{
  "schemaVersion": 1,
  "lessonId": "state-02",
  "contentHash": "0a7645bdac396ac8fd5de8325968a22ca7d46e85fe4e484664f1e46880575d33",
  "executedAt": "2026-10-20T10:45:28.661784+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Before/after: 4.0 0.6399999260902405\nBefore/after: 4.0 0.6399999260902405\nExpected tree mismatch\nPASS: state-02\n",
  "notebookCells": [
    {
      "sourceHash": "cc691a3a883d9e3de3ba95715941564601e11032419b3b90da42657c937572ad",
      "outputs": []
    },
    {
      "sourceHash": "42445c22554f4b55527826770edc960a554785349877c98f35607bc48b57b23e",
      "outputs": []
    },
    {
      "sourceHash": "e674b33d812dd2f21f6a6f89b48dd60fe47109414b375da0121a559e60ec1ba2",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before/after: 4.0 0.6399999260902405\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "754c9dd27a99b8b03d7d0af10d419355209313069e44267ba94bef124b71f8f4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before/after: 4.0 0.6399999260902405\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "dd4234a5517e6ffd4e196e6a603f42d6e9d4b53bf280e427c2d9cef200d534f6",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5lpSwAStH7do/dX+sU6fOMv1/vPLS67NvCa6+8Z/RuHHj+falb2r83wYAKJtZcKvKbLh5PI+RI0bHqy8NiK+GfROTJ0+JObN/6nUye/acGD/ux5g8aUo0avzTNW61omol16oLtqUofvrbtvoi9o39YVwWrKTZQzttvski27HFlh3jlRcHxIdDhsaGHdYveZxk6223iIaNGkR5SH+zp+ezotfmAphykEKTFMD8+OOPWW2YFi1alEczgHI2+PW348F7H49Tfn9srLlW2/JuDgAABSgNMbrlhruXOJRn0qTJJQHMioQ8yfdjfohOG+wUSzJ+/I8LbWvdulVUdgIYgHKSCpgll19yTbYsaNiXX2cfTg0aNohXBvUthxYCAFCVPdPvxbjx2jtilZYt4rQ/HB+bduoQzZo3iVq1amX7e/7quGx2pJg7d4Ufa87/13Jp0qRxSdHexVln3XaL7XFemQlgykHxcKI0FXXz5s3LowlABbDOemvF3vt3W+S+Rx/smwUvO++2U6Wr7g4AQOXw7NMvZeuzzz8tduyy7UL7h389oswea9XVWmbrevXrxgUXnxWFSABTxpZW0+X111+P++67L7vdo0cP081CgRg3bnw2frZJ08bRtGmTbNs2226RLYsLYFq0aBbnXnjGSm4pAACFYsL4Cdm61f+HI/Ma+NqgGDd2/DKdr+b/F8idtYjhTC1brRJrr9suvvhsWAz96NNYv/16UWgKdhakNAvR999/X7LMmjWrZCrZebenOi3zGjZsWBaapGVRsxil6afPOOOM6N+/f0ydOrVk+5gxY+Lyyy+PnXfeOXus1PvFLEhQuT38wBNxzh/+li3P9nsx2/bIg31LtqX9xe6785HYd4/DszUAAFQE7dZeI1vff9cjJUOEinu+/PW8y5b5fGkoU/Ll518tcv8xJ/TK1meecn4229GCUrHfdD09bdr0qIoKtgdMp06d4quvFv6luPfee7Ol2E477RQvvvjTH1alkUKbhx56KC655JKsWnMKWpLx4/+XHLZp0ybrBbPOOuus8PMAys87b74ffR7tN/+2t97PlmL7HrBnObQMAACW7uDD9osnHv1v9sXh4DfeiQ02+lnWK+bNQe/GxptuGM1bNIt33x5S6peyy87bZ9fHf/r9hbHNdluWzFp00mm9s57gu+zeJY498eu49qpb4vCDjo2fbbButF1j9ahevVpWpDfNkjRz5szYedcdq+Qw/IINYPLyxz/+MQt30lCj4cOHx9ixY7MeL6uuumpsvPHG2bCjnj17mloWqoA0drW041eP+d0R2VJab3/803hcAADIyxprtom7Hro+rrrs+nj37Q/ipedejdVarxpHHn1IHNn7kDjuqN8v0/m67LxDnHn2SfHw/X2yaa2nT5+Rbf/tMYdlAUxy9HE9Y+vOm8e9dz4cb7/5XtZbpm69OtGyZYvotteu8YtddsxqIVZFRXNT0RIq9ZTW8xb2BQAA4H8+/vjjbL3BBht4WSjX36GCrQEDAAAAsLIIYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAACAKquoqCjmzp2bLbCsin930u/RiqqxwmcAWEZ1/3yh16yATf3L2eXdBACggNSuXTumTp0akydPjgYNGpR3c6hk0u9N8e/RitIDBgAAgCqrYcOG2Xr06NExadIkPWEoldTrJf2+pN+bpFGjRrGi9IABAACgymratGnWiyEtw4cPz7aVxXASqra58wxZq1+/fjRp0mSFzymAAQAAoMqqVq1atGnTJsaPHx8TJkyI6dOn6wVDqX5v0rCj1PMlhS/p5xUlgAEAAKBKS388N2vWLFugvKgBAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAORMAAMAAACQMwEMAAAAQM4EMAAAAAA5E8AAAAAA5EwAAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAORMAAMAAACQMwEMAAAAQM4EMAAAAAA5E8AAAAAA5EwAAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAAhgAAACAyk0PGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICc1cj7AYCIvo8/HW8Pfi8+/OCT+OyTL2LmzJlx/kV/iB77dvXyAAAAFAABDKwE//nXTTFq5Oho0rRxtFileXYbAACAwmEIEqwE51z4++j73H3xwoDHY/9f9fCaAwAAFBg9YGAl2GbbLbzOAAAABUwPGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnNfJ+ACDi4QeeiHfefD97KT775Its/ciDfWPwG+9ktztuvnHse8CeXioAAIAqSgADK0EKX/o82m/+bW+9ny3FBDAAAABVlwAGVoILLj4rWwAAAChMasAAAAAA5KwgA5gpU6ZEv3794qKLLooDDjgg1l133ahWrVoUFRVFr169yuQx5s6dG7fffnt06dIlWrRoEXXq1Il11lknjj/++Bg2bFiZPAYAAABQORTkEKQ33ngjunbtmtv5Z8yYEfvuu2/07ds3+7lGjRpRt27d+OKLL+Lqq6/OgpmHHnoodt1119zaAAAAAFQcBdkDJmnatGnWO+XUU0+NO+64IzbeeOMyO/fpp5+ehS8peLn88stj4sSJMWHChBgyZEhstdVWMWnSpNhvv/3i66+/LrPHBAAAACquguwBs8MOO8TYsWPn23bttdeWybk//fTTrJdLcu6558bJJ59csm+jjTaKJ598MjbccMP47rvv4pxzzolbb721TB4XAAAAqLgKsgdM9erVczv3nXfeGbNnz4769evHSSedtND+5s2bR+/evbPbDzzwQFaPBgAAAKjaCjKAydMzzzyTrXfcccdo2LDhIo/p1q1btk7hy2uvvbZS2wcAAACsfAKYMvbBBx9k6w4dOiz2mHn3pbowAAAAQNVWkDVg8lJcbDdp06bNYo9Lw5OaNGkS48ePj5EjR5bq3J07d17k9hTgtG/fPntsqCxa16ld3k2gHHm/AgCgKlvcaBgBTE5/VNSrV2+Jx6b9KYApDmwKzdk9fxqqRYHavLwbAAAAsHIJYCqJAQMGLLFnzOIStopq0rjybgHlaeS06f4BClhle78CAICyoAZMTn9ULG12o+L9jRo1KssmAAAAABWQAKaMA5jiQGXEiBGLPW7y5MnZ8KOkdevWZdkEAAAAoAISwJSxjTbaKFu///77iz1m3pmPljRbEgAAAFA1CGDK2C677JKtX3nllZg0adIij3nyySdLCvFut912Zd0EAAAAoIIRwJSxQw89NKpXr56FL1deeeVC+8eOHRvXXXdddvuAAw5Y6mxJAAAAQOVXsAHMuHHj4vvvvy9ZZs2alW2fPn36fNt//PHH+e43bNiwKCoqypbzzjtvofOut956cdxxx2W30/4UwqRzJh9++GF069Ytvv3222jQoEFccMEFK+W5AgAAAOWrYAOYTp06xSqrrFKyvP7669n2e++9d77te+211zKf+5///GcWtMycOTNOOumkLGxp3LhxVh9m4MCBUb9+/XjooYdijTXWyOGZAQAAABVNwQYweapVq1b06dMnbr311thpp52ymZFSL5i11lorjj322KwI76677lrezQQAAABWkhpRoNJQouXRrl27mDt37lKPS0OUevbsmS0AAABAYdMDBgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJzVWJ47zZkzJz76YGiMHPFtTJs2LbrvvXvZtwwAAACgUAOY++56JG645vYYN3Z8ybZ5A5gJP06MIw45IebMnhM33nFFNG/RrOxaCwAAAFDVhyD9469XZsvYH8ZFvfr1oqioaKFjGjVuGO03XC++/uqbeKbfi2XZVgAAAICqHcAMfG1Q3Hvnw1G3bp3455UXxMtvPBFNmzVe5LG77fHLmDt3brwx8M2ybCsAAABA1R6C9MC9j2U9Xnqf0Ct+scuOSzy242YdsvVnn3y54i0EAAAAKJQeMO+982G23nv/bks9tmGjhlG/fr34/vuxK9Y6AAAAgEIKYH4cPyHqN6gXDRs2KNXx1atXj9mzZq9I2wAAAAAKK4BJPVqmTpkas2bNWuqxEydOypbGTRqtaPsAAAAACieAWWudNWLOnLkx9KNPl3rs80+/nBXh3aD9eivaPgAAAIDCCWC67LxDFqrcdN2dSzxu5Dej4qrLb8gK9u68205l0UYAAACAwghgDjh471itdat46fn+8YdTz5+vJ8y0adPj88+GxS033B2H7N87xv4wLtZeZ83Yo8cuebUbAAAAoOpNQ12nTu248tqL45gjT4unn3ohnun3Ysm+7TbbveR26iXTslWL+OdVF0aNGqU+PQAAAECVVeoeMMk6660V9z16U+x7wJ5Rq3atLGyZd0mBS499u8ZdD14fa7Zrk1+rAQAAACqRZe6i0qx50zj7gtPjzLNPig+GDI0x330fc+bMieYtmsVGG28QdevWyaelAAAAAJXUco8RqlmrZnTcrEPZtgYAAACg0IcgAQAAALDsBDAAAAAAFWUI0uYbdlnmkxcVFcXgD55f5vsBAAAAFGQAk2Y5AgAAACDHAOb62/61xP2TJk2OIe9+GA8/8ETMnTM3/nDOydnMSAAAAACFrtQBzBZbdVzqMT//xXbx68P3j9/2PDmuveqWuOvB61a0fQAAAACVXpkX4W3WvGmc9eeT46thw+Pm6+8q69MDAAAAVDq5zIK0+VYdo3btWvHsf1/K4/QAAAAAlUouAUya/aioWrX4dvR3eZweAAAAoFLJJYD5cMjQmDZ1WtSpUyeP0wMAAAAUdgDzwfsfxdln/jXrBbPpZh3K+vQAAAAAVXcWpKN7nrzE/dOnz8iGHI357oeYO3du1KxZM3577GFl0UYAAACAwghgBr/xTqlPulrrVnH2+afHRhu3X952AQAAABReANP7+F5L3F+9RvVo1KhB/GyDdWPTTh2yIUgAAAAALEsAc8KSAxgAAAAAVuIsSAAAAAD8jwAGAAAAIGcCGAAAAIDyqAGz+YZdyuTkqRDv4A+eL5NzAQAAAFSpAGbu3LkrvyUAAAAAhRTAXH/bv1Z+SwAAAAAKKYDZYquOK78lAAAAAFWUIrwAAAAAORPAAAAAAFTUACYV6v1x/IQYPeq7GDXy28UuFdnkyZPjwgsvjI4dO0bDhg2zJd3+y1/+ku1bHsOGDctmf1racuihh5b58wEAAAAqUQ2YJXnlpYFxz+0PxrtvD4lp06ZX2mmohw8fHl26dInPP/88+7lu3bpZqPTuu+9my2233RYvvPBCtG3bdrkfo0WLFlG9evVF7mvSpMlynxcAAACowgHMVZddH7feeE+pp6muqNNZz549O3r06JGFL61atYpbbrkldt9992zfU089FUcccUS2Lx3z5ptvRrVqy9dRaNCgQdGuXbsybj0AAABQ2ZQ6WXh9wJtxyw13Zz06Tv79sfHgE7dm25s2axKPP3133HzXVfHbYw+PRo0aRpOmjePK6y6OJ569Nyqi22+/Pd55553s9gMPPBBdu3YtGRq0xx57xIMPPpjtS8ekYwEAAABWSgDzwD2PZQHFUb0PjcOPPCjWXuennh2pd0ibtq2j42Ybx7EnHhn3PHJj1K1bJ87/0z+iTt06URHdeutP4dFOO+0UO+yww0L707bi7WkoEgAAAMBKCWCGvPdhtt73wO4L7Jl/mNFqrVvFmWefFD98PzZuveHuqGimTp0ar776anY79XZZnG7dumXrV155JbsPAAAAQO4BzLixP0at2rVilZbNS7ZVr14tpk2bsdCx2+24ddSsWTNeeXFAVDQfffRRzJkzJ7vdoUOHxR5XvC/Vi/n444+X67EOOuigaN68edSqVSurNbPbbrvF9ddfH9OmTVvO1gMAAABVOoCpV69u1Klde75t9evXjymTpyw0G1KqE5PCmm+/HRMVzYgRI0put2nTZrHHzbtv5MiRy/VYb7zxRsyYMSNq164d3333XTz99NPRu3fv2HzzzePTTz9drnMCAAAAVXgWpNTz5csvvoqZM2dFzZo/3a3NGq3jow8+iSHvfRRbbNWx5NjRo76LyZMmZ7VgKpqJEyeW3K5Xr95ij5t334QJE0p9/jp16sQxxxwTBx54YGy22WbRuHHjbPtXX30V1113XVxyySXx4Ycfxq677ppNd92oUaNSnbdz586L3D