WeatherForecast.ipynb

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  "cells": [
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      "metadata": {
        "id": "YsFA06zOcmWy"
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      "source": [
        "Weather Forecast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZ8mT6jq4gLx"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from zipfile import ZipFile\n",
        "import os\n",
        "import numpy as np\n",
        "from torchvision.datasets.utils import download_url\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import torch.nn as nn\n",
        "from collections import OrderedDict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTb8XR5d4_uO"
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      "source": [
        "DataSource \n",
        "\n",
        "We will be using data from  Max-Planck-Institute for Biogeochemistry at https://www.bgc-jena.mpg.de/wetter/ and as per documentation at https://www.bgc-jena.mpg.de/wetter/Weatherstation.pdf we get the following metadata about Weather Data table"
      ]
    },
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      "metadata": {
        "id": "4Ext8DmKIbh6"
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/T3/AA8K+LH/AEZr8f8A/wAGvhX/AOW1ec/8E9/+U3H/AAUM/wCvj4d/+o49ez6nrfin4lf8FCfE/g/T/HHiPQvCnhbwHp2p3Vpp6We1NRvb27SJw0sEjZWGyclSSp8wEjpSv7yj3v8AhFy/G1l5tDatFzfS34yUfzd35XPJvhL+0N4z+C2o6teaN+xl+0nLeazNJJPcal4p8PahKiPPNceTG8+suUiEs8rBFIA34HAUDuP+HhXxY/6M1+P/AP4NfCv/AMtqzv2c/wDgpN/aXwN8IX/i61l1XVdUvl097+1MMLalDP4in0WxvEt1OT5wjhuHIVIws37sscJXW+H/ANvtI/Geu6dregS2CXXj2fwP4WJvINmpyW1r5tzNK4YmJIzDdMxKnAjVV3uQtOHvaR2V/wAHGL385R+/1ConFyc976+rTl+UW/kYf/Dwr4sf9Ga/H/8A8GvhX/5bUf8ADwr4sf8ARmvx/wD/AAa+Ff8A5bV7r4e8X6j8cfgdJqOjLqXhDVNYt7i2gkvIF8/TpVd4fOVXVlcAqXjJUq67DjDYr50+Hnizxrqfi34+61P8TPGtx4W+EXiazsbG38rTC93b2em2V7qcbM1uis0pnljBLoEKjBQVEpqPNzfZXM/RNJv5OS+8cYOVlHq7L1abS+dn9xrf8PCvix/0Zr8f/wDwa+Ff/ltR/wAPCvix/wBGa/H/AP8ABr4V/wDltW5of7eekeCvDcNjqOi/EXW5NI0PwxfXep3kGmi7um1uc2tqkqQyRR/avNU+akUaoM5QEECrmpf8FGPC48V654c07RdbvfE3h2HVLm901zDG/l2BiSQRMrusskskqxxIvLOkgOwITWs4uM3Te6v/AOS/F911fy1Ig+eKnHZ2/wDJldff0+4+D/8Agsb+2Z8Q/H1l+zANU/Zl+MPhAaN+0L4P1W0Oqah4fk/tm4iluDHp8HkajJiebJCGXZENp3SLxn7I/wCHhXxY/wCjNfj/AP8Ag18K/wDy2rzP/guNqS6z4Z/Y1vEjnhS6/ab8BTLHNGY5EDS3JwynlWGeQehr37/goH408R+BPAvgK58NeJNU8N3Gq/EHw9oV5JZJA5uLS91GG3njPnRuBmN2wygEHnNJpqcab3lKMfnKSivxeoRacHUWyi5fKKcn+C0ON/4eFfFj/ozX4/8A/g18K/8Ay2o/4eFfFj/ozX4//wDg18K//LauD/ba/aU+IfwAtfjf4O0jxjrUt14Z+G9v4/8AD+vNa2ZvdKmN5PayWkuIPJliby0dC8e/iYFmAXHtGofEzxJ8Ff2y/ht4En13VPFfh74maNq8xTUIbf7Rotzp620qypJDFGWhkWd0YSbiH8raVBZSRXMotbSvb5cyf4xa9fLUcvdbi91b8VFr8JJ/8HQ5P/h4V8WP+jNfj/8A+DXwr/8ALaj/AIeFfFj/AKM1+P8A/wCDXwr/APLatjX/AI7+IPgl+23d2PiDXby++HPiuzkstPtp4YcaPq9vaC+WKN1RXZbi2F2drs2GtEC4LnPl9n8UPjR4r/Z9+IGuaP4x1Sbx34f+JmqafpmnC1s/slxb2ZaRNK2+Tny5Ahh8zPmDfu35GaiMk99LK78lzxhr/wCBKX+HXdpOpRaTf3efuSn/AO2uOtve8rs7j/h4V8WP+jNfj/8A+DXwr/8ALaj/AIeFfFj/AKM1+P8A/wCDXwr/APLap5v2pNR+M37UvwEPg/xFd2vw88aaRqWp3UEEMP8AxNZEsrW6hWRnRnVUWdMhGUlmcHO0YuftUfFPxx+yt+0X4N8YpqWt+KPhr40ceELvw1HHbL/ZOtXDKNNu4n8sSmKaUG2kDuyo00UmAoernGUZqElq3b57Jf8Abzslbq1ey1URkpQc47JX+W7evZXbv0Ttd6GZ/wAPCvix/wBGa/H/AP8ABr4V/wDltR/w8K+LH/Rmvx//APBr4V/+W1RftMan8R/2Mfh74M+KF38QPE/jHw34Juw/xF0mS2siNR064/dyXduI7dJA1nI6SLGrAyQo6ndJtJk/aRh+Lfwc+B5+Lmm+MfE15c+FNUPi3xB4MdNPa2utBUs9zpkT+QHE0NufMR/My8sJUtscbZcopXk7JOzb2W2vpZ3utLJ9VYpRbajFXbV0u++nrdJWf8y6O4v/AA8K+LH/AEZr8f8A/wAGvhX/AOW1H/Dwr4sf9Ga/H/8A8GvhX/5bVq+P/CnxV+NP7O/iHxx8OviD4g0HxL4gEPiDwjo9xDZfYUtUVHisZt9u0ii8jUmRixeFrj5SPL56r9kH4s3/AO1d4esPi5b3viLRvCXiPS4YtL8M3iQKsMi7vtFxLiPzPN80tEAJNm2AOAfM4vkkm4yVmt0+l1/mmmlqmtdLNxzppSjqns112/R3Te62u9DgP+HhXxY/6M1+P/8A4NfCv/y2o/4eFfFj/ozX4/8A/g18K/8Ay2rjvj/+0B8RvBXxk/an0/SNX8aXun+CPh7p+u6ALAaUIfD13Nb6lK8p8/ZJIC1pF8p80ABxjLCj4Y/Gv4i3X7QfhzQ5vEHjvxTpurfBXT/FNxbWX9kxNDqU87xPdFpVh7AYRSVBGdlY+0VpP+VX/wDJakvypS8rtdLtaTi42v1/zpx/OpH7n5X7H/h4V8WP+jNfj/8A+DXwr/8ALaj/AIeFfFj/AKM1+P8A/wCDXwr/APLao/2WfjH45+J3/BMbwn4j1fVPFU/inXvhqnii48YBdORY79rcSeSsQHD5+YD7OYtgIL7sCuU8e/tY+PX/AGQ9EsI/EU2l/Erwz430fwd4x1K2tbfzX/4n1jp8sqxvG0cZvLe6S4TCfKkp24IGOidNxrew+1dL75crfona9+jTVzPnXIqj2f8AldL1a28007HX/wDDwr4sf9Ga/H//AMGvhX/5bUf8PCvix/0Zr8f/APwa+Ff/AJbVe8J/HjxR8Jv2uPHXhDxHrd54g8P6/am58FLeRwJJb31tHbC6sA8caFxJ9ttZI9+5htn5wvHWf8E7fHfiX4j/ALMEGu+L9fu/Eet3Gva7by3U8MMIWO21a7tYkRIkRQqxQIOhJOSSc1kmvZxqdJRUl6P/AC/4a6aKejcezt+F/wCvPTdM4X/h4V8WP+jNfj//AODXwr/8tqP+HhXxY/6M1+P/AP4NfCv/AMtqz/iZ8VviNcfsheC/ih4S1vVf+Eh1rUG8VzaPGkU8OqaaLae+/soJIjbC1rbpCrRFG8xi5JLGtXxL+0BrPxf/AGuvh5p3hPxhf2Pw78S+BNe1qM6dHbn+07iA6QYLgSSRO21Ev3AVSBuB3A4AqKlRQTb3jdtdrRcvx5ZJea7ajtql3289bfqn6PvoRf8ADwr4sf8ARmvx/wD/AAa+Ff8A5bUf8PCvix/0Zr8f/wDwa+Ff/ltXHfsO/ED4o/tHfst/D/XtS8ceOPD3iPWvBljfTatq8OkPpuvXmoWV0ivbxwxMUeCdIpgp2ZBCtGVNdb+y/rnin4++P/G2oQfEvxTc/DO3tZfC3hWe6azS78R6jbP/AKbrUbwwRnyYpVEESpiN/LndlZHjxvVg4TlTf2b3+X/Bsv8At5dE2pTTSfe34/8AAu/SL62T4H9rb9vb4o65+yn8TbK5/ZF+Ouk2154T1WCW+udT8MtDZo1nKrSuE1RnKqCWIVWbAOATxXL/APBNn9un4m+Ef+CdXwD0mw/ZP+N/iOx0v4ceHrS21aw1Lw2lrqkcemW6LcRCXU0lEcgAdRIiuAw3KpyB9I/En4mXHxl/4JZ+LfFN7HHDfa38NdRubtIxhFnOnS+aF/2d4bHtiuf/AGB57u0/4Ip/BGewv7rS762+CugT293bBDLbyJoduysu9WXIIHVSPasMTUVCE6ktVFN6eXY1w9N1pxprRyaWvmQf8PCvix/0Zr8f/wDwa+Ff/ltR/wAPCvix/wBGa/H/AP8ABr4V/wDltXO/D79orxz8M/gX+yx431TxTrPjH/hb50bSPEWnXttaDdNf6VJdG8t2ihjaNo5osshYx+U0nyggMOp8Zf8ABSSKb4V6xqfhrwzd3mt6aPDVz9lN5bvELXWb77JG3mhvLaWNknV0UsodVw5BLDpqUnCcqe7jLkf+LT/NO/n6256dVSjGfSUVJejul+Ka/pEH/Dwr4sf9Ga/H/wD8GvhX/wCW1H/Dwr4sf9Ga/H//AMGvhX/5bV6P4c/bP0jxV8Trvw3ZaLqtxJpXiGLwtqckTJNJYXz2KXhLRoWJt0WWKN5s4EjgAFQXFv8AaM/a80b9mvU4YtY0+6ubf7CNRnlhni8xYftUNu3lxFt8jIZg7cBQin5ixCnKOvLb7W3npzflr6Gj0vfpv99vz09Ty3/h4V8WP+jNfj//AODXwr/8tqP+HhXxY/6M1+P/AP4NfCv/AMtq7sft8eFtN8R3tjrWkeIdBtrDxLqHhaXULpLd7UXNnp0mpSP+7lZ/KNrE7BtmcjaQD0ofCb9ofV/ix+1jpEVpJrdl4J8R/DiPxNZadfw2igyPeRrHODHulVmikGUd8DIwoO6in78oxj9pX+XJKav/AIoxdu/pdhU9yMnL7Lt/5OoO3pKSv29Tk/8Ah4V8WP8AozX4/wD/AINfCv8A8tqP+HhXxY/6M1+P/wD4NfCv/wAtq6X4iftXarpnin48aPc6N4j0zRfhj4at9Si1bRJdPl1FjJa3E8kkEd0TF5qrHlFlVkzEdww22rlv+3todnq11pkvhnxnM+n63pHh03RSyK3NzqdtDPaOMTjCsJkVztXYxPAXmiPvOy/u/fJ2S9W9Egl7qu/7z+UUm36JM47/AIeFfFj/AKM1+P8A/wCDXwr/APLaj/h4V8WP+jNfj/8A+DXwr/8ALaumuf8Ago74VuJrq10zRdd1DVdM07VtUv8ATdsa3aR6bqMmnTQworMJ7iS4hmEUaHDiM5ZSVDen/EH44WXgrxp4P8Mw20l74h8ctc/2ZbOxgjWO2g86aSVyCUVQUXAVmLSKMY3MotYqa2f+bX5p/cweknF7r/JP8mvvR4V/w8K+LH/Rmvx//wDBr4V/+W1H/Dwr4sf9Ga/H/wD8GvhX/wCW1T/CX9pO9/at/ay0ZdEu/Fej+DdJ8LprQt4ZbWKOe9GoX1jdQXane8ipJamMBOAyFg2CCb/xu/ag8Vj/AIaL0HSrO88Nv8M/B66ppOuwvazv9qeyubkP5UgcHmNQFaNhhGJxuAEzmo0vavb3n8otp+W6djSnTc63sFveK+ckmvPRSVzJ/wCHhXxY/wCjNfj/AP8Ag18K/wDy2o/4eFfFj/ozX4//APg18K//AC2ro9G/4KDaBY6z4b8IalY6nB421j+z7dIb9oLa3ma506e/89pUZ1SMR2k+Rt3bwqhMNkegan+1HoXhv9nSz+JGs2Gt6Rp14lqq6dc2uzUBc3M6W0NtsJC+Y88iRglgh3BtwX5q1qU3CUoPdO3z/r/Lc56VRVIRmvtK69GeOf8ADwr4sf8ARmvx/wD/AAa+Ff8A5bUf8PCvix/0Zr8f/wDwa+Ff/ltWh4Z/a31rwT8VvinYeIYtS1G3tPGFtpGh2V5NY239mo+gQag0IeLmQPIJQg/eyFpVBIUEro+EP28/+E58fzy6b4Y1OfwV/wAK1sPiDbXjTW0VzNHd/anWIxvKoVttuF5bG5jkhRuOakt/JN+V4uWvyT+aNbO1/wCviUPzaOe/4eFfFj/ozX4//wDg18K//Laj/h4V8WP+jNfj/wD+DXwr/wDLavZ/2ff2kdK/aDbxJDYW8lvdeFryCzu8SrNBN59nBdxyQyrw6GO4QE4HzKwGVwzcJqP/AAUF0bTfFi6NP4T8S2U8/i+bwRbXt7NZQ6fNqUUX2hlMiztIsf2ZZJg/lEYiZf8AWbUammp+z62v8rpfnJL1aRKknD2nT/gN/lFv0TZyf/Dwr4sf9Ga/H/8A8GvhX/5bUf8ADwr4sf8ARmvx/wD/AAa+Ff8A5bV3HgX9uew+I91rEWleEvErv4TFmfEcc6xwS6V9onmj3qrsPOjjSB52dSA0LRtH5hbaMXxF/wAFJ/DfhTw14N1e78N+IZ7X4haPbeIPD8dmYZLi5sZ7yxtA8iu6CORW1KzZkDN8spwSVK0o+8+Vb3t+Df4pNrvZ22Y3pq/62/zV+11cwf8Ah4V8WP8AozX4/wD/AINfCv8A8tqP+HhXxY/6M1+P/wD4NfCv/wAtqvftFfti6jd/DzWIfCdp4o8MeJPCHifwzp2vedFp0o09r/ULIPZSkyTI5a0uVZmh3bRPHtcMGC+sftGeL77R4/BugadczWNx448RwaNJdQuUkt7dYJ7ufYw5V3htZIwwwVMgYEEChK6T7tL77a+mu4N2u30Tb9Em389Njxn/AIeFfFj/AKM1+P8A/wCDXwr/APLaj/h4V8WP+jNfj/8A+DXwr/8ALatLTv8AgqJ4O8S/EbW/Dnh3RNd199A8Wz+BJ5reW33nVYYt75gMnnLaCTELXLIFWQ5I8vMg5TwF/wAFpfh94h+HXhfxbr/hrxZ4S8O+LfAus+PrK7u1t7grZaU0QvEdIpGYSfvl8vgh9rfd+XdKnFw9onpa9/Llc7/+Apy9ClCXP7O2t7W8+blt/wCBaeps/wDDwr4sf9Ga/H//AMGvhX/5bUf8PCvix/0Zr8f/APwa+Ff/AJbVynhP9oDxf+0Z/wAFArLwi914z8FeCvG/wUl8UWmmm+tkntLh9Rt44LuOS3LPFcLHIQyb2TOPvDNe8/sE/HTVf2j/ANkrwf4s10Q/29cxXOn6q8KbIpryzuprK4lRf4VeW3dwOwYCtOSSjeStvo99JTg/ucPxXnbPmTk1HW1temsYyX3qX4Pyv5j/AMPCvix/0Zr8f/8Awa+Ff/ltR/w8K+LH/Rmvx/8A/Br4V/8AltX1bRUlHyl/w8K+LH/Rmvx//wDBr4V/+W1fMXxf/aF8WfHn/guX+wt/wlHwb8f/AAj/ALKt/iD9m/4Se60uf+1vM0ODf5P2G7uMeXsXd5mzPmrt3fNj9Sq+DP2/f+U5f/BPr/r3+JP/AKY7OgA/4J7/APKbj/goZ/18fDv/ANRx6+odQ/Y08A6n448V+I5bTxGNX8cJBFrksXirVYo7+OFXWKIxLciNY1WSQbFULh2GOTXy9/wT3H/G7n/goYe/2j4df+o49fY918ePBFj4tvtBn8X+GrfW9MltoLuwl1KFLm2kuTi3V4ywZTKeEBHzHgZNKybsO7Wpy2v/ALEfwu8SG4E/hWGCG6bTHkt7K9ubK2zpuPsOIoZEjUQ7QFAUDAAORxWja/so+ALHVY7+DQBBfQaxca9Bcx3twk1tezm4M8sTiTdH5hurjcqEK3mtkc16JRTvrf5/PT/Jfcuwvs8vT/h/83977lOy0G207Q106BZYrVIjCuJn8wAjBPmZ37u+7O7POc815ron7FPw78PfDrxv4UtdN10aF8R57m68RQS+JtUmk1CW5GJ3Er3BkiMg4bymTIwOgAr1aik0rt91Z+mjt6XSfyQ02rW6O68n39fM8l1H9h/4b6tqd3eXGl63JcX99peozt/wk2qAST6YQ1i2BcYAiZVYKBtZhlgx5q34M/Y4+Hnw+1K5vdJ0a+tr68e+e4um1q+muLj7bcC5uUeR5mdkeYb9hJVSW2hdxz6fRVJtO631/FJP70kn5JE8q5eXpp+F7fdd27XZ8Hf8F3FCad+x8AAAP2oPAoA/7bXNfX3xq/Z/8LftC6TpNj4rtNRvLbQ9UttasltNXvNOMV3byLLBKWtpYyxR1VgGJGR0r5B/4LvDNh+x/wC37UPgX/0dc19qeNPid4c+HM+mR+INd0nRH1q4NpYLfXaQG9mCM/lR7iN77VZto5wpPap6p9mmvVPT5328xrZryd/S2vytv5HDeLv2Jvhv498D+LPD2s6NqeoWHjmOODXpZdf1E32pwxgiOF7vz/tHkpubbEJAg3uQo3NnYs/2ZfCFlqep34ttam1XVdLk0aTUrjX9Qnv7a0k5eK2uXnMtqGYKxMDoSyI2dyKRzPx5/bO8M/DD4Ka34t8Oar4U8YT6NaLqLWUWvRxebaCeOKWYOiyHCeYMnbt3FVLLuBrt9U+PHgzR7FbibxNozxvctZKILlZ3adYGuGiCpuYusCtKRjIRSx+UZodrXf8AXT7un4DV72X9df8AJ/czHv8A9lLwNrOsS32o6ZqGrXEuo2Grj+0davr2OG7ssfZp445ZmSN12jJQLv537snNLw5+xt4C8IrINNtfEloJvEUniuXb4r1Y+bqUhJeZs3PzBsnMZzGe610OmftD+Ada8V2mg2njXwpc63fxwS2thHqsDXN0k8Mk8LRxhtzh4opZFKghljcjhSR2NVrF32f/AAz/ADjH/wABXZE6NW6W/CzX3WlJejfdnlmmfsWfDbQde8Malpug3elXPgw37aMthrN9aQWRvpGkuyIo5ljbzGY53KcDAGAoA1PiF+zF4P8Air4I8M+Hdfttc1DTPCF3ZX+mZ8Q6jHcpcWbI1vNLcJOJp5EZFbdM7lmG5ixJJ7+iktLNdGn807p+qeq7Mq7187/jo/vW/c5P42fBDw1+0T8L9U8GeLrO61Hw3rcP2e+tYdQubJrmPuhlgkSTB7gNz3qz43+FGifEb4W6j4M1mK/vPD+rWDaZeRDUrmKe4t2TY6NcJIJsspILb9xyck5NdHRUyipJxkrp7+YRk4tSi7NbeXoYfhL4c6V4F+Hlj4W0tL630bTbJdPtkbULiWeGFV2KondzNkLgBi+4YGDwKzvgf8DvDX7OXw2sPCHhCzu9O8PaXv8Astrcajc35gDMWYCS4kkkxuJON2Bk4rraK0lKUpOUndvfz9SYxUYqMVZLY8u8S/sZ/D3xd4i8e6rf6ZrMt98TdLj0XxI6eItSiXULOMSKkIRLgLCFWWUAxBDiRhnBNReGP2Kfh54N8Uwa3pun+ILbVLbw4nhKK4/4SjVXMeloSUtwGuSBtJJD43g8hs16tRUcqs1bf/Jr8pSXpJrqym29/wCtn+cYv/t1dkeV+FP2L/AHgb4TW3gXSLTxNYeE7PQj4ag06LxZqwjgsMg+WrfadysAAolB8wJlA4Ulab4u/Ym+Gvjiw1K31HQr1/7a1DTNV1CeDW7+2ub+603yvsU000cyySNEYYiCzHcYwW3HmvVqKptt8z33+d7/AJ6+upNl/X3fkkjzpP2UvA39vxarPpmo3+o22st4gtri/wBavrx7O+a1e0M0Jlmbyf3Dsm2Pao4IG5VI1/gp8DPDX7PPgj/hHPCdpe2Oj/a7i+EFzqd1qDLNcTPNMwe4kkcBpZHcgNjLHAGa66iktFb+tLJfgkvkhvXf+t/8397PLfAX7MmneH/DXh3R9Tlvbiz8B6nNdeGms9WvLMQwMHEUU8UTokoijleELJ5iMihiAWIEGl/sPfDPQNQ8N3OmaHqOkP4Q0i70HR007XtRsobCyu3je4iSOKdU+d4oyWILAxrgjAr1mik4pqzX9Ws/vTafk2O7vf8Are/5njfh39gj4Z+E/AWgeFtOsfFdr4e8LWL6dpNgvjPWjDYwvayWuEBuz8ywyuqOcuhO5SrAMLHhP9kDwv8ABbSNPf4f2d9pmp+G9Bk8PeHob/xDqd5puk2z+UMLbSzvENvkxHIQOQm3eoYmvXKKcryvfrv57/5v733Eunlt+f6I8P8A2jPAGn/Bz/gnD8QPDVhIw03w38OdTsIpZmG5ki02Vd7npk4yT6k1zv8AwTP8JWPj3/gkP+z1ouprcvp+q/CLw1a3K291LayNG2j2oYCWJlkTIOMqwPvXoX7bqhv2L/i8CAQfBWsgg9/9Bmrgf+CYnifTvBX/AASP/Z31fV7610zStM+Efhq6u7u5lEUNtEmjWzM7seFUAEkmlVtNP2mqe/YdO8WuTdbdzufB/wCxT8NvBGkaDp9poV7d6f4W0ttG0e11TWr/AFSDS7VoPsxSFLmaRY2MGYjIoD7GZd2GIMFr+wp8LLSN1Xw1M4fSrDRP3ur30pS0sZfNs41LTEqYZPmR1w6n+KtrXP2ivD9vdaGLDXfCBh1DWZNIvE1TWf7PuoXjtpJ3iggMTNNcqFRjA3lnymaTdhQr73w4+MfhH4w6dLeeE/FHh/xNawLE8sul6hFdpEsiCSMsY2ONyEMM9Qcircm25t+r/FX/APAm16vuyLRSUVt0++35x/BeRS8L/AHwl4K1ie+0nS3sJ7uaG6uFhvJ1hupoolhSaWPfsklEaRrvZSxEaZJ2rjO+Kv7LHgT416pqN74k0SS+utX0tNFvZItQurU3VmkzTJE/kyICElZnU/eUscEZqWD9qb4aXXh251ePx/4Nk0yzjuZp7ldYtzHCls+y5Zju4EL/ACyE/cPDYp4/ae+HcihofGvhq7jy2+S1v47iOALKsLNKyEiNRK6IWcgBmAzk1KV2kvl91vy09NCnonf0+53/AAdn6nNaX+wf8LtHvrKeHQtTY6frcviGGObxDqU0P22WzNlJI0T3BR1a2ZojGymPax+Xk1v+Cf2X/BXw58baX4h0bTr+01PRdF/4R2xJ1i9lgt9PDq4txA8pi2qyrtJTKgAAgcVaH7Rfgx7yO3TXIpp54/Ogjit5ZGu48SHzIQqHzUxDKd6blAQnOKWw/aR+HupvEkHjjwnJLPfW+lpH/asAkN5PEs0NttLbvOeNldY8byrAgYog7Wcell+Dil914ryuglrdS+f387/H3vXXzKvjz9l/wP8AEu/8SXWsaNLNceLtOGlau8GoXNqby3EUsIU+VIu1vLnlTzFw+1yN2OKwE/YU+GaXUkw0rXTLLqem6y7HxRqpLXenxJFZy83P/LNI0GPuttBYMRmug0v9qb4b6+Mab458L6tMZltxb6fqEd3ctKzzIIxDEWkLFra4G0Ln/R5f7jY7Dwz4m07xp4estX0i+tdS0vUoVuLW6tpBJDcRsMq6sOCCD1ojp7y7r707r5p6rs9UEnf3Zdn9zVn8mkk+6R5/4P8A2O/h74CJbSdHv7WdpNQka4Ot38lxJ9vuDdXaNK0xdo5JyZPLLFFYkqBk11/i/wCF+heOrzSLnUrEyXegTPPp1zDPJbXFk7xtE+ySNldQyMVIzgjqDgVv0UdFHog6t9X+u5wPgH9mDwR8LvG8XiHw/o82l6nBpEehp5Oo3X2YWiSvMqfZzIYS3mSyOZCm9mdiWJJpPHn7L/gf4l33iS51jRpZrjxdpw0rV3g1C5tTeW4ilhCnypF2t5U8qeYuH2uRuxxXf0UpJSXLLVa/jdv723f1Y4ycZc0dHpr6WS+6yt2sux5XpP7FXw40XxcNeg0bUv7ZQWQivJde1Caa3+xwS29uYmec+WywzTRlkwXWVw5YMc9l4j+FHh7xf8P5fC2qaamoaHOqh7eeR3ZiriRX8wtv8xZAHEm7eHAYHIzXRUVTk3uTFKNrdNPkeVN+xR8OH+IV54rfR9Uk8Q3139ulvX8Qai7+d9j+wllBnwm62AjYKAGCqSCVBFSD9gf4SW/g5PD6+EIm0dNCPho2z6hdusmnYlAt3JlJdVE0u0sSybztK17BRSt/Xya/JtejY76WON+H/wAAPCnwv8Y6z4g0XT7mDWPEIiGo3U+oXN09z5caRIT5sjAERxRrkAEiNASdoxmaf+yj4E0wal5ek3jvqniD/hKZZJtXvZpYdTxt+0wu8paBiuVIhKKVJUjaSD6LRRfW/wAvxT/NJ+qT3QrK3L0/4DX5Nr0bRxdp+z34QsfENzqsOlSR6hfRQwXkwvbjOoJFK8sYuBvxOVeWQgyBjh2HQ4rm9e/Yf+GPiXw5ZaTdeHbgWGlwQWlgkGr31u2nQQXEVzFBA8cytDEs0EDeXGVQ+RECCEUD1iihaO6/r+rv7xv+v6+R5z8Q/wBk7wH8Uo9VXWNIvGOuy2NxqLWer3tg97NZSJJayyNBKhaSNo48OTuIjUEkKBWr8Y/hlL8QtE0h7CaK31rwzqlvrOlyzlvL86LcjRuRkhZYZJomYAkCUsASMHsaKFpZro7/ADXX8EHdd018mrNfceWRfsYfDu38Ya3rtto+p6ZfeJb86tqkOna/qNlY3t8Y1jN21rFOkBuMKh87yxJuRH3blDDkPCv/AAS1+Bng2y8LWtn4NvJLHwXoeoeGtIsr3xHql7aW+m35zd2rwzXLxzRy8bhKr/dUDG1cfQVFJRSXKlpt8rNW+5teja2Hd3vfX/g3/PX1PDrP9hjwf8LPE0XjbwDpV3D8Q9C8Lv4U0G71jxVq91ZWlkSGS3kiknkRoldVcDyy2UGCvUdx+zR8CNO/Zl+A/hjwJplxNe23h2zED3cwCy30zMZJrhwOA0srySEDgFzXc0VXM9bv+rt/K7k2+7d3cmy6f1ol+CSS7LRBRRRSGFfBn7fo/wCN5f8AwT6/69/iT/6ZLOvvOvgv9v0Z/wCC5f8AwT69rf4k/wDpjtKAF/4J7/8AKbj/AIKGf9fHw7/9Rx69H0v9mnWf2j/G/wAal8T/ANoeHPDHijxppUiQTaYyXmqabpltYtGsU5kwkUt1DNk+WXCM+CpdWXzj/gnuP+N3P/BQzn/l4+HX/qOPX1B8WP2xfBXwW17+ztcPiITf2pY6IJLTQL27ge+vSotrZZI42RpG3pkAnaHBbaDmiLtNPq9EvNyi16u6sl1u1bUGrxflq35Wd/Rdb9LbnqdFYPw4+I2nfFLw22qaYmoxQJdT2bx31jNZTxywytFIrRyqrjDIcHGGGCCQQa3qGgv/AF6BRRRQAUUUUAfB/wDwXe/5B/7H/wD2dB4F/wDR1zX1T+0B8D9Q+M2r+ALqx1uw0ceCPE0XiN0udMa9+3eXbzwiEETReXxOzb/n5UfLXyr/AMF3hmw/Y+5PH7UPgX8f311X2j8Q/ir4d+FGmpd+IdVttLglEjIZMlmWONpJGCqCxVI1Z3bGEVSzEAE0bNPs016p3X4jSbTj3TT9Gmn+DPl+7/4Jea1q3gzR9DvPiLpJtdM0XxBpDvb+FpIpJv7V1a21JpBm9YL5bWypjDbgzHIOBXd+LP2G77xV8YtR8aN43e3vtZvobjULCLTXSwuoYNPns7eMos4k3KbiSRmMhWT5UZCqjHoWq/tM+HbXxZrOj6dBrHiO48MRxy67JpFmbqLRlkjEqCVgRvkMbLJ5MIkmCMjFAHUt1fw88d6b8UvAGheJ9Glkn0fxHp9vqljK8bRtJBPGssbFWAKkqwOCMjvUuEZ0/Zte6v8A25uX4u9n622BSafMuv8Akl+Vvw7ni37Hn7Fmsfst+IVub7xnp/imzg8HaJ4Pt410A2M8UWlm68qUyi5kVty3TArsH3Qd3WvoGiitJzcnzS3f66kqKW39aW/JBRRRUjCiiigAooooAKKKKACiiigAooooAKKKKACiiigDzD9tv/kzD4u/9iVrP/pDNXkf7CXw5vPi9/wRR+BnhfT9cn8N3mvfBzw3ZR6lDAJ3tN+j2oLBCRnjI4ZTg8MpwR6z+3HCLj9in4wRlnUP4J1pSVYqwzYTcgjofeuS/wCCTv8Ayiy/Zp/7JV4X/wDTRa0mk1ZjTad0YVr+wDr+j+Om1fT/AB/pkNs3jq28c/Yp/Dss4E0WlDTTD5hvRI29FVy7FjuByDnNetfsr/BS6/Zw/Z98L+CLzVrPXZfDNiliL+2042C3Kp0YxGWUq3r85yfSuw17xVbeHb7S7e4h1KWTWLr7HAbXT57qON/LeTdM8SMsEe2Nh5kpVNxVd251B0qpNqHIttPwSS+5JIlxXNzPe34Xb/Nv5nxT4N/Yh8R/tEfs/wBxpWvalP4Kn0/xH45udLt7vQWaZf7Ym1S3huJc3C+bELPUXkEa+WS5j3MDGVPr2p/sP2mt+KzrV7qthd3OpeD7TwdrNrLpsjaffwWss0kUiW/n7V5uZwySGVWBTI+U7vbtJ1q0162kmsriK5iimltnaNshZI3aORD7q6sp9watVMEo6x8vwTX5Sf3+SKm3NWltd/K8lJ/jFfd5s+R3/wCCUWj3V9C9xrlh9lkJub6ytdOvLK2kvsTY1C1WG+U6fdZmO57MxBwWDBgRt6TVf2AtTe+gnsfHxMv/AAkHhvxFdzano5vJ7yfR7eC3wzrcR/69YFZjglXLH5gdtfSlFOHuW5eji16xd4/c/wAktkkKXvOTl9pNP0krP71/nuz508LfsFz6T4d8JJf+K7a61rwX401nxfYXlnpc1lBJ/ab3zTW0sS3TO21b+ULIsqnKISpG5W9v+GvgW1+GfgfTtDsorOC20+MoiWlsLaBcsWOyME7Rlj1JPqSck7lVY9atJtam05LiJr63hjuZYA3zpG7OqOR6MY3A/wB00LRWX9aW/IHq+Z/1dt/my1RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8Gft+/8AKcv/AIJ9f9e/xJ/9MdnX3nXwX+36M/8ABcv/AIJ88/8ALD4k/j/xI7SgBf8Agnv/AMpuP+Chn/Xx8O//AFHHr239r7wD4q+Inxr+CdtongnVdW8MeG/Gq+KPEepW11p8MUawadeRW4KTXEcsjC5kt2O1GwsfGcYrxL/gnv8A8puP+Chn/Xx8Ov8A1HHr7pvNbstPl8u4vLWB8Z2ySqpx64JoWkoy6ppr1TTX4pP5A9Yyj/Mmn6NNP8GTwW0dqrCONIwzFyFUDLE5J+pPNPqO1u4r6ASwyxzRt0dGDKfxFSUAFFFFABRRRQB8H/8ABd7/AJB/7H//AGdB4F/9HXNfYfxQ+BnhX4zG2/4SXShqX2SC4tEH2iaENBcBVnhfy2XfFIEQOj5VgoBBr47/AOC73/Hh+x//ANnQ+Bf/AEdc1913upW+mqpuLiGAOcKZHC7j7ZoBeR8+/Ab4beJf2WPiT8WrQeGdU8UaL8QPFs3i/SNR0+a2HlNcWtvFJZXCyyo0Zje3OxwGQxMgyGXafd/CGmy6N4S0uzmt9PtJrS0ihkgsY/LtYWVACkS/wxgjCjsAKuJfQyXTQLNE0yDc0YcFlHqR1qWiOkVHskl6RVkvu0+XqD+Jy7tt+rd3+Ovz7WsUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5h+23/wAmYfF3/sStZ/8ASGavFf2OdL8Ra5/wQu+DVj4SspNR8SX3wV8PW2n28eoHT3klfRrZRtnDKY2GSQQ68gDcudw9o/biDn9ir4weWVEn/CE61sLDKg/YJsZ9q5H/AIJO/wDKLL9mn/slXhf/ANNFrSkrqzKjJxakuh5lZ/C/40aP42htm8PeMJ/Dtt8SI9XiOneIrSyh/sSXRfIuIDbC9AEQvt0giLOQzhl5BavZv2EPhp4j+HX7Nfhu38aJ4th8Zy6fBFrceu+IH1eUTxJsLI4uJolBAB/dsNx+Zstk17NRVRdocnp+CUfyX33Zm4rmUuyt+Lf33b/A+Lfhn+yb8SPh2vhvSYdNuE8K6Z4v8Talrdi2qi8Gsw313qMljMsb3CgpCksLFHdWMkofYWhBNPVfgT8ZvDN/rNvpVt8VtWiiuPA8VjfS+MrUSXUNrcqNbkYfbI1V5LUFZPkXzGJKAk7j9vUVMVytPs4v15dk/Lv5fIqWt/O/4u7PhvRB468PftPeEND8V2nxCX+05vHcunaTH4tRJb/TRdWUuntmO9CHy45nWPzHEke7ACheNv4Zfs2/G3yYj8Sdc1fxDqA0nw21vfaJfxRT2U9oxa/tvMkljwZ24llWM+fHIVJTauPrq68M6ZqOtW2pz6fYT6jZqVt7t4EaaAHOQjkblByeh71eV1fOCDtODg9DQl7ij2t/5Lez9Xe77vy0B6yk+7b+93t6LZeR8TaD+zr8Wj+zZ8PNJuNJ8aSeKH0S+j8Tzav4xOoXFpfrYG3tSitetB8zuWE0bMY2jVzGXbes+ifsy/FTXJ/G2uahpOtaT4w8cfCzTPD41OPxWYZtP1i1g1VXkZrebh2lms2DRgqvmSEPlW3fadFE1zKaf2rfKyktO3xa+i7a3Cbi4NfZ/wA4y176xX3vufFPjL4WfGrVNb11otC+JTWt1qXga7tTb+MbaMotpcD+2ggN+vlq8AYOi4WUsCAzZI9B+HX7OHjbwv8AGrU59R1TxHd6LYeJ7S/8O3/9qm6lj0iPTIrZtPupbid5nXzhcSOpjcySSRyGTfkp9K0Vop++593fy+zpbsuVW8nJbNmMYJQVPolbz2a3+evnZ7pBRRRUFhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfBn7fv/ACnL/wCCfX/Xv8Sf/THZ19518F/t+/8AKcv/AIJ9f9e/xJ/9MdnQAv8AwT3/AOU3H/BQz/r4+Hf/AKjj10n/AAUL8MfDW6/aE+CfhzVF8I/8JH49+JWn6nqzai1sbp7PTLC5uokJf5xCZrW3RV+6Xlbuxrm/+Ce5/wCN3H/BQz/r4+HX/qOPX3nQtJxn/K0//AWmvxSB6wnD+aMo/wDgSa/U+P8A4nfHnUf2VPGnxX8KfD628Fw2Pg7wva+JtI8NjS3N5rWualeag32KIRTJkSvCgASIlWuAxJAwbaftpeOdU0LStVi0mO11K+8ReINO1Pw6tt9on8O6Vp0N+Ev58EP5jz2tsRkhHW8RFUkiQ/Q3gb4K6V4A+I/i/wAU2lzqc+qeNpbeXUPtM4kiXyI/KiWJcDYoTjH49SSevrOcJSpuKlZ2tft1/Db02sUnHn5raXu/PSz/AB97/E3e60PH/wBgnW9c8Q/sleCL3xLrkfiPX7vSra51K+S28lZLmWFJZgPmZXxJI4LJhcggAbcV7BRRXRWnGc3KKsn07GVOLjFKTu+4UUUVmWfB/wDwXe/5B/7H/wD2dB4F/wDR1zXd/wDBYC60W1+D3wyOqN4aW5/4Wn4WNp/bLxpDxqcHmElsnYF5fH8Oc8Vwf/Bd44sP2P8A3/ah8C/+jrmvvGlb34S/llGX/gMlK3zta/Qd/dlH+aMo/wDgUXG/yvc+af2FviPoHin4ifEPQ/EVv4Yt/j54XvxZeMZrMRi41W1ID2F3DyZBZPbtHsiJIicSIxZwXf6Wooqr6Jdkv68r9ibat93/AEvl+W/cKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPMP22/8AkzD4u/8AYlaz/wCkM1eJ/sg+P9W+E/8AwQd+FHirQnsU1nwz8CdF1azN5A09uZbfQYJVWRFdCykpg4YHnrXtX7cUph/Yq+MDhHkKeCdabav3mxYTcD3rzH/gnl8ONP8AjD/wRn+A/hTVnu00rxJ8G/Dum3v2aXypWgl0W2SRVbB2kqSMjkZ4weazrKThJQ3tp6mtCUI1IyqK6TV/TqZOs/tw+M7bxlY6NZ2mha1ol5F4Zl1DxXplu62Xh4ajPPHdJMGlkQuixRMhLYQXUZkUqAXxfDP7TXxA1r9sLT4769RfCOm23ibS9KVLD5PGDwXmjpbSxyKwXzCbiWHcoKA28zhQrEJ9geFfD0XhHwxp+lQS3E8Gm20drHJOwaV1RQoLEAZOBycVfrWolztwenv2/wC3klHy9zVrzet2kznpKShFT392/na/Nrv7ztf00tdnxFZft2fEa18D/DDX9Q1PwLpA8d3Ph6z1Sw1SIfadIvb64aO8tkjjmUosSsoUTN5oe3lDAgny7nwd/a98R/GX9o3wLqFx4o0C08NNa+MdDuoLG0M9pf3lh4gsrC3fcJSVkeArJjJCq0jfdOV+0aKE1zXt1k/lKPKl6R1a82r7XKkr7dor7m23/wBvaX9PM/PrwB/wUTvPCXwk0eTQU8BWFhoQsmvdP0TS00/SFhn8TXOnTPGDMzY+zRPNthyInVnlcqyofe/2Atc0TXZfjcfDd1pbW5+I9/MotQu2JpLOybcyLggltxIOCTuzzmvoqilT91yb1vGUf/ApQl+HJZLTcdT3kktFzRl90ZR/Hmu2fEnwJ/ak8f6X8Ofhvo+q6oNRu/Eeu69beItdaxjt20V4pb1re1YzSeSssskR4bG1I9gQM6MGar/wUN8b+C7/AFm01rXPhSmoaLceB7SeCJJjGJdbuVtrwq7XCsUiBaaPKAlcBuPmP29RRHSSb7xb9E9V5X2f9XHqtOz/AB6/I+PPBP7fWv8AjP4s6ZoreJvhxpukm/8AGOlXt9LbPJH5mj3VultKD9rUJvin3SISc7CVKg4EXwK/bb+KHxsiW81fQ9I+HNzC+hSDTdZAjS7sbyzilu7xd7Cc7JZZViUKFU2jLKRuZo/pnxL8FtK8VfGDw143uLjU01fwpaXdlYxxThbYx3Xl+cHTHzE+TFznjYMdTnr6SXuq+9l96er/AO3u2yWnZofxSfRuVvJPZf8Abq0v1373+JdB/bx8da7+zZ8PPFkviHwXc6t4+0S+1aCLSNIZLe3ktLAyPE8811Ih/wBIaPMajzmVmRFJVpFj+G/7X/jjxi3xa8Y6ZrOl3Vy/w/0vX/C+hyaPNcW4uRp93cSquyVHki+0RSxlgRuYoA3Cg/b1FE7tTS0crW/u/Ff1vdf+A+ZcJJSg2rqO676p6+lmvn5HxT4y/wCCjuvaTreu2tt4j+Gtsunal4GitmuIXwYNauBBeBz9qG5ohmRGXaAEKsDgtXoPw6+PvxL1v41an4c1lvDlvN4b8T2mi3EX2b7FFrmnPpkU0moWsTSyzhmupXCDLxLHbyIzlwzr9K0VopLncraN3t2Xu2Xy5ZevM9LpMxjFqCg3ra1/Oz1+9p/Ja2ugoooqCwooooAKKKKACiiigAooooAKKKKACiiigAr4M/b9/wCU5f8AwT6/69/iT/6Y7OvvOvgv9v3/AJTl/wDBPr/r3+JP/pjs6AF/4J7/APKbj/goZ/18fDv/ANRx69o/a1+L/wAT/hP4y8IQeH9U8K2tt458caV4U0u0u9Hku5hbyRPPe3LyLcoN6xQ3BRNoA8oElg2B4v8A8E9/+U3H/BQz/r4+Hf8A6jj19S/GL9mG1+M/xX8BeLLzxV4q0yb4dajJqumafYix+xS3MlrPaM8oltpJG/c3EqjbIuN2Rg0Je/FvZNN+iabXzSa+YP4JpbuMkvVp2fydn8jgfjl+1RrvwD/Z5+IjXOr6BrnxO8I2M93ax2mh3UtihmlkXTEuo45W8tpcRqQZlJyXAC8Dodc/b4+HXgzwrdavqurXj6Zp1pqdzLqNlpk9zb3Q0x0i1AwrGryOsLuQSFIIRypYIxE3xQ/Yj8OfE298VzjXPE+gnxtq+ka3rC6bJalbq5017doD+/glwrLawI6jhlQ4ClmY2PCf7FHgrwZ8K9X8FWaX3/CNaxp+o6XJaN5GYre+d3mRZBGHbHmMFMjOQPU81LcuVvrb8UtPvd152vpsVFRc1zaK+vo3r93TsrrXc0/BP7WXg74ieK5NF0iTXbrULfV5tDnT+w7xUtbqG3iuJBI5j2ogjmiPmMQhMiqGLECvSq84+Bv7Lfhj9n3WNYv9DbVJrnXI7VLl725E5LQW0VuZQdoPmSrDE0rHJdkBPQCvR60korSP9f1v5ba2u8qbk1eej/r8tvPfS9kUUUVJZ8H/APBd7/kH/sf/APZ0HgX/ANHXNfUf7SWpfEu102yh+HOn2tzPPb3jTXEjwAwXKxD7IrCZgPIaViZWUO4SMhF3OGX5c/4Lvf8AIP8A2P8A/s6DwL/6Oua+8KBp2dz5/wDhD8ZfGP7TnjT4nf2Hq9j4Z0r4beIpfCVlF9gW5bV9Qt7eCW5mud5yLbzJ/LSOIxyERs5l+cKvsPwpm8Q3Pwu8NSeLY7OHxXJpVq2tR2n/AB7pemFfPEfJ+QSb8cnjHJrk4f2aLTw78QvEviDwx4h1zwq/jSdLzXbOxW2ktr66WJIftSiWJzFO0UcSMyEKwjUlS3z16Doei23hvRLPTrOPybOwgS2gjyTsjRQqjJ5OAB1oj8CvvaN/VL3muyb1S7WVlYTVm0tru3pfRPu0tG+ru+paooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzD9tv/kzD4u/9iVrP/pDNXnH/BNnx3o/ww/4JAfs9+INf1C30vRtI+Efhm5u7qYkJDGukWuTxyT2AAJJIABJAr0X9uCRYf2LPi+7sqIngnWSzE4AH2Cbk15V+wF8I9E+PH/BGP4BeEfEUEs+j658I/C8M6xSGORMaVaOjo3Z1dVZTzgqOD0pPbQatfU6jwz+2ZHa/Hnxx4b1940sLPUdF0zwvbR6VcWmo39zfWk07QOszYdgsEj79saqiPu+6TV3TP8Agop8LNV1eKwj1XWVupLzT7Aq2hXoRJ726ns7dC/lbObq2ngYhiqSRMrEcZXXv2DPDHi34nX3i7WNd8T6trF1f6bqkBuRYmLT7qyt57ZJYkW2AJeC5njdZN6kSEqFIBFj4pfsJeCvi/Naz6tPriXtno13o0V1aXEdvIizzJOlwAse0TW8qloGChYzI+FOeGt1fa/4JL8XK6v0TTs2mmnre29l/wCBX1+Sjay3vdaKzXY3f7QvhvTvghqfxEuW1e28L6PaXF9dSy6TdJcJBBu8yQW5j81lARmBVDuUblyCDWVc/te+Aba5S0bV5k1Sf+zhbabNYz217efb3dLMxRSohZZGjkG4fKvlybyuxsSftIfC+98V/sleNvBXhezjlvtV8LXmhaZA8wiQPLavBHuduAo3Ak88A8E8Vx2i/sO+H/FTaH4m8USa5L440+x0aO3vnuLcy6K+nNJLEkAjiERHmTz7yyvvEhBOAoDjZz1+FOPrZ83NbzVlbor63FO6gnH4mpeia5eW/k7yv1dtOpY1b9t/wdpPjbVUuPEZtrHwvomrahrej/8ACN3s+oWr6dNbLdSfaY2MWIUuIy0Co7yLPFKjbPvnxE/b28HeE7bShpces67eahrmkaRJawaVcrLapqOXhnZGQMUaJXKlQdzLt6g4z/Ev/BObwn4i13xBqSeJPGWnXnirStc0vVpLWSxzff2v9lW6uG8y1bEoSzto49uEVYgNhJJNmP8AYA8Ot4judVuPFXjW7vZpNCnieSSxUWsujhxbSIEtVyWEkgkD7lO87QmBiad+WHPunDm7fG3NLrblty9delkk57y5f79vuSp3+d3L00v17T9pH9oS0/Z08AaXrt1pmqamuq67pehxw2lq80iNe3kNsHZVGQF83OMZJAUDJqpqn7XvgnSfEdzpsl3fvJZammh3EkVlI6Qai9n9tSyIA3ecYCrYCkbnRCQ7Ba3vif8ABHSvix8PbDw5qN1qkVvpl9p2pW11DMpuUuLG5huYHLOrBj5kKbtyncCfXNc14a/ZB8OeC9c1zUdHv9a0+88R3Ed9fyqbaaWa7S0jtPtPmSws4do4YSw3bGaPJX5nDTLmSny6u7t6e7a//k/z5d1cas0vT8df/tdOqvqnYo6T+338K9Z8N2+rw+IL1dNudDs/EqTy6LfRKNMunMcF226EbYmdSCTjbjLYHNaI/bS+HEL3/wBt12fSYdL1ufw5dXOpaZdWdrFfwxGZ4TNJGsefLBZW3bW4CkkgV55/w7B8MD4dt4YHjr4iDTm8EWPgDd5mmeaNPs5WlifP2LHnZdlZsbSD90HmqvgP9hofEf8A4Tmw+KFtf3vh/UfH1x4psNKa7t3tdSQ2scEUk/lRiQMGV5PLDhd2w4OMDSprOSp7Wk1fyqRS++Dbt3XRJgrJXl3S07cjb/8AJ0l6ebO7f9vX4c2yab9qvNatn1lNMmsIv7HuZpbqHUvOFjKEiR2VZmglVQwVgy7WVSyg6Vz+2L4MtNbt9Pl/t+C6vjJFZxXGj3FtLezx6eNRa3SKVFl80WpL4ZAAyuhIdWQZE37DmgDwh4U0S08R+KtNs/B1/pt/YG2Fgru1gW8hJS1qQ6/MAzEb22j5xzmPXf2D/C2v/E4eK5Nc8WxakPEr+KCI7qDBnexawaAuYTL9n8h5AI/M+XzG2lRwJqrSapvX3uW/a0eW/nrK9uy7kxunFy7K/rre3lpG1+77Fj4Sftt+FfiH8L/hxr1/Drei3nxL0ZtX02xk0a8kaQR2Yu5kDJGykrFkqM5k2nYGqC2/4KH/AAnutGl1Ia7qq6ZB4ftPFUt6fD+o/ZoNMure4uILqSTyNsaOlrOBvKnegjxvZVNvw3+xT4c8J+EfAukWWt+KkX4co0OiXL3MDzQQnT/7P8ogxFCvkY6IDvG7OS2eb8Af8E2fBngvwDrPhW917xf4o8Oa94I0/wAA3dhq0tnsbTrJbhYGDwW0UgmAuZcvuwSQdoIFXVacp8m32f8Aya9//JUvn5DgtI83z+9fpd/ceufDj4w6P8TtQ1mxsftdtqfh6WGLUbK7i8ue1M0KTxEgEgho3BBBOCGU4ZWA6que+Hvw6t/h3YywwXl3emYIGeeOCI/Ku3O2GONMnkk7cknrgADoaJWvoTDmt724UUUVJQUUUUAFFFFABRRRQAUUUUAFFFFABXwZ+37/AMpy/wDgn1/17/En/wBMdnX3nXwZ+36P+N5f/BPr/r3+JP8A6ZLOgA/4J7j/AI3cf8FDP+vj4df+o49fb/izxbbeEfDGrapKGuI9HtZbuaGF0ErLGhcqN7KoJAONzAepA5r4g/4J7/8AKbj/AIKGf9fHw7/9Rx69F+LH7E/ijx7cftB63baH8Ov+Ex+IdsNN8H6pcX03m6VB/ZTWDSyyCzLQS/v7pj5XmbllCFsCsqvO4SVPezt69PzNaKhzx9ptdX9Op9G/CX4i23xf+FvhzxXZ2l7p9r4l0y21SK1vFRbm2SeJZVjlCMyhwHAIDEZzgnrXQ14t+y3+z3rHwX+IvxD1G+sfDmmaPr0mmW+h2ek3ss4t7SzsUgxKrQRKshl81sqXyrJk5Wvaa6q3Jzt0/h6ehzUXPkXtN7a+vUKKKKyNAooooA+Dv+C7wzYfsf8At+1D4F/9HXNfV/x4+Oz/AAan0C1t9IGrXviCS8WJZLo2sMS21lPduWk2PyVh2qNvJYnICmvlH/gu9/yD/wBj/wD7Og8C/wDo65r7G+Mvhm98W+CJbLTLHS7vU5ZUS1m1CFJotNZjsa6COCGaNGdlX+IgKSAxImV7ablQtf3tv6/r9GUfhb8Ybj4peJdat4NAu9P0vRBFbyX1zcxsZrxhvkt0SMsCIlMe594w7lNuUbHcV418Hfgl4i+FnxP1OJL25k8KpdrLppe+aRRZmwhjeCSInm4e+E9y8xBLeacsSxUey1pK2lv6/r+m93nC9ve/r+v6tsFFFFSUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5f+28gk/Yu+LysAyt4K1kEEZBH2GauN/4JRzpbf8ABK39mt5HWNF+FXhfLMcAf8Si1712f7bf/JmHxd/7ErWf/SGavD/2SfhdffG7/ggz8J/Bulw6TPqXin4F6JpVqupytFaLLNoVvGjSMscjBVLBshGPy8CoqScYtpXZdKKlNRk7Jvft5n1pqGv2OlTW8dzeWtvLdv5duksyo07/AN1AT8x5HA9a5D4OftA6N8YvA8etpFc6AJdY1DQxZ6rJBHcG5sr2aylUeXI6NmWFiu1jkFehOK8Rl/4J+St4iubuK08O2WleIPBlt4d1bRLK7e1t7a8iuZLiS7hmS3EjtO0iGRx5Mha1hbcTgpxXi7/gn98U/Gfws8RaDJeeAtPm1ay8ZQW0lvqFw8kE2s6ub+3ZpzZh/LjUpvSMIfMiU7pFOF0aSkl096/la9vXmtp6rszKDco3at8P3Na/+At2fo+6Pq6++MunS3XiSx0S3uPFGt+FJreDUNK06e2W5ieZY5AMzyxxgiKRZCGcHb0ySoPUDU7ZmwLiAsH8vHmDO7+79favkX4rfsF+MvGvhf4zJa2Hwzn1b4rCwvBLcPND9ku4bWyhYGQW0jbBJatKr4LMzLlUI3V0PxP/AOCeY+Ini/4mNa3WieH9D+IeltfxG1tA97o3id7GbT21SJtqji2eIggq/mRluCxNRNtRuld2/Gydvz172XW60ppStzOyf4J/5XV/K76WPpbStdstet3lsLy1voo3MbNbyrIqsOqkg4B5HHvXMfA7402Px18CSa9ZWGpaTHBqeo6VNa6h5Qnhmsr2ezlz5Tum0yQOVIY5Ug8HIGB+zp8Dbj4Sm9vb620+DVNR0zTdPuDZXjzQv9jikjTanlRIihX2jCbiqqGYhVC/MXi7/gml4+8d+DNS0S6t/hzZJrtt4/g1C5TUrm5aRtevvtdg5RrNfM+zsE3bmG0oCmcCrmlGbjHVWev5feRTvKEXLRtq/krO/rZ2PumS6iidVaSNWY7QCwBJ64ql4m12XQvDV/f2enXeuXNnbPcQ6fZSQpcXzBSVijaaSOIM5GAZJETJ5YDJHyb4u/YU8a+JviL4i1VtF+Gr2Ot/EDw34wWN9TuA6xafawQXasPsRBlk8k45w6yEMVwc8xrn/BOr4j6l4Hu9Ji0z4YL/AGhZePbG5V9aujHLHrl0Z7BG/wBA+ZLcBFZSMLsXZnAxEm+STW6vZd7Ri197bj/27frYulZygpaJ2v5Xcr+tkk/+3rdD61+MHxus/gt4Z0PVNS0rVrqHW9b03QvLtfIZ7KW+uY7WKSXdIoMayyoGMZdhnIDCpo/jPpthrGk6ZrcE/hvVtf1C8sNMsb+e2aa+W2EjmdPJlkHlPGgcbiHAkQOqOdo+c9b/AGHPHkugXRDeDNc1m78YeFvE8NxqWozxtpNvpf8AZ3n6fDILV2MebOd42AQE3TblU7mZvh39hTX/AAtr2i+Ib3S/h8G8L+NPFPiOK0jWa+jFlqgmeLaotVYzRu6kwquGIO2TOK091c3M9m/utTsvvc9b20e6szKDlKEXbV20/wDA7/lHS19Vtqj63g1K3uSgjnhkMgJUK4O4DqR61NXzv+xL+zjpXw4u9b1iysrBvDkGo3g8C7tKazutK0u+kW9uYdsirIim7lkQLhcR28IxX0RSaslfdq7Xa/T1S37O61td0ndu219H38/n08vuCiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvgv9v0Z/4Ll/8E+va3+JP/pjtK+9K+DP2/f+U5f/AAT6/wCvf4k/+mOzoAP+Ce//ACm4/wCChn/Xx8O//Ucevs5fjH4Ub4ot4IHiLRz4wSzGotov2pPtwticef5Wd3l543YxnjOa+Mf+Ce//ACm4/wCChn/Xx8O//UcevWv2o/BXi/Rf23fhF4w8GaVeXb67out+B9ZvYYd8OkRTi3vba8n7BIns5goP3nmVOr0lfniujv8Ak7ffKyfZO/Qf2X30/NX/APJbv1sj1KT9r74WxT2MTfEDwkJdUubmzs0/tOItdz2+ftEUYzlniw28DJXac4wa7Twh4z0f4g+HrfV9B1XTdb0q73eRe2FylzbzbWKttdCVOGBBweCCO1fMnxH+H+r61/wUB+Eug+HLfxDofh74deC9bnGtP4eubrT/ALddSWMMUX2p0EBnMCXTElycscgk4rgviJ4c8b/shTePPDHwqufijNY6X4Sudbt518LPqya74r1XVZpnujKtmySNGWaSWKErGqTKNqhacGpcv97mt/265LbpfldvNx6O6JK17dOW68pW69WuZX8ubW6s/uuivjPxZ8b/AIs3njjxno1lpfxMv9H0zWbxtMvW8OT2CXdrb6HanylkS1WRzNqNzI0JTarG0kVpgmEapD8WPizruofDCafVfi7p2n6lpOn6H48Ft4EdZtL1N0WVNQtA9kxMcj280Nw2JI4Eu4jiJwdpTXO0l15f/J02vPdWemjlHvpMnaPM+0n/AOAtX/O/mk+x9r0U2FDFCilmcqANzYy3ucd6dQNHwd/wXeH+gfsf/wDZ0PgX/wBHXNfeNfB//Bd7/kH/ALH/AP2dB4F/9HXNfRv7WnifxX4c134Wp4XuvFNrb6l4uis9dOj6KdSUaa1pcl2nIgl8lBKsGJDswT1IyKOqXdpfe0vu11DpKXZN/cm7LzdtPM9N8a+PNE+G3h+TVvEOsaZoWlwukcl5qF0ltAjOwRFLuQAWZgoGeSQBWtX54fE/4gfGb4vfAJfDmo6b8SNWn8UeGPEP2+KfwTPam3u7XWrOPTCCLRBHJJZ/aJNrnEm3cFxgV698QPiJ8W5Pipf6NZ2fje58FSaxHHp2sx6W1tMEGkXD3CTrFb+cYVuvsxidUQPLujaTYMNE58tP2lr7u3XR8u3e6uujTRUY3ly3/q1z6xor5N/Yp+JHxW+L3xMs5PHF58RvD1vZeCvDuo3mnX/hRNOsL3WHivotTh86W0ztDi0l8uOVWVgMfIWQ/WVbVIcknG99/wANDOMr9O3+dgoooqCgooooAKKKKACiiigAooooAKKKKACiiigAooooA8u/bii8/wDYq+MCbmXf4J1pdynDDNhNyD2Ncj/wSd/5RZfs0/8AZKvC/wD6aLWuw/bb/wCTMPi7/wBiVrP/AKQzV4n+xtf6/p3/AARC+Asnhe4vLTX2+FHhGKzmttMl1JomfT7FCWgiZZWj2sd5jIdU3MvzKKUnZXHFXdj67psc6TM4R1YxttcA5KnAOD6HBB/EV8M+Mfiv8e9H8I3/ANn8OeNbPXtDsLi+hNjDd6tpuozjVU2pb77VbiTNskgMV1sKxyDZ5zYkG3oA+KnhH4f/ABc1DRG+JcHiG/8AHlvrdgl5pcM+dKefTfMijV4W35tWuFKRbmT7OVUo4Gbpx5pJdHFv7pRjZ9tG33tHYmb5b+UlH703deWiV+7R9m0kkixIzMwVVGSScAD1r42T4l/FvxR8UbPRf7a+K2i6ZefE7VtHOoxeCh+40FtFeS0m3yWDQrGl95SpM4IJJVy/OOb+NvjD41eLrLxnomm6b8TUtPEujeNNJgC6a4uIrmKSNNJlWVYUgt0dBceW0bN5kbKXcylAuTn7imlvHm++KlZ+eqT7O5rCCdVU2/tcrf8A29JX9PdbXlbufbbeNdGTxXb6EdW00a3d2b6hBp5uU+1TWyOiPOsedxjVpI1LgYBdQTkitOvjL4nfET4w+F5PAOkeBIviFc2E3hOUaut94bz5DxarpKPOJfsw2XY099UMULsC5iT90zY3aviPx38V9K/aB0eytJvH1n8ObrxwbO3ng8PzX1w+knw9LLObhpLeSSKJdUWJY5ZNrMXdQWQLjWaUfvkvuly7ee676vTY5qU3OHM1bS9v+3btefb7vU+uKK8Q+B2q/ED48/sI6TLqepa74L+Jes+H2hl1G50YWN7p2oBWVZmtbiEKDvCsVMW0gnAwRXg3h7x1+0NqPw58T6jrEPxP0+8XQI/Eum2dlpti91FO1o9lJpHzWx3ul5G16CAXMcka4YfK0vSUo9vx329Em35ebSNkrpPv+G1r+rdl572PuaWdIdu91Te21dxxuPoPenV8PeIdQ+KnjXxVp9vqNr8SoNK8FfFKC8gm0/TJ/wDSdGl0NyArzQ+ZdJHqDyJuZPlypbaFXbY+MXi/4+WHwm+I+t2134v0bxdo994iOlWOk6BNq4vrMRyHR1giWB4d3y2rOymR2aSeNgBzER1jzeV/laDt6+/Zr+69RP4oxXW33tzX3Winf+8lY+2aK5HRfDGp6r8JZrEeLPEjalqlvNJBrN1Z21vqFiZizx4h+zpGpiDBVWSIthBv3NuJ+UrjxZ8eLnwJ4dl1H/hY0GvyaVqGk38Vvp8f2Iahp9pJD/aH7i2M2bu7YSQIzLA8cKkkBtrxVn7NSb+yr+u97east7bq3W1Uo+0cUtObv0239dX6J36X+26K+HV/aN+MXiA+N10i48ZJ42tfhx4a1vRfDE/hyNYV1y4m1GGeOcNb+ZBBM9pEG82VPLjZ3BQjK9H4r8a/G628JW1zHB4ptvFH9teJ4/EKWmmSXFraaRFDqB0qSyXynjlmLppewRh3k82bepwwWsS/Y899eW+2t+V2du/+QUI+15baXtv0uubX8vXRXPr+ivKvgA3ibVP2W9O1HxH4g8T3PiXWdEivbqXU9Mh0680q5a0QSxJB9nj2BJldgJkdgWIJKhQPluy/aE+OHib9k7wn4k8Pan471u51b4W2GuX+pQ+E0nuF1+S4sDEltDHaDz0mgkvjKqRuqLDEQY9+W0nDlqyot6xaTfTVTe//AG416tJXuZ0pe0pqouqvrv0/+Sv6Jn3zRXyP8SLj4z6P4l8cW9p4j8Ztp+nWelX/AIS1eLQBfXN1E0kst9DJaQQRQyXBBECpMYyiLC4y/mM3Fan4v/aJtNLubZNO1G9vrS0u7jZbJrMFnrai2swj21w8DT2F6rmXbb3CXFs7PIfM/d4WIatr+v6/G/S12U1a3n/kn+v9aX+7aK+QvGPxK+L/AIW+Jdvp9zefESXSX+IurWU01h4US8jGgPoM01pJ5kNm2FjvxGqODuLfJIX6VleAdX+OviX4ffDO51fXfHAh8U+Arq81q7u9BNlfaB4l+z2CRRvawWazCJWF9MI5lKPISrPt8pKmEuaPMl0i/wDwKLlb1VrNfzNK+pUo2nyPu15aS5b+nXT7Otj7Soqj4YN4fDWnnUHEl+baP7S4i8oNLsG87Mtt+bPy5OOmT1q9Wk48snG97GcJc0VLa4UUUVJQUUUUAFFFFABXwX+37/ynL/4J9f8AXv8AEn/0x2dfelfBn7fv/Kcv/gn1/wBe/wASf/THZ0AH/BPf/lNx/wAFDP8Ar4+Hf/qOPX3nXwZ/wT3/AOU3H/BQz/r4+Hf/AKjj16N+0V40ufgJ/wAFGPhzrjXet3mmfELwlrHh+10canOLK41u3ltJ7PbBv8lZZYGukL7chY8k4UkJP31Hvf8ACLa+9pRXm0O3ut9rfmk/uTbfkj6tor4s+Jfw4v8Awr+1/wDBjwFpU/i7xG9t4d8R+LPEcCeLb62j1CQyWVvEZC85/cia7laOPkII1Cj5ak8B/tNeK/2QdLi8I+NdVsvG13YeJ7W01K7jv7q9bRl1W9uJ0sDdTIC5sLBreQNKWlnj5KRDaxFJWTb0bsu1+bk/9Kur7aataXJJ626avulbmv8AdbTfVWVrtfZ9FfJ3xD/bH+IHij9k7QvHXhXR/DWmJ8SbbRxoC3moSG9sDquo21rB5kaoQ7CC5ErMCFjdSuJANx+pPDepNrPh6xu3e0ka6t0lL2splgbcoOUcgFlOeDgZHarcWk79G0/Jrdf1+TJur2Xr8i7RRRUjPg7/AILvf8eH7H//AGdD4F/9HXNfeNfB/wDwXe/5B/7H/wD2dB4F/wDR1zX1R+0F8bNU+Dmr+ALXTdFsdY/4TXxLH4dkNxfta/YjJbXE6zDET7wPs5BX5fvZB4xR1S7tJerdl+LDo5dEm36JXf4I9Ior4a/aR/bp1n4kfsx3aW2iXfhTUtd0HV9a02803xFNHNazaPrNlYyxu8ccbBJWuAwIJBQEMnOB6144/bluvDfxY1PwDHounQ+KtMvoLfabqW7iu4ZtNuL1HiVIlZmVrfZKG2LEkiyFyCFMSqRjT9rJ6a/cnZ/imreVylBuXKt/+Bf8j6Lor56/Zk/bV1r9p7xnp1vpPhPRbbQbrwpoPiy6vJNfZrm0i1OG9PkCFbcrJLFNaBCDIgKybsgrsb6FradOUJOMt1p9xnGalt/VwoooqCgooooAKKKKACiiigAooooAKKKKACiiigAooooA8u/bi3/8MVfGDytnmf8ACE61s3fdz9gmxn2rkf8Agk7/AMosv2af+yVeF/8A00Wtdh+23/yZh8Xf+xK1n/0hmryH9hf4ox/BT/giX8EPFkn2A/2B8GfDt4iXtwbeCWRdGttiM6q7LubavyqxywABOBSbsrscU27I+raK+Z/Cv7e174u+Ldt8PYNDtrfxRceKL3QPtN0Xit1it9Ih1QTtAf3qPIk6RCJmBBSVyfkKVgWf7bHj7wToXx/8Q+ItE8IXh+GFhDqFlotnr0oE3l6Na31xElwbc71zNJiQRjJCDaAS1NbX6WUvk0mn+Py6ijq1Fb3t872/r7z63or5t8S/t2ajY/FPxB4Pt9C0aDVdJ12LSbQtqE13LqET6L/anmiBIVKlSUjcF9kat5hkPCGt+yz8d2/aA/aVtvFNldalbaP4v+E+geIho76pJdWun3E91eFwse7y1lChUZkRS2wZ6U4Lmlyr5+V4ykvvUX6dSZSSp+06aW87yjH/ANuv5o+m6K+Zvjn+3rrHwF+P1x4S1fwtosejxXOkTrqj6xKr/wBlXss1tLfNH9nIUW91HFHIu8gLcxOWGcVDo37e2tWvj7QtF1/w9oFi+o69b+HNQFnqU08WkXM+kS6mvnXEkMaB1WNEeEKWVZBIWAKoyh7yvHvb56L82l66bp2qfutqXRXfolf8tbb2s+qv9P0V8ffAv9u7xn4j8LeBdF1LSdI1Dxp41g8R6sklqbie2t4NPvDFHCyKm9XkeRETJIWOGRyWK7T6x+zt+0p4s+OHj/xDpl74L0rQNO8NPZx3sza+bm7je60y1vkjESQeW5Q3DRuwm2/uwylw2A4q97dP82vzTT7dd1cem/8AX9fj8me00V4V4+/at8QeB/jrrngibw94dhmhs7LV9Fu7zW5LeLU9PZpxqE0h+zsImtFh3MuWDedDlk38ed6d/wAFK9WubK3jXwro11qN1/wiFzEj6nNZRTW2v3r2WY98DSP9nkTdvMaiVGUgJkZUHzW5erS+bdl+Kavsmmnswn7ral0V/lZP8mvvXc+uaK+fPDf7a1546+O03w1stBt9M8R2Wp6xYXd7PO11pyjT7XTbnMbKI2d5U1W2G07dhjuPveWN1f8AZX/bb1/9qXxdZQWHgnTtL0Sbw5o/iS6urrW5BcwQ341CPZHCLYrKyT2OATIivFMHyCNhIPmipR2aTXo72/JjmnFtS6b/AH2/rue1aX8K/D+jfEfVPF1tpscXiPWrWGxvr4SOXuIISxijIJ27ULuVAHBkc/xNnoa8F+Ov7YmrfBTx38Q7E+FtM1LSPh/4Gj8czXJ1lre4uofMu1lgWMwMgcLaOVYyYYsoO3lhyUP/AAUG8Q3Wmtff8INYwaXdeOYfA1ndjUp551nmaB47iW1FspWPyZXJXzN3nIkY4k8xSl77UIddvnPkXpebsu7FU9y8p/1aHP8AhCz8lp5H0/q+lQa7pVzY3SGS2vImhlUOyFkYEEZUgjgnkEGs/wCH/gDR/hX4K0zw54esItL0PRrdbWxs4iTHaxKMLGuSSFUcAdAAAOBXzX4n/wCCgXijwRrkllrngZ9JW3n+yreyEy6bdS/ariGNJ7mNm/sxpo4oJIxeRqmbkIZAymrHw9/4KBa34t8eaL4en8HGz1DVri8tJ7bWFudEvdKmh0s3yLJE8U0csUhSRBcQTOmBkbiGVY54qnOr0W/y1/C/yv5q7cXzKHV3t+v9dbO17O31JRXyZo//AAUk1/Vvhw3i2P4eWE2j2fwt0/4o30UWvyNdx29xDdySWkKC1ImlVrYBSSgcOSdhAB9t+AnxZ1/4lzX6a1okFhBFY6fqFnewOyxXYuY3aSIRyYk/dPGR5hAWQOpXBDquzpyTkn9l2frdr84vXbQlySaXfX8Iv8pRdt9fU9GoooqBhRRRQAUUUUAFFFFABRRRQAV8F/t+/wDKcv8A4J9f9e/xJ/8ATHZ196V8Gft+/wDKcv8A4J9f9e/xJ/8ATHZ0AH/BPf8A5Tcf8FDP+vj4d/8AqOPX3FrHhDSvEOr6Vf3+m2N7faHM9zp1xPAsktjK8bxM8TEZRjHI6ErglXYdCa+Hf+Ce/wDym4/4KGf9fHw7/wDUcevePiF+0X4g+Fn7cui+Etav9Ftvh3r/AII1PxDbObB/tq3unz2qTxGbzdpQw3SyBRHuJRhk0r+8o97/AIRcvyTt56Dt7rfp+LS/XXyuz1n/AIVB4S/4WT/wmf8Awi/h3/hMBbGy/t3+zYf7S8g/8svtG3zNn+zux7VFrfwR8F+Jdal1LUvCPhfUNRnuIruS6udKglmkmiAWKUuyli6BVCsTlQBgjFfO/wAQf2kPiV8MvjJ8JfCWueJvCOmzeO7PXNa1Yt4bmkl060tBb/Z4UVbo7phJdwxu3Ich9qrxUsH7eWv+HP2RPHPiTxXplvofxF8LaTq2s2+m/wBi3jK1jFPdR6ZeXVoHMtoLtIFcQzSq4+fn5G2pvljzJbXt8m7pdL3T03um+jY1Hmklf4rK/rtfytZX21S6pH0KnwZ8Hx6M+nL4U8NLp8skcz2o0yAQu8cnmxuU27SySfOpxkNyMHmujggS2hSONFjjjUKiKMKoHAAHYV4Xeft3eFfCHw3n1HV7p72+0uz1CO7nsLOQ2V3qOm2zTahbQH5mJiMcwJ5UGKRdxZGA6/8AZU1zxb4v+DGk6/4zvornVvEMEOpfZE0ltO/snzIIy9rtdmZ1SXzdrthijKDkjc2nLrKK2Vnfo73tbvdJtPay81fJTTjGX81/XS179rNpNb3fk7ej0UUVJZ8Hf8F3jiw/Y/8Af9qHwL/6Oua+1vGXwz8N/EWbTJPEHh/Q9dk0W6F7pzajYxXTWFwAVE0RdT5cgBI3Lg4J5r4q/wCC73/IP/Y//wCzoPAv/o65r7E+J/xr8P8Aweu/DsGvTX0EnirUhpGmi3sJ7rz7oxSSrEfKRthZInILYB24zml1S81999PnfbzDo35O/pbX5W38jLsv2TvhZpwthb/DT4fwCzhntrfy/D1ovkRTyCWaNMR/KskgDuBwzAE5PNbVx8FfBt3HGkvhLwzKkVy16ivpcDBJ2j8ppRleHMfyFupXjOOK8V+P/wDwUA0DRPgHq3iHwDrFpea5b6dNq9hFqWi3phvre0vYLW9CqfKLPE8yxkBiyuw+VsEV6Trf7Vfg/Rbm7tRcaldalp90LO7sItPmS5tXNpJeBpFkVdiNBE7K7EK5XYpL/LQ5JR5pPT8LX1+V/wAUxpNuy3/4H+X4G34E+AngX4XazLqXhnwX4T8OajNapYyXWl6Rb2c0luhJSEvGgYxqWYhScAk4HNdZXmXgL9r7wH8T/GNjoWgahquqXupabYazbvb6JfPbNZX0M81rcNOIvKjjkW2mAaRlAdNhw7Kp9Nq5KSdpdCU09V6/5BRRRUjCiiigAooooAKKKKACiiigAooooAKKKKACiiigDy79uKXyP2KvjA+132eCdabaoyzYsJuB71wn/BL/AMOad4v/AOCSv7Oml6tYWWqaZf8Awm8MQ3NpdwLPBcIdItco6MCrKe4IxXe/tt/8mYfF3/sStZ/9IZq8x/4J2fEbS/hB/wAEbvgH4p1s3iaP4f8Ag94bv717WzlvJo4Y9GtmdliiVpHwoJwqk4B4qZSSTctioptpR3Pc7X4FeCLGa5kg8G+FYZLy4ju7hk0m3UzzRqUSVyE+Z1UlQx5AOAcVc/4VV4XEt5J/wjegeZqNmNPu2/s+HN1bBdggkO35o9o27DlccYxXnEf7enw6uLLVJ7e58R3a6Lc/ZLxbbw7fzPC/9njUQdqxElDasrhx8pLBQdx21W8X/t0eGdB1Dw/Z2NjrWqXmq+Mv+ELvbe3snmk0q7Fk96wk2bgf3IRl2FgfMHo22+VuXs+rtp62S+XvJX813IUly+0T0Sbv5RTb+5Jv5Hcxfs2fDqDV5tQTwD4KS/uJElluV0O1E0rpEYVZn2ZJWJmQEnhWK9DirXgz4D+B/hzraan4e8GeFNB1GOzXTkutO0i3tZ1tVYuIA6IGEQYlgmdoJJxmuE8c/t8fDT4c6bqeoatq19DpGk3Oo2M2oQ6fNcQPcaehkvYUEas7NAiSlsLj9xMASY2A9Aj+LOlTfDq/8UpBrv8AZenJcPKj6LeRXjCBmVyls8YmcEoSpVCJBhk3BgTLmox9o3ZWvfytv6Nde3kUotvkS62t5p7eqfToy/4j+H2geMZJH1fQ9I1V5bSSwdryzjnL28jIzwksDmNmjQlOhKKSOBWdq/wR8F+INcfU7/wh4XvtSkuo717u40qCWdriNQkcxdlLGRVVVVs5AUAHArhbn9urwAmjWOo28viC9sNQ0SLxFDdQ6JdC2NjJbSXSyGVkWMN5MbN5W7zOVGwllBu337avw40y81aO41u7ht9D0nT9evb06Vdmxg0++8wW10bgReUIm8qTLbsIEYvtAJq+Vxly2s4/g7v7tU/mn2ZN01fo/wAVov1X3rujsR8HPCK23kjwr4bEP2p77Z/ZkO37Q+7fNjbjzG3vlup3tk8mpvCvwq8L+Bdd1PVNE8N6Bo+p62yNqN3Y6fDbz35RQiGZ0UNIVUBRuJwAAOBXE6r+2V4D0ayF7PqF3/Zc93qOn2d9FaPNDqF1p6TPeW8IQF3kjW2uTjbh/Ik2FttdL4D+OPh/4mfDiXxXoravc6JHALlJZdHu7Z7qIwpOrwxyxq8yNG6lWjDKxyoJZSBnzJJyvokm/JPZvsnbQpp3UXu27eq3+ae/mb+oeFNL1fVob+703T7q+t7eaziuZbdHmigmKGWJWIyEcxxllBw2xcg4FeU/Gj9iDwX8TvClhpukaN4a8JS2WoaVdtdWOgWxkmg0+8ivILM4C/uPNhTK9Nu4AAnIdN+318Lbf4fWviiXXdQh0i+0KDxNbvLol9HLPpk0kUa3SRNCHaNXmiD7VJj81CwUMpLvEn7dvw58JW2rXOoX+rWVn4ea1TWZrnR7q2OkNdTvBbLPFKiygyyIdoCH5Xjc4SRGapLkak9LNfen+kr/ADugScvdWt/1t+at+B6CfhJ4VfR4tObwz4eNhBPJdR2x06HyY5pN3mSBNuA7b33MBk7mz1NLofwm8K+GPF1z4g03wz4f0/Xry0isJ9SttOhiu57aL/VQtKqh2jTPyoThewFecR/t4+ALi3014bu4uZNbt5rvSo4JLeX+2IYYoZJGtWWUpcMBPGPLiZpCxYBDtbGtP+2V8P4NWSw/tLVWvZNcvfDaQroV9ubULOza9nt/9TgH7OjOpJCyAfIWPFEnyX5tLJt+isn8ldJ9r2Jj70VJap/8F/o38r9BdW/Zc0jxN+0dqHxA1k6frUd9oFroK6Ve6ZFPFAtvcy3CSq7ZO/zJnzxjhOhXJ6nTfgl4M0ay1i2s/CPhi0tvEN39v1SKHSoI01K53BvOnAXEsm4A73ycgHPFefad+358Ntd8N2Wq6bfa5fWuoaLN4lgH9h3du82lwwW88t6izxx+ZCqXUAzHuLO5RQzq6r6D4h+Mfhvwzpfh+7utUjMfiydLbRVgie4k1OR4nmVYkjBZ/wB0jyEgYVEZiQATT5XFctttPvk3b5yT+at00G03zPrr/wCAxSv8o29E/MZcfAvwRdvO0vg7wrK11GIZi+k27GZBvwrZTlf3j8Hj529TUp+DHg42UVsfCfho20EvnRxf2ZBsjk8sxbwNuA3lkpkc7SR04rxP4Lft+6Td/CSbU/HN5brrenN4h1DUF0vTJoI7PSdM1e4sTeywyO7xrtiQlCzOzCTapCMFx/Df7ecU3xa1ZvEGur4a8MaZ4q1XQlS78P3P2W4tbHSmvWl+2cLHMPLmmJYlDFHsCbiJDCcXddLOT7WTUW/k3b1T7Mpxa9b8vzab/JX9Ld0e8+C/2efAHw21GG78O+BvB+gXdvYLpUU2naNbWskVmpJW2Vo0BEILMRGPlG48c1u+GPBWjeCLQ2+i6RpmkQEKpjsrVLdCFGFGEAGAOB6V5lqf7V2j3mu+ELe3vdS0Ea9qlvbLFqfhW+uP7XjuLG4uYYoJoysdvIyxb90u/aI2jaNXdSu5+y7+0Ja/tO/CW38WWemappNvdXd3bpBfWzwSbYbiSJW+YYOQgJ2kgEkZ4rRqV3fdOz/PX7/XXzJutGuqTXpql+W+2nkeiUUUVIwooooAKKKKACiiigAooooAK+DP2/f+U5f/AAT6/wCvf4k/+mOzr7zr4M/b9/5Tl/8ABPr/AK9/iT/6Y7OgA/4J7/8AKbj/AIKGf9fHw7/9Rx6+sPjB+zN4b+N3xJ+H3inWvt39ofDjUZ9R01IJdkVw00DRNHMMZePPlybcj54YycgYPyf/AME9/wDlNx/wUM/6+Ph3/wCo49fYPxn+OmhfA/wHrmvanI94NAszfXFhZyQm8ePKrlUkdB1YcsyjnrSbtr2BuydzLuv2brG9/aksvivL4g8RSatp2gT+HLbSibX+zIraeaKaVgPI8/zGkgiO7zsfIBjHFYPxl/Yl8OfGWbxxJJrXibw/J8RLbTrXXG0uS1/0pLGQtDjz4JQuVYxuB8rJ2BJarX7Mv7Y3hz9qfR9Mu9E0zX9MOp6RFrCw6lDCjwxuIyEfy5HAceauQMjg8+vrVb1sPUoSVGorON9O122/xbZlRxFOunVpu6dtfRJL8EkeF6x/wT/8I3vwd8U+B7DVvEehaL4tGqLdvYfYhcxDUZpJrsJJJbuSGaaUAPv2iQgYwu323S7H+zNMt7bzZJ/s8SxeZIFDybQBuIUBQTjPygD0A6VPRWK0Vv60NXq7vz/HcKKKKAPg7/gu9/yD/wBj/wD7Og8C/wDo65r65+MvwMtPjRqXg+6uta1zSJPBeuR6/aDTjb7bmdIZYQsvnQyZj2TScJtOSPm4r5G/4Lvf8g/9j/8A7Og8C/8Ao65r7wZggySAPU0dU+zT+ad1+IdGu6afo1Z/ej5rl/4Jg+E73w9pmlXnjL4gXthpel6vpKRyS6cvmxanfwX9wXKWancJreLYVIAUEEHJNdX4l/YV8KeLPiXJ4wvdV8Sy+I571L2e6M1uUuPLs5bSCNoTCYdsSTSvGQgdZJGbfyRXZeNPj1pPgzxp4F0dre81GPx9qVzpVnqFm8ElpaTQWlxdMJiZA+CltKoKK+GXDbetddd63Zafp7Xc95awWiruM0kqrGBnGdxOMVLjGUORrTXT1d3+Lv8AMfM079f6X6Hkn7M37FWi/ss6213ovijxjq8P/COab4XjtNXntJoYbTTzObbaY7eOTeouJQcuVII+XgV7LXHp8WYtRlvbaPTtc0l7eGZxf6npUkVjFsUnezkqCvGfvDI7jrWb+yZ8WLn46/szeBPGF7cafc3/AIi0S1vrt7Hi3894lMgQbmwA+4YLEjGCa0cnNuT12/G/+TJsk/X9LHoVFFFSMKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPL/wBt+RYv2Lfi8zMFVfBOskknAA+wzc1xf/BKi0iv/wDglP8As3QTxpNDP8J/DEciOMq6nR7UEEdwRXaftvxrL+xb8XlZQyt4J1kEEZBH2GbiuL/4JU3kOn/8Ep/2bZ7iWOCCH4UeGHkkkYKiKNHtSSSeAB60pJNNS2Gm07oXQv8Agmr8PvDHgbU9A06+8WWdrqvhePwlNNHqQ88WqSySJIGKczBZPK3MCDEioVKg5vWv/BP/AMKWWtX2oQ694ugubvxFB4oi8m4toYrC9i0/+zgYY0gCIrW2EI2nGAVKkV7RpPiPTtegWWxv7K9ibOHgnWRThtp5BPRhj68U99dsY9XTT2vLVb+RPNW2MqiZk/vBM5I4POO1Vd83N1/4PN+av8hX0a6f8C35aHi3in/gn/4O8QfDjxp4VstT8Q6BpPjqTUZr8aebTzopL8P9raOSa3kf948kr4cuoeQkAYUL7VpNg2maVb2slzPetBEsbTzhBJMQMbmCKq5PU7VA9AKyr7x7EJdPOmWkuvW11em0u7mwurUxaUojdzLNvlRigKKm2ISSbpFOzaGZeP8AgP8AtMW3x50611Wy0W7sPD2qaUus2GpTXcDLJA0roqyxht8UhVVkxhlCOMsGBUJa+7/VktPw27iatr/V29fm3v8Af1OVH/BO/wADQeHdL0q2vvEtrp+jzarJbQLdRSxot+vltGEkiZVEEWIoGUB4oxtDHc27ifDn7Acs/j3xr4V1i88R3Hwu1rwJofg8Ty6javd6xDZz6g88E+Ig6RvFeLEDH5Z2q4G3CGvp2fxNbv4auNUsMaxDDC80aWUsbm5KgnYjMwTcSMDcwGepHWsj4LfFnT/jl8HvC/jXTIbuz03xXpVrq9tBeBFuII7iJZUSQKzKHCuAQGIz0J60QWrt05b/AHSUb/8Ak1vmVJ3S+dvLWLdu2qX9I881b9g3wbqnh+30lLzXrPS9N1DVtW0q1hlgMej3mpR3SXNxDviYlsXt3sWUui+e3y4CBe88J/Ds/Cr4G2XhXTJ9R10aBoy6bZPePCtzdrFD5cYdkSOMMQFGQqjvXVyXkMThWljVjnALAE4GT+QrP8XeIbjw34fuLyy0m/1+5gKAWNjLbpPKGdVJBnlijAUEsdzjhTjJwDnOkp05Uukkk/kml913941L34ze6vb5tN/e0j5m+Fn/AATni8T/ALLXgvw9491nxNp3irRvAVp4Jnl0+6snGmQI9rLcrbk27RsJpbK2JeRXbbEgGwls+r+JP2QfD3iDxd4j1+LUtd0nXPFlja2eqX9i1slxO9sjJFcq7QsYpgjbd0WwEKvy/KK6P4i/Gqy+G3xB8D+HbrTdUu5/Hl/cabZ3Ft5JgtZobOe8xNukVwHjt5QpRWG4ANtBzUnh3406Vqus6To2pRy+HfE+p6Q2tPol/cWz3ljArpG3mmCWSLIdwuUkZSVbDHBrac/aTfdtu3m7ydvxb+9kxbilrsl92iX5JfgeUax/wTK+Guu2etWl1Dcvp3iRJDqtgtjpyWV9cPHDGL1oRahFu1WH5bhAsg8x/mPy7dvxV+w1oHiPxrFrtt4l8X6LcweKbjxgsVlLZtD9vn019NkOJreQlDBIx254c5Bx8tezQ3sNy+2OaKRtofCuCdp6H6VLWcoKSaa3VvldO3pdLTyQ02o8vT/gNX+5tfNni3hL9hbwn4P0HwBbQ6jrl3qHw58OSeFNM1W7Wzlu5tOcQfuplNv5LEG2gYMsasCnXDMG7bx/8C9G+Id54PvLqW/ttT8CX/8AaOjX1vIvn20ptpbV871ZXV4ZpUYMp+9kYYAjs6K0lJyd33v87t/m2/VvuSkvwt8rW/LT0PB/AP8AwT48I/D3VF1G31jxJe6uBqkR1C8FjJO0GoX76hLDgWwQLHcyytGwUSKJGBdgaqeJ/wDgnF4S8by3MeteJPGWqaZf+ItS8R3mmzyWItrt7/TpdOuLZytqJPI+zTSKAriQFs+YSBX0HRUcqty9LNfJtSa+bSb89SuZ35r63v8AOzV/udvT0R5VrP7KVnr8XgRLrxf4ylX4fana6pp4aSzIuHt4JrdEmzbcqyTybimx2O35vlFdB8D/AIF6V8AfC8mjaNeatc6d500sEN7OsgtBJPNOY02qvyh5n5bLEBQWO0V2tFW5N3b6u/zdtfwRKilZLokl6K7S9NX94UUUVIwooooAKKKKACiiigAooooAK+DP2/f+U5f/AAT6/wCvf4k/+mOzr7zr4L/b9/5Tl/8ABPr/AK9/iT/6Y7OgBf8Agnv/AMpuP+Chn/Xx8O//AFHHrpP2rv2H/if8Rfhx8aP+EW1Gy/tX4l6df2MeiS6/K2lGeW4tIra/US2xktZ0srcrKkUhidzkJnDDm/8Agnv/AMpuP+Chn/Xx8O//AFHHr7H1j46+DPD3xB/4RO+8T6Ja+J/sL6n/AGVJdot59lT78/lZ3eWO7YwPWptHmUn/AMOt38tLv0v0G7uLj0/pL89PM+Wv+Ca37F/jr9mj47+P9b8TaDouiaJrtla2ekRadqS3Iiight0CyIEXa5ZJWyNwIKgnIr7RrgNP/as+GWq+CbHxLB8QfBr+HtTYpaan/bEC2l0Qyqdkhba2GZQSDjLAd61/FXxp8K+Cbq+h1TXLG0bSoVudQJYsmmQtnbLcsoKwRkAkPKVUhWOcKcdeLxM69T2lTc5MHhIYemqVPZf8MdRRXN+NPi94c+H91YW2p6pEl9qqu9jZW8b3V5eogBd4oIlaWRVDKWZVIUEEkZq34D+ImhfFHw6ur+HNX0/XNLeaW3W7splmhaSJzHIgZeCVdWUjsykHkGuZa7HU9LeZs0UUUAfB/wDwXe/5B/7H/wD2dB4F/wDR1zXsf/BQrwLq3xCtvAVnpOlW+oNbanez3EmpaodN0iGF9OubVhcTfZLtQ5+05iDwEF0++pAD+N/8F3hmw/Y//wCzofAv/o65r7xqKkFOPK9i4TcHzI+PviL+y34p/aN/Zq+D+keENOT4cQ+E57030Gqag322Bf7Mv9NQqTayC6WZ7gSu0oheSJiTtdyF4bx38GfEOi/FDT77WPhV4dmsvDeleHlvPD3h/W5ILWe5spLuQw6JbvpuLm3xdYljMtqG27JF2Zd/vqitnNur7V7uSl81e35mSilT9mtuVx+Ttf8AIRQFUADAA4HpS1k+NvHmh/DTw5NrHiLWNL0HSbdkWW81C6S2gjLsFUF3IUFmIAGeSQK1qgrbQKKKxLr4leHbLxbNoE2vaNDrlvZDUZdPe8jW6iti/lidoydwjLkLuIxnjOaOtv67h5m3RXF/Ej44aX8PNJ0e9WGbWbfV9csdC8ywmhdbWW7nSBHky4+QM652hm56d67ShaptdHb52T/Jp/MHo7fP81+jCiiigAooooAKKKKACiiigAooooAKKKKAPMP22/8AkzD4u/8AYlaz/wCkM1eL/sV/DW6+MH/BE34A+HLJYGuNT+FfhBQZb+4sNirp9i7Ok8AMkUqqpaNlBw4TIIyK9m/bii8/9ir4wJuZN/gnWl3KcMubCbke9cj/AMEnf+UWX7NP/ZKvC/8A6aLWk1dWY07O6PFpP2cdW8J/tTeE/DWrw6D4+1m98D+L4Uv9eVQSl1faZJbRXVzDZqsspC3J3eUjMqyH52DO234c/wCCY3iex+EviHwlq/j271y91hbW4tPFb3csOp6bex+H4dIa4CKm6QgxyyKDcBSLlwykqS/2bTZZ0h273VN7bV3HG4+g96lQXJyPtb1XO5r5pvfd23KVSSlzLu382rP5W6bW6HzH4y/Y617Wh8Lrux8J/DCDVPBuq6bd6rN9smja8t7XTbuyMcb/AGRiRi4UKjBQFVgWOQB5Z4X/AOCYfjnT/h5oOhhPh54ek0/wJZeHdQuNLvrh/wC1Lu11a3vvLlX7JEXtZ4oDDIzHeFmf5GA5+8aK29pL2ntftczl83Fxf4Pba5lyR5FT6JRj8oy51+PXexyfhrwbP4c8OeIfsmi+H9M1HWbme+aCzmdYbq4eJFMk0vlBi7MvLiM4ULwSOfj/AMI/8E0PHdh8MtF8OXsPw7tY9J8C+FdEZ7bUbmdZ9X0bU/tjXBVrNMLIhZBLkyKWPyEdfuyioh7kueO+n/kqkl9yk/w7DkuaLg+u/wB6l+aX49z4+0/9hbxjb/FPTtYn0H4Zz6ZY/FTUvHfkf2lOG+x3ejS2PkhfsO3zTcSLKwztOwNktwOFn/4Jq/E9vgjp3hdbH4XtcRfD1vC12765eGKfUF1KC6guT/xLyWWOOJgrEb1Z8AAc1990UU/c5OX7KSXooezX/kv467lTfM5N/ad3/wCB+0/9K/DQ+U9M/Yu8aaD4s8BX8kXgvxJN4b8c674m1TUtR1CaK+1Kz1G21GKO3OLWTcYRexRYZghjtlxt4Red+Fn7Ctx8EfC/gnVPG+keA9S07wr8PpPB3iG2tbO41UGH7Wkhe3RbUSSq0HmRkFEKbujrmvs6ip5Vppt93wuNmuq5ZNW8wbevnv8A+Bc//pST+R4f+wv+zy3wP+FVqNTSyu9Zit10Sz1EWYgupNFs3eLTYpSRuL+RtkcH/lpM/Ar3CiitJzcneTu/6/Hv5kRikrL+v+G2XkFFNSdJJHRXVnjxuUHJXPTPpTqkoKKy/EXjjRfCFrBPq2r6ZpkF1ew6dDJd3SQrNdSuI4oFLEAyO7KqoPmYkAAk1qULa/8AX9aoPIKKKKACiiigAooooAKKKKACiiigAooooAK+DP2/f+U5f/BPr/r3+JP/AKY7OvvOvgv9v3/lOX/wT6/69/iT/wCmOzoAX/gnv/ym4/4KGf8AXx8O/wD1HHrX+N154ol/ax+PXj7SbL4o6e/gf4Z2Oh+GxpXhKe4HiK883ULqeK2MtnKJ/wB61kn7g9s5IGRkf8E9/wDlNx/wUM/6+Ph3/wCo49fedZVqSqRcb20a/wDAk4v8G18zSnU5Xqrq6du9pKX5pH57f8Kn0KD9m34c/BOTwt8T7PUdc+G+keDfFOu2/g3XZ4f7OZVE9jEwg+xw3UsjTB552j+zpIWdiQq12nwp8J+L/hd+xV8YPB914a1TW/i34s8QeJooraS1kMOrte3M8Wm3DTkeWLJLI2aGQttjSExn512V9q0VviWq7qc60nzJpdpSUml2/K7va975UHKl7Np3cHFpvvGPLd+ul/RK9krfG37Fvwq1b9j340+NdP8AHx8Q69cr4c8MaF4X1yHSrq/ivdNsNLSCa3R4kfy5ft32uZ42wzCZH+YAlfqr4ZeG7Xwx4PhitdGi0D7ZNPqM9iknmeTcXMz3ExZhwWaWV2bHGWOOMVv0VVSrKpJznu/6+97vu9SKdNQioR2VvwVvy/DQKKKKzND4P/4Lvf8AIP8A2P8A/s6DwL/6Oua+jf2tPE/ivw5rvwtTwvdeKbW31LxdFZ66dH0U6ko01rS5LtORBL5KCVYMSHZgnqRkV84/8F3v+PD9j/8A7Oh8C/8Ao65r7xo6p9mn62advR7MOkl3TXpdNXXmr3XmfAfiP9ov48RfBOeW2X4pv4jvPA/i67jZPh9P51tq9nqECaKqoLHAeeDzSUYESDLALxjutS+OfxX0T4lavHaH4i6npNn428OWFik/gicQ3Ol3VrAdRkZ0s1JjjlaYmQEeUy4Jxha+wqKUVZRXZp+vv81n6r3f8IS1T81Jel0lf1W68z4D/aC1P40+LP2WLCx1LQvF2qavrXg/UV8e6fDo1zdJBqwubT7NHYpHGxdQxuVXyNyNCu9ySFY+vftk/Ev4jeFfiMD4IuPHkenN8NvEOppDpfhWTUbdtZhNmdMQsbWQrM++6HklgXCAFeOfffiF8WPDfwosoLnxJrVho0FwWCPcybFIRdzsfREUbmc4VRySBXQ9aU05Wtpbm281JfhzJrtZFwkoyTavs9etmvztZ+p8d+G/jF8YNf8AjtpOjXtp44m8ANJo93rl/L4YubC5hSfSr554YXS3jZkF8lkJAm6SISEEhd22z+z5p/jLxr+1Ho8vxG8Oaxcxt4R16wju77RJjay2n/CQI+mrcyGPyVunsYopGjYh8gkqGyK+u6K1cl7ZVUtnLTp70XD8E9P+CZKL9j7K/SOvX3ZKX4ta/Lscd4p+APhDxfolnptzo0drZWOpW+rxRabPLpo+1W8iyQyMbdoy+11U4bKnaMg12NFFQtrfP52Sv9yS9EuxXn/X9asKKKKACiiigAooooAKKKKACiiigAooooA8w/bb/wCTMPi7/wBiVrP/AKQzV5b/AME9/DeseMP+CMnwI0vw9rtz4Y16++Dfh2HTtWt4opZNPuDott5coSVHRtrYJDKQRkV6h+3Fv/4Yq+MHlbPM/wCEJ1rZu+7n7BNjPtXI/wDBJ3/lFl+zT/2Srwv/AOmi1qKkOaLje1+25UJcslLseLeHvHX7Q2o/DnxPqOsQ/E/T7xdAj8S6bZ2Wm2L3UU7Wj2UmkfNbHe6XkbXoIBcxyRrhh8rT+IdQ+KnjXxVp9vqNr8SoNK8FfFKC8gm0/TJ/9J0aXQ3ICvND5l0keoPIm5k+XKltoVdv3DRWvN+8VT006aSUk/W0Ur+bfVkcq5HT6NNedpRcX67t/d2R8TfGLxf8fLD4TfEfW7a78X6N4u0e+8RHSrHSdAm1cX1mI5Do6wRLA8O75bVnZTI7NJPGwA5i+q9F8ManqvwlmsR4s8SNqWqW80kGs3VnbW+oWJmLPHiH7OkamIMFVZIi2EG/c24nrqKzcU4cj7JfcrX9Xu33Gm1Lm82/vd7ei2S2t6HxJceLPjxc+BPDsuo/8LGg1+TStQ0m/it9Pj+xDUNPtJIf7Q/cWxmzd3bCSBGZYHjhUkgNteBf2jfjF4gPjddIuPGSeNrX4ceGtb0XwxP4cjWFdcuJtRhnjnDW/mQQTPaRBvNlTy42dwUIyv3FXPaX8K/D+jfEfVPF1tpscXiPWrWGxvr4SOXuIISxijIJ27ULuVAHBkc/xNm1J8zb62+VubZbK7kr9PdWmgWXKkun46x366KNvm+7PmH4p+NPjdY+F9SuLCDxTb+KovGGtwXMdjpklzZw+HEt702EtuPKeOSYhbBxtDytNI6EFNyr6n4B1D4kH9iC51qzv9X8QfFDUvCzajYW3iHTYtNa21U2K7bVoRDAUj+0qTiUFhvYF9oUD2yis+X91Knf4lFX6qyadvXdve/XUvn/AHsaltnJ26O7vZ+S+Ff3bLpc+Y9L1Lx5b+Kfh23274i33gzV4FfxaLvTJor2wuRp7+WqbIluFSS4BaXZ8iOsKrtR3U+T6h8bP2h/CnhnxDdX8fxAk1rTfBNtqWnWNn4Pa+gvL/8Ate6jRGaO0bdPJp4tGmhR8ozuQI8HH3pRW0p3qe0tbVu3TVNfhe69EZRjan7O99Er9dGn+NrPvdnx/wCIfEnxuvfgxrWsWUnjC3+IEXiC/t5tKj0vFgdFbWIhFPbl4SDNHpKl4xHmR5pXDozrsXK+LXj74yeAvGevR6HrHxGvdL/4Q/xRquk29n4OuL6K2v4l09tLtTPLaPJNIZHvwofa0iqqlW2B3+1aKx5bbPo198bX+T95dU762ZrGVpXavrf8b29OltrW7Hw7rGt/FTwp4K/aK8VaaPilD4o13RrTWfCqQeH5LpIbhdHstsUNs1uzlxeCaMwbSwAkLKM766L4sfEb4r+BPHay6Efif4l07Tbzw3c+fc6BuF9a3epynUYo4La0jQ+VZyBGM5EsfkRELuLyN9gUVrKSc1NKyTTt0suno9n3RjCNqfI3fSzfV3tr+D+8+LPD+o/E34N/CnxILNvihL4gn+Lcc5Emh/2ktxo9z4gxIq7bZz5J052dpE4iCqA6Ebaz/iN8Rf2gtH/Z0tdT06DxlB44XwBrt54ojtdEkvfsniGPyDp8VhCYZFmBnM8arCro8A3uSdr19x0VEVaHJ6fhBRt6ac3r+Oif7x1Hrdt26aycn+dv+BoeNfsx/EPxX4p+JfxL0nX18TTaPo19p7aDeatoMum/aYJdOt3nCOYIklC3PnA4BKElTgAAey0UVUnciEeVWvcKKKKRQUUUUAFFFFABRRRQAUUUUAFfBn7fv/Kcv/gn1/17/En/ANMdnX3nXwX+37/ynL/4J9f9e/xJ/wDTHZ0AL/wT3/5Tcf8ABQz/AK+Ph3/6jj17F8cv2rfEWj6d8dra38O6cnhv4V6NLJea0mvTW968j6RJehIYUg/1qs1sp/fJhZ9wbcuw+O/8E9/+U3H/AAUM/wCvj4d/+o49fYOo/s6/D7WLHxHa3fgXwbdW3jCZbjXoptFtnTW5VAAkulKYnYAAAybjgD0rKrT9pCUHs019/wDwLmtGpyTjO17NP7jwb4N/tYa14AvPB/we1LSLjUfF9lLo/hR9ZutTkvo57k6G+pXU9zI6K7SpDBuK7iZHnT51BZh7Z+zb8an+PXw8u9Zex+xNY65qmiFlJMN59hvprT7TFnny5PJ3gc43Yy2Nx0tP+AXgTSZ7+W18FeEraXVbpL69eLR7dGvLhI/KSaQhPnkWP5AzZIXgHHFdLpWlWuhabBZWNtb2dnaxiKGCCMRxQoBgKqjAAA6AV01KnPOU311++34J3t6+SOanTcIRgnskvW1/zuvu82WKKKKzNAooooA+D/8Agu9/yD/2P/8As6DwL/6Oua+pv2jfjzqHwPg8Orpnhe88TXXiG8uLRVheRUtjFZXF0C4iilkPmNAIl2oRulXJHAPyx/wXeP8AoH7H/wD2dD4F/wDR1zX1b8f/ANmfw1+0emiJ4n0/S9btNBnkuYNM1jT4tS0q4ldPL8ya2kG13RS2xwVZC7YOGIMVFJxtF2ZcHFO8lc5fWf2i/GfhSx+DsOpeD9Lh1n4oanJpl3Zzak9u2hP/AGdd36BgIpPMdUtTHIoIAc/KSKyPgB+17c/tPyeG9JuNCuPCjeNvBz+K7S5sdVFzNaJHdR28kRLQqA2ZFZWAORnIUjnv/Bv7LPgnwt4G8MaFeaHp3iSHwdJNNo1xrVpDez6Y0pfd5DOv7kBJGjUR7QsYCD5RitPwV+zx8P8A4barYX/h3wN4O0C+0uzbTrK403Rba1ltLZn3tBG0aApEX+YopCk84zWzcfaN292708uVpfdKz9FYySkoWv71lr58ybfzjp5bnE/HP9lXVvi38M7/AMMW/jzVobXVIpI55dTt0vnichfKlj2GLmJhv2Pujc4DqQMH2WJDHGqli5UAFjjLe/FOoqCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPMP22/+TMPi7/2JWs/+kM1eQ/sL/FGP4Kf8ES/gh4sk+wH+wPgz4dvES9uDbwSyLo1tsRnVXZdzbV+VWOWAAJwK9c/bil8j9ir4wPtd9ngnWm2qMscWE3AHc1wn/BL/wAOad4v/wCCSv7Oml6tYWWqaZf/AAm8MQ3NpdwLPBcIdItco6MCrKe4IxSle2hUWk02ZCftiyeNvH2h6Rd+H5P7T0X4lJ4Tdotdu9JIMuitfQ3clogJmjKTeW1vcHarqJOXRQvqP7KPx08Q/tB/DGPxTrPhvSvDtldGWO1S01eS/lleG5ngm3q1vEEXMKshDOWEhyEK/NuTfs1fDm41SO+k8AeCXvYrxNRS4bQ7UypcohjScNsyJFQlQ+dwU4BxW74C+HPh74V+Hk0jwvoOjeG9JjkeVbLS7KKzt1dzuZhHGqqCxOScZJpr4LddNfPlim7ebTfztskQ78yfS36yf5NL5X3bv8X/ABV/b5v9M1f4afGO10bV08Nav4G8Q6na6Emsz4vgLzSorV7u3CeVFInnlncLI0SvIoLY59m1P9rLxn4f+K2heFbjwXpk39u6rfada6jNqMlgs8dvpa363AtzFJIilvNgKuQd0QcZVxj1W9/Z+8BajrkOp3HgjwjPqVstykN3Jo9u08S3P/HyFcpuAl/5aYPz/wAWaTUf2ffAWsXFjNd+CPCF1LpZlNk82jW7taebH5UnlkplN8YCNtxuUYORxUTUnBxg7Ozt1s227/L7n22KVr3f9aWPm2+/4KM3fj7wTo90ng7ULDRfGnhy1v7Wdrm5S2kku9Ka8Nm2p2e5tPugWREMsSFgyvG5ZlStD4UftT+PvFnjLW7fQdC0C8sYPhn4b8Q+HNE1HX7j7Vd3l5DqUpt3unidpHYWm0yMCcR7yCS2PedJ/Zk+G2gaGdLsfh74HstNMItvskGhWscHlCMxCPYEC7RGSgXGNpx04qa6+B+haZo9tF4Y0/SPB+oabaQ2GnX+maRaCXT7aJiy20atGVWHDSJsAGFlfbtYhhpUs/acml37vkrS+96q1+z7u66wb6LXzd4v5XSadt7+lsD9lv8AaDuf2mPBcfim207T7Xw1f2dnLYTxXkks8s7wJJcxSRtEoTyZHMX3iS8cgZUK4r1GsjwL4JsPh34VtdH0yJYrW13twqqZZHdpJJGCgDc8jO7EAZZia16qbTleKsTBNKzdwoooqCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4M/b9/5Tl/8ABPr/AK9/iT/6Y7OvvOvgv9v3/lOX/wAE+v8Ar3+JP/pjs6AF/wCCe/8Aym4/4KGf9fHw7/8AUcevp/xN+2Z4C8IfFSXwZqF7q8Ovw6nYaO0Q0a7aI3F9HLJbASiPyyrrBL84bapQgkHivmD/AIJ7n/jdz/wUMHf7R8Ov/UcevpX4ufsS+C/jV8RL7xRrB1iPVdQttJtpGtbsRIi6bfSXsDKNpIdmleN2zkxMVUrkkrXmj2ur+l9bedtvMenLLvbT1/y6v0sPsf22vAmoXEsCHxStzFosXiBbeTwzqCTz2ctybaJ44zCGkZ5RgRqC5BU7cMCfWLeYXNukgDqJFDAOpVhkdweQfY1xniD4BeH/ABJ8ZfDXjmdLpNX8LabdaXaRRSBLWSKeSCT94mPnMbW6mPkBSzHBOCO2qlblV99fzdv/ACW1/O4n8Tttp+Sv+N7eVu2pRRRSAKKKKAPg/wD4Lvf8g/8AY/8A+zoPAv8A6Oua+xPif8a/D/weu/DsGvTX0EnirUhpGmi3sJ7rz7oxSSrEfKRthZInILYB24zmvjr/AILvHFh+x/7/ALUPgX/0dc19c/GX4GWnxo1LwfdXWta5pEngvXI9ftBpxt9tzOkMsIWXzoZMx7JpOE2nJHzcUdV2ur+l1f52vYOku9nb1s7X8r2v5FX4a/tTeBvi5qun2Wg6xLdXOrWV5f2ay2FzbC5htJ0t7ooZY1BMM0saOvUM2McHFP4f/tE237QfhU6l8OJNO1WDy4LmO8vZv9CuIZfNA2PCXIlBj+aJwrpuXcqkgVgfB79hrRvgz4s8LavZeLvGmoyeErDWdOtIL57Dypo9UvIry5MnlWsblhLDHsKsoAXBDZNdN8Cv2XfDf7P2ravqWkG5utX8QWllaarqFzDbR3Opm187y552gij82dvPk3yuCzfLk8Cktve3/wDtn/7by/Nv5PS7/rp/mZHjrQvHOp/Ff4T6idNuZYdL1W+fX30jVAmnw2z2FxFEZYpZI2mPnPCRtjcrgkY7+vUUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeYftt/8mYfF3/sStZ/9IZq8x/4J2fEbS/hB/wAEbvgH4p1s3iaP4f8Ag94bv717WzlvJo4Y9GtmdliiVpHwoJwqk4B4r039t5xH+xd8XmYhVXwVrJJJwAPsM1cX/wAEqLSK/wD+CU/7N0E8aTQz/CfwxHIjjKup0e1BBHcEVFTm5Xyb9LlQ5eZc23U6bSf21fBHjJr+00S81y4v7S7Gmvt8N39wtvcNp41BGZEjBaM2zpIGBCtuCBt5ArO8A/t2/D/VfDOlG/8AFDXt5Jouh6tc6jB4cv7CxuIdWfybK5VJFk8mOaZWUI8jNGQQ7fKWqz8Ff2GfB37P7iTwzd+ILa4Tw7H4aSaa6SdkgjkleObDxlWnUS+WHYEeXHGpBC88dYf8EwfDOneCf7Aj8d/EX7ENA8PeHFYyaWZEttEunurIg/YsbzJI28kYYHAC9a1XLzvt7vr9q/p0at3s72Rn73L5r/OP6X+a00evUeM/2rkuPjZ8L9E8J3lhqmi+KPFGqeGtamfTbh1WW00+9nZbe6DLDvjuLTy3GJMneo2sjY7L4jftJ+FfhT40XQNam1WHUpNDvfEMSw6Vc3Ec1pZmMXBR442VpE82P90CZDvGFNcvpX7Enh3w7rvhG60rXfFGl2fgvxPqHiyw06B7RrVrq+S6W6R99u0hic3ty+1XUqz/ACsFUKOx+LPwH0L4zax4Tv8AVxeJdeDdVGrWL20ojLt5UkTwy8HfC6yHcnG4qp7VCTsr/P7vyT19FbqmW7czfTp97t82rK+yevSx5F+0T/wUB0bwp+z/AOMte8F3kR1/SdO1htIk1fS52sbzUdMtXubqyZA8UnmIscqsCVAaKUAs0bLXpA/as8G29+IbjUvKt49aTw1PqJTFlFqrRLILQyZ4fDBd2Nm8iPdv+WuY8X/sDeD/ABN8PvHPhi01HX/D+leP5dTn1BdN+xiWGTUQ/wBsaKSW3kYeY0sr7XLqGkJAGFC34/2IPBkkbW13Jq2oaVN4jj8W3GmzyQ/ZbrVERR9pcLGrZaRFmKKwTzOQoHy0lzfhH/wK65vlbmt58vS4St07y+63u/O9r+XNboVb39s3w14gg8HXWj6vd6bpviu+01rC+vPC99e2muWl68scCwTRMqQPM0YKyTE7VKFoyJENRfDD9vDwt458L6RqWo2HiPQl8Q+L77wdp32nRrnZLd295c2yB5FQpHv+zMcuQoY7M7sCrJ/Yk0SD4d+B/Clp4o8ZWOgfDy+0680W0intGWFLGQPbwOXt2LxqFRMsfMKoPnyWLZHjn9jK1n+HOo+BdIGo3Hh3xTq02sXN5cax5Nx4bum1NtTWezRIcswuJpnVmcMpSFTuXJS9NbfzP/wH3Ld7Stzf3U9ba6qXT0X3+/f1V+V9G1pfqvZPh74+sPid4Stdb0tb9bC93GE3llLaSuFYru8uVVfacZU4wwIIyCDW1UOm6dBo+nW9paxJBbWsawxRoMLGigBVA9AABU1DtfQSv1CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4M/b9/5Tl/8ABPr/AK9/iT/6Y7OvvOvgv9v04/4Ll/8ABPr3t/iT/wCmO0oAX/gnv/ym4/4KGf8AXx8O/wD1HHr7vW6iaVoxJGXU4KhhkHGcY+lfCH/BPf8A5Tcf8FDP+vj4d/8AqOPXd/FH9iHxj8RPjb4v8SPpHw9ktvE3jTw1qfnSarcLdnRtIEcphYCyIE0lwr5UOVMb7S/GKS+KMejau+ybSb+Sd7dkw+y32T+dk3b52t6n1bqev2OiW8c17e2lpFK4jR5pljV2PRQSeSfSpG1S2R2U3EAZWCEGQZDHoPr7V8TeJ/8Agn98V/F3wh8SeHpLv4e6bNrVh4vjtltL+4ZbS41q/klQec1nuECWrRoyRrGd6EkyKcDtdA/Y08T/APDUWl/EbVPCPwreK6iW31zTU1O5nje4tYgLDWYt9kA1+vm3cLbwCsMkeJWZTlxd7X0vv5f10tvdN2V7EtL21s2vXaz+avfs1ZXbR9UyyrBGzuyoijJZjgAVANZs2GRd2xGQP9avU9O/esS98LS/Ez4Tz6L4w0zSkm13TGs9XsLa4a9s1MsZWWNJJIo2lTkgM0aEjkqOlfOP7Iv/AATin+C2s2M/jI+D/EcEOhaGJ4oNPBVta0+2nshdKHQARratCqn7xfexC8ZEnzST6Wt573+6yt3v0B2smvP9Lffrftbr0+rINRt7koI54ZDICyhXB3AcEj1qavEf2Ov2Qrb9mvwtFa31voF3f6JJfaZoFxZ2gVtN0V76ae3tgxUMH2SIJNvys0a9doY+3U9Lf1/W2/np5h1a/r+r7eWp8H/8F3v+Qf8Asf8A/Z0HgX/0dc192y3McDKryIhfO0MwBbHJxXwl/wAF3v8AkH/sf/8AZ0HgX/0dc19IftX/AAG1T43638L7jTdO8M38XgzxdFr1+NXuXhJt1tbmFkh2wS7nYzqdrbFITk0uqXdpeibSb+S1DpJ9k2vNpNpfN6fM9fWRXOAyk4B4PY9K57Wfi54Y0GymuLnXdMWG3lWCYpOspidt2FYLkqTsbr/dPpXzp+yv+xz48+CPxA8C6hqNr4HtrHQdE8S6Zqp03VrmWW6fUNTt7uy2q1pGJEhigMfzMuzfhAQOe7/Yx/Zm8Q/s6QalHrOsm+s73S9MtLTTzqLammjvb/afNht7mWCO4Np++Tyo53laPa+GG4ikrtXen/7TX5JP5+Wr0u/66f5nV67+0BEnjr4eWGjQ2WraP441G80+S/FyyPatBZXFyNsezDgmAqSWXGeh7ej1ka74A0HxTrmlapqeiaRqOpaFK02m3d1ZxzT6fIylWeF2BaNipIJUgkEitemIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDy79uCNZv2LPi+jqro/gnWQykZBH2Cbg1x3/BKOZLf/glb+zW8jrGi/CrwuSzHAH/Eote9dn+23/yZh8Xf+xK1n/0hmrxL9kH4ZX3xp/4IP/CbwhpkGlXGo+KPgXomlWq6nI0doJZtCgjRpGWORgqlg2QjHjgVFSTjFyirtdO5pSjGU1GTsm9+3mfXQmRiMOpycDnqfSqkHiTTrprlYr+ykayfy7gLOpMDf3X5+U8dDXx7qP7B/wAQodY1O80vTPhvYzT3Xgi5s3TWrpGtjo91HNfkEWPytOieWpHLgDftAxRpP7CfxF01rhtV0j4UeNLLV/CN94d1nQ9Svrm10q+knvpLtCkcdk/lwq0r8MJJF3MN7FvMXSatJxjrrJeqUbp/9vS93y3MqT5oKU9HaOnq0mv+3Vr52t1PrfW/Fb6ZfabDa6bd6tHeXX2e6mtZ7ZU0pPKd/PmEkqMUyipiISPukU7NoZl1I7qKVAyyRspxghgQc9Pzr4+8KfsFeMfB2m+DdOGj/DfWX8F+PP8AhIDr95qM66t4i0/7Dc2w+2H7E+btVnSI/vGR44V5X7g4qz/4Jm/EXTfghF4bttN+F1vqUXwn1HwQs8es3axpqs86vBeAiwzsiQNh8eYpcqoxkk0v5a/dyc34y93ye/YaT69l97m4v7o+96ab6n3w86RglnVQvBycY4z/ACpsd1FKgZZI2U4wQwIOen518l+If2FvFr/EyHX7HTfh9cpL8SrXx7cQ3N5NE0af2BHpl3AjC0fc7yoZC5CiRW+YA157Z/8ABM34i6b8EIvDdtpvwut9Si+E+o+CFnj1m7WNNVnnV4LwEWGdkSBsPjzFLlVGMkqOtr/Py9zm+fve4vPXYFrb0X4z5bfKPvv7j72uLuK0UGWWOIMdoLsFyfTmsrxR4vOh6BNeafp914iuY9pSw0+e2W4nUuFZlM8sceFBLHc44UgZbCngP2oP2fbr9ov9nSXQHj0WDxbYG01fRbm7BubSw1e0kS4t5CxQO0fnRhWIQM0bOMc4rwzVv+CZuoad+zj8QfCGm23gTVdV1MNa+FLi/ia1XTLaa6XULgSSJBIyH7bJOyLGpASK35BBw18TUun4rT8b302sr3voJO8FLq7adu9/JL5t6WtqfY8N1FcHEckbkEj5WB6cH8q5FPjVYt8f2+HjafqaakNB/wCEgS9IiNnLCLgQNGCHMgkDMpIZAuDwx5A+aT+wl8Ql+IE+q6avgfwu+ofEPVvE82paZq1x9utbC80ObTlWMfY1DTJcSi4MZZUJQHduOR3X7Of7KXij4OfGjwlrV1Z+DY9L0T4eReEdQubG9mN7fXkc8cn2gxtbqGV9jElpdwZz97k0qPvShz6Jq79XTlK3/bs+WPnfTS46vuxkoau+noqkY3/7ehzS8lv0v9DtqdsjurXEAaMhWBkGVJ6A+ma4r4oftC6L8L20LfFc6wut+IrPw07abLBJ/Ztzcv5cbThpFYIGwDtDMM/dxkjgPiJ+xRaeM/2j9Q8UwJodv4e8V6Xbr4jtXs1e5udTsJHbTbyPKldyfaJSzMd2ba2xkA4810n/AIJ+eMPCvgL4X6fZzeG7/W/Ar+HrG/1O51i4g/tCy0u5MxdYktSolYl3AfcQ07jzcAl1Tbcoc23NG/8Ah5rS9NF90lLdOKc9Iz5dWoyt/i5br8X96ts0z66tPEFhf3d1BBe2k09kcXEccys9v1++Acr0PX0NYuo/E+ysZ4Z0iNz4f+xXN7d69FeWv9n6cIdh2SkyiTLqzMCkbIBE+9kym75a8V/8E3PGXjD4JeFvB7eMrLTJ/CHg/VPDJ1SCSWSXxPJdS2riW9XYu1JPszNMoaQs874PGW674p/sXar8TfjXZeIY/Dnw60WyvtB17StbmgnllvLmW/s7KCKQg2yrMENmoO5kwqpgEjFRXc4wk6erSdvN627b2T6NaF0lFzipuyur+S0v+Z9CeEfH2keOPC2m6zpt9bz2GrWcd/bSbwC8LoHVsduCPpWvHIsqBlYMrDIIOQR618cP+x0r33wl8Mtp/hu08TeE9G/4RvxH9i0aZ7DUfDV3ZmK8h+0vCiPI9zaI4x8yeYAyjzNzfYWnadb6Rp8FpaQQ2traxrDDDEgSOJFGFVVHAAAAAHAArprRgpy9m7q7t6f8Nb53XS756cpOK51Z2Tfl5fn8rPqTUUUVkaBRRRQAUUUUAFFFFABRRRQAUUUUAFfBn7fv/Kcv/gn1/wBe/wASf/TJZ19518Gft+/8py/+CfX/AF7/ABJ/9MdnQAf8E9x/xu4/4KGf9fHw6/8AUcevvOvgz/gnv/ym4/4KGf8AXx8O/wD1HHr0H9oLWPjj4S/aY1OTwxN4v1bwXBd+GtUitbSytWt5IJrq5stQsEcwF8j/AEe6dyxMcasdyggUr+9GPdpfe7a+S3fkh292Uuyv/wAD1b0X+R9QeMfGej/Dvwtf65r+q6domi6VC1ze39/cpb21pEoyzySOQqqO5JAq/bXMd5bxzROskUqh0dTlXUjIIPcEV8U/tLWnxVvPg58S/AeuXPjrXbOcaL4a0y607w99uk1+11CWFNTvpmtrUhUiWe4j2IEMcVsHbJcPX2vDEsMSogCogCqAMYAqktL/AC/X9V+upLeqS/ra363+Q6iiikMKKKKAPg7/AILvDNh+x/7ftQ+Bf/R1zX3LrfiCw8NWX2nUr6z0+23BPNuZliTceAMsQMnsK+G/+C73/IP/AGP/APs6DwL/AOjrmvq748fs26H+0LFZR61eaxax2dpe2JSymRFmgvIhDcI29GwWi3IJE2yKHcKw3GgatfU9CopltbpZ20cMahY4lCKvoAMAU+h+RMb213CiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHl37cUXnfsVfGBA7oX8E60u5ThlzYTcj3rkf8Agk7/AMosv2af+yVeF/8A00Wtdh+23/yZh8Xf+xK1n/0hmry3/gnv4b1jxh/wRk+BGl+HtdufDGvX3wb8Ow6dq1vFFLJp9wdFtvLlCSo6NtbBIZSCMioqS5YuVr27blQjzSUb2ufTl7ew6ZZTXNzNFb29ujSSyyMESNFGSzE8AAAkk1X8N+JNO8ZeHrHV9Iv7PVNK1S3S7s7y0mWe3u4XUMkkbqSroykEMCQQQRXyb+zlr/xv8Z6lcS+OLv4geGbF9Lh8TRgaZaSSWjfZHtJtJIFqxkkW7ia8XClyksS8r8p4Xwt+0P8AHe28CQ6teL8U7jV4fBvgW9lsJvAE4WbVZ76SLXIyq2IbcLdY2kjQjyt5ZdvGNVH33C/8vprzX9bONn2809M+Z8vNb1Xzjb/0q/p2tr9x61470Tw54i0fR9Q1jTLHVvEMkkWl2U90kdxqLxxmSRYUJ3SFI1LMFBwoJPFSeMfGWkfDzwtf65r+qafomi6VC1ze39/cJb21pEoyzySOQqqO5JAr5Sk1X4geMPjN8GdS8Y6X4sGoaV8TNfFzaWfh+aTS9K006fq1tp85njgOEeKS1Jkklx5lwwbaRsTf/bzufiD4h8PfFPw3ZWPiBvCN98LtQGkf2HpJ1C41bWZFuY5LaQLFK64i+z+Wqhd5ml5YoNuM5tQ5l1T/AAjza/l62N6UFKryN6XSfzny3Xl19Ls+i9B8d6J4p1fUNP0zWNL1C/0kQtfW1tdJLNZiZPMiMiKSU3p8y7gNy8jIrVr4n8R/Fr4qwePYtM0a6+KmneHhqfhCG2aDwLM6WtncQTrqaBnsGO1NsTM0hZonIBIGUq/41u/2jNJ8M/EAaJqGq3fjTw/qOpReHRdacs1r4h09dJYWJOyKOzjme62TtJuB85Xi2LEyqu00k522i2vW3Jt/4Gvula9jnouUoQct5JP0vzb/APgL++N9z7HknSJ0VnVWkO1ATgucE4HrwCfwrP8AC/jLSPG9jPdaLqmnavbWt1PYzS2Vyk6RXEMjRTQsVJAkjkVkZTyrKQQCK+YPHPhfxS/x3+E/iHTdW+Lmq+GdPv7ue9tJdOKC1kfSdkaOskIkIklV9zy/JG9wyqyA4FX4c/Ef4r+JPG19b+Jx4/Hgy0u/FsMV9H4fms9Rkniv4BpA2RW6M0H2Np2jkCGOVkAdmOAYm+VtPWyk/wDwG2nq9kurt87j70IzXVpfen08ravp2e6+u6K+GPhR8cP2gdIazfxlB45k1pPE/hOy1LT7fwfJPp0dvdafb/2wYporU74YrkznzVkKxsuN+3AP3PWkoWu77Nr7lF3Xl7ys+6fYlS1S7pP721b1027NBRRRUFBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfBf7fo/wCN5f8AwT6/69/iT/6Y7OvvSvgz9v3/AJTl/wDBPr/r3+JP/pjs6AD/AIJ7/wDKbj/goZ/18fDr/wBRx6+86+DP+Ce//Kbj/goZ/wBfHw7/APUcevcfiH+2Pr3gvxb8T4IfDPhyTQPhvJZ2bajea/Nby6he3dmJre0jhS1kLStPNZxbQ2SLoFQzARsmxpXdj6Cor5bH7Y0nxJ1fSdEn0LzLyD4lP4Y8yDXrrRnVbTTpdS+2PFEC7RBoJYXt5W2OYn3ZGYx1X7J/7ZWpftGah4atdR8J22gy+IvA9p42zb6o14LSG6naOCJ8wx8uqO4I7IwIqoLmtbrZr0cOdP5xTfytvdKZPlvfpv8A+B8n/pVl872tZv3qiiikMKKKKAPg7/gu9n7B+x//ANnQ+Bf/AEdc19418H/8F3v+Qf8Asf8A/Z0HgX/0dc19UftBfGzVPg5q/gC103RbHWP+E18Sx+HZDcX7Wv2IyW1xOswxE+8D7OQV+X72QeMUdUu7SXq3Zfiw6OXRJt+iV3+CPSKK+Std/wCCmOqaR8JLrxKPAdkZ08IeJvFVtanXWKy/2Few2txAz/Z/l83zt0bgHG0BlGeOwuP239S0f4hajomo+ELSKLSfFmieFbi4g1hpSz6rbwzRTIhgXIjadVZSRkAsD/DRD3uXl+00l5ty5F/5Np/wAl7t2+ibfpFcz+5O59C0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5d+3EXH7FXxg8oIZP+EJ1rYGOFJ+wTYz7VyP/AASd/wCUWX7NP/ZKvC//AKaLWuw/bb/5Mw+Lv/Ylaz/6QzV47+xJ8Tbv4K/8EOfg54xstPt9VuvCnwQ0LWEsp7k20d19n0K3lMZkCOU3BCM7WwSOKmc1GLlLZF06cpyUI7vQ+saK+Z/H37fWseCPElvoX/CFafdazPF4Wvdqa2/2b7NreotpykyfZ8rLDMu7aUxImSGBBUYfib/go34l8E/Dnxv4s1L4d+dZfDvUtS0jXLCwvZJGgkstNa7e4juZYolkhecCFVEYkMciTkAbo1uz97+7e/8A264p+tnKK07+TM4SU0nHra3/AG8nJffFNn1pRXz18TfjR4o8JfEL4fW+r6Dot9caxrNxaaZcad4rvLKFWOg3l4HubRIyk0Ze2nhAlZgMJMqhwFXz/wAdf8FBbrX/ANnrRdU1fwOYpfFnw7g+IsFrpfjC6smigS6sEmt2u4YI5o3U3sJQqNswWVG2qDuS1m4LdWX3qT/KMn8gi+ZKS2av8r2/No+xaK+XrD9sLxL4V8Y/Fi313/hGPL0fx/YeF/D1t9pna5lhntdJLbYViLzOv21pNiH5mO3KL89UvAX7bviT4q/GP4X2tra+H9I0bVx4ptPEEFxfN5sdzpWowWAKkKQA5dpFRuT5igt8uWIPnjGUdpJP74qX5NfeOXutp/1q1+af3H1dRXyD8Ov277rTvBPxBhsPCz2Unwv0fXPE2pxal4gu9W/tEW2s6raGC3u5wJPnOnTvllKwLLBGqFQMdd4N/b0vvEPj+PTLvwfFZ6bc+OLTwZBc/wBpsbkfatDj1eKd4GhAUqsixPHvOGyQSBglP940oa7P75ci++Wi+/YVR8jal0vf/t2Kk/ui0/mfSFFFFAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4L/b9/5Tl/8ABPr/AK9/iT/6Y7OvvSvgz9v3/lOX/wAE+v8Ar3+JP/pjs6AD/gnv/wApuP8AgoZ/18fDv/1HHr7IHwJ8Dg66R4N8Kf8AFUXUd7rP/Ept/wDibzxlTHNcfJ++kUomGfJG1cHgV8b/APBPf/lNx/wUM/6+Ph3/AOo49fU/iL9rfwV4X8aeJfD9zNr8mqeEYFudVS18PX93FaRtbS3SMZYoWj+aKGTb83zMFQZdlUrRajSb0Ro3f7Lnwy1C8FxP8OvAk9wJpbgSyaBaM4kljMUr5Medzxkox6spwcjitHwN8CfA/wAMNVF94a8G+FPD18LCHShcaZpNvaSizhz5VtujQHyUydsedq5OAK4vUf2rvD+uy6XZ6RriaVqc3jGPwncQXmh3Gol7tIBeS2YMDhIpHtfnWdnaNM8qzApWZrP7fPw6vfhXquvab4luNOWHQdU1y3u73wzqNwkUFhMttc3Bt1RJJ0hneMMiMrMD8pAywE7RvH5eaUeZW8uV39NdndluaXK+v6ya/wDSl967rT3GivLdP/a48F2Xiefw1qetTDX9J1Ox0DUJP7Gu7a1/tC6t0nhRWZWRRIjhgPMbbuALZq/8MP2pvBPxn8SppXhjUrrV55NMt9ZEsen3CW5s7gzC3uBI6BTHKYJtjAkN5Z7Fc1be2v8Aw1/y19CFLRN9r/e7fnp6nodFFFIo+Dv+C7x/0D9j/wD7Oh8C/wDo65r7W8ZfDPw38RZtMk8QeH9D12TRboXunNqNjFdNYXABUTRF1PlyAEjcuDgnmvir/gu9/wAg/wDY/wD+zoPAv/o65r66+L/7QPhv4I3WkW2t3RS/14zjTrRGjWa+aGPzJEiDsoeTaRiNSXbPyqcHCduo4pt2RnTfsd/CO505LOT4WfDiS0jtriySBvDVkY1guHEk8QXy8BJXAZ16OwyQTV2X9l34Zz3sly/w68Cvcy3dvfvK2gWhd7i3ULbzE+XkyRKAEfqgAAIrtrO5W9tIplV1WVA4DrtYAjOCOx9qkqtU/T/O/wCevqSmpK62f6hRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5d+3FKLf9ir4wSEOwTwTrTEKpZjiwm6AdTXk/7B/wxX41/wDBEz4J+Dnv5NMi8VfBXQNIluo4hK8Ec+iW8TsqngttY4zxnHB6V61+23/yZh8Xf+xK1n/0hmrzj/gmz470f4Yf8EgP2e/EGv6hb6Xo2kfCPwzc3d1MSEhjXSLXJ45J7AAEkkAAkgVM4xlFxlsy6cpRkpQ3Wx6rov7MHgeDwBp+g6v4S8I65FaCzkma50O2KXVzaqFhuGjKkCRMZVuSnYiuok+HXh6bWZ9RfQtGfULobZro2URmmHl+Xhn25P7v5OT93jpxXjfh39s230n4z/EHSvFU32Hw5oup+HdK0WVNAvorn7RqoMccdwCHOGnMaiUpGi7wG5Ga6C0/bk+HWo+IrTSra+8QXWoX11qNlBBB4a1KWR5tPuY7a8TYsBYeVJKmSQBtO8EoCw0bdSTvq3e/rZX/AAcb+TXSxjGMacUo6JWS/G35P7n2Z0/iD9nrwbq+mWiQeFfClrfaPvl0a6Oi28jaPcNCYRPANo2OEO3KlSV+XOK5r4PfsX+Bvhn+z/ovw+1Lw94a8VafpejWmh3U2o6JbMdWgtRiH7QhUq5H3ueNxZgBmsX4p/t/eDvh78NPHGtwQazfX/g3w7deJBppsJI7i+tYZZYDIikAhBNGVO8KQpV8bWUnU1D9rPw94a+IN9Z6xrn2GJdJ0q5t9En8O3ltqHn315LaQEXEjeVN50wSJYUjDRsjM7lXGyYav3ftW+bXNb5/EtOt77aXL3Vr9n8L8v8A8lFq/fTfXvNR+C/g7WJ7iW78J+GrqW7khlnebS4HaZ4dnlMxK/MU8qPaTyvlpjG0Yj0r4HeCtCFv9h8H+FrP7JdyX8HkaVBH5NzIQZJlwg2yMQNzjk4GTXGXv7cHw90jxhc+Hr2/1S116xF99rsBpNzcS2jWcME9wrmFHQMsFzbyjDHckqld1dj4i+N3hjwv8KrfxrdarD/wjd5DazWt3GrP9rFy0aWyxqBuZpXljVFAyWdR3oWquttPx2++2ne2gPRpPz/Df7r69r+ZHpX7PngHQdPW0sfA/hCytEmmuFhg0a2jjWSaQSTOFCAbpHAdj1ZgCcmop/2bvh3c+IH1aTwD4Lk1WXU11t71tEtjcPfqnlrdmTZuM4T5RLneF4zivJfjT+1xd6n8YPB3w68G6hNoXiPVteuNK1uS50R9Sk0dotON9EjIjiMrKrQsXDkeWZMFWBKet+Ovj74b+HHxA0jwxqkmrDWddsbvUbGG10i7vBPDa+X553QxuoZfNj+UkMdwwDS5kkpbdvwl92l/lcbTba/rW6/r1OzorxT/AIeG/CZ9O1K9i8RXtzp2k2NtqVxfW+i301mtvcwNPbyeekJjxKqsqAtl5B5YzIQpvy/ts+CIvE+t6AG1mfxL4Zgnu9Z0a2097m+02CKO3laV0j3bwUurcqIy7P5mFVijhas+bk69vTf7uvbqLpfp/X9I9corxzw1+174Zg8QHStT1y41PUNU8WX/AIZ02Kx8J6lbGC5t7Y3Zs5Qwk3SLbqz+f+7ilAyqjHPKfEb/AIKFeF/B3h7xf4k0nVz4o03QPD1tqcOj2fh29jvFkkvriyaR7lyI2UTxGJ4RGssD28u8ksqKlrytdUmvmub8tdNLa7ag1bm8v/kuX89Neum+h9HUV514o/ak8KeCvEGnadqz6tp0moyafCstxp0sUUUt/M8FpExYA75JYym1QShK79gZSeJ8G/trad8SPAmia9HLd+Eba/8AHU/hLbq3hu8uvt7RX1zZrEjRsi27yPAMTSFo4z8rKWIwLV287fO6VvX3lpvqrLVA9E2+1/lZv7rJ67aas97orxnxF+318M/CXhGz8Qalq95baDqunzatpl+thLPFqtnDLFFNcQiJWdkQzRNkqCUcOoZAWHongD4qaH8Tpdai0e7lnuPDt/8A2ZqUE1tLbzWdx5MU4jdJFVgTFPE4OMEOPeha7f1rb83b10B6bnRUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8F/t+/8AKcv/AIJ9f9e/xJ/9MdnX3pXwZ+37/wApy/8Agn1/17/En/0x2dAB/wAE9z/xu5/4KGDv9o+HX/qOPX0l4g/Yy0XxRD8R477xN4vmT4m6rZapqIE1rH9l+yLboltDttxm3ZLZEeObzQ6vIDw7Z+bf+Ce//Kbj/goZ/wBfHw7/APUcevpnxz+1Quh+LfGej+H/AA7d+LL34eWdvd69bWt3HFdIbiNpIYoIm/1rlF3NuMa4ICs7goFbX+vJ/oPzMDQf2ANA8MeMrPWtP8X+N7WSx8R3XiqK1SSwNqt9PYzWRYRm0OFSGd9irjBCA5VFUc8//BLvwxJ4JsNAPjz4jGw0/wAKjweuZNLLyWRuo7qUsfsPMkzxIJGwAyjoDzVn4of8FJ9D+Evhj4x6tqng3xX9n+CurWWlauqS2PmXv2qG3mjuLcNOMxCO5jYlyjcONp2Guj+O/wC2f/woSxv7y98B+KdWsNPudH09riwubBlmvdSuYraG1RXuFYyLJNEXyAoWRSC2acH8Eo/3UvnHlivnGVvRpCl9q/W9/lLmf/k2v4mL8Rv2EG1KXW9e0Pxn4ml8U3PidvG1nDqT2Q06TVE0wadbxTeVaCX7LHEqEBGEgZd28nFaX7HP7NWvfs33epWMurPqHheXTrK2s49R07S7fVI54Q6NufTra3ia3EZjEauHcYblR8pd8Sf27tF+EGk+P/8AhJPDXiPS9a+HvhZ/Gd1pTyWjy6lpaGRZJ7WRZjG2xo2VkdkYMU4w6k9B8MP2rdP+IHxci8CX/h7xD4Y8U3HhuPxXDa34t5UlsXmEJO+CWQJIshClH2k5yu4KxBR092HVLT/CmtO9lBry5XfYVXa8+n37xf3NzXrz+Z6pRRRQM+Dv+C7xxp/7H/v+1D4F/wDR1zX1h8f/ANmvw/8AtKaPFpnidri60Zbe4tp9Me3tbmyvBMEG+SK4hlUyR7MxuAGXe/PNfJ//AAXe/wCQf+x//wBnQeBf/R1zX1T+0j+0ha/s1ab4YvtQ0HWNX0/xJ4hsfDjXNk8Cx6ZNeTLBBLP5siYiaV0TK7iC68c5pOKk1B9WkvVuyXzbHGTjea6Jv5JXf4He6BosPhvQrLTrYym3sIEtojJIZH2IoUbmPLHAGSeTVuvDdD/bgtPE2tfE3SdP8GeJbnWvhfr9j4fu7AXNisupSXiwtDNbkzhfKImUZkKHcki4yhFe2Q3qSYVmRJsAtGXBZCexxVOTn+8et7P70n+TuQoqHuLpp92hNRWJ4w+I2h+ALWCfWNSt7CK5cxxNJkh2HUDAPSuE8Q/tA6lb/FXwfpmj6XZax4f8UzrELuK4/fRw+TcPLdkdEjikit4sOB5jXS7Wyu1ktWku9v6/rz2Keib7K/yX9fotT1aiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8w/bdIX9i/4vEkADwVrOT/24zV5R+wF8I9E+PH/AARj+AXhHxFBLPo+ufCPwvDOsUhjkTGlWjo6N2dXVWU84Kjg9K9V/bhiS4/Yr+L8ciq6P4J1lWVhkMDYTZBFcZ/wSpvIdP8A+CU/7Ns9xLHBBD8KPDDySSMFRFGj2pJJPAA9aTGt9De1r9ibRPE3ibxTqup+J/F1/c+LLrQ7+dXeyRLW40i4S4tJYglspB8xF3hy6sOAoqt8N/2GNH+GXxWsvFtp4x8b3l3Y6hrepLaXT2DW0j6tNFPdq2y1WTb5kKFMOCvIyRXar+0Fotx8a9F8FW0VzeTa9ot9rdrqlvLBLp7JaXFtBNCWWQyCUNdxHGzbjPzZGK6+TxBYRar9ga9tFvvL877OZlEuz+9tznb74xTjouaPW/z1s/8A0lL/ALdXREvVcr6NffbT8G/vfU+fo/8AgmR4GGj3Wny6740ns77w1qHhW7R723DXtreuzyvM6wB5Zw7FhI7MSwBfeQDW94o/YS8N+NvHr+ItX8Q+LtRvxa6fDbiWe12Wc1jqL6jbXUZFuGEqXEjcFjFswnl7RivUPE3xN0Lwfq/h+x1HUrW2uvFN41hpkbSDN3KsElwyrzziKJ2z7D1rZub6Cy2+dNFFv+7vcLu+maI6crX2dvJ3b087yb9XccnzXUvtb+a03/8AAUvlY8N1v/gn94Z1j4jX3iuPxJ4wsNc1ZNUGoz272J/tB7+0tbOSVw9swDR29lbJGE2qBFllcsxPXSfst+Hr/wDZv0T4Y6hc6tqei+H7XTrezvJ5IlvkksJIZbS43RxrH5sckETg+XtJQZUgkHu73xPpumWb3FzqFjb28ahmlknREUE4BJJwATUx1a0AY/arfCAFv3g4B6E896I6K0f7v/kt+X/wG7t2B6tN9L/ja/32VzyNP2KPD0Xxz0/4iJrvidPEtpqr6vdSBrQx6rI1gNPVJlNvkRpbjaoiMZyzMSSc1F8bfg/4n8eftUfDfxBpUl3p2h+H9G1yx1DUrSe2FxBJeC08rZHMjhgPszEnbwSnUbhXr9hrllql3cwWt5a3M9mwS4jilV3gY54cA5U8Hr6Vy/xQ+NNj8KvFPgnSrzT9Tu28c60dDtZ7YRGKzm+yz3Iabe6vsK28gBRXO7GQBzSsnaL9PvVkvxC9k5dk36Jat/I8v8J/8E1/h94R8C+KvCsVz4iuvC3izR9O0WbSrieBorKOxUi3mhkEIm84OTIWkkcF+cdq7Cx/ZN0PSfiIni2y1bXbHxH/AGZNo0t7a/ZYTPaSNE/lNGsAjG2SIOrKqsGd/mw2K9Et/FGmXc08cWo2MslrJ5UypcIzRPz8rAHg8Hg88GpRrNmwUi7tiGGR+9XnnHr68VXM3Ln69/VNP702n3TtsHTl6f5W/JpPyavufOPjX9jvWPDnxe8Ear4W1PxBqNo3xEvfGuv3d3c2KyacJ9GudO2W6+QN65liO1w7bUc7ySAdK1/4Js+CbXTvEFkNa8XvZeJdDfRb2Brm2ILSajcanJeBvI3i4e8up5TljF8+BGFAA9+n1O2tiBJcQRlgSN0gGcdfyqC88T6Zp3l/aNRsYPNdY08ydF3s33VGTyT2HelFWtbpovJcvKkv+3dBybd79dX5vmc7v/t53PGfiH+wL4a+JfiC+1XUPEvjVdRv5dKna4S8t2lhk068a9gaJpIGMQMxDOkZVG2rhRjNWJP2FvDqeG7LSLbxH4xs9O0/xWvi+KBLi2kUXQv7i/MR8yBv3TT3Mhb/AJaFQq78KK9pjuI5ZGRXRmQ4YBgSp965DwP8arHx18VvGnhGHT9TtL/wSbI3M9wIvIvFuomkjeEo7NgbGB3qhBHQjBpJLp35vndO/rdL7uwnqte1vk0019zaPLPGX/BNXwL4y+GOl+Em1XxVp+keHdGudA0IWs1qZNCsp5IWkigMkDg/JbxRBpRIyopAIJLH0v4Q/Ae0+D/ivxxrFvrmvaxdePdXj1q+XUPs3l286WlvaAQiGGMhDFbRZDlzkE5GTXY3OsWlnZS3M11bRW8GfMleVVSPBwcknA59a5z4w/F+w+D3wW8ReOZrW81rS/Dmkz6xJDprQvPdQRRGVvKMjpGx2KSMuAfWhz5bzb6O78m7v72r/IqMHNqEVrfT12OsornNB+I8eqw3l1eadeaLpVvDbTwajfz2y218s0YfKbJWddhOxvNSPLfd3j5qe/xR0IeOrXwymo2s2uXumPq8FpHKrPJaq6R+YOehZwFPQ4bB4NVJNS5Xvrp6Jt/gm/kZwmpxU47Pr62t+aOgorD8P+OYtQ0HR7nVbWTwzqGsRqyaVqVzbG7hkIGYSYZZIndSQD5Ujr6E1sQ3UVyMxyRyD/ZYHvihookooopAFFFFABRRRQAUUUUAFfBf7fpx/wAFy/8Agn17wfEn/wBMdpX3pXwZ+37/AMpy/wDgn1/17/En/wBMlnQAf8E9/wDlNx/wUM/6+Ph3/wCo49en/GT9iC7+Pvxk0nxdrfhbwVpXjLw14ltr3RvHmlXssGuWmj294k4sX2Qo0vmxK8TRPI0QWd25I2t5h/wT3H/G7j/goZ/18fDv/wBRx6+vvGP7SPgD4e+MW8Pa94y8N6PriWZ1B7G81COGdLYEKZyrEERAkAuflBIyaSajOM+q1Xqtb/h91+jYNOUHDo9/yt6a7d7dUjwn49/8E+tT+Mnjv9oK7/tTTk0L4veBrfRrKycOGt9YjtL6zN5IQCAogmt1XGTwxwNq51PjF+y58QfFvwv+EWgafL4U1q58M+KtL8U+MLvUtSuLD+1ZrPM22BUtp8g3SwkByu2OILzxXud/8YPC2mN4WE3iDSQPG9wLXQGW5V11iQ28lyFgYEiTMMUkmRxtUnNZvjP9pP4ffDrxg/h/X/GnhnRdbjszqL2V9qMUE0dqCFM7KxBEQJALn5QSMmnH3JJrdOL+cG5L7m7tdkr6JBL31Z7WkvlNKDfztZPu31Z84/tU/sK/EH9o60+IevO/gxPFfjnRLDwTb2EmrXSadpGgx3wur0eeLRnmubnLg/uURQsS5O1mb1b4EfAXxL8JP2mPidr0kWgDwh41/sufTYred2udOe3tDDPCQ0YyjS4dQG2AMx2qzNn2jTdSt9Z06C7s7iG6tLqNZoZoXDxzIwyrKw4KkEEEcEGpqa91cq6X/F3a+bvJ+bfcTfM+Z63/AMkl+CS9EFFFFIZ8H/8ABd7/AJB/7H//AGdB4F/9HXNfVX7XXwIH7TX7NHjTwKtytjeeINMkh0+8Of8AQL1cSWtwMcgxTpFIMd0r5U/4LvDNh+x/7ftQ+Bf/AEdc1941M480XHYqEuWSl2Pm/wCHH7GviHwn+0t4V8fXms6XIlx4TSy8a20COBq+tQTyT211EDwI1e+1End8w/0cD7vHP/th/sS+Lvjr8R/iFrOgaf4EB8UeB7Hw3p13qOpXFtdwXkF/NcGZ/LtJNihJQFdXZ8p0UHI+sKK0ctU1pZya/wC3ua//AKU0uysuiIStFp63UU+/u8rX/pKv317s8k/Zm+DviH4T+M/ijdata+HbDSPFviOHWNFtNJvZZ1tIRptlaSI6NBEsbGW1eTCbgfN5IOc9lq3wO8Fa98TbDxrfeD/C174y0qLyLLXp9Kgk1Ozjw42R3JQyouJJBhWA+dvU1py+PNDh8bw+GX1jS18R3Fk+oxaWbpBeSWqOsbziLO8xq7opfGAWAzk1rUk9I26JJekVy/pZ/MLbrvv89f8AghRRXGeBPjz4e+InjjVPDuny3Q1TS7Zb1454GjE1u1xPbCVCeq+dbTpg4b5M42srMuthvRXe3+en56ep2dFFc/L8TdIh+KMHg5pbka7c6ZLq8cZtpBE1vHLFE7CUrsLB5oxtDFuckYoW6j1f6Jt/gm/RB0v/AF2/M6CiiigAooooAKKKKACiiigAooooAKKKKAPMP22/+TMPi7/2JWs/+kM1eL/sV/DW6+MH/BE34A+HLJYGuNT+FfhBQZb+4sNirp9i7Ok8AMkUqqpaNlBw4TIIyK9m/bijM37FXxgQO8ZfwTrQDrjcv+gTcjORn61yP/BJ3/lFl+zT/wBkq8L/APpotaTV1ZjTs7o8p8ef8E+viP4is5LWTUfCfioReDvF+gWl/r2oNHfR3WsS2b20srQWAjlMQtm3z7FkPmD5XYM79R4Z/Yi8cQeE7XTdX1nRbu5Hjm18b3OofbpprmQRW0cb6dloBuibYbfzDj/RnIMeflP0x4t8d6J4Bt7ObXNY0zRotRvYdOtHvbpIFubqZwkUCFiN0jsQqoMlicAGtWnDRe70svul7T7+azfdWT0bupe9o/7z/wDA1yv8Lpdtba2t8ffDL/gnNf8Agb4P/BC3Xw/8MrXxl8M703OrSxo9xa6qRpV5Yxv5xtkkco08LhXQfLFtDDCmvZf2xf2a3/aP8B6Omm/2Lb+KvC2tWus6Le6naieC3dX8u4RhtJKy2sk8RAH/AC0B7V60k6SSOiurPHjcoOSuemfSnUTtJ3l3v81b/L+kCdtu1vk7/wCbPkrwL/wT21n4XeFfCenaO3g2WTwrr4ihu3g8q+Hh21hvY9OtEuDBIVnj+0gOxQ7laXaysQwpfAP/AIJu6t4C/wCEHTxJ/wAITe2tn4btfDni6CCB7n+210yZJ9JnVpYxl45DNvDgfI0ajdsBP2FTZp0t1BkdUBYKCxwCScAfUk4o6tvVv87Wv67/ADd9xNJq3lb5J3S+T29EeEfsf/skX/7O2l+G4dZvbfWtT8MeH5fDq6ut25l1KJrlJvNlhEaKJHZDI5dpWDyPtfDMX43xN+xL4zn8V6PeWl74b1O4s/iZfeMZtdvb2aDVI9PurS8t1t4wLeQGS2W6jjjUuEaO2TlCcD6W8PeO9E8W6rq9hpWsaXqV7oFyLPU7e1ukml06cosgimVSTG5R1ba2DhgehrTmnS3TdI6ouQuWOBknAH4kgfjSklK1+34OSm9u7V9Lde5XM7Nd/wD5Fw/CLa18uyPjPxj/AME0PEfjn4a+BdNu9V8Mx618LfBSaLodywkuIdU1m3urC6tNRulaMFFSWwJZV8xiL64G7rvXXP8AgmVqFn+zX8RfBum23gXVdT1eN7LwtcX0bWg020nuRf3KySpBI6H7ZJOUWNSAkUHIIOPrvS/GWka54g1TSbLVNOu9U0QxLqNnDcpJcWBlTfGJUB3R70+ZdwGRyMitIkAZPAFU5PV97v5yfM35N7X7abaE20S9PwVl8lul313PjXxp/wAE2tW+J/ivxPf6pY+BNOh1DxmdR06KGSS7VNCvNFt9K1SyZWt4xFI4jknQJuUyrEWIwxqp4k/4Jt+MtR+FEeiQX3gU6zfeEtY8N6vqyQNa3V9JLaW1hp8rSiB3MS21srTRrsJlI2vsBB+qvB/x38EfELV7bT9A8YeGNbv7y3uLu3t7DU4biWeG3n+zzyKqMSUjn/dOw4V/lODxXV1CjaHKtmkvklypfKz/AO3m3uy4zcZqfWL+53v+Ol+6S7I+O9T/AOCf3i74gS/FO91tPAmj+IvGOh6ZZ6Prum3E91eWN1b20kFxuL28bRrPFIYXkjkLvGzZUZCj174A/AvXfhl+0B8SfEd3pXhLSNB8YW2krY2+kXkkksMlpBJFIJIzbRIFO5drKzEgcgV7NTXnRJURnUPJnapOC2OuB3rSU25OT6mUIKMFBbL/AIH+R8o2f7DHiKzh8bXdro3w307XvEHxDi8SWmoxlri5TSvtVncSQCR7T9xOZLRJAAksfmYJ+b5xx2n/APBOT4jxfDn4jaPqOoeCddXxP4Z8QeHtBsby/umtdCa/1C+vI7gO1sx3Mt5HFJtQELZQ7S2cL9n+E/GOkePNGGo6Hqmnazp7TS24urG5S4hMkUjRSpvQkbkkR0YZyrKwOCDWlWSglTjTWyVl6cih/wCkr723uzdVGpup1cuZ+vM5/wDpX4JLY+Sj+xr8RZfiXqmtxW/w90u31i88LXl15F9LPdO2lwXCzsjvZBUlMkkISUo5McbDEbFWWH9nD9g7xh8HfGnhzWdYh+HmsS6FoF/4cALTyuLaTURdQ4d4ckmFpYSOBGCuPMGQfrugkAZPAFaOXvc73u383Hkfy5dLbGMIKMPZra0Y/KMuZfjqfFPw5/4Jy+PPDX7OuveDbzWPDFveeKPh5YeB/NtbyeSLwxJay3p+12WbdDJkXaShSIistunzEEMvQfBH9ifxx8Kvi74W1sWfgS0sdL8a+Jtd1KWy1W4+1XNhqSyeREF+xorururOjMqAxghmPT6P8IfGzwb8QbyzttB8WeG9auNQguLq1isdShuHuYbecW88iBGJZI5iI3YZCuQpIPFdLLOkO3e6pvbau443H0HvTTalz90198rv8V+aY5K65X0d/R8vL/6S/wBR1FZugeMdI8V3WpwaXqmnalNot2bDUI7W5SZrG4CJIYZQpJSQJJG21sHDqcYIrSqfMYUUUUAFFFFABRRRQAV8Gft+/wDKcv8A4J9f9e/xJ/8ATHZ19518F/t+/wDKcv8A4J9f9e/xJ/8ATHZ0AL/wT3/5Tcf8FDP+vj4d/wDqOPXokPgnxL8Zf+Ci3xRv7e58WeDtG0jwLpPhex1c+HJUS9aS5vp71rO5uYjAzIXtRlRICVBwQK87/wCCe/8Aym4/4KGf9fHw6/8AUcevvOplBN3fn+KcX+DafqVzPlcV1t+ElJfikfnt8bvhDc+CPhf8KNV+HvhHxY/hL4FeNfD2keHdJfw1q82uiwt7lLXULlYWh8xo5LaaZjKV2slupDfNitr9qLVPHmqfGz9obxz4N8P+OWvvCXwx0zRPD0Q8L3SjX3a5vbm/itXmhAkk2vbJmLcQQCFfbivu+inL3ouM9buUte8oKEvk0ndebQRdpXXaK+UZ88V8n18jkPgD4I8P/DL4J+FvDXhSwn0zwz4c0yDS9LtZvM3w28CCKMEyfO3yoMM3LDB5zXX0UVpUm5ycnuzKnDkgoLogoooqCz4P/wCC73/IP/Y//wCzoPAv/o65r6Q/a5+Luo/Dj/hXek6Pcahbah4x8Y2WnySWVi97OllCsl7efu0R22vBavEWC/L5wOQcGvm7/gu9n7B+x/8A9nQ+Bf8A0dc19u6p4K0rWvFGla1d2MM+qaIs6WFw4Ja1EwVZdvbLBVBPXGR3OTqn2afrZp2fk7WfkHRrumvS6tf1W6Pj+P40/GPVtQ8PXdx/ws/R01Sy8Y3l5aQ+DWlS08uZJdFhJ+yOVkWFtgDEGVkcYfgmPwT8cPjf4k8eeDNO1Cx8cyeG7/SPC+p+LLqfwncafPYyzwap/aUFuwt0OVuY9LWSNd0sUcsjAj5mH2xRRD3X3+H/AMl5tPSV1zd+VBP3k1tfm1/xW1/7ds+XtdnyX8Erjx5q/wC2L4NvPF2jeIjaWWkeNbHTdSm0m4Ea6fJq+mNpq3U3l+XHPJb20hVZGWRljBYb2Ocfwl8XfiyfHej2l/qHxS/se88aeKtPvZX8DyZt9Jt0mk02QOLHgMywqkhz5oYj5m5H2ZRUOD9kqSe0XG/XWXNzf4t9fNjvq33lzfg1y/4ddF5LseLfs1eFpfjj+z78PfF/j/StatPHWo+H7CbWortLzSZob0Qp5yvakx+WRJu+XYB+GKuJ8Eb34Pp8T/GFn4w8beItc8S2M1xDHcWGn3cmmNFHK0MVpHBbQyTBNwCRTSSZI4IZ5Gb12q1jrNnqdzcw213bXE1k/l3EcUqu0DYztcA5U45wa0rNVJScVa99ul+wU3y2vra3ztseBfsx+C/FPxM0LWbb4q6bdSXemXUJ0+9tr/Ure1vo3tot7pDcCO4gbep3xM0qKxO2Q5Kr6fafAnTtO+LWjeLbbU9Zhl0LRJ9Bt9O8yKSzaCaWGVmcvGZjJugjwfNxgHg5rt6YbiNbhYjIglZS6puG4qCATj0BI/MU3K8lNaP/AIDT+9N/eRGNo8r1/wCHv+aQ+iiipKCiiigAooooAKKKKACiiigAooooA8w/bb/5Mw+Lv/Ylaz/6QzV4R+y94n1XwR/wQD+GeuaHqVxo+taJ8A9I1GwvYEjeS2nh8PwyxuFkVkbDKOGUgjtXuv7cRdf2KvjAYlRpB4J1rYGO1SfsE2ATg4H4GvPP+Ca3gjSviV/wSC/Z70DXLOPUdG1j4ReGrS9tZCQlzC+j2oeNsEEqwyCO4JHQ1nWjKUJRi7No1oTjCpGc1dJq6PMvjv4p+JXxB8MwWUdt411/wk914A1S31EeGJGvoNRXXYZ9QMUUVsC8EdrDHM7tGY42JG45KLreEPix8Y/Fuv8AjODW7Tx5beH9HtvEtv4dvIPDtxaX17fQ6gRp7SIIFDp9kMZiYp5MpMm/eVFfX/h/QbTwtoVnpthF5Flp8KW9vFuLeXGoCquWJJwAByauVrNRbko6Jub/APAkop+sUvd7O172156ClCEVLVrkv58u69JP4u+u19PiLwrD8dvBkXxP1O6tPFUnjXxBP4bu5Y4bdbjTLO2+yaVHq5sW2ENLF/xMBFErMSyZKMWBab4leMfjvbfGbxZB4ZsvEOo+BNOfzNCtLy01Czvb+IwWRmMF6scmJo7j7SI472Mxyh3+dEjDV9r0dapzvUVS219Om1rei6dU27PaxGFqfJe/m9/X1fU+WrOb4p2HiP4q6LqHinxpPbeCPt2q6NqVnYWc8uswXtor2NoI47Zi8lpKtyu1UDti3Zi/mEV5Nq/in4yfFWTwTp2qyfFb7FpsHgTxHqlxH4VlsJFvJJrsarF/x6KriIrZO0OGMZO4jAOPufwT4B0T4b6GumaBpdjo+noxZbe0iEUanAHAHAAAAA6AKAMAAVr1nBcri73s6bXrTbf/AJM2ubvyrqaTfMpLa6mv/A7flZ8vRczS00PiPxF44+I3gD47/EXXfCmieO45df8AHHhi3FpN4TuZ7O9s30u3gu5XlW3bKRMpDyxSBEaPrg4NXxB4Z+NHxE/Z+8HXWt6p45tdbW58I3nifS7LTpTPBfQ6xbvqU0bNCAYhGZCYYFki22yODgnzPuaiqj7r5v7yl6WS93/C2rtd7WtYU/eVtvdt+Ld/Xpfto7pnyPrHxO+LWo/tRLpUMHj7/hWf9o6JJqckugPFPZW0umX7TRwTxQKZR9uSwExjaR497DKIW20/FfxN+K/gb4jaaNNtvihr+j6Dq+g2NzcXehGSTW7GSOUXsywwQLEB+8jLO7CRJIsbI03FvsSilHSSl2kn62to/J2s+6bCWqa7xcfS99fVX0fkux8G/A3wP4q8T/FTSrHT7Xx94Iv7mx+J0Ntr8vha6gTSZr7xNbXVjKWurcwfvIo2lRZAQ6qcA5r6Q/Yv1zxl4++F9t4o8az6va6nqdtBaSaPdCDybGe2jEFzNGY41ZhNcJM6sWKmMxFQuSK9Q8W+ENL8eaBNpes2FtqWn3BRpLedA6MyOro2OzK6qykchlBGCBV2wsINLsYba2hit7a3QRxRRqFSNQMBQBwABxiikuSmqa6K3/k0pfL4reatfZBP3pOXdt/fbTz207dN2fDvjH41ftBWs+sN4X8P+OnutZ0bxIbaHUNGaVtPu4NVhFi+5okt43+wtdeVGhdJfLhLNIzE16B4j8KeJrX9rf4deIbDVfitqvg+3i1aKW3lsCIbKWWPSxBE4khEpjbZdMZZjlSZUV1GFr6noop+7GK6pNX73bevpey7JJbBP3pN9/6/T+mfH2geLfjZqvgnwvr8OoePb2V/FfiTwrqmn3fh+Gwn+xzXt5Fo+rNG9pGwSBVsyXVRG0UrSMGKGuu/ZvtfizD8QUg8Z6zrjyaT4k1eyuftNiZLfWdIVXGnSh44o7eJyi28rSRksZJJoygGBH9J0UQ93z0t+X43T17StskE/e8tb/np6Wa06OKa1vf5j/aA8ZfEfTfjv8QodA1H4mJoeh+AYdZ0qx0Tw5BPDqOqq18sltDc3Fo8bysgtW8oSbt23GMsrcv4C+LPxrt/igugXemeLI/CZ8V2hvNY/sWeeWy0p9EDskDzwB5g2qrsdvKLRo78RpsK/YlFFP3dN/8A9pv/ACXovNhU95t7f/sqP36N37tn5+/DTx/8Uvhd4HvNK0TSfi00Eml/EK9e0TwdJDIdTbxEJdMuUlmswBLJb3FxIqZKOq5WJmAB6HxDqHxU8a+KtPt9RtfiVBpXgr4pQXkE2n6ZP/pOjS6G5AV5ofMukj1B5E3Mny5UttCrt+4aKKfuOL35f/k+e/qrcq7R0CfvKUf5ub/yaPK/zb9ddz5N8OfEf4p65+1F4l0rW4/HY+FtjruoxxXaaDNbXKD+y9Kks4YXhgR5rUXD6t+9TfmSGJGkIIDcDY/HD9obwvot7da9D49k16x8OeDbttPsvBz3tnLfT6pPFqyLJDaMHYWIgeVI5CImdyu3Ax94UUQ91RW9lFetla79evor33E1o13/AM0/0t83awkbiWNWGQGGRkEH8jyKWiigYUUUUAFFFFABXwZ+37/ynL/4J9f9e/xJ/wDTHZ19518F/t+/8py/+CfX/Xv8Sf8A0x2lAC/8E9/+U3H/AAUM/wCvj4d/+o49e2/EP9tfWvBXxW8U+H7Twfp+q2egeI/Dvhm3uf7ZaGW9utVMZdTH5DBPs8UqTN8x3IeMGvEv+Ce5/wCN3P8AwUM/6+Ph1/6jj19e3v7NXw51LW59TufAHgm41K61JNZmu5NDtXnmvkTYl0zlNxnVBtEhO4DgHFJfFFva6v5q6uvnG6v0dg+y11s7etnZ/fb5Hg3xD/4KVS+F/hjr+t2nh/Tpb/w1B4nvLqxW9luWmh0a8ks1ddsS+XHczROgkkKhGUhVmPFdpaftX+KtU/adT4c2vhXw4UvbG21zT9Uk8QN5V1ppUi6+VYCftkLvbYhBKtHcrJ5ijg+gW/7Lfwys9GbTofhz4Ei094p4GtU0C0WFo55DJMhQR42ySEu4xhmOTk1Jpv7M3w30a4sJrP4feCLSXSrubULJ4dCtY2s7mZFjmnjIT5JHRVVnXDMqgEkAU43Vr69/N9PTpf0aVr3RLW9tNXbyTt+WqXk7u7R0vizV7PRvCGp397JtsLSzluJ3VyuIlQsxDA5Hyg8g18O/sWeDfiHN+zj8CPiPfa34h8P6Xp1hJ4t8WXOoeJbzVLvxTb3lpPJBp62ju8e0PcwlXciRPs8aIp3kj7Vl+FHhaf4cL4Ofw14ffwilounrobadCdNW2UBVgFvt8vywAAE27QAOKp3nwH8Daj4B07wpceDPCk/hfR3iksNHk0i3awsWibdE0UBTy0KNypVRtPIxQlaUnf8Alt5W5r373vHTbRp6Mbd4pet/na1u3XXdXTVmj5o/ZC+J+t6L+0j+0dpPjmbXbdZ7HR/HcdrqTGM6dbXNnNaTwwYb5YY300gFcZJLHDMTXnH7BWreKNS8Qfs3aTe+J/Euj6zrHgy7+I2um+1+71VfFNlcgRpYvFNIyRGCbULV1kI3AQKiEgvj7W8Sfs4/Dzxj4l1TWtX8B+DNV1jXLEaZqN9eaJbT3OoWg5FvNIyFpIh2RiV9qm0v4CeB9CvNBnsPB/hmwl8LWrWGjta6bDD/AGXbM6OYIdqjy4i8UbbFwu6NGxlQQ6btJOW1krLokpr9Yb78rT3JmrxcV1/Xlv8A+369Lxa1R8ef8F3v+Qf+x/8A9nQeBf8A0dc19b/HX4xN8INP8Mi2sU1PUvFPiOw8P2ds0vlA+fLmaTOD/qrZJ5sY58rGRnI+R/8Agu8cWH7H/v8AtQ+Bf/R1zX1t8WPgBo3xn8b+DNV19INQsfBd3c6jb6Zc20c9vcXUlu1ukzBweY45ZtvHWTPYUuqfS6v5pNXXzV0Po+9nb1to/k7M8etv+CgOqa7qGkHSPCWkXun63D4quLeaTXWjcR6LOscbsq27YF1G6yLzlQ6/eBzUWg/8FFbnxJ428FeGh4SXTtT8f2fh+/064a+N1bWUOq22qXI+0YjQh400qVdqnDvNGoZeTXtMv7Mfw2nFoH+HvgdxYQT2ttu0K1P2eGdQs8afJ8qSAAOo4YAA5p2jfs0fDjw5M0mn/D/wTYSPaW9gzW+h2sTG2t23W8JKoP3cTconRDyAKIaP3tfh+5c3N/4F7vpZ23CeqfLo/e+TduX/AMB971urnh/wk+Pt/wDGX9tbwySdV0yOw0XxpoOpWKX0x02/udL1jSrZLuOEt5ZJEkuG2l18x4yzBcnV8LftxeI/EPjDTNFbwVpEc+r+KvEPhaCYa9IUWTSVmczMPs2QsqQt8oyVYgcj5h7bY/BzwhpnifTdbtvCvhu31nRraWy0+/i0yFLqxglbdLFFIF3Rxu3LKpAY8kGsiP8AZc+GUN3HOnw68CJPDdXN7HINAtA6XFypW4mB8vIklUkO3VwcMTUWl7JQT1UZK/8AecuZSt6Nq3S/kN25m+8r/wDbtmuX5e7r1t5mL8HvHXiH9o74TeC/iBo2sQ+G9J8X6JY6yulT6ct5JbCeFJTGZt67sbsZ2jpnFcr8Ofhn4n+DPxA+KHjvWPD/AIQsbNNKaDQYdO8Szy/aLWCa8uwLn7Rawx28jy3Mju5kmVfN2gqseZPa/BfgjRfhv4WsdC8O6Rpeg6JpkQgs9P061jtbW0jHRI4owFRR6AAVc1XSrXXtLubG+tre9sryJoLi3njEkU8bAqyOpyGUgkEEYINaVrOUnS03t5X+/ZafeFN6KNTVaX87O/l1PEvgR8cfE/7TGl6sLae48Dar4fuYoL7TdV8NzQ3kXmwRyDcksmxl3F9ssEkkcgHDAhgOhg+D+vw/tSaB4zmGgXVjY+E7vQr+9DvBf3U8tzazIwhERTy1EDjmbIMnA659C8NeDdI8GWzw6PpWm6TDKQXjs7ZIFchQoJCgA4UAD2AFaVNtKanFbX/GLi/wb6kRT5OWTv8A8Bp/ml0CiiipKCiiigAooooAKKKKACiiigAooooA8w/bb/5Mw+Lv/Ylaz/6QzV5B+w78SLr4N/8ABD/4NeLrKwt9UufC3wR0LV0s57g26XX2fQreUxmQI5TcEIztOCeleuftxTC3/Yq+MEjByqeCdaYhVLMcWE3QDkn2FcF/wTI8J6V47/4JHfs7aNrmmafrOkal8JPDNveWN9bpcW11G2j2oZJI3BV1I6ggg1FRScWoOz6GlJxU05q6vqvIjm/b21PStXvYL/wXaRQaZdeE4biSHWjI2zX7lLaIopgUFoXYlwSAygYOScU7D9vTW/E/iy70XQNB8L6pfX+k6/qegS3OsS2Gn3zaVqEVoYWumhbcJUmR/NjjKRssiZcKXr2G4/ZT+F120hl+G3gGUzG0Mhfw9aNvNoc2pOY+fJIHl/8APPHy4pde/ZW+GHim3aHU/hx4C1KJ/O3JdeH7SZT5z75shoz99/mb+8eTk1pOzk3HRXl9zilFf9uyvLz2ehlSuoJT1dl82t/v/DoeLaH+1/qnxc0P4a+Jn8JQ2dh4k8bHQtOt18V3dle6dOtjeb/7RtoohG5WWGRDbSPIgBjl++qquZp//BTTWW+EUXiu7+H+nxIfhxe/EaSCLxAz7YLSRUltgTbDMjblKNjHXOMDP0Fe/szfDfUdYn1G4+H3gifULrUF1ea5k0K1aaa9WPylumcpkzCP5BITuC8ZxxVI/sg/CY6ULH/hV/w7+xLpz6QLf/hG7PyhZO+97Xb5ePJZ/mMeNpPJGaL63t30/wC3LL7p+95rTyGl37Jf+Ttv74Plt0ep5ze/txatb/EaTw+nhDTMj4iReA0nbXHBKzaImqw3ez7N1w4jeLPy4LB36VyGn/8ABTTWW+EUXiu7+H+nxIfhxe/EaSCLxAz7YLSRUltgTbDMjblKNjHXOMDP0Rq/7PHw/wDEEtvJf+BvB169pNFcQNcaLbSmGWKPyonUsh2skYCKRyqjAwOKzD+yD8JjpQsf+FX/AA7+xLpz6QLf/hG7PyhZO+97Xb5ePJZ/mMeNpPJGaUdLX1t+PuWXp7/vvy93YF0v2X389398PdX37kX7SPxy1D4JfDvRNe0vRrLWn1XxDo+iPb3V81mI01C+gsxKHWKTJRp1bbt5CkZHFeJeMP2k9c8b/tQeB7TS7K50/UvCfi/XvCur2C69Oulap5egpqEcp2ptlAWeDG+ItHIJFBwCx9v/AGhP2dLP46/DDSvCkd3BoemaXq+l6qkUVhHNEwsLqK6ig8skKIy8MYIH8IIGMgjYvv2fPAWqPYNdeCPCFy+lPcSWTS6NbObNrgETtHlPkMoZg5XG/cc5zUSUnGdnZ3ly+StCz+T51brfyQ4OySlror+vvX/9tfy9T5/8Ff8ABTBviF8Hb7x/Y+FZbbRdFm8MW13p00pe+uX1qGwmBgYAKVhXUYgPlPmtFKo8vANT/Ab9svxVr2jeFdJ8QWuiS+JvHHjzxB4dtJraWSS30y2srnV2WSZCEY5j01oo1DDeQSSNpFe/eG/gT4I8G2umwaP4N8K6VBo0cUOnx2ek28CWMcZJjSIKgEaoWYqFwAWOMZq0fhP4VMFzF/wjXh/yr26+3XCf2dDtnuNzP5zjb80m53bcecsxzkmtZuLk3FaX/DT/ACf32uSk+Wz3/wCA1+bXrY+a/hp/wUb1v4neN00aLwc+lbtfvfC8F/cxT3GkX9/ay3UDobyAP9kaSW3Ro0uIV3xyqVdmYJV3Tv8AgoD4n1X9n5/H0HgfQ2j0uTTdK1nTn16VJ9O1mbU1sLyw3fZcH7MWVi5xvJA2qPmr3e++BnhuGwuzoekaN4Z1iaDy7bVtO0m0F3YSCIxRyx74mQtGpwodWXAxgjimL8APCepeBZ/D2uaDoniXTr2/fVb2HU9OguIby8aYzGdomUpvEmCpxldq4PGamOnxa/1/w9/+3Uut3PV3jt/w/wDwEv8At5vWx4Zpv7Zvinxh+1t4f8Ix6doWi6HYX3iDStcM2oyObiay/ssxSRP5S8FNQGFYAl+NwABbz7wh+3tqX7LPwVvF1rRNV8WI+t+OX0u6n1ua7uyuneImtYYZ2lR3WEJcoN+9zHHB909F+udI/Z/8B+H9SkvbDwT4Rsbya+fU3nt9Ht4pXu3AVrgsqAmVgqgv94hRzxVLTv2XPhlo9zBNafDrwJazWpuzDJDoFojQ/azm62kR5HnE5kx/rP4s0o6Rinuo2fnKyu/LW9uyB6tvpzXXlH3rL7mrvrY8e8V/tP8AxA039ojwH4duNL0PSdButa1KC+v7ueS1/tS0g0pLpZliYM8AV5ZFKycs1urAhGqh8I/2vdZ8Q+OvEfgbw34Vt7bXdJfxNq0ra34nu760u2stW+yCKO5mQvEkjuXICmO1TYiIyldv0WfhN4VNvaRHwz4fMWn3H2u1T+zodttNs8vzUG35X2fLuGDt4ziqWl/ALwJokbpZeCvCVmkl1PeusGj28Ya4nx58xwnMkmBvbq2BkmqjZTTe1mvV2Vn8nrbZ7bbD1hbrdP5K91800r7r8/AvFn7bH/Ccarr3hF4be10/W9K8VQafrOl6lPAYZ9KlhtsR3AEZeR2uOsJHkyRGPc7ZK+d/BP8AaS8V/BnwR4r8YSHUPGGn6R4Q8B6trMWra7csNOtbi0kGoX8QZZRuSMee6KEEnlMSdxyfrvUv2c/h7rM8kt54E8GXUsr3EjvNols7O1xnz2JKcmTc28/xZOc5rGv/ANlDwdZadNaeGNF0LwTBqUC6fq66JodjbnWNPWCSFbKVvJLCJFkymwqUKAD5SytNNuMZJ6t8n/kvPe/lLmV7a2Xew3ZuN9k5/dLltbzSi7X6vtcv6F8X7o/BnW/GmtW+iaZp1jHe6hZSjUmNtPp0QdobmWVo18oSRqJDgMEVhy1eI+Gv+CjV3q/i3SvCtz4et7bxHr+saJptnK7SxW8ceoWE1488kUgWUCL7PLGAQvmtJB/q/MwPp+Tw9YS6AdKeytZNMa3+yNaPErQNDt2+WUIwU28bcYxxXO2f7P8A4D09pmt/BPhGBriKGCUx6PbqZY4WDwo2E5WNgGUHhSMjBqlZT7q8fuTfN85Kyv01sTduHZ2l6XaXK/SLu7ddLng+u/tE+Pm+NPjPw3r+n+FYtK034erqhsrDxHc20txdvcalCyx3ccIeN2WzUZQb4SWILMoxd8Gftj+Ip77wl4fs/B+mRS+L/CGj+IfCovvEVzcS6mkjRrqEMsrW7OZLOKSKQuS7SiVSdpLEe66r8HPCGuqFvvCvhu9C2r2AE+mQyAW753w8qf3bbmyvQ7jkc1Z0X4a+HPDdvosWneH9EsIvDdu1ppKW1jFEulwsqq0cAVR5SFVUFUwCFA7VME0mm/6vUev/AIFH0UWla9x1feleOn6O1NaevLK/fmu9UreV/snftUa1+0rpXhzW5PC76V4e8W+H216zndmSWwYToi2kquAZZDFIjl4xsRkdD/Az43iH9szxFoN58R2n8M+GrPSvAfie38LR3s2uTvPqFzcppklt5dqlqS7MmoMPLWTcZIURdwk3p7noXgXRPC15PcaZo2ladcXTO80traRwvMXYuxYqASWYljnqTnrWSvwI8Dp/be3wb4UH/CS3keoavjSbf/ia3MZUpPcfJ+9kUohDvlgVXB4FNfEn0s/vck1r5RvFfJu7uwfW3dflb8Xr+VkfN1/+3fcfHDwd478F2GiTaZ4n0rw74nur65TVpdP+ypp17Lp6SQvErSrLI6+ZtBBixjexwTH8Mf2rrn4faZaQXtpJc61ceEPAkiX+oeIr6+GqvqctzbHZY7GVLlDBNIRDl7nK+Y67AV+kB+zx4AW1uYB4G8HCC9aVriP+xrbZOZVCylxswxcABs/eAAOcVW0/9mD4aaQ2bT4eeBrU/Z7W0zFoNqn7m1bdbRcR/chbmNeiHlQKKXuq0tb8l/8At1zvbtdSS+V+tkVPefu6Jc9vLm5LX725X9/zPCfhJ+274m+Mn7QXgfTobTw9pWh32k+JodYtZ79hML/Ttbs9MQqQhADmQsiHn9/gk7Ruxfhn/wAFB5dL+BXjbVLLwnc2tt8JPDL+JNVs7/XbrWLnU0+3anC0FteTgSyNt06Uh5Afmljj2qFzX0/pHwL8EaBLDJYeDvCtk9vNNcxNb6TbxmKWZlaWRSqDDOyIWI5YopOcCo9D/Z+8BeGNPtbTTfBHhHT7SxZmtobbR7eKO3LS+cxRVQBSZfnOOrfN15oj8KT3V9fVtrTyul8vkO6u3bt9yt+aT+88p8D/ALbmo+IPijZ+H9R8J2tla6j49vfAsF1DqjSy+ZBpMmppcNE0K4DRxmMrvJVznJA58Q/b9/5Tl/8ABPr/AK9/iT/6Y7Ovr+3/AGbvh3aeIItWi8BeC4tVg1N9aivE0S2W4jv3TY92sgTcJ2T5TIDvI4JxXx/+36f+N5f/AAT6/wCvf4k/+mO0oXwRT3SV/NpK7+bu/K9ibPmb6a/m7fcrL5MX/gnv/wApuP8AgoZ/18fDv/1HHr2j9rX4v/E/4T+MvCEHh/VPCtrbeOfHGleFNLtLvR5LuYW8kTz3ty8i3KDesUNwUTaAPKBJYNgeL/8ABPf/AJTcf8FDP+vj4d/+o49fUvxi/ZhtfjP8V/AXiy88VeKtMm+HWoyarpmn2IsfsUtzJaz2jPKJbaSRv3NxKo2yLjdkYNCXvxb2TTfomm180mvmN/BNLdxkl6tOz+Ts/kcFD+0v4p+E3x/i8K+P9X8MXGlQ6K95JNYaTNBd3V3d6uLPSYY18+QFpo1mUoFyXj3blXKjr9d/bl+GvhOTWoNX12TTtQ8NvOmr2LWc09xpnk/ZvMaUQq4VMXlqQ+dpE64J5xF43/Yt8O/EP4w33jfVNa8ST6vPLo89nGHtRBpEmmS3Mtu0A8jccm7uA4laQEScBSFI8p+JH7AWvn4oa94h8JawlnqN8f7Qsrq8s9Nv4NQ1GSVZZptTFzaSSiMGC0WNbExHbbRglTHG6lLaMan967673i36XcX5KNt3Z1ErzlD+60vwkvycfV32V/q7RtUXW9JtrxIrmBLqJZRHcRNDLGCM4dG5Vh3B6VZpsYYRrvIL4G4gYBNOoZK2CiiigZ8H/wDBd7/kH/sf/wDZ0HgX/wBHXNfUf7SXxd8V/C3TbJfCfhK88TXt5b3kwaO2mniSWGINDbMIhlGuJGVBI5WOMB2YnaFb5b/4Lvf8g/8AY/8A+zoPAv8A6Oua+8aBp2ep8/8Awh+NPjD9qHxp8Tn0LU7TwnpPw58RS+FLK3eyW7fVNQt7eCW4lumYg/ZxJP5aRw+W5EbP5vzqq+w/CmbxDc/C7w1J4tjs4fFcmlWra1Haf8e6XphXzxHyfkEm/HJ4xya5OH9mi08O/ELxL4g8MeIdc8Kv40nS812zsVtpLa+uliSH7UolicxTtFHEjMhCsI1JUt89eg6Hott4b0Sz06zj8mzsIEtoI8k7I0UKoyeTgAdaI/Ar72jf1S95rsm9Uu1lpYT3fa7t6X0T80tHpvd9S1RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHmH7bf/ACZh8Xf+xK1n/wBIZq8m/YN8cp8Mf+CJ/wAEvET6lBoyaH8FfD981/NpU+qxWIj0S3bzntYGWadExuaONlZgCAy9R6z+23/yZh8Xf+xK1n/0hmryz/gn78Nbb4zf8EXPgb4Rvb3UNOsfE/wY8P6Vc3NiYxcxRTaJbxuYzIjoG2scFkYDPSoqc3I+TfoaUeX2kefa6v6Hpg/bL8A2GtXOk3usXi6hpz6VDdP/AGLeJB5mpkJYkP5ZUCeQ7F+Y4b5Scg1jfCP9unw18T/h34b1ltP1ux1DxdqGo2WkaRJZMl5eizeYyOobC7Vih3M27aGYICWKqaGuf8E+NE1+5v5ZvHHj1H1Gbw5PLsbTQA2hzLPZ4zZnG6RQZP73Rdg4rS0X9grwXoKeGzDda5JN4Pvb270aW4kgn+wJdtM09uqPEY2iYzn76s/7uP58oDWs7e9y93b00s353vdLokYUrqMefeyv62d/0IZf+CjPwoTwxq2tR6vrt1pOh6TBr19d2vhrUriC1sJrVruO5d0gKrG0KO2SRyjL9/5a62T9qfwhJ9sjtJ9Tvr3T9Sh0m4s49OnjnguJbUXcYcSqgRGtyHEjkJzt3bvlrlPFH7CmieK9B+Jmmz+LfGkVr8VNBt/D2qLE9gDaW8MDwBrcm1O12jkkBL71y3CrgYjuP2CdAv8A4gXviS88WeNL2+vtbs9feGZ7A2v2m105tPjzELUK6+UQ+JA22VEdSpWplu+Xyt9+v4fiXsvv++yt8r3v5Fv4OftPP8aP2gZNP0W90rU/AWq+BdM8W6NdRWcsN25urm6ibzGd8FSsKEL5aMpLA57RWn7U0vhn9pHxx4P8Rz6c1nZz6JaeGLeytHS+v7m+gu5HgYtIyyMBavJuAjVUVy3Clqk/Zw/Ye0H9mPxVb6novibxlqcdp4dt/DFvZarcWs1vBaQTSSxbdlukgZTK4zvwQckFssbPiL9i3w74o+Nl94+u9a8SNrs99p+pWQV7UQ6RPZQXNvG8A8jcd8N5cxuJWkBWU4CkAitPdS7u/pzt/fy2sxO95vyXL6qCWvk53b8noR3/AO3v8MrCK8T+2rq41DTbmKxvdPt7Caa5sbqW/k06K3mCqUjeS8ikhTcwV2XKkrhqj8V/8FBPhb4MZ/tut3uyBb9ppI9LuWjiFjPHbXY3bMO0VzLFAyx7m82RVxnOPM/G37AGt+HviVqviDwJrC6bfXCf2hZT3Flpl5He6s95cXktxqX2m0klEQmmUp9heJlCkYBVGEcf/BP7XvFviHWLbxNqdpeaF4w1DUpPElklhpyWgtbiaS5hOnzJbLfpcLcrayt9pnkjzE2A4EYWaeqXPvrf1VrW9XfV9LaJjqaN8u19PS3Xz220vda7nrmo/tu+AtE8U/2DfS+IINfW8n099Ng0K7v7lLiG1ivHi22scoZvs00coCk7lJxyrAa/xs/aJtPgv48+HWhXGl6tqEvxB1mbSYpLS0knW18uxubos+0E8+RgD0LHopNU5v2VNNl+I3hDxMPEXiOK78G3Vzd29vDHYRWt209uLeQSotsDjYDjyyhyxJJ4xvfF74GaX8Zb3w1dX95q2n3nhPUJdS0+40+dYpEkks7izcEsrcGG5lHGGB2kEEU+3yv+v9ai6P0dvXW36HHeJ/29vhp4P0TUdV1DVr2PRdNi1WY6hFYTXENwulyCLUPKEas7+Q27d8vzCORk3KjEbMf7X/gB/EE2lNq17Ff22p2mjTRTaTeReTd3USzW0TFogFMqOpTJwSwXO7iub179gXwfqnwk8YeCbDUdf0DQfGceqx3Kad9jWW1GpSSSXXlSSW7thnmmKiQuq+YcAbU219c/YO0nV/Eup6zL408dyXepeIdH8UTRq2mqjXWlwxxW6D/Q+I2EKFxnJI4ZQcVMH8PNt7t/S65reivbzKl15f71vu9y/q/i/A2LT9vP4WTeCIfEdx4km0vR7jT9Q1OGfUtLu7ETQ2DFbzYJYlLvEQcouXIBKggE0l9+3R4A0bxkvhy/k8R2viJnaMaXHoN5e3W5bVLvAW2jlyTA+8AZJ2uMbkYDzP4Of8E8dP8AHnwA0bQPi7ZX19c6ZF4gsotKF9C9paR6pPPvljeFFcy/Z5dgLMdm58DJ3V6qv7JOmJ8R/B3iceJfEyXnguWaW2gjj0+K2vDNb/Z385EtQTlCxzGUOW6kBQKjvr/W/wClv62T8vP9Lfr+HzwvjJ+374O+FPgTxnqQg1m81LwpoWqa7Fp/2CRJtSh0+f7NcNGpAIRJyilmC5Vw67kO6ui8bftkeBPhnZahL4ivdZ0l9I8NyeLb2KTQb93t9NjkWOSbCQncUZhuRcuoO4qF5rhdX/4Jj+BtdtrqC613xrJBfaXruj3S/bbdXu4NXmWa682QQeZK6vHGUZ2YgRqGLAYPS/GH9h7w78a9A1Oy1TxD4vt59Z8L6l4Rvb22ubf7RPY30qyy/wCsgdFdWUbCqgKONpGAIje0ebfW/wB0+W3z5L/9vW6FNR5nbbp6Xje//bvPbztcueIv24fh34X8SatpF3f6yupaNqVto1xGuhXpj+2XQtzawCbyvJ3zfaofLy4Dbjz8rY7v4a/E/Sfito13eaTLIw06/uNLvIZU2S2l1BIY5YnHTIYcEEqwIZSQQT5nffsJeGdeHiwazrnibXP+Es1fStek+2fYgNPvtNS2S1ngEdsgyPssRZZPMRjuBXaxFeseDvCMHgvSWs4Jpp0aVpS0iRpgt2CxqiKBxwFHqckkm1azv/Xw/rzf+S67ozXNZd+v43/Sz9dNmatFFFIoKKKKACiiigAooooAKKKKACvgz9v3/lOX/wAE+v8Ar3+JP/pjs6+86+C/2/f+U5f/AAT6/wCvf4k/+mOzoAX/AIJ7nP8AwW4/4KF+1x8Ox/5bj19V+IP2hY5/jBe+APCmlHxL4p0ixh1LVw10LWw0WGYuIBcTbXYSzeXIUjjjdsIWbYpVm+VP+Ce//Kbj/goZ/wBfHw7/APUcevoDwZ8CfFXwV/aV+Kfi7w/a6Hr+l/Fa50/U5he6jJZ3GlXdrZR2Rj+WGQSW7RwxuCCGV2cbSG3BL4rPbX7+i8urv5W6j6P+v6/p9C98Pv2xbL4m+J7Pw1pvhvWLbxhcC/nuNIv5IojpVpaXsti11cyIZFSOWeGRYQm9pMEhQquyZR/4KD+EzZazbQ6Vrd94o8Ny6xDqugWzWwu7U6WIGu3DyyxxMmy5tnQ7wXW4QlV+cJL4X/Z38T/DH9qfxR8S7S50/wAUT+NvCmlaFqEVzMbF7a70+W8eOSPajqLeQXjhgPnjMSkCXedvmN9+xf8AE7wv4J1Xwtplp4D8W6P8RtYv/FHxAn1LxJe6LJq99dyAGyjiisbgnT44EiiKebHJMkQVmRS+9Wk1ZaNp/wDgXNaP/bvKm33drNXVq91Su9Vdf+A8t5fNSaivJXadmz6n+GXxC0z4ufDfw/4r0WSWXR/E2m2+q2LyxmN3gniWWMsp5UlWGQelblQabYx6Xp1vbQxQQxW8axJHDGI441UABVUfdUAYA7Cp61qOLm3Dbp6GNLm5Fz7219QoooqCz4P/AOC7vFh+x+e//DUHgX/0ddV92y3McLqryIjPnaGYAtjk4r4S/wCC73/IP/Y//wCzoPAv/o65r2f9vfS7bU/F3wHMmlafq1xbfEWCVUu4i0cUZ06/RnZhG+xd7xDJXG5kHcUfajHu0vvaX4XB6RlL+WMn68sW7fO1j6G+0x+cY/MTzAASu4ZAPQ4pEu4pHZVljZlJUgMCQR1FfAOnfskapoP7RuueDpbLwnrGteKPAPiQwPdCZdP0AX+s+fbWcMwgZ2S3jkO1cRnCMVCLgL1w/wCCeXjiw8R31wY/AuvPN4u8La02q6hqU8V/qdtpllb21486rZsBPcmF2KB2VxKQz+pS96EJy05rfL95KEv/AAFLmfe/u3s2FT3ZTiteW/z9yMo/+BN2Xa2tr2PtNWDqGUggjII6Glry39kD4T+Ivgr8Jr3Q/Ei6LHdN4j1rU7SPS7yW6t4bS81K5u4IgZIYipjjnWMqF2jZwSK9SoDq15v89/mFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5d+3FAlz+xV8YI5EWSOTwTrSujDKsDYTAgjuK82/4JyfEPRvhH/wAEcfgJ4p8RX8Ol6B4c+Dnh3UtRvJc7La3i0W2eRzjnhVJwOa9L/bb/AOTMPi7/ANiVrP8A6QzV4r+xx8B9N/ah/wCCFfwe+HWr3E9np3jX4I6Bo89xBgy2wm0S3QSKDwSpIYA8HHNZ1XJQbhvbT1NKKg6kVU+G6v6dT1ZP2rb3S9O8Javr3gfWNB8O+NLjy7a9nu4Wl0eL7LNdebqMQIFuCkJACPLhmAfYeKZYfto6K/ww8IePr7S7/TPAXjm8s7PS9WnZRJGt7IsVjcXEPWKG4eSJVO5mXz4vMSPL7LNp8PvHPxL+Fa+CfHNvoFlYXWly6Trl9pN8839sRSWzwN5MTwqYA+8OcsxQrsBcHfXnGs/sYeLPHv7JHw++CWv32kDQvCNzosWq65bSuZtXsdIuIJ7dYoNn7uac2sIly5WMM+wyHGN3yqo19nmjbzi2+dvbVKzWier912OdObhFvR8ruu0ulvW9nutF72rt9SVSfxLpsV3cwNqFks9lH5txGZ1DwJgHc4zlRgg5PrXzl8A7bxr8dL3xQf8AhYPxa8JaZpGo20Vo114UTSrrUmSEpcyldW0+QeTNKvmKluNsalcOC5jjz9S/YL1+68DWulNd+HL7U9F8dXPi601y6kl+2ajbTa3HqkmnzgRny1kWOO3d1aQFbeI7MfIsL4kns7a+Ttr8k27bu1rXNJaJ23/4f9Ul21veyZ9P2Wu2Op2sU9teWlxBOpeOSOZXSQA4JBBwQPaln1uytdThspby1jvLhS0UDSqJZQOpVc5I+lfKniD9hbxXqf7QVr41ktvBWsWs/inVfENxpt9fTLHp0dzokOnR28B+yuJA80IuZSwQB5GIDkBjQ/Z7/wCCfPjLwR8G9S8OeMNS8N6tqut6LodourR3lxdT+G7vT9MSw82zaSFC4Vka5iY+WVluJcg/eaHKXI5JapRdu7ad43/u7X2fdFRUeblbstde1no7ee/deZ9b2HiTTtV3/Zb+yufKlMD+VOr7JByUODww9OtZ1x8TNCtviDb+Fn1K1Gu3OnSaslr5g3/ZkkSMyEZ4BZwAe+Gx0NfLuqf8E+Nfm8GfD+2GneDNR1jwtdeFYr+e81OdILiz0eWViIo1tCqsyzSKqlckSlXlKqAZv2fv2FfGHwv+KkXirVrD4dXc6Q6/YrAtxNciC1vdck1G3RHe1QjbbzPbsMAR4BXzFJWtGkqlr3XNJX7pQvGXlzSaVtWrMht8l7a2i7eblaS/7dSvfrdH1dqPiGw0i0S4u76ztYJGCJJNMqI7HkAEnBJrA1P4v6Ymq+KNJ0lJfEXiPwpYx3t1o1hNAt3IZVkaGFTNJHEsjiM48x0UblLMoOa+XPCP/BOnxP4a/Z3+H/hO3074baPqmjaFf6X4kuLBnL6nNLYmzt5FuWtA7RhHkV0KI+3YqyBVKsuk/wDBPvxvb/DH4k2F/b/DXVNd+IXgOz8MTzzPOVF1BYXFplpWt2cozPbytJtLExsPLHysJqaKbXS1vO/N+Vl/4EuxrTUXKCk9G9fLWP33u/u31Pr5PEtkt1b2s9zb2t/cQ+etnLPGJwuMn5QxzjByRkcHmrkNxHcKGjdHUgEFTkYPSvlaH9h7xlP4P1XSbrVtDkfWvFugeLJLz7ZM81gunQaekmnRZg+aGQ2LxiQ7cRXkuYyQVdf2J/2NfG37N3ivwJLqdr4JtNL8P+ApPDGqDR9TuJZLu9+2pPFKqPaRK8axqyhmYMpkIC45OnKuflv1f/pHN/6V7nr72zRzxlL2fM1rZaeskn9y97Zdtz6gm16xt9VjsJL20S+lQyJbtMoldRnLBc5I4POOxriP2fPj0f2gtJ1TVrPQbzTdAt72a002/nuoZP7UEM80EjCNGLxYeEkBxysiEHO5V8z8b/sd+JfEuveLUi1uwNl4n+IGj+No9RlmkF/p0Nitjv09EEZUpJ9ieMNvAEd5LlDgh+m/ZH+AWvfAL9mCXwjd2XhHTdf+1arcRHTJJLmwdrm6nmhaTMMDttWVFYbeiYDdMY8zVJztrbb5Rf5tx+V9mjayc1G+l9/m1+Vn87dGeuWHiLT9VjZ7W/s7lEl8hminVwsnXYcH73I461h/Gb4q2nwW+EXiXxjdWV9q1l4X0u41e4tbAxG5nhhjaRxH5johO1Tjc4Bx1r49u/8AgnP8TH06zs3Hw31OyNt4HW6gutRuIo4ptC1JrmdoFjstiJJA5hRdgO1VDHGSbPiz/gnf8Rtd0PU7Cxg+HelDVdX8aTyzxavdblstZtLmK1h2iyGRHJMjvHkIPLypY1VRPlnybrmt525bel238ovuOjyucPabNxv5Jpt/dZL1Z9iXfjWU+GdK1PT9G1PWE1SW2XybaS3SS1imZQZpPOlRdkYbc4Qs5CkIjthToQ+JNOuL6e1jv7J7m1ZVmhWdTJCW+6GXOQT2z1r5R+I/7Bni7xHqtpf6TF4Js0spPBwi0k3c0VmJNG1T7bcX4ZbU7bmSH/R0CoPl+9JjCri+Mv8Agmp4l1L4F2HhpZfDPifXdDvpWs9V1jVJ7cXNvNr1rqkzSpHav+/dbcAsxlO/JVlDtWjUeZ225pL5WVn8/v3u9LLnpOfIufe0b+rWv3PT5ad39ceIviVofhXxNoGj3+o21vqXieaWDTYGkG+5aKJpZNoznCqvJ7EqO4rT1bW7LQbdZb68tbKJ3EavPKsasx6KCSOfavlLw5+wt4usfjLa+Krmy+Hqx6N421jXtKtTPNdJbWV9YRwIADbIEkjuI/NMa/KfMJEgYc+mfte/s9+JPj5PoUGjT6BDp1pY6vbXovd0dyz3dmbaLy5BFLiLDyeYoCMwK4cAMrY1JONJTiruydvNpPl9U9L9zopxjKpySdldq/km1e3mle3merW3iWaTxRf6fPpV9ZWlpFA8GpTS2/2a/eQuGjiVZTMGj2Dd5kaKfMXYXw23VBBGRyDXw/rn/BPz4l+JPD2rw6jYfDC/v9T0HwTpks76vdr9quNC1Ka5mmkY2TMWeCRI1fJYlTkIpAH2d4P8Kab4H8MWWk6RpenaLp1lHshsbCFYba2HUqiqqgLknoB9K3nGKbUXdJtLzXf5mMJSaTkraL8ldfJ6GlRRRWZYUUUUAFFFFABXwZ+36f8AjeV/wT6HbyPiT/6Y7SvvOvgz9v3/AJTl/wDBPr/r3+JP/pjs6AO48Zf8EuPEEH7XXxS+MHw4/aA+I3wr1j4w/wBkt4jsNM0XQ9StJm02yWztjH9us5nQCMMSA3LSNnjaFvf8MK/Hj/o9L4t/+EZ4R/8AlXRRQAf8MK/Hj/o9L4t/+EZ4R/8AlXR/wwr8eP8Ao9L4t/8AhGeEf/lXRRQAf8MK/Hj/AKPS+Lf/AIRnhH/5V0f8MK/Hj/o9L4t/+EZ4R/8AlXRRQAf8MK/Hj/o9L4t/+EZ4R/8AlXR/wwr8eP8Ao9L4t/8AhGeEf/lXRRQBwnx1/wCCP3xB/aXj8IL43/a3+LWtr4C8T2PjPQgfC/hi3+watZMzWtz+605N+wu3yPujbPzKeK7v/hhX48f9HpfFv/wjPCP/AMq6KKAD/hhX48f9HpfFv/wjPCP/AMq6P+GFfjx/0el8W/8AwjPCP/yroooAP+GFfjx/0el8W/8AwjPCP/yro/4YV+PH/R6Xxb/8Izwj/wDKuiigA/4YV+PH/R6Xxb/8Izwj/wDKuj/hhX48f9HpfFv/AMIzwj/8q6KKAD/hhX48f9HpfFv/AMIzwj/8q6P+GFfjx/0el8W//CM8I/8AyroooAP+GFfjx/0el8W//CM8I/8Ayro/4YV+PH/R6Xxb/wDCM8I//KuiigA/4YV+PH/R6Xxb/wDCM8I//Kuj/hhX48f9HpfFv/wjPCP/AMq6KKAD/hhX48f9HpfFv/wjPCP/AMq6P+GFfjx/0el8W/8AwjPCP/yroooAP+GFfjx/0el8W/8AwjPCP/yro/4YV+PH/R6Xxb/8Izwj/wDKuiigA/4YV+PH/R6Xxb/8Izwj/wDKuj/hhX48f9HpfFv/AMIzwj/8q6KKAD/hhX48f9HpfFv/AMIzwj/8q6P+GFfjx/0el8W//CM8I/8AyroooAzPG3/BOP4zfEXwZq/h7Wf2yfi3eaPrtlNp19b/APCH+E4/PgmjaORNy6YGXKsRlSCM8EGqfwo/4Ji/Fv4H/C3w14K8L/tifFrS/DPhDSrXRNIsh4R8KzfZLO2hWGCLfJprO+2NFXc7MxxkknJoooA3/wDhhX48f9HpfFv/AMIzwj/8q6P+GFfjx/0el8W//CM8I/8AyroooAP+GFfjx/0el8W//CM8I/8Ayro/4YV+PH/R6Xxb/wDCM8I//KuiigA/4YV+PH/R6Xxb/wDCM8I//Kuj/hhX48f9HpfFv/wjPCP/AMq6KKAD/hhX48f9HpfFv/wjPCP/AMq6P+GFfjx/0el8W/8AwjPCP/yroooAP+GFfjx/0el8W/8AwjPCP/yro/4YV+PH/R6Xxb/8Izwj/wDKuiigA/4YV+PH/R6Xxb/8Izwj/wDKuj/hhX48f9HpfFv/AMIzwj/8q6KKAD/hhX48f9HpfFv/AMIzwj/8q6P+GFfjx/0el8W//CM8I/8AyroooAP+GFfjx/0el8W//CM8I/8Ayro/4YV+PH/R6Xxb/wDCM8I//KuiigA/4YV+PH/R6Xxb/wDCM8I//Kuj/hhX48f9HpfFv/wjPCP/AMq6KKAD/hhX48f9HpfFv/wjPCP/AMq6P+GFfjx/0el8W/8AwjPCP/yroooAP+GFfjx/0el8W/8AwjPCP/yro/4YV+PH/R6Xxb/8Izwj/wDKuiigA/4YV+PH/R6Xxb/8Izwj/wDKuj/hhX48f9HpfFv/AMIzwj/8q6KKAD/hhX48f9HpfFv/AMIzwj/8q6P+GFfjx/0el8W//CM8I/8AyroooAP+GFfjx/0el8W//CM8I/8Ayro/4YV+PH/R6Xxb/wDCM8I//KuiigA/4YV+PH/R6Xxb/wDCM8I//Kuqngb/AIJea6f2u/hn8YviP8fPiJ8Vtb+EcWrx+HLLVNG0TTbW2Op2otbov9hs4WkzGqEBicFARjLBiigD/9k=)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wvHWjhS5ACk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
          "referenced_widgets": [
            "2e9b67998f6d4eedb772baec5b5557d1",
            "d042c0a40324487b9e466c9f6d86f89d",
            "5b6ec48f1638457caf1f6fbff59bf23a",
            "0ec002664b6b48ebbe9a8fde514348b2",
            "bc82e58982fa47dfa5822d33202c647c",
            "d698062c54d74273822ec06fcf0ae2cb",
            "fd8f18163d0145ba95d1fa8996090c73",
            "322f996281334854a2f5ac4f9df9a0ad"
          ]
        },
        "outputId": "01228782-dc07-4c09-8773-7ba94b3efcfe"
      },
      "source": [
        "dataset_url = \"https://github.com/amandatta16/WeatherPrediction/blob/main/Dataset/WeatherDataSet2009To2019.zip?raw=true\"\n",
        "download_url(dataset_url, './data','WeatherDataSet2009To2019.zip' )\n",
        "df = pd.read_csv('./data/WeatherDataSet2009To2019.zip')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://raw.githubusercontent.com/aman-user/WeatherPrediction/main/Dataset/WeatherDataSet2009To2019.zip to ./data/WeatherDataSet2009To2019.zip\n"
          ],
          "name": "stdout"
        },
        {
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=28775750.0), HTML(value='')))"
            ]
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "      <th>rain (mm)</th>\n",
              "      <th>raining (s)</th>\n",
              "      <th>SWDR (W/m²)</th>\n",
              "      <th>PAR (µmol/m²/s)</th>\n",
              "      <th>max. PAR (µmol/m²/s)</th>\n",
              "      <th>Tlog (degC)</th>\n",
              "      <th>CO2 (ppm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>01.01.2009 00:00:00</td>\n",
              "      <td>996.50</td>\n",
              "      <td>-7.78</td>\n",
              "      <td>265.65</td>\n",
              "      <td>-8.62</td>\n",
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              "      <td>3.40</td>\n",
              "      <td>3.18</td>\n",
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              "      <td>431.7</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>01.01.2009 00:10:00</td>\n",
              "      <td>996.52</td>\n",
              "      <td>-8.02</td>\n",
              "      <td>265.40</td>\n",
              "      <td>-8.90</td>\n",
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              "      <td>3.11</td>\n",
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              "      <td>434.4</td>\n",
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              "      <th>2</th>\n",
              "      <td>01.01.2009 00:20:00</td>\n",
              "      <td>996.57</td>\n",
              "      <td>-8.41</td>\n",
              "      <td>265.01</td>\n",
              "      <td>-9.28</td>\n",
              "      <td>93.4</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.02</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.89</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1309.80</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.50</td>\n",
              "      <td>136.1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>7.15</td>\n",
              "      <td>436.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2009 00:30:00</td>\n",
              "      <td>996.53</td>\n",
              "      <td>-8.51</td>\n",
              "      <td>264.91</td>\n",
              "      <td>-9.31</td>\n",
              "      <td>93.9</td>\n",
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              "      <td>3.01</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>1310.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.63</td>\n",
              "      <td>171.6</td>\n",
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              "      <td>7.09</td>\n",
              "      <td>435.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2009 00:40:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.31</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.07</td>\n",
              "      <td>94.2</td>\n",
              "      <td>3.26</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1309.19</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.50</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>7.38</td>\n",
              "      <td>427.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date Time  p (mbar)  ...  Tlog (degC)  CO2 (ppm)\n",
              "0  01.01.2009 00:00:00    996.50  ...         7.23      431.7\n",
              "1  01.01.2009 00:10:00    996.52  ...         7.20      434.4\n",
              "2  01.01.2009 00:20:00    996.57  ...         7.15      436.2\n",
              "3  01.01.2009 00:30:00    996.53  ...         7.09      435.1\n",
              "4  01.01.2009 00:40:00    996.51  ...         7.38      427.1\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GK8MTrECC1F6"
      },
      "source": [
        "need to add month column to dataframe since months highly effect the temperature, to do that just gonna read the datetime and extract month from that and add that as an extra column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "h5EGWtODCzsM",
        "outputId": "35d9ae00-9acb-4a1b-dc3b-3aa218251424"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['Date Time'], format='%d.%m.%Y %H:%M:%S')\n",
        "print (df['date'].dtype)\n",
        "df['month'] = df['date'].dt.month\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datetime64[ns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Date Time</th>\n",
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              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
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              "      <th>SWDR (W/m²)</th>\n",
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              "      <th>max. PAR (µmol/m²/s)</th>\n",
              "      <th>Tlog (degC)</th>\n",
              "      <th>CO2 (ppm)</th>\n",
              "      <th>date</th>\n",
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              "      <td>996.52</td>\n",
              "      <td>-8.02</td>\n",
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              "      <td>2009-01-01 00:10:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>996.57</td>\n",
              "      <td>-8.41</td>\n",
              "      <td>265.01</td>\n",
              "      <td>-9.28</td>\n",
              "      <td>93.4</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.02</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.89</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2009 00:30:00</td>\n",
              "      <td>996.53</td>\n",
              "      <td>-8.51</td>\n",
              "      <td>264.91</td>\n",
              "      <td>-9.31</td>\n",
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              "      <td>0.19</td>\n",
              "      <td>0.63</td>\n",
              "      <td>171.6</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.09</td>\n",
              "      <td>435.1</td>\n",
              "      <td>2009-01-01 00:30:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2009 00:40:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.31</td>\n",
              "      <td>265.12</td>\n",
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              "      <td>94.2</td>\n",
              "      <td>3.26</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1309.19</td>\n",
              "      <td>0.34</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>7.38</td>\n",
              "      <td>427.1</td>\n",
              "      <td>2009-01-01 00:40:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "             Date Time  p (mbar)  ...                date  month\n",
              "0  01.01.2009 00:00:00    996.50  ... 2009-01-01 00:00:00      1\n",
              "1  01.01.2009 00:10:00    996.52  ... 2009-01-01 00:10:00      1\n",
              "2  01.01.2009 00:20:00    996.57  ... 2009-01-01 00:20:00      1\n",
              "3  01.01.2009 00:30:00    996.53  ... 2009-01-01 00:30:00      1\n",
              "4  01.01.2009 00:40:00    996.51  ... 2009-01-01 00:40:00      1\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9OV6Fsy5qN_"
      },
      "source": [
        "If we closely monitor the data above, in column Date Time, data is getting recorded after timestamp of 10 minutes that means sensor records data 6 times in an hour.\n",
        "That means in a day or 24 hours 24 * 6 = 144 readings in a day.\n",
        "\n",
        "Now we will use past 4 days readings or 144 * 4 = 576 timestamp to predict data of next day that means after 24 hours = 24 * 6 = 144 readings\n",
        "\n",
        "so for a input variable of 576 reading the target label will stand at 576 + 144 = 720 readings\n",
        "so \n",
        "for each i\n",
        "    X[i+0:i+576] should predict Y[i+720]\n",
        "\n",
        "Preparing dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mju5tVxBaNQ"
      },
      "source": [
        "od = OrderedDict()\n",
        "od[\"p (mbar)\"] = \"Pressure\"\n",
        "od[\"VPmax (mbar)\"] = \"Saturation vapor pressure\"\n",
        "od[\"VPdef (mbar)\"] = \"Vapor pressure deficit\"\n",
        "od[\"sh (g/kg)\"] = \"Specific humidity\"\n",
        "od[\"rho (g/m**3)\"] = \"Airtight\"\n",
        "od[\"wv (m/s)\"] = \"Wind speed\"\n",
        "od[\"month\"] = \"month\"\n",
        "od[\"T (degC)\"] = \"Temperature\"\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5Jy_9R8Al6j"
      },
      "source": [
        "Selecting the features putting most impact\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIYwVFFDArtC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b2802dbf-86cb-4df3-ba36-501913f83c0c"
      },
      "source": [
        "\n",
        "reqFeatures = [i for i in od]\n",
        "df[reqFeatures].head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>996.50</td>\n",
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              "      <td>996.52</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.94</td>\n",
              "      <td>1307.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1</td>\n",
              "      <td>-8.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>996.57</td>\n",
              "      <td>3.23</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.89</td>\n",
              "      <td>1309.80</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1</td>\n",
              "      <td>-8.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>996.53</td>\n",
              "      <td>3.21</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.88</td>\n",
              "      <td>1310.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1</td>\n",
              "      <td>-8.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>996.51</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1309.19</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1</td>\n",
              "      <td>-8.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   p (mbar)  VPmax (mbar)  VPdef (mbar)  ...  wv (m/s)  month  T (degC)\n",
              "0    996.50          3.40          0.22  ...      0.66      1     -7.78\n",
              "1    996.52          3.33          0.22  ...      1.03      1     -8.02\n",
              "2    996.57          3.23          0.21  ...      0.72      1     -8.41\n",
              "3    996.53          3.21          0.20  ...      0.19      1     -8.51\n",
              "4    996.51          3.26          0.19  ...      0.34      1     -8.31\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bd5QDgUEGI7j"
      },
      "source": [
        "Visualizing Data\n",
        "\n",
        "we will plot Temperatue on Y axis against each feature plotted at x axis over entire timeframe "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufxSZK3eGIAY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "1592745f-b270-4736-b537-21e4d3e6106f"
      },
      "source": [
        "def temperatureToFeatureVisualization(df):\n",
        "    fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
        "    tempKey = \"T (degC)\"\n",
        "    i = 0\n",
        "    length = len(reqFeatures) - 1\n",
        "    while i < length:\n",
        "        key = reqFeatures[i]\n",
        "        y = df[\"T (degC)\"].to_list()\n",
        "        x = df[key].to_list()\n",
        "        axes[i // 2, i % 2].plot(x, y)\n",
        "        axes[i // 2, i % 2].set(xlabel=od[key], ylabel=od[tempKey])\n",
        "        i += 1\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "temperatureToFeatureVisualization(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uvvjiC4wYMQK5ubkQiUSwsnrZDMHJyQmpqan19omKihJr5ysUCmUWHyHSkpZXjA3RDzG7jzNcOxvDSFcLEZ7W2BuXjvsZL9DdqgM6GetiYA9LRN/LUnS47d7yv+5i8XB3mOhrIy2P+r/UJc+ygsoJArQ8oTj+395w7Wwso2gIITVa1UQqNzcXU6ZMwaFDh6Qe0KpVq3Do0CGcOHECJSUlsLGxQVnZy+Yh48aNw5AhQzBt2rQmj0NV30TZ3Xv2AkO/OAcA6Gigjb/mhMHWVB/H7mTgrV+vAQDilw2Gka4Wnj4vQciak4oMl/yLb2sCUwNtnEvIQdyicHRUoUmx5P1elFVZQeVE+9XShOLe8iHQ16HhYQlpCbk1karL3Nwcjx49atOJG7Ju3Trs3bsX0dHRMDAwgIGBAbS0tJCRkcF9O5WSkgIHBwepn5sQebr2OB9vfH+Z+5xfLEL/dafxmo8twt07c8s9lxxTRHikCbVH+vrgz5tYNYqPztQJv0GyKCuonGhfWppQJH42lIaIJUQJSJRgbNq0ifu5srISMTExYtXR0hAVFYWdO3ciOjoapqam3PKxY8di69atXOe99PR09OnTR6rnJkTe3t1xHaWiKrFlZRVV+D02Db/HpikoKtJSJ+5noeeqE7iycAAlGZB9WUHlRPvQkqSCaigIUU4SNZGaOnUq97OWlhZcXFwwa9YsdOzYUSpBPHnyBPb29ujatSuMjavbRurq6uLKlSvIzMzEm2++ieTkZOjo6GDLli3o169fs8ekqm+irIrLK+C++GXNRJirBc4l5CgwIiINyasj6g1hmfmiFAUlIrgpSZtvWb8XZVlWUDmhvlqSUFxa0B/WJvoyjIYQIo13o0QJRkMzsyr7bK1UcBBls+7YA2w5lYhQF3NcSKyejfvwnF5wt+6ALguOKDg60hoTAu2xM6a6xmmUry2ixgnE1tf84XR3+WAY6LSqRapUyfq9qGplBZUTitOSpCJlzTAZRkIIqUtufTAGDRqE69evN7uMENIwxhi2nKoeRrMmuQAADxsTRYVEpKAmuQCAvdfTkZpbjN2zQwBU/5/XcF98DN++6Yd+3S2hrcbtw6msIE2RNKloqDaQEKJamkwwysvLUVpaisrKShQWFnIFZkFBAYqKiuQSICHK7kWpCB/8cRPZwjJsGu8DezODets0VFj2cqn/ra6+tiZKRJUyiZPI3tXH+ej26d8oq6iqt27Wr9fQrbMxds4KgpkKjTwlCSorSGMoqSCkfWoywVi9ejWWLVsGHo8HE5OX37R26NAB8+bNk3lwhCiTmOQ87L3+BFNDu6Cb1cs29fuup+Ofu5kAgLC1pzDU0wp+jh2x8vA9HJkTBnebDgCARcPdseKvu9x+5xNf9ruwNdVH+vMSSi7UQEPJxTAva4ABh28/w5KDd7B5go8CIpMdKitIjcoqBueFzTf5/HRYD4zxs4OpgXol24SQahL1wZg9eza+/vprecQjNdS2lkhTUVkFPGoNGfvVG76I4FsDAO48LcCwTecBAD4OpohLfS62b0374aspeRiz9ZLYOudOhhjuZYOnz0vw5zV6XtXZ95P88evlxzjzMBs/TQ3AuYQcTAi0h4ul/DqAy/q9qGplBZUT0iNJTcUYPztMCHSAr4Mp1VYQosTk1gdDlQoMQmShsLRC7PM7v13Hf/q7YN6gbjCs1Xl33VhvVFUxLDt0F+cTczBSYMOtq5tcAEBSdhG+OJEgu8CJ0pjxy1WsG+uNMw+zMWVbLADgSX4xvnnTX8GRSQ+VFe2LJElFdytjvNHTAa8KbGGiry2HqAghykCiBCMhIQFz5szBzZs3UVpayi3Py8uTWWCEKBMrEz0M87LG4VvPuGWbTybCRF+bax4FAL9dTsWbwY74YHA3nE/MgWtnYxSVVeC1ry4oImyiZD7486bY54rKZiuQVQqVFepPkqTCQEcTr3rbYHygA7ztTKi2gpB2SKIEY+bMmZg9ezaWL1+O33//HZs3b4aTk5OMQyNEuUSN8xZLMABg5eF7Yp9/vJCMHTGPMTOsK4Dqb6iXHbqDh5lCucVJVMeJ+1lIySmCk4WhokORCior1JOkHbX5tiYYH2iPV71tYKxHtRWEtGcS9cHw9fXF9evXwefzcfv2bTDG0LNnT8TExMgjxlahtrVEFgpLRdh7PR1LDt5pcP0oH1tcfZyP1LxiAEDPLma4kkzf3pJqNZ3565LXOP+yfi+qWllB5UTTJEksjHS18KrABhMCHMC3o2G3CVEHcuuDoa1d/U2EsbExUlJSYGVlhZwcmnmYtD/GetqYHOKEzBel+Op0Ur31e+PSxT5TckFqe7uvMxbtj1d0GDJDZYXqk7S2wtveFBMD7THcywaGuoqfRJIQolwkeiv07t0bubm5ePfdd+Hn5wcdHR2MHz9e1rERolRWH7mHY3cycOqDvpjeqwuuPs5HDCUQpAW6dZbfiFGKQGWF6pIksTDW1cJrvrYYH+DADb9NCCENabaJFGMMGRkZsLauHpIzLS0NBQUF8PT0lEuArUVV30TaagrgrZG+GORuhRJRJfqvP43MF2UKjoyoipiFA3D8XiY+2SdeixG/bDCM5PAtsCzfi6pYVlA5IVli4efYERMCHTCMbw19HU05REUIUSS5NZEKDw9HfHx1gWhvbw97e/s2nZS0D19EJ2BD9ENMCXHCklfclXIkkbKKSnT79Gi95TeXDBIbUvFm2su5Ld7efl0usRH100FfG2v+vl9vuaiiCtBVQEBSRmWFatgZk4oFe283uY2JvjZe87HFhEAHsYlFCSFEEs0mGDweD3Z2dsjJyYGFhYU8YiJqYkP0QwDATxdT8NPFFEwItMd7A9xgZaKn4Mheqqo/6TIAwHvZP7A11ccwL2s8yhYi+l6WfAMjaul8Qk69OVUAIP5pAcJcOykgIumhskL5SVJbEehkhgk97THU0xp62lRbQQhpHYlqMIyMjCAQCBAREQEjIyNueVRUlMwCI6rv/Mf9MOG7y0jLqx41Z2dMGq4k5+HPt4JhbqTYr2v/vJqGD3ffwoRAe8zp74L/hrshNa8YC/fdxoXEXABA+vMSfHv2kULjJOpl2V8Njz72IKMQlsZ6mPFLLNaO9kaws7mcI5MOKiuUU3OJRUcDbYz2tcN4Oc8sTwhRXxIlGHw+H3w+X9axEDVj19EA5z7qjxk/x+Lk/SxMDnHCtgspWLD3Nr6dpNjZiz/cfQtAddIDAH5OZujj1gnbp/fE+n8eYsupREWGR9RUWl4JzA11MMjDCjtjUrnlKw/fw/sDK5CWV4IJ312W27C10kZlhXJpLrEI7mqOCT0dMNijM3S1qLaCECI9Es2DoYqo857yuJCYgze+v4KxfnbILy5H9L0sRPCt4GhuiCkhTujcQf5NpuoWvMZ6Wpgb7oa/b2cgJoVGhiLy52RugJTc6vlTbi8dhOLySqn/btB7UZy63o+mEgtzQx2M8bfD+AAHdFGTCR4JIdIlt07eaWlpmD17Np48eYIbN27gxo0bOHXqFP773/+26eSkfQh1sYCfY0f8ee0JhntZo6uFIY7czgAA/HAuGT27mkFUWQVdLU2sHOkJezMDbt/rqfm4/CgX5RVV0NbUQB+3TvCw6dCmDuMHbz6tt6ywtALLDt1t9TEJaaua5AIA+Ev/AQDcXzFEpdrBU1mhWIwxdFlwpMF1Ya4WGB/ggHD3ztDR0pBzZISQ9kaiGoyIiAhMnDgRn3/+OW7evImKigr4+Pjg9u2mR6FQJHX9ZkpVZb4oxfSfYxGf/gLu1h2w+BV3fBGdgEuPchvc3q2zEYrKKhuc9di5kyG87U3R27UT+vewRAc97QaO0LjwqDNIyBK26joIaasPBrlh3T8PJdr2y4m+GOZlLbVzy/q9qGplhTqVE43VWrzT1xmvB9jD0ZxqKwghkpHGu1GiBMPf3x9Xr16Fj48P4uLiAEDsZ2WkTgWHuigVVWLLycR6/RtcLI3gZWeCh5mFiE9/Ibauf3dLzO7rjA562sgvLseR28+w73o6CsuqR+LR0dRAL1cLDPGwwhg/O2hoNF+zkVVYitnbr+Pa43zpXRwhLfD9JH+k5hVj+V9N15pF8K3w1Rt+UjuvrN+LqlZWqEM50VhisTXSFwN6dIa2JtVWEEJaRm5NpLS0tFA7D8nPz4eadt0gMqSnrYkPBnfDsTsZYjUIZRWVKBNV4RUvG0wN6YIrybm4/CgPqXnFOJ+QA7uO+ninrwu6WRkjqKs5lo/wRHZhGY7fzcTf8c9w8n4WTt7Pwkd7bmGcvx0+GNQNlh30cCkpFxo8oGdX8RF5LI31MFJgQwkGUZgZv1yVaLsjtzOQlC2EVQc9GMphIr62orJCfhpLLL590w+DPKzkHA0hhIiTqMQaO3Ys3nrrLbx48QLff/89tm7dihkzZsg6NqKmtkz0xeCNZwEAIwQ2uPP0BY7EP8Ph28/qbVteWYVfLj3GL5ceAwD6duuEAd0t0cO6A0YIbDCxpwPS8orx1ekk7IxJxR9Xn+CPq0+wY0ZPTPz+CgBgeq8uGOZlDT0tTehpa0BfRxMHblT3w9j7TgjWHXuAi0kNN9UiRNEGrD8DAPhxij/6d++s4GiaRmWFfDSWXKjq6GOEEPUj8ShSO3fuxP79+8EYw8iRIzFx4kRZx9Ym6lD1rc5WHbmHb88+wqYJPnjV2wbCsgrEpxcgMUuI7MIyZBWWIbuwFFmFZbj1pEDR4RKicOc+6ic2AEJryOO9qEplhaqVE5RYEELkQW59MGrUbNqWEXzkRdUKjvbmWUEJglefBAB8NKQbRgpsUVHJ8OR5MXKE5UjKEqK4vAIBTmYQllVAU4OHjIJSbDmZyPW/IESdGelqQVjrWd8y0QfDvWzadEx5vRdVpaxQpXKioeTi5pJBMNFv2SAXhBDSHLn1wUhNTcXMmTNx5kx1VX2/fv3wzTffwMHBoU0nJ+1X7fbka48+wNqjDxrc7rtzyfIKiRClsmd2CP6z8zo8bExwPjEH7/9+A26djeHWWXlnWqayQvqo1oIQoookGl5i0qRJGDhwIDIzM5GRkYEBAwZg0qRJso6Nk5CQgJCQELi5uSEgIAB37tyR27mJbHTQ08bf74XBxkTyicS6WxnDzFBHbFkE3wozw7pgxQgP/DDZH6tH0SzCRD0M3ngWvg4dseF1AZa84o6KKoZBG87i7tMXze+sIIosK9SxnGgouUj8bCglF4QQpSdREykPD496L2tPT0/Ex8fLLLDa+vfvj0mTJmHKlCnYvXs3/ve//yE2NrbJfVSp6ru9Y4zh2uN8ZBWWYenBO8gqLJN43+TVESgRVYKxl7UiTc1iS4gq+us/vbD+nwc49SAbWho8xHwysF6yLQlZvxcVWVaoWznR0HuMEgtCiDxI490oUQ2Gi4sLHj58OTHUw4cP4erq2qYTSyorKwtXr15FZGQkAGD06NFIS0tDYmJiM3sSVcHj8eDvZIYIvjViPhmIpFURiBrnjRBnc9iY6KG7lTFsTfUb3LfLgiNwX3wMHkuOYcq2GJSKKhvcTkuC+TEIUQaBTmYY62cntmz45vP4ZJg7ZoZ1QUUVw4l7mQqKrmmKKivUqZy4kfackgtCiMqTqA+GUCiEt7c3QkJCAACXLl1CSEgIRo0aBQDYu3evzAJMS0uDtbU1tLSqQ+XxeHBwcEBqaipcXFy47aKiohAVFSUWM1FNmho8jPK1wyhfu3rrCktFYABe2Xwej3OLueUWRro4/SAb3RcdrbdPzy5mcLfpAE8bE5gZ6eBJXjGO38tCam4RnhaUoryiSpaXQ4jEulgY4o+3gwEAn4/1xpRtMTj9IBsA8Mb3lxHibAEAcLfpoLAYm6KoskJdyglKLAgh6kKiBGPSpEli7Wjl2f9CUnPnzsXcuXO5z3Z29f84JarPWK96xJQzH/ZDUrYQp+5nYYinFToa6MBjybF62/dyscCNtOe4kpxXb52OpgY8bDsgLvU5t2ysnx2sTfWhq6WBz4813PGcEFnxsTcV+/zj5AB0XXgEAJD5ogz74tLRs4sZPGxMFBFes5S9rFDmcoKSC0KIOpEowZg8ebKs42iUvb09nj17hoqKCm6W2NTUVBqVhMC5kxGcOxlxn5NWRWDzyWYPriMAACAASURBVARsjE7glm2f0RNVVQypecW48/QFCktFMNHXRnllFX67nIqHWYVix/zzmnK2xybtg4+DKX6PSYVtR304mRvi5P2setv0crFQQGSSUVRZoerlBCUXhBB1I1GC8ezZM2zcuBEJCQmoqHg5LvvBgwdlFlgNS0tL+Pr6Yvv27ZgyZQr27NkDOzs7sWpvQoDqplXvD3RDck4RDtx4ivsrhgAANDR4cLIwhJOFodj2IwS2AIDi8gr8HpOGVUfuoaKqesyDoK5muPyofq2HJI7MCYO7TQeUiiqhq6WBHGE5Vv99D3/dfIbySmqORRp399kL7IxJa3KbYV7Wcoqm5RRVVqhyOVE3uRjGt8aXb/gqKBpCCJEOiUaRCg4Ohp+fHwIDA6Gpqcktf+ONN2QaXI0HDx5gypQpyM3NRYcOHbBt2zbw+U0PR6rMo4MQ5VRWUYllh+5iz7UnKJOgX0Yft044m5ANI10tFJZW/zF1ecEAWDUx9O7dpy8Qsemc1GImqmfB0O5Y/ff9Vu3bx60Tfp4W2Opzy/q9qMiyQhXLibrJRfLqCKWfnJAQov7kNpO3l5cXbt261aYTyZuiCw6iuorLKxCX+hwOZgYwNdCGtqYGXpSI8DBTiPLKSuhoaqJnVzNoa74chK2sohK6WpoNHq+8ogp/XktDWl4Jtp5JktdlEDUysIclerlYYFKwEzTaMCKarN+LqlZWKLKcqJtcUJMoQoiykNtM3n5+fkhISJDb0LSEKJKBjhZC67Rz19PWhGWHxmsmGksuAGDyjzG49ChXavER9WdprMvNB/PFeAHXnE/ZUVkhGUouCCHqTqIEY+7cuQgJCYGrqyv09F7+kXXy5EmZBUaIOjj1IIuSCwIA8HfsiCGeVlh5+F6z264exUeYaycUlopgbqQrh+ikg8qK5lFyQQhpDyRKMCIjIzF79mz4+/uLtaslhDStg97LX7G54W6Y3dcZ2poaYIwhW1iGBxmF+ObMI5xPzFFglEQerj7Ox+7ZIdDR0sDiA3ea3HZAj84AoFLJBUBlRXMouSCEtBcS9cHw9vbGzZs35RGP1FAfDKJKGGO4kpyHgzef4vaTAmhp8qCnpUm1H2pm99vB8LY3hesnf4st19LgcSOYAcBIgQ02jveR+vll/V5UtbJCnuUEdegmhKgKabwbNZrfBAgNDcWNGzfadCJCSON4PB6Cuppj1Wt8HPpPL+x7JxQ7ZwVhXribokMjUjRm6yXce/YCx97vLbZ83zuhMNHX5j7vv/EUaXnFdXdXelRWNCwxS3zG8JhPBlByQQhRaxI1kTp37hy+//57uLi4iLWrvX79uswCI4QAX9OoU2rn1S0XMIwvPpfF5G0xKCgRwcHMAKn/JhYPMwthb2agiBBbjcqKhg2MOiP22dK48QEjCCFEHUiUYGzZskXWcRBC6mCMobi8EgDwircNDt18quCIiCQCncwQk1J/ksYPB3fD58ceAAAO334mti6vqBwAYG2ixyUYdUcyUwVUVtRH/S4IIe2RRAlGnz59IBKJkJqaCmdnZ1nHRAhBdbMpF0sjJGYJW5VcGOhowtJYFym54k1tFg13R6moEt+cScKL0opG9iatsXi4OwK7mGHl4bsIcDLD5pOJ3Lqa5KKuE/P64O/bz7Dun4cYH2iPpOwi5AjLoNWG+S4UhcoKcZRcEELaK4n6YJw+fRqOjo7o168fACA2NhaRkZEyDYwQAuyaFYStkX7YMtEHB/4vtEX7FpdX1ksuAGDFX3dhY6oHvp0Jtk/vifVjvdsU48Aelnjd375Nx1AVfbt1anTdT1MDMDXUCZ62Jvh9VjDmDeqG3m6Nbw8AJvra+ODPm1j3z0MAQH6RCDnC6vkvKpsff0PpUFnROEouCCHtiUQJxvz583Hu3DmYm5sDAAICAhAXFyfTwAgh1cOUDvG0wnAvG3jbm+KL8QKpHPe/u27iQmIuIn+4gnl/tm3Un+h7WbiX8ULi7a2amLBQ2T0vFnE/G+uJVwBP2RaLLguO4FLSy5G/zj7MbvJ4BSUixKU+5z4v/+su97OOpkSvZ6VCZcVLtWsvBPamCoyEEELkT6ISrLKysl51t46OjkwCIoQ0TllndL71pID7+XV/e1xZOKDRbTNelHI/62mrzh/RXSwMcSOtOhkIcOqImIUDG9xuwneX8fR5CYRlLW9+FuZqgUAnM3wxXqCSowxRWVGtbtOo/S2sfSSEEFUnUR8MPT09CIVCrsC7ffs29PX1ZRoYIaRhlsa6yCqsbkZjb6aPtLwSbt22qQGYui220X1r+nTI0kdDumHO75J9a10qqpJpLE0x1NFE0b+d6CWRnFMEoLpvy9ZIP+jrND6R3M8XU5pc35BQF3P8Or1ni/ZRNlRW1EdNowgh7VGTXx9OmDABAPDpp59i0KBBSE9PR2RkJMLDw7Fy5Uq5BEgIEVd76NLayQVQ3ab/8JxejTZDqptc6GtrwsxQB2tG8bllX73hi+i5vevuKpEbi8PhtzIaFxKrmwktjOiOqaFOcDI3gI5W22orTn3QF3MbmReko4F2g8ubsmoUHxZGLf92/c0gRyRlFyFXWIYw14ZHevrm7CNsjE5o9ljravV/uZqSj5IWJDzKhMqKl+rWXhBCSHvUZA3G/fv3AQCDBw+Gm5sbjh49CsYYli1bRiOEEKIgg9w749rj/AbXfbT7FpdELBjaHbN6d0X68xIM23QeBSWietvfXT4YANBlwREAwIDulhjqaYXXv73cqtgEy4+LfV515D6GelrhrT7OeNXbBj7Lj6O8suW1FnGLwtHRUAdj/e0Qdfyh2LpfpwdiX1w69l5Pb9Ex3/u9dRPCDfKwwuivL6KTsS4Oz+mFwM9OtOo4AMRGByurqMLTghI4dzJq9fEUhcqKaqxOx3yqvSCEtFdNJhi12wB36dIFs2fPlnlAhJCmRfCtsfrv+w2uS8wSwtpED2tGe6G3qwV4PB7sOhrg5pJBAIBnBSXYdiEF3559BOBlYlHDy84UBSUixCTnYbBHZxy7k9nmeP+Oz8Df8Rk4n5CD32b2xIsSEab/fLXBbWvPIWFrqo/05yWYEuKEjobVNQ3WJvpYO9oLH+25xe2z9UwSV2MiqeP/7Y3wDWfFlmlq8FBZ1fzITaO/vggAyC4sq1eD1FJnanUCf6evM7paGLbpeIpCZUW12r9Pj1ZFKDASQghRrCYTjFu3bsHMzKzecsYYeDwe8vLqTyZFCJGt5tr2Lxrujj6NDI9qbaKPhRE9cDUlD9drjV5UY0P0Q1x9XP177WppzCUYK0d64tP98c3G1rmDLn6Z1hNWJnq4mpKHb848gpOFAf64+gSHbz/jJpibF+6GKgb4O3WErak+UvOKEdTVHCWiSngv+wcd9LSQ/rz6j/dhXuKzXj8tePlHfZirBc4l5DQbV10WRrqY0asLvj+fDADQ0dLAgxVDsOVkItbXqSFpiqiR2hhrEz28KBG1qI/Hh4O7qWTHboDKCgD1OvVrqOA8JoQQIi08VrdOtxYPDw8cOXKksdVwdHSUSVDSYGdnhydPnig6DEJkxnnhkXrfuF9eMABWJs0PA5uYVYhhm86Db2uC3bNDEJOch3HfXOLWu3U2ws6ZQbiVXoD9cen4dJg7Pjt8F/tvND7h3+Lh7hghsIG5kW69dZkvStFzlXhTou8m+WPmLy9rMiyMdGDX0YAbqQmoHtL2/Mf9oFVryNaCEhHi0wsQ4lw9FOqxOxn439EHSM4pgoWRDnKE5c1e/6Lh7ljx75Cw1xeFw+zfGhLGGJKyi7D3+hN8dTqp2eO0xVg/O/x57eU76uHKoW3up9IcWb0XVbWskOb9qN33gppGEUJUmTTejU3WYOjq6iptwUBIe3dn2WB0X3QUAHByXh90bUHbfRdLY7h2NsLTf2sJAruIf/v87Zv+MDfSRb9ulujXzRIA8HqAQ5MJxrReXRpd17mDHu6vGIJx31zihrStnVx0tzLG/YzCesnB5QaGuzXR10aoy8vO1UM8rfHjhRQk5xThVW9bxKcXcM2sGlOTXMwNd+OSC+Dl7OkBXcwAGSYYs3p3xcKIHghxMcd/d7VtHhJl0N7Liia+pyOEkHapyQSDXpqEKC89bU0ce783sgvLWpRc1KioZNDQ4IExVq8vxi+XHuO9ga4w0X85OlOwszliPxmIgM+iWx3vwXd74cjtZ3jnt+sAgFe8bbBurBd0tTTrjb6zaLi7xMd+klc9Y/nbfbrC8t8RtGJT8jB266UGt5/RqwtCXS245Kmu/zXSx6Ut9LQ1uGF5R/vaAQD2xb1M2GRdeyFL7b2sqP37k7ya+l4QQkiTJVp7nYGVEFXRzcoYvRoZKrU5rp2N8SS/BHee1p+F+8cLyRhcpxM0AHQy1oVhrT4gCZ8NxYH/C8W95UMkPm8E3xpLXnGHXUd9LHvVA7pa1cf7+70wse3MDCUbejbzRSmeFlRP3mdZa3jeAKf6fQIA4Os3fPHpcPdGkwsAuJ9RKNG5W6L2nB9VjOF8Qk6zM32rCiorXlLVfjSEECJNEk20RwhRPxMDHXDo5lMcu5OBpFURcF4oXouhpdnwH0oX5w/ADxeSMdTTCtqaGvC2N23xuaeGdsHUUPEmVT2sOyBlzTBkvijFsE3nsGj/HQzxsG62U/vpB1kAgJECm3rrvOxMcOtJAbpYGOK7SX7g8XhtHgbW0dwAGjweN/GepLp1NsaDzOrEJfL7K8gtKoe+tiZmhHXBf/q7tikmoji1a97ur5A80SaEEHWmunXyhJA28bDtAADYfDIRf1xNq7d+4+uCBvczMdDG3HA39LDuIJO4OnfQw4DunSEsq0CpqOlRmESVVdgVWx37vEHd6q03/7d/RScjXbhYGrcqubgwvz93HAB4nFssllzM6t1VouPUJBcAYGdmgAmB9vjz7WDMG9RNpZtHkZf0tFs2ezshhKgrKtUIaac66L1sgrRg720A1U2gAKBrJ0P4OHRUSFwA4PTvfBANJT61TfspFtdTn2NaaBexGc5rfB3ph4k9HfBVpG+Lzv/F+OrkaufMINia6uPqpwMR3NW8wW1r5hSR1P0VQ3Dg/0KxepQXPG1NWrQvUS5VtUZxE7SiJo8QQtRVk8PUqjIappaQ5p16kIV919PhZG4AKxN9jA+wV4rx+0tFldwIWfHLBsNIt+HWnDXNU/bMDoafY8N9LqRpxs9XEX2v+ckH+bYmuJ1ewH1e9qoHhGUVGOzRGS6WxrIMsUn0XhTX1vtBQ9MSQtSRNMoKhddgbNq0CZ6enuDz+fDy8sL27dvF1q9cuRLOzs5wdnbGJ598oqAoCVFP/bpZYtMEH8wd1A0TezooRXIBiDc1WXLgToPb1P5uxMyw/twbsvDVG76waGCej7oO/aeX2OfALmb4v34uCk0uVBmVE4QQoloU3snbw8MDFy5cgImJCdLS0uDj44Pg4GA4Ozvj7Nmz2LlzJ27dugUtLS2EhoYiJCQEw4bRN0WEqDu3zkZ4mCnE7L4N93H4/lz1LNze9qaw66gvl5h0tDSwd3YItl95DGFZBXZcSZVov+5WlFi0hTKWE7WbR+17J0Sm5yKEEFWj8BqMAQMGwMSkuh2yvb09rKyskJZW3e56165dePPNN2FoaAhdXV1MmzYNO3fuVGS4hBA5CXPt9O9PL2tVbqQ9x4RvL2Nj9EN8duQeAOCbSD9oa8rvVeZgboCFET2w5JWG5+kIdDLDi1KR2DIaurRtlLGc8FhyjPtZkf2VCCFEGSk8wagtOjoa+fn5CAgIAACkpqaKzQ7r5OSE1NSGvzGMioqCnZ0d908oFMolZkKI9GW+KMUP56trKDL+neNCWFaBkV9ewKVHudgYnQAAWDOKDysTvUaPI0sVlS+/wV431hsjBDaYGdYFu94KwsTvLiskpvZAWcqJkmZGOCOEkPZM5k2kgoODkZCQ0OC6uLg42NvbAwBu376NqVOnYteuXTA0NGzxeebOnYu5c+dyn+3s7FoXMCFE4WpGtQKAS49yYKSnhYyCknrbjfJV3O+5oa4WFg13Bw/AaF9bjPF7GcuKEZ547auLAIDvJ/krKELVocrlxI4ZPdt8DEIIUTcyTzAuXbrU7DZ3797F8OHD8eOPP6JXr5edIx0cHPD48WPuc0pKChwcHGQSJyFEeXS3MsbJ+9UT6H15KglfnkqChdHLuSjWjOJjpI+twuePmN6rS4PLfRw64tGqCDAAmkrScV6ZqVo5UVRWwf0c4mIh03MRQogqUngTqXv37iEiIgLffvstwsPDxdaNHTsWv/76K4qKilBWVoYff/wR48ePV1CkhBB5CexSf8jZHGE5ACB6bm+MD3RQ+knNNDR4lFxIibKVExuOPwQAGDcyfDIhhLR3Ck8w5syZg4KCAnz88ccQCAQQCAQ4dqy681zfvn3x+uuvg8/no0ePHggPD8fw4cMVHDEhRNb6uHVCb7dO8HEwxfgAeyyt1aGahnptf5StnBD+W4OxcXzDs90TQkh7RxPtEUJUwrOCEmhq8GBprJhO3eqA3oviWns/SkWVSMoWwsOGZmInhKgfaZQVVL9LCFEJ1ibymeuCkOboaWtSckEIIU1QeBMpQgghhBBCiPqgBIMQQgghhBAiNWrbB0NXVxedOnVqfkMZEgqFMDIyUmgM8kDXqT7awzUC7fc6s7OzUVZWpsCIlEtLyglVfmYodsVR5fgpdsVRdPzSKCvUNsFQBu2lQyVdp/poD9cI0HWSllPle0mxK44qx0+xK46qxw9QEylCCCGEEEKIFFGCQQghhBBCCJEazaVLly5VdBDqLDg4WNEhyAVdp/poD9cI0HWSllPle0mxK44qx0+xK46qx099MAghhBBCCCFSQ02kCCGEEEIIIVJDCQYhhBBCCCFEaijBIIQQQgghhEgNJRitdPToUfj7+8PLywtBQUG4efMmAGDq1Klwc3ODt7c3QkNDERsby+1TXFyMCRMmwMXFBW5ubti9e7eiwpdYY9dZ4+TJk9DU1MTGjRu5Zep0nYwxLF26FG5ubuDz+ejXrx+3T1ZWFoYMGQJXV1d4enri7NmzigpfYo1dZ0xMDIKCguDj44MePXpg7dq13D7K/v85Z84cODk5gcfj4caNG9zyhIQEhISEwM3NDQEBAbhz506b1ylSS6+ztLQUI0eO5N5H4eHhSExM5PZTxedXEZT1eWhIa34XlEVTz6sqPKuDBg2Cl5cXBAIBwsLCEBcXB0A17n2Nbdu2gcfjYf/+/QBU474DgJOTE7p16waBQACBQIBdu3YBUI17X1ZWhnfffReurq7g8/mIjIwEoBqxN4uRFsvLy2NmZmYsPj6eMcbY2bNnmYeHB2OMsQMHDjCRSMQYY+zQoUPM0dGR22/ZsmVs8uTJjDHGHj16xDp16sRycnLkGntLNHWdjDH2/PlzFhAQwIYPH842bNjALVen69y4cSN77bXXWFlZGWOMsWfPnnH7TZ06lS1ZsoQxxlhMTAyztbVl5eXl8g2+BZq6Tm9vb3bgwAHGGGO5ubmsU6dO7M6dO4wx5f//PHPmDEtLS2OOjo4sLi6OW96vXz+2bds2xhhjf/75J/P392/zOkVq6XWWlJSww4cPs6qqKsYYY5s3b2Z9+vTh9lO151dRlPV5aEhrfheURVPPqyo8q/n5+dzPe/fuZV5eXowx1bj3jDGWnJzMgoODWVBQENu3bx9jTDXuO2Os3vNeQxXu/fvvv8/effdd7rmv+RtDFWJvDiUYrRAbG8tcXV3FlhkbG7Nr166JLcvOzmZaWlpcwuHu7s4uXbrErR87diz77rvvZB9wKzV3nZGRkezAgQNs8uTJYgmGOl2nra0te/DgQYP7GRoaiiUcAQEB7Pjx4zKNtS2auk6BQMB+/vlnxhhjqampzNbWlrs2Vfn/rF3IZGZmMmNjY+53r6qqinXu3JklJCS0ep2ykPQ664qNjRX7wkPVnl9FUIXnoSGtfUaUSe3nVdWe1W3btjFvb2+VufeVlZVswIAB7OrVq6xPnz5cgqEq972hBEMV7r1QKGTGxsasoKBAbLkqxC4JaiLVCq6ursjNzcXFixcBAAcPHkRhYSFSUlLEtvviiy8QEREBLS0tAEBqaiocHR259U5OTkhNTZVb3C3V1HXu3r0bGhoaePXVV+vtpy7XGR8fj8zMTBw4cAA9e/ZEz549uarX3NxciEQiWFlZccdR1etMSUnBtm3bsGjRIjg4OMDNzQ2rVq3irk3V/j8BIC0tDdbW1tzvHo/Hg4ODA1JTU1u9Thm1JN4vvvgCI0aMAKCaz68iqNrz0BBVvYaa51WVntVJkybB3t4eixYtwq+//qoy9z4qKgqhoaHw8/PjlqnSfQeq7z2fz8f06dORnZ2tEvc+KSkJZmZmWLVqFfz9/REWFoYTJ06oROyS0FJ0AKrIxMQEu3fvxoIFCyAUChEcHAx3d3fuYQCA7du3448//lDaNouSaOw6hUIhoqKicPr0aUWHKBWNXScAVFRUoKSkBFeuXEFKSgpCQkLQvXt32NnZKTjqlmvquV2zZg1Wr16NiRMn4tGjR+jTpw/8/f25+0BU26pVq5CYmIgTJ04oOhRCmlX7eS0pKVF0OBL75ZdfAAA///wzPv74Y6xYsULBETUvPj4ee/bsUem/Vc6ePQsHBweIRCJ8+umnmDx5skrc+4qKCjx+/Bju7u5Ys2YN4uLiEB4ejsOHDys6NOlQdBWKOigtLWWmpqZc9dXvv//OXFxc2OPHj8W2U5WmJo2puc6NGzcyS0tL5ujoyBwdHZmhoSHr2LEjW7hwIWNMfa4zISGBGRkZsaSkJG7dmDFjuGsxMDBQierjxtRcZ2xsLNPR0RFbN2bMGPbtt98yxlTn/5OaSDUc7+eff878/PzE2ogzpvrPrzyowvPQEFVuItXQ86qKz6qenh7LyMhQ+nv/1VdfMSsrK64819XVZZ06dWJfffWVSt73p0+fMiMjI5V47rOzs5mGhgarqKjglvn7+7Ndu3YpfeySoASjlZ4+fcr9/Mknn7BRo0YxxhjbtWsXc3FxYSkpKfX2WbJkSb3OstnZ2XKJt7Uau87a6vbBUKfrnDlzJvvyyy8ZY9Wdnx0cHNiVK1cYY9XXXbsDnI2NjVJ2gKutoeusqKhgHTt2ZCdOnGCMVb/07O3t2cWLFxljqvP/Wbcdbp8+fcQ6yfn5+bV5nTJoyXWuX7+e+fr6sry8vHrHUcXnVxGU/XloSEueEWXS2POq7M9qfn4+S09P5z7v27eP2drasqqqKpW59zVq98FQ9vvOWHU/htrJ6Pr161lYWBhjTDWe+/DwcHb48GHGWHX5am5uzp48eaISsTeHEoxWmjFjBuvWrRtzdnZmkZGR3AOupaXF7OzsmLe3N/evZsQdoVDIxo0bx7p27cpcXV3Zrl27FHkJEmnsOmurm2Co03Xm5OSwV155hXl4eDAPDw8u2WCMsYyMDBYeHs5cXFyYu7s7O3nypKLCl1hj13n8+HHm6+vLvLy8WI8ePdj69eu5fZT9/3PWrFnM1taWaWpqMktLS+bs7MwYY+z+/fssKCiIubq6Mj8/P3br1i1un9auU6SWXmdaWhoDwLp27cq9iwIDA7njqeLzqwjK+jw0pDW/C8qiqedV2Z/VlJQUFhAQwDw9PZmXlxcbMGAAl+Cpwr2vrXaCoez3nTHGkpKSmEAgYHw+n3l6erJXX32VJScnM8ZU494nJSWxvn37cs/O7t27GWOqEXtzeIwxpuhmWoQQQgghhBD1QKNIEUIIIYQQQqSGEgxCCCGEEEKI1FCCQQghhBBCCJEaSjAIIYQQQgghUkMJBiGEEEIIIURqKMEghBBCCCGESA0lGIQAcHJyQrdu3SAQCODu7o4vv/xS0SERQoha2bt3L/z8/CAQCNC9e3f0798fVVVVze63dOlSlJaWyiSmusdevHgxfvvtN5mci5D2hObBIATVCcb+/fshEAjw+PFjeHl54dy5c/Dy8gIArhDU0JBvTl5RUQEtLS25npMQQqTt2bNn4PP5uHbtGhwdHQEA169fh4+PD3g8XpP78ng85Ofnw9TUtEXnlOS93dpjq4K2lB+KKnuozFMfVINBSB2Ojo7o1q0bJk6ciNGjR2Pw4MHw9PTEs2fPcOzYMfTq1Qt+fn4IDAzEqVOnAAAJCQkIDQ2Ft7c3+Hw+Pv30UwDAoUOH4OXlBYFAAE9PTxw4cAAA0LdvX+zfv58755gxY/DTTz8BAKZMmYJp06ahd+/e8PT0BAD8+uuv6NmzJ3x9fdG7d2/cvHlTjneEEELaJjMzE5qamjAzM+OW+fr6csnFBx98gICAAAgEAvTu3RsPHjwAALz99tsAgLCwMAgEAmRlZWHKlCnYuHEjd5wPPvgAS5cuBVBdI1H3vd3aYwuFQkybNg2enp7w9PTEsmXLuHP27dsXH3zwAcLCwuDs7Mwdq66ZM2di3bp13Ofk5GRYWVlBJBLhxIkTCA4Oho+PDzw8PPDDDz9w29WUAyEhIXBzc8PkyZNRUlICAMjKysKoUaPA5/Ph6emJb775htvPyckJH3/8MQIDAzF58uR68fTt2xf/+c9/EBAQABcXF8ybNw813zP37dsXc+bMQXBwMAYNGgQAWLduHQIDA+Hr64shQ4bg8ePHABov21auXIkePXpAIBBwX9gB1Ync8+fPuTgsLCyQkpLSYMwikQjz589HYGAgBAIBxo0bh/z8/AbvL1FiCp1HnBAl4ejoyOLi4hhjjN26dYsZGxuziRMnMmtra5aRkcEYYywpKYkFBQWxgoICxhhjCQkJzMrKipWWlrI5c+awVatWccfLzc1ljDHm5eXFLl68yBhjrLKykuXn5zPGGOvTpw/bt28ft/3o0aPZtm3bGGOMTZ48mXl5ebEXL14wxhg7f/48Gzp0KCstLWWMMXb27Fnm7u4uq1tBCCFSV1lZyUaNGsU6duzIRo4cydauXcuePHnCbZKX1wAAIABJREFUrc/KyuJ+3rlzJxs8eDD3GQD37mSs+h25YcMG7vO8efPYkiVLGGOMLVmyROy93ZZjf/TRR2zixImssrKSCYVCJhAI2O+//84Yq36Hjxw5kolEIlZcXMycnJy4d31tFy5cYB4eHtznxYsXs7lz5zLGGMvLy2MVFRWMseoyw8HBgaWlpXFxuLu7sxcvXrCKigo2fPhw9tlnnzHGGBs3bhybP38+Y4yxzMxMZmdnxy5dusQYqy7Lpk+fzqqqqurFUhN3//79WXl5OSsqKmJ+fn7st99+49YNHjyYlZeXM8YY++2339iMGTO4GH/55RcWERHBGGu4bMvLy2MmJiasuLiYMcZYUVERKykpafA+m5ubs+Tk5AZj/uyzz9jy5cu5bZcvX87eeeedBq+HKC+qhyLkX6+//jr09fVhYGCAH3/8EfHx8dDX10fnzp0BAEePHkViYiJ69+7N7aOhoYHU1FT07t0bH374IYRCIfr06YOBAwcCAAYMGID33nsPY8aMwaBBgyAQCCSKZezYsTA2NgYAHDhwADdv3kTPnj259Xl5eSgpKYG+vr60Lp8QQmRGQ0MDe/bswf3793HmzBn8/fff+Oyzz3D16lW4uLjg+PHj2Lx5MwoLC1FVVYW8vLxWnysiIoJ7bwNo9bGjo6Oxfv16aGhowNDQEJMmTcLx48fx+uuvA6guM7S0tKClpQWBQICkpCQEBweLHSMkJAQVFRWIjY2Fv78/fvnlFxw6dAgAkJubi+nTp+Phw4fQ0tJCbm4u4uPjYWdnBwAYN24cVw5Mnz4dmzZtwsKFCxEdHY1r164BACwtLTFq1ChER0cjKCgIQHXtR1PNziZNmgRtbW1oa2sjMjIS0dHRmDhxIgAgMjIS2traAID9+/cjNjYWfn5+AIDKykruGA2VbZWVlXB1dUVkZCQGDRqEYcOGcdfSnNox79+/HwUFBdizZw8AoLy8HE5OThIdhygPSjAI+deuXbvEEoD4+HgYGRlxnxljCA8Px44dO+rt6+rqipCQEBw/fhxbtmzBxo0bceTIEURFReHOnTs4deoUJk+ejDfeeAMfffQRtLS0xF7WdTsw1j3v5MmTsWrVKmleLiGEyF337t3RvXt3vPXWWxgyZAgOHjyIMWPG4N1330VsbCycnZ1x69YtsS9y6mro/Vn7nVn759TU1BYduyl1/2jX09PjftbU1ERFRUWD+02dOhXbtm2DUCiEhYUF1/T17bffRkREBPbs2QMejwdfX98mO7M3ljTUXV77+iVRe/+6Zc+CBQswa9asevs0VrZdvnwZFy9exOnTpxEUFISdO3ciLCwMmpqaLSrzNm/ezDXTIqqJ+mAQIqHBgwcjOjoat27d4pbFxMQAqO6D0blzZ0yaNAlr167F5cuXAQD379+Hh4cH3n33XcyePZtb7uLigitXrgCobpN7/vz5Rs/76quvYvv27UhNTQVQ3XHx6tWrMrlGQgiRhfT0dFy4cIH7nJ+fj+TkZDg7O6OgoADa2tqwtrYGYwxbtmwR29fY2BgFBQXcZxcXF+7dm5ubiyNHjjR63pYeu7aBAwfihx9+AGMMRUVF+PXXX1v1R++bb76JP//8E1u3bsW0adPE7oGjoyN4PB7Onj1br2/d7t27IRQKUVlZiW3btnE14wMHDsR3330HAMjOzsbevXsRHh4ucTzbt2+HSCRCSUkJduzYwR23rpEjR2Lr1q1cjY9IJEJcXByAhsu2wsJCZGZmIiwsDIsWLUKvXr247WuXeXv37kVRUVGj8Y0cORIbNmxAcXExAKC4uBh37tyR+PqIcqAaDEIk5OLigh07duCtt95CcXExysvL4ePjgx07dmD37t3Yvn07dHR0UFVVha1btwIAFi5ciAcPHkBHRwcGBgb4+uuvAQAfffQRXn/9dfD5fHh4eIg1f6orLCwMa9euxWuvvYaKigqUl5dj2LBh8Pf3l8t1E0JIW1VUVGD58uVITk6GgYEBKioqMHnyZIwYMQIAMH78eHh4eMDc3BwjR44U23fevHkIDw+HgYEB/vnnH8yaNQtjxoxBjx490LVrV65pUEP4fH6Ljl3bokWLMGfOHPD5fADVTVfHjRvX4mu3sbFBYGAgDh48KNYhe82aNXjnnXewYsUKCASCeuVAQEAABg8ejOzsbAQHB+P9998HAGzatAmzZ88Gn88HYwyffPJJk2VIXT169EBoaCjy8vIwYsQIjB8/vsHt3njjDeTm5qJfv34Aqv8Pp02bBh8fnwbLtoKCAowZMwZFRUXg8XhwdXXlOppv2LABc+bMwaeffophw4bB3Pz/2bvv8KjK9G/g30kmmfRGEgKkQRIIpJIQhIAEpBcbZVWWJihu3mXVjauwKiv6QxZbbKjIKriKsqIi6oK4oiCISFF6DWVIISG9Z5Ipz/tHzJiQNiQzc6Z8P9fFRebMKfecOTl37nOe5zk92o1v6dKlqK+vx0033aS/u7J06VLExMQY/BlJehymloiIiMiCLFiwAImJifqiwlhGjx6Nhx9+uFWhRWRsbCJFRERERERGwzsYRERERERkNLyDQURERERERsMCg4iIiIiIjIYFBhERERERGQ0LDCIiIiIiMhoWGEREREREZDQsMIiIiIiIyGhYYBARERERkdGwwCAiIiIiIqNhgUFEREREREbDAoOIiIiIiIyGBQYRERERERmNRRUYGzZsgEwmw9atWwEAhYWFmDRpEqKiohAbG4s9e/ZIHCEREUmNuYKIyLJZTIGhVCrxr3/9C8OGDdNPW7ZsGYYNG4asrCxs2LABs2fPhlqtljBKIiKSEnMFEZHls4gCQ6fT4b777sPrr78OhUKhn75582b86U9/AgCkpKSgd+/e+OGHH6QKk4iIJMRcQURkHeRSBwAAmZmZGDFiBJKTk/XTSkpKoFarERQUpJ8WHh6O7OzsdteRmZmpf11QUNBiWSIie1dUVIT6+nqpw+iy7uYK5gkios4ZI1dIXmCcPHkSn332WbfbzGZkZCAjI0P/Ojg4GLm5ud0Nj4jIZgQHB0sdQpcZI1cwTxARdc4YuULyJlJ79+6FUqlEVFQUwsPD8fPPP2Px4sXYvHkz5HI5CgoK9PMqlUqEhoZKGC0REUmBuYKIyHpIXmCkp6cjPz8fSqUSSqUSw4YNw7p165Ceno5Zs2Zh7dq1AIBDhw4hLy8PaWlpEkdMRNR9QgjklNbif6cKcOBSidThWDzmCiLTEEJAWVyD4mrrbT5JlkfyJlIdee655zB37lxERUXB2dkZGzduhJOTk9RhEZGdEUKgXqP77Z8WVSoNCipUGNrXD3IHGarqNSitbkBJTT2yS2vx6s4sKEtqb2gbOzPSEBnoAQBQa3WoqdegtkGLnl4uUGt1qKhTo6JODbmDDP0CPEzxMa0WcwVZoopaNXLKauHj5gRvVyd4KBr/5FJrBVQaLVQNWjRodfBzd4abs2F/jgkhoNEJODl27fpwYaUKd//rZ1wqqmnz/T+lReDRiQPg6CBrMV2nEziaW44ADwWc5Q5QyB3gLHeAs6MD5F2MhWybTAghpA7CFNi2lojaotUJVNSpUVrTgLLaBhRV1eOJz0+grLZxWNObo/xxrqAKhVWWezVv7ZxkTIq98c7JPC+2xP1BxiKEQHW9BpUqDSpq1SivbcDsdw4YvLynixw+bk5wcnCAo4MMWYXVBi3Xx8cVf58SDa1OQKMV0Oh0UGsFNFodNLrGYkSjbZy261whjudWGLTecQN7QvdbMVOtUuPX7HKDP8u6ucmYEMPBE6yZMc6NFn0Hg4jsQ2GVCt+evoYVX56CWtv5NQ9/DwUOPD4Wjg4y1Gu0OJpdjvf3X8G3p6+hQavrVix7s4q7tbyp9fJ2QVKYj9RhENkkIQRyy+pwqbgG/9pzCT9eaH0+8PdwRnF1Q4vXpTUN0BlwuTYq0AMDe3nB1ckRrs6OcHSQobSmAQUVKlSq1PoiwVB55XVY8tERg+c31M4z17q8rIdCri9wdEJApdbB00Xe6q6LEAJCAD9fLoFWJ1Cl0qDytzu1lSo1rpTU4r/H8/Xzuzs7IsTPDS5OjjiTX4lX7krE5LheXY6TTIsFBhF1ixBCf9VOKxqvlpXUNOBycQ0uFFbjXEFVm0m6O4qr6xHx+HajrtOYnBxlBhVK7Xl3/hCMHdjTiBERUROVWot9F4qRV16H4uoGVKnUUMgd4erkiJd3nu90+Z5eLi0KjPAe7kgO84W3qxO8XBqbQ3m5OqGnlwJF1Q1wkTsgqqcnahs0ePST4/jy2FX9st6uTvjukTT4eyja2hSultfhaE45HGTAU1+ewrVKy72z2uRG7tzciJoGLc4WVOlfr/r6DKJ7eelfuyscEejpYpJt041jEykiOyeEQG2DFpUqNSrrNLhWqcKVkhooS2pxpaQG569VI7v0xvoT2Cp/DwVeuycR5bVqFFSoUFJTjzd2XTR4+V7eLojq6QkfVyeo1FoM69cD/QLcEeLnhj4+rnBxcjRh9DwvXo/7w76U1TRg8P99a/D80wf3wXMz41tceS+racDCfx+Cu7Mcl4qqcbVCpZ/XWe4ArU5AKwR0OoGtR6+2t+pWDj4xFgEeCtRrdKht0KK2QYOiqnqkb/wVBZUqwz8kAQBuTegNTxc5AjwUCPBs/Bfi64Z+Ae4mP8/aAmOcG1lgENkYnU6gql6DvLI65JbV4kJRNU7mVeBEXgVySuukDs8uJAR7Y1hED9Q1aBHgoUBEoAcGh/qgl7erpHHxvNgS94dtO55bjm3H8xHi5waNVof391/BpeLfOzfLZMCOh0bB00WOBo0OdWotHB1k+uZLbd1VCF+2zZwfgUxk659HYFAvLzg5yiCTyTpfwM6wDwaRFdP81legaQSOwiqVvgCQyQAZAJ0QOJNfhSPZ5TiSU9buyB9kGo4OMvT0VKCntwuCvFzQ08sFvbxdEOTd+LO/hwIqtRZCAAN7eXI0FSILIYTAbWv2tflefLA3psX3wszkEPi5O5tk+/4ezhjYy8vi+3TZqzveaH1s9PRSwNXJEYN6e2FiTBDCerij6Rp805V4rU6gqKoeMgATY4IgkwEanYBWJ6CQO7BYaYYFBpEJaHUCuWW1OH21Eukf/ip1ONSOiAB3+Lo5w8fNGT5uTvB1c4KPmzOyrlVhclwvjBvYs9VwjURk+U7nV7Y5fURkD/TydsXVchXe/fES3BVyeCjkcHeW//6zwhFerk7wc3OGt6sTHH47Bwgh4OkiR5VK0+n2i6sb9MVFgKcCRRY8Kp01G9DTEzlltaht0HZ7XU39W5Qltdh+oqCTuVvzcpHj+IqJ3Y7DVrDAIDKASq1FaU1Di38lNQ0oramHsqQWp/Iqbvi5B2RcLk4OcHeWw03hCDenxv89XZzQx8cVwb5N/9wQ4uuKHh4KFg5ENszLpfVzUFycHLDvwo091FImA1ydHOHi5AgXuYO+uHBzdkRkoIdBw76yuDCdc9eqOp8JQExvL5y62nbRaSyVKg3qNVoo5OzjAbDAIDuj0wlUqtT6IqFKpUFVvQY19RqU16pRWlOPkpoGlDUrInLL2G/BHJzlDujbwx2BXgqE9XCDj6sz3H+7mujmLIebsyPcnB3hrmj6WQ53Z0e4KeRwdXJkwUBEeiF+blCungoAOJpTjroGLYZH9IBOJ1Cr1qKmXoPqeg1Kaxrwh7f3o73eqELgt07XLa+Q1zZokcPBL6yGqYqLm/r64VhuOVTqxibPsU99g/49PREf7I3oIC/4ujfeBevj44KwHu5dfkCiNWKBQVanXqPFidwKHFSWYt+F4javSI2NDkSQtwsKKlRQltQgu7S2W8OGkvH09FLgh0fHcCQPIuqWmnoNzuRXIr9CBSfHxqdLOzn+9oRpuQPq1Vo8/vkJXLyu71qgpwJ3pYSgpl6L9fsud3n7TQ/nJNsV28cLt0T3RKCnAv4ezvBybRyGuOmfh0IOjU7gkLIUhy6X4UReOY7lVmDTwZxW65I7yBDdyxMjIv0R38cH8cHeCPZ1RUGlCtPf/An5FS1HC3v6thjMTw030yc1PhYYZHYarQ61ai1UDVrUqRuvDNX99rrp59KaBlwqqsa2EwUorr7x28vfnS00QeTUHb5uTkgK9cXyaYNYXBBRt+zNKsLcdw92adnCqnq8/v0FI0dEtuhkXiVO5rV/98PRQaYvNpqKj2H9eqBerUXhb03jhBC4XFyDSpUGV0pqcTLvkkHb/s+hHPi5O8PFyfG3ZnoOjU31mv3c1HzPEu/gs8Ago9FodSioVCG7pBZXSmuRXVqLwsp6fbOjkuoG5JWzuZE9UMgdEB/sjadujUFsH2+pwyEiGyCEwO7zRXhr10X8kl0mdThkhSYM6ol5w8Nxsagax3LKcbagqt0BAdryxJSBqFNrUfHbE8fLa9XYk1WE0pqGzhcG8OjEAZDJZFi+9WSn857Jr8RfNhn2lHZHBxm0OoHMPyRgelKwQcuYGgsM6pAQAqU1DbharsLVijpcLa9DUVU9ymobC4ay2qbOzg0o5+1iu+DoIMN9I/tiSLgf+gW4m+UBcURk37KuVWH8y3ukDoOs3P9OX8P/Tl/r8vIyGTAi0h+HlKXYda7whoaO7+3tgtsT++BSUXWn86aPjkBKuC9Uah1U6t9aefz2s0qtRV2DFiqNFnUNOlwtr8P+S41NxTM2H8OBS6VICPHBPUNDJB02lwUGoUGjw/dnC3GxqBpFVfUorFKhoEKFa5X1KKqqR8Nvz2sg+zEpJghpAwIQ29sboX5u8HZrPSILEdGNKKtpgKeLvEvPi6m5rpP1f/8yEukf/oKc0jq4OzvisUnRGBHpj7ve3o8SA68mE92oldvOGDzvmAEB2HDvUKjUWkQv34GrFSokPP0/g5Y9k1+JxyYOgE40XujVicbnYgGN/ze9FjqgTq3Fo58e0w+L/PHhHHx8OAe/XCnDczPiJHs+E5/kTXhz9wU8v+Oc1GGQBN6ZN6TxLoSvK4fWswM8L7bE/WEezftLjBsYiDWzkzq961mv0UKnA+SOMjjIZHjpf+fw5u6L5giXyCYo5A44+Pi4Ll0gNOuTvNVqNbKzsxEREdGtDZLl0OkEiqvrUVvf/QfUkPX5cekYBPu6SR0G2RjmCmru1NUKLNhwSP9655lCRC/foR9CtolKrUVlnRpfHL2KZ7cbfpWYyNgCPRVwcnSATAY4yGT6/zU6Hcpr1Kiq7/xBi4bq7e2C6F5ekAGQyWRwkEG/PQeZDND/DMjQFE9jTI4yGRwcGpdz/G0eFydHeCjk8HSRY+zAnpK2PjCowNi9ezdmz54NuVyO7OxsHDp0CK+++io2btxo6vioC8pqGuCmcISzowMKq+rxY1YxHvnkmNRhkQSG9fPDqP4BuDkyAIN6e1nkSBNkO5grqLnHPj2GzYc7vwpar2lsQmKoPj6uHDCETKZp9Kd+/u5YPKofSmoacK1ShWuVKpTWNOifi1KlUqO4+sab440eEIDl0wahoEIFXzdnDOrtZeyPYBEMKjCWLVuGvXv3YubMmQCAlJQUHDliWM92Mo+CChX2nC/C+z8rOxxSjWxPdJAnHh4XheH9/NlXgiTFXEHNBXm5tDn94XFROH21EgIC569VIbvkxooFFhdkDpeKa7Bsywmjr3f3uSLsPveD/vWuv41GX393o29HagYVGFqtttXtbmdnZ5MERJ0rqa7H+n2X8cYutke1dVGBHrhSWotZycGYGt8LQ8L84Cy3nyeBknVhrqDmMiYMQEKIDxb9+3CL6a/szMIrO7MkiorIctyR2BvBvq5Sh2ESBhUYLi4uqK6u1g93deLECbi62uYOkZIQAtX1GlyrrEdhpQq/XCnDS9+elzosMhFXJ0ek9Q9ASl8/DA33Q/8gD3a0JqvGXEF7zhdh3Z5LuFpeh14+LkjrHyB1SESS6OvvjogAD0QEuCO6lyemxfeGk0QjOknBoAJj+fLlmDBhAvLy8jBnzhzs3LkTH330kaljs3lCCGw+nINXd2bh6nWPiCfbEeTlgiW3RCK2jzeigzz5zAiyWcwV9k1ZXIN5639/uval4hrsu1AiYURExuUgA3QGjr0qA3BLdCBm3xRq0pgsVacFhhACcXFx+PDDD7Fjxw4IIfD0009zhJAOCCFQVa9BUVU9iqvqUVzdgKIqFYqq67H7XBFOXWUfCVv3xZ9HICHER+owiMyGucK+1DVo8eGBKyivVWPNrgtSh0NkFM5yB3go5FBrdahStR4tytDiAmgssA15qJ6t6vQ5GE1J4+TJzh9rbklMPb55pUqNA5dKcTy3vLGQqG58KF1+hUo/AgHZrtfuGYx+/u4I8eVD6Mh6mPK8aI25gs/B6NjV8jo8/J+jOKgslToUIqsU4ucKNyc5rlWpcG9qXyy5JdIqRnM0y3MwZDIZgoODUVxcDH9//25tzFoJIfBrdjmyrlXhWG4FPvsll0+3tkMKuQPWzknGmOhAqUMhsjjMFbbjq2NX8ZdNHP2LqCN+7s64JToQtyb0RmKIDwoqVMgprUVOWS22/JqHE3kVyCn9fcSzl3eex5xhoejhoZAwavMxqA+Gh4cHEhMTMWXKFHh4eOinZ2ZmmiwwS3Ekuwx3vvmT1GGQROYMC0VZrRrP3hELHzeOhkPUEXvOFbbiZF4FiwuiNjwwqh8atDqcK6jCybwKlNY04NNfcvHpL79f6Y/p7YXsktoWD+O7c3AfRAZ6YOzAQLspLgADC4y4uDjExcWZLAiVSoW7774bp0+fhqurKwIDA/HWW28hMjIShYWFmDdvHi5evAiFQoE333wTo0aNMlkszW3YdxlPf3XaLNsiyzB6QAAWj+qHpFBfdsYmukGmzBWWmidsRV2DFh/8rMSq7Wf10/4xbRCe+S9zIBEAvL3nUqfzlNeqEdPHC3183HClpAbLpw2y2/6YnfbBMAeVSoXvv/8ekydPhkwmw5o1a/Dpp59i9+7dWLhwIUJDQ7FixQocOnQId955Jy5fvgwnp47bvXen/ZgQAnPePcDRL2zUpJggDO3rh7hgb0QGeMDXnXcmyD5Yc58DS8sT1kal1uKTX3JRWKlClUqDIeG+8HN3hkYrUFhVj799cqzF/D29FLhWyf6ERO0J9XPDkHBfDOvbA0lhPgjxc7OZoeaNcW40qMB45pln2pz+j3/8o1sbb8/hw4cxc+ZMKJVKeHh44MKFCwgKCgIADB06FKtWrcK4ceM6XEd3ds7TX53Chn3KLi1LlunNPyYhIcQHfXw4Jj/ZL1P/QW3OXCF1nrA2f99yApsOZksdBpHVuT2xN26JDsT4QT3h5mxQwx+rZ5ZO3gBQVVWl/1mlUmH79u0YPnx4tzbckVdffRW33347SkpKoFar9UkDAMLDw5Gd3fokmZmZ2aKdb3V114YGu1pex+LCRvi5O6O8tgE7Hh6F/j09pQ6HyOaZM1dImSesgVYnsPyLk/joAIsKovb4uTtjdP8AJIb6YGhfP0QEeNjVw/BMyaAC44UXXmjxesWKFViwYIEp4sGqVatw4cIFfPfdd6irq+t8gd9kZGQgIyND/zo4OLhL23dX2Ed1aou+fuhm+Hso4O/hrH+SMBGZj7lyhdR5whq8/O15g4qLiAB3XCyqMUNERObVy9sFdw7ug4QQH/T2dkWInyu8XZ3494GZdOmv6R49euDSpc47u9yoF198EVu2bMHOnTvh5uYGNzc3yOVyFBQU6K9OKZVKhIaa7qmIVjA8MTUzd1gY/jgsFNFBXlKHQkTXMUWusIQ8YcmO5ZRj/oaDKK9VGzQ/iwuyRgGeCiydFI2Y3l4I9FTA180ZDvwDzqIYVGC89tpr+p+1Wi0OHjzY4na0MWRmZmLTpk3YuXMnfHx+73E/a9YsrF27Vt95Ly8vD2lpaUbddnNfHcs32bqpe5ZOisYfhgTDz513KIgskalzhaXkCUtVr9Hi9jf2SR0GkVF8dN9NSA73tZmO0/bGoE7e9957r/5nuVyOyMhILF68GL6+vkYJIjc3FyEhIejXrx88PRvbyisUChw4cADXrl3D3LlzcfnyZTg7O2PNmjUYM2ZMp+vsageV8GXbbngZMi5XJ0fUqbUAgOdnxmNmUjCvTBAZgak7NZsyV1hSnrBkXxzNw1u7L2LOsDAkhvhg2us/Sh0SUZtu6uuHxyZFIyHYG3L2e7AoZhtFqq0ns1r601pZYFiH9+5NQVKYL7xcOh5Okoi6z9R/UFtbrrDFAgMA1FodvjlVgBVfnkZxNYeaJWn08nbBw+OikBrhj94+rnDkhUKrYbZRpCZMmIBff/2102nWrq5BK3UIdmP0gACsnZPMh9kR2RB7yRWmplJrUVRVj2O55dhzvggn8ioxflBPhPdwQ15ZHfIrVVh8cz+E+7u3ufx/DmZj+Ren9K/De7hBWVJrrvDJzowf1BN/mzAA/Xt6sPky6XVYYDQ0NEClUkGr1aKqqgpNNzsqKipQU2N7HcP2XSiWOgS7sftcEdRaHQsMIhtgb7nClH7NLsP0N39qNf1MfmWL1x8dyMaikX3xp7QIBHgqoNHqsOXXPHx57CoOKUsBAI9OHIAXvjnH4oK6xNFBhv/9dRT6+buzcKAb1mGB8c9//hNPP/00ZDIZvL299dO9vLzwyCOPmDw4c4sL9u58JjKKn5bdAk82iyKyCfaWK0xJp+u01bLeuz9exrs/XkZymC9+uVLW6v0XvjlnzNDIRv15TAQyxg9gEyYyKoP6YKSnp+Ott94yRzxG05X2YzqdQL/Ht5soIvsytK8f1Fod0voHICHEByG+bujj4wpXZ96xIJKKqfscWFuusNQ+GGqtDucKqqAsqUFBhQrltWrUNmhRVtuA/Io6XCisRnF1g9RhkhXp6++ObQ+OtJsnUVP3mK0PhjUljO44llsudQhWZWSkP8YP6omkUF9E9/Lk0y+J7Jy95ApTc3J0QGwfb8T2aX1XXa3V4UJhNb48dhVv7b4IAPBsKHOoAAAgAElEQVT3ULAzNwEAvnskDREBHlKHQWRYgZGVlYUHH3wQx44dg0ql0k8vLS01WWBSOJrDAqMjc4aFYmSkP5JCfRHo5SJ1OERkYewlV5iaEALF1Q24Wl6HvPI6XC2vw6XiGpzKq8CZgio0aHQt5mdxYV+mJ/XB0knRCPRUsG8EWSyDCoz7778f6enpeOaZZ/Cf//wHr7/+OsLDw00cmvkdY4HRrrfnJmNijHEfrkhEtsVecoUpabQ6RD7xtdRhkAXgMO5kzQwqMCorK3HXXXdh5cqViIuLw9tvv42bbroJjz/+uKnjMysPF7ZNbM7N2RGODjKE+LohIqDt4RCJiJrYS64wFp1OoLS2AbX1WtQ0aFDboEGVSmPQsvHB3sgY3x955XV44vOTJo6UTGnT/cMwONSHoyqSTTHoL2onp8bq2dPTE0qlEkFBQSgutr0hXaODvKQOwaJ8eN9N2HWuCP383dmmk4g6ZS+5wlAXCquw+1wRxg/qCX8PBRwdZPo/Iue+ewB7s7q+b1beEYt1ey7hv8fzjRUumVAPd2esX5CCAUGeLCTILhhUYIwaNQolJSVYsmQJkpOT4ezsjLvvvtvUsZkdH7TX0p3NxmIfMyAQ3m68TUtE7bOXXNGZBo0Ob/9wES99ex4AsHLbGf17Xi5yPDg2qtvbuG3Nvm6vg4xvUkwQHh4fhb7+7lDIWUiQ/ep0mFohBAoKCtCrVy8AQE5ODioqKhAbG2uWALuqK0Ns/X3LCWw6mG2iiKyTu7MjXpiVgClxvaQOhYi6yZTDslpjrjDV/jikLMWstfvbfK+nlwLXKusR09sLk2ODoFLr4OPmhMhAD4T3cIeDTIb5Gw7icjEfUGjpPvnTcAwJ82VHa7I5Zhumdvz48Th5srGNZ0hICEJCQrq1UUultPMTeh8fV0T19EBvH1dEBXpgYkwQevu4Sh0WEVkJe8kVnUkM8cETUwbilytlUGm00OqEvjnUI+MH4FJxDd798RJOXa1stWxqRA8Eeir0BcaQMF8EeCowqJeX/o4ImdfdKSF46tYYPseJ6AZ0WmDIZDIEBwejuLgY/v7+5oiJTMBBBlz/gFh/D2csHNkXE2OC0M/fnVdhiKjLmCt+5+TogPtH9cP9zaaNeXE3iqrqMTE2CN6uTnhwbCTOFlTBUyFHUXU9Tl+txGFlGXacKmixruN5FXh4XBQW39wP94/qh+jlO8z7YezMg7dE4qFx/flUa6JuMugOhoeHBxITEzFlyhR4ePze2TczM9NkgUlh/6USqUMwqrg+3hjYyxPF1Q34MasYDVod3J0dMS2+N/6QEoykUN7aJSLjsZdccaPKahqQU1qLm6P84e3a2JfNzVmOpFBfAEBUT0+kRvjjvpuBCS//gPPXqnFzlD/2ZhWjQaPD8zvO4atj+TiT3/qOB3XPO/OGYGSUPzteExmZQQVGXFwc4uLiTB0LGcG4gYGICPBATYMG350pxObDFQCAm/r6YdaQEEyJC4KbM4fjJSLjY65o2/lrVdDoBFIjOr6z06DR4fy1agBoNcIUi4vue3tuMkZG+sNdwRxIZGoG/ZY99dRTpo6DumlGUjAmxwbh5Z3nsfNMIYDGPhV/uSUSM5ODEdaDz7EgItNirmjb2YIqAC2LBJ1OQKXRtrjgs/PMNbPHZssOPTEOAZ4KqcMgsksGFRg5OTlIT09Hbm4ujh49iqNHj2LXrl3461//aur4yABzh4WhUqXGfe8fhpOjDPOGh+H2xD4YHOIDB7YjJSIzYa5o7d8/KfHUl6cAAD9eKMYrO89jcKgv5q8/CKDxrnOApwKTY3vh/334q5ShWrUN96YgLSqAOY/IQnQ6TC0ATJkyBbNnz8YLL7yAY8eOQaPRYPDgwThx4oQ5YuySrgyxFb5sm4miMY8xAwLwxNSBiAz0lDoUIrJAphymFrC+XGHq/VGv0SLmH99AoxN4curAFs/DoK7zc3fG3sfGsKkTkYmYbZjawsJCzJkzBy+99FLjQnI55HL+YluKiTE9MT81vNP2vUREpsRc0ZJC7oi+/u7IKqw2uLg48PhY9PRyweL3D+N/p9lkCgAeGhuFv9wSCbmjg9ShEJGBDDrzy+VyNL/RUVZWBgNufJCJ/d/tMbgzKRgevIpDRBaAuaK1+anheHLrSYPnf2v3RWh1wu6LiwvPTmZBQWTFDPrLdNasWXjggQdQWVmJd955B2vXrsV9991n6tioDemjIzAzOZjPrSAii8Nc0dodg/vcUIHx3k9K0wVjoeYOC8OK22L47AkiG2JQgfHII49g06ZNqKiowP/+9z9kZGRg9uzZpo6Nmtn8wHCkhPO5FURkuZgrWvu3HRYMnXl04gAsHtUPTrxDQWSzDOrk3aRpVmv4I9dWOnkvSA3H/aP6oY+Pq9ShEJGVM3Wn5ibWkivMsT8Kq1QY+ux3Jt2GNTj2jwnwdnOSOgwiMoAxzo0GXT7Izs7GxIkT4erqCldXV0yePBnZ2dnd2jC1z99DgRdmxiPr2clYcVsMiwsisgrMFa0Ferog69nJePXuRKlDMavX7hmMy/+cAuXqqVCunsrigsjOGFRgzJs3D+PGjcO1a9dQUFCAsWPHYt68eaaOTS8rKwupqano378/UlJScOrUKbNt21xmJAXj2FMToFw9FYefHIdZQ0J4+5iIrIqUucKS84STowNuT+wD5eqpeGlWgn769MF9EB1kO8OK//LkOH1BcVtCb4u/g0VEpmPQX7BFRUV49NFH4e3tDR8fH/ztb39DcXGxqWPTe+CBB7B48WKcP38eS5cuxYIFC8y2bVNyc3bEkjGROPbUBLz0hwR4u/IKDxFZLylzhbXkibEDA9HD3Rl9fFyReVciJgzqKXVI3XJ+5WR9UdHDg0/NJqJGBhUYkZGROH/+vP71+fPnERUVZbKgmissLMThw4cxZ84cAMCMGTOQk5ODCxcumGX7puAsd8CikX2x57Ex+NvEASwsiMgmSJUrrClP+Lg5o6eXC4qq6/Hm7gt47XvLi7EzTXfblaunwlnOO+1E1JpBo0hVV1cjISEBqampAID9+/cjNTUV06dPBwBs2bLFZAHm5OSgV69e+oc1yWQyhIaGIjs7G5GRkfr5MjMzkZmZ2SJmS3TP0FA8ODYSvbzZr4KIbItUucLa8kRJTT0aNDo8v+OcJNvvivMrJ7OYICKDGVRgzJs3r0U7WnP2vzBURkYGMjIy9K+Dg4MljKZt3z2ShogAD6nDICIyCUvPFVLmiSqVGsn/txMNWp3Zttkd6+YmY0JMkNRhEJGVMqjAmD9/vqnjaFdISAjy8/Oh0Wj0T4nNzs5GaGioZDF1RYCngsUFEdk0qXKFpeeJl/53Dq9bQVOof06Pwz1DLWOfEZF1M6jAyM/PxyuvvIKsrCxoNBr99C+//NJkgTUJDAxEUlISNm7ciAULFuCzzz5DcHBwi9ve1uDf9w6VOgQiIpOSKldYcp54Z+8lfXHh76FAcXW9xBG1xuZPRGRsBhUY06dPR3JyMu644w44OjqaOqZW3n77bSxYsACrVq2Cl5cXNmzYYJLtPHtnLJ74/KRR1+noIMO/7x2KQb29jLpeIiJLI2WuMFeeuBHbjudj5bYz6OPjitfuGYwZb/0kdUh6Xy4ZgfhgH6nDICIbZVCBUVNTgzVr1pg6lnYNGDAA+/fvN/l2UiP8jbo+mQxYeUcsRkYZd71ERJZIylxhrjxxI9btuQgA2HT/MOSW10ocDXBx1RQ4OvDZFERkegYVGMnJycjKyjLb0LRSCfNzM9q6XJwc8OrdgzGRneSIyE7YS64wVKWqsZnYqBd2SRZD+ugILJ0ULdn2icg+GVRgZGRkIDU1FVFRUXBxcdFP//77700WmBQcjHhl59M/pSK2j7fR1kdEZOnsJVcY6nJxjWTb/u9fRjIHEZFkDCow5syZg/T0dAwZMkSSPhjWhh3miMgeMVdI79TTE+GuMCi1ExGZjMFnoWeeecaUcdgM5eqpUodARCQZ5grzW3VnHGbfxOFlichyGHSZfcSIETh69KipY7F6LC6IyJ4xV5jXk1MHQrl6KosLIrI4Bt3B2Lt3L9555x1ERka2aFf766+/miwwazI41Aef/78RUodBRCQp5grz2JmRhshAPriViCyXQQWGlEPUWrr7RvbFk9MGSR0GEZHkmCtMi4UFEVkLgwqMtLQ0qNVqZGdnIyIiwtQxWY2lk6KRPpr7g4gIYK4wlRMrJsDTxUnqMIiIDGZQH4zdu3cjLCwMY8aMAQAcOnQIc+bMMWlgli59dASLCyKiZpgrjOvVuxOhXD2VxQURWR2DCoxly5Zh79696NGjBwAgJSUFR44cMWlglmxoXz8+uIiI6DrMFcYx+6ZQKFdPxe2JfaQOhYioSwwqMLRabavb3c7OziYJSGqrp8d1Os/mB4abIRIiIutiT7miM1nXqrq03LGnJmDVnZ3nISIiS2ZQHwwXFxdUV1dDJmt80vWJEyfg6upq0sCksv9SSYfvX/7nFDNFQkRkXewpV3Qm89vzNzT/3sfGIMTPzUTREBGZV4d3MO655x4AwJNPPokJEyYgLy8Pc+bMwfjx47Fy5UqzBGhu9Wpdu++tm5usT5xERNTIHnNFZ74+WWDwvBsX3cTigohsSod3MM6ePQsAmDhxIvr3748dO3ZACIGnn37aZkcIKa6ub/e9CTFBZoyEiMg62GOuaIsQAm/vuYSeXgqDl/l1+Xj4udtnMzIisl0dFhjNr9b37dsX6enpJg9IasfzKtp9T6sTcHTgHQwioubsMVe05emvTuO9n5QGzdsvwB3fPzLapPEQEUmlwwLj+PHj8PPzazVdCAGZTIbS0lKTBSaVBk37TaRYXBARtWaPuaItRVXt3wFvbs3swZgW39vE0RARSafDAmPAgAHYvn27uWKxCE6OMqi1otX0k09PlCAaIiLLZ4+5oi19fDvv0H7sHxPg7cbnWhCRbeuwwFAoFAgLCzNXLBZhRlIw/nMop8W052bEwUNh0IBbRER2xx5zRVvW7bnU4fvK1VPNFAkRkbQ6HEVKiNZX8m3dvovFrab9YUiIBJEQEVkHe8wV1zueW97h+ywuiMiedFhg2OMTWB8ZP6DVNA5NS0TUPnvMFdd7+qvT7b7H4oKI7I1BT/K2J77XDRf4+j2DJYqEiIisgUqtxS9XylpNn5EUzOKCiOwSOxZcRyFvWXNN5LMviIioA9HLd7SaljG+Px4cGyVBNERE0uMdjOsMDW851KKznLuIiIja1t7Q5iwuiMie8a/n6+RXqqQOgYiIrMScdw+0msZmUURk7yQvMF577TXExsYiLi4O8fHx2LhxY4v3V65ciYiICEREROCJJ54weTyBngr9z3F9vE2+PSIi6pil5YnmDl5u+RBBFhdERBbQByMmJgb79u2Dt7c3cnJyMHjwYAwfPhwRERHYs2cPNm3ahOPHj0Mul2PEiBFITU3F1KmmO4FX1qn1P7+7YIjJtkNERIaxtDzRZOPPV1q8vrRqism3SURkDSS/gzF27Fh4ezfeKQgJCUFQUBBychofdPfxxx9j7ty5cHd3h0KhwMKFC7Fp0yaTxqPV/T6ee4CHooM5iYjIHCwtTwCNz/54cutJ/euDj4+FgwOHNCciAiygwGhu586dKCsrQ0pKCgAgOzu7xdNhw8PDkZ2d3eaymZmZCA4O1v+rrq7uUgzN29Py+RdERJbFEvIEACz/4mSL14FeLl1eFxGRrTF5gTF8+HD4+/u3+a/pChQAnDhxAvfeey8+/vhjuLu73/B2MjIykJubq//n4eHRpXjPX+t6wiEiohtnbXkCADb+/HsR8+WSEV1eDxGRLTJ5H4z9+/d3Os/p06cxbdo0rF+/HiNHjtRPDw0NxZUrv7dxVSqVCA0NNUmcREQkDWvLE0KIFq/jg31Muj0iImsjeROpM2fOYMqUKVi3bh3Gjx/f4r1Zs2bhgw8+QE1NDerr67F+/XrcfffdEkVKRERSsLQ88fjnvzePusiO3URErUheYDz44IOoqKjA0qVLkZiYiMTERHzzzTcAgNGjR+Ouu+5CXFwcBg4ciPHjx2PatGkmjeeR8f1Nun4iIroxlpYnCirqAABBXi5wZMduIqJWZOL6e702Ijg4GLm5uTe8nE4nsPPMNYzqHwAXJ0cTREZEJI2unhdtVVf3R0WtGqeuViA10t8EURERScsYuULy52BYGgcHGSbEBEkdBhERWShvNycWF0REHZC8iRQREREREdkOFhhERERERGQ0LDCIiIiIiMhobLaTt0KhQEBAQJeWra6u7tYDmKwVP7d94ee2L9XV1airq0N9fb3UoViMjvKEtRwnjNO4GKdxWUucgPXEao44i4qKup0rbLbA6A57HWmFn9u+8HPbF3v93F1lLfuLcRoX4zQua4kTsJ5YrSVONpEiIiIiIiKjYYFBRERERERG47hixYoVUgdhiYYPHy51CJLg57Yv/Nz2xV4/d1dZy/5inMbFOI3LWuIErCdWa4iTfTCIiIiIiMho2ESKiIiIiIiMhgUGEREREREZDQuMZrKyspCamor+/fsjJSUFp06dkjokk3jwwQcRHh4OmUyGo0eP6qfb+udXqVS444470L9/fyQkJGD8+PG4cOECAKCwsBCTJk1CVFQUYmNjsWfPHomjNa4JEyYgPj4eiYmJuPnmm3HkyBEAtv+dN9mwYQNkMhm2bt0KwPa/7/DwcAwYMACJiYlITEzExx9/DMB+vm9DGbo/3n33XURFRSEiIgL3338/1Gq12WLs6LzVnFKphKOjo/47T0xMxMWLF80WJ9D+cXc9KfdnSUlJi33Uv39/yOVylJaWtphPiv1pjNxsjn3bVpyGHqeA+fZte/vT0OMUMN+x2lashh6rgGX8/rciSG/MmDFiw4YNQgghPvnkEzFkyBBpAzKRH374QeTk5IiwsDBx5MgR/XRb//x1dXVi27ZtQqfTCSGEeP3110VaWpoQQoh7771XPPXUU0IIIQ4ePCj69OkjGhoaJIrU+MrKyvQ/b9myRcTHxwshbP87F0KIy5cvi+HDh4thw4aJzz//XAhh+9/39b/bTezh+74RhuyPS5cuiV69eon8/Hyh0+nErbfeKtasWWO2GDs6bzV3+fJl4e3tbba42tLecdec1Pvzei+88IKYNm1aq+lS7M/u5mZz7du24jT0OBXCfPu2vf1pyHEqhHmP1fZiba69Y1UIy/j9vx4LjN9cu3ZNeHp6CrVaLYQQQqfTiZ49e4qsrCyJIzOd5geyPX7+Q4cOibCwMCGEEO7u7iI/P1//XkpKivj2228lisy0NmzYIBISEuziO9dqtWLs2LHi8OHDIi0tTV9g2Pr33VaSsofv+0YYuj+ef/558cADD+hfb9u2TYwYMcKssTbX/LzVnCX8gWHIH26Wtj+jo6P154XmpNyfXc3N5t63HX3f7R2nQph/33a1wJDiWO0otvaOVSEs4/f/emwi9ZucnBz06tULcrkcACCTyRAaGors7GyJIzMPe/z8r776Km6//XaUlJRArVYjKChI/154eLjNffZ58+YhJCQEy5cvxwcffGAX33lmZiZGjBiB5ORk/TR7+r7j4uKwaNEiFBUV2cX3fSMM3R/Z2dkICwvTv5b6WGk6b7WlpqYGKSkpSEpKwjPPPAOtVmvm6Fofd9ezpP35008/oaysDNOmTWvzfUvYnzfye2tJ+7aj4xSQft92dpwClrU/OztWAen36fVYYJBdWrVqFS5cuIB//vOfUodiNu+//z5ycnKwcuVKLF26VOpwTO7kyZP47LPP8OSTT0oditnt2bMHx48fx6+//gp/f3/Mnz9f6pDICDo6b/Xq1Qt5eXk4dOgQdu7cib179+Kll14ya3zWdty9++67mDdvnv6P9+YsYX9aq87yq9T71tqOU6DjYxWQfp+2hQXGb0JCQpCfnw+NRgMAEEIgOzsboaGhEkdmHvb0+V988UVs2bIFX3/9Ndzc3NCjRw/I5XIUFBTo51EqlTb52QFg/vz52LVrF4KDg236O9+7dy+USiWioqIQHh6On3/+GYsXL8bmzZtt/vtu+ixOTk54+OGHsXfvXrv6HTeEofsjNDQUV65c0b+W6li5/rx1PYVCgcDAQACAn58fFi5ciL1795o1xraOu7bmsYT9WV1djc2bN2PhwoVtvm8J+xO4sdxsCfu2s+MUkH7fGnKcNs0n9f4EOj9WAen3aVtYYPwmMDAQSUlJ2LhxIwDgs88+Q3BwMCIjIyWOzDzs5fNnZmZi06ZN+Pbbb+Hj46OfPmvWLKxduxYAcOjQIeTl5SEtLU2qMI2qvLwcV69e1b/eunUrevToYfPfeXp6OvLz86FUKqFUKjFs2DCsW7cO6enpNv1919TUoLy8XP9606ZNGDx4sM1/3zfK0P0xY8YMfPnllygoKIAQAmvXrsXdd99t1ljbO281V1hYqB/hpr6+Hlu2bMHgwYPNFmN7x931LGF/AsDHH3+MhIQEREdHt/m+1PuzyY383kq9bw05TgFp962hxykg/f5s0tmxCljO8dqCVJ0/LNHZs2fFsGHDRFRUlEhOThbHjx+XOiSTWLx4sejTp49wdHQUgYGBIiIiQghh+58/JydHABD9+vUTCQkJIiEhQQwdOlQIIURBQYEYP368iIyMFIMGDRLff/+9xNEaj1KpFCkpKSI2NlbEx8eLsWPH6juR2fp33lzzTt62/H1fvHhRJCYmiri4OBEbGytuu+02cfnyZSGEfX3fhmhvfyxatEh88cUX+vnWrVsn+vXrJ/r16ycWLlxo1hHHOjpvLV++XLz11ltCCCE+++wzERMTI+Lj48WgQYPEkiVLhEqlMlucHR13lrQ/mwwfPlysX7++xTSp92dXcrMU+7atODs6ToWQZt+2FWdHx6kQ0h2r7X33QrR9rAoh/fHaGZkQQkhb4hARERERka1gEykiIiIiIjIaFhhERERERGQ0LDCIiIiIiMhoWGAQEREREZHRsMAgIiIiIiKjYYFBRERERERGwwKDzGrKlClYs2ZNq+kJCQnYsmWLBBHZtiFDhmD37t2dzvfzzz8jLi4OgwcPxjfffIMpU6bg3LlzHS7zj3/8Ax9++CEAYPfu3dixY4cxQiYi6rItW7YgOTkZiYmJiI6Oxi233AKdTmeSbTU/T168eBFJSUkYPHgwNmzYgPvuuw+7du26ofXJZLIWD4EzhY7O7TNnzsR7770HgOd36j651AGQfVm0aBFWrVqFJUuW6KcdPnwY+fn5uPXWW026bY1GA7m8a4d8U4JycDBvTd6dmG/Ev//9b8yePRt///vfAQATJ07sdJlnnnlG//Pu3btRXl6OSZMmmSxGIqKO5OfnY/Hixfjll18QFhYGAPj1118hk8lMsr3t27frf/7000+RkpKCt99+GwBw7733mmSb3dU85o7w/E7dxTsYZFa33XYbcnJycPz4cf209evXY968eSgpKcGYMWOQnJyMmJgYLFmyRP+H/XvvvYdbbrkFt912GwYNGoRRo0ZBqVQCALRaLR599FHExsYiNjYWf/nLX9DQ0AAAWLBgARYuXIhRo0YhNja2VTwrVqzAjBkzcMsttyA6Ohq33norSkpKWrw3ceJExMbGIj8/H9988w1GjhyJ5ORkDB06VH+FKisrCyNGjEBCQgLi4uLw5JNPAgC++uorxMfHIzExEbGxsfjiiy8AAKNHj8bWrVv1cTS/ctRWzB988AFuuukmJCUlYdSoUTh27Fib+/enn37Sb+vee++FRqPRv1dQUIA//OEPGDp0aIsYV69ejY8//hhr1qxBYmIiysvLER4ejqNHjwIA8vLyMHPmTMTFxSE+Ph7Lly/Xx/nKK6/g6NGjWLt2LT788EMkJia2SExEROZy7do1ODo6ws/PTz8tKSlJX2CEh4fj0UcfRXJyMiIjI/HCCy/o58vKysLUqVORkpKC+Pj4Fnfa9+/fj5EjRyIhIQHx8fH683jTefL999/Hyy+/jC1btiAxMRGnT59ucY6vqKjAfffdh9jYWCQkJGDhwoXtfoY333wTQ4cORd++fbFhwwb99ObnZKDl3enRo0fjkUcewahRoxAaGorly5dj+/btGDlyJMLDw5GZmdnmes6ePYvU1FTExMTgjjvuQGVlpX6+js7vS5YswapVq/Tznjt3DiEhIS3yDREkfY442aWMjAzx0EMPCSGEqKurEz4+PuL06dOirq5OVFVVCSGE0Gg0YurUqWLTpk1CCCE2bNggnJ2dxenTp4UQQjz33HNi/PjxQggh3nzzTZGWliZUKpVQq9Vi8uTJYvXq1UIIIebPny/i4+NFZWVlm7E89dRTIiAgQOTn5wshhEhPTxf333+//r1evXqJgoICIYQQFy9eFMOGDRMVFRVCCCGysrJEUFCQUKlU4sEHHxSrVq3Sr7ekpEQIIUR8fLz46aefhBBCaLVaUVZWJoQQIi0tTXz++ef6+WfMmCE2bNjQZsw//vijmDx5slCpVEIIIfbs2SMGDRrU6rPU19eL4OBg8e233wohhPjmm28EALFr1y4hhBATJkwQu3fvFkIIoVarxcSJE8XmzZv123z55Zf16woLCxNHjhwRQggxevToFp+tsLCw1TJPPfWU/jslIpKCVqsV06dPF76+vuKOO+4Qzz//vMjNzdW/HxYWJubOnSt0Op0oKioSISEhYt++fUKj0Yjk5GRx5swZIYQQNTU1Ii4uThw8eFCUlJSIwMBAsWfPHv02ms7vzc+T158Dm5/jFyxYINLT04VWqxVC/H4OvR4A8eKLLwohhDhz5ozw8PAQarW61baEECI5OVl/bk9LSxMzZswQGo1GlJaWCi8vL/HnP/9Z6HQ6kZubK9zd3fW5p/l6hgwZIt555x0hhBDHjx8Xzs7OLfJQe+f3s2fPirCwMKHRaIQQQjz44IPimWeeMeQrIjvCJlJkdosWLUJaWuBwSN4AACAASURBVBqef/55bNmyBQMHDsTAgQNRW1uLpUuX4scff4QQAoWFhYiNjcXdd98NAEhNTcXAgQMBAIsXL8aTTz4JrVaLnTt3YsGCBVAoFACA+++/H2+88QaWLl0KAJg1axY8PT3bjWfq1KkICgrSr3f69On696ZMmYKePXsCAHbs2IELFy5g1KhR+vcdHByQnZ2NUaNG4dFHH0V1dTXS0tIwbtw4AMDYsWPx0EMPYebMmZgwYQISExMN2kfNY/7iiy9w7Ngx3HTTTfr3S0tLUVdXB1dXV/20s2fPQi6X67c9YcIE9OvXDwBQU1OD7777DteuXdPPX11d3Wk/i+rqavz444/45ptv9NMCAgIM+gxERObk4OCAzz77DGfPnsUPP/yAr7/+Gs8++ywOHz6MyMhIAI35RyaTwd/fH9OnT8fOnTvh4+ODU6dO6XMNAFRVVeH06dMoLCzEgAEDcPPNN+u30fwOiSH++9//4sCBA/omth2dQ//4xz8CAKKjoyGXy1FQUIDg4OBOtzFz5kw4OjrC19cX/fr1w7Rp0yCTydCnTx8EBARAqVS2yD+VlZU4evQoFixYAACIi4vDyJEjDfo8AwYMwKBBg/DFF19g4sSJ2LRpE06cOGHQsmQ/WGCQ2Q0aNAiRkZH46quvsH79eixatAgAkJmZicLCQhw4cAAuLi7IyMiASqW64fVf397Ww8Ojy8s3X1YIgfHjx+Ojjz5qtUxUVBRSU1Px7bffYs2aNXjllVewfft2ZGZm4tSpU9i1axfmz5+PP/7xj3jssccgl8uh1Wr1y1//Oa/f7vz581vckr7RzyKEANDYmdvFxeWG10NEZC2io6MRHR2NBx54AJMmTcKXX36JjIyMNueVyWQQQsDPz69FE6Qm27ZtM3W4LTQ/Pzs6OuqbHXWWM65frr31dORG+qo89NBDeO6551BUVITx48frL8QRNWEfDJJEU2fvgwcP4q677gIAlJWVISgoCC4uLigoKMAnn3zSYpn9+/fj7NmzAIB33nkHY8aMgaOjI8aNG4f3338fDQ0N0Gg0eOeddzBhwgSDY9m+fbv+yv4777yjvwNwvYkTJ2Lnzp0t+o8cPHgQQGP73Z49e2LevHl4/vnn8fPPPwNovKvQ1J8kPT1dPz0yMhIHDhwAAFy+fBk//vhju/Hddttt2LhxI7KzswE0djg/fPhwq/mio6Oh0Wj0/UJ27tyJixcvAmgsWMaMGYPVq1fr57969Spyc3M73DceHh4YNWoUXnrpJf20oqKiVvN5eXmhoqKiw3UREZlSXl4e9u3bp39dVlaGy5cvIyIiQj+tqa9baWkpPv/8c4wdOxYDBgyAl5dXiz4PFy5cQGlpKVJTU5GVlYW9e/cCaDz/lpaW3lBct912G1588UV9n8K2zqGdaZ4zDh482Ond5854eXlh8ODBeP/99wEAp06dajcPtXV+nzBhAgoKCrBy5coWg7YQNWGBQZK46667cO7cOcyaNUt/tf6hhx7CgQMHEBMTg7lz57b6Qz81NRVLly5FTEwMvvzyS/1oHYsXL0ZSUhKSkpKQmJiI8PBwPPzwwwbHcvPNN2P27NmIjo7GlStX2r1TEBkZiY8++ggPPPAAEhISMHDgQLzyyisAGkcQaRrm9a677sLatWsBAI8//jhiYmIwePBgfPDBB1ixYgUA4LHHHsOuXbsQFxeHv//97y2aP7UV3/PPP48777wTCQkJiImJwX/+859W8zk7O+Pjjz/GX//6V8TFxeGjjz5CQkKC/v0PP/wQFy5cQGxsLOLi4jB9+nR9h/aOfPDBBzh8+DBiYmKQmJjY5jDDd955J44ePcpO3kQkGY1Gg2eeeQb9+/dHYmIibr75ZsyfPx+33367fp6AgAD9IB1LlixBamoq5HI5/vvf/2LLli2Ij49HTEwMFi1ahLq6Ovj6+uLzzz/HsmXLEB8fj6SkpBZFjCFefvll1NfXIy4uDomJiXj88cdv+LOtXLkSb7zxBhISErB+/XrExMTc8Dqu9/7772PdunWIjY3Fk08+2aL5b3Ntnd9lMhkWLVqEwMBADB8+vNuxkO2Riaa2E0QW7L333sPWrVtbjLxkDCtWrEB5ebm+UCAiItsUHh6OrVu3GtwXjjo2bdo03HXXXZg7d67UoZAF4h0MIiIiIjJIU6d5BwcHzJ49W+pwyELxDgYRERERERkN72AQEREREZHRsMAgIiIiIiKjYYFBRERERERGwwKDiIiIiIiMhgUGEREREREZDQsMIiIiIiIyGhYYRERERERkNCwwiIiIiIjIaFhgEBERERGR0bDAICIiIiIio2GBQURERERERmNRBcaGDRsgk8mwdetWAEBhYSEmTZqEqKgoxMbGYs+ePRJHSEREUmOuICKybBZTYCiVSvzrX//CsGHD9NOWLVuGYcOGISsrCxs2bMDs2bOhVqsljJKIiKTEXEFEZPksosDQ6XS477778Prrr0OhUOinb968GX/6058AACkpKejduzd++OEHqcIkIiIJMVcQEVkHudQBAEBmZiZGjBiB5ORk/bSSkhKo1WoEBQXpp4WHhyM7O7vddWRmZupfFxQUtFiWiMjeFRUVob6+Xuowuqy7uYJ5goioc8bIFZIXGCdPnsRnn33W7TazGRkZyMjI0L8ODg5Gbm5ud8MjIrIZwcHBUofQZcbIFcwTRESdM0aukLyJ1N69e6FUKhEVFYXw8HD8/PPPWLx4MTZv3gy5XI6CggL9vEqlEqGhoRJGS0REUmCuICKyHpIXGOnp6cjPz4dSqYRSqcSwYcOwbt06pKenY9asWVi7di0A4NChQ8jLy0NaWprEEUtLCAG1Vid1GEREZsVcQURkGJVaK3UI0jeR6shzzz2HuXPnIioqCs7Ozti4cSOcnJykDsus6jVaLP30OOSODvjn9Dg88fkJbD6ciyAvF/x74VAMCPKUOkQiIkkxVxARNfrq2FX8ZdMRPDFlIO4f1U+yOGRCCCHZ1k3IVtrWFlapMPTZ79p9P8TPFQq5I/69cCj6+LiaMTIisja2cl40Fu4PIrI14cu26X9Wrp7apXUY49woeRMp6piXixNev2dwu+/nlNbhQmE1Rqz+HuHLtmH/xRLYaM1IRERERFbAoptI2TutTiB6+Y4bWuaef/2s//nSqilwcJAZOywiIiIionaxwLBgjt0sDvo9vh0AkBzmiw/vuwkuTo7GCIuIiIiIqF1sImXhPksf3u11/HKlDNHLdyB82TZcLKo2QlRERERERG1jgWHhksP8oFw9FesXDDHK+sa+9APCl23DF0fzONwtERERERkdCwwrcUt0Tzw/I95o63voP0cR9cTXeOGbs7haXme09RIRERGRfWOBYUX+kBKCnRnGfXjUG7suInX195i//iCO5ZQbdd1EREREZH9YYFiZyEAPKFdPxS3RgUZd7w/ni3D7G/sQvmwbdp0r5FC3RERERNQlHEXKSq1fkIK6Bi3ufHMfzhZUGXXd9244BADwdJFj/YIUpIT7GXX9RERERGS7WGBYMVdnR+x4eBRKaxqQ9H/fGn39VSoNZq3dr3/9zcOjMCDI0+jbISIiIiLbwSZSNsDP3RnK1VOxb9ktJt3OxFf2IHzZNpTWNJh0O0RERERkvVhg2JA+Pq5Qrp6KPwwJNul2kv7vW4xY/T3O5FeadDtEREREZH1YYNig3DLTDzubV16Hya/uRfiybfjq2FU+U4OIiIiIALAPhk366P5heO27LGR+e94s2/vLpiMtXq+eHoe7UkIgk8nMsn0iIiIishy8g2GjHhwbhcv/nIK7U0LMvu1lW06g79+349FPjkHDOxtEREREdsXgAkOtVuPixYumjIWMTCaTYfWMeJxbOQnLpw0y+/Y/+SUXkU98jfBl2/Dlsatm3z4RmR9zBRERGVRg7N69G2FhYRgzZgwA4NChQ5gzZ45JAyPjUcgdsWhkX1x4djIeHhclSQwPbjqC8GXbEL5sG0Y+9z3qGrSSxEFEpsNcQUREgIF9MJYtW4a9e/di5syZAICUlBQcOXKkk6Wsj04n8NYPF3GuoApaIaDTCXx9ssCgZa/vbiDTT5dd97r5PG0vdP28TfPJrnu/5fZbbqf1un5fh6dCjv/P3p2HRVW2fwD/zsKmuOSKCoiCOwiKmLupuaSmZotWLrhrv95K22gxl0xto81KK6U3NbPUtyxNi8Rcck1x3zBGQEEUTAEFZnl+fwDHGRhggJk5M8P3c11ezDnnOefcZ2acZ+45z5KVpyvjamwr5cYdtHtjGwAgZlIE+rWx7qzkRCSP6lJXEBFR2SxKMPR6PQIDA03Wubu72yQgOZ1OvYV3t5+r1L5CFFsubYP5UtVW0azhRR4N98WS0SHQCwEPtUqmqIioMqpLXUFERGWzKMHw9PREdna29Cv4iRMn4OXlZdPA5NC6cS08O6AVTqfeglqpgFKpwKWMHJy8XDDfw9gIP9xTs+zKsrLjJlVlwKUSd0Is5KZSwsNNCa3OgC93/4NbufLd1Sjyw98p+OHvFGl5+bjOGNCuMdxUHI+AyNFVl7qCiIjKZlGCMXfuXAwaNAiXL1/GuHHjEBsbi2+//dbWsdmdu1qJ2QNbyx2GLP4zoBX0BoHAV7fKHYqJmWuOSI+7tayHLyd0QS1PNxkjIqLSVJe6goiIyqYQosw2PBBCIC0tDbm5udi2bRuEEBg8eHCJ2+COxtfXFykpKeUXJBNavQHTvjmMneeuyR1KmTY91QOd/e+ROwwip2LLz0VnrCtYTxCRqwmI2iI91iwdVqljWOOz0aIEIyQkBCdPnqzSieyNFUfVvL3tLD7f6fhDTXZpfg8+eaITmtRhMwyi8tg6wXC2uoL1BBG5GkdJMMpt2K5QKODr64vr169X6UTkXJ53kqZihy/dQPclOzDkw134+1ImysmXichGWFcQEVERi/pgeHt7IywsDEOHDoW3t7e0Pjo62ipB5ObmYuzYsTh9+jS8vLzQqFEjfP755wgKCkJ6ejomTJiAixcvwsPDA5999hn69OljlfNS6dQqJTRLhyFXq8eU/x7C3oQMuUMq09m0LDz8+T4AwMtD2uLxrn6oW4Oj1xDZky3rCtYTRETOw6IEIyQkBCEhITYNZPr06XjggQegUCiwbNkyTJ06FTt37kRUVBS6deuGbdu24dChQ3jooYeQmJgINzd29LUHTzcV1k7thqxcLeZtPoVNRy7LHVK53t52Fm9vO4uHO/vinUc6QqWswhBdRGQxW9cVrCeIiJxDuX0w5HD48GE88sgj0Gg08Pb2RkJCAnx8fAAAXbt2xeLFi3H//feXeQy2rbWNzJx8/HA4GUt+PSt3KBYb3bkZ3n0klIkGVXuu9LnIeoKIqCRH6YNh0R2MhQsXml3/xhtvVOnkpfnoo48wcuRIZGRkQKvVSpUGAAQEBCApKanEPtHR0Sa34bOzs20SW3VXr6Y7ZvQNxPQ+LXEgMRNjv9gvd0jl2nTksnTnZd8r/dkhnMhG7FlXsJ4gInJcFiUYWVlZ0uPc3Fxs3boV3bt3t0lAixcvRkJCAv744w/cuXPH4v3mzJmDOXPmSMu+vr62CI8KKRQKdGtZH5qlw5CceRuDPtiFO1q93GGVq/uSHQCAD8aE4qFOfI8QWZO96gq564mTl29i2Y4E6AyGCu9b2TYDlW1qUNlGClVp2uAs11hZlb++Sr4WVbg8Z4m10pfoJNdXcM5K7leFk+Zq9fB0U1V6/6qwKMF49913TZbnz5+PyMhIqwfz3nvvYdOmTYiNjUWNGjVQo0YNqNVqpKWlSb9OaTQa+Pv7W/3cVHl+9WrgzJtDkJOnQ4d52+UOxyKz1x/D7PXHAACxc/oiqJF3OXsQUXnsUVc4Qj3x87Er2HYqDWqlAkpFJZte2nc3VD7Myu1o56dFmj3eXuerys52v0ZnibPS56vkjlV49e3/nFZyRxlZlGAUV79+ffzzzz9WDSQ6Ohrr1q1DbGws6tatK61/9NFHsXz5csyfPx+HDh3C5cuX0bdvX6uem6yjpocamqXDcDtfh/ZvOEeiAQD3R/8JAHi4sy8WjuyAmh6V+m9BRMVYu65wtHoi7oX74Fevhs3PQ0RkKeM+GHLdvQAsTDA+/vhj6bFer8fBgwdN2rtWVUpKCp5//nm0bNkS/fr1AwB4eHjgwIEDePvttzF+/Hi0atUK7u7uWLNmDUcGcXA13AsSDeM3uTPYeCQFG48UdGryqe2JnS/eJ/3n1BsEsvN0qOPF9x5RaWxZVzhiPfFB7HnUKvaDhLnGDKW1cDDXTMNc2dIaSJg/rvnSZo9r9lwV2N/imEo5bqllzR3XzHNlfvcKxFrKtVq8sgKvoaO+B8yWs/x5qVisljf1sfS9WerzWtX9LV5pjdfQsvd2hWJ1ABYlGEePHr27g1qNsLAwTJ8+3WpB+Pr6lvrGa9y4MX777TernYvsR7N0GFJv3sHA6F3IztPJHU6FpN3KRdu52wAUzBbet3VDvP/7eQBAvzYNsSoyotK3gYlclS3rCkeqJ4ruWjjDsN0kL3PVRGk1h7k6pfSy5o5bSukKxWD5cW11bRWL18xxLX8aSq3HzZctJQgzpSsWQ2llq3ZcuVk0TO3169fRoEGDctc5Eg4/6FhytXq8+ctprD1QcmQXZ/bT//VER986TDbIKdj6c9HZ6oqqPB+ZOfnQG0pWn/b+cmP2y19pZR3gy25FPiod4cuu2ePy854cmFMNUzto0CAcOXKk3HVEpfF0U+Gth0LwxL3+GPbxHrnDsZqRn+4FUHBXY1rvlri3ZX3Ot0HVVnWqK+rVdJc7BCIih1VmgpGfn4/c3Fzo9XpkZWVJt6dv3ryJnJwcuwRIrmXBz6flDsEm4s5dQ9y5awCAZ/oH4bEIP/jew86fVD2wriAiImPKsjYuWbIEdevWxcmTJ1GnTh3UrVsXdevWRUhICMaNG2evGMmFRD8WKncINvfxjgT0ejsOLV/ZgiEf7kKuE8wPQlQVrCuIiMiYRX0wZs2ahc8//9we8VgN+2A4LiEEnvzqAP66mCF3KHb1UKdmeP/RUCjZhIpkYuvPRWerK1hPEJGrcZQ+GBYlGM6IFYfju56dh1V7EvHZzotyh2J3v/ynF4Kb1ZE7DKpm+Llois8HEbkaR0kwymwiVeTChQt44IEH0LRpU9SrV0/6R1QVDbw98NKQtri4eCg+GOP6TaeMDf9kDwKituDhz/8yOxINkTNiXUFERICFCca0adMQGRmJe+65B3/++SceeeQRvPDCC7aOjaoJlVKBhzr5QrN0GI7MHSh3OHb196UbCHx1KwKituBo0g25wyGqEtYVREQEWJhg3Lp1C2PGjIFSqURISAhWrFiBH3/80daxUTVUr6Y7NEuH4eybQ9CvTUO5w7Grhz77CwFRWzD3x5Nyh0JUKawriIgIsHAeDDc3NwBArVq1oNFo4OPjg+vXr9s0MKrePN1UiJnUFQBw844WoQuqz2zuq/dfwur9lwAAB18bgEa1PGWOiMgyrCuIiAiw8A5Gnz59kJGRgaeffhrh4eFo0aIFRo4caevYiAAAdbzcoFk6DF9O6CJ3KHbX9a0/EBC1Bd/s08BFx2MgF8K6goiIAAtGkRJCIC0tDU2aNAEAJCcn4+bNmwgODrZLgJXF0UEc118J1zFv8ym89VAIuraoWAfQg4mZeGzFPhtF5hwOv34/Gnh7yB0GOSFbfi46Y13BeoKIXI2jjCJlUROpgQMH4uTJgnbhfn5+8PPzq9JJqXpbuScRF9KzpUShSR1PvPVQMPq0agi1quybak3qsLlQl0WxAIDXhrbD1N4toFBwXg1yDKwriIgIsKCJlEKhgK+vL9vRktUse6KzyXLqzVxM/vowgl77FU9/ewQJ6dml7utXrwa2PdcbdbzcbB2mw3tr6xm0eKVgBKqAqC14ZdMJ5OsMcodF1RTrCiIiKmLRHQxvb2+EhYVh6NCh8Pb2ltZHR0fbLDByXV7uKum23ZGkG3j717M4kJgJAPjleCp+OZ4KAHhzZAeM6tQMtTxNk4m2PrVxbN4gAMDJyzcx/JM9dozeca07mIR1B5OgVACbnuqJML+6codE1QzrCiIiAixMMEJCQhASEmLrWKga6ux/D9bP6A69QeDP8+mYvf4Ybt7RAgDm/nQKc386hTC/uhjXrTm6BtSDf/0aJvsHN6uDX/7Ti0mGEYMARn26FwAQ1Mgbvz7bG27lND0jsgbWFUREBFjQydtZsfOe80q/lYtVezVY/ufFEtvq1nDDhG7N0bZJbbRvUhs1PdTYcfYquraojyZ1PLFm/yUs2nJGhqgd38ZZPdDZvy77bFRj/Fw0xeeDiFyNo3TytijBSE5OxqxZs5CSkoL4+HjEx8cjLi4Os2fPrtLJbYkVh/MzGAT+upiBD2PP4/Cl8me59qvnhYHtfNChaW14uavw1NojdojS+Tze1Q8z+waief2acodCdmbrz0VnqytYTxCRq3GUBMOidhMzZszA2LFjpXH4g4ODsWrVqiqdmKg8SqUCvVo1wIZZPXD2zSFYPi4cQzr4lFo+OfMOVu1NxPM/HMNTa4+gdWNvuLNpUAnrDiaj77s7ERC1BSv3JOLf2/lyh0QugnUFEREBFiYY6enpGDduHJTKguJqtRpqtUXdN4iswtNNhSHBPlg+PhynFgzGR2PDyt3n/NVs5Os5qlJZ3vzlNMIW/o42r/+KbSfTkKfTyx0SOTHWFUREBFjYyVutVpvMInzjxg3OKkyyqemhxsiwZhgZ1gyXMnLw3Pp4HE36V+6wnFqezoCZa/4GUNAx/O2HQ9DZ/x7216AKYV1BRESAhQnGo48+ihkzZuDWrVv46quvsHz5ckydOtXWsRGVq3n9mvjfUz0hhMDhSzewbEcC/jx/Te6wnFpCejYe/rxgEsT/6xeIR8P9ENCA/TWofKwriIgIqMAoUuvWrcOPP/4IIQRGjRqFJ554wtaxVQk771Vft3K1+OVYKtYfTsaxZN7ZsIZQv7p4pHMzDO/YFPfUdJc7HKoke3wuOlNdwXqCiFyNo3TyrtAwtUVFnaHZBCsOAoBzaVn4/nAy/nf0MjJz2JnZGga2b4zRnZqhf7tG8FCr5A6HKsBen4vOUlewniAiV+MoCYZFnbyTkpIwePBgeHl5wcvLCw888ACSkpKqdOKKuHDhAnr06IHWrVsjIiICp06dstu5ybm18amFucPbY/8rA/Dx453kDscl/H76KmatPYI2r29D2MLf8MvxKzAY2M6e5K0rWE8QETkOixKMCRMm4P7778fVq1eRlpaGAQMGYMKECbaOTTJjxgxMnz4d58+fx8svv4zIyEi7nZtcg7taiRGhTaFZOgwJbz2ApaM527A1/Htbi6e/PYqWr25F9G/ncPVWrtwhkYzkrCtYTxAROQ6Lmkh16NChxK9BwcHBOHnypM0CK5Keno6goCBkZmZKI5Q0adIEe/bsQVBQUKn78dY3WUqnN+BI0r/47lASNh25LHc4LuG9R0MxKqwp1JyHxKHY+nNRrrqC9QQRUQGnaiIVFBSE8+fPS8vnz59Hq1atqnRiSyUnJ6NJkybSWOoKhQL+/v4lbrtHR0fD19dX+pednW2X+Mj5qVVKdG1RD9GPhUGzdBiOzh2IhLcewHfTu+HTJzrDy439DCrqhR+OIei1XxEQtQUJ6Vlyh0N2IlddwXqCiMixWDRMbXZ2NkJDQ9GjRw8AwL59+9CjRw+MHj0aALBp0ybbRWihOXPmYM6cOdKyr6+vjNGQMysaJalby/oAgGEdm0CnN2Dkp3tx6sottGxQE/9cz5EzRKdyf/QuAIB/vRrY9lxv1HDnxGuuytHrCtYTRET2YVFNP2HCBJN2tPbsf+Hn54fU1FTodDrp1ndSUhL8/f3tFgORWqXElmd6m6y7/O8d9Fy6Q6aInE9S5m20f2M7AGDz0z3R0beuzBGRtclVV7CeICJyLBYlGBMnTrR1HKVq1KgROnfujDVr1iAyMhIbN26Er69vme1qieyhWV0vqX3j35du4OHP/wIA+N7jhdg5ffHn+Wv4z7qjyNcZ5AzTIY1YthcA8N/JXdErqAFUSscezpQsI1ddwXqCiMixWNTJOzU1FR9++CEuXLgAnU4nrd+8ebNNgyty7tw5REZGIiMjA7Vr10ZMTAxCQsoeBYid98iRXPn3DrafSsPaA0lISC+93ff4bs2xev8lO0bmGL6Z3BV9WjeUOwyXZ+vPRTnrCtYTRESO08nbogSje/fuCA8PR9euXaFS3e3w+uSTT1bp5LbEioMcVWZOPr47lIQ/z13DgHaNcFhzA33bNERgQ2/c26KeNDnZ+7+dwyc7EmSO1r5m9G2JVx5oJ3cYLsvWn4vOVlewniAiV+NUCUbHjh1x/PjxKp3I3lhxkCt5ZdNxrDuYLHcYdvPF+HAM6uAjdxgux9afi85WV7CeICJX4ygJhkXD1IaHh+PChQtVOhERVd6S0R0R98J9codhN9NX/42AqC0IiNqCHw5Xn8TK2bGuICIiwMI7GCdOnED//v3RqlUreHp6Sut37HDcEXT4yxS5qvSsXHR96w9puY6XG27e0coYkX3Ur+mOn57uCd97asgditOy9eeis9UVrCeIyNU4yh0Mi0aRGjduHGbNmoUuXbqYtKslIvtrVMsTmqXDoNMboFIqpD4buVo9Dmky8d3BZGw5kSpzlNaXkZOPXm/HAQAm9QzAC4PaoKYH59RwJKwriIgIsDDBAICFCxfaMg4iqiC1yrSFo6ebCr1bNUTvVg3xKYCim5NrDiRh7o8nZYjQdmL2ahCzVwMAeP/RUIwMa1ri+SB5sK4gIiKLauSePXsiPj7e1rEQkRUpFAV3N8Z3aw7N0mE4MX+Q3CHZxPM/HEPQa7+i77tx+PtSJixo9Uk2wrqCiIgAC+9g7N69G1999RWCgoJM2tUeOXLEZoERkXXV8nST2mMeSbqB0Z/9ONcV+AAAIABJREFUJXNE1nUp4zYe/nwfAGBkWFM8d39rtGhQU+aoqhfWFUREBFiYYCxbtszWcRCRHXX2vweapcOQp9Njy/FUzPn+mNwhWdVP8VfwU/wVAMCLg9vgsS5+aFjLQ+aoXB/rCiIiAiwcRQoAtFotkpKSEBgYaOuYrIKjgxBVTHLmbaw5cAkr/vxH7lBsxstNhb+i+uOemu5yhyILe3wuOlNdwXqCiFyNo4wiZVEfjJ07d6J58+bo168fAODQoUMYN25clU5MRI7Fr14NvPJAO2iWDoNm6TCcXDAYo8Kayh2WVd3R6tHpzd/xyqYTcofiklhXEBERYGGCERUVhd27d6N+/foAgIiICBw9etSmgRGRvLw91PhwbCdolg7D36/f71LJxrqDSQiI2oJZa/7GzduuP4eIvbCuICIiwMI+GHq9vsTtbnf36tnEgKg6qu/tgQ/HdsKHYzshOfM2Xv/xJP48f03usKrs15Np+PVkGgBgQvfmeH5gG9Sp4SZzVM6LdQUREQEWJhienp7Izs6WJvQ6ceIEvLy8bBoYETkmv3o18N/JXQEAJy/fRGTMIVzPzpM5qqr7Zt8lfLPvEgBgWu8WmNk3EPW92TG8IlhXEBERUE4TqccffxwA8Prrr2PQoEG4fPkyxo0bh4EDB2LRokV2CZCIHFdwszo4/Pr9SFwyFDGTIuQOx2q+3J2I8EWxCIjagj0XrssdjsNjXUFERMbKvINx9uxZAMDgwYPRunVrbNu2DUIILFiwwClGCCEi+1AoFOjXphE0S4dBqzfgh8MpePV/rtGRetzKA9LjPS/3g+89NWSMxjGxriAiImNlJhhFt7kBoEWLFpg1a5bNAyIi5+amUuKJe/3xxL3+uJOvxzvbzyJmr0busKyi19txAIDl4zrjvjaN4Ommkjkix8C6goiIjJWZYBw/fhz16tUrsV4IAYVCgczMTJsFRkTOz8tdhXkPdsC8BzsgMycfPZfuwB2t3mxZv3peSM68Y+cIK2fmmoKZqXsG1cfkni3Qu1VDuKstGpTPJbGuICIiY2UmGG3atMHWrVvtFQsRubB6Nd1x5s0hAIBcrR7PfRePbafSpO3JmXfw/Yzu0OkN2PB3CjYdvSxXqBbbm5CBvQkZAIDhHZvgsS5+6BFYH2pV9Uo2WFcQEZGxMhMMDw8PNG/e3F6xEFE14emmwvLx4QCA9KxcvLzhODQZt9Gkjif86tVAj6AGWPpwR+xNuI4Nf6dgy4lUi489uENjbD91tcwyLRvUxD/Xc6p0DcX9cjwVvxy/G+cj4b5495GOJs2HXBXrCiIiMlZmgiGEsFccRFRNNarliZhJXUusd1cr0a9tI/Rr2wjva/XYeS4dMXs1OJBYdnOb7aeuYnSnZpg9sDVe+OGY2fL/XM+Bu0qJfL3BatdR3Ia/U7Dh7xR89mRnDGrf2KXvarCuICIiY2UmGJyBlYgcgaebCkOCm2BIcBNk5+nwx5mrePa7+FLLbzp6GYM6+GD9jO4AgKSM2/ByV6GGuwo3bucj7tw1bD+Zhn3/ZEBvsO2X46fWFvTXWDiyAyZ0D7DpueTCuoKIiIwphIv+9OTr64uUlBS5wyAiG7p5W4vtp9Pw0objpZY5+NoANKrlaXbbv7fz8ceZdPx6Mg27zl+z+I5Gm8a1cO5qVqVibtekNtZM6SrLJH78XDTF54OIXE1A1BbpsWbpsEodwxqfjUwwiMglXM/Owy/HrmD+z6dLbIt74T60aFCzzP1v3tFi+6k0bI6/Uu6djcUPheDB0Cao6a7G6v2XMG/zqUrFfGL+INTydKvUvpXBz0VTfD6IyNUwwbAxVhxE1VfazVzM/ekkfj9d0Nl7/ysD4FPH/F0MczJz8hF75iq2nUzDngvXzd7Z8FArMbB9Ywzv2BR9WzeEl3vBnBhCCGw7mYZZhU2jLKFQAEdeH4h7arpbvE9l8HPRFJ8PInI1TDAKffzxx/jiiy+gUCigUCjw0ksvYdy4cdL2RYsWISYmBgAwduxYvPXWWxYdlxUHEVlDVq4WO86mY+uJVMSdu4Z8Xclkw9NNiYHtffBYF1/0DGwApfLuyFEXr2Vj2n8PWzxq1WtD22FyrxZQKa0/+pSzfi6yniAisoyjJBhldvK2hw4dOmDv3r2oU6cOkpOT0alTJ3Tv3h2BgYHYtWsX1q1bh+PHj0OtVqNnz57o0aMHhg2r3BNGRFRRtTzdMDKsGUaGNUNWrhZ/nEnHL8dTTfps5GoN+PnYFfx87Aqa1fXCw52b4ZFwP/jXr4HAht7Y8cJ9AIDsPB06v/m72SSlyFtbz+CtrWcAAG+O7IAn721ukrBUR6wniIici+wJxoABA6THfn5+8PHxQXJyMgIDA7F+/XqMHz8eNWsWtJ2ePHky1q1bx4qDiGRRy9MNozo1w6hOzXDzjhaxp6/il+NXsPvCdegK+2xc/vcOPt6RgI93JAAAJvdsgRcHt4GXuwreHmqcX/SAdLxcrR6LtpzGmv1JZs8396dTmPtTQf+OYR2bYNnjnarFvBrFsZ4gInIuDjUwe2xsLG7cuIGIiAgAQFJSksnkTQEBAUhKMl8RR0dHw9fXV/qXnZ1tl5iJqHqq4+WGh8N9ETOpKw6/fj/eeaQj+rRuiOLf/1ftTUS7N7YhIGoL3t1+1mTOCE83FRaNCoFm6TCcfXMI/q9fYKnn23I8FS1e2YpOC3+D1obzdzg61hNERI7P5glG9+7d0aBBA7P/kpOTpXInTpzApEmTsH79eumXqIqYM2cOUlJSpH/e3t7WvAwiolLVreGOx7r44ZvJXfH36wOxZHSI2XKfxl1Ei1e2ovObv0NXLEnwdFPhxcFtoVk6DCcXDMbjXf0BAMVbR924XdAnxJWwniAici02byK1b9++csucPn0aw4cPx6pVq9CrVy9pvb+/Py5duiQtazQa+Pv72yROIiJrqFfTHY939cfYCD98/ZcGC8wMm5uZk4+g134FAHwwJhQjQ5uZ9LPw9lBjyeiQEolKTp4OR5P+RdcW9Wx7EXbGeoKIyDpGhjXFT/FX0L9tI1njkL2J1JkzZzB06FB88cUXGDhwoMm2Rx99FKtXr0ZOTg7y8vKwatUqjB07VqZIiYgsp1AoMKlnC2iWDkPikqHoGVTfbLnZ64+h5atbERC1BT8cTjZbpkhNDzV6tWoAd7XsH912xXqCiMgyysJ2ukqZ++vJXks988wzuHnzJl5++WWEhYUhLCwM27dvBwDcd999GDNmDEJCQtCuXTsMHDgQw4cPlzliIqKKUSgUWDu1m5RsdGha22y5Fzccx85zrtX8yRpYTxARWaZozqemdS2f+8kWZJ8Hw1Y4vjkROQut3oAvd/+Ds6lZWDCig80m3OPnoik+H0Tkam7n6/DtgSSMifBDLU+3Sh3DJebBICKq7txUSjx1X5DcYRARkZOr4a7G1N4t5Q5D/iZSRERERETkOphgEBERERGR1TDBICIiIiIiq3HZTt4eHh5o2LBhifXZ2dlON7mSs8XsbPECzhcz47U9Z4vZknivXbuGvLw8O0Xk+EqrJyzhbO+PqqpO18trdU28VstZo65w2QSjNM44aoizxexs8QLOFzPjtT1ni9nZ4nV21e35rk7Xy2t1TbxW+2ITKSIiIiIishomGEREREREZDWq+fPnz5c7CHvr3r273CFUmLPF7GzxAs4XM+O1PWeL2dnidXbV7fmuTtfLa3VNvFb7qXZ9MIiIiIiIyHbYRIqIiIiIiKyGCQYREREREVmNUycYzzzzDAICAqBQKBAfHy+tv3DhAnr06IHWrVsjIiICp06dkrYFBASgTZs2CAsLQ1hYGNavX2/RfraOubT15cVl65grE68jPse5ubkYNWoUWrdujdDQUAwcOBAJCQnSPunp6RgyZAhatWqF4OBg7Nq1y6JtcsV73333oUWLFtJz/MEHH9gt3tJiBoBBgwahY8eOCAsLQ+/evXH06FFpmyO+j8uKV873cVn/vwAgJiYGCoUCP/74o7ROzvews9uyZQvCw8Ph4eGB5557zmSbwWDAf/7zHwQGBiIoKAjLli2z6TZ7u3btGh588EF07NgR7dq1w8SJE3Hnzh1p+6JFixAYGIjAwEC89tprJvtWdpucNm7ciJCQEAQHByM4OBgajQaAa7yW5qSnp6Nx48YYNWqUyXpXe10//vhjBAcHIyQkBB07dsSaNWtMtrva9ZbFHt+zLCKc2J9//imSk5NF8+bNxdGjR6X1/fr1EzExMUIIIX744QfRpUsXaVvxssbK2s/WMZe2vry4bB1zZeJ1xOf4zp07YsuWLcJgMAghhPjkk09E3759pX0mTZok5s2bJ4QQ4uDBg6JZs2YiPz+/3G1yxdu3b1/xv//9z+zxbB1vaTELIcSNGzekx5s2bRIdO3aUlh3xfVxWvHK+j8v6/5WYmCi6d+8uunXrZvIekPM97OzOnTsn4uPjxWuvvSaeffZZk23//e9/Rf/+/YVOpxMZGRnC399fnDx50mbb7O3ZZ58Vs2fPFkIIodPpxODBg8Wnn34qhCh4H7Zv315kZ2eL3NxcER4eLn755ZcqbZPTkSNHRJs2bcTly5eFEELcunVL5OTkCCFc47U0Z9SoUWLy5Mli5MiR0jpXe12FECI2Nlb8+++/QgghkpKSRP369UVCQoIQwjWvtyz2+J5lCadOMIoYV8JXr14VtWrVElqtVgghhMFgEI0bNxYXLlwoUdZYefvZMuay1pcVlz1jtjTesso6ynMshBCHDh0SzZs3l5Zr1qwpUlNTpeWIiAjx+++/l7tNrnjLSjDsFa8QZcccExMjQkNDhRCO/z4uHm9ZZeWMV6/XiwEDBojDhw+XeA84wnvY2c2bN69EgjF06FCxbt06afnFF18Ur732ms222dtzzz0npk6dKvR6vbh9+7bo3bu3+Omnn4QQQjz11FNiyZIlUtlPP/1UPPnkk1XaJqcnn3xSrFixwuw2V3gti/vqq6/E7NmzRUxMjEmC4WqvqzkdOnQQcXFxQojqcb1F7P09qyxO3UTKnOTkZDRp0gRqtRoAoFAo4O/vj6SkJKnMhAkTEBISgilTpuDatWsW7yeHsuJy1JgBx3+OP/roI4wcORIAkJGRAa1WCx8fH2l7QEAAkpKSytwmV7xFoqKiEBISgjFjxuCff/4BUPa12MuECRPg5+eHuXPnYvXq1QAc+31sLl7jbY70Po6OjkbPnj0RHh5ust4Z3sPOKikpCc2bN5eWjZ87W2yzt7lz5yIhIQE+Pj5o1KgR2rVrhxEjRpQbp7Ncn7HTp08jKSkJffv2RadOnTB37lzo9XoArnetiYmJWL58Od56660S21ztWouLjY3FjRs3EBERAcD1r9eY3PWpMZdLMMqza9cuHD9+HEeOHEGDBg0wceJEuUNyOY7+HC9evBgJCQlYsmSJ3KFYxFy8q1evxtmzZ3H8+HH07t0bw4cPlzFCU9988w2Sk5OxaNEivPzyy3KHU67S4nW09/HJkyexceNGvP7667LG4Yy6d++OBg0amP2XnJwsd3g2Vd61f/fdd2jfvj1SU1Nx5coVnD9/Hl999ZXcYVdKedeq0+lw9OhRbNu2DXv27MFff/2Fzz//XO6wK6W8a508eTKWLVsGLy8vuUO1Ckv/D584cQKTJk3C+vXrUbNmTRkjJrXcAVibn58fUlNTodPpoFarIYRAUlIS/P39AUD66+bmhueeew6tW7e2aD+5lBVX7dq1HTJmR36O33vvPWzatAmxsbGoUaMGAKB+/fpQq9VIS0uTfuXVaDTw9/cvc5tc8QIFzyVQ8OvE008/jRdeeAEZGRmyx2ts4sSJmDlzJjIyMpzifWwcb/369R3ufbx7925oNBq0atUKAJCWlobp06cjNTUVs2bNctj3sCPYt29fpff19/fHpUuXpEmrjJ87W2yztvKu/bPPPsMXX3wBlUqFWrVq4ZFHHkFcXBymTp0qxVnE3DVUdJstlXet/v7+GD16tPSle/To0di3bx+efvppp3gtjZV1rTdv3sTx48cxZswYAEB2djZu376NAQMG4I8//nC61xWw7P/w6dOnMXz4cKxatQq9evWS1jvj9VaWI3zPkti9UZYNFG+n3LdvX5MOLuHh4UIIIbKzs006db7//vuid+/e5e5nj5jLWl9WXPaK2dJ4Hfk5fv/990Xnzp1FZmZmibITJ0406QTbtGlTqRNsWdvkiFer1Yq0tDRpecOGDcLf39+ia7FlzDdu3JA6TwohxP/+9z/RrFkzqaO6o72Py4rXUd7HZfUZKd4HwxHew87OXB+MmJiYEp14jx8/brNt9vbggw+KuXPnCiGEyM/PFw8++KBYvHixEEKIuLi4Ep1cf/755yptk9PatWvFmDFjhF6vF1qtVowYMUK88847QgjXeC1LU7wPhqu9rkIIcfr0adG8eXOxbdu2Ettc8XrLYs/vWWVx6gRj+vTpolmzZkKlUolGjRqJwMBAIYQQZ8+eFd26dROtWrUS4eHh0n/2ixcvirCwMBESEiKCg4PFiBEjRGJionS80vazR8ylrS8vLlvHXNF4HfU5Tk5OFgBEy5YtRWhoqAgNDRVdu3aV9klLSxMDBw4UQUFBon379mLHjh0WbZMj3uzsbBEeHi6Cg4NFx44dRf/+/UV8fLzd4i0tZo1GIyIiIqS4BgwYYPLl2NHex2XFK/f7uKzPgyLFEww538POLjY2VjRr1kzUqlVLeHt7i2bNmkkdnXU6nXjqqadEixYtRMuWLcWHH34o7WeLbfb2zz//iEGDBong4GDRrl07MXnyZHHnzh1p+4IFC0SLFi1EixYtRFRUlMm+ld0mF71eL55//nnRtm1b0aFDBzFz5kyRl5cnhHCN17I0xRMMIVzrdRVCiPvvv1/UrVtXqi9DQ0NNkg1Xu96y2ON7liUUQghh//smRERERETkiqpdJ28iIiIiIrIdJhhERERERGQ1TDCIiIiIiMhqmGAQEREREZHVMMEgIiIiIiKrYYJBRERERERWwwSDyIysrCx4e3tjypQp0rrNmzdj9uzZpe7z448/Yv/+/dLy4cOHpZlUy6NQKPDvv/+a3fbhhx8iLS3NwsiJiKiiFixYgKlTp0rLe/bsgUKhwM6dO6V1M2fOxNy5cyv02V5cgwYNoNFoqhitdXXp0sXkOomsgQkGkRnr169HeHg4Nm3ahOzsbADAiBEj8MEHH5gtr9PpSiQYXbp0wfr166scCxMMIiLb6tevn8mX7Li4ONx7770l1vXv399qn+1ErowJBpEZK1euxMsvv4w+ffpIFcnXX3+NUaNGAQB27tyJDh06YMqUKQgLC8PatWuxefNmvPvuuwgLC8NXX32FnTt3IiwsTDrmihUr0Lp1a3Tu3BlvvvkmFAqFyTk/++wzdO3aFS1atEBMTAwAYOHChbhy5QrGjBmDsLAwxMfH2+kZICKqPrp164YrV64gJSUFQMFn/BtvvCElGKmpqUhKSkL37t1NPts1Gg3q1q2LefPmITw8HEFBQdi6dat03M2bN6Ndu3bo2LEjXnrppVLPv3//foSHhyMsLAzBwcH4/PPPAQCRkZGYPHkyevTogdatW2PixIm4c+cOgII77dOmTUPXrl3RsWNHTJ8+Hfn5+QCAtLQ0PPbYY+jatStCQkLw+uuvS+f666+/pPNMmjQJOp3Oek8kURFZ5g8ncmCnTp0SzZo1EzqdTvz000+ie/fuQgghYmJixMiRI4UQQsTFxQmFQiF27twp7Tdx4kTxwQcfSMtxcXEiNDRUCCHEiRMnhI+Pj0hNTRVCCPHGG28I4/9+AMR7770nhBDizJkzwtvbW2i1WiGEEM2bNxdHjx614RUTEdGAAQPEN998I3Jzc0WLFi2EEEIEBgaKO3fuiLVr14p+/foJIUw/2xMTEwUAsWHDBiGEEL/++qto3bq1EEKIq1evinr16olTp04JIYRYsWKFACASExNLnHvEiBHi22+/lZYzMzOFEAX1Svv27cWtW7eETqcTw4cPF2+99ZYQQohp06aJ//73v0IIIQwGg5gyZYp45513hBBCDBo0SKqftFqtGDx4sPj+++9FXl6e8PX1Fb///rsQQojt27cLACIuLs46TyJRId7BICpm5cqVmDBhAlQqFYYOHYrExEScOXOmRLmWLVuib9++Fh1zx44dGDJkCHx8fAAA06ZNK1HmySefBAC0bdsWarWazaKIiOyoqJnUgQMH0LVrVwAFdzb27duHnTt3ol+/fmb38/T0xOjRowEA3bt3x8WLFwEU3JXo2LEj2rdvDwCYMmUK3N3dSz33m2++iYULF2LPnj245557pG2PPfYYatWqBZVKhSlTpiA2NhZAQb+/orvmnTp1wu7du5GQkICcnBz88ccfePbZZxEWFoYuXbogISEB586dw9mzZ6FWq3H//fcDAAYNGoSWLVta4dkjMqWWOwAiR6LVarF69Wq4ubnh22+/BQDcvn0bK1euRHBwsElZb2/vSp+nePMooKCSKqJSqXjbmojIjvr164eVK1fC398f9913HwCgb9++iIuLQ1xcHL7++muz+3l4eEif6SqVCnq93mw5c5/7RZ577jmMHDkSsbGxePXVVxEcHIzPPvuszOMIIbBx40a0bt3aZHtRv8H9+/eb1CsAcPz48QrFRVRZvINBZGTz5s1o2bIlLl++DI1GA41Gg/3792P16tXQarVl7lu7dm3cvHnT7LZ+/fph+/btSE9PB1Bwl8RSZR2XiIisIyIiAunp6Vi7dq1JgvHdd98hNTVVuqthqe7du+P48eM4e/YsAGDVqlVSH4nizp07hxYtWmDatGl49dVXTQYM2bBhA7Kzs6HX6xETEyPdfRg1ahTefvtt6ceoGzduICEhAd7e3ujXrx+WLl0qHaOof0nbtm2h0+kQFxcHAIiNjZXuuBBZExMMIiMrV66UmioVadeuHZo1a4asrKwy9x0/fjy+//57dOrUCV999ZXJtqJOdj179kTnzp2Rm5uLOnXqWBTTM888g2nTprGTNxGRDbm5uaFXr17IyspC27ZtAQCtW7dGVlYWevXqBTc3twodr2HDhli1ahUeeughhIaG4sKFC6hfv77ZssuWLUOHDh3QqVMnvP7663j//felbRERERg8eDDatWuHunXr4rnnngMAfPDBB/Dy8kJYWBg6duyIAQMGSEPgrl27FgkJCQgODkZISAhGjx6NjIwMuLu7Y/369Zg9ezZCQkLw7bffIjQ0tBLPFlHZFEIIIXcQRNVBVlYWatWqBQD46KOPsG3bNvz6668yR0VERI4qMjISYWFhUlJB5CzYB4PITqKiorB3715otVo0bdoUK1askDskIiIiIqvjHQwiIiIiIrIa9sEgIiIiIiKrYYJBRERERERWwwSDiIiIiIishgkGERERERFZDRMMIiIiIiKyGiYYRERERERkNUwwiIiIiIjIaphgEBERERGR1TDBICIiIiIiq2GCQUREREREVsMEg4iIiIiIrMahEoyYmBgoFAr8+OOPAID09HQMGTIErVq1QnBwMHbt2iVzhEREZE3PPPMMAgICoFAoEB8fX2q5lStXolWrVggMDMS0adOg1Wot2kZERPbnMAmGRqPBl19+iW7duknroqKi0K1bN1y4cAExMTF44oknWHEQEbmQRx55BHv27EHz5s1LLZOYmIi5c+di9+7dSEhIwNWrV/HFF1+Uu42IiOThEAmGwWDA1KlT8cknn8DDw0Na//3332PmzJkAgIiICDRt2hR//vmnXGESEZGV9enTB76+vmWW2bBhA0aMGAEfHx8oFArMnDkT69atK3cbERHJQy13AAAQHR2Nnj17Ijw8XFqXkZEBrVYLHx8faV1AQACSkpJKPUZ0dLS0nJaWZrIvEVF1d+3aNeTl5ckdRoUlJSWZ3OEwrgvK2lYc6wkiovJZo66QPcE4efIkNm7cWOX+FXPmzMGcOXOkZV9fX6SkpFQ1PCIil1HenQJXx3qCiKh81qgrZG8itXv3bmg0GrRq1QoBAQHYv38/pk+fju+//x5qtRppaWlSWY1GA39/fxmjJSIie/P398elS5ekZeO6oKxtREQkD9kTjFmzZiE1NRUajQYajQbdunXDF198gVmzZuHRRx/F8uXLAQCHDh3C5cuX0bdvX5kjJiIie3r44YexefNmpKWlQQiB5cuXY+zYseVuIyIiecieYJTl7bffxl9//YVWrVohMjISa9asgZubm9xhEdnU0aQbCIjagne2nZU7FCKbmzFjhtRUafDgwQgKCgIATJ06FZs3bwYAtGzZEgsWLEDPnj0RFBSEhg0bYsaMGeVuIyIieSiEEELuIGyBbWvJms6lZWHwh7vwxvD2mNyrhU3P9dnOBLyz7Rya16+BP1/sZ9Nz5Wr16PNOHL6Z0hVtfWrb9FwkP34umuLzQURUkjU+Gx36DgaRo4g9cxUAsHr/pXJKVp1WV5DzJ2Xetvm5lv95EelZeRjy4W6bn4uIiIiqByYYRA7mg9jzAAB73Fv8Zp/tEyYiIiKqXphgEFWAwcVaFGbm5MsdAhEREbkYJhhEFnh3+zkAwKUM2zdbIiIiInJmTDCIiIiIiMhqmGAQEREREZHVMMEgIiIiIiKrYYJBRHaz72IG9AbX6ihPREREpphgkNXdvKO1y3kysvMQELUFf128bpfzUdX8dioNj3+5H5ExB+UOhYiIiGyICQZZVdzZdIQu+A2vbDpu83PNWP03AOCJLw/Y/FxUdZqMHABAQnq2zJEQERGRLTHBIKs6nnITAHBIc8Nu5yLn8MWuRABA6s1cmSMhIiIiW2KCQU4rX2+QOwSqgOvZeXKHQERERHbABIOsqmim65w8ncyREBEREZEcmGCQVX1/OBkAm8EQERERVVdMMMiq0rPYDIaIiIioOlNbWlCr1SIpKQmBgYG2jIeILKTTG5CTp0d2vg45eTpk5xWAkCSWAAAgAElEQVT8LfinR06+8Tq99Nh4nSuLO5eOXkEN4Kbi7yhERET2ZFGCsXPnTjzxxBNQq9VISkrCoUOH8NFHH2HNmjW2jo/I4bR4ZQvUSgVUSgXclEqoVAppWa1UQq0qeqyASqmEWqmAulgZlVIBN5XpclE5Y/+39sjdhCBfLyUQ2Xk65Okq18m9prsKNTzU8Paw+PcFp7P9VBpmrP4bHZrWxpZnessdDhERUbVi0TeMqKgo7N69G4888ggAICIiAkePHrVpYOScqsMszYPb+0BnENAbDIV/BXR6AZ3BUPC4cJ1Wb0C+Tne3jEFApzeYLOsL/5Vmy4lUeLop4e2hRs3CpMCntidqeqhQ00ONmu5F6wuXC8sUPFaZ7FfTQ40abioolXeTmICoLfZ4yuyuaI6UU1duyRwJERFR9WNRgqHX60s0jXJ3d7dJQESObvn4cKsez2AQ0Iu7SUfwvO3StoS3HoCaTXzIxV24cAETJ07E9evXUadOHXz99dfo0KGDSZmYmBh89NFH0nJKSgr69OmDTZs2QaPRIDAwECEhIdL2jRs3skkvEZFMLEowPD09kZ2dDYWi4JfPEydOwMvLy6aBEVUXSqUCSijgpiq5jckFVQczZszA9OnTERkZiQ0bNiAyMhKHDh0yKTNp0iRMmjRJWg4ODsaTTz4pLdeqVQvx8fF2i5mIiEpn0beXuXPnYtCgQbh8+TLGjRuHgQMHYtGiRbaOjazo6i0OG0tEjic9PR2HDx/GuHHjAAAPP/wwkpOTkZCQUOo+Bw4cQHp6OkaMGGGvMImIqALKTTCEEAgJCcHatWsxf/589OjRA3v37kX//v3tER9ZwW+n0nDv4j/w4g/H5A6FiMhEcnIymjRpArW64Ia6QqGAv78/kpKSSt1n5cqVGD9+PNzc3KR1OTk5iIiIQOfOnbFw4ULo9SVHSYuOjoavr6/0Lzs72/oXREREljWRGjhwIE6ePIlZs2bZOh6ygaKOrsdS/pU5EnJkGdl5cFcr4aZSwl2lNOkMTuQocnJy8N1332H//v3SuiZNmuDy5cto1KgRMjMzMWbMGLz//vt46aWXTPadM2cO5syZIy37+vraLW4iouqk3ARDoVDA19cX169fR4MGDewRE1nZf/dpAADnr/LXOipd+KJYqx5PoQDcVUq4q5XwULMvCZnn5+eH1NRU6HQ6qNVqCCGQlJQEf39/s+V/+OEHdOjQAe3bt5fWeXh4oFGjRgCAevXqYfLkyfj2229LJBhERGQfFtX63t7eCAsLw/Tp06VfgIx/Baqq3NxcjBo1Cq1bt0ZoaCgGDhwotb9NT0/HkCFD0KpVKwQHB2PXrl1WO2918e9trdwhUDUkBJCnMyArV4fr2flyh2MzOXk6BERtwcHETLlDcUqNGjVC586dpXmVNm7cCF9fXwQFBZktv3LlSkyZMsVkXXp6OrTags+5vLw8bNq0CZ06dbJt4EREVCqLmkiFhISYDP9nC9OnT8cDDzwAhUKBZcuWYerUqdi5cyeioqLQrVs3bNu2DYcOHcJDDz2ExMREk7a3RPb0zLqjJnNPeBvNM+HtoYa3Z8H8FN6Fc1HU9FDDQ62URmFzVBtmdofxjBxCGD++u1BqGQgYDEC+Xo98nQH5elHwV2dAvk6P+T+ftlnsctrwdwoAYPb6eOyNYt+0ylixYgUiIyOxePFi1K5dGzExMQCAqVOnYsSIEVJn7nPnziE+Ph5bt2412X/Pnj144403oFKpoNPp0L9/f7z22mt2vw4iIipgUYIxb948mwbh6emJoUOHSsvdunXDe++9BwD4/vvvpbsZERERaNq0Kf7880/cf//9No2JqDSbj12p8D5uKoU0MV5R4uHt6VaQpBROllfLs+CvsU/jEqBQAAoooFAASqPHAKBUKAq3FzRnVBY8KFwu3F74uGg/hdE6Y10C6lXi2bCcqyYYusKJEu9oS3YqJsu0adMG+/btK7H+q6++KlEuKyurRLnRo0dj9OjRNouPiIgqxqIEY+HChWbXv/HGG1YNpshHH32EkSNHIiMjA1qtFj4+PtK2gIAAs6OLREdHIzo6Wlp29NFB0m/louviP/Dd9G7o1rK+3OFQBZxf9ABy8nTILvyXI/3VIztPi+w8PXIK12cV/jUto0PmjXzk5GXhdn7ZX0rf3X7OTldFlfX2r2cBAJk5rtsMjIiIqCIsSjCMfzHKzc3F1q1b0b17d5sEtHjxYiQkJOCPP/7AnTt3LN7P2UYHWXOgIEla8PNp/Ppsb5mjcX437djPxF2thLvaHffUrPps9nqDQE6+cRKix6hP90rbf/lPLxiEgBAFTZOEECj4wfzuOoNBFG4raKYkhOljgyjYDnG3GVPRsaav/rvK11Dd5esNcodARETkUCxKMN59912T5fnz5yMyMtLqwbz33nvYtGkTYmNjUaNGDdSoUQNqtRppaWnSXQyNRlPq6CJOpbDxunHbdqq80IW/2e1cAVFbrHIchQJwUynhUTjSUtE/Y8HN6ljlXERERET2YlGCUVz9+vXxzz//WDWQ6OhorFu3DrGxsahbt660/tFHH8Xy5csxf/58HDp0CJcvX0bfvn2tem5yfo939bPp8dcdTLb6MYWA1AkaeVY/PBEREZEsLEowPv74Y+mxXq/HwYMHTfpFVFVKSgqef/55tGzZEv369QNQMK75gQMH8Pbbb2P8+PFo1aoV3N3dsWbNGo4gRSUsGd3Rpse3RYJhiaJO3kWdtaVO3YUduosvK3B3PQr/KhVGHb6NjsN59IiIiMgWLEowjh49encHtVqaE8NafH19S20q1LhxY/z2m/2av5D1GH85VpqMhFT0pffu31LLKU2XjcsZ6//+TmkkpeKjJgElR04yGVWpcMXd/Uoex9g9NdygN4iCf6KgT4NeFCxbGzt5U3FbT6Sif9tG8HRTyR0KERGRWRb3wSg+i/f169dtEpCcDiZm4rEV+/DSkDZ46j7zkzyR5ez55fifazl2O9fKyAgIUbKTdVFnbK3eAL1BQKsX0BnuPtYbDNAZBHR6UfjXeLngsd4g8MUu6zY/tFTPpTugVimgVirgplIWPlbCrfCvWlW43tx2k8dKuCkL/qpVCrgplVAVywjnrI+HUlmQKKqUisKkUQGVsiDpU0mPFVApC5YVheuMyxQkoQqoFCg8nqLEuVzJb6fS8NTaIwhvfg82zuohdzhERERmWZRgDBo0CEeOHCl3nbPbdf4aAODnY6lMMKzgl//0kkYxKhrJqGgUJOP1EIDBXLnC0Y4KvrgXjYh0t9yz38XLcl2jP/tLlvPaWn1v94KkSG9Ans6A7DyDlPwUrdcWJkZVvVmz6ehl6wRdzSTfKBhZLznztsyREBERla7MBCM/Px+5ubnQ6/XIysqSmjHdvHkTOTn2+8XYFeUUzn9wNq3kpFGuwtYjIMmVYFSG8S/uCpNf7e/+8l60nJ4lT4/v4yk37XauMwuHwCCKmpgVJJ16g5CSR72hIMEsKiOEgN5gpkxhwimtL9xnwqqDdrsWe4o7mw5AvvcIERGRJcpMMJYsWYIFCxZAoVCgTp27XxZr166N559/3ubBubKVexLlDsGlvPdoqJQAm/y4Xjj3g7QopNXS44JlIW033b9gae5Pp6oUnxCAToji0ZVrdKdmUv+Ooi/SeoPpl2rjL9cly5p+GS++Tq4vql7u7D9QGXsSXK9pKhERuZ4yE4x58+Zh3rx5mDVrFj7//HN7xSSbZXEJAIAzqbdkjoQq6oUfjtntXC0b1ESuVo9cnaHgr1Zf5SZDpalIUyJ3lRIeaiU83FTwdFPC000FD7USXmoV1Mq7/ReUirt9FORKMFJu3IYwuWtxt+lb0V2IovV6g5Ca1pk8LmpSJ+4mXUWPiYiISD4W9cGoDskFWd/gD3aVO4rT3ccoMZKT8X4m+xhtl8OOF+4rsU6Igo7c+XoD8rR65OkMhf/0yNOW8lhXsmy+zoAvd1fu7la+3oB8vQFZeboqXqHt9Xo7Tu4QiIiIyEYsSjAuXLiAZ555BseOHUNubq60PjMz02aBkfxEsV+W73bMLvy1uNgvzcWHGj531XX7lxSnUCjgrlbAXa2Et0el5q+UGCcY/ds2ku6S5GoNyC1MUnILE5NcrR46W90+ISIiIqoEi74JTZs2DbNmzcLChQvx3Xff4ZNPPkFAQICNQyNbCIjaIncINvH5k50Lm8ygsNOwMGl+c7efgoDeuExh4lTUN0EUrtMbUNjcpmD/VXvvfun/6+J1qJXKguFTlQXDtioVBUO1KhUKk+ZIamXBMKrGf1VKhckwrGVZFRlh0+fN+P2gWTqszLKi2HNZWt8P407aReuFEBj4wS6bXgsRERE5BosSjFu3bmHMmDFYtGgRQkJCsGLFCtx777149dVXbR0fObFmdb3uDkuLu8PTCmE6b8TdoWpNyxatMy5bmgdCmtj0WowTjCe+PGC14yoVKExW7iYljkqhKJhvwpFjJCIiIvlZlGC4ubkBAGrVqgWNRgMfHx+XnGivOkhcMrTcX82rwvgX8b1R/a1+fGGUlAS99qu0fvHWM2ZGTSo56lLRsKgm2wvvaJhsN/pbvNPwwpEd7s7kbRDSBHnGs3ubbjcUDrF696+u8Pw6feFfo/J/Xcyw+vPmaJ7uV3KeGYUCZSaRFVE0YAMALPn1jHUOaoFcrb7gjlhRs0LD3cclOrCbNDE0HXJXCOORwu7eTSMiInIGFiUYffr0QUZGBp5++mmEh4fD3d0dY8eOtXVssvr99NUS60r7Wm7u+7rZdaUcwZ7NlvYmlPzyWlq+YXZ1Ba4rZP72gmZAFsZW1tenoi+epX0Jtefs16G+dUusq8jrXV5+N/yTPdLjU1dKzk1h7rilvoaVeL2Kmm+plQqoVAXLSuP1RuvURrNnFzX5Kjq6UlGyc38R4wTA1lb8ab/3Rtu52+x2LiIiIkelEMV75hYjhEBaWhqaNClogpKcnIybN28iODjYLgFWlq+vL1JSUiq0j6v2TyAi11NenxlzKvO56Mr4fBARlWSNz0aL7mAMHDgQJ0+eBAD4+fnBz8+vSid1BtGPhZosm0vDSsvMzOVsxde8tOG49Hh6n5Zl3i2w5Bdr46FcjX/tB4BP4y5K5d4Y3l4aBlZROKu08RCxpcVbsNL8FRuvfaOKE9KRqRcHtzH/firlzWdudellBT6MvVD54KxIoUDBnZDC92TRXZGimc4L1hcsS7OgK0ubHV2B0zLNZTOlVwsAZT/nJdZVoOXT139pKhGV47tw4QImTpyI69evo06dOvj666/RoUMHkzI7d+7EAw88gDZt2kjr9u3bBy8vLwDAypUrsXTpUhgMBvTv3x+fffaZ1LyXiIjsq9wEQ6FQwNfXF9evX0eDBg3sEZNDGN3Z16bHN04wxt3b3HR+CJP5IIySAKBwDgiFaZJgVBYKlNhmnGAs/OV0mXEV31epuDvvRNEXvxLnVRSLUQbuKmWJdea+zEnbyvhSZ26Tcfv3E/MH3Z0JXJoZXEizgBvPKC6MZxIXJdeV2EcAvd+5O0fE/5npq2BNxglGZX4Rr4iy7hAW9VvQV3Cmc0czd3h7mx7fVROMGTNmYPr06YiMjMSGDRsQGRmJQ4cOlSjXpk0bxMfHl1ifmJiIuXPn4siRI2jcuDFGjhyJL774Av/3f/9nj/CJiKgYi+5geHt7IywsDEOHDoW3t7e0Pjo62maBye30FfO/gJbVfr70vgxlf/Xu8679Jh0L9a0jzTpd9KXYeA6Loi+/xl98xd3vxyajQglx94t60chQgNZu12Is7sX7Sqwr61mv6GvVbckf0uPb+XrLzlN8Q2ESVjCTtmX9KK5n55V5jtI67Ffm2m/eLuW1q9R7vnrS6g0Vfq2AivXvcjXp6ek4fPgwfvvtNwDAww8/jKeffhoJCQkICrIswd6wYQNGjBgBHx8fAMDMmTOxePFiJhhERDKxKMEICQlBSEiIrWNxKEM/3i13CDZxLKVkp2FX0HPpDrud697Ff5RfyEq6LIq127lCF/5mt3O5qlZGI5uRZZKTk9GkSROo1QXVkUKhgL+/P5KSkkokGBcvXkTnzp2hUqkwadIkPPXUUwCApKQkNG/eXCoXEBCApKSkEueKjo42+WEsOzvbFpdERFTtWZRgzJs3z9ZxVGuPhvtKDUOKN90xaeZj/qFJk5zihAA2H7siLT8Y2lTax3w/i9KbFhnfsSgeY9H634xG3xrYvrGZ8sUmvisxXGfp24qG7CxaTs68Ix21dyvzzfeEyXMmSqwv73qKHEi8O2t914B6Fu1TsL70PjmllTcYBE5cvgm/el6oV8O9nPJ37zyZrDe662S63nTN2bS7s623aVzL5Jhmj1vG9ZTWuqloH03GbfMFXEjdGm5mXotS/n+KsvtxmdtW/O5ZddK5c2ekpKSgTp06SElJwdChQ9GgQQM89thjFh9jzpw5mDNnjrTs62vbprBERNWVRQlGcnIyZs2ahZSUFMTHxyM+Ph5xcXGYPXu2reOrFn74236jmPxslGzYmrmhfm1l9wX7zctyUJNZfiErSM68Y5JE2dq5q1nlF6Iy/VtaMzMqlZ+fH1JTU6HT6aBWqyGEQFJSEvz9/U3K1a5dW3rs6+uLxx9/HLt378Zjjz0Gf39/XLx4t6+ZRqMpsT8REdmPRQnGjBkz8MQTT+Ddd98FAAQHB2P8+PFMMKhM/+lvZjK10gpXsC+BQmHaQfm5+1sVlrd87omKtHt/77fz0uMXB7cpWcDC4xSctwLzWFjh2GWVX7Tl7iR0rw9rV1i24q+FJeXn/1z2AAO20rCWR4lBE4pGnio+kILpoAZ3BzJQFl5k0UhWRfspFa7b7NBeGjVqhM6dO2PNmjWIjIzExo0b4evrW6J5VGpqKho3bgylUomsrCz88ssvmDJlCoCCfhu9evXC/Pnz0bhxYyxfvtzl52oiInJkFiUY6enpGDduHN5///2CndRqqb0sUWmeH2TZF/HKMk0wWtv0XMviEpCrLZjS29YjO9nTE/f6I3jedmx+uheCm9Wx6bmME4zTCweXmKVaelw0q7XBtNmcKNaEznjGbINx8zmDwBNf/X97dx8XVZ3vAfwzwyg+JKYZScKAPEjKowomYKumFndVcjHzYU3JRdFdb69d7tW6q7XWmrW5cVfr9UpNo9KNtRVyfdmDrZqme7HgJvnQEyrDkOJTlgQ3iWF+949xDvPEcBjmzIHh8369eDHnnN+c3/f3m+FwfjO/h4+lvK780KhouWz9+73RLiZKcDWTmPNxxwkTXJ2n6BPncQX+YPPmzcjJycG6desQFBSEwsJCAEBubi6ysrKQlZWF4uJivPzyy9DpdDCZTJg1axYeeeQRAEBkZCSeeuopZGRkAAAmTJiAvLw81cpDRNTdyWolWL+2tvruu+9c9sP2J/PH6tEzIAA9dVr01GkRqNOiZ4BW2rZ7rNMi0GHb9nigzXkCtJZPQm2n7CxelqZoWWa+XKro+buDgX164sL1G2qH4XV9eupw7lllp6dtLV9fGRzUy+lG3ToWSJoVzTrm4WYjxdUNvv0xAbOLS+CLB+WtUG79VsR2tXO7bYfj1t/+KjY2FqWlzteprVu3So+XL1+O5cuXt3qOxYsXY/HixYrER0RE7SPrv/ysWbOQl5eHuro6bN26FZs2bUJubq7Ssalq7QzfzZo1Otx50DB1Lmuy4rBk+/8iLfI2tUOhdjr2+0mKnt/d+h6taVn3A2h9qDcREVHXpBEyv4ooKirC7t27IYTAjBkzMG/ePKVj6xBPljm3vVFIj3J9I+l+gTb3VWn7XNtZie4eOtD9Mz3M05pfefV3buNSSnLYrU773JfTTVlc7Dth0/c9wU33Ho8X3HM4VnPt/xA2sE+r8bQ8z/MbRk/fX21l6Wm8nr8vnVVdbZAeR9zWx6M85daPLwfHq8WThRE9uS76M9YHEZEzb1wbZfdTmDt3rjRozp+/qrf6n7Pf+iwv28aGP6mo+d5neZ0875uBtp/Xul6AkdqnO0xZS0RE1F3JamAYjUYsXrwYhw8fBgBMnDgRmzdv9tk0gJWVlVi4cCGuXr2K/v3747XXXkNcXJyiec6723XZPFkh2fI8+4Pbj1V7EJV7Wg3Qu0cAevfUoXdPreVxjwDVZrnJHTdUeux+BfT2r3AMAJs/Oic9zhsf6fA8N+d0+zq5OdaO19cb+bl7oq/j7FCeNw+u3/eVtE/OTFwdKcdz730pbfcL1EnfbWhg6ZrUaDKj2dUgCiIiIuowWQ2MBQsWYOrUqXjrrbcghMDWrVuxYMECHDp0SOHwLPLy8rBkyRLk5ORg165dyMnJQVlZmaJ5fmnzSbXjDbCrWxvHmyGnGyAffOljFkDDT81o6CSLcZ272iCrrlxVjnN9ume42iBr+tc2XycXmbkugyfvCffncHkel+E55C1jCl455W6rrlyd2ZO8AeCiw4B5Oa+3nG9OXSX5eUKI3fGWNJqWfbbHbfZbUzm93tIxDV79V1WbcXWENK0uABMbRURE1AXIGoMRFxeH06dP2+2Lj4/HqVOnFAvM6vLly4iOjsa1a9ek2axCQkJw9OhRp3nSbXV0DAYRUWfGMRgdx/ogInLmjWujVk6i6OhofP11y0JjX3/9NWJiYjqUsVw1NTUICQmR1t3QaDTQ6/UwGu3ngy8oKEBoaKj0U19f75P4iIiIiIiohawuUvX19UhKSkJ6ejoAoLS0FOnp6cjOzgYAlJSUKBehTPn5+cjPz5e2Q0NDO3S+njqtXbcJW7Yr/zqydqVw6IVhcy7Lo+s/NnUoPk+dXHOf3bbcAftye3jF/WGf9Pjzp+93cz6Z+bpJ9uQ/TuGtcksL+8s/Znb4fHbpvBCf/fnknMu7r4Xs2Pxw0gbbbyNXZsaih1YLXYAGugAtemhv/g7QoEeAFjrtzd8BGui0lv26m/t76twf7xGgRdTv323J97Y+0mrg1nUsLKuGWx4DaFl3w7owoM26G2ab/cJmnQ5rGl8uGkhEROQp2WMwFixYYLftK2FhYaitrYXJZJK6SBmNRsUHmAcGaGEyW1YYbmo2u1xUy1uey06QBqFKq/ne3NOybePmTqfntLL/6b0tKyh/cPoStNqWRhJgvQGybzhpNPaPtRqb/uk39zs9z+E+VenF1MIGWKY6jby9L3r1CFA0L+q6nn//q7YTeQlnxyIiIpLZwFi4cKHScbQqODgYo0aNwo4dO5CTk4Pi4mKEhoa6HX/hDT80mhQ9v630qEEu91tXEXbHuqqwu+PY27L9H3//rN3xeeoLhyldbcNsbU0HOUtIODa6emi1OPHN9y7T2KazP26/13Uapz1tppFzHk/ydllfnuTdRhlaq37bmFt9idp4fe3yaeUktrtbe1/bp2ktmBa/mRiFpmaBn0xmmMxmmJoFmpoFTGYzmprNaGoWN39bHpuazTCZhfTYuv+nZrPlWLNA083zcNA1ERGRM1mDvGtra/GXv/wFlZWVMJlabrz37NmjaHBWX331FXJycvDtt98iKCgIhYWFSEhwv9I2B3kTkT/jIO+OY30QETnz2UJ72dnZGD16NGbMmIGAAN93RYmNjUVpaanP8yUiIiIiovaR1cBoaGjASy+9pHQsnYonnw62h+23JXLzksZYiJauUdbBoYB9lypLGsvGiCdbBl6fWHNfS7cS0fIc265Wwvb5wn5Mh8v0Nnn+24aPpHU4PvzPCQ4xCrvuTXbna6MMrZUZdvF4VgabU9nF0/IcmWVw+To5lscmT4cy28Vskx42+TuXz6H7klP5XNVr22Wwy7OVMtvXq3MZ7LtEiVbL51SvbsoAaZ+wydu5Xt85WSvlnRk32GUZ7N877ahXhzJ8XHVNSpcSPsDmPMLufG7rtbW/KYcYz15pABERUWcnq4ExevRoVFZW+mxq2s4g/dkDANzdRFqOtnqD4nQT6erm2GLY6vfsbm7bSt8RiWs+8N7J2jDxz4d8lhdRa94/fdF+8gLYL5QH6Zj9Inu26a1pAOcJEGwZvv0/m2MtebhKr7GZOEHjeH6bPK3P0frhbF9EROSfZDUw8vPzkZ6ejpiYGPTq1Uvaf/DgQcUCU1tt3Q2v3ti7c0+0ZZC35uZdRas3KDazOFn2298w2d6MtNygaFD8aUs/uodSQh1mfXK8obKfYcrunC5vwGxuiGzydHeTBRc3eY7nk07n8gbMIUabMjvOgtVSFxq78rkqc6v16vAcOBx3VQbnenVI7zAjl3MZHKZCdqpX5zIAjvWkcTqfy3r1tAx2r4FnZXB8TaGBZ2VwSL/taBWeefcL3Nm/F/7nvyZBSbbfRpavnuyzvIiIiDorWQ2M+fPnY9myZUhJSVFlDIYafNW4AIBtOamKnt+2gfH8g0mK5kXUGQzo2xMAcGufnipHQkRE1P3IXqjg6aefVjKOTmfF/bEAbNd6sHxiau2mIK0NgZZPy+0fu187Iv8t300XS9TdZI8cguPG7/DbycPUDoWIiKjbkdXAyMjIQEVFBZKTk5WOp9NYv893i3MRkXdptRo88wv3U1kTERGRMmQ1MI4cOYKtW7ciOjrabgzGp59+qlhgapuWGGL3LYVWo5H6kFu/mbB+GwHHfWjlmwybfZsOn1W3gETkFWmRt6H03Ldqh0FERNRpyGpgdLcpagHgpXmjFD0/GxhE/uG2WzjOo6MqKyuxcOFCXL16Ff3798drr72GuLg4uzQHDx7E448/jvr6emg0GkydOhXPPfcctFotDAYDoqKi7BZgLS4uRlRUlK+LQkREkNnAGD9+PJqammA0GnnBJiJqxUObSm1mBrv5DabWedYzpxnP4DwTmO23pLaznPmjvLw8LFmyBDk5Odi1axdycnJQVlZml2bAgAH429/+hsjISNy4cQOTJ0/GG2+8gZycHABAv379UFFRoUL0RETkSFYD49ChQ5g3bx50Oh2MRiPKysqwYcMG7NixQ/+E2cQAABKQSURBVOn4uoUPTl+UbjJsb0akfS4GkbvaZ5u2ZTC6euUi6g4eyRiKvScsC/udu1rvtPCf2dyyJo7zAnz2i/e1vqCk/7p8+TLKy8vxwQeWNXpmzpyJ5cuX48yZM4iOjpbSjRw5Unrcq1cvJCcnw2Aw+DpcIiKSQVYD4/HHH8eRI0fw4IMPAgBSU1Nx/PhxRQPrTpZs/1+1QyAiD40OHwDDc1MVzUMIAbMAon7/rqL5qKGmpgYhISHQ6Sz/jjQaDfR6PYxGo10Dw9bFixexa9cu7N27V9rX0NCA1NRUNDc3Y8aMGVi1apXTtOoFBQUoKCiQtuvr6xUoERERyWpgNDc3O3WN6tmT/Y695b9nJ0mfYpqlTy5bPs00Wx/bfKpp3We++RGnubX0Anjhn1+rWDoi6iiNRoMAfhsJAKirq8P06dOxcuVKpKSkAABCQkJw/vx5BAcH49q1a5g9ezZeeOEFrFy50u65+fn5yM/Pl7ZDQ0N9GjsRUXchq4HRq1cvaWAdAJw8eRK9e/dWNDC15RR+Aq11Fqmbvy3bLV2QtFJXJZvjWnnpbf1ipLL/5NjAIKLOKiwsDLW1tTCZTNDpdBBCwGg0Qq/XO6X94YcfkJmZiQceeMCuoRAYGIjg4GAAwMCBA7Fo0SK8+eabTg0MIiLyDbcNjLlz56KoqAirV6/Gfffdh/Pnz2P+/PnYv38/3nzzTV/FqIpPqq7BfLNbgtkspMdEROQ9wcHBGDVqFHbs2IGcnBwUFxcjNDTUqXtUfX09MjMzkZmZidWrV9sdu3z5MgYMGIAePXqgsbERJSUldmM2iIjIt9w2ML788ksAwP33349hw4bh/fffhxACTz31lN/PJvX505ku91v7QlsaHC3dlKz7hLnlmFm0nn78+kM+K8vDY8Ox/Vi1z/IjImqPzZs3IycnB+vWrUNQUBAKCwsBALm5ucjKykJWVhY2bNiATz75BA0NDSgpKQEAzJo1C6tWrcLRo0fx5JNPIiAgACaTCffeey9WrVqlZpGIiLo1tw0M2648Q4cOxbJlyxQPqLOz9oUO6EJzRg7saxkvM+yOW1SOhIg6YsitvXH++x/VDsPrYmNjUVpa6rR/69at0uNVq1a12mjIzs5Gdna2YvEREVH7uG1gnDhxAgMHDnTaL4SARqPBtWvXFAuMvCclYgAA4Gcxt6scCRERERH5O7cNjNjYWLz7rv9Ni9jd3BNzOyqf+Tf0CNCqHQoRERER+Tm3DYzAwECEh4f7KpZu5c7+vXDh+g2f5cfGBRERERH5gtu7TtEdlpFVyYOjLVPT3jW4n8qREBERERF5j9sGBlfrJiLqPDKib1M7BCIiojax3wwRURcROzgIABDcL1DlSIiIiFrHBgYREREREXkNGxg2htzaW+0QiIhaNemuYADAonFDVY6EiIiodao3MDZu3Ij4+HgkJCQgMTERO3bssDu+du1aREVFISoqSvGVWX8xcggADrwmos4pYlBfGJ6biqXjo9QOhYiIqFVup6n1hbi4OPzrX/9C//79UVNTg5EjRyItLQ1RUVH46KOPUFRUhBMnTkCn0yEjIwPp6emYOnWqojHZrmBORERERETyqf4NxqRJk9C/f38AQFhYGAYPHoyamhoAwM6dO/Hwww+jb9++CAwMxKJFi1BUVKRmuERERERE5IbqDQxb+/fvx3fffYfU1FQAgNFotFvoLyIiAkaj0eVzCwoKEBoaKv3U19f7JGYiIiIiImqheAMjLS0NgwYNcvlj/aYCAE6ePIlHHnkEO3fuRN++fdudT35+Pr755hvp55ZbbvFmMbwuqHcPu99ERERERP5A8TEYpaWlbab5/PPPMW3aNLz66qsYN26ctF+v16O6ulraNhgM0Ov1isTpawvTI3Dq/HWsmjpC7VCIiIiIiLxG9S5SX3zxBX7+859jy5YtmDJlit2xWbNmYfv27WhoaEBjYyNeffVVzJkzR6VIvatHgBZ/mTMSt3PBLCIiIiLyI6o3MB599FFcv34djz32GJKTk5GcnIx9+/YBACZMmIDZs2cjISEBw4cPx5QpUzBt2jTFYtFqLbNHBaheK0REREREXZNGCCHUDkIJoaGh+Oabb9r1nKv1jUhZux8lv07HKP0AhSIjIlKHJ9dFf8b6ICJy5o1ro+rrYHQmg24JhOE5ZdfYICIiIiLyZ+wMREREqqqsrER6ejqGDRuG1NRUnD592mW6bdu2ISYmBlFRUVi8eDGamppkHSMiIt9iA4OIiFSVl5eHJUuW4Ouvv8Zjjz2GnJwcpzRVVVV44okncOTIEZw5cwaXLl3Cli1b2jxGRES+xwYGERGp5vLlyygvL8f8+fMBADNnzkRNTQ3OnDljl27Xrl3IysrC4MGDodFosHTpUhQVFbV5jIiIfM9vx2BcuXIFoaGhaofRpvr6+k6/KKAnWK6uheXqWjwt15UrVxSIpmNqamoQEhICnc7y70ij0UCv18NoNCI6OlpKZzQaER4eLm1HRETAaDS2ecxWQUEBCgoKpO0LFy50if8TSvPXv5P2YB2wDqxYD8DFixc7fA6/bWA0NjaqHYIs/jqLCcvVtbBcXYu/lktp+fn5yM/Pl7ZZjxasB9YBwDqwYj3AKx+8sIsUERGpJiwsDLW1tTCZTAAAIQSMRiP0er1dOr1ej+rqamnbYDBIadwdIyIi32MDg4iIVBMcHIxRo0Zhx44dAIDi4mKEhobadY8CLGMz9uzZg4sXL0IIgU2bNmHOnDltHiMiIt8LWLNmzRq1g+ju0tLS1A5BESxX18JydS3+VK60tDQ88cQTeP7551FWVobCwkLccccdyM3NBQDExsZiwIABCAoKwqJFi7Bx40bExcVhzZo1CAgIcHtMTt7EegBYBwDrwIr10PE68NuVvImIiIiIyPfYRYqIiIiIiLyGDQwiIiIiIvIaNjCIiIiIiMhr2MBQwY0bNzBjxgwMGzYMSUlJmDJlitOqtV1dYWEhNBoNdu/erXYoXtHY2Ijly5cjJiYGCQkJ0qrDXd27776LUaNGITk5GfHx8Xj99dfVDskjjz76KCIiIqDRaFBRUSHtr6ysRHp6OoYNG4bU1FScPn1axSjbz1W5usP1w5vkvge2bduGmJgYREVFYfHixWhqavJxpMqSUw8HDx7EmDFjMGLECMTFxWHlypUwm80qRKuM9lwPhBC49957ceutt/owQuXJrYOTJ09iwoQJGD58OIYPH46SkhIfR6osOfVgNpuRn5+PESNGIDExERMnTvSra21r/zcdeXxtFORzP/74o3jnnXeE2WwWQgjx4osvivHjx6sblBdVVVWJtLQ0MXbsWPH222+rHY5X/Pa3vxXLly+XXrPa2lqVI+o4s9ksBgwYID777DMhhOV1CwwMFHV1dSpH1n6HDx8WNTU1Ijw8XBw/flzaP3HiRFFYWCiEEOLvf/+7SElJUSlCz7gql79fP7xNznvg3LlzIiQkRNTW1gqz2SymT58uXnrpJR9Hqiw59fDpp5+Ks2fPCiEs77OMjAzpOf6gPdeDF154QeTm5or+/fv7KDrfkFMHDQ0NYujQoeLIkSNCCCFMJpO4fPmyL8NUnJx6ePvtt8WYMWPETz/9JIQQ4o9//KOYNWuWL8NUVGv/N2115NrIBkYnUFZWJsLDw9UOwyuam5vFpEmTRHl5uRg/frxfNDDq6+tFv379xPXr19UOxavMZrMYOHCgOHz4sBBCiM8++0zceeedorGxUeXIPGd7obx06ZLo16+faGpqEkJYynvHHXeIyspKNUP0iLt/AP50/fA2ue+B559/XuTl5Unb77zzjsjIyPBprEry9G/hN7/5jfjDH/7ggwiV1546OHXqlLjnnnvEmTNn/KqBIbcOXnnlFTF37lw1QvQJufWwe/dukZSUJOrq6oTZbBYrVqwQv/vd79QIWVHu/r905NrILlKdwIYNG/DAAw+oHYZXFBQUICMjA6NHj1Y7FK85e/YsBg4ciHXr1iElJQX33HMPDhw4oHZYHabRaLBz505kZ2cjPDwc48aNw+uvv46ePXuqHZpX1NTUICQkBDqdDoClvHq9HkajUeXIvMufrh/eJvc9YDQaER4eLm1HRET41fvEk7+FixcvYteuXZg2bZqvwlSU3DpoamrC4sWLsXnzZlnrqHQlcuvg888/R2BgIKZNm4bk5GQsWLAAV65cUSNkRcith+nTp2PChAkYPHgwQkJCcODAATz99NNqhKyajlwb2cBQ2bp163DmzBk8++yzaofSYadOnUJxcTFWr16tdiheZTKZUF1djREjRqC8vBwbN27E7NmzcenSJbVD6xCTyYS1a9eipKQE1dXVOHDgAB5++GFcvXpV7dBIJn+6flDnUVdXh+nTp2PlypVISUlROxyfeuqpp5CdnY3hw4erHYpqTCYT9u/fj82bN+P48eMYMmQIli1bpnZYPldeXo5Tp07h/PnzuHDhAiZNmoSlS5eqHVaXwQaGiv785z+jpKQE7733Hvr06aN2OB125MgRGAwGxMTEICIiAseOHcOSJUvw8ssvqx1ah+j1emi1Wvzyl78EAIwcORJDhw7FyZMnVY6sYyoqKnDhwgX87Gc/AwCkpqYiNDQUx48fVzky7wgLC0NtbS1MJhMAy6BNo9EIvV6vcmTe4W/XDyXIfQ/o9XpUV1dL2waDwW/eJ0D7/hZ++OEHZGZm4oEHHkB+fr6vQ1WM3Do4fPgwXnzxRURERGDcuHGoq6tDRESEX3yC356/h4kTJ2LIkCHQaDSYP38+jh07pkbIipBbD2+88YY00F+r1WLhwoX48MMP1QhZNR25NrKBoZKCggIUFRXhn//8p9/MUrFs2TLU1tbCYDDAYDBg7Nix2LJlS5f/5GPQoEGYNGkS9u3bBwCoqqpCVVVVl/+Ey3qR/eKLLwAAZ86cwdmzZxEbG6tyZN4RHByMUaNGYceOHQCA4uJihIaGIjo6WuXIOs4frx9KkPsemDlzJvbs2YOLFy9CCIFNmzZhzpw5aoSsCLn1UF9fj8zMTGRmZvrdN9Fy6+DIkSOorq6GwWDA0aNHERQUBIPBgNtvv12NsL1Kbh089NBDKCsrQ11dHQDLbINJSUk+j1cpcushMjISBw8exE8//QQA2Lt3L+Lj430er5o6dG3s0MgQ8khNTY0AICIjI0VSUpJISkoSY8aMUTssr/OXQd5CCHH27FkxYcIEER8fLxITE8WuXbvUDskr3nzzTalM8fHx4q9//avaIXlkyZIlYsiQISIgIEAEBweLqKgoIYQQX375pRg7dqyIiYkRo0ePFidOnFA50vZxVa7ucv3wltbeA7/61a/EP/7xDyndli1bRGRkpIiMjBSLFi2SZo7xF3LqYe3atUKn00nvq6SkJLF27Vo1w/Yque8Fq6qqKr8a5C2E/Dp44403RFxcnEhISBCZmZnCaDSqFbIi5NTDjRs3RG5urrjrrrtEQkKCmDJlijTLmj9o7f+mt66NGiGEULL1Q0RERERE3Qe7SBERERERkdewgUFERERERF7DBgYREREREXkNGxhEREREROQ1bGAQEREREZHXsIFBpCKDwYBNmzbZ7YuIiEBFRYVKERERERF1DBsYRCpy1cAgIiIi6srYwCByQ6PR4JlnnsHdd9+NiIgI7N69G88++yxSUlIQExODQ4cOSWm3b9+OxMREJCYmYurUqTh//jwA4LXXXsPkyZMxd+5cJCQkICUlBefOnQMALF26FF999RWSk5ORlZUlnaukpARpaWkYOnQo1q5d69MyExEREXUEGxhEbbjlllvw8ccfY9u2bZg/fz5CQkJQXl6OdevWYcWKFQCAU6dOYcWKFXjvvfdw4sQJpKenIzc3VzpHWVkZ1q1bh5MnT2Ly5Mn405/+BADYtGkTYmNjUVFRgT179kjpv//+e5SWlqKsrAzr16+XGitEREREnR0bGERtmD17NgAgJSUFDQ0NmDNnDgBgzJgxqKysBAB8+OGHyMzMxJAhQwAAv/71r3Hw4EE0NzcDgPRthPXx2bNn3eY5b948AMCgQYMQGRmJqqoq7xeMiIiISAFsYBC1oVevXgCAgIAAp22TyeTyORqNxuU52nqep+mJiIiIOgs2MIi8YOLEiXj//fdx4cIFAJauT5MmTZIaJa0JCgrC9evXfREiERERkU/o1A6AyB/Ex8dj/fr1yMzMBACEhYXhlVdeafN5iYmJiIuLQ3x8PCIjI+3GYRARERF1RRohhFA7CCIiIiIi8g/sIkVERERERF7DBgYREREREXkNGxhEREREROQ1bGAQEREREZHXsIFBRERERERewwYGERERERF5DRsYRERERETkNWxgEBERERGR1/w/dwZmX6rR7OIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x640 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uquF-ZbmCicY"
      },
      "source": [
        "Scale data using minmaxscaler\n",
        "\n",
        "as we are going to feed the input features to deep learning\n",
        "model we need to make sure that features value range are uniform so that gradient descent algorithm has smooth descent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nheXZOUdDEiV",
        "outputId": "0bad3ffd-1b5e-46d7-abed-b7840e4f5dbb"
      },
      "source": [
        "input_features = []\n",
        "\n",
        "for i in od:\n",
        "  if i != \"T (degC)\":\n",
        "    input_features.append(i)\n",
        "\n",
        "features = df[input_features]\n",
        "target = df[[\"T (degC)\"]]\n",
        "target.index = df[\"Date Time\"]\n",
        "features.index = df[\"Date Time\"]\n",
        "\n",
        "print(features.shape[0])\n",
        "print(target.shape[0])\n",
        "print(df.head())\n",
        "print(features.head())\n",
        "print(target.head())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "578189\n",
            "578189\n",
            "             Date Time  p (mbar)  ...                date  month\n",
            "0  01.01.2009 00:00:00    996.50  ... 2009-01-01 00:00:00      1\n",
            "1  01.01.2009 00:10:00    996.52  ... 2009-01-01 00:10:00      1\n",
            "2  01.01.2009 00:20:00    996.57  ... 2009-01-01 00:20:00      1\n",
            "3  01.01.2009 00:30:00    996.53  ... 2009-01-01 00:30:00      1\n",
            "4  01.01.2009 00:40:00    996.51  ... 2009-01-01 00:40:00      1\n",
            "\n",
            "[5 rows x 24 columns]\n",
            "                     p (mbar)  VPmax (mbar)  ...  wv (m/s)  month\n",
            "Date Time                                    ...                 \n",
            "01.01.2009 00:00:00    996.50          3.40  ...      0.66      1\n",
            "01.01.2009 00:10:00    996.52          3.33  ...      1.03      1\n",
            "01.01.2009 00:20:00    996.57          3.23  ...      0.72      1\n",
            "01.01.2009 00:30:00    996.53          3.21  ...      0.19      1\n",
            "01.01.2009 00:40:00    996.51          3.26  ...      0.34      1\n",
            "\n",
            "[5 rows x 7 columns]\n",
            "                     T (degC)\n",
            "Date Time                    \n",
            "01.01.2009 00:00:00     -7.78\n",
            "01.01.2009 00:10:00     -8.02\n",
            "01.01.2009 00:20:00     -8.41\n",
            "01.01.2009 00:30:00     -8.51\n",
            "01.01.2009 00:40:00     -8.31\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jLHRGjwBsbp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0919284a-eda1-4913-ae56-5295fa5f3807"
      },
      "source": [
        "\n",
        "inputScaler = MinMaxScaler()\n",
        "outputScaler = MinMaxScaler()\n",
        "\n",
        "\n",
        "features = inputScaler.fit_transform(features.values)\n",
        "target = outputScaler.fit_transform(target.values)\n",
        "\n",
        "\n",
        "print(len(features))\n",
        "print(len(target))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "578189\n",
            "578189\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tHMpnfXCxMi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4db3d2ec-836f-4e46-91e8-f210c892d913"
      },
      "source": [
        "split_fraction = 0.815\n",
        "train_split = int(split_fraction * len(features))\n",
        "train_split"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "471224"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGEk6BBQGlm5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac157ec5-2518-40f4-bab4-e94e0fa58d37"
      },
      "source": [
        "x_train = features[0 : train_split - 1]\n",
        "x_val = features[train_split:]\n",
        "y_train = target[0 : train_split - 1]\n",
        "y_val = target[train_split:]\n",
        "\n",
        "print(x_train.shape, y_train.shape)\n",
        "print(x_val.shape, y_val.shape)\n",
        "\n",
        "print(\"#\"*100)\n",
        "print(y_train)\n",
        "print(\"#\"*100)\n",
        "print(x_train)\n",
        "print(\"#\"*100)\n",
        "print(y_val)\n",
        "print(\"#\"*100)\n",
        "print(x_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(471223, 7) (471223, 1)\n",
            "(106965, 7) (106965, 1)\n",
            "####################################################################################################\n",
            "[[0.25261237]\n",
            " [0.24863161]\n",
            " [0.24216288]\n",
            " ...\n",
            " [0.39724664]\n",
            " [0.39542213]\n",
            " [0.39392934]]\n",
            "####################################################################################################\n",
            "[[0.80649869 0.03900032 0.00460155 ... 0.73950133 0.99722463 0.        ]\n",
            " [0.80669326 0.03788602 0.00460155 ... 0.74321291 0.99726153 0.        ]\n",
            " [0.80717969 0.03629417 0.00439239 ... 0.74934898 0.99723061 0.        ]\n",
            " ...\n",
            " [0.85309855 0.0889844  0.02991006 ... 0.63054865 0.99727649 1.        ]\n",
            " [0.85300126 0.08818848 0.02781845 ... 0.63192553 0.99726452 1.        ]\n",
            " [0.85309855 0.08739255 0.02551767 ... 0.63318268 0.99722962 1.        ]]\n",
            "####################################################################################################\n",
            "[[0.39210483]\n",
            " [0.39011445]\n",
            " [0.38878753]\n",
            " ...\n",
            " [0.38397744]\n",
            " [0.38895339]\n",
            " [0.39160723]]\n",
            "####################################################################################################\n",
            "[[0.85290398 0.08659663 0.02238026 ... 0.63449969 0.99725455 1.        ]\n",
            " [0.85329312 0.0858007  0.02091613 ... 0.63629561 0.99723959 1.        ]\n",
            " [0.85436326 0.08516396 0.02007948 ... 0.63776228 0.99722762 1.        ]\n",
            " ...\n",
            " [0.92752213 0.08309456 0.01505961 ... 0.67035829 0.99726851 1.        ]\n",
            " [0.92800856 0.08532315 0.01589626 ... 0.66619773 0.99723959 1.        ]\n",
            " [0.92781399 0.08643744 0.01694206 ... 0.6639229  0.99722363 0.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05eSaQVEL5it"
      },
      "source": [
        "create data loaders\n",
        "\n",
        "we need custom dataset for LSTM which uses seq of readings too\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s557EnCFPSYX"
      },
      "source": [
        "class TimeseriesDataset(torch.utils.data.Dataset):   \n",
        "    def __init__(self, X, y, xSequenceLength, ySequenceLength):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        self.xSequenceLength = xSequenceLength\n",
        "        self.ySequenceLength = ySequenceLength\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.X.__len__() - (self.ySequenceLength)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        #if len(self.X) > (index+self.xSequenceLength) and len(self.y) > (index+self.ySequenceLength-1):\n",
        "        #print(\"len(self.X) =\",len(self.X), \"index =\", index, \"|\", \"index :\", index, \" + \", \"self.xSequenceLength :\",self.xSequenceLength, \" = \", (index+self.xSequenceLength), \"| len(self.y) =\", len(self.y), \"(index+self.ySequenceLength-1)\", \"| index :\", index, \" + \", \"self.ySequenceLength-1 :\", self.ySequenceLength-1, \" = \", (index+self.ySequenceLength-1))\n",
        "        return ((self.X[index:index+self.xSequenceLength]), (self.y[index+self.ySequenceLength-1]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiUUnfNRIEf_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a38a75ae-4047-470a-bca6-a506335c9e3f"
      },
      "source": [
        "oneDayReading = 144\n",
        "xSequenceLength = oneDayReading * 4\n",
        "ySequenceLength = xSequenceLength + oneDayReading\n",
        "\n",
        "\n",
        "\n",
        "for x in range(ySequenceLength):\n",
        "    if x == xSequenceLength - 1:\n",
        "        print(\"*************************\")\n",
        "    print(x_train[x], \"==\", y_train[x])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.80649869 0.03900032 0.00460155 0.08451503 0.73950133 0.99722463\n",
            " 0.        ] == [0.25261237]\n",
            "[0.80669326 0.03788602 0.00460155 0.08167896 0.74321291 0.99726153\n",
            " 0.        ] == [0.24863161]\n",
            "[0.80717969 0.03629417 0.00439239 0.07884288 0.74934898 0.99723061\n",
            " 0.        ] == [0.24216288]\n",
            "[0.80679054 0.0359758  0.00418323 0.07827567 0.75066599 0.99717776\n",
            " 0.        ] == [0.24050423]\n",
            "[0.80659597 0.03677173 0.00397406 0.08054453 0.74752312 0.99719272\n",
            " 0.        ] == [0.24382153]\n",
            "[0.80659597 0.03693091 0.00397406 0.08054453 0.74695441 0.99719072\n",
            " 0.        ] == [0.24448499]\n",
            "[0.80649869 0.03788602 0.00397406 0.08281339 0.74354216 0.99717975\n",
            " 0.        ] == [0.24813402]\n",
            "[0.80649869 0.03963706 0.0037649  0.08735111 0.73701697 0.99717676\n",
            " 0.        ] == [0.25526621]\n",
            "[0.80649869 0.03963706 0.00397406 0.08678389 0.7370469  0.99717776\n",
            " 0.        ] == [0.25526621]\n",
            "[0.80649869 0.03836358 0.00439239 0.0833806  0.74147685 0.99718673\n",
            " 0.        ] == [0.25045613]\n",
            "[0.80679054 0.03629417 0.00460155 0.07827567 0.74949864 0.99721765\n",
            " 0.        ] == [0.24183115]\n",
            "[0.80766612 0.03486151 0.00460155 0.07543959 0.75485648 0.99720369\n",
            " 0.        ] == [0.2363576]\n",
            "[0.80766612 0.03454314 0.00439239 0.07430516 0.75668233 0.99718374\n",
            " 0.        ] == [0.23436723]\n",
            "[0.8077634  0.03454314 0.00418323 0.07487238 0.75626328 0.99717477\n",
            " 0.        ] == [0.23486482]\n",
            "[0.80883354 0.03470232 0.00418323 0.07543959 0.75638301 0.99719471\n",
            " 0.        ] == [0.23519655]\n",
            "[0.80951454 0.03533906 0.00397406 0.07714124 0.75404831 0.99719172\n",
            " 0.        ] == [0.23801625]\n",
            "[0.80951454 0.03533906 0.00418323 0.07657402 0.75407824 0.99716579\n",
            " 0.        ] == [0.23801625]\n",
            "[0.81000097 0.03517988 0.00439239 0.07657402 0.75485648 0.99719072\n",
            " 0.        ] == [0.23735279]\n",
            "[0.8098064  0.03470232 0.00418323 0.07543959 0.75647281 0.99717676\n",
            " 0.        ] == [0.23552828]\n",
            "[0.81009826 0.03470232 0.00418323 0.07543959 0.75704152 0.99716579\n",
            " 0.        ] == [0.23503069]\n",
            "[0.81107112 0.03422477 0.00439239 0.07430516 0.75895717 0.99716878\n",
            " 0.        ] == [0.23337204]\n",
            "[0.81214126 0.03422477 0.00418323 0.07430516 0.75937622 0.99718873\n",
            " 0.        ] == [0.23337204]\n",
            "[0.81233583 0.03454314 0.00460155 0.07430516 0.75826873 0.99718773\n",
            " 0.        ] == [0.23469896]\n",
            "[0.81194669 0.03406558 0.00481071 0.07260352 0.76009459 0.99717078\n",
            " 0.        ] == [0.23254271]\n",
            "[0.81126569 0.03374721 0.00481071 0.07260352 0.76075309 0.99716878\n",
            " 0.        ] == [0.23154752]\n",
            "[0.8118494  0.03311047 0.00501987 0.07033466 0.76377623 0.99716878\n",
            " 0.        ] == [0.22856195]\n",
            "[0.81243312 0.03215536 0.00501987 0.06863301 0.76796672 0.99719272\n",
            " 0.        ] == [0.22424946]\n",
            "[0.8132114  0.03215536 0.00481071 0.06863301 0.76838576 0.99720169\n",
            " 0.        ] == [0.2240836]\n",
            "[0.81369783 0.03263292 0.00460155 0.07033466 0.76632045 0.9971987\n",
            " 0.        ] == [0.22657157]\n",
            "[0.81496254 0.03215536 0.00481071 0.06920023 0.76865515 0.99719571\n",
            " 0.        ] == [0.22458119]\n",
            "[0.81583812 0.03167781 0.00481071 0.06749858 0.77146877 0.9971987\n",
            " 0.        ] == [0.22192735]\n",
            "[0.81554626 0.03151862 0.00460155 0.06749858 0.77194768 0.99719471\n",
            " 0.        ] == [0.22126389]\n",
            "[0.81544897 0.03135944 0.00481071 0.06693137 0.77209734 0.99716779\n",
            " 0.        ] == [0.22109803]\n",
            "[0.81651912 0.03056351 0.00501987 0.06466251 0.77592864 0.99718773\n",
            " 0.        ] == [0.217449]\n",
            "[0.81720012 0.03056351 0.00460155 0.06579694 0.77631776 0.99720867\n",
            " 0.        ] == [0.21728313]\n",
            "[0.81739469 0.03199618 0.00418323 0.06976744 0.77021162 0.99718973\n",
            " 0.        ] == [0.22391773]\n",
            "[0.81827026 0.03151862 0.00439239 0.06749858 0.77302523 0.9971638\n",
            " 0.        ] == [0.22126389]\n",
            "[0.81924312 0.03183699 0.00418323 0.06863301 0.77221707 0.99718274\n",
            " 0.        ] == [0.22259081]\n",
            "[0.81972955 0.03342884 0.00418323 0.07260352 0.76569188 0.99717676\n",
            " 0.        ] == [0.22988887]\n",
            "[0.82109155 0.03438395 0.00481071 0.07317073 0.76260888 0.99720369\n",
            " 0.        ] == [0.23386963]\n",
            "[0.82245355 0.0339064  0.00522903 0.07146909 0.76518303 0.9973064\n",
            " 0.        ] == [0.23171339]\n",
            "[0.82313455 0.03231455 0.00543819 0.0680658  0.77143883 0.99740912\n",
            " 0.        ] == [0.22524465]\n",
            "[0.82430197 0.03326966 0.00460155 0.07146909 0.76784699 0.99736624\n",
            " 0.        ] == [0.22955714]\n",
            "[0.82595583 0.03517988 0.00460155 0.07543959 0.76153132 0.99736425\n",
            " 0.        ] == [0.23718693]\n",
            "[0.8268314  0.03581662 0.00460155 0.07657402 0.75943608 0.99735328\n",
            " 0.        ] == [0.23984077]\n",
            "[0.82877712 0.03645336 0.00460155 0.07827567 0.75793948 0.99725056\n",
            " 0.        ] == [0.24232874]\n",
            "[0.82945812 0.03677173 0.00460155 0.07884288 0.75668233 0.99723959\n",
            " 0.        ] == [0.24398739]\n",
            "[0.83120926 0.03740847 0.00460155 0.08054453 0.75482654 0.99721964\n",
            " 0.        ] == [0.2468071]\n",
            "[0.83247398 0.03756765 0.00501987 0.08054453 0.75488641 0.99723061\n",
            " 0.        ] == [0.24730469]\n",
            "[0.83344683 0.03740847 0.00501987 0.07997731 0.75587417 0.99722962\n",
            " 0.        ] == [0.24664123]\n",
            "[0.83403055 0.03772684 0.00501987 0.08054453 0.75491634 0.99721167\n",
            " 0.        ] == [0.24796815]\n",
            "[0.83510069 0.03820439 0.00522903 0.08111174 0.75371906 0.99721167\n",
            " 0.        ] == [0.24979267]\n",
            "[0.83529526 0.0391595  0.00501987 0.0833806  0.75057619 0.99719671\n",
            " 0.        ] == [0.25327583]\n",
            "[0.83655998 0.03979624 0.00585652 0.08394782 0.74857074 0.99717576\n",
            " 0.        ] == [0.25609554]\n",
            "[0.83753283 0.03947787 0.00627484 0.08224617 0.75036667 0.99719272\n",
            " 0.        ] == [0.25460275]\n",
            "[0.83850569 0.0391595  0.00627484 0.08167896 0.75147415 0.99717277\n",
            " 0.        ] == [0.25377343]\n",
            "[0.83870026 0.03979624 0.00606568 0.0833806  0.74928911 0.99716978\n",
            " 0.        ] == [0.2562614]\n",
            "[0.83889483 0.04106972 0.00585652 0.08678389 0.74524829 0.9972336\n",
            " 0.        ] == [0.26073976]\n",
            "[0.84015955 0.04170646 0.00585652 0.08791832 0.74366189 0.99732037\n",
            " 0.        ] == [0.26306187]\n",
            "[0.84181341 0.04218402 0.00585652 0.08905275 0.74234488 0.99728247\n",
            " 0.        ] == [0.26521811]\n",
            "[0.84317541 0.04218402 0.00627484 0.08791832 0.74324284 0.99730341\n",
            " 0.        ] == [0.26488638]\n",
            "[0.84473198 0.04218402 0.00627484 0.08791832 0.74369182 0.9972705\n",
            " 0.        ] == [0.26505225]\n",
            "[0.84580212 0.04170646 0.00669316 0.08621668 0.74596666 0.99737023\n",
            " 0.        ] == [0.26306187]\n",
            "[0.84716412 0.04122891 0.00669316 0.08508225 0.74818163 0.99731239\n",
            " 0.        ] == [0.26123735]\n",
            "[0.84813698 0.04075135 0.00669316 0.08451503 0.74982789 0.99733931\n",
            " 0.        ] == [0.25991043]\n",
            "[0.84862341 0.04075135 0.00669316 0.08451503 0.75015714 0.99733533\n",
            " 0.        ] == [0.25974457]\n",
            "[0.84901255 0.04059217 0.00690232 0.0833806  0.7512347  0.99734929\n",
            " 0.        ] == [0.25874938]\n",
            "[0.84842884 0.0402738  0.00690232 0.08281339 0.75207279 0.99733134\n",
            " 0.        ] == [0.25758832]\n",
            "[0.84774784 0.04059217 0.00690232 0.08394782 0.75024694 0.99728347\n",
            " 0.        ] == [0.25924697]\n",
            "[0.84648312 0.04138809 0.00732064 0.08451503 0.74719387 0.99729942\n",
            " 0.        ] == [0.26206668]\n",
            "[0.84541298 0.04154728 0.00752981 0.08451503 0.74602652 0.99728646\n",
            " 0.        ] == [0.262896]\n",
            "[0.84405098 0.04218402 0.00773897 0.08564946 0.74354216 0.99733333\n",
            " 0.        ] == [0.26505225]\n",
            "[0.84346726 0.04282076 0.00794813 0.08678389 0.74090814 0.99734231\n",
            " 0.        ] == [0.26770609]\n",
            "[0.84259169 0.04329831 0.00815729 0.08791832 0.73896256 0.99737322\n",
            " 0.        ] == [0.26936474]\n",
