{
  "schemaVersion": 1,
  "lessonId": "optimization-05",
  "contentHash": "d4f1f65e9be5e5231003b756f6a7dcaefb895c32fab266192c9e5f10120a7db0",
  "executedAt": "2026-10-20T10:45:33.435928+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "projection / residual: [3. 0.] [0. 4.]\nprojection / residual: [3. 0.] [0. 4.]\nPASS: optimization-05\n",
  "notebookCells": [
    {
      "sourceHash": "026084b1346b2fd20420d7bd1cec36a9318c45369b5cf256a89cac9e3512dac1",
      "outputs": []
    },
    {
      "sourceHash": "e8d743febda0de9b926deb0aa41e0d9aed52efe4f8c16946c488cca708e969f7",
      "outputs": []
    },
    {
      "sourceHash": "084042e4e4af6334d1ac0ef6ffeea2d0cdc24f6ce5589da4023a11170821129b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "projection / residual: [3. 0.] [0. 4.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "ff9b427003117879fae5a0631d9c43d54d75bf49eda3aa675291f903cc472ffd",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "projection / residual: [3. 0.] [0. 4.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "bb9bd8b9896a845acd93f52e17e3d755fa0ae4bc754d06b7f5c4adfb0a775287",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Wq1FZ3N3d5cSxUzbrHwCYFXVfAAAwDwIYwAQOHzymA5g7WjbOsa23t7cEBPjLtasRNukbAJgVdV8AADAXAhjABGJj48TP31eHK7mRkpIi7h78egAAo1D3BQAA8+ETFmAC+QsE6VWQ4uLic2x74fxFiYmJlZCQgjbpGwCYEXVfAAAwHwIYwASq16yinzeu35pj2/lzF+rnOvVqGN4vADCjk4evyeyJ1gudN25TUlp1KWfzPgEAAOMRwAAm0Pm+e/Rw90mffiXR0THZtlux7G/5euosXS+mS/cONu0jAJin7stGSU5KzXIstGSA9B9aT/8OBgAArsfT3h0AYLx27VvLvB8XyKb126T//U9Lzz5dJSkxWR/7e/UGOXfmvKxasU42rN2ig5rWbVtKk2YNuDUAYEDdl4tnswbhnl7u8vSoxuLr78U1BwDARRHAACbxwSdvy4gho/Q0pI/e/8Kyf8jTr1o+GCjN7mgkY95/zW79BABXRd0XAADMjQAGMInAwAD5YvqHsnTRMl3nZffOfZKQkKiPeXh6SK3a1aTH/V2kU9d7GP4OAHmMui8AAIAABjARVVegQ+e2+pGamioREZGSmpKqV0ny9OTXAQAYgbovAABA4RMXYFLu7u4SHFzA3t0AAJdG3RcAAJCBVZAAE+jctq/07/NMrts/+uBg6dKun6F9AgAzoO4LAADIwAgYwATOnb1gqfeSG2EXLsmF8xcN7RMAuDrqvgAAgOsxAgZAFqo+jKoXAwC4NdR9AQAA/0UAAyCT5ORkCb9yTfz8fbkyAHALqPsCAACsYQoS4IKio2MkKjI6y6gWNa1IfTCwRu2PioySX+YtlqSkJKlavZKNegsAroW6LwAAwBoCGMAFfT9jrnz5xcxM+65djZBOd/fJ1flq+lHnbvca1DsAcF3UfQEAANkhgAFc1PUjXVSgkt3Il/8KLVZEeve9Tz8AALlH3RcAAHAjBDCAC3pgQC/p0r19+hdpadK5XT8JLlhAvv1xcrbnuLu7S0CgvwQE+NuuowDgIqj7AgAAckIAA7igwMAA/chQv2EdKRCcX4qXKGrzvuzdu1d++eUX2bp1qxw4cEAuXbok165dE39/f6lUqZLcc8898uyzz0rx4sVt3jcAyCvUfQEAADkhgAFMYNq3n9jtvWfPni1jx461fO3t7a3Dl4iICNmyZYt+fPrppzJr1izp0qWL3foJALeKui8AACA3WIYagKHq1Kkj7777rqxevVrCw8MlISFBj4CJjY2VefPmSYUKFSQ6Olr69u0rJ06c4G4AcCrUfQEAALnFCBjAZJISk+TggSMSduGSxMXF3bA4b5du/9SRuQ29e/e2ut/X11d69OghNWvWlCpVquhA5vvvv5fXX3/9tt8TAGyBui8AAOBmEMAAJgpePv9kmsz7YYHExsbl2F6tnJQXAUxOKleuLMHBwXL16lU5c+aM4e8HAHmFui8AAOBmEMAAJpCSkiKDnxwhWzbt0H+xLVgoWMKvXNUrHxUuUkiuXY2QhIRE3dbPz1fyFwiyaZFeFb4oajoSADgD6r4AAICbRQ0YwAR+/XmJbN64XQqFFJTv5k6Rv/6er/erpamXrJgrf29dIlO+/khq1akuKamp8tzQJ2XRsh8M609SUpIe7TJz5kzp2LGj3lewYEEZOHCgYe8JAHmFui8AAOBWMAIGMIHfFy/XU4qeHfKYVK9ZJctxDw8PadS0vkz/7lN55tFhMurV96RchTJSpWrFPO1HyZIl5ezZs1n2qzowahWkkJCQPH0/AMhr1H0BAAC3igAGMIHDB4/q53bt78q0PzU1NdPXnp6eMuyVZ+WBnk/Kt1//IGPez9uCuEWKFJHk5GRd/DcyMlLvq1+/vnz00UdSq1atXL1Gs2bNrO7fs2ePVKtWTaKiovK0z84qJibG3l0wPe6Ba96DtUtOy+YV1utVdX+iigSHevB7SAVVqVkLvCcmJHJt7MAVfhcFBgbauwsAkCeYggSYQHRUjPgH+Imfv59ln5eXl8TFxWdpW7V6ZfH1zSdbN+/M835s27ZNLly4IBEREXL58mWZNm2ahIWFyV133SVPPvmkrlUDAI7qzNFImT/tkNVj9VqGSvN7S9i8TwAAwHkwAgYwAVVUNyEhIdO+oPwBEn7lmi7AWyA4f6ZjqWlpEn45vTCuUQoVKiSPPfaYtG3bVk9Bmjp1qtStW1cGDRp0w/PWr19/w5Ex/JUsM66H/XEPXOMeqLov33y4TpKTMo8cVEJLBshjLzcWX3+v234fV+Hm7pZln7ePN/892BG/iwDA/hgBA5hAkdAQiYmO1Y8M5SuU1c+qOO/19u7eLwnxCeLn72uTvpUpU0Z69Oiht6dMmWKT9wSAm0HdFwAAkBcIYAATqFYjvfDu3j0HLPtatGqqP1R8/MEk2b1znyQlJunw5c1X39MFe+s1yF1NlrxQokT6sP3Dhw/b7D0BILdWLTiebd2XfoNrS+mKBbiYAAAgRwQwgAlkhC1/Ll1h2derT1cJLVZEzp8Lk4H9npWmde+R/n0GyfGjJ/WqSI899bDN+nfs2DH9zPBoAI7m5OFrMnviLqvHGrcpKa26lLN5nwAAgHMigAFMoMWdTeTLmR9Lt54dLft8/XzlyxkTpE69mjqcyXgUKx4qE74YKzVqVb3t91Wv99+Vlv5r37598uuvv+ptVYwXAByFqvsyefTGbOu+9B9aT48YBAAAyA2K8AImoJaXbti4bpb9pUqXkK9nTZSwCxflwvmLEhgUYKkNkxfUakeqOO4zzzwj7du3l4oVK4q7e3ruq1Y/mjdvnowaNUri4+PFx8dH3nzzzTx7bwC4HdR9AQAAeY0ABoCEFi2iH0Y4cOCADBkyxLL0dVBQkCQmJkpUVJSlTWhoqHz33XdSo0YN7gYAh0DdFwAAkNcIYAAYRoUt8+fPl5UrV8qGDRvk7NmzcunSJT1kXxXerVWrlnTs2FEGDBig2wKAI6DuCwAAMAIBDGBCCQkJEhkRLcnJyTdsp+rB3A413ahbt276AQDOgLovAADAKAQwgEnExcXLzGmzZemiZXL61Nkc26tRKlv2LrdJ3wDAEVD3BQAAGIkABjCBqMgoeeyh5+XokRP6A0Zu5LYdALgK6r4AAAAjEcAAJvDlF9/IkcPH9WpIfR/qIXfdfYcULhIiHh4e9u4aADgE6r4AAACjEcAAJrBy2d96StHw1wbL/f2oxwIA16PuCwAAsAV3m7wLALu6GHZZ3N3d5L4eHbgTAHAd6r4AAABbYQQMYAJB+QMlMTFRfHx87N0VAHAo1H0BAAC2wggYwATqNagl0VExEnbhor27AgAOg7ovAADAlghgABMY+PgDuuDulM9n2LsrAOAQqPsCAABsjQAGMIHqNavI6HdfkcW//SlvvvKunD511t5dAgC7oe4LAACwB2rAACbQuW1f/ezu4SGLfvtDP4KCAsU/wC/bc9SqSQv+nG3DXgKAbVD3BQAA2AMBDGAC585eyLIvIiJSP24UwACAq6HuCwAAsBcCGMAE3vrfK/buAgDYHXVfAACAPRHAACbQtXt7e3cBAOyKui8AAMDeKMILAABcHnVfAACAvRHAAAAAl0bdFwAA4AiYggQAAFxWfGyyTB69SZKTUrMcCy0ZIP2H1qPoOAAAsAkCGMDFNKjeWj+XLV9a5i2cmWnfzVCrIG3ZuzzP+wcAtqz78sPn++Ti2Zgsxzy93OXpUY3F19+LGwIAAGyCAAZwwQ8c1z//dxsAzGLd0jOyfU2Y1WP9BteW0hUL2LxPAADAvAhgABfz5cyP9bOvr0+WfQBgprov86cdsnqscZuS0qpLOZv3CQAAmBsBDOBiGjaum6t9AOCq4mKSZPLojdR9AQAADoVVkAAAgMtQUy5njt9O3RcAAOBwCGAAAIDLWLXguGxeccbqMeq+AAAAe2IKEuBizp+zXnDyVhQrHppnrwUAtqj7MnviLqvHqPsCAADsjQAGcDGd2/bNk9dhGWoAzoS6LwAAwNERwAAuJq+WnGbpagDOgrovAADAGRDAAC5m4V9zrO7ft+egvPPmB3q75/1dpUnzBhIaWlh/HRZ2STat3yrzfligv37jnZekWo3KNuw1ABhT96X7E1WkdMUCXF4AAGB3BDCAiyleomiWfWfPnNfhS0jhQjL5q4+kcJFCmY6XLV9amjRrIP0e7ilPPTJU3nnjA5n181Qb9hoA8r7uS72WodL83hJcWgAA4BBYBQkwgelTvpXoqBh54+2XsoQv11MBjWoTGRkl0yd/a9M+AkBe133p82x1Xc8KAADAETACBjCB9Wu3iK9vPqlbv2aObVUb1VadAwDOXPcln5+HXfoGAABgDSNgABMIv3z15s+5Em5IXwDA6Lov/QbXpu4LAABwOAQwgAnkLxAkcXHxsnP7nhzbqjaqbVD+IJv0DQDysu5L4zYlpVWXclxUAADgcAhgABNQBXbVcP0xb34oVy5nP7Il/MpV3UbVTFDnAICz1X3pP7QedV8AAIBDogYMYAJPDOovf/2xSo4dPSk9Ow2QHvd3kcbN6kuR0BB9/GLYZdm8YZv8/ONCiYiIFJ98PvocAHC2ui++/l526RsAAEBOCGAAEyhdpqRM+HysjHhhlF7haOb02fph7cONf4C/jJvwlj4HABwJdV8AAIAzYwoSYBJNmzeUnxbMlN797pOg/IE6bLn+ofapY/MWzpBmLRrZu7sAkAl1XwAAgLNjBAxgImrK0atvvqgfZ8+c1zVflIKFgqVEyWL27h4AWEXdFwAA4AoIYAAT6NfjcXETN3lvwijL1CIVuBC6AHB01H0BAACuggAGMIGjh0+Ip5cndV0AOB3qvgAAAFdBDRjABAqFBIuHh4e9uwEAN4W6LwAAwJUQwAAm0KBRHYmNiZUTx07ZuysAkCvUfQEAAK6GAAYwgYcf6SOenp4y/v3PJTU11d7dAYAbou4LAABwRQQwgAlUqVZJ3vvoTdm+ZZcM6DtIlv2xWq5cDtcfcgDA0VD3BQAAuCKK8AIm0KB6a8v2vj0HZcQLo3I8x83NTbbsXW5wzwAgM+q+AAAAV0UAA5gAI10AOAPqvgAAAFdGAAOYwJczP7Z3FwDghqj7AgAAXB0BDGACDRvXtXcXAOCGqPsCAABcHUV4AQCAXVH3BQAAmAEjYACTSkpMkmvXIvV2gQJB4uXtZe8uATAh6r4AAACzIIABTCQuNk7mzv5Vliz6S44cOiapqenLULu7u0nFyuWlQ+d20rtvV/H187V3VwGYAHVfAACAmRDAACZx4tgpGfLMq3Lm9LksqyKlpKTJwf1H5NCBo/Lzjwvkk0nvSplypezWVwDmQN0XAABgJgQwgAnExsTKoMdfkgvnw8TDw11at71Tmt7RSIoWLaKPX7hwUTau3yrL/1wtp06ekUGPD5e5v30tfv5+9u46ABdF3RcAAGA2BDCACcz6dp4OXwqFBMsnk96T6jWrZGnTo3dn2b/3kDz/9Cty4fxFmf3dz/LYUw/Zpb8AXBt1XwAAgBmxChJgAiv+WiNubm7y+ujhVsOXDNVqVJbXRw/TU5TUaBgAyGvUfQEAAGZFAAOYwKmTZ8Xb20tatW6eY9s772qm26pzACCvUfcFAACYFQEMYAJJiYl6mWk1CiYn7u7u4u3jLclJSTbpGwDXNf29LfJY65/1s0LdF7iSZb1q6cfVPZtv6fy1z9yrzz+34hdxJFvffET369gPX9i7KwDgcghgABMoXCREYqJjdYHdnJw8cUaio2KkUEhBm/QNgDm4Wt2XX2fs04/LF2Ls3RUAAOAkCGAAE2jQqI6uuzBu7KeSkpKSbbvU1FT58N3P9Ieghk3q2bSPAFxPgUL5pGipAMlfMJ/MHL9dLp7NGlZ4ernL06Mai6+/lziT32Ye0I/LF2Lt3RXYiV/xsvrh7pOPewAAyBUCGMAEHhp4v55atP7vzfLIA4NlzaoNkpCQYDmutv9evUEfW7t6o7i7u8lDA3rbtc8AnF/PJ2rK2G/ukZCifrJ5hfUReP0G15bSFQvYvG/A7Wr26QL9yF+pFhcTAJArLEMNmEDFyuVl+KuD9QiYvbsPyAvPvKpDlqD8Qfp4ZESkpKam6VEyyvBXn9PnAMDtou4LAABAOgIYwCT6PtRDSpQqLp98OFmOHTkhKSlpcjX8WqY2FSuVk+eHPyUt7mxqt34CcB1fjtkkG5dZH/ni5e0um5af0VOUug6oJqsXnZA1i07I+VNRorLgEuWCpE238tKsXWmr54/ou1SuhMXKIy83kIatSsiS2Qdl66pzep+nt7uUr1ZQWnYuIVXqFrJ6vioOrLw0oaVUrVvYaptxL6yWgzsvS9cBVeW+gdX1PlVQeN3vpyxtPnhxTZbzpq/okYurI3JgxyXL+Tc6Jzd9dVWqIOy1fVukXO9npGzPJ+TMktkStnaJxJ4/KckxUVJ7xMdSuPHdlvYxZ4/L6cXfy9XdGyX+Spha91zyFS4mheq1kNJd+ku+QkWtvk/suRNyasE3cnXvZom/fEEkLVW8AguId4EQCa7RUEJbdJSgCjUynaMK1Sr13/pKgms2yvKaybHRcmL+NLm44S9JuHxePPwCJKhCTSnddYAUrNk42+9ZFfXd9tajevvun3Zn2+5G7x9xaKdc2rRcIg7ukNiL5yQpIlzcvb3Fr2hpCWnQSkp1fFC8AvNLXlOFheMvnZNqz74joc3ukZO/zpBLG/+SuEvnJCUuRhp/MFcCy1XN8/cFAGdBAAOYSMtWTfXj8MGjsm/PQQn/J4ApWLCA1KhVlVEvAPKMGlF3ZG+41WOq7kvxskFy8tA1Pfpu4hsbZMfa8+Lh4Sbe+TwkPjZZju0L14+wM9HS7ZH08MOauOgkGfvMCjl3Mko8PN3E28dTYqOSZM+mMP3o+FAF6flYnTz7vlStmqBgH4m8mj6N0y/QSzw9mdFttNTkRNn21uMScWCbuLl7iIefv8h/ijafXvS9HJ75gaSlptc6c/f20c+xZ4/rx/nlv0it4R9JwdqZ/8gQvmuD7HxvsKQmpt9TNw9P8fD1k4SrlyQh/KJEHdunw5SgZ0bnur8qxNk26hGJC0sPIN08PSUtKUmubFstV3b8LVUef12MtOW1hyzb6jqoOjUpsVH6e1GPcyvmS/1R08SvWBlD3j8pKkI2jeijgy31vXv4+BryPgDgbAhgABOqVKWCfgCAUVYtOC5XsilQq+q+qNEvyvJfjklqSpo8MqK+NG5TSrx9PCT8Uqx89/EO2bnugiz87oAeBaNWSrLmt5n79YiZJ0c2kgatSugw5NK5GJn9+U59/uLvjkqlGqFSu6n1kQ8364Hn6uhHxqiUZ99uarpRKfagRr6IpEm1Z0ZLaIsO+gN9UkykpCUn6ePnVy2QQ1+/J+4+vlK22yNSvE0P8SlYRI+AUaNijs2ZqEdi7P5wqDT56GfJF/Lvz8OBL9/R4Utg+Wo6GAmqVFsXo09NSpL4y+fk0qYV4uae+xW60lJSZPf4YTp8UaNeqjz+moQ2ay/uXl4SF3ZaDn09Tg599a64eRhXeFqNcglt2UmCqzeQRK/08MM/n49c3btFjs35TCKP7JE9H78sjd+fY8j7H//xCx36qMArpMFd+ntPuHZZPLwpWAzA3PiTDQAAsFvdFzVaRYUYLTqU1eGLUrCwnzwzqokUCMmnZoLI5pXWpzHp86OT9CpKTe4uZRmJUri4v37NMlXSp1j89OUe7rCTS4mPlRrPvyvF7+5hGU3h5R8k3vkLSXJcrBz6+n29r9aLH0j53s9IvkKhOkRxc3eXgFIVpPZLEySk4V2SHBslpxbMtLxuYkS4xF1In1JW5bHXJH/lOpbl0FVooEaIlLlvoJTuMiDXfb248S+JPJz+81/zhfel2J1d9GspvqGlpNZLEySofA1JTYgTo9R5daIUbdEhPYT6h7uXtxSq21zqjZom3gUKSdTRvXLtwHZD3l8FWnVf+0KKNG1n+d59CoSIp5/1IBUAzIIABjCZSxevyKxvfpKRI8bIoMeG64faVvvUMQC4HXExSTJ59EZJTkrNckyNYuk/tJ7lA65SsUZBqd7g3w+JGby8PaRGw1C9feZoRLbvV6FGQanZKL3d9Tw83OXePulBz9njkXLmePavAcfnX7JCplov11MjW5KjIySgdCUJadgq29codldX/Xxlx1rLPo98fiLu6f8cTrh6OU/6GrZ2qX4OqlhTQurfmeW4u4enlOv9tNiLp6+/FKjeUG9f27/VkPcoWLd5lpo5AACmIAGmkZKSIhM/niazZv4kycnJel/Gqkfqw9CShcvkkw+nyEMDe8ugIY+Jh0f6X6IBILfU75SZ47fLxbMxVuu+qJEqqobK9cpVK5jt66kRMEpMVPo0E2uq188a3mSoWKuguLnreqpy4uA1KVku74uOwjbyV6uX7bFr+7fp59gLp2TN43dl2y71n+lK8ZfOW/Z5+OSTgrWaSvjOdbJv4usScXC7HimjwpNbrVsSeTR9xFVwrewL2heo0UjXmklLSf//sRH/LV5c97suWBxxdJ8kR1211Li5XoIqVGyAAlXrG/K6AODsqAEDmMSbr7wrSxct0/8o8/b2kmo1qkho0fS6BWEXLsn+vQclMTFJZkybLWEXLsqYcSPt3WUATlj3ZfOKM9nWfSldsUCW/b7+2f9TRBXlVVKSs46m+W9IY42a0uTn76UDnMir8Tn0Ho7MOyj7oC7h6kX9rAKGRCshw3+lJmb+Wag2aLTsev95iTq2X6+EpB6q0G9A2SoS0uBOKd62l57SlFtqxSHl+uk//+Xh7aNXIUq8lvcjT1MS4mTne8/plaAyqLDHMyC/uHum//emigqr66XaGsE7f/b3CwDMjAAGMIFVy9fKkoV/6e0H+veSJwcNkKD8gZnaREVGyZdffCPfz5yrR8Pc06GN3Nm6uZ16DMCV6r6EFPXLVPcFuFkqEMlOWmp6QFe4aTupPfyjm35ttTR1o/d/kKu7N8jlbWsk4uBOiTqevlqQeqillFVIU7RFR6e4cSd+nqrDF7X6Ufk+g8W/VjPxDikmQUFBljZ7P31VLqxeqIsUG+KfaV0AgMwIYAAT+HnuQj3NqP9jfWXIsKestgkMCpRhrzwrnl6eMnPabH0OAQyA2637klGn5fq6L3np2uXsR7YkJqRIbEz6tJOg4MwjZdzd3fQS2EmJ6UsWW5NxrlEyRvgoqh+q7k2WPkQb2wdX4FOgkH6Ov3j2ll9D/XwWrN1MPxQ1MuTK9rVydM5nEnvmmOz/4k0JrtlYF5LNiVf+gpJw+YJewjo7KYkJeqlmq325bgqwaqdGy/xXckxUtq8d9vcS/Vyu51O6gHBUVNa2aoltAIDtEU8DJrBvzwH9j8tHHn8gx7aqjbu7u+zdvd8mfQPgunVfMjIXj39WJzLC/u3Zf5A8sueqrv+ilK2SefqTX2B6LZrwi9anYMTFJsn5k9l/yLXkSbcxgMAvwNuynV0/ju1Pn86C7OX/p95I9ImDEn/lQp5cKlX/pUjTtlJ7+AT9tZquE5HLFYOCKtTUz1f3bMq2zbV9W7Kt/+IZ8O9IlYRsvp+Iw7uzfe2Ma6CW1bYmOS5GIm9wPgDAOAQwgAlEXIuSwMCALNOOrFFtVNvIiGib9A2A69Z9KVct2PD3P7LniuzbmnWkQWpKmvz543G9XaJcUJYCvKUqpH+9bbX1URO//3A42xE9SkYx4ZioxFvue2ipAMvS21ut9EON0Fk86+Atv75ZhDa7R4cWaakpcmj6e5YpSdm5fuRJatKNRxi5+1w3ckpVdM5Nf+64Vz+rpaivX3EpQ1pKihz/6ctsz1dLX7t7p7/vxQ1/Zj0/NVVOzJ+W7fmefukBTtQJ6z87x3+cpJf1BgDYHgEMYAIBgf4SHR0tcbE5F9tTbVRb/wA/m/QNgGvWfWncpqQULZVz6Hu7/AK8ZNJbG2XT8jOS/E+x3kvnY+TzURvk+P5r+usej2ddDrdJm1L6ec/mi/LL1/v0NColKiJB5k3dIwu/O6BfOzslyqd/yN3w12lJiLu1lWw8Pd2l/p3F9fai7w/qICsj9LlwKko+f2ODnL7BEtz45zr6B0rlR17R25c2LZPt7zypV0ZSQUfGKK3Y86fkzNI5sunlvnLm9zmWSxdxcIdsGNpdTv42U6JPH8l0TuSxfbL3k1cty1UH/7N0c06KNGmnV1FS9nz0klxYs8iyAlNc2BnZPX6oRB7ZJe7ZrLLk7umlR98oJ36eJmHrllqCopizx2XXuCESfTL7YK5Q3TvSz533pYSt+90y0ib+SpgcmDpGTi2YKV6BWQtiAwCMRw0YwASqVqskG9dvlblzfpP+j/a5YduffvhNUlJSpUrVijbrHwDXqvsSWjJA+g+tJ7M+22l4P7oOqCarFx6XKe9s0ktdqxEl19dNaf9AeanbvFiW8+5oX0Y2LDstB7ZfkgXfHJCF3x4Q3wAvifvn3F5P1pRdGy7IwZ2Xrb5v667l5fCuK7JtzTl5bt0CCQz2EQ+P9L9rjZvTPtf97/lEDd0HVctm8tubxMPTTX8PcTHJ4uPrKc//r5l88OKaW7gy5lKsVRdJTUqUQ9P/pwvQbt29Udw8vcTT119PuUn7JwBRCjduk+ncmFNH5Mg3H+qHm6enePoGSHJctKQlpwcX7l7eUn3wWL1qUW6oGi41h42XbaMe1XVp9n7yiuz7/A295LWu3eLuLlUef11Ozp8u8Zes/2GkwoND9BQmVUdGhTiqX2pUTEpstA6D6rwyUba99aj1c/s9J+G7N0ji1Uuy56Phuj/uPn6SEps+pa5k+756BMz5lb/l+voCAPIGAQxgAh27tpMN67bIZxO+FE9PD+n7UA9d5+V66q99P876RT796EtdL6Zzt/Qh1ABwM3VfVAjy9KjGlik6RlOhyeuTWuupOttWn5PLF2L0yJXy1YKlReeSUrVeeoHW/3L3cJMh7zaXpXMO6dEzatSM+t1XvUERubdPJanRMFQHMNlpcncpETeRVb8dl9PHIiQiPN5Sb+ZmFCzsJ69/cZf8NvOA7Nl4QSKvJYh3Pk+p27y4dH64ik1GEbmKEm176tEfaoRL+M51EnfhjA48VGCRr2QFCapYQ0Lq3ymF6rW0nKP21Rz6oVzdu1kij+zRoUViZLgehZKvWFkJrtVESnV8UPyKpo+Yyi3fwsWl8Qc/ysmfp8nFjX9J/OUL4ubhJYXq3ymluw6QgjUb6wDmRiszNXz3ezn+42S5sn2N7pOqS1O4YWsp1+tJ8SteNvtzCxeTxu/PkWNzJ8mVraslMeKKDmFUgeES9/SWIk3byb6Jr9/U9wMAyBtuaepfUQBcmvrP/OlHh8nmDdvSV3ooFCwNG9eVosWK6ONhFy7J5o3bJfzKVd22cbP6Mvmrm1/K056aNUtfuWL9+vX27opDyFj1IjCQD2/cg7y38rdj8u2EHVaPPfxiXbmra3nDL/uIvkvlSlisPPJyA2nRvozVNvx3YD+rH71TkiKvZtpXpvtjUvHBF+zWJ7PivwMAcByMgAFMQIUuEyaOkdEjx8mfS1fKlcvh8seSFZnaZGSx7Tq0llHvvGSnngJw9rovrbqUs3mfAAAAnAEBDGASfv5+8v6Et6T/owdk6aJlsm/PQbkanv7XyeCCwVKjVlVp3+luqV6zir27CsCJ676owBcAAABZEcAAJqOCFvUAAGet+wIAAOCMWIYaAADkaNWC43qZZGv6Da4tpSuyrC0AAMCNMAIGMIGkxCQ5fvyUeHl5Srny1otVZjh+7KQkJSXrdqr97QoPD5cFCxbIsmXLZOvWrXLy5ElJTEyUkJAQqVu3rvTr108eeOAB8fDwuO33AmCeui83s9QzAACAI2AEDGACfyxdIf26Py7fz5ibY9upX3yj2y77Y1WevHdoaKgMHDhQvv32W9m3b58kJydLvnz55Pz587JkyRLp37+/3HHHHXL58uU8eT8AeYu6LwAAAHmDAAYwgYwwpdN99+TYtluvTrrWw1+/500AowKXhg0bysSJE+Xw4cMSHx8vkZGRcu7cORk+fLi4u7vLxo0bpVevXnnyfgDyDnVfAAAA8g5TkAATOHL4uLh7uEvN2tVzbFuvfi3x8HCXI4eO5cl7q6lHbdq0ybK/WLFi8sEHH0hwcLC8/vrrsmrVKlm7dq0eDQPAMVD3BQAAIO8wAgYwgUsXr0hAgH+uarp4eXtJQGCAXL50JU/e21r4cr0nnnjCsr158+Y8eU8Arln3BQAAwJkRwAAm4OnhIfFx8blur9qqqQe2oOrBXD9dCYD9UfcFAAAg7xHAACZQrHioJCYmyb49B3Nsu3f3AUlISJTQooVt0jc1RSlDnTp1bPKeALJH3RcAAABjEMAAJtCwST39oWrSZ1/dsF1GGzc3N2nUtL7h/VLLUav6L0rlypWldevWhr8ngBuj7gsAAIAxKMILmEC/h3vK3Dm/yro1m+SlIW/KS689J0VCM49wuRh2ScaN