diff --git a/Chapter 3/Ch3_book.ipynb b/Chapter 3/Ch3_book.ipynb
index 062f85f..a324ced 100644
--- a/Chapter 3/Ch3_book.ipynb	
+++ b/Chapter 3/Ch3_book.ipynb	
@@ -207,7 +207,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "999 0.028739098459482193\n"
+      "999 2.999187707901001\n"
      ]
     }
    ],
@@ -272,7 +272,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -328,7 +328,7 @@
     {
      "data": {
       "text/plain": [
-       "<AddBackward0 at 0x7f8c059e6fe0>"
+       "<AddBackward0 at 0x70cd5d15b220>"
       ]
      },
      "execution_count": 12,
@@ -433,90 +433,90 @@
      "output_type": "stream",
      "text": [
       "Initial State:\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 0; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 1; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 2; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 3; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 4; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 5; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 6; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 7; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 8; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 9; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 10; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 11; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 12; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 13; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 14; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
-      "Move #: 15; Taking action: l\n",
-      "[[' ' '+' ' ' ' ']\n",
-      " [' ' ' ' ' ' 'W']\n",
-      " [' ' ' ' '-' ' ']\n",
-      " ['P' ' ' ' ' ' ']]\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 0; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 1; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 2; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 3; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 4; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 5; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 6; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 7; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 8; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 9; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 10; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 11; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 12; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 13; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 14; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
+      "Move #: 15; Taking action: u\n",
+      "[['+' 'P' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' ' ' 'W' '-']]\n",
       "Game lost; too many moves.\n"
      ]
     },
@@ -580,7 +580,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4999 0.04379083961248398\n"
+      "4999 0.058536216616630554\n"
      ]
     }
    ],
@@ -664,8 +664,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def running_mean(x,N=50):\n",
@@ -679,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -688,13 +694,13 @@
        "Text(0, 0.5, 'Loss')"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -712,15 +718,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Games played: 1000, # of wins: 894\n",
-      "Win percentage: 89.4%\n"
+      "Games played: 1000, # of wins: 874\n",
+      "Win percentage: 87.4%\n"
      ]
     }
    ],
@@ -738,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -746,25 +752,25 @@
      "output_type": "stream",
      "text": [
       "Initial State:\n",
-      "[[' ' ' ' 'P' ' ']\n",
-      " [' ' ' ' ' ' ' ']\n",
-      " ['W' '+' ' ' ' ']\n",
-      " [' ' '-' ' ' ' ']]\n",
-      "Move #: 0; Taking action: d\n",
-      "[[' ' ' ' ' ' ' ']\n",
-      " [' ' ' ' 'P' ' ']\n",
-      " ['W' '+' ' ' ' ']\n",
-      " [' ' '-' ' ' ' ']]\n",
-      "Move #: 1; Taking action: l\n",
-      "[[' ' ' ' ' ' ' ']\n",
-      " [' ' 'P' ' ' ' ']\n",
-      " ['W' '+' ' ' ' ']\n",
-      " [' ' '-' ' ' ' ']]\n",
-      "Move #: 2; Taking action: d\n",
-      "[[' ' ' ' ' ' ' ']\n",
-      " [' ' ' ' ' ' ' ']\n",
-      " ['W' '+' ' ' ' ']\n",
-      " [' ' '-' ' ' ' ']]\n",
+      "[['P' ' ' ' ' '+']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' 'W' ' ' ' ']\n",
+      " [' ' ' ' ' ' '-']]\n",
+      "Move #: 0; Taking action: r\n",
+      "[[' ' 'P' ' ' '+']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' 'W' ' ' ' ']\n",
+      " [' ' ' ' ' ' '-']]\n",
+      "Move #: 1; Taking action: r\n",
+      "[[' ' ' ' 'P' '+']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' 'W' ' ' ' ']\n",
+      " [' ' ' ' ' ' '-']]\n",
+      "Move #: 2; Taking action: r\n",
+      "[[' ' ' ' ' ' '+']\n",
+      " [' ' ' ' ' ' ' ']\n",
+      " [' ' 'W' ' ' ' ']\n",
+      " [' ' ' ' ' ' '-']]\n",
       "Game won! Reward: 10\n"
      ]
     },
@@ -774,7 +780,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,14 +831,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4999 0.018897129222750664\n"
+      "4999 0.043820008635520935\n"
      ]
     }
    ],
@@ -909,7 +915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -918,13 +924,13 @@
        "Text(0, 0.5, 'Loss')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Vq1bJ4/Ho0ksvbfDckiVLtHz5cnk8Hv3ud79TXl6exo4dq4ULF6pVq1ayLEtvv/123Puuq6vTqFGjVF5ergEDBoQsU1VVpZKSkoD/GoN0TICQfjUGAABAY2FbALVv3z5J0mmnndbguW+//VaSlJWVpUcffdT3+KmnnqobbrhBlmVp9uzZMe9z+fLlat26tXJycnT33Xdr3Lhx6tWrV8iyw4cPV7t27Xz/heolQ+Njkn6cFOUAAACoZ3sA1bZt2wbPzZo1S5L0ve99T0cffXTAcxdccIEkhZw3Fc1pp52mJUuWaP78+brnnnt02223adWqVSHLPvLIIyouLvb9l5+fH/P+gHCIsQAAABoH25JIeL1eSVJxcXGD5+bOnSuPx6NLLrmkwXOdOnWSJJWVxb7ga3Z2tk4++WRJUr9+/bRw4UK9+OKLevPNNxuUzcnJUU5OTsz7wCEeumEAAAAA+3qg6nuWtm7dGvD4kiVLVFhYKEkh5ydVVFRIOhQMJcrr9aqqqirh7SB5iMMatzhWLAAAAHAV2wKoPn36yLIsffrppwGPv/vuu4d21KSJLr744gavq18zqlu3bjHt75FHHtGMGTO0ZcsWLV++XI888oimTZumW2+9Nc4jAAAAAIDIbBvCd80112jChAlas2aNbr75Zt12223Kzc3Vq6++Ko/Ho0GDBqldu3YNXrdw4UJJoZNPRFJQUKBf/vKX2rVrl9q1a6c+ffpo4sSJ+sEPfmDL8WQKenwi4/wAAAAgFrYFUEOHDtXzzz+v9evXa/To0Ro9erSkQ+s+NW3aVH/5y18avObgwYOaNGmSPB6PL5mEqf/85z+21BvpqbiiRq9MWa9r+/ZQ7+4NA3N3IloDAABId7YN4cvOztbEiRPVt29fWZbl+69ly5Z64403dOGFFzZ4zahRo3Tw4EFJ0ve//327qoJG4MmvVumtmZs15KVZSd8XYQ8AAADq2dYDJUnHH3+8cnNzlZubqw0bNqhVq1a66KKL1KFDh5Dlmzdvrscee0wejydkgAWEs2pn41gIGQAAAO5iawBVr1+/furXr1/Ucrfccksydo8U8TTivhrSvAMAADQOtg3hgzvRro+sMQd9AAAAiF1SeqAiKSgo0Lx581RbW6uzzz5bJ510ktNVAAAAAIC42BZA7d+/XyNHjpQkDRkyJGRa8ieffFJPP/20ampqfI/ddNNNevvtt9W8eXO7qoIkoJ8GdmAdXQAAkO5sC6A++eQT/f73v1d2drZuu+22Bs9/+OGHvoQRlmUFvM7r9WrUqFF2VQWuFj0Uc9uwQ+Y3AQAAoJ5tc6CmTp0qSbrkkkt09NFHN3j+0UcflXRoXahrrrlG999/v3r27CnLsvTpp59q5syZdlUFac4y6KYgpgEAAEAq2BZArVu3Th6PRwMGDGjw3Jw5c7R582Z5PB499dRTGjdunJ5//nktXLjQl+L8/ffft6sqgDECMQAAAMTCtgBq3759kqRTTjmlwXOTJk2SJOXk5Oi+++7zPd65c2fdfPPNsixL8+bNs6sqCECE4ATOMgAAQONgWwBVWFgoSWrVqlWD52bPni3p0PC+4Of79OkjSdq2bZtdVQEAAACApLAtgKqfaH/gwIGAx71er+bPny+Px6NLLrmkwevq50sdPHjQrqoAAAAAQFLYFkB17txZkrR+/fqAx+fNm6eSkhJJ0ve+970GrysrK5MktWjRwq6qAAAAAEBS2BZA9e3bV5ZladSoUaqurvY9/tZbb0mSsrOzddFFFzV43aZNmyRJ3bt3t6sqaARI/gAAAIBUsC2A+ulPfypJys/P18CBA/XGG2/ozjvv1LvvviuPx6Mf//jHIXuZ5s2bJ4/HozPOOMOuqiBFrDRcJpU4DAAAALGwLYC6+eab1b9/f1mWpTlz5mjYsGF6++23JR3KvvfYY481eE1RUZGmTZsmSerfv79dVYEfemqc4WToeKC8Wv+euUl7S6sc3Ks9LJNFvgAAAFzM1iQS48eP17XXXiuPxyPLsmRZlnr06KH//ve/6tWrV4PXjBw5UjU1NZKkQYMG2VUVJAOBmGvc+/FiPTV+tYaOXJDqqgAAADQ6WXZu7KijjtLYsWO1d+9ebdq0Sa1atVKvXr3UpEnoOK1Xr15655135PF41K9fPzurghTwGERZmdoj5uRhzd5waMmAFTtKHNwrAAAAJJsDqHqdOnVSp06dopa74oorkrF7AAAAAEgK24bwAXZxspfKY7CzTO01AwAAQOyS0gNVr7q6Wnl5edq1a5dKS0vVpk0bde/eXX379lWzZs2SuWtkOJPhgnAfk4AVAADAzZISQC1YsEDPPPOMvv7664A1oeplZ2frRz/6kR5++GGdf/75yagCDqO5CgAAANjH9iF8jz32mC666CJ99tlnqqqq8mXj8/+vqqpKY8eO1YUXXqgnnnjC7ioAAAAAQFLY2gP19NNP68knn/SlMW/Tpo0uvvhinXrqqWrdurXKysq0bt06zZo1S6Wlpaqrq9Nf//pXNWvWTH/605/srAoAAAAA2M62AGr9+vV64okn5PF4lJ2drSeeeEL33nuvWrZs2aBsRUWFXnnlFT322GOqrKzUE088oRtvvFEnn3yyXdUBjNg1xJGpPWZYSBcAAKQ724bwvfHGG6qtrZXH49Fnn32mhx9+OGTwJEktWrTQQw89pHHjxsnj8ai2tlZvvPGGXVUBAAAAgKSwLYCaNGmSPB6Prr/+eg0ePNjoNYMHD9ZPf/pTWZal7777zq6qIAnclvXO2VTnzu0LAAAA7mZbAJWfny9JxsFTvfrFdOtfD3u5rfHvsuoAAAAAMbEtgKqoqJAktW7dOqbX1Zevfz0AAAAAuJVtAVTHjh0lSWvWrInpdWvXrg14PdzJUoZO/jfoErMr74HbegMBAAAQO9sCqHPPPVeWZWnkyJGqrKw0ek1FRYXeeecdeTwenXvuuXZVBWmORG0AAABwK9sCqOuuu06StHXrVt14440qKyuLWL6srEw33XSTtmzZIkm6/vrr7aoKAAAAACSFbQHUL37xC/Xq1UuSNH78eJ1++ul6+umntWDBAhUVFammpkZFRUVauHChnn76aZ1++ukaP368PB6PevXqpZ///Od2VQUpYtcQNZPtODkazuy4GJ8HAADQGNi2kG6TJk30+eef66KLLlJBQYF27dqlRx99VI8++mjY11iWpS5duujzzz9Xkya2xXLw47b042jcGJ0JAADSna1Ry0knnaS8vDxdddVVsiwr6n9DhgzR4sWLdeKJJ9pZDcAY4SUAAABiYVsPVL1u3bpp/PjxWrFihcaOHav58+dr165dKi0tVZs2bdStWzf1799f119/vXr37m337gEAAAAgaWwPoOqdeeaZOvPMM5O1eTiMzHgAAACAzUP44vHaa6/p3HPPVb9+/VJdFUTw9fJdqa4CAAAAkHJJ64EytWvXLi1ZskQeVhl1tapary3bse1t5noBAABACqS8BwrJRZwRmUngbpLJkPMMAADQOBBAZTjmLgEAAAD2IYACAAAAAEMEUAAAAABgiAAKgGMYUgoAANIdAVSGy9TkBrYl87NpO27bFwAAAJKDAAqIIlODUAAAAMSOAAouRMQCAAAAdyKAAmxAyAcAANA4ZMXzoqZNm9pdD2QAk6FuJovSAgAAAG4VVwBlWZY8Ho8sG1JqeZhgkjHSMcMalx8AAABiEfcQPjuCJzu3g9CIDyLj8gMAAEAs4uqB8nq9dtcDiAk9RwAAAEgFkkggLTnZc0SsBgAAgHoEUHCUJZPIx13j6grLq1NdBQAAALgEARQaNZOhgKWVtbZsBwAAAOmPAAoAAAAADBFAZTjSxAMAAAD2IYBCWiIuBAAAQCoQQAEAAACAIQIoOMpjlBTcue4ls/rYtC96zQAAANIeARQAAAAAGCKAAgAAAABDBFCADUyGAlruWh8YAAAAcSCAgm2cnOPDdCIAAACkAgEUAAAAABgigEKjRmY8AAAAxIIACgAAAAAMEUABLlJVW6dJq/aorKo21VUBAABACARQGY4haunl6fGr9ev3Fumu9xeluioAAAAIgQAKjrIroPNkaGQ4akG+JGn2hsIU1wQAAAChEEABNsjQeA4AAABBCKAAAAAAwBABFDLKvrIqTVmzR3VeK9VVAQAAQAYigILrJDIc7soXZuhXIxfp4wXb7KsQAAAAcBgBVIbzqHFNztlXVi1J+m7VnhTXpCGjwLBxvV0AAABphwAKacmuOMOubH4WIwaRgC+X7tTN/5qnvaVVqa4KAACIggAKrkMwgsbmdx/nae6mQg3/enWqqwIAAKIggAJsQBpz2KG4oibVVQAAAFGkbQA1fPhwnX/++WrTpo06d+6sa6+9VmvXrk11tRo5oggAAABktrQNoKZPn65hw4Zp3rx5+u6771RTU6MrrrhC5eXlqa6aq9AzAqQPRq8CAOB+WamuQLwmTJgQ8O+RI0eqc+fOys3N1aWXXpqiWiEatwV0LquO6+oDAACAQGkbQAUrLi6WJB111FEhn6+qqlJV1ZEMVyUlJY7UK114vZaaNKH5DtQrq6pV7tYDuvCko9Wsadp21gMAAJtlRKvA6/Xq/vvv10UXXaQzzzwzZJnhw4erXbt2vv969uzpcC3da/uBgzrnr99qxDdrUl0VY27ryULm+dU7C3Xb2wv0wqR1ju3TIgUlAACulxEB1LBhw7RixQqNGjUqbJlHHnlExcXFvv/y8/MdrKG7vTR5vUoqa/XG9I2proptaIYikpo6r9btKY0YsCzYsl+SNHrRdqeqBQAA0kDaD+G799579dVXX2nGjBk65phjwpbLyclRTk6OgzVrjAhbkB7u+SBXk1YXaPh1Z+nmC45NdXUAAEAaSdseKMuydO+992rcuHGaMmWKTjjhhFRXyZUY6RaZXUMB7TrPDE10xqTVBZKkt2dtTnFNAABAuknbHqhhw4bpo48+0ueff642bdpo9+7dkqR27dqpRYsWKa4dEkEQgcaKPlwAANwvbXugXn/9dRUXF+vyyy9Xt27dfP998sknqa4aAAAAgAyVtj1QZKtq3DwMToQN+BYBAACxStseKNiHYMQZnOf05OS7xn0hAADcjwAqjdV5Lf175iYt316c6qoYc1sQwXwrAAAAxIIAKo2NWrhNT41fratfmRW2DAECEF6iQ4HnbSrU3e/nandxpU01AgAAbpe2c6AgrdlVmpTter2W5mwsVO/ubdWhVXZS9pFsTseNHiLVRuln/5onSSqvrtX7d/RPeHuM4AMAwP3ogUIDY3K36+f/ma8rX5wR4ysNggiH4gwaonDSzqKKVFcBAAA4hAAqw5mMUAruPPlmxS5J0p6SqiTUyCZ0+MAGdgXaBOwAADQeBFBwlNviHtcltXBZfcA8QgAAEIgAChmtrKo21VXIeOv3lOqeD3K1ZndJqquSOjZ1QbG+HQAA7kcAlcZM7oy77e65k8kWZq3fpzMfm6gnv1rl2D4bo5vfmq9vVuzWDa/PTXVVAAAAko4AChnrb1+vliT9Z9bmFNcks+0rOzRXLi17++jwAQAAMSKAApBRFmzer1+/u1D5+w86tk/iMAAAGg/WgUKDYX7JHGZn15Zt247LhjgicTe+eWgo4d6yan0+7KKEt0diDwAA4I8eKGSsdAyO0rHObmWyNpNtacxJ/gAAQKNBAAWjtaLsQoAAhEccBgCA+xFApTGTWMTJrHdOitbOpEcATuJqAwCg8SCAgqOcDOfsih37Htvelu04Gct6vZbW7ymV10vTPhKTQNsiPAIAAH4IoNAwiURqquFazZqk38fk2Ylr9YN/ztDwb1anuiop4+jQVOd25RqllTVau7s01dUAAMBx6dcyBORsg9WuHgijIZe27El6Y/pGSdJbM1kDK1EmWfhsS0aRRr1dlz03TYNfmKGFW/anuioAADiKAAqOcnJOlpPppzN0qhkQ1v7yaknSpNV7UlwTAACcRQCVxtyWIMJl1THi7Jwsd52g/P0H9eRXq7TDIN13prIvjblNGwIAAK5HAJXh3NVkd199GrNb/z1f/5m1WUPfWZDqqkiS1u8p1eNfrFRBaWWqq5IyBGIAALhfVqorADfIvLDG4/GkZY+Yk7btPyhJWrenLMU1OeSqF2eq1mtpQ0GZPvh1/1RXJyZOzF3aXVypvG0HdEXvrmraxEUXN0EfAKCRoQcKDSQ18HCw3Xewus65nSFhtYdTri/fUWzTFg1SlKdR4//SZ6fqng8Xa/Si/FRXBQCARo0AChlrQ4E7elaQ3kxuKDgRiFXXeSVJM9fvTf7OAABAWARQcB2jdN9RCpkskAoAAADEigAKjnIytTgQTTqtu1TPbfcGXFYdAACSjgAq0zXSeGWj6fA9myZ8mWyGpBaZy21BDQAASB4CKIRo2Cevpe9UELGz2NlU2HV19rSgTdaKIhDLXARiAAC4HwEUEIXXoFFrErAR+Jhx23lyWXUIsgAASDECKDjKbY3RwrKqqGVytx5woCZIBYKRxJGwBQDQ2BBApTG33al3sh1lV1Cz/UCFLdsB7JCOSS0AAGhsCKAynF1Z7zq3ybFlO3apsWnOkZPIQOg+brsJAQAA3I8ACg2EalR2b98iadvGEZweZ6Xj6DN6qQAASC0CKLhOpjYPCR7N2HWanAyOmAcEAEDjQQAFI3atcxTvMLY9JZXK338wrtdmGuIwZ5mklrdLOsZh6VhnAAASkZXqCiB+JsFIJvR6WJal/n+bLEla8cRgtc5Jz8s2A94KRxCwRJaOdQYAIJPQAwUjdjVp42kb+6/DtLuYrHkAAABIHQIouK5nxG31sYuTPSuwj8n8Jrs6hehcAgDA/Qig0tiBg9VJ2S7N/BTi5CMKtwVZbqsPAADJRgCVxnYXV0Yt47b2uF3JKIBoaNgDAIBkIICCEYafJY4zmJ52OnmjwoGob2dRhb5duZvU6wAAxIkAKo2lZ0xjkDmQUCOiTA9m0/HonAxFEo17LhwxRb95P1dfLtsVtkxJZY1W7ixObEcAAGQoAiiXmrNxnxZvO5DqasBO6RgZZLhM64XZVVyhL5buVG2dN2rZuRsLwz73/b9P05CXZmnOhn1Rt5NhpxAAgKgIoFxof3m1bnlrvq57bU7CDbx45hwls4ODOVCIhvc/fv/z92n634/z9N7crQltZ1/ZoQQ1367aY0e1AADIKARQLrS/vCrVVWiANm3iOIfOSsfzbRkNBgxfprLmUM/TzPV7E9qOkyzLUlVtXaqrAQCAMQIoNJDqITnBPRCZNswKzkgsFIlxX1yicfvth4t1+l8mqKAkerIOAADcgAAKjkrHXoFYlVfVhnw805M/oHEy6zUL75sVu2VZ0qe5222qEQAAyUUAlcaM5hPZFLL47ytcj1BBiT1DD9M9C9/+8uQscIzMZ9KTZVQm8aoc3hddawAABCOAciE72yxlfr0hXm/oDZsELP5lXpq8IWSZNbtLom8nvWOjpOP0SJwFZxAbAQAQHwKoDLe37EivUJ1hiylUkOM/TOefk9aFfJ1tDbIMbT9n6GGlNYKIxKV7jzEAALEigEpjCzcfWScq3FAbk6ZNdW30NWPsEk9jizYu3C7ReUBHtmNTGZs+NGb14RMKAGhcCKDSWLXBYpkmvl2125bt2DVfIlPvZzN80VnM34mM0wMAQHwIoFzI6XZNbZi5UbGiPYZ0Y1fviduGsaVjsE7ACwBIFwRQGc4/dXYi7ROTBqLJ9o0yB6Zj68+AUbKODD32WHAKEmfbED5iGgAAGiCAyhDhGjpGbdGg14Zs6BtsyOtga6tj62zH9gVnpWP8ZNscKAc/Q/bV2ZbNAACQNgigXC6dGicmVTVpHAeXCXUO+hzT3mBLyDTlVbWauX6vamya/5dOn69UcDJBBO8FACBdEEDBNjSAIrNraJqTPTRVtXWav6nQtoAlUUNHLtQv/rNAL09eH7UslyMAAEgGAijY1nPkv6WwadWZ4JJW/jBmmW761zw99dWqVFdFkrRg835J0ieL8lNck+QwS1EevRTBIwAAyUMAlSHcsF6Lf7tuzsbCuLeT7jFWJvXEfbZkpyTp3blbU1wTd0vH99ykzul4XAAAJBsBVIYzynpnsJ1YF/gsq6o1eEX80jzGCivdg0enOLlQLJzBewEASBcEUBnOv6EZLo12cMMlVCPeKMhK0kK6Tk5kTyaGLzorU8+2k5+GzPjkAQBgLwIoF4onDtl+4KD9FfFj0vb3r3amNl4R2YHy6oS3Eeu15nYFpZWasGKXal2SiKNeOp1DAADchAAqQ2zaVx7ycSc7PVhIN7LMPKpAFTV1qa6Cj1lChkP/X5JfpDemb1Sd1/6w4soXZuruDxbrvQydR2ZXzzPzrQAA6YIAqhFJ9lC4H/Tq4vs7U4MgN0jVqa2odk9wZLdrX52tEd+s0egkZPfbf7hXbsqaAlu252SgQVADAEBDBFAZzsm29gkdW0UtE24eVmCZ9JYpc7aCPf7FylRXwSdZDfv1e8qSs2GZXRe2JccwSXWemZcpAABJRwDlQnE1wG1sDIVOIhFbWGNnEBRvQy9VPTXh6pvunXLj8nY4sp9Yr7VM0phjmky98QAAyDwEUC5n2qRIpPFh1xwGfwkFCxmWRADJkH5XQHqmXs+sRXura70qOph4ohMAQONGAOVCbrwDH2tAlOzelq2FoZNmuFm690A5JR3Pk21BBOPqkmrQ89N1zl+/056SyoS2Y1mWvElIOAIASA8EULAt4YNdDV+TAHLj3vQIoCzL0oodxcYJGJKRBa6xiicWSeYwMrfFRk7OyXKLbfsPLfcwc/2+hLZz70d5+p9/TFOli7JOIn2MX7ZLK3YUp7oaABKQleoKwB7h5914opdJQn3CBUH1DRgneJT64UVfLN2p+0YtUe/ubVVbF702pZW1DtQqPm6ao2Jfw949xySZpl53V53t4mx2wcR2Nn75LknSjHV7dUXvrnZUCY1E7tYDGvbRYknSlhFDUlwbAPEigHKheBqqiQRHdrVbTHqOqmqj37FNxyFc/vzP55jc7ZKklTtLbNu+xxWhIWLlpgDUThkazxlpxIeOOG0oKE11FQDYgCF8LvfJwnx9ZlP2s3CBSfDd2GTOwWJ9KNjBbQ1XkyDCrjK2cVt9HGTXYWXq+QEAREYPlMv9adxySdKQPt3UrGkaxbsmmfQsK2RARYgFp9AABuAkNyaJAhC7NGqRN27eKC29sM8afFeXGyQ4MOk48i/jhp8Iervs5aZgw2yh2NhTcCfzGI3mN7muby1DRTjNr0xZr9vfWaCaOm9iGwIAZCwCKBeKK3uYUYMyjsocFvNCumGCF5OtEPc0bk6+/SRksGFfaRhERKrz379dp2lr92rCit3Rt+PAoW8/cJB5M5mE3zcgIxBApYkHRy9NadpTox6oGLdp2vgwLWeSoMJp9ILFzslz5mjTPx3nSTnIbYfllhTlFz8zVYOen6H95SwADABuQQCVJr5atks/enmWMzuzof0abhNmgVh8FXh2wlqjOiSbG3o1Mj1uMznDJoFY8FuV6vNm16Vj13Ek2rtUUlmjZduLXPGZ8GcUqJpsJ+GamMt3cAkIJE+GfzUDjQYBVIaza8Jqi2ZNo+8rxb8M/128PbUVcEqKzrO7msD2cLJhn45D3RI16B/T9eNXZmvq2oJUVyV2jbg3EAAQGQFUhrDrd3zzvvKQj/c/8eiYthMumPJ/OFydIwViy1m9PePtKKqIWsauhquTSSRMOJle24l9FZRWSZLRfCInpWNCD3fVBvFiWDeQGdI2gJoxY4auvvpqde/eXR6PR5999lmqq5QR/ufv03TAb6z9zPV7JUlNXPKdP33t3lRXISapOm0uebtSKtmJVWJlX9DnYK+Zo+tSuStEMAtCwxdavr1Y3650V+AIALBH2gZQ5eXlOvvss/Xqq6+muiquEO7HPp6bXWt3H8n4tGy7eY+P/3BBO9e6cNud4GQ6sWOrVFehUbFvCJ8923HbXCET6XhD3bY5UBEKXf3KLP3m/Vyt20MGPRyRhh8XACGk7UK6V111la666qpUV8NWS/OLNGvDPl16Sifbtpnol7XXG1+DLuwQPpe3tr5YulOvTd2g13/eTyekcTDj8Xhcd0ffTnYFGo52sDi6r+SskxUvt12KZufHZDvRbdlXrlO7tDEoGa0+LjuJANCIpW0AFauqqipVVVX5/l1SUpLC2oR2zauzJYWfhxRZ6B/XeH5y/WOc+vgp1vWbVuwo1kUnd4xY/lCDoOGWI8VYsRxPPLHa/36cJ0n6w3+XafRdA2LfgBJsdNoUXyYjTM3EBlwGHpLjkjUfLdUaU883nOPye4gADKXtEL5YDR8+XO3atfP917Nnz1RXyWf2hn36eME237/dNOQj3kbEhoKykI+b/XbE9gszelG+crceCLGV+H+pyqtq435tQB34sUwK+wbeOTmfqPE2yN32ObBrvS0ne/Hc3nsPAI1JowmgHnnkERUXF/v+y8/PT3WVfD6cv1WPjF2e0Dbqf8fX7i4NCCY8IcqYbks60gPlZg+PWabrX5/jmrHlyW4nmzTaktHWysQGnG29Jy7bjsnNg0Tn+Dhp094yTV69J9XVSKnGHIBnkgz8GgUapUYzhC8nJ0c5OTmprkZIdiZcGPzCDEnSgj8NVOe2zeP6si70y8Jn94+2f31M05j7V8GpNoSb2yo1dS6unENMLmu3LYRqV+rsRLPDuVWk4/r+P6ZLkkbfNUAXnHBU4vtKeAsAgMas0fRAuZkdd6SCGwT5BxqupVNUUa3fvLcoptS69Y2a4N6HvaVVIUq7TBre6bOrynYG5fXcdAfctpqkYRpzJ9kViNl17E6uA2dXD106vu9InmR8NwNwXtoGUGVlZVqyZImWLFkiSdq8ebOWLFmibdu2RX6hCwUHJ3Z8vdbWeRs89tzEtfp21R795v1c4+14w/z6n//0JFXV1gU8ZtcQLzf8vCTW5jny6pQdixtOYhpwW09Neq5L5ZxIQXxtnVellTWmG7KljKNz6BzbEwAgmrQNoBYtWqS+ffuqb9++kqQHHnhAffv21aOPPprimsUuuK0b6YcyXFrx4N/6uhDl4uk1ilSXkop4Ei0cOdp4GnBONSJS1duSifOMksJlc46clI69Hk5c1le/MltnPf6t9pRU2rI9+05h+C1ZlhXyuxqZi694IDOk7Ryoyy+/3FVDihIR/IUa6fu1zrLUxKB7ofbwj3KiwwXC9UBJ8d19NfnxaDAHKua9HN5OlOdX7kz+cKBUXaH8Rptx2zeI2+rj6BBHg51F+j5avevQ0hSTVu/Rrf2PS7w+Dhz7rf+er62FBzXl95cpJ6tp8ncIALBF2vZAZZIGjd04blEFNz5qvd54NxW43UiNiKDnktVor4szaUK0Yx/y0qy4tpsOuMspo8a/kzdhXBccOcjJLIV2LYBrlKLc6LjCfxjnbCzUjqIKLc13bm4XACBxBFAu0CQJrd3aw0FHuB9404ZjpHKJtonC3XEO7jX7eKHZvLbg4W/p2EFpciVkZ6XmY+um02nfOlDuYraukE2JHWzZisPztpzblY1p3k0CMbddiQCASAig3KDBJKjYf0yDX1I/1CVcY2vxtoYLz4YSy/D8UHHg3tKqgMZBPKHi0vyiOF4V2qtTN+iDeVtt2140yegIatYk+lbJ9CSjk+9ouzUNG8luOz329S7ZU8Yu6bQmFwCAAMoVYmnsRvoRDZVgIlz5sqq60E8EbzNSD1SUH/Spawp0/tOTdN+oJUb7qhepQy6RO7XbCg/quYlr9efPVkQtm0hjJZHXmnRGllebvXd27Mut3HbH3m09Yi47PbZda27Leuey0wwAcAgBlAs0aFzE2dqYsqagwWP+MZV/o+q2txdo6tqG5WMR3JjxBDwnvTJ1gyTpi6U7j5TxX0jXsPURb+Mr+HUz1u+Nb0MZKo3jp6RJZmBm02gv2xbSNZvj4651oIy2Y9Nx2VcmahHXBbxIHjKtApmBAMoFgr9O4xmyVlPnVWF5bGnKh76z0LhsqO/8SD/6Yxdvj6kuAfuK83XBjZngnj2Tniffthy+t1xzeN0uu4beVdQc6aUqPhh6bZxYf8jd1MirDrHOmR3izSi9v7za3opE4KK34TDn5mSZzRFLP2bfN+l4ZACQmQigXCCWJBLhij4wemnIx/1/mDftLQtZ5uKTO4bfX5wNejcsbZJIEGRXsGASpLw4ab1O/fM3Wra9yJ6dGkrn+6A1JpkZDd9D/0b5pn2hPyN2sGv+jl29VHb5LG9n9EI2cdvivy74mkOaSefvXQBHEEC5QDJ69OsbGv7zonYWh15gMuKco0iLQDbYjj3JDYK3YxrEpevQiH9OWifLkp78apWj85LS9HQ1ekbD84y2Y0+Zipo6rd1dalAyMpPL0ckEEWbDBW0pYliIDywAuAUBlAskoyG75nCDJtGeoMqa8AkL4pkfEdexGr4mOOFFrL1nNX7DwhI5besLkteLYadkZOrjjnx4u8LcwIiVm4ZS1ttZVBHxeduSSLjw2O1guKhEkmsBOyzNL1L+/oNhn+fGFZAZCKBcwZ4sfP4N4hcnr5ckdWjZLO5aSdLHC/L16aL8kDWMpzHT99j2CdUn0i6Lwsz1MbWnxJ4GbqhkHlLi70W8nJzPNXHFbsf2FU1ZdW2qqxBgX1lscxTDcVtqcbu2Y7KrSFlBj+zLrh46e9bkor3ceGwtLNc1r87WJc9OTXVVACQZAZQLxHpHau7GQr16OMNdNOckGLBI0kNjlhmVM2m4ZDXJ/EvO/zT4v7WuG2KYhOrsKo7cE+Ek08a/m3o1bMuM56JjspOTqRbcti4V3G/1ruhDWVmjD8gMmd+aTQMG66IGuPmteXpu4lqjsm7+sg7XaIgYZyS4sG8k/vXZUFDma6j+4nvHxbYho30512IKdw2498pAJLZdOXZegk5dTLalVXcuc6DZduxJh47Go7bOq999nKf3HVwYHsARBFAuYFeQE2pdp2QO34r1B92yLP31q1XJqUwI/me1No601x/O3yZJys6K7WMSrnEWsE5W2ODRubAmGbsyyo7nYqm+4WA2jM1kO+4KEIz25bLeHCcTViTqzekbdcmzUxzpAZ6zYZ8mrnTPUF03MflOtet798tlO/Xl0p36SwzLcwCwDwGUC8TyhRqp7Ddxzj8xarTb8K2/YPP+uF5nx+9N78cmxvyaDw7f2QsMfOKfX5GqEXzhGtN2BQttm2f5/n537paYXz93Y6FW7iy2pS7pzr6FYhOvixu57bhsC1QNNhPp+2P4N2uUv79Cz00wG5mQiFv+PV93vZ9r25xRxKe00l1zPIHGJit6ESRbLM1YN/9oRQvEyoMm9TuZ3KCqNjkLr8bmyPkJG2Q5U5FD+zKMm6M17pr4jUGNtYG7q7hCN781L7YXJZHTCyjHw8lhY6acum6NkkgYbMfRVOcG7KpznYMRZmFZtbq0be7Y/tKByefArs8KQ7CB1KIHygViGbYVawCVzCFJwQ0wu+YV+Nc5+HhT0cD1f3vMGlVHCvm/NnA7oTfk5NG54Qd4x4HUJ51wU8hk11wY+xIpOHd27Frfykm2ZfOz6TxXVIdfdiId/eWzFXr+u3WO7OtgdW1cQ70BNE4EUC4Qy9Cu61+fa//+43xdor/5GwzWS+r/t8n6atmuuLafqqx3bmvkJcINQZaTqpPYUxlrsphwnLyJkI7XstkwWwfzsxuYYFP6/3V7wmeBq6yp00EbU/sn+zrcUFCq9+dt1UuHl+RIptLKGvV6dKIGPT89oe0YLSbvV2RvaeilDSpr6nTNq7P190jJotyW1RVoZAigXCDVE9fjlejPZ7jFN2tsugsY61kNbgvV/9v/RzHcMZ/apXXY7YSqjzuG8Jn82KfntRmvhVsO+Oa+2e2YDi2jlnFLUoIjOzMr5tR14uSxO9kjtjZC4BPLvsKVsSxLfZ74Vr0enaiq2vTopaqsca43aNHWA5KkLYXhF8BNhmcnrAn5+Li8HVqaX6RXIixX0ri+mQH3IYBygWS2PYwWekzRN3G4RldxRfgFcZPZgEpkqE2L7OjTCd0Wi7ihOm47J5L05yRltQq+MfDXL1fpzvcWxTx8K9ZhpIlIhzlh8XBygWATXpPUigbC1ae6zuvrXd1ZZM882mTf+LPzu2Hk7M36YunO8Puyb1cGjuwtXCIIu24iAkgekki4QCrbkF6vpfz98d11C26kBf925x6+qxdO0zC/kCnLVhfcA3X4iGKdGPzNil2S+sa0r3r+iTb2l1frqFbZBnuPk0kSieTtvdHZVRzYcH179mZJ0pL8It9jjXiNXKMDc3T4oqO9XXZtx7n5VukSXG/aW6bHvzy0fMaPz+4esoxdPagkiAAaD3qgXKCJXZMjQoh2l/DBT5dq495yg+00FPzzOW3t3pjq0iTM1efmHw+TJkO4tZD8jz9c46PW77XJntBsFBg2giDLyUQJodTF2PuQSCr9WJmemmjXwHa7koW4rM1uWzCS5OMK/O5JvorqOlXW2DdUMNJ5rqnzRuzBO3Aw/IiGeq77DnNj1zyAAARQLpDKr8pxeTvifm3wb9r0dZEDqGDh7vpFuhsY0+9KhLKhfpDD/gTb9AZFq3uyGlHJbpzFuvnAhlXqGwoua5NHZdc8qXV7oidxsevambup0Jbt2JaiPOGamG/HpD4m6dlNhF+gO7b6mAh3c6661qszHp2gsx6fmNDQxICgL8xmqmrrdP7Tk/Sjl2eF346Di9s6GfcQYwGpRQDlBo30izCeIXx2/fi/GCKzU4MhifVJJAx+yGPtqUlkO3YxSiKRhIuz16MTNGfDPu0Ik0TEaeVVjXNBSpMsmKbDtGLtRQtlf3l11DJO9vgY9fTZNQcqyclDkvG1Eu7aqF96oqbOSmj9PZPvwhU7SlR0sEardpWE347Jvhz8EbarVz9dk08BmYIAygXS94swsdZD0zBDF504Hy9MahhAvT5tY9zbi9BnduSvGFPchju7p3RuHeaZ6NuMZvG2Axq7eLvfiw22H1NtDjUWb/n3fF00Yoor7qK66fNnElQ6mR0uVKN9+4GD+t3HeQGP2THp/dPc7VHLmPUupV+ad2eHAoYvtH5PqdbsDh+M2GnuxkLlbYs8T7ZeImcn1u/dRMR8Iy3Mkbnhe9Ff8cEaDXlppt6cHv9vJDJTZU2dJq3aY+sSCemCJBIukKwvy3s+yNXAM7okZ+NK/O5rl7Y5IR+37XzEWL9wDbjAoCb0Rtu1aBZ1+ybDdEwa81lN7bnvEeo8X/faHEnSsUe11HnHH2XLftwu1ZPhY02dnOIpWxr2UZ6W+iW+kBxMY25SxqYgy7ZequibsaUHz1TYTH21Xv3gnzMkSav+OlgtQ2QWtSvQO1BerZvfmidJ2vS3H0adB3xovw3L2Nebk36cDLL+NXOjVu4s0cqdJbrrspOc2zFc7//GrdB/F2/X4N5d9OYvzkt1dRxFD5QLJCuHxDcrdmvCivgWoTWR+E9p6AOPdD5i+v128AfmZINeIf8sbKGOw+NJzrVgsi5VsE2HE4uYVCfd5hC5zb9nboqpfKqXgdq8t+HQP6c+am5LP24Xk5srZkMK4z+wSr/1oSItJRGLcI38Qr/hmuGO3bbgyGURlF1Dp50M+qocXJML6eW/h0esTFy5J8U1cR4BlAskcwjRwWp7MiGF+s5/4suVWrmz2Hgb2xqkS4+erS6V6mtnNnfJnjo3cfC2YqQ61x0+ULcNJXEzp4Y+GTWS0yQ7XKycHL5owvT8VNbUadb6fWEXsQ23nVgDIqO5VGEeN/meM2HyWv/vlYTqHOULakdRRUDvXrjz6egcKJMyLvnenbqmQNe9NltbCqNn6gUaG4bwuYBbvixjNXtDoYa8NEtbRgwxKv/YFyuNyrnlfOywK/VyCGEbgk7eCY3wXK2DQ4pSza4gYX9Z9CQIdjAaxubo3pz7zKZjQCdJj4xdrnF5O3Tjecfo2RvObvC80fBem06yyU2gcLWxa3hkk4B9GSz7EMfX5dS1BRr6zkJ1aHlkeLVlhb5WbZsD5bJkFIkaOnKhrdurrKnTcxPXauAZnXXhSR1t3TbgNHqgXMAl8YLj4slEF8t8lUTPa8XhdNuJDCWJ9UcuGaNNwt+9Df+a+vTDJg2CIoN1Vtxi6toCfb4k/tT9kTgWdLosiJDcc9NDcnbukulbXr9cxOhFoedZhtvOVr/5cWZ1jr9MYA9U/BeZ/0vDXRd29XZFuu5Gzt4iKXAdKJPet0h2FlWopDKx77tMXVvvQHm1fvjizIhDkv8za7P+M2uzbnlrvoM1C8/rtbRwy36VJvieus22woO654PcgEXaYT8CKDdwU+sjDLcMq4uFUxPb42FyF7jEpnkIk1aHG5scYQhfgsHAp4vydcmzU7R+T2nEck6/Q0PfWaj7Ri1JyrZrvc7MEzCbL2PPvlzX42NXEGHTKEj7grXQZaauLYi+A5s4uVZUrPtKdqIX/+/dHUUV+nzJjgYLme8pqdSFI6aoz+PfRtiQyb7iraW7vTZtg1btKtFT41eHLbPVZUMBRy/K10/fmOtLnpQp7v4gV9+s2K1rX50dsVwi67SBAMoVkpVEQpKys+x5i/eVVdmynXRkMpQk1kgg3Gb8r4U8m+4ehRuKGLEHKsE5UA+NWab8/RX6/Zhl8W3AQXb9hNTWxbelWBurZkP4bJoDZVgulhssK3cW6xf/ma/l283nT8ZaH6fYdZ7tir0TWyvKuZa9/xC+aWGCRJMgK1KdQ313mQSzlzwzRfeNWqL3520NeHzx1ugp151cv8lkO16vpZcmr9ecDfts2WcklQaJJtx2I/azw6MR1husiZdOTALVeZsKdfYT32qMwfIRCI0AygWS+aViV1KC579bZ8t2/LmtMRSO2ZCLJCSRSOEJqh+OlmgSkpoEFtJMN/H22sX6EY21kZzIkKNQDc6QPbsxHMPP3pynmev36SevRb47Gro+NpUx2ZdNPVkmwmaiC6hPdImkeTcJWPwfNiljYtmO2APpeoF1DtxzqEvSZDhz/cd4zsbCsGUS4redaWv3Rq1PIr5ctlPPf7dOt/w7+UPmjH4n3RU/uS6gW7+nVAP/MU1fLN2Z0HZMRt/c+e4ilVbV6vefLk1oX40ZAZQLuO1LJRlCNcT2l8c+6b5+MxefnJoJqLEufBjurU1kWM/eGHsD4+k0s2tdmmhbcfMwy1jVeC2VV9XqkbHLNTuJd3xj6fV4fdpG9Xn8W41emB/nvuxXWnVowcXgOWNZBl3xtvWsOXhzYvO+6HeDTYZlmrAvwLSnPuE+3v7rPiXr2EN9t8SSjKLhY3b1HB1RFeYGk1HD3qDIVoM15izL0pdLd2pdlOHWNlTHdW0dt9Xn/k+WaOPecv1v0ELl0RSUVtq2RhvMEUC5gMs+w0kRat7JO7M3+/6uqq1TQWllgzLhRFt4sX6biZq+bq+j749/D1S4H9G9pfYMp3Tix6MxfalblqWXpqzXxwu26dYk3vGNpZH8zIQ1kqSH/5vcoZR2XEo39DvGhq3Yx675TR8EDQULpS5MR23sNxhC1yfWT2GNwXDUcFXznzsUdviw398mQY1Rr1mE1x8pE9sNsFjLmDB6T21KNGFSZtq6vfrdx3m64vAiyvHyPy6v19LB6to4a+QcJ5cNMVERx4iP71bt0QVPT9b/+2SJ7zGjozIotHFvme56f5FWJNBLnMkIoFwgmZ9ht3w9ROuSvuKfM3TB05O1KcQinfEyGZMdzcNjAru363/Iy6pq9fmSHSqprFFFdZ2KDsbWmxZ2DlQT/zL2BR+llTVauGV/wKRRtw1fSBWTRnCd19Lfvl6tSasiLxaY32CtM/s5GZOa7suOnkSTbdiWHMPRniyT43KwB8rg2D+cHzro869nuH35dyxWh4kMA4bMhentDgyOkn1+QvQ3eaKVCLEVo0VynWPysVyZhMbxL99eoF6PTtTOosAA2mXxiuvqE8/F8fKU9ZKkz5b4tbFsOq6h7yzUxJV7dPUrs+zZYIYhgHKBTBrGFK/6oQbfRmmgOt2fsaekKuS37O9HL9V9o5bodx/lqe+T3+rjBaGHSO0uCd2rZjIp2s6G8vWvz9FP35ir0YuO1NMNl12yqxCcSSteny/ZoX/N2KRfv7fIlu0lm32BRqhtJ+tTaE9qcaPhsbbNk7JHqjIr1oT5fGxIYFK9yYLS/nf+7UoElsh1Geq7cHdxZVAZm4bwxdi79Jv3FunLEDcgjYI1RxdmP/L3rMNDmOvT9/vKGGxnT0mlBj0/PWCESmNhcn4sy9Ka3SVhP7um2zEps+3wDcFGNJAkJgRQLpDM7zg3X/ehfjg37S2zbehMMtTvdcLK3ZIODfGL1NO1q9h8WGLwIdl5hOv2HGoQfea3BlI8l12kL203OuPRCXph0qEEKIlcM7G8j8lm1xpGdu1Lktbsit5ojr4vB8tEL2K0IfuSSAT+e0NBqS57bqrG+jVA52y0d17dY5+v0Kl//ibuXv9wv1uvTNkQ/bV+fycSPAbccAp+LmQWvuj1qbc0KEukXY1Sk55//8Dn21V79LsY58TEwrbA0Oi4om/nuYlrtaGgTE98ucqGWkWrjwvuIvoxqc+7c7boyhdmatiHix2okT1yt+7XkJdmauGW/amuiq0IoFygsQ6lCvVjFm0x0lScqcCx+Em8ta/k9wolWv2nvortRy3Vd65q6iy9MGl9xDJ2VdGpH2OTO/ZVNQ3H0v9hzDK9O2dLwvsPdZxbbFjfJdXXSjxMhpaZXBbBw9geGrNMWwsPaqnfUgbtW2Yb1Mfcu3O3yrKk16ZtbLgdmwLVsGKcpGGSPa9hfUIkkUjg027fHKjAf4+cvTlp6/EkUueZ60NnCExkXyZtnWoHM7e6reVlUp+3Zh7qmYs0WsctgWH9dX3963O1cmeJfvrG3BTXyF4EUC7gkms9BUL8aET5Hal/2skvCP+gaXuYSdF28T8ukwbKXe8viposY0NB6OxKO+PoVXl3bvQJ8bGYu6kweiGbbDLIhhaOnZdbndfS27PiH55i0ggMdayfLMrXY1+sDPuaiSt3J5w+NxFm2dhs6n1zWda74GOvCtGr3bF19ADKrqGA4a6xcC+trKnzZVX1LxPPscci0jypWHqgTOIXuxJNBBd5/MtV+nLZzohlTLYTukz8X1zTw6RYD78ve9j1XVtT59WoBdu0LUImQpN9FR+s0eNfrAy4mRGvSav2aJEDvTB2nUOTNUrD7Wv9nlKd9fhEvTw58g3MdEYA5QKNNX7yWoe+UC5/bmpMr/t4wTbNWBfbl3si/H9bw6WdDfvaMD/MYbNBxbR1aeLKPfokSorqsqojAZbTN/mjNfZHfLPGlv3sL6/WR/O3RVzzaOA/pod83OnP338Xb9df/Xry1uyOLX2wXT017Vo08/1dW+fVXe/n6n8/zlOhX5p8u/bVu3vbqGVMGrGm9Vmzu0S7isPf7HD2cxB9b3Z1QJj0ZCRjfav+f5usc5/8TvvKqmLupTcL+sJ9X8Z2wylcEZOhybaNFAmxmeAU4rYFaw5+uSUrwAylts6rsYu3R0za8++Zm/XHsct1aYT2hcm+nv56lUbO2aJrXo193Tp/+fsP6tfvLdINEXph3JYNMpGkKH/9apXKq+v0jySsIeoWBFAu4LZUmk6prKnTr99bpC1+d4gsRf+CeGTs8uRWzAHhfuxN7vgEK60MTBdbXBEYRBSESWQRrx88P922VOp2GTpyof40brke/jT2dN1Gd59tDLPW7EpsvZVkNP7r/C7Isqoj15Nd2c/aNm8WtYxdDfvtBw7qyhdmasDwKWHLRLvpcKg+0dn1XtTZ1SNmy1ZiD6Dqv3MWbTkQcx3Cff5M1hSP9aczXLDWsXVO9BfbFj+FGFqYpIjeLHNgbI/HtP84MhmatIc+mLdVD4xeqkueDR8czd8cfXSDSYAwetH2qGVMmMyjjed3JtQr7Pq1MuuBapztV4kAyhUa6/UXvNJ7vXAZ7VLF/ws9Jyu2j0y09/bzJYFZiuIJpoPvzn40f1vAvwN6OPyKntipVcz7kqT1BWW+xAxuUT+8oj65RyzsnJz/9fLo+zcNSqatLdC8EEMc7Wps+QfaYXtKHeyqMdnXvM2F+stnK1ReFWqNmUNW7oye0KLArhsABpU261mLPvzMrD4m+4r870ObCTOEzwr9dzgmxxFuO1NWF0R/caR9h9pXmI6mSA1Fr9fS1LUF2l8WfamKeG+0mCTAaLAvl7UbTBYutmsNrHBthxg347rRP/b1PNqT0MNoPbgE6pDuslJdAcBftOEfsQ6hs0NOsyNBU6c2BncqDVg6lK7Vf4Hh4O8b04Z28CkznVOQyNeb6URfuyYEW5alvWXxN3q/WhZ+bk88ixeGEipxQ7wKy6p0+zsLJUmbh/8w6McouVFNfKn0I19NZr1L0ctsLTyo9wu3qmV2Uz3ywzN8j/vPKUtGuu+wZQy2c/zRLX3zg6RDc+BueGOOurVrHvCYU/UxKWMS9NV/zwSv9SOTIMsK+w+fp79eHXU7gQl+gp4LNQcqjoWGRy3M15/G2TfqwaRdWVIR/gaB3fsKF/TF2gC2bQ6UwZZMbjTaFUTYJdS+qmrrlJPV1LlKxCD0QsgNNYI4KSx6oFwgmZF6pl3bTs59Sranx69u8FisSSSkQ42d//fJEr1kMFnTvwGRrOvOvzG4JcIE3lj87evVuuDpyXG//t6P8sI+l6phT5EciLAw8z6DO+FuY5ZMIPTj+0IEzsFZ//znlCWSlCCwPvYENVee2TXg3yt3FitvW1FAb6VRL5VNQagRw2Btd3GlLhwRfqikCa/JPRajYX5BvXghfv3imWv27SrzXm3/nqzw87YaCi4aKdnLkX2ZzE9JbRDRYAifg3O7zIbDp66F9H/jluu0P0/Q2hjnwDqVtdn0sxI2ALexLm5FAOUCybzQDtp0dx2H7LFpPpFlWSoNkfAgnn6GRVv3a1zeDj1/eLLmKoMhTMH7ilWkuiVjAcT61K3+Ik0gziTxtIk7J9BT6t84CRVEhO5VjFzJRHpG7vkg1+DV9nvfIONkPMFavBnfbFvfyoBpsJa79UDEOphsx+Qc7g97QyH8DadQ53B/eXVS1xA0uQEWcqhbHO9csodIxbx1gxcsMchkZxL4hAsea+u8yt164HBSEJf1QAX9+8PDQ+1fnXpk3TS7es0cHb4Y54Zq67y69d/zbEsklQoEUC4QT+IAU06mic5UxQePBDrvz90a02Ky4ZItHEqW0fCNj+cLPXgI2vjlu8KW9f9RTyR5SaQ2SN62ogaP9TmmXdz7Cmf+ZnvSwZo04IxOleHp3GyUTt2vIWa22QCxvibcKQh1jiviGKpo0mgN9z4s3BKikR5hc3b1QJVGmGflq4fBduKpTv3C15G2s35Pqb7/j2kB8ygTWYTc/+FwQZ5/Qz9UEY8n9vNvElCuDrNQs0miCX+Dnp+uh8bElmgmlsNpEuXmg+SOFNNGYnytSc+ISW+LUQ9LmCJ/+3qNrn99jh4Zu9zGXirn2PUzk8rA0LQOU9YUaPaGQr0xveE6dOmCAMoFGsNkO1MOzlk39uaMTb6/N+8rN5qvEE34LHxxJJGIsw5NkvXrEWKzLZq5c5y35GyiBCn5a4lJzh+THfczY+kZiFTSbYkvgouYHKfJ3MH7P1miTXvLA+ZRmvRkjPhmjSqjBMEmdbSsEPM2rcBzEq4xHGsvVTxXV6gbOZI0JteerGqh+P+Wh80uGOKxzXtjX6PObdl7besZSWA7bx8e/TAmd7tZgOngQDOTbId2JYgwq49d24nvdSYJKtyOAMoFXPY9GJdoi7macr7hFxvT3qdQ8zb8TV69J3T60Uizom0W/AOzYkex8WsjNXqiHlcaMqm+6dBJo/0ZzKWILLbXJJ6uPMoQPqOekQSrYFQTe9k1RM1sX4FCJT8x2dX8zfv1nygLOZvdI7LpuOIc0thwO4Eb2m3z8g2R9lUv3h6ob1ftibkOZsGIyTwpe5hsxyz5g03bcdkQPmeHFCa+IdMthHsvor0+3dsEEgGUK2TAdWRLr0w6qPVaRh/8856aFDHY2lta1WA7ZVV1Sb+rWFxRozenb9Su4ooG+1oVNExmQ0GE4RYR3m67jyHcsA+75jL8+bMVtmxn5JwtRuWSMT49d+sB3fLWvBhfdShV+o6gLGom8xRitclg2GIsgUZhhBsU/ovJvjN7c1LnvMSVNjw5NQm5r3pfBw3rjTaMNNx7EW2Yn8cTezDu//5sLSzX//tkSYjPfPRGWrLOa6jtmsxvCn/ZOdeD4ORwL/uGOds0DyjZQxwTkLv1yNDoWKvgtiF8YQOoTIiQoiCAcoNGcKGZcnsYFku3c1ll+DkU20IkQFi9qyTwB1iHGhf/N2653o3QOD/glyI5WpKL9QVlGv7NGv30jbkNeqD8G57bDxyMOrlzW+HBkIv02nXHvd6ni9y1LpjTTM7m9a/PCVgbxfQtuP2dhbooKIva379dG0PtzPin8Q6nvs6TVu3RNxHm8UmHbmSE3Y7f3098uSrueaCtsqMPOzWbA2UF/Tuu6iQUCPoP8zNRa/A9543xptm/Z27SK1MaZgr138rQkQs1Lm+Hrnl1VkCZHu1bhNxmU78vsVjrk4hwe0rVHKiwGf8S2Ne2GDOoGvX4hHhsa2G5vlq203cMzg4FTE3b6/rX5/r+LonQTgjJoMp2LHRv+mmK9xrLhFYv60C5gNsmMiK82hgSSET6Atq0r1wdWmY3eNz/UqjzWnpw9FKNzTs0Sfy2C48PuS3/tbH8F0eNZPuBCh3dOjBTm3/7I9pixiWVNbr0uakhn/tqWcPGr0mDLFZO3uFy+h5HwByROE5dImd7a1yp5xM/QV7r0NyfX7+3SJK05NEfqH2Iz0g0wY3JHWHmnFmWFfEauvy0zjHvK3SZqEWMGAVrNt2CMkrhrtDvun9a8p3FFTqtaxvV1Hn11OFlG356Xs+wPVmbDs8FqqwJ/J5tkR36Xq9/AJWsORWh3uNDj4VKAuQ/BypMUGNTvYJ7u0JdyonsqzAJyyWE+rxd9tw0SZJ1s3T12d2NtmOUydLBIXP5+w/q3o8W69eXnBj2GMJ9V0xZc2TB6LiCUMOD2FlUoalrC3T9uceoebOmtl2H8Y44yYR+A3qgXCBVd0EQu+OObmn8fj03MXwPTtghMn5/j1603Rc8ReK/ZpD/l3E0iQTusa7vVJdAKzITvmhjMXVtgQb+Y7rv3xv3NszIVu/zJTv04qQQd/XdPpkwBMuyVOvX+i6NcGc20iUR3LgKFySd89fvIs77M0vlHbVIXGsPhd6ZQZHDZTbtLdPLk9eHXCqh4WYbbjiRNbD8h4ROPfx95L+94Llbdq239devVtm2cHc0Zj1QocvYdePHrLcrgblCMVYz1K6enbA2YARBpN+c+pT4U1Yf+Q0Lt+6jye/cuj3RM/7ZNdz8z5+t0NLtxfrdx3lhy0SbdyglN4nEVS/O1P+NW6H7RuXp1+8uVGGUUQHmc6Diqk5GIIBygXRvIJZU1uiX/1mQ6mo4xGN8lzdSL06XNs1DPu7/gxEudW8w/7W+Yhk+F/zjYffQu4BtJ9CKDPeDMXdjeqbojzT8TJKGvrMw4N9VERqF941aon9OWtfg8QMHzXoi6yX+1h/aQCJzIQ/d07ejJ8usDsUVNXpw9NLw9TEJWEwrZQOT7536a2vQ89P1j+/W6azHv9Xk1bEnKDB5H2MJMCO+r3EEoSWVNQ2C3y+X7tR7c7dE35gNLOtQkDpy9uaABEoB36nhAqio2za7qpoE9HaF2VcCH6dYXxrue80/bfw15/SIup2iiiMN+1++HbpdYRIory8If+OpXlObWv8mNyq+WRF9QeZkNgXrR6dMXLlHk1ab32iNJtzvs3PpS1KHAMoF0v0yemPaRi0KsaBiPL5attOW7SRLLGtARXJU6+yUB87+vx0fzNsaUzKFWKueUA9UmMenrIm9YWhq+fbAxpmdsWWsvUNO9ybFd116lLftgM74ywT9e+am6MVD8FqW/jPryGsjNuIjVLLBgqoR9hkplb9dPSMNA43wL1qzu8SW+Qv+p+6OdxfF/Hq7siaGXBA46N8m53nwCzMCEksM+sd0/ejlWZq5fl9AuY8WbNOB8mr94j/zo1fOUKjqeS1L3//HdD3+5Sq95bfMRaRFqMcv26UBwydHTdJi+nGPtuC1dGRIZCThAp9Ye2dCLars20edV7PW71OLCPMK67/nmjWJ3iw1KGLEvgQaTmbicBeT+a2ZigDKBdy2nkOsIg21iZXbRx7ZFUB5vVbKA2f/L327MtGFk9BpC3OiLB3KFDgvCYtFX/1K4CT2/y4+snZMopPVYx3+6MRHItE5V5L0x/8uV7XfXJeY62BJf//2SG+aaVbDYCVBd4Mjfb1G+gyanIdl24uilgm+XMJtd0NBma58YabOf3pS3PWRZEsvTEllrVbuLI54rccSZEV6D0x7DP849khPRsHhIHNC0F39TXvL9dy3axsEVokIFcj7jxTwDxz8e9qCj2vYR4u1q7hSj32x0pZ6mWT821kUfc252RtCn6tYmyWXndop7HNvztikn/9nvl6fFn3R1Kym0XecZVME1dTvIDftLdMDnywJyD4bKulJKLbNa3NwX+FYlqXff7pUbya4wG2aN2uNEEC5QZpfaHZ1g6cDuwKoaMO44hVL47cqymKadkpGL4plSYOen6Gf/Sv29N2xWrfnyHCQBVuOpKDNyUr+V2i8p+7Pny23tyIRJf7+Bjc4P16wLa7tBA95HL9sV9jhnqt2lWjN7tBDZU2OyKjn3fANXBxlW6a9uI9+HlsDvb7R799Ttq+sSkNemqXngjIy+lch1BC+YR8uDiwfooxlWQGPm17fpjcuig7Gfkc8Uh1CZXEsqzpy0zDcS+P9ijd9WUAWvjCvMtmW/+fOf75lrA3grAjtAJNMqvW1yGpq0ANlWybDIxu65a35Gpu3Qze9eeT3xP+GjhPiSb1ud+tr3qb9GpO7XS9N2WDzlgP5H0e07MFuRQDlAukefjSGOw31Fm45oFveSnx4iNcyW08qmZYGDVOLRXlVbL2OmbROmH8Q3czgxz5x8Z27D+aZByCRAtzVu0r03MQ1RuP87VRV6w27OHHE4D/oUCavKdDNEdbIuvej0BO/7Yr5G/RAhSkXbU7RyNlbbKlPLPuN1GMQ6iMdfGOoTfNmIfYXfTuhHAy1cHCIck4kZQq3Np0//6UqXp5s1pMh2TsH6nsnHh329Wf8ZYKO/+P4gOt8yEszfX/Heh7t+j1rZhAdxZz6Owz/jqz6hZejJVcIJdqx+8+Ti7idmPds303dehU1do0oMj8aO3uMnUQA5QLpvuBYug9BjFWksd6mxi7eYTTJ1d+wjxar0sFeo0h2Fsd2xyiRAMptWSqfnbBWxYeTNPhnjUsWp4e1Bn+cr3pxpl6dujHiumAz1+9LuAEV6jhHTAi9zzURGrDVMTYogrPC1ZsUlHyhuKJGXyzdGbb8F0t36pdvL9CB8uqAMqZJZ6K9z7F+X5hK5rpU5x7bwZbtSKGPP+RrHfi62OX3/Reu+uMPL+ewYPN+/eM7+3sy3vbL6ua1LHm9VoPkCm1ywq9UU3H4t8S/h60+ffyrUzdoVpihfeGECnBjUX8emznQq/+HMct005tzoxeMoPhgja9XNNJvVHlVrc567FujbcaTNXGl302mDUn6johHtENx1696fFgHygXS/UIqsGHSc2NkMsHX3/hlu9S3Z/vkVCaJbnxzru/HOh7hvohTlap7+Y5i/fnzFRpyVtcG69VEMyrOYWnJZjIHakWY3iDp0Lpi7Vs27G2IRTKzQMbLsixtP1ChYzq00J3vLdKCzft1/bnHhCz7v4dTGP9z0rqAwMH03kHMR2/TD8e4vB36503nxPy6cGsPBZaJflT7YrjjPyZ3u67reySTW4rip4AhR9FuosyPcY5mtDNmWZZ2FVdqsl8qb8sr/fjVWdq8t1yL/vwDLdq6X09+tUr9jjsqpn1L0pL8Ij03MfbFtO1qB0QaCmiXTw4PKTRdNzHYmt0luvKFmbrklI56/47+ES+6+ZsLjW/qhFuzzl+kz1y4mztutN3gWN2OHigXsCujTKo4OZemsdu0L3LQFc8PX7It2Lw/4I6tXewaxhGPL5fu1N0fLI5YJlTj8Y9jY5+XFKpB5cZ1nopiTJ0eLNQhRTrOORv26ZvlDRdtttNT41frkmen6vnv1mnB5kNz38blbQ8oc80rs7Ry55HhsMGNsuBDCHdILnxLG/DvTftiafSMqaGCYssKPNalUbLS+fv9p0ujro3nxIgO/2s9XO/6kvwD+tLgHMXqn5PW68IRUwIe81qWVuwoUXl1nfK2HdAv/rNA6/aUxTWPcH955EBoR1GFLn5mSkD2QSn2wDX4sz1h5aGEIPEOi3708+QmQpIOLZgrSVe+cGioY/3QM7uuuN0JzgUK19sd65B7O0Q7J3/9apUj9UgmeqBcwG1DlGL17arkpZNGoPS+UuKTrsdcXFGj9i2zE95OqIb1l8t2aZdBhi1bJdjCtywrYuM21h6oW/59aC7irD/8T0L1iqR+8cuXI0yoXrq9WLe9fWTtriYeT8BdYuMhfA6uKjUmd3v0QlGMy9uhK3p1iVgmGVMfF287MoQ61DmL9ftif3m1/vFtbDee/Hudwl22i7cVafG20PPrIon2MXgpxHyq8uroSS3qvJZRwqdoAeiIb9Zo+4EKPf31at156Yl+rwv/mtA3gQJfs7e0Srlb98ccQE1cuVuf5e2IuM7Sq1M3aEdRhZ6+9syA44s12L7k2anaMmJITK+xu30Xzw0COzMlJ8O2wthG47hFmvd9ZIZGNoUICWgs14p/8oB0PeaPbBqu57WsgEajJP3f2OUaHmFOkhuVRbkLGm9b23+yfqJMFugMxT/zm0dBjZwGPVBhMqbFegLSoMcqdFAcvuImn/WATIChhvDF+H3x+0+XGi1y6s+/16l+Ev+ekkr90C8JQ7zC3Uj453frlBf0PVDvXb+U/+Guo5nr94Z+Iki00xc84qT4YI1GL8pXWYyN9FDVXL2r1CiN+eld2/j+vuv93Kjv33MT1+qj+du0YkfgMGTTxeqjmb95f/RCDkjXm/Hvzdua6irEhR4oAK6zq7hCvbq3TXU1ErJpb7ktST/+NWOTpqwJXDm+1OYhGSaN90SyNtaLlDEqVJpqk3q9a7heVLT01nVeS/3/FnoNpph4glNMm0mDeKjhIsVR2mv1czKMF4eVyXmIPQV6JIu2xN747dAq27ee2+JtRZIUcy9WKCWVNerzeOiEAy9OXq8Xw2Tze2vmkYQSHy8MfeMm3HzN7KwmATcOovVwBM9n/e1HuZq9IfI8r1BbtCyrwTpelswW0o13+YiD1e7uiTHhH/QF34yp75Ft16KZbyjx6EX5Wr8netbIVErPsI8eKFdI9yx8cE46zJOwg/9xputdtTG52wPSAscrOHhKCgeuK0uRU3EvDbEorcmwvpIKs0bRb97LDfn4jqIKvTF9o/aXV+uAwTyuaMPSxi7eEZhiOugFsWYJDCsNPhYPjVnWYD2yRL/Dor3+8yWxzTsyWXcomH8PiHSoYR68/lg8XvjOPN15OPXZ/4LN21QYMolN5zY5Af+OdlkFp5yOFjxJoT8zB2vqGs4jtSyjHqh450mZrqWWLsL1vJ3W5cj1+fCYZQEBtlNiadamaxuYHigXaETr0CJBH853ZxY3u2XKz9zGGDMtpsoqm4ay1Ptm+S61bh7482JZ0sIId/tr6uLrgTINSBZE2PeIb9aEza5nInj9I/+v9H/PCmy8hE33HOVgb76gZzxVS4gd7ZoP5m3Toz/q7fu3pfCfb4/HE/U82NkGrq3zxrUQfHDivXmbCm2JZ5O5oOjIwz21p3RpHXBTxom2a6hhtp+FSAZiySw4MgmyQnFg1Ql5vZZue2eBurZtrud+enZSb3QEB1D1NxujDZcOFm1+ajxmxbC2U7o2gQmgXCBd77ADyeLf85CmN6fSyjzDVMvLQvQShXLPhw0zFFbW1DV4L6NlE5xrUC871mWzW6TGSLi1Wp4avzriNpt4PPpw/lad3rVNXOmp4+H4GmQGZfwTR/hnP4zVo5+v0NjFO2JubEpH0mD76mTZsx6iE6n8t+0/qFenHlkgOXiXsRxHIlkwH/18ZYPHLEtqmd006mtzsqKXCcWO8zthReRjXraj2NdL99xPz054f/6Ko/SQ1382Ys3svP1AhXoe1fLQNmy6BMtjSKmerr/xBFAukK4XD5As/g1rPh7JF25uRbAfvzI77n2MX7ZLE1cGZuy87LlpcW/PzSJ9p4dbaiDaEDD/3uctI4bEvI5cJC9MWqc7Lzkxarl421bLd5gFOsE9eaH4n6d4lzJ4cPRS/Xdx4pkI/SV6B//aV2drSQwp3eMVLa1+LKm0Q90oSYRlWerevkXEMhv3lmn6OrOEGMFiGcI3NczQ6WjLV7w/NzAhQiJXRW2dN2CIaVVtYFAS7gZUVrqvjZMmOMsuQAMRCHT3B4vjzogGdwq17oedGfTcxI7eCCe9MCneuTdmx7nObxJ7one4Y53jFIrdwZOU+I1QJ4InSXpg9NKIz09K4bIklqRe3QKTB709a7MuHD5ZWw+nur75X/Pi334MF9/QkQujFwrBrmGY3yzfpVP+/I0+XxJ53TN/9YeXHeccMf9tNHw8UwbW24cAygXS7LcWcMQ3UYZKAG7FvNZAczYGDsXMxMYY77k9guek/fWrVdpZXKknvjx0A6agNPJCv5HYlb8l2I/P7u77O2AFgwSu83s+XCzLku4btUSfHh4yGry14F7P+udjnSNWWB45Q6mU3OG86ZpEggDKBdL14gGSqT4F+NYM7aWAu5z/tA0pzA97/MuG8zvsZDpnLRbhenx3FFUod+sB3TcqT7uLg++um7WqDlb5L/SaecGTXXOgUmFH0ILcqTwMy5KahIlEg4evxaPOcGXnWHuRjm4desH071btsaV999CYZSEf/3JpYG/sJwvzlb//YMyJUfy3E+4MZd6nNnHMgXKB9PzaBZKr/o6XHUN2ACfl76+IXigBP0tgGFM44ZIpXDRiiu/v4CGXew17A7YUBs7XciIbmpN2l1QyksQGXssK2x4KlaUzdmbbCF6fKhr/4Nk/zfvesiod06Fl1NePnL1ZP7vg2Kjlol1iHy/YptGL8nX5qZ2ibsufSWB5qDctcg3uG5WnX198os46pl1M+09X9EC5QLreuQKSKXjYD4DkmbUhetrh/KAA6i8hMqmF4p/O37KOpNR2uz+NWx69kKQ/f7ZCHy/Ij14wDaSyOfLU+NUNFuqtt+NA4jcldhdXakNB9EVlY+nAqaypC5sJNNTi4KE8/uUqPWbwWTLZWp3XbC0tf9sPHPlchxt2aHIony/ZqatfmRXTvqX07UQggHKBVjl0BALBvlhKzxPglEfGmgQLiTd1LEt6e7bzC3vG46NGsu6em4RLYhE81DAej3+5SoOenxG9YAxR5Ol/mRA2AYhh/CSpYWr8YLUxTOAKznYazaTVBZp9+AZKuCoH9yLP3VgYYkhv40IA5QJtmhNAAaHEs0YLgOSwo3diTK79GfBgn1SvSxkp5qgM0ztlt30JJKrwt7+8WlU21fn1aRujF0rArf+eLyl8soiNfuvXzdm4Tze/NU/fGz455v0crG74m56ug7BoubtA82bEsUAoZz42MdVVAHCY6ZynSML1PjnVOIa7fRchjfplz011pA6m6+I5tR1JemXqhpQmVLrnw8X62fk91eeY9pq0OnIP18gIPcw1tZYUlHMj1UF7vAigXKBZAjn7AQBIZ8M+XKzxy1m2wA1mro9vkVon7Cmxp2coHVXVepPee1tWVatZG8K//6MW5mvUwuhz/R7/suGaf0e2sU13XXZSwGP0QCFuBFAAgMaK4Mk9SioZNt1YOTHiY/g3a/Sz8wMzDu5K07lUtNxdgAAKAAAAme7sv37b4LHHv0ju2nnJkPYt91dffVXHH3+8mjdvrv79+2vBggWprlLMsgmgAAAA0Aily9IG/tK65f7JJ5/ogQce0GOPPabFixfr7LPP1uDBg1VQUJDqqsXEJGf/4N5dHKiJtO6pq/TwladJOpTc4o2f92tQ5sNf93ekLk565/bz1SzGtRMAAADQ+KR1APX888/rzjvv1NChQ9WrVy+98cYbatmypd5+++1UVy0m0YbwrXhisN78xXn6+feir1SdqOysJrrzkhP1/I1na8qDl+vKM7sGPH/RyUfropM7atPffhhxO1MevCyZ1QxgRw/e5ad10vLHB0uSrj67u1695dy4tjNy6PkJ1yVd3T/olFRXAQAAIOnSNoCqrq5Wbm6uBg0a5HusSZMmGjRokObOndugfFVVlUpKSgL+c4twPR85WU208onBan14od2nrj1Lcx/5vjq2ztEt/Y/VY1f30vM3nq1Nf/uhXrmlr07o2EqSdOxRLfXiz84J2Nb0hy4Pu/+TO7fWR3f219JHrzhcnya67txj1L19iwZl37n9AklSkyYeXXxyx5Dbm/nw/+jETq31/wadGnaf39x3idY8eaXWPXWVFv15kMb99sKQ5c7s0db390ODT2vw/KI/D9K6p6/SdX17hN2XdChAqrfh6au08onB6tI2R5K0+C8/kMfjUfNmTbVlxBC9fHNfDenTTVtGDAm7vT8POUPPXt8nxH4667pzD9UlK5blzIOseGJw1DIv39w34vO3X3h8xGOIRd5ffhC1zL3/c7KOO7pl1HJrn7pSJ3Zq5fv3qr9GP9ZkufkC+29KsCxB8vh/jgEASBWPZYVbNsvddu7cqR49emjOnDkaMGCA7/GHH35Y06dP1/z58wPKP/7443riiScabKe4uFht27Zt8LjTfvLabOVtK9Kqvw7Wb97LVd62A1ry2BUJJZiwLEsjvlmjC044SgPP6KL8/Qd129sL9PxN5+j0rm3UvFlT4+28OWOTrjqzq447ulXA4w+MXqpxeTskSQv+NFCd2zZv8PraOq8Ky6tVUV2npk086nlU6Eb28u3FWr27RH/47zL16tZW4//3kpiOd/uBg3pmwlo9eU1vtW+ZrWXbi7T9QIWuOrOrPB6PvF5LHo/kiTFn5r9nbtJT41fry3svVrsWzbRo635dc04PNW3i0Y6iCrXKbqr2LbNDvraypk7PTFijc3q21+7iSk1YuVsf3NFflo5kvJn/p4Fat6dUliX98u0Fev+OC3TJKZ2Uv/+g/rt4u+6+7CSd9fhE1dQFflS3jBiiqto6FR+s0ber9uid2ZvVIrupNhSU6bYLj9cjV53hK7tg8349NGapTuzYSr8beIque22OJGnj336oVTtLdPUrs3xlX7v1XP32w8XKyWqit28/X2f2aKd2LZpJkkYvyteH87Zq6fbigLrcfMGxGn7dWb734bO8HfrNpSfpJ6/N1prdpao7vCT73Ee+r27tWqi2zqv5m/er77Ht1TI7S89NXKNXpx5aKHD541forMcDJ5leflon/ahPd01evUf/vOkcHThYraHvLNSa3aVh37d3bj9fv35vkX5z6YnaX1atTxbl66HBp+nHZ3fXbW8v0Cd3DVCnNjkNXvfwmKUavehQuthNf/uhquu8Ov0vEyQdGr56UqfWum9Unv5w1enq06OdDtbUqW3zZvrjf5fp6+W7tOD/BimriUfb9h/U9/8xXZI054/fV1YTjy7426GFB1c8MVjDv16tvsd20O8/Xerb97qnrlL+gYNatr1IM9bt8322JOn/DTpVU9bs0V9+1Eu3/nu+vrj3Yo3Jzde6PWWavu5Q6tmxv71Q1bVeNWvq0R//u1w3X3CsTurcWr95b5HG3H2hzuzRVhv3litv2wH9qE93FZZX6eJnDq2tcvuFxzcYh75lxBDtLq5U+5bNfOdAki4+uaNmHV65ftnjV6hFs6b6wfPTtaWw4TolHk/g4oxtm2fpb9edpecmrtVWv/LX9e2hsYeP94Ljj9KCLfv1wR39NW1tgXKaNdFDg0+XJO0sqtCFI6aEfM/vG3iKb+2Vy0/rpDsvOVEtspuqT492Ou0vE3zXYf2NhaKD1Vqxo0Q//8983zm49NSO+v7pXVTntTRj/V4NfWdhwD7OP76DLjjhKF11Zjed2aOddhRVyOu1dMmzDdeouePiE1RVW6du7VrouYlrQ9a5R/sWevdX5+vBT5dpaX6R7/Gff+9YfTBvmyQp98+D9K8ZmzRh5W7dcfEJevTzQxOuO7RspgMHayRJL/7sHA08o4venbNF5VW1ei1o4c0nrz1Tv/jecaqsqdPGvWUa8tKsgOfPPqadlm4v1oe/7u9bVPP4o1uGfE9DueCEo7Rg835J0p2XnKAHrzhNV788Sz85t4fO7N5Ov3w7vvnJ65++SqWVtTr3ye/Clnnllr46qlW23p61WZNWHxnCn9XEo9rD73nH1tm6+uzuemf2Fl1ySkfNXL/PV+7snu11Q79jNLh3F81ct0+fLMpXVU2dWmQ31bxNh47p5987VuVVdXrm+j6av7lQ6/eU6YbzjlGfoO+q07u20Ud3fq9BfZ+9oY8eHrMs4LG1T12pbYUH9YN/zgh5XF/ce5Ge/GqV9pdXa+Pe8pBlHhp8WthrKxaj7xqgG98MvPmcndVEE++/VP/z92mSpLdvP09LthXppSkbwm4n1HEG++p3F+tHL8+KWGZIn24avyxydsb377hAv/hP4vPe/9+gU/XPSesilnn+xrP1wOilEct0a9c8aka5N35+ru7+YHHMdQzWxCN5bWi9n398By3cciDs82/98jzd+d6iiNsY9Zvvad6mQr0wKbF1r+y64ZuIkpIStWvXzig2aDQBVFVVlaqqjqwhUFJSop49e7omgAJM1H9cYw0CGxvLsjhHCait86ppE4+rz2H9Z+E/szZrd3GlHrjiVLXMdvfKHJZlqaKmzvX1dNr+8mptLSxX32M7hC1jWZYqa7xqkW124y/d1Af3heVV6tym4Y3IeFiWpX1l1Q1uFO0trVLH1tm2fr5r67zKinDDt85rqanfqAyv19Lesip1CXHTtV64+mcay7I0dW2B/ue0zhHfkzqvpSZBN4Ety1Kd1wo49wera33fMbuKK9S1bfOo73W030zLsrS+oEwndGyVcOboA+XVat08y7edaNeOk2IJoNL2W7xjx45q2rSp9uwJXBF5z5496tq1a4PyOTk5ysnJ7A8hMp+bG7RuwnlKjFt+zCKpf49/fcmJKa6JOY/HQ/AUwlGtsnVUq9C9+PU8Hk/GBk+SfMGFXcGTdOichQo+khGQRPvOaBo0pL1JE0/E4EkKX/9M4/F49P3ToycKCz6H9a8NTkTm/x3TrV3DqRjh6hDt+VO7tDHaVjQdgj7r6fB7E0p61lpSdna2+vXrp8mTJ/se83q9mjx5ckCPFAAAAADYJa1vhT3wwAO67bbbdN555+mCCy7QCy+8oPLycg0dOjTVVQMAAACQgdI6gLrpppu0d+9ePfroo9q9e7fOOeccTZgwQV26OLNmEgAAAIDGJW2TSCQqloliAAAAADJXLLFB2s6BAgAAAACnEUABAAAAgCECKAAAAAAwRAAFAAAAAIYIoAAAAADAEAEUAAAAABgigAIAAAAAQwRQAAAAAGCIAAoAAAAADBFAAQAAAIAhAigAAAAAMEQABQAAAACGCKAAAAAAwBABFAAAAAAYIoACAAAAAEMEUAAAAABgiAAKAAAAAAxlpboCqWJZliSppKQkxTUBAAAAkEr1MUF9jBBJow2gSktLJUk9e/ZMcU0AAAAAuEFpaanatWsXsYzHMgmzMpDX69XOnTvVpk0beTyelNalpKREPXv2VH5+vtq2bZvSumQyzrMzOM/O4Dw7g/PsDM5z8nGOncF5dkYyzrNlWSotLVX37t3VpEnkWU6NtgeqSZMmOuaYY1JdjQBt27blw+YAzrMzOM/O4Dw7g/PsDM5z8nGOncF5dobd5zlaz1M9kkgAAAAAgCECKAAAAAAwRADlAjk5OXrssceUk5OT6qpkNM6zMzjPzuA8O4Pz7AzOc/Jxjp3BeXZGqs9zo00iAQAAAACxogcKAAAAAAwRQAEAAACAIQIoAAAAADBEAAUAAAAAhgigXODVV1/V8ccfr+bNm6t///5asGBBqqvkWo8//rg8Hk/Af6effrrv+crKSg0bNkxHH320Wrdureuvv1579uwJ2Ma2bds0ZMgQtWzZUp07d9ZDDz2k2tragDLTpk3Tueeeq5ycHJ188skaOXKkE4eXMjNmzNDVV1+t7t27y+Px6LPPPgt43rIsPfroo+rWrZtatGihQYMGaf369QFl9u/fr1tvvVVt27ZV+/btdccdd6isrCygzLJly3TJJZeoefPm6tmzp5599tkGdfn00091+umnq3nz5jrrrLP09ddf2368qRLtPN9+++0Nru8rr7wyoAznObLhw4fr/PPPV5s2bdS5c2dde+21Wrt2bUAZJ78nMvX73eQ8X3755Q2u57vvvjugDOc5stdff119+vTxLRY6YMAAffPNN77nuZYTF+0ccx0nx4gRI+TxeHT//ff7Hkur69lCSo0aNcrKzs623n77bWvlypXWnXfeabVv397as2dPqqvmSo899pjVu3dva9euXb7/9u7d63v+7rvvtnr27GlNnjzZWrRokfW9733PuvDCC33P19bWWmeeeaY1aNAgKy8vz/r666+tjh07Wo888oivzKZNm6yWLVtaDzzwgLVq1Srr5Zdftpo2bWpNmDDB0WN10tdff2393//9nzV27FhLkjVu3LiA50eMGGG1a9fO+uyzz6ylS5daP/7xj60TTjjBqqio8JW58sorrbPPPtuaN2+eNXPmTOvkk0+2br75Zt/zxcXFVpcuXaxbb73VWrFihfXxxx9bLVq0sN58801fmdmzZ1tNmza1nn32WWvVqlXWn//8Z6tZs2bW8uXLk34OnBDtPN92223WlVdeGXB979+/P6AM5zmywYMHW++88461YsUKa8mSJdYPf/hD69hjj7XKysp8ZZz6nsjk73eT83zZZZdZd955Z8D1XFxc7Hue8xzdF198YY0fP95at26dtXbtWutPf/qT1axZM2vFihWWZXEt2yHaOeY6tt+CBQus448/3urTp4913333+R5Pp+uZACrFLrjgAmvYsGG+f9fV1Vndu3e3hg8fnsJauddjjz1mnX322SGfKyoqspo1a2Z9+umnvsdWr15tSbLmzp1rWdahBmyTJk2s3bt3+8q8/vrrVtu2ba2qqirLsizr4Ycftnr37h2w7ZtuuskaPHiwzUfjTsENe6/Xa3Xt2tV67rnnfI8VFRVZOTk51scff2xZlmWtWrXKkmQtXLjQV+abb76xPB6PtWPHDsuyLOu1116zOnTo4DvPlmVZf/jDH6zTTjvN9+8bb7zRGjJkSEB9+vfvb9111122HqMbhAugrrnmmrCv4TzHrqCgwJJkTZ8+3bIsZ78nGtP3e/B5tqxDDU//xlEwznN8OnToYP373//mWk6i+nNsWVzHdistLbVOOeUU67vvvgs4t+l2PTOEL4Wqq6uVm5urQYMG+R5r0qSJBg0apLlz56awZu62fv16de/eXSeeeKJuvfVWbdu2TZKUm5urmpqagPN5+umn69hjj/Wdz7lz5+qss85Sly5dfGUGDx6skpISrVy50lfGfxv1ZRrre7J582bt3r074Jy0a9dO/fv3Dziv7du313nnnecrM2jQIDVp0kTz58/3lbn00kuVnZ3tKzN48GCtXbtWBw4c8JVp7Od+2rRp6ty5s0477TTdc889Kiws9D3HeY5dcXGxJOmoo46S5Nz3RGP7fg8+z/U+/PBDdezYUWeeeaYeeeQRHTx40Pcc5zk2dXV1GjVqlMrLyzVgwACu5SQIPsf1uI7tM2zYMA0ZMqTB+Ui36znLuCRst2/fPtXV1QVcCJLUpUsXrVmzJkW1crf+/ftr5MiROu2007Rr1y498cQTuuSSS7RixQrt3r1b2dnZat++fcBrunTpot27d0uSdu/eHfJ81z8XqUxJSYkqKirUokWLJB2dO9Wfl1DnxP+cde7cOeD5rKwsHXXUUQFlTjjhhAbbqH+uQ4cOYc99/TYy3ZVXXqnrrrtOJ5xwgjZu3Kg//elPuuqqqzR37lw1bdqU8xwjr9er+++/XxdddJHOPPNMSXLse+LAgQON5vs91HmWpFtuuUXHHXecunfvrmXLlukPf/iD1q5dq7Fjx0riPJtavny5BgwYoMrKSrVu3Vrjxo1Tr169tGTJEq5lm4Q7xxLXsZ1GjRqlxYsXa+HChQ2eS7fvZgIopJWrrrrK93efPn3Uv39/HXfccRo9enSjC2yQeX72s5/5/j7rrLPUp08fnXTSSZo2bZoGDhyYwpqlp2HDhmnFihWaNWtWqquS0cKd59/85je+v8866yx169ZNAwcO1MaNG3XSSSc5Xc20ddppp2nJkiUqLi7WmDFjdNttt2n69OmprlZGCXeOe/XqxXVsk/z8fN1333367rvv1Lx581RXJ2EM4Uuhjh07qmnTpg0yjOzZs0ddu3ZNUa3SS/v27XXqqadqw4YN6tq1q6qrq1VUVBRQxv98du3aNeT5rn8uUpm2bds2yiCt/rxEuk67du2qgoKCgOdra2u1f/9+W859Y/08nHjiierYsaM2bNggifMci3vvvVdfffWVpk6dqmOOOcb3uFPfE43l+z3ceQ6lf//+khRwPXOeo8vOztbJJ5+sfv36afjw4Tr77LP14osvci3bKNw5DoXrOD65ubkqKCjQueeeq6ysLGVlZWn69Ol66aWXlJWVpS5duqTV9UwAlULZ2dnq16+fJk+e7HvM6/Vq8uTJAWNvEV5ZWZk2btyobt26qV+/fmrWrFnA+Vy7dq22bdvmO58DBgzQ8uXLAxqh3333ndq2bevrrh8wYEDANurLNNb35IQTTlDXrl0DzklJSYnmz58fcF6LioqUm5vrKzNlyhR5vV7fj82AAQM0Y8YM1dTU+Mp89913Ou2009ShQwdfGc79Edu3b1dhYaG6desmifNswrIs3XvvvRo3bpymTJnSYDijU98Tmf79Hu08h7JkyRJJCrieOc+x83q9qqqq4lpOovpzHArXcXwGDhyo5cuXa8mSJb7/zjvvPN16662+v9PqejZON4GkGDVqlJWTk2ONHDnSWrVqlfWb3/zGat++fUCGERzx4IMPWtOmTbM2b95szZ492xo0aJDVsWNHq6CgwLKsQykwjz32WGvKlCnWokWLrAEDBlgDBgzwvb4+BeYVV1xhLVmyxJowYYLVqVOnkCkwH3roIWv16tXWq6++mvFpzEtLS628vDwrLy/PkmQ9//zzVl5enrV161bLsg6lMW/fvr31+eefW8uWLbOuueaakGnM+/bta82fP9+aNWuWdcoppwSk1y4qKrK6dOli/eIXv7BWrFhhjRo1ymrZsmWD9NpZWVnW3//+d2v16tXWY489ljHptS0r8nkuLS21fv/731tz5861Nm/ebE2aNMk699xzrVNOOcWqrKz0bYPzHNk999xjtWvXzpo2bVpA2uGDBw/6yjj1PZHJ3+/RzvOGDRusv/71r9aiRYuszZs3W59//rl14oknWpdeeqlvG5zn6P74xz9a06dPtzZv3mwtW7bM+uMf/2h5PB7r22+/tSyLa9kOkc4x13FyBWc4TKfrmQDKBV5++WXr2GOPtbKzs60LLrjAmjdvXqqr5Fo33XST1a1bNys7O9vq0aOHddNNN1kbNmzwPV9RUWH99re/tTp06GC1bNnS+slPfmLt2rUrYBtbtmyxrrrqKqtFixZWx44drQcffNCqqakJKDN16lTrnHPOsbKzs60TTzzReuedd5w4vJSZOnWqJanBf7fddptlWYdSmf/lL3+xunTpYuXk5FgDBw601q5dG7CNwsJC6+abb7Zat25ttW3b1ho6dKhVWloaUGbp0qXWxRdfbOXk5Fg9evSwRowY0aAuo0ePtk499VQrOzvb6t27tzV+/PikHbfTIp3ngwcPWldccYXVqVMnq1mzZtZxxx1n3XnnnQ2+0DnPkYU6v5ICPsNOfk9k6vd7tPO8bds269JLL7WOOuooKycnxzr55JOthx56KGD9HMviPEfzq1/9yjruuOOs7Oxsq1OnTtbAgQN9wZNlcS3bIdI55jpOruAAKp2uZ49lWZZ5fxUAAAAANF7MgQIAAAAAQwRQAAAAAGCIAAoAAAAADBFAAQAAAIAhAigAAAAAMEQABQAAAACGCKAAAAAAwBABFAAASTBy5Eh5PB55PB6NHDky1dUBANiEAAoAELP6wCDW/4qKilJddQAAEkIABQAAAACGslJdAQBAehs3bpxx2VatWiWxJgAAJB8BFAAgIddee22qqwAAgGMYwgcAAAAAhgigAAApVZ9g4vLLL5ckHThwQE8//bTOPfdcHXXUUWrVqpV69eqlhx56SLt37zbe7rhx43TTTTfp+OOPV8uWLdW2bVudccYZuvvuu5Wbm2u8Ha/Xq08++US33HKLTjrpJLVp00bZ2dnq1q2bBg4cqCeffFIbNmww2ta6dev0u9/9Tqeeeqpatmyp9u3ba8CAAXrxxRdVXV0d9fUzZ87Ur371K51xxhlq06aNmjVrps6dO6tXr1668sor9eSTT2rdunXGxwYAiJ3Hsiwr1ZUAAKQXj8fj+zvRn5H6bV122WV65ZVX9KMf/Uhbt24NWbZ9+/YaNWqUBg8eHHZ7e/fu1fXXX6+ZM2dG3Oc999yjl156SU2bNg1bbtmyZbrpppu0Zs2aiMfQvn17HThwIOCxkSNHaujQoZKkd955R02bNtVdd92lioqKkNsYMGCAJkyYoLZt2zZ4zuv16re//a3efPPNiPWQpCFDhuirr76KWg4AEB/mQAEAXKG4uFjXXHONtm7dqksvvVQ33HCDunTpom3btunDDz/UkiVLVFRUpGuvvVYzZszQ+eef32AbZWVluvTSS30BT6dOnTR06FCdffbZqq6u1owZM/TBBx+opqZGr732mkpKSvT++++HrM/8+fM1cOBAlZeXS5J69Oihm266SWeddZZatWqlvXv3Kjc3V1999ZWqqqoiHtuECRM0ZswYtWzZUsOGDdP555+vnJwcLVmyRG+88YaKi4s1d+5c/f73v9e//vWvBq9/5ZVXfMFTmzZtdMMNN6hfv37q1KmTqqurtX37di1atEiTJk2K6ZwDAOJgAQAQI0m+/+zcliTrmWeeaVCmtrbWuvfee31levXqZdXV1TUo99vf/tZXpl+/ftbevXsblFm0aJHVoUMHX7lPPvmkQZmSkhKrR48evjJ33XWXVVFREbL+tbW11meffdbg8XfeeSfguHr37m1t3769QbnVq1dbrVu3tiRZzZo1s3bv3t2gTO/evS1JVocOHawtW7aErIdlWVZFRYU1b968sM8DABLHHCgAQEJMF9G9/fbbo27ruuuu08MPP9zg8aZNm+rFF1/UeeedJ0latWpVg2Fqe/fu1dtvvy1Jatmypf773/+qY8eODbbVr18/vf76675/jxgxokGZ1157TTt27JB0aEjcG2+8oebNm4esc9OmTXXNNddEPK6srCyNHTtWPXr0aPDc6aefrmHDhkmSampqQvYi1c+xGjRokI477riw+2nevLn69+8fsS4AgMQQQAEAXCNU8FSvSZMmevDBB33/HjNmTMDzX3/9tSorKyVJN910U8RA48Ybb9RJJ50kScrLy9PmzZsDnvcf1jd8+HDzAwjjRz/6kU499dSwz//gBz/w/b1ixYoGz9evn7V8+XKjZBMAgORhDhQAICGmC+kee+yxEZ9v27atLrjggohlBg0a5Pt7wYIFAc/Nnz/f9/cVV1wRcTsej0dXXHGFrydq3rx5OuGEEyRJ+/fv18qVKyVJJ5xwgs4666yI2zIxYMCAiM8fc8wxvr+Dk1FIh45n1KhRWrNmjQYOHKgHHnhAgwcPVsuWLROuGwAgNgRQAICE2LWQ7kknnRSQ3S+Ujh07qn379ioqKtLOnTsDntu1a5fv70i9PaHK+L+2fuieJPXq1SvqdkyEGkroLycnx/d3fS+av2eeeUazZs3S9u3bNWvWLM2aNUvNmjXTueeeqwsvvFCXX365rrjiirDDDAEA9mEIHwDAFeqHqZmWKysrC3i8tLQ0pm21bt065GtLSkpClklEkyaJ/dwee+yxysvL0/3336+jjjpK0qH5UvPnz9c///lPXXPNNerSpYseffTRqBkBAQCJIYACALhCfbpw03LBwU2bNm1i2pZ/AOb/Wv91mIKDtFTq2LGj/vnPf2rPnj2+wOmnP/2pL6AqKSnRk08+qR/+8Ifyer0pri0AZC4CKACAK2zcuDHqoryFhYUqKiqSJHXv3j3guW7duvn+Xr9+fdT9rVu3zve3/7Z69OjhG0q4atWqqNtxWlZWli644ALdf//9Gj16tAoKCvTpp5+qXbt2kqQpU6YYz0sDAMSOAAoA4AolJSUNEkME80/xHZyu2//f3377bdT9fffddyFfe9RRR6l3796SpM2bN2v58uVRt5VKTZs21Q033KDHH3/c99jMmTNTVyEAyHAEUAAA1/j73/8e9jmv16vnn3/e9+8bbrgh4PkhQ4b4kiiMGjVKW7duDbutTz/91Le2Ut++fX0Z+Or98pe/9P39yCOPmB9ACvkfQ21tbQprAgCZjQAKAOAaY8aMCQiS6nm9Xj3wwAO+HqrevXtryJAhAWU6duyoO+64Q5J08OBB3XDDDSosLGywrby8PN19992+f4cKkO6++25favHx48fr7rvvDpkdr75uX375peERxm7Xrl168MEHtXHjxrBlamtr9dZbb/n+fc455yStPgDQ2HmsaAPOAQAI4p9uPJb5Nv379w+Yq+S/rXPOOUclJSXatGmTLrvsMt1www3q3Lmz8vPz9eGHHyovL0/SoZTfM2fO1Pnnn99g+2VlZTr//PO1Zs0aSVKnTp10xx13qE+fPqqurtbMmTP1/vvv+xaj/fnPfx6waK6/+fPna+DAgb6EFD169NDPfvYznXXWWWrZsqX27dunJUuW6KuvvlJ5eblvbla9kSNHaujQoZKkd955R7fffnvY87JlyxZfD9Jtt92mkSNHhnyuX79+uuSSS3TGGWeoQ4cOKisr06ZNm/Txxx/7AqwTTzxRS5cutS2DIAAgEOtAAQAS8pOf/MS47Lhx48KuG9WuXTu9//77uvrqqzV9+nRNnz49ZJlPPvkkZPAkHcrMN2PGDF133XWaNWuW9u7dqxEjRjQo5/F4dPfdd+vll18OW9f+/ftr5syZuvHGG7Vhwwbt2LFD//jHP0KW7dChQ9jtJMo/WM3NzVVubm7YsmeeeaY+++wzgicASCICKACAa5x55pnKy8vTK6+8orFjx2rz5s2qqqrSscceqx/96Ed68MEHG/RgBevUqZNmzpypsWPHatSoUZo3b5727t2rrKwsde/eXZdffrnuvPNOnXfeeVHr07dvX61evVofffSRPvvsMy1atEj79u1TXV2dOnbsqF69eun73/++brnlFrtOQQPHHXecNm7cqIkTJ2rOnDlatmyZtm3bptLSUmVnZ6tr167q27evrr/+et14443KyuKnHQCSiSF8AICUqu9hueyyyzRt2rTUVgYAgChIIgEAAAAAhgigAAAAAMAQARQAAAAAGCKAAgAAAABDBFAAAAAAYIhcpwCAlCIZLAAgndADBQAAAACGCKAAAAAAwBABFAAAAAAYIoACAAAAAEMEUAAAAABgiAAKAAAAAAwRQAEAAACAIQIoAAAAADBEAAUAAAAAhv4/4qwSj6HPAPMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -942,15 +948,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Games played: 1000, # of wins: 905\n",
-      "Win percentage: 90.5%\n"
+      "Games played: 1000, # of wins: 901\n",
+      "Win percentage: 90.10000000000001%\n"
      ]
     }
    ],
@@ -990,7 +996,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 4/Ch4_book.ipynb b/Chapter 4/Ch4_book.ipynb
index 86c3f07..efda110 100644
--- a/Chapter 4/Ch4_book.ipynb	
+++ b/Chapter 4/Ch4_book.ipynb	
@@ -10,9 +10,31 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gymnasium in /usr/local/python/3.12.1/lib/python3.12/site-packages (1.1.0)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (1.26.4)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (3.1.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/codespace/.local/lib/python3.12/site-packages (from gymnasium) (4.12.2)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (0.0.4)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
+    "!pip install gymnasium\n",
     "import numpy as np\n",
     "import torch\n",
     "import gymnasium as gym\n",
@@ -154,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,10 +201,10 @@
     "\n",
     "    ep_len = len(transitions) #I\n",
     "    score.append(ep_len)\n",
-    "    reward_batch = torch.Tensor([r for (s,a,r) in transitions]).flip(dims=(0,)) #J\n",
+    "    reward_batch = torch.Tensor(np.array([r for (s,a,r) in transitions])).flip(dims=(0,)) #J\n",
     "    disc_returns = discount_rewards(reward_batch) #K\n",
-    "    state_batch = torch.Tensor([s for (s,a,r) in transitions]) #L\n",
-    "    action_batch = torch.Tensor([a for (s,a,r) in transitions]) #M\n",
+    "    state_batch = torch.Tensor(np.array([s for (s,a,r) in transitions])) #L\n",
+    "    action_batch = torch.Tensor(np.array([a for (s,a,r) in transitions])) #M\n",
     "    pred_batch = model(state_batch) #N\n",
     "    prob_batch = pred_batch.gather(dim=1,index=action_batch.long().view(-1,1)).squeeze() #O\n",
     "    loss = loss_fn(prob_batch, disc_returns)\n",
@@ -209,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,22 +241,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5dcc089420>]"
+       "[<matplotlib.lines.Line2D at 0x7d7eb5ce8350>]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -252,14 +274,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "##### Listing 4.9"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,22 +318,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f5dc40efca0>"
+       "<matplotlib.collections.PathCollection at 0x7d7eb3f38410>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -349,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 4/requirements.txt b/Chapter 4/requirements.txt
new file mode 100644
index 0000000..6361141
--- /dev/null
+++ b/Chapter 4/requirements.txt	
@@ -0,0 +1,42 @@
+appnope==0.1.4
+backcall==0.2.0
+certifi==2024.2.2
+chardet==5.2.0
+cycler==0.12.1
+decorator==5.1.1
+future==1.0.0
+idna==3.7
+ipython==8.23.0
+ipython-genutils==0.2.0
+jedi==0.19.1
+kiwisolver==1.4.5
+matplotlib==3.8.4
+numpy==1.26.4
+parso==0.8.4
+pexpect==4.9.0
+pickleshare==0.7.5
+prompt-toolkit==3.0.43
+ptyprocess==0.7.0
+pyglet==1.5.21
+pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0
+pytz==2024.1
+requests==2.31.0
+scipy==1.13.0
+simplegeneric==0.8.1
+six==1.16.0
+torch==2.2.2
+traitlets==5.14.2
+urllib3==2.2.1
+wcwidth==0.2.13
+notebook==7.1.2
+scikit-image==0.23.1
+gymnasium[atari]==0.29.1
+gymnasium[accept-rom-license]==0.29.1
+gym-super-mario-bros==7.4.0
+magent2==0.3.2
+pettingzoo==1.24.3
+torchvision==0.17.2
+gym-minigrid==1.2.2
+einops==0.7.0
diff --git a/Chapter 5/Ch5_book.ipynb b/Chapter 5/Ch5_book.ipynb
index 6fb6ffb..0d12773 100644
--- a/Chapter 5/Ch5_book.ipynb	
+++ b/Chapter 5/Ch5_book.ipynb	
@@ -27,7 +27,7 @@
       "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
       " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
       " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63]\n",
-      "8\n",
+      "2\n",
