0.7354 - val_accuracy: 0.7879\n", "Epoch 8/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.4840 - accuracy: 0.8519 - val_loss: 0.6382 - val_accuracy: 0.8129\n", + "98/98 [==============================] - 2s 20ms/step - loss: 0.5076 - accuracy: 0.8454 - val_loss: 0.7631 - val_accuracy: 0.7718\n", "Epoch 9/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.4588 - accuracy: 0.8592 - val_loss: 0.6317 - val_accuracy: 0.8145\n", + "98/98 [==============================] - 2s 18ms/step - loss: 0.4784 - accuracy: 0.8530 - val_loss: 0.6311 - val_accuracy: 0.8144\n", "Epoch 10/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.4333 - accuracy: 0.8668 - val_loss: 0.6162 - val_accuracy: 0.8158\n", + "98/98 [==============================] - 2s 18ms/step - loss: 0.4539 - accuracy: 0.8601 - val_loss: 0.6196 - val_accuracy: 0.8163\n", "Epoch 11/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.4090 - accuracy: 0.8732 - val_loss: 0.6221 - val_accuracy: 0.8168\n", + "98/98 [==============================] - 2s 18ms/step - loss: 0.4287 - accuracy: 0.8672 - val_loss: 0.6179 - val_accuracy: 0.8187\n", "Epoch 12/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.3870 - accuracy: 0.8798 - val_loss: 0.6102 - val_accuracy: 0.8223\n", + "98/98 [==============================] - 2s 23ms/step - loss: 0.4056 - accuracy: 0.8738 - val_loss: 0.6109 - val_accuracy: 0.8214\n", "Epoch 13/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.3665 - accuracy: 0.8857 - val_loss: 0.6207 - val_accuracy: 0.8188\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.3846 - accuracy: 0.8802 - val_loss: 0.6212 - val_accuracy: 0.8184\n", "Epoch 14/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.3471 - accuracy: 0.8916 - val_loss: 0.6093 - val_accuracy: 0.8255\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.3654 - accuracy: 0.8855 - val_loss: 0.6000 - val_accuracy: 0.8247\n", "Epoch 15/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.3289 - accuracy: 0.8975 - val_loss: 0.6279 - val_accuracy: 0.8172\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.3458 - accuracy: 0.8917 - val_loss: 0.6993 - val_accuracy: 0.8014\n", "Epoch 16/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.3137 - accuracy: 0.9020 - val_loss: 0.6321 - val_accuracy: 0.8162\n", + "98/98 [==============================] - 2s 21ms/step - loss: 0.3311 - accuracy: 0.8961 - val_loss: 0.6548 - val_accuracy: 0.8102\n", "Epoch 17/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.2966 - accuracy: 0.9074 - val_loss: 0.6565 - val_accuracy: 0.8145\n", + "98/98 [==============================] - 2s 22ms/step - loss: 0.3099 - accuracy: 0.9031 - val_loss: 0.6293 - val_accuracy: 0.8196\n", "Epoch 18/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.2818 - accuracy: 0.9118 - val_loss: 0.6323 - val_accuracy: 0.8200\n", + "98/98 [==============================] - 2s 21ms/step - loss: 0.2953 - accuracy: 0.9079 - val_loss: 0.6438 - val_accuracy: 0.8211\n", "Epoch 19/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.2674 - accuracy: 0.9168 - val_loss: 0.6637 - val_accuracy: 0.8115\n", + "98/98 [==============================] - 2s 21ms/step - loss: 0.2791 - accuracy: 0.9131 - val_loss: 0.6675 - val_accuracy: 0.8170\n", "Epoch 20/20\n", - "98/98 [==============================] - 2s 21ms/step - loss: 0.2534 - accuracy: 0.9209 - val_loss: 0.6497 - val_accuracy: 0.8188\n" + "98/98 [==============================] - 2s 21ms/step - loss: 0.2659 - accuracy: 0.9176 - val_loss: 0.6525 - val_accuracy: 0.8187\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 134, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -3976,23 +4046,23 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 34/Unknown - 0s 7ms/step - loss: 0.6577 - accuracy: 0.8151" + "34/34 [==============================] - 0s 7ms/step - loss: 0.6699 - accuracy: 0.8135\n" ] }, { "data": { "text/plain": [ - "[0.6576907336711884, 0.81506974]" + "[0.6698637008666992, 0.8135458827018738]" ] }, - "execution_count": 135, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -4018,7 +4088,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -4042,7 +4112,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -4077,323 +4147,326 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:5 out of the last 5 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:6 out of the last 6 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:7 out of the last 7 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:8 out of the last 8 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:9 out of the last 9 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:10 out of the last 10 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:From :6: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n", + "Instructions for updating:\n", + "Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).\n", + "WARNING:tensorflow:5 out of the last 5 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:6 out of the last 6 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 7 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 8 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 9 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 10 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:11 out of the last 11 calls to .distributed_function at 0x7f230438bb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:11 out of the last 11 calls to .predict_function at 0x7f1b13f7ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { @@ -4401,7 +4474,7 @@ "text/html": [ "\n", " \n", " " @@ -4430,7 +4503,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -4458,7 +4531,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -4466,7 +4539,7 @@ "text/html": [ "\n", " \n", " " @@ -4486,7 +4559,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -4494,7 +4567,7 @@ "text/html": [ "\n", " \n", " " @@ -4514,7 +4587,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -4522,7 +4595,7 @@ "text/html": [ "\n", " \n", " " @@ -4549,7 +4622,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 101, "metadata": {}, "outputs": [ {