InceptionV3.ipynb

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      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n",
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      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
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      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
      "Loading required package: lattice\n",
      "\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    lift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    progress\n",
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     "data": {
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       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'brain-tumor-classification-mri'</li><li>'keras-pretrained-models'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'brain-tumor-classification-mri'\n",
       "\\item 'keras-pretrained-models'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'brain-tumor-classification-mri'\n",
       "2. 'keras-pretrained-models'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"brain-tumor-classification-mri\" \"keras-pretrained-models\"       "
      ]
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     "output_type": "display_data"
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   "source": [
    "# This R environment comes with many helpful analytics packages installed\n",
    "# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n",
    "# For example, here's a helpful package to load\n",
    "library(keras)\n",
    "library(magrittr)\n",
    "library(reticulate)\n",
    "library(tidyverse) # metapackage of all tidyverse packages\n",
    "library(e1071)\n",
    "library(caTools)\n",
    "library(caret)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "list.files(path = \"../input\")\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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    "tags": []
   },
   "source": [
    "# Data Preparation and Organisation"
   ]
  },
  {
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   "source": [
    "set.seed(1212)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2a81324",
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    },
    "tags": []
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   "outputs": [],
   "source": [
    "train_data_glioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/glioma_tumor\")\n",
    "train_data_meningioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/meningioma_tumor\")\n",
    "train_data_pituitary <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/pituitary_tumor\")\n",
    "train_data_notmr <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/no_tumor\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1d4f17",
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     "start_time": "2023-04-04T17:16:38.215297",
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    "tags": []
   },
   "source": [
    "**Volume of data in each class**"
   ]
  },
  {
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   "id": "44e56e75",
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    "execution": {
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "[1] \"Glioma ::  826\"\n",
      "[1] \"Meningioma ::  822\"\n",
      "[1] \"No_tumour ::  395\"\n",
      "[1] \"Pituitary ::  827\"\n"
     ]
    }
   ],
   "source": [
    "print(paste(\"Glioma :: \",length(list.files(train_data_glioma))))\n",
    "print(paste(\"Meningioma :: \",length(list.files(train_data_meningioma))))\n",
    "print(paste(\"No_tumour :: \",length(list.files(train_data_notmr))))\n",
    "print(paste(\"Pituitary :: \",length(list.files(train_data_pituitary))))"
   ]
  },
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   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "**Creating various working directories**"
   ]
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   "execution_count": 5,
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   "source": [
    "train_dir<-'./train';dir.create(train_dir)\n",
    "test_dir<-'./test';dir.create(test_dir)"
   ]
  },
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   "outputs": [],
   "source": [
    "train_dir_glioma<-file.path(train_dir,\"glioma\");dir.create(train_dir_glioma)\n",
    "train_dir_meningioma<-file.path(train_dir,\"meningioma\");dir.create(train_dir_meningioma)\n",
    "train_dir_notmr<-file.path(train_dir,\"notmr\");dir.create(train_dir_notmr)\n",
    "train_dir_pituitary<-file.path(train_dir,\"pituitary\");dir.create(train_dir_pituitary)\n",
    "\n",
    "test_dir_glioma<-file.path(test_dir,\"glioma\");dir.create(test_dir_glioma)\n",
    "test_dir_meningioma<-file.path(test_dir,\"meningioma\");dir.create(test_dir_meningioma)\n",
    "test_dir_notmr<-file.path(test_dir,\"notmr\");dir.create(test_dir_notmr)\n",
    "test_dir_pituitary<-file.path(test_dir,\"pituitary\");dir.create(test_dir_pituitary)"
   ]
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item ⋯\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. ⋯\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "401. TRUE\n",
       "\n",
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      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "192. TRUE\n",
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       "194. TRUE\n",
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       "196. TRUE\n",
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       "198. TRUE\n",
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       "200. TRUE\n",
       "201. TRUE\n",
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       "203. TRUE\n",
       "204. TRUE\n",
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       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
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       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
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       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
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       "260. TRUE\n",
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       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
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       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
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       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
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       "43. TRUE\n",
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       "51. TRUE\n",
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       "55. TRUE\n",
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       "64. TRUE\n",
       "65. TRUE\n",
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       "71. TRUE\n",
       "72. TRUE\n",
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       "75. TRUE\n",
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       "80. TRUE\n",
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       "88. TRUE\n",
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       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
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       "119. TRUE\n",
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       "156. TRUE\n",
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       "158. TRUE\n",
       "159. TRUE\n",
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       "181. TRUE\n",
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       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
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       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "203. TRUE\n",
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       "206. TRUE\n",
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       "208. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE"
      ]
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       "\\begin{enumerate*}\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
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       "60. TRUE\n",
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       "78. TRUE\n",
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       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
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       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
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       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