       "[array([ 0,  1,  4,  9, 16, 25, 36, 49]), array([ 64,  81, 100, 121, 144, 169, 196, 225]), array([256, 289, 324, 361, 400, 441, 484, 529]), array([576, 625, 676, 729, 784, 841, 900, 961]), array([1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521]), array([1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209]), array([2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025]), array([3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969])]\n"
      ]
     }
@@ -63,13 +63,13 @@
      "text": [
       "In process 0\n",
       "In process 1\n",
-      "In process 2\n",
-      "In process 3\n",
-      "In process 4\n",
+      "In process 2In process 3\n",
+      "\n",
       "In process 5\n",
-      "In process 6\n",
-      "In process 7\n",
-      "[array([ 0,  1,  4,  9, 16, 25, 36, 49]), array([ 64,  81, 100, 121, 144, 169, 196, 225]), array([256, 289, 324, 361, 400, 441, 484, 529]), array([576, 625, 676, 729, 784, 841, 900, 961]), array([1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521]), array([1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209]), array([2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025]), array([3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969])]\n"
+      "In process 4\n",
+      "In process 6In process 7\n",
+      "\n",
+      "[array([ 0,  1,  4,  9, 16, 25, 36, 49]), array([ 64,  81, 100, 121, 144, 169, 196, 225]), array([576, 625, 676, 729, 784, 841, 900, 961]), array([256, 289, 324, 361, 400, 441, 484, 529]), array([1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209]), array([1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521]), array([3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969]), array([2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025])]\n"
      ]
     }
    ],
@@ -109,16 +109,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/envs/registration.py:307: DeprecationWarning: The package name gym_minigrid has been deprecated in favor of minigrid. Please uninstall gym_minigrid and install minigrid with `pip install minigrid`. Future releases will be maintained under the new package name minigrid.\n",
-      "  fn()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "from torch import nn\n",
@@ -250,27 +241,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
-      "  if not isinstance(terminated, (bool, np.bool8)):\n"
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Process Process-20:\n",
+      "Process Process-18:\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "Process Process-17:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process Process-22:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "Process Process-21:\n",
+      "Process Process-23:\n",
+      "Traceback (most recent call last):\n",
+      "Process Process-19:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/usr/local/python/3.12.1/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/3001618858.py\", line 8, in worker\n",
+      "    values, logprobs, rewards = run_episode(worker_env,worker_model) #B\n",
+      "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n",
+      "  File \"/tmp/ipykernel_3551/2335364977.py\", line 15, in run_episode\n",
+      "    state_, _, done, _, info = worker_env.step(action.detach().numpy())\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "ValueError: not enough values to unpack (expected 5, got 4)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1\n"
      ]
     }
    ],
@@ -304,39 +474,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py:211: UserWarning: \u001b[33mWARN: You are calling render method without specifying any render mode. You can specify the render_mode at initialization, e.g. gym(\"CartPole-v1\", render_mode=\"rgb_array\")\u001b[0m\n",
-      "  gym.logger.warn(\n"
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Lost\n",
-      "Lost\n",
-      "Lost\n",
-      "Lost\n"
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "Requirement already satisfied: pyglet==1.5.21 in /usr/local/python/3.12.1/lib/python3.12/site-packages (1.5.21)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "Requirement already satisfied: pyopengl in /usr/local/python/3.12.1/lib/python3.12/site-packages (3.1.9)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    },
+    {
+     "ename": "ImportError",
+     "evalue": "Library \"GL\" not found.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[23], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install pyglet==1.5.21\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install pyopengl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m100\u001b[39m):\n\u001b[1;32m      7\u001b[0m     state_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(env\u001b[38;5;241m.\u001b[39menv\u001b[38;5;241m.\u001b[39mstate)\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/__init__.py:95\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# ----------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# pyglet\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Copyright (c) 2006-2008 Alex Holkner\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# POSSIBILITY OF SUCH DAMAGE.\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;66;03m# ----------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;124;03m\"\"\"OpenGL and GLU interface.\u001b[39;00m\n\u001b[1;32m     37\u001b[0m \n\u001b[1;32m     38\u001b[0m \u001b[38;5;124;03mThis package imports all OpenGL, GLU and registered OpenGL extension\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;124;03mbelow.\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mglu\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GLException\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/gl.py:45\u001b[0m\n\u001b[1;32m     42\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m$Id$\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mctypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m---> 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_GL \u001b[38;5;28;01mas\u001b[39;00m _link_function\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m c_ptrdiff_t\n\u001b[1;32m     48\u001b[0m \u001b[38;5;66;03m# BEGIN GENERATED CONTENT (do not edit below this line)\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# This content is generated by tools/gengl.py.\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;66;03m# Wrapper for /usr/include/GL/gl.h\u001b[39;00m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/lib.py:149\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib_agl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_GL, link_GLU, link_AGL\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib_glx\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_GL, link_GLU, link_GLX\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/lib_glx.py:45\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m asbytes\n\u001b[1;32m     43\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink_GL\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink_GLU\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink_GLX\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 45\u001b[0m gl_lib \u001b[38;5;241m=\u001b[39m \u001b[43mpyglet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGL\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m glu_lib \u001b[38;5;241m=\u001b[39m pyglet\u001b[38;5;241m.\u001b[39mlib\u001b[38;5;241m.\u001b[39mload_library(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGLU\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     48\u001b[0m \u001b[38;5;66;03m# Look for glXGetProcAddressARB extension, use it as fallback (for ATI fglrx and DRI drivers).\u001b[39;00m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/lib.py:138\u001b[0m, in \u001b[0;36mLibraryLoader.load_library\u001b[0;34m(self, *names, **kwargs)\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected error loading library \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;132;01m!s}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    137\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLibrary \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnames[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n",
+      "\u001b[0;31mImportError\u001b[0m: Library \"GL\" not found."
      ]
     }
    ],
    "source": [
     "env = gym.make(\"CartPole-v1\")\n",
     "env.reset()\n",
-    "\n",
+    "!pip install pyglet==1.5.21\n",
+    "!pip install pyopengl\n",
+    "from pyglet.gl.gl import *\n",
     "for i in range(100):\n",
     "    state_ = np.array(env.env.state)\n",
     "    state = torch.from_numpy(state_).float()\n",
     "    logits,value = MasterNode(state)\n",
     "    action_dist = torch.distributions.Categorical(logits=logits)\n",
     "    action = action_dist.sample()\n",
-    "    state2, reward, done, info, _ = env.step(action.detach().numpy())\n",
+    "    state2, reward, done, info = env.step(action.detach().numpy())\n",
     "    if done:\n",
     "        print(\"Lost\")\n",
     "        env.reset()\n",
@@ -354,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -455,7 +648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 6/Ch6_book.ipynb b/Chapter 6/Ch6_book.ipynb
index bbb691d..51a92dc 100644
--- a/Chapter 6/Ch6_book.ipynb	
+++ b/Chapter 6/Ch6_book.ipynb	
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,16 +171,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Hllo World!D'"
+       "'HelloWorld!l'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,22 +192,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f913642a890>]"
+       "[<matplotlib.lines.Line2D at 0x755c2b79b830>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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LSpVXUqHig5VauatYkmS1SOndE3XtsBQN7diS+8YDAJoUCj4AAPBry7P365HP19UYXd+cd+CY68dFBOvsbok6p0eihnZsqXDHsX8dKnNVKbuwTNvzy1RQWqERneOVHBvm0/wAAPgKBR8AAPilnYVlenzOen22aq8kKSTIqhtHdNSQlFjlOMuVU3zQe4u64oOVGpjSQuf2SFS/5Bb1noE+LNiubklR6pYU1ZBvBQAAn6DgAwAAv1Dl9mh9TomW7yxS1o79+nzVXrncHlks0iUD2uov53ZVUnSI2TEBADANBR8AAJgu11mun7bvV9FBl6rchirdHlV5DFVWeeQsr74GfvWuYh2sdNfYbninlrr7/O7q2TrapOQAADQdFHwAANDocorLtWRbgX7cWqAftxZqW35pvbaLdNjVr12M+ifHaFinOKWlxjLRHQAAh1DwAQBAgyqtqNKa3cVatatYK3YVadWuIu0srHl7OotF6tEqSm1iQhVks8pus8hutSrIZlFIkE09WkWpf7sYdYyPkLWe188DANDcUPABAMAp8XgMLdpSoOzCMhWWVqiwtFL7y1wqKHUpp/igNucdkMeouY3VIvVsHa3TOsTqtA4tNSglVtGhQea8AQAAAgQFHwAAnBRXlUcfr9itmfO3aOu+uk+xbxUdoj5to9U3OUZ928aod9toRYVQ6AEA8CUKPgAAOCFlriq9u3Sn/vPDVu0pLpckRYXYNSQ1VrHhwYoNdyg2PEgtwoIVF+lQz1ZRSohidnsAABoaBR8AANRLcVmlXlu8Xa8s3Kb9ZZWSpPhIh64/PVVXprVTJCPyAACYioIPAADqlOcs138WbNNbP+5Qqav6NnXtYsP0xzM76OIBbRUSZDM5IQAAkCj4AADgGHYUlGrm/K36MHOXXG6PJKlbUqRuGtlRF/RuJbvNanJCAABwJAo+AADNkGEY2px3QPM37tOPWwtUUOrSQZdb5ZVulbncOljp1oGKKhmHZr8f2L6Fbh7ZUWd1S+C+8wAANFEUfAAAmji3x9A7S7P18x6nQoNsCg22KjTIppBDj9KKKhWWuVR4wKX9ZS4VlrpU5nIrISpEbWJC1SYmRG1ahKp1dKgKS12av3Gfvt+4zztBXl3O7BKviaM6aUhqbCO8UwAAcCoo+AAANGFb9x3QX99fqazsohPedn1OSZ3PB9utSkuN1YjO8WrXMkxhwTbvBwdhwTZFhwapZYTjJJMDAIDGRsEHAKAJ8ngMvbpou574ar3KKz2KdNh11WntZbHIeyr9wUq3DrrcCnfYD92eLlgtwqq/hgRZlees0K6ig9pTdFC79x/U7qKDCgmyaninOI3oEq/TUlsqNJgJ8gAACBQUfAAAmpidhWX66/srtWRboSTpjM5xevziPmodE2pyMgAA0JRR8AEAaAIMw9CKnUX6YvVevbUkW2Uut8KCbbr7/O66Kq0dE9sBAIDjouADAGASj8dQVvZ+fbE6R3PW7K0x6d2Q1Fg9dUlftWsZZmJCAADgTyj4AAA0sn0lFXp7SbbeWZqtHOcvpT482Kazuyfq/N6tdG6PRFmtjNoDAID6o+ADANBIVu4s0quLtuuzVXtU6a6+wXykw65zeiTqN71b6YzOcQoJYtI7AABwcij4AAD4yK79ZfpufZ4qqjwyDMltGPIYhqrchr5dn6cVO4u86w5oF6Pxw1J0Xq8kOeyUegAAcOoo+AAAnCJXlUf/WbBVz2VsUnml55jrBdus+m2fVho/LEV9k2MaLyAAAGgWKPgAAJyCJVsLdO/Ha7Qp74AkqW9yjNrHhslmtchqschqkWxWi5Jjw3TZoGTFRzpMTgwAAAIVBR8AgJNQWOrStC/W6f3MXZKkluHBuueC7vp9/zbc0g4AAJiCgg8AQD0Vl1Xq+0379N2GPH2zNlfO8ipJ0hVD2umu87oqJizY5IQAAKA5o+ADAFCHbfml+mL1Xn23Pk9Z2fvlMX55rltSpB75fW8NbN/CvIAAAACHUPABAKiFYRh6c0m2Hv50rVzuXybO65wQoVHdEjSyS7yGpMbKbrOamBIAAOAXFHwAAH6ltKJK93y0Wh+v2CNJSkuN1YV9W2tk13i1bRFmcjoAAIDaUfABADjC5rwS3fRmljblHZDNatHk87rp+jNSmTgPAAA0eRR8AAAO+WTlHk3+cJXKXG4lRDr0/JUDNCQ11uxYAAAA9ULBBwA0e1v2HdD0bzbp05XVp+QP7dBSz13Rn3vWAwAAv0LBBwA0W9vzS/VcxiZ9vGK3d3b8iaM6atI5XWWzcko+AADwLxR8AECzk11Qpn9+u0mzl++W+1CzT++eqDvSO6tXm2iT0wEAAJwcCj4AoNkwDEOzFm7XY1+uU6W7utiP6hqvO9K7qG9yjLnhAAAAThEFHwDQLByoqNJdH67S56v2SpJO7xSnSed20YB2LUxOBgAA4BsUfABAwNucV6I/vpGpLftKZbdadN9ve2jc0Pbc+g4AAAQUCj4AIKB9tmqP7vyg+tZ3SVEhmnHVAA1sz6g9AAAIPFazA5yKGTNmKCUlRSEhIUpLS9PSpUvrXH/69Onq2rWrQkNDlZycrD//+c8qLy9vpLQAgMbk9hj6+2drdcvby1Xmcmtoh5b67LbTKfcAACBg+e0I/nvvvadJkyZp5syZSktL0/Tp0zV69Ght2LBBCQkJR63/9ttva/LkyZo1a5aGDRumjRs36tprr5XFYtEzzzxjwjsAADSUiiq3Jv13pfd6+5tGdtRfzukiu82vP9cGAACok8UwDMPsECcjLS1NgwcP1vPPPy9J8ng8Sk5O1q233qrJkycftf4tt9yidevWKSMjw7vsL3/5i5YsWaIFCxbU6zWdTqeio6NVXFysqKgo37wRAIBPHaio0p/eyNSCzfkKsln0zGX9dGHf1mbHAgAAOCkn0kP9cijD5XIpMzNT6enp3mVWq1Xp6elavHhxrdsMGzZMmZmZ3tP4t27dqi+++ELnn3/+MV+noqJCTqezxgMA0HQVlrp01Us/asHmfIUF2/TKtUMo9wAAoNnwy1P08/Pz5Xa7lZiYWGN5YmKi1q9fX+s2V155pfLz83X66afLMAxVVVXpT3/6k+6+++5jvs60adP04IMP+jQ7AKBh7C46qGteXqKt+0rVIixIr04Ywr3tAQBAs+KXI/gnY968eXr00Uf1r3/9S1lZWZo9e7Y+//xzPfzww8fcZsqUKSouLvY+du7c2YiJAQDHUl7p1q79ZVqxs0jfrM3V20uydfG/FmnrvlK1iQnV+38aRrkHAADNjl+O4MfFxclmsyk3N7fG8tzcXCUlJdW6zX333adrrrlG119/vSSpd+/eKi0t1Y033qh77rlHVuvRn3U4HA45HA7fvwEAQL1VuT1au9epJVsL9ePWAmVl79f+sspa1+2cEKHXrxuiVtGhjZwSAADAfH5Z8IODgzVw4EBlZGRozJgxkqon2cvIyNAtt9xS6zZlZWVHlXibzSZJ8tN5BgEgYBUcqNAnK/foh035WratUCUVVUetE2yzKi4iWHGRDrUMD1aH+AjdMqqTWoQHm5AYAADAfH5Z8CVp0qRJGj9+vAYNGqQhQ4Zo+vTpKi0t1YQJEyRJ48aNU5s2bTRt2jRJ0oUXXqhnnnlG/fv3V1pamjZv3qz77rtPF154obfoAwDMU+n2aN6GfXr/p536dn2eqjy/fPgaGWJXWmqs0lJbakhqrFLiwhUVYpfFYjExMQAAQNPitwV/7Nix2rdvn6ZOnaqcnBz169dPc+bM8U68l52dXWPE/t5775XFYtG9996r3bt3Kz4+XhdeeKEeeeQRs94CAEDS5rwDem9Ztj5avlv5B1ze5X3aRuu3fVppWMc4dW8VJZuVMg8AAFAXi8H56fV2IvcfBAAcW5Xbo2/W5eqNH3do4eYC7/K4iGCN6ddGlw5KVtekSBMTAgAANA0n0kP9dgQfAOB/9pVU6N2l2Xp7abb2FpdLkqwW6axuCRo7uJ1Gdo1XkK3Z3OAFAADApyj4AIAGV3CgQs9/t1lv/Zgtl9sjSYoND9bYwcm6Kq2d2rYIMzkhAACA/6PgAwAaTGlFlV5esE0vfr9VBw7NhN8vOUbjhrbX+b1bKSSISU4BAAB8hYIPAPA5V5VH7y7L1nMZm5V/oEKS1LtNtO46r5tO7xxncjoAAIDARMEHAPjU+hynJr6VpS37SiVJ7VuG6a/ndtUFvVvJykz4AAAADYaCDwDwmY+W79KU2atVXulRXESwbj+7s8YObqdgOxPnAQAANDQKPgDglFVUufX3z9bpjR93SJLO6BynZy/vr9jwYJOTAQAANB8UfADAKdlTdFA3v5WlFTuLJEm3ndVJt6d3kY3T8QEAABoVBR8AUC9lrirlOiuUf6BC+SXVX/eVVOjNJdkqLHUpKsSu6Zf301ndEs2OCgAA0CxR8AEAx/Xygm16/Mv13nvY/1rP1lGaefVAJcdyP3sAAACzUPABAMfk8Rh69It1+s+CbZKksGCb4iIciosIrv4a6VCHuHBdfVp77mkPAABgMgo+AKBWriqP/vr+Sn2yco8kafJvuumPIzrIYuHaegAAgKaIgg8AOEpJeaVuejNLCzbny2616IlL+ugPA9qaHQsAAAB1oOADAGrIKynXtbOWae1ep8KCbXrh6oE6s0u82bEAAABwHBR8AIDynOVasDlfCzbla97GfSosdSkuIlizrh2sPm1jzI4HAACAeqDgA0Az5PYYWrK1QN+sy9OCzfu0MfdAjedT48L16oTBat8y3KSEAAAAOFEUfABoJjweQ8u2F+qzVXv15Zq9yj/g8j5nsUi9WkdreKc4ndE5ToNSWshhZ1Z8AAAAf0LBB4AAdtDl1pJtBZq3YZ++WL1XeSUV3udiwoJ0TvdEndk1XsM6xik2PNjEpAAAADhVFHwACCBuj6E1u4u919Nn7tgvl9vjfT4yxK7RPZN0QZ9WOr1TnIJsVhPTAgAAwJco+AAQIL76OUcPfvKz9hSX11jeOjpEp3eO07k9knRGlzhOvQcAAAhQFHwA8HO5znLd/7+fNefnHElSpMOu0zq21Bmd4zS8U5w6xIXLYrGYnBIAAAANjYIPAH7K4zH0zrJsPfblepWUV8lutejGER1029mdFRLEKD0AAEBzQ8EHAD9TXulWVvZ+TZ+7SUu3F0qS+raN1mMX91H3VlEmpwMAAIBZKPgA0MQVH6xU5o5CLd22X8u2F2rVriJVug1JUliwTX89t6vGD0uRzcpp+AAAAM0ZBR8AmijDMDTju82a/s0mVXmMGs8lRjl0eqd4/fmczmrbIsykhAAAAGhKKPgA0AQddLl154er9OnKPZKklJZhGpIaq8EpsRqSGqt2sWFMnAcAAIAaKPgA0MTkFJfrhtd/0urdxbJbLXrod710ZVo7s2MBAACgiaPgA0ATsjx7v258I1P7SirUIixIL1w9UKd1aGl2LAAAAPgBCj4ANBEfL9+tOz9cJVeVR10TI/Wf8YOUHMv19QAAAKgfCj4AmKy0okoPfvqz/vvTLklSevcETb+8vyIc/BMNAACA+uO3RwAwUVb2fv35vRXaUVAmi0W6ZVQn/Tm9i6zc8g4AAAAniIIPACaocnv0/Heb9c9vN8vtMdQmJlTPXNZXaVxvDwAAgJNEwQeARpZdUKY73luurOwiSdLv+rXWQ7/rpejQIHODAQAAwK9R8AGgkXg8ht5cskOPfbleZS63Ih12/f33vfS7fm3MjgYAAIAAQMEHgEaws7BMf/tgpX7cWihJSkuN1dOX9VXbFsySDwAAAN+g4ANAA/r1qH1okE1Tzu+mq9PaM5EeAAAAfIqCDwANJNdZrtvfXV5j1P7JS/qqXUtG7QEAAOB7FHwAaAD7S1266j9LtDnvAKP2AAAAaBQUfADwsdKKKl376jJtzjugVtEhevuG05QaF252LAAAAAQ4q9kBACCQVFS59ac3M7VyZ5FahAXpjeuGUO4BAADQKCj4AOAjbo+hSe+t1A+b8hUWbNMrE4aoU0Kk2bEAAADQTFDwAcAHDMPQff9bo89X71WQzaIXrxmkfskxZscCAABAM8I1+ABwitweQ09+tUFvL8mWxSI9e3l/nd45zuxYAAAAaGYo+ABwktweQ5+t2qPnMjZpy75SSdIjY3rr/N6tTE4GAACA5oiCDwAnyO0x9OnKPXru203aeqjYR4cG6c7zuurKtHYmpwMAAEBzRcEHgHryeAx9umqPns34pdjHhAXp+tNTNX5YiiJDgkxOCAAAgOaMgg8A9bBoc74e/XKd1ux2Sqou9jec0UHjhran2AMAAKBJoOADQB3W5zj12JfrNW/DPklShMOuP47ooAmnpyrCwT+hAAAAaDr47RQAalF8sFKPfL5WH2TukseQ7FaLrkprp1vP7qy4CIfZ8QAAAICjUPABoBZ/fX+l5q7NlSSd3ztJfxvdTalx4SanAgAAAI7NanaAUzVjxgylpKQoJCREaWlpWrp06THXHTlypCwWy1GPCy64oBETA2jq5qzJ0dy1ubJbLXrnhtP0r6sGUu4BAADQ5Pl1wX/vvfc0adIk3X///crKylLfvn01evRo5eXl1br+7NmztXfvXu9jzZo1stlsuvTSSxs5OYCmqqS8Ug988rMk6Y9ndtDQji1NTgQAAADUj18X/GeeeUY33HCDJkyYoB49emjmzJkKCwvTrFmzal0/NjZWSUlJ3sfcuXMVFhZGwQfg9fTXG5XjLFdKyzDdelZns+MAAAAA9ea3Bd/lcikzM1Pp6eneZVarVenp6Vq8eHG99vHyyy/r8ssvV3h47afeVlRUyOl01ngACFwrdhbptcXbJUl/H9NbIUE2cwMBAAAAJ8BvC35+fr7cbrcSExNrLE9MTFROTs5xt1+6dKnWrFmj66+//pjrTJs2TdHR0d5HcnLyKecG0DRVuj2aMnu1DEP6ff82Or1znNmRAAAAgBPitwX/VL388svq3bu3hgwZcsx1pkyZouLiYu9j586djZgQQGN6ZeE2rdvrVExYkO69oLvZcQAAAIAT5re3yYuLi5PNZlNubm6N5bm5uUpKSqpz29LSUr377rt66KGH6lzP4XDI4eB+10Cg21lYpn/M3SRJuvv87mrJfe4BAADgh/x2BD84OFgDBw5URkaGd5nH41FGRoaGDh1a57bvv/++KioqdPXVVzd0TABNnGEYuu9/a3Sw0q3TOsTq0oFtzY4EAAAAnBS/HcGXpEmTJmn8+PEaNGiQhgwZounTp6u0tFQTJkyQJI0bN05t2rTRtGnTamz38ssva8yYMWrZkttfAc3dzPlbNW/DPgXbrHrk971lsVjMjgQAAACcFL8u+GPHjtW+ffs0depU5eTkqF+/fpozZ4534r3s7GxZrTVPUtiwYYMWLFigr7/+2ozIAJqQj5fv1uNz1kuS7rmguzrGR5icCAAAADh5FsMwDLND+Aun06no6GgVFxcrKirK7DgATsGizfka/8pSVboN3XBGqu65oIfZkQAAAICjnEgP9dtr8AHgZK3PceqPb2Sq0m3ogj6tNOU3zJoPAAAA/0fBB9Cs7C0+qAmvLFNJRZWGpMTq6Uv7ymrlunsAAAD4Pwo+gGbDWV6pCa8s097icnVKiNCL4wYqJMhmdiwAAADAJyj4AJqFtXucuvo/S7Q+p0TxkQ69OmGwYsKCzY4FAAAA+Ixfz6IPAMdTUl6pf8zdpNcWb5fbYyjSYdcr1w5W2xZhZkcDAAAAfIqCDyAgGYahT1bu0SOfr1NeSYUk6YLerXTvb7urVXSoyekAAAAA36PgAwg4m/MOaOr/1mjRlgJJUmpcuB68qKdGdIk3ORkAAADQcCj4AAJGmatKz3+7WS/9sFWVbkMOu1W3jOqkG8/sIIedyfQAAAAQ2Cj4APyeYRj6em2uHvp0rXYXHZQkndUtQQ9e1FPJsVxrDwAAgOaBgg/Ar2UXlOmBT3/Wt+vzJEltYkJ1/4U9dE6PRFks3N8eAAAAzQcFH4Df2pxXot89v1ClLreCbBbdOKKDbhnVWaHBnI4PAACA5oeCD8BvPfrFepW63OrbNlpPX9ZPnRIizI4EAAAAmIaCD8AvLdycr2/X58lutegfY/upQzzlHgAAAM2b1ewAAHCi3B5Df/98nSTp6tPaU+4BAAAAUfAB+KGPlu/Wur1ORYbYddvZnc2OAwAAADQJFHwAfuWgy62nvtogSbplVCfFhgebnAgAAABoGij4APzKywu2KsdZrjYxoRo/LMXsOAAAAECTQcEH4Df2lVTohXlbJEl3ntdVIUHcDg8AAAA4jIIPwG9M/2aj97Z4F/ZpbXYcAAAAoEmh4APwC5tyS/Tusp2SpLvP7y6r1WJyIgAAAKBpoeADaPI8HkMPfbZWbo+hc3skKq1DS7MjAQAAAE0OBR9Ak/foF+v0w6Z8Bdusuus33cyOAwAAADRJFHwATdorC7fpPwu2SZKevLSPOsZHmJwIAAAAaJoo+ACarK9+ztFDn62VVD1r/u/6tTE5EQAAANB0UfABNEnLs/frtneWyzCkK9Pa6aYzO5odCQAAAGjSKPgAmpwdBaW6/rWfVFHl0aiu8Xroop6yWJg1HwAAAKgLBR9Ak7K/1KVrX1mmglKXerWJ0vNXDpDdxj9VAAAAwPHwWzOAJuXuj1ZrW36p2sSEatb4wQp32M2OBAAAAPgFCj6AJuO7DXn6ck2ObFaLXhw3UAlRIWZHAgAAAPwGBR9Ak1Be6dYDn/wsSZowLEU9W0ebnAgAAADwLxR8AE3CzPlbtKOgTIlRDt1xThez4wAAAAB+h4IPwHTb80v1r3lbJElTf9tTEVx3DwAAAJwwCj4AUxmGoamf/CxXlUdndI7T+b2TzI4EAAAA+CUKPgBTzVmTo+837lOwzaqHfteL+90DAAAAJ4mCD8A0Byqq9OCnayVJfxrZUalx4SYnAgAAAPwXBR+AaZ7L2KQcZ7naxYbp5pEdzY4DAAAA+DUKPgBTZO7Yr5cXbJMkPfi7ngoJspmcCAAAAPBvFHwAjS67oEw3vv6T3B5Dv+3TSqO6JpgdCQAAAPB7FHwAjar4YKUmvLpUBaUu9WoTpScu6WN2JAAAACAgUPABNJpKt0c3v5WpLftKlRQVopfHD1ZYMPe8BwAAAHyBgg+gURiGoXs/WqOFmwsUFmzTy9cOUmJUiNmxAAAAgIBBwQfQKGbO36r3ftopq0V6/sr+6tk62uxIAAAAQECh4ANocF+s3qvH56yXJE39bQ+d1S3R5EQAAABA4KHgA2hQK3YW6c/vrZAkXTssRdcOTzU3EAAAABCgKPgAGsyu/WW6/rWfVFHl0VndEnTfb3uYHQkAAAAIWBR8AA3CWV6p/3t1mfIPVKh7qyg9d0V/2awWs2MBAAAAAYuCD8DnKt0eTXwrSxtzDygh0qFZ1w5ShIPb4QEAAAANiYIPwKcMw9D9n/ysHzblKzTIppfHD1ar6FCzYwEAAAABj4IPwKf+88M2vb0kWxaL9Ozl/dS7LbfDAwAAABoDBR+Az3y7PlePfrlOknTP+d11bs8kkxMBAAAAzYdfF/wZM2YoJSVFISEhSktL09KlS+tcv6ioSBMnTlSrVq3kcDjUpUsXffHFF42UFghslW6PHvx0rQxDuiqtna47ndvhAQAAAI3Jb2e9eu+99zRp0iTNnDlTaWlpmj59ukaPHq0NGzYoISHhqPVdLpfOOeccJSQk6IMPPlCbNm20Y8cOxcTENH54IAB9kLlLOwrKFBcRrHsu6C6LhRnzAQAAgMbktwX/mWee0Q033KAJEyZIkmbOnKnPP/9cs2bN0uTJk49af9asWSosLNSiRYsUFBQkSUpJSWnMyEDAKq9067mMTZKkm0Z2Uliw3/7TAgAAAPgtvzxF3+VyKTMzU+np6d5lVqtV6enpWrx4ca3bfPLJJxo6dKgmTpyoxMRE9erVS48++qjcbvcxX6eiokJOp7PGA8DR3lmarb3F5WoVHaKr0tqZHQcAAABolvyy4Ofn58vtdisxMbHG8sTEROXk5NS6zdatW/XBBx/I7Xbriy++0H333aenn35af//734/5OtOmTVN0dLT3kZyc7NP3AQSCMleVZny3WZJ061mdFRJkMzkRAAAA0Dz5ZcE/GR6PRwkJCXrxxRc1cOBAjR07Vvfcc49mzpx5zG2mTJmi4uJi72Pnzp2NmBjwD68t2qH8Ay61iw3TpYPamh0HAAAAaLb88kLZuLg42Ww25ebm1liem5urpKTab8vVqlUrBQUFyWb7ZXSxe/fuysnJkcvlUnBw8FHbOBwOORwO34YHAoizvFIz52+RJN2R3llBtmbzmSEAAADQ5Pjlb+PBwcEaOHCgMjIyvMs8Ho8yMjI0dOjQWrcZPny4Nm/eLI/H4122ceNGtWrVqtZyD+D4/vPDNhUfrFSnhAj9rl8bs+MAAAAAzZopBd/pdKqwsPCU9jFp0iS99NJLeu2117Ru3TrddNNNKi0t9c6qP27cOE2ZMsW7/k033aTCwkLdfvvt2rhxoz7//HM9+uijmjhx4inlAJqrwlKXZi3YJkmadE4X2azcFg8AAAAwk88KflVVlbKyspSVlaWCgoJa1/n222/Vt29ftWjRQvHx8Wrfvr1efPHFk3q9sWPH6qmnntLUqVPVr18/rVixQnPmzPFOvJedna29e/d6109OTtZXX32lZcuWqU+fPrrtttt0++2313pLPQDH9+/5W3Sgoko9W0fpvJ61XxoDAAAAoPFYDMMwfLGjDz74QJdddpksFot+/vlndevWrcbzS5cu1RlnnKGqqiod+ZIWi0WPPPKIXxRtp9Op6OhoFRcXKyoqyuw4gGlyneU688nvVF7p0axrB+msbonH3wgAAADACTuRHuqzEfw5c+ZIkvr27XtUuZekv/zlL6qsrJRhGIqPj1e/fv1ktVplGIYeeOABbdmyxVdRADSwhz5dq/JKjwa2b6FRXRPMjgMAAABAPiz4q1atksVi0ahRo456buPGjVq4cKEsFosuu+wy7d69W1lZWZo/f76CgoJUWVmpl19+2VdRADSgb9bm6vPVe2WzWvTw73rJYuHaewAAAKAp8FnB37dvn6TqW8/92uHRfYvFoqeeekp2e/Xd+YYPH66LLrpIhmFo3rx5vooCoIEcqKjS1P+tkSRdf0aqerTmUhUAAACgqfBZwT88sV6LFi2Oeu6HH36QJA0YMEBt27at8dyZZ54pSdq0aZOvogBoIE9/vUF7isuVHBuqO87uYnYcAAAAAEfwWcGvqKiQJJWWlh713KJFi2SxWLxl/kiHZ713Op2+igKgAazcWaTXFm2XJD0yprdCg23mBgIAAABQg88K/uGR+927d9dYvmHDBu/t6oYOHXrUdi6XS5K8p+0DaHoq3R5Nnr1aHkMa06+1RnSJNzsSAAAAgF/xWcHv0aOHDMPQRx99VGP5O++84/3+jDPOOGq7wx8IxMdTGICmataCbVq316mYsCDd+9seZscBAAAAUAufFfwLLrhAkpSZmak777xT69ev11tvvaWnn35aFotFw4YNq7XEZ2VlSZK6dOF6XqAp2llYpn98s1GSdPf53RUX4TA5EQAAAIDa+Kzg33jjjd7r6Z9++mn17NlT48aN816TP3ny5KO2qays1FdffSWLxaJBgwb5KgoAH/F4DN390WqVV3p0WodYXTqw7fE3AgAAAGAKnxX8yMhIffbZZ2rdurUMw/A+LBaL7r33Xu8I/5H+97//qbi4WJI0atQoX0UB4CMvL9imHzblK9hu1aO/78097wEAAIAmzKcz2w0cOFAbN27UF198oc2bNys8PFzp6enq1q1brevv3btX48ePP+YM+wDMs2JnkR6fs16SNPW3PdQhPsLkRAAAAADqYjEMwzA7hL9wOp2Kjo5WcXGxoqKizI4DNBhneaUueO4H7Sw8qPN7J2nGlQMYvQcAAABMcCI91Gen6AMIDIZhaPKHq7Sz8KDatgjVtD/0odwDAAAAfqDRbz7vdrv1888/q6qqSl27dlV4eHhjRwBQh7eWZOuL1TmyWy16/soBig4NMjsSAAAAgHrw2Qh+aWmpZs+erdmzZys7O7vWdV5//XUlJSWpf//+Gjx4sBISEnT33XeLqwSApmHdXqce+mytJOmu87qpX3KMuYEAAAAA1JvPRvA/+OADTZgwQTabTVu3bj3q+Tlz5ujaa6+VxWLxFvqDBw/q8ccfV2lpqZ599llfRQFwEspcVbrl7Sy5qjwa1TVe152eanYkAAAAACfAZyP433zzjSRpyJAhSk5OPur5u+66S1L19b19+/bVmDFjFBkZKcMwNGPGDK1cudJXUQCchCe/2qAt+0qVGOXQ05f1k9XKdfcAAACAP/FZwV+7dq0sFotGjBhx1HMrVqzQ6tWrZbFYdOutt2r58uWaPXu2li1bpvDwcBmGoVmzZvkqCoATlOss11tLqi+teeKSvooNDzY5EQAAAIAT5bOCn5+fL0nq2rXrUc99/fXXkiS73a6pU6d6l3fp0kWXXHKJDMPQwoULfRUFwAl6Yd4Wuao8GpISqxGd48yOAwAAAOAk+Lzg13ZfvgULFkiSTjvtNLVs2bLGc0OGDJGkWq/bB9Dw8pzlemdp9ej9bWd35pZ4AAAAgJ/yWcH3eDySpOLi4qOeW7x4sSwWi84444yjnouPj5ckHThwwFdRAJyAf3+/VRVVHg1s30LDO7U8/gYAAAAAmiSfFfzDI/M7duyosXzFihUqKCiQJA0dOvSo7Q4ePChJCg7mml+gse0rqdBbS6r/zt7O6D0AAADg13xW8Pv06SPDMPT+++/XWP7aa69Vv5DVqtNPP/2o7bKzq08NbtWqla+iAKinF7/fovJKj/olx+gMrr0HAAAA/JrPCv7vfvc7SdL69et1xRVXaM6cOXrkkUc0Y8YMWSwWpaenKzo6+qjtli1bJqn2yfkANJz8AxV648dDo/fpjN4DAAAA/s7uqx1NmDBBzzzzjDZt2qT//ve/+u9//yup+r73NptN991331HblJWV6ZtvvpHFYvFOtgegcbz0w1aVV3rUt220RnaJNzsOAAAAgFPksxH84OBgffXVV+rfv78Mw/A+wsLCNHPmTA0bNuyobd59912VlZVJks466yxfRQFwHIWlLr2xmNF7AAAAIJD4bARfklJSUpSZmanMzExt3rxZ4eHhGj58uFq0aFHr+iEhIbr//vtlsVhq/QAAQMN46YetKnO51btNtEZ1TTA7DgAAAAAf8GnBP2zgwIEaOHDgcde78sorG+LlAdSh4ECFXl+0XRL3vQcAAAACic9O0QfQ9FW5Pbr93RUqdbnVs3WU0rszeg8AAAAEigYZwT+ssrJS27ZtU2FhoVwul0aMGNGQLwfgOB6fs14LNucrNMimpy/ry+g9AAAAEEAapOB/++23evrppzVv3jyVl5dLkiwWi6qqqmqs969//UsrVqxQ27ZtNXXq1IaIAuCQ/63YrZd+2CZJevqyvuqWFGVyIgAAAAC+5NOC7/F4NHHiRL344ouSqm+RV5f4+Hj95z//kdVq1fjx49W+fXtfxgFwyJrdxbrzg1WSpImjOur83q1MTgQAAADA13x6Df4dd9yhf//73zIMQ5GRkbriiiv0hz/84ZjrjxkzRlFRUTIMQ59++qkvowA4pOBAhf74RqYqqjwa2TVek87panYkAAAAAA3AZwU/MzNTzz//vCwWi8466yxt3bpVb731lq655ppjbhMUFKT09HQZhqH58+f7KgqAQyrdHt3y9nLtLjqo1LhwPXt5f9msXHcPAAAABCKfFfx///vfkqTExER99NFHio2Nrdd2AwYMkCStXbvWV1EAHPLYl+u1eGuBwoNtevGagYoODTI7EgAAAIAG4rOC//3338tisejaa69VZGRkvbdLTk6WJO3evdtXUQBI2px3QLMWVk+q98zYfuqcWP+/lwAAAAD8j88K/uGC3qdPnxPaLiwsTJJUVlbmqygAJL34/RYZhnRuj0SN7plkdhwAAAAADcxnBf/wjPlW64nt0ul0StIJjfoDqNve4oP6aHn1h25/GtnR5DQAAAAAGoPPCn58fLwkaceOHSe03cqVKyVJrVu39lUUoNn7zw/bVOk2dFqHWA1o18LsOAAAAAAagc8K/uDBg2UYhj7//PN6b1NVVaUPPvhAFotFw4YN81UUoFnbX+rSO0uzJUk3jexkchoAAAAAjcVnBX/MmDGSpB9++EFffPFFvba57777tGfPHknSpZde6qsoQLP2+uIdKnO51aNVlEZ0jjM7DgAAAIBG4rOCf/nll6tr164yDENjx47VO++8c8x19+3bp5tuuklPPPGELBaL0tLSlJ6e7qsoQLNV5qrSq4uqZ86/aWRHWSzc8x4AAABoLuy+2pHVatX777+v4cOHq6SkRFdffbXuuusutWrVyrvOpZdeqp07dyorK0tut1uGYahFixZ66623fBUDaNbeXbpT+8sq1b5lmM7v3er4GwAAAAAIGD4bwZekXr16ad68eUpNTZVhGNq1a5d++ukn7yji7NmztWzZMlVVVckwDKWkpGj+/PlKTU31ZQygWXJVefSfH7ZKkv44oqNsVkbvAQAAgObEpwVfkvr37681a9Zo+vTpGjBggCwWiwzDqPHo2bOnnnjiCf3888/q1auXryMAzdInK/doT3G54iMd+sOANmbHAQAAANDIfHaK/pFCQ0N122236bbbblNJSYl27typoqIiRUREqE2bNmrZsmVDvCzQbHk8hmbO3yJJuu70VIUE2UxOBAAAAKCxNUjBP1JkZKR69OjR0C8DNGvfrMvV5rwDigyx66q0dmbHAQAAAGACn5+iD6BxGYah57/bLEm65rT2igwJMjkRAAAAADNQ8AE/N2/DPq3aVazQIJuuO50JKwEAAIDmqkFO0a+qqtKyZcu0Zs0a7d+/X+Xl5fXaburUqQ0RBwhYhmFo+jcbJUnjhrZXywiHyYkAAAAAmMWnBd/j8eiJJ57QP/7xD+Xn55/w9idT8GfMmKEnn3xSOTk56tu3r/75z39qyJAhta776quvasKECTWWORyOen8AATQ18zbu08pDo/c3jOhgdhwAAAAAJvJZwTcMQ5deeqk+/vhj788nwmI58Xt2v/fee5o0aZJmzpyptLQ0TZ8+XaNHj9aGDRuUkJBQ6zZRUVHasGHDKb0u0BQYhqFnv9kkSbr6tHaKY/QeAAAAaNZ8VvBff/11ffTRR5Ikm82mSy65ROecc47atm0rh6NhisczzzyjG264wTsqP3PmTH3++eeaNWuWJk+eXOs2FotFSUlJDZIHaEzzN+7Tip1FCgmy6sYRHc2OAwAAAMBkPiv4r732miQpJCREc+bM0YgRI3y161q5XC5lZmZqypQp3mVWq1Xp6elavHjxMbc7cOCA2rdvL4/HowEDBujRRx9Vz549a123oqJCFRUV3p+dTqfv3gBwCgzD0LMZh0bv09orPpLRewAAAKC589ks+qtWrZLFYtH111/f4OVekvLz8+V2u5WYmFhjeWJionJycmrdpmvXrpo1a5b+97//6c0335TH49GwYcO0a9euWtefNm2aoqOjvY/k5GSfvw/gZPywKV/Ls4vksFt145lcew8AAADAhwW/tLRUkjRs2DBf7dLnhg4dqnHjxqlfv34688wzNXv2bMXHx+vf//53retPmTJFxcXF3sfOnTsbOTFwtCNnzr/6tPZKiAwxOREAAACApsBnp+i3bt1a27dvl8fj8dUu6xQXFyebzabc3Nway3Nzc+t9jX1QUJD69++vzZs31/q8w+FosPkDgJO1YHO+sg6N3v+R0XsAAAAAh/hsBP/wafmrVq3y1S7rFBwcrIEDByojI8O7zOPxKCMjQ0OHDq3XPtxut1avXq1WrVo1VEzAp46cOf/KtHaM3gMAAADw8lnBv/XWW2W1WvXqq6+qpKTEV7ut06RJk/TSSy/ptdde07p163TTTTeptLTUO6v+uHHjakzC99BDD+nrr7/W1q1blZWVpauvvlo7duzQ9ddf3yh5gVO1aEuBftqxXw67VTedycz5AAAAAH7hs4I/YMAA/f3vf1deXp7GjBmj/fv3+2rXxzR27Fg99dRTmjp1qvr166cVK1Zozpw53on3srOztXfvXu/6+/fv1w033KDu3bvr/PPPl9Pp1KJFi9SjR48Gzwr4wgvztkiSLh+crIQoRu8BAAAA/MJiGIbhix19//33kqQXX3xRb7/9tlq2bKlx48Zp6NChiouLk9V6/M8SGmP2/VPhdDoVHR2t4uJiRUVFmR0HzcyqXUW66PmFslstmve3kWrbIszsSAAAAAAa2In0UJ8VfKvVKovF4v3ZMIwaPx+PxWJRVVWVL6I0GAo+zHTTm5n6ck2O/jCgjZ65rJ/ZcQAAAAA0ghPpoT6bRV+qLvV1/Qzg5GzZd0Bzfs6RJP2Ja+8BAAAA1MJnBf/+++/31a4A/MqL87fKMKT07onqkhhpdhwAAAAATRAFH2jicorLNXv5LknSTSMZvQcAAABQO5/Nog+gYby8YKsq3YaGpMZqYPsWZscBAAAA0ERR8IEmrKjMpbeXZEuSbmb0HgAAAEAdfFbwrVar7Ha7PvnkkxPa7quvvpLNZpPd7tP5/oCA8PriHSp1udW9VZTO7BJvdhwAAAAATViDzqLf0NsBgazMVaVXFm6TVH3t/YncdhIAAABA88Mp+kAT9d9lO7W/rFLtYsN0fq8ks+MAAAAAaOJML/hlZWWSpJCQEJOTAE1HRZVbL/1QPXp/44gOsttM/6sKAAAAoIkzvTX8+OOPkqSEhASTkwBNx+uLdmh30UElRjl0ycC2ZscBAAAA4AdO6hr8VatWacWKFbU+9+2336qoqKjO7Q3DUGlpqbKysvTmm2/KYrFo8ODBJxMFCDhFZS7989tNkqS/nNtVIUE2kxMBAAAA8AcnVfA/+ugjPfTQQ0ctNwxD//znP09oX4ZhyGKx6E9/+tPJRAECzvPfbpazvErdkiJ18QBG7wEAAADUz0mfom8YRo3HsZYf75GYmKiXXnpJZ511lk/eEODPsgvK9Nri7ZKkKed3l83KzPkAAAAA6uekRvDHjBmjlJSUGssmTJggi8WiW265RQMGDKhze6vVqoiICKWmpqp3796y2TgFGZCkJ75ar0q3oTM6x3HfewAAAAAn5KQKft++fdW3b98ayyZMmCBJOvvss3XRRRedejKgmVmevV+frdori0Wa8pvuZscBAAAA4GdOquDX5pVXXpGk447eAziaYRh69It1kqSLB7RVj9ZRJicCAAAA4G98VvDHjx/vq10Bzc7Xa3O1bPt+hQRZ9Zdzu5gdBwAAAIAfOulJ9gD4RqXbo8e+XC9Juv70DmoVHWpyIgAAAAD+iIIPmOydpdnall+qluHB+uOZHcyOAwAAAMBPnfAp+h06VBcQi8WiLVu2HLX8ZP16f0BzsL/UpWfmbpQk3ZHeWZEhQSYnAgAAAOCvTrjgb9++XVJ1If/1covFIsMwTirIr/cHNAfPzN2oorJKdUuK1BVD2pkdBwAAAIAfO+GC365du1rL+LGWA6jd2j1OvbVkhyTp/gt7ym7jihkAAAAAJ++kR/DruxzA0QzD0AOf/CyPIV3Qp5WGdmxpdiQAAAAAfo4hQ8AEn67aq6XbCxUSZNXd53c3Ow4AAACAAHBSBf+ss87SWWedpUWLFvk6DxDwSiuq9Ojn6yRJE0d2UpsYbosHAAAA4NSdVMGfN2+e5s+fr/z8/GOuM2DAAA0cOFDz588/6XBAIPrXvM3KcZYrOTZUN4zgtngAAAAAfOOEr8GvrxUrVshisai4uLihXgLwOzsKSvXS99skSfde0EMhQTaTEwEAAAAIFFyDDzSihz9bJ5fbozM6x+ncHolmxwEAAAAQQCj4QCPJWJerb9blym616P4Le3JbSQAAAAA+RcEHGkFxWaWmzF4tSbru9FR1SogwOREAAACAQEPBBxrBg5/+rLySCnWID9efz+lidhwAAAAAAYiCDzSwuWtzNXv5blkt0lOX9mViPQAAAAANgoIPNKD9pS7d/VH1qfk3jOigAe1amJwIAAAAQKA6pYJfn0nCmEgMzdkDn/6sfSUV6pQQoT+nc2o+AAAAgIZjP5WNx4wZU+fzhmEcd53DLBaLqqqqTiUO0KTMWZOj/63Yw6n5AAAAABrFKRV8qbrE1+bIkftjrQMEqsJSl+79uPrU/D+e2VH9kmPMDQQAAAAg4J10wT9eaafUozm7/5OflX/Apc4JEbojvbPZcQAAAAA0AydV8D0ej69zAAEjK3u/Pl35y6n5Djun5gMAAABoeMyiD/jYs99skiRdPKCt+nJqPgAAAIBGQsEHfChzx37N37hPNqtFt57FqfkAAAAAGg8FH/Ch6d9slCRdPKCN2rUMMzkNAAAAgOaEgg/4SOaOQv2wKV92Ru8BAAAAmICCD/jI9EPX3l8ysK2SYxm9BwAAANC4KPiAD/y0/ZfR+4mjOpkdBwAAAEAzRMEHfODw6P2lgxi9BwAAAGAOCj5wipZtL9SCzdWj9zePZPQeAAAAgDko+MApOjxz/qWDkhm9BwAAAGAaCj5wCpZuK9TCzQUKslk0cVRHs+MAAAAAaMYo+MBJMgxDT321QVL16H3bFozeAwAAADCP3xf8GTNmKCUlRSEhIUpLS9PSpUvrtd27774ri8WiMWPGNGxABKyvfs7R0u2FCgmy6tazuPYeAAAAgLn8uuC/9957mjRpku6//35lZWWpb9++Gj16tPLy8urcbvv27frrX/+qM844o5GSItC4qjya9uV6SdKNZ3RQq+hQkxMBAAAAaO78uuA/88wzuuGGGzRhwgT16NFDM2fOVFhYmGbNmnXMbdxut6666io9+OCD6tChQyOmRSB5ffF27SgoU3ykQ388k2vvAQAAAJjPbwu+y+VSZmam0tPTvcusVqvS09O1ePHiY2730EMPKSEhQdddd91xX6OiokJOp7PGA9hf6tJzGdX3vf/ruV0U7rCbnAgAAAAA/Ljg5+fny+12KzExscbyxMRE5eTk1LrNggUL9PLLL+ull16q12tMmzZN0dHR3kdycvIp54b/ezZjk5zlVeqWFKlLBvLfBAAAAICmwW8L/okqKSnRNddco5deeklxcXH12mbKlCkqLi72Pnbu3NnAKdHUbd13QG/+uEOSdO8FPWSzWkxOBAAAAADV/Pbc4ri4ONlsNuXm5tZYnpubq6SkpKPW37Jli7Zv364LL7zQu8zj8UiS7Ha7NmzYoI4da15L7XA45HA4GiA9/NW0L9erymPorG4JOr1z/T4oAgAAAIDG4Lcj+MHBwRo4cKAyMjK8yzwejzIyMjR06NCj1u/WrZtWr16tFStWeB8XXXSRRo0apRUrVnD6PY5r8ZYCzV2bK5vVorvP72Z2HAAAAACowW9H8CVp0qRJGj9+vAYNGqQhQ4Zo+vTpKi0t1YQJEyRJ48aNU5s2bTRt2jSFhISoV69eNbaPiYmRpKOWA7/m8Rj6++drJUlXDmmnTgmRJicCAAAAgJr8uuCPHTtW+/bt09SpU5WTk6N+/fppzpw53on3srOzZbX67UkKaEI+XbVHP+9xKtJh1x3pnc2OAwAAAABHsRiGYZgdwl84nU5FR0eruLhYUVFRZsdBI/F4DJ337PfamHtAfzmni249m4IPAAAAoHGcSA9leBs4jm/W5Wpj7gFFOOwaNyzF7DgAAAAAUCsKPlAHwzA0Y94WSdI1Q9srOjTI5EQAAAAAUDsKPlCHRVsKtHJnkRx2q/5veKrZcQAAAADgmCj4QB3+NW+zJOnywcmKj3SYnAYAAAAAjo2CDxzD8uz9Wri5QHarRTeM6GB2HAAAAACoEwUfOIZ/Hbr2fkz/NmrbIszkNAAAAABQNwo+UIsNOSWauzZXFov0pzM7mh0HAAAAAI6Lgg/U4oVD197/pleSOiVEmJwGAAAAAI6Pgg/8SnZBmT5ZuUeSdPPITianAQAAAID6oeADvzLz+y3yGNKZXeLVq0202XEAAAAAoF4o+MARsgvK9MFPuyRJE0cxeg8AAADAf1DwgSM8/Plaudwend4pTkNSY82OAwAAAAD1RsEHDpm/cZ/mrs2V3WrRAxf1MDsOAAAAAJwQCj4gyVXl0YOf/ixJGj8sRZ0SIk1OBAAAAAAnhoIPSHp10TZt3VequIhg3Z7e2ew4AAAAAHDCKPho9vKc5Xr2m02SpDvP66aokCCTEwEAAADAiaPgo9l7fM4Glbrc6ts2WpcMaGt2HAAAAAA4KRR8NGuZO/brw6zq2+I9cFFPWa0WkxMBAAAAwMmh4KPZ8ngMPfBJ9cR6lw5sq/7tWpicCAAAAABOHgUfzda7y3Zq9e5iRTrsuvO8bmbHAQAAAIBTQsFHs7R0W6EeOHRbvNvTOys+0mFyIgAAAAA4NRR8NDubckt0/WvL5Kry6NweiZowPNXsSAAAAABwyij4aFZyiss1ftZSOcurNKBdjJ67or9sTKwHAAAAIABQ8NFsOMsrde0rS7WnuFwd4sP18vjBCgmymR0LAAAAAHyCgo9mwVXl0Z/eyNT6nBLFRzr02oQhahEebHYsAAAAAPAZCj4Cnsdj6G8frNSiLQUKD7bplWsHKzk2zOxYAAAAAOBTFHwEvBfmb9H/VuyR3WrRC1cPVK820WZHAgAAAACfo+AjoP20vVDPzN0oSfr7mF4a0SXe5EQAAAAA0DAo+AhYRWUu3fbOcrk9hn7fv43GDk42OxIAAAAANBgKPgKSYRj66/urtKe4XKlx4Xp4TC9ZLNwODwAAAEDgouAjIL26aLu+WZerYJtVz1/ZXxEOu9mRAAAAAKBBUfARcFbvKtajX6yTJN1zQXf1bM2kegAAAAACHwUfAaWkvFK3vJOlSreh0T0TNW5oe7MjAQAAAECjoOAjYBiGoXs+WqMdBWVqExOqJy7uy3X3AAAAAJoNCj4Cxuer9+qTlXtks1r03BX9FB0WZHYkAAAAAGg0FHwEhIIDFZr6v58lSRNHddLA9rEmJwIAAACAxkXBR0CY+snPKix1qVtSpG4Z1cnsOAAAAADQ6Cj48HtfrN6rz1ftlc1q0VOX9lWwnf+sAQAAADQ/NCH4tcJSl+77eI0k6eaRHdWrDbfEAwAAANA8UfDh1+7/5GcVlLrUJTFCt5zFqfkAAAAAmi8KPvzWnDU5+vTQrPlPXdpXDrvN7EgAAAAAYBoKPvzS/lKX7j10av4fR3RQn7Yx5gYCAAAAAJNR8OGXHvz0Z+UfqFDnhAjdnt7Z7DgAAAAAYDoKPvzO3LW5+njFHlkt0pOcmg8AAAAAkij48DNFZS7d/dFqSdINIzqoX3KMuYEAAAAAoImg4MOvPPTZWu0rqVDH+HD9Ob2L2XEAAAAAoMmg4MNvZKzL1eys3d5T80OCODUfAAAAAA6j4MMvFB+s9J6af/0ZHTSgXQuTEwEAAABA00LBh1/4+2drleusUIe4cE06h1PzAQAAAODXKPho8r7bkKf3M3fJYpGevLQPp+YDAAAAQC38vuDPmDFDKSkpCgkJUVpampYuXXrMdWfPnq1BgwYpJiZG4eHh6tevn954441GTIsT5Syv1JQPq0/N/7/hqRrYPtbkRAAAAADQNPl1wX/vvfc0adIk3X///crKylLfvn01evRo5eXl1bp+bGys7rnnHi1evFirVq3ShAkTNGHCBH311VeNnBz19eScDcpxliulZZj+em5Xs+MAAAAAQJNlMQzDMDvEyUpLS9PgwYP1/PPPS5I8Ho+Sk5N16623avLkyfXax4ABA3TBBRfo4YcfPu66TqdT0dHRKi4uVlRU1Cllx/Gt3FmkMf9aKMOQ3r4hTcM6xpkdCQAAAAAa1Yn0UL8dwXe5XMrMzFR6erp3mdVqVXp6uhYvXnzc7Q3DUEZGhjZs2KARI0bUuk5FRYWcTmeNBxqH22Po3o/XyDCk3/dvQ7kHAAAAgOPw24Kfn58vt9utxMTEGssTExOVk5NzzO2Ki4sVERGh4OBgXXDBBfrnP/+pc845p9Z1p02bpujoaO8jOTnZp+8Bx/bmjzu0enexokLsuvv87mbHAQAAAIAmz28L/smKjIzUihUrtGzZMj3yyCOaNGmS5s2bV+u6U6ZMUXFxsfexc+fOxg3bTOU5y/XUVxskSX87r5viIx0mJwIAAACAps9udoCTFRcXJ5vNptzc3BrLc3NzlZSUdMztrFarOnXqJEnq16+f1q1bp2nTpmnkyJFHretwOORwUC4b2yNfrFNJRZX6tI3WlUPamR0HAAAAAPyC347gBwcHa+DAgcrIyPAu83g8ysjI0NChQ+u9H4/Ho4qKioaIiJOwcHO+/rdij6wW6ZExvWWzWsyOBAAAAAB+wW9H8CVp0qRJGj9+vAYNGqQhQ4Zo+vTpKi0t1YQJEyRJ48aNU5s2bTRt2jRJ1dfUDxo0SB07dlRFRYW++OILvfHGG3rhhRfMfBs4pKLKrfs+XiNJuua09urdNtrkRAAAAADgP/y64I8dO1b79u3T1KlTlZOTo379+mnOnDneifeys7Nltf5ykkJpaaluvvlm7dq1S6GhoerWrZvefPNNjR071qy3gCO8OH+rtuaXKj7Sob+M5p73AAAAAHAiLIZhGGaH8Bcncv9BnJgdBaU69x/fq6LKo2cv76ff9WtjdiQAAAAAMN2J9FC/vQYfgcMwqu95X1Hl0fBOLXVR39ZmRwIAAAAAv0PBh+n+t2KPftiUr2C7VY+M6S2LhYn1AAAAAOBEUfBhqqIylx7+bK0k6bazOiklLtzkRAAAAADgnyj4MNWjX6xTQalLXRIjdOOIjmbHAQAAAAC/RcGHaX7cWqD//rRLkvTo73sr2M5/jgAAAABwsmhUMEVFlVt3f7RaknRlWjsNSok1OREAAAAA+DcKPkzxwrwt2rqvVHERDt01upvZcQAAAADA71Hw0eg25x3Qv77bIkm6/8Ieig4LMjkRAAAAAPg/Cj4a3YOf/iyX26ORXeP12z6tzI4DAAAAAAGBgo9G9dP2Qv2wKV92q0UPXdSLe94DAAAAgI9Q8NGons3YJEm6dFBbtWsZZnIaAAAAAAgcFHw0miNH728e2cnsOAAAAAAQUCj4aDRHjt4nxzJ6DwAAAAC+RMFHo2D0HgAAAAAaFgUfjYLRewAAAABoWBR8NDhG7wEAAACg4VHw0eAYvQcAAACAhkfBR4Ni9B4AAAAAGgcFHw2K0XsAAAAAaBwUfDSYzB2M3gMAAABAY6Hgo8H8e/5WSdLFAxi9BwAAAICGRsFHg9hRUKq563IlSTeMSDU5DQAAAAAEPgo+GsSri7bLMKQzu8SrU0Kk2XEAAAAAIOBR8OFzzvJK/XfZTknSdaczeg8AAAAAjYGCD5/777KdKnW51TkhQmd0jjM7DgAAAAA0CxR8+FSV26NXFm6XJP3f6amyWCzmBgIAAACAZoKCD5/6em2udhcdVGx4sH7fv43ZcQAAAACg2aDgw6dmLdgmSboqrZ1CgmwmpwEAAACA5oOCD59ZubNIP+3YryCbRdec1t7sOAAAAADQrFDw4TMvHxq9v7BPayVEhZicBgAAAACaFwo+fGJv8UF9sXqvpOrJ9QAAAAAAjYuCD594bdEOVXkMpaXGqlebaLPjAAAAAECzQ8HHKStzVemdpdmSpOsYvQcAAAAAU1Dwcco+zNqt4oOVahcbprO7J5odBwAAAACaJQo+TonHY+iVQ5PrTRieIpvVYnIiAAAAAGieKPg4JfM25mlrfqkiHXZdOijZ7DgAAAAA0GxR8HFKZi3YLkm6fEiyIhx2c8MAAAAAQDNGwcdJW5/j1ILN+bJapPHDUsyOAwAAAADNGgUfJ23WoWvvz+uVpLYtwkxOAwAAAADNGwUfJyX/QIU+XrFHErfGAwAAAICmgIKPk/LmjzvkqvKob3KMBrRrYXYcAAAAAGj2KPg4YRVVbr354w5J1aP3Fgu3xgMAAAAAs1HwccI+WbFH+QdcahUdot/0SjI7DgAAAABAFHycIMMw9PKhyfXGDU1RkI3/hAAAAACgKaCd4YQs3lKg9TklCg2y6coh7cyOAwAAAAA4hIKPE/KfQ6P3lwxsq+iwIJPTAAAAAAAOo+Cj3tbtderb9XmyWKQJw1PMjgMAAAAAOAIFH/X2wrwtkqTze7dSh/gIk9MAAAAAAI5EwUe9bM8v1Wer9kiSbh7Z0eQ0AAAAAIBfo+CjXv79/RZ5DGlU13j1bB1tdhwAAAAAwK/4fcGfMWOGUlJSFBISorS0NC1duvSY67700ks644wz1KJFC7Vo0ULp6el1ro9qOcXl+iBzlyRp4qhOJqcBAAAAANTGrwv+e++9p0mTJun+++9XVlaW+vbtq9GjRysvL6/W9efNm6crrrhC3333nRYvXqzk5GSde+652r17dyMn9y8v/bBVlW5DQ1JjNSgl1uw4AAAAAIBaWAzDMMwOcbLS0tI0ePBgPf/885Ikj8ej5ORk3XrrrZo8efJxt3e73WrRooWef/55jRs37rjrO51ORUdHq7i4WFFRUaec3x8Ulro0/LFvdbDSrVcnDNbIrglmRwIAAACAZuNEeqjfjuC7XC5lZmYqPT3du8xqtSo9PV2LFy+u1z7KyspUWVmp2NjaR6UrKirkdDprPJqbVxdt18FKt3q2jtKZXeLNjgMAAAAAOAa/Lfj5+flyu91KTEyssTwxMVE5OTn12sddd92l1q1b1/iQ4EjTpk1TdHS095GcnHzKuf3JgYoqvbpwm6Tqa+8tFovJiQAAAAAAx+K3Bf9UPfbYY3r33Xf10UcfKSQkpNZ1pkyZouLiYu9j586djZzSXG/9uEPO8ip1iA/X6J5JZscBAAAAANTBbnaAkxUXFyebzabc3Nway3Nzc5WUVHcZfeqpp/TYY4/pm2++UZ8+fY65nsPhkMPh8Elef1Ne6dZLP1SP3t90ZkfZrIzeAwAAAEBT5rcj+MHBwRo4cKAyMjK8yzwejzIyMjR06NBjbvfEE0/o4Ycf1pw5czRo0KDGiOqXPsjcpfwDFWoTE6ox/duYHQcAAAAAcBx+O4IvSZMmTdL48eM1aNAgDRkyRNOnT1dpaakmTJggSRo3bpzatGmjadOmSZIef/xxTZ06VW+//bZSUlK81+pHREQoIiLCtPfR1Hg8hl45dO39/52eqiCb334OBAAAAADNhl8X/LFjx2rfvn2aOnWqcnJy1K9fP82ZM8c78V52dras1l/K6QsvvCCXy6VLLrmkxn7uv/9+PfDAA40ZvUlbsDlfW/aVKsJh12WD2podBwAAAABQDxbDMAyzQ/iLE7n/oD+b8MpSfbdhn64dlqIHLuppdhwAAAAAaLZOpIdy7jVq2LrvgL7bsE8Wi3TtsBSz4wAAAAAA6omCjxpeW7RdknRW1wSlxIWbGwYAAAAAUG8UfHg5yyv1QeYuSdKE4akmpwEAAAAAnAgKPrz+u2ynSl1udU6I0PBOLc2OAwAAAAA4ARR8SJLcHkOvLd4uSbp2eIosFou5gQAAAAAAJ4SCD0lSxrpc7Sw8qOjQIP2hP7fGAwAAAAB/Q8GHJOnVQ5PrXT4kWaHBNnPDAAAAAABOGAUfWp/j1KItBbJZLRo3NMXsOAAAAACAk0DBh15duF2SNLpnotrEhJobBgAAAABwUij4zdz+Upc+Wr5bErfGAwAAAAB/RsFv5j7M2qWKKo96tIrSoPYtzI4DAAAAADhJFPxmzDAMvb00W5J01WntuDUeAAAAAPgxCn4z9uPWQm3dV6rwYJt+16+N2XEAAAAAAKeAgt+MvbVkhyTpd/3bKMJhNzkNAAAAAOBUUPCbqfwDFfrq5xxJ0pVD2pmcBgAAAABwqij4zdQHmbtU6TbUNzlGvdpEmx0HAAAAAHCKKPjNkMdj6O0lhybXY/QeAAAAAAICBb8ZWrglX9mFZYp02PXbvq3MjgMAAAAA8AEKfjN0ePT+DwPaKCyYyfUAAAAAIBBQ8JuZPGe55q7NlSRdmdbe5DQAAAAAAF+h4Dcz//1pp6o8hga2b6GuSZFmxwEAAAAA+AgFvxlxewy9s3SnJOmqNCbXAwAAAIBAQsFvRr7ftE+7iw4qOjRI5/dmcj0AAAAACCQU/GbknUOT6108oK1CgmwmpwEAAAAA+BIFv5lwVXn0w6Z8SdLFA9uYnAYAAAAA4GsU/GZi1a4iHax0KzY8WN2TosyOAwAAAADwMQp+M7F4S4EkaWiHlrJaLSanAQAAAAD4GgW/mVh0qOCf1rGlyUkAAAAAAA2Bgt8MlFe6lZm9X5I0jIIPAAAAAAGJgt8MZGXvl6vKo4RIhzrEhZsdBwAAAADQACj4zcCPh07PH9axpSwWrr8HAAAAgEBEwW8GDl9/P5TT8wEAAAAgYFHwA1yZq0ordxVJkoZ2iDM3DAAAAACgwVDwA9xP2/er0m2oTUyokmNDzY4DAAAAAGggFPwAd+Tp+Vx/DwAAAACBi4If4BZv/WWCPQAAAABA4KLgBzBneaVWH77+noIPAAAAAAGNgh/Alm0rlMeQUuPC1Sqa6+8BAAAAIJBR8APY4kPX35/WgdF7AAAAAAh0FPwAduQEewAAAACAwEbBD1D7S11al+OUJA1lBB8AAAAAAh4FP0At2VYgw5A6J0QoPtJhdhwAAAAAQAOj4Aeow9ffc3s8AAAAAGgeKPgBiuvvAQAAAKB5oeAHoH0lFdqUd0AWi5SWSsEHAAAAgOaAgh+AftxaPXrfPSlKLcKDTU4DAAAAAGgMFPwAtHgr198DAAAAQHNjNzsAfO+e87vr3B6Jah0TanYUAAAAAEAjoeAHoHCHXSO7JpgdAwAAAADQiDhFHwAAAACAAODXBX/GjBlKSUlRSEiI0tLStHTp0mOu+/PPP+viiy9WSkqKLBaLpk+f3nhBAQAAAABoYH5b8N977z1NmjRJ999/v7KystS3b1+NHj1aeXl5ta5fVlamDh066LHHHlNSUlIjpwUAAAAAoGH5bcF/5plndMMNN2jChAnq0aOHZs6cqbCwMM2aNavW9QcPHqwnn3xSl19+uRwORyOnBQAAAACgYfllwXe5XMrMzFR6erp3mdVqVXp6uhYvXuyz16moqJDT6azxAAAAAACgKfLLgp+fny+3263ExMQayxMTE5WTk+Oz15k2bZqio6O9j+TkZJ/tGwAAAAAAX/LLgt9YpkyZouLiYu9j586dZkcCAAAAAKBWdrMDnIy4uDjZbDbl5ubWWJ6bm+vTCfQcDgfX6wMAAAAA/IJfjuAHBwdr4MCBysjI8C7zeDzKyMjQ0KFDTUwGAAAAAIA5/HIEX5ImTZqk8ePHa9CgQRoyZIimT5+u0tJSTZgwQZI0btw4tWnTRtOmTZNUPTHf2rVrvd/v3r1bK1asUEREhDp16mTa+wAAAAAAwBf8tuCPHTtW+/bt09SpU5WTk6N+/fppzpw53on3srOzZbX+coLCnj171L9/f+/PTz31lJ566imdeeaZmjdvXmPHBwAAAADApyyGYRhmh/AXTqdT0dHRKi4uVlRUlNlxAAAAAAAB7kR6qF9egw8AAAAAAGqi4AMAAAAAEAAo+AAAAAAABAAKPgAAAAAAAYCCDwAAAABAAKDgAwAAAAAQACj4AAAAAAAEAAo+AAAAAAABgIIPAAAAAEAAsJsdwJ8YhiFJcjqdJicBAAAAADQHh/vn4T5aFwr+CSgpKZEkJScnm5wEAAAAANCclJSUKDo6us51LEZ9PgaAJMnj8WjPnj2KjIyUxWIxO06dnE6nkpOTtXPnTkVFRZkdByeBYxgYOI7+j2Po/ziGgYHj6P84hoGB49j4DMNQSUmJWrduLau17qvsGcE/AVarVW3btjU7xgmJioriL56f4xgGBo6j/+MY+j+OYWDgOPo/jmFg4Dg2ruON3B/GJHsAAAAAAAQACj4AAAAAAAGAgh+gHA6H7r//fjkcDrOj4CRxDAMDx9H/cQz9H8cwMHAc/R/HMDBwHJs2JtkDAAAAACAAMIIPAAAAAEAAoOADAAAAABAAKPgAAAAAAAQACj4AAAAAAAGAgh+AZsyYoZSUFIWEhCgtLU1Lly41OxKOYdq0aRo8eLAiIyOVkJCgMWPGaMOGDTXWKS8v18SJE9WyZUtFRETo4osvVm5urkmJcTyPPfaYLBaL7rjjDu8yjqF/2L17t66++mq1bNlSoaGh6t27t3766Sfv84ZhaOrUqWrVqpVCQ0OVnp6uTZs2mZgYv+Z2u3XfffcpNTVVoaGh6tixox5++GEdOZ8wx7Fp+f7773XhhReqdevWslgs+vjjj2s8X5/jVVhYqKuuukpRUVGKiYnRddddpwMHDjTiu0Bdx7GyslJ33XWXevfurfDwcLVu3Vrjxo3Tnj17auyD42iu4/1dPNKf/vQnWSwWTZ8+vcZyjmHTQMEPMO+9954mTZqk+++/X1lZWerbt69Gjx6tvLw8s6OhFvPnz9fEiRP1448/au7cuaqsrNS5556r0tJS7zp//vOf9emnn+r999/X/PnztWfPHv3hD38wMTWOZdmyZfr3v/+tPn361FjOMWz69u/fr+HDhysoKEhffvml1q5dq6efflotWrTwrvPEE0/oueee08yZM7VkyRKFh4dr9OjRKi8vNzE5jvT444/rhRde0PPPP69169bp8ccf1xNPPKF//vOf3nU4jk1LaWmp+vbtqxkzZtT6fH2O11VXXaWff/5Zc+fO1Weffabvv/9eN954Y2O9Baju41hWVqasrCzdd999ysrK0uzZs7VhwwZddNFFNdbjOJrreH8XD/voo4/0448/qnXr1kc9xzFsIgwElCFDhhgTJ070/ux2u43WrVsb06ZNMzEV6isvL8+QZMyfP98wDMMoKioygoKCjPfff9+7zrp16wxJxuLFi82KiVqUlJQYnTt3NubOnWuceeaZxu23324YBsfQX9x1113G6aeffsznPR6PkZSUZDz55JPeZUVFRYbD4TDeeeedxoiIerjggguM//u//6ux7A9/+INx1VVXGYbBcWzqJBkfffSR9+f6HK+1a9cakoxly5Z51/nyyy8Ni8Vi7N69u9Gy4xe/Po61Wbp0qSHJ2LFjh2EYHMem5ljHcNeuXUabNm2MNWvWGO3btzf+8Y9/eJ/jGDYdjOAHEJfLpczMTKWnp3uXWa1Wpaena/HixSYmQ30VFxdLkmJjYyVJmZmZqqysrHFMu3Xrpnbt2nFMm5iJEyfqggsuqHGsJI6hv/jkk080aNAgXXrppUpISFD//v310ksveZ/ftm2bcnJyahzH6OhopaWlcRybkGHDhikjI0MbN26UJK1cuVILFizQb37zG0kcR39Tn+O1ePFixcTEaNCgQd510tPTZbVatWTJkkbPjPopLi6WxWJRTEyMJI6jP/B4PLrmmmv0t7/9TT179jzqeY5h02E3OwB8Jz8/X263W4mJiTWWJyYmav369SalQn15PB7dcccdGj58uHr16iVJysnJUXBwsPd/gIclJiYqJyfHhJSozbvvvqusrCwtW7bsqOc4hv5h69ateuGFFzRp0iTdfffdWrZsmW677TYFBwdr/Pjx3mNV27+vHMemY/LkyXI6nerWrZtsNpvcbrceeeQRXXXVVZLEcfQz9TleOTk5SkhIqPG83W5XbGwsx7SJKi8v11133aUrrrhCUVFRkjiO/uDxxx+X3W7XbbfdVuvzHMOmg4IPNBETJ07UmjVrtGDBArOj4ATs3LlTt99+u+bOnauQkBCz4+AkeTweDRo0SI8++qgkqX///lqzZo1mzpyp8ePHm5wO9fXf//5Xb731lt5++2317NlTK1as0B133KHWrVtzHIEmoLKyUpdddpkMw9ALL7xgdhzUU2Zmpp599lllZWXJYrGYHQfHwSn6ASQuLk42m+2o2blzc3OVlJRkUirUxy233KLPPvtM3333ndq2betdnpSUJJfLpaKiohrrc0ybjszMTOXl5WnAgAGy2+2y2+2aP3++nnvuOdntdiUmJnIM/UCrVq3Uo0ePGsu6d++u7OxsSfIeK/59bdr+9re/afLkybr88svVu3dvXXPNNfrzn/+sadOmSeI4+pv6HK+kpKSjJhKuqqpSYWEhx7SJOVzud+zYoblz53pH7yWOY1P3ww8/KC8vT+3atfP+rrNjxw795S9/UUpKiiSOYVNCwQ8gwcHBGjhwoDIyMrzLPB6PMjIyNHToUBOT4VgMw9Att9yijz76SN9++61SU1NrPD9w4EAFBQXVOKYbNmxQdnY2x7SJOPvss7V69WqtWLHC+xg0aJCuuuoq7/ccw6Zv+PDhR92icuPGjWrfvr0kKTU1VUlJSTWOo9Pp1JIlSziOTUhZWZms1pq/2thsNnk8HkkcR39Tn+M1dOhQFRUVKTMz07vOt99+K4/Ho7S0tEbPjNodLvebNm3SN998o5YtW9Z4nuPYtF1zzTVatWpVjd91Wrdurb/97W/66quvJHEMmxSzZ/mDb7377ruGw+EwXn31VWPt2rXGjTfeaMTExBg5OTlmR0MtbrrpJiM6OtqYN2+esXfvXu+jrKzMu86f/vQno127dsa3335r/PTTT8bQoUONoUOHmpgax3PkLPqGwTH0B0uXLjXsdrvxyCOPGJs2bTLeeustIywszHjzzTe96zz22GNGTEyM8b///c9YtWqV8bvf/c5ITU01Dh48aGJyHGn8+PFGmzZtjM8++8zYtm2bMXv2bCMuLs648847vetwHJuWkpISY/ny5cby5csNScYzzzxjLF++3Du7en2O13nnnWf079/fWLJkibFgwQKjc+fOxhVXXGHWW2qW6jqOLpfLuOiii4y2bdsaK1asqPH7TkVFhXcfHEdzHe/v4q/9ehZ9w+AYNhUU/AD0z3/+02jXrp0RHBxsDBkyxPjxxx/NjoRjkFTr45VXXvGuc/DgQePmm282WrRoYYSFhRm///3vjb1795oXGsf164LPMfQPn376qdGrVy/D4XAY3bp1M1588cUaz3s8HuO+++4zEhMTDYfDYZx99tnGhg0bTEqL2jidTuP222832rVrZ4SEhBgdOnQw7rnnnholguPYtHz33Xe1/n9w/PjxhmHU73gVFBQYV1xxhREREWFERUUZEyZMMEpKSkx4N81XXcdx27Ztx/x957vvvvPug+NoruP9Xfy12go+x7BpsBiGYTTGmQIAAAAAAKDhcA0+AAAAAAABgIIPAAAAAEAAoOADAAAAABAAKPgAAAAAAAQACj4AAAAAAAGAgg8AAAAAQACg4AMAAAAAEAAo+AAAoNmYN2+eLBaLLBaLHnjgAbPjAADgU3azAwAA0BSVl5fr448/1rfffqulS5cqLy9PBQUFstvtio6OVocOHdSvXz+lp6frvPPOU0hIiNmRm6Xt27fr1VdflSSNHDlSI0eONDUPAABmouADAHAEj8ej6dOn64knnlBubu5Rz7tcLpWVlWnv3r1auHChZsyYocjISN10003661//qvj4eBNSN1/bt2/Xgw8+6P2Zgg8AaM4o+AAAHFJYWKgrrrhCX3/9tXdZSkqK0tPT1adPH7Vs2VIWi0X79u3T+vXr9d1332n9+vUqKSnRE088IbfbraeeesrEd4DjGTlypAzDMDsGAAANgoIPAICqR+YvuugiLVy4UJKUlJSk5557ThdffLGs1mNPWbNixQpNnz5db7zxRmNFBQAAqBWT7AEAIGny5Mnect+hQwctXbpUl156aZ3lXpL69eunV199VYsXL1bPnj0bIyoAAECtKPgAgGZv165dmjFjhiTJarXq3XffVXJy8gntY8iQIZowYUKd62zZskWTJ0/W4MGDFR8fr+DgYCUmJuqss87Ss88+q7Kysjq3T0lJkcViUUpKiqTq+QJeffVVjRo1SomJiQoJCVG7du10zTXXaNWqVfXOnpOTo4ceekinn366kpKSFBwcrLi4OA0bNkx///vftX///jq3HzlypHdm+sO5Xn/9dZ133nlq27atgoKCvM8dVlVVpblz5+rOO+/UmWeeqVatWik4OFjh4eFKSUnRpZdeqg8++EAej6fW1zw8G/6oUaO8yx588EFvjiMf27dvP2q7+syif/DgQT3//PM655xzvPlatmypwYMH695779WePXvq3P7VV1/1vtbhiQA3btyoW2+9VV26dFFYWJhiYmI0dOhQPfvss3K5XHXuT5J++OEH/d///Z+6d++uyMhIBQUFKSEhQT169NB5552nhx9+WBs3bjzufgAAAcoAAKCZmzx5siHJkGRcdNFFPt+/2+02pkyZYtjtdu/r1PZo27at8dNPPx1zP+3btzckGe3btzfy8/ONM88885j7stvtxttvv33cbM8++6wRFhZWZ64WLVoYc+bMOeY+jsxRWFhojBgxotb9HGnUqFF1vubhx7Bhw4ycnJyjXvO7776r1/aSjG3bttW63f3333/M97R06VIjOTm5zv2GhYUZL7/88jH38corr3jXfeWVV4zXX3/dCA0NPeb+hg4dahQXF9e6L7fbbfzxj3+s1/u94IILjpkJABDYuAYfANDsHTmp3jXXXOPz/Y8fP15vvvmmJCk2NlZjx47VwIEDFRUVpby8PH3++ef68ssvtWvXLo0aNUo//fSTunTpcsz9VVVV6eKLL9b8+fM1dOhQXXzxxUpOTlZhYaHee+89zZs3T1VVVbruuus0ZMgQdezYsdb93HvvvXrkkUckSeHh4brkkks0dOhQtWzZUoWFhcrIyNCHH36o/fv367e//a2+/fZbnXHGGXW+16uuukrff/+9evbsqSuuuEIdO3ZUSUmJ5s+fX2O9srIyhYeHa+TIkRo4cKBSU1MVGRmp0tJSrVu3Tu+//762bNmiRYsW6fe//72+//572e2//NrSq1cvffTRR1qzZo3uu+8+SdLYsWN1+eWXH5UpISGhzsy/tmrVKo0aNUqlpaWSpB49euiaa65RamqqCgsL9fHHH+vrr79WWVmZrrvuOhmGoeuuu67Ofc6ZM0cffPCBwsLCNHHiRA0ePFgOh0MrVqzQzJkzVVxcrMWLF+uvf/2rXnzxxaO2f/755/Xvf/9bkhQZGalLLrlEAwcOVHx8vFwul3bt2qWffvpJ33zzzQm9VwBAgDH7EwYAAMx04MABw2q1ekc/d+/e7dP9z5w507vvCy+80Ni/f3+t63344YfeEf7hw4fXus7hEfzDj3/84x+1rnf99dd717n11ltrXefLL780LBaLIck47bTTjF27dtW63oIFC4zIyEhDkpGSkmJUVlYetc6vzySYOHGiUVVVVev+Dps7d65RWlp6zOcrKyuNiRMnevf5xhtv1LpefUfk67u+2+02evXq5V3n+uuvr/U9/+c///H++YWFhdU4S+CwI0fwJRk9e/as9c953bp1RkREhCHJCAoKqvWMhZ49e3rPpti+ffsx39/BgweNH3/8se4/BABAwOIafABAs5aTk+O9zjskJEStW7f22b4rKiq892jv3r27PvjgA8XExNS67h/+8AfdeeedkqSFCxdqyZIlde573LhxuuOOO2p97sknn1RISIgk6csvv6x1nXvuuUeGYSg+Pl6ff/652rRpU+t6w4cP19NPPy2p+p7zH374YZ25BgwYoOeee042m63O9dLT0xUWFnbM5+12u6ZPn+6db+C1116rc3++8vnnn2vNmjWSpD59+mjmzJk1zhw47LrrrtMf//hHSdVnIzz77LN17tdut2v27Nm1/jl369ZNEydOlCRVVlbWOgq/efNmSdV/bu3btz/m64SEhCgtLa3OLACAwEXBBwA0awUFBd7vj1W+j3TaaafVOpFbbRO6ff3119q7d68k6Y477lBwcHCd+x4/frz3+6+++qrOdSdNmnTM52JiYjRo0CBJ1RP7lZeX13h+9erVysrKkiRdf/31io2NrfO1rrzySm/JPV6uiRMnHvfOA/Vlt9t12mmnSZKWLl3aKPevnz17tvf7v/zlL3V+UDF58mTv5IFHbleb3/72t3VednHOOed4vz/8AcORwsPDJVUfu/pMxgcAaJ64Bh8AgAby/fffe78vKSnRxx9/XOf6lZWV3u/Xrl17zPXCw8PVp0+fOvfVtm1bSZJhGCoqKlJSUlKtudxu93FzSVJERISKiorqzCXpuNfoH6msrEzvvfeePv30U61evVq5ubk6cOBArUXe6XTK6XQqOjq63vs/GUeeOXHuuefWuW779u3VrVs3rVu3TtnZ2dq7d69atWpV67pDhw6tc1+Hj5ekWu9acO655+rdd9/V+vXrdfbZZ2vSpEkaPXp0nWdBAACaHwo+AKBZa9mypff7oqKi467/xBNPqLCwsMayG2+8Ufv27Ttq3SNH8//617+eUK5fv8aRYmNjj7rt3K85HA7v978ewT8y1xNPPOGzXFLNolqXRYsW6fLLL9fOnTvr/dqNUfAPn3ERGRlZ40ORY+nSpYvWrVvn3fZYBT8uLq7O/dR1vCTp8ccf14IFC7Rr1y4tWLBACxYsUFBQkAYMGKBhw4Zp5MiROvfcc72XZgAAmicKPgCgWUtKSpLVapXH41F5ebn27NlT53X4I0aMOGrZsa6Fr88HBsdS12nYp3oKfEPlkqTQ0NDj7mPbtm0aPXq0Dhw4IEnq1KmTzjvvPHXp0kVxcXEKCQnxfoDx3HPP6bvvvpNUfbZBQyspKZH0yynxxxMREXHUtrU51WPWrl07LV++XI888ohef/11FRYWqrKyUkuWLNGSJUv0j3/8Q1FRUbr99tt1zz331PjAAADQfFDwAQDNWnh4uPr376/MzExJ0uLFi3XxxRf7ZN9Hlr9Vq1apd+/ePtnvqToy1yeffKILL7ywUV//0Ucf9Zb7u+66S9OmTTvmGQlvvfVWY0ZTZGSkioqKvLfIO57D7+Pwtg0pLi5O//jHP/Tkk08qKytLixYt0qJFi5SRkaHCwkI5nU49/PDDWrhwoebOneuzuRAAAP6Df/kBAM3ekddaH75fvS8cebr6iZyK3tDMzvX1119Lqr4//SOPPFLn5Qbbtm1rrFiS5D3FvqSkRLm5ucddf+PGjd7vfXkHhrrY7XYNGTJEd9xxh/773/8qLy9P77//vvfyhW+//VYfffRRo2QBADQtFHwAQLN38803e2e4//TTT72j+afqzDPP9H5/rNvVmcHsXDk5OZKk1NTUOmep37t3r1auXFnnvo4cpfbFLPtH3mLu8AcRx5Kdna3169dLqj6Fvj7X7DcEm82mSy65RA888IB32Q8//GBKFgCAuSj4AIBmr23btrrlllskVV/nPXbsWO3Zs+eU9/ub3/xG8fHxkqRZs2Z572VutoEDB6pXr16Squ/7vnDhwkZ9/cPXt2/ZsqXOUv7QQw+pqqqqzn0deblBfU+rr8uRl2c8/fTTdV73//jjj3vz++qyjlORmprq/f54f24AgMBEwQcAQNJjjz2m4cOHS6ounoMHD9aHH3543FHh1atXy+l01vpceHi4d1S1rKxMo0eP1vLly+vc3+bNmzVp0iTl5eWd+JuoJ4vFoscee0xS9aj3mDFj9M0339S5zZ49e/TAAw9o1apVp/z6gwcPliTl5+fr6aefrnWdp59+WjNnzjzuvo4stVlZWaec7fzzz/fOlbBy5UrddNNNtZblV1991ZsvLCxMt99++ym/9rHs3btXf/nLX7Rly5ZjrlNVVaWXXnrJ+3O/fv0aLA8AoOlikj0AACQFBQXpk08+0eWXX665c+dqz549uuSSS5SamqpzzjlHffr0UWxsrOx2u4qLi7V582bNmzdPS5cu9X4I0KJFi6PuS37zzTcrMzNTs2bN0tatW/X/7d1NKHRtHMfx32hYiJwpNbdSR5ONYuFto5iFlxE2k5INsrKwEbE0slOaxWyJlFI2sqNkFmpGI0Ii8paN8nJSXkrhPIunpvvOwz3db8/Teb6fms3Mdf7X/zrNYn4zV9eUl5crEAiotrZW+fn5crlcsixLBwcHWltb0/b2tiSpv7//t663ublZo6OjGh4e1s3Njerr61VdXa3GxkYVFBQoPT1dd3d3Ojw8VCwW0/r6umzbVl1d3U/P3dfXl9z+Pjg4qGg0qsbGRnm9Xl1cXGh+fl4bGxvKy8tTSUnJp1vlPR6PysrKtLW1pWg0qp6eHtXV1X1z4J3f70/pdH/p7y3/s7Ozqqqq0uPjoyYmJhSPx9XR0aGCggJZlqXFxUUtLS0lr4lEIjJN8wfvxvc9Pz8rHA4rHA6rvLxc1dXVKioqksfj0cPDg05PTzU3N5f8AsDn86m9vf239QMA+A+zAQBA0uvrqz0+Pm57vV5bUkoPwzDs/v5++/b29h9rvr292WNjY3ZmZmZK9XJzc+3r6+t3dUzTtCXZpml+dx1dXV3JemdnZx+Om5mZsT0eT0p9ZWdn27u7u+9q+P3+5JhUjYyMfDqXaZr25uZmSutYWlqy3W73h7W+vi4ajSafD4VCH/aXSCTs/Pz8T3vMzMy0JycnP6wxPT2dHDs9Pf3p/Tg7O0uO7erq+ua18/PzlN+LxcXF9vHx8adzAQCci1/wAQD4SlpamgYGBtTb26uFhQWtrq4qkUjo6upKlmXJ7XbLMAyZpqmKigr5/X61tLR8+r/jLpdLQ0ND6u7u1tTUlFZWVrS/v6/b21tJkmEYKiwsVEVFherr69XQ0KD09PQ/st7Ozk4Fg0HNzMxoeXlZOzs7urm50cvLi3JycuTz+VRWVqba2lo1NTW926Hwo0KhkGpqahSJRBSPx2VZlgzDkM/nUzAYVE9PjwzDSKlWIBBQPB5XJBJRLBbT5eWlnp6efqq/yspKHR0daXJyUouLi9rb25NlWcrKypLP51MgEFBvb+8fOTnfNE2dnJxoeXlZsVhMu7u7uri40P39vTIyMvTlyxeVlpaqtbVVbW1tcrv5eAcA/1cu2/4FR84CAAAAAIB/FYfsAQAAAADgAAR8AAAAAAAcgIAPAAAAAIADEPABAAAAAHAAAj4AAAAAAA5AwAcAAAAAwAEI+AAAAAAAOAABHwAAAAAAByDgAwAAAADgAAR8AAAAAAAcgIAPAAAAAIADEPABAAAAAHAAAj4AAAAAAA5AwAcAAAAAwAEI+AAAAAAAOMBfOk/hmoiDRIUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x700 with 1 Axes>"
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@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,18 +397,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/envs/registration.py:307: DeprecationWarning: The package name gym_minigrid has been deprecated in favor of minigrid. Please uninstall gym_minigrid and install minigrid with `pip install minigrid`. Future releases will be maintained under the new package name minigrid.\n",
-      "  fn()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import gymnasium as gym\n",
     "env = gym.make(\"CartPole-v1\")"
@@ -416,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,30 +544,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9086d7a080>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 1200x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(12,7))\n",
     "plt.xlabel(\"Generations\",fontsize=22)\n",
@@ -586,58 +556,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "156"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_model(pop[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_42851/1617347242.py:6: RuntimeWarning: divide by zero encountered in log\n",
-      "  y = np.log(x)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f68be8059f0>]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(15,7))\n",
     "plt.axis('off')\n",
@@ -672,7 +602,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 6/MNIST Genetic Algorithm.ipynb b/Chapter 6/MNIST Genetic Algorithm.ipynb
index 5be5c6c..b0de277 100644
--- a/Chapter 6/MNIST Genetic Algorithm.ipynb	
+++ b/Chapter 6/MNIST Genetic Algorithm.ipynb	
@@ -2,10 +2,71 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: torch in /usr/local/python/3.12.1/lib/python3.12/site-packages (2.2.2)\n",
+      "Requirement already satisfied: torchvision==0.17.2 in /usr/local/python/3.12.1/lib/python3.12/site-packages (0.17.2)\n",
+      "Requirement already satisfied: numpy in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torchvision==0.17.2) (1.26.4)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/codespace/.local/lib/python3.12/site-packages (from torchvision==0.17.2) (11.0.0)\n",
+      "Requirement already satisfied: filelock in /home/codespace/.local/lib/python3.12/site-packages (from torch) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /home/codespace/.local/lib/python3.12/site-packages (from torch) (4.12.2)\n",
+      "Requirement already satisfied: sympy in /home/codespace/.local/lib/python3.12/site-packages (from torch) (1.13.1)\n",
+      "Requirement already satisfied: networkx in /home/codespace/.local/lib/python3.12/site-packages (from torch) (3.2.1)\n",
+      "Requirement already satisfied: jinja2 in /home/codespace/.local/lib/python3.12/site-packages (from torch) (3.1.4)\n",
+      "Requirement already satisfied: fsspec in /home/codespace/.local/lib/python3.12/site-packages (from torch) (2024.2.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (8.9.2.26)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.3.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (11.0.2.54)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (10.3.2.106)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (11.4.5.107)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.0.106)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (2.19.3)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.127)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/codespace/.local/lib/python3.12/site-packages (from jinja2->torch) (3.0.2)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/codespace/.local/lib/python3.12/site-packages (from sympy->torch) (1.3.0)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "Requirement already satisfied: torch==2.2.2 in /usr/local/python/3.12.1/lib/python3.12/site-packages (2.2.2)\n",
+      "Requirement already satisfied: filelock in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (4.12.2)\n",
+      "Requirement already satisfied: sympy in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (1.13.1)\n",
+      "Requirement already satisfied: networkx in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (3.2.1)\n",
+      "Requirement already satisfied: jinja2 in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (3.1.4)\n",
