LLMs-Pharmaceutical / Git / [404218] /Code/PennyLane/Benchmarking Devices II/lightning.kokkos 25 qubit CPU 65.76s (4) kkawchak.ipynb

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[404218]: / Code / PennyLane / Benchmarking Devices II / lightning.kokkos 25 qubit CPU 65.76s (4) kkawchak.ipynb
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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "rqYMIG5_jAmf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ce7a6a09-debc-4b17-e8fe-9be155700dd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1701142962.9254959\n",
            "Tue Nov 28 03:42:42 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane custatevec-cu11 pennylane-lightning-kokkos\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "30MBcn8AjAmh",
        "outputId": "92543c53-22ff-4b1c-9c71-5d71cf871b4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.6  0.6  0.72]\n",
            " [0.6  0.14 0.17]\n",
            " [0.72 0.17 0.14]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def create_data_point(n):\n",
        "    \"\"\"\n",
        "    Returns a random undirected adjacency matrix of dimension (n,n).\n",
        "    The diagonal elements are interpreted as node attributes.\n",
        "    \"\"\"\n",
        "    mat = np.random.rand(n, n)\n",
        "    A = (mat + np.transpose(mat))/2\n",
        "    return np.round(A, decimals=2)\n",
        "\n",
        "A = create_data_point(3)\n",
        "print(A)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZNWu6j_jAmh"
      },
      "source": [
        "Let\\'s also write a function to generate permuted versions of this\n",
        "adjacency matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9AlMlrsVjAmi",
        "outputId": "f649f139-f5de-454d-f57d-406a7ab1bb77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.14 0.17 0.6 ]\n",
            " [0.17 0.14 0.72]\n",
            " [0.6  0.72 0.6 ]]\n"
          ]
        }
      ],
      "source": [
        "def permute(A, permutation):\n",
        "    \"\"\"\n",
        "    Returns a copy of A with rows and columns swapped according to permutation.\n",
        "    For example, the permutation [1, 2, 0] swaps 0->1, 1->2, 2->0.\n",
        "    \"\"\"\n",
        "\n",
        "    P = np.zeros((len(A), len(A)))\n",
        "    for i,j in enumerate(permutation):\n",
        "        P[i,j] = 1\n",
        "\n",
        "    return P @ A @ np.transpose(P)\n",
        "\n",
        "A_perm = permute(A, [1, 2, 0])\n",
        "print(A_perm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Js9CqRTZjAmi"
      },
      "source": [
        "If we create [networkx]{.title-ref} graphs from both adjacency matrices\n",
        "and plot them, we see that they are identical as claimed.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Q7N1C_MKjAmi",
        "outputId": "7192ba01-6671-4536-c896-20657c013a46"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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EdLRnLF++HOnp6Rg4cKDoKIrGwo5eS1xcHIKDg00+DPu08ePH4+HDh5nbxxARkWls2rQJXl5emDNnDqZMmYLTp0+jadOmomM9J2PRRJcuXYStxjUXLOzotQQFBUGv1+dpYVe5cmV07doVM2fOhF6vz7PzEBFZi9u3b6NLly7o3LkzvL29ERoaikmTJsHe3l50tBcKDg7G+fPnuWgiG1jY0WuRJAnly5dHxYoV8/Q8AQEBuHz5Mv755588PQ8RkSVLT0/HTz/9hOrVq+PYsWNYs2YNtm7dmudt+OvSarWoUKECWrVqJTqK4rGwo9eSsY1YXk+ubdiwIZo3b45p06ZBluU8PRcRkSU6duwYGjRogAkTJmDQoEEICwtD9+7dFbM4IisxMTFYvXo1F01kE/+EKNciIiIQGhqap8OwTwsICMDRo0dx8ODBfDkfEZEliI2NxahRo9C4cWOoVCocPXoUv/32G1xdXUVHy5YVK1YgLS0NgwYNEh3FLKhkdn9QLi1fvhz9+vVDZGQkihYtmufnMxgMqFWrFipWrIhNmzbl+fmIiMyZLMtYs2YNxo4di4SEBHz33XcYNWoUbG1tRUfLNlmWUbt2bbzxxhtYv3696DhmgT12lGuSJKFOnTr5UtQBgFqtxoQJE7B582aEhYXlyzmJiMzR1atX0a5dO/Ts2RO+vr4ICwvDRx99ZFZFHWAcPg4JCeGiiRxgYUe5Isty5vy6/NS7d2+ULFkSM2bMyNfzEhGZg9TUVEydOhXe3t4ICwvD5s2bsX79epQuXVp0tFzRarUoV66c0P1pzQ0LO8qVS5cu4e7du/le2Nnb22Ps2LFYvnw57t27l6/nJiJSsqCgINSpUwdff/01xowZgwsXLqBjx46iY+VabGwsVq1ahaFDh3LRRA7wT4pyRZIk2Nvbw8/PL9/PPXz4cDg4OODXX3/N93MTESnNo0ePMHjwYDRv3hxubm44deoUpk2bBmdnZ9HRXstff/0FnU7HRRM5xMKOckWSJPj6+gppOFxdXfH+++/jjz/+QHx8fL6fn4hICWRZxuLFi1GtWjVs3LgRf/75Jw4ePIhatWqJjvbaZFnGn3/+iY4dO6JUqVKi45gVFnaUY+np6di7d2++D8M+7aOPPkJSUhLmzZsnLAMRkShhYWFo0aIFBg0ahLfeegsXL17E8OHDLWbI8sSJEzh79iwXTeSCZfwGUL46ceIE4uLihBZ2pUqVQu/evfHzzz8jLS1NWA4iovyUnJyML774ArVr18a9e/ewe/duLF++3OL2T9VqtShTpgzatm0rOorZYWFHOSZJElxdXVGvXj2hOSZMmIA7d+5g1apVQnMQEeWHXbt2oWbNmpg+fTomTZqEkJAQoV+w80pcXBxWrlyJoUOHwsbGRnQcs8PCjnJMkiT4+/sLvx+St7c32rdvj+nTp3ObMSKyWPfv30evXr3Qtm1blClTBufOncPkyZPh6OgoOlqeWLlyJZKTkzF48GDRUcwSCzvKkcTERBw+fFgx3xInTpyIkJAQ7Ny5U3QUIiKT0uv1mDt3LqpVqwZJkrB06VLs2bMHVatWFR0tT2m1WnTo0MFs770nGgs7ypEDBw4gLS1NMYVds2bN0KBBA0ybNk10FCIikzlz5gx8fX0xatQodO/eHZcuXUK/fv2gUqlER8tTJ0+exKlTp7ho4jWwsKMckSQJpUuXRpUqVURHAQCoVCpMnDgRe/fuxcmTJ0XHISJ6LQkJCfj4449Rv359JCYm4sCBA5g3bx4KFy4sOlq+0Gq1KF26NN566y3RUcwWCzvKkYxtxJT0rfGdd95BpUqVMH36dNFRiIhy7Z9//oGXlxd+//13fPfddzh16pSQm8CLkpCQgL/++gtDhgwRPofbnLGwo2yLjIzE2bNnFTMMm8HGxgbjx4/H2rVrce3aNdFxiIgAGO/5mR23bt1Cly5d0KVLF3h7eyM0NBSffvop7O3t8zihsqxatQpJSUlcNPGaWNhRtu3ZswcA0KpVK8FJnjdw4EAULlwYP//8s+goRESYNGkSvvzyS9y+ffulr1u3bh28vLxw7NgxrF27Flu3bkWFChXyKaWyaLVatGvXDmXLlhUdxayxsKNskyQJ3t7eKF68uOgozylQoABGjx6NBQsW4NGjR6LjEJGV2rx5M4oXL46dO3eiZs2aL522otfr4eXlhSFDhuDixYvo1q2boqa55KfTp0/j+PHjXDRhAiqZNwCjbJBlGeXLl8e7776r2F6xR48eoWzZsvj000/x1VdfiY5DRFZGr9ejc+fOaNCgAb7++mvRcczKBx98gH/++Qc3b97k/LrXxD89yparV6/i1q1biptf9zQPDw8MHjwYv/32GyZMmIACBQqIjkREVmTLli24ePEiNm3ahLt372L27NlIT09HvXr14O/vj2LFisFgMFjMfq6mkpiYiOXLl2Ps2LEs6kyAv12ULZIkwdbWFs2aNRMd5aXGjx+PqKgoLFmyRHQUIrISGQNfNjY2UKvVOHDgAFq3bo1Lly7h0qVL+Pzzz9GvXz8AYFH3AqtXr0ZCQgKGDBkiOopF4FAsZUu3bt3w4MEDHDhwQHSUV+rRowdOnjyJS5cucZ9BIso3a9euxR9//AEnJydUqVIF06dPh8FgwOHDh9GpUyf8+OOPGDlyJHvt/qNx48YoXLgwtm3bJjqKReBvFr2SXq/Hnj17FD0M+7SAgABcvXoVGzduFB2FiCzI0aNHcffu3eduY5LRP9KsWTNERERg27ZtaNGiBWxsbGBnZ4cmTZpgyJAhWLFiBQD22j3t7NmzOHr0KBdNmBB/u+iVTp8+jejoaLMp7OrXrw9/f39MmzYN7JAmotd18uRJ1KxZE126dEHz5s3RtWtXXLhwIfN5lUoFg8GAYsWKZRYohw4dynze3t4e0dHR8PDwgE6nY7v0lHnz5qFEiRLo0KGD6CgWg4UdvZIkSXBxcUHDhg1FR8m2gIAAHD9+HEFBQaKjEJEZi46OxpdffommTZvi4MGD+P3333Hp0iVMmDABZ86cAWAc1ci