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       "71. TRUE\n",
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       "91. TRUE\n",
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       "93. TRUE\n",
       "94. TRUE\n",
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       "\n",
       "\n"
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       " [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[91] TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
      ],
      "text/latex": [
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gliomavector <- c(list.files(train_data_glioma))\n",
    "meningiomavector <- c(list.files(train_data_meningioma))\n",
    "notmrvector <- c(list.files(train_data_notmr))\n",
    "pituitaryvector <- c(list.files(train_data_pituitary))\n",
    "\n",
    "# all other dir 600->train and rest test except for notmr -> 300 train and rest test\n",
    "\n",
    "file.copy(file.path(train_data_glioma,gliomavector[1:600]),train_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[1:600]),train_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[1:300]),train_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[1:600]),train_dir_pituitary)\n",
    "\n",
    "#filling the test_dir \n",
    "file.copy(file.path(train_data_glioma,gliomavector[601:length(gliomavector)]),test_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[601:length(meningiomavector)]),test_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[301:length(notmrvector)]),test_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[601:length(pituitaryvector)]),test_dir_pituitary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d56e205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.073513Z",
     "iopub.status.busy": "2023-04-04T17:16:57.072281Z",
     "iopub.status.idle": "2023-04-04T17:16:57.130041Z",
     "shell.execute_reply": "2023-04-04T17:16:57.128545Z"
    },
    "papermill": {
     "duration": 0.075979,
     "end_time": "2023-04-04T17:16:57.132230",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.056251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Train Dataset :: \"\n",
      "[1] \"Glioma train ::  600\"\n",
      "[1] \"Meningioma train ::  600\"\n",
      "[1] \"Notmr train ::  300\"\n",
      "[1] \"Pituitary train ::  600\"\n",
      "[1] \"----------\"\n",
      "[1] \"Test Dataset\"\n",
      "[1] \"Glioma test ::  226\"\n",
      "[1] \"Meningioma test ::  222\"\n",
      "[1] \"Notmr test ::  95\"\n",
      "[1] \"Pituitary test ::  227\"\n"
     ]
    }
   ],
   "source": [
    "#printing the len of the train and test data\n",
    "print(\"Train Dataset :: \")\n",
    "print(paste(\"Glioma train :: \",length(list.files(train_dir_glioma))))\n",
    "print(paste(\"Meningioma train :: \",length(list.files(train_dir_meningioma))))\n",
    "print(paste(\"Notmr train :: \",length(list.files(train_dir_notmr))))\n",
    "print(paste(\"Pituitary train :: \",length(list.files(train_dir_pituitary))))\n",
    "\n",
    "print(\"----------\")\n",
    "print(\"Test Dataset\")\n",
    "print(paste(\"Glioma test :: \",length(list.files(test_dir_glioma))))\n",
    "print(paste(\"Meningioma test :: \",length(list.files(test_dir_meningioma))))\n",
    "print(paste(\"Notmr test :: \",length(list.files(test_dir_notmr))))\n",
    "print(paste(\"Pituitary test :: \",length(list.files(test_dir_pituitary))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555c0c2d",
   "metadata": {
    "papermill": {
     "duration": 0.015682,
     "end_time": "2023-04-04T17:16:57.163741",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.148059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test and Train Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66b38f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.198076Z",
     "iopub.status.busy": "2023-04-04T17:16:57.196831Z",
     "iopub.status.idle": "2023-04-04T17:17:05.871636Z",
     "shell.execute_reply": "2023-04-04T17:17:05.869310Z"
    },
    "papermill": {
     "duration": 8.695283,
     "end_time": "2023-04-04T17:17:05.874935",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.179652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_datagen<-image_data_generator(rescale=1/255,\n",
    "                                   zoom_range=0.2,\n",
    "                                   height_shift_range=0.2,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   fill_mode='nearest')\n",
    "\n",
    "test_datagen<-image_data_generator(rescale = 1/255)\n",
    "\n",
    "train_generator<-flow_images_from_directory(directory=train_dir,\n",
    "                                           generator=train_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           seed = 123)\n",
    "\n",
    "test_generator<-flow_images_from_directory(directory=test_dir,\n",
    "                                           generator=test_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           shuffle = FALSE,\n",
    "                                           seed = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b4fd22c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:17:05.909498Z",
     "iopub.status.busy": "2023-04-04T17:17:05.907855Z",
     "iopub.status.idle": "2023-04-04T17:17:05.931217Z",
     "shell.execute_reply": "2023-04-04T17:17:05.929243Z"
    },
    "papermill": {
     "duration": 0.043077,
     "end_time": "2023-04-04T17:17:05.933651",
     "exception": false,
     "start_time": "2023-04-04T17:17:05.890574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[223] 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[445] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[556] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[593] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[630] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[667] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[704] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_generator$classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4a8f6e",
   "metadata": {
    "papermill": {
     "duration": 0.020503,
     "end_time": "2023-04-04T17:28:03.290594",
     "exception": false,
     "start_time": "2023-04-04T17:28:03.270091",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# InceptionV3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "560717ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:03.333733Z",
     "iopub.status.busy": "2023-04-04T17:28:03.332415Z",
     "iopub.status.idle": "2023-04-04T17:28:03.425549Z",
     "shell.execute_reply": "2023-04-04T17:28:03.423950Z"
    },
    "papermill": {
     "duration": 0.117644,
     "end_time": "2023-04-04T17:28:03.428039",
     "exception": false,
     "start_time": "2023-04-04T17:28:03.310395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_datagen = image_data_generator(\n",
    "    rescale=1/255,\n",
    "    zoom_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    width_shift_range=0.2,\n",
    "    fill_mode='nearest',\n",
    ")\n",
    "\n",
    "test_datagen = image_data_generator(\n",
    "    rescale=1/255\n",
    ")\n",
    "\n",
    "train_generator = flow_images_from_directory(\n",
    "    directory = train_dir,\n",
    "    generator = train_datagen,\n",
    "    class_mode = \"categorical\",\n",
    "    target_size = c(299, 299),\n",
    "    batch_size = 20\n",
    ")\n",
    "\n",
    "test_generator = flow_images_from_directory(\n",
    "    directory = test_dir,\n",
    "    generator = test_datagen,\n",
    "    class_mode = \"categorical\",\n",
    "    target_size = c(299, 299),\n",
    "    batch_size = 10,\n",
    "    shuffle=FALSE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "410ea2e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:03.471637Z",
     "iopub.status.busy": "2023-04-04T17:28:03.470405Z",
     "iopub.status.idle": "2023-04-04T17:28:06.469513Z",
     "shell.execute_reply": "2023-04-04T17:28:06.467693Z"
    },
    "papermill": {
     "duration": 3.024138,
     "end_time": "2023-04-04T17:28:06.472681",
     "exception": false,
     "start_time": "2023-04-04T17:28:03.448543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "conv_baseV3 = application_inception_v3(\n",
    "    weights='/kaggle/input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',\n",
    "    include_top=FALSE,\n",
    "    input_shape=c(299,299,3)\n",
    ")\n",
    "\n",
    "freeze_weights(conv_baseV3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4580081",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:06.518017Z",
