gaussian2S.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using device: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Using device: \u001b[1;36m0\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "from scipy.stats import norm\n",
    "import plotly.graph_objects as go\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, StepLR, OneCycleLR\n",
    "from pathlib import Path\n",
    "import matplotlib.animation as animation\n",
    "from sklearn.datasets import *\n",
    "\n",
    "\n",
    "\n",
    "from rich import print\n",
    "import imageio\n",
    "\n",
    "\n",
    "from utils.Models import MLP\n",
    "from utils.Datasets import BBdataset\n",
    "from utils.utils import *\n",
    "seed = 233\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device = 0\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "\n",
    "experiment_name = \"gaussian2S_change_epsilon_norm\"  # 你可以根据需要动态设置这个变量\n",
    "# log_dir = Path('experiments') / experiment_name / 'test' / time.strftime(\"%Y-%m-%d/%H_%M_%S/\")\n",
    "log_dir = Path('experiments') / experiment_name\n",
    "log_dir.mkdir(parents=True, exist_ok=True)\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成初始和目标密度样本\n",
    "def gen_data(m=4, num_samples=1000):\n",
    "    mus = [(m, m), (m, -m), (-m, m), (-m, -m)]\n",
    "    source_dist = np.random.multivariate_normal([0, 0], [[1, 0], [0, 1]], num_samples)\n",
    "    target_dist = make_s_curve(n_samples=num_samples, noise=0.01)[0][:, ::2] * 2\n",
    "    return source_dist, target_dist\n",
    "\n",
    "# 生成二维Brownian bridge\n",
    "def gen_bridge_2d(x, y, ts, T, num_samples):\n",
    "    sigma = 1\n",
    "    bridge = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    drift = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    bridge[0] = x\n",
    "    for i in range(len(ts) - 1):\n",
    "        dt = ts[i+1] - ts[i]\n",
    "        dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        drift[i, :] = dydt\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        bridge[i+1] = bridge[i] + dydt * dt\n",
    "        bridge[i+1, :] += diffusion\n",
    "    return bridge, drift\n",
    "\n",
    "# 主函数\n",
    "def gen_2d_data(m=4, num_samples=1000, epsilon=0.001, T=1):\n",
    "    if not isinstance(num_samples, int):\n",
    "        num_samples = int(num_samples)\n",
    "    source_dist, target_dist = gen_data(m, num_samples)\n",
    "    ts = torch.arange(0, T+epsilon, epsilon)\n",
    "    source_dist = torch.Tensor(source_dist)\n",
    "    target_dist = torch.Tensor(target_dist)\n",
    "    bridge, drift = gen_bridge_2d(source_dist, target_dist, ts, T=T, num_samples=num_samples)\n",
    "    return ts, bridge, drift, source_dist, target_dist\n",
    "\n",
    "ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Km2Ttmj/JXN6xJg9/PDDFb+FL80VKXUllz5c33zzzYOOu+mmmwB/XG3JggULeOyxxwYdd+utt475W56zzjqLWCzG9ddfTz6fH7SvFOvSpUtZsGAB3/jGN0in08PO0dnZOeo1zj33XNrb2/nd735X3pbNZrn11lt3GV93d/eg33Vd58gjjwQol7wurTW0p1bkvvXWWykWi+Xfb7nlFhzH4ZxzzilvG+t9H09s5557Lh0dHdx+++3lbY7j8J3vfIdoNMopp5yyO09HCCHesNK3+QPbsGQyyW233Tbmcyxfvpy2trZBc1jz+Tw/+MEPBh03njZnPO+xy5Yto7Gxke9973vYtl3e/pOf/GSXj89ms8PayAULFhCLxQYtv1BVVbXH2qL29vZB1Vr7+vr42c9+xuLFi8tD20oJ4sD2KJPJVBwJMdbYli1bRlNTE9/73vcGPbd7772XF198cdBnELF/kZ4eMWYf+9jHyGazvOMd72DRokXYts2KFSu4/fbbmTdvHpdeeikARx11FBdffDG33noriUSCU045haeffpqf/vSnvP3tbx/UC/HhD3+Yj3zkI1x44YWceeaZrFmzhvvvv79cunNXqqur+da3vsWHP/xhjj76aP7lX/6F2tpa1qxZQzab5ac//Sm6rvPDH/6Qc845h8MOO4xLL72U1tZW2traePjhh6muruZPf/rTiNe4/PLL+d///V8++MEPsmrVKmbOnMnPf/5zIpHILuP78Ic/TE9PD6eddhqzZs1i69atfOc732Hx4sXlXrDFixdjGAY33HADyWSSYDBYXktid9i2zemnn85FF13Ehg0b+O53v8uJJ544aPLtWO/7eGK74oor+P73v88ll1zCqlWrmDdvHr/73e944oknuPnmm4nFYrv1fIQQ4o0666yzyr3uV155Jel0mh/84Ac0NTWNeWTBlVdeyf/+7//yvve9j2uvvZaZM2fyy1/+sjxZvtQTMZ42Z+nSpQB87nOf473vfS+BQIC3ve1tFRfeDgQCfPWrX+XKK6/ktNNO4z3veQ9btmzhtttu2+WcnpdeeqncLhx66KGYpsmdd97J9u3bee9731s+bunSpdxyyy189atfZeHChTQ1NQ3rLRmrgw46iMsuu4xnnnmGGTNm8OMf/5jt27cPSjTPOuss5syZw2WXXcanP/1pDMPgxz/+MY2Njbz66quDzjfW2AKBADfccAOXXnopp5xyCu973/vKJavnzZvHxz/+8d16PmIa2IuV48QUc++996oPfehDatGiRSoajSrLstTChQvVxz72MbV9+/ZBxxaLRfXlL39ZzZ8/XwUCATV79mz1mc98RuXz+UHHua6r/uM//kM1NDSoSCSili9frjZt2jRiOeZnnnmmYmx33323Ov7441U4HFbV1dXqmGOOUb/+9a8HHbN69Wr1zne+U9XX16tgMKjmzp2rLrroIvW3v/1tl89969at6vzzz1eRSEQ1NDSoa6+9Vt133327LFn9u9/9Tp111lmqqalJWZal5syZo6688kq1bdu2Qef/wQ9+oA444ABlGMagc86dO1edd955FWMa6R49+uij6oorrlC1tbUqGo2q97///aq7u3vQY8d630eLbWjJaqWU2r59u7r00ktVQ0ODsixLHXHEEeWSqSWlUqqVSqcyQiltIYQYj0olq++++2515JFHqlAopObNm6duuOEG9eMf/7hiueWR3nc3b96szjvvPBUOh1VjY6P65Cc/qX7/+98rQD311FODjh1rm/OVr3xFtba2Kl3Xx1S++rvf/a6aP3++CgaDatmyZeqxxx4b9n48tGR1V1eXuvrqq9WiRYtUVVWVqqmpUccee+ygpRiUUqqjo0Odd955KhaLDSqDPVobPFLJ6vPOO0/df//96sgjj1TBYFAtWrRI/fa3vx32+FWrVqljjz223EbedNNNFc85UmxDS1aX3H777WrJkiUqGAyquro69f73v1+9/vrrg465+OKLVVVV1bCYRiqlLaY2TSmZNSyEEEIIsTtuvvlmPv7xj/P666/T2tq6t8MRQoxAkh4hhBBCiDHI5XKDKpjl83mWLFmC67rlSfpCiH2TzOkRQgghhBiDd77zncyZM4fFixeTTCb5xS9+wfr16/nlL3+5t0MTQuyCJD1CCCGEEGOwfPlyfvjDH/LLX/4S13U59NBD+c1vfsN73vOevR2aEGIXJrRkteu6fP7zn2f+/PmEw2EWLFjAV77yFVl8UAghxF4h7ZJ4I6677jrWrVtHOp0ml8uxatUqSXiEmCImtKfnhhtu4JZbbuGnP/0phx12GCtXruTSSy+lpqaGa665ZiIvLYQQQgwj7ZIQQuyfJrSQwVvf+lZmzJjBj370o/K2Cy+8kHA4XF79VwghhJgs0i4JIcT+aUJ7eo4//nhuvfVWXnrpJQ466CDWrFnD3//+d2666aaKxxcKhUGr53qeR09PD/X19eVFv4QQQkw8pRSpVIqWlhZ0fUJHQk+q8bZLIG2TEELsK95Q2zSRiwCVFkDUNE2Zpqk0TVNf+9rXRjy+tBiU/MiP/MiP/OwbP6+99tpENhOTbrztklLSNsmP/MiP/OxrP7vTNk3o8Lbf/OY3fPrTn+bGG2/ksMMO49lnn+W6667jpptu4uKLLx52/NBv05LJJHPmzGHLli3E4/GJCnOP8TyPrq4uGhoapsQ3oxLvxJJ4J5bEO7ESiQTz588nkUhQU1Ozt8PZY8bbLoG0TZNN4p1YEu/Ekngn1htpmyZ0eNunP/1p/vM//5P3vve9ABxxxBFs3bqV66+/vmLjEgwGCQaDw7bH4/Ep07DYtk08Hp8SfzgS78SSeCeWxDs5ptvwrfG2SyBt02STeCeWxDuxJN7JsTtt04Q+u2w2O+wGGoaB53kTeVkhhBCiImmXhBBi/zShPT1ve9vb+K//+i/mzJnDYYcdxurVq7npppv40Ic+NJGXFUIIISqSdkkIIfZPE5r0fOc73+Hzn/88V111FTt27KClpYUrr7ySL3zhCxN5WSGEEKIiaZeEEGL/NKFJTywW4+abb+bmm2+eyMsIIYQQYyLtkhBC7J+mzowlIYQQQgghhNgNkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtDbhSU9bWxsf+MAHqK+vJxwOc8QRR7By5cqJvqwQQghRkbRLQgix/zEn8uS9vb2ccMIJnHrqqdx77700NjayceNGamtrJ/KyQgghREXSLgkhxP5pQpOeG264gdmzZ3PbbbeVt82fP38iLymEEEKMSNolIYTYP01o0nP33XezfPly3v3ud/Poo4/S2trKVVddxeWXX17x+EKhQKFQKP/e19cHgOd5eJ43kaHuEZ7noZSaErGCxDvRJN6JJfFOrKkS53iNt10CaZsmm8Q7sSTeiSXxTqw3EueEJj2bN2/mlltu4ROf+ASf/exneeaZZ7jmmmuwLIuLL7542PHXX389X/7yl4dt7+zsxLbtiQx1j/A8j2QyiVIKXd/3a0RIvBNL4p1YEu/ESiaTezuECTHedgmkbZpsEu/EkngnlsQ7sd5I26QppdQejGUQy7JYtmwZK1asKG+75ppreOaZZ3jyySeHHV/p27TZs2fT3d1NPB6fqDD3GM/z6OzspLGxcUr84Ui8E0vinVgS78RKJBLU19eTTCaprq7e2+HsMeNtl0Dapskm8U4siXdiSbwT6420TRPa0zNz5kwOPfTQQdsOOeQQfv/731c8PhgMEgwGh23XdX1KvBAAmqZJvBNI4p1YEu/EmkrxToUYd8d42yWQtmlvkHgnlsQ7sSTeifNGYpzQZ3fCCSewYcOGQdteeukl5s6dO5GXFUIIISqSdkkIIfZPE5r0fPzjH+epp57ia1/7Gps2beJXv/oVt956K1dfffVEXlYIIYSoSNolIYTYP01o0nP00Udz55138utf/5rDDz+cr3zlK9x88828//3vn8jLCiGEEBVJuySEEPunCZ3TA/DWt76Vt771rRN9GSGEEGJMpF0SQoj9z74/Y0kIIYQQQggh3gBJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJak6RHCCGEEEIIMa1J0iOEEEIIIYSY1iTpEUIIIYQQQkxrkvQIIYQQQgghpjVJeoQQQgghhBDTmiQ9QgghhBBCiGlNkh4hhBBCCCHEtCZJjxBCCCGEEGJam7Sk57//+7/RNI3rrrtusi4phBBCjEraJrG/2dbezdZXtrOtvXtvhyLEpDIn4yLPPPMM3//+9znyyCMn43JCCCHELknbJKa79rYuerpT1NXHaGlt4Pd3PMaf/riCuoYQqaTD/AUzaW1tIBy2CIYs5sxtYsnSA/d22EJMiAlPetLpNO9///v5wQ9+wFe/+tWJvpwQQgixS9I2iemmva2LDetfBxQHL5rNk0+8wC9++lcSiQymoVMVDdG5I4mnFAtVA5te6mLtc6+UH69pYJoG4aogM5vrOOigWcya08iRiw+QREhMCxOe9Fx99dWcd955nHHGGdKwCCGE2CdI2ySmgwfvW8kD963i+XWvkExkUChCQYuqaJiuzgRK7Tw2lcoBoOkaylPDzqUUFIsuxUSWvkS2P4ECXdeYf0Az73z3yRx3wiG0tDZMynMTYk+b0KTnN7/5Df/85z955plnxnR8oVCgUCiUf+/r6wPA8zw8z5uQGPckz/NQSk2JWEHinWgS78SSeCfWVIlzd0jbtG+TeEe2rb2b++55mq6uFGv+uZEtW3YMOyaXt8nlbdA0NG34ObT+7ZqmjWlmtwI2b+7gGzfcQSQc4LgTDue0Mxdz8KLZzGypf+NPahfk72FiTcV4d9eEJT2vvfYa1157LQ8++CChUGhMj7n++uv58pe/PGx7Z2cntm3v6RD3OM/zSCaTKKXQ9X2/MJ7EO7Ek3okl8U6sZDK5t0OYENI27ft/exJvZc89+zJ3/PpR8nn/b043YMHC8Scdmq7RPDMGWuUen1155ZVXue2Hr1JVFebQI+Zyxplvoq6+etznGSv5e5hYUy3eN9I2aUqp8f/Fj8Ef//hH3vGOd2AYRnmb67pomoau6xQKhUH7oPK3abNnz6a7u5t4PD4RYe5RnufR2dlJY2PjlPjDkXgnlsQ7sSTeiZVIJKivryeZTFJdPXEfaCabtE37/t+exDvcX+9fxVe/9CuKjvOGz6VpGgsW1vHyph72xEfAUCjA+e84novee0q5aMKe7AGSv4eJNdXifSNt04T19Jx++umsXbt20LZLL72URYsW8R//8R/DGhWAYDBIMBgctl3X9SnxQgDlhlPinRgS78SSeCfWVIp3KsS4O6RtkngnwkTG+7l//xF/e3D1uB9nmjqOU2EokA6WFaCpqZrtHYPn/eyOXNbm9l8+wm9//Sh19dU0N9fy/g+ezqlnLHljJx5A/h4m1lSK943EOGFJTywW4/DDDx+0raqqivr6+mHbhRBCiMkgbZOYKlav2si3b/oD6194bdyPveAdx3PxZWdx+68f4bnVmwkEDOobq4nXxliyZAGz5tTgOiaO4/K7Ox7jmX9sQCko5IvYdnG34vU8RVdnkq7OJDf+9x0cfMhsKXog9imTsk6PEEIIIYQYXans9K9+/lfWv/AaxaI75seGQiZLlx3MBy45o1xi+uOfetew4zzPY8eOHTQ1NaHrOkuWHjhoPZ/tHb387o7H6Ozs4/VXd9DTnRr38+jpTnHdR7/LZ/6/90m5a7HPmNSk55FHHpnMywkhhBC7JG2T2Bf8/o7H+OVP/0pHRy/eOAoMmKbBW047iq/e8KHdvnZLa0O5V6altWFQovLgfSv55z83YdsOyUSa1Ss3kskURjpV2auv7OC6q/+PE085gv+64bLdjk2IPUV6eoQQQggh9qLf3/EY377pTuzCGIeWaRCPV3Hsmw/h7ReeMKG9KWeevYwzz15W/r29rYv/+5+7+NsDu55nVCg4/O2B1XRsu5Ef/ezTExajEGMhSY8QQgghxF7S3tbFT3/8wJgSnkgkyNvecRyLlyzk4EWz9sqcmZbWBv7rhss4YME9/OCWe8b0mOfXbuWD77uen/36MxMcnRAjk6RHCCGEEGKStbd18eQTL3L/fc+wY3ti1GN1XWPhga1cevnZnHr64kmJb1cuu+Jc4vEoN934O9xKVeKGeGl9G1/50s/5/Jf+dRKiE2I4SXqEEEIIISbRw39dzU3f+B1dO5K7LBkdr63ifR84nTOXv2mfq4Z24UUnc9wJh3L7rx/hvj8/QzKZGfX4v9z1Dwxd55LLlu9zz0VMf5L0CCGEEEJMkva2Lr759d/S1dk36nE1NVVc9C+ncM55x+zTCUJLawMf/9S7+Pin3sUdv3qYn//kr3R2Jkc8/u47n2T1qk1c9bHz9+haPkLsiiQ9QgghhBCToL2ti8/++49GTXh0XaN1ViNXXXP+PjOUbawu+pdTMUyDH37vHnp70yMe99qrnfzPt+6UtXzEpJKkRwghhBBigj3819V89zt389qrnSMeU1sb5bzzj+Wd7z5pyiYDF150MgC/v+NxNr+8bcTjtrX38MXP/ZQf/OSTkxWa2M/pezsAIfZFdk+C7NY27J7E3g5FCCHEFNfe1sXvbn9s1IRH0+AT//4uPnrdO6ZswlNy4UUn841vX8mJpxyOpo183No1W/jwv944eYGJ/ZokPUIMkVi7nva77mfbPQ/Rftf9JNau39shCSGEmMJ6ulOs/ufGUY85/cw3DVoPZ6praW3gGzd/hPPffjy6PnLms26dX85aiIkmSY8QA9g9CRIr14BSBBtqQSkSK9dIj48QQojd9slrb8Ebpapzc0stX73hQ5MX0CS6+LKzmDdvxqjHvLS+jW9943eTFJHYX0nSI8QATiqDm7cxY1E0XceMRXHzNk5qcBlOGf4mhBBiLD7z6R+QTGRH3G+aOt+99dpJjGhytbQ2cOF7TiZgGaMed8/d/6C9rWuSohL7I0l6hBjAjFVhhCycVBrleTipNEbIwoxVlY+R4W9CCCHG4uYbf8fDf10z4v6qaJCv/PeHpvwcnl258KKTueSy5aMek0rl+N0dj01SRGJ/JEmPEANYdXHiy44CTaPQ1QuaRu2yo7Dq4oAMfxNCCDE2X/vyL7n914+Mesw3bv7IlCtLvbsuu+JcPv2ZizCMkT963v2HFdLbIyaMlKwWYoj4EYuItDbjpDKYsapywgM7h78FG2rLw98KXb04qcyg44QQQuy/Vq/ayP33PoNSIx9zwTuOZ8nSAycvqH1AqZz1N/77jor3Jp3O86lrv8+vfve5SY5M7A+kp0eICqy6OJG5rcMSmbEMfxNCCLF/e27NFgoFZ8T9VdEgF1921iRGtO847oRDmTW7ccT9m1/exs03SlEDsedJ0iNEBSMVKtjV8DchhBCi/fWRh2jpusZVH3v7tJ/HM5KW1gau+tj51DfERjzmj394Qoa5iT1OhrcJMURi7XoSK9fg5m2MkEV82VHEj1hU3j/a8DchhBD7t/a2Lu6795mK+0xT50OXn8OFF500yVHtW049YwkHHzKbKy/7Fp3bk8P25/NF7v3L01x2xbl7IToxXUlPjxADjLVQwUjD34QQQuzfrr7ifyjki8O26zpcevk5fOiKc/ZCVPueltYGjj/+sBH3//y2v/LwX1dPYkRiupOkR4gBxrpOjxBCCDHUt77xO7a191TcV11TxbFvXlRx3/7qoEWzRtyXz9t89zt3s629exIjEtOZJD1CDCCFCoQQQuyObe3d/OnOJ0fc39QUp65+5Hks+6PjTjiU+obqEfe3t3ezYf3rkxiRmM4k6RFigKlWqGCkggtCCCEm14b1r5PNFiruMwyNSy8/Z78tXjCSltYGPvUf7yYcsSrudx2Pp1a8MMlRielKChkIMcRUKVSwq4ILQgghJs+aZ18ecd+73vuW/WYR0vE69Ywl9PSkufH62yvuf/C+Vbz9wqNpamqa5MjEdCM9PUJUsK8XKhhrwQUhhBCT44nH1lbcbpo673nfKZMczdRy3AmHMLO1vuK+fKHIX/78j0mOSExHkvQIMQVJwQUhhNh3PHjfStraKhcwOPX0xTKsbRdaWhv4wAdPR9Mq79+yqV0KGog3TJIesd8rzYtJb351n5kfs6u5OlJwQQgh9g3b2rt54vHnK+4zTJ1/+9j5kxzR1HThRSdz6BFzK+4r2A493alJjkhMNzKnR+zXSvNiCt0J3GwOoypMsL+Ywd6aHzOWuTqlgguJlWsodPVihKx9uuCCEEJMV9//vz+TTucq7jvooFnSyzMOJ59yJM8/t3XYdqfoUiw6eyEiMZ1IT4/Yb5XmxXgFG8+2QSm8go1rF/fa/JjxzNWJH7GIlguWM/Pc02i5YDk1UsRACCEm1epVG/n7CHN5AE485fBJjGbqO/KoAwiFhldyc12P73/3T3shIjGdSNIj9ltOKoOTzqKUQjkuejgErocRDA6aHzOZZaHHO1dnXy+4IIQQ09mrW3dQLLoV91mWwTnnHTPJEU1tS5YeyFnnLK24b91zr/DgfSsnOSIxnUjSI/YLlRKXbHsHdm+SXPsO3EIBJ50BQ8ctFMrzYxJr19N+1/1su+ch2u+6n8Ta9RMap8zVEUKIqWPO3CYMo/JHqWOOPViGtu2GSy5bTlU0NGy7p+CfqzbthYjEdCFzesS0V2mOTKS1mfT6TQSqozjZHE42h3IcdDOCYQWoXXaU/9gBQ82cVJrEyjVEWpsnrGdF5uoIIcTUsWTpgTTNiANq2L5DDp8/6fFMBz3dKZQ3/H4CNDZWT3I0YjqRpEdMa0PnyJQSFw1w8zbh1ma8YhGvYFPo7aPxhKOJHjgPqy5Odmsbbt4m2FBbHmpW6OrFSWXGnIQUU2myeQc8D90wxrTY6VRZHFUIIfZ37W1dvPZaFwsWDl9jJpvJ74WIpr5t7d0UCsWK+2bPkQVKxe6TpEdMa6U5MkMTF6A8jMyMRfHyBYK11eWEBwYPNTNj0TENNbN7EuVkJfP6NnqeW0dmew9eJocRCROsH1tlOKsuvk8mOwOf374YnxBCTKarr/ifEfdVVQUnMZLpI5stQIX1enQN2ttkrR6x+yTpEdPaSIlLuLUZBaMOIxvvULOBw+g0XcPJ23g1YbxCf2U428Yr2CMOkdvXE4qxlNIWQoj9xYP3rWRbew+aXnlFzfkLWiY5oulhztwmwuEg6dTgMuCegj/f/RRz5jZx6hlL9lJ0YiqTpEdMa6MlLlZdfJfDyMY61GzoMLrCji7s3gRaTQRcDz0cwsvlUZ5HMZUtV2MrnTfb1rFPJxQjDROcyPlNQgixL7v7zidH3BewDA5eNGsSo5k+liw9kGOPW8TfHlg9bF9vb4rf3f4YBx8yW4pEiHGTpEdMe6MlLmMZRjaWY4YNo6uphs4ePLsIhl6uxpbv6EQzTbqfeRY3lS73Crl5m0Asss8mFCMNExzP/CYhhJhObLvyvBOABQtb5UP5G9DcXFdxeyadp68vS093Su6vGDcpWS32CxO9ns3QUtPKtrFqa9CtAJppoFwXTdPRQ0HMaITUCxvxCn4S4ebzFLp7UGhjWptnb5BS2kIIMdiyow8acd/7//W0SYxk/6EUaDrU1cf2dihiCpKkR4g9wKqLE120EDdfIPPaNtx8gbqjj6L+hGU0nXwcwcYGogvnUTVvNlZdrV8eO2iR7+ymmOhD2UWyW18n39WzTyYUpWGCaJpfCELTpJS2EGK/dtyJh1XcPndeI2eevWySo5leTjz5cPQR5koddsR86eURu2VCk57rr7+eo48+mlgsRlNTE29/+9vZsGHDRF5SiAlTaYHTksTa9aTXb8JJZ3CSKYqpDKkNL1PoTlB14DyseAw8Fz1g4hYKaKZJfkc3ubYO3LwNgHJd8m0dONn8PplQxI9YRMsFy5l57mm0XLCcmn1ozpEQYyXtkthTXlj3SsXtumbQ3tY1ucFMMzOaa6mqGr5AKYw+rFCI0Uxo0vPoo49y9dVX89RTT/Hggw9SLBY566yzyGT2nWE7QoxFYu162u+6n233PET7XfeTWLu+vK80yd+1i3iOi6ZrKNfFK9hkNm0BIL7sKLyiS66tAw2N2KEH4mZz4Hpg6GgBE+V6/T9uhWXu9g0TPUxQiIkm7ZLYU7q6+tAqdEZ09/TR052a/ICmkZ7uFJ7yKu575qkNklSK3TKhhQzuu+++Qb//5Cc/oampiVWrVnHyySdP5KWF2GMGVi4zY1GcZB89K1aVCw2UJvkb4VC5Upsq2OhBC892cFIZnHQGN5/HydugaYRCIQLxapx0BhS4tl/WWjNNMIx9rpCBENOFtEtiTznwoFZ/jsmQxMdxXIpFZ+8ENU0Uiw75nF1xXzKRkUIGYrdMavW2ZDIJQF1d5aocQuyLnFSGYiqLQuH27fALFXgevc++wIzTji9P8ncLBTB0vFweLWDiFWz0aIjUxs30PvlPvGLRLwKQTlNMJDGiUQKxKMW+NDguKOXv70vhFQpSGU2ISSDtkthdDY01FbfHYhECASmO+0YEAibBkEU2Uxi2z/M8SSrFbpm0f5We53HddddxwgkncPjhh1c8plAoUCjs/APv6+srP9bzKndz7ks8z0MpNSViBYl3rDJt27B7e/25N7qGbgbQDJ30xs3UHHUIVm0NNUuPJLHqObSg5Q9xMww8TxGoryW98RXcYhHleoBC0zRcx0MrFtHMEJoVQOVyaAETI1aFl8vjOS6O40zqc5W/h4k1FeOd7sbSLoG0TZNtqsT798fWoekamqahaaBpGuhgmhq1ddF9Nv6pcH9r66IsWDiTdWu3Dru/9Q0xTNPYZ+OfCvd3oKkY7+6atKTn6quvZt26dfz9738f8Zjrr7+eL3/5y8O2d3Z2YtuVuzn3JZ7nkUwmUUqh6/t+YTyJt7JiKo2XK6CHgwD0bN5CsdFfPwcATceqi2PrOmZbO8FiAZpqMU9cip4rkG3vIN/egecq8l1dBOw8XjTUX2sTQEMzTTQNDMuAUBQ3aKIZBngehGJoAZPeTJrsjh0T9jyHPl8tFCTjFuXvYYJMtXhLPSDT2VjaJZC2abJNmXg1mwUL69F0jeaZMdA0lKdonlmHYbrsmIT3790xFe6vYcK733scVlDR15ftv7861bEwLbPqMUxH7u8eMtXifSNt06QkPR/96Ef585//zGOPPcasWSOvUPyZz3yGT3ziE+Xf+/r6mD17No2NjcTj8UmI9I3xPA9N02hsbJwSfzgS73DJdRvIrXrOn6MTsrAa6gh3JQlpGnZf1u+t0TQsdIKNdcxobcGqrcHuTeIEHDw9gLetixAGRjxCV7IPY0cPuq6jBnbHB0wMy6KqqRknk6GQymHV1mDWxChmcuhKp7YqSrSpqfwQuzdJrq0DgHBrM1Zt5aEVu/t89ZCFuegAmg48UP4eJsBUi9eyrL0dwoQaa7sE0jZNtqkSb0vLTF7e9He/B0IpXt7Ug1IK5QVwHYOZLfV7O8SKpsr9PeOsJg47/CB+9uMH6ezaDsokFI5y3PFLWHTIwr0d3oimyv0tmWrxvpG2aUKTHqUUH/vYx7jzzjt55JFHmD9//qjHB4NBgsHgsO26rk+JFwL87leJd+JMZLx2T4LkqufQlCLUUEu2fTu5V9vwHA/lOH5PjAF4CieVIf6mwwnV15JYu96v3pa3QXk4uTxVc1pB1zBCQTRNwwhaKF3HcxxQoOk64eZGAuEgGlDUe/HyeQq5HF7exoiE6XnsKbxsjvgRi0isXU/nQ09g9/rfcFi1cRpPO554f9louyeBk8pgxqpGnQc08Dhg0PMtptJkX36FbFMTpmnu8lz7Avn7nThTIcbdMd52CaRt2humQrzV1RGUp0D3O/L9hEfhOB69PWlaZzXu7RBHNBXuL0DrrEb+4/97L+tf3ITrmNQ3VE+JAgZT5f6WTKV430iME5r0XH311fzqV7/irrvuIhaL0dHhf0tdU1NDOByeyEuL/dBYP/iPpFSFLdhQi1d0cDNZlIJAdRV2TxLlueimidL9gtKZlzbTVRUhvX4TKEWwoRa7uxc3k6PQk8Cqq0EVHcxgkEBtnEBVmGLGL1OtGTpKKbJtHeR3dPnbCgYoRSBeQ9XcVpxUmsTKNZhVEXpWrKSYTKEHAoCiOKCCXLato5x0GSGL+LKjysnQQAOTMyNkEZ47u/x8NV3HjFVR7Opmx/2P+onaKOcSYqqSdknsKdlsAcPQ8dTgRQZM06CuPraXopqe6uqraWpqmhIfysW+a0L/em655RaSySRvectbmDlzZvnn9ttvn8jLiv3QaOvojFWpCpuTSuPZRb/6mhUg2FCHURVG043+QgYmRjiIpuskVz9PMZXFjEXRdB2rvhYjEgbXo9CVQAuYxJccRqAqjJu3CVSFaTrteCLzZlHo2EG+vQOKDpppoPev1ePl83jFImYsipu3sTt7cLJ5NA10K4De37Xr5nLk+hOeUtKFUiRWrhm2gOrAstul4zIbN6PpOk4qjfI87O4EXn8FutHOJcRUJu2S2FMikSBKDV9V7ehjD5oSvRFC7G8mfHibEBNt6Af6Ug/JeNe5serixJcdRWLlGpxUGs0wMCNhtEAAw7LwCkWUq9DDJsHGeoxwiEJHJ1rAxO7u9dflKdgE6+M0nHIc6Dq9xTwzFxyAk+gr90LZiT5yr7ZhVFX5i5gW/XLVWiAAuUI54fLyBX9eUWMdZiRUTsboX7rU6P9WenBvTZRCV++wctcDe7EGHhc7eD7Zra9T6OpFKQ8jGCRYVzPquYSYyqRdEnuKGTDRNG3Y35RhGHspIiHEaKSQvJjyRvpAvzsf1uNHLCLS2kyurYO+DS+T3rgF+6UtoGsEqv15MIFYFOV5ZDa/ivI8dCtAoavXL6lpmsTfdDjRA+bgeR7p/uoyVl0cqy5OYu16uh9/mnxHJ5oV8Fe10zWU66GKRfSAiWboFHr7MEMW8SX+uZzjlw2b01NzpD/sTHkuhR3dmDXVKNtPlEpzdkoG9mKZsShOKu0PX1t8GPHFh/kLqDoOmSefxkllCAw4Zui5hBBCQE93X8Xtd/9xBRdedJL09gixj5GkR0x5I32g390P69m2DjofWkGhuwdV9AsYhJqbMENBiqksnuNS6OpB03WCTfXYXb1opk54ZjNKedid3dg9Ccx4NeBXXfPSWTzX9XukDB09FPSruWk64AIKVXSxGuoItc7A3tGNcl1S6zdhRKsGJWMAxXSG1PpNFLoTFJMpQKF1dmPVxmk67Xi/pPaQOU6lXqxCVy9GyKJ22VHlpNCqi+N5HlWdnfDiyxWPEUIIsVNdfQxd1/DcwT09qb4cTz7xIhdedNJeikwIUYkkPWLK29UH+vGwexL0rFhFMdmHbhi4joPyPIo9SQLzZqNbRcJzWnCzOazGOnTdoNDZ44840yBYGy/3MnlKkXjuBTJbtvnr7yiFk81RNbcVTdcpdHbjZrLohoEWDKFbJkZ1hHzbdvSQRaixbthQvVIy0/3bv+Dm83j5ArppgOH3cBkhi3Br87CiBaWCBJHW5lGLPUTnzSJ+4AK8dHZKVG8TQoi95eBFswlYBk5u+GKJ7W1deyEiIcRoJOkR08JYPtCPhZPK4OZyAP4cnaKDcl1c26awowtQpDdu8YeDZXMYVWG8vL9Se357J042h1UdJdveQfK5F+nLpIn0ZQnWxdF0HTebw+7uxaqvxS3YeIUiRlUIN5enmMhS7B++podDaLruzxvq7CHX1lF+Tr3PvkBu23ZQClV00CMhNAVmOISbzfnD8gZUlKuUOI3Gqq1Br6/drfsnhBD7k2g0Qi43eLFEXddoad031+gRYn8mSY+YNip9oB9vGWszVoURDlPsS6NcF80wUI6DKhaxexMox/WHp+l6udiAZhqg63iFIsVigqoD5pBevwnlOIDCs4vkt+1AD1p+qWrPo9DVC66HHgjg5W28vO0v9NDPyxfIvtaOZuigoHvFKhQQaW0uV11D0/AcBzeTQ7MCZF/fBvjHgqJqTitesQiaRjGVHTbHaeC9sRN9FHZ0kwuZMGBBVCGEEJX1dKeor6+mu2vw3B7DNDjuhEP3UlRCiJFI0iOmrdIQLyedRTMMapYcRsNxS0d9jFUXp+74pXQ+tMJPcpQH/eWkPcf1h6l5HkTCfuKTy6PQdm73NNLrX8ZzHAJ1cbyMB64L+IkMukZoVgtmtIrUCy/h2gWUbZcKsvk0DQBVsCEQINTShB4wSKxcgwYoTxGc0UCxO4EyDDzPQdlF6C+k4CT7UMojA35iZhfRdJ1seweRua2D7o2btykm+3AyOdAgWxfDeq2D1redOQGviBBCTB919TFczx22Tk/Rdvj7o2u56F9O3UuRCSEqkaRHTEulMtZ2Xxo3k8Ur2BQefBygYuJj9yTKRQIirc3M/dd3+mvgrF1P+sVNeIXBiYlXsMHwkxOKfsIBGgpFMZVCFV2K6QzEBi92qOkG2S2v+YUWIiECdXHsbTsGBzOgAVWOQ2F7F8xoQNP8ZbWMkAVKYc5pwUn2Uczk/BLbmo7yXDzXT8DsniS6aaAHLcz+RVSrD14AUC7xrYcsiq+m/PNWR0FBYvXz1Bx2MNED5uzu7RdCiGmvpbWBmTPr2fhS+7B9t97yFxqb