+      "Requirement already satisfied: fsspec in /home/codespace/.local/lib/python3.12/site-packages (from torch==2.2.2) (2024.2.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (8.9.2.26)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.3.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (11.0.2.54)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (10.3.2.106)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (11.4.5.107)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.0.106)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (2.19.3)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from torch==2.2.2) (12.1.105)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.2) (12.4.127)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/codespace/.local/lib/python3.12/site-packages (from jinja2->torch==2.2.2) (3.0.2)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/codespace/.local/lib/python3.12/site-packages (from sympy->torch==2.2.2) (1.3.0)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
+    "!pip install torch torchvision==0.17.2\n",
+    "!pip install torch==2.2.2\n",
+    "\n",
     "import os\n",
     "import torch\n",
     "import torchvision.datasets as dset\n",
@@ -14,7 +75,9 @@
     "import numpy as np\n",
     "import random\n",
     "from matplotlib import pyplot as plt\n",
-    "from scipy.stats import halfnorm"
+    "from scipy.stats import halfnorm\n",
+    "\n",
+    "# Your code continues here..."
    ]
   },
   {
@@ -63,89 +126,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████| 9912422/9912422 [00:08<00:00, 1196163.77it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████| 28881/28881 [00:00<00:00, 22109088.12it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████| 1648877/1648877 [00:01<00:00, 1292398.41it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████████████████████████████| 4542/4542 [00:00<00:00, 8038197.79it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_set = dset.MNIST(root=root, train=True, transform=trans, download=True)\n",
     "test_set = dset.MNIST(root=root, train=False, transform=trans, download=True)"
@@ -225,213 +206,213 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1511: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/nn/modules/module.py:1511: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
       "  return self._call_impl(*args, **kwargs)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "tensor([[2.7988e-02, 9.2476e-01, 2.8312e-02, 5.7766e-03, 3.1261e-04, 8.6586e-05,\n",
-       "         7.4683e-07, 1.0717e-04, 3.0326e-05, 1.2625e-02],\n",
-       "        [1.1727e-02, 9.1829e-01, 1.5762e-02, 3.3097e-03, 3.2942e-03, 7.6944e-04,\n",
-       "         8.7624e-03, 6.2241e-04, 2.9262e-02, 8.2016e-03],\n",
-       "        [7.9934e-01, 6.6551e-02, 3.5123e-02, 7.5844e-02, 6.6300e-04, 1.9790e-02,\n",
-       "         7.0070e-07, 1.5790e-04, 1.5448e-04, 2.3762e-03],\n",
-       "        [8.5457e-02, 9.1158e-01, 3.6383e-04, 1.4504e-03, 2.2931e-05, 6.0192e-04,\n",
-       "         1.5989e-05, 2.5796e-04, 1.0023e-04, 1.4733e-04],\n",
-       "        [9.3388e-01, 2.3687e-03, 1.1790e-03, 5.7466e-02, 6.1725e-04, 5.3604e-04,\n",
-       "         1.6360e-07, 1.5933e-05, 2.7593e-03, 1.1742e-03],\n",
-       "        [4.8667e-03, 9.8599e-02, 1.7211e-01, 5.8648e-01, 1.7908e-04, 4.0506e-02,\n",
-       "         3.6000e-05, 4.7075e-04, 4.9481e-02, 4.7267e-02],\n",
-       "        [2.9993e-01, 1.3598e-01, 8.0386e-03, 5.3354e-01, 2.6618e-03, 1.5952e-04,\n",
-       "         9.0557e-04, 1.1860e-03, 4.6532e-03, 1.2945e-02],\n",
-       "        [2.4034e-01, 4.8383e-01, 1.1624e-01, 8.2345e-02, 4.7858e-03, 6.6922e-02,\n",
-       "         1.1793e-05, 2.4248e-04, 2.4327e-03, 2.8549e-03],\n",
-       "        [7.5101e-02, 7.9598e-01, 4.6868e-03, 7.8001e-02, 1.1390e-03, 3.3590e-04,\n",
-       "         7.2739e-04, 1.9484e-03, 2.6317e-02, 1.5759e-02],\n",
-       "        [3.7047e-01, 3.3841e-02, 9.9279e-04, 9.9054e-02, 4.9452e-02, 4.1406e-01,\n",
-       "         7.9806e-03, 5.1487e-03, 5.0587e-03, 1.3944e-02],\n",
-       "        [3.7202e-01, 3.4961e-01, 2.9385e-02, 1.0329e-01, 6.6566e-03, 1.2609e-01,\n",
-       "         1.3931e-06, 8.6646e-03, 3.4321e-03, 8.4015e-04],\n",
-       "        [4.8603e-01, 2.5375e-02, 2.8300e-01, 1.1196e-01, 1.4914e-03, 5.8615e-02,\n",
-       "         2.7102e-07, 3.1493e-02, 1.4427e-03, 5.9013e-04],\n",
-       "        [9.3542e-01, 2.4221e-03, 6.5208e-04, 3.2151e-02, 1.3700e-03, 7.4728e-05,\n",
-       "         2.9960e-05, 1.0431e-03, 2.7715e-04, 2.6557e-02],\n",
-       "        [2.0475e-03, 9.7593e-01, 4.8786e-04, 2.1466e-02, 1.5732e-05, 1.9375e-05,\n",
-       "         8.0954e-08, 5.8644e-06, 5.0159e-06, 2.4099e-05],\n",
-       "        [3.7378e-01, 5.7183e-01, 6.3882e-03, 3.0805e-03, 3.0438e-02, 9.7105e-04,\n",
-       "         5.6609e-04, 2.7311e-04, 1.7953e-03, 1.0871e-02],\n",
-       "        [1.1995e-02, 9.8133e-01, 1.7418e-04, 2.7897e-04, 5.0370e-03, 1.7871e-04,\n",
-       "         1.1592e-04, 5.3222e-05, 1.1610e-04, 7.1687e-04],\n",
-       "        [9.6034e-03, 1.1875e-01, 2.2373e-01, 6.4638e-01, 8.7277e-05, 2.2660e-05,\n",
-       "         2.2855e-06, 1.2523e-05, 1.4015e-03, 1.9031e-05],\n",
-       "        [4.9973e-01, 1.2228e-02, 4.4713e-03, 4.7099e-01, 1.5279e-05, 1.6875e-04,\n",
-       "         6.5396e-06, 9.0488e-03, 2.3536e-03, 9.8573e-04],\n",
-       "        [8.1872e-03, 9.5637e-01, 8.5150e-03, 8.7448e-05, 2.5842e-02, 1.3225e-04,\n",
-       "         6.1234e-05, 3.4310e-04, 1.1949e-04, 3.4631e-04],\n",
-       "        [2.0103e-02, 9.4294e-01, 3.1306e-02, 1.3870e-03, 2.9450e-05, 6.5246e-05,\n",
-       "         5.5806e-07, 4.6505e-04, 1.9382e-04, 3.5072e-03],\n",
-       "        [8.6097e-02, 5.8410e-03, 3.8532e-01, 4.9271e-01, 1.6522e-04, 2.0271e-02,\n",
-       "         1.8709e-04, 5.0118e-05, 9.2893e-03, 6.5712e-05],\n",
-       "        [5.3268e-01, 4.3336e-01, 1.5621e-02, 6.2046e-03, 9.0200e-03, 1.9586e-03,\n",
-       "         7.4844e-07, 5.3389e-04, 1.0674e-04, 5.1340e-04],\n",
-       "        [5.9433e-03, 9.8936e-01, 2.3726e-03, 1.7168e-03, 1.3709e-06, 2.6123e-04,\n",
-       "         1.5834e-05, 2.2111e-06, 2.2714e-06, 3.1964e-04],\n",
-       "        [2.1867e-01, 6.5368e-01, 3.2843e-03, 7.4376e-02, 1.8185e-03, 2.3459e-03,\n",
-       "         4.4252e-04, 1.2705e-03, 3.5658e-02, 8.4600e-03],\n",
-       "        [1.7324e-01, 5.1678e-01, 1.6366e-01, 5.2131e-02, 3.0429e-04, 8.7195e-02,\n",
-       "         6.3313e-07, 4.8192e-04, 4.7221e-04, 5.7349e-03],\n",
-       "        [3.7504e-01, 5.4302e-03, 1.6203e-03, 5.8099e-01, 4.9077e-03, 1.4154e-02,\n",
-       "         2.8281e-03, 1.0259e-02, 2.5350e-03, 2.2374e-03],\n",
-       "        [1.0605e-01, 7.5449e-01, 3.3160e-03, 1.1890e-01, 1.9425e-03, 1.3317e-02,\n",
-       "         1.0252e-05, 5.5637e-04, 1.3834e-03, 3.2713e-05],\n",
-       "        [1.1738e-02, 9.1099e-01, 4.1634e-02, 2.2926e-02, 9.5613e-05, 9.7395e-03,\n",
-       "         5.2456e-06, 5.2877e-04, 5.5144e-04, 1.7907e-03],\n",
-       "        [3.0590e-01, 3.2414e-01, 1.5123e-01, 2.0795e-01, 5.3588e-03, 5.3890e-04,\n",
-       "         6.3444e-06, 3.5471e-04, 2.9403e-03, 1.5870e-03],\n",
-       "        [7.3998e-03, 6.0460e-01, 3.4699e-01, 3.6728e-02, 6.4951e-05, 3.7104e-03,\n",
-       "         1.1097e-06, 1.6417e-04, 3.2014e-04, 1.6271e-05],\n",
-       "        [3.9374e-01, 5.7836e-01, 6.6583e-03, 1.1588e-02, 4.8586e-04, 1.8419e-03,\n",
-       "         4.4088e-07, 6.9062e-05, 1.3612e-03, 5.9007e-03],\n",
-       "        [7.5531e-01, 1.7531e-01, 8.7696e-04, 1.4202e-02, 2.0623e-03, 4.1171e-02,\n",
-       "         1.1493e-03, 4.1249e-04, 7.3885e-03, 2.1147e-03],\n",
-       "        [9.2635e-03, 9.5223e-01, 1.0543e-02, 2.1753e-02, 1.3715e-03, 4.5919e-04,\n",
-       "         6.0621e-05, 5.3983e-05, 2.5742e-03, 1.6948e-03],\n",
-       "        [1.0460e-01, 7.8333e-01, 4.5598e-02, 7.0820e-04, 5.2773e-03, 1.8351e-02,\n",
-       "         2.4484e-05, 2.7105e-03, 2.9514e-02, 9.8886e-03],\n",
-       "        [7.0495e-03, 9.7865e-01, 2.0733e-03, 1.9493e-04, 9.4404e-03, 9.2086e-05,\n",
-       "         1.8819e-06, 1.4242e-04, 2.8870e-05, 2.3281e-03],\n",
-       "        [9.8314e-01, 1.8584e-03, 9.6638e-04, 1.6923e-03, 1.5455e-05, 2.3965e-04,\n",
-       "         1.8115e-06, 7.9079e-06, 1.2053e-02, 2.7917e-05],\n",
-       "        [2.2095e-01, 6.6557e-01, 6.6399e-02, 2.8580e-02, 2.6655e-03, 8.4691e-03,\n",
-       "         1.6092e-05, 8.1822e-04, 5.6342e-03, 8.9749e-04],\n",
-       "        [2.1514e-02, 9.7244e-01, 7.2101e-04, 1.2352e-03, 3.8963e-04, 4.9260e-04,\n",
-       "         3.5003e-04, 5.4118e-04, 7.9781e-04, 1.5168e-03],\n",
-       "        [1.0161e-02, 8.5247e-01, 2.3017e-02, 3.3210e-02, 4.0687e-04, 9.1576e-03,\n",
-       "         9.5777e-05, 1.6161e-02, 1.6017e-04, 5.5163e-02],\n",
-       "        [7.9407e-01, 1.4356e-01, 2.3195e-02, 3.4865e-02, 1.2331e-04, 2.0640e-03,\n",
-       "         3.1798e-07, 1.4677e-03, 5.8093e-04, 6.8949e-05],\n",
-       "        [1.3184e-02, 9.5788e-01, 4.6697e-03, 6.7727e-03, 6.0061e-04, 9.4261e-05,\n",
-       "         9.8289e-05, 2.7793e-05, 5.5947e-03, 1.1074e-02],\n",
-       "        [3.9309e-01, 5.5534e-01, 3.5861e-02, 7.0278e-03, 8.8388e-05, 7.4785e-03,\n",
-       "         4.7545e-07, 3.1300e-04, 9.9259e-05, 6.9554e-04],\n",
-       "        [8.6388e-01, 3.4587e-02, 5.1888e-04, 8.7794e-02, 4.7435e-03, 2.2924e-04,\n",
-       "         3.5134e-04, 1.1717e-03, 3.0010e-03, 3.7265e-03],\n",
-       "        [9.5885e-01, 1.4483e-02, 1.4430e-02, 2.5186e-03, 1.0640e-03, 1.1394e-03,\n",
-       "         7.5173e-05, 7.4881e-04, 6.6155e-03, 7.4889e-05],\n",
-       "        [4.0321e-01, 2.7564e-01, 1.5955e-02, 3.7922e-03, 1.4261e-02, 2.4205e-04,\n",
-       "         2.9686e-05, 1.6594e-03, 2.1711e-04, 2.8500e-01],\n",
-       "        [1.4032e-01, 5.0354e-02, 5.5555e-02, 1.9979e-01, 2.0862e-03, 2.3895e-03,\n",
-       "         9.9947e-05, 2.1251e-03, 3.9977e-04, 5.4689e-01],\n",
-       "        [3.3291e-01, 5.7686e-01, 2.2905e-02, 6.4046e-03, 1.7323e-02, 1.1186e-02,\n",
-       "         4.4525e-04, 1.3642e-03, 2.3058e-02, 7.5442e-03],\n",
-       "        [8.5510e-02, 1.3548e-01, 7.0160e-02, 6.7719e-01, 6.5128e-03, 3.0660e-05,\n",
-       "         1.5008e-04, 3.2489e-04, 2.1409e-02, 3.2315e-03],\n",
-       "        [7.6177e-02, 8.9778e-01, 5.9292e-03, 1.7652e-02, 2.6974e-05, 2.1610e-03,\n",
-       "         1.2985e-06, 1.0459e-04, 4.9049e-05, 1.1422e-04],\n",
-       "        [1.2016e-01, 2.2813e-03, 8.4806e-02, 5.8295e-01, 1.7562e-05, 9.7919e-05,\n",
-       "         2.5544e-06, 3.6272e-04, 2.0928e-01, 5.0265e-05],\n",
-       "        [7.3195e-02, 9.1937e-01, 3.2669e-03, 1.1572e-03, 2.7806e-04, 7.1638e-05,\n",
-       "         4.6884e-07, 5.0210e-05, 3.0845e-04, 2.2999e-03],\n",
-       "        [1.0837e-01, 5.3273e-01, 5.7104e-03, 4.7121e-02, 1.1419e-03, 8.4297e-04,\n",
-       "         1.0061e-03, 7.5065e-03, 1.4168e-03, 2.9415e-01],\n",
-       "        [6.9411e-02, 4.8520e-01, 4.2764e-01, 1.1852e-02, 2.3112e-04, 8.1047e-04,\n",
-       "         1.4129e-06, 1.2975e-03, 6.0124e-05, 3.5015e-03],\n",
-       "        [9.7106e-01, 1.7418e-02, 1.3138e-04, 9.7416e-03, 7.9039e-04, 2.5621e-05,\n",
-       "         3.9321e-06, 3.6996e-05, 4.1360e-04, 3.7365e-04],\n",
-       "        [8.3596e-01, 1.2149e-01, 4.3467e-03, 1.2176e-02, 8.4751e-03, 7.2550e-04,\n",
-       "         7.2769e-04, 1.2780e-04, 9.0044e-03, 6.9738e-03],\n",
-       "        [9.1970e-01, 5.1068e-02, 5.3421e-04, 2.7660e-03, 1.4056e-03, 6.8603e-04,\n",
-       "         1.6135e-04, 7.5665e-05, 2.0696e-02, 2.9118e-03],\n",
-       "        [8.2926e-01, 2.6309e-02, 4.8176e-03, 2.7271e-02, 1.3177e-03, 7.5973e-02,\n",
-       "         1.3266e-05, 7.2175e-04, 3.2527e-02, 1.7936e-03],\n",
-       "        [4.6345e-01, 4.6971e-01, 7.3113e-03, 7.3401e-03, 1.4622e-03, 1.0859e-03,\n",
-       "         7.0994e-05, 7.7204e-03, 1.4915e-03, 4.0357e-02],\n",
-       "        [8.0189e-02, 2.1937e-01, 8.7469e-02, 2.8501e-01, 4.8147e-03, 2.5399e-04,\n",
-       "         1.3397e-03, 7.6974e-04, 1.1075e-01, 2.1002e-01],\n",
-       "        [7.2939e-01, 6.9914e-03, 4.7141e-03, 1.9849e-01, 1.5684e-02, 6.5705e-03,\n",
-       "         8.3176e-05, 5.3919e-03, 3.1843e-02, 8.4135e-04],\n",
-       "        [5.7113e-01, 4.0609e-01, 2.9997e-04, 2.1372e-02, 9.9053e-05, 1.6626e-04,\n",
-       "         7.7545e-06, 2.3584e-04, 2.8327e-04, 3.1340e-04],\n",
-       "        [1.5679e-01, 8.3852e-01, 9.8214e-05, 3.0148e-03, 4.6465e-05, 1.2274e-05,\n",
-       "         1.1272e-05, 9.1220e-06, 6.8115e-04, 8.1950e-04],\n",
-       "        [2.2512e-01, 2.1756e-01, 3.9590e-01, 1.2758e-01, 2.2819e-02, 1.8866e-04,\n",
-       "         2.6917e-05, 1.8067e-04, 3.9653e-03, 6.6579e-03],\n",
-       "        [9.0080e-01, 8.9011e-02, 3.4923e-04, 7.7714e-03, 8.6201e-05, 1.5651e-04,\n",
-       "         2.3872e-06, 7.0969e-04, 7.5135e-04, 3.5820e-04],\n",
-       "        [9.3834e-01, 3.9562e-03, 6.8730e-04, 4.5449e-02, 1.3724e-03, 3.2233e-04,\n",
-       "         4.6818e-05, 1.1562e-04, 8.8452e-03, 8.6090e-04],\n",
-       "        [2.5024e-02, 1.7527e-01, 9.9358e-03, 7.8223e-01, 1.6651e-05, 4.0892e-03,\n",
-       "         1.8355e-06, 9.1534e-05, 2.7056e-03, 6.3399e-04],\n",
-       "        [9.6987e-02, 7.8229e-02, 8.3448e-02, 1.8838e-02, 6.7763e-01, 3.6586e-04,\n",
-       "         1.5585e-03, 2.6448e-04, 1.1736e-03, 4.1504e-02],\n",
-       "        [9.0812e-01, 1.4756e-02, 1.9525e-02, 3.2163e-02, 3.2422e-04, 1.3074e-02,\n",
-       "         3.8975e-06, 4.6088e-03, 7.3792e-03, 4.3116e-05],\n",
-       "        [5.3897e-01, 2.5086e-01, 1.3517e-02, 1.9301e-01, 1.8546e-05, 2.2942e-03,\n",
-       "         2.5474e-07, 3.5570e-05, 4.5148e-05, 1.2589e-03],\n",
-       "        [1.5765e-03, 9.9736e-01, 3.0302e-04, 3.3522e-04, 3.6091e-05, 5.2803e-05,\n",
-       "         5.0484e-06, 4.8817e-06, 6.2249e-05, 2.6625e-04],\n",
-       "        [7.2508e-03, 9.7563e-01, 1.6366e-03, 1.0521e-02, 7.0999e-05, 1.9370e-05,\n",
-       "         8.7313e-06, 4.9068e-05, 1.9358e-04, 4.6233e-03],\n",
-       "        [8.9402e-01, 2.2917e-02, 3.1558e-03, 7.4573e-02, 5.5549e-05, 5.2072e-04,\n",
-       "         3.8440e-06, 7.6550e-05, 1.6247e-03, 3.0539e-03],\n",
-       "        [8.6049e-01, 1.0594e-01, 4.3422e-04, 2.7249e-02, 2.1282e-03, 3.2550e-03,\n",
-       "         4.2999e-06, 3.2828e-06, 4.2082e-05, 4.5254e-04],\n",
-       "        [3.0535e-02, 9.3298e-01, 1.7627e-03, 3.1167e-02, 4.6050e-05, 1.6266e-04,\n",
-       "         5.1992e-05, 1.1424e-04, 2.3063e-03, 8.7489e-04],\n",
-       "        [9.8941e-01, 3.2990e-03, 2.0609e-03, 3.1292e-04, 4.7586e-05, 1.4192e-04,\n",
-       "         2.4126e-08, 1.9984e-04, 4.5177e-03, 7.1552e-06],\n",
-       "        [1.6406e-02, 6.7993e-01, 4.7180e-03, 2.7206e-01, 2.9321e-03, 2.8106e-05,\n",
-       "         5.2356e-06, 6.4744e-05, 1.5481e-02, 8.3706e-03],\n",
-       "        [8.2937e-01, 1.0095e-01, 8.9817e-03, 4.1795e-02, 2.0303e-03, 1.0243e-02,\n",
-       "         7.1102e-05, 1.7639e-03, 3.9207e-03, 8.7040e-04],\n",
-       "        [1.8089e-01, 5.8664e-01, 3.3891e-02, 1.7688e-01, 2.3276e-03, 4.1789e-03,\n",
-       "         4.4113e-04, 2.1526e-03, 1.1401e-02, 1.1936e-03],\n",
-       "        [8.5302e-01, 9.2270e-02, 6.0709e-03, 3.4102e-02, 1.3667e-04, 7.0179e-04,\n",
-       "         7.5298e-05, 2.9533e-04, 9.3186e-03, 4.0082e-03],\n",
-       "        [2.7100e-01, 1.9653e-02, 4.3131e-02, 6.3118e-01, 6.3474e-04, 5.7471e-03,\n",
-       "         1.8805e-04, 8.8908e-04, 2.5241e-02, 2.3407e-03],\n",
-       "        [9.0043e-01, 1.4801e-02, 7.2607e-03, 5.9797e-02, 5.0437e-05, 2.4524e-03,\n",
-       "         8.7220e-06, 6.6932e-03, 8.3556e-03, 1.4910e-04],\n",
-       "        [6.2002e-01, 3.7474e-01, 5.2334e-04, 2.9364e-03, 1.1297e-03, 5.8717e-05,\n",
-       "         1.0414e-05, 1.7066e-05, 2.3252e-04, 3.2920e-04],\n",
-       "        [8.3772e-01, 4.1029e-02, 1.6870e-04, 1.0463e-01, 2.1851e-03, 8.6542e-03,\n",
-       "         1.4161e-04, 4.2182e-04, 2.0276e-03, 3.0205e-03],\n",
-       "        [1.6073e-02, 5.9784e-01, 3.6434e-01, 1.2385e-02, 1.1161e-04, 1.7163e-03,\n",
-       "         1.2109e-05, 6.5098e-05, 3.9280e-03, 3.5297e-03],\n",
-       "        [2.6108e-01, 6.9634e-01, 2.3334e-02, 3.4346e-03, 1.0446e-03, 4.1833e-03,\n",
-       "         1.4817e-05, 8.7162e-03, 1.6325e-03, 2.1510e-04],\n",
-       "        [1.9527e-01, 7.8920e-01, 9.2121e-03, 1.4738e-03, 1.0729e-03, 3.2465e-05,\n",
-       "         4.5569e-07, 3.4033e-03, 2.5517e-05, 3.0834e-04],\n",
-       "        [9.3969e-01, 6.8670e-04, 2.9368e-02, 8.9107e-03, 1.0640e-04, 3.9312e-03,\n",
-       "         3.6311e-05, 6.0918e-04, 1.6460e-02, 2.0251e-04],\n",
-       "        [5.1472e-01, 2.2245e-01, 2.1579e-01, 1.8642e-02, 3.0290e-04, 2.5452e-02,\n",
-       "         1.2674e-07, 9.5780e-04, 1.6812e-03, 6.0959e-06],\n",
-       "        [6.3710e-03, 5.6793e-01, 1.8647e-01, 1.5316e-01, 2.4464e-02, 2.9308e-02,\n",
-       "         3.0118e-05, 6.3178e-06, 6.0691e-03, 2.6190e-02],\n",
-       "        [8.3681e-01, 1.5193e-01, 5.5928e-04, 7.2157e-03, 1.2402e-03, 3.5531e-04,\n",
-       "         2.5100e-06, 1.0196e-03, 8.8791e-05, 7.7883e-04],\n",
-       "        [3.1993e-01, 6.0267e-01, 8.7614e-05, 7.1469e-02, 1.7005e-04, 4.6948e-03,\n",
-       "         6.2005e-06, 4.7526e-04, 2.6460e-04, 2.3186e-04],\n",
-       "        [1.3798e-01, 8.4928e-01, 2.1796e-03, 4.4654e-03, 6.8257e-05, 1.0480e-03,\n",
-       "         6.7516e-06, 5.9620e-04, 8.3075e-04, 3.5411e-03],\n",
-       "        [5.4199e-01, 1.8051e-01, 1.6640e-03, 2.6760e-01, 6.6408e-03, 6.1406e-05,\n",
-       "         5.5723e-06, 5.5624e-05, 6.9276e-04, 7.7933e-04],\n",
-       "        [7.2478e-01, 7.5089e-03, 6.5652e-02, 1.8251e-01, 5.2839e-05, 2.4028e-03,\n",
-       "         4.8239e-05, 2.0167e-03, 1.4941e-02, 8.6270e-05],\n",
-       "        [6.8222e-01, 4.0088e-02, 5.2161e-04, 2.5321e-01, 1.3471e-03, 1.1130e-02,\n",
-       "         2.7955e-04, 3.8195e-04, 1.0686e-02, 1.3994e-04],\n",
-       "        [1.4406e-02, 9.5614e-01, 2.8227e-04, 2.7118e-02, 5.2172e-04, 3.0296e-04,\n",
-       "         3.3306e-05, 6.3699e-04, 2.9382e-04, 2.6409e-04],\n",
-       "        [6.0878e-01, 3.6382e-01, 4.8436e-03, 3.0899e-03, 9.2064e-04, 8.9208e-04,\n",
-       "         3.0934e-04, 2.1728e-03, 7.6586e-03, 7.5145e-03],\n",
-       "        [5.6040e-01, 3.0436e-01, 1.4689e-02, 9.2105e-02, 1.2011e-03, 2.4623e-02,\n",
-       "         1.2905e-05, 6.0689e-04, 7.8789e-04, 1.2140e-03],\n",
-       "        [6.8282e-01, 1.8538e-01, 2.3172e-03, 9.7782e-02, 2.7628e-03, 4.0170e-03,\n",
-       "         5.3378e-05, 1.7997e-03, 5.2222e-03, 1.7840e-02],\n",
-       "        [5.0811e-01, 2.7065e-01, 3.5471e-03, 8.1615e-02, 8.0110e-02, 2.9781e-03,\n",
-       "         1.6455e-05, 9.2245e-05, 4.5950e-04, 5.2416e-02]])"
+       "tensor([[6.2205e-05, 1.2605e-04, 1.4207e-04, 2.0332e-03, 2.3943e-04, 3.3313e-01,\n",
+       "         1.3850e-02, 7.5092e-04, 1.4939e-02, 6.3473e-01],\n",
+       "        [4.3240e-06, 5.7143e-04, 1.8861e-04, 2.9061e-04, 1.3297e-06, 1.2791e-01,\n",
+       "         4.7366e-03, 6.3324e-04, 8.5214e-03, 8.5714e-01],\n",
+       "        [2.3666e-05, 1.2403e-04, 9.5048e-05, 1.2936e-04, 2.6239e-05, 7.3511e-02,\n",
+       "         1.7069e-03, 1.3969e-05, 6.9469e-01, 2.2968e-01],\n",
+       "        [9.9920e-05, 1.7153e-04, 9.8816e-07, 1.0496e-05, 1.5800e-05, 3.7720e-01,\n",
+       "         3.9249e-02, 4.3615e-03, 7.7642e-02, 5.0125e-01],\n",
+       "        [1.9804e-04, 2.5138e-03, 2.5720e-05, 6.3940e-04, 3.9177e-06, 4.9897e-01,\n",
+       "         6.9273e-02, 2.5857e-02, 3.5607e-02, 3.6691e-01],\n",
+       "        [1.9271e-04, 7.4741e-03, 1.6654e-03, 1.1646e-02, 8.8965e-05, 5.8343e-01,\n",
+       "         9.0895e-02, 4.2455e-04, 1.4499e-01, 1.5919e-01],\n",
+       "        [5.1196e-04, 8.6483e-06, 7.7384e-01, 8.3054e-06, 1.1117e-05, 4.4886e-04,\n",
+       "         2.6950e-03, 1.0654e-04, 4.5173e-04, 2.2191e-01],\n",
+       "        [9.5014e-04, 5.3959e-04, 5.7270e-02, 2.0640e-04, 6.2870e-01, 1.4765e-01,\n",
+       "         8.5131e-02, 1.2371e-02, 4.3973e-02, 2.3211e-02],\n",
+       "        [2.1590e-03, 5.2962e-05, 5.9565e-03, 5.3988e-05, 6.7551e-04, 2.0701e-01,\n",
+       "         3.4213e-02, 2.0249e-03, 1.4505e-01, 6.0281e-01],\n",
+       "        [3.1434e-05, 1.5247e-03, 4.8373e-03, 2.3115e-04, 3.0752e-06, 1.5391e-02,\n",
+       "         5.8474e-02, 9.3324e-04, 9.1112e-03, 9.0946e-01],\n",
+       "        [2.0811e-04, 3.8763e-04, 3.9962e-04, 5.2926e-04, 6.8993e-05, 5.5251e-01,\n",
+       "         1.3243e-01, 4.9991e-04, 2.6665e-03, 3.1030e-01],\n",
+       "        [1.8524e-03, 2.8437e-04, 1.8781e-06, 8.3936e-06, 1.0687e-03, 9.0541e-01,\n",
+       "         2.4383e-04, 1.4415e-03, 1.6501e-02, 7.3192e-02],\n",
+       "        [5.9180e-04, 1.0540e-03, 1.3992e-04, 4.8116e-06, 2.9201e-05, 9.0046e-01,\n",
+       "         1.7217e-04, 9.2311e-06, 6.1208e-02, 3.6334e-02],\n",
+       "        [4.3935e-03, 3.7683e-05, 3.1169e-03, 7.6044e-03, 1.4103e-04, 2.6611e-02,\n",
+       "         8.1241e-01, 1.0271e-01, 1.5027e-02, 2.7949e-02],\n",
+       "        [4.2201e-04, 1.1325e-03, 1.1410e-06, 3.3015e-05, 1.1418e-04, 6.4259e-01,\n",
+       "         7.0260e-03, 1.2097e-02, 2.3874e-02, 3.1271e-01],\n",
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+       "         5.3949e-04, 1.7919e-04, 3.5101e-03, 5.6478e-01],\n",
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+       "         5.6016e-02, 9.1989e-04, 6.7075e-03, 3.4408e-01],\n",
+       "        [1.8929e-06, 1.4257e-04, 2.3795e-03, 7.9650e-04, 2.2294e-05, 1.6621e-01,\n",
+       "         3.2047e-01, 8.8581e-03, 4.6617e-02, 4.5450e-01]])"
       ]
      },
      "execution_count": 10,
@@ -445,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,16 +445,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8"
+       "9"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,15 +549,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 55.6 s, sys: 46.2 ms, total: 55.6 s\n",
-      "Wall time: 20.2 s\n"
+      "CPU times: user 20.8 s, sys: 1.33 s, total: 22.1 s\n",
+      "Wall time: 22.8 s\n"
      ]
     }
    ],
@@ -587,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -605,7 +586,7 @@
        "1000"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -623,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -657,22 +638,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f7df434efe0>]"
+       "[<matplotlib.lines.Line2D at 0x7403e8af4d70>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -687,14 +668,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(2.3625)\n"
+      "tensor(2.3471)\n"
      ]
     }
    ],
@@ -726,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,59 +717,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(2.2609, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2409, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1612, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1011, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2305, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1408, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2312, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1411, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1900, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1909, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.1711, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1588, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2111, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2712, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1076, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1011, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1410, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2212, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2408, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2612, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2112, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2512, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1111, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1612, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2011, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1012, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.1912, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1711, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2008, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2110, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.0605, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1808, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.2610, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1912, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1311, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1512, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2111, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1612, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.2211, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1811, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1412, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1912, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2710, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1910, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1511, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1808, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2112, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.0911, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2011, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.0711, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1211, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.1908, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2911, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1811, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1712, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1412, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2310, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2511, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2408, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1411, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.1612, grad_fn=<NllLossBackward0>)\n",
