524 lines (524 with data), 198.0 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "1c058fba-3280-4ecb-bb4d-7cb12f4233d2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696823476.2998607\n",
"Mon Oct 9 03:51:16 2023\n"
]
}
],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"%matplotlib inline\n",
"# !pip install pennylane\n",
"\n",
"import time\n",
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "c31c991e-c216-4e0f-c730-413ba3e155a8"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
],
"image/png": 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2jK2mnCaS8vJyvOeee1J/d3Z2Yr9+/XD27NlEv//973+PvXr1ws8++yx1rLa2Fm+88UbiNpw6dQo7OjpSn9bWVldEYjb4jSbU888/j3V1dcTpwJkQRQtzArtap5WAoO0nz7xfl9D9S1Lo/kW4GIkdqP1lRI6KEqT2bH5PYyaFXtuboSv2+MQTT+guKm5vb2e2TipnieT06dOYn5+Pq1atSjt+++2344QJE4iuccUVV+CUKVPSjtXW1mIwGMTi4mIcOnQo/uxnP8NPP/3U8BqzZs3KcoU5JRLSwa9OqL///e9ZK45LiorwwIEDtu+tva7IkywTtNN6Wae/tre3Z7mBRMrasgu1v4zcdQAPeCK4ab5H3tUStG1vA+NsSO18Zb1OKmeJ5NChQwgA+Oabb6Ydnz59OpaXl1v+vrGxEQEgK6ayYsUKXL16Ne7atQtXrVqFl112GY4cORLPnDmjex2aFondwV9SVJSVDhxMksm5ABZaJy9N1o+krQcriwTgVSoE7LRdbt6jV7XStG3XS/XluYeLCkkkBpg6dSpeeeWVlue1tLQgAOArr7xC1C6mtbY0g3/dunWm5zp1c/kJrKwH0TJ3zCBCbTE9dx1AHwSoQVbxH5LndvseeS9izbx3MJmcYSUPeCg/OUskblxbn332GRYWFuITTzxBdK++ffviwoULic5lUmtLZ/DW1dWZCtG6ujpH9/cTWE0gP8SM9DKlVBcZb3LRc9cBVCHAQurxHztWgpv36HWMpb29HUcQbCqHyKcaQc4SCWIi2D5t2rTU352dndi/f3/LYPuSJUswEAiYxj5UtLa2Yl5eHq5evZqoTSxqbekNfloWiQgarRuwtB5Edj/pZUopSgiLikp0yYUHmpqaMBwekXX/bdu2UetHJ1aCk/coQqkYUjJT3fTSInGIlStXYiAQwKVLl+KePXtw6tSpGAqF8MiRI4iIOHnyZJwxY0bW7yKRCP7oRz/KOn7ixAl84IEHcMuWLXjw4EF85ZVXMBwO45AhQ/DUqVNEbaJda8tsEKgxEq0QDQJZjMSv2+Nmwg/WA20YxyXKMJF26+2qeXXsbtu2jWpiAU8rwWuLRAWJolRT3bWzauZaGxkjIcT8+fNxwIABWFBQgOXl5bh169bUd9FoFGtra9POf//99w019i+++AKvv/56LC4uxm7duuHAgQNxypQpKWIiAauV7Xo4cOCA46wtL/2/LCCy9UAb+plS5kFv0n6haaHqWU1uiI23lSBCrMxKUVIJ72nIzu5SgN4OpTlPJKKBJ5GoWL9+va11JKJoWxLOoG+RmKfhWglZs5gLvTa6W3zJe9yKZO0aKUoque4yIJJYLEZFOZBEwhleEAkJtIPJjmbn9xhKriI7U+pxV4KblfVAuxyMF1aCyNauSq5lkJ0m3AsA+4ZCVIhQEglniEYkerGQSk0FXyPNLldiKLkKvUypoqISR6vmWVoPXddsTlpNc11ZDyJZCaLAaD6Xgf1tJ4wgiYQzRCMSo1hISVGRqWaXazGUXEVDQ0PKrel01Twr66G6ugYVJYSJJIB0wnMr+EW2EngjFotleRisys3b7TdJJJwhApGo7iiryr5m9XpkDEVsmMU07ApZFhYJYsJ6SKQje59J5hc4cSXrzVercvN2lQNJJJzhJZHouaMUSATijAaTntARIYcekW98xm+xINoxDRbFJFkRVC7CrSs5M3ZktGGdtEh8AqdEQkOQ6bmjgklfqZ3BRGqRsBK+POMzbrOVvCAgFgKaRTFJVi6zXIRbV7Je7MjKfW0Hkkg4wy6R0BKaVsJ/rs3BZJYdw1rQ047PmAl7p5o97XRZO2ApoGnGHqRFQgaarmTt+6OZmCCJhDPsEgktoWnljrI7mMwGIctAPM1JpSfsI5FKjMViGI/HHQk6lZQqKqJUXUt24CcB7ef9V3hZm1Zzd9GiRUTtMGovDeVAEgln2OlwmkLT6lpGOwdaIXMQsg7E04zPpFsbuzAze6irLpS1Zq9HSl4Kcr8IaK/2X3FDArxT363mlFU7eLRXEgln2Olw2kFtVou1nC5mdHovGkSVrbXXYKK0ubbIYZCYENJJaRkxAbGC3zbIYp2uq45RGvW9vEh915u7obw8DID3e5EgSiLhDtYWiZmmRXuxltPFjG5BgxDT4whmriDFUrPXX1wnhmvpXF9PkW0pKpiXF0KnLkfehSHN4hkKJGpombWDV3slkXCG0xiJldC0Y77SEi411dXYMy8PfwCAzwL5Yka3oEGI6cLfPDgdDo801WD1g9uqhSO2a8nvsHJRpVuKm1wTPI/Ud7O5rM7d+vp6onbwStWXRMIZdomEVGjyNrfXr1+P+Zo2AQCWAOD/B+aLGWnCLSF2CRnrOlRm99IPbrdjZsxFZNeS30CSFZf9Xpxns5Eu4uW1p4qdFHxpkeQgnK4jIRFkPFea9y4s1N0Pvlij6YjuVkmPIyiYWGHtzIIwCm5HIlGh+8CvIEnLzrYU7bsc9ayDkqIiDCkKE4vbzlwm9VbwKGQpiYQzWKxs573S3Gr3Rd5xALeIx+MYi8WwoiLq2IJgHdymkWrqt9X5RiBNb9Y/rwYBehMrDGa16FhY3HbmMqm3gkchS0kknMGCSHhbJFb7wQ+55BKq9+MJt1YUbSuMxsJGLxdHsoCdBZfZluJCBAgQ9QWrlHkzOJnLpGOOpYdAEglnsKq1xXMfBiuL5H/+53+o31N0sNL2adTMol13y2vYWXBpZCk2NTVZvi/eln5bWxtWRiKoQPa2uL0hkaUlqjUpiYQzWBEJ730Y3OwHn0tgqe3TWKHup1XudmB3waUTbZy3pV9TXY09FQUBAKs040n7N411WCwUHkkknMG6+i+vALeb/eD9BHvppXS1fRo1s6yuEQ6P8KWLi9eCS16Wvkpac5PPshwA45Ao9x6nQF6sV7dLIuEMEfYjoQm7+8H7Bc7SS+lq+zwsEkUJ+tbFhcheceJl6WvdaDWQ2BY3zbWlKK7Ii/XyAEkknJFrRIKYO9lAWjhLL7VvMZC3w/nCRr1rJBZL1gjv4hJlbPEo4aJaIu1JMtGSV7SiwjF58XDRSSLhjFwiklzdt53EEmhubsZFixYxjz/QcOG0t7dnrc5PkEg7VdKjiVzLNCNBphttLgD2VBSMRiKurssjaUASCWfkEpHk6r7tJHGFLgGn1m4itxicaNluNeIucpyOAHEmpEcTrDPNRLF0tGDlRpMWSQ4iV4gkl/dtJ4krdAm4p5F0XYLXWrZfSsun938zJkqbxKmQntfvgAQs3GhmSQM0SDXnieTJJ5/EgQMHYiAQwPLycmxsbDQ8d8mSJRnmP2AgEEg75+zZszhz5kwsLS3F7t2745gxY2y9AK+JhJYmJsq+7axgJHQTpVT0Caa+vt60X2lo2W7en19Ky3dZhFUZ87HK9diiZemIaNGYQc/aGXvddTiuKr2P5Q6JOli5ciUWFBTgM888g7t378YpU6ZgKBTCo0eP6p6/ZMkSLCwsxMOHD6c+R44cSTtnzpw5GAwG8cUXX8R33nkHJ0yYgBdffDGePHmSqE1eEQnteIYIFgmrydzc3KxbMqUrzuCs6J+beApNTZqWxsuy/xOE3TtN4Cf+Vii495zHtPxg0ZhB++5puqZzmkjKy8vxnnvuSf3d2dmJ/fr1w9mzZ+uev2TJEgwGg4bXO3v2LJaWluLcuXNTx44dO4aBQABXrFhB1CaviIRFPIPnanotWE1m/a13o6623lXhNsNLpNXprIUpq7RqGll2rN4DbwuHtiKYs0Ry+vRpzM/Px1WrVqUdv/3223HChAm6v1myZAnm5+fjgAED8MILL8QJEybge++9l/q+paUFAQB37tyZ9rvKykq89957da956tQp7OjoSH1aW1u5Ewkr64EkOMhigrCazCTXdRpnsBKOZm4x0VansyY1VmnVbvuRxXtgnfloNP9ou6ZzlkgOHTqEAIBvvvlm2vHp06djeXm57m/efPNNXLZsGe7cuRM3bdqE3/3ud7GwsBBbW1sREfGNN95AAMCPP/447Xc333wz3nLLLbrXnDVrVtogUT88iYR1PEPPVcJqgrASqqTXdRNnyCahPyFJoJ7HehVS8CA1lvdwk3DA4j2wyny0mn/SIiGEEyLJxJdffomXXHIJ/uY3v0FEZ0QigkViVWTRzqp0UguD1QRhJVTtXtdJnCGbhMi2fRXJIuFFanYFPum4dKMI0H4PLOOMJPOPpms6Z4nEiWtLDz/84Q/x1ltvRURnrq1MeBEjWbNmDSqQXXahDyQqipJMfjsWBssJopKiVxYJDcTjcduLGUVJ3eXVT6QC32m8xmnCAc33wMpToK3bpdbq0pt/NNet5CyRICaC7dOmTUv93dnZif379zcMtmfizJkzOGzYMPzP//xPROwKts+bNy91TkdHh/DBdnVglWkGjPZvkslkx8KwM0FINcnGxkbNQkB3uxkagaewtqvZi5S6y7OfrAQ+7yQEmu+BlcIVi8VQyZjrNQC4y4CgaGTx5TSRrFy5EgOBAC5duhT37NmDU6dOxVAolErpnTx5Ms6YMSN1fl1dHTY0NGBLSwtu374db731VuzevTvu3r07dc6cOXMwFArh6tWrcdeuXXjjjTf6Iv1XJYK5ALgsqa2QmrF2BzzJ+aSapN55ABUIcAV1ocpTWDvV7EXYvlgUUvPS5UfrPbDIfIxGIlnbYPexqTjaRU4TCSLi/PnzccCAAVhQUIDl5eW4devW1HfRaBRra2tTf993332pc0tKSrCmpgZ37NiRdj11QWJJSQkGAgEcM2YMNjc3E7fHKyJxY8Y6McGtJgipJql3XlfBwbloN8ZDAl7CWhR3lVN4TWoiJSE4Be2yKFZKnNu6XUbIeSIRDV6vbOe1wY/ZBHG353bXeQCv+kZg6EEUzT4Tflm1LVISglvQImUrpS8Wi1FqcTokkXCG10TiBG1tbbo7IpKY4HoThFSTtDoP4AHfCQw9eK3Zq6Cx0JA3CfndqqMNrypOSCLhDD8SSU11NYYUJStYX1JU5CrI6NYi8fumTKLBTeDaq9Iholp1XsKLihOSSDhDRCIx0yIzNRx1+091S1CnGg6pJqm/KVMQAZRzXmA4gdG7dusm8qKEi/ZZRLHqrMDDYuO1q6MWkkg4QyQiIVkbwirXnVST1DsvHB6JTU1Nrp79XIOVxeAmcM07VuHHwolebALHk1wlkXCGSERCsjaEdHGTU5AOdr9onKLCymLgVZCShkbuZQFL0vZnnsdjEzgvkyQkkXCGKERCGpRTA+1aTaoMAEOK4vudEM8F2NkSmFVBSjvrhkiex4tMLTfrniKRKNFcc9M2r7e8lkTCGaIQCanLSk+TCoLzQLsEH+gv5KzCxD7t+hZDS0sLFhWVpP2mqKgEDxw4YHk/KxKiZUV4tXbEzbonRUnso8KqaKoIW15LIuEMUYjEyiJZv369EJtXSTiD/kLO3phYyGllkcxDgGUIMI9Y2JvFvGhaEV5YJLSyDOcxmEeizFFJJJwhCpEg6qcJBgFSdXpGhMNEVovX8MsCOl4gW8j5F2oxEi3crBsiBe+1I7TWPfVUFOopuaJseS2JhDNEIhK9NMEySBR3Ww6AQUURQtsxgh+zd3jAeiEn/awtMyKnbUXwXjtCyyKJRiJpbaa5P4/Xc1QSCWeIRCQqGhoaDE1vJak58d5OlwQibT8rEpzsxuhE2NshchZWBM9MPjfrnrTnsWizV1teayGJhDNEJBIr8/jKb37TlSZFw/W0bt06rKurSxVozKU6SyzgRHDb/Y0dIvf7CnQ3655YP6cXCxAzIYmEM0QkEiPz+E8AWfsaRCsqiAcoDdfT/v37dTOJFi9e7NgVcy7AiUCz8xt9Im9Gq/pnfl8PJPK6Jy/7VhIJZ7glElaBZT3zOACQva+BDZOZhuspQSJBTM8+CmIo1FdaJARwIlxIfpMeU2nDRDZYevUBv1gbEu4hiYQznBIJ60VHeuaxmyAeDdeT1ba6I0aM1K3DVVRUIoUYY6S/3xpM7BHjfaxKZvB5A0kknOGUSHgtOopGIthTUfDHScJwmlZII+Wzrq7O9BozZszAPn2KM8hPQYACrKoaR6tLJAxQXV2DihIUwjKUGXzewo5cU0DCE8TjcVjT0AB/7OyE2wDgIgC4DQD+0NkJaxoaYN++fdTu8+rmzbDw7Fn4VfLYaxnnvJr8d/DgwabXuuSSS0yvYPV7AIBRo0aZXmPo0KHQ3t4GAIUAMD15/M8A8DX4xz82UOsXCX2sWLEcysqGJP+qzPg2CgAA+/fv59KWSZMmwyuvbAWA5QDwIQAsh1de2QoTJ/47l/tL2AAHYst5OLFIeC06yrxPDST2enaaVkgj5bMrRpLtvrKqIVVfX++0KyQIIUL2HM82SNeZPqRF4gOo2r1T68DpfZYDwNUAMBkABiT/PdbZCV999RX861//srzeihXLYezYa9KuMHbsNbBixXLiNjU1bYGiou5p1ygq6g5NTVs0Z+lrwxLsMXToUKiuroH8/HshMWJaAWA55Of/Aqqra2DIkCEWV3CPlpaW5P/YWUXt7e0wfvwNMGzYMKipqYGhQ4fC+PE3EM0DiQxwILach9sYCY1FR2ZaVeZ9ygCwJwBOB8BXdWIzJBoajbTE9evXp60jUe8NAvjnjXCuaK9erxHhMQ7k4ldzyGA7ZzglEhqLjkgyv/TuY5S5VVER9Ty4WVU1DvPyQmmur7y8kKfBdj8GfmmQnpfrGIx20gyF+mIsFqNSGFFUhUUESCLhDLfrSIwmK4kgsJP5FY/HU1lT+rEZJVke21sNzWttWA9+0l79SHp6aG9vz1q4ClCGAIWYyORz/lxela5X4QfLVhIJZ9Be2U66vsRJcTej3zyeupc4GpqX2rB2ovtNe3VKeqIJt65+n4cAaxAgntbvdkriG1+bb+n6WCzGpNAjC0gi4QzaREJqZTjN/NKLzfRMVgU+18uT6Gnz4fAIJn3DQnCzLtTIE9YVj9e4Evy8StfrKYZl0FWRW5SCqZnIeSJ58sknceDAgRgIBLC8vBwbGxsNz120aBFGIhEMhUIYCoVwzJgxWefX1tZmmM+A1TZeLE0isWNlOC03rRczqUxpSf7QulkhW5ufi3l551PtG5aC24nLRlS3nfUeLHFXZE7bhWqkGOgqhgBYCYBrAHCuxXz1CjlNJCtXrsSCggJ85plncPfu3ThlyhQMhUJ49OhR3fMnTZqECxYswJ07d+LevXvxjjvuwGAwiB999FHqnNraWhw/fjwePnw49bEzmGgSiV0rw03mV6briPfmQqIhXXBl1ppSMH3ty+OoKD0xEonavk9XP89Fu7sW2nsGa9IT3W2nH3Dvg127QmbvH8KiDpkZzBQDPWWvLWmRaM9XADAWizm6PyvkNJGUl5fjPffck/q7s7MT+/Xrh7Nnzyb6/ZkzZ7BXr164bNmy1LHa2lq88cYbHbfJK4sEkW65aRGD3DyRrs1n1pp6GgECGlLp6qOKiihxH3UJ7rK0a6h/001rtVYIvA46W0FvTCb6alfac3npnjOz6PQUQ3VRsNZCCUKiCrdIyFkiOX36NObn5+OqVavSjt9+++04YcIEomscP34cu3fvjv/7v/+bOlZbW4vBYBCLi4tx6NCh+LOf/Qw//fRTw2ucOnUKOzo6Up/W1lZqRILozMqgGZj2e1lwp+gS8nNNtPTszDY7RSUTgkXBzIKIib8VKoLbSCHYtm1b1nsV3SJREY/HMRaLYSSin57ulXvOqv/UDeZUxbAZ3BVO5YmcJZJDhw4hAOCbb76Zdnz69OlYXl5OdI27774bBw0ahCdPnkwdW7FiBa5evRp37dqFq1atwssuuwxHjhyJZ86c0b3GrFmzMjQkoEokPDa1ES1DRxQkihb2NNDSN5kKjUgkanl9q+rH2sWZRiB9d6pCsG3bNlNt3W8uzUxFx0syJLHotIrhsmT/e70fOwkkkRhg9uzZ2Lt3b3znnXdMz2tpaUEAwFdeeUX3e9YWiQoWlgHr0vV+R3t7O0YilQaC6QFToUEitNy4kpy6b6y09W3btmE4PNL2dUWBl+45EhKzsyCYZK7zUgJzlkjcuLbmzp2LwWAQm5qaiO7Vt29fXLhwIdG5ou2QSFIuhXXper+joiKadGF1aelW5dVJhJYb7dmJ+8bqfpmVDMLhEcRzRBR47Z4jtehUxTBaUeEoQYa3EpizRIKYCLZPmzYt9XdnZyf279/fNNj+29/+FgsLC3HLli1E92htbcW8vDxcvXo10fmiEInVQHOaLnwuwijOcM01/wezKxf3QTvBcieuJKfC0lxbF6OSgRNkKkteuufsJqk4dV3zVgJzmkhWrlyJgUAAly5dinv27MGpU6diKBTCI0eOICLi5MmTccaMGanz58yZgwUFBfjXv/41Lb33xIkTiIh44sQJfOCBB3DLli148OBBfOWVVzAcDuOQIUPw1KlTRG0ShUisBhqv0vW5hEz3olHZDkUJEQstJ9lxXYTwWwSox65V3ubuG2MCetxTLd4pjNx7Bw4c0F1IytO60o4V2oVPvVACc5pIEBHnz5+PAwYMwIKCAiwvL8etW7emvotGo1hbW5v6e+DAgRmTPvGZNWsWIiJ+8cUXeP3112NxcTF269YNBw4ciFOmTEkREwlYEwnJoCQZaLlmkXiVMJCIo+hnD9kBqSBpa2tLWkLaMawgwDgEWGj57vS0deOEAjHSfo0geryHVRqyF0pgzhOJaGBFJHZ8oqQDjWbpet5QicMqC8npde0SEq806erqmmQ1ZG26cG9MrGsJWFpCehaQcUKBuBYJiXvP61X6rO4vLZJzALSJRBVs0UiE2CdKOtCc+Ge9ThXW0/ISQvRpV5NV1BpTWliXCQFi943fKxlYZWf9/Oc/95QcWQf9eSuBkkg4gxaRGBV3ayfUQOwMNBJtmlTQsiYaPS0voZHXIEAzJor3zbU9Wb3WXklgXbjQuVvDb5UM9AV1G2ZXCfDGXcc6DZnH+jItJJFwBi0iMSruVmPiqtLC7kCzIgArQctDoyfRyLVxA9J6RV6njJKC5PndttVPlQyyragyTGTRLUerBaN+t0hU8Hpfkkg4gwaRWLqmCCwSFVYDjYQARPFHW2vk07HLSgliRUWUynVFCjZ3xUi0KceJGIlI1hMP6Nfe0o7RmmTfeOOuY+EuzNwbhxfpSyLhDBpEohcsb4aukgrLLFxVdkBCAFaCdtGiRVy0L7JS4vbv7ReLBDEhPKuqxmUITwWrqsYJ6YbiIey0u32mj9F2BKgyVZJYgqa7MNPVrWRY4KwrUkgi4QzaFkkbJNxZ2kGjUBo8pALU6rz6+npuGr3R3t0JgeH83n4LNsfjcayvr8f6+nqhiE6FkaWrVyySBkjGqFf9RMP9pHV1VwFgbwCuFSkkkXAG7RhJGWSXme6tKFTKTNtx6ZgJWp4avb47Q8FE1pbze/st2Cw69CzdhEtOYda/flMGSKHOr+kA2JDsO97rvySRcAYtImlvb0/tVMhq0NghACtBy3sSa7U8mvd2oz16nRptByzbau2CfBVZxNDslMz3C9ra2nBkOJz2TAoktubVdi7rihSSSDjDyx0SncCuENYTtM3NzRiLxbKK/vHS6L22JvywBkUFj7aS7a/OLgZFUjLfDpF6qSDUVFdjb0XJ2viqzKFy6fRZJJFwhpc7JGb+lmTAuBHCekIpEoliLBbjMukyn9Gr1FU/rEFRwaOt5EkRbLPi9J5VUUJZ9dGMxrvXCkJjY6Pp/J8L5IsR3VYLlkTCGbRXtpMsLNQKVKcDxokQ9kqAej3BtfBTxhfPtuonRagLR9n3kfGzateamI9ZrxWEEUmXlpFHws4cd1stWBIJZ9AmErOFhXqkUVJUxKW8tJcC1OsJrgXNNSisXSg818voJ0UEMFFYkn0MTf9Zyces1wpC1/2NLZL169cTjRcatbkkkXAGq6KNehZDppYx12Lg0Rz8Xi3i83qCa9uxZs2a1D7cbtqjZ2GFwyOplz33ou/UcdvU1GTLinRLqvrPSj5mvV6kqt6/ChJZm1qPRBAAR4bDtq/lJtYqiYQzeO1HoqdlrEkOjMwBsyl5vL6+nvr9eQqltrY2DIdHmE7wcHgEUxeXntAvKipxlTWmXz8siABKzqXI0qi0QIrsZyXfd4X1+LYiSvX+T4P+OjI7Soa0SHwIXkRitPpdO2D0FjPSXAHLWyhVV9dYbnGrKEGmQtFtADcTVgKL9vPwyHBzY03QdFvqPasd0mcxvu0QpTY++ioAPgCAQUVx5KZ2Wy1YEglneGmRICTSAoPJgcJ6BSzPtNt0gVuDiS1tM7e4rWFqEVkJfVKftRbWqbIPED2PXeFNM8ON1t4wVv3r1NWnfVY7Y5bF+LZDlDQr/Lq9liQSzuBBJGqQXdGQhqplhBQFS4qKUoOFR7yER9ptusBtT5KG1toamTzOzofNwm9OtnjP+NpeZrDR3hvGqn/D4RHU2m5nzNIa307dZTTnl9NrSSLhDB5EopqpTyetDj0tQ61/tQkSsRO1YrBf92TXn4TxlMbetTaha1LSzoJi5TfXT5Uls7C8zGAz3xvGvG/03g2PMvlegkcAP7Nfac0BSSScwWPP9kxLIw4J/6l2ojU2NmZXCAXAhQQTUtRSH8YFG8tQ68OuqhqnkwU1gkoWFAu/uX6qbBUCLDS9tpcZbE4XHVpZUIk91oM6pFrlSwVIC5bvy2gpAK34qCQSzmBNJHb2Yw/l5aUXewTAQHJA6YF3xVa7MAqeZra3qmpcUtgvRNplxFnGhZqamjRZadbX9jJF1WkZFCsLatu2bagt7Jj41CTfpf8skkyljFWCSuZSgDJIuL1pxUclkXCGFxYJQnrsw+ocI82cVcVW2hZOpp9X+7d+UJ6+24dlXIj02mJbJK9mCUnS9nZl5z2gex0/wEgpO3DgAHVFJHO+Z2Zv6skIu5BEwhluiIRU4Fql8jlZgMSiYqsXgeAuTXmTZ0KWJ7xcF6J3bzPFg9SC8roIJw1YWV40FZHM+W60nsxNfJQ5kXzxxRf40UcfZR1/7733nFzO93BCJHbrY1ml8jlZgMSiYqsXgeAuQpye8TzNyWcwz4LyG7wUukb3bmpq0hWSdi0or4pwugVvS9H3Fsnzzz+P/fv3x+HDh+OVV16JW7duTX131VVX2b1cTsAJkTgtqGY20ewuQKJdsdXrWlxdCxcXYnaqsEK9/IjX4C10tdaznXvzsKCculJpuWC9iF1lznc1RuJ0AWImmBLJ8OHD8ciRI4iI+NZbb+Hll1+Ozz77LCIilpWV2b2cIzz55JM4cOBADAQCWF5ejo2NjabnP/fcczhs2DAMBAJ4xRVX4N/+9re078+ePYszZ87E0tJS7N69O44ZM8bWwLJLJDTKF+jByQIkmhVbvQwEd2nKCibWNfTGzLiPn/ztJOCVaefWXcnSgnLatsbGRltJDlbwQonSm+++ydr65je/mfZ3W1sbVlZWYl1dHReLZOXKlVhQUIDPPPMM7t69G6dMmYKhUAiPHj2qe/4bb7yB+fn5+Pjjj+OePXvwN7/5DXbr1g3ffffd1Dlz5szBYDCIL774Ir7zzjs4YcIEvPjii/HkyZNEbbJLJKw3r7KjLW7bti2Zfpm5wMx+xVYRiiv+9a9/9bwNrME7DkXLXUlzkV/2TplkbdNfUFmFAE+7tpK8il2ZJaK4AVMiufbaa/Gdd95JO3b69Gm89dZbMT8/3+7lbKO8vBzvueee1N+dnZ3Yr18/nD17tu75t9xyC95www1px0aNGoU//elPETFhjZSWluLcuXNT3x87dgwDgQCuWLGCqE2iWCRG9zLa3TASiaZNqHB4BG7YsMGVkPK6QKDXFVx5gEUcysi6EUE5UKFPAgomVtUbt82KeGiV2smFhAEtmBDJ8ePHERGxtbUVDx8+rHvO5s2bSS/nCKdPn8b8/HxctWpV2vHbb78dJ0yYoPubiy66CH//+9+nHXv44YfxW9/6FiIitrS0IADgzp07086prKzEe++9V/eap06dwo6OjtSntbXVFpEgui+oZgW9YP7Y667DqqpxGROxDAF2ZQmjeDyOixYtwvr6elsTy+vJJJLgYwHaz2dl3YhEzMYVk6t02xaLxXSIxypL0f0zNTQ0YF1dHa5fv57Sk3sDJkQyfPhwQwLhhUOHDiEA4Jtvvpl2fPr06VheXq77m27duuF///d/px1bsGABfv3rX0fEhOsLAPDjjz9OO+fmm2/GW265Rfeas2bN0hmc9oiEZnE2PegF88+D/GSqpp421jWh3BbiQ/Q2+4aWVSTian/agt3KuhGFmMkTQ7qORSLRjGfLzOrLzFIkK5ZpBJF28aQBJkRyxx134IABA3Dv3r1px3fu3Inf+c537LfSAUQhEhoWiQoWAlfPddYMVtpYPDWhwuGROmXTgxgOjxBKqBrBrVUkskCgKdjtLBb00l2JSFoxuattkUhU59nYlu/3sgYaCzCLkTz88MNYVFSEr7/+OjY3N+PNN9+MiqLgd7/7XceNtQNRXFuZ4FVGnhR6wXx1wZL5mpGuQnldk60NM1NpRRGqVnBK0qILBFqC3U+LBUmKO2rbFovFDJ6tCrPregXR7YZiolhuNME02P7YY49h9+7dsVu3bjh+/HjL1FvaKC8vx2nTpqX+7uzsxP79+5sG2zOJbvTo0VnB9nnz5qW+7+joYBpsZw1nFslczM/vo7MbIbuSIyJCRIGQ6WKjJdjXrVuX/P08omf1erGgGYFmts34PS7EzLpeNLY4tuNyFNFlqgcmRHLkyBG89957sUePHhgOh/H888/HlStXumqoE6xcuRIDgQAuXboU9+zZg1OnTsVQKJRa2zJ58mScMWNG6vw33ngDzzvvPJw3bx7u3bsXZ82apZv+GwqFcPXq1bhr1y688cYbmab/2oHTQacXzO+KkWRrY6owShTQUyegeEKVNUQKLlu52JwKdv3sJzXpQtwaV3YJ1A7xuIWVAtLQ0ICxWAwrKqLE7fcaTIikR48eWFZWhi+//DIiIq5duxYLCwvx8ccfd95Sh5g/fz4OGDAACwoKsLy8PG11fTQaxdra2rTzn3vuORw6dCgWFBTg5ZdfbrggsaSkBAOBAI4ZMwabm5uJ2+OWSPTIwm4JlUzoBfPHVVVlZW1VVEQxFoul3btrAqr7fngvVHlBJIuElYvNOPupS6EQVbghkhMoC5ecmWJnRFxd1aqVZD/7w7pnQiR6bp7t27fjBRdcgP/xH/9hr4U5BqdEYkYWTkuoZEJv0llNxOwJ6L1Q5QkRgsusCI1k6+BcAw3rgyQBw2jLA0UJIcBc380lrtV/Dx48iJdeeqnby/gaTonEiCwqIxHMjHEg8FuwqCIej2syuLwTqrwhQnCZlYtNJNedn2DHOlSJq6GhQUMe/ut37mXkRTaDecAJkVitbs/MukKgu2UuaYqrCELVK3gZXPbKIhFRM/YaTvssnbT91+9yPxLOcEIkVvW2WFskdv3vLIKTfshc8RKsXGwiuO5owM4YcjPenFpx2QSkZkD6o98lkXAGC4skWlHBrISKl1qpyIv9RAMra5D2dXkrBXbGEI3x5ma+pJP2Lkxkx/lj7Esi4Qy3MRI9smBVQqW5uRnr6uocaVg0IPpiPxHBysXm9rpeKQV2xhCt8ebUitMj7UgkO1OSFDxJWxIJZzglEhKyoCVEjNcOtHOzSKR/PrfghVJgZwzRHG9urTgapM2yNp8eJJFwhtt1JDyCusZrB8q4+WtlxhBb8NRWaQlpu222M4ZYjDevEjBoLQewA0kknCFaiZRMkNYpYu2WyBWLRLREAVIXE812uxXSem2ORKKW488ri8RL8Ny/SAtJJJwhOpFYTfq6ujpu/lo/ZwyJmihg5WJi0W63Qrq6uia5UC89+FxUVGLZLjtjyM/jTQXrHVWNIImEM0QnEt6bIZnBz+tSREwUIHm37Eut2BPSXW0uw8yCoABBrKiImv7ezhjiNd5YWqnSIjlHIDqRINLVzNwIJq+0ercTXVQ3iXG59IS1uWjRImbtdiqkuyxkd+2yE69gFdvgNZ5Z76iqB0kknOEHIqGlmdFwafDU6mlNdFETBSKRStP3UV9fz7zddoV01xgSrz/tgtd4Zr2jqh4kkXCGH4hEhVvNzI1A9UKrpzXRRbBIMq2qbBdR+vYAFRVR6u2m5cKxIkA/BMK9GBM8s8YkkXCGn4jELoyFl/3Jw1urpz3RvQrcGllVXW6tXZi5iyWAgrFYjFq7abtw2tvbk+XVg9z7kxZEtVJpQRIJZ+QikZgJDvdBVj4aHO2JziNwq6fxG1lVkUg0oz/jmKgyOzetP2m0W68NihLEcHiE4/fW3t7uq42eMiGClWrVPjfWiyQSzshFIjFzCbkRTDy1elYTnYV7wYi403eszH6GSCRK3J9O253dj21ZFpD2/dsVYF5v4esGIqYX01oFL4mEM3KNSEgFsBMBwDv9V8SJrgcj4g6HR5haVbFYjHl/Zlt2ahXb9LZWVY3zbWq3U5iNZ68WrtJaBS+JhDNyjUh4+H55aaF+WLdCVnmAPqkbtUXdlEm9Xnr7zNuqKEFdKzbXoe1/Lxeu0lxzIomEM/xIJHpBdH3BIZ7v1wm0u9aJ5kaxIm4eu1SmCz8lSwhWVY1LtuEB07YCTM+ZMaOFHevCy4WrNFfBSyLhDD8RiZ7/tG/R100Eh9guIVKIWt4E0doiaWpqYt72LuGXvdpc321lZD29ysyK9QJ2x41XSpjWkpQWiU/hJyLJ9J9eCQomUjC993ez9CmLWN5EC5JYDit3YJfwm2spBOPxuKGFlLBkcseKRbQ/bninBOsphiVFRVRWwUsi4Qy/EEmm/7TZQrtUBQdrV5Bba8GKgPzgqvMyltMl/JYRCUGjtrqxYkWrqIzobNywGGtmfaMXWA8pCpYUFaW9H5m15QP4hUgy/adrUgPN2wVVTq0FUgLyYiGkU6HoRSqsHYvErK1OyFBkl6PTcUMrU9AqjdcqsL5+/Xq5jsRP8AuROLFIeLXJrFaUmgBAulAvc8LyskhEFopWyI6RpJdbISnvrsIOGYrscnQ6bmhZl1ZpvKzLy+cskbS1teGkSZOwV69eGAwG8a677sITJ06Ynj9t2jQcOnQodu/eHS+66CL8+c9/jseOHUs7T/vC1c+KFSuI2+UXIkHMriLaFSPxJqhupfV1fZS0SWm1UC9zkvNYT2IlFEV036hIF35KRt+XoaKEqI8JP7gc3YwbN9YlSRrvunXrEABwnsk5bpCzRDJ+/HgcPnw4bt26FV9//XUcPHgwTpw40fD8d999F//t3/4NX3rpJdy/fz9u2LABhwwZgj/4wQ/SzgMAXLJkCR4+fDj1OXnyJHG7/EQkelVE9bK2eGnR1msoXk1+1xsBqlLCORweaUpAmdqYUy2RVPhbPUckEvWFpaJm/QA8iIlyK3Fmwt0Ptaq82s/EytoYGQ6ntakMAHcB3fLyOUkke/bsQYBEKqSKtWvXYl5eHh46dIj4Os899xwWFBTgV199lToGALhq1SrHbROZSIwEYaa25GWZCj2tL0EcNTrEEtf83742S/qcdt1UVkJRUXoaWioigUS407Ks/GCRqGC5n4leHES1uPUsEiVJFlqXVzB53GlgXQ85SSSLFy/GUCiUduyrr77C/Px8fOGFF4ivU19fj3379k07BgDYr18/LCoqwpEjR+LixYvx7Nmzhtc4deoUdnR0pD6tra3CEQmtejtuQSJ09LS+hPXRniXIElpy4v+XXDIkuZKavrvKru/e2rKaJ7ywRLR+DtpFFv1SwoYVzOIgeptZBRXFkGDUADst5CSRPPbYYzh06NCs48XFxfjUU08RXeP//b//hwMGDMBf//rXaccfeeQR3Lx5M+7YsQPnzJmDgUAA//CHPxheZ9asWRlCD4QjElr1dpzCSeA5Ho9b7ugHsA0z9/nW+vSLikrwwIEDrtruVFPWE4qK0jvZPnHdN5kwEu5FRSXUA+Mil7BhHdPSxkGaIZFFGdeQQlNTU5YyOCLp0uKxf7uviOTBBx/UFcraz969e10TSUdHB5aXl+P48ePxyy+/ND135syZeOGFFxp+L7pF4tUez1q4ycYxdnVVJUkkmHbdxHfDEWAeFW3Wqe9eTyhGIqoGL777RoX+c7DdiEqkCsB6SlA4PDLNrU4D6jirypB36t/qONP2Dc+57Ssi+eSTT3Dv3r2mn9OnT7tybR0/fhxHjx6NY8aMIQqiv/zyywgAeOrUKaJnEC1Gwjot0Apufd/6ri5tJpH2us3YVf8pTnwPlu3PFIpW7htRs7m0z+GHwDgt6ClBCeVFoWotNTc3owKAvZPEoHoOekMi3mE0Hnjt3+4rIiGFGmx/6623UscaGhosg+0dHR14zTXXYDQaxc8//5zoXo8++ij27t2buG2iEYnXFgktoaMVZPF4HOvq6jTXzd4TA2AkJnYLtL6HlfCm6bs3ct+0tLQI69bJhJ8C425g9ZyKEqQWv3E6T3nt356TRIKYSP+96qqrsLGxETdv3oxDhgxJS//96KOPcNiwYdjY2IiIiY4YNWoUXnnllbh///609N4zZ84gIuJLL72E9fX1+O677+K+ffvwqaeewvPPPx8ffvhh4naJRiSI/LQWRLrb8epdT3u867rZe2Ik/i4zvYe+22JEltuChe/e2FIRP5sr0fcKJtyImS5HJWeIxHpd0wPUiFONBzr1HLB2B+YskbS1teHEiROxZ8+eWFhYiHfeeWfagsSDBw8iAODGjRsREXHjxo0ZGmvX5+DBg4iYSCEuKyvDnj174te+9jUcPnw4Lly4EDs7O4nbJSKR8NBaaG/HSxKgr66uSWZqma/ZMEJXuxZiIuZiThTsCyX6Q8PvErDpfab+nSuurfRyMdnraBLrmtw9b2ZGpZexTDPkLJGIChGJRAVLrcVMo3ai0ZNo6O3t7US7BurByqIRaUW/aII5ve/UveHpxKREQltbGxYVlWSQZRkChJJjxv3zajMqqyARE+HhObALSSScwZtIaAZnnV6L9na8djR0p9p8l/De5Lk14DeLBNEovTmI4fAIIdvrBMaB9mIEWOhK2Whubk65s1QrpB0AazK8JV6s99KDJBLO4EUkNBcZur0WbY3a7vWcuM66hPd0IawBvy3Gy7Yy02tyiZooQAqSLY+dPKPeXMuMi7yaPF5fX8/o6exDEgln8CISmosM3V7LrkZNe88QPddZRUXUcpKTxFh4add23H8ipQinb24lfqIAKayUGadCXjvXNoHYcREtJJFwBg8ioZnSS+taJBq1nRXudjX0trY2w2KIRoK3S3h7W/VYCzP3n4il6f3oliMBq02pMudaDYgbF9FCEgln0CISI+HX1tZGtTQCrQWLJBq1nRRXuwF6o2tnBkv1rtHU1KSpICyGgNaDiKXp/ZYoYAe03Y16c60dslezW7mVvXjPkkg4wy2RWMUraqqrLYu1NTQ0EA802gsWjTRqpxoeSYCerEiitctFpNIcmRC1NH2uWiSI9pUZUpet0VxTN24zgpfFVyWRcIZbIjGLV2gHYg0A9sk0iR3uz8xjwSJLzdV64dga3ws4kUvT+y1RwC6sFAw7Lkc3c83L4quSSDjDDZGQaCyqaayXKti7sBBDimJ7oNlZsMg6RdgJrC2SuOaYP10uIpemF7lqLw/Yddk6sSq8LnUkiYQz3BCJVbwiM+8cIVFq+gHNoHQz0FgHellqrvpVgoOYWECWGy4X0UvTi+waZAWWLlstvC6+KomEM1haJPF43NA0Zr03AY1aUCw1V71rFxWVoKKEmBCXF8iV0vS5BF7JBtIiOcdAK0Zi5EM1Mo3NtuNk7TqiFZCnAe21/ehyIXEdqpt+qcHZXI9RiAyeyQY8i69mQhIJZ7glElIfqp4wZjXQ/J7i6QeXC6nrUO+8qqpxWFU1zleE6QYiLchE5JdswKtkvB4kkXAGrXUkToQfq4GWyymeZuApsEhdh2bn+YEw3UDEBZmI/JMNvHjPkkg4Q4TqvywG2rnkPmElsMj2VjEm6nOV0FWIvmdLLhO5JBLOEIFIWID24iyRQVtgWRETqevQ7y5GNzjXSdRrSCLhjFwlEhU0F2eJCBYCi6S0ibRIzOEVifpZIaIJSSScwYtI1P0MrMoq8IB2sonufrACbYFFKvxJXYcsXYw8hKaIC1r14HeFiDYkkXAGayJpa2vDcVXpW5wqAPjta67BWCzGlVT0JpvfNWbaAouUmEhdhywCuzyEpugLWo3v5U+FiDYkkXAGayKpqa7GUF5eqgzKQgAMZAhyXimB2ZNNjE2i3MJIYEUilWnaNEmRPrUaASkxkQZsaQZ2eQhN0Re0Ina9z4aGBt8rRLQhiYQzWBKJ2X4GvAu56WvuueHDN1olb/a3VqDpW2oBBFjIXJO2CysLzE4laaf38HpBq7Fl7W+FiCYkkXAGSyLJrLfTDO7ra7ltS/Zkq0JRNolyC1VgVVREDfbuLtPVrvW077y8EGq3oxXF3278Hnchre1zRc82y35fc3NCIaIJSSScwYpItG4SlTjWJCe4F4Xc1q1bZzDZFlITQCKAvLJwlwZvdr4IyRFaGD9fWZIs3bu7RM42s35+/ytENGBHrp0HEsKhvb0dJk+aBGsaGlLHfgwAnwHApcm/XwOA2zS/eTX57+DBg5m16+zZswCgQD7cA52AABAFgFchHx6ETgCor6+H/v37w+DBg2HIkCHM2sEaLS0tyf9VZnwTTf67HwCGpP7eunWr6fn9+/d33R/xeBxaWlqo9O3QoUOhuroGXnnlXujsVN/jSgB4GwCWQ9fIug06OxEaGibDvn37TO+b2T79e7wK+fm/gLFjazwdH8bv988AUAYAk1NHRo+OwooVy/k0zM/gQGw5D9oWid5mNiFIZGpB8t8g8N/zWdXkrsywPtS/RdK63YC2ReKmX1hlV+nFhMCBK8qsfaIW0LR6vyNGlAvXZi+Qs66ttrY2nDRpEvbq1QuDwSDeddddeOLECdPfRKPRrMny05/+NO2cDz74AGtqarBHjx5YXFyMDzzwAH711VfE7aJJJFalox999FF84oknMFpRkfZMrLO21OyWaEUF9snPx7kAuAwA53IiMd4w3+sk2+3BKk2VdXaVGhNySoYk7ROxjIjR+yoqKuGaAuxmHQ/rNUA5SyTjx4/H4cOH49atW/H111/HwYMH48SJE01/E41GccqUKXj48OHUR9sxZ86cwSuuuALHjh2LO3fuxDVr1mDfvn3xoYceIm4XTSKx2swmFoulzuUxQfX2jHayta/fQJLFpdVU9c6vqIi66hfecQa7ZChyHMQK+vu8VNp6HjeC3M1e7Lz2cc9JItmzZw8CADY1NaWOrV27FvPy8vDQoUOGv4tGo/iLX/zC8Ps1a9agoih45MiR1LE//elPWFhYiKdPnyZqG0+LJBqJOL6uk0FvtGd0NBLhpmV6WbIik6ytdpSMRNItYDduEavMp7q6Ot12OO0vu64o0TOzSKB9n6TPQ0OQu9mLndc+7jlJJIsXL8ZQKJR27KuvvsL8/Hx84YUXDH8XjUaxb9++WFRUhJdffjnOmDEDP//889T3M2fOxOHDh6f95sCBAwgAuGPHDt1rnjp1Cjs6OlKf1tZWakSCiBiNRLJjIABYBvZTfN0Meq93aPNbyQrabih9jb8NE+617D6hRWSklq6fLRI9kD6PW0GunVfNkMjEjBPOK9I5SUP5ykkieeyxx3Do0KFZx4uLi/Gpp54y/N3TTz+N69atw127duHy5cuxf//++P3vfz/1/ZQpU/D6669P+83nn39uqlHNmjUrbbKqH1pEEovFUoH1lPAHwF1gP8XXzaD3es9oP5WsYCVUs91N+im6VVXjkq43Oum7es+nJ5iqqsYl18t0ucPy8kJYVTWOijDjbY1aufdoKFfqvKrKmONVBPOKxPVNy+3lKyJ58MEHdYWy9rN3717HRJKJDRs2IADg/v37EdEZkbC2SNTBOk+jrTixBNwOei8tEr9pu6zcPPrZVUbZZPT7y8oqTOzSGMhoXwB79y42/A2N+7KClXuPhnLV3NyMCmRXp+gNiYxMNxaJmgxDw+3lKyL55JNPcO/evaaf06dPO3ZtZeKzzz5DAMB169YhojPXViZYLEiksYUujUHv1Z7RfvO/sya+eDyOdXV1pn3Cor/MrML0Z44jwJrkv/pWkx3LyGtr1Mi9R0O5cnsNozlZGYlQVfx8RSSkUIPtb731VupYQ0ODZbA9E5s3b0YAwHfeeQcRu4LtR48eTZ3z9NNPY2FhIZ46dYromiyIhMYWujQGvVd7RvvNIkFkX6nWen0L3f6yul99fb0Oebl/b6K/e7fKlVsFz2hOxmIxV9fNRE4SCWIi/feqq67CxsZG3Lx5Mw4ZMiQt/fejjz7CYcOGYWNjIyIi7t+/Hx955BF866238ODBg7h69WocNGgQVlZWpn6jpv9ef/31+Pbbb+O6deuwuLjYs/RfxHS/sNsUX1oWhRdrAfy21W97ezvVrC09GK9vUZKWQPZ3FRVRR/eysgr1qxy7tyRFt0bdKle0XMaZc5K2KzpniaStrQ0nTpyIPXv2xMLCQrzzzjvTFiQePHgQAQA3btyIiIgffvghVlZWYp8+fTAQCODgwYNx+vTpWR3zz3/+E7/zne9gjx49sG/fvvjLX/7SkwWJLPLDvbIoaEDUldF60PPpRyLu1pHoob29PWs9S4JAChCgEDMzuoqKShy3gcQyyCa2x3PeIlHhRrli5TKmed2cJRJRQYtIWOaHi7i6mBR+aDsvn36XkJ2HXTEJRL3CmZGIeyKzsgr1yL5btx6YWfwQIIhFRSXU7msnm0vErXNZKXg0ryuJhDNoEInXazYknIOnBm3l9qmvr6cqNEmtwuxSK+mWkfo3abu2bduG4fCIrPu2tLQQW6k0M79YkRErJYnGdSWRcAYNIvF6zYZbiKj18QJPn75Xbh9SwZTeF9pMLrK+0BP+4fDIVEULO5YfDSuRVzkSESGJhDPOZYvkXJ5oKkSvicUTbndfJE83Nu9nWu+EVzkSESGJhDNox0h4r9lwg3N5omnBU7iLnoSg1xeKEjIteonoNN1Y3/KjYSX6VbmjBUkknEGLSGgH4Fi7m871iaaFF8Jd1CQEo8rJVm4mZ+nG7CwSv7ub3UISCWfQXkfiVkDwcjed6xNND6IKdy9gd68TZ+nGxpafWR0wEpzripIkEs5gtWe7U/ByN5FMtHM5CC+RgB03k5N0YyPLb1xVFZ4H+Wnnngf5OK6qirjtfnQ304IkEs4QiUh4a1FGE23sddcxs4rskpMkM29hx81kN93Y6J2q95wLgPXJj1mpdqMx4ucFvW4hiYQzRCISVu4ms4kWTRaL0060cVVV1K0iu+sC/LafCSuIQKR2kxHcughJt2IgdQOfiy5LSSScIRKR0LZIzCaa3nfRSAS3bdvGxCqyuy7A6wqyXkMkIuWdjKBuDpemyED25nA11dUYVBScDoCvMnQDO4WXSoAkEs4QiUgQ6fh11QFstr+BUSxmRDhM3Sqym4Xjl3pNLOEVkZoJPx6aPel21Y2NjbpWy0KXCg8NiLA+SxIJZ4hGJG78unoD2GhCOv3OyQS1CtjGYjFb59POKBPBfaSFF0RqZQHx6iOSXQQREUeGw7pWC8lOhawhwvosSSScIRqRqHCi/WkH8LLkhDKakGbfjQyHqWa7WAnGSCRq63xawkwk95EWtImUhAS60m27LKC8vBBWVFzLtY9IswmtFCGvlAJR0o4lkXCGqERiF5kDuNmF1dHU1ETdNI9EophdVbYPGhUE5LHaXNQ4DC0iJSVK8/sp3PvIyr1rZbWMCIeZtc0KoqzPkkTCGU6JRDR3iN4AroGEua83Ia0ma3NzM9bX12N9fT2VVN3EDnDppdIBahBgl+4EYx3gFT0OQ4NISYmya9V5pgW0ybSPrGpvOYWVe9dK61eLRHoBaZGco7BLJCIE0vSgN4DboSvTJbOtRpO1paXF9vOR9InxXhxd7i29e7AK8PphJz83RGqHKPXLl7QhwCDTPmLt7jJ79yIvNhShbZJIOMMukVgF0ry0VIwG8Mirr8a6ujpcv3591m8yJ6uTQCHpb/S3mk24t3i7lES3SFQ4JVI7RJnoCwUBemveTRkC9DTto4RS4I1LUOTFhiK0TRIJZ9jpcMvUxIoKZoPHjKDU7/RiGyVFRcRtcmKW2/mN3r7oCfdWuycCXOSS7m5hlyirqsYhQCDj3SxPvh+9febLTK/LQ6Fy4n7lCadKAI2+k0TCGXY63CyQpgBgb0WhnvJnd1FhTXU1NjU1JdaRRCK2rAsngUK7v+nSlJdhl3vLG5eS6CXd3cIOUer1ReI37Uky0R5XMBHbyrZ0YrEYc21cVPeyW9B8LkkknEHDInkc6K+9UGHmNjL7jrV14fQ3IrqUcrWEhhOijMfjBjGTOAI8kGGtZL8/u8qLE4iwToMFaD6XJBLOcBoj0cYheiqKbU2eBCT58kbfqRsJ2W2Tk0Ch3d/ksktJRGhLwpMSptk7MvouEolSU6iM3DuiZEXRBu3nkkTCGXaJRC+QVpksfEh7cFu5jcy+U7VKu21yEii0+5tcdymJBicLL83ekdF3iRRvdwqVlXtHlHUaWpiRHilx034uSSSc4XQdiVG2E82UPzcWSTwed9UmJ+4eu79xcg/R1u/4AW4WXpq9o8zvaGjVJFmRolgkRqTnJIVeWiQ+hyhb7RoJSDMysCIKEdIQaUGkAKufyIx3TMqN8kIqTEVYp6FtRybplRQVOYp10HwuSSSc4fVWu1YC0owMSIkiF4LJXgRYMwlD1NpcZuC98NKN8kLq3hFBQbIivXkOLAuaz5WzRNLW1oaTJk3CXr16YTAYxLvuugtPnDhheP7BgwfTOlT7ee6551Ln6X2/YsUK4nZ5XWuLVEDacTHkGni7M4wIo6pqnJC1uczgVZacU7elnffs5bi3Ir1lFmRoBhrPlbNEMn78eBw+fDhu3boVX3/9dRw8eDBOnDjR8PwzZ87g4cOH0z51dXXYs2fPNAICAFyyZEnaeSdPniRulxdEomq6DQ0NXAWkX8E7wGoUU0isnxAnbZkUfsqSE8VtZQUWFglN5CSR7NmzBwHSi6mtXbsW8/Ly8NChQ8TXKSsrw7vuuivtGADgqlWriK9x6tQp7OjoSH1aW1tdEwmpz1zPjSVaBoqIsJq06spmGrELKw0e4NWM42LU5jKDn7LkRHBbkcKI9NQYiay1RRmLFy/GUCiUduyrr77C/Px8fOGFF4iu8dZbbyEA4BtvvJF2HACwX79+WFRUhCNHjsTFixfj2bNnDa8za9YsXXeYEyKxGwDOdGPNBXYLGXMNepM2CJC2S55C+B7MYBVTAJiOAM3YVXhSfItEhZ9coH5oqxHpHThwwHMyzEkieeyxx3Do0KFZx4uLi/Gpp54iusbdd9+Nl112WdbxRx55BDdv3ow7duzAOXPmYCAQwD/84Q+G16FpkRjFN0aGw8QLqcqSAlF0U94LaC0MvUkbAMCnNX3fGxI75LkJxFtbJJn1qAJYVTWOwdNL+AVGpOclGfqKSB588EFd7V772bt3r2si+eKLLzAYDOK8efMsz505cyZeeOGFxM/gZj8SLTE0A+AajZWRqYUY+fl3ZWjSIpvyvGBm6UUjETw/L8/cknNp1RnFFPr0+bruLoKSSCREg6+I5JNPPsG9e/eafk6fPu3atfXnP/8Zu3Xrhp988onluS+//DICAJ46dYroGZwSiUoMuyCxgZRW6CkA+HOwt5Bq/fr1wpvyvGBk6akVBKaDRWwJ3MWZ9GIKkUilqaXil/fmpzUwEs7hKyIhhRpsf+utt1LHGhoaiIPt0WgUf/CDHxDd69FHH8XevXsTt82tRVIGiV0ItUIvCIBRHa3YLxkpXqG5udmytAsA4CawiC25tEhUaF0TJOsxeAtpO/cTaUGnBHvkJJEgJtJ/r7rqKmxsbMTNmzfjkCFD0tJ/P/roIxw2bBg2Njam/W7fvn2Yl5eHa9euzbrmSy+9hPX19fjuu+/ivn378KmnnsLzzz8fH374YeJ2uUn/taqx9WqGVuynjBSe0BNyZvXFloP+NsJqjEQl6MpIhJpgt4qdRJNjgcd7dUIKuVoxV0IfOUskbW1tOHHiROzZsycWFhbinXfembYeRF2AuHHjxrTfPfTQQ3jRRRdhZ2dn1jXXrl2LZWVl2LNnT/za176Gw4cPx4ULF+qeawQ3RGJVpO4BA63YDxkpPKEVclbWRrSiAvvk5+PCJGlohWme5v92NvQihVHspG/R17kKabukIFJ9qlyB6C7CnCUSUeGGSKwmaFBRhNb4RJgMen1Yk7Qu9FyAelZd2be+hf/nmmvSjmVmdNEQ7PqxkyhXIe2EFESsmOtX+MVFKImEM9yubK+MRLCnouBcyF7fIOIAQxRrMugJuXYdayOzfVqrTk9D750kpMxsOhqCXS92wktIO7kfKfmIoFiIDr+4CCWRcIZTItETxmoa78hwOG0Vv2gQaTKQrlp3+vvM9xOLxbi2XwSLBNFgQzZIuAO/fc01OK6qippikauE5CcXoSQSznBKJLpasKJgtKKCUUvpTFBak4GmsHCTzWaloU+HjGw6B+/H6ll5Z+MZ3c8suSDTJZi5duk8cO8KFMnSZQE/uQglkXCGEyLxohotrQnqdjKwEBZustks34WL90P6rLyz8fTuR5JcoPbVJWCcsu5mLLOwdEWybqRFImEIJ0TCWzOhOUGtJoPVvt4s3WJOs9mM6nBVuXw/dp+Vdzaeej81k82qnV1rYayJ125f0Rayolo3flkLJomEM0S3SFjcy6xqqdnEFVUj09PQFUi4apy2U9RnVdumEpaddnathTGvCuDkOWkrVyLF8bTwy1owSSSc4TZGwlozYWH9GLlGQopiOnG99hFbuTn0Mrmcvh+vn1UPelr6yHDYVjtJFtE6Gcs0iVdkElch+lowSSSc4ZRIeGkmLCeVOhlIN9jyaoI7cXO4fT8iCjMjLV2x0c729nYsKSrKqjgdAvdl+GkpVyKSuN8giYQz3K4j4aGZsLZ+7ExcL3zEbtwcbt6PSP5wksWvpO1sb2/HaEVFFsk2NTW5Gsu0lCsRSdxvkETCGV7v2U4C1taPnYnL20fspVDxwh9u5L6zIvsRSReX+qmMRDAWi5n2DysliMZ1RSJxP0ISCWf4gUhUsLR+7E5cXj5iEdwcLJ9VJY5t27aZkhYJocbjcYzFYrrWhmjBYCv4JagtKiSRcIafiIQlRJ24uerm0Iv7WNUHIyF7J25AkdZqZEL0oLaokETCGbyIROTJqqK5uRnr6+sty5LwRi66OczqgxmRpRXZNzY22iJdUddqSLiHJBLOYEEkWtLww2T1oo12iFVUa8kpSFfjG7nvjLT0ETZTgUVdqyHhHpJIOIMmkegJ5JKiIuEnK0+B4oa0csXNYRn3sem+0+4sSWqR5KrLUCIBSSScQZNIMgXyXBsT2yvwFihSC7buc9JFgXqkXAzZO0cGIbFwUQsRkhgk2EESCWfQIhI94bAGuspRqPtixBlOVidxGCuBsmjRIurb1YpMrLygF/cJ5eXZWhSoR8pBACzRXAMgsdAwc1sD+S5yG5JIOIMWkegJ5ObkpCzLmNhllCerG3eRkUD5E2SXGncbl1D7aJOGVEXVglknRxjFfUgXBVoRwbOQ2OrZbJfOXExiIIEfEl/cQhIJZ7C0SBASpSeCkF2yu6SoyNa1WVbk1RMogaSGTNMF1djYmE1OALhQIC2Yd+KB07iPlSVJ0vZcS2Kwgh8SX2hBEglnsIiRqAL5cXAXIyEZ+DRcFHoChYXbo6a6OouceidJSxQt2C8xHLc7S2phl8z8qtH75d3SgCQSzqBJJEYC2WlAk2TgazVTt3EYVaDU19e7arcerASfCFsT+y1uwNs15WeN3m/v1i0kkXAGi3Ukdqvq6oF04Kvn0YzDqNecC+mxDDeTToQsIStNWoQ22gGpa4qWBeFnjd5v79YtJJFwBuuV7U61RjsDXy0L7iYOo0VbW1vWJldlABgyCdxawUuNkFST9qvWauSaomlBiNw3JEQpcvtZQBIJZ7AmEqcBTbsWCYsdFPWIyY0bw6ssITuadC5lMtG0IETU6O0SZS69WyvkJJE8+uijOHr0aOzRowcGg0Gi35w9exZnzpyJpaWl2L17dxwzZoyuxjVp0iTs1asXBoNBvOuuu/DEiRO22sar1paT7BySgU97grPU3Lwqy27neXIlk4n2exRRo7dLlLnybkmQk0Ty8MMP4+9+9zu8//77iYlkzpw5GAwG8cUXX8R33nkHJ0yYgBdffDGePHkydc748eNx+PDhuHXrVnz99ddx8ODBOHHiRFttE7n6L8nApz3BeWiePEudOH0ev5djYfEeRdLo3Yx7v79bEuQkkahYsmQJEZGcPXsWS0tLce7cualjx44dw0AggCtWrEBExD179mBmts/atWsxLy8PDx06RNwmkYlEhdXApznBRdQ83SDXnocULJ5bJI1eRFebSJBEgogtLS0IALhz586045WVlXjvvfciIuLixYsxFAqlff/VV19hfn4+vvDCC4bXPnXqFHZ0dKQ+ra2twhOJFWhPcJE0TxrItechBavnFkGjP1cVBFJIIkHEN954AwEAP/7447TjN998M95yyy2IiPjYY4/h0KFDs35bXFyMTz31lOG1Z82alSZw1Y+fiUQFrQkukuZJA7n2PKTI9ec+VxUEEtghkvPAQ8yYMQN++9vfmp6zd+9euPTSSzm1iAwPPfQQ3H///am/jx8/DhdddJGHLaKHIUOGwJAhQ1xfp3fv3vC3detg3759sH//fhg8eDCV63qFXHseUuT6cy9fsQL+feJEmNzQkDpWM3YsLF+xwsNW+Q+eEskvf/lLuOOOO0zPGTRokKNrl5aWAgDA0aNH4YILLkgdP3r0KJSVlaXO+eSTT9J+d+bMGWhvb0/9Xg+BQAACgYCjdp1roEVMoiDXnocUufrcuU6UvOApkRQXF0NxcTGTa1988cVQWloKGzZsSBHH8ePHobGxEe6++24AABg9ejQcO3YMtm/fDldffTUAAPzjH/+As2fPwqhRo5i0S0JCQjzkKlHyguJ1A0jx4Ycfwttvvw0ffvghdHZ2wttvvw1vv/02fPbZZ6lzLr30Uli1ahUAAOTl5cF9990Hjz76KLz00kvw7rvvwu233w79+vWDm266CQAALrvsMhg/fjxMmTIFtm3bBm+88QZMmzYNbr31VujXr58XjykhISHhO3hqkdjBww8/DMuWLUv9fdVVVwEAwMaNG+Haa68FAIDm5mbo6OhInfOrX/0KPv/8c5g6dSocO3YMIpEIrFu3Drp3754659lnn4Vp06bBmDFjQFEU+MEPfgB//OMf+TyUhISERA4gDxHR60b4HcePH4dgMAgdHR1QWFjodXMkJCQkXMOOXPONa0tCQkJCQkxIIpGQkJCQcAVJJBISEhISriCJREJCQkLCFXyTtSUy1HyF48ePe9wSCQkJCTpQ5RlJPpYkEgo4ceIEAEDOlEmRkJCQUHHixAkIBoOm58j0Xwo4e/YsfPzxx9CrVy/Iy8sj+o1an6u1tVWmDGdA9o0xZN8YQ/aNMZz0DSLCiRMnoF+/fqAo5lEQaZFQgKIocOGFFzr6bWFhoRz0BpB9YwzZN8aQfWMMu31jZYmokMF2CQkJCQlXkEQiISEhIeEKkkg8QiAQgFmzZsly9DqQfWMM2TfGkH1jDNZ9I4PtEhISEhKuIC0SCQkJCQlXkEQiISEhIeEKkkgkJCQkJFxBEomEhISEhCtIIpGQkJCQcAVJJJzw2GOPwbe//W04//zzIRQKEf0GEeHhhx+GCy64AHr06AFjx46Fffv2sW2oB2hvb4fbbrsNCgsLIRQKwY9//GP47LPPTH9z7bXXQl5eXtrnZz/7GacWs8WCBQvgG9/4BnTv3h1GjRoF27ZtMz3/+eefh0svvRS6d+8OV155JaxZs4ZTS/nDTt8sXbo0a4xot9nOFbz22mvwve99D/r16wd5eXnw4osvWv5m06ZNEA6HIRAIwODBg2Hp0qWu2iCJhBO+/PJLuPnmm+Huu+8m/s3jjz8Of/zjH2HhwoXQ2NgIX/va16C6uhpOnTrFsKX8cdttt8Hu3bvh73//O7z88svw2muvwdSpUy1/N2XKFDh8+HDq8/jjj3NoLVvEYjG4//77YdasWbBjxw4YPnw4VFdXwyeffKJ7/ptvvgkTJ06EH//4x7Bz50646aab4KabboL33nuPc8vZw27fACRKgmjHyAcffMCxxXzw+eefw/Dhw2HBggVE5x88eBBuuOEGuO666+Dtt9+G++67D37yk59AQ0OD80agBFcsWbIEg8Gg5Xlnz57F0tJSnDt3burYsWPHMBAI4IoVKxi2kC/27NmDAIBNTU2pY2vXrsW8vDw8dOiQ4e+i0Sj+4he/4NBCvigvL8d77rkn9XdnZyf269cPZ8+erXv+LbfcgjfccEPasVGjRuFPf/pTpu30Anb7hnSu5RIAAFetWmV6zq9+9Su8/PLL04796Ec/wurqasf3lRaJoDh48CAcOXIExo4dmzoWDAZh1KhRsGXLFg9bRhdbtmyBUCgEI0aMSB0bO3YsKIoCjY2Npr999tlnoW/fvnDFFVfAQw89BF988QXr5jLFl19+Cdu3b09754qiwNixYw3f+ZYtW9LOBwCorq7OqTEC4KxvAAA+++wzGDhwIFx00UVw4403wu7du3k0V2iwGDOy+q+gOHLkCAAAlJSUpB0vKSlJfZcLOHLkCHz9619PO3beeedBnz59TJ9z0qRJMHDgQOjXrx/s2rULHnzwQWhuboYXXniBdZOZ4dNPP4XOzk7dd/7+++/r/ubIkSM5P0YAnPXNsGHD4JlnnoFvfetb0NHRAfPmzYNvf/vbsHv3bsfVunMBRmPm+PHjcPLkSejRo4fta0qLxAVmzJiRFczL/BgN8lwH676ZOnUqVFdXw5VXXgm33XYb/PnPf4ZVq1ZBS0sLxaeQ8DNGjx4Nt99+O5SVlUE0GoUXXngBiouL4emnn/a6aTkHaZG4wC9/+Uu44447TM8ZNGiQo2uXlpYCAMDRo0fhggsuSB0/evQolJWVObomT5D2TWlpaVaw9MyZM9De3p7qAxKMGjUKAAD2798Pl1xyie32ioC+fftCfn4+HD16NO340aNHDfuitLTU1vl+hZO+yUS3bt3gqquugv3797Noom9gNGYKCwsdWSMAkkhcobi4GIqLi5lc++KLL4bS0lLYsGFDijiOHz8OjY2NtjK/vAJp34wePRqOHTsG27dvh6uvvhoAAP7xj3/A2bNnU+RAgrfffhsAII10/YaCggK4+uqrYcOGDXDTTTcBQGL3zQ0bNsC0adN0fzN69GjYsGED3Hfffaljf//732H06NEcWswPTvomE52dnfDuu+9CTU0Nw5aKj9GjR2eliLseM47D9BK28MEHH+DOnTuxrq4Oe/bsiTt37sSdO3fiiRMnUucMGzYMX3jhhdTfc+bMwVAohKtXr8Zdu3bhjTfeiBdffDGePHnSi0dghvHjx+NVV12FjY2NuHnzZhwyZAhOnDgx9f1HH32Ew4YNw8bGRkRE3L9/Pz7yyCP41ltv4cGDB3H16tU4aNAgrKys9OoRqGHlypUYCARw6dKluGfPHpw6dSqGQiE8cuQIIiJOnjwZZ8yYkTr/jTfewPPOOw/nzZuHe/fuxVmzZmG3bt3w3Xff9eoRmMFu39TV1WFDQwO2tLTg9u3b8dZbb8Xu3bvj7t27vXoEJjhx4kRKngAA/u53v8OdO3fiBx98gIiIM2bMwMmTJ6fOP3DgAJ5//vk4ffp03Lt3Ly5YsADz8/Nx3bp1jtsgiYQTamtrEQCyPhs3bkydAwC4ZMmS1N9nz57FmTNnYklJCQYCARwzZgw2NzfzbzxjtLW14cSJE7Fnz55YWFiId955ZxrBHjx4MK2vPvzwQ6ysrMQ+ffpgIBDAwYMH4/Tp07Gjo8OjJ6CL+fPn44ABA7CgoADLy8tx69atqe+i0SjW1tamnf/cc8/h0KFDsaCgAC+//HL829/+xrnF/GCnb+67777UuSUlJVhTU4M7duzwoNVssXHjRl3ZovZFbW0tRqPRrN+UlZVhQUEBDho0KE3uOIHcj0RCQkJCwhVk1paEhISEhCtIIpGQkJCQcAVJJBISEhISriCJREJCQkLCFSSRSEhISEi4giQSCQkJCQlXkEQiISEhIeEKkkgkJCQkJFxBEomEhISEhCtIIpGQEBQrVqyAHj16wOHDh1PH7rzzztT+GhISokCWSJGQEBSICGVlZVBZWQnz58+HWbNmwTPPPANbt26F/v37e908CYkUZBl5CQlBkZeXB4899hj88Ic/hNLSUpg/fz68/vrrKRL5/ve/D5s2bYIxY8bAX//6V49bK3EuQ1okEhKCIxwOw+7du2H9+vUQjUZTxzdt2gQnTpyAZcuWSSKR8BQyRiIhITDWrVsH77//vu5+5ddeey306tXLo5ZJSHRBEomEhKDYsWMH3HLLLbB48WIYM2YMzJw50+smSUjoQsZIJCQExD//+U+44YYb4Ne//jVMnDgRBg0aBKNHj4YdO3ZAOBz2unkSEmmQFomEhGBob2+H8ePHw4033ggzZswAAIBRo0bBd77zHfj1r3/tceskJLIhLRIJCcHQp08feP/997OO/+1vf/OgNRIS1pBZWxISPsXYsWPhnXfegc8//xz69OkDzz//PIwePdrrZkmcg5BEIiEhISHhCjJGIiEhISHhCpJIJCQkJCRcQRKJhISEhIQrSCKRkJCQkHAFSSQSEhISEq4giURCQkJCwhUkkUhISEhIuIIkEgkJCQkJV5BEIiEhISHhCpJIJCQkJCRcQRKJhISEhIQr/P+Hbb38tIDSMAAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
],
"source": [
"import pennylane as qml\n",
"from pennylane import numpy as np\n",
"from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# Set a random seed\n",
"np.random.seed(42)\n",
"\n",
"\n",
"# Make a dataset of points inside and outside of a circle\n",
"def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
" \"\"\"\n",
" Generates a dataset of points with 1/0 labels inside a given radius.\n",
"\n",
" Args:\n",
" samples (int): number of samples to generate\n",
" center (tuple): center of the circle\n",
" radius (float: radius of the circle\n",
"\n",
" Returns:\n",
" Xvals (array[tuple]): coordinates of points\n",
" yvals (array[int]): classification labels\n",
" \"\"\"\n",
" Xvals, yvals = [], []\n",
"\n",
" for i in range(samples):\n",
" x = 2 * (np.random.rand(2)) - 1\n",
" y = 0\n",
" if np.linalg.norm(x - center) < radius:\n",
" y = 1\n",
" Xvals.append(x)\n",
" yvals.append(y)\n",
" return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
"\n",
"\n",
"def plot_data(x, y, fig=None, ax=None):\n",
" \"\"\"\n",
" Plot data with red/blue values for a binary classification.\n",
"\n",
" Args:\n",
" x (array[tuple]): array of data points as tuples\n",
" y (array[int]): array of data points as tuples\n",
" \"\"\"\n",
" if fig == None:\n",
" fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
" reds = y == 0\n",
" blues = y == 1\n",
" ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
" ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
" ax.set_xlabel(\"$x_1$\")\n",
" ax.set_ylabel(\"$x_2$\")\n",
"\n",
"\n",
"Xdata, ydata = circle(500)\n",
"fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
"plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
"plt.show()\n",
"\n",
"\n",
"# Define output labels as quantum state vectors\n",
"def density_matrix(state):\n",
" \"\"\"Calculates the density matrix representation of a state.\n",
"\n",
" Args:\n",
" state (array[complex]): array representing a quantum state vector\n",
"\n",
" Returns:\n",
" dm: (array[complex]): array representing the density matrix\n",
" \"\"\"\n",
" return state * np.conj(state).T\n",
"\n",
"\n",
"label_0 = [[1], [0]]\n",
"label_1 = [[0], [1]]\n",
"state_labels = np.array([label_0, label_1], requires_grad=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqAg65FbTnyx"
},
"source": [
"Simple classifier with data reloading and fidelity loss\n",
"=======================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"id": "ZyKIWD9bTnyx"
},
"outputs": [],
"source": [
"dev = qml.device(\"lightning.qubit\", wires=1)\n",
"# Install any pennylane-plugin to run on some particular backend\n",
"\n",
"\n",
"@qml.qnode(dev, interface=\"autograd\")\n",
"def qcircuit(params, x, y):\n",
" \"\"\"A variational quantum circuit representing the Universal classifier.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): single input vector\n",
" y (array[float]): single output state density matrix\n",
"\n",
" Returns:\n",
" float: fidelity between output state and input\n",
" \"\"\"\n",
" for p in params:\n",
" qml.Rot(*x, wires=0)\n",
" qml.Rot(*p, wires=0)\n",
" return qml.expval(qml.Hermitian(y, wires=[0]))\n",
"\n",
"\n",
"def cost(params, x, y, state_labels=None):\n",
" \"\"\"Cost function to be minimized.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): array of state representations for labels\n",
"\n",
" Returns:\n",
" float: loss value to be minimized\n",
" \"\"\"\n",
" # Compute prediction for each input in data batch\n",
" loss = 0.0\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" for i in range(len(x)):\n",
" f = qcircuit(params, x[i], dm_labels[y[i]])\n",
" loss = loss + (1 - f) ** 2\n",
" return loss / len(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Bz83H0xTnyx"
},
"source": [
"Utility functions for testing and creating batches\n",
"==================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"id": "i1-TLseuTnyx"
},
"outputs": [],
"source": [
"def test(params, x, y, state_labels=None):\n",
" \"\"\"\n",
" Tests on a given set of data.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" predicted (array([int]): predicted labels for test data\n",
" output_states (array[float]): output quantum states from the circuit\n",
" \"\"\"\n",
" fidelity_values = []\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" predicted = []\n",
"\n",
" for i in range(len(x)):\n",
" fidel_function = lambda y: qcircuit(params, x[i], y)\n",
" fidelities = [fidel_function(dm) for dm in dm_labels]\n",
" best_fidel = np.argmax(fidelities)\n",
"\n",
" predicted.append(best_fidel)\n",
" fidelity_values.append(fidelities)\n",
"\n",
" return np.array(predicted), np.array(fidelity_values)\n",
"\n",
"\n",
"def accuracy_score(y_true, y_pred):\n",
" \"\"\"Accuracy score.\n",
"\n",
" Args:\n",
" y_true (array[float]): 1-d array of targets\n",
" y_predicted (array[float]): 1-d array of predictions\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" score (float): the fraction of correctly classified samples\n",
" \"\"\"\n",
" score = y_true == y_pred\n",
" return score.sum() / len(y_true)\n",
"\n",
"\n",
"def iterate_minibatches(inputs, targets, batch_size):\n",
" \"\"\"\n",
" A generator for batches of the input data\n",
"\n",
" Args:\n",
" inputs (array[float]): input data\n",
" targets (array[float]): targets\n",
"\n",
" Returns:\n",
" inputs (array[float]): one batch of input data of length `batch_size`\n",
" targets (array[float]): one batch of targets of length `batch_size`\n",
" \"\"\"\n",
" for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
" idxs = slice(start_idx, start_idx + batch_size)\n",
" yield inputs[idxs], targets[idxs]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B5s1nRL2Tnyy"
},
"source": [
"Train a quantum classifier on the circle dataset\n",
"================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "0d9e51c1-d449-45bd-adba-92e08b97e1ed"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.467081 | Train accuracy: 0.410000 | Test Accuracy: 0.357000\n",
"Epoch: 1 | Loss: 0.254146 | Train accuracy: 0.670000 | Test accuracy: 0.601000\n",
"Epoch: 2 | Loss: 0.210061 | Train accuracy: 0.650000 | Test accuracy: 0.599000\n",
"Epoch: 3 | Loss: 0.293175 | Train accuracy: 0.600000 | Test accuracy: 0.560500\n",
"Epoch: 4 | Loss: 0.170896 | Train accuracy: 0.680000 | Test accuracy: 0.678500\n",
"Epoch: 5 | Loss: 0.142287 | Train accuracy: 0.790000 | Test accuracy: 0.735500\n",
"Epoch: 6 | Loss: 0.164525 | Train accuracy: 0.745000 | Test accuracy: 0.695500\n",
"Epoch: 7 | Loss: 0.293692 | Train accuracy: 0.545000 | Test accuracy: 0.600000\n",
"Epoch: 8 | Loss: 0.132510 | Train accuracy: 0.815000 | Test accuracy: 0.762000\n",
"Epoch: 9 | Loss: 0.177017 | Train accuracy: 0.730000 | Test accuracy: 0.713000\n",
"Epoch: 10 | Loss: 0.123784 | Train accuracy: 0.800000 | Test accuracy: 0.774500\n"
]
}
],
"source": [
"# Generate training and test data\n",
"num_training = 200\n",
"num_test = 2000\n",
"\n",
"Xdata, y_train = circle(num_training)\n",
"X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
"\n",
"Xtest, y_test = circle(num_test)\n",
"X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
"\n",
"\n",
"# Train using Adam optimizer and evaluate the classifier\n",
"num_layers = 7\n",
"learning_rate = 0.6\n",
"epochs = 10\n",
"batch_size = 32\n",
"\n",
"opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
"\n",
"# initialize random weights\n",
"params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
"\n",
"predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
"accuracy_train = accuracy_score(y_train, predicted_train)\n",
"\n",
"predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
"accuracy_test = accuracy_score(y_test, predicted_test)\n",
"\n",
"# save predictions with random weights for comparison\n",
"initial_predictions = predicted_test\n",
"\n",
"loss = cost(params, X_test, y_test, state_labels)\n",
"\n",
"print(\n",
" \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
" 0, loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"for it in range(epochs):\n",
" for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
" params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
"\n",
" predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
" accuracy_train = accuracy_score(y_train, predicted_train)\n",
" loss = cost(params, X_train, y_train, state_labels)\n",
"\n",
" predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
" accuracy_test = accuracy_score(y_test, predicted_test)\n",
" res = [it + 1, loss, accuracy_train, accuracy_test]\n",
" print(\n",
" \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
" *res\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YWspC-9BTnyy"
},
"source": [
"Results\n",
"=======\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "ZPszGYA3Tnyy",
"outputId": "5f5a39b5-8859-47b1-f4f7-cdeb354bea0e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.123784 | Train accuracy 0.800000 | Test Accuracy : 0.774500\n",
"Learned weights\n",
"Layer 0: [2.73107099 1.62161438 3.07138144]\n",
"Layer 1: [3.70420024 1.081219 3.42608893]\n",
"Layer 2: [3.50629619 1.15774143 3.09361175]\n",
"Layer 3: [ 3.58109406 -1.81735392 -1.92568195]\n",
"Layer 4: [-0.42554993 1.74276134 -0.47367074]\n",
"Layer 5: [1.48463303 1.83547825 0.44335683]\n",
"Layer 6: [-1.48957045 -1.21376033 0.19225792]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
],
"image/png": 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4IYDRN954I9XV1VGpVAqct3btWkomk3TgwIHQfi1cuDDAg5Tb7sc+9jFqbGwMxav7779f4V/CKJPJ0FFHHeVhOhHRzp07CYDCt8lY9vLLL9OiRYuoUCgEjtfV1QXGzXQ9kZ6/jak8it3LAaxevRqzZs3CcccdhwsvvBD19fX46le/imOOOSZw3oc//OHA9/vvvx+pVApdXV347W9/632WLVuG+vp6zwq3a9cuvPzyy57rNKerrrrK2rfHH38cP/vZz3DVVVcpcXKVuJMdPHgQO3fuxNve9jacfPLJ3vGjjjoK73nPe/Dtb38bf/jDHwLXXHrppYF75XI5HDx4EM8++ywAP37vwQcfxF/+8pfIfZk1axZOO+00/Nd//RcA4Dvf+Q6SySTWrFmDX/3qV3jqqacAMCtqNpsdl/vcX/3VX6GpqSnwDABz0Y5CH/rQh5BMJgPHpk+f7v3/l7/8BaOjozjllFOQTqfxgx/8QGnDNo7lzJP+/n4sX74c2WzWO1ZfX49LL70UzzzzDH7yk58Ezn/f+94XsLScccYZICLFtfSMM87Az3/+c7zyyivGsXAcBytWrPDe209/+lOMjo5i7dq1ICJ873vfA8De26JFi7z5EXW96Oihhx7CMcccg/POO887Nm3aNHzoQx/Snl9fXx+IEZs6dSqWL18e6X1zS/bAwABefPFF6/kxTQ7FOH1ocBoIYt0LL7yA3/72t8jlcnjxxRfx5JNPlvt4AICxsTH853/+J971rnd5bf72t7/F6OgoisUinnrqKfziF78IXKPD4UqotrYWF198sXJ8vM85nn1m9erVnpUJYN4AjY2N3rUHDx7Erl278La3vQ1HH320d94pp5yCs88+29p+TDG9lsi0RqPS/fffj1wuh6ampgDur169GgcPHvT4l4lqN51O449//CMefvjhip9BpF/+8pf44Q9/iIsuuijgbdfV1YXW1lblfBHLfve73+H5559HLpfT8qY6Kpe/jSkaxe7lAG6//XaceuqpmDJlCubMmYP58+fDcYL6iClTpiguHE899RSef/55zJ49W9suTyrDmZ558+YFfp81a1Zgg9YRd6GsVt3h3/zmN3jxxRcxf/585bcFCxbg1Vdfxc9//nMsXLjQO3788ccHzuN95vGQ+Xwe559/Pnp6enDrrbfirLPOwtve9ja85z3vQW1tbWh/crkc+vv7ATAhrb29He3t7Whubsajjz6KOXPm4Ec/+hHe8573jOu5bc9go5NOOkk59tJLL+Gmm27Cli1b8Itf/CIQm66L/bX1oZx58uyzzyrupYDvmvTss88G5ox8bw7axx13nHL81VdfxfPPPx9w25Upl8vh+uuvx0svvYRHH30URx11FNra2rB06VI8+uij6Orqwre//W28613v8q6Jul509Oyzz2Lu3LmKAMMzJMt07LHHKuc2NTVh7969xntwOumkk3D11Vfjc5/7HO69917kcjmcd955eO973xu7lh9CinGa0aHA6SeeeAKf/OQn8Z//+Z+KsF9pnoOnn34aRIR//Md/xD/+4z9qz/n1r38dUKrocLgSOuaYY7TJGsf7nOPZZ+Rr+fX82l//+td46aWXtJhnwsGYYnqtkmmNRqWnnnoKe/fuNYZehPEb1Wj3Ix/5CP7t3/4NZ599No455hi8+c1vxrve9S685S1vqei+pv0JAObPn68Iwg8++CDWrVuHH/7wh4E8PVGVwOXytzFFo1joBrB8+XIvK66JamtrFQbv1VdfxezZs3HvvfdqrzlS4qxMlgW+CBOJBHbs2IHHHnsM3/jGNzAwMIAPfOAD+OxnP4vHHnsM9fX1xraz2Sx6e3uxf/9+PProo8jlckgkEshms3j00Udx9NFH49VXX/UsBhP1DDYStX6crrjiCmzZsgVXXXUV3vjGNyKVSiGRSODCCy/UJuoYbx/GQ6Z7V9qnbDaLv/zlL/je977nvTeACeOPPvoonnzySfzmN78JvLfJXC/jHevPfvazeP/734+vf/3r2LlzJ6688krcdNNNeOyxx4wJnGKaWIpxOpwmCqd///vfI5/Po7GxETfccAPmzp2LadOm4Qc/+AGuu+66iktn8euuueYaFItF7TmyMKnD4UpI1041nnM8uHMo94eYYjrcqNy1Lic7fPXVV9HV1YVrr71We/6pp55aUb+itjt79mz88Ic/xMDAAL75zW/im9/8JrZs2YL3ve992Lp1a0X3jkqPPvoozjvvPKxcuRJf+tKXcNRRR6GmpgZbtmzBl7/85UhtlMvfxhSNYqF7HDR37lzs2rULZ555ZihAnHDCCQCYhkx0FfzNb35j1YBzd7Mf//jHWL16tfG8qNqrWbNmYcaMGRgZGVF+e/LJJ+E4jmL9jEqdnZ3o7OzEP/3TP+HLX/4y/vqv/xr33XcfLrnkEuM1XCh7+OGHMTw87NUvXblyJe644w4cffTRqKurw7Jly0LvfSgyt+7YsQMXXXQRPvvZz3rH/vSnPwWyepdD5cyTE044wfgOxbYmipYvX46pU6fi0UcfxaOPPoo1a9YAYO+tt7cXjzzyiPedU9T1oqMTTjgBP/nJT0BEgXf99NNPV/wMtjmzePFiLF68GJ/85Cfx3e9+F2eeeSY2bdqEdevWVXzPmCafYpwOUrk4PTg4iNHRUXzlK18JrGeeNd5GpmfmY1xTUxM6ZpVQJfvBeJ9zomn27NmYNm2aFvPGg4MxxfRaoqamJoXHevnll/HLX/4ycGzu3Ln4v//7v4qxxYQh5bQ7depUvPWtb8Vb3/pWvPrqq/jIRz6CzZs34x//8R9xyimnlIVT4v4kk7xPPPDAA5g2bRoGBgYCXkxbtmxRrjX1odr8bUyM4pjucdC73vUuHDx4EDfeeKPy2yuvvOJNztWrV6Ompga33XZbQGsdlj2RU1tbG0466SRs3LhRmexiW7xeqW1BJJNJvPnNb8bXv/71QOr/X/3qV/jyl7+MbDaLxsZGa79E+t3vfqdo4zOZDABYy0+ddNJJOOaYY3DrrbfiL3/5C84880wATBjft28fduzYgc7OTmtd06jPX01KJpPKc992222KxjUqlTNPuru7MTQ05MVPAyzD95133okTTzxRG+NTTZo2bRo6Ojrwr//6rzhw4EDA0v3SSy/hC1/4AubOnYujjjrKuybqetFRsVjEL37xi0ApoT/96U/o7e2t+BlMc+YPf/iDEtO+ePFiOI5jnc8xHX4U4zSjSnGaW2DFa19++WV86UtfinRfnuVWfubZs2fjrLPOwubNmxWGGYBS7qwcqmQ/GO9zTjQlk0msXr0aX/va1/Dcc895x59++ml885vfPIQ9iymmyaO5c+cq8dh33nmnwne9613vwve+9z0MDAwobfz+978PzVsDMAzR4UfUdkdHRwO/OY7jVW3geFsOTh111FHIZDLYunVrwL374YcfVnL4JJNJJBKJwJg888wz+NrXvhb5OavN38bEKLZ0j4Py+Twuu+wy3HTTTfjhD3+IN7/5zaipqcFTTz2F+++/H5///OdxwQUXYNasWbjmmmtw00034dxzz0V3dzcef/xxfPOb38TMmTND7+E4Du644w689a1vRSaTwcUXX4yjjjoKTz75JJ544glv4XNL8JVXXolisYhkMokLL7xQ2+a6devw8MMPI5vN4iMf+QimTJmCzZs3489//nOglnFU2rp1K770pS/h7W9/O+bOnYsXXngBvb29aGxsRHd3t/X6XC6H++67D4sXL/Zi4Nra2lBXV4dSqRQpnruc568WnXvuudi2bRtSqRRaW1vxve99D7t27QqNhQ6jcubJ2rVr8a//+q84++yzceWVV6K5uRlbt27Fz372MzzwwAOKi+1EUC6Xw//7f/8PqVQKixcvBsAY6fnz52NkZESpmRl1vejosssuwxe/+EW8+93vxsc+9jEcddRRuPfeezFt2jQAlVm25s6di3Q6jU2bNqGhoQF1dXU444wz8KMf/QiXX3453vnOd+LUU0/FK6+8gm3btiGZTOL8888v+z4xHVqKcZpRpTi9YsUKNDU14aKLLsKVV16JRCKBbdu2lRWa09rair6+Ppx66qlobm7GokWLsGjRItx+++3IZrNYvHgxPvShD+Hkk0/Gr371K3zve9/D//zP/+BHP/pR2c8JmNd2WEz4eJ9zMuj666/Hzp07ceaZZ+LDH/4wDh48iC9+8YtYtGgRfvjDHx7q7sUU04TTJZdcgr/927/F+eefj66uLvzoRz/CwMCAgtFr1qzBv//7v+Pcc8/F+9//fixbtgx//OMf8f/9f/8fduzYgWeeeSYU15ctW4Y77rgD69atwymnnILZs2ejUChEbveSSy7B2NgYCoUCjj32WDz77LO47bbbkMlkvNw7mUwGyWQSN998M55//nnU1taiUCgY84/cdNNNOOecc5DNZvGBD3wAY2NjuO2227Bw4UL83//9n3feOeecg8997nN4y1vegve85z349a9/jdtvvx2nnHKKktdm2bJl2LVrFz73uc/h6KOPxkknnYQzzjij6vxtTC5NUpb0w5J4GRJbCZWLLrqI6urqjL/feeedtGzZMpo+fTo1NDTQ4sWL6dprr6XnnnvOO+fgwYPU09NDRx11FE2fPp3OOuss+vGPf6yUOZBL0XD69re/TV1dXdTQ0EB1dXW0ZMmSQCmRV155ha644gqaNWsWJRKJQAkGaEos/OAHP6BisUj19fU0Y8YMWrVqFX33u9+NND5yH3/wgx/Qu9/9bjr++OOptraWZs+eTeeeey7993//d9iwenT77bcTAPrwhz8cOL569WoCQI888kjguK5kmOn5+bnr169X7qsbF5nC5sjvfvc7uvjii2nmzJlUX19PxWKRnnzySeWdRh1HoujzhIho3759dMEFF1A6naZp06bR8uXL6cEHH9TeQy5/ZeqTruSPif7jP/6DAChlxy655BICQHfddZf2uijrRS4ZRkS0f/9+Ouecc2j69Ok0a9Ys+ru/+zt64IEHCAA99thjgWsXLlyo3Fcu80PESvq0trZ6JXe2bNlC+/fvpw984AM0d+5cmjZtGjU3N9OqVato165d1jGJqfoU4/Shx+nvfOc71NnZSdOnT6ejjz6arr32WhoYGFDGwIQf3/3ud2nZsmU0depU5Tn37dtH73vf++gNb3gD1dTU0DHHHEPnnnsu7dixw/qMYaRb20RmfCjnOU0lw6LsM6aSYR/96EeVa3W4/8gjj9Dpp59OU6dOpblz59I///M/09/93d/RtGnTwgckppgOQzKVDDOt0YMHD9J1111HM2fOpBkzZlCxWKSnn35au1ZeeOEF+vu//3s65ZRTaOrUqTRz5kxasWIFbdiwIVBCVUf/+7//S+eccw41NDQQgAA/EqXdHTt20Jvf/GaaPXs2TZ06lY4//ni67LLL6Je//GXgPr29vXTyySdTMpmMVD7sgQceoAULFlBtbS21trbSV77yFS1vc9ddd9G8efOotraWTjvtNNqyZYsWe5588klauXIlTZ8+PVAaMyp/G1N5lCA6jNS4McUUU0xl0MaNG/Hxj38c//M//6OUjooppphiej3Q2972NjzxxBPaeM+YYoopppgOD4pjumOKKabXBL300kuB73/605+wefNmzJs3Lxa4Y4opptcFyTj41FNPob+/H2edddah6VBMMcUUU0yRKI7pjimmmF4T9I53vAPHH388MpkMnn/+eWzfvh1PPvmksRRUTDHFFNORRieffDLe//734+STT8azzz6LO+64A1OnTjWWMIoppphiiunwoFjojimmmF4TVCwW8c///M+49957cfDgQbS2tuK+++7DX/3VXx3qrsUUU0wxTQq95S1vwb/+67/if//3f1FbW4s3vvGN+MxnPoN58+Yd6q7FFFNMMcUUQnFMd0wxxRRTTDHFFFNMMcUUU0wxTRDFMd0xxRRTTDHFFFNMMcUUU0wxxTRBFAvdMcUUU0wxxRRTTDHFFFNMMcU0QRTHdFvo1VdfxXPPPYeGhgYkEolD3Z2YYoppAoiI8MILL+Doo4+G48S6yHIoxsiYYjqyKcbHyinGx5hiOrKpHHyMhW4LPffcczjuuOMOdTdiiimmSaCf//znOPbYYw91N15TFGNkTDG9PijGx/IpxseYYnp9UBR8jIVuCzU0NABgg9nY2HiIexNTTDFNBP3hD3/Acccd5633mKJTjJExxXRkU4yPlVOMjzHFdGRTOfgYC90W4u5AjY2NMWDGFNMRTrH7X/kUY2RMMb0+KMbH8inGx5hien1QFHyMg3NiiimmmGKKKaaYYooppphiimmCKBa6Y4oppphiiimmmGKKKaaYYoppgug1JXT/13/9F9761rfi6KOPRiKRwNe+9jXrNYODg2hra0NtbS1OOeUU3HPPPRPez5hiiimmyaYYH2OKKaaYzBRjZEwxxXQo6TUV0/3HP/4RS5cuxQc+8AG84x3vsJ7/s5/9DOeccw7+9m//Fvfeey8eeeQRXHLJJTjqqKNQLBYntK+lUgn79u3DKaecgnnz5lXt3Immw6kvNgrrazWeo9pt7N+/H3v27MEb3/hGdHV1VdRepTTZ71W+n+n+5bzD19LcPBQU4+PE0+HUlyhUybobb9uVtvF6wkf5nkQ0bnw8VM/xWqLXCkbG+Dg5dLjzkDJG7N69G4lEAvl8ftLH91DykCZ8tPXrsOQh6TVKAOirX/1q6DnXXnstLVy4MHDsr/7qr6hYLEa+z/PPP08A6Pnnn490/ujoKBWL3QTA+xSL3TQ2NkZERCMjI9Tf30+lUin0XPG8ySBbvw8nGh0dpe5iMdDX7mKRxsbGQn8bT/v5bDbQhu39jI6O0sps1rs+KbQFgOa0tND+/fvHPRa2vuzZs4fa2jom/L3yPgwNDSnzqKVlTuB7Npunu+66i7LZvHKcv0NbG9V+hnLX+eFOk4WPROWNnW19yvhoOvdQ4ON4cWUyydTfffv2TQg+tre10fDwsHdOFHwU2zhU+Dg6Okp5Aacn6r2GzWtHevbuYpEefvhham9r046xbvy7CgXqKhQm7DmONHwkOjx5yGrho3zuRNORgo+TxUOWi48yRjjumq/G+Nr6smfPHuqQsGgiMXJoaMiKj0NDQ9TX16dgNx9j3fjPaWk5LPDxiBa6c7kcfexjHwscu/vuu6mxsTHyfcrdbIrFbkommwnYTsABArZTMtlMq1at1goSjpMOnOs46XELGHzyDgwMKIvJtMBM/S4WuyPfd7Kou1ik5mSStgN0AKDtADUnk9RdLIb+RqR/fvlYd7FIacehjIYRjMK0jo6OBhZ4EqCU2xfep5Tb3ngo2sbhEJCqynvVjZ1OQAZqCdjs3i+j3J99d9xz08qaKBS6lLnIrslM2Nw80pjKycJHovLGzrQ+V69apd0k044TODftOFXZPEdGRujOO++k3t7eSPhow5XDjUz9ndPSUjY+ysd525sAKkgYGVX4W71qFdUeQnzkv89paVHuXel7NeFj2LwuANQk3T+dSAQYzVmafUh+h+lEgmqr9Bw6OtLwkejw5CHHi4+mc8vFSBMPeaTj40TzkLOam8vGR0eDj00A1brXVkpR8LG7WNTev5J3a5o7un7UArQ5Ij6mNfjYVSgo7zAFUOYwwMcjWuieN28efeYznwkc+4//+A8CQC+++KL2mj/96U/0/PPPe5+f//znkQdzZGTEffHbCSDhcwsBDjlOk0GQEM9VhRSTgCEzjjqrJhduVq1aTYVCV+A30aqu7/c2AhBJUxqmLaumxpX3dXuwo7RNeC7Tb/lcLvD8rfPn05mdnYFjXHOWAagZKiM4M522bi75bNYDqW2WPu3cubPisTBtDiuzWWpva6P6RGLc75VIL1i3tbXT8PCwVlkDNBHQTUD4vAJalbkO1FuuKVX0DDY60pjKicJHosox0rR2bwFjLJokBpJvkuK5GURnAGTGcWBggPr6+pQ177jr3sQM2TAn6hyMItBWg2z93RARH5cvW0Z33XWXctwBY4i6DRhpE/5GRkbY+wbo2kOEj/39/dSxbFlV3quOaVyZzXqWGBPzN2J59t2a+b7eck1pHM8RRkcaPhIdfjxkNfBxm3tuFAFYJ1jrrJpcwDEpO480fCyHhzx9yZKyechy8NG23sezvsOUCyMjIwEecjzvNgwfRSWurFRYWQE+bgeoPiJGHip8jIVuiT796U8HJgf/RBnM/v5+9/xBAvoJGCImfPB2bIJENOF3dHSUVq1aTb61kAvXqlUTaCYmyNcQUEfABpKFeb/fB6T7HiAA1N/fb3zmMLf0KC7rYVb5sDE+IC2oA8I9dL/pNq1azYJtchxKWBatiWktlUoKoPeE9AkA9fT0aJ/TttHoNo5RF4TU+Vv+exXJF6w3EVCQ2naIWbV187o39P76uX6N5Zr+ip7BRkcaUzlR+EhUOUbytTsIUD9AQ2CCm43R4ZukTUgR8ZFv8pxhFP8qax7M2lrnrm2ZAbFhjm0OjsfVu1x8FMfZ1N+tmuMmpt5khe6M+M507+fOO+/0rq0UH8WxMSl6o+Jjpe+Vk8g07tXcIwPQmGY8ei33v1Mzxv22Po/jOcLoSMNHosOPhxTX7QhAfQDlheujrLVB27mSW7poMXSgx8dmMGtiPUBrwISdww0fOe810TykPDaV8pBR8dG23sPGt1x8JIDugOrOPV6MtOEjV+LqxqlcfCSArrH1uYJnsFEsdLtUiWvQeCzde/bsIVUQThOwhqIJEtGEXybI1hKzKG4nJgg1kl6IuUPqE4gpAsaIC0cDAwOGa8OtiSMjI9TW1k6OkyKdZT7MZV3vlsz6GeZOb9NSJjQL+BbN4rQx8OUyrQCor69PAfR/ttzn7rvvDgCjTSvISbdxrATbGLngsN5rY3weDH4b3cSUOLK3RsEwr+8Mvb9+rg9arokt3VFoovCRqHKM3LNnj8LkpcEYuSibpFXgcPGRb/IZMIaR/9VZDXSCWDeYkMTXKMfHcrX9nOnRWTttrt46HOBjZ3MVtWHkDdJxHT6GHY+CkTrhr6+vj4h8pvIAQA9Z7iHjI1G0mHIdPnaDMchccLBajcvAx+3CPWTrVrN7XB4PE9MYJpRbFU8VPoeNjjR8JDr8eEg+lzgecSEvKj4SIggc/f0KPm4Hc+NtNMwtnSDWDdAmHJ74aMPISizd1eYhdfjY3tZGY2NjAXyMcg85RKpSfCQwZeoMqDxkpRgZBR9T7vzTjVO5+EiIoHgq8xmiUCx0u3TttdfSokWLAsfe/e53T1gSjGKxmxKJNDGBQRQebG62PcSEj1tCzxMtqf55D7n/mwT7Aumt390kCvO+gLzNPb6Nkslmamvr0MZgqAJzBwE7iCkO1kt9VJ8lm81r3JKZVV52p5etPRykt7kLbht85joFpn0Uf5vh9mW30JEoGsSy3TOzWe/9bIJvxeObp9inBoCm19QExrC7WAzEoui0grJL13owjXiHfB6Y4LDY837w3yuQolwubxxfnXAfXRgWj+0mP1xCvH8zAStD2tP32Y/p3qYNuRiPG9qRxlROFj4SRR+77mKR0okEbZc2Rhtj0eOuG5sQODw8HFgXQNB9Wbfmw4QkUVjkjGoAc5JJyruuyjamZy5Aw+6z9sPuVq1lRF08MLmKiqFGfKzF/vI4wCj4SGDKxUoxUif85bNZr68iRpaDj2NjY6Ex5TqX1xGAbgU865OIkYs1925y+6SL2ZQxUrZOlsPsrYcfsyjfvzukPb7PidekNe81zKW4XIw80vCR6PDkIXmOAVHYsc2rBWC8wjaAUo4Tei4XkMX2ueLLJNwXoLd+87VXDXxcAMbHDKByfGxy+2Ry2Q7wkLmcER/T7vPx325xj8tjMx4eUoePKcfxXLtt+JgCaAr02FcJPobxkDq8SYG52pvGdzz4KB7rRPn4yEMsdH3O4NDj42tK6H7hhRfo8ccfp8cff5wA0Oc+9zl6/PHH6dlnnyUiorVr19Lf/M3feOfv37+fZsyYQWvWrKGf/vSndPvtt1MymaSHHnoo8j2jDqYaFy1brbmVUBQk0gRMJYDH3oJqaqaTTuBoaZlDRKIQBAq6+uqEIpuwz4TjUqlEY2NjSjZp0UIuWp/1cbypwHP4/5fjWhzsVzATdtBa35SW4osQtE7xj05DOxYBAHSLthmgmYbjGfe6jRs3Uj6Xo1r4CSB0wvP0mhqtFtcR+qQVCARNrxxjlXHvxTfFLoBymrEDHOrr64vkbeDPa5u3xjXefGWKJ/+ecmJAJjzvJb1AztfEaYFrpkyZFvguzsdqZN4/EpjKQ4GPRNHGTtZ4ywwLn+vy5lojrZs5LS2U1pzHk8pwfLxdWvu6Td+GAZwx5RlRZUaxqbEx8F1memRtvoxFlTJsvF/cVVQNNQK1t5+hCJnt8N25TfiYh+8KbVNymBgbRfhDEB9LpRJ1FQoeRvKEOVHwkcdLbkc4PhIFkxGZMLJFMwY5929/f7/V42BoaMjrj40J3wqf2ROxW75/LRjDfQB6hrcRTCkhXtOSSlFmyRLtfCQaf3bpIwEfiV4bPKRuLpnwcS6Y0iwhvNPZzc3adTmnpcXDR65QE9fdoGa9R7G0Hm74WBL+FzNhy+0npe+i9T5vWJsZ+PhYCQ+Zhh4fu4Xrent76czOzlB8TLptTTQ+ch5SHgcH8HjIauHjNeI4CcnSysXHNBjfcLJ8XTJ5WODja0ro/ta3vqUsSgB00UUXERHRRRddRPl8Xrkmk8nQ1KlT6eSTT6YtW7aUdc+og6nGRcsC7xgF47tBPqMkMkwJAo6Vzst4i/Guu+4Szmsi36osuv9yIcYWH1tPgCMJtwnp3gUCNnuWRX3StVGvj/5nquY8UbAO69dWAkBtbR2ucJ8h2a2ZJaVzaCuCGjK+gDeCae5k0OHa0G3w43ECC9bNArtZs2i7AbpLAwLdYEAFBK0pMhhzZvmmm27S/s5Bdzd8QF8Dg9Uol9Nawrql8xrd33a7bTXCt+LolSeqt0Gx2O2GENjeJ/sUi900PDwc0BiWSiXq6+tTFDuNjU3S9QkCFkvHFgS+L1y4KOAqVY3M+0cCU3ko8JEo2tjJ7mwywzKGYHy3vNny/9uWLtWuPy5MfuYznyGAMaMpqO5xIvNqs+TWwxcWReZNFmYLYHjBk3TJa1vnwr7AgBFRw1sA30MpGGoUrBDwDYDaNEo3jo9pcG8Y//dmOLQJfiynCSPfChUL2zTjo8PHzNKlyvOX4LvH2sZmEOH4yAV77lkRhpEb4ONjSnhXpiQ/XIkgZzuO4o4P+MxcqVTyMJL/Pzw8bBVe0oa5xD+LW1s95Qan8WaXPhLwkei1wUPqFIRR8ZELXcdJ5/I5vW7dOgJAH3TXopysSxbube7qnEc6nPCxX/i/o61NcaUXBf1O9/yS1AaPp5czZ3N84FgYxkNeARUfuwA6XTrGDUEHpOOHCz6G8ZATgY8cI0Ueshx8lJUpHQgqmw81Pr6mhO5DQZVbuomYENxEQUteDQEzSExoxs4pEEtUVStNwDMI4BkTZeYpQ0yY7yJmIdQlupL7JApKLOtiW1u7Ubj1XdHZNb29uuRYuljfJgKSWpf1bDZv6Zfonr4+9FyTqzffDMIW+CJA0fQ58GPCRbcrOZnTBgTBWmyXb3imTaGnpyf098thdvWJqunl560xnGOL45e9IHylTC0B15LshSEL2SYaHR3V1ufu6+tz55ZjmIMOAUsD1xWL3Z4mdbwZ2o8UpvJQUCWWbnLntOw6Vg+2qaegujNmwCx8MiMzR1i74vGMu2a6AM86vhcqg2d0YYQvLLaHMG+ypUJe2yYX9jQ0XjOStULXL84k+2vYPP9PRYKSmpJ9J7l7yWI42t8BVu6mEaqQxzGS46OIhVHwURQSwyxZYb/Nlfok46OYjMiGkf2a38VQIdt7kBnBWjD3WNHKODOdVvJy6MhU33d4eJh6enpYZmrNXHLAvDvkuS1bm7RjEQEjY3ysnKKOHcsJpFcQivhYh/Lw8Qz43hs6jNwHhqEpQOuSHDZ3voHDDx9L0r0qyfgfhX8EQFPdPurwcUQ4JvKQOr5SbHupYZwOFT7aeMhq4OOclpayeEhdfe6+vj5asnBhaCiEMi6HAB9jodtC5Qwmc6NNkS9k6oToMEHnTNLXNK6lcIG4S3OfKQScTizWWhb8mwjIU9BKGS7csvhcf7H654W7sLe2Bq2W3O1XF0MuWlnb2trda7a6f/VW8RkwuzLagEnchDZu3OgpFMRr+GYk3qNGc0xc2DxGksfLcOYzKljVQNWw2jYu8dn6hfPkOE1+zhVXXBE6rnzc77zzTvr85z+vuHcD3FUnQ46TjmxVZq6w8lytpWw2T8uW8XdumoPBteE4KZo7d17oc0TNTBkzlZVT1LHj8Yp83WyCqvSyZX3VCeQp+HF4ujXTpblPDZjwtwT6mLG8dN8ozNtu4VydC7sOCxSrsOSCqcM2OWtw+DoOW0/23zk+drS3C4oxH3sqwcd6qDGr8riEjXNLOq1lrER85Ba9KBip85S69dZbrcpR7nGwdu1a6li2TLGw8O8ZsPryUawmYfV9T9d4B4jjInt2rQeo3nFocWtr+FhEwMgYHyuncnJe8Jhik4KwUnyshb6EVbO7htOae6UBegC+11CYS3Q18FF3TTn4KHoxNieT1O6WPrN5NIluzZXyjyZ87HbHXtxjrnGvkxUqXHl2uOGjjYc0/c7Hvaenh77yla/QtClTxo2PRHq3+FrAWgIygcMDH2Oh20LlW7qDrrBMKB4m39V7PAyS7bcSsVjyAeF+PaQKOcyqzhOlsWPhwi1vL+iWvI2A20OvO++882jLli2KBitoPeUfP57Yt2CGKwPCXBnDFiAAejP85BXiOxSv2Q/V0tMwY0ZoNs8NAK2GyujXAtRVKBAR6ZnqZNKr+Wjqd0MiQS3pdOg568HKVjiac9RMpGHJ/cTwh6DAy75PC1zD329YvU3WluwKy7wiHIfX6DbNwTXu91FSQzUyxEr09bvrILZ0TxaVY+mWXWG7wRKMXSMdD2N2TPPe5PXCrymBMRMDwv16NOs0Az8xUXMy6dWtjcK8AX7oh+jCnpe8lMTvV111lZKBVhcjydetnAzHvjeE7Tfm3y8AY0p4AjTxPfJ3sB++pwH/1E+frsVlER8J4RgZlpjJho9ivHQYRqag1jnWZWrOQF/yS5zLDvTCzkzpmrDSRmL2alk4SsFPnGRbG6PQuyL3QO99EFu6J5bKwcfNAC2X3l0ezN15vXCsEnyMgp0lsKz6HxTWSSNUl90C2HquFj6GPU+5+MgxkvOQUWpdizgVlX+Mgo/c00rGFL7Odd8PF3yMzkP6lvRy8XGadE0UHtKBapRqEvpUKT5y7wRZwRFbuieZKovpzhOrib2egtZlno21UgYp7LdBAvrce4sTyiFgEQENbp/838oRbh0n5VkzgwJzIvQ6/mlpmUP79+9Xxo3HauzcuVNZZNnsSlcQW0ByEjrRTf06MIZip7SAly9bFpr0jIOzmCzBBGanL11K8+bODSzSBrCNZDd8TasDBtqzDSDTVSjQ6OgodRUKarxPoUB9fX2hwJGqr/fcuFLS/VMIMuY8E7r4LLWAF8eTh0OONrN4hoBaNyGazQPiNm8e9vX1hSY0870k1pMvHIttXWe51273u6l0mSjY1NDSpafHQvckULkx3XkwF8n1wrzkydAa6xhGVRLLZyrlB7AYN7nuLV/Hi8CsCwuhUQqUwbxxBZ7KEPJ1FjZfHSoUupTELWH4SMQwkuXPCHozJRJpaxgPtwyEYbcumYwOI+sAmjG9nsSxm4IkXQ4VHw+AMT46jEwDdFYuN2583A4Wr8mtRryfIkbOaWmhtOMY8VHE7QyCe0ha6PugZV7sFPrHBRTd+IqeCybX0yhrw1SWRxzP0wCqSyQCAsN413hMeqok50ULmMecPHcbxoGPNuzcA5ZsUYePKfe3k6Xfq4OPdoHYAVv/JnwU435FjFyZzVK949ACaCzLbuJaUyhMJfwjkR4fU45DC047jRa5VlUuhK53x349GKZMND7Wz5gRGR+j8JCi0Mu9DJpQHj7eJvSPZ8I3YSTnIScCH6cgOJ41AJ2+dGksdE82VRbTzeOsZeE3QeZySFEEctNvuljvveRbEbuFc68lALRz506v74VClytgZUjNsM6YQy6gi6C2Y8cO4X6661pJZDJ5BnYbhWXV5p9isZv27dtHM1tmB44vhuO5qoyNjdHs5ubA7xkwzW2bu5hkjZpuQ5BLeR0A04rJWkjOsJ9mWfxiuQsvgY/jUEdbm9X1HGAa5i7p3g5AbZlMIG5wbGzMS1yi2+DGACpqkiwF51t47Xhm7d5EACiXU8vAiQnNbr31Vs1c7XbnKoh5W+iy/HMBJUr5vW+QnNQvSibzmKmsnMqN6ebaf3n+JoT/UwgyOynYXStNlm6d5XIvfGZBdLsrwWceOUbySgEp+FZIuW8O/Lgyvv6Gh4c9JitckcRxujZymIaKkcF1VSh00b59+zQhT365vbGxMWpunqX8DqRo6dI2o1VWh5EzW2Yra1+uYiAmR1ppeZcpxxkXPpqSTuVzOe8d2fCRf24xtMXPs2XlXev2zwFCk/XIdewBNRaTu5DL84+PrU3w+QbMZSjHu8Zj0lMlOS90HiTc2lwpPob91irdqwDG40TBRyLyknKF4SMX0EUh+dZbb/Usl7KruAO2/jlO8woVUchWx5vP+7M0pcPSiYQn4OsqxDSCJe/iyVwr4SH3atYh5ycPF3wkIhoaGvLc9KNiZLn4OA1C1nhdgmChYs9ibz/V4+NSw/w7HtHwcYPh3dgwMha6q0jl1ukOximvp0RiBtXVNbjZn7eTXypJFnS4O6/MAKUpGNMtWyUbyFyHW2buuGATjFMoFMSY8CDz1tbWQbt27dJaMLlWjT2TTkjuE+Y168fdd99tTZbAsmWnlXFqaGjyAGFkZITa2jrIcVSL58yW2R64r+jsVMoO1YJpC2WGS1xYotbUllBn48aNnluP6FZpc3PRtQWwpDspCTia4SeBKECvqetoa/PGULfpzJs3L9AvnZvNyQBN974fILuQCwJqXIub+bxSqeRa3nRzlb/nDQTsIyBYYiyZrCWmsEqRPSN/O8lW8CiZzGOmsnIqJ2ZR1JqvB2hGIkENdXUeE2FSaDnCX5lBS0GtrcrXTAP0SYfkDK083lDMfMsxUo6zlZm3tqVLaYUbFiJjiVrVQp6v/cp6kl0pdaTPjVBD8+cvkPBRxdGWljmee/rSpaeTr/Dln1rK5c6yluHjGGlLyijiowNmXbFhpCl5z3jwccnChV7fK8XHVjCrvnieLS71ONjrJ/NMwKa5ys/bBVUY46VwHPj5REzj2q4ZmyhZemN8rJwqxcdtANUnElQ3fTrVC5bFMHxcAHOZqgz0GMmTgYXNuzB8JCKvBKAOHzva2mjXrl1aQZTzkAXpmoJwP3n9R8FHjtmy51JTQ0OAhzx9yRKjC/fIyAj19fVRuqEh8HsGLAZ59apV4+YhuRD4wAMPUHexOGH42AQ/QVul+MizftswkuNwVHwEmFVZl9lePC+sYg8/5wboFfpR8bEf9lJr41njRLHQbaVyBlMXp2wWRnjd40vIZ8oypFoCu8icvdzm2l0in7l7h/vXz0o9MjIiJUbjMeE7SR/DHRRk1Gfj168X7s/7tFfpv84C6XsMLCamcAgK1O3tZ0hjLD/7Hcp9FsOhYfguRHdAjVHiQKpbWLLrl7JQ3c2nVCrR2rVrvTYrcQFbA1BKqFHogTlAQ8L3MOaNyJyQR7zWlDX0NMhja7I+z/La5GBsEi7UBHzBuZpKNQsZ9NMk50ZIpZppyZLTQ9673SMkbKOOmcrKKerY6SwAto0WYLFzQJBpENuYBT2zZ7P8iBlaP+D+3Sn8PjAwoGR55XHh1/Lzd+4MLTeir2ohY3R0fCQScyPICqwUAY5UHUCPzblcPnAvFpbUR34+hATpEh4WCl1Kf2yKBREf+/r6aNHChV6bpndjSs5zOOCjrpyRLhN/Cv4+s8iSrMeWTbgBvuCUhhoGMTOdprvuusuaTCjK2Ixnjcek0mThYwFMISSvDV5ruRKM5MK2Dh9lHnI7fHzcCT+G21haSiohxq8NxNO6/dmr6bvJ+sgxNw19iN+ZnZ2BcZbvrYtZbgULT+L9mSgecnh4OBDGWCk+yn2rFj42uQoIG0Y6iIaPs4S2ZYFefjYbPs5ubvZCLxughkKk6+utySgHIo5PpWucKBa6rVTJZiNquMwMCU9cNkg+c8TjqzeQH/M6Qr517wICmKZv586dXnZVsxWlj1SrOssUrbpvF4i5xZcnLKXTM0nvLp+Rzs+QzCTqLJDBjLwbtPdkXgNcaSE/e0G9D1JUhOOdtBRM47VBAgoOMAMDAwFrvE5LOYLgpsJJToYjg8xyC1MkZ6iU65AvdY+HgXdYQh5eV9JeM9Fxa6FvI713xhwCPut9t80Tfak5f57dfffdghInQ7qY7VwuT6VSSajfLmfk7wi9R1gWypiprJzKHTsdPoZttIPu3xuEOatLisaZTxkfw7TaOgarFqBZTcH68QUEk2lFFZZKpZLWxdsP+ykPH4mIbrzxRgrDx0RiBpnx8YCwtsO8oxzSJzx0FLdzvWLB37dsCcN0rqiHOz4mwBIN8b7rMvGnhf9t80RXOUPs9+mCN4Wu3ykwSxARUT6XC/SNW7p4GUob41+NNR6TT5OFj5vhuyXLZfyuEc7niW2jYORHocfHfDarKAgqxUiTZbZbODcDjTXeYH0UeUhTyFHKcWiNYT0UdPcS+jMC36NAPicqD8kTdnFLdzk8ZFR8LEIV0NsNzxwVH1NgyoYoGFkv9F2Hj3MA+qzw3TZPwvrd3tZG+/fv95Q4YRipw8eUe43NFd6EkbHQXUUa72ZjtnTcIhwXLYkFYszNHaTW3HaoqWmWp92zW1EWkMzIJRJpam5W4+9UJlAWlgYpmPxKTOImZ2xPE9BIPpMpPqvaTw44uhrOrE9j0j0/SHq35/DxGAIoK1uT4NCYsLBN2lSuqb0DqqVN1LiOjIxQR1ubVks4p6WFxsbGvPgn2QWsS1rgOgDqMRwXmXxx05HP2yQ9Y9hmfsop86V3UUPAWwm4l4JJ13RZ7YPxow899JDbhl5QyGbzQrhC+FzRZ76vJeD6SPNsItb565nGM3a20A3+G9eoZ8AYMt06dMCEZRkfTW2vhy9kyZtzrXRMZgJlYWkQwWQ8fB35pflkjKwllguhPHxU570OHy8gMxaG3yuYiFN/zsKFSwJ9KBa7qVDocte+um+JFnsurOezWSNGauMscfjh4xLBYg93zlwJ0NVgnhAiEy5a+wLuvZJHhClBEM8XYOq3+Hw6i2ktQNdHuHYi1vjrnSYTH7eBJchKw4yRYkIyW/u8/J+IhelEwrMqloORsoJsUOiX7LlR667FA7ALd+K8HR0d9QwbHn8GXxkgWoR1bs82V+i8NO66c8J4yDC+UHznYTykLrlZCkyIFeObdf0c0hwrBx/5eZ1C+yaMlCtYzAQLXXwHQPfCD2+IgpFcmA7Dx2A4l7nvct1wgMXoN5Y51ypd47HQbaFqbDZBYWSQgGvIcVhiMXZ8EwUZFYf07oNqsh19vesU+a6KJuZKL/yw2G9fWNqzZw/pXd4XCX0Vf8sT8G3NcVCYBZLHIKrKAF18+s3ud9ntOTzedxESlAiM6XpyMIOySNAB6MsacKaIg6bunCbHUVyWZODloDAyMkKnL12qjTPnTKW44c1wgYZnuGxOJmlOS4sWmDra2iTLkxkQb7SACy9Lxj5riJW9C8ZaizW6efxTZ+eZ0jkJam6erVzHrOfB2uzZrMjw263VfIMYHh4WrtXlRWBW8ole569XGu/YyRvtLfBLsfDfNsFnIB3DWtUl29FmkpXWZ5jVQD7GM3FzXNDhYwEOtXqhPzqMXEF+yFA0fCQiymbzESzUYfi4jfwKFibvqHpiSk1+zgj5yla9lTyZbKZCoctVCKj7luM0UWfnmYrCQE6Cmc9maWho6DWBjys6O704df6ZjmCfM/DdTTk+ninF/jvwsxEH55Bbkgl+bXbxflEsMSI+ijH1KajWM24ln6g1/nqmycbHamOkaQ2YrMgiRq5etUpZJ10Ixt3KvNIK+O7w4sc257mw2iTkB5Gt1LJ7tqisOADfI8B0r3ogYCEXS0xF4SF5Qk6Zf8znckoctY6HHBoaor6+Pok/88NOxPjm68Aycs9AsMoNVzTL+JjPZqUcHeFeYjZX7CYpFr4LLCN7uRiZAJSEyDp8DIZzRQsH5d4IQ0NDgdhv3fjEMd2TRNXYbMbGxtzEN0Hma+XKVW4SM//Y3Lnz6LbbbnO/m+NWxUyJMjOTy+WF2GITc7XVcJx9uHWCufzWERPSxdjBWtJZ0oOJsbjAFp5op7NzhXC+6ZnXE3dvZExhiZiFR7b8hI8b+02t83wiwhPd6OI75XMaIFnMHIfaXUZPl5giDz9OSLS+paEmy+GAMKelhR5//HFrjcoVlnrfJRe46qGWHZteU0Npd+MKlhXbS7LFrlDokuZwgoDp7jvaTTq3WcbYc6GEW+rYO1q2jLuIl2+tzmbzlEjUKX3kiaMmep2/Xmm8Yyda5mRGo6tQUAQSHvMWZv0JyyTLM7TaXHr7NcfENWbHx+0EdGrmv4yR4aXydu3aZU1UGMTH64gJysOk4qMtD8gG8q3kGena00Kv9d3eTW03kCys53J56u/vp6GhIXUfg3NI8ZEz5LfAF+pTCFqbAhZAsD0gbP4mwITzD8LPzCyX4FkPxign4FvpeL9sMdth+JjPZqkukVCsirKlbSLW+OuZDgU+ch6yUozsaGuzxtiGlWjk66yrUPBcufl8T8P3JOpEuJs2wITHsGdZ0dkZGIMwz6aUO4bcYjoGNRFYWBvXwbeGL5b4+VMtPORdd91FgFlZ0ZJOK5jCecihoSHlHclx5iIfasJHk5JPztLOhXqbQjoKDym+10WAolDVYWSd+452Qx9aUO+el0cQH0ulkjUPggkju4tFSjkOXY6gJV/c76uxxmOh20LV2mxMych4Ka62to7AS2bM014KzhtfMBa1e7o6rna39uuk4747ORcSs9m81KeggBTOYHEGjQv93cTczuW4xlr3ExaDyO81RepPgZiXQIqAk4Vxk607zeQrPA5QWJ3nMHdC/jGdY8ooaUwkAl8DK95nDvTJKTLwy1kQMU1de1tbIPMz16x2FQrGLL/dYFknF0vP5YBlYhYBS1dWLJvNexlA/Xm9iZiAIbYpCwqqsoNZz/d77/nWW2+lxsYmMpU5CiN9IsO8laEkipnK8VC1xo7HWslzefWqVZTPBS3DDlhyHdNateEj/023OXMXs52atVwePoYrG4MYacbHlpY55Dj1wrkmfJxKKkZmiAm77eRbsAtkLsvH259NqrJgRoQ+hP2+RjsOxmSdQi6OycJH0UV2H4Jx2QBo2pQptGvXLu28EQVjjo+iJVJh5BB099RlAp4JVj6KP/+tt95KTY2NZVtiiAwKKNcDy0YxPlZOE42PvBSXXAM+CkaKuRnkOtc2fLzOMP85RppK4PHyUDZLKU/oegCqRVp0TU4nElQLGGO0xWeuSSQC/eEW0xT8xFuO4V4OfEXDzJBElvL994Lzpf59eVij3D+jW3tI5m75OZtgxscUfCUbf99iCVtPgeg4NG3KlFB8jMpDKs+SzdLw8LA310xu9zI+8nvKGNnljTGLC+/r69PzvhaM1OFje1ubVxquWms8FrotVA3AtMVe6+ob65OR+cLuJz7xCWptXRSYILlcUMBgrr5ceNlLquUiQ9zNVxZqWNkukyujyGSZGKyN0jMPKcDD2tpE0RhUaECOM6KLiNVnFp9Lfk7+f0/ofUxayA3wY5BMYGLKKMnjOqMkv7jbcg8x8UaUeC9508vAr0OYghrLyuMU5Y2DZzHt7e31NupgqaBu8hMvDRJjsKdJc8Sk7JhDatZ5s6BvI5l5iEIxU1k5VRMjdXPZARRmM+XOYdO8/8QnPiHUyHaFIWkDHRkZobalS73Nma8LHVOWTiQCG3Y0fLQluhQx0oaP4dZw9kkTpPAZtgYbiFm9B93zNpO+xCNvf4/hXrZ48O2W33drx8GPezd4t0wiPoqxpCaMXHgaU5aEuS/KWZ159l7Rml0P0DyogoV8vzlQE/4pwoxUAzmMYnycXJpofARYgtZyMXLeKacE5lAl+Mj5CZ1Q09HWprViF9xref4F0zraKKz1MQTjqQHVC8QmxNdp1jMvz7UIrIa9+Fzyc3I+cI93PBoPuVgjoIsKRfGdhCkMTLypbOmOgo/5bDbSvJpIfBR5yAyC2KfDR4IeI9Ngwr/c15mSCz7Pi3Go8TEWui1UDcD0g/sHyZyMLMxtULR8yJYMh1h84GYCWJz4vn37BIufI/yVhdaU9/spp5xKGzduDMlGK/fJ1u8SBWMKebb23YYx6JfOHyQWo50iVj5MvNcoAaq7PpCgpUtPdxUY6917riHHqRcytvPx0zPC9YashvxEXekDrgUVgWsUKnB3IugOxEHoGvjaWltM0Vb378aNG62ZTde4//+92z85sUUYMxqmpYTUjs/MbyKfmRffzXay1/uuoUQiHZifjpOipUtPF1zO2SebDbdei9r7yVznr1eqJkYOIpiQ7N8t83G9sA6bwGLYxLnigMUHbobvWrh61aqAO5voepeCykTwtfMP//APZeKjTZHIMTJNfj4KEz5uJTM+dkj3MuHjqew5nZTbxm4CPkCJxAwPHxl2cq8hHUaKVQ3EfYnHlXPlm87TSByHUVKVo53kly3zn70SfFy0cKGXeMyGjz3CvJEZt2pg5KBwvmipEe+13tJmDYIu6KLr6f33369YOcPcIWN8nFyaSHzcC3vZLxEjU1CVN9XAR4Ap7MUa2Lb1Yy3P5K6VtHuMr205IZsnzCHInw2C4UUjQMuFNV9y1+EqJAgIWr6BBJ2+dCk1J5O0HqCbwUJBUo7j5Yo40bvGzkPe4rWr3wPWg/GPec1YMEuyZHwQQm5EzNvm4gPHMRs+JgDP2mw7f6LxUTzfhI/bYU9yJ4d38jj1vr4+LywHQh+qyUPGQncVqRqAqU9Gxq0Y4Qs4+EkSywouJ1ebSiyBmV/GK2iFiWIl4R+H5s/nMbGmPtURcwnPkN5NcSYx5mmY1Ky9YQzoGOmZRc5QiBbTqeTXkQ2ez0r0qMeDSb70/ZABIQGmAeUnjkGTvRzMxUUUxjMI36BENyuAxbWsEBJI2DSVi1tbrRsb35j4RsTdr65GODAnoCoWmtzjMxIJr9Qa74sfGsDdVvm82E5+Nv7wJHfhc6OOZAZdF6ety+5sqnU8Eev89UrVwkidK+IJlrkqfpJgDJa45poAmgrG1PB1octYPsOy7vjHAWjBfJ7V34aP24gpRLmQK2LkmcSE6x2kKqh0a2ADmfGxixje8j7J+KhiIfsuuVoWuqiz843Wvqg1vgvkZ1AfI7XqRrfbR1EYz1CYEjho5Vfj/mz42JhI2GP7oMfH3bBb4U6G6oJaD1BjXZ3nzj7oniu6vYpZ+LnFpgBmhbPN87C5KZfjqXXHTCRdXhFbrGI11/jrlSYSHxfBT+AXBSNrwQRsGz7KCp5y8BGw16Wf4l5bgL5+80wwA8UORBf0eIz2Cs01jRL+NcGhoAEr+Htz8ywFH1euXEVvDCT5isZD2vgebrHPIJjAS2chFzHyVKnPXYWCtQ41x8d6MM8GXlXGpvjgXjq3IxoPqcPHFFhytXrH0fCQ4fiYhl3hagrvbEmnqQGqEUyXy6JSjIyF7ipSuYCp05AUi92KFc+PZ7YxWzsJ6CXgfMt5oGBMrXieqVa4GGt3gBiTY+9TZ+cKIXlWUgKXtPS9xv27lZhiQBbS64mBH7fo65gxniBItJgmiCkgMiS7LCeTzRrFw3b3u0OmmMbGxiYiIm2MvRiDwxfzjETC0yLr6hCagGwD/CyinhDuLux8NktT4Wt3RWt6RvjLwZ1rn2VwK7j3FLWD/H+bVQVQFQuzpO98o2AbQ73wG7fy8XfVR1HKEIXPz5OVd6zLSB6WN6Ha6zwmn8oZO5MGubtYVBg9rugJm6s7AeoF6PwIc1qMqZXPi2JB3QTQlAgCchAfExTEVJDeW+kbxCzWQVxKJBqpoSEtWKe5Ekt2a+fKzfXCfbmCVndNivxkb/5a8XFPj5FtbR2aeHb9OLB64RzX5b0lDAs2kL8/svHmyrNSqUSNdXU0A0ELnoiPYgbfSvAxilXlGwjGFuoEIp6kqRFB3BX/9gM0DL+Gdtj8tSlJ5bXjIJg0SJtXJEIseLlrPKYgTSQ+OvBjn8Mw8oII82u8+MhdfdMh7YjJz/g5SWntpKXvjrve+sGUA7Iw1wAmxHM8yCCoVFWF1/UuPs5wsab6+KgqJvVYd63wHGnXmh4dI30BXAwtbWpoiIyPJozk58k4GJWHlPFR9545Rp4mtSm2PQKmfFkuXFdueCeguq9zI5hctaFSjIyF7ipS1ME0WdmGhoYsi4czXTqXPYd8Vzvunh0mOKfJ19iJ59ncIfk9xLhcvYWms/NM75n9mN4NxAR7LlTrNHPbSc02rrqHh/eznoC50jUOMdd6EzDpjt9ApphGU1IfBynKwwm4aCmMVrFIw8PDdOmll4aCQL8ETnxhc2tOQtO2aCHf6/5f7zi0GfpanJtdwJItNnPgu8zLm1dzMunVuNwApuXsB2MIpwN0CfwMpM2BvohWMz5PZWtXJ5nKeaXTMy3v3fzbwMCAJr5cPc/mJhQzlZVTlLEL0yBHiSvTeV448K2V3G0uTAuehi/Ey+cNInxDL4Ep3hyPeSsHH7cTw1iOkToGL0Pjx0fu9SRek7dcU9IcA5nivr/4xS9K+MgZVl1StmD/i8XuAD6aYrzZOPG+fIM4w+uXJVNxW8RHsVbtFSgPH0V3Vp0VrslxyIEfX1kCiyc04SP3jHLgWwtvh15Ib4JeQZCqqwudm2G/3XjjjQF8NM7vGB8njCYaH0WhQp47eUwePhKCAm/OMJ/FRIc878EGBIVqU2y6nG1cXkeyknbE+00MTVwcuCachxwvPoqGpHCM5O97aGiIjjvuhDIwckPF+LgG5lj9Ieg9fsJ4SB0+3gnQGwCa5h73hFn4PGQSPj7yuSrjdhNYmI3OM8IJmZs2jOSJ/6yeoyEYGQvdVaSog2mysvmasTC3Wp2Qwr/3u+fbktiUhHN055kWfqd7D1lo6SLVMlFLnZ0rPE2sH6t+gMyCPe9TvXDv9cSYQ87AsvFKJGwZepuUa9j3guF8U1m0GeTHNF4jtAEhOZ1ZAMy4QCHG1fFsnUpsHaBYyMV4nH7pN+6auBug89xj1yEYxyVazHkHS/C10Ss6O7VWd74BcYCVNyw58cQigE6XznHAXOm5a/y17l+/zN168pVIsrdCgljogd9eOj2T9u/fH6HevDwn9pLeZXav9p2L9WvHs85jUinK2IVpkG1xZboNmH/n64fHzoUxhbcguB7k8xxomAgwpdOAd22l+Ehkx8hrKQwf7RnM25RrfOWt6Zp+zbEE+VYcESP5ehOZVF3Zxoy7Dllehra2dg8f1SodPOO7vJfJ/WO/MYu/qtTlsZq6906Iho8ExmiKuGhSfkbFx51CX+a7iasWQF8mqRWqNSgJ0COPPGKspczLf8lrZ6+h73ul87x9KMbHCaPJwMe9UC2LDpiVkBANH0UhRZe0KwwfS1Ctol1Qvf9qADp96VJPgBGfzeRdwvsuWoXrAaqrrQ24Km8HqN7NTj4Itjds9O7N8U80LEXhIceLjxwjMxLuFQjY7MkIXOjr6+sTjBDlYKQeH5mHrR0fCb6F+fSlS434aOMhZXx8o+b3LgQT4HEecvEixnuvh69o13l2yPc80y0ZJ+MjVwDw88T1MwqVp+C8u2mdhWFkLHRXkaIMpj2xjvm3YF1ibg0pCdfuJp5dvKVljruI5CQ1XaQyPrXEXPr4eY3EwEYWUiB9ZAGa92lIAY5sNi+cJzOYuvJQ8v34mIy416+hSsdRr5E0Wbrl5Bl+/GAi0UhhTOoHBfDph5ANMpfTJhppche36LJDAuDwskSi5lG8cQaqtpiDiQkc2pYuDbii8TZEFzFuxd4JX7NYL2U/1cW+crd4EbB4bAzL6JyyvKfdxIQLltmcW6qHh4cNWtp5hvYypFfAZLT3jS05E0e2sbNpkG1WOP4bn7Mig8hrgzYnkzSnpYXSiYTiJtcFlSEVs7ByASYDNUOuvMFXjo9E5WOkOOdHCPiAZW2F/WbCQpMlR2YSM0I7OiZ1u3QfjunM3Z25W8punlwpUCB/L+MJ2cTwKpHh1Zcda5Teewq+Czn/2PBxG3ws3C3MtZJwnCdfKwcfxfhqW53tBQDdC9A7wCw/+VzOY8Z1ZfO4JUpuL6PpWwrmbNYxPk4cTRY+EnyM5HxJFHzkJURlISQDITM5mADVIZ0j4mM7fP5CFKBLYMJ/XrqWZ9zn5/UL1xP05aH8+wV5ySwS1Afm2aJXxm+m6B6fE4GPgwJ+8TZHiOe68d3RK8XIcHxMaRIEjwcfTTxkVHzsFu4n8pD5XI4aXcWJab7fC8aHTwfzbBgYGNDiI4+t14VfyNUkuJIrzGIeW7oniaIMpmrRCApqbW3tihWPx5qOjY25ib9SFBSm0wHwKBa7af/+/UKsoCgwdpHZrVsHQlM0i1uMxdU9j1ruKagIkC3xuvJQTQTU0NSpvJTUXlKZTp4sTrZ41oWOMQMvf2xbWuZoLKc8y2632+YHCLjZfXYOZuFMqilWxQYUuk3MgWrpll3SvwGV8edJSqK6G/IkHbrfxOs2wFcm2DKLip8aMKZybGyM2traLe+Jf3IkKz+y2ZW0ceNGz92HCfFp8ksh8Xdp8/rwM/4nEmkqFLqqss5j0pNt7GyWmv7+fq0lj4dczGlpURRPaWlddBeLtH///kCyLcBPcqhzWeTXy+urBmxDrxeu8RO+VIqP28hnvMIwMkWJxBShfZ1gLitUo+Cj6Gm0ze1TLZnxsdldRzcTq++dIpXZExnSaygM05kyM2zNcsZ1LwUznvdL58nulmxM5fJwCYBuCMGuMHwMw70BVIaPZ7mxg7aM6uLHAej0JUsCxxa1tnpVRviaWYxgDhCbVVOM9RTrmo9njcdkpsMdH7nLtiyEpIQ20tLcnAKW/E88n+cu0AnQulJP3JLP49Xleau7pgEgxjfUu1iyl1QBuJaYkC3ywrVkT+bq85B6fEy59xoPPuownfPiIg9eLka+9vBRTKLGFZNjY2PUbrE4808OakWejmXLaO3atbRz504iIq8GeBq+4mDQ0rd6qGGXcUz3JFI1LN06Kx6P9+ZWPjnxAo9/k5NqjIyMUG9vLx111DHEgMcmgBxL5gyIJuuh3KZNQ8iFJ8dwvQ48OIDolAWNmvMQsU1fQaFaTgvE3HT2kWr1n0PAfuIAzFw5g0oSwPEYHJkZFzPU6oDiOKmPHMRETbSoZdNpeeunTw98l611zcmkFbBOht5ynoCq6bYBnwh4SXee270+eoltRnyu6TdO9R2avDLCBHu2AcdC98TSeC05pVKJxsbGlJhGXu5jeHhY0WLz/AkmfJw3bx41wC6AHAtzlQH5msVGfIuKj3wepzVt6PBsO5lr24vrIQo+qpnKVQUux8ewLOljpGNSfQ+Xxe7zycpc7sVkWrPHSffiz+R7egX3LFUZMX/+AjrttCBzuRhOoI6wDR+vhj6xUAPUZJJh7cjMoIyPYUmvuKXScfu8CZqKGa4QxdeM6pERTbDXZTmvZI3HZKaJwkduKe7r66Pl7e3Kb1HwMaoQkkI0rwm+dkTctSUl3LFjh1KWTMZtZomX8chkcBJLGOp4RDsPqcdHhxivMh583O6exzFyUDhuS3hswsj1kfBx6dLT6aSTgjmRysXHHrCEeymMHx+nw8etWvdYlNjqXjAre0aYl3uhKgZsGBnGI4vn6bKcl7vGRYqFbguVH9OtWrN5RsqdO3dSf38/DQ0NaYVwHUhy0iVqC2YODxNAwlx9xeN7KWgdT7nn2pK4gYB7Sc1UbTp/KwGnWfq2k3y3Ta6dFfvkax/r6lL0wAMPaMeuVCpp4rRNVvg8hTGpvFyECAZcoxtl41oP5oazHszCIGuixbiUApjmWcdwFRCsq8mP53O5gLuWrh89GvBZ3NqqxNDYslNyTam4CS9ZuJCIyOC5kSK2Segs1XorYbHYTaOjo1KptyibJ587fphG7D45cVROzKJOgyxm7C2VSsxVTCq7YmIiOekSETU1+sq7MAHENMdlr5O9HgZVgo83E1M4DVOQGTJdczL5VQFsCiyO22Z8HB4e9uLMxfHT4yNfk5wxNGGk/2lpmUPt7Wdo2uGM5KDmN/FZ6okJ6FsJWO/FIor7Y6HQJeyxBbc/m0jNh5IhHlMuKiii4CPHvVbp+ZJl4mM9QPMN+KizTKbAmEbvu+N47YdZCUdHRxWFlG1ec8FeDNOI8XHiaLz4SERaHtKkpKwEHyvFSF297CULFxLgC9DXWNo/4TgmTN4L1Q2dX1OEQwmt8SgMU7il2cTX+hjZ2Nik3V9KpRK1tbUL+YZEnkWXt8aMjytXrtKE30XJi6THSBHbzPho8gYoEPMGEPAxm7XiYwmMJ5V5yErwsRF+biQer93b20tEdoyM4hUhYuSZgVJv9jkth2lUEx9jodtCUQeTx7SKL3XVqtWKtiy4OIJCBk+oINPIyAi1tXUo1zDGhJfkCtPemRg7ffzHjh07aMmSDKmaPJuGkCd8CM8kzcBQrCur6xt3KxTBbS+pdb9ZH8W6zSIT71teM8QEO25