4TcmoUaPQvn17/PPPP9i1axdkWUZ0dDTu37+PFi1awMHBwWpvafJfSUlJWLZsGQYPHgw7OzvRcSwGCzt6JUmS0KJFC7P64L311lvw9vbmNmNE9Fru3LmDvXv3olevXqhUqRJat26NOXPmQK/X44cffgBgXDRhMBig1+thZ2eHb7/9Fo0bN8Zbb72FNm3aoHLlypBlGT179hT80yjLmjVrEB8fz0UTJsbCjl4qOTkZBw8eNJth2AwqlQoBAQHYunUrQkNDRcchIjMVGRmJN95445ntvVq1aoUOHTrg/Pnz+PvvvwEYi7uMxVp169bF4sWLsXPnTrzzzjtYvnw5du7cqcibu4uk1WrRpk0bq91pI6+wsKOXOnToEHQ6ndkVdgDQs2dPlCpVCjNmzBAdhYjMVO3atXH9+nWcO3fumcfbtWuHKlWqYMuWLTAYDEhOTsaCBQtw/PjxzNe0bt0aH3zwAdq1a5ffsRUvJCQER44c4aKJPMDCjl5KkiQUL14cXl5eoqPkmL29PcaNG4cVK1bg7t27ouMQkRny8PBAjx498Msvv+Dx48eZj7/xxhsoU6YMrl+/DlmWERISgi+++ALLly8XmNZ8zJs3D8WKFUOnTp1ER7E4LOzopSRJwptvvmm2k32HDRsGJycn/PLLL6KjEJGZ+vrrr3H9+nVotVokJydnPl6pUiWEhITAYDCgYcOG+PHHH/Hll18KTGoeuGgib7Gwoyw9fvwYp06dMsth2AyFChXCyJEj8eeffyI2NlZ0HCIyQ2XKlMGUKVMwc+ZMLF26FAkJCUhNTcWhQ4fQu3fvzG2w+vfvDw8PD8FplW/dunWIiYnB0KFDRUexSNx5grK0bt06dO/eHbdv30bp0qVFx8m1e/fuoXz58pg6dSoCAgJExyEiMzVu3DisX78exYsXR0JCAhISErB69Wo0adJEdDSz4ufnhwIFCmDXrl2io1gkFnaUpffffx/79+9HWFiY6CivbciQIdi5cyeuXbv2zOo2IqLs0ul0uHjxIo4ePQqDwYD3339fdCSzExoaCm9vb6xduxbdunUTHccisbCjLFWuXBnt2rXDb7/9JjrKawsLC4OXlxcWL16MAQMGiI5DRAp169YtnDt3Dm+99VbmECuZztixY7Fy5Urcvn2bX7LzCOfY0Qtdv34dV69eNev5dU+rXr06OnbsiOnTp3M7HyJ6Tnp6OmbOnAkvLy98+umnkGWZbYWJJScnY+nSpRg0aBCLujzEwo5eKDAwEGq1Gi1atBAdxWQmTpyI0NBQbN++XXQUIlKQ4OBg1K9fHxMnTsSQIUNw+PBh2NnZme3dAJRq/fr1iI6O5qKJPMahWHqhnj174ubNmzhy5IjoKCYjyzKaNGkCR0dH7Nu3T3QcIhIsJiYGkyZNwp9//om6devijz/+QP369UXHsljNmjWDvb09JEkSHcWisceOnmMwGBAYGGgxw7AZVCoVJk6ciP379+PYsWOi4xCRILIsY+XKlahWrRpWrFiBWbNm4ejRoyzq8lBYWBgOHDjAnSbyAQs7es65c+fw6NEjiyvsAKBz586oXLkypk+fLjoKEQlw5coVtG3bFr1794afnx/CwsLw4YcfZu7zSnlj3rx5KFq0KLp06SI6isVjYUfPkSQJBQoUQOPGjUVHMTkbGxtMmDABGzZswNWrV0XHIaJ8otPp8N1338Hb2xvh4eHYsmUL1q1bh1KlSomOZvFSUlKwZMkSDBw4kIsm8gELO3qOJElo1qwZHBwcREfJE/3790eRIkXw008/iY5CRPlg//79qFOnDiZPnoyxY8ciNDQUHTp0EB3LamzYsAFRUVFcNJFPWNjRM3Q6HYKCgixyGDaDk5MTxowZg4ULF+Lhw4ei4xBRHnn06BEGDhyIFi1aoHDhwjh16hT+97//wdnZWXQ0q6LVauHv748qVaqIjmIVWNjRM44cOYLk5GSLLuwA4IMPPoBarcacOXNERyEiE5NlGQsXLkTVqlWxadMmaLVaHDhwADVr1hQdzepcunQJ+/fv56KJfMTCjp4hSRKKFi1q8Q1gkSJFMGTIEMyePRtJSUmi4xCRiVy4cAHNmzfHkCFD0L59e1y8eBHDhg2DWs3LnQjz5s1DkSJF8M4774iOYjX4m07PkCQJrVq1sopGcPz48YiJicGiRYtERyGi15ScnIzPP/8cderUwf379yFJEpYtWwZPT0/R0ayWTqfD4sWLMXDgQIuds61Eln/1pmyLiYnB8ePHLX4YNkP58uXRvXt3zJw5E+np6aLjEFEu7dixA97e3pgxYwY+//xznDt3Dq1atRIdy+pt3LgRjx8/xrBhw0RHsSos7CjTvn37YDAYrKawA4CAgABcv34dGzZsEB2FiHIoIiICPXr0QLt27VC+fHmEhITg66+/hqOjo+hoBOOiiebNm6Nq1aqio1gVFnaUSZIkVK5cGeXKlRMdJd/UrVsXrVq1wrRp07jhN5GZ0Ov1mDNnDqpVq4a9e/di2bJlkCSJqy4VJDw8HHv37uWiCQFY2FEmSZKsqrcuQ0BAAE6ePMn9Y4nMwOnTp9GkSROMHj0aPXr0wMWLF9G3b1+oVCrR0egp8+fPR+HChfHuu++KjmJ1WNgRAOD27du4dOmSVRZ2bdq0Qa1atbjNGJGCxcfHY9y4cahfvz6Sk5Nx8OBBaLVaFC5cWHQ0+g+dTodFixZhwIABHBYXgIUdAQACAwOhUqng7+8vOkq+U6lUCAgIwPbt2xESEiI6DhE9RZZlbNy4EV5eXvjzzz/xww8/4NSpU9BoNKKjURb++ecfPHr0iIsmBFHJnFhEAPr27YtLly7h+PHjoqMIkZaWhkqVKsHf3x9LliwRHYeIANy8eRNjxozB5s2b0aFDB8yePRvly5cXHYte4c0334ROp8OBAwdER7FK7LEjyLJstfPrMtjZ2WHcuHH466+/cPv2bdFxiKxaWloaZsyYAS8vL5w8eRLr1q3D5s2bWdSZgStXriAwMJCLJgRiYUcIDQ3FgwcPrLqwA4ChQ4fC2dkZv/zyi+goRFYrODgY9evXxyeffIKhQ4ciLCwMXbt25eIIMzF//ny4ubmhW7duoqNYLRZ2BEmS4OjoaPVzVgoWLIgPPvgAWq0WMTExouMQWZXo6GiMHDkSvr6+sLOzw7Fjx/DLL7+gUKFCoqNRNqWmpmLRokXo378/nJycRMexWizsCJIkQaPRcPUSgDFjxkCn0+HPP/8UHYXIKsiyjL/++gvVqlXDihUr8Msvv+Do0aOoV6+e6GiUQ5s2bUJkZCQXTQjGws7KpaWlYd++fVY/DJuhRIkS6N+/P3755RfodDrRcYgs2pUrV9C2bVv06dMHzZo1Q1hYGMaMGQMbGxvR0SgXtFotfH194e3tLTqKVWNhZ+WOHj2KxMREFnZP+fjjjxEREYEVK1aIjkJkkXQ6HaZMmQJvb29cvnwZW7duxdq1a1GqVCnR0SiXrl27ht27d3PRhAKwsLNykiTB3d0dPj4+oqMoRrVq1dC5c2fMmDEDBoNBdBwii7Jv3z7Url0b3377LcaOHYvQ0FC0b99edCx6TfPnz4erqyu6d+8uOorVY2Fn5SRJQsuWLTn08R8BAQEICwvDtm3bREchsggPHz7EgAED4O/vDw8PD5w+fRr/+9//UKBAAdHR6DWlpaVh4cKF6NevH/8+FYCFnRWLi4tDcHAwh2FfQKPRoEmTJpg2bZroKERmzWAwYMGCBahWrRo2b96MefPmISgoiPOwLMiWLVvw4MEDDsMqBAs7KxYUFAS9Xs/CLgsTJ07EgQMHEBwcLDoKkVkKDQ1F8+bNMXToUHTo0AEXL17E0KFDoVbz0mNJtFotGjdujJo1a4qOQmBhZ9UkSUK5cuVQqVIl0VEU6e2330aVKlUwY8YM0VGIzEpSUhI+++wz1KlTB5GRkQgMDMTSpUvh6ekpOhqZ2I0bN7Bz50721imIregAJE7GNmK8o/uLqdVqzJ49myv1iHJgx44d+OCDD3Dv3j18+eWX+OSTT+Dg4CA6FuWRBQsWoGDBgnjvvfdER6En2GNnpSIiIhAaGsph2Fdo3bo1qlatKjoGkeLdu3cPPXr0QLt27VCxYkWEhITgq6++YlFnwdLT07FgwQL07dsXzs7OouPQEyzsrFRgYCAAoGXLloKTKB9XDBNlTa/XY/bs2ahevTr27duH5cuXY/fu3XjjjTdER6M8tnXrVkRERHAYVmFY2FkpSZJQu3Ztznkholw7deoUmjRpgjFjxqBnz564ePEi+vTpw+kdVkKr1aJhw4aoXbu26Cj0FBZ2VkiW5cz5dfR6oqOjodfrRccgylfx8fEYN24cGjRogJSUFBw6dAh//vkn3N3dRUejfHLr1i1s376dvXUKxMUTVujSpUu4e/cuC7vXsGvXLvz8889ITEwEAHTt2hVDhgyBi4uL4GREeUeWZfz999/48MMPERUVhf/9738YO3Ys7OzsREejfLZgwQK4uLigR48eoqPQf7Cws0KSJMHOzg5NmzYVHcXsHDlyBB999BFOnz6Nt956Cx07dkRYWBhWrVqF06dPY/HixaIjkgUxyEB8GhCdBkSnArFpQJoBMMA43GKnBlztAHd7wN0OKGgHqPNoFPTmzZsYPXo0tmzZgo4dO+K3335D+fLl8+ZkpGgZiyb69OnDL7MKxMLOCkmSBF9fX65iyiZZlqHX6zF9+nR8++23aNeuHebMmQNvb284OTkBAE6cOIEWLVrgf//7H4oXLy44MZm7hHTgWgJwPRFIl42PqQDIL3htVCpwzdhxDFsVUMEZqOgCuJiodU9LS8OsWbPwzTffwN3dHevXr8c777zDeXRWbPv27bh79y6HYRWKhZ2VSU9Px969exEQECA6itlQqVS4dOkSli5dim+++QbDhw9/bi6Rg4MDKlSogIiICBZ2lGtROiA0DojUPV/Ivaio++/j6TJwJQG4nAB4OgA1XIHC9rnPc+TIEYwYMQKhoaEYM2YMpkyZgoIFC+b+gGQRtFot6tevDx8fH9FR6AW4eMLKnDhxAnFxcZxfl0OSJMHe3h5DhgyBu7s7ZPnfy2lSUhKmTJmChIQEVKxYUWBKMld6GQiJAfY+BB7qjI9lVci9Ssb7HuqAvZHG4+pzeLDo6Gi8//770Gg0cHBwwPHjxzFr1iwWdYTbt29j27Zt7K1TMPbYWRlJklCoUCHUr19fdBSzEhERgdKlS8PDwwOAsRcvOTkZBw4cwOrVqxEWFoYFCxbA1dVVcFIyN9GpwNHHQOKTxdW5Lej+K+M44QnAvRSgYWHjXLyXvkeWsXLlSowbNw7Jycn49ddfMXLkSN7LkTItXLgQBQoUQM+ePUVHoSyo5Ke7HsjitWjRAm5ubvj7779FRzErN27cgJeXFyZPnox27drhxIkTOHPmDIKDgwEAw4YNQ79+/WBnZ8e5R5RtD1KAw4+MRVheNsSqJ//4egDFHF/8msuXL+ODDz6AJEno3r07Zs2ahZIlS+ZhKjI3er0e5cuXR/v27fHnn3+KjkNZ4FCsFUlMTMThw4c5DJsL5cuXx48//oitW7eiVq1amD59Os6dO4eWLVti7dq1GDJkCOzt7VnUUbY9SAEOPTKucM3rb9cyjOc59Mh43qfpdDp8++23qFmzJq5cuYKtW7dizZo1LOroOTt27MCdO3c4DKtw7LGzIjt27EC7du0QFhaGatWqiY5jlh4+fIi4uDgkJSWhaNGimQslMj5GLOwoO6JTgX2RxmIrv6kBtPA0Dsvu3bsXI0eOxNWrVzFhwgR8+eWXKFCggIBUZA46d+6MO3fu4OTJk6Kj0Euwx86KSJKEUqVKcVP711C0aFFUqlQJJUuWRPHixaHX62EwGKBSqfDf70j8zkQvopeNc+pE/XYYAByJTMegIUPRsmVLeHh44PTp0/jhhx9Y1FGW7t69iy1btrC3zgywsLMiGduIsVcp9wwGAz799FP06NEDd+7cgY2NDQwGY7+LWm38OGXsRsE/Z3qRC7HGhRIiy/5Egxo2Zb0xf/58BAUFwdvbW2AaMgcLFy6Ek5MTevXqJToKvQJXxVqJyMhInD17FhMmTBAdxayp1Wp4enrCzc0NcXFxAABbW+PH6MSJE1iwYAESExMREREBjUaD3r17o0qVKiIjk4JE6YyrVEVTq9VoN+Qj+HuqoObXe3oFvV6P+fPno1evXihUqJDoOPQK/EhbiT179gAAWrVqJTiJ+fvwww+xYsUKeHl5ATDuvTts2DA0bdoUGzZsgK2tLfz9/XH8+HH06dMHoaGhghOTUoTGGVenKoEKKoTGik5B5mDXrl24desWh2HNBAs7KyFJEmrUqIESJUqIjmL2bG1t4eDgAAB49OgRAgICsHPnTlSuXBlly5ZFVFQU+vXrl7mC9sMPPxScmJQgId24o0RWQ7BpOh2Wff8JhtUrid6VnfBpp0Y4G7Q728c/tGk1PuvcBH2qOKN/DTd81sUXIYf2ZPl6GcY8Cek5+znI+mi1WtSpU4f3PzUTLOysgCzLmfPryHRkWcYXX3yB4OBgzJgxAyEhIdixYwdq1aqFdu3aAQA+/fRTnDp1CmFhYYLTkmjXEl7eWzd7/EBsmfcTmr7TB4Mm/wK1jQ2+H9AeYccOvvLYq3/6BrNG90KRkmUw4Kuf0CvgO5SrXgtR9+++9H2qJ7mIsnLv3j1s3rwZw4cP57xhM8E5dlbg2rVruHnzJgs7E0tISMDly5fxzTff4L333gMAFClSBH379sWWLVtw4cIFVKhQAV9//TVSUlJecTSyZAYZuJ6YdW/d5dPHcGjTKvT7fDo6v2+cB9u8a3+Mf9Mby76fiO//PpzlscNPBWPdrG/R/8uZ6DRsXI5yyTDm8nYF1Lxm0wssWrQIDg4O6N27t+golE3ssbMCkiTBxsYGzZs3Fx3FotjZ2SE8PBylSpV65vF169YhISEBHh4ecHBwQJ8+fVCnTh0xIUkR4tOA9Jcsgw3etg5qGxu07vPvHCZ7R0e07DkE4SeP4NG921m+d8v8WXArWhwdhnwEWZaRnJizLrh0GYjncCy9gMFgwLx589CzZ09ul2hGWNhZAUmS0LhxY27gbWKOjo5455138MsvvyAwMBDh4eFYvnw5Vq5ciXbt2sHT0xNqtRpFixblEIaVi057+fPXQ0+jZMUqKFDw2RWHles0BADcCD2T5XvPHwpEpdoNsG3hrxhcuyj6VSuIofVKYPvi2dnPl5rtl5IV2b17N27evMlFE2aGQ7EWTq/XY8+ePRgzZozoKBZp2rRp+OCDD9C/f3+4uroiPDwcffv2xZQpU0RHIwWJTjXOZ8uq0y76QQTcPJ9f2OT+5LGoB/de+L6EmGjERT3CpROHcP7wHnQf+zWKliqLPWsWYcGXY2Bja4c2fUe8NJsKQEwqAOfs/zxkHbRaLWrVqoWGDRuKjkI5wMLOwp05cwZRUVGcX5dHHB0dMWfOHNy4cQMXLlyAr69v5spjWZbZU0cAgNi0l9+QODUlGXb2Ds89bu/gmPn8i6QkGYdd46MfY9ycVdC83QMA0LhDN4xvXRPrf/3ulYWdDCDmFT2KZH0iIiKwadMmzJo1i+2YmeFQrIWTJAkuLi5o1KiR6CgWy8nJCdWrV0fXrl1RokQJGAwGFnX0jLRXbApr7+iEtFTdc4+n6lIyn8/qfQBga2eHxh26ZT6uVquh6dQDjyPu4OHdW6+dj6zP4sWLYWdnhz59+oiOQjnEws7CSZKE5s2bw87OTnQUq6FWq1nU0TNeVTe5FyuBmMiI5x6PfvJY4WIlX/g+F7fCsHdwhIt7EdjY2DzzXKEingCAxNjo185H1iVj0USPHj3g5uYmOg7lEAs7C5acnIwDBw5wGJZIsFc1tOW96uDetXAkxcc98/jl00eNz9eo8+LjqtUoX6MO4h4/RFrqsysgop/MyytUuOhr5yPrEhgYiOvXr3PRhJni59mCHT58GDqdjoVdPtPr9TAYDEhL48QlMrJ7RUvbuEM3GPR67F6hzXwsTafD3jWL8IZPI3iULAMAeHj3Fu5eufjMe3079YBBr8e+dUsyH0tNScGBjStQuooXChd/cW9fTvKRddFqtfD29kbjxo1FR6Fc4OIJCyZJEooVK4YaNWqIjmJVdDodmjRpgsGDB+Ojjz4SHYcUwNUOiErNegFFFZ9GaNKxO/76cRLiHkeiePnK2LduCR7euYEPpi/IfN1vY/vjQvB+rLv975Fa9xmBwJXzseCLUYi4Fg6PUmURtH4ZHt69iU8XbX5lNhUAN87UoCcePHiAv//+Gz/99BOnlJgpfk+zYBnbiPHDmb8KFCiAWrVq4aeffmKvHQEA3O1fvioWAMb8vBQdhozF/g3LsPDrD6FPS8OkRVvg1bjZS9/n4OSEb1bvgV/n3tizeiGWTQ2ASq3GZ4u3oq5/u1dmkwG42Wf/ZyHLtnjxYtja2qJv376io1AuqWRZflV7Q2YoKioKHh4eWLhwIQYOHCg6jtU5d+4cateujRUrVnArHkJsKiBFik6RtTeLGXsVyboZDAZUqVIFGo0GS5YsefUbSJHYY2eh9u7dC1mW0apVK9FRrFKtWrXQtm1bTJs2DfzuRAXtAFuFdpzbqoCCnJRDMF43rl69ykUTZo6FnYWSJAlVq1ZFmTJlREexWhMnTsTZs2chSZLoKCSYWgVUcDbOZ1MSFYy51EoLRkJotVp4eXnB19dXdBR6DSzsLFTG/DoSx9/fH3Xr1sW0adNERyEFqOjy6nl2+U2GMRdRZGQkNm7ciGHDhnFetpljYWeBbty4gStXrrCwE0ylUiEgIACSJOH06dOi45BgLraAp4Nyeu1UMOZx4TAsAViyZAnUajX69esnOgq9JhZ2FigwMBBqtRotWrQQHcXqdevWDeXLl8eMGTNERyEFqOGqnF47GcY8RLIsQ6vVolu3bihSpIjoOPSaWNhZIEmS0KBBA24FowC2trYYP348Vq9ejZs3b4qOQ4IVtgeqKGTos0pBYx6iffv24cqVK1w0YSFY2FkYg8GAwMBADsMqyODBg+Hq6oqff/5ZdBRSAC9XwNlG3JCsCsbhV69CggKQ4mi1WlStWhVNmzYVHYVMgIWdhQkJCcHDhw9Z2CmIs7MzRo0ahXnz5iEqKkp0HBLMRgU0KiK2sGtY2JiD6OHDh9iwYQOGDx/ORRMWgoWdhZEkCU5OTmjSpInoKPSU0aNHw2Aw4PfffxcdhRTA3R7w9cj/4k4FQONhPD8RACxduhQA0L9/f8FJyFS484SFadeuHWRZxo4dO0RHof8YOXIkNmzYgJs3b8LR0VF0HFKABynA4UfGhQx53RCrYSzqPPmrR0/Isoxq1aqhXr16+Ouvv0THIRNhj50F0el0CAoK4jCsQo0fPx4PHz7M/IZMVMwRaOEJFLDJ2/O42BrPw6KOnhYUFITw8HAumrAw7LGzIPv370eLFi1w+vRp1KlTR3QceoFu3bohJCQEFy5cgI1NHl/NyWzoZeBCLBCeYBwuNUWjnHGcKgWNCyU4p47+q2/fvjh27BguXbrE+XUWhD12FkSSJHh4eKBWrVqio1AWAgICEB4ejk2bNomOQgpiowJqugH+RYGiDsbHcnuZzXhfUQfA3xOo6cqijp73+PFjrFu3josmLBB77CxIkyZNUK5cOaxatUp0FHqJ5s2bIzU1FYcPH2aDSi+UkA5cSwCuJwLpT1rorHryZL0eUKuhUqlg+2RP2oou3FGCXm7WrFmYOHEi7t69i6JFi4qOQybEHjsLERsbi2PHjnF+nRkICAhAcHAwDh06JDoKKZSLLVDLDehUEnizGFDPHajoDBSxBwrZGp8vZGv8f6f4CMz5eBDKxYajU0nj+1jU0ctk7DTx7rvvsqizQPz4W4h9+/bBYDCwsDMD7du3h5eXF6ZPnw4/Pz/RcUjB1CrA1c74D5xf/Jokl8LosnEFTrRsjPo1quRrPjJPhw4dQlhYGGbPni06CuUB9thZCEmSUKlSJZQvX150FHoFtVqNCRMmYNOmTbh48aLoOGTmChQoAB8fH/YAU7ZptVpUrlyZ+4lbKBZ2FkKSJPbWmZHevXujRIkSmDFjhugoZAH8/Pxw8OBB0THIDERFRWHNmjUYNmwY1GqWAJaIf6sW4M6dO7h48SILOzPi4OCAsWPHYtmyZYiIiBAdh8ycRqPBjRs3cPfuXdFRSOGWL18Og8GAgQMHio5CeYSFnQUIDAyESqWCv7+/6CiUAyNGjICDgwN+/fVX0VHIzGk0GgDgcCy9VMaiiS5dusDT01N0HMojLOwsgCRJqFu3LooUKSI6CuWAq6srRowYgd9//x3x8fGi45AZK168OCpVqsTCjl7qyJEjCA0N5U4TFo6FnZmTZZnz68zYRx99hMTERMybN090FDJzGo2GhR29lFarRcWKFdGyZUvRUSgPsbAzcxcuXMD9+/dZ2Jmp0qVLo0+fPpg1axbS0tJExyEzptFocObMGSQkJIiOQgoUHR2N1atXc9GEFeDfrpmTJAkODg6Zc2zI/EyYMAG3b9/G6tWrRUchM+bn5we9Xo+jR4+KjkIKtGLFCqSnp3PRhBVgYWfmJEmCn58fnJycREehXPL29ka7du0wbdo0cIc/yq1q1arB3d2dw7H0nIxFE507d0bx4sVFx6E8xsLOjKWlpWHfvn0chrUAEydOREhICHbt2iU6CpkptVoNX19f3s+OnnP06FGEhIRw0YSVYGFnxo4dO4aEhAQWdhagefPmqF+/PqZNmyY6CpkxPz8/BAcHQ6/Xi45CCqLValG+fHleK6wECzszJkkS3N3d4ePjIzoKvSaVSoWJEydiz549OHnypOg4ZKY0Gg3i4+MREhIiOgopRGxsLFatWsVFE1aEf8tmTJIktGzZEjY2NqKjkAm8++67qFixIqZPny46Cpmp+vXrw87OjsOxlGnFihVITU3FoEGDREehfMLCzkzFx8cjODiYXesWxMbGBuPHj8fatWtx/fp10XHIDDk5OaF+/fpcQEEAjIsm/vzzT7z99tsoUaKE6DiUT1jYmamgoCCkp6ezsLMwgwYNgru7O37++WfRUchM8UbFlOH48eM4d+4cF01YGRZ2ZkqSJJQrVw6VKlUSHYVMqECBAhg9ejQWLFiAx48fi45DZsjPzw+3b9/GrVu3REchwbRaLcqVK4fWrVuLjkL5iIWdmcrYRkylUomOQiY2atQoyLKMuXPnio5CZsjX1xcA2Gtn5eLi4rBy5UoMHTqU87CtDAs7M2QwGPDRRx9hwIABoqNQHihatCgGDRqE3377DcnJyaLjkJkpWrQoqlSpwsLOym3cuBE6nY6LJqyQSuat7s1SWloa7OzsRMegPHL16lVUqVIFc+bMwfvvvy86DpmZIUOG4OTJkzhz5ozoKCSALMvQ6/UICgpCy5YtRcehfMbCjkih3nvvPZw+fRoXL17kUArlyMKFCzFs2DBER0ejUKFCouOQIAaDgfeus0L8GydSqICAAFy5cgV///236ChkZjQaDQwGA4KDg0VHIYFY1Fkn/q0TKVSDBg3QokULTJs2DexYp5yoUqUKPDw8eKNiIivEwo5IwQICAnDs2DEcOHBAdBQyIyqVivezI7JSLOwUbOvWrdi5c6foGCRQu3bt4O3tzW3GKMc0Gg2Cg4ORlpYmOgrlkRUrVuDw4cMAjPPpiAAWdoo2efJkDB48GKtXr84ciuOH17qoVCpMmDABW7ZswYULF0THITPi5+eHpKQknD17VnQUygPJyckYMWIEPvvsMzx48IDz6SgTfxMU6sGDBzhx4gTefPNNjBs3Dl9//TUAToa1Rr169UKpUqUwY8YM0VHIjNStWxcODg4cjrVQ3377LQoXLoy0tDR06NAhs4Dnl39ilaBQq1evho+PD5YsWYLhw4dj2rRpaNGiBY4cOQLA+OHlhHrrYG9vj7Fjx2L58uW4d++e6DhkJhwcHNCgQQMuoLBQc+fOxdy5c/HHH38gMTERH330Ee7evcsv/8TCTqlWrlyJzp07AwC++eabzMY5ICAAhw4dglqt5nZiVmT48OFwcnLCL7/8IjoKmRE/Pz8cOnSIXwItzMKFC+Hq6oqOHTuiZs2a+OeffxATE4P33nsP586dA8CeO