torch_DTW_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e0609c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddba5bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f320012eb10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "torch.manual_seed(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea3965a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dtwtorch(exp_data, num_data):\n",
    "    r\"\"\"\n",
    "    Compute the Dynamic Time Warping distance.\n",
    "    \n",
    "    exp_data : torch tensor N x P\n",
    "    num_data : torch tensor M x P\n",
    "    \"\"\"\n",
    "    c = torch.cdist(exp_data, num_data, p=2)\n",
    "    d = torch.empty_like(c)\n",
    "    d[0, 0] = c[0, 0]\n",
    "    n, m = c.shape\n",
    "    for i in range(1, n):\n",
    "        d[i, 0] = d[i-1, 0] + c[i, 0]\n",
    "    for j in range(1, m):\n",
    "        d[0, j] = d[0, j-1] + c[0, j]\n",
    "    for i in range(1, n):\n",
    "        for j in range(1, m):\n",
    "            d[i, j] = c[i, j] + min((d[i-1, j], d[i, j-1], d[i-1, j-1]))\n",
    "    return d[-1, -1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2c68c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get experimental data\n",
    "x1 = np.linspace(0,1.0,num=100)\n",
    "xEnd = np.log((1.0 + (2.0*np.exp(1.0)) - np.exp(1.0))/2.0)\n",
    "x2 = np.linspace(1.0,xEnd, num=50)\n",
    "\n",
    "y1 = np.exp(x1) - 1.0\n",
    "y2 = 2*np.exp(x2) - 2.0*np.exp(1.0) + np.exp(1.0) - 1.0\n",
    "x = np.concatenate((x1,x2))\n",
    "y = np.concatenate((y1,y2))\n",
    "\n",
    "exp_data = np.zeros((len(x), 2))\n",
    "exp_data[:, 0] = x\n",
    "exp_data[:, 1] = y\n",
    "\n",
    "exp_data = torch.from_numpy(exp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4bc672a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define numerical model\n",
    "\n",
    "class NumModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        betas = torch.nn.Parameter(torch.tensor([1.5, -1.5, 2.0, 2.0]))\n",
    "        self.register_parameter('betas', betas)\n",
    "        self.register_buffer('x1', torch.linspace(0, 1, 100))\n",
    "        xEnd = np.log((1.0 + (2.0*np.exp(1.0)) - np.exp(1.0))/2.0)\n",
    "        self.register_buffer('x2', torch.linspace(1.0, xEnd, 50))\n",
    "        \n",
    "\n",
    "    def forward(self):\n",
    "        y = torch.empty((150, 2), dtype=self.betas.dtype, device=self.betas.device)\n",
    "        y[:100, 1] = torch.exp(self.betas[0]*self.x1) + self.betas[1]\n",
    "        y[100:, 1] = 2.0*torch.exp(self.betas[2]*self.x2) - 2.0*torch.exp(self.betas[3]) + np.exp(1.0) - 1.0\n",
    "        y[:100, 0] = self.x1\n",
    "        y[100:, 0] = self.x2\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2784a6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NumModel().double()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d813f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot sample outputs\n",
    "with torch.no_grad():\n",
    "    num_data = model()\n",
    "plt.figure()\n",
    "plt.plot(exp_data[:, 0], exp_data[:, 1], '.', label='Experimental data')\n",
    "plt.plot(num_data[:, 0], num_data[:, 1], '-', label='Numerical model')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Strain (mm/mm)', fontsize=16)\n",
    "plt.ylabel('Stress (GPa)', fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb525007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial dtw distance tensor(181.0964, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print('initial dtw distance', dtwtorch(exp_data, num_data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e202497b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 181.0963600836811 1.3137145042419434 [0.099]\n",
      "1 114.30538334031836 1.3164467811584473 [0.09801]\n",
      "2 105.35630849105426 1.4083573818206787 [0.0970299]\n",
      "3 111.02453651116477 1.403062343597412 [0.096059601]\n",
      "4 107.6314307498145 1.4228935241699219 [0.09509900499]\n",
      "5 95.85245670091734 1.4449238777160645 [0.0941480149401]\n",
      "6 84.35276251209876 1.4381763935089111 [0.093206534790699]\n",
      "7 73.99183635426874 1.4511003494262695 [0.09227446944279201]\n",
      "8 66.90704069882563 1.4346270561218262 [0.09135172474836409]\n",
      "9 63.784140506177465 1.438981294631958 [0.09043820750088044]\n",
      "10 63.71365441346665 1.4429066181182861 [0.08953382542587164]\n",
      "11 63.894057998917916 1.454115629196167 [0.08863848717161292]\n",
      "12 61.619850621995994 1.4706811904907227 [0.08775210229989679]\n",
      "13 55.72454296994877 1.431835412979126 [0.08687458127689782]\n",
      "14 48.23516309543106 1.4590873718261719 [0.08600583546412884]\n",
      "15 41.32413848153443 1.4550340175628662 [0.08514577710948755]\n",
      "16 35.89399305336098 1.4912919998168945 [0.08429431933839267]\n",
      "17 32.128684216359005 1.4474663734436035 [0.08345137614500873]\n",
      "18 29.80853093184986 1.4274139404296875 [0.08261686238355864]\n",
      "19 29.59921577698014 1.4279024600982666 [0.08179069375972306]\n",
      "20 28.534258170737672 1.4408857822418213 [0.08097278682212583]\n",
      "21 26.887803222048735 1.430593490600586 [0.08016305895390458]\n",
      "22 24.36476027122211 1.4364542961120605 [0.07936142836436554]\n",
      "23 21.447613874928656 1.422297477722168 [0.07856781408072187]\n",
      "24 18.602684167187203 1.4140245914459229 [0.07778213593991465]\n",
      "25 16.342890738791308 1.4176583290100098 [0.0770043145805155]\n",
      "26 14.730478825200551 1.4383444786071777 [0.07623427143471034]\n",
      "27 13.433578631034608 1.4347233772277832 [0.07547192872036323]\n",
      "28 12.371558260765728 1.4282317161560059 [0.0747172094331596]\n",
      "29 12.828118608329657 1.495133638381958 [0.073970037338828]\n",
      "30 12.326887519032182 1.4282402992248535 [0.07323033696543972]\n",
      "31 10.657158536956706 1.4049773216247559 [0.07249803359578533]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "908 0.45533692122641617 1.4433395862579346 [1.0774370091953371e-05]\n",
      "909 0.455379685135953 1.530303955078125 [1.0666626391033837e-05]\n",
      "910 0.45524739167106654 1.4360089302062988 [1.0559960127123499e-05]\n",
      "911 0.45538576574821543 1.4303054809570312 [1.0454360525852263e-05]\n",
      "912 0.4552827341983622 1.4541008472442627 [1.0349816920593741e-05]\n",
      "913 0.455446874792548 1.426544189453125 [1.0246318751387804e-05]\n",
      "914 0.4555046447191755 1.4229462146759033 [1.0143855563873926e-05]\n",
      "915 0.45531945506149435 1.4061784744262695 [1.0042417008235187e-05]\n",
      "916 0.45547604648537565 1.41862154006958 [9.941992838152836e-06]\n",
      "917 0.4555997978621051 1.460914134979248 [9.842572909771307e-06]\n",
      "918 0.45548056825544664 1.4614856243133545 [9.744147180673594e-06]\n",
      "919 0.4552492850690892 1.426445484161377 [9.646705708866858e-06]\n",
      "920 0.4553352681647554 1.4268016815185547 [9.55023865177819e-06]\n",
      "921 0.4552080070716344 1.4061877727508545 [9.454736265260408e-06]\n",
      "922 0.45521123807986974 1.4122734069824219 [9.360188902607805e-06]\n",
      "923 0.4553126710016991 1.4211230278015137 [9.266587013581728e-06]\n",
      "924 0.4552076578413856 1.4135150909423828 [9.17392114344591e-06]\n",
      "925 0.4554889793468092 1.4706995487213135 [9.08218193201145e-06]\n",
      "926 0.45554468333692966 1.3900172710418701 [8.991360112691336e-06]\n",
      "927 0.4553848031206927 1.4220058917999268 [8.901446511564422e-06]\n",
      "928 0.4553612495114648 1.3847801685333252 [8.812432046448778e-06]\n",
      "929 0.4554675934530448 1.4250624179840088 [8.72430772598429e-06]\n",
      "930 0.4553591893242513 1.4301180839538574 [8.637064648724447e-06]\n",
      "931 0.4553347030280312 1.4255051612854004 [8.550694002237203e-06]\n",
      "932 0.455400825293808 1.4627349376678467 [8.465187062214832e-06]\n",
      "933 0.45526277914318936 1.4071910381317139 [8.380535191592684e-06]\n",
      "934 0.4554513364736182 1.4571568965911865 [8.296729839676756e-06]\n",
      "935 0.4555415036969055 1.480888843536377 [8.21376254127999e-06]\n",
      "936 0.45543083291773695 1.589968204498291 [8.131624915867189e-06]\n",
      "937 0.4552530705926249 1.5257422924041748 [8.050308666708517e-06]\n",
      "938 0.45532246425186823 1.5544898509979248 [7.969805580041432e-06]\n",
      "939 0.4552318110885283 1.4814822673797607 [7.890107524241017e-06]\n",