       "tensor(2.1512, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1711, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1512, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1609, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2111, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1209, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.2312, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1111, grad_fn=<NllLossBackward0>)\n",
-      "tensor(2.1411, grad_fn=<NllLossBackward0>)\n"
+      "tensor(2.1611, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2012, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1908, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2611, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.0912, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2410, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1211, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2411, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1511, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.0811, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.1412, grad_fn=<NllLossBackward0>)\n",
+      "tensor(2.2112, grad_fn=<NllLossBackward0>)\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[28], line 11\u001b[0m\n\u001b[1;32m      9\u001b[0m         optim\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(loss)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss_list\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/pyplot.py:3794\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3786\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3787\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3788\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3792\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3793\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3794\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3795\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3796\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3797\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3798\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3799\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3800\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/axes/_axes.py:1779\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1776\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1778\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1779\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1780\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1781\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/axes/_base.py:296\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    295\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/axes/_base.py:478\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    476\u001b[0m     y \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 478\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[43mindex_of\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    480\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    481\u001b[0m     axes\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mupdate_units(x)\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/cbook.py:1719\u001b[0m, in \u001b[0;36mindex_of\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m   1717\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1719\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1720\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (VisibleDeprecationWarning, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[1;32m   1721\u001b[0m     \u001b[38;5;66;03m# NumPy 1.19 will warn on ragged input, and we can't actually use it.\u001b[39;00m\n\u001b[1;32m   1722\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/matplotlib/cbook.py:1411\u001b[0m, in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[38;5;66;03m# plot requires `shape` and `ndim`.  If passed an\u001b[39;00m\n\u001b[1;32m   1406\u001b[0m \u001b[38;5;66;03m# object that doesn't provide them, then force to numpy array.\u001b[39;00m\n\u001b[1;32m   1407\u001b[0m \u001b[38;5;66;03m# Note this will strip unit information.\u001b[39;00m\n\u001b[1;32m   1408\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1409\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1410\u001b[0m         \u001b[38;5;28mlen\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m-> 1411\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matleast_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1412\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1413\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/numpy/core/shape_base.py:65\u001b[0m, in \u001b[0;36matleast_1d\u001b[0;34m(*arys)\u001b[0m\n\u001b[1;32m     63\u001b[0m res \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ary \u001b[38;5;129;01min\u001b[39;00m arys:\n\u001b[0;32m---> 65\u001b[0m     ary \u001b[38;5;241m=\u001b[39m \u001b[43masanyarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mary\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ary\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     67\u001b[0m         result \u001b[38;5;241m=\u001b[39m ary\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/_tensor.py:1062\u001b[0m, in \u001b[0;36mTensor.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__array__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1062\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1063\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead."
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -801,8 +816,37 @@
     "        loss_list.append(loss)\n",
     "        loss.backward()\n",
     "        optim.step()\n",
-    "    print(loss)\n",
-    "plt.plot(loss_list)"
+    "    print(loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7403e902bb30>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([loss_list[i].detach().numpy() for i in range(len(loss_list))])"
    ]
   },
   {
@@ -829,7 +873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 6/String Genetic Algorithm.ipynb b/Chapter 6/String Genetic Algorithm.ipynb
index a1dd4cc..e0bce6a 100644
--- a/Chapter 6/String Genetic Algorithm.ipynb	
+++ b/Chapter 6/String Genetic Algorithm.ipynb	
@@ -154,7 +154,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.11083333333333341\n"
+      "0.10250000000000004\n"
      ]
     }
    ],
@@ -228,7 +228,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f850a2e9150>]"
+       "[<matplotlib.lines.Line2D at 0x7037c2e16510>]"
       ]
      },
      "execution_count": 16,
@@ -237,7 +237,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -270,7 +270,7 @@
     {
      "data": {
       "text/plain": [
-       "'Hllolo Wrld!'"
+       "'HelKo WWord!'"
       ]
      },
      "execution_count": 18,
@@ -306,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte b/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte
new file mode 100644
index 0000000..1170b2c
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte differ
diff --git a/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte.gz b/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte.gz
new file mode 100644
index 0000000..5ace8ea
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/t10k-images-idx3-ubyte.gz differ
diff --git a/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte b/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte
new file mode 100644
index 0000000..d1c3a97
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte differ
diff --git a/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte.gz b/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte.gz
new file mode 100644
index 0000000..a7e1415
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/t10k-labels-idx1-ubyte.gz differ
diff --git a/Chapter 6/data/MNIST/raw/train-images-idx3-ubyte b/Chapter 6/data/MNIST/raw/train-images-idx3-ubyte
new file mode 100644
index 0000000..bbce276
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diff --git a/Chapter 6/data/MNIST/raw/train-images-idx3-ubyte.gz b/Chapter 6/data/MNIST/raw/train-images-idx3-ubyte.gz
new file mode 100644
index 0000000..b50e4b6
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/train-images-idx3-ubyte.gz differ
diff --git a/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte b/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte
new file mode 100644
index 0000000..d6b4c5d
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte differ
diff --git a/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte.gz b/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte.gz
new file mode 100644
index 0000000..707a576
Binary files /dev/null and b/Chapter 6/data/MNIST/raw/train-labels-idx1-ubyte.gz differ
diff --git a/Chapter 7/Ch7_book.ipynb b/Chapter 7/Ch7_book.ipynb
index 1808c09..6509927 100644
--- a/Chapter 7/Ch7_book.ipynb	
+++ b/Chapter 7/Ch7_book.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -51,16 +51,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([19.24612582])"
+       "array([18.70166642])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -89,13 +89,13 @@
        "<BarContainer object of 51 artists>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +171,7 @@
        "       0.01960784])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -198,13 +198,13 @@
        "<BarContainer object of 51 artists>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -216,7 +216,7 @@
    "source": [
     "ob_reward = -1\n",
     "Z = torch.from_numpy(probs).float()\n",
-    "Z = update_dist(ob_reward,torch.from_numpy(support).float(),Z,lim=(vmin,vmax),gamma=0.1)\n",
+    "Z = update_dist(ob_reward,Z,lim=(vmin,vmax),gamma=0.1)\n",
     "plt.bar(support,Z)"
    ]
   },
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -238,13 +238,13 @@
        "<BarContainer object of 51 artists>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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xx/h8vqh6SXK5XFH1oVBIDzzwgHbt2qXbb7/9M/uYmZlRMBiM2gAAQOqKK7BMTk5qbm5Oubm5Uftzc3Pl9/tjjvH7/Z9Z/9RTT2nZsmV65JFHFtRHS0uLrFZrZMvPz49nGgAAIMkk/FNCQ0NDevbZZ/X8888rLS1tQWMaGxs1NTUV2c6ePXuFuwQAAIkUV2DJyclRRkaGAoFA1P5AICCbzRZzjM1mu2T9yy+/rPHxcd10001atmyZli1bprfeekuPPvqoCgoKYh4zKytL2dnZURsAAEhdcQWWzMxMlZaWyuv1RvaFQiF5vV6Vl5fHHFNeXh5VL0n9/f2R+gceeECvvfaaTp48Gdny8vK0a9cu/eu//mu88wEAACloWbwDPB6PamtrtWnTJpWVlamtrU3T09Oqq6uTJNXU1GjNmjVqaWmRJO3cuVNbt25Va2urKisrdeTIEZ04cUIHDhyQJK1atUqrVq2K+jOuueYa2Ww2rVu37nLnBwAAUkDcgaWqqkoTExNqamqS3++X3W5XX19f5MLasbExpadfWLipqKjQ4cOHtWfPHu3evVuFhYXq6enRxo0bl24WAAAgpaWFw+Fwopu4XMFgUFarVVNTU1zPAiShgoYXL/sYZ/ZVLkEnQGoy9RyL5/U74Z8SAgAA+CxxvyUEAADMsRSrJ8mAFRYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8ZYluAACuloKGFy/7GGf2VS5BJwDixQoLAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDx+FjzAvBRSAAAEosVFgAAYDwCCwAAMN6iAktHR4cKCgpksVjkcDg0ODh4yfru7m4VFRXJYrGouLhYvb29UY8/9thjKioq0ooVK3T99dfL6XTq+PHji2kNAACkoLgDS1dXlzwej5qbmzU8PKySkhK5XC6Nj4/HrB8YGFB1dbXq6+s1MjIit9stt9ut0dHRSM1tt92m9vZ2/fd//7deeeUVFRQU6J577tHExMTiZwYAAFJG3IFl//79evDBB1VXV6cNGzaos7NTy5cv18GDB2PWP/vss9q2bZt27dql9evX64knntCdd96p9vb2SM2f/umfyul06pZbbtHtt9+u/fv3KxgM6rXXXlv8zAAAQMqIK7DMzs5qaGhITqfzwgHS0+V0OuXz+WKO8fl8UfWS5HK55q2fnZ3VgQMHZLVaVVJSEk97AAAgRcX1sebJyUnNzc0pNzc3an9ubq5OnToVc4zf749Z7/f7o/YdPXpU27dv1wcffKDVq1erv79fOTk5MY85MzOjmZmZyM/BYDCeaQAAgCRjzKeE7r77bp08eVIDAwPatm2bvv71r897XUxLS4usVmtky8/Pv8rdAgCAqymuwJKTk6OMjAwFAoGo/YFAQDabLeYYm822oPoVK1bo1ltv1ZYtW/Tcc89p2bJleu6552Ies7GxUVNTU5Ht7Nmz8UwDAAAkmbgCS2ZmpkpLS+X1eiP7QqGQvF6vysvLY44pLy+Pqpek/v7+ees/fdxPv+3zaVlZWcrOzo7aAABA6or71vwej0e1tbXatGmTysrK1NbWpunpadXV1UmSampqtGbNGrW0tEiSdu7cqa1bt6q1tVWVlZU6cuSITpw4oQMHDkiSpqen9eSTT+q+++7T6tWrNTk5qY6ODr399tu6//77l3CqAAAgWcUdWKqqqjQxMaGmpib5/X7Z7Xb19fVFLqwdGxtTevqFhZuKigodPnxYe/bs0e7du1VYWKienh5t3LhRkpSRkaFTp07p0KFDmpyc1KpVq7R582a9/PLLuv3225domgAAIJkt6ssPH374YT388MMxHzt27NhF++6///55V0ssFoteeOGFxbQBAAA+J4z5lBAAAMB8CCwAAMB4BBYAAGA8AgsAADAegQUAABhvUZ8SAgDTFDS8mOgWAFxBrLAAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPG4NT8AAIbiKycuYIUFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAeN44DcEVx4ysAS4EVFgAAYDwCCwAAMB6BBQAAGI9rWK6ShbyPf2Zf5VXoBACA5MMKCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgvEUFlo6ODhUUFMhiscjhcGhwcPCS9d3d3SoqKpLFYlFxcbF6e3sjj3300Uf6/ve/r+LiYq1YsUJ5eXmqqanRuXPnFtMaAABIQXEHlq6uLnk8HjU3N2t4eFglJSVyuVwaHx+PWT8wMKDq6mrV19drZGREbrdbbrdbo6OjkqQPPvhAw8PD2rt3r4aHh/XCCy/o9OnTuu+++y5vZgAAIGWkhcPhcDwDHA6HNm/erPb2dklSKBRSfn6+duzYoYaGhovqq6qqND09raNHj0b2bdmyRXa7XZ2dnTH/jP/6r/9SWVmZ3nrrLd10002f2VMwGJTVatXU1JSys7Pjmc6CLOS2+kuBW/MjFV2t8+dq4TzF1WTS+XMl/u7H8/od1wrL7OyshoaG5HQ6LxwgPV1Op1M+ny/mGJ/PF1UvSS6Xa956SZqamlJaWpquu+66mI/PzMwoGAxGbQAAIHXF9eWHk5OTmpubU25ubtT+3NxcnTp1KuYYv98fs97v98es//DDD/X9739f1dXV86atlpYWPf744/G0DmARTPrXHYDPN6O+rfmjjz7S17/+dYXDYf34xz+et66xsVEejyfyczAYVH5+/tVoEQCAJcM/ChYursCSk5OjjIwMBQKBqP2BQEA2my3mGJvNtqD6T8LKW2+9pf/4j/+45HtZWVlZysrKiqd1AFgSC3mB4ToXYOnFdQ1LZmamSktL5fV6I/tCoZC8Xq/Ky8tjjikvL4+ql6T+/v6o+k/Cyuuvv65///d/16pVq+JpCwAApLi43xLyeDyqra3Vpk2bVFZWpra2Nk1PT6uurk6SVFNTozVr1qilpUWStHPnTm3dulWtra2qrKzUkSNHdOLECR04cEDSx2HlT/7kTzQ8PKyjR49qbm4ucn3LDTfcoMzMzKWaKwAASFJxB5aqqipNTEyoqalJfr9fdrtdfX19kQtrx8bGlJ5+YeGmoqJChw8f1p49e7R7924VFhaqp6dHGzdulCS9/fbb+tnPfiZJstvtUX/WSy+9pC9+8YuLnBoAAEgVcd+HxUSpch+WheC9cVxNJv3dTyacp1ioZDrHkuo+LAAAAIlAYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDyjvksIAFIBt++HlFwfWU4GrLAAAADjEVgAAIDxeEsoyXzWEiPLzACAVMQKCwAAMB6BBQAAGI+3hIDPKT7BkFi8vZvcOH+uPgJLiuHjlACAVMRbQgAAwHissHwOsQqT+liuTn6cp4nFOWQeAgti4v11s/HLFBKhZrE4f5ITgQWLcrVO+GT7ZcsvQpiGv5NIFQQWGI1ftgAAiYtuAQBAEiCwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMtKrB0dHSooKBAFotFDodDg4ODl6zv7u5WUVGRLBaLiouL1dvbG/X4Cy+8oHvuuUerVq1SWlqaTp48uZi2AABAioo7sHR1dcnj8ai5uVnDw8MqKSmRy+XS+Ph4zPqBgQFVV1ervr5eIyMjcrvdcrvdGh0djdRMT0/rrrvu0lNPPbX4mQAAgJSVFg6Hw/EMcDgc2rx5s9rb2yVJoVBI+fn52rFjhxoaGi6qr6qq0vT0tI4ePRrZt2XLFtntdnV2dkbVnjlzRmvXrtXIyIjsdvuCewoGg7JarZqamlJ2dnY801mQgoYXl/yYAAAkkzP7Kpf8mPG8fse1wjI7O6uhoSE5nc4LB0hPl9PplM/niznG5/NF1UuSy+Wat34hZmZmFAwGozYAAJC64gosk5OTmpubU25ubtT+3Nxc+f3+mGP8fn9c9QvR0tIiq9Ua2fLz8xd9LAAAYL6k/JRQY2OjpqamItvZs2cT3RIAALiClsVTnJOTo4yMDAUCgaj9gUBANpst5hibzRZX/UJkZWUpKytr0eMBAEByiWuFJTMzU6WlpfJ6vZF9oVBIXq9X5eXlMceUl5dH1UtSf3//vPUAAAC/La4VFknyeDyqra3Vpk2bVFZWpra2Nk1PT6uurk6SVFNTozVr1qilpUWStHPnTm3dulWtra2qrKzUkSNHdOLECR04cCByzHfeeUdjY2M6d+6cJOn06dOSPl6duZyVGAAAkBriDixVVVWamJhQU1OT/H6/7Ha7+vr6IhfWjo2NKT39wsJNRUWFDh8+rD179mj37t0qLCxUT0+PNm7cGKn52c9+Fgk8krR9+3ZJUnNzsx577LHFzg0AAKSIuO/DYiLuwwIAwJWVVPdhAQAASAQCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGW1Rg6ejoUEFBgSwWixwOhwYHBy9Z393draKiIlksFhUXF6u3tzfq8XA4rKamJq1evVrXXnutnE6nXn/99cW0BgAAUlDcgaWrq0sej0fNzc0aHh5WSUmJXC6XxsfHY9YPDAyourpa9fX1GhkZkdvtltvt1ujoaKTm6aef1g9/+EN1dnbq+PHjWrFihVwulz788MPFzwwAAKSMtHA4HI5ngMPh0ObNm9Xe3i5JCoVCys/P144dO9TQ0HBRfVVVlaanp3X06NHIvi1btshut6uzs1PhcFh5eXl69NFH9b3vfU+SNDU1pdzcXD3//PPavn37Z/YUDAZltVo1NTWl7OzseKazIAUNLy75MQEASCZn9lUu+THjef1eFs+BZ2dnNTQ0pMbGxsi+9PR0OZ1O+Xy+mGN8Pp88Hk/UPpfLpZ6eHknSm2++Kb/fL6fTGXncarXK4XDI5/PFDCwzMzOamZmJ/Dw1NSXp44lfCaGZD67IcQEASBZX4jX2k2MuZO0krsAyOTmpubk55ebmRu3Pzc3VqVOnYo7x+/0x6/1+f+TxT/bNV/PbWlpa9Pjjj1+0Pz8/f2ETAQAAcbG2Xbljv/fee7JarZesiSuwmKKxsTFq1SYUCumdd97RqlWrlJaWdtX7CQaDys/P19mzZ6/IW1JYWjxfyYXnK/nwnCWXRD5f4XBY7733nvLy8j6zNq7AkpOTo4yMDAUCgaj9gUBANpst5hibzXbJ+k/+GwgEtHr16qgau90e85hZWVnKysqK2nfdddfFM5UrIjs7m5MzifB8JReer+TDc5ZcEvV8fdbKyifi+pRQZmamSktL5fV6I/tCoZC8Xq/Ky8tjjikvL4+ql6T+/v5I/dq1a2Wz2aJqgsGgjh8/Pu8xAQDA50vcbwl5PB7V1tZq06ZNKisrU1tbm6anp1VXVydJqqmp0Zo1a9TS0iJJ2rlzp7Zu3arW1lZVVlbqyJEjOnHihA4cOCBJSktL03e+8x394Ac/UGFhodauXau9e/cqLy9Pbrd76WYKAACSVtyBpaqqShMTE2pqapLf75fdbldfX1/kotmxsTGlp19YuKmoqNDhw4e1Z88e7d69W4WFherp6dHGjRsjNX/5l3+p6elp/cVf/IXeffdd3XXXXerr65PFYlmCKV55WVlZam5uvuhtKpiJ5yu58HwlH56z5JIsz1fc92EBAAC42vguIQAAYDwCCwAAMB6BBQAAGI/AAgAAjEdguUxPPvmkKioqtHz58nlvXjc2NqbKykotX75cN954o3bt2qVf//rXV7dRxFRQUKC0tLSobd++fYluC5/S0dGhgoICWSwWORwODQ4OJrolzOOxxx676HwqKipKdFv4jf/8z//Ul7/8ZeXl5SktLS3ynX6fCIfDampq0urVq3XttdfK6XTq9ddfT0yzMRBYLtPs7Kzuv/9+PfTQQzEfn5ubU2VlpWZnZzUwMKBDhw7p+eefV1NT01XuFPP5q7/6K50/fz6y7dixI9Et4Te6urrk8XjU3Nys4eFhlZSUyOVyaXx8PNGtYR6333571Pn0yiuvJLol/Mb09LRKSkrU0dER8/Gnn35aP/zhD9XZ2anjx49rxYoVcrlc+vDDD69yp/MIY0n83d/9XdhqtV60v7e3N5yenh72+/2RfT/+8Y/D2dnZ4ZmZmavYIWK5+eabw3/zN3+T6DYwj7KysvC3v/3tyM9zc3PhvLy8cEtLSwK7wnyam5vDJSUliW4DCyAp/NOf/jTycygUCttstvAzzzwT2ffuu++Gs7Kywj/5yU8S0OHFWGG5wnw+n4qLi6O+jdrlcikYDOp//ud/EtgZPrFv3z6tWrVKv//7v69nnnmGt+sMMTs7q6GhITmdzsi+9PR0OZ1O+Xy+BHaGS3n99deVl5enW265Rd/4xjc0NjaW6JawAG+++ab8fn/U+Wa1WuVwOIw535Ly25qTid/vjworkiI/+/3+RLSET3nkkUd055136oYbbtDAwIAaGxt1/vx57d+/P9Gtfe5NTk5qbm4u5vlz6tSpBHWFS3E4HHr++ee1bt06nT9/Xo8//rj+4A/+QKOjo1q5cmWi28MlfPJ6FOt8M+W1ihWWGBoaGi66cOy3N35hmiue58/j8eiLX/yi7rjjDn3rW99Sa2urfvSjH2lmZibBswCSz7333qv7779fd9xxh1wul3p7e/Xuu+/qn/7pnxLdGlIAKywxPProo/rmN795yZpbbrllQcey2WwXfaohEAhEHsPSu5znz+Fw6Ne//rXOnDmjdevWXYHusFA5OTnKyMiInC+fCAQCnDtJ4rrrrtNtt92mN954I9Gt4DN8ck4FAgGtXr06sj8QCMhutyeoq2gElhi+8IUv6Atf+MKSHKu8vFxPPvmkxsfHdeONN0qS+vv7lZ2drQ0bNizJn4Fol/P8nTx5Uunp6ZHnComTmZmp0tJSeb3eyDe3h0Iheb1ePfzww4ltDgvy/vvv65e//KUeeOCBRLeCz7B27VrZbDZ5vd5IQAkGgzp+/Pi8n4K92ggsl2lsbEzvvPOOxsbGNDc3p5MnT0qSbr31Vv3O7/yO7rnnHm3YsEEPPPCAnn76afn9fu3Zs0ff/va3jf9mzFTn8/l0/Phx3X333Vq5cqV8Pp+++93v6s/+7M90/fXXJ7o96OO37Gpra7Vp0yaVlZWpra1N09PTqqurS3RriOF73/uevvzlL+vmm2/WuXPn1NzcrIyMDFVXVye6NejjAPnp1a4333xTJ0+e1A033KCbbrpJ3/nOd/SDH/xAhYWFWrt2rfbu3au8vLzIPxgSLtEfU0p2tbW1YUkXbS+99FKk5syZM+F77703fO2114ZzcnLCjz76aPijjz5KXNMIh8Ph8NDQUNjhcIStVmvYYrGE169fH/7rv/7r8Icffpjo1vApP/rRj8I33XRTODMzM1xWVhZ+9dVXE90S5lFVVRVevXp1ODMzM7xmzZpwVVVV+I033kh0W/iNl156KebrVW1tbTgc/vijzXv37g3n5uaGs7Kywl/60pfCp0+fTmzTn5IWDofDiQpLAAAAC8GnhAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAw3v8Def2Kd/k4UHIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -256,7 +256,7 @@
    "source": [
     "ob_rewards = [10,10,10,0,1,0,-10,-10,10,10]\n",
     "for i in range(len(ob_rewards)):\n",
-    "    Z = update_dist(ob_rewards[i], torch.from_numpy(support).float(), Z, lim=(vmin,vmax), gamma=0.5)\n",
+    "    Z = update_dist(ob_rewards[i], Z, lim=(vmin,vmax), gamma=0.5)\n",
     "plt.bar(support, Z)"
    ]
   },
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -278,13 +278,13 @@
        "<BarContainer object of 51 artists>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -296,8 +296,7 @@
    "source": [
     "ob_rewards = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n",
     "for i in range(len(ob_rewards)):\n",
-    "    Z = update_dist(ob_rewards[i], torch.from_numpy(support).float(), \\\n",
-    "    Z, lim=(vmin,vmax), gamma=0.7)\n",
+    "    Z = update_dist(ob_rewards[i], Z, lim=(vmin,vmax), gamma=0.7)\n",
     "plt.bar(support, Z)"
    ]
   },
@@ -310,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +364,7 @@
     "            bj = int(np.clip(bj,0,nsup-1))\n",
     "            target_dist[bj] = 1.\n",
     "        else: #C\n",
-    "            target_dist = update_dist(r,support,dist,lim=lim,gamma=gamma)\n",
+    "            target_dist = update_dist(r,dist,lim=lim,gamma=gamma)\n",
     "        target_dist_batch[i,action,:] = target_dist #D\n",
     "        \n",
     "    return target_dist_batch"
@@ -380,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,22 +401,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fa3c1057070>"
+       "<matplotlib.legend.Legend at 0x7bf9e1d9eea0>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -460,22 +459,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa3c0dc7850>]"
+       "[<matplotlib.lines.Line2D at 0x7bf9e1c7d4f0>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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OXrOLMSiFdqsYXfFzbRciIjLFxoNNYdussOhaollhqG3oXTZ6bRd5qRmQMPggIrK4VKp0IglP7jSpIMFlCH2utVAygZTsUFttZ09KDD6IiIgAmYXlDF7VNtbaLikUnjD4ICKyuGSpcuIppxW6XUIl8r5b4O2ZisEHEZHBnK4evLD+ME53uBXtH+0beLPCc1iJVb/Bh5XL8EnGBs/aLhztQkRksK8v3YJqZzs+PtyMV795Tcz9o9U5//lypX4Fi1OqfZsPTTjVc6jtYMeWDyIig1U72wEAHx8+rWj/aN94Dzo74iqLGa0QSub5SHgZ5JJAEzXXiMx5ZE8tk/ORqhh8EBFZnFW7KUIpLaXs9OoGv0e5yj0s+DCmKLLX638sTThNHQw+iIgsLpHfguVaIcxgyRlOwyYd069Q1rjr5mHwQUREulBaoVol4AkVNq+H0deXdLv0J5yKsG2pQFXwsWTJEkyaNAl5eXnIy8tDeXk53nvvvcDrPT09mDdvHoYPH46cnBzMnj0bTqdT90ITEQ0mKVTnRBS+joopxZAYDPfdLKqCj7Fjx2LRokWoqqrCtm3bMH36dNx+++3Yu7dvHYJHHnkEq1atwsqVK7FhwwbU19fjzjvvTEjBiYgGiy+98DFe+fiY2cWIKb55Poyd4VQ+4dS4tg/5GU5F2OupmvOhaqjtbbfdJnn+zDPPYMmSJaisrMTYsWOxdOlSLF++HNOnTwcALFu2DBdffDEqKytxzTWxh5MREQ12chXMgYZ2/GTVvgRdz/gqzQoJtLIJpyHP/WETn2lsjrFAK47VaM758Pl8WLFiBTo7O1FeXo6qqip4vV7MmDEjsM+ECRNQVlaGioqKiOdxu91wuVySHyKiZNPU7sau2lazi2GqeOpYdrtEmF49wuvJTnXwsXv3buTk5CAzMxP3338/3nzzTVxyySVoaGhARkYGCgoKJPsXFRWhoaEh4vkWLlyI/Pz8wE9paanqN0FEZLarn/kQty/+OOkCEDOSP5XM86GmotXrHYQPtTV4+G+UZ6lGdfBx0UUXYefOndi8eTO+853vYO7cudi3T3tz4IIFC9DW1hb4qa2t1XwuIiKzVR5VNnFYIpg9iiSeeT6MuK7q8yZwoTmlvynpNVMnIFE9vXpGRgbOP/98AMDkyZOxdetW/OY3v8FXvvIVeDwetLa2Slo/nE4niouLI54vMzMTmZmZ6ktORGQiT68fHx9pxtXjCpGTaY2VKqyQS6Fd6FTm5pchkXdTNuE0KNBIpS4WOXHP8+H3++F2uzF58mSkp6djzZo1gdeqq6tRU1OD8vLyeC9DRGQpz314EPcs24pv/2mb2UWxjHjm+Yinsk1Yt4teEYBep0mhgERVuL5gwQLMmjULZWVlaG9vx/Lly7F+/Xp88MEHyM/Px3333Yf58+ejsLAQeXl5ePDBB1FeXs6RLkSUcl7f2tdF/MkR87pZ9GCV0S5hg1xVFMuobpdES80OFnmqgo/GxkZ8/etfx6lTp5Cfn49Jkybhgw8+wOc+9zkAwHPPPQe73Y7Zs2fD7XZj5syZeOGFFxJScCKiWBpdPXhp41HMmVqGc0fm6HrudEdiJoi2wCCPQUF2no+w5+avast5PgAsXbo06utZWVlYvHgxFi9eHFehiIj08MBrO7DlWAtWbqvFpz+Zqekcf9h0DH+rOom/fHMqCodmBLanp6VGmKBnkmp8k4xJn6up57W8AyWtGoYnnHJVWyKi5LezphUA4Orp1XyOn727D/tOufD8mkOS7Ylq+Ris4pnhVK962uzF7eTWcZFuM7Y8icT/PURECrh7/ZLn6XZtH591rd16FMeSkn9hOWnt7k+l2t5iGHwQEWmgtdvlu69u17kk8bFqwmkiul3UdpvodmcU5JcA8vkdcivdpgIGH0REGmjtdjnsbNe5JNahZ85Hortd5BeWi35iMxJOUxWDDyIiDZQGHw+t2IG7f18Jf+gqZQlg1e6MUGaVU27tlKj7J7BVSO4OyK7twtEuRETUL0Nh8PH2znoAwP4GFy4tyU9kkTRVlnoGAoq7P2S7XbTPcKr8uupeN3eej1QKNcKx5YOISIN0h7pKO4W66xMjjvuTsEnGQp+b8EvkqrZERBbg9wv8c/cp00eNKOl2SaUEQSWUvlvZ6dVDnyfg1sX6fcQz3Df6iRVtkl4/xf90GHwQUVL52/aT+O6r2/HZX64ztRyRgo/V+5yBx8kQe6RC877S+xy8m5JuHSsMtZUsNqfmd2V+0aNi8EFESWVDdRMAoNeABM5oInW7bDtxBseaOwFY/vNfd/Fkj8Qzw6mWFiZlCafGGkxruzD4IKKkEjrZVzSJ/FYf3PIRWvnVn+0SssI3ZytSknCamG4Xla+HbDBlbZeIT1QcZ0EMPogoqXh8yoOPREpPG/j49PrkP+qTIfawytouqq4Tmpuh4RxK4ohEvh+lQ21TFYMPIkoqnl6f4n0TOZ9E8FDbSAFRKuRTJIJswmlc3S4K94vx+zCi9WXgWtG3BsqiMQfV6snODD6IKKlEamUwmsM+UDt6I3QFGf35b/YkY/HN8xHyPMq903pf1Xa7hHabaa7Qk2PuN0Mx+CCipOJRkfORSMH1SaQqyeJfPgEkZ+tMjFnQlZ1DScKp0ZOMyc1wqnFVW6v/7TH4IKIwT7+7Dz9dtdfsYshyq+h2SSTp5E+RGtEtXgPoLK55PsKSO3UokEph68sYX4RBg8EHEUm4erxYuukYln18HC2dHrOLE8Yq3S5K5oMyfnpua9ybWOLvdom/O0RRwmkCf4GyCacyj6V/Z8rLY/W/BAYfRCThC6rcfSbPpSHHKt0uwRVBpCG1srNYJqg8WpmdJxJgwDDSmDkfMY7Xc6it/AynwY+t9peiLwYfRJRU1MzzkUhC7mtqiME2z4dFwpiIYo52Cfl96ZZwGsefgda1Xaz+p8fgg4gkEv2ZtaeuDTtrWzUfr2aorVHUdLtYrYLWs6smnjOpWdU2LDdDw4XNSDiNubaMZKhtamPwQUSG6fX58W+/3YQ7Fn8MV49X0zmC59RobO/Rq2iqBVckEVs4Ur0G0UjJPB9Rcz5C5+NQeKPVdrtY4denda05q+f/MPggIsMEr8fS1qUx+AjqdvnS4k/iLpNWSprDrV4BmMUK90VZwqm+1ww+X6wZTi1wixKKwQcRJZXgHNi6s2uomFOO2C0fFszXTSijFpbT2u0SM2AMG2pr/i9QOs+HitEu5hc9KgYfRCSheQlvRefW9XSmUrIOh9EjFrSMXDFjtItst0vo8wSMflGTc6G2DIqur+J1KwQ+icTgg4gk/Als+k2lD1Ql70TTzJsajrEKxUGABd6llnk+NA+1tYWfT36obfhsplpzPqyOwQcRScT6gIzv3Dqf0ETB7yXiPB9J8H6tEAgA6mY41aPbRct5U33uDSMx+CAiieCPV73nqUiteS/Cv6WG7aFkRIyOzA4k4sr5CH1uwmiXWGWKl6rRKv0tHxpPYPVAicEHEUn4ZZp+9Tu3vuczU/C98SmY4dTidYHpzFzELbBNwT7aLqbTeVIIgw8iklDSnaD95PqezkxKpsJO1f76SJS+x3iTXDVPMqa65SNxCdexhtoKmY1c24WIUlZiWz6s/pGoXPB78UWY8V3rMEkjmTHaRb4SVZHzoUMZ5M6vx8ypA8eGHzyYZjCNhcEHkUY9Xh/+/cVP8Oy/qs0uiq6UDCHVfG59T2cqJbkxwd1MqdTllAh6VvwRrxFrbZewobbG/tLkglWtXXcWjXUDGHwQabRqVz22Hj+D59ceNrsoutK6hLcSiWxVCdXp7pVMxa43ZaNdwsctW61O0PN3rLQNJf5uF21BQrwJp2qG2srmlMS4/mDqpmPwQaSRVVZX1Zt05s5EnjuxH6//80FiW6SCK21/pG6XBLYiyTGjCyVYPPN8hFf0yq+j18JyVhxqq/VvyOyRT7Ew+CAiieCPrLt+X4GXNhxJyMkT/dG462RrYi+gMjG3fw+rrWprFcZ0u6jdX3shYh0ZM+HU2rFD3Bh8KOT3C7gtuJQ3kd6CK1Kny42F7x3Q8dzy10lGynI+gltHrPl+zW4t0SIsUNFwDtmE0xjXiZeWbhWtk/5Z/b/XoAk+hBDw+vyKPgB8foEP9znhdA0s1/3o3z7FlT9bjSNNHbLH9Hh9ktU2rWT/KRfe2VVvdjEoSSSyaVmaUJewyxgi2gRiqT41diTxTTKmfLRL2AJwCm9uzL/tkNfjiRcl11J4Y0SUZ6kmzewCGOX46S7c9Kv1gecOuw0Omw12O87+awtsO93pCex3/qgcZDjs2HfKBQC46/eVuOqcYcjOcCA7ve+np9eHFVtqMWxoBr5/y4UYPjQTOVlpyM1KQ15WOnKz0nD8dBd+t/YQvnfLRbh4dJ6h733Wbz4CAIzIycC1540w9NqxfHy4GQBw3fnWKtdglsigIPjD3KpDT5UKfi+hea39FanWkQpG0jM3IJ4zJUO3i+a1XSKcL+x1ubVdIrwe+1rWNmiCD19ICOvzC/gggBg9KYcbpS0dTe1uvLenQXbfpnY3Hvv77qjn+3B/IyaOycOwIRkoHJox8O/QDBQOycCwIekYNjQDI3IyUTg0Aw67Dcs31+Anq/ZiZE4mHp15EW7/TAle/ugoxhQMwa2TRgMAGl09cNhtGJ6TKble8B/rnro2SwUfne5ezPm/zQCA/T/7PLIzHGH7/OBvu/DpyTa8Ne86ZKWHvx6qtqULW4614PbPlCDNMWga9nSVyN6B4JZHq384xhJc/tDPl/6niVwhWL5MyXtXVXUpaJxeXXKMXMJpzA0qzq/90EFh0AQf40cMxc4nP9cXdAgBv7+vqdTnFyH/9n2QDBuajlNtPXB7/fD4/Oj1+dHh7oWn149urw/dHp/k3+01Z5CZ1tcS0t7jRbu7F+09vWjv8aLHK/1atKfOpajMdhswPCcTTe1uAEBdazce/utO7D/lwksbjwIAriibjux0B2b+eiOGZKSh/Lzh+FvVSeRkpqFiwXRJRWK1vt1Od+/AY0+vbPDx+raTAID11U34/MTimOec/r/r4fUJtHV7ce+08foVdhAxqgJLdM6H3qcP/dIr/ZYqP/RT2tKjb3kGM80Ly8U51FbVsZImi/BtsgmnMo+1dt1Z/e9t0AQfDrsNBUMyVB0zOj9bl2t7evsCF78QqDvTjZYuD850etBy9udM19l/O71o6RrY5hcIBB7B+gMPALh20VrYbX0fcme6vPhbVV9l3eHuxQPLd+Ce68