4px6xpK9EJkQohJJesReMd5hZ+PlpDI46SxuJosGGFUR3GyO5Op15Q/+peuXqrXZvQnAn9NSe8xiAtEqij0J0DVw+isVlLiuX3QNwNDB9QAFmr94qac0VLE4OKj+ZMYr5FGeR2RWM5phUOzuQdlO5SeiFF6+QO61bQQb6wi3NqOgfy2hDJppEp49k/QLm/Acpzzsze8tUoRbmzGjVegBs1xgAXau7VPo6vXXCDIMNPzFT1UxRXrDy1jxailkIIQQozjjrDfx98efH7Y9nc5z2w/v5+BDZkvpaiH2EZL0iEk3UmWxPclzXX++S66AGatC2TaaruPkbRLPPk926+u4eRtN13AyWZx0Fj1gAhqFrl52PPAoZrSKYiqDEY2gEo4/n6fEzynQQpZf5a1gl4exKaXQTWPYkAcAVSziZHKY1abfO2NZ6AELT9PR+ufo4Lh+0jLo8Qrl+hWCSkUbEs8+T3rjK+S3deJ5Lrguqr9KHJoGrkcxmcKqrRlWxrtU4htdBw2U56EAN1/Acx2SL24i37FjQl4bIYSYLs48exnf+9+7K+7b/HI7D963iosvWz7JUQkhKtH3dgBi/1IadlaqLIZS/jC0nsS4z5Pd2lbxcYm16+l69ElUsYhyHYq9SZxsHq9g4yT76F39PK5dxAiHcPIFiok+P1Gx+gsNuA6e46KHQ6AUbjZHIF7dv5CojlEVwWpqRAtaBKqq0EyzPA8HFF6ugJsv+MPeYMA+QNcxqyIAFFMZ7J4kRlUEIxTqX+jUQrMsymPpdP+aWiCAZhrlnhrAT9xyOZxUyk+UwE94dN0vmhAI4Gaz5Ds6QdPKZbxLJb6LqSz5bdv9xEd5uOkMynUxY1GqZs/c7ddGCCH2J285vfIQNsfxePzRtVK+Woh9hPT0iEnlpDLloVWa7q8tUxp2NdahVMl1G0iueq5iT9HApKpq/hx45TXs7l5QGkrTwNWwe3pxczk0Baq/l0PTNDzbRjle/5o7OsWeJEopVL6IZ5oY4SBGOEyoZQbKttENAyNk4RaKmFURisk+3FwePDWkl6afYaBbAbACOIk+ArEqlKcAhW7qOJ5COUV03fCLI6D8c+l+r4+TypSLETipDHaij2IiiWcXBwyxAzwPzTSwaqpRrkvdMUuIHjhv0P31Ey/lD2FrqMNO9OFkspjRMLHGht1+bYQQYn9zwkmHsfKZtRX3tbf30NOdkiFuQuwDpKdHTCozVlUeWqU8b9iwq10pptIkVj03Yk9RKakyY1H/g3tNDaD5vTLxarSACY6LV7DRgpafKGg6ZjSCV3TwXMcvQe15KM9FtwIYQQszWkXdCUdjRMLk2rbjZPM0nXY8s979VlovOItQazNuNucnKQNohulfB/x1dVwPe0cXyvUwqqooJvuwuxMUMzl/eFzRRQF6KOivv2MF0AMBjHCIQE2M9PpNfi9XeweFrl6/1LWn/OfR3yuEofvX6e5fJNVzByUtibXr2X7fI/7+bA43myM8owEzEu5/bTK79doIIcT+aPGbFjJ7bmPFfYlEimJxhDmbQohJJUmPmFSloVVo/tyZgcOuxsIfOjYgqYlFcfN2edjX0KTKy+X84gIBE033580AaLqGl8uhGTqBmhjNZ5/KrAvPZfa730rVAXP8BUlzBdxsDi0SRrcsCju6sbt7cNJp7O4eMltfx6qLY8aqSG/cMrjsdImuEaiOoQX85MXvtQHwyG/bjio65UVGUX4PkaYg2FSPVVONVVNN1QFzqJo3m3DLDNy8Ta6tg77nXkTTtSEX0/yKcgrwPPSgRaA6Wk6UYEBPmKGjh4KookOhqwe7uxczGqHqgLm7/doIIcT+6oQTD6+43XMV9/7l6UmORghRiQxvE5OuNBF/tOptI1V308PBclJjxqLDeiNKSVVi5RoKXb0YoRBmdRRVdPDy+fK6O0qBphSe42IGTMKtzVh1ceyeBGrFKjTT8AsXeAonkQTXpbCjE9AwY1G8XJ7EP9dRfehBfknsnL8Gz9CeHjwP3Qpi1VYTnNGEl8uRbd/ur9ljDKkGV36Ii93Vi1UfxwiHwHPRA+HycwVwsvnhQ+g8DzNWjWbohJrq0YMWeiAwaIharq3DL27QUI+m6xQ6u/HyBZTnUbv0SApNtcSPOAwvnZ2wynpCCDHd1NVXY+g6jucO23ffX1ZyyWXLZYibEHuZJD1iryhNqK9ktOpugViU6NIjSa56rj+psYb1RsSPWIRZFcHu7MFqrMPJZOlZsRInmycQMCmmM/5inpSKUO9MPpxUBs+20QwDPIVSCjwPL1/AcxzMqgiaUmhWADeTJfvK6wQb6/yExzTBHlCmur/oQcNxx9D37DqUXfCH2G3v7B8+51W+OY6D6ylCMxqJHrxgQAJnEVu0EAA3l/N7hwbSNOLLjqTQts1fgycQGJQUJtaup2fFSn8uUF+aYHMjVn0tuB4zzn4LkXmz2LFjB1ZtDXp97TheTSGE2L85jothGDjO8KTHtot87f/9iv/9/jV7ITIhRIkkPWKfMrS6m5NKk1i5hkhrM2a8GoCaww+matbMEXuKKiVNs979VpxUhmJfiq6/P4MZi/q9K4aBk0qXe0LMWJWf8CiFHgn783Q0zS8W4Hk4yZQ/b6Z/LFvvqjVE5s9BD5j+gqUlhkF04TySeGiGTnjubDIbN5Pv6PQru0GFstT9FOC69P5zHa5dpGbJEaiCjZ1KkVq/iVz7dpy+9ODH6DroGsGaasIzmwYlSrXLjvLvy8o16AGT0MwmCtu7yLdvJzxzBvUnHUP0gDl4Q5KwiV5LSQghpgvTNIhVhyh02hX3P/fsZlav2siSpQdOcmRCiBJJesQ+ZbTqbqWkB0buKbJ7EvSsWIVyHMyaapRdKCdNkbmt2D0JjJCFsguDhsd5rkt2axtmrIqaJYeRu+dhvFRq8DwdXQfP83/6f7d7ktg9z4Gm++Wu+9fI0YMBlG2jdOh58p9+RTVdx1MelHpolCqv94NhDBriBoDr0rfmBdIbXkazLL9inGUNKltd5nkYoRBWYx3RA+YMGz6Y3dpWvq+B/rLZhc4e6o9fSk2FdXgmYy0lIYSYLmKxMK2t9XR39VX8Lsu2HV7dukOSHiH2IilkIPYp463uNnS9nt5nXyC3bTuF7l5yr7XhOu6gQgelOT9e0SG7tY1iKosRi7Ljwcdpu/NeXv/tXwB/8c5hhQk8Dwzdr+4GO5MWND+pCQSw6uvQgxbKcf0FSwE9YPi9VpkMhfYdg8/Zf43hRQkGXLZg46bTePkCTl+qcu8QED1wAdED5ozpvirbxorHCLc2D7+nvck9spaSEELsL+rqqznrnGUE++ddVnL3H1dMYkRCiKGkp0fsU4YXItg5Z2fo8KuhvRHRRQvJbNzsV2kzDJTnUejoJDSjcVjSVOxLUUykUEB++w5000QzTYp9aQoP9fjr7VQafuZ6O3MhpcD1FwNFA69QwO1fU0fTDUKtM8j0pTBjVXhFB7s3OWLCYoTDuFSYp1O6TuWHlWlWgMj8WRXvS6mXZqT7OtSeWEtJCCH2N+9410mseXYLD9y7suL+F9Zt5Ue33sNlV5w7yZEJIUCSHrEPGmt1t6Fzf5Krn8dzXIIzGih2J1Cui/I8ogfOL58jvflVtv3pQX9uzgCea2OGgrh2EZXJjjNiBXr/2j5FFz1k+YuVdifQgmb/QqKpcvGEYTQNIxJCKYVbTFc+ZjSmQbChjvT6TYRmNI44JyrS2lwu2VCqVlfxdAN6hSpVyBNCCFHZOy48gcceXkM+P/z9Xin42W0P8KalB8owNyH2AhneJvZJVl2cyNzWET+YD12EVLOCOOk0XqGAphThOS1YdXHCM5uILz4U8HtAXrvj7mEJT/mcfWlUoTD+YBUYYQszEsZqqCXcMoNwywyUpwi1NOEVXYo9vaM8XqF0DeWMcQE7XYNAwP9/yyJ24PzyGj52Z0/FdYwSzz5P+1330/X3Z+hduYZsWwd2T4Lk2vUk164fNHTNqq15Q2spCSHE/mrJ0gM565xlI+4v5B0+fd33efivqycxKiEESE+PmKIG9ka4jkv+9W14RQcMHSeVRo9EsKqrqDnykPL6Oz0rVuJm8iOfdOAwskpr7oxAC5iAjpPN4mSy5Hd0EaiOEaipRrcsNCsw0qi2MqcnCaW5QhUv0j/UTtP8ogkaYAUINtRiVkXKvTFWY92wXhpN10hvfAWU5xdtKNh0PvQEynUp9leBs2praDj1eGiqxe5NYlXHaDjlONz+Xq9Kc3+EEEIM99kvvB8NjbvurDyHJ53O8+2b/sDBh8yWtXuEmESS9IjdsrfLGZfm/vSsWEW+vQPPcdAtC90ycXMFvGwWNxgg+dyLOLkCocY6nGwe3TRwnVESGo3+OToaKG/EOTgDKbuI67r9xwNFh2J3L3aij3y6j1BXwh9WZpowQm+Oly8MXC5oSEwammmiGTrBlhm4fSmUq9ACBqANmqMTPWAOTiZLYuUacts6/WFujXVkX2tHua4/J0nzr6fp/UUZdJ1iMkXPk6twD55HbksbXt72y3Sj0C1LKrgJIcQ4XHzZWTz9j/Vsa++puL9jWy+/u+Nxrvn4OyY5MiH2XzK8TYxbYu162u+6n233PET7XfeTWLt+t84ztPLaeMWPWET98UsxIhGMoIUZjaCZpv/hHkDXyW/vouuxp+h87Cm8YhHleeiBCtV1AgZ6KEggXuMnPY67M+HRRq6sVuZ6w4sNuC6e6wGav29Xw9dGyq80Dc3QsZrqsbd3lRdQVY4LKOKLD6XlguXl0tPxIxYRXbQQr1DA7ukl+cJLFHsTOLk8WtBC2Q6q6ODZNm7BxssXUK6Lk8qQ2bwVlMKMRSkm+ygm/UIMUsFNCCHGrqW1gdPOWDLqMb/5xUO0t3VNUkRCCEl6xLgMLSCwux+G91TiFG5txorHQNPx7CJe/5wczTRxUxk0w0DTdZxsHjedwXMcvKLtJzK6Doa/qCeuIlATo+GUN2PVxv3tJUr5x4xktJzI60+edtVjNNrpTQMjHMLe3oWbz+O5Lm4mS7EnQb6jk56n/Tk6JXZPgr7n1vsV6GBnQmYX/YSpFIsCHBflen5CqGkoV/lJTn/iqGmA65bnBlVcI0gIIcQwF150EjNb6kbc73mKq6/4n0mMSIj9myQ9YlyGFhDYnQ/DQxMnr2DT/fg/SG9+ddzxWHVx6o5fRqAm5n9wdz30gAmGgWvbeI7jLwqaL4CuE5k7C6sujhYwicydRWTWTKyGesyaamaceTKBaBWarqNHIoMv5I3S67OrEXBjGCI3Wm+SclyKiSRewfYTGGfAIqaeR35HJ50PrSgnnrm2DorJPjzXT2g0XS9fQ9NAKwVc2u75vVHhmTPQA361OQxjZ+iGIRXchBBinFpaG7jm4++gviE24jHb2nu441cPT2JUQuy/ZE6PGJc9Uc54Z+JURW7bDpy+FJ5dZPt9j+CcdMy4541EWptpOu0EnHQGM1pF15OryG55zS8hDRAJg+ehBy0C0Qi6aWD39pF9rX3nPBxDZ8djT+GkMv6H/pGGolVau6e8j10nQCMZbY6RBniVdwPguBQ6u+jb8DJurkDqhZdw0lmUbffPUfLn7aCUP/RP09HDIXTT8MtkZ/NohkGhqwcvZFLMFTGsIoGaakDhpDKjrusjhBCislPPWMLBh8zmC/95G+vWba14zPe++ycam+KcuovhcEKIN0aSHjEuoy0eOlZmrArPtklv6sSzbX+Zm6AFhl5eU2as5xu6EGdodguFjk70YMDPZ2wbL5dHDwYwI2GKqQz5bTvKw7f8rg8Nig75V9t3fUFv5OxDD1j+0Lmh+ctoleBG2lfarvB7d0pGSKxU0aHj/kehv4KdEQnhea7/u+f0F0Pwh/oZ0QiGZeFkc6iCnxgFamKEZzaQy+cwlEbD8cvKFdv2ZsEKIYSY6lpaG/jhzz/NGSd9inR6eAXRbMbmq1/+BevWvsKJJx8ua/gIMUEk6RHjNpbFQ3dN21kiWtPQNA2zqgonlcZJZcZ0ztIwOa9gl0sx961djyo6fvlnt1jumTHjNWim6Sc8SvllpIuOn1yMZfhZScAcsSiBVyyCpvu9R4N2DDl/ab6QO1p1uPF3G6nSHB4HHLuIFgigSqfR/NLaVk01aDpGyALdQIUdPLtI1dxW0DWMcBiVLhCojpVfA0l2hBDijbvx5iu56vJvV3zbz6QL/PJnf+MPv3ucd1x4Itd84p2TH6AQ05wkPWK3WHXx3f4w7KQy6FaAyNxZ5Ns6/A/mnsJJ9mFEQsOGypXKY+vRyLDzFLoTfm+R64Gh+3mU56Gy9uBzdCfK1d2CDXUUtnf6Zaa9MRQZKAmYaEqh3BF6e5TyEw2nOMpwNQ3dslD9821GpFG5F6g/SdxlzEr5w9v6FzI1+kteWw11OKkM9ccvI1Adw3Nduh590h+mGKvCzed2OVxxb5crF0KIqWjJ0gM58eTDefzRdSMek8va3Pn7v3PSKUdIj48Qe5gkPWLSleYFoRRWUz2F7V0oz0MzzWFD5QYOX9NDFu4hC2hqagKg0JvESaVBAyMSwcvlAeWXrR7aE9Nf8Uw5Dtl8Hl3Xx7b4qN4/qcY00DyFGlqJrb+ctNJ1sIsYoSBOxqFiL42hoxkGetDCzeUxamK4qczwniEYORnT/PvnZHL+ULsxJGymZfpzejyvP7EM+1Xv+u9zaV2fQlcCYiHiS48YMZkZOpxQ1u4RQoix+/in38ULz2+luys14jG5rM29f3lGkh4h9jCp3iYmXWleEJrfkxGa0UDjyW9m1rvPK681A5XLY2c2bcHuTZJYu56eFc+gXBfPdnAzWbSAiW4FMWNVaJY1pAy0Qg+Fds6R0XX0UBAjFiWyYG7lQHUNIxL2CyBURVCus3N+jdn/fYHqT4TsIgCubY+cTLkeRjiEVV+Lcl3cdGbkOUIjJDzoup9OjTHhwVPoVRG/LLVbObGMH7GIlguW03zOqdQdu4Saww+ueKo9Va5cCCH2Vy2tDXzo8nMwjNE/fv3l7qf4n5v+MElRCbF/kJ4esVeMZV5QqcpbsKG2vzx2FV4qTa6tg94n/4lXdDBCoXJZ6kB1DD0QwCsWsRPJwfNrSvNwNA09aBGa0Yjnethd3RR7kyNModH8ks8BE8/pX2S0dNDAnqQBw9RUvjDq83b60jjJkb/hG5ViZwW2cfByBQI11YRbZxA7eEG5QMFAVl0cM15NeseOEc8z/PWIUujqHfMcLCGEEHDhRSeTSKT5wffuGXHqput6/PoXD/Haq52cdfZSzjx72eQGKcQ0JEmP2Gt2NS9oeHnsDLplkt3aRm7bdjRdRynPr4nQn/jUH7+UxNr15F5v39mY9Pf4eK5bXqwUTcPu7MJzXJSTqdzweJ4/ZC7vVz57QyWpS8ZTNGEI3bL8xVUzuXE9zs3lUJ5LMdlH5pXXCdbHd2tY2p4oVy6EEAIuu+Jc4vEo3/7mndj9IwWGUgoef3Qtf39sLbf98H6+8e0raWltmORIhZg+ZHib2CeVJstHFy0ETaPQ1QuaRnjWTPLtHWh6//wY06+mFohXE19yGEZVhMyWVwcnJ/3Vy/A8NF1DKUWuvcNfsNRxUMXKDc7Oxyu/0ttY5gBNEM0KEKiP70ya9JEXMx32WF3DzRVQnodn23gFm54VK0muXT/q0DS7J0F2a1v5mIHDEkuvh6zdI4QQu+fCi07mN3/4HCeceOiQ4diDKQWbX97Ge97xFT73nz+iva1r8oIUYhqRnh6xzxk6WT66aCGRlmb0aITtbe0UvS0EZzRQ7E70V0HzfxLPvoCTzfnFAXR958KjgBEJE5k1EyedpdDdSyBahW1XWFNnH6WHghS6enYmPeNJwAwTigV0KwCuh1OwKfYm2fG3vxOoiQ26vyWVChZEWpuxqmM0nHIcumFI9TYhhHiDWlob+OZ3ruJb3/gdt//ykVGPLRZd/nb/ah596DlaZtUzo7GW899xnAx9E2KMJOkR+5Shk+WdVJr0+k1UH7wAM16N3tONEbLQlMKc00KhuxevN0kgFsWqr8XZkvYTA13vXyzULx+tmSZ2IuX3kLgenuNMmYQHy+wverAbAWv4BRg0DS9voxkaxe5eNNPEaqin0N3DjgcfJxCvwYxFcA9ZQCRToPvxf6Dpevk16HzoCYxQCOV5O5Ogua17/rkKIcR+6OOfehddnUke/uuzeLt4r3eKLq9u2cGrW3bwzNMb+OXP/sa1n3wngYBJXX1MhsAJMYJJGd72f//3f8ybN49QKMSxxx7L008/PRmXFVNQabK8GYuWJ8u7eRsnlQEgEIsSX3okaBpOKoMRCGBGq7Dq/cn1VkNt/zo2Hp7j7qw7kOij0LGDQvt2PNvG6UvvuaA1bVzDzcbNHmVonbar62ooT6FbATyniJu38YoOejiIm8vhpNIozyMQi4BS9D33Ih1//hv5jk4KPQns3iSaZWH3JlGOI1XbxLQh7ZLY1/zXDZdx2ZXnMKO5Fm2X7+07rX/xNa66/Ntc82//y2c+/SN+dOs9rHtuiwyDE2KICe/puf322/nEJz7B9773PY499lhuvvlmli9fzoYNG8rrrQhRsqvJ8sVUmuiAIValxTXt7l6UruPlcuhVYZRdRDkuk9KdoxR6VRVeOjPx1xpqtOIKukagphojHPJ/NwwMyyLz2usUe5O46SyeXUQPWuhBC89TOIkEGAH0UBBVdCh09WBWhQEwa6qlapuYFqRdEvuqy644l3POO4YN61/jJz+8n5c2vD6m+jdKQTZbYMOLr/HS+tf4/e2P0dBUw2GHz6euNkpPb5rGxmpmz2kim83T0BQp/623t3WxYf3r9HT3UVdfzcGLZklvkZiWJjzpuemmm7j88su59NJLAfje977HX/7yF3784x/zn//5nxN9ebGPKRUoGG0+SHjubDIbN1Po6sUIWeXJ8r3PvUj3P9eQS6Qxw2Hqjl9K/IhF9L3wEj3/WO0XG1AKDB09GPSTAT2AKvSXkda0nXNiDANc9w1VUxtI5fN7prrbSEY694g9QKAHg4SamygmkijHxaqpxslk0BQoz/PXFwLwPJSncJJ9AFj1dXiRMIXObr/YQ1UEqzaOsguoYECqtokpT9olsS9raW2gpbWBU09fwupVG/ni537Kju2JMT9eKejpSdPTk+al9W3D9pumwcKDGvn5bY9QWxdj1TMbSfVly81hOGJxxJEH8LYL3izzhcS0MqFJj23brFq1is985jPlbbquc8YZZ/Dkk09O5KXFPqjS5PiBZZMH7td0ndjB84kvPgyrLo7dk6Dz4SexVZFAXw6nL03nQyswqyKkX9rs9+qU3rFdDy+bA11D0wb8iQ9IcKzaGorJPtQIpULHTdMmtlPJMAevDTQGylMUOrv9hVDzBQrdvYMWQzVCIQLNMQo7uil09mBEQpiBKpRdwIpXozwPXI8ZZ78FJ5MlsXLNsERUiKlG2iUxlSxZeiB33/dVPvmx7/L00y9RtMfXDlTieh6O47Dm2S2oCl+c5bI2Tz+1nqefWs/tv36Eq6+5gFe37mDO3CaWLD3wDV9fiL1lQpOerq4uXNdlxowZg7bPmDGD9evXDzu+UChQKOxc3LGvz//m2fM8vJFWrt+HeP3fnk+FWGFy47V7k/T2FyiwGuI4qQy9K9cQapmBVVtTcX9m6+tUH3UonueReX0bhd4EKh5FC5gox6XQm6DvhZewE33+B/RhT1D5yZCuD/qwj6FjVEX8xU5dl9zrHX6vz+7o7z1SxeENkRrw84aNM+FBgbJtCt02oNAsa/A90qCYTqNHQoRmzqDuuDcRbJmBt/Fl1PrN5PuTm/jSI4nMmwVAqGUGubYOAP/Yvfx3Lv/eJtZUiXO8xtsugbRNk03iHe7Gb3+EZ/+5iQfv/ycP//Wf9PXldrtt0TQNTfP/u6uZ3c+v28rHPvK/6LqO53nE66q44ILjePMJh1FXH2NmS/1uRjF28vcwsaZivLtrn6redv311/PlL3952PbOzk6/vPA+zvM8kskkSil0fd9fAmky4y3s6CahPALVUX+tnOoqin1p9LZ2gsXCLvdnshky0RAFy0DZGpgmmhZAVy6Z6iqUYw2/6MDhbJoGuu6/2Rs6oUPmA5B7fRuFhhq8Sn9fmuYvgFopISpPMlWjZjV2NDza8gsTo/95awETzTDxCjbgQVWwPwHcWeAh5xSJtcym0FRLzs7jxKMET1iKyhfQw0EKsSg7duwAIP3K62Q2bcGzHfTnTKoWzifanxDtDfLvbWIlk8m9HcI+Q9qmySXxVtYyq5qLL3sLb3v7m3j99S7Wv/gqzzy1ftyjtDVdo3lmDDStYk/Prjz66Coee3wVM2bUcvyJh3H8iYeP+xzjIX8PE2uqxftG2qYJTXoaGhowDIPt27cP2r59+3aam5uHHf+Zz3yGT3ziE+Xf+/r6mD17No2NjcTj8YkMdY/wPA9N02hsbJwSfziTGa8dCOKtWgd9/nweJ5UBTWdGa4vf07OL/elMgUwyi3I9In1Zf6FR06R51iz09ZuxE+nKyUepl8c0MCNhtEAAty9NjauRffV1jIJNIF/0r1fhzV+PhPBsB3TQNL2/OAJohtE//Gvk0telXp5wb3ryEp+ASSBWRTGRxKiqwrCgmMr6PVG67kfkKT8x0sCsjRNs7yQeCGLWxHDSGeKBIFZ9PVZtTfm0dm+S3IsvU61Uf4GJDLz4MvEDFww6bjLJv7eJZVkVvkiYBsbbLoG0TZNN4h1dU1MTiw5ZyBlnvplt7+rma1/5Jc+t3oLjju0bcK3/i7GXN/XsnNu5Gza91M0Tj2/i1FO38NWvX7bb59kV+XuYWFMt3jfSNk1o0mNZFkuXLuVvf/sbb3/72wH/5v7tb3/jox/96LDjg8EgwWBw2HZd16fECwH+m4nEO1yovpbaZUf5pY67EuV5IaH62jHtN00TqyZGPqChZwpoARMjHCJUF6fhxGPYft8jfo+G6v9AjwI0zOooTjoDnoebyaGbRXTTxIyE8PrnFuE4aCNUClB5G83Q/bLR9C+aHTAxQ0HcfB61i1Fn2oCfyeKks2gKvGzOv7am9Rd3sPDy/nA3NNDNAFqhQH5bjr41LxBoqKP3uXXYqTzmgDlXdk+C7KatuOksgdo4XraAbgVxUmm8dBa9/zXaG+Tf28SZCjHujvG2SyBt094g8Y5N66xG/u/719He1sWTT7xIe1sXHR09vPD8q3R39mFXmreq+02lUmq3enqGeuhvazjrLf/Oeee/mcVLFk5I9Tf5e5hYUyneNxLjhA9v+8QnPsHFF1/MsmXLOOaYY7j55pvJZDLlqjli/xE/YhGR1uYRq7eNtt+MVRGsj6Pnc2imgSo6uNk8fRs303zGSQB0//0ZnJS/OKlmWRjBAFXz55DauAUnmQJcPM8j0jqH2MELSK3fhFew/Tf9kcaIet7wfUUHp5ja9Ro541hnYY8pOmAF0CMRvHweN5vFCAWJLFpA9cIDKCT76FvzAk46g25ZgEJT0PfiRjTThIhFsCGOm8qQWLkGJ50hvX4TxVQWu6ubQme3fxwQqKmWCm5iSpJ2SUw3La0NXHjRSeXfS2Wo1z23hTXPvkzHth481+PgQ2dRHYvS09tFTU2ObNbGskwKBZuivZtzW4F0Ks/tv3yE23/5CA2N1Xzy39/NqWcs2RNPTYg9ZsKTnve85z10dnbyhS98gY6ODhYvXsx99903bBKpmN4GlqqOzG0d8bhSolNajLT0u1UXJ3rwAjqfXoVnF/1ERCl6VqzCrIrQcNxSAHqffhbPdjDCQZTrUSgtoGka4Ck006CwvZO+DS8TX3YUPStW7n7Z6l09bg+Vwx63/qFsRjgEGsQOORA3lab3n2sxQhah1ma/4p3rogdMgo31uNkcru2gVUdws3l/QdKeJMnVz2OELKy6eH8lOAfDMAb0pgkx9Ui7JKa7nWWvF9Pe1kVPd4q6+hgtrQ14nsf6FzfhOiaO4xIImNTVx9je0ctzz27m9Vc72bixjZ6eFLqusWN7Am8cPUJdnX188XM/IV4blWpvYp8yKYUMPvrRj444bEBMf7sqVQ07k6Jsewfp9ZsqHhtuacYIh9GtrN+9aZp42Ry9Tz+LEQ6TXL0OI2gRbpmBk0pTTGVwM1mcbA4U6CELIxzGzWTpfXoNVQvmUuxLVa78ti/TtZHX5wFQCs910TUAjcL2TsxwyJ+Lk+xDZXIEG+tQjosRDqGU8u9LOkN+eydmyr9fRjiIpvmPc7N59IAJpk6wqb48r0cWKBVTlbRLYn9RSoAGqquvpqmpadBQoZbWhkFJSilZKhYd/rlqI3+++ym2tfWM6Zq27fKJa27hzOVLueSy5bLYqdgn7FPV28T0Y/ck6FmxEuU4mDXVKNsmsXINkdbm8oflUlLkpLPYvUkC1VHCrc04qfSgY81YFXrARLkeStPxUmmUUuQ7u+m45294+QJ6yK9QZtXWUOhNUp5Nozw0w/BLP2sadncPha7u/t6OAFowiDegJO0+baSEp1S0wdDBccE0MEKWPxQw4FLc3oZXdFCeh9VUTzGZwu7uRTN0wnNmlUfjea6H1t9LpVv+YqSa1T+fQdMxY1GUbcsCpUIIMY0NTJaWLD2Qy644l9/f8Ri3fvcvJJOZXT4+l7W5+84nefC+VXz0urdz4UUnT3TIQoxq35+xJKa0xLPPk9u2A7snQe7Vdtyig5u3y8PX7J4Eif71ecxYFOW4FPvSOOmM38Mw4FiAYGM9oHCz/hoFmmn6i5EWbDQrgCo6FLp6yG3vws3kCFSFsRrqAA03ncUtOn5i4C9S4Fecdj2Ujv+7YfjHG1Pgn8bAyXyGjm4FIGD213Dwn5sWCKAFTAodnSjPT/w0wN7RTaAuTqChDqUgu/lV7EQfuhVANww0XcfJ5DBiUdA0nFSaQE01gZpYf2U9TRYoFUKI/cyFF53Mbb/8NJ/+zEWccOJhRGPhXT4ml7O58fo7+NGt90xChEKMTHp6xISxexKkN76Cpuug6yilKGzvIjSjodxD4KQyuHmbYEMtha4ePMeBok32tW0EqqMEamKYsSoSa9fTu3INBeURCkdwnJTfQ6OB6+loulb+QO7lCriGgR60UJqGyubLxQ80TUPpOlZjPcWeXly7iCoWoVTgxnWxexOTOx9nV8PVBh4aDuEVbIxICN0w8BS4uRyRebNwkmns/jlM/ho9BroZoGruLAqdPX6eZ5iYtTXY3b0YVgC7u9fvPdNccF2cdBalgW4a4Hq4qQxNZ56EbhiDXrNKhSiEEEJMf37RhJPLPTef+fQPePiva3b5uNt+eB/ZTIELLzpJhruJvWIKfJ0tpionlUF5HsHmxvIin8rziB54QPkDsxmrwghZFHoS2L1JNNMAXccrOhT70sQWLQQo9wYFqqMEaqsxrABG0AJN9z+cF2zQdIxQ0E8idB03lyff1uHPWQkF0UNBzKqIvw5QMoWbzVdOblxvzElIJZoVKFc4G9sDxl7lzcvly+W3nWwOL59H13W/tLZSfvJXV0PV7BaqDpiLbgUI1Ndi1cYxqmOE57RgBC0008TN5fGKDqChBwIEaqpBKZTjoOk6oeZGlOehGwaRua1YdXGsunj5/4UQQojrb7yc899xHIGAMepxTtHjN798mC989ic8/NfVkxSdEDtJ0iMmTCmhMUyD8OxWgvW1hGfOIL740PIxVl2c+LKj8PI2bi6PYVmEW5uJLpiLVVtDuKW53BtkxqrQdI1gXQ1GOISbL/hzcoIWumlid3ZT7Etj1dYQnTcLMxLGKzp4/R/iwy0zcPJ57J4kTrJv5DLV4Fd7M00/IRlrTXhdw4hV+cPlnF0s4FNi+D0q46YUynFRjosWDGJGQmihIMp1KfYkyXV0UujuwbOL9D33Il6xiJNMkd/eiR60iL/pcIxQCOV5KNclUB8nUBVGtwJYtXHCs1vR++cEybwdIYQQo/nsF97P/9zyUd7xrhNHTX5c1+P5ta9wy//+idWrNk5ihEJI0iMmUCmhKc0JMSIh6o9fWrGXQDN00DQ/QTFN3EIBzTDwXLecPPk9R8ofXhWNEohXUzV/DrGDDiAyf7af/IRChFub/Z6Klhl+D0YsSnh2K65dxM1k/cU6d8XzKJdkLiVH5ijfYum6X6jB9YeJjdnuruVjGgRnNGDVVmNGwmiWhduXRg9YoIEqFnESSTyniB4wqZrbSqipHjMcouGU42h56xnMevd5NJz8ZkIzGsBT6EGL6MJ5BKqj/npHMm9HCCHEGC1ZeiD/8bn38j+3fBRzlPZSKXjt1U5u/uYfpMdHTCqZ0yN228C1d0b6YLyrBUlLhQzMiJ+sFLZ3kXut3e9lqKqi69En/bLVy46id+Uain1p0HRqlhxGev0m8FwKvRnsHd3+IqMaZLa8RqhlBsq2serjGKEQTiqNKhbRNB09aOEWK6xSPZCnwOtPXjTNf5d2vZ0V0obSQDdNnN7kyOc0dP+8A4fU7W65bMfFKzpYNTWA8ktRuw66ZaIHTKymBpy+FJpuYMaiaLqOVV9LoasX3fAbI6suzozTjsdefChOKoMejdDZ0000V0TXNMIDKuwJIYQQY7Fk6YF85fpLuOFrvyHRW7nKm1KK9te7+OXP/8bBh8yWOT5iUkjSI3bLWNbeKSnNBRmqVOjASWcJNTcS0HX0gEnmldcJxGvK6+0kVq6h5YLlfs9NWzszWlsI1ddiRqvY/sBj2F09fiLR/2He7k3ipLNY9XGaTjuhv/x1hkJvkm13P4BynZGTFxjc+6JpO49TauQCB67nxzGaoQkP7DrpMYwRe46cvjQNJx2DGa2i69Gn/Pk5/cP4dFNHi0XxikUKO7owa6px0xlQHt6Q85Ven97nXqTnuXUUUnnMkIUCSXqEEEKM26lnLMFxXL70//0Md4Qh3KlUjnXPvcLX/t+v+N/vXzPJEYr9kQxvE+M2sMx0sKEWlCKxck25cthYJNaup/2u++n+x2rs3iTZ9u0oz8PL5ft7JerQdH1Q2WqrtoZgUz1WbQ0AkdZmv/Ka8nt4cPxy1JoV6J+HomFURcqT72sXH0r8TYej6X5J5hEptbOk9Xh6Ykar+KZp9K8WumsaYJp+BTbLrDynqH84YCDqz7fRAyZ6KISmQbF/aFpoVjPK9cht76LvxY1k2zpwc3m6Hn2SxNr1g05n9yRIrHqu/zWN79ZrKoQQQpScefYyzjv/2F0et/Lpl/jcv/9oEiIS+ztJesS47SwsEB2WmIzFwKQpPLPRn0OSTJHv6EQzTazaOMouoDzPnws0wmT6XP+H+HKCAn6S4nm4+QJ2dw/b73t40Af8lreewZwPvJOm00/EamoYOREZa4IyRpppoAcCuz5Q18EwMaNVfrntXGFw4qVpaFYA3bLQDJ1iOkPPilUoxyUyp5XQjEbMcIiqAw8gt7XNn9NjmuB5KMdFD4cqJjSl19QIh3frNRVCCCGG+uwX3s/l/3YudQ2xUaew/u3B1VLYQEw4SXrEuO0sLJDeZWJSydCkKdzaTCBeQ90xS5j17rfSeNrxoGnkOzpx8zah2a04qQx2hfkyqpQQDOw+V4DnoYeCaLo+6AO+3ZNANwyCjXX+Oj8jlZZ23N2fbzNU/6Kn/hC8XfyT08CsCqOHg+A4aKaBFgzsTOr6e5M0XceqjWN3J8ht206hu5fca20oXcfJ5el95lly27Zj9yT65y/5vVZ2Zw+aFRyW0JReUzeXG9dravckyG5tkx4hIYQQFV12xbn88Cef5Myzl/kjGUbw9a/dPnlBif2SJD1i3AZWZSt09Y67ylelpCkQixA9cJ5/7iMWEV20sL8nI03PipW89ts/s+3uB0i/8nr5PEZVhPI76KBKMQotYBJsrMeqry1/wC8Nqdt2z0N0r1iJVygM7iUaaHerqlXUXwRBKbRd9PbowSChxnq/5LWn/PLXtuM/TU3zn6dSBGpi1B5zFPm2bWi6jmYYKM8j374dN51BN02/Z8lxwS6WEzg3myO/bfuwhMaqixNfemT/a5oY02s68H6233X/sCFzQgghBPgLmn7k6rcSj0dHPGbL5g6+9v9+OYlRif2NFDIQu2VXVdlGU0qaEivXUOjqxQhZgz5g2z0JvzKbYaAcF5TCs228gk1m0xbsAxcQqq9FNwwC8Zg/xM1xIRxCN3T0UAgzHMKKV5d7LDzXHTQPyUml/aFkQ4sT9CcWmqb51y719pQquO0OXQcUngIrEsZ2HD/eoYeFggTi1WAYOH3pndf1PL/3qpTcOA5uLk+xuxflKYIzGih2J/rLZXtokRCh5kY8z8XNZIddx0mnqT/x6EH320llCLc2UxcOUBsIYVVHR31Nh87rKhWciEjFNyGEEBW0tDbw7veezA9uuWfEY+7989McfczBfq+QEHuYJD1it41UlW0sRkuads4vCYHroYdDqIKNHrTwbMcfllVfixmrIlgXx7WLGMEgbqGAYQWILlpIev2mQQmVbhi4eZtgQ215zkoxncOIRHDJ9S8mqvwExfX8FXo0ygmLphuga+jBAG5qeCIxKtdDuR4YOsEZjSgNit2JwcfoOkY4SLE32Z/MeBjVUdxMbsBBCmX7hRuKyRRdT67y70G8hvCcFr9stQI9EMBJpbGqYxS2d/vV33QdNPqfe4xISzMwuAqfHrJwD1lA5Jg3oe9iQdaBQxTdbB7NCuKk0n7BCUl6hBBCVHDZFeey6pmX+OfKTRX3F4suN15/O6ZpcOoZSyY5OjHdyfA2sdeUqqoN/ZBcnl9SKICh4+Xy/n8LNrpllodllXqMDCuAm8tjWAFqlx1Fw3FLablgOTPPPY2WC5ZTc8QizFgVmq5T2NGNkyvgpNLopk4gGiF28AGEZjZhhMN+9TfDX8tHK5W11nQ0Q8eqrSEyfw6aaaJZgfEPgXM9PLtIIF6zc7haKbnwPJxs3q9OV1+LETAJ1dcSXTgXoyYGAdPv7Rk4L8hxcZJpctt2kH29wx+StvQI6o5fCppGMZXFCAb6h8R5/cPlXNA0zFhVxSp8mU1bKs6dGsqMVeHZRTKbt5LZ+jqZzVvx7OKY53UJIYTYP333B9dx+JHzRtzf15fjxv++g/a2rskLSuwXJOkR+5yByYwetEDT0C0LPWgRXTi/XLIa/B6joQlO6RwDE6psWwduPk++s5v0xs0UuhNUHXQAZjSCsm3CM5sI1MTQAgGqDphDaEaDX+RA09AMnWBzIxgG6Q2bUa7r9wztxrQfN5/HSabKVdj83iT/RJqmUexJYoRDGOGQX8K7UCQys4nqQw5E6y9TrRnGzmF3/VXZ3HQGt2CTWu9/e9ZywXJaLziLuhOO9o8rJUwKPNsG/Op3he4EXtHBKxb7ExlnHBXb+osqaIN/F0IIIUbzw59+ivMuePOI+3u6U/zkRw9MYkRifyDD28Q+aeDwN8910Q0DPRohUSyM+1ylHo1ArAoMA7uzm2IqTfblVwm1zsDu7O4fChciWF+LV3QodPWWe0U006SwvRvVP/9HtwL+QqC7Ud