atsp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class ATSPInstance:\n",
    "    def __init__(self, filename):\n",
    "        self.name = \"\"\n",
    "        self.dimension = 0\n",
    "        self.distance_matrix = None\n",
    "        self.parse_instance(filename)\n",
    "\n",
    "    def parse_instance(self, filename):\n",
    "        edge_weights = []\n",
    "        reading_edge_weights = False\n",
    "\n",
    "        with open(filename, 'r') as file:\n",
    "            for line in file:\n",
    "                line = line.strip()\n",
    "\n",
    "                if \"NAME:\" in line:\n",
    "                    self.name = line.split()[1]\n",
    "                elif \"DIMENSION:\" in line:\n",
    "                    self.dimension = int(line.split()[1])\n",
    "                elif line == \"EDGE_WEIGHT_SECTION\":\n",
    "                    reading_edge_weights = True\n",
    "                elif reading_edge_weights:\n",
    "                    if line == \"EOF\":\n",
    "                        break\n",
    "                    edge_weights.extend(map(int, line.split()))\n",
    "\n",
    "        self.distance_matrix = np.array(edge_weights).reshape((self.dimension, self.dimension))\n",
    "\n",
    "    def print_instance(self):\n",
    "        print(f\"ATSP Instance: {self.name}\")\n",
    "        print(f\"Dimension: {self.dimension}\")\n",
    "        print(\"Distance Matrix:\")\n",
    "        print(self.distance_matrix)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gurobipy as gp\n",
    "from gurobipy import GRB\n",
    "\n",
    "def solve_atsp(instance):\n",
    "    try:\n",
    "        # Assume que instance.dimension e instance.distance_matrix estão disponíveis\n",
    "        n = instance.dimension\n",
    "        dist = instance.distance_matrix\n",
    "\n",
    "        # Cria o ambiente e o modelo\n",
    "        model = gp.Model(\"ATSP\")\n",
    "\n",
    "        # Cria as variáveis x[i][j] binárias\n",
    "        x = [[model.addVar(vtype=GRB.BINARY, obj=dist[i][j]) if i != j else None for j in range(n)] for i in range(n)]\n",
    "\n",
    "        # Cria as variáveis contínuas u[i] para a formulação MTZ\n",
    "        u = [model.addVar(vtype=GRB.CONTINUOUS, lb=0, ub=GRB.INFINITY) for i in range(n)]\n",
    "\n",
    "        # Define o sentido de minimização no modelo\n",
    "        model.modelSense = GRB.MINIMIZE\n",
    "\n",
    "        # Restrições de grau: Cada cidade deve ter exatamente um arco de entrada e um de saída\n",
    "        for i in range(n):\n",
    "            model.addConstr(gp.quicksum(x[i][j] for j in range(n) if i != j) == 1, name=f\"out_{i}\")\n",
    "            model.addConstr(gp.quicksum(x[j][i] for j in range(n) if i != j) == 1, name=f\"in_{i}\")\n",
    "\n",
    "        # Restrições MTZ para eliminação de subciclos\n",
    "        for i in range(1, n):\n",
    "            for j in range(1, n):\n",
    "                if i != j:\n",
    "                    model.addConstr(u[i] - u[j] + (n - 1) * x[i][j] <= n - 2)\n",
    "\n",
    "        # Restrições de limites para u[i]\n",
    "        for i in range(1, n):\n",
    "            model.addConstr(u[i] >= 1)\n",
    "            model.addConstr(u[i] <= n - 1)\n",
    "\n",
    "        # Fixar u[0] a 0\n",