      "940 0.4552820652266573 1.5062501430511475 [7.811206448998606e-06]\n",
      "941 0.455220140490254 1.430091381072998 [7.73309438450862e-06]\n",
      "942 0.45526101876314523 1.5228183269500732 [7.655763440663533e-06]\n",
      "943 0.4552477239134185 1.5146808624267578 [7.579205806256898e-06]\n",
      "944 0.4552352295992091 1.4780657291412354 [7.503413748194328e-06]\n",
      "945 0.4552793340081488 1.4801461696624756 [7.428379610712385e-06]\n",
      "946 0.45520672547245394 1.504739761352539 [7.354095814605261e-06]\n",
      "947 0.4552910585895316 1.4643235206604004 [7.280554856459208e-06]\n",
      "948 0.45520518416944433 1.4531550407409668 [7.207749307894616e-06]\n",
      "949 0.4553888365095353 1.5106770992279053 [7.13567181481567e-06]\n",
      "950 0.4553982604481221 1.499800443649292 [7.064315096667513e-06]\n",
      "951 0.45524237771963705 1.4957003593444824 [6.9936719457008374e-06]\n",
      "952 0.4554552023556849 1.4799244403839111 [6.9237352262438286e-06]\n",
      "953 0.45556266322646255 1.4804015159606934 [6.8544978739813906e-06]\n",
      "954 0.45549906557040953 1.4467508792877197 [6.785952895241577e-06]\n",
      "955 0.4552847255334462 1.4265799522399902 [6.718093366289161e-06]\n",
      "956 0.4554584803109698 1.442873477935791 [6.650912432626269e-06]\n",
      "957 0.45561309148374884 1.4432191848754883 [6.584403308300007e-06]\n",
      "958 0.45559790741418676 1.4379124641418457 [6.518559275217007e-06]\n",
      "959 0.45543302990913215 1.4591476917266846 [6.453373682464836e-06]\n",
      "960 0.4552683197030959 1.4456114768981934 [6.388839945640188e-06]\n",
      "961 0.45542033992837994 1.4729835987091064 [6.324951546183786e-06]\n",
      "962 0.45541648377279637 1.4324400424957275 [6.261702030721948e-06]\n",
      "963 0.45526773351414046 1.4301691055297852 [6.199085010414729e-06]\n",
      "964 0.4554047905456772 1.412208080291748 [6.137094160310582e-06]\n",
      "965 0.45550856804886747 1.4452550411224365 [6.075723218707476e-06]\n",
      "966 0.4554600922926166 1.4141738414764404 [6.014965986520401e-06]\n",
      "967 0.4552774368209486 1.4077692031860352 [5.954816326655197e-06]\n",
      "968 0.4554195008505995 1.4263675212860107 [5.895268163388645e-06]\n",
      "969 0.4555507625907697 1.444352149963379 [5.836315481754758e-06]\n",
      "970 0.4555324397824825 1.4643747806549072 [5.77795232693721e-06]\n",
      "971 0.4553822456335898 1.441765308380127 [5.7201728036678385e-06]\n",
      "972 0.4552802331773285 1.452840805053711 [5.66297107563116e-06]\n",
      "973 0.45538631903049165 1.4179799556732178 [5.606341364874848e-06]\n",
      "974 0.4553510189699047 1.4518496990203857 [5.5502779512260996e-06]\n",
      "975 0.4552272983180339 1.4732906818389893 [5.494775171713839e-06]\n",
      "976 0.45535416141409013 1.526667594909668 [5.439827419996701e-06]\n",
      "977 0.4553609767322589 1.4509572982788086 [5.385429145796734e-06]\n",
      "978 0.45524237178640636 1.4219934940338135 [5.331574854338766e-06]\n",
      "979 0.4553828325526161 1.4202210903167725 [5.2782591057953785e-06]\n",
      "980 0.4554646728583849 1.431182861328125 [5.225476514737425e-06]\n",
      "981 0.45541665364902945 1.4147963523864746 [5.173221749590051e-06]\n",
      "982 0.45525421591921206 1.444077968597412 [5.12148953209415e-06]\n",
      "983 0.45540499550730956 1.4434332847595215 [5.070274636773208e-06]\n",
      "984 0.45552204336686847 1.4687778949737549 [5.019571890405476e-06]\n",
      "985 0.4555102464452508 1.40065336227417 [4.969376171501421e-06]\n",
      "986 0.45538485975368875 1.40883469581604 [4.919682409786406e-06]\n",
      "987 0.45525239724939826 1.4014925956726074 [4.870485585688542e-06]\n",
      "988 0.455406201828146 1.4504194259643555 [4.821780729831657e-06]\n",
      "989 0.45544571022075303 1.413222074508667 [4.7735629225333405e-06]\n",
      "990 0.455370554403944 1.4519245624542236 [4.725827293308007e-06]\n",
      "991 0.45522954323811127 1.4492111206054688 [4.678569020374927e-06]\n",
      "992 0.45533403148947804 1.4117484092712402 [4.6317833301711775e-06]\n",
      "993 0.45534475310465644 1.4448847770690918 [4.5854654968694655e-06]\n",
      "994 0.4552483200624127 1.4510905742645264 [4.539610841900771e-06]\n",
      "995 0.4553386187689274 1.473484754562378 [4.494214733481763e-06]\n",
      "996 0.45540471113433795 1.4452013969421387 [4.449272586146945e-06]\n",
      "997 0.45536079424543574 1.4521021842956543 [4.4047798602854755e-06]\n",
      "998 0.45525740137697346 1.4377965927124023 [4.360732061682621e-06]\n",
      "999 0.4553397638211564 1.4471628665924072 [4.3171247410657945e-06]\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-1)\n",
    "lmbda = lambda epoch: 0.99\n",
    "scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda=lmbda)\n",
    "# optimizer = torch.optim.LBFGS(model.parameters(), lr=1.0)\n",
    "for t in range(1000):\n",
    "    t0 = time()\n",
    "    # Forward pass of the model\n",
    "    num_data = model()\n",
    "\n",
    "    # Compute the dtw loss\n",
    "    loss = dtwtorch(exp_data, num_data)\n",
    "\n",
    "    # Zero gradients, perform a backward pass, and update the weights.\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    scheduler.step()\n",
    "    t1 =  time()\n",
    "    if t % 1 == 0:\n",
    "        print(t, loss.item(), t1-t0, scheduler.get_last_lr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77ede62e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZ/93yeFQ6pMuGdttfK9Q2jkvf79ldFspFp2QjOOaLjp2pYhJSCHQz9s9QeXSjUoAGAxZP7pTp05lzZo17N69m3vvvZeIiAh8fX1ZvHgxOl3O3e+effbZbJePGjWKp556irVr1zJ16lQAtm/fztmzZ2nXrh2tWrXKdr/77ruP5cuXs2nTJh5//PEs64cOHZpjLDfy6KOPZikP4OfnR1BQEH///TcjRozIMrFpw4YN8fb2JikpiQsXLlCxYkUAwsPDARg0aBDlypXLci4fHx969erFV199xZYtW6hdu3am9S1btqRt27ZZ9mvXLmMI74kTJ/J1jWlpaSxfvpwNGzYQGxtLSkpKpr5me/fuzddxr/b3338TGxuLl5dXtqMoW7duTceOHXPsy+VwOPjpp59Yt24dJ06c4MqVK64YNU3j6NGjpKSk5KuUw6FDh1i2bBn79u3j4sWLrn5YsbGxQMb1d+7cOc/HFcJdTGYLl6OW0SAyo4r27uavs2VXU0ChA57sWuffjWO2wNl9YPTB0WoobNjujpBdJEkqxYICyqDTyJQo6TWNOgHFvwZPfkoAGI1GlixZQps2bVwdmT/66KMsf9yv1bhx4+suP3LkiGvZvn37gIyO2F27ds12v8TERADi4uKyrAsICCAgIOD6F3Id9erVy3Z5pUqV+Pvvv6+7PjY2litXrriSJOe1hIeHs23btmz3O3nyJJD9teR0Lmdye+XKletcSfZiY2Pp3bs3hw8fznGbixcv5vm413K+p7Vr184xkWncuHG2SVJiYiL9+vVj+/brf3lfunQpz0nSzJkzmTRpEg6HI8dtCuL6hSgqoVGxLA//gW+MM0CDw0FDaTvwFSKDLXwVGcN/I0/w+ZZo/hsZzcwBzQk5Oj9jx5aDwbvo52q7liRJpVignzczBzRnYth+7Eqh1zRmDGhW7O8i3YzbbruNWrVqceTIEfz8/HjooYduuE9Od6ycBSsvX77sWmY2mwGIj48nPj7+use1WCxZlpUpU+aG8VxPTn90nXePbrT+6jsyzms5duwYx44du+5583Itzrt2V58rt4YPH87hw4fp0KEDU6dOpWXLlvj7+2M0GrHZbK7/3ixnAlepUqUct8mpYOnYsWPZvn07DRs2ZMaMGXTs2JGAgAA8PDwAqFGjBnFxcVit1jzFtHnzZiZOnIher2fmzJncd999riRO0zQmTZrE22+/nefjCuEuJrOFz8PX8oPxAzw1G2vs7XjhUB+2mDO+T/4becL1j3iHgi/C1xHisQbQoMNz7gv8KpIklXIh7WrRvUElYhJSqBPgU6oTJIDXX3+dI0eOoNPpMJvNjBkzhi+++OK6+8THx2d7N+n8+fMAmR5FlS1bFoDHHnuMJUuWFGDkRc95LV988QVPPfWUm6OBM2fOsGHDBnx8fFi9ejX+/v6Z1p86darAzuW89uslus73/2o2m43vv/8egJUrV9KwYcMs68+ePZuvmL799lsAXnnlFcaPH59lfUFevxBF4fSpGBYZZuGvXWGPox4vW0dhQyMmIQWFytIdZLhudcb/NOwLAbdBMfgHgdRJugUE+nnTqV7FUp8gbd68mQ8++AAfHx8iIiIoX748//3vf/npp5+uu9/ff/993eUNGjRwLWvSpAkA+/fvL6Co3aeor+XavlLXcj7aa9SoUZYECQqmL5KT8z119nnKTnafi/j4eJKTk/H398+SIEFGW9rt9myPd6Prd87pllN/o4K8fiEKXdplWm5+mpq6eGIcVXgyfRypeLq6fDi7gziV5zIP6f+pTdfpeffEnA1JkkSpkJSUxLBhw3A4HLz33nvceeedzJ+f8Wz7qaeeuu4dg08//TTb5QsWLACgd+/ermXdunUjICCAvXv3Zin4WNI8+OCDACxZsoQLFy4U+vm8vTOS9Owe3V29/vz589k+qnv33XcLLJZGjRpRs2ZNLBZLtlPB7NmzJ9s+R84Yk5KSsr2O68WY2+vPrpDlunXrJEkSJYbpYhKXvn4U4/m/SPXw5wnreC7gl6nLh7M7iP6ffzwMMazHW0uHwBZQu4ubr+BfkiSJUuGll14iJiaG3r17M2rUKAAGDx5MSEgI58+f5+mnn85x3/379/Pqq6+6+nrYbDYmTpzIrl27KFeuXKbRb15eXkybNg2Ahx9+mPDw8Cx/0Pfv389rr73G1q1bC/oyC1Tbtm0ZNGgQFy5cIDg4mN27d2dab7fb2bhxI4899limSuX5FRQUBMDBgwezTVqbNm1KhQoVOH36NG+//barXVNTUxk9enSW+G6GTqdj7NixQMYj2qs7rp88eZJhw4ZhNGadOKp8+fI0bdoUm83GmDFjSE9PBzLaatasWYSGhrr6Jl2rbt26AK7K29dyDgR45513iI6Odi2PioriiSeewMvLzZWHhciF0J0n2frhY1Q4s4UU5cmWdvP5dvyjLBvZkcjxPQlpV8u1bUi7WkSO78l3T7RijO+GjIWdXoAb3HUtUgVafOAWUhLqJJVEzno39evXz1SV+NqfuXPnuvYJCwtzFQWMi4vLdLyLFy+qatWqKUB9+eWX2Z7LWXG7YsWKql27dq4q1jqdTi1btizbOMePH++qo+Pv76/atWunWrdurfz9/V3LncUUlcpccft6blQnKacimzeqwZNTHaHLly+r4OBgV8y1atVSHTp0UM2bN1fe3t6u5Vd/dp11koYNG5btuTZs2JCpHtLVn+c777xTAapcuXKqQ4cO6o477lAhISGufefNm+c6Z9WqVVXbtm2Vr6+v0jRNffHFF651ub2+67HZbKpfv36uYzZq1Ei1bNlSGQyG61bc/vHHH5Wmaa73vm3btq7PzBtvvJFjLJs3b85U3bt79+7qjjvucH1OzGazq3K8h4eHat68uWrYsKECVJMmTVz1p3Kq2yR1koqO1EnK3pnEFPXR60OVmuyrrG+WV8MnTM9dbb7dS5Wa7KvU+42Usqa5FhdWO+fl77fcSRLF0tGjR9m6dWuOP84aPOfOnXPdJVqwYAHVqlXLdJwKFSrw1VdfoWkao0ePdvX7uFpISAi//PILTZs25dChQ6SmpnLnnXeyYcMGHnnkkWzjmzlzJlu3bmXw4MGUKVOGvXv3EhMTQ40aNXjiiSdYtWqVa66y4qxs2bKsWbOGb7/9lj59+pCSksKff/5JQkICt99+O6+99ho7d+4ssLsYS5cuZfjw4fj6+rJr1y42bdqUaZj9888/z5IlS2jZsiUXL17k2LFjtG3bltWrVxd453K9Xs+KFSuYOXMmDRo04MSJE5w7d45hw4axc+dOV6mEa/Xv359ffvmFzp07Y7FYOHz4MLfddhtLlixx3WXMTrdu3Vi6dCnt27cnLi6OzZs3s2nTJldHb19fXyIjIxk6dCi+vr4cPnyY9PR012i67GpZCVGcpGz7ghcNKwB43fYkGxytXLX5nExmC9uOJ2D6Z4QbSsH2f4b9d3gaDNnfiXUXTaliMtVuCZOUlISfnx9msxlfX9/rbpuamkp0dDRBQUG5+mPjcDhISkrC19f3ukUQxc2pU6cOJ0+e5Pjx465HIaLgyee5aFzbznn93hG5Z7VaWb16Nf369cv2sewt6fAvqO8GoykHc20D+NA2EMiozRc5vieBft6ERsUyIWwfDgU6jYy6SBWjYfF9YPSBsQcz1UYqrHbOy99v+cYSQgghRL6YzBb+2hGB+mEEmnJwosaDfGT/N0FydtQ2mS2uBAky6iJNDNtP6paPMha0fKxYFI+8ltRJEkIIIUSehUbF8ln4On4wTkHTLJyp1JW6I74g8ootS22+7KbJqsNpvKJ/BTToWDyKR15LkiQhhBBC5InJbGF2WCQ/GGdRUbvMX44gHjv9JOuu2FxD/K+W3TRZTxrWZPxPw35QMftpjtxNHrcJIYQQIk9Oms7zhfE9auvOc9JRmSfSX+Wy8szUSftq19ZFCtAuM8gYmbGyGBWPvJbcSRK3rBMnTrg6ugohhMglu5WWO17GS3eCi6osw62vkfBPscjrTaB+9TRZzY5/hmFbGgS2hNrZV5kvDiRJEkIIIcQNmcwWouOv0HL3G/jErMem82Jk6qtEq8BcT6Ae6OdNYBkdhC/KWFDcikdeQ5IkIYQQQlyXc/j+S/r/0dkQhgMdhkFfMS+w5w0nUDeZLUQnJBMUUCZjm/3L4co5KFcNmj5QtBeSR5IkCSGEECJHzuH7g3TredkQBsCb1hE8H9gz207aV8tSG+nBZoTschaPfAb0xbvOlHTcFkIIIUSOohOSCdaieNuwEICPbQ+wxH5Xjp20nbKrjfTzilA4tx+MZaDNsMIO/aZJkiSEEEKIHDW07OEj4zz0muI7Ww9m2x6+YSdtyL420gj9qoz/aVU8i0deSx63CSGEECILk9nC+SM7aR4xDJ1mZZ29La/bnkSv6XLVSfva2kj1tdPcqd+DQkPr8GwRXMHNkyRJCCGEEJmERsXyaVgE33tMQadd4bx/W5o/9j+WJDqu20n7as7aSBPD9mNXiqcNGXeRtMb9i23xyGtJkiSEEEIIF5PZwgdhm/nBOJNKWhIHHLUZbHqONQYvOtW7cXJ0NWdtpLjYaNqEbwMH0GV04QReCKRPkhBCCCFcYs+YWGScRS1dPDGOKgxLH49Zed+wo3ZOAv28aXvuezSHFWp1ghptCzjiwiN3koQQQgiRwWqh1ZZn8dDFcl6V53Hr+FxV075alrpIaZch6suMlZ1fKsTgC57cSRKihKpTpw6aphETE+OW8/fo0QNN09i4caNbzl/YYmJi0DSNOnXqFMjxFi1ahKZpDB8+vECOJ0RBMpktbDt6ltRlQ/E48zvphnKMsI7nlKqS62rakNGXqcs76xn8xe90eWc9oVGx8OdiSDNDxfrQ4O4iuJqCU+yTpM2bN9O/f3+qVauGpmmsWLHiuttv3LgRTdOy/Bw6dCjTdsuXL6dJkyZ4enrSpEkTwsPDC/EqRG45//Df6L3u1asXmqaxaNGiIotNCCFKo4zE5jfivh6J14l12HSeeDz+Pf99bTjLRnYkcnxPQtrVuuFxsquL9GbYHmzb/ike2fkF0BX7tCOTYh9tcnIyLVq0YN68eXna7/Dhw5hMJtdP/fr1Xeu2b99OSEgIjz/+OHv37uXxxx9n0KBB/P777wUdvrgJU6ZMQSl14w1vUfXq1aNhw4YYjcW7Yq0QovhyJjav6r/jYcNmbErHqLQXMJVvRaCfN53qVczVHSTIvi5SH20HhstxUKYS3P5IIVxB4Sr2fZL69u1L375987xf5cqVKV++fLbr5syZQ3BwMBMmTABgwoQJbNq0iTlz5rBs2bKbCVcUEL1ez969e1m+fDkDBw50dzjF0m+//ebuEIQQJVx0QjJP6n7mWcNPAEywPcU6extGJKTkOjlyurYuEvw77J/2z4DRq+ACLyLFPknKr1atWpGamkqTJk2YNGkSPXv2dK3bvn07Y8aMybR9nz59mDNnTo7HS0tLIy0tzfU6KSkJAKvVitVqvW4sVqsVpRQOhwOHw3HD2J13T5z73IoeeeQRvv32W6ZOncqDDz6IlsMs0blt0+xIOxeMG70HJbWdr461IOJ2HqOw2uHadnY4HCilsFqt6PX6Aj/frcz5nX+j7/6SoOGZlXQ2LgVgpvVRfrD3QKdBdT+PPF9fgI+Bt+5vwqSVB3Eo6Ko7QDNdDMrog63lUMjj8QqrnfNyvFKXJAUGBvL555/Tpk0b0tLS+Oabb7jrrrvYuHEj3bt3B+Ds2bNUqVIl035VqlTh7NmzOR535syZTJ06NcvydevW4eNz/R7/BoOBqlWrcuXKFdLT03N9LZcvX871tqWF849HSEgIW7ZsYf/+/Xz99dcMGDAg03Y2mw2A1NRUV8IKMGrUKJYtW8b8+fMZPHhwluO/8847zJo1i9dee43x48cDGe189fKnn36at99+mzVr1nDx4kXq1avHmDFjeOihhwCIjY1l1qxZrF+/nsTERBo2bMiECRPo06dPtteklCIsLIwlS5awd+9ekpOTCQwMpE+fPowdOzbLZzEyMpL+/fvTpUsXVqxYwfz58wkNDSUmJoaAgAD++usvAG6//XZOnTrF3r17qVUra3+BDRs28OWXX/LHH39w8eJF/P39ue2227jvvvsYOnQonp6eAFgsFlatWsXq1avZt28fJpMJgKCgIO6//36ee+45ypQpk+X4zvcgJSUl03uQk8Ju5+TkZD755BPCw8OJiYlBr9fToEEDQkJCGDFiBAZD9l93W7du5b333uPPP/8EoEWLFowdO5Z69TKK3TkcjmyvLyUlhc8//5yVK1dy/PhxbDYb9erV4+GHH+aZZ55xta9TamoqkPEFnZv2yi/n90Z6ejoWi4XNmze73itRsCIiItwdQr4lpkG5C7t54NxcAD633cNn9v5oKAYFOdi9dT2783HcMsDkVhCfqvHIuZ8hGaL9OrNv4458x1rQ7ZySkvtSBqUuSWrYsCENGzZ0ve7UqROnTp3i/fffdyVJQJY7E0qpHO9WQMYjubFjx7peJyUlUbNmTXr37o2vr+91Y0pNTeXUqVOULVsWL68b325USnH58mXKlSt33ZhKI90/nfr8/Px4/fXXeeaZZ3j//fcZOnSoax3g+oPn5eWVqf2d/XOuXe7k/MPl6elJuXLlXO3sXJ6SkkKfPn2IjY2lWbNmaJrGgQMHeOqpp9Dr9XTo0IHg4GCuXLlC48aNsdls7N27l8cee4zVq1fTq1evTOezWq0MGTKE//3vfwBUq1aNWrVqcfToUT7//HN++ukn1q9fT4MGDVz7OJNuvV7PsGHDWL16NfXq1aNJkyakpqa6rsvZHmXLls1yrS+++CILFiwAoGLFitx+++3Ex8ezbds2IiMjeeihh6hUqRIAf/31FyNHjnQl840bN8ZsNnPo0CH279/PL7/8wubNm/H2znzr3fke+Pj4XPd34OrPc2G1c3x8PH379mXfvn3odDqaNWuG1Wpl165d7Nq1i3Xr1rFy5cosv3/fffcdjz/+OA6Hg4oVKxIUFMTBgwcZOHAgb7/9tqudr72+uLg47r77bg4ePIjBYKBOnToYjUYOHTrE5MmTiYiIYM2aNZnazHluo9F4w++M/Lj2eyM1NRVvb2+6d++eq+8dkXtWq5WIiAiCg4NLZJ/AH3adZtWPYSw2zkOnOThRrT93PzifJpdSqeXvQ6BfAXxezh/E+MVfKE1HzUHvULNCnTwforDaOU//SFElCKDCw8PzvN9bb72lGjVq5Hpds2ZN9cEHH2Ta5oMPPlC1atXK9THNZrMClNlsvuG2FotFHTx4UFksluw3cDiUSrvi+rFbktSl83HKbknKtLzY/jgcuW63G6ldu7YC1JYtW5TValV169ZVgPr2228zbXfXXXcpQH311VeZlg8bNizb5U6TJ09WgJo8ebKy2+3q0qVLym63u5YbjUbVs2dPde7cOdc+77zzjgJUYGCgat++vXrkkUdUUlKSUkopu92unnnmGQWo9u3bZznf+PHjFaBatWqldu/e7VqekpKiRo0apQDVtm3bTPts2LBBAUqv16vKlSurbdu2udZd/RlytlV0dHSm/efMmaMA5ePjo7755htlt9td6y5cuKBmz56tzp8/71oWExOjvv/+e3X58uVMxzGZTGrgwIEKUFOmTMlybXfccYcC1IYNG7Jp6X8VRTs/9NBDClBNmzZVx44dcy2PiopSVapUUYB69dVXM+1z+vRpVbZsWQWo8ePHK6vVqpRSKj09XY0ZM0YZjUYFqNq1a2e5ns6dOytAPfLII+rs2bOudadOnVLdunVTgBo3blym/b766isFqGHDhl23vfLr6nZWKhffOyLf0tPT1YoVK1R6erq7Q8mzM4kpqu+E+cr8ZlWlJvuqiEl3qAbjV6oziSk3fdytx+L/PU7Ys0pN9lUqdGi+j1lY7ZyXv9+l7k5Sdnbv3k1gYKDrdadOnYiIiMjUL2ndunV07tzZHeGBNQVmVHO91AHl3RNJ/kw8Ax5ZH8fcLIPBwBtvvMGIESOYNm0aISEhhd63wmAwsGTJEipXruxaNm7cOObNm8fp06eBjMdYzrs9Op2Od955h6+//pqdO3e6HmtBxt2NDz/8EF9fX3788Udq1KjhOqa3tzcff/wxUVFRREVFsWXLFrp165YpFrvdzieffEKnTp1cy250R8BisTB9+nQAPv30U4YMGZJpvb+/f6Y7ogC1a9emdu3aWY5VtWpVFi9ezI8//si3337L5MmTr3vuvCjIdj569ChhYWEAfPPNN67HZABt27bl448/ZtCgQcyfP59JkyZRrlw5IKN9rly5Qrt27Zg5c6ZrH6PRyAcffMBvv/3merR5tVWrVrFt2zbatWvHN998k+kxXo0aNQgNDaVBgwZ8+umnTJs2LcsdOCHcyXRiP18bZ+KrpRDlaMAL1pdIQ09MPjpqO4VGxbqG/us0mNOvMvft+yFjZZeSVTzyWsW+BMCVK1fYs2cPe/bsASA6Opo9e/YQGxsLZDwGGzp0qGv7OXPmsGLFCo4ePcqBAweYMGECy5cv54UXXnBtM3r0aNatW8esWbM4dOgQs2bN4tdff+Xll18uyksTufD4449Tv359Dh8+zLffflvo5+vbty/VqlXLtEyv19O8eXMAHn300Sx90MqXL09QUBCQ8fl0Wr16NWlpafTp0ydTguSk0+m49957Adi0aVOW9X5+ftx///15in/r1q1cuHCBatWq8dhjj+V6P4fDwcqVK3n++efp27cv3bp1o2vXrgQHB6NpGkePHs3Tc/wbKch2joiIQClF165dadWqVZZzPfTQQ9SoUYPk5GS2bt3qWr527VoAnnvuuWxjHDVqVLbLnQnZ8OHDs+3nFBgYSLt27bhy5Qq7du3K9hhCuEViLC3WD3PNx/Zk+iuk4pmnatrXyq420tm1c8BhhdpdoHqbgovfDYr9naQ//vgj08g057+Chw0bxqJFizCZTK6ECTI6K44bN464uDi8vb1p2rQpq1atol+/fq5tOnfuzHfffcekSZN44403qFevHqGhoXTo0KHoLuxqRp+MuzH/cDgcJF2+jG+5cpn64RRbxvz9cuWGXq/njTfeYOjQoUyfPp3Bgwfn2AG3IFx9F+Jqzv4711v/999/c+XKFdeyffv2AbBjxw66du2a7X7nzp0DMvq4XKt+/fp5vnP2999/A9C+fftcf3YSExPp168f27dvv+52ly5duuEghdwqyHY+cuQIAE2aNMl2H51OR6NGjTh9+jRHjhzh7rvvzrRf48aNs90vp+XO9/WTTz5h6dKl2W7jPHZ276sQRc1ktnD6VAytfn0Uw+U4ksoEMfzieJIok6dq2tm5tjZSWVJ4RP9PeZISNgVJdop9ktSjR4/rFhS8tuLyq6++yquvvnrD4w4cOLD41N/RtMyPqxwOMNozlpWEJKmQDR48mLfffpvDhw/zzTffMGLEiEI7V05JgLMD/Y3WX/1ZNZvNAJw6dYpTp05d97wWiyXLsuxGlN2Is0NiTjXCsjN27Fi2b99Ow4YNmTFjBh07diQgIAAPDw8g4xFSXFxcgQ7DLch2diZMVz+6u5ZzBOHVI0ad+zkTs5z2uZbzfd2/f3+O53PK7n0VoiiFRsXyTth2lhqnY9CdItm7Gr5Pr+JH/IlJSKFOgE++EyTIWhvpEf0GfDULVv/6GOv3LqCrcB/5CyyKPb1ez5tvvgnA9OnTrzucObs/oldLTk4u+ABzULZsWQBef/11lFLX/Smo6VWc/W0SExNztb3NZuP7778HYOXKlQwYMIBq1aq5EiSbzXbd0hjFgbOdz58/n+M2zjt2zva5er/4+Phs98npeM79nI/5rvcj87QJdzKZLbwVtpOvjLNorDvFOVWeexNfxYR/nqtp5yTQz5uZA5qj1zQM2HjS8AsAxq4vlYp/5Jf8KxC3hEceeYQmTZoQHR193YTCefclpz98x44dK4zwsuV8/JObOw4FpWnTpgBERUXlqmBhfHw8ycnJ+Pv7Zyqd4bR//37sdnuBx1mQnOUTDh48mO16h8Phmrvx6lILzv+/dl5HJ+ejy2u5430VIj9Onr3AZ4bZtNQd55Iqy5D0iUSrysQkFFz/QoCQdrWIHN+TX+5KIFC7CGUqw+0hBXoOd5EkSZQIOp3ONbrqrbfeyvHRT926dYGMJOFap0+fdnXWLQr33HMPHh4erF69mqNHjxbJObt06UJAQABxcXG5mmLHOfIqKSkp20dD7777boHHWNB69+6NpmlERkaye3fW8ndhYWGcPn2aMmXK0KVLl0z7QcYot+x88skn2S53Fjb97LPPXAUihSh27FZa7niZzvqDXFbeDE0fz1FV46Y6aV9PoK8X9Y99lfGiwzNg8Lz+DiWEJEmixHj44Ydp3rw5J0+ezDRK6WrOef5WrFjB6tWrXctNJhOPPfZYkVYerlatGi+//DJWq5U+ffqwcePGTOuVUuzcuZPnnnuOEydOFMg5vby8eOONNwB45plnWLZsWaZHj5cuXeLDDz903WkrX748TZs2xWazMWbMGFdFeLvdzqxZswgNDXU9eiuubrvtNlfiMnTo0Ext+eeff/LSSxmdR1944YVMj9ueffZZypQpw++//84bb7zh+mxYrVZeeeUVDhw4kO35HnzwQTp27MihQ4fo379/lruTaWlprFq1iieeeKJAr1OI3DJdukL8N8Pxio7ApvNkpPUV9qm6N91JO9M5zBa2HU/AZP7nH1cnNsC5fWAsA21Lz2dfkiRRYmia5rqblNMjoMaNG/Pkk09is9m45557qFu3Lq1ataJWrVqcP38+x2HdheXtt99myJAhREdH07NnTwIDA+nQoQMtW7bEz8+PDh068Omnn+ZpupobefHFF3nuuedITk5m8ODBVK5cmfbt2xMUFESlSpUYO3Zspr5ZM2fORNM0PvvsM9fw9apVqzJ+/Hhef/31TDXGiqtPPvmE5s2bs3//fho0aEDLli1p2rQpbdq0wWQy0atXL6ZMmZJpnxo1avDZZ5+haRpvvfUWgYGBtG/fnqpVq/LBBx+46k1dS6fTERYWRqtWrfj111+pX78+9evXp2PHjjRt2hRfX1/uvffeTEm6EEUldOdJNn0whEoxP5Ou9Gxt8yEfvjaKZSM7Ejm+JyHtsk5hlOdzRMXS5Z31DP7id7q8s57QqFjY9nHGytaPg4//TZ+juJAkSZQoAwYMoGXLltfdxlnEr169esTFxREfH88zzzzD9u3b8zTqqyAYDAa++eYbVq1axQMPPABkFDc1mUw0aNCAF154gY0bN2bqK3OzNE1jwYIFrFq1invvvRdN09i7dy9Wq5U77riDBQsWZKpR1L9/f3755Rc6d+6MxWLh8OHD3HbbbSxZsoRp06YVWFyFqVKlSmzfvp1p06bRuHFjjhw5wsmTJ2nXrh0ff/wxq1evzrYQ52OPPcb69evp2bMnqampHDp0iObNm/PLL78QEpJzn4rAwEC2b9/OggUL6N69OxcuXGD37t1cvnyZ9u3bM3XqVDZs2FCYlyxEFqbEFC7/OJ5H9BuwK42