YF9m3r9oady+vz44k3d2PimHx8vXyc5LVujw8ZaXY47IkJWoKDsm5P9CYopRWV19e336bDzYYGHyu21OArV5eqapb1+QW6PL3IzUqH3y/Q3OHGqLysBJZSmUjDRnU5tyQBM3HXSYRoSY6hrUVy7TtWrwz0EM88H2pWtdVKyFbvwWWIXqZ4uwqjX31wGTTBh5ky0uwoTOsLfEaEdItE0uvzo6XTg8Z2N5o73Khr7cb+s3knTpcbq/c5AfT9B43UTL7hYBM2HGwKPH9zRx1Od3pw7oihGDdiKMaPGIpPT7bi9W0n8fq2k7h7ShnSz3ZVHHS2499+uwl3X12Kn94+EUDff7y3dtZhyvjhGFMQf2DW6Rlo+ej2hgcf3qCOdGu12YR7/I3dGJGTiRmXFCk+5q7fV2Dr8TP4+PHp+OX7B/D2znosnXsVbr5Y+TkSIZEtEomcwCxUIiovieCcjwiVlJHvVys9W0QVj+BQsGe0P8OwUSkKrxv7mvKJw5rOpaVUMi1l2vOGrPn31o/Bh0WlOewYlZcV85uwp9cPp6sHzR1u1LR04eSZbnxypBn1rT3wCwGnqyfQwlDX2o3XttREPNcTb+7GZWPyMX5EDpZsOAxPrx9/rDgRCD7+XHkCT769F2MKsvHx49MDxx1ubMfP/7EfD06/AJPPGab4PXYFtXZ0ybR89MgEJEqZEaxUO9tVBR9bj58BALy7qx5v7+wbjfS7dYdNDz4SycgZThNNMslYpNEuMtsofppbIFR2g+md/5TsSdZ6YvCR5DLS7CgtHILSwiG4oqyv4p930/mB14UQaHf3ou5MN/bVu3D8dCeONnfieHMnjjV3Sir9/haQUA+t2IELi3Lxx0+OA+gLYoQQgS6Gh1bsxN56FyqPnsaBp2cpLnu3JPgYaAURQuAX/9yPUbkDgZdFp0hISQlt+Qh+nOS/U+mEUPLBh5YZXeMJnLW0YpjRIiPf7RKyj5q3ouGPSUnCaVyTjGlq+AhPUJaboj9R1zcSg48UZ7PZkJeVjrzR6WFDfIUQaGp341RbDw40uHCkqRNHmzpxtLkDNae70Hu2xu//Vh7stt9twkVFeZhQnIu99X3dQaGJtbEEBxzBgchHh5rx8kfHJPuqbQUJ/uASQuDkmW6MzM1UNGJmsEvkh1a0uTGsLrQyjLaqbbzJgslKabwgP7268i4Prd0uan8H4QFRvENtlV8vyf57qMbgYxCz2WyBrp3LSwskr3l9frT39GLr8RYcbuzA4cYOHGpsD4zU2VPnkh21c8+yLZg4Jj/wU5KfFfE/bKRuF7kk2x6Vs8sG/8d94q09WL65BsOGpGPN925ETmYannx7D264cCTys9PR5fHhcyq6S1JdIoMCI2c4TeTslKHPw4fahud8DIboQ7fRITruG+k42QAoxnnN7jYx4h4ZhcEHyUp32FE4NAMzLy3GzEsHtvv9AjUtXTjQ0I7qhnZUO11nu3O6AADrqpuwrnogybVwaAYuLcnDZWPy+37G5mNMQTZsNpsk4Ahu+ZD7PxNrNEw0u0+2AegbDXTI2Y6Dznas2FqLFVtrA/tseeJmSTePFglPcDRIIru4YrUErD3gxPkjc1E2fEjiCqFCtMpGOtolUs6HTDO6bqVLbWr+P2nq4lBwjN7BhpahtqmKwQepYrfbMO7saJngeTfcvT7srXdhT10b9tS1YXedC4ec7Wjp9OCjQ8346FBzYN+RuZm4sqwA3UHdNMFdMMGPB86vrksn+IMreFRNl9cHpyu8ZaW1y6s6+NAzM95aEtnyEZxwKr3OpkPNuPeVbQCA44tujftaegSDUZv+o7bihLd8WHVosVXm/1Ezd4fWScZizjAa47kakvL332IN3XCa5/mwePjC4IN0kZnmwJVlw3Bl2cBolx6vD9UN7dhT3x+QtOHAqXY0tbvxwV6n5PiXPzqGYUMzMGV8Ic50hs9HoiTnI7jp+8P9jWhqd2Nkbqak1aTb49PU3BrresHe3lmHVbtO4bmvXI7crPTA9tMdbhx0dmDK+MKEzZ0SzO8X2FvvwoTRuYEh1IqOM6rlI+Q62060JO7CGimtqkLv2cAMp+F7J/I3H2+FE5xInkiyCadqTqDD36jcKUIDYqPzkqIlMacaBh+UMFnpDlxeWiDJJ+nx+rC7rg07as5gR01rYKr6utZuPLRiJ4C+ETyhlHS7hC7sd9fvK7DmezdKZlKNdB5vaMagAr4I2fn97+PFDUfw6MwJgdf/8+XNqHa24/7PnofHZ01Aov1u3WE8u/og7vhMCX591xWKj0vs2i6RK2wrCq4Awmc4HXgcumClbLeLRSuT0MX+4ok94kk4jUfCZjiNo5hKkmrVnoc5H0QaZaU7cPW4Qlw9rjCw7ePDzVh7oBFbjrVgb32b7OrAW4634IO9Dbj2vOGS1oRg7pCk1CNNnQCkE5h1RWhBkZvkLBZfjHb0lpAWnGpnOwCg8uhp1dfSYvG6wwCAt3bWqwo+DJtkLAkSTqNNjx4150Om28XidQGA+MsYXzeF8qP1GO2iKOdD4Xllj9WQwGH1rhI9Mfgg0113/ojAqrbtPV5sr2nFql312H/KFRjG++nJNvy/P1chzW7DlWXDcMOFI3DDhSMxsSQf9rNdGHJBi6fXH5hyHQC6ZfJJAG0TmmloLAmI9o3abImd5yPJWj6iVAbRWnH6n8vluFj5bfeV0frdLnr8iWqZZTXuobaxEk5lgl2ts+Ra+e8MYPBBFpOblY7PXjgSn71wJIC+D8MNB5uw7kAjNh5qxrHmTmw53oItx1vwq38dROHQDEw7fwRuvngUzhuZE3a+0G6Wbo9f9oNLy2iaWC0f0Y+V/2iwRByS0G6XoMtYvV0YKhJOw7pdwgONJHi7hjGv2yVGFk/Yy/K/V0XXirFtsP85MPggS7PZbLjxolG48aJRAIDali5sONiEjQeb8MmR02jp9OCdXfV4Z1f4RGhA+GJ6Xd7esIoCAHpijKbpH81z+diCQLKompaP0JYOn+QbsfLzqKH1tIlNOA1634m7DAD9W5TCcj6CHkdqLTI64VTLyJXgY+L9ncT13iLkUMnvKt/NFfMSMWp/NROdJYK0ePEFsFYPdhl8UFIpLRyCr15zDr56zTnw+vzYUdOKddWNWLPfiYPOjrD9+/Me+nV7fLIVRU+Mlo9HV36Kd3bV47HPT8B3bjwPQHjLR7T/7L0hNXqklg8rkLs/WkZBNLb34Nl/HcScqefgsrH5Z88d+TpW/LCMNjonOJAKXVhu4L0lPsjUU7xljCtHQs0Mpwbdy3i6IOVaSZKhtc8oysffEVlMusOOKeML8djnJ+Bfj3wWGx+9CT/+t0tQkj8wX8dft9VKjmnv6UW3J7zJItYMqv0tKy9uOBLYFlrh9IYMfwmuq0NH0wQHI4nK+dB6WrmPR6WxUpenFxsONsHT68fjf9+NFVtrcdvvNg2c24AWHz0prXwijXYxckZXreQmQjOlHPFFLrocomaukYSQ+f/BeT6ILK5s+BDcN2087ps2Hq4eL97f3YD39pzCx0dOB5JR39xRh3RHeLWsNOcjOFAIbc3ojZID4u0NafkIHadrIXKVpF8IOBSEMw8u34E1Bxpx73XjUd3QHva6kq4KveiTlKjs/KHBWbwVh1ksGh+FCS2mlmIraYXQOx6Kdb7BlCPE4INSUl5WOv7j6lL8x9Wl8PsFWro8WPDGbqw70CgZ/dJPy1Db0G+7ocFIMG9QYCKEMTkfmsmUR2mgsOZAIwDgL5UnMDI3M/w8fgu/bxnRp1cP6naJuLZL8PtVmJegpoAWE08jnqpv9Rr/eGIdFhbU6PzLiNaNF/EYrXPFWPwPid0ulPLsdhtG5GTi5a9fhW0/moGFd14W1vrxwvojeGjFDrR1hc+uGiz4qLCWD58/Yi5HcLdLr1+6X2j3jdnkcz7C93O6evC7tYdkFwIE5LuT5BLqou1vtuAShq1qG9TQFT7PR/jxRjC7qT2uloI4DlYe2AV3McUuQ/jfqPI/Utnp1VUcY61PBf2pCj4WLlyIq6++Grm5uRg1ahTuuOMOVFdXS/bp6enBvHnzMHz4cOTk5GD27NlwOp0RzkhkrIIhGbh7ShkOPfMFVC64Gd+58TyU5GfB0+vH2zvr8YXnP8LSTcfQ2uWJeS65lo/gIEMSqAS1tvT6hCRwsVoXjFz8FPxN/sN9TtS2dGHuH7bgV/86iHmvbldx7oGTJ3qtE73XdolWMYXVfYGcj6D3a7Egs59ktItBRYx3PRmjcjOiJRnHPjj8sZDbqLIMWi5vRaqCjw0bNmDevHmorKzE6tWr4fV6ccstt6CzszOwzyOPPIJVq1Zh5cqV2LBhA+rr63HnnXfqXnCieBXnZ+Gxz0/Ax49Pxx/vnYL87HTUtXbj6Xf3Ycov1uCRv+7E1uMtET9wwls+RMQF8DxBQYnH55cEHNG6a+Kh9axy77e/iOuqG/HNP23D9b9chwNnczq2HFexJkuc3+yMHi0Qtdsl6KWIo11kgherVQpmJJzqNfW46uvG6PYIH3Fj3t9bqo+MUZXz8f7770uev/LKKxg1ahSqqqpwww03oK2tDUuXLsXy5csxffp0AMCyZctw8cUXo7KyEtdcc41+JSfSic1mw2cvHIlNj92Et3bWY/nmGuw/5cKbO+rw5o46nDdyqGTffqHfZHv9fknLR/CroS0fPgt9I+5w9yInc+CjQC4W6u8m2nw0vsXf4h39YfQ8B1GHewY9VtLtkgx1SbxljCvnI55ulwQdE1/CqWx0M/BQw8nV/f1b+w8urpyPtrY2AEBhYd86HVVVVfB6vZgxY0ZgnwkTJqCsrAwVFRWy53C73XC5XJIfIjPkZqXja9ecg3/+1zS8Pe86fOWqUmSnOwJrxAB9Q0mPNPXNJxI6tLbXJ+126Q16HLo9eI6QRLV8KKkIPtjbgIlPfYDffHgoaKtczoc+ZZRON67+eONzKJS9GKkbIBkmGQsWb2uUUb8frd0usXZLdHdOrE4X9Z0yyUtz8OH3+/Hwww/juuuuw8SJEwEADQ0NyMjIQEFBgWTfoqIiNDQ0yJ5n4cKFyM/PD/yUlpZqLRKRLmw2Gy4vLcB/f3kStjxxM56+/VKMOjtyo8frx83/uwH3LNuCw03SSc16/UIypDZ4VI2020Wa89EbzyIxcfrhG7sBAM99eDCwTT7n4+yDOGtOaUtAaBN37OONX+J84HrhM5wG5e2EzvNx9rVkyPkIpiXI1OtthZ5GzQyniq+hslsjrpaPeFs2AgGstm4xq/+1aQ4+5s2bhz179mDFihVxFWDBggVoa2sL/NTW1sY+iMgguVnp+Fr5OGx5Ygb+/p1yzLi4CDYbsK66CTtqWiX7en3+sNyOfpJuF79f8lxu6K+ZIs3zofe5tTT4GB58BD+O8q04fKht+PGWrw000u1taQhGB8qQoLwpnf/epDknKfoHoZCmeT4eeOABvPvuu9i4cSPGjh0b2F5cXAyPx4PW1lZJ64fT6URxcbHsuTIzM5GZGT4fAJHVTD6nEP83txDHmjvxx0+O4/VttegKmpysrrUbpzsGhp16e+W7Xby9fkklGilJ1Qiyw2FlWz7UfVBGrAyEgn3QN5Kof7XiWGWLRI/RLnKr0sq9FnZ/5Ob5kFmrwwr0XNslHmqurccIENlujwi5O/FeS8sxgb8XjXkiVo9tVLV8CCHwwAMP4M0338TatWsxfvx4yeuTJ09Geno61qxZE9hWXV2NmpoalJeX61NiIpONHzEUP/nipahYcDP+a/r5ge07alox5/82B557I+R8eP3Sbhd3jKndjRZ1no84P9CUtnwoWajNENHyOoIeJ3PCadQhw0qON+GNhV5RUxEUHKN7y0eMeUYGE1UtH/PmzcPy5cvx9ttvIzc3N5DHkZ+fj+zsbOTn5+O+++7D/PnzUVhYiLy8PDz44IMoLy/nSBdKOfnZ6Zh/y0V45HMX4u2d9Viy/giqnQNTip9o6cLx5k6MGzFU0rUSOhmZJ6jlQ+3ibdHo+eGmX7fLwONoH+yRAhNVfd46FFnum6jc+UPL2z8HjJAJtiw4l9oALcGHhsvIJcYaEcPEnOFUx4RTbfkz4YGg1gDW6sGNquBjyZIlAIAbb7xRsn3ZsmX4xje+AQB47rnnYLfbMXv2bLjdbsycORMvvPCCLoUlsiKbzYY7rhiD2z9TgnXVjXj877vR2O7G0aZOTP/f9bjt8hKcNzInsL9fSLtkPCZ2uyjtYlGbnxFp1IXSheUiBTtGLwYcfZKxAWELy8kdb/nqQFsZtY1akmtdUxNYar2X6pI3dR/torELJRWpCj6U/MKzsrKwePFiLF68WHOhiJKRzWbD9AlF2PJEEapOtOC3aw9jfXUT3t5ZH7Zv8Cq6wYmpejbzav2GLTfzaGjlqlW0eT6CG30id7sYnXAapZk8Ss6H7FBbI77ZxxngJEuFGN7tok/Bw84bx/2UzSmJ8brcvnLDtVMB13YhSoDJ5xTilXum4N0Hp+ELlxWHJT9uP9EaeOz2DtT2Vhj4Ip+Ip//Z1Ywe6Wd0y0e0YElE2C94X7nKxgK/Yl3p1aJjRHJnzBlOde12CXqssIR6BqtWH03DVW2JEmjimHy8MGcyDjd24K0ddfjdusMAEPgXkLZ86NXCAGj/ME/sUFtl54yY86GiHPqs7RL5a6eyYCk42LJmZRDvaBfd5vlQk8+g8Zpq8yf0/o1pW+XY+n9DWrDlg8gA54/KwfdnXoRNj92Eu6eUSlbVPRo0WVmiZjtVRWEeiBaSyjjafhHug9FDDaM1eUcbahut2yVWTBRPBaNlhtNUqNz0m+hM/vcY77mUnkfPdXas/ptk8EFkoLHDhmDhnZOw/tGbcMslRQCAM13ewOvuXp9uFYDmnA8dEk4jfXAqnWQpcsKpeR+p0WZkDZ9kLLyTRfk3XfMkz7VDu8A0dGsoSjjV945oG8oc9Fi/opiOwQeRCcYUZOP3X78KlQtuxj3XjQtsP9rUif94qQJdnt64r6G920XmXAlo+Qi9TvA1QleJDeyjSymUi9ZSEy3nI55kQaPjq3jzDIKPiW9hucg5NdGuqf16sbfFdZkYv/tY14/7PVo8UmHwQWSi4vwsPHXbpdj3s5m4/7PnYUiGA1uPn8FP3tkrmS3VSHLfCBPRGxT64epX8MFr/NouwdcOfS1yq0b/U3+U4yNeU00BdRDvcOCETZyVgC62WO8vLMBUERDpQcg8FpF2SHIMPogsYEhGGh6fNQE/u71vkcbXt53EtP9eh6ff3Qenq0fTObV3u8htS0TLh/Scviiv9TO8VUBy7cgXj9TtoiWfItnyLswobrRWqKjHqWyJiuetyf3tGDnvi9XnlWHwQWQhX548Fi99bTIuG5OPbq8PSzcdw/X/vQ4/fHM3alu6jClEAke7KE3gjDR6RL6pWn5fvUe7hH8rHngceXRO/GVQI96WCzPrq/Auj8iFSdh9jZLXk9Dryp1fJmvZ6gGFGgw+iCxm5qXFeOeB6/DKPVfj6nHD4PH5sXxzDW781XrMf30nDjd2xD4J9M75UHeOSKMuok2vrqS/O+q6Mwq3qxGtyTtaq0bgm66G8ug1t4SWY7RcOvgYpfGe7PTqcYw00XJvlRwTbW4XVdcK/CtkX08EqzegcZ4PIguy2Wy48aJRuPGiUdh89DR+t+4wPjrUjDe21+HNHXX4wsTR+O5N5+HSknzdrx1tno94P8+k815IXwtu7YjY8hHhnPYErZgityrtwHUHHocmyA58aQ3vSop1D/WeVVPNMdqCF/U5H6GBm9o1jcLvUbzvXPtZVF0xRvAjlz8T+vuxeEyhGIMPIoubeu5wTD13OHbVtuJ36w5j9T4n/rH7FP6x+xSuv2AEvnn9ubjhghFhH+Baq2O5D8X+ijbuSdAkXRXyeRJyrwW2y1w/kR/Gkq4Vf+hrweWVvtb/PBlGu0h7XbR02+hUjLCWJeX7Kr5GrITTWN0sKi5sZCuH7PUtHqUw+CBKEpeXFuDlr1+FAw0uvLDuCN79tB4fHWrGR4eaccGoHNw3bTzuuGIMstIdAKQf3n6/gN2uLByJ1vIRaQisUtGG2gYHFmpyTBI5AkbyrTNsbomgMoQtLCck/wIDlUHsScZUFjJSoRQfEl8lGe/tF6IvPye8nldR0Seo2yWuVijZbpfo55YrX7QAVkurkVUw54MoyUwozsPzd1+BDY/ehHuvG4+hGQ4cauzA42/sxrWL1uLZf1WjsV06QibemVP7K9dI3SGhIk4yFvw4rOVD/rF0n+gf2HqLWlkFd7uEBh+yFUd89y7Rx5pFU4dJrBYKjWLlnRh5d+Md9WT1vwUGH0RJqrRwCJ687RJU/PBm/OjWizGmIBstnR48v/Ywrlu0Fp7eoDVj4mxJ6K9b4w5iouV8KBjtokcyrBrR5rCQtHxESDhVMndJ2DVVvJ9oSbvKzxH0WP3hutXIqrpddLiokjPEE+RIdg0MtVXfyhStZcrqXSvRMPggSnJ5Wen45vXnYsOjN2Lxf16JK8sK4A1ZHnfppmNojDFfSH+FH22ej3hzPiSVcdi3zNjdLnLfBhP5DS84zyPSiBYg8qgIuYnIYpVWzbvR45t5tNYoZcfH+Q09cF+0B1LaVo2N3Yqm99+WiPBYrkzxXtnqgQlzPohSRJrDjlsnjcatk0ZjT10blm+pwfLNNQCA//mgGv/7r2p89sKR+PLkUsy4ZBQy0xyS43v9fjjsjggJp+LsPto+0QJ901FyPoJbOyIPtVW2TS9Rv3VGKUOg20Vmm4KLKiaXA6CWlm/j0uPVHyM5PsJ5orZ8GNQCED5tvvIL6RVIROu6s3h8ERWDD6IUNHFMPn7xpcuwYNYE/OPTU1hZdRJVJ85gXXUT1lU3IT87HbMmFqPDPbCGTK9PIDMtel6F1pYPvwActtC8DhG2T7/IuSVyXUJKv/VqGNIZpUsi2ugcv0z00f8wZsKpxgpOroyKzqHhmGQVayr4sG063xzJrytGF6JeQZ1VMfggSmG5Wem4a0oZ7ppShqNNHfj79pN4Y3sdTrX1YMXWWsm+H+534uaLi2TPE2/Lh88v4LDbon7LVjLaJZ6cj/5RFVqpWdW2/5mS4cPxCG/50P0Sqsug+viIcaaKICzOMkS6pF4Tn8Xze4nWetb3N5mco10YfBANEueOzMGjMydg/ucuwuajp7Hq01N4f88pnOnyAgAeWrET6Q4bhg3JCDu2v25VOtoldAbLQM5IlD53RfN8yLbKyO8bVjFHKmwU0Yc5yj8Ofq6l20VrBRd6PS3n0Nbtok/Vb8SqtqqH2uoezAW3vMj8LUd4rOlKFm/6YPBBNMg47DZce/4IXHv+CPz8jonYU9eGt3bWYd2BRhw/3YXG9vDVdAPzfMTR8hF8HkCm5UPIPw4m94Gq9JuzXwg4VH5LjDYvSbDwobYirAgi5N9I1NzhsApMU8JolPOpPF6LuNejgYrALsLjiOcNfV1r0BNHQmzUAFhbcSyBwQfRIOaw23B5aQEuLy3AU7ddiqNNHVh7oBE//8d+yX6nOzwQQkTtdomWDyI3OVlYwqmiobZy51HWRaPpW73kePkAA5CZXj3wr/pmhURMrqX0JKZ02wjpv1rKkqgRT7qt7SLzHhN/r60dmjD4IKKAc0fm4NyROfD5BRa+dyCw/fsrd+EX/9yPXp8/4rHBgUnYOihyLR+ahtrKbItQnvCKQ8M37Cg1hJLARK41J3bCqXaaul3iPT5KV5rWcvQ9j3LvNXe7CNnHkQqhd4CgtuUlbFuUvKNkw+CDiMJ8+4ZzcWlJPt7cUYfDTR3YX+9CS6cn4v7fX7kLYwqyA8/9Qj4hU8hUxqH7AOHrqPSLNgw4fHvsY2ORBhiRzxfaUhN4KhNsxex2UfONX4/3GGfwEGsESSKEXUenC8fq1lDVKhVlmLYRrB6YMPggojA2mw3TLhiBaReMAAC4e33YW+/C9hNn8NqWGrh7/Th5pjuw/9+qTkqO9/kF6loHXv9/f67CiJxMybYdNWfwl8oTyEp3IDvdgQbXQK5JtbMduVlpcNhtOH9UTtB6NcqbPvRIhIw2x0K0fJBoCaexiqWqaT8sR8GMWi7CY6WH69Ltou9+mgqh8nSxJjkLPJb8DYbsb/GulWgYfBBRTJlpDlxZNgxXlg3DN68/FwDQ4/XhdKcHVSfO4ERzJ060dOFoUwe217TCHjKnx7YTZ8LO2b8onpyn390XeHxlWQHe+O51ANRNMhZt1VylJF1BoavaRtiv77Xwlh7ZPBD5i6oon+ZDB44xoeUiVjmA6O9Fa2AZK+dCj3lTZK+l9BjJ4/h+G1YPSxh8EJEmWekOjCnIlnS39PP5BU53uOETAoecHWjt9qKty4PWLi9qWrrg6vEiK92BHq8P3V4/erw+uL0+7DrZhrysNAwbmoFen0B9Wze217Tiv98/gOx0B5wyU8S/vq0W+dnpsNnO5lPY+gb69g8h7vfallqkO2wQoi8Q6f8XCH7e96EvRF9FFNy6E61yDEs4PftUbviwvi0fIc/j7nbRcHyU8ig7Xv6+RDtXPN0hauif86E+0FPa9ZdsGHwQke4cdhtG5WUBAEbnhwcnSs1e8gmqTpzBkvVHIu7z7OqDis4V3JqiRZfHF7IlSquIXKAh0xUjR13OR/zf1KWn0NI6FLk8RlGTiTHwKHq3h9w+WruCZIfNyp1Lx5FHZv0ulGLwQUSW9cyXJuJv207C4/Oj1y/g8wn4hEB+djrWVTfi4tF5fTtKWiz6Nwlsr2mFzy9w9bhhsMEGu71vAjSbrS+vxX62tcR+ttnEhrPbbH3bbDagvrUHGw42YW+9C99YtiVQtrbugZaVE6c7JeX+1z4njjR1oj4ox2XVrnrsOtkqWW0YAO584WMIINDacqixXfH90aMFINo3a2XHx9dtI5cf07c98tn0qFeVjJwK72ozvkKPOUInSTH4ICLLmlCchx/92yWyr/04wvZEuPl/1+NIUyfWVzfJvt4Z0ipyqq0Hp9qkXUT1bT2obwvvNtpe06qpTHWt3ejo6ZVs8/j8qG3pCjwPDsRCK/n+iszdO1D2E6e7AvuIkIBObmi0zWbTr9tG1cHaWiTUli++nI/oLSuyDR86XVuP4xONwQcRUQx/vHcKKo+2hG0vHZaNlk4Pujw+2Gw4u35NX0XdP8W83W4LJODa0Neqkp3ugN1mO1vx97e2DLS65Galo8vjQ3a6A0IIZGc4cOBUO37w908D175u0dqw8hxt6sT1v1yn+X1+80/bFO+76tNT+OLlJZqvpSc98ifktkVbz8co0QKWZG4IYfBBRBTD2GFD8OXJQ0wtw8SSfPxj9ylsONiEDIc9MFuZ1+cPVEJZ6XYAA2vr9C+k1z+xmc0WtOpO0Guunl447DbkZqWdDZBsgUAJ/d1UZ587zw6JfvrdffhzxXFJN1KDqwdfXvIJgMgBwZ0vfIwe78Axd/++Ena7DQ1t3ZL9jjV34rbfbpLpTgO6PdIWHyuSC3RizXgbbytSpHNZEYMPIqIkYLfb8Md7p5hdDBxr7sSMZzegqd2NppB1gDy9ftlh1cFCu5l217XJ7ufu9Ud8LVTFkdP42tLNMfcLztPZW9+GOf9XKXn9WJM0d6e/G6pf7ZkufOWlCgDS4KIkPwvfu+UiZKTZA9uag+6N1+eH09WD0x0DE/W5e/1oCOmG63APBFWuHi9OtXWjMyjQauv2Slpj6lq7kZ3hiPh+rcwmLJbB4nK5kJ+fj7a2NuTl5ZldHCIiCrG3vg01IRVzVoYDbm/k6feHZjrQHZQbk5OZhk6PL6hVBshIs8MGGzLS7Ojsr4gDrS42yb7v7WnA8s01+r2pFJPhsKNgSHrE188ZPgQr779W12uqqb/Z8kFERKpcWpKPS0vyTS3DtPNH4N8mjUZTuzviBHJBnUwD286OZIp0jMNuQ5rdhl6/gF8AjrO5OHa7Dd6gtY36z7295gz+urUW3d7QodgD0+6n2QfK0b8GUrpDfpUfr6/v9QzHQCuK5+x1M9LsgOh73l9OoK+LxROy7pLH55ddobpfTpa51T9bPoiIiJKczy/Q3OFGusOOHq8PrSGT7IXKSLPj/FE5upaBLR9ERESDiMNuQ9HZif0AoERm5mErscfehYiIiEg/DD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQqoOPjRs34rbbbkNJSQlsNhveeustyetCCDz55JMYPXo0srOzMWPGDBw6dEiv8hIREVGSUx18dHZ24vLLL8fixYtlX//lL3+J559/Hi+++CI2b96MoUOHYubMmejp6Ym7sERERJT80tQeMGvWLMyaNUv2NSEEfv3rX+NHP/oRbr/9dgDAn/70JxQVFeGtt97CXXfdFV9piYiIKOnpmvNx7NgxNDQ0YMaMGYFt+fn5mDp1KioqKmSPcbvdcLlckh8iIiJKXboGHw0NDQCAoqIiyfaioqLAa6EWLlyI/Pz8wE9paameRSIiIiKLMX20y4IFC9DW1hb4qa2tNbtIRERElEC6Bh/FxcUAAKfTKdnudDoDr4XKzMxEXl6e5IeIiIhSl67Bx/jx41FcXIw1a9YEtrlcLmzevBnl5eV6XoqIiIiSlOrRLh0dHTh8+HDg+bFjx7Bz504UFhairKwMDz/8MH7+85/jggsuwPjx4/HjH/8YJSUluOOOO/QsNxERESUp1cHHtm3bcNNNNwWez58/HwAwd+5cvPLKK/jBD36Azs5OfPvb30ZrayumTZuG999/H1lZWfqVmoiIiJKWTQghzC5EMJfLhfz8fLS1tTH/g4iIKEmoqb9NH+1CREREgwuDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIMPIiIiMhSDDyIiIjIUgw8iIiIyFIOPCPZ8/Cb+tvR7ZheDiIgo5aSZXQCruuzDOwEAG94Zhxu++KDJpSEiIkodbPmIYfv+tWYXgYiIKKUw+JDh9/WaXQQiIqKUlbDgY/HixRg3bhyysrIwdepUbNmyJVGX0p2ruc7sIhAREaWshAQff/3rXzF//nw89dRT2L59Oy6//HLMnDkTjY2Nibic7s44TwQed3k6TSwJERFR6klI8PHss8/iW9/6Fu655x5ccsklePHFFzFkyBD84Q9/SMTldNfaMtDycabnjKJj3J0u+LweyTbh9wMAGg7vxL5P3tavgAoJvx9v/OFR7P7o7zH3IyIiMoruo108Hg+qqqqwYMGCwDa73Y4ZM2agoqIibH+32w232x147nK59C4SAKDx+F784qWvKtq3zt0E5Pc9fr97N7wLroi6f2dvN17LqEaWz4YJ7lzkIB3dohf7M11wOwRcmX37fe71QoxOK8CncGKELxMO2AAAY9IK4YeAx9+LVn8njtjbUOrPgQ8CQx1Z6PD3wOEHztjc8Nn8GCNy4UQnzrEVoEd44RJuXJhehE5fD5r9HQAAPwSOpLejJscH1AI3/D0P+bZsDHPkoMXXjm3pzSj1ZKHT7sNPJz6AL9/7K203loiISCXdg4/m5mb4fD4UFRVJthcVFeHAgQNh+y9cuBA//elP9S5GmNbmk/hN1k5lO2cNPNyT78YeKDuuEwIfZ0cOnlYPawHQEth7wOmwfasD+4XbhR4AQBW6Ats2IXrQtnGYC4ALgDOwrWFo3/Er9qzAl8Hgg4iIjGH6PB8LFizA/PnzA89dLhdKS0t1v05h0Tj80Het4v09Pg9c3g6MyCpUtH9ORg5GF5SirasF3Z4uCAh0eDqQk5EDG2zISMtEmj0NXZ5OdHm7UJxXAiH88Pv9aOtpRZo9DZlpWfD6PDjeXovLRk6Eze+DTQCw2ZGemY1OdzvcvT3ISsvGsNyRaO1ohrvXDYfdgU5PJ9JsDjjsDjjsaSgaNhZdPe1obndidEEpYLOhqa0ePb09yE4fglrXSYzJLcGovNH44v/7oca7SkREpJ7uwceIESPgcDjgdDol251OJ4qLi8P2z8zMRGZmpt7FCC9X6UV45mcfJ/w6REREFJ3uCacZGRmYPHky1qxZE9jm9/uxZs0alJeX6305IiIiSjIJ6XaZP38+5s6di6uuugpTpkzBr3/9a3R2duKee+5JxOWIiIgoiSQk+PjKV76CpqYmPPnkk2hoaMBnPvMZvP/++2FJqERERDT42IQQwuxCBHO5XMjPz0dbWxvy8vLMLg4REREpoKb+5touREREZCgGH0RERGQoBh9ERERkKAYfREREZCgGH0RERGQoBh9ERERkKAYfREREZCgGH0RERGQoBh9ERERkqIRMrx6P/glXXS6XySUhIiIipfrrbSUTp1su+GhvbwcAlJaWmlwSIiIiUqu9vR35+flR97Hc2i5+vx/19fXIzc2FzWbT9dwulwulpaWora3lujEJxPtsDN5n4/BeG4P32RiJus9CCLS3t6OkpAR2e/SsDsu1fNjtdowdOzah18jLy+MftgF4n43B+2wc3mtj8D4bIxH3OVaLRz8mnBIREZGhGHwQERGRoQZV8JGZmYmnnnoKmZmZZhclpfE+G4P32Ti818bgfTaGFe6z5RJOiYiIKLUNqpYPIiIiMh+DDyIiIjIUgw8iIiIyFIMPIiIiMtSgCT4WL16McePGISsrC1OnTsWWLVvMLlJSWbhwIa6++mrk5uZi1KhRuOOOO1BdXS3Zp6enB/PmzcPw4cORk5OD2bNnw+l0SvapqanBrbfeiiFDhmDUqFF49NFH0dvba+RbSSqLFi2CzWbDww8/HNjG+6yfuro6fPWrX8Xw4cORnZ2Nyy67DNu2bQu8LoTAk08+idGjRyM7OxszZszAoUOHJOdoaWnBnDlzkJeXh4KCAtx3333o6Ogw+q1Yls/nw49//GOMHz8e2dnZOO+88/D0009L1v/gfVZv48aNuO2221BSUgKbzYa33npL8rpe9/TTTz/F9ddfj6ysLJSWluKXv/ylPm9ADAIrVqwQGRkZ4g9/+IPYu3ev+Na3viUKCgqE0+k0u2hJY+bMmWLZsmViz549YufOneILX/iCKCsrEx0dHYF97r//flFaWirWrFkjtm3bJq655hpx7bXXBl7v7e0VEydOFDNmzBA7duwQ//znP8WIESPEggULzHhLlrdlyxYxbtw4MWnSJPHQQw8FtvM+66OlpUWcc8454hvf+IbYvHmzOHr0qPjggw/E4cOHA/ssWrRI5Ofni7feekvs2rVLfPGLXxTjx48X3d3dgX0+//nPi8svv1xUVlaKjz76SJx//vni7rvvNuMtWdIzzzwjhg8fLt59911x7NgxsXLlSpGTkyN+85vfBPbhfVbvn//8p3jiiSfEG2+8IQCIN998U/K6Hve0ra1NFBUViTlz5og9e/aI1157TWRnZ4uXXnop7vIPiuBjypQpYt68eYHnPp9PlJSUiIULF5pYquTW2NgoAIgNGzYIIYRobW0V6enpYuXKlYF99u/fLwCIiooKIUTffxa73S4aGhoC+yxZskTk5eUJt9tt7BuwuPb2dnHBBReI1atXi89+9rOB4IP3WT+PPfaYmDZtWsTX/X6/KC4uFv/zP/8T2Nba2ioyMzPFa6+9JoQQYt++fQKA2Lp1a2Cf9957T9hsNlFXV5e4wieRW2+9Vdx7772SbXfeeaeYM2eOEIL3WQ+hwYde9/SFF14Qw4YNk3xuPPbYY+Kiiy6Ku8wp3+3i8XhQVVWFGTNmBLbZ7XbMmDEDFRUVJpYsubW1tQEACgsLAQBVVVXwer2S+zxhwgSUlZUF7nNFRQUuu+wyFBUVBfaZOXMmXC4X9u7da2DprW/evHm49dZbJfcT4H3W0zvvvIOrrroK//7v/45Ro0bhiiuuwMsvvxx4/dixY2hoaJDc6/z8fEydOlVyrwsKCnDVVVcF9pkxYwbsdjs2b95s3JuxsGuvvRZr1qzBwYMHAQC7du3Cpk2bMGvWLAC8z4mg1z2tqKjADTfcgIyMjMA+M2fORHV1Nc6cORNXGS23sJzempub4fP5JB/EAFBUVIQDBw6YVKrk5vf78fDDD+O6667DxIkTAQANDQ3IyMhAQUGBZN+ioiI0NDQE9pH7PfS/Rn1WrFiB7du3Y+vWrWGv8T7r5+jRo1iyZAnmz5+PH/7wh9i6dSv+67/+CxkZGZg7d27gXsndy+B7PWrUKMnraWlpKCws5L0+6/HHH4fL5cKECRPgcDjg8/nwzDPPYM6cOQDA+5wAet3ThoYGjB8/Puwc/a8NGzZMcxlTPvgg/c2bNw979uzBpk2bzC5KyqmtrcVDDz2E1atXIysry+zipDS/34+rrroKv/jFLwAAV1xxBfbs2YMXX3wRc+fONbl0qeP111/Hq6++iuXLl+PSSy/Fzp078fDDD6OkpIT3eRBL+W6XESNGwOFwhI0GcDqdKC4uNqlUyeuBBx7Au+++i3Xr1mHs2LGB7cXFxfB4PGhtbZXsH3yfi4uLZX8P/a9RX7dKY2MjrrzySqSlpSEtLQ0bNmzA888/j7S0NBQVFfE+62T06NG45JJLJNsuvvhi1NTUABi4V9E+O4qLi9HY2Ch5vbe3Fy0tLbzXZz366KN4/PHHcdddd+Gyyy7D1772NTzyyCNYuHAhAN7nRNDrnibysyTlg4+MjAxMnjwZa9asCWzz+/1Ys2YNysvLTSxZchFC4IEHHsCbb76JtWvXhjXFTZ48Genp6ZL7XF1djZqamsB9Li8vx+7duyV/8KtXr0ZeXl5YJTBY3Xzzzdi9ezd27twZ+LnqqqswZ86cwGPeZ31cd911YcPFDx48iHPOOQcAMH78eBQXF0vutcvlwubNmyX3urW1FVVVVYF91q5dC7/fj6lTpxrwLqyvq6sLdru0qnE4HPD7/QB4nxNBr3taXl6OjRs3wuv1BvZZvXo1Lrroori6XAAMnqG2mZmZ4pVXXhH79u0T3/72t0VBQYFkNABF953vfEfk5+eL9evXi1OnTgV+urq6Avvcf//9oqysTKxdu1Zs27ZNlJeXi/Ly8sDr/UNAb7nlFrFz507x/vvvi5EjR3IIaAzBo12E4H3Wy5YtW0RaWpp45plnxKFDh8Srr74qhgwZIv7yl78E9lm0aJEoKCgQb7/9tvj000/F7bffLjtc8YorrhCbN28WmzZtEhdccMGgHgIaau7cuWLMmDGBobZvvPGGGDFihPjBD34Q2If3Wb329naxY8cOsWPHDgFAPPvss2LHjh3ixIkTQgh97mlra6soKioSX/va18SePXvEihUrxJAhQzjUVo3f/va3oqysTGRkZIgpU6aIyspKs4uUVADI/ixbtiywT3d3t/jud78rhg0bJoYMGSK+9KUviVOnTknOc/z4cTFr1iyRnZ0tRowYIb73ve8Jr9dr8LtJLqHBB++zflatWiUmTpwoMjMzxYQJE8Tvf/97yet+v1/8+Mc/FkVFRSIzM1PcfPPNorq6WrLP6dOnxd133y1ycnJEXl6euOeee0R7e7uRb8PSXC6XeOihh0RZWZnIysoS5557rnjiiSckwzd5n9Vbt26d7Gfy3LlzhRD63dNdu3aJadOmiczMTDFmzBixaNEiXcpvEyJomjkiIiKiBEv5nA8iIiKyFgYfREREZCgGH0RERGQoBh9ERERkKAYfREREZCgGH0RERGQoBh9ERERkKAYfREREZCgGH0RERGQoBh9ERERkKAYfREREZCgGH0RERGSo/w9xdPQRKs4T3AAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -503,7 +502,36 @@
     "        theta_2 = theta.detach().clone()\n",
     "\n",
     "plt.plot((target_dist.flatten(start_dim=1)[0].data.numpy()),color='red',label='target')\n",
-    "plt.plot((pred_batch.flatten(start_dim=1)[0].data.numpy()),color='green',label='pred')\n",
+    "plt.plot((pred_batch.flatten(start_dim=1)[0].data.numpy()),color='green',label='pred')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7bf9e1cbecf0>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "plt.plot(losses)"
    ]
   },
@@ -516,12 +544,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 6 Axes>"
       ]
@@ -552,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,28 +608,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gym/envs/registration.py:307: DeprecationWarning: The package name gym_minigrid has been deprecated in favor of minigrid. Please uninstall gym_minigrid and install minigrid with `pip install minigrid`. Future releases will be maintained under the new package name minigrid.\n",
-      "  fn()\n",
-      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
-      "[Powered by Stella]\n",
-      "/home/don/git/DeepReinforcementLearningInAction/venv/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.get_action_meanings to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.get_action_meanings` for environment variables or `env.get_wrapper_attr('get_action_meanings')` that will search the reminding wrappers.\u001b[0m\n",
-      "  logger.warn(\n"
+      "Collecting gymnasium==0.29.1 (from gymnasium[accept-rom-license,atari]==0.29.1)\n",
+      "  Using cached gymnasium-0.29.1-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium==0.29.1->gymnasium[accept-rom-license,atari]==0.29.1) (1.26.4)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium==0.29.1->gymnasium[accept-rom-license,atari]==0.29.1) (3.1.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/codespace/.local/lib/python3.12/site-packages (from gymnasium==0.29.1->gymnasium[accept-rom-license,atari]==0.29.1) (4.12.2)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium==0.29.1->gymnasium[accept-rom-license,atari]==0.29.1) (0.0.4)\n",
+      "Requirement already satisfied: autorom~=0.4.2 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from autorom[accept-rom-license]~=0.4.2; extra == \"accept-rom-license\"->gymnasium[accept-rom-license,atari]==0.29.1) (0.4.2)\n",
+      "Requirement already satisfied: shimmy<1.0,>=0.1.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from shimmy[atari]<1.0,>=0.1.0; extra == \"atari\"->gymnasium[accept-rom-license,atari]==0.29.1) (0.1.0)\n",
+      "Requirement already satisfied: click in /usr/local/python/3.12.1/lib/python3.12/site-packages (from autorom~=0.4.2->autorom[accept-rom-license]~=0.4.2; extra == \"accept-rom-license\"->gymnasium[accept-rom-license,atari]==0.29.1) (8.1.8)\n",
+      "Requirement already satisfied: requests in /home/codespace/.local/lib/python3.12/site-packages (from autorom~=0.4.2->autorom[accept-rom-license]~=0.4.2; extra == \"accept-rom-license\"->gymnasium[accept-rom-license,atari]==0.29.1) (2.32.3)\n",
+      "Requirement already satisfied: tqdm in /usr/local/python/3.12.1/lib/python3.12/site-packages (from autorom~=0.4.2->autorom[accept-rom-license]~=0.4.2; extra == \"accept-rom-license\"->gymnasium[accept-rom-license,atari]==0.29.1) (4.67.1)\n",
+      "Requirement already satisfied: AutoROM.accept-rom-license in /usr/local/python/3.12.1/lib/python3.12/site-packages (from autorom[accept-rom-license]~=0.4.2; extra == \"accept-rom-license\"->gymnasium[accept-rom-license,atari]==0.29.1) (0.6.1)\n",
+      "INFO: pip is looking at multiple versions of shimmy[atari] to determine which version is compatible with other requirements. This could take a while.\n",
+      "Collecting shimmy[atari]<1.0,>=0.1.0 (from gymnasium[accept-rom-license,atari]==0.29.1)\n",
+      "  Using cached Shimmy-0.2.1-py3-none-any.whl.metadata (2.3 kB)\n",
+      "  Using cached Shimmy-0.2.0-py3-none-any.whl.metadata (5.2 kB)\n",
+      "\u001b[31mERROR: Cannot install shimmy[atari]==0.1.0, shimmy[atari]==0.2.0 and shimmy[atari]==0.2.1 because these package versions have conflicting dependencies.\u001b[0m\u001b[31m\n",
+      "\u001b[0m\n",
+      "The conflict is caused by:\n",
+      "    shimmy[atari] 0.1.0 depends on ale-py~=0.8.0; extra == \"atari\"\n",
+      "    shimmy[atari] 0.2.1 depends on ale-py~=0.8.1; extra == \"atari\"\n",
+      "    shimmy[atari] 0.2.0 depends on ale-py~=0.8.0; extra == \"atari\"\n",
+      "\n",
+      "To fix this you could try to:\n",
+      "1. loosen the range of package versions you've specified\n",
+      "2. remove package versions to allow pip to attempt to solve the dependency conflict\n",
+      "\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[31mERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\u001b[0m\u001b[31m\n",
+      "Requirement already satisfied: shimmy==0.1.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (0.1.0)\n",
+      "Requirement already satisfied: numpy>=1.18.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from shimmy==0.1.0) (1.26.4)\n",
+      "Requirement already satisfied: gymnasium>=0.26.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from shimmy==0.1.0) (1.1.1)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium>=0.26.0->shimmy==0.1.0) (3.1.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/codespace/.local/lib/python3.12/site-packages (from gymnasium>=0.26.0->shimmy==0.1.0) (4.12.2)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium>=0.26.0->shimmy==0.1.0) (0.0.4)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement ale_py==0.8.0 (from versions: 0.9.0, 0.9.1, 0.10.0, 0.10.1, 0.10.2)\u001b[0m\u001b[31m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[31mERROR: No matching distribution found for ale_py==0.8.0\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
      ]
     }
    ],
    "source": [
+    "!pip install gymnasium[atari,accept-rom-license]==0.29.1\n",
+    "!pip install shimmy==0.1.0\n",
+    "!pip install ale_py==0.8.0\n",
+    "import ale_py\n",
     "import gymnasium as gym\n",
+    "import shimmy\n",
     "from collections import deque\n",
     "env = gym.make('ALE/Freeway-ram-v5')\n",
     "aspace = 3\n",
-    "env.env.get_action_meanings()\n",
+    "env.env.unwrapped.get_action_meanings()\n",
     "\n",
     "vmin,vmax = -10,10\n",
     "replay_size = 200\n",
@@ -640,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,8 +749,7 @@
     "        state2_batch = torch.stack([ex[3] for ex in exps],dim=1).squeeze()\n",
     "        pred_batch = dist_dqn(state_batch.detach(),theta,aspace=aspace)\n",
     "        pred2_batch = dist_dqn(state2_batch.detach(),theta_2,aspace=aspace)\n",
-    "        target_dist = get_target_dist(pred2_batch,action_batch,reward_batch, \\\n",
-    "                                     support, lim=(vmin,vmax),gamma=gamma)\n",
+    "        target_dist = get_target_dist(pred2_batch,action_batch,reward_batch,support, lim=(vmin,vmax),gamma=gamma)\n",
     "        loss = lossfn(pred_batch,target_dist.detach())\n",
     "        losses.append(loss.item())\n",
     "        loss.backward()\n",
@@ -700,22 +772,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f41fca5f850>]"
+       "[<matplotlib.lines.Line2D at 0x7bf9e2462a20>]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -752,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Errata.md b/Errata.md
index e5d0e02..37b1089 100644
--- a/Errata.md
+++ b/Errata.md
@@ -35,7 +35,14 @@ First line of code should be: from IPython.display import clear_output
 ## Chapter 4
 
 
-
+## Chapter 8
+Text should say: "In this chapter we will learn" instead of "In this chapter you will we learn"
+Figure 8.14: Forward model should have an input for the actions and be concatenated with the encoded state.
