{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3. Parameterisation"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Up to this point of the pipeline, a sentence is still represented as a :term:`string diagram`, independent of any low-level decisions such as tensor dimensions or specific :term:`quantum gate` choices. This abstract form can be turned into a concrete :term:`quantum circuit` or :term:`tensor network` by applying ansätze. An :term:`ansatz <ansatz (plural: ansätze)>` can be seen as a map that determines choices such as the number of :term:`qubits <qubit>` that every wire of the :term:`string diagram` is associated with and the concrete parameterised quantum states that correspond to each word. In ``lambeq``, :term:`ansätze <ansatz (plural: ansätze)>` can be added by extending one of the classes :py:class:`.TensorAnsatz` or :py:class:`.CircuitAnsatz` depending on the type of the experiment.\n",
    "\n",
    ":download:`Download code <../_code/parameterise.ipynb>`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum case"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "For the quantum case, the library comes equipped with the following ansätze:\n",
    "\n",
    ".. _tbl-ansatze:\n",
    ".. csv-table::\n",
    "   :header: \"Ansatz\", \"Description\"\n",
    "   :widths: 20, 60\n",
    "\n",
    "   \":py:class:`~.IQPAnsatz`\", \"Instantaneous Quantum Polynomial ansatz. An IQP ansatz interleaves layers of Hadamard gates with diagonal unitaries. This class uses ``n_layers-1`` adjacent CRz gates to implement each diagonal unitary (see [Hea2019]_)\"\n",
    "   \":py:class:`~.Sim14Ansatz`\", \"A modification of Circuit 14 from [SJA2019]_. Replaces circuit-block construction with two rings of CRx gates, in opposite orientation.\"\n",
    "   \":py:class:`~.Sim15Ansatz`\", \"A modification of Circuit 15 from [SJA2019]_. Replaces circuit-block construction with two rings of CNOT gates, in opposite orientation.\"\n",
    "   \":py:class:`~.Sim4Ansatz`\", \"Circuit 4 from [SJA2019]_. Uses a layer each of Rx and Rz gates, followed by a ladder of CRx gates per layer. \"\n",
    "   \":py:class:`~.StronglyEntanglingAnsatz`\", \"Ansatz using three single qubit rotations (RzRyRz) followed by a ladder of CNOT gates with different ranges per layer. Adapted from the :term:`PennyLane` implementation of :py:mod:`StronglyEntanglingLayers`.\""
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "In the example below we will use the class :py:class:`.IQPAnsatz`, which turns the :term:`string diagram` into a standard :term:`IQP circuit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import BobcatParser\n",
    "\n",
    "sentence = 'John walks in the park'\n",
    "\n",
    "# Get a string diagram\n",
    "parser = BobcatParser(verbose='text')\n",
    "diagram = parser.sentence2diagram(sentence)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "In order to create an :py:class:`.IQPAnsatz` instance, we need to define the number of :term:`qubits <qubit>` for all atomic types that occur in the diagram -- in this case, for the noun type and the sentence type. The following code produces a :term:`circuit <quantum circuit>` by assigning 1 qubit to the noun type and 1 qubit to the sentence type. Further, the number of IQP layers (``n_layers``) is set to 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+Jd6UdWk9oDa1vGgedNu1a1cWi7PJrl27BIDMmjUr2brU8pLeJd5EeVFsbKx4e3uLq6ur/PfffzrrUhsrSS/x5uyv7KF5QG3Tpk2TnZynlhfNg25Tu8Sbsk7zgNqUblGYWl5+++03ASDTp0/P6XDzjWnTpolKpZK9e/fqLE8tJyLpX+JNWZPWA2pTy0tKD7olyg80V0Gm9IDa1MZLSg+6JcNK7RaFaX22fP/998kedEuU12keUJvSLQpTGy9Pnz4VNzc3nQfdUvZjAT8HqNVq8fHxSfEeuJs3bxYLCwv55Zdf5Pr16zJo0CBxcHCQR48eicj/CswpPWSNsiYyMlKqVauW4hck6eUltYesUdYFBweLnZ1dil+QpJeXkSNHiqmpqQQGBhojdKIc5+vrK+bm5nL69Gmd5emNFc7+yj5v3ryRevXqScmSJZN9QZJeXjQF5ncfskZZd+/ePSlcuLC0atUq2Rck6eVl6tSpolKpZN++fcYIPU/bu3evqFQq+fbbb3WWp5cTTYG5SJEiyQrMlHUTJkxI8QuS9PLy6NEjKVGihNSvX1/evHljjNCJctR///0nrq6u4u3tLXFxcTrr0hsvP/74owCQzZs3GyP0PE1zFeSoUaN0lqeXE7VaLV26dBE7OzsJCQkxRuhEOSoxMVHat28vDg4Ocvv2bZ116Y2Xy5cvi5WVlfj4+HBiaw5hAT8HLFq0SADIhg0bkq2rV6+ezgMFExMTpVixYvL9999rl2lObqZNm5Yj8eYHarVaunfvnuotivTJi2b2V0BAQI7EnB+8fPlSKlWqJFWqVJFXr14lW59eXuLj46Vp06ac/UX5wpo1awSALF26NNk6ffZhqZ3cUNZ8+umnYmlpKX/++WeydfrkZdasWZz9ZWCaqyBTu0VRennRnNzY29snO7mhzLt161aqk1T0GSuaW7zUrFmTs78MaOfOnalOUtEnL+fPnxdLS0sZNGhQjsRLZCyxsbHSsGFDKVasmISHhydbn954UavV0qtXL7G2tparV6/mWNx5XVqTVPTZh/EZOJSffPvtt6lOUtFnvKxfv14AyE8//ZQj8eZ3LOBns+PHj4uZmZmMGDEi2brY2FgxNTVNdpLu4+MjHTp00FmmGVjvXl5MmTN37lwBINu3b0+2Tt+8JL28OK2HrJJ+EhMTpWPHjlKwYEG5efNmsvX65uXx48dSsmRJqVevHmd/UZ514cIFKVCggAwYMCDZjIeMfLbMnz8/xcuLKXOWLVsmAGT16tXJ1umbF81DVu3s7NJ8yCrpR61WS58+fVK8ClJE/7ykdXkxZVxat4nMyD5M8wwczv4yDM1tIrt165alz5ZffvlFAMiyZcuyO2Qioxk2bFiKV0GK6D9eNA9ZLVeuXJoPWSX9pHWbyIzswzTPwOnUqRPvgkB51m+//ZbqROGMjJcvv/xSzMzM5Pjx49kZLomICSjbhIWFoWvXrmjYsCHmzJmTbP3Tp0+RmJiIokWL6iwvWrQoHj58qLNs0qRJaN++PXr37o3bt29na9x53ZEjRzB27FiMGzcOXbp0SbZe37yYmppi48aNcHBwwEcffYTo6Ohsjz0vmzlzJn799Vds2LABFStWTLZe37w4OTlh586duHLlCnx9fbM9bqKc9uTJE3z00UeoXr06Fi9eDJVKpbM+I58tX375JXr16oWBAwfi6tWr2R57XnbmzBn4+vri888/R79+/ZKt1zcvKpUKv/zyC0qWLInOnTvj1atX2R16nvbTTz9h/fr1WLVqFTw8PJKt1zcv9vb22LVrF+7du4eBAwdCRLI99rxKRDBw4EA8ePAAu3btgr29vc76jOzDPDw8sHLlSqxduxZ+fn7ZHnte9vLlS3Tq1AmlS5fGL7/8kqXPlv79+2Po0KHw9fXF2bNnsz12opzm7++PxYsX46effoKXl1ey9fqOF2tra+zcuRMRERHo3bs3EhMTsz32vEpE4Ovri6tXr2Lnzp1wcnLSWZ+RfVjFihWxbt067N69G99//322x06U027fvo0+ffqgffv2mDhxYrL1GRkvc+fORcOGDdGtWzeEhYVla9z5HQv42SQ2NhZdunSBhYUFtm7dCnNz8yz1Z2JigrVr16Jo0aLo3LkzoqKiDBRp/nL//n18/PHHaNGiBb777rss91e4cGHs3LkTN2/exGeffcYT+kzav38/Jk+ejClTpuDDDz/Mcn916tTB0qVLsXLlSixfvtwAERIpQ0JCAj7++GPExMRgx44dKFCgQJb6U6lUWLFiBSpUqIDOnTsjIiLCQJHmLw8fPkTXrl1Rr149zJ8/P8v92dnZYdeuXQgPD4ePjw/UarUBosx/jh8/jpEjR+Krr75Cz549s9xf1apV4e/vj61bt+KHH34wQIT507x587B161b4+/ujSpUqWe6vV69eGDFiBEaOHIkTJ04YIML8R61Ww8fHB48ePcKuXbtga2ub5T4XLFiAunXrokuXLslO9olyswsXLmDIkCEYOHAgBg8enOX+ypYti02bNuHgwYOYOnVq1gPMp5YvX45Vq1bh559/Ru3atbPcX4cOHTB58mR88803OHDggAEiJFKGqKgodOrUCUWLFsXatWthYpK1srC5uTm2bNkCMzMzdO3aFbGxsQaKlN7FAn42GT58OIKCgrBz5044Ozun2MbR0RGmpqZ49OiRzvJHjx7BxcUlWXvN7K8HDx5w9lcmxMTE4KOPPkLBggWxadMmmJqaptguo3nx8PDAqlWrsH79evz000/ZEnte9vfff6NXr15o164dJk+enGq7jOalf//++Pzzz+Hr64szZ84YPG4iYxg/fjyOHTuGrVu3omTJkim2yehYsba2xq5du/DixQvO/sqEuLg4dOvWDSKCbdu2wcLCIsV2Gc1LxYoVsWHDBvz666+YOXNmtsSel/3777/o1q0bGjVqlOJVkBoZzUvXrl0xbtw4jB07FkeOHDF43Hnd4cOHMW7cOIwfPz7FqyCBjOcEAObMmQNvb2907dqVs78yYcaMGfjtt9+wYcMGVKhQIcU2Gc2LhYUFtm3bBrVaje7duyM+Pj5bYifKSZqrID08PODn55fsShWNjI6XVq1aYcaMGfjuu++we/fu7Ag9Tztz5gyGDx+OYcOGpXgVJJC5z5YpU6agXbt26NWrF/7++29Dh02U40QE/fv3R2hoKHbv3p3sKkiNjI6XokWLYufOnbh06RK++OKLbImdAN4DPxssX75cAMiqVavSbVuvXj3x9fXV/pyYmCjFixfXeTDEu3bs2CEAZM6cOQaJNz9Qq9Xi4+OT6j1w35WZvHz11Vdiamoqx44dM0jM+UFkZKRUq1ZNKlSokOweuCnJaF7Se7gUUW6yefNmASDz589Pt21m9mGHDh0SExMTmThxoiHCzTd8fX3F3NxcTp06lW7bzORl6tSpolKpZP/+/QaJNz948+aN1K9fX0qWLJnsHrgpyWheEhISpGXLllKkSBG5d++eocLO8+7duyeFCxeWVq1aSUJCQpptMzNWHj16JCVKlJD69evzGTgZsG/fPlGpVPLtt9+m2zYzeTl58qSYmZnJ8OHDDRIvkbHEx8dLs2bNxNnZWf75559022d0vKjVavnoo4/Ezs5OQkJCDBZ3Xvfff/+Jq6urNGzYUGJjY9Nsm5l92PPnz6VChQpSvXp1iYqKMljcRMYwe/ZsASA7d+5Mt21mxsvKlSsFgKxYscIg8ZIuFvAN7OzZs2JhYSFDhgzRq/3mzZvF0tJS/P39JTg4WAYPHiwODg7y8OHDNF83fvx4MTExkcOHDxsi7Dzvp59+EgCyYcMGvdpnJi/x8fHSpEkTvQ/q8ju1Wi3du3cXGxsbuXbtml6vyUxeNAd13t7e6R7UESnV1atXxdraWnr16qXXgxoz+9ny/fffC4BkDyyilPn7+wsAWbp0qV7tM5OXxMREad++vTg4OMjt27cNFXqeNmjQILG0tJQ///xTr/aZycvTp0/Fzc1NatasKdHR0YYKPc+Kjo6WmjVrSpkyZeTZs2fpts/sPuz8+fNiaWkpgwcPNlToedrt27fF3t5eOnTooNeDGjOblyVLlggAWbNmjaFCJ8pxo0aNElNTUwkMDNSrfWbGy6tXr8Td3V0qV64sL1++NFToeZZmsparq6tek7Uyuw+7du2a2NjYSI8ePfjAdMq1AgICxMTERCZMmKBX+8yOl88++0wsLCzk7NmzhgibkmAB34DCw8OlePHi4uXllaFC4U8//SSlSpUSCwsLqVevnl5/6AkJCdKqVSspXLgwZ3+l4/jx42JmZiYjRozI0Osyk5fHjx9LyZIlpV69epz9lY65c+cKANm2bVuGXpeZvJw6dUrMzc11vkEmyi0iIiKkbNmy4uHhIa9fv9b7dZkZK2q1Wrp06SJ2dnYSHByclbDzvAsXLoilpaUMGDAgQydzmcnLixcvpEKFClKtWjWJjIzMSth53rJlywSA/PLLLxl6XWbycvnyZbGyshIfHx+e0KdBrVZLnz599L4KUiMzORERWbVqlQCQ5cuXZzbkfCEyMlKqVq0qFStW1OsqSI3Mfrb0799fChQoIBcvXsxK2ERGsXHjRgEgCxYsyNDrMjNebt68KQULFpROnTrp9cVafjZs2DAxNzeX06dP6/2azH62bN26VQDIvHnzMhsukdHcvXtX76sgk8rMeHnz5o289957Urx48XSL/ZQxLOAbSFxcnDRq1EhcXFwkLCwsR7b57NkzKVOmDGd/peHff/8VZ2dnady4scTFxeXINv/880+xtLSUTz/9NEe2lxsdPnxYTExMZOzYsTm2Tc3sL39//xzbJlFWJSQkSJs2baRQoUJy586dHNnmq1evpEqVKlKpUiXO/krF48ePpVSpUlK3bl2JiYnJkW1qZn91796dxeJUnDlzRszNzWXo0KE5ts3169cLAFm0aFGObTO3WbhwYYaugjSEIUOGcPZXGjRXQdra2sr169dzZJsxMTFSp04dKVWqlDx58iRHtklkCFeuXBErKyvp3bt3jn3+/vrrrwJAvvvuuxzZXm60evXqDF0FaQhff/21mJiYyJEjR3Jsm0RZ9fr1a/H09JSyZcvqdRWkIYSFhUnRokWlUaNGOVaHyw9YwDeQ4cOHi5mZmZw4cSJHtxsUFCRWVlbSt29fntC/Q3MP3BIlSsijR49ydNu//PKLAJBly5bl6HZzg3v37kmRIkXkgw8+yNC3v1mlmf1laWkpFy5cyLHtEmXFpEmTRKVSycGDB3N0u5rZXx07duTsr3do7oHr5OQkoaGhObrtbdu2CQCZO3dujm43NwgPD5dixYpJgwYNcvx2aV9++aWYmZnJ8ePHc3S7ucGxY8fE1NQ0w1dBZlVsbKx4eXlx9lcq5syZIwBk+/btObrdBw8eiKOjozRv3lzi4+NzdNtEmfHs2TMpW7aseHp6ZugqSEOYPHkyn4GTCs2EuYxeBZlV8fHx0qJFCylSpIjcv38/x7ZLlFmZvQrSEE6cOCFmZmbyxRdf5Oh28zIW8A1g7dq1AkAWL15slO1v2LCBs79SMHjwYLG0tJTz588bZftDhw4Vc3NzOXPmjFG2r0Sae+C6ubnJ06dPc3z7SWd/6fNgQyJj2rVrlwBI80FB2WnPnj0CQKZPn26U7SvV6NGjxdTUVI4ePWqU7Y8bN46zv96huQrS1dVV/vvvP6Nsv3HjxuLs7Cz//vtvjm9fqf755x9xdnaWJk2aGGX2VVhYmLi4uHD21zs0V0GOHz/eKNv/448/xNTUVMaMGWOU7RPpKyEhQVq3bi2FCxeWu3fv5vj2ExMTpV27duLg4CB///13jm9fqTS3rM3JqyCT0jwDp1atWrwLAime5irIjRs3GmX7fn5+AkDWrVtnlO3nNSzgZ9HFixelQIEC0q9fP6POgP/qq6/EzMxMjh07ZrQYlGT58uUCQFatWmW0GGJjY6VBgwZSrFgxvR6qk9ep1Wrx8fERKysruXz5stHiCA0NFScnJ2nWrBlnf5FihYSEiJ2dnXTp0sWony1Tpkzh7K8kNm/eLABk/vz5RoshISFBWrZsydlfSQwfPlzMzc3l1KlTRovh0aNHUqJECalfvz6fgSPGvQoyqZMnT3L2VxKaqyAzeg9cQ/vxxx8FgGzZssVoMRClZ+LEiWJiYiIBAQFGi+H58+dSvnx5qV69ukRFRRktDqUw5lWQSV26dEkKFCggn3zyCe+CQIoVGBgopqamMnLkSKPFoFar5ZNPPpECBQrIpUuXjBZHXsECfhY8efJESpcuLbVr1zbKt79JxcfHS5MmTcTZ2Vn++ecfo8ZibGfPnhULCwsZMmSIsUOR//77T1xdXcXb2zvHL+lXmp9++kkAyPr1640dihw9elRMTU1l9OjRxg6FKJmXL19KpUqVpEqVKvLq1SujxpKYmCgffvihODg4yO3bt40ai7FduXJFrK2tpVevXkY/WdPM/uIzcETWrFkjAGTJkiXGDkXOnTsnFhYWMnjwYGOHYnSDBg0y6lWQSS1evFgAyNq1a40dilFproIsU6ZMjt0DNzVqtVp69eol1tbWcvXqVaPGQpSSnTt3CgCZNWuWsUPRPgOnR48eRj/+MLZRo0aJqampBAYGGjsUWbdunQAQPz8/Y4dClIzmKsimTZsafdJidHS01KpVS0qXLs1n4GQRC/iZFB8fL82bNxdHR0d58OCBscMREc7+EhF5+PChFC9eXLy8vBRTMD916pSYm5uLr6+vsUMxmuPHj4uZmZl8+eWXxg5FSzP7a9OmTcYOhUgrMTFROnXqJAULFpSbN28aOxwReTv7q0KFClKtWjWJjIw0djhGERERIWXLlhUPD48cvwduai5fvixWVlbi4+OTb0/oNVdB9u/fXzG/g5UrVwoAWb58ubFDMZply5YZ/SrIpNRqtfTr108KFCggFy9eNHY4RqFWq6Vv375GuQdual6/fi01atSQcuXKSUREhLHDIdIKDg4WW1tb6dq1q2I+W7Zu3SoAZN68ecYOxWg2btxo9Ksg3/XFF1/wGTikOG/evJF69epJyZIlFXPb4Pv374ujo6O0aNHC6F8o5GYs4GfSmDFjxMTERH7//Xdjh6Lj/PnzYmlpKYMGDTJ2KDlOcw9cFxcXCQsLM3Y4OpYsWSIAZM2aNcYOJcf9+++/4uzsLI0bN1bUPWiTzv66cuWKscMhEhGR7777TgDIr7/+auxQdGhmf3Xv3l0xJ7M5JSEhQdq0aSOFChWSO3fuGDscHevXrxcA8tNPPxk7lBz35MkTKVWqlNSpU8foV0G+67PPPhMLCws5e/assUPJcWfOnBFzc3NFXAWZVExMjNSuXTvfzv5atGiRAJANGzYYOxQdd+7ckUKFCknbtm35wHRSBCVdBfmur7/+WpH1h5xw5coVsbKyUsRVkEnFxcXJ+++/L0WLFuUzcEgxPv30U7G0tJQ///zT2KHo+P3338XExES+/vprY4eSa6lEREAZcuHCBdStWxeFCxdG3bp1jR1OMkFBQXj06BGOHDmC5s2bGzucHLNw4UKMGDEC5cqVQ/ny5Y0djg4RQWBgIExMTPDff/+hUKFCxg4px3Tt2hU7duxAnTp1UKRIEWOHoyM6OhonTpxAnTp18Oeffxo7HMrnbt26hcqVK8POzg5eXl7GDieZa9euISwsDDt27MBHH31k7HByzOrVqzFgwACULl0alStXNnY4yZw4cQJxcXG4f/8+ihcvbuxwcszAgQPxyy+/wNPTE0WLFjV2ODpiY2MRGBiIihUr4saNG1CpVMYOKUeICCpVqoTbt2+jadOmsLCwMHZIOh49eoSgoCAMHDgQK1euNHY4OSYsLAxubm6wsLBAo0aNjB1OMjdu3MCDBw+wevVq9OvXz9jhUD731VdfYcGCBahWrZriPlMTExNx5MgRFCtWDKGhoTA1NTV2SDmmTp06uHjxIho1agRra2tjh6Pj2bNnuHDhArp27Ypt27YZOxzK544cOYIPPvgARYsWhaenp7HDSebPP/9EREQELly4gNq1axs7nFzHzNgB5EY1atRAnz598ObNG4P09+rVKwQEBKBJkyZwdHTMcn+NGjWCqakp6tevb4Doco82bdrg5MmTBuvv7t27uHTpErp27WqQ/jp06AA3N7d8VbwHAB8fH4MWL86fP4/o6Gg0adIky33Z2dmha9euaN26ddYDI8qi8uXLY8CAAXj58qVB+ouJicG+ffvg7e0NFxeXLPen+VKhcePGWe4rN/nggw8M9jkAAP/88w/OnTuHTp06wcws64dhbdu2hbOzM4oVK2aA6HKPnj174tWrVwbr7/Lly3j69Ck++OCDLPel+Wx5//33803xHgBUKhWGDx+O48ePG6zPw4cPw9HRETVr1sxyX3Z2dihfvjw+/vhjA0SWexQrVgyDBw/G48ePDdJfQkICdu/ejfr166NkyZJZ7q9u3bqoW7cuWrRoYYDoiLKma9eu+Pfffw3W37Vr1xAaGoq2bdsapL+uXbuibt26+ap4DwBDhw7FwYMHDdZfYGAgrK2tUa9evSz3ZWdnBzc3N/Tt29cAkRFlzXvvvYcePXogMTHRIP09ffoUgYGBaNmyJQoWLJjl/po1a4YCBQqgevXqBogu/+EMfAUICQlBlSpVcPLkSTRs2NDY4dD/W7FiBT777DOo1Wpjh0JJ+Pj44P79+wYtEBDlRWFhYShRogT279+PNm3aGDsc+n9btmzBxx9/jMjISNja2ho7HPp/vr6+OHnyJIKCgowdCiXh6ekJb29v+Pn5GTsU+n9RUVGws7PD5s2b0aNHD2OHQ6RoEydOxKZNm3D37l1jh0JJvP/++3Bzc8PatWuNHQqRop06dQre3t4IDg6Gu7u7scPJ90yMHQARERERERERERERESXHAj4RERERERERERERkQKxgE9EREREREREREREpEAs4BMRERERERERERERKRAL+Hnc/fv3oVKp0nwoW2BgIFQqFV68eJFjceV3zIsyMS9E+uFYUSbmRZmYF2ViXpSHOSHSH8eLMjEvRPrhWMk4FvAJDRo0QHh4OOzt7QEA/v7+cHBwMG5QxLwoFPNCpB+OFWViXpSJeVEm5kV5mBMi/XG8KBPzQqQfjhVdZsYOgIzPwsICLi4uxg6D3sG8KBPzQqQfjhVlYl6UiXlRJuZFeZgTIv1xvCgT80KkH44VXZyBr3CvX7+Gj48PbG1t4erqih9++AFNmjTBiBEjAAAqlQq7d+/WeY2DgwP8/f11lt24cQMNGjRAgQIFUK1aNRw7dky7LullKYGBgejfvz9evnwJlUoFlUqFqVOnZu+bzIWYF2ViXoj0w7GiTMyLMjEvysS8KA9zQqQ/jhdlYl6I9MOxkvNYwFe4MWPG4NixY/j1118REBCAwMBAXLp0KVP9jBo1CpcvX4aXlxfat2+PZ8+eJWvXoEEDLFiwAAULFkR4eDjCw8MxevRoQ7yVPIV5USbmhUg/HCvKxLwoE/OiTMyL8jAnRPrjeFEm5oVIPxwrOY8FfAWLiorCqlWrMG/ePDRv3hzVq1fHmjVrkJCQkOG+fH190aVLF7i7u2Pp0qWwt7fHqlWrkrWzsLCAvb09VCoVXFxc4OLiAltbW0O8nTyDeVEm5oVIPxwrysS8KBPzokzMi/IwJ0T643hRJuaFSD8cK8bBAr6C3blzB3Fxcahfv752WeHChVGpUqUM9+Xl5aX9v5mZGerUqYOQkBCDxJnfMC/KxLwQ6YdjRZmYF2ViXpSJeVEe5oRIfxwvysS8EOmHY8U4WMDP5VQqFUREZ1l8fLyRoiEN5kWZmBci/XCsKBPzokzMizIxL8rDnBDpj+NFmZgXIv1wrBgeC/gKVq5cOZibm+PcuXPaZc+fP8etW7e0Pzs5OSE8PFz78+3btxEdHZ2sr7Nnz2r/n5CQgIsXL8Ld3T3F7VpYWCAxMdEQbyFPYl6UiXkh0g/HijIxL8rEvCgT86I8zAmR/jhelIl5IdIPx4pxmBk7AEqdra0tBg4ciDFjxqBIkSJwdnbGxIkTYWLyv+9dmjVrBj8/P3h5eSExMRFjx46Fubl5sr4WL16MChUqwN3dHfPnz8fz588xYMCAFLfr5uaGqKgo/P777/Dw8IC1tTWsra2z7X3mNsyLMjEvRPrhWFEm5kWZmBdlYl6Uhzkh0h/HizIxL0T64VgxEiGjCw4OFgBy8uTJZOsiIyOlT58+Ym1tLUWLFpU5c+ZI48aN5csvvxQRkbCwMGnZsqXY2NhIhQoVZP/+/WJvby+rV68WEZF79+4JANm4caPUq1dPLCwspEqVKvLHH39ot3H06FEBIM+fP9cuGzJkiBQpUkQAyJQpU7Lx3SvX8uXLRaVSpbiOeTGevn37SqNGjVJcx7wQ/c+///4rAGT//v3J1nGsGM/mzZsFgERGRiZbx7wYz7Bhw8TDwyPFdcyL8Xh4eMiwYcNSXMe8GEdkZKQAkM2bN6e4jjkh+p8JEyZImTJlUlzH8WI8jRo1kr59+6a4jnkh+p+TJ08KAAkODk62jmMl56lE3rkpEeW4kJAQVKlSBSdPnkTDhg3Tbd+kSRN4enpiwYIF2R9cPrZixQp89tlnUKvVerVnXnKGj48P7t+/j+PHj+vVnnmh/CosLAwlSpTA/v370aZNm3Tbc6zkjC1btuDjjz9GZGQkbG1t023PvOQMX19fnDx5EkFBQXq1Z15yhqenJ7y9veHn56dXe+Yl+0VFRcHOzg6bN29Gjx490m3PnFB+NnHiRGzatAl3797Vqz3HS854//334ebmhrVr1+rVnnmh/OrUqVPw9vZGcHBwqre1SYpjJXvxHvhERERERERERERERArEAj4RERERERERERERkQLxIba5UGBgoLFDoBQwL8rEvBDph2NFmZgXZWJelIl5UR7mhEh/HC/KxLwQ6YdjJXtxBj4RERERERERERERkQKxgE9EREREREREREREpEAs4BMRERERERERERERKRAL+ERERERERERERERECsSH2GZSaGgonj59apC+njx5gvbt2+PJkye4dOmSQfoEAEdHR5QqVcpg/eUGhswLALRv396gOQGYl6wqXbo07O3tOVYoTzLkWHn16hXat2+PV69ecbxkkSHzEhsbi/bt2+Ovv/6CpaWlQfoEmJescnFxQb169ThWDMCQealXrx5cXFyYlyzKjn1YbGws80J5kiHHi4ODAxo1asTzSQMwZF48PDzg4ODAfRjlSdlRqwwNDUVMTIxB+gQ4XjJLJSJi7CBym9DQULi7uyM6OtrYoaTJ2toaISEh+WZgMC/KlBvykt9yQsqUG8YKkP/GC/OiTLkhL/ktJwDzokS5ISdA/ssLKRPHizLlhrzkt5yQMuWGsQJwvGQWZ+BnwtOnTxEdHY3169fD3d3d2OGkKCQkBH369MHTp0/zzaBgXpRJ6XnJjzkhZVL6WAHy53hhXpRJ6XnJjzkBmBclUnpOgPyZF1ImjhdlUnpe8mNOSJmUPlYAjpesYAE/C9zd3VGrVi1jh0HvYF6UiXkh0g/HijIxL8rEvCgT86I8zAmR/jhelIl5IdIPx0rexIfYEhEREREREREREREpEAv4REREREREREREREQKxAI+EREREREREREREZECsYBPRERERERERERERKRALOCn4ObNm3BxcUFkZGSm+3Bzc8OCBQsMF5TCvPfee9ixY4exwzC4fv36oVOnTsmWBwYGQqVS4cWLFzkeEzEvRBnB8aJMzIvyMCfKxLwoE/NCpB+OFWViXigvMEStUl/379+HSqVCUFBQtm/L0H7++We0b9/e2GEYXJ4q4Pfr1w8qlQoqlQrm5uYoU6YMvv76a7x58yZD/YwfPx7Dhw+HnZ0dgPy5U9+2bRsqV66MAgUKoHr16ti/f7/O+kmTJmHcuHFQq9VGipCIiIiIiIiIiEi5sqtWmV+FhoaiXbt2sLa2hrOzM8aMGYOEhATt+gEDBuDSpUs4ceKEEaM0vDxVwAeA1q1bIzw8HHfv3sX8+fOxbNkyTJkyRe/Xh4aGYu/evejXr1/2Balwp0+fRs+ePTFw4EBcvnwZnTp1QqdOnXDt2jVtmzZt2iAyMhIHDhwwYqRERERERERERETKlZtqlXFxcdm+jcxKTExEu3btEBcXh9OnT2PNmjXw9/fH5MmTtW0sLCzQq1cvLFq0yIiRGl6eK+BbWlrCxcUFJUuWRKdOndCiRQscPnwYALB27VrY2tri9u3b2vaff/45KleujOjoaADA1q1b4eHhgeLFi6e7rW7dusHS0hJubm744Ycfkq2Pjo7GgAEDYGdnh1KlSmH58uXadZrLUXbu3ImmTZvC2toaHh4eOHPmTFZ/BfD390fjxo0BAF26dIGtra12Z6GPhQsXonXr1hgzZgzc3d0xffp01KpVC35+fto2pqamaNu2LTZv3pzleImIiIiIiIiIiPKi7KhV+vv7w8HBAbt370aFChXg5eUFAHj48KG2zZ07d9CxY0cULVoUtra2qFu3Lo4cOaITm5ubG6ZPnw4fHx8ULFgQgwcPThZ/YmIiBgwYgMqVKyM0NDTLv49du3ahc+fOsLa2RoUKFbBnzx69XhcQEIDg4GCsX78enp6eaNOmDaZPn47FixfrfPHQvn177NmzBzExMVmOVSnyXAE/qWvXruH06dOwsLAAAPj4+KBt27bo3bs3EhISsG/fPqxcuRIbNmyAtbU1AODEiROoU6dOmv2GhIQAAFq2bIm//voLU6dOxTfffAN/f3+ddj/88APq1KmDy5cv4/PPP8fQoUNx8+ZNnTYTJ07E6NGjERQUhIoVK6Jnz546l35kluZSnOnTp+P48eMIDQ3F6NGj9XrtmTNn0KJFC51lrVq1SvblQr169fLcJSkAsHfvXtja2ur8a9OmjbHDyveYFyL9cbwoE/OiPMyJMjEvysS8EOmHY0WZmBdSCkPWKqOjozFjxgysXbsWv/zyCwBgwoQJ2vVRUVFo27Ytfv/9d1y+fBmtW7dG+/btkxXh582bBw8PD1y+fBnffPONzrrY2Fh069YNQUFBOHHiBEqVKpXl38GKFSvQvXt3XL16VfveIyIi0n3dmTNnUL16dRQtWlS7rFWrVnj16hWuX7+uXVanTh0kJCTg3LlzWY5VKcyMHYChaXbKCQkJiI2NhYmJic7M8WXLlqFGjRr44osvsHPnTkydOhW1a9fWrn/w4EG6Bfz169cDAAYNGoSKFSuiYsWKCA4Oxty5c3UuZ2nbti0+//xzAMDYsWMxf/58HD16FJUqVdK2GT16NNq1awcA+Pbbb1G1alX8/fffqFy5cpZ+D5ovAapUqYJatWrB19cX06ZN0+u1Dx8+1BkMAFC0aFGdb/EAoFixYvjnn3+gVqthYpJ3vgtq2rQpli5dqrPs3Llz6NOnj5EiIoB5IcoIjhdlYl6UhzlRJuZFmZgXIv1wrCgT80LGlF21yvj4ePj5+aF+/fqwtLQEAFy5cgXnz59HvXr14OHhAQ8PD2376dOnY9euXdizZw98fX21y5s1a4ZRo0Zpf75//z6At18AtGvXDrGxsTh69Cjs7e0N8vv48MMP0bNnTwDAzJkzsWjRIpw/fx6tW7dO83Wp1Ss16zSsra1hb2+PBw8eGCReJchzBXzNTvn169eYP38+zMzM0KVLF+36QoUKYdWqVWjVqhUaNGiAcePG6bw+JiYGBQoUSHMb9+7dS7asYcOGWLBgARITE2FqagoAqFGjhna9SqWCi4sLHj9+rPO6pG1cXV0BAI8fP85yAb9AgQI6D8RwdXVNtu2ssrKyglqtRmxsLKysrAzatzHZ2NigfPnyOsv+/fdfI0VDGswLkf44XpSJeVEe5kSZmBdlYl6I9MOxokzMCxlTdtUqzczMULduXZ1ldnZ2CAkJQb169RAVFYWpU6di3759CA8PR0JCAmJiYpLNwE9tInPPnj1RokQJ/PHHHwat+1WoUEH7fxsbGxQsWDBbapaaWxDlBXln2vT/0+yUPTw88Msvv+DcuXNYtWqVTpvjx4/D1NQU4eHheP36tc46R0dHPH/+3CCxmJub6/ysUqmgVqtTbaNSqQAgWZvMMDPT/W5GpVJBRPR6rYuLCx49eqSz7NGjR3BxcdFZFhERARsbmzxVvCciIiIiIiIiIjIUY9UqR48ejV27dmHmzJk4ceIEgoKCUL169WQPqrWxsUnx9W3btsXVq1cN8rzOpFKqWepTC02tXqlZl1RERAScnJyyGKly5LkCflImJiaYMGECJk2apH1wwenTpzF79mz89ttvsLW11blkBABq1qyJ4ODgNPstU6ZMsmWnTp1CxYoVtbPvczMvLy/8/vvvOssOHz6sfSCGxrVr11CzZs2cDI2IiIiIiIiIiChXMmStMiEhARcuXNBZFhkZCXd3dwBva5X9+vVD586dUb16dbi4uGhvj6OPoUOHYtasWejQoQOOHTuWwXdqeF5eXvjrr790ZusfPnwYBQsWRJUqVbTL7ty5gzdv3uSpmmWeLuADQLdu3WBqaorFixcjMjISffv2xRdffIE2bdpgw4YN2LJlC7Zv365tr3lYa2JiYqp9au6RtmLFCty6dQtr1qyBn5+f3g+JVbovv/wSBw8exA8//IAbN25g6tSpuHDhQrIdyIkTJ9CyZUsjRUlERERERERERJS7GKpWaW5ujuHDh+PcuXMICQkBAFSvXh316tUD8PZWNTt37kRQUBCuXLmCXr16ZfiuH8OHD8d3332HDz/8ECdPnsziO8+ali1bokqVKujbty+uXLmCQ4cOYdKkSRg2bJj2GQDA23pl2bJlUa5cOSNGa1h57h747zIzM4Ovry/mzJmD69evw8bGBjNnzgTw9o965syZ+Oyzz+Dl5YXixYujTZs2MDMzw5EjR9CqVSsA/7uljeYSD803WQEBAVi1ahVcXV0xbdo0nQfY5mYNGjTAxo0bMWnSJEyYMAEVKlTA7t27Ua1aNW2bsLAwnD59WvtA37zC398/xeVNmjTR+xZEZHjMC5H+OF6UiXlRHuZEmZgXZWJeiPTDsaJMzAspjSFqlcDbh7WOHTsWvXr10j7T4fvvv9eu//HHHzFgwAA0aNAAjo6OGDt2LF69epXheEeMGAG1Wo22bdvi4MGDaNCgQRZ/A5ljamqKvXv3YujQofDy8oKNjQ0++eQTTJs2Tafdpk2bMGjQIKPEmF3yVAE/tZ3yuHHjkj0AQmPkyJEYOXKk9mczMzNMmDABP/74o3ZQPH78GLa2trC1tdV57bZt21CrVq0U+03pkpSgoCDt/93c3JJ9UDg4OBjkw6Nfv36oUaOGzhOrO3XqlKG+u3Xrhm7duqW6ftGiRejXrx9KlCiRpViJiIiIiIiIiIjyouyqVWp89NFH+Oijj3Dp0iXUrl0brq6u2nVubm74448/dNoPGzZM5+eU6pcp1SzfjSkrmjZtqvPzixcv9H5t6dKlsX///lTXX79+HUFBQdi6dWtmw1OkPFXAN5TPPvsML168wMuXLxEWFgY/Pz80b97c2GEpirOzs8EGLhEREREREREREaVMU6uMjIyEnZ2dscNRrPDwcKxduxb29vbGDsWg8vw98DPDzMwMEydOxNGjR1G/fn3Y2Nhg0aJFORpDmzZttLP+3/2nUqlSXae55CYtqb3W1tYWJ06c0Cu+UaNGoWjRoll9m0RERERERERERJQGTa3S2MX7DRs2pFpTLFOmTKrrqlatmm7fQ4YMSfX1Q4YM0Su+Fi1aJLtKIS/gDPw0dOrUCZGRkUbZ9sqVK7VPo36XlZVVqusKFy6cbt9Jb+XzruLFi+sVHxEREREREREREeW8fv36GeVZnB06dED9+vVTXGdubo74+PhU1z179izNvqdNm4bRo0enuK5gwYIZCzSPYQFfobKzkF6+fPls65uIiIiIiIiIiIjyHjs7u0xfBZBeAd/Z2RnOzs6Z6juv4y10iIiIiIiIiIiIiIgUiDPwsyAkJMTYIaRKybFlNyW/dyXHlt2U+t6VGhflX0r+m1RybNlNye9dybFlN6W+d6XGlVOU+v6VGldOUPJ7V3JslD8p+W9SybFlN6W+d6XGRfmXkv8mlRyb0rGAnwmOjo6wtrZGnz59jB1KmqytreHo6GjsMHIM86JMuSEv+S0npEy5YawA+W+8MC/KlBvykt9yAjAvSpQbcgLkv7yQMnG8KFNuyEt+ywkpU24YKwDHS2apRESMHURuFBoaiqdPnxqkr3v37qFr165YtWoVPD09DdIn8HbwlipVymD95QaGzMvOnTsxY8YMXLx40SD9aTAvWfPNN98gPDwcK1euNEh/QP7MCSmTIcfK48eP0aZNGyxatAgNGzY0SJ9A/hwvhsxLQEAAxo8fjxMnTsDa2togfQLMS1bNnj0bly9fxubNmw3SH5A/cwIYNi8ff/wxatasibFjxxqkPyB/5sWQOYmOjkajRo3w/fffo2XLlgbpE8ifeSFlMuR4Wbx4MQ4ePIjffvvNIP1p5MfxYsi8fPrpp3B1dcX06dMN0h+QP3NCymTIsRIUFISBAwdi+/btKFOmjEH6BDheMosz8DOpVKlSBvuDs7KyAgBUqlQJtWrVMkif+ZUh83Lx4kWoVCrmxAAMmZciRYogMjKSeaE8yZBjJSwsDMDbB5dzvGSNIfNy+/ZtAICnpydsbW0N0md+Zci8ODk5wdrammPFAAyZF2trazg5OTEvWWTInERFRQEAypQpw7xQnmTI8eLi4gJLS0uOFQMwZF5sbW1RpEgR5oXyJEOOlZiYGABAlSpV4O7ubpA+KfP4EFsiIiIiIiIiIiIiIgViAZ+IiIiIiIiIiIiISIFYwCciIiIiIiIiIiIiUiAW8PO4+/fvQ6VSISgoKNU2gYGBUKlUePHiRY7Fld8xL8rEvBDph2NFmZgXZWJelIl5UR7mhEh/HC/KxLwQ6YdjJeNYwCc0aNAA4eHhsLe3BwD4+/vDwcHBuEER86JQzAuRfjhWlIl5USbmRZmYF+VhToj0x/GiTMwLkX44VnSZGTsAMj4LCwu4uLgYOwx6B/OiTMwLkX44VpSJeVEm5kWZmBflYU6I9MfxokzMC5F+OFZ0cQa+wr1+/Ro+Pj6wtbWFq6srfvjhBzRp0gQjRowAAKhUKuzevVvnNQ4ODvD399dZduPGDTRo0AAFChRAtWrVcOzYMe26pJelBAYGon///nj58iVUKhVUKhWmTp2avW8yF2JelIl5IdIPx4oyMS/KxLwoE/OiPMwJkf44XpSJeSHSD8dKzmMBX+HGjBmDY8eO4ddff0VAQAACAwNx6dKlTPUzatQoXL58GV5eXmjfvj2ePXuWrF2DBg2wYMECFCxYEOHh4QgPD8fo0aMN8VbyFOZFmZgXIv1wrCgT86JMzIsyMS/Kw5wQ6Y/jRZmYFyL9cKzkPBbwFSwqKgqrVq3CvHnz0Lx5c1SvXh1r1qxBQkJChvvy9fVFly5d4O7ujqVLl8Le3h6rVq1K1s7CwgL29vZQqVRwcXGBi4sLbG1tDfF28gzmRZmYFyL9cKwoE/OiTMyLMjEvysOcEOmP40WZmBci/XCsGAcL+Ap2584dxMXFoX79+tplhQsXRqVKlTLcl5eXl/b/ZmZmqFOnDkJCQgwSZ37DvCgT80KkH44VZWJelIl5USbmRXmYEyL9cbwoE/NCpB+OFeNgAT+XU6lUEBGdZfHx8UaKhjSYF2ViXoj0w7GiTMyLMjEvysS8KA9zQqQ/jhdlYl6I9MOxYngs4CtYuXLlYG5ujnPnzmmXPX/+HLdu3dL+7OTkhPDwcO3Pt2/fRnR0dLK+zp49q/1/QkICLl68CHd39xS3a2FhgcTEREO8hTyJeVEm5oVIPxwrysS8KBPzokzMi/IwJ0T643hRJuaFSD8cK8ZhZuwAKHW2trYYOHAgxowZgyJFisDZ2RkTJ06Eicn/vndp1qwZ/Pz84OXlhcTERIwdOxbm5ubJ+lq8eDEqVKgAd3d3zJ8/H8+fP8eAAQNS3K6bmxuioqLw+++/w8PDA9bW1rC2ts6295nbMC/KxLwQ6YdjRZmYF2ViXpSJeVEe5oRIfxwvysS8EOmHY8VIhIwuODhYAMjJkyeTrYuMjJQ+ffqItbW1FC1aVObMmSONGzeWL7/8UkREwsLCpGXLlmJjYyMVKlSQ/fv3i729vaxevVpERO7duycAZOPGjVKvXj2xsLCQKlWqyB9//KHdxtGjRwWAPH/+XLtsyJAhUqRIEQEgU6ZMycZ3r1zLly8XlUqV4jrmxXj69u0rjRo1SnEd80L0P//++68AkP379ydbx7FiPJs3bxYAEhkZmWwd82I8w4YNEw8PjxTXMS/G4+HhIcOGDUtxHfNiHJGRkQJANm/enOI65oTofyZMmCBlypRJcR3Hi/E0atRI+vbtm+I65oXof06ePCkAJDg4ONk6jpWcpxJ556ZElONCQkJQpUoVnDx5Eg0bNky3fZMmTeDp6YkFCxZkf3D52IoVK/DZZ59BrVbr1Z55yRk+Pj64f/8+jh8/rld75oXyq7CwMJQoUQL79+9HmzZt0m3PsZIztmzZgo8//hiRkZGwtbVNtz3zkjN8fX1x8uRJBAUF6dWeeckZnp6e8Pb2hp+fn17tmZfsFxUVBTs7O2zevBk9evRItz1zQvnZxIkTsWnTJty9e1ev9hwvOeP999+Hm5sb1q5dq1d75oXyq1OnTsHb2xvBwcGp3tYmKY6V7MV74BMRERERERERERERKRAL+ERERERERERERERECsSH2OZCgYGBxg6BUsC8KBPzQqQfjhVlYl6UiXlRJuZFeZgTIv1xvCgT80KkH46V7MUZ+ERERERERERERERECsQCPhERERERERERERGRArGAT0RERERERERERESkQCzgExEREREREREREREpEB9im0mhoaF4+vSpwfpydHTEnTt3YGVlZZA+AcDR0RGlSpUyWH/5jYmJCZydnY0dRp5gyPESFxcHU1NTXLp0ySD9ARwrlDeJCJydnaFSqYwdSq5nyH1YWFgYHB0dceXKFX7mExER5WHm5uYoVKiQscPIEwx5LGZqaoq4uDieT1KeZMixcufOHTg6OuLmzZuIiYkxSJ8Ax0tmsYCfCaGhoXB3d0d0dLRB+/3kk08M2p+1tTVCQkI4MDJJrVbj8ePHxg4j18uu8VK7dm2D9cWxQnmRSqXC48ePISLGDiVXy659mLe3t0H7436MiIhIWeLj4/H8+XNjh5HrZdex2JYtWwzWF4/DSAmya6x07tzZoP1xvGQOC/iZ8PTpU0RHR2P9+vVwd3c3djgpCgkJQZ8+ffD06VMOCjIqpY8XjhUiSovS92EA92NERESUdyn9WIzHYaQUSh8rAMdLVrCAnwXu7u6oVauWscMgyhU4XogoN+M+jIiIiMh4eCxGpB+OlbyJD7ElIiIiIiIiIiIiIlIgFvCJiIiIiIiIiIiIiBSIBXwiIiIiIiIiIiIiIgViAZ+IiIiIiIiIiIiISIFYwE/Bs2fP4OzsjPv372e6jyZNmmDEiBEGi0lpxo0bh+HDhxs7jGwjInj8+DEA4O+//4aIGDkiwzDE33ZaAgMDoVKp8OLFCwCAv78/GjdunC3byq9+/vlntG/f3thhUC6g2W/duHEjw/uxd/cV745tIqVQqVTYvXu3Qfp69eoVPD09oVarDdIfERHlXyKC58+fIzY2Nk+dT6ZH3/PNvF4vyQhD/i6ePn0KZ2dn/Pvvvwbpj5Qju2s57zLkMXZOCg4ORokSJfD69Wtjh2Jwea6A369fP6hUKqhUKpibm6NMmTL4+uuv8ebNG737mDFjBjp27Ag3NzcAwP3796FSqRAUFJQ9QStMeHg4evXqhYoVK8LExCTFD5PRo0djzZo1uHv3bs4HmI1EBOvWrYOnpycmTZoEEUGFChXg6emJdevWGfXAKzv+tkl/wcHBGDp0KNzd3VGkSBFUqFABn3zyCc6cOWPQ7YSGhqJdu3awtraGs7MzxowZg4SEBO36AQMG4NKlSzhx4oRBt0t5h2ZfUbJkSQDAyJEjUaFCBdSoUUPv/di7+4oGDRogPDwc9vb22Rl6tjh79iw++eQTlC9fHkWKFIG7uzuGDh2K69evGzs0rfj4eCxfvhwtWrRA8eLF4eLiggYNGmDevHmIjo7OkRiePXuGqVOnom7dunByckKpUqXQrl07bN68OdOffdnRZ1ZERESgd+/eKFiwIBwcHDBw4EBERUVp1xcsWBDm5ubYsGFDjsdGRER5Q9LzyaVLl+K///5TzPlkerLjfDO3TwJRwjFaUiKCyZMnw9XVFVZWVmjRogVu376tXe/o6AgfHx9MmTIlx2Oj1LGWYzgzZsxAgwYNYG1tDQcHh2Trq1Spgvfeew8//vhjzgeXzfJcAR8AWrdujfDwcNy9exfz58/HsmXL9N6BRUdHY9WqVRg4cGA2R6lcsbGxcHJywqRJk+Dh4ZFiG0dHR7Rq1QpLly7N4eiyj4jgiy++gI+PD65du6az7tq1a/Dx8cEXX3xh1IMu/m3nnPj4eO3/Z82ahfr160OtVmPevHk4duwYVq9ejbJly6JDhw4YP368QbaZmJiIdu3aIS4uDqdPn8aaNWvg7++PyZMna9tYWFigV69eWLRokUG2SXmLiODcuXMprtN3P5bSvsLCwgIuLi5QqVQGj9nQNGNXrVZj+PDhaNOmDYoWLYrFixfj+PHjWLJkCWxtbeHt7Y3FixcbfPuJiYnpzuCOi4vT/v/u3buoVasWFi9ejK5du2Lbtm0ICAjAiBEj8Pvvv6Nq1aq4deuWweNMGkNAQAAqVqyIP//8E6NHj0ZAQAB27tyJDz/8ENOnT0erVq0yPIslO/rMqt69e+P69es4fPgw9u7di+PHj2Pw4ME6bfr168f9KxERZUpuOJ9MD883/8dYx2hpmTNnDhYtWoSff/4Z586dg42NDVq1aqVTCO7fvz82bNiAiIiIHI2N0pZbxlbScwQliouLQ7du3TB06NBU2/Tv3x9Lly7VmQiZJ0ge88knn0jHjh11ln300UdSs2ZNERGZMmWKAEj2b/Xq1SIism3bNnFyctJ5/b179wSAXL58WURELl68KACkatWqYmFhIS4uLjJ27FiJj4/XvqZx48YyfPhwGTNmjBQqVEiKFi0qU6ZM0ekXgKxYsUI6deokVlZWUr58efn111+z/Ds4evSo9n25u7uLlZWVeHl5yY0bNzLcV+PGjeXLL79Mcd2aNWukRIkSWYxWOdauXZvi38a7/9auXWuU+LLjb7t27doyd+5c7c8dO3YUMzMziYyMFBGRf/75RwDI7du3ReTt76h27dpia2srRYsWlZ49e8qjR4+0r9f87T1//lxERFavXi22trYCQC5evCiPHz+W2rVrS6dOneTNmzcSEREhvXr1EkdHRylQoICUL19efvnllyz9njTjdceOHdKkSROxsrKSGjVqyOnTp1NsrxnPAGTJkiXSvn17sba21o5XPz8/KVeunNy8eTPF1z9+/Fhq1qwp8+bN0y67f/++fPjhh+Lg4CDW1tZSpUoV2bdvX7qx79+/X0xMTOThw4faZUuXLpWCBQtKbGysdtmxY8fEwsJCoqOj9fmVUD6i734so/uKlMa2vb29HDx4UCpXriw2NjbSqlUr+e+//7L8Ho4ePSp169YVa2trsbe3lwYNGsj9+/dTbKsZ75s3b5b3339fLC0tte9j9OjRUrduXQkPD0/xtX///beUKVNGtm3bpl02ZcoU8fDwkLVr10rp0qWlYMGC0rJlS+0+LCWa38Wvv/4q7u7uYmpqKvfu3dNpo+l3xYoV4ubmJiqVSkREXrx4IeXLl5dvvvlG1Gp1iv0vX75cSpcuLREREdplpUuXlhkzZkj//v21++O0YhR5+3k+bNgw+fLLL6VIkSLSpEkTERH5888/pXDhwrJnz54UXxcfHy/9+/eX9u3ba5fFxsbKsGHDxMXFRSwtLaVUqVIyc+ZM7frM9JnRfbcGANm1a1eabUREgoODBYD8+eef2mUHDhwQlUolYWFhMmzYMPHw8JAHDx4IAPn777/T7ZOyn4eHhwwbNszYYVASkZGR2v0uEelS+vlkegx9vqn5bE/675NPPhER/eolz58/l4EDB4qDg4MAkDp16khQUFCW32dKx3s9evSQV69eadtk5BhNc5x88eLFZMdoJUuWlGXLlqUZT1o1l6TUarW4uLjonL+/ePFCLC0tZdOmTTpty5QpIytXrky3T8oZ2VHL0fzd7d27V6pXry6WlpZSrVo1nXOCp0+fyscffyzFihUTKysrqVatmmzcuFGnn9TOEd49xp48ebK4uLjIlStXsvS7cHV1FQDSoUMHvcdISjTnYCmJjY0VS0tLOXLkSJZiVZo8OQM/qWvXruH06dOwsLAA8PbWL+Hh4dp/8+bNg7W1NerUqQMAOHHiBGrXrp1mn5p7o1epUgVXrlzB0qVLsWrVKnz33Xc67dasWQMbGxucO3cOc+bMwbRp03D48GGdNt9++y26d++Oq1evom3btujdu7dBvyn96quvcOHCBZiZmWHAgAEG6xcA6tWrh3///TfH7sGVnUQEc+fOhYlJ2kPCxMQE8+bNU8SsCUP8bTdu3BiBgYEA3v4OTpw4AQcHB5w8eRIAcOzYMRQvXhzly5cH8HZm6/Tp03HlyhXs3r0b9+/fR79+/fSK9+HDh2jUqBGqVauG7du3w9LSEt988w2Cg4Nx4MABhISEYOnSpXB0dDTAbweYOHEiRo8ejaCgIFSsWBE9e/ZM9xvYqVOnonPnzvjrr78wYMAAPH36FJMnT8auXbtQsWJF7Nq1C9WqVUOxYsUwadIkfPDBB7hx4wY2bdqEGTNmIDIyEgAwbNgwxMbG4vjx4/jrr78we/Zs2NraphvzmTNnUL16dRQtWlS7rFWrVnj16pXO7T7q1KmDhISEVGdaU/6k2Y+lR6VSwd3dHf/991+mPweBtzNB5s2bh3Xr1uH48eMIDQ3F6NGjs/QeEhIS0KlTJzRu3BhXr17FmTNnMHjw4HRn/o8bNw5ffvklQkJC0KpVKwQHB8Pf3x+7d++Gi4sLli5digoVKsDNzQ0//fQTKlWqBHNzc6xYsQJjxozR2affuXMHu3fvxt69e7F3715cvHhRr9/F7NmzsXLlSly/fh3Ozs7J2vz999/YsWMHdu7cqb0l36xZs1C7dm1MmzYNL1++RO/evbWXZi9atAht2rTBoEGD0KhRIyxYsECnvx9++AF16tTB5cuX0a1bNwBI9/N4zZo1sLCwwKlTp/Dzzz8DAIYPH44ZM2agffv2CA4ORuPGjeHk5ITu3btj5MiRmDNnDn7++WcEBwfj6NGjAIBFixZhz5492Lp1K27evIkNGzboXM6bmT41MrPv1seZM2fg4OCg/TsHgBYtWsDExERnX1qqVCkULVqUtykjIqIMyY3nk+nJ6vlmyZIlsWPHDgDAzZs3ER4ejoULF2rXp1cv6datGx4/foyffvoJAFC5cmU0b97cIPWSd4/3jh07hlmzZmnXZ+QYbePGjTp9Jz1G+/zzzzF06FDcvHkzyzHfu3cPDx8+RIsWLbTL7O3tUb9+/WS3da1Xrx6PZRTMkHXKMWPG4IcffsCff/6JQoUKAfjfFclv3rxB7dq1sW/fPly7dg2DBw9G3759cf78eZ0+UjpH0BARDB8+HGvXrsWJEydQo0YNg/wO3N3dDT5GNCwsLODp6Zn3xoDxvjvIHp988omYmpqKjY2NWFpaCgAxMTGR7du3J2t75swZKVCggGzZskW7rGPHjjJgwACddu/OwB8wYIAAkAsXLmjbLF68WGxtbSUxMVFE3n6L5e3trdNP3bp1ZezYsdqfAcikSZO0P0dFRQkAOXDgQOZ/AaI7A1/zzdu+ffsEgMTExGSor7S+DX758qUAkMDAwCzFqwS3b9/O0ExVc3PzHP+nUqlSjMXU1DRZW1NT02TrVCqVqFSqFNuZmZmJmZmZdryYmJik+prUXp/az5plRYsWlS+++EJnBkP79u2lf//+Bs2lZrwmnXFw/fp1ASAhISHJ2iedgT9ixAiddcuXL5cuXbqIyNvZupaWluLn5yeXL1+WgQMHiqmpqRw9elRERLy9vbVjt3r16jJ16tQMxz5o0CBp2bKlzrLXr18LANm/f7/O8kKFCom/v3+Gt0F5V2b2Y5nZV6T0s7m5uZiYmGR5/6jZD6W0X0urvWaflTQWzbKk79HMzEy7L9XEnvT/mveQ9H1p2qc1Ax9AmjPCpkyZIubm5vL48WOd5cWLF5e//vpLRN4eW3h5ecnZs2dlz5494uLiIo0bNxYRkSNHjkj9+vW1rytdurT06dNH+/OFCxcEgIwfPz7VGBo3bqyd5aNx69YtcXFxkfj4eElISJCKFSvK4MGD5fLly7Jo0SIxMzPTzoabNGmS9hhm+PDh0qxZsxRnpGW2z4zuuzWg5wz8GTNmSMWKFZMtd3JykiVLlmhn4IuI1KxZM1P7cDI8zsBXHs7AJ0pZbjifNPb55rvberetSqVKdvxmZmYmPXr00B6LlStXLlOzdZOaMmWKWFtb68y4HzNmjM6xVkaO0ZLOen73GE2tVouzs7MsXbo01Xj0nYF/6tQpAZDsitdu3bpJ9+7ddZZ99dVX2pnUZHzZUafU1P2Sfh7/8ccfAkC+//77VGNp166djBo1SvtzSucIIm+Psbdt2ya9evUSd3d3+ffffzP0nlOjmYGvObfSZ4ykJK0Z+CIinTt3ln79+mUlVMUx07POn6s0bdoUS5cuxevXrzF//nyYmZmhS5cuOm1CQ0PRqVMnjB49Gt27d9cuj4mJQYECBdLs/969ewCgMyOwYcOGiIqKwr///otSpUoBQLJvplxdXbWz9zWStrGxsUHBggWTtTEEV1dXAG+vHtDEl1VWVlYAYJSHtxjas2fPMtT+yy+/RJkyZbIpmpStXbsWL1++RI8ePRAXF4c//vgDpqam6N27t067iIgIzJkzBw0bNkT79u21y/38/ODk5IQePXpol0VHR+Prr7/GyJEjcffuXdy7dw+1a9dGQEAAxowZg2+//RYtWrRAw4YNAbwdN/v27UNYWBiio6OhUqmQmJiIcePGwdXVFbdu3cLChQvx/fffw9raGmfOnMGmTZsAAM2aNdOZcQEAQ4cORZcuXXDp0iW0bNkSnTp1QoMGDQzy+0o6tpL+/VeuXDnV1ySdmQkAf/31lzaeQ4cO4f3338ewYcMAAEuWLNG+N802nj9/DgD44osvMHToUAQEBKBFixbo0qWLwb6p1rCyssoTY48MJ6P7sf79+2PTpk167StSGttbt27F/PnztW2CgoKwcuXKZDPFM2rdunW4cOECKlWqhMqVK6NWrVqpPjz32bNnmDx5MkaMGIFy5cppl//8889477334OnpiV27diEmJga9evUCALx48QITJ07E5MmTUaRIEcyZMwddu3ZF2bJlsW/fPly6dAnffPONtq8dO3bgjz/+SDNmCwuLdMd46dKl4eTkpP05IiICkZGRqFatGgDgt99+w+7du1G/fn0AgK+vr3YWWtL9i0bS7WmOR9KbkfbuzJ2//voLdevWhZmZGYKDgxEWFgY/Pz+Ym5vD09MTe/bs0bZ1dXXFlStXALy9T/wHH3yASpUqoXXr1vjwww/RsmXLLPWZ0vvSd99taNy/EhFRRuWG88n0ZMf55rvHkBoLFiyAq6urTtuff/4ZNjY26Nu3L44dO4Zt27bBxMQEu3fvBgB4e3sjNjYWd+7cyfJ7dXNzg52dnfbnpLWajB6jaa7C1nj3GM3FxSVbajxp4bGM8mRXndLLy0v7f805k+aq3MTERMycORNbt25FWFgY4uLiEBsbqzMWgeTnCBpfffUVLC0tcfbsWYPdKeFd2TVG8uIYyJMFfBsbG+0tP3755Rd4eHjoPPDh9evX6NChA7y8vDBt2jSd1zo6OiY7Sc4sc3NznZ9VKlWyB9vp08YQNCf3huxbUyhIWpDIrYoUKZKh9p999pn2byynnD9/Hi9evMDUqVMBvM2lh4cHLC0tdf62GzZsiObNm2Pnzp06XzKdOnUKIoLPP/9cp9+NGzfCyckJf//9Nz799FN0794d/v7+aNGiBYYNG4YpU6agYsWKeP36NUqXLo1WrVphyZIlcHJyQmhoKFq1aoUuXbrA09MTgYGBWLhwIT799FM4ODjA2toau3btwuvXr3HixAmEhYWhePHi2m23adMGDx48wP79+3H48GE0b94cw4YNw7x587L8+0o6tvT9+7exsdH5OSEhQftFVVxcnM56CwsL7SVvarUaQUFBGDNmDADg008/RatWrbBv3z4EBATg+++/xw8//IDhw4enuX0XF5dkl7M9evRIuy6piIiIPDH2yHAyuh87fvy43vuKlMb2b7/9ptNm9+7dWLFiRbJ9TEZ9/vnnuHz5Mg4ePIjffvsNBw8exOHDh/Hee+8la3v//n1MnjwZffv2haenp3b53r170blzZ7Rp0wa3bt2CSqXSxvXy5UtMnDgRffv2hZOTEyZOnIivv/4azs7OePz4McLCwnTew927d9Mt4FtZWaV7m5+U9i9JD8Tf3cckve3WpUuXkn3mvHv8ACDdy/HT28eZm5vr9PtuDJUqVQIA1KpVC/fu3cOBAwdw5MgRdO/eHS1atMD27dsz3WdK78uQxy4pnRgkJCQgIiICLi4uOrcp4/6ViIgyKjecT6YnO8433z2G1Ni6dSs8PT112gYEBMDBwQGff/45IiMjcerUKQQGBuL69evo1KkTNm3ahKpVq+r0k1lp1WEyeoxWokQJPHjwQK++s0JzPvjo0SPtJAfNz0mPgwEeyyiRMeqUc+fOxcKFC7FgwQJUr14dNjY2GDFiRLIH1b57jqDxwQcfYNOmTTh06FCyL/IMKTvqoBEREToTvPKCPH8PfBMTE0yYMAGTJk1CTEwMRAR9+vSBWq3GunXrkp1w16xZE8HBwWn2qfmmPOmJ8qlTp2BnZ4cSJUoY/k0o1LVr12Bubo6qVasaO5QsK1euHGrUqKHXPQs9PDwUsSMw1N9248aNcfToURw/fhxNmjRB4cKF4e7ujhkzZsDV1RUVK1YEANy4cQPPnj3DrFmz0KhRI1SuXFmvb0k1cbi7u6Np06b477//dNY7OTnhk08+wfr167FgwQIsX748s78Sgytfvjz++usvAG9nfAQEBODs2bNITEyEn58fXrx4gVevXmHUqFEoXrw46tatq31tyZIlMWTIEOzcuROjRo3CihUr0t2el5cX/vrrL53f6+HDh1GwYEFUqVJFu+zOnTt48+YNatasacB3S7mdZj+WHpVKhYIFC8Lc3DzTn4PZrWbNmhg/fjxOnz6NatWqJbu3aHreHbubN2/GjRs3EB8fjxkzZgAAnjx5ggEDBqBjx44p3rM+uzk6OiIuLk77JZ23tzfmzJmDmJgYhIWFafcZp0+fxsSJEzFy5EiDx5D096R5LoCfnx8SExNx9uxZHDp0CPHx8VizZg0OHDig88yTggULokePHlixYgW2bNmCHTt2ICIiIkt9ZicvLy+8ePFC55kGf/zxB9RqtXZGHfD2XqF37tzh/pWIiDIkN55PpscQ55uaCU+JiYkZ2natWrXw8OFDmJmZoWTJkgDenl+VL18+22YCa2T0GK1Pnz7ZGo9GmTJl4OLigt9//1277NWrVzh37pzOLGzgba2GxzLKZcg65dmzZ7X/f/XqFQBon0116tQpdOzYEX369IGHhwfKli2LW7du6R1nhw4dsHHjRnz66afYvHlzBt+lceXFMZDnC/jA24efmJqaYvHixZg6dSqOHDmCZcuWISoqCg8fPsTDhw8RExMD4O0DI69fv57mt1uah8XNmTMHN27cwK+//oopU6Zg5MiR6X5g5xZBQUEICgpCVFQUnjx5gqCgoGQ7jBMnTqBRo0bamXa5mUqlwujRo9P91k+tVmPUqFHpzrTMKYb4227SpAkOHToEMzMz7S0KmjRpgg0bNqBx48badqVKlYKFhQV++ukn3L17F3v27MH06dP1jvW7776Dh4cHmjVrhocPHwIAJk+ejF9//RV///03rl+/jr1798Ld3T2rv5Z0hYWFoXLlyslmu7+rQ4cO2LZtGyIiIlCnTh2MGzcOjRo1gqWlJQICAlC7dm18/PHHeP78OXbt2qV93YgRI3Do0CHcu3cPly5dwtGjR/V6Xy1btkSVKlXQt29fXLlyBYcOHcKkSZMwbNgwWFpaatudOHECZcuWzRUH/pRzNPux9IgI4uLisvQ5mF3u3buH8ePH48yZM3jw4AECAgJw+/Zt7fg5f/48KleujLCwsDT76dy5M1auXIn4+Hh06dIFHTp0QJUqVWBtbY0XL16gWLFiaNGiBYoXL57sQU3p8fPzQ/PmzdNsM378ePj4+KTZxsTEBB06dMCSJUsAAAsXLsTly5dha2uL6tWr44MPPsCxY8cwYMAALFy4MN1tvqt58+bw8/NLs03NmjURExODo0ePwsrKCv7+/pg8eTIsLS3Rv39/dOrUCbNnz8bq1asREBCgncn1448/YtOmTbhx4wZu3bqFbdu2wcXFBQ4ODpnuUx/67rtT4u7ujtatW2PQoEE4f/48Tp06BV9fX3z88ccoVqyYtt3Zs2dhaWmZ7ESYiIgoLbn1fDI9WT3fLF26NFQqFfbu3YsnT54gKipKr+22aNECXl5e6NSpk/YBrVeuXMHEiRNx4cIFw7/RJDJ6jFavXr0M9a/PMVpKVCoVRowYge+++w579uzBX3/9BR8fHxQrVgydOnXStouOjsbFixe1tzckZTJUnXLatGn4/fffce3aNe3VM02bNgUAVKhQAYcPH8bp06cREhKCzz77TPvFlL46d+6MdevWoX///ti+fXvW3rQBhIaGIigoCKGhoUhMTNSpXWrcv38fYWFhOg98zgvyRrU5HWZmZvD19cWcOXOwf/9+REVFoUGDBnB1ddX+27JlCwCgevXqqFWrFrZu3ap9veZD2Mzs7R2HNDP1rl+/Dg8PDwwZMgQDBw7EpEmTcvidZZ+aNWuiZs2auHjxIjZu3IiaNWuibdu2Om02b96MQYMGGSlCw+vTpw98fX0BINkXMZqffX19c+wbdn1k9W8bABo1agS1Wq1TrG/SpAkSExPRpEkT7TInJyf4+/tj27ZtqFKlCmbNmpWhW92YmZlpL3ts1qwZHj9+DAsLC4wfPx41atTA+++/D1NT0xz5Zjc+Ph43b95M955o5cuXR7du3dCzZ09ER0fjm2++watXr/Dff/9hz5492L9/P168eAF/f3+dSzkTExMxbNgwbcGoYsWK2gPAtJiammLv3r0wNTWFl5cX+vTpAx8fn2SX0G3atClPjT0ynD59+mi/iHv3xFDzs7OzM968eZPhfUVOsLa2xo0bN9ClSxdUrFgRgwcPxrBhw/DZZ58BeHtCcvPmTcTHx6fZT9OmTVG+fHkMGjQIarUay5Ytw8uXL/Ho0SMsX74cFy5cQEREBH788cd0n3vzrqdPn6Z779Xw8HCEhoam29fkyZPh5+eHAwcOoFy5ctp7xoeHh2PmzJl48uQJbty4oXNSpq87d+7g6dOnabZRqVSYPXs2PvnkE9y/fx9t27bFkydP8ODBAwQHB2PJkiV48eIFAgMDda62s7Ozw5w5c1CnTh3UrVsX9+/fx/79+2FiYpLpPvWh7747NRs2bEDlypXRvHlztG3bFt7e3smu+tq0aRN69+6d7N6gRERE6cmN55Ppyer5ZvHixfHtt99i3LhxKFq0qPb3kx6VSoX9+/fj/fffx7fffgvg7QSJBw8eoGjRooZ/o+8w9jFaar7++msMHz4cgwcPRt26dREVFYWDBw/qHM/++uuvKFWqFBo1apSpbVDOMEQtBwBmzZqFL7/8ErVr19b+XWlu4zRp0iTUqlULrVq1QpMmTeDi4pKpv9muXbtizZo16Nu3L3bu3Jn5N20AkydPRs2aNTFlyhRERUVpa5dJv9jbtGkTWrZsidKlSxsx0mxgrKfnKtnevXvF3d1dEhMTReTtU6AByJMnT0RE5OLFizpPTVai7I5x//794u7uLvHx8dnSv7Go1WpZu3at1KhRQwBo/9WoUUPWrVsnarXa2CFmybt/2zlB6eMlvfhiY2OlQ4cO4u7uLps2bZIXL16IiMjz589l1apVUrVqVfnnn39yLN5r166Js7OzNg6id2n2Y+7u7pnejxljX2FoERER8t5778l7770nv/32m7x+/VpERB49eiQ//vijeHp6SlRUVLr9ZPc+7NChQ1KoUCEZPny4XL16VRITEyUxMVGCgoKkT58+8tVXX2V7jDNmzJAiRYrIvHnztPuzN2/eyKFDh8Tb21t27typiD6z07Bhw6Rq1apSuHBhuXv3rrHDof/n4eEhw4YNM3YYlERkZKQAkM2bNxs7FCJFyuvnk+nJjmNIY51P6nuMprTz3fr168uGDRuMHQYZ2Ltj6+jRowJAnj9/rm2jtL/FlGR3jLGxsVKqVCk5efJktvRvTPliBn5GtWvXDoMHD0ZYWBj+/vtvzJ07Fx4eHtl+r7Xc5PXr11i9erX2qoS8QqVSoW/fvggKCsJ3330HlUqF27dvIygoCH369Mk1lzqmJunfNunHwsICu3fvxtdff43Zs2fDwcEBlpaWcHJywvr167Fo0aIcffZFeHg41q5dq33CPNG7NPuxgIAAAG9vd5LR/Vhe2FcUKlQIx44dQ/fu3TFq1CjY2NjA0tISpUqVQmBgIFatWpXqA5tyUsuWLXHx4kVERkaiUaNG2gdkt2nTBiVKlNBeCpudJkyYgF27diEgIADlypWDhYUFrKysMHLkSPTt2xcdO3ZURJ/ZLS4uDkuWLNE+64iIiCijkp5PDh06FMWKFctT55PpyQvHkBpKOEbLqKdPn+Kjjz5Cz549jR0KGVheGlvZKTQ0FBMmTEDDhg2NHYrB5a3qqwGNGDECQUFBaNCgATw9PbF27doc3f6QIUOwfv36FNf16dMnzXXp3c+3atWqOk9JT2rZsmV6PV26a9eu6bbJzVQqlfZWSZonhecVI0aMMHYI6Zo5cyZmzpyZ4rrXr1+nWnRr1KgRDhw4YPB4VCoV+vXrh379+iEqKgoRERFwcnLK8PMf0hvX+tyLO6/dx42yj+YEsXLlypnaj2VlX2Fra5vqupiYmFTHzoEDBwx6ua+FhQW++uorfPXVV3j58iVevXoFZ2dnnWdKKEGZMmWwevVqrFq1Co8ePYKJiUmOXB6eVKNGjXDo0CHExsbi8ePHsLOz07k1mFL6TE1anxv6fjZYW1ujR48ehg6NiIjyIZVKhUKFCsHS0jLPnU+mJyfON9OqaTg5OeHJkycprtO33qGRk8doJ06cQJs2bVJdr8/zAxwdHfH1118bMixSECXUctL6O03rPA9I/2/YEMfz5cuXz7P7XBbw0+Dp6Znp+6xm1bRp01J9GGHBggXTXJee/fv3p3r/4JwuGBClZMiQIejevXuK66ysrLQPc0lpXXaztbVNsziZlvTGNVFeERQUlOq6tA7sihcvnk0RAfb29oq/csXExASurq5GjcHS0hIlS5ZUfJ/vSu9zg4iIiPKOtGoa5ubmBq935MQxWp06ddI8hiZKqkmTJhCRHN9uWn+n6RXw08Pj+bSxgK9Qzs7O2hngqa3PrDz3IAfKcwoXLozChQsbOwyDS29cE+UVeXXWAylXXv3cICIiouTyYk3DysqKx9CkeNn5d8rj+bTxHvhERERERERERERERArEAj4RERERERERERERkQLxFjpZEBISYuwQUqXk2HILKysrvP/++8YOI89Q6t+kUuMiyioRQePGjWFmxo96Q1DyvkLJsREREeVXDg4O8PDwMHYYeYZSj3eUGhflX0r+m1RybErHs/pMcHR0hLW1Nfr06WPsUNJkbW0NR0dHY4eRa8XExOD48ePGDiPXyw3jhWOF8iKVSoVjx45h7Nixxg4lV8sN+zCA+zEiIiKlefHiBa5cuWLsMHK93HAsxuMwUoLcMFYAjpfMYgE/E0qVKoWQkBA8ffrUIP3du3cPXbt2xapVq+Dp6WmQPoG3g7dUqVIG648oMww9Xr755huEh4dj5cqVBukP4FghotQZeh8WEBCA8ePH48SJE7C2tjZInwD3Y0RERJQ3GfpY7NNPP4WrqyumT59ukP4AHoeRMhh6rAQFBWHgwIHYvn07ypQpY5A+AY6XzGIBP5NKlSplsD84KysrAEClSpVQq1Ytg/RJpCSGHC9FihRBZGQkxwoR5RhD7sNu374NAPD09IStra1B+iQiIiLKywx5LGZra4siRYrwfJLyJEOOlZiYGABAlSpV4O7ubpA+KfP4EFsiIiIiIiIiIiIiIgViAZ+IiIiIiIiIiIiISIFYwCciIiIiIiIiIiIiUiAW8PO4+/fvQ6VSISgoKNU2gYGBUKlUePHiRY7Fld8xL8rEvBDph2NFmZgXIiKi/IGf+crEvBDph2Ml41jAJzRo0ADh4eGwt7cHAPj7+8PBwcG4QRHzolDMC5F+OFaUiXkhIiLKH/iZr0zMC5F+OFZ0mRk7ADI+CwsLuLi4GDsMegfzokzMC5F+OFaUiXkhIiLKH/iZr0zMC5F+OFZ0cQa+wr1+/Ro+Pj6wtbWFq6srfvjhBzRp0gQjRowAAKhUKuzevVvnNQ4ODvD399dZduPGDTRo0AAFChRAtWrVcOzYMe26pJelBAYGon///nj58iVUKhVUKhWmTp2avW8yF2JelIl5IdIPx4oyMS9ERET5Az/zlYl5IdIPx0rOYwFf4caMGYNjx47h119/RUBAAAIDA3Hp0qVM9TNq1ChcvnwZXl5eaN++PZ49e5asXYMGDbBgwQIULFgQ4eHhCA8Px+jRow3xVvIU5kWZmBci/XCsKBPzQkRElD/wM1+ZmBci/XCs5DwW8BUsKioKq1atwrx589C8eXNUr14da9asQUJCQob78vX1RZcuXeDu7o6lS5fC3t4eq1atStbOwsIC9vb2UKlUcHFxgYuLC2xtbQ3xdvIM5kWZmBci/XCsKBPzQkRElD/wM1+ZmBci/XCsGAcL+Ap2584dxMXFoX79+tplhQsXRqVKlTLcl5eXl/b/ZmZmqFOnDkJCQgwSZ37DvCgT80KkH44VZWJeiIiI8gd+5isT80KkH44V42ABP5dTqVQQEZ1l8fHxRoqGNJgXZWJeiPTDsaJMzAsREVH+wM98ZWJeiPTDsWJ4LOArWLly5WBubo5z585plz1//hy3bt3S/uzk5ITw8HDtz7dv30Z0dHSyvs6ePav9f0JCAi5evAh3d/cUt2thYYHExERDvIU8iXlRJuaFSD8cK8rEvBAREeUP/MxXJuaFSD8cK8ZhZuwAKHW2trYYOHAgxowZgyJFisDZ2RkTJ06Eicn/vndp1qwZ/Pz84OXlhcTERIwdOxbm5ubJ+lq8eDEqVKgAd3d3zJ8/H8+fP8eAAQNS3K6bmxuioqLw+++/w8PDA9bW1rC2ts6295nbMC/KxLwQ6YdjRZmYFyIiovyBn/nKxLwQ6YdjxTg4A1/h5s6di0aNGqF9+/Zo0aIFvL29Ubt2be36H374ASVLlkSjRo3Qq1cvjB49OsU/4FmzZmHWrFnw8PDAyZMnsWfPHjg6Oqa4zQYNGmDIkCHo0aMHnJycMGfOnGx7f7kV86JMzAuRfjhWlIl5ISIiyh/4ma9MzAuRfjhWcp5K3r0pEeW4kJAQVKlSBSdPnkTDhg3Tbd+kSRN4enpiwYIF2R9cPrZixQp89tlnUKvVerVnXnKGj48P7t+/j+PHj+vVnnmh/CosLAwlSpTA/v370aZNm3Tbc6zkjC1btuDjjz9GZGQkbG1t023PvOQMX19fnDx5EkFBQcYOhZLw9PSEt7c3/Pz8jB0K/b+oqCjY2dlh8+bN6NGjh7HDIVK0iRMnYtOmTbh7965e7fmZnzPef/99uLm5Ye3atXq1Z14ovzp16hS8vb0RHByc6m1tkuJYyV6cgU9EREREREREREREpEAs4BMRERERERERERERKRAfYpsLBQYGGjsESgHzokzMC5F+OFaUiXkhIiLKH/iZr0zMC5F+OFayF2fgExEREREREREREREpEAv4REREREREREREREQKxAI+EREREREREREREZEC8R74mRQaGoqnT58apK979+4BAG7evAkrKyuD9AkAjo6OKFWqlMH6yw0MmZcHDx5ARHDp0iWD9KfBvGTNs2fPEBUVZdC85MeckDIZcqw8fvwYAPD3339zvFCeZMjx8uTJE0RHR3OsEBHlc4b8bHn48CFiY2N5PmkAhsxLVFQUnj17xs98ypMMOVZu3rwJAAgODkZMTIxB+gQ4XjKLBfxMCA0Nhbu7O6Kjow3a78CBAw3an7W1NUJCQvLNwMiuvNSuXdug/TEvhmHIvOS3nJAyZddY+eKLLwzaH8dL1piamqJChQoQEWOHkqvxs4WIiAwtN3y2APnv8yU78nL58mXs37/fYP3lt5yQMmXXPqxr164G7Y/jJXNYwM+Ep0+fIjo6GuvXr4e7u7uxw0lRSEgI+vTpg6dPn+abQcG8KJPS85Ifc0LKpPSxAnC8GEJiYiJu374NlUpl7FByNaWPF44VIqLcR+mfLUD+/HxRel7yY05ImZQ+VgCOl6xgAT8L3N3dUatWLWOHQe9gXpSJeSHSD8cKkf44XoiIyND42aJMzAuRfjhW8iY+xJaIiIiIiIiIiIiISIFYwCciIiIiIiIiIiIiUiAW8ImIiIiIiIiIiIiIFIgFfCIiIiIiIiIiIiIiBWIBPwU3b96Ei4sLIiMjM92Hm5sbFixYYLigFOa9997Djh07jB0GERFRriEiePjwIQDgzp07EJEMvf7Zs2dwdnbG/fv3AQCBgYFQqVR48eKFgSMlyhqVSoXdu3cbpK+IiAiUKFECr1+/Nkh/RERERLmRIWqV+rp//z5UKhWCgoKyfVuG9vPPP6N9+/bGDsPg8lQBv1+/flCpVFCpVDA3N0eZMmXw9ddf482bNxnqZ/z48Rg+fDjs7OwA5L8T5BUrVqBRo0YoVKgQChUqhBYtWuD8+fM6bSZNmoRx48ZBrVYbKcrs0a9fP3Tq1CnZ8vz2N6A0zAuR/jhelEdE0KhRI5iYmGDEiBEAAE9PTxQtWhSrVq3Su5A/Y8YMdOzYEW5ubgCABg0aIDw8HPb29tkUefY5e/YsPvnkE5QvXx5FihSBu7s7hg4diuvXrxs7NK34+HgsX74cLVq0QPHixeHi4oIGDRpg3rx5iI6OzpEYnj17hqlTp6Ju3bpwcnJCqVKl0K5dO2zevDnDXwBlZ59ZMWPGDDRo0ADW1tZwcHBItr5w4cJ477338OOPP+Z4bEREGcXjMGViXsiYsqtWmR9duXIFPXv2RMmSJWFlZQV3d3csXLhQp82AAQNw6dIlnDhxwkhRZo88VcAHgNatWyM8PBx3797F/PnzsWzZMkyZMkXv14eGhmLv3r3o169f9gWpcIGBgejZsyeOHj2KM2fOoGTJkmjZsiXCwsK0bdq0aYPIyEgcOHDAiJESEREpm4jgiy++wMmTJ5Ote/LkCT799FN88cUX6RZOo6OjsWrVKgwcOFC7zMLCAi4uLlCpVAaP29Di4+MBAGq1GsOHD0ebNm1QtGhRLF68GMePH8eSJUtga2sLb29vLF682ODbT0xMTHfSQVxcnPb/d+/eRa1atbB48WJ07doV27ZtQ0BAAEaMGIHff/8dVatWxa1btwweZ9IYAgICULFiRfz5558YPXo0AgICsHPnTnz44YeYPn06WrVqleFZ6dnRZ1bFxcWhW7duGDp0aKpt+vfvj6VLlyIhISEHIyMiIiIyjNxUq0x6PKo0Fy9ehLOzM9avX4/r169j4sSJGD9+PPz8/LRtLCws0KtXLyxatMiIkRpenivgW1pawsXFBSVLlkSnTp3QokULHD58GACwdu1a2Nra4vbt29r2n3/+OSpXrqydSbV161Z4eHigePHi6W6rW7dusLS0hJubG3744Ydk66OjozFgwADY2dmhVKlSWL58uXad5nKUnTt3omnTprC2toaHhwfOnDmT1V8B/P390bhxYwBAly5dYGtrq91Z6GPDhg34/PPP4enpicqVK2PlypVQq9X4/ffftW1MTU3Rtm1bbN68OcvxEhER5VXr16/XOaBMiZ+fHz766CPtzJyk//z9/QEA+/fvh6WlJd577z3t696dNebv7w8HBwccOnQI7u7uGf78T0tgYCDq1asHGxsbODg4oGHDhnjw4EGKbTXHOFu2bEHjxo1RoEABbNiwAQAwduxYnDt3DiEhIZgzZw5atWqFqlWromnTppg7dy4uXLiAH374Adu3b9f2N3XqVHh6emLdunVwc3ODvb09xo8fn2a8mt/Fnj17UKVKFVhaWiI0NFSnjabflStXokyZMihQoAAA4OXLl2jVqhU6d+6MoKAgDBkyBA0aNECNGjXQvXt3HDhwABMmTEDLli3x/PlzbX9ubm6YOXMmBgwYgEaNGgEAdu7cmWacTZo0ga+vL0aMGAFHR0e0atUKAHDhwgX07NkT/v7+2LdvH3r06IGaNWuiTp06GDp0KK5cuYISJUqgZ8+e2r7i4uLg6+sLV1dXFChQAKVLl8b333+vXZ+ZPrPzeFXj22+/xVdffYXq1aun2uaDDz5AREQEjh07ZrDtEhEREeWU7KhVao53d+/ejQoVKsDLywsAtLfsBN7etrNjx44oWrQobG1tUbduXRw5ckQnNjc3N0yfPh0+Pj4oWLAgBg8enCz+xMREDBgwAJUrV052TJ0Zu3btQufOnWFtbY0KFSpgz549er1uwIABWLhwIRo3boyyZcuiT58+6N+/f7Jj7vbt22PPnj2IiYnJcqxKkecK+Eldu3YNp0+fhoWFBQDAx8cHbdu2Re/evZGQkIB9+/Zh5cqV2LBhA6ytrQEAJ06cQJ06ddLsNyQkBADQsmVL/PXXX5g6dSq++eYb7Um2xg8//IA6derg8uXL+PzzzzF06FDcvHlTp83EiRMxevRoBAUFoWLFiujZs6dBZhdpLsWZPn06jh8/jtDQUIwePTpTfUVHRyM+Ph6FCxfWWV6vXr08d0kKERGRoYgI5s6dCxOTtA+3TExMcPv2bfz3338IDw9HeHg45s2bB2tra+0xyYkTJ1C7du10txkdHY158+Zh3bp1Wf7810hISECnTp3QuHFjXL16FWfOnMHgwYPTnfk/btw4fPnllwgJCUGrVq0QHBwMf39/7N69Gy4uLli6dCkqVKgANzc3/PTTT6hUqRLMzc2xYsUKjBkzRueqhDt37mD37t3Yu3cv9u7di4sXL+r1u5g9ezZWrlyJ69evw9nZOVmbv//+Gzt27MDOnTu19/icNWsWateujWnTpuHly5fo3bu39vY5ixYtQps2bTBo0CA0atQo2fOONMd+GzduBAB8//33yY793rVmzRpYWFjg1KlT+PnnnwEAw4cPx4wZM9C+fXsEBwejcePGcHJyQvfu3TFy5EjMmTMHP//8M4KDg3H06FEAwKJFi7Bnzx5s3boVN2/exIYNG7S3W8psnxrZdbyqLwsLC3h6evK4k4iIiHI9Q9Yqo6OjMWPGDKxduxa//PILAGDChAna9VFRUWjbti1+//13XL58Ga1bt0b79u2TFeHnzZsHDw8PXL58Gd98843OutjYWHTr1g1BQUE4ceIESpUqleXfwYoVK9C9e3dcvXpV+94jIiIy1dfLly+T1Svr1KmDhIQEnDt3LsuxKoWZsQMwtL1798LW1hYJCQmIjY2FiYmJzsy3ZcuWoUaNGvjiiy+wc+dOTJ06VeeE+MGDB+kW8NevXw8AGDRoECpWrIiKFSsiODgYc+fO1bmcpW3btvj8888BvJ1xNn/+fBw9ehSVKlXSthk9ejTatWsH4O0MpKpVq+Lvv/9G5cqVs/R70JxUValSBbVq1YKvry+mTZuWqb7Gjh2LYsWKoUWLFjrLixUrhn/++QdqtTrd4kRuovkbSioxMdFI0ZAG80KkP44XZbhz5w7++uuvdNup1Wpcv34dffr0gY2NDZ4/f47Tp0+jZs2a2gPw8+fPw8LCAh06dNC+7unTpwCAnj17wtzcHP/88w/i4+OhVqu1n/lmZmbYsWNHlh52FRcXh5cvX+LUqVM6xejUHmavmSlkY2MDf39/7QSHkJAQFCxYEEOGDMGzZ89w7tw51KhRA7a2tpgxYwYeP36MAQMGwNraGg8fPkSzZs1gZ2eHmzdvIiYmBjExMdrfh4uLC549e5Zm3PHx8ViyZAk8PDzSfG9r166Fk5OTdtm6detw8OBBAMCoUaNw7949/Prrr3j8+DEGDx6sPY7r168fJk6ciG+//Vb7Ws2x36VLlwAADg4OyY793lWhQgXMmTNH+/Pt27dx//59fPrpp0hMTETnzp3RpEkTLFy4ECdOnMDIkSMxceJEWFhYoGfPnjh06BCaNm2K0NBQVKhQAd7e3lCpVChdunSW+9TIruPVjChWrFiqV30QESkJj8OUiXkhY8quWmV8fDz8/PxQv359WFpaAnh7n/jz58+jXr168PDw0DkWnj59Onbt2oU9e/bA19dXu7xZs2YYNWqU9uf79+8DePsFQLt27RAbG4ujR48a7NlbH374ofaqz5kzZ2LRokU4f/48WrdunaF+Tp8+jS1btmDfvn06y62trWFvb5+njh3zXAG/adOmWLp0KV6/fo358+fDzMwMXbp00a4vVKgQVq1ahVatWqFBgwYYN26czutjYmK0l1Cn5t69e8mWNWzYEAsWLEBiYiJMTU0BADVq1NCuV6lUcHFxwePHj3Vel7SNq6srAODx48dZPiEqUKCAzgMxXF1dk21bH7NmzcLmzZsRGBiY7PdiZWUFtVqN2NhYWFlZZSleJdH8DSV17tw59OnTx0gREcC8EGUEx4sypFdgfldcXBxUKhX+/PNPlCtXDsWKFdOuU6vV2uOLtJiamsLGxkb7s6WlJWJjYzMUx7ssLCxQsmRJnDt3Dk5OTnB0dESxYsXSPV5694GkkZGRKFmyJADg0aNHKF68OEqUKAEAqF69us7lvAUKFNDeNx94e8xhZmam87M+cSc9zkpJ6dKldYr3ERERiIyMRLVq1QAAv/32G3bv3o369esDAHx9fbWXO7u6uurcQgdAsu0VKVIk3eOvd6+s+Ouvv1C3bl2YmZkhODgYYWFh8PPzg7m5OTw9PXUuMXZ1dcWVK1cAvP1C4YMPPkClSpXQunVrfPjhh2jZsmWW+kzpfRnyeDUjrKyscuzhwUREWcHjMGViXsiYsqtWaWZmhrp16+oss7OzQ0hICOrVq4eoqChMnToV+/btQ3h4OBISEhATE5NsBn5qE5l79uyJEiVK4I8//jBo3a9ChQra/9vY2KBgwYIZrlleu3YNHTt2xJQpU7THvEnltWPHPFfAt7GxQfny5QEAv/zyCzw8PJI99O348eMwNTVFeHg4Xr9+rfMEZ0dHx2QnY5llbm6u87NKpUr2ALWkbTSXoqf3kDV9JD3J1fSd3gPy3jVv3jzMmjULR44cSfEEOCIiAjY2NnmqeA/o/g1p/Pvvv0aKhjSYFyL9cbwoQ5EiRTLUfvHixfDx8UGbNm2wc+dOnVvU9O7dGyKivTUL8Pa+9E2bNsWmTZvg4OAAf39/jBgxQqcYu3v3bnTu3Fnv+0qm5fLlyzh48CB+++03nDlzBocPH9a5J7/G/fv3UaZMGfj5+cHT01O7vG3bttoH2I4YMQIqlQrz588H8PbSVwcHB6xcuRJOTk4oUaIEdu7cCWdnZ0ydOhW7d+/WeQ+jR4/GhQsX0ozXysoq3dv8JP2yA3h7BWPSk6O4uDidNkln7l26dCnZONPn2E+fGDTHVnFxcTA3N9fp990YNLP7a9WqhXv37uHAgQM4cuQIunfvjhYtWmD79u2Z7jOl92XI49WMiIiIQLly5XJ0m0REmcHjMGViXsiYjFWrHD16NA4fPox58+ahfPnysLKyQteuXZM9qPbd41GNtm3bYv369Thz5gyaNWuW4e2nJqWaZUaOLYODg9G8eXMMHjwYkyZNSrFNRESEzkSd3C7v3PckBSYmJpgwYQImTZqkfXDB6dOnMXv2bPz222+wtbXVuWQEAGrWrIng4OA0+y1TpkyyZadOnULFihX1mh2XG8yZMwfTp0/HwYMHU/0m7tq1a6hZs2YOR0ZERJQ7lCtXDjVq1Ej3NnMqlQo1atTAlClToFarsW7dumSFZ32OT7JbzZo1MX78eJw+fRrVqlXT+TJBH+XLl9feUsjb2xubN2/GjRs3EB8fjxkzZgAAnjx5ggEDBqBjx44p3rM+uzk6OiIuLg6PHj3SxjlnzhzExMQgLCwMK1asAPD2eHLixIkYOXKkwWNI+nvSPBfAz88PiYmJOHv2LA4dOoT4+HisWbMGBw4c0Ll9Y8GCBdGjRw+sWLECW7ZswY4dOxAREZGlPpWCx51ERESUFxiyVpmQkJBsUktkZCTc3d0BvK1V9uvXD507d0b16tXh4uKivT2OPoYOHYpZs2ahQ4cOOHbsWAbfafa4fv06mjZtik8++UR7DvGuO3fu4M2bN3nq2DFPF/ABoFu3bjA1NcXixYsRGRmJvn374osvvkCbNm2wYcMGbNmyBdu3b9e2b9WqFc6cOZPmvdA0l1itWLECt27dwpo1a+Dn55flh8QpxezZs/HNN9/gl19+gZubGx4+fIiHDx8iKipKp92JEydSvEyFiIiI3hbmR48ene5sEhFB2bJlceTIESxbtgxRUVHaz17NQX2rVq1w/fp1g10lmBH37t3D+PHjcebMGTx48AABAQG4ffu29sTg/PnzqFy5MsLCwtLsp3Pnzli5ciXi4+PRpUsXdOjQAVWqVIG1tTVevHihfd5O8eLFtQ9z1Zefnx+aN2+eZpvx48fDx8cnzTYmJibo0KEDlixZAgBYuHAhLl++DFtbW1SvXh0ffPABjh07hgEDBmDhwoXpbvNdzZs317nfaUpq1qyJmJgYHD16FFZWVvD398fkyZNhaWmJ/v37o1OnTpg9ezZWr16NgIAA7cyiH3/8EZs2bcKNGzdw69YtbNu2DS4uLnBwcMh0n/oICwtD5cqVcf78+Qz9LjRCQ0MRFBSE0NBQJCYmIigoCEFBQTrHnffv30dYWFiy5zERERER5UaGqlWam5tj+PDhOHfuHEJCQgC8vTVlvXr1ALy9Vc3OnTsRFBSEK1euoFevXhm+inL48OH47rvv8OGHH+LkyZNZfOdZc+3aNTRt2hQtW7bEyJEjtedMT5480Wl34sQJlC1bNk9dvZnnbqHzLjMzM/j6+mLOnDm4fv06bGxsMHPmTABv/6hnzpyJzz77DF5eXihevDjatGkDMzMzHDlyBK1atQLwv0uENZd4aE5YAwICsGrVKri6umLatGmKnK2UGUuXLkVcXBy6du2qs3zKlCmYOnUqgLcna6dPn9Y+0JeIiIiS69OnD86fP59i0dbExARqtRpWVlb4999/ERUVhQYNGui0Wb16Nfr164fq1aujVq1a2Lp1Kz777LOcCh/A24dA3bhxA2vWrMGzZ8/g6uqKYcOGaeOIjo7GzZs3de5Zn5KmTZuifPnyGDRoEFatWoVly5Zh3rx5iI+PR+HChREeHg5nZ+dMXc349OlT3LlzJ8024eHhye73mZLJkyejXr16eO+999CmTRsEBwfj4cOHKFSoENRqNSZOnAhHR8cMxwi8nQ2kefhwalQqFWbPno1PPvkEx48fR9u2bfHkyRM8fPgQxYoVw8uXL7F8+fJkD+Kzs7PDnDlzcPv2bZiamqJu3brYv3+/9gqQzPSpj/j4eNy8eTPT9xidPHky1qxZo/1ZM1Pq6NGj2mWbNm1Cy5YtdR7MS0RERJRbGaJWCbw9Th87dix69eqlvSXU999/r13/448/YsCAAWjQoAEcHR0xduxYvHr1KsPxjhgxAmq1Gm3btsXBgweTnbPklO3bt+PJkydYv369Tj2ydOnSOlcWbNq0CYMGDTJChNlIKBk/Pz9p2bKl9udNmzaJra2t9ueLFy8KALl48aIxwtNLdsf49ddfy6BBg7Kl78xiXpRJ6e9Z6fFR/pEb/hZzQ4xKpFarZe3atVKjRg0BoP1Xo0YNWbdunajVar362bt3r7i7u0tiYmI2R5x9IiIi5L333pP33ntPfvvtN3n9+rWIiDx69Eh+/PFH8fT0lKioqHT7ye6/xUOHDkmhQoVk+PDhcvXqVUlMTJTExEQJCgqSPn36yFdffZXt8c2YMUOKFCki8+bNk3/++UdERN68eSOHDh0Sb29v2blzpyL6zE4eHh4yZMgQKVWqlJw8edLY4ZCIREZGCgDZvHmzsUMhMrjccJyTG2I0NKW/Z6XHR3nHu7XK1atXi729vfbn3PC3mBMxXrt2TZydneXFixfZtg1jyPO30Pk/9u48LKrq/wP4+yIMMqIgm+CCmrjgCoKpCIlpKplblluIS2WaWi74NST3MBdcw11+LmhqJuGSCmWikkuKoiWguKCFuKBIICrb+f3hl/mKbAMOzB14v56H54F7z5z7uXw459w53Dm3ND777DO89dZbSElJQXR0tFofy65srKysMG/ePG2HQUREJHuSJGHYsGGIiorC8uXLAUC1TIinp2exD1rN1atXL4wePbrYpWrkrGbNmjh27BgGDhyIKVOmoFq1ajA0NIStrS3Cw8MRGBhY6EO0ylP37t0RGRmJ1NRUuLm5QaFQQKFQwMPDA3Xr1lV9IrEsTZ8+HT/99BPCwsLQqFEjKBQKGBkZYfLkyRg2bBj69u0rizrLWmpqKqZPn45OnTppOxQiIiIircmdq0xNTdV2KLKWmJiIrVu3wsTERNuhaFSFX0KnNPT19eHr64uQkBAMGzYMLi4uWLlyZbnG4OHhgRMnThS478mTJ4W+uZ0+fTqmT59eZN1FfTz60KFDcHNzKza+KVOmFFuGiIiI/keSJFhbWwN48YBbdSfuXzZx4sRSH7+o8f/p06cwMjIqcJ+61wbqUigUmDRpEiZNmoSUlBT8+++/sLKygqGhocaOoQkNGzbEpk2bEBgYiHv37kFPTw+1atUq1xjc3NwQGhqK58+f4/79+6hevTpMTU1lV2dh5s+fr/o4eEFxHDp0qNg6TE1Ny33ZKCIiIiK5yZ2r1Lbt27cXem1maWmZbz36XPXr10dQUFCRdY8ZM6bQpbo9PT3Vek5WRX1mEifwi9CvXz+t/Wdr48aNqgfXvcrIyKjQfWZmZsXWHRUVVei+OnXqqBUfERER6Zaixv+iJvDL8trAxMRE9nfH6OnpwcbGRqsxGBoaol69erKv81VjxozBwIEDC9xX2N8bERERERVvxIgRWnkWZ58+fdC+ffsC9xkYGBT6XCwDAwM8fPiwyLrnzp0Lb2/vAvfVqFGjZIFWMJzAl6myfLNsZ2dXZnUTERGRPHH8p/JmZmam1s0lRERERKQbqlevjurVq5fqtcVN4FtZWcHKyqpUdVd0XAOfiIiIiIiIiIiIiEiGOIFPRERERERERERERCRDXELnNcTExGg7hELJObayJudzl3NsZU2u5y7XuKjykvPfpJxj0xVVqlRB48aNIYTQdigVglz/JuUaFxERFU/OfbicYytrcj13ucZFlZec/yblHJvccQK/FCwsLKBUKuHp6antUIqkVCphYWGh7TDKDfMiT7qQl8qWE5InXWgrANvL68rOzkZcXBwkSdJ2KDpNF9oL2woRkW7RhbEFqHzjiy7kpbLlhORJF9oKwPZSWpzALwVbW1vExMQgKSlJI/XdvHkTH3zwAQIDA+Hg4KCROoEXjdfW1lZj9cmdpvMSHBwMPz8/REZGaqS+XMzL65kxYwYSExOxceNGjdQHVL6ckDxpuq3cv38fHh4eWLlyJTp16qSROgG2F5IHTbeXhQsX4sKFC9i5c6dG6gPYVoiIdI2mx5ZVq1bh8OHD2L9/v0bqy1XZxhdN5+WTTz6BjY0N5s2bp5H6gMqXE5InTbeVqKgofPzxx/jxxx/RsGFDjdQJsL2UFifwS8nW1lZjf3BGRkYAgKZNm6Jt27YaqbOy0mReIiMjIUkSc6IBmsyLubk5UlNTmReqkDTZVhISEgAAdnZ2bC9UIWmyvVhaWkKpVLKtEBFVcpocW6ytrWFoaMixRQM0mRdjY2OYm5szL1QhabKtPH36FADQvHlz2Nvba6ROKj0+xJaIiIiIiIiIiIiISIY4gU9EREREREREREREJEOcwCciIiIiIiIiIiIikiFO4Fdw8fHxkCQJUVFRhZYJDw+HJEl4/PhxucVV2TEv8sS8EKmHbUWemBd5Yl6IiEjTOLbIE/NCpB62lZLjBD7BxcUFiYmJMDExAQBs3rwZpqam2g2KmBeZYl6I1MO2Ik/MizwxL0REpGkcW+SJeSFSD9tKXvraDoC0T6FQwNraWtth0CuYF3liXojUw7YiT8yLPDEvRESkaRxb5Il5IVIP20pevANf5p48eQIvLy8YGxvDxsYGS5Ysgbu7OyZOnAgAkCQJISEheV5jamqKzZs359kWGxsLFxcXVK1aFS1btsSxY8dU+17+WEp4eDhGjhyJlJQUSJIESZIwe/bssj1JHcS8yBPzQqQethV5Yl7kiXkhIiJN49giT8wLkXrYVsofJ/BlburUqTh27Bj27t2LsLAwhIeH4/z586WqZ8qUKbhw4QI6duyI3r174+HDh/nKubi4YPny5ahRowYSExORmJgIb29vTZxKhcK8yBPzQqQethV5Yl7kiXkhIiJN49giT8wLkXrYVsofJ/BlLC0tDYGBgfD390fXrl3RqlUrbNmyBVlZWSWua/z48RgwYADs7e2xZs0amJiYIDAwMF85hUIBExMTSJIEa2trWFtbw9jYWBOnU2EwL/LEvBCph21FnpgXeWJeiIhI0zi2yBPzQqQethXt4AS+jF2/fh0ZGRlo3769apuZmRmaNm1a4ro6duyo+l5fXx/Ozs6IiYnRSJyVDfMiT8wLkXrYVuSJeZEn5oWIiDSNY4s8MS9E6mFb0Q5O4Os4SZIghMizLTMzU0vRUC7mRZ6YFyL1sK3IE/MiT8wLERFpGscWeWJeiNTDtqJ5nMCXsUaNGsHAwABnzpxRbUtOTsbVq1dVP1taWiIxMVH1c1xcHNLT0/PVdfr0adX3WVlZiIyMhL29fYHHVSgUyM7O1sQpVEjMizwxL0TqYVuRJ+ZFnpgXIiLSNI4t8sS8EKmHbUU79LUdABXO2NgYH3/8MaZOnQpzc3NYWVnB19cXenr/+7/L22+/jYCAAHTs2BHZ2dmYNm0aDAwM8tW1atUqNG7cGPb29li2bBmSk5MxatSoAo/boEEDpKWl4ciRI2jTpg2USiWUSmWZnaeuYV7kiXkhUg/bijwxL/LEvBARkaZxbJEn5oVIPWwr2sE78GVu8eLFcHNzQ+/evdGtWze4urrCyclJtX/JkiWoV68e3NzcMHToUHh7exf4B7xgwQIsWLAAbdq0QUREBPbt2wcLC4sCj+ni4oIxY8Zg0KBBsLS0xKJFi8rs/HQV8yJPzAuRethW5Il5kSfmhYiINI1jizwxL0TqYVspf5J4dVEiKncxMTFo3rw5IiIi0KlTp2LLu7u7w8HBAcuXLy/74CqxDRs24LPPPkNOTo5a5ZmX8uHl5YX4+HgcP35crfLMC1VWCQkJqFu3Lg4ePAgPD49iy7OtlI9du3Zh8ODBSE1NhbGxcbHlmZfyMX78eERERCAqKkqt8sxL+XBwcICrqysCAgK0HQr9V1paGqpXr46dO3di0KBB2g6HSNZ8fX2xY8cO3LhxQ63yHFvKx1tvvYUGDRpg69atapVnXqiy+v333+Hq6oro6OhCl7V5GdtK2eId+EREREREREREREREMsQJfCIiIiIiIiIiIiIiGeJDbHVQeHi4tkOgAjAv8sS8EKmHbUWemBd5Yl6IiEjTOLbIE/NCpB62lbLFO/CJiIiIiIiIiIiIiGSIE/hERERERERERERERDLECXwiIiIiIiIiIiIiIhniGvhEVOZu376NpKQkjdT18OFDpKWl4fz58xqpDwAsLCxga2ursfqIqGLRZB928+ZNAEBUVBSUSqVG6gTYj5F8aLK9pKen48GDBxzziYgqOU2OLWlpaXj48CHHFiLSKZzAJyqEkZER3nrrLW2HofNu374Ne3t7pKena7ReJycnjdWlVCoRExPDiy4iyqes+jA3NzeN1sd+7PVYWFjA3t5e22HovLJoL3Fxcfjhhx80Vh/bChGVF1NTU7Rp00bbYei8shhbLly4gIMHD2qsPo4tRFTWOIFPVIinT5/i+PHj2g5D5yUlJSE9PR3btm2T5eRITEwMPD09kZSUxAsuIspH7n0YwH5ME5KSkhATE6PtMHSe3NsL2woRlafHjx/j4sWL2g5D53FsISLiBD4RlRN7e3u0bdtW22EQEZUK+zAi9bG9EBGRpnFsIaLKjA+xJSIiIiIiIiIiIiKSIU7gExERERERERERERHJECfwiYiIiIiIiIiIiIhkiBP4REREREREREREREQyxAl8ogIIIXD//n0AwLVr1yCE0HJEmvHw4UNYWVkhPj6+TOoPDw+HJEl4/PgxAGDz5s3o3LlzmRyrsjp8+DAcHByQk5Oj7VCIiCoEIQRSUlKQnp5e4jH/1XH11XGQSC4kSUJISIjG6ps2bRqvRYiKIIRAcnIynj9/XqHeTxZH3feb7u7umDhxYrnEJHea/F1kZGSgQYMGOHfunEbqIyL54AQ+0UuEEAgKCoKDgwO+/vprCCHQuHFjODg4ICgoSKsXXiNGjIAkSZAkCQYGBmjYsCH+85//4NmzZ2rX4efnh759+6JBgwZlF2gFFR0djbFjx8Le3h7m5uZo3Lgxhg8fjlOnTmn0OF988QWcnJxgaGgIBweHfPt79uwJAwMDbN++XaPHJaKyNWLECPTr1y/fdk74ak/uuKqnp4dt27YhLi4OjRs3RuvWrdUe818dV11cXJCYmAgTE5Myjl7zTp8+jeHDh8POzg7m5uawt7fH2LFjcfnyZW2HppKZmYn169ejW7duqFOnDqytreHi4gJ/f3+kp6eXSwwPHz7E7Nmz0a5dO1haWsLW1ha9evXCzp07S32dWBZ1llZ8fDw+/vhjNGzYEEZGRmjUqBFmzZqFjIyMPOWqVKnCaxGiArz8fnLNmjW4c+eObN5PFqcs3m/q+nWOHMadlwUHB6N79+4wNzeHJEmIiorKs1+hUMDb2xvTpk0r99iIqGxxAp/ov4QQ+OKLL+Dl5YW//vorz76//voLXl5e+OKLL7R60dWzZ08kJibixo0bWLZsGdatW4dZs2ap9dr09HQEBgbi448/LuMoK4bMzEzV9wsWLED79u2Rk5MDf39/HDt2DJs2bcIbb7yBPn36wMfHR6PHHjVqFAYNGlTo/hEjRmDlypUaPSYRUWUihMCZM2cAvLgz+WXqjvkFjasKhQLW1tb56pSj3HEuJycHEyZMgIeHB2rVqoVVq1bh+PHjWL16NYyNjeHq6opVq1Zp/PjZ2dnF3sH98qTxjRs30LZtW6xatQoffPABdu/ejbCwMEycOBFHjhxBixYtcPXqVY3H+XIMYWFhaNKkCc6ePQtvb2+EhYUhODgY7733HubNm4cePXrgyZMnJaq/LOp8HbGxscjJycG6detw+fJlLFu2DGvXrsX06dPzlOvcuTOvRYheoQvvJ4vD95v/o61xpyhPnjyBq6srFi5cWGiZjz76CBEREbL6BzwRaYAgrYuOjhYAREREhLZDqdS2bt0qABT7tXXrVq3EN3z4cNG3b988295//33h6OgohBBi1qxZBca7adMmIYQQu3fvFpaWlnle7+TkJBYvXqz6uW/fvkJfX1+kpqYKIYT4+++/BQARFxcnhHjxO3JychLGxsaiVq1aYsiQIeLevXuq1x89elQAEMnJyUIIITZt2iSMjY0FABEZGSnu378vnJycRL9+/cSzZ8/Eo0ePxNChQ4WFhYWoWrWqsLOzE//3f//3Wr+nmzdvCgBiz549wt3dXRgZGYnWrVuLkydPFlg+MjJS9btavXq16N27t1AqlWLWrFlCCCECAgJEo0aNxJUrVwp8/f3794Wjo6Pw9/dXbYuPjxfvvfeeMDU1FUqlUjRv3lz8/PPPJTqPWbNmiTZt2hS479atWwKAuHbtWonqpMrln3/+EQDEwYMHtR2KTsvtIyIjI1+rnoL6cCHy95uloakYKxN1x/ySjqsFjYMmJibi8OHDolmzZqJatWqiR48e4s6dO699DkePHhXt2rUTSqVSmJiYCBcXFxEfH19g2dyxcefOneKtt94ShoaGqvPw9vYW7dq1E4mJiQW+9tq1a6Jhw4Zi9+7dqm25Y9TWrVtF/fr1RY0aNcSgQYPE8ePHC/1bzP1d7N27V9jb24sqVaqImzdv5imTW++GDRtEgwYNhCRJQgghHj9+LOzs7MSMGTNETk5OgXGuX79e1K9fXzx69Ei1rX79+sLPz0+MHDlSde1SXFvp3LmzGDdunPjyyy+Fubm5cHd3F0IIcfbsWWFmZib27dtX4OsyMzPFyJEjRe/evVXbnj9/LsaNGyesra2FoaGhsLW1FfPnz1ftL02dJb3OyQVA/PTTT0WWKcyiRYtEw4YNhRBCpKamCgAiICCA1yJEr5D7+8niaPr9Zm5/9fLX8OHDhRAv+toJEyaIqVOnipo1a4patWqp3n/lSk5OFh9//LEwNTUVAISzs7OIiop67fMsbAz7999/VWVKMu7kjv2RkZH5xp169eqJdevWFRlP586dxZdfflmic8j93V64cKHA/V26dBFff/11ieokelVERIQAIKKjo7UdCgkheAc+EV7cLbF48WLo6RXdJPT09ODv7y+Luyb++usvnDx5EgqFAgDg7e2NxMRE1Ze/vz+USiWcnZ0BACdOnICTk1OeOjp37ozw8HAAL34HJ06cgKmpKSIiIgAAx44dQ506dWBnZwfgxd168+bNw8WLFxESEoL4+HiMGDFCrXjv3r0LNzc3tGzZEj/++CMMDQ0xY8YMREdH49ChQ4iJicGaNWtgYWGhgd8O4OvrC29vb0RFRaFJkyYYMmQIsrKyinzN7Nmz0b9/f/z5558YNWoUkpKSMHPmTPz0009o0qQJfvrpJ7Rs2RK1a9fG119/jXfeeQexsbHYsWMH/Pz8kJqaCgAYN24cnj9/juPHj+PPP//EwoULYWxsrJHzAgBbW1vUqlULJ06c0FidRESVRe6YXxxJkmBvb487d+6oPa4WJD09Hf7+/ggKCsLx48dx+/ZteHt7v9Y5ZGVloV+/fujcuTMuXbqEU6dOYfTo0cXe+f/VV1/hyy+/RExMDHr06IHo6Ghs3rwZISEhsLa2xpo1a9C4cWM0aNAA3333HZo2bQoDAwNs2LABU6dOzXP9c/36dYSEhODAgQM4cOAAjh07hs2bNxf7u1i4cCE2btyIy5cvw8rKKl+Za9euYc+ePQgODlYtDbBgwQI4OTlh7ty5SElJwUcffaRaxmDlypXw8PDAp59+Cjc3NyxfvjxPfUuWLIGzszMuXLiADz/8EACKXZt5y5YtUCgU+P3337F27VoAwIQJE+Dn54fevXsjOjoanTt3hqWlJQYOHIjJkydj0aJFWLt2LaKjo3H06FEAwMqVK7Fv3z788MMPuHLlCrZv355nGcPS1JmrNNc5pZWSkgIzM7M82ywsLHgtQvQSXXw/WZzXfb9Zr1497NmzBwBw5coVJCYmYsWKFar9W7ZsQbVq1XDmzBksWrQIc+fOxS+//KLa/+GHH+L+/fv47rvvAADNmjVD165d8ejRo9c+t4LGsAULFqj2l2Tc+f777/PU/fK48/nnn2Ps2LG4cuXKa8dcEm+++Sb7Z6IKRl/bARDJwfXr1/Hnn38WWy4nJweXLl3C3LlzYWlpWQ6R/U9sbCzOnj0LQ0ND5OTkICsrC5Ik4b333sPq1avzlL158yZWrFiBYcOG4fjx4zh+/DjCw8NRrVq1PGXT09Px22+/ISAgAHfu3EFWVhacnJywdOlS3LhxA9u3b4eNjU2e19y8eRM3b94EALi5uWHRokVYunQpqlatqvoI4caNG6FUKnHq1CnVm8ncZWGWL1+ummC4ffs2HB0dVRd9mlyb39vbG7169QIAzJkzBy1atMC1a9fQrFmzQl8zdOhQjBw5UvXzhg0b0KVLF7Rq1QrXr1/HkCFDsGTJEnTq1AkBAQE4evQofH190bRpU7Ro0QK///47evbsidu3b2PAgAFo1aoVAOCNN97Q2Hnlql27Nm7duqXxeomo7Bw4cCDfP/Oys7O1FE3lpe6YL4RATEwM1q9fj7S0NLXG1YLGwczMTLi6uuKPP/4AALRu3Rr79+/PN3aXxJMnT5CSkoKsrCyEhoaqth84cKDA8g8fPgQAODs74+7duzh06BAAYN++fWjTpg1CQkJw7do1rFq1CkOHDkWtWrWwfv16XLt2DVu2bIG5uTkeP36MWbNmwdraGmfPnkVGRgY6deqE48ePAwBatmypuimgMJmZmVi9ejXatGlTaJmMjAxs3bo1z3VWUFAQDh8+DACYMmUKbt68ib179+L+/fsYPXo0mjZtCuDFEnO+vr6YM2eO6rXvvvsuPv/8c9X+gIAAnDt3Du+//36hMTRu3BiLFi1S/RwXF4f4+Hh88sknyM7ORv/+/eHu7o4VK1bgxIkTmDx5Mnx9faFQKDBkyBCEhoaiS5cuuH37Nho3bgxXV1dIkoT69eu/dp25SnOdUxrXrl3Dd999B39//3z7eC1C9D+68H6yOGXxfjN3XNy3bx+USqVq+507d2BpaQlLS0vVpL2trS0WL16MuLg4XLt2DREREViwYAGePn0KAJg0aRJOnTqFH3/8EaNHj36tc83JycHmzZtRvXp1AMCwYcNw5MgR+Pn5ASjZuPPqA2hfHnemTZuGZcuW4ejRo6rXlAf2z0QVDyfwifC/N7bq+uabb8p9fdvcifDc9WJzj79lyxZs2bJFVU4IgaysLOjp6SEoKAhBQUGq10uSlOeJ9LllJ06cCCEEhBA4fvw4cnJyEB4ejszMTFSpUgUXL15Ulc/Ozs53x8i0adMgSZIqNh8fH9XPudvefvvtPHdcAMDYsWMxYMAAnD9/Ht27d0e/fv3g4uKikd9X69atVd/b2NgAAO7fv1/kG9vcfyTk+vPPP1XxhIaG4q233sK4ceMAAKtXr8aOHTvyHCM5ORnAiwfRjh07FmFhYejWrRsGDBiQJx5NMDIy0sqDk4io9Lp06YI1a9bk2XbmzBl4enpqKaLKqaRj/rx585Cdna3WuFrQOAhANSGQWyY7OzvfG/6SkiRJ9U/x3IfxFnZtkjtu//TTT9i7d69qe+71wvHjx1Xje+6DSYUQyMnJwdy5cyFJErKysjB//nzo6emp1q//6quvVHVlZ2ejWrVqRcasUCiKHQ/r16+fZ1Lr0aNHSE1NRcuWLQEA+/fvR0hICNq3bw8AGD9+vGry5+WxONfLx8v9/RR39+arn6z4888/0a5dO+jr6yM6OhoJCQkICAiAgYEBHBwcsG/fPlVZGxsb1XXTiBEj8M4776Bp06bo2bMn3nvvPXTv3v216izovNS9zimphIQE9OzZEx9++CE+/fTTfPt5LUL0P7rwfrI4ZfF+89Vx8eVjSZKUZyzMysrC7du3ER4erhpnJk6cCH39F9NWrq6ueP78Oa5fv/7a59qgQQPV5D3woh+9f/8+gJKPO7mfws716rhjbW2tqru8sH8mqni4hA4RAHNz8xKVj4mJQUZGRrl+eXl5oU+fPsjMzERmZiaysrLQokULrFmzRlUmOTkZLVq0QL9+/ZCVlZXn9UOGDMGgQYPybMvMzISjoyMWLFiAfv36YfXq1bh//z4MDAxUD16Kjo5W1W1iYoIhQ4bg+PHjiImJUd31l3sn3q+//goAePDgATIyMlR3IAIvPlKZkJCQ5/fo4eGBW7duYdKkSbhz5w66du362ssK5DIwMFB9n3uxWNzD8l6deMjKyoKRkRGAF3cEvrxfoVCoPk6ak5ODqKgo1VJDn3zyCW7cuIFhw4bhzz//hLOzs+qjn5ry6NEj2d21Q0RFq1atGuzs7PJ81alTR9thVTolHfMbN26s9rha0DhoYmKSp8yPP/4IAK99XZCTk4Pz58/Dz88P7du3R9WqVXH8+PECy8bFxQEAzp07l2d7z549sX//fmRkZGDChAn48ssvVfuSkpIAvLhTPPca4M6dO8jIyMCMGTPQpk2bPHUtWbKk2OXijIyMip2wKmgsrlq1qurnV8fjl495/vx51Vic6+XrgVzFLV1R3PWAgYFBnnoLi6Ft27a4efMm5s2bh6dPn2LgwIH44IMPXqvOgs5L3euckrhz5w66dOkCFxcXrF+/vsAyvBYh+h9deD+pjfebr46LuV9vvfUWJkyYkGdbnz594OXlhYyMDMyfPx916tRBXFycatzcsWMHrly5gqlTp752vl4dG17+p3tJx526deuqXXd5Yf9MVPFwAp8IQKNGjdC6dWu11ixs06YNGjVqVE6RFR3L9OnT8fXXX+Pp06cQQsDT0xM5OTkICgrK9wbZ0dER0dHR+erp3Lkzjh49iuPHj8Pd3R1mZmawt7eHn58fbGxs0KRJEwAvPlL58OFDLFiwAG5ubmjWrJladxLkxmFvb48uXbrgzp07efZbWlpi+PDh2LZtG5YvX17om0RtsLOzU30U1tXVFWFhYTh9+jSys7MREBCAx48f499//8WUKVNQp04dtGvXTvXaevXqYcyYMQgODsaUKVOwYcMGjcX17NkzXL9+HY6Ojhqrk4iossgd84sjSRJq1KgBAwODEo2r5cnR0RE+Pj44efIkWrZsmW8d3uK8Os7t3LkTsbGxyMzMVH1q4MGDBxg1ahT69u1b4Jr1Zc3CwgIZGRm4d++eKs5Fixbh6dOnSEhIUI2vJ0+ehK+vLyZPnqzxGF7+PeU+FyAgIADZ2dk4ffo0QkNDkZmZiS1btuDQoUN5ng9Uo0YNDBo0CBs2bMCuXbuwZ88ePHr06LXqLGsJCQlwd3eHk5MTNm3aVOD1cUZGBq9FiF6ii+8ni6OJ95u5NzyVdMnAtm3b4u7du9DX10e9evUAvHh/ZWdnp7FnphWmpOOOHD9J+ddff7F/JqpgOIFPhBdv0r29vYv9z3hOTg6mTJkim487fvjhh6hSpQpWrVqF2bNn49dff8W6deuQlpaGu3fv4u7du6o1A3v06IHLly/n+2i5u7s7QkNDoa+vr/rYtbu7O7Zv347OnTurytna2kKhUOC7777DjRs3sG/fPsybN0/tWL/55hu0adMGb7/9Nu7evQsAmDlzJvbu3Ytr167h8uXLOHDgAOzt7V/311KshIQENGvWTLUecWH69OmD3bt349GjR3B2dsZXX30FNzc3GBoaIiwsDE5OThg8eDCSk5Px008/qV43ceJEhIaG4ubNmzh//jyOHj2q9nldu3YNUVFRqtxFRUUhKioKGRkZqjKnT5+GoaEhOnbsWLpfABFRJZY75hdHCIGMjIwSj6vl4ebNm/Dx8cGpU6dw69YthIWFIS4uTjXW/PHHH2jWrFm+T769qn///ti4cSMyMzMxYMAA9OnTB82bN4dSqcTjx49Ru3ZtdOvWDXXq1FE9zFVdAQEB6Nq1a5FlfHx84OXlVWQZPT099OnTR7Wm8ooVK3DhwgUYGxujVatWeOedd3Ds2DGMGjUKK1asKPaYr+ratSsCAgKKLOPo6IinT5/i6NGjMDIywubNmzFz5kwYGhpi5MiR6NevHxYuXIhNmzYhLCxMddfj0qVLsWPHDsTGxuLq1avYvXs3rK2tYWpqWuo61aHudU5hr3V3d4etrS38/f3x4MED1d/+y+Li4ngtQvQSXX0/WZzXfb9Zv359SJKEAwcO4MGDB0hLS1PruN26dUPHjh3Rr18/nDp1CgBw8eJF+Pr65lmipyyUdNx58803S1S/OuNOYR49eoSoqCjVP0quXLmieu/4shMnTqiWbCOiioET+ET/5enpifHjxwNAvjsncn8eP368rP7Drq+vj/Hjx2PRokU4ePAg0tLS4OLiAhsbG9XXrl27AACtWrVC27Zt8cMPP+Spw83NDTk5OXkm693d3ZGdnQ13d3fVNktLS2zevBm7d+9G8+bNsWDBggIfaFZUrDt27ECLFi3w9ttv4/79+1AoFPDx8UHr1q3x1ltvoUqVKti5c+fr/VLUkJmZiStXrhS7LqCdnR0+/PBDDBkyBOnp6ZgxYwb+/fdf3LlzB/v27cPBgwfx+PFjbN68GaampqrXZWdnY9y4cbC3t0fPnj3RpEkTtR9W+Mknn8DR0RHr1q3D1atX4ejoCEdHxzyfXNixYwc++uijPA+CIiIi9Xl6eqr+af3qmJ87qWJlZYVnz56VeFwtD0qlErGxsRgwYACaNGmC0aNHY9y4cfjss88AvHhI/ZUrV5CZmVlkPV26dIGdnR0+/fRT5OTkYN26dUhJScG9e/ewfv16nDt3Do8ePVI9rL4kkpKSil2nODExEbdv3y62rpkzZyIgIACHDh1Co0aNVGvGJyYmYv78+Xjw4AFiY2PRr1+/EsUIvHjwZO5yQYWRJAkLFy7E8OHDER8fj3fffRcPHjzArVu3EB0djdWrV+Px48cIDw9HixYtVK+rXr06Fi1aBGdnZ7Rr1w7x8fE4ePCg6nkFpalTHepe5xTkl19+wbVr13DkyBHUrVs3z9/+y06ePMlrEaJX6OL7yeK87vvNOnXqYM6cOfjqq69Qq1Yt1e+nOJIk4eDBg3jrrbdUDyb38fHBrVu3UKtWLc2f6Cu0Pe4UZt++fXB0dFQ9xHzw4MFwdHTM80/2U6dOISUlRbVkGxFVEIK0Ljo6WgAQERER2g6l0svJyRFbt24VrVu3FgBUX61btxZBQUEiJydH2yG+lgMHDgh7e3uRnZ1dbseMjIwUAERkZGS5HbMkiovv+fPnok+fPsLe3l7s2LFDPH78WAghRHJysggMDBQtWrQQf//9d7nF++DBA2FmZiZu3LhRbsck3fTPP/8IAOLgwYPaDkWnyb0PE0I3YpQjTYz52hhXNe3Ro0eiQ4cOokOHDmL//v3iyZMnQggh7t27J5YuXSocHBxEWlqaWnWV5d9iaGioqFmzppgwYYK4dOmSyM7OFtnZ2SIqKkp4enqKSZMmlXl8fn5+wtzcXPj7+6vG/mfPnonQ0FDh6uoqgoODZVFnWUpNTRUAhLGxMa9FiApQ0d9PFqcsxkVtXeeoO+7I7Tps4MCBws/PT9thUAUQEREhAIjo6Ghth0JCCN6BT/QSSZIwbNgwREVF4ZtvvoEkSYiLi0NUVBQ8PT115qOOhenVqxdGjx5d7Efq6X8UCgVCQkLwn//8BwsXLoSpqSkMDQ1haWmJbdu2YeXKlfkeXFSW4uPjsXr1ajRs2LDcjklEVBG9POZ7enqicePGJR7zK8K4WrNmTRw7dgwDBw7ElClTUK1aNRgaGsLW1hbh4eEIDAzM91BXbejevTsiIyORmpoKNzc31cPkPTw8ULduXcyePbvMY5g+fTp++uknhIWFoVGjRlAoFDAyMsLkyZMxbNgw9O3bVxZ1lodRo0bxWoSoAC+PLWPHjkXt2rUr1PvJ4lSEcTGXHMadksrIyECrVq0wadIkbYdCRBqmr+0AiORIkiTVg9rs7Oy0HI1mTZw4UdshFGv+/PmYP39+gfuePHlS6ESCm5sbDh06pPF4JEnCiBEjMGLECKSlpeHRo0ewtLSEkZFRieoZM2YMtm3bVuA+T09PtdYXdnZ2hrOzc4mOS0REhZMkCSYmJlAqlaUa819nXDU2Ni5039OnTwsdZw4dOgQ3N7dSH/dVCoUCkyZNwqRJk5CSkoJ///0XVlZWMDQ01NgxNKFhw4bYtGkTAgMDce/ePejp6ZXLUgovc3NzQ2hoKJ4/f4779++jevXqeZbRk0udhSnqGqsk11EuLi6aDIuowpEkCTVr1oShoWGFez9ZnPJ4v9miRQvcunWrwH2WlpZ48OBBgfvWrVuHjz76SO3jlOe4c+LECXh4eBS6X53nBygUCnz99deaDIuIZIIT+EQkO2PGjMHAgQML3GdkZKR6UFJB+8qasbFxkRMuRZk7d26hD06sUaPG64RFREQ6KCoqqtB9RU3g16lTp4wiAkxMTGBiYlJm9WuCnp5evjXZy5uhoSHq1asn+zpfVdw1FhGRLjh48GChz3kxMDAodF9pJ9/LY9xxdnYu8rqAiCo3TuATkeyYmZnBzMxM22FonJWVleqTHURERJXtrkzSvop6jUVElUv9+vW1HYLGGRkZ8bqAiArFNfCJiIiIiIiIiIiIiGSIE/hERERERERERERERDLEJXSIqFzExMRoO4QCyTUuIpIXOfcVco6NKie5/k3KNS4iIiqeXPtwucZFRBULJ/CJCqGnp8f1yjXAwsICSqUSnp6e2g6lUEqlEhYWFtoOg4hkSBf6MID92OsyNDSU/YNbdYEutBe2FSIqLwYGBqhZs6a2w9B5HFuIiDiBT1SonJwc3L9/X9th6DxbW1vExMQgKSlJI/XNmDEDiYmJ2Lhxo0bqA15cFNra2mqsPiKqODTdh4WFhcHHxwcnTpyAUqnUSJ0A+7HX9fz5c6SkpGg7DJ2n6fYyePBgODo6Ytq0aRqpD2BbIaLyk5mZieTkZG2HofM0PbZ88sknsLGxwbx58zRSH8CxhYjKHifwiajM2draauyCxtzcHKmpqWjbtq1G6iMiKo4m+7C4uDgAgIODA4yNjTVSJ5GcaLK9KJVKWFpacswnIqrkNDm2GBsbw9zcnGMLEekUPsSWiIiIiIiIiIiIiEiGOIFPRERERERERERERCRDnMAnIiIiIiIiIiIiIpIhTuATaUF8fDwkSUJUVFShZcLDwyFJEh4/flxucVV2zAsR6TL2YfLEvMgT80JEuox9mDwxL0RUVjiBTyRTLi4uSExMhImJCQBg8+bNMDU11W5QxLwQkU5jHyZPzIs8MS9EpMvYh8kT80JEpaGv7QCIqGAKhQLW1tbaDoNewbwQkS5jHyZPzIs8MS9EpMvYh8kT80JEpcE78IlK4cmTJ/Dy8oKxsTFsbGywZMkSuLu7Y+LEiQAASZIQEhKS5zWmpqbYvHlznm2xsbFwcXFB1apV0bJlSxw7dky17+WP1oWHh2PkyJFISUmBJEmQJAmzZ88u25PUQcwLEeky9mHyxLzIE/NCRLqMfZg8MS9EJFecwCcqhalTp+LYsWPYu3cvwsLCEB4ejvPnz5eqnilTpuDChQvo2LEjevfujYcPH+Yr5+LiguXLl6NGjRpITExEYmIivL29NXEqFQrzQkS6jH2YPDEv8sS8EJEuYx8mT8wLEckVJ/CJSigtLQ2BgYHw9/dH165d0apVK2zZsgVZWVklrmv8+PEYMGAA7O3tsWbNGpiYmCAwMDBfOYVCARMTE0iSBGtra1hbW8PY2FgTp1NhMC9EpMvYh8kT8yJPzAsR6TL2YfLEvBCRnHECn6iErl+/joyMDLRv3161zczMDE2bNi1xXR07dlR9r6+vD2dnZ8TExGgkzsqGeSEiXcY+TJ6YF3liXohIl7EPkyfmhYjkjBP4RGVAkiQIIfJsy8zM1FI0lIt5ISJdxj5MnpgXeWJeiEiXsQ+TJ+aFiLSFE/hEJdSoUSMYGBjgzJkzqm3Jycm4evWq6mdLS0skJiaqfo6Li0N6enq+uk6fPq36PisrC5GRkbC3ty/wuAqFAtnZ2Zo4hQqJeSEiXcY+TJ6YF3liXohIl7EPkyfmhYjkTF/bARDpGmNjY3z88ceYOnUqzM3NYWVlBV9fX+jp/e//YW+//TYCAgLQsWNHZGdnY9q0aTAwMMhX16pVq9C4cWPY29tj2bJlSE5OxqhRowo8boMGDZCWloYjR46gTZs2UCqVUCqVZXaeuoZ5ISJdxj5MnpgXeWJeiEiXsQ+TJ+aFiOSMd+ATlcLixYvh5uaG3r17o1u3bnB1dYWTk5Nq/5IlS1CvXj24ublh6NCh8Pb2LnAQXrBgARYsWIA2bdogIiIC+/btg4WFRYHHdHFxwZgxYzBo0CBYWlpi0aJFZXZ+uop5ISJdxj5MnpgXeWJeiEiXsQ+TJ+aFiORKEq8u4EXlLiYmBs2bN0dERAQ6deqk7XDovzZs2IDPPvsMOTk5apV3d3eHg4MDli9fXraBVXJeXl6Ij4/H8ePH1SrPvFBllZCQgLp16+LgwYPw8PDQdjj0X7t27cLgwYORmpoKY2PjYsuzDysf48ePR0REBKKiotQqz7yUDwcHB7i6uiIgIECt8sxL2UtLS0P16tWxc+dODBo0SNvhEMmar68vduzYgRs3bqhVnn1Y+XjrrbfQoEEDbN26Va3yzAtVVr///jtcXV0RHR1d6BJQVH54Bz4RERERERERERERkQxxAp+IiIiIiIiIiIiISIb4EFsiDQkPD9d2CFQA5oWIdBn7MHliXuSJeSEiXcY+TJ6YFyKSA96BT0REREREREREREQkQ5zAJyIiIiIiIiIiIiKSIU7gExERERERERERERHJECfwiYiIiIiIiIiIiIhkiA+xpQrl9u3bSEpK0khd6enpcHZ2xvnz5zVSXy4LCwvY2tpqtE6502ReatSogbp162o0L5UxJ1TxCSHg4OCAKlWqaDsUnafJPuzx48dwdnbGpUuXULVqVY3UCVTOfkyTeTEyMkLDhg05tshM/fr1YWJiou0wdJ4m28qzZ8/g7OyMx48fs71QhaTJ9lKlShU0bdqU7yc1QJN5qVu3LmrUqME+jIh0CifwqcK4ffs27O3tkZ6ertF6nZycNFqfUqlETExMpRngyyovO3bs0FhdlS0nVDlIkoSoqChkZ2drOxSdVlZ9WKdOnTRaX2Xrx8oqLyEhIRqrq7LlpCzcunUL9erV03YYOq2s2sq5c+c0Wh/bC8lBWbWXw4cPa7S+ytZeyiovq1at0lhdlS0nRFT+OIFPFUZSUhLS09Oxbds22NvbazucAsXExMDT0xNJSUmVZnCXe14qY06ISH1y78OAytmPyT0vlTEnJE9ybysA2wvJB9uLPMk9L5UxJ0RU/jiBTxWOvb092rZtq+0w6BXMCxHpMvZh8sS8EKmHbYVIfWwv8sS8EFFlxofYEhERERERERERERHJECfwiYiIiIiIiIiIiIhkiBP4REREREREREREREQyxAl8IiIiIiIiIiIiIiIZ4gQ+ERFRBSSEAADExsbi2rVrqp/V8fDhQ1hZWSE+Ph4AEB4eDkmS8Pjx4zKIlKj0nJycEBISopG6Dh8+DAcHB+Tk5GikPno9Qgg8f/4c9+7dYx9GFZYkSezDiIiIqFicwCd6yYgRI9CvX7982/nGT7uYFyL1jRgxApIkoV69egCAyZMno3HjxmjdujWCgoLUmgTz8/ND37590aBBAwCAi4sLEhMTYWJiUpahl4nTp09j+PDhsLOzg7m5Oezt7TF27FhcvnxZ26GpZGZmYv369ejWrRvq1KkDa2truLi4wN/fH+np6eUSw8OHDzF79my0a9cOlpaWsLW1Ra9evbBz584STZyWdZ2a8Pz5czg4OECSJHTp0kW1vWfPnjAwMMDXX3/NsUWLcvswPT09xMbG4scff2Qfxj6sWJWhD+vTpw9sbW1RtWpV2NjYYNiwYbhz545qf24ftn379nKPjf6H71vkiXkhIl3HCXwiIqIKQgiBM2fOFLjvr7/+gpeXF7744osiJx7S09MRGBiIjz/+WLVNoVDA2toakiRpPGZNy8zMBADk5ORgwoQJ8PDwQK1atbBq1SocP34cq1evhrGxMVxdXbFq1SqNHz87O7vYux8zMjJU39+4cQNt27bFqlWr8MEHH2D37t0ICwvDxIkTceTIEbRo0QJXr17VeJwvxxAWFoYmTZrg7Nmz8Pb2RlhYGIKDg/Hee+9h3rx56NGjB548eVKi+suiTk35z3/+g9q1axe4b8SIEQgODi7niCjXy33Yq/0N+zD2YYXFUFn6sC5duuCHH37AlStXsGfPHly/fh0ffPBBnjIjRozAypUryzUuIiIiKgeCtC46OloAEBEREdoORadFRkYKACIyMrLUdQwfPlz07ds33/ajR48KACI5Obn0AQrNxKhr5J6XypgTqri2bt0qAJTqa9OmTUIIIXbv3i0sLS3z1PtqW9u0aZMwMTERhw8fFs2aNRPVqlUTPXr0EHfu3Hntczh69Kho166dUCqVwsTERLi4uIj4+PgCy968eVMAEDt37hRvvfWWMDQ0VJ2Ht7e3aNeunUhMTCzwtdeuXRMNGzYUu3fvVm2bNWuWaNOmjdi6dauoX7++qFGjhujevXuRfUTu72Lv3r3C3t5eVKlSRdy8eTNPmdx6N2zYIBo0aCAkSRJCCPH48WNhZ2cnZsyYIXJycgqsf/369aJ+/fri0aNHqm3169cXfn5+YuTIkcLY2FjUqlWr2H6sc+fOYty4ceLLL78U5ubmwt3dXQghxNmzZ4WZmZnYt29fga/LzMwUI0eOFL1791Zte/78uRg3bpywtrYWhoaGwtbWVsyfP1+1vzR15uZyz549wt3dXRgZGYnWrVuLkydPFlhHbt8NQPz000+FnverDh48KJo1ayYuX74sAKh+D7lu3bqlqpdjS/ljH8Y+rDAVrQ/LVdI+7GV79+4VkiSJjIwM1bbcPuzatWulqrOyk/v7FiEq5/gi97xUxpxQ5RARESEAiOjoaG2HQkIIfc38G4CIiIi0SQiBxYsXq1XWzs4O27ZtgyRJ2Lt3L5YtWwZra2vcvHkTP//8M5o3b46bN2+qyicmJgIAbt26heTkZDx48ADp6emYN28eFixYAD09PUyePBljxozB8uXLS30OWVlZ6NOnDwYPHoxFixYhMzMTFy9exN9//13gHaH//PMPAMDb2xvTp0/HvHnzYGhoiLCwMPzf//0ffv75Zzx9+hTz5s1DYGAgsrKy8Mknn2Dr1q3YunUr5s6di0mTJqFt27aQJAnJycm4du0atm/fjrVr1yIlJQVjx44tNu709HQsXLgQGzduhLm5OaysrPKVuXbtGvbs2YPg4GBUqVIFALBgwQI4OTlh7ty5ePz4McaNG4cjR47gjTfewODBg3Ho0CEcOnQIx48fx/LlyzFnzhxVfUuWLMG8efMwffp0rFixAgEBAYiPj0fbtm0LjXPLli0YO3Ysfv/9d9W2CRMmwM/PD71790Z0dDTGjh2L6OhodOnSBXXr1oWFhQXWrl2L5s2b4+jRo+jSpQtWrlyJffv24YcffoCtrS3+/vtv/P33369VZy5fX1/4+/ujcePG8PX1xZAhQ3Dt2jXo67/+Jeu9e/fw6aefIiQkBEqlssAytra2qFmzJpKTk1/7eFQy7MPYh7EPU9+jR4+wfft2uLi4wMDAQLXd1tYWtWrVwokTJ9CoUSONHpOIiIi0SNv/QSDega8pmvrPfJUqVUS1atXyfFWtWpV3TJSS3PNSGXNCFVNcXFyp71zlV/FfRd29CkBERUUVmptZs2YJAwMDcf/+/Tzb69SpI/78808hhBCjRo0SHTt2FKdPnxb79u0T1tbWonPnzkIIIX799VfRvn171evq168vPD09VT+fO3dOABA+Pj6FxtC5c2fh6OiYZ9vVq1eFtbW1yMzMFFlZWaJJkyZi9OjR4sKFC2LlypVCX19fzJo1SwghxNdffy2mTZsmhBBiwoQJ4u233y7wjtvS1pl79+rGjRtVdeXeJR8TE5PvOCW9Az8nJ0f07NlTzJs3L8/x9PT08o0tkiRxbNEC9mHswypTH5ZL3T4s13/+8x+hVCoFANGhQweRlJSUr4yjo6OYPXu22nXS/8j9fYumYtQ1cs9LZcwJVQ68A19eeAc+0Su6dOmCNWvW5Nl25swZeHp6aikiApgXouI8fPiwROXnzp2LZcuWoVevXhg5cqRq+7Rp01C7dm18+eWXqm1RUVGYMmUK9u7dC2NjYxw+fBjfffcdfv75Z1WZiIgIzJ49G7/++utrnceiRYvw22+/wcnJCW3btoW7uzvMzc0LLHv37l189NFHWLFiBVq2bKna/vXXX6NHjx5wc3PDunXr8OTJE0yePBkA8ODBAwwePBjbt2+HtbU1Pv/8c4wbNw4tWrTAli1bcOzYMfzf//2fqq41a9bgxx9/LDJmhUKB1q1bF1mmfv36sLS0VP386NEjpKamquLev38/QkJC0L59ewDA+PHj8csvvwAAbGxs8t0R/vLxctf1fvToUZExODk55fn5zz//RLt27aCvr4/o6GgkJCQgICAABgYGcHBwwL59+1RlbWxscPHiRQAv1lh+55130LRpU/Ts2RPvvfceunfv/lp1FnReNjY2AID79++jWbNmRZ5bcb777jukpqbCx8cnz/Z27dph27Zteba9++67iIuLe63jUcmxD2Mfxj6seFOnTsXHH3+MW7duYc6cOfDy8sKBAwfyPN/ByMio3B4eTAXj+xZ5Yl6ISJdxAp/oFdWqVYOdnV2ebbkfcSbtYV6IilbYBFFhvv/+e3Tu3Blbt27N88a/SZMmEELg7bffVm3T03vxzPu33noLpqamuH37NgwNDfOU+ffff/O9rjTefvttXLhwAYcPH8b+/fuxdetW/PLLL+jQoUO+svHx8aq4HBwcVNtNTU3x5ptv4u2338a+ffsgSZIqrpSUFACAi4sLLC0tce/ePQwaNAhWVlY4fvw4Ll68mOccDh48WGzMRkZGxT4cs1q1anl+zsrKQtWqVVU/Z2Rk5CljbGys+v78+fP5+r+Xl0zIJYp4sGdhMRgZGamOb2BgkKfeV2No2rQpAKBt27a4efMmDh06hF9//RUDBw5Et27d8OOPP5a6zoLOK/d3WtwDNdXx22+/4dSpUzA0NMyz/Y8//sC8efOwZcsW1bbnz5+/9vGo5NiHOai2sw9TP4bK0oflsrCwgIWFBZo0aQJ7e3vUq1cPp0+fRseOHVVlHj16lOefLVT++L5FnpgXItJletoOgIiIiF5fo0aNir2DEngxoVCjRg0YGBggKCgo36SNo6MjoqOjyypMtTg6OsLHxwcnT55Ey5Yt8f3335fo9XZ2dvjzzz8BAK6urti5cydiY2ORmZkJPz8/AC/uYh01ahT69u1b4HrPZc3CwgIZGRm4d++eKs5Fixbh6dOnSEhIwIYNGwAAJ0+ehK+vr+ruW016+ffUtGlTGBgYICAgANnZ2Th9+jRCQ0ORmZmJLVu24NChQxgxYoTqtTVq1MCgQYOwYcMG7Nq1C3v27MGjR49eq86ytHLlSly8eBFRUVGIiopSTWo6Ozur/iYA4NmzZ7hz5065xER5sQ/7H/Zh6qlMfVhBcv8x8PI/HZ89e4br16/D0dFRW2ERERFRGeAEPhERUQUgSRK8vb2LLSeEQEZGBtatW4e0tDTcvXsXd+/exdOnTwEAPXr0wOXLl7XyEM+bN2/Cx8cHp06dwq1btxAWFoa4uDjY29sDeHG3dLNmzZCQkFBkPf3798fGjRuRmZmJAQMGoE+fPmjevDmUSiUeP36M2rVro1u3bqhTpw7Wrl1bohgDAgLQtWvXIsv4+PjAy8uryDJ6enro06cPVq9eDQBYsWIFLly4AGNjY7Rq1QrvvPMOjh07hlGjRmHFihXFHvNVXbt2RUBAQJFlHB0d8fTpUxw9ehRGRkbYvHkzZs6cCUNDQ4wcORL9+vXDwoULsWnTJoSFhanu6Fy6dCl27NiB2NhYXL16Fbt374a1tTVMTU1LXac6EhIS0KxZM/zxxx8l+l0ALx7s2LJlS9VXkyZNALy4G69u3bqqcqdPny7wzmAqe+zD/od9GPuwV505cwYBAQGIiorCrVu38Ntvv2HIkCFo1KhRnrvvT58+DUNDwzzbiIiISPdxCR0iIqIKwtPTE/Pnz0dsbCwkScqzHEHuz1ZWVrh//z5cXFzyvHbTpk0YMWIEWrVqhbZt2+KHH37AZ599Vq7xK5VKxMbGYsuWLXj48CFsbGwwbtw4VRzp6em4cuUKMjMzi6ynS5cusLOzw6efforAwECsW7cO/v7+yMzMhJmZGRITE2FlZYUqVaqUOMakpCRcv369yDKJiYm4fft2sXXNnDkTb775Jjp06AAPDw9ER0fj7t27qFmzJnJycuDr6wsLC4sSxwgA169fR1JSUpFlJEnCwoULMXz4cBw/fhzvvvsuHjx4gLt376J27dpISUnB+vXr8ywZAQDVq1fHokWLEBcXhypVqqBdu3Y4ePCgapmS0tSpjszMTFy5cgXp6emoUaNGiV+vjh07dqBbt27Yv39/mdRPRXu5D9PT08uz9Aj7MPZhr9LlPqyklEolgoODMWvWLDx58gQ2Njbo2bMnvv766zxLg+3YsQMfffQRlEpliY9BREREMqalh+fSS6KjowUAERERoe1QdJouPP1dF2LUNLmfs9zjIyqpnJwcsXXrVmFvby8AqL5at24tgoKCRE5OTrF1HDhwQNjb24vs7OxyiLhsPHr0SHTo0EF06NBB7N+/Xzx58kQIIcS9e/fE0qVLhYODg0hLSyu2nrLuI0JDQ0XNmjXFhAkTxKVLl0R2drbIzs4WUVFRwtPTU0yaNKnMY/Tz8xPm5ubC399f/P3330IIIZ49eyZCQ0OFq6urCA4OlkWdLyuLvDx48ECYmZmJGzduvHZdHFtKL7cPa926Nfsw9mFq0cU+rCxosg+rrHSh79aFGDVN7ucs9/iISisiIkIAENHR0doOhYQQXEKHiIioApEkCcOGDUNYWBiAF0sFxMXFISoqCp6ensU+qBAAevXqhdGjRxe7zIOc1axZE8eOHcPAgQMxZcoUVKtWDYaGhrC1tUV4eDgCAwPzPRBRG7p3747IyEikpqbCzc0NCoUCCoUCHh4eqFu3LmbPnl3mMUyfPh0//fQTwsLC0KhRIygUChgZGWHy5MkYNmwY+vbtK4s6y1p8fDxWr16Nhg0bajuUSi23D4uKikKzZs3wwQcfsA9jH1Yk9mEvsA8jIiKquLiEDhERUQWUO8nVrFkz2NnZlfj1EydOLPWxi1pa4OnTpzAyMipw36FDh+Dm5lbq475KoVBg0qRJmDRpElJSUvDvv//Cysoqz3IDctCwYUNs2rQJgYGBuHfvHvT09FCrVq1yjcHNzQ2hoaF4/vw57t+/j+rVq8PU1FR2dRZm/vz5mD9/fqFxHDp0qNg6nJ2d4ezsrOnQqJQkSYKhoSFq1arFPox9WLHYh7EPIyIiqsg4gU9EREQaFRUVVei+oia/6tSpU0YRASYmJjAxMSmz+jVBT08PNjY2Wo3B0NAQ9erVk32drxozZgwGDhxY4L7C/t6ICsM+rHTYh5Ue+zAiIiIqCifwiYiISKNKc7cs0eswMzODmZmZtsOgCoJ9GJU39mFERERUFK6BT0REREREREREREQkQ7wDnyqcmJgYbYdQKDnHVtbkeu5yjYuI5EXOfYWcYytrcj13ucZFlZec/yblHBtVTnL+m5RzbGVNrucu17iIqGLhBD5VGBYWFlAqlfD09NR2KEVSKpWwsLDQdhjlRhfyUtlyQpWDEAKdOnWCvj6H+tehC30YUPn6MV3IS2XLSVlo2rQpzM3NtR2GTtOFtgKwvZA8sL3Iky7kpbLlhIjKH9/VU4Vha2uLmJgYJCUlaaS+4OBg+Pn5ITIyUiP15bKwsICtra1G65QzTedlxowZSExMxMaNGzVSH1D5ckKVgyRJ+P3335GVlaXtUHSapvuwsLAw+Pj44MSJE1AqlRqpE6h8/Zim87Jw4UJcuHABO3fu1Eh9QOXLSVm4cuUKLC0ttR2GTtN0W0lPT4ebmxu+/fZbdO/eXSN1AmwvJA+abi+rVq3C4cOHsX//fo3Ul6uytRdN5+WTTz6BjY0N5s2bp5H6gMqXEyIqf5zApwrF1tZWYwNnZGQkJElC27ZtNVJfZabJvJibmyM1NZV5IaJyo8k+LC4uDgDg4OAAY2NjjdRZWWkyL5aWllAqlRxbqELSZFtJS0sDADRs2JDthSokTbYXa2trGBoasq1ogCbzYmxsDHNzc+aFiHQKH2JLRERERERERERERCRDnMAnIiIiIiIiIiIiIpIhTuATEREREREREREREckQJ/CJiIiIiIiIiIiIiGSIE/hEWhAfHw9JkhAVFVVomfDwcEiShMePH5dbXJUd80KkHrYVeWJe5Il5kSfmRX6YEyL1sb3IE/NCRGWFE/hEMuXi4oLExESYmJgAADZv3gxTU1PtBkXMC5Ga2FbkiXmRJ+ZFnpgX+WFOiNTH9iJPzAsRlYa+tgMgooIpFApYW1trOwx6BfNCpB62FXliXuSJeZEn5kV+mBMi9bG9yBPzQkSlwTvwiUrhyZMn8PLygrGxMWxsbLBkyRK4u7tj4sSJAABJkhASEpLnNaampti8eXOebbGxsXBxcUHVqlXRsmVLHDt2TLXv5Y/WhYeHY+TIkUhJSYEkSZAkCbNnzy7bk9RBzAuRethW5Il5kSfmRZ6YF/lhTojUx/YiT8wLEckVJ/CJSmHq1Kk4duwY9u7di7CwMISHh+P8+fOlqmfKlCm4cOECOnbsiN69e+Phw4f5yrm4uGD58uWoUaMGEhMTkZiYCG9vb02cSoXCvBCph21FnpgXeWJe5Il5kR/mhEh9bC/yxLwQkVxxAp+ohNLS0hAYGAh/f3907doVrVq1wpYtW5CVlVXiusaPH48BAwbA3t4ea9asgYmJCQIDA/OVUygUMDExgSRJsLa2hrW1NYyNjTVxOhUG80KkHrYVeWJe5Il5kSfmRX6YEyL1sb3IE/NCRHLGCXyiErp+/ToyMjLQvn171TYzMzM0bdq0xHV17NhR9b2+vj6cnZ0RExOjkTgrG+aFSD1sK/LEvMgT8yJPzIv8MCdE6mN7kSfmhYjkjBP4RGVAkiQIIfJsy8zM1FI0lIt5IVIP24o8MS/yxLzIE/MiP8wJkfrYXuSJeSEibeEEPlEJNWrUCAYGBjhz5oxqW3JyMq5evar62dLSEomJiaqf4+LikJ6enq+u06dPq77PyspCZGQk7O3tCzyuQqFAdna2Jk6hQmJeiNTDtiJPzIs8MS/yxLzID3NCpD62F3liXohIzvS1HQCRrjE2NsbHH3+MqVOnwtzcHFZWVvD19YWe3v/+H/b2228jICAAHTt2RHZ2NqZNmwYDA4N8da1atQqNGzeGvb09li1bhuTkZIwaNarA4zZo0ABpaWk4cuQI2rRpA6VSCaVSWWbnqWuYFyL1sK3IE/MiT8yLPDEv8sOcEKmP7UWemBcikjPegU9UCosXL4abmxt69+6Nbt26wdXVFU5OTqr9S5YsQb169eDm5oahQ4fC29u7wEF4wYIFWLBgAdq0aYOIiAjs27cPFhYWBR7TxcUFY8aMwaBBg2BpaYlFixaV2fnpKuaFSD1sK/LEvMgT8yJPzIv8MCdE6mN7kSfmhYjkShKvLuBF5S4mJgbNmzdHREQEOnXqpO1w6L82bNiAzz77DDk5OWqVd3d3h4ODA5YvX162gVVyXl5eiI+Px/Hjx9Uqz7xQZZWQkIC6devi4MGD8PDwKLY820r52LVrFwYPHozU1FQYGxsXW555KR/jx49HREQEoqKi1CrPvJQPBwcHuLq6IiAgQK3yzEvZS0tLQ/Xq1bFz504MGjSo2PLMCVVmvr6+2LFjB27cuKFWebaX8vHWW2+hQYMG2Lp1q1rlmReqrH7//Xe4uroiOjq60CWgqPzwDnwiIiIiIiIiIiIiIhniBD4RERERERERERERkQzxIbZEGhIeHq7tEKgAzAuRethW5Il5kSfmRZ6YF/lhTojUx/YiT8wLEckB78AnIiIiIiIiIiIiIpIhTuATEREREREREREREckQJ/CJiIiIiIiIiIiIiGSIa+ATERHJxO3bt5GUlKSRuu7fvw8AuHbtGs6fP6+ROgHAwsICtra2GquPqLQ02V4ePHiA9PR0thUN0GRe0tPT8eDBA+blNWk6JwBw8+ZN5oWIiIionHACn4iISAZu374Ne3t71eSIpnzxxRcarU+pVCImJoYTLa9BoVBACKHtMHRaWbUXJycnjdVVGdtKWeQlLi4OP/