Xrc/zRGQFgALppA3Z10VaHL4Kjq8HTQ8di/YfooVNU9cbXy9ylJSUhJ+fH2azOVdzt0VHRxMUFJSrOZScE2r6+vqWjDpJJZS0c9GQdi4a17ZzXr93RO5ZrVZWr15Nv379itXcbbHhk6m1dw4Ar1if5gd7DwCWjexIp3oVC+Qc244nMPiL3zMtm2OcxwP6bdDsIRj4ZYGcBwqvnfPy91u+sYQQQoiSbvsCV4I01fq4K0Eq6I7azrpITjW08/TX/VOItsvoAjtPcSFJkhBCCFFCmcwWjq39BNZOAGBfg+dZ7MiYYaIgO2o7XV0XCeBpw2r0moJ6d0JgiwI7T3FR7CtuCyGEECKr0KhYNq/4go8MH4MGh+oOp/mjbxN5T2qBVNPOSUi7WnRvUInTp0/RNnwL2IAuLxf4eYoDSZKEEEKIEsZktvDbiq+Yb5iPXlMstd3JG3/3JjIplUA/70JJjq4W6OdN4K4fwGaBaq0hqHuhns9d5HGbEEIIUcJc2ruKeYa5GDU7K+ydmWR7AruiwKtpXy1TbaS0K7Dz84wVXV/OmIO0FJI7SUIIIUQJYTJbuLhvHY03PYdOs/OzvQP/sT6HA12hVdOGjEd7zqH/Og2Wt9xNq9RE8K8Hje4tlHMWB5IkCSGEECVAaFQsYeHfs8g4C52Wzv5yXflPwjPY0RdKJ22na2sj6ZWNqgcXggZ0eQl0+gI/Z3EhSZIQQghRzJnMFr4PX87Xxvfw1tLZYG/BqISn+X5UN1LSHYXWSRsgOiHZlSAB3KfbRqB2kXTvyni0eLRQzllcSJJUhKRupxCiqMj3TekSf2g7XxlnUVZLZYu9Gc9ax5CGgZR0R4EVisyJszaSQ4GGg2cNPwGQ2uZpPErJRLY5kY7bRcBgyMhF09LS3ByJEOJW4fy+cX7/iBLM9BfN1g/HV7Pwu6MRI63/IQ2PQu2DdLWrayPdpdtNfV0c6Yay+HZ9utDP7W7y21MEDAYDZcqU4eLFi5QrVw69vvQ+vxVCuJ/dbufixYuUKVNGkqQSzGS2cO7on9z+6xB0aWYSyrdg5LnRpOJZqH2QsuOsjVRmyfsQDx4dngIvvyI5tzvJb08RCQgI4NSpU0RHR+Pn54e3tzd6vR4tm2GTDoeD9PR0UlNTZa6rQiTtXDSknYuG3W4nNTWV9PR0kpKScDgcBAYGujsskU+hUbF8Eb6GZcbp6LQkLvg1I+DZn1ib5lGohSKvJzBxD8TvAr0HdHyuSM/tLpIkFREfHx+CgoI4f/48ly5dIiEhIcdtlVJYLBa8vb2zTaJEwZB2LhrSzkVDKUVKSgply5albNmyVK5cGQ8PD3eHJfLBZLbwaVgE33m8TSUtiQOO2gw5P5rVaR5FUijy6jiiE5IJCiiTcc7IDzNWtBwM5aoWSQzuJklSEfLw8KBGjRoopbBarTgcjmy3s1qtbN68me7duxerGaZLG2nnoiHtXDSsViubNm2id+/ekhyVcGdiDrPE422qaIkcctRkSPoELlGGmISUIkuQrq2LtCDYi7uPrgU06PxSkcRQHEiS5Aaapl33S0yv12Oz2fDy8pI/KoVI2rloSDsXDb1ej91ul7t1JZ35NC1+exyDdoFjjmoMSZ/IJXyLrJM2ZK2L5FBg2fAh6IEm90HFekUSR3EgSZIQQgjhZiazhdOx0bT67TEMSbFc9qnJ45fGk4BfkXfSvrYuUnXiuVe3LeNFKZ3INieSJAkhhBBuFBoVy/thkSw1voVBF8cV7+qUe/oXwrQAt3TSvrouEsBThtUYNTtpNbvhWb11kcVRHMhQEyGEEMJNTGYL74dFssT4NvV1cZxR/tyT+ComLYBAP2861atY9KPYrqqLVJ7LhOg3AuDZY2yRxlEcyJ0kIYQQwk1OnzrJEuPbNNSd5qyqwOD01zmpKhVpJ+3sOOsi2X6bic9faVD1dqjb023xuIskSUIIIUQRM5ktnD51kua/DsHrnwTpkfRJxKjAIu2knV1crmH/3g44+k3Giq4vwy04KECSJCGEEKIIhUbFMjsskiXGt/DSxZFoqMRjKROIUVWLvJP2tXFdPez/f63+orXlIlQIgsb3F3k8xYEkSUIIIUQRMZktrgSpgS4Ok/JnSMoEPnhuACnpDrdU0nbGdfWwf52yUeXAf0EDOr8I+lszXbg1r1oIIYRwg4w+SP8mSI+kT+KkqkpKuoNO9Sq6La5rh/0/oN9KdS2BdK8APFo+5ra43E2SJCGEEKIoXDlPy/VDMF6TILmzD5LT1cP+dTh4Tv8jAKntnsPD6OXW2NxJSgAIIYQQhchkthC1/2+sX/bDePEoKV5VeCz9DVeC5K4+SFe7eth/H10U9XQm0g3l8O36jFvjcje5kySEEEIUkoxO2lv41vg2Rl0cKV5V8Bn5C98aqrmlUOT1hLSrRff6AZRb/BZcBI8uo8CznLvDcitJkoQQQohCkNFJewtLjW9xm+4MZ5Q/j5knsNRQjUA/72KTHF0tMH4bXDwAxjLQ4Vl3h+N28rhNCCGEKASnT8VkSpAeSX+DaFWZmIQUd4fmYjJb2HY8AZPZkrFgy+yM/7YdAT7+7gusmJA7SUIIIUQBck5Wm1Eo8gxxqiKPpk8iVlUpFp20na6ti/RFj3Tuit0Geg/o9Ly7wysWJEkSQgghCkhoVCwfhG3mW+PbeOnOcMlQmSHJE4lVlYtNJ23IWhfJoUAfOQf0QMvB4FvNneEVG5IkCSGEEAXAZLbw4T8J0m3/3EF6LHkic0c94NZCkdm5ti5SUy2GHvo9KE2H1mW0+wIrZop9n6TNmzfTv39/qlWrhqZprFix4rrbh4WFERwcTKVKlfD19aVTp06sXbs20zaLFi1C07QsP6mpqYV4JUIIIUqzuJNHWWac5kqQMuZiq+wqFFlcEiT4ty6S03OGlQCkNnwA/Ou6J6hiqNgnScnJybRo0YJ58+blavvNmzcTHBzM6tWr2bVrFz179qR///7s3r0703a+vr6YTKZMP15et27BLCGEEDfhUgwtfx1MkO4csY5KhKS/wali1gfpalfXRaqrnaGfbicA3j3HuTmy4qXYP27r27cvffv2zfX2c+bMyfR6xowZrFy5kp9++olWrVq5lmuaRtWqVXN93LS0NNLS0lyvk5KSALBarVit1lwfJzecxyvo44rMpJ2LhrRz0ZB2LjpXt7XJnMr56AO03DQcwxUTST61ePTSK8Spiug0mH5/YwJ8DMXyfRnQMpBOQRXwXD0a3QmFo/7d2P0bQDGJtbA+03k5XrFPkm6Ww+Hg8uXL+PtnHsp45coVateujd1up2XLlkyfPj1TEnWtmTNnMnXq1CzL161bh49P4fwrISIiolCOKzKTdi4a0s5FQ9q56Exb8iu7os/wrcdMDFoi5wzV2F13HE87/IhPtVPJS1Hm3F+sXv2Xu0PNJDEN4lM1KnkpArUEep1YAUCk1p5Lq1e7N7hsFPRnOiUl9yUYSn2SNHv2bJKTkxk0aJBrWaNGjVi0aBHNmzcnKSmJuXPn0qVLF/bu3Uv9+vWzPc6ECRMYO3as63VSUhI1a9akd+/e+Pr6FmjMVquViIgIgoODMRqNBXps8S9p56Ih7Vw0pJ2LjtVq5YefI/gr5jTfecygonaZvx21GJo8geU9+nOXX/HtuvHDrtNMXXnQNez/l/pR6LDjqN2VTg+/5O7wMimsz7TzSVBulOokadmyZUyZMoWVK1dSuXJl1/KOHTvSsWNH1+suXbrQunVrPv74Yz766KNsj+Xp6Ymnp2eW5UajsdC+kArz2OJf0s5FQ9q5aEg7Fw19YjRLje9SXkvmL0cQj6dPwExZ4szp1AoonlN5mMwWJv2TIAFUUGZqn/wfaKDrPg5dMf3cFPRnOi/HKrVJUmhoKE8++SQ//PADvXr1uu62Op2Odu3acfTo0SKKTgghRElkMls4t28jD599B0/Nwi5HfYanv8ZlfIptJ22na4f9P2H4BS/NypWKt1O2bg+3xVWclcokadmyZTzxxBMsW7aMe+6554bbK6XYs2cPzZs3L4LohBBClEShUbGEh3/PQuO7eGppHPNuwYjE0VzGq1gVisyJc9i/Q4EvyTyuz+jrk955DGjaDfa+NRX7JOnKlSscO3bM9To6Opo9e/bg7+9PrVq1mDBhAnFxcSxevBjISJCGDh3K3Llz6dixI2fPngXA29sbPz8/AKZOnUrHjh2pX78+SUlJfPTRR+zZs4f58+cX/QUKIYQo9kxmC6vCv+Ur4wd4a+lstjfn2cSXWTaqZ7ErFJkT57D/iWH7GaKLwFezYC5bD/9WD7g7tGKr2CdJf/zxBz179nS9dnaeHjZsGIsWLcJkMhEbG+ta/9lnn2Gz2Xj++ed5/vl/555xbg+QmJjI008/zdmzZ/Hz86NVq1Zs3ryZ9u3bF81FCSGEKFES9/zEF8b38dRs/GZvxSjraNLwcBWKLClC2tWie1AZAr54HtLAL/g10BX7koluU+yTpB49eqCUynG9M/Fx2rhx4w2P+eGHH/Lhhx/eZGRCCCFuCQd/pNGmUWiajTX2drxofRErBnQaxboPUk4Cj/0AaRehfC1o9pC7wynWin2SJIQQQriDyWzhctQy6m8dh6bsnKzWl9HRg7GiR0Px1v1Ni/0jNsi4juiEZIICyhBYRg/b/hnF3eVl0EsacD3SOkIIIcQ1QqNi+WPFPGYZPkfTFNE17iPoiUVsvJzO8XNJHN+zg4fb1HB3mDcUGhXLhLB9rrpI37U9SvukOChbBVo+5u7wij15ECmEEEJcxWS28PeK93nP+Bk6TbHUdifBx0MwXU4n0M+bDkH+lM9aNq/YMZktrgQJQFN2qvz1zwClzi+CsfgWvSwuJEkSQgghnJTCun4WU4xfA7DQ1peJtiexKY2YhNxPZ1EcXFsX6T7dNmpr57F6+kPbJ9wXWAlSYI/bUlJSsFgs+Pv7o0m9BSGEECWMKTEFx7pJ1Dq4EIAPrQ8x1z4A0Ip9ocjsXF0XSYeDFwwrALC0fRajRxn3BldC5CtJSkpK4scff2Tz5s1ERkZy8uRJUlNTAdA0DX9/f1q3bk23bt3o3bs37dq1K9CghRBCiIL0/c5oHD+N4RH9BgBWVHmBebFdAFUiCkVm5+q6SH2136mnM5Fm9MW323PuDq3EyFOStHPnTubPn8/y5cuxWCzZDs1XSpGQkMC6deuIiIjgzTffpFmzZjz11FM8+eST+PiUrExcCCFE6Wa6aMb7p2fpr9+BXWmMt40kLLYLYaM6lZhCkTkJaVeL7vUr4rdoMiSCZ5fnwatgJ2UvzXKVJB05coQJEyawYsUKlFIEBATw4IMP0r59e9q1a0dgYCD+/v54e3tz8eJFLl68yMGDB9m5cydbt25l27ZtvPzyy8yYMYMpU6YwcuRIdFK8SgghhLulp+C1fCj99TtIV3pGW1/gF0cHQJW4QpFXyzTs37QBEo+ARzno8Iy7QytRcpUkNW3aFICQkBCGDRtGr1690Ov12W5buXJlKleuTKNGjRgwYAAAcXFxLFu2jE8++YRRo0Zx4cIFJk6cWECXIIQQQuSNyWwh9sxZWkY+S4W4HViUB89ax7DJ0QKgRPZBcso87F/xR6W38AdoPxK8K7g7vBIlV0nS0KFDmThxIvXq1cvXSapXr864ceMYM2YM3377rXTsFkII4TahUbG8G7aVr4yz8NRFk24oy7Z284nc6EFJ7oMEWYf9d9f24J/0Nw6DN7pOz19/Z5FFrpKkhQsXFsjJ9Ho9Q4cOLZBjCSGEEHllMluYE7aJ74wzqa+L44Iqx4iUCXzWsQ+RHSEmIaVE90HKPOxf8ZIhHICzDR6jWpkAt8VVUknFbSGEELeMM9EH+d44lZq6eEzKnyHpEziuqhOTkEKnehVLbHLkdPWw/y66/bTWHSNNGdF3fcndoZVI0ntaCCFEqWYyW9h2PIH443/SMuJRauriiXZU4eH0yRxX1Ut0/6NrOYf96zWNF/+pixRbZyBVqtV2b2AlVIHcSTKZTPz1119cvHgRq9Wa43byqE0IIURRcnZibs4xFnm8i167QmK523gk4T+cU34luv9RTkLa1eIun+ME/PA3Smek/oBJ7g6pxLqpJOnQoUM8//zzbNy48brbKaXQNE2SJCGEEEXG2Ym5g3aAL4yzKaulsttxG4FDfmKFV4US3//oegJ2zQVAa/UY+BX/iXiLq3wnSadOnaJbt25cuHCBrl27cuTIEeLj4xk8eDCnTp3i77//Jj4+Hm9vbwYMGIDBIN2fhBBCFJ3ohGR6azuZa5yHp2Zjq70pI63/YeEVTzpV8S5VyVGmukiXD8CJDaDpoesYd4dWouU7c5k5cyYXLlxgxowZjB8/nm7duhEfH88333wDgM1m48svv+TVV1/FZDKxZs2aAgtaCCGEuJGmZ5azwDgXnab4xd6Ol63PY9M8S03/I6fMdZEgsuanVANo8QhUqOPm6Eq2fHfcXrduHWXLlmXMmOyzVIPBwNNPP813333H+vXrmTVrVr6DFEIIIXLLlJhCbPhk/H57FZ2mWGa/i+eto7FpnqWu/9G1dZEaE0O185tRmg66jnVvcKVAvu8kxcXF0aBBAzw9PQFcFbjT0tJcywDuvvtu6tSpw9KlS3n99ddvMlwhhBAiZ6E7Y0j7aRxD9REAHLjtaXrcO5VvL1hKZf+jzHWR4IV/6iJdqH0PAQG3uSmq0iPfd5J8fHwy9TPy8/MDMpKna5UvX56YmJj8nkoIIYS4IdOFRHx+eoah+ggcSmOydRj3HegJmlYqaiBlx1kXCaCBdoq++igAVLf/uDGq0iPfSVLNmjU5c+aM63WTJk0A+PXXXzNtl5CQwOHDhzPdXRJCCCEKVNplvH8YfNVEtc/ztb0PdqWISUhxd3SF5uq6SC/8UxfpVNVgKtVr5d7ASol8J0ldu3YlPj6es2fPAjBw4ECUUowbN47//ve/HDhwgPXr13PfffeRmprKXXfdVWBBCyGEEJDRJ2fngcOkf3kP5c9uJVl58oT1VX5ydAZK9kS1uRXSrhbbn65Jf/0OAGre/6abIyo98p0k3X///Wiaxs8//wxAmzZteO6557hy5QrPPPMMt99+O8HBwezYsYMKFSowc+bMAgtaCCGECI2KZdA7oQSE3ofHub2kelRgW9dFbFe3A5TKQpFXc1YSN5ktVN4zHw0FDfpC4O3uDq3UyHfH7eDg4CzVtefPn0+HDh1YunQpMTExeHt707VrV1599VVq1qx508EKIYQQkJEgLApfxf883qGKlshpFcDwKxP4pn1PItuX/Ilqb+TqYf9B2ll+8/o+465H91fcHVqpUuAVHocOHSqVtYUQQhSqhIOb+M44DT8thUOOmgxLf41z+JeaiWqv59ph/6P0K9ApO6lBd+FVo417gytl8vS4zWq18u6779KqVSvKli1L2bJladmyJTNnziQtLa2wYhRCCCGAjATh743f0fTXofhpKUQ5GjAo/Q3O4X9L9D+CzMP+a2tneVAfCcDRRi+4MarSKdd3kux2O3379mXDhg0o9W9Rhr/++ot9+/bxyy+/sH79epl+RAghRKEIjYpl14qPmWH4LzrNwYGynRh+4RmS8Sj1/Y+u5hz271Dwgn4FBs3BRntLGjbq5O7QSp1cZzSff/4569evx2g08txzz9G9e3eUUmzevJlPP/2UrVu38sknn/Diiy8WZrxCCCFuQabEFE6tnM67xu8B+MHWndcvPM0Po7qSku4o1f2PruUc9v9p2G+uu0jWbq/cMtdflHKdJC1btgxN01iyZAkPP/ywa/lDDz1E586defTRR/nuu+8kSRJCCFGw7Da0VWMZZ8hIkD6x9WeW7RFAIyXdQad6Fd0bnxuEtKtF/5gdGA44SK3Tk+De97o7pFIp132SDhw4QOXKlTMlSE4hISFUqVKFgwcPFmhwQgghbl0ms4Udh2JJXfIIVY8uw6E03rAOZ5btUUC7ZfogOV095J+LJ/A5+AMAXnfJlF+FJdd3khITE+nYsWOO64OCgti5c2eBBCWEEOLWFhoVy/thkXxhfA8v3QlsOk92tJrF0m1VAHVL9UGCzEP+dRr8Wu8H6io73NYLarZzd3ilVq6TJKWUaxLb7Oj1+kwduoUQQoj8MJktfBa+jh+M71JHd45LqixPp77CR90fIbJ76a+BdK1rh/xX5xy1Tq0EDbhjvFtjK+1kKJoQQohiJf7vrfxgnEJF7TKxjkoMt77GCVXtlqiBlJ2rh/wDvKBfiUFzkFitO+XlLlKhylOS9Mcff1C3bt1s1znncMtpvaZpHD9+PI/hCSGEuFWYzBYSd6+k2ZbR6LRU/nIE8UT6qyTgd8v1P7ra1UP+a2rnGKDfAoCt26tujqz0y1OSlJqaSkxMzHW3yWm9pml5OZUQQohbSGhULPtXfMAUwyJ0muJg2Y48duEZLuN5y/U/upZzyP/EsP08r1+JUbNjCuhMYONu7g6t1Mt1kvTVV18VZhxCCCFuUabEFC6ufJ3pxh8BWGbryeQLT96SNZByEtKuFj2rpBDwVSQoCLxvirtDuiXkOkkaNmxYYcYhhBDiVmRLR7/yOZ4zZCRIH1gH8pH9QW7lGkhXM5ktRCckExRQhsA980HZoN6dUKuDu0O7JUjHbSGEEEXOZLYQe+YsLbe9SOVTW7ApHRNsT/GDvQfALd0HyenqYf81tXg2eS3NKG4oI9qKTJ4muHWHzZs3079/f6pVq4amaaxYseKG+2zatIk2bdrg5eVF3bp1+fTTT7Nss3z5cpo0aYKnpydNmjQhPDy8EKIXQghxrdCoWAa88z98l92H56ktWPXebO2wgDBHT4Bbvg8SZB32/5x+BTplI63WHXIXqQjl6U7S6dOnOXPmDL6+vjRq1CjTuieeeCLH/YYPH0737t3zFWBycjItWrRgxIgRPPTQQzfcPjo6mn79+jFy5EiWLFnC1q1bGTVqFJUqVXLtv337dkJCQpg+fToPPvgg4eHhDBo0iMjISDp0kA+fEEIUFpPZwtfhP7Pc4z2qaReJV348aXmNz7o8QGSXW68GUk6uHvZfQ4vnYf1mAI42fp5mbozrVpPrJMnhcBAcHMyRI0f44YcfsiRJixYtQtO0LAUlNU1jx44dHDhwIF8j3Pr27Uvfvn1zvf2nn35KrVq1mDNnDgCNGzfmjz/+4P3333clSXPmzCE4OJgJEyYAMGHCBDZt2sScOXNYtmxZtsdNS0sjLS3N9TopKQkAq9WK1WrN83Vdj/N4BX1ckZm0c9GQdi4aJaWdL/z5E98bp1JWS+WYoxrDra9xWlXi+LkkOgT5E1DLFyje11EUbV3Dz9M17H+UfgVGzc4WR3Nq1+9crNumIBVWO+fleLlOktasWcPhw4e5++67GTBgQLbb1KpVi6eeeirTsnXr1hEZGcmaNWvylOzk1/bt2+ndu3emZX369GHhwoVYrVaMRiPbt29nzJgxWbZxJlbZmTlzJlOnTs2yfN26dfj4FM5z84iIiEI5rshM2rloSDsXjeLazolpUP3cb9x5YTE6TbHN3oRnrS+TRFk0FMf37ODC3+6OMm8Ku60HBWlEnrjguou0v9IDXNy6nt2Fetbip6DbOSUlJdfb5jpJCg8PR9M0xo4dm+M2NWvW5PXXM0+017NnT7p27UpYWFiRJElnz56lSpUqmZZVqVIFm81GQkICgYGBOW7jLIiZnQkTJmS69qSkJGrWrEnv3r3x9fUt0GuwWq1EREQQHByM0Wgs0GOLf0k7Fw1p56JRnNv5f3+cxLZqEr0MvwDwu9/djDg/mDQM6DR46/6mPNymhpujzL2iaut+QOqK0RgP2LHU7MZTQ0cX2rmKo8JqZ+eToNzIdZK0c+dOfHx86NGjR56C6dy5M+XLl+f333/P034349rHes5HgFcvz26b6z0O9PT0xNPTM8tyo9FYaL8khXls8S9p56Ih7Vw0ils7n01IIGD10wQbdgHwrnUQn51/gLBRnUt8DaRCb+vEWIx/fw+Ad6/XoRi9r0WpoNs5L8fKdZIUGxtL7dq1MRjyXjWgWrVqnD59Os/75UfVqlWz3BE6f/48BoOBihUrXneba+8uCSGEuAlJJsoufYhg/QHSlJH/WJ/lZ0cnAKmBlINMdZE2vwcOKwTdAbU7uTu0W1KuM57k5GTKli2b43qTyYSHh0e268qWLUtycnLeo8uHTp068dNPP2Vatm7dOtq2bevKHjt16kRERESmfknr1q2jc+fORRKjEEKUZiazhXNHd9F840jKXjnDBVWOken/4U/VAJAaSDm5ui5SHe0c672+zajT0/P1G+0qCkmuk6QKFSqQkJCQ4/rr3YWJj4+nfPnyeQrM6cqVKxw7dsz1Ojo6mj179uDv70+tWrWYMGECcXFxLF68GIBnn32WefPmMXbsWEaOHMn27dtZuHBhplFro0ePpnv37syaNYv777+flStX8uuvvxIZGZmvGIUQQmQIjYrll/AlzDPORa+lklSmDtvbLWDvWjOgpAZSDq6ti/SiIQydspNapydeUhfJbXKdJNWrV4/ff/+d+Ph4KlWqlOsTnDt3jujoaDp27JivAP/44w969uzpeu3sPD1s2DAWLVqEyWQiNjbWtT4oKIjVq1czZswY5s+fT7Vq1fjoo48y1Vjq3Lkz3333HZMmTeKNN96gXr16hIaGSo0kIYS4CSazhf0rPuC/xq8xaA622Zvw/MUxrG7VmshWUgPpeq6ui1RXO8MDuox/tB9p/BK3uzGuW12uk6S77rqL33//nU8++YQ333wz1ydYsGABmqbRq1evfAXYo0ePLLWXrrZo0aIsy+644w7+/PPP6x534MCBDBw4MF8xCSGEuIbDjlrzOtONGZOh/2DrzkTbU1gxEJOQQqd6FSU5uo6ggDKuukijDWHoNUWEvQ3NGklfJHfK9bQkzz77LB4eHsycOZPffvstV/v8+uuvvPPOO3h4ePD000/nO0ghhBDFk8lsYcehWFK/HUy1vxcCGSPYXrE9gxWD9D/KpUA/b2YOaE5j3Wn667YD4LhjvCSWbpbrJKl69epMmTKFtLQ0+vbty8svv8yhQ4ey3fbvv//mpZdeol+/fthsNiZPnkyNGiWnBoYQQogbC42K5YF3luOz9D68jq/BrvNgW6v3+MzxIKBJ/6M8CmlXi7Cmm9FpCstt99CnV+8b7yQKVZ7G87/22mtcvHiR9957j48//piPP/4Yf39/ateuTZkyZUhOTubkyZNcvHgRyKg9NG7cOMaPlxmLhRCiNDGZLSwJ/5EVHrMJ1C5yQZXj2dRxfNTjcSJ7SP+jvHAO+6/viKbS0Z8BDe/gSe4OS5DHJAlg1qxZ3HXXXUybNo1t27Zx4cIFLly4kGW7zp0788Ybb9CnT58CCVQIIYT7Of+gexz5me+NU/HW0jnqqM6T1nHEqirS/yiPrh72/4VxNsF6oNkAqNLE3aEJ8pEkAfTu3ZvevXtjMpnYtm0bcXFxXL58mXLlylG9enU6d+5MYGBgQccqhBDCjTL+oP/FC7pwxhr/BxpstLfgReuLXMZH+h/l0dXD/ptrJwjW78KuNC60GUNldwcngHwmSU6BgYGZhtYLIYQonUxmC1PCdjHX8Cn99TsAWGjryzv2wVjRS/+jfLh62P9Yww8ArHB0pZqjmiRJxcRNJUlCCCFuDadjT/CdcRotdCewKj2TbE8Qau/JvEdbUbGsp/Q/ygfnsP+WHKGnfi82pWO+bQDfyt24YiNXSdLOnTtp3759gZwwJSWFmJgYmjSR561CCFGcOfsfNbAdpfWa4eh1Z7moyvJc+hh+V43Raxpt6lSQ5CifnMP+a/w0A4Dljjt4ZkAvac9iJFclADp27Ejfvn1vatqOS5cuMWPGDGrXrs3//ve/fB9HCCFE4QuNiqXLO+v5duFcyiy9D33yWcxl6zEg/S1XgiSP125eSKVYuuj249AZ6TlyFiHtark7JHGVXN1JGjduHPPmzWPdunXUqVOHRx99lH79+tG6dWu8vLxy3C82NpbIyEhCQ0NZu3Yt6enptG7dmv79+xfYBQghhChYJrOFiWF7eUkfxsuGMAA22FvSeOgPLPMsJ8P7b5LzDl1QRR8CN2TcRdK1fpzKtRq6OTJxrVwlSe+++y4vvfQSkydPZtmyZcyYMYOZM2ei1+tp3LgxgYGB+Pv74+npSWJiIhcvXuTQoUOuCXGVUjRu3JhJkybx6KOPFuoFCSGEuDknzyYw1/Ax9+p/B+Bz2z28Y3uUby/r6VTZW5Kjm3D1kP9uun1847EV9J7QbZy7QxPZyHXH7Ro1arBw4UJmz57N119/TWhoKLt27WLfvn3s27cv232qV69OcHAwTz75JF26dCmwoIUQQhQ8k9lCXOxxbt/0LD76faQrPa/bnuQHew8Z3l8Arh7yD4qXDRldT5KbP04Zv+pujU1kL8+j28qXL8/o0aMZPXo0qampREVFcfLkSRISEkhNTcXf35/KlSvTsmVL6tSpUwghCyGEKGihUbEsC1/BZ8bZ+GiJXNH78ZRlNDscjaT/UQG5esh/D91e2uiOkqqM/F3vKdq6NzSRg5sqAeDl5UW3bt3o1q1bQcUjhBCiiJnMFnas+ITvjF/gpVk57KjB02mv8NGo+0lJd0j/owLiHPLvUIox/9xFWmLvzT216rg3MJGjXE9wK4QQohSy21BrXudD4wK8NCu/2VvxUPoUTqpKpKQ7ZIqRAuQc8t9b/yctdCdIVp4E3P2qtG8xJsUkhRDiFmQyWzgVF0eLHWOpFrsJgHm2+/nA9jAOdNIHqZCEtKnBgN9/gQRQ7Z/mga4t3R2SuA5JkoQQ4hYTGhXLovBVfGr4AE/deWx6b35vMZ0Pt1fHgZI+SIXAOey/8YUIKiQcBE9fyvYc6+6wxA1IkiSEELcQk9nCphUL+Z/xE8poacQ6KvFs+n9YeMcQIu9AaiAVAuewf03Z+dVjChV0QOcXwcff3aGJG5AkSQghbgEms4Xo+MtUinqfBcbPAIi0N+UF60skklEgUvofFbyrh/0P0m8mSHeWC6oc1iYjqOru4MQNSZIkhBClXGhULDPCfme2YT719bsB+K+tLzNtg7Gjl/5Hhcg57N8DK6P/qV6+wHYfvZJ0VK3k5uDEDUmSJIQQpZjJbOHz8LWEGWdTT2ciTRmZaH2ScNUdB0j/o0LmHPY/WPcb1bULmJQ/yxy9eUqS0hJBkiQhhCjFEvf8TLjxDXw1C2eUP8+kj2Wfqsu8R1tRsayn9D8qZIF+3rx7Xz16rFkBwDzbACYPaC1tXkIUSpJkt9v5448/OHPmDK1atZLK20IIUYQy+h9doVn0QhptfQdNU+x0NGRU+ssk4Ide02hTp4L8oS4iA22rQEsitVxtXhgxmUB/X3eHJHIp38Uk165dy4ABA/juu+8yLT9z5gwdOnSgc+fODBw4kNtuu42pU6fedKBCCCFu7Iddpwl+ZzWXvh6M79aZaCiO1RrEUOskV4Ikj9cKn8lsYdvxBM6eOwtb5wLgFTxJEqQSJt93khYvXszKlSuZNGlSpuVjxozhzz//xM/Pj6CgIPbt28e0adPo1auXTHIrhBCFKDENFq1czw/GD2msO0W60jPVNoIXHnqLDcjw/qLiHPLvUDDO8D0vGMxQuQk0e8jdoYk8yvedpKioKPz8/GjdurVr2cWLFwkPD6dSpUocOXKEP//8k2XLlqGUYs6cOQURrxBCiByUu7CblR6TaKw7Rbzy49H0SXxrv5OYhBQC/bxliH8RuHrIfwBmRuh/AeBih1dAp3dzdCKv8n0nKT4+nlq1amVatmHDBmw2G48++iiVKmWMbRw4cCCBgYHs3bv35iIVQgiRLdOlK1h/fZsB5xaABn84GjAqfTTnqSDD+4uYc8g/wCjDSspoaexx1MXi151O7g1N5EO+k6SUlBT0+sxZcWRkJJqmcdddd2VaXqNGDfbt25ffUwkhhMhB+Na/8F/7PHfo/gJgS4UHGXl2AKn/1D+S/kdFyznkv6pK4DH9rwB8YAthVqUybo5M5Ee+k6QqVaoQExODzWbDYMg4zNq1a9HpdHTr1i3TthaLhTJl5AMihBAFKf7I77Rd9xg1dfFYlAcTrE/x49muhI/qTEq6Q/ofuUGgnzczBzSHH1/CU7Oxw9GEex54VN6HEirffZK6deuG2Wxm2rRpXLlyhYULF3Lo0CE6duxI+fLlXdtZrVaOHj1KtWrVCiJeIYS4pTlHTSVu+5KK391LTS2eGEcVHkyfxgpHVxwKUtId0v/IDZzvTc9Klxlk2AzAbY/OIqR9bTdHJvIr33eSJk6cyIoVK3j77bd5++23Xctff/31TNtFRESQlpZG586d8x+lEEIIQqNimRz2J2/qv6azYT0Av9lbMcY6iiQy7tbrNKQPkhtcPaLtI+M87tPboX4fAhp3d3do4ibk+05S06ZN2bBhA/369aNBgwbcdddd/Pzzz9x9992Ztvvmm2/w8/OjX79+Nx2sEELcqkxmCx+FbeQ741QGG9bjUBof2B7mWK8vSNbKAqCheOv+JnIHqYhdPaKtkRbLffptAMS3f8XNkYmbdVMVt9u3b89PP/103W2WLVt2M6cQQggBXNy3jh89JlJRu0yiKsNo6wtscrRgWQ1/Isf35Pi5JI7v2cHDbWq4O9RbztUj2v5j+AGAn+wdCdDVReawLdlk7jYhhCimXNOLxHxFk60z0TQH+x11eNb6MqdVZdfw/kA/bwJ8DFz4290R35qcI9pacJRg/S7sSmOu/WG+kceeJV6+kyS73U5ycjIeHh54eXllWhcVFcWXX37JmTNnaNOmDWPGjKFcuXI3HawQQtwqQqNieTvsd941fIavPgqAEzUeYNDxB0lRRhneX4w4R7TV+GkGAMsddzDywd7y3pQC+U6S3n//fSZOnMicOXN48cUXXctXrVrFgw8+iN1uRynFzz//TFhYGL///juenp4FErQQQpRmJrOFr8N/ZoVxLnV1Z0lTBqbZhvPCwOn8pmkyvUgxFFLxBOj249B50OPJ96hcs9aNdxLFXr47bq9ZswadTsfgwYMzLX/ttdew2WyEhIQwZ84cmjRpwr59+/jyyy9vOlghhCjNTGYL247Fc37TF4QZ36Su7ixxqiIh6W9mTC9ywSLTixQjJnMq244nYEpMgV+nAKBr9wSVazZwb2CiwOT7TtLx48epWrUqFStWdC07ePAgBw8epF27dixduhSABx54gHr16rFkyRKee+65m49YCCFKodCoWKaFRTHVsIiB+s2gwQZ7C8ZYR5FIOZlepJjZfk5jzOzNOBTco9/BfONu8CgL3ca5OzRRgPJ9Jyk+Pj5LgciNGzcCGfO1OdWqVYv69esTHR2d31OxYMECgoKC8PLyok2bNmzZsiXHbYcPH46maVl+mjZt6tpm0aJF2W6Tmpqa7xiFECK/TGYLX4SvIcz4JgP1m7ErjXetITxle8WVIEn/o+LDZE4l9IQOhwIDNv6j/x6Ay62fhbIynq00yfedJL1ez+XLlzMt27JlC5qm0aNHj0zLfX19850khYaG8vLLL7NgwQK6dOnCZ599Rt++fTl48GCWCXYB5s6dyzvvvON6bbPZaNGiBQ8//HCWmA4fPpxp2bUd0IUQoihcjlrGSuMkymhpnFflecn6AjscTZj3aCsqlvWU/kfFzMkLKSg0AB7Wb6Ku7iwJypfjdYbSwc2xiYKV7ySpXr16HDx4EJPJRGBgIBaLhTVr1lCuXDnatGmTadvz589TqVL+susPPviAJ598kqeeegqAOXPmsHbtWj755BNmzpyZZXs/Pz/8/Pxcr1esWMGlS5cYMWJEpu00TaNq1aq5jiMtLY20tDTX66SkJCBj2hWr1Zqna7oR5/EK+rgiM2nnoiHtnD2TOZXY85docfA9GuxfDBpsszdhtPUF4imPToPbq5cj0C/jH283aj9p56JT3c+IhsKTdF42LAdgvv0BRgT4S/sXoML6TOflePlOkh566CH27dvHvffey4gRI1i1ahVJSUmMGDECne7fp3jnz58nJiaGO+64I8/nSE9PZ9euXYwfPz7T8t69e7Nt27ZcHWPhwoX06tWL2rUzz51z5coVateujd1up2XLlkyfPp1WrVrleJyZM2cyderULMvXrVuHj0/h9BOIiIgolOOKzKSdi4a087+2n9PYFp3AfONcyuliAPit7P08nTAQO3o0FIOCHOzeup7deTy2tHPRCKmrUeHkGqpoiZxyVCK9Zo98vV/ixgr6M52SkpLrbfOdJI0bN45ffvmF33//nT179qCUok6dOkybNi3Tds6K23feeWeez5GQkIDdbqdKlSqZllepUoWzZ8/ecH+TycQvv/zi6kTu1KhRIxYtWkTz5s1JSkpi7ty5dOnShb1791K/fv1sjzVhwgTGjh3rep2UlETNmjXp3bs3vr6+eb6267FarURERBAcHIzRaCzQY4t/STsXDWnnzEzmVFZ/OJufPT7DV0vhoirLWOvzTBv+IhuA2Isp1PL3cd1Byi1p56JjtVox/hJOn4TVkA7efSYxpf197g6r1Cmsz7TzSVBu5DtJ8vHxITIykpUrV3L06FFq1qzJAw88kOWuilKK0aNHExISkt9ToWlalmNeuyw7ixYtonz58jzwwAOZlnfs2JGOHTu6Xnfp0oXWrVvz8ccf89FHH2V7LE9Pz2zrPBmNxkL7QirMY4t/STsXjVu9nU1mCzHnE6n8+zt8ZlwEwC5HfV5IfwkTFYkzp9OpXkVqBdxc4d1bvZ0Lm8ls4djZy9Q68zP69MtQpRkBnYaCLt/joMQNFPRnOi/HuqlpSfR6PQMGDLjuNi+//HK+jx8QEIBer89y1+j8+fNZ7i5dSynFl19+yeOPP46Hh8d1t9XpdLRr146jR4/mO1YhhMhJaFQsH4Vt5CPjR9TTZXzPfGHrxyzbI9gwyPD+EiI0KpYJYfuorC6w0TMCNOCuNyVBKsUK7J11OBzEx8cTGxtbUIfEw8ODNm3aZHkeGRERQefOna+776ZNmzh27BhPPvnkDc+jlGLPnj0EBgbeVLxCCHEtk9nC2hWL+dFjIm10R0lSPjyTPoaZ9iGuBEmG9xd/JrOFCWH7cCgYbQjDS7Oy09EIU+Vu7g5NFKKbnuB29erVfPjhh2zbto3U1FQ0TcNms7nWv/322xw4cIC5c+fma4Tb2LFjefzxx2nbti2dOnXi888/JzY2lmeffRbI6CsUFxfH4sWLM+23cOFCOnToQLNmzbIcc+rUqXTs2JH69euTlJTERx99xJ49e5g/f36e4xNCiGuZzBaiE5IJqmDE8etUvjQuBGC/ow6jrKOJVVVkeH8JE52QjENBPS2OQfqNALxjfYRXLlgILC93AUurm0qSXn31VWbPno1SCg8PD4xGY5ahdYGBgbz55pv06NGDp59+Os/nCAkJ4cKFC0ybNg2TyUSzZs1YvXq1a7SayWTKcvfKbDazfPly5s6dm+0xExMTefrppzl79ix+fn60atWKzZs30759+zzHJ4QQV3M+kqnOOeYZP6aF7gQAX9n6MNM2mHQyJqdtU6eCJEclSFBAGXQajDN8j15TrLO3YQ8N5DFpKZfvx23Lly/n/fffp1q1avz8888kJyfTrl27LNs9+OCDAPz444/5DnLUqFHExMSQlpbGrl276N69u2vdokWLXJW+nfz8/EhJSWHkyJHZHu/DDz/k5MmTpKWlcf78edauXUunTp3yHZ8QQsC/j2T6ajtY5TGRFroTJKoyrG0+m7fsw10JkjxeK3kC/bz5rKeirz4Ku9J43zaIt+5vIu9jKZfvO0nz589H0zR++OGHTCPFrlWhQgWCgoKkU7QQotQ7eTaBt/RfMNiwAYAoRwNGp7/A7Jb3EtnLh5iEFHm8VlIpRfCZTwFIqPcgj3kF8nCbGm4OShS2fCdJu3fvpmbNmtdNkJwqVarEvn378nsqIYQo1kxmC2eP7aHFlpfoaDiCQ2kssN/Hh7aBoBlciZEkRyWPs39Zwys7qRizBfSe+Pd7k/Jb/3J3aKII5DtJSktLo3z58rnaNiUlBb1en99TCSFEsRW68yS7V37MZMPXeGvpJOn9ed7yLFsczeTRWgnn7F+mlIOfPV6nog5oPxL8agCSJN0K8p0k1axZk2PHjmVUHr1OYSaz2cyhQ4do2rRpfk8lhBDF0tnz5/H56RneMW4HYLO9OePSRvHFqL6MSnfIo7US7Ooh//fpdtBUd5LLypvkFs9R0d3BiSKT747bffr0wWKx8OGHH153u2nTpmGz2bj33nvzeyohhCg2TGYL244nkHB4O37f3EV//XZsSsc71kcYZn2N88qPlHQHnepVlASpBHMO+Tdi4z+G7wH4zHYv0cnynt5K8n0n6bXXXmPx4sVMnDiR+Pj4TEUbHQ4H+/fvZ86cOSxatIhKlSoxevToAglYCCHcJePxy18M162hrWEpHpqd0yqAl9Jf4E/VAECqZ5cSziH/IboN1NadJ175scjRj8fkvb2l5DtJql69OitXrmTAgAF88MEHfPDBB651zsdvSin8/f0JDw+nYkW5QSmEKLlMZgvvhW3lc8Nn9NJnzPW+xt4eU4932furCVDSB6kUCfTz5r376nLHmuUAzLMN4I0BbQn0885SD1CUXjdVTPKOO+5g//79vP/++4SHhxMTE+NaV61aNQYMGMBrr71G9erVbzZOIYRwC+foJv2J9fzi8RqVNDNpysh02xCW2HuxrE4tIsc3kuH9pYjzPe+d+D3ltCQs5erw7IhpBPr7ujs0UcRuelqSwMBAZs+ezezZs0lOTsZsNlO2bFl8feXDJIQo2UKjYpkc9iev6L/jScMvoMFhRw1esr7AYVXL9WhNhveXHs4RbRXVJTZ5zgcNvPtOw1sSpFtSvpMknU6Hv78/cXFxeHp6AlCmTBnKlClTYMEJIYS7mMwWFob/QphxPk10JwH42tabd+yDsSgPebRWCl09om2MYTk+Whp/OuoTWC0Ymf781pTvJKls2bLUq1fPlSAJIUSpoRQpWz/nR+N0vDQrF1Q5XrE+w3pHa5mYthS7ehLbEH1G1fQZ1kf5j0xie8vKd5LUqFEjzp07V5CxCCGEW5nMFk6djuX2XW9Q78Ra0GCT/XbGWZ8lnvIyMW0p5xzR9prhO/SaYq29LbtpLKMVb2H5rpM0cuRIYmNjWbVqVUHGI4QQbhEaFctrs+ZS5/veeJ1Yi11nZHeTV3nS9porQZLHa6VboJ83X/RIp7d+Fzal4337I/Ke3+LyfSdp5MiR7N69m0cffZTp06fz+OOP4+/vX5CxCSFEkTBdSOTyytdY7LEagCOO6oxJf5H/9hnOlj7IyLVbhVLcFTsPgIQGISy+d4i857e4fCdJdevWBcBisTB27FjGjh1LQEBAjh23NU3j+PHj+T2dEEIUKOcw7/paHOV+epanDAcBWGwLZoZtMKl4EpOQIpWzbwHOz0Lji+upEPcHGMtQ9b6pUE7e91tdvpOkq2siOcXHxxMfH5/t9pqm5fdUQghRoJyVsx/V/cYkwxK8tXQuqHK8Zn2aXx1tAKmcfatwDvnXKxsRHpOooAM6vwjlqrg7NFEM5DtJio6OLsg4hBCiSJjMFmaHRfKZ4QuC9X8CsMXRnJhu77Nh/UWkcvat4+oh/0P0v1FHd4545Ye92VNUdXdwoljId5JUu3btgoxDCCEKlfORivHoalZ7vE6AlkSaMvCuLYQv7X1ZWrc+ke19pP/RLcQ55L8sKYw2hAEwx/YQ9ybpqFrJzcGJYiHfSdLixYupUqUKffr0ueG269at4+zZswwdOjS/pxNCiHwLjYrl7bDfeV2/hBDDRtDgb0ctXraOksrZtzDnkP9n9D9TUbvMcUcg/3P05AV5zCr+ke8SAMOHD2fGjBm52nbmzJmMGDEiv6cSQoh8M5kthIX/wCrjBEIMG3EojU9s/XnQOt2VIMmjtVtToJ83c/pV5il9xqjG9+yPMm1AS/ksCJebmrtNKVWg2wkhREFwPloLKm/Asf5tlhm/QKcpTjkqMdb6HFGqkVTOFgDcd/Fr0NJJqtSGyY+9IpW1RSY3PcFtbpw9e1bmdBNCFAnnaKUGxPKhcQGNdbGgQaitB9NtQ7iCj1TOFpjMFs4e203LPd+iAb73vYOvJEjiGrlOkmJjY7MM+zebzWzevDnHfSwWC5s2beLIkSN06NAh30EKIURumMwWXg/by1O6VfzH8AOemo0Lypeo26cw8Y9A7DJyTfBvIv254T00vYNTVXtRs2Z7d4cliqFcJ0lfffUV06ZNy7Rs//799OzZ87r7OR+1vfzyy3mPTgghcsH5eM1yPppvjW/RQXcIgAh7ayZYR/Jxyz5E9pKRa+LfYf/ttYP00u/GpnQMj+3HErNFPhcii1wnSXXq1KF79+6u15s2bcLX15eWLVtmu72maXh7e1O3bl1CQkLo2rXrTQcrhBDXchaGHKDbzGTDYsrpLFxRXky3PU6ovQd6TScj14RLdEIySjkYb1wKwFL7XRx3VCUmIUU+HyKLXCdJw4YNY9iwYa7XOp2O5s2bs2HDhkIJTAghbsRktvB+WCQLDF9ytz4KgChHA8ZZn+OkqiKP1kQWQQFluE+/g5a6E1xRXnxkGyDV1UWO8t1xe8OGDfj5+RVkLEIIcUOukWsBZUja9T/WeEygonaZdKXnQ9vDfGa/l48ebSMj10S2An00ZviGgQU+tfXnklZeEmmRo3wnSXfccUdBxiGEEDfk7HDrqy4z3fgV/fU7XIUh/2N9loOqjoxcE9lyJtfNor/E13IGe9lAuvWfwmNVZQJjkbMCLQFw5MgRZs+ezc6dO0lPT6d+/fo88cQT3HfffQV5GiHELcjZ4fYu7Q9meCykkmbGpnTsDXqCxw7fQarSy+M1kS1ncl1Bmdno+QFooA+eQoeGNdwdmijmcp0krVu3jiFDhtChQwd++umnLOs3bdrEPffcg8VicY1o+/vvv/npp5945ZVXeOeddwouaiHELSc27gzvGxYwQB8JwBFHdf5jfY6J3Qaz4UEZuSayd/Ukti8bllNOs7DPEURA7f4Eujs4UezlelqSX3/9lQsXLjBo0KAs69LT0xk2bBgpKSn4+Pjwyiuv8MknnzBkyBAA3nvvPbZt21ZwUQshbhkms4W/N/1A65/vZoA+ErvSWGC7j/7pb3OQeq7EqFM9eWwisnJOYltPi+NR/XoA3rIOIeZCqpsjEyVBru8kbd26FU3TuP/++7OsW7FiBbGxseh0OtauXUvnzp0BeOaZZ6hTpw5vvfUW//3vf13LhRAiN8K2HcD+ywQe1m8C4LxHLZ678hS7HLfJozWRK85JbCcalmLQHKyzt+EPmshoNpEruU6STp8+Tb169fD19c2ybs2aNQD06NEjSyL0n//8h3fffVfuJAkh8uTCX7/Qce0oqukv4lAaC+19+TAthGWj7iAl3SGP1kSuBPp5s7B7Mj1/341V6Zllf0ySa5FruU6S4uPjadGiRbbrtm/fjqZp9OvXL8s6Pz8/ateuTVxcXP6jFELcEkxmCydN52h+4H0q7vsGNIh2VOEV6zP8oRoBkJLuoFO9im6OVJQEJrOF6PNJdD0xB4CERkNY0m+wJEgi13KdJOl0Os6fP59leVJSEkeOHAHIcX62ChUqEBsbm88QhRC3gtCoWH4M/45Zxs8ooyUA8JWtD+/aQrDgBSBF/0SuOUe0PaTbSGfjAdIN5Qi8bzKUkQRJ5F6uO24HBQVx6tQpTp8+nWn5r7/+ilIKDw8P2rZtm+2+8fHxVK1a9eYiFUKUWmfPn8Px40t86/E2NbQEYh2VGJz+Bum9Z5KuZfxRkz5IIrecI9o8VSrjDN8D8H7qfZhskmCLvMl1khQcHIzNZuP5558nNTVjVEBSUhIzZ85E0zR69eqFp6dnlv0uXrxIdHQ0NWpIPQohRGYZI9e+x+/Lbjyqz5ji6BtbL+5On8U2R2Nur16eyPE9WTayI5HjexLSrpabIxYlgXNE29P6VVTREjnpqMwiW29iElLcHZooYXL9uG3MmDEsXLiQn3/+mcDAQBo0aMDRo0cxm80AjBs3Ltv9wsLCAOjSpUsBhCuEKC3Ct+5FWzOBB/RbgYy+R69Zn2anagz8+2hNJqYVeRUUUIaq2iWeMfwMwCzbI9g1D3lUK/Is13eSatasSXh4OP7+/pjNZqKiokhMTETTNN56660cpymZN28emqbRt2/ffAe5YMECgoKC8PLyok2bNmzZsiXHbTdu3IimaVl+Dh06lGm75cuX06RJEzw9PWnSpAnh4eH5jk8IkQdKcWnnd3Rbdy8P6LdiVxqf2u6lb/os/uDfBEkerYn8CvTzZmm9CHy0NP5wNGCt6iifJ5EveZqW5M477+TEiROsXr2aEydO4OvrS+/evalfv36221+4cIERI0agaRpdu3bNV4ChoaG8/PLLLFiwgC5duvDZZ5/Rt29fDh48SK1aOd96P3z4cKZyBZUqVXL9//bt2wkJCWH69Ok8+OCDhIeHM2jQICIjI3PsfC6EuDkmcyrnTEdpvmcaFY7/AhocctTkVevT/KXqATDvkVYyMa24eaa/qHt6JQCe/WYS2biLfJ5EvuR57rZy5coREhKSq20rVqzI6NGj8xzU1T744AOefPJJnnrqKQDmzJnD2rVr+eSTT5g5c2aO+1WuXJny5ctnu27OnDkEBwczYcIEACZMmMCmTZuYM2cOy5Yty3aftLQ00tLSXK+TkpIAsFqtWK3W/FxajpzHK+jjisyknYuG1Wpl+1nY+uE0XjcswUtLwa7pmWe9n3m2B7D+8zWk0+D26uUI9PNy7SdyTz7PGYn4yYRk2kVOwAuFo8kDNGqT8ZSjINtF2rpoFFY75+V4BTrBbUFLT09n165djB8/PtPy3r1737A4ZatWrUhNTaVJkyZMmjSJnj17utZt376dMWPGZNq+T58+zJkzJ8fjzZw5k6lTp2ZZvm7dOnx8Cuc5d0RERKEcV2Qm7Vy4rJcT6Hf6K7ob9wGw11GX16wjaVCzBrbYjCf+GopBQQ52b13PbncGWwrcqp/n7ec0Qk/o6KHbQzePSGwY2EA3UlavLrRz3qptXdQKup1TUnLfgb9YJ0kJCQnY7XaqVKmSaXmVKlU4e/ZstvsEBgby+eef06ZNG9LS0vjmm2+466672LhxI927dwfg7NmzeTomZNxtGjt2rOt1UlISNWvWpHfv3tlWIb8ZVquViIgIgoODMRqNBXps8S9p58KT8S/6KzQ98z98/3obg95CqjLygW0gC+39sKNnUq+2jPP3IfZiCrX8fVx3kET+3MqfZ5M5lTGzN6PDzkTDUgAW2u6m750hhfK5upXbuigVVjs7nwTlRrFOkpw0Tcv0WimVZZlTw4YNadiwoet1p06dOHXqFO+//74rScrrMQE8PT2zLXFgNBoL7ZekMI8t/iXtXLBCo2L5PHwdMwxf4K/LGDCx09GQ16xPE60y5l3Xaxr1qvgS6OdNrYBy7gy31LkVP8+nzWYcCobo11NfF8cFVY75tvu53ZxeqJ+vW7Gt3aGg2zkvxyrWSVJAQAB6vT7LHZ7z589nuRN0PR07dmTJkiWu11WrVr3pYwoh/mUyW4hOSKas3s7pldNYbVyBp2blivLiXdujmKv14OTpjC8mGbkmClpQQBn8tGTGGP4HwBzbQyRrZWXIv7hpxTpJ8vDwoE2bNkRERPDggw+6lkdERHD//ffn+ji7d+8mMDDQ9bpTp05ERERk6pe0bt26LJPzCiFuzDn9Q0uO8I7xC/5jyJincaO9Ba9bnyCOSrxQzs7G/3QnzpwuI9dEgQv08+b7RluoGH2ZI47qhDp6SSIuCkSxTpIAxo4dy+OPP07btm3p1KkTn3/+ObGxsTz77LNARl+huLg4Fi9eDGSMXKtTpw5NmzYlPT2dJUuWsHz5cpYvX+465ujRo+nevTuzZs3i/vvvZ+XKlfz6669ERka65RqFKKlMZgszwn5nqv47Hjf8CkC88mWadSg/OToBGjoNKnkpAv285NGaKHAmswXTif20OpnRF8kW/Dabbu8lCZIoEMU+SQoJCeHChQtMmzYNk8lEs2bNWL16NbVr1wbAZDJlmjw3PT2dcePGERcXh7e3N02bNmXVqlX069fPtU3nzp357rvvmDRpEm+88Qb16tUjNDRUaiQJkQvOR2tBFX0w7w5nncdEqmiJAITaejDDNpjLWlkg49Ha9PsbU+bcX26MWJRWzruYnxneR9PbOFOpG026PXjjHYXIpWKfJAGMGjWKUaNGZbtu0aJFmV6/+uqrvPrqqzc85sCBAxk4cGBBhCfELcP5R6myusA04yJ663eBBiccVXnd9iTbHU3RaxrhozqRku6gToAPAT4GVq+WJEkULOcktp21fQTr/8Sq9AyLu5/FZovcRRIFpkQkSUII9zOZLbwetpchugheMXxPOc2CVen5K2gEQw53w6KMrk7ZLWpWcO0nBfdEYYhOSEZTdt4wfgPAN/ZgjjqqEZOQIkmSKDCSJAkhrsv5eM16Zj/fG6fQWncMgF2O+kywPsXUbg+z/kEfYhJSpFO2KDJBAWUYrF9PQ91pLqmyzLENcE2KLERBkSRJCJGj0KhYpoTt4nl9OM/of8aos3NZeTPL9gjf2u9Cp+ldiZEkR6IoBXqkMsknHKzwgW0gyVo5GdEmCpwkSUKIbJnMFlaFL2W18UuCdOcAWGNvx1TbMEzKX+odCffa9B6e1kSs/g3o13cioyr7ymdRFDhJkoQQLs5Ha/W8rmCIeJ3FHj8DcFZVYLJ1OGsd7Zj3aCsqlvWUR2vCLUxmC6bj+2i18zM0wNjvHTrdJoWAReGQJEkIAWQ8Wns9bC+P6SIY90/HbLvSWGzvzWzbw1zBB72m0aZOBUmOhFs4R1d+bngvY8h/5e5Uu+0ud4clSjFJkoQQmMwWloavJMy4kNt10QDscdTjeIdpvBWpw46Sx2vCrZxD/rtof9FLvxur0jP09P18I0P+RSGSJEmIW5jJbCH2jIkqUe8SbvwOnaZIUj7Msj3CMvudfNuoE5FdZeSacL9rh/wvtvfmmCNQhvyLQiVJkhC3qNCdJ9mx8lMmGr6lkmYGDcLsXZlhfYwE/FzDqWXkmigOggLKMET/Gw10cVxUZZlre1CG/ItCJ0mSELeg89H7qPnzM4QYDwBw3BHIG7Yn2KGa4gB5tCaKnUCPVCa6hvw/LEP+RZGQJEmIW4TJbOHk2Qs0PfFfAqLmU1mXTqoy8rHtQb6w30M6Rhm5Joolk9mC+mUK1axmrBUbcs/dE3hehvyLIiBJkhC3gNCoWH4JX8JUw1eU050HYKO9BW/YhnNKZQyflpFrojgKjYrli/A1/GL8BjTYWm8sPerLkH9RNCRJEqKUctY8Kp92hvI/jmGRxx8Zy5U/021DadH7cc6sOQIyck0UU84RbV8YlmLU7Pxqb8UzW3yJ7CIj2kTRkCRJiFLIOZ3ISN3PjDKspIneik3p+Mp+N3NsD5GMN4/XqEDk+J4yck0UW9EJyXTT9nLXP0P+37YNwa6UjGgTRUaSJCFKGZPZQsSKRaw1LqaWLh6AbfYmTLYN56iqASAj10SJEFTByGTDYgAW2fsQrQJlRJsoUpIkCVEKOB+t3WY4j+e6ifzXuCFjufLnLesQVjk6oNM0QEauiZIj8O+vQWciQfnykW2AfHZFkZMkSYgSLjQqlqlhf/CcfiVt9D/jqdlIV3r+a7+HebYHSMELvaYRNqoTKekOebQmij2T2cLp2GjabHwHHWDoM5XPq9wpn11R5CRJEqIEMyWmsHHFQtZ5LKGGlgDAZsftnOk0hdmb0zJNJ9KiZgU3RyvEjTnnZ3vX8Cnt9Mlc8GtGxY7D6aTTuTs0cQuSJEmIEsb1aE1nwnvdeD4xRgJwWgUw3fo4ax1tWdagJZGdZToRUbI4R7PdzjEG6jcDMDI+hPmX0+QzLNxCkiQhSpDQqFimh0XxvD6ctvrVeGh20pSBz+z3ssB2P6l4SqdsUWJFJySjlIMpHosA+MHWnT8d9WQ0m3AbSZKEKOacd47KGDV2rpjPbx7fUUVLBGC9vRXnukxm7iZLpkdr8gdFlERBAWV4WL+ZlroTXFbevGt7REazCbeSJEmIYszZP+N2jjHZuJjZxmMAnHRUZprtcX5ztGFZ/duJ7CSP1kTJF+iZzrQy/4N0+Mj2IBe1CpL0C7eSJEmIYspktvBh2GbeM3zHQ/otAFxRXsyzPcCX9r6kY5RHa6LUMJktqDVTqZZ+EVuFetzV902eqFJePtfCrSRJEqIYcT5aCyqvJ33LPH7z+JgyWhqQ0T/jXVsIF7QKOJB6R6L0+Hd+tkUZ87PdNo47GgS6OywhJEkSorjIeLT2F8HaH7xuXEJtLR40+NNxG1Osw/hL1UOvaYRLvSNRimSMaPuLRYbFGDU7EfbWPBtZnsiuMj+bcD9JkoQoBkxmC4vCV/GN4Ru66A8AcFZV4EDT//D07rrYFVLvSJRK0QnJ3Kn9SXf9PtKUgbdkfjZRjEiSJIQbmcwWTsWdpvIfH/CzcRl6TZGmjHxmv4dPbfexsE0PIvtIp2xRegWV1/OG8RsAFtr7cVJVlRFtotiQJEkIN/n+9+Mc+HEuYwz/o7yWDBqssrdnpm0wp1Vl6ZQtbgmBBxaCdp6zqgLzbA9IXztRrEiSJEQRcXXKruiDZ3QEbVa9xiCjCYC/HbWYahvKTtUEh5JO2aL0M5ktxJ08RuvN76MDPPu+xcJKPeSOqShWJEkSogg46x015CSTjEvoojuAvw4SlC8f2gbynb0ndvTMe7QVFct6yh8KUao5fx8+MMyjrd5CQoWWBHR4jE6a5u7QhMhEkiQhCpmz3tFM/Q88rN+E7p9+R1/Z72a+7X4uk9H3Qq9ptKlTQZIjUao552drzSEe0G/DoTSeODeIz5JS5bMvih1JkoQoJCazhZOmeCrs/Yz1Hl/g80+9ox/tnXjX9gj9unYgJTIalEwnIm4d0QnJaMrONI+vAfjO3oO/HHVkNJsoliRJEqIQhO6MYefKTxhn+J5A7SJosMtRn7esQ9it6qPXNEZ0rcOIrnVk5Jq4pQQFlGGI/lea6E6SqMrwvi1ERrOJYkuSJCEKgKtTdkAZPE5tpcnP/yHEGAPAKUclZtkeZbXqgENpWe4aSXIkbiWB+iu87h0GNnjfNgiz5id3UUWxJUmSEDfJ2Qm1NiYmGJbSW7+LijpIUt7Msz3A1/Y+pOEhnbLFLc9ktqD/8TUq2y5jrXw79wRP5PnK5eT3QRRbkiQJcRNMZgvvhW3lDX04Q/S/YtTs2JSOpY67mGN9iIv4AtIpW4jQqFi+D1/Oco/lAGy67RV61a/s5qiEuD5JkoTIB5PZwsmzCZTf+wUbPD6nnGYB4Fd7K2baBnNX126YpVO2EEDG78vrYXtZafwKgFBbDyZuMBDZQeZnE8Wbzt0B5MaCBQsICgrCy8uLNm3asGXLlhy3DQsLIzg4mEqVKuHr60unTp1Yu3Ztpm0WLVqEpmlZflJTUwv7UkQp8P3vJ5j77hvUWdqNRgfnUk6zsM9Rh8HpE3nK+gox1GBE1zpEju/JspEdiRzfk5B2tdwdthBuE52QzKO6X2mqO4lZ+TDL9ohrfjYhirNifycpNDSUl19+mQULFtClSxc+++wz+vbty8GDB6lVK+sfns2bNxMcHMyMGTMoX748X331Ff379+f333+nVatWru18fX05fPhwpn29vLwK/XpEyZOpUvaJCFqumsAgYxyQ0Sn7PVsIq1RH7EonnbKFyEY9HwvjDN8DGZ21L+IrI9pEiVDsk6QPPviAJ598kqeeegqAOXPmsHbtWj755BNmzpyZZfs5c+Zkej1jxgxWrlzJTz/9lClJ0jSNqlWrFmrsouRzdsq+nWNMNC6lve4Q/jq4pMryse1Blth7kY5ROmULcR1Vdr4DWgr7HXX41t5LHkGLEqNYJ0np6ens2rWL8ePHZ1reu3dvtm3blqtjOBwOLl++jL+/f6blV65coXbt2tjtdlq2bMn06dMzJVHXSktLIy0tzfU6KSkJAKvVitVqze0l5YrzeAV9XJHZjdrZZE7ls/B1fGwI5R79TgBSlZGv7H35xNafJMoAoNPg9urlCPTzuu7xblXyeS4axbGdTeZULh7eRsvdSwAIeHguiz0bU8vfh0A/r2IVa14Ux7YujQqrnfNyvGKdJCUkJGC326lSpUqm5VWqVOHs2bO5Osbs2bNJTk5m0KBBrmWNGjVi0aJFNG/enKSkJObOnUuXLl3Yu3cv9evXz/Y4M2fOZOrUqVmWr1u3Dh+fwrllHBERUSjHFZld3c6JaRCfqlHTkEiDsytYa9yIUbPjUBr/s3fnA9tAmgRW4LIpY44pDcWgIAe7t65nt7suoISQz3PRKC7tvP2cxg8nYIXHG6CDP7y7EXfCDOzgApSK35fi0talXUG3c0pK7vvCFeskyUm7ZtJDpVSWZdlZtmwZU6ZMYeXKlVSu/O9Q044dO9KxY0fX6y5dutC6dWs+/vhjPvroo2yPNWHCBMaOHet6nZSURM2aNenduze+vr55vaTrslqtREREEBwcjNFoLNBji39d284/7DrNeyv/5Andau4x/ExZLRU0+M3eilm2RziiaqLT4PvHujMFiL2Y4voXsciZfJ6LRnFqZ5M5lTGzNzNYH0FzXQxJyofnEgezvMudpeL3pTi1dWlWWO3sfBKUG8U6SQoICECv12e5a3T+/Pksd5euFRoaypNPPskPP/xAr169rrutTqejXbt2HD16NMdtPD098fT0zLLcaDQW2i9JYR5bZEhMg12nLuPnqTj68wds8FhBJS3jF2ivox4n27zGmP+3d+dxUZb74/9f9yysCqaooaiIictxDVJxt9RSTy5Hk+ycXLLFQ5+T6anz0crUOv3KyrXcjydbTDHDNKWUElnM3FCz1PKjKEqggbIpIjNzff/gN5PIiEAww8D7+XjwKK97e9/vGe55c8113ff3dTHfNJW/uV9dANt/RdnI+9kxqkOeL2RnU0/l3DRY+xF+U76kZt+oUb831SHXtUFl57k8+6rWRZKbmxshISHExMQwatQoW3tMTAwjRoy47Xbr16/niSeeYP369QwbNuyOx1FKceTIETp27FgpcQvX8NmhC7yWpDHyyAKmGTcx25ABwFlL46IZa5burO8QRmJ/L3m+mhDl0NLPm/81bqCedpXjlha2wdoym024mmpdJAFMnz6dxx9/nNDQUMLCwli1ahUpKSlMmTIFKPoaLDU1lY8++ggoKpDGjx/P4sWL6dGjh60XytPTE19fXwDmzp1Ljx49aN26NTk5OSxZsoQjR46wdOlS55ykcLi0rGvEf/khX7ltJFhXNJ3/oqrHYtNoNpr7YcJgu6j7+3pKcSREOfjn/kS4fjcAswongmaQ2WzCJVX7Iik8PJzMzExee+010tLS6NChA9HR0bRo0QKAtLQ0UlJSbOuvXLkSk8nEs88+y7PPPmtrnzBhAmvXrgUgKyuLp59+mvT0dHx9fenatSvx8fF069bNoecmHMt6v6M21w5TZ/frrDAeBSBLebPcNJwPzYO5obljAZmiLEQFpV3Jo27UVOoA19qP5YWQCdILK1xWtS+SACIiIoiIiLC7zFr4WO3evfuO+1u4cCELFy6shMiEq4g8kML6zV/wT30kDfQ/AnBNubPGPITVpmHk4I1e09gcEca1Gxa5qAtRAZEHUvjhi0W8YfyRHOXFriZ/Z2SrBs4OS4gKc4kiSYiKsPYc1b+WjM/Wl/jCreheRzeUng2WBzD3nsaC2MsoNFvPUedmdzk5aiFcU1p2Pm9H7eEbt0gAFpjG8PH2dLp3lOezCdclRZKokSIPpPBeVCzP6T+nuz6etnqFRWlstvRmoWk0F1QjPmnZijnZv9GqSw9aNfaRC7kQf0ByxlX+V7+eu7Q8Tlia87F5EGaKns8mv1vCVUmRJGoE2/PV/LzR5V3k+tYX+dZtF+6aCYCd5hDeNY3lF9UMKBpz1Ly+F5nu0L1lfZnGK8QfFFzwIz0NcQC8XPgEZvQyo024PCmShMuzPl/tLpXN3w1fMsH4DRP0NwD4ztyed0zhHFat0f3/9x/9fVC2R424668QTmcuxG/3TAAizQNIUsEy+UHUCFIkCZeWlp3PW1F7+ad+GxP1O/DWCkDBQUsw802PsNfyJ6CoMIq6ZVC2PHdJiD8uLTufgrhFBF46Dp716TdhKeuvesjkB1EjSJEkXFJadj4pv6Zz19HVxLn9Fx8tH4CjliAWmsYQ3HMU+/ecBZQMyhaiikQeSGFJ1G52ui0EDfa3fp5udzflbmcHJkQlkSJJuJzP957kzPYFPKXfRj3tKmhwwtKcBaYxxFhC0Gs63uzTkkl9WsqdsoWoImnZ+cyMOsYyw0d4awUcsATz2IFWJDwgs9lEzSFFkqj2bIOyfTW8f/iQfnELGW0oer7aKUtTFplG87XqhlnpSoyDkIu1EFUjOeMq/bTDPKQ/gEnpeKXwCUxKk9lsokaRIklUa5EHUpgdlUS4bhfPGrbgo2WBBsmWxiw2jWarpScWdLw/risN6rhLr5EQDtLSV+M141oA1piH8LNqLrPZRI0jRZKodqw9R3V0hRz/Yj673LbSRLsMwAXlx3umv7DJ3AczeqBoUHZI4F1SHAnhQP5Hl4H2G2mqPotNo2U2m6iRpEgS1UpRz9EhwnWx/N2wlbnGKwCkqfq8bxrJRnN/JvUJhsRkUEouzEI4waXkH/Dbsxgd4PHwO6y5q7/04ooaSYok4VQ33wQSUz4nt7zDbrcvuVsrKo5+VfVZZhrBZ+Z+FOCGXtOY1DuQSb0DZVC2EE4Quf8czbY9QyNdIbHmLlyy3Ee4PJ9N1FBSJAmnsd4E0qhu8DfDt0zzjGa2IROAVNWA5abhbDT3x6QZsYAMyhbCydKy8/l+ywrCjce5roy8aprAr5t/om+bRvL7KGokKZKEU6Rl5zM36iCTdN/yjGEbjbQsKIRU5cdS0wg2mftyAyN6TWPzLTeBFEI4R8qvabxk+ASA90yjOK8agzyfTdRgUiQJh7F+tRbkCwV7/0Oc2zIaatlA0YDspaYR+PaYSOR3FzDLTSCFqHY6nFyMt5bDaYs/q83DAGRGm6jRpEgSDhF5IIXXog7wmO4bgg3bCNRyQIPzloa8bx5JlLkPFs1IYt/WTOjbWsYbCVHdpCbhffRDAGabnrD19MrECVGTSZEkqoy156iuyuP8ljdIcIumvpYHQIpqxJl2U3jmaGsKlF7GGwlRjaVdyaPu5/+gDgo6PsI7A/8hf8iIWkGKJFElIg+k8HbUHibqv2aCfgcvGIqerXbW0pil5hFsNvfm4/t6s3uIl1xshajGIg+kcPyL+cw1/kiO8iK2UQQjfD3l91XUClIkiUpx81R+XV46eVtmkuC2Cy+tAICfLQEsNY1gu6UHZvS2cQz+crEVotpKy85nYVQ8MW6RALxjGsun0el06yTPZxO1gxRJ4g+zTuVvwm/83bCVcEM8kw2FAPxgaclS00h2WkLQNJ3dqfxCiOopOeMqswwfUlfL54ilFevMA7HIbDZRi0iRJCrE2nPk7aZn1eYdvG3YykhdIgbNAgoOWNrwvmkkcZZOgIZe04iSqfxCuJS2OXuor9+PSemYWfgkFnQym03UKlIkiXKz9hy14RzPGrYQY9yHTlMAxJs78r5pJF16DyMxMRlkKr8Qrqkgj/q7Xwbgv+ahnFAtpBdY1DpSJIk7KvboEOCzzZ+zyrCFgfrDtnVizCG8bxrBUXUPek1jsTw6RAiXlrfjNepkn8fkE8Dw8YvpmK3J77KodaRIEqWy9hpZlOIB/RFm3xXDJrcjAFiUxnZLd5aaRvILzbEoeXSIEDXBjm92MPDQKtDgqczHeCj5GuH3NXd2WEI4nBRJ4rbSsvN5JeoID2t7mWL8kna685AHN5SeKHMfVpn/zBnVRB4dIkQNknYlD//4Geh1im3mHsSauxAf9SN9gxvK77aodaRIEiWkZedzLj0D7+Mb2GVcSTPdbwDkKQ/WmR+gIOQZFu+/ilnJeCMhapr8PSvopDtDjvJibuHjAJiVzGgTtZMUSaKYqO+OkRy9mIn6r2mg5YIOMpQPH5ge4mPzQK5qdUm8vxuP3I+MNxKipsm+QODRBQDMMz3KbxT98SMz2kRtJUVSLWcdlN3KPRvvpJUMPvQhdQzXgaLnqq0y/5lNln7kKzcZbyRETffV/6IrvEpGvc5EXnwAkPuaidpNiqRaLPJACqs3f81Tuu2E6hNw08ygwQlLM5abhtvujv3+uK40qOMuvUZC1GCXD0VR/+Q2lM6A37gVJHi0lN5iUetJkVTLpGXnk/xbHo2uJNFg61t845ZkW7bP0paV5ofZZe4CaEDRX5EhgXfJRVKIGuzzvScI+/qfoMHyG0NpkFKH8PvkkUFCSJFUi2zcd4a4rR/wlH479+hOc4++aBr/t5Z7WW56mCQVzNN9gohLTC42KFsulELUXGnZ+eRsn0MTw2XOWRqx2PQXTDKbTQhAiqQazTreKMgHPH9aT4/YxYw1Fs1UK1BGPjf34T/moZxRTYCiXqNJchNIIWqVSz/vZbx+BwCvmJ6gADeQ2WxCAFIk1ViRB1KYH5XAeP0O2uu/wVe7iq8Gl1UdPjYP5iPTIDLxRVf0rZoMyhaiNjKbaH/wVfSa4gtzTxIsnQCZzSaElRRJNcTNjw4xZP4MW2eT4JaIu2YC4KylMWvMQ/nM3JfruAPIQ2eFqOVy4t7D59IxCgx1+f8Kiu6JJF+zC/E7KZJqgKJHh/xAN+0ET+u3c7/+MOH6omWHLK1ZZRpGjCWUJ/vcQ2FiMshNIIWo9b6M+577494EDWbnP8rkId3p1LSe/MEkxE2kSHJR1p6jOnoz+75YxlbjV3TQnQWKBmPHWEJZaRpGkgoGZLyREOJ3aVnXqPvNv/DWF7Df0oZIcz90X/1M4owBcl0Q4iZSJLmgyAMpvB21h3G6bxlviGGBMQuA68rIJnNf/mMeyuDevTiamAyUnKUmF0Eharfcgxvorz9KgTIws/BJFDp59IgQdkiR5AJuHW9k2TqHPW6JeGiFAFxU9fjQNJhPzQ+QRV3pNRJC3N61y7Q69G8AlppGclo1BWSwthD26JwdQFksW7aMli1b4uHhQUhICAkJCaWuHxcXR0hICB4eHgQFBbFixYoS63z++ee0b98ed3d32rdvz+bNm6sq/D8k8kAKvd76lpVrVnFq/iAaftSPcfpYPLRCjlkCef5GBL0LlrDCMtJWIFl7jfx9PQlr1UAKJCHE73bOQp+fSXadVqyyjABksLYQt1Pte5IiIyN5/vnnWbZsGb169WLlypUMGTKE48eP07x58xLrJycnM3ToUJ566ik++eQT9uzZQ0REBA0bNmT06NEA7N27l/DwcF5//XVGjRrF5s2bGTt2LImJiXTv3t3Rp1hCWvZ1TmVrHDubzg9fLGKH8Wta61KB38cb/cc0hAOqDaCh1zQ2yyw1IcQdZB6LocGRT1Bo+I5dTqxvJ+ltFqIU1b5IWrBgAZMnT+bJJ58EYNGiRezYsYPly5fz5ptvllh/xYoVNG/enEWLFgHQrl07Dh48yLvvvmsrkhYtWsSgQYOYOXMmADNnziQuLo5Fixaxfv16x5zYbUQeSGFRVBx/1ccQePrvvGHMAyBPebDR3J8PzA8ypHcYSbeMN5JZakKI0kQeSOGeL2fSQAefmAfidtGf8Oby6BEhSlOti6QbN25w6NAhZsyYUax98ODBfPfdd3a32bt3L4MHDy7W9uCDD7JmzRoKCwsxGo3s3buXadOmlVjHWljZU1BQQEFBge3fOTk5ABQWFlJYWFie07qttOzrxH2xhni39zBqZgDOWxqy1vwgG839ycULnQZ/6x7A37oHkHL5Gs3re+Hv61FpMdQm1pxJ7qqW5NkxSstzWvZ1ZkYdw1f9k+cMm5lveoRrUccIa3kX/r4ejg7V5cl72jGqKs/l2V+1LpIyMjIwm800bty4WHvjxo1JT0+3u016errd9U0mExkZGfj7+992ndvtE+DNN99k7ty5Jdp37tyJl1flDHY8la1xwByMxaCx39KGNaYhxFhCUWgoNDQUY1taOLxnl22bTOBwpRy99oqJiXF2CLWC5Nkx7OX5VLaGRem5gg9zTROKGhVsjI6lta9ycIQ1h7ynHaOy83zt2rUyr1utiyQrTdOK/VspVaLtTuvf2l7efc6cOZPp06fb/p2Tk0OzZs0YPHgwPj4+dz6JMkjLvs6yE/HcXzCfVBoCoNPgs6e7c73QbOs1EpWjsLCQmJgYBg0ahNFodHY4NZbk2TFKy7P12mK5qR7SaTB26AC5plSAvKcdo6rybP0mqCyqdZHk5+eHXq8v0cNz6dKlEj1BVnfffbfd9Q0GAw0aNCh1ndvtE8Dd3R13d/cS7UajsdJevOZ+Rt78S0dmRh0DVXQRe/MvHQlt6Vcp+xf2VeZrKG5P8uwY9vJsvba8FPUj5pvuuN/cr66ToqwZ5D3tGJWd5/Lsq1oXSW5uboSEhBATE8OoUaNs7TExMYwYMcLuNmFhYXz55ZfF2nbu3EloaKgtMWFhYcTExBQbl7Rz50569uxZBWdRPuH3NSes5V1sjI5l7NABchETQlSK8Pua0ze4ocxmE6IcqnWRBDB9+nQef/xxQkNDCQsLY9WqVaSkpDBlyhSg6Guw1NRUPvroIwCmTJnC+++/z/Tp03nqqafYu3cva9asKTZrberUqfTt25d58+YxYsQItmzZwjfffENiYqJTzvFW/r4etPZV0g0uhKhU1vunCSHKptoXSeHh4WRmZvLaa6+RlpZGhw4diI6OpkWLFgCkpaWRkpJiW79ly5ZER0czbdo0li5dSpMmTViyZIlt+j9Az5492bBhA6+88gqzZs2iVatWREZGVot7JAkhhBCieqj2RRJAREQEERERdpetXbu2RFu/fv1ISkoqdZ9jxoxhzJgxlRGeEEIIIWogl3gsiRBCCCGEo0mRJIQQQghhhxRJQgghhBB2SJEkhBBCCGGHFElCCCGEEHZIkSSEEEIIYYcUSUIIIYQQdkiRJIQQQghhhxRJQgghhBB2uMQdt6sjpRQAOTk5lb7vwsJCrl27Rk5OjjxhugpJnh1D8uwYkmfHkVw7RlXl2fq5bf0cL40USRWUm5sLQLNmzZwciRBCCCHKKzc3F19f31LX0VRZSilRgsVi4ddff6Vu3bpomlap+87JyaFZs2acP38eHx+fSt23+J3k2TEkz44heXYcybVjVFWelVLk5ubSpEkTdLrSRx1JT1IF6XQ6AgICqvQYPj4+8gvoAJJnx5A8O4bk2XEk145RFXm+Uw+SlQzcFkIIIYSwQ4okIYQQQgg7pEiqhtzd3Zk9ezbu7u7ODqVGkzw7huTZMSTPjiO5dozqkGcZuC2EEEIIYYf0JAkhhBBC2CFFkhBCCCGEHVIkCSGEEELYIUWSEEIIIYQdUiQ5ybJly2jZsiUeHh6EhISQkJBQ6vpxcXGEhITg4eFBUFAQK1ascFCkrq08eY6KimLQoEE0bNgQHx8fwsLC2LFjhwOjdV3lfT9b7dmzB4PBQJcuXao2wBqivHkuKCjg5ZdfpkWLFri7u9OqVSv++9//Oiha11XePK9bt47OnTvj5eWFv78/kyZNIjMz00HRuqb4+HgefvhhmjRpgqZpfPHFF3fcximfg0o43IYNG5TRaFSrV69Wx48fV1OnTlXe3t7q3Llzdtc/c+aM8vLyUlOnTlXHjx9Xq1evVkajUW3atMnBkbuW8uZ56tSpat68eWr//v3ql19+UTNnzlRGo1ElJSU5OHLXUt48W2VlZamgoCA1ePBg1blzZ8cE68Iqkufhw4er7t27q5iYGJWcnKz27dun9uzZ48CoXU9585yQkKB0Op1avHixOnPmjEpISFB/+tOf1MiRIx0cuWuJjo5WL7/8svr8888VoDZv3lzq+s76HJQiyQm6deumpkyZUqytbdu2asaMGXbX/9e//qXatm1brO2ZZ55RPXr0qLIYa4Ly5tme9u3bq7lz51Z2aDVKRfMcHh6uXnnlFTV79mwpksqgvHn+6quvlK+vr8rMzHREeDVGefP8zjvvqKCgoGJtS5YsUQEBAVUWY01TliLJWZ+D8nWbg924cYNDhw4xePDgYu2DBw/mu+++s7vN3r17S6z/4IMPcvDgQQoLC6ssVldWkTzfymKxkJubS/369asixBqhonn+4IMPOH36NLNnz67qEGuEiuR569athIaG8vbbb9O0aVOCg4N54YUXyM/Pd0TILqkiee7ZsycXLlwgOjoapRQXL15k06ZNDBs2zBEh1xrO+hyUB9w6WEZGBmazmcaNGxdrb9y4Menp6Xa3SU9Pt7u+yWQiIyMDf3//KovXVVUkz7eaP38+V69eZezYsVURYo1QkTyfOnWKGTNmkJCQgMEgl6CyqEiez5w5Q2JiIh4eHmzevJmMjAwiIiK4fPmyjEu6jYrkuWfPnqxbt47w8HCuX7+OyWRi+PDhvPfee44IudZw1ueg9CQ5iaZpxf6tlCrRdqf17bWL4sqbZ6v169czZ84cIiMjadSoUVWFV2OUNc9ms5nHHnuMuXPnEhwc7KjwaozyvJ8tFguaprFu3Tq6devG0KFDWbBgAWvXrpXepDsoT56PHz/Oc889x6uvvsqhQ4f4+uuvSU5OZsqUKY4ItVZxxueg/BnnYH5+fuj1+hJ/lVy6dKlElWx19913213fYDDQoEGDKovVlVUkz1aRkZFMnjyZzz77jIEDB1ZlmC6vvHnOzc3l4MGDHD58mP/5n/8Bij7MlVIYDAZ27tzJ/fff75DYXUlF3s/+/v40bdoUX19fW1u7du1QSnHhwgVat25dpTG7oork+c0336RXr168+OKLAHTq1Alvb2/69OnDv//9b+npryTO+hyUniQHc3NzIyQkhJiYmGLtMTEx9OzZ0+42YWFhJdbfuXMnoaGhGI3GKovVlVUkz1DUgzRx4kQ+/fRTGVNQBuXNs4+PD8eOHePIkSO2nylTptCmTRuOHDlC9+7dHRW6S6nI+7lXr178+uuv5OXl2dp++eUXdDodAQEBVRqvq6pInq9du4ZOV/yjVK/XA7/3dIg/zmmfg1U6LFzYZZ1iumbNGnX8+HH1/PPPK29vb3X27FmllFIzZsxQjz/+uG1969THadOmqePHj6s1a9bILQDKoLx5/vTTT5XBYFBLly5VaWlptp+srCxnnYJLKG+ebyWz28qmvHnOzc1VAQEBasyYMeqnn35ScXFxqnXr1urJJ5901im4hPLm+YMPPlAGg0EtW7ZMnT59WiUmJqrQ0FDVrVs3Z52CS8jNzVWHDx9Whw8fVoBasGCBOnz4sO1WC9Xlc1CKJCdZunSpatGihXJzc1P33nuviouLsy2bMGGC6tevX7H1d+/erbp27arc3NxUYGCgWr58uYMjdk3lyXO/fv0UUOJnwoQJjg/cxZT3/XwzKZLKrrx5PnHihBo4cKDy9PRUAQEBavr06eratWsOjtr1lDfPS5YsUe3bt1eenp7K399f/fWvf1UXLlxwcNSuJTY2ttTrbXX5HNSUkv5AIYQQQohbyZgkIYQQQgg7pEgSQgghhLBDiiQhhBBCCDukSBJCCCGEsEOKJCGEEEIIO6RIEkIIIYSwQ4okIYQQQgg7pEgSQgghhLBDiiQhRJU7e/YsmqYRGBjo0ONeunSJunXr8tBDDzn0uMK++Ph4NE3jlVdecXYoQpSJFElCuKCUlBSmT59Ohw4d8Pb2xtPTk+bNm9OzZ09efPFFduzYUWKbtWvXMmfOHM6ePev4gJ1k7ty55OXl8eqrrzo7lEoza9YsNE3js88+c3Yo5da3b1/69u3LwoUL+fXXX50djhB3JI8lEcLF7Nq1i5EjR5Kbm4ter6dZs2Y0atSIy5cvc/r0aZRSNGjQgIyMjGLb9e/fn7i4OGJjY+nfv79DY05NTeWBBx6gadOmfPvttw455pkzZ2jbti29evUiNjbWIcd0hK5du/LTTz+RkZGBj4+Ps8Mptx07dvDQQw8xZcoUli9f7uxwhCiV9CQJ4UJycnIIDw8nNzeXYcOGcfr0aZKTk9m3bx+nTp3i8uXLrF27lu7duzs71GKaNm3KyZMnHVYgAaxcuZLCwkImTJjgsGNWtdTUVI4cOUKfPn1cskACGDRoEE2aNOHjjz8mJyfH2eEIUSopkoRwIdHR0bYehI0bN9KiRYtiy+vVq8eECRPYvn27kyKsHgoLC1m7di3u7u6MGjXK2eFUmm3btgHw5z//2cmRVJxOp+ORRx7h6tWrrF+/3tnhCFEqKZKEcCFnzpwBIDg4GC8vrzJts3v3bjRNIy4uDoABAwagaZrtZ+3atUDJwdWrV6/mvvvuo27dumiaViyGefPm0b9/f5o1a4a7uzsNGzbkoYceum1xVtrAbWscAF999RV9+/albt26+Pr6MmTIEA4fPlym87xZbGwsly5donfv3vj6+t42J/3798dsNjNv3jzatWuHp6cngYGBzJkzB5PJBEB+fj6zZs3innvuwcPDg1atWvH2229jb6TCxIkTbTk9d+4cf/vb32jcuDF16tQhLCyMmJgY27rHjh1j9OjRNGrUCC8vL/r27cv3339f6nnZK5LmzJmDpmnMmTOHzMxMIiIiCAgIwNPTk86dO7NhwwbbuufOnWPSpEk0adIET09PQkJCbvuaBQYGomkaZ8+eJS4ujoEDB1KvXj3q16/PqFGjOHXqlG3drVu32nq37rrrLsaNG1fqmCNr/JGRkaWerxBOp4QQLuO9995TgPL19VVXrlwp0zZJSUmqV69eysfHRwGqQ4cOqlevXraf6OhopZRSycnJClAtWrRQU6ZMUYBq1qyZCg0NVfXq1bPtb/LkyQpQderUUcHBwSo0NFT5+/srQAHqrbfeKhHDzfu+lXW75cuXK03TlL+/v7r33nuVt7e37TgnTpwoV55efvllBaiZM2faXR4bG6sA1a9fPzV69GgFqHbt2qk2bdooTdMUoCZNmqTy8/NV9+7dlV6vV506dVKBgYG2eF999dUS+50wYYJtmZ+fn/L29lYhISHKz89PAcpgMKhvv/1WJSQkKG9vb1WvXj0VEhKifH19FaC8vLzUjz/+aDfm/Px85eXlpYKDg4u1z549WwHqueeeU/fcc49yc3NT9957r2ratKkt1g8//FCdPHlSNWrUSHl5eRWLSa/Xq5iYmBLHa9GihQLUggULlF6vV40aNSr2uvj7+6u0tDS1YMECBaiAgADVuXNn5e7urgDVpk0blZ+fb/dcsrKylKZpytPTUxUUFNzp5RTCaaRIEsKF/Pzzz0qn0ylAhYSEqE2bNqmsrKwybduvXz8FqNjYWLvLrYWMXq9X3t7easuWLbZl165ds/1/dHS0+v7775XFYim2fXx8vPL391d6vV793//9n919l1YkeXl5qQ8++MDWnpOTox544AEFqPDw8DKdo9WAAQMUoDZt2mR3ubVIMhqNKiAgQB0+fNi2bPfu3crNzU1pmqaGDx+uOnbsqE6fPm1bvm7dOgUod3d3dfny5WL7tRZJRqNRPfrooyonJ0cppZTZbFYREREKUJ07d1aBgYFq+vTptgLh+vXr6uGHH1aAGjt2rN2Yt23bpgA1ffr0Yu3WIsloNKoBAwaoixcv2pa99dZbtoKmW7duJWJ65plnFKC6detW4njWIsloNKr58+crs9mslFLqypUrqkePHgpQw4YNU15eXmrdunW27VJSUlRQUJAC1LJly+yei1JKBQcHK0Dt3bv3tusI4WxSJAnhYt544w1bYQEoTdNUmzZt1MSJE9WGDRvU9evX7W5X1iIJUPPnz69QbP/5z38UoN544w27+y6tSPrHP/5RYtkPP/xg6zkrD+uH9J49e+wutxZJgNq8eXOJ5ePGjbPlNikpqcRya5EQFRVVrN1aJPn7+6urV68WW5aVlaU8PDwUoLp27VqiyDx58qQClI+Pj92Yrb17u3btKtZuLZI8PT1VampqsWUmk0kFBATcNqYrV67YYsrMzCy2zFokjRgxokQsO3bssOVv6tSpJZavWLFCAWr48OF2z0Wp39+PGzZsuO06QjibjEkSwsW89NJL7Nq1i6FDh+Lm5oZSip9//pm1a9fy6KOPEhwczO7du//QMcaPH1/q8t9++43Fixfz2GOPMXDgQHr37k3v3r1ZtGgRAEePHi33MZ988skSbR07dsTDw4Ps7GwyMzPLvC/r7Q/q169f6nr169dn5MiRJdq7dOkCFE2379q1a4nl1jbrGLFbjRs3rsSYMV9fX1q2bAnApEmTio3zAmjTpg2enp7k5OTYPdfo6Gh8fX3p3bu33WMOGTKEJk2aFGvT6/V07NjxtjHVq1fPFlNycrLd/U6ePLlEmzU/t1t+p/zA76/Nb7/9dtt1hHA2g7MDEEKU34ABAxgwYAD5+fkcPHiQffv2ER0dze7du0lJSWHo0KEkJSXRtm3bcu/bz88PPz+/2y7fuXMnY8eOJTs7+7brXL58udzHbdWqld32hg0bcv78efLy8mjQoEGZ9nX9+nUA3N3dK3zMsizPy8sr935PnDhR6vKUlJQS53r06FFSUlIYO3YsRqOx0s/lxIkT5ToX6z7vtPx2+wTw9PQEigbGC1FdSU+SEC7M09OTPn368MILL7Br1y7i4+Px9vYmPz+f+fPnV2if3t7et12WlZXFo48+SnZ2NuPHj+f777/nypUrmM1mlFK22VuFhYWVdlydrugypcpx31trL0VWVlap691uhqC1l+dOy28XU2XvtyxT/x15Ljf3gpW2vLTXzFpIl1aQC+FsUiQJUYP07t2biIgIAPbv31/p+//qq6+4cuUKYWFhtptW1qtXz1bInD9/vtKPWRGNGjUCKtajVR1t27YNnU7HkCFDnB1KpbG+Njf3SglR3UiRJEQNExQUBMCNGzeKtd86BqYirM99CwsLs7u/ioxFqgrWMTMnTpxwbiCVICMjg/3799OjR48a0+tiHUcHcO+99zo5GiFuT4okIVxIRkbGHb92+u677wBo3bp1sfbKGANi3cfFixdLLMvMzGTNmjUV3ndlsg5uPnjwoJMj+eO2b9+OxWJx6bts3+rkyZNkZ2cTFBRUYrC5ENWJFElCuJBPPvmELl26sHr16hIzoLKysnj11Vf55JNPgKIZVDez9jBZ77xdEX369AFg48aNfPPNN7b2tLQ0Ro8ebbtLtbMNGjQITdNITEx0dih/WE14FMmt9uzZA8DgwYOdHIkQpZMiSQgXomkaP/zwA08//TR+fn4EBQXRvXt3goODady4Ma+//jpKKV544YUSzywLDw8HYN68ebRp04Z+/frRv39/vv766zIfPyQkhDFjxlBYWMigQYNo3bo1Xbt2pXnz5iQlJfHWW29V6vlWVGBgIPfffz+nT5/m0KFDzg6nwgoLC9m5cyfNmze3TeWvCayPI3niiSecHIkQpZMiSQgXEhERwa5du3jxxRfp2bMnZrOZI0eOkJqaSosWLRg/fjwJCQm88847Jbbt06cPn376Kd26dSM1NZX4+Hji4uJIT08vVwzr1q1j1qxZBAYGcu7cOdLT0xkzZgwHDhygc+fOlXWqf9jTTz8NFMXrquLj48nJyalRvUgXL14kNjaWTp06cd999zk7HCFKpanyzKsVQggXYTab6dixI+np6Zw7d466des6O6RymzZtGosWLSI6OrrGzGybPXs2r732Ghs3buSRRx5xdjhClEqKJCFEjbVlyxZGjhzJG2+8wUsvveTscMotODiY1NRUMjMz8fDwcHY4f1h2djaBgYEEBwezb98+Z4cjxB3JHbeFEDXWiBEjePfdd+945+3q6pdffnF2CJXq3LlzTJ06lREjRjg7FCHKRHqShBBCCCHskIHbQgghhBB2SJEkhBBCCGGHFElCCCGEEHZIkSSEEEIIYYcUSUIIIYQQdkiRJIQQQghhhxRJQgghhBB2SJEkhBBCCGGHFElCCCGEEHZIkSSEEEIIYcf/A/6sZZg6hwSUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "with torch.no_grad():\n",
    "    num_data = model()\n",
    "plt.figure()\n",
    "plt.plot(exp_data[:, 0], exp_data[:, 1], '.', label='Experimental data')\n",
    "plt.plot(num_data[:, 0], num_data[:, 1], '-', label='Numerical model')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Strain (mm/mm)', fontsize=16)\n",
    "plt.ylabel('Stress (GPa)', fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40ede598",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}