xMcU41rifDcSUEmvIcVJULHbTxo0bSd48RC0uX/gyUDjQlEvQCBGyNtvIcEEt+yC2q0sq0YxgyQgxsZAJhDLQl5LICOAkA5afsVkGedl7IdpGk83mDeX2pmjyG6TIlBAlThQ0cRRl7HSWmtWrVqnujsL8lTfQDjdhoUwjIyPU0damvaY2mdTObXHtmJhBU93PyvFRxMhrLdcMExO8o2Bkxp37mylYNrISfLyFmIIgrG81xBK9bSDGALLsufPnc2wW+yuub65c0yVLFBXIeiW0useaPAGaSXX1LA8fCUH3x3LwUcRmOfklx8eM1Haj9D3d0ECAXZmbz2bVeQ8E3CnFvahgmOsxPk4cVYqP3cUi7du3T3u8XIzMZ7PUJOVsaU4madqUKTTNMsfCMLJfc36pVKKhoSHKLFkSEM6ihGJwd3fRPVnNQM4/NndxGR+3E6uGMFe6rxkjgxUoONZE4SFVfCwUugQFJ++zzP+EYWRwv8nl8gElix4fxTZ1GBnEx2KxW5+UDGpuIpmHrAY+rlu3ztuTZIxs1syXA7B7UuSzWaXcHo83PxT4GAvdFip3s+HWhIGBAVq6tI2iu3b7k5/HeqvW7TDXwZRmodpKzDQI19xCjlNP2WxeysS+2wWZWsM9MhQUhgrub7WGpG+cGbJp9c6nYGI1XWzjXBItGp2dZyqKDz9b7qPEXMh1DLAI1DPIBzf+O+vTVVddpV3cHFx0VulUXR195jOf8ax4d955pxezbKJ169ZRFIZrt/v3RAQ30jd2dipZ1Lm7o5hNXXSp1G2se6EvbbPX/b9HAE3P/ainh/xNhW9YsqKEb3pRXKrC5ogs9DhkKv0RW3ImjsoZOxEf25YujezaLTJovLSKbL0xXTM1mdRusLYa4GJs1y1giQbz2WwZ+JgmudYrw8BMSN4JESMHLfN/DfkYuYlUfEwQ8CBVjo8F8vFRh5G6rLq6/nKmV2eVdiiTaaO+vj7PiseTK5rWLMdH+/j4GX55HWEbPuah4uP5KA8fxZI8vVJbHB8PwBcsFkhtiN9tYUth81du1wHzkNKdG+PjxFEl+MgVYtXAyJWu15Dp/KaGBmM9extGivkBGgCvkouYjX03QHM17afBhB/uAbgBQeGuFswj0BfsZB7Btv4vJuASCsfHj5HIm+sw0ncr30fl8ZAqPnIeUvUq4mUXVat0Y2MT3XXXXZF5yN7eXmG8bHz2bvfvQuLGv0Khy5szMj6aeMhq4WNvb6+X42DQPXY5zAlOdfkDCGrf5Pmrs9ZPFj7GQreFyt1sogvKpvp7azzXWp+5s8XD6QQQ7j5iEpZFzZlNeBkh4HLNeRkKMmU+41UodFEud1bI+XuIWTd0fUuS+iy22B12b7lkgh/rktFcr7OI8BjEDcLvbJyvuOIKbc3gFFRLdgKgjyHo4mKqwynOHRHsbAyXqGUsAbQHajwKr/GqA0wx+UXYxmoqWSa2yUvkBDXD/e7Y68Y9SY7TRL6r+XryXcJlJt4873t7e71NKJvNG0M8qr3OY/KpnLGLKiibatyvEdYUZ+yiCCbyBstDNEwa76B3SqX4aFYCZbN5+sEPfuCGYuiYuFFiyiqHzPgt3ienWWcpAjoC9y0PH02xkRwjU8LvfE06pK/bLWN6wh2bYCiJLuyKY6T6+wct+OAnGNoJFhd6GoLz4LT5842MYVR85PNS5/kj4+Pdd9/ttdUPvwyTbKl2wErYcDfz9TBjsGne84zCV1xxBfX29npW8XKTBJW7xmMKUiU8ZDUxst6dQ2H4WAlGJkOulQWYITDBW+aRWiXMXCx87yoU6KycmHBV5qWvJzMPWS4+mjFSTaAWlYdU8fGKK64gFSM3kVp+khsqrouMj3zuLFvG4/s3kN2ocY3QXskd02BoZWNjMP9REY7CQ1YLH3fu3OlZuvm+XoA+EWpTY2Ng/w+7R9jcn2x8jIVuC5ULmNEFZb1rd7BcGD/P5q69gIB/F84XsyZuIp1bCnfda29f7go/MsgUSG9dPo6AtaHPdtxxJwrZp1NunzoloJlLzOU4I7XPQVTsjy3zrW/RMI8rByHdb2L2cpVh4yA6MDBACai1J2vBahLuBhOEU/BdFHnsSceyZZRyWL3E3QjGnBAxsJzT0hJot8YCZvWA5xq2SOpTAaDFMJcwuwWsJM2pYMxcHqprPHfPTUHdcDNSm7VgG213sSiET4SXgFu69HRiAC8z5BlynLSm5JL6TuUEhLIQI29K1VrnMflUzthFFZRNrt1yPdk1sAtFCwD6d+F8cWPWabw72tq8HBHl42OCgCuIxVubMbKnp4dyOa4k2kDAUgpWbciQ7zIuW4fl7Ly22G97SZlwfNxNQSu8zLCJe1aCdPXLgUXk17RtpKDHE2tn2bION4HlBhKVAlxptmrVak3bYc/N4x6TtBGgmRoGvwb6mtmngeHjaWDuix3Q42N3sUhNjY1qckownJXxEWCJedLuXhA2b++55x6a1dSkKHQzAKVdr4uw63X5O+Q9Rlb8VmONxxSkcseu2hj5Qcs84/zCDUK7NozM53I0PDxMvb29tGThQsXVPeW2qUsI+wbw+tkOJSVeL+kKzFdeeaXHQzKMzJDqdj2V9DxkJfgYxpvfIrQdhjV2fGRGCRNGziM/Xpu35eNjNpsX+OndJOPj6OioRolry51UT7x8K8No8doCscSYwXfkIEV5OAEespr4yMMnuMIxbO42NTZqlUWb3PvbMFJOeDkZ+BgL3RYqZzCDmZvD3TpYCRWTW+FeUq0mc0gFHRGMuHbLJOQzqwBPpa/2V7cg86TXDi7QXOeDg54hGiOVQeW/lSi8TrctdqedeJ3toAfBKKkMq84dSDyuCvKOU+8BW1ehoAjdU6Tv3QDtQ3Cz0VlTROvwotbWQImi7WDuV0mYLXEZob1GqJpAHdjcIfRF7lO99J0DVkY6noFvLSf4AHYD/DImQY2s/r2x+KappCYkSXnxVcVityZMoYmAWsE9N2i5y+Xyoe6p413nMQUp6tiJWUltcVhy3WUeC0bQu63NgWbThx+n245wBrYXvucInzeV42OtBsuCdavLw0eOkR8w9MeGj6BgNvBK8FGHkdw18RqP+WM5TGSGcor0vUDMVVMum2O+11133UWMWW2UxjvMk8t/blY9Qs/gi3NwVMI7x/A/Z8h44iEZI3UeRd+Ar2wVmTvTnGxva2MutlCxnWfU1SbgcvvD3XPlON58Lhfj4yRSJTxktTHSQbCsHHfv5oJPFIzkwruOhzTG00JvpfQ98mz4yM/R4aPMQ94pHSsHH7tJ5SF1StUC6UNuouEjEUXEyNXEvDvFY7Jxwu/zwMCAy0/xUmrifhTmDZAR2pOxNWUYT19RKwqt1cTHrkKBuotFr33TnKyBHh+5AYhjpFyfvgmMbz8U+BgL3RYqZzDVet26pAhNBDiKhS7I7GRItfamyRxPEhZPFwQAnhBgZGREir81gZEtjvwOUhm3BAHLDW3vFs6TfzNlAR609KWRGIg50jm6DJM6d6Brhfej/p7N5j1tlynhyY4dOwjwa11zzd92+JnIZWtKTgdUGjBSLHEADQvAo9v0TBlDC/At1brNMAHG7HFPCN5GCcxyIrc5At+NsQfBbI9BV3M9aIf9zkul6Ep3dHa+0XrtRK3zmIIUdezkerS6xIM8i6lSIkxYFxmo1sk0mOCtuyaKC1xJWE/9/f1VwEeQXx4sQ/L8Bc41tD0R+LiBfOarEnycQYwZljFSzUOiSyjKk6LdeafIFOeFexdC+qFTQIt7perJxdruo6AHlHl8RDddMXGlDrfrwTAyn83S2NhYYE73uveR3X4H3ePvQ9AKKbqai+fL4TvGOWsoJeVEvHYi1nhMKlXCQ1YbI7mbuHh+HiqvETZneA4Zjo/9/f1e/HCY+274/N5Lem+hj5I+fKREPg7Kv5lcqaPgI1PkB3lIU5UbXciNjodU8ZFIn3S5WOz2eEjWHxEfRc8nGSNzFI6Ppvw3HcSSdYbhYxRlBWi+W3as2vhYKpW8MQmbQzaMGxsbUxK2OhGvnYg1jrJafh1S5ZZuIr12zq8bzMsRMJcRLpjL5ZTkxT2DmCui6ELOF0EHqfF0QQD4/Oc/T8cddwL54GZjHPUL7tJLL6VgmZrtxFzcT5aeN6xt+bewZ+f3kRMVdXltdnauEGJ6bUDLXXhkRUaG2EawjRyniXK5vPZd68rvrF61ynMJ5wvapq2WrduitlqOufog/BqFYhu6TW9Qc1/eF1s5h5TjeKWcxPPEZ9G5jYkW9EWtrS7Qqe+NWa5Fa6B+jokZI0ulEvX29noJRFQFl/naaq/zmIJUiaWbwBg+eQ6JdYNLpRK1u3GtYkKzsLk7A6ClgLaUTQfUfAzieuNtnHLKqWVgmH7++cm+0jR5+KiLpZ5DomLW32ui4mMX6a09m0IrbujwkYi7iHOrzgayJ/pZQHpmU44l559rqdzcENxNV8Q3G27rMFK+RoeRSbA6twCz3utK4zQnk9TU2EgzoMd2UTkkjzdPRmcThmJ8nDyq1NJNmBiMnObOQd280CU84xjJrz/1lFO8vtgSrYXNQZ/f4oKtDh/LwcgwLOEK0HB89HlIGx8uhtzoElCG4yN/dzJGqvhoey6Qat0Ow8cPkl9q0oaPtj1iN3meiUgGFNzVwMfMkiXUVSgYk/xxD9OoGCfykDZ87Ovrm7A1jrJafh1S5THd4uKuJy7gyjGmekuemUHwE9DoFkEPqW4rDAAcJ01TpkwjtV7hYg0YpQngjKfZkpjNrnTP0WWGLJAfD+K3rSaJkxUEaeUaxkzO0jxbLTGm8IC3UNTMkzaXIr98z6JFiwPXRo0HJnLjaZpnBa7Pw6E+CzBEictKNTRo4/oa4Scz0W16Mlhx6ze3TJv6JLrZyu6LGYRbytMCIKYTCdrsjkPwnTjuHA7fTMK0jWooR3l1uWWKmcrKqZKYbnED5dpxQI2h0mmpw+ZuAsEEPiPCfH4vVGtPBswVcxtY9t2gm59DLPxBxqMUAbx2/OGCj12kVyCmSYwzbGtrl86Jgo9ELE49OHbl4CMRw8jm5tlSO7awqDCmz1/vjlNDqotmhoBGamhIh7bT4L5/ERdtGXFNGMmt4+L/MkZOAcPmlnSa0o6juF7OTLP+2pSjUfBRVJiWU3dWphgfK6dKY7onEyM/4P5WBzWutQAWCteYSARC6Th/sRh61/VTLfN32TJeOcGEj/pEwOEYaQo3WURR8FHlIcOtvD5GThQ+dpO9vK49PxTDR9k9neGjfzyqsUtv7c8iUVV8rIHPQxaksXXg5yCYCHzMZ7OR3xtRLHRXlcoFTJ3riFxPjxN30clns16Sre3edXoGIZtVMzSLMWwtzbPoa1/7msJc1dRMdwGpify4me3EmDWdMMvcVlgSIRXwiIj6+jgYFEif1bGLZNfKYrGb9u3b5yoadG4x+5Vr/Nqz28mP/xaT96wPLDJeViGcYbuWdOUZTO/KRtlsnnQJJ7LuM5abgZQnZJteU0NTobrTpuALEAlA2aRT7nF5A43CzIluZDr3xTrL9de6fzcIv6l1wfkGooZgJBLpQMZxsa6weMyckMSh4eHhst5fzFRWTuWMnW4+dbS3G9fcyMgItbe1UX0iEcBHYwyhm4H0Dug3agC0orOT7r77bi9ngf+ZIqzhQffYIsMcW+Hinp4hJKocH8fGxmjfvn2UTs+U7mvCxwIBQ+7/PGOtbMnwMTIKPiYS9e7/Kka2tXWUvb6I9BgZJW7QzGxeQ4lEmlKpZgp6XIltM4bST1oX3DdbmmcpAksUS3cYRkZx75Z/LyHI9AEMy3XuxSlAyagrY+TIyIgnFGWkeyYBeuSRR8p6dzE+Vk6V8JAyRuZzubJ5yCgYmdNg5Fm5HD3yyCPULpXVmwKfDxkU7tEFfXJZHj7XJFjhxQRbdnzkMctBPjGch+wiNRbaIVafOxo+ElGE0Like81E42MzASstfTFVQrqGgJRgbNMlBE0SS2rsGOSKhCY5W4FMce0cv6qNjwSVhxwPPhKx2vUNBnxMAGW9w1joriKNt9wDoC8NJZ+TASur0A9QDg45BmZOJ9Qvh0NXgFk+a4UJx91X/umf/kk4fzv5sS+DwjGdMKtmhxa1d761MQwQblauV7Nbb3Xvyy2WuwPX2NyQE4n6gJBGRPTQQw+Rn3hHjan3wTgIclFLTIlkS7jUCdWttclxrLU2mxobvf9t4CTXne1w47KJ2AbCSlX4Fmr+N8yNTASoUqnkxbheLYCe2CeuMOC/bzX8HhwrNQSjUOgy1Kn35x9zLzcx2lOpra2jrHcYM5WVUyUYqcQjRsDIlQD1gcUiKm7iLiPHN3gucMihGxnhXCJfQfe+971Pmpf9FJyrOnzsIR1DWB4+btdezyo/8Ky7NnwUMV2PkWIySKIo+MhDjyYDIzlD6PfDcZpo2bLlIdewZ+/sPJPsY6z3gsrlWK6OkZER6u3tpRtvvDFQElK0ykTBSO6yuBugSy0YiQi/b4fevfjMzs5AGTUdv8EFmmmadZAC21vKoRgfK6dDwUNWipE8k7TMQ3784x8nwFekc0+QQWGucsWRnD3dhPWV4GN0HvJe9zgfo3AeUsbHkZERITGZ7G2UEfozOTykH2YTxEg1j1EQ94LlvsJz67S1dSjfdTxkWDv1Ifgo8pDvOcT4ODY2Rn19fV6iYt0a6Ghri/z+YqG7ilSpa9B6MKFjA5iAlc/llHP4i94EVVPYHIGZC6tLt3PnTu2EYwuWL/BwIGpraycic3weEQkWdbENXVbcTgI2uzGFfOHyfujci/jzc2uSebG3t3eE1HIV24LQrwtC2yzH0m2LLwb0mR3PaG/XxqosEuaNLXsj/3ALlviedO9/Kpg7mK5P3I3MVJuQz7sLBNAT+xRm6RZ/b28/Q5ORvJ7mz28NjLvvPqZuaExoCN8A4uy8k0OVYGRY+Tx+DsfIvVC10bM060nGR9P85Fl4h4aGDPhIAjbZGbXy8ZFInyCMl8rjlg3ukWTDx2ayxSBms3mjIkuPj2tca/dkYaS63z388MMu0xhkNpmAPpccJyUximb8Fb2g+LvSjUU61UxThe/lYCSfd+vhZ2euNEnamZ2dgYy768HyFazo7FTWklyuSSyVE3YPMQu1jWJ8rJwOFQ85HowM4yG7wYR7ANayZu2u4GLCyPLwkbmcl8dDPugeD+chs9mo+MiTO4L8UluTw0PKnk+FQhc1NjYb8HEpARtcwdyOj2E8pDoete54q4qIzhB8FOfd7HHi4xnt7ePCx+5i0eMhw/oQ9R3GQncVqZIkGDKDyL/ns1kvhb74oruhxjY0gbk48MRRIskZLgksKYHsTjmnpYWak0lPCAoCRDcBDaGgEcW9gj9P9Ky4ckbEbvLd3mWLpUMsrqeb9LElKUqnZwb6oxPU2L07KGil2hgKROUkmrFpKRe1tgYAjTP7pprfXZrFbwIGnilSR7rNO51IBBjJUwE6QepDPpdTYpH27NkTsKZzDaGsMKiFX45B/p1rEJubZpLOZZcnGIwyptyqFLaRlPMOY6aycipn7Pbs2RNaPm94eFhhCMWM0tXEx6bGRm9DvsU7Ls63Fs0xlVELIz0+Epmz0WaEPh4oAx85c6hipJgMMjo+DpMte201MXLZso4APjJGzyF9Hd5g8iN1b1Pb1lFwLAYJWEOOk6KpNdMC7Z4ITVlIyfI4MjJCfX19Xjy2I2ChLgFQLVjoUErz+5yWFm1pSjGBljimpr3hhOOOU9YBwReIenp6Ir+/GB8rpyOBh9Qle50Dv8yoaQ7aeMjy8bFcHjJDvjddpfjYJOCjjyv67OoTh49EFMBI5vYdho9iX81t871MR/54rCfmTcArYYiKiAVuP/xjPHu5SCIPacPHjPu7jI+ch5zV1DRufIySeT/qO4yF7ipSueUeHKgMYrM7yeodx5t0/EXz2DGT1doU5yhOphGwGotirWY5brcFSQGYthGLcXGMQNTZeWbkMWKukLzdQcsC3y79bgJdfj5373mQWOyJvyBaWubQ/v37lXEJz8jLS0OEx8mUG9PN4ovSyjimUs1KLNbyZcvcpE1mtyyxhJFOgOXgNKupScv42zZv8ZMAqDmVChzjZcO4plt0RRs0tJUR2lu8aFGghiPAXN56vO96l10+TjbNrz1mf+JicmIKUjlj19HWpnX75sxee1tbgCGcLHwkgGoC9Uw5jqW0+JjJLIs8PkF8PED2zLjiGomKj7sJuJxY4je/DR6qIY5LNHzsJhuWl4ORo6OjLpMoj2U9NTSkA2s1m81TItEo3ZtjBbf+++XAmBtoUtM2i2cMw0eThayxvj5wbHpNTeC7jI+ckUwJc2szzBUeMkuXEgBaIP0ufjfuDRJGmpjGv//7v1fmuLh2HnjggcjvL8bHymmieciH3Pdp8iyzYSRPIpVHUHC35X4BmFeeTgHf5GavjkLl42O5PCQvd9hDQHAdl4ePPGN5iphge2jwkYho4cLF0r11+OjzS36+Ejlmeyq1tMyxYKQs1MvfQU1Ns6i9/YzAsba2di0PGQUfOUaG8ZDjxUfOQ4bN76g8ZCx0V5HKGUybu4Jocd4OplnskCYVr7XIJ4ZJ09JdLFIjzEXn5eyr+wFq8oRs8ZoHiWnxgkxHZsmSwP10iQg4DQ0NKfEgYclvgkkbTLVn+fmJQLvt7R20du1arWtcFBcdli2X199V42R08Thhz86JCd26hEt+/8/K5bw4bZtbVj9AdyBYhksGpxVC7IpuLMI27zXCsVowC/gm6DXdjYlEYF6Lc0sEPd73+UJJEbnvS43zg21U69at88bctqFls3nl/flCQ21ZcVUxU1k5VVoyTLfJAfCS2GwaJz7qMkOb8JEAehygKUqG15x2XS9ZcrrybCaMGBsbc+cqpI8Zp/zEX+HWZuZGiMB1GzduDLVuRcfH9aRLsCNjZBR8ZIx1mtR4yCAD3N7O47jDQ58Yg3mH1Ebw3aVSLQGlrH4suHt+0EKWhG/dy4AxiyZ8TDtOQECR5xbHSJ5cSMbHmVKbupCih+DnyuBz3raeBgYGqKmx0aiwjSoUEcX4OB6aKB5yE1ShRazVbcPI1atWaas58OttGfzhroc0VD60qbFR4U1MOFE+PpbLQ1YTHznfOPn4WCx20/e//31Kp8XkuGH4SBJGyry/Q52dK0J5SHaNzjPKIYbR/FitGzK4ieTQ0pktswM8pA0fL730UiUPgI6HrAY+ciOAzpou5jaw0REvdH/xi1+kE044gWpra2n58uW0Z88e47lbtmxRFl5tbW3ke5WrpQwDKV6SpL2tjZqTScpAdQmSE7UMDAwYgWp2c7PWatQNc/ZVDtpvetOb3PEwacrgxb2ZEhHo4j2mT6+T2g0KS46TokKhS4hbROj5ALzao2JmVt2Y2DWVQcABZiiZGcXY+bBEXub7qtZbHreaArw63oOG98Pf+3r47jX8/W4AS4zTUFdn1aLaNu+d7ndxnujceFNgtTvFeR2W2dcBFHe0JrDNeTuYtTH4jp4muZQH92DQlQRJJpupUOhy61kmSLeRsOylm7w5HIWOJKZyMvGRKPrY2fBxKfxNs7tY9NzPKsVHXgO5HHzk7XZ2dgpjYvbKsGGE+ntCalfFqWw27yoy263ncrwWmchq4WMiUUeFQpfx+SrDR77XdJDsDsqYNm6JGozw3Lw8kMgQ1hHgGF3K1T6Z71GKiI8ZBBnJcvGRt8G/iy67T4O58QaY2HTaUyboyks1J5Ne1YoEVIXtbIA+C3+Pj0IxPk48PhKVx0OG4aOIZSaM7CoUKB1yvQ0jIf0uZ+APiws2Y4gdHx0nRZ2dKzTlD824diTgYzLZ7FYhmhHhmblVXsbIDQRMo7q6hsg8pPkeO93vYv95NZqgEvNEl0+z4SPAwhuj8JAThY8OWIgnD3mLgpFHtNB933330dSpU+nuu++mJ554gj70oQ9ROp2mX/3qV9rzt2zZQo2NjfTLX/7S+/zv//5v5PtVEo8TJkgBzGVhpZvoxHRuynGUck9inITtXiX4Gno+4e6AHEOsupYnkfLqKnPmV5eIYGU2S21tHUr8i59RMZiNlseBLFlyuqYueZL0WXRrA/E2UUBMX7sx5YLPGheMGBCk0zNpbGzMmOQjLJGXSDYNaQ/UjJ4iE3cL2Ga63n1fjnQeQa0jaEuCY9u8+xHUZg9agFD+jceQybVEbXNymzvvfHeyWaTLQM5dnnTv2/cq4JvSbvfd1hOQD4z9RMTkHM402fhIVD1LN3cbK5VK2rjFicbHbQClHYdmtsi1Us0xeyaMyGZXUn9/v1DiUZzf9S7eqRhZX5824GNKOreZgEwAjyYKH4n0iZAqx8cozK3IwHF3/2vcsXOk83ibqtI4jMxJnNz3WwY+Dki/60rZRMFH/j0B5krcCH328TktLUSkLy/Fre8Z+EqC3WDeTSm3bzYraKVr/HCnwxkfiaLzkDt27LCeJwoX/CNnDrfNRTsPGcJjuHNLhxMsCWJ72fgIOIqhpLV1MTFhXcbIIA955ODjte7fTAR85O1wfPSVxlF5SLs1XVcNSY/rYfiYgu/lY+MhJwofG8Fc2HXzOIyOaKF7+fLl9NGPftT7fvDgQTr66KPppptu0p6/ZcsWSqVSFd+v0syTcpmRDIIZ/WxCUbqhgRoTCcVtZ05Li1AyyXz9NWAMpAi6DhDQbDImV3JhgeNpeLibp7gARiG7EskL7A7SWyBPIkB0mxRBNk0s5iSoEGhqmmUARDOI6QQ1fT3wcEtoFPfmqOd6QoHbj3Z3TnzW/SueMxOguwDqdb/vhT7uZe3atdo+c+CPuqmKMbNh84n3mc9rXbZUW6b1fvd5Eso70Y8b3xTEDS1amRE1RtxGRwpTOdn4SFTe2OnwsQmHBz4CoObmWS4e8eRa5nmmr+U6SvoYuDHN73p3v6j4KDONrx18tDFz7cSYyU3E3PsTUv/ayE+EuZdMmd11zJKIkeYkTqqlO0o4kMhI7oXqcrvU0kYf9MmsjN5KAtPMMZLPSVs8rmyNrOYaP5zpcMdHomg8pA3fgPBkU1Es6lwJaeIhBzVzbAR+LeXgfs3X2KhmvUbHx0ymTYtxzc2zSQ0DCvKQRw4+bnX7kCZgkaZ/ZxLQRwBPNluQfmffdYkUdTxkOJ8lKgnCQ4JEHlKHjyLuhWHkZOCjLka8Wmv8NSV0//nPf6ZkMklf/epXA8ff97730Xnnnae9ZsuWLZRMJun444+nY489ls477zz68Y9/HPme5QKmTrPCLZfdxSINDQ0FXn6YZTEDvTtbPpeLFB/JazXzWrS68zNgGvhrwLQ9YgZVXutTXACcsVjj3UdeYAXyLZfcAtlIQK3gUm5338lmg5mBywExItK4Z3Kw2UyMoWPAY9Ji2bR84nUjIyPU1tbulrKQNaQZ8rS7rqb2AYBWw8/gqGyK8Jk90xxY3t7u3d8UAtBVKCibdwqsbNh64VgtEEjsJj4wz+p8JVSw6wZoGEELvA3IMvA1kxcb55DrIaDZFPwEamGb0jVl18o8EpjKQ4GPROWN3eGEj+0CPor3DGIMr8SgWlz0llKde12ze1z3+wZi1omasvCRJ6nhVC4+lkol6uvrI8epkdrOEGPmJhIfBy3P+QD5jLkcY8/7lxe+q+6MgBN4ZpOVy6/3KyVgg4+bfI8Mm08fBOgbUBnJPBijWBKw1OhWiWAWah6jaGJAw/DxZsu19WUkuiKK8XGy8JEoGkbakkDdfffdXs152U3Xgd6gIl7vzV+3komJh+wGsw5+FKDlmhhkzkMGM41PDD5ms/nA/bPZvOL5dDjiIxFRNruSHKeemKeODR+vI6akWE1M4NblEuoiJgxzr1NxvJk3qiiUlouPLGHneuFYLZmrIbHEeDoeMi/gY5RqPflJwsdrYC57pqMjVuj+xS9+QQDou9/9buD4mjVraPny5dprvvvd79LWrVvp8ccfp8HBQTr33HOpsbGRfv7zn2vP/9Of/kTPP/+89/n5z38eeTBF4owcj0fW1YTl5RjkeAOxNJNRgCmVaGU2S/VuEhfvesfxknXxz8psltauXaudZHuhxjRkwDScsgu8GIsx4p0vLrBwYNu4cWMoEF155ZXaBBdE5YOYnxFTB+4+E2vSYvnxLBu0z2KK5wxqHTPka3CDzDPf/EybYgmg0yLMASJzLcIVnZ2BuSRu3uL/XYUCrejs9Ppj0kTy89NgTKg8b1dms4E2REumAz+fAH+eh6CbQ/44yZuC6nZrHl9d7FQYHQlM5WTgI1F1MPJQ4WPacaixMR24Tza7kvr6+mjdunUajBkjvaVArhdrxz9AJ9QfGnwkIlq2bDnZypZNHD46pM+my3/vJBafHda/k0PHM4qr55Ilp1MuF2TWdYmHWtJpBdt0rrYOQCvAyjDqMHJmOq2ENHArpjynH5K+y3NcxseuQkHpy2bDtbqSPmEU4+Pk4iORHSMdIFCrmM+x7mKRbrzxxtC509vbq+cf3Tkq8499fX1KiSXmlSGvZzXMxl+jE4uPPT09Sv4fTocjPhKZ6mA7EfBxAQHLKEyoto3n+PHR/79Q6KLOzhVCf7YR80LKaK+tcz/l8pCThY+AWhYyjGKhO4Refvllmjt3Ln3yk5/U/v7pT39amiSIPJhhpBOMdO6NXADi302aGJMwxeMVtkMvOIVpNnnssXicJzXYBj+JB+9TEQ4lA8BwTSiwRSnzBERNNmEGkGilH0Bz585T3tPo6KgmyVvGBZBgZkq/TJhsZeHJ5A5QMJaGjcP5YNbmsPfxQYDq3IyP4hwYEd5Df39/5IzQ6YYGakgkggK+41A+lyMif1PimkgHaqxMGqBOsIRuU6T257S00Gc+85lAG/wjfxefp8VQ6qelZU7gvRSL3W6iJXmsM4Hr2to6yi73RvT6ZSplsuEj0cRg5GThY1O6RWAudExBGG4UyXep48fLyaJ7dejvdnxkeDBefIxyfjXwUV/zNkW+q7yszMgQK2W5hvxkQWF7BbfwiOM54r0HOYttWFvpdAslEnXEmGSf6eR4IuMjn1NpqErTDECNiYRSZmxmOk1/+7d/q61Tv9eAj3OgLxfJYxbF9ZOW8D0FphQIMK8C5pdDMT4yOlT4SKRi5GaoSp+uQiEgXJjwccnChYHr+JycmU6H8o8ir+HzfzyPgn595XJ5d+8O5w+PFHxUhWg9PhKF8ZAJUvGxlXx8FEsamvr4ZsN4DhLAFC9RnzWbzdPSpae7iuZgDiceMx+sCAHSKWIcpOhUOEZ8vOqqq8jGQ04kPnKlfrk85BErdFfiHqSjCy64gC688ELtb9XSUopkE4xE7RwHVjnmQEyk5UCfAdVBUHsjZ1tdoJmgopZdrIPrxVD09VHetXjLoDsGBrxRmdZSqSQIT2rCC7ZY9ckmiPRJLnTnqi5NMrgz8JfrlPq1EmXAbCQ5hjJaHLfM0KsMflhMluhpMAo1tjufy3na37DYLDFDrm7+yTHgclIg+fwEmIZ9g9t+D4Kb/3bo6yfefffdSrv7AaqXXEjLr7/uArNQc7NcOhKYysnAR6LqY+Rk4aNf85PPI9mVcT3pk0Byy8FuLZ6wRD52/IuKj6o7H7eusL+JBEu2JlNUfBwdHbUmETs0+Ci3Hd6/Zcs6hHuosaK5HMsC39PTY2lrDfkKPP17EedoCaA7LRjZkk5TczJJGwC6HWo97g0CPorX6fAxLV07p6VFi482xStQnvVGpBgffZpsfCSyv2PuATNe/hHwy4bpsvXziio8VIKtHR47rF9ft956KwWto0cmPhKRW1XFXDpWVAjYMbKHguV8ZV47Ye1jsH0VI7PZvOcFG4aPbNyc0HcTfJ5wK7sNH008ZBR8FLOXR1k7k42Prymhm4glwrj88su97wcPHqRjjjnGmAhDpldeeYXmz59PH//4xyOdX+lmIyYksCWt6Onp0Sa/6oa+BmKYW8QMsIQtOsFpTDNBM2AxOWn49RnF9sT+bwWLqWhGUHBvTCSo3dUO2YCNJa+RwcNPTBGWBMuUzVpeJEFmTAdmLMNjNpunW2+91duwstk8hbkTie4qvtbVBFQJTVspqkeSDsCeyGHdunWBTTQDdQNMJxKRsj3qvovzj1uE8tkspeAnQglTCMhxXdxNvgDVNSgFP8GVLklMOhFMlCQDX9A1TPQcGPSu4VrbSulIYCqJJh8fiSobu0OFj+yzlcxMwSLSM00OsXwQKp5wt0vWbp78jLIM/7g1oL+/X0gkqcdHfc3abmJu7hy/1o8LH7PZPCUSPATJbDXKZivHR3vmWxkfM9L3cOvZvHnzpezwnOn2+8aUu1GY/ZL0f7CvMj7qvL7kOSvPUxG7F0OTNT+RoK5CYVz4eAC+cFWCn+hKXEOVUoyPjCYLH4miYSR/x729vVXBxw3u3FwkzWH+2QQunMs8XBR83E1MiDwy8LFY7Kb777/fc2tn429295azhdt5yCnkW8ELmnbrQ/u4bt06AR+3GdpIkS+8R8FHveKZYyR7NymyezWwucbn6XjwsTmZpHRDA4nvVcRIEz6KWH3llVdOGj6+5oTu++67j2pra+mee+6hn/zkJ3TppZdSOp32yjj8zd/8TSCzc09PDw0MDNC+ffvo+9//Pl144YU0bdo0euKJJyLdr1zA1CW1ymvKg6lZwH2XSA6sY/DdJ2StZEECQ11stg6kdedlwATvjDSReRIBEcy5BjTwfLmclwBpeHhYAbZcLq8sALZ4ufAkLsZ+ZSHLZCrxFXR95EyYHBOTIGApATdQMEbGBjzB/kRzlTdoBd0DGdjLcXQVCvRG153WJFh3QhV0m+HX2yQBYPo11/Nx7Ovr05Yrk89PuPOI4MfVcADlc1aeX2k3cY8uSUwtGBPA57ecwMLXoGak+e1v/OIcq4SOFKZysvGRqLyxO5T4qDKKOqZgL+mTd7FkkDpmMKjhHyOTxTUKPhKJGLmVdEIgd582KZqi4eN2Cpbk4hjJy/WMDx+jWbptsfBcEBf710CJRNAtMZ2eabnXbkNbzeQncBL3HwmvDfgYhpF8nsr4SGCJMk1ZpceDj/J6EfvKE2JVSjE+Tg4+EkXDSJ3n23jw0QvLkdrUK5VUt2EmHFaOj9w7pq+vT4khbmvrCCSNPLT4mCImuF5BcnmuujpTIrFt2v5E5yHDsJS/C97HO0hWGge9k0z3aqVo+LhGe72IkdEUnWw/Web+HQ8+iiG1Oow04WNa+l6plZvoCBe6iYhuu+02Ov7442nq1Km0fPlyeuyxx7zf8vk8XXTRRd73q666yjt3zpw51N3dTT/4wQ8i36vScg/yBJCTAmU0YNjkOOQIwGorIi9aLeX2REuqqN3hbkFh7m3iBOQbAI/x3Sa0X+84tOKMM5RFIMdd8g1h165dnst0FK1auZonljyNa//2klqaol76zoW4DcJvZs3cwMCAVE4hzBXV3BYXfGUFiC6OWkwcZbKq9EHdgDPQey7ISVN0zNsGsM1X9mjgoQgpME24fM9ugIYs84u/01Kp5Lqd2RPFEZG7ccgbfRPZQhKi0pHCVBJNLj4SVVYybLLxcbHCKIqWVNF74hYBE0SloOwC6VtJfLdrkWlZT45TT2ecsUIRsv2kM/6xbDZPfX19NDQ0FCGLOet7ud4dQXw8QCoDLCslKsdHIjERkczIzZXaMlnF9yp9cpxaJUEm++6E9K3ffdYMBZ+PW8jksdW7nor42A/m9ZWWMLIJjEFMQGXyRHw0uU+K+GjLUC3i45yWFnW9gAlXOkG9XIrxcXLwkSgaRnKPMvmcSvFxG1y3c/f8QelazkP6VWtsLuLR8DGbzWvjoDs7V9DSpacHjrW1ddCuXbsOIT6CgJnSd4eAB92+TAvFyBtvvDGAj9F5SJvXkCP1J8gnJZPNtHDhEksbfZHwUa7QY8JItmeoXg2+Z1Ta/QjzBU5Z+Njf30/3339/+Bx3z9fhYwq+oXG8GHnEC92TSeUMpi12wBQbLZ/Hs57aXNiucf83lSJZBFVjVGNpc+3atYHNnG8Am6EpGSWUpZLdnms1Ezzp/rYYjls+SwaaAnFtIk/OEJX8xS67JZaIxcY4pC9V4bigdot0XRDIfWuKv6HkcmeR3hV1SmhbvRKQ/MM//APNmzs3dE6E/TbggtO97vfFra2qi6I7/mJfueaQ0+joKM1qbqYUmBtZp/y+wQR5vkErmz58TfkggkK37MpORNRuUSZET4RUuaKG05HEVE42RR27Q42P0VzJuRVVz5y0ti6ivr4+JfOr46RJZlpaWuYIJVdkt+daCYtSxLPWst8z5Lvp7aYgw8KYsnLmuhkfiVhMps4tcnz4uH//fqqpmS6Nb4b8RJM6S7eoAGFt33bbbTR37jyhDRMO6DMFs1CCfhITaKrlHdMkezjIOSJEfNwGViJMtnx3w3fBTY0TH21hFzI+hglXOkG9HIrxsXKaKB6yWvjIQ8DkNrvd44u1ruR7pbVWGT5yl+/oGJkU8LHZxaWtxHBtIvERxCoqyDxkiphwSWTGyDtIVh5yhQRLpGbjIWX+h2Mk6+uWLVto48aNAkaW733JMJJ5COjw0a+FHnwOGSOffvppF/NTxEJGg8Y3X5Dn+5ugHECK2tz2Ta7gsvdrFB4yCj4Gvk9wIt5Y6LZQOYMZZZMslUpeYhfTeSe7f23xuvJHbi8HvWuRE9KmLgu4eG4JfszvXXfdRUDQNUQ3mcVjGwDaB9BMLZAHwdhEYqyTPPZsActuQWvc39aQmn0YBOx0z5NddZgCoKZmuracwmmntdL8+QuU51iy5HTq7DzTUN/Q8Z6VW0TmtLRoM5WLc6K9rY2ak0m6BWwzXQ8m9Mqu3A5AjzzyiNFFcTeYtjqlqdPaXSzSVKiKmq3Suxy0zMuEdD1nRMX55W9ywXZGhPkVdFkyC0JRQhJsFDOVlVPUsTvU+KjOnxzp8i7YEsaIpCqESiQyROxjEgZ1WARizIpcHs8h38W7VpsoSO6XiJGHAh+XLeugq666SnC7ZJ90eiYtXLhEwkdTLH2CWlrmuEw776ceB5iVSmTCm0hNzubQI488oskwXEssJnU3AWvIcVKK54wJH3cjKEBXAx+J/LJDcjtcibRly5Zo6wpmpjUqxfhYOVWbh5RLd8nnLK0AHzmmim2OAZTSupKnKCzpoEg2fOQ8ZHkYuYGAfZq1zZVm1cbHW4R7hPXThJG1JGcoZ+XUWM6MaDxkQRh39ZlbWua41RfM+NjW1u4K07IiNziOnZ1nKuVZfQzeTmEYyTwaGkm2YgdDAeyhRxlpfvLv+ipF4TxkFHwMfK8AI2Ohu4pUDS0l3yR5IoUomheeaTwDjZuv6wYxPDzsaXrE9kZgz66aktrkiQpEMk1WXVw4t4LqJrN47Gr4ydhuAGiRx0iyTzZrjs3VuSHps+Xa3Ca5tk0U2rZpz21oaNIAxKgCfqedtpA2btzogYIuaQev75tEihbDoRRAre674Jue6X3t2rVLKZ+UBIvFl5UqvORBOS6KIoDx0IQPGvpkS7Q2Q+oTd7kUhXyxBE8zWDy7zpNiaGiIWlsXad6BvOHFlu5DRdWydPOEXdXCx1KpRH19fdSxbJk0f0YIsMXTqcKljpHTJwwbJbXci+iqJ+IOScdAZosKw6awGvQmjGQJLA8dPjY2NtFVV13l7YEqPpqYex5ruMD6ztLpID4yhrRRaZOXARPx0aZkmWx85HOLu/xug76E08ps1h6mg9jSfSip2jxkFHzMw0+iZsNHnfV8D0Ct1jwOfigGL/Mp45I5oeLeENwxYeSge+xmUitP+Bg5efgo95OPy+nS+boxVK3fOh4yGI9twsjF7t+TQ9+XHNLE2uMCcjBcJ5fLewLrwEC0et9+bXKucN5AeswOd5fXeQmloJYBi8JDRsXHwPfY0n1oqdJ4HNMmKSaTSicSSsxsHkGB2ZS4TASVUqlEHW1tlHYc5X5Gy6l0Hk9UIJIJ3DOaRdEMP3GXPJl1SZFM1vOwCa9zQ9LXheXMcg/BqwMtu012U3DzaCJgKqnaU11ZBhXwE4m0ovXzGTW9BYkzb/yvHDe/DSyOtbtY1MZ5pWAuA8bnGrcS21wU/c0x6JbGM0uKfarXvEPx3ibPBzEZCh+bze49ZKDdBNCUgLunToPshySISVt0SVJsFDOVlVMlMd1hyQN52Eq18JGI3FqxKVItBSbviXbpvKlaoVsf+tBNeldtnpRGtuKoQqqJweHWTROFYaSKj+uJudNPPD5yplw/dh8Mfeag14BDjEkMuj62tMwx1ATPGNpkzLbNi6YSfGysAj6K48P3ThEjZd5Cu3eAMaGioBXj4+TTRPCQJnzshp+8rxx8VHlIh4AZoWsj+JlLjhPGA5mSJEbBSLXUVRheVMpDlo+PMkalCJhl6Ks8hgWlXZmHDMZH25TEHCNVPslxmkLw0ezVlc0yA5itEgXHSL8041bpfJPngNnSXY4nro6HjIKPKeiTR1eCkbHQXUUqFzDF7HrauC73xQ4NDSnWYtlVWBSUSvA3e537w9jYWCBZwKBt8sIX9nZKk1qcdDKTbIofFxN18ZhuXVIkW5zRooULtVpKW1xvqVQyWJfDFreYfdIhoIVUZlEuy2DvBycbWPGxuNn9+yWoGyzPDG97l/I4rnHn2kpNZmgdiOksOaZN23E/aagg5oS8W3ne8rklz6lRgFo8Zckm8q2GssbZ/14odCkuUWEa7/Gu85h8Kmfs5OyjjosXMkae2dlZdXwMJuMbtGBDyf1cQyJTI+Mjkazss7W7XohX5EyIyIDKDIvK6JrmtQ0jdZnTJxsf9WXFbrY8M3cr30rMBTzohp7N5iO8S7VN5uIZnpRpPPiYkvAxXSY+EvkYea2EkV1ue5vgW3fk9SJ+7yoUqKsQ9L4oJ1tvjI+V00TwkDp8XOzOCxs+9vX1Ge87p6XFU6rbyvaxsBOOj763mZxQURVmbUKXjJEFCioxw8NMFi5cNEn4yAVtjpGzCFhFQYzUjWE0HjLIP9rKi3EM/RLJClyW0DLsOfRlwBynXpN93txf1dLNz9clpOP8nT6B3Hh5SI6PXABfpMHHFun7eDAyFrqrSJVuNtwlI8yVEmDxYO1gCc54YqpBaQLJ1+o0MDqrNK+fbNR+w7dOi7HDtokYtihM10TNqNmYSGgzCNoEWLEOKXeL8etDmsF548aNdOONNwp91SXzEbWH4UxxOWVzOPN2HH+v7gn8eMeyZTQ6OmpPFqEZxzz8OMF8LqetbaiL6a5150gG8Gp27wZz550BZqU5AKZdlC2UHNSizltZAOPPlw24tclWM55JeYG3joL14fVeEDaKmcrKqZKxixL6MBH4qLdIy3kXMsJ3btXwMUOc8zxhmMqsmYVmnYIoqtAKbDDOa7WUzkPEvH3uJREjDyU+9vT0aN6JzdIN9x7DgeOtrYtoaGiI2traQ++pKwPG6qlvIgDW2sCcZHxsBrwcG9cCNAXMC0hnaQH8xJTlzFsdRop7qFzjdoPbhzwEpfrOncaM2FGz9cb4WDlNFA/J8ZFn0udzU1QK6a7LZ7Pa+/H16GcoP0D6slli0lvR6qtawM34WC5GRhdaE4nGiPjIw4xu9rCpHHwEQO3ty+lDH/qQ+91UESNDQQHzmtB29QlkbZbu49y/XMHI+rJsWUeEnDj6MmBs74DEX4VjpK/YzpCa6K6GWBLNDcTCC4LKATEkazw8pCxjiBjp5TSCj8+chxwPRsZCdxWpUsC0Be+vW7fOmxhD0iQZgZ8dWhaYHc3EM91vDGqMg6jtWQlWbkqM+WqAv4HzsmD5bFaJjzQtCtGSMTw87GXlPgC/tqTOCsAVAKbFtWfPHg3oqG6ZohUoimaTkx83LCsY+Pe50nG7ZaS/v5+yWQ1D58Z08/dZD2bFk+dIX18fdReLlHKc0DEPlAFzwaQJPpPH2xH7r9PgjY2NUVehQAnorSabDffvhb/RL1+2jJocxyrgi2N09913e883ErjvYOhYn3LKvEAMcCXuZpxiprJymiiMnAh8DDIeY5r13ih9X0msnApnqOpJtGLwsjdE5OYfWBw6F0V8LJVKtHbtWqlfo8RcuEUmTWZu1XnNaszmhX6LoRnB7/o8GOH46Au248NHMa8Jx0eW3Mchc+mcDcRcyoPMfV9fn5sZORV6z2DsaTP5DOHSQDumfYQTx0eOjXI1CB1Gyt5kDlA2PpZKpYAAxsMrBqV1wT8cl2ckEpTPZu0xwDE+TihNND6SBiPz0PNYmZB3zu93s9c+T+ho9jDz8dG3dLP1yhTfjpOitrZ2KpVKroKsIzJGEBFt3LiRVHzsJn24GcfIKPioe6ZEYO3b8PGBBx7w2g+WG5MxMqe5XzQe0k9+tobCMbKe2L4RFKj7+voilFir17RZSxwf+/v7tV6kOozcv3+/EIcu70EOMU8lsQ98X93p9adj2TKqd5zQ8rZ8fMLwUSeA8w/Hvo5ly7y2xoORsdBdRaoUMG0vEfCLusuJV/rd77LAzL/LLhajo6NWN+LjwIS79QAtBGiq1DbPpL0dLInGYun3fC7nlQfLwJycQx4D0ZrFNU6bwSynYvs8EZvsRsIXVj6bpSlISqXGMqSrSShq3xhQpCVQSREwhQqFLheMOShxUBPdJzmo7XQBIuECUhD8eDyOLlFHMBmGD/i1YG4wpoQOIpDoYgdTANUZxlHMlMuZZ67BtQFIPpejJsnlN+XOP3Fu8Xd1Dfw6opz5DPRJEvB57XfxHHktsE+4Gxn/+PU87d4HJoqZysppIjGyFqDLMT58JDIp7UTG44Pkx+8tIKaRl7PKJokxDXuIlYnxf8vl8kJ5MC7UhVsFRHz0+8XxKiPdW03ew8uhBJV724m5OQZx0Y83VPNghOEjH7uTTjqZouEjS5qmS0TX0jInBB85rorPXEuM6RffU9CN1R87nUUuRaoShY+jb0XP5VjsYlR8LJVKtGThwoowclZTk4KPutKNOiUp338/4B7jFskwoQwAnb50aeh5MT5OLE00Pm6CGrbXB32y272Gd64KpSBWtorjyG5iibrq3XW+kFiOB3m9LnK7uYdYgq/gOvczYBdI5qF0NZ/1+NhMDIdlfA4mgBR5SF945feW8ZELmhkFH5mru4wrjpc4bs+ePQIPEoaRlxAAam/voHJ5SF8pYMJIOZRGxkh1T2LPUUOQkhmzcdzkfef4SBSdh2xvX+6Om7wHFcSpTD4vd42Xm0PHK4s8ZFR83A6fV7B55o4XI2Ohu4o0ns1Gl+wiDWaB5K4LYkZqnQu2rkD8lVdeGdAGcreIDFTBrAmgUwF6APqELCLD4ICVGdP93uQ4XqmxMQRjh3ibnHnQLQruirwJwRi4NdCXFhsaGlLaWATQUkVjaNYWjoyMuJkL5WsyHnCqGW9NTHmvBxBLlpyutMnrFZprTjraUjGiomErggoMUdOtix2sl77noWaQnw4WMqAr5aEDz/vvvz8wF+X3wt+VWJYB8IXmgJuj6yUhk8mNJ5idnb+jsHey2/2tIfQ8OUFRtdf5650qHbvR0VEvD4UuBGY7WLy3HLZQLj4SceFSZXSYwLmcmCvdlcK65jFnMtPQTOYsso77/xjpSn5xjNAzVbUEXO/+v4mCMXD6MjXZrMwkZwi437JmfIvCwMCAFR/Ve9jx8a677lKYp5aWOZ6bqR4fE5p+5EnNEnyNxxQHvRd0cYMnEtAmHZMzs38w4K3AyYSP/p4SjpG8xux64d4iRtpKN+rwsatQCMT62hK27XZ/a7CcF+PjxFK18VHkIUV81GHkBgEfxXcuJ2TUrUvfTZiHqTxAvjLQhIGnk28d1/2ecaefWvKLY0T5+KhPVDs0NKRpx2b1BXHloRkfC8SE/hSl0zOl38LaZm7sfX19rut8dB6SjZ0OI2U820qiAsPHyL2k4mOCgDdIx/IUBR+J9Bgp4qM9x8YI+a72slKGe0qwTOoiRcXHJthzUG1w11DNODEyFrqrSOMVuk1xr2Jtzp07d1KHW4dZTj6mS1QlbuK7du3yJospqQv/f5YGnOXJNcPy+273e7fbnzXwN3cuLJoybTvw0/tvd/+XXUTD2mgGaJ7bj6u9Z9FbN4NuTCCmFeVuUCIATKNoCYx6SRToS6US9fb2evEgRHZ3dpN1f5t83NXqhdVJ56Aijw+P0b8Deovzvn37tFrCXbt2BeL5TRq/j0K1MC63hByI42OL47377rtp7txThUzTOjeyJvJdTfn4OqTX5NZGiuuOmcrKqdKx6y4WKe04ileNA6bUE9fG4tbWUHzcAJDszsaZOH9dbiZ9QhfT9zDGzJaUptudf2uIK4fULLmqYs5n6vjveouQPhttMwHzKBzHekhfrkePj4nEDKoEH4mIdu7cST09PUqpTPPYbSLgKKlfqmU63P1TTH6nK73GXVDVkj25XJ727dundaPct2+f4BHFPnk4npKTBIw8WZrPTY2NtGPHDoqCkaba3KL3U29vL8096SSqhTlrebd0rQO9p5SuVFk113hM48fHjAYfZR6yt7dXyd1i4yHntLRI+Civyw2k4uMs4X/TOq6z/F4i32K9wcWWDVXFR1M7fuLHMO+52xVsYALqFWSuG36A7J55Hwys9Up4SOB4qV8qPoZjZIn8Ouk6q3w4PpqUxoVCl6YkmWkcPkqyC74t2VtUfOSu5iefeKInSIeFs4rX5jUYaSrnOJ41HgvdFhqva9AaMEuzmISKC0iiO6ScEICDovi9Fsw1WxRk0w3MyicKSSUwLThP6iILvvL5BJ9hON/y+xrY4ySi/qZTEnS0tVmtrYBoQdAtUoccJ6xkjwyEXOsWBnbrrYm5bMne5PqsPMaKv5f2tjZFoyZnj98GRIrxroWfvVG2JovKjE1QE6KFtZ1w+7sezDK/Ab4XhGnO6GLK5XP3QlYSOIb/+aY7Jlw+6B4/UTrPIeByEkG72us8psrGTsTHPBheBRR4LjaEzaEgPuot0y0tczSJZDjj0aS9xp9vYYyZ6bc1EbDE9pv4+xj51ploDEr4bzuJCdjycx96fNRbx5qJW94bG5sUfDQl+IG1xnAN6erT6pQZjpOmmprp0vvJUAKNVISjYCQP4+L42JxMUoclGaY8v+346MeUy8cLQEAZMOgeP1E6z4EfuhHj48TRePGxBBbuVw0eMuPOJc4PzmlpCUm0ZQpTseHj+ZbfbUnBysXHoADY2ro4xNpqL1fF8lPonrtgeB6eqPGh0LYdJ2U1AIRjZDg+Ag61tbVHwkh7Hgw9PuqVGZtIdXsPaztBTPnhK6QZv+4Y54wNHzmOdkiJoB3pL/9wg5d47aVQw2o74Cu2wjAyFrqrSJUA5ujoqGJpPREODQuTRBQgxZcpx0yUSiUve2xUIfchzbGw88Xftlt+rweUGHR54ou/cTe73ZrfeGI1cZxmptPe/1uhL4eVBtswFsOhpGIFDXc1Vi3duyk8OYe/ydhKUNm0lKb6rJ1gyhRdXLxuIxWT0+nGn7uxh7nU8GM8w70onNdCryE/Hv7GLW/kYc+Xz2apOZmkTTBn8M0gGNLgK1W461iJ1M1aV7/TIbYJ8ZqRu4kzJdVe5zExKnfsRkdHlc0xg6CgICqQRIwMw0fTulu0SJfcLJxBqvy3erJlpw3+xrPc7naPnyT8rs7v6dMbhO+D5GfIFdtPkx7HWgg4zdL/Q4eP4b8t0NYB1iX4aWvroPnzebxn2Dsw3Ut25+dMrRynye6xW8DIRAg+hmFyPpultOPQaRHxkQtNrWD7690AXSFdq9tfHbetHvc735djfJw4qgY+OjAnMrXxkHfddReF7c/Vx8ftlt8/GGFtRsHHMB6A/88zlYv30OEYj+mOyj+Kx4aEPpgxMkoJUzNG2pQFnQRs1io+dRjpV+GoBB/l33gVEBEfaw3jsID0LvI8hlwfSmXDR8+bR+NhOxegAYBuhMqnjkLFawegE/i6QrS47ljoriJVstmYYzIcKrouafxFtre1WdvjRedlIWsQvpAlumm/wz1uEsrqpk0zuh2FJe1K1dVp44fEic9/k2O3+WQWJ71c7oQvEjlWWeeG7WeOdaR72LSwWynILG6nYHKOoNtLLpenvr4+JW7FlExCp1Xk9zK9D17yS3SRke9x//33e8K2bfwbEF4Pdqv73eSxsAmqZtAB6NPw3RRli70DFsMthwrkhQR//H1npPllirtZrN3ARDDnrmW6hCi1ZMpkWq11HhOjcsfOFDoiusTyuVqviXmVieNjOAOxgILhB+8IvWbq1OmauZciJoCZyug4Eh6FMS1yXCLHso8L18rl8vg+chLZkt/o3eYTBMywjNXtVA4+trcvrxI+nmzpV39gLevwce7ceRR85rB3kAi511bhmE1R4L8Hx4KP7VIImYyRHBej4iPHe9GdXXSlLEBVqHJ3yVqEVwsZ7xqPyadq4GMK5iR9Nh7SxD+KRhI10VYl+MgxUMQMeZ1HDd+Jgo9EeoxMky88i/goumHLgh/Hbhs+Xk7m/WA76UphtbV1KNbn8jHS1i9ubddj5J49e4TKGuPFR7EfJnzcZBjjeaS31rN9hWWAD3otZbN5CsPHKN6fOnwUQzB0GDlHaiO2dE8SlQuYNm2+g3rKws/SHCWJiRzHoNNgNyuLyTwBH3jgAcrncspkXARmRd4ENW43CeYCypPAzIU+lqwTjAkQ6+rq3NvDNFbifTNun/ji6ISfGGQn/NjuK664ggCb23kQABoaGsmP8dHHBcplJ2xlE8bGxjQ1eMMBgVvzTS4yc1palOR2CzTjzwUXnUWbNMdtmR0vAVOA1IApdjhzZ3oOXk6Of7qLRc917WbhWl1Yga4fe4Wx4596JCgZWbDpiVyrO2YqK6dyxs5amkOeU+3tVusAx8fgXFDLCTKmMBrT8cADD1Aul5fOTbufTaSWhaklls13OzGrDHdRlJnSAjFr8xRimbVNrps1EeY3yE/qw5VNneRbv3cScDUBELwB1lva5UKkQ0EhXbRA+PjY29vrvYfx4eO/W/pVIlO+Dpa0jSvo+Hiud9+H/A6ahXmht6oEj9tc4mfQMXCoDn4libB9V5dPw3eFLQ8fD2jOWYRguFDY/tqDialDG1OQJgIfxWM2HlIXB6ta+PaSHMZSPj5miCVRM2HkFGJYZcJHLsR2kh0fa8n3ZgvDSI6PfN2niJXC4t5zvcQTnJWHjyCmQOBhJzKWsHbWrVsXeBdRMHLfvn2aLN62cJmgt1MQI7lCoVr4KPbDho+XEOOxa8juRaAmBy0Wu4XwB8a76vBxwfz5FAUjF0Gt3GQLY42CkbHQXUUqd7Oxx61dQ/Kksrnk9ff3U0s6HarBTsEXaJeAWTv5+aK1ek5Li9e26IbUXSxSI9SYBgB0ujA5ubv4MFS3jDnS9wz0LqMb4CdsC7PGinHPABP8jLWie3u99ooat/OEANg3gykWpkAEM/37Cpad6FA8GEShTixvlnIcWgPfjS8DvffATGGMdC4yGfiW/+0IKlxMcSqilVC2qvCY7m3wPSXWQ5/hdAB+KEFzKmV9Z9zixefU6OioZ8XhHzHesOS+Z5s7/HUQ4iOVeRemjbWvr0rXeUw+lTN2ttqzPIO/iGe6mvIijYyMUENDmoL1RjmDpWPaFhPwXvLjwIOMX0vLHK9tPp+z2TwxJjATmF9BxobXIB0hlpRMLmmzmtSs5mIpMFHos1k2tlIwFptfe4N0/rYAPrLrTZZ6/mzvJMaEy5YRtWwZF7qj4KN/XjslEjymj1vnw6xjc8i3UjnSPTIUVJzK7qaytaXbfTa4fQhaVfyYbn6cu3XqMyRz5rommYyEj+Kc0lXo0OGjjTmU43xFfLQxoba1VckajylI1cTHa6B6J0bBx3RDA9VDtfD5RgoZ144nk6u0CR9ZsrNNxOpSi21xHsuGj4soOj6myMa7sTUr42OC1FrRojAZBR9BwKepXHzs7++nXC5v5SH98mYbiHkeHSe8Ix0+zpTGSMTIAvllE8ePj8GY7m3k59NZT8FQJz6mA8RkHruXVUNDWindqCtlx72FRXxc3NpKYRgp5orh+HjllVdStTAyFrqrSNW2dPuxKVuVBcevN23I02tqvP/DNuAUmCBTkwi6IPJslToaGxujroLr4iFNug74DIHMIPD/O6F3q9O5jPbD7jInC4AAaJGUwVjMdi5qiMfchSn2dbnwvQSWvCkN0KkWDaL6HvTnycLlYvhl1RwwLwGRGYL7nlLuOOpcZLj7t1iPVXbJ3wBmYckL13BBWe5Td7FI+/fvDzxPUupTxu2rrEBxALrB8s462tsDc0rnJidm1h0BvJqKGeiVEq2Ge9neBwClbFQ113lMPlXTksM/c+En/JE1zSJG6uuZ2uZGvXeenCCLZzyXKWihlRmVJe7fvaQvy8JxX+cKyZlCkVnsJ3sMn8zcMGuNzj2vWOyW9iVdea08cWYL2Oh+D0tG6eNj8B2YcVR9V10ULC+ks44tJl63XE3+w59JzBqsG+MGAtrdMfPL1GSzEjNX7PbKmunnE9z+bRLGwlfy2by3Fre2BjynouAjx3IHaqWPJve4zvoJS19ifJw8mgh8BEBLoc8FE8ZDOpq50WIsj5iYJHycIqytqPgYJba8RDI+trW1axMv+pUJouAjiJWZrAQfzf3NZvPSeRwf+ZjqlL4p8pO8yQnSqo+PY2NjmhhxOZQp4/ZVttaH5xNJJBqV8mC6UN0kUl4Cy1uE9jPQ85B5wzqyVdIpByNjobuKNL6YbtltQ9S4BZkm3eKcgmQgW3lzMkmnzmNxaybtTL8waVKOQx3LlgXKtuhIjP1YvmyZNsYhCX18mA7E5ckrC9A8OYEDUGMiQdfAT0ZjEtT5R87GKWqhuotFahKsu+vBrMR54d5NAK0U+jsGcdMJahBntsymrkKBmpNJT/A1aenqhRrV28EE+lr3/3aoLi01YEK5uMDbpQy33P170P3LtdKmce6U7tFdLNLw8LA2dqhUKrEydVK/U2C1vWUFSgpMOZCH3q09A5+xHB4etjIPcl/3Qp8D4EvQz3OAxdsz7bqskY5WJmy86zwmRpXGLMpzKA9fyNAJEipGyi5zLAvqG95wTOha9b2NWJmaZcs6QjFSxEeWOVxmUHksY4b0MdgO2d0WS9L/BwhwKJFodPu7m4L7iPxM7CPXjRU9PdR9aT0xBURe6kva0tf13nP5luHwcjk+syvHXtaS7/4prv+TiTGD/p7Z2ion/+FeZYNCv8L63amMTRg+6iz3/vsMxohyL5yZFnwEWPiQrYSYjI+AWendDz0+5nM5SicSFZXAGe8aj8mnauFjN/ySoTq+Shay5ZC07WCVRo55A6vNfAC+Yt8YDumkrPhI5GNke/tySiR0eVbM+NjYKIb9lIOPLFM5UzSuJ5XXVvGxs/NMJbyFY2R0fLSF/ujwcTvZyi6y59Dh43ZiQvFUCRNqiAnlvgKira1duMfE4SORj5GOo8PI6Zp33UxMEA+XjVpbFwd4SFN/5RK8Jh6yz4CP3MO3GhgZC91VpEo2G122QFVDLjMl6gafQD21IaG1+oYJuKIrEp9cOhodHVU0oWFt32A4vsh17bC5jIoCta6OtAMWc6FzSRdrgesUCaOjo9RVKCht5sBcyVPuQkrAd9fm/R0DKCdpZxfDoUbXU2A9QHd6v+kBwBQ/3SH1px3MNV98LgC0ceNGWikkHSMEE511a/otj7NSkkzQgD/00EPamrnyu7Z5IPyD5r11g8dfsz44UBUIYl9FZmBQuN8IQL3uhysYwjKim+JDC4WuSC6T413nMTEqd+x0GfkLwjotGOZ3ECM5IxEWz2fzNgom55JJF39X6T1ZvWszw8UEa3FvUOuksu+LKFguT3ym7aFKBH2sYJqAb5PvqthIdtd2vp+JMZXjyXYcjNFmTK6Y9Ii9L1/oli3d3DXdVoN3BoW5dd55551ezdxoHmvbAlYXjk1h+LgG9tKdsrDEsXC9gI8lqEptsQ8cH7n3mrjHdhUKMT5OIlUDHzPuHAozTHS4ifrkfZU084P/1u99D1dQhvGQUS25Ya7dqkKtUnwE6V3SfXwsFrtpYGBAwcho+DiF/DjucvBxhOyl0kwx1DI+thMwTDI+bty4UbLYTxw+8mP657F5ac2Xnoe/L7EfjqRAUPvL+V0+13mY5E73r1f5xICPfK1VAyNjobuKNJ7NZnh4WJg4/JMhPdMkLjq1DEIRjrdxd+iyoEIt9m4rByK7t4luzOIs56C+1HD80ksvJRvAyxtHLUDpREKxptZCdaHjz6VL788t3eKz7HafpR4+c5OBz+zwxchrYPL+8uM73e8fhR9vzEE9IVnEeX1B05jxWumD8Bkusf4wV4wsam2llOMoCdJ4v8PKbdkSp4ll2ADmLcDLiMj95jEy8vG9CDKTG6CPA+djWO8qLKII9auh1gqvBWhWU5Nao9x9HhEQS6USXXXVVXTeeefRli1bJn2dv96p0rEbHh72lDPihifnbbglMDd4XFr4huwL6DZvowNGjFRd28KtuWEZX/3M2mFCaYaYC+M2YhaONIlMEGP6aqVnaiKfEVWTx+kt3RuIWV14bCRnVmvdZ+AWEVMc8073O7ekLCWf6VXxceFC7l5qGjdu4Rl075mioOv2BwjgMYYZ95nl2P1N5FtqymNqzzhjBckM/NKlfF6GMdZ+fCG5GMn3CxM+isJyVHwcBfOckvfSRqj7ZQqgpsbGQHKtGB8PLVU6dqVSifr6+pQwsQz0hgk+70bBlPthvBznIX1sDVdQmnjIIEaGW3KZMFXQ/vaJT3zC0g8dPqaoPHwk0gnrqqXbhI8ZYkpJ7iZdLj5yjEyTjoesHB+ZYqS1dZHrXr6A1AovleNje3uHMmaFQldIfXfTPNgrtROeJ4Phvbm/nN8dhT6fVFR8JCIaGBigv/mbv6H3ve99ZYXdVLLGY6HbQtXYbMRkE0FG8A5Si8p3E3MZCVq9k0i5JZRYtkqbJnQlwi3dOkunqYSUCOqm+DGundfF5PaBuY5C+kSJWyrA32B0JcbkslS69nZK32XXPW5lkwX8Zum8RYCUOZuBqJjgjSea48LnDdC7vPAEa7Kw2aoZg+mWcW5yHGOJMAdM8F8D31sgBV8QNzF9skIiA9/60um2uR6q0kdUJjju+xH7Wu/GrQ/CZ0q7wJjKgHt+IkGrVq40xqRxZcvTTz+taKdNcWcTvc5frzTesZMTOvI5sxfwMM//FFx8TIVuyMPDwyHeRmkCVhBjitZrMVKvxY9SZ9qc8TWdnukyWHIinJOIWXfltR92H/GZuBJXjdfTx3Tr2twp/N+puccm8mMHuYAv9zlHanIhR/i7mfy6u6K76A2kLw90HanWrDPJLy/GP9Ol61TBP5yprSHG2G6gIAPvhL6DvzFg5Gkh+EgCRp4MFct1+GgqrTm7uVlrpeH/dxeL9P3vf18JyQrL7TJRa/z1TNXkIfPZbGBPvQMqDzEHLJFuGE8U5CF14XXNxLKZmy3dKqbYMIYr83SCFtu3mavyROEjud+DwrpYliocH/VCe3R8LBDwOVJ5fod8L4By8JFb0uX2TpG+J6Xr5HfNsc6Ej1NIxsdEIh0yZqbkkxn3XuuJKSIaSZ1zsjLBUZTnHM+3VgEf9+3bF1u6Dzeq5majup07bkyqHPdg3uwXt7Z67eWzWapLJJTYhmnS93w2q9RSNWXJXAp9TBqPH7scQaE6437fDBX8HTDGmbctZ2M1aWF7e3u9Mio6V2vxmm0R2usXvjtQBbyUe7wVvoCfhxoP1exeqxNip0CNMUmCaZx1meZr3N911n4ODB0Anene9zqw8mgf04xzU2Ojdmyuh97VcRNvv72dUo7jxdTrNvCC2w4QXntdrqXOvSzk8mdvPOMMpU9h77VUKtGKM85wM837n1owYGQCd3ATlTOsTvY6f71RtTHSZwYdzbu114Jta+vw2uvsXEF+oh7+CX5Pp2ca8VFlRJaSnkFlG3ciUSf9xsuEbSfHSWvcFx1iFgAixqD0k1/CRs8EifgYleENZi/XMVb9xOPI9RZ2h4IZ2sOSwi0gZtUShdgGzXvgzOJSTVsp93x1bftM71xiQngzMQH9agI+RjIT6seMymOjY6CDsdrMcnQLMevNegqWHmLXihiZMeBjHsEqFQDoGxp8rgQfh4aGKN0QrEucASjtODS9pkbZx1IIVjGZ7DX+eqOJw0eXn9HwEBn4wohsCOkQ6nqzvCgpUhN0TQt819Wa1mNkNwU9UUzC1GTjYxSlgA0f9UJ7+fiYISbEriHu9m7GxwQxJagcJ58ipixMamQI3p9WAtrc+25wx+295Mek8w+PFS8fH7PZvIuR17jPojMkFgi43v1fnmeOsW3uZSGXiGxsTEtthONjqVSizJIlVCNdUwsmmMvrJ47pPsQ0EZtNqVTyMueFu/Woi/8Tn/gE9ff308DAQGCylcCES+6ebBLkuIVQZ+keBWgewgU10d06A72bU29vr1ez2RZTZFoo3cUi1cLP2Gpyez4Qob28209bvHLKTcI2aDlP58Ytx+GJ417JGGwAc4/RMXL8uxj77EDdYKdq+iQqUE449thAu0n4tdh1c6cAvUbxeKgWHf4cAwMD1NvbS729vTQ0NERzWloCfbKFNNx5551e5l4ZGP2+69dQnJ13cmgixo7jWzg+6rLMOrRx40ZNXC5n2PKkZ2DY5s9dDc2WYZ31tZt46at0ukXzWzC+cOfOnULZLN09wmPiSqWSq7ytJZ/BDXfttO83w9b7ZrPcU8sWs6f7jbs8mgTocuMCN5Afoygzcvx7L/nWNYdUBY3ORTUoINTW1kltzyFgv9cPXi3EgY/BumSj14Rg5Lp16yrGR69EJVScPxnhe0yMj5NDE81DGq3Z0CvJ9fgoCrXmtSqGq+gxch/5JapMwpQdH/v7+2nZsg5N+5Xi4wHyE2iaBGsbPoYL7dnsZOJjlFwZPDfHJtJbyv+BfIu6zgJux8fTTlsotZskc311jsE6JfoHSVXS+ArjnTt3Ul9fHzWlmwP3O9EdnzB8HBkZ0fKQnL8O9ew15DIYzxqPhW4LVQswxQy4RFHqea+RjusTR4iWZLHElM4NfAOCybXkLJkZMAZiAZg1V84qzoU9W307vhlkoNe0Hg+967Fc/msz1E3DKFhLbldin/lfWx1VOb7UdN5W4dgo7LHWu6V3FMXa3w/BRRsqQ+W454hKhwyCY8Xfua5PJiVBxnC+bfz5pwCmoEk7juLW2KJxabd5MNx4443e79x1X46LNK2hnp6eSV/nr0eqxtiNDx9LBHyU/PJc7ONryHkbYgkVnYvjhkDyGH0VilpiloF6UrOKM2Fv3bp1bnt6xWlQANZZ65uIMS5BJkh1Fd9MKjMVzoiasv0H613rx7yvr0/y1ApjXvlvo8TcVG2Mo/iO7NYsdg5zP9RblBzyrfeyMC5+wuIKOSNqZjrFaiFR8JFj5GehehVxa3U5+Cgq3034aNpjYnycHJpoHtLm4VeCLj+NyO+IaywcIx0nFagMUih0SZjCcSTh/hWzijMBy4aPXFDy16yMjykC6qgyfAwXms34WCCb0D45+Mjf0dYI93mH+7dAek+iDul8Wb6oBB9T7jszPUfUkIDPkmwxb2jg+2Iw7BZwQg14onJKxEgefhtmyDPlMhjPGkcli//1ROMFTF12x2Kxm4aGhoTJqysqH6y3akqssxgOjYDFTS+RJi93+RVBWJyMY2NjimC03W1LZ+3e6/7/3ve+lwCzQMddGffC7I4MmMt/yZsJt+J3QuP67grqY2NjtNy1rsvPf4vUB5srik2DLD53lKziawDaAz+5SaS+CP+bzlkH39Kdz+WoOZmkHrAQAd04yIASdm/5GU4+6aTQZ5w3d27gnrOamhSmkruIy20UQt4rfxcFqa3g99jSfShpPGNXKT6yUlrhWnkmMDtuG33k14vlHzlrajCb+djYmMbdEQRcQXpr914S8dEU3x109dZb6wG1hji3MqkKCW6h6iQTI0pEdP/99xv6vSlw3zDBnSiKF4L4W5SsuXD7sJLUsTbdQ4x5NJ1zN3FGOZfjVqj1BNxOwPGWuTDN0jaLpxSrhYTh48aNGwNKXQds35grPa+DYEgWWfCRz4UwfIwt3YeWqsFDyrlNuotFDyN51mZdUlM+X2qhUbI7fL1zjB0iNVO2vC6CMd6seojsTswTThak4+w7V6Qz66Yq0PH4dXbOXjLhoykhmhkfdxPgKHk1OEay8QzDx3BMmlh83E7AHvITiEbByO0RzvG9gXI57ir+UWKW9/Hgo/hedcqG4G8bN26Ukk47xARsuR+Nbj8eIobB1xLAsFSHj0Rk5CFtxjLxnVZzjcdCt4XGC5ja4u7JZlq1arWGocuQ46SpUOgqu1QN38TFWGPZjU1kEERt4gb4ib0OwNeuyxlYdWW+FkNI4CZZqkXX93aorsum8l+mclabNPfnyRDkTSkP0D6oQn9TY6PRwi6SrlZmCn4Na9EN3VY/W44lKcCPgZdrBPJ3RmDa6TBGjn94YpzuYlFrwRbbLCcGXjz/C1/4QugzcoVFf38/3X///ZTU9CMFvbu96b1yN1+Te7kDXpc4KGjEMd2TS+MZu8nCR18rv578zLRBNzY5m7lvMdlAjGnjwvKgdJwzGDpvpMXEM+6GJzXLk5ysxlT+y+z6vkm5f7HYTfv27dOMVzsxl8lRCjLHjmLtEQV39b3pEvLMIWYh4m6Wtvqwy0mNZ8wQMFVjeeLvjIgxhzZm1cXcQpdQXjDMgs37dJyl7SlKqTAbPhIxt+Bbb73Vc0dPaTAyU0V8nDZliuK1lkIc0z2ZNN6xk6vMcN5p9apViuEiA72XmW5u3gGQmmRLjjWW14WfzVwN3+GJvQal43JiMBkjcwRsUkpUBTGjRAyzgpbORKKe5s6dFxEf2f1zuXzg/hwj5dhhhsn7aGLwsZnKw0ddnPRm8pXNOoyMEqvOPjz5LNsnouCjrd1+Up/D/IwiPvqeYGlNP1Kk7hVJ6buPj3w+mDAyCdWrN47pPsQ0HsAMX/yOtqh8S8scb7JwIcaWBOe9uk0Zfhx2yv0+gmBGc9GiLLux6ZJwaLWlUOPFiYJC66DUNoG5ZcuJx0zXy8KxmO1YPHc7gqW55kATg5xIeInHdPflpKuV2QrQfqiC/AHDeKUMY8aFYB0zlYGvxODCepjbOt+AdcoO+fz1bl8SlnbXgLnAr4fPvAH6zOk6hcVit267qX1RK6lL3sZrzIprSHa55PN4eXu7ArrNzbPj7LyTSJWO3WThI7OocEFOFuxAfgmWERItOarFRK57KjNVpjI2jMnUl+/aRvqa46Mka/nN1weZPxkfg4qNQQqWneGJj3ifOSOnvy8nNSkoiCU2GyZmiRAZ2wOG8Uq792rWjFszsZrkMoOeIb9sEE8IFBb37SsweH3e8Gvq3Xvq3kmQgdwMH+dFIdqGjx1tbZ53VBhWVwMf208/PXCtA+aFFOPj5FE1eEjdPHEAanYcbZK8sbExBR9lJXs+IFz9u2atiVbelLumx4OPXGDS59SQcSYcI9Xs4FHxkYjKwMg5VD18dIhlIpe9m8LwcSoBs8ksBKuKVp5NPZlsplSq2YJjwfrl9ioX9eQ4U4V7mc77IPmhV3wP5PMtXGHB5lXC0v6CwPxJSIK4LHSLa4i7mPdA5b8dMP44zl5+CGk8gGmOSwzf0KOVr/HPPwF+PWhRsOPuFCcClJMmV3exSA8//HBgMoqC417oY4SjuKqNjo5SV6GgTGgO+ryuXkrus8Cg6IRenXDMx8aUWVuu+StaJRa1tnpJRUxkKtWxXliw28Hct02u9GFCMwC6BKDboSohAH1cvFjaTGzPtLmKlnEnpN0UoGQJ55nw14CXcFKfUQQof66a+5GQrg+bB6JiaFQaY/HaewE6H6w+eDkaSk4xU1k5VTp2k4WPzELtkDn290Ri1pYgI8XxMdguF1I3keo+ae6DjI/M6iozSzzGcZQYsxdktEQGRcfU6Zg/f2xMyXTMfT7llHmR8TGblcthbiNfGbadwtzow98dCLiEmEu47GoIMsd8ZrTt2RU0IrNnirf33zsXtj9UJj7aEqRVGx89647jxPg4yVQNHlKeJ4MI5y3ENRtWHtZfdx0K3ohVGeTkaJXhY7ggJfbZjJEHqJr4KI6PGSM3a/vb2rqIduzYERDeZRKFe1URcK0wHpXi4273799QsB44KJvl4To6jJTrl0fFx+OEe5iwV+w3F7Y/RExZqpaULBS6Au/ln//5n6X3resHSA45u86CkXsj4mM+lyt7ncZCdxVpYizd4QkZdMH74e4qLqjAUcqSAEzIkSdXczJJc1paAhnCdYJ2PpultWvXet+NLslCn7uLRUo7jjbr9hhYDfGoGwYHrYGBAS24iTFtilXbPa7rL7eG66z0OtIpAfgzicLrerCEbXNhz7jOrRE7hd+4qzpPNiIDBX/WMU17YtIx3bguFcAmrXnXYdnLe+HXRGwE6IMA3Yxgcr7R0dFA3GJYrMw6gO6MMA9EhkGsx1gO0xGFYqaycqq+pbua+FggFv9lE+xUBo65t8sZcDeRbOmYO3ee8N3eZ7+vm0itid1NrIZ4NOZUZOrkREtEomKjQPpkOo6xzyz7rWql15HesqNjzNa77c51v/P4zzDmqpd8Bou5YvrJmPaSnlndq23viiuuiDAXuIJGJzh0ELf+bQDbO6cCtEjCxzUuNq0Bi52V8dGGXwCrAx7j42ufJsLSbUsCJWOk7DnIQ9fYuohSSqueKsXHtrZ22rhxo3AsGkY6Tpr03knVw0eiKBhZMGBTHQX474ow0pHGbz35ieh2kx0f+V650z1e8o75ArQOIwsUrF8eFR9PE67XvZ9aYkoKPn48EV0H+dVDGt3n2k7AGi85ny6/S/ic7A30vceCkZnDBB9jodtC1Yvp9plBlqzAPKF0CU70CzY4wZNIeXFmslXRNLlugCrUJRAsK+HXPLRPUt0EFwW404T7mDaMnp4er73R0VHKZ7OB/ulcR0L7ZjhmyuoeRrLLlk4odsCS0dkyz9ZDrXPNx4DHtegs+BkEhW4x1l7r4uhe0yTMh4zU5smWvorPtkg6xt9px7JlXnIW/v5lS7ro/XC6ZR7wTbi7WPTa5f3rj3htVIqZysqpOjHd0fGxt7dX2RTNDM1mYpu9TbAzbe43kE6oe+CBBzxloIiPNkYwqGjgllSRyRMtSvr+ivhIRLRnzx4lHlEtfRb2jPqEb36s4QatG6COVJd/k1Dc597LxvCLGen9ZHXBDPCqqyljBjlTOUpB5lB1c/Szz5vKkJ3sjYm4z+rw8bPQ76kL5s+n+gR7HlFIDsPIGB9f+1StmO7APJHeN/+YkuSpRgPR6mzDx0TI+rTjY19fn1D+qxyMzJCKj/VCO/r+3nnnnQHhOgwfg/cLwyFTBm+Q7KJtIxUjZcUe/95Pdnzk4TAcH/1kZz5G6iz4leIj35/Kywwf/CwmoEs6lqD58xe4ez8fV/7+wyzpfgiEaLyScW6lK0McDvgYC90WGi9gmtxcCoWuUMt1W1sHDQ8PB9oaHR2lbFZkLgzgBZUZME0uXv6KZwjnLs99fX2KZXcKfKs4B/+05M7raw3VGIr1Up9sAt7qVatodnNzqHvd6OioV2olzKIsCqDd0u9yVvcopEsWJz4jF/TDGKsMmNCuTXpXKlE+m/U8EcTnrwcTknniBzFuXOetwAV63vaihQsp5Ti0Hn7stq2c2lb4ipNa9xqdS3wGoC7oLem1YO7+/DkaLPOAezcMDw97FiLeP5tCo5zMvEQxUzkeGs/YlYOPLIGNv+HKGKniI9+0o9RNNTGcW93vYgZcXXkYkB+vGOyzyIj5+Dgo9YmXyvqg0F44A7Nq1WrBBTNFOlfL0dFRamhIW55xBqkMVrfwezCrexQy1wDmDJXIxBZIlwyRjadoNeGMHyR3TRNzfrL7vjLS+KhxmT7DegsBCYH52+r+9fdm0aNMh4+z3L9rAPqGBgfnuPi4CWpGXREjbQk6x4OPOsVVGMX4WDlVg4fUhdp1FQraZK9cadPR1qbgYzCJ2GTjY5pUy3gYRo4PHwG41ngzPvJxsWPkNRSOj6rywEZqIjo54RzHSBM+OuTnuFCTnZVKJXc/5GNebXzc5l5/DfnZzE3jt0a4N58HvJKELlN6F5VjSa9HMhQjuadFVHxct27dhOFjLHRbqFqbjZy4QW+ZERcQW1T65BDhLifXwM/Ad5phcvEyWtcZJh0vQSUKe41gLshin8/s7Ay41Ihaw29Ar/HnwplOGG0V7mdLJFYqlbRafvm8wGYFKC74clb3qGTKcJ4R+jAGlelywDwMdH1NOQ51FQqBTZY/lxyzx9vSPTtn2HYK18oMnmhROdUt+WXzFtBlsM8gqDzo0vTT1LbWMu+GPojXippKrsTJQ1UCpRA9ZECkmKmsnKoxdtHwUd5wgxip4qNoZdVp71PEGBUdA8cZ0euk44yh8UtQyVbqYFK/zs4zDfh4vqaP8l6gU8q2CverJaDB0H+/nzbPgYULTaXUROaP7S19fX2R36nZ5T8j9UOXDIh7Kaj9zWbzQsynHCc6qhlPc1usHngp5Dr2/7Jlywkwl8kMw0dZ2dgMJniL5xxrwMhMBfhILka2g+3ZYZb0qBgZ42PlNJE8pCyMi3sxf9f8HR96fGwmYJay1uWY3oceekj4vVx8rBXumSHfMm62rjNX9nCMtOOjz3+Xw0PqMVKOtzbh4w3avjpOSlPhY6LxsYPCx09UsuqqfGT+f/bePTqu6zoP33NBEnwAmAFACn7JD1EvPjECAQqhBhxrIvyQIm2aR+02ie20lp20taV4eYmWmsZxKNFJJCIRl1PFlFDK5hLdBBHluImDiLDUkLHzIJDEDRvbxcSiV+k83KwCK7bzsNPa+/fHuXvuPvvsc8+dwQAEqXvWmgXMnfs499xzvruf30bbeDDuee76+bdlwMgs+FhaA3zMle5AW+2XTcKmqof4Ub6DbRUL5+BEkCi4A/39WAJ/3jYBNU1W+QKnT8MKBCafVwKMVk+yEwDvg4RpuwhJ7rDsRwkShXiRbfd5X/cylmzpUX4MALuiCEcOHMDp6emGd9dXnsvn6fblASEivvTSS87C7uzoUAWdQvxMNgXuaXhoqGHBlmQ7PGdP5lz7zvcU2AqqJI3Yv2dP494G+vvV0HRO2laDbCXJ6vHn+zM8xxGWBw4AuL1UwpJgZe3r6MBid7e39jd9OgDwk+yYrKRBuVDZelvNsbNLiOh4F0VF5uHW8LGOAKdQkgElCjIXOC+hnkd4CYltNSGo8eHvMTTCiouPuveHctyoTE9nvE32o4RJSCDdY5acaI0U7DGMoi6sVKq4uLiIu3fvwyjqQuN9kB4dW+GVzYeRy8vLrEwXH3NZY72IJIRt3dqFAFSfXL8n8qLpBmgtZL+I/rzMezHJNbSPi6Je3Lnz1kbUzOTERGqJR/Tgo4/4co7ho4//4xK4SrwPH3t7erAbdCMvfx/fJo7LgpE5Prbe1kqG9BmEilHUkOlaxcekTFY78HEOCR+np6cb97FyfOzARHnMho+7d+9lfeUYeR4BHmi8W3R81MnImpUh3dKYnWgUbRcfzV9iD9fvaWhomEWKrR0+TkxMKiUetXOT514aZOx3jZmXFNnwaOo9D2eQITsBMuFjAdzKQKGWK91tbKsNmH4GXx7WorEKusyqHR19uGfPfpyensa5ubnGQp+fn3dyfjXFDcAo6E8//TSmKUm+cGytniQ/Ny9jJpWnAhgBg641y37zKf9b4/y4K2Czh0shhd8b316GxNhA+c4UdqcZEKTVi+53CozQNAWmJFnnhg3WcTUwno4iGGND2j3RC5Q8utr/2nG+Fy7/+I6lEjSXL19Wx0gaQtK84TzckueepR03Pz/v5O1zjzalS2hztwSAd4Ee2t9MykAuVLbe1gtGZsFHXveaPEdnz55FW6iRAoEt9AwO3hHojx6OrdUk13PUfh5dT0ABbXIwX5i6FHCpn5wdV/OaaNsTYdqMpSHRISFLE5K1sj/Gi0RCsxa6SNeh8aBwQ/2e7LJfzRijtbxM/vGP4cTEJF6+fBnHazXrmGbxERlGPsDwMRRKPnLggHVdDR8BjNGxCK5RdETs1yxG5vjYelsrfExLr1sJPi4sLDCj5vrEx507bxXXzIaPhcJWdtwyAtzjnNuPj1I57kGAQtP4aO55Ck2o/sMYxkcyjqw/fHSNrNKLnqUPyXs9iUBIr2WuyZDHGT4iGN1jPeBjrnQH2moDZrjcjcmRcb09bokBH3sigfIZSH+x/wuwraK+/Y6DawEK1YieAruMGb0IyFsNYHLgaLKTAFMGPQy9CkmIPL9mFYwiJhdVOe4z1ZAeFYCyQ3wf6O9XvQl0z6H7lffDfxtR7olKuczMzCB/gZJ3O8RW2gXuGHXG/QuxqG8tFHB4aKgBLHNzc3j06FG87eabLc93kDEVXGW/WqngeK1mseQ3QnliPgBurKG+ylB4el5ZFP5Z2a8M4V65UNl6Wy8YuVJ8NMemXecIAkxlCEU87pDqZLsHsvgnil3iiaHrk2AUqombKMn2NatovEdcsKUyWGcwior4hjfsRJvADNCU6LHHMvGm6LmS/nt+jJ3LR+JWde4pinqxVhvHoaFhTARlrkA8ILZLYZ/nZZYwCUU9HTju3kZUAKKJvpiensZbd+5sGh9RYCThY19Hh/quozDJED7y76uBkTk+tt7WCh99z/2CNVeax0dEX561XLNTCHAEC4X0kO524uOJEycEjwfhSRZ8nEQ3n13zBNv4mFSr4AY4WyFfGT5yhdeHjyPOPa03fDTVdMhIQ/0M9SExyACYyKpkHMvOPdN4chnyCXAdb5MA+OvrBB9zpTvQ1uJlo+d1ECjYuSdmv8eQCF4KhS7cufOWVOIoWtz3MhDmk+6KmKAFANxz++1OqHERbJIODtBkFMiqkNGLoBNcMq4aGKtUJ+hh6ANgvAvn4752x+c+H1hUnMjtDCTkZ1VwQ6+LYOdmW4sztgDT/XKPAx9L31i8Kb4mv6fq2BguLy87Bg9Z/9t3b9KIEEFSozzkgeEfIl9ZXFzEhx56yAGv0Pj2dXTg9lLJ2n+8VsM3jo055xqv1XB+ft4652Lcd408Lm1Mp8EGT/m8Qi0XKltva4WRUWQT8EiMrFSqK8LHJJTtAtrT7Araa2AX+nMgjfA1NDSi4mO6sCEFLkATan2T2FZDKs1jwvm00laTCPDraJTnbkxCJtOEvDq6Ap8eWmjGOfKei2OkuS6RBCEmQlzaeJxAqRSMjRkBzDV6SAUiS14m98BkEX7NZ8+e/Q1yqscff7wpfCTiyyLYQuF4rdbwoMvzVZVcbQ0feWWKTBFqTWJkjo+tt7UYu8mJCez1pM/RM07yrJPwaYCtQXxElAzfIXwENKHeq4+Pt912OxrCRX7tMiZh8EX04yPlZRewUOjBMKGcxEciQCMFuFV8vIIJURz39KaNx4/iesTHAwcO4szMjEgL61PO7zfIyOiGWm2cedBd44aUIauge7NvDODjSmTIVVe6//7v/x7//M//3Nn+p3/6p62cbl23ZgGT52+k5XLw9t/+239TgGMHAvy8ZRlz8z/cCahZKpeWlpywYY1QjH96tm51BICDAPhuMQllGHYWK1IZ/HnlPCSdCyMNIQQAXwJD1sW3877SoiJlmBR88hIUABq5eecz9FsuztnZWas0Gu8HNyCElNxdALitUMBqpdKYO3TOIgC+HUzu/HEwSmdx2zaHuK2vowMjsI0I02IcMH7ejqcZEm+4TAfg47kR7HEugt8YkxYhQBZQzpwrQ+N8BgItqkGOaSeYcmZ8bK5WTneOkXprBR+/+MUvYk9PycE7gDEEONnAyJXgo5tXR0JZmlAi+7MfAd7i4KNOaKMJdFyoep1y/hoaQcgIJbYwQp9hBHgB3ZIs/FxckFvERAmebVy/u7vEDBhp/dYF8NnZWbx48aJyD8MIcH/KeHAveA0Bjlq558kxh9Gwrt8b98GUmuvp6VVK0PXG/SAjgkzVQtS9YSW0iZmScFf+Pi2w+wvhI+GURazG8HF2dtZKDcuKjxwHfb/tgsSI3SxG5vjYelttfFxaWsI7ymWH5HYHmPJ19IyXl5fx7rt5+HR2fHRzrUP4uElZ+2uLjwCg4PpNCLCAAF9EGb2TRPesDT7apdF4Pzibd6v4OImGRO77EeBjmIaPiYGgffhozy/5nErxRzfIOKWQ4/e7ho+I6DjAWsXHlciQq6p0P/vss/jqV78aBwcHcd++ffgHf/AHjd/uuOOOZk+37lvWwXTBIwxoi4uLcTjxhnjSUV7HVGMS8uMqlcMxmcNUvODsMgA0OSVYT05MYJ9QhHrBeJQfA1MuaiOYcGhOeFbs6nKAvBOMMkyNh3WMgl4jWpJs3QoZybji/tfrdRweGmoQodWU43shUQ612tYRAB6N//8P4OaUp1m/5OLkxGMaMcMWAOwXY/Fh5ZqdALijt7fxfAk8RkHPS//Qhz7k5JkP9PdjQVxLU1CXwY0o4N5weY898TiXxVgfVfpWhcQYkwZqmvBALwoKxU+rnxgSaIkEg/pxtdjLc4x0Wyv4iGhqrHZ0dGKSR3gBTZh1Up+Ujm0VH40X3RdS+Bga5W4j2mQ+Rdy2rQeNJ/pG6176+weUihNnEGAUXe+PTsZjhNai6JMb/YRo8HFoaJiVcakpx/bG90BCmcZGW0WqeQqwTQisPo/LD6CWC0iRWX5P0+b4Hil6gTxTfJ9OBCg0nm/iGRpz5g9AhPv334GXL19mRE58rhXY2J9n40D9Xlauz7099v11xdhYA9vrrOHjJCTvnlbxkfITNYMqQvK+Ii+4hpGdALhZ8I1cDfbyHB/dJp0XzhzyPKeLFy9iqbu78W60+GXAZi9fXFxkOFFGzTvrw0c315rw8Rk0WLwBAd6HifzaG6+5TsyOj4TZ7cHH97znPYiYsL5XKtzTv0M5nofFrz4+IiL29PSiiUSS2LMZwwSfPnz8NALcIPbtwN2796n4aO6jvfiYzIEd6JYr24YuflcR4N3sWPecPuMTlyFXgo8rkSFXVekeHBzEr3zlK4iI+Id/+Ie4Z88e/NjHPoaIiOVyudnTtdT+03/6T/i6170OOzs78eDBg3jx4sXU/X/1V38Vb7vtNuzs7MS9e/fib/7mb2a+VtbB1MFDz+XQLYe6pewjH/mIUn82/Rj+8eVna6VN6Ps9ALhP/FYDo6CVCoWG0i1ziWaUc5YhISsrsgmdxSM+zELYZXkM3/EkXGjh4p3xh4dcmbyTdOvXKBhFXrv3MuiM4l1iLOilKA0FESRgsri4mNr/u0ZHVXb4XwA3x4/yADVw2QpJqoEPnDj5Ch8bUoovgE1SkSWsXubFaGR1odxt3/zi49lsDdpm1nmWdrUxci3xETHb2DWDj4jNeEAMiUql0ho++pl2tbIm9P0eBNgnfisjkd1w4dU+94xyTo2pVpbBkv03OexDQ8MWPmYbryiltnVv3J9OJAGLSDXDYzmKPOrAvveq87yNp6MDbW+IZiiILHw0+2l1Z4s4OnpXSpk5KbRGDtNuFPVidzcpC+l1Z9cCH2kdpJW+oQ9/X20S+9K7m/BReoratcazthwf3cadF9KQo7Eoa+9O35w4e/assi5Wio8JnugYWRLbs+DjMppQ7fbgI4BtzM2GkVEcGbM6+FgolLBWG0dEFJ5uea0i2tFUWfGRxl7uW8Risc+ZB6XSdoyiEhqM5IbRleGj8Zj7ogAor/sC2mlHdOxp9Zw+fJTrIG0t3DE46Di/2iFDrqrSvXv3buemDx8+jEePHl0TK+Wv/Mqv4KZNm/Dpp5/Gz33uc/jOd74TS6US/u///b/V/X/3d38XOzo68LHHHsPPf/7z+JM/+ZO4ceNG/B//439kul6WwWymnJcpWM89MukkBa94xavjPA0uZKSXQTjCwLorZkeVL3rNW9wXT8BO0MnIuLLKQ9/OgxEwzsW/TYFRwKticmsh4PTR8s6KMbkYb8Ti7js+tOhugUQoooWs1dumSABSeDvACDOkdCf5Kv5rPRxf597AflQ2g+dN+fYtKhELFCFQh0QYXFhYyKSgZyFf4WOdJXxHskb6PDk+tntimJRh9De/4Q0IAHgTm2dp49lMa6dQeTUxcq3xETE8ds3io+2RCZXEKuDg4FBT+Gh+N8KIwWFtX81bTMpqJ7pkZORlSe7FrjixiACEGRTGt4B6GPgrA/03QguVk+TNrXIhjwXcvZsMBj5B8aHGvrOzs4F621xI7MDe3h24vLzM8HE0cK2H49/8+2j46NvXzBkpmHJPmWHBfe655wICaHq/1wIfEXWM7PTg4+7bDXHeG+Lz+Yg8c3xcn/hIjPRp84fmyOFKBbuiqOHZTpOJtm7pEp7qduDjFdQVwT7Ulb4s+DiLAOdYHxYwrTZ0CB8BphxjLiLi/fffn3r8jTe+PnXtA9zS6EOz+AiwCWu1cVxcXMSjR4+ye1k5PiIiHjgwnLqvi4/UT9qv3hi/sbGqNfbN4KM5j0+3OR84dgrtHHe/p7sZfLxrdBRvev3rLT2kXTLkqirdb3zjG/FP/uRPrG3f/OY38V/9q3+FHR0dzZ6u6Xbw4EF817ve1fj+rW99C1/1qlfhz/7sz6r7v/nNb8bv/u7vtrbdeeed+GM/9mOZrpdlMG1ChPTyNjYbLSDAQTbRtAm4TZmgAcGVbdRCjVeS98CV1YsXLzqexwGwwzd2gfGsTsXfQ6VRjscLZj/owkgW1vC0FxB5oB9g55ZedAA9553Cs4ncJkRcMxx/TwuZ5gucM4T69j3iuW/pUZmdncWlpSWrjAK91GfB1IjdCf50gLS5UNaO6+jAe+6+Wy05VlKMJ6Hn6IvCAAB8TcbxbKa1U6i8mhi51viIGB67ZvAxYV6lD734OXM3x0cq+ZIdH+3zaNb4LMQxaec295IonmV2HA8ZvBL/1oV26Hp34BpdaPIjXWEkCyNuWDEfbOxL+Oh6ysqY5HTy8xdEeaE0oR3Q5HinzwkNH9MVhrRnnnhN9Aiy45gIe1XUcg/HIFp1fOTPUp7/JLj4qHnDc3x023rGxysQlhVmZmac0kgHIV2BaD8+trI9Kz4CJko7rbtdaHB+Kv6eXj7KYM4eaxvHyOeffz71+GPHjmE6xiSpTc3j4y6xn5ZH3ho+IiI+9NBDgfOF8DHZd2ZmpgV85MaVtLkg34Mmt7y3dwe6JdM6G9EBvK0EH18RWGfrQun+2te+hoiIX/7yl/Gv/uqv1H0+85nPZD1dS+2b3/wmdnR04K/92q9Z29/2trfh93zP96jH3Hjjjfj4449b237qp34K9+/fr+7/jW98A7/61a82Pl/+8peDg6l7cnQh0bAkyjwSWnhlTOqiFuMFd2/822lxPvLE2AJBFSJnEkVgPNdZS5uk/fb2+O/c3BxOTkxgqVDAM2C83UfAMIlvEBM+i1BSZAslAsATYCuQvNF1teP7Y+bsEIP5ZgCvFz3E7t4VRQ4TunYtAKMMZ7VeZ/F0+/omy9HU6/WGFfBIvE2GoI+BC041MKDVC4CD8e994lmVAJxc/8mJCRwaHGzMB+65Hujvd/JiQrVFfwAAuwoFLHV3O959YjJPixy4GuHlVxsj1wIfEZvHyGbw0bXEl9AuX1VDE27Yh4ZM6/ubwkezXQojUgAgT4nfW+z/7anG8fV6Pc6XK6IRVqgO6yZr7fiFEs17QiGHJ5ArkLxNTEw6oYF0bH//QIPtNRQSWalUrfPW63XmnfGPjal9S4JxSEAGTLxb/ugHex6lnc/HrDzrnNNOeXgC3VDZcTT548m2XujAkzGu7VxFfEQMY+RmMClY1UrF8vaEDNs5Pv6atX294CP3dPuiIaqVCvYq70NyAJTBTuejyD5/JE8r+Eh53Np5Q+WlQvg4hQA9uGEDhS/71nvZ03+Oj8l1JUYm17WP37hxSwZ8NJh24MCIdc6s+Ji8/86n3F/z+IgYNiiE8THZt1KpNo2Ptg7Ti64BhRRzl5djYmIS9+4dRBklwUPyeQvh4/8Hphxuf6nkeMN9MiQ5KUNs/rKtitI9ODjoBcq1an/xF3+BAIC/93u/Z20/cuQIHjx4UD1m48aN+F/+y3+xtj3xxBN4ww03qPt/4AMfEJMIMg2mIeMpoWu1M0KiW0JAI2ogCw+fjHKiH0aTG3hU+S3yEmPJklJpL+XQb/SJwIQra6RlAIDvjf/KRXEJXGWP+keh2FaheiEgLC8vO6zmlLfWFxlmWa18RhWSUCwAwL5iES9fvtw4b8j7Si+2KUgs0oOge4tJwSVluKzsVwSblA7RGBS0mtbFeMxC5Wgo90u+yCPQy8zQcyh1d6P2DOlFDsr3Y8eO4fT0NH7qU58KGjvkM8waseDbJxLjqRHVrTWR2tXGyLXAR8TWMLJ5fCSMlPsTRhKJTXP46Cd+kfuGBKEs+ZBVTAQR/tvO+O97478+AU3mN0ZI7L9pYXfLy8sKq3kNAZ7EKDLhlkNDI6gLQ4ONY7q6ivjiiy9a585WW/ZetL0zvmsRhs+ieRdKEiWTpy3nUZLTrQncacLmMyn5pFX0pxPQfSXGH+45aQc+akJeFoz0caTsy/HRaesZH8lA/mEwrONyPpWiyCkftwSu7EXPWMpXrqf73bgyfPTleoc83VnwkX/3YeQlT/85PuqKKSLi5cuXcePGLc51C4Uijo1VU0pUVpEMC5s3b20RH6l/hJGkaK4cHxH9BoV0fDze2Lejo489m+z4WCrJaJtI/KXPJJr3s+Gsmp2dxU996lPs+LBxgY91O2TIS+Di+GrhY2al+1//63+Nr33ta/ELX/iCtf2zn/0s/pN/8k+ynmZFbS1AsxVPN6IRdJLJ7hIi7NmzX4AHUfDL/LMOTEhiauz/SyjBqaenFxcWFhqkKFpuchGM1f1JSNihz4HO4kdK2CbQFUStvNQO0InEIkgs7r7csjlIrLm0rQuMB5YrkLItLi42QpN85z5QtsdqQCwoeilt2bjRWljVSkVVosvsvi5AYpH2sdWejP8nZbgHXO+HrHVO82i8VnPOSaUMNK8K35eAopkyCiTwUZ7+uXPnGqW9qmNj2NfRgcfBGCyOgiu8bdm4EbfG/3tD7ZV79c3Xcvz9SOCct4rnKb1Ia10y7Gpj5FoJla1gZPP4GMJIeuHT72F89OXelUrb42Mo1/ocGkFBY9KNMGHOlUKNXT7FCG3UV5cAJ/EC+0Ln65gIZ7RvFxovwTNqviKijY++c3/0ox9FPwkSIFcwJbP8gQMHlfsnkqEITYkaLnzOK9eaxIQJ+AJS3XHZn5mZGWceGYOCfb6enj602dClsGnfi1sj1y/s7d69D+v1uoWPVLpmZGioLfjoE/K0+sucb8R3XmnYzvFx/ePj5MSESrhaBMDenh48ceKE9awnwZW9esHIGWRgrwHgBogwgiIackjbWbF37/5GXWV/bjJ5jx/GJLSYSA01jOxQ8KEZfCQDLKBhRCds1pTFOXTxcR9yBVLDyMQj/CDaZF7mvKOjh3B09C6BR7LEWGRhCrUwPg7G1yLc0QzEreEjojEoyFJpxsCg42Nfn810PjExGVdVag4fEbFRn3t6erpB2Dg2Vo0j2B5AgN9A+Z6emJiM+7CVXdM1QnPiUGorlSFfyfCxKNbSauFjZqUb0YTV9Pf346c//WlcXFzEN73pTRhFEf7Tf/pPmzlNy22twoN4yzqY2S1cYTKhhEiA7+MKoFHUawHK8vKyk7twO5gSVhvBhE6chsSbLMONB+O/rwM9p/ZJSHKC6xCunSzDzCl0uRj35wFIlNIiuPWhpRCiMRXWIMm95ouJwu66oghvAb1c1974ugeHh60xLG7bZu1L+d10X+8A+6VXjs/D76dUKFj3Qp74bgB8fbwtLcSPalr/xE/8RGOMl8G1bG8vlSzFQs7HM5AtT8w3vrJWuhTeyLASCmnUSPG0XPoye54hY8FxMMYMCOyXJZSyXTmLVxMjrwY+IrZCNhnCRy6Q+PZ/n/g9Gz66dVvLWCh0Y3d3KRYMHsPEu1IT+w42jnHZy8lLxAlgQl6fDeIcxutvBLaNaISUC+gKv7YCSU2vilFDO7cwEWCMN6eIpnatVrZmBxrvuD2Ojz/+OKYLie8Qz+SZuB89nntKnoXhMCkheUJ8a5fw8cSJE3jgAEUEPIka8dJzzz3nxUfzbNJzJp9++unG+K4WPk6BLuQtLy87ObycbyT0Du6ChAMjx8f1i4+I2T13WQjXptg+TwLgRAM7bKMnX9t6bnINjbK9Lcakx9B4eu9lv2sY2SW2t4KP8hxlTEKXu9Dglo0lEt+zYSSvOW7WfBR14cTEJFYqVSwUutCQp7m5xgB7W8DHt7J7JQ92Ob6HBB9NihA/D38WtwbX7tzcHB49ehQ/9KEPYRo+jo4eahgU3VSeMD4ODt6By8vL6thOTEzi5cuX2XZtDhbj39JTkTTi0JXKkEVIiHnXCh+bUroRET/4wQ/i5s2bcePGjfhd3/VdwXIL7W4HDx7Ed7/73Y3v3/rWt/DVr351KhGGBPTv+I7vaCuRGqIkC3InpvE4dMYLKZQzeFpM9DDjL2KyUDT2cJ8SPQ+G8Iv/RqHe+3bvtrbLsKfB+C9X5pbAVeZJ4SxCYlGSfblj/35cWFiwFr9ab1zkZnD2br5QAAyD9+FKJTW0moQhvrDIonwabC86z6WTISlyfA8eOIAARkHXPPHVSqXxzNLKtwwPDTljXAc7TN5X35Vqm4cEM+qLNr4kCJIFU56HK/RkhEgjZtP6Wq/X8ZadO537REhY9n35/zxtwmdU0EpNtLrOs7SriZFrjY+IrZBNhvDxGQznBvKww+bwMfFoz6MthEhBqYwAL6Dh1uDbqwjwGbRzzQGNkqoJoPweltAVVGtoBCJSQovid1ODWuIj3ZPryZdeda3OrcHHpCRQGfWyNVVnHBMvuq/ky5b4GBl9oHnW34IA0yg9TSRghfDR5K9LDwlFB5jSQRopjs2Qny78VypVRFxdfMQARu6N38Uap0cE4HjDi/H2SQD8QI6PjbZe8RExnKN6BOLKMoVCkJfntJh7pHxkx8gjGMZHUqTn0cVI+b2d+Oj24/bbd+PCwgIiYqoMqWNkH0pmdVIAFxYWYjZz8ur7I5aax8craHuw7fsyHnNAY8R0o5UqlWrjmaVhZEJOquOjhjnN4CNhtTa2PNLg3DlfbjrXh7ihlrzx9Hz0MHN65q3IkFcDHzMr3V/5ylfw/vvvxy1btuDQ0BBu3boVf+VXfiXr4W1rv/Irv4KdnZ340Y9+FD//+c/jj/7oj2KpVGrUfXzrW9+KDz30UGP/3/3d38UNGzbg1NQUfuELX8APfOADbS8ZhpjFk7MQLzC+sNI83ZwQgS9iVwClScFBm4ce1cBVOkuFAnaCXmeaQpS4gFEGf51pLmRMKteSipc8TxGMx5YskppHwVdvnOc0PwbGsk+K2NGjR4N1uB+N//Iwnay53fSpVio4MzODc3NzKrso90zQQj516pTLmC48+1mI1QAMCyiBrjZ2FEEggYe89ADQAETfdc6dO4f33XefA07ckrgMgCNibOjeQwBG5CVZmCirAHgK3JJ0V9uTsx4wcq3xEbFdnm4NH9P2557u5vBRT/GpoVb72XhdNSGthLbFvoxu6DwJZfweJpXr2EKFFoJfKm3P4LFJG68LaISnLqRyL0ePHmVM5qFjk3EMP8tRq0+VSrURwlqv13FmZoaVLaMPeZsS74nMS5deq6Qf96b255FHHrHw0R037gWUwl4Zs+BjvV5vvGvS8FFGKmnvBg0jffjYMJyO2YRGIwD4grhejo/rFx8Rw3LHArjvwzRP9/Px/8fBpD3Y2JcFI9Px0RhIqWa1ZrDj4eSri48AkYUP2joP1xw/ggYfq0gkZ9PT0/jjP/7jgeMetcawWXycmJhsRCuSDFmpVK19NG/8qVOnVM+yT4ZMizI7evRoi/g4aZ0nzahD+OjOwfPsWK1Wu/1uaIcMeTXxMbPSvWXLFiyXy/jJT34SERF/67d+C3t6evCxxx7Leoq2tV/8xV/E1772tbhp0yY8ePAg/sEf/EHjt2q1ij/yIz9i7f+rv/qreOutt+KmTZtwz549+Ju/+ZuZr9XMYFYqVWViUi7HZGPSTE9P4+DgkLJvMZ7cZPkqoBvSwhefbqXkoWwrKQ9GJSiCIRqxlf18YD8qN+Yre5XmcfXVGydBRSrCcpH5jrtXXJvaXaOj2AUiZBwMQQ0ANPJVstSe1rwZnHH2fDwmMgSbXoCjkK4w889Afz+WBLtpEfSUgcMA+ET8/7YtW6xxojQCemZ8fOXzLbP+0Rzw5dqnAZiWn8M9ScNDQ9gdb5eGJN6HRu5jFGF1bKzt69zX1gtGriU+ImYfO/Myz4aPs7OzcU5dScHITfExreGjm+LjE5JC4Y+UZ5guZBlvwTMYrk9Knge9rEulUrXGsrnau9IrL40bacc+4KxdoxB3oR0uXkLDaPtM4zlq6z3d25Tca7LPeQQ44oQXJgrCeUzPwU/usb9/ICb0k0L765QxmUTjrQ/jIyeiTMNHnmfoK/Hkw8g0fEQ03p5Sdzd2KRhJ/+f4uH7xEVHnlOkF4+GeBLuk0dDgoOrB2wRJOp3kkcnu6eahvistobg2+MjxQcOY9JrjcpwSLEjK/PqOu9dZtwYfi5iE4x/H9uCj/T7IjpGjmGZUDOPjHjRlKiU+zmMSlSbHaLHxW3d3KTBXuFJP80DnI1kNGVLDx76ODhwZGrp6Svcv//IvO9v+6I/+CF/5ylfiv//3/z7raa651sxg+nM5fjr+/32NSePPn9mNiSVvDHXrYA19JBGTExOWghrK50377XT8XZ5DChwUAh06H4VEpZXk8nkUQiHS3eB64WuQIdcYEuWVFtfS0hLeNWpbIyMwOeC+mqqIYUv18Xghkyf8JLiejwIAPvfcc9b5ngSXCK4MJp9ZI7cre65Pgp5MPQAwOfa+PkVsfDVwKkURbtm40dq/CC6AhUgp1HrpzPsvWev5OC+Da4SIlHO0Y537Wo6R6WNnLNEaPlKumy28+HKwTW5hEU1etRQQ0vExEWh4SJsv9D0U4n46/i6Pp7xFIyS6tcfTlVt/WRdohEy6wktIMO72jNOZeDzTju1BgMjCR5dkKELzvjrpJS9CDHuBoqjIvDwn0c1BLDj4aM41jnrO5SZ0Bciy5z4Bk9QDO9w9hI/bmsBHAEOulqZAay2Ej4iGRInzuhBGal72HB/XpjUrQ0rj+CQA/nT8//sYDmgcPuV4rhbBjWLc56mNrGGkraCGUoPSfptVjl8tfDS/+8OYsxhRXXxMco6z4SMi4qc+9Sns6ekV97UDAX5+RfgIcFywi+sYSaH2yfmeRJcIrhz3vYzZ8JHGVUs9oHfAk6hXZtoU/0YRE/YcHBt7o3jXZ5ursrUqQ6bhYxaMXNWcbtm+9KUv4e23377S06zb1sxgurkc7sTs7x9oPDw7f0ZaBEP19szEHK/VrMmwvLzcyMVut6fbJ3BQrqGW08bP1wXpZa8AoFHnUFPcNWWuGDhnHdzyKTKn41K871NPPYUzMzO4vVRSQ+CjwOIL5WTR4iVmyBqYl+JJcPPg6TqTExNYjCJ8NyQ59HzsfSXitFz0m8AIhPJlzFMKqFyZT5FXQyTjvhJxx3PPPZcpdN6XhyTzVmWjkNhHAXCa3esVMEaLrnjuyrzLdq3zZluOkaYlVnee6+bm8FGInJuDzTFSEqnp+FirjTv4aMplATu+XZ5uTQCKHHxMJw6S4Zb2Pc3Ozqbkx9ew+VIx9bjfPsbhqPF8Zmdn8eLFi7EwWUQtxJM/v/Q5oAvWQ0PDjDm3hsYbc9I7RxIjyk8jL3dmC4G++5ZC+00o8wk7oIj7IGrU3m4HPs7NzQWFw5XgIyLie97zHiSM5O+CUTCM5zk+rl1rRYa8AMaxMa/MJ6rrzjl8iNyWZDVSzrlc9BIAdkGHda7+/gGrbCqiwcgk/aOdnu7VxkcT5p1eK1sLk05TNOvoemLT8dEYEWj/9DB42cLcJ5JdvIbG8Fy2xrWnp7dxjYQw893oYmQZbaLNNHx8ABOPeRn9KQXab8QtsuzMAT4eXIbMEjrfKkbytFNaOxivuwK4TrwQRq6p0o2ImWuZXYutmcF0rVQuoy6vE+qCAy04LmD4F5+P8XRxcdGwqIIdRqFZ1X3hGAP9/db2MvgFDn59H4V/BIB3jY5iz9atqcRYREDmU6I5WzUA4M74r5cEAfS64CNgcqQQTA1T+Xsz+R20+EN5f1SaS+Zqa6U/+jo68J6773bqkctP2n3L6wP467qmGWEoymCObaMoBx9JEeUnSeDTcs6bqYfoi0IYhyRvrZXcnNUUKhFzjET0WfHdHD5jyT/sEZ5m0PaW+/DxNgtv9X7sRJtd27XA9/cPOGV09O1lTOpH24LI2Fi1ce20sjy12jh2dxfRL9wBjo1VG7lrrvCp5cTvDIzTbOP/m2++TRw7gibX/pnGtUNCtq/eNCc2Sjve3of2879HX3rpJSUagn/S7ltTHuzxm4DIqlyRhi/cAEj4eP/993vXgoaR7cBH+c4gfJzP8fGqtFZkSHpGPtlgcmLCUtDpWc6ALctw+WACIuyAIhpj4WkEmEotO2jWQgnT8NEm0Ep+S/K924uPpjSgxMeStW4TAjKfcu6W3jJpJOn46PJQpOFjOsbJsc6Kj5oM6VOAx8aquLS05HBjrAwfAZNUpWaMMGTAnou/myiHZvERUc/XTzNk8ObjPIJ4rbUqQ6650n09t2ZfNrXaeAw4RzyT78PoCkZlNBYgXvswnT6fFCvfZBiv1awyWVKpHB4awoWFBa/F/fLlyzgiwsbTJiOFs8zPzzvHDQ0O4iGmKMk8o10A+EuQGBFI4acSZ8fBKOa74v01y26ap5srvcTozZX+jZBY/4kV3KvMMhKHpaUlhzRNGiu4gYODK4Xkn0/pfxQfq3nCb005DuIx03Ja3vrWt6beH/9tCVwrOxkrtHmXRWBMYwB+/vnnG54gX5ucmHDqzVLO24Ymnt1K13nektbM2NlC1XkF45bQLV1F+IhoSG6KmfAx8VK4+Gis/yVMY9euVKqi3Enygr98+bLwmKcLWpVKFZeXl3F5edkhyRkaGsEXXniBXadDnHMXAvwSciE58V7wnGrKYdYiA9I8UckYVSrVOLT0OHLhuru7Ny6dcy+mCWsSH+W9TkxMYq02rhoyKpVqAx+TkFNtjiT9NzVg5XMENOH0afdt39/ExKSFj77KFQWBL1q1jhFIhDeJZSGMTMNHqr/uywWl4334eFOOj1elNTt247UadgPg20F/x2tOAiLkq4KRlWRaXVb2ct6MssYVVBsfh4aGGzKkxMhabRz3779DrMl24aOUnzvRlBhMDAmJUVTmVJOnnBi8faHozeFjqdQf4yMZNMIYuRJ8REQRlu/HR3MuN1ooPRJIx8ckGsF/f/ZvWqj5CFJJSA0fQwp1Gkt6SIb0cS6VY5xsVYbMle42tmYB07W8+0IAtTwzKWD46PMN6NRTJoOmTB+6804cGhy0ttELn8Ixzp0751jdjoCx4qdNxn179jjXI8VeTvQn2eTeBWAdU47/9hWL1vYOMDXHZR+W4kVTBJd8pAZurpwcF/nyOgz6i46UzH27TWmKpaUlHOjvd8LQS1Hk5FnJWq4A0PDWEqnOFbBroJ9n/dCs3cV4DOV9F8Em9ZFC3fPPP596f/w333UjMELcHfv3Wy/rNIER0Z/zPgWuIWagv98Je+NEgTwk6Blx7Hr05FzPrZmxe+mll3B7/w3iJcwx0vVqNo+PtNYSL4WGj/Llfuedh5xcZXrha2VoktzBI2jKXqUJIttwbOyNCqNuVYRJn0Hjdbkp3meXGCujXA4PHxTbIzT5ebIPS/GYFMU46bnv2rhs2LBZXMsv5O3eva+Bj+Y9aL/rOjr6sFYbd64hvdXJc+AEcbzGLxfwyp4504EmJNX2mtmkPrZQl6VWMidA06p1FAkjOzoaxmhqIaVau7amZEUATmpZjo/rszUzdlqkgpS5aqBXkhkVz5eXqEtKjIaNZdR8GGlIgP0YSTLk6uHjFTT1qsk4WRbYZL4Xi31iO+0vvdrEnSS962OYBR/d66SzpO/evQ9feOGFFeHjxMQkvvDCC2xbGj7q0UIJKWl2fEREPHBgGNPuT39Hu2H2GzZsblS1oBYqO0bya/q1dRkyhI/H2bG5p/sqtmZfNsmk0TwxWXJjODC5+Q8EKBUo4CIk5GS+ycCBcGRoSH3hVysVfPzxx3Hvnj3WtSYnJnC8Vmt4nUPCSFGwZnPSMHncAxB7csEtL8aJaaS1nudvL0FCMCaFEq7EaeF59XodR4aGnJdXX3xOellxZXY3JAJVf6mUOh48LMYnaA3092MxJr0ri/6TV+I8AwQNKKSyysdhJDZ68DY7O9sYd8mGHgHgpo4OLEKYuK4LbCKeUEmb+fl5q+Y4NzDsAFeI6AJjPODzemZmRrXwX2Lfb9Tu7SrnLF7vrZmxo7UwBUm5vgQP24GPnJjtHGrM27xxZbpSqWIUaaHuVXzqqafwxIkTjmciYXsN5X+b94HxTrvnd4+dRaNIS4GFjK7bxHZigY/YeZYwIc+R3qHEm66F5tG4lErb0TUSd6LOKk8koBGWSmR49HvW6BqVSlUVtMzYkvFVF6xtIY8Lnb6Q8eT/oaERBx8REQf37lWrRZBHeyvYlRpCGEnvn5BCT7W4JT5qSlYPGKMrr7qR4+P6bK3g4xnP/MrC0UNKusYv4Av7TYsu4zLk0NCIulaHhkbw2WefdUjRsuOj4ejIjo+IBttbwcgyO8cSEhGaPU6bkMKo0/DRhJRLfOxDg7vSINyLSek0ul7r+DgxMcnKoKXh43lM3kEuSaRd2SKMjyafnMZdcwgWMGFuT3vmXY3rTUxMpqRNJdEQrgxgG2+OxPP/OBic7i+VrH6n4eN747+D4E/Hbccaz5XuQGtPTjdNzHelThg/MJIC/z4EKOI26MAxMXmqlYqa0yDD2dLAmj41MN7ovo4OHK/VGsdH4CckiwDwvpRzS2st/ebrz4NsGzcuAOvDKBjCg7Lof3f8Ny0MLyQASQ/8AICT26fdl4w8CF2nWqlgB+hKJwDgu0EPJSOg6AITUjasnMOX7w/KmNH35557Dg8ODwfvj54HpQRwhVrbn8L6tWvz8dFC2klopXIqVmRB/OH7S0PEWrHzvlzbSurQTkCEUcPLEGILD+HjxzARbnZYx1QqVe8cMGF+oRquUqC5hFw5TAQWuheujEaYeN/1cjfufYcs+g+K7Y+x81AfRtGEWUuBzETCpOEjYppXQcsdH0BjBPEZSOxnmbWmbaVSxYTkTSduM5+auC4f7754TrgCvZbPOj8/r2Luyfj//Xv3Wr+FMJJKQYaINrcWjPDbLD5Wx8ZyfFzHbSX4SJEUJHO9KzDnNFmDPHj7Lay95OCCLy/WDfnNgpE1BHiyCXyk3+9LObfEEvotC0YuYsJ+DpiET9+Kbo73RgQwazzNGBFmGpdRSkQkxrF6Zfi4sLCAGzduwXR8fDe6eJ2UQzTKb3Z8TPok3yvm+y/+4i/iK1/5muA9Js9jKjbcDKfun4T1+8dDIx48ODzcFD5G4JYgXlfs5dd7yzqYS0tLyqSRnpgsBASdmAZMvaX+BruefDkTqyVvZDnlYcz84gTWZCEib+8k2CEV9Xod3/nOdzrCCCnoRTBKsHZu7UVAAomvP4+CXtpKXr8M/hBoLWSKQkOJAdsrALGFNwJG4Zb3BOD3QJMwGxK0OIsi/51yujeBokyzZ3+8ib7IOXEc7Jx57rn+0Ic+5O0XQEIUxHkF0van3wbE/ch56SOOOeyJmCgr49MbRXhweDjI8NvKOs+b27KM3dLSkmqYWQbAPkcg8OPjBuiIOTMkaQ8/B4UV20IBrxzBm1siRxMQjqDtybBrps7NzeHMzIxCRmaEzyTf2l8OzPV0p/XnUfRVyLDHooy+8D5ffhph5P333x/ow2Y077QRTHLuNQOJ64GW+Oi7xn333ReYEzsx4UGRXq0Iba/3g6h5ejSMoKoRD8RYZ1W8mJjAs2fPNu4xhJH0rgsRbU61iI+9UZTj4zpuK8XHHWw9hYhQC+A6RkqFApObCBsKqJXYq9XGnb4l0Zs81SMrRobwsYxkwDS4NOo5t4YBpLT5+vNeDONjGnZkkyH9198aj/NtaAjX6Hde13q18XE3Gq+9VMr7MHk/kiKbHR/tiF7KmTfvFdtrndY3qrs+i7YO5NufqoVoaWVFrEKk4mOpUGgaH6tjY5mqRDSzxqnlSnegZR3MhNiGJrEksiECmp2oF6kny7xrlRodvQtnZmbw3LlzcS1wE34sJ0sRjNWbGrecZiEck9saihWzuhHoH/Eco22rVioqo3lISSPPuqZQ8/HxnYPquSK6Hv8sLy9g1yKiEt6/btGPMpiwP98z8N2nfNEipBssGuPK9kvzhs/Ozlokblq+Px1fhqQWOeWsyzBLyyDD+kDpCzIsZySFNI7PS/rfN7fkOAXndEaBspl1nje3ZRk7UmL4s54FwHPxs6ISN6/R6nPGL1QAE/kh8XF7/w344osv4lNPPYXHjh2Lt9+ORvHmtVdtplxEbrUPEbPVPdtsz0TiHT7iOWZKPX+lUhXkOaHQPC40ljDdC6yf48CBEcsI4Xq0QkbiAtuHvDi8f7egJtiPjh5Sxj9rSoEUbD+Y4bldcuYM9/RIfER067kSRu5l+Dg5MdGo6JGGkfw9qlX36I0ijHJ8vG5bO/BxCgCfiv/fCa5izdMfpCywvVTCF198sREi/hM/8RPxupX4aJRNPi/s9ZmFlFFXrFzPrR8LtfNUKhIfyRkVwo7W8REArPDq1vCR+jCOiWGSjBdrgY+PB46vYqv4KPPOk3N0srx0YqyX+s4kau/RJH0h2d+kfEXsHrW0sqhBXtkufNRC61eyxqnlSnegZRnMZGGcRLcA/c74L1m3fsMzwSkP0YDUo/Hk6QLAHb29apkkgEQZ5JPF52XlxBpc+a2JSUdK1NvjvzzE5o6YiC0tpE7mQWhKXhkAx8AVWIpg2MQfDCyI94G+kHhfhoeGGv3muVI1sOtVW1ZhMN7lMbC9yn1gBC7OCE/3cQkSg8CWjRsdb9rhSgW7oshiFCeit1CJgrT7m4HEI14EvzdcMqzTMxk5cKDB2n4+nm9FdtyLL77okMLVwIRZkmApQUpj5v3Upz7VAEPtfmpgDBg7xdjS3PYZIGYD45PGxtvKOs+b3kJjxw1P9yhrKAJTvQAA8DeAv7zNZ4i9UEmBeQeYKI0pMIaukkNmw89hK4RcqLQ9CboF3RhENYHmKZQ54yHPhPGo26y0vOa0fQ870BVYimjCH6cwxOwdEsiiqMsKH7RJbGqY5CBqY9KJxoPfx/bZi5oQafZLBF7pTatUDjuMwH7yPHmPFOrtEzpnMcmj1D09PnysVirYFePWGQUfFxYWcLxWU6O/OEby97L2LtRINfnN5Ph4bbd24OMl9jx/A1zFugpJ+oPExyJIGVKTPxN85GVAXTxrFiO/vzH3qQ0O3iHOKY95ALPhYxkN2ZmGkTvjdZ6GHf8i0I+OFvGxhMa7PIZ2LvkYuiHZYXzUK1VkxceQN34GQ/hYqVStPtPzMPOWG2/OozEomBz5hYWFmAFfzrcaGn2HlO+kvwsLC85zTkg19bSyOTDy+sY24yPXH1a6xnnLle5AyzKYCTDV0BfKZ3tSJuPtfBHZ1h8qh0WEY0XQlaoimHxejclcelmppIQE9CfFpNPyvEkQCRFmacdQO3fuHD700EM4EtfipuvL/vDvXoEBEjbztL5wFnbp8deIRiike1yc63jKc9CUz8XFRZyZmcHq2Jh6f5Ps+pq3pAjhsmDvgCT327dPf6mkkrhRuM1JZQzIy0/z6Cd/8iexv7fX2qcMRhDQSCZkWA55hHo8fT0JQ6g+TwAAoyFJREFUoOa1S6GVxp/GKUT2lpaP1co6z5veQmPHjX/jYAxbMnqFOBSOg8GzbZCQCdJnABLBkvBxBgC3qjm/xM5NGDyM0uuCKD0JugXdX1bFJ4j4haDh4RH1OOrLiRMncPfuveL6sj/0f3oOXKKQppO7ufVhQ2PSE38mxbkoSkE+h0nn2ufOncOZmRmlti3dXxkTzxCx/ErBusiOSydo8v3eW+pbMT6eO3cO3/zmN+OrX/lKa7+G8q1wa3CMzPHx+m7twMcy2JEOh8BVMjrBOAY4Ps6CLbvcqq5RGx+50u3i2TIa72hzGMlxLkSY5TsOMZEhDxwYEdf3YWQaPj4V6Icpa9U8PkZolO5xcb7s+Hj27FmcnZ3F+fl5pc52hAbbQ/i4HQEeCdzjO1J/L5X6vSRulBppFGg5BgU8ceJE45m9+c1vxle+8tVinzIab/ozTv44x0efx7wARZyACBEA960CPtKzb8ca5y1XugOtOU932uSmHMM+1GvmGetPBxSxFucm8N/lxNDKiJSViUIe3g+DW0+0v1TCTaArfJ1yAjPBQQuRK7L+DAvWbK026faY/Zs+pe5u7GH1RWWdSbkg6mCUPhJGfKFWs7Oz1ktNWrcWwZRDIyblaXENUvLp97T+0L5UsssXHj8IdujYk6Ar//RXi06g3zeI+6GPz7onQaUG5iVfFtfvAMCPf/zjjqe72NVlEa0BpJNM0Nqge5TA9wyAFVan9bMLEiVf66f2/CPIPTlr1bJ6ckJrmqdraGuHKhiMWeslFOLnCogybCzxYHxYweUNqAs0ncgFkSjqbYSu27XI+TFmnlcqVatUilabdHT0LlHnljwKXGAL1aJeQOMtl/0nA+8VByPNNv4/5RvOYRKtxXPwKC/v0UBfKGSUBF5dCE3y88+jXUe3LJ4L/07Gbm28SQD3Cd5hfOwDo/jK9+ddo6NYrVSc93Cxq8v6vhb42BmfQ/YxDR+5crXSNZ43f2sXPnJyV59sQVF6Lms553+hNarVUG4WH4l0TPM2l1HDR/ucOkZSDXBqGkaaygrJ9y1butDGyCwpQ75yYePYOj5Oi2twzGsGH83zMJgojSSjmB0fI/TjI70//fgYNo7U0Hj37T5s29YTe8ltI8jevftxdPSQtc1H4pfoVk+iNlc/Cdkq7TSLj1x/aMca5y1XugMt62AGmfes/I7koXeL77dBhOOQkAFQfUUt5KyogO5Af7/VLwpnU720HR24ecMG1dvs834vLCx4w8UvgaugI+q1SYvxMWkKdklZEEUwHi/eJ8nGOgCAPx//7/N0ax6MCAAXwPam0zXu9TwHbd8uzz0tgas0UugYglHcZyEJoSVFXx7Dxzrk6U7rMz8fzTf+fDZv2OCdY6dOncK3vOUt+JGPfAQR0cn5oSZTHBbAENPx64eYz2+CJIUC2dg+CLoiTt/znMW1aVlzFrti5cH3nLfGc+x8YE7fGs/X49Zz13A3Qpckp+gwsiahiz4vUIc1v9I8O5VKFS9fvuwJhbyEGiusVpvUCFqk2J9HXfgpo54zV8LEezKl9H8AAS43+uz35PgEOS5IEpkRcZb4hLdZa5z8HugPo+65uhRfV6tDqwlktXh7l+c6ieAYwsczoBOYFcEYPWWd7lKhgAcPHMD77rsPjx07ZtV2Xw18LID9vq5DQt72yRR8zJXutWlXCx9pTlLaglurW6+h3Bw+ltA2bnGsWGbdS/BxeXk5JVw8O0bair1PwS6hrlQP4NrgoyQJy4qPvvShJeW6afhIxlB5DOFjj2fceF76JU+f70X73rXnswHlO7hQKOHExGQmGdJNb6B7vID2/bQPH6vAIupyT/fat6yDGQqZmYofNuUmnoYk2b/OAHGzAFQt2b9ZghQi95ny7D8FRtE7AknZEt8E5nkOxMbqO69UeNV9QM+toHv0CQzHwRDQDPT3Y6lQwAfB1Nl7EIwQtAnsGqbcO1+DJKTbEpbADkc5DkkYP3nhfPdB+0bxPto9+QS3csrYUMmQd7BnJffVvCNaXXN5fgpFS7sveT0twmLLxo3Wd+7Z8T1/Upjm5uaCc+SCmBe0Vo6CKRFxWPSnEwAPjY6uyjrPm9uyjN3y8rKXQVTOtVCuFZ1j1nruEnfTich8GOkn93kQjSeDwvE8xlWWJ0346DunK8xp1yUhQ14z3QMA8Mn4HJ2xAj+FRgC7F40wtAujqAsrlWpjDGzvE3nIpRBFwir1zwi+CYloGlMt94D5ct9rnuuWPeetohHqiJxII7AjZcEWvDdARyZ8PM/mnLZfFozk39uNj9q7kwy2J0DHx4KyBla6xvOmt6uFj3w+kIySrNF03GkeH6fiNf4DnnXt4iPiWmGkVDjp+3FcfXw8jgafSEbKio/ouSfdUOLHx3p8DCnXWfGxD5Nc77Rz86hH373JZ+saVk3Zs+R71nSt6enpYFUIDR+lDHmTgpHNyJC50t3G1sxgpoXM8HDmtAnSvW2bM0kkARpZabxe1zgkQgvr5sRr3EtL52gmzyFUDkuGdvN9SJCZ9owJf7GQYs5DuAGgkRteFguGfycBR/POp4Wj8PBuAMDxWg1v6Ovz1ikHMIou9Vnek/ad8q3ohcjDWyhPfBnskBjfWEsh7zAQ07MJT9TI8+YznPe02O6LsCix775IB55L0xVFllFEI5vjRgxen5bfq7zvMpjcyPFabdXWed7s1szYVcfGsFeZjxGbgyGMlGtMJ7NJLwHGMdLNmSNioWT/xBMREgCTPOkQqZobtsj3OR9vn/ZcM7sHQM+5S4QeEnBc71PaPRaRe7gmJibx8OG7nXJuLlMweWW0e5LbKHTzOLsuFwrpOfGQV58n6VXW/W+EDvzp+P+uQiEVH0PlNrNiZA2y4SOFlA8PDTXetT58rLK1sgSGiCsLPt7Q17dqazxvdrta+KjNyX0QYUeDTygbProe6fbgI2KYeLJ9GOmLklltfDTnqtXGM+JjBwJ8xjOWreAjxs9qMHWcdXK9eTZPJMllLT6elwbznfu02O4zrJYa33mkg6ZXmbreI03j42rJkLnS3cbWzGBqITMj8WTmytZw/FCdsOn+fvzoRz9q7Y9gFK+ymBhpoMsJrBySGHCJvzi9/hXQPad9kCh/MzMziGjnIvnqQ0srPZ/49JkE41Xg1wwp//v27MHZ2dlGzrO8xwiMsMQFnMXFRZyenm7Un00LRymDCUkBSMLwlpeXHWK03WCYl6fAKN3cc8KNJWRZu6Tcv7boeTg1f5mmvWgvgF7bvLenx7kehdscDpyXe3FCL/s55fnTuJHBQ97rPXffrZbmoXlBqRZl8fcMJCz08tnTWpmens7r0K5BaxYjJU8A5SpmwUjiS6B9JyDCCHrQ9Wak53rTvJiYmFRy5rjAInPuuNDgz5OemZkR5cj0+qeuJV/Ls5xEk2PIw8jTPfk7d97C6sjWUPeODCIXcCi8Lym75hOiSEjb01hn9Gz1kNFPIme1TfosmZBJwLukjIEmFJLgz8M3fYLwc5gI3sfFuVwhjPBxEsLlLZvByDqk46OGkXK9cHwkMk7CPR7BlePj+mjN4qN0DoyKOZWGj8SVI+ffSWte8bUUxkc3rLs9+IjIc3b1EovNYeRecd10jHzta1+HzeAj9ZfkxzA+3txYZ/RsXXzsRICH0SimU+h6licxwX3KGc+Cj2W0w/uzlqA8jdlqm/P0qsOBc3NPd8goM+c8//TyZM3hY7MyZFayyVzpbmNr9mVDAMFrxVVjgCwDqJMDIKmPPDw0pJJn9YFRBt8LRsntBCWkOK4fyvuRFg5dBH9os3zxT0KS5zBy4ADOzs7is88+6yh0dB8+S742ySkcvEdcc2OhoHqWqSwXhTylKYH0vywLEzqOf5eCCbE2+sLqe2LPySXlmfMFz70gB8EYLkaV50qGgy0bN2IRjEHiNHuGPCVBC2Hv6+jA6tgYPvXUUzg9PY0jBw40rnEJ9Nz5vo6OxvWyRlgcBTt0R5JQkBWf960TTA6kDPOvQmJwIpZ6niefxdrfmLspREatrvO8Ja2ZsSNcmoLEULcIphxSt7JeJEZuKxQsfLwExnvDj7kVCtgNgD0Nr45tIecCVLoAQB6Lsvj9JPqVQHPsgQMjODMzgz09vWK/MkZRyZOv+AwmpWikkLsPZTmuQmEjamHTGzduEaF5afdYR1fwCh1zzDrOh4++kNFCoSf+/xK6xhISGDUh+CCacEb9mfb3E4vvY2iER3p+G9BmCDbC8RGBkft278bp6WmsViqN9xVhuGaILhUKjZzurBj5VAo+1ut1HBkasgzlZdC95rvZuTlG5vi4/tpKZchFMHn+xQz4WIoi7O3pSXWavBdMydUOJax4rfHxwIGDYr8aApxMyen2YSSV5eqzzmfClnWMTFJCs+FjpVIVfW0NHxNjaGhsr8RjKcswpuHjLBqSNfe5Jvj4DJoogQfQGEI70TCdU3/cEPYo6sWhoRE8d+4cDg0Nx+lEdP6TqBOOljDJ6ZaGVZ/B4igaxdwYGDhG1ut1HB4awiKTIZvFx1ZkyHbjY650B1qzgEmhMOfB9jpGyuTojcFUeogJQKWyxr8fAuMF5NuqY2ONySHDuimc+QLbvwNsa+ljYEKrd0FSL/v7AfBj8W9b4k9D6Iv314i2eM6aLJ2VRRC4Mf5bjMPt6bO9VMLLly+r90gfEnBm2f9dHmVPKvRlcJVd2Z566qnU677+ta+1n0s8F4ggJ80LYlukk0VPLwhZw7OTXaPYxPhK8rntyjU/+9nPOlbEtPMfFPvx+agZgUKgdyH+nwhmOKlglry2C6CHc7Zjnectac2MnQ8fs2IkRcBIPNwlvlcB8DIYTzjfzllSQ6GN5tOBRjBxBZVt20hgeR8mnpUt8QdQZxwvYn//gIOPduksnyAm12IBu7tL1rZSaXsDHxHDBJ9GSKP/j7B+dnpCIcuoCei8ET76rvna175e3McImnqxdTQCY9oYLKBU1OmZfvzjH0eXFGkLJmVzRKSBgjn8PSPft0Nin/FaDd84NubgtYZpWp43x0eaC/zYED7yUErCyBwf119rVYZ8AprHxyxOk8b8BcBaZnykUGYZns3xkTCwCwF2xX83oMmTvoBZ8REgsnJ6Z2dnlfrNIY+tOf8TTzzhGD45RjaDj1HE+1pGnaAtjI+2MTTt3UOfUTS4N4sGK9Puv46a0WNiYhJfeOGFeMylQaQY/62y/7OMLz/POALcZf1eq43j2Ngbleulnd82woyNVb0yZCv42KwMObUK+Jgr3YHWLGBevHixAXgFsJWkNGWLP2yyVB6HxKNpPDf2xK2B8S7K/FjEZIL6WLrvj//fxLbxfbgQW1B+5995njjd09mzZ50wKS4UaJOcPA8nwVUub9m5E5977jn1HrOE8pFXmowPR5V7koIWKbuSUTELsQ2NG4AJ4Z4BQ2qTdv83QRIlIGtdh0LpnwDA12UcXzKOjIKdFkAGGclqOzc3h0ePHsW5uTkc6O9Xow86tb4xsNIMJFlADwAaBDPNWCmr2nxICaXMhcrWWzNjx/ExAte7nQUjSfjk+NgDSfk8jo8nwc2PpRb25AAC3I+6oHKr+B4p+9B3NxT67NmzTsjcK15Becc+QYwLfa4HZHh4xLHIh2vi1sX/JFwfde5HluiZmJhMxcew4FZg/w+jESzfHRiDd2NHRx9WKlXnukNDI+jmCvZhkvN9Gkk4rsb1XelDmHM6noO9UYQR2ISnCDpG1ut1nJ6ebnjJKcSbY2QnuPWXpTAnMTIrPgIk/CY5Pq6/1szYLS0tNaLyWsVHOrYLTOTFBTBGHylTZcfHsphv9J0qEGxCG+/os1Os9ZXh4+7de9l3Hz6Q4dDFx507b3FkyObwcQoTfHSNf1nxERGxUgmFZEt8PIzGMHkicP83NSKpNBlSr+jRF29/AgFel3F8eQg+Twtwa71zfKzX68Ljzg0WFKmQ9I0bLlaCjyuRIduNj7nSHWjNvmwmJyawVCjg8Rj4eiFMxDIrHm5PoeB4cijUWnqVI3DZUCmMuDo2prJ0F8EA8CQAvp31Uzs3gF4WpRcYMQwkeeJ8wm8CU7ReLoDQi2NSuV5fRwcerlQcAPMpgTsgCbmPwJ9Lveu224wnXoSf3zU66uQa83Eer9UsT/ljYEp67GJ9LsWfrPffBXaUAG+hUHqARMkPja9vG3kR0/JYLl++7Hi/OzdsSL+uh8U+BHoE0ouLi3jLzp2Net0UWlwDvcb8DrBz4hvrLKXmYi5Utt6aGTuOj8CeZ7MYuVusqS0bNjhKTQgf6/U6bu+/AV0BgEIVJ9EIWJqgUsREaCyhETw1YaaMdg4k92R0IsAvoFv7Nktu3KRzvSgq4tDQsCMc+IWcHez/MdTy+G67bRfOzMw4oZWjo3c5BHTcS1arjSue8i4EuAmNYHaG9YFfM8TyW1BrumZX9M19+UpiSo+hTCEKYeTy8jKO12qZvN9SmGvWkzM3N4ezs7N47tw5PHr0aMPIT5iY4+P6aM3iY19Hx4rx8WGwCVg1L3nRg480p2ZnZ2NFUhqyimgwj3Dt7ZiOkSVMShu2Ax9DSnJ78TGKeuP7kP0w3x966KGm8XF5eVm5LkUJjGISMj+GegqO7/67rEgq3v7zf/7PgWObGV/fNpM7HpIhzb0n19uwoTP1upoM2Qw+zs7O4r7duxvRrCEZcgAUwuk24WOudAdaK/mKZ8DkbtH/WcNoi2ATcRFr9/sCx589exZnZmbw0OhoUy/7OzLsE7p23fP/FOjWfS0XvRhPft9C0upbT05M4LPPPoug/HYT+5+817TQtJdOdWzMIZwrFQrYCa7iT56JNHKwSUjKEMjwe9/9D4ArhPEWCqWnfkSe85fB9mrTcW8H4yWX4bmhPBbu/Q6lMhBYSUMF9wBpOewa+34EiXFBG/sI/DXmc0/O6rSsY8fxkZf7ahYjy9AcPo4cOICnTp3y4uNu1QMzjsb7QqVy0hThUC1oIu6SXpNeNCVmuHW/jLoAGGHifQgTC5GXJQn1loLbLvZ/R/xdyxOMcGysGqgj7nolXPIbLcfzFgTYGo8Fv6bMA6T8wLJ3HYdTBRKvkYaRMkyX8HEzGG/hb4D+DvJhJPfuUA4nx24f94XEyLLS1zR8LOb4uO7a1cJHjOfz2zPiY5LiIteqD9d2IcChDPttDfzeDD6eiXGnhC5G1jArPo6NVTPjYxKeruWRRzg8fLBpfEQ0GOkf80k00UYFdI0enajnT08iV1JlO3r0KKZh5I4dA6wv2juojDYhKGHr29F4yfk7BVTjKG+aDGm/45JqHD4Zsll8pLRKjosSI3eDTmSce7rXqLWSr3gFEqWbXrSy7NczYJQN/sDpZfkbAhxPi3PR5wo7LgK39jRZR8+DrnABJAqq79xHA7/zvOkHIPF6+14SWs4yFwSaqW8dsX3nIcmZps9rwYRrU1647MtjbN+mvMNs8W0vlSwLshYaz8PvtfsnhfdjNKaKRS0U0l6Ix+RJsC3cGqhMAuDPK9vLYCICsub5yb75UhkWFhYQ0QCmHJtNYMrXWP2LhVmNfb8IhnTrY/F8K8apFRQBoZXfyXMWV7dlHTuOj4vseTeLkcfFugzh41YwL1sfPl4BwHMA+NbGdbhHOaTMzWKYHIZYZx9A26ujMcouoysAbhL7yT75ardyQe4Smpw9ft4IAd7C9k3zgPjq8+qMw9QqlSomJDvUv6PsnriQuczOqxlCjICcho9p90AG0EuQTkjlw8dOMPiqGWHTGsdurXoHz+3WMFKmTYTwcReYOt33Qo6P66FdC/iYKFplhiWEW1fQn9MNaKJX0vDv+wO/N4OPiDpRW4QJo3Z78THxdLcfHxERt20roo2PGnkaD8H33f9zjTHVMDKpta7fhyG4PIM6waVmFPh5ZXs5Pt41MqS1BL/18m1pMmQz+EjOtAdAlyE5ieZq4WOudAdaq55uqXQSdT2fHGUA/CQAvgvSFTVf+azHxDFyn4uQTqgxBWHyrXOB37l3m/c7a75FtVLB8VqtMdHPi+tlYRg8Ax7WbrCFK+qLJvjUwLZuUT+mlb4ToMmQ7yUwHuui0g8Zfv8OMEJgv+gHV1Jlo9Bc6cWmZ8zHqA4Jky49a+pPLxigKqX0Uwt91HKTeN+0VIZSoYCTExPW2uB112UokC/UUnvuACZXZ2Zmxlt+h4Nwu9Z53uzWiicHwS1Xp2FkDYzhbxu4nBaENSF8fB/YWEG/E7aUrfNKK3+o1Eodk3qxIU+OFJ64UKsJo+YzMTGJo6N3sX7x64WUzTOYCEOa4Fll+15gxy+hG07J+059mHb6TfjosqeTx4nGWIaaJiWFTO5gEY0HRxfAZEvKv2mpApEzP46z82bBx15I8BGhOYwkAbAMfu6LEEbyEl85Pl47bb3j4zsaxxBOEZaEcrp5mkgWDFodfKxUqtckPiJqivASGu++Dx/5/b8DAbahnfttno1PTvOH0mtGBXoulLvPI5J60RhTSyn9tI0MIRnSREa5aQiFQqlR0rLd+CgxUsPHaqWSs5evZWslp5sUyDFwQx+64gfJGa4j0F/wNUgsLR3sXGmWeukh1s5L+dw0CSPQS5TRPmXlPnj/qD51M/VK+QLR6pSW4n1DVtphyM7aTb9rCjoJVL464stK3ylch/oWqnmdFoovlVStUb6gfOnSS9M3RjeCHekQqoFeZ8fOzMxkEtKIkCTteVMfF1l/pCGDWpZweo347qWXXsqFyjVureQsEo6VwMWWbWCHyAIYY5bPqEb4SJiRFR8RTH5rkZ33SQDcIKz3XdCB/rqzJFxQTqPcp4wUhuh6Q9IVeomRdh4cKa4hL/sshuuoXoj/HmG/uTmR5n7GUa+Rm3ipKYfOLo1zJd4vJKD7Qk1tAUxry8vLTh4lQISviYXSduIjPz4LRi4vLzfIfEIYeR70yDSOkTk+Xjut3fjYA83jI0Xk+fDRfDQlV1MAi/F2vnZ9+DcZ+P3li49chkz6F+qLJL7kfTTPpr9/wDu/tHxqU988bYy2ogkd56HlWYzR5viZmRmHFE8LPQ+R2jUjQ2bBx6HBQTw0Omr1izCSV1vKgpG50t3G1uzLRlMg+cPbIb5TaHGawjgyNGSdKwI/KUZWD/GCmIQyNLsMBpwJ5DvE79aEBMC94L4cNoIJXeLU/WmhGgsLCzgs7pU+afcxDLpAzRfYodFRLBUKQYFqFBQSt3g8SnGNYDrncMwYyyMb0vrBw+/59lmlH5o1cGlpqSG48TrwIWVfPqsnAv2cZcfu273bCdHRnmEI5Ch3Sr7s6buPOdV3TxSlURb9Gujvz9Tfla7zvCWtmbHTLMkl9r9c94MAeDYwv/ft3t1Yu1nxMQtGTsdr7BKQEMf7VkYTRmc8BRs2bFb2oe+TaAQbW3E3XtkIjVBzvLHdF5a3vLwsCHv49dKEnxlMF6pIMO1G11OknZPCELmwWWa1YLX3BCnTRwJ96ULXozPr9CGEj/ey57ca+CjnXzsw8vHHH0+NTGumgkaOj+untRMfAVwZkkp1+uZCtVLBU6dOeeXHJOLD5+kOkWrROh2x+kU1txNPqsRH+lTx5YCPUeTi44EDw+xcWUqJyRB8iZF+fEQ0GJkwpx+J7z+k6Guh5U8E+jnbOH737n1Ozrv2DEO8HM3IkFnw0cfb1ApG5kp3G1urL5tnn33WLUkFSvhtDIK+F/H09HRjApUh8ZT7JhPV3Q55iKXgQGXJ7hgcdJi8q2Nj+MILL+D+PXuQQFtO+p5CwWK1lvvQ9yxWdSp1MDc3h0899VSDXV162kl5DVL/x+Eh/GWWpqD7ztMBgL8gFiKxyj+Q4XgSomR5tbrSDy0nZ3JiolFv8Hz8DCn8vwxutEIRTM60NCDcHujncfCHrVv9bgLk6vV6g2leKkMD/f3qPFDzD0EJgY+/k0GFGyR8/ZUtFypbb62MXb1ed5QMTVkmgqu0NUsv4zNs3xA+XmFrNitGbgODj5UKF+wMKc9LL70kcHMrGgHpAhrhowf7+m6wjnMVVLO2QwQ0vBTMuXPnUPci9WIimKULz1FUxFpt3PFGpJeNcc+zYUMnywskgbMTTRgikSmFBNYqyvJBdl6kP2eR4yN5Q4hbpZ34KIlPs2JkqAJFtVLBUqGgR6YpAl+Oj9dGaxc++mRIijT04tjsbOP9fJNnvu5zcrqfwXB4t63oGcV4G3Z3l6w+Dw2N4MLCAs7Pz+OePfstrEs+ndjba1cyaAc+1ut13L17H+oVKtYWH00Y+CY0ueeJ8lkobETbK5+Gj6T0+jDSj4+IJoTb1BsnLOZEcvTcedg5RXlJI8LtgX4eRztv3s9KTi2Uc96sDKnhYy8opcDYxVrFyFzpbmNrFjApb0G+QHl9OFQepixNIh/y5MQElqIoSHwmw46aERwiSPKJZY2/tJw0mvTLy8t47tw5vHXnTuyNIvvFEEVYHRtzxintRU9NI0/oBOMBoMXkZQUXJbiMkJquGPvGtgvsnD5aoGV2rE+4i8AQNsjt5cBz5+MFAHgLuAYNAH9JtPs85+fpCrKfNL73BcaDgJ1YIiN2zvOQkFTIfMUs94uoh9NHADgOxnDBlaMlMF5Rvu+k3C8vibMqrVneC07qRBh5HtLxKjR3CJ/eHZizEh+bwcgs+HgGTFqRDFHfAB04XqthvV7Hp556Cvfs2e9Y/6OoF8fGqs44hZoJqZbEO51ovEwkQGmMt0Yo4kIs9S9doLogtvMwxLL4jcInuZBGxEayL53KNnk+3ZND+ELEn2Wwn3G78bEZjOQsuoSRj0FSY76vo6NhsPFGdCl57Dk+XhutHfjYLhlyW0FPtbCjeaRCnIYDPCfYHLuwsODgI6JR+AzmkYJn5+3WauOrgo9u6PnVwscjaCv7iEmYdpn1TVeAt2zpxihKMx748ZHGzJz/FuUZX0KNwCz9PrW0Kz4XOhHgvvh/3UBB+JgYNCLnnOQVb1aG1KJGGs6/xpxPjOsrwchc6W5jyzqYGj19GRKvZohYbCuInGpRl1pOoDTh9AgA3gr+8lFccKgC4Ay45Z14o8keAn3u6fHtMz8/33ROmbz3h8EVoArxPfNtBw8csEhk6F5Ghoa8FrC0vtP9kwWMhyHW4+v3gCvwUTjY0OCgtb0DbM9bWvj97Oxsg6G+K37GF5RnQgQTtP2CZ769Flzyvk4A7N66tXG+UASBVDieBKNwSKPAeK2GMzMzqfPfB2bVSgV7CoXG/ZI3apL3Azy13eV+uSdnVVqWsfOVf0urWsDnB7Hz+zByYWEhMz5+AAB/ApKqDyGMHAFDeBnCR7lmNLIX2xuuCzMy0igLPrpemLL4vheloDU0NIJnz551CAxnZ2dZmTAp4EXefifhqQvsNx6GuB+NQHUSXRKiCPfvv0Ns60ATqukKYLJRaGINDK5uBaOEnBfzYaX42NvTg1tjxSUrRnKDzKfBDRce6O/HU6dO5fh4nbZ24GMWjMwiQ4Z4BcznNAKcZZiRplgBmuiUGZTlnXi7ePGiwAgdQyqVahAf7X3Cnm9ELfT8auCjzMeW+DjNxlv2rwNPnTqlcFbYxoM0xnCDkVF8TBcaI4AkuaujiWDg5HY+j/5rUTP2bt3azc6ZHkVQr9eZMcbPnN6qDLm4uIi37tzZeB9w7CszfMQVYmSudLexZR1Mqh+nPTCE8MvZIcZgodpc8Dp37py3zmgnGKC+HYxi9jC45aNIkGgmfIIEmlDIelcUWWV4tH1K3d2p+RJpFsx6vY5Dg4PYBYLyHxKyNxrH3u5u677vuftuyysgFUPynNfADWWn50j3MAzGmCLDEOeV81YhycmrVipYjCK8F0xJl4dBYa33CNgUeiPP78un7wM3h5U/Z+q3phjw56eVKZHPi4+ByiKfwZMjnzfPzzzO+siP6YqfuxSs5bnJ296OdZ43t2UZOw0fi/F6y4KP2tzXMPLs2bNBfDwer+Fu0EvsdcRrU5tzafh4BcKCcVcUNbzxPmGmS0QJNYOPMzMzWCptRyO4HUcjQB/HhMU7YbqV3p/Qd3P8XnS9MJRjSMJYCZPwR+7hnkfXwzKM5