Capacitated-Vehicle-Routing-Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capacitated Vehicle Routing Problem\n",
    "Below is the example of selecting the optimal route for a single vehicle with limited capacity to serve N customers with varying demands<br>\n",
    "The vehicle only carries a single product and so this problem can be designed as a single commodity network flow problem <br>\n",
    "\n",
    "Based on https://www.youtube.com/watch?v=7_-Xuq2xKdc but with a modified flow balance constraint and directed graph to illustrate the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd = np.random\n",
    "rnd.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10 # number of clients\n",
    "xc = rnd.rand(n+1) * 200 # X coordinates for the customers including the main source (depot or warehouse)\n",
    "yc = rnd.rand(n+1) * 100 # Y coordinates for the customers including the main source (depot or warehouse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dmflaZj4LHKB6CMyyscAxArgF+DQnT3O97I/RAv35GHBTZr5W73O0bl/2xwcW7FMCb63f/wgnZqxd9seoVcWFe2a+AHwBOEQV6q8Ce4BXGoLkMNUZ/iA5PzOPANTL8+r2Zg9HGbS+AfwO1ZkTDGifIuIa4IXMfGzepoHsD/BO4Bfq4cx/j4ifqdsHtT8AnwI+HxHPU+XEjXX7IPepqeLCvR6L3kT16+LbgXOonuc6Xyn3gC7q4SjLWURsA44B08ebmuy2rPsUESPANuBPm21u0ras+1NbCZxLNUzxx8BdEREMbn+g+m3khsy8CLgBuLVuH+Q+NVVcuAPvB57NzNnMfB24B3gP1a9Zx2fBHMQHiLx8/NfEenn8V+SBfjhKRGwBPgRMZD34yWD26UepTigei4jnqGp+OCIuYDD7A1Xd99RDFd8G3qCabGtQ+wOwhSoTAP6WE8NJg9ynpkoM90PA5RExUp9lXAk8BTwAXFvvswW4t0/1LdVOqrrh5Pp3Ah+tr/ZfDrx6fPhmuYuIq4DPANdk5lzDpp3AdRFxVkRcDGwEvt2PGhcrM5/IzPMycywzx6jC4tLMfInBPUb/QHWtioh4J3Am1SyKA3d8GrwI/GL9/gpgf/1+UI/Rwvp9RbcbL+CzwNPAXuCvqa7qv4PqH+ABqv9jn9XvOk9R/+1U1wtepwqJ64G3Abuo/jHuAlbX+wbwZao7Fp4Axvtdfwt9OkA1zvlo/fpqw/7b6j49Q313w3J6NevPvO3PceJumWV/jBY4PmcCf1P/d/QwcMWgHJ9T9Onnqa7BPQbsBn56UI5Rqy+nH5CkApU4LCNJQ89wl6QCGe6SVCDDXZIKZLhLUoEMd0kqkOEuSQX6Pwcaxg7WbcS2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xc[0], yc[0], c='r', marker='s') # draw the warehouse as a red square\n",
    "plt.scatter(xc[1:], yc[1:], c='b'); # plot the customer locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = [i for i in range(1, n+1)] # customer nodes\n",
    "V = [0] + N # source node with all customer nodes\n",
    "A = [(i, j) for i in V for j in V if i!=j] # all possible arcs between the nodes\n",
    "c = {(i,j): np.hypot(xc[i]-xc[j], yc[i]-yc[j]) for i,j in A} # cost of each arc is the euclidean distance between the nodes\n",
    "Q = 20 # vehicle capacity\n",
    "q = {i: rnd.randint(1, 10) for i in N} # demand for each node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gurobipy import Model, GRB, quicksum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using license file /Users/bijoythomas/gurobi.lic\n"
     ]
    }
   ],
   "source": [
    "m = Model('CVRP');None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = m.addVars(A, vtype=GRB.BINARY)\n",
    "u = m.addVars(N, vtype=GRB.CONTINUOUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.modelSense = GRB.MINIMIZE\n",
    "m.setObjective(quicksum(c[a] * x[a] for a in A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addConstrs(quicksum(x[i,j] for j in V if i!=j) == 1 for i in N) # only one outgoing edge is selected\n",
    "m.addConstrs(quicksum(x[i,j] for i in V if i!=j) == 1 for j in N); # only one incoming edge is selected\n",
    "m.addConstrs((x[i,j] == 1) >> (u[i] - q[i] == u[j]) for i,j in A if i!=0 and j!=0); # flow balance constraints\n",
    "m.addConstrs(u[i] >= q[i] for i in N); # flow into node i must be >= the demand at node i\n",
    "m.addConstrs(u[i] <= Q for i in N); # flow on an arc cannot exceed the vehicle capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (mac64)\n",
      "Optimize a model with 40 rows, 120 columns and 220 nonzeros\n",
      "Model fingerprint: 0x3020c815\n",
      "Model has 90 general constraints\n",
      "Variable types: 10 continuous, 110 integer (110 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [2e+01, 1e+02]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 2e+01]\n",
      "Presolve added 205 rows and 25 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 245 rows, 145 columns, 1354 nonzeros\n",
      "Variable types: 55 continuous, 90 integer (90 binary)\n",
      "\n",
      "Root relaxation: objective 3.039835e+02, 40 iterations, 0.00 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  303.98354    0   23          -  303.98354      -     -    0s\n",
      "H    0     0                    1074.7736522  303.98354  71.7%     -    0s\n",
      "H    0     0                     637.8412129  303.98354  52.3%     -    0s\n",
      "H    0     0                     525.8818956  303.98354  42.2%     -    0s\n",
      "H    0     0                     508.7059270  318.17513  37.5%     -    0s\n",
      "     0     0  318.17513    0   24  508.70593  318.17513  37.5%     -    0s\n",
      "H    0     0                     477.8512583  318.17513  33.4%     -    0s\n",
      "     0     0  322.81593    0   24  477.85126  322.81593  32.4%     -    0s\n",
      "     0     0  339.79451    0   28  477.85126  339.79451  28.9%     -    0s\n",
      "H    0     0                     475.9030486  339.79451  28.6%     -    0s\n",
      "     0     0  341.89992    0   27  475.90305  341.89992  28.2%     -    0s\n",
      "     0     0  341.90537    0   27  475.90305  341.90537  28.2%     -    0s\n",
      "H    0     0                     471.8019086  351.32015  25.5%     -    0s\n",
      "     0     0  356.13310    0   23  471.80191  356.13310  24.5%     -    0s\n",
      "     0     0  356.72120    0   23  471.80191  356.72120  24.4%     -    0s\n",
      "H    0     0                     469.7367681  361.39327  23.1%     -    0s\n",
      "     0     0  362.48197    0   25  469.73677  362.48197  22.8%     -    0s\n",
      "     0     0  363.55297    0   28  469.73677  363.55297  22.6%     -    0s\n",
      "     0     0  363.55297    0   28  469.73677  363.55297  22.6%     -    0s\n",
      "     0     0  368.41594    0   28  469.73677  368.41594  21.6%     -    0s\n",
      "     0     0  369.15239    0   28  469.73677  369.15239  21.4%     -    0s\n",
      "     0     2  369.15239    0   28  469.73677  369.15239  21.4%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 1\n",
      "  Gomory: 5\n",
      "  Cover: 11\n",
      "  Implied bound: 19\n",
      "  MIR: 13\n",
      "  StrongCG: 1\n",
      "  GUB cover: 1\n",
      "  Zero half: 2\n",
      "  Relax-and-lift: 8\n",
      "\n",
      "Explored 1428 nodes (14666 simplex iterations) in 0.38 seconds\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 8: 469.737 471.802 475.903 ... 1074.77\n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 4.697367680848e+02, best bound 4.697367680848e+02, gap 0.0000%\n"
     ]
    }
   ],
   "source": [
    "m.optimize();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_arcs = [a for a in A if x[a].x > .99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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yTVPHjx7S4ciLatiwoTw9PeXl5SWn03nN9WvUqKGTJ09mPN6yZYuGDh2qXbt2qWTJkln4jgIAgJxGwYQCyzRNLd19SlPWHNTFxDQlpzv078/k6U7HVfsevZCoPy4m6qeoMyru467hLauqQ+1yGUXTwoULZRiGzp49qzlz5mj//v2qWLFiTr8kAMgxtWvX1s6dO/Xuu++qTp06mjBhgvr37880EwAAwC0aN26c3nrrrYzH8+bN05tvvqkxY8aoZMmSWrx4sYYOHaoePXrI399f3bp108svv6yoqChFRUUpOTlZAQEBqhFYU7rJDJNHmftUPPQ5xf48Xelxp+Xi6i6P8g/Is4K/DBebSnYarbhfZ+nU9Kcle7pci5eXX5OekqQijbopdtWnOjXtabkWKiafwJZKCF8mu9OUt4tT/YYP18GDB2UYhpo0aaLZs2dfc31XV1eVLl0643GxYsXk4uJy1XMAACBvMm72K5fg4GAzPDw8B+MAOeNMfLJeCItU9Kl4JaU5/vmA/+HlZlNg+SL6sGstlSnipRo1amjv3r2S/v7w/PDDD7O+NIACIzo6Wv369ZOfn5+++OKLf1zmxOFwyGaz5Uw4AACAPMI0TZ04cUJRUVGKjo7O+Pfo0aOqUqWKAgICFBgYqMDAQAUEBKhChQoZP+5pPmm9jl5IzLGszkundWbms3Jzc5Orq6sSEhJ0+vRplSlTJscyAACAnGEYRoRpmte9DwwTTChwdh6LVZ8vdyg13Sm788YF680kpzsUcTxOLaf8puG13bV37165ubnJMAzVqlVL7du3z+LUAJB7BQQEaOvWrZo0aZKCg4P11ltvafDgwXJxufb+dSkpKZo2bZqSk5P1+uuvW5AWAADAevHx8YqJibmqTIqJiZG3t3dGidS2bVu99tprqlatmjw8PG56vuDKRfXHxUTd5DfEWcYwpEIp52SappKTkyVJfn5+OnDggEqUKCE3N7fsDwEAAHIFJphQoOw8Fqtes3coOT3zU0s34u4ilT/8vd4Y2E0hISHy9PTMsnMDQF7z+++/q1+/fpo+fboCAgKuKZkuX76sHTt2aNq0aTp16pTCwsJUqVIli9ICAABkL7vdroMHD141kRQVFaULFy7I398/YyopICBAAQEBKlGixG1dZ+exWPWes+O2VujILG93m+b2rat5U8Zq5syZSklJUf369ZWenq6DBw+qRYsWatOmjUJDQ1W2bNlszwMAALLXzSaYKJhQYJyJT1bLKb8pMTXrP3D7eNi0ZvhDKlPEK8vPDQB5jdPplN1ul7u7+033e+WVV1SlShUNHDgwh5IBAABkD9M0dfbs2Yz7I/27SDpw4IDKlSuXUSL9ezrpnnvuue60951cv8n763QyLjnLznkjFYt567cRTWUYhsaPH6833nhDmzZtUsOGDXX27FmtXr1aK1as0M8//6xKlSopNDRUbdq0Uf369eXqykI6AADkNRRMKPBM01TXz7dp14m4214W72ZcXQwFVSqq+QPqc4N7ALiB9PT0q5ZMadWqlXr27Knu3bsrIiJC7u7uqlmzpoUJAQAA/llSUpL27t17zVSS0+nMKJD+XSj5+/vLx8cnR3It2fWn3vghJlunmLzcbJrQoYY61C6f8VxMTIz8/f2v+X9hu92u7du3a8WKFVqxYoWOHz+uRx55RKGhoWrdurVKlSqVbTkBAEDWoWBCgWfVB20AwLU2bNig2bNny8/PTw0aNNDKlSuVlpamiIgIdenSRW+//bbVEQEAAOR0OvXHH39cVSJFR0fr5MmTqlq16lVTSQEBASpTpoylPzg0TVNPfrFNEcdz5w8rT58+rVWrVmnFihVas2aN7rvvPrVp00Zt2rRRcHCwbDZblmcGAAB3joIJBVp2LhXw1zevyqdGM/nWbCVJqlDUSxtebsYUEwD8l9jYWIWFhenAgQPav3+/kpOT1b59ew0ZMkQhISHq06ePhgwZotOnT+vZZ5/VggUL5O3tbXVsAABQgMTGxl4zkRQTE6PixYtfVSIFBgaqatWqV01l5ybZvTT82uFNVbrInd93OD09XZs3b9bKlSu1YsUK/fXXX2rVqpXatGmjVq1aqXjx4lmQGAAAZIWbFUwsfos8oUePHlq7dq0SExNVunRpvfLKK+rfv/8tHRt+PE4XE9OyOeHfLiamKeJ4nIIrF7ul/efOnas+ffroiy++uOXXAwB5idPp1JQpUzR+/Hi9/vrrWrZsmZxOpzw9PfXGG2/I29tbw4YNk8PhUOHChVWkSBGdPn1a9957r9XRAQBAPpSWlqb9+/dfM5V0+fJlBQQEKCAgQLVq1VKvXr1Uo0YN+fn5WR05U8oU8dLcPvXUc/YOJadnXcnk5WbT3D71sqRckiQ3Nzc1bdpUTZs21XvvvacTJ05o5cqVCgsL0+DBg/XAAw9kTDfVrl07S+9XBQAAsg4FE/KE1157TbNmzZKHh4f279+vpk2bqnbt2goKCvrHYxdGnMzSD9Y3k5zu0IKIk7dUMMXFxemdd96Rv79/DiQDAGu4uLjo7bffVvny5fXll1+qXLlyGjx4sA4fPqx58+Zp48aNkiSbzaawsDCdOHGCcgkAANwx0zR16tQpRUVFXVUmHT58WJUrV864T9LgwYMVGBioSpUq5ZuVKIIrF9PX/eqp95c7lJruvKPl8lxdDHm4uWhun3q3/EPK21GxYkUNHDhQAwcOVGpqqjZu3KgVK1boqaee0qVLlxQaGqrQ0FA98sgjea70AwAgP2OJPOQ5Bw4cUNOmTfXRRx+pS5cu12z/8ssvNXPmTNWvX1+zZs1SiuGpwi0HyavK31N8zpRExf46UylHwiXDkE/gw/Jr1F2Gy9/rPV/Z87PidyyR80qc3MtWVfHWz8m1yF2SpOQ/div2lxlyJMaqkH8zpZ0/nrFEXnrcaV1cMVX283/Iz8dTLVq0UFhY2A1fx6BBgxQYGKgFCxaoR48eTDAByPcOHTqkUaNG6emnn1ZcXJy2bdumDz/8UJJ08eJF3X///frhhx/UsGFD2e12ubr+53cw58+fV4kSJfLNFz8AACDrJCQkKCYm5pol7jw8PK65T1L16tXl5eVldeQccSY+WS+ERSrqz/jb+tGlt7tNAeWK6MOutVSmiHXv2dGjRzOW0tu4caNq1aqlNm3aKDQ0VIGBgXw+BAAgm7FEHvKFZ599Vl9++aWSk5NVu3ZttWnT5ob7bt++Xb1799aZs+dU8dEXdHHlVJUbMleGYejC8imy+fip7MAvZKan6Nyit3TFt4R8a4cq6eBWxW9dqLs6jZZrsbKK37pQF5ZNVOmeH8iRFK/zSyeoeJth8r6vvhIiflLC7pXyqdFMknRpwzx53V1bxXu+o92vN9ee3btumG/Hjh0KDw/XZ599pgULFmT5ewUAudF9992nBQsWyG63a/v27Rl//86dO6enn35a3bt3V8OGDeVwODLKpfj4eL333nvat2+f9u3bp48//litWrWy8mUAAACLOBwOHT58+KoSKSoqSmfPnlX16tUzSqQOHTooICBAd911l9WRLVWmiJfmD6iv7yNPafIvB3UxMU3J6Q7d5HfGMoy/l8Mr7uOuFx+uqsdrlbO8wLnnnns0ZMgQDRkyRMnJyVq/fr1Wrlypjh07KjU1VaGhoWrTpo1atGihwoULW5oVAICChoIJecZnn32mjz/+WFu3btX69evl4eFxw30rVaqkAQMG6Ex8sorWfljnV38mZ+IlyZCSj4arwgthcnHzkNw9Vbju40qIXCXf2qFKiFylwg06y61EBUlSkZAuurx1oezx55RyIlpuJSrKp1ojSZJv3cd0eefSjGsaLjbZ489JiXG6YjfUqFGj62ZzOBx69tln9fHHH7OONIACydXVVdWqVVNKSoqCg4NVu3ZtlStXTlOnTpX095I2knTkyBGFhYXp6NGjmjlzpqKjozVjxgw1aNCALw8AAMjnzp07d1WJFB0drd9//12lS5fOmEjq0aOHAgICdO+998pms1kdOVcyDEMdapfX47XKKeJ4nBZEnFT4sTidjEuSq4shwzBkmqbsTlMVinoruHJRdQmqoKBKRS0vlq7Hy8srY7m8jz76SIcOHdKKFSs0ffp09erVS3Xr1s24d1P16tVz5WsAACA/oWBCnmKz2dSoUSPNmzdP06ZN0/PPP3/d/UqXLi1JSneYsrn/fRNSZ3qynMkJksOhPz/p9Z+dTadcC5eUJDnizyluzeeK+3XWf53NlD3hohxXYuXqWyLjWcMwZPuvx0Wb9dOljV/r+KwX9NCqd/TqKyPUr1+/a7J99tlnCgwMVIMGDW73bQCAPK948eJasWKFVqxYoVq1aql48eIZ21xdXZWenq7ly5crPj5eI0aMUIkSJVSqVCnt2bNH7u7uFiYH8P/s3XdcFHf6B/DPsCxIEQWxo4KxoBQhIPaKoruWaCJYExV7i3qnxnhGk1hykURUrKiol1iiJjlzya4FC9GoUVBkARVFQUDEAqL0LfP7w5974TBqEmAon/frda+4zOzMZ/cS2dlnnudLRFSaCgoKEB8fX6wrSaPRoKioyFhI6tSpE6ZMmQIXFxfUrFlT6siVkiAI8Ha0M66jpNUb8DCnEFq9CLlMgL21OeSyynUDpCAIaNWqFVq1aoU5c+YgNzcXJ06cgEqlgkKhgCAIxu6m3r17w8rKSurIREREVQ4LTFQp6XQ6JCYmvnI/uUwo1v4vs6kLwVSOJrP3GNdc+i2ZjT1sOgfA2qVXyXNm3YXu6UPjY1EUof/NY5m1Leoo3oeF3ASfdTbDiCED0L179xKL1R8/fhwRERFQqVQAgMzMTFy+fBnR0dFYv379K18TEVFV8nzcqVarxenTp+Hg4AAnJyfcvHkTd+7cgbe3N7y9n435PXz4MEaMGIEaNWpAFEXekUpERFSJGAwGJCcnl1gnKSkpCS1btjQWk+bOnQt3d3c0biz9aLaqTC4zkXRdpbJgZWWFQYMGYdCgQRBFEVevXoVKpUJwcDBGjRqFzp07GwtOLVu25L9fREREpYAFJqrw7t+/jxMnTmDgwIGwsLBAeHg49u7diz179rzyufbW5tDpDcbHptZ2qOHoiazj21C7+7sQzGpA9zgD+qcPUaOpG2p6KPD49Ncwq9ccZnWbwVCQi/yky7By7gqLN7yReWwz8q6fhUXLDnga9SP0OVnGY+deOwPzRs7Q2daFY6N6zzqcXjCmYefOnSgoKDA+fvvttzFs2DBMmDDhL75TRESVl1wuR1FRES5dugQnJyc8evQI0dHRWLRoEQDg5MmTSE1NRfv27QGAXwgQERFVYI8fP4ZGoyk24i42NhY2NjbGdZIGDx6MxYsXo3Xr1uxOplInCALatm2Ltm3bYt68eXjy5AmOHz8OlUqFoKAgWFhYQKlUQqFQoGfPnrCwqFrFNiIiovLCAhNVeIIgYNOmTZg6dSoMBgOaNWuGNWvW4K233nrlc+UyEzSxs8TN3/zMfuDfkBWxE3e3TYOhKB+mtRqgVsd3AACWrTvDoC3Awx9WQZd9HybmVqjh6AEr566QWdZC3SELkXlsCx6q1sDapRfMHdoaj1uUnoCs8FCgKA/v7G2EtWvXwsnJqUSm2rVrF3tsZmYGGxsb1KpV68+9QUREVYSvr6/xzzdv3kTbtm1hZ2eHmzdv4siRI6hbty569+4tYUIiIiL6La1Wi4SEhGLrJMXExCArKwsuLi5wd3eHu7s7Ro4cCTc3N9jZ2UkdmaopGxsbDB06FEOHDoUoioiJiYFarcZnn32G4cOHo1u3bsaCU/PmzaWOS0REVGkI4m/nh/0Pb29vMTIyshzjEJW+Bd9ewYGoVLzkX/VSIwiAv5cDVr3TruxPRkRUhcXFxUGpVGLYsGE4deoUBg8ejGHDhsHFxUXqaERERNWOKIpIT08vsU5SQkICmjRpYuxKev5PJycnmJhUrvV8qPrKysrCsWPHoFaroVarUbt2bSiVSiiVSnTr1g3m5uZSRyQiIpKUIAhRoih6v3AbC0xU1V1MysTYHReQV6Qv83NZmsnwr/E+xoVTiYjoz7t//z727t0LR0fHEl2rXIOJiIiobOTm5iIuLq5EMUkQBGNH0vNiUtu2bWFpaSl1ZKJSYzAYcPnyZahUKqhUKsTHx6Nnz57G7qamTZtKHZGIiKjcscBE1ZooiugedBIpWfllfq6mdpaImNeTX3oSEZWhoqIiHD16FDVq1ECfPn2kjkNERFQp6fV63Lp1q9g6SRqNBmlpaXB2djYWkZ4XlOrXr8/rHKp2Hj58iKNHj0KlUuHw4cNo2LAhFAoFlEolunTpArlcLnVEIiKiMscCE1V7311KxeJDsWXaxWQhl2HlUFcM9XQos3MQEdEz4eHhCAwMRL9+/fDFF19wHTsiIqKXePjwYbFupJiYGMTHx8Pe3r5YR5K7uztatmwJU1Mu10z0v/R6PS5evAi1Wg2VSoUbN26gT58+UCqV6N+/Pxo1aiR1RCIiojLBAhNVe6IoYsTW84hKzoLOUPqLMQmiAe0d7fDNlM68q4+IqJw8efIE8+fPh1qtxpYtW6BQKKSOREREJKnCwkJcvXq1RDEpNze3xDpJrq6uvEGD6C/IyMjAkSNHoFKpcPToUTRr1sy4dlOHDh1YqCUioiqDBSYiAOnZ+egTHIHcwtLvYjLRF6HWL+vw7Vfb0bx581I/PhER/b7jx49j4sSJ6NGjB4KDg2Frayt1JCIiojIliiJSUlKKFZFiYmJw69YtNG/evEQxqWnTprwRjqgM6XQ6nD9/HiqVCmq1GsnJyfDz8zN2N9WrV0/qiERERH8aC0xE/y8yKRPvhl1Avrb0ikwWchn+Fdge537YjeXLl2PTpk145513Su34RET0ajk5OVi4cCG+//57bNq0CYMHD5Y6EhERVQMGgwGiKEImk5XZOZ48eYLY2Nhi6yRpNBpYWloWKyK5u7ujTZs2MDc3L7MsRPR60tLScPjwYahUKhw/fhytWrUyrt3k7e1dpn9nEBERlTYWmIh+IzIpE2N3XkCh1vCXxuWZmggwl5tg1zgfeDvaAQAuXryIp5ta/QAAIABJREFU4cOHY+DAgQgKCuLFHRFROYuIiMCECRPQoUMHrFu3DnXq1JE6EhERVUE//vgjVq5cifT0dCxcuBAjRoz4y+PmdDodbty4UawrSaPR4P79+3BxcSnRlWRvb19Kr4aIylJRURHOnj0LlUoFlUqFjIwM9O/fHwqFAv369ePnVSIiqvBYYCL6H+nZ+ZjzTTRiUrP/VDeTpZkMbo1rYc1wDzSsZVFs2+PHjxEYGIg7d+5g//79HJlHRFTO8vLysHjxYuzduxchISEYNmyY1JGIiKgSioyMxMGDBzFy5Ei0a9cOBoMBJiYmyM/Px6JFi+Dq6oq3334bH3zwARwdHbFo0SLjPi8jiiIyMjJKrJN07do1NG7cuERXUvPmzdntQFSF3LlzB2q1GiqVCidPnoSrq6tx7SYPD49X/h1CRERU3lhgInoBURTx7+g0rD6WgEe5RcjX6vGS/xwgCM/G4dWxMsPf+rbCEI/GvzvHXBRFhISEcGQeEZGEzp49i8DAQLi5uWHDhg2cfU9ERK/leZFoxYoVCA4OxvLlyzF16lTodDqYmpoiMjIS69atw6effgpHR0d8//33CAoKwtmzZyGKYrFrhIKCAsTGxhZbJ0mj0UCv15foSHJxcYG1tbWEr5yIylthYSF+/vlnY8Hp8ePHxlF6ffv2Re3ataWOSERExAIT0cuIooio5Czsj0pBZFIWUrLyYGoiQBAEiKIInUFEE1tLeDvaIsCrCbya2b72ArnPR+YNGDAAX3zxBUfmERGVs/z8fHz88cfYuXMn1qxZgxEjRnCRcyIieqUbN25g2bJlcHV1RUpKCkJCQowFpoSEBAQGBuLYsWOwsLBAamoqlEolLl68WOzzfkFBAT766CMcPXq0WDHJ3d0dDRs25O8jIiohMTHRWGw6c+YMPDw8jN1Nbm5u/HuDiIgkwQIT0R+g1RvwMKcQWr0IuUyAvbU55LI/36L++PFjTJgwAcnJyfjmm2/wxhtvlGJaIiJ6HRcuXMD48ePRsmVLbNq0CQ0bNpQ6EhERVWBqtRpHjhzBwIEDsXHjRnz33XfGbXq9Hm5ubggPD0ejRo0AAI6Ojrhw4UKJbtnXGZlHRPQi+fn5OHXqlHHtpsLCQmN3U58+fVCzZk2pIxIRUTXxsgITP+kS/Q+5zAQNa1mgqZ0lGtay+EvFJQCoXbs2Dh48iLFjx6JTp044ePBgKSUlIqLX5ePjg0uXLsHV1RXt2rXDrl278LKbbIiIqHrbtm0bvvjiC7Ro0QKCIMBgMAB4VjCSyWRwcHBAdHQ0tFotAMDGxgZ37twpcRwWl4joz7KwsIBCoUBISAhu3ryJ48ePw8XFBZs2bUKjRo3g6+uLL7/8EvHx8fxcS0REkuGnXaJyIAgCZs2aBZVKhQULFmDWrFkoLCyUOhYRUbVibm6O5cuX4/Dhw1i9ejUGDhyI1NRUqWMREZHERFE0FpAAID4+Hubm5tixYwe+/PJLnD59Gr169UJiYqKxYDRs2DB8//33SElJQXR0NDp27Mi1UoiozAiCgNatW2POnDk4evQo0tPTMWfOHNy8eRMKhQJOTk6YPn06fvzxR+Tm5kodl4iIqhEWmIjKkbe3Ny5duoS7d++ic+fOSExMlDoSEVG18+abb+LixYvw8fGBp6cntm3bxrs+iYiqiZycHJw/fx5bt27FrFmz0LNnT9jb2xs7kQCgqKgImZmZOHToEGQyGby8vBAQEIA33ngDycnJKCwsxMSJE+Hp6YmhQ4diyJAhaN++PVq0aCHhKyOi6sTa2hqDBg3Cpk2bkJSUhJ9++glOTk5YvXo1GjRogH79+mHt2rW4ceOG1FGJiKiK4xpMRBIQRREbNmzAp59+io0bN2LYsGFSRyIiqpZiYmIQGBgIOzs7bN26Fc2aNZM6EhERlQK9Xo/ExETExMRAo9EgJiYGMTExSE9PR5s2beDu7g53d3e4ubnBzc0N9evX/91jLV68GJaWlpgwYQJCQ0MxcuRItGjRAkVFRcjJyYGdnV05vjIiopd78uQJwsPDoVKpoFarYWlpaVy7qUePHrCwsJA6IhERVTIvW4OJBSYiCUVGRmL48OFQKBT44osvUKNGDakjERFVOzqdDkFBQVi9ejU+/fRTTJkyhWtmEBFVIg8ePChWRNJoNIiPj0f9+vWNRaTn/2zRogVMTU1feUxRFCGKInQ6HVJTU2FtbY169eqVw6shIio9oigiJiYGKpUKKpUKV65cQffu3Y0FJycnJ6kjEhFRJcACE1EF9vjxY0ycOBG3b9/G/v378cYbb0gdiYioWoqPj0dgYCAsLCywfft2NG/eXOpIRET0GwUFBbh69WqxriSNRoP8/PxiHUnu7u5wdXVFzZo1pY5MRFShZGVl4dixY8buJjs7OyiVSiiVSnTt2hXm5uZSRyQiogqIBSaiCk4URWzcuBGffPIJNmzYAH9/f6kjERFVS3q9HmvWrMFnn32GJUuWYObMmexmIiIqZ6IoIjk5uVgRKSYmBrdv30aLFi2KdSS5u7vDwcEBgiBIHZuIqFIxGAy4dOkS1Go1VCoV4uPj0atXLyiVSigUCjRp0kTqiEREVEGwwERUSURFRSEgIAD9+/fHl19+yZF5REQSSUhIwIQJEyCKIsLCwtCqVSupIxERVUnZ2dnQaDTFRtzFxsbC2tq6xHg7Z2dn3l1PRFRGHj58iCNHjkClUuHIkSNo2LChsbupc+fOkMvlUkckIiKJsMBEVIlkZ2dj4sSJSExMxP79+9GiRQupIxERVUsGgwHr16/Hp59+ioULF2Lu3LmQyWRSxyIiqpR0Oh0SEhKKrZMUExODR48ewcXFpUQxqU6dOlJHJiKqtvR6PS5evGhcuykxMRG+vr7G7qaGDRtKHZGIiMoRC0xElcxvR+atX78eAQEBUkciIqq2EhMTMXHiROTn5yMsLAxt27aVOhIRUYUliiLu3btXrCNJo9Hg+vXrcHBwMBaRnheSmjdvzlGkREQVXEZGBg4fPgyVSoVjx47B0dERCoUCSqUSHTp0gKmpqdQRiYioDLHARFRJcWQeEVHFYDAYsGXLFnz00Uf4+9//jvnz5/NCmoiqvby8PMTFxRXrSNJoNBBFsVgRyd3dHW3btoWVlZXUkYmI6C/S6XQ4f/68sbspJSUFfn5+UCqV6NevH+rVqyd1RCIiKmUsMBFVYtnZ2Zg0aRJu3rzJkXlERBJLTk7GpEmTkJmZiR07dsDNzU3qSEREZc5gMODWrVvFikgxMTFITU1F69ati422c3d3R4MGDSAIgtSxiYioHKSlpUGtVkOtVuP48eNo1aqVce0mb29vdqkSEVUBLDARVXKiKGLTpk1YunQp1q9fj+HDh0sdiYio2hJFEdu3b8eHH36IWbNmYeHChTAzM5M6FhFRqXj06BE0Gk2xYlJsbCzq1KlTYp2kVq1acdF3IiIyKioqwi+//AKVSgW1Wo379++jX79+UCqV8PPz4/p6RESVFAtMRFXEpUuXEBAQAD8/P6xevZoj84iIJJSamoopU6YgNTUVO3bswJtvvil1JCKi11ZUVIRr164V60iKiYnB06dPS3Qkubq6onbt2lJHJiKiSiY5ORlqtRoqlQqnTp2Cm5ubce0mDw+Pv9zdVFhYCL1eD0tLy1JKTEREL8ICE1EV8nxk3o0bN7B//360bNlS6khERNWWKIr46quvMG/ePEyePBkfffQRzM3NpY5FRGQkiiJSU1OLFZE0Gg1u3rwJJyenEsWkZs2acbwdERGVuoKCApw+fdq4dtOTJ0+Mxaa+ffuiVq1af/iYn3zyCY4dOwaDwYBx48Zh3LhxnCxARFQGWGAiqmJEUcTmzZuxdOlShISEcGQeEZHE0tPTMW3aNNy8eRNhYWHw8fGROhIRVUNPnz5FbGxssa4kjUYDc3PzYkUkd3d3tGnTht3wREQkmcTERGN30+nTp/Hmm29CqVRCoVDAzc3tlTc7/Prrrxg4cCAePHiAiIgIrFy5EqGhoWjWrFk5vQIiourjZQUm0/IOQ0R/nSAImDZtGjp06ICAgACcOnUKwcHB/JKAiEgiDRs2xPfff499+/Zh0KBBGDt2LD755BNYWFhIHY2IqiC9Xo8bN24UKyLFxMQgIyMDbdq0MRaRhg4dCjc3N9SrV0/qyERERMW88cYbmDlzJmbOnIm8vDycOnUKarUaQ4YMQVFREZRKJZRKJXx9fVGzZs0Szy8sLETHjh0BwDhuT6PRFCsw5ebm4vPPP4e5uTn8/f3RqlWrcnt9RETVBTuYiCq5J0+eYOLEiRyZR0RUQWRkZGDmzJnQaDQICwtD586dpY5EJCmt3oCHOYXQ6kXIZQLsrc0hl/21NReqk/v375foSIqPj0fDhg2LdSW5ubmhRYsWkMlkUkcmIiL600RRREJCgnGUXnR0NNLS0kqMvvvqq68QGhqKixcvonPnznjrrbcwduxY45qB165dw/bt2/HkyROYmJggJSUF3333HUfoERH9CRyRR1TFPR+Zt2TJEqxfv54j84iIKoCDBw9i1qxZGDFiBFasWMHFh6naEEURkclZOBCVgsikLKRk5sFUZgJBAEQR0OkNaGJnCW9HW/h7NYF3M1uu+YNna1PEx8cXKybFxMRAq9WWWCfJxcXlhXdzExERVTX5+fkwNTWFXC43/uzkyZNYvXo1/vWvf0Eul2PUqFFYvnw53N3dodVqIZfLMW/ePFhaWuJvf/sbateujWnTpqFnz578voSI6E/giDyiKu75yLyOHTtyZB4RUQUxbNgw9OzZE7Nnz4a7uzu2b9+OHj16SB2LqMyIoojvL6chODwBj3KLkK/V4/m9bFqDvti+tx7m4vajXPwYk446VmaY26cVhno2rhaFJoPBgOTk5BLj7ZKSktCyZUtjEWnu3Llwd3dH48bV430hIiJ6kReNnL548SIcHBxga2sLrVaLdu3a4eTJk3B3dzcWok6cOIHNmzfDxsYGAHD+/Hm89dZbAJ59ZuHvViKi0sECE1EV4unpiaioKEyaNAkdO3bE/v37OWOYiEhC9vb22L17N3744QeMGjUKQ4cOxT//+U9YW1tLHY2oVKVn52PON9HQpGUjr0j/6ifgWTdTXpEeeUX5+Me/Y/FNZArWDPdAw1pVZ+2yx48fQ6PRFOtIio2NhY2NjXGdpMGDB2Px4sVo3bo1x/YQERG9BltbW0RFRQEAYmNjcf/+fTRp0sS4PTY2FnK5HO7u7jAxMcHTp0+Rk5MDHx8fAGBxiYioFLHARFTF2NjYYN++fdiyZQu6dOmCkJAQjBgxQupYRETV2uDBg9GtWzfMnTsXbm5u2LZtG3x9faWORVQqLiZlYtzOCyjUGqAz/P747ZfJ1+oRlZyFPsER2DXOB96OdqWcsmxptVokJCQYi0jPC0pZWVlwdXU1diWNHDkSbm5usLOrXK+PiIioIlEqlbhw4QKcnZ1Ru3ZtTJ06FR06dDBuv3btGjw9PaHValGjRg0cPXoUTZs25e9fIqIywDWYiKqwy5cvIyAgAL6+vggODn5hazkREZUvtVqNKVOmQKFQICgoyDi2g6gyupiUiffCLiBf+3pdS6/DQi7DV4EVs8gkiiLS09OLFZE0Gg0SEhLQpEmTYuskubm5wcnJCSYmJlLHJiIiqrJu3LiBli1bYv369bC1tUVAQAASExOxbNkybNmyBdbW1hg8eDC6du2KBQsWcDweEdGf8LI1mFhgIqrinjx5gsmTJ+PatWscmUdEVEFkZ2dj/vz5OHz4MEJDQ9G/f3+pIxH9YenZ+egTHIHcwtIrLj1nZS5D+Nweko7Ly83NRVxcXIlikiAIxvF2z4tJbdu2haWlpWRZiYiICCgsLIS5uTkAYNSoUUhNTUXjxo1ha2uLjz/+GPXq1TPuW1BQgK+//hr9+/eHg4ODVJGJiCoFFpiIqjlRFLFlyxZ89NFHWLduHUaOHCl1JCIiAhAeHo6JEyeiV69eWL16NWxtbaWORPRaRFHE8NDzuHQn60+PxXsZUxMBXs1ssW9SxzK/y1iv1+PWrVvFikgxMTFIS0uDs7OzsYj0vKBUv3593vlMRERUCRw/fhy3b9/GuHHjYGpafJWQtLQ0LFiwAEeOHEGjRo2gVCqhUCjQuXNnyOVyiRITEVVMLDAREQAgOjoaAQEB6NWrF9asWcOReUREFcDTp0+xcOFCHDp0CJs2bcKgQYOkjkT0St9dSsXiQ7HIKyr97qXnLOQyrBzqiqGepXdX8cOHD6HRaIxFpJiYGMTHx8Pe3r5YR5K7uztatmxZ4ssoIiIiqlr0ej0uXLgAtVoNlUqFxMRE9OnTB0qlEv3790fDhg2ljkhEJDkWmIjI6PnIvKtXr+LAgQMcmUdEVEGcOnUKEyZMQKdOnbB27VrUqVNH6khELySKIroHnURKVn6pH/ve7oWwcu2Fmu36AQCa2Frg5/m9jB1D+fn5OHv2LHx9fV96nMLCQly7ds1YRHpeUMrNzS3RkeTq6opatWqV+mshIiKiyufevXs4fPgwVCoVwsPD4ejoCKVSCaVSiQ4dOkAmk0kdkYio3L2swMQVZ4mqGRsbG+zduxfTp09Hly5dsHfvXqkjERERgJ49eyImJgb29vZwc3PDd999J3UkqqIKCwsxYcIENGvWDDVr1oSnpyfUavVrPz8yOQuPcovKMOF/PcotQlRyFgDg2rVrcHV1hZ+fHwoLCwE8K3bduXMHP/30Ez777DN07PhspJ61tTVGjRoFlUoFW1tbzJw5E+fOncPjx49x5swZbNy4EVOnTkWXLl1YXCIiIiKjBg0aYNy4cdi/fz/u37+PtWvXQq/XY9q0aahXrx5GjhyJr776Cg8ePJA6KhFRhcAOJqJqjCPziIgqpl9++QWBgYHw8PBASEhIsQWJif6q3NxcBAUFYdy4cWjatClUKhVGjhwJjUYDR0fHVz5/wbdXcCAqFS+5jPjT/reDSRAAfy8HtH16GTNmzEB+fj7MzMzw1ltv4e7du9BoNLC0tISbmxtatWqFb7/9FpaWlvj73/+OadOmlX5AIiIiqrZSU1ON3U3Hjx+Hs7Ozce0mb29vmJjwPn4iqprYwUREL+Th4YHIyEg8efIEHTt2xPXr16WOREREALp06YLo6Gg0bdoU7u7u2LdvH152UxDRH2FlZYWPP/4Yjo6OMDExwcCBA+Hk5ISoqKgX7r9z50507doV8+bNg62tLUIm9Ufezf/ehGYoyMVD1VqkhryL1PXvIevnryAa/rs2U86Vo0jbOhUpwcOR8c1H0GXfN27Lv30ZaaFTcSc4AJlHNxU7rzbrLtK/XogvR3XC+PHjkZeXB1EUodfrYWZmhk8++QQ3btzA3bt3ceTIEWi1WixevBgODg5cnJuIiIhKnYODAyZOnIjvvvsODx48wGeffYacnByMHz8eDRo0wHvvvYd9+/YhMzNT6qhEROWGBSaias7GxgZ79uzBjBkz0LVrV+zZs0fqSEREBMDCwgJBQUE4dOgQli1bhrfffhv37t2TOhZVQRkZGUhISICLi8vv7vPrr7+idevWSM+4j5o+b+ORep2x6Pnwp2AIJjI0mrIVDcevQ8HtS8i5chQAkJdwDtnnDqDe0H/AYfYemDu44OEPqwAA+rxsPPh+JWp3H4Mm7++Bae2GKEyNN57z8c9fw8LJE83+9g2mz5wFJycnmJmZAQDs7e3Ru3dv1K1bFwBw4cIFREZGYurUqWXyHhERERH9lpmZGXr37o2goCDExcXhwoUL6NSpE/bs2QNHR0d06dIFK1aswOXLl3mjGBFVaSwwEREEQcDkyZMRHh6Ojz/+GJMnT0Z+fukv3E1ERH9chw4dcOnSJbRt2xbu7u746quveJFKpUar1WL06NEYO3YsnJ2df3e/Zs2aYdKkScjK18HWsy/0OZkw5D6GPjcL+bciYes7CSZmNSCzqg2b9kOQe/VnAMDT6MOw6eQPuX0TCCYy1OocgKKM29Bl30d+YiTk9k1h5dwVgswUNdu/BZm1rfGcgokMuuz7MMl/jMXLP8etW7eQlpaGsLAwvP3228b99Ho9pk+fjpCQEI6mISIiIkk4Ojpi2rRp+OGHH3D//n0sXboUDx48wPDhw9GoUSMEBgbi4MGDyM7OljoqEVGp4hUYERm1a9cOUVFRyMnJQYcOHTgyj4iogjA3N8eKFSugVqsRFBSEQYMGIS0tTepYVMkZDAa8++67MDMzw/r161+6b4MGDQAAWr0ImVmNZ8/X5j8bd6fXI3X9e7gTPBx3gofj0eH1MOQ9+/JEn30fWeGhxm0pa0YCEKF7+gj6nEyY1rQ3nkMQBMh+89i2VyAAEcnb56BHBy+EhYXB3t4e7777Lrp162bcb+PGjXB3d0enTp1K6Z0hIiIi+vNq1KgBPz8/rFmzBgkJCTh9+jQ8PT2xfft2ODg4oGfPnvj888+h0Wh44xgRVXqmUgcgooqlZs2a2L17N7Zt24auXbtizZo1GD16tNSxiIgIgJeXFyIjI7Fy5Up4eHjgn//8JwIDAyEIgtTRqJIRRRETJkxARkYGVCrVa69ZJJcJ+O33IDKbuhBM5Wgyew8EE1mJ/WU29rDpHABrl14ltumy7kL39GGxTPrfPJZZ26KO4n1YyE3wWWczjBgyAN27d0eLFi2KHef48eOIiIiASqUCAGRmZuLy5cuIjo5+ZeGMiIiIqKy1aNECs2bNwqxZs5CXl4dTp05BpVJh8ODB0Ol0UCqVUCgU8PX1Rc2aNaWOS0T0h7CDiYhKEAQBkyZNQnh4OD799FOOzCMiqkDMzMzw8ccfIzw8HBs2bED//v1x584dqWNRJTNt2jRcvXoV//nPf2BhYfHaz7O3NodObzA+NrW2Qw1HT2Qd3wZDYR5E0QBtVjoK7mgAADU9FHhy7gCKHiQDAAwFuci9dgYAYPGGN7QP7yDv+lmIBj2eRv4AfU6W8di5185A9+QhdAYRjo3qPetwkpUsYu3cuRNXr15FdHQ0oqOj4e3tjaVLl2LFihV/6r0hIiIiKiuWlpZQKpVYv349bt26hWPHjsHZ2RkbNmxAo0aN0KdPH6xevRrXrl1jdxMRVQosMBHR72rXrh0iIyONI/OuXbsmdSQiIvp/7dq1w6+//ooePXrAy8sLmzdvhsFgePUTqdpLTk7Gli1bEB0djQYNGsDa2hrW1tbYvXv3K58rl5mgiZ1lsZ/ZD/wbRIMOd7dNQ8qaEXjw/WfQ52QCACxbd4ZNx2F4+MMq3Fntj7vbZyA/MRIAILOshbpDFiLr1E6krB0JXdZdmDu0NR63KD0B9/71N9wOegfvDB2CtWvXwsnJqUSm2rVro0GDBsb/mZmZwcbGBrVq1forbxMRERFRmRIEAc7Ozpg7dy6OHTuG9PR0zJo1CwkJCfDz80Pz5s0xY8YM/PTTT8jLy5M6LhHRCwkvq4Z7e3uLkZGR5RiHiCoiURSxbds2LFq06LVG5t2/fx/16tUrp3RERBQXF4fAwEBYWVlh27ZtaN68udSRqApb8O0VHIhKRXncVCsIgL+XA1a9067sT0ZERERUQYiiiLi4OKhUKqhUKkRFRaFLly5QKpVQKpUlRgYTEZUlQRCiRFH0ftE2djAR0Sv9dmReSEgIcnJyXrifXq/H5MmTMWXKFIwdO7acUxIRVV8uLi745ZdfoFAo4OPjg5CQEHYzUZnx92oCC3nJUXVlwUIuQ4BXk3I5FxEREVFFIQgCXF1dsWDBApw6dQqpqamYNGkSrly5gu7du6NVq1aYM2cOjhw5goKCAqnjElE1xg4mIvpDdDodRFF84WLgmzdvRnBwMGJjYzFnzhxkZGRgx44dsLa25gL0RETl5Pr16wgMDIRMJsP27dvRsmVLqSNRFSOKIroHnURKVtmvz9jUzhIR83rycwQRERHR/xNFEVeuXDF2N8XExKBHjx5QKpVQKBRwdHSUOiIRVTHsYCKiUmNqavrC4lJaWhoKCwuhVCohl8sxduxYyGQymJub80shIqJy1Lp1a/z8889455130KlTJ6xevRp6vV7qWFSFCIKAuX1awdKsbLuYLOQyzO3Tkp8jiIiIiH5DEAR4eHhg0aJFOHPmDJKSkjB69GicP38ePj4+aNu2LebNm4cTJ06gqKhI6rhEVMWxg4mI/hJRFPHvf/8bO3fuhLe3NzZu3Ij27dtDFEW8+eabmDVrFuzt7aWOSURULSUmJmLChAkoLCxEWFgY2rRpI3UkqiJEUcSIrecRlZwFnaH0F2MyNRHg1cwW+yZ1ZIGJiIiI6DUZDAZERUVBpVJBrVbj6tWr6N27t7G7ycHBQeqIRFQJvayDiQUmIvpLioqKMHToUAwaNAhTp06FTqfDgAEDsHTpUri6usLGxqbE/mZmZhKlJSKqfgwGAzZv3owlS5Zg3rx5mDdvHkxNTaWORVVAenY++gRHILew9DvkrMxlOD63JxrUqlHqxyYiIiKqLh48eIAjR45ApVLh6NGjaNy4MRQKBZRKJTp16vTCCTVERP+LI/KIqMw8fPgQ+fn5GDduHAAgKioKtra2eOONN2BjY2NcZF6j0WD37t0YM2YMtm3bJmFiIqLqxcTEBNOnT0dkZCSOHz+OTp06ITY2VupYVAU0rGWBXeN8YCEv3VF5FnIZdo3zYXGJiIiI6C+qW7cuxowZgz179iAjIwObN2+GXC7H3LlzUa9ePQQEBGDHjh1IT0+XOioRVVIsMBHRX9KoUSN4enqiT58+2Lx5M3bv3g17e3vj3fEmJia4evUq5s+fj7i4OAwbNgzr1q1DSEiIxMmJiKoXR0dHHD16FJMnT0avXr2wbNkyaLVaqWNRJeftaIevAn1gZS6DIP61TiZTEwFW5jJ8FegDb0e7UkpIRERERAAgk8nQqVMnLFu2DFFRUbh69SqUSiXUajXatm0LLy8vfPTRRzh37hzXcCWi18YReURUKr7++mtH+YAmAAAgAElEQVSEh4djwIAB8Pb2hpOTk3Fb9+7dMXjwYEydOhXW1tb45ptv8OjRI0yfPl3CxERE1VdKSgomT56Me/fuISwsDJ6enlJHokruu8MnMHf/FVg6OCNfa/jDz7c0k8GtcS2sGe6BhrUsyiAhEREREf0erVaLc+fOQa1WQ6VSIS0tDX5+flAqlejXrx/q1q0rdUQikhDXYCKicmEwGJCcnIxTp05h/PjxAIAPP/wQqamp2LVrF0xMnjVNDhkyBJ6enli6dKmUcYmIqjVRFLFr1y4sWLAAU6ZMweLFi2Fubi51LKqECgsL4eHhgZUrPwMcvbH6WAIe5RYhX6vHSy41IAjPxuHVsTLD3/q2whCPxhAEofyCExEREdELpaamGotNJ06cgLOzM5RKJZRKJby8vIzf7xBR9cACExGVq6NHj8LT0xOCIGDhwoWYNGkSvL29IZPJEBoainXr1nH9DyKiCuLu3buYOnUqbt26hR07dqB9+/ZSR6JKZsWKFfj1119x6NAhCIIAURQRlZyF/VEpiEzKQkpWHkxNBOM2nUFEE1tLeDvaIsCrCbya2bKwRERERFRBFRUV4cyZM1CpVFCpVHj06BH69+8PhUIBPz8/2NlxtDFRVccCExFJoqioCCNHjsTatWvh4OAAjUaDoUOHIiwsDN27d4fBYOBdL0REFYAoiti7dy/mzp2L8ePH4+OPP0aNGjWkjkWVQGJiIjp06ICoqCg0a9bshfto9QY8zCmEVi9CLhNgb20OuYy//4mIiIgqo6SkJGN3U0REBNzd3aFUKqFQKODh4cEbh4iqIBaYiKjciaKInJwcjB49GrVr14ZCocCKFSswZcoUzJo1S+p4RET0AhkZGZgxYwbi4uIQFhaGTp06SR2JKjBRFKFQKODr64v58+dLHYeIiIiIyllBQQEiIiKMBaecnBwoFAoolUr06dMHtWrVkjoiEZUCFpiISFLvv/8+mjVrhqZNm8Lf31/qOERE9AoHDhzA+++/j1GjRmHZsmWwtLSUOhJVQPv378fy5csRFRUFuVwudRwiIiIiktiNGzegVquhVqtx5swZeHl5GdducnFxYXcTUSXFAhMRVSiiKEKr1UKr1cLKykrqOERE9AIPHjzA7NmzcfHiRWzfvh3du3eXOhJVINnZ2Wjbti0OHDiAzp07Sx2HiIiIiCqYvLw8nDx50rh2k16vN3Y3+fr6wtraWuqIRPSaWGAiogqnoKAAHh4eWLRoEd577z2p4xAR0e84dOgQpk+fjrfffhufffYZLwQJADBr1iwUFhYiNDRU6ihEREREVMGJoojr168bi02//vorOnbsaCw4tW7dmt1NRBUYC0xEVCHFxsbC398fHTt2xPr169nNRERUQWVmZmLu3Lk4ffo0tm3bht69e0sdiSR08eJFDB48GHFxcbCzs5M6DhERERFVMk+fPsWJEyeMBSe5XG4cpdezZ0+O6CaqYF5WYDIp7zBERM+5urri4sWL0Ov18PHxQXx8vNSRiIjoBezs7LBr1y6EhIRg7NixmDp1Kp48eSJ1LJKATqfDlClTsGrVKhaXiIiIiOhPqVmzJt566y1s2bIFd+7cwaFDh9C0aVOsWrUK9evXh0KhQEhICG7evCl1VCJ6BRaYiEhS1tbW2LVrF+bNm4cePXpg165dUkciIqLfMWDAAMTGxkKn08HNzQ1HjhyROhKVs40bN6J27doYM2aM1FGIiIiIqAoQBAFubm5YsGABTp06hZSUFEyYMAGXL19Gt27d0KpVK8yZMwdHjx5FQUGB1HGJ6H9wRB4RVRgcmUdEVHkcPXoUkydPhq+vL7788kvUrl1b6khUxtLS0uDh4YEzZ86gdevWUschIiIioirOYDDgypUrxlF6Go0GPXv2NK7d1KxZM6kjElULHJFHRJXC85F5BoMBPj4+iIuLkzoSERH9Dj8/P2g0Gpibm8PV1RU//vij1JGojM2ZMwfTpk1jcYmIiIiIyoWJiQk8PT3xj3/8A7/88guSkpIwatQonDt3Du3bt4eLiwvmz5+PEydOoKioSOq4RNUSO5iIqELauXMn5s+fj6CgIIwbN07qOERE9BInT57ExIkT0blzZ6xdu5Zr81RBKpUKs2fPhkajQY0aNaSOQ0RERETVnMFgQFRUlLG76fr16+jduzeUSiUUCgUaN24sdUSiKuNlHUwsMBFRhRUXFwd/f3/4+Phgw4YNHJlHRFSB5ebmYtGiRThw4AA2bNiAoUOHSh2JSkleXh5cXFwQGhqKvn37Sh2HiIiIiKiEBw8e4MiRI1CpVDhy5AgcHBygVCqhVCrRqVMnmJqaSh3xD9HqDXiYUwitXoRcJsDe2hxyGYeRkTRYYCKiSis3NxczZszAhQsXcODAAbi4uEgdiYiIXuLMmTMIDAzEm2++iZCQENStW1fqSPQXLVq0CElJSdizZ4/UUYiIiIiIXkmn0+HChQtQqVRQq9W4desW+vbtC6VSif79+6NBgwZSRyxBFEVEJmfhQFQKIpOykJKZB1OZCQQBEEVApzegiZ0lvB1t4e/VBN7NbCEIgtSxqZpggYmIKr3nI/NWrVqF8ePHSx2HiIheIi8vD0uWLMHu3buxdu1a+Pv78+KnkoqLi0PPnj2h0Wgq5IU4EREREdGrpKen4/Dhw1CpVAgPD8cbb7wBhUIBpVIJHx8fyGQyybKJoojvL6chODwBj3KLkK/V4yVf10MQAAu5DHWszDC3TysM9WzMay0qcywwEVGV8HxkXvv27bFx40aOzCMiquDOnz+P8ePHo02bNti4cSMLFJWMwWBAjx49MHLkSEyfPl3qOEREREREf5lWq8W5c+eMazfdvXsX/fr1g0KhQL9+/cp1AkN6dj7mfBMNTVo28or0f/j5FnIZ3B1qYc1wDzSsZVEGCYmeeVmBiYMbiajScHFxwcWLF2FiYoL27dsjNjZW6khERPQSHTt2xOXLl9G6dWu0a9cOX3/9NV52cxNVLDt37kRRURGmTJkidRQiIiIiolIhl8vRvXt3/POf/0RMTAwuX76Mnj174ttvv0WLFi3QsWNHfPrpp4iMjITBYCizHBeTMtEnOAJRyVl/qrgEAPlaPaKSs9AnOAKRSZmlnJDo9bCDiYgqpd+OzBs3bhzbgYmIKrjIyEiMHz8ejo6O2Lx5Mxo3bix1JHoJURSxefNmdO7cGe3atZM6DhERERFRmSssLMSZM2egVquhUqnw6NEj9O/fH0qlEn5+frC1tS2V81xMysR7YReQr/1zhaUXsZDL8FWgD7wd7UrtmETPcUQeEVVJcXFxCAgIgJeXFzZu3Ahra2upIxER0UsUFRVhxYoV2LRpEz7//HPeIFDBFRYWwtzcXOoYRERERESSuH37NtRqNdRqNSIiIuDu7g6lUgmlUol27dr9qWuZ9Ox89AmOQG5h6RWXnrMylyF8bg+Oy6NSxwITEVVZubm5mDVrFs6dO4cDBw7A1dVV6khERPQK0dHRGD9+POrXr4/Q0FA0bdpU6kjVml6vNy5sLIoii35ERERERP+joKAAERERxrWbcnNzoVAooFQq0adPH9SqVeuVxxBFEcNDz+PSnSzoDKU/OtzURIBXM1vsm9SRn+mpVHENJiKqsqysrBAWFoaFCxeiV69eCAsL4/oeREQVnIeHBy5cuIBu3brBy8sLW7Zs4d/dEsjNzcXKlSuxYMECfPDBBygoKDBeiPL/DyIiIiKi/6pRowb69euHtWvX4saNG4iIiEC7du2wdetWODg4oFevXggKCkJsbOzvfpb+/nIaYu9ml0lxCQB0BhExqdn4d3RamRyf6EXYwUREVUZ8fDz8/f05Mo+IqBKJi4vD+PHjUbNmTWzbtg1OTk5SR6o2AgMDUVBQgPHjx2P9+vVITEzE/PnzMXbsWADsZiIiIiIieh25ubk4deoUVCoVfvrpJxgMBuMovd69e8Pa2hqiKKJ70EmkZOWX+vkf/hgMmY09bLu/CwBoYmuBn+f34md5KjXsYCKiaqFt27a4cOECTE1N0b59e8TGxkodiYiIXsHFxQVnz55Fv3790L59e6xfvx4Gg0HqWFVeRkYGEhISsHz5cvTt2xeHDh3Cl19+iaCgIAwZMgRPnjzhBSkRERER0W8kJSVBqVTC1tYWDRo0wMyZM6HT6WBlZYUBAwZgw4YNuH37No4cOYKWLVti3bp1aNiwIfr27Yv5qzbjwdOCcsn5KLcIUclZr9zP0dERFhYWsLa2hrW1Nfz8/MohHVU1LDARUZXyvyPztm/fzjE/REQVnKmpKRYsWIAzZ85gz5496NWrF27evCl1rCqtfv36GDJkCA4ePIiCgmcXuv369UNsbCzq1q2LU6dOSRuQiIiIiKiCmT59OurVq4f09HRER0cjIiICGzduLLaPIAho06YN/v73vyM8PBx3797FjBkzcO6eiPwifbnkzNfqsT8q5bX2/c9//oOcnBzk5OTg6NGjZZyMqiIWmIioSho7diwiIiIQHByM9957Dzk5OVJHIiKiV3B2dsbp06cxZMgQdOzYEcHBwdDry+cirDrq2rUrwsPDsXLlSmRnZ0On0wEAfHx88O2330qcjoiIiIioYrl9+zYCAgJQo0YNNGjQAP3790dcXNwL9z116hQcHBwQGhqKyZMnI3L7R8iNPW7cLuq0yDqxHakbxyNl3Rg8OrweBm2hcXvezQu4GzYLd4KH495X81B0/7ZxW9G9RKTvmI07q/3x4N+fQ9QXGbfp87KRsf8TBI/pAjs7O3Tr1o0TIqhMscBERFXW85F5ZmZm8Pb2hkajkToSERG9gkwmw9y5c3H+/Hn8+9//Rrdu3XDt2jWpY1Upzzt7O3bsiNDQUMTFxWHQoEHYunUrNmzYgNDQUCiVSolTEhERERFVLLNnz8a+ffuQl5eHtLQ0qNVq9O/f/3f3v3fvHrKzs5F0JwV1FO8j8+hm6Aue3QCddWoHtJlpaDh+HRpPCYX+6SNk/7IXAFB47yYeqdaiTv+ZaDJ7D6w9FLh/cBlEnRaiXov73y2HlUsvNJm9F5bOXZB3/azxnE8ufA9ZzTpoNmcPUu+mY+XKlS8dfT169GjUrVsXfn5+uHLlSim9U1SdsMBERFWapaUltm/fjkWLFqF3794cmUdEVEm0aNECJ0+exKhRo9C1a1esWrXK2GFDf81vLzAdHR3x7bffYt68eUhPT0dOTg7GjBmD4cOHS5iQiIiIiKji6dGjB+Li4mBjYwMHBwd4e3tjyJAhv7u/XC7HkiVLkF1oQK3WHSCY1YDuUSpEUUTOlSOw9Z0EmUVNmJhbolbnAORePQ0AyLlyBDU9+sO8UWsIJjJYu/lCMJWj8O41FKZdBwx61Gz/FgSZKaycu8K8YUvjOQUTU+hzsiA+fYDsQgO6dev2uwWm3bt3IykpCcnJyejVqxf69euHx48fl+6bRlWeqdQBiIjKw3vvvYf27dvD398fJ0+exObNm2FtbS11LCIiegkTExPMnDkTAwYMwMSJE3Hw4EGEhYXB1dVV6miV0rVr1xAZGQkHBwcIgoAuXbrA1PTZ5cDgwYMxePBgiRMSEREREVVMBoMB/fr1w5QpU3D27Fnk5OQgMDAQH3zwAVatWvXC59SpUwempqbQ6osgCIBgag6DtgCGvGyI2kKk75zzm71F4P9H2emy7yNXcwJPon7872a9DvqcTACAzLpOsaKRzKae8c82Hd7G4zN7kLJ7MTr8uBzTp07BwoULX5ivS5cuxj9/+OGH2LVrF06fPo1Bgwb90beHqjEWmIio2mjTpg0uXLiA999/H97e3ti/fz/c3d2ljkVERK/g5OSE8PBwbN26Fb169cLs2bPxwQcfQC6XSx2t0tDpdBg2bBg6d+6Mp0+fon79+vjPf/6DgIAA+Pj4AABiYmLQsmVL1KhR46VjNIiIiIiIqpvMzEykpKRg5syZMDc3h7m5OcaPH4/Fixf/boHpOblMwG+H6ZhY2kAwNUejiRtgWtO+xP6mNnVRo3MAanUuOVWg4I4G+pxHEEXR+Jld/+QBTG0bPju2uSXsfCfCov9kbFLWQ8BgBdq3bw9fX99XvkZBEDj1h/4wjsgjomrF0tIS27Ztwz/+8Q/4+vpi27Zt/OVJRFQJCIKAyZMnIyoqCmfOnEGHDh04I/wP2L17N1xdXREaGor169fj7bffRr169bB7927Ex8fj6tWr+PHHH2FhYcHiEhERERHR/7C3t4eTkxM2bdoEnU6Hx48fY9euXWjXrt2rn2ttDp3eYHwsCCawbueHrOPboM99NpJO9/Qh8m9FAQCs2/XD08tqFN69DlEUYSgqQN7NizAU5sG8sTNgIsPTyB8gGvTIu34WhekJxmPn3bwAbdZdaPUGNG1gD5lMBplMViLTnTt38Msvv6CoqAgFBQUICgrCw4cPi3U1Eb0OFpiIqFp699138fPPP2Pt2rUYM2YMnj59KnUkIiJ6DU2bNoVarcasWbPQt29fLF26FEVFRVLHqvDatm2LlJQU3Lp1C3Xq1EH37t3h7++P2rVrIygoCM7Ozpg+fbrUMYmIiIiIKqzvvvsOhw8fRt26ddGiRQuYmpoiODj4lc+Ty0zQxM6y2M9se42HqW1D3PvX33FntT8y9i2GNjMNAGDesCXqKGYh8+hmpKwZgbtbJiFXEw4AEGRy1B26CDma40hZMwK5V0/DslVn43F1mXeRsXcxbgcNQ/euXTB9+nT07NmzRKanT59i2rRpsLW1RePGjXH48GGo1WrUqVPnL7xDVB0JL7tz39vbW4yMjCzHOERE5SsvLw/vv/8+Tp8+jQMHDnBkHhFRJZKWloapU6ciKSkJO3bsgLe3t9SRKrQlS5bg2rVr8Pf3h7+/v/Hnfn5+WLVqFTw8PCRMR0RERERUdS349goORKWiPIboCALg7+WAVe+8uruK6HUIghAliuILL7jZwURE1drzkXkfffQRfH19sXXrVo7MIyKqJBo3bowffvgBH3zwAQYMGIAPP/wQBQUFUseqsD788EMMHDgQx48fx4wZM3Ds2DGcO3cOsbGxaN26tdTxiIiIiIiqLH+vJrCQlxxVVxYs5DIEeDUpl3MRscBERARgzJgxOH36NNatW8eReURElYggCBgzZgyuXLmC69ev480338T58+eljlUhWVhYYPTo0Zg+fTqcnZ0xd+5c7Ny5E2vWrIGFhYXU8YiIiIiIqizvZraoY2VWLueytzaHVzPbcjkXEUfkERH9Rl5eHmbPno2ff/6ZI/OIiCoZURRx4MABvP/++xgzZgyWLVvGwskraLVayOVyqWMQEREREVV5311KxeJDscgr0pfZOSzkMqwc6oqhng5ldg6qfjgij4joNVlaWmLr1q3GkXmhoaEcmUdEVEkIgoCAgABoNBqkpqaiXbt2OHPmjNSxKowX/T5jcYmIiIiIqHwM9WwMt8a1YGoilMnxTU0EuDvUwhCPxmVyfKIXYYGJiOgFno/MCwkJwejRozkyj4ioEqlbty727duHzz//HAEBAZg9ezZyc3OljiUpURSRnJwMrVYrdRQiIiIiompJEASsGe4Bc3nZfCVvLjfB2uGeEISyKWARvQgLTEREv8PZ2Rm//vorrKys4O3tjStXrkgdiYiI/oChQ4ciNjYWWVlZcHd3x8mTJ6WOJJldu3Zh+PDhkMnKZ2FhIiIiIiIqqWEtC+wa5wMLeel+LreQy7BrnA8a1KpRqsclehUWmIiIXuL5yLwlS5agT58+HJlHRFTJ2NnZ4V//+hfWrl2L9957D9OmTat2XakPHz7EwoULsWnTJpiY8OM/EREREZGUvB3t8FXg/7F353FV1/n3wM/d2HdQEURUBESEC4KKytX8pqnkuEy5ZG6ZlmVOmpYtWo7zq2xatMYsTVNbJk0nl9IWzcoLAgrKLuICyCLKjsDlrp/fH+ad7mCuwIflPB8PHiPc7cCQwj339XoPhL217J7X5cmlEthby/D53IGI7OHWRAmJbh9/wyQiug2PPvoo1Go11q9fz5V5RERt0Lhx45Ceng6dToeQkBD89NNPYkdqMcuXL8e0adPQv39/saMQERERERGulUw/LIqGr73prqeZ7KxkiPB1xeElw1kukWhYMBER3abrK/McHBwQERHBlXlERG2Mi4sLtmzZgo0bN2L+/PmYN28eqqurxY7VrNRqNX766SesXr1a7ChERERERPS7zMxMDA3vi99enYg3JvWDj6st7KxkuNXxSRLJtWLJx9UWr0/shx3zo9DV2bZlQhPdAAsmIqI7YGtri02bNuG1117DyJEjsXHjRq7MIyJqY0aPHo309HTI5XL069cPBw8eFDtSs9DpdFiwYAHWrVsHJycnseMQEREREXV4er0er776KgYMGICCggI4OjpiUng3HH1+BD57bCAmR3RDLw97KGQS2CqksLOSwVYhhUImQS8Pe0yO6IbPHhuIo8+PwKTwbpDcqpEiamaSmz0xGhkZKSQlJbVgHCKituPMmTOYPHkygoODsXHjRj55R0TUBh05cgTz5s2DSqXC2rVr4ebWflZLvPnmm4iNjcV3333HXzyJiIiIiFqBadOmYffu3TAajQAAf39/5OTkNLqe3mhCWa0WeqMAhUwCDwdrKGScFSFxSCSSZEEQIm90Gb8riYjuUmBgIBITE+Hk5ITIyEikpKSIHYmIiO7Q//3f/yEtLQ3Ozs4ICQnBvn37xI7UJC5cuIB3330X69evZ7lERERERNRKvPnmmxgyZIj5fRcXlxteTyGToquzLbq72aGrsy3LJWq1+J1JRHQPbG1tsXHjRqxatQqjRo3Cxx9/fE8r8/RGEy5Va3Cxoh6XqjXQG01NmJaIiG7EwcEBH3zwAXbs2IFly5bhkUceQVlZmdix7pogCHjmmWewbNky9OzZU+w4RERERET0u549e8Lb2xuPPvoounfvDh8fH7EjEd0TudgBiIjag+nTpyMiIgJTpkzBr7/+ik2bNmHHjh348ssv8euvv/7pq8cFQUBSfiV2JRcgKa8SBRX1kMukkEgAQQAMRhN83OwQ2cMVkyN8EOnryleiExE1E5VKhdTUVKxcuRIhISH44IMPMHnyZLFj3bH//Oc/yM/Px3PPPSd2FCIiIiIi+gO1Wo24uDicPn0a1tbW0Ol0Ykciuic8g4mIqAlpNBosXrwY33//PUpLSyGRSLB371488MADFtcTBAF7ThVh7eEclNfpoNEbcbPBJ4kEsFXI4G5vhSUjAzAp3JtFExFRM4qPj8fcuXMRHByMDz/8EF26dBE70m2pqalB37598dVXX0GlUokdh4iIiIiIfmc0GhEZGYnly5dj2rRpYschum08g4mIqIXY2trinXfeQV1dHRoaGqDRaLB06VKLtXmXqjWY9kkCVuzLQEGlBvW6m5dLwLVppnqdEQWVGryyNwPTPknApWpNM382REQd1+DBg3Hq1Cn07t0boaGh+PLLL+9pBWpLWblyJUaPHs1yiYiIiIioldm8eTMcHR0xdepUsaMQNRlOMBERNbHnn38ea9euhclkgiAIkEql2L17NyZNmoQTeRWYs+04tHoTDKa7f6JSLpXAWiHF9jkDEdnDrQnTExHR/zpx4gQee+wx9OrVCx9//DG8vLzEjnRDycnJiImJQWZmJjw8PMSOQ0REREREv6usrESfPn3w448/IiwsTOw4RHeEE0xERC1o2bJl+OKLL7B06VIMHjwYCoUCr776Kk7kVWDWp8dRpzXeU7kEAAaTgDqtETM/PY6kvIomSk5ERDcyYMAAJCcnQ6lUIiwsDNu2bWt100xGoxELFizAW2+9xXKJiIiIiKiVWbVqFSZNmsRyidodTjAREbWA4qp6jFp3FHVaY5Pft721DIeXDEdXZ9smv28iIrJ06tQpPPbYY/Dy8sLGjRvh4+Mjap6ZM2dCo9EgIiICP/zwA3799Vee0UdERERE1IpkZmZixIgRyMrK4ovBqE3iBBMRkYgEQcDinanQ6k3Ncv9avQmLd6a0ulfTExG1R+Hh4Thx4gQGDx6M/v3745NPPhH179/ExETs3bsXL7/8MkaMGMF/C4iIiIiIWhFBEPDss89i5cqVLJeoXWLBRETUzPacKkJGcfU9r8W7kcINc3H1wimkFVZjb0pRk98/ERE1plAosHLlShw5cgSbNm3CAw88gLy8PFGylJeXw2i8Nh37j3/8Ay+88IIoOYiIiIiIqLG9e/eipKQETz31lNhRiJoFCyYiottw3333wcbGBg4ODnBwcEBgYOBt3U4QBKw9nIN6XdOvxvsjjd6I9w7l3NYr13U6HR5++GH06NEDEokEv/76a7NmIyJqr0JCQhAfH4+RI0ciMjISH374IUympplW1RtNuFStwcWKelyq1kBvbHy/giCguroaAGBnZ4dx48Zh6dKlTfL4RERERER0bzQaDZYuXYr3338fcrlc7DhEzYLf2UREt2n9+vWYN2/eHd0mKb8S5XW6ZkpkqbxOh+T8SkT2cLvldaOjo7F48WJMnjy5BZIREbVfcrkcy5cvx/jx4zF37lzs2rULW7ZsgZ+f3x3djyAISMqvxK7kAiTlVaKgoh5ymRQSCSAIgMFogo+bHSJ7uGJyhA8ifV1RWVkJo9EIV1dX7NixAw888EAzfZZERERERHSn3n33XYSHh+P+++8XOwpRs2HBRETUxFatWoWsrCzY2Nhgx67/AA4ecH9wCay7+gMADFfLUXloIxoKMiCxsoXTgAlwihwPABAEE2oS/oPa1B9haqiDTQ8l3EYvhMzWEQBQm3EEVUc/h6BrgNPAiRaPW5WfhXH3L0V9aQFsbW3x6KOP4r333muUz8rKCosXLwYAyGSy5vxSEBF1GEFBQYiNjcX777+PQYMGYcWKFVi0aNEt/54VBAF7ThVh7eEclNfpoNEbcX0YVW+ynH69UFaH3PI6fJd2Ce72VvjbCD88u3gx1rz5Ji1Mw1YAACAASURBVGxsbJrrUyMiIiIiojtUWFiItWvXIikpSewoRM2KK/KIiG7TSy+9BA8PDwwdOvSWa+X279+PadOmYejq/bDtPRAVhz4GcK1AKt29GorOPdHtme3o8sjruHpiHzQXkgEAV5O+Rf3ZBHSZvgbdnvkMUhsHVPz0EQBAV3YRFT9ugMe4pej2zGcwampgvFpmfszyQ5vgNmgCampqcP78eUyZMqV5vhBERHRDMpkMzz33HOLj4/HNN99g2LBhOHPmzJ9e/1K1BtM+ScCKfRkoqNSgXvffcunPCAJQrzOioFKDV789jUtBU1Cpbfoz/oiIiIiI6O698MILePrpp9GzZ0+xoxA1KxZMRES34a233sKFCxdQVFSEJ554An/5y19w/vz5P71+dHQ0Ro0eg8IqLez7/R/0V3IBALpLZ2HU1MAl+hFIZAooXDzhEDYadafVAICrKd/DZdhMyJ08IJEr4Bw9HfVn4iCYjKjPjoNt7wGw6d4PErkCLqqZgOS/f41LpHKUFOTh0uUrcHBwQFRUVPN+UYhayO2cLUbUmvj7++PXX3+99kKDoUPx9ttvw2i0nEY6kVeBkWt/Q3J+5V2f06fRG5GcX4mRa39DUl5FU0QnIiIiIqJ7FBsbC7VajRdffFHsKETNjivyiIhuw6BBg8x/nj17Nr766iscPHgQixYtuuH1PT09UVarhVwmhVRuDcGgg2AywlB9Bcar5bi4dup/ryyYYN2tLwDAWF2K0m9etyiOIJHCWFcJY2055I6dzB+WWtmYV+cBgHvM31Ct/gJ+vXrC3c0NY8eORXh4OKysrGBlZQVra2vzn6+/6XQ65OTkoFOnTo0u++ObRCJpoq8kkaWamhpMnz4deXl5cHR0RGFhIWbNmoWXX34Z9vb2MJlMkEql+Oabb7Bt2zZotVq89957CA4OFjs60U1JpVIsWrQIDz74IObNm4fdu3dj69at6Nu3L07kVWDWp8eh0d9dsfRHBpMAg9aImZ8ex+dzB97WOXxERERERNQ8jEYjFi1ahLfffhv29vZixyFqdiyYiIjugkQiueVUhd4o4H97GbmTB+QuXeD95Cc3vI3MyQPuMc/C5vfCyeIyBzfoywvM75v0DTBqrprfV7h5w3PCMozFSeRnnsSnn36KOXPmwGQyQafT3fCtqqoK//znP2Ftbd3oMq1WC51OB71eD4VC8afl042Kq3u97G5uK5fLWYS1QU5OTti3bx80Gg0cHBzg5+eHyMhI8w/iUqkUeXl5+OSTT/D444+jtrYW/+///T+sX78e7u7uIqcnurVevXrh8OHD2LRpE4YPH475z76AffqQJimX/kijN2L2tuM4vGQ4ujrbNul9ExERERHR7dmyZQscHR0xderUW1+ZqB1gwUREdAtVVVVITEzE8OHDIZfLsXPnThw9ehTr1q276e0UMkmjszSsugZAamWH6oTdcIz4CyQyOfTlBRAMOlh3DYBj2FhU/fYZPMY9B7lzZxjrq6EtPA27gCjYBQ5FyWdL0VCQCWuvAFSrvwQEk/m+azN+gWtAJF5+dTkyT8Thp59+wvr162948LtWq4UgCOjduzc2bNiAYcOGwdra+oYFjSAI0Ov1f1pA3ejtdi6rqam569ve6M1gMLS60utGl8lksrv7RmzHZDIZHBwczNN0AwcOBADz9NJXX32FqKgoPPzwwwCAt99+G0VFRSyYqM2QSqVYsGABxo4dizFv/wCNvRaQNv2P4Vq9CYt3pmDH/CgW7kRERERELayyshKvvvoqvv/+e/48Th0GCyYiolvQ6/VYsWIFsrOzIZPJ0KdPH+zduxeBgYE3vZ2HgzUMRpPFxyRSGTo9/Coqj2xB0cePAwY95O7d4DJsJgDAccB4AAIu71wJY20FZHbOsAtSwS4gCladfOH2wFMo+/YdCLoGOA2cCJmjh/m+G3KTkXtkM/w3zoOvry927Nhxw3IJAAIDA5Gfnw8AGD16NAAgNzcXPXr0aHRdiURiLkdas5tNat1LcVVfX3/PZdof3//j17O1lF43epNKW+6YRkEQIJFIsHXrVowaNQpubtdWfF3/gTwjIwOTJ0+GTqeDlZUVunXrhrKyshbLR9RUkstlkLj7ouybdyBz8oDr73/3NxWDSUBaYTX2phRhUni3Jr1vIiIiIiK6ub///e+YMGECwsPDxY5C1GJYMBER3UKnTp1w4sSJ277+qlWrzH/2cbPDBVMX+L74nfljckd3dJrwwg1vK5FI4TRwEpwGTrrh5Q4h98Mh5H7z+85D/jty7fGXZejlYY8jS++7Zca8vLxbXqetkUqlsLGx+dNSrbUwGo33XHr97+UNDQ0WE2FNcb9yubzJSy8bGxssX778T8urf//739izZw9sbS3XezU0NJjXIAKAnZ0dDAZDs/9/RdSUBEHA2sM5qNc17Wq8/6XRG/HeoRz8tb8Pzp49i969ezfr4xEREREREZCZmYkvv/wSWVlZYkchalEsmIiImlFkD1fkltc1WpXXHCSSa49HrZtMJoOtrW2jEqU1EQQBBoOhyabArl9+fTXj/5JIJFCr1fD19UVAQIBFDolEAldXV2g0Guj1elhbW6O4uBidOnWyuI/i4mJERES0+CTY7dxWJpNxPQIhKb8S5XW6FnmslnocIiIiIiK69rvr4sWLsXLlyka/qxK1dyyYiIia0eQIH3yXdqnZX7EOALYKGaZE+DT741D7J5FIoFAooFAoYG9v3yKP+dlnn+Hhhx+GnZ2d+WPXJ50eeOAB7Nq1Cw899BAaGhpQVlaGnj17Wty+S5cuOHny5D1Ngl1/uz4Ndje3vdFlJpOpxYutu7lvng/WfE6dOoVxD01HaVE+bHtFAn/oG+vPHUfV0c9hqL4CKw8fuI1eCKvO176/CzfMhWP4GNRl/AJjbQVsAwbDffTTkMivrSy9mvIDahL+A1PDVVh36wu30Qshd3THha3LAABKpRISiQRbtmzhIcNERERERM1k3759KC4uxlNPPSV2FKIWJ7nRK4mvi4yMFJKSklowDhFR+yIIAoa9/QsKKjXN/ljd3ezw27L7OClBbY7RaIS7uzuys7Ph6ekJANi+fTsMBgPmzp0LvV6PGTNmoLy8HDqdDrNmzcL8+fNFTn37jEYj9Hp9k06DNfVt//d8MDHOBbud2yoUihY9H+xuHDp0CI899hhef/11zJgxA0ajEf7+/rAKGwdd4AOoP5uAsv1vwynqYdgFDMaVr19D54dfhZVnb9Rl/ooq9ZfwfmIjJHIFCjfMhdTKBp2n/B0ShQ1Kd6+GtW8oXIfNhCYvFWX73kLnqath5eGLyiNboLuSC88ZbwEA8teM44o8IiIiIqJm1tDQgL59+2LTpk0YOXKk2HGImoVEIkkWBCHyRpdxgomIqBlJJBIsGRmAFfsymnWKyVYhw5KR/iyXqE2SyWQoKSmxOD9r0qRJMBqN5tJj7dq1OHfuHABg6NChYkW9KzKZDDKZrNWfD3ajtYj3WmxdPx+sKUuzPzsfrLVMgxUWFqKsrAwLFy7EqlWr8MQTT0Cv10MRHAOJANj3icbVE3sBALWpP8IxbAysvQIBXDtnrzr+a2iLs2HTPQQA4BgxDnKna2s2nIdMQcWhjXAdNhN1Wb/CIXQkrD2vFUgu981GwbppMFRdhtylCwBAbzS19LcREREREVGH8u677yIsLIzlEnVYLJiIiJrZpHBv7EwqQHJ+JQympj+MSS6VILSbMyaGeTf5fRO1lP8tX5ycnCze9/b2hrc3v8ebk1wuh1wut1hT2Nrc6Hywppjoqq2tbbJpsIaGBhiNRmi1WtTV1eHll1+Gs6sbnOUyGPTXXmggc+oMADBUX0Fd+hHUJH/330/SaICxtsL8rszxvzvcZU6dzZcZaytg3cXPfJnUyhZSW0cYasvNBVNlPc9iIiIiIiJqLoWFhVi7di1OnDghdhQi0bBgIiJqZhKJBOumhmHk2t9g0Db9FJO1Qor3p4ZzeomI2j0xzge7Ux999BGeffZZ83TTpEmTsP/b7wD89wUGxppSyF27Qu7UCTZDpsB5yJ+fj2S8WmpxO5mDGwBA5uAGQ80V82UmXQNMmquQO7ibP2YwNv2LGoiIiIiI6Jrly5djwYIFjc4IJupIWvcSeyKidqKrsy22zxkIW4WsSe/XViHD9jkD4enculdvERF1FC4uLvDx8cH69etRUlKCDRs2QGGlQEXiXggmI+rPHIP2Ug4AwEE5GldPfQ9t8RkIggCTrgH1507ApK0339/VkwdgqCmDUXMV1fFfwy5IBQCw7zsctWmHobt8AYJBj6rftsPaK9A8vSSzd0VRQV6Lf/5ERERERB1BbGwsjh49ipdeeknsKESikgjCn7+yMTIyUkhKSmrBOERE7VtSXgVmbzsOrd50T+vy5FIJrBVSbJ8zEJE93JowIRER3auqqiqkpaUhJSUFqampiDsWjwsVDTBUlcC217VzUeVuXnAdNhOaC8moOvoF9JXFkMqtYN2tL9xjnoXU2g6FG+bCMXwM6jJ+gaG2Anb+g+A2+mlIFddeVHD11EHUJH4DU0MtrL2D4DZ6IeROHgCAulMHIUv9Bg0aDTZt2oQpU6aI9vUgIiIiImpPjEYjBgwYgOeffx6PPPKI2HGImp1EIkkWBCHyhpexYCIialmXqjVYvDMFaYXV0OjvfGWenZUMId7OWDc1DF2dbZshIRER3Q5BEJCXl2cukq7/b2lpKUJCQhAWFgalUomwsDC8qK5HXoXmju6/cMNcuMf8DbY9wu44m7S2FKXbFmHAgAFQqVSIjo7G4MGD4eDgcMf3RURERERE//XJJ5/gs88+w9GjR3lcAXUINyuYeAYTEVEL6+psix3zo7A3pQjvHcpBeZ0OGr0RN+n7IZFcW4fnbm+F50YFYGKYN3+IISJqQRqNBpmZmRZFUmpqKhwdHc1F0qOPPop//vOf8PPzg0xmuRJ1YFEq8isLb/p3fVORSICH7wvHy/8oQHx8PNRqNVavXo2TJ08iKCjIXDhFR0ejS5cuzR+IiIiIiKidqKqqwsqVK/H999/zeRkisGAiIhKFRCLBpPBumBjmjeT8SnydXICkvEoUVNZDLpVAIpFAEAQYTAJ8XO0Q2cMVUyJ8EOHryh9giIia2eXLly1KpJSUFOTm5sLf3988kTRx4kQolUp4eHjc1n1OjvDBd2mXUK+788nVO2WrkGFKhA9cXFwwduxYjB07FgDQ0NCApKQkxMbG4tNPP8W8efPQuXNnREdHm0snPz8//jtDRERERPQnvv32W0yYMAHh4eFiRyFqFbgij4ioFdEbTSir1UJvFKCQSeDhYA2FTCp2LCKidslgMCAnJ8diKiklJQU6nc5ivZ1SqURQUBCsra3v+rEEQcCwt39BQeWdrcm7G93d7PDbsvtuWRQZjUZkZmZCrVYjNjYWarUaRqPRXDapVCqEhoY2msYiIiIiIuqoamtrYWNjA7mccxvUcfAMJiIiIiLq0GpqapCWlmZRJGVlZcHLy8uiSAoLC0O3bt2aZYrnm5OFWLEvo1mnmGwVMrwxqR8mhXe749teP1PqetmkVqtRXFyMwYMHm0ungQMHwtaW5/8RERERERF1FCyYiIiIiKhDEAQBFy9etCiSUlNTUVJSgn79+lkUSSEhIXB0dGzRbNM+SUByfiUMpqY/jEkulSDC1xU75kc1WUFWWlqKuLg4c+mUkZEBpVIJlUoFlUqFIUOGwM3NrUkei4iIiIiotRIEgaukqcNiwURERERE7Y5Wq0VmZqZFkZSamgpbW9tGK+78/f1FXfVmMpmQlpaG9z7+FCc6jYHG0PQFk721DD8vuQ+ezjZNft/X1dXVITEx0bxWLzExEb6+vuaVeiqVCj4+Ps32+EREREREzS0vLw+ZmZkIDg6GTqdDQECA2JGIRMWCiYiIiIjatNLS0kZnJZ07dw69e/e2KJKUSiU6d+4sdlyzL774Ap9//jni4uKg0+mg1+ux4esf8EG6AI2+6Vbl2Spk+HzuQET2aNlpIr1ej5SUFPOEU2xsLGxtbS3OcQoKCoJUyvMEiYiIiKj1u3TpEmJiYuDr6wudToeQkBD07NkTo0aNgp+fn9jxiETBgomIiIiI2gSj0Yhz5841WnFXV1fX6Kykvn37wsam+aZ1msLQoUORkJAAk8kEAOjTpw9Onz6NpLwKzN52HFq96Z7W5cmlElgrpNg+p+XLpRsRBAE5OTnmskmtVqOqqgpDhw41Tzj1798fVlZWYkclIiIiImrklVdegcFgwFtvvYULFy7g6NGjyMzMhKurK5577rlW//sHUXNgwURERERErU5tbS3S0tIsiqSMjAx07ty50Yo7X1/fNrnzPDc3F0FBQdBqtbC3t8eGDRswa9YsAMClag0W70xBWmH1XU0z2VnJEOLtjHVTw9DV2bapozeZ4uJixMbGmgunc+fOITIy0jzhNHjw4BY9C4uIiIiI6M9s374d+/fvx/bt2+Hg4AAASE5Oxptvvgl3d3ds2LBB1NXbRGJgwUREREREohEEAYWFhY1W3BUVFSE4ONiiSAoNDYWzs7PYkZtEfn4+HnzwQURGRmLv3r0wGo0oLS21eNWjIAjYm1KE9w7loLxOB43eiJv8eA6J5No6PHd7Kzw3KgATw7zbXPFWXV2N+Ph4qNVqqNVqnDx5EoGBgRZr9bp06SJ2TCIiIiLqgPR6PZ5++ml4enoiJiYGgwcPNl82fvx4fPTRR/D29hYxIVHLY8FERERERC1Cp9MhKysLqampFoWSQqFotOIuICAAcrlc7MjN4uTJkxg/fjyef/55PPvsszh16hRycnIwderUG15fEAQk51fi6+QCJOVVoqCyHnKpBBKJBIIgwGAS4ONqh8gerpgS4YMIX9c2Vyz9Ga1Wi6SkJPOEU1xcHDp16mQum6Kjo9G7d+928/kSERERUeskCAIkEglSUlKwf/9+VFdXw8vLCwMGDEB9fT0WLFiAvLw8sWMStTgWTERERETU5MrLyxtNJeXk5KBXr14WZZJSqYSnp6fYcVvMgQMHMGfOHGzatAmTJk26q/vQG00oq9VCbxSgkEng4WANhUzaxElbJ5PJhMzMTItznPR6vblwUqlUCA0NbbflJBERERGJr6ioCL/99hvy8/Oxc+dOhIWFYcaMGRg5cqTY0YhaHAsmIiIiIrprJpMJ58+ftyiSUlNTUVNTg9DQUIuppODgYNjatt7zgJrbRx99hNWrV2PPnj2IiooSO067IAgC8vPzzWVTbGwsCgsLERUVZS6dBg0a1KG/74iIiIjo7lyfWrrOaDRanLF0/fKamho4OTmJEZFIdCyYiIiIiOi21NXVIT093aJISk9Ph7u7u0WRpFQq0aNHD0ilHWOq5lZMJhNefPFF7Nu3DwcPHoSfn5/Ykdq1srIyxMXFmUun9PR0hIaGmlfqRUdHw83NTeyYRERERNTK1dXV4erVqzhz5gyGDx9u/rhOp4OVlRUaGhoszlAl6ohYMBERERGRBUEQUFxc3GjFXUFBAYKCgiyKpNDQULi6uoodudVqaGjA7NmzUVRUhH379sHd3V3sSB1OfX09EhMTzRNOCQkJ6N69u8U5Tr6+vmLHJCIiIqJWZvbs2ZDJZLhy5QpmzJiBadOmWVy+YcMGuLm5Nfo4UUfCgomIiIioA9Pr9cjOzm604g4AwsLCLCaTAgMDoVAoRE7cdpSXl2PChAnw9vbG9u3b+erGVsJgMCA1NRVqtdpcOllbW5vLJpVKhb59+3ICj4iIiKgD279/P959913s27cPP/74I3755RcMGTIE6enpWLhwIXr06IELFy5wcwN1eCyYiIiIiDqIyspKpKamWhRJ2dnZ6N69e6MVd127drXYN0535vz58xg7diwmTZqEN998k790tmKCIODs2bPmlXpqtRoVFRUYOnSouXSKjIyElZWV2FGJiIiIqIVMnz4dM2fOxNixY7F27Vp89NFHeOWVV5CVlYVTp05h165dcHZ2FjsmkehuVjDJWzoMEREREd07k8mE3NzcRivuKioqEBoaCqVSicGDB2PBggXo168f7O3txY7criQkJGDSpEl47bXXsGDBArHj0C1IJBIEBAQgICAAc+fOBQBcunQJsbGxiI2NxTPPPIOcnBxERERApVJBpVJh8ODBPMiZiIiIqB0SBAEmkwkLFizAsGHDoNPpcPHiRRw6dAi+vr4wGAyYN28esrKyMHjwYLHjErVqnGAiIiIiauU0Gg0yMjIsiqS0tDS4uLhYTCSFhYWhV69enKRpZnv27MGTTz6Jbdu2ISYmRuw41ERqamoQHx9vXqmXlJSEgIAA80o9lUoFT09PsWMSERER0T0yGo2QyWQArr1wTyqVoqamxuLFRREREdiyZQvCwsLEiknUanBFHhEREVEbUVJS0uispNzcXAQGBjZacefm5iZ23A5n3bp1eOedd7B//370799f7DjUjLRaLZKTk81r9eLi4uDm5mYum6Kjo+Hv7881k0RERERtzDPPPIOqqiqsWbMG3bp1A2BZOr388ssoKSnBp59+KmZMolaDBRMRERFRK2MwGHDmzJlGZZLBYEBYWJi5RFIqlQgKCuLZMCIzGo147rnn8PPPP+PgwYPo3r272JGohZlMJmRlZZknnNRqNbRarcWEk1KphFzOLeRERERErdWJEycwYcIEPPTQQzh69CgmT56MFStWmC/PzMzEqlWrsHHjRr6gj+h3LJiIiIiIRFRdXY3U1FSLIikrKwvdunVrNJXk7e3NiYhWpr6+HtOnT0dNTQ2++eYbuLi4iB2JWomLFy9CrVabS6eLFy8iKirKXDoNGjQIdnZ2YsckIiIiot+lpqbi+++/x4svvoj4+Hi88847KCoqwtKlSzF58mRs3boVISEhiIy84XPpRB0SCyYiIiKiFiAIAvLy8iyKpJSUFJSWliIkJMSiSAoJCYGDg4PYkekWLl++jL/85S/o06cPNm/ezEkyuqny8nIcO3bMXDqlpaUhJCTEvFIvOjoa7u7uYsckIiIi6tD+uA7PYDBg586d2Lp1K4qLi1FbW4uLFy+KnJCodWHBRERERNTEGhoakJmZaVEkpaWlwcHBwbza7nqZ1Lt3b/MvMNR2ZGdnIyYmBjNnzsSqVas4WUZ3rL6+HsePHzev1EtISEC3bt3ME07R0dHw9fXl9xYRERGRyOrr6+Hp6YmvvvoKDz74oNhxiFoVFkxERERE9+DKlSuNzko6f/48/P39G6248/DwEDsuNYHr+9jXrFmDxx57TOw41E4YDAakpaVZrNVTKBQW5zgFBwdDKpWKHZWIiIioQ/n3v/+NXbt2Yc+ePWJHIWp1WDARERER3Qaj0YicnJxGK+4aGhoaFUl9+/aFtbW12JGpGXz11Vd49tln8e9//xsjR44UOw61Y4Ig4Ny5c+YJp9jYWJSVlWHIkCHm0ikyMpJ/1xARERE1M6PRiLq6Ojg5OYkdhajVYcFERERE9D+uXr2KtLQ0iyIpMzMTXbt2tSiSwsLC4OPjwxVWHYAgCFizZg0++ugjHDhwACEhIWJHog6opKQEsbGx5tLpzJkz6N+/v3ml3pAhQ+Ds7Cx2TCIiIqI2q7q6GlZWVrCxseHveUS3gQUTERERdViCIKCgoKDRirtLly4hODjYokgKCQnhK9Y6KIPBgKeffhonTpzAgQMH4OXlJXYkIgDXyvD4+HjzhNOJEyfg7+9vcY4Tv1+JiIiIbo8gCBgzZgxmz56N6dOnix2HqE1gwUREREQdglarRVZWVqMVdzY2No1W3Pn7+0Mul4sdmVqBq1evYsqUKQCAr7/+Go6OjiInIvpzOp0OJ0+eNJ/jFBcXBxcXF3PZpFKpEBAQwFfjEhEREd3A/v378eKLLyI1NRUKhULsOERtAgsmIiIianfKysoaTSWdPXsWfn5+FkWSUqlEly5dxI5LrVRxcTEefPBBDBw4EB9++CFLR2pzTCYTTp8+bZ5wUqvV0Gg0FhNO4eHh/N4mIiKiDk+r1SI4OBgbNmzAAw88IHYcojaDBRMRERG1WUajEefOnWs0lVRbW9vorKTg4GDY2NiIHZnaiPT0dIwbNw5PPfUUli9fzokPajcuXrxocY5Tfn4+Bg0aZC6dBg0aBHt7e7FjEhEREbWoNWvWID4+Hvv27RM7ClGbwoKJiIiI2oTa2lqkp6dbFEkZGRno1KlToxV3PXr0YCFAd+3w4cOYPn063n//fTzyyCNixyFqVhUVFTh27Jh5rV5qair69etnMeXk4eEhdkwiIiKiZlNUVASlUonExET4+fmJHYeoTWHBRERERK2KIAgoKipqtOKusLAQffv2tSiSQkND4eLiInZkakeysrIwYsQI7Nq1C8OGDRM7DlGL02g0OH78uHnCKT4+Hl5eXhbnOLHEJyIiovZk5syZ8PHxwRtvvCF2FKI2hwUTERERiUan0+H06dONVtzJ5fJGU0mBgYE8J4SalMlkglQqtfhYfX09ampq4OnpKVIqotbFaDQiLS3N4hwnmUxmMeHUr18/yGQysaMSERER3bH4+HhMnjwZ2dnZcHBwEDsOUZvDgomIiIhaREVFRaMi6cyZM+jZs2ej85L45D41l61bt0Kj0WD48OHo27cvpzCI7pAgCDh//ry5bIqNjcWVK1cwZMgQc+kUGRnJM++IiIio1TOZTBg4cCAWL16MGTNmiB2HqE1iwURERERNymQy4cKFC41W3FVVVSE0NNSiSAoODoadnZ3YkamDWLZsGY4cOYLx48fj559/xrRp0zBu3Dj4+vqKHY2oTbt8+TJiY2PNpVN2djbCw8PNE05DhgzhOlMiIiJqdbZs2YItW7YgLi6OLzwjukssmIiIiOiu1dfXIz093aJISktLg5ubW6MVdz179my0joyopdTW1mL+/PlYv3493N3d8cMPP+Dw4cPo2rUr5syZA3d3d7EjB7jNDwAAIABJREFUErUbV69eRUJCgnnC6cSJE+jVqxdUKpW5dPL29hY7JhEREXVg1dXV6NOnD7777jtERESIHYeozWLBRERERLckCAIuXbrUaMXdxYsX0adPH4siSalUwtXVVezIRI1Mnz4dvXv3xurVqwEABw4cwM8//4xRo0Zh7NixEASBr1wkagY6nQ6nTp2CWq02l07Ozs7mlXoqlQqBgYH874+IiIhazNKlS1FdXY3NmzeLHYWoTWPBRERERBb0ej2ys7MtyqTU1FQIgtDorKQ+ffpAoVCIHZnopq4XR4cPH8aBAwcQExODUaNGAQBWr16Nc+fO4bPPPhM5JVHHYTKZkJ2dbV6pp1arUVdXZy6coqOjER4ezn9fiIiIqFlkZ2dDpVIhMzMTnTt3FjsOUZvGgomIiKgDq6qqajSVlJ2dDR8fn0Yr7ry8vPjqcmqTrhdMV65cwdatW1FRUYGYmBgMHz4cWq0Wf/3rX/H555/Dzc1N7KhEHVZBQYHFOU65ubkYOHCguXCKioqCg4OD2DGJiIiojRMEAWPHjsXo0aOxZMkSseMQtXksmIiIiDoAk8mEvLw8iyIpNTUVZWVlCA0NtSiSQkJCYG9vL3Zkort26NAheHt7w97eHr6+vrj+M61EIsG5c+ewb98+HDp0CBMmTMAvv/wCiUSCr776imeEEbUilZWVOHbsmHml3qlTpxAcHGwx5dSpUyexYxIREVEb8+233+KFF15AWloap6WJmgALJiIionZGo9EgIyPDokhKS0uDk5NToxV3fn5+fFKd2pXXXnsNu3fvRnR0NPLy8rB48WKMHTsWBoMBcrkcAFBXV4eTJ0/iu+++g1wux+uvvy5yaiK6FY1GgxMnTpgnnI4dO4auXbuayyaVSoWePXty0paIiIj+lFarRXBwMD788EOMHj1a7DhE7QILJiIiojaspKSk0Yq73NxcBAYGWpRJSqUS7u7uYsclalaXLl3ClClTsHPnTnh5eWHv3r2YN28efvrpJ/Tv3x86nQ5WVlbm//1j6UREbYvRaER6erp5wkmtVkMikVhMOIWEhEAmk4kdlYiIiFqJt956C3Fxcdi/f7/YUYjajZsVTPxtm4iIqJUwGAzIyclptOJOr9ebi6TRo0dj+fLlCAoKgpWVldiRiVqcTCZDQEAAjEYjAGDixIkoKSnBpEmTkJycDA8PD+Tm5mL9+vVYs2YNyyWiNkwmkyEsLAxhYWFYtGgRBEFAbm4u1Go11Go11q9fj5KSEgwZMsRcOg0YMAA2