zwg8bqq2x5Kau24uPjAx8fH43VV9nyQkTaU6VKFejpcTEKouIIIfi+RUY4gU9ERCQDSUlJSE9Px7Zt22Bvb6/tcAoUExMDT09PJCUlcZLlNWRkZECSJG2HodPk3l4qa1thXuRH7jkBKmdeiEh7srOzkZOTo+0wiGRPkiS+b5ERTuATERHJiL29Pdq2bavtMIh0AtuLPDEv8sOcEBEREekufm6IiIiIiIiIiIiIiEiGOIFPRERERERERERERCRDnMAnIiIiIiIiIiIiIiQnJ+P8+fMAgD/++APJyclajog4gU9ERERERERERERUiUVGRmLUqFGwsbHBF198AQAYMWIEateujVGjRiEyMlLLEVZenMAnIiIiKgdCCNy9excAcP36dQghSvT6hw8fwsrKCvHx8QCA8PBwSJKEx48fazhSotfj5OSEkJAQjdR1+PBhODg4ICcnRyP1ERFR5SWEwNOnT5GUlIRr166V+FqMqKISQuCbb76Bs7Mzjhw5Ah8fH9W+3bt3Y+bMmThy5AicnZ3xzTffsO1oASfwiYiIKpARI0agX79++bZzsld7hBBwc3ODnp4eJk6cCABwcHBArVq1EBgYqPYFsJ+fH/r27YsGDRoAAFxcXJCYmAgTE5MyirzsnD59GsOHD4ednR3Mzc1hb2+PsWPH4vLly9oOTSUzMxPr169Ht27dUKdOHVhbW8PFxQX+/v5IT08vlxgePnyI2bNno127drC0tIStrS169eqFnTt3lvqNU1nU+ToaNGgASZLyfDVv3ly1v2fPnjAwMMDXX3/NPkyLOLYQkS4TQiAoKAgODg44d+4cDh06hMaNG8PBwQFBQUGcjKRKz8/PDzNmzMCcOXNw48YNeHt7q/Y1bNgQPj4+uHHjBubMmYMZM2bAz89Pi9FWTpzAJyIiIiojQgh88cUXiIiIyLfvwYMH+OSTT/DFF18U+8YxPT0dgYGB+Pjjj1XbFAoFrK2tIUmSxuPWtMzMTABATk4OJkyYAA8PD9SqVQurVq3C8ePHsXr1ahgbG8PV1RWrVq3S+PGzs7OLvYM7IyND9f2NGzfQtm1brFq1Ch988AF2796NsLAwTJw4EUeOHEGLFi1w9epVjcf5cgxhYWFo0qQJzp49C29vb4SFhSE4OBjvvfce5s2bhx49euDJkyclqr8s6tSEuXPnIjExEYmJiRg4cCDeeOONPPtHjBiB4ODgco+LiIh0X+61mJeXF/766688+/766y94eXmpdS1GVFFFRkaqJu9nzpyJKlWqFFiuSpUqmDlzpmoSn8vplC9O4BMRERGVkW3btiEgIKDIMgEBAXj//ffz3YUsSRI2b94MADh48CAMDQ3RoUMH1etevfN18+bNMDU1RWhoKOzt7WFsbIyePXsiMTHxtc8jPDwcb775JqpVqwZTU1N06tQJt27dKrBsfHw8JEnCrl270LlzZ1StWhXbt28HAEybNg1nzpxBTEwMFi1ahB49eqBFixbo0qULFi9ejHPnzmHJkiX48ccfVfXNnj1bdYdcgwYNYGJikudjvQXJ/V3s27cPzZs3h6GhIW7fvp2nTG69GzduRMOGDVG1alUAQEpKCnr06IH+/fsjKioKY8aMgYuLC1q3bo2BAwfi0KFDmD59Orp3757ngV4NGjTA/PnzMWrUKLi5uQFAsZPO7u7uGD9+PCZOnAgLCwv06NEDAHDu3DkMGTIEmzdvxs8//4xBgwbB0dERzs7OGDt2LC5evIi6detiyJAhqroyMjIwfvx42NjYoGrVqqhfvz6+/fZb1f7S1Jmby+DgYHTp0gVKpRJt2rTBqVOnijyvkqpevTqsra1hbW0NIyMj6Ovr59nfu3dvXLlyRaPHJCKiyuHla7FX/5mf+3NAQAC2bdtW7rERycGqVatga2sLX19ftcr7+vrC1tYWq1evLuPI6GWcwCciIiIqA0IILF68GHp6RV9u6enpIS4uDnfu3FHdhezv7w+lUglnZ2cAwIkTJ+Dk5FTsMdPT0+Hv74+goCAcP34ct2/fzvMR2NLIyspCv3790LlzZ1y6dAmnTp3C6NGji73z/6uvvsKXX36JmJgY9OjRA9HR0di8eTNCQkJgbW2NNWvWoHHjxmjQoAG+++47NG3aFAYGBtiwYQOmTp2a506469evIyQkBAcOHMCBAwfUuuMnPT0dCxcuxMaNG3H58mVYWVnlK3Pt2jXs2bMHwcHBiIqKAgAsWLAATk5OmDt3LlJSUvDRRx+pls9ZuXIlPDw88Omnn8LNzQ3Lly/PU9+SJUvg7OyM77//HgDw7bffFjvxvGXLFigUCvz+++9Yu3YtAGDChAnw8/ND7969ER0djc6dO8PS0hIDBw7E5MmTsWjRIqxduxbR0dE4evQoAGDlypXYt28ffvjhB1y5cgXbt29XLbdU2jpz+fr6wtvbG1FRUWjSpAmGDBmCrKysYnOgrgULFsDc3ByOjo7466+/8k2w2NraombNmho7HhERVQ4luRbz9/fnXfhU6SQnJ+P777/HqFGj8OzZMzx58kT1levp06d5tj979gwjR47E999/n+dmFipb+sUXISIiIl1y4MABGBsb59mWnZ2tpWgqr+vXr+PPP/8stlxOTg4uX76MTp06wcDAAE+fPsU///wDa2trDBgwAACQkJCAKlWqoGnTpqrX5a7D7uzsjCpVqiAlJQWZmZm4du0aPvroIwDA48ePsWvXLpw7d67U55GdnY2UlBTs3LkT+/btU22fP39+geVzl8t58uRJnjvlk5KSIIRAly5dkJ6ejoSEBNSqVQsKhQI+Pj548uQJ3N3dYWBggH/++QdvvPEGFAoFkpKS8OTJE1y6dEn1+6hevToePnxYZNyZmZlYvXo12rRpU2iZjIwMbN26FZaWlqptQUFBOHz4MABgypQpuHnzJvbu3Yv79+9j9OjRqhyMGDECvr6+mDNnjuq17777Lj7//HOcP38eAGBqaoqjR4/mydurGjdujEWLFql+jouLQ3x8PD755BNkZ2ejf//+cHd3x4oVK3DixAlMnjwZvr6+UCgUGDJkCEJDQ9GlSxfcvn0bjRs3hqurKyRJQv369V+7zlze3t7o1asXAGDOnDlo0aIFrl27hmbNmhWZA3V88cUXaNu2LczMzHDy5El8+eWXOH/+fL4+rLyeO0CF49hCRLqmJNdily5dQqNGjWBgYFAOkRHJw9OnT/H8+XPMnj0bs2fPLrBM7idLC3Lp0iV07ty5jKKjl3ECn4iIqILp0qUL1qxZk2fbmTNn4OnpqaWIKqfiJphf5eLiAmNjYwQFBaFDhw5wdXVV7du9ezdMTU3xzjvvqLbdvn0bu3btwrvvvouqVavir7/+wq+//ooPP/xQVebq1avYu3cv+vbt+1rncujQIcTExKBBgwaoX78+mjZtmm8iL1dKSorq4a9169ZVbQ8ODkbLli3RpEkThIeHw8zMTLVkTGpqKtauXYt33nkHJiYmCAoKgouLC+rUqYPff/8dV65cQf/+/VV1RUZGIj4+vsiYFQoFWrduXWSZ+vXr55m8f/ToEVJTU9GyZUsAwP79+xESEoL27dsDAMaPH49ffvkFAGBjY5PvrqNXj2dubo779+8XGcOrn6z4888/0a5dO+jr6yM6OhoJCQkICAiAgYEBHBwc8vwTxcbGBhcvXgTw4h8K77zzDpo2bYqePXvivffeQ/fu3V+rzoLOy8bGBgBw//59jUzgT548Oc9xgoKCcOrUKZw5cwaGhoaqfe+++y7i4uJe+3hUehxbiEjXlOZarHbt2mUUDZH8XL9+HX///XepX5+amqrBaKgonMAnIiKqYKpVqwY7O7s82/755x8tRVN5mZubl6j8f/7zH3h5eaF79+4IDg7Os0RNQkIChBB57tQODw/Hrl27MHfuXJiammLz5s04efJknjIhISHYu3dvnm2lsWjRIly4cAGHDx/G/v37ERQUhF9++SXPmvy54uPjsX79evznP/+Bg4ODavtff/2Fzz77DB4eHpg4cSIkSVLFlZKSgrVr18LHxweWlpbYsGEDli1bBisrK8yePRtPnjzJcw7e3t747bffiozZyMio2GV+qlWrlufnrKws1Vr4wIs79F8u8/I/Lc6fP5+vnb16154kScU+PLegGIyMjFTHNzAwyFPvqzHk3t3ftm1b3Lx5E4cOHcKvv/6KgQMHolu3bvjxxx9LXWdB55X7Oy3uvErLwsICQgjo6+vn+f0+f/68TI5H6uPYQkS6pqTXYrNnz87XzxFVZOHh4QgODsa5c+fy3Jjx5MkT1KpVC8CLpTwdHR3zvC42NhbOzs6oXr16ucZbmXENfCIiIqIy0KhRI7Ru3brYdVclSULr1q0xa9Ys5OTkICgoKN/Es6OjI6Kjo8sy3GI5OjrCx8cHJ0+eRMuWLVXrvKvLzs5O9TF2V1dX7Ny5E7GxscjMzISfnx8A4MGDBxg1ahT69u1b4Jr1Zc3CwgIZGRm4d++eKs5Fixbh6dOnSEhIwIYNGwAAJ0+ehK+vb567xzXl5d9T7nMBAgICkJ2djdOnTyM0NBSZmZnYsmULDh06hBEjRqheW6NGDQwaNAgbNmzArl27sGfPHjx69Oi16ixvuZ9qeDn/z549w507d7QVEhER6Sh1r8X09PTQpk0bNGrUqJwiI5KHNm3aoGrVqggLC0O1atXyfOUyMjLKty80NBRVq1Yt9tOupDmcwCciIiIqA5Ikwdvbu9g7lYUQeOONN/Drr79i3bp1SEtLw927d3H37l08ffoUANCjRw9cvnxZKw+KunnzJnx8fHDq1CncunULYWFhiIuLg729PQDgjz/+QLNmzZCQkFBkPf3798fGjRuRmZmJAQMGoE+fPmjevDmUSiUeP36M2rVro1u3bqhTp47qYa7qCggIQNeuXYss4+PjAy8vryLL6OnpoU+fPli9ejUAYMWKFbhw4QKMjY3RqlUrvPPOOzh27BhGjRqFFStWFHvMV3Xt2hUBAQFFlnF0dMTTp09x9OhRGBkZYfPmzZg5cyYMDQ0xcuRI9OvXDwsXLsSmTZsQFhamWgJo6dKl2LFjB2JjY3H16lXs3r0b1tbWMDU1LXWd6khISECzZs3wxx9/lOh3AQCnTp3C8uXLcfHiRdy4cQPbt2/H2bNnUa9evTwPrT19+jTXJCYiohJT91osJycHU6ZMKfaTe0QVTc2aNTFkyBCsXbtW7efaZGdnY+3atRg6dGie6zUqW1xCh4iIiKiMeHp64o8//ihw0lZPTw85OTkwMjLCP//8g7S0NLi4uOQps2nTJowYMQKtWrVC27Zt8cMPP+Czzz4rr/ABAEqlErGxsdiyZQsePnwIGxsbjBs3ThVHeno6rly5onp4bWG6dOkCOzs7fPrppwgMDMS6devg7++PzMxMmJmZITExEVZWVqhSpUqJY0xKSsL169eLLJOYmIjbt28XW9fMmTPx5ptvokOHDvDw8EB0dDTu3r2LmjVrIicnB76+vrCwsChxjMCLdUaTkpKKLCNJEhYuXIjhw4fj+PHjePfdd/HgwQPcvXsXtWvXVj1j4NVnEFSvXh2LFi1CXFwcqlSpgnbt2uHgwYOquw5LU6c6MjMzceXKFaSnp6NGjRoleq2hoSF27tyJ2bNn4/nz52jYsCGaN2+eb/3hHTt2oFu3bti/f3+J4yMiosrt5Wux3GuvXLk/jx8/ns/zoEpr3Lhx2LRpE/z8/DBz5sxiy/v5+eHvv//G559/Xg7RkYogrYuOjhYAREREhLZDoZesX79eSJKk7TDoFcOGDRNubm7aDoNI4yIjIwUAERkZqe1QCqULMcpRTk6O2Lp1q2jdurUAoPpq3bq1CAoKEjk5OWrVc+DAAWFvby+ys7PLOOKy8+jRI9GhQwfRoUMHsX//fvHkyRMhhBD37t0TS5cuFQ4ODiItLa3Yesr6bzE0NFTUrFlTTJgwQVy6dElkZ2eL7OxsERUVJTw9PcWkSZPKPD4/Pz9hbm4u/P39xd9//y2EEOLZs2ciNDRUuLq6iuDgYFnU+bKyyMuDBw+EmZmZuHHjxmvXVRn7MF04Z12Ikag0pk+fLho2bKjtMEho7lqMqKKaN2+eACDmzJkjsrKyRFpamqqdnDt3TgghRFZWlpgzZ44AIObNm6fliCsfLqFDREREVIYkScKwYcMQFRWF5cuXAwCioqIQFRUFT09PtT+u3atXL4wePbrYpWrkrGbNmjh27BgGDhyIKVOmoFq1ajA0NIStrS3Cw8MRGBiY76Gu2tC9e3dERkYiNTUVbm5uUCgUUCgU8PDwQN26dTF79uwyj2H69On46aefEBYWhkaNGkGhUMDIyAiTJ0/GsGHD0LdvX1nUWdbi4+OxevVqNGzYUNuhEBGRjnr5WszZ2RkeHh6Ii4sr8bUYUUXl6+uLefPmYdasWXjjjTewePFi1b6bN29i/vz5eOONNzBr1izMmzcPvr6+Woy2cuISOkRERETlQJIkWFtbA3jxULXSvFmcOHFiqY9f1PIoT58+hZGRUYH7Dh06BDc3t1If91UKhQKTJk3CpEmTkJKSgn///RdWVlYwNDTU2DE0oWHDhti0aRMCAwNx79496OnpoVatWuUag5ubG0JDQ/H8+XPcv38f1atXh6mpqezqLMz8+fMxf/78QuM4dOhQsXU4OzvD2dlZ06EREVElJEkSjIyMYGFhATs7O22HQyQbkiTh66+/hoeHB1avXo2FCxeq9n344YeoWrUqhg4dis8//xxOTk5ajLTy4gQ+ERERUSUQFRVV6L6iJvDr1KlTRhEBJiYmMDExKbP6NUFPTw82NjZajcHQ0BD16tWTfZ2vGjNmDAYOHFjgvsL+3oiIiIhIO5ycnBAYGAh/f39s374dEyZMwJYtW9C7d28+sFbLOIFPREREVAnwTjMqb2ZmZjAzM9N2GERERERUAjVr1oSjoyMAoF27dpy8lwGugU9EREREREREREREJEOcwCciIiIiIiIiIiIikiEuoUNERCQjMTEx2g6hUHKOTVcYGBigQ4cOEEJoO5QKQa5/k3KNq7zI9fzlGld5kPO5yzk2Iqp4GjRoACsrK22HQSR7enp66NChg7bDoP/iBD4REZEMWFhYQKlUwtPTU9uhFEmpVMLCwkLbYeiszMxMnD59GpIkaTsUnaYL7aUythXmRX50ISdA5csLEWlPfHy8tkMg0gk5OTk4ffq0tsOg/+IEPhERkQzY2toiJiYGSUlJGqnv/v378PDwwMqVK9GpUyeN1Am8mAyytbXVWH1EpaHp9rJw4UJcuHABO3fu1Eh9QOVsK5rOy+DBg+Ho6Ihp06ZppD6g8uVF0zlJT0+Hm5sbvv32W3Tv3l0jdQKVLy9EREREJcEJfCIiIpmwtbXV2ARGQkICAMDOzg5t27bVSJ1EcqLJ9mJpaQmlUsm2ogGazItSqYSlpSXz8po0mZO0tDQAQMOGDZkXIiIionLCh9gSEREREREREREREckQJ/CJiIiIiIiIiIiIiGSIE/hERERERERERERERDLECXwiIiIqkfj4eEiShKioqELLhIeHQ5IkPH78uNziquyYF3liXuSJeZEf5oSIdB37MSL1sK2UHCfwiYiISONcXFyQmJgIExMTAMDmzZthamqq3aCIeZEp5kWemBf5YU6ISNexHyNSD9tKXvraDoCIiIgqHoVCAWtra22HQa9gXuSJeZEn5kV+mBMi0nXsx4jUw7aSF+/AJyIiqmSePHkCLy8vGBsbw8bGBkuWLIG7uzsmTpwIAJAkCSEhIXleY2pqis2bN+fZFhsbCxcXF1StWhUtW7bEsWPHVPte/shjeHg4Ro4ciZSUFEiSBEmSMHv27LI9SR3EvMgT8yJPzIv8MCdEpOvYjxGph22l/HECn4iIqJKZOnUqjh07hr179yIsLAzh4eE4f/58qeqZMmUKLly4gI4dO6J37954+PBhvnIuLi5Yvnw5atSogcTERCQmJsLb21sTp1KhMC/yxLzIE/MiP8wJEek69mNE6mFbKX+cwCciIqpE0tLSEBgYCH9/f3Tt2hWtWrXCli1bkJWVVeK6xo8fjwEDBsDe3h5r1qyBiYkJAgMD85VTKBQwMTGBJEmwtraGtbU1jI2NNXE6FQbzIk/MizwxL/LDnBCRrmM/RqQethXt4AQ+ERFRJXL9+nVkZGSgffv2qm1mZmZo2rRpievq2LGj6nt9fX04OzsjJiZGI3FWNsyLPDEv8sS8yA9zQkS6jv0YkXrYVrSDE/hERESUhyRJEELk2ZaZmamlaCgX8yJPzIs8MS/yw5wQka5jP0akHrYVzeMEPhERUSXSqFEjGBgY4MyZM6ptycnJuHr1qupnS0tLJCYmqn6Oi4tDenp6vrpOnz6t+j4rKwuRkZGwt7cv8LgKhQLZ2dmaOIUKiXmRJ+ZFnpgX+WFOiEjXsR8jUg/binboazsAIiIiKj/Gxsb4+OOPMXXqVJibm8PKygq+vr7Q0/vf//TffvttBAQEoGPHjsjOzsa0adNgYGCQr65Vq1ahcePGsLe3x7Jly5CcnIxRo0YVeNwGDRogLS0NR44cQZs2baBUKqFUKsvsPHUN8yJPzIs8MS/yw5wQka5jP0akHrYV7eAd+ERERJXM4sWL4ebmht69e6Nbt25wdXWFk5OTav+SJUtQr149uLm5YejQofD29i7w4mjBggVYsGAB2rRpg4iICOzbtw8WFhYFHtPFxQVjxozBoEGDYGlpiUWLFpXZ+ekq5kWemBd5Yl7khzkhIl3HfoxIPWwr5U8Sry5KROUuJiYGzZs3R0REBDp16qTtcOi/NmzYgM8++ww5OTnaDoVe4uXlhfj4eBw/flzboRDJWkJCAurWrYuDBw/Cw8Oj2PLu7u5wcHDA8uXLyz64SmzXrl0YPHgwUlNTYWxsXGx55qV8jB8/HhEREYiKilKrPPNSPhwcHODq6oqAgAC1yjMvZS8tLQ3Vq1fHzp07MWjQoGLLMydUmfn6+mLHjh24ceOGtkOhl7z11lto0KABtm7dqlZ59mNUWf3+++9wdXVFdHR0ocvavIxtpWzxDnwiIiIiIiIiIiIiIhniBD4RERERERERERERkQzxIbZERESE8PBwbYdABWBe5Il5kSfmRX6YEyLSdezHiNTDtlK2eAc+EREREREREREREZEMcQKfiIiIiIiIiIiIiEiGOIFPRERERERERERERCRDXAOfiIiIqAi3b99GUlKSRuq6efMmACAqKgpKpVIjdQKAhYUFbG1tNVYfUWlpsr2kp6fjwYMHOH/+vEbqA9hWiIh0kSbHlrS0NDx8+JBjy2vSZE7KSmXLS05ODrZu3Yrs7GyN1Hft2jUAwJ49e2BjY6OROqtWrYqhQ4dCkiSN1FeZcAKfiHSKpaUlcnJytB0GkewJIfDmm2+iSpUq2g5Fp92+fRv29vZIT0/XaL1ubm4arU+pVCImJqZSvUnRpJo1a6JRo0baDkPnlUV7iYuLww8//KCx+thWiIh0S1mMLRcuXMDBgwc1Vl9lG1vK6vpY0ypbXn799VeMHDlS4/XOmDFDo/U1bdoUzs7OGq2zMuAEPhHplAcPHuD27dvaDoNI9iRJwh9//KGxOzAqq6SkJKSnp2Pbtm2wt7fXdjgFiomJgaenJ5KSkirNGxRNS05OxvXr17Udhs6Te3thWyEi0j0cW+RH7jkBKmde3nrrLTg5OSEpKQnnzp2Dubm5tkNS+eeff+Dk5ISWLVvCwcFB2+HoJE7gExERERXD3t4ebdu21XYYRDqB7YWIiDSNY4v8MCfyUrVqVQQHB8PJyQlDhw7FoUOHZPFp7OfPn+ODDz5A1apVsWvXLujrcyq6NPgQWyIiIiIiIiIiIiIdZmtri127duHIkSPw9fXVdjgAgPHjx+PixYsIDg6GpaWltsPRWZzAJyIiIiIiIiIiItJxb7/9NhYtWoSFCxdi9+7dWo1l/fr12LhxI9auXct1718TP7dARERUAT1+/BgAcObMGRgZGaFNmzaoWbOmdoMiIiIiIiKiMjV58mScO3cOI0eOhL29PVq2bFnuMZw6dQrjx4/H559/jhEjRpT78Ssa3oFPRERUgURGRmLUqFGq9SjnzJmDLl26oHbt2hg1ahQiIyO1HCEREREREVVGK1euhJeXl7bDqPAkScLGjRvRqFEj9O/fX3VzV3m5e/cuPvjgA7z55ptYtmxZuR67ouIEPhHpDCEEUlNTkZqaimvXrkEIoe2QiGRDCIFvvvkGzs7OOHLkCMaPH6/ad+7cOcycORNHjhyBs7MzvvnmG7Yfov8SQiAlJQXp6eklHlsePnwIKysrxMfHAwDCw8MhSVK5v0kiKo4kSQgJCdFYfZ9//jmePHmisfqIiKjiE0Lgxx9/xJYtWwAAV65cgbW1NVJTU9Wu46uvvsKECRPKKsQKpVq1aggODkZSUhKGDRuGnJyccjluRkYGPvzwQwghsHv3bigUinI5bkXHCXwikj0hBIKCguDg4ICQkBBERUWhcePGcHBwQFBQECciiQD4+flhxowZmDNnDm7cuIExY8ao9jVr1gw+Pj64ceMG5syZgxkzZsDPz0+L0VY+I0aMQL9+/fJt54Sv9owYMQKSJEFPTw/btm1DXFwcGjdujNatW6s9tvj5+aFv375o0KABAMDFxQWJiYkwMTEp4+g17/Tp0xg+fDjs7Oxgbm4Oe3t7jB07FpcvX9Z2aCqZmZlYv349unXrhjp16sDa2houLi7w9/dHenp6ucTw8OFDzJ49G+3atYOlpSVsbW3Rq1cv7Ny5s9TXI2VR5+v6+eef0b59exgZGaFmzZr5+i87OzssXbpUK7EREZUUr8PkITw8HJ07d4YkSQAAHx8fTJgwAdWrV1ft79u3L2xsbFCtWjU4ODhg+/bteerw9vbGli1bcOPGjXKPXxc1atQIO3bswM8//4y5c+eW6LWrVq1CgwYNULVqVbRv3x5//PGHWq+bMmUKzpw5gx9//BE2NjalCZsKwAl8IpI1IQS++OILeHl54a+//sqz76+//oKXlxe++OILTuJTpRYZGamavJ85cyaqVKlSYLkqVapg5syZqkl8LqdDlZUQAmfOnAEA1ZvIXOqOLenp6QgMDMTHH3+s2qZQKGBtbZ2vTjnKzMwEAOTk5GDChAnw8PBArVq1sGrVKhw/fhyrV6+GsbExXF1dsWrVKo0fPzs7u9g7wTIyMlTf37hxA23btsWqVavwwQcfYPfu3QgLC8PEiRNx5MgRtGjRAlevXtV4nC/HEBYWhiZNmuDs2bPw9vZGWFgYgoOD8d5772HevHno0aNHie9KL4s6X9eePXswbNgwjBw5EhcvXsTvv/+OoUOH5inj7u6ONWvWICsrq1xjIyIi3bVt2zbV8jm3b9/GgQMH8qyNfvLkSbRu3Rp79uzBpUuXMHLkSHh5eeHAgQOqMhYWFujRowfWrFlT3uHrrJ49e2LevHmYM2cO9u3bp9Zrdu3ahcmTJ2PWrFk4f/482rRpgx49euD+/ftFvm7Lli0ICAjAypUr4eLioonwKZcgrYuOjhYAREREhLZDoZesX79eSJKk7TAqva1btwoAxX5t3bpV26ESac3IkSOFra2tyMrKUm27evWqqn2kpaXlKZ+VlSVsbW3FqFGjyjtUnRMZGSkAiMjIyNeqZ/jw4aJv3775th89elQAEMnJyaWuW1MxVibqji0FfW3atEkIIcTu3buFpaVlnnpfzeemTZuEiYmJOHz4sGjWrJmoVq2a6NGjh7hz585rn8PRo0dFu3bthFKpFCYmJsLFxUXEx8cXWPbmzZsCgNi5c6d46623hKGhoeo8vL29Rbt27URiYmKBr7127Zpo2LCh2L17t2rbrFmzRJs2bcTWrVtF/fr1RY0aNcSgQYPE8ePHC/1bzP1d7N27V9jb24sqVaqImzdv5imTW++GDRtEgwYNVNdhjx8/FnZ2dmLGjBkiJyenwDjXr18v6tevLx49eqTaVr9+feHn5ydGjhwpjI2NRa1atYptK507dxbjxo0TX375pTA3Nxfu7u5CCCHOnj0rzMzMxL59+wp8XWZmphg5cqTo3bu3atvz58/FuHHjhLW1tTA0NBS2trZi/vz5qv2lqTM3l3v27BHu7u7CyMhItG7dWpw8ebLQcxJCCADip59+KrJM7jHr1KkjNm7cWOD+1NRUAUBs27ZNGBoail9//bXYOokqq+nTp4uGDRtqOwydp4nrHF6HaVZpzjk9PV306NFD9fPixYuFs7Nzsa979913xciRI/Ns27Jli6hbt67GY6zIsrOzRf/+/UWNGjVEbGxsseXffPNNMW7cuDyvr127tvj2228Lfc25c+eEoaGhGDVqVKHXa1R6vAOfiGRLCIHFixdDT6/orkpPTw/+/v68C58qpeTkZHz//fcYNWoUnj17hidPnuDJkyd5lpPI3Zb79ezZM4wcORLff/89kpOTtRg9UfnLHVuKI0kS7O3tcefOHSQmJsLf3x9KpRLOzs4AgBMnTsDJyanYetLT0+Hv74+goCAcP34ct2/fhre392udQ1ZWFvr164fOnTvj0qVLOHXqFEaPHl3snf9fffUVvvzyS8TExKBHjx6Ijo7G5s2bERISAmtra6xZswaNGzdGgwYN8N1336Fp06YwMDDAhg0bMHXq1Dzj7PXr1xESEoIDBw7gwIEDOHbsGDZv3lzs72LhwoXYuHEjLl++DCsrq3xlrl27hj179iA4OBhRUVEAgAULFsDJyQlz585FSkoKPvroI9XyOStXroSHhwc+/fRTuLm5Yfny5XnqW7JkCZydnXHhwgV8+OGHAKB6ZkFhtmzZAoVCgd9//x1r164FAEyYMAF+fn7o3bs3oqOj0blzZ1haWmLgwIGYPHkyFi1ahLVr1yI6OhpHjx4F8OJBffv27cMPP/yAK1euYPv27arllkpbZy5fX194e3sjKioKTZo0wZAhQzRyN/z58+eRkJAAPT09ODo6wsbGBh4eHvk+Bamvrw8HBwecOHHitY9JREQVy5kzZ/Dhhx9i4cKFqm179+5F3759VT+fOHFCdU1VlJSUFJiZmeXZ9uabb+Kff/4pdjyn/9HT08PmzZtRu3Zt9O/fv8jnDmRkZCAyMhLdunXL8/pu3brh1KlTBb7mwYMHeP/999G6dWusWrVKJz6Nqmv0tR0AEVFhrl+/jj///LPYcjk5Obh06RJGjBgBU1PTsg+MSEb++ecfPH/+HLNnz8bs2bMLLFOrVq1CX3/p0iV07ty5jKKjlx04cADGxsZ5tmVnZ2spmspL3bFFCIGYmBh89dVXePr0KYKDg/HOO+9gw4YNAF7ks2rVqvjyyy9Vr/nnn38AvJgoNzQ0RHR0NDIzM1G7dm0EBQUBeNEeQ0JC8ryupJ49e4aUlBTcuHEDK1euVG0/f/58geX//fdfAICtrS2OHTuGY8eOAXjxUfV69eph4cKFSEhIwN69e9G1a1fUrFkTS5cuxa1btzBr1izUqFEDSUlJ8PLygpmZGU6fPo1nz57ByspK9fuoXbs2jhw5UmTcmZmZWL16Ndq0aVNomYyMDGzduhWWlpaqbUFBQTh8+DCAF+uq3rx5E3v37sX9+/cxevRoNG3aFMCLNY59fX0xZ84c1WvfffddfP7556r9AQEBOHfuHN5///1CY2jcuDEWLVqk+jkuLg7x8fH45JNPkJ2djf79+8Pd3R0rVqzAiRMnMHnyZPj6+kKhUGDIkCEIDQ1Fly5dcPv2bTRu3Biurq6QJAn169d/7TpzeXt7o1evXgCAOXPmoEWLFrh27RqaNWtWZA6Kk7um8OzZs7F06VI0aNAAS5Ysgbu7O65evZrnQXS1a9fGrVu3Xut4RETlhddh5eeNN95Ar1698O2332LatGkAgB9++AGBgYGqMrdu3Sp2Av+HH37A2bNnsW7dujzba9eurarj5X+MU9Fq1KiBkJAQtGvXDiNHjsTu3bsLnGhPSkpCdnZ2vveQtWrVQmxsbL7yWVlZGDx4MJ4+fYo9e/agatWqZXYOlRkn8IlIth4+fFii8r///juMjIzKKBoieSrq7onyeD2pr0uXLvnW6zxz5gw8PT21FFHlVNKxJTw8HHfu3IGZmRn++ecf1ST9/fv3oVAo8Ntvv6nKpqWlAQCOHz+OKlWqIDk5GZIk5ZlY//fff5Genp7ndaVhamqKn376CcbGxqhWrRpMTExgYGBQYNncddz//vtvPHr0SLX91q1bMDU1xW+//Ya7d++ievXqSEhIQEJCAgwNDSGEwMmTJ6FQKJCdnY2TJ09CqVTi3r17qFKlCiIiIlR1JSUl4enTp0XGrFAo0Lp16yLL1K9fP8/k/aNHj5CamoqWLVsCAPbv34+QkBC0b98eADB+/Hj88ssvAAAbG5t8nyp6+Xi5b1Jf/h0U5NVPVvz5559o164d9PX1ER0djYSEBAQEOOdx6wAAjVRJREFUBMDAwAAODg551pO1sbHBxYsXAbz4h8E777yDpk2bomfPnnjvvffQvXv316qzoPPKfUDc/fv3X3sCP/e5BL6+vhgwYAAAYNOmTahbty52796Njz76SFXWyMio3B4eTET0ungdVn4sLS3xwQcfYNy4cTh79izq168PhUKBmjVrqso8ffq0yIneo0ePYuTIkdiwYQNatGiRZ1/ue36OQSXXtGlTBAUFoV+/fli4cCG++uqr167Tx8cHx44dw6+//op69eppIEoqCCfwiUi2zM3NS1T+8OHDsLOzK6NoiOQpPDwcXbp0wblz5/JM3Fy7dg0ODg4AgHv37qFatWp5XhcbGwtnZ2dUr169PMOt1KpVq5avj8qdDKbyU9KxRalU4r333kNwcHCeu5Q++ugjCCHw/fffq7bltseTJ0/C1NQUmzdvxsSJE/Pc8R8SEoL+/fur9SmA4ly4cAGHDx/G/v378eeff+KXX35Bhw4d8pWLj49Hw4YNERwcrOoXgBd3p+c+wHbixImQJAnLli0D8OIj66ampggNDYWlpSXq1q2LU6dOwcrKCrNnz0ZISIhqiRsAWL58ORYtWoSUlJRC4zUyMir2I9Wv9lVZWVl53uBnZGTkKfPy3ZTnz5/P18YK+qdGcUvuFRRD7mRBRkYGDAwM8tT7agy5nwho27Ytbt68iUOHDuHXX3/FwIED0a1bN/z444+lrrOg88r9nRb3UGB15P4zoHnz5qpthoaGeOONN3D79u08ZR89eoRGjRq99jGJiMoDr8PKl7GxMfr27Yvt27ejYcOGGDJkSJ79FhYWhS7leezYMfTu3RvLli1TPfT2Zbn/iH/5H/6kvr59++Lrr7/G9OnT4ejoiB49euTZb2FhgSpVquDevXt5tt+7dw/W1tZ5tu3atQv+/v5YtmwZ3N3dyzr0So1r4BORbDVq1AitW7dWaw38Nm3a8E0kVUpt2rRB1apVERYWhmrVqqm+lEqlqszL23O/QkNDUbVq1WLvhiWqaHLHluJIkoQaNWrAwMAAQUFB+SaeHR0dER0dXVZhqsXR0RE+Pj44efIkWrZsmeefCeqws7NT/SPB1dUVO3fuRGxsLDIzM+Hn5wfgxZqmo0aNQt++fQtcs76sWVhYICMjQ/Um0tXVFYsWLcLTp0+RkJCgWsLn5MmT8PX1xeTJkzUew8u/p9znAgQEBCA7OxunT59GaGgoMjMzsWXLFhw6dAgjRoxQvbZGjRoYNGgQNmzYgF27dmHPnj149OjRa9VZlpycnGBoaIgrV66otmVmZiI+Pj7PEkAA8Ndff8HR0bFc4iIiIt3z0UcfYefOnfj555/x7rvv5tlX2HVUeHg4evXqhYULF2L06NEF1vvXX3/BwMAg3535pL7Zs2fDw8MDQ4YMUS2fl0uhUMDJySnP0og5OTk4cuQIOnbsqNp26dIljBo1CkOHDn2tpSFJPZzAJyLZkiQJ3t7exd5RlpOTgylTpvBBKVQp1axZE0OGDMHatWvVXsczOzsba9euxdChQ/N8lJWoMsgdW4ojhEBGRgbWrVuHtLQ03L17F3fv3lUtE9OjRw9cvnxZKw+CvnnzJnx8fHDq1CncunULYWFhiIuLg729PQDgjz/+QLNmzZCQkFBkPf3798fGjRuRmZmJAQMGoE+fPmjevDmUSiUeP36M2rVro1u3bqhTp47qYa7qCggIQNeuXYss4+PjU+CddS/T09NDnz59sHr1agDAihUrcOHCBRgbG6NVq1Z45513cOzYMYwaNQorVqwo9piv6tq1KwICAoos4+joiKdPn+Lo0aMwMjLC5s2bMXPmTBgaGmLkyJGqj6Fv2rQJYWFhqjsCly5dih07diA2NhZXr17F7t27YW1tDVNT01LXqY6EhAQ0a9YMf/zxR4l+F8CLfziMGTMGs2bNQlhYGK5cuYKxY8cCgOohwMCL5XoSEhLyPOCOiIjoZT169EB2drbqH9Wv7jt16lSe9y9Hjx5Fr1698MUXX2DAgAGqa69Xl747ceIE3NzcuHzua6hSpQq2bdsGMzMzvP/++/mWI5o8eTI2bNiALVu2ICYmBmPHjsWTJ08wcuRIAEBycjL69++Pxo0bY8OGDZyLKQecwCciWfP09MT48eMBIN+d+Lk/jx8/nmsXUqU2btw43L59W3XHbHH8/Pzw999/qx7sSFTZeHp6qpacenVsyX0DYmVlhWfPnsHFxQU2Njaqr127dgEAWrVqhbZt2+KHH34o3+DxYlmf2NhYDBgwAE2aNMHo0aMxbtw4fPbZZwBerAl75coVZGZmFllPly5dYGdnh08//RQ5OTlYt24dUlJScO/ePaxfvx7nzp3Do0ePsHTp0hI/kCwpKQnXr18vskxiYmK+ZVkKMnPmTAQEBODQoUNo1KiRas34xMREzJ8/Hw8ePEBsbCz69etXohiBFw81TkpKKrKMJElYuHAhhg8fjvj4eLz77rt48OABbt26hejoaKxevRqPHz9GeHh4nrsBq1evjkWLFsHZ2Rnt2rVDfHw8Dh48CD09vVLXqY7MzExcuXKl1GsDL168GIMHD8awYcPQrl073Lp1C7/99luef/iePHkS3bt3z3dXPhERUS59fX0MGTKkwH/We3h4QF9fH7/++qtq25YtW5Ceno5vv/02z7XXqw+e37lzJz799NMyj7+iq1mzJkJCQhAXF4dPP/00zxKDgwYNgr+/P2bOnAkHBwdERUXh8OHDqFWrFrKzs/HRRx/h8ePH+Omnn/J88pvKkCCti46OFgBERESEtkOhl6xfv15IkqTtMEgIkZOTI7Zu3Spat24tAKi+WrduLYKCgkROTo62QyTSunnz5gkAYs6cOSIrK0tcvXpV1VbS0tKEEEJkZWWJOXPmCABi3rx5Wo5YN0RGRgoAIjIyUtuhFEoXYpQjTYwtBw4cEPb29iI7O7scIi4bjx49Eh06dBAdOnQQ+/fvF0+ePBFCCHHv3j2xdOlS4eDgoOpDilOWf4uhoaGiZs2aYsKECeLSpUsiOztbZGdni6ioKOHp6SkmTZpU5vH5+fkJc3Nz4e/vL/7++28hhBDPnj0ToaGhwtXVVQQHB8uizrKUmpoqAAgLCwu+dyEqxvTp00XDhg21HYbOk/t1jtzjKwuaOueAgADRvXv3Er3m4MGDwt7eXmRmZhZZrjLmpbR27dolAIhly5apVf7rr78Wenp6IjQ0tGwDozz4EFsikj1JkjBs2DB4enri/fffR3x8PHbv3o1GjRrxo1pE/+Xr6wsAmDFjBgIDAzFgwADVvtjYWISGhmLdunW4ffs25s2bpypPVFm9PLZ4eXnhzJkzOHjwYInGll69eiEuLg4JCQmoV69eGUdcNmrWrIljx45h1apVmDJlCq5evQqFQgFJktCjRw8EBgbme6irNnTv3h2RkZGYO3cu3NzckJaWBuDFJyWGDx8OHx+fMo9h+