    "        model.addConstr(u[0] == 0)\n",
    "\n",
    "        # Otimizar o modelo\n",
    "        model.optimize()\n",
    "\n",
    "        # Verificar se uma solução ótima foi encontrada\n",
    "        if model.status == GRB.OPTIMAL:\n",
    "            print(f\"Comprimento ótimo da rota: {model.objVal}\")\n",
    "            print(\"Rota ótima:\")\n",
    "\n",
    "            # Imprimir a rota a partir do vértice 0\n",
    "            current_vertex = 0\n",
    "            visited = [False] * n\n",
    "            for count in range(n):\n",
    "                visited[current_vertex] = True\n",
    "                for j in range(n):\n",
    "                    if current_vertex != j and x[current_vertex][j].x > 0.5:\n",
    "                        print(f\"{current_vertex} -> {j}\", end=\" \")\n",
    "                        current_vertex = j\n",
    "                        break\n",
    "            print()\n",
    "\n",
    "            # Retorna a solução na forma de uma matriz 2D\n",
    "            solution = [[x[i][j].x if i != j else None for j in range(n)] for i in range(n)]\n",
    "            return solution\n",
    "        else:\n",
    "            print(\"Nenhuma solução ótima encontrada.\")\n",
    "\n",
    "\n",
    "    except gp.GurobiError as e:\n",
    "        print(f\"Erro no Gurobi: {e.errno}, {e.message}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Erro durante a otimização: {e}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (linux64 - \"Gentoo Linux\")\n",
      "\n",
      "CPU model: Intel(R) Core(TM) i9-14900K, instruction set [SSE2|AVX|AVX2]\n",
      "Thread count: 32 physical cores, 32 logical processors, using up to 32 threads\n",
      "\n",
      "Optimize a model with 307 rows, 289 columns and 1297 nonzeros\n",
      "Model fingerprint: 0xf9c39470\n",
      "Variable types: 17 continuous, 272 integer (272 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 2e+01]\n",
      "  Objective range  [3e+00, 7e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 2e+01]\n",
      "Presolve removed 33 rows and 1 columns\n",
      "Presolve time: 0.00s\n",
      "Presolved: 274 rows, 288 columns, 2538 nonzeros\n",
      "Variable types: 16 continuous, 272 integer (272 binary)\n",
      "Found heuristic solution: objective 151.0000000\n",
      "\n",
      "Root relaxation: objective 2.350000e+00, 51 iterations, 0.00 seconds (0.00 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0    2.35000    0   22  151.00000    2.35000  98.4%     -    0s\n",
      "H    0     0                      89.0000000    2.35000  97.4%     -    0s\n",
      "H    0     0                      42.0000000    2.35000  94.4%     -    0s\n",
      "     0     0   18.26786    0   27   42.00000   18.26786  56.5%     -    0s\n",
      "     0     0   18.26786    0   27   42.00000   18.26786  56.5%     -    0s\n",
      "H    0     0                      39.0000000   18.26786  53.2%     -    0s\n",
      "     0     0   28.00000    0   29   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   29   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   33   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   33   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   16   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   17   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   19   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   36   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   36   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   27   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   27   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     2   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 13\n",