2t0Nl/Hk+hCn7yUcoaPNfDK2Rwcnl00yR6yIHUHnko2fYO+p57sTyvSNnFnSfU9XICZlRFyuWoI63NROa2UuxLoRsmWAE/WQK8gk33ipWkXtqM3d2L3d2LHrQwolVQFx22eGklTiqDbllUHTDHHz5nGDipjAxvE0IIMSYnnnQY99z91IjTZR968J9cctlZNM+sm9zAxLQlSY/YZw2dM+R5HuzYMeiYgXNSjJBFfNlRxPt7e0p2zj+pwu3oQrescm+N3dlNwynHoRsGZqyKvg0v0/nwk34BAA2/iEIut7MTQ9PQqsJ+NbbxJj26v1iql8vhKcNPFko9MADF/mTGcfBch8SqtRjhEPnX2tEDJuHZLeRf3+YnXKXHlGJQfkJVfdABFLp6BycfuuZXc7MsPNtGKUXqpc046Sxa0EIVi3i5PG7Bxg0a7Lj/Uewlh9Fw3NIRn0ppCKKy/Xk94y1bLoQQYv928KLZ1NbF6OlOVdyfTuf53R2P89FrL5jkyMR0JcPbxJRk9yRIrl1Pz4pVg+akDF10EwaUyE72oVy/YIEeMDFrqv0J/IZBZG4r2Ta/R8XN5fsf2F9eeuC3UErhpbPgOGOOVbMCGNVRNNPEyWTx8rZfvW200WAKvFyenqdW4aSzmLEogVgVodktaKFgxflEbiZHvqNzUPIRbm32F3t1PdxcHuV6/sKnnn/xQFUEMxIpFzrwXJf89k52PPg4XU+uGjG8N1q2XAghxP6tpbWB97zvLaMe88jfnmVbe/fkBCSmPenpEVNOct0Gkquew06kKCb7CLXMIKDraFYQu6ubXFvHoA/fpQ/oPStW4rkemoJgYz3KLpQThNIQOOU4GJaJZ2h4dnFnYmIFxrdgqa5hRKOY1VGCkRi6rpHP7RhXsgTgpnM4ZoBcWwdONoeXL6CKDlrA8NfvKekvbe05zqDkw6qL03ja8fSsWIWby2GEw1QfuYjEqrU4qTRuPo9yPL/QARqapvu9UdkcydXrqD54wYiJzBspWy6EEEKcefZSnn5qPc89t5miPXxo9bb2bjasf41DD2/ZC9GJ6UaSHjGlFFNpcqueQ1OKYGMdTipNoaPTH6rW5Zd67l6xEgWDhrmVPqAnnn2e9MZX/BLSAbOcIGS3tvlD4GqqKSbT6Lrhz/vX3fLCortMeAwDDB3dNGk86ViqjzqEzp5uorkifes2kGvvGP9cf03DtR3cbA7Q/IcrhSq65f3o/rwjTdNpPPWEcjGHoc+9lJxk2zr8RU89Dy9T8GPSAAVKeeA4/rwlx9vlHJ03UrZcCCHE/q2ltYF3vedkOruSvPrKjmH7lYLf3/EYhx7+3r0QnZhuZHibmFK8XKG8PowZDhGc0YByXQodnQCEZjahB8yKw9ysujhNp53ArHefR8OJR1O77CjCrf56NX5Jaw0n2YcWCfkVzpQiWBdHDwagOHoPjWYaBOLVGKaJpmlE5s3Cqq2h0J2g58lV9L24ETy1s1Q17Cw9PRpdRxVtlOf5yU3/NnQNzP7FVT2FbujUHnMUtYsPrXiaUjU7oL+oQ4SqA+aUq+JZjQ2gaXiFIp7nYUbCBGKRinN07J4E2a1tw+6vEEIIMV6nnrGE5eeMvBjpxg2v09PdN4kRielKenrElKKHg/78nFQaMxbFCJhYdbV4xSKh5kbM/lLPwybzD5Bt6xhW/ADAzdvkd3T1L+apYcaiVC2cT3bLq+Sd7oqJjxYOouwietBCFWy0gF8wQDcM7N4kmU1bCCb7/GFt5RI1/f/VdEJzmslv2+Ff01N+b1F/1ThQ/VWxNXQrgHJdvKKDHvD/X7dMXM0lMquZujcvHTHhGahU1CHYUOsvKmqaaBqEGmrRw0HyuSyGESBQE6s4R2cshSOEEEKI8TjnvGP42Y8foFAY3s7mCw6pVK7Co4QYH+npEVNKIBYlvvTIwRPojzmKYH0cZfs9IqNVErN7EvSsWImbzfn7laJnxUp6VqzCKyUmSoHr4WayJFatpZjOlss9D6VyBfwKAMqfv1MXJ1gfx4xV+QlGroCTyfoJzUBWgGBDLU5f2k9sBuw24tVoAdPv5fFc0PqHndF/oAbBFn8xVd00UEWH5Oq1JNaur/h8B/bKlIs6pNJ+gkV/LmYYmKEgVn0tM846edCaRwPPNXQx00o9akIIIcR4tLQ28MEPnVVxX7HoSk+P2COkp0dMOTWHH0zVrJmDJtCb0SoSK9f0r7djlXsp7J7EoOMSzz5PbtsOdEOnmEwTqI/jZvPgubiZnJ8BlOa3lHpcCt6Ic3E000ALBFCA3Zsk2FBH9KDZAOWkyrMdNF0fPCfI8yim0njZfHmNHDedRTkObiaH1r+QKIaBEQwCCq/oEKip9quvuQo3X8CqrSHU3IiTSpfX5yn1zozUKxNfdhSJlWtwUmkCNdWA8tfdCVnEFi2g5vCD0fXh34cM7CXSdB0zFh21R00IIYQYq8uuOJe/PbiazZu2Ddu3+p+bWH7OCXshKjGdSNIjphy7N4mXzg6qGFZpMdOuJ1eRXr+p/KE/umgh6Y2v+AmIrqOUorC9C6sujnJ1vGLKnytT6l33FMpx/EFm+vAS0QB6JILmeQRnNGB3J1CuR2rDy2S3vkbN0iMJzWzC2dIGKP8cpR4fpfDyBT/JMnR0TYNoBCeRBNf1e3YUfkU2w0Y3/V6ZumOWEF98KOmNr9Dz9GpCzY0VE5ChvTIDk6KhhQ2gf7HRXSz+OrCXSNbmEUIIsacdfsT8iknPK1s62NbeTeusxr0QlZguJOkRU0r6ldfJvfgyXoU5JVZdvDxfp5jKUkwkMWtiRFpm4KTSJFevwy0UMePVuH1plOeiPI/qQw9CDwfZ8cCjuPkh44k9BYZGZHYrufYOVMHeuc/wiwxoloVnF/GKRaxgNVZ9f5Kx6jnCSw7FbtyKk+jzS2CX9BcgAPCyeWy7iKboX6x0cJU4z3EI1MUJhoLEFx+KVRcneuA8+l7YMGICsqtemaFV16y6eMXFXwey6uJEFy0kuXodxXSOQCwia/MIIYTYYw5eNKvi9kKhyIb1r0nSI94QmdMjpoxSYYCR5pQM7N0IxCIoz5+X4xUdzFiUYjpDsTeB3dmN57oY4RDhmTOILz4UM1qFEY3urKimaWiBQLkEtW7oGKEgmmWhhUN+r43r4RWKoBSarmNEwlj1O5MMN2+j6zpNZ5yEVV/nzwsydDTLRA+H+quw9f+4rj9/p2Tg4qOOi4ZG/fHLBg3Ziy5aOOLioAN7ZXY1z2msEmvXk16/CeV66KZBdNHCYfN+hBBCiN113AmHEo9Hh213HY+f3/bAXohITCeS9Igpw0ll8Gynv7z0zsTCSWXK+0vlrPWg5a81U7Dx7CKFngROKovyPJTj4uXyFPtSVB/pf2hPrFxDsLaa6AFz0ENBNCtAqKUJ3bLQTBNPKTy7iB4MYJgmRlUEPRzEaqjDqq2h7vhlBOvjw5IMPRyk5vCDmfm2MwjEY/6wOsfDy+bB8wjPnknswHlEZrcQqKvFiPYnJaVKbxoQMKlaOJdwazOJtetpv+t+tt3zEOn1m4guWsjMc08bVnig1Cvj5gvktnUOS4rGa2BCGWpuxAhZpNdvkiIGQggh9piW1gYu/7dzMQPDP56+tKGdB+9buReiEtOFJD1iyjBjVeiWiZPKVOy9GNi7oQcCmJEwmmHgpNJ4eRtQ/VXR/F4UVXSwuxODkiWrpppQyww0XUfZRazaOEZVGCeZRtPACFqgPDRNwwgGCc1oBE1HN3Qic2fhFd1yz0t86ZEAZF9t96/nejsXOgUwDXRd93uZQkFCDbVEDpg9uJcHDV3XSb+0ha0//wM7HnxsUE9Xev2m8tymgZXa9nSvzMB7pOk6mhWkmEyRa+vY7XMKIYQQQ1140ckcdti8Yds9pfjnPzdNfkBi2pA5PWLKsGprqFo4H158eViVNvB7N0qVyQpdvQRqYtQdu4RwSzPZ9g623/cIXqGIpmsowwDPI/PyK1QfeuCwtX/CM5uoOfIQCj0JslteQzkORP0ud8/10BQEG+tRdgHPLtK9YiXKU2i6RvTgBdQuPpTM69vo+cdqCqk8yi5STGcwIiF008RzHDy7iFsolp9LbNFCUus3EWyqp9iXxis64HnooSBuLoeby6McFz0U9Ku4DZinM3DtIU3XcPM2gVikXNktvX4T1Qcv2O2enoEJpev4i8Eqz/OfN8haPUIIIfaYaDRccbtlycdWsfvkr0dMKdF5s4gfuGBY9baSoZXJBs5x6f77M9g9CZTnd6ZoIau/l8YYlCwZIYtQazPJ59aT27YdTdcJzmggEDDxig6xgw8g37Yd5Xr9tQgUesAsFxTIbX2NqnmzSKx6DkyNYEOcfPsOv5cHzV8Q1FNohkfdMUcRbmkur+uTePYFIrNb8IoOxWQfubbtKMdBN02MSBinL01hexeB6mqUXcAIWXiuO6hSW2FHF3ZvYtD8olJyBAy7N2NRSih7Vqwi339PQjOb0APmsFLZQgghxO5qb+ti8+b2ivte2zpysR0hdkWSHjHlWLU16PW1w7YPXJMnMrd18GPq4tSfeDQ7HngU5XpopokRCmIEA+XjB5a87nr0Sdx8vn/dHo1idwJzTguqUCR+xCF4hx6E3dmD8lwSz75QHvZVSjDszh6cdBbqYnhFF6u+jkJXD8px8PIKpfznERvS+zKwx0nTNDTDQDkehEw0x0EPWuAp7K5uAjUxapcdhW4Ygyu11VRDZw9Osg8jWF8eBpht7xhUwntg5buxiB+xCA3Y/te/E2yswwyHUJ4na/UIIYTYY3q6UxQKTsV9z699hfa2LlpaGyY5KjEdSNIjpoWRFuIcqOG4pQB0//1piqkMKuuhB4Nk2zoGlXHObm2j0J3AyxdQRQfPcVCG4ScRkfCg5EHTNTy7OKx0tJ1KYfcmKRTzZPIOZlWEYEM9mqHj5gvoAZO6YxYPKxtd6k3JtXVghMNUH3EwqRc24mZz6FYAK16NEQ5Rf/wywv29K3ZPYlCypGwbq7YGzTSHDZ2rtG7PeJKVcGszVjyGsm1U0JK1eoQQQuxRdfUxamoi9HSnhu3r68uyYf3rkvSI3SKFDMSUN3QhzqGlrAeqPngBgepqwjMaiS6cTyAWGXas57q42RzKddEjIfAUXtFBKfwFTgckD3rABNSgAgaxRQvJv9ZOoDqKZgb8pCiZovaYo4gvPcIvsKDrpNZvIrF2fYVnpPprHShiBy+g6cyTCDXVY0ajBGpi1B+/jJojFg2byzSwfHXTaScw693nlSu7hVuaBxUiGFr5bqwqXUvW6hFCCLGntLQ2cMRRB1TcpxRsfrny0DchdkV6esSUt6uFOIceqzyPYFO9X6EtGBh2rG4YGFVhvIINrocRCaObJo2nvJlAdYzksy8MupZbKFJ//FIC1bFBc3PCrTMoeA5VsRhOKksgWkXvyjX9PSPRcm+LWRVBN4zy3Bw9YBKZ1Vze33LBcqoPXjDqXJyR5jKNNHSu1EPjuS7ZrW2YsSrMePWY7vdI1xJCCCH2hFgsMuK+V1+ReT1i90jSI6a8gZXFBn6grzTkaizHmrEqgnVxXLuIEQziFgoYVoBwazNQOXkwqiIU+1IU+1IYVZH+YzJo1VWgCgT638CHJmepl1+l/a770U0TzdBxcnmq5rQOS94ic1t3mVyUhueNtG9osQarsb5/7pI/JLBm6ZHQNHyu1HivJYQQQrwRow1fe2HdVpnXI3bLhA1ve+WVV7jsssuYP38+4XCYBQsW8MUvfhHbtifqkmI/tashVwPXrxnL8KzSMYYVwM3lMaxA+ZhKj7ca69n2p7/y+u/v4fXf38O2Pz2I1VjvF0DoS5evYVRFQCkKPQmU55HZ2obT10ext49iKo2TK+BmcuX9Y50vM/D5jSZ+xCJaLljOzHNPo+GU47A7uwcPCVz1HMVU+g2+GkLsu6RdEmJqOGBBM4GAUXHf9u29bFj/+iRHJKaDCevpWb9+PZ7n8f3vf5+FCxeybt06Lr/8cjKZDN/4xjcm6rJiPzXSkKuRChzsanjWaMcM3Oe5LjsefJxisq9/fo9GMZki39ZBwxknoWfSzGhtId++na5Hn8TJ5nCzOYrJPtxMDs0wMKIRcBy8QsGvzuZ6FdchqmS0Ag4Dq9kNHO5WKtYwtNcp39WLnivsmRdEiH2QtEtCTA2BgElVNEyid/gXccWiS093316ISkx1E5b0nH322Zx99tnl3w844AA2bNjALbfcIo2LmBBDh1wNLXAwtGLZGx0uVk4ecjkAdMsCwMu7ONk8umEQbKoHKMdRNbcVu7sXJ5dHixp4RRscB0wTL5MlNKORGWe/Bd0wdjlfZrTnN3Cx0krV7EYa5ud5HtlX27GqozJ8TUw70i4JMTXU1ccIhy0SvcP3GYZGXf3Y5qAKMdCkVm9LJpPU1dVN5iXFfmToMK9SgYM3WrFsNGasCiPsrxzt2TaeXUQpMCOh8rC0oXFY9bVoholZFcaMhFGAm/V7fWqWHEb0gDljmsMz0vPL9Sc8o1WzqzhMr6GOvnXr6bj3Ydrvun+EynJCTC/SLgmx72lpbaB1VuU5O5GqEAcvmjXJEYnpYNIKGWzatInvfOc7o36bVigUKBR2Dq/p6/O7Lz3Pw/O8CY/xjfI8D6XUlIgVple8yXUbSKx6bmfPxtIjCbc2o4csiql0uaqaHrLQo5E99pzNeDW1x72JHX99nGKiD9Cw6uPUHrcUsyaGvbkLO1cEXRsUhxmLED14AekNL6N0g4CpE198GHX/f3t3Hh9Vfe9//D2TZCYBsi81CYsEEWqRxSBBQQVEEKWKWv3dXutPLBeX4lativX2otcFqVirtlWs/YFWL+rVKlpXCiquVRIWUVYFgUQkZN/IZGa+vz+QlMhMMiE5s5y8no9HHm3mnMm8GUY+vDnnfE/RqJCzOfv0Cvjr8xsj736P3FlpktOhuOTeat5XLU9tfZsV2lJ+dKwS837wrxuyrvpYJt4hV1aqfHWNqlq9Tol5P5ArPbVb3qvuZqfPbzSKlZxdEcpckphN4RZredcUb9XuXbvVt1+NRhUOjnScDsXK++t2x8vhdBy4UbdDcjgcklPKy0vXUbkZUZs/Vt7fg2Ix75HqdOmZO3euFixY0O4+Gzdu1NCh/zqVprS0VGeeeaYuvPBCzZ49O+jz5s+frzvuuOOwx8vLy2PiQlO/36+amhoZY+R0Rv8tkOySt6WuXpXrN0jxDsVlp8nX1KTa9RuUkZQg3w8HqWHbdvnr6uV0xavPMQNV3dIs7e14ycuWunr5m5rlTHIrIblP0P3qGxvUlJEsX5JLjrg4uY4dqOacdFWsXqPy7V8rsalFfuOV2e+R0+dVXJJbfY4ZKN/RfRWfnSbnd6/hTe6jvSHkOlSgX199UoIakhPVsL9JcUlJ8u1vkpITVdWyX/WBfn5ivJr31qjG+OXp1Vu1DoeU0lsttfVylpbJ3RKd1/nY5fMbrWpqaiIdIWRWziWJ2RRusZT3lZc+1KefbFZmVm9V/P0TnThmiH484+RIx2pXrLy/+X1TNeiYTDmcDh2Vmyw5HDJ+o74DMjo9K8MpVt7fg2Itb1dmk8OYA7dBDFV5ebkqKira3aegoECu765vKCsr04QJEzR27FgtWbKk3Tc00L+m9evXTxUVFUpLS+tMzIjw+/0qLy9XdnZ2THxw7JK3cWeZ9rz+ttxZaQfuveP3q3lftY6aNlG9+ufJU1Xzrwv6QzxqEejIUeqwIYft56mq0TcvvyUZ03q0RQ6HMk8dq32rPlZtvEO9m1rk2VMu4/PJlZGu9BOHK3PsCUf0HgT6tQR6LNT8h/7cspffUm28QxmJifLVNUoOh3LPmRLVR3rs8PmNVtXV1crMzFRNTY1SUqL7/Hkr55LEbAq3WMm7tmSbbrz2UbW0+HTMsZn6cmulXK443ff7KzTyhGMiHS+oWHl/n3/mHT1w/4tyOBwadEyGvtxWKWOMsrNT9Mjj1ys3LzPSEQOKlff3oFjL25XZ1OkjPdnZ2crOzg5p39LSUk2cOFGFhYVavHhxh2+m2+2W2+0+7HGn0xkTvxHSgcOv5LVOoLyulD6KT3TJV9dw4LqWugbFJ7rkSukjp9OpxMx0KTO0+89IB64NqileL4cxSvxugYCa4vXq3Tf3sOts/PWN8h+yClrCwXvr7KuSf79HzrQ+8lbslTPOKTmkuHin6j7bJFdyHyV9t6BCqIKt1JaYmS6PwyFvXYO8DodcGWlKH/5D9e6bG/INRBMz05VeOFx16zfIs69G8d+tHJfYifctEuzw+Y1WsZDxICvnksRsioRYyPvS3z5QU5NHDqdDXq9fXp9P3kavPlu3XSeMPjbS8doVC+9vXHyCjN9ITskYyRgj4zeqrm7Uls27ld83tP/mIyEW3t9DxVLermS07Jqe0tJSTZgwQQMGDNDChQtVXl7euu2oo46y6mXRAwW68WZHSz235+ACAYcu53zwJqHf/5nBVkFzZWcoLtElX2Oj4lq8csbFyREXL7+RPN/s1d4V7yshNfmwVdWCOZKV2g6uMHdwgYeOyk/qsCHKSEpQekIiq7fBlphL6C5lpfu08fOvD3vcGMnr80Ugkf2Ule4L+Ljx+yU5whsGtmBZ6Vm+fLm2bdumbdu2qW/ftqtsdPKMOqBDodx7J1TBikygm4QGK1x9Cvqrpb5BNSXrZPx+GUnxGWlqqaw+sIJbVqaMp7nNEtrtCVbEvr9S2/eX5W7vPj6H8lQeWOhAknr1z4uJf+0BOou5hO5SWVEnjydwuXnj1U816/KzwpzIfjZ+cXiplKTEJBert+GIWPY3m5kzZx44FBngC7CCKyMtpKWeQ/k531/Oub0jR2nHD1XeuVOVe9Yk5Z07VanflYrUYUOUOW60sk4pUuIPsmRavDJ+vxKPylZ8krtTS2gfWsSM399axCQFXZb7+0eHAi1dLR04ba5s2Zva8/rbqvznGtVs2Nyl9w+IVswldJeMzGQ1N7cE3LZrZ7mWv7E6zInspax0n3Z+XR5w28BBecrLD7ycNdCesC1ZDcSSzh45CnYj04TkPsqZWCDvqB+pqXSPKj4sljM+rk1xCXQEKdDPD3REKSn/qKBHpUI5Ta9tMUpTw/4mVQe5fgkA8C8uV/C/QpWUbNMZZ44OYxp7qayoU11dY8Bt/fpTeHBkKD1AEMGKTFd+lpHavfbIU1ndWrQktSldwYpYe9czdXSa3qHFSE7HgSWuy6sDXr8EADigsqJOmVkpKt9bHXC7xxP4KBBC801ZhVo83oDb+vfPCXMa2AWlBwijXvlHtV5++f3V2w69/sbv8UhyyOlKCLg4waGClaFQFng49LS5uOTe8u1vCvnoEwD0VBmZycrISFZmZooqKusO296//w8ikMo+Ghub5fcHPu00L0qXqkb0o/SgRzv0yIrVRza+v6iAkQKeZhaf3FsNXx2450jvggEhLXgQ7KhUR6fptS1G1VJyotIKj+coDwC0Iy8/S9PPGauvt38bcLtnf/TftDaarV//VdBttbWBT3sDOkLpQY8V6spm3aG9JaddGWltTjPzNTbJ4Tiw9Kl8vnaXzA5FR6fpHSxGntp6VbXsV+qggiP+dQJATzFx8ii99vdPVFp2+I1x/7G8RLN/MT0CqWJfWek+bfp8Z8BtDodUX9cU5kSwC9alRY8U6spm3eVgqQm0yprU9jQzxcWpdTGpuLhOLXhwpFwZaerVP08JyX0sew0AsBtXYkLAx0t3V2hN8dYwp7GHyoo6VdcEXtU0ISFew0fyD3M4MpQe9EgdlZDu5vf5JGPUXFkdcOW2Q5fJ9tY1KCE1WQmpKQdKUAdLZgMAIqOgIPBNbb1en956ozjMaeyhpcWruiCnsE08fYRGFQ4OcyLYBae3IeqE4zqbztyAtKtqNmxWTfF6eRub5Gtskre+Qe6MtMOKzPevv5EUtuuNAACdl5YW/Oj42uKtKivdxz1lOun9VRsCrtzWr1+mrpjz4wgkgl1QehBVwnWdTSgrm3WHlrp6NRWvl8MY9R6QL09FlYzfr6zTTlKfgv4Bcx2a4UjzBCqO4Vy0AQB6gozMFMU5HQG3bd/xrSor6ig9nVBWuk+vvvKxvn+/YIfToVNOG857iS6h9CBqdHSxf3fr7A1Ij4S/qVm+/R4lfneDUFdmupr3VckZF9ftr3VQoOIoKWyLNgBATzFkaF/17u0OvNFIDyx8Xn958qbwhophH32wUTXVh59m7pBD4075UQQSwU64pgdRI9zX2UjfXcA/IL/ThcdTWa3Gr0s7XPjAmeRuPY0u0LU83S3QAg2VH65W5YfFYVu0AQB6irz8LE2YNCro9k1f7GJBg04oK9132FGeg6oDlCGgMyg9iBqHXmcTjoJwpKo/26SyZW/qm9dWqmzZm6r+bFPQfROS+yitcLjkcKh5X5XlixIEKo7exv3yNTWFXCY9VTUhFToAgDT1rNFBt/l8fu38em8Y08S2PXuqAj7udDokBWlDQIg4vQ1RI1zX2XTFkZyClzpsiHr3zQ3L9TSBFmiI75UoyRHSog31O3araeOX8nMaHACEJCEhXomJrqDb33j1E517/rgwJopNZaX79MWGHQG3paT11pCh/cIbCLZD6UFUCcd1Nl1x6E1EDx41CeXGoR3dILS7BCqOmSePlpE6LJOeqho1bNuulDBdUwUAdpCRmaw+yYlBt69b+xWruIXg9v98Qt+UVR72uNPh0IljjlVuXmYEUsFOKD2IOuEqCEcinEtdH6lgxbGjMumta5Df421zGlwohQ4AerLcvEwNO36g/vnR9oDb/X6j55a+o+t/9ZMwJ4sdy99Yrc/W7wi4LSMrWccO4SgPuo5reoBOOPQmouG4RudIBVqgoaNFG+KTe8vpipe3riGqr6kCgGhz8vgfKT2tV9DtX325J4xpYk9J8TYZ/+HX7DidDuXlZig5OSkCqWA3lB6gk9KOH6q8c6cq96xJyjt3qlJtcs2LKz1VvY8ZGPWFDgCiTUZmii74P6cG3b5h/Vd6+x9rwpgotlRX1wV8PC7OqalnjVZGZkqYE8GOOL0NOALRfApeV/Q5uq/SBg+Sv74xKq+pAoBoddl/TNO3e6r18osfHbatsdGjBfc8oyE/7Me1PQF8+s/NAR8fNDhPMy44RXv3sgIeuo4jPQDacKWnHtG9iwCgp/v1f12s06ecEHBbdVWD/vjQsjAnin4/Oed21dftD7jtlNOOD3Ma2BmlBwAAoJscdVR60G0r3lqjF55bFcY00e0Xs3+v3bv2BdzmcsVr2tljwpwIdkbpAQAA6CbjTx0mpzP4X68e+9PfVVYa+C/6PclfHntNJau3Bd1+6sThnAqIbkXpAWzIU1mtxq9L5amsjnQUAOhRRhUO1sTJI4Jur6lp1O3/+UQYE0WfstJ9evrJlUG3O53SL645J4yJ0BNQegCbqf5sk8qWvalvXlupsmVvqvqzTZGOBAA9yt0LZik3NyPo9vVrt2v5G6vDmCi6zP/v/1FjQ+DreCTpwp9O5CgPuh2lB7ART2W1qlevk4yROytdMkbVq9dxxAcAwuzaG89vd/uDv/tbmJJElzXFW1W8emvQ7ScWHatf/uqCMCZCT0HpAWzEW9cg336P4pP7yOF0Kj65j3z7PfLWNUQ6GgD0KEOG9lVmVnLQ7fvKa/Xc/7wdxkSRt6Z4q2687lH5A9yIVJKmn1ukhx+9Nsyp0FNQegAbiU/urbhEl7x19TJ+v7x19YpLdCk+uXekowFAj5KXn6Wfz54mhyP4Pg898GKPuWnpPf/9tOZc/qAaG5oDbu/TJ1E/nz0tzKnQk1B6ABtxZaQpbfQIyeFQ874qyeFQ+ugR3HMHACLggotO1X9ceVbQ7V6vX/NuW2L71dz+8threuWlj+T3B9/n3PPHcR0PLBUf6QAAulfa8UPVK/8oeesaFJ/cm8IDABE06/IDpefPj7wWcLvH49MN1/xJz/ztv8IZK2zWFG/VM0+/IxP4jDZJUnZOqi646JTwhUKPxJEewIZcGWnqNSCfwgMAUWDW5Wdp6HH9gm7fsX2vfnzmr8OYKDxeeG6Vfn3T46qrbQy6T2Jigm64+UKO8sBylB4AAACLXXzJ6e1uL/+2VmdOukVrioOvbBZLXnhulf7w+5dUVRV8IZ3MrGT9z/O3aeLpI8MXDD0WpQcAAMBiZ5w5WuNPHdbuPtVVDbr6iod02y1/CVMqa6wp3qq//Pl1NTV5gu6TnZOqPy+5kSM8CBtKDwAAQBgsfPBK3Tl/plJSkoLu4/MZrXhrja6+4qGYXODg7X+s0Y3XPqrKfXVB9+nXP5tT2hB2LGQAAAAQJmecOVo/Ov5o/dv5d8nj8Qbdb/UnW/SL/3hQ5/1kvM44szDqC0JZ6T69/uonemrJP4Ie4UlIiNNZ04t06awpUf/rgf1QegAAAMIoLz9Ld9x9qe74zZPav78l6H579lRp0Z9e0dv/WKMfn3eyLrjo1DCmDN0DC5/Xyy9+pKbGwPfgOeiin07QNb88L0ypgLYoPQAAAGE2cfIoDflhP911x1Mq+XRb0P38fmnr1lI98vDLKiut0AUXnRIVR0kOHtl5/plVqqqq73D/jKxklqVGRFF6AAAAIiAvP0t/eux6/eLy37dbfHw+o/r6/Xpu6Tta/ekWnTPjpIgc9TlYdNat+VLr127X/v3BFyr4vp9ESVlDz0XpAQAAiKA/PXa9/u9P52vLptJ292tp8Wnrlt36/cIX9MqLH+rUicM1cFCehgzta2mhKCvdpz8+vEzvrlgvr9fXqefGxTk1/Zyx+vnssyxKB4SG0gMAABBhTy69VXfe/le9uuyf7e7n9xn5fT5t2rRbmzbtltPpUK9ebk06Y5RmzprabeXn4FGdFcvXaMdX38jv79zz4+IcGja8QFfOma5RhYO7JRPQFWEpPc3NzSoqKtK6deu0Zs0ajRw5MhwvCwBAQMwlRKPf3H6JjjtugJ5aslzf7q2W32c6fI7ff+DUt5df/EivvfKJjspL19EDc+Vv8aq+yaPkPm4dN+xopaX1UUZmio4dkq+4eGltyTbt2lmu5v0eNTV5VFVVp/T0ZA0fWaCS4q166ol/qKkx9NPXDjV8+EBdde05lB1ElbCUnptvvll5eXlat25dOF4OAIB2MZcQrS646FSdNO44Pf/ce3rhuVVqbmd1t+/zen3avXOfdu9se3+fD977ovX/uxMTNHrMAH22tlT19fvl97ctVk6n47DHOmPcKT/S/Q9ddcTPB6xi+c1JX3/9db311ltauHCh1S8FAECHmEuIdnn5Wbr2l+fp2l+ep9z8DCUluRQX3z1/ZfN4vPp2T7XqAhQeSUdceBIS4jR5ygkUHkQtS4/0fPvtt5o9e7Zeeukl9erVq8P9m5ub1dz8rzXea2trJUl+v1/+zp5MGgF+v1/GmJjIKpHXauS1FnmtFSs5O6uzc0liNoUbef/lvJ+M19iTf6jNm3ZLMlr61Ep9/tnXOvLjMJLD4ZDDceB/u/pP38kpSRozZqgGD8nT8cMLNPKEY7r9feDzYK1YzHukLCs9xhjNnDlTV155pUaPHq0dO3Z0+Jz58+frjjvuOOzx8vJyeTxHdl5pOPn9ftXU1MgYI6fT8oNoXUZea5HXWuS1Vk1NTaQjdLsjmUsSsyncyNtWXLx03LBcSdKd9/5Ma4q3am3Jl9q0aad83s7/BdDhdOio3GTJ4ZA5wqM6qWm9NfSH/XX6GaOUkZnS+vjevXuP6Oe1h8+DtWItb1dmU6dLz9y5c7VgwYJ299m4caPeeust1dXV6dZbbw35Z99666264YYbWr+vra1Vv379lJ2drbS0tM5GDTu/3y+Hw6Hs7OyY+OCQ11rktRZ5reVyuSIdIWRWziWJ2RRu5G3f1Gk5mjptnN5ZsVZP/L+3VLp7nxoamzt+4nccDodkjL7cViljApceV0K8vF6f/N/bnpOTqvMvHK/JUwqVm5fZpV9HqPg8WCvW8nZlNnW69Nx4442aOXNmu/sUFBRo5cqV+uijj+R2u9tsGz16tC6++GI98cQThz3P7XYftr8kOZ3OmPiNkA78YUJe65DXWuS1VizljYWMB1k5lyRmUySQt2OTzjhBQ4/rr8qKOrW0ePXXJcv12frtamnxSTJq3t+igJ3GKRlz4MhnoCM9/3bxBJ02cYR2fr1Xe/dUqbK6Xi5XgkaOKtCQof0icoNRPg/WiqW8XcnY6dKTnZ2t7OzsDvd76KGHdNddd7V+X1ZWpqlTp+rZZ59VUVFRZ18WAICAmEvoqfLys1pLyKjCwSor3afKijplZCbrow++0MsvfaSG+ib5fEYOGTU0NMtnfMrITFHvXsnq06eXtm7Zrab9LUpL7aV/+9npuuCiU1p/HmAnll3T079//zbf9+nTR5I0aNAg9e3b16qXBQAgIOYS7O7QEnRw6euDJejg46W7y7V7d5n69s1Tft/sNkUpEkdxgHAJy316AAAAEF6HlqCDcvMyFRfvU05OZtB9ADsKW+k5+uijg14wBwBAuDGXAKDniP4rlgAAAACgCyg9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1ig9AAAAAGyN0gMAAADA1iwtPa+++qqKioqUlJSk9PR0zZgxw8qXAwCgQ8wmAOh54q36wS+88IJmz56te+65R5MmTZLX69WGDRusejkAADrEbAKAnsmS0uP1enXdddfpvvvu06xZs1ofP+6446x4OQAAOsRsAoCey5LSU1JSotLSUjmdTo0aNUp79uzRyJEjdd9992nYsGFBn9fc3Kzm5ubW72trayVJfr9ffr/fiqjdyu/3yxgTE1kl8lqNvNYir7ViJWdnMJuiP6tEXquR11rktVZXclpSer766itJ0u23367f/e53Ovroo3X//fdrwoQJ2rJlizIyMgI+b/78+brjjjsOe7y8vFwej8eKqN3K7/erpqZGxhg5ndG/RgR5rUVea5HXWjU1NZGO0O2YTbHx2SOvtchrLfJaqyuzqVOlZ+7cuVqwYEG7+2zcuLG1hd1222264IILJEmLFy9W37599b//+7+64oorAj731ltv1Q033ND6fW1trfr166fs7GylpaV1JmpE+P1+ORwOZWdnx8QHh7zWIq+1yGstl8sV6QghYza1L9Y+e+S1FnmtRV5rdWU2dar03HjjjZo5c2a7+xQUFOibb76R1PY8abfbrYKCAu3cuTPoc91ut9xu92GPO53OmPiNkCSHw0FeC5HXWuS1VizljYWMBzGbOhZLnz2JvFYjr7XIa52uZOxU6cnOzlZ2dnaH+xUWFsrtdmvz5s0aP368JKmlpUU7duzQgAEDjiwpAAABMJsAAB2x5JqelJQUXXnllZo3b5769eunAQMG6L777pMkXXjhhVa8JAAA7WI2AUDPZdl9eu677z7Fx8frkksuUVNTk4qKirRy5Uqlp6db9ZIAALSL2QQAPZNlpSchIUELFy7UwoULrXoJAAA6hdkEAD1T9F+xBAAAAABdQOkBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2Zlnp2bJli84991xlZWUpJSVF48eP19tvv23VywEA0CFmEwD0TJaVnunTp8vr9WrlypUqLi7WiBEjNH36dO3Zs8eqlwQAoF3MJgDomSwpPfv27dPWrVs1d+5cDR8+XIMHD9a9996rxsZGbdiwwYqXBACgXcwmAOi54q34oZmZmRoyZIiefPJJnXDCCXK73Vq0aJFycnJUWFgY9HnNzc1qbm5u/b6mpkaSVF1dbUXMbuf3+1VbWyuXyyWnM/ovlyKvtchrLfJa6+Cfu8aYyAbpRsym2Pjskdda5LUWea3VpdlkLLJr1y5TWFhoHA6HiYuLM7m5uaakpKTd58ybN89I4osvvvjiK0q+vvzyS6vGREQwm/jiiy++Yv/rSGaTw5jQq9LcuXO1YMGCdvfZuHGjhgwZohkzZqilpUW33XabkpKS9Pjjj+vll1/Wp59+qtzc3IDP/f6/plVXV2vAgAHauXOnUlNTQ40ZMbW1terXr5927dqllJSUSMfpEHmtRV5rkddaNTU16t+/v6qqqpSWlhbpOO1iNrUv1j575LUWea1FXmt1ZTZ1qvSUl5eroqKi3X0KCgr03nvvacqUKaqqqmrzBg4ePFizZs3S3LlzQ3q92tpapaamqqamJiZ+I8hrLfJai7zWIq91mE3tI6+1yGst8lqrJ+Xt1DU92dnZys7O7nC/xsZGSTrs3ECn0ym/39+ZlwQAoF3MJgBARyy5Yumkk05Senq6Lr30Uq1bt05btmzRTTfdpO3bt+vss8+24iUBAGgXswkAei5LSk9WVpbeeOMN1dfXa9KkSRo9erTef/99LVu2TCNGjAj557jdbs2bN09ut9uKmN2OvNYir7XIay3yRh6zibxWIK+1yGutnpS3U9f0AAAAAECsif4FuQEAAACgCyg9AAAAAGyN0gMAAADA1ig9AAAAAGwtZkrPli1bdO655yorK0spKSkaP3683n777UjHaterr76qoqIiJSUlKT09XTNmzIh0pJA0Nzdr5MiRcjgcWrt2baTjBLRjxw7NmjVLAwcOVFJSkgYNGqR58+bJ4/FEOlqrP/7xjzr66KOVmJiooqIiffLJJ5GOFND8+fN14oknKjk5WTk5OZoxY4Y2b94c6Vghu/fee+VwOHT99ddHOkpQpaWl+tnPfqbMzEwlJSXp+OOP1+rVqyMdKyCfz6ff/OY3bf7buvPOO8WaN4Exm8KDudR9mE3Wi4W5JPW82RQzpWf69Onyer1auXKliouLNWLECE2fPl179uyJdLSAXnjhBV1yySW67LLLtG7dOn3wwQf693//90jHCsnNN9+svLy8SMdo16ZNm+T3+7Vo0SJ9/vnneuCBB/Too4/q17/+daSjSZKeffZZ3XDDDZo3b55KSko0YsQITZ06VXv37o10tMO8++67mjNnjj7++GMtX75cLS0tmjJlihoaGiIdrUOffvqpFi1apOHDh0c6SlBVVVUaN26cEhIS9Prrr+uLL77Q/fffr/T09EhHC2jBggV65JFH9Ic//EEbN27UggUL9Nvf/lYPP/xwpKNFJWZTeDCXugezyXqxMJekHjqbTAwoLy83ksyqVataH6utrTWSzPLlyyOYLLCWlhaTn59vHn/88UhH6bTXXnvNDB061Hz++edGklmzZk2kI4Xst7/9rRk4cGCkYxhjjBkzZoyZM2dO6/c+n8/k5eWZ+fPnRzBVaPbu3WskmXfffTfSUdpVV1dnBg8ebJYvX25OO+00c91110U6UkC33HKLGT9+fKRjhOzss882P//5z9s8dv7555uLL744QomiF7MpPJhL3YfZZK1YmUvG9MzZFBNHejIzMzVkyBA9+eSTamhokNfr1aJFi5STk6PCwsJIxztMSUmJSktL5XQ6NWrUKOXm5mratGnasGFDpKO169tvv9Xs2bP117/+Vb169Yp0nE6rqalRRkZGpGPI4/GouLhYkydPbn3M6XRq8uTJ+uijjyKYLDQ1NTWSFBXvZXvmzJmjs88+u837HI1efvlljR49WhdeeKFycnI0atQo/fnPf450rKBOPvlkrVixQlu2bJEkrVu3Tu+//76mTZsW4WTRh9lkPeZS92E2WS9W5pLUQ2dTdzcxq+zatcsUFhYah8Nh4uLiTG5urikpKYl0rICWLl1qJJn+/fub559/3qxevdr89Kc/NZmZmaaioiLS8QLy+/3mzDPPNHfeeacxxpjt27fH1L+obd261aSkpJjHHnss0lFMaWmpkWQ+/PDDNo/fdNNNZsyYMRFKFRqfz2fOPvtsM27cuEhHadfSpUvNsGHDTFNTkzHGRPW/qLndbuN2u82tt95qSkpKzKJFi0xiYqJZsmRJpKMF5PP5zC233GIcDoeJj483DofD3HPPPZGOFbWYTdZhLnUvZpO1YmkuGdMzZ1NES88tt9xiJLX7tXHjRuP3+80555xjpk2bZt5//31TXFxsrrrqKpOfn2/KysqiLu/TTz9tJJlFixa1Pnf//v0mKyvLPProo2HL25nMDz74oBk3bpzxer3GmMgNl1DzHmr37t1m0KBBZtasWWHNGkwsD5Yrr7zSDBgwwOzatSvSUYLauXOnycnJMevWrWt9LJqHS0JCgjnppJPaPHbNNdeYsWPHRihR+5YuXWr69u1rli5datavX2+efPJJk5GREbWD0ArMpujIy1zqXswm68TaXDKmZ84mhzGRW5KnvLxcFRUV7e5TUFCg9957T1OmTFFVVZVSUlJatw0ePFizZs3S3LlzrY4qKfS8H3zwgSZNmqT33ntP48ePb91WVFSkyZMn6+6777Y6aqtQM1900UV65ZVX5HA4Wh/3+XyKi4vTxRdfrCeeeMLqqJJCz+tyuSRJZWVlmjBhgsaOHaslS5bI6Yz8GZsej0e9evXS888/32ZVpEsvvVTV1dVatmxZ5MK14+qrr9ayZcu0atUqDRw4MNJxgnrppZd03nnnKS4urvUxn88nh8Mhp9Op5ubmNtsibcCAATrjjDP0+OOPtz72yCOP6K677lJpaWkEkwXWr18/zZ07V3PmzGl97K677tJTTz2lTZs2RTBZ+DCbrMVcigxmk3VibS5JPXM2xVsVLhTZ2dnKzs7ucL/GxkZJOuwPDqfTKb/fb0m2QELNW1hYKLfbrc2bN7cOlpaWFu3YsUMDBgywOmYboWZ+6KGHdNddd7V+X1ZWpqlTp+rZZ59VUVGRlRHbCDWvdGCpxYkTJ6qwsFCLFy+OmsHicrlUWFioFStWtA4Wv9+vFStW6Oqrr45suACMMbrmmmv04osv6p133onaoXLQ6aefrs8++6zNY5dddpmGDh2qW265JeoGy7hx4w5bZnXLli1h/7MgVI2NjYf9txQXFxfWP2sjjdlkLeZSZDCbrBNrc0nqobPJoqNQ3aq8vNxkZmaa888/36xdu9Zs3rzZ/OpXvzIJCQlm7dq1kY4X0HXXXWfy8/PNm2++aTZt2mRmzZplcnJyTGVlZaSjhSTaz53evXu3OeaYY8zpp59udu/ebb755pvWr2jwzDPPGLfbbZYsWWK++OILc/nll5u0tDSzZ8+eSEc7zFVXXWVSU1PNO++80+Z9bGxsjHS0kEXzaQSffPKJiY+PN3fffbfZunWrefrpp02vXr3MU089FeloAV166aUmPz/f/P3vfzfbt283f/vb30xWVpa5+eabIx0t6jCbwou51HXMpvCJ5rlkTM+cTTFReowx5tNPPzVTpkwxGRkZJjk52YwdO9a89tprkY4VlMfjMTfeeKPJyckxycnJZvLkyWbDhg2RjhWyaB8uixcvDnpudbR4+OGHTf/+/Y3L5TJjxowxH3/8caQjBRTsfVy8eHGko4Us2ofLK6+8YoYNG2bcbrcZOnRo1FzYHEhtba257rrrTP/+/U1iYqIpKCgwt912m2lubo50tKjEbAof5lL3YDaFR7TPJWN63myK6DU9AAAAAGC16DnZFAAAAAAsQOkBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGv/HyBzfPNCTB/WAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_source_and_target(sour, targ, left_title=\"Source distribution\", right_title=\"Target distribution\", save_path=None):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].scatter(*sour.T, c='#D27685', s=10, alpha=0.6)\n",