            "[0.84229983 0.04361668 0.00794813 0.08848554 0.737885   0.99734729\n",
            " 0.        ] == [0.27052579]\n",
            "[0.84191069 0.04409424 0.00794813 0.08961997 0.7362986  0.99738319\n",
            " 0.        ] == [0.27201858]\n",
            "[0.84074326 0.04457179 0.00815729 0.09018718 0.73435302 0.99728646\n",
            " 0.        ] == [0.27367723]\n",
            "[0.84074326 0.04600446 0.00857561 0.09302326 0.72983328 0.99719172\n",
            " 0.        ] == [0.27865318]\n",
            "[0.84084055 0.04743712 0.00941226 0.0947249  0.72519381 0.99721565\n",
            " 0.        ] == [0.28396086]\n",
            "[0.84025683 0.04775549 0.01003974 0.09415769 0.72414619 0.99724457\n",
            " 0.        ] == [0.28495605]\n",
            "[0.84025683 0.04807386 0.0102489  0.0947249  0.72300877 0.99724956\n",
            " 0.        ] == [0.2861171]\n",
            "[0.84074326 0.04743712 0.0102489  0.09302326 0.72528361 0.99730341\n",
            " 0.        ] == [0.28379499]\n",
            "[0.83947855 0.04711875 0.00941226 0.09359047 0.72582238 0.99728048\n",
            " 0.        ] == [0.28263394]\n",
            "[0.83870026 0.04759631 0.00941226 0.0947249  0.72411626 0.99730142\n",
            " 0.        ] == [0.28412672]\n",
            "[0.83870026 0.04727794 0.00899393 0.0947249  0.72501422 0.99728347\n",
            " 0.        ] == [0.28313153]\n",
            "[0.83870026 0.04775549 0.00857561 0.09642655 0.72351762 0.99725953\n",
            " 0.        ] == [0.28479018]\n",
            "[0.83860298 0.04807386 0.00836645 0.09756098 0.72232033 0.99727748\n",
            " 0.        ] == [0.2861171]\n",
            "[0.83870026 0.04807386 0.00857561 0.09756098 0.72208088 0.99728347\n",
            " 0.        ] == [0.28628297]\n",
            "[0.83879755 0.04807386 0.00815729 0.09812819 0.72232033 0.99729344\n",
            " 0.        ] == [0.2861171]\n",
            "[0.83889483 0.04823305 0.00815729 0.09869541 0.72184142 0.99733233\n",
            " 0.        ] == [0.2866147]\n",
            "[0.83938126 0.04839223 0.00794813 0.09926262 0.7215421  0.99727549\n",
            " 0.        ] == [0.28727816]\n",
            "[0.84035412 0.04918816 0.00836645 0.10039705 0.71950672 0.99729145\n",
            " 0.        ] == [0.289932]\n",
            "[0.84113241 0.04902897 0.00857561 0.09926262 0.72043461 0.99728247\n",
            " 0.        ] == [0.28926854]\n",
            "[0.84093783 0.04966571 0.00857561 0.10096427 0.71821964 0.99724856\n",
            " 0.        ] == [0.29159065]\n",
            "[0.84054869 0.05030245 0.00899393 0.10153148 0.71648358 0.99725654\n",
            " 0.        ] == [0.29341516]\n",
            "[0.84006226 0.04998408 0.0092031  0.10096427 0.7168727  0.9973064\n",
            " 0.        ] == [0.2927517]\n",
            "[0.83996498 0.0498249  0.00899393 0.10039705 0.7175312  0.99729843\n",
            " 0.        ] == [0.29208824]\n",
            "[0.84103512 0.05030245 0.00878477 0.1020987  0.71645365 0.99726153\n",
            " 0.        ] == [0.29358103]\n",
            "[0.84103512 0.05046164 0.00878477 0.1020987  0.71609447 0.99726951\n",
            " 0.        ] == [0.29407862]\n",
            "[0.84152155 0.05030245 0.00899393 0.10153148 0.71690263 0.99729344\n",
            " 0.        ] == [0.29341516]\n",
            "[0.84220255 0.05046164 0.00878477 0.1020987  0.71663324 0.99726153\n",
            " 0.        ] == [0.29391276]\n",
            "[0.84181341 0.05014327 0.0092031  0.10096427 0.71714209 0.99726651\n",
            " 0.        ] == [0.2932493]\n",
            "[0.84220255 0.04998408 0.00899393 0.10096427 0.71783052 0.99724358\n",
            " 0.        ] == [0.29258584]\n",
            "[0.84220255 0.04966571 0.0092031  0.09982984 0.71881828 0.99728546\n",
            " 0.        ] == [0.29159065]\n",
            "[0.84229983 0.05046164 0.00878477 0.1020987  0.71663324 0.99721366\n",
            " 0.        ] == [0.29407862]\n",
            "[0.84229983 0.05078001 0.00941226 0.1020987  0.71555569 0.99721167\n",
            " 0.        ] == [0.29523967]\n",
            "[0.84220255 0.05078001 0.00962142 0.10153148 0.71558562 0.9972336\n",
            " 0.        ] == [0.29523967]\n",
            "[0.84210526 0.05062082 0.00962142 0.10153148 0.71597474 0.99723061\n",
            " 0.        ] == [0.29474208]\n",
            "[0.84210526 0.05062082 0.00941226 0.10153148 0.71606453 0.99721366\n",
            " 0.        ] == [0.29457621]\n",
            "[0.84181341 0.05109838 0.00941226 0.10266591 0.71465773 0.99721865\n",
            " 0.        ] == [0.296069]\n",
            "[0.84122969 0.05125756 0.00941226 0.10266591 0.71375977 0.99720867\n",
            " 0.        ] == [0.29673246]\n",
            "[0.84161883 0.05173512 0.00983058 0.10323313 0.71271214 0.9971997\n",
            " 0.        ] == [0.29805938]\n",
            "[0.84229983 0.05173512 0.00983058 0.10323313 0.71268221 0.99720568\n",
            " 0.        ] == [0.29855697]\n",
            "[0.84278626 0.05125756 0.00983058 0.1020987  0.71447813 0.99725156\n",
            " 0.        ] == [0.29673246]\n",
            "[0.84268898 0.05125756 0.0092031  0.10323313 0.71423868 0.9971997\n",
            " 0.        ] == [0.29689832]\n",
            "[0.84346726 0.05237186 0.00941226 0.10550199 0.71133527 0.99719371\n",
            " 0.        ] == [0.30038149]\n",
            "[0.84239712 0.05253104 0.00983058 0.10493477 0.71049717 0.99718574\n",
            " 0.        ] == [0.30104495]\n",
            "[0.84229983 0.05237186 0.01003974 0.10436756 0.71094615 0.99720867\n",
            " 0.        ] == [0.30038149]\n",
            "[0.84191069 0.05205349 0.01003974 0.10380034 0.71181418 0.99721865\n",
            " 0.        ] == [0.29922043]\n",
            "[0.84229983 0.0518943  0.00962142 0.10380034 0.71226316 0.99721366\n",
            " 0.        ] == [0.2988887]\n",
            "[0.84268898 0.05237186 0.00941226 0.10550199 0.71118561 0.99722662\n",
            " 0.        ] == [0.30038149]\n",
            "[0.84317541 0.05221267 0.0092031  0.10550199 0.71178425 0.99722164\n",
            " 0.        ] == [0.29988389]\n",
            "[0.84259169 0.05237186 0.00899393 0.1060692  0.71082642 0.99721665\n",
            " 0.        ] == [0.30054735]\n",
            "[0.84239712 0.05221267 0.00899393 0.1060692  0.71142507 0.99726253\n",
            " 0.        ] == [0.29988389]\n",
            "[0.84288355 0.0518943  0.00857561 0.10550199 0.71259242 0.99724358\n",
            " 0.        ] == [0.29872284]\n",
            "[0.84346726 0.05237186 0.00836645 0.10720363 0.7112754  0.99718673\n",
            " 0.        ] == [0.30038149]\n",
            "[0.84239712 0.05284941 0.00878477 0.10777085 0.70938969 0.99720269\n",
            " 0.        ] == [0.30204014]\n",
            "[0.84239712 0.05269023 0.00899393 0.10720363 0.70995839 0.99724158\n",
            " 0.        ] == [0.30137668]\n",
            "[0.84200798 0.05237186 0.00878477 0.10663642 0.71076656 0.99721665\n",
            " 0.        ] == [0.30038149]\n",
            "[0.84142426 0.05253104 0.00857561 0.10720363 0.70992846 0.99719272\n",
            " 0.        ] == [0.30104495]\n",
            "[0.84113241 0.05316778 0.00857561 0.10890528 0.70825227 0.99719371\n",
            " 0.        ] == [0.3027036]\n",
            "[0.84084055 0.05396371 0.00857561 0.11060692 0.70591757 0.99719172\n",
            " 0.        ] == [0.30519157]\n",
            "[0.84054869 0.05444126 0.00878477 0.11117413 0.70421144 0.99719172\n",
            " 0.        ] == [0.30701609]\n",
            "[0.83977041 0.05491882 0.00878477 0.11230856 0.70253524 0.99717676\n",
            " 0.        ] == [0.30850887]\n",
            "[0.83986769 0.05491882 0.0092031  0.11174135 0.70247538 0.9971977\n",
            " 0.        ] == [0.30867474]\n",
            "[0.83928398 0.05539637 0.0092031  0.11230856 0.70100871 0.99717876\n",
            " 0.        ] == [0.31000166]\n",
            "[0.83860298 0.0561923  0.00962142 0.11344299 0.69867401 0.99719272\n",
            " 0.        ] == [0.31248963]\n",
            "[0.83831112 0.05555556 0.00983058 0.11174135 0.70044    0.99720568\n",
            " 0.        ] == [0.31033339]\n",
            "[0.83831112 0.05460045 0.00899393 0.11117413 0.70292436 0.99722363\n",
            " 0.        ] == [0.30751368]\n",
            "[0.83860298 0.05444126 0.00794813 0.11230856 0.70340327 0.99719571\n",
            " 0.        ] == [0.30701609]\n",
            "[0.83870026 0.05444126 0.00752981 0.11344299 0.70355293 0.99720269\n",
            " 0.        ] == [0.30685022]\n",
            "[0.83860298 0.05539637 0.00690232 0.11627907 0.70085905 0.99718274\n",
            " 0.        ] == [0.30983579]\n",
            "[0.83772741 0.05539637 0.00732064 0.11571185 0.70046993 0.9972356\n",
            " 0.        ] == [0.30983579]\n",
            "[0.83733826 0.05396371 0.00690232 0.11344299 0.70400192 0.99722762\n",
            " 0.        ] == [0.30568917]\n",
            "[0.83655998 0.05428208 0.00627484 0.11514464 0.70304409 0.9971997\n",
            " 0.        ] == [0.30635263]\n",
            "[0.83617083 0.05396371 0.00606568 0.11457742 0.70379239 0.99724358\n",
            " 0.        ] == [0.30535744]\n",
            "[0.83626812 0.05364534 0.00564735 0.11457742 0.70478015 0.99724158\n",
            " 0.        ] == [0.30419638]\n",
            "[0.83626812 0.05364534 0.00522903 0.11514464 0.70472028 0.99721167\n",
            " 0.        ] == [0.30436225]\n",
            "[0.83548983 0.05412289 0.00501987 0.11684628 0.70271484 0.9971997\n",
            " 0.        ] == [0.30618676]\n",
            "[0.83510069 0.05475963 0.00522903 0.11854793 0.70073932 0.99722164\n",
            " 0.        ] == [0.30817714]\n",
            "[0.83412783 0.05475963 0.00522903 0.11798071 0.70046993 0.99718673\n",
            " 0.        ] == [0.30801128]\n",
            "[0.83383598 0.055078   0.00501987 0.11911514 0.69972163 0.9972007\n",
            " 0.        ] == [0.3088406]\n",
            "[0.83286312 0.05491882 0.00522903 0.11854793 0.69939238 0.99720967\n",
            " 0.        ] == [0.30867474]\n",
            "[0.83257126 0.05491882 0.00501987 0.11911514 0.69930258 0.99718574\n",
            " 0.        ] == [0.30867474]\n",
            "[0.8315984  0.05539637 0.00501987 0.12024957 0.69753659 0.99718075\n",
            " 0.        ] == [0.31016752]\n",
            "[0.83082012 0.05571474 0.00501987 0.12081679 0.69648897 0.99719571\n",
            " 0.        ] == [0.31099685]\n",
            "[0.83013912 0.05555556 0.00501987 0.12024957 0.69672843 0.99720768\n",
            " 0.        ] == [0.31049925]\n",
            "[0.83013912 0.05571474 0.00501987 0.12081679 0.69615972 0.99718773\n",
            " 0.        ] == [0.31099685]\n",
            "[0.82906898 0.05571474 0.00522903 0.12024957 0.69589033 0.99718873\n",
            " 0.        ] == [0.31083098]\n",
            "[0.82906898 0.05555556 0.00501987 0.12024957 0.69639917 0.9971977\n",
            " 0.        ] == [0.31033339]\n",
            "[0.82867983 0.05539637 0.00501987 0.11968236 0.6965189  0.99721964\n",
            " 0.        ] == [0.31000166]\n",
            "[0.8281934  0.055078   0.00501987 0.11911514 0.697357   0.99720369\n",
            " 0.        ] == [0.3088406]\n",
            "[0.82780426 0.05475963 0.00481071 0.11854793 0.69813523 0.99722363\n",
            " 0.        ] == [0.30784541]\n",
            "[0.82760969 0.05444126 0.00460155 0.11854793 0.69876381 0.99723061\n",
            " 0.        ] == [0.30701609]\n",
            "[0.82673412 0.05460045 0.00439239 0.11911514 0.69804544 0.99721466\n",
            " 0.        ] == [0.30734782]\n",
            "[0.82585855 0.05475963 0.00439239 0.11968236 0.69708761 0.99721565\n",
            " 0.        ] == [0.30817714]\n",
            "[0.82537212 0.05539637 0.00418323 0.121384   0.69532162 0.9972007\n",
            " 0.        ] == [0.30983579]\n",
            "[0.82488569 0.05491882 0.00460155 0.11968236 0.69636924 0.99725056\n",
            " 0.        ] == [0.30850887]\n",
            "[0.82420469 0.05396371 0.00439239 0.11798071 0.69855428 0.9972356\n",
            " 0.        ] == [0.30568917]\n",
            "[0.82391283 0.05380452 0.00418323 0.11798071 0.69921279 0.99724158\n",
            " 0.        ] == [0.30485984]\n",
            "[0.82225897 0.05380452 0.00397406 0.11798071 0.69846449 0.99723859\n",
            " 0.        ] == [0.30485984]\n",
            "[0.82060512 0.05316778 0.00397406 0.11684628 0.69951211 0.99726352\n",
            " 0.        ] == [0.30303533]\n",
            "[0.8213834  0.05253104 0.0037649  0.11571185 0.70157742 0.99720867\n",
            " 0.        ] == [0.30104495]\n",
            "[0.82186983 0.05205349 0.00355574 0.11514464 0.70337334 0.99720768\n",
            " 0.        ] == [0.29922043]\n",
            "[0.8220644  0.05205349 0.00355574 0.11514464 0.70337334 0.99723759\n",
            " 0.        ] == [0.2993863]\n",
            "[0.82313455 0.05221267 0.00355574 0.11514464 0.70349307 0.99726153\n",
            " 0.        ] == [0.29971803]\n",
            "[0.82313455 0.0530086  0.00334658 0.1174135  0.70103864 0.99724856\n",
            " 0.        ] == [0.30237187]\n",
            "[0.82235626 0.05380452 0.00334658 0.11911514 0.69846449 0.99720269\n",
            " 0.        ] == [0.30485984]\n",
            "[0.82128612 0.05364534 0.0037649  0.11854793 0.69825496 0.99720768\n",
            " 0.        ] == [0.30469398]\n",
            "[0.82041055 0.05348615 0.00355574 0.11854793 0.69840462 0.99720269\n",
            " 0.        ] == [0.30419638]\n",
            "[0.82109155 0.05475963 0.00355574 0.121384   0.69505223 0.99723959\n",
            " 0.        ] == [0.30817714]\n",
            "[0.82216169 0.055078   0.0037649  0.121384   0.69472298 0.9972336\n",
            " 0.        ] == [0.30900647]\n",
            "[0.82255083 0.05539637 0.0037649  0.12195122 0.69400461 0.99722662\n",
            " 0.        ] == [0.31000166]\n",
            "[0.82313455 0.05587393 0.00397406 0.12251843 0.69316651 0.99718374\n",
            " 0.        ] == [0.31116271]\n",
            "[0.82323183 0.0561923  0.00418323 0.12365286 0.69190937 0.99725156\n",
            " 0.        ] == [0.3126555]\n",
            "[0.82293997 0.05571474 0.00418323 0.12195122 0.69325631 0.99725554\n",
            " 0.        ] == [0.31099685]\n",
            "[0.8227454  0.05555556 0.00418323 0.12195122 0.6935257  0.99721167\n",
            " 0.        ] == [0.31066512]\n",
            "[0.82293997 0.05523719 0.00439239 0.12081679 0.69457332 0.99719172\n",
            " 0.        ] == [0.30966993]\n",
            "[0.82303726 0.05523719 0.00439239 0.12024957 0.6949325  0.99719671\n",
            " 0.        ] == [0.30917233]\n",
            "[0.82323183 0.05539637 0.00439239 0.12081679 0.69439373 0.99726253\n",
            " 0.        ] == [0.30983579]\n",
            "[0.8227454  0.05475963 0.00460155 0.11968236 0.69580053 0.99726951\n",
            " 0.        ] == [0.30817714]\n",
            "[0.82284269 0.055078   0.00460155 0.12024957 0.69526176 0.9972725\n",
            " 0.        ] == [0.3088406]\n",
            "[0.82284269 0.05539637 0.00481071 0.12024957 0.69439373 0.99732236\n",
            " 0.        ] == [0.30983579]\n",
            "[0.82216169 0.055078   0.00481071 0.11968236 0.69463318 0.99732037\n",
            " 0.        ] == [0.30917233]\n",
            "[0.8220644  0.05539637 0.00481071 0.12024957 0.69379509 0.99730042\n",
            " 0.        ] == [0.31016752]\n",
            "[0.82255083 0.05555556 0.00522903 0.11968236 0.69379509 0.99735527\n",
            " 0.        ] == [0.31033339]\n",
            "[0.82391283 0.05587393 0.00522903 0.12081679 0.69349576 0.99729743\n",
            " 0.        ] == [0.31132858]\n",
            "[0.82439926 0.0561923  0.00543819 0.12081679 0.69256787 0.99732635\n",
            " 0.        ] == [0.31248963]\n",
            "[0.82449655 0.05635148 0.00585652 0.12081679 0.69211889 0.99729045\n",
            " 0.        ] == [0.31315309]\n",
            "[0.82488569 0.05682904 0.00585652 0.121384   0.69134066 0.9972735\n",
            " 0.        ] == [0.31414828]\n",
            "[0.82556669 0.05746578 0.006484   0.12195122 0.68996378 0.99728945\n",
            " 0.        ] == [0.31613866]\n",
            "[0.82624769 0.05762496 0.00669316 0.12251843 0.68939507 0.99730042\n",
            " 0.        ] == [0.31696799]\n",
            "[0.8268314  0.05762496 0.00690232 0.12195122 0.68966446 0.99730242\n",
            " 0.        ] == [0.31696799]\n",
            "[0.82673412 0.05762496 0.00711148 0.121384   0.69005358 0.99728247\n",
            " 0.        ] == [0.31663626]\n",
            "[0.82741512 0.05746578 0.00732064 0.12081679 0.69038283 0.99734131\n",
            " 0.        ] == [0.31647039]\n",
            "[0.82867983 0.05730659 0.00711148 0.12081679 0.69143045 0.99733632\n",
            " 0.        ] == [0.31580693]\n",
            "[0.82916626 0.05746578 0.00690232 0.121384   0.69113113 0.99730441\n",
            " 0.        ] == [0.31647039]\n",
            "[0.82926355 0.05762496 0.00711148 0.121384   0.69068215 0.9973463\n",
            " 0.        ] == [0.31696799]\n",
            "[0.8295554  0.05794333 0.00711148 0.12195122 0.6902631  0.99732735\n",
            " 0.        ] == [0.31746558]\n",
            "[0.83004183 0.05858007 0.00732064 0.12308565 0.68858691 0.99730042\n",
            " 0.        ] == [0.31962183]\n",
            "[0.83062555 0.05921681 0.00752981 0.12422008 0.68706037 0.99727948\n",
            " 0.        ] == [0.32161221]\n",
            "[0.82965269 0.06001273 0.00794813 0.12478729 0.6848454  0.99735328\n",
            " 0.        ] == [0.32376845]\n",
            "[0.82877712 0.06064947 0.00836645 0.12592172 0.68257056 0.99735826\n",
            " 0.        ] == [0.3259247]\n",
            "[0.82965269 0.06160458 0.00878477 0.12705615 0.68074471 0.99730242\n",
            " 0.        ] == [0.32841267]\n",
            "[0.82965269 0.06176377 0.0092031  0.12705615 0.6800862  0.99731039\n",
            " 0.        ] == [0.32907613]\n",
            "[0.8302364  0.06271888 0.00962142 0.12819058 0.67817055 0.99729145\n",
            " 0.        ] == [0.33156411]\n",
            "[0.82994455 0.06208214 0.01003974 0.12648894 0.6795175  0.99731239\n",
            " 0.        ] == [0.32990546]\n",
            "[0.82936083 0.06240051 0.00962142 0.12762337 0.6785896  0.99727848\n",
            " 0.        ] == [0.33073478]\n",
            "[0.82906898 0.06335562 0.01045806 0.12819058 0.67607531 0.99725854\n",
            " 0.        ] == [0.33355449]\n",
            "[0.82867983 0.06383317 0.01150387 0.12819058 0.67457871 0.99726053\n",
            " 0.        ] == [0.33504727]\n",
            "[0.82731783 0.06399236 0.01171303 0.12762337 0.67386034 0.99726452\n",
            " 0.        ] == [0.33521314]\n",
            "[0.82702598 0.06494747 0.01213135 0.12932501 0.6714957  0.99723261\n",
            " 0.        ] == [0.33786698]\n",
            "[0.8268314  0.06558421 0.01275884 0.12932501 0.66978958 0.99725056\n",
            " 0.        ] == [0.33969149]\n",
            "[0.82673412 0.06558421 0.01317716 0.1287578  0.66993924 0.9972715\n",
            " 0.        ] == [0.33952563]\n",
            "[0.82712326 0.06574339 0.012968   0.12932501 0.66940046 0.99724657\n",
            " 0.        ] == [0.34035495]\n",
            "[0.82692869 0.06717606 0.01380464 0.13102666 0.66592834 0.99722463\n",
            " 0.        ] == [0.34416985]\n",
            "[0.82760969 0.06749443 0.01464129 0.13045944 0.66559909 0.99721765\n",
            " 0.        ] == [0.34499917]\n",
            "[0.82790155 0.06622095 0.01443213 0.12819058 0.6685923  0.99722363\n",
            " 0.        ] == [0.34168187]\n",
            "[0.8281934  0.06542502 0.012968   0.1287578  0.67080727 0.99723161\n",
            " 0.        ] == [0.3391939]\n",
            "[0.8281934  0.06494747 0.01192219 0.12932501 0.67170523 0.99725056\n",
            " 0.        ] == [0.33819871]\n",
            "[0.82838798 0.06558421 0.01171303 0.13102666 0.67047801 0.99720269\n",
            " 0.        ] == [0.33952563]\n",
            "[0.82790155 0.06797198 0.01338632 0.13386273 0.6644916  0.99724258\n",
            " 0.        ] == [0.34616023]\n",
            "[0.82838798 0.06749443 0.0140138  0.13159387 0.66595827 0.9972346\n",
            " 0.        ] == [0.34483331]\n",
            "[0.82799883 0.06765361 0.01359548 0.1327283  0.66526984 0.99718673\n",
            " 0.        ] == [0.34549677]\n",
            "[0.8295554  0.06797198 0.01422297 0.13216109 0.66506031 0.99720568\n",
            " 0.        ] == [0.34649196]\n",
            "[0.83130655 0.06685769 0.01359548 0.13102666 0.6684127  0.99725255\n",
            " 0.        ] == [0.34334052]\n",
            "[0.83218212 0.06622095 0.01234051 0.13159387 0.67032835 0.99729344\n",
            " 0.        ] == [0.34151601]\n",
            "[0.83325226 0.06590258 0.01150387 0.13216109 0.67143584 0.99737621\n",
            " 0.        ] == [0.34068668]\n",
            "[0.83393326 0.06542502 0.01129471 0.13159387 0.67275285 0.99739716\n",
            " 0.        ] == [0.33935976]\n",
            "[0.83529526 0.06494747 0.01108555 0.13045944 0.6747583  0.997452\n",
            " 0.        ] == [0.33770111]\n",
            "[0.83655998 0.06415154 0.01087639 0.12932501 0.6770032  0.99744901\n",
            " 0.        ] == [0.3358766]\n",
            "[0.83685183 0.06383317 0.01066722 0.1287578  0.67781137 0.99737422\n",
            " 0.        ] == [0.33504727]\n",
            "[0.83860298 0.06367399 0.01108555 0.12819058 0.67885899 0.99736125\n",
            " 0.        ] == [0.33471554]\n",
            "[0.83977041 0.06367399 0.01192219 0.12648894 0.67945763 0.99728845\n",
            " 0.        ] == [0.33454968]\n",
            "[0.84181341 0.06367399 0.01213135 0.12648894 0.68023586 0.99732834\n",
            " 0.        ] == [0.33471554]\n",
            "[0.84210526 0.06367399 0.01171303 0.12648894 0.68041546 0.99726951\n",
            " 0.        ] == [0.33454968]\n",
            "[0.84259169 0.0635148  0.01129471 0.12705615 0.68122362 0.99731039\n",
            " 0.        ] == [0.33388622]\n",
            "[0.84278626 0.06303725 0.01087639 0.12705615 0.6824209  0.99728048\n",
            " 0.        ] == [0.3325593]\n",
            "[0.84395369 0.06303725 0.0102489  0.12762337 0.68286989 0.99728247\n",
            " 0.        ] == [0.3325593]\n",
            "[0.84473198 0.06303725 0.01003974 0.12819058 0.68319914 0.9973084\n",
            " 0.        ] == [0.33239343]\n",
            "[0.84580212 0.06255969 0.0092031  0.1287578  0.68451615 0.99733433\n",
            " 0.        ] == [0.33139824]\n",
            "[0.84696955 0.06255969 0.00836645 0.12989223 0.68514472 0.99732037\n",
            " 0.        ] == [0.33106651]\n",
            "[0.84774784 0.06240051 0.00773897 0.13045944 0.68583316 0.997455\n",
            " 0.        ] == [0.33073478]\n",
            "[0.84813698 0.06176377 0.00773897 0.12932501 0.68729983 0.99750187\n",
            " 0.        ] == [0.329242]\n",
            "[0.84949898 0.06160458 0.00815729 0.12819058 0.68840732 0.99748392\n",
            " 0.        ] == [0.32857854]\n",
            "[0.85008269 0.0614454  0.00857561 0.12705615 0.68915562 0.99750586\n",
            " 0.        ] == [0.32808094]\n",
            "[0.85154198 0.06112703 0.0092031  0.12535451 0.69062229 0.99750885\n",
            " 0.        ] == [0.32708575]\n",
            "[0.85212569 0.06112703 0.00941226 0.12422008 0.69098147 0.99748791\n",
            " 0.        ] == [0.32691989]\n",
            "[0.85290398 0.06080866 0.0092031  0.12422008 0.69196923 0.99746497\n",
            " 0.        ] == [0.32625643]\n",
            "[0.85358498 0.06064947 0.00857561 0.12535451 0.69250801 0.99743904\n",
            " 0.        ] == [0.3259247]\n",
            "[0.85416869 0.06064947 0.00794813 0.12648894 0.69265767 0.99735726\n",
            " 0.        ] == [0.3259247]\n",
            "[0.85475241 0.06064947 0.00773897 0.12648894 0.69289712 0.99737721\n",
            " 0.        ] == [0.3259247]\n",
            "[0.85582255 0.06064947 0.00752981 0.12705615 0.69331617 0.99741311\n",
            " 0.        ] == [0.3259247]\n",
            "[0.85718455 0.06096784 0.00711148 0.12819058 0.69301685 0.99736923\n",
            " 0.        ] == [0.32675402]\n",
            "[0.85835198 0.06112703 0.00711148 0.12819058 0.69331617 0.99744702\n",
            " 0.        ] == [0.32691989]\n",
            "[0.85903298 0.06112703 0.00732064 0.12819058 0.69331617 0.99736724\n",
            " 0.        ] == [0.32725162]\n",
            "[0.86010312 0.06064947 0.00752981 0.12705615 0.6949325  0.99745899\n",
            " 0.        ] == [0.3259247]\n",
            "[0.86029769 0.06064947 0.00752981 0.12705615 0.69508216 0.99744702\n",
            " 0.        ] == [0.3259247]\n",
            "[0.86127055 0.06064947 0.00773897 0.12648894 0.69544135 0.99745699\n",
            " 0.        ] == [0.3259247]\n",
            "[0.86146512 0.0603311  0.00773897 0.12592172 0.69654883 0.99745599\n",
            " 0.        ] == [0.32476364]\n",
            "[0.86224341 0.06001273 0.00794813 0.12422008 0.69777605 0.99740613\n",
            " 0.        ] == [0.32376845]\n",
            "[0.86311898 0.05985355 0.00815729 0.12422008 0.69825496 0.99742707\n",
            " 0.        ] == [0.32360259]\n",
            "[0.86350812 0.05985355 0.00857561 0.12308565 0.69876381 0.99748392\n",
            " 0.        ] == [0.32327086]\n",
            "[0.86360541 0.05905762 0.00941226 0.12024957 0.70088898 0.99754475\n",
            " 0.        ] == [0.32094875]\n",
            "[0.86341084 0.0582617  0.00983058 0.1174135  0.70298423 0.99749987\n",
            " 0.        ] == [0.31862664]\n",
            "[0.86379998 0.05746578 0.0102489  0.11514464 0.70525906 0.99745899\n",
            " 0.        ] == [0.31630453]\n",
            "[0.86379998 0.05698822 0.01066722 0.11344299 0.70642641 0.99746397\n",
            " 0.        ] == [0.31497761]\n",
            "[0.86379998 0.05682904 0.01087639 0.11230856 0.70714478 0.99746397\n",
            " 0.        ] == [0.31431415]\n",
            "[0.86428641 0.05603311 0.01066722 0.11117413 0.70932982 0.99739716\n",
            " 0.        ] == [0.31199204]\n",
            "[0.86525927 0.05523719 0.0102489  0.11003971 0.71175432 0.99733233\n",
            " 0.        ] == [0.30966993]\n",
            "[0.8650647  0.05555556 0.01003974 0.11117413 0.71118561 0.99733931\n",
            " 0.        ] == [0.31033339]\n",
            "[0.86555112 0.05523719 0.00962142 0.11117413 0.71202371 0.99730042\n",
            " 0.        ] == [0.30950406]\n",
            "[0.8650647  0.055078   0.00941226 0.11117413 0.7122033  0.99723261\n",
            " 0.        ] == [0.30900647]\n",
            "[0.86623212 0.05475963 0.00962142 0.11003971 0.7136999  0.99724059\n",
            " 0.        ] == [0.30784541]\n",
            "[0.86632941 0.05428208 0.00941226 0.10947249 0.71498698 0.99728447\n",
            " 0.        ] == [0.30651849]\n",
            "[0.86623212 0.05412289 0.0092031  0.10947249 0.71555569 0.99729643\n",
            " 0.        ] == [0.30585503]\n",
            "[0.86516198 0.05412289 0.0092031  0.10947249 0.71495705 0.99737123\n",
            " 0.        ] == [0.3060209]\n",
            "[0.86341084 0.05396371 0.00941226 0.10890528 0.71480739 0.99735627\n",
            " 0.        ] == [0.3055233]\n",
            "[0.86311898 0.05332697 0.00899393 0.10833806 0.71639379 0.99730042\n",
            " 0.        ] == [0.30353292]\n",
            "[0.86379998 0.05237186 0.00815729 0.10720363 0.71926726 0.99723859\n",
            " 0.        ] == [0.30054735]\n",
            "[0.86379998 0.05078001 0.00815729 0.10380034 0.72393666 0.99720568\n",
            " 0.        ] == [0.29540554]\n",
            "[0.86477284 0.04950653 0.00752981 0.1020987  0.7285462  0.99719671\n",
            " 0.        ] == [0.29076132]\n",
            "[0.86409184 0.04934734 0.00690232 0.10266591 0.7284564  0.99717078\n",
            " 0.        ] == [0.29042959]\n",
            "[0.86379998 0.04966571 0.00669316 0.10380034 0.72728905 0.99725455\n",
            " 0.        ] == [0.29159065]\n",
            "[0.86379998 0.05046164 0.00690232 0.10550199 0.72504415 0.99718574\n",
            " 0.        ] == [0.29407862]\n",
            "[0.86379998 0.0498249  0.00773897 0.10266591 0.72681014 0.99722164\n",
            " 0.        ] == [0.29225411]\n",
            "[0.86302169 0.04855142 0.00773897 0.09926262 0.73052172 0.99722263\n",
            " 0.        ] == [0.28777575]\n",
            "[0.86331355 0.04839223 0.00732064 0.09982984 0.73124009 0.99725455\n",
            " 0.        ] == [0.28711229]\n",
            "[0.86350812 0.04743712 0.00690232 0.09812819 0.73411356 0.99725754\n",
            " 0.        ] == [0.28396086]\n",
            "[0.86263255 0.04648201 0.006484   0.09699376 0.73662785 0.99723261\n",
            " 0.        ] == [0.28080942]\n",
            "[0.86263255 0.04584527 0.00606568 0.09642655 0.73875303 0.9971977\n",
            " 0.        ] == [0.27832145]\n",
            "[0.86272984 0.04504935 0.00606568 0.0947249  0.74114759 0.99720169\n",
            " 0.        ] == [0.27583347]\n",
            "[0.86282712 0.04441261 0.00564735 0.09359047 0.74363196 0.99722563\n",
            " 0.        ] == [0.27301377]\n",
            "[0.86360541 0.04504935 0.00522903 0.09585933 0.74192583 0.99720568\n",
            " 0.        ] == [0.27533588]\n",
            "[0.86263255 0.04632283 0.00522903 0.09869541 0.7370469  0.99719671\n",
            " 0.        ] == [0.28014596]\n",
            "[0.86195155 0.04680038 0.00585652 0.09869541 0.73525098 0.99719272\n",
            " 0.        ] == [0.28197048]\n",
            "[0.86146512 0.04632283 0.006484   0.09642655 0.73680745 0.99720169\n",
            " 0.        ] == [0.2799801]\n",
            "[0.86136784 0.04520853 0.00627484 0.0947249  0.74033943 0.99717776\n",
            " 0.        ] == [0.2761652]\n",
            "[0.86136784 0.04473098 0.00606568 0.09415769 0.74201562 0.99718973\n",
            " 0.        ] == [0.27417482]\n",
            "[0.86058955 0.04473098 0.00564735 0.0947249  0.74141698 0.99721565\n",
            " 0.        ] == [0.27450655]\n",
            "[0.86029769 0.04536772 0.00585652 0.09585933 0.73929181 0.99718374\n",
            " 0.        ] == [0.2766628]\n",
            "[0.86010312 0.04504935 0.00606568 0.0947249  0.74018977 0.99724059\n",
            " 0.        ] == [0.27566761]\n",
            "[0.86020041 0.04584527 0.006484   0.09585933 0.73764554 0.9972356\n",
            " 0.        ] == [0.27865318]\n",
            "[0.85990855 0.04616364 0.00732064 0.0947249  0.73683738 0.99718574\n",
            " 0.        ] == [0.27931664]\n",
            "[0.85971398 0.04568609 0.00711148 0.09415769 0.7379748  0.99718973\n",
            " 0.        ] == [0.27798972]\n",
            "[0.85932484 0.0455269  0.00690232 0.09415769 0.73857344 0.99719571\n",
            " 0.        ] == [0.27716039]\n",
            "[0.85903298 0.04457179 0.00669316 0.09245604 0.74129726 0.99718274\n",
            " 0.        ] == [0.27400896]\n",
            "[0.85796284 0.04393505 0.00669316 0.09132161 0.74321291 0.99720967\n",
            " 0.        ] == [0.27152098]\n",
            "[0.85660084 0.04218402 0.00606568 0.08848554 0.74833129 0.99720668\n",
            " 0.        ] == [0.26521811]\n",
            "[0.85533612 0.04154728 0.00564735 0.08735111 0.75036667 0.9971977\n",
            " 0.        ] == [0.26239841]\n",
            "[0.85416869 0.04075135 0.00564735 0.08621668 0.75231225 0.99721366\n",
            " 0.        ] == [0.25974457]\n",
            "[0.85309855 0.03995543 0.00522903 0.08508225 0.75473675 0.99720369\n",
            " 0.        ] == [0.25659313]\n",
            "[0.85309855 0.03884113 0.00481071 0.08281339 0.75865785 0.99718673\n",
            " 0.        ] == [0.25228064]\n",
            "[0.85329312 0.03820439 0.00439239 0.08224617 0.76117214 0.99717876\n",
            " 0.        ] == [0.2496268]\n",
            "[0.85309855 0.03724928 0.00439239 0.07997731 0.76470412 0.99720568\n",
            " 0.        ] == [0.24564604]\n",
            "[0.85222298 0.03740847 0.00418323 0.08111174 0.76344698 0.99735826\n",
            " 0.        ] == [0.24664123]\n",
            "[0.85173655 0.03836358 0.00522903 0.08111174 0.75994493 0.9974161\n",
            " 0.        ] == [0.25029026]\n",
            "[0.85154198 0.03852276 0.006484   0.0794101  0.75949594 0.99739217\n",
            " 0.        ] == [0.25078786]\n",
            "[0.85144469 0.03820439 0.00711148 0.07770845 0.76075309 0.99735328\n",
            " 0.        ] == [0.24946094]\n",
            "[0.85095826 0.03756765 0.00711148 0.07657402 0.76251908 0.99726253\n",
            " 0.        ] == [0.24730469]\n",
            "[0.85193112 0.0359758  0.00711148 0.07317073 0.76871502 0.99729045\n",
            " 0.        ] == [0.24100182]\n",
            "[0.85241755 0.03549825 0.00627484 0.07317073 0.77113951 0.99728945\n",
            " 0.        ] == [0.23851385]\n",
            "[0.85183384 0.03549825 0.00564735 0.07430516 0.77054087 0.99729942\n",
            " 0.        ] == [0.23884558]\n",
            "[0.85183384 0.03549825 0.00564735 0.07430516 0.77054087 0.9972715\n",
            " 0.        ] == [0.23884558]\n",
            "[0.85134741 0.03454314 0.00585652 0.0720363  0.77401299 0.99726651\n",
            " 0.        ] == [0.23486482]\n",
            "[0.85056912 0.03326966 0.00585652 0.06920023 0.77877219 0.99734829\n",
            " 0.        ] == [0.22939128]\n",
            "[0.85066641 0.03295129 0.00564735 0.06863301 0.78032865 0.99724358\n",
            " 0.        ] == [0.22773263]\n",
            "[0.85017998 0.03231455 0.00564735 0.06749858 0.78275315 0.99723161\n",
            " 0.        ] == [0.22491292]\n",
            "[0.84979084 0.03088188 0.00543819 0.06466251 0.78808106 0.99724856\n",
            " 0.        ] == [0.21894178]\n",
            "[0.84940169 0.03088188 0.00501987 0.06466251 0.78853004 0.99723061\n",
            " 0.        ] == [0.21827832]\n",
            "[0.84949898 0.03135944 0.00501987 0.06636415 0.78640486 0.99725156\n",
            " 0.        ] == [0.22060043]\n",
            "[0.84949898 0.03247373 0.00501987 0.06863301 0.78164566 0.99720169\n",
            " 0.        ] == [0.22574225]\n",
            "[0.84949898 0.03231455 0.00585652 0.06693137 0.78260349 0.99723759\n",
            " 0.        ] == [0.22474706]\n",
            "[0.84949898 0.03024514 0.00606568 0.06182643 0.79095453 0.99724059\n",
            " 0.        ] == [0.21579035]\n",
            "[0.84959626 0.03056351 0.00564735 0.06296086 0.78987698 0.99722662\n",
            " 0.        ] == [0.2169514]\n",
            "[0.84881798 0.0327921  0.00606568 0.0680658  0.77990961 0.99722363\n",
            " 0.        ] == [0.2274009]\n",
            "[0.84833155 0.03470232 0.00794813 0.06863301 0.77266605 0.99723061\n",
            " 0.        ] == [0.23519655]\n",
            "[0.84745598 0.0359758  0.0102489  0.0680658  0.76712862 0.99723859\n",
            " 0.        ] == [0.24100182]\n",
            "[0.84706684 0.03581662 0.01108555 0.06579694 0.76778712 0.99726651\n",
            " 0.        ] == [0.2401725]\n",
            "[0.84677498 0.03565743 0.01045806 0.06693137 0.76805651 0.99727748\n",
            " 0.        ] == [0.2396749]\n",
            "[0.84551026 0.03613499 0.00983058 0.06863301 0.76614086 0.99728646\n",
            " 0.        ] == [0.24116769]\n",
            "[0.84414826 0.0359758  0.00983058 0.0680658  0.76623066 0.99733533\n",
            " 0.        ] == [0.24050423]\n",
            "[0.84327269 0.03629417 0.0092031  0.07033466 0.76428507 0.9973074\n",
            " 0.        ] == [0.24216288]\n",
            "[0.84200798 0.03740847 0.00941226 0.0720363  0.75994493 0.99729743\n",
            " 0.        ] == [0.2463095]\n",
            "[0.84093783 0.03852276 0.0102489  0.07373795 0.75518573 0.99729244\n",
            " 0.        ] == [0.25111959]\n",
            "[0.83938126 0.04011461 0.01108555 0.07543959 0.74928911 0.99726751\n",
            " 0.        ] == [0.25692486]\n",
            "[0.83763012 0.04106972 0.01213135 0.07600681 0.74518842 0.99729643\n",
            " 0.        ] == [0.26073976]\n",
            "[0.83626812 0.04282076 0.01275884 0.07884288 0.73857344 0.99728646\n",
            " 0.        ] == [0.26737436]\n",
            "[0.83480883 0.04425342 0.0140138  0.07997731 0.7331258  0.99730042\n",
            " 0.        ] == [0.2728479]\n",
            "[0.83334955 0.04536772 0.01464129 0.08167896 0.72896525 0.99728546\n",
            " 0.        ] == [0.27682866]\n",
            "[0.83101469 0.04711875 0.01568709 0.0833806  0.72270945 0.99728945\n",
            " 0.        ] == [0.2827998]\n",
            "[0.82916626 0.04791468 0.01610542 0.08451503 0.71938699 0.99728845\n",
            " 0.        ] == [0.28578537]\n",
            "[0.82867983 0.05109838 0.01798787 0.08848554 0.7098686  0.9972735\n",
            " 0.        ] == [0.29623486]\n",
            "[0.82780426 0.05523719 0.02175277 0.09132161 0.69765632 0.99731239\n",
            " 0.        ] == [0.30966993]\n",
            "[0.82692869 0.05842089 0.02468103 0.09302326 0.68903589 0.99730441\n",
            " 0.        ] == [0.31912423]\n",
            "[0.82566398 0.06112703 0.02802761 0.09359047 0.68143315 0.99732037\n",
            " 0.        ] == [0.32741748]\n",
            "[0.82401012 0.06383317 0.03053754 0.09529212 0.67421952 0.99731039\n",
            " 0.        ] == [0.33488141]\n",
            "[0.8227454  0.06733524 0.03472077 0.09642655 0.66538957 0.99731139\n",
            " 0.        ] == [0.34466744]\n",
            "[0.82109155 0.06653932 0.03513909 0.09359047 0.66676644 0.99732735\n",
            " 0.        ] == [0.34234533]\n",
            "[0.81953497 0.06813117 0.03660322 0.0947249  0.66242629 0.99732037\n",
            " 0.        ] == [0.34665782]\n",
            "[0.81856212 0.0709965  0.04015896 0.09529212 0.65566165 0.99731737\n",
            " 0.        ] == [0.35412174]\n",
            "[0.81768655 0.07242916 0.04162309 0.09585933 0.65203987 0.99725554\n",
            " 0.        ] == [0.35793664]\n",
            "[0.81651912 0.0678128  0.03743987 0.09302326 0.66185758 0.99724657\n",
            " 0.        ] == [0.34599436]\n",
            "[0.81437883 0.06431073 0.03430245 0.09018718 0.66952019 0.99732136\n",
            " 0.        ] == [0.33620833]\n",
            "[0.81360054 0.06701687 0.03743987 0.09132161 0.66272561 0.99727649\n",
            " 0.        ] == [0.34367225]\n",
            "[0.81262769 0.0709965  0.05061703 0.07770845 0.65404532 0.99744004\n",
            " 0.        ] == [0.35412174]\n",
            "[0.81223854 0.07179242 0.05124451 0.07827567 0.65186028 0.99733134\n",
            " 0.        ] == [0.35644385]\n",
            "[0.81155754 0.07593123 0.05689186 0.07770845 0.64300039 0.99731039\n",
            " 0.        ] == [0.36672748]\n",
            "[0.8104874  0.07640879 0.05982012 0.07430516 0.64147385 0.99729244\n",
            " 0.        ] == [0.3680544]\n",
            "[0.80990369 0.07274753 0.05793767 0.06920023 0.64922626 0.99730042\n",
            " 0.        ] == [0.35893183]\n",
            "[0.80873626 0.06940465 0.05480025 0.06693137 0.65652968 0.99732834\n",
            " 0.        ] == [0.34997512]\n",
            "[0.80727697 0.06701687 0.05124451 0.06749858 0.66110928 0.99732535\n",
            " 0.        ] == [0.34383812]\n",
            "[0.80542854 0.06733524 0.05270864 0.06579694 0.65988207 0.99735527\n",
            " 0.        ] == [0.34450158]\n",