5kyJBo3779fM+rMmjQtLxbQHlqXWf+MJfCUtnerwAAYFk0bNhwxXrArLVuu5gzZ258OvTzkm1bbt0pm2r6+v/cWtILJvX4uOSiK7PbP9tg3Sg0q666alxzzTXRpUuXkvAlWXPNNeOiiy6Ku+/+qS7OsGHD4rLLLivHlgIAAAAVrgfMNttuEc889UJW12XDDutn2w44eO+4986H481B78buO+0fa67VNr7+6puY8OPEbMakgw7ZJypyEjVlypTFHjfvvtL2UimNAw44ILbbbrt47bXX4pFHHonzzjuvVPcbMGDAEnvGLC5hq6gmjSvvFlCeRi4wbJHCUtnerwAAoCyUugfMzrvuGL2P7xWrtGpRsq316qvGRZeeE/Ub1IsJEybG++9+GD+On5CFL0f2PjR27/bLqGhWX331RdaDWdA333xTcrt169Zl2obi0OTzz//XmwgAAACoukrdA6Zho4bR+4ReC23v8svtY7On74lXXx6Y1X5p2KhBdN5uy2i7xv+Cjook1UupVq1aNhPS+++/H127dl1sbZXigsIbbLDBSm4lAAAAUJABzJI0btIouvXYNSqDunXrxvbbbx8vv/xyPPnkk3HGGWcs8ri0L9lhhx2y+5Sl4uFEa621VpmeFwAAAKjkQ5CmTp0WVUWvXj/15HnppZeyWiwLSttSQJP07Nlzmc6dihIvycMPP1zymHvvvfcynRsAAACo4gHMLtvvE+f98eIYNPCtqOwOP/zw6Njxp2mz999//+jXr18WnKQl3U7bknTMYYcdNt99X3zxxazGTVpuvfXWhc694447xgUXXBBvvfVWNiNUseHDh8c555wTv/rVr7Kf27RpE6eddlrOzxQAAACoVEOQpkyZGn0e/W+2tFq1ZXTba9fYc6/dYs12baKySXVdHn/88Wyq6FQIN9WBKR5mNHXq1Gy9zjrrZMekY5dFClrOPffcbEn3TVNRz5o1KyZMmDBfHZrUE6ZJkyZl/MwAAACASt0D5o/nnhqbdNwo6yUyetS3cfN1d8a+exwWhx90bDx47+MxccLEqEzatm0b7777btZbZdNNN83CkrRssskmcf7552f70jHL6p///Gcce+yxscUWW0SrVq2y6aynT5+e9Xjp3r173HLLLfH2228r7AsAAAAFpGju0oqWLOCb4SOjzyP94qknns1uZycpKoqaNWvEjl22zXrFbLfj1svcc4QVm9K6uLBvZXFUl4fLuwmUo7u3/9DrX8Cm/uXs8m4CAABU/ABmXm+/+V70ebRfPPvfl2LSxMlZEJM0bdYkdu/2y9hzr11jgw1/VpbtZQECGCojAUxhE8AAAFCIViiAKZaKzb7w7KvxxGP/jYH9B8fsWbNLCtUO/uD5smkpiySAoTISwBQ2AQwAAIWo1DVglqRWrVqx2x6/iKuu+3s88uQdsWGH9UtmFQIAAAAodKWeBWlpUs+X1APmhWdeiWnTppfVaQEAAAAKO4D54vNh2bTUT/V5JsZ890O2LfV6qV+/Xvxyt52i+967l1U7AQAAAAongBk3bnw81efZeOKxp2PoR5+WhC7VqlWLLbfuFN332T1+scuOUadO7TzaCwAAAFB1A5hn+r2YDTEa8OqgmD17dkl9l3Zrr5FNPZ1mPGrZapU82woAAABQtQOYM085L5vVKAUvjRs3yorudt9nt9ho4/b5thAAAACgUAKY6tWrxXY7bpPVddmxy7ZRs2aZ1e8FAAAAqNJKnaI8/crD0bRpk3xbAwAAAFAFVSvtgcIXAAAAgJwDGAAAAACWjwAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgIoSwOy4Vbf4+dbd4+uvvsm3RQAAAACFGsDMmD4jZs+eHWus2SbfFgEAAAAUagCzSssWMWfOnHxbAwAAAFAF1SjtgVtvu0U88sAT8cH7H8dGG2+Qb6sAAACgCvrg/Y/imqtuiXff/iBmzZoV6/1s7Ti01wGxa9dfLPW+c+fOjddeeT1eev61ePetITFq5LfZOdqu2SZ269olDj3iwKhdu/ZKeR7k2APmyN/+OurVqxt/v/CKmDJ5ynI8FAAAABSuQQPfil6/PiHeefP92HX3n8f+B/WI78eMjTNPOT9uv/m+pd5/xowZ8bujz4zHH+6XjVLZ98Dusdd+e8T0adPj3/+6MY469MSYOnXaSnku5NgDpqhatTj7gtPjL3++JPbvfkQcfNi+0XGzjaNZ86ZRrdric5zVWrdajmYBAABA1ZF6qvzlnH9GtaJqcdOdV8b67dfLth99fM847IBj4t+X3xA777ZTtF591cWeo1q16nH8yb+JAw/eOxo1bliyfebMWXH6iX+Ol1/oH/ff/Uj0POrglfKcyCmA2XPnX5XcTonavy65dqn3KSoqisEfPL+MTQIAAICqZdDAt2P41yNir327loQvScOGDeLI3ofGuWf9Lfo82i96H99rseeoWbNG/OaYwxa5/cijD8kCmDffeFcAU9kDmDTWbFktz30AAACgqhn8xtvZepvttlxo37bb/7TtrUHvLvf5a9T46c/76jWqL/c5qCABzBPP3ptvSwAAAKCK+vqrb7L1Gmu2WWhfi1WaZzVXi49ZHo89/GS27rzdFivQSipEALOkcWgAAADA4k2aODlbN2hYf5H76zeoX3LMsnr15YHx0H19Yq111oy99+/mn6Gyz4IEAAAAVLxprf9wyvlZsHPJv86PWrVqlXeTWNEeMAsaO3Z8jBoxOqZNmx6bb7np8p4GAAAAqrzini+L6+UyedLk+WY2Ko0P3v84jj3q9GzW4qtvvCTWWW+tMmkrFSSASVWVr73qlhj68WeLnOlowo8T46zTLshuX3z5uVlFZwAAAChkxbVfUp2XDTusP9++78f8EFOmTI0Om7RfxvDltJg7Z25cfdM/Y6ONS39fKsEQpNtuuidOOf5P8fFHn2YzHBUv80qJXa3atWJg/8Hx3NMvlXV7AQAAoNLZfMuO2Xrga4MW2tf/1Z+2bVbK0SXF4cuc2XPi3zf8IzbedMMybi3lGsCkcWVXXnp9VK9ePU4987h4YcDj0bxF00Ueu0f3XbJgZsBrg8uyrQAAAFApbdV5s2jTtnU89cRzMfSjT0u2T5w4KW6+7s6oWbNmdN9rt5LtY777Ib784qts/7w+HDI0C19mz5qdhS+bduqwUp8HK2EI0t23P5Ste/7m4Di014FLPLa4Jsyn/z9MCQAAAApZjRo14py//D6O++3v46hDT4zd9vhF1KtfL557+uUYNXJ0nHLGcdG6zWolx1912fXR59F+cf5Ff4ge+3bNtv04fkIWvkycMCm23WGrGPja4GyZV8NGDeKQnges9OdHGQYwb735XrY++NB9l3pss+ZNo27dOvHt6DGlPT0AAABUaVtus1nccte/45qrbo6nn3ohZs2aFev+bO046fTeWSCzNKlQb6q7mvR/5Y1sWdBqrVcVwFT2AGbs9+OiXr26WbhSGmnqq8mTp6xI2wAAAKBKSYV2/3PDJUs97oKLz8qWeaUeMm9/rNZqla8BU6du7Zg2fXrMmTNnqcemqanTOLXU9QkAAACg0JU6gGm7xupZheUvv/h6qce++tLALKhJXakAAAAACl2pA5gdduqczWx05633L/G4iRMmxpWXXRdFRUXx819sVxZtBAAAACiMAObgw/aLxo0bxeMPPxVX/PPaGD/ux/n2T5k8Jf775PNxyAG945uvR0bLVi1inwP2zKPNAAAAAFWzCG+jxg3jn1f9JU7sfWbcfvN98/WE+cW2e2U9X+bMmZv1kqlfv15ccsUFUadO7bzaDQAAAFD1esAkm2+5adz54HWxdefNY/bsOdmSApfUG6b49pZbd4rb7rs6q+wMAAAAwDL0gCm21tprxtU3/TNGjfw23nnzvRjz3Q8xe86caN6iWXTafOOsWC8AAAAAKxDAFFutdatYrfUuy3t3AAAAgIJR6iFI1/3n1rjjlvtKfeK7b38wuw8AAABAoSt1D5jr/n1rNszosCMOKtXxd932QIwe9V30Pr7XirQPAACAAlb3zxeWdxMoR1P/cnZhFuEFAAAAoAIFMBMnTopatWrmdXoAAACAwg5g3hr8bkyaODlatlolj9MDAAAAVI0aMI8/0i+eeLTffNsm/Dgxju558mJPNnfu3JgwYVJ88fmwKCoqim2226JsWwsAAABQlQKYUSNGx+A33plv28yZMxfatjhrrbNmHH1czxVvIQAAAEBVDWA236pj9I7/zWCUppSuV6/uEmdBKqpWFA0a1I/1frZ2dv9q1dT4BQAAAFhsALPFVh2zZd4Apm69utH7BNNKAwAAAJRJALOgJ569N6pX16MFAAAAILcApvXqqy7zyQEAAABYhgCm2LRp0+ORB56I/q++EaNGfhvTp02PPs/cU7J/4sRJ8eqLAyKKiqLrnjt7jQEAAICCt0wBzCdDP49TjvtjjB71XTbldJKmm55XKsJ7wzW3x1fDvonmLZrFVttsVvAvMgAAAFDYSl3UZcKPE+N3R5+Z9XpZv/16ccoZx0b9BvUXOi4FMnvtt0cW0Lz0/Gtl3V4AAACAqhvA3HXbAzHmu++zmZHuuP+abDrqOnVqLfLY7XfqnK3ff/fDsmspAABAgfjg/Y/ihKPPiB227BadO+0Whx90bDz91PPl3SxgZQxBeumF17LeLSf//tioXr36Eo9de501o3qN6jH86xEr0jYAAICCM2jgW3Hcb38ftWvVit32+EXUq18vnnv65TjzlPNj9KgxcfiRB5V3E4E8A5hvvh4ZNWrUiPYb/Wypx6agJtWCmTRp8vK0CQAAoCDNmjUr/nLOP6NaUbW46c4rs/IPydHH94zDDjgm/n35DbHzbjuZpRaq8hCk2bNnR82aNRYqurs406ZOizp16qxI2wAAAArKoIFvZyMJuu75y5LwJWnYsEEc2fvQmDlzZvR5tF+5thHIOYBpsUrzmDp1Wowf9+NSjx360acxffqMWG21lsvZLAAAgMIz+I23s/U222250L5tt/9p21uD3l3p7QJWYgDTafNNsnW/vs8t9dibb7g76ymzpSmoAQAASu3rr77J1mus2WaRX4rXq1e35BigigYwBx2yTza19PVX35b1cFmU6dOnxz//9u945qkXolq1ojjw4L3Lsq0AAABV2qSJP9XRbNCw/iL310+1Nv//GKCKFuHdaOMNsmrbt998X/Q6+PisS9yUyVOzff+65JoYOWJ0Nl5xwoSJ2bajj+8Va67VNr+WAwAAAFS1ACZJU1CnKdBuvOaOeOn517JtaajRHbfcn91OPWTS9NO9j+8VvznmsHxaDAAAUEUV93xZXC+XyZMmR6PGDVdyq4CVHsAkRx/XM3rs0zUee/jJeOfN92PMmB9izuzZ0bxFs+i4+Sax9357xOptViuTxgEAABSS4tovqc7Lhh3Wn2/f92N+iClTpkaHTdqXU+uAlRrAJKuu1jLr5QIAAEDZ2XzLjnHz9XfFwNcGxe7dfjnfvv6vDsrWm225qZccqnIRXgAAAPK1VefNok3b1vHUE8/NN/nJxImT4ubr7oyaNWtG9712888AhdIDBgAAgLJXo0aNOOcvv4/jfvv7OOrQE2O3PX6R1eF87umXY9TI0XHKGcdFayUfoDACmMmTpsTLL/aPT4Z+HhN/nBizZs1a/MFFRXHeX89cwSYCAAAUji232Sxuuevfcc1VN8fTT72Q/c217s/WjpNO750FMkABBDCPPtQ3Lv3bf7LCT8XSzEcLSjMjpe1pLYABAABYNqnQ7n9uuMTLBoUYwDz/zMtxwdk/vQHUqVsnNum4YbRo0SyqV6+eZ/sAAAAACieAufXGe7L1NttuEf+44vxo0OCn+ekrs8mTJ8fll18eDz74YHz++efZtnXWWSf222+/OPXUU6N+/eV/jjNnzoxrrrkm7r777hg6dGjMmDEj1lhjjejevXucccYZ0aJFizJ8JgAAAECVCGA++/TLn4YUXXRmlQhfhg8fHl26dCkJXurWrZsNm3r33Xez5bbbbosXXngh2rZtu8zn/vHHH2OXXXaJQYN+miauVq1aWbXyjz/+OFtuvfXWeOaZZ2LTTU0fBwAAAIWg1NNQ165VK+o3qBctW60Sld3s2bOjR48eWfjSqlWrePLJJ7PeMFOmTIm+fftGy5Yts33pmDlz5izz+Q877LAsfGnQoEHccccd2bknTZoU/fv3j/XWWy/GjBkT3bp1i4kTJ+by/AAAAIBKGsCste6aMXXqtGyp7G6//fZ45513stsPPPBAdO3aNevdk5Y99tgjG5KUpGPSscsi9Zrp06dPdjsNQTr00EOzqeSSzp07ZwFP7dq1Y8SIEXHppZeW+XMDAAAAKnEAc8BBPWL2rNnR59F+UdmlIUDJTjvtFDvssMNC+9O24u1pKNLynHuttdaKX//61wvtTz1gDjzwwOz2soY7AAAAQBUPYLp23yW6771bXP73q+OpPs9EZTV16tR49dVXs9upt8vipCFCySuvvJLdp7RSbZdk9913j2rVqi3x3F9++WV8+umny9R+AAAAoAoX4U3O/9tZsVrrVePsMy+Kqy6/MTbaeP2oX7/e4u+Qivb+9cyoSD766KOSui4dOnRY7HHF+1K9mFQ4t1OnTks999ixY2PUqFGlPncyZMiQrFcMAAAAUHUtUwDz+MNPxT13PpTdHj3q22xZlFRLJc0oVFQBA5hUe6VYmzZtFnvcvPtGjhxZqgBmec9dGql+zKKkAKd9+/aVrqBvg6bl3QLKU+s6tf0DFLDK9n4FAJQv146FbWIlvHZs2LDhigUwr7w0MM4/+x9ZsFK3bp3YpONG0bxF06hevXpU1n+8evUW33tn3n0TJkwo93NXNRfetkt5N4FydGH494fFObtn5R3my4p5YvPPvYQF7J0TjynvJkCF5f8HVUWpA5hbbrgrC1+22XaL+McV50eDBvXzbRnzGTBgwBJ7xiwuYQOgcpk0rrxbQHkZOW26F7+AuZYDqPpKXYT306Ff/DSk6KIzK3X4Mu+H25QpUxZ73Lz7GjVqVO7nBgAAAAoggKlWrSjqN6gXLVutEpXZ6quvvsiaLQv65ptvSm63bt263M8NAAAAFEAAs8667WLq1GnZUpmlgrXF00O///77iz0uFbdNUo2bDTbYoFTnbtasWay22mqlPvfSZksCAAAACiyA2e+gHjF71uzo82i/qMzq1q0b22+/fXb7ySefXOxxxft22GGH7D6ltcsuPxUX7devX1YzZ0nnbteunSmoAQAAoACUOoDp1mPX2Gu/PeLyv18dT/ap3DM09OrVK1u/9NJL8dprry20P217+eWXs9s9e/ZcrnN/+eWXcc899yy0//PPP4/7779/uc4NAAAAVE5FcxfXTWMB5/3x4mz9wnOvxqSJk6PVqi1jo43Xj/r1Fz/dcqSivX89Myqa2bNnxxZbbBHvvPNOrLrqqnHLLbfEbrvtlu3773//G0