    "    axs[1].scatter(*targ.T, c='#37306B', s=10, alpha=0.6)\n",
    "    axs[0].set_title(left_title)\n",
    "    axs[1].set_title(right_title)\n",
    "    axs[0].grid(True, alpha=0.3)\n",
    "    axs[1].grid(True, alpha=0.3)\n",
    "    axs[0].set_xlim(-8, 8)\n",
    "    axs[0].set_ylim(-8, 8)\n",
    "    axs[1].set_xlim(-8, 8)\n",
    "    axs[1].set_ylim(-8, 8)\n",
    "    fig.show()\n",
    "    if save_path is not None:\n",
    "        fig.savefig(save_path)\n",
    "\n",
    "plot_source_and_target(source_sample, target_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_gaussian2d(bridge, colors=True):\n",
    "    def get_color(point):\n",
    "        x, y = point\n",
    "        if x > 0 and y > 0:\n",
    "            return 0\n",
    "        elif x > 0 and y < 0:\n",
    "            return 1\n",
    "        elif x < 0 and y > 0:\n",
    "            return 2\n",
    "        else:\n",
    "            return 3\n",
    "\n",
    "    # 生成数据\n",
    "    color_list = ['#37306B', '#66347F', '#9E4784', '#D27685']  # 对应于目标均值的颜色\n",
    "\n",
    "    # 创建3D图形\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    # 为每个样本绘制bridge\n",
    "    for i in range(bridge.shape[1]):\n",
    "        x = bridge[:, i, 0].numpy()  # X坐标\n",
    "        y = bridge[:, i, 1].numpy()  # Y坐标\n",
    "        z = ts.numpy()               # 时间作为Z坐标\n",
    "        c = color_list[get_color((x[-1], y[-1]))] if colors else '#D27685'\n",
    "        ax.plot(x, y, z, color=c, label=f'Sample {i+1}', alpha=0.3)\n",
    "\n",
    "    # 添加标签和图例\n",
    "    ax.set_xlabel('X')\n",
    "    ax.set_ylabel('Y')\n",
    "    ax.set_zlabel('Time')\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_gaussian2d_gif(bridge):\n",
    "    def get_color(point):\n",
    "        x, y = point\n",
    "        if x > 0 and y > 0:\n",
    "            return 0\n",
    "        elif x > 0 and y < 0:\n",
    "            return 1\n",
    "        elif x < 0 and y > 0:\n",
    "            return 2\n",
    "        else:\n",
    "            return 3\n",
    "    colors = ['b', 'g', 'r', 'c']  # 对应于目标均值的颜色\n",
    "    fig, ax = plt.subplots(figsize=(10, 8))\n",
    "    ax.set_xlabel('X')\n",
    "    ax.set_ylabel('Y')\n",
    "    ax.set_xlim(-10, 10)\n",
    "    ax.set_ylim(-10, 10)\n",
    "    # 定义更新函数\n",
    "    def update(frame):\n",
    "        ax.clear()\n",
    "        ax.set_xlabel('X')\n",
    "        ax.set_ylabel('Y')\n",
    "        ax.set_xlim(-10, 10)\n",
    "        ax.set_ylim(-10, 10)\n",
    "        for i in range(bridge.shape[1]):\n",
    "            x = bridge[:frame + 1, i, 0].numpy()  # 注意：frame + 1\n",
    "            y = bridge[:frame + 1, i, 1].numpy()  # 注意：frame + 1\n",
    "            if x.size > 0 and y.size > 0:  # 确保x和y不为空\n",
    "                color_index = get_color((x[-1], y[-1]))\n",
    "                ax.plot(x, y, color=colors[color_index], alpha=0.5)\n",
    "                ax.scatter(x[-1], y[-1], color=colors[color_index], alpha=1, s=10)\n",
    "    # 创建动画\n",
    "    ani = animation.FuncAnimation(fig, update, frames=len(ts), interval=100)\n",
    "    # 保存为GIF需要花费6分钟左右\n",
    "    ani.save(log_dir / 'brownian_bridge.gif', writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000000, 1, 7])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples = 10000\n",
    "ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=num_samples)\n",
    "# print_debug(ts, bridge, drift, source_sample, target_sample)\n",
    "start = torch.cat(torch.split(source_sample.repeat(len(ts)-1, 1, 1), 1, dim=1), dim=0)\n",
    "times = ts[:len(ts)-1].repeat(num_samples, 1).reshape(-1, 1, 1)\n",
    "positions = torch.cat(torch.split(bridge[:-1, :], 1, dim=1), dim=0)\n",
    "\n",
    "scores = torch.cat(torch.split(drift[:-1, :], 1, dim=1), dim=0)\n",
    "# print_debug(start, times, positions, scores)\n",
    "raw_data = torch.concat([start, times, positions, scores], dim=-1)\n",
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算均值和标准差\n",
    "mean = torch.mean(raw_data, dim=0)\n",
    "std = torch.std(raw_data, dim=0)\n",
    "\n",
    "# 使用Z-Score标准化进行归一化\n",
    "normalized_data = (raw_data - mean) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">==========<span style=\"color: #808000; text-decoration-color: #808000\">model</span>==========\n",
       "</pre>\n"
      ],
      "text/plain": [
       "==========\u001b[33mmodel\u001b[0m==========\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">batch_szi<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e:2000</span>00, channel:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, length:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "batch_szi\u001b[1;92me:2000\u001b[0m00, channel:\u001b[1;36m1\u001b[0m, length:\u001b[1;36m7\u001b[0m\n"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">MLP</span><span style=\"font-weight: bold\">(</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcin<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcs<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ModuleList</span><span style=\"font-weight: bold\">(</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcout<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>relu<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ReLU</span><span style=\"font-weight: bold\">()</span>\n",
       "<span style=\"font-weight: bold\">)</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mMLP\u001b[0m\u001b[1m(\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcin\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcs\u001b[1m)\u001b[0m: \u001b[1;35mModuleList\u001b[0m\u001b[1m(\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcout\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m2\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mrelu\u001b[1m)\u001b[0m: \u001b[1;35mReLU\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 50\n",
    "batch_size = 200000\n",
    "lr = 1e-4\n",
    "\n",
    "# 组合成data\n",
    "train_ds = BBdataset(normalized_data)\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=20)\n",
    "\n",
    "model = MLP(input_dim=5, output_dim=2, hidden_layers=2, hidden_dim=256).to(device)\n",
    "loss_fn = nn.MSELoss()\n",
    "loss_list = []\n",
    "print('='*10+'model'+'='*10)\n",
    "print(\"batch_szie:{}, channel:{}, length:{}\".format(*next(train_dl.__iter__()).shape)) # batch_szie, channel, length\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4142479646dd43449804255d35dad09d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training (lr: X)  (loss= X):   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train(model, train_dl, optimizer, scheduler, loss_fn):\n",
    "    losses = 0\n",
    "    for training_data in train_dl:\n",
    "        training_data = training_data.to(device)\n",
    "        # x: (batch_size, channel, length-2), y: (batch_size, channel, 2)\n",
    "        x, y = training_data[:, 0, :-2], training_data[:, 0, -2:] # 不同模型要改\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "\n",
    "        pred = model(x)\n",
    "        loss = loss_fn(pred, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        losses += loss.item() / len(train_dl)\n",
    "        \n",
    "    return losses\n",
    "from torch.optim.lr_scheduler import OneCycleLR\n",
    "\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=20)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)\n",
    "epochs = 100\n",
    "# scheduler = OneCycleLR(optimizer, max_lr=1e-4, total_steps=epochs*len(train_dl))\n",
    "scheduler = None\n",
    "\n",
    "epoch_iterator = tqdm(range(epochs), desc=\"Training (lr: X)  (loss= X)\", dynamic_ncols=True)\n",
    "model.train()\n",
    "model.to(device)\n",
    "for e in epoch_iterator:\n",
    "    now_loss = train(model ,train_dl, optimizer, scheduler, loss_fn)\n",
    "    loss_list.append(now_loss)\n",
    "    cur_lr = optimizer.param_groups[-1]['lr']\n",
    "    epoch_iterator.set_description(\"Training (lr: %2.5f)  (loss=%2.5f)\" % (cur_lr, now_loss))\n",
    "epoch_iterator.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), log_dir / 'model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n5+dDlhuzU1xcHD799FM8+uijGDlyJHr27In58+fjqaeeUtucOXMG9957L0pKStCjRw+MHz8eX3/9NXr06NEKXbw2jevkMOUQERFpiSQC4NPbZrPBYrGgvLwcYWFhrXru9PcP4p3dBUibOAi/nTCwVc9NRETUmbXl5zfAZ1d5JXHFYyIiIk1iyPGCKx4TERFpE0OOF+qcHD/XQURERL5hyPGi8e4qxhwiIiItYcjxgk8hJyIi0iaGnBbinBwiIiJtYcjxgnNyiIiItIkhx4uGOTkcySEiItIWhhwvGp5dxaEcIiIibWHI8aJhMUCO5BAREWkLQ44XfEAnERGRNjHkeCGhYSTHz4UQERGRTxhyvGiYkyM4KYeIiEhTGHK84OUqIiIibWLI8aJxxWOmHCIiIi1hyPGi8Snkfi2DiIiIfMSQ44WkrnjMlENERKQlDDleNK547N86iIiIyDcMOV7wKeRERETaxJDjhfpUB6YcIiIiTWHI8UKWOZJDRESkRQw5LcRnVxEREWkLQ44X6pwcP9dBREREvmHI8aLx7irGHCIiIi1hyPFC5mMdiIiINIkhx4uGp5Dz7ioiIiJtYcjxgosBEhERaRNDjhcNE485J4eIiEhbGHK80OsYcoiIiLSIIccLXf3MY5ebIYeIiEhLGHK80NeHHDcn5RAREWkKQ44XOrnur8jFkENERKQpDDlecCSHiIhImxhyvFDn5CiKnyshIiIiX1xVyFmxYgXi4+NhNpuRmJiI3bt3X7F9WVkZ5s2bh5iYGJhMJgwaNAibNm26pnO2F47kEBERaZPPIWfdunVIS0vDkiVLsG/fPowaNQopKSkoKipqtr3D4cDEiRORl5eH9evXIycnBytXrkTPnj2v+pztqXEkhyGHiIhIS3wOOcuWLcOcOXOQmpqKYcOGITMzE8HBwVi1alWz7VetWoXS0lJs2LABN910E+Lj43Hrrbdi1KhRV33O9tSwTg5HcoiIiLTFp5DjcDiwd+9eJCcnN55AlpGcnIydO3c2e8xHH32EpKQkzJs3D1FRURg+fDheeOEFuN3uqz6n3W6HzWbzeLUV9e4qrpNDRESkKT6FnAsXLsDtdiMqKspje1RUFKxWa7PHnDx5EuvXr4fb7camTZuwaNEi/OlPf8Lzzz9/1efMyMiAxWJRX3Fxcb50wyeck0NERKRNbX53laIoiIyMxD/+8Q8kJCRg+vTp+P3vf4/MzMyrPmd6ejrKy8vVV0FBQStW7Il3VxEREWmT3pfGERER0Ol0KCws9NheWFiI6OjoZo+JiYmBwWCATqdTtw0dOhRWqxUOh+OqzmkymWAymXwp/apxJIeIiEibfBrJMRqNSEhIQHZ2trpNURRkZ2cjKSmp2WNuuukmHD9+HMolIyG5ubmIiYmB0Wi8qnO2p4aRHDcf0ElERKQpPl+uSktLw8qVK7FmzRocPXoUc+fORVVVFVJTUwEAs2bNQnp6utp+7ty5KC0txfz585Gbm4uNGzfihRdewLx581p8Tn/S1088dnPiMRERkab4dLkKAKZPn47i4mIsXrwYVqsVo0ePRlZWljpxOD8/H7LcmJ3i4uLw6aef4tFHH8XIkSPRs2dPzJ8/H0899VSLz+lPXCeHiIhImyQhtH8dxmazwWKxoLy8HGFhYa167tzCCtz+5y/QvYsRexdNbNVzExERdWZt+fkN8NlVXnEkh4iISJsYcrzg3VVERETaxJDjhSxxnRwiIiItYsjxgs+uIiIi0iaGHC84J4eIiEibGHK8aFgnRwhAYdAhIiLSDIYcLxpGcgCO5hAREWkJQ44X+ktCDuflEBERaQdDjheeIzm8w4qIiEgrGHK84EgOERGRNjHkeME5OURERNrEkOOFJElq0OFIDhERkXYw5LQA18ohIiLSHoacFlCfX+VmyCEiItIKhpwWaBzJ4d1VREREWsGQ0wINIzmK4EgOERGRVjDktICu/tEOnJNDRESkHQw5LdAwkuPinBwiIiLNYMhpAd5CTkREpD0MOS2g1/EWciIiIq1hyGkB9e4qN++uIiIi0gqGnBYw6XUAALuLIYeIiEgrGHJawGyo+2uqcbr9XAkRERG1FENOCwQZ6kZyahlyiIiINIMhpwXM9SHH7uTlKiIiIq1gyGkBXq4iIiLSHoacFjDzchUREZHmMOS0QGPI4eUqIiIirWDIaQFz/S3kvFxFRESkHQw5LdAwJ4eXq4iIiLSDIacFGm4ht7sYcoiIiLSCIacFOCeHiIhIexhyWkC9hdzBkRwiIiKtYMhpAXUkh5eriIiINOOqQs6KFSsQHx8Ps9mMxMRE7N69+7JtV69eDUmSPF5ms9mjzQMPPNCkzaRJk66mtDbBdXKIiIi0R+/rAevWrUNaWhoyMzORmJiI5cuXIyUlBTk5OYiMjGz2mLCwMOTk5KhfS5LUpM2kSZPw5ptvql+bTCZfS2szDSGnhnNyiIiINMPnkLNs2TLMmTMHqampAIDMzExs3LgRq1atwsKFC5s9RpIkREdHX/G8JpPJa5sGdrsddrtd/dpms7Ww+quj3l3FkRwiIiLN8OlylcPhwN69e5GcnNx4AllGcnIydu7cednjKisr0adPH8TFxWHq1Kk4cuRIkzZbt25FZGQkBg8ejLlz56KkpOSy58vIyIDFYlFfcXFxvnTDZ1wnh4iISHt8CjkXLlyA2+1GVFSUx/aoqChYrdZmjxk8eDBWrVqFDz/8EG+//TYURcGNN96IM2fOqG0mTZqEt956C9nZ2XjxxRexbds2TJ48GW5386EiPT0d5eXl6qugoMCXbvis8XIVQw4REZFW+Hy5yldJSUlISkpSv77xxhsxdOhQvPbaa3juuecAADNmzFD3jxgxAiNHjkT//v2xdetWTJgwock5TSZTu87ZaRzJ4ZwcIiIirfBpJCciIgI6nQ6FhYUe2wsLC1s8n8ZgMGDMmDE4fvz4Zdv069cPERERV2zTnnh3FRERkfb4FHKMRiMSEhKQnZ2tblMUBdnZ2R6jNVfidrtx6NAhxMTEXLbNmTNnUFJScsU27cmsPtZBgaIIP1dDRERELeHzOjlpaWlYuXIl1qxZg6NHj2Lu3LmoqqpS77aaNWsW0tPT1fbPPvssNm/ejJMnT2Lfvn247777cPr0aTz88MMA6iYlP/HEE/j666+Rl5eH7OxsTJ06FQMGDEBKSkordfPaNIQcoC7oEBERUcfn85yc6dOno7i4GIsXL4bVasXo0aORlZWlTkbOz8+HLDdmp4sXL2LOnDmwWq3o2rUrEhISsGPHDgwbNgwAoNPpcPDgQaxZswZlZWWIjY3F7bffjueee67DrJVj1jf2p9bpRpBRd4XWRERE1BFIQgjNX3+x2WywWCwoLy9HWFhYm3yPgb/fBKdbYMfCnyA2PKhNvgcREVFn0taf33x2VQuZ9Zx8TEREpCUMOS1kUu+w4pwcIiIiLWDIaaEgY/1aOXwSORERkSYw5LSQernKwZBDRESkBQw5LaQuCMiRHCIiIk1gyGmhIM7JISIi0hSGnBYy1T+/qoaXq4iIiDSBIaeFeLmKiIhIWxhyWijEVLc4dGWty8+VEBERUUsw5LRQty5GAEBplcPPlRAREVFLMOS0UPeQupBTwpBDRESkCQw5LdS9fiSnpNLu50qIiIioJRhyWqh7l7onovNyFRERkTYw5LRQN16uIiIi0hSGnBaKqB/JKalkyCEiItIChpwWigitG8mpcbpRUev0czVERETkDUNOCwUb9QgPNgAAzpXV+rkaIiIi8oYhxwexliAAwLmyGj9XQkRERN4w5PigZ9e6kHOGIYeIiKjDY8jxQc9wjuQQERFpBUOODxpCztmLDDlEREQdHUOOD2IbQg5HcoiIiDo8hhwfNMzJ4eUqIiKijo8hxwex4WYAQKGtFk634udqiIiI6EoYcnwQ0cUEo16GIgBrOdfKISIi6sgYcnwgyxJiLXWjOZyXQ0RE1LEx5PioYV4O77AiIiLq2BhyfMRVj4mIiLSBIcdH6kgOQw4REVGHxpDjI66VQ0REpA0MOT6K6xoMADhdUu3nSoiIiOhKGHJ81D+yCwCg4GI1ap1uP1dDREREl8OQ46MeISaEmfUQAjh1ocrf5RAREdFlXFXIWbFiBeLj42E2m5GYmIjdu3dftu3q1ashSZLHy2w2e7QRQmDx4sWIiYlBUFAQkpOTcezYsasprc1JkoQBkSEAgONFlX6uhoiIiC7H55Czbt06pKWlYcmSJdi3bx9GjRqFlJQUFBUVXfaYsLAwnD9/Xn2dPn3aY/9LL72EV155BZmZmdi1axe6dOmClJQU1NZ2zFWFGXKIiIg6Pp9DzrJlyzBnzhykpqZi2LBhyMzMRHBwMFatWnXZYyRJQnR0tPqKiopS9wkhsHz5cvzhD3/A1KlTMXLkSLz11ls4d+4cNmzYcFWdamtqyClmyCEiIuqofAo5DocDe/fuRXJycuMJZBnJycnYuXPnZY+rrKxEnz59EBcXh6lTp+LIkSPqvlOnTsFqtXqc02KxIDEx8bLntNvtsNlsHq/21BByTnAkh4iIqMPyKeRcuHABbrfbYyQGAKKiomC1Wps9ZvDgwVi1ahU+/PBDvP3221AUBTfeeCPOnDkDAOpxvpwzIyMDFotFfcXFxfnSjWvWv0ddyDl5oQpuRbTr9yYiIqKWafO7q5KSkjBr1iyMHj0at956K95//3306NEDr7322lWfMz09HeXl5eqroKCgFSv2rlfXYBj1MhwuBWcucr0cIiKijsinkBMREQGdTofCwkKP7YWFhYiOjm7ROQwGA8aMGYPjx48DgHqcL+c0mUwICwvzeLUnnSyhX0TdejmcfExERNQx+RRyjEYjEhISkJ2drW5TFAXZ2dlISkpq0TncbjcOHTqEmJgYAEDfvn0RHR3tcU6bzYZdu3a1+Jz+0DAv58i59p0PRERERC3j8+WqtLQ0rFy5EmvWrMHRo0cxd+5cVFVVITU1FQAwa9YspKenq+2fffZZbN68GSdPnsS+fftw33334fTp03j44YcB1N15tWDBAjz//PP46KOPcOjQIcyaNQuxsbGYNm1a6/SyDYzqFQ4AWPnlSZTXOP1bDBERETWh9/WA6dOno7i4GIsXL4bVasXo0aORlZWlThzOz8+HLDdmp4sXL2LOnDmwWq3o2rUrEhISsGPHDgwbNkxt8+STT6KqqgqPPPIIysrKMH78eGRlZTVZNLAjuT+pD1Z+eRJFFXYcKCjDrYN6+LskIiIiuoQkhND87UE2mw0WiwXl5eXtOj9n3r/2YePB81g4eQh+eWv/dvu+REREgaCtP7/57KprMCym7g05ep7zcoiIiDoahpxrMLynBQCw51QpAmBAjIiIKKAw5FyDcfHdYNTLOFdei2O8lZyIiKhDYci5BkFGHX7UrzsAYFtOsZ+rISIioksx5FyjhruqtuZe/insRERE1P4Ycq7RT4ZEAgB2nCjB91ZOQCYiIuooGHKuUd+ILpg8PBpCAGt25Pm7HCIiIqrHkNMKfp7YGwCw+UghnG7Fz9UQERERwJDTKn7UrzsiQowoqXJg9Vd5/i6HiIiIwJDTKgw6GY/fPhgAsHpHHhSFa+YQERH5G0NOK5k2pidCTHqcLavBvvyL/i6HiIio02PIaSVmgw7JQ+vutNr8XaGfqyEiIiKGnFY0cVg0AGDLd4V8zAMREZGfMeS0olsH94BRJ+PUhSqcKOZjHoiIiPyJIacVhZj0SOpf95iHT4/wkhUREZE/MeS0spTr6i5ZfXTgHC9ZERER+RFDTiu7/boo6GQJOYUVeHXbCX+XQ0RE1Gkx5LSyiBAT5t3WHwDwwb6zfq6GiIio82LIaQMP3NQXAHCsqBLHiypRVu3wc0VERESdD0NOG+jWxYihMWEAgORl2zBp+Zeodbr9XBUREVHnwpDTRmbWP7QTAKy2Wnx1/IIfqyEiIup8GHLayD1je6lPJweA/9t3xo/VEBERdT4MOW3EpNfhhbtG4JP5N0OSgE2HrDhexAUCiYiI2gtDThsbGhOGG/p0AwAcOlvm32KIiIg6EYacdjAwKgQAkFvIkRwiIqL2wpDTDgZFhQIAXt16AofOlPu5GiIios6BIacd3BDfTf1z2rsH+LgHIiKidsCQ0w6GxYbh3V8kAahbIPDIOZufKyIiIgp8DDntZFzfbki5LgoA8GLW9yiptPu5IiIiosDGkNOOfp7YB5IEfHnsAma/uZuXrYiIiNoQQ047unVQD7wxeywA4PBZG7ZzFWQiIqI2w5DTzn4yJAozbogDANz/xm4UlFb7uSIiIqLAxJDjBz/q113985+35PqxEiIiosDFkOMHNw5oDDnv7z+Lh9fs4URkIiKiVnZVIWfFihWIj4+H2WxGYmIidu/e3aLj1q5dC0mSMG3aNI/tDzzwACRJ8nhNmjTpakrThMhQM9b/Mkn9+j9Hi/CPL0/6sSIiIqLA43PIWbduHdLS0rBkyRLs27cPo0aNQkpKCoqKiq54XF5eHh5//HHcfPPNze6fNGkSzp8/r77eeecdX0vTlLHx3fDfI2PUr9/eeRqVdpcfKyIiIgosPoecZcuWYc6cOUhNTcWwYcOQmZmJ4OBgrFq16rLHuN1uzJw5E8888wz69evXbBuTyYTo6Gj11bVrV19L05y//fx6nHzhvxDfPRhVDjcmLtsGp1vxd1lEREQBwaeQ43A4sHfvXiQnJzeeQJaRnJyMnTt3Xva4Z599FpGRkXjooYcu22br1q2IjIzE4MGDMXfuXJSUlFy2rd1uh81m83hplSxLWHLndQCA8+W1+I6rIRMREbUKn0LOhQsX4Ha7ERUV5bE9KioKVqu12WO2b9+ON954AytXrrzseSdNmoS33noL2dnZePHFF7Ft2zZMnjwZbre72fYZGRmwWCzqKy4uzpdudDg/HhyJmwdGAABW78jDmYu8rZyIiOhatendVRUVFbj//vuxcuVKREREXLbdjBkzcOedd2LEiBGYNm0aPv74Y+zZswdbt25ttn16ejrKy8vVV0FBQRv1oP2M6GkBAHyw/yweXvONn6shIiLSPr0vjSMiIqDT6VBYWOixvbCwENHR0U3anzhxAnl5ebjjjjvUbYpSN+dEr9cjJycH/fv3b3Jcv379EBERgePHj2PChAlN9ptMJphMJl9K7/AmD4/B37eeAAB8b61AcYUdPUIDq49ERETtyaeRHKPRiISEBGRnZ6vbFEVBdnY2kpKSmrQfMmQIDh06hAMHDqivO++8Ez/+8Y9x4MCBy15mOnPmDEpKShATE9Ps/kA0opcFH/9mvPr1/2Tu4LOtiIiIroFPIzkAkJaWhtmzZ2Ps2LEYN24cli9fjqqqKqSmpgIAZs2ahZ49eyIjIwNmsxnDhw/3OD48PBwA1O2VlZV45plncPfddyM6OhonTpzAk08+iQEDBiAlJeUau6ctw3ta8Itb+uG1L07idEk1Xt12AiEmPWbc0BtGPddtJCIi8oXPIWf69OkoLi7G4sWLYbVaMXr0aGRlZamTkfPz8yHLLf9A1ul0OHjwINasWYOysjLExsbi9ttvx3PPPRdwl6Ra4tGJg/DFsQs4et6Gl7JyAAA1Djd+cWvTy3pERER0eZIIgGsiNpsNFosF5eXlCAsL83c51+zIuXJMeWW7+vWY3uH44Fc3+bEiIiKi1tfWn9+8BtIBXRdrwS2DeqhfHygoQ1FFrR8rIiIi0h6GnA7q9Vlj8e2S2zEqLhxCAOOWZuOL3GJ/l0VERKQZDDkdlFEvwxJkwB2XPN/qmX8f8WNFRERE2sKQ08HNvjEeL/3PSADAieIqxC/ciPiFG/FK9jE/V0ZERNSxMeR0cAadjJ+NjUP/Hl08ti/bkst1dIiIiK6AIUcj7h3Xu8m2s2U1fqiEiIhIGxhyNOLhm/vh/+Z6rio9/sXPsfHgeT9VRERE1LEx5GhIQp9ueH7acESFNS6SuPD9g/jeasPzH3+Hi1UOP1ZHRETUsXAxQA0SQmDDgbN4dN23HtunjY7F8hlj/FQVERGRb7gYIDUhSRLuGtMLs5P6eGzfebIE1Q4XtuYUQVE0n12JiIiuCUOOht3/g5AjBDB/7QE88OYevLe3wE9VERERdQwMORo2IDIUeX+cgn89nAgAKKqwY8t3hQCA17ad9GdpREREfseQEwBuHBCBCUMiPbbxYhUREXV2DDkB4rHbB2NgZAgmD48GAJy6UIWMTUdRZOODPYmIqHPi3VUB6Lfv7MdH354DAISZ9Uj/r6HNLiZIRETkT7y7iny26L+H4eeJvREZaoKt1oX09w9hzLOb8fqXnKdDRESdB0NOAOoRasILd43A1iduw6z6O7AuVjuxZmeefwsjIiJqRww5ASzYqMezU4fjvV/WPQ6ioLQGSzd+hz9vyUWOtcLP1REREbUtvb8LoLZ3Q3w36GUJLkVg5ZenAABfHivGi3ePhN2lYHhPi58rJCIian0cyekkHrq5LwCgexcjAGBffhkm/vkL3P3qDj7zioiIAhJDTieRPnkovn9uEvYumogh0aHqdrtLwYGCMv8VRkRE1EYYcjoRs0EHAPjFrf08tqeu3oN/7joNu8uNsmqO6hARUWDgOjmdUMNTzL/IvYAP9p9Vt8dazCitdmDzglvRu3uwHyskIqLOgOvkUKtreIr50ruGY1hM4w/VufJa1DoV3PLy51j4fwcRAPmXiIg6MYacTizYqMfHvxnf7L61ewpQaLO3c0VERESthyGnk5NlCX0jugAAenUN8tj3o4xs3PHX7Ri6KAtZh8/7ozwiIqKrxpBDeO3+BMxK6oOPfj0evbt5zsU5dLYcNU43fvvOAf8UR0REdJUYcgiDokLx7NTh6NbFiHd/kYRRceFN2jjcCufoEBGRpjDkkIdoixnv/SIJ/XrUXcLqGd54Catv+iYs25KLWqfbX+URERG1GG8hp2aVVTtgq3Ghd/dgLPnwMNbsPO2xP3loFH7UrxsevrnfZc5ARER0ZW39+c2QQy2y+1Qpfvbazibbv39uEmqdbgQZdTDpdX6ojIiItIohpwUYctrHkXPlmPLK9mb33Ta4B1anjmvnioiISMu4GCB1GNfFWpo8EqLB1pxiDFn0Cf74yff40+YcztshIiK/0/u7ANKWn42Nw8ffnsfEYVEIMenx9q7TKKt2AgBqnQoyt50AABh0Mn47YaA/SyUiok7uqkZyVqxYgfj4eJjNZiQmJmL37t0tOm7t2rWQJAnTpk3z2C6EwOLFixETE4OgoCAkJyfj2LFjV1MatbH+PULw1cKf4Ok7r8PjKYOxf9FE7Fj4E/z3yBiPdsu25GJ//kU/VUlERHQVIWfdunVIS0vDkiVLsG/fPowaNQopKSkoKiq64nF5eXl4/PHHcfPNNzfZ99JLL+GVV15BZmYmdu3ahS5duiAlJQW1tbW+lkftTJIkxIYH4W8/vx5fPvljj313v7oDy/+Ti81HrKhx8PIVERG1L58nHicmJuKGG27A3/72NwCAoiiIi4vDb37zGyxcuLDZY9xuN2655RY8+OCD+PLLL1FWVoYNGzYAqBvFiY2NxWOPPYbHH38cAFBeXo6oqCisXr0aM2bM8FoTJx53HLVON65/bguqfxBqwoMN+J/reyG3qBI/GdwDs2+MhyRJfqqSiIg6gg418djhcGDv3r1ITk5uPIEsIzk5GTt3Nr29uMGzzz6LyMhIPPTQQ032nTp1Clar1eOcFosFiYmJlz2n3W6HzWbzeFHHYDbocPjpFDx9xzAY9TJM+rofsbJqJ17ffgpf5Bbj6X9/h6zDVgBA5rYTeODN3ah2uPxZNhERBSCfQs6FCxfgdrsRFRXlsT0qKgpWq7XZY7Zv34433ngDK1eubHZ/w3G+nDMjIwMWi0V9xcXF+dINamOyLOGBm/ri8NMpyHl+MmYm9m7SZu4/92HzESv++Mn32JpTjA8PnPNDpUREFMja9BbyiooK3H///Vi5ciUiIiJa7bzp6ekoLy9XXwUFBa12bmo9xvpRnLvG9PTYHh1mBgA88r971W15F6qw8eB5/DX7GBRF80s3ERFRB+DTLeQRERHQ6XQoLCz02F5YWIjo6Ogm7U+cOIG8vDzccccd6jZFUeq+sV6PnJwc9bjCwkLExDTeoVNYWIjRo0c3W4fJZILJZPKldPKjhD5dMbKXBQfPlOO9XyZhUGQoRj272aPNa1+cVP88Mi4ctw7q0d5lEhFRgPFpJMdoNCIhIQHZ2dnqNkVRkJ2djaSkpCbthwwZgkOHDuHAgQPq684778SPf/xjHDhwAHFxcejbty+io6M9zmmz2bBr165mz0naI0kS/vehRHw47ybcEN8NlmDDFdv/7bNj+NeufNhqndh5ogTHCivaqVIiIgokPi8GmJaWhtmzZ2Ps2LEYN24cli9fjqqqKqSmpgIAZs2ahZ49eyIjIwNmsxnDhw/3OD48PBwAPLYvWLAAzz//PAYOHIi+ffti0aJFiI2NbbKeDmmXJciAUXHh6tdPpAzG/9ucgz//bDRyCivw6tYTSOzbDbtOlWJP3kXsybuI331wCAAQZtZj1++SEWTks7GIiKjlfA4506dPR3FxMRYvXgyr1YrRo0cjKytLnTicn58PWfZtqs+TTz6JqqoqPPLIIygrK8P48eORlZUFs9nsa3mkEXNv7Y+Zib0RHmyEEAK//clABBl1WPnFSSzddNSjra3WhWFLsvCvh3+EsCA9rou1+KlqIiLSEj6gkzoURRFYuuko3th+6rJt/t89o3DqQiXW7z2Dv8+8Hgl9urVjhURE1Fr4FPIWYMgJPJnbTiDvQhWemzYcSzcexeodec22M+plDIsJw52jYvHg+L7tWyQREV0ThpwWYMgJbCeKKzHhT9u8tuvTPRgLJw1Br67BGNGLl7SIiDq6DrXiMZE/9O8RgrSJgxDfPVjd9mjyILz5wA0e7U6XVGPuP/fhjr9tx5bvCvHnLbkY/+JnOHy2vL1LJiKiDoAjOaQpX+QWo9rhwqThdWsqrdp+Cs9+/N0Vj4kIMeGrhT+GSc+7s4iIOhJermoBhpzOraTSjv/+63acL7/yU+t/cUs/PDlpCHSyBCEEHxBKRORnDDktwJBDTreCzUcK8e43BRjeMwwGnYx3duej0GZv0lYnS+jdLRhvPTgOcd2CmzkbERG1B4acFmDIoct54r1v8d7eM83umzIiBitmXt/OFRERUYO2/vz2eTFAIi15PGUwBkaFQC/LTebufG+1+akqIiJqD7y7igJaVJgZj9zSHw+O74tf/3gAAOCRW/oBAGocbn+WRkREbYwjOdRpLEgeiHvG9oLDpeAfX5xEtZMhh4gokHEkhzoNvU5Gn+5d1Ad9VnMkh4gooDHkUKcTbKwbwHS4FLgVzc+7JyKiy2DIoU4n2Ni4KGC1w+XHSoiIqC0x5FCnY9LLaFgHsIbzcoiIAhZDDnU6kiQh2FA3msM7rIiIAhdDDnVKQfXzcjj5mIgocDHkUKcUZKz70WfIISIKXAw51CkFG+pGcni5iogocDHkUKfUuFYO764iIgpUDDnUKTXcRs67q4iIAhdDDnVKwVz1mIgo4DHkUKcUHmwEAJRWOfxcCRERtRWGHOqUosJMAIAiW62fKyEiorbCkEOdUlSYGQBQaLP7uRIiImorDDnUKUWG1oecCo7kEBEFKoYc6pQaL1dxJIeIKFAx5FCnFG1puFxVi/Iap5+rISKitsCQQ51SdJgZg6JC4FIElv8nF0IIf5dEREStjCGHOiVJkjB/wiAAwJtf5WHWqt1495sC5JdUQ1EYeIiIAoHe3wUQ+cuUkTEorhiGp//9Hb48dgFfHrsAADDpZYzoacHAqFD06hqEXl2DEB1mhiRJiA03I8YSBFmqC0pERNRxSSIAxultNhssFgvKy8sRFhbm73JIY44VViDrsBWbDltxoqgSDrdyxfZGfd0AqFEnIzLMhMhQEww6GWFmA2LDzXC6BXqEmmAJMiAixAhLkBGhZj0sQQaYDTooQqBbFyMMOg6kEmmZWxEorrCrc/zId239+c2QQ3QJtyKQV1KF/fllKCitxpmLNSi4WI3z5TWQIOF8eQ2c7tb5X8YSZIAlyIBQsx46WUKwUYcap4LuXYww6mTIMtAjxIQuJj0MOhmhZj3cikCtU0G/Hl0gAOgkCYOjQ6GTJehlCV27GGF3uiFJEsKDDJBljjYRtZVn/n0Eb36Vh3F9u+GXt/ZDRa0LvboGY0h0KLqYeKGkJdr685vvAtEldLKE/j1C0L9HSLP73YrAmYvVsLsU6GQJRTY7iipqUWV3o6TSjvIaJ2qcbtQ6Fdhqneq2iloXymucsLsUyBKgCKC8xtmmd3bpZAnBBh1MBhkmfd1/zXodzPVfmw0yKu0uhJkNcCoCkaEmRIeZYQkywO5yQ5YlhJr0CDHrIUsSHC4FliADDHoZ4fWjUgadjPIaB4SoW2DRqJehlyVU1LoQbTGjyu5C12AjwxYFpLW7CwAAu0+VYvepUnW7US9jVC8L8kqqkXJdFAZFhaK82om4bsEYGhOGgZEh/H+inTDkEPlAJ0vo072L+vXlwlBzhKgbhTHpZVysdqC0ygFbrQu2WieEEKiodUGSJNhqnBAA3G4FRRV21DjdcLoVlFQ6YNTLUARwvqwGBp2M4ko7CkqrYdDJcCkKap2Nl9rcikCF3YWKDrAUkFEnw6SXPQIXBBARYkLXLgZcrHbiYpUDUWFmdA8xwqSXIUGCWwgEGXQw6mUI0TD6pYdeJ0MnS7A73QgLMsDpVqCXZXTrYkRctyDIkgSTQYew+lGy0ioHIkPN6qVGomslhICuPqj0CDVBAtR/yDhcCvbkXQQAvP11fpNju3cx4rqeFoSa6z6Cz5fV4Mb+EegeYsRtgyNhd7lxoqgKQ2JCEWMxQ4KEoPqHCpNvrupy1YoVK/Dyyy/DarVi1KhR+Otf/4px48Y12/b999/HCy+8gOPHj8PpdGLgwIF47LHHcP/996ttHnjgAaxZs8bjuJSUFGRlZbWoHl6uIqpTXu2EXifBoJNRWuWoH1Vyw+5SYHe6UXvJf201zvpLZG4YZBlFFbWw2mpRUeuCWa+DWwhU1rpQ5XCh1umGXq4LZwBQUb9dUQTCg41wKwLW+ueAuRWhjlZ1JJIEGGQZkgTIkgS54b9y3Z/NBh3CzAa4FAVGvQ4OlxtOd938qVqnG7YaJ8KCDAgy6mB3KjDoJOh1dSNXBp0MvU6CXpbV7Yb6D0CdLOFCpV2duA4ATpdAeLABBp2Mc2U1cLgVhJr1iLUEocLuQqhJD0uwARernDDqZbjcCkLq53WVVTtR7XAhIsSEiBATgo06hNSHOVN9iNPLMpxuBU63gCQBtU43BIAwswF6WYIAUGV3weFWoJMkhJr1EADqPg0EhAAEAINOht3lRrdgI2y1ToQHG+FyCzjdCmRZgq7h71GWIEv1X8t1k/J1kgRJAmw1TigCqKitCwCWIAPCggww6WXYXQqMOhlVDhfsLgVhZj0KbbXqPDZZkqAIAXv9yKjTraB7fZ9lSYJbETDoJEiSBCEEFFEXPhr6IiBgkGU43ArKqp0IMevhcNX9Q0AC4HDX1SNJdT+3EupqvvRnRpbqLgW7FIFquxsmQ917XlbjxNjn/wNJAo4+Owlmgw5CCOSVVOPUhUqcuViDk8VVOHOxGvml1RgaE4bzZbU4dLYcNU63Tz+7Bp2Emwf2wMheFpRWObAn7yLsTjeemjwEKddFX8P/Ff7X4ebkrFu3DrNmzUJmZiYSExOxfPlyvPfee8jJyUFkZGST9lu3bsXFixcxZMgQGI1GfPzxx3jsscewceNGpKSkAKgLOYWFhXjzzTfV40wmE7p27dqimhhyiPxPCAG3IuBSBGRJQq3LDb0s4ezFGnQx6euClqvuUp7d6UaVw4WC0hoAQHiwAREhJuSXVqPKXveB13DO0ional1uhJj0KKt2wFbjAgA43QokCbC7FBh0dR/qFyodOFNajUqHC5f+ZpMkQPuzD+lqNASUa9FwN6W7mfPEWszYkT6hxedyuBR8e6YM+/Mv4uzFGnTrYkKVw4UTRZUorrTj4JlyhAfXhdmWGBgZguD6+T+hJj0E6v7h0SPEhPIaJ2IsZtw8sAeS+ndvcY3tqcOFnMTERNxwww3429/+BgBQFAVxcXH4zW9+g4ULF7boHNdffz2mTJmC5557DkBdyCkrK8OGDRt8q74eQw4RNRCicSTC6VbgUgSCDTqUVjvgcClQ6vcr9aGs4V//FXYXahxu6OS6+UduRcCkr5u3JFB3ScJW40RJpQPdQ+pGr5xuAZeiwOUWcLjr/utS6kZQXG4F7vpLlF2MuvqRk7pzNowE1DrdiLGYodfJqHG4ceZiDRQhIAEoq3EiyKCra6+TUGV3q3O4IsNMKKt2Ir+0Gg6Xovaz1umu6zigjugpAnArdUGw2lE3giBJgFIfSC1BBtQ43JDqP8glqW6UQ6qfhyUB6uhShd0FnSzBoJOgiLpzKPUjKJdz6fmaCwk/JEu4ZFSp9ejkpt+/NcLvzMTeWHrXiGs7ySVcbgV6nQxreS0+PHAWI3pZAAHsybuIb06XwlbrQq+uQdh48LzP5zbpZYwfEFE/705BfEQwiivsOF9ei4GRoXjgxni4FAXnympR7XBh4rCoNl8qo0OFHIfDgeDgYKxfvx7Tpk1Tt8+ePRtlZWX48MMPr3i8EAKfffYZ7rzzTmzYsAETJ04EUBdyNmzYAKPRiK5du+InP/kJnn/+eXTv3nzytNvtsNsbJxrYbDbExcUx5BARtTIhBOwuBWaDrn7uk9Tkg09cEhxd9eGp4bKlXpZhNtRdRmsIOg6XAodbQVD9ORvmtlTUuhBq1kOSgMpaF1yKQJBRB7O+bj6KLAFuIVBW7YSEugm+DZfkZEmqD1OAhLpk1XCeYKNOvdTXcBkM9W2LK+3QSXVzXho+DQUuueylALWuusu+0RYz7C4FSn3/BOrm1/hrzaySSjv2nr6IkxeqUFHrRLXDjTCzASVVdnQx6lFcacf7+85e9fmHxoTVX5I2oLjSjriuwfj7zOtb9c6xDnV31YULF+B2uxEVFeWxPSoqCt9///1ljysvL0fPnj1ht9uh0+nw97//XQ04ADBp0iT89Kc/Rd++fXHixAn87ne/w+TJk7Fz507odE0nW2VkZOCZZ57xpXQiIroKkiTBbKj7PXy5tZ0aRoBkSNB7mR+rk+sCRRDqGl46Gbzh+wCAKaT5E+kBRIW1fBJumNmg/rlrF2OT/ZGh3te4saDxHCZvHWxH3UNMuN3LnJwX7hqBc2U1cCsCtloX/nO0EKeKq2DUyzDoZAghEBsehE2Hz+N0STUMOkm9geHoeZvHuYptdgRrbAJ0u9xdFRoaigMHDqCyshLZ2dlIS0tDv379cNtttwEAZsyYobYdMWIERo4cif79+2Pr1q2YMKHptc709HSkpaWpXzeM5BAREVEjs0GHfpfcBZrQp/m5ro+nDIaiCHXi9akLVThQUAZbjRM1DWtzCe2t9O5TyImIiIBOp0NhYaHH9sLCQkRHXz5NyrKMAQMGAABGjx6No0ePIiMjQw05P9SvXz9ERETg+PHjzYYck8kEk8nkS+lERER0BZeu3dOvR4hHONIqnxaNMBqNSEhIQHZ2trpNURRkZ2cjKSmpxedRFMVjTs0PnTlzBiUlJYiJifGlPCIiIiKVz5er0tLSMHv2bIwdOxbjxo3D8uXLUVVVhdTUVADArFmz0LNnT2RkZAComz8zduxY9O/fH3a7HZs2bcL//u//4tVXXwUAVFZW4plnnsHdd9+N6OhonDhxAk8++SQGDBig3mJORERE5CufQ8706dNRXFyMxYsXw2q1YvTo0cjKylInI+fn50OWGweIqqqq8Ktf/QpnzpxBUFAQhgwZgrfffhvTp08HAOh0Ohw8eBBr1qxBWVkZYmNjcfvtt+O5557jJSkiIiK6anxAJxEREflFW39+80EuREREFJAYcoiIiCggMeQQERFRQGLIISIiooDEkENEREQBiSGHiIiIAhJDDhEREQUkhhwiIiIKSAw5REREFJB8fqxDR9SwaLPNZvNzJURERNRSDZ/bbfXwhYAIORUVFQCAuLg4P1dCREREviopKYHFYmn18wbEs6sURcG5c+cQGhoKSZJa7bw2mw1xcXEoKCjoFM/E6kz9ZV8DE/samDpTX4HO1d/y8nL07t0bFy9eRHh4eKufPyBGcmRZRq9evdrs/GFhYQH/g3apztRf9jUwsa+BqTP1Fehc/ZXltpkizInHREREFJAYcoiIiCggMeRcgclkwpIlS2AymfxdSrvoTP1lXwMT+xqYOlNfgc7V37bua0BMPCYiIiL6IY7kEBERUUBiyCEiIqKAxJBDREREAYkhh4iIiAISQw4REREFJIacK1ixYgXi4+NhNpuRmJiI3bt3+7skn33xxRe44447EBsbC0mSsGHDBo/9QggsXrwYMTExCAoKQnJyMo4dO+bRprS0FDNnzkRYWBjCw8Px0EMPobKysh170TIZGRm44YYbEBoaisjISEybNg05OTkebWprazFv3jx0794dISEhuPvuu1FYWOjRJj8/H1OmTEFwcDAiIyPxxBNPwOVytWdXvHr11VcxcuRIdUXUpKQkfPLJJ+r+QOlnc/74xz9CkiQsWLBA3RYo/X366achSZLHa8iQIer+QOlng7Nnz+K+++5D9+7dERQUhBEjRuCbb75R9wfS76f4+Pgm760kSZg3bx6AwHpv3W43Fi1ahL59+yIoKAj9+/fHc8895/EQznZ7bwU1a+3atcJoNIpVq1aJI0eOiDlz5ojw8HBRWFjo79J8smnTJvH73/9evP/++wKA+OCDDzz2//GPfxQWi0Vs2LBBfPvtt+LOO+8Uffv2FTU1NWqbSZMmiVGjRomvv/5afPnll2LAgAHi3nvvbeeeeJeSkiLefPNNcfjwYXHgwAHxX//1X6J3796isrJSbfPLX/5SxMXFiezsbPHNN9+IH/3oR+LGG29U97tcLjF8+HCRnJws9u/fLzZt2iQiIiJEenq6P7p0WR999JHYuHGjyM3NFTk5OeJ3v/udMBgM4vDhw0KIwOnnD+3evVvEx8eLkSNHivnz56vbA6W/S5YsEdddd504f/68+iouLlb3B0o/hRCitLRU9OnTRzzwwANi165d4uTJk+LTTz8Vx48fV9sE0u+noqIij/d1y5YtAoD4/PPPhRCB9d4uXbpUdO/eXXz88cfi1KlT4r333hMhISHiL3/5i9qmvd5bhpzLGDdunJg3b576tdvtFrGxsSIjI8OPVV2bH4YcRVFEdHS0ePnll9VtZWVlwmQyiXfeeUcIIcR3330nAIg9e/aobT755BMhSZI4e/Zsu9V+NYqKigQAsW3bNiFEXd8MBoN477331DZHjx4VAMTOnTuFEHWhUJZlYbVa1TavvvqqCAsLE3a7vX074KOuXbuK119/PWD7WVFRIQYOHCi2bNkibr31VjXkBFJ/lyxZIkaNGtXsvkDqpxBCPPXUU2L8+PGX3R/ov5/mz58v+vfvLxRFCbj3dsqUKeLBBx/02PbTn/5UzJw5UwjRvu8tL1c1w+FwYO/evUhOTla3ybKM5ORk7Ny504+Vta5Tp07BarV69NNisSAxMVHt586dOxEeHo6xY8eqbZKTkyHLMnbt2tXuNfuivLwcANCtWzcAwN69e+F0Oj36O2TIEPTu3dujvyNGjEBUVJTaJiUlBTabDUeOHGnH6lvO7XZj7dq1qKqqQlJSUsD2c968eZgyZYpHv4DAe1+PHTuG2NhY9OvXDzNnzkR+fj6AwOvnRx99hLFjx+Kee+5BZGQkxowZg5UrV6r7A/n3k8PhwNtvv40HH3wQkiQF3Ht74403Ijs7G7m5uQCAb7/9Ftu3b8fkyZMBtO97GxBPIW9tFy5cgNvt9vhhAoCoqCh8//33fqqq9VmtVgBotp8N+6xWKyIjIz326/V6dOvWTW3TESmKggULFuCmm27C8OHDAdT1xWg0Ijw83KPtD/vb3N9Hw76O5NChQ0hKSkJtbS1CQkLwwQcfYNiwYThw4EBA9RMA1q5di3379mHPnj1N9gXS+5qYmIjVq1dj8ODBOH/+PJ555hncfPPNOHz4cED1EwBOnjyJV199FWlpafjd736HPXv24Le//S2MRiNmz54d0L+fNmzYgLKyMjzwwAMAAutnGAAWLlwIm82GIUOGQKfTwe12Y+nSpZg5cyaA9v3sYcihgDRv3jwcPnwY27dv93cpbWbw4ME4cOAAysvLsX79esyePRvbtm3zd1mtrqCgAPPnz8eWLVtgNpv9XU6baviXLgCMHDkSiYmJ6NOnD959910EBQX5sbLWpygKxo4dixdeeAEAMGbMGBw+fBiZmZmYPXu2n6trW2+88QYmT56M2NhYf5fSJt59913885//xL/+9S9cd911OHDgABYsWIDY2Nh2f295uaoZERER0Ol0TWa2FxYWIjo62k9Vtb6Gvlypn9HR0SgqKvLY73K5UFpa2mH/Ln7961/j448/xueff45evXqp26Ojo+FwOFBWVubR/of9be7vo2FfR2I0GjFgwAAkJCQgIyMDo0aNwl/+8peA6+fevXtRVFSE66+/Hnq9Hnq9Htu2bcMrr7wCvV6PqKiogOrvpcLDwzFo0CAcP3484N7XmJgYDBs2zGPb0KFD1ctzgfr76fTp0/jPf/6Dhx9+WN0WaO/tE088gYULF2LGjBkYMWIE7r//fjz66KPIyMgA0L7vLUNOM4xGIxISEpCdna1uUxQF2dnZSEpK8mNlratv376Ijo726KfNZsOuXbvUfiYlJaGsrAx79+5V23z22WdQFAWJiYntXvOVCCHw61//Gh988AE+++wz9O3b12N/QkICDAaDR39zcnKQn5/v0d9Dhw55/M+1ZcsWhIWFNfmF3NEoigK73R5w/ZwwYQIOHTqEAwcOqK+xY8di5syZ6p8Dqb+XqqysxIkTJxATExNw7+tNN93UZImH3Nxc9OnTB0Dg/X5q8OabbyIyMhJTpkxRtwXae1tdXQ1Z9owXOp0OiqIAaOf39homUAe0tWvXCpPJJFavXi2+++478cgjj4jw8HCPme1aUFFRIfbv3y/2798vAIhly5aJ/fv3i9OnTwsh6m7jCw8PFx9++KE4ePCgmDp1arO38Y0ZM0bs2rVLbN++XQwcOLBD3qI5d+5cYbFYxNatWz1u1ayurlbb/PKXvxS9e/cWn332mfjmm29EUlKSSEpKUvc33KZ5++23iwMHDoisrCzRo0ePDneb5sKFC8W2bdvEqVOnxMGDB8XChQuFJEli8+bNQojA6eflXHp3lRCB09/HHntMbN26VZw6dUp89dVXIjk5WURERIiioiIhROD0U4i65QD0er1YunSpOHbsmPjnP/8pgoODxdtvv622CaTfT0LU3aXbu3dv8dRTTzXZF0jv7ezZs0XPnj3VW8jff/99ERERIZ588km1TXu9tww5V/DXv/5V9O7dWxiNRjFu3Djx9ddf+7skn33++ecCQJPX7NmzhRB1t/ItWrRIREVFCZPJJCZMmCBycnI8zlFSUiLuvfdeERISIsLCwkRqaqqoqKjwQ2+urLl+AhBvvvmm2qampkb86le/El27dhXBwcHirrvuEufPn/c4T15enpg8ebIICgoSERER4rHHHhNOp7Ode3NlDz74oOjTp48wGo2iR48eYsKECWrAESJw+nk5Pww5gdLf6dOni5iYGGE0GkXPnj3F9OnTPdaNCZR+Nvj3v/8thg8fLkwmkxgyZIj4xz/+4bE/kH4/CSHEp59+KgA06YMQgfXe2mw2MX/+fNG7d29hNptFv379xO9//3uPW93b672VhLhkCUIiIiKiAME5OURERBSQGHKIiIgoIDHkEBERUUBiyCEiIqKAxJBDREREAYkhh4iIiAISQw4REREFJIYcIiIiCkgMOURERBSQGHKIiIgoIDHkEBERUUD6/1RXcLxsRhMtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(loss_list)+1), loss_list)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_num_samples = 4000\n",
    "test_ts, test_bridge, test_drift, test_source_sample, test_target_sample = gen_2d_data(num_samples=test_num_samples)\n",
    "# print_debug(test_ts, test_bridge, test_drift, test_source_sample, test_target_sample)\n",
    "pred_bridge = torch.zeros_like(test_bridge)\n",
    "pred_bridge[0, :] = test_source_sample\n",
    "model.eval()\n",
    "sigma=1\n",
    "with torch.no_grad():\n",
    "    for i in range(len(test_ts) - 1):\n",
    "        dt = (test_ts[i+1] - test_ts[i])\n",
    "        test_source_sample_reshaped = test_source_sample.reshape(-1, 1, test_source_sample.shape[-1])\n",
    "        test_ts_reshaped = test_ts[i].reshape(-1, 1, 1).repeat(test_num_samples, 1, 1)\n",
    "        pred_bridge_reshaped = pred_bridge[i].reshape(-1, 1, pred_bridge.shape[-1])\n",
    "        x = torch.concat([test_source_sample_reshaped, test_ts_reshaped, pred_bridge_reshaped], axis=-1)\n",
    "        x = (x - mean[:, :-2]) / std[:, :-2]\n",
    "        dydt = model(x[:,0,:].to(device)).cpu()\n",
    "        # print_debug(dydt)\n",
    "        dydt = dydt * std[0,-2:] + mean[0,-2:]\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(test_num_samples, 2)\n",
    "        pred_bridge[i+1, :] = pred_bridge[i, :] + dydt * dt + diffusion\n",
    "        \n",
    "        \n",
    "        \n",
    "        # dt = ts[i+1] - ts[i]\n",
    "        # dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        # drift[i, :] = dydt\n",
    "        # diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        # bridge[i+1] = bridge[i] + dydt * dt + diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(pred_bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(pred_bridge[0, :, :], pred_bridge[-1, :, :], left_title=\"Source Sample\", right_title=\"Predict Sample\", save_path=log_dir / 'pred_bridge.