2vGwk6hjh8/jm7dugEA9Ho96tevj5kzZ8LFxQUtW7bEZ599hqSkJMEpKb8UKlQI77//Pv744w/ExcWJjkNmQqPRICIiAjdu3BAdhUzo66+/xrhx4wAA6enpqFKlCn777TfExcVh1KhRuHfvHnvurBj/5hXozJkzqFWrFry8vAAgc9eBevXqYceOHZg8eTKWLl2KYcOGITU1VWRUykcfffQRkpOTodVqRUchM+Hr6wsAHI61ILGxsWjTpk3mVoO2trYAgKZNm2L9+vVISUlB9+7dcenSJZExSSBuKaYwsixDpVK9cC/Yp7eHWblyJebPn4/AwEARMUmQwYMHY9euXbh27Rrs7e1FxyEzUKNGDTRt2hR//PGH6CiUhzKuD/v378eIESNQoEAB7N+/HwULFhQdjfIZe+wURqVSQa/XP1fUAc+uiO3VqxfWr1+fn9FIASZMmIC7d+9i5cqVoqOQmeCNii1LVvclzLg+NG/eHKtXr0a1atVY1Fkp9tgpWEbvXU6fI8vWqVMn3LhxA+fOnePvAL3SkiVLMHDgQERFRcHd3V10HDKRrK4Ber0eNjY2SEpKQoECBQQkI9HYY6cwiYmJSE9PB4BnPrT/rb95QbdeAQEBOH/+PHbs2CE6CpkBPz8/AMi8VRKZJ1mWsXv3bty/fx/Av9eA/14bMuZks6izXuyxU5hvvvkGsiyjUaNGqFy5MkqWLAkXF5dnXrNmzRq4uLigffv2glKSSLIso3HjxihQoAD27t0rOg4pnCzLKFGiBIYMGYKpU6eKjkO59Ouvv+KLL75Ajx498Oabb6JBgwaoUKHCM1/yDQYDVCoVv/hbORZ2CqLT6eDk5IRixYrBwcEB5cqVQ7169eDj44Nq1aqhQoUKcHFxQcOGDTFixAiMGjVKdGQSZP369ejWrRuOHTuGBg0aiI5DCtetWzc8fPgQ+/fvFx2FcqlBgwZwd3dHUlISbt68CR8fH7Rr1w6+vr6oVq0aHBwccOrUKXzxxRfYuHEjHBwcREcmQWxFB6B/HT58GLVr18a8efOQmJiIv//+G/v27cPq1avh4eGBmjVromzZsjh//jzeffdd0XFJoC5duqBy5cqYPn061qxZIzoOKZxGo8Fnn32G1NRUrqY2Q+Hh4XB0dMSYMWPQqVMnSJKEOXPmYOrUqShfvjzeeust+Pv74/vvv4darWZRZ+XYY6cg+/btw/z58/HRRx890wsTHh6Obdu2QZIk7N69G2+88QbOnz8vMCkpwR9//IFRo0YhPDwclSpVEh2HFOzYsWNo1KgRgoOD0ahRI9FxKIdiYmKwZ88elC9fHnXr1s18PCwsDLNnz8a2bdtgb2+Py5cv48SJE8+8hqwPCzsFSUlJwa1bt1CmTBk4OTm98F52LVq0QP369bkhPCE5ORnlypVD9+7dMWfOHNFxSMHS0tLg6uqKKVOm4OOPPxYdh3Lh6etBeno6bGxsMufSxcXF4c0334SjoyOCgoJExiQF4KpYBXF0dESVKlXg5OQEAJkfYlmWodfr8eDBAwQFBaFjx44iY5JCODk5YfTo0Vi0aBEePXokOg4pmJ2dHRo1asT72Zmxp7/k29raQqVSwWAwZP5z6tQpzrsmACzszIJKpYKNjQ2KFCmCgwcPokWLFqIjkUKMGjUKKpWKPXb0Shk3KuYgjeVQq9VQq9V48OABOnfujB49eoiORArAoVgiMzdmzBisXLkSt27d4r2rKEs7duxAu3btEB4ejjfeeEN0HMqhV92UnjetpwzssVOgjK71p0VERECv1wtKREo2fvx4REdHY/HixaKjkII1adIEKpWKw7FmKqNo0+v1mdeH1NTU554nYmGnQBnd6xlSUlIwbtw4DB48mMUdPadChQro3r07Zs6cyd8PypKrqyu8vb1x8OBB0VEom86ePYugoCCsX78e586dA2DcWSLj+rBkyRIMHToUV65cERmTFIb3sVMAvV6PdevW4fLly4iMjISrqyt8fX3RrFkzODs7Q6fToUCBAnB0dMzcLoboaQEBAahfvz42bNiA7t27i45DCuXn58fdSszEmDFjsG3bNty+fRtVq1aFvb09ypUrh4EDB+Ltt98GAFy6dAmHDx/mHsD0DM6xU4ChQ4di69atcHNzQ4UKFZCSkoLIyEh4enqiW7du+OCDDwD8u7kz0Yu0atUK8fHxOHr0KIdl6IVWrFiBvn374uHDh/Dw8BAdh7KwadMmDBkyBPPnz0fbtm1x8OBBnD59GocOHcKdO3fQunVrfP/991CpVLhx4wbKly8vOjIpCAs7wXbt2oX+/ftjw4YN8PX1RUxMDG7evInz588jMDAQu3btQt++fTF58mTeTZxeKmNy/N69e7lyml7oxo0bqFChAv7555/MXh9Snl69eqFIkSKYPXv2M4+Hh4dj1apVmD17NkaMGIEpU6YISkhKxjl2gu3atQsajQa+vr4AADc3N9SuXRt9+vTBwoUL8c0332D58uUICwsTnJSUrm3btqhZsyamT58uOgopVLly5VCqVCkuoFAwWZZRvXp1HD58+Ln7U1apUgVfffUVPvvsM2zYsAGPHz8WlJKUjIWdYLVr18aZM2ee25w7oyN18ODBqFixIjZv3iwiHpkRlUqFgIAAbNu2jVvO0QupVKrM+9mRMqlUKnTu3Bk6nQ6//vorIiIinntN586dERsbi8uXLwtISErHwk6wt99+G9WqVcPUqVOxceNGREVFQZblzMIuISEBV69e5d5/lC09e/ZEmTJluOUcZUmj0eD48eNISUkRHYWy4O3tjffffx8//fQT3nrrLSxYsAA3b95EXFwcEhISsGfPHqSkpKBx48aio5ICcY6dAoSEhOCTTz7Bjh07UK5cOTRr1gx169bFvXv3sHPnTri6uj7Xo0eUlZ9++gmffPIJrl+/jtKlS4uOQwpz6tQp1KtXDwcPHoRGoxEdh17i/v37+Pzzz7Fy5Uo4OzvD19cXJ06cgKenJ95//32MGDFCdERSIBZ2CnLr1i2sXLkS27dvx5UrV1CjRg34+Phg2LBhqFSpkuh4ZCbi4+NRpkwZDBs2jPPt6Dnp6elwc3PDV199hYkTJ4qOQ9mQkJCANWvW4Ny5c3jjjTfg5+eHWrVqcfU7vRALOwWLjo7m/YkoVyZNmoQ5c+bg9u3bcHV1FR2HFKZ169ZwcnLCpk2bREchIhPjHDsFc3d354bdlCsffvghdDod/vzzT9FRSIE0Gg0OHz7M9oXIArGwUzh2tVNulChRAv369cMvv/wCnU4nOg4pjEajwePHj3Hp0iXRUeglWHhTbrCwU5i0tDTREchCfPzxx7h37x7++usv0VFIYRo3bgy1Ws19YxXuwIEDSEpKYoFHOcLCTkG2bNmCtWvX8kNMJlG9enW8/fbbmDFjBgwGg+g4pCAFCxZE7dq1eT87BdPpdOjatSu+++47jtxQjrCwU5A5c+Zg6dKl/BCTyQQEBODChQvYtm2b6CikMLxRsbL9/fffePToEfr37y86CpkZFnYKodPpEBQUhDfffFN0FLIgGo0GTZo04W1P6Dl+fn64fPkyHjx4IDoKvYBWq0WzZs1QrVo10VHIzLCwU4jg4GAkJSWxsCOTythmLCgoCEePHhUdhxQk4+bEhw8fFpyE/uvy5cvYs2cPhg8fLjoKmSEWdgohSRI8PDxQq1Yt0VHIwrz99tt444032GtHzyhdujTKli3LBRQKNH/+fLi7u6Nr166io5AZYmGnEJIkoVWrVlCr+VdCpmVjY4MJEyZgw4YNuHLliug4pCB+fn6cZ6cwqampWLRoEQYMGABHR0fRccgMsYpQgNjYWBw7dozDsJRn+vfvj6JFi2LmzJmZjxlkIDYVuJEInI4G9kUCu+8DO+8b/70v0vj4jUTj6wxcrG1xNBoNTp06haSkJNFR6Il//vkHDx8+xLBhw0RHITPFLcUU4J9//kGXLl1w/fp1lC9fXnQcslBTp07Fd999h4s37iDWoQiuJwLpTz79KgAvagieftxWBVRwBiq6AC62+ZOZ8tbZs2dRp04d7Nu3D82bNxcdh2Dc7i0lJQUHDhwQHYXMFJtnBZAkCZUqVWJRR3mq9/DRSK/cGCfSikCV9mwhl9W3u6cfT5eBKwnA5QTA0wGo4QoUts/DwJTnvL29UahQIRw6dIiFnQJcvXoVkiRh6dKloqOQGeNQrAJIksRhWMozehkIiQFO6Vzh1aQFgKwLuVfJeN9DHbA30nhcPfv8zZaNjQ2aNGnCeXYKMX/+fLi5uaFbt26io5AZY2En2J07d3Dx4kUWdpQnolON8+XCE4z/r1bbmOS4GbVceAIgPTCeh8yTRqPB4cOHuTuJYKmpqVi4cCH69+8PJycn0XHIjLGwEywwMBAqlQr+/v6io5CFeZBiXACRpM/b8ySmG8/zICVvz0N5w8/PDzExMbhw4YLoKFZt8+bNiIyM5KIJem0s7ASTJAl169ZFkSJFREchC/IgBTj0CDAg98Ou2SXDeJ5Dj1jcmaOGDRvCxsaGw7GCabVa+Pr6wtvbW3QUMnMs7ASSZZnz68jkolOBw4/yvqD7LxnG83JY1rw4OzvDx8eHNyoW6Pr169i1axd3miCTYGEn0IULF3D//n0WdmQyehk4+jj/i7oMBgDHorigwtzwRsVizZ8/H66urujevbvoKGQBWNgJJEkSHBwcMvdsJHpdF2KBRL24wg4AEtKBC3ECA1COaTQaXL9+Hffu3RMdxeqkpaVh4cKF6NevHwoUKCA6DlkAFnYCSZIEPz8/roAik4jS/bv6VbTweCCKQ7JmI+PLJXvt8t+WLVtw//59DsOSybCwEyQtLQ379u3jMCyZTGiccacIJVABCI0VnYKyq0SJEqhYsSILOwG0Wi0aN26MmjVrio5CFoKFnSDHjh1DQkICCzsyiYR0IFKX9RBsmk6HZd9/gmH1SqJ3ZSd82qkRzgbtzvbxD21ajc86N0GfKs7oX8MNn3XxRcihPVm+XoYxT0J6zn4OEkej0bCwy2c3btzAzp072VtHJsXCThBJkuDu7g4fHx/RUcgCXEt4eW/d7PEDsWXeT2j6Th8MmvwL1DY2+H5Ae4Qde/VKyNU/fYNZo3uhSMkyGPDVT+gV8B3KVa+FqPt3X/o+1ZNcZB78/Pxw+vRpJCTwLy2/LFiwAAULFsR7770nOgpZEJUsy1y/JkDTpk1RrFgxrFu3TnQUMnMGGdh8z7iX64tcPn0Mk95uhH6fT0fn9ycAAFJTUjD+TW8U8vDE938fzvLY4aeC8XkXX/T/ciY6DRuX42y2KqBTSUCtlDFiylJoaCi8vb0RGBiIli1bio5j8dLT01G2bFm88847mDNnjug4ZEHYYydAfHw8goODOQxLJhGflnVRBwDB29ZBbWOD1n3+He6xd3REy55DEH7yCB7du53le7fMnwW3osXRYchHkGUZyYk5681Jl4F4DseaherVq8PNzY3Dsflk69atiIiI4DAsmRwLOwGCgoKQnp7Owo5MIjrt5c9fDz2NkhWroEDBQs88XrlOQwDAjdAzWb73/KFAVKrdANsW/orBtYuiX7WCGFqvBLYvnp39fFwdaxbUajU0Gg1vVJxPtFotGjRogNq1a4uOQhbGVnQAayRJEsqVK4dKlSqJjkIWIDrVOJ8tq0676AcRcPMs8dzj7k8ei3rw4nuXJcREIy7qES6dOITzh/eg+9ivUbRUWexZswgLvhwDG1s7tOk74qXZVABiUgE4Z//nIXE0Gg1++OEH6PV62NjYiI5jsW7duoXt27dDq9WKjkIWiD12AmRsI6ZSceIRvb7YtJffkDg1JRl29g7PPW7v4Jj5/IukJBmHXeOjH2PktPno/P4E+HZ6D58t2YrSVbyw/tfvXplNBhDzih5FUg6NRgNbW1vcunVLdBSLtmDBAjg7O6Nnz56io5AFYmGXz+7fv4/z589zGJZMJs3w8uftHZ2Qlqp77vFUXUrm81m9DwBs7ezQuEO3zMfVajU0nXrgccQdPLz76gLgVflIOfz8/PDo0SNUqFBBdBSLlZ6ejgULFqBPnz5wcXERHYcsEAu7fBYYGAgAXHVGJvOqusm9WAnEREY893j0k8cKFyv5wve5uBWGvYMjXNyLPDcsV6iIJwAgMTb6tfORcqjVaqjVvCzkpe3bt+Pu3btcNEF5hp/gfCZJEmrXrg1PT0/RUchCvOpDXN6rDu5dC0dS/LMbuF4+fdT4fI06Lz6uWo3yNeog7vFDpKU+uwIi+sm8vEKFi752PiJrotVqUa9ePdStW1d0FLJQbHPzkSzLmfPriEzF7hWf4sYdusGg12P3in8naqfpdNi7ZhHe8GkEj5JlAAAP797C3SsXn3mvb6ceMOj12LduSeZjqSkpOLBxBUpX8ULh4i/u7ctJPiJrcfv2bWzbto29dZSnuCo2H4WHh+POnTss7MikXO2AqNSsF1BU8WmEJh27468fJyHucSSKl6+MfeuW4OGdG/hg+oLM1/02tj8uBO/Hutv/Hql1nxEIXDkfC74YhYhr4fAoVRZB65fh4d2b+HTR5ldmUwFws3vNH5AURZZlLvzKpYULF8LJyQm9evUSHYUsGAu7fCRJEuzs7NC0aVPRUciCuNsD1xJf/poxPy/FqlJfYv+GZUiMjUa5arUwadEWeDVu9tL3OTg54ZvVe7Bs6kTsWb0QuuRElPeqg88Wb0WdFm1fmU0G4Gafgx+GFOvBgwcoUKAAChYsKDqKWdLr9Zg/fz569+7NP0PKU9xSLB+98847iI6Oxr59+0RHIQsSmwpIkaJTZO3NYsZeRTIvV69exYoVK7Bjxw5cuHABRYoUQdWqVdGvXz+88847cHR0FB3RrGzduhUdO3bE8ePHUb9+fdFxyIJx9ks+SU9Px969ezkMSyZX0M64J6sS2aqAghwXMCtRUVEYPnw4vL29sWzZMtSqVQszZ87Et99+C0dHR/zvf//DggULXn0gesa8efPg4+ODevXqiY5CFo5Nbj45efIkYmNjWdiRyalVQAVn4ErCy29UnN9UMOZSK7TopOfJsowffvgBkiRhwYIFaNOmDTw8PDKf79OnD2bNmoU//vgDo0aNEpjUvNy9exdbtmzB7NmzOT+R8hwLu3wiSRIKFSrELnjKExVdgMsJolM8S4YxF5mPq1evYs+ePfj666/Ru3fvF77Gzc0NOp2OiyhyYNGiRXBwcMjyz5TIlDgUm08kSYK/vz9sbVlLk+m52AKeDsZeMiVQwZjHhb/uZsXT0xMXLlyAv7//c8/JsowLFy5g6tSpLFByIGPRRK9evVCoUCHRccgKsNnNB4mJiTh8+DBmzpwpOgpZsBquwN4HMqCAXhT5SR4yL4UKFUKVKlXwzTff4Ntvv4WNjQ2uX7+O69ev49SpU9i+fTvKlSuHgQMHsrcum3bv3o2bN2/y3nWUb1jY5YODBw8iNTWV8+soz6Snp+P36T8iPNkeHYd/LHxbqCoFgcK8zYlZ+vXXXzFlyhSULVsWNWrUgKurK6Kjo1GoUCF07doVI0aMQOnSpUXHNBtarRa1a9dGgwYNREchK8HCLh9IkoRSpUqhatWqoqOQBbp06RIGDBiA48ePY9LnX6CgnQpJejELKVQAnG0BL444ma3mzZujcuXKuHnzJk6dOgUA8Pb2RoMGDeDs7Cw4nXmJiIjApk2b8Ouvv7KHk/INC7t8kLGNGD/YZEoGgwFz587FxIkTUbp0aRw6dAiNGzdGdCqwL1JcYdewMGDDX3WzVqpUKZQqVQq+vr7PPJ5x21O2ZdmzaNEi2Nvbo0+fPqKjkBXh4ok89vDhQ5w5c4bDsGRSt2/fRps2bTBmzBgMGTIEp0+fRuPGjQEYd6Lw9cj/hRQqABoP4/nJvC1evBjdunXDpUuXABiH+gFjQceiLnsMBgPmzZuHnj17wtWVE04p/7Cwy2N79uwBALRq1UpwErIEsixj2bJlqFmzJi5evIhdu3bht99+e26IrJijschSI38KPDUAPw/Ak5sRWITExETcunULDx48AIAsV/OnpaXlZyyzIkkSbty4wUUTlO+4pVgeGzZsGI4cOYLz58+LjkJm7uHDhxgxYgQ2btyIfv364ddff4Wbm9tL3xOdChx9DCTq8y6Xi61x+JU9dZYjLi4OCQkJKFmyZJavSUhIQLt27bB9+3a4uPCGhf/VrVs3hIeH4+zZs+zlpHzFHrs8JMsydu/ezWFYem3//PMPvL29ERQUhHXr1mHp0qWvLOoAY7HVujhQ5cl111SXl4zjVClo3AuWRZ1lKVSo0EuLOgBwcXHJ3FGBnnX//n38888/GD58OIs6yncs7PLQtWvXcPPmTRZ2lGuxsbEYNGgQunTpgsaNGyM0NBRdu3bN0TFsVEBNN8C/KFDUwfhYbi81Ge8r6gD4ewI1XblQwloYDIbM/86Yc9eqVSts3LhRVCTFWrx4MWxtbdG3b1/RUcgKcVVsHpIkCTY2NmjevLnoKGSG9u7di4EDByI6OhqLFi3CgAEDXuvbf2EHoGlRICEduJYAXE8E0p9MxFDhxaton37c9smetBVduKOENTAYDIiPj4dKpULBggWfuTdixpy7Dh06YMSIEaIiKlLGookePXpkq1edyNTYPOchSZLQqFEjFCxYUHQUMiPJycmYNGkSfvnlF7Ro0QKLFy9GuXLlTHZ8F1uglhvg7QrEpxvn4cWkAjFpQJoBMMDYlW+nBtzsADd741BrQVtAzd45qxESEgIfHx/4+fmhSJEisLW1Rc2aNVGjRg2oVCqULFkSjRs3xsOHD/Ho0SN4eHiIjqwIe/bswbVr17Bs2TLRUchKcfFEHtHr9fD09MTo0aMxefJk0XHITBw/fhz9+/fHjRs38MMPP+DDDz8UvosEWa+VK1fi9OnTuHbtGtLT0zO3F3N3d0daWhp0Oh2io6OxadMmdOzYUXRcRXjvvfdw4cIFhISEcH4dCcEeuzxy5swZREVFcX4dZUtaWhq+++47TJ06FT4+Pjh16hSqV68uOhZZuV69eqFXr14AAJ1OB51OBwcHB4SGhuL27duws7PD3r17BadUjgcPHmDjxo2YOXMmizoShoVdHpEkCc7OzmjUqJHoKKRwoaGh6N+/P86ePYsvv/wSn332Gezs7ETHIsqk1+vh4OAABwfj6pu6deuibt26AID27duLjKYoS5Ys4aIJEo5jPHlEkiQ0b94c9va8DwS9mF6vx8yZM1GvXj0kJycjODgYX3/9NYs6UhwbG5vnHsuYxRMdHY2wsDDo9Xl4s0QzkLFoonv37ihcuLDoOGTFWNjlgeTkZBw4cIDDsJSl69evo2XLlggICMCoUaNw8uRJ1K9fX3Qsohz78ccfMWTIEBw+fFh0FKH27duHK1eucKcJEo6FXR44fPgwdDodCzt6jizLWLBgAWrVqoWbN29i7969mDlzJpycnERHI3qhyMhIBAcHIyUlBYCxZ+rpNXfjxo1DzZo18eeff4qKqAharRbVq1eHRqMRHYWsHAu7PCBJEjw9PeHt7S06CinI/fv30alTJwwdOhQ9evTAuXPneI9DUrxVq1bB19cXAwYMQFBQENRqNVQqVeZNiosVK4Z3330XgYGBgpOK8/DhQ2zYsIE7TZAisLDLA5Ik4c033+QHnDKtW7cO3t7eOHHiBDZt2oT58+ejUKFComMRvVLr1q0BAAUKFMB7772HMWPGICoq6pm5oC4uLihUqBAePnwoKqZQS5YsgVqtRr9+/URHIWJhZ2pRUVE4efIkh2EJgHFieZ8+fdC9e3e0aNEC58+fR6dOnUTHIsq26tWrw9bWFp9//jnWrl2LEydOwNfXFzNmzMCFCxeQlJSEadOmoU6dOlY5pUCWZWi1WnTr1g1FihQRHYeIhZ2p7d27F7Iso1WrVqKjkGA7d+6Et7c3tm3bhuXLl2Pt2rW8Oz+ZpXr16mHFihVo2rQpgoKCMHbsWKxYsQLt27eHu7s7Dh48iF69esHFxUV01Hy3f/9+XL58mYsmSDG484SJjRw5Env27MGlS5dERyFBEhMTERAQgN9//x2tW7fGwoULUbp0adGxiHJt6tSpWL9+PU6dOpX5WFJSEnbs2AF3d3f4+PhY7b6ovXv3xqlTpxAWFsbpN6QI7LEzsYz5dWSdDh8+jNq1a2PJkiWYO3cudu7cyaKOzN67774LGxsbGAyGzMecnJzw7rvvwt/fH25ubs88Zy0ePXqE9evXc9EEKQoLOxO6ceMGrly5wsLOCul0OkyaNAlNmzaFp6cnzpw5g5EjR7KxJ4tQvXp1bNiw4ZnfZ5VK9cxtT6xxT+OlS5cCAPr37y84CdG/uKWYCQUGBkKtVqNFixaio1A+OnfuHPr164ewsDBMnToVAQEBL7xTP5E5K1OmzHOPWfMXl4xFE127duXcWVIU6/uKlYckSUL9+vXh7u4uOgrlg/T0dPzwww+oX78+ZFnG8ePH8emnn7KoI7ICBw4cwKVLl7hoghSHhZ2JGAwGBAYGchjWSly+fBnNmjXDF198gY8//hjHjx9H7dq1Rccionyi1Wrxxhtv8CbjpDgs7EwkJCQEDx8+ZGFn4WRZxty5c1GnTh1ERkbiwIED+OGHH+Dg4CA6GlG+yFgkYc03VHj8+DHWrVvHRROkSCzsTESSJDg5OaFJkyaio1AeuXPnDt566y2MGjUKAwYMwJkzZ+Dr6ys6FlG+69KlC2bNmiU6hjDLli2DwWDAgAEDREcheg4LOxORJAlNmzaFo6Oj6ChkYrIsY8WKFahZsybOnz+PHTt2YO7cuVZ5M1YitVqNhIQE7Nu3T3QUITIWTbz77rsoWrSo6DhEz2FhZwI6nQ5BQUEchrVAjx49wnvvvYe+ffuiffv2OH/+PNq2bSs6FpFQGo0Ghw4dssrh2EOHDiEsLIyLJkixWNiZQHBwMJKSkljYWZgtW7bA29sbe/fuxZo1a7BixQqueCaCsbB7/PixVe6wo9VqUblyZd7WihSLhZ0JSJKEIkWKcFWkhYiLi8PQoUPRqVMnNGjQAOfPn0f37t1FxyJSjMaNG0OtVuPQoUOio+SrqKgorFmzBsOGDbPKGzKTeeBvpglIkoRWrVrxg24B9u/fj1q1amH16tWYP38+Nm3ahOLFi4uORaQohQoVQq1atayusFu+fDn0ej0XTZCisRJ5TbGxsTh27BiHYc1cSkoKPv74Y/j7+6NcuXI4d+4chgwZwlsZEGXBz88PBw8eFB0j32QsmujSpQuKFSsmOg5RlljYvaZ9+/bBYDCwsDNjJ0+eRN26dTFnzhzMmDEDe/fuRYUKFUTHIlI0jUaDy5cvIzIyUnSUfHHkyBGEhoZy0QQpHgu71yRJEipWrMhCwAylpaXh22+/RePGjeHk5ISTJ09i/PjxHFInygaNRgMAOHz4sOAk+UOr1aJChQpo1aqV6ChEL8Ur2GuSJIm9dWYoLCwMvr6++Pbbb/HZZ58hODgYNWrUEB2LyGyUKVMGZcuWtYrh2OjoaKxevZqLJsgs8Df0Ndy9excXL15kYWdGDAYDZs2ahbp16yI+Ph5HjhzB5MmTYWdnJzoakdnJuJ+dpVuxYgXS09MxaNAg0VGIXomF3WsIDAyESqWCv7+/6CiUDTdv3kSrVq0wbtw4jBgxAqdPn0aDBg1ExyIyWxqNBidPnkRycrLoKHkmY9HE22+/zRXyZBZY2L0GSZLg4+MDDw8P0VHoJWRZxqJFi1CzZk1cu3YNgYGBmDVrFpycnERHIzJrfn5+SEtLw/Hjx0VHyTNHjx5FSEgIF02Q2WBhl0uyLHN+nRl48OABunTpgsGDB6Nr1644d+4cWrZsKToWkUXw9vZGoUKFLHo4VqvVoly5cmjdurXoKETZYis6gLkKCwtDREQECzsF27BhA0aMGAGVSoW///4bnTt3Fh2JyKLY2NigcePGFruAIjY2FqtXr8Znn33GRRNkNvibmkuSJMHBwQF+fn6io9B/xMTEoH///ujatSuaNm2K8+fPs6gjyiN+fn44fPgwDAaD6Cgm99dff0Gn03HRBJkVFna5JEkSNBoN52kpzO7du1GzZk38888/WLp0KdavXw9PT0/RsYgslkajQUxMDMLCwkRHMSlZlvHnn3+iU6dOKFmypOg4RNnGwi4X0tLSsG/fPg7DKkhiYiJGjx6NNm3aoGrVqjh//jz69evHLcGI8lijRo1gY2NjccOxJ06cwNmzZ7logswOC7tcOH78OOLj41nYKURwcDB8fHywcOFC/Pbbb9i1axfKlCkjOhaRVXB2doaPj4/FLaDQarUoW7Ys2rRpIzoKUY6wsMsFSZLg5uaGunXrio5i1VJTU/HFF19Ao9GgcOHCOHPmDEaPHs1JzkT5zNJuVBwXF4eVK1di6NChsLGxER2HKEd4BcwFSZLQsmVLfuAFiouLQ6NGjfDjjz/i22+/xcGDB1GlShXRsYiskkajwbVr1xARESE6ikmsXLkSycnJGDx4sOgoRDnGwi6HEhIScOTIEQ7D5pE7d+7gwYMHSEhIAGCcwPwihQoVQrNmzXDs2DF8/vnnsLXlnXuIRNFoNABgMb12Wq0WHTt2RKlSpURHIcoxFnY5FBQUhPT0dBZ2JibLMkaMGIFWrVqhdevWaN68Oa5du5bl4oeMPV99fHzyOSkR/VfJkiVRoUIFiyjsTp48iVOnTnHRBJktFnY5JEkSypYti8qVK4uOYjHOnj2Lhg0bIjQ0FAsXLsSkSZPg7OyMgQMHAnhxr51areaKVyIF0Wg0FrEyVqvVonTp0njrrbdERyHKFY5f5VDGNmIsKkxn/fr1KFGiBBYvXozChQtDo9GgUqVKaNu2LS5evIhq1aqJjkhEr+Dn54eVK1ciMTERzs7OouPkSnx8PP766y9MmDCBc6jJbLHHLgfu37+PkJAQDsOawLx587B9+3YAwFtvvYUhQ4agcOHCmc8nJibC3d0dHh4eoiISUQ5oNBro9XocO3ZMdJRcW7VqFZKSkrhogswaC7sc2LNnDwBwE/nXIEkSKlasiBEjRmTeqd7X1zdzyy+9Xg8AePToEezt7eHk5GSRWxURWRovLy+4ubmZ9XCsVqtF+/bteR9MMmss7HJAkiTUqlULxYoVEx3F7CQlJaFr165o3749Ro8eDT8/P4SGhgLAM4VbxhD3kSNH4OXlBWdnZ96XjsgMqNVq+Pr6mu0CilOnTuHEiRNcNEFmj1fMbJJlOXN+HeXMrl27UKxYMcTGxuLixYsYP348fH19cebMGcTExDxTuGX897Fjx9CqVavMx//66y+Eh4fne3Yiyj6NRoMjR45k9rybk3nz5qFUqVJo166d6ChEr4WFXTZdvnwZt2/fZmGXC0WKFMHq1aszh2EBwN3dHYmJiUhPT3/mtbIs4+7du4iIiMi8T52Pjw8GDRr03GuJSFn8/PwQFxeH8+fPi46SIwkJCVixYgWGDBnCe2KS2WNhl02SJMHOzg5NmzYVHcXs1KtXD+3btwfw7xy6Dh064Nq1a7h27RqAf29polKpcP36dSQlJWHKlClo0qQJmjZtCp1OBy8vLzE/ABFlS4MGDWBnZ2d2w7GrV69GQkIChgwZIjoK0WtjYZdNkiShSZMmcHFxER3FrGXcQsDOzg7VqlXD0aNHAeCZ28ecPn0aDx48QFRUFM6dO4dff/1VSFYiyhknJyfUrVvX7Ao7rVaLdu3aoWzZsqKjEL029jlng16vx549e/Dxxx+LjqJY8fHxuH79OooVK5atxSVVq1ZFSkoKHj9+DMD4Z5xR9HXr1g2lSpXCu+++m6eZicj0/Pz8sHbtWtExsu3MmTM4duwY/v77b9FRiEyCPXbZcPLkScTGxnJ+XRZmzZqFcuXKoU+fPqhevTr++OMP3L9/HwBeeKuSjOHYVq1aZd7L7umbgZYoUYJFHZGZ0mg0uHXrFm7fvi06SrbMmzcPJUqUQIcOHURHITIJFnbZIEkSChYsiAYNGoiOojg7duzA3Llz8ccff2D16tUYM2YMfvrpJ3z33XcAjKtc/7slWEYRV6ZMGciyjIiIiHzPTUR5w9fXFwDMYjg2MTERy5cv56IJsigs7LJBkiT4+/vzg/8Ce/bsQcGCBfHee+/By8sLkydPxrBhw3Dw4EHMnj0bAJ679UHG/1eoUAEhISHcno3IghQrVgxvvPGGWRR2a9asQXx8PBdNkEVhYfcKSUlJOHToEIdhX8BgMECWZZQvXx7JycmZj/fv3x9NmjTB0qVLcfv27cyCOD4+HsC/PXZdu3bFrVu3ULx48fwPT0R5RqPRmEVhp9Vq0bZtW5QvX150FCKTYWH3CgcPHkRqaioLuxdQq9UoWbIkgoODn5lPU6xYMXTs2BG2trb4559/AAAbN27EyJEjERISkvk6e3t7FC1aNN9zE1He0mg0OHv2bOaXOSU6d+4cgoODudMEWRwWdq8gSRJKliyJatWqiY6iSOPGjYNer4dWq31moUSHDh1gMBhw69YtAMYbgB45csRsJlQTUe75+fnBYDAgODhYdJQszZs3D8WLF0fHjh1FRyEyKRZ2r5CxjRjngWXthx9+wK+//orAwMBnFkp4eHjgypUrAIB+/fph//79mTcqJiLLVbVqVRQpUkSxw7FJSUlYtmwZBg8eDDs7O9FxiEyKhd1LPHr0CKdPn+Yw7CsMGjQI7du3x8cff4yVK1dCp9Ph0qVLuHXrFnr06JH5utKlSwtMSUT5RaVSwdfXFwcPHhQd5YXWrl2L2NhYDB06VHQUIpNjYfcSe/bsAYBnNqOnF1uxYgV8fHwwevRo+Pv7o2HDhqhUqRKHOYislJ+fH4KDgxW5x7NWq0WbNm1QoUIF0VGITI7373gJSZLg5eWFkiVLio6ieM7Ozvj999/x4YcfIiwsDKVKlYK/v7/oWEQkiEajQWJiIs6dO4e6deuKjpPp/PnzOHz4MNatWyc6ClGeYI/dS2TMr6N/paWlISgo6Ll70wFAgQIFUK9ePfTt25dFHZGVq1+/PhwcHBQ3HDtv3jwUK1YMb7/9tugoRHmChV0Wrl27huvXr7Owe0poaCgaN26Mjh07IiEh4bkdJYiIMjg4OKB+/fqKWkCRnJyMpUuXYtCgQVw0QRaLhV0WJEmCjY0NmjdvLjqKcHq9HjNnzkS9evWQkpKCvXv3wtXVlSuFieilNBoNDh48qJgvgevWrUNMTAwXTZBFY2GXBUmS0KhRIxQqVEh0FKGuX78Of39/BAQEYPTo0Th58iTq1asnOhYRmQE/Pz/cu3cPN2/eFB0FgHHRxJtvvolKlSqJjkKUZ1jYvYDBYEBgYKBVD8PKsoz58+ejVq1auH37Nvbu3YsZM2bA0dFRdDQiMhO+vr4AoIjh2AsXLuDgwYPcaYIsHgu7Fzhz5gyioqKstrCLiIhAp06dMGzYMPTs2RPnzp3jkDQR5ViRIkVQrVo1RRR28+bNQ9GiRdG5c2fRUYjyFG938gKSJMHZ2RmNGjUSHSXfrV27Fu+//z7s7OywefNm3oeOiF6Ln5+f8JWxKSkpWLJkCYYNGwZ7e3uhWYjyGnvsXkCSJDRv3tyqGoCoqCj07t0b7733Hlq2bInz58+zqCOi16bRaHD+/HnExMQIy7B+/XpER0dz0QRZBRZ2/5GSkoIDBw5Y1TDszp07UbNmTWzfvh0rVqzAmjVr4OHhIToWEVkAjUYDWZZx5MgRYRm0Wi38/f3xxhtvCMtAlF9Y2P3H4cOHkZKSYhWFXUJCAkaOHIm33noL3t7eOH/+PHr37s3bmBCRyVSuXBmenp7C5tldvHgRQUFBXDRBVoNz7P5DkiR4enrC29tbdJQ8dejQIQwYMAARERGYO3cu3n//fRZ0RGRyKpUKGo1GWGE3b948FClSBO+8846Q8xPlN/bY/UfGNmKWWuTodDp8+umnaNasGTw9PXH27FmMHDnSYn9eIhJPo9Hg6NGjSEtLy9fzZiyaGDhwIBwcHPL13ESisLB7SnR0NE6cOGGxw7Bnz55FgwYN8NNPP2Hq1Kk4cOAAKleuLDoWEVk4Pz8/JCcn4/Tp0/l63o0bN+Lx48cYNmxYvp6XSCQWdk/Zu3cvZFlGq1atREcxqfT0dPzwww9o0KABVCoVjh8/jk8//RQ2NjaioxGRFfDx8YGjo2O+D8dqtVo0b94cVatWzdfzEonEwu4pkiShSpUqKFu2rOgoJnP58mU0bdoUX3zxBT7++GMcO3YMtWvXFh2LiKyIvb09GjZsmK/3swsPD8e+ffu4aIKsDgu7p2TMr7MEsixj7ty5qFOnDh4+fIgDBw7ghx9+4DwTIhLCz88Phw4dgizL+XK+efPmoXDhwnj33Xfz5XxESsHC7ombN2/i8uXLFlHY3blzB23btsWoUaMwcOBAnD17NnPPRiIiETQaDR48eIBr167l+bl0Oh0WL16MAQMGcH9rsjos7J4IDAyEWq1GixYtREfJNVmWsWLFCnh7eyM0NBQ7duzAnDlz4OzsLDoaEVm5Jk2aAEC+DMf+/fffePToERdNkFViYfeEJEmoX78+3N3dRUfJlUePHqF79+7o27cvOnbsiPPnz6Nt27aiYxERAQDc3d3h7e2dLwso5s2bh6ZNm6J69ep5fi4ipeENigEYDAZIkmS23+42b96MYcOGIT09HWvWrEH37t1FRyIieo5Go8GBAwfy9BxXrlxBYGAgli1blqfnIVIq9tgBOH/+PB4+fGh28+vi4uIwZMgQvP3222jQoAHOnz/Poo6IFMvPzw8XLlxAVFRUnp1j/vz5cHd3R9euXfPsHERKxsIOxmFYJyenzDkg5mDfvn2oVasW1qxZgwULFmDTpk0oXry46FhERFnSaDQAjHty54XU1FQsWrQI/fv3h5OTU56cg0jpWNjBWNg1bdrULFZPJScnY/z48fD390e5cuVw7tw5DB48mFuCEZHilS9fHiVKlMizeXabNm1CZGSk2U6rITIFqy/sUlNTsX//frMYhj1x4gTq1auHuXPnYubMmdi7dy8qVKggOhYRUbaoVCr4+fnl2cpYrVYLjUaDGjVq5MnxicyB1Rd2wcHBSEpKUnRhl5aWhsmTJ6Nx48ZwcnLCqVOnMH78eKjVVv/XR0RmRqPR4Pjx49DpdCY97rVr17B7927uNEFWz+orA0mSUKRIEcVusxUWFgZfX19MmTIFn3/+OYKDg+Hl5SU6FhFRrmg0Guh0Opw6dcqkx50/fz7c3Ny4gIysHgs7SUKrVq0U1/tlMBjw888/w8fHBwkJCThy5AgmT54MOzs70dGIiHKtTp06cHZ2NulwbFpaGhYuXIh+/fpx0QRZPWVVM/ksNjYWx44dU9ww7I0bN9CqVSuMHz8eI0eOxKlTp9CgQQPRsYiIXputrS0aNWpk0gUUmzdvxoMHD7hogghWXtjt378fer1eMYWdLMtYtGgRatWqhWvXrmHPnj34+eef+Q2UiCyKRqPBoUOHIMuySY6n1WrRpEkT1KxZ0yTHIzJnVl3YSZKEihUrKmJl6YMHD9C5c2cMHjwY3bp1w7lz5+Dv7y86FhGRyfn5+eHRo0cIDw9/7WNdv34du3bt4qIJoiesvrBTQm/d+vXr4e3tjaNHj+Lvv//GwoUL4erqKjoWEVGeaNy4MdRqtUmGYxcsWIBChQrhvffeM0EyIvNntYXd3bt3ERYWJrSwi4mJQb9+/dCtWzc0bdoU58+fR+fOnYXlISLKD4UKFULNmjVfewFFxqKJvn37okCBAiZKR2TebEUHECUwMBAqlUrYcOfu3bsxaNAgJCQkYOnSpejbty93jyAiq+Hn54fdu3e/1jG2bt2KiIgILpogeorV9thJkgQfHx94eHjk63kTExMxevRotGnTBtWrV0dISAj69evHoo6IrIpGo0F4eDgePnyY62NotVo0atRIsfchJRLBKgs7WZaFzK87cuQI6tSpg4ULF2L27NnYuXMnypQpk68ZiIiUQKPRAECu59ndvHkTO3bs4KIJov+wysIuLCwMERER+VbYpaam4vPPP4efnx+KFCmCM2fOYNSoUYq7KTIRUX4pW7YsypQpk+vCbsGCBXBxcUGPHj1MnIzIvFnlHDtJkuDg4AA/P788P1fGUGtoaCi+/fZbfPLJJ7C1tco/diKiZ2Tczy6n0tPTsWDBAvTt2xfOzs55kIzIfFlll5EkSdBoNHl641+9Xo8ff/wR9evXh16vx7Fjx/D555