/Uy3UUV4+z3U0/B+DRo0SC9NrVZC+uKLL8TTM+dfSc2aNbO6f8+ePVKtWjWJiooyoKfOJyYmxt5dMD1nvAdnjkZmW/elXstQqX9XiFP9N+aM98BVpKVmncaamJDoVD8/rsIV/jsIDAy0dxcAIE8QwAAmUKp0CXlh+NMy/r3PZfmfa2Tl8rVSpWpFKV6iqD5+7uwFOXjgiKSmpOqvnxv6lJQtX9rQPo0ZM0amT5+ut9955x25++67DX0/ADcWH5ssM8btkuSk9N8D1ytc3E/6PFtdj44DAADArSGAAUziwQG9xc/fTyaM+0Kio2J0PZj9ew/pYxkFdwMC/eWFl56RHr07G9qX9957T9544w29PWLECHnttddyfe769etvODKGv5JlxvWwP2e4B+p3wPcTNsvl83FZjnl6ucug0U2lcGgBcVbOcA9cjZt71rDO28ebe2FH/HcAAPZHAAOYSPdenaRd+7vkr99Xyo6tu+Xy5XD9F+1CIQWlbv2a0vbeu/Ry1UaPfMkIX4YPHy7vv/++oe8HIGfUfQEAADAeAQxgMipg6dazk37Y2qhRo+Ttt9/W2y+99JKMGzfO5n0AkNnJw9eyrfvSuE1JadWlHJcMAAAgDxDAALAJtdrR//73P739yiuvyLvvvsuVB+wsLiZJJo/eaLXuS2jJAOk/tB51XwAAAPIIy1ADMNyrr75qCV9GjhxJ+AI4SN2XmeO3y8WzMVbrvjw9qrH4+nvZpW8AAACuiAAGgKHUaBdVdFd566239IpHAOyPui8AAAC2RQADwDCnTp2yFNlVxX4nTZokRYsWzfYxZMgQ7gZgA9R9AQAAsD1qwAAwTGpqaqbpDmFhYTdsHxERwd0ADEbdFwAAAPsggAFgmLJly+rgBYBjoO4LAACA/TAFCQAAk6DuCwAAgP0QwAAAYALUfQEAALAvAhgAAFwcdV8AAADsjwAGAAAXRt0XAAAAx0ARXsDFvPXae3nzQm5u8tbYl/PmtQDYDXVfAAAAHAMBDOBifpu/VNzc3KyuPqT254Y6V7UlgAGcG3VfAAAAHAcBDOBiOt93rxq8YtXK5WslKjJafHy8pVqNylIktLDefzHskuzfe0gSEhIlKChQWrVpbttOA8hz1H0BAABwLAQwgIt5+71Xre5//aUxOnwZ8Hg/efTJByUwMCDT8ejoGPlqyvcyc/psSUlJkTHjRtqoxwDyGnVfAAAAHA8BDGAC839aJEsXLZPHn3lYBj3/mNU2AQH+8vywJ8XLy1OmTf5WGjWpL/f17GjzvgK4fdR9AQAAcDysggSYwC8/LRJ3dzd5+JE+ObZ96JH7df0XFdoAcD7UfQEAAHBMBDCACRw/dlICAgKyTDuyRrVRo2GOHT1hk74ByDvUfQEAAHBcBDCACaQkp0h0dLSu85IT1UY91DkAnAd1XwAAABwbAQxgAuUqlJHU1DT57usfc2yr2qSmpupzADgP6r4AAAA4NgIYwARUMV311/Gpk76RT8d/KVFR0VnaqFEvEydM1W1UDRgK8ALOg7ovAAAAjo9VkAAT6NWnq6xZsV7+Xr1BLzM9+9ufpGr1ylIkNEQfvxh2WQ7sOySJiUk6qLnjzib6HACOj7ovAAAAzoEABjABNaJl/MQxeoTLrG9/koSERNm5fY/er6jQRfHw9JC+D/aQ54c+aTkGwHFR9wUAAMB5EMAAJuHl5SkvjnhGHn7kfvnr91Wyd/cBCQ+/qo8VLBgsNWpVlbvvaSWFixSyd1cB5BJ1XwAAAJwHAQxgMiGFC0nfh3rYuxsAbhN1XwAAAJwLRXgBAHAy1H0BAABwPgQwAAA4Eeq+AAAAOCemIAEmcvrUWfljyQo5fPCoREZESXJycrZtVRHeKTMm2LR/AHJG3RcAAADnRAADmMSUiTNk2uRvJDU1zbLq0Y2wChLgeKj7AgAA4LwIYAATWLzgT5ny+Qy9XbhIiDRr0Ug/e3p42LtrAHKJui8AAADOjQAGMIEfvp+vn1u1uUPGTXhLvLy97N0lADeBui8AAADOjyK8gAkcPXxcTyl69c0XCV8AJ0TdFwAAAOdHAAOYgApf/AP8pEhoiL27AuAmUfcFAADANRDAACZQtnxpiY9LkMTERHt3BcBNoO4LAACA6yCAAUyge69Oesnp3xcvt3dXAOQSdV8AAABcCwEMYAI97u+SXoB37GeyecM2e3cHQC5Q9wUAAMC1sAoSYAJqCerKVSrI9i275OlHh0mdejWkZu3qui7MjTz17ECb9RHAv6j7AgAA4HoIYAATmDJxhi7EmzGtYce2PbJz+94czyOAAWyPui8AAACuiQAGMIH6DevIP/kLAAdG3RcAAADXRQADmMC0bz+xdxcA5AJ1XwAAAFwXRXgBAHAA1H0BAABwbQQwAADYGXVfAAAAXB9TkACTSUxMlHV/b5a9uw/I1StX9b7gQsFSo1ZVad6ikXh7e9u7i4CpUPcFAADAHAhgABOZO+dXmfTpVxJxLdLq8fwFguTZIY9Jzz5dbd43wKyo+wIAAGAOBDCASXwyfop8M32O/mu7UrhIiIQWLay3wy5ckksXL8u1qxHyv9ET5OyZC/L8sCft3GPA9VH3BQAAwDwIYAAT2L51l8ycNltvt27bQgYNeVwqVCybqc3xYydl4oRpsuKvNTJz+mxpeVdTqdegtp16DLg+6r4AAACYC0V4AROY893P+rnzfffK+M/GZAlflHLly8j4z96RLt3u1aNkfvh+vh16CpgDdV8AAADMhwAGMIGd2/eIm5ubPDc052lFg198UrfdvnW3TfoGmBF1XwAAAMyHAAYwgavhERIYGCCFixTKsa1qo9qqejAA8h51XwAAAMyJAAYwAV/ffBIbGydJiUk5tlVtVFt1DoC8Rd0XAAAA8yKAAUygfKWykpKSIksW/pVjW9UmOTlZnwMg71D3BQAAwNwIYAATuKd9a/3h7/0xn8jK5Wuzbbdm5XoZN/ZTXQPm3g5tbNpHwNVR9wUAAMDcWIYaMIGefbrK/J8WyeGDR2XY4JFStXoladq8oYQWK6KPX7xwSTau3yr79hzUQU3lqhWkx/1d7N1twGVQ9wUAAAAEMIAJeHl5yudTx8mw596Q3Tv3yf69h+TAvsOZ2qjgRaldt4Z8+Onb+hwAt4+6LwAAAFD4hAWYREjhQvL1rImy7I/VsnTRMtm396BcvXJVHwsuFCw1alaV9p3uljbtWoq7O7MTgbxA3RcAAABkIIABTEQFK+3a36UfAIy3bukZ2bzijNVj/QbXltIVC3AbAAAATII/cwMAYIAzRyNl/rRDVo81blNSWnUpx3UHAAAwEQIYwCSio2MkNiY2x3aqjWoL4PbqvswYt0uSk1KzHAstGSD9h9bTq40BAADAPAhgABNQdV9aNe4sb7/xQY5tX3tpjG676gbLVQPIue7L5fNxWY55ernL06Mai6+/F5cQAADAZAhgABP46/eV+vm+Hh1zbNutVyf9AfLPpSts0DPA9axacJy6LwAAAMiCAAYwgYP7D4u7u5vUa1g7x7ZNmjXQxXr37bVeuwJA9k4eviazJ+6yeoy6LwAAAOZGAAOYwMWwyxIQECD58vnk2NbXN58EBgbI5UtXbNI3wJXqvkwevZG6LwAAALCKZagBE1BTihISE3PdXrVV5wC4ubovF89mLWBN3RcAAAAojIABTKBIaGFJiE+QY0dP5NhWtYmPi5fChQvZpG+AK6DuCwAAAHJCAAOYQL2GtfTzV19+n2Pbr6Z8p5fHrdcg/RwAt173pV7LUGnVpRyXEAAAAAQwgBn0eaC7niKxZMFfMv69zyUhISFLG7Xvw3cnyuIFf+mv73+gmx16CrhO3ZfCxf2kz7PVdaAJAAAAUAMGMIEq1SpJ/0f7yDdf/SCzvvlJFsxfKo2b1ZfiJYrq4+fOXpBN67dJVFS0/vqB/r2kRq1qdu414Nx1XwaMqC35/PjfLAAAANLxL0PAJIYMf1q8vL1lxtTvJTIySpb9sTrLh0kPD3cZ+PiDMmjIY3brJ+AqdV9Klg+0eZ8AAADguAhgAJNQ0yCeHfKYdO3eXhb8slR2bN0tly+H6/0hIQWlTv2a0rV7BylZqri9uwo4dd2Xxm1K6rov0dHpI8oAAAAAhQAGMJlSpUvIoOcZ4QIYUfcltGSA9B9aj7ovAAAAyIJVkAAAyKO6L0+Paiy+/l5cTwAAAGTBCBjAZFJTU2X/3oNy7myYxMfHS5du7e3dJcBl6r6UrljA5n0CAACAcyCAAUzkh+/ny9RJ38jV8GuWfdcHMJERUfLIg4MlNSVVpn37iRQKKWinngLOWfcFAAAAyA5TkACTGDf2U/0Iv3JV/Pz9rNaoCMofKNWqV5JTJ8/In0tX2qWfgCOi7gsAAABuFwEMYAIb1m6WOd/9LL6++eTDT9+W1ZsWSnDB/Fbb3tvxbl3nYtOGrTbvJ+CIqPsCAACAvMAUJMAE5s75VY94eWrwQGnT7s4btq1bv6Z+PnLouI16Bzg26r4AAAAgLzACBjCBXTv26eduvTrl2DYwKFD8/f3k8uVwG/QMcGzUfQEAAEBeIYABTCDiWqT4B/hJYGBArtp7eHhISnKK4f0CHBl1XwAAAJCXCGAAE1AjWuJi4yQ5OTnHtlFR0fqRv0CQTfoGOCLqvgAAACCvEcAAJlCuQmlJTU2Tg/sP59h2+R+r9YfPqtUq2aRvgCOi7gsAAADyGgEMYAKt27bUocr0Kd/dsN25M+flswlTdcHetve2sln/AEdC3RcAAAAYgQAGMIHe/bpJseKhsmr5Onll6OhMI2Hi4xPk6JET8vXUWfJgr6ck/MpVKV+hjHTs2s6ufQbsgbovAAAAMArLUAMmkC+fj3w6+T15+tFh8seSFfLn0pWWY3fUb2/ZVqNkioSGyIefjRFPT349wFyo+wIAAAAjMQIGMIkKlcrJD79Mlx69O4u3j7f+sHn9QwUuXXt0kO9/+lLKlC1p7+4CNkfdFwAAABiJP3EDJlKwULCMfHu4vDxyiOzdc1AuXbwsqampUiikoNSoVVV8ffPZu4uAXVD3BQAAAEYjgAFMyMvbS+rWr2mT94qNjZXVq1fLtm3bZPv27fpx7NgxPepmwIABMmPGDJv0A8gOdV8AAABgCwQwAAy1adMm6dChA1cZDom6LwAAALAVAhjABKKjY+TIoePi5+8rlatUyHTs0sUr8v6YT2Tjui3i5u4md7ZuLkNfflYKFiyQZ+8fHBwsdevWlXr16unHuHHjZPfu3Xn2+sCtou4LAAAAbIUABjCB+XMXyscfTJbefe+TV958wbI/OTlZBj02TI4dPalHAihLFvwlhw4c1cV4vbxu/1dEy5YtJTw8PNO+yZMn3/brAreLui8AAACwJVZBAkxgw7ot+rl957sz7VdLUh89ckIHLQMe7ydPDR4ofn6+cvTwcZn/08I8eW8PD488eR0gL1H3BQAAALZGAAOYwOmTZ/VzpcoVsgQwbm5u8sSgATJk2FPy1LMD5dVRL+rRMMt+X2Wn3gLGou4LAAAA7IEABjCBq+HX9MgW/wC/TPu3bdmpnzvfd49lX9t7W+lQ5sjh4zbvJ2AL1H0BAACAPRDAACaQkJAoqampmfadOHZKoqNipETJYhJatIhlv7e3twQGBkhUZLQdegoYi7ovAAAAsBeK8AImkL9AkIRfuSpXLodLoZCCet/G9Vv1c+261bO0T0pKEn//zKNlHEWzZs2s7t+zZ49Uq1ZNoqKibN4nRxQTE2PvLjic+Nhk+WLUBklOyhxGKoWL+0mPJytJdHTeBY/cA/vjHthPWmp6YffrJSYk8jvaDlzhv4PAwEB7dwEA8gQjYAATqFa9sn7+9usf9HNcXLz8NOdXPdWoYZN6mdqqkEYdL1Q4PagBXKXuyw+f75PL5+OyHPP0cpcBI2pLPj/+JgEAAADj8K9NwAS6dG8vf6/eIN9+/aOsXrFOYmLi5NLFyxIQ6C9t770rU9ttW3bp5/IVyogjWr9+/Q1HxvBXssy4HulW/nZMtq8Js/qz029wbalWp3ie/pxyDxwL/x3Ynpu7W5Z93j5qiisjGeyFaw8A9scIGMAE2rW/S7r16qhHAZw4flqHLz4+3vLG28MlIMA/U9s/lizXI2MaNKprt/4CeenUkWsye2J6sPhfjduUlFZdynHBAQAAYDhGwAAm8eY7I6Rr946ya/sePfKlSfOGugDv9ZISkyQoKFCvitSiVVO79RXIK3ExSTJ59CardV9CSwZI/6H1dOAIAAAAGI0ABjCRuvVr6kd2vLy95I13XrJpnwCjqBFfM8dvl7Az0Vbrvjw9qrH4+ntxAwAAAGATTEECALikVQuOy+YVZ7Kt+1K6YgGb9wkAAADmxQgYAIa7evWqpKSkWL5OTk7WzwkJCXL58mXLfi8vL8mfPz93BLeNui8AAABwNAQwAAxXr149OXnyZJb9c+bM0Y8MrVq1kpUrV3JHcFuo+wIAAABHxBQkAIDLoO4LAAAAHBUjYAAY7sSJE1xl2AR1XwAAAOCoGAEDAHAJ1H0BAACAIyOAAQA4Peq+AAAAwNERwAAAnBp1XwAAAOAMCGAAAE6Nui8AAABwBgQwAACnRd0XAAAAOAsCGACAU6LuCwAAAJwJAQwAwOlQ9wUAAADOhgAGAOB0qPsCAAAAZ0MAAwBwKtR9AQAAgDMigAEAOA3qvgAAAMBZEcAAAJwCdV8AAADgzAhgAABOgbovAAAAcGYEMAAAh0fdFwAAADg7AhgAgEOj7gsAAABcAQEMAMBhUfcFAAAAroIABgDgsKj7AgAAAFdBAAMAcEjUfQEAAIArIYABADgc6r4AAADA1