NjZiRyciIiIRFBcX4+2330ZCQoLYUYg6DE4wERER/Q+dTofMzEx07doVnp6ezfIY1dXVSEtLsyiSsrKy4OXlZbHeTqlUolu3blwHRB1acXExjhw5ghkzZgAApk6dCjc3N3z00Ufm6yxbtgwA8M4776CoqAiVlZXo16+fKHmJqOVcuXIFcXFx5tIpKysL4eHh5sJp6NChcHFxETsmERERtYBZs2bB29sbb775pthRiNoVrsgjIiK6TYmJiVi4cCEMBgP8/f3x5ptvonfv3nd9f4IgID8/v9GKu8uXLyMkJMSiSAoJCYGjo2MTfjZEbV9mZiZiYmJQW1uLFStWYMmSJaivr0dwcDAef/xxrFixAgBw5MgR/PzzzzxriaiDq62tRUJCgnml3vHjx9GzZ0+Lc5y6desmdkwiIiJqYgkJCXjooYeQnZ3N36uJmhgLJiIiIgCXL1/G4cOHkZubi7lz58LLy8t8mclkglQqxbRp0zBq1Cg8/vjjeOqpp9CpUycsW7YMTk5Ot7z/hoYGZGVlNVpxZ29vb1EkhYWFoXfv3jwzgug2qNVqJCQkICYmBo8++ihefPFFTJs2DWfPnsX999+PxYsXY+zYsVi/fj3Ky8vx5Zdf8r8tIjLT6/U4deqUecIpNjYWjo6OFuc4BQUFcVKYiIioDTOZTBg0aBCeffZZ89YDImo6LJiIiKhD0Wq1OHnyJOLj49GlSxeMGzcOzs7OWLJkCc6cOYMuXbrA3d0ds2bNQmhoqLlcqqmpwYoVKzB06FBMnToVR48exZ49ezBx4kQMHz7c4jFKS0uRkpJiLpFSU1Nx7tw59O7du9GKu06dOon0lSBqH6qqquDi4oK9e/di5cqV2LBhA1QqFdLS0vDFF1+gqKgIdXV12L17N89cIqKbEgQB2dnZ5gmn2NhY1NTUIDo62lw69e/fHwqFQuyoREREdJs+/fRTbN68GXFxcXzRCFEzuFnBxN/AiYio3Xn33Xfx3XffYdCgQUhISMD58+cxYsQI1NTUYOfOnXB0dMSKFSuwadMmrF+/HkajEVKpFNXV1bCzs4PRaAQA9OrVC1ZWVigsLLS4/5qaGsyaNQsajQZKpRL3338/nnvuOfTt25cHixM1g+vnp0ycOBGXL1/G3/72N6jVaoSGhmLatGkIDQ1lsUREt0UikSAoKAhBQUGYP38+AKCwsBCxsbGIjY3F559/jgsXLmDAgAHmCafBgwfDwcFB5ORERER0I9XV1XjllVfw7bffslwiEgF/EycionbFZDJh8uTJWLJkCWxtbREfH4/NmzcjMzMTubm55l3MMTExePzxxwEAUqkUAODm5gYbGxuUlpYCADw8PCCXy6HT6Swew8HBAQcPHuQPr0QiePLJJ6HVajFo0CBoNBq88cYb6N+/v9ixiKgN69atG6ZNm4Zp06YBuDY1eezYMajVaqxevRonT55EUFAQVCqVuXTq3LmzyKmJiIgIAP7xj38gJiYGkZE3HK4gombGgomIiNoVqVQKf39/8/ve3t44ceIE/vGPf2DFihXmj0dERODy5csAAJlMBpPJBHt7e/Tq1Qvx8fHQarWwsbFBYmIiRo4c2egxiEg8vXv3xoULF/DBBx+YnxAmImoqLi4uiImJQUxMDIBrZywmJSVBrVZj8+bNmDt3Lrp06WIum1QqFXr16sUXnhAREbWw7OxsbN++HRkZGWJHIeqw+AwZERG1ax9//DHGjRsHLy8vyGQyc6lkbW0NT09PXLhwAcB/S6NHHnkEp06dwo4dOxAfHw+ZTIYuXbqIlp+oIyssLERdXZ3Fx7RaLT7++GPs2bPHvN6KiKg52djYIDo6Gi+99BIOHjyI8vJyfP311wgPD8cPP/yAYcOGwdvbG1OmTMEHH3yAU6dOmdftEhERUfMQBAFLlizBSy+9xN/ZiUTEgomIiNqt9PR0HDt2DFOmTAEAREZGYt++fQCA4uJihIeHQ6PRAADi4+ORlpYGhUKB999/H0eOHMGCBQswe/ZsBAYGivY5EHVUO3bsQERERKOCydraGrt378aYMWNESkZEHZ1MJoNSqcTChQuxY8cOFBYWIi4uDuPGjUNGRgamT58ONzc3jBkzBq+//jp+++03888bRERE1DQOHDiA3NxcPPPMM2JHIerQJIIg/OmFkZGRQlJSUgvGISIiujWTyYRz584hNTUVKSkp8PPzw6OPPgpra2sA117JJJFIsGjRIqhUKnPB9OOPP2Lv3r2wt7fH6dOnMWTIELzyyiuoq6vDvn370KNHDwwZMkTMT42owxMEAW+99RY2bNiAAwcOICQkROxIRER3rLS0FHFxcVCr1VCr1cjMzERYWJh5pd7QoUPh6uoqdkwiIqI2SavVol+/fvjXv/7FF54RtQCJRJIsCMINDzpjwURERK1aXV0d0tPTkZKSYi6UMjIy4OHhAaVSibCwMIwaNQpRUVGQyWTm23311VdYtmwZHnroIZSUlMDBwQGvv/46SktLsXXrVgQHB2PcuHHw9PQU8bMjoj8yGAxYuHAhEhMTceDAAXh7e4sdiYioSdTV1SEhIQGxsbFQq9VITExEjx49LM5x8vHxETsmERFRm/DWW28hLi4O+/fvFzsKUYfAgomIiFo9QRBQXFxsUSSlpqaioKAAQUFBCAsLMxdKoaGhcHFxuen9ffjhh9ixYwf++te/okePHhg4cCCfrCZqxa5evWqeNvz666/h6OgociIiouaj1+uRkpICtVptLp3s7OygUqnMpVNQUJD5jEgiIiK65tKlSwgJCUFCQgJ69+4tdhyiDoEFExERtSp6vR6nT582l0jXCyWpVGpRJCmVSgQGBkKhUIgdmYiaUXFxMR588EEMGDAAH374If+bJ6IORxAEnDlzxlw2xcbGoqqqCkOHDjWXTv3794eVlZXYUYmIiEQ1e/ZsdO3aFWvWrBE7ClGHwYKJiIhEU1FRYVEipaamIjs7Gz169LAoksLCwuDp6QmJRCJ2ZCJqQenp6Rg3bhwWLFiAF198kX8HEBH9rqioyOIcp3PnzmHAgAHmlXqDBw/mtCcREXUoCQkJeOihh5Cdnc1/A4laEAsmIiJqdiaTCRcuXLAoklJSUlBZWYnQ0FCLIqlfv36ws7MTOzIRiezw4cOYPn061q1bh+nTp4sdh4ioVauursaxY8fME04nT55EYGCgxTlOXbp0ETsmERFRszCZTIiKisKiRYswc+ZMseMQdSgsmIiIqEnV19cjIyPDokhKT0+Hi4tLoxV3vXr14vkBRNTItm3bsHz5cuzatQvDhg0TOw4RUZvT0NCA5ORk84TTsWPH0KlTJ3PZFB0djd69e3MylIiIWj290YSyWi30RgEKmQQeDtZQyCyfR9i6dSs2bdqEuLg4PsdA1MJYMBER0V0RBAElJSUWRVJqairy8/MRGBhoUSaFhobCzc1N7MhE1MoJgoBVq1bh888/x8GDB9GnTx+xIxERtQsmkwkZGRnmc5zUajWMRiOio6PNpZNSqYRMJhM7KhERdXCCICApvxK7kguQlFeJgop6yGVSSCSAIAAGowk+bnaI7OGKyRE+CHCVISgoCPv27cOAAQPEjk/U4bBgIiKiW9Lr9Thz5kyjFXcmk6nRVFKfPn14yDQR3TGdTof58+fj9OnT+Pbbb7nKiYioGQmCgPz8fPNKPbVajaKiIkRFRZknnAYNGgRbW1uxoxIRUQchCAL2nCrC2sM5KK/TQaM34iZPTUMiAWwVMki0tehek46DH67iZC6RCFgwERGRhaqqKqSlpVkUSadPn4aPj49FkRQWFgYvLy/+AEdE96yqqgoPPfQQHB0d8eWXX8Le3l7sSEREHU5ZWRni4uLMpVN6ejpCQ0OhUqmgUqkwdOhQTqQTEVGzuFStweKdKUgvqka9znjHt7eRS6H0ccG6qWHo6swXRxC1JBZMREQdlCAIyMvLa7TirrS0FCEhIRZFUr9+/eDg4CB2ZCJqhy5evIiYmBjcf//9eO+997ieiYiolairq0NiYqJ5wikxMRHdu3c3TzipVCp0795d7JhERNTGncirwJxtx6HVm2Aw3WRk6RbkUgmsFVJsnzMQkT34ggiilsKCiYioA9BoNMjMzLQoklJTU+Ho6NhoxZ2fnx+f4CWiFnHy5EmMHz8ey5Ytw+LFi8WOQ0REN2EwGJCSkmKxVs/GxsaicOrbty8PVyciott2Iq8Csz49Do3+zqeW/oytQobP57JkImopLJiIiNqZy5cvW5RIKSkpuHDhAgICAiyKJKVSCQ8PD7HjElEHdfDgQcyZMwcff/wx/vrXv4odh4iI7pAgCDh79izUarW5dKqoqMDQoUPNpVNkZCTP5iQiohu6VK3ByLW/oU7bdOXSdfbWMhxeMpzr8ohaAAsmIqI2ymAwICcnx2IqKSUlBTqdrtFZSUFBQbC2thY7MhERAODjjz/G3//+d+zZswdRUVFixyEioiZSXFxsPsdJrVbj7NmziIyMNE84DR48GE5OTmLHJCIikQmCgKmbEnDyYuU9rcX7M3KpBBG+rtgxP4rnRhM1MxZMRERtQE1NDdLS0iyKpKysLHTt2rXRijsfHx/+AEVErZLJZMJLL72EvXv34uDBg/Dz8xM7EhERNaPq6mrEx8ebV+olJycjICDAYq2ep6en2DGJiKiFfXOyECv2ZaBe17TTSyVfvgj7fiPgqBwNW4UMb0zqh0nh3Zr0MYjI0s0KJnlLhyEi6ugEQcDFixctiqTU1FSUlJSgX79+UCqVCA8Px5w5cxAaGgpHR0exIxMR3ZaGhgbMmTMHhYWFOHbsGNzd3cWOREREzczZ2RljxozBmDFjAABarRbJyclQq9X47LPP8OSTT8LNzQ0qlcpcOvn7+/PFUkRErZyDg4PF+xqNBk8//TT+9a9/3fK2giBg7eGcJi+X/pdGb8R7h3IwMcz7lv+uPPHEE/jtt99w9uxZfPrpp5gzZ06zZiPqKFgwERE1I61Wi8zMTIsiKTU1Fba2tuaJpClTpuCNN96Av78/ZDKZ2JGJiO5KeXk5Jk6ciK5du+Lw4cOwsbEROxIREYnA2toaQ4YMwZAhQ7B8+XKYTCZkZmYiNjYWhw8fxmuvvQadTmeeblKpVFAqlZDL+fQEEVFrUltba/5zXV0dunTpgsmTJ9/WbZPyK1Fep2uuaBbK63RIzq9EZA+3m15PqVRi6tSpWL58eYvkIuoo+BMcEVETKS0tbXRW0rlz5+Dn52debTdu3DgolUp07txZ7LhERE3m/PnziImJwYQJE7BmzRpIpVKxIxERUSshlUoREhKCkJAQPPXUUwCA/Px8qNVqxMbG4pNPPkFBQQGioqLMpdOgQYNgZ2cncnIiIrpu9+7d6Ny5M1Qq1Q0v37ZtGzZv3oyoqChs2bIFgpUdrO97Ara9rm3UMjXUoeLIZjScTwIkEtiHjoJL9HRIpNdeZFub+hOqj38DU20lrLwC4D5mEeTO15430eSeQsWhjTDWVcAheITF4+ori3Hh4AcY+k4eHG2tcf/992Pnzp03zLhw4UIA4AvhiJoYCyYiojtkNBpx7ty5Rivu6urqoFQqoVQqMWLECCxevBjBwcH84YWI2rWEhARMmjQJr776qvmJQyIiopvx9fWFr68vZsyYAQAoKyvDsWPHoFar8fLLLyMtLQ0hISHmCaehQ4dy7SoRkYi2b9+OWbNm3XQNXWJiImbPno2ysjL0nbIM5w9+AO+F2yGRSFB2YC1k9i7wevITCPoGXNn9d9Q6esAxfCzqc+JRHb8LnR9+FXI3L1TH70LZ/n/Cc+Y7MNZXo3TPG3CPeRZ2/lG4mvwdrp76Hvb9rhVNVUe/gG3PcIQteh/fLxqCpKSklvqSENHvWDAREd1EbW0t0tLSLIqkjIwMdO7c2bzi7oknnkBYWBh8fX25S56IOpQ9e/bgiSeewLZt2/Dggw+KHYeIiNooDw8PjB8/HuPHjwcA1NfX4/jx41Cr1Vi/fj1mzJgBHx8f84RTdHQ0f/YmImohFy9exG+//YYtW7bc9Hq+vr6YP38+9EYTDH4qGGvXwVRXBUgAzYUk+CzeCanCGrCygdOAibia8gMcw8fiasoPcBo8GQoPHwCA85ApqInfBUP1FTRcTIfCozvs+0QDABwHTEDNiT3mx5RIZTBUX0FeQSFkCitER0c33xeCiG6IBRMREa4dQFlYWNhoxV1RURGCg4PNZdKMGTMQGhoKZ2dnsSMTEYlq3bp1ePvtt/HDDz8gIiJC7DhERNSO2NnZ4b777sN9990HADAYDEhNTUVsbCz27t2LpUuXwsrKylw2qVQqBAcHc0UrEVEz+OyzzxAdHY2ePXve9Hqenp4AgLJaLaxsrq05Nek1MGmuAkYjCtfP+u+VBRPkTp0AAMbqK6g8vAmVR/5YYAkwXC2HsbYCckcP80clEglkf3jfdcRcVKk/R+HWJeh3aA2WP78Mc+fOvcfPmIjuBAsmov/P3p3HRVngfwD/PHMy3AOonIKK4IGAMhzCjEqRrWnmkbpm5tFaW61XW6217a9zq5f9WrW1/ZWZ4VaW2WZrrZaarg2H3HjhfSGICjIiDNccz+8PtmcljDyA4fi8X6951cDMM9+ZF4ozn+f7/VKP09jYiKKiIuzbt69ZoKRQKBAdHY3o6GhMnjwZL730EsLCwrhwmIi6FYvNjoqaBlhsIpRyAT6uaijlN/6BnM1mw5NPPomdO3ciIyMDwcHB7VgtERERoFAoEBMTg5iYGCxevBiiKOLEiRPSHqcVK1bg8uXLSExMlEInnU4HtVrt6NKJiLq8v//971i2bNkN395iE3Ftg6ncvRcEhRJBizdIO5euJXf3gXvi9Bb7lQDAajoPa3WFdF0URdiuuS531cJ73CI4q+R4JV6BWVMmYNSoUQgNDb3heono9vBTUyLq1i5fvtyiK+nYsWPo168foqOjERUVhT/84Q+IioqSzrYhIupORFFE7lkTNuWdQ+4ZE85V1kIhl0EQAFEErDY7grycoQvRYlpMEHTB2p8dOVRbW4sHHngAV69eRXp6Ojw9PTv42RARETWdwT5w4EAMHDhQOlO9rKwM6enpMBqNWLRoEY4ePYoRI0ZIe5xGjhzJKQRERDcpIyMDpaWlmDZt2g3fRykXIIr/va5w9YJTyHCYvl8Lz1GzIaicYL1yEbbqCjj1HQa36HG4YvwYqt79oeoVDHu9GXVnCuAySA/NAB0qd7yL2qMZ0AyMR3XeN7DVmKRjm4+kQe0/CKJ3b3h7Nb2PkctbhlhA08nGdrsdoijCYrGgvr4eKpWK3a9Et4kBExF1C3a7HSdPnpRCpB8DpaqqKkRFRSEqKgoGgwG/+93vEBERAY1G4+iSiYjalSiK2FxQihU7j+GyuRF1Fpv0Rs9itzW77akKM05fNuOb/WXwdlFhaUoYJg8PaBY0Xbx4Effeey8GDRqEzz//HCqVqiOfDhERUav8/Pxw//334/777wcAXL16FZmZmUhLS8Mbb7yB3NxcDBw4UBqpZzAY4Ofn5+CqiYg6t/Xr12PKlClwc3O74fv4uKphtdmbf23CkzDtScX5tY/B3lgHhYcvPBKmAgCcwxNht9SjYstyWKsuQaZ2gVNINFwG6SF39kCvSctQueM9VGxdCdehyVAHDpGO21h2DKada3C+oRYPf+KPVatW/ewov7Fjx2LPnj0AmoKzRx55BLt375bGsRLRrRHEayPln9DpdGJubm4HlkNE9MvMZjMOHjzYrCvpwIED8Pb2lnYl/fjfkJAQno1CRD1OWVUdlmwsxIHSKtQ22n75Dj+hUcoRGeiBlTOi4eehwdGjRzFu3DjMnj0bL774IpeqExFRl9PY2Ii8vDxprF5aWhq0Wm2zPU5hYWH8HUdE1AbueOvfOFVh7rDH6+/jgl2/H9Nhj0fU0wiCkCeKou6632PARESdlSiKOH/+fLMRd/v27UNxcTEGDRrULEiKjIyEVqt1dMlERA6Xc6YSc1Oz0WCxw2r/+X/n/RKFTIBaKcOTw9X446Mz8frrr2PevHltWCkREZHj2O12FBUVIS0tTQqd6urqpLBJr9dj+PDh3MdKRHQLnvnHPmzKK0ErHzu3GUEApsUEYvnUqPZ/MKIeigETEXV6FosFR44cadaVtG/fPgBoFiRFRUVh0KBBUCqVDq6YiKjzyTlTiYfWZaPOcvNdSz9HtDRgWYIrHrt/bJsdk4iIqDMqLi6Wwiaj0Yji4mLEx8dLoVN8fDxcXFwcXSYRUaeXc6YScz7MvqVpCjfLWSXH3+fFQRfi1e6PRdRTMWAiok7FZDJJ3Ug/BklHjhxB3759W4y48/Pz45gKIqIbUFZVh5QVe2BuaPs3cS5qOXYuHQ0/D+6vIyKinqOyshLp6elS4LRv3z5ERERIHU56vR4+Pj6OLpOIqNMRRRGj3tyNc6a6dn+svl7O2PPUGH52RNSOGDARkUPY7XacPn26WZBUWFiIyspKDBs2rFmQFBERwbMBiYhukSiKmLFmL/KLTbc1Fu/nKGQCYoK1+GxBAt+4ERFRj1VbW4ucnBwYjUYYjUZkZmYiICAABoNBCp1CQkL4u5KICMCX+SV4/p8H27WLSaOU47XJEZg8PLDdHoOIGDARUQeoq6vDwYMHmwVJ+/fvh6enZ4uupP79+0Mmkzm6ZCKiboNv3oiIiDqe1WrF/v37pQ4no9EIhUIhjdQzGAyIiIi4rfc+q1evhs1mw4QJEzBgwIA2rJ6IqH2Joohfv78XeWd5EhxRV8eAiYja1IULF1rsSjp9+jTCw8ObBUmRkZHw9vZ2dLlERN1ae46fuPDJMrhEJMMt6m4AQJBWgx+eTuYbOCIiousQRREnT55stsepvLwciYmJUugUGxsLtVp9Q8ezWq343//9X2zatAkqlQqZmZkAgOrqapw4cQLR0dH8nUxEnVp7j/H+fukY+Ho4tfmxiai51gImRUcXQ0Rdh9VqxdGjR1uMuLNarVKQNG7cOCxbtgyDBw+GSqVydMlERF3S4cOH8cQTTyAvLw+9evXCm2++icmTJ9/QfXPPmnDZ3NjOFTa5bG5E3lnTLy7Q3bVrF5566imcOHECPj4+WLZsGR555JEOqZGIiMhRBEFAaGgoQkNDMW/ePABNJ+elp6fDaDRiyZIlOHz4MEaMGCGN1EtMTISnp+d1jyeXy7Fs2TK4urpi//79AIDCwkJs2LABe/bsQW1tLZKTk7Fy5UpOiCCiTsnPQ4P1c+Mwe1026ixtFzJplHKsnxvHcImoE2DAREQAgKqqKuzfv79ZkFRUVITAwECpI2nhwoWIjo5GQEAAz5QjImojVqsV9913H377299ix44d2LNnD+69914UFBQgLCzsF++/Ke9cm75Za02dxYbP8861GjBZLBZMnjwZy5cvxyOPPILc3FwkJycjPj4eUVFRHVInERFRZ+Hr64upU6di6tSpAJq6jzIzM5GWloY333wTOTk5CA0NxXPPPYcpU6ZAofjvxzQ/vufauXMnpkyZAgDYsGEDrly5gqysLJSXl2PZsmXYuXMnxo4d2+Kx6+vr4eTED1+JyLF0IV74aH4c5qRmo8Fiv61xeXIZYGuox9OJPr940hsRdQyOyCPqYURRxNmzZ1uMuLt06RIiIiKajbgbNmwYXF1dHV0yEVG3dvDgQSQkJKC6ulr6IGns2LGIj48DoSLaAAAgAElEQVTHK6+80uL2qampWLt2LRISEvDBBx+gXnCCe8pvoRnQ1K1urzejctda1J/MBQQBLpF3wVP/AASZHABQs287qrK/hL3GBJV/GLx/tRAKj94AgLrTBajc8R5s5kq4Dk1GY/lZaUSexXQel7e+DWv5aXi6OOHOO+/Exo0bW9R38eJF+Pr6wmw2w9nZGQAQGxuLJ598EjNnzmyX15CIiKiramxsRH5+Pnx8fBAaGtri+yaTCXfccQe++uorBAcHY+/evXj//fexZMkSqNVqLFq0CAsXLsT48eNht9sBADKZDNu2bcOHH34Io9GIp59+GgsXLoRSqezop0dEJCmrqsOSjYXYX1J1SyfIOavkGNzHBf9cNhlCXRUiIyPx5z//GXfffTdPgiZqZ62NyGMPNVE3Vl9fj7y8PHzwwQdYtGgRRo8eDa1Wi6SkJLz//vtoaGjAzJkzsW3bNlRVVWHv3r1499138dhjj2HkyJEMl4iIOsD1TvYRRREHDx6Urh88eBCTJk3CDz/8AFEUkZWVhfDwcJRdvAS3uCm4vO1t6TgV/1oBQSaH/6Pvw2/e26g/nY+afdsBALXHMlGVuQm9J/8RgYs3QB04FBVblgMAbLVVKN/8GjxHPYigRRug8PRDQ0mRVMOVHz6Gpt9whDz5GU6fLcbChQuv+3z69OmDmTNn4sMPP4TNZkNmZibOnj0LvV7fZq8ZERFRd6FSqZCQkHDdcAkAioqKIJPJEBwcDJvNhpKSEhw9ehTTpk3D6NGj8dxzz+Huu5t2JQqCAJlMhtLSUrz00kuYO3cuioqK8N133zX7dwURkSP4eWjw2YIEvDY5AkFaDZxVcvxSLiQITcFSkFaDP0+KwBeP6dHHTQ2r1Yr8/HxMmjQJEydO7JgnQETXxRF5RN3EpUuXWnQlnTx5EgMHDpQ6kiZOnIioqCj06tXL0eUSEdF/DBo0CL1798abb76JpUuXYvfu3dizZw+Sk5Ol25w7dw7btm3Dzp074e7uDm9vb8yfPx+XahqhHX4Xyr/7G+zmK4AA1J3KRdCSjZAp1YDKCe6xk1Bd+C3cho9DdeG3cB85DUqfIACAR+J0XM3cBGvVJdQXH4DSpy9cBjUFQW6x9+FqzmapBkEmh7XqEmA2ocYqtBoYzZw5E7/5zW+wePFiAMD//d//ISgoqD1ePiIiom7t1KlTGDZsGABg+/bteOedd7B7924olUq8+OKLOHz4MEaNGgWgKWASRRGbN2/G6NGjMXbsWCgUCgiCgKtXrzryaRARAWj6e2ry8EBMig5A3lkTPs87h9wzJpwz1UIhE6S/x6x2EUFaZ+hCtJgeE4SYYK3UpZSUlIRNmzYBaArpn376aUc+JaIejwETURdjs9lw7NixZkFSYWEh6uvrpSApJSUFTz31FIYMGQK1Wu3okomIegxRFGGxWFBXV3dTl/Hjx2P16tV44YUX4OPjg6CgIBw/fhwTJ05EXV0dLly4AKvVisbGRpjNZgBAVFQUtv6QDbmqabeC3VIHe101YLOhZPVD1xRlh8K96cQCW9UlmHaugWnXB9dWDWv1ZdhqKqFw85G+KggC5Ndc1ybPxxXjRzj7wRKM/vZ1LHvmKcyfP7/Fa3DkyBHMmDEDmzdvxl133YXjx49jwoQJ8Pf3x/jx49vw1SYiIuq+vv/+e6xYsQIHDhyQfn/u2rULAQEB0qg7Nzc3fPvtt3j00UdhsVigVCpx4cIFlJaWIjw8HAqFAiaTCVFRUSgpKXHk0yEiakYQBOhCvKQ9ShabHRU1DbDYRCjlAnxc1VDKrz94a/To0fjHP/4BPz8/qFQq7nklcjAGTESdWHV1Nfbv398sSDp06BB8fX2lXUmPPfYYoqKi0LdvX86cJSL6CVEU0dDQcNOBT21t7U3f58f7CYIAjUYDjUYDZ2dn6f9bu7i5uWHBggXS9RUrViA5ORn33nsvNBoNTp8+jaVLl6KxsRGCIMDLywvbt2+HIBdw7YQ9uXsvCAolghZvkHYuXUvu7gP3xOlwHZrc4ntW03lYqyuavXa2a67LXbXwHrcIGqUMryeq8OtJ4zFq1KgWI30OHjyI8PBwaVxPeHg4xo8fj23btjFgIiIiukEJCQmYP38+9uzZg++++w4vvvgiRo0ahQ0bNmD79u3o378/tm3bhqlTpwKAFDpdvnwZjY2NCAgIANDUBW21WuHu7g6g6fc73zcSUWejlMvg56G5odtOnDgRFy9exB//+EcsXboUc+bMwZdffgmZjJtgiByBARO1i5s584Ca/pF/7ty5FiPuysrKMHToUClMmjNnDoYNGya9OSAi6mrsdjvq6+vbPej58b719fVQKBQ3Ffb8eHF1dUWvXr1u+n63skB7//79CAsLg91ux9/+9jfYbDa88847UhfqkSNHUFdXh8mTJyMxMRFfffUV/P39YbHZYbXZpeMoXL3gFDIcpu/XwnPUbAgqJ1ivXIStugJOfYfBLXocrhg/hqp3f6h6BcNeb0bdmQK4DNJDM0CHyh3vovZoBjQD41Gd9w1sNSbp2OYjaVD7D4JV2wsh/r2bOpzkLUOs4cOH4/jx49i1axeSk5Nx6tQpfPPNN/jDH/5wCz8xREREPZOLiwumTJmCKVOmAGiaZCGKIk6dOoVVq1ahrKwMixYtwvTp02GxWGA2m+Hp6Yn+/fvjyJEj+PWvfw0AOHDgABQKBQYMGAAAUrjEoImIuqqgoCC8/PLLAICVK1dizJgxeO211/D88887uDKinokBE7UJURSRe9aETT/OTq2shUIugyAAoghYbXYEeTXNTp0WEwTdNbNTe5qGhgYUFRU1C5L27dsHtVotBUnTpk3Dq6++ioEDB0Kh4B9Tan82mw319fWQy+Vwcmoat1VTU4MjR46gtrYWWq1Wmv1+Lbvdju3bt2PdunUYOnQoHn/8ce746mJsNluHdPX8+P8NDQ1wcnK6qcDmx4unpyf8/f1v+n7XC0E6m48++ghr166FxWKBwWDAjh07mo04DQ8PR2lpKfz9/ZGamip9XSmXIcjLGSeuOZbPhCdh2pOK82sfg72xDgoPX3gkNJ3d7ByeCLulHhVblsNadQkytQucQqLhMkgPubMHek1ahsod76Fi60q4Dk2GOnCIdNzGsmMw7VwDNNZi6qf+WLVqFfr169fiuQwYMADr1q3DokWLcPbsWXh4eGDWrFl4+OGH2/x1IyIi6il+/PfM4sWLpR2HPzpw4ADeeecdvPvuu3B2dobNZkN1dTUAYNWqVXjqqacwcODAZve5dOkS5s2bB71eD4PBgNjYWOl9ABFRV6FSqfDFF18gNjYWMTExGDdunKNLIupxBPHauSo/odPpxNzc3A4sh7oaURSxuaAUK3Yew2VzI+osNrTyIwVBADRKObxdVFiaEobJwwO6ddBUUVHRYlfS8ePHMWDAAGlfUlRUFKKiotCnTx9Hl0s9lNlsxj333IPs7GzMnDkT69atw9WrV7F27Vp88cUXcHNzg7u7Ox5//HEkJyc3O9sxPz8fzz77LKZPn45Dhw6hpqYGa9asgd1uZ3v6LbqR/T2329Vz7XWr1XpLYc/NdvX8eHFycuLPRht75h/7sCmvpNXfv21FEIBpMYFYPpVzzomIiDqrXbt24dFHH4WzszPuvvtuLF++vMVt6uvrsXXrVhiNRqSlpaGoqAjDhw+HwWCAXq9HUlISPD09HVA9EdHNS0tLw9SpU5GRkSF1bBJR2xEEIU8URd11v8eAiW5VWVUdlmwsxIHSKtQ22m76/hqlHJGBHlg5I/qG56x2VjabDSdPnmwx4q66urpFkDR06FBoNF37+VL3YrVacfDgQRw+fBiffvoptmzZgoyMDDz77LPYvn071Go1Vq9ejd27d+Mf//gHbDabdAble++9h4yMDKxfvx6HDx/GM888gxUrVrTYydJViaKIxsbGDgl6frwA6LCwR6PRQK1Wd+ugvyfIOVOJOR9m39Lv4pvlrJLj7/PipGW8RERE1HmZTCY4Ozs364r+OdXV1cjKyoLRaITRaEROTg769+8vdTjp9XoEBgZ2QNVERLdm9erVWLNmDTIzM+Hi4uLocoi6ldYCJs7eoluSc6YSc1Oz0WCxw2q/tVOm6yw25J01IWXFHqyf23U+rKqpqcGBAweaBUkHDhxAr169pCBpwYIFiIqKQkhICD+4pU5PoVAgOjoap0+fRn19PQCgd+/eCAoKQmpqKvz9/XH8+HHMnj0bAKTuE5PJhAsXLmDkyJEAAA8PD4SHhyM/Px+hoaHtMtddFMWf3d/TFuPbfnr5cWzgrQQ9Li4u8PHxuen73sr+HurZdMFaeLuoUNtY1+6P5eOqRkywtt0fh4iIiG6fVnvjv7Pd3NyQkpKClJQUAE1d9fn5+UhLS8Nnn32G3/3ud3Bzc5MCJ4PBgEGDBvH9LhF1Gk888QRycnKwYMECfPLJJ/z7iaiDMGCim5ZzphIPrctGneX2z5S22kVYG2yYvS4bH83vXCGTKIooLS1ttiepsLAQJSUlGDJkiNSZ9MADDyAyMpLjA6jL02g0sFgsAJp2qMyaNQuPP/44fH194eLigieeeKLZ7QVBQGNjo7QnTK1WQ6lUSl041y4Q/uSTT24oAPql2zQ0NEClUt1SR4+Hhwd8fX1v6r5OTk7cg0adniAIWJoShuf/ebBdu5g0SjmWpgzkGzUiIqIeQKlUIj4+HvHx8fj9738Pu92Oo0ePSiP1Xn/9dVRXVzfrcBoxYgRPliIihxEEAe+++y6SkpKwatUqLFmyxNElEfUI/NSMbkpZVR3mprZNuHStOosNc1KzsXPpaIeMy2tsbMThw4db7EuSy+WIjo5GdHQ07rvvPrzwwgsIDw/nB87UaVmt1lbDGrvdjjvuuOO6P8NOTk5obGwEAJw/fx4ffPABMjIy4Ofnh6+//hozZsxAQUEBbDYbFAoFVCoVXFxcUFVVJR2joaEBzs7OzY5rs9mwZ88eAM1Hv3l7e990UMT9PUTXN3l4ADbmnkPeWdMtdxa3RiETEBnogUnRAW1+bCIiIur8ZDIZBg8ejMGDB+ORRx4BAJSUlCAtLQ1GoxF///vfcerUKcTGxkodTgkJCXB1dXVw5UTUk2g0Gnz55ZdISEhAdHQ0xowZ4+iSiLo97mCiGyaKImas2Yv84vb78ComWIvPFiS069nRlZWVLYKko0ePIiQkRBpx9+N/fX19eaY23bJf2t9zu90817vY7fZWwxpvb2988skn1z2zMCcnBwsXLsTevXtx7tw5REZGwmQyAQCKi4sRGxuLixcvNrvPt99+i9deew0//PADamtrERcXh3//+9/w8fHpkNeYiP6rrKoOKSv2wNzQ9l1MLmo5vl86Br4eTm1+bCIiIuoeTCYTMjIypNCpoKAAQ4YMadbl1Lt3b0eXSUQ9wI4dO/DQQw8hJyeH++OI2gB3MFGb2FxQioPnq9olXAKaxuXtL6nCV4WlmDz89v/yt9vtOHXqVLMgad++fbhy5QoiIyMRFRWFpKQkPP7444iIiGjRdUHdT2v7e9pyp8+P962vr4dMJmt1bFtrFy8vr18c+/bTi1KpvOVQ1GKxoLq6GmazGQAQHR2NDz74AOHh4fj2228xc+ZMAE1hU2FhISZOnIikpCT4+Phg7ty5qKmpwa9+9SuGS0QO4uehwfq5cZjdRmNsf6RRyrF+bhzDJSIiImqVVqvF+PHjMX78eABAfX09cnJyYDQasXbtWsyfPx99+vSRwiaDwYD+/fvzpE4ianN33XUXlixZgqlTp+KHH36AWq12dElE3RY7mOiGiKKIUW/uxjlT+ywQv/DJMrhEJMMt6m4EaTX44enkZvtbUlNT0adPH9xzzz3XvX9tbS0OHDjQLEjav38/vLy8mnUkRUdHo1+/fhyx1UnY7fZ2C3iud/8b3d9zM3t9funSVcYpTp06FVlZWWhoaECvXr3w8ssvIyIiAk8//TQAIDAwEMuXL4ebmxuOHDkCo9GIBQsWAADOnDmDrVu3wsPDA/fcc89NLRMmoraXe6YSc1Kz0WCx39ZJIQqZALVShvVzO9eORCIiIuqabDYbDh48CKPRKF0ANOtwioyMhFwud3ClRNQdiKKIadOmwcvLC2vWrHF0OURdWmsdTAyYeqDPPvsML730EoqLi+Hr64vU1FQYDIZW75NzphJzPsxut+Xh1wZMzio5/j6v6cMss9mMOXPm4KuvvkJKSgq2bduGsrIy7Nu3Dzt37sRnn32Gixcvwmazwc3NDSkpKRg1ahSio6MRGRkJLy9+IHYzrt3f0x4Bz08vFosFTk5O7Rrw/PTCcJGIeoKyqjos2ViI/SVVt9TN5KySY1iAB1bOiHbIbkQiIiLq/kRRxOnTp6WRekajERcuXMDIkSOl0Ck2NhYaDf8tQkS3prq6GvHx8Vi6dKl0kiwR3TwGTCTZsWMHfvOb32Djxo2Ii4tDWVkZACAgoPWl3c/8Yx825ZWglR+X23JtwCQIwLSYQDwwQMQ999yDiooKWCwWqFQqeHh4wG63Izo6Gr1794azszPmzJmDESNG4I033sCmTZtw5MiR9imyg93u/p5bCYha29/TFgHPT++vVqs5DoGIqJ2IooivCkvxlx3HcNnciDqLrdXf44LQNA7P20WFJ+8Kw6ToAP4dTURERB2qvLwcaWlpUuh06NAhREdHSx1OSUlJnJhARDfl6NGjMBgM+PrrrxEfH+/ocoi6JAZMJElMTMTDDz+Mhx9++Bdvm5qairVr1yIhIQGr/vYeRJULvMc+Bs2App8le70ZlbvWov5kLiAIcIm8C576ByDImtrZa/ZtR1X2l7DXmKDyD4P3rxZC4dG00LPudAEqd7wHm7kSrkOT0Vh+VgqYLKbzMO9YjapT+5vVI5fLUVRUhIEDB173A6/Kykp4e3ujoqIC3t7et/tStXCz+3vaogPo2v097RHwtOX+HiIi6pxEUUTeWRM+zzuH3DMmnDPVQiETIAgCRFGE1S4iSOsMXYgW02OCEBOs5e8CIiIi6hRqamqQlZUFo9GItLQ0ZGVlISQkBAaDQQqdgoKCHF0mEXVy//znP7Fw4ULk5OSgT58+ji6HqMtpLWDqGstBqE3YbDbk5uZi4sSJCA0NRX19PSZNmoQ333zzZ1vOs7Ky8ODs2QhevAGm/G9xedvbCHhiPQRBQMW/VkDu4gn/R9+HaKnHpS9eQo2bD9yGj0PtsUxUZW5C7/v/Bwovf1RlbkLFluXwnf2/sNVWoXzza/C+ZzGcByagOu8bVBdsg0tEMgDgyg8fwykoCqVp32HH9u/wwQcfoKCgADU1NaipqZE+9Prp/p6vv/4aPj4+OH78OPbv398mIc+1972d/T3u7u7o06fPTQdEXWV/DxERdV6CIEAX4iXtUbLY7KioaYDFJkIpF+DjqoZSzvGhRERE1Pm4urrizjvvxJ133gkAsFgsKCgoQFpaGj7//HMsWrQILi4u0kg9g8GAQYMGcTQ6ETVz3333ITc3FzNmzMCOHTugVCodXRJRt8EOph7k/PnzCAgIQExMDL7++msolUrcd999GDNmDP785z+3uH1qaipeffVVGPMO4I639sBca8a5t+5H4O8+AgSg5G/zELRkI2RKNQDAXLQH1YXfwveB13Hx8xfgHJ4Et6ixAABRtOPcW9Pgv+D/UF98ANUFW+H30Fv/+Z6I0r/NhYf+AbhF3Y2Kr9+CIFdgmI8cCotZCnmqqqpgs9muu79HpVKhvLwc/v7+8PPza5cxb05OTlw2SkRERERERNRJiKKIo0ePSh1ORqMRV69eRVJSkhQ6jRgxAiqVytGlEpGD2Ww23HvvvQgPD8eKFSscXQ5Rl8IOJgIAqUtp4cKF8PPzAwA8+eSTePXVV68bMAGAr68vLDYRggDIlE4AALulDva6asBmQ8nqh/57Y9EOhXsvAICt6hJMO9fAtOuDa44mwlp9GbaaSijcfKSvCoIA+TXXtcnzcTXtIxwuyIanhztmzZqFGTNmtAiIftzfU15ejtGjR+N3v/sd/vjHP7bFS0VEREREREREnZwgCBg0aBAGDRqEBQsWAABKS0ulsOmTTz7BiRMnEBsbK3U4JSQkwM3NzcGVE1FHk8vl+OSTT6DT6RAbG4sHHnjA0SURdQsMmHoQrVaLwMDAm96roJQLLZaCy917QVAoEbR4g7Rzqfn3feCeOB2uQ5NbfM9qOg9rdYV0XRRF2K65LnfVwv/eJdj1+zE4eSAPKSkpmDt3LkJDQ1scy2QyYezYsZg4cSLDJSIiIiIiIqIeLiAgADNmzMCMGTMAAFeuXEFmZiaMRiNefvll5OfnY/DgwVKHk16v504Woh5Cq9Vi8+bNuPPOOzF06FBERUU5uiSiLo9DaXuYefPm4a9//SsuXboEk8mElStXYsKECa3ex8dVDavN3uxrClcvOIUMh+n7tbA31EIU7bCYylBffAAA4BY9DlczN6Gx/CwAwF5vhvlIGgBAM0AHS0Uxao9mQLTbUJ27BbYak3Rs85E01F8ph4+rGlpt06Lx642mu3r1Ku6++24kJSXhjTfeuK3XhYiIiIiIiIi6H09PT4wbNw6vvfYajEYjLl++jJUrV8LX1xfr1q3DoEGDEBYWhocffhgffvghTpw4gdbWSRBR1xYZGYm3334bU6ZMQWVlpaPLIery2MHUw/zpT39CRUUFwsLC4OTkhOnTp/9i549SLkOQlzNOVZibfd1nwpMw7UnF+bWPwd5YB4WHLzwSpgIAnMMTYbfUo2LLclirLkGmdoFTSDRcBukhd/ZAr0nLULnjPVRsXQnXoclQBw6RjttYdgznd62Fdt3j6NOnD1atWoV+/fq1qGvz5s3IycnBoUOHkJqaKn29qKgIffv2vY1XiYiIiIiIiIi6IycnJ+j1euj1eixbtgw2mw2HDh2C0WjE9u3b8ac//Qk2m61Zh1NUVBR3MhN1IzNnzkROTg5mzZqFb775hn++iW6D0NpZGTqdTszNze3AcqizeuYf+7Apr6TFqLz2IAjAtJhALJ/KNlUiIiIiIiIi6jiiKOLMmTPSHqe0tDSUlpYiISFB2uMUFxcn7bkmoq7JarXirrvugl6vxyuvvOLocog6NUEQ8kRR1F33ewyY6EbknKnEnA+zUdtoa/fHclbJ8fd5cdCFeLX7YxERERERERERtaa8vBzp6elS6HTw4EFERUVJHU5JSUnw8uJnGERdzaVLl6DT6fD2229j0qRJji6HqNNiwES3TRRFjHpzN86Z6tr9sfp6OWPPU2MgCEK7PxYRERERERER0c0wm83IysqSOpyysrLQt29fqcNJr9dzdD9RF5GdnY0JEybAaDQiPDzc0eUQdUoMmKhNfJlfguf/ebBdu5g0SjlemxyBycMD2+0xiIiIiIiIiIjaisViwb59+2A0GqXQSaPRSHucDAYDBg8eDJlM5uhSieg61q5di7/85S/IysqCm5ubo8sh6nQYMFGbEEURv35/L/LOmmC1t/0yJoVMQEywFp8tSGD3EhERERERERF1SaIo4tixY9JIPaPRiCtXriApKUkKnWJiYqBSqRxdKhH9x6OPPoqKigp88cUX/FyS6CcYMFGbKauqQ8qKPTA3tH0Xk4taju+XjoGvh1ObH5