+zqCMiesLPzw8nTpxAcnJyjt63bds23Lt3j8OwRC9gdYVdWloa9u3bl6fDsFevXkXz5s0xadIkjB07FidOnICPj0+enY+IyBxpNBqkpaXhxIkTOXqfVqtFgwYNUKdOnbwJRmTGrK6wO378OOLj4/OksJNlGX/88Qdq166NiIgIBAUF4ccff4SDg4PJz0VEZO5q1qyJggUL5mg49tatW9i+fTt764iyYDHjggYZiE8DotOA6FQgNg1IMwAGGKtXOzXgagecuPkQ3g01qONT16Tnv3v3LoYOHYodO3ZgxIgRmDFjBlxcXEx6DiIiS2JjYwNfjQah1+/gRuLL2253e8DdDli4aDEKFCiAnj17io5PpEgq2VS7MAuSkA5cSwCuJwLpT34SFYAX/VAqAAZZhkqlgq0KqOAMVHQBXF6zvF21ahU++OADODo6YsGCBWjXrt3rHZCIyMJltN0Xo1JgY+8IQIYKqizb7ozHkxPi8SAkGCPeaf3abTeRJTLbwi5KB4TGAZG6rAu5V8l4n6cDUMMVKGyfs/c/fvwYH3zwAdasWYOePXtizpw5KFy4cC6SEBFZB1O03ZBlQKXKddtNZMnMrrDTy8CFWCA84TUahf/IOE4VF8DLFbDJxiYQ27Ztw5AhQ6DT6TB37lwOCxARvYRS2m4iS2dWhV10KnD0MZCoz7tzuNgCDQsb53O8SHx8PD7++GPMmzcP7dq1w/z581GyZMm8C0REZOaU0HYTWQuzKewepACHHxm/neVlYNWTf3w9gGKOzz4XFBSEgQMHIjIyEj///DOGDh3KPV6JiF5CCW03kTUxi9udPEgBDj0yrpLK6ypUhvE8hx4ZzwsAKSkpCAgIQIsWLVCqVCmcO3cOw4YNY1FHRPQSottuImuk+B676FRgX6TxA5vf1ACKR4VhWO/uuHz5MqZOnYpx48bBxsZGQBoiIvMhuu1u4clhWbJOil4srpeN8zJEVZ56gwEnYmzgWMAZJ0+ehLe3t6AkRETmQ3TbbQBwLAp4sxgXVJD1UXSPXUiMcQWVSLJswBvOMmoXZi8dEVF2KKHtBoAqBYGarqJTEOUvxc6xi9Ipo2FQqdS4kmSDqFTRSYiIlE8pbTcAhMeDbTdZHcUWdqFxxhVOSqACEBorOgURkfKx7SYSS5GFXUK68a7kMoA0nQ7Lvv8Ew+qVRO/KTvi0UyOcDdr9ymPcvXoJi74Zh8+6+KJXZUd0K6NC5O0br3zf/RtXM19/5ewJAMYckTpjLiIierGMtjtVAe02wLabrJMiC7trCf9+45s9fiC2zPsJTd/pg0GTf4HaxgbfD2iPsGMHX3qM8JNHsH3Rr0hJiEepytWzfe7Fk8fBxub5NSWqJ7mIiOjFMtpupbTbANtusj6KK+wMMnA90fhN6/LpYzi0aRV6f/ID+n8xHa37DMc3q/agaKlyWPb9xJcep37rt7EkNAY/SSFo2qVPts59Zt9OnAnaiQ5Dxz33nAxjLoNil5oQEYmT0XaHK6jdBth2k/VRXGEXnwakP/kABm9bB7WNDVr3GZ75vL2jI1r2HILwk0fw6N7tLI9T0L0wnFwKZvu86WlpWPjNR+gw+CMUL1fpxa+RgXh26RMRPSej7VZauw2w7SbrorjCLjrt3/++HnoaJStWQYGChZ55TeU6DQEAN0LPmOy8WxfMQmJsNLp++MXL83GFFRHRczLabiW22wDbbrIeyivsUv+dXxf9IAJuniWee437k8eiHtwzzTkj72PdL1PQc8KU5xqjp6kAxLBxICJ6TkbbrbR2G2DbTdZFcYVdbNq/dytPTUmGnb3Dc6+xd3DMfN4Ulv/wCYqVrYhWvYa+9HUygJi0l76EiMgqZbTdSmu3AbbdZF0Ut6VY2lMbC9o7OiEtVffca1J1KZnPv67wU8EIWr8MX68KhFr96jo3TcTGh0RECpfRNiqx3X46H5GlU1yP3dOfPfdiJRATGfHca6KfPFa4WMnXPt+yqRNRvWFTeJapgMjbNxB5+wbioh4BAGIiI/Dw7q0s8xERkVFG26jEdvvpfESWTnE9dk9XmuW96uD84b1Iio97Zg7F5dNHjc/XqPPa53t07xYe3rmJD3wrPPfc/wa/jQKFXLE0NOaF+YiIyCijbVRiu/10PiJLp7jCzu6pT1/jDt2w6c8Z2L1Ci87vTwBg3Ili75pFeMOnETxKlgEAPLx7C6nJSShVuVqOzzfif1qkJic981jI4T3Yvug39P9ixnPHtGPrQET0nIy2UYnt9tP5iCyd4go7Vzvjps0ygCo+jdCkY3f89eMkxD2ORPHylbFv3RI8vHMDH0xfkPme38b2x4Xg/Vh3+987UCbGxWL7ot8AABdPHAIAbF88G86F3ODs6oZ2A0cDAOo0b/NchsS4GACAV+PmqFy7fubjKgBudib+gYmILEBG2620dhtg203WRXGFnbs9cC3x3/8f8/NSrCr1JfZvWIbE2GiUq1YLkxZtgVfjZi89TmJsNFbN+PKZxzZrZwIAipYul9lA5IQMwM0+x28jIrJ4T7fdSmq3AbbdZF1UsiwraqOV2FRAihSdImtvFjN+MyUion+x7SZSBsXNOihoB9iqXv06EWxVQEHF9XESEYnHtptIGRRX2KlVQAXnf3efUAoVjLnUSgtGRKQAbLuJlEFxhR0AVHT5d/cJpZBhzEVERC/GtptIPEUWdi62gKeDcr75qWDM48KufCKiLLHtJhJPkYUdANRwVc43PxnGPERE9HJsu4nEUmxhV9geqKKQ7vMqBY15iIjo5dh2E4ml2MIOALxcAWcbcd36Khi78L0KvfKlRET0BNtuInEUXdjZqIBGRcQ2Dg0LG3MQEVH2sO0mEkfRhR1gvJu5r0f+NxAqABoP4/mJiChn2HYTiaG4nSey8iAFOPzIOBk2rwOrYWwYPB3z+ERERBaObTdR/jKbwg4AolOBo4+BRH3encPF1tiFz297RESmwbabKP+YVWEHAHoZuBALhCcYu9xNET7jOFUKGifbcl4GEZFpse0myh9mV9hliNIBoXFApC73jUTG+zwdjPc64rJ4IqK8xbabKG+ZbWGXISEduJYAXE8E0p/8JFk1Fk8/bvtkX8OKLrwrORFRfmPbTZQ3zL6wy2CQgfh041yOmFQgJg1IMwAGGCfU2qkBNzvAzd44B6OgLTeFJiISjW03kWlZTGFHREREZO0Ufx87IiIiIsoeFnZEREREFoKFHREREZGFYGFHREREZCFY2BERERFZCBZ2RERERBaChR0RERGRhWBhR0RERGQhWNgRERERWQgWdkREREQWgoUdERERkYVgYUdERERkIVjYEREREVkIFnZEREREFoKFHREREZGFYGFHREREZCFY2BERERFZCBZ2RERERBaChR0RERGRhWBhR0RERGQhWNgRERERWQgWdkREREQWgoUdERERkYVgYUdERERkIVjYEREREVkIFnZEREREFoKFHREREZGF+D/ksS5hFattBwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "\n",
        "# interpret diagonal of matrix as node attributes\n",
        "node_labels = {n: A[n,n] for n in range(len(A))}\n",
        "np.fill_diagonal(A, np.zeros(len(A)))\n",
        "\n",
        "G1 = nx.Graph(A)\n",
        "pos1=nx.spring_layout(G1)\n",
        "nx.draw(G1, pos1, labels=node_labels, ax=ax1, node_size = 800, node_color = \"#ACE3FF\")\n",
        "edge_labels = nx.get_edge_attributes(G1,'weight')\n",
        "nx.draw_networkx_edge_labels(G1,pos1,edge_labels=edge_labels, ax=ax1)\n",
        "\n",
        "# interpret diagonal of permuted matrix as node attributes\n",
        "node_labels = {n: A_perm[n,n] for n in range(len(A_perm))}\n",
        "np.fill_diagonal(A_perm, np.zeros(len(A)))\n",
        "\n",
        "G2 = nx.Graph(A_perm)\n",
        "pos2=nx.spring_layout(G2)\n",
        "nx.draw(G2, pos2, labels=node_labels, ax=ax2, node_size = 800, node_color = \"#ACE3FF\")\n",
        "edge_labels = nx.get_edge_attributes(G2,'weight')\n",
        "nx.draw_networkx_edge_labels(G2,pos2,edge_labels=edge_labels, ax=ax2)\n",
        "\n",
        "ax1.set_xlim([1.2*x for x in ax1.get_xlim()])\n",
        "ax2.set_xlim([1.2*x for x in ax2.get_xlim()])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "eUhWZ9_3jAmi"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "\n",
        "def perm_equivariant_embedding(A, betas, gammas):\n",
        "    \"\"\"\n",
        "    Ansatz to embedd a graph with node and edge weights into a quantum state.\n",
        "\n",
        "    The adjacency matrix A contains the edge weights on the off-diagonal,\n",
        "    as well as the node attributes on the diagonal.\n",
        "\n",
        "    The embedding contains trainable weights 'betas' and 'gammas'.\n",
        "    \"\"\"\n",
        "    n_nodes = len(A)\n",
        "    n_layers = len(betas) # infer the number of layers from the parameters\n",
        "\n",
        "    # initialise in the plus state\n",
        "    for i in range(n_nodes):\n",
        "        qml.Hadamard(i)\n",
        "\n",
        "    for l in range(n_layers):\n",
        "\n",
        "        for i in range(n_nodes):\n",
        "            for j in range(i):\n",
        "            \t# factor of 2 due to definition of gate\n",
        "                qml.IsingZZ(2*gammas[l]*A[i,j], wires=[i,j])\n",
        "\n",
        "        for i in range(n_nodes):\n",
        "            qml.RX(A[i,i]*betas[l], wires=i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glOVsqRvjAmi"