RDAAAAcCnVfAAAA4IoIYAAADoW6LwAAAHBFBDAAAIdB3RcAAAC4KgIYAIBDoO4LAAAAXBkBDADA7qj7AgAAAFdHAAMAsDvqvgAAAMDVEcAAAOyKui8AAAAwAwIYAIDdUPcFAAAAZkEAAwCwC+q+AAAAwEwIYAAAdkHdFwAAAJgJAQwAwOao+wIAAACzIYABANgUdV8AAABgRgQwAACboe4LAAAAzIoABgBgM9R9AQAAgFkRwAAAbIK6LwAAADAzAhgAgOGo+wIAAACzI4ABABiKui8AAAAAAQwAwGDUfQEAAAAIYAAABqLuCwAAAJCOKUgAAENQ9wUAAAD4FwEMACDPUfcFAAAAyIwABgCQ56j7AgAAAGRGAAMAyFPUfQEAAACyIoABAOQZ6r4AAAAA1hHAAADyBHVfAAAAgOwRwAAA8gR1XwAAAIDsEcAAAG4bdV8AAACAGyOAAQDcFuq+AAAAADkjgAEA3DLqvgAAAAC5QwADALhl1H0BAAAAcocABgBwS6j7AgAAAOQeAQwA4KZR9wUAAAC4OQQwAICbQt0XAAAA4OYRwAAAbgp1XwAAAICbRwADAMg16r4AAAAAt4YABgCQK9R9AQAAAG4dAQwAIEfUfYGr+nbbTvF9Y4xUGf+ZOJOTV6/pfquH2gYAAI7P094dAAA4Puq+ALZxLS5eJq7fqLcHN2siBXzzcekBAHARBDAAgBui7gtcWVA+H6kcUkiKBwWKI4iIj5exK9bo7Yfr1ck2gPH0cNf9ztgGAACOjwAGAJAt6r7A1d1Xvap+OJsSQUGyc8gz9u4GAAC4CfzJBABgFXVfAAAAgLzDCBgAgFXUfcHtumf6N7LmxCl5vXVLeblVC5m4fpPM2blHjoVfFXc3N6ldLFSeatxQetWqnqvzJ23cIj/u2itHr4TLtfh4+aFfb+lavYql/aIDh2TG1h2y5cw5uRIXK0HePlK9SIj0b1BXHqhTW9zd3awW4X1y/gIpXSC/HBz2nNV+xCYmydTNW+XXfQfk4KUrEpWYIIV8/aRRqRLyZOP60rZihRtehx3nLsiXm7bI3ydOybmoKHETNz3lqU6xUOleo5rcV62q7lvG95uh6kcTM73O9X1UhXczjh8YOljKBBfI8r5RCQkyacNm+XX/QX3N4pOTpWhAgLQsV0aeb95EahUNtdpfVZD41LUI+bJ7F+lTu6ZM3LBJ5uzYLUfDr4qnu7vUK15UhrZsLvdUuvH3nR1VOFj5/dGHpGrhwjJu9VpZfOCQvjZBPj5yR5nS8tKdd0j9EsVu6fUBAHBUBDAAgCyo+4K8lJiSIp1nzpLVx0+Kh7ubBHr76ABFBRLqser4Cfm0Swdxc8sakCgJySnS/uvvZN3J0/p89SH9+qZJKSny5M8LZM6uPfprdaxAvnz6PdacPK0fs3bslh8e6C2BPj431fd9YZekx3dz5OS1CP21Co4CvL3lQnS0LNh/UD+ebtJQJnRun+Xc1NQ0Gfnncpnw93rLPl8vT8nn6SFHroTLoctXZO7ufXL+teG61kuwr6+E+PnJ5dhY3VZtq+83Q4i/X677feDiZen6zSw5HRGpv/b28NDvrb6Pk9t36evxQcd7ZFDTRtm+RnRiorSd/o1sPnNWvDzcxcfDUyITEmTV8ZOy+sRJmXRfZxnQoK7cqpNXI2Tg3F/lfFSU5PP01H28FBMrv+w7IAsOHNQB0AN1a9/y6wMA4GgIYAAAmVD3BXlt6qZtEpuUpEOK/vXqiJ+3l1yIipa3l62Ur7fukGmbt0nN0CLyVJOGVs+fvHGLpEmaTOrWSe6vVVOfr1YLUsGO8tZfKy3hy5A7msrwls11WHH+yhWZuWO3jFm9VlYcOyFPz18o3/ftmet+h0VHS6eZ3+u+dqpSSUa0aiF1ixUVb08PuRoXp0fbjF2xWvevUkihLGHGO8tXWcKXPrVryIg7W0j10ML665jERFl78rR8s22nDnUUFRBdP7Ll76cftTqyJSfRCYnS8/sfdPhSxN9fPunSQTpXrayL9argZ9ii3+WPw0f1c/mCwdK+ckWrr6P67+PpqUcadahSUbw8POTQpSvyxPzfZNPpszJ88R/SrUZVyZ/v1lZqGrHkT/H39pJfHu4r7SpW0KOAdl8Ik+d+WyIbT5+Rp39ZKDVDQ/VIKQAAXAE1YAAAFtR9gRHUSJSPOt2rR4qo8EQpGhggX3TrLD1qVNNfj1m+WhKSk7MdifFVz24ysEE9y/lqxEiRAH85Fxkln/2zbLMKQN5r39YyUkSNVHm2cQN59962+uuf9+7Xozly651lq3T4ovo498H7pXGpEjp8UdRolRdbNJOpPbrqr99buUaSU1It56opPx+sWau3n23WWGb07m4JXxR/b289hee7Pj30Skx56cvNW/U0LzV6Zt5DfXRIkrFSUsVCBeWnB++3TO95belf2b6OCs0WDXhQT/NS4YtSuXAhfb4asaLuy+KDh2+5nzFJifJr/35yb+WKlulhalrUwgEPSNngApKUkiqjl6285dcHAMDREMAAACyo+wIjlAgKlEca1LN6bGSbO/Wzmnaz7Ohxq22qFQ7JVOvlevP37tcf1NX0lddat7Ta5pkmjXTtE+WHXXtz1WcVBs3auVtvD7+zebbTo1T9FjUlSk2d2XbuvGX/d9t3SUpqmp4K9Xbb1mJLc//5HjtXrSINSxbPclyFKSNbp1/3/Zcuy87zF6y+TrfqVTOFRhkK+/tLk1Il9PaeCxdvuZ/dq1eTGqFFsuwP8PGWF+5oqreXHjqil+YGAMAVMAUJAKBR9wVGaVWurNUCuEq1IoUlNMBfwqJjZNvZc9KxSqUsbZqXKZXta287mx561ClWVAr5Wa+RokZ/tCpfRocvW8+ey1Wft549L3FJ6SNyun8754Zt1UgQRRWuVaNklHWnTuvnu8qXtYzasYXE5BTZHRamt++uUC7bdqpfaoSMConUNVHX778alUz/XqwpFhion9VUrFvVJof+KalpaTogurNc+tcAADgzAhgAAHVfYCi14s+NlAgK0gHMxegYq8fViIvsXIpJP6dk/qAbvkfJ/PnT22fzHv+lCsNmUH3LjbikpH/PiYrWz7dSw+V2hMfF6VAlp2vi6+Wli/yq7+1SdHrR3/9SI3uykzGlKSn132lXeflzoX4mbvb6AwDg6AhgAMDkqPsCR3f9SkC2khFiKJdGjtDTYm5GdlOWAACAeVEDBgBMjrovMJoqlJub46qo7s3KGB1zNjJ9ueXsnIlIX0a6cC7fo2jgv+1OXrt20/1S06r0uVdv/tzbUdDX1xJY3eiaqNE6V2LTpw8VDsj98ta2+rm4/ljGtQQAwNkRwACAiVH3Bbaw+sRJPdLKmgMXL8uF6PTpOvVLZC0Ym5MG/xSZ3XHugoT/Eyj8V0pqqqw6fjK