      "  Gomory: 5\n",
      "  Cover: 17\n",
      "  Implied bound: 27\n",
      "  Clique: 1\n",
      "  MIR: 38\n",
      "  Inf proof: 19\n",
      "  RLT: 11\n",
      "  Relax-and-lift: 35\n",
      "  BQP: 1\n",
      "  PSD: 9\n",
      "\n",
      "Explored 6667 nodes (38576 simplex iterations) in 0.47 seconds (0.47 work units)\n",
      "Thread count was 32 (of 32 available processors)\n",
      "\n",
      "Solution count 4: 39 42 89 151 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 3.900000000000e+01, best bound 3.900000000000e+01, gap 0.0000%\n",
      "Comprimento ótimo da rota: 39.0\n",
      "Rota ótima:\n",
      "0 -> 6 6 -> 15 15 -> 5 5 -> 14 14 -> 3 3 -> 4 4 -> 7 7 -> 8 8 -> 16 16 -> 1 1 -> 12 12 -> 9 9 -> 10 10 -> 2 2 -> 13 13 -> 11 11 -> 0 \n"
     ]
    }
   ],
   "source": [
    "x = solve_atsp(instancia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "def plot_atsp_solution(instance, x):\n",
    "    \"\"\"\n",
    "    Plota a rota ótima da solução do ATSP como um grafo.\n",
    "\n",
    "    :param instance: Instância do problema ATSP, contendo a matriz de distâncias.\n",
    "    :param x: Matriz de variáveis de decisão da solução do Gurobi.\n",
    "    \"\"\"\n",
    "\n",
    "    # Cria um grafo vazio\n",
    "    G = nx.DiGraph()\n",
    "\n",
    "    # Adiciona os nós ao grafo (cidades)\n",
    "    n = instance.dimension\n",
    "    G.add_nodes_from(range(n))\n",
    "\n",
    "    # Adiciona as arestas da solução ótima\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if i != j and x[i][j] > 0.5:\n",
    "                G.add_edge(i, j, weight=instance.distance_matrix[i][j])\n",
    "\n",
    "    # Definir a posição dos nós em um círculo para visualização\n",
    "    pos = nx.circular_layout(G)\n",
    "\n",
    "    # Desenhar o grafo\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    nx.draw(G, pos, with_labels=True, node_color=\"skyblue\", node_size=500, font_size=10, font_weight=\"bold\", arrows=True)\n",
    "\n",
    "    # Desenhar as arestas com as distâncias\n",
    "    edge_labels = {(i, j): f\"{instance.distance_matrix[i][j]}\" for i, j in G.edges()}\n",
    "    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)\n",
    "\n",
    "    plt.title(\"Solução Ótima do ATSP\")\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (linux64 - \"Gentoo Linux\")\n",
      "\n",
      "CPU model: Intel(R) Core(TM) i9-14900K, instruction set [SSE2|AVX|AVX2]\n",
      "Thread count: 32 physical cores, 32 logical processors, using up to 32 threads\n",
      "\n",
      "Optimize a model with 307 rows, 289 columns and 1297 nonzeros\n",
      "Model fingerprint: 0xf9c39470\n",
      "Variable types: 17 continuous, 272 integer (272 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 2e+01]\n",
      "  Objective range  [3e+00, 7e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 2e+01]\n",
      "Presolve removed 33 rows and 1 columns\n",
      "Presolve time: 0.00s\n",