vTpcHNzwzfffIPp06dDCIGsrCw0b94cX3zxBfr27SuLOstDv3790KlTJ22HQUREOuyzzz7D48ePkZqaiurVq6v1midPnmDTpk3Q1+d0pqYMHDgQZ8+ehbe3NxwcHIp8GG1ISAi++eYbfPvtt+jevXv5BUngXzwR6QxJklC9enVUr14ddnZ22g6HSFYkScLXX38NDw8PrF69WrVeNAA4OzujatWqGDp0KD7//HM4OTlpMVIieZEkCSYmJlAqlaUaWyZOnFjqYxsbGxe67+nTp4Wu7Xro0CG4ubmV+rivUigUmDRpEiZNmoSUlBT8+++/sLKygqGhocaOoQkNGzbEpk2bEBgYiHv37kFPTw+1atUq1xjc3NwQGhqK58+f4/79+6hevTpMTU1lV2dh5s+fj/nz5xcax6FDh9Sqh2vfExHR69LX1y/xTUUffPBBGUVTuX377be4cOECBg4ciMjIyAJvTImNjYWXlxcGDBiAadOmaSHKyo0T+ERERBWIk5MTAgMDMXnyZLRs2RKzZs1Cly5d0Lp1az6wlkhmoqKiCt1X1AR+nTp1yigiwMTEBCYmJmVWvybo6enBxsZGqzEYGhpq/FMXZVHnq8aMGYOBAwcWuI8PAyQiIqqc9PX1sXPnTjg7O+P999/HiRMn8jz/6N9//0W/fv1Qr149bNq0iSshaAEn8ImIiCqg3Ls327dvj86dO2s3GCIqED9NRuXNzMwMZmZm2g6DiIiIZMbCwgLBwcHo1KkTxo0bh40bN0KSJOTk5GD48OFITEzE2bNn1V7uiDRLT9sBEBEREREREREREZH2tG3bFuvWrcP//d//Yd26dQBeLK8TEhKC7du3o0mTJlqOsPLiHfhERERERERERERElZyXlxfOnj2LL774Ag8ePMCsWbMwa9YsvPfee9oOrVLjBD4R6RR9fX0oFApth0Eke0KIIh+QSSUTExOj7RAKJefYdEWVKlXyrPNJr0euf5NyjYuIiIon1z5crnGVBzmfu5xj0wVLly7FxYsXMXPmTLz33nuYOXOmtkOq9DiBT0Q6JSsrCxkZGdoOg0j2JElCWlqatsPQeRYWFlAqlfD09NR2KEVSKpWwsLDQdhg6Kzs7G8+ePdN2GDpPF9oL2woRkW7h2CI/upAToPLlRZMMDAywe/duHDx4EP3794eeHldg1zZO4BMREREVwtbWFjExMUhKStJIfWFhYfDx8cGJEyegVCo1Uifw4o2Ura2txuojKg1Nt5fBgwfD0dER06ZN00h9ANsKEZGu0fTY8sknn8DGxgbz5s3TSH1A5RtbNJ2TslLZ8qJptWrVwsiRI7UdBv0XJ/CJiIiIimBra6uxi/+4uDgAgIODA5c4ogpJk+1FqVTC0tISbdu21Uh9RESkmzQ5thgbG8Pc3Jxjy2vSZE6IqHj8DAQRERERERERERERkQxxAp+IiIiIiIiIiIiISIY4gU9EREREREREREREJEOcwCci+q/4+HhIkoSoqKhCy4SHh0OSJDx+/Ljc4iKSG7YVeWJe5Il5kSfmhYiINI1jCxGVFU7gExGVgIuLCxITE2FiYgIA2Lx5M0xNTbUbFJEMsa3IE/MiT8yLPDEvRESkaRxbiKg09LUdABGRLlEoFLC2ttZ2GESyx7YiT8yLPDEv8sS8EBGRpnFsIaLS4B34RFRhPHnyBF5eXjA2NoaNjQ2WLFkCd3d3TJw4EQAgSRJCQkLyvMbU1BSbN2/Osy02NhYuLi6oWrUqWrZsiWPHjqn2vfyRx/DwcIwcORIpKSmQJAmSJGH27Nlle5JEGsC2Ik/MizwxL/LEvBARkaZxbCEiueIEPhFVGFOnTsWxY8ewd+9ehIWFITw8HOfPny9VPVOmTMGFCxfQsWNH9O7dGw8fPsxXzsXFBcuXL0eNGjWQmJiIxMREeHt7a+JUiMoU24o8MS/yxLzIE/NCRESaxrGFiOSKE/hEVCGkpaUhMDAQ/v7+6Nq1K1q1aoUtW7YgKyurxHWNHz8eAwYMgL29PdasWQMTExMEBgbmK6dQKGBiYgJJkmBtbQ1ra2sYGxtr4nSIygzbijwxL/LEvMgT80JERJrGsYWI5IwT+ERUIVy/fh0ZGRlo3769apuZmRmaNm1a4ro6duyo+l5fXx/Ozs6IiYnRSJxE2sa2Ik/MizwxL/LEvBARkaZxbCEiOeMEPhFVGpIkQQiRZ1tmZqaWoiGSL7YVeWJe5Il5kSfmhYiINI1jCxFpCyfwiahCaNSoEQwMDHDmzBnVtuTkZFy9elX1s6WlJRITE1U/x8XFIT09PV9dp0+fVn2flZWFyMhI2NvbF3hchUKB7OxsTZwCUblgW5En5kWemBd5Yl6IiEjTOLYQkZzpazsAIiJNMDY2xscff4ypU6fC3NwcVlZW8PX1hZ7e//5P+fbbbyMgIAAdO3ZEdnY2pk2bBgMDg3x1rVq1Co0bN4a9vT2WLVuG5ORkjBo1qsDjNmjQAGlpaThy5AjatGkDpVIJpVJZZudJ9LrYVuSJeZEn5kWemBciItI0ji1EJGe8A5+IKozFixfDzc0NvXv3Rrdu3eDq6gonJyfV/iVLlqBevXpwc3PD0KFD4e3tXeDF0YIFC7BgwQK0adMGERER2LdvHywsLAo8pouLC8aMGYNBgwbB0tISixYtKrPzI9IUthV5Yl7kiXmRJ+aFiIg0jWMLEcmVJF5dwIvKXUxMDJo3b46IiAh06tRJ2+HQf23YsAGfffYZcnJytB0KvcTLywvx8fE4fvy4WuXd3d3h4OCA5cuXl21gRDKTkJCAunXr4uDBg/Dw8Ci2PNtK+di1axcGDx6M1NRUGBsbF1ueeSkf48ePR0REBKKiotQqz7yUDwcHB7i6uiIgIECt8sxL2UtLS0P16tWxc+dODBo0SNvhEMmar68vduzYgRs3bmg7lP9n787jqqj3P46/DwIq4g6CekVNTTEVEpdASSvLzJ8p7hqSqGkaplexUnIpM5fcL+6R4hZuYGW3tEi8eTMXzFsppmkuFWqamooFwvz+6HquBCrq4cwBXs/Hw8eDM/Od77yn6cx8z+fMmcENHn74YdWoUUPLly/PU3vOLQAcAVfgAwAAAAAAAADggCjgAwAAAAAAAADggHiILYBCLSkpyewIQIHAe8UxsV8cE/vFMbFfAAC2xrkFgCPgCnwAAAAAAAAAABwQBXwAAAAAAAAAABwQBXwAAAAAAAAAABwQ98AHAMBBnDlzRhkZGTbpKzU1VZJ09uxZ/fTTTzbpU5KqVq1qs74AAACAW7l8+bIuXrxos/7++OMPpaWl2XR8XKFCBZUsWdJm/QHAX1HAB5DvTpw4obNnz9qkr/vuu08VK1bU3r17bdKfJHl4eMjHx8dm/RUEttwn+aWo7ZdTp06pZs2a+v33323ab1hYmE37+9e//qXg4GCb9gncDVsex6pWraoWLVpwbnEw9erVk4eHh9kxCjxbvlf++OMPdenSRRkZGbxfANhF9+7d9dFHH9m0z127dmnDhg0262/IkCGaN2+ezfoDgL+igA8gX504cUK+vr5KS0uzab+zZ8+2WV9ubm5KSUkpMh8c82uf2FpR2y/FixdXlSpVdO7cOcXExMjNzc3sSFaGYWjEiBH66aefVLFiRbPjAPl2HJs/f77N+ipqx7D8cPDgQQr49yi/3iu2LHxJvF8A3FyNGjUkSWPHjlVgYKC5Yf5i48aNWrx4sTUjAOQXCvgA8tXZs2eVlpamlStXytfX1+w4OaSkpCg0NFRnz54tMh8aHX2fSEVzv5QvX14JCQkKDAxUQkKCVqxYIYvFYnYsSdLUqVP13XffacOGDapfv77ZcQCHP44VxWMYHJOjv1ck3i8Abm327Nn6z3/+o5iYGA0ZMkTe3t5mR5Ik7d+/X6tWrVL37t0VGRlpdhwAhRwFfAB24evrq8aNG5sdAzdgnzieRo0aKSYmRr169VLTpk01bNgwsyPpk08+0ZgxYzR69Gh17tzZ7DhANhzHgLzhvQKgoHJ1ddW6desUEBCgbt26KTExUa6urqZmunDhgkJCQlSzZk3FxMQ4zEU3AAovJ7MDAACA/+nZs6dGjhypkSNHatu2baZm+eGHH9SzZ089/vjjmjhxoqlZAAAAUDRVqVJF69ev186dOzVy5EhTs2RlZalPnz765ZdflJCQIHd3d1PzACgaKOADAOBgpkyZolatWqlbt246efKkKRnS0tLUuXNnlStXTqtXr1axYsVMyQEAAAC0aNFCc+bMUXR0tGJjY03LMXHiRH344YdatWqVateubVoOAEULBXwAQJ7NnTtXYWFhZsco9JydnRUXF6eSJUuqS5cu+v333+26fsMwNHDgQH333XeKj49XhQoV7Lp+AAAA4K+ef/55hYeHa9CgQUpOTrb7+j/44ANNmDBBr732mp566im7rx9A0UUBHwCQJ4ZhaP369dYrXr777jt5e3vr0qVLee7jlVde0dChQ/MrYqHi6emp+Ph4ff3114qIiJBhGHZb99y5c7Vq1SrFxMTIz8/PbusFgOsMw9Aff/yh06dP6/vvv7+jY+C5c+dUqVIlHTt2TJKUlJQki8WiCxcu5E9Y4C5ZLBZt3LjRJn19/PHH8vf3V1ZWlk36AxyRxWLR/Pnz1bBhQ3Xu3Fm//PKL3dZ96NAhhYaG6umnn1ZUVJTd1gsAEgV8AAVA37591alTpxzT+UBuX0lJSWrVqpX1IU2jR4/W0KFDVbp0aev8jh07qnLlyipVqpT8/f21atWqbH1ERkYqNjZWR48etXv+giggIEALFy5UTEyMFi9efEfLzps3TzVq1FCJEiXUvHlz7dq1K0/Lbdu2TSNHjtSIESPUq1evu4kNFAicWxxT3759ZbFY5OTkpIMHD2r9+vWqU6eOGjVqpBUrVuSpkD9p0iR17NhRNWrUkCQFBQUpNTVVZcuWzef0tvfll1/q2WefVe3atVWxYkX5+vpq8ODB2r9/v9nRrDIyMrR48WK1adNGVatWlbe3t4KCgjR9+nSlpaXZJcO5c+c0YcIENW3aVJ6envLx8VH79u0VFxd311+A50efd+v6cSm3f7t375YkPfnkk3Jxcckx9gIKmxIlSig+Pl5Xr15Vz549de3atTwve7fj40uXLikkJESVK1fW8uXL5eREKQ2AfXHUAQDkycqVK623zzlx4oQ2bdqkvn37Wud/8cUXatSokTZs2KCvv/5a4eHhCgsL06ZNm6xtPDw81LZtWy1YsMDe8Qusvn37asiQIRo6dKh27NiRp2XWrFmjESNGaPz48dq7d6/8/PzUtm1bnTlz5pbL/fjjj+revbsefvhhTZ061RbxASDPDMPQzp07Jcn6ZfF13377rcLCwvTiiy/esnialpammJgY9e/f3zrN1dVV3t7eOfp0RBkZGZL+fEji0KFD1a5dO3l5eWnevHn617/+pfnz58vd3V0tW7bUvHnzbL7+zMzM217BnZ6ebv376NGjaty4sebNm6euXbtq3bp12rJli4YPH67ExEQ98MADOnTokM1z3phhy5Ytuv/++7V7925FRkZqy5Ytio+P1//93/9p4sSJatu2ra5cuXJH/edHn/fi+pdQN/4bMGCAatasqSZNmljb9e3bV3PnzrVbLsAs1apV09q1a7Vt2zaNHj06T8vc7fjYMAz169dPJ06cUEJCQoH8MhhAIWDAdAcOHDAkGdu3bzc7Cm6wePFiw2KxmB2jwEtOTjYkGcnJyXfdx7PPPmt07Ngxx/StW7cakozz58+bmq+guZttTktLM9q2bWt9/dZbbxlNmjS57XJPPfWUER4enm1abGys8be//c3mGQuzP/74w2jRooVRuXJl4+eff75t+2bNmhkvvPCC9XVmZqZRpUoVY/LkyTdd5urVq0azZs2MatWqGWfOnLFJbuQUFxdnSDIuXbpkdpQCjXNL4bR8+XJD0l39W7p0qWEYhrFu3TrD09MzW79/3adLly41ypYta3z88cdGvXr1jFKlShlt27bN0/H1drZu3Wo0bdrUcHNzM8qWLWsEBQUZx44dy7XtDz/8YEgy4uLijIcfftgoXry4dTsiIyONpk2bGqmpqbku+/333xs1a9Y01q1bZ502fvx4w8/Pz1i+fLlRvXp1o0yZMsYTTzxxy/8Xr/+3eO+99wxfX1+jWLFixg8//JCtzfV+lyxZYtSoUcM6Pr5w4YJRu3ZtY+zYsUZWVlau/S9evNioXr268euvv1qnVa9e3Zg0aZIRHh5uuLu7G15eXrd9v7Rq1cp44YUXjGHDhhkVK1Y0WrdubRiGYezevduoUKGC8f777+e6XEZGhhEeHm506NDBOu2PP/4wXnjhBcPb29soXry44ePjY7z55pvW+XfT5/V9uWHDBqN169ZGyZIljUaNGhlffPHFTbfJMAxDkpGQkHDLNrlJT083PD09jddffz3b9OPHjxuSjO+///6O+4TtjRkzxqhZs6bZMQq1mTNnWo+jt3M342PDMIwpU6YYkoz4+Ph7zgsAd4sr8AEA2ezcuVPdunXLdgX2e++9p44dO1pff/7559mu+LqZixcv5ngAarNmzfTjjz9a702M23N1ddX69eslSd26dct25eFfpaenKzk5WW3atLFOc3JyUps2bW56Bb9hGIqIiNB//vMfxcfHy9PT07YbAAC3YRiG3nrrrdu2s1gs8vX11c8//6zU1FRNnz5dbm5u1nPS559/roCAgNv2k5aWpunTp2vFihX617/+pRMnTigyMvKetuHatWvq1KmTWrVqpa+//lo7duzQwIEDb3vl/yuvvKJhw4YpJSVFbdu21YEDB7Rs2TJt3LhR3t7eWrBggerUqaMaNWroH//4h+rWrSsXFxctWbJEo0aNyvaLhCNHjmjjxo3atGmTNm3alKeHPKalpWnq1Kl6++23tX//flWqVClHm++//14bNmxQfHy89u3bJ0maMmWKAgIC9Prrr+vixYt65plnrLfPmTt3rtq1a6fnnntOwcHBmj17drb+ZsyYoSZNmuirr75St27dJOm244LY2Fi5urrq3//+txYuXChJGjp0qCZNmqQOHTrowIEDatWqlTw9PdW9e3eNGDFC06ZN08KFC3XgwAFt3bpV0p/PeXn//fe1du1afffdd1q1apX1dkt32+d1UVFRioyM1L59+3T//ferV69ed3R7j7x6//33de7cOYWHh2eb7uPjIy8vL33++ec2XyfgiIYPH67evXurX79++vrrr2/a7m7Gx9Kfv8YZM2aMxowZo5CQEJtmB4A74Wx2AADIi02bNsnd3T3btMzMTJPSFG733Xef2rdvr8mTJ+vll1+WJK1du1YxMTHWNsePH79tAX/t2rXavXu3Fi1alG16lSpVrH3c+IEZt+bt7a0NGzaoVatWGjFihKKjo3Ntd/bsWWVmZsrLyyvbdC8vLx08eDDXZRYvXqyYmBgtXbo0T1/MAIUF5xbHceTIEX3zzTe3bWcYhlJSUvTPf/5Tv/32m8aMGaMRI0bo5MmTOnnypHbt2qUyZcroo48+si5zvajzySefyN3dXV9//bUyMjLUq1cv6wMQH3nkEb377rvZlrtTly5d0sWLF+Xp6Wm9bUylSpW0f//+XO9Zf/r0aUnSE088oZIlS1qP0bGxsXr44Yf1n//8R6tWrdLYsWP14osvqlq1alq5cqW+//57ffbZZ/Ly8tLly5e1ZMkSVatWTYcPH1ZGRoZ69+6tkydPSpL8/f2VmJh4y9wZGRmaP3/+LR9anp6eruXLl2f7gnfFihX6+OOPJUkjR47UDz/8oPfee09nzpzRwIEDVbduXUl/3tYlKipKr732mnXZp556SkOGDLHOj46O1p49e9S5c+ebZqhTp46mTZtmfX348GEdO3ZMAwYMUGZmpkJCQtS6dWvNmTNHn3/+uUaMGKGoqCi5urqqV69e2rx5sx555BGdOHFCderUUcuWLWWxWFS9evV77vO6yMhItW/fXpL02muv6YEHHtD333+vevXq3XIf3KmYmBi1bdtWf/vb33LMq1Klio4fP27T9QGOymKxaMmSJdq/f79CQkK0Z88elS9fPke7uxkf//DDD+rZs6eeeOIJvf766/mSHwDyigI+gALhkUceyXHf9J07dyo0NNSkRIWXp6enunbtqhdeeEG7d+9W9erV5erqmm0wfPXqVZUoUeKmfWzdulXh4eFasmSJHnjggWzzSpYsKUl2e7BdYRIYGKi5c+dq8ODBatq0qZ599tl77nPHjh0aOnSohgwZku2ZBkBRwLnFcZw7d+6O2g8YMMD695QpUzRlypRs87ds2ZJjme7du2d7feN98q976qmn7ihHbq5/+Z1XixcvzvVB5fHx8da///rrhBuvvB40aFC2eV27dr2j9bu6uqpRo0a3bFO9evVsxftff/1Vly5dUoMGDSRJH3zwgTZu3KjmzZtLkiIiIvTJJ59IkipXrqzz589n6+/G9V3/hcKvv/56ywx//WXFN998o6ZNm8rZ2VkHDhzQTz/9pOjoaLm4uMjf31/vv/++tW3lypX1n//8R9KfXxg8/vjjqlu3rp588kn93//9n5544ol76jO37apcubIk6cyZMzYt4P/444/avHmz1q5dm+v8kiVLMsZCkeLm5qb4+Hg1adJEzzzzjD744AMVK1bsnvpMS0tTSEiIypcvr9WrV99zfwBwryjgAygQSpUqpdq1a2eb9uOPP5qUpvBzd3dXx44dtWrVKtWsWVO9evXKNt/DwyPHh/Hrtm3bpg4dOmjWrFnWh97e6PoHdG7TcncGDRqk3bt3a9CgQWrQoEGOgoaHh4eKFStmvbLzutOnT8vb2zvbtFOnTqlr165q1qyZZs2ale/ZAUfDucVxVKxY8Y7a165dW7Vq1dLbb7+d7RY1ERERMgwj2wNev/jiC3Xv3l379+9X2bJltXbtWk2YMEEHDhywtvn44481YMAAm+z/b7/9VklJSfrkk0908OBBrV69Otfb+pw8eVKBgYHavHlzti+7+/Tpo/DwcD366KMaP368LBaLJkyYIEn67bffVL9+fe3YsUMVK1ZU06ZNtW3bNnl4eGjGjBnavHlzti8vXn/99Vy/HLhRyZIlb3ubn1KlSmV7fe3atWxf5Kenp2drc+MvW/bu3Zvjfebi4pJjHcYtHk58swzXLwpIT0+Xi4tLtn7/muH6LwIaN26sH374QR999JE+/fRTde/eXW3atNH69evvus/ctuv6f9PbPRT4Ti1dulQVK1bU008/nev8X3/9lTEWipz77rtP7777rtq1a6cJEyZo4sSJ2ebfyfjYMAwNHDhQhw8f1o4dO3K9oh8A7I174AMAcvXMM88oLi5OH374YY4rEh988MFshY/rkpKS1L59e02dOlUDBw7Mtd9vv/1WLi4uOa7MR95YLBbNmzdPjRo1UufOna23f7jO1dVVAQEB2W6ZkJWVpcTERAUGBlqnpaenq1u3bjIMQ+vWrZOrq6vdtgEA/qpWrVq3vQpc+vMYWKZMGZUsWVLr16/X3/72N1WtWtX6r0WLFjp27Fi2adeLmVWqVFHVqlVVvnx5WSyWbG2uf4Fw47S7/de2bVtNnjxZe/bsUcOGDfXpp5/m2u560ahSpUrZpjdo0ECpqamqWrWqnnzySW3atEmXLl1SpUqVtHTpUklSsWLF9Oqrr6pTp07y8/NT1apVVaZMGbm4uGTrq3Tp0vmyvzw8PJSenm4thrVs2VLTpk3T1atX9dNPP2nJkiWS/vzyJCoqSiNGjLB5htq1a1tvu3T9uQDR0dHKzMzUl19+qc2bNysjI0OxsbH66KOPsv3KrEyZMurRo4eWLFmiNWvWaMOGDfr111/vqU97MAxDS5cuVVhYWK5fgvz+++86cuSIHnzwQbvmAhxB27ZtNWnSJL3xxhvauHFjtnl5HR9Lfz4nY9WqVYqJicnTeQkA7IECPgAgV23btlVmZqb1A+xf5+3YsSPbvaK3bt2q9u3b68UXX1SXLl106tQpnTp1KsdP4j///HMFBwdbr3DDnStRooQ2bNigq1evqmfPnjkekDdixAgtWbJEsbGxSklJ0eDBg3XlypVst1wYMWKEdu7cqfXr11t/5g8AZrFYLHl6iKxhGEpPT9eiRYt0+fJl67nm6tWrkv48P+3fv/+mvxLLTz/88INGjx6tHTt26Pjx49qyZYsOHz4sX19fSdKuXbtUr149/fTTT7fsJyQkRG+//bYyMjLUpUsXPf3006pfv77c3Nx04cIFValSRW3atFHVqlWtD3PNq+joaD322GO3bDN69Ohcf0F3IycnJz399NOaP3++JGnOnDn66quv5O7uroYNG+rxxx/Xtm3b1K9fP82ZM+e26/yrxx577KbPernuwQcf1NWrV7V161aVLFlSy5Yt07hx41S8eHGFh4erU6dOmjp1qpYuXaotW7ZYv8iZOXOm3n33XR08eFCHDh3SunXr5O3trXLlyt11n3nx008/qV69etq1a9cd/be40WeffaYffvgh2y2kbvTll1+qePHiOQqSQFHxyiuvqHPnzgoLC8txb/u8jI+TkpI0cuRIjRgxQj179rR3fAC4KW6hAwDIlbOzs3r16qU+ffrkmNeuXTs5Ozvr008/Vdu2bSX9+dC9tLQ0TZ48WZMnT7a2bdWqlZKSkqyv4+LirLcCwN2rVq2a1q1bp8cee0yvvPKKpk+fbp3Xo0cP/fLLLxo3bpxOnTolf39/ffzxx9YHd8XGxmrevHlasGCBgoKCzNoEAMgmNDRUb775pg4ePCgnJ6dstx6xWCwyDEOVKlXSmTNnchy7li5dqr59+6phw4Zq3Lix1q5dm+Pe8PnNzc1NBw8eVGxsrM6dO6fKlSvrhRdesOZIS0vTd999p4yMjFv288gjj6h27dp67rnnFBMTo0WLFmn69OnKyMhQhQoVlJqaqkqVKt3VPZnPnj2rI0eO3LJNamqqTpw4cdu+xo0bp2bNmumhhx5Su3btdODAAZ06dUrly5dXVlaWoqKi5OHhcccZpT8fanz27NlbtrFYLJo6daqeffZZ/etf/9JTTz2lX375RadOnVKVKlV08eJFLV68OMeDqkuXLq1p06bp8OHDKlasmJo2bap//vOfcnL689q2u+kzLzIyMvTdd9/d0/3pY2JiFBQUdNN76r/77rt65pln5ObmdtfrAAoyi8WiZcuWqXnz5urUqZP1webS7cfHJ0+eVPfu3dWqVStNnTrVzM0AgJwMmO7AgQOGJGP79u1mR8ENFi9ebFgsFrNjFHjJycmGJCM5OdnsKLly9Hz5wVbbHB0dbTzxxBN3tMw///lPw9fX18jIyLhlu6K4X+7WrFmzDEnGu+++m6f2e/bsMYoXL27069fPyMrKyud0+Ku4uDhDknHp0iWzoxRojn6McPR8jiwrK8tYvny50ahRI0OS9V+jRo2MFStW5Om4tWnTJsPX19fIzMy0Q+L88euvvxoPPfSQ8dBDDxkffPCBceXKFcMwDOP06dPGzJkzDX9/f+Py5cu37Se//1/cvHmzUb58eWPo0KHG119/bWRmZhqZmZnGvn37jNDQUOPvf/97vmecNGmSUbFiRWP69OnGyZMnDcMwjN9//93YvHmz0bJlSyM+Pt4h+sxvv/zyi1GhQgXj6NGjZkfBf40ZM8aoWbOm2TGKpO+++84oU6aM0alTpzydC65evWo0bdrU8PHxMc6cOWOHhABwZ7iFDgDgrgwaNEgPP/ywLl26lOdlrly5oqVLl8rZmR+A2cqwYcPUu3dv9e/fX19//fUt2/7yyy/q3LmzGjVqpHnz5t32oYUAYG8Wi0V9+vTRvn37VK9ePXXt2lWHDx/Wvn37FBoamqfjVvv27TVw4MDb3qrGkZUvX17btm1T9+7dNXLkSJUqVUrFixeXj4+PkpKSFBMTk+OhrmZ44oknlJycrEuXLik4OFiurq5ydXVVu3bt9Le//c0uv7gbM2aMEhIStGXLFtWqVUuurq4qWbKkRowYoT59+qhjx44O0Wd+O3bsmObPn6+aNWuaHQUw3f33368VK1Zo48aN2X4ZnBvDMBQREaGvv/5a8fHxPAQagEOiggIAuCvOzs6Kioq6o2W6du2aT2mKLovFoiVLlmj//v0KCQnR7t27VaFChRztrl27pp49e+rq1avasGGDSpQoYUJaAMgbi8Wi4sWLy8vLS7Vr177j5YcPH37X677V7VGuXr1602e4fPTRRwoODr7r9f6Vq6ur/v73v+vvf/+7Ll68qN9++02VKlVS8eLFbbYOW6hZs6aWLl2qmJgYnT59Wk5OTtZbUthLcHCwNm/erD/++ENnzpxR6dKlVa5cOYfr82befPNNvfnmmzfN8dFHH922jyZNmqhJkya2jgYUWE8//bTGjRunsWPHqnHjxmrXrl2u7RYvXqyYmBgtXbpUAQEBdk4JAHlDAR8AgALOzc1NCQkJatKkiZ555hlt2rQpx72RX3nlFW3btk2ffvqpqlWrZlJSAHB8+/btu+m8WxXwq1atmk+JpLJly6ps2bL51r8tODk5mf5Q9OLFi9v8HJcfff7V888/r+7du+c672b/vwG4vfHjx2vv3r3q3bu3du/eneML4R07dmjo0KF64YUX1LdvX3NCAkAeUMAHAKAQqFmzpt599121a9dO48eP1xtvvGGdFxcXpxkzZmjWrFlq3bq1eSEBoAC4myv+gXtRoUKFXH89B+DeODk5acWKFWrWrJk6d+6sHTt2WG8/lpqaqi5duqhZs2aaOXOmyUkB4Na4Bz4AAIXEE088oUmTJmnSpEnauHGjJOnrr79W//791bt3bw0bNszcgAAAAIAdlStXTgkJCTp69Kj69+8vwzCUnp6ubt26SZLWr18vV1dXk1MCwK1xBT4AAIXIyy+/rD179igsLEybN29WaGio6tSpoyVLlvDQWgAAABQ5DzzwgJYuXaru3buradOm+uGHH7Rr1y5t27ZN3t7eZscDgNuigA/ALlJSUsyOkCtHzWUPjrztjpzN0VksFi1dulQPPfSQWrRoofLly+vTTz+Vm5ub2dEAm3PUY4Wj5kLR5cj/TzpyNgCFR7du3fTyyy9r1KhRMgxDCxcuVGBgoNmxACBPKOADyFceHh5yc3NTaGio2VFuys3NTR4eHmbHsJuCsE+kordfbKl06dLauHGjoqKiNHDgQNWsWdPsSIBNFYTjGMewe1etWjWVKVPG7BgFWkF4r0i8XwDYx6RJk3T16lVVqFBBAwcONDsOAOQZBXwA+crHx0cpKSk6e/asTfobO3asUlNT9fbbb9ukP+nPD7c+Pj4268/R2Xqf5Jeitl9srU6dOlq7dq3ZMYB8Yevj2NSpU/XVV18pLi7OJv1JHMNs4eTJk6pevbrZMQo0W79X0tLSFBwcrMmTJ+uJJ56wSZ8S7xcA9lGsWDHNmTPH7BgAcMco4APIdz4+Pjb7UFaxYkVdunRJjRs3tkl/RZUt9wkAmMGWxzFPT0+5ublxbkGhZMv3yuXLlyVJNWvW5P0CAABgJ05mBwAAAAAAAAAAADlRwAcAAAAAAAAAwAFRwAcAAAAAAAAAwAFRwAeA/zp27JgsFov27dt30zZJSUmyWCy6cOGC3XIBAAouzi2Oif3ieNgnAAAAuaOADwB3ICgoSKmpqSpbtqwkadmyZSpXrpy5oQAABRrnFsfEfnE87BMAAFAUOZsdAAAKEldXV3l7e5sdAwBQiHBucUzsF8fDPgEAAEURV+ADKDSuXLmisLAwubu7q3LlypoxY4Zat26t4cOHS5IsFos2btyYbZly5cpp2bJl2aYdPHhQQUFBKlGihBo0aKBt27ZZ59340+2kpCSFh4fr4sWLslgsslgsmjBhQv5uJADArji3OCb2i+NhnwAAAOQPCvgACo1Ro0Zp27Zteu+997RlyxYlJSVp7969d9XPyJEj9dVXXykwMFAdOnTQuXPncrQLCgrS7NmzVaZMGaWmpio1NVWRkZG22BQAgIPg3OKY2C+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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import AtomicType, IQPAnsatz\n",
    "\n",
    "# Define atomic types\n",
    "N = AtomicType.NOUN\n",
    "S = AtomicType.SENTENCE\n",
    "\n",
    "# Convert string diagram to quantum circuit\n",
    "ansatz = IQPAnsatz({N: 1, S: 1}, n_layers=2)\n",
    "circuit = ansatz(diagram)\n",
    "circuit.draw(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "This produces a quantum circuit in :py:class:`lambeq.backend.quantum.Diagram` form.\n",
    "\n",
    ".. note::\n",
    "\n",
    "   Lambeq also includes other circuit ansätze. See :py:class:`~.ansatz.CircuitAnsatz` for further reference.\n",
    "\n",
    "Conversion to :term:`pytket` format is very simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <iframe srcdoc=\"\n",
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       "&lt;head&gt;\n",
       "    &lt;meta charset=&#34;UTF-8&#34;&gt;\n",
       "    &lt;!-- Download Vue 3--&gt;\n",
       "&lt;script type=&#34;application/javascript&#34; src=&#34;https://cdn.jsdelivr.net/npm/vue@3&#34;&gt;&lt;/script&gt;\n",
       "&lt;!-- Download Circuit Renderer with styles --&gt;\n",
       "&lt;script type=&#34;application/javascript&#34; src=&#34;https://unpkg.com/pytket-circuit-renderer@0.7/dist/pytket-circuit-renderer.umd.js&#34;&gt;&lt;/script&gt;\n",
       "&lt;link rel=&#34;stylesheet&#34; href=&#34;https://unpkg.com/pytket-circuit-renderer@0.7/dist/pytket-circuit-renderer.css&#34;&gt;\n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "\n",
       "\n",
       "\n",
       "    &lt;div id=&#34;circuit-display-vue-container-8d04c943-04df-418f-8265-6151df9e8a5c&#34; class=&#34;pytket-circuit-display-container&#34;&gt;\n",
       "        &lt;div style=&#34;display: none&#34;&gt;\n",