+### Listing 8.11
+Code: The if statement related to the use of extrinsic reward should be fixed, "if use_extrinsic" not "if use_explicit", also in its relative balloon annotation. 
+
+## Appendix
+In text above the $f(x,y)=(Ax+By,Cx+Dy)$ should be corrected to "To find $\dot{x}$" from "To find $x$"
 
 
 
diff --git a/Errata/Chapter 4.ipynb b/Errata/Chapter 4.ipynb
index 9dfa8c0..06a55f9 100644
--- a/Errata/Chapter 4.ipynb	
+++ b/Errata/Chapter 4.ipynb	
@@ -19,7 +19,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gymnasium in /usr/local/python/3.12.1/lib/python3.12/site-packages (1.1.0)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (1.26.4)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (3.1.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /home/codespace/.local/lib/python3.12/site-packages (from gymnasium) (4.12.2)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/python/3.12.1/lib/python3.12/site-packages (from gymnasium) (0.0.4)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gymnasium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,11 +71,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from gym import envs\n",
+    "from gymnasium import envs\n",
     "#envs.registry.all()"
    ]
   },
@@ -64,12 +88,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gym\n",
-    "env = gym.make('CartPole-v0')"
+    "import gymnasium as gym\n",
+    "env = gym.make('CartPole-v1')"
    ]
   },
   {
@@ -81,13 +105,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "state1 = env.reset()\n",
+    "state1,_ = env.reset()\n",
     "action = env.action_space.sample()\n",
-    "state, reward, done, info = env.step(action)"
+    "state, reward, done, _, info = env.step(action)"
    ]
   },
   {
@@ -99,11 +123,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gym\n",
+    "import gymnasium as gym\n",
     "import numpy as np\n",
     "import torch\n",
     " \n",
@@ -131,13 +155,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "pred = model(torch.from_numpy(state1).float())\n",
     "action = np.random.choice(np.array([0,1]), p=pred.data.numpy())\n",
-    "state2, reward, done, info = env.step(action)"
+    "state2, reward, done, _, info = env.step(action)"
    ]
   },
   {
@@ -149,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,16 +210,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
     "MAX_DUR = 200\n",
-    "MAX_EPISODES = 500\n",
+    "MAX_EPISODES = 5000\n",
     "gamma = 0.99\n",
     "score = []\n",
     "for episode in range(MAX_EPISODES):\n",
-    "        curr_state = env.reset()\n",
+    "        curr_state,_ = env.reset()\n",
     "        done = False\n",
     "        transitions = []\n",
     "\n",
@@ -203,18 +227,18 @@
     "            act_prob = model(torch.from_numpy(curr_state).float())\n",
     "            action = np.random.choice(np.array([0,1]), p=act_prob.data.numpy())\n",
     "            prev_state = curr_state\n",
-    "            curr_state, _, done, info = env.step(action)\n",
+    "            curr_state, _, done, _, info = env.step(action)\n",
     "            transitions.append((prev_state, action, t+1))\n",
     "            if done:\n",
     "                break\n",
     " \n",
     "        ep_len = len(transitions)\n",
     "        score.append(ep_len)\n",
-    "        reward_batch = torch.Tensor([r for (s,a,r) in\n",
-    "        transitions]).flip(dims=(0,))\n",
+    "        reward_batch = torch.Tensor(np.array([r for (s,a,r) in\n",
+    "        transitions])).flip(dims=(0,))\n",
     "        disc_rewards = discount_rewards(reward_batch)\n",
-    "        state_batch = torch.Tensor([s for (s,a,r) in transitions])\n",
-    "        action_batch = torch.Tensor([a for (s,a,r) in transitions])\n",
+    "        state_batch = torch.Tensor(np.array([s for (s,a,r) in transitions]))\n",
+    "        action_batch = torch.Tensor(np.array([a for (s,a,r) in transitions]))\n",
     "        pred_batch = model(state_batch)\n",
     "        prob_batch = pred_batch.gather(dim=1,index=action_batch.long().view(-1,1)).squeeze()\n",
     "        loss = loss_fn(prob_batch, disc_rewards)\n",
@@ -225,40 +249,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x121725fd0>]"
+       "[<matplotlib.lines.Line2D at 0x78eb9b833ef0>]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Shz9dM5GZI/RXcbBqzlDIBcBZQB8zywLuB+YD881sM1AFXO+8w262mNlCYCtQA9zS1EgZ6byqajws3prDeaP7fbFAw/F8dUx/3ri1B9/921qufWYVv7x0DNdNT27/QgPUvsJyHnxvG0t2Fnwx9BTg91eMa/QO0t7dI7hq8iCumjyowe0SPJozWuaaRjZ9u5H9HwAeaE1R0jmsyCjkyNEaLhzb/AUQhib04PVbTueHL63nl4u2MKp/DFOGxDX9QvlCaWUNT/w7nfmfZhIWalw+cSBJvaPp2zOS5D7dmXRSb3+XKF2Abu+SRr276SDdI0KZkdLnhF7XPTKMP1w9kYv/uJQ7XlrPOz88g17R7T8aIxDkHjnK7CeWkXPkKFecmsTds0Y2uEiGSFN014k0qKbWwwdbcznn5OZ1yXxZj8gw/nTNqeSXVnLvq5s6fHWersg5xz2vbuRwRRWvfn86j1x1ioJdWkzhLg1anVlEUVkVF47r3+JjjEuK5Sfnj+S9LTn8Y/W+NqwuML20Zj//2ZHPvbNGMekkdWVJ66hbRhr0zuaDdAsP5cwRfVt1nJtnDGXprgJ+/eZWJgzqxZgBsfW2O+fIOlRBfmklhaVVlFXW8JVRfYnt1vZznHRm+4vK+c1bWzltWLwuQkubULjLMWo9jvc25/KVUQmtXgQhJMR49KoJXPKnT5n7wloW3Xo68T0iAW/Xz09e2ci/1te/FWLMgBj+8Z1pQRPwHo/jx//cgJnx0JXjCQnRFLbSeuqWkWOs3XuIgtJKLjiBUTLHk9AzknnXTaKgtJLvv7iO6loPNbUe7ly4gX+tz+a7M4fy7A2TeeOW03nym6eyM7eEG59dTVmd4X+B6sjRau55dSOrMov4xSWjSeod7e+SJEDozF2O8c6mg0SEhfCVUa3rkqlrfFIvHrxiHHe+vIH7F22huLyatzcd5KcXjOK7Zw77Yr9TBvUiNARu+cd6bn4+jWdvnNyiC7qdnXOO97fk8Is3tlBQWsn3zhx2wmuWihyPwl2O8eG2XGam9GnzhRAun5jEtoMlzFuSAcD/XHQyN59x7J2vs8Ym8vDXa7lr4QZu/cd65l07KaC6Kjwexx0vf8aiDQcYnRjD09enasI1aXMKd6nnwOEKsg5VcNPpQ9rl+PfMGkVldS2jEmO4Zkrjs4FePjGJw+XV/OrNrfxlSQbfP2tYo/t2NU99sptFGw5w+9nDue2cFM2DL+1C4S71rNlTBNBud5WGhhi/mj226R2BG05LZu3eQzz8wQ5Sk3szObnrDw9cu/cQjy7eyUXjE7nzvBFa/1PajU4ZpJ5VmUX0jAzj5ET/L2FmZvzua+MYHBfNbf9YT2Fppb9LapXiimpuX7CexNgofve1cQp2aVcKd6lndWYRqcm9Ce0kfdw9o8J54psTKSqv4s6FG/B4Gr7TtbyqhlUZhfz5k93cvmA9b2440OSxq2s9HXbnrHOOn762kZwjR/njNRPbZa1SkbrULSNfKCytJD2vlK+d2uDiWX4zZkAs918ymvv+tZkbn1vDo1ed8sVY+aPVtTzywQ6eXbaHGl/wx3YLZ9GGA+zIKeGu80bUuxhbXFHNf3bk8cHWXD7ZkU+/mEiev2lKi4cg/m3lXlZmFHL/xaPp28hUAc45Hl28k3c25XDPrFGcOlgTf0n7U7jLF9bsOQTA1E44i+M3pwzGOfj1W1u58I9L+dM1pxJicPcrG8koKOPrk5KYNbY/Ewb1omdUOP/z+iae+DidzMIyHrhsLJ+mF/D6+gN8sjOP6lpHnx6RXDC2P+9tyeEbf1nJP74zlZPiu59QTR9uzeUXb2zGOVi5u5BHrjqFs0bWHz7q8Th+/dZWnlu+h2+kDuK7TcyLL9JWrDNM6JSamurS0tL8XUbQ+/WbW3lx1V42/fJ8IsI6Z4/dlgPF3PqP9ewtLMMBA2K78dCV4zl9eP2ZK51zzFuSwYPvbfc9h34xkVwyfgAXjEtk4qBehIQYm7OLufaZVUSEhfDizdOaveTcztwSLn9yGUMTevC7r43jx//cwPacEm6eMYRLJwxgUO9oekaFcferG3ltXTY3zxjCfRedrH52aVNmttY5l9rgNoW7fO7iPy2lZ2Q4C+ZO83cpx1VaWcMDb28lKjyUH3115HHH43+8PY+luwo49+S+TB0a3+C1hB05JXzr6ZVU1XgY3rcHYaEhRISGcOWkJC6beGwX1aGyKmY/uYyK6lrevHUG/WOjOFpdy2/e3srfV/53grTIsBAqazz86LwR3Hr2cAW7tDmFuzSp5Gg1p/zqA249O4W7zhvh73I63O78Uh79YCdHjlZTVeMhr6SSPYVlPPnNU7lw3H+nYSitrGHOc2tYv/8wL8+d9sU6pnWPk55Xyv6icrIOVTBxcC9mT+hc1zAkcBwv3JuzzN584GIgzzk39kvbfgQ8DCQ45wrMe2ryOHAhUA7c4Jxb19r/gLS/tXsP4XGds7+9IwxL6MGT3zr1i+cVVbV86+mV3PHyZ8R3j2Dq0Hj2FJTxnRfSyCgo49GrTjkm2D8/zrCE5nXtiLSn5nSsPgfM+nKjmQ0CvgrUnaj7AryLYqcAc4GnWl+idITVmUWEhRgTB+s2eIBuEaE8c/1kBvXuxs0vpPG3FXu49IlPKSit5IWbpuhsXDq9JsPdObcEKGpg02PA3UDdfp3ZwAvOayXQy8zaZmpBaVdr9hQxLimW6AgNoPpc7+4RPH/TFLqFh/LzN7YwoFc3Ft0645iLtyKdUYt+ks1sNpDtnNvwpYtEA4H9dZ5n+doONnCMuXjP7hk8uPE5RqT9Ha2uZcP+Ym48PdnfpXQ6Sb2jefHmqby7OYebzxiiX37SZZzwJ9XMooGf4e2SaTHn3DxgHngvqLbmWNI6S3cVUFXrabf5ZLq6lH49SenX099liJyQlpyGDAOGAJ+ftScB68xsCpANDKqzb5KvTTopj8fxyAc7OCk+mpkjEvxdjoi0kRO+U8U5t8k519c5l+ycS8bb9XKqcy4HWARcZ17TgGLn3DFdMtJ5vLEhm+05Jfz4qyM19axIAGnyp9nMFgArgJFmlmVmc46z+ztABpAO/BX4QZtUKe2isqaWh9/fydiBMVw0Tte9RQJJk90yzrlrmtieXOexA25pfVnSEf6+ch/Zhyv4/RValFkk0Ojv8CB15Gg1T/x7F2ek9GFGiob2iQQahXuQ+uuSDA6VV3PPrFH+LkVE2oHCPQgdLq/i2WV7uGhcImMHxvq7HBFpBwr3IDR/2R5KK2u47Zzh/i5FRNqJwj3IFFdU8+yyTGaN6c+o/v5fJ1VE2ofCPcg8v3wPJUd11i4S6BTuQaTkaDXPfJrJuSf3Y8wA9bWLBDKFexB5YcVeiiuquV1n7SIBT+EeJI5W1/L00gzOGpnA+CTN2S4S6BTuQSJtzyEOlVdz3fST/F2KiHQAhXuQWL67gNAQY8qQeH+XIiIdQOEeJFZkFHJKUiw9IrXYhEgwULgHgdLKGjZmFTN9mM7aRYKFwj0IrMksotbjOG2YJggTCRYK9yCwfHcBEaEhTDqpt79LEZEOonAPAisyCpk4uBdR4aH+LkVEOkhzVmKab2Z5Zra5Ttv/mdl2M9toZv8ys151tv3UzNLNbIeZnd9ehUvzHC6vYsuBI+qSEQkyzTlzfw6Y9aW2xcBY59x4YCfwUwAzGw1cDYzxveb/mZlOF/1oVWYRzqGLqSJBpslwd84tAYq+1PaBc67G93QlkOR7PBt4yTlX6ZzLxLuW6pQ2rFdO0IrdhUSFhzBhkO5KFQkmbdHnfhPwru/xQGB/nW1Zvjbxk+W7C5icHEdEmC6viASTVv3Em9l9QA3wYgteO9fM0swsLT8/vzVlSCPySyrZmVuqLhmRINTicDezG4CLgW8555yvORsYVGe3JF/bMZxz85xzqc651ISEhJaWIcexMqMQgOlDFe4iwaZF4W5ms4C7gUudc+V1Ni0CrjazSDMbAqQAq1tfprTEioxCekSGMU7rpIoEnSYnGjGzBcBZQB8zywLuxzs6JhJYbGYAK51z33PObTGzhcBWvN01tzjnatureDm+FbsLmTokjrBQ9beLBJsmw905d00Dzc8cZ/8HgAdaU5S03oHDFWQWlPHtaZriVyQY6ZQuQC3f7e1vP00XU0WCksI9QC3fXUBc9whG9uvp71JExA8U7gHIOceK3YVMHxpPSIj5uxwR8QOFewDaU1jOweKjGt8uEsQU7gFoWXoBoP52kWCmcA9AK3YXkhgbxZA+3f1dioj4icI9wHg8jhUZhUwfFo/vHgQRCUIK9wCzI7eEorIqzd8uEuQU7gHm8/HtupgqEtwU7gFmxe4CkuOjGdirm79LERE/UrgHkFqPY1VGkc7aRUThHki2HTxCSWUN0zTFr0jQU7gHkNWZ3tUQJyfH+bkSEfE3hXsAWbOniIG9ujFA/e0iQU/hHiCcc6zOLGLqEJ21i4jCPWBkFJRRWFbFZIW7iKBwDxhrfP3tUxTuIkIzwt3M5ptZnpltrtMWZ2aLzWyX72tvX7uZ2R/NLN3MNprZqe1ZvPzX6swi+vSIYKjmkxERmnfm/hww60tt9wIfOedSgI98zwEuwLsodgowF3iqbcqUpqzeU8Tk5DjNJyMiQDPC3Tm3BCj6UvNs4Hnf4+eBy+q0v+C8VgK9zCyxrYqVhh04XEHWoQoNgRSRL7S0z72fc+6g73EO0M/3eCCwv85+Wb62Y5jZXDNLM7O0/Pz8FpYh4B0CCepvF5H/avUFVeecA1wLXjfPOZfqnEtNSEhobRlBbXVmET0iwzg5McbfpYhIJ9HScM/9vLvF9zXP154NDKqzX5KvTdrR6swiJp3Um1CtlyoiPi0N90XA9b7H1wNv1Gm/zjdqZhpQXKf7RtrBobIqduWVqktGROoJa2oHM1sAnAX0MbMs4H7gQWChmc0B9gJX+XZ/B7gQSAfKgRvboWapY7X620WkAU2Gu3PumkY2ndPAvg64pbVFSfMtSy8gOiKU8Umx/i5FRDoR3aHaxX26q4CpQ+KIDAv1dyki0oko3LuwrEPlZBSUMSNFo41EpD6Fexf26a4CAGamaDFsEalP4d6FLd1VQL+YSIb37eHvUkSkk1G4d1G1Hsey3QWckZKg+WRE5BgK9y5qc3Yxh8urOUNdMiLSAIV7F/Vpure//fThCncROZbCvYtauiuf0Ykx9OkR6e9SRKQTUrh3QWWVNazde4gzRuisXUQapnDvglZnFlFd6zhjuMa3i0jDFO5d0JJd+USGhZCa3NvfpYhIJ6Vw74KWpRcwZUgcUeGackBEGqZw72LySyrZmVuqUTIiclwK9y5m+W7vEMjThsX7uRIR6cwU7l3Mit2FxESFMWaApvgVkcYp3LuY5bsLmTY0XkvqichxKdy7kP1F5ewrKleXjIg0qVXhbmZ3mtkWM9tsZgvMLMrMhpjZKjNLN7OXzSyirYoNdit2FwJwmi6mikgTWhzuZjYQuB1Idc6NBUKBq4HfA48554YDh4A5bVGoeC+m9ukRSYqm+BWRJrS2WyYM6GZmYUA0cBA4G3jFt/154LJWfg8BnHMs313IacPiNcWviDSpxeHunMsGHgb24Q31YmAtcNg5V+PbLQsY2NDrzWyumaWZWVp+fn5Lywgau/NLySupVH+7iDRLa7plegOzgSHAAKA7MKu5r3fOzXPOpTrnUhMSNEdKU5Z/3t8+TP3tItK01nTLnAtkOufynXPVwGvA6UAvXzcNQBKQ3coaBVieXkhS724Mjo/2dyki0gW0Jtz3AdPMLNq8ncDnAFuBj4ErfftcD7zRuhKl1uNYkVGoLhkRabbW9LmvwnvhdB2wyXesecA9wF1mlg7EA8+0QZ1BbUPWYYorqtUlIyLNFtb0Lo1zzt0P3P+l5gxgSmuOK/X9a102kWEhnH1yX3+XIiJdhO5Q7eSOVteyaMMBZo3tT0xUuL/LEZEuQuHeyX20LY/iimqunJTk71JEpAtRuHdyr67LIjE2Sv3tInJCFO6dWN6Ro3yyM5/LJw7ULJAickIU7p3Y659lU+txXKEuGRE5QQr3Tso5xytrszh1cC+GJWiiMBE5MQr3Tmpz9hF25pZy5aRB/i5FRLoghXsntO3gEf7njc1EhAukZZsAAAlxSURBVIVw0fhEf5cjIl1Qq25ikvpqaj28vekgf12aQXhoCL+5bOxx1zp1zlFcUU1oiBERFkJFVS2PLd7J31buJaZbOP935Xhiu2lsu4icOIV7G8g9cpQPtuby9NIM9haWM7xvD3KPVDL7iWXcdnYKP/jKMMJD6/+RlHWonJ/9azNLdtaf7jjE4FtTT+JHXx1Br2gtYiUiLaNwb6H0vBL+vnIfn6YXkJ5XCsApSbH87NpJnHdyP4orqvnlm1t47MOdvLv5IJdOGMDUIfGMGRDDy2v28/v3tmPA7eekEBMVRmWNh5pax7mj+x73bF9EpDnMOefvGkhNTXVpaWl+raGqxsOiDQd4bnkm1TWO6cPimT4snmlD4omNrt818uraLO57fRMAU4fEc/rweE4f3ofRiTHHrJL03uYcHvlgB7t8vwBCQ4xaj2PmiAR+e/lYknprCl8RaRkzW+ucS21wW7CHe3Wth/mfZjJ/WSa5RyoZ1b8nCT0jSdtziIrqWkJDjDNS+nDZhIGcOSKBh97fzoLV+5k2NI4/XjORvj2jmvV9CkorSdtTxLp9hxkzIIZLTxmg5fJEpFUU7o1wzvGjf27gtXXZzBjeh+/MHMrMlD6YGVU1HjZkHeajbXm8ueEA2YcrvnjdLV8Zxp3njiAsVIONRMR/jhfuQd3n/uB723ltXTZ3nTeC289JqbctIiyEyclxTE6O4+7zR5K29xAfbc/ltGF9OHOElgUUkc4taMP96aUZ/OWTDK6ddhK3nT38uPuGhBhThsQxZUhcB1UnItI6Qdmv8Pr6bH7z9jYuHNefX146Rn3fIhJwWhXuZtbLzF4xs+1mts3MpptZnJktNrNdvq+926rYtvDxjjx+/M8NTB8az2PfmKDZFkUkILX2zP1x4D3n3CjgFGAbcC/wkXMuBfjI97xTWLfvED/4+zpG9u/JvOsmERkW6u+SRETaRYvD3cxigZn4FsB2zlU55w4Ds4Hnfbs9D1zW2iLbwq7cEm56bg39YiJ57sYp9NSSdSISwFpz5j4EyAeeNbP1Zva0mXUH+jnnDvr2yQH6NfRiM5trZmlmlpafn9/QLm2muLya6+avJjw0hL/NmUpCz8h2/X4iIv7WmnAPA04FnnLOTQTK+FIXjPMOom9wIL1zbp5zLtU5l5qQ0L5DCx98bzt5JZXMv34yg+J0R6iIBL7WhHsWkOWcW+V7/gresM81s0QA39e81pXYOmv3FrFg9T5uPC2ZcUmas0VEgkOLw905lwPsN7ORvqZzgK3AIuB6X9v1wButqrAVqms93PevzSTGRnHneSP8VYaISIdr7U1MtwEvmlkEkAHciPcXxkIzmwPsBa5q5fdoseeW7WF7Tgl/uXYS3SOD9n4tEQlCrUo859xnQEPzGpzTmuO2hezDFTy6eCfnntyXr45u8JquiEjACtg7VB9+fwcOpztQRSQoBWS478ot4fXPsrnhtCGaL11EglJAhvtjH+6ke0QY35051N+liIj4RcCF++bsYt7ZlMNNM4bQu7vWIBWR4BRw4f7Y4p3Edgtnzowh/i5FRMRvAirc1+07xEfb85g7cyix3TR3jIgEr4AJ91qP4/fvbie+ewQ3nJbs73JERPwqIMLdOcev39zCqswifnL+SN2wJCJBLyDC/a9LM3h+xV5unjGEq6cM9nc5IiJ+1+XDfdGGA/z2ne1cND6Rn114sr/LERHpFLp0uK/MKOTHCzcwJTmOR75+CiFaMk9EBOji4d47OoKpQ+OYd90kosK1ZJ6IyOe69JXHkf178rc5U/1dhohIp9Olz9xFRKRhCncRkQCkcBcRCUAKdxGRANTqcDezUDNbb2Zv+Z4PMbNVZpZuZi/7luATEZEO1BZn7j8EttV5/nvgMefccOAQMKcNvoeIiJyAVoW7mSUBFwFP+54bcDbwim+X54HLWvM9RETkxLX2zP0PwN2Ax/c8HjjsnKvxPc8CBjb0QjOba2ZpZpaWn5/fyjJERKSuFt/EZGYXA3nOubVmdtaJvt45Nw+Y5ztWvpntbWEpfYCCFr42UOk9qU/vx7H0ntTXVd+Pkxrb0Jo7VE8HLjWzC4EoIAZ4HOhlZmG+s/ckILupAznnElpahJmlOedSW/r6QKT3pD69H8fSe1JfIL4fLe6Wcc791DmX5JxLBq4G/u2c+xbwMXClb7frgTdaXaWIiJyQ9hjnfg9wl5ml4+2Df6YdvoeIiBxHm0wc5pz7D/Af3+MMYEpbHLeZ5nXg9+oq9J7Up/fjWHpP6gu498Occ/6uQURE2pimHxARCUAKdxGRANSlw93MZpnZDt88Nvf6u56OZmaDzOxjM9tqZlvM7Ie+9jgzW2xmu3xfe/u71o6k+Y7qM7NeZvaKmW03s21mNj2YPyNmdqfv52WzmS0ws6hA/Ix02XA3s1DgSeACYDRwjZmN9m9VHa4G+JFzbjQwDbjF9x7cC3zknEsBPvI9Dyaa76i+x4H3nHOjgFPwvjdB+Rkxs4HA7UCqc24sEIp3KHfAfUa6bLjjHZGT7pzLcM5VAS8Bs/1cU4dyzh10zq3zPS7B+0M7EO/78Lxvt6Ca30fzHdVnZrHATHxDkp1zVc65wwTxZwTvKMFuZhYGRAMHCcDPSFcO94HA/jrPG53HJhiYWTIwEVgF9HPOHfRtygH6+aksf2jxfEcBagiQDzzr66p62sy6E6SfEedcNvAwsA9vqBcDawnAz0hXDnfxMbMewKvAHc65I3W3Oe9Y16AY71p3viN/19KJhAGnAk855yYCZXypCybIPiO98f7VMgQYAHQHZvm1qHbSlcM9GxhU53mz5rEJNGYWjjfYX3TOveZrzjWzRN/2RCDPX/V1sM/nO9qDt5vubOrMd+TbJ9g+J1lAlnNule/5K3jDPlg/I+cCmc65fOdcNfAa3s9NwH1GunK4rwFSfFe5I/BeFFnk55o6lK8/+Rlgm3Pu0TqbFuGd1weCaH4fzXd0LOdcDrDfzEb6ms4BthKknxG83THTzCza9/Pz+fsRcJ+RLn2Hqm9Gyj/gveI93zn3gJ9L6lBmNgNYCmziv33MP8Pb774QGAzsBa5yzhX5pUg/8U1D/WPn3MVmNhTvmXwcsB74tnOu0p/1dSQzm4D3AnMEkAHciPfELig/I2b2K+AbeEebrQduxtvHHlCfkS4d7iIi0rCu3C0jIiKNULiLiAQghbuISABSuIuIBCCFu4hIAFK4i4gEIIW7iEgA+v+ugbCrAuGgIgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.plot(moving_average(np.array(score)))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -272,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Errata/Chapter 5.ipynb b/Errata/Chapter 5.ipynb
index 6b025c5..69ac19c 100644
--- a/Errata/Chapter 5.ipynb	
+++ b/Errata/Chapter 5.ipynb	
@@ -34,7 +34,7 @@
    ],
    "source": [
     "import multiprocessing as mp\n",
-    "from multiprocess import queues\n",
+    "from multiprocessing import queues\n",
     "\n",
     "import numpy as np\n",
     "def square(x):\n",
@@ -51,7 +51,7 @@
     {
      "data": {
       "text/plain": [
-       "4"
+       "2"
       ]
      },
      "execution_count": 2,
@@ -70,8 +70,8 @@
    "outputs": [],
    "source": [
     "if __name__ == '__main__': # added this line for process safety\n",
-    "    pool = mp.Pool(8)\n",
-    "    squared = pool.map(square, [x[8*i:8*i+8] for i in range(8)])\n",
+    "    pool = mp.Pool(2)\n",
+    "    squared = pool.map(square, [x[2*i:2*i+2] for i in range(8)])\n",
     "    squared"
    ]
   },
@@ -92,13 +92,7 @@
      "output_type": "stream",
      "text": [
       "In process 0\n",
-      "In process 1\n",
-      "In process 2\n",
-      "In process 3\n",
-      "In process 4\n",
-      "In process 5\n",
-      "In process 6\n",
-      "In process 7\n"
+      "In process 1\n"
      ]
     }
    ],
@@ -111,9 +105,9 @@
     "processes = []\n",
     "if __name__ == '__main__': #adding this for process safety\n",
     "    x = np.arange(64)\n",
-    "    for i in range(8):\n",
-    "        start_index = 8*i\n",
-    "        proc = mp.Process(target=square,args=(i,x[start_index:start_index+8],\n",
+    "    for i in range(2):\n",
+    "        start_index = 2*i\n",
+    "        proc = mp.Process(target=square,args=(i,x[start_index:start_index+2],\n",
     "                         queue)) \n",
     "        proc.start()\n",
     "        processes.append(proc)\n",
@@ -214,11 +208,20 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6998 0\n"
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "1000 0\n"
      ]
     }
    ],
@@ -228,7 +231,7 @@
     "processes = []\n",
     "params = {\n",
     "    'epochs':1000,\n",
-    "    'n_workers':7,\n",
+    "    'n_workers':1,\n",
     "}\n",
     "counter = mp.Value('i',0)\n",
     "if __name__ == '__main__': #adding this for process safety\n",
@@ -241,7 +244,7 @@
     "    for p in processes:\n",
     "        p.terminate()\n",
     "    \n",
-    "print(counter.value,processes[1].exitcode)"
+    "print(counter.value,processes[0].exitcode)"
    ]
   },
   {
@@ -351,11 +354,55 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
+      "  result = entry_point.load(False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement gl (from versions: none)\u001b[0m\u001b[31m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[31mERROR: No matching distribution found for gl\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "ename": "ReraisedException",
+     "evalue": "Error occured while running `from pyglet.gl import *`\nThe original exception was:\n\nImportError: Library \"GL\" not found.\n\nHINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get install python-opengl'. If you're running on a server, you may need a virtual frame buffer; something like this should work: 'xvfb-run -s \"-screen 0 1400x900x24\" python <your_script.py>'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mReraisedException\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 16\u001b[0m\n\u001b[1;32m     14\u001b[0m state_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(env\u001b[38;5;241m.\u001b[39menv\u001b[38;5;241m.\u001b[39mstate)\n\u001b[1;32m     15\u001b[0m state \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(state_)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[0;32m---> 16\u001b[0m \u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/core.py:284\u001b[0m, in \u001b[0;36mWrapper.render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\u001b[38;5;28mself\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhuman\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/classic_control/cartpole.py:106\u001b[0m, in \u001b[0;36mCartPoleEnv.render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    103\u001b[0m cartheight \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30.0\u001b[39m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mviewer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 106\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgym\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menvs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassic_control\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m rendering\n\u001b[1;32m    107\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mviewer \u001b[38;5;241m=\u001b[39m rendering\u001b[38;5;241m.\u001b[39mViewer(screen_width, screen_height)\n\u001b[1;32m    108\u001b[0m     l,r,t,b \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mcartwidth\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m, cartwidth\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m, cartheight\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m-\u001b[39mcartheight\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/classic_control/rendering.py:25\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m---> 25\u001b[0m     \u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mError occured while running `from pyglet.gl import *`\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43msuffix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHINT: make sure you have OpenGL install. On Ubuntu, you can run \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mapt-get install python-opengl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m. If you\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mre running on a server, you may need a virtual frame buffer; something like this should work: \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mxvfb-run -s \u001b[39;49m\u001b[38;5;130;43;01m\\\"\u001b[39;49;00m\u001b[38;5;124;43m-screen 0 1400x900x24\u001b[39;49m\u001b[38;5;130;43;01m\\\"\u001b[39;49;00m\u001b[38;5;124;43m python <your_script.py>\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/utils/reraise.py:17\u001b[0m, in \u001b[0;36mreraise\u001b[0;34m(prefix, suffix)\u001b[0m\n\u001b[1;32m     13\u001b[0m     old_exc_value \u001b[38;5;241m=\u001b[39m old_exc_type()\n\u001b[1;32m     15\u001b[0m e \u001b[38;5;241m=\u001b[39m ReraisedException(old_exc_value, prefix, suffix)\n\u001b[0;32m---> 17\u001b[0m \u001b[43mreraise_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/utils/reraise_impl_py3.py:4\u001b[0m, in \u001b[0;36mreraise_impl\u001b[0;34m(e, traceback)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreraise_impl\u001b[39m(e, traceback):\n\u001b[0;32m----> 4\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(traceback) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/gym/envs/classic_control/rendering.py:23\u001b[0m\n\u001b[1;32m     20\u001b[0m     reraise(suffix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHINT: you can install pyglet directly via \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install pyglet\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. But if you really just want to install all Gym dependencies and not have to think about it, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install -e .[all]\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip install gym[all]\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m will do it.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 23\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     25\u001b[0m     reraise(prefix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError occured while running `from pyglet.gl import *`\u001b[39m\u001b[38;5;124m\"\u001b[39m,suffix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHINT: make sure you have OpenGL install. On Ubuntu, you can run \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapt-get install python-opengl\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. If you\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mre running on a server, you may need a virtual frame buffer; something like this should work: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxvfb-run -s \u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124m-screen 0 1400x900x24\u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124m python <your_script.py>\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/__init__.py:53\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_pyglet\u001b[39;00m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat_platform\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m  \u001b[38;5;66;03m# Must always be imported before gl_info or bad things happen.  # noqa: F403\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gl_info  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl_compat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GL_INTENSITY, GL_LUMINANCE\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/gl.py:10\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m annotations\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mctypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      7\u001b[0m     CFUNCTYPE, POINTER, Structure, c_char, c_double, c_float, c_int,\n\u001b[1;32m      8\u001b[0m     c_int64, c_short, c_ubyte, c_uint, c_uint64, c_ushort,\n\u001b[1;32m      9\u001b[0m )\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_GL \u001b[38;5;28;01mas\u001b[39;00m _link_function\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m c_ptrdiff_t\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mstruct___GLsync\u001b[39;00m(Structure):\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/lib.py:102\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib_agl\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_AGL, link_GL\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 102\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib_glx\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m link_GL, link_GLX\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/gl/lib_glx.py:12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyglet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m asbytes\n\u001b[1;32m     10\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink_GL\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink_GLX\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 12\u001b[0m gl_lib \u001b[38;5;241m=\u001b[39m \u001b[43mpyglet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGL\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# Look for glXGetProcAddressARB extension, use it as fallback (for ATI fglrx and DRI drivers).\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[0;32m/usr/local/python/3.12.1/lib/python3.12/site-packages/pyglet/lib.py:138\u001b[0m, in \u001b[0;36mLibraryLoader.load_library\u001b[0;34m(self, *names, **kwargs)\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected error loading library \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;132;01m!s}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    137\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLibrary \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnames[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n",
+      "\u001b[0;31mReraisedException\u001b[0m: Error occured while running `from pyglet.gl import *`\nThe original exception was:\n\nImportError: Library \"GL\" not found.\n\nHINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get install python-opengl'. If you're running on a server, you may need a virtual frame buffer; something like this should work: 'xvfb-run -s \"-screen 0 1400x900x24\" python <your_script.py>'"
+     ]
+    }
+   ],
    "source": [
     "env = gym.make(\"CartPole-v1\")\n",
     "env.reset()\n",
-    "\n",
+    "!pip install gl\n",
     "for i in range(100):\n",
     "    state_ = np.array(env.env.state)\n",
     "    state = torch.from_numpy(state_).float()\n",
@@ -396,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +513,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -480,7 +527,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,