            "[0.8050394  0.06685769 0.05480025 0.06125922 0.66113921 0.99731837\n",
            " 0.        ] == [0.34317466]\n",
            "[0.80406654 0.06399236 0.05354528 0.0567215  0.6676644  0.99734031\n",
            " 0.        ] == [0.33521314]\n",
            "[0.80280183 0.05905762 0.04831625 0.05501985 0.67912838 0.99736624\n",
            " 0.        ] == [0.32128048]\n",
            "[0.80202354 0.05730659 0.04685212 0.05331821 0.68382771 0.9974181\n",
            " 0.        ] == [0.31564107]\n",
            "[0.80085611 0.0561923  0.04580632 0.05275099 0.68622228 0.99740314\n",
            " 0.        ] == [0.31232377]\n",
            "[0.80036969 0.05523719 0.04476051 0.05275099 0.68849711 0.99741112\n",
            " 0.        ] == [0.30950406]\n",
            "[0.80017511 0.05428208 0.04329638 0.05275099 0.69083181 0.99739516\n",
            " 0.        ] == [0.30668436]\n",
            "[0.79929954 0.05348615 0.04162309 0.05388542 0.69289712 0.99742109\n",
            " 0.        ] == [0.30386465]\n",
            "[0.79871583 0.05380452 0.04120477 0.05501985 0.69178964 0.99745899\n",
            " 0.        ] == [0.30485984]\n",
            "[0.79793754 0.05396371 0.04078645 0.05615428 0.69098147 0.99752181\n",
            " 0.        ] == [0.30535744]\n",
            "[0.79774297 0.05539637 0.04162309 0.05842314 0.68667126 0.99748392\n",
            " 0.        ] == [0.31016752]\n",
            "[0.79764569 0.0561923  0.04162309 0.06012479 0.68472567 0.99747295\n",
            " 0.        ] == [0.31232377]\n",
            "[0.7968674  0.0561923  0.04120477 0.060692   0.68406717 0.99750187\n",
            " 0.        ] == [0.3126555]\n",
            "[0.79657554 0.05666985 0.04120477 0.06182643 0.68269029 0.99746996\n",
            " 0.        ] == [0.31398242]\n",
            "[0.79647826 0.05714741 0.04162309 0.06239365 0.68128349 0.99740713\n",
            " 0.        ] == [0.31564107]\n",
            "[0.79599183 0.05762496 0.04162309 0.06352808 0.68002634 0.99738718\n",
            " 0.        ] == [0.31680212]\n",
            "[0.79589454 0.05762496 0.04078645 0.06466251 0.67975695 0.99734131\n",
            " 0.        ] == [0.31696799]\n",
            "[0.79521354 0.05762496 0.04015896 0.06579694 0.6794277  0.99734031\n",
            " 0.        ] == [0.31696799]\n",
            "[0.7955054  0.05794333 0.03953148 0.06749858 0.67900865 0.99733134\n",
            " 0.        ] == [0.31746558]\n",
            "[0.7961864  0.05842089 0.03932232 0.06863301 0.67796103 0.99725455\n",
            " 0.        ] == [0.31895837]\n",
            "[0.79462983 0.05842089 0.03911316 0.06920023 0.67727259 0.9972735\n",
            " 0.        ] == [0.31912423]\n",
            "[0.79443526 0.05746578 0.03743987 0.06976744 0.67963722 0.9972735\n",
            " 0.        ] == [0.31613866]\n",
            "[0.79375426 0.05762496 0.0361849  0.07260352 0.6785896  0.9972366\n",
            " 0.        ] == [0.31696799]\n",
            "[0.79336511 0.05858007 0.03576658 0.07487238 0.67613517 0.99724856\n",
            " 0.        ] == [0.31945596]\n",
            "[0.79258683 0.0582617  0.03472077 0.07600681 0.67661409 0.99728845\n",
            " 0.        ] == [0.31846077]\n",
            "[0.79132211 0.05810252 0.03430245 0.07657402 0.67655422 0.99724956\n",
            " 0.        ] == [0.31796318]\n",
            "[0.78957097 0.05778415 0.03367496 0.07714124 0.67643449 0.99721067\n",
            " 0.        ] == [0.31729972]\n",
            "[0.78850083 0.05889844 0.03283832 0.08111174 0.67293244 0.99727449\n",
            " 0.        ] == [0.32078288]\n",
            "[0.78840354 0.06096784 0.03430245 0.0833806  0.66748481 0.9972366\n",
            " 0.        ] == [0.32691989]\n",
            "[0.78772254 0.06080866 0.0334658  0.08394782 0.6677542  0.99722762\n",
            " 0.        ] == [0.32625643]\n",
            "[0.78655511 0.0614454  0.03388412 0.08508225 0.66580861 0.99725156\n",
            " 0.        ] == [0.32791508]\n",
            "[0.78509583 0.06192295 0.03388412 0.08564946 0.66395283 0.99725554\n",
            " 0.        ] == [0.32940786]\n",
            "[0.78441483 0.06240051 0.03388412 0.08678389 0.66239636 0.99730042\n",
            " 0.        ] == [0.33073478]\n",
            "[0.78412297 0.06319643 0.03451161 0.08791832 0.66027118 0.99729843\n",
            " 0.        ] == [0.33305689]\n",
            "[0.7825664  0.06399236 0.03492993 0.08905275 0.6575773  0.99729543\n",
            " 0.        ] == [0.33554487]\n",
            "[0.78295554 0.06415154 0.03472077 0.08961997 0.65742764 0.99729543\n",
            " 0.        ] == [0.3358766]\n",
            "[0.78276097 0.06415154 0.03430245 0.09018718 0.65730791 0.99727848\n",
            " 0.        ] == [0.3358766]\n",
            "[0.78276097 0.06415154 0.03367496 0.09132161 0.65727798 0.99726153\n",
            " 0.        ] == [0.3358766]\n",
            "[0.78227454 0.06446991 0.0334658  0.09245604 0.65620043 0.99726651\n",
            " 0.        ] == [0.33670592]\n",
            "[0.77993968 0.06478828 0.03304748 0.09415769 0.65470382 0.99733134\n",
            " 0.        ] == [0.33736938]\n",
            "[0.77838311 0.0646291  0.03242    0.0947249  0.65434464 0.99728945\n",
            " 0.        ] == [0.33703765]\n",
            "[0.77770211 0.0646291  0.03158335 0.09585933 0.65413511 0.99728147\n",
            " 0.        ] == [0.33687179]\n",
            "[0.77663197 0.06494747 0.03053754 0.09869541 0.65272831 0.99730242\n",
            " 0.        ] == [0.33786698]\n",
            "[0.77517268 0.06526584 0.03032838 0.09926262 0.65138136 0.99731837\n",
            " 0.        ] == [0.33886217]\n",
            "[0.77381068 0.06590258 0.03074671 0.10039705 0.64913646 0.99732735\n",
            " 0.        ] == [0.34085255]\n",
            "[0.7737134  0.06685769 0.03095587 0.1020987  0.64710108 0.99726352\n",
            " 0.        ] == [0.34300879]\n",
            "[0.77381068 0.06813117 0.03137419 0.10436756 0.64410788 0.99727748\n",
            " 0.        ] == [0.34649196]\n",
            "[0.7737134  0.06892709 0.03200167 0.10493477 0.6418929  0.99728447\n",
            " 0.        ] == [0.34897993]\n",
            "[0.77293511 0.06940465 0.03242    0.10550199 0.64060582 0.99731438\n",
            " 0.        ] == [0.35014099]\n",
            "[0.77176768 0.06940465 0.03200167 0.1060692  0.64012691 0.99730541\n",
            " 0.        ] == [0.35014099]\n",
            "[0.77040568 0.0698822  0.03200167 0.10720363 0.63857045 0.99730242\n",
            " 0.        ] == [0.35130204]\n",
            "[0.76923825 0.07067813 0.03179251 0.10947249 0.63620581 0.99726253\n",
            " 0.        ] == [0.35345828]\n",
            "[0.7682654  0.07131487 0.03158335 0.11117413 0.63432009 0.99731837\n",
            " 0.        ] == [0.35511693]\n",
            "[0.76797354 0.07163324 0.03074671 0.11344299 0.63351193 0.99726452\n",
            " 0.        ] == [0.35594626]\n",
            "[0.76670882 0.07226998 0.03074671 0.11457742 0.63168607 0.99734829\n",
            " 0.        ] == [0.35743904]\n",
            "[0.76456854 0.07242916 0.03116503 0.11457742 0.63024933 0.99743505\n",
            " 0.        ] == [0.3581025]\n",
            "[0.76359568 0.07195161 0.02823677 0.11854793 0.63072825 0.99748292\n",
            " 0.        ] == [0.35694145]\n",
            "[0.76252554 0.06876791 0.01819703 0.12819058 0.63719357 0.99742109\n",
            " 0.        ] == [0.34831647]\n",
            "[0.76242825 0.06685769 0.01338632 0.13216109 0.6412344  0.99732834\n",
            " 0.        ] == [0.34317466]\n",
            "[0.76126082 0.06653932 0.01150387 0.13499716 0.64150379 0.99737621\n",
            " 0.        ] == [0.34234533]\n",
            "[0.76116354 0.06685769 0.01066722 0.13669881 0.64057589 0.99737123\n",
            " 0.        ] == [0.34317466]\n",
            "[0.76038525 0.06749443 0.01066722 0.13840045 0.63886977 0.99734031\n",
            " 0.        ] == [0.34483331]\n",
            "[0.76009339 0.06829035 0.01108555 0.13953488 0.63680445 0.99728746\n",
            " 0.        ] == [0.34698955]\n",
            "[0.75931511 0.06860872 0.01150387 0.13953488 0.6358167  0.99729444\n",
            " 0.        ] == [0.34781888]\n",
            "[0.75863411 0.06717606 0.01129471 0.13669881 0.63866024 0.99728247\n",
            " 0.        ] == [0.34433571]\n",
            "[0.75775854 0.06558421 0.01003974 0.13499716 0.64237182 0.99729344\n",
            " 0.        ] == [0.33952563]\n",
            "[0.75649382 0.06717606 0.00962142 0.13953488 0.63776228 0.99725754\n",
            " 0.        ] == [0.34416985]\n",
            "[0.75552097 0.06765361 0.0102489  0.13953488 0.636565   0.99724557\n",
            " 0.        ] == [0.34516504]\n",
            "[0.75532639 0.0678128  0.0102489  0.1401021  0.63593642 0.99726253\n",
            " 0.        ] == [0.3458285]\n",
            "[0.75445082 0.06844954 0.0102489  0.14180374 0.63396091 0.99724557\n",
            " 0.        ] == [0.34765301]\n",
            "[0.75386711 0.06940465 0.01108555 0.14237096 0.63162621 0.99725953\n",
            " 0.        ] == [0.35014099]\n",
            "[0.75299154 0.07020057 0.01171303 0.14293817 0.62968062 0.99727649\n",
            " 0.        ] == [0.3519655]\n",
            "[0.75192139 0.07004139 0.01192219 0.14237096 0.62935137 0.99730441\n",
            " 0.        ] == [0.3519655]\n",
            "[0.75133768 0.06956383 0.01108555 0.14237096 0.63027927 0.99722064\n",
            " 0.        ] == [0.35047272]\n",
            "[0.75046211 0.06956383 0.01087639 0.14293817 0.62983029 0.99724158\n",
            " 0.        ] == [0.35063858]\n",
            "[0.74997568 0.07020057 0.01045806 0.14520703 0.62833368 0.99732735\n",
            " 0.        ] == [0.35213137]\n",
            "[0.74939196 0.0709965  0.01066722 0.14634146 0.62626837 0.99724158\n",
            " 0.        ] == [0.35412174]\n",
            "[0.74812725 0.07115568 0.01066722 0.14690868 0.62549014 0.99724657\n",
            " 0.        ] == [0.35445347]\n",
            "[0.74764082 0.07115568 0.0102489  0.14747589 0.62528061 0.99726552\n",
            " 0.        ] == [0.35445347]\n",
            "[0.74686254 0.07115568 0.01003974 0.14804311 0.62492143 0.99728945\n",
            " 0.        ] == [0.35445347]\n",
            "[0.74647339 0.07195161 0.0102489  0.14917754 0.62288605 0.99728247\n",
            " 0.        ] == [0.35677558]\n",
            "[0.74569511 0.07242916 0.01045806 0.14974475 0.6216289  0.9973064\n",
            " 0.        ] == [0.35777077]\n",
            "[0.74540325 0.07211079 0.0102489  0.14974475 0.62192822 0.9972735\n",
            " 0.        ] == [0.35727318]\n",
            "[0.74520868 0.07211079 0.00983058 0.15031197 0.62207788 0.99727549\n",
            " 0.        ] == [0.35694145]\n",
            "[0.74413853 0.07242916 0.00983058 0.1514464  0.6209704  0.99728247\n",
            " 0.        ] == [0.35777077]\n",
            "[0.74316568 0.07242916 0.01003974 0.15087918 0.62058128 0.99725854\n",
            " 0.        ] == [0.35777077]\n",
            "[0.74287382 0.07242916 0.01003974 0.15087918 0.62046155 0.99730541\n",
            " 0.        ] == [0.35777077]\n",
            "[0.74277653 0.07258835 0.01045806 0.15087918 0.61980305 0.9973064\n",
            " 0.        ] == [0.3586001]\n",
            "[0.74248468 0.0730659  0.01066722 0.1514464  0.61878536 0.99729244\n",
            " 0.        ] == [0.35959529]\n",
            "[0.74267925 0.07354346 0.01150387 0.15087918 0.61782753 0.99729444\n",
            " 0.        ] == [0.36092221]\n",
            "[0.74209553 0.07433938 0.01254968 0.15087918 0.61597174 0.99740613\n",
            " 0.        ] == [0.36274672]\n",
            "[0.74229011 0.07465775 0.01275884 0.15087918 0.6153731  0.99743306\n",
            " 0.        ] == [0.36357605]\n",
            "[0.74258196 0.07529449 0.01338632 0.1514464  0.61420575 0.99755273\n",
            " 0.        ] == [0.36506883]\n",
            "[0.74287382 0.07577205 0.01380464 0.15201361 0.61315813 0.99742608\n",
            " 0.        ] == [0.36639575]\n",
            "[0.74326296 0.07593123 0.01380464 0.15201361 0.6131282  0.99748292\n",
            " 0.        ] == [0.36672748]\n",
            "[0.74355482 0.07609042 0.01380464 0.15258083 0.61261935 0.9974161\n",
            " 0.        ] == [0.36739094]\n",
            "[0.74297111 0.07593123 0.01338632 0.15314804 0.61291867 0.99738818\n",
            " 0.        ] == [0.36672748]\n",
            "[0.74248468 0.07593123 0.012968   0.15371526 0.61261935 0.99745101\n",
            " 0.        ] == [0.36689335]\n",
            "[0.74287382 0.07656797 0.01380464 0.15371526 0.61160166 0.99739017\n",
            " 0.        ] == [0.36822027]\n",
            "[0.74277653 0.07688634 0.01443213 0.15314804 0.61088329 0.99735627\n",
            " 0.        ] == [0.36904959]\n",
            "[0.74297111 0.07720471 0.01526877 0.15314804 0.61022479 0.99737023\n",
            " 0.        ] == [0.37004478]\n",
            "[0.74297111 0.07720471 0.01568709 0.15201361 0.61022479 0.99733832\n",
            " 0.        ] == [0.37004478]\n",
            "[0.74229011 0.07688634 0.01610542 0.15031197 0.61085336 0.99731139\n",
            " 0.        ] == [0.36904959]\n",
            "[0.74277653 0.07704553 0.0167329  0.14974475 0.61052411 0.99732834\n",
            " 0.        ] == [0.36971305]\n",
            "[0.74277653 0.07593123 0.01610542 0.14861032 0.6130384  0.99742907\n",
            " 0.        ] == [0.36672748]\n",
            "[0.74219282 0.07386183 0.012968   0.14917754 0.61707923 0.99733533\n",
            " 0.        ] == [0.36158567]\n",
            "[0.74199825 0.07322509 0.01171303 0.14974475 0.61839624 0.99732535\n",
            " 0.        ] == [0.35992702]\n",
            "[0.74199825 0.07274753 0.01150387 0.14917754 0.61923434 0.99744502\n",
            " 0.        ] == [0.35893183]\n",
            "[0.74190096 0.07274753 0.01150387 0.14917754 0.61923434 0.99744802\n",
            " 0.        ] == [0.35893183]\n",
            "[0.74190096 0.07290672 0.01087639 0.15031197 0.61908468 0.9974171\n",
            " 0.        ] == [0.35893183]\n",
            "[0.74199825 0.07322509 0.01108555 0.15087918 0.61812685 0.99740214\n",
            " 0.        ] == [0.36009288]\n",
            "[0.74190096 0.07386183 0.01150387 0.1514464  0.61677991 0.99742308\n",
            " 0.        ] == [0.36175153]\n",
            "[0.74160911 0.07433938 0.01150387 0.15258083 0.61582208 0.99740214\n",
            " 0.        ] == [0.36274672]\n",
            "[0.74092811 0.07449857 0.01150387 0.15314804 0.61495405 0.99740214\n",
            " 0.        ] == [0.36341018]\n",
            "[0.74092811 0.07449857 0.01171303 0.15314804 0.61492412 0.9974161\n",
            " 0.        ] == [0.36341018]\n",
            "[0.73976068 0.07449857 0.01171303 0.15258083 0.61450507 0.99739616\n",
            " 0.        ] == [0.36341018]\n",
            "[0.73830139 0.07449857 0.01171303 0.15258083 0.61414589 0.99737222\n",
            " 0.        ] == [0.36307845]\n",
            "[0.73723125 0.07449857 0.01129471 0.15371526 0.61354725 0.9974161\n",
            " 0.        ] == [0.36324432]\n",
            "[0.73635568 0.07449857 0.01108555 0.15428247 0.6131282  0.99737721\n",
            " 0.        ] == [0.36341018]\n",
            "[0.73489639 0.0741802  0.01045806 0.15428247 0.61333772 0.99735028\n",
            " 0.        ] == [0.36241499]\n",
            "[0.73382625 0.07402101 0.00983058 0.15484969 0.6130384  0.99735527\n",
            " 0.        ] == [0.36224913]\n",
            "[0.73295068 0.07386183 0.00962142 0.15484969 0.61315813 0.99735726\n",
            " 0.        ] == [0.36158567]\n",
            "[0.7313941  0.07433938 0.00983058 0.15598412 0.61136221 0.99736524\n",
            " 0.        ] == [0.36307845]\n",
            "[0.72983753 0.07529449 0.01066722 0.15655133 0.60890778 0.99740114\n",
            " 0.        ] == [0.3652347]\n",
            "[0.72857282 0.07561286 0.01213135 0.15484969 0.60777036 0.9974161\n",
            " 0.        ] == [0.36606402]\n",
            "[0.72711353 0.0762496  0.01317716 0.15428247 0.60606423 0.99739815\n",
            " 0.        ] == [0.36739094]\n",
            "[0.72575153 0.07577205 0.01338632 0.15314804 0.60648328 0.99746198\n",
            " 0.        ] == [0.36639575]\n",
            "[0.72448682 0.07513531 0.01254968 0.15314804 0.60732138 0.99746597\n",
            " 0.        ] == [0.3647371]\n",
            "[0.72341667 0.07529449 0.01234051 0.15371526 0.60648328 0.9973802\n",
            " 0.        ] == [0.3652347]\n",
            "[0.72263839 0.07529449 0.01254968 0.15371526 0.60630369 0.99747095\n",
            " 0.        ] == [0.36506883]\n",
            "[0.72263839 0.07497612 0.01213135 0.15314804 0.60684247 0.99738818\n",
            " 0.        ] == [0.36440537]\n",
            "[0.72195739 0.07529449 0.01192219 0.15428247 0.60585471 0.99729643\n",
            " 0.        ] == [0.3652347]\n",
            "[0.72078996 0.07640879 0.01254968 0.15598412 0.60316082 0.9972705\n",
            " 0.        ] == [0.36788854]\n",
            "[0.71962253 0.07672716 0.01317716 0.15598412 0.60187375 0.9973084\n",
            " 0.        ] == [0.36888373]\n",
            "[0.71903882 0.07672716 0.01380464 0.15428247 0.60190368 0.99738918\n",
            " 0.        ] == [0.368552]\n",
            "[0.7177741  0.07609042 0.01422297 0.15258083 0.60268191 0.99743904\n",
            " 0.        ] == [0.36722508]\n",
            "[0.71631482 0.07593123 0.01464129 0.1514464  0.60256218 0.99752879\n",
            " 0.        ] == [0.36672748]\n",
            "[0.71553653 0.07593123 0.01505961 0.15087918 0.602203   0.99745699\n",
            " 0.        ] == [0.36672748]\n",
            "[0.71534196 0.07656797 0.01526877 0.15201361 0.60064653 0.99731837\n",
            " 0.        ] == [0.368552]\n",
            "[0.7143691  0.07720471 0.01526877 0.15314804 0.59897034 0.99721964\n",
            " 0.        ] == [0.37004478]\n",
            "[0.71407724 0.07768227 0.01568709 0.15371526 0.59786285 0.99727549\n",
            " 0.        ] == [0.37120584]\n",
            "[0.71359082 0.07688634 0.01568709 0.15201361 0.59929959 0.99728147\n",
            " 0.        ] == [0.36921546]\n",
            "[0.71310439 0.0762496  0.01485045 0.15201361 0.60040708 0.99725953\n",
            " 0.        ] == [0.36772267]\n",
            "[0.71203424 0.07545368 0.01443213 0.15087918 0.60169415 0.99736225\n",
            " 0.        ] == [0.36573229]\n",
            "[0.71193696 0.07513531 0.0140138  0.15087918 0.60250232 0.9972725\n",
            " 0.        ] == [0.3647371]\n",
            "[0.71106139 0.07481694 0.01359548 0.15087918 0.60259212 0.99726153\n",
            " 0.        ] == [0.36423951]\n",
            "[0.71067224 0.07433938 0.01359548 0.14974475 0.60360981 0.99737621\n",
            " 0.        ] == [0.36291259]\n",
            "[0.71047767 0.07386183 0.01317716 0.14917754 0.60474722 0.99727748\n",
            " 0.        ] == [0.3614198]\n",
            "[0.71047767 0.07338427 0.01254968 0.14917754 0.60570505 0.99731737\n",
            " 0.        ] == [0.36025875]\n",
            "[0.71067224 0.07258835 0.01171303 0.14917754 0.60732138 0.99732535\n",
            " 0.        ] == [0.35843423]\n",
            "[0.71008853 0.07242916 0.01108555 0.14974475 0.6076207  0.99729843\n",
            " 0.        ] == [0.35777077]\n",
            "[0.7096021  0.07226998 0.01066722 0.14974475 0.60765063 0.99726851\n",
            " 0.        ] == [0.35743904]\n",
            "[0.70872653 0.07258835 0.01045806 0.15087918 0.60669281 0.99727549\n",
            " 0.        ] == [0.35826837]\n",
            "[0.70901839 0.07242916 0.0102489  0.15087918 0.60714179 0.99724457\n",
            " 0.        ] == [0.35777077]\n",
            "[0.70814282 0.07242916 0.01003974 0.1514464  0.60654315 0.99721266\n",
            " 0.        ] == [0.3581025]\n",
            "[0.70668353 0.07226998 0.00983058 0.1514464  0.60630369 0.9972735\n",
            " 0.        ] == [0.35760491]\n",
            "[0.70600253 0.07195161 0.00983058 0.15087918 0.60666288 0.99731837\n",
            " 0.        ] == [0.35694145]\n",
            "[0.70580796 0.07195161 0.01003974 0.15087918 0.60681254 0.99729743\n",
            " 0.        ] == [0.35660972]\n",
            "[0.70502967 0.07195161 0.01003974 0.15031197 0.60657308 0.99726751\n",
            " 0.        ] == [0.35660972]\n",
            "[0.7048351  0.07195161 0.00941226 0.1514464  0.60639349 0.99725355\n",
            " 0.        ] == [0.35660972]\n",
            "[0.70473781 0.07163324 0.00941226 0.15087918 0.60714179 0.99729444\n",
            " 0.        ] == [0.35578039]\n",
            "[0.7041541  0.07147405 0.0092031  0.15087918 0.60708192 0.99723759\n",
            " 0.        ] == [0.35561453]\n",
            "[0.70366767 0.07147405 0.0092031  0.15087918 0.6069622  0.99733034\n",
            " 0.        ] == [0.35544866]\n",
            "[0.70357039 0.07115568 0.00899393 0.15087918 0.6075309  0.99725554\n",
            " 0.        ] == [0.3547852]\n",
            "[0.70269481 0.07163324 0.00878477 0.1514464  0.60630369 0.99731438\n",
            " 0.        ] == [0.35578039]\n",
            "[0.70250024 0.07115568 0.00878477 0.15087918 0.60732138 0.99730541\n",
            " 0.        ] == [0.35461934]\n",
            "[0.70269481 0.07131487 0.00878477 0.1514464  0.60690233 0.9972735\n",
            " 0.        ] == [0.35511693]\n",
            "[0.7021111  0.07147405 0.00878477 0.1514464  0.60639349 0.99730242\n",
            " 0.        ] == [0.35544866]\n",
            "[0.70133281 0.07131487 0.00878477 0.15087918 0.60654315 0.99736425\n",
            " 0.        ] == [0.35495107]\n",
            "[0.70123553 0.07163324 0.00878477 0.1514464  0.60576491 0.99733433\n",
            " 0.        ] == [0.35578039]\n",
            "[0.70113824 0.07195161 0.0092031  0.15201361 0.60492682 0.99728746\n",
            " 0.        ] == [0.35660972]\n",
            "[0.70113824 0.07226998 0.00899393 0.15314804 0.60414858 0.99723759\n",
            " 0.        ] == [0.35760491]\n",
            "[0.70094367 0.07195161 0.00878477 0.15258083 0.60483702 0.99729444\n",
            " 0.        ] == [0.35660972]\n",
            "[0.7000681  0.07195161 0.00857561 0.15314804 0.60447784 0.99730541\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69948439 0.07195161 0.00836645 0.15314804 0.60423838 0.99735527\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69899796 0.07195161 0.00815729 0.15371526 0.60402885 0.99734729\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69909524 0.07195161 0.00794813 0.15428247 0.60402885 0.99737821\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69880339 0.07195161 0.00794813 0.15428247 0.60390913 0.99729543\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69880339 0.07211079 0.00773897 0.15484969 0.60363974 0.99726352\n",
            " 0.        ] == [0.35694145]\n",
            "[0.69860881 0.07242916 0.00773897 0.1554169  0.6028615  0.99724059\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69880339 0.07242916 0.00752981 0.15598412 0.60292137 0.99723959\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69880339 0.07242916 0.00732064 0.15655133 0.60274178 0.99724657\n",
            " 0.        ] == [0.35793664]\n",
            "[0.69831696 0.07274753 0.00732064 0.15711855 0.60190368 0.9972336\n",
            " 0.        ] == [0.35876596]\n",
            "[0.69773324 0.07226998 0.00732064 0.15598412 0.60262205 0.99730341\n",
            " 0.        ] == [0.35760491]\n",
            "[0.69763596 0.07195161 0.00711148 0.1554169  0.60340028 0.99733732\n",
            " 0.        ] == [0.35660972]\n",
            "[0.69763596 0.07226998 0.00690232 0.15655133 0.60271184 0.9973443\n",
            " 0.        ] == [0.35743904]\n",
            "[0.69763596 0.07242916 0.00732064 0.15655133 0.60244246 0.99736425\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69724681 0.07226998 0.00752981 0.15598412 0.60235266 0.99730541\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69656581 0.07242916 0.00752981 0.15598412 0.60205334 0.99731737\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69646853 0.07242916 0.00773897 0.15598412 0.60178395 0.9972705\n",
            " 0.        ] == [0.3581025]\n",
            "[0.69646853 0.07242916 0.00794813 0.15484969 0.60202341 0.9973084\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69637124 0.07226998 0.00815729 0.15484969 0.60226286 0.9973084\n",
            " 0.        ] == [0.35743904]\n",
            "*************************\n",
            "[0.69578753 0.07195161 0.00794813 0.15428247 0.60265198 0.99728646\n",
            " 0.        ] == [0.35677558]\n",
            "[0.6953011  0.07211079 0.00794813 0.15484969 0.60202341 0.99730042\n",
            " 0.        ] == [0.35727318]\n",
            "[0.69520381 0.07195161 0.00815729 0.15371526 0.60250232 0.99731737\n",
            " 0.        ] == [0.35677558]\n",
            "[0.69500924 0.07179242 0.00815729 0.15371526 0.60265198 0.9973064\n",
            " 0.        ] == [0.35644385]\n",
            "[0.69423096 0.07163324 0.00794813 0.15371526 0.60283157 0.99731339\n",
            " 0.        ] == [0.35578039]\n",
            "[0.69403638 0.07163324 0.00773897 0.15371526 0.6028615  0.99733533\n",
            " 0.        ] == [0.35578039]\n",
            "[0.69296624 0.07147405 0.00794813 0.15314804 0.60259212 0.99737023\n",
            " 0.        ] == [0.35561453]\n",
            "[0.69209067 0.07147405 0.00815729 0.15314804 0.60226286 0.99741311\n",
            " 0.        ] == [0.35561453]\n",
            "[0.6912151  0.07147405 0.00836645 0.15258083 0.60193361 0.99744203\n",
            " 0.        ] == [0.35561453]\n",
            "[0.69072867 0.07147405 0.00857561 0.15201361 0.60187375 0.99745599\n",
            " 0.        ] == [0.35544866]\n",
            "[0.69082596 0.07147405 0.00878477 0.15201361 0.60181388 0.99743505\n",
            " 0.        ] == [0.35561453]\n",
            "[0.6912151  0.07131487 0.00878477 0.1514464  0.60232273 0.99745799\n",
            " 0.        ] == [0.35511693]\n",
            "[0.69209067 0.07115568 0.00878477 0.1514464  0.60310096 0.997453\n",
            " 0.        ] == [0.35461934]\n",
            "[0.69286896 0.07131487 0.00899393 0.1514464  0.60301116 0.99741012\n",
            " 0.        ] == [0.35511693]\n",
            "[0.69364724 0.07147405 0.00941226 0.15087918 0.6029513  0.99742907\n",
            " 0.        ] == [0.35561453]\n",
            "[0.69452281 0.07163324 0.01003974 0.15031197 0.60289144 0.99741112\n",
            " 0.        ] == [0.35611212]\n",
            "[0.69520381 0.07195161 0.01108555 0.14861032 0.60268191 0.99737721\n",
            " 0.        ] == [0.35677558]\n",
            "[0.69569024 0.07226998 0.01254968 0.14690868 0.6022928  0.99740513\n",
            " 0.        ] == [0.35743904]\n",
            "[0.69549567 0.07226998 0.01422297 0.1440726  0.60214314 0.99744702\n",
            " 0.        ] == [0.35777077]\n",
            "[0.69559296 0.07195161 0.01464129 0.14293817 0.6030411  0.99746397\n",
            " 0.        ] == [0.35677558]\n",
            "[0.69627396 0.07179242 0.01505961 0.14180374 0.6037894  0.99748292\n",
            " 0.        ] == [0.35627799]\n",
            "[0.6973441  0.07147405 0.01485045 0.14123653 0.60474722 0.99744702\n",
            " 0.        ] == [0.35561453]\n",
            "[0.6980251  0.07131487 0.01485045 0.14066931 0.60564519 0.99743705\n",
            " 0.        ] == [0.35495107]\n",
            "[0.69919253 0.0709965  0.01422297 0.14123653 0.60648328 0.99746996\n",
            " 0.        ] == [0.35445347]\n",
            "[0.69948439 0.07067813 0.01338632 0.14237096 0.60738124 0.99748092\n",
            " 0.        ] == [0.35345828]\n",
            "[0.69919253 0.07035976 0.01234051 0.14293817 0.60809961 0.99743206\n",
            " 0.        ] == [0.3524631]\n",
            "[0.69890067 0.07020057 0.01150387 0.1440726  0.60797989 0.99746896\n",
            " 0.        ] == [0.35229723]\n",
            "[0.69958167 0.07004139 0.01150387 0.1440726  0.60878805 0.99744403\n",
            " 0.        ] == [0.35179964]\n",
            "[0.70016539 0.07004139 0.01171303 0.14350539 0.60914724 0.99744403\n",
            " 0.        ] == [0.35163377]\n",
            "[0.70094367 0.07020057 0.01213135 0.14293817 0.60887785 0.9974161\n",
            " 0.        ] == [0.35229723]\n",
            "[0.70084639 0.07020057 0.01275884 0.14237096 0.60881798 0.99744303\n",
            " 0.        ] == [0.35229723]\n",
            "[0.70016539 0.07020057 0.01317716 0.14123653 0.60860846 0.99749289\n",
            " 0.        ] == [0.35229723]\n",
            "[0.70084639 0.07020057 0.01338632 0.14123653 0.60887785 0.99749289\n",
            " 0.        ] == [0.35229723]\n",
            "[0.70035996 0.07035976 0.01338632 0.14123653 0.60866832 0.99751084\n",
            " 0.        ] == [0.3524631]\n",
            "[0.70035996 0.07035976 0.01317716 0.14123653 0.60863839 0.99747295\n",
            " 0.        ] == [0.3524631]\n",
            "[0.70191653 0.07035976 0.01317716 0.14123653 0.60926696 0.99742009\n",
            " 0.        ] == [0.3524631]\n",
            "[0.70327853 0.07035976 0.01338632 0.14066931 0.60980574 0.99737422\n",
            " 0.        ] == [0.3524631]\n",
            "[0.70405681 0.07035976 0.01359548 0.14066931 0.61001527 0.99734231\n",
            " 0.        ] == [0.3524631]\n",
            "[0.70532153 0.07067813 0.01380464 0.14123653 0.60965608 0.99730042\n",
            " 0.        ] == [0.35345828]\n",
            "[0.7068781  0.07067813 0.0140138  0.14066931 0.61028465 0.99725854\n",
            " 0.        ] == [0.35345828]\n",
            "[0.70843467 0.07067813 0.01443213 0.1401021  0.61118262 0.99730042\n",
            " 0.        ] == [0.35329242]\n",
            "[0.70940753 0.07004139 0.01443213 0.13896767 0.61273908 0.9972715\n",
            " 0.        ] == [0.3519655]\n",
            "[0.71086682 0.06940465 0.01380464 0.13783324 0.61504385 0.99728746\n",
            " 0.        ] == [0.34980926]\n",
            "[0.71203424 0.06829035 0.01338632 0.13669881 0.61782753 0.99728646\n",
            " 0.        ] == [0.34715542]\n",
            "[0.71310439 0.06765361 0.01359548 0.13499716 0.61968332 0.99727449\n",
            " 0.        ] == [0.34549677]\n",
            "[0.71397996 0.06717606 0.01338632 0.13386273 0.62129965 0.99727449\n",
            " 0.        ] == [0.34416985]\n",
            "[0.71553653 0.06685769 0.01317716 0.13329552 0.62261666 0.99728845\n",
            " 0.        ] == [0.34334052]\n",
            "[0.71728767 0.06653932 0.01338632 0.1327283  0.6240534  0.9973064\n",
            " 0.        ] == [0.3425112]\n",
            "[0.7191361  0.06526584 0.012968   0.13045944 0.62806429 0.99731339\n",
            " 0.        ] == [0.3386963]\n",
            "[0.72059539 0.06446991 0.01213135 0.12932501 0.63048879 0.99731737\n",
            " 0.        ] == [0.33654006]\n",
            "[0.72215196 0.06383317 0.01171303 0.1287578  0.63270376 0.99732336\n",
            " 0.        ] == [0.33471554]\n",
            "[0.7232221  0.0635148  0.01171303 0.12819058 0.63372145 0.99730242\n",
            " 0.        ] == [0.33405208]\n",
            "[0.72419496 0.06335562 0.01192219 0.12762337 0.63449969 0.99726651\n",
            " 0.        ] == [0.33355449]\n",
            "[0.72565425 0.0635148  0.01150387 0.12819058 0.6348888  0.99733732\n",
            " 0.        ] == [0.33372035]\n",
            "[0.7266271  0.06255969 0.0102489  0.1287578  0.6372235  0.99730242\n",
            " 0.        ] == [0.33139824]\n",
            "[0.72789182 0.06208214 0.00899393 0.12989223 0.63886977 0.99731937\n",
            " 0.        ] == [0.33007132]\n",
            "[0.72954568 0.06208214 0.00836645 0.13102666 0.639648   0.99734729\n",
            " 0.        ] == [0.32990546]\n",
            "[0.7307131  0.06208214 0.00836645 0.13102666 0.63997725 0.99733533\n",
            " 0.        ] == [0.33007132]\n",
            "[0.73188053 0.06176377 0.00836645 0.12989223 0.64138406 0.99734031\n",
            " 0.        ] == [0.32891027]\n",
            "[0.73324253 0.0614454  0.00857561 0.1287578  0.642731   0.99739017\n",
            " 0.        ] == [0.32808094]\n",
            "[0.7341181  0.0614454  0.00878477 0.12819058 0.64326978 0.99735128\n",
            " 0.        ] == [0.32791508]\n",
            "[0.73577196 0.06112703 0.0092031  0.12705615 0.64464665 0.99741112\n",
            " 0.        ] == [0.32708575]\n",
            "[0.73723125 0.06112703 0.00962142 0.12592172 0.64539495 0.99745101\n",
            " 0.        ] == [0.32691989]\n",
            "[0.73869053 0.06112703 0.01003974 0.12535451 0.64569427 0.9973792\n",
            " 0.        ] == [0.32725162]\n",
            "[0.74014982 0.06096784 0.01066722 0.12422008 0.64677183 0.99742408\n",
            " 0.        ] == [0.32675402]\n",
            "[0.74092811 0.06064947 0.01045806 0.12365286 0.64787931 0.99742707\n",
            " 0.        ] == [0.3259247]\n",
            "[0.74199825 0.06017192 0.01003974 0.12308565 0.6497351  0.99754275\n",
            " 0.        ] == [0.32426605]\n",
            "[0.74297111 0.05969436 0.01108555 0.12081679 0.65114191 0.99766342\n",
            " 0.        ] == [0.32310499]\n",
            "[0.74404125 0.05953518 0.012968   0.11684628 0.65224939 0.99768237\n",
            " 0.        ] == [0.32244153]\n",
            "[0.74520868 0.05953518 0.01275884 0.1174135  0.65284803 0.99755472\n",
            " 0.        ] == [0.32227567]\n",
            "[0.74618154 0.05921681 0.01192219 0.11798071 0.65377593 0.99752181\n",
            " 0.        ] == [0.32161221]\n",
            "[0.74666796 0.05921681 0.01213135 0.1174135  0.65419498 0.99767738\n",
            " 0.        ] == [0.32144634]\n",
            "[0.74695982 0.05921681 0.01275884 0.11627907 0.65437457 0.9977462\n",
            " 0.        ] == [0.32144634]\n",
            "[0.74734896 0.05905762 0.012968   0.11571185 0.65479362 0.99770631\n",
            " 0.        ] == [0.32111461]\n",
            "[0.74861368 0.05858007 0.01275884 0.11514464 0.65652968 0.99763151\n",
            " 0.        ] == [0.31978769]\n",
            "[0.74987839 0.05794333 0.01171303 0.11514464 0.65889431 0.99757168\n",
            " 0.        ] == [0.31763145]\n",
            "[0.75036482 0.05682904 0.01066722 0.11457742 0.66185758 0.99760259\n",
            " 0.        ] == [0.31431415]\n",
            "[0.75085125 0.05635148 0.01003974 0.11457742 0.66344398 0.99761555\n",
            " 0.        ] == [0.3126555]\n",
            "[0.75085125 0.0561923  0.01045806 0.11401021 0.66362357 0.9977462\n",
            " 0.        ] == [0.31248963]\n",
            "[0.75124039 0.05635148 0.01129471 0.11230856 0.66362357 0.99774819\n",
            " 0.        ] == [0.31282136]\n",
            "[0.75153225 0.05587393 0.01254968 0.10947249 0.66497052 0.99780503\n",
            " 0.        ] == [0.31149444]\n",
            "[0.75240782 0.05539637 0.01234051 0.10833806 0.66673651 0.99778708\n",
            " 0.        ] == [0.30983579]\n",
            "[0.75289425 0.05523719 0.01192219 0.10833806 0.66751474 0.99775318\n",
            " 0.        ] == [0.30917233]\n",
            "[0.75328339 0.05475963 0.01150387 0.10833806 0.66889162 0.99775417\n",
            " 0.        ] == [0.30784541]\n",
            "[0.75367254 0.05444126 0.01045806 0.10947249 0.66966985 0.99770631\n",
            " 0.        ] == [0.30701609]\n",
            "[0.75376982 0.05412289 0.00983058 0.11003971 0.67062768 0.99776814\n",
            " 0.        ] == [0.30585503]\n",
            "[0.75464539 0.05348615 0.00899393 0.11003971 0.67257326 0.99777711\n",
            " 0.        ] == [0.30403052]\n",
            "[0.75561825 0.0530086  0.00941226 0.10833806 0.67430932 0.99775318\n",
            " 0.        ] == [0.30253773]\n",
            "[0.75688297 0.05284941 0.00941226 0.10777085 0.67538687 0.99768137\n",
            " 0.        ] == [0.30187427]\n",
            "[0.75756397 0.05205349 0.00878477 0.10720363 0.6777515  0.99781401\n",
            " 0.        ] == [0.29955216]\n",
            "[0.75921782 0.05141675 0.00899393 0.10550199 0.68038552 0.99766243\n",
            " 0.        ] == [0.29739592]\n",
            "[0.76077439 0.05109838 0.00878477 0.10493477 0.68182226 0.99772426\n",
            " 0.        ] == [0.29640073]\n",
            "[0.76184454 0.05062082 0.00878477 0.10323313 0.6840971  0.99783196\n",
            " 0.        ] == [0.29441035]\n",
            "[0.76262282 0.0498249  0.0092031  0.10096427 0.68664132 0.99776315\n",
            " 0.        ] == [0.29192238]\n",
            "[0.76359568 0.04934734 0.00941226 0.09982984 0.68822772 0.99778609\n",
            " 0.        ] == [0.29059546]\n",
            "[0.76544411 0.04918816 0.0092031  0.09982984 0.68972433 0.99772725\n",
            " 0.        ] == [0.28976613]\n",
            "[0.76719525 0.04886979 0.00941226 0.09869541 0.69113113 0.99782398\n",
            " 0.        ] == [0.28893681]\n",
            "[0.76923825 0.04823305 0.00983058 0.09642655 0.6941842  0.99786786\n",
            " 0.        ] == [0.28644883]\n",
            "[0.77118397 0.04807386 0.00941226 0.09642655 0.69553114 0.99775318\n",
            " 0.        ] == [0.28595124]\n",
            "[0.77157311 0.04727794 0.00941226 0.0947249  0.69807537 0.99780902\n",
            " 0.        ] == [0.28313153]\n",
            "[0.77264325 0.0466412  0.00941226 0.09415769 0.70020054 0.99780204\n",
            " 0.        ] == [0.28130702]\n",
            "[0.77429711 0.04648201 0.0092031  0.09359047 0.70163728 0.99776913\n",
            " 0.        ] == [0.28047769]\n",
            "[0.77536725 0.04584527 0.00899393 0.09245604 0.70373253 0.99780603\n",
            " 0.        ] == [0.27865318]\n",
            "[0.77634011 0.04520853 0.0092031  0.0907544  0.70663594 0.99787983\n",
            " 0.        ] == [0.27583347]\n",
            "[0.77828583 0.04441261 0.0092031  0.08848554 0.71004819 0.99781002\n",
            " 0.        ] == [0.27301377]\n",
            "[0.78003697 0.04393505 0.00962142 0.08735111 0.71187405 0.99777113\n",
            " 0.        ] == [0.27168685]\n",
            "[0.78149625 0.04361668 0.0102489  0.08508225 0.71361011 0.99776614\n",
            " 0.        ] == [0.27052579]\n",
            "[0.78295554 0.0434575  0.01003974 0.08508225 0.71507678 0.99769035\n",
            " 0.        ] == [0.2695306]\n",
            "[0.78383111 0.0434575  0.00941226 0.08678389 0.71498698 0.99766941\n",
            " 0.        ] == [0.26986233]\n",
            "[0.7846094  0.04297994 0.00941226 0.08508225 0.71702236 0.99779107\n",
            " 0.        ] == [0.26803782]\n",
            "[0.78480397 0.04202483 0.01087639 0.08054453 0.72064414 0.99782997\n",
            " 0.        ] == [0.26422292]\n",
            "[0.78499854 0.0402738  0.01359548 0.0720363  0.72651082 0.99793767\n",
            " 0.        ] == [0.25808592]\n",
            "[0.78626326 0.03963706 0.01464129 0.06863301 0.72965369 0.99789479\n",
            " 0.        ] == [0.25526621]\n",
            "[0.78694426 0.0391595  0.01505961 0.06693137 0.73159927 0.99791872\n",
            " 0.        ] == [0.2534417]\n",
            "[0.78743068 0.03884113 0.01652374 0.06409529 0.73297614 0.99787285\n",
            " 0.        ] == [0.25228064]\n",
            "[0.78888997 0.03836358 0.01736038 0.06182643 0.73513125 0.99788581\n",
            " 0.        ] == [0.25062199]\n",
            "[0.79122483 0.03804521 0.01736038 0.060692   0.73773534 0.99784792\n",
            " 0.        ] == [0.24879748]\n",
            "[0.79248954 0.03740847 0.01715122 0.05955757 0.74004011 0.99783395\n",