       "            &lt;div id=&#34;circuit-json-to-display&#34;&gt;{&#34;bits&#34;: [[&#34;c&#34;, [0]], [&#34;c&#34;, [1]], [&#34;c&#34;, [2]], [&#34;c&#34;, [3]], [&#34;c&#34;, [4]], [&#34;c&#34;, [5]], [&#34;c&#34;, [6]], [&#34;c&#34;, [7]], [&#34;c&#34;, [8]], [&#34;c&#34;, [9]]], &#34;commands&#34;: [{&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*John__n_0&#34;], &#34;type&#34;: &#34;Rx&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [6]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [10]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*park__n_0&#34;], &#34;type&#34;: &#34;Rx&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*John__n_1&#34;], &#34;type&#34;: &#34;Rz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]], [&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*walks__n.r@s_0&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]], [&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_0&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]], [&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*the__n@n.l_0&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [10]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*park__n_1&#34;], &#34;type&#34;: &#34;Rz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*John__n_2&#34;], &#34;type&#34;: &#34;Rx&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]], [&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_1&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [10]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*park__n_2&#34;], &#34;type&#34;: &#34;Rx&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]], [&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*walks__n.r@s_1&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]], [&#34;q&#34;, [6]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_2&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]], [&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*the__n@n.l_1&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]], [&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_4&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [6]], [&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_3&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]], [&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_5&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [6]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]], [&#34;q&#34;, [10]]], &#34;op&#34;: {&#34;type&#34;: &#34;CX&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [10]], [&#34;c&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]], [&#34;q&#34;, [3]]], &#34;op&#34;: {&#34;type&#34;: &#34;CX&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]], [&#34;q&#34;, [6]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_6&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [9]], [&#34;c&#34;, [0]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [3]], [&#34;c&#34;, [5]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]], [&#34;q&#34;, [4]]], &#34;op&#34;: {&#34;type&#34;: &#34;CX&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [6]], [&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;params&#34;: [&#34;2*in__s.r@n.r.r@n.r@s@n.l_7&#34;], &#34;type&#34;: &#34;CRz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [2]], [&#34;c&#34;, [4]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [4]], [&#34;c&#34;, [7]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]], [&#34;q&#34;, [5]]], &#34;op&#34;: {&#34;type&#34;: &#34;CX&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [6]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]], [&#34;c&#34;, [6]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [5]], [&#34;c&#34;, [9]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]], [&#34;q&#34;, [8]]], &#34;op&#34;: {&#34;type&#34;: &#34;CX&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [8]], [&#34;c&#34;, [3]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]], [&#34;c&#34;, [8]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [7]], [&#34;c&#34;, [2]]], &#34;op&#34;: {&#34;type&#34;: &#34;Measure&#34;}}], &#34;created_qubits&#34;: [], &#34;discarded_qubits&#34;: [], &#34;implicit_permutation&#34;: [[[&#34;q&#34;, [0]], [&#34;q&#34;, [0]]], [[&#34;q&#34;, [1]], [&#34;q&#34;, [1]]], [[&#34;q&#34;, [2]], [&#34;q&#34;, [2]]], [[&#34;q&#34;, [3]], [&#34;q&#34;, [3]]], [[&#34;q&#34;, [4]], [&#34;q&#34;, [4]]], [[&#34;q&#34;, [5]], [&#34;q&#34;, [5]]], [[&#34;q&#34;, [6]], [&#34;q&#34;, [6]]], [[&#34;q&#34;, [7]], [&#34;q&#34;, [7]]], [[&#34;q&#34;, [8]], [&#34;q&#34;, [8]]], [[&#34;q&#34;, [9]], [&#34;q&#34;, [9]]], [[&#34;q&#34;, [10]], [&#34;q&#34;, [10]]]], &#34;phase&#34;: &#34;0.0&#34;, &#34;qubits&#34;: [[&#34;q&#34;, [0]], [&#34;q&#34;, [1]], [&#34;q&#34;, [2]], [&#34;q&#34;, [3]], [&#34;q&#34;, [4]], [&#34;q&#34;, [5]], [&#34;q&#34;, [6]], [&#34;q&#34;, [7]], [&#34;q&#34;, [8]], [&#34;q&#34;, [9]], [&#34;q&#34;, [10]]]}&lt;/div&gt;\n",
       "        &lt;/div&gt;\n",
       "        &lt;circuit-display-container\n",
       "                :circuit-element-str=&#34;&#39;#circuit-json-to-display&#39;&#34;\n",
       "                :init-render-options=&#34;initRenderOptions&#34;\n",
       "        &gt;&lt;/circuit-display-container&gt;\n",
       "    &lt;/div&gt;\n",
       "    &lt;script type=&#34;application/javascript&#34;&gt;\n",
       "      const circuitRendererUid = &#34;8d04c943-04df-418f-8265-6151df9e8a5c&#34;;\n",
       "      const displayOptions = JSON.parse(&#39;{}&#39;);\n",
       "\n",
       "      // Script to initialise the circuit renderer app\n",
       "\n",
       "const { createApp } = Vue;\n",
       "const circuitDisplayContainer = window[&#34;pytket-circuit-renderer&#34;].default;\n",
       "// Init variables to be shared between circuit display instances\n",
       "if (typeof window.pytketCircuitDisplays === &#34;undefined&#34;) {\n",
       "    window.pytketCircuitDisplays = {};\n",
       "}\n",
       "// Create the root Vue component\n",
       "const app = createApp({\n",
       "    delimiters: [&#39;[[#&#39;, &#39;#]]&#39;],\n",
       "    components: { circuitDisplayContainer },\n",
       "    data () {\n",
       "      return {\n",
       "        initRenderOptions: displayOptions,\n",
       "      }\n",
       "    }\n",
       "})\n",
       "app.config.unwrapInjectedRef = true;\n",
       "app.mount(&#34;#circuit-display-vue-container-&#34;+circuitRendererUid);\n",
       "window.pytketCircuitDisplays[circuitRendererUid] = app;\n",
       "    &lt;/script&gt;\n",
       "\n",
       "\n",
       "\n",
       "&lt;/body&gt;\n",
       "&lt;/html&gt;\n",
       "\"\n",
       "            width=\"100%\" height=\"100%\"\n",
       "            style=\"border: none; outline: none; overflow: auto\"></iframe>\n",
       "</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pytket.circuit.display import render_circuit_jupyter\n",
    "\n",
    "tket_circuit = circuit.to_tk()\n",
    "\n",
    "render_circuit_jupyter(tket_circuit)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Exporting to :term:`pytket` format provides additional functionality and allows interoperability. For example, obtaining a :term:`Qiskit` circuit is trivial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytket.extensions.qiskit import tk_to_qiskit\n",
    "\n",
    "qiskit_circuit = tk_to_qiskit(tket_circuit)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. note::\n",
    "\n",
    "   To use ``tk_to_qiskit``, first install the ``pytket-qiskit`` extension by running ``pip install pytket-qiskit``. For more information see `the pytket documentation <https://cqcl.github.io/tket/pytket/api/#extensions>`_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classical case"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "In the case of a classical experiment, instantiating one of the tensor :term:`ansätze <ansatz (plural: ansätze)>` requires the user to assign dimensions to each one of the atomic types occurring in the diagram. In the following code, we parameterise a :py:class:`.TensorAnsatz` instance with :math:`d_n=4` for the base dimension of the noun space, and :math:`d_s=2` as the dimension of the sentence space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import TensorAnsatz\n",
    "from lambeq.backend.tensor import Dim\n",
    "\n",
    "tensor_ansatz = TensorAnsatz({N: Dim(4), S: Dim(2)})\n",
    "tensor_diagram = tensor_ansatz(diagram)\n",
    "\n",
    "tensor_diagram.draw(figsize=(10,4), fontsize=13)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Note that the wires of the diagram are now annotated with the dimensions corresponding to each type, indicating that the result is a concrete :term:`tensor network`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix product states"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "In classical experiments of this kind, the tensors associated with certain words, such as conjunctions, can become extremely large. In some cases, the order of these tensors can be 12 or even higher (:math:`d^{12}` elements, where :math:`d` is the base dimension), which makes efficient execution of the experiment impossible. In order to address this problem, ``lambeq`` includes :term:`ansätze <ansatz (plural: ansätze)>` for converting tensors into various forms of :term:`matrix product states <matrix product state (MPS)>` (MPSs).\n",
    "\n",
    "The following code applies the :py:class:`.SpiderAnsatz`, which splits tensors with order greater than 2 to sequences of order-2 tensors (i.e. matrices), connected with :term:`spiders <spider>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHVTmgLsFBQWx++67x4wZM2LhwoVlHltYWBg//elPy9y2Kvj68MMPN6jWVdOD69Sps8Z9q44Hs3jx4g1aZnUyaNCgWLFiRbz00ksREfHss89GixYt4owzzogFCxaUzip87rnnSv+mEVHmxENLly6NOXPmxNy5c2PvvfeOr776Kt5///31ev65c+fGXnvtFaNGjYonnniiTDgZEaUr0YcffniNwwVsqMWLF6+1TyL+2yv6hK879thjS3sw4r9fCezXr98Gr6dW9Zb1FBWpvPoXUshl/86aNSsOOeSQqFev3iYfmgbWJRc9fOONN8bTTz8dd999dxx22GGlsylhU51yyiml4WTEf8OfLl26lOnXhx56KJYtWxbHH398zJ49u8xl//33j5KSkhg1atR6P6ft44rzk5/8pMz6qGHDhjF06NCYN29ePP/88xERUa9evdL7Fy5cGHPmzIkaNWpE3759Y8yYMWtd7s9//vN1PufSpUvj0EMPjVtuuSXuvPPOjToviKynrGo7g/LTTz+NwsLC0unf63pM8+bNS6+v7ZOSpk2bRsR/0/XVv2K9xRZbrHEw6dUfuyFWLWfZsmVl/lNF/K+hi4qKNmiZ1cmqYPjZZ5+NwYMHx7PPPhu777579OrVKxo3bhzPPvts9O/fP5599tnYfvvto0mTJhHx35XWsGHD4oEHHojJkyevsdz13VgaMmRILFy4MF588cXYZZdd1rj/tNNOi8cffzxOOeWUOOecc2KXXXaJffbZJ4444ogy/bc+ioqKYtmyZWu9b+nSpfqENaxrvbah66lVvbW2/rOeIlfKq38hhVz176oPRqdNmxZPPvmkb9iQM7no4T59+pT+fPTRR8d5550Xu+66a0yYMCE6deq00cuFdfXr559/Xnp94sSJERGx5557rnM5M2fOXO/ntH1ccdaW66z6ZuEnn3wSEVG6T7/bbrvFggULyjx29UlrqzRv3vwbj0/+y1/+MhYsWBB/+ctfNnrW+OpZz9dVxx6ptjMoN8Y3fbUgy7KNfuy3WRVSrW1q79SpU6OgoCC22GKLDVpmddKyZcvYdttt49lnn43FixfHmDFjYtCgQVFYWBgDBw6MZ555JubMmRMTJkwoc5D5I488Mq6++urYb7/94i9/+Us89dRT8fTTT5ces3LVCW6+zeGHHx6FhYVxySWXxJIlS9a4v2nTpjF27Nh47rnn4vTTT48FCxbEz372s+jSpUvpgXzX1xZbbLHOKeBTp05d69Rxqrfy+srUqnXQutZTEWv/6gJsCl/5oyrLRf/OnTs39txzz3j//ffjscceW+vJc6C8VMQ6+JhjjonFixeXnqAHNta6+nX1ffNVP999993x9NNPr/WytmMFrovt48pj4cKFceKJJ0ZExBFHHBEPPfRQjBw5Mp5++ukYNGjQWjOabwsGDzrooCgqKoorr7xyoz+YadasWUTokVWqbUDZsWPHKCkpKf2UZG0qy/F6ViX/awurXn311dhmm22cIOdbDBo0KMaPHx9PPPFELF++PPbYY4+I+O9xbl555ZX45z//GVmWlW7If/nll/H3v/89jj766LjpppviyCOPjMGDB8eee+5Z5qsB6+NHP/pRjBgxIp599tn4/ve/v9Yp2jVq1IjddtstLr300njppZfijTfeiIULF37j8S7WZtXBfr8+43Py5Mkxbdq02GmnnTZoebC+WrduHW3atIlXX311jftW3ab/AHJnVTj53nvvxaOPPrpBxzuHymrVh/tz585NXAmV3dpmwG2orbfeOiL+Gxrtueeea72s7/EnI2wfV6S15TqrTjbcsWPHeOaZZ0oP4XfyySfHD37wg9h7771jzz33jEWLFm3Ucw4aNCiefPLJ+PDDD2P33Xf/xpPorEvz5s31yGqqbUB50EEHRUTEZZddViYt/+ijjyIi4jvf+c4Gf702V3bbbbeoV69eXH/99VFcXFx6+xNPPBGffPLJBn2KU10NGjQoSkpK4uKLL4727duXfkVk0KBBsWzZsrjsssuiZs2aseuuu0bE/z5h+/onKdOnT9+oYzn98Ic/jPvuuy9eeuml2Hfffcscs3TVgXFX17Vr16hXr94Gb4wdccQRERHxhz/8ocztq67rFXLpiCOOiI8//rj0DHkR/z17/R//+Mdo1KhR7LfffgmrA8hf8+bNi7322ivefffdePjhh2PfffdNXRKst5UrV65z9tEf//jHiPjf2WxhXVZN2NmUMPuwww6LOnXqxEUXXbTWb77Nnz9/nYfTWhfbxxXjT3/6U8yfP7/0+vz58+Omm26KRo0axcCBA9c5g/Zf//rXOo8/uT522223eOqpp+Kzzz4rPT/JhtIj/1Ntj0G51157lZ7Be968efH9738/ZsyYEdddd11ERPziF79IXOH/NG7cOC655JL4+c9/HnvuuWccccQRMXXq1Pj9738fXbt2jTPPPDN1iZXebrvtFoWFhTFx4sQYMmRI6e3bbrtttGrVKt57773o169fNGjQICIiGjRoEHvvvXeMGDEi6tWrF717947PP/88br755ujQocNGTeE+9NBDo1atWnHYYYfF4MGD45///GdsvvnmceKJJ8aUKVNi7733ji233DKWLFkS999/fyxYsGCNE+p8m+9973vx/e9/P66++uqYP39+9O/fP0aPHh233XZbHHXUUWs9BiaUl3PPPTcefPDBOPLII+Oss86KNm3axH333Rdjx46NW2+9tfT/F1RG8+fPL90RXnXWzRdffLF0JvsBBxwQPXv2TFYffJO99torxo8fH0cccUTMmzcvRowYUeb+AQMGbNCsH6hICxcujLZt28bBBx8c3bt3j5YtW8aMGTPisccei3HjxsUee+xRLme2J7/17t07CgsL49JLL4158+ZF/fr1N/gbkW3bto0//elPccIJJ0S3bt3i6KOPji233DJmzZoVb7/9djz22GPx3nvvlTlx7rexfVwxmjVrFn379o1jjz02IiLuuOOOmDRpUtx6661RVFQUu+yyS+kxcv/0pz9F7969480334x77rknevToEW+//fZGP/cuu+wS//rXv2KfffaJ3XbbLZ599tkNOgSfHvmfahNQrpoJt3py/pe//CV69eoVd955Z5x99tlRv3796NWrV7zwwgvRtWvXVKWu1dlnnx1NmzaNa665Jn7605/G5ptvHocddlj87ne/8/Xu9dC4ceP4zne+E+PHj1/jeEyDBg2Ke++9d43bR4wYEeeee2488cQTcdddd8XWW28dl156adSqVat0xbehDjzwwHjkkUdKp5SPHDkyjj766LjzzjvjrrvuilmzZsXmm28e2267bTz00EPxgx/8YIOf48EHH4zhw4fHiBEj4p577ok2bdrEb37zmzj33HM3qmZYX02bNo2XX345zj333Ljhhhti4cKFse2228Zf//rXOPzww1OXB99o3rx58etf/7rMbc8991w899xzEfHfnRYBJZXV66+/HhER9913X9x3331r3H/HHXcIKKm0ioqK4tRTT40XX3wx/vWvf8WXX34ZDRo0iO222y6uv/76OOmkkxxzmG/Vvn37uP322+Pyyy+Pn/zkJ7FixYo45phjYrfddtug5Rx77LHRpUuXuOqqq+Lmm2+OL7/8Mpo1axbbbLNNXHLJJdGqVasNWp7t44px+eWXx0svvRQ33HBDzJw5M7p06VLm5DWNGjWKG264IX74wx/G/fffH3/9619jxx13jH/84x9x2223bVJAGfHfWd6jRo2KvffeOwYOHBjPPvtstGvXbr1+V4+sJqsmHnvssSwisttvv/0bH/f6669nEZG9/vrrFVRZ1ailvOTja2Ld8vHvnY+vibXLx791Pr4m1i3f/t759nr4Zvn4987H18Ta5ePfOh9fE+tWVf7ed9xxRxYR2XPPPfetj61Mr6ky1VKZVJtjUK46wGiPHj0SVwIAAAAArJL3X/G+7777Yty4cXHdddfFDjvsUGnOgFRcXFx6Fql1WdvJU6h+lixZUuaAv+uyoV83gPUxf/78tR4kfHW1a9eOJk2aVFBFsP70L1WZ/qWq08PkK73Nt/m2HpH1rF3eB5SnnHJKFBQUxCGHHBJXX3116nJKTZ48eYMP2kv1dP/996/XMS+zr51xHMrDGWecEXfdddc3PmbgwIHx/PPPV0xBsAH0L1WZ/qWq08PkK73Nt1mfHmFNeR9Qzps3L3UJa9WqVat4+umnv/ExH374YZxyyikVVBGV1eDBg7+1VyBXfvnLX8ZRRx31jY9p3LhxBVUDG0b/UpXpX6o6PUy+0tuVx5AhQ2LIkCGpy1jDt/WIrGft8j6grKzq1q0be+655zc+xpRwIiJat24drVu3Tl0G1dS2224b2267beoyYKPoX6oy/UtVp4fJV3qbb/NtPSLrWbtqc5IcAAAAAKDyEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJFMzdQGV1cSJE1OXUClqyJV8fm38Tz7/nfP5tfFf+fw3zufXxv/k6985X18XZeXz3zmfXxv/lc9/43x+bfxPPv+dK8Nrqww1VEYCyq9p1qxZFBUVxVFHHZW6lIiIKCoqimbNmqUuo9xUtvEl9/QwVZn+parLpx7Wv9VPPvVvhB6ubvQvVZ0ezq18G9/yUJBlWZa6iMpm0qRJMXv27E1aRv/+/eOMM86IH/7wh5u0nGbNmkX79u03aRmVTXmM77HHHhtbbrllDBs2rHyKoozLL788xo8fH/fff/8mL0sPr+lvf/tbXHzxxfHaa69FjRo1yrEyIiI++OCDOOKII+Luu++O7bbbbpOWpX/XbqeddorzzjsvfvCDH5RTVazuqKOOim7dusUFF1ywycvKtx4uj/699NJLY+LEiTFixIhyqorVPfzww3HZZZfFuHHjNnlZ+da/EZvew++++278+Mc/jvvuuy+6dOlSjpUREVFcXBx9+vSJiy66KA444IBNWpb+XbvDDz88evXqFeecc045VcXqhg0bFp9//nnccccdm7wsPbymv/71r3HttdfG6NGjN7mWfBzfTWUG5Vq0b99+kxulsLAw2rVrF7169SqnqvJHeYzvZpttFk2bNjW+OdKiRYsoKioyvuuwqT381ltvRUREr169BJQ5sGpMu3btqofXojzWwQUFBdG+fXvjmyP169ePZs2aGd+1KI/+bdasWdSvX9/45sjYsWOjoKDA+K7DpvZwcXFxRER069Yttt9++/Iqi/9v1fhuueWWengtymMdXFRUFC1atDC+OdK0adOYM2eO8V2HTe3hl19+OQoLC41vjjhJDgAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgTGzx4sXRsWPHKCgoiNNOOy11OXnH+JavDz74IC688MLo169fNG/ePBo0aBDf+c534tJLL41FixalLq/KM74Vzzoit4xvbhnf3DPG5cd7XG4Z34pn/ZBbxjf3jHFuGd8NJ6BM7MILL4xZs2alLiNvGd/ydfvtt8c111wTnTp1igsvvDCuvPLK2GabbeJXv/pVDBgwIJYsWZK6xCrN+FY864jcMr65ZXxzzxiXH+9xuWV8K571Q24Z39wzxrllfDecgDKh8ePHxx/+8Ie4+OKLU5eSl4xv+Tv00ENjypQp8Ze//CVOP/30GDp0aNx///1xwQUXxIQJE+K2225LXWKVZnwrlnVEbhnf3DK+uWeMy5f3uNwyvhXL+iG3jG/uGePcMr4bR0CZSHFxcZx44omxzz77xCGHHJK6nLxjfHNjp512ioYNG65x++GHHx4REe+8805Fl5RXjG/FsY7ILeObW8Y394xx+fMel1vGt+JYP+SW8c09Y5xbxnfj1UxdQHV1zTXXxPvvvx8PP/xw6lLykvGtWFOmTImIiJYtWyauJD8Z3/JnHZFbxje3jG/uGeOK4z0ut4xv+bN+yC3jm3vGOLeM78YzgzKBTz/9NC666KK48MILY6uttkpdTt4xvhWruLg4LrnkkqhZs2YceeSRqcvJO8a3/FlH5JbxzS3jm3vGuOJ4j8st41v+rB9yy/jmnjHOLeO7acygTGDo0KHRsWPHOOuss1KXkpeMb8U688wzY/To0fHb3/42ttlmm9Tl5B3jW/6sI3LL+OaW8c09Y1xxvMfllvEtf9YPuWV8c88Y55bx3TQCygo2YsSIePrpp+PFF1+MWrVqpS4n7xjfivXrX/86rr/++jjppJPivPPOS11O3jG+5c86IreMb24Z39wzxhXHe1xuGd/yZ/2QW8Y394xxbhnfTSegrEDLli2Ls846K/bbb79o1apVfPTRRxERMXXq1IiImD9/fnz00UfRrFmzaNSoUcJKqybjW7GGDRsWw4cPj2OPPTZuuumm1OXkHeNb/qwjcsv45pbxzT1jXHG8x+WW8S1/1g+5ZXxzzxjnlvEtJxk5Ubdu3ey6664rc9u8efOyiPjWy5VXXpmo6qpjwIAB2ZAhQ8rcZnzLz2mnnZb17NlznfdfdNFFWURkxxxzTFZcXFyBleWH22+/PYuIbOXKlWu93/humjfffDOLiOy1114rc7t1RPkpLCzMbrrppjK3Gd/ys9NOO2UnnXRSmduMb/k56aSTsp122mmN241x+bjpppuywsLCdd7vPW7TvPbaa1lEZG+++eZa7ze+m2blypVZRGS33357mdutH8pPz549s9NOO63Mbca3/AwZMiQbMGDAGrcb4/Jx3XXXZXXr1l3jduNbPsygrED169ePBx98cI3bZ82aFaecckrss88+cfzxx0fPnj0TVFf1Gd+K8Zvf/CYuvvjiOProo+P222+PwkLn2ipPxjd3rCNyy/jmlvHNPWOce97jcsv45o71Q24Z39wzxrllfMuHgLIC1apVKw499NA1bv/ss88iIqJTp05rvZ/1Y3xz74YbboiLLroo2rdvH3vuuWfce++9Ze5v2bJl7LXXXomqq/qMb25ZR+SW8c0t45t7xji3vMfllvHNLeuH3DK+uWeMc8v4lg8BJbDexo4dGxERkyZNimOOOWaN+wcOHGjjdxMYXwDylfe43DK+AFR1AspKYKuttoosy1KXkbeMb/m58847484770xdRt4yvmlYR+SW8c0t45t7xrh8eI/LLeObhvVDbhnf3DPGuWV8N4wDkwAAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkI6AEAAAAAJIRUAIAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkI6AEAAAAAJIRUAIAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkI6AEAAAAAJIRUAIAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASKZm6gKAyqdZs2bRuXPn1GXkrVq1akX//v1Tl5G3CgsLo3///pFlWepS8lKWZdG3b98oKipKXUre2nrrraNx48apy8hbjRs3jq233jp1GXmrqKgo+vbtG1mWRUFBQepy8k6WZdG/f/8oLDTPJFf69+8ftWrVSl1G3urcuXM0a9YsdRl5q2XLlrF8+fLUZeStOnXqRJ8+fVKXkbe8swFrWLRoUUyYMCF1GXlr3rx58frrr0eNGjVSl5KX6tSpE6NHj47FixenLiUvLV26NEaPHp26jLw2ZcqUmDJlSuoy8tbkyZNj6tSpqcvIW1mWxejRo2PZsmWpS8lLixYtitGjR0fdunVTl5KXatSoEePGjYsvv/wydSl566233rKNlkMLFiyId999N3UZeWvu3LnxzjvvpC4jbwkogTU0aNAgFi5cmLqMvLVw4cJo0KBB6jLy1qqx1cO5sWpc9XDuWAfnlnVwblkH55Z1cO5ZB+eWdXBu6d/c0r+5JaAE1rDZZpvFggULUpeRtxYuXBibbbZZ6jLy1qqxtXGWG6vGVQ/nzmabbaZ/c8g6OLesg3PLOjj3rINza8GCBfo3h/Rvbunf3BJQAmto2bJlLFq0KObOnZu6lLw0efLkaNmyZeoy8lb9+vWjfv36MWnSpNSl5KXJkydHROjhHGrZsqX+zSHr4NxaNbZ6ODcmT54c9evXdxzgHLIOzp05c+bE4sWLrYNzqEWLFjF79mxfo88R2xC5JaAE1tC7d++IiHjttdcSV5KfxowZUzrGlL/CwsLYcccd9W+OjBkzJoqKiqJbt26pS8lbvXv3jg8//DDmzZuXupS8M3fu3Pjwww+tg3No2223jaKiIuvgHBkzZkzstNNOTpKTQ71799a/ObJqXK2Dc6d3795RXFwc48ePT11KXrIfl1ve2YA1dO7cOZo0aRJjxoxJXUremTdvXnzwwQfRt2/f1KXktb59++rfHFm1c1yzZs3UpeStVesHO8jlb9WYWgfnTs2aNWPHHXe0Ds6RMWPG6N8c69u3b/znP//xIVEOjBkzJpo2bRqdOnVKXUre6tGjR9SrV886OAemTJkS06ZNsw7OIQElsIaCgoLo06ePneMcGDt2bETYOc61vn37lm5EUL5ee+01/ZtjW2+9dTRu3Ng6OAdee+21aNKkSXTu3Dl1KXmtb9+++jcHpk6dGlOnTrUOzrFV4ztu3LjEleSf1157Lfr06RMFBQWpS8lbqz4ksg4ufz7kzD0BJbBWq2agZVmWupS8MmbMmGjcuHFsvfXWqUvJa2ag5cb06dNj8uTJNsxybNWHRGY/lL8xY8bYOa4Affv2jUmTJsWMGTNSl5JX7BxXjK233joaNWpkHVzOsizzIWcF8U2i3BgzZky0bds2tthii9Sl5C0BJbBWffv2jTlz5sQnn3ySupS8Yue4YqzaeLBxVr5Wjaedi9zzIVH5y7LM12MryKoxtg4uX2PGjIk2bdpEmzZtUpeS1woLC31IlAMff/xxzJkzxzq4AvTt2zc+//zzmDlzZupS8optiNwTUAJr1adPn4iIGDVqVOJK8sfixYvjlVde8cZWQfr27RujRo0S8JSjZ555JrbYYgs7xxWgb9++MXv27JgwYULqUvLGhAkT7BxXkLZt20br1q1tQ5SjLMvimWee0b8VpG/fvvHyyy87E3I5euaZZyLCCXIqwqr1hHVw+Zk/f36MHTvWOjjHBJTAWjVt2jQOPvjguPLKK2PlypWpy8kLN998c3z11VdxzDHHpC6lWjjhhBNi3LhxpRvEbJoZM2bEbbfdFieccIIZwBVgzz33jPbt28dll12WupS88dvf/ja23HLL2GOPPVKXkvcKCgrihBNOiNtuu80MnnIyatSoGDduXJxwwgmpS6kWjjnmmPjqq6/illtuSV1KXlixYkVcccUVccghh0TTpk1Tl5P32rdvH3vttVf87ne/i5KSktTl5IXrrrsuiouL40c/+lHqUvKagBJYpwsvvDA+/vjjGDFiROpSqrzFixfH5ZdfHsccc0x07NgxdTnVwr777hu9e/eOYcOGmUVZDq688sqoVatWnHnmmalLqRZq164dF1xwQTzwwAPx7rvvpi6nynvnnXfiwQcfjAsuuCBq166dupxq4cwzz4xatWrFlVdembqUKi/Lshg2bFj06dMn9tlnn9TlVAudOnWKH//4x3H55ZfHkiVLUpdT5Y0YMSI++eSTuPDCC1OXUm1cdNFF8c4778QjjzySupQqb/78+XH11VfHySef7PiTOSagBNbpO9/5Thx88MExfPhwsyg30c033xyzZ8+OCy64IHUp1UZBQUEMGzYsXn75ZbMoN9GMGTPiT3/6U5x55pnRuHHj1OVUG0OGDIl27drFJZdckrqUKu+SSy6J9u3bm8FegZo0aRJnnHFG3HjjjWZRbqJRo0bFK6+8EsOGDTODvQJdcMEFMWvWrLj55ptTl1KlrVixIoYPHx6HHHJIbL/99qnLqTZ23nnn2GuvveLiiy82i3ITXXfddbFkyZI455xzUpeS9wSUwDcyi3LTmT2ZjlmU5cPsyTTMoiwfZk+mYxblpjN7Mh2zKMuH2ZPpmEW56cyerFgCSuAbrZpFeckll8SyZctSl1Ml3XjjjWZPJrL6LMqRI0emLqdKmjZtmtmTCa2aRXnRRRelLqVKWhXumD2ZxuqzKKdNm5a6nCpp5MiRZk8mtGoW5Q033JC6lCpp6dKlZk8mtPosyhUrVqQup0q65pprzJ6sQAJK4FtdfPHFMW3atDj66KOjuLg4dTlVyqhRo+L888+Pn/zkJ2ZPJrLvvvvGbrvtFkcffXR88MEHqcupUhYsWBD7779/NGzY0OzJRGrXrh2XXnppPPzww3HVVVelLqfKueqqq+Lhhx+O4cOHmz2ZyM9+9rNo2LBhHHDAAbFgwYLU5VQpH3zwQRx99NGx2267mT2ZSKdOnWLo0KFx/vnnO1zMBiouLo6jjz46pk2bFsOGDUtdTrU1fPjweP/99+PEE0/0baIN9MQTT8Qll1wSP/vZz8yerCACSuBb9ejRI+677754+OGH48wzz/Tmtp7eeOONOPjgg2PQoEFx9dVXpy6n2iooKIiHHnoomjVrFoMHD44ZM2akLqlKWL58efzgBz+Ijz76KJ566imzJxM66qij4vzzz49f/OIXDrexAe6555745S9/GRdccEEcddRRqcuptho3bhz//Oc/44MPPohDDz00li9fnrqkKmH69OkxePDgaN68eTz88MNmTyZ0zTXXxKBBg+Lggw+ON954I3U5VUKWZXHGGWfEI488Evfdd1/06NEjdUnVVp8+feLOO++Mu+66y7e5NsArr7wShx12WBx00EExfPjw1OVUGwJKYL0cdNBBccMNN8T1118fl19+eepyKr1PPvkk9t133+jWrVs89NBDUatWrdQlVWtNmzaNkSNHxvLly2PfffeNr776KnVJlVpJSUkcd9xx8cILL8Rjjz3ma1mVwPDhw+PYY4+NY489Nv71r3+lLqfSGzlyZBx33HFx3HHHOclQJfCd73wnHnvssXj++efjuOOOc8KGbzF//vzYd999Y8WKFfHUU09FkyZNUpdUrdWqVSseeuih2GabbWLfffeNTz/9NHVJld7vfve7uOGGG+LGG2+Mgw46KHU51d6PfvSjuOqqq+Kyyy6LP/7xj6nLqfQmTpwY+++/f/Tu3Tv+8pe/RI0aNVKXVG0IKIH1NnTo0LjwwgvjvPPOizvvvDN1OZXWF198EYMHD44GDRrEk08+GZtttlnqkoiI9u3bx1NPPRWffvppHHLIIY6p+g3OOeecuPfee+Oee+6J3XffPXU5xH9nAt9yyy0xePDgOOSQQ2LcuHGpS6q0xo0bFz/4wQ9in332iZtvvtnMs0pi0KBBcc8998S9994b5557bupyKq1ly5bFIYccEp999lk89dRT0b59+9QlERGbbbZZPPnkk9GgQYMYPHhwzJo1K3VJldadd94Z559/flx00UVx8sknpy6H/+/ss8+Os88+O84444x48MEHU5dTaU2dOjX22Wef2GKLLeLxxx+PunXrpi6pWhFQAhtk2LBhceKJJ8YJJ5wQV1xxhWNSfs2bb74ZgwYNigULFsTIkSOjefPmqUtiNT169Ii//e1v8e9//zu+973vxeTJk1OXVKksXLgwTj311Ljqqqvi2muvjcMOOyx1SaymZs2acf/990f37t1jn332icceeyx1SZXOo48+Gvvss0/06NEj7r///qhZs2bqkljNYYcdFn/4wx/iyiuvjFNPPTUWLVqUuqRKZfLkyfG9730vXn755fjb3/4W3bt3T10Sq2nRokWMHDkyvvrqq9h9993jzTffTF1SpVJcXBxXXHFFnHDCCXHSSSc5uVsldMUVV8SRRx4ZRx11VFx33XVms3/NmDFjYvfdd48sy+Kf//ynwxslIKAENkhBQUHceOONceaZZ8a5554bO++8c7z//vupy0pu+fLlcdFFF0Xv3r2jRo0a8cwzzzgpTiW16667xt///veYOHFidO/ePW677TbHVY2IF154IXr27Bl33HFH3HDDDXH66aenLom1qF+/fjz55JOx8847x8EHHxw/+tGPYs6cOanLSm7OnDlx5JFHxiGHHBI777xz/P3vf4+ioqLUZbEWP/3pT+OGG26IO+64I3r27BkvvPBC6pKSy7IsbrvttujevXu8//778eSTT8auu+6auizWomPHjvHMM89EjRo1onfv3jFs2DDHVY2I999/P3beeec499xz48wzz4wbbrjB7PVKqLCwMG6//fY48cQT44wzzojdd989Pv7449RlJbd06dI455xzYsCAAdGwYcN45plnom3btqnLqp4ycqJu3brZddddl7qMvDVgwIBsyJAhqcuo9l5++eWsS5cuWZ06dbIrrrgiW7lyZeqSknjjjTey7bffPqtZs2Z24YUXZsuWLUtdEuth3rx52ZAhQ7KIyPbZZ59s8uTJqUtKYuHChdlpp52WRUT23e9+N/vwww9Tl8R6KCkpye65556scePGWcuWLbPHHnssdUnJPProo1nLli2zxo0bZyNGjMhKSkpSl8R6+PDDD7Pvfve7WURkp59+erZw4cLUJSUxadKkbJ999skiIjv22GOzefPmpS6J9bBs2bLswgsvzGrWrJltv/322RtvvJG6pCRWrlyZXXHFFVmdOnWyLl26ZC+//HLqklhPzz77bNahQ4esqKgou+6667Li4uLUJSUxZsyYrFu3blnt2rWz3/72t9mKFStSl1StCShzRECZWwLKymPx4sXZ2WefnRUUFGT9+vXL3n777dQlVZhFixZlF110UVazZs2sZ8+e2fjx41OXxEZ48sknsy222CLbfPPNsz//+c/VZsOkpKQkGzVqVNaxY8esXr162bXXXlttN06rsmnTpmX7779/FhHZkUcemc2YMSN1SRVmxowZ2ZFHHplFRHbAAQdk06ZNS10SG6i4uDj7wx/+kNWrVy/r2LFjNmrUqGoTMK9YsSL785//nG2++eZZmzZtsieffDJ1SWyE8ePHZz179sxq1qyZDRs2LFu0aFHqkirMhAkTsr59+2YFBQXZ2WefnS1evDh1SWygBQsWZKeeemoWEdnAgQOz999/P3VJFWbBggXZOeeckxUWFmY77bRTtdqHrcwElDkioMwtAWXls2o25ao3uHvvvTdbunRp6rJyYsKECdlpp52WNWzY0KzJPLH6bMo2bdpkF154Yfb5559XyHOXlJRkH374Yfbqq69mH374Yc53zufNm5ddf/31WY8ePcyazBOrz6asVatWdthhh2WjRo2qkMC5ovu3uLg4GzVqVPZ///d/Wa1atcyazBOrz6bs2bNndv3111fITMKK7t8sy7LPP/88+/Wvf51tscUWZk3midVnUzZs2DA77bTTsgkTJqQuKyeWLFmS/eUvf8kGDhyYRYRZk3li1WzKiMj23HPP7IEHHsjbfZvXX389O/nkk7PNNtvMrMlKSECZIwLK3BJQVk5Lly7N7r333my33XbLIiJr2rRpdtZZZ+XFp3GLFi3K7rjjjqxfv35ZRGStWrXKzj///OyTTz5JXRrlaPz48dnQoUOzBg0aZAUFBdl+++2XPf744znZcCkpKcnuvvvurGfPnllElF569uyZ3X333eW6o1xSUpK98sor2ZAhQ7J69eplNWrUyA455JDsqaeeMmsyj8yZMyf7wx/+kHXr1i2LiKxz587Z5Zdfns2cObPcn6si+zfLsmzmzJnZ7373u6xTp05ZRGTdunXL/vCHP2Rz5swp1+chneLi4uypp57KDj744KxGjRpZvXr1smOPPTYbPXp0ufdTRffvihUrssceeyzbb7/9soKCgqxBgwbZ0KFDffMiz3zyySfZeeedl7Vs2TKLiKx///7ZHXfckRezKidOnJidddZZWdOmTbOIyHbbbbe8noxQHS1evDi76667sp133jmLiKxFixbZOeeckxcfYn/11VfZLbfcku20005lJiNMmjQpdWl8jYAyRwSUuSWgrPzef//97Oyzzy7dkBk4cGB24403Zm+88UaV+ZRq+vTp2aOPPpqdcsopWcOGDbOIyPbee+/s4YcfzpYvX566PHJowYIF2Z///OfSDZktttgiO++887KRI0dmX3755SYvv6SkpPS4j4WFhWV2kFddP+200zZpJ3nJkiXZyy+/nF1++eWlsyW32mqr7NJLL/VV2DxXUlKSvfTSS9lRRx2V1alTJ6tVq1b2f//3f9ndd99dLrPEKqJ/V81su/vuu0tnS9apUyc7+uijs5deesmMyTw3bdq0bPjw4dmWW26ZRUTWo0eP7PLLL89efvnlbMmSJZu07Iro3yz772z1kSNHZuedd17pbMnevXtnf/7zn7MFCxZs0rKp3JYvX5499NBD2d57751FRNawYcPslFNOyR599NEqcxiOFStWZG+88UZ24403ls6WbNq0aXb22WfnxcQDvtnbb7+d/fSnP80aNWqURUS2xx57ZDfddFP21ltvVZlzDkydOjV76KGHspNOOinbbLPNssLCwux73/te9re//a3K7ItWRwVZ5tSluVCvXr244oornAU1R3beeefo0qVL3HHHHalL4VssXbo0Hn300bjlllvi3//+d6xcuTLq168fffr0if79+0f//v2jX79+0axZs6R1rlixIt56660YPXp06eWzzz6LiIh27drFUUcdFSeeeGJ06NAhaZ1UvPHjx8ef//zneOCBB2Lu3LlRUFAQ2267bfTr16+0h7t27RqFhYXrvcx77rknfvzjH3/r4+6+++44+uijv/VxWZbF5MmTS3v31VdfjfHjx8eKFSuiXr16sc8++8TJJ58ce+211wbVSdU3d+7cuOeee+L222+PCRMmRERE8+bNy/Rv7969o379+uu9zPLu34iIRYsWxdixY8v08KxZsyIiomfPnnHcccfF0UcfHU2aNFnvOqn6SkpK4umnn46bb745nnrqqViyZEnUqlUrevXqVdq//fv3j3bt2q33MnPRvyUlJfH++++X2YaYOHFiZFkWTZo0icMOOyxOOumk2GGHHda7TvLDJ598ErfeemuMGDEiJk+eHBERHTp0KLMO3n777aNWrVpJ65w9e3aZ9e9rr70WixYtipo1a8Yuu+wSJ510UhxyyCFRp06dpHVSsZYsWRIPPvhg/PnPf47Ro0dHcXFxNGjQYI39uNTvzcuXL48333yzzDp40qRJERGx1VZbxY9//OM4/vjjo3379knr5NsJKHNEQJlbAsqqafHixfH666+XefOYOXNmRER07tw5evXqFW3atIlWrVpF69ato1WrVqU/N2nSZJOClcWLF8eMGTNixowZMX369DL/fvDBBzFu3Lhy2fEhf2VZFh988EGZ/n3nnXciy7Jo1KhR9OnTJzp06LBG7676uXbt2qXL2X777ePdd9+NkpKSdT5fYWFhdO/ePd58880oKSmJWbNmrdG706dPjylTpsRrr70W06ZNi4j/7vis3r89e/ZMvuND5TB37tx49dVX49VXX43Ro0fHmDFjYsGCBVGjRo3o2bNndO/evbRnv/7v5ptvHgUFBRvVvxERX3311VrXv9OnT4933nknJkyYULrj07dv39Kdnsqw40PlsGLFipgwYUKZdfCnn34aERFt2rSJ3r17R9u2bdfaw82bN48aNWpsVP8WFBTE8uXL19iGWPXzp59+Gq+99lp8+eWXUVBQEN27dy+zDu7SpUsUFBRU1DBRia3+QeLo0aPLfJC40047RZcuXda6/m3VqlUUFRVt9POWlJTE3Llz11j/zpgxI6ZOnRrjx4+Pjz76KCIiWrZsWaZ/d9xxx016bvLH1z9IHD16dMyePTsiIrbZZpvYYYcdYosttlhrDzdp0mST1oOLFi1a6/p3xowZ8f7778frr78ey5Ytizp16sSOO+5YJjxt06ZNeQ0BFUBAmSMCytwSUOaHLMvis88+K/209u233y59w5k/f36Zx9asWbN0I61BgwZRu3btqFWrVum/TZo0iVmzZsWKFSti+fLlsWLFili6dGl88cUXMWPGjPjqq6/KLK9WrVqly9tqq61Kd4R79eoVdevWrchhoApbsGBBvPbaazF69OgYO3ZsTJ48OWbMmBEzZ85cY+e3SZMm0bp16ygqKoqxY8eu93M0a9Ys5s6du8bymjVrVrrxt8MOO5RuiLVq1apcXhv5r7i4ON57773SHY0PP/wwpk+fHtOnT48lS5aUeWy9evWiVatW0bBhw9LQcX20bds25syZs9bltW7dOlq3bh1bb7116c7EtttuGzVq1CiPl0c1MGPGjNLA/Y033ijdYV2107xKYWFhNG/ePJo0aRITJ05c7+VvvfXWMWfOnJg7d+4ay2vZsmW0atUq2rVrF717947+/ftHnz59okGDBuXy2sh/S5cujfHjx5d+cPTZZ5+Vhi8rVqwo89jNN988WrVqFS1atIi6deuW2QZu3rx5zJ07t8w28PLly2PBggWly1u5cmWZ5TVs2LA0POrRo0fpjM6tttpKoM56ybIsPvnkk9JtiHfffbd0P+6b9rsaNGhQpn9r1aoVDRs2jLlz55b276r9uJkzZ8aMGTNiwYIFZZZXu3bt0m3gVftx/fv3j+985ztm+VZxAsocEVDmloAy/y1ZsiRmzpy51tk2ixYtiuXLl5fZCGvVqlXMmjUrateuXfqGV6dOnWjevPlaP4ne1E/y4JsUFxfH7Nmz1/pp73/+858YOXLkei/rxBNPjN69e5eZldmiRYvSGZlQ3rIsi4ULF651ts3bb78dTz755Hov68c//nH06tVrjRkVm222mXUwObN8+fL44osv1lj/vvXWW/HII4+s93IOO+yw2GGHHdaYFd+sWTNBOjmTZVnMnTt3rdsQs2bNimXLlpVu/y5fvjyaNWsWM2fOLPPhfe3ataN+/fprnVHcsmXLqFevXuqXSR5b/Ztra9uP+3qY3qxZs/jyyy/LBJd16tSJFi1arHU/rnHjxrYh8pSAMkcElLkloASqqo8++ii23nrr9X78hx9+GJ07d85hRbD+9C9Vmf4FgMrLkfIBoAJ16tQpevbs+a3HVC0sLIztt98+OnXqVEGVwbfTv1Rl+hcAKi8BJQBUoIKCgvj5z3/+jSdoiPjvQe3PPvtsX2GhUtG/VGX6FwAqLwElAFSwo446Kk477bSIiDV2gFfN7DnttNPiqKOOqvDa4Nus3r9fn4mmf6ns9C8AVE4CSgCoYAUFBXHdddfF3XffHd26dStzX/fu3eOee+6J6667zuwdKqXV+7d79+5l7tO/VHb6FwAqJyfJyREnycktJ8kB8sXMmTOjVatWccUVV8TBBx8cnTp1smNMlZFlWey5556xfPnyuOOOO/QvVUqWZXHLLbfE0KFD49VXX40+ffroXwBIpGbqAgCgOlu1M7zNNts4WyxVTkFBQdSvXz/q16+vf6lyCgoKonXr1hER0aFDB+EkACTkK94AAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkI6AEAAAAAJIRUAIAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkI6AEAAAAAJIRUAIAAAAAyQgoAQAAAIBkBJQAAAAAQDICSgAAAAAgGQElAAAAAJCMgBIAAAAASEZACQAAAAAkUzN1AQBQnWVZFgMHDoxatWqlLgU2Svv27VOXAButVq1aMXDgwMiyLHUpAFCtmUEJAAkVFBTECy+8ECtWrEhdCmyUSZMmxaRJk1KXARtlxYoV8cILL0RBQUHqUgCgWhNQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAVBGLFy+Ojh07RkFBQZx22mmpy4ENon+pSj744IO48MILo1+/ftG8efNo0KBBfOc734lLL700Fi1alLo8AMg7AkoAqCIuvPDCmDVrVuoyYKPoX6qS22+/Pa655pro1KlTXHjhhXHllVfGNttsE7/61a9iwIABsWTJktQlAkBeEVACQBUwfvz4+MMf/hAXX3xx6lJgg+lfqppDDz00pkyZEn/5y1/i9NNPj6FDh8b9998fF1xwQUyYMCFuu+221CUCQF4RUAJAJVdcXBwnnnhi7LPPPnHIIYekLgc2iP6lKtppp52iYcOGa9x++OGHR0TEO++8U9ElAUBeq5m6AADgm11zzTXx/vvvx8MPP5y6FNhg+pd8MmXKlIiIaNmyZeJKACC/mEEJAJXYp59+GhdddFFceOGFsdVWW6UuBzaI/iWfFBcXxyWXXBI1a9aMI488MnU5AJBXzKAEgEps6NCh0bFjxzjrrLNSlwIbTP+ST84888wYPXp0/Pa3v41tttkmdTkAkFcElABQSY0YMSKefvrpePHFF6NWrVqpy4ENon/JJ7/+9a/j+uuvj5NOOinOO++81OUAQN4RUAJAJbRs2bI466yzYr/99otWrVrFRx99FBERU6dOjYiI+fPnx0cffRTNmjWLRo0aJawU1qR/ySfDhg2L4cOHx7HHHhs33XRT6nIAIC85BiUAVEJLliyJWbNmxZNPPhlbb7116WW33XaLiP/OTtt6663j1ltvTVsorIX+JV8MGzYsLr744jjmmGPi1ltvjYKCgtQlAUBeMoMSACqh+vXrx4MPPrjG7bNmzYpTTjkl9tlnnzj++OOjZ8+eCaqDb6Z/yQe/+c1v4uKLL46jjz46br/99igsNLcDAHJFQAkAlVCtWrXi0EMPXeP2zz77LCIiOnXqtNb7oTLQv1R1N9xwQ1x00UXRvn372HPPPePee+8tc3/Lli1jr732SlQdAOQfASUAAMBqxo4dGxERkyZNimOOOWaN+wcOHCigBIByJKAEgCpkq622iizLUpcBG0X/UlXceeedceedd6YuAwCqDQdSAQAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQEmV1KVLl6hfv37qMgA22dKlS6NXr16x2WabpS4FAAAgiZqpC4CN8dVXX8XUqVNTlwGwyebMmRPjx4+PBg0apC4FAAAgCTMoqZK6desW48aNi+XLl6cuBWCTvPLKK1GzZs3o1KlT6lIAAACSEFBSJR1++OExb968GDlyZOpSADbJvffeG4MHD44mTZqkLgUAACAJASVVUo8ePaJ79+5x3333pS4FYKN99tln8corr8QRRxyRuhQAAIBkBJRUWUceeWQ8+uijMXHixNSlAGyUyy+/PIqKiuLAAw9MXQoAAEAyAkqqrJ/+9KfRoUOHOOyww2Lx4sWpywHYIA8++GDcdNNN8fvf/94ZvAEAgGpNQEmVVb9+/XjggQfi448/jp/+9KepywFYbx999FEcf/zxcfjhh8fJJ5+cuhwAAICkBJRUad27d48bbrghbrvttrj00ksjy7LUJQF8o88//zwOPPDAaNmyZdxyyy1RUFCQuiQAAICkaqYuADbVkCFD4vPPP49f/epXMXHixPjzn/8c9erVS10WwBpefvnlOPjgg6N+/frxj3/8IzbffPPUJQEAACRnBiVVXkFBQQwbNiz++te/xsMPPxwDBw6MadOmpS4LoIzbb789dt999+jWrVu89tpr0a1bt9QlAQAAVAoCSvLG4YcfHv/+979j2rRp0atXr7jrrruipKQkdVlANTdlypQ46qij4vjjj48hQ4bE008/Hc2bN09dFgAAQKUhoCSv7LjjjjF27NjYddddY8iQIdG3b994+eWXU5cFVEOLFy+O3/zmN7HNNtvEv/71r7j99tvj5ptvjtq1a6cuDQAAoFIRUJJ3WrduHQ888EC8+OKLUVJSErvsskscccQR8dlnn6UuDagGSkpK4r777ouuXbvG8OHD49RTT40PP/wwjj32WCfEAQAAWAsBJXnru9/9bowdOzZuv/32eP7556Nz585x6KGHxnPPPeds30C5mzt3bvz+97+PLl26xJFHHhk77rhjvPfee3HFFVdEw4YNU5cHAABQaQkoyWuFhYVx7LHHxocffhjXXnttvPfeezFo0KDo3r173HjjjbFgwYLUJQJV3BtvvBEnnHBCtG3bNs4777zo169fjB49Oh599NHo3Llz6vIAAAAqPQEl1cJmm20Wp556arz77rvxzDPPRNeuXeP000+PNm3axNChQ+Ppp5+OFStWpC4TqCKmTp0aN9xwQwwYMCB69eoVI0eOjAsuuCAmT54cI0aMiH79+qUuEQAAoMqomboAqEgFBQUxaNCgGDRoUEyePDluvvnmGDFiRNx8883RuHHj+P73vx8HH3xwDB48OIqKilKXC1QiH3zwQTz66KPx6KOPxpgxY6JmzZqxxx57xMMPPxwHHHBA1KzpLRUAAGBj2Jui2mrXrl0MHz48LrnkknjzzTfjkUceiUcffTTuueeeqFevXuyzzz5x0EEHxaBBg6Jt27apywUq2PLly2PcuHHxz3/+Mx599NF49913S9cNd999d3z/+9+Pxo0bpy4TAACgyhNQUu0VFBTEDjvsEDvssENccsklZWZJHXPMMRER0aFDhxg4cGDpZauttnI2XsgzS5cujTFjxsQLL7wQL774YrzyyiuxZMmSaNSoUey///5xySWXmF0NAACQAwJK+JouXbrEOeecE+ecc07MmjUrXnzxxXjxxRfjhRdeiLvuuiuyLIt27drFrrvuGgMHDow+ffpE165do06dOqlLBzbAF198EW+++Wa89NJL8eKLL8aYMWNi2bJl0bBhw/jud78bv/nNb2LgwIGxww47+Po2AABADtnjgm/QvHnz+MEPfhA/+MEPIiJi3rx5pWHGCy+8EPfdd1+UlJREzZo1o2vXrtGzZ8/SS48ePaJNmzZmWkJiS5cujYkTJ8aECRPKXL744ouIiGjatGnsuuuucfnll8fAgQOjR48eUaNGjcRVAwAAVB8CStgAjRs3jgMOOCAOOOCAiIhYsGBBvP3222VCjyeeeCIWLFhQ+vjVQ8uePXvGdtttF/Xr10/5MiAvZVkWkydPXiOI/OCDD6K4uDgiIjp16hQ9evSIoUOHln6Q0Llz5ygsLExcPQAAQPUloIRN0KBBgxgwYEAMGDCg9LYsy+Lzzz+PCRMmlIaXTz/9dNxwww1RUlISBQUF0alTp+jZs2d06tQp2rVrF+3atYv27dtHu3btolmzZmZdwjosW7YspkyZEpMnT47JkyfHpEmTYtKkSfHee+/F22+/HfPnz4+IiEaNGkWPHj1i0KBBceaZZ5Z+ONCgQYPErwAAAICvE1BCOSsoKIitttoqttpqq9KZlhERS5Ysiffee69McPnII4/E5MmTY/ny5aWPq1u3brRt27Y0sPx6gNmuXbvYfPPNU7w0yKni4uKYPn16afi4KoBc/frMmTPL/E7Tpk2jXbt20bVr19hvv/1KZyq3bdtW0A8AAFBFCCihgtSrVy923HHH2HHHHcvcXlJSErNmzVprGPOf//wnRo0aFdOnT4+SkpLS39l8883XCC1Xv962bduoW7duRb9EWKcsy2L27NnfGD5OnTq19KvYERH169cv7entt98+9t9//zL93q5dO2fUBgAAyAMCSkissLAwWrZsGS1btoyddtpprY9ZsWJFTJs2ba3hztixY+ORRx6J2bNnl/md5s2bR8uWLaNRo0all8aNG3/r9c0339zx+FinpUuXxpdffll6mTdv3lp/Xv36vHnzYurUqbF06dLS5dSqVat0pnCHDh1i1113XSN0b9SokVmQAAAA1YCAEqqAWrVqxZZbbhlbbrnlOh+zePHiNY7NN3v27NKw6LPPPos333yz9PrChQvXupyCgoLYfPPN1zvQ/Pr1oqIiAWcllWVZrFixIr766qtvDRXXdX31kHF1NWrUWGtPdOjQIRo1ahRbbLFFmQCyRYsW+gQAAICIEFBC3igqKoouXbpEly5d1uvxK1asiPnz5693MPX++++XuW/ZsmXrXHbNmjWjTp06G3SpXbv2Bv/ON11q1KgRBQUFUVhYuMa/a7ttbf+uPnsvy7IoKSlZ49+13bauf1esWBHLli1Ldlm+fHlkWbbOv1vDhg3XCBm7du36jcH0qp/r169vtiMAAAAbRUAJ1VStWrWiWbNm0axZs436/bV91XfevHmxZMmSDQrMli5dGvPnz1+vx69+fMKK0qtXrxg/fnyFP++GBLYNGjSIZs2arXfwuyqIXD1kbNCgQdSoUaPCXycAAAAIKIGNUrdu3WjVqlW0atWqwp6zuLh4vYPPb5vduD4zHktKSqJOnTqxfPnyb5xtub4zMmvVqrVeIWLt2rXNRgQAAKDaEFACVUaNGjWiqKjImZsBAAAgjzhDAQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMnUTF1AvmrQoEFkWZa6DACAnKpZs2bUqFEjdRkAAFRhZlDmSHFxcSxYsCB1GQAAOTVnzpyoWdNn3gAAbDwBZY7suOOO8fzzz6cuAwAgZxYtWhSvvfZa7LjjjqlLAQCgChNQ5shBBx0Uzz//fLz77rupSwEAyIk777wzli5dGgcddFDqUgAAqMIElDkyZMiQ6NKlSxx++OGxZMmS1OUAAJSrd955J37+85/HySefHJ07d05dDgAAVZiAMkeKiori/vvvj48//jhOOeWUKC4uTl0SAEC5mDt3bhx++OHRuXPnuOaaa1KXAwBAFSegzKHu3bvHn/70p7jrrrti0KBBMXny5NQlAQBskueffz569uwZ06dPj/vvvz/q1auXuiQAAKo4AWWODRkyJJ577rn45JNPYvvtt4+HH344dUkAABtsxYoVccEFF8SgQYNi6623jrfeeiu23Xbb1GUBAJAHBJQVYODAgfHWW2/FoEGD4tBDD40TTzwx5s+fn7osAID18p///Cd22WWXuOKKK+LSSy+NUaNGRbt27VKXBQBAnhBQVpAmTZrEgw8+GLfeemvce++90bZt2/jZz34Wn376aerSAADWkGVZvPTSS3HIIYdEt27dYs6cOfHyyy/HeeedFzVq1EhdHgAAeURAWYEKCgri+OOPj48++ijOOOOMuPvuu6Nz585x6KGHxiuvvBJZlqUuEQCo5lasWBH33Xdf9OnTJ3bdddd4//334+abb4633347+vTpk7o8AADykIAygdatW8fw4cNj8uTJccMNN8Tbb78dO++8c/Tv3z/++te/xuLFi1OXCABUMzNmzIgrr7wyOnbsGEceeWQ0atQo/vGPf8Q777wTJ554opPhAACQMwLKhIqKimLo0KExceLEeOKJJ6KoqCiOOOKIaNq0aRxwwAFx2223xcyZM1OXCQDkoSzL4r333ovLLrss+vXrF61bt45f/epXsddee8Vbb70VTz/9dOy7775RWGhzEQCA3CrIfK+4Uvnwww/j8ccfj8cffzxefvnliIjo379/HHDAAXHggQdG165dE1cIAFRVxcXF8corr5Rua3z00UdRv379GDx4cBxwwAHxve99L5o1a5a6TKgwf/vb3+LAAw+MmTNnRosWLVKXAwDVloCyEps1a1b8/e9/j8cffzz+9a9/xZIlS6JLly6x//77x3e/+93o37+/DSkAYJ2yLIuPP/44XnnllXj22Wfj73//e8yZMydatmxZ+uHnHnvsEXXr1k1dKiQhoASAykFAWUUsWbIkRo0aFY8//nj885//jGnTpkVEROfOnWPAgAExYMCA6N+/f2y33XbOrAkA1dTixYtj3LhxMXr06HjllVfilVdeidmzZ0dERPfu3WP//fePAw44IPr06eOr2xACSgCoLASUVVCWZTFp0qQyOx9vvvlmFBcXR4MGDaJfv37Rv3//GDBgQPTt2zcaNWqUumQAIAcmT54cr7zySuk2wRtvvBErV66MzTbbLPr27Vv6IWbfvn2jcePGqcuFSkdACQCVg4AyTyxatCjGjRtXGliOHj065syZExERHTp0iO7du0f37t1ju+22i+7du0fXrl2jTp06iasGANbHl19+Ge+++2688847pf++8847MWvWrIiI6NSpU+mHkwMGDIju3bv7RgWsBwElAFQOAso8lWVZfPjhh/Hqq6/G22+/XbojM2XKlIiIqFGjRmy99dZlQsvu3btH586do2bNmomrB4DqadGiRTFx4sTS9+1VYeTa3r+7d+8e22+/ffTv3z9atmyZuHKomgSUAFA5SKLyVEFBQXTp0iW6dOlS5vYvv/wy3nvvvdIdn3feeSduvPHG0hkYtWvXjq5du0bXrl2jY8eOZS7t2rUTXgLAJlq8eHF89tln8cknn5RePv7445g4cWJ88sknseqz41XfgDj66KNLA8ltttnGNyAAAMg70qZqplGjRqVf/1rdF198UeYrYx988EG8+uqrMWXKlCgpKYmI/87aaN++/RrBZYcOHaJjx47RpEmTKCgoSPGyAKDSKCkpiWnTppWGj59++mmZMHLGjBmlj61Tp0506NAhOnToEAceeGBpENmtW7fYbLPNEr4KAACoOL7izTdavnx5TJo0aZ07WV9++WXpYzfffPPSwLJt27bRqlWraN26dbRu3br05+bNmztrKABV1vLly2PGjBkxY8aMmD59ekyfPr3058mTJ5e+Vy5fvrz0d7bYYovSD/O+/uFe69atvS9CQr7iDQCVgxmUfKPatWtH586do3Pnzmu9f968eWVCy1U/P/fcczF9+vTSE/WsUqNGjWjRosUaweXafq5bt25FvEQAqrksy2LBggVrBI5r+3lt72stW7aMVq1aRdu2bWO//fYrE0ButdVWUa9evUSvDAAAqgYBJZukcePG0bhx4+jVq9da71++fHnMnDlznTt6EyZMiJEjR8aMGTNi5cqVZX63UaNG3xpitm7dOho1auSr5QCsobi4OGbNmvWNgeOqn5csWVLmd4uKikrfb1q3bh3dunVb63tR06ZNnS0bAAA2kYCSnKpdu3a0a9cu2rVr942PKykpiblz565z53HKlCkxbty4mD59eixYsGCN52jUqFGZS8OGDdf7ev369QWcAJVQSUlJfPXVV/Hll1+WXubPn79B11cdR3mVZs2alYaMnTp1il122WWtH341aNAg0asGAIDqR0BJpVBYWBjNmjWLZs2aRY8ePb7xsYsWLSoNLlcdB+zrO6Tz5s2Lzz77rMxtK1asWOvyatSosUGB5tevb7755o4fBrAWK1as2OBAcfXrX3311TqXXVRUtMa6uWXLlrHNNtuUXm/cuHGZ8LFFixZRu3btChwBAABgfQgoqXLq168fnTp1ik6dOq3372RZFkuWLPnWHePVb5s+fXqZ61//+t8qBQUF0aBBgw0KOBs0aBD16tUrvdStW7f0X2EnkFJxcXEsWbKk9LJ06dJYsmRJLF68OObPn79BAePixYvX+TyrrzdXrSO33HLL9VqPNmzYUNAIAAB5REBJtVBQUBBFRUWlxxTbGMuWLSvd8V6foPPTTz8tc33hwoXr9Tx16tQpE1p+PcRc3+sb8juOnwaV04oVK9YICtcWHq7v9fV5zNePB7w2BQUFaw0Rt9lmm2/9gGbVzHPrHQAAYBUBJaynOnXqRIsWLaJFixYb9fsrV64sPZbaV199tcHBwurX58yZ8433L1++fINqq1Wr1iaHnPXq1YvatWtHzZo1Sy81atTIyXXHDGVjlZSUxMqVK6O4uDhWrlxZeinv68uWLdvkoHDp0qVRXFy8Qa9vff7Prj6De0M/+FgVNm622WZmewMAAOVGQAkVpGbNmtGkSZNo0qRJzp+ruLj4GwOSjQlHv/rqq5g5c+Y3Pr6iFBYW5jQA3ZjrBQUFpZeIKHO9PO5b39+tW7duLF26NLIsK71ERJnr5XHf+vzuqjCwIgLB9b2+qrZcKywsXK/gr3Hjxps8W3rV9Tp16gjvAQCAKklACXmoRo0apV9pryhZlq01GCqPcKkilrFqttrGLHPFihXfGNhtzO0ba8CAAfHKK69s9O9/UxC6obcXFBRErVq11gh2NyT8LSoqWuv9uQicy3MZNWrUEBYCAACsJwElUC5WhVG1atVKXUre2Jiws6SkJAoLCzcqWAQAAIAUBJQAlZTgEAAAgOrAEe4BAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACAZASUAAAAAkIyAEgAAAABIRkAJAAAAACQjoAQAAAAAkhFQAgAAAADJCCgBAAAAgGQElAAAAABAMgJKAAAAACCZgizLstRFAAAAAADVkxmUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQjIASAAAAAEhGQAkAAAAAJCOgBAAAAACSEVACAAAAAMkIKAEAAACAZASUAAAAAEAyAkoAAAAAIBkBJQAAAACQzP8Do2oM2575hF8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1300x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import SpiderAnsatz\n",
    "from lambeq.backend.tensor import Dim\n",
    "\n",
    "spider_ansatz = SpiderAnsatz({N: Dim(4), S: Dim(2)})\n",
    "spider_diagram = spider_ansatz(diagram)\n",
    "spider_diagram.draw(figsize=(13,6), fontsize=13)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Note that the preposition \"in\" is now represented by a :term:`matrix product state <matrix product state (MPS)>` of 4 linked matrices, which is a very substantial reduction in the space required to store the tensors.\n",
    "\n",
    "Another option is the :py:class:`.MPSAnsatz` class, which converts large tensors to sequences of order-3 tensors connected with :term:`cups <cup>`. In this setting, the user needs to also define the *bond dimension*, that is, the dimensionality of the wire that connects the tensors together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import MPSAnsatz\n",
    "from lambeq.backend.tensor import Dim\n",
    "\n",
    "mps_ansatz = MPSAnsatz({N: Dim(4), S: Dim(2)}, bond_dim=3)\n",
    "mps_diagram = mps_ansatz(diagram)\n",
    "mps_diagram.draw(figsize=(13,7), fontsize=13)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. rubric:: See also:\n",
    "\n",
    "- :ref:`lambeq.ansatz package <api_ansatz>`\n",
    "- `Example notebook tensor.ipynb <../examples/tensor.ipynb>`_\n",
    "- `Example notebook circuit.ipynb <../examples/circuit.ipynb>`_\n",
    "- `DisCoCat in lambeq <./discocat.ipynb>`_\n",
    "- `Extending lambeq <./extend-lambeq.ipynb>`_"
   ]
  }
 ],
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