uIiIiIiIiIyFHOnz+PtLQ0KXQ6fvw4dDqdNFJv5MiRcHd3d3SZRD1WQ0MDRo8ejUmTJmHZsmWOLoeoU2HARG0q90wlZq/LRp2l7UImjVKOj+bHQRfCpZhERERERERE1L1VVVUhMzNTGqmXl5eH8PBwqcNJr9fD19fX0WUS9SglJSVISUlBeno6vL29r3sbq9UKk8mEXr16dXB1RI7DgInaXO6ZSsxJzUaDxX5b4/IUMgFqpQzr5zJcIiIiIiIiIqKeqaGhAbm5uVKHU3p6Onx8fJrtcQoNDeXoLqJ21tDQAFEU4eTUcsKS1WrF6tWrsXHjRuzevfu6tyHqjhgwUbsoq6rDko2F2F9SdUvdTM4qOYYFeGDljGj4eWjaoUIiIiIiIiIioq7Hbrfj0KFDUoeT0WiExWJp1uEUFRUFhULh6FKJepT6+noMHToUkyZNwltvveXocog6BAMmajeiKOKrwlL8ZccxXDY3os5iQys/UhCEpnF43i4qPHlXGCZFB/DsGyIiIiIiIiKiVoiiiOLiYhiNRil0OnfuHBISEqQOp7i4ODg7Ozu6VKJuq6ysDM899xyKi4uxdu1a9OvXz9ElEXUIBkzU7kRRRN5ZEz7PO4fcMyacM9VCIRMgCAJEUYTVLiJI6wxdiBbTY4IQE6xlsEREREREREREdIsqKiqQkZEhhU4HDhxAZGSk1OWUlJT0s3tkiOiX2Ww2yOVyAEB2djaeeeYZ9O/fH2vXroVMJoPVamUXIfUIDJiow1lsdlTUNMBiE6GUC/BxVUMplzm6LCIiIiIiIiKibqm2thZZWVnSSL29e/ciKChIGqlnMBgQHBzs6DKJugSr1YoHH3wQTz31FC5evIhnn30Ws2fPxtNPP+3o0og6HAMmIiIiIiIiIiKiHsRqtWLfvn3N9jip1epme5yGDh0KmYwnBBNdz8cff4xHHnkE/fv3x7p16xAXFwcAOHfuHBoaGnDhwgUAwNmzZzFr1ixHlkrUrhgwERERERERERER9WCiKOL48eNS2JSWlobLly8jKSlJCp1iYmKgVqsdXSpRp/HUU09hz549yMnJQVZWFp544gmkpKTg2LFjuP/++/H+++9jz549+Mtf/oIlS5Y4ulyidsGAiYiIiIiIiIiIiJopKytDWlqaFDodO3YMMTExUodTYmIi3N3dHV0mkUO98sormD17NkwmE0aOHIni4mL07t0bW7duxaJFi/Dss8/i4YcfdnSZRO2GARMRERERERERERG16urVq8jMzJQ6nHJzczFw4EAYDAYpdPLz83N0mUQdym63S6Mk33vvPaSmpiOdDSIAABarSURBVCIpKQn/+te/sHz5ctx7770AmroEBUFwZKlE7YIBExEREREREREREd2UhoYG5Ofnw2g0wmg0Ij09HV5eXtJIPYPBgIEDB/JDder2rg2PQkNDcenSJRQXF8PT09PBlRG1PwZMREREREREREREdFvsdjuKioqkkXpGoxENDQ3Q6/VS6BQdHQ2FQuHoUonalCiKKC8vh06nw4QJE3Dy5Em88847GDBgAARBkAIodjFRd8SAiYiIiIiIiIiIiNpccXGxNFLPaDSiuLgY8fHxUodTfHw8nJ2dHV0mUZvYvXs3kpOTmwVJPw2VSktLIYoiAgMDHVUmUZtiwERERERERERERETt7vLly8jIyJA6nPbv349hw4ZJHU5JSUnw8fFxdJlEt+Xq1atwd3eXwiWz2YzvvvsOBQUF+PTTTxEaGoo77rgDzzzzjKNLJbptDJiIiIiIiIiIiIiow9XW1iI7O1vqcNq7dy8CAgJgMBik0Ck4OJhjxajLsNvtWLVqFZKSkhAXF4dLly5h48aNOH36NAICAjBt2jRotVrExcVh+/btCAoKcnTJRLeltYCJA1GJiIiIiIiIiIioXTg7O2PMmDEYM2YMAMBqtWL//v0wGo3YsmULnn76aSiVSils0uv1iIiIgEwmc2zhRD9DJpNh/Pjx0Gq1AICcnBycPHkSer0ekydPhlwuR2lpKby9vdFacwdRd8AOJiIiIiIiIiIiInIIURRx8uRJaaReWloaysvLkZiYKO1x0ul0UKvVji6VqIXS0lKkpKTg2WefxUMPPQQAKCoqwpo1a6DVavHCCy84uEKi28cOJiIiIiIiIiIiIup0BEFAaGgoQkNDMW/ePADAhQsXkJ6eDqPRiEWLFuHo0aMYMWKE1OWUmJgIDw8PB1dOBJSXlyMsLEwKl77//nvs3r0bbm5umDFjBgBIe5qIuiN2MBEREREREREREVGnVV1djczMTGmPU05ODkJDQ5vtcfL393d0mdRDDR48GFOmTMHJkyeh1WoREhKCu+++G9HR0Y4ujahNtNbBxICJiIiIiIiIiIiIuozGxkbk5+dLI/XS0tLg6ekphU0GgwFhYWHsGqEOceHCBXz55ZcwmUyYNm0atFotevXq5eiyiNoMAyYiIiIiIiIiIiLqlux2Ow4fPix1OBmNRtTV1UGv10uhU3R0NJRKpaNLJSLqchgwERERERERERERUY9RXFwsdTcZjUacOXMG8fHx0li9hIQEuLi4OLpMIqJOjwETERERERERERER9ViVlZXIyMiQxuoVFhYiIiJC6nBKSkriWDNqU6IockwjdQsMmIiIiIiIiIiIiIj+o66uDjk5OdJIvczMTPj7+zfb4xQSEsKAgG6aKIqoqqrCN998gwcffNDR5RDdNgZMRERERERERERERD/DZrNh//79zfY4yWQyKWzS6/WIiIiAXC53dKnUBZSUlCA2NhYff/wx7rzzTkeXQ3RbGDARERERERERERER3SBRFHHq1ClppJ7RaMTFixeRmJgohU46nQ5OTk6OLpU6qd27d2PmzJnIyspCcHCwo8shumUMmIiIiIiIiIiIiIhuw8WLF5Geni6FTocPH8bw4cOlsXqJiYnw9PR0dJnUibz11lvYsGED0tLSoNFoHF0O0S1hwERERERERERERETUhqqrq7F3716pwyknJwf9+/eXRuoZDAYEBAQ4ukxyIFEUMXPmTGg0Gqxbt447vahLYsBERERERERERERE1I4sFgvy8/OlDqe0tDS4u7tLYZNer8egQYMYMvQwZrMZCQkJePzxx/HYY485uhyim8aAiYiIiIiIiIiIiKgD2e12HD16FEajUQqdampqoNfrpdBp+PDhUCqVji6V2tmJEyeQmJiIr776ComJiY4uh+imMGAiIiIiIiIiIiIicrCSkhIpbDIajTh9+jTi4uKkDqeEhAS4uro6ukxqB//617/w6KOPIicnB35+fo4uh+iGMWAiIiIiIiIiIiIi6mRMJhMyMjKk0KmgoABDhgyBwWCAwWBAUlISevfu7egyqY28/PLL2L59O3bt2gWVSuXocohuCAMmIiIiIiIiIiIiok6uvr4eOTk5UuCUkZEBX19faaSewWBAv379uMepi7Lb7Zg0aRKCg4Px17/+1dHlEN0QBkxEREREREREREREXYzNZsOBAwekkXpGoxEApJF6BoMBw4YNg1wud3CldKOqqqoQGxuL559/Hg899JCjyyH6RQyYiIiIiIiIiIiIiLo4URRx+vTpZnucLly4gJEjR0qhU1xcHJycnBxdKrXi0KFDGDNmDL777juMGDHC0eUQtYoBExEREREREREREVE3VF5eLoVNaWlpKCoqQnR0tNThlJiYCK1W6+gy6Sc2bdqEZ555Bjk5OfDx8XF0OUQ/iwETERERERERERERUQ9QU1ODrKwsaaRednY2+vXr12ysXmBgoKPLJADPPPMMCgoK8O2333LMIXVaDJiIiIiIiIiIiIiIeiCLxYKCgoJmXU6urq5S2KTX6zF48GAIguDoUnscq9WKX/3qV9DpdHjjjTccXQ7RdTFgIiIiIiIiIiIiIiKIooijR49KHU5paWm4evUqkpKSYDAYYDAYMGLECCiVSkeX2iNUVFRAp9PhrbfewtSpUx1dDlELDJiIiIiIiIiIiIiI6LpKS0ubdTidPHkSsbGxUofTyJEj4erq6ugyu628vDyMGzcO//73vzFkyBBHl0PUDAMmIiIiIiIiIiIiIrohV65cQUZGhhQ65efnY/DgwVLgpNfr0adPH0eX2a2sX78er732GrKzs+Hh4eHocogkDJiIiIiIiIiIiIiI6JbU19cjNzdX6nDKyMhA7969pT1OBoMB/fv35x6n2/TEE0+gpKQEmzdvhkwmc3Q5RAAYMBERERERERERERFRG7HZbDh06JC0x8loNEIURam7yWAwIDIyEnK53NGldimNjY1ITk7GuHHj8Pzzzzu6HCIADJiIiIiIiIiIiIiIqJ2IoogzZ85II/WMRiPOnz+PkSNHSmP14uLioNFoHF1qp3f+/HnExcXh/fffx7hx4xxdDhEDJiIiIiIiIiIiIiLqOOXl5UhPT5dCp4MHDyI6OlrqcEpMTISXl5ejy+yU0tPTMWXKFGRkZGDAgAGOLod6OAZMREREREREREREROQwZrMZWVlZ0h6nrKwsBAcHSx1OBoMBQUFBji6z03jnnXfw3nvvITMzEy4uLo4uh3owBkxERERERERERERE1GlYLBYUFhZKHU5paWnQaDQwGAxS6DR48GDIZDJHl+oQoihi3rx5aGhowIYNGyAIgqNLoh6KARMRERERERERERERdVqiKOLYsWNS2GQ0GnHlyhUkJSVJodOIESOgUqkcXWqHqaurg16vx4MPPoilS5c6uhzqoRgwEREREREREREREVGXcv78+WYdTidOnIBOp5NG6o0cORJubm6OLrNdnT17FvHx8fj000+RnJzs6HKoB2LARERERERERERERERdWlVVFTIyMqTQKT8/H+Hh4c32OPXp08fRZba5nTt3Yvbs2cjOzuaeKupwDJiIiIiIiIiIiIiIqFtpaGhAbm6u1OGUnp6OXr16SWGTXq9HaGhot9hftHz5cnzxxRf44Ycf4OTk5OhyqAdhwERERERERERERERE3ZrdbsehQ4dgNBqli81mg16vl0KnyMhIKBQKR5d600RRxPTp0+Hp6Yn333/f0eVQD8KAiYiIiIiIiIiIiIh6FFEUcfbs2WZ7nEpKSpCQkCB1OMXHx0Oj0Ti61BtSXV2NhIQELF68GI888kirt7XY7KioaYDFJkIpF+DjqoZSLuugSqk7YcBERERERERERERERD1eRUUF0tPTpdDpwIEDiIyMhMFggMFgQFJSEry8vBxd5s86duwY9Ho9tmzZgoSEBOnroigi96wJm/LOIfeMCecqa6GQyyAIgCgCVpsdQV7O0IVoMS0mCLpgbbcYHUjtjwETEREREREREREREdFPmM1mZGdnSx1Oe/fuRd++faUOJ4PBgL59+zq6zGa2bNmCJ554Arm5uejduzc2F5Rixc5juGxuRJ3FhlY+8ocgABqlHN4uKixNCcPk4QEMmqhVDJiIiIiIiIiIiIiIiH6B1WrFvn37pB1OaWlpUKvVzQKnIUOGQCZz7Li5//mf/8H3mXnwn/pHHCq7itpG200fQ6OUIzLQAytnRMPPo2uMCaSOx4CJiIiIiIiIiIiIiOgmiaKI48ePS2GT0WhEZWUlkpKSpNBJp9NBpVJ1aF1Zpyow810jRJkConDrYZdCJkCtlGH93DjoQjrvaEByHAZMRERERERERERERERtoKysTAqb0tLScOzYMeh0OqnDaeTIkXB3d2+3x885U4mH1mWjznLzXUs/R6OU46P5DJmoJQZMRERERERERERERETt4OrVq8jMzJTG6uXl5SEsLKzZWD1fX982eayyqjqkrNgDc0PbhUs/clHLsXPpaI7Lo2YYMBERERERERERERERdYCGhgbk5eVJXU7p6enw9vaWwia9Xo+BAwdCEITr3r+4uBiBgYEt9jyJoogZa/Yiv9gEq/3nP9e/VQqZgJhgLT5bkPCztVHPw4CJiIiIiIiIiIiIiMgB7HY7ioqKmu1xamhokAIng8GAqKgoKBQKiKIIrVaL2NhYbN68Ga6urtJxvswvwfP/PIjaxrbtXrrwyTK4RCTDLepuaJRyvDY5ApOHB7bpY1DX1VrAdOvbv4iIiIiIiIiIiIiIqFUymQwRERGw2Ww4evQoLl68CIPBgClTpuDIkSOYM2cOvLy8MHbsWCxevBgNDQ0wGo2Ijo7G2bNnATR1L63YeazNw6WfqrPY8Jcdx9BaY8pPrV+/HoIgYO3ate1YGXVGCkcXQERERERERERERETU3fn7++P555/Hd999h7q6OsyaNQuzZs0CAFy+fBkZGRlYvXo1GhsbYbfbcfLkSYSFheHTTz9F0IgxuGxu7JA6L5sbkXfWBF2I1y/e1mQy4fXXX8fQoUM7oDLqbBgwERERERERERERERG1sylTpgAAcnNzUVJS0ux73t7euPfee5GamgoAcHJyQn19PRQKBWbNmgW1uxfUYx6Bpn/TpDJ7vRmVu9ai/mQuIAhwibwLnvoHIMjkAICafdtRlf0l7DUmqPzD4P2rhVB49AYA1J0uQOWO92AzV8J1aHKzOiym8zi19W0k/e8ZuGnUuPPOO7Fx48affU7PPvssFi1ahM8//7xNXiPqWjgij4iIiIiIiIiIiIioE0hJScErr7yCpUuXQqFQYOXKlaipqUEf/XRc3vq2NLqu4l8rIMjk8H/0ffjNexv1p/NRs287AKD2WCaqMjeh9+Q/InDxBqgDh6Jiy3IAgK22CuWbX4PnqAcRtGgDFJ5+aCgpkh7/yg8fQ9NvOBJf/CdKSkqwcOHCn601Ozsbubm5+O1vf9uOrwh1ZgyYiIiIiIiIiIiIiIg6gcceewzPPfccwsLCEBwcjAULFsAOAdYBBthqKmE3X4HNbELdqVxo71wAmcoJchdPuMdOgvnwDwCA6sJv4T5yGpQ+QRBkcngkTkfjxdOwVl1C3clcKH36wmWQHoJcAbfY+yB31UqPL8jksFZdwplzJZArVdDr9det02az4fHHH8df//pXyGSMGXoqjsgjIiIiIiIiIiIiIupkfH19AQAVNQ1QOTkDAOyWOtjrqgGbDSWrH/rvjUU7FO69AAC2qksw7VwD064PrjmaCGv1ZdhqKqFw85G+KggC5Ndc1ybPxxXjRyj5cCkidryBPzz9FObPn9+itr/97W+IjIzEyJEj2/AZU1fDgImIiIiIiIiIiIiIqJOy2EQIwn+vy917QVAoEbR4g7Rz6Vpydx+4J05vsV8JAKym87BWV0jXRVGE7ZrrclctvMctgrNKjlfiFZg1ZQJGjRqF0NDQZsf5/vvvsWfPHmzduhUAUFlZiYKCAhQWFmL16tW3+5Spi2DvGhERERERERERERFRO7Naraivr4fNZoPNZkN9fT2sVusv3k8pF/Cf1UsAAIWrF5xChsP0/VrYG2ohinZYTGWoLz4AAHCLHoermZvQWH4WAGCvN8N8JA0AoBmgg6WiGLVHMyDabajO3QJbjUk6tvlIGqxXKyCKIry9tE0dTvKWIVZqaioOHz6MwsJCFBYWQqfT4YUXXsCf//zn23mJqIthBxMRERERERERERERUTt79dVX8dJLL0nXP/74Y7zwwgt48cUXW72fj6saVpu9+dcmPAnTnlScX/sY7I11UHj4wiNhKgDAOTwRdks9KrYsh7XqEmRqFziFRMNlkB5yZw/0mrQMlTveQ8XWlXAdmgx14BDpuI1lx2DauQbnG2rx8Cf+WLVqFfr169eiJk9Pz2bXVSoV3N3d4eHhcbMvC3Vhgnht9PkTOp1OzM3N7cByiIiIiIiIiIiIiIjoWne89W+cqjB32OP193HBrt+P6bDHo85LEIQ8URR11/seR+QREREREREREREREXViuhBtsz1M7UkQmh6P6JcwYCIiIiIiIiIiIiIi6sSmxQRBo2y5C6k9aJRyTI8J6pDHoq6NARMRERERERERERERUSemC9bC20XVIY/l46pGTDA7mOiXMWAiIiIiIiIiIiIiIurEBEHA0pQwOKvat4tJo5RjacpACB01j4+6NAZMRERERERERERERESd3OThARgW4AGFrH3CH4VMQGSgByZFB7TL8an7YcBERERERERERERERNTJCYKAlTOioVa2z8f6aqUMq2YMZ/cS3TAGTEREREREREREREREXYCfhwbr58ZBo2zbUXkapRzr58bB18OpTY9L3RsDJiIiIiIiIiIiIiKiLkIX4oWP5sfBRS2/7XF5CpkAF7UcH82Pgy7Eq40qpJ6CARMRERERERERERERUReiC/HCzqWjEROsveVuJmeVHDHBWuxcOprhEt0ShaMLICIiIiIiIiIiIiKim+PnocFnCxLwVWEp/rLjGC6bG1FnsUEUf/4+gtA0Ds/bRYUn7wrDpOgA7lyiW8aAiYiIiIiIiIiIiIioCxIEAZOH/397d7MKARjFcfi8xiRSGrEQMpegZi7EykW6Fu6BZknNRkrMvHYWQv0jH9PzbM/m7H91zlGdnR7W1c28Lq5mdXk9r9n8odbXWrXWqvdez8tex6Otmo5HdT45rsnJSFjiywQmAAAAAAD4x1prNR3vvp66e1os6+7+sZ4WvYaDVnvbGzUc+JjD9xKYAAAAAABghQwHa3Wws/nba7DiJEsAAAAAAAAiAhMAAAAAAAARgQkAAAAAAICIwAQAAAAAAEBEYAIAAAAAACAiMAEAAAAAABARmAAAAAAAAIgITAAAAAAAAEQEJgAAAAAAACICEwAAAAAAABGBCQAAAAAAgIjABAAAAAAAQERgAgAAAAAAICIwAQAAAAAAEBGYAAAAAAAAiAhMAAAAAAAARAQmAAAAAAAAIgITAAAAAAAAEYEJAAAAAACAiMAEAAAAAABARGACAAAAAAAgIjABAAAAAAAQEZgAAAAAAACICEwAAAAAAABEBCYAAAAAAAAiAhMAAAAAAAARgQkAAAAAAICIwAQAAAAAAEBEYAIAAAAAACAiMAEAAAAAABBpvfePh63dVtXNz60DAAAAAADAH3HSe99/b/BpYAIAAAAAAIC3nMgDAAAAAAAgIjABAAAAAAAQEZgAAAAAAACICEwAAAAAAABEBCYAAAAAAAAiLzsB/YyBl+EVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "G = nx.DiGraph()\n",
    "nodes = {n: 'depot' if n == 0 else f'{n} needs {q[n]}' for n in V}\n",
    "for i, j in selected_arcs:\n",
    "    G.add_edge(nodes[i], nodes[j])\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "edge_labels = {(nodes[i], nodes[j]): u[j].X if j!=0 else u[i].X - q[i] for i, j in selected_arcs}\n",
    "nx.draw_networkx(G, pos, node_size=800)\n",
    "ret = nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, label_pos=0.5)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}