      },
      "source": [
        "We can use this ansatz in a circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "0oMs3Tu0jAmi",
        "outputId": "9188aa15-2497-4fb4-f970-a1acbaca985e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 58400x2600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "n_qubits = 25\n",
        "n_layers = 2\n",
        "\n",
        "dev = qml.device(\"lightning.kokkos\", wires=n_qubits)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def eqc(adjacency_matrix, observable, trainable_betas, trainable_gammas):\n",
        "    \"\"\"Circuit that uses the permutation equivariant embedding\"\"\"\n",
        "\n",
        "    perm_equivariant_embedding(adjacency_matrix, trainable_betas, trainable_gammas)\n",
        "    return qml.expval(observable)\n",
        "\n",
        "\n",
        "A = create_data_point(n_qubits)\n",
        "betas = np.random.rand(n_layers)\n",
        "gammas = np.random.rand(n_layers)\n",
        "observable = qml.PauliX(0) @ qml.PauliX(1) @ qml.PauliX(3)\n",
        "\n",
        "qml.draw_mpl(eqc, decimals=2)(A, observable, betas, gammas)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIHRQVnDjAmi"
      },
      "source": [
        "Validating the equivariance\n",
        "===========================\n",
        "\n",
        "Let\\'s now check if the circuit is really equivariant!\n",
        "\n",
        "This is the expectation value we get using the original adjacency matrix\n",
        "as an input:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZCFN2RkSjAmj",
        "outputId": "5004a4da-3d68-4520-93a4-9f48fa5bdc2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for A: -0.00033458730167252085\n"
          ]
        }
      ],
      "source": [
        "result_A = eqc(A, observable, betas, gammas)\n",
        "print(\"Model output for A:\", result_A)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncmkAwH-jAmj"
      },
      "source": [
        "If we permute the adjacency matrix, this is what we get:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KjwEdmGXjAmj",
        "outputId": "190cd09d-2591-4ec5-856d-02b95cbba82b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A:  0.5159090985451731\n"
          ]
        }
      ],
      "source": [
        "perm = [2, 3, 0, 1, 4]\n",
        "A_perm = permute(A, perm)\n",
        "result_Aperm = eqc(A_perm, observable, betas, gammas)\n",
        "print(\"Model output for permutation of A: \", result_Aperm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOIqQmysjAmj"
      },
      "source": [
        "Why are the two values different? Well, we constructed an *equivariant*\n",
        "ansatz, not an *invariant* one! Remember, an *invariant* ansatz means\n",
        "that embedding a permutation of the adjacency matrix leads to the same\n",
        "state as an embedding of the original matrix. An *equivariant* ansatz\n",
        "embeds the permuted adjacency matrix into a state where the qubits are\n",
        "permuted as well.\n",
        "\n",
        "As a result, the final state before measurement is only the same if we\n",
        "permute the qubits in the same manner that we permute the input\n",
        "adjacency matrix. We could insert a permutation operator\n",
        "`qml.Permute(perm)` to achieve this, or we simply permute the wires of\n",
        "the observables!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "MgYVIMP6jAmj"
      },
      "outputs": [],
      "source": [
        "observable_perm = qml.PauliX(perm[0]) @ qml.PauliX(perm[1]) @ qml.PauliX(perm[3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pb21hBrLjAmj"
      },
      "source": [
        "Now everything should work out!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tpzF6L3TjAmj",
        "outputId": "48f068aa-5d95-4f7d-d677-73b0324651e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A, and with permuted observable:  0.5353897330672126\n"
          ]
        }
      ],
      "source": [
        "result_Aperm = eqc(A_perm, observable_perm, betas, gammas)\n",
        "print(\"Model output for permutation of A, and with permuted observable: \", result_Aperm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wblJk9mjAmj"
      },
      "source": [
        "Et voilà!\n",
        "\n",
        "Conclusion\n",
        "==========\n",
        "\n",
        "Equivariant graph embeddings can be combined with other equivariant\n",
        "parts of a quantum machine learning pipeline (like measurements and the\n",
        "cost function). [Skolik et al.\n",
        "(2022)](https://arxiv.org/pdf/2205.06109.pdf), for example, use such a\n",
        "pipeline as part of a reinforcement learning scheme that finds heuristic\n",
        "solutions for the traveling salesman problem. Their simulations compare\n",
        "a fully equivariant model to circuits that break permutation\n",
        "equivariance and show that it performs better, confirming that if we\n",
        "know about structure in our data, we should try to use this knowledge in\n",
        "machine learning.\n",
        "\n",
        "References\n",
        "==========\n",
        "\n",
        "1.  Andrea Skolik, Michele Cattelan, Sheir Yarkoni,Thomas Baeck and\n",
        "    Vedran Dunjko (2022). Equivariant quantum circuits for learning on\n",
        "    weighted graphs.\n",
        "    [arXiv:2205.06109](https://arxiv.org/abs/2205.06109)\n",
        "2.  Quynh T. Nguyen, Louis Schatzki, Paolo Braccia, Michael Ragone,\n",
        "    Patrick J. Coles, Frédéric Sauvage, Martín Larocca and Marco Cerezo\n",
        "    (2022). Theory for Equivariant Quantum Neural Networks.\n",
        "    [arXiv:2210.08566](https://arxiv.org/abs/2210.08566)\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HFTIJBPqph2f",
        "outputId": "196e3820-bd0c-45bc-d19e-5aeaaa3fb667"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1701143028.6823854\n",
            "Tue Nov 28 03:43:48 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}