            " 0.        ] == [0.2468071]\n",
            "[0.79375426 0.03677173 0.01736038 0.05785593 0.7433027  0.9977821\n",
            " 0.        ] == [0.24382153]\n",
            "[0.79560269 0.03581662 0.01694206 0.0567215  0.74767278 0.99775417\n",
            " 0.        ] == [0.23984077]\n",
            "[0.79696469 0.03454314 0.01652374 0.05445264 0.75303062 0.99780404\n",
            " 0.        ] == [0.23453309]\n",
            "[0.79793754 0.03342884 0.01589626 0.05275099 0.75734084 0.99770232\n",
            " 0.        ] == [0.2302206]\n",
            "[0.80007783 0.03263292 0.01547793 0.05161656 0.76156126 0.99769035\n",
            " 0.        ] == [0.22657157]\n",
            "[0.8009534  0.03183699 0.01526877 0.05048213 0.76494358 0.99766043\n",
            " 0.        ] == [0.22325427]\n",
            "[0.8009534  0.03120025 0.01485045 0.04991492 0.7676674  0.99764448\n",
            " 0.        ] == [0.2202687]\n",
            "[0.80260726 0.03056351 0.01443213 0.04878049 0.77090006 0.99753179\n",
            " 0.        ] == [0.217449]\n",
            "[0.80387197 0.03008596 0.0140138  0.04821327 0.77398306 0.99757666\n",
            " 0.        ] == [0.21462929]\n",
            "[0.80581769 0.0296084  0.01380464 0.04764606 0.77649735 0.99753478\n",
            " 0.        ] == [0.21280478]\n",
            "[0.80727697 0.02913085 0.01359548 0.04707884 0.77919124 0.99743705\n",
            " 0.        ] == [0.21048267]\n",
            "[0.80854169 0.02881248 0.01338632 0.04651163 0.78107696 0.99739815\n",
            " 0.        ] == [0.20898988]\n",
            "[0.80922269 0.0286533  0.01359548 0.04651163 0.78185519 0.997455\n",
            " 0.        ] == [0.20849229]\n",
            "[0.81009826 0.0286533  0.01359548 0.04594441 0.78248376 0.99739317\n",
            " 0.        ] == [0.20816056]\n",
            "[0.81087654 0.02849411 0.01380464 0.0453772  0.78344159 0.99735926\n",
            " 0.        ] == [0.2074971]\n",
            "[0.81126569 0.02817574 0.01359548 0.04480998 0.78511778 0.99741909\n",
            " 0.        ] == [0.20583845]\n",
            "[0.81175212 0.02769819 0.01317716 0.04480998 0.78745248 0.99743206\n",
            " 0.        ] == [0.20351634]\n",
            "[0.81243312 0.02722063 0.01275884 0.04424277 0.78987698 0.99741211\n",
            " 0.        ] == [0.20119423]\n",
            "[0.81262769 0.02642471 0.01234051 0.04367555 0.79313957 0.99747793\n",
            " 0.        ] == [0.19771106]\n",
            "[0.81243312 0.02562878 0.01171303 0.04254112 0.79661169 0.99750586\n",
            " 0.        ] == [0.19389617]\n",
            "[0.81350326 0.02515123 0.01129471 0.04197391 0.79978449 0.99742807\n",
            " 0.        ] == [0.1909106]\n",
            "[0.81418426 0.0243553  0.01087639 0.04140669 0.80340627 0.997454\n",
            " 0.        ] == [0.18726157]\n",
            "[0.81544897 0.02371856 0.0102489  0.04027226 0.80762669 0.99737023\n",
            " 0.        ] == [0.18328081]\n",
            "[0.81632455 0.02308182 0.00983058 0.03970505 0.81106887 0.99735926\n",
            " 0.        ] == [0.17996351]\n",
            "[0.8166164  0.02260427 0.00941226 0.03913783 0.81331378 0.99731438\n",
            " 0.        ] == [0.1776414]\n",
            "[0.81622726 0.02212671 0.0092031  0.03913783 0.81516957 0.99728048\n",
            " 0.        ] == [0.17548516]\n",
            "[0.81632455 0.02180834 0.00899393 0.03857062 0.81693556 0.99732535\n",
            " 0.        ] == [0.17366064]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f389COOIEue"
      },
      "source": [
        "\n",
        "\n",
        "train_dataset = TimeseriesDataset(x_train, y_train, xSequenceLength, ySequenceLength)\n",
        "val_dataset = TimeseriesDataset(x_val, y_val, xSequenceLength, ySequenceLength)\n",
        "\n",
        "batch_size = 256\n",
        "\n",
        "train_loader = DataLoader(train_dataset, shuffle=False, batch_size=batch_size, drop_last=True)\n",
        "val_loader = DataLoader(val_dataset, shuffle=False, batch_size=batch_size, drop_last=True)\n",
        "#test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uRw0ZikNQAi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "612f7f9d-f476-4d8a-f381-e125ee177833"
      },
      "source": [
        "dataiter = iter(train_loader)\n",
        "dataiterVal = iter(val_loader)\n",
        "sample_x, sample_y = dataiter.next()\n",
        "sample_x_val, sample_y_val = dataiter.next()\n",
        "print(sample_x.shape, sample_y.shape)\n",
        "print(\"here\")\n",
        "print(sample_x[0], \"==\", sample_y[0])\n",
        "print(\"here\")\n",
        "print(sample_x_val.shape, sample_y_val.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([256, 576, 7]) torch.Size([256, 1])\n",
            "here\n",
            "tensor([[0.8065, 0.0390, 0.0046,  ..., 0.7395, 0.9972, 0.0000],\n",
            "        [0.8067, 0.0379, 0.0046,  ..., 0.7432, 0.9973, 0.0000],\n",
            "        [0.8072, 0.0363, 0.0044,  ..., 0.7493, 0.9972, 0.0000],\n",
            "        ...,\n",
            "        [0.6965, 0.0724, 0.0079,  ..., 0.6020, 0.9973, 0.0000],\n",
            "        [0.6964, 0.0723, 0.0082,  ..., 0.6023, 0.9973, 0.0000],\n",
            "        [0.6958, 0.0720, 0.0079,  ..., 0.6027, 0.9973, 0.0000]],\n",
            "       dtype=torch.float64) == tensor([0.1737], dtype=torch.float64)\n",
            "here\n",
            "torch.Size([256, 576, 7]) torch.Size([256, 1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqL7_QrNED-9"
      },
      "source": [
        "Using Gpu to expedite learning process."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoO9bf7mECV4",
        "outputId": "ae528e74-3b4a-41ba-8429-269e4316cbbe"
      },
      "source": [
        "def get_device():\n",
        "    if torch.cuda.is_available():\n",
        "        print(\"using GPU\")\n",
        "        return torch.device('cuda')\n",
        "    else:\n",
        "        print(\"using CPU\")\n",
        "        return torch.device('cpu')\n",
        "\n",
        "device = get_device()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "using GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbnUaSNbM-7F"
      },
      "source": [
        "Creating Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9-KFmI8NAmZ"
      },
      "source": [
        "class WeatherNet(nn.Module):\n",
        "    def __init__(self, input_size, output_size, hidden_dim, n_layers, drop_prob=0.5):\n",
        "        super(WeatherNet, self).__init__()\n",
        "        self.output_size = output_size\n",
        "        self.n_layers = n_layers\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.input_size = input_size\n",
        "        self.lstm = nn.LSTM(input_size, hidden_dim, n_layers, dropout=drop_prob, batch_first=True)\n",
        "        self.dropout = nn.Dropout(drop_prob)\n",
        "        self.fc = nn.Linear(hidden_dim, output_size)\n",
        "        \n",
        "        \n",
        "    def forward(self, x, hidden):\n",
        "        x = x.float() \n",
        "        batch_size = x.size(0)\n",
        "        lstm_out, hidden = self.lstm(x, hidden)\n",
        "        lstm_out = (lstm_out[:, xSequenceLength-1, :])\n",
        "        out = self.dropout(lstm_out)\n",
        "        out = self.fc(out)\n",
        "        out = out.view(batch_size, -1)\n",
        "        out = out[:,-1]\n",
        "        return out, hidden\n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        weight = next(self.parameters()).data\n",
        "        hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),\n",
        "                      weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())\n",
        "        return hidden\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9VZIadxPMWc"
      },
      "source": [
        "initialize model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Anyfxl6bO79a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cb45f18-d95f-4af2-e018-89363f3cfddf"
      },
      "source": [
        "input_size = 7\n",
        "output_size = 1\n",
        "hidden_dim = 64\n",
        "n_layers = 1\n",
        "model = WeatherNet(input_size, output_size, hidden_dim, n_layers)\n",
        "model.to(device)\n",
        "print(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:63: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WeatherNet(\n",
            "  (lstm): LSTM(7, 64, batch_first=True, dropout=0.5)\n",
            "  (dropout): Dropout(p=0.5, inplace=False)\n",
            "  (fc): Linear(in_features=64, out_features=1, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "muZ4uN-qQXBg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dfcfeff-024e-4487-e931-3c57b2781c4e"
      },
      "source": [
        "lr=0.0005\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), weight_decay=1, lr=lr)\n",
        "print(model.parameters())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<generator object Module.parameters at 0x7fc2c2b22f50>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbN9FmzSQvZx"
      },
      "source": [
        "Start training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "groxxjTXQnbY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dc1b210-e1fe-48ee-a003-e2f7e3d214a3"
      },
      "source": [
        "epochs = 4\n",
        "counter = 0\n",
        "print_every = 400\n",
        "clip = 5\n",
        "valid_loss_min = np.Inf\n",
        "\n",
        "model.train()\n",
        "history = []\n",
        "for i in range(epochs):\n",
        "    h = model.init_hidden(batch_size)\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs_d, labels_d = inputs.to(device), labels.to(device)\n",
        "        counter += 1\n",
        "        h = tuple([e.data for e in h])\n",
        "        optimizer.zero_grad()\n",
        "        output, h = model(inputs_d, h)\n",
        "        loss = criterion(output.squeeze(), labels_d.float().squeeze())\n",
        "        loss.backward()\n",
        "        nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
        "        optimizer.step()\n",
        "        \n",
        "\n",
        "        if counter%print_every == 0:\n",
        "            val_h = model.init_hidden(batch_size)\n",
        "            val_losses = []\n",
        "            model.eval()\n",
        "            for inp, lab in val_loader:\n",
        "                inp_d, lab_d = inp.to(device), lab.to(device)\n",
        "                val_h = tuple([each.data for each in val_h])\n",
        "                out, val_h = model(inp_d, val_h)\n",
        "                val_loss = criterion(out.squeeze(), lab_d.float().squeeze())\n",
        "                val_losses.append(val_loss.item())\n",
        "            model.train()\n",
        "            print(\"Epoch: {}/{}...\".format(i+1, epochs),\n",
        "                  \"Step: {}...\".format(counter),\n",
        "                  \"Loss: {:.6f}...\".format(loss.item()),\n",
        "                  \"Val Loss: {:.6f}\".format(np.mean(val_losses)))\n",
        "            history.append(np.mean(val_losses))\n",
        "            \n",
        "            if np.mean(val_losses) <= valid_loss_min:\n",
        "                torch.save(model.state_dict(), './state_dict'+str(counter)+'.pt')\n",
        "                print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(valid_loss_min,np.mean(val_losses)))\n",
        "                valid_loss_min = np.mean(val_losses)\n",
        "\n",
        "        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/4... Step: 400... Loss: 0.010930... Val Loss: 0.166267\n",
            "Validation loss decreased (inf --> 0.166267).  Saving model ...\n",
            "Epoch: 1/4... Step: 800... Loss: 0.055517... Val Loss: 0.094106\n",
            "Validation loss decreased (0.166267 --> 0.094106).  Saving model ...\n",
            "Epoch: 1/4... Step: 1200... Loss: 0.019792... Val Loss: 0.063869\n",
            "Validation loss decreased (0.094106 --> 0.063869).  Saving model ...\n",
            "Epoch: 1/4... Step: 1600... Loss: 0.041260... Val Loss: 0.053760\n",
            "Validation loss decreased (0.063869 --> 0.053760).  Saving model ...\n",
            "Epoch: 2/4... Step: 2000... Loss: 0.005444... Val Loss: 0.053705\n",
            "Validation loss decreased (0.053760 --> 0.053705).  Saving model ...\n",
            "Epoch: 2/4... Step: 2400... Loss: 0.092467... Val Loss: 0.057599\n",
            "Epoch: 2/4... Step: 2800... Loss: 0.043216... Val Loss: 0.056948\n",
            "Epoch: 2/4... Step: 3200... Loss: 0.102310... Val Loss: 0.053536\n",
            "Validation loss decreased (0.053705 --> 0.053536).  Saving model ...\n",
            "Epoch: 2/4... Step: 3600... Loss: 0.093777... Val Loss: 0.055524\n",
            "Epoch: 3/4... Step: 4000... Loss: 0.073380... Val Loss: 0.060652\n",
            "Epoch: 3/4... Step: 4400... Loss: 0.092067... Val Loss: 0.061707\n",
            "Epoch: 3/4... Step: 4800... Loss: 0.094961... Val Loss: 0.059854\n",
            "Epoch: 3/4... Step: 5200... Loss: 0.118371... Val Loss: 0.061483\n",
            "Epoch: 4/4... Step: 5600... Loss: 0.047950... Val Loss: 0.062379\n",
            "Epoch: 4/4... Step: 6000... Loss: 0.100630... Val Loss: 0.071189\n",
            "Epoch: 4/4... Step: 6400... Loss: 0.051036... Val Loss: 0.070868\n",
            "Epoch: 4/4... Step: 6800... Loss: 0.043712... Val Loss: 0.064142\n",
            "Epoch: 4/4... Step: 7200... Loss: 0.060341... Val Loss: 0.064496\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWhhqenp4TrK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40d9c426-a597-4e28-c56d-0c6c63044e62"
      },
      "source": [
        "print(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.16626658216006604, 0.0941058221666552, 0.06386854639045435, 0.05376029302852077, 0.05370521677324144, 0.057599154749642556, 0.056948045392574025, 0.05353638551431829, 0.05552370384997718, 0.0606523563859196, 0.06170743313657978, 0.05985406994674473, 0.061483451489188025, 0.062379209104545294, 0.07118902334688142, 0.07086822382323107, 0.06414155261794453, 0.06449562420196135]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9rFv8rrZcAr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6e2c0ea6-797f-49fb-8122-dbcf3f546e2a"
      },
      "source": [
        "def plot_losses(history):\n",
        "    losses = [x for x in history]\n",
        "    plt.plot(losses, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.title('Loss vs. No. of epochs');\n",
        "\n",
        "plot_losses(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lILtzTf6pD8L"
      },
      "source": [
        "Checking unseen data from https://www.bgc-jena.mpg.de/wetter/weather_data.html,\n",
        "For that We have to feed the model past 120 days of data gets its output and compare it with 121st day and plot the same\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKJuYct7Zmvu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256,
          "referenced_widgets": [
            "806da831c54b418e8ef30a254c950c98",
            "1408081f783f498da768ba2db0a14388",
            "bff000db43f54248acd47d3cfcee7403",
            "059b348cb14f4333927bc80d68426b8b",
            "5e9fcfb00b134e9597a64e42dd5e26f3",
            "3fd9232f6d69458f98140b98a9051ed4",
            "46ebe34910654e8d9a90785e15de7636",
            "4749c46af2524b84b5854a60e881254b"
          ]
        },
        "outputId": "8ea0ed0c-cb54-474e-d917-9bd290b30c46"
      },
      "source": [
        "dataset_url = \"https://github.com/amandatta16/WeatherPrediction/blob/main/Dataset/single_2021.csv.zip?raw=true\"\n",
        "download_url(dataset_url, './data','single_2021.csv.zip' )\n",
        "zip_file = ZipFile('./data/single_2021.csv.zip')\n",
        "zip_file.extractall()\n",
        "df = pd.read_csv('./data/single_2021.csv.zip', encoding = \"ISO-8859-1\")\n",
        "print(df.head())\n",
        "print(df.shape[0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://raw.githubusercontent.com/aman-user/WeatherPrediction/main/Dataset/single_2021.csv.zip to ./data/single_2021.csv.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "806da831c54b418e8ef30a254c950c98",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=208256.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "             Date Time  p (mbar)  ...  Tlog (degC)  CO2 (ppm)\n",
            "0  01.01.2021 00:10:00    978.24  ...        13.48      434.7\n",
            "1  01.01.2021 00:20:00    978.24  ...        13.44      435.4\n",
            "2  01.01.2021 00:30:00    978.26  ...        13.39      434.2\n",
            "3  01.01.2021 00:40:00    978.25  ...        13.32      434.3\n",
            "4  01.01.2021 00:50:00    978.26  ...        13.22      435.8\n",
            "\n",
            "[5 rows x 22 columns]\n",
            "5387\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kycXsGaS0WpX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "0f5837b3-770e-49ac-bb67-dfe4ca3ba17d"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['Date Time'], format='%d.%m.%Y %H:%M:%S')\n",
        "print (df['date'].dtype)\n",
        "df['month'] = df['date'].dt.month\n",
        "df.head()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datetime64[ns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "      <th>rain (mm)</th>\n",
              "      <th>raining (s)</th>\n",
              "      <th>SWDR (W/m²)</th>\n",
              "      <th>PAR (µmol/m²/s)</th>\n",
              "      <th>max. PAR (µmol/m²/s)</th>\n",
              "      <th>Tlog (degC)</th>\n",
              "      <th>CO2 (ppm)</th>\n",
              "      <th>date</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01.01.2021 00:10:00</td>\n",
              "      <td>978.24</td>\n",
              "      <td>2.01</td>\n",
              "      <td>276.90</td>\n",
              "      <td>-0.82</td>\n",
              "      <td>81.4</td>\n",
              "      <td>7.06</td>\n",
              "      <td>5.75</td>\n",
              "      <td>1.31</td>\n",
              "      <td>3.66</td>\n",
              "      <td>5.88</td>\n",
              "      <td>1235.72</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.63</td>\n",
              "      <td>210.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.48</td>\n",
              "      <td>434.7</td>\n",
              "      <td>2021-01-01 00:10:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01.01.2021 00:20:00</td>\n",
              "      <td>978.24</td>\n",
              "      <td>1.96</td>\n",
              "      <td>276.85</td>\n",
              "      <td>-0.79</td>\n",
              "      <td>81.9</td>\n",
              "      <td>7.04</td>\n",
              "      <td>5.77</td>\n",
              "      <td>1.27</td>\n",
              "      <td>3.67</td>\n",
              "      <td>5.89</td>\n",
              "      <td>1235.93</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.37</td>\n",
              "      <td>199.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.44</td>\n",
              "      <td>435.4</td>\n",
              "      <td>2021-01-01 00:20:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01.01.2021 00:30:00</td>\n",
              "      <td>978.26</td>\n",
              "      <td>1.88</td>\n",
              "      <td>276.76</td>\n",
              "      <td>-0.77</td>\n",
              "      <td>82.5</td>\n",
              "      <td>7.00</td>\n",
              "      <td>5.77</td>\n",
              "      <td>1.22</td>\n",
              "      <td>3.68</td>\n",
              "      <td>5.90</td>\n",
              "      <td>1236.34</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2.75</td>\n",
              "      <td>207.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.39</td>\n",
              "      <td>434.2</td>\n",
              "      <td>2021-01-01 00:30:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2021 00:40:00</td>\n",
              "      <td>978.25</td>\n",
              "      <td>1.78</td>\n",
              "      <td>276.66</td>\n",
              "      <td>-0.71</td>\n",
              "      <td>83.5</td>\n",
              "      <td>6.95</td>\n",
              "      <td>5.80</td>\n",
              "      <td>1.15</td>\n",
              "      <td>3.70</td>\n",
              "      <td>5.93</td>\n",
              "      <td>1236.77</td>\n",
              "      <td>2.63</td>\n",
              "      <td>3.31</td>\n",
              "      <td>206.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.32</td>\n",
              "      <td>434.3</td>\n",
              "      <td>2021-01-01 00:40:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2021 00:50:00</td>\n",
              "      <td>978.26</td>\n",
              "      <td>1.62</td>\n",
              "      <td>276.50</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>86.1</td>\n",
              "      <td>6.87</td>\n",
              "      <td>5.91</td>\n",
              "      <td>0.95</td>\n",
              "      <td>3.77</td>\n",
              "      <td>6.04</td>\n",
              "      <td>1237.43</td>\n",
              "      <td>2.16</td>\n",
              "      <td>3.28</td>\n",
              "      <td>192.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.22</td>\n",
              "      <td>435.8</td>\n",
              "      <td>2021-01-01 00:50:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date Time  p (mbar)  ...                date  month\n",
              "0  01.01.2021 00:10:00    978.24  ... 2021-01-01 00:10:00      1\n",
              "1  01.01.2021 00:20:00    978.24  ... 2021-01-01 00:20:00      1\n",
              "2  01.01.2021 00:30:00    978.26  ... 2021-01-01 00:30:00      1\n",
              "3  01.01.2021 00:40:00    978.25  ... 2021-01-01 00:40:00      1\n",
              "4  01.01.2021 00:50:00    978.26  ... 2021-01-01 00:50:00      1\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asFC85oA0fFW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "outputId": "a4d105fd-af8b-4887-e23c-ab0b7df71b60"
      },
      "source": [
        "temperatureToFeatureVisualization(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x640 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO0hQkz2yIUz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f9c2d6a-8315-4de8-a1ee-363155b8d4ac"
      },
      "source": [
        "input_features = []\n",
        "for i in od:\n",
        "  if i != \"T (degC)\":\n",
        "    input_features.append(i)\n",
        "features = df[input_features]\n",
        "output = df[[\"T (degC)\"]]\n",
        "output.index = df[\"Date Time\"]\n",
        "features.index = df[\"Date Time\"]\n",
        "print(features.head())\n",
        "print(output.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                     p (mbar)  VPmax (mbar)  ...  wv (m/s)  month\n",
            "Date Time                                    ...                 \n",
            "01.01.2021 00:10:00    978.24          7.06  ...      1.02      1\n",
            "01.01.2021 00:20:00    978.24          7.04  ...      1.56      1\n",
            "01.01.2021 00:30:00    978.26          7.00  ...      2.08      1\n",
            "01.01.2021 00:40:00    978.25          6.95  ...      2.63      1\n",
            "01.01.2021 00:50:00    978.26          6.87  ...      2.16      1\n",
            "\n",
            "[5 rows x 7 columns]\n",
            "                     T (degC)\n",
            "Date Time                    \n",
            "01.01.2021 00:10:00      2.01\n",
            "01.01.2021 00:20:00      1.96\n",
            "01.01.2021 00:30:00      1.88\n",
            "01.01.2021 00:40:00      1.78\n",
            "01.01.2021 00:50:00      1.62\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SG71ngoCyaBI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2333cd5-7057-4ed2-fa44-91a537d68084"
      },
      "source": [
        "features = inputScaler.transform(features.values)\n",
        "\n",
        "output = output.values\n",
        "print(features)\n",
        "print(\"################\")\n",
        "print(output)\n",
        "\n",
        "\n",
        "print(\"################\")\n",
        "print(np.squeeze(output[712:725]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.62885495 0.09726202 0.02740013 ... 0.52761232 0.99726053 0.        ]\n",
            " [0.62885495 0.09694365 0.02656348 ... 0.52824089 0.99731438 0.        ]\n",
            " [0.62904952 0.09630691 0.02551767 ... 0.52946811 0.99736624 0.        ]\n",
            " ...\n",
            " [0.61640237 0.04998408 0.00669316 ... 0.62734592 0.99733533 0.09090909]\n",
            " [0.61708337 0.05030245 0.00690232 ... 0.62692688 0.99736724 0.09090909]\n",
            " [0.61747252 0.05078001 0.00711148 ... 0.62560987 0.99746896 0.09090909]]\n",
            "################\n",
            "[[ 2.01]\n",
            " [ 1.96]\n",
            " [ 1.88]\n",
            " ...\n",
            " [-5.36]\n",
            " [-5.31]\n",
            " [-5.21]]\n",
            "################\n",
            "[0.1  0.08 0.05 0.05 0.07 0.14 0.17 0.2  0.18 0.17 0.19 0.3  0.18]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUnLncX2L4m9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a1893c5-9f37-4e6b-c0cd-1472d6d2239a"
      },
      "source": [
        "model.load_state_dict(torch.load('./state_dict.pt'))\n",
        "predict_dataset = TimeseriesDataset(features, output, xSequenceLength, ySequenceLength)\n",
        "print(\"((((((()))))))\", predict_dataset.__getitem__(0))\n",
        "predict_loader = DataLoader(predict_dataset, shuffle=False, batch_size=batch_size, drop_last=True)\n",
        "\n",
        "predicted = []\n",
        "actuals = []\n",
        "h = model.init_hidden(batch_size)\n",
        "for inputs, actual in predict_loader:\n",
        "    print(inputs)\n",
        "    print(actual)\n",
        "    inputs = inputs.to(device)\n",
        "    h = tuple([e.data for e in h])\n",
        "    out, h = model(inputs, h)\n",
        "    out = out.cpu()\n",
        "\n",
        "    predicted.append(out.detach().numpy())\n",
        "    actuals.append(actual.detach().numpy())\n",
        "    break\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "((((((())))))) (array([[0.62885495, 0.09726202, 0.02740013, ..., 0.52761232, 0.99726053,\n",
            "        0.        ],\n",
            "       [0.62885495, 0.09694365, 0.02656348, ..., 0.52824089, 0.99731438,\n",
            "        0.        ],\n",
            "       [0.62904952, 0.09630691, 0.02551767, ..., 0.52946811, 0.99736624,\n",
            "        0.        ],\n",
            "       ...,\n",
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            "       [0.72789182, 0.07927412, 0.        , ..., 0.59917986, 0.99751583,\n",
            "        0.        ]]), array([0.2]))\n",
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            "\n",
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            "\n",
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            "         ...,\n",
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            "\n",
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            "       dtype=torch.float64)\n",
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            "        [ 0.5200],\n",
            "        [ 0.5500],\n",
            "        [ 0.5800],\n",
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            "        [ 0.6600],\n",
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            "        [ 0.5800],\n",
            "        [ 0.6600],\n",
            "        [ 0.7300],\n",
            "        [ 0.7400],\n",
            "        [ 0.7200],\n",
            "        [ 0.6700],\n",
            "        [ 0.6400],\n",
            "        [ 0.6200],\n",
            "        [ 0.5800],\n",
            "        [ 0.5200],\n",
            "        [ 0.5100],\n",
            "        [ 0.5100],\n",
            "        [ 0.4600],\n",
            "        [ 0.4100],\n",
            "        [ 0.4300],\n",
            "        [ 0.4400],\n",
            "        [ 0.3100],\n",
            "        [ 0.3200],\n",
            "        [ 0.4200],\n",
            "        [ 0.4100],\n",
            "        [ 0.3200],\n",
            "        [ 0.1600],\n",
            "        [ 0.0500]], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Cb2mNJ1PEk5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee079987-d5cb-413c-e3c4-612519df1daf"
      },
      "source": [
        "print(len(predicted))\n",
        "print(len(actuals))\n",
        "x1 = predicted[0]\n",
        "print(x1.shape)\n",
        "x1 = x1.reshape(-1,1)\n",
        "print(x1.shape)\n",
        "x2 = (actuals[0])\n",
        "print(x2.shape)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "1\n",
            "(256,)\n",
            "(256, 1)\n",
            "(256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnEZW_AM2m6W"
      },
      "source": [
        "#x2 = scaler2.inverse_transform(x2)\n",
        "x1 = outputScaler.inverse_transform(x1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3mzSV2At_B2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b2f423d-c759-4686-91e5-60e6715f19b6"
      },
      "source": [
        "for x in range(len(x1)):\n",
        "  print(\"Actual = \", x2[x], \" and predicted =\", x1[x])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual =  [0.2]  and predicted = [-0.3533442]\n",
            "Actual =  [0.18]  and predicted = [-0.3533442]\n",
            "Actual =  [0.17]  and predicted = [-0.3533442]\n",
            "Actual =  [0.19]  and predicted = [-0.3533442]\n",
            "Actual =  [0.3]  and predicted = [-0.3533442]\n",
            "Actual =  [0.18]  and predicted = [-0.3533442]\n",
            "Actual =  [0.1]  and predicted = [-0.3533442]\n",
            "Actual =  [0.1]  and predicted = [-0.3533442]\n",
            "Actual =  [0.14]  and predicted = [-0.3533442]\n",
            "Actual =  [0.25]  and predicted = [-0.3533442]\n",
            "Actual =  [0.28]  and predicted = [-0.3533442]\n",
            "Actual =  [0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [0.28]  and predicted = [-0.3533442]\n",
            "Actual =  [0.28]  and predicted = [-0.3533442]\n",
            "Actual =  [0.29]  and predicted = [-0.3533442]\n",
            "Actual =  [0.35]  and predicted = [-0.3533442]\n",
            "Actual =  [0.42]  and predicted = [-0.3533442]\n",
            "Actual =  [0.4]  and predicted = [-0.3533442]\n",
            "Actual =  [0.38]  and predicted = [-0.3533442]\n",
            "Actual =  [0.37]  and predicted = [-0.3533442]\n",
            "Actual =  [0.3]  and predicted = [-0.3533442]\n",
            "Actual =  [0.34]  and predicted = [-0.3533442]\n",
            "Actual =  [0.31]  and predicted = [-0.3533442]\n",
            "Actual =  [0.26]  and predicted = [-0.3533442]\n",
            "Actual =  [0.29]  and predicted = [-0.3533442]\n",
            "Actual =  [0.27]  and predicted = [-0.3533442]\n",
            "Actual =  [0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [0.42]  and predicted = [-0.3533442]\n",
            "Actual =  [0.43]  and predicted = [-0.3533442]\n",
            "Actual =  [0.44]  and predicted = [-0.3533442]\n",
            "Actual =  [0.5]  and predicted = [-0.3533442]\n",
            "Actual =  [0.42]  and predicted = [-0.3533442]\n",
            "Actual =  [0.38]  and predicted = [-0.3533442]\n",
            "Actual =  [0.36]  and predicted = [-0.3533442]\n",
            "Actual =  [0.45]  and predicted = [-0.3533442]\n",
            "Actual =  [0.4]  and predicted = [-0.3533442]\n",
            "Actual =  [0.39]  and predicted = [-0.3533442]\n",
            "Actual =  [0.34]  and predicted = [-0.3533442]\n",
            "Actual =  [0.35]  and predicted = [-0.3533442]\n",
            "Actual =  [0.3]  and predicted = [-0.3533442]\n",
            "Actual =  [0.37]  and predicted = [-0.3533442]\n",
            "Actual =  [0.39]  and predicted = [-0.3533442]\n",
            "Actual =  [0.36]  and predicted = [-0.3533442]\n",
            "Actual =  [0.46]  and predicted = [-0.3533442]\n",
            "Actual =  [0.52]  and predicted = [-0.3533442]\n",
            "Actual =  [0.54]  and predicted = [-0.3533442]\n",
            "Actual =  [0.68]  and predicted = [-0.3533442]\n",
            "Actual =  [0.68]  and predicted = [-0.3533442]\n",
            "Actual =  [0.55]  and predicted = [-0.3533442]\n",
            "Actual =  [0.58]  and predicted = [-0.3533442]\n",
            "Actual =  [0.65]  and predicted = [-0.3533442]\n",
            "Actual =  [0.71]  and predicted = [-0.3533442]\n",
            "Actual =  [0.75]  and predicted = [-0.3533442]\n",
            "Actual =  [0.74]  and predicted = [-0.3533442]\n",
            "Actual =  [0.72]  and predicted = [-0.3533442]\n",
            "Actual =  [0.76]  and predicted = [-0.3533442]\n",
            "Actual =  [0.76]  and predicted = [-0.3533442]\n",
            "Actual =  [0.82]  and predicted = [-0.3533442]\n",
            "Actual =  [0.85]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.88]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.76]  and predicted = [-0.3533442]\n",
            "Actual =  [0.73]  and predicted = [-0.3533442]\n",
            "Actual =  [0.67]  and predicted = [-0.3533442]\n",
            "Actual =  [0.68]  and predicted = [-0.3533442]\n",
            "Actual =  [0.77]  and predicted = [-0.3533442]\n",
            "Actual =  [0.82]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.85]  and predicted = [-0.3533442]\n",
            "Actual =  [0.83]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.91]  and predicted = [-0.3533442]\n",
            "Actual =  [0.85]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.86]  and predicted = [-0.3533442]\n",
            "Actual =  [0.87]  and predicted = [-0.3533442]\n",
            "Actual =  [0.87]  and predicted = [-0.3533442]\n",
            "Actual =  [0.95]  and predicted = [-0.3533442]\n",
            "Actual =  [1.1]  and predicted = [-0.3533442]\n",
            "Actual =  [1.14]  and predicted = [-0.3533442]\n",
            "Actual =  [1.22]  and predicted = [-0.3533442]\n",
            "Actual =  [1.17]  and predicted = [-0.3533442]\n",
            "Actual =  [1.09]  and predicted = [-0.3533442]\n",
            "Actual =  [1.05]  and predicted = [-0.3533442]\n",
            "Actual =  [1.02]  and predicted = [-0.3533442]\n",
            "Actual =  [1.01]  and predicted = [-0.3533442]\n",
            "Actual =  [1.04]  and predicted = [-0.3533442]\n",
            "Actual =  [1.04]  and predicted = [-0.3533442]\n",
            "Actual =  [1.02]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.96]  and predicted = [-0.3533442]\n",
            "Actual =  [0.97]  and predicted = [-0.3533442]\n",
            "Actual =  [0.97]  and predicted = [-0.3533442]\n",
            "Actual =  [0.95]  and predicted = [-0.3533442]\n",
            "Actual =  [0.94]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.92]  and predicted = [-0.3533442]\n",
            "Actual =  [0.91]  and predicted = [-0.3533442]\n",
            "Actual =  [0.91]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.95]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.93]  and predicted = [-0.3533442]\n",
            "Actual =  [0.89]  and predicted = [-0.3533442]\n",
            "Actual =  [0.88]  and predicted = [-0.3533442]\n",
            "Actual =  [0.87]  and predicted = [-0.3533442]\n",
            "Actual =  [0.85]  and predicted = [-0.3533442]\n",
            "Actual =  [0.83]  and predicted = [-0.3533442]\n",
            "Actual =  [0.81]  and predicted = [-0.3533442]\n",
            "Actual =  [0.78]  and predicted = [-0.3533442]\n",
            "Actual =  [0.77]  and predicted = [-0.3533442]\n",
            "Actual =  [0.78]  and predicted = [-0.3533442]\n",
            "Actual =  [0.77]  and predicted = [-0.3533442]\n",
            "Actual =  [0.74]  and predicted = [-0.3533442]\n",
            "Actual =  [0.73]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.71]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.61]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.65]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.63]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.74]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.66]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.66]  and predicted = [-0.3533442]\n",
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            "Actual =  [-0.26]  and predicted = [-0.3533442]\n",
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            "Actual =  [-0.31]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.3]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.27]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.26]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.3]  and predicted = [-0.3533442]\n",
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            "Actual =  [-0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [-0.29]  and predicted = [-0.3533442]\n",
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            "Actual =  [-0.19]  and predicted = [-0.3533442]\n",
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            "Actual =  [-0.02]  and predicted = [-0.3533442]\n",
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            "Actual =  [0.58]  and predicted = [-0.3533442]\n",
            "Actual =  [0.62]  and predicted = [-0.3533442]\n",
            "Actual =  [0.61]  and predicted = [-0.3533442]\n",
            "Actual =  [0.66]  and predicted = [-0.3533442]\n",
            "Actual =  [0.67]  and predicted = [-0.3533442]\n",
            "Actual =  [0.72]  and predicted = [-0.3533442]\n",
            "Actual =  [0.69]  and predicted = [-0.3533442]\n",
            "Actual =  [0.68]  and predicted = [-0.3533442]\n",
            "Actual =  [0.63]  and predicted = [-0.3533442]\n",
            "Actual =  [0.57]  and predicted = [-0.3533442]\n",
            "Actual =  [0.58]  and predicted = [-0.3533442]\n",
            "Actual =  [0.66]  and predicted = [-0.3533442]\n",
            "Actual =  [0.73]  and predicted = [-0.3533442]\n",
            "Actual =  [0.74]  and predicted = [-0.3533442]\n",
            "Actual =  [0.72]  and predicted = [-0.3533442]\n",
            "Actual =  [0.67]  and predicted = [-0.3533442]\n",
            "Actual =  [0.64]  and predicted = [-0.3533442]\n",
            "Actual =  [0.62]  and predicted = [-0.3533442]\n",
            "Actual =  [0.58]  and predicted = [-0.3533442]\n",
            "Actual =  [0.52]  and predicted = [-0.3533442]\n",
            "Actual =  [0.51]  and predicted = [-0.3533442]\n",
            "Actual =  [0.51]  and predicted = [-0.3533442]\n",
            "Actual =  [0.46]  and predicted = [-0.3533442]\n",
            "Actual =  [0.41]  and predicted = [-0.3533442]\n",
            "Actual =  [0.43]  and predicted = [-0.3533442]\n",
            "Actual =  [0.44]  and predicted = [-0.3533442]\n",
            "Actual =  [0.31]  and predicted = [-0.3533442]\n",
            "Actual =  [0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [0.42]  and predicted = [-0.3533442]\n",
            "Actual =  [0.41]  and predicted = [-0.3533442]\n",
            "Actual =  [0.32]  and predicted = [-0.3533442]\n",
            "Actual =  [0.16]  and predicted = [-0.3533442]\n",
            "Actual =  [0.05]  and predicted = [-0.3533442]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPwKqAraUTee"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}