     "iopub.status.busy": "2023-04-04T17:28:06.516574Z",
     "iopub.status.idle": "2023-04-04T17:28:07.226421Z",
     "shell.execute_reply": "2023-04-04T17:28:07.223834Z"
    },
    "papermill": {
     "duration": 0.742324,
     "end_time": "2023-04-04T17:28:07.236641",
     "exception": false,
     "start_time": "2023-04-04T17:28:06.494317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "inception_v3 (Functional)           (None, 8, 8, 2048)              21802784    \n",
      "________________________________________________________________________________\n",
      "global_average_pooling2d_4 (GlobalA (None, 2048)                    0           \n",
      "________________________________________________________________________________\n",
      "dense_11 (Dense)                    (None, 256)                     524544      \n",
      "________________________________________________________________________________\n",
      "batch_normalization_99 (BatchNormal (None, 256)                     1024        \n",
      "________________________________________________________________________________\n",
      "dense_10 (Dense)                    (None, 4)                       1028        \n",
      "================================================================================\n",
      "Total params: 22,329,380\n",
      "Trainable params: 526,084\n",
      "Non-trainable params: 21,803,296\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model<-keras_model_sequential() %>%\n",
    "    conv_baseV3 %>%\n",
    "    layer_global_average_pooling_2d(trainable = T)%>%\n",
    "    layer_dense(units=256, activation=\"relu\",kernel_regularizer=regularizer_l1(1e-8)) %>%\n",
    "    layer_batch_normalization() %>%\n",
    "    layer_dense(units=4, activation=\"softmax\")\n",
    "\n",
    "\n",
    "model %>% compile(\n",
    "    metrics=\"accuracy\",\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    optimizer = optimizer_adam(learning_rate = 0.01, decay = 1e-6),\n",
    ")\n",
    "\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "631aa7e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:28:07.283325Z",
     "iopub.status.busy": "2023-04-04T17:28:07.281942Z",
     "iopub.status.idle": "2023-04-04T17:31:37.109106Z",
     "shell.execute_reply": "2023-04-04T17:31:37.107381Z"
    },
    "papermill": {
     "duration": 209.85265,
     "end_time": "2023-04-04T17:31:37.111967",
     "exception": false,
     "start_time": "2023-04-04T17:28:07.259317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "history<-model %>% fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=20,\n",
    "    epoch=20,\n",
    "    validation_data=test_generator,\n",
    "    validation_step=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b154de06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:37.170613Z",
     "iopub.status.busy": "2023-04-04T17:31:37.169176Z",
     "iopub.status.idle": "2023-04-04T17:31:37.552495Z",
     "shell.execute_reply": "2023-04-04T17:31:37.550300Z"
    },
    "papermill": {
     "duration": 0.408397,
     "end_time": "2023-04-04T17:31:37.555310",
     "exception": false,
     "start_time": "2023-04-04T17:31:37.146913",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Final epoch (plot to see history):\n",
       "        loss: 0.3428\n",
       "    accuracy: 0.885\n",
       "    val_loss: 0.2518\n",
       "val_accuracy: 0.9 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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MlQMPjKK68oXR0AtBmCHSQwA0PvS09Z3TW3u07beLkgSf+qqr2uuMwtNDGPBQA0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NmzZeLQ0hhBDyKxs2bHjzzTfbtm07YMCA\n1atXe7GSwYMHP6NT5L7d4yxp/2znT48Uvp/8KokjdiVQRETE6NGjv/zyS+b11+TGTYBhmD3/\nyFevvDl4cLVq1bxdHUIIIeQPnE5nv379//hjNaPR0LCw/SdOLlu2rFXr1osWLvTKpO4zZ84s\n/n5cEuVYUuB94YhdXr377rvz58+P0uvI8qVkyaJKHDdr1qxPP/3U23UhhBBCvmdmYlKtE2ce\n+i/q30N/vPwK/PGX/OcG+vN8ec06WLt+8xsDow8cfnTjWifOzExMynVHsxuUDaw2Neup+fJE\nQsjiuzZb4p4hLzQpa9RygrpyjcZfLD/z6GvVLPPuZZP7seXG5n4dmkQEqMuUjRk0aXXWRJ2P\n7eft8rqhF1JPz3pWE9zjoX5c1rMf9G5bPkCr0BhqN+ux9GiKu72cwH1xakfHuBAFzwaWjxzw\nf78X7BuLI3b50KFDhw4dOthsNlmWNRqNt8tBCCGEfFWaJF1xPDLXt1IFYapszwlodaDVmQBM\nj24MkJaH+TI7Tm/zZtNx523Do1UcABz4eK4u4p1ewarh0R1WBrw8b81X5VWuHYvGjOrVoFen\n1MrKx6+aI4s329bsdLpCpx9+XRdKb08b9fqSmxnRAADwwXOP6WfqhTtVapb/sdnKQ98+91BP\nw+o9s9hW7/t5f8QYHaumj3qtYXy5xIuN9QoAmNGsR98v5nzVvOqZNZ93G9WtQh/TuIr5XjsU\ng12+qVSq3DdCCCGEUAkQ0nB6GLfo3e23/mhfAahj1B/XnpszHAAqDfpwzutvdwhWAUBs5Ecj\nvu542CJWVj7+V/z1DYP2WbT7/1lYT8sDwNPP6vQhXdxfenw/gWolIQyvUqsfWHnZfPmTn86k\n/nJjde/yGgB4qlHjnQHB70w+cfizugBg7Lp40oCWABA38tf4cUv3Xs0ADHYIIYQQQlkYLvCb\nJuUGf7AW2g9LOT32pKj7o2tFABgxasi2NSsnnzh75cqlI7tzmZL26pJzmrID3KkOAJSBndqV\nUbpnO8tXP4n/bOfVsX3KZ570I6xudJThzZUn4bO6ABA5oEbWlkEcAwValxGvsUMIIYRQcTOy\nbCVB8dB/GrMJEm6DlG21YlmCOwnK1JRHN64kKIzs48+cPqTZ1G4ppz666pB2j1kW1vT7SgIr\nOa53jA5/+dPFJjawccfXvl2+MOceCEsAHrihIYRnACC//VBKH+qHZQmlmSeUBZ0HhttwxA4h\nhBBCxW1ISNCQkIcngj19mjZr0cJBGLnbi1C5Cly/zqxcztltGzZurFsjtsD7Cqw+MU74YdSO\ns4e33OxzuA0ApJ4Zvf6q/bZ9bSjPAIA1MZdAVrFnjGXZnKOWT+I1PAA4Mw6tTLJVyX8/IY2a\nOK2fL7xteTVMAwBUyph2Li18UI2cX5UvOGKHEEIIoRIhLi5u08aNT9WoDr/Oh0/Gwdyfa1Wp\nvGH9+rp16xaqX0Y1vVOF9X063VY2+TS2DAAIgU9RWZyyZPvVG5f3bPylZ4v3AeDUxcQn3YsR\n3npWA5WpVZO+Kzfu3rPp9zeatQ3QcDn3wxLIuHwuIeGB+3YNlT/pX9U4tFGPJet3Ht6z+eOX\nGuy1h3z7v5qFOroH4YgdQgghhEqKWrVqbfr771u3bl25cqVChQrh4eEe6bbhF/1sVf5XZ/wK\n97lbXfh7GyZfeeejl78zc/ENWk5YebJs75qfNKrRPiUl60K67BhF+b+P/TGk/4dvdG0B2vCe\n/1v+wx+vfZRjP01GdrG+OzCmYU/T1V+z9cTO/O+fgDeHj+zVLsnBxjVo99v+H5sahEf3WGCE\n0gJdm+cjJElKTU31dhWeRwgJDAxMSUmRZdnbtXieWq1Wq9X++t4BgMFgcDgcdrvd24V4Hsdx\nRqMRAFJTU6U8TEPgc1QqFc/zZrPZ24UUiYCAAIZhLBaLzWbzdi2e5/5wJiXlPu2ZL9JqtUql\n0ul0mkwmz/bs8WUz09PTPduhm06nK4pufRGeikUIIYQQ8hMY7BBCCCGE/AQGO4QQQgghP4HB\nDiGEEELIT2CwQwghhBDyExjsEEIIIYT8BAY7hBBCCCE/gcEOIYQQQshPYLBDCCGEEPITuKQY\nQgghhIoJLhFR1HDEDiGEEELIT2CwQwghhBDyExjsEEIIIYT8BAY7hBBCCCE/gTdPIFQoycnJ\nP/7447Fjxwgh8fHxgwYNCggI8HZRCCGESikMdggV3NatWwcMHJiRkUHCwoDClq1bf549e97c\nuU2aNPF2aQghhEojPBWLUAGlpaUNfPNNi05PZ82Wf1sqL1xKf/gpQ60ZMGiQ2Wz2dnUIIYRK\nIwx2CBXQ+vXrzWlp8ohREF01sykmVn5nRGpy8saNG71aGkIIoVIKgx1CBXTp0iUAgGo1Hmit\nXhMALl686I2KEEIIlXYY7BAqII1GAwCQ/uBZV7Pp/pcQQgih4oXBDqECatSoEQDAyhUPtK5c\nDgB48wRCCCGvwLtiESqg+vXrd+rUae3yJeTmddq0OQCQbVvovr0vvvhifHy8t6tDCCFUGmGw\nQ6jgZs6cGR0d/d3334t7/gEAhVL5znvvjRgxwtt1IYQQKqUw2CFUcIIgfPjhh8OHDz979iwh\nJCYmRqVSebsohBBCpRcGO4QKS61W16lTx9tVIIQQQnjzBEIIIYSQv8BghxBCCCHkJzDYIYQQ\nQgj5CQx2CCGEEEJ+AoMdQggh5NusVuvJkyfNZnPumyJ/h8EOIYQQ8lXXrl3r07dvUHBwjRo1\ngoKCXujW7dSpU94uCnkTTneCEEII+aTr16+3atPGlJ5OO3WBylXg1s0969a2bdfur3Xratas\n6e3qkHdgsEMIIYR80ldffZVmMtHvZkFMrLtF7vqiOKjfhAkTVq5c6d3akLf4f7Djed7bJXge\nIQQAeJ6XZdnbtXgey7IAQAjxy/cOABiGYVnWL4/O/d4BAMdxDOOHV3qwLMswjF++d3DvB4t/\nfzj97NC2bNtG6z2VleoAAMLC5BYtd69bK8uyIAiF7J9SWsgeUPHz/2Cn1+u9XUJR0Wq13i6h\nSLh/uzAM46/vHSFEqVQqlUpvF1KE/PXDCQCEED/+ZAKAIAiFDwQllp+9d+nmdAgIeLg1IECW\nJPDEwTqdzkL2gIqf/we75ORkb5fgeYSQwMDA1NRUvxyxU6vVarVakqTU1FRv11IkDAaDw+Gw\n2+3eLsTzOI4zGo0AkJaWJkmSt8vxPJVKxfO8v957GBAQwDCM1Wq12WzersXz3B9OP/uNEBER\nfv7C+YdH1c6d0+r04KFff0FBQYXvBBUnPzxXghBCCJUGL/XoQc+fg3lzwOUCAKAU1qyGvf+8\n1KO7X14LgfLC/0fsEEIIIb80ZMiQPXv3bv11HrNurVyhInP7ppyQUKt27f/973/eLg15DSZ6\nhBBCviolJcUvr0jJI4VC8eagQcFBQXJyEhw5JCck6LTawYMG+dmlhChfMNghhBDyMampqe+/\n/35UZGRMTEzlSpX69et3/fp1bxflBf/9999rr76qcTl/fr7VP71fWtC5XSW1atiwYevXr/d2\nachr8FQsQgghX5KSktKmdetr1693jqpSs2bcFZN5+fr1O7Zv/2v9+piYGG9XV6ymT5+u4rgd\nr3YP1agBoF7ZkPZVKtWbv/jLLye1b9/e29Uh78BghxBCPsNsNm/fvv3SpUtly5Zt3Lhx+fLl\nvV2RF0yfPv3a9eu/v9ixXZVK7paRDeo0W7hy7Nixy5Yt82ppxe3A/v2tK4a7U52bVsG/EF3l\n6wOHrVarWq3O4bXIX2GwQwgh37BmzZr3x4xJujeHhVIQRo4aNXLkSPf8c6XHxg0bngsvl5Xq\nAKB6UGCvalXn7txpt9v9e4bIh4hOp5J7+Pe4mucAwOFwYLArnfAaO4QQ8gH//vvvoIEDQxmy\nununK0P77XytR9PyZSdOnDhnzhxvl1bcUlJSIvQPz4AdodO5/HfyyyeJjY3ddeOWmG3OSAqw\n+cr1sLJl3TNKolIIgx1CCPmA7777TqNQ/N2za7sqlcpqNQ3KlV31Yqc6ZUO+/eab0rbuU7ly\n5c4kPxzgTiUnC4IQGBjolZK8pV///tdM5lfXbLhmTgeAu1bbkA1b/r2V0K9//9I2jouyYLBD\nCCEfcPTI4cbhYYEqVVYLS0iX6Cq3ExLu3LnjxcKKX5euXQ8nJM44eCQrz/518cqKMxc6dOig\nUCi8WVmx69Gjx+jRo9dfulp11vzgb36K+G72L8dP9+nT5+233/Z2achr8Bo7hBDyAZQC88gY\njHtUprSN2A0dOnTrli3vbd0199ip2iFBF9NM/95KqBAR8cknn3i7NC/44IMPXnjhhb///vvK\nlSvh4eFNmzatW7eut4tC3oTBDiGEfEDNWrV27dqZaneUUQruFpnSPy9cDgkODg0N9W5txUyl\nUv2xZs28efNWLF++4dKl8uHhI1/uNXz4cI1G4+3SvCMmJqZevXpKpdLpdJpMJm+Xg7wMgx1C\nCPmAIUOGbN68uePyPyY1e65OaMhVs/n/du8/cCthwoQJpXBVUI7jBg4cOHDgwIK8mFLisHu6\nojzjFZRlvbZ3VApgsEMIIR/QqFGjb775ZuzH/2u9eJW7hee44cOHDx061AvVZGUjh4MAEKcI\nkkQliYgiABCXk7jv07TbAQCoTBwO9+uyEhURRZAlAAAKkBWzKGVEx/29iCJku98TJIk4xexV\nEEpBfKAlk9NJJNejzQ4AXb4PtbgQQoUHJ2ohQBXCEzfgOOB590PKcqBUSrHVoM5TxVApKuEw\n2CGEkG/o2bNn69at//7770uXLpUrV65p06ZVqlTJ0ytlmYgOkGXidIJTJJIEogMkiXE4QJLA\n6XQnM4kQmcqsJUPlcFCnk4gOIsvucEbsNqCUiGL2lPxaZ5EAACAASURBVIY8iVJitz3URmwP\nt+TUgd6AwQ4BBjuEECq5JIk4ReJwgNNJXE6w20Ndzj51akG1GOJywt3b5OYVkCQiOojTRSUX\n47CDJIPoIJILnE4iio+NC08iAwAAwV8MPgrP8CIAwH+/CCFUbIjdRmw2sFkZ0UHsdnDYid1O\nHA7isIPdRkQHEUUQReKwM6JIRQfJfiISoZyVvkst0WNhsEMIlXTbt29ftmzZpYsXw8qVa968\n+auvvsp6YnBCFMW5c+fu3r07JTk5Miqqb9++9evXL2BfkkSsVsZmJZYMsFoYm5VYrcSSQWxW\nYrMSu53YrO6zmXnvEqeXBQCqVD7wneC4h+88YFnKP2buOsIyD1ygBkAI4TjO6XRmtcgKAbLN\nIONyuTZs2BCiVj1bPiyrNdlm337tRkxMTExMTKEP5sErCLOTZXjkBPdDQ63u8desvoj9kfs/\nMNghAAAg/j0BkuSnK8wQQgIDA1NSUmRZ9nYtnqdWq9Vqtb++dwBgMBgcDof90Z/Lvo/jOPdC\nRqmpqZInRptkWR45cuSiRYv0SqFqGeONDEtCekZ8rVrLV6woU6ZMYXq+efPmi926Xbx0KcKg\nD1GpzqSkWp3Ot99+e+zYsY/dnthsxJKudIqs1epIvksyMojNymRkEKvFHeAKU0xJRjnefZ0+\nZRiqUADDgkIBLEs5nrIsYTnKc0AYKggAAAqBEgIMQxXC6tWrN23fPrNtc0W2KHYxzdR7zYbh\nw4d36twl8yUAlGUha8FTjqMcXxQH4v5wJiUlPWmDvXv3du7c+ZdObV+Oq5q9vdrPCyrE116+\nfHlRVOUBlBKHXavVChqtE8Dj050EBQV5tkNU1HDEDiFUci1btmzRokX942tMbtFIw/MUYM6R\nE8M37xg3btyMGTMK0/OoUaNuXr/2W+d23WOjASDV7hi1eceG3xZ0rlW9XnQUk5FO0tNJRjqT\nkU4y0onFkv0uS+HJ3ZYEVBAIw8oKBbAc5XngeeA4qlBQwoCgBIahgkAZFnie8vztxLtjP//8\n1eqx7SMr3e8BoPbchTH1G8xZsKDAZexctHTpqbM/tW+ZvVGW6aGExJuUSCElbu4992Ceknt4\nMFjFseJj770tIQihShVVqoDnIdt4JCq1MNghhEquxYsXRxj037RuyjEMABCAAbVr7L5x8/ff\nf//qq6+USmWuPTyEOEViMpmuX41ISlje9fnWlSu628sohXkd2wAAnDsB50549CDyVR+hghIE\ngSoUoBAor5AFAXgF8HxmC8dRXgGCQFmW8grK84TjqFJFGQZ4vgBzpJ3etWvFmfPudHu/CgAd\nz99ITCzMocTGxrpkece1Gy0qRmQ1brpyFQDi4uIK03MRiYmJYRhmy5XrXaIjsxqvm9PPJKe+\n0aWaFwtDKF8w2CGESq4rly7VDwniHrx46KmwsktOnbt169YTJ/uQJCYjnZjTiMnEpptJupmk\nm5l0E0k3u69M0gD82K7l41/raU5KU6w2qlIFV6hIVWqqVFGViiqVVFCCUkUFQVYI7gcgKClf\nJGchc1C+fHkAOJ2c0hXupxmHJF1IMzWu36AwPb/wwgtfTpw4YP2WH9o0a1Wpgkzp0tPnxu3a\nV71a3DPPPFPYuotAaGhohw4dZv/5Z3mddljdeI2CP3j7zrCN2xiG6du3r7erQyivMNghhEou\nrU6XZLM81JhktQGATqcjNhuTbibmNMZsImYTMZvYdDMxpRGrJV+3KRSKSi1rNFSlkbU6qlJd\nS0qe9vPPd+vUT+r8QpIgJCoEE8vDZ5+Q7Vv379tXuXLlYqoqz6pUqVKrZs1vDx5tU7livbIh\nACBK0ntbd6XZ7N26dStMz0aj8dfffhs0cEDXFWsFjpNk2SXL1ePi5v/yq0fufSkK06dPN5tM\n43funLBrn5LjbE6nQa//YebM2NhYb5eGUF5hsEMIlVxNmjadP3fuoTuJdUND3C12l6tJhfDX\natesvGgOKYYrnxiGajSyRkc1WqrTswYDYyxjYzmq0coaLVVrHpo8bO7EiXOPnoTxE6FMwP3W\nl3vRrZt37txZAoMdAHzz7bfdXujaeMGyRhHlg1XKfxMSr5vMPXr06NChQyF7btCgwd59+5cv\nX378+HGFQlGvXr0uXbqU2FQHAAaDYcXKlZs3b967d29ycnJsbGyPHj0CAwM91f/du3dPnTql\nVCqrVaum05XcVTCQT8NghxDyJEopIQWaqUOSGEsGMaURUxprNrnH4aZXLDtt1BBFtlOxSo5r\nXjEcAB6/llSBOGU5yWo7mZSSDtDqxR58mTJUp6danazTU7Um+4wYKpWK5XmX2fykrpKTk4EQ\nMBgfaC1TBgBK7F3eNWrU2LN335QpU/bt3XP2blJs7Tqfvv56x44dPdK5SqXq06ePR7oqNq1a\ntWrVqpVn+0xNTf3kk08WL17snspApVS+M3z48OHD+WI/+Y78HgY7hJBnbNu2berUqSdPnGAY\nJj4+fsz77z/99NOPbkYkFzG5c5uZmNNYs4mkpTLpZpKRDo9M38OCx2bnoryCGgyyTk91eqoz\nyHr9gTPnJn3/w7/nzqeLIiHkxRdfnDBhAoSGFubGQrPZDJTCubMQm+3+gDNnAKAkT3ATFBQ0\nadKkgIAAhmEsFostPytZoVxJktSrZ88jR468UbNa2yoVHZL024kzX375ZWJi4uTJk71dHfI3\nGOwQQh4wderUSZMmldPrXqxSUaJ04+HDr3XvPmXsx91btSBmE2M2EVMam24iJhOxZBRhHSyb\nOdKmN0p6PdXpqd4g6wxUp6eP3EJbu0btxS/2uHHjxt27d6Ojoz1yakyr1QIhzLSv5E8+g7By\nAACXLjI/fCszTAHu4UX+4a+//vrv0KFprZoMrRvvbukRG91v3aZffvll2LBhFStW9G55yM9g\nsEMIFYIsE4vl9plTl/5YNat9y1erx/AMCwAUKAEC5kRYtaQodkt5BTUYZb2B6g2yXk/1Bllv\nlPVGqtHka4SPEBIREREREZH7pnljMBiAUu7yJVffV2mVSCJJ9NJFJcdZZVmtVntqL8i37Nmz\nh2WYN2pVz974Zp2ai06e2bt3LwY75FkY7BDyALPZTAjx46uhidXCuCcNMd+bPcScxqSbiSUD\nJCka4NdObR/Y3kMLYlGVStYbqcEo6/TUYJQNxswROJXKI/17XL169QDgo6fr37JYDt25KzBM\n/XrxlNJvDx5xfwmVQjabTcGyKu6BX7h6QQEAVqvfrlmCvAWDHUIFRyldvnz5lK++unzlCgBE\nVqny3pgx3bp1K+DdA95GbFZ3eiNmE5Oe7p5JhGSkk3Rz0a5GTwjVaKnBKLnTm95A9UZZb6AG\nY/HP61ZI7dq1q16t2sR9B0c3qDOjdTNJlhedPDvr8LEWLVpgsCu1oqKibE7nfwmJ7gll3HZf\nvwUA0dHRT34dQgWBa8X6JFwrtoQYO3bsrFmzqgcHdYysRAHWXLh8Jin5nXfeedJ6o27eXSuW\nOEViSmPS00m6iaSbWbOJmDPDXPZVs4oEw8haHTUYqcEouc+i6gzUYKR6Q37XS/AWlUrF87z5\nyXfFAkBCQsKoUaM2bdrkfkoI6d69+8SJEw0GQ7HUWHD+ffNErmvFFp3bt283bNCgklYzr0Pr\n2qHBAPDXxSv9/toUXD58565dHOeBERatVqtUKp1OJ64VizDY+SQMdiXB2bNnmzRp8mLVyHkd\n27iXRnDKcu81G9ZeuPzPnj2RkZFPemHxBDtitzGmNGLKnLyXMaURs4lJN5Fi+Z0tU3oo4e7J\npOSOvfuoy5WnxjKyTk+1Ok/d4uoteQl2bidOnDh27BjP87Vr1/aVURkMdkXnr7/+evutt8zp\n6eX1OpvLlWK1VQgP/23RIk+trobBDmXBU7EIFdDmzZtlWf74uYZZC17xDDO2UcPV5y5u2bIl\nh2DnYZLEpJuY1FSSlsKY0hhTGklLZcxppKhHBLPOn2p1VKfffODgL6vXOKgcU8Zodoh/Xbpy\nJ8Py+eefK1q3L+JhwBKqRo0aNWrU8HYVqKR4/vnnn9q/f/78+SdPnlQoFA0aNHjttdfwRmlU\nFDDYIVRA7gHF8jpt9kb306IaFaCUMZuYlCQmJZlJSyWpyUxqKmNOe3T6N48hhKo17rnfZJ2e\n6g1Uq5P1Blmro1pd9kUXGjdvI9ZrOGXKlB3HTzAMU6tWrR8/+OC5554rqsIQ8jXBwcHvvfee\nt6tA/g+DHUIFVK5cOQA4nZz8VFjZrMZTd5MBIDw8vPD9E6fTneFI8l0mJTnzcdHcxEBVallv\noDq9rNdTrZ7q9JlPH0xvOWvZsmXbtm11Oh0hxGQySUV6vwVCCKHHwWCHUAG1b99+3Lhxo7fu\nXt71+VCNGgBuZ1je3bZbrVK1a9cuv72RjPTMobjku2xKCpOSRMwevlaGshw1GKjeeNtmO3v7\nzvUMiyq07NNt2wZWiaSsx34UlOSVQB+1a9euXbt2paSkREdHd+vWLTg42NsVIYRQoWCwQ6iA\nwsLCJk6c+N6779ac89sz5coCwD83b9td0vSvvw4JCcnplQ4HuX2Tv5PApCSRlGQmJZlJSyEO\nh6cKoyyXOeWbsYx74jdJZ6AGA9VobTbb0KFD//zzTwBQcpzd5dJOmfbFxIm9evXy1N59hcVi\nGTx4yIYN6wGAKBRUFCd9+eWXkya99NJL3i4NIYQKDoMdKl0kSfLgkFLv3v/P3n1HR1F1AQB/\nb8r2vukhlDQCRJr03pEiolJUIIJSFfjAhqhUERQFQRQEQUUsiKBglCK9CCi9l4SE9LYl28u0\n749ggGwSQjLJFt7veDzJZHbmLTu7e+eVe8e0bt16+fLlF86fhxD2HTho5syZ9y5zgwwDjQbM\nqMeMesxowIwGzKBnbVaCp/ceJxazKg2r0rBqNavS3InkpDJQQSK9OXPm/PHHH6+3b/16+8fV\nItG5/ML/7Ts8Y8aM+Pj4Ry3L2uzZs3fv2Q1GvwhGPMfJpODGDduKT6ZNn56QkNC8eXNvtw5B\nEKSaULoTv4TSnTysnJycDz744NDBgwaDoWHDhqNGj540aZJAIODr+AAAwHGYxQz1OqzYgBl0\nmMGAGfWYxczXygYXw9glUlnDaFaj5TRBrFrDqjQPVYDBarUmNG48JLrBpiF3R4p1dkf82o2D\nhg5dvXp1zRtZklECAGA0Gn15jl1xcXGTJk3oXr3B7HsyDhoM8Plhzw8btnLlyooeWPV0J/4I\npTvxXyjdCVIK9dghge/SpUtPDRnicjgGxTQMrx95Kq9g4cKFu3bt2r59e7VjO+hyYgY9/G9N\nA27UQ15XNugdDhFBSP+ru+CgmC7f/2wWis+cPVvtshZpaWkut7tPo/r3bgySiFuFBl+9erWm\nLfYrqampNE2DNu3u26rRcDGxV69d81KjEARBeIACOyTwzXrrLYJhDr04slmQtmTLZ6fPv3Xg\n6DfffDNp0qSqHAHabZiuCNMX4Xodpi/C9Dpos/LWPhxnlSpWE8RqtKxGy2qCBia9CPS6Q6OG\nle4iJvHnmjSec+R4dnZ2tSvWkyQJAHDSZaNPJ80QfrXioeZK/imA211mO3S7CanUCw1CfEle\nXt7p06eNRmPjxo3btm2L+XxW7UKadtNMhLebgfgIFNghAa6goODU6dNvdWhTGtUBAKa1abn2\n/KU///yz3MAOOp2YrhArKsCLCkuCOeDgrVA3J1dgwSGMSkMpVaxGy6q1rFJVph5DvsncWFi2\nK7HmJcNjYmKUCsWW6ynjWybi/3X7XdHpzxcWjRv8ZLUP648SEhIkMpnjwD5u4CAA//vHT00B\n6Wnt+k/2atMQb2IY5sMPP/xi9Wrqv6D/sebNV65Y8dhjj3m3YZVId1Mjb2eFC4X7Wz32aN2f\nIRVAgR0S4IqKigAA0ar7ynRCAKKVitS8PAAA4DjMaMAL87HCAqyoANcV8pZnRCxhNVpWrWHV\nd//PkaRSqWRcLnfFlSHi4uNP//23naIl5N136JHMbJFIWL9+/Yoe9UACgeB/M2YsXLjwyV9+\nn9m2VbhMejIn7/3j/0okkldeeaXah/VHQqHwf9OmLVmyBM56kxsxEmi04NJFbOPXUpls4sSJ\n3m4d4jWLFy/+7LPPQNdu4OlhQKUCF85f+XbD088+e+Lvv30zFc4pu3N0RraBYdLdVNK1lE1x\n0d5uEeJ9KLBDAlxJ5pFbxcX3buQASAwJHhsXK/npW6wwH3oMyT0sjiBYtfZuGKfRcmrtQ61s\nuFdSUtLevXuTknd/3q9nmEzqZphPT5379eat0aNHi6t7zBJTp05lWXb5smVP/rKjZEvTJgnf\nLv+02sO7/mvmzJkAgOUrVrhO/VOyJSExccXy5ZGRkV5tF+I1FotlzZdfgk6dwcLFdzY1imbj\nG5tenbRhw4a3337bq60rx+9myytZea7/VkD+UqRrKBS8pZJ7t1WI16FVsX4JrYp9KIMGDjSn\np/3+7OD6SgUvB+TkclYTxKq1rLZkYlwQK1dUlGHEk1KpdLlczkpruS5duvTT5ctZlo1UyIts\ndidNd+/W7duNG2UyWSWPqiKdTnfq1CmdThcXF9emTRuC4O0Gz19WxZYqKio6deqUXq+Pj49v\n06bNA1PhoFWx/uuBq2JPnDgxZMgQMGc+6NXn3u3Y6Oe6xcX+8ssvtd/Gh7CiyLC4oKjM9/cg\njXp9eDBR3fVV5UKrYv2Or/XYsYc2r04+cjbLgickths7bVy0pJwWcrQx+Zsvd524UuTA60cn\nDp/8SscoNN8ZuR/LYvm5RE4Wlpu9b0BPgbNDtY/EKVWMRssGhbDaIEYbzGmDOGGtl+5+6623\nnnzyyS1btty8ebNXeHivXr0GDhzI18GDgoIGDBjA19H8WnBwMI//sIhfoygKAABIj5XyAtJd\n4059HjEc93Ze4beG4jLbx4eHftaovi1A7zqQqvOtwC5t23uf/pwx+tWpL6npP9d+8e5M9w9r\nX/Vcj7R/8Rsbr6rH/++NGDl7eOvnS1+ftfaHz0JIX1+4hNQ2yDBYXg6RlYFnZ2I5mbDkY/oh\ncSIxGxTMBocywaFMUDAXHMrxm+6uypo0aTJv3jyvnBpBHkEJCQkQw7h/T4Ku3e5uzcvjMjKa\n9elT8ePqlJVlX87MPWC13bsRg2BR/XqzGzWgqvWhhwQYXwrsOPfyn6/FPP/J8D4xAIDYpXB4\n0tIfcsaOibyvN47jXGvP6pq+vWRAhxAAQEzcvOTh0zZmW99sxM8oG+JnWBYvyCNup+EZ6Vhu\nNmTohz6AQsmGRbAhYUxoGBMUwimUD34MgiABJyQkZOhTT23fsYPTBoFnhwOpFFy5jH36MYbj\nY8eO9XbrAAAgl6JHZ2Rfct5XflAA4ap6YUnhod5qFeJrfCiwc5mOZDqZKX3vzFwWqrq0kq04\ncyh/zKiY+3fkWA7ggjv9cxATYxAybCDPFEQ8QbOJyEjD028RmenwIecDsQolE1GPDQ1jQsLZ\n0PBqL3FAECTALFu2zGKx7Nv4Ndj4NSQFHOWWq9Ur1q2Lj4/3dtPASbtjbEaO/v6pqxoC31Q/\nsp0EfYghd/lQYOe2XQQANJWQpVuaSIjdF01g1H27QSj6X8+oz5avPP7OuGg5e3jLMlKR+FL9\nu+uAFi1adOrUqZKfQ0ND16xZUxet9waVShWQa19K0oHiOK5Wq+/7A8tyGenctcvczWtcYcFD\nHFEghJFRsEFDGNUARDUgZF5eNYZhGI7jNVzf6ptKq2IoFIHZg15ycZa9MgNFybMTi8UiUa3P\nIq17JRdn5a+dWq3evXv3/v37Dx8+bDAYmjZt+txzz2k0mrpqY4V+KtRNuJ3tvH+1XLRYlNy0\ncWOJGPz32hEEwe/FSdMPPQaCeJ0PBXasywYA0BJ3p8oFkThtLWflYMeXZ/x+ctaHb88AAECI\nPTtn3r0T7PR6fU5OTsnPEEIeK777Gt/Ph15Dd147ys3euM5evchev8LZbA960B1QJofRsVij\nGKxRLAwNAz72b1XtsmD+IoDfdyDQn11gf7BU5bXr169fv3796qAxVcFw3Ky0jGVZOWW2d1Yq\nfktMCCbJezfy/pUXkIkXAp4PBXaYQAwAMNKs7L/rUk8xuKrsvHXGnffu5LddnUatGdU3RMJe\n/XvHwg+mEovXv9BEVbLD4MGDW7ZsWfKzVCq1VTkU8CMQQolEYrfbA7LHTiAQkCTJ2mzO86ex\n61dheiqo4oxghYJtEM3VbwQaNuK092QT9bHcDSKRiGEYv57mTHNclsud5nKnuVxpLleay33L\n6SrJpwUhZDjORN250ZfheAMh2VAobCAQNBQKGomEDQSCcAHpp4EtSZI4jleeqsZ/SSQSCKHb\n7fbri7MiGIaJxWL/+kawMOzYtNt7issudB0VpPmsQT2h2237b7muUCgkCIJhGH4vTpZlyftj\nR8T3+VBgR0ofA+DIDQcdJbwT2KU4aGUXVZndDJfW3LBh37/6tByHAIAWfZJeTd674fN/X/ji\nzg1W7969S3cO4Dx2EonE6XQG3u0UpGksPwdcuchevYRXYRSAI0i2XhRdvxHTMJoJDb/7Bx8L\n5u4lEAjcbrffBQf5FH3UZj9ms5+yO267KapqNxXFDJPtdv9tue/bVARhK4mos1TSUSJuK5GI\nMT8L8wIyzRsAQCwWQwgpigrIJ0gQhFgs9qOndttNjcnMuX7/UgkMgjmhwVODNKzLde8zwXGc\nIAiWZXl/gnI5ynjsZ3wosBOpekYIvtxzrLDP4CgAAGU7/6/F/UyfsDK74UIR4CgTw8r/69gz\nOGlcKqzr5iK8ggyN30ohrl8hbt2ENP2AcBVCJiSMaRhDN4xmIqNAQI+LeVcRzRyz2f+22Y/Z\n7LdcvKXycnLcCZvjhM0BACAhbCkWdpJKO0rFnfwwyEOQ2rDfapuSnWek71sqIcOwL6PC+8t5\nyFKOBDAfCuwAFLwxLOHNb+fvC3+rmZr6/YtlkvDeSfVkAIC0rd8ftivHJT0JAFAlTGoiO/vO\ne6umvNAvRMxcPZ68Kd895tNW3m49Ui0ch2feJq9eJFKuQ5frATvjOB3VkI5rTMc05tBNZG3K\no+gdZstvJvM5u7O2B/spjjtld56yO1cWAQmG9ZdLn1LK+8hlwkCfhogg5eIA+LRI/1Ghrkyy\nhyiS+KFhVBOhd9JqIn7Ex0qKccze71b8vPdfvRPGtOg++bUJsVICAHD0lVErDPW2bf6oZC+3\n6ca3X35/5nqa3oHXaxDbd+TEQa3LduyVCOChWH8vKYbpCokb18grFzBT2fzpZVAQwug4Kr4J\nE9e4Dko+1IGqlBTzCj3D/G6y/GYy/2N3PGwGIRmGaQkcQlgy9Z5lWRGEQggLaLqAoh/2U0aO\nY0/IZU8p5T2lUoEv9eGhkmL+64ElxXyBmWFfzcnbbbaW2d5eIt7YIFJb8eiETCYTiUQURZlM\nJn6bhEqK+R0fC+z49ogHdm63e8OGDQcPHszLz4+LjX3hhRf6eDt/OrSYyasXyWuXsaLCB+7M\nATD70N8/pt6+cOUKj/VMvc7XAjs3x/1usmwpNh21OegqfCCQEDYTCRsJyEYCQSMBGS0UNBII\nggkcVFAr1sVxmW4qk6IyXO5Mis5wu885XDlVm56vxPEhCln89av/fL8p9dat8LCwvn37jh07\n1lsTulFg5798P7C77nSNzcr1nPMwRq38MDy08jscFNghpQLnyxIpo6CgYOjTT6empGCRkaw2\n+OaRI8nJySNGjlz12Wd1n84AMjSRepO4cpFITwVV7mWEANRXyAoNhrS0NF9IEBp48ih6k7H4\na31xmaynnnAIE0XCdhJRB6mkh1SqwB/iEhJCGCcUxAkFQHa3ikw+Rf/rcBy22v+xO244KxyF\nNzHMJqMJhEaCocPA6VM3d+08+M47Gzdt2vHbb1qttuptQBAft9tsfTUnz8zc9/EogHBJeEiS\npuwiQgSpBArsAtbs2bNvpaeD9+azvfsAAFiXC6z5fMvPP3ft0uW5556ro0ZwHJ6bTV4+T9y4\n+oApdDh+OCf/ePrt2R3b3rtZgOEAJcnkG8uBA1bbN4bifVZr5UOuUQJysELWVSrtKBXLeL0f\nCCOJIaR8iEIOAMil6L9t9p1myz6LzVlRl2FMLIiJZUc+DzJu35z77nvvvRfAuceRR4qb5d4v\nKFqrN5a59CNJ8uuoiNaSQJh/gtQlFNgFJovFsmvXLq7/AND7v7FXoRBMm4GdOP7zli11ENhB\nm5W8dJ68dB4rNlS6H2TCI6mmj1EJzdbOmLnnn7MTWyZq7ynJsDstQywSRUdH13aDHxFmht1k\nLP7GUJzhrmwkNIQghihkz6iUbSSiOpjgFkESw1WK4SqFjWV3m607zJb9Fpu7ogivQUPu2++3\nnT39jMHYVxOYFSCQR0cmRU3Iyj1rLzsxo4tUsr5+RCWT6hCkIiiwC0y5ubk0TYO4+4cvcZyN\njU1LT6/FE3McnpFOXjxLpt4AlY7ucUoVldCMeqwVq75Trmf8+PHbt29/amvyp326tw4LKbLZ\nPzp5+o/UtEmTJgVkgaM6lkfR6/TGjcZiC1PhULiawAfJZU8r5Z2lEtwbi1KlGPasSvGsSmFm\n2F1my3azZZ/JUk7VEAi5x9u+kFvYwmieHqwZJJd5pbUIUkN/mCwzcgtMHh+VrwRp5oQGEeiq\nRqoFBXaB6U6lTo+FI9BgUCmVtXFGrNhIXDonuHwBWi2V7MaJxHRCM3ez5mxEvTJ/atu27cqV\nK9+ZPbvrpi0EhtEsCyEcPnz43Llza6PBj44bTtcXOsM2s8Vd8bBrW4n4JY1qiFIu8I3vEgWO\njVQrR6qV7Z8cktajF+g/oNzdLjicL2fmNhKQrwZpnlMrUYYUxF+4OW5eftF6fdmPaAmGrYwM\nG6pE6ZyQ6kOBXWAKDw+Pi49P3ZnMDX0GqP6beHvmNHfjeu+pU/k8E8OQt26SF87gGemgkgWV\nGEY3jKYTW9Kx8Rxe4VX33HPP9erVa9euXenpQ4BzLgAAIABJREFU6SEhIe3bt3/88cf5bO0j\n5qTdsarIsNdireiFkWDYMJVinEaVKPLRFN/9WrZYu3QJFx4JmjevaJ90N/VGbsFHhbqJWvXL\nGrX8YRZ2IEjdS3dT47NyLzrKDr8miIRf1QtP8NU3I+IvUGAXsOJiY1N27YJjR3NPPQ2Cg8GN\n62DXnwDCVq34SeYMzSby4jnBxbPQVjbl0r3YoGCqWQuqWXNOWqVs6SEhIVOmTJFIJIGaqqZu\nHLTalxfqTtorTFoRJxS8pFWPUCoean1r3Zs+ffpv27cXvj6dGzAIxDcGbhcUSbhevYHH6HwR\nzXxQoPtCZ5ykVU/QqpRochLik7YVm9/MK/CcEfGCWvlheCiqvILUHArsApPL5Tp08GDnyHAr\nTV/47hsAAIZhT8dH772dtWXLlieffLL6h+Y44vYt8vwZIi2lksQlHCmgE5q6H2vFRkZV/1zI\nQ+IA2GO2Li/Sn/PoDCjVUSqeGqTpK5f5xRdIcHDwnt27582b93tyMpe8AwDQonXrt3t2OxtS\nb73eaPCYnFTMMB8V6r7UGydoVZO0ahUK7xCfYWKYt3ILfzWVTYIoxbCPI0KHqxReaRUSeFBg\nF5iysrLsDsewhHZTWjfXOxy5VlusWiUmiKe2/n7t6tXqHRM6neTl8+S5U1hxZR1pbESk+7FW\ndEIiJ0Clb+oOy4HfzZZPi/RXK8gJh0EwUCGfqlU/LhGXu4PPioyMXL9+vcPhSE9PDwsL02g0\nAIDeAEwN0vxQbFpdpM+iymbDMTHMJ4X6tXrjBI16slatJlB4h3jZUat9Wk6+Z17upiLhhqiI\nWFQoDOEPCuwCU0lefjtFAwC0YnFpAhE7RQuED/29jumKBOdOEVcuwIqrBVAEwTVvRbd4nAkK\nqW6rkeqgOW5rsXmlzpDqkbC+hBDC59TKV4LU0f4caovF4qZNm963BYPjNaqxauV2k2WVzuAZ\n0VoYdnmRfp3eOEmrnhyEeu8Q73Bx3KL8onUGo+fipRc1qkXhISK06AfhFQrsAlNUVFRYaOiW\n6ynT27Yk/8sWcdNg/Cc3f3jVk9ixLJGWIjj7L555u5KFETaKWnzi1M+3Mg6deFVZO0tukXK5\nOe4no+mzIkNmBQG3DMPGaVVTtJrgwO2yIiAcplI8q1LsMls+KdRf8gjvrCy7rEj/lcE4WauZ\npFX7+JxCJMBccbqmZOdd87gsVTj+SUToU2j1K1IL8Pnz53u7DbWI4zjfqcjJIwihRCJxOBwV\nlfqFEMpksu9+/e1ARrZWLLJRVHJK2sQ9ByiIrV69umQwq7Lju92C86dFf/4mOH8GMxVXvrMA\nxxmWXXvqXFxcXGJiYjWf0j1IkiRJMlBfOwCASCRiGKYm5TScHPeNwTQhK3ebyWIqb6ajCsen\nBWvW1Yt4QiGT1mEFOQzDSpIOOp3OuqxDDQGIEwqTNKoWYlGam8r3+Ld1cdzfNvt3RhMNuMfE\nompndSFJEsdxV+VlVPyWWCyGEFIUFZC1XkouTrvdXjenoznuc51hcnZegcc/ZjeZdEvDem15\nnRQhEAgIgmBZlveLUyKR8HtApLahHruAlZSURNP0ksUfjPjtz5ItTRISvl6+PCYmppJHQYtZ\ncPYUefEMrDioumkwRspkUsHdKuydIiMAAKmpqTy1HamQlWW/NRSv1hmK6PLzP2txfEqQ+qVH\nMusHBKC/XNZfLttrsX1SpPPM5l/MMEsKdGv1xle06vFadV2GvMgj5YrTNSMn/7zHGiYhhHPC\ngidq1WjwFak9KLDzCSaTadeuXbdu3dJqtV27dm3WrBkvh33ppZeeffbZs2fPFhQUxMbGtmzZ\nkiAqfMXxwnzBqZPEjSsVVoyAkG4Usy27YOz6VZcnJMUI7o66FrtcAN3Y1bIimlmnN3xrMBVX\n8AKFkcTUIE2SWoUyJvSVS/vKpfutto8L9Wc8cr4YaGZRgW61zjglSD1eq+a3Bi7yiHNz3LJC\n/SqdgfLosU4UCdegNHVI7UOBnff9+eefb7z+uk6vL/kVw7AXXnjho48+EvAx1V2pVPbs2bPy\nffD0W8J/j+OZFZYa44QiKrEF1botq9KEnzvHgcVrz11c2qtr6Q5rzl4AAHTp0qXmDUY83XZT\nq3WGn4wmZwWDm1ECclqQZpRa6SN1I3xEb5m0t0y612JbWqjz7DsxMMwHBbo1euNkrWa8RvUI\ndnDWGYfDcePGDYFAEBsby8vHms86ZXfOyMm76bGMCYNgapB2VrBW8MjfdCF1AAV2XnblypUJ\n48fHqpQbRwxtHxFWYLN/8s+Zb77/XqFQLFiwoHbPzXHkzWvkP3/jBXkV7cKqNO427enEFhx5\n5+O4VatWTz755GfJyRlmy7ONYzEIf72R+uuN1IEDBrRr1652G/zoueR0fVakTzZbmQpCumiB\nYEaIdphSTqKQrgJ95dI+cukes3Vpoc5zaYWBZhYXFK3RGyZp1RM0aGkFz+x2+8cff/zl2rU0\nRQEAZHLFW2++MXHiRDzgVijbWPaDgqINhmLPpa/RAsHKemEd/C3NEOK/UGDnZevWrcMA+HPE\nkAiZDAAgEyjXPNGryO74+uuvZ82aVUuDm5BhiCsXBP8ex4yGivZhIqPcbTvSsY2BR8SwevXq\n6OjoL9es2XHzFgBAQJJTp0596623aqOpj6yrTteigqK9FltFOySIhDOCNEOVchyFdA8CAXhC\nIeuvkO00W5YWlpPqz0gzHxbovtQZJ2hV47VqTcCFHV7BcVzSiy8ePnQIdOsBOnYCNG3b99fc\nuXOzsrIWL17s7dbx6Q+zdU5+Yba77OJ0AsLJWvWs0CCU0ASpS7AuV67VPd8vS9W7Vy+RvujI\n6OH3btx46eqkXfv379/fvIL6mBBCrVZrMBjYims/lP9Ayk2ePyM4fRJaLeXvgWFUfBOqTQcm\nPLLyQ5nN5uvXr3Mcl5CQwG+WE4lEEtglxZRKpcvlqmjNb4ab+rBQ96vJ7HnrX6KtRDwtSNNf\nLvPBUR2CIFQqFQDAaDQyFU3W9CqWA39YLJ8U6K5VkPZPgmGj1IopWk3UPcuDSonFYpIkzeay\nxQMCg0ajwTDMZrM5HBUWo6u6AwcOjBw5Eox7GSSNu7OJ48AHC+DBA6f+/bdBgwY1P8VDKbk4\ndTodj8dMdbln5xUespZzA9ZMJFwRGdZSXLb2XS2RyWQikYiiKJPJxO+Rg4KC+D0gUttQj52X\nYTjuGVozLAcAgLze5EG3mzz7r+D0CVjBRzZHCqgWranH27OKKkVpCoUCjb3yq5CmlxUZNhmK\nPaddAwAgAH3k0mlB2o5SNKZTfRgEQxTywXL5Tov140KdZ++dnWW/0hd/azA9rVRMDdY0QSUB\nquvIkSMAAPDsPXetEIJhI7j9+44dO1b3gR2/7Cy7rMjwpd7g9rgDE0D4eoh2WpAGTZBAvAIF\ndl7WokWLH7+/lFZsilbdCac4ALbdSJVKJPHx8bycArqcgrOnyDMnKwzpxGKqdXt3q7acGEUM\n3mFimM91hnX6Ynt5XbAEhE8r5dOCtX4RZFAUxe89SW3AIBiskA1SyHaaLZ8U6i97hHcUx20p\nNv1SbOojl74SpOkslfj6U/I9NpsNEgQnkd63VaEEAFgsFYwY+IkdJsvc/MJcj1p2AIC2EtGK\nyPB4f3irIoEKzRT2ssmTJ2MEMWDLjq3XU3Is1tN5BaN27Np/O3PylClCYU1XxUO3W/DP39J1\nqwTHDpYb1XFSmatbb9ukGa5O3VBU5y07TJaOKekrigyeUR0BYZJG9W9co9X1wn0/qvvzzz+7\ndu8ukUolUmnvPn0OHjzo7RY9AARgkEJ+ILbhd/Ujyx0y4wDYa7E9nZ7VJSV9vd5oZh5u5sMj\nrlGjRhxNg5s37tt65TIAoPJsmr7sosP5zO2s8Vm5nlGdmsA/iQj9M7oBiuoQ70Jz7Lzv8OHD\nr82YkZmdXfIrSRCTJk9+7733Klk49sA5dtDlEpw5SZ7+B7rKn8jFBgW72naimyQC35sn/ujM\nscuj6LfyCnabrZ77QACGKOWzQ4Ji/ORLYtGiRStXrsQiI9l2HQDLYieOs4UFc+fOnTZtmreb\nVlVHrLbP9caDFS9YkWLYc0GayeGhDZkALMwA+J5jl5ub2659e6pePXbOAlC/AQAAXL6EzZ8T\nJhL++88/Nb9xfVg1nGOXRdGLC4rKnfyKQTBKpXwvLNiLy27QHDukFArsfILT6Tx48GBqampw\ncHDHjh0fOPukksAO0hR54azg5DFoL//7iQ0OcbXpSDd9DPhqXtZHIbBzulxrc/PfLygqtxOo\np1z6bkhQi7qadl1zN2/e7NK1K9e5C5izAJAkAAC4nPDdt/GLF06fOhUZ+YCFOD7losO5Smeo\nJMUMAKCdRDxGrRyokAdYehR+AzsAwPbt26dNn+5yu2G9KEBTbG6uWqP5YdOmtm3b8nL8h1Lt\nwM5IM5/q9Bv0xe7yLolWYtFHEaGtvP1uRYEdUgrNsfMJIpFowIABNTwIZGjy/JlKQjomJNTd\nsTsdV04GE6QupTicE1Nu/V1ez9DjEvF7oUFdpH5Ww2PPnj0cy4IJk+9EdQAAoYgbP5GeMnH/\n/v1JSUlebd3DaS4WfRUVcdtNfaEz/GQ0ucr7Lv/X7vjX7ng9t6C3XDpUKX9CLpP46m2Sdw0d\nOrRt27Zr1669fPmyQCB4fNSoiRMn8ruIvlY5Oe4rnXGlzmAqb4m3hsDfDQkarVb54Pp05FGG\nAruAwDDk5fPCk8egufx7NSY03N2pGx0Tj0I672I4bpXOsKzI4PToatUQ+PthwcNVSn98he70\ngoSF37c1LAIAUFhY6I0W1VRDAflxROjboUE/GIo3GkyZVNkUZQAAN8ftMlt3ma1iDPaTy4Yq\n5H0UMpSxrIzIyMiFCxd6uxUPzclx3xtNq4r05a6QICF8UaN6K1irJnxuKguCoMDOz3EceeOq\n4OhBrLj8VMNMaLi7Sw+6USwK6bwul6InZ+eesJUzyDVEIf8oIjTIb78kQkNDAQAgKxNE3zMp\nPjMDABAeHl7Bg/yAFsenB2unBmmP2mzfGUw7LVa6vA48B8vtMFl2mCwEhM1Ewu4yaTuJqJNE\ngsqU+SM7y35vNK3SGfLLC+kAAP3ksvfDg6MDujYa4tdQYOfH8NtpokN7KyoIxgYFuzt1p+Kb\noJDOF+w0W2bkFhjpsgM6kSS5NCKkn1zmlVbxZeDAge+//z6zehW3YBGQygAAwGyCa1cLxeK+\nfft6u3U1hUHQXSbtLpNmUtT3ZusmnVFXXgceAIDmuAsO5wWHEwAgwGArkaiLVNJJKm4nlaCe\nPN9nZtgNhuIvdQZDBbm1O0rF80KDH0fFwRDfhhZP+CWiMF96/Aibcr3cv7JqrbtzdyqhmZ+G\ndAG2eMLFcXPzi77Wl30uGARjNer3QoICo19n1apV77//PlSp2FaPA47DzpzirNZPPv7YvybY\nPZBYLOZw4ve8vB0m6y6LxVK1BChCCNtKxF1kkq5SSWuxiPDVNybviyd8SiWLJwpo+mt98QZD\ncblz6QAAjUXCOaFB/X34BgwtnkBKocDOz2DFRsHRA+SNq6C8F45VKN2du1NNm/vsiteqCKTA\n7obTNSEr17N6VSOhYFVkWPvAuvU/ceLEsmXLL1y8ADGsTevWb775ZqtWrbzdKJ7dW1LMxXH7\nLNbtJssei9VRUQE4D1IM6ygVd5NKByll9clyqpZ50SMY2P1jd6zXG/80W8st9wIAaCQgZ4YE\njfD5oswosENKocDOb0CnQ3DiqODcKVDePSUnlrg7dXe3aO2DeekeVsAEdpuMpnfzCjy/8p8P\n1i6rF0FS5dcq9Wu+Xyu2hsqtFWtn2b0W2z6r7ZjN7lkJvhKPiYRPKhWDFDIfSWn76AR2Dpbb\nZjKv1xuveBQdKdVYJPxfkOYZnw/pSqDADimF5tj5A4YRnD8tOHGk/OoRpMDdtgPVthOHJvP6\nDCfHvZ5TsKW47CesFMM+DA+Z1LC+y+VyPigASE9P37ZtW2pqanh4ePfu3Xv06MFX8/bt23f0\n6NHCwsK4uLjhw4dHRUXxdeRHkwTDnlLKn1LKAQCZFHXc5vjbZv/bZs96UJB3yem65CxaXFAU\nLxQMVsgHK+WPieo6be+j5qrTtbnY9JPRXFzxjUczkfC1EO1guRzlMUH8ET89dlEt+yQlJSWN\nGdE42LdSqgZArw9x66bwwF/lL3rFcSqxhatzD07quzM/qsHfe+wyKerFjBzP8qPNxaJ19cJj\nhILSyhOVHOSLL774YPFiyu3G5HLWZgMsO2DAwHXr1opENXqLWa3Wl19++cCBAxDDoETKWi1C\nkWjB/Pkvv/xyTQ5b6tHssatIFkUfs9mPWe1HbLaKlliWES8UPKtSDFMpvDJKG8A9dnkU/ZvF\nus1svWitsLIIAKC1RPRasLafXOZ3ER3qsUNK8RPYtW6oPJdhhpiwbf8RSUljnn+2t4b0iTle\n/hscAADw/FzhgT14TlY5f4MQa97K2r4Lo1TVebtqnV8HdgestsnZeWVWv0IAJmnVc0KDBRgE\n95QUq+ggBw8eHDFiBHi8DXjtTRARCex28OMm8MOm8ePHL1mypCbNmz59+k+bN4OxL4GRzwOh\nCGRmwGVLwaWLycnJ7du3r8mRS6DAriIpLvcxm/2o1fa3zVHRostSEIC2EvEwleIphVxTh0lw\nAi+ws7LsHybLLybLMZutkmmQAgwOUchf1qja+O20VxTYIaX4mmPHXjv2xw8//PDT5h1pxS5S\nEjno+VFjkpKGdmvm3fjOT4MDaLMK/z5EXjxX/gqJiEhnz/7q5i0rqRXr1/w0sOMA+LRI/1Gh\nrsz3hwbHv6gX3kcuLd3ywMBuzJgxfx0/zm7eBsT3fM3MfUd05nTqzZvVLrJpsVjiGzeme/QC\n78y5Z6sZGzls6IAn1q5dW73D3gsFdg/EcuC8w/mnxZpsMqc/aKxWgMGeUulwleIJhUxY+zO9\nAiawK6TpPRbbbrP1iNXmrPQ7LpwkxmpUY9SqYL/NIlkCBXZIKb7m2GFNugxZ1GXI+1/YTu7c\n+sMPP/y8adn2DUsVDR4flTQmacyYDnEank4U4CDDkKdPCk4ehe5yZtazao2rWx86PgH6w2Te\nR4qZYV/NydtttpbZ3lIs+iYqop7g4YbVrt24wSY0uS+qAwC0buM8eiQjIyM+Pr56jUxLS6Mp\nCrRqfd9WuYKNi792vfzUOQjvMAhaS0StJaI5oUFXna4/zJY/zNZrFUzhd7PcHot1j8WqwLEn\nFfIRKkUHiQRN/KrINZd7j9m622I953jwMuXOUsnLWvUAudRns88gSPXwvHgCYtKOg1/sOPjF\nt89snThqwq4bZ9a8f+bLRTNj2w+aOPXNN0Z14/d0AQa/nSY6sBvTl5NmiROL3W07UW06cP6/\n6DXwXHe6XszMTfOIxUeplR9FhFajo0UoEADP/jynAwBQ7e46AICgZHmNx5Gh0yH0t+q0gaGp\nSNhUJHwrJOiWy/272bq12HTTIzNOCTPD/mA0/WA01ROQw5Ty4Sqljyyk9boCmj5mtf9tsx+2\n2sut/FZGIwE5TKUYrlI2esjbLQTxFzwHdlkXDm3dunXrtq3HrxVAiDfuMHD4iOFB+pPrN2x6\nc/Qfu28c37ewI79n9BaWAzzeN2O6ItHBv/Dbt8r5G467W7dzd+jCifx18kdg2222TsnOs94/\nJi6A8MOI0DHqahY779C+fcqPP3IZt0GDhnc2URS2b29IeHj9+vWr3dS4uDilWm3et4cbMvRu\nWpyUmyA1pcPEidU+LFJzMULBzGDNzGDNRYfzl2LzbyZLAV3+YotsN7WiyLCiyJAoEg5Vyocq\nFQ0evQBFRzMl646P2ewpFYTCZWgI/GmV8uX69eLcFeY3QZDAwM8cu1un927bunXrtm2nUvUQ\nYnHt+g8fMXzE8GHNo+QlO3CMdV7rRh+mat22Oh3xqaV5WgaGeTo9a3ZI0BOKmi5HhRRF/ntc\n+M+xcrPTMQ2jnb2eYLVlpzhACLVaLZpj510cACuLDEsKi8oM+kSS5NdREa0lFS5ffeAcu/T0\n9B69ejkJkh2dBBKagKIiuPkH7sb1zz777Pnnn69Jm9evXz979myY+Bg3fCTQBoErl7EfvpNh\n2NHDhyMiImpy5BJojh0vGI47arNvM1n+MFmsD3qPtxSLnlLKn1Iqosia3qj77Bw7G8tedLjO\nORznHM5zDmdGlZMFKnG8t0z6tFLeSyaVCMiKKk8EADTHDinFT2AHIYQQi23bb/iI4cOHD2tZ\nX+G5z+HnGg/YLbUXn6356aquNoIDlgPPZWYftNggANODtbNDtNVOX0ncuinatwuay3kfshqt\nq2d/Ojq23AeiwM7rHCw3PSdvu8lSZnsXqWR9/QhtpSPmVUl3cu7cuZmvv37l0qWSX1Uazfy5\nc0eNGlXDZgMA1q9fv3jJEst/oUnL1q1XLF/erFmzmh8ZoMCObw6W22Wx/FJsPmS105V+VkMA\nWkvEA+TS3nJZM5Gweh9JvhPYZbupFDd1zem85nRdcLpuutzMw3xV1SfJJxSy/nJpR6mE/O/z\nuZKSYgEABXZIKX4Cu3c+2TB8+LBWDao58FR7aiM4WFqo+7hQX/prF6lkXVTEw66ownWFwr07\n8exMzz9xIrG7c3d3yzaVlAVDgZ13ZbuppMycSx4T3idr1fPCgh84F7sqgR0AgGGYy5cvp6Sk\nhIWFtWzZUibjLVuh2Ww+f/58UVFRfHx8YmIij2txUGBXS4po5jeT+Zdi83nHAy4bAEAoQfSU\nSXrLZd2lEvXDfDR5JbCzMGwmRWW6qTS3+6bLfd3lSnG5q1iE914EhI+LRX3ksv4KWZPyJiCi\nwK56UGDnd3grKaY7s3324i+oMeu/HdoAALCvf6s5ROLMectHtAvm5fjVw3twcM3p6nHrdpmh\ntzCS+CoqokPVEiBBl0vw9yHBuVPAMybDMKp5a1eXnpz4AYdCgZ0X/WN3jMvMKbo/U50AwmWR\nYc+pyumr9lTFwM4focCutt10ubcWm3aYrJ6LdTzhELYWizpJJc3FwhYi0QNn49VeYOdguXya\nLqTpApouoJhcisp0U5kUlUlRBrr61wkOYXORsItM0lkq6SARSyutkY0Cu+pBgZ3f4WfxhCll\nXXyHKSaofGnCnfeVpnVcxorNz/+VrL+YPqWJmpez+IImIuHKiLC37i8Amk/RT6dnzQkNmhKk\nqbzrg7x+RXhgD7SVTYoBAGAi6rn6DmJCQvluMsKn742mWXkF7vtD+1CC+LZ+hP+mNkX8SLxQ\n8E5o8DuhwZecrh0my3aTuZIJZwzHnbI7TtnvRGkqHG8uFrUQC5uLRA0EZDCOB5GEqLr9tWaG\ndXCsneXMDGNjWQfHFdNMMcMUs6yRZopZtphmjAxjZJg8in7gTMGqE2OwuUj0uETcWSruKJHI\ncZ9Iho8gvoOfHrvliUGzMxseuHm0c9jd7zZn0alesV1vRi3QXZ5V81NUTy31+lx1usaVl95i\nkEL+aURouWMfWLFRuG8nkV7OuldOLHZ37OZu3Q5U+RMW9djVPZrj5uQVrjcUl9neSizaWD8y\n/GHmraMeO//l9R47T+cczh0myx9mS9WXFNxLgmFaHAshSS2OK4RCBoBit7v0g8XNsXaWAwCU\nVFa1MiwDgINlXTwN9VQFCWETkbCVWFTyX7xQUL3Mc6jHrnpQj53f4alWrIhUjD965fMOZbaf\nntWiw4oi2pVb81NUT+0FB2aGnZ6T/6e57Nz5SJL8ol5Y53uygkGGEfz7N3niGGQ88hdgmLvl\n4+7OPbmHLACKArs6ZqSZCdl5hz2qTA5TKVZEhj1spjoU2PkvHwzsSl1zug5YbQes9pM2u7sO\nAy9+4RDWJ4kEkTBeKGwsFDQWChqLhLxU3UCBXfWgwM7v8DMUy3CcQFnOZFVcggPg5cijJgld\nKxEMwI9xjVYVFM3Pyb93tVoORT17O3tmWPDs8FASQph5m9i1AxYVeh6BDY9kBz4FwiOrkWa0\nZLa7UCgMyMCOIAgAAISwll67h3Xd4XwuPTPt/nRZOITzI8P+F1qdKaQYhhEE4SPPjl/4f8uB\nBQJBoF6cGIb55mvXUihsqVS8BoCdZQ9brHtNln1my+2qpXnzCgEGo0hBAyHZUCisLyAbCASx\nImFjkVBU6VS5asMwDNTaN4LXlbz1eL84A/JdHPD46bFb3FizSN/hRk5ylPDuKCTrzns6MuaI\n4k3jrQU1P0X11EGfwTGz5flrKXkew7LtpJLvbl9veOakZ71XKJXBAUOwVm2qPvbqCcfxgOwR\nAQBgGFYSufrCE9xpKB59I8V8//xuJYF/3zhugEZVvWNiGMZxHF/rlnwKhLDk69MXXrvaACGE\nEPrRt12uy33WZjtrsZ212s7abLl1G+fhEAaRRDBBRAqFoQIyQiAIJclwIRkuEDQQCiMEgros\nj1ZycQbqlVnysclxHL8XJ03TgRoKBzB+AjvD5U+jWr6BR3V5/bVxnZvHSjAq/eo/G5d/uC/F\nPv+fnLltvbYwtm6G84po5tXs3INWe5ntcppeefX0CzkZdzdBSDd9zNmz/wPXvVYODcXWjVVF\nhkUe+YejBYLvG0TG1aCgExqK9V++PBRbFYU0fcHhuu50FdK0gWF1NF1E03qG0dFMRaO3chzD\nARRjUAAhCaEUwyAAShyXYFAMMRmOyTGs5GcljilxXIVjahxXE4QSwxS+tLIBDcVWDxqK9Tv8\nDMVqEmdeScaHT3p3/vQjpRtFmoQFP/0yx3tRXZ0JJvCfG0at1Rvfzy+698PRQhAvNe+wJyji\n06tnNJSbCQpx9RvEREZ5salIFTlYbmZu/rbist/fPWTSr6LCVahiL+KfQgiir5zoK5d6/snK\nsnKVCsMw1uHgXKjuFoL4K95qxTYcMP1UxuTLJw+fu55hZ4jw6GY9urdR4HXYz+5VEIDJamXf\njFvj3exl2X2Jmn+OqH8wKGQ55ezfpg26+JepAAAgAElEQVRAAYE/yKGocZm55zwywSZpVB+G\nh5D8pfNFEN8hwzA1QWAYZsMw3yoohiDIw+AtsAMAAChI7Ng3sePdDRxrt9iAQi6p+DEBAi/I\nE+75o2VB3hGMeLNpqw1R0ff+tVAgGi0QDcsrXBwW8lCJ4JG6d8Rqm5CVZ7h/GFGAwY/DQ19Q\n+1xtFQRBEAS5V+1OgMje97Q2uEmtnsLrIE0LD++XfL8BL8gDAEhY+ovLp34+d0xDlZ2kvLXY\n3CX19k6PDCmI71itM4zMyCkT1QUT+G8No1BUhyAIgvg+fnrsOMb6+YwJG/ef1jvuS9WWn5kB\nxU15OYVvwnOzRbt+xwxlZ+M+lZ/TQpv2SkLLA/b7hvMKafrFzNxnVYrFYSEa1HXnSxwsNyMn\n/1dT2Ul1rcSib+pHRJIPqMWEIEhd4jju559//vXXX1PT0qLq1evbp8/EiRMFguovaUKQgMFP\nj925hT2mf77ZrGoUH07fvn07oXnLFs0TCH0u1PRcvWM3L6fwNZCihPt3S378xjOqY1Ua+8gx\n2oFPbY5usCIyzHNd2LZic+fU9J+NpgBMd+GfMilqQFqGZ1T3nEqRHF0fRXUI4lPcbvfwESOm\nTZt2+OrVrPDIk9k5CxYs6NGrl8Fg8HbTEMT7+Omxe2fVFW3iopvH3+UYa7RM3eXz796NkjsK\nDyc2GmiNKGf5lb/DszNFu3/HjB4fIhjmbtnG3a03R5IAAAjAKLWyj1z6Rm7BbvN99WF1NDM1\nJ/97o2lpRGgTEcoS5E27zdbpufnG+zPVERDODtFOD9Z6q1UIglRk3bp1hw8dAuNeZke/CDCM\nBQDs+yt1yaJ58+atWrXK261DEC/jp8fuqNnd8LnBAACIy8aESA6c1QMAxCHdvxvbcNGwr3g5\nhY+AlFv01x+SzRs9ozomJMw+ZoKr9xPc/R08oQSxqX7kyvK67k7aHb1vZSzIL7IFYjo63+dm\nuXfzCpMyc8pEdcEE/lujKBTVIYhv+mXrVhgTC5LGgdIaFX36cd17bt+xg6KqUzAXQQIJP4Gd\nmoCU5c7bqX09ac6OnJKfGzxTrzj1U15O4SswHM/JLltMAsfd7TvbR7/MhIRW9LgX1MqjsY36\neKSPojjuc52hc8rtP+7v0kNqWyZFDU7PXKc3lhkQby0R7Ytp2EFSowzSCILUnqysLK5RdNmt\nsXFOhyNQ8w8jSNXxE9iNj5SnfvNhlosBAEQNiczeua5ke/7+Al6O7zs4HHf0fxLcU8qQjahn\nS5ro6tb7gTnqIkjipwb1vqwXHuyxbCKHosZl5oy8nX3DifKC1oU/TJZeqRmemepGqZXJjepH\nkLymAUIQhFcqtRro9WW36ooghimVaPU68qjjJ7Cb9PUER9GvMUH1051MTNJ4e+GmjuPe+njh\nzMHLLmuazeLlFL6DjYh0t24HAOAIwtWtt+35sWzQQ1TXeFalOBHXaIJWhXvkuT1gtXW7dfvV\n7PwCmi73sUjNURz3foHupaxc0/05TUQQLo8MWxEZJkD5hxHEt/Xt3RteOAcunr+7KTcH2/tX\np06dJJLAT5uKIJXjp2civPvSc9vCF6xNxiCQhk/6acbWUSs+Oclxipj+W3dP4uUUPsXdtSfm\nsLs6dWNVmmo8XInji8NDR6qUb+UVnL0/HwrLgS3Fpj/MlleC1FODNFLMhyotBoBrTtfUnPyL\nHh11TUTC9VER8TUo/4ogSJ157bXX/ty5s/C1/3F9+oG4eJCbi+36UwC4hQsWeLtpCOJ9kKug\n8PPDYF0uChMIyXt6OsxZN9NtoqaN65Ne7f7whULyleAA2GI0zc0vMpRXLl2D468GqSdrNQKs\n7D8ihFCr1RoMBjYQV11IJBKJRMLva0dz3Gqd4aNCvWel8xEq5ccRIZI6jKGVSqXL5XI6y8aX\nAaCkzjoAwGg0MuVd1f5OLBaTJGk2l82MExg0Gg2GYTabzeHw9aJi+fn5CxYs2L5jB01RGI53\n7dJl0aJFCQkJlTyk5OIM1El4MplMJBJRFGUymfg9clBQEL8HRGobD4Edx5gkAnX7H1MOjYzh\npU088vHAroSeYRblF/1UbGbKey1ihYLXgrXPKOX3Dt2iwO6h3HC6puXke86ok2LYxxGhw1UK\nXs5SdSiw818osPMpFEVlZ2eHh4eLRKIH7owCu+pBgZ3f4aGXAuLK15to0r4+VfNDPZq0OP5p\nZNjR2IZDFHLPv6a63K9k57W9mbZOb3Tx0L36aGE5sE5v7HWrnHUS8ULBruj6dR/VIQjCF5Ik\nGzVqVJWoDkEeHfwMP805urN51rRXP9uhdwXgPXrdiBMKNtSP+K1RVAtxOR9SWRT9bl5hx5T0\nr1F4V2U3Xe6B6Znv5hWWGX7FIHglSLM/tiFKDY0gCIIEGF7m2IG+ffuytOHg4XMAikLDg0Xk\nffFienp6zU9RPX4xFFsGB8CvxeYPCoqyqPLXxoaRxKtBmukx0bTZhIZiy1XMMMuL9BsMxW62\n7OUdIxSsigxvK/HmLT4aivVfaCjWf6Gh2OpBQ7F+h59VsSKRCICIQYMieDnaIw4C8KxK8aRS\n/rWheFWRodAj9Uk+Rc/JK1xaqB+hVo5TKRqjbqd70Bz3raH44yK9gS4bVWAQTNCo3wsLFqGE\nJgiCIEiA4iewS05O5uU4SCkBhJO16rEa1fdG0+dFhhyPOjkWhtmgM2zQGbpIJS9r1U/IpcQj\nH6/ss9jm5RfedLk9/9RIQH5WLxzVk0AQBEECG8qw79NEEI7XqJJUys3Fps90hgx3OWUQj9ns\nx2z2CJJ4UaMaoVTUE5Ce+wS8q07X/IKigxab559wCF/SKOeEhuA0deXKlcLCwtjY2KioqLpv\nJIIgCILUNn4Cu8oH9VGNlxoSYDBJo3pBrdxWbF6hM6SW1yOVS9FLCnQfFeraicXPqBRPKeWa\nB5U4CwAsB/ZZrV/piw9bbeXOFe0oFb8fFtJCLNqxY8e7c+YU5OWVbO/WrdtHH30UGxtbl61F\nEARBkNrGz+IJWOkgIC+nqB5/XDxROZYDe63Wrw3FBy3lhzIlSAi7yyTPKBUDFTK/K19RlcUT\nVpbdXGz+SmdMc5cT5gIAGgrIeWEhgxUyAMC2bdsmT56MNWjIDhsBtEHg2hVs6y8qqeTIoUOh\noaG19TQqhhZP+C+0eMJ/ocUT1YMWT/gdfnrs5s+ff9/