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j0hfNXbKmR4zbQw89NOyn8MW1IsWh5OKb65tuumlQuxtvvBHw5tUWLV26lEcffXRQu1tuuWXcn/KcfvrpRKNRrr/+enK53KCfFWNduXIlS5cu5Zvf/CapVGrIc3R2do56jne84x20tbXxu9/9rvRYJpPhlltuGTO+7u7uQd/rus4RRxwBUCp5XdxraLJ25L7lllsoFAql72+++WZs2+btb3976bHx3veJxPaOd7yD9vZ2fvvb35Yes22b733ve0QiEU488cQ9uRwhhNhrxU/zB/ZhiUSC2267bdzPccYZZ9Da2jpoDWsul+PWW28d1G4ifc5EXmNXrVpFfX09P/zhD7Esq/T4z372szGPz2QyQ/rIpUuXEo1GB22/UFFRMWl9UVtb26BqrX19ffzv//4vy5cvL01tKyaIA/ujdDo97EyI8ca2atUqGhoa+OEPfzjo2u655x5efvnlQe9BxNwiIz1i3D75yU+SyWR497vfTUtLC5ZlsXr1an7729+yePFiLrroIgCOPPJILrjgAm655Rbi8TgnnngiTz/9ND//+c9517veNWgU4mMf+xgf//jHee9738tpp53G+vXrue+++0qlO8dSWVnJt7/9bT72sY9x1FFH8aEPfYjq6mrWr19PJpPh5z//Obqu8+Mf/5i3v/3tHHrooVx00UU0NzfT2trKQw89RGVlJX/+859HPMcll1zC97//fT7ykY+wdu1a5s+fzy9+8QvC4fCY8X3sYx+jp6eHk08+mf3224+tW7fyve99j+XLl5dGwZYvX45hGNxwww0kEgkCgUBpL4k9YVkWp5xyCueffz4bN27kBz/4AW9+85sHLb4d732fSGyXXnopP/rRj7jwwgtZu3Ytixcv5ne/+x1PPPEEN910E9FodI+uRwgh9tbpp59eGnW/7LLLSKVS3HrrrTQ0NIx7ZsFll13G97//fT74wQ9y1VVXMX/+fH75y1+WFssXRyIm0uesXLkSgP/4j//gAx/4AD6fj3POOWfYjbd9Ph9f/epXueyyyzj55JN5//vfz2uvvcZtt9025pqeV155pdQvHHLIIZimyR//+Ec6Ojr4wAc+UGq3cuVKbr75Zr761a+ybNkyGhoahoyWjNeBBx7IxRdfzDPPPMO8efP46U9/SkdHx6BE8/TTT2fhwoVcfPHFfPazn8UwDH76059SX1/PG2+8Mej5xhubz+fjhhtu4KKLLuLEE0/kgx/8YKlk9eLFi/n0pz+9R9cjZoF9WDlOzDD33HOP+uhHP6paWlpUJBJRfr9fLVu2TH3yk59UHR0dg9oWCgV13XXXqSVLliifz6cWLFigrr32WpXL5Qa1cxxH/fu//7uqq6tT4XBYnXHGGWrz5s0jlmN+5plnho3trrvuUscff7wKhUKqsrJSHX300erXv/71oDbr1q1T73nPe1Rtba0KBAJq0aJF6vzzz1d///vfx7z2rVu3qnPPPVeFw2FVV1enrrrqKnXvvfeOWbL6d7/7nTr99NNVQ0OD8vv9auHCheqyyy5TO3bsGPT8t956q9p///2VYRiDnnPRokXqrLPOGjamke7RI488oi699FJVXV2tIpGI+vCHP6y6u7sHHTve+z5abLuXrFZKqY6ODnXRRRepuro65ff71eGHH14qmVpULKU6XOlURiilLYQQEzFcyeq77rpLHXHEESoYDKrFixerG264Qf30pz8dttzySK+7W7ZsUWeddZYKhUKqvr5eXXPNNer3v/+9AtRTTz01qO14+5yvfOUrqrm5Wem6Pq7y1T/4wQ/UkiVLVCAQUKtWrVKPPvrokNfj3UtWd3V1qSuuuEK1tLSoiooKVVVVpY455phBWzEopVR7e7s666yzVDQaHVQGe7Q+eKSS1WeddZa677771BFHHKECgYBqaWlRd9xxx5Dj165dq4455phSH3njjTcO+5wjxbZ7yeqi3/72t2rFihUqEAiompoa9eEPf1ht3759UJsLLrhAVVRUDIlppFLaYmbTlJJVw0IIIYQQe+Kmm27i05/+NNu3b6e5uXlfhyOEGIEkPUIIIYQQ45DNZgdVMMvlcqxYsQLHcUqL9IUQ05Os6RFCCCGEGIf3vOc9LFy4kOXLl5NIJPi///s/NmzYwC9/+ct9HZoQYgyS9AghhBBCjMMZZ5zBj3/8Y375y1/iOA6HHHIIv/nNb3j/+9+/r0MTQoyhrCWrHcfhC1/4AkuWLCEUCrF06VK+8pWvyOaDQggh9gnpl8Te+NSnPsULL7xAKpUim82ydu1aSXiEmCHKOtJzww03cPPNN/Pzn/+cQw89lDVr1nDRRRdRVVXFlVdeWc5TCyGEEENIvySEEHNTWQsZnH322cybN4+f/OQnpcfe+973EgqFSrv/CiGEEFNF+iUhhJibyjrSc/zxx3PLLbfwyiuvcOCBB7J+/Xoef/xxbrzxxmHb5/P5Qbvnuq5LT08PtbW1pU2/hBBClJ9SimQySVNTE7pe1pnQU2qi/RJI3ySEENPFXvVN5dwEqLgBoqZpyjRNpWma+q//+q8R2xc3g5Iv+ZIv+ZKv6fG1bdu2cnYTU26i/ZJS0jfJl3zJl3xNt6896ZvKOr3tN7/5DZ/97Gf5xje+waGHHsqzzz7Lpz71KW688UYuuOCCIe13/zQtkUiwcOFCXnvtNWKxWLnCnDSu69LV1UVdXd2M+GRU4i0vibe8JN7yisfjLFmyhHg8TlVV1b4OZ9JMtF8C6ZummsRbXhJveUm85bU3fVNZp7d99rOf5XOf+xwf+MAHADj88MPZunUr119//bCdSyAQIBAIDHk8FovNmI7FsixisdiM+MOReMtL4i0viXdqzLbpWxPtl0D6pqkm8ZaXxFteEu/U2JO+qaxXl8lkhtxAwzBwXbecpxVCCCGGJf2SEELMTWUd6TnnnHP42te+xsKFCzn00ENZt24dN954Ix/96EfLeVohhBBiWNIvCSHE3FTWpOd73/seX/jCF7j88svZuXMnTU1NXHbZZXzxi18s52mFEEKIYUm/JIQQc1NZk55oNMpNN93ETTfdVM7TCCGEEOMi/ZIQQsxNM2fFkhBCCCGEEELsAUl6hBBCCCGEELOaJD1CCCGEEEKIWU2SHiGEEEIIIcSsJkmPEEIIIYQQYlaTpEcIIYQQQggxq0nSI4QQQgghhJjVJOkRQgghhBBCzGqS9AghhBBCCCFmNUl6hBBCCCGEELOaJD1CCCGEEEKIWU2SHiGEEEIIIcSsJkmPEEIIIYQQYlaTpEcIIYQQQggxq0nSI4QQQgghhJjVJOkRQgghhBBCzGqS9AghhBBCCCFmNUl6hBBCCCGEELOaJD1CCCGEEEKIWU2SHiGEEEIIIcSsJkmPEEIIIYQQYlaTpEcIIYQQQggxq0nSI4QQQgghhJjVJOkRQgghhBBCzGqS9AghhBBCCCFmNUl6hBBCCCGEELOaJD1CCCGEEEKIWU2SHiGEEEIIIcSsJkmPEEIIIYQQYlaTpEcIIYQQQggxq0nSI4QQQgghhJjVJOkRQgghhBBCzGqS9AghhBBCCCFmNUl6hBBCCCGEELOaJD1CCCGEEEKIWU2SHiGEEEIIIcSsJkmPEEIIIYQQYlYre9LT2trKv/zLv1BbW0soFOLwww9nzZo15T6tEEIIMSzpl4QQYu4xy/nkvb29nHDCCZx00kncc8891NfXs2nTJqqrq8t5WiGEEGJY0i8JIcTcVNak54YbbmDBggXcdtttpceWLFlSzlMKIYQQI5J+SQgh5qayJj133XUXZ5xxBueddx6PPPIIzc3NXH755VxyySXDts/n8+Tz+dL3fX19ALiui+u65Qx1Uriui1JqRsQKEm+5SbzlJfGW10yJc6Im2i+B9E1TTeItL4m3vCTe8tqbOMua9GzZsoWbb76Zq6++ms9//vM888wzXHnllfj9fi644IIh7a+//nquu+66IY93dnZiWVY5Q50UruuSSCRQSqHr079GhMRbXhJveUm85ZVIJPZ1CGUx0X4JpG+aahJveUm85SXxltfe9E2aUkpNYiyD+P1+Vq1axerVq0uPXXnllTzzzDM8+eSTQ9oP92naggUL6O7uJhaLlSvMSeO6Lp2dndTX18+IPxyJt7wk3vKSeMsrHo9TW1tLIpGgsrJyX4czaSbaL4H0TVNN4i0vibe8JN7y2pu+qawjPfPnz+eQQw4Z9NjBBx/M73//+2HbBwIBAoHAkMd1XZ8RvwgATdMk3jKSeMtL4i2vmRTvTIhxT0y0XwLpm/YFibe8JN7yknjLZ29iLOvVnXDCCWzcuHHQY6+88gqLFi0q52mFEEKIYUm/JIQQc1NZk55Pf/rTPPXUU/zXf/0Xmzdv5le/+hW33HILV1xxRTlPK4QQQgxL+iUhhJibypr0HHXUUfzxj3/k17/+NYcddhhf+cpXuOmmm/jwhz9cztMKIYQQw5J+SQgh5qayrukBOPvsszn77LPLfRohhBBiXKRfEkKIuWf6r1gSQgghhBBCiL0gSY8QQgghhBBiVpOkRwghhBBCCDGrSdIjhBBCCCGEmNUk6RFCCCGEEELMapL0CCGEEEIIIWY1SXqEEEIIIYQQs5okPUIIIYQQQohZTZIeIYQQQgghxKwmSY8QQgghhBBiVpOkRwghhBBCCDGrSdIjhBBCCCGEmNUk6RFCCCGEEELMapL0CCGEEEIIIWY1SXqEEEIIIYQQs5okPUIIIYQQQohZTZIeIYQQQgghxKwmSY8QQgghhBBiVpOkRwghhBBCCDGrSdIjhBBCCCGEmNUk6RFCCCGEEELMapL0CCGEEEIIIWY1SXqEEEIIIYQQs5okPUIIIYQQQohZTZIeIYQQQgghxKwmSY8QQgghhBBiVpOkRwghhBBCCDGrSdIjhBBCCCGEmNUk6RFCCCGEEELMapL0CCGEEEIIIWY1SXqEEEIIIYQQs5okPUIIIYQQQohZTZIeIYQQQgghxKwmSY8QQgghhBBiVpOkRwghhBBCCDGrSdIjhBBCCCGEmNXMfR2AEEIIIYSYOj3dfXR2ZKitq6SpuY621i56upPU1EZpaq7b1+EJURZTlvT893//N9deey1XXXUVN91001SdVgghhBiR9E1ithqYyHS09/Lcs1uIREOsfXojXT1dvPF6nIaGahYvmUdbWzf5XIHKyjDve/9bOenUFfs6fCEm3ZQkPc888ww/+tGPOOKII6bidEIIIcSYpG8Ss8HA5Abg3r8+zaOPPE/7jl50XSOfK5BO50rtNV1j6bJaEokM8d40r2zcvutnGrz80hv89Cf3UB2rZPGiBpYe2MzCRQ2sWHnAlF+bEJOp7ElPKpXiwx/+MLfeeitf/epXy306IYQQYkzSN4nZ4Ctf/gWPP/w8ruviKshm8riu2uPnUwoymTybNrQBbTz91AYADEOjpraS4084lANb9uO4Ew6RaXBixil70nPFFVdw1llnceqpp0rHIoQQYlqQvknMZG2tXfy/D/w36VRu7MaTwHEUnTsT3PnH1QBEK0Oc+LYjOPzI/WUUSMwYZU16fvOb3/DPf/6TZ555Zlzt8/k8+Xy+9H1fXx+A9wmG65Ylxsnkui5KqRkRK0i85SbxlpfEW14zJc49IX3T9Cbxju62H9/Dj390L+BNVZsoTdPQNO+/e1rDN5XK8de/PM3df3maUNjPAQc0cdqZqzj2+EOY31S7Z086Avl7KK+ZGO+eKlvSs23bNq666ioeeOABgsHguI65/vrrue6664Y83tnZiWVZkx3ipHNdl0QigVIKXZ/+1cAl3vKSeMtL4i2vRCKxr0MoC+mbpv/fnsQ71OOPPMdTT26guytBoeCwdNmeJxaartE4PwqahtqLqXADpdJJ/vSHh/jbA//g3e89gSOWL52U5wX5eyi3mRbv3vRNmlJqcv7id/OnP/2Jd7/73RiGUXrMcRw0TUPXdfL5/KCfwfCfpi1YsIDu7m5isVg5wpxUruvS2dlJfX39jPjDkXjLS+ItL4m3vOLxOLW1tSQSCSorK/d1OJNG+qbp/7cn8cKOtm42btgOKH7yo3vYsqV9Up4XvBGepctqeHVzD+V4CxiLVfDjn18zaSM+8vdQXjMt3r3pm8o20nPKKafw/PPPD3rsoosuoqWlhX//938f0qkABAIBAoHAkMd1XZ8Rvwig1HFKvOUh8ZaXxFteMynemRDjnpC+SeIth8mM96G/reNnP7mPHW09pFIZJn3Wke4VLFBKTdpIz0C9PSlu/t5dfPWGiyftOefy38NUmEnx7k2MZUt6otEohx122KDHKioqqK2tHfK4EEIIMRWkbxLTWVtrF7+7/VFaW7tIJaemSEE5/O3+dWQyP+AznztfqryJaWPKNicVQgghhBBDtbV28eQTL3Pv3U/z/PrXJvW5/X6TUCjA4v0baTlkAQsX1tPV3UVjUxfKdqmtr6K7M8G27TvJZQt0dSVwnb0fAVr9+Euc987/5KOXnsnFl75jEq5EiL0zpUnPww8/PJWnE0IIIcYkfZPYlx762zpu/Mbv6Ny5d8VDdF3D9Bn4TIOqqgpWHdPC2886Cp/PpKY2WhpxcV2XnTt30tDQMGiq0MBNTv/nu3fy5OMvkcnkRzrduDiOy6033829dz/D57/wISltLfYpGekRQgghhNgH2lq7+J/v3rVXCU9jY4yzzjmW4958KIWCPSTJGa+m5rrSMV+74WLWrd3EG1t3ks9ZZHMF/nb/Gl57tQPbdiYc47atnVx+yXf44L+czJVXv2fCxwsxGSTpEUIIIYSYQsVRlX889TLbt3Xu0XNEoiGOXL4/Z7/zOE46ZfnkBgisWHnAoJGZCz56Om2tXfz+9sd49OHn2PbGxOJWCn77q4d5y4mHy4iP2Cck6RFCCCGEmCIP/W0dv/zfv9PW1k1Pd3LCxy9YWMeZZx3DMce27NGIzt5oaq7jk59+N+89/y30dCf56pd/weuv7Rz38Y7j8stf/F2SHrFPSNIjhBBCCDEF1q3dxDdvuIOenuSEy0UvWFjHl75ywZQnOsMpToX7zR++yAP3ruGLn/8Z493y5/FHXuAnt9zD2886ap9fh5hbpn9BbiGEEEKIGe6hv63j69f/lu6uvgklPLoO7//wSdxx55c57Igl0y5ROO3MVXzmc+fj94//c/Rbb/4rl1zwLR7627oyRibEYDLSI0QZWD1xrL4UhUIOGhr2dThCCCH2oeL+OzvaeiZ0XFNTDd+/5cppl+js7r3nvxWA39/+KFtebR/XMd3dSf77a7/hoIMXTPvrE7ODjPQIMcniz2+g7c77aL/nIXr+sY7ECxv3dUhCCCH2oQfuXcv6dZvJZa3xHaDB2e88hj/89T9nTELw3vPfyje/83FOP3PluI9JxNP813/+irbWrjJGJoRHkh4hJpHVEye+Zj0oRaAuBkoRX/scVk98X4cmhBBiH2hr7eLnP70P2x7flDbT1Lnk42fx/335/5U5ssnX1FzHxz9xDkv2b0TTxnfMmqdf4ROXfpff3/5oeYMTc54kPUJMIjuZxslZmNEImq5jhEI4OQs7md7XoQkhhNgH/ue7d5HJjG+Ex+czuPrf3sfFl769zFGVT1NzHZf+61kcevjicSc+bW09/OgHf5HER5SVJD1C4I3QZLa27vWIjBmtwAj6sZMplOviZLMYQT9mtGJyAhVCCDFjtLV28ciD68fV1vTpfPfmT/Ce895a5qjK76RTV/Cf/3UhH7/iHAxjfJlPKpnlvrufkaluomwk6RFzXnENTuud97PtN3fS9eTaPX4uf02M2KojQdPId8VB04itPAJ/TWzS4hVCCDEz/M/37sS2nbEbavDpz5w3q/avaWqu44KLz+D7P7pyXJXdXFfR0RHfo72LhBgPSXrEnFZcg1NIJLFTKXI7u9n5wGNjJj6jjQzFDm+h6Z1n0Pj2k6g5ZgVVhx1UpuiFEEJMV+vWbuLhv41vlOfD/+9k3nv+W8oc0b6xYuUBXHXNe9DH8Y6zo72XX//ywfIHJeYkSXrEnGYn0xSSGexMFg0wwiGU45BY9+KIU92KI0M77n6QtjvvI/78hiFt/DUxwgub8EUj5b0AIYQQ087vb3+Uyy/5Do7jjtn2sMMXl0o+z1bvPf+tnPfBt42r7d8fWMdPbrm7vAGJOUmSHjGnmdEKdFPHtQpgmmDb6AE/ynGGLT4wuDpbtVedbc16qc4mhBAC8BKeb3/jd6hxFGs76OAFfPiCU2dMWeq98enPvI+DWprHbqjgD3c8Lmt7xKSTpEfMaHtbgMBfE6NqxWFouo6TzqAAsyKMGQkPW3xg9+psZjRS9upsE7nGySrIIIQQYuLaWru460+rse2xR3hOPf1NXP+NiznplOXlD2yauP6blxAM+sZs193Vx6c/8YMpiEjMJWOvLBNimoo/v4H4mvU4OQsj6Ce26khih7dM+HnqjvM2UkusexHlOJiRMNWrjhy2+MDA6mxmNIKdTE1KdTarJ46dTGNGKwaddyLXOFn3QwghxJ7p6U6yo7VnzHbhsJ/Lrzx3TozwDNTUXMcnP/1uvvOtP2BZ9qhtt76+k9t/9RDv+8CJUxSdmO0k6REz0u7TzOxkivia9YSbG/eoUlrdcSupPGjpsInHQMXqbPE168l39WIE/SMmSOM1UrIykWuc7PshhBBi4goFm76+zJjt3nPeW+ZcwlNUXL/0w+//mWQyO2rbv//tWUl6xKSRpEfMSMVpZoG66tI0s3xXL3Yyvcdv8v01sXEdGzu8hXBz45gJ0ngMTFbMaAQ70UfP6rWl5x/vNZbjfgghhBi/HW3dXPmv3xuzXVVVBe85b3ZWahuv957/Viorw3zh2p+N2m79uld5+O/PcsjhTVMTmJjVZE2PmJF23wR0sqaZjZe/JkZ4UfMeJxTFtTfZ1nacnIVjO2S3tZLv7iW7o4PeZ1+a0DXu6/shhBBz2XPPvsqlF91IoTD2Wp73f+htc3aUZ6DTzlxFU3PtmO2+/c3f0dPdNwURidlOkh4xIw3eBLQXNG2vp5mVQyGZIvNG26DCAgNLXnevXoudyZJv70S5LpphoOk66U1bAMZ9jTPlfgghxGyzo62b1Y+/SE9Pasy2CxbV89FL3z4FUU1/ba1d1NRGx2zX1ZVk+3ap5Cb2nkxvEzPWZE4zmywDCxKkt++g57kXyCdzmP1rdcLNjUPW3uC6uLaNoZlohol/Xh3KVdjJ9ISucTreDyGEmO16upN07oyPq+3nv/Ch8gYzg/R0JzENg5raCD3doyeMjzy4nlNPO3aKIhOzlSQ9YkYb7zqcqTCwIIGma9g5C2oiBOpiOMk08TXr0WDI2hs7nUX3+9BNA7OqEmVZoGmlqWkTucbpdD+EEGIuKBRsMhlrzHbvfPfxrFh5wBRENDPU1EapiARpOXghqx9/adS227d38uw/N/OmVQdOUXRiNpLpbULsZk/2utm9epqybazeOJrpG7SfDzBk7Y0ZCVN99HKMcMjb70empgkhxIzh85nYjjNqmyOOXMK1X5RRnoGamus4+9xj0TSNurrKUds6tsvzz22ZosjEbCUjPWLOGzglLdPavkd73QypnlZVCZ09OJkMyjRxkmmMoJ9QcyMKhpS8rjq8ZVwls4UQQkwvL7/4OspVo7b510+eO0XRzCwnnbqCgw5ewAP3/ZObv3fXqG2fePRFLvjomVMUmZiNJOkRM9JIm3lO1OApaTpOLocvWjHhvW5237RUWRb+6ipswyDfFcccsJ+PvyZGuLmRbGs7AKHmRoDSOexkuvS8kgQJIcT09sQYU7N0HeK9Yxc5mKuamus47Yw38b+33U86lRux3fPPv866tZtkiqDYY5L0iBkn8cJGEmufm/BozO6KU9LcvPc8hb4UVm8Cf+3E9ropJmDBBU2kX3kNO92JGQlTf9LxpEI+qn1B/JWRQc8x3IgSUHrMtSxAQ/f79uoap8JkJaBCCDHTtLV28ew/t7Bg0cjTs6qqI/zlrqc46OAFUqp6BE3NdRy5Yn9WPzZ6AnnvX5+RpEfsMUl6xIxSSKbIrn0ObUD1s+FGY8bzRtxOpsl3x70Ew3FB03BtGzvRhxHwD9nrZrjnLI4U5XviOOks6Bq6z0do8X5UHXYQ+Z07CTc0oOu7ls/tvv7HTqboWb0G0NB9Bma0gtTmTlCK8KL9QKnSNRbjni4JxsCRsumenAkhxGT7+U/uxyoURm3TUF9FOp2jpzspSc8oznnncWMmPbnc2AUjhBiJJD2irCZ7FMDN5nFyFsG6kUdjup5cS2LdC7i2iy8aHvGNuOs4OJksKIUeCuJmc+i6jlIMWm/jr4kN++a+WH7asQq4eQsnnwfbwdE0ep5ci9WXRD/8ICxfgGBtdem8Q9b/RCNktrejaeCvaSS7Y6eXiLmK7But+Opq0A2D3mdfIrt127RJMIZL3sY7HVAIIWa6ttYu7rvnmTHbZTN56upj49qTZi6LxSLohobrjLw+auvrHVMYkZhtJOkRZVOOUQA9FBi0dmb30ZiuJ9ey84HHUK6L7veB64z4Rlw3DIyKEG7ews3mvDLRkTD1Jx6LrzJaes7E8xvoWb0G3WcOenNfLD9thIK4VgGK1XsMAxyb5AuvUEglcf1Bqgdc++7rf+xkCjMcBDSs7l7svqQ38gQ4eQunrR1frJL0pi1DYtiXCcZwydt4pgMKIcRs8OQTL5HLFdB0bdR2lbEIZ7/zOBnlGYPPZ1IRDpJMZkds8+rmtimMSMw2UrJalMXuowDFKVoTKQM9HF80QmzlEaBp5Lt6B5V3tnriJNa9iHIcjHAIDbAzWQrJzKDiAEVmtIJATQw94AddQzkOTs6ikEoTXtRMprWdtjvvY+ffHye7YydOwR62/LSTz4NSoAANNPr/v1LoPt+Qa/fXxLw1PAOuofb4VdQcvxLlujh5y1v5quugaaBAOS5OvoAZjQyKwU6m96jE9mQYmLwVy28PTECFEGI2a2vtHrPNvMYY//lfF3LSKcvLH9AMV1MbpWm/2lHbFAoOP7nl7imKSMw2MtIjyqKcowBVhx1ExX7zh0ybs5NplON4SYxtg2niZrLopj7sG3F/TYxIyzIyDzyGsh00U8eMhElt2ExwXn0pafPX1VLoS5Hv6MKsCGOnM7hWgUIqTaRlGakNmzGCftxcHvDyHzQNDB3d78cMBLC64oOuPXZ4C+HmxiHXYFaEabvzPgrxPvRQEAoFFBpGMIhu6ljdvShdx81mMYJBMm3tpDZs3idT3orJ2+7lt2WURwgxF9j9I/KjOea4g2WEZ5yamus4913H862Nd+COUgL81794kLefdbTcVzFhkvSIshhuCtdkjgIUSz/vfk4zEsZ1XZx0BjedQTMMYisOG/GNeLip0Rut6R+5cXJ58j1xrM4enJyFGY2A4+Cvqya/s5vM9jacdA5QdNz3CP7qGNFDD6DykIOIv7SB7OutKMfxEpVwmIJdINsVxwwFx3Xtkf0XUnPsm9j5wGPeGqOAH19FGF9lBCMaoe+5l3ELBcCbitf79LN7VGJ7soyUvAkhxGzX1Rkfs81BLQvLH8gs8t7z38q2rTv5za8eHrFNKpXjySde5r3nv2XqAhOzgiQ9oiz2xSjAwHPauo4WM6hacSi1x60c8Zh8bwI77c0f1oMB3Gwe1yqgBfy4VoH0lq2ltmZFCHQDZVhoPh+gsLp76HniGXzV1fiiYWrfcjT+aJS+zVtIbthCrqMLPZ7CV1FB38ZXqeuPZaT1TlZPnHBTIzXHryT9ymsox8GMhIm0LKPvuQ2g6xihIOg6rlUg39074RLbMLkFJoZLQIUQYrZr2z769Da/3+C4Ew6eomhmj/M/9DZu/+0joxY0SKUyUxiRmC0k6RFlsy9GASZyznh/gQJlOyjXxc1boGtohkF2exvewpz+ZTX9r72aUmiGju73ecekMmjKQPcZOFaB3LY2Iiceh/vsC2imjmaagIadzrDz/kew0xlC8+qHLYxgp9KDpqpVrTiUcFMjZrQCO5mm9x/r0PTiuRUKG2DEEtujXbeUmRZCiD3X1trFjh2jJz0nnbJCpmDtgY0btqOhUeyDh9PUNPraHyGGI0mPKKt9MQownnOWCi0YOnow4JWu1jVvPZCC5MubwTCo2H+hV5XNMLB6EqAUKpPFtQoox/a+d1zyO7vRfCZ6wI/V2YOdyXkv2q6LZujgKlzLpvuxp/FVRigk0wTnN+DrH6HJtXeSWPcCRjBQSoRSGzZTedDS0rUYoRC5zm4vVgBdw6gIo5nmuEfTRiszbcZG3lxPCCHELhs3bCPeO7RAzkCHHrZoiqKZbRSuO/p6KdMnb1/FxMlfjZiTBhZacK1C/349gKNA17D7UqBr5FyXQG0MN5vDF/WmmfU+/SxWb6J/Q1O80SGfiWtZXkGEgB8zHMSKJ7wRGeVVdFMKcJW3ximdLRVGUJaFZhjYubxX/rpQKE1Vy7a2l0atlAbYzq6LUOCLRGg47a3ohoHrOOiGgdUTH3VD1pEKTEjSI4QQ4/PsulfHbBMI+qcgktnnoJYFBAL+MTYiHXkUSIiRSNIj5iTXcUAp8j1x/NUxrO5ecF0wdDRACwVAgdXTSyHRh24ahBbthy9SwfxzTsNJZ8h39tD5+NO4+bxXElvXMEI+AtVV1By/Cuv+R8G2wHXRfSau62KEApiRCvx1Nvn2TvLtnZjRCHo4iL2zC7svie73YYZDoGl0r16La1k4VoFCsSS11r8nhOat67E6e3BdZ1xV3MpdYEIIIeYCy7LHbBMOB6YgktmnqbmO6poIO9p6RmwTi0WmMCIxW5R1n57rr7+eo446img0SkNDA+9617vYuHFjOU8p5pjh9qgZad+a4uNdT66l65EnsTPeaEuuYyf+6lipUpvmM/HFKkED3efDVxXFtR1SG7ew7Y6/sOPPD6CA8OL9vIEe08SIVKCbJrguruMQO7yFxReeR+UhBxBsrMeMVWH4/ZgVYQqpNFZXL6Dh2jZWoo/M69u9pAtw8xaFRB+u7XhtexNeqWq7v5NVyvtyHAqJBJ1PPMPOBx7D6kuNuSfScHsESZlpMZdIvyQmg08f/e2T328yX9ad7JG21i5So2xQCvDcs1umKBoxm5R1pOeRRx7hiiuu4KijjsK2bT7/+c9z+umn89JLL1FRIZ8si70z3IJ8YNhF+sWiBYVkGjuVxlddRcWiZrIdXahCgboTj8VXGaHj3of69wfQcHMWut+Hnc70JxwaumFSSCTpWb2G2uNXoQf9uDkLbBs94McI+HHSGTJbW9EjYWqOOpLY8UfjpjJk2trpe24D2bYONF3HP6+OQnevV+JaKTRN99baNNTh5nLYuRxOJoumVCkh2p1yHDRD9zY1TWdwCzaaP4DV1U22tX3YZCZ2eAtmRRirswd/fQ2R/aWkqpg7pF8Se+uhv63j8cdfHLVNtDJMTW10iiKaXTZu2DbG1Db44+8e57QzV0qhCDEhZU167r333kHf/+xnP6OhoYG1a9fy1re+tZynFrOY1Zsg39ZB1yNPoWmgh4I4mSxdjzyF7vOh+wyv4lmij57VazArwnTc/yh2og+lFMp2KPQmyPt92H1J3FyentXPUPuWY/A31NH3/AYvEXFdlKZQpRdfhascsBW5ji7iz72Em7O8xMM00AN+FBo7H3wCNB0zGqawpJnI4sX4KyPUHbcSX6SCjr897o0e5S0vHtdFFQqlCnHZ1nbMaNhLphwHZRgj3gvd58dJptFNAzdvke/qoRDvQ7ku3avXoGDINLeuJ9eSWPcCru3ii3qbrUr1NjFXSL8k9kZbaxd/uespCgVn1HYBvylvyPeYNmYhg76+DD3dSbnHYkKmdE1PIpEAoKamZipPK6a5iewZk3p9O9mXXyXf3kmhLwV6/xocQ0cBZiiIryZGob0L5di4jsuOex/C6uzeVXcacLM5sjt2oqGh+X1ous7OBx6l0JdCKYXu96MANz14LwCVzQPgWBZ9z29ED4fQA35vSlq8DwDNMND9PgrpNKlEHO35TfgiYWKrjiTc3IhyXTJbt3uz1CwLUIPrYjsOTiqL0r0fDSpeUFRsb2igXC+Ggk2+sxvNNAjOb0D3mUM2K+16ci07H3gM5brofh+4jlRvE3Oa9EtiInq6k6RTOQKB0d8+1TfEpiagWSgWG3vEVTd0GUkTEzZlSY/runzqU5/ihBNO4LDDDhu2TT6fJ5/Pl77v6+srHTtW1j8duK6LUmOXWpwu9mW8Vm8CO5km29ZOauOru6ajrTyCqsMOGvaYXE+c1ObXqLAKOLbj1W6xbZSug3LBMChkctjWTnTTB7qOclxybR0oNbTSi8pbYOgYho5tu1hxbyTIjEbAtr2y1ACmMXzioRRONovZ2IARqSC/sws0DSMcQlkWhWwOx4xCwMDJW/SuWU+2fae3XscqeJXfwEtsdotPOQ44gK4PP7VNKTB0XNtFGaAZJnooiJ1KoxmGt4lptIJ8VxyrL4UZq8TqTRBf9yKu42BUhMG2KWSyKN3A6kuhV0bk77eMZmK8s914+iWQvmmqTed4q2siVESDvPpqG5ruvYhrmoamef8trpSuro5My/hhet9fANM0qKgIkkzlgOHv78KFdTTOr5mW1zDd7+/uZmK8e2rKkp4rrriCF154gccff3zENtdffz3XXXfdkMc7OzuxrNHnd04HruuSSCS8kYIxFjlOB/sq3tTr20lvfg03l6eQSmOGQvjrq3GyWfqee4GakA9fdGhllmxHF2ml0CJBrGwItyLQP1Li0UwT5SrvxdFnohsGejCAFe/z9trZnQa6349mmCgrg6qsQPMZXi4S9OFkgaA5eBRmGJlc1ktgIkHQdXRTg0AI19Swwn5681k03QC7gL7lNQrREBACt/851R78AzYM73ema4QWNpNLplG2TSHoFVPIZDOYPg09GqS3kCO1cyf5nd0k/Tp2bZV3jUYAN2/hi/jpLeQwdu6Uv98ymmnxFkdAZrPx9EsgfdNUm87xGiac8JYD6OrsxHG8EQlN12icHwVNQ/W/rvsCip07d+7LUEc0ne8vgGHaHHXs/mx9vQMY/v6uWLVY7u8kmWnx7k3fNCVJzyc+8Qn+8pe/8Oijj7LffvuN2O7aa6/l6quvLn3f19fHggULqK+vJxaLTUGke8d1XTRNo76+fkb84eyLeK3eBNmXX6VSKTADpHs70DMWFdEoejBEvitOtS9IuKFhyLE500/i+ZcJp3L4Mnlcq4Cbt7yERNcwQkGU44JS3gal1TUU4gnyySyubXv76gygB/xoOce7D65CrwiichZOOrtrdKV4X/bgkwUFYJhElIbKZlCuixEKedPylPKe2x677OlwNL8P3WdiBAPUN+/nlc7O5TDz3l5BaBBUOvUnHUfV0v0BsHwBXOMFLJX3ih7kvf2B5h27ipql+4/776E4SmdGK/BXV+1R/JNB/r2Vl98/u/cYGW+/BNI3TbXpHu8BB+zPjtb7Bo1EoBSvbu4pzSro7cnTMEw/Nh1M9/vb0NDAKaem+NbX76CnJzXk/moaXP3ZFXJ/J8lMi3dv+qayJj1KKT75yU/yxz/+kYcffpglS5aM2j4QCBAIDK1rr+v6jPhFgPfiJ/GOzE1lcIubghZsjIDfm0aWt3ByecygH39lZFA8xTU/eiRMZNkSjJdfxQ34vSlnfl//NDUDwzTxz6um0JfC8PnId+zESWfRA35QykuING8jUS8YhdJddNU/tJvNo/KW9wLrM721QqaJpmk4ubyX+OjarhEaAKM/TmeYpEjT0H0mKpHxCi74fd5GpK6XmO1JIlU6bTAAtoObs3BtG6cviVuwQSmv2pvmbYha/P1aPXHcVIZoyzJSGzZj6zpazKBqxaHUHbdyQMij/z0MVzFvXxZBkH9v5TMTYtwTE+2XQPqmfWE6x1tbV4njuKVRB/TiTgKq9FginqZ9R8+0XWg/ne8vwMmnvYmWQxbyX//5K9au2VS6v7oOH/jQybxp1YH7OsRRTff7u7uZFO/exFjWpOeKK67gV7/6FXfeeSfRaJT29nYAqqqqCIVC5Ty1mKZ23xzTqAij7CSFZAZfNDxkz5iBb7L1oB8OXsr8c0/HTWXo27SF5IubsLp7vLU00Si4LmY4SOURB9P33Ev4KsL4a6vJ98SxehIYAZ83DawvhSrYYONVR7MdbwqcUqXNPzXTBKUINjViJRLYfSn0YADHsqDgoOk6yrEHJ0EDKYWybdxCwZvC5irvvwObFzcaHWX63NCbaKC5CmXoaIaB3b8WadDzKG9dUHzNeuxUetDGpZGWZYSbGnEdB90wsHri49qnx+qJE1+z3iurXVeNnUwNKZQwsO14i1MIMZWkXxKTwTRNID/iz23bZeOGbdM26ZkJmprr+P6PruSfa17hn2tf5Jhj4c1vPYwVKw/Y16GJGaqsSc/NN98MwNve9rZBj992221ceOGF5Ty1mKaKm2PG16wn39WLvzJCzdHLCTc1DnmDvPub7EIyRXrza3DAUsxoBbltbfiiYfRQgHx7J7n2DgyfiRmpoO+5l7EzOSoWNaPpOoGaGLiK2PJD6Hr8adD0XWt1ikUKismL63qbjBZsME3cfB5l928cmsmB66IH/eg+H046i3JHnqKmbBtVKHhraHTNm9Kmad45DR3QMCsj/c/jUKpu0B/DEIaOv74WCgXsVAZlOyRfedX7dFHXYMAhVk8cZds42RxGMFBKVFIbNgMMSoRiq46k8tChn5wNTF7sZBqnf5RO03XMaIR8Vy92Mj1iojodRoOEGEj6JbG3erqTBEN+Eon0yI200v+IvbT8Tcto2q+ShoaGGTESIaavsk9vE2J3scNbCDc3jjkSMPRNdgVuMuVNddO00s98uo5ummRe34YvVkWoaR75njhOJovV3Yu/thqruxeUi53OYKezoKlxja5oGvgb68k/v8GrimZqqEwWN5fHV1WJ5vdR6OoZPHozEtebdqaH/LiO65W29pmYoQCqUMDNuSgNcFy0gB9lFYYUYDCjEex4H24+D2j46/vL7Dr2kBiU7VBIptF8PgL1taVEJdfeSWLdC4MSofia9QSb5g06fvfkJdKybNAonZ1MYQT9mNFd5UUnMhokxL4g/ZLYWzW1USKREB30jtxIwUEto68VE0JMLUmZxT7hr4kRXtQ86hvhgVPhlOt6yY7fxIxWDPmZm80CoByXfFcPut+H7vNhZ7IkN79OrqMTJ5uj78VNuJaFyhfGFacq2PS9uNErmpDL4Wa88+Aq8jt2UujsT3hMw/sa8wkVbs7y9hVyXJxsnlxHN046g7JtKNjguqhsbtiKc3ZfCiMcQg8EMMIh3EwOO51FM4b5/KJ/I1Y3b5Hf2Y2dzWMnU2iGgWu7mNFIKRFychZ2ctenlrsnLyhFasNmIi3LQNPId/WCpg2ZjlhMVEd7biGEmMmamus4+bTlY7Z7/JHnyx+MEGLcJOkR01ZxKtzAN9mRZUvwV1cN+ZkVT3pv7js6yby+ndSGVymkUuR7E940MAVGNEIhmRq+6MAoVDbfv0p1lEa2M77RHvCSmuIaItftX0s0/mNd20Y3dNDAtQu4luVNbRuOrmOn0+R2dpHa/BqFZIaqFYfii4YHJJNDR2yKyYvm9+Nkst5/cxa+SAXVq46k7s1H0fTOM6jabdra0ER16HMLIcRMd/Gl78Ac44Ouv/9t3RRFI4QYjynbp0eIPTFwKpweCRMv5If8rG/jq7Tf/eDQg20HLWCglINm6OTaOrwS1+Xi9o/MaIw/idkDTiKJ5vehnP5iCgPXJe1OuWi6j0BDHW4+j2boVB60FIDEuhex052Ykf4CEtVV0L/vgRmtwLUs0lu6S0ufNJ9J9+q1XuntoB8FQ0bqdl+zZQT9Q0aDhBBiNmhqruGNrZ0j/rwvkZnCaIQQY5GkR0x7/poY/pqYtwvvbpuR+Wti5Np2eutfdqdp6H4fju3gFmyvtHXpZ5QhMfGeVA8EwdBwxjmFbk8o1/WKIuj6yAkPgOPiUsDq6vGOUyla77wfN5vFzlnoho6/oY5Qc+MwB2v9x3jnU9kcirHX6ox3zZYQQsxkuezoH6JFolINUIjpRJIeMaNZPXHSr28b8efKdtB9Bu7uCchICY+u79n+OT4T3Wd6yZXroGyF7jMJNNaj5x1UcS3QZOnfhFUVYzYMb6RpuOtyXJxszmunFKlXtqD7TDB0lO1i9SbIbW+n+rg3QUM1QP/6KR8V+y8Cx8HOW+S2t2EEAqNWbisqJqpCCDEbtbV2kerfnHQkDY2xqQlGCDEusqZHzCiFZIrMG21YPXEAep99CTvRN/IBrttfnppdm5IOpOvooeCu79UeJDyGjqZp/eWsveIB2DZuNke+vXNX8YPJpNSudTyui24YoO+aX671FzoYckx/KWylayir0F8mG5xcju7Hnybb7k3VKK7NUVYeIxxEcx0008TJ52WtjhBizuvpTmIYo7+F6mgbpbqbEGLKyUiPmDESL2yk57kXyCdzmP0llNObtqDpurfnze4FCkwDoyKM7jOxehyvOtrulEI5rne8644+5U3XAM1LjHTDa+8z0XXN29MHhq24VjYDNkV1LQvNNFCmiaZ5pbbdQbGoQe1Vtn9tlKGXKuM5lkXhn88T002qjzh4yNqc2JsOw+rslrU6Qog5r6Y2SkUkSDI58odaLzz/Om2tXbJBqRDThCQ9YtoYuBHm7m+mrZ448bXPgakRqIvhJNMk1r2IazsE5jeQa23HdQbMr9Z1cBwKfSnMcNBLagq7ZTSahmYaBJsayLd3ojRv9GPEaWKuAhRoGmZVBLu3D6wCezA2tGcMw0u8CsNvhqpsh+gRLUT2X0Tv0+uxunpQxoDNUIfjuGg+DeU46MEA6Brxtc9Rsd/80tqcbKu3Y31x3Y+s1RFCzHVNzXUc1LKA9h2jj+b8+zW38IvffH6KohJCjEamt4lpIf78BtruvI8ddz9I2533EX9+w6CfF0soG6FQaU2Jchx0U8fwmYQX7wf+ATm863pJiut461lcZ5iyzl4CY3X34jouummMXZoaQCnsnsS4NjedXP1J1ygyW7bhi1RQc/SR+GpiBJsbMYPBwY0G3gbNS3g0n0mwvhZfZPC+OpnWdnrXrKfr8Wdou/M++ja+OrmXJIQQM9Spp79pzDabNrbR1to1BdEIIcYiSY/Y54bbCDO+Zn1p3Q7sWmPiZLOlNSVmJEzVisNA03DzBYJ1tRDwD3puzTBRbv8UNsXgN/zKGx1xUhkoFHAy2X2QyEyA40Jh9OlzTipNx/2PkHhuA04uh5PMoEcr0Hw+r4Gu7cqbdB1/bTWarmMEA+ihEE42W1qrs/vvpZBIsvOBx2i76/5hE1MhhJhLTjtzFTV1kTHb/fbXD5c/GCHEmCTpEVPG6omT2do6KJmBXaM4ZjTirY3RDax4ktSm10tt/TUxYiuP6N+MNA6aRvWqIwnOqye8aAFVyw9h/jmn4a+q9KZz9a/zUYWCt86mv9rZkIESpcA0vKSg2GaGc12XbEcndryPQm8ca8dOlKbQAv7BCU99LWZlBNd2yHf3knrlVZxsntjKI/DXxAZtUFqI92Gn0ijHwYxGhk1MhRBirvnxzz4zZpu1T78yBZEIIcYia3rElIg/v4H4mvXeFLWgn9iqI4kd3gLsGsXJtHXgpDM4mRzKddj5yGr6XtpYahtqbiSSSVMTrqBiv/l0rV5D/J8v4BYKaLpOePECL+Exxti7ZiClMHw+nKksQFBmhZ7ErrLbxVLWlo1ZHYOIhmvl8VdV4auuou/lzaXiC67j4uTypbU7xQ1KU5s7wfX2OtIDAUCBbmAnU0NKVo+2LksIIWabpuY69l86Hxh5z57Nm9r4/e2P8t7z3zp1gQkhhpCkR5Td7tOkdt/Y0l8TI9KyjJ0PPIaybZRy0QzDm3pmFYivWY+dSpN4bgMJHMAgv3QhvWueK1VNU3aB1IbNYJrjT3j6OeUoKb0vDdhnSDP0UllqPeDDXxWlkMyg+31egYJiRTtNA6VwslmSG18lePwqrHgfhb4Ubt7qHyVTuHmLzLYdKNtGMwwybe2EFzUDoye2QggxW73zXcdy152Pjtrmlpv/wnEnHCKV3ITYh2R6myi7gdPXikUIBi6WBwg3NeKLVRFsrMfw+70pVI6LEQhQSGbofvwZch2d2NksuY5Oup9Yg5vNeW/aB47SDFeWeg5T9q5KdLrfhwKcXJ5g0zz8Nd5GpKV1ThqgFLm2dnY++AStf7wXJ50GFPh8YHplul3LQg/48VVGSG3YjNUTH9e6LCGEmI0OPnQx2u51cnaTiGf43e2PTU1AQohhSdIjyq44fc1Opkbc2NKMVuCLhtF0Hc1negmNoePk8161tFQaNNANA6WU9/MSNWh0Qwww4L5kd+wktek1Cr29dK9eS2Zbq/eD4jqf/v+mtmyj89F/YHX3eI+5CiyrNIJmVISpWLwfoebGUvKabW2nkEii+QMjJrZCCDEb1dRGaWisGbPdw39/Viq5CbEPSdIjys5fEyO26sj+IgS9pSIEA9d8FNvoAT+63w+ahhHwY/h9RJYuQrneehM7m8fNeYmQFg55B8/82gPlFfCjhQLe/j6uV6YbFKpg7yrjXSzgoBvelDjHHrrZK3iV8nJ5lEspec20tdO9eg1WvI/0lq3kunuHTWyFEGI2mt9UywlvPmTMdj3dSTZu2DYFEQkhhiNresSkGmkhe3Gjy+F+Vjwm3NxYauM6DrpheKWT4310/2Od94ZdDRjVsR30SNhLgia4jmdOsXfbo6iY4CjlFTrA2VXKWrk46Yw3LW53moYZq8TN5bG6uvFVRYm2LCO5YTO6zyQ4v4F8Rxe5tg5C8+dRe/xKKWYghJgTjn/zYRx00Bo2vLx9xDa5nEVPd3IKoxJCDCRJj5g0Yy1kLxYtmMgx4K0J8sWiFFIZ7wFdBw1UoYCyC2imCUETZVleYiQGcxyUOzDpGfAz1/XW8pgGoIGm4eQttN1vo6ZhhIIEYl5J8NrjVxHqT1Djz75EoK4an65jVoTJd/ZQe/xKqqSIgRBiDjnnXcex4eU7Rm1z6w//CiCV3ITYByTpEZNipAptZkW4NGKze8IzWlU3oDQqZEYrCNTE0EwT3bHQQ0HIW2ghExT4Y5W4jovV1YPMdRtDf5U2wBvlKRaBKPSP9rjurv2K+tdX4boo10UPBtADfqpXHTkooSmu1zKjEZRl4Y9FS2WvhRBirjj2+EOIVoZI9o1cETTem+a73/4jIImPEFNNkh4xKYoV2gJ11aWF7Ok3Wum49yHQ9GFHcQYe4xYKoGkUkhniz75IZuv20uhPpGUZ4UX7UXh5Ezga2DYKr6iBrhv462rItraj6TpKChoMofl8XuU2pTBCQW/krKoSpRwKXb1eI6XAGZAMuS66aWBWRjHDISoO3J/KA5YMSV6La7Hia9aT7+rFCPqHrNcSQoi5YH5TLf/ykVO5+ft/HrVdPlfglz//u5SwFmKKSdIjJsXACm1mNEK+J46TzuKrCOOvHbo3z8Bjsq3t2JksrlUAIP7si/hjlZjRCNkdHWTufwQjGkUzdIxIBSRS4Do4mSx6rBKrqwc7lUYpSXiGo1wHJ+uABr5YlJpDlpPZup3cjp3eyE9x9Kc4AtRf9tu1FZqhU7XiUOqOWzni84+2XksIIeaS085cyW0/vodcbvTtE9rautm4YZskPUJMIaneJibFkAptjosRDuGvrR6xhHFxU9LiBpi634dZEaLQl8LO5cm8vo1CTxwnm6cQT2B191KIJ9ArwgTmzwNdp9DbR66zG2UVhq82NhwNxtxUYTZx3NK0tXxHF/nObpTtoopT2EzTWye1O1eBppf24hmNvyZGeFGzJDxCiDmtqbmO5SuXjavt72XfHiGmlCQ9YtLEDm+h6Z1nMP8dJzPvzLcRqI2NujcPeJuS+qurqFiykIrFCwjOnweuIt/RhXL715sohbJtNJ8P+vfo0U0Dw+9HMww0XZ9YEqPYNaox1zguqY1bUMrFrAh799ayBm/wOoBbKODkLLKt7WS2tspmo0IIMYaPXXoW+nAfJO3m2X++yrq1m6YgIiEESNIjJlnxE//I/gvH3JsHvCluZiQMroNyFXYiiRbw9S+eV/2jMl5bVSh4SU/OItfeiZPNoVwXNcIbdjECpXByeS+JHGMNlJNKY6dS7HzwCVrvvJ+2O+8j/vyGKQpUCCFmnpraKMedcPCY7Wzb4fFHX5iCiIQQIGt6RBmNZ61HcVpc54OrsXrbcG0bTdNQSqErhRGtwOlLgePiOhaE/f2jNN4Gm+Oe0iYGsXsTaH5f//49u63pKTIMlONgxZPoPhPd7wPXGbI2SwghxC5NzXWcfe6xvPzSG2Puy/ObXz5IU3OtVHITYgrISI8oq/Gs9Qg3N2IE/firKzGDAXTTRDdN0DRUzgLDK52sBwKg6Wh+r+2cWpdTBsryKuZpplEaTRvIjITRdB1N1/sTHhc7k6WQzAxamzVeVk9cpsgJIeaEk05dwac/894x2zmO4ls33M63v/m7KYhKiLlNkh6xz9nJNMpV+CqjoEAPBdENg1DTPMyKMP5YjGjLMsIL5mME/WiA1ds35tQsMQ4KlO3s2tRV18E0Qddws3kv8bRt7GQaO5fHyebQTX3I2qyxxJ/fQNud97Hj7gdlipwQYk447cxVHLl8/zHbuS789pcPc/UnfzAFUQkxd0nSI/aZ4if/ruNgBP24eW9Ux83mwNBRSuGrqsQXrUBZFnooBLqOa9uofF72Id0bpgGAbhrofrM0amaEQ5ihAEYoBD7DKyPu9ld/sx2U41Bx4P4Tmtq2+ya0KEV8zXoZ8RFCzHqnv30Vuj6+WQmrH3+JD77nK2WOSIi5S5IesU8M/OS/65En8dfXogf86H4/aBpGwI/h91F7/Epqjl9FIZmh7+VXcNKZESuNiQmw+++hz8RXVYlZUwW6hspboOsYFSFvvZSuofn6S1rrGmakgsoDdn1yOZ4pa8VNaM1oZMTy5UIIMRsdd8IhNM6vGXfi89prHZx8wtW0tXaVOTIh5h4pZCCm3O6f/NvJFFZnN3UnHoduGLiOg24YpeIHVk8cq69v1xt1MTkMAzdvkc9b6IaBZppoAT+mL4hhGijbQXccr5KepuPm8vhjVaWpbV1PriWx7gVc28UXDRNbdSSxw1uGnGb3jWtHKl8uhBCzTVNzHZ/81Lu47cf3semV7ePaLSGTsfjg+77GRz92JqeduVI2MBVikkjSI8rO6okPquBW/OQ/ULdr49J8Vy+6YRBe1Dzk2O7Va3HT2X0U/Sw2cMTM5yNQXYUTqaD+TUfii1TQ9ciTFBJJ7EwWZRXQfSZVKw7FXxOj68m17HzgMZTrjlnVrVihL75mPfmuXoygf9jy5UIIMRuddOoKDjp4AQ/cu5af/eRestnCmMfkcwV++IO/8Je7/sGZZ63imGMPpqY2KgmQEHtBkh5RVvHnNxBfsx4nZ6HpOpEDFhNevGDIJ/+arlHoS5La8kZplKdv46sk1r2I1Rufu5uJThG3UEAPBUsFJSKL98NOZ7wROd1AN3ViKw6j9riVWD1xep9ej1soYFSE0fqruqEb2Mn0sMlMuLmxVCAuJOWuhRBzTFNzHRdcfAannbmSz171I159dceYxyhXse2Nnfz4h3dzz5+fZtGSeZx97rGcdOqKKYhYiNlHkh5RNgOnsWHo5No7ye7oILTpNYLNjVid3eS7enEtC9Bov/8RnHQWIxwCTcNO9HkL7Mexs7XYS7ZD5o1WWNJMrq0dJ5ki3Nw4aJ8lgMzWVuIvbMDq6UXZDk4yjR4MoBxnxKpuAxNfI+hHgSQ9Qog5qam5jm985zI+dsE36elOjesYpWD79i40XeN3v32Ugw5eICM+QuwBeTcpyqY4jU3zByh096IZOrqho2y7tIan7s1HYQSD6MXqbUrh5vIU4n24BRs9FMTwmeAzZV+ecivYWIk+2u9/lO2/v5utv/g9mdZ2wouaybS203bnfbTeeT+9z3iJrB70o1zXKy6hFJED98dOpgcVNZDKbUIIMVhTcx2f/dz7CYX9Ezpu2xudrPvnq/zof/4ihQ6E2AOS9IiyKS1gT3gJDGhoholZVYmT8xbP+yqjKNfFCATAcb0pVo7tbZapgZvP47ouGhq+WBQ9MLFOQkyMsgq4rovu81FIJOlZvYbUljdKiYsvGvYa6vrgkuG6Tt+LrwzZh2e4ym2FZIbUptdJbXlDNisVQsxJJ526gl/e/nmW7D9vQse5rst996zhkotu5Ce33F2m6ISYnSTpEWVTXMCumSbKdVGOg682hrKsUvWuYmLk5PNg6DiZDKq0+kPDzVk4yTSqUMDJF9B0XUZ8yi1v4VgWuA6FvjRWZ08pcfHKivtKm5nqPp83HVEpComkN71twGjOwMptynXJtrZTiCfofGQ1b/zfH9h2x19ks1IhxJzU1FzHr3//BQ5q2W/Cx3Z39nHrzXdz8Ue+wZ1/eIJ1azeVIUIhZhdZ0yPKKnZ4C+HmRnqffYn0pi24VgHHcUtVwIBSZS+U8jbD1DQvuVHK2xRT09ACPlAKNVbZ6mJSJHv57BWVy+PoGmhprGRyUOEJMxzCylv9I3MBzEiEQjzh5aKOU6rGZyfThBc1l36/2R2dFPpSmJEwTi7f//u2cPPWiJXfhBBitrv+mx/jgg/eQDI58SqlLz6/lRef34ph6qxcdSDnvPNY5jfVjlnprae7j86ODLV1lbI+SMwZU5L0/M///A/f+MY3aG9v58gjj+R73/seRx999FScWkwD/poY804+nq5QoH9fF4fUhs2YkQpih7cQO7wFsyJMx70PYVZV4otUkN3RgZ1IegmPaaDpBqq/IMKoldxcF2QgaHLoOr5YFbltbURalpHasJl8Vy++qiiVhx1E6pUtaLqO0Z/0KAUYxpB9eIqJb2rT6/Q8vQ4zGiG7rc2bypi30AP+0malkvSIqSL9kpgumprr+PwXP8T1X/k1fX2ZPXoOx3Z5+qkNPP3UBnRdo7m5lhWrDuCgloW88UYHbdu7SWeyVIRDBAI+TNOmoz1NuCLI2ecey0EHL2Djhm2AxkEt+0kiJGalsic9v/3tb7n66qv54Q9/yDHHHMNNN93EGWecwcaNG2loaCj36cU0YfXESW3YjBEMEOgvUz3w030nncHJFwjU16DpA0ZqNFCOiypkUZqGWwiMfTKpbj0pjEAAf20NdjJFuKmRyoOWDtpvKTCvnvia9djJFL6qSkBhJ9PD7sPjr4kROWAxfS9tLE1ldLM5NJ+Jm7dks1IxpaRfEtPNSaeuIFYd4evX/5adHb2kU/k9fi7XVWzb1sW2bV3Ak0N+rukaS5fV0rqtj0DAx9pnNuG6LrbtoOs69Q2VXPnp93DQwQvo6U5SUxsFKP1/SYjETFX2pOfGG2/kkksu4aKLLgLghz/8IX/961/56U9/yuc+97lyn15ME3YyTSGZwRcN4xYKg6ZAZVrb6Vm9lkKiz5tCVRXFtR30YACUi5uzAND8PnSfsY+vZG7Q/F7BCKu7B93nw3Uc/DUxrHgfqU2v46+vKY3gZFvbATAqwqU9loYbsRm4Sake8OPYDrrfjx6QzUrF1JJ+SUxHK1YewKUfP4u/3PUUPT1JXn7xjbKeL5cvkM1agx5zXYcdbb1c+9mfEAr5MUwd11FomiIUClJbV8m57z6e957/1rLGJkQ5lDXpsSyLtWvXcu2115Ye03WdU089lSefHPrpg5i9Mm3eAnarpxfd78MMh/BVRXEdx3sT7DMINs0j396J1d2LbnhTq4xQkMwbbeg+g9DCZmzXhh09+/pyZiddB00HQ0fTNdxCgUI8gVFRQdcjT9L5yJNkt+1A2TaaaRJ702GEF+03aA+e2KojCS9qHvEUxUTJTqZxHWfUJEmIcpB+SUxnJ526ojTCUijYXPuZW4nH92zK297aPSFKpy26uvq46Zu/Z+OGbVx48Rky6iNmlLImPV1dXTiOw7x5g0syzps3jw0bhlZryufz5PO7hnT7+voAr0Sj67rlDHVSuK6LUmpGxApTF6/VmyC5YTNGVbR/GpuFaztUH70cdB07ZxGoi2HqOkY4jNXVja++BmtnN04iheb3YVZGMCIh7M7uGVNyUA34mvY00KMVYBpofj9mrBJVKOCLRvDXxshsbye/s9ur1hapwM3m6F37HOnXt2OGg/jrYtjJNL1r1hNsmoe/umrEU5mxSsxY5aDH9uRvUP69lddMiXOiJtovgfRNU22ux9s4v4bG+TUA/NvnP8Cvf/kgr73aTjqz51PeBtI0DU3z/rsnHartuPz5zqd4+smXOefdx3H0MQdTUxtlflPtpMS3u7n+91BuMzHePTWtqrddf/31XHfddUMe7+zsxLKsYY6YXlzXJZFIoJRC16f/W/Opije/s5u4cvE1N6AcB9e2cbM50pEQ2UKOdDRIOpfFCIWwCzksHOjo6B9SB19zPQVDp3dbG7mAiVMdKVusk82KhGZGXQUN0HWsSJDQfvPR5tWR2rgFMxyg0N2NbVtQFQbdwAiaaBVVONkchuYSrKxA0zVUZQWFvhR6axuBwuR0zqORf2/llUgk9nUI04b0TVNL4t3lkMOb+PRnzyWZzGLbDmvXvMLTT+5diX9N12icHwVNQ7l797HcQ39/hiceW8f8plpOPOkIjli+dK+ebzjy91BeMy3evembypr01NXVYRgGHR0dgx7v6OigsbFxSPtrr72Wq6++uvR9X18fCxYsoL6+nlgsVs5QJ4XrumiaRn19/Yz4w5mqeC1fAHftC9DnLYK38zkwfMxrbsJfXUUkWyC+9jnyr7dBMomeL3gV2gzD+293Aj0UQstmcWMRQokM2gz4RKI4yhPqTU37xEfzmfjq68gYfoLt3WhdcfztnSirgB8YtCWsaaD7fGga+INhzOLvNZkGTS/9XgeyehO7iiCMMgo0EfLvrbz8/tm5EfBE+yWQvmmqSbyDDSyucdzxy3n2zZv54rU/oy+ZxbEd3NEqmg5D0zRQilc393hVUSfByy918MSjmzjj7as44x2rWP6mZZPyvCB/D+U20+Ldm76prEmP3+9n5cqV/P3vf+dd73oX4N3cv//973ziE58Y0j4QCBAIDK3Opev6jPhFgPdiIvEOFqytprp/AXtuRxe6qRNbcRjB2moAqo84GF+kgh1//hsqEEDl+z85dV0v8bFtVC6PpkDTdDTXnfZJRJE24Gta0rXSRqNuXxIbG6srQbCuxusYh2M7KDRqjl9JeGGztxFpV7xUta34ey2KP79hyLqf2OEtWD3xQdXg9oT8eyufmRDjnphovwTSN+0LEu/I3rTqQK759/P4y11PkU7n2PZGJz3dfaPu5jBI/84PSqm9HukZKJezuPOPq7nrT6s54S2HcfW/vW/S1vzI30N5zaR49ybGsk9vu/rqq7ngggtYtWoVRx99NDfddBPpdLpUNUfMDbHDW7BTaRLrXkQ5jrfGJ1JRqv4Vf34DVk+v17j4GlzcnBTvH6TSNFDTf4RnZtEAhW6aaAG/l9DYDprPRNd1HEMHZ8A91zQwdIINtdQdtxJ/TaxUmGC45MXqiZc2ng3UVZdKldupNKkNm4ckQkJMBemXxEw3sOBBTW2UjvZe3ti6k3A4wDNPb+TBB9b1FyJQGIaBbTu4k5jgjEYpePzRF3jx+df52MffIZXexLRR9qTn/e9/P52dnXzxi1+kvb2d5cuXc++99w5ZRCpmt1379Pi9ctU9cTrufRhQ2KkMrlUA10ULBcFnQKF/n57+pEe5rjcqMUlD8XOONsK967+/rm2jMlmoDIGm4ebyYBqQ3S3JVIpQ0zw03RjXZqJ2Mo2TswjUVaPpurcx6Y5OEutexAj6ByVCxT2bhCg36ZfEbNDUXFcaSWlqrmPFygMAOO3MVVx48Rls3LAdUBzUsqCUFD3y8HqeW7cF0zQIBnwc2LIfCxbU8/JL20jEU8TjaWzbmZT4entTfPO/b+eNN3by6c+8b1KeU4i9MSWFDD7xiU+MOG1AzA0D3/xa8T7ynd04qQxo3noSAHQdZRXQA35c18II+FH9m5Qq8N6ga6CHg6iCDQV7n13PjGEYXpEBxx09YSzYuAUbN2iC41DoTYA5zMuDroOrMMLeZqIjTV0rMqMVGEG/t/9S/6a0uqmjHAczGiklQsU9myTpEVNF+iUxmw1MiIrfr1h5AO98zwn8c80rbHtjOwsW7sebVh0IQFtrV2nU6MXnX+e/v/Yb0qncXsehFPz2lw+TzeT5/Bc/vNfPJ8TemFbV28TsVXzzm++JY3X3oix71+iDq0qjOJpp4otGMBvDVB3RQvdT69D8JnZvH7ZlUVwhoxmGlxBN0XD9jKWD5vOhsEujOmPRAn4UoGsammmgh7wk07W8AhOubVO96kgAelavRdk2ZlUlysoPGbEZuCFpvqvXS4xWHEZyw+ZBiZA3AlhRppsghBCiaPmbltG0X+WgAgm7jxqZpsEvf/F3Xt/STjZr9Rc8UOPtRoa4649PsmxZE+d/6KRJuAIh9owkPWJKFN/8dj/2NG4uj+b3eWvobRvlOOg+H65tAwqzIkzt8SsJNTeSeG4DuY7OXZUAlMLNZL1vdR2zuhJNg0KPlNcdVsHBLWS9pHJA0YLRaJqGyls4Be/34WbzKA0vQdU0KpYtpurwFjoeXE12RwearlPoS+KrrQbHJdvaPmiNT+zwFsyKMFZnD/76GiL7L8SIVHiFLdo70QyDqhWHyiiPEEJME7tvkhqPpwCNZ9dt5ne/eRTHmXj2c+M3fs/69Vv42g0XT37AQoyDJD1iyhTf/Hbc+xCarqM0jVxru7e4UtPw11ZTveJwYssPKb0BjhywmMz2NpTteOt6wgNKFSqFm8vjFgr75oJmklIltrGTHjeX95LK/mOUbZcKGJiRCgpdPaS2vEHypVe8REjXUK5Lrq0DIxig85Gn0AyjNN0NGDQFzk5nBhS2eAHXdkht2IwZqZBiBkIIMU3sPkUO4KRTlrNwYQN//uNqWlu7SCYnNgXu7/evY8urX+XSfz2bg1r2m7TqbkKMhyQ9YkpF9l+I/ZZjSm+CQ82NhPabT3hhM6FhFrKHFy9AMwwAVGHAJ0u6Dq6Lm937OcdzguOiB/zolSHseJ+XxIwxT0Hz+9DwkiB/bYxAXQ1mpIJ8Vy/xZ18k39WDchUqmwcNlOPiFgq4VoFAYz0oRc/qtbiFAppG/xQ4i/ia9ZgV4f7CFgEC/VPcpJiBEEJMf+89/60cd8Ih9HQn2dHWzTeuv52+vsy4j3/t1Xb+499+wgEHNnPhxWdw0qkryhitELtI0iOmXOzwllHLHA+kGwZmtAInk8N1BlSUkSpuE+baDqbP51Vl0zXID0169GAAtDQAqmCXNq5zbQczUoGdTKHpGrk2b2NH3TRxXQeVt7wy16aBZugUunsxFzST72jFzeTQ/SaFRApfbQxchdXZM6SqmxQzEEKImaE4CnTYEUs47cxV3P6rh/jprfcSj6fHdbzrKja90srvbn+Ugw5eICM+YkpM/12IxKzkr4kRXtQ86A2u1RMns7WV1JY3yGxtxeqJY0Yr8EUqMCsjGBUDFrr3ry8RE6AUmuZt8MoI87HdgoNmGrv2SNI0MA3cbI70th2gaUQO2B8n543uuPk8qr/AgRmt8A4r2Dh5C6u7B2VZYOgovPVb+R070XQNf31Nqaqbcl0pZiCEEDPY+R86iXsfuoF3vvv4cR/juorNr7TS050sY2RC7CIjPaIsrJ74uEZyioqlj/M9cZx0FiMcIlAbw19fi5OzKPTEcawC1ES9UsqOlKueKF9VlNrjVtK1eg1WvA8YZi8GpTAqwjipDLrPKO3WrVwvYYq0LCM4r56ux59GQ0OvjOBksijLopBIeoUS+kuL29kAZqQCpWkUuuNetT1Nw4hGvGmO6cygqm7Vq46UUR4hhJjBLrj4dB579Dl6ulPjap9IZPjHUxuoqY3KaI8oO0l6xKQba++W3Vk9ca+9VcDNW16BAsvCTmXIbNtBYF4doeZGUm+0opsG/poYha6ecZdgFh4nn6eQzqCbJrquM+Tu6Tr+WCV1Bx9E4p8vYKdS2OnsrmIFjktqw2Z8kQqMipD3uyrY6D4fjm3vqgyn614hg1AA3TSxuuPetDmlUMrFSaaweuITmuYohBBi+mtqruOzn3s/373xj+zY0TOuY269+a88+tB6Wd8jyk6mt4lJVUxgUIpAXTUoRXzNeqye+IjH2Mk0hWQGXBdVsNFDQXBcNENH2TZGIICTy3mFC6wCdiola3r2gJvJ0f3YP7DTGTTDQAsMqIRnmvhrYvhqY6Q3v4adTmMn0+A4oGvohoGby3m/JyBQE8NfW01oQRO+ygiaaaIH/JjRCL5YJbrfh6Yg2DgP5bpoKHTTJNjYgHKV99wMP81RCCHEzHXSqSv4n1uv5JTTx5/AvLJxO7/8xd9pa+0qY2RirpOkR0wqO5nGyVmY0UhpgbqTs0pvcoeTaWunEE+Qa+/EsSzsZMpbB+K4aKZJvrOLXEeXN5KgFG7OGw3CZ4Ihf8IToRwHN5cntGA+0aWLibQsxayM4o9FwTDIbmsj19GFsgdMfXNcXNvBTmdx8zlCzY3EVh2J4ffhZHMYwSC+qko0XfdGc2xv6qERChFbcSih+Q34a2KEFjZh+ExZuyOEELNcU3MdX7vhYs5913Hjaq8UbHx5Ow/cu7bMkYm5TN4xikllRitGXKBeLFRQHPWxeuIknt9A33MvY1ZF0QN+NE1HOQ6aYWBGwkQPOaB/xMEFQ/dGEIqJTsEecUG+GIaGtx5KKey+FEY4iGsVUI6DLxohUF0Jjkuhr38udn+pcJTqLxwB6N5j4eZGqlcdSd2bj2K/885i3ulvxVcV9UpWF2x8VZXUHr+SyP4LqTl+FUY45P0eNU3W7gghxBzx+S99mKs/+14MY+zCQ4WCza9/+ZCM9oiykTU9YlL5a2LEVh05ZIF6prV90Doff30tVmc3hUQSK95HcH4DwfpaXKuA1ZsgduQhBOprSG/djlus1KaU9/67YDPsy6fP9BIhMTwF2C4uyis0kUqj+/1ofj9GpAKrr7+Cjut6Ja2LNA3db+JvqEfXdXqffYns1m2l36ViVxnybGs7wKA9l2TtjhBCzF3nf+gkNm9u464/PTnm/tjx3hT/9Z+/4vs/unJqghNziiQ9YtLt/iYXoO3O+0rrfHLtnaRf346/vhZ/XS2FvhT5ji7MijC4Dpquk9q0hcQLGyn09qJrGvh9OAUbVSiMeF5d11E+EyWJzygURiiEk8ng5i00M49Sir6XN6GUwq0Mg1I4qTQUU0ulwDBQto0WDJLetAXdZxKoqx6yqehICc1oPxNCCDG7ff6LH2ZeYzU/+/H9FMboo9c8/QoP3LuG085cNUXRiblCpreJshi4QH3gOh+rN4HVm8DNWxS6e7B64vjrqlGuS76zB7fgAArdZ+KLhr16BbqO7vMx/PDOLr5YJb76mvEFaBp7e4kzkrIdnL4k2E7/dDfDK1ZgO96aKb3/JUExqFiEaxWwE0mC+zWiXDWhNVtCCCHExZe+gy/+57+Mq+2X/uNnPPS3dWWOSMw1kvSIsiuu87G6e8l39aBcb5G8k7PItXeQ39mNrzLCvFPfTO3xK9H9XhUw0Lz1O67CV1M1esU2XUMZOoWd4yiR6TO9DTjn4uamA++hq9CL66M0DaMihB7w70p8dM2bMqjr6IaBEamgYmHzoDVbVncvKBfXGWbPn367r+USQggxN5125ipaDl4wZjvXhRu+9htZ3yMmlSQ9ouyK63yU6+Lm8miGieYz+9fpAEqhGQah5kZCzY0YQT+Ztg6yOzpwchauXaDQHd+1D8xwXIXVtrNUOWxUBRtVrAA3BxiVEW+fnGEox/V+D8UEsHhLNA00HU0pr3S4UphBf6lyG5pGassbZHfsxE6l6XrkSeLPbxjy/PHnN9B2533suPtB2u68b9g2Qggh5o6rrnnPuNrF42l+f/tjZY5GzCWS9IgpETu8hXlnnkSwsQFfZQTd58MIBzHDQcKLmtH9fuxkGn9NjEjLMuxEEjfvLZT3xSpRGqMnKXNx1GacNMPbLHQ4ynHxxSoxq6JeAuQ46MEAms/0kh369yY1TWIrDvMS2MNbCC5owk5lUK6LYxUoJJJD9mPakz2bhBBCzG4rVh7Am1YtG1fbe+9+mh1t3WWOSMwVkvSIKRPZfyG1bzkaIxhEuS64Cn9DHRpgBP24jkNmayu+SAV60O+9UXcVbr6Ak8vvmnY1nIEJkezdM4idTA8//UzXqT12BfuddzZNZ51C5WEHYkYjBGpi+GJV+CojBOpqCDc30nDaW6g9biXQX2p83YveqJqrcLM5rHgfhWRm0NqePdmzSQghxOz3g1s/RX1D1Zjt+hIZNm7YNgURiblAqreJKVWs7Nb77EukN21BuV45an99LV2PPImTs1COg5PKoBkGeiiIk854+/Ho4xvN0f0+3Gx+9EYaY5bOnBU00A0D3e/DzuUHT2UD7GyOrkeexE5lyPcmYF4NwcY6nGQat+BQe/zKQeWnAbKt7diptPf70LyTuHkLlDto09GBezaZ0cigPZuEEELMbdd/42Nccel3yedHrspaKDj8Y/UGDjmsaQojE7OVfCQuppy/Jsa8k49nv/POZv47TqbuxOOwOrtL06A0DVzXRTMMVN5C95n9b7A1b8NMXQfTQPP5hj65rmMEg2OP9syFhAe89VO6hmv3j/Qo5a0Q7f9Kb34dxyr0l6R2cPIWbsHGjEZQrouvMjp8qWlN8+6/pqFsB5QakhwV13KhaeS7emVjUiGEECU1tVFWHX3gmO3u+evTrH78hSmISMx2MtIj9pni3i2Zra04OctLeHQds6oSvbMHM1qBrzKCm7fQ01nsVMo7UNfQdB1N10EzQfMSHGVZ4Lo4mSx6IICbye7Dq5tCuu4lMcNQto2r6yhsLxF0BrTTtP4paN6omGvbOPk8hXgfjq6POCoTam7EXx2jkOjzps1pXpU9q7uX+PMbiB3eUmorG5MKIYQYTlNzHWefeyxtrd28tqV9xHYF2+GfazZxzLFH0rxf/RRGKGYbGekR+9zAaVDKdVGWhb+6CiMYxMlZ6AE/jae/lZrjV+GrjOCLVRGa30DNUcvxVVWh+33eaEN/8uMWbNzcGNPbZpMREp4i5ThoSqGbu33GoZSXFOXyuLYNyitkkG3rILez29s8doQkperwg/BVRdE0DT3oJ7SgCd1nDluoYOCeTUIIIUTRSaeu4Fvf/Tgj1Nop6WiP09OdnJqgxKwlIz1inytOg4qvWU++qxcj6Kfh5BMIDRghyLS2k9u+A90w8FWEqVpxKJUHLSXf2UUhkUTZDkopVN5LdjRdB0NDFUbeP2ZOMAwCtdU4+Tx2Jjd8G03zNih1FRgGwaZGdEPH6uwuJTADfw/xNetxchYAejhIeL8mzJBXnCLf1VuqwieEEEKMpam5jvnz69i+feQ9ebLZPIXCOLakEGIUkvSIaWGkaVD+mtig0se+WBVGX5rUhs1UHrSU2Koj6Vm9BuJ9Xm2C/o+LlHJLIz9zlRb0o2k6gXl15HZ2QzI1fDufiWaYONksmqFjhoP4KsLku3rpffYlslu34eQsNF3HyeXwRSsI1FVjdfdSSCSx0xmMgF8KFQghhJiwttYu8tbIxQyK1v1zM29aNfYaICFGMrffFYp9zuqJk9naitUTH3Ea1K7SxxVouoYZrSiVPo4d3sJ+551N/VuPIdBQi+7zeet9DB3m0qdCu+9T5DPxV1Xir67CivdhJ5MjFm9Qto1r5b0iB45Ltq2DTFsHmq6T3rSlVGBC2TZWbwLN70fTdfy11RjhEDiuFCoQQgixR3q6k9TVj12++vZfP1z+YMSsJiM9Yp+JP7+hNFXKCPqJrTqytAje6omXRn12rflJoyorsJPpQfv6mNEKGk4+gdjyQ+l6ci29z6xHDbcvzWylabv2KdI0/I31mD4TzTTx1VYTX/ci2KMkgK5C8xkYlSGvAES6D9tOElm2mOwbbbv22amqhM5u7ERfaWQnUBuj7sTj0A1DChUIIYSYsJraKDU1UaprIvT2DD8jAaCvL8sD967htDNXTWF0YjaRpEfsE8Upa27eS3jcvEV8zXrCzY2D1o2UkqFVR9K7Zj2FvhRoOv6GutK+PgMTpthhLSRf2kQhnQHGHi6f0TQNM1YJtoOTz6OsAmasklD/iFd2RydOaweaoaOGy3k0DSMSxs3mMStCVCxdRCGfI2j6vGp6NTGyW1vJ98QJ1MRQVh5/dQzNNEtrr6pXHUlk/4VTfulCCCFmh2IVt02vtI7Z9m/3/1OSHrHHJOkR+4SdTJPvjuNalldG2dDR/X6yre30rF6Dsm3MqkqU5SVDTe88g2DTPPTWNqorIvQ8+lRp2pWdTNGzei0a0PvcS1jdPXNjHx5dR/f58DfUkd/ZRcHuw05nSG16Dd3vQ/cHcAsWmqahBo4GDThe2TaaaaD5/KS3tmH5DYyePjTXpWv1GlDgZLLYqTSBmhgNJx8/qMCEjOwIIYTYWyeduoItW3Zw6813j9rujdc7aGvtoqm5booiE7OJJD1iylk9cZKbtmD3Jb3NRMMh3GwOx3bo2/gq2R070Q2dQiKFrzYGrsJOpgkumE+gkEfP2YP29XFsh9yODtrvfhAr3jc3Eh4Ax/EqrHX3oBkGejCAm7NwrAJOJgd62tvUtVjSWmO3e6NQjotREQbHxor34VRH0QzD2+8onaVi/4U4qTTKdak78bjSqI4kO0IIISbT2886ml//4kFSqREqjQKtrV30dCcl6RF7RAoZiCkVf34DW3/xezof/QeuZeHm8jjpDJrPxAj6ybV6C+jRdZRS5Du6SsULigbu62Nn8+TaOkApNJ85dDRj1lPguiirgLJdL7EpFjVw3V2lqJUCNDBNjEgYM1aJGYmg6RpGKEigoR7d9KGbBmYkDLrhPY3j4K+tBk1HH2sjBSGEEGIPNTXXccppbxq1jWU5vPTC61MTkJh1JOkRU8bqidOzeg2FRBLd5wPD8N6gaxq+yihmxKvO5q+rBlfh2jbKdYkcsP+gkQV/dRWxVUeCppHd1oabt3Adh0IiOfeSHkWpNLcq9E8VHHgPFBjRCvCZmLFKGk46jmBDPaF5dYTm14Om46QzXkNdw7VsrN4+3Hwet2CDYUgpaiGEEFPizLOOGrPNiy9snYJIxGwkSY+YMnYyjZ3JoWlgBAMYwSBooBwHTdeJrTgM5Sqsrl4c2/bW9VSEiC0/ZNDzWL0J/JVRqlYcjhEOesmTUt76oNFoo/9MDwcndkHmNBn5KE5fGynfc13McAhfRRhfNIpyXcxoBD3gR/f7cHMWhb4Uyna8+2Bo6D4TpcDqSUgpaiGEEFMiHh+5eltRW1v3FEQiZiNZ0yOmjBmtwAwHsZMpXKuApmvofj/+6hjzznwb/lglPU8/i+pPeFBQ6EvTt/FV6o5bCUDq9e1kX34VN2eBcnHyeXSfgbJc1MBFK8Mt3B9tEEjXCS/aj9SGV8c/WrT73jj7kOb3oVx3wFQ2vP9qGqBhVoQxI2H89TWlqYGa349bsHEti3xHJ8pVGBVhKubNQzMMCr0Jao5eQeSAxZLwCCGEmAJj96tbX5NiBmLPyEiPmDL+mhg1x6/CVxXFLRRwCzb+aq8iWGT/hdjJNGg6mt+HEQxiVnprThLrXsTqiWP1Jkhvfq1UtU3TdZx0FmW76KEgut8EXQdNQwsG0EzDW8gPpWl0w9I00HQCtdVovgmM3kyHzU9177o0XQfDQDMMNNMramBEK9ADAcxoBb7KSKm8dGzVkRSSaZIbNuNmsqAUStPQTANVKIBhoCwLMxKWhEcIIcSUOahlP4JB36ht+voy/O72R6coIjGbyEiPmFKxw1sINzeSbW0HINTcWHpTbUYr0E0dZTsY4RDKKqAZBk7ewk6mcZXCtezSZpn+2mqyO7tRVg6nUADDS5hUoYCmFHooBLqGk8l6JZutEfbtUQpch9QrrzGeT5n2mq55U/L2JmkqDWppoIFrFbyCBgCGDv1TBmvfcjSVBywplZe2euK7rrA/EdRMA+UqlKGhbJd8Zw+BWFSmtAkhhJhSTc11rDqmhccfeX7Udk898TLvO19Ge8TESNIjppy/Jjbsm2l/TYyqFYeRf+Ax7L6UN11L01BOgr5NW4itOAzdb2In0/iiEazuXjTAV12Fm8ujbAfNMAgfuAQnkcS1XYyAn0Ig5ZWyHoXu93uFE0ZKjAbSvCpo2IVRp8zp4eCwo0uaaWJGI9iJJMqx96zEdvGY4nqegVwFhlfxrvKAJYQXNQNe5bz4mvVY8SSF3rg3+81nemt5lFfpTdMgsmwR9SccJQmPEEKIKXfc8QePmfS4riulq8WESdIjppW641ZipzN0P/Y0SrmAQrkuPavXYoRDVCxbAi+/Sr6rF5SLWREivLC5f21KATuZouHNx2BGK0obaPY++xI7H3x85JPqOnooiJPJjtpm1343GpruJT5quNEa0wTXxS04MHCYXtfAVaj+OJXrjJzwDNlTZ6R2w61dUt5ol26UKq5ZPXHia9Z7UwPrayj09UHB8ooxFK9L1zEqQlhdPYOezuqJy2akQgghpsRxJxyC6TOwC86IbSLREDW10SmMSswGZVvT8/rrr3PxxRezZMkSQqEQS5cu5Utf+hLWWBW2xJxXecD+mFWVGIEAZiiIGY2gHIfEsy8RqI0x/9zTmf+Ok5l35kn4a2LYyRS6zwTXwYyEh5RWrl5+CIH6mpFPaBhg295ox2gG7H+j8paXlOga6DqaaXozzXwmvmgFmqFDobBbQlKcTmbii1WWSk0PSwEB/+jFEkZbpwRULFtUSlLsZBonZ2FGI5ihIMHGBm/NU3+xA83vI7ywiUBdLU7Om04I3uhQ2533sePuB2m78z7iz28Y/R4JMY1JvyTE9NfUXEco6B+1TcGyZZRHTFjZRno2bNiA67r86Ec/YtmyZbzwwgtccsklpNNpvvnNb5brtGIWMKMVmEE/dl8SPRgC20YP+FGOg5vN46+uQq+tBsBOZ4ivWU++qxcj6Kd61ZFkWtuJr1mPk7Mwgn5iq44ksnQx+fauYaeDGT4TJ19A9/vRHOVVjttd/1Q7ze/3fu663tQ0NO+jAw2vCINpeNPkHO88ms8HPhNsp3Ru5dhYXb3DT00Db02OqyBv9Rcn0AdPuyuOOik1fKU5XcNfEytVvCve02LVNjMawfCZhPebT3jpYpIvvoIe9OOvqSKdzaLpOoW+JKktb+waHaqrxk6miK9ZT3jAOiwhZhLpl0Q5PPvPzWx7YzsLFvbxplUH7utwZoWamgjJ5MizL9p39EgFNzFhZUt6zjzzTM4888zS9/vvvz8bN27k5ptvls5FjGrg2h4nnUEP+Esll/VQYFDbcHNjaWF+qLkRgO13/NXb46eqEmXl6XxwtTfyYuiDEw1NA0PHCAVQ6Zy3Z9Booz1KoeyCl5AA6P3V4WwHpQO6jnJc3EJ+wN45CiMYQOULqOKnyYqRR5VMA3+syluDZNvoPtMr5qAA20YzzcFJ2YDpbZrPBE3DjFRQ++ajB2/oWhMj0rKMxLoXsdOdmJEwtcevourwFoLz6voTxziOAU7OouvxZ0C52NkcFQub0XQdMxoh39VLtrVdpruJGUn6JTHZvnvjH7j3r0/T2BRl+xtx3nbycj7/pQ/v67BmvFNPX8lPbr13xJ8rhazpERM2pWt6EokENTWjTDMSol9xlCKx7kWU401bq1p5BPlopNSmuDC/OKJTSKXJ7dhJtq3d22emL4lZFSXf3YMR8KPrOqVUwzTQNG+foGDTfDJbt2H3jbApmqGD43rrX5zd5hgX1/QUbG+0x/BjhINeYQXHRjkOTiaL3l9Ku7RWpzhCowGhIGRy3veOS6Ev5SVNuu590Z/bGLqXdA0c3FEKPRhAD/gJL16A1dkNmk5qw2bMSAXh5kbsZJpMWzupDZtxbQfd1Im0LKPq8BZgV0W99PYdpP75LD7TVyoU4aSz5HviBPqnEbpWge7Va1CuQtN1IgcsJrb8UEl+xIwl/ZLYU+vWbuIvdz5FOpOltj5IMpXjL3c9RTgc4FOffd++Dm9GO+7Nh46a9ICSNT1iwqYs6dm8eTPf+973Rv00LZ/Pk8/nS9/39XkVt1zXxR1pKtA04rouSqkZEStM/3hrjllB5MD9S6MKZlWUXGcnruti9Sbo7Z965a+LkWnbSfr+R3BsBxwHXdfAdcm0doDj4BZsr/hAwIeyitPSdAKN9aBruI7j5RL9+97gDLgnxalquuG1cZWXlOx+3xSovIVrO96UNMe7v8pxUT6voIEqJk+FQumYUsKj4Y0mWZY3AhWpwE1nUY4DmkZgfgP5zm40TfOep3/Ex3UcKBTI7uggUF1VKuKw88EnMIJBXMvC6k3gq4wQap6H3b9HT+TA/fFXVwFgxioxEklcF4xoGHQNX20MK5VBOS65rl40XUeh0Hwmru2Qa+8ks6OD5KbXqDluJVWHHVSGv4KRTfe/393NxHhnu/H0SyB901SbKfHef+8akqksWv/aSk3TUErx+zseZ8Giet79vrfs4wiHNxPu70svvOb12Xj31VvCqpVWotu2M22vYSbc34FmYrx7asJJz+c+9zluuOGGUdu8/PLLtLS0lL5vbW3lzDPP5LzzzuOSSy4Z8bjrr7+e6667bsjjnZ2dM2Khqeu6JBIJlFLeJ/vT3IyJN2hCIY+7M1uKt9DVS1y5+Cq9Igc5TaEiodI2O8px0Qwd1+wfYaF/wT5eyWjd70P3+UiFfKBs8hVB3KAP8F5c1e4jOtD/3F6yVFpPUxqxGVBFzTC8xEgLY0VD6D5v5ETz+8h3dHmxqMCQ43S/D3TdS9CKG46GA/3tFZZPx62KgKmjXLe0zkfz+9BNH8ouEAz5MQwNJ+QnF4/jM4PokSj5Qg5Nh7xro1VWUOhLobe2ESjseiNnFfLkQz56czmMUAgnl4XGGgKHtaDrOnYmS/qlTRihAFZXNypWAa5LwdRIPfcCNSHvOqfKjPn77TfT4k0kEvs6hHErZ78E0jdNtZkQ76ub23hl4xaWLqtF0zUa50e9LRb6pz8/ufpZDj50PjW1lfs40qFmwv395z9fYumyWoBh76/PZ7J9exuGOXKFt31lJtzfgWZavHvTN0046bnmmmu48MILR22z//77l/5/W1sbJ510Escffzy33HLLqMdde+21XH311aXv+/r6WLBgAfX19cRisYmGOuVc10XTNOrr62fEH85MjtcOhHDXvgB9adANtO6EV8Gtv0KaWyhgBIM42Ty+mhjWzm5v3x+89S2BWAXB/eaT3vQ6Tt7CTOe9EYx8HjebGzUOze/HjIRBKQq9Q//xmVWVXklqQPObVGoGTlsnvqpKgpqBk8ngZvNDnzfgxwwGvNEkpXBzFrrfBE3H/P/bu/P4KMt7beDX8zyzZp0sE8gCgSAlUhAwKUGhlU0QoeJy9JzW+krLq9VSyzlqBWr7Uk9VpGB7qrWK1Re1WiuVKnrEBQFlEY8SloIQNkEgYck2k8k223OfPyYZEzKZmZA8s+X6fj75I5NnZq5Mhtz8ct/3705LgdfmgLuxpW2GSfhmiRQZilGFUFuhOp3QewFz7gA4z9dAtjchJWcAJElCU6sHqqsFyampgHACkowB+Xn+mR4AULOz4ayth1LxJdRqm68JRMlo/wyOq94O+eAxeOsaoNQ3QlJ8SwTN2WZ4HE3I0JuQlJNzcT/cixDP7994yGswBO+eFEu0HJcAjk2RFut5P9q4B089sQ5VVb72/lLbH6+OHa2DaPsj1vlzzfB6dMiJ4O/EcMX663umqhaf/89xNDf7/qAQ6PXV62QUFOQhJycrmlEDivXX90Lxlrc3Y1OPix6r1Qqr1RrWtZWVlZgyZQpKSkqwevXqkC+m0WiE0Wjscrssy3HxgwB8/ziZVzvteU1ZGcgoHQPbzr2+ltWKAn1KMmSzCc5zNb5DS1OSoDOboE9Ngqzo4KquAQAY0lJhzMmG8/QZCI8XOqMexpzB8NodcDe1wOP2+BoadFzi1nbGDiQJ6ZcVI2VwASSjAefe29yl8FGMegiXAUIIGCxpUE+e82W2ZsLb2ASn1wsRoOiB0wVVUaBLMkExm4F0wNPSAtXpgrumHqrHDV1SEgABr9Pla2wgyZAVGVAkSKoXHnsjmhqbfQODEGg9cxbm3AHQmYxwO11w1TfAmJGGjNIxMLV1wOsodeggWL5xCdTG5i6NCtpf87pPyv1L+wwDsgGXCzqTAYa0lIi/j+L1/RsPeeMhYzstxyWAY1M0xGreqsoarF2zFXW1Dv+sA/yT/8J/m7PVBY/HG3P528Xq6wsA9XWN8Li9QV9fWVb830MsiuXXN5B4ytubjJrt6amsrMTkyZNRWFiIlStXorq62v+1gQMHavW01I+0b8DvuFHf2+qCaUA2UoYXwTJ2pL99tazIMOcNQMrwIiQNKUDNxzsAIWDOtaKl8iycx05Cl5riX8GmmMzwtrR+3bhA/XoZW8vJKjgrz0Ixm5F8yRA07KuA6nb7CwFXTT10KclQUpLgcrohVBWm3Bx4m31NAbyNzV2/mbaW1/B44GlqAdoOFvWcq4EkS5D0viYKkizBnDsAANB8+hyE2wWoKiRFB8OAbLjO1UKfngbJYIDrfDXcdXZ47A7fbFFKEnQmY6cmBoF0bAne3Wtev+cAmo586RuAJAkZpWPYzIBiHscl6q26WgcaGpr9Mw7dMRj00Ot5/vvFcLs98HiCL1vT63Xs3kY9ptm/yA0bNuDo0aM4evQoCgoKOn0t1C8LonAZMi0wZFqQVJiPtBHDurRSNmRa/IVR++3NX1XC2+qCMTsDqtsNT7OvWYAhIx1uu93XJEACFJMR3qa2AkWSIOkUCCHgcTS2nWXT6OsQl5oCb3Ozbw+OTge1uQW6lCQMmDO97T9VFZCEgLO2HqrLDdG2v6jTfiCdztfeOjUFSlISXOerfZ3YIAC9AYb0NEhKE9T283qEgCkns23/T9uSOnsDIEtQks1wna+FZND7lgWoqq/oyx8I4XKhseIo0kYMu+gixZBpwYCpV8I1diRbV1Nc4bhEvZWZlQqvV4XLFeA8tw5UobK72EWy2ZoCHkHXkSyDry/1mGbzWPPmzfNNRQb4INJCe/Fz4X/AL7y940GdqtMF1eWGbDRANuhhyMoEFBmirZiQDXpAUaCzpEOfYfEVP5IE2aCHpMhwOxp9Hde8KiSDwbfkTK+DUFXIioLkIQXIvKIEQlWhtjohyTJkvR5Ksq9DGuS24sftBjweuOvtcJ477+vIBgCSDOHxwN3ggGIy+ootRzMgSci6shSWktGAJMFVZ4ek00ExGX3n6DQ1w9vU4m/cICky4PX6CrRWFzyOJs1eb6JYxXGJ+oJer0CnU4Je09ToxI7tByKUKNEIKErw/55aMlI4y0M9FvuL94j6mCHTAkvpGECS4Hb4Zmh0yUmQ9ToIlwsmaxaM1iwoKSkwF+Qi97vTUfi9uUgqzAe8KrxOJzyNzVBdHkiyjKShgyApCjyORnhaWv1FRUvVWQBA+qgRGHDNFJgG5sCQafEfMKqYzdClpQCyBMlg8B0uKoSvK5tXhaTX+Q5jlQC11QnFZELO1d9G/twZyJs7EwJAY8VRCK8KWacgaVih7z9wbrevkFJVqB6P7zEFgLaMiskAXWoyXHU2NH9VCVedLZo/DiKiuFFX64DZbMSISweFvHbtmi2oqqyJQKrEMqJ4EAxGfdBrRo8ZGqE0lEi44JT6FVedDR5HE5LyB3bZD+SsqYdiMiBn6kSYL1gS56qzwV1rg6Qo/kNHheqFISsTA6ZOhN6ShtqtnwGSBMVkhC7JjIZ9hwAZcOmNSCkaDM+3x6N60ydQXS4IjxeQPVBgBGQZutRkCJcbnpYWoG0ts6TIkBUdJIOAPiMdA66ZjJSiwf7vw9Z2TpFpoBUeRyMcXxz2FUfJSRAer28pnKpCMRmhmE3wOJqgmAzIKB3j3+vUfrCrpXQM0r75jWj+aIiIYl5mViqSU0yw2bo5zLqDqjN13HdykUJtVr905JDIBKGEwqKH+g3bvoou/9G3jC6GLjUZ+pRkAIA5f2Cn/UDtWirPwllT1/ZZ25I0WUZasW9vTNrwIjgOHPV1ijMa4G5sRkvVOTjhhXzwGDJKxyApfyAUkwHmgVZ43F546m1QXb5GBG67A/r0VMhuN1RI0Kcmw9vqhOp2w5CRjpypE/0FDwB4HE3+fUmSLEOXmgJnnR0QApIkQUlOgldpher2wHrVBKR22O8EAFXr3geE8DVLsDeg7pOdMOUNiMSPgYgobuXlZ2POdRPw6subQl7rdXvhdgff+0NdbXivHE2NwY+O8J3ZQNQzLHqoX+g4M2LMzoDH0ehrd93Y5O/6ppgMEECXPSq2fRU4995meBocX98oAVAFmo6fgm1fBZLyB0Kf6ju7R6iA82w1JFmGPi0FaHHBtnMvJABCFdClp8F14rRvKZrH62tk4PHAbbNDMZmQNX4ssq8oQUulb3lcx0KsXcd9SbrUFHgcjdCnJkOSJXgam+FtaYVQVehTU2AcYPU3fADgb+QAWYL7bA2E1wPVq8K+5wvgm5do9SMgIkoIU6aPgyUjBYvvew52e/f7I/V6hR3ceqiqsgbvrd8JVVWDXscmBnQxuKeH+oX2mRFdaop/ZsTtaIZ99xf+QghCwLZzb6c9Lq46G+o+KYfnwjbTAoAsQXi9vmIK8O8TctX4DkI1DsiGYvTtn/G2+g5ZU0wGtFb5Gg0IpwsQArLJCNlggD49DbnXzUDe7GkwZFqQProY6aOLAzYKMGRakFJ8CbytLrSerfY3Nsi5+jswDbBC1ushSQAkCTUf74BtX4X/vrq24sh5rsa3gbttGYF9/2G0nK3u8lxERNTZuJLhGHVZYdBrnE4PZ3p66FDFKZw+dT7oNUnJBowoDr2niuhCLHoooqK1eb7jzIhQVd+BpjoZoq2jWXsh1LGzmavOhsYjJ+BpbAQkGZJB7y8QAACqgLuhEc62fUKW0cXImzsTOdMmwZybA0Wvg1CFfy+NOX+gr1Bpaf26XbUiQ7hckIx6KGYzjBnpYX0/tn0VaKw4CtXjhaTI/rN3LKOLkXP1t6FPT4FpgBXJhfldijlDpgXJw4sgVNW3N6ltD5Grtg62Xf+Eff+hPnvdiYgS1WVjg8+Me70qbLbed8rsT+pqG+F2Bz+j58pJo7hPii4Kix6KGNu+ClStex9n1m9C1br3O80+aK1jxzZnTT0gSbCMGwVdSlKnQqi9s1l71tr/2Q1PYxOg+oqLjuuIJaMewu2Bx9HkbzHdPkOTeWWprztcQ2OnwzuT8gbCkJkBQ04WJJ0CtJ0yrRgM0Kcm+ffcBNNxqZ451wrFZERjxVF/USMrCiDJMGRlBCzmACBj7EiYcwdAn54KyLKvDbfJCEmSYSv/Jzu6ERGFcNmYob4Z9SC+PFYVmTAJwm5zhLxm3OXDI5CEEhEXm1JEdLenJinAfhWtWEYXdzmoVElJhm3nXn/ntozSMQDQqaiA6vUVAQL+dtOQFQiXBwICkqKg+cSpTo0GLKOLYcobALmyCgPy82DKygDgm3HSpSRBJwQUkxHOczW+80fTU/2FUSgBmxjU1MPjaIIh0xJwv097MdfOkGlB5pUlqN36GYTbDdlkhMGaBTU1Bd5qm/+xiIgosHElw5GalgSHo6Xba86frY9govi3q/xYyGsys1IikIQSEYseiohQ/1GPlqT8gWj/Q117w4D2jf7tWc35AwFZQfqoEVDMRtSX74Orps631E0CIIDGIydgGftNAPi6qMpIh9HthKHDkrX2GSfbzr2QJBnmvIFIGT4UlrEjg74O7a22danJIYuajs/RsZi78PEto4uhS07Cufc2+5ouWFLR2NLSpUAiIqLA8nIzcchR2e3Xvzx2NoJp4tvaNVuwZ9eRoNdIEmCxsOihi8OihyIinNkHLXQsFi48m8ZgzYKrurZL57bAndGS/IWJp8WJmi2fQgIg63QwZGVAeFXU7zmAlq9O+R8vveQyICejS6ZAM07BBGy1HaKoCfc5fOcHlbU9lg1INcFSMpqzPEREYcgfnINDh7svevbvO46qyhruQQmhqrIGb7/xCTye4F3bzGYDO+LRReM7hyIi3NmHvtSxWJBkGd7WVuhTk2HMzoCrth62XfthHJAdcLldsKwZY0ei6chxCI8HuvQ0CJcTqgCajnwJWa/7+vHK/wndpBIgJyfg6xHO997dssC8uTNDFjXhPkd7geRqaES9uxXpw4pC3oeIiICiYQOx6cPuvy4E8Phja/D4kz+JXKg4VFfrQGVlTcjrcvOy2K6aLhqLHoqYns5whKvjbE77Y15YLDjP18JVb/dv7peNBgiPB4rRGHC5XbCs7fthbDv3+mesUoYPhePQMUgGA7zNLZAMBngcTZBbnL363oItC0wqzO+z19CQaYHOkobG88FbhRIR0deKhuWGvKZ851HO9oTgdnvgcIQ6kBSYevXlfB3porHooYgKd/YhXAGXfo0u7lospKcB1bXw2BugGA1QnS5IOh28Tid0yeZuN/t3l/XCoggA7PsOounLWkiS7697uvRUyGZjr76/aC0LJCKi0EYUD0JOTvCjBlpbXThUcYr/WQ8inL1PebkZmH/nrAikoUTFltUUty6czel4Hs2F5/IIlxOGDAsknQ7OmnrIRgMsl4+CYtD7W1j3dLmdIdNywWyLryWC8He1DtHLNMznuLDVdnc5o3UGEhFRf5WXn43rbrgi5HXLH/lbBNLEr93lh0NeUzq+OAJJKJFxpofiVqilXxfuy8mZeiXMFyxZC7Q0LpBQ13kcTZANeiQXFQJeL6AocDsaofZyeRsQ3rLA7ma8iIhIW+PLLsUn2/cEvcZW34Q1f92MW74/JTKh4sjmD3dj29b9Ia/7RnF+BNJQImPRQ3Er1NKv7oqFC/fnXFT3tAsKivYswuXslKW3y9vCyRkLZyAREfVXmVmpGDgwEwf2B1+iVb7zCIueC1RV1uDZp9ejtcUd9DqTyYAJV46MUCpKVFzeRnErnKVfXZeg9UywJXTtX2/+yteu9MIslpLLoE/V/jyB9hkvXWqKf8bL2+qCx9Gk+XMTEfV3uXlZGDmqEFKIJc27y49i84e7I5QqPhyqOIXjX54Jed2YccOQm5cVgUSUyDjTQ3GtLzvCBVrCFmwJ3YXn/lhKxyBv7syvDxG1pOF8BLqhsdkBEVF0jbv8EuTnZ+LUqe7bLjc0NOP3K9dixKWD2NSgzcYPQheBGZYkTLt6XATSUKLjTA/Fvd7O5gC+JWxV697HmfWbULXufdj2VQBAl4YI7QWF6vUGnAEC0KetpMPRk2YHRETU9zKz0nD3PddBloPP9pw/Z8MvHng+QqliW1VlDTZv3BPyuvuX/Bsys9K0D0QJj0UP9XvBlrB1V1DIihJTS8oso4uRN3cmcq+diry5M5HOJgZERBE1edpYTJgQ+ndvxYFT2PDezggkim33/+wZeL1q0Gumz7gck6eOiVAiSnRc3kZxK9zOa6EEW8LW3UGlrjpbzC0p6+szkIiIqGf0Rn1Y123dsh9XX1OqcZrY9ZM7/gtffhn6bJ5xJZdEIA31F5zpobjU3XK0i9HdErYLDyrtuGyNS8qIiOhCJaXDw7puy+a9WLtmi8ZpYtODi57Hrp1HQ16n1+twxUR2bKO+w5keijuuenuftmhuL2A6nukTTgHTl00UiIgo/t3y/Sl4fc0WnPyqOuh1ra1u/OmJtwAAN93ynUhEiwm7y49g84d7wrr22u9+C3n52VDV4EvgiMLFmR6KO1q0aL7YPTF90USBiIgSx389tQAFBaG7szU1teK5Z9ajqrL7jm+JpKqyBr/+5UtQVRHy2vT0JNz+o5kRSEX9CYseijvhLEe7GCxgiIiot/Lys7Fg4Vzk5KSHvLa+vhFPPbEuAqmia+2aLVhw5x9w7mx9yGsVRcadP/ku23pTn2PRQ3HHkJEelf007QeRth9MGu3HISKi2DRl+jg89Og8GAxKyGs3frAbDy56PmFnfNau2YI/PfEWzlSFLngAYP6ds3DTLd/WOBX1R9zTQ3Ep0vtpbPsquhxEarmIttB99ThERBTbxpUMx+RpY/HBu+Uhr934wW7s23sc/3H/TZgyPXEO4qyqrMFbb+5AU1NrWNdPnzEOP7pzlsapqL/iTA/FrUgtRwt2jk80HoeIiOLDDTdNgt4Q3t+Xz5+z4dln1mN3+RGNU0VOXa0DhytOhXVtcooJP/nZXI0TUX/GoocohL5qnKBFAwYiIopd40qG4+Z/Db8724kvz+C/Vq7F5g93a5hKe7vLj+Dee/6E/3v74xCh+xYAAH5yz1zu4yFNcXkbUQgdGyf05iDSvnocIiKKHz+790bkF2RjxbI1Ia8VAjhUcRq/W/E6IAEjigfFXSHwxO/+gb/+ZVOP7jNnbhn38ZDmONNDFEJfHUTKA02JiPqnKyaORFp6UtjXV5+341eLX8D/W/JCXM36rPnr5h4XPLl5mfjlr2/TKBHR1zjTQxSGvmqcwANNiYj6n7z8bIwvK8aHH+wK+z4ejxf7953A8kdegyUjBeNKhmuYsPfuvedP+GTbgR7dx2TS4413/lOjRESdcaaHKEx91TiB5wEREfU/P/nZdUhOMfX4fjZbI+772TNYu2aLBql6b3f5EVw365c9LnhSUkz4aMfvNUpF1BVneoiIiIg0lpefjV8uvRVP/P4NnDtbD1UNc4c/gOZmJ1Y+tgaHKk5h3vyZUd/nU1VZg7paB1b/+V3s+ORgj74XALBkpOD//+V+jdIRBcaih4iIiCgCpkwfhxGXDsKhitPYu/soXvvrR2F3NxMCeOuNHdjy0T5c+90y/Mst345K8fP8s+ux7h+foKbaBlXt+f2Tkoy48+5ro164Uf/DooeIiIgoQvLys5GXn40p08Zi0OCcsLq6dWSrb8RfX9qINX/djLIJl2LO9RM07fJWVVmDQxWnAQj8/W8fY9fOoxf9WEajDj+YNw033hx+G2+ivsKih4iIiCgKbrrlO7DZGvHnp9f3+L4ej4rt277A9m1fwGQ24PLSS3D/oltw7mw9Tn51HoMLczo1P2hfkpaRmQIlzP/9Pf/sevz9tS1w2Jvh9V7EtE4HuflZ+MH/mc7W1BQ1LHqIiIiIomT+ndfCYknB0398C42O1ot6jNYWFz7ZegA3bv01JFmCBEBRFAwebMXUGePQ3OTE3j3H4Gx1w2TSY/L0kZg85VvIL7B2epyqyhrs2H4Q27ftw8H9J1Ff39jr72/U6CG49fZpcXnmECWWiBQ9TqcTZWVl2Lt3L3bv3o2xY8dG4mmJiIgC4rhEseSmW76DKyaOxNo1W/HKSxt79VhCFRAAVNWDY8fO4NjTZ/xfkyRAkiS0uhrx4vObodPr4HGrkCXAYNSjtqah1zM67TKyUnHHj2dxKRvFjIi0rH7ggQeQl5cXiaciIiIKieMSxZq8/Gzc8x834OnnFkJvUDR5DiEAAUCogMPRiroaBxrsTbDZmnD+nK3PCp78giw8/+J9LHgopmhe9Lz77rv44IMPsHLlSq2fioiIKCSOSxTLxpUMx38+Mg+DBluh6LQpfrSi0ymYPXcC1r79EJeyUczRdHnbuXPncMcdd+DNN99EUlJSyOudTiecTqf/84aGBgCAqqpQL6YvYoSpqgohRFxkBZhXa8yrLebVVrzk7KmejksAx6ZIY17gqqljkG5Jxqqn3oZXFfhi/1d99tiSJPmXufXVn74vLx2Gm26+CiOKC5Cbl9WnrwXfD9qKx7wXS7OiRwiBefPm4a677kJpaSlOnDgR8j7Lli3DQw891OX26upquFwuDVL2LVVVYbfbIYSALEdk5WCvMK+2mFdbzKstu90e7Qh97mLGJYBjU6Qxr09eQRpu+tcJ+PyzQ0hJkVFT09AnjyvJEgbmpgKSBNHDQ0UDychKw90LrkVmVhoAL86fP9/7kB3w/aCteMvbm7Gpx0XP4sWLsXz58qDXHDx4EB988AEcDgeWLFkS9mMvWbIE9957r//zhoYGDBo0CFarFRaLpadRI05VVUiSBKvVGhdvHObVFvNqi3m1ZTAYoh0hbFqOSwDHpkhj3q9Nn5GDb476BupqHfjsfw7iH3/fBlt9E9RwTzQNQJIkQAgcO1oH0YvHMegVfGNEAW64cQqKL73koh8nFL4ftBVveXszNvW46Lnvvvswb968oNcUFRVh06ZN2LFjB4xGY6evlZaW4tZbb8WLL77Y5X5Go7HL9QAgy3Jc/CAA3y8T5tUO82qLebUVT3njIWM7LcclgGNTNDDv1/ILrMgvsGL0mCLMml2GQxWnUVfbAJutETu2H8TRw5Vobe3BjKPc1tBAiLBnegYMzMCtt02FotOhsbEZeXlZyM3LQmZWakT27vD9oK14ytubjD0ueqxWK6xWa8jrnnjiCTz88MP+z6uqqjBz5ky89tprKCsr6+nTEhERBcRxifqLvPzsTkXG/DuvRVVlDQ5VnMKO7Qex+cM9cDiaIUmAXq/A5fJe9HOlppox9vJLMOOaElx9TWlfxCeKKs329AwePLjT5ykpKQCAYcOGoaCgQKunJSIiCojjEiWi9kJoyrRxmDd/Bja8V47PPq2AV1WhyDIyslLQ2uqByaRDc5MTe3YdBSRAr1MwZGgOBgzMgqp6UVPdgPPnbDCa9MjPz8a//NtVmDJtbLS/PaI+E5HDSYmIiIhIW3n52bh9/kxcfU0J6modAZefVZ6uxunTVSgoyEN+QecZ0qrKmm7vRxTvIlb0DBkypFcb5oiIiPoSxyVKVBcug+soNy8Lis6LnJysHt2PKN7F/o4lIiIiIiKiXmDRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFCY9FDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFCY9FDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFCY9FDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQtO06HnnnXdQVlYGs9mMjIwMXH/99Vo+HRERUUgcm4iI+h+dVg+8du1a3HHHHXj00UcxdepUeDwe7N+/X6unIyIiColjExFR/6RJ0ePxeLBw4UKsWLEC8+fP998+cuRILZ6OiIgoJI5NRET9lyZFz65du1BZWQlZljFu3DicPXsWY8eOxYoVKzBq1Khu7+d0OuF0Ov2fNzQ0AABUVYWqqlpE7VOqqkIIERdZAebVGvNqi3m1FS85e4JjU+xnBZhXa8yrLebVVm9yalL0fPnllwCAX//61/jd736HIUOG4PHHH8fkyZNx+PBhZGZmBrzfsmXL8NBDD3W5vbq6Gi6XS4uofUpVVdjtdgghIMux3yOCebXFvNpiXm3Z7fZoR+hzHJvi473HvNpiXm0xr7Z6Mzb1qOhZvHgxli9fHvSagwcP+quwBx98EDfddBMAYPXq1SgoKMDf//53/PjHPw543yVLluDee+/1f97Q0IBBgwbBarXCYrH0JGpUqKoKSZJgtVrj4o3DvNpiXm0xr7YMBkO0I4SNY1Nw8fbeY15tMa+2mFdbvRmbelT03HfffZg3b17Qa4qKinDmzBkAnddJG41GFBUV4eTJk93e12g0wmg0drldluW4+EEAgCRJzKsh5tUW82ornvLGQ8Z2HJtCi6f3HsC8WmNebTGvdnqTsUdFj9VqhdVqDXldSUkJjEYjDh06hEmTJgEA3G43Tpw4gcLCwotLSkREFADHJiIiCkWTPT1paWm46667sHTpUgwaNAiFhYVYsWIFAODmm2/W4imJiIiC4thERNR/aXZOz4oVK6DT6XDbbbehpaUFZWVl2LRpEzIyMrR6SiIioqA4NhER9U+aFT16vR4rV67EypUrtXoKIiKiHuHYRETUP8X+jiUiIiIiIqJeYNFDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFCY9FDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFCY9FDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQWPQQEREREVFC06zoOXz4MObOnYvs7GykpaVh0qRJ2Lx5s1ZPR0REFBLHJiKi/kmzomfOnDnweDzYtGkTysvLMWbMGMyZMwdnz57V6imJiIiC4thERNQ/aVL01NTU4MiRI1i8eDEuu+wyDB8+HI899hiam5uxf/9+LZ6SiIgoKI5NRET9l06LB83KysKIESPw0ksv4fLLL4fRaMSqVauQk5ODkpKSbu/ndDrhdDr9n9vtdgCAzWbTImafU1UVDQ0NMBgMkOXY3y7FvNpiXm0xr7baf+8KIaIbpA9xbIqP9x7zaot5tcW82urV2CQ0curUKVFSUiIkSRKKoojc3Fyxa9euoPdZunSpAMAPfvCDH/yIkY9jx45pNUxEBccmfvCDH/yI/4+LGZskIcIvlRYvXozly5cHvebgwYMYMWIErr/+erjdbjz44IMwm8147rnn8NZbb+Hzzz9Hbm5uwPte+Nc0m82GwsJCnDx5Eunp6eHGjJqGhgYMGjQIp06dQlpaWrTjhMS82mJebTGvtux2OwYPHoz6+npYLJZoxwmKY1Nw8fbeY15tMa+2mFdbvRmbelT0VFdXo7a2Nug1RUVF2Lp1K2bMmIH6+vpOL+Dw4cMxf/58LF68OKzna2hoQHp6Oux2e1z8IJhXW8yrLebVFvNqh2NTcMyrLebVFvNqqz/l7dGeHqvVCqvVGvK65uZmAOiyNlCWZaiq2pOnJCIiCopjExERhaLJjqUrrrgCGRkZuP3227F3714cPnwYP//5z3H8+HHMnj1bi6ckIiIKimMTEVH/pUnRk52djffeew+NjY2YOnUqSktLsW3bNqxbtw5jxowJ+3GMRiOWLl0Ko9GoRcw+x7zaYl5tMa+2mDf6ODYxrxaYV1vMq63+lLdHe3qIiIiIiIjiTew35CYiIiIiIuoFFj1ERERERJTQWPQQEREREVFCY9FDREREREQJLW6KnsOHD2Pu3LnIzs5GWloaJk2ahM2bN0c7VlDvvPMOysrKYDabkZGRgeuvvz7akcLidDoxduxYSJKEPXv2RDtOQCdOnMD8+fMxdOhQmM1mDBs2DEuXLoXL5Yp2NL+nnnoKQ4YMgclkQllZGT777LNoRwpo2bJl+Na3voXU1FTk5OTg+uuvx6FDh6IdK2yPPfYYJEnCv//7v0c7SrcqKyvxgx/8AFlZWTCbzRg9ejR27twZ7VgBeb1e/OpXv+r0b+s3v/kN2PMmMI5NkcFxqe9wbNJePIxLQP8bm+Km6JkzZw48Hg82bdqE8vJyjBkzBnPmzMHZs2ejHS2gtWvX4rbbbsMPf/hD7N27F9u3b8f3v//9aMcKywMPPIC8vLxoxwiqoqICqqpi1apV+OKLL/D73/8ezzzzDH7xi19EOxoA4LXXXsO9996LpUuXYteuXRgzZgxmzpyJ8+fPRztaFx9//DEWLFiATz/9FBs2bIDb7caMGTPQ1NQU7Wghff7551i1ahUuu+yyaEfpVn19PSZOnAi9Xo93330XBw4cwOOPP46MjIxoRwto+fLlePrpp/HHP/4RBw8exPLly/Hb3/4WTz75ZLSjxSSOTZHBcalvcGzSXjyMS0A/HZtEHKiurhYAxJYtW/y3NTQ0CABiw4YNUUwWmNvtFvn5+eK5556LdpQeW79+vSguLhZffPGFACB2794d7Uhh++1vfyuGDh0a7RhCCCHGjx8vFixY4P/c6/WKvLw8sWzZsiimCs/58+cFAPHxxx9HO0pQDodDDB8+XGzYsEFcddVVYuHChdGOFNCiRYvEpEmToh0jbLNnzxY/+tGPOt124403iltvvTVKiWIXx6bI4LjUdzg2aStexiUh+ufYFBczPVlZWRgxYgReeuklNDU1wePxYNWqVcjJyUFJSUm043Wxa9cuVFZWQpZljBs3Drm5uZg1axb2798f7WhBnTt3DnfccQf+8pe/ICkpKdpxesxutyMzMzPaMeByuVBeXo7p06f7b5NlGdOnT8eOHTuimCw8drsdAGLitQxmwYIFmD17dqfXORa99dZbKC0txc0334ycnByMGzcOf/7zn6Mdq1tXXnklNm7ciMOHDwMA9u7di23btmHWrFlRThZ7ODZpj+NS3+HYpL14GZeAfjo29XUlppVTp06JkpISIUmSUBRF5Obmil27dkU7VkCvvvqqACAGDx4sXn/9dbFz507xve99T2RlZYna2tpoxwtIVVVxzTXXiN/85jdCCCGOHz8eV39RO3LkiEhLSxPPPvtstKOIyspKAUB88sknnW7/+c9/LsaPHx+lVOHxer1i9uzZYuLEidGOEtSrr74qRo0aJVpaWoQQIqb/omY0GoXRaBRLliwRu3btEqtWrRImk0m88MIL0Y4WkNfrFYsWLRKSJAmdTickSRKPPvpotGPFLI5N2uG41Lc4NmkrnsYlIfrn2BTVomfRokUCQNCPgwcPClVVxXXXXSdmzZoltm3bJsrLy8Xdd98t8vPzRVVVVczlfeWVVwQAsWrVKv99W1tbRXZ2tnjmmWcilrcnmf/whz+IiRMnCo/HI4SI3uASbt6OTp8+LYYNGybmz58f0azdieeB5a677hKFhYXi1KlT0Y7SrZMnT4qcnByxd+9e/22xPLjo9XpxxRVXdLrtnnvuERMmTIhSouBeffVVUVBQIF599VXxz3/+U7z00ksiMzMzZgdCLXBsio28HJf6Fscm7cTbuCRE/xybJCGi15KnuroatbW1Qa8pKirC1q1bMWPGDNTX1yMtLc3/teHDh2P+/PlYvHix1lEBhJ93+/btmDp1KrZu3YpJkyb5v1ZWVobp06fjkUce0TqqX7iZb7nlFrz99tuQJMl/u9frhaIouPXWW/Hiiy9qHRVA+HkNBgMAoKqqCpMnT8aECRPwwgsvQJajv2LT5XIhKSkJr7/+eqeuSLfffjtsNhvWrVsXvXBB/PSnP8W6deuwZcsWDB06NNpxuvXmm2/ihhtugKIo/tu8Xi8kSYIsy3A6nZ2+Fm2FhYW4+uqr8dxzz/lve/rpp/Hwww+jsrIyiskCGzRoEBYvXowFCxb4b3v44Yfx8ssvo6KiIorJIodjk7Y4LkUHxybtxNu4BPTPsUmnVbhwWK1WWK3WkNc1NzcDQJdfHLIsQ1VVTbIFEm7ekpISGI1GHDp0yD+wuN1unDhxAoWFhVrH7CTczE888QQefvhh/+dVVVWYOXMmXnvtNZSVlWkZsZNw8wK+VotTpkxBSUkJVq9eHTMDi8FgQElJCTZu3OgfWFRVxcaNG/HTn/40uuECEELgnnvuwRtvvIGPPvooZgeVdtOmTcO+ffs63fbDH/4QxcXFWLRoUcwNLBMnTuzSZvXw4cMR/10Qrubm5i7/lhRFiejv2mjj2KQtjkvRwbFJO/E2LgH9dGzSaBaqT1VXV4usrCxx4403ij179ohDhw6J+++/X+j1erFnz55oxwto4cKFIj8/X7z//vuioqJCzJ8/X+Tk5Ii6urpoRwtLrK+dPn36tLjkkkvEtGnTxOnTp8WZM2f8H7Hgb3/7mzAajeKFF14QBw4cEHfeeaewWCzi7Nmz0Y7Wxd133y3S09PFRx991Ol1bG5ujna0sMXyMoLPPvtM6HQ68cgjj4gjR46IV155RSQlJYmXX3452tECuv3220V+fr747//+b3H8+HHxj3/8Q2RnZ4sHHngg2tFiDsemyOK41HscmyInlsclIfrn2BQXRY8QQnz++edixowZIjMzU6SmpooJEyaI9evXRztWt1wul7jvvvtETk6OSE1NFdOnTxf79++Pdqywxfrgsnr16m7XVseKJ598UgwePFgYDAYxfvx48emnn0Y7UkDdvY6rV6+OdrSwxfrg8vbbb4tRo0YJo9EoiouLY2ZjcyANDQ1i4cKFYvDgwcJkMomioiLx4IMPCqfTGe1oMYljU+RwXOobHJsiI9bHJSH639gU1T09REREREREWoudxaZEREREREQaYNFDREREREQJjUUPERERERElNBY9RERERESU0Fj0EBERERFRQmPRQ0RERERECY1FDxERERERJTQWPURERERElNBY9BARERERUUJj0UNERERERAmNRQ8RERERESU0Fj1ERERERJTQ/he/18ddiDzRGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(test_bridge[0, :, :], test_bridge[-1, :, :], save_path=log_dir / 'gt_test_bridge.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b047f6287784bcd991ed4ad0ec3ad1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_gif_frame(pred_bridge, log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}