A2v1+tYrVSwK4rwfQD4SwB4m9jfJ2ATDmn4OA46preKj+8QzzgNI9cCH0n5l/3M8XF9tCxj5yv/VmPPqx0y5Pz8vBcfNzb25dEoT6JrICNFi3KCXUVKtqEhyl1OD1k3oc/pvA9mHz0/OOQBr9frODg4hEbpvBcBHo3xkTzwXQ2MbD8+coV1OMZIjo8+tvhdjXE14eHFuO8/jQC7M+EjIg/flhisMZr3IcBdrG+aEYF+I0W9Ln6j5+dGNknvtY85/WMtYqQ0YvlkyI2QRGauBCNzpbuNLctgBsMe4u9lBeyKYAsKw0NDeNfoqFcxJQFPEy5Oim20fQGS/EVZoivkYUVMFmuW8Kbn2f/aPmnHhzw8S0tLTk3nCJKXTRmSOtNFsElkOgFcoT+KcHhoCA+NjlrKrza2nL28CxICvDIYIYrCBXfFvz8ASXgqB4yyODd950ycWiNPdxFcS5zmXR4Ak6+oCZvDkK4Y8GeklSmh57K4uGgxuGfJrQ/Nu0Z6xtgYbhPEdfQsKCyoJAwrvvsZHhrK2XlXsYXGLis+kqWZz48SuCzMaRgpSSez4OMy2CX2DsXnbhYfs3g+3wfGSJYmzPiwM4sH3HhCehBgn7jXCGU+YeJRuRL/LaIUZiuVKs7OzuLo6CFMamtrxDq8xjYgwCjaoZMkdNXi/iX57lIAkxhJWDs3N5c6D3t7elR8HAfXsDIJCXN9s/j4qHjGPoycn59fVXyk/HV5b/Q9x8f10dqFjwi6DOkQvK4IHyM0iqfEpzpSznGlUsVabdzx9Pq8rHbpwHRPt2Ewf57979vH3V6pVK170hTQpaUlh1vD3DMZI8tIdaabx8dOdj4NH/k9dCEp08m5H0CDm73IDaaugloW5zfnCeFj4um278Ng8w50+0xpSxrrPBlRfF5w/oyXUYauT0xMWvjoDT9vEiM5PlK6WZoMeYeIPm0VI3Olu40ty2BSjofvgZ2OJ0cPAN4gJkAnGG8LVwTTAHgkZswGMEqdVlrkCNhCB/fSzs7Optbq1CyFpPBRSIa06hPoj4J9b2VIGND7IEyIlObhQTShdEXQSUS8+UyQvdY3kZJNgfFQd4HNvE7X+n523H+Jt/P77hDfJycmcGZmpjGGUiiMID2fDjFMwkOfI+w+HoZEgOQCGeU9+s61S3nGXWCMGyPDw2rZmTKYNIW053vH4KCTg0jzTguv2wW6kaFMzzYmSuFEWuocyJD3lQuVrbfQ2IXw8YH4/5PgRn50AGBJ4EIII1vBR/57WgmoED4+A3q0TKlQELjghm1Gcdhmq/joeg3qaEIWf0Bsl0LsudTfiQfDZiAfQSM4JqzCRhiTXjFAN19Q917zcWwWI0OKCxmM7wX7nXCT6GsWfJwC3bvdBYA927bhCy+80DZ8rI6NefHxeDwuPmKtHB/XRwuNXYjVnvCRjJADYs42K0OG8DFqrE9/Hq6W0kLrWGJkwkpdw8SjbCvsCVO5xI8yJlUiKHy7eQ84tUqliq7S6StZ1iw+nmH7PohGMbUxPrnW97N7HEb7njvUcU2UZjvX3XyPMsuQ6Z7re9G8N+hd8rDSvxomXB2+c+1ynjFAF27Z0oUvvPCCkg61CxPDRHIeOTd9MqQmk54BI8cXYfVlyFzpbmNrRukOgVwExrq+AK61nD7kbUkTvELsqlrYeCMfmeWmTE9PNzys2kudrPWSql9ajsjbXIyvcxqSkiW07yQAPhvo94MpE56EKl9YvC83bxbCIZ8ACdEOeZMp1FwKTvx7B9tfhoUdHB62XjxSaaZwQHou2ayU6ZY4ClF7l/KMNMOA5pHv7elR89JHwZAG9RQKuKO3V813vCXwfItR5E2b4OF1p9nvaV4haktLSw1Wy6zeyVbWed70llXpDuEjYckhSPgSWsHI1wTmjppWAy7eZMHH5eXlxtrma0auvw4wRlfCx4fBJVs7AOnCcho+8nF2hdIQC3G6t8H2RlCooJaXPRlvjzFhE3nG9XxBGd0jIwZkuHQaRobwcRtAoxTMh8ElzNHwsQgu4Rl500+CW7rpJjCYuhJ8HK/V1IgGik7j+Hg+NM/jsc3x8eq2lXq6JT6Ow8pkyBA+Pti4nj8PlxTrubm5hpxjE2KZz8TEJD777LPx9ycxMb5JbzB5mym8PKk7nex7MLVfRtHV+8vH2RcW7ydAy4qPZFwkg6qGkWXxXWMHLyFA5MXHJBTdDunOKkP67qO7u8SiF55QnlGv0vcu5FFLUdQbby/FH77/7QiwFUul7RhFJWUsSkgGlgiKOAFRZhmyFXwkGVIjAm4WI3Olu40ta3g5WZxl6PgmAIvkpAjG4zvuAcbQRJkCE842oEyQItg5QAi2sj45MeEVHvmkJeWRhy1F8XcSGt8HRpDZCIniyIVO/n0u7stsfB7pISBPxmlP32dmZhphUY0+g80qeIQdtwjJi2caEs9v2kutHPdDy/EcBOP9KIIdsh4q3ya9YQB+Ft1Tp06lzsPQi3lhYaGhvGqGgD4A3Bnv+/73v98hdYvAEJ698MILlsLwbkgI5vi+Pu+KClxgk1FYhBYx2Qu/N17z/Dzo1viZmZnG2BCzv5x/krl+pes8b3rLIlRq+NgLiXIjMZJ7IJvFyAVoHh8fiOfiPXffnRkfqaYnrYkiJEo1lWDcAAYr0/CR6km3io/EAEv1yl2h9DHP9lY8OW6oIMBNCPAbmHi7jTC1FYwwHXk8+tJ7wMnQ+Plr7D59LYSPh0ZH8fLly6n4WAXAt8f7P/roo9jb0+Pgya/92q9Z+PgwJBFc7cLHYiCyg/CR+FN8GEnjlePj1W1Zxo6i+OS8KIFNnEr4OAyAPwDtx8co/j4BEXbIdRv14t133+P1cNuEWEaBTDzYPLLnAhpFbRuacoJTCPDrqIdlkxFvDo2SSZ5eGe4coTEuusok4ePQkKwjTmHfpFzzUl+LmJTwOpYRH8voz4MfRKO8FzEJWU8Ptffho3tu853LRFrz50+b67344osKi7j0qO9EkiFNbjzvRxSH2QMmuepTaEj2qmJfGit+/sTAsgE6rOpEaTJkq/hI48WJgOW7J0uJ46xrnFqudAdaM0Rq0rodgc0+uQS64LUMNjCqAmoUWbUYl0FXhvj16HwAdq1qh7AjEI50IT6v7/64sKiFBVJ4FC0Cn/BJIeLSC0z5GfLck5CU7doKgB9mi0Z6mjaAnylcY/TkOZ6+ZxWyKHMhkecrynEqgslblY2HaXm9FYLggVhC0wwMZECRCsR4rdYQSjfH+46CeekTyN0L6S/017z61Y167g3gAreEjdYvPo7foTxDnpcrrdfUH8lOnCV0spl1nje3ZSVS07x//NktgW6Q0jBNU16KkCjVzeIjvWA15TqEj1NgjGkS11YLHwkjCX84Q/Y+RVguWPVWtZJcPGfRR3YjlefjCLAFXUG5jBH04ARECY+I2Ie+SyWaG2d84dJyfx5NNF6rqXPirtHRxjFZ8DENIwf6+y18rIFtNGoHPh4Rx2rvmUkwRp0QRub4ePVblrFbXl52PHjEZdAqPmpOoBA+0vWWwSje/PexMZ7LLXObD8f7+Qx7v4EaK7VRUBFNuoqm5JGCeRoTgjV5HvrOw8MTpdllyObnr2KiXHchwIfZNSW2+ZnC9fuvo117m/pKYdTpnmcNH02fesV9mJD1SqXq7M/xcWlpKSZ/k8YUOww/iUxIC0OPYoMKL+nWF58/QlM+ku7tcDy2U5hEXfnP/9xzzwWdbFyGbCc+Itiy/2rgY650B1rWwdTypLWJoClcZbAtOuPggquPuY9enu8DvyBKQofPGxBUHsEWHO6//348duxY4xgiTwspegCJZ4t7hEiY0azx/TGJUpbQKwrhLCvjXAJwSNjIihUqRfAqz/icD9y3XLCUj+/bn8KDtGiEHb29uAlcELmhrw/n5+cb4Docg5XvXnwhadzL8jCYUEjJhjoJgCcCcwUA8Gd+5meQhEctlHfOcxzv0zi43ihioOZGhlBYaSjPqdl1nje3ZRUq5Zy+ScwlHz52gusBGge/kpuGj5og2tvTgwsLCyvGR369t73tbS3hI2E4x8dSoYA39PU1QqOlNZ4bDTVhmYTHqLDB2t4lcgc3btxifbdzCf3K842MER3i6y+DjY8aC7jExxBvRRo+3nP33bi9VFKrUnzH6GhmfDwCYVx/GJLwdImRRJK3Enz8mDhW688ymHdaCCNzfLz6rRkZUqYWrAQfsyjoHB8BAG+L5xWd7ziYiMZDo6NBT6n5+FJYZuPvxy18NL9nOS9hDHlgE6KxQqGEfX03xEo1V5rNJ2wQoM+G+DpFDHliOT4iYkOG9N//q5Tfw+W0eMsy/mlh/mNjb0RDhmnjeLHYZ5XWTZjmfffyaKAfD6Px7AOSZzz5VINz5f7772+UUMwiQ15r+AiZzvgybs2+bOr1Os7OzjYULJo0GpkX91bQhyw6CEntbFpM5M2QAFuOJ9d20GuLjtdqiOifYCEhow7GyiqFPe7JSZu4t95yC/bGCnUJXGF5oL8fL1++3PDk8kVCDK1pQhJ/AYXy3XkOErVQaOKZlHOSB8uKSigUcP+ePQ5oPvTQQ6n3cvToUUREPByXzaE6s0fApCnQ2FyIt/WAS9pWSOkrgAllTevDTeJ8ZUgY4fsA8LByfi2CowPslzcPk9U83RTN8AwA/nrgHhYWFjI/u9yTs/qtmbEjfKxWKtgV1zueguz4yDGSKzJzc3NBfNwNLglRGQxRG68M0Qo+0jqQGFmGMIEW4eMZMJUINAx/49iYNzRaw8g6JLltp8EN259i+0luiTR81JRnwkdNQFLxMYrw1p07nRxEzvSdFR9JMdDwsQhuTewQPtL9pfVjWHnGhJFF5fzN4qP0dDfGkc3tnw7cB2Fkjo9Xv7UiQw4PDTXwkZOjyudIkX5ZZEj+jtXwsQSAbwS/DBnKCTaftBrVS6jnOKcrrJ2dW+Nc4TMIMIZabeixsTfGiqYbFm0I1tL6fRptpTo97FurNBMmKZP1sOlTRi0C6bWvfYODj1nGf3Z2lpUVo9z44/EYcC/5hfh32m6Or1S4Uuy7l+lAPyTxWhkTRvg+BOgPnL95GfJawsdrSuleWlrCH/qhH8Lu7m4sFov49re/Hb/+9a+nHlOtVsUEAPyxH/uxzNds5WWztLSEbxwbs645CMmL3Bcm9LDnwdNi0rxFZNmnGotpXoW0CaZ5yXshYfbVCseXCgXshAT40zw5styYFPYoL0ae40jg3JoVLE1gknkvaSQKMqdd/l4E49nQjBH0/6HR0YYVMeTJOXv2rGPp5ufiOSt87O4FO99rB+h5oZMQZnIvimdMx/F9bhVjUVaOK4Ir8JLgyRnhKcR/eXm54bELPUNpfVSVrSZIglpd5+utXQ18RGx+7DR83MmeuQ8f7wP/eg/hYycAHoj/nwK9ZqcPg9LC2ctsPw0jixA2BNJvfG1qGL5SjFyP+NhfKuHTTz/dMj5yI4rPYJMFH4uQvWZ8CCPlWDSLj8UocrBsvFaz8JGMB1kwMsdH064VfEREfOmll9TyqJcgMeQ0K0NqVRloLZbBhONmwR+/ssTztkmB7MWkRvUkuqHRRbRrVfuUMF7HWq8N7e/fkcD56+I7eXJ1pZJjpO1Vdu8/yWn3jU8PuoRjSQRSqdTfwMcsnm4TGq5HOtnHEhEbGRgeRYCp2LgRYcI0r9UbD/WjKJ4xHcf36fHMlcjC6qwyJKVGyrW6HvHxmlK6v+u7vgsHBwfxD/7gD/DTn/403nzzzfiDP/iDqcdUq1V85zvfiX/1V3/V+DQDfq0A5uTEhPqSjeL/mwkTokkmc1iPgAmxIGAMhkAyUhU5wXrBeFjoWvTpBJP/cB7CQqNP6KL8NQprTvNi8N/5S8V37prnXGl95azXfDx8JAoLCws4OzuLCwsLbsi32D8Ck/98BvQcz8mJCbyhr0+9l4H+fqxWKk4eYS8kBptLoL9oG+eHJF+lKvYrQ2L91uoh0/zk+/Fxq7Px3VYo4JaNG63z+8b7feCmEcgx+dSnPuUQu6WdU1of00rgreY6X2/tauAjYvNjdzXwMYvCOTs7q+JjEXTlsQMSnogQRobw8TwkparahZFcGVwP+LipowO3QXvxsQh2moJPGQnhYwR26O1KMbKzww7dz4qPmvB46M47nTzwoMeeYWSOj6ZdK/iIqJdHLUJi5FsJRpJy+iDYxscsMmSSG61xQ1xCN9+a8qDPx9+bUdiLuGfP/vj3Kwjgq85wxcLH5PcltNnS5fm5Mpicx5TN8ve1Uqk2npOdK+7e/8TEpIWRbqksyQi+EU2eu36uWm08VuSlstqJlcph3LnzVtRLopF8dQldIkyulNM1H1b2K2OSj04GFDkPIrGfNG7QGG9FE85vz5UaFNZchlxrfLxmlO7Pf/7zCGCHlv7Wb/0WFgoF/Iu/+AvvcdVqFX/8x3+85es2C5ghopZtgcnAP2VIQh+JFGF62oR2SGAMCX00ydQJBokQQd7TbeAKRfKal8BW+iKx/0B/P37qU59SvbWyf9KLw18qmoAmhSR+Lq3OdJqAzvsUIlE4e/Ys3rJzp3q/vE/aS7GvowPfODbmgMaOvj48GMj3pvkgz9kLRhgmjwsJxg+A8e48ACYKolQoNMZjCgyxnNUHAPwF8Avqs2AL5pcvX7a8Tr4XtQZkFGI8Pz/vDZntBDe0qAiAI0NDzjOR5UvOnTvnhMi2e52vt3a18BGxubG7WviYReGs1+sqPpYhwUeODw+Dq4jy68pw8yz42E6M5P1+OeCjz2BD9+bDRypDQ8aWS2CMLLL/Y3H/s2Dkiy++2DI+IpoQ45mZGaxWKt6ylD6PvQ8jqQRejo+mrUd8XFpaUold+VwPRbZoGFmtVBrPvOU0mhgfXcVRKlnkPf1xdMPJpcJ8CW1PcLJvf/8Azs/Ps2vQb1mqLJBi6FeI/cohGRE0T2+5MRZ+z7O5f62E1+LiIp44cQJvu+12535tYjne94SwrlYbx1ptXBxXwN7eHcHxSe5Z5qr3xs8pqfltwtE50/xGpuxfQoC9Sv/H4v77jBmzjb6Mjh5y7qMac4HQgWstQ64VPl4zSvepU6ewJBie/+///b/Y0dGBH//4x73HVatV3L59O/b39+OePXvwoYcewr/7u7/LfN1mXzZ3xNYW30uWXuS+3/c2yr6Yz12jo04xeB8wqt4OT5gECadnQKfTp7yyOgA+5blmWZnoXXE/OFs6kZuF+kdeFV+4OhkEHoREuJILaosY58ZiBSNMASRe/2ZIFCR5j08QqkE4PLEqQmv7SyXcFudv+fqyL3BO6c3jn/FazZpD1PcpSGqqkyDpC0k9DqZG916Rq54WkgsAOHLggEXSwdvkxISXGfqk8gwjMOH6JJhqhEqyzM9qlHxYj+1q4SNic2MXIrJaTXzkcz+EkfV6veE5OQ+ekkyQYOTbletq4eZp+Jilf9VKBXujKIiRI/DywscR0POpJYZp+Dg5MdEoJyb7zvPDZZqNDyOHh4Ycr2KagjQ8NGQpg9TS8LExVuJesmAkf/Y5Pq4vfJycmAjy2NAnK0ZKI1YaoWsr+BgmT6sjwNvjbb6cZlICpxBgK27Y0NmYl0ahpDxu12Nts4hX4xBpX5lEMgiMoKtUF+PzD7Br8TGfRKNwGuwL5VhLfLSNFW7uuflew1D49vz8PI6NVUXfNiLAfYHnsS/1vDxU/8ABu7yarezzvlNueBHdEHIZtn8cC4Ve3LNnfwMf6/U6PvXUU975eDVkyNXGx2tG6f7gBz+It956q7N9x44d+Eu/9Eve45588kl8/vnn8dKlS3jmzBl89atfjd/3fd/n3f8b3/gGfvWrX218vvzlL2cezMTylTKBAr/Pz887eWudYCzsaRacvo4OPDQ66lLtT0xY7NbULl68mEqnf0H0bxRMWS7KpQjlcc/NzTVy9MizklYHb2lpCcdrNatPvpfK5kIBj4IbvliOx2Uo/i7Dp6TXvxkSBV5q7Xzg3tPIyiIALBYKjleqJ3DOE4ExmWX/k9Ann3kWgCPildOQCOoFUIR09tzUUmbx8/AZfXgYcNp9PQqxN0o5n1r+Duxa6llzc651oXKt8BGxdYxcS3x05mM8DxYWFhyMrFYq6ks9hJE8v/xKvE66CgU8ngEjNHxMC7t+6aWXLKUwhJH/ND7nywUfFyAhOPONCRHL+fARMawk18X5Qhj50ksvqfhoRSgpGJUVHy2PPWTDyNC1tZbj49rho+S/kXMwhKEhfCwC4JaNG9U8Vk2GTMNHs08oV5oUvwIWCl1oFN8r6FeMnxEYWUCTA+wP4X7ppZdEbjX97lNA/ykCjFrnMQoveWpfH297EJPc8aRvWXKs+XjZYejnA+NGdcH1vg8NjWAUlZxxANgeOO+JwJjMIjcYaBgZzuvnIeSn0TZm2M+Gv9dUY4+CZ9p6abcMudr4eNWV7gcffFBMHPfzhS98oWXQlO3FF19EAMAvfvGL6u8f+MAH1D5kGUxSaGqgexnK8fdd2gTr6MBqpYLDQ0NYjJls+STg4WyaBUeGpvHQC22/yYkJNX+4Mz73cTAC2Tyk5xH7JjqFGHKBTwvRpAXNJ/8ZyPbSAfCTIm0ENyevVChYoainTp3CW3buxC4Weq1ZdqXwGWIjvtHTfzJU3AS6F2i7Mm8IAEJGDu7pfvHFF9X5ubS0FPQ03ivGtwqAb1D6S2M0OTGhlnobiJ+3JqAjJl608xmf9aQ4XxbhGFOuL9t6FSrXGz4ito6R9MyvNj4i2uG7vv3SMLIMCaHhPOjpL63iI4LNJk594cLBgxnXzbWIj7eDiQggdvI0fOTzJoSRNKYrwcdpZYzTMNJXCpPw0YdRzeJjqxiZ4+P6w8croEfykSzA2frluuzt6QniIz3z7XE5Vg37suLjBujASMnFNgohkXXNo16jm/7XlcCdO28Rx0xiEg5uPNbHjh1DRKnUXkGjLAOGy4QBGk9tXdlnI8q85UKhhBMTk7i4uIhPPfUU3nzzLVgobMXEkGB73hG1Ul9ZWOB5qLnW95tQ95RvR9eDT+Hj6UYO7unW8GBpaSlDObGjmEQT0KeKAO9Geh9JfPRyhsDVlyFXAx+vutL913/91/iFL3wh9fPNb36z5fAg2f72b/8WAQCff/559feVeLpJ6X4SXEW1Q3yXIT8OOQAoL2Uwgh5Z7I8dO5aae6BZcfo6OoI5Q33FYqMfPNSOC0BUu9p3Dgn8feDJg5uddYS2JTBCSRH8AlbIunWf8gwKYMJRtefBv0sBXYZZamWz+L13QVKPnPr/YTAvytAzrop9uPIwAK6gTC9i+l/WsZbzIRSuCGC8Stvi8aL6naFcSgSXkZ57hSTjOH/mvlQBuvdqPE587r/pTW9Kff6z8nug3uJ6FSrXGz4irtyTs1b4CKCXd6Hmw0dSPNPm/Uaw16jEyBIkxHCrgY+ToIeCEkbSGFwr+PgMGIJGLbKAC0oSH6U3f4dyXvKYtAsfO8HUM94KhuQypOz7yrM56QoMo5rBx0Ewnn4yApHR+kd/9EeDGJnj4/rDxzPxnE9zdsiUMfl7VnzUSgRSy4KPTwLghJqb3Cm+awriDfHvuhJoyl75WLCvNPDdVWqJQE0nZzMGgCuYsJr7FMj70CUTK+Dw8EHU87FNCgyv342olfoKsX9Pxf3stPoeRSXs6ZHEa9wQQcdXlb7R/zuUMSGl3IxPf/+AOj+TUmRpfadPDwJsi8fkOAIUcR9EqfiIIBxyKfgo10saRvYIfKRoziwy5Grg41VXurM2IsL4wz/8w8a2c+fOBYkwZPvMZz6DAIB/8id/kmn/ZgZzcXERI0gUIwr76okfvgQ4XrPWATdIhDB68BR6SZ/9e/Z48w1CQmPaZOuKBcLzkC5MFEAX+tKETc0DKYW2SdDred8OxotCL4+0vlF4PAk6F+J+9UYRlkA3JBS3bVNfPnws6bplzyKvsj6MDCf1CjXBXHvGg2BYjLvAePwAjHdlHgDHQc9R4S/aRuiruI8QQElCNjn2vrkSAq00SyG91E+CXt/4EiQKjKyvTPd9vXu6s7arhY+IzY0dsfJyfCyy+caV5JXiYwRuCSxqIXwMEbIRC3cII7tgdfCxD/R0nZsgETDWOz7u37vX6vv2UsmLkRo+8nMRRmoEaPS9XfhYAoPF0pPimyunPdtnlGfNW1Z8LIIfH0MYmePj+sXHK2AMNJYsIIyIVNM7zYi3FvjIccRWss/H1/MpaQXUFeMo5RjbI+sqtURC9iS6RG43IcBCvF9I+b0Qf6fyZBcwMShIg4BhEB8ePugdS7tcV4Qu+zeV66qj5pHv7x/AKJIkaK4hwtxjN5p62eR1nkYTcaARoPHvZRUP7Hug8fUZM6hfZesal9qMj4jpGFkD844srjN8vGaUbkRT8uGOO+7Aixcv4mc+8xm85ZZbrJIPf/7nf4633XYbXrx4ERERv/jFL+LDDz+Mf/iHf4hf+tKX8L/+1/+KN910Ex4+fDjzNZt92YzXapYXBMBYxsdrNXV/CW6yBvN0/D8p85rVUWshEhwuJMmyOmR5moV0YeIJUMqMxaVSzgOo5EOUB8f7zsfgolgIdTChlJoHZCxeUI+BXVJAE2rJGxHy3I4cOKAaMmhxkxXuEuiW6Bl2r2RgCeVR87EnoqUyGGMN1UEfZM//AgD+ACTCs0ryJCxzfD5oVvQRcC3jAIDvec97Uvue9hs9D98c1Zii+fyjTxl0g0UJ0qMhtFBYX7vWhUrEq4OPiM2N3fLysuO1LkPCQi7bSvCxCIaUS2shfORrdiUYeRO4GFEAgx/8vFnxUVOm5wHwleI6k2AUwz5Y//hIFQdCWMLxsQZGgS6CMcaehiQt4QwkJSo/BquHj+QxeeSRR1L7LvGMto8qz1qulZXgYxl0/hceHZXjY46PWmsVHx9r9DtrKPV9aHvFATs66Pt5tPOp6ZgHsFAoYa02bo2BW8ubrjWPAK8U64g8xDUE6ELD0H0BKTxcV/ofY8f7Pb2VStXBSLvU2ul4X2kQoO9JbvX09LSFj1kMEeZ+dqAp1UbK8HkE2ImJsYBqkV9Au+a5SwLH54P5fRndCICdqDPBh2XIVvGR1ovEyCNg861o+EgYSL+tJT5eU0r30tIS/uAP/iB2dXVhT08P/pt/82/w61//euP3L33pSwgA+Nu//duIiHjlyhU8fPgw9vX1YWdnJ95888145MiRVavTvbS0pND5Rzheq+Hy8nIjV07L3ZJCimat95XI0kIoQ5bKQ6Oj2Cmu0QlGICSgDXlLaGJzVvHeKHJCE7mHobFNhCiS0EZCKgf7qrJo+sAIWVpY6hvHxhySkK3x7+9Vzo+QvEw2Fwo4wthnqcmXIb1spiF5sdGYyOcSeoE9AC57OMTPYgP7zp9D8NlkmA9aKA/vF52HGJNlyOYuACuyg4NWw7sUYH/kpRre+ta3OuMUus8bwX7+ztx7mbDzIl4dfERsHiO1sMjxWk0lfLxa+Fiv1/Geu+9uC0YSNnwMEmG3JM5bDsxbqdTyNaKxpPcB4BuV67QDH3n5IWoaPiIAngPjmSZlXsNH/px91+0CFx+19c6Vj9XGR/rt3LlzODI0pApvG8Af7p4Fo1aKj2+BQHRUjo+N33N8TFqr+JjIK1lDqbmy+CgCTMXs43ade6PknWx8r9XGrXmbKLUPiOsjJjWlpYf4HkwYvRN5nZi63XrkW9l+/nzsKOrCoaERBx/dUmtnEOAcmjzoOXVMpAzpv+4DmHi9uZc8QtuTzb3t/ueS7ummfetsvC+gbzzSZMieFeIj9W16ehrvv/9+bBYfT0ASRbJW+HhNKd1XozUzmC6ZQ1JbTy44yvugyVyGhMG2BrpVssYmjawBSxOEAzMJataEji0347WaQxJUBDf0kQQ6uSi0/ENOMsMJE3rB5EDeCIBbY6FNtuXl5UaueVbhKQKdvGa8VnOsX6Px35An532gLzh6TlMAeAeAKoyPgcndlr+FcugBkhwsGstH4/srgT9/vQYuYJUKBbxrdFTN06JnrnmGtX4dHB5WGZOpv0SYJOfhIM2H6Wl1nSwuLqokLVot3pAnkT63gBHu6buvDE871nne7NZsSRwnVDyukyyx7GriIyK2DSM5PvL1xTGyC0w5rzR85Ji2HvBRe05TYLzCJXANCTvi3yU+0nNJu24VbHw8GveZ7k/LYdfCxEuFAo7XaqoBvFl8LIp5q2HkO5TtVQD8JZoTCkauBj7uA4PXZADau3t3jo9r1F5O+NjdOB9XzkjplTWvZVj0dPyd5zbz8O2NCPAKLBS6LKIyRE2pzaJcurnmRIK2vLyM/f0DCNYY3a6cXxoRADmRGM/vThTXIwhwCKWH33wfQ4APO79VKoczXJe89zSej8b3WEI9f90NE6cIAg0fa7VxVqs7WyrA8PDBVBnyVAv4SGOpYaQ0LmXFxwIAPrRG+Jgr3YGWdTBD5QMkKQRnOORKWVbvsubdKIkJrJFtVMfGggIOz0M8Ca6QVAabaZcrjwTkRGS1BDqzrzahNXZjHyHQ+cA4UTgODzWhMSuBHpY8wBbjEbBfMNwTcxfoOY9R/JEvIs7SKK+5E5IcdR4WRop22pzQWJo3gQEQ+XJGNC9JzTLdo4xFf6mEdwwOWrliU5DkUqLo1zwkJXv4XONWQq0mYhmS3MS+jg7csmGD9WxCJEUXIDHsdAKoUQrtXOd5c1uzGJkWZsbXDK29duEjrdEQPnJhtlWM1IiMaPslSDCSSNGy4CMi4vT0NEZw9fHxtHhOHB/n4/NoGNkBOj5SJQSJkSUweCPx8Zy4P21eLIOS/gSAO3ptMiLCyGbwsQCApe5u7FaMMsNsTOk5TIFReqvi/BwjVxsfy2Ai0Xwhxe1Y43lz2/WEjyOxMTu9r5LA7CS6CqZGAEbbL8X/U53vJdTYzzWMNLnmESbKJIVySw/x+Xi7Lq8n4dxTaIe4lzHJ6Zb52J1x/0npPWLJ+ba3+i7UyeWMZ9oot7a+0N8/oHjfi2hCu+uYsMUfb1w/uUdNP1lWxrUzrouebCPDgYnidQ0Fvb071H719PTiodFRy4jkkyGz4CNiGCNLhQJ2toCPRTAe77XAx1zpDrSsgxkO/ziiLm4qzQBgXtDkrfNZZh6AsEBFE6mvowPvuftuxxoUKovClTZgC+FI/L/0HnCSGQot5uBOFlgO7iNDQ84YprEby3vVwiz5PRw9etT5fRkSo4ATlg6Al8F+OfFQKurb7YGxT/utOjZmXXMDGADaKfpSAkPUxPvvY2fcCzrj+SfBfjnzlySRntQB8KfBVdydUiKgM6EiJDkxe0H3/nMuA9WKH59/kT3TfnF9raaorwzKc889t6rrPG9uaxYjfWt2Vnmeq4GPvwHp+Dg5MdG4ZlaMjADwYTAKVxH8+NgHiSJYh+bwEREtz9bVxkc+piSk8n6lKQ/a9oWFBUeg2giArxd92QGA71buz4eR/Z5nxRWYKlNcQvjY29OTGR8p4qCvoyOIkWuBjyPDw5nCJVtd43lz21rh4xUAfAoSGbAd+Eje72FRuzskQ7pkXdX4L5W60tizJQEXKbrkkbUV1KGhEWcME+dXWVxfKtdaGHoirxM+ur9fQkP+ppGRjaNRZHXCt6Rv5DFP81rrv42NVcV1N6DJ4d4ptpfQsIfze/ARoPWjey8PI1f4KxW67hm0c8BNv/bs2a+cw/xfBt1Jt5oyJP90gM5roeHjviY93M2uccRc6Q62dnm6fTkPs7OzePHiRWcR3waR15tMnxA4N0IMowiPALPqZCiL8g4w4ZByYWgsgVzoIMvSHDSfV4eIlreD2I07AZywv55CIfXckphnEYwlTeZvdIIJmWyET7HF2BjPmFRioL+/kfuYFq6SJujOzc3h448/bhY46CGgRUhK4VD/fSVExsAjtIvxkGyjmodteGgI7xKWSQ5q/F7eC8brVIoi7C+VGqUZ+DxrGGDYCydtztFnZGgIn3vuOTx69Cj+zM/8DD700EN4kDHBA5g5yNcHH/+seTitrPO8ua1dnhwtb5bwUSo+vhd5FnwcZsdo+JilrKLESPIcZcXHndAaPiImGHkcbAIxS9kMYLyGj7NgjBrcoJCGj/I5DfT3YxHC5Rx9bLVHjx7Fer2O9Xodjx07hgXw4yPNh5Bnewek46N8pu3GRwrnP1yppGJkiEjOh49PP/00Pv7447h/z54cH9dpW2189BnifNE2zeAjgCFP7Ioiy9OeRYbUvcQ96CrE3OtNZGU74+/N5R4j8jTP42g83btQKvqh0lc6cdkiArwLXYV7M/I63VroPMmQJmSdcsP9eeG+3yjHm2RIk5Puhsmb733iHjQCtB2oGTQARpwxCffZRP+EymGej/d9NJ4vqyFD7t+zB0+cOIGPPPII/sRP/ERT+NgKRuZKdxtbazndyeL2MyEmoGFyUUrOxO+CDrwEdi4NKU9pk28u/v7roOdRUFiylq8jc4dq8f6lKHI8oACJRVUTUgYhHdzphcFzRzQ2Ql9NyghcZZyHXVUrFeyG5GUkBbgnIVtYKF/wFN7YiqebPr09PY2+pO1fANdC1wUG2M6ePRu8HmdFlgzqqofN8yIlReEsuC/2gf5+/Mmf/EnvPAMAfOtb36qSXPC5cIT1o6+jA984NubMxQgAK4F7nhJzYDXWed7s1krOYta8WcotLMm5CjpLfb1ex/vuuy+4DuuQjo8AJjIlK0b+PLhYEsJHinBpBh8RdYzU1gphTFZ8lBh5SVnvZdCNHVxpDBkTfJ5uLvCUurqwK3AeDR+LYDzBJ06cyISPfLxbxUc63wlwjboD/f14+fJlfPzxx1Pn2p133tkUPo7XanjP3Xc75wzdc46Pa99WGx8REW/o6/MyNLeKj5dAx8dlsCM4dHyUIeamznSptF3MVXJGUW1t/lsZAX4p/l9X9p566ikVHyWHkpufHal95OHglUo1/v3DmDCLSwX3ScwSOm97uin8u3lPN4AJ996/v8z6kra/VpKtG2+7bXeGa0nWeP++FyAsG58APSpzvFbDY8eOpeLj93zP9+CxY8cwM0ZGEe7o62saH7nhvRmMzJXuNrZmyz1ohGkaEyIt7rCH3Hh0JXFGBG4oXV+8nTzdO0Fn/SZPzIgIGRro78eSsFIV43PykMvTAPhBMXl9YZJRygTXwjrJukSMjRxMq5WKZXFVlWZ2jpmZGeyI+xEChL2QLqDy0C8tjLE3XuQblPMUIVEQzgA0hMmQR+jEiROp5UNCIV6yTja90H15Lm+HsKLQGY/7lXhMu6IIu7dsUefZGLgv7TKkhxrxbV3KfOqAhGBOjvEAJAIBF0javc7zZrdmMVJTGEuS2VRJi9DWrbbuFxcXdYUTbM6JED7OzMx4++rDSFL2JN60Gx8RXYyUNXs1pdmHj2fAH3ZK9zICyjtHyenm9yv33SxykmntFvm4xMrtWuCjHO9m8JEwsgbg9OESu5fhoSG87eabnblWAuOJbxUfN4Kel0vPNMfH9dFWEx8Rwx7yVvGxDDo+TrI15JMhnwTAqvAIT0xMNmTIpGQVyb16CHniVU5TUJPzh/BxaGiY8StdQul15+cwfeXKeUhZ3otpSrydguqGekdRL/b17UATMi4V5SJSCL7RJQrxuTSCtERRvu22XajlYddq4zg0NJx6bJJTb+6vUqk6ekwERZyACBH01IZ242MEfhZ+DSN7WsBH65wZMTJXutvYWnnZnDt3rhFKjIiMhIBPoAhvv303s8j7wzboPNQo9FEKVPT9ArgEApKIhiZU1jqpMrxjFhLFP0RWwEmH6IVxQ1+fQ6hDzLKyLS4uNjy1sgYfD5+WC+T555+3+psmgM2Aq2Bq7OVnwB/qXYBEwea/dQDgArsm5Z34hFzpPZoCu9Ys/U7ebt+YHAX75SwZ4mksNOV6EpI6v9J6Pq7sr90D95r5PJRFsFlV+TM54hmXHwe7lBrN/RLYAoGsNdnudZ4305odO14GqV6vN0is5Hra+frX43/8j/8xdd1q+IiI+B2jo6mW8xBLN3/htoqRRCa2Fvg4OzuLp06dauAFvw5hvhwnjo8I2TBSYps0BtO5NIykSClfGTbCsHbj43HP70+CrcA0i490j1q5tiwYWYaV4WPa2PSIa+f4ePXaauLjc88915CNfOv22LFjzjVWio+NEqkBGbLO1rPt7T2DJoS5iElIua7MdnUVMYpkLnIJjTKZKOm8drccT46PJuydX+e4io9JX6lvId6mGZTeep29/Azqod4RJp7psvitAwEWrHExn/OpY5f85uZhJ9GSx9FOA6BjBxHgpMXoLp2KVYjwJQXrVhMfOVFaqzJkGj7y47NiZK50t7E1W2PRVxpsYmIytrAdQRNSQ5Y8slj5Fw3lulGjmqjFGPxOx39L4HoWfx0AhxrXSBZ3ef9+q+9BEo948klvKRfCfMdKEo5qIF+S7pUzFWovCF5CRgsz4vcUCnm8QAs4trzReXh4pwz9Og6G8GwQAO8V5ycyHno5TbNrcmHSR/4TQWJFpnElw8mF+HhpZa6BeWkSk3cHQKNGvI/1cRl0Lxzl0vjGi8CwVfZkLjz4LJePgp7Hdl/81ydsc4FgNdZ53uyWdey0OUjKzuTEhMs9AUl+cdo88oUYDvT3YxcYoa+xvsHGEh9m3TE46PS/WYx8KOO1VhMf+XXkOMn7CWEk5SjLOt2EkVo4fjcA3hav1a2Mh4OTlTUMp9BefJyZmVE94UUw+BhBEt740ksvNY2PVGatFYwMjXUWfKT3ljancnxcP2018TECwO5t21Ln0iOPPNJ2fOyKo0l4y4qPSRrmT6OdH60rszMzM0qN6whNaLcrM3N8TI6Tedg8BNwfqm57g7PwNpl88aGhYVWGrFSqcR1yMiAcR0N4NogA94rzk6JMHvZpq7/JvjpBWk8PVWmgcSV28wuNcXXD7stoDBplNPpJR6Mmuk+G9Dln1gIffRhJ+snHPHMxDR8liXI71zhirnQHWzvqdIdr7G1ELe/CsBImk4RbzZaXlx027OGYYGV2dhafffZZBWhqaICqiKOjd1l9D5J4KDnX5MkMWUW5NbRerwcts1Sbb3JiAktR1ChhI61lI2C8uY7A6fG+aAJcqVCwjqdjNYDZ0dvrXIu+71OAw7onMS4R+ImXRuP76ooiLMT7SKtgB5i8FTXMFVhealzzV2N9LALgTYFn5xPqyIJ40XM8Wbd943EL2ARU9Ey0Ouc8jwwA8Fjg3JpA0M51nje7ZR07lXk0A2nZRtBDIW8B/9pH1DFyZHgYZ2ZmYnz0X/PQ6KjT/1YxksgXrwY+1uM1pY2TVjpSrkcpiPPxlRhZUK7TOA4Afziwbnkdc3q3tIqPEYAhPVMwcgASfCzGWNEqPqbdzxEwXvLDyjlCUQWt4GPWfuX4uLZttfFxKyTyhDR4rRY+9pdKDtFUM/hoK8NELJZOlkYY+cgjj8T7+8nGELk8vg9dzqQ+NJ72OpqcbTcUfnl5uYGRbhi8XduaH0/Huk64SPyV2/el3pdbx7wTDfP7SUxyzumzGwuFHjROvaOoedVHR+9y9BWjhwwgZ2GvVKre+Rni3QjhI4IuQ4bw8UZIFGaOkZcgO6Gg79wPQJ7TfVVb+9jL0xbTu9GEj/BF6DIS8vwQsp6dPXsWh4ZGrEk2NDSCo6OHnOMTVkWd+VEj8fBNPipVcdfoKJKQUwR/TjRvktRLLtTp6emmSnTxPOMz4rr8ntQFOTGBCwsLjoVTAkwZEsH2djAvuikwXpkjEM7PPA62ENsXk6nxvpBXT27fALZQHcpP5wRqANmYcUPAKI8hZXwyHn/5ws8CxhfArXEcgVLHl43/AIStoNVKJWfnXcOWZeyy5B365uC7wS1hVQI9fYHngs/OzuL8/LwrWA4N4aHRUXXeFoGV9ApUV8iCkadOncLtMTPr1cLHDeDmtvlqY2s1x6tjYzgzM+N9X5wBoxhTGCHHR8KrLkiINYPRRgB45/Cw46XOio9nwCgcoWuRgAWBfdPmZugaZOgtg23wDaUcNIOPnDCrBLqSn+Pj1WurjY8PxGtDzpNW8bFaqWB5/34vPhJZYDvwcWZmBkdH78IQqRlvhI8+WZvjYzbSsg1oWNV1WdsmSNbzwDUZ0nbC1dAoyHSNG9EYG6bQhIgfwSSHPd2TTk4zk7IqFXiKapXbO9E43EgX6A6MSx25R90nQ4acKyF8RNBlyCz4SDI1x0htzjeLj6QX5OzlV6m1r0532gQ34R67dxP9f1fq/pVKlZ0zii1tGgFFGZMQmkVMahOa68l8BY3EIzT5KOzpPkjCSdKOXVpaapRPkYDeC+blQflM/FxpSqGv5t65c+dwZmZGfaloQiQ1+QLkSh797/OubAP3JdWn7EfXn56exte/9rUN0jAuvHKPS5ndX8gKyAnUAPSavFmBsajcDxkXaCy0uuoRgEoq1wdKSaXY8BFSNroB8DPxd62MXG8UYXVsLNO6li0XKltvWcYuFHqYRUF6/Y03NuZWFqOOT0mhObwXTGic3P+TtI6UfK5mMZKE0IchUTpXgo8RQIMzJCs+Svzg43T27FknTcVniOSNY6SGj2dAz4WOwBgAfJ50jo+IiHNzc/jqV7yiKXxECAuD9PsFCI9h2jyrgj/8nRtHtRz3DmgvPgKYiCsyLstz5/h4ddpa4eOtO3e2FR8BjJF7NfER0WBkT6GArxeK4vb+G1R8NJGjERolVtb7jgQ+EmGbTy4nZbcsfk9Kh83MzDj1sSuVaiYZ0sjj0iFH30+inte9DWWouK1ERw18rNfrcWUYygXXFPwz8XcqY4YYqlNuwtDtFFdtfp4PzLNd4MfHLDKkxDANH2mOUY56q/jY19GBIyy1tJmWK91tbO3ydB84cBDdEHLb85zUBkxnJIyirvg651Ovaa43rixsA85EbCGFK405PO2e+SSXxBmykRC6F1yPygZl0YVeOHXxP4L9oqJPSNHmTb4AuZJL/9dAZyPu7OhQ76EKhohI5tP5hFffvWKG/Y5D4l3h88q3/yjoFu1OcCMDBvr78dDoKPZGkSPYUp4mCQFvAdcrxUPfiSiG5qCsJU4f7Xn29fQ45DKt1J+llguVrbd2eHKqY2PYKxl64/lC+8zNzTXmcojZ+giEBYIiGKW7KubWTrZGNXxEzIaR7cbHTQB4Q1+fii/N4KNvTQ0PDeHCwkKmZ84xUsPHK+BnbO8vFp17uAPah4+Y8dnzudUqPmrh79WxMbxj/37sinPY+RytQ5LD2G58fL04l4wUyPHx6rRrFR+n4mv0i3nF8ZH63i4ZUnLhyOMT7/FedFm5N2Ff3w1i22vjv2keXVkiiyufyadSSVe0ebOdcNIhR99rqDG2d3TI+4oQ4A40RG3HnXEhQkxdwdfuFzGsOxyP+1bGkAypGYiLYIgzS1HkyJDbSyW8Y//+TDJkGj7Ozc015tji4mLQuSTxUZZAXit8zJXuQGtHne5KxSzWDRs2i8VUQ2PtKuLExCRbqKEFQeyLIe86WbPIApYs7L6+HV7St6wtK3EGNQ6wL4FtRdXCQorx9jLo5dEcpkEGBHTeGhgrWihHg784sni6015WoFy/BMbKJsO7uCCVxYNN28qge6EpDLcMSekcRMTxWs31fIDNMsn7PTQ46Ci1+3bvxoWFBceaHQqvLIJN1tIb90vLm886rh0A+OKLLzZyYKWAeu7cuUwvfWq5UNl6azZnUQs9nJ+fx96eHmftnIznz0B/PyImmBMSGOnlnbae6IWv4c6Ovr6mPTaytRMfAQA3RRH2KjnKJWgOH9uNkRo+hlJg6DMaX5vugzBrpfhIwqCGkRGbW3TPzeJjBIB37N9vbbtl50587rnnENGuFJHj48u7tQsffcSpRQDcsnEjIrYXH3nEHKWKEOYM9PenEr9lbc1gpMv+fQ+CRRLspmQSGZjrFSeHF5eXZ1kXePi54ULyhbvz/kl8TPd0p8n4/FNGE/5ewigqOSmmSVSopuBLnYCXAXND+m2verlxvaWlJdxeKqkYqTGRRwB46tQpZ340K0NWU/ARUScfXO/4mCvdgbbSOt0uO+A2ARQGLBYWFsRC1ev4mQXB2QjTFu6F+O8R9fdCoQs5m7pkXUTUrZj8Nz7JZVkymTsma12XwPak+hbLNkhyF+kzAkkZLi7EEBDUIMkH5tZgeR++F8d4rWa9AMuQCHCDkP6yOgJ2PjK//qE773SUWTrmXGAceF54D7h5ruV4O1kH+QtwvFbDTWL/GhiBUgsDrY6NNYhEJLsynXdhYQF7e3rcMJ2Y4OhI/Ix859aIObSw8WK83VE2urudZ6eROWURBHKhsvWWdey00MPxWs1ZD3Ke0gsS0cYcH7N1DVxjmbaeyJru4y0oRlGjOsQUuJUNqPkwUvNeLUKi7IfwsQ9cT2paGF8IHzl+tBsja5B4O2qQhLf6MPK059q9xWJb8LFUKGAH+MuTSWxIw0cEg5Eyfao6NoYvvPCCFx8REe8aHfUq/sfBePerynlzfLx+WrvxUUYElti6bSc+cm4YzWBXrVRcRnUFI9slQ7opnJOYMG3TeKSRFfNxq6LxHHNP93G0Q9VrmIULyVe1qFYbZ044Cvkmef4mcS9SOT6CNheTucdSqR9feuklhc2djqFo2XBeOEABN3hJ3ZL7oHm5TZAOSxnSh2OtyJBFMDJ/Gj4i+rmX1jM+5kp3oLXysqHQmkqlqrAD9sULfjr+2PnVibfcZSSsVKrKgppEvxWPLzQqF1BHQwYhF5ttHKjVxp3a4ponnBh0y2KilsD2tCImAEuAWgbzUgiFQhXjkhj04YuiDLaiCWAsb5KtkARs6V3yMYaO12rOgpRKbtrLSr486Pp7d+9uXO8SuOHbOxTQ6APX60WCo6/sAa85Kcf9XtF/LQxUEi1pvyHqDKgyrLEGCdswsan7wulOggt6EfjL5nSJfLRO8JPHtHud5820ZseOhx6q8wuSygQfU9YtHaOF9sq5orFxS0XvAluvXNDcqZybv4i1MlPyBU19/bDS104Aq+62XKdccA5hpLZmOD5K7NIYXVeKkbwPoVJv50DHRwAjuLcLH8+ApzxZ7NnQxl3iI59HcgwG+vuD+CjnyD133+2kCewCwG2FQo6P12FbTXycYxggSxauFB99Mo0v9FeuQc1goCkwWWVI3XtcRiPvpqdkbtnSbZ3bln870ZWHxzHhQ7KdWBIffVWLarVxD3s5/6SFgZPMftw6plLheoVL7AawA10PdgllXnir+DgFgE/B6sqQWzbaRhKJj7xfHCOXwX1nrDd8zJXuQGv1ZRNmM7cL0evlFMxnaGi4kWvnhrCfRDe3xYStm/0KCLBL/F5CPQyn3PhuyNk6HSCZmJi0LJfLy8uNsBP5YqDJL1nBu6Jk8Ydy9aJ40vNzcy9NEQw4PwmAPwDGw3IYXAFay6PMUuKCXoAUwvMbADgM/tDFMdBDbd7Nvh+P+1dVgKoIroDcCCOLrcicZM4nhFPZjOeffx7f8pa3NPa9B5IatUXIxhLp+40U+8XFxQb5HdXq1dIE+As3FFY2PT2NTz31FN6wfXvqfj/AtgUt9ymhQrlQ2XpbKUamGa+0Z6cpMpSPnIWNm9ZTX0cHFsD1EpchyVPz4U6awnWY1bFeXl7GakyMJjGSiNF8+HiezecQRsqwc8JfwsdLYEo7doKJ1NGMDCvBSMqnuwCJd0LDyBLo5EwcH++FtcPHxcVFvO+++xp9l/j4DGTLfc2Kjz5FqgjGWJnj4/XXVhMftee3Unwsg8ELkmn2gutt7AA9LafMvpcKBcfDKPGR+ptVhkzk3wdYf0K5zJHi/OKe7CKacl1nEOAH0BAZH8bESWWna2r4mFbujOOjUdxn0DjefOHdY6gTrPHvx+P+VVHLC7erIZFOcBSjqCsmbG4NH3mEartkSNInKEfbJ0NyfEQMY+TRo0fxVa94Reo+VwMfc6U70FoFzDCb+enGIuvvH3CO9xFRaEr5xMQkfuITn8Du7pK1va9vR5xHzhXsUAmFuuc735Z8QnlrALZlkOe68cWghkJFUeq56+I6JMDJFwhAUo9Ve0a+Bcn7TWQVpDA/B8baLK+9OwYlCRibQA9d8d1bV6GApW7bOktKawL0/uM/+MEPOh7nMgC+ERIjBbeC+8Yg9Ju8RppVUbOe+vovLZ++/T7GtgVz1BS21ZWu87ytHCN9z+sBsPO5ZdMw0see+4lPfMJZTzv6+nDzhg2OsBdi/uW4wxWuJdBLEj7++OOp5yMBh/pP+Ci9261gZCNMU6zRdmOkzPEmjJTRAiXQFekNYIwfWfHxQTBpR1qoeBZ8PHHiBN5z993O9W4AExkg8TFtDE57tmv4WA28L7minuPj9dFWCx9PQ5JK4SvJlRUfFxYW8NSpU9gvyKU2b9jQUslS7bsPH6kWdhYZUpN/7XBzmZJZjPfJQqRGH6nglhEAMIqKTk53SM6X+JgYCGYR4DkkUmP72rvRrStexKTMl+yffm+FQpejE0xMTLLa483j4zgk0UbtkCHl+bPiIx/TLBi5nvARgnu8zNvqebrtBZ01B4aaBNTEAjiFRqGfwiSPhfchK9GCRi5htvGcZfLKpC04jeVy5MABazFoZDV3DA6mLwL2/xHwl5PpAD2sSRMUpXeN5zNqOdTdhUIjhylkgChA8vIiBts0hYNARpsL20slbx3NCACLXV1eYjrev1CeZNpvu0G3dtdS5gK9aHnOohXapngQS+AnjeN9yz05V6etlieH1lg78JHm2hQkOdobPfOF1l4W3OEKly/Ebu/u3ann40o33dvI8DD2xOzXaRhJuXJpgjkABEtuhTCSjI2ad40wUtYKhvj7odFRc08C8+Xz5t6zED5ykiduzKMWwsdbb7451VtH16uD8Yak9dvn6dbwsTeKMEq5rxwfr7+2mvhIiguvOtAKPiJmx8ishIbyuw8fQximyZBzc3PMY0vy7TJKD/HQ0DCG5V2eR025167nWEuv1JXpRJEnfDSGggK6UamdODp6qPEsTDm0NL2Bk8WdDtyb8cxrMmQIH/ft3o0bFfyi3/l8OBXAFd9vESjRZE3gI5+zaRippSRdTXzMle5AW8nLRmMzT3K67dCV2dlZLyFDKInfLhlAc4UzJF5Rtrfu6Z5jB4ZCS27ZuRNPnDjhWObHazXc0durLoabwHhdQkosV5BDyqMkLaJ2z913q4Ii5VrSoi6DeWFYxEpgh7ycOHECQy8P6l9WkifNukZA76ttGFKWL8TfiUCNwoQkcFE+TjEu7UBEdX1gQvhDz0a77hl2bs3qrj3zy5CQxdDnhr4+vGt01CEV6oz3lXMqVJ82FypbbysZO/WlCSaKxCLyifGxFbZcwkftBaut1/MZ5rZUuELrOe23Rx55RMXIgf7+hqGOj08PGM9wtVIJCuY8Dy/URw0jl5aW1IgZnmtJz3BfvPY4RpYKhQaWzszMBPEx63jKucFbFnyMMpyfE0z6MJKEu8fie6ZyjSF89CnqU9AefNyycSPeOTyc4+M6aKuBj1Ww86tXgo+Li4tqDXgfRmYt3ce/rwQf02RIUyKsiFzGLkAXRhBZ+JjN051OQuaTIe+++x7UlOlabRwRuR6wD40H+ziSY6xQKDX2Q0xkSL8izfuXjUi5FXwMcXJcgGz46JMhi4HzZ8FHRD1yQ2LkZXBTmrZs3Ih3jY46a2st8DFXugNtJYCph8KUMSFoSBaIsXJVY4ZyN49aa66SzhfqLLqLlD5lB6jsnG763hn/fx4B3o4AWxGgYDFZXvEsuCLYAk4RbItWqVDAuw8fxoOx96PxkgDj0aFFtnfPHiyK+pSUW0nXqUHY+tqlhE4iGlbvkiBTIEFRI36Twic9O0RUX1wcMGT/tHDRPvAzCfvqtcrahr2BsbhXXF8LpSSiKEmEEoEJMZoJXOPtkAihNBfks6V741b3kGB+F5jQ+8mJCaxWKg6TawcYDxPfJnOB2r3OX+5tpRjpCIrgknzV63WsVipu7nIKyYkUQvmcmmXX81nhJaZJ3NnB1i95xy+APm9bxcjBvXtTa5WeOHECq5WKKpiX2TWuQGsYGco/bgYjs3jutMgBDR/l3EDMjo99ALg1MBazYHvmLin3NjkxgX/8x3/sGCUGAPDpwPm3FgqOEDoAK8fH7wMTet/X0YH9xWKOj+ugrRU+hgirZNOU9KwYSdgi8azMvpfAKDHPAOCjsDr4eOfwMDqM2xBZ+Dg2VlWcX5TTTbJuDUNRoD4ZslYbjzmQErmdlOlE6ae0zrLVVxnpSjJkeoQs758bUp/Gtq7Vs5b4+AoI4+O9kA0ffTLkHYHzd0WRZcikucDffb668D6MJFLWN0FCBLpDEFqSkXs18TFXugOtHS8bl83cruN999334IEDFAbjJ2SQLbGgaXnakt2RL8oe7OiQljlZ2ixCQ8DmlhQYhYID+rKcSicYdk0C27R8RhK2eO6PVAKjlP+fhLAllYTCZkiCeN8iUEKj4u08bycCN2SHCJPktTSmxRrY9WMR9ZdjSHhN+30z6BEKdbDJlLSXONWuzWK5ps8OAPwFSARmn4cKEZuKbqCx5uFwBM50bZ91WrZcqGy9tQsjh4eGHANbX0cH3nP33TgcCE3W8JHmr5aDSPO3DHpZnWJ3t5cRnP7/BTAepw6xnyYUf1JZ65sA8D5IhIi0tXQBkvBuDR81bgUAk5NJ524WI7OQqFFOY1aMrFYqKvs4jQ33cGj4WIaE5IkwshV8DOWlHvH8TsdRbqFqCIKwp1s+q71glJR24CN9z/FxfbTVxsfJiQl89tlnW8bHM6BH96RhZA8AdnbYZF3Sm7gPEkLFVvAxqwyZBR+3998g1p1k8n4SQ57jNBnSd0xC6ng6vo5WzSgSud8RuhWJqFywvNYyuop8QqRMDrtm61mH8HGT5/cs+NgLYXyU3AJchqRn68uxblaG7AGwy95B4ulfDXzMle5Aa+fLRvN83333PVgqbcekdnc6IQM1d7FrFq9OBOhRFmUJAbbE/z+KSQj5OQR4L9unE12W814E6MQqRA2FkjwPXGmTjMA+i9Z73vMeZ4H4yrOMxLnTJOwtLCzgQH9/Q4irgb/8hSbIcKISjc2XrI7/IgAQx44dawDxeK2mhqvvBY/npqMDD42OptZ6zVqvtQ8SC2JNuVYxBpmdGZ5LiPzpAujWbo2xtAiG8V2W5PHVNdZe8kUwL3Dex7T+3X///ak5OLLlQmXrrV1jl6W8ktcz6cFHmh/a2usE88KVwl4JALfE/3+M4cI5AHwvJEJHEZJ8WilU1AAcfES2BviHXvC+e8uKj9WxMYsFVlaXaBYjQ/g4OzvbGGcfRpJx7yMf+UjjGWvh6j2QkKzJdT8yPIwzMzMNkh2Jkc3Us97DxtpXx7iLXcP3TGZmZoLC3S5oDR/fB7bgKud0CB8RwikSOT6uXVtNfDx0552WJ7FVfOS4kgUjuyHhb+AOE46RRUhkhFbwUV5zxfjIWNO5DNnfP4BJ5CfldDPHGBRxAqJUGdIntyee6/ehLa/byjnhIyLGpXrdcHWAvajJ+R0dfTg6eojlr5sPT03NWs86hI8lMDLkKwLPJAs+jirzTeOsOA4Gk6vsWA0f+bxuhwx57NixzGszV7rb2FYCmJLQQsvZNgo3Kcf+RcmJMhC1xT6PAPaiq9XGcXT0LrF4D6GpQci3jSvb9qf2p3ENSKyWfDITcQ9NYN/Eftvb3oaFeP/jEBYWJGmOrPHns6r6wrU1cKfvUsjj93oRTOkdKQRevnzZeTEO9PdjKTIhTzIigCvXGrmJfDmS8HtUudebIFG6ffk6hfgZhXLgQ+RP9Ont6XGuQezlS6IPzrMR+WY0py95+j4j+pjWP05OlaXlQmXrbSVEQRIf5dohhfH2wHz14SPNj3kwig0/93ithneNjlrbDoFJn7D2U7b1gfHkpPUpDR+3xsedZ/v6zvOqV7wCt0I2fDx37pzKWNwKRobwcX5+3nledL8Xwa3uQOudyqhJjOwpFJxrydA+iZGa8rAIpgSZvM8CAL6OjZ9GTNcBBn+ycIRkIRHdLjw2afhI1/dhZFZ8RAiTAeb4uHatXfiIaDBSruUs+Cg9dhoz+jy4Mo2GkdsVPHhJmZOlQJ8AjDyk4WMBkqiZkAx5w/btmfHx7NmzKj6OjVVZ391wdZ8MefHixXg/fx64Od/WeD9/rjbHR6kn9PcPYBSV0JQItsO1uXKdVYacAdcxlgUfN8VzLjTOWfFRYjRnL9fwsU98Xy8yZK50t7G1Apg+QrRabVzUC+RF78+gsWKV0A0r6XTyuhNP90nUavo999xziMiV89NoPNpkKZPsjJ1iW5cHJM5b1/ItOiLuIeXQ8fBCwiIrF35ooWqLjYMNLfjjYFvQKFybv8zIUy49D50dHWpo9T1gk4/58qd4f5aXl9XyCwfK5dTQFZ5/8+vgKg0RmNJgI8JLXoKEDOICGAGsCxLLdJr3rwhg5dn7nu/+PXusaw4PDTlEcjznhzxsaflm8uVAuUZk/W4QucX1ldP614wXBzEXKlfSmh07H+HPeK3mWLgB7Hxhmtc0X3vjeS1z7WgunQT95Svxkbw1GsNuMb6GDCG+BXSsOs+ulQUfAdIjU1rBx3ZhpFY/twjG2OatpwphfJT98WHkeK3mxUiOj+chqQ/Oj9912204FFfB0PCR5tlWcJmaQ/gYCsE8OGwbwaUhU861cmDMQvjIn2Ww7F2Oj2vW2oWPMqrjPHvuhI+anFVW1h6fS5pys3/PnoYhk2NkFXR8LCnbaQ6m4eMRz/ykvmWRIa8mPlbHxnADdGCkcCT1lozH9kkAHGlEsurK+VQ8hkQ6KfuzvLwce8D5PRTw0OhoKj5yjosnwMVHAMCb3/CGTPhYgMTgcgZWjo9ShqxWKlY+drP4KOe1hpGcQ+OmQP9WKxIoV7oDrZWXTZJrbROiGYsXX3RPsUl3HgEeRzeHehKNYu1OAqPYb0SjIE8hJ3EgJd0taeBf+DZT+WNi3yWUyn1vqR97xKIrxYuzJhZ2SXwvx3+LIt+jJ7AQJAO21ny1KF966aWmcv80BsUoQx+9+VPiXotge3IIJKUHqSD6WwPjLSkCYHHbNpc8BVxrNFn5CLieAZ38YqC/vwF8WvhPEYzArRG28HAinj/aTCkGja21VChYL9fitm2Neyoq/RsaHMy8VqnlQmXrrdmx8xH+RGKOPBU/byof9QHQvbQasQpdZyMY4W+KXYvXtZVlA1PnqbKN768Jr51x/0L4uBdcb3oEScQQ9b070Ed+n+3CyDRPlfytDK0rfOO1mkqWxAXRxcVFE2rOvH0SH8tgsI3CzOU524WPV8AIsQ6HRxQ5IZJnwK6pLudaVoxMK5Ej7yfHx/XR2oWPsoYxEZ0RPv4SAB4U8zUt0o9YpW/S1ohi7AkpUVJekrwxGj5uAhsfeyHJF84qQ3LMuxr4+CQYb7gtV0ZOP8YgwoJQzil0PSs+doOdf5wFH+U7s1V8/I+QYHwxfm7yGa0EH7kMKedau2VIiijyYSQf0ywtV7rb2JoFzHCJgiNoQsG5AisX7AVMSoohanndS0tLODg4JI6bREOsYJOvJUaAB+L9QjW6aRvVBOS5LokhIYrIkJD0oQp6/b0imHAWyUZIAgivkU3HS48Wz/1JC6ek5qvVS/0ishyfVfS02H4+3h46Lkv+FL+HQ6OjFpATEJwEQyAhAbMYj3OoXBt9KIzofQC4rVDAzphcSQqtFIJ28eJFp7SO3Nfr3TlwAHvj8hA0RiHW5KNHjzaekfayGxkawueeew5nZmawn3nefh0SayUfOxlqvBrrPG9Ja2bsQmthClyPpQyD46Q5vnW3tLTkeDh9AijhAp+z6rpWtm2A5KVdAx33+BpKw8cy2JEpzeAjHa+NqVbHmloaRoZqZcvfSDBqFh+zzAtuiAzhYwlMaHsoRLxVfJRh9xIj0+qRRwAOPiJkx0gfPi4sLOD8/DyWursbhpocH9dHayc+klwyDwkZlcRH2ocbCeXaW1paclilfazoiAYXQuHCUl66ArZSkwUfJwHwpyG7DFkGHR/pmlnwsVUZUuKjZAGXY7UMgCOe0HU+jlpYcyv4eAYAxwBUjqE7oHl8vABG5txWKOAmQaJXABPZs1J8BDAecDnX2iFDfvSjH8XhoaEGQ7kPI6tjY5nI01pd47nSHWjNvmxCxArm04EmjPwMAuzAhKzsfPx7OoP50tISI3+QTIiTjWvxF7Ud7u4zCJxDo3ifw0RB7woex8mGsrJak0AsLWX0faeynef+nBe/A6TXpPTl/qX1VVpuSVA6HzhOgreWP8UBAyDx+PNzj4AJ67Gss5CA2W0BIDoNifVX5gruBmMZfxBMeYaRAwcaL5fJiQmrjNoUGCVgI0CD8C3tXjjIZvEgas9vfn7eIZej/EhfyPA2cMOMV2ud5y1pzYxdlrXQAclLcQf7P+u6o9rSdBxfO5wwzKfI+M5/DhKcI4PXNkgiX9KOnYbm8HHP7bc3hY8RGO8FP6fEyFDNXi33L9RX/hsJRs3iI2JSKsc3L/bu3u0w0YfwkT7txkdEdObXVNyX/lIpOMclPmYda/4Mc3y8dlq78ZGiGErgEjmGvNGc8NUplQom2oZfj2NkiBiLUmZmwWAlyUuUhpMFHzluhdZCAYxSqeGjVPSy4GMII1eKj8jGhMZLO+7EiRPOtVvBx+fjMeoB+z3Iq+lkwcciJHNOYuQHAPAdYOTXw4yobiX4SM92JfiIqMuQZIDwYeQdLUQBNbvGc6U70Nrv6X5H/HcKAZ5X9p1Et1xAEfv7BxqTqVI5HLgGzxVPiBZMbgeVLZA1um3rvjEM8O8RAlwS1zPK/U2QKMQhi9Rb3vIWnJubw5EDByxr5nkwHpIeSMIqj0KiWKaFKNXAhPikhQv5FnoNXKtoH5i8xD5wraXUF5nP8lgMKr3d3d45kQYY98ZjNx1/dwjXwPbSAfjLNshw2DLoIURV0Ak1IkjIfuQ5z549m3pNEoCPQqI8cSu3Zn1u9Ik9P1994AiS/Ff5W29PT9MWylbXed6S1k5PzjvYPHpemd8aw24R7DSNkHAoGcTpRV2v1/HWnTtdxmkAp95xAcKC3SIk3hAicAnh43333YfT09O4b8+eID4eB5OnR5b6NIwsA2DJU2eWmlbfNK1WdhVs7OSRN83gIyLiI488EsTI45DkwGfBx/9PmT/N4OMkGEI4aeCQYb7yvOfOnUv9/QK4+HgF7ND2EEbm+HjttHZHAlH6BrHcy31984gwMlgKUKwjjpH79+xR5aV+cD2q0pCVho/DkMiQoYijt7zlLQ4+ksGhCww2EznvuyAhh1sNGTINH33vKsJjeVwXmPfMyPCwc21SurPiozTWalEMFc/8kfjIowfaIUNmwcczkFQIaQUfEXUZsgTpGNlK6k2zazxXugNtZTndXLEtoV4LG9D2ii+jZCcEKGMUmTztRKmXxyVKsGFKLCPPJ7ePLYvzd6NhTyeveRldL3oxvodldj2j5G8Ek5MoBS/acQlc7wMJrD6LE/3thsSa6wtRIpDTPCmUJ+1b6CfBFZ4nAfAyKOy7YCzBvWDns8jjeXgKkaIQaElwluNC5yt57vGSuJ7vvBSKH7IQ8lAbDj41sT+98GZnZ9WcGQI/2n+SbZPPlT5lsKMXsoIygFFAuOdRhtJqzK/tXud5M63VnEWp2Mo6rzRfuJCzDK5AUYZEoeT46BPatoL/RT0/P+/M0xvA9RZouXDF+D40Jt+eeO36UkI+rKyPCAB/XjkX368Q94XjgA8jaS1q+Fiv11VjhVYrmwS4GaXPA9AaPvqwjLwyPG/7auNjbxRhBOnh89oc5ylSGj4CuLWPfRgZwsfjYDgRyIOopRo0g5E5Prbe2oGPfZCQuEosuCTmgVwDEiNDXtN7wa/MaPg4CcYwxddJWVk3afi4CYzX+hnQ87J9MuSToCvRhBmrLUOm4eMyuAY7kiHl2g9hZLVSaQofffcna5iH8BEhHHnQLhlSppDuBdeQI43fPnzMIkPeC3aEBb2bqRTZauFjrnQHWisvG43y3yjckjWcCt77PNbTmOR1G2KzY8eOsXP6jtuFmnJcr9eZQeA4GkbzI+JcIU/9LuQe8tsgcsJVOgCwL4ocC1UWqzwxJb6SLaoS2ACVZiEjwUJjAJWeBRL2o3ihDQ8NYZH1mxbhEbYwJZBqFsBepgCMDA01GCw1ZaEHbGZkzkTL81bpHneJ6z0JLjBxIAp51tLGUyOO8uUV0st/ERICLDqvVq6pAK6wQH0iRmLZZ02YoO8yt5PvFwqrbXWd5820ZsdOmz8UWaKFwWUJSaS1yvHRd9wu0F/UPG+vGOfdnlHOFVLUupV7IbzT8PEZSOpKy2N2gI6PN4IJFyYBS+JLmrBx9OhRtdxXBEbQ6QPXmzAyPOzFxylIsKpVfDwDOnFZJwD29fQ4KQbrAR994aE+jKzF15URHBo+3rJzZ7Bfvt8kRhbAeCL5NlnqMYSROT623tqBj6SsabhS9szDB8UaofX6nve8J4gRfI3Iuc3x8QK4hsRW8JHLkBEA3jU6aillPhmyBjbL9XlIIoLI+7raMmRvvMb27tlj4eMzkJAntiJDUl1xqhKhyZA+fDwDbq47QEJslhUfEcKRB+2SIQkfpQxZB70yRbtkyAIYwzrfJks9thMfc6U70FoJLyfrCFlbAB6M//oU2SK64d61eB+XNdx8yqiHiUfo84DPzs56DALAjsmSk06fCGfYYrofEtCSZazSAP4M+K2V/Ni9gcX/drFotPA7n3I2MjSk1tgmoZYDKeVw3pvh3uTvdbDzegoA+DD7XfMekQcnNJaPQlLS4xkwAPz2wDFp4/kAu2etXMbc3JxlAeYvNX5eWfohJCz4hALtxdsHbl4bV3LKEA6rbWWd5y1pzYZPSnx8MDBHi+AqgWRF13CD5qKmPEYp851IhjSBgB+zEkXtY6DjY9oxU577fJVyXAgjOe5oSv4Y6Czqd+zfvyb4iGCH/xPpDim47cTHK5CQvrWCj11CwPZhJHkWZVRXGj5qbP6yX77qGhILfVEZZPQOhdU2u8bzZrdWZcinn3668ZxDiiwvbSUjMXyylUZWS2srDSM1fOTHrAQfaT3OzMw0VUUhLWKSH9sOGVIasCIweHrq1Cmnz4djD/VqyZD74rJbhAU09lI5z2qQHQU3LWhzhuNWIkP68DGEkUVojwyZho8kS4RkyFzpbmPLOpi+2tyjo4fQ1MAG9CuyBes4ozg/Ge+j1dXuRBMOXraO6+npjf8PE7GNjVXFNctovOMhTzd93mfOyRbYNJv09Xod5+fnM1vsfYuKXh6LbP8QQBEQ+nJKRsAtI6TV2Kb6hhLABiHOGSoUUu/tB9j98d81axuFJFUVAOgDW6H1Xe9IfI57xPlJEJMe/ruaGM/q2JhaLoMsgJw44zw7b1Zh4cOgs2xSORFfWG6Iwb2LjV1amFAuVLbesoxdWm3urfE6uuCZ17IkFF/bGm50ghFqyuI4sl57rfIMH2Vfy5BY/7OQuqQJIRwfOdGL75jTnvssgW1EaBYj+T3xfQiDeGmatcRH+l3WcZ0EwEOwcnwkwS5ifyU+FiGMj5TbzeezDyPHazXsBCN8c7Kj0FzinjMNI8tg3ifPgF6qLXT+LvCH1Ta7xvOmt2ZkSCcCqL8fuwsF/P7A3OaffXE9eHrmGnZsUuZSJyQEt2kYqfWTY/JK8PFesOdhVhmyptxjEWwjQjtlyC6w8bEIRkZCtBnPKQ+8VYx8r2e8fDLkPOjh5VkMKhDfr8RHAN3D3weJ9znLeGbFx7WUIUPnfhD8qVmtrHFEzJXuUMs6mG5t7pOYKNv0KaMW9m1I1XoQoIBzc3PsXLJWNn1OoizVVSpRnU5e5us8AjyAUVRs1O3W+0o52+X4+y50ve99yJX8CLY69QWPg0tkwGuT+hZllvqP3ILHLXEkIMkQ7a1grHY+cPEpb5TPgWiTmsyDm+Pd292d2m9fSYYy+Ekp0s63NfB7F+g5S1puTCcYj5ZKBNLRgeO1miNUhmor8n4RiUiW0CQCeM3zJl8s8jxZyhrRObRSRc2u87y5LcvYqaQmUYQ39PVZz1cjfJkCo0QX4vVJ5/IZXDSehkOjozheq1kK1vl4fhaFFdtHUlVma0SSvJAQssvTJ65ESaKXEP75CJNkmHNWjDwPRmAsgp1Dt17wkTy2DtZAerpBqJwh4eMVsMPQfWV1fPhYBFPH1Sc8ahh5aHTU6hudN4SP3CgTwsiblHNl8TpGAPhJSMfIHB9bb1nHTuLOSWVelkE3lD0ICYkYhYA3i5FU650w8jFIynMFCaoKBexU1tdK8JGulUWGDP1+BprHxy7w5yUfEds1fERcfzJk2m+Um/0MGNm5FO9bA51/YgASI6YmQ9J8agUfCSNXW4bMgo/8vtuBj7nSHWhZBlNnLCcWck2xNcqw+T4Z7594o90QcN1DPj09jbOzs1ipVJkSfQkB9qJUykdHD1nMlWFPtmQvL6MhUisjAOCtUGhY2PnEJs8nBxuf4BKBsaCRMumb+FshATVuieOLzUeoUQVXiOfX0o7h+Rv0gimDHgK4vVTCXpnHAwlYayy+IVD0jcO2QgF3gRuq1NfRgUODg1gdG3OAlVvy6uDm+CyAC6bVsTEHHEOKgey3JLzyHcfDi3yC/vennCc0ng9DMtdyT/fqtNDY+dhyNcGhF8wL/wGwlULuaZHhjb71QvjIhdAnwShZmlKehdmXPgVw2XnLkKQ6FEHHOw0fNZx4BowAkRUfyYiQhpG/4Nm+oNzj1cLHZwCCQrYvIuKOwcGGt0ReT+IjnT8LPmpjdijOObUEvAzKAY3rsjhvmmexXq+rGLkIdqrSeeVcIU/OLjAGrZ3seq2s8bz5WzMypKZ0SCXidmDKMLQHI6uVSmM+fxoMjvE5X+ruxhdffDEzPgKsDB/lmKTJkGRs890jNyKsBB81DArhI2JrGMmfaztlyA2g4+PI0BAuLCw4pTMlfhBGkjxIPB5ShtxeKmEpLoPbCj4SRrZLhqTfWsHHEiTyQu7pXoOWZTDd2tw+xfbDKJVhgHE03m+jSN9///2NB3vq1Cl2nkU0+dZ15Ar6889T2bEpdp1JTBT85Fr9/QONsjDpOdsfQ4AZNEYD3tcyAvRgQdxDJxjg44yLnCFTChgAhvGc6jfeHpj4BKoEns+AAb0HwBZIfSGYNUiE2Ls8HgcJhGRtzVKfUgpzZAQg4NByTtMsa97rxP2QCgMHeQr5pPPPBK5Hyk0/mPJDAIBve9vbnH6sJE9r/549LhsrG2NZisOXg6aRPPWBzUxK23vBzMssoZNZ13ne9BYaO63Uivay0xhqx+M1RPNMw0duYLIEgpjFXr6QJ8El1gEwnh6tbJac5xcgIXfpEucog8E2zXPax0o2yTHRcGIDZMNHjgcaRlJOHnl4pBA/CInwJUPwrwY+7o3DY33PQHqZGgrywkIjmsGaQ7VaY9zr9bpFshPCtdOQhG6XAPA32HllTnUWj4wco+PxeDhCt8ix5vPFh4+kTJTAxkLKWZS4WWZ/6f3S6hrPm781I0PS3MmKjwNgKq3weXbs2DFENEorL8WnEWtxGZLmM615XoGAPhR54ZvjBTD17edgZfiI2JwMmYY/AEl51VbwcSdbN0Wwo21C+IjYGkaWIXEWaUptq7LYodHRVHxEtGXIrPhYBFuGbBc+tlOGLIORJ1rFx5EDB1a0xqnlSnegtebp9pGR1dAtxdWHRkm2Pc2l0vb4/wK6YeqdODp6l0KIdhgBTsT/l9HNBS/igQMjoq/0eYZdj35bRoCquEaER8Hkbz8S/yUhtxhFeM/ddzvWMroIJ4DYBQkgX4GEVdFSnKIIb7v55sYC1hgMCUCyeGI7IQkLTAu9kqFCvhrfBBY8f4d71Z4BnZE3rZ88xEcDF7oOkZhJRVJaog9nGJcqmDIesp+jkD2PtTdm0JQWVKoN6ssFR8TGS58Eg2HQiS02KX2k5y+t6pNghAaAhNwvDy9fndaKp1t7masCDNiGE/qQchiBYcOV82LLhg34RiHI1ADwWUjwRxOwiLgrbX3Sb0tgcoxlzjnh0SNgyCXn2PHVSgWXlpa8QhgnEcuCj/sZkc4s6J7Zjez/NBwogwn5pzSStcDHK6Dn4qX1lYeJawpqvV7H6elpqzyWbz5myT9Nw8gqJBh5PnCujcozLBUK3lB17vHjhqPD4HKSFCHJXxwTfSwAYF+x6OCjrGs+MzPT8hrPm7+14uluFh/lnOXsy1RWkD//TjAM4XLeUfjzlOd6IW8lcaisFB+blSFJYZbyx02ve13jHD4W7Kz4SOO2o7e3KXxEbA4jKeqgVXzMIkOuR3zcBK5CXCoUGljYDhlyH1x9fMyV7kBrPqebcqkBbcU2FNbdhUlO9XE0tbZ3IUA/ajWzN2zYLELKy2JRRgjwsHqtAwcOYnrOdo/4rQtfDwXcAjb5DL9eBIDvjv8WowiPgFHaNEFRU5Q1S2Z1bEz1PNXBeGfIy10GW0DVQI2TvMkwmvNgXnLzWh8qFZyfn08Fi6efftpSgDlAkGWVL/5OBVwIFBcWFhxm41C5As4I3SjDE0UN8C+D/kLaCAkJRRl05YXydhCScGDZb64A8WPpXDQuXPBGdIljNojjtfxe/tkAJqSULLMXwC6Rwr35vB8rWed5c1szOd2+ELkshE9lSFIdtoIRui6Bn310A9um5UfuBj0/8kC57LV8U1+eAZs74QIYD+wWSOrD8mvxF/3IgQPYFUUNxV+uJyJCahUfERJDEx0/DGF85GPA+RyuxM9HE1iz4OPc3JyFUVKAagYj7xodbRofEV2MHBocbIScRuBnct4K2THyGUhy0bUwWN97c2FhARHD+AhgKwg+4ZA+N8bzkUoPARjPFOEjf/45Pq5eazan+xlwFZQsZFK9YDDpDBiMuinGgaKyvooAuHnDhtRyfRHYlVXoehEokRlgG0ePQ+v4WB0ba1qG9IWMnzhxoi34eBRE6a2M+Dgch22HwvK5DLkSfGyXDLmW+NgXRQ2nSRo+IrZXhrxa+HjNKN3Hjh3D7/iO78AtW7ZgsVjMdMy3v/1tfP/734+veMUrcPPmzfid3/mdmQqd85Z1MJeXl7FWG2cPlROaXUGTww3oD+u+CQFeQr08mE9R/xdows01hvNi3IdJBJhH432/gACADz30ELph7pNolHdAyaZegwiXISHHKIPuJdqnLJpe8V2ClcxXOQ6G0ZFC3TSwohcQlZe4BGGPbp0tIPJ4Pvvss05/N8X3YYFGf38jx1kKU46HlYEblUSSfdJITCQoSnDRmgQcfk7+/yXQX0jSAu17rqPx/ZIXjB/TXyxiDztmCswLv6qMt2yTExNYiiIHpGsQl7GBxIrPlQMZIucbZ/pdkrOsZJ2v57aeMXJ5eRnHazXrOXdAIjiECPFuAmNJl/OY1qrv2R+L54ovP3IAjLFtFhJB7KbXvz61LFUpJr8BcEPodgFYLKz8WpGy7h18HBvDxx9/fEX4iGDnsl2J7zGEj3y800oBlsT3NHzU7pmwzpernAUjs+AjYnaMrIlr0XcpxPkwchckBJRSUe4Eg2d8/2EIR+D48LEcz0Xu6aTndi/o9ctDGJkWWp51ja/3tp7xEdHFSFK2suAjYZSGkWnP/V9Ago/avI4gYcXmGHlrzCjOr83TgFrFR7kuAQC3i+9pMiSFjPfEHtKrgY97wfWmchKxrDLkSvARsb0ypA8fZR9awcdSd7cqQ46ysfa1VmRIMqBcLXy8ZpTun/qpn8Jf+IVfwPe+972ZAfPnfu7nsFgs4ic+8Qn8kz/5E/ye7/kefMMb3oD/8A//kPm6zQzmxMQkRlERAY4gwCfR9T4D+hXoC+gqz0fi37LUzPadd6PYL8Jf/MVfjP+fwiRPnB/DAA6SgvUA4TDuLmXBkeB14sQJREQrtNLnwdGIKAisyHN5XvSlCrpFTiMaofNKtkOyIMp70EKxSsq+PIyH5yTxsaLFf+zYsUyCo69xFlFuVaa+bBPjI0kwtE9aGBGVfJCs5mnWdz7evNHLUANpaTWvg/vy4+M4OzvrzBEu9GfxhF0PQuV6x0hioqVSK0+C6332zaULoAuGNMfThNEsc1sTNChkW76gZf4dCZxZStL48HGQzWUuKLaCj8+AqSEt+6KStYHNYC4xkpcCJG8G/56Gj2UwwqfEpfWEkbzMlkak1gxGVisVtSTOauGjdi4fyVwaRlIKUFrL8XFtZEiOkZ8EV6HzzaWPiTVO84UcEyvBRxldEQE0ZMgjYL+TPwz+cqhZ8JFCoyW+9MW/T09PI2JzMuRa4qNPhvQqnJ59X074GHo3++5trWTIduPjNaN0U/vIRz6SCTC//e1v4yte8Qo8fvx4Y9vf/M3fYGdnJ/7yL/9y5utlHUw/K/hxBABRCiwJ3Y6iXjRe5+PK8aGQ9AsYVsy7UHrAd+/eF1+zF+0wctMXq351PHkpxyhkcfWR3HRBUseQ13W2PDhRpFqUtHwOWqhVSKzBl8B9SVEIqsxrSfOge62d9XrDIhYq45NmpaR96AXSSsuad8Ot5QQkfWBbLh9l//ueK/WVg/SjgWO6UjzMCflguqIFkJC9lVPGWpsjxMiZpV0PQiW19YiRofC66elprI6NOS89CiX0GftCTK4U1hjCLMK7EiRGQs2A1wF6KSuOka3gYw/YDPvjtRqWCgULH7eBIcKRTZv7JJTy0j0aPm6CpI5pCCObwcepjPuvF4x0KkJAojxcAMCnMjxbLritBT7WxbmiwFhr80QacHwtx0fT1kKGlM+QsO/g8LAXH31zPLQGs+CjZijct3u3um60VJ9m8DGtr1QODbE5GXKt8LFZjHy5yZASH69AUpfdd8zePXu8/V8LGXI18BGaekrroGUFzJdeegkBAD/72c9a2w8fPoz333+/97hvfOMb+NWvfrXx+fKXv5xpMF0Gc1v5nZ2dVUqBAU5MTOLo6CE0Odza8US+JpVjKjUWUsynPNshPjc432eVSUnAHPJ0+yzt5J3moOl4jAIeSY2M5yQA7rDuwQDpVkhqIGrn14gtQkyNs7OzVu5HaN/FxcXUfJhWrZOy/6F+a5bnk+z7GcgQnh/n+vB9ngocs2/3bu/zTIxU/n4/wPo40N+PpQDDr5wjzbSXo1C5lhiZhUjGR5ZyaHQ0tWyWJhTwCJegIKRsA3BD6kYD5zkC9ppaKT6mEcdoTc59Cr0rifsgfBwaHEy9hnxmzeBjFlxaTxi5U4xRWXmWIYyUIYqrjY+z7FzVSkUNX20XRub4mLTVliF9z3tmZsaLj0Xws0PXYGX4KEPE0zBypfiYdv8cHxGblyGbwcdthQLuvv321PPnMmRzMqQWwh3CSIqO1dp6kiFzpRsRf/d3fxcBAP/yL//S2v6mN70J3/zmN3uP+8AHPuCASJbBDNW/5g9QPtTl5WUslfo9x59EN/+6hoZZnPbxKeYRpoemn0ETWi5KkXkAbzPYOd0+i6u2eHz5a60qSVIolfnJPC8mC8s3QjYrJR0XMj5wj5XGHDpeqzV1v7I1wzA5CsaT9kD8HDjYSONFUTzXtLIMi6B7iUqQ7YUQKqNRjCIcGRryWiGzhI1nbS9HoXItMTLk6Q7hY78oZSWP37xhg9WXGtgkaZrgWYSkXrSGdzKkzifU8mM2AHhrRa8lPtK40Zrx4WPaNVrxdDeDS+sJI3vAYHqj/rHAR/KElZR5RPuuNT4eB/Pe5RFkq4WROT4mbbVlyNB60fCRc63I47X83yz4SIpdyCtNGDkNYXzcDH58rAbmu4aP2nhkbe3Gx2Yx8lqSIYkpfiUypK906EqMCutFhrxmlO4HH3xQBSf++cIXvmAds9qA2aqVElEymBvlt6OjDycmJoPHGsV7O0rlOYp6cWysinNzc3jffffF45JFMR/17GsU60ql6vQVoIj7IFIn7xwA/jyAl2XQRxRBoUWacN2OxgGxWfDVcjjUmn3CGkbHlUFnH5Y1GldrofP+1xTgKhUKXnIS6oPGlOnrq/aSGQe9HEmWF4IWIsYVFG2cVqKEpLX1KlReTxiprTfN0qy15eVl3F4quYIhUzo4RmYRPCNI6kVLvJNlW56BcLmc+8EIbxo773itdlXwETFcZjCtyWdWbgIffbi0njCSsCYrPgLY5Zj4vmuNj75xWg2MzPExaaspQ7aKkcvLy1itVPRQ4Pj4ZvExFNkjMTKU6kMypLbe7j58uHEPTv/BrYTSztZOfLzeZEiONZJEt1kZ0mdU2ruKGLke8fGqKt1//dd/jV/4whdSP9/85jetY1Y7NEi2ZgbTFz6edXEsLy/j2Fg19fg0xX5hYQGHhobZ8ZJBPdlX6+v2/huc8AsqQyHB++jRo/iRj3zEWyqLPrUY0LMK12vZtP6O12p41+goaqChHZempPK2Ggs9C9gtLCxkMkpkNV7Il4xWiqmZF4IWItZMLna72noVKq8njFyp8JA1nNAnuI7XahYJYARg5U1zjPP11ac4l9mxVAf12LFjVi3Uaw0fEfU++5RO3zHrGSOp7Foz+JjW17XAR97ntWo5PiZttWXIlWBklnJRaYr9wsICDrPj05R4ra8D/f1u3rlHhrz//vvx2LFjTsi4VO7KYCqnXCv4eGh0FIcGB68LGVJizUplSG3uaVVxrjUZ8ppRultpzZJgTE1NNbZ99atfXTUSDN5WujjSjs+i2NPxCwsLmff1hV9ELSwECdzNLqK1btp4Z3mGK7GQtrOtxNvfbPMJBVy5b6Wtdr9Dbb0Kla209Y6Rq4mPiGHBleNjSMDVQjl9GHm94iNidqXTd8zVXt9r1ZccH9d/W+/4iLj6MmRW3GsWI9shQ5LH+1rGR98233EvFxkybe6ttlywmq2ZNV5ARIRroF25cgWWl5fh13/91+H48ePw6U9/GgAAbr75Zujq6gIAgNtvvx1+9md/Fr7v+74PAAAeffRR+Lmf+zk4ffo0vOENb4D3v//9cOnSJfj85z8PmzdvznTdr33ta1AsFuGrX/0q9PT0rM7NtdD+7M/+DL74xS/CzTffDLfcckvb9pX7A0BTx67kunm7dtr19mzX6zpvpuUYabesc7SVuUzHbNiwAf7f//t/OT7mzWrX27Ndr2u8mZbjo93Wuwx5va2hvCXtenu2Ta3xVTcBtKn9yI/8iGUdoc9v//ZvN/YBAPzIRz7S+P7tb38b3//+9+PAwAB2dnbid37nd+Li4mJT172eLLx5y1ve9HY9rPMcI/OWt7ytRrse1niOj3nLW95Wo12Xnu6r1darlTJvectb+1q+zltv+djlLW/Xd8vXeOstH7u85e36bs2s8WiN+pS3vOUtb3nLW97ylre85S1vecvby65tuNodWO+NAgG+9rWvXeWe5C1veVutRus7D/xpvuUYmbe8Xd8tx8fWW46Pecvb9d2awcdc6Q60r3/96wAAcOONN17lnuQtb3lb7fb1r38disXi1e7GNdVyjMxb3l4eLcfH5luOj3nL28ujZcHHPKc70L797W/DX/7lX0J3dzcUCoXg/l/72tfgxhtvhC9/+cvXZf7O9X5/APk9Xg+t2ftDRPj6178Or3rVqyCK8qybZlozGHm9zzuA6/8er/f7A8jvUbYcH1tvOT7aLb/Ha79d7/cHsHr4mHu6Ay2KInjNa17T9HE9PT3X7WQEuP7vDyC/x+uhNXN/uQentdYKRl7v8w7g+r/H6/3+APJ75C3Hx9Zajo96y+/x2m/X+/0BtB8fc5Nl3vKWt7zlLW95y1ve8pa3vOUtb6vUcqU7b3nLW97ylre85S1vectb3vKWt1VqudLd5tbZ2Qkf+MAHoLOz82p3ZVXa9X5/APk9Xg/ter+/a7W9HJ7L9X6P1/v9AeT3mLer014OzyS/x2u/Xe/3B7B695gTqeUtb3nLW97ylre85S1vectb3vK2Si33dOctb3nLW97ylre85S1vectb3vK2Si1XuvOWt7zlLW95y1ve8pa3vOUtb3lbpZYr3XnLW97ylre85S1vectb3vKWt7ytUsuV7rzlLW95y1ve8pa3vOUtb3nLW95WqeVK9wrbBz/4QTh06BBs3boVSqVSpmMQEX7qp34KXvnKV8KWLVvgnnvugT/7sz9b3Y6uoC0vL8MP//APQ09PD5RKJbj33nvhb//2b1OPeeMb3wiFQsH6/Nt/+2/XqMfh9sQTT8DrX/962Lx5M9x5550wPz+fuv+zzz4Lt99+O2zevBn27dsHs7Oza9TT1lsz9/jRj37UeV6bN29ew942137nd34H/tk/+2fwqle9CgqFAnziE58IHnP+/HkYGhqCzs5OuPnmm+GjH/3oqvczb9c/Rub4mOPjems5Pl477XrHR4AcIwGuPYy8nvER4OphZK50r7D94z/+I7zpTW+Cf/fv/l3mYx577DH40Ic+BCdPnoSLFy/Ctm3bYGJiAr7xjW+sYk9bbz/8wz8Mn/vc5+BTn/oUfPKTn4Tf+Z3fgR/90R8NHvfOd74T/uqv/qrxeeyxx9agt+E2MzMD733ve+EDH/gA/PEf/zEMDg7CxMQE/PVf/7W6/+/93u/BD/7gD8K9994Ln/3sZ+F7v/d74Xu/93vhT//0T9e459lbs/cIANDT02M9r//1v/7XGva4ufZ3f/d3MDg4CE888USm/b/0pS/Bd3/3d8Pdd98N//2//3d4z3veA+94xzvg3Llzq9zTvF3vGJnjY46P663l+HjttOsdHwFyjLzWMPJ6x0eAq4iRmLe2tI985CNYLBaD+33729/GV7ziFXj8+PHGtr/5m7/Bzs5O/OVf/uVV7GFr7fOf/zwCAC4sLDS2/dZv/RYWCgX8i7/4C+9x1WoVf/zHf3wNeth8O3jwIL7rXe9qfP/Wt76Fr3rVq/Bnf/Zn1f3f/OY343d/93db2+688078sR/7sVXt50pas/eYdf6uxwYA+Gu/9mup+7zvfe/DPXv2WNv+5b/8lzgxMbGKPcsbb9cjRub4mOPjem85Pl4b7XrER8QcIxGvPYx8OeEj4tpiZO7pXuP2pS99Cb7yla/APffc09hWLBbhzjvvhN///d+/ij3T2+///u9DqVSC4eHhxrZ77rkHoiiCixcvph77sY99DLZv3w579+6F//Af/gP8/d///Wp3N9j+8R//Ef7oj/7IGv8oiuCee+7xjv/v//7vW/sDAExMTKzL5wXQ2j0CAPzt3/4tvO51r4Mbb7wR/vk//+fwuc99bi26uybtWnuGL+d2LWFkjo/X3trK8dFt19ozfDm3awkfAXKMBLi21leOj3pr1zPc0M5O5S3cvvKVrwAAwMDAgLV9YGCg8dt6al/5ylfghhtusLZt2LAB+vr6Uvv7Qz/0Q/C6170OXvWqV8GlS5fgwQcfhMXFRfj4xz++2l1Obf/n//wf+Na3vqWO///8n/9TPeYrX/nKNfO8AFq7x9tuuw2efvpp2L9/P3z1q1+FqakpOHToEHzuc5+D17zmNWvR7VVtvmf4ta99Df7hH/4BtmzZcpV6ljfZriWMzPExx8f/v737CWnyjwM4/hFzVpjIaGwRBK4/HqKYCslz2YIFmR0siGiELS+eOgghDEHEIOgQXUaHDv29FBWGB8GKpVCBg9wqMQ1mZgS1Q5KN6JL7/E6//X5L52/z5+Oj8/0CDz57HN8vc2/58LBH+oiVtJb6KEIjRdZWI+njwparkVzpXkAoFJp3U4A/v3L98q0VZu+xra1NDh8+LPv27ZPTp0/LnTt35NGjRzI5ObmMu8ByMQxDzpw5Ix6PR3w+n/T29orD4ZBr165ZvTSsQsXeSPqIf6OPKESx91GERuIf9DF/XOlewPnz5+Xs2bOLnuN2u5f03C6XS0REksmkbNu2LXM8mUyKx+NZ0nMuRb57dLlc826e8Pv3b5mZmcnsJR8NDQ0iIpJIJGTnzp0Fr3e5bN26VUpLSyWZTGYdTyaTOffjcrkKOt9qS9njn8rKyqS2tlYSiYQZS1xxuV7DyspKruIsQbE3kj7Sx8XQRyym2PsoQiOLtZH0cWHL1UiG7gU4HA5xOBymPHd1dbW4XC6JRCKZQP748UOi0WhBd6/8v/Ldo2EY8v37dxkZGZH6+noREXn27Jmk0+lMBPPx+vVrEZGsPxJWsNlsUl9fL5FIRI4dOyYiIul0WiKRiJw7d27BnzEMQyKRiLS3t2eOPX36VAzDWIEVF24pe/zT3NycjI6OSlNTk4krXTmGYcz7Fx2r+TVc7Yq9kfSRPi6GPmIxxd5HERpZrI2kjwtbtkYWepc3ZJuentZ4PK49PT1aUVGh8Xhc4/G4plKpzDk1NTXa29ub+f7SpUtaVVWlfX19+vbtW21ubtbq6mr99euXFVv4T42NjVpbW6vRaFRfvHihu3fv1kAgkHn88+fPWlNTo9FoVFVVE4mEXrhwQV+9eqVTU1Pa19enbrdbvV6vVVvIcu/ePS0vL9dbt27pu3fvtK2tTauqqvTr16+qqtrS0qKhUChz/suXL3XDhg16+fJlHR8f1+7ubi0rK9PR0VGrtvCfCt1jT0+PPn78WCcnJ3VkZERPnTqlGzdu1LGxMau2sKhUKpV5r4mIXrlyRePxuE5PT6uqaigU0paWlsz5Hz580M2bN2tHR4eOj4/r1atXtbS0VAcGBqzawrpR7I2kj/RxtaGPa0ex91GVRq61RhZ7H1WtayRD9/8UDAZVROZ9DQ4OZs4REb1582bm+3Q6rV1dXep0OrW8vFz9fr++f/9+5Refp2/fvmkgENCKigqtrKzU1tbWrD8IU1NTWXv+9OmTer1etdvtWl5errt27dKOjg6dnZ21aAfzhcNh3bFjh9psNj1w4IAODw9nHvP5fBoMBrPOv3//vu7Zs0dtNpvu3btX+/v7V3jFhStkj+3t7ZlznU6nNjU1aSwWs2DV+RkcHFzwfff3noLBoPp8vnk/4/F41GazqdvtznpPwjzF3kj6SB9XG/q4dhR7H1VppOraa2Qx91HVukaWqKoWdm0cAAAAAADkg7uXAwAAAABgEoZuAAAAAABMwtANAAAAAIBJGLoBAAAAADAJQzcAAAAAACZh6AYAAAAAwCQM3QAAAAAAmIShGwAAAAAAkzB0AwAAAABgEoZurGt3796VTZs2yZcvXzLHWltbZf/+/TI7O2vhygDAWvQRAHKjkShEiaqq1YsArKKq4vF4xOv1Sjgclu7ubrlx44YMDw/L9u3brV4eAFiGPgJAbjQShdhg9QIAK5WUlMjFixflxIkT4nK5JBwOy/PnzzOxPH78uAwNDYnf75eHDx9avFoAWDn0EQByo5EoBFe6ARGpq6uTsbExefLkifh8vszxoaEhSaVScvv2bYIJYF2ijwCQG41EPvhMN9a9gYEBmZiYkLm5OXE6nVmPHTx4ULZs2WLRygDAWvQRAHKjkcgXQzfWtVgsJidPnpTr16+L3++Xrq4uq5cEAKsCfQSA3GgkCsFnurFuffz4UY4ePSqdnZ0SCATE7XaLYRgSi8Wkrq7O6uUBgGXoIwDkRiNRKK50Y12amZmRxsZGaW5ullAoJCIiDQ0NcuTIEens7LR4dQBgHfoIALnRSCwFV7qxLtntdpmYmJh3vL+/34LVAMDqQR8BIDcaiaXg7uXAIg4dOiRv3ryRnz9/it1ulwcPHohhGFYvCwAsRx8BIDcaiX9j6AYAAAAAwCR8phsAAAAAAJMwdAMAAAAAYBKGbgAAAAAATMLQDQAAAACASRi6AQAAAAAwCUM3AAAAAAAmYegGAAAAAMAkDN0AAAAAAJiEoRsAAAAAAJMwdAMAAAAAYBKGbgAAAAAATMLQDQAAAACASf4Ce7EO2KtWxWIAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"print(\n",
" \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
" loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"print(\"Learned weights\")\n",
"for i in range(num_layers):\n",
" print(\"Layer {}: {}\".format(i, params[i]))\n",
"\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
"plot_data(X_test, initial_predictions, fig, axes[0])\n",
"plot_data(X_test, predicted_test, fig, axes[1])\n",
"plot_data(X_test, y_test, fig, axes[2])\n",
"axes[0].set_title(\"Predictions with random weights\")\n",
"axes[1].set_title(\"Predictions after training\")\n",
"axes[2].set_title(\"True test data\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sYQWz6IdTnyy"
},
"source": [
"References\n",
"==========\n",
"\n",
"\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
"quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
"\n",
"\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
"optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
"\n",
"\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
"for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
"503-528.\n",
"\n",
"About the author\n",
"================\n"
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "8FGVwF_QUYza",
"outputId": "75ecd903-d97c-40d1-8438-06a428eb4043"
},
"execution_count": 74,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696824036.60448\n",
"Mon Oct 9 04:00:36 2023\n"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 0
}