9fS7fQ7VThX2VxQdufqWfZqXT69asPHZCYhP/nZp0MyNn1NLbN0ut0lS7aPqyzcuPnsi23erjJyX5n+lDub0meU1dm2yPHU8/ppbotlafBgAAZ0QAAwAmFReTJJNHb5LkpKw1HEJLBkj/ofWYRoE8cSYiUmZu22H12P9WrtbPhfx8pU357IuyZket1OPl4S6JKSny/qq/rbaZsmmrXk5a6VO7Rq5eVy0T3bdOTb09Ye16OR5+9Ybt/xv+PFy/th6JopbgfvOvFbn8bkTyX7ck9dW4W1v9p3et9O9xwf6DOpj6L7Vc9tgVaywrTNkr4FDLgu+/eCnL/pjERPlk7Qa9fW+lCpI/Xz479A4AgLxHAAMAJkTdF9iSWor5hYVL5ctNWy2FalUgMvjXRTJ39z799Wut75R8Xp63VMj1uWZN9Pan6zbK678vkyuxsZbViSZt3iYvL/lTf92zZvUbruzzX6Pbttavr4KQu76cITO37pBr14UiagWgxQcPy6M//SJ3T5uZ6dzyBQvKsBbN9fbn6zfJI3N/yRQ2qJBh4f5D0uu7HyQyPsGyX4UNGcVzZ27bKUkpKTd9TZ5o1EDKFwzWI1zUCk6/7TuoRwEpR6+ES+9ZP8rmM2f11/9r31bsxc/LS7p+M1v+PHxUUv+puaOWte4yc5YcC78qnu7u8ubdd9mtfwAA5DWK8AKACVH3Bbb0ROP6suHUGRmyYIkMW/S7BPp465EhGbOSBjaoK880aXjLr/9W27t0vRO1zPRHf6/XI1ZU6KOCjZR/3kQtazypW6ebet2igQGyZOBD0nf2XNl/6bI8/ctCeebXhfq1VbgRlZC+/LRSoWBwlvNH3X2XxCYlycT1m2TOrj36oUKHfJ6e+vtXSywrGc8ZnmzUQI+ambJxi3y9ZbsU9vcTD3d3KREUKMufGJhjv1XB4HkP9pGu38yS0xGR0mf2XPHx9BBfTy/9vhlTez7oeI+0r1xR7GVch3Yy6q+VOoTx9fIUL3cPiUxIsPTv8/s6St3iTD8CALgOAhgAMBnqvsDWVC2VRQMflM/Wb5Ifdu6Ro+HhEuDtrWuVPNm4odyfy2lB2fHy8JAZvbtLr5o15Out22XLmXN6OWa1jHLNIiHycIN68mDd2uJ+C6spVS5cSDYMekJm79wt8/ful53nw+RKXKwenVE2uIDUDC2iw50eNatlOVe9nwo51JSgLzdtkbUnT+uRPyq8qViooNQrXlR61KieZbnn4Xc2l6B8PvL9jt1y8NJlORcVZQmrcqtqkRDZMvgpmbRhs/y2/6AcvnJFh0Gl8gdJq/Jl9aih2sXSa8XYS5ng/LJh0ON66pgaSaSW/lZLYzcvU0peuvMOafhPfR8AAFyFW1p2VfEAwIk0a9ZMP69fv97eXXEIUVHpK4gEBgZmqfvyztMrJOxMej2M/9Z9eWNya/H197JZP814D8zknunfyJoTp+T11i1lZJtWDnkP1AiTQb8ukkqFCsquFwbZsHeubfWjd0pSZOa6OWW6PyYVH3xBfN8Yo7/+/dGH5M5yZe3UQ/PgdxEAOA5qwACASVD3BcjbJbABAABuBgEMAJgEdV+AzE5evSY/7Nqjt5uUKsnlAQAAhqIGDACYAHVfgH/tC7sk7aZ/o+vEKKoezZONG3CJAACAoQhgAMDFqbovk0dvkuSk9GVo/1v3pf/QeuLmdvPFSQFnlZSaosMXtZpRs9Il9VLHZYIL2LtbAADAxRHAALCJmJgYmTBhgvz0009y9OhRva9ChQrSs2dPGTp0qPj7U3/BCNR9gT398Vh/h7wBdYoVlbh3Rtq7G6bFtQcAmBUBDADDnT59Wlq3bm0JXnx9fXUwsHPnTv2YOXOmrFixQkqVKsXdyGPUfQGQG3FJSTJiyZ96OejSBfLLQ3Vry8P16oi3pwcXEACAPEIRXgCGSklJka5du+rwJTQ0VBYvXqxHw8TGxsqiRYukSJEi+phqk5qadYoMbt2ZY1Eye+Iuq8catykprbqU4/IC0F5cuFSmbd6mV4XacOqMDP5tsdT8+HOZvnmbJCancJUAAMgDBDAADPXNN9/Ijh079PbcuXOlQ4cOut6IenTs2FFPSVJUG9UWeSM+NllmjttF3RcAubLowOEs+05HRBLEAACQhwhgABhqxowZ+rlVq1bSsmXLLMfVvoz9aioS8saKX07KpXOxWfZ7ernL06Mai6+/F5cagEWJoMBsrwZBDAAAeYMABoBh4uLi5O+//9bbarRLdjp16qSf16xZo8/B7du35ZLV/f0G15bSFVntBUBmb7VrLfk8b1wa8GaCGL/iZbPs8y/BtEcAgLlRhBeAYfbv32+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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "a1880e46c837722a31591ba36ea0a36c4a182a1580a8ae1dc3e362fdc265dfab",
      "outputs": []
    },
    {
      "sourceHash": "35c243d7edf7f8a893f0460c74fbde2198a6df64aa6b420dc0f3296a474a2a09",
      "outputs": []
    },
    {
      "sourceHash": "c943ebd8966f2c418efa94e8b7a246117f59a8266c1d26a83b278726d7032b58",
      "outputs": []
    },
    {
      "sourceHash": "205b962939660d6c0a53e92d21428de75ace7afb9a81eff3943c976e9122ae16",
      "outputs": []
    },
    {
      "sourceHash": "62bba43fb07c8f5e61d3acc92d5f7ec921ed4a2d69077961bb4ff48ff3ecdfc4",
      "outputs": []
    },
    {
      "sourceHash": "2797b9e2895680b200dc303d84b15ba90fdbfdb6632884b54441fe2fcf148fc3",
      "outputs": []
    },
    {
      "sourceHash": "32946f14cc9245ab94369547fbeecdf99dd193c510fed94392a1bab00e21de76",
      "outputs": []
    },
    {
      "sourceHash": "32c4e3e54c4d1371ef0ef39ac3469c1c70cf247f57d72e3f69023eb625606984",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}