cccUSMHj06OnbsGIMHD55vqu0XX3wxunTpkt1O9ysOXObVo0eP6NOnT1aU99prr42DDjooO8fAgQOz0OWTTz7J6sV89NFHK1zxvngWpMXNkgRA5XJUl4fLuwmUk7u3/9BrX8Cm/uXs8m4CABVlGurHH+mXzYJUnNeMHvVttixK8XFFFTSASWHI448/ngUpqUdK165dS4YZTZ06NVuvs8462THzhi+ldccdd2RDkQYNGhSHHHJIFujUqlUrJk2alO1fZZVVom/fvqYbBAAAgAJR6gBmz712Sx1aqoy2bdvGu+++G5dddlk89NBDWRCTbLLJJrHffvvFaaedFvXrL990240bN86GMV199dVx9913x9ChQ2PGjBmx/vrrR/fu3eOMM87IQhgAAACgMJR6CBIVkyFIAFWLIUiFyxCkwmYIEkDVV+oivAAAAAAsHwEMAAAAQM4EMAAAAAAVpQhvsR++HxuPPvRkvP3me/Hd6DExdeq0kpmRFpRmQerzzD1l0U4AAACAwghgXnz+tfjzGX+NKVOmLjF0Kd6XbgMAAAAUulIHMMO++DrOOvX8mD59Rmy/4zaxQ5fO8bfzL48GDevHqWceF9+PGRsDXxscbw1+N5o0bRzH/O6IqFOnTr6tBwAAAKhKNWDuvO2BLHzZvdsv48rrLo4DfrVXtr127dqx937d4jfHHBY33nFFXP6fv2bDkvo+9nR067FLnm0HAAAAqFoBzODX386GFB159CFLPG6nX2wXJ53WO4a891Hcc8dDZdFGAAAAgMIIYL777vuoVr1arLPeWiXbUiAzc8bMhY7tsc/u2b5+fZ8ru5YCAAAAVPUAJpXTbdiwwXyFdevWrROTJ0+OOXPmzHdsvfr1okGD+jH86xFl21oAAACAqhzArNKyRUyeNGW+ba1Waxlz5syNzz/9cr7tUyZPiYkTJ8X0adPLrqUAAAAAVT2AabNG65g1a1Z8/dU3Jds23nTDbP3AvY/Nd+wdt9yfTUW9etvWZdlWAAAAgKo9DfUWW3WM/q+8EQP7D4411myTbdtn/27x+MNPxUP39YkRw0fFBhuuF58M/Tw7Lg1V6rrnznm2HQAAAKBq9YDZdfcusfmWm8bXw/7XA2bTTh3iiN/+OuvtMuC1QXHrjffEay+/nv2cApueRx2cV7sBAAAAql4PmNZtVosbbr9ioe2/O/Xo2Krz5tmMR9+O+i4aNmoQ226/VXTba9eoUaPUpwcAAACossokIdm68+bZAgAAAMAKBDCn/e7sbDLqU844NtoorgsAAABQ9jVgXn6hf/R/5XXhCwAAAEBeAUzTZk2jVu1ay3p+AAAAgIJX6gBm403bx6SJk2P0qO8K/kUDAAAAyCWAOfjQ/bL11VfctEwPAAAAAFDoSh3AbLF1pzjjTyfGU32fjdN+9+f4cMjQfFsGAAAAUGizIO25869+ukP16vHic69mS+06taNxk0ZRvdqic5yioqLo88w9ZddaAAAAgKocwIwcMXqhbdOmTsuWxUkBDAAAAEChK3UAc95Ff8i3JQAAAACFHsD02Gf3fFsCAAAAUOhFeAEAAABYPgIYAAAAgIoyBGlBc+fOjQk/ToypU6dltxdntdatlvchAAAAAAozgHnlpYFxz+0PxrtvD4lp06Yv8dg0C9LgD55fkfYBAAAAFFYAc9Vl18etN96zxB4v8yrtcQAAAABVWakDmNcHvBm33HB31KhRI0445bex/U5bx/579oqmzZrEbfdeHd+P+SEGvDoo7rvrkahWvVpccPFZsfY67fJtPQAAAEBVKsL7wD2PZUOKjup9aBx+5EEl4Uq1atWiTdvW0XGzjePYE4+Mex65MerWrRPn/+kfUadunTzbDgAAAFC1Apgh732Yrfc9sPsCe+YuVHT3zLNPih++Hxu33nB32bQSAAAAoBACmHFjf4xatWvFKi2bl2yrXr1aTJs2Y6Fjt9tx66hZs2a88uKAsmspAAAAQFUPYOrVqxt1ateeb1v9+vVjyuQpC82GVL169Sys+fbbMWXXUgAAAICqHsCkni+TJk2KmTNnlWxrs0brbD3kvY/mO3b0qO9i8qTJUVSWLQUAAACo6gHMWuu2izlz5sanQz8v2bbl1p2yqaav/8+tJb1gZsyYEZdcdGV2+2cbrJtHmwEAAACqZgCzzbZbZGHLvHVdDjh476hdu1a8Oejd2H2n/aPnr46L3XbaP1587rVsxqSDDtknr3YDAAAAVL0AZuddd4zex/eKVVq1KNnWevVV46JLz4n6DerFhAkT4/13P4wfx0/Iwpcjex8au3f7ZV7tBgAAAKg0apT2wIaNGkbvE3ottL3LL7ePzZ6+J159eWBW+6VhowbRebsto+0aq5d1WwEAAACqdgCzJI2bNIpuPXYti1MBAAAAFO4QJAAAAABWYg+YNBX1wP6D48MhH8fYH8Zl25o1bxobdtggtum8edSsVXM5mwMAAABQ9SxzAPPAvY/FtVfdEuPH/bjY4UjHnNArDvy1GZAAAAAAljmAuej8y+Kh+/pk01EnzVs0jZatVsluf/ft9/HD92OzYObvF14Zn3z8eZx9weleZQAAAKDglTqAeabfC/HgvY9nt3fZ/efZjEhrr9NuvmO+/OKruO7ft8bTT70QjzzYN7bedvPYZfcuBf8iAwAAAIWt1EV477vr0SgqKop9D9gz/n75eQuFL8laa68ZF192bnZM6iWT7gMAAABQ6EodwAz9+LNsfdxJRy312GNPPDILa4rvAwAAAFDISh3AzJ0zJxo2bJDNdrQ0zVs0y45N9wEAAAAodKUOYFZv2zomT5kS06dPX+qx6Zh0bNs1V1/R9gEAAAAUTgDTrfsuMXvW7Hj8kX5LPTYdk47tuucuK9o+AAAAgMIJYH7dc//YfMtN49K//Tub4WhxHn2ob3bMFlt1jEN67l9W7QQAAACo+tNQ33TdndFxs43jk6Gfx4Xn/DOuv/q22HKrTtGyVYts/3fffh+D33gnvh39XVb/ZdNOHeLGa+9Y5Ll6H9+r7J4BAAAAQFUJYK77963ZzEZJmmL621HfRd/Hn57vmLQ9mThxUtx8/V2LPZcABgAAACgkpQ5gNtti0/j//AUAAACAPAKYG++4YlnOCwAAAMCyFuEFAAAAYPkIYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnBRvAzJw5M6688srYZpttomnTplG/fv1o3759nHHGGfH999+v0LmLioqWumy//fZl9lwAAACAiq1GFKAff/wxdtlllxg0aFD2c61ataJmzZrx8ccfZ8utt94azzzzTGy66aYr9Dgp2EnnXpTmzZuv0LkBAACAyqMge8AcdthhWfjSoEGDuOOOO2Ly5MkxadKk6N+/f6y33noxZsyY6NatW0ycOHGFHufhhx+O0aNHL3J57LHHyuz5AAAAABVbwQUwL7zwQvTp0ye7fc0118Shhx4aNWr81BGoc+fO0bdv36hdu3aMGDEiLr300nJuLQAAAFAVFFwAk4YXJWuttVb8+te/Xmh/6gFz4IEHZrdvv/32ld4+AAAAoOopuAAm1XZJdt9996hWbdFPPw0/Sr788sv49NNPV2r7AAAAgKqnoAKYsWPHxqhRo7LbHTp0WOxx8+4bMmTIcj/eqaeeGq1atcoK8bZo0SJ22mmnbFjThAkTlvucAAAAQOVTUAFMqutSrE2bNos9bt59I0eOXO7He/vtt7NCvvXq1YsffvghXn755Tj99NNjo402itdff325zwsAAABULgU1DfW8sxqlUGRx5t23PL1VDj/88DjggANi2223jWbNmmXbvvvuu6ymzHnnnRfffPNNdO3aNd56661o165dqc6ZCgQvSuqh0759+xWesQmAiqFB0/JuAeWldZ3aXvwC5loOoOpo2LBh5ewB88QTT0RRUdFyLynwWNluu+222HPPPUvCl6Rly5ZZ75dUgybNujRu3LhyaRsAAACw8tUo1BRqypQpiz1u3n2NGjUq0zakniwHHXRQ3HXXXfHoo4/G3Llzs6BoaQYMGLDY8y0pYQOgcpk0rrxbQHkZOW26F7+AuZYDqPoqfACz2267xZgxY5b7/vMOJ1p99dUXWQ9mQWmIULHWrVtHWUuhSQpgfvzxx6w2TCrQCwAAAFRdFT6AqVmzZpkFFGlI0GqrrZbNhPT+++8v9rh5Zz5a0mxJAAAAAKVR4WvAlLVddtklW/fr1y8b/rMoTz75ZLZOBXLXW2+9Mm9D8XCiNLypefPmZX5+AAAAoGIpuACmV69e2frLL7+Me+65Z6H9n3/+edx///3Z7Z49ey7z+RcX6hRL00/fd9992e0ePXqUqv4LAAAAULkVXADTpUuX6N69e3b7mGOOibvvvjtmz56d/Txw4MDYY489Ytq0aVm9mNNOO22h+w8bNmyJMyyl6afPOOOM6N+/f0ydOrVke6pjc/nll8fOO+8cs2bNynq/mAUJAAAACkOFrwGThzvuuCMbijRo0KA45JBD4ogjjohatWrFpEmTsv2rrLJK9O3bd7mq0X///ffx0EMPxSWXXBLVqlUrmUVp/PjxJce0adMm6wWzzjrrlOGzAgAAACqqggxgGjduHK+99lpcffXVWQ+YoUOHxowZM2L99dfPesekHiwphFkef/zjH6NTp07ZUKPhw4fH2LFjsx4vq666amy88cbZsKM0tMlUgwAAAFA4iuYurWgJFVqa0nrewr4AVG5HdXm4vJtAObl7+w+99gVs6l/OLu8mAJCzgqsBAwAAALCyCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAAABDAAAAEDlpgcMAAAAQM4EMAAAAAA5E8AAAAAA5EwAAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAORMAAMAAACQMwEMAAAAQM4EMAAAAAA5E8AAAAAA5EwAAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkrEbeDwAAwNJN/cvZXiYAqML0gAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZ6ahBgAgV199OTz+c8WNMWjg2zF16tRYo13b2P9XPeKAX+0VRUVFpT7PxAkT445b748Xn301RnwzKmrVqhWt26wa3ffePfY5oFvUrl071+cBACtCAAMAQG4+/2xY9Dr4+Jg+bXrs2vXnsUrLFvHKSwPjb+dfHl98Niz+8OeTSx2+/Hq/o+Ob4SOj0+Ybx34H9YgZM2ZE/1feiL9feEW88Owrcc3Nl0a1ajp4A1AxCWAAAMjNReddFpMmToqrrv97bL/jNtm24048KnofeWrcd9cj0XXPnWPTTh2Wep6H7u+ThS+H9DwgTj/rhJLtM2fMjF6/Pj7eGPhWvP3m+7H5lpv61wSgQvIVAQAAuQ09emvwu7Hl1p1KwpekZq2aWQiTPPLAE6U61zfDR2Xr7Xfcer7t6Vydt9syuz1u7PgybD0AlC0BDAAAuRj8xtvZepv/D0jmlYYR1a1XN94c9G6pzrXuemtl61dffn2+7akHzMD+g6NOndqxSceNyqTdAJAHQ5AAAMjF11+NyNZrrNlmoX3Vq1eP1VdfNb74/KuYNWtW1Kix5MvSvffvFk/2eSbuuu2B+PCDobHxJhtmNWBee/n1mDJlalx82bnRslUL/5IAVFgCGAAAcpFqvyQNGtZf5P76DerHnDlzYsrkqdGoccMlniv1cLn+tn9lNWX6PNov3h78XkmQ86tD9y1VHRkAKE8CGAAAltu1V92y0LZDeu4fDRstOVBZVmPHjo9TjvtjjBs3Pivo23GzjWPa1Gnx4nOvxWX/uDpefrF/3P3QDdGgwaLDHgAobwIYAACW23X/uXWhbT322T0LYBo0bJD9PGni5EXed/KkyVFUVBT16tdd6uNc+rd/x3vvfBD3PXZz/Gz9dbJtKWzZ/1c/TUd9yUVXxb13Phy/OeYw/5oAVEgCGAAAltvbH7+02H1rrLl6tv76q28W2jd79uwYMWJ0rN5mtaXWf0lSrZfGjRuVhC/z2mLrTtn64w8/XcbWA8DKYxYkAABysfmWHbP1wNcGLbTv7Tffj6lTpsbmW25aqnPNnDkzJk+enM16tKDi6adr1aq5wm0GgLwIYAAAyEW7tdeIzbbYNAa9/na8+vLAku0pRLn6ypuy23vvv+d890k1Xr784qtsPa9U82XWrNlx/TW3z7d9+vTpceP/byvuCQMAFZEhSAAA5OaP554SvX59Qpx6/Nmx6x5dYpVVmscrLw2Mzz/9Mg46ZJ/ouNn8sxfdd+cjWV2Z3sf3imN+d0TJ9t+d+tt4+633s7Dl9f6DY5OOG2Xhy2svvxGjRo7Ofu6+127+JQGosAQwAADkZp311oo77rsm/nPFjfHqiwNj6tRpsWa7NnHWOSfHAQfvXerzbLDhz+Keh26Im667Mwa98Xbcd/cjUaN69Wi7Zps47qSj4rAjDoqahiABUIEVzZ07d255N4Ll17lz52w9YMAALyNAFXBUl4fLuwmUk5te2NdrDwBVmBowAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAORMAAMAAACQs4ILYGbNmhXPP/98XHrppXHIIYfEhhtuGNWrV4+ioqL4+c9/XqaP9fjjj0e3bt2iVatWUbt27VhjjTWiV69e8d5775Xp4wAAAAAVW40oMN9880388pe/zP1xevfuHddff312u1q1atGwYcMYPnx43HbbbXH33XfHDTfcED179sy9HQAAAED5K7geMEmDBg1iu+22ixNOOCFuuumm2Hnnncv0/JdddllJ+HLWWWfFuHHjYvz48fHll19mPWJmzpwZv/nNb+KNN94o08cFAAAAKqaC6wGThgFNmDAhG3JULA1JKispaDn//POz20cddVRcdNFFJfvatWsXDz30UHTq1Ck++uijOOOMM+LFF18ss8cGAAAAKqaC6wGThgPNG76UtUceeSQLeJI//elPC+1PtWBOP/307PZLL70Uw4YNy60tAAAAQMVQcAFM3p555pls3b59+1hrrbUWecwee+yx0PEAAABA1SWAKWNDhgzJ1h06dFjsMauuumq0aNFivuMBAACAqqvgasDkbcSIEdm6TZs2Szwu7f/+++9j5MiRpTpv586dF7k9BTipt83EiROXo7UAVDQNmpZ3CygvPssBoGpIsyAvigAmp4unevXqLfG44v3F9WIAILnwtl28EAAAVVCFD2CeeOKJ6N69+3Lf/9xzz43zzjsvKrsBAwYssWfM4hI2AAAAoPypAVPGioOQKVOmLPG44v2NGjUq6yYAAAAAFUyF7wGz2267xZgxY5b7/ksbClTWVl999Rg7dmxJLZjF+eabb7J169atV1LLAAAAgPJS4QOYmjVrlswYVBmk2Y/ef//9bFmc0aNHZwV4i48HAAAAqjZDkMrYLrv8VDzxo48+imHDhi3ymKeeemqh4wEAAICqSwBTxvbZZ5+Sui4XXXTRQvtnzJgRl156aXZ7p512inbt2pV1EwAAAIAKpiADmB9//DEbAlS8TJ8+Pds+c+bM+baPGzdukfcvKirKll69ei20r0mTJtnMS8kNN9wQZ599dsnU1F9//XXsv//+8cEHH0SNGjXiH//4R67PEwAqk5eefy0u/su/otfBx0fnTrtFpw12imuvuqW8mwUAUBg1YPKw1157xUsvvbTQ9v79+8cqq6xS8vOaa6652GFES3LqqafG0KFD4/rrr4+//vWv8be//S2bHSkFP8V1bVI4s9VWW63gMwGAquOOW+6PNwe9Ew0a1I9VWjaP4V8tuaA9AEBlUpA9YFaG6667Lh577LHo2rVrNG/ePKZOnRpt2rSJww8/PAYPHhw9e/Ys7yYCQIVy3ElHxqP97oqXB/WN4048qrybAwBQpgqyB8yLL764QvefO3duqY7r0aNHtgAAS7fZFpt6mQCAKksPGAAAAICcCWAAAAAAciaAAQAAAMhZQdaAAQBWvkVNKX1Iz/2jYaOG/jkAgCpPAAMArBTX/efWhbb12Gd3AQwAUBAEMADASvH2xy95pQGAgqUGDAAAAEDOBDAAAAAAOTMECQCoEF549pV44dlXs9sjvhn107bnXo2RI0Znt9utvUYcefQh5dpGAIDlJYABACqEoR99Fn0e7Tfftk8+/ixbks237CiAAQAqraK5c+fOLe9GsPw6d+6crQcMGOBlBAAAgApKDRgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAgZwIYAAAAgJwJYAAAAAByJoABAAAAyJkABgAAACBnAhgAAACAnAlgAAAAAAQwAAAAAJWbHjAAAAAAORPAAAAAAORMAAMAAACQMwEMAAAAQM4EMAAAAAA5E8AAAAAA5EwAAwAAAJAzAQwAAABAzgQwAAAAADkTwAAAAADkTAADAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAOSsaO7cuXPzfhDy06pVq5gyZUp06NDBywwAAADlLP19fsMNNyy0vUa5tIYy07RpU68mlcqQIUOytdAQwHsjgGtHCokeMMBK1blz52w9YMAArzyA90YA144UDDVgAAAAAHImgAEAAADImQAGAAAAIGcCGAAAAICcCWAAAAAAcmYWJAAAAICc6QEDAAAAkDMBDAAAAEDOBDAAAAAAORPAAAAAAORMAAMAAACQMwEMsEjnnXdeFBUVxc9//vMyf4V69eqVnTut85QeY8HlxRdfLPPH6dix40KPc+utt5b54wDlz3tj6e29994LvTem1w+oetL14vL+H19Z14VQEQhggEonhRvpA760YUrTpk2jVatW2VKrVq3FHjds2LA4/vjjY5111om6detGixYtsguK2267LebOnbvY+6Xjis9frZq3VaBqvDeOHj06HnjggfjjH/8YXbt2zY5blpB53vPXrFlzuZ4TAFQlNcq7AUDFlEKF9ddfP9ZYY42oaNKF/0svvZTdLk0PnYcffnipxz399NOx3377xaRJk7KfGzZsGOPHj88eJy3pj5BHHnlkkX9EPPvssyW327VrF1999dVyPCugMiik98Zrr702zj///OVuzy233FJyOz1OcdsA5rXaaqtl76tpDVWdr2qBRTrhhBPi448/jttvv73Kv0IpMCkOX7beeuv44IMPYsKECdnPl19+edSoUSP69u0bp512Wnk3FShnhfTemHq6tGnTJrp37x7nnHNOPPjgg+XdJKAK+tvf/pa9r6Y1VHV6wAAFL/1hkcKWli1bZkFL8+bNs9ekTp06cfLJJ8fEiROzY6655po48cQTY9111y341wyo+v70pz/FueeeW/LzrFmzyrU9AFDZ6QEDFUSnTp2ybxsvueSShfZ9+umnJePuN99880XeP21P+//+978vtO+ZZ56Jgw46KNq2bRu1a9eOxo0bxzbbbBP//Oc/Y/LkyctdaPKpp56K3XbbLZo1axb16tWL9u3bx5///OcszEhd4dP905CcpUnfqqbHKT5PKmp7xRVXxJw5c+Y7rvicxd3YU9f4FS2ym55/Gl6UHHvssSXhy7xSCFO/fv3sj4+77rprmc4PrBjvjeXz3pik3n8Ay2rmzJnZNWZ6z0pDutN150477RT333//MhfhHTduXNx0003ZdezGG2+cXaela9nUOy/1Xp53GPiifPLJJ3HMMcfEBhtskL2Ppi/XVl999dhiiy2yns1vvvmmf2BWKgEMVBC/+MUvsvXzzz+/0L55t73zzjvZh9G8xo4dm22f9zzJ9OnT49BDD41dd901+9D75ptvsg+tFDq8/vrr8fvf/z623HLLGD58+DK3NwUte+yxR1Y7JbUnFXD8/PPP48ILL8yG8aT6KaXtzn/AAQfEK6+8khW6nTp1arz77rtZ6HHEEUfMd2wqjDtvMccUihQXeCxNkd1Fee2117LHTNLzWZR08bDDDjtkt9PzBVYe743l894IsDxmzJiRXXema8whQ4ZkQW4a1v3yyy9nIUoKQ5Y0scGCUuj8m9/8JruO/fDDD7P7pgkPRowYkdWx2mWXXeIPf/jDIu/73HPPxaabbhrXXXddDB06NAuGUggzatSoLHi57LLLst7NsDIJYKCC/ZGRgoj0AbGoAKZRo0bZN58LfpOZfk7b0zcMm222Wcn24447Luuxseaaa2bFEFMokj4Ep0yZkn1jkD6UPvroo9h3330X+kZ1SVIx2hS0JOnbhzR7UDp3Cnbuu+++7IPtggsuWOp5Hn/88bj++uuzb0lSiJSCnO+//z77oE1SjYV5w6f0wZ1m5dh2222zn08//fTs53mX4n2llS4OinXo0GGxxxXvm/d4IH/eG8vnvRFgeaRAo3///vHvf/87u+ZM71/purD4/SuFIcsSerRu3TobBv7GG29k16/pejEF0ulLxbQ9Bc+p9/cTTzyx0H1T2DNt2rTs2njAgAFZOJTun7alnjGp1/mGG27oH5qVSgADFcSOO+6YfUtQ3DulWEr6U8CSumaecsopi+wlU/xz6t5ZvXr17Hb6oLn55puzGTvS/VO3zhTQJOmb0F/+8pfZtxGpG+bgwYOjT58+pW5rmpK0uM3pG4kU8CTpQ/DAAw+Mhx56aKFeOouSjkkfwqkLaHHbUtfSG264oWSo1T333BN5St+gFE+Xmr4VWZzU1TVJFxOLG7YFlD3vjeXz3giwPNIXcldddVUcf/zxJddVq666avb+lXo8J6m2VOqlXRpHH310Nqwy9dhOvbiLpevXtP2vf/1rSU+ZeY0ZMyY+++yz7HYKg9LQ+3QtXXwdvN5662Vh9amnnuofmpVKAAMVRBrmksajFneZLJZ6XHz33XexySabZOHGgvvn/Xne4Ufpgy45+OCDF1uHJfWo2WeffbLb/fr1K1U7Uxf4VKm+uEBj6ga6oC5dupQM2VmSFGos2JW+WI8ePbL1e++9F3lKBXaTJYUvC+5PIQywcnhvLJ/3RoDlka7tinu7LKq+YJJ69KX6hGVhzz33zNap183s2bNLtjdo0KDkGjX1wIGKQgADFUhxgDJvwFLcuyX1WEndJNO3CGnYUOpSXvyhUhyIzBvApKFMyW233ZbdZ3FLGppUPBVzaRQXK0s9bdI304uzpOK9xdK3GYsKcIq7nCapqyhQ2Lw3/o/3RqAiS1/CLe7aLl3HpppUSep9XVppqPuZZ56ZXTemouSpx3hxgfHiIURpeNK8va9Tbaydd945u92zZ8+st3UqVJ6Og/IkgIEK+EdGGoJU/AFRHMAU71uwIGXxOk2hPG8Nk5EjR5b01vj2228XuxQPpyntB1LqjVM8ZCdVkl+c1DV0aVIPnKXNvrFgPZw8vl0vzfOfd/+S2g2UPe+NK/+9EWB5LO36r3hId7oGLY3HHnssm2XzH//4RxbapJAl9UpO170pzElD7YstOEQ8zZ6U6r+k2TlTwd305WC6hks9ztMwqOJh6LAyCWCgAtluu+2y8a2pSFjqwZK6UqY6LemCu7i3yYLfBBev04dK8djWpLgbZhqHm+rILG1Z1ilK532sqnChkD7QlxTCpGJvSfrgTjOMACuP90aAwvPDDz9kvVdS0dx0HZyueVOYUvzlYuoNPnDgwJLjF5xdKYU9KbRJw51SHcU0S2fqnZN6c6fJIlIdGPW0WNkEMFCBpB4lnTt3zm6nD5m33norK2aWulwW99RYsAfMCy+8MN/2Yml4UXG3zbKUvnFIiqvIL05l+VZh3l5DS5rhqHjfkmZKAvLhvRGgclja9V/x/uKhSEvy5JNPxo8//hhNmjTJbqdr3QW/BFtafZf0hWEaipR6wKSwJl1Xp8kiUq+aNJvSkUceWereOFAWBDBQwcwbsMxb/6XYWmutlRXVTcFKCmmKA5YFA5jtt98+W/ft23ehbwRWRPEMHKmHTXGdmUVZ1h41y6J4bHFZPK/0zXoaJ5ykD/dFSd+2FD/XXXfddYUfE1h23htX7nsjwPJI13+Lew+at4Zh8cQTSzJ8+PBsvf766y+29/GyFvNNw5f23XffLIRJ0peJr7766jKdA1aEAAYq6B8Zb7/9dsmHw7wBzLw/n3322dm6bdu2WTfKBaftS1KB3gWn5ltQGvKUQobS2HTTTbMPwuSiiy6KOXPmLHRMGja1pHBmRRVPWV2aqa6XJn2gF0+LeO211y7ynOn1S+OK01CwQw45ZIUfE1h23htX7nsjwPJIocnNN9+8yH1p2E/SvHnzkgK5S5J6viSffvpp1ltlQV9//XU21H5x17alnd1ycUWDIQ9+26CC2WqrrbKp81KwMWjQoKx3RvGwpAX/ECke95oqzi8ojZU96qijsttp3Ovxxx8fQ4cOLdmfCjimIU5pSsB11lkn3nnnnVK38cILLyz5luNXv/pV9gGYzJo1Kx588MHsm4VUpDcvG2+8cUnvnuJvR1ZEuiBIr3nqgrrHHntk39AUfyty5ZVXxvnnn5/9fOyxx8a66667wo8HLDvvjSv/vTF9DqXpYouXVI+hWArt591nZhGgODRJ15zXXHNNSWiSer307t077r333uznVAB3SRM5FEu9jlM4koa9//rXv57vevOJJ57IrnUXF56kaanTe+Kll14aH3zwQUltxNQ7J13/HnroodnP6fpvp5128o/HSiOAgQqmZs2aJcOH5i0+Oa8Fhxst+HOx9OF3zDHHZLevvvrq2GCDDbIeH+mbhxTspOFEKVxIBWaXpaju/vvvH2eddVZ2+4EHHog111wzC1yKe5OkaVLPOeecbH9pPmCXVSrIltr/xRdfZI+dxhGnYVlpmbcYW2mlc6TeRulDON0/TWmYvklOdXdOOumkLKzq1q1b9iEOlA/vjSv/vTH9sbPKKquULMW1xZLf/e538+1LM5QApC+rttlmmzjuuOOyiQvStNHpuvD666/PXpz05eAJJ5xQqhcqfelVfL356KOPZu9r6fosXW927949C3huu+22JdbvO/3007P6fem9Mc2YlK6p0/VvGnaUbqf7pzbCyiKAgQpo3kBlUeFKughOIcGSjin+gyWFMGla6/SB97Of/SwLWlL1+PRhk8KdM844I/uWIN1eFmn4Ufr2IXUhTR+G06dPz3rSpG810oV+8fjf4u6jZSk9zksvvRT77LNP9lqkb0a++uqrbFlSYeClfcvy/vvvZxcOa6+9dvZ80oVD+lbklltuiT59+mSvJ1B+vDeu/PdGgGVRq1atrC5LCmU32mijbChQ+oJrhx12yGYcuvHGG5fpS7/U6/quu+7KeoOnYUPpS7EUxJx88snx3nvvZY+xKGkCi/vvvz8LglK9mRQUp+vf1L50nxNPPDHrGZN6bcPKVDRXpTYgB6mraPqgTcFP+rAtD8Uf8GmmqDRN98qQvmlOf+yk0KZXr14r5TGByqNQ3xvT46RwKIX0aegrABQiPWCAMpcK/z788MPZ7d13390rDOC9EQAKngAGWC5p/P9NN90UI0aMKJkJKRVlTN1EU1HgNISnffv2sddee5X7K5zak77xTUse02N37Nix5Pyp9wtQuLw3/s/ee+9d8t6Yer8AQKGrUd4NACqnAQMGxL///e/sdqqNkgrWjh8/viSMSVNjpxmRyrNuSipAuaA09respaJuCz5WKvYGFB7vjf+TirMv+N6YakEAQKFSAwZYLqkAb6pInwr8pumbx40bl11Yp5mWUmX6VPQsjwK8ABWZ90YAYHEEMAAAAAA5UwMGAAAAIGcCGAAAAICcCWAAAAAAciaAAQAAAMiZAAYAAAAg8vV/pDMKz+cQ43QAAAAASUVORK5CYII=",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "c77c55b13ddaa12e9407ad04c91a1913908c4953edf31b5b7f7d2e4aeae1b951",
      "outputs": []
    },
    {
      "sourceHash": "a62af19283a7004cef09b033a546bc22faed5d824beeea37dc8c3eab603dcc25",
      "outputs": []
    },
    {
      "sourceHash": "f103821413e4471f8c6df76137c8fc46f80ade583c0ee14b6f19c8c1c1230747",
      "outputs": []
    },
    {
      "sourceHash": "50648ed0f912fd70fbf4529b4b2aab781e094b3b760253c5d21cc23e88de6048",
      "outputs": []
    },
    {
      "sourceHash": "27abbc2a93b978ab72e25a90e03e1177e20aada2b6f4c3405e977114441991e4",
      "outputs": []
    },
    {
      "sourceHash": "2636cc70770dd2b17e6a753b6ec1931549383e01cb02bcb4da3a37d332022238",
      "outputs": []
    },
    {
      "sourceHash": "109f12307f63022bea6313483e1fe722b126325d0123f5fb05d9daae409f1354",
      "outputs": []
    },
    {
      "sourceHash": "16d0031c11c1b7a02abf263e5b1357520c0e235bb1b6a9201513f66e10512ea9",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected tree mismatch\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}