vHJ2bdnX7zzsMMHL+msW8nAIpgUHQ\nXy57QiE3SCTLb93+yWgylff1SXHcPottn8UmxmAXqaSXXNZDKokNiJJZN13u7wzFPxabLEz5\nK4IVODYzWDtRqy6p+spx3IKFC7FGjdg1XwGhCAAAOnZiO3QyTpvyxRdfLFy4sC4bjyAIgiC1\nip/Abt68eZ4bV3z8T+/47itWnnl33ChezoLcK04s/iAi9L3QoO3F5jV64xWnq9zdHCy312Lb\na7EBAEIIoodM2l8h7SaVqPxqoNbGsqftjj0W626ztaIsMAAADIJhSuX8sOBg4u6zu337dl5u\nLpj0yp2orkTTZiC+8bG//67VZiMIgiBIHavFxRPi0PZfLWyZOOPTw6Yl3ZUoJUetEEI4Uq0c\nqVaesTt+NVm2myye6VFKFdL0lmLTlmITAWGcgGwhFpX8lygSiT3K0XqXi2XT7I7LFtsph+OQ\n1X7W4Sy33lopGYY9p1JMDNI08lg7cmdQSSYrs52TyaxGA6+tRhAEQRAvq91VsZJ6EgjxxpJH\ncZ1mHXtcIn5cIl4YFnzMZt9WbN5psZU7RFuC5rhrLvc1l3tzsRkAcCfOk4hjBIIokqgvIBsI\nyBCi1ldMuziukKLzaVpHMwU0nUtRWRSd6aayaDrfTVVxYmYDATlBq35epVTg5U8ljIqKIkiS\nvnQRDB5yz7mdWMrNxh078vA0EARBEMRn1OKXN0sVfTrnPClrFUb62eT9iuh0ujVr1pw/f55l\n2cTExClTpkRE+FZOZhzC7jJpd5n0E47ba7Emm60HrTbPVL1llMZ5924UQdhAKKhPElqCUGKY\nmsCVGKbC8ZIfSAgBADIMK5M8jwXAzDAAABPDcADYWNbGciaWLaaZYoYxsWwxwxTTjIFhCmim\nktCzKrrKJBM06v5yWeW9jXK5/OmhQ7du28bFNwZPPwswDBgNYNnHrMk0evTomjQAQRAEQXwN\nP4Fdx3J6Pti8lIsZemeb9z7n5RRed+jQofEvv2yxWOK1GhzCr44f/27jxtVr1gwaNMjbTSuH\nAMJBCvkghZzhuAsO10Gb7YDFdtbhpKu8QtnJcTecrhsVTN3zFgLC1mJRb7l0kFxW9ZIbixcv\nTr99+/TnK7GvvwIaLZefBzlu5uuv9+/fv1ZbiyAIgiB1jJ90J+UFdoAQaR7vPXrpu88LvDd9\ni690J1artW2bNnKW+WnIEy1DgwEA1/WG53fszna6/j11qu5Xg0MItVqtwWB4qFqxZoY9YrUd\nstnP2B03XG7Ke2loHlYDAdlTJu0hk3SVSisacq0cx3Hbtm07fPhwSYLi559/PjExkfd2VhFK\nd+K/ULoT/4XSnVQPSnfid/jpsTtx4gQvx/FZe/fu1en1Xz4zuCSqAwAkaDVf9OvR68dtycnJ\n48aN827zqkiBY4OV8sFKOQDAxXFXHK4LTucFh/Oiw3ndZ+I8HMIIAdlILGogFIZxbAOSbCeV\neC6JeFgQwmHDhg0bNoyXRiIIgiCIb+Jtjp3uzPbZi7+gxqz/dmgDAMC+/q3mEIkz5y0f0S6Y\nr1N4UUZGBgCgTVjIvRvbhIcCANLS0rzTppoRQthaImotuZMBxM1xt1zuTIrKdNMZbncWRWe4\n3ZkUVVGuuJqAAGgJPJggQgkihCCCCTyMJIJwPJwk6pFkBEkopdIHJihGkEcQy7KbN2/eunXr\nzdTUyPDwfv36vfLKK2IxqoDsH86cObNq1aqLly+LRaI2jz/+xhtvoNqGSG3gqaRYyrr4DlNM\nUPnShDvDZJrWcRkrNj//V7L+YvqUJmpezuJFMpkMAKBzOMNk0tKNOocDACCXy73WLP4IIGwi\nEjbxmLVmYVgjy5QsfShm2WKaMTKMmeUAAE6Wdd7TyefiOAEAEEIpBkmICSEQQUhCTI5jKhxX\nYpiKwJU4psQwpV+l0EMQH+F2u18YNerwoUNYRAQbHVtYWHD2ww9/+vnnP5OTvVJABXkoq1at\nev/996FMxjZpCig6devWbb/+uvHbb3v37u3tpiGBhp/AbsPT79jErY7cPNo57M69Y+slW9Je\nO9Urtuuc4eumXJ7Fy1m8qGvXrhDCz06fWzegT+nGFf+eAwD06NHDa82qfXIck+NYfRIlrEEQ\nL9uwYcPhQ4fAuJfZ0S8CDOMAAEePZC6c+95773311Vfebh1SmevXry/64APQpi07Zz6QKwAA\nbE42NeuNV6dNO3fmDOpzRfjFTyKST1NNsUmfl0Z1JUTBbT+b3Lg4ZSUvp/Cuxo0bjx49+rtL\n1/pu/m39+cvfXLzy1NbfPzt9fsiQIe3atfN26xAECXzbfv0VNmwIxowFpdWfu3bjevf9c+fO\ngFyIE0h27NjBMgw3882SqA4AACLrseMn6YuKjhw54tWmIQGInx47huMEynLqkOISHAD+J2l5\nxdKlS2NiYpZ98snRvw4CAMQi0axZs6ZNm+btdiEI8kjIys7mEpuD+zNHgpgYas+uwsLC+vXr\ne6ldyIPl5ORgYjEbHn7f1pgYAEBWVpZ32oQELn4Cu6kNFYvWvpc1NzlKeHf6FOvOm//5dXm9\nN3k5hdcRBPHqq69OnDgxNTWVZdm4uDiBoJxYFkEQpDZo1GqjTld27XpREYSwJMUM4rPUajXr\ndAKLBdw7J7uwEACg1Wq91iwkQPEzFDt52xxYvKdZQq8Fq77dd/jY8aMHf1j74ROPNUnW0zM3\nT+XlFD6CJMkmTZo0a9YMRXUIgtSlfn37clcugX//ubspNwfbvbNd+/YKhaLixyHe16dPH8Bx\nYOPXoHTBmdsNv98oEAq7du3q1aYhAYifHjtN4swryfjwSe/On353uoBIk7Dgp1/mtA2EdCcI\ngiDe9b///W9HcnLu7De5Hr1AXDzIz8P+2i0A4INFi7zdNOQBunbtOnTo0O3bfoHXrnIdOwGn\nCzu0n83JeXfBApT+F+Edb3nsGg6Yfipj8uWTh89dz7AzRHh0sx7d2yhw7xWdQBAECSAajWb/\n3r2LFi3aum2b88A+giC6de/+/sKF8fHx3m4a8mBr1qxp27btpytX6jZ8BQCIjo+fs3HjwIED\nvd0uJADxU1LMZwVqktvqlRTzFxKJJLATFKOSYv7LF0qKsSybl5cXHBzM+4QQVFKsDuh0OoFA\nwPvoOSophpTiZ44dAEB3ZvuEZ/uO3Z5R8uu+/q06Dhqz5d8ivo6PIAiCAAAwDIuMjETTfP1U\nUFAQmhOJ1Cp+AjtTyrr4Ds9+nXyGFN1TeeLA5uc7x625FpidLgiCIAiCIL6Gn8DuTuWJzJyv\nnrhT+a71ki1pmcfbS5xzhq/j5RQIgiAIgiBI5VDlCQRBEARBkADBT2D3KFSeQBAEQRAE8XG+\nVnmCPbR5dfKRs1kWPCGx3dhp46Il5bcw/e+tP+w8fvVGjrJe46dfntHvMU3NngGCIAiCIIjf\n863KE2nb3vv05xMdnpkwb0aS7Nb+d2euLbe7T3fm6xlLf9S2HfjeB3P7N3Gunv/aJTvFyxNB\nEARBEATxX75UeYJzL//5WszznwzvEwMAiF0Khyct/SFn7JhIaZkdVy/fWW/ggilDHwMANG38\n4e28eSdTzI+1QBX3EARBEAR5pNVu5Qk5dJgtdoVcUpUjuExHMp3MlL6RJb8KVV1ayVacOZQ/\nZlTMvbu5LSdOW9wThsf9twGbMf99vp4FgiAIgiCI/+ItsAMAAChI7Ng3sePdDVl/PR095Drl\nzKjKo922iwCAphKydEsTCbH7ogmMun838ykAQOiVP2dt/uNWviO0QczgpGkDWoaV7jBz5syj\nR4+W/FyvXr3t27dX+wn5OI0mkGcW4jgewBnPSZKUyWTebkUtUqvV3m5CLQrgKxMAIJVKpdKy\n4yQBI7BfO5Ik+X2CFIWmOfkffgI7jrF+PmPCxv2n9Q763u35mRlQ3LSKB2FdNgCAlrg77S+I\nxGlr2bJLjMsMAFi++ujISVNeChVeO/LLl/OmuD7fNDQqkL8mHzU5OTm1UTEJQRAEQQIbP4Hd\nuYU9pn9+Jq5j33jVtb9OZD8xZKgQOK8cPAA1PVdv3ljFg2ACMQDASLMy/M7SWj3F4KqyX+0Y\ngQMAes6b93SCGgDQuEmLvOMjtq++PHRJh5Idhg8f3rVr15KfJRKJ1Wqt8fPzORBCqVRqs9kC\nrNSvyWRasmTJtxs32qxWnCA6d+68ZPHi5s2be7tdPBOLxTRNB+StMI7jYrEYAGC32wOykDFJ\nkjiOB2SdXwCAVCqFELpcroC8ODEMC9RvBACAUCgkSZJhGH7r/LIsS5Lkg/dDfAk/gd07q65o\nExfdPP4ux1ijZeoun3/3bpTcUXg4sdFAa0RVu/RJ6WMAHLnhoEtzpqQ4aGUXVdkWS+IAONG9\ngbx0S/twyRFdbumvnTp1Kv05UAvJlwR2LpcrkL47rVZrv/79U1JSQOcuIKEpY9Af+2t39x49\ntm3deu9rGgCEQiFFUQEZHBAEURLYuVwuhmG83Rz+QQghhAH52gEAJBIJhJCm6YB8ggRBSCSS\ngHxqAACCIEiSZFk2UJ8gUnX8pDs5anY3fG4wAADisjEhkgNn9QAAcUj378Y2XDTsqyoeRKTq\nGSHA9xwrLPmVsp3/1+Ju3Ses7G7q/moC23vTdOd3jjmUY5fHxADEz3311VcpN2+C9+aB95eA\nUWPAtBnsN5tYpXLW2297u2kIgiAI4h/4CezUBKQsd7ru29eT5uzIKfm5wTP1ilM/repRoOCN\nYQmp387fd+ZGXtrlr+cuk4T3TqonAwCkbf3+m++S7+yFy2cNjTvwwdzfjpxOvXHxl89mHbGS\nYycn8PJEEC/au3cvbNgQ9Opzd1NQMPvk0OvXrmVnZ3uvXQiCIAjiN/gJ7MZHylO/+TDLxQAA\nooZEZu9cV7I9f3/BQx0nduSiV55suvnTua+8tShF1WnR8ldL2pdzYNcfO4+V7tZ0zJIpg2N2\nr/9k1txPThRop3+4upNKyMsTQbxIbzByQR5ZD4ODAQAGg8ELDUIQBEEQf8PPHLtJX09Y2POT\nmKD6N4oyY5LG22dP6Tgu9JlG1LJllzXNPnmIA/2/vTuPj6K+/zj+mdl7E7JLNkAUEIVwRKAK\nWLUIQiAIWqUgqEUkIIcWbSseaC3etfozxQoqFIoCQqlAPblEAeWw0FqtigoIBUtEQSDn5tjN\nHvP7Y0M4xJbYXWb3m9fzr+x3JpPPPL7z3bx35juzmmXA6DsHjD6xuffMRb2PW816WcEdlxX8\nz3UjmbRq1fLf27ZHo1HRj/m8sWe3pustW7Y0ry4AAFJGfM7YndGn8MOXn7zyko66Jmln3Pzi\npP7/eGHq3Q9Oq2mdv2j1zXH5E1DesKuvjh46KLNnSvjIQ3M+eF9fsezSSy/1+fhaEQAA/jst\nQc/LqPhy5xdVznM7nmXTErH5U6XwXbE+n6+kpESlu2Ijkci48eNXrlihZ2dHc9prxcXG9m0t\nss9YsXzZ2WefbXZ18eTxeILBoJI3r1mtVq/XKyKlpaVK3hXrcrlsNltFRYXZhSREZmamrutV\nVVXxfWRGkogdnIcPHza7kIRIT093Op2hUKi8vPy/r90Qaj/SWUnxOWP3bRmtO5zXyeRUh9Ri\nsVjmzZ07e/bsSzt1arH7X+e7nJMmTdqy+a+KpToAABInrl8pBvxvNE27+uqrb7jhBrfbrerZ\nVgAAEidRZ+wAAABwmhHsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4A\nAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAgIlJZ\nWVlUVGQYhtmFAPj+CHYA0Nj97W9/y7/sMq/X26ZNmxbZ2Q8//HBlZaXZRQH4PqxmFwAAMNPK\nlStvHDtW83qN664Xr7d668fPPvvshk2bVq9aZbfbza4OQMMQ7ACg8YpEIr+6916tZavojFnS\nJENE5LoRsmLZJ08WLliwYPz48WYXCKBhuBQLAI3X9u3bD+zfHx06rC7Vxfz4Kr1Zs3Xr1plX\nF4DviWAHAI1XWVmZiEhW1nGtmhb1ZZWUlppSEoD/BcEOABqvVq1aiYjs3n1cazCo7/vyrNat\nTSkJwP+CYAcAjdfZZ5/drXt3/eWlsvWjuqZgQKY9Ga2sHDZsmKmlAfg+uHkCABq1Z55++idD\nhxZP+oXWoaPRNFPfuSNaUjJ69OhBgwaZXRqABiPYAUCj1rFjx79v2fL0009v3rz50Df7z73o\nojFjxvTr18/sugB8HwQ7AGjsPB7P/fffn5mZqet6VVVVTU2N2RUB+J6YYwcAAKAIgh0AAIAi\nCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAA\nAIog2AEAACiCYAcAAKAIgh0AAIAirGYXkHAul8vsEuJP0zQRcTqdhmGYXUv82Ww2EdE0Tcm+\nExFd1+12e6wTFaPrdZ8VnU5nNBo1t5hEsNlsFotF1SMzdkzGBqB6Ygenqn1ntVpFRNf1+O6g\nkqNYeeoHO7vdbnYJiWK325UMdhaLRUQ0TVO172L/YJQMdvU7ZbPZlDw4dV2P5XKzC0mIWPdZ\nLBaFD05V+y72rhL3gzMcDsdxazg91A925eXlZpcQf5qm+Xy+iooKJT9Oud1ut9sdjUaV7DsR\n8Xg8wWAwEAiYXUj8Wa1Wr9crIn6/PxKJmF1O/LlcLpvNVlFRYXYhCZGZmanreiAQqKmpMbuW\n+IsdnKq+q6SnpzudznA4HPcdVPUcp8KYYwcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACg\nCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0A\nAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCII\ndgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAA\niiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgB\nAAAowmp2AUhJ4XB4+fLlW7duNQyja9eugwcPttlsZhcFAEBjR7BDg+3cuXPsuHGf79ghui4i\nEo1OffLJuc8/n5uba3ZpAAA0alyKRcPU1taOHDVq176v5N77ZdVbsmqN3PfgngMHRo4aFQwG\nza4OAIBGjWCHhlm7du2/9+yJ/uI2uWygOJzicEj/AdHb7vxy797Vq1ebXR0AAI1ayge7QFlp\nddQwu4pGZPv27SIiP7zwuNaLLxaRzz77zIyKAABAnWQLdtH1i5+985ax146a8MATc/ZUh//z\n2oHiLeNuHLPwYPXpKQ4iYrFYRERCx3dNba2IWK1M2QQAwEzJFez2vHzfU0u2XHz1hAcnFaTv\nXjfl9tnR717ZiNbM/NV0f4TTdadVjx49RETWrTmude1bInLBBReYUREAAKiTTKdYjNrfL9ne\nbsTUa/LbiUhOoXZNQeGir8aMapl20tU/nD/lQ09f+WbV6a2ysevVq9eFF130j+f/aFSUS99+\nouuy/m1t6eJuPXr07dvX7OoAAGjUkijYBcs3FgUiEwe0jL10eHt1S5/2wfoDo0a2+/bK5f96\n5bHVgceeH3bXyBODXXV1dThcd6HQMAxN0xJatiliO6Vp2unfO03TFi5YcNfkycsX/1kW/znW\n+OMrr5w6dWrdVdp4/IkTflCPKbkwTOEAABCKSURBVH13GhzbdwrvoJK7Vo++S0WN4W0TpyiJ\ngl1t1VYROdd99Dm3uW7r6q3lMvLENaO1+397/6JB98xu7z5JkpgyZcqmTZtiP7dq1eq1115L\nVMVma9q0qSl/1+fzLXv99U8//fT99983DOOCCy7o2rVr3P+KxWLx+Xxx32ySsNlsaWknPxWt\nBq/Xa3YJCaTwkSkibrfb7XabXUWiqN13NpstvjsYCoXiuDWcHkkU7KLBKhHxWY9O+8uyWcKV\ngW+v+Ubh/WXdbx3fI8uIlJ6++nC8Ll26dOnSxewqAADAUUkU7HS7S0RKw9H0I1f0ikMRi9d+\nwmoH/zZj3vbsWfP7ftd2Jk6cOGLEiNjPNputvLw8IeWaStO0jIwMv98fjf6H20tSldPpdDgc\n0WjU7/ebXUtCpKWlhUKh2tpaswuJP4vFkp6eLiKqHpwOh8NqtVZVVZldSEJkZGRomhYIBJR8\n2Hjs4FTyP4KIuFwuu90eDofje3AahqH22XclJVGws6V1Fdn4eU24taMu2O2qCXt6nXhIHdq0\ntda/f+ywIfUtK28asSbtvJde/E3sZYcOHeoXRSKR0lIFz+rFZlGEQiEl/3fGvnbWMAxVrwIY\nhhGJRJTcO8Oou0s9HA5HIhFzi0kEq9Wq67qSfSdHJiWrfXAquWsi4nA4ROm3TZy6JAp2Tm/e\nmfZZb757MP/K1iISqvroPX/t1fnZJ6zWruDXvx9ad+Aa0Yo773rokim/vaa5ytMmAAAATkUS\nBTvR7HcN7zR5/kNrz7i7c9PQshlPus/oX9AqXUT2vPSnDdWeGwuuEhFnizY5Lep+IzbHztum\nbdtsleehAwAAnIpkCnYiOdc9ektw2uKnHigOaO3O6/PoIxNid1J89fYbK0paxYIdAAAATkqr\nnxOjJIXn2Pl8vpKSEiXn2MWetqBq34mIx+MJBoOBwEnu+E51Vqs1NtW6tLRUyTl2LpfLZrNV\nVFSYXUhCZGZm6rpeVVVVU1Njdi3xFzs4Dx8+bHYhCZGenu50OkOhUNzvDsnKyorvBpFoyfWV\nYgAAAPjeCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAA\ngCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2\nAAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACK\nINgBAAAogmAHAACgCIIdAACAIgh2AAAAirCaXQBERDZs2LBgwYKdu3a1aN68d+/eN998s9Pp\nNLsoAACQYjhjZzLDMCZPnjx8+PAV69fvsDk27fni0Ucf7dmr1759+8wuDQAApBiCnclWrFgx\nf/58GXRFdMkrMv3Z6II/y28e/2r//jvvvNPs0gAAQIoh2Jls6dKluscrd0yW+muvvXpHBw95\n5513Dh06ZGppAAAgxRDsTLa3qCja5myx2Y5r7dDRMIyioiKTigIAACmJYGcyT0aGXlZyYmtJ\niYh4PB4TCgIAACmLYGeyvLy8aFGRbNp4tMnv15e/1uqss9q1a2deXQAAIPXwuBOTTZgw4cUl\nS/Y+dJ+Rf5l07iIlJfqKZUZpyeMvvKBpmtnVAQCAVMIZO5M1adJk9apV148YYXl7rTw1VV6Y\n2zG7xauvvDJo0CCzSwMAACmGM3bm8/l806ZNKyws3L17d4sWLTIzM82uCAAApCSCXbKw2+25\nublmVwEAAFIYl2IBAAAUQbADAABQBMEOAABAEerPsUtLSzO7hPiLPQnF7XYbhmF2LfFns9lE\nRNd1JftORCwWi8PhsFgsZhcSf7pe91nR5XIpeXBarVaFj8zYG4vdbq/vR5XEdkrVvou9bVos\nlvjuYCQSiePWcHqoH+yUfIeK0XVdyf+d9Q/wU7jvNE1Tcu+O7TtVD05V+66eqjsYOziV3LVj\nxXcHlRzFylM/2Pn9frNLiD9N0xwOR2VlZTQaNbuW+HO73W63OxqNKtl3IuLxeILBYCAQMLuQ\n+LNarXa7XUSqqqqU/KzvcrlsNpuqR2ZmZqamacFgsKamxuxa4i92cKrad+np6RaLJRKJxH0H\n3W53fDeIRFP8swsAAEDjQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwS0mGYRQXFyt5S6yI\nzJs3Ly8vb+TIkWYXkigVFRXBYNDsKhJix44deXl5eXl5RUVFZteSEIFAQNXbKkVk6NCheXl5\nS5cuNbuQhAiHw8XFxWZXkSiFhYV5eXm333672YXAfIo/7sRisWRlZZldBRrGarX6/X6v10vf\npZxvvvkmlns8Hg/dl3Kqq6v9fr/NZqPvUpHf7w+Hw/QdOGMHAACgCIIdAACAIhS/FItU1LZt\n2/z8fJ/PZ3YhaLCMjIz8/HzhafWp6dJLL62qqmrTpo3ZhaDBcnNzq6ur27Zta3YhMJ/GN8EB\nAACogUuxAAAAiiDYAQAAKII5dkgu32yZMuHxT45tGTtv6RCf06x6cIrmTxztfGTWT5u5jjRE\n1y+euXzjP7/0Wzp1uXDML25s6+bdJkmd0HeMweRnhEtfnTP7jc0fFwf0M1q3HzzqZwO7ZR9Z\nyNBr7OhvJJeyj8pcvqtum9C5vqVNE5uJ9eAUGLs2Pf/q12XXHDNhd8/L9z21ZO8Nt/58bNPw\nytkzptxeu2j2rVwgSD4n6TvGYPJ767G7Fm3LGHPTLzudmbZ13YszH7q15tkXhrROF4YeCHZI\nNge3VXjP7dmzZ+f/viqSwMEt0+555t3iytrjWo3a3y/Z3m7E1Gvy24lITqF2TUHhoq/GjGqZ\nZk6VOJmT9x1jMOlFgl/O+uBwn8emXtW5qYi079R1/3vXvTbz0yGPX8zQgzDHDsnmo4pg027e\nSE3FgYNl3LCd/Lydr5nyyP9NfeKeYxuD5RuLApEBA1rGXjq8vbql2z9Yf8CMAvGdTtp3whhM\nepHAv9ucc84VbTOONGjdPI5QWaUw9CAinLFDsvmwMmS8+/S1z+wIGYY1rdnA62+7+aofmF0U\nvpM9o2VOhkRqj5uAVVu1VUTOdR+9fpfrtq7eWi7Kfv1vSjpp3wljMOnZPb2nTetd/zJUuWPu\n15VtbuwoDD2ICMEOSSVS+1WlxXZ2Vs8nFj3iNfx/XzX3d3Puc7RfMKaT1+zS0ADRYJWI+KxH\nLwhk2SzhyoB5FeFUMQZTy973Vz09fW6o7eVTBrUShh5EhGCHpGKxt1y6dOmRV47e1929c/UH\nbz/36ZipvcwsCw2k210iUhqOplsssZbiUMTitZtaFE4JYzBV1JZ+PveZp9/4sKTP8Im/vb6f\nU9OEoQcRYY4dkly3Fq5QxSGzq0DD2NK6isjnNeH6ll01YU8XTvmkJMZgEvLvXffzm371sZxX\nOGfeHSP7x1KdMPQgIgQ7JJWynTPGjb/1QG30SEN0w9fV3nM7mFkTGs7pzTvTbnnz3YOxl6Gq\nj97z13bPz/7Pv4VkwBhMfka0+rf3zHT0/+XMB27qmHXcFEmGHoRLsUgqGW2v81X/7J6HZv/8\n+n5ereaDNX/aWNXkgfH8U0k1mv2u4Z0mz39o7Rl3d24aWjbjSfcZ/QtapZtdFv47xmDyqz64\naFt16Mau7g/ef7++0erKOb+zl6EHEdEMg/vZkUSCpZ/Nm7Xorx/vCliatG3fZcjYm350Fu9K\nyS5Su2/o8FuufW7xDc3ddU1GZM2CaUvWvFcc0Nqd1+dnd0zISeNjZDL6dt8xBpPcgXen3FT4\nyQmNGa1//acZF4sw9ECwAwAAUAVz7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbAD\nAABQBMEOAABAEQQ7AAAARRDsAKSSxblZrqb5ZlcBAEmKYAcAAKAIgh0AAIAiCHYAEsiI1Eb4\nPmoAOF0IdgC+j8q9Gyf9dOBZzbyOtMxO3fo9PHtV9Jilbovec9bHz952ZVaa22axN2vdueDu\nGYdDR1f55u9LR17+o2bedHuap8MP8x+Zv/6E7e//66JrB1zga+J0e5pdfPnIv/zj0LFLaw5s\nvmnwJb4Md5qv5UWDCtbsq0rcngJACtEMg0/TABqm6uvXzsu5tkhrOfLG4TlZlo/X/+UvG/ac\nXzDvwxfGxFZwW3RHbnb5tkMDrim4sL1368aXlm0qyu51975NT1hEDr0/NedH99Q4cq4fPaRt\nk5pNry9cu6Ms/771a37TJ/brB959NKfvg0bWDwtuGNjcUvLK889tq3D/8fMvxp2TsTg3q2Cv\n74euItuVNw/t2eHQP98onLPS1uxK/zfL+JwKAGIAQAM91Nlnc+duPlxT3/LqHeeLyKO7y2Iv\nXbomIr/8y/a6xdHQ3J91EZEx678yjOi1zd02d+7G/VWxhZHQoTu7ZWm6c2N50DAMIxrMb+p0\n+QZtr6yNrVBTvD7Tpmdf/KJhGC928onIRQ+vr//TK69rJyIbyoKJ3msASH58xAXQMOHqz36z\nraTTxBd+5HPWN17xwHQRWfKHnfUtaS1GTR/eqe6FZh311Ktui/7mvZtrDr+y9GB1xwnzeme7\nYwt1a9aUP48xooEH39wnIv6vnlpbGuhROL1Tmi22gjOzz2t/ePb+cVl1G7O4XvpVr/o/1OGq\nliJSGT32UjAANFIEOwANEyh5I2IYnzx5oXYMh7ePiJR/Ul6/mrfj9cf+ltWZ8+NMp3/vO4HS\n1SLStuCcY5emty4Qkf1vHRCRil3viMgl/Vocu0LvcRNvGV/3+Dp7evdWdkv9Is2qxXX/ACCF\nWc0uAECq0e0i0vXuub/rd+YJSxye84++0E7MWzZNjGjwpJvUNKuIGGFDRKLBqIjYv/Xrx6zs\n/K5FANDIEewANIwz8wqLNilc1nHgwJ71jeGaHS8v+zj7PHd9S9nnS0QG1r+MBPcuLw6k/aCP\ns6lT5PkvFv1bujevX1q5b6GItOjfQkQyOnQXWfPX9w5Lm4z6Fd6+Z+LC4qbznnssobsGAKmO\nS7EAGsbqzHno3MxdC0evO1Bd3/jirT8ZMWJE0THvKFUH5k1+/V9HXkUX3z3EH4n2fbSPK2vY\n1c3cO2aP23IoEFtmhEseH/mcpjseuLK1iGS0ufe8dPvff3nXF4FIbIXa8i0F0+eseO9oEAQA\nnBRn7AA02KRVM+d0GHl5uy5Dfzq4R/vMT99esnDNzq5jFo5qfvSMXVrLHtOHdd4+YuyFOZ6P\n1y99Zf0XzS+8beHlZ4nIH5bf/9YlU/q26zF63NBz0ms2vDLvzW2l/aas6+91iIhm8bz+p1va\nD53eNafPjTcMzLaVvTpn1v5I2oyXxpi1vwCQMsy+LRdASir7fPXNQ/pke9Pt7sxO5/d6cM4b\noejRpS5dO2fI27uWP9Ezt6XTass8s+P1dzy1vzZSv8LX7y766YALfRkuq7NJu+55D89754Tt\n/+uNWYN7d8lw2xxpTbv3u27h5v2x9hc7+Zze/setubiPiKwsqTEAoNHjAcUA4s9t0bMHr9vz\nap7ZhQBA48IcOwAAAEUQ7AAAABTBzRMA4m/o8OHeC5qZXQUANDrMsQMAAFAEl2IBAAAUQbAD\nAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABTx/yI1sQTI\nM2pqAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history;plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a94d788c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:37.603060Z",
     "iopub.status.busy": "2023-04-04T17:31:37.601597Z",
     "iopub.status.idle": "2023-04-04T17:31:37.619445Z",
     "shell.execute_reply": "2023-04-04T17:31:37.617851Z"
    },
    "papermill": {
     "duration": 0.044523,
     "end_time": "2023-04-04T17:31:37.621841",
     "exception": false,
     "start_time": "2023-04-04T17:31:37.577318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  0   1   2   3 \n",
       "226 222  95 227 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(test_generator$classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3de5aad",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "563d4120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:37.670031Z",
     "iopub.status.busy": "2023-04-04T17:31:37.668645Z",
     "iopub.status.idle": "2023-04-04T17:31:42.657688Z",
     "shell.execute_reply": "2023-04-04T17:31:42.655752Z"
    },
    "papermill": {
     "duration": 5.016535,
     "end_time": "2023-04-04T17:31:42.660756",
     "exception": false,
     "start_time": "2023-04-04T17:31:37.644221",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_class\n",
       "  0   1   2   3 \n",
       "323  87  80 280 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_class <- function(vec) { return(which.max(vec)-1) }\n",
    "\n",
    "pred <- predict(model, test_generator)\n",
    "pred_class <- apply(pred, 1, max_class)\n",
    "\n",
    "table(pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8e7f44d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:42.707173Z",
     "iopub.status.busy": "2023-04-04T17:31:42.705811Z",
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    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "   pred_class\n",
       "      0   1   2   3\n",
       "  0 216   9   0   1\n",
       "  1 104  58   8  52\n",
       "  2   2  11  72  10\n",
       "  3   1   9   0 217\n",
       "\n",
       "Overall Statistics\n",
       "                                          \n",
       "               Accuracy : 0.7312          \n",
       "                 95% CI : (0.6984, 0.7622)\n",
       "    No Information Rate : 0.4195          \n",
       "    P-Value [Acc > NIR] : < 2.2e-16       \n",
       "                                          \n",
       "                  Kappa : 0.6288          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : < 2.2e-16       \n",
       "\n",
       "Statistics by Class:\n",
       "\n",
       "                     Class: 0 Class: 1 Class: 2 Class: 3\n",
       "Sensitivity            0.6687  0.66667  0.90000   0.7750\n",
       "Specificity            0.9776  0.75988  0.96667   0.9796\n",
       "Pos Pred Value         0.9558  0.26126  0.75789   0.9559\n",
       "Neg Pred Value         0.8033  0.94708  0.98815   0.8840\n",
       "Prevalence             0.4195  0.11299  0.10390   0.3636\n",
       "Detection Rate         0.2805  0.07532  0.09351   0.2818\n",
       "Detection Prevalence   0.2935  0.28831  0.12338   0.2948\n",
       "Balanced Accuracy      0.8232  0.71327  0.93333   0.8773"
      ]
     },
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   ],
   "source": [
    "conf_mat = table(test_generator$classes, pred_class)\n",
    "confusionMatrix(conf_mat)"
   ]
  }
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