      "Presolved: 274 rows, 288 columns, 2538 nonzeros\n",
      "Variable types: 16 continuous, 272 integer (272 binary)\n",
      "Found heuristic solution: objective 151.0000000\n",
      "\n",
      "Root relaxation: objective 2.350000e+00, 51 iterations, 0.00 seconds (0.00 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0    2.35000    0   22  151.00000    2.35000  98.4%     -    0s\n",
      "H    0     0                      89.0000000    2.35000  97.4%     -    0s\n",
      "H    0     0                      42.0000000    2.35000  94.4%     -    0s\n",
      "     0     0   18.26786    0   27   42.00000   18.26786  56.5%     -    0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0     0   18.26786    0   27   42.00000   18.26786  56.5%     -    0s\n",
      "H    0     0                      39.0000000   18.26786  53.2%     -    0s\n",
      "     0     0   28.00000    0   29   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   29   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   33   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   33   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   16   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   17   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   19   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   36   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   36   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   27   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   27   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0   10   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     0   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "     0     2   28.00000    0    8   39.00000   28.00000  28.2%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Learned: 13\n",
      "  Gomory: 5\n",
      "  Cover: 17\n",
      "  Implied bound: 27\n",
      "  Clique: 1\n",
      "  MIR: 38\n",
      "  Inf proof: 19\n",
      "  RLT: 11\n",
      "  Relax-and-lift: 35\n",
      "  BQP: 1\n",
      "  PSD: 9\n",
      "\n",
      "Explored 6667 nodes (38576 simplex iterations) in 0.48 seconds (0.47 work units)\n",
      "Thread count was 32 (of 32 available processors)\n",
      "\n",
      "Solution count 4: 39 42 89 151 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 3.900000000000e+01, best bound 3.900000000000e+01, gap 0.0000%\n",
      "Comprimento ótimo da rota: 39.0\n",
      "Rota ótima:\n",
      "0 -> 6 6 -> 15 15 -> 5 5 -> 14 14 -> 3 3 -> 4 4 -> 7 7 -> 8 8 -> 16 16 -> 1 1 -> 12 12 -> 9 9 -> 10 10 -> 2 2 -> 13 13 -> 11 11 -> 0 \n"
     ]
    },
    {
     "data": {
      "image/png": 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IDh8Wpzs4OLBx40YKFixIlixZKFas2DfXACVF4cKFsbe355dffuHp06dYWVmxdevWeGtqvmb48OGsXr0aFxcXBg0ahKWlJX///Td58+aNmz4HH0a4Ro4cyfjx43FxcaFJkybcvXuXhQsXUr58eTp16vTFPnbt2sW7d+8++5wegEqVKmFjY8PatWtp27ZtgnIfPHiQsWPH0qRJEypVqkT69Onx8fFh2bJlREZGxj3XRwgh0jz9baQmhBDJZ//+/UqPHj2UwoULK+nTp1dMTEyU/PnzKwMGDPhku9v3798r48ePV+zs7JR06dIpuXPnVkaOHKlERETEO+/jrZkVRVEePnyoODo6xm2L+9tvvykHDx6MtzVzrFOnTin16tVTjIyMFEApUaKEMn/+/LjXnzx5ojRv3lzJlCmTkjFjRqV169bKs2fPPrst8uXLlxVnZ2clffr0ioWFhVK7dm3lzJkzCf78vHv3Thk3bpxStGhRxdzcXMmQIYNStWpVZcWKFfG2i4515swZpWzZsoqJiUm8PF/amrlfv37xjvn6+iqAMn369HjHY7ex3rx5c9yxW7duKY6Ojkr69OkVa2trpWfPnsrVq1cVQFm+fPk3P7Zr164pNWvWVMzMzJScOXMqEydOVDw8PD67LfKCBQuUwoULK+nSpVOyZ8+u9OnT56vbViuKojRu3FgxMzP76hbI3bp1U9KlS6cEBATEHfva1sw+Pj7KmDFjlEqVKinZsmVTjI2NFRsbG6Vhw4bxtgdXlP99zv39/b/5uRBCiNRGpShJfBqXEEIIrYiJiaFYsWJs3bo13jodIYQQQnydrJkRQgg9MzIywtnZmfXr1+s7ihBCCGFQZM2MEELo0ZIlS1Cr1Xh6elK/fn19xxFCCCEMiozMCCGEHp05c4Z+/fqhVqvp3bu3vuMIIYQQBkXWzAghhBBCCCEMkozMCCGEEEIIIQySFDNCCCGEEEIIgyTFjBBCCCGEEMIgSTEjhBBCCCGEMEhSzAghhBBCCCEMkhQzQgghhBBCCIMkxYwQQgghhBDCIEkxI4QQQgghhDBIUswIIYQQQgghDJIUM0IIIYQQQgiDJMWMEEIIIYQQwiBJMSOEEEIIIYQwSFLMCCGEEEIIIQySFDNCCCGEEEIIgyTFjBBCCCGEEMIgSTEjhBBCCCGEMEhSzAghhBBCCCEMkhQzQgghhBBCCIMkxYwQQgghhBDCIEkxI4QQQgghhDBIUswIIYQQQgghDJIUM0IIIYQQQgiDJMWMEEIIIYQQwiBJMSOEEEIIIYQwSFLMCCGEEEIIIQySFDNCCCGEEEIIgyTFjBBCCCGEEMIgSTEjhBBCCCGEMEhSzAghhBBCCCEMkhQzQgghhBBCCIMkxYwQQgghhBDCIEkxI4QQQgghhDBIUswIIYQQQgghDJIUM0IIIYQQQgiDJMWMEEIIIYQQwiBJMSOEEEIIIYQwSFLMCCGEEEIIIQySFDNCCCGEEEIIgyTFjBBCCCGEEMIgSTEjhBBCCCGEMEhSzAghhBBCCCEMkhQzQgghhBBCCIMkxYwQQgghhBDCIEkxI4QQQgghhDBIUswIIYQQQgghDJIUM0IIIYQQQgiDJMWMEEIIIYQQwiBJMSOEEEIIIYQwSFLMCCGEEEIIIQySFDNCCCGEEEIIgyTFjBBCCCGEEMIgSTEjhBBCCCGEMEhSzAghhBBCCCEMkhQzQgghhBBCCIMkxYwQQgghhBDCIEkxI4QQQgghhDBIxvoOIIQQQiSVRlHwD9fwIiyaF2HRhETHoIlRUBupSG9sRA4LY3JYGGNjrkatUuk7rhBCCC1TKYqi6DuEEEIIkRhvozRcCYjAOyCCCM2HX2NGQMx/zvnvv83UKkpbm1HK2oyMJupkTiuEEEJXpJgRQghhMCI0MRx9GsrVwEhUQGJ+gcWeXzKrKXVyWmKqlpnWQghh6KSYEUIIYRB8g6PY8+gdYdFKooqYj6kAS2MVDfNmwM7KRFvxhBBC6IEUM0IIIVK8S/7hHHwSmujRmC+JbadeLkvK2phroUUhhBD6IGPsQgghUrTYQga0U8j8t52DT0K55B+upVaFEEIkNylmhBBCpFi+wVFxhYyuHHwSim9wlE77EEIIoRtSzAghhEiRIjQx7Hn0Dl1vqKwC9j56R6Qm5pvnCiGESFmkmBFCCJEiHX0a+t2L/RNCAUKjFY481e0IkBBCCO2TYkYIIUSK8yZSw9XASJ0XMrEU4GpgJG+jNMnUoxBCCG2QYkYIIUSKczUwQufTyz6mAq4ERCRzr0IIIb6HFDNCCCFSFI2i4B0QkeRRmXUjXBlZxoaRZWxY/2vPBF+nAN4BEWjkiQVCCGEwjPUdQAghdEGjKPiHa3gRFs2LsGhComPQxCiojVSkNzYih4UxOSyMsTFXo1Yl9xiA+Br/cA0RmqQVFP/sXMf1g7uS3HeE5sP3TQ4L+fUohBCGQH5aCyFSlbdRGq4EROAdEBF3Q2wE/HefKiPgSuCHv5upVZS2NqOUtRkZTdTJHVd8xouw6CRdF/ivL7un/0aeEuV5+/Ipb18+S3L/UswIIYRhkGlmQohUIUITw/7H71h0M4hzL8PjvbP/8Ya7MfGuUzj3MpxFN4PY/1i2500JXoRFJ/qXkyY6mo2j+qAyMqLt5EWojJJWmBqR9GJKCCFE8pO3noQQBs83OIo9j94RFv2hgEnsBKXY868FRvLwbRQN82bAzspEqxlFwoVEx3xSgH7L4b+n8++NS7SZtIgsOfMmue8YIDRaClohhDAUMjIjhDBol/zD2fgwWCvPI4l93sjGh8Fc8g/XRjyRBJqYxH0ln9y6wvHlcynVoDWlG7T67v6jE9m/EEII/ZGRGSGEwbrkH87BJx8edKit28/YdmLbLWtjrqWWRUKpjRK3IcPLB7eJ0Wi4cXg3t47uBeB9xIdi9MaRPYytmpeRntcxy2CVoPaME9m/EEII/ZFiRghhkHyDo+IKDl05+CSULKZqmXKWzNIbG32yaUNCREd++oyYmOhooqKjSei4nRFgaSyTFoQQwlCoFEU21BdCGJYITQxLbwVpZWrZ16gAS2MVPR0yY6qWG9zkciUgAs9/Q76rjakNy/Dm+b+UcGpG+z+XJupal9zpKWVtRkhICNeuXcPb25srV67g7+/Ppk2bMDGR4lYIIVIKGZkRQhico09DdV7IwP/W0Bx5Gkr9PBl03JuIpe9tkQd0bcfdi2cICAhAURRU//8cIrVazfv376WYEUKIFESKGSGEQXkTqeFqYGSy9acAVwMjqZLDQp5Dk0xszNWYqVVJfnAmwIi9l5N0nbGi4ezBfWjev487FlvQVKtWDUtLyyRnEkIIoX0yzUwIYVCOPwvl3MvwBI3KHFo8jcN/T//sa5MuPEdtnLD3c1RApezm1PxRbmSTy+Yrvvgo6VFIvsX4sV/n9M/vULduXd69e0dMzP9W7mTIkIGWLVvi4uJCvXr1yJIlS7JlE0II8XkyCVwIYTA0ioJ3QESip5dZZspK7mJl4/2JnTqUEArgHRCBRt770bnz58/ToEEDfmpQg+TeIVkBSlmbUbZsWS5cuMAPP/yAWv2/0bhWrVrxzz//0K5dO2xsbKhSpQoTJ07k4sWL8YoeIYQQyUemmQkhDIZ/uCZJU48KVXek9fgF39V3hEbBP1yj9/UcqdXZs2cZP348Bw4coEiRIiycOY2MWUy5HhSl87VR8GFUpkRW07iphAULFuTChQvUrVuXO3fukCNHDjw8PFCpVDx58gRPT088PT2ZMWMGY8aMwdraGmdnZ+rXr4+TkxM2NjbJkFoIIYT8VhZCGIwXYdFJuu7G4T1c89qJeQYrfixcEqe+v/Jj4RJJ6l+KGe06ffo048eP5+DBgxQtWpQNGzbQqlUr1Go1kZoYfN4FEZpMu9bVyRl/GuGPP/7ImTNnaNeuHdWqVYsbzcuVKxdubm64ubnx/v17zp07x/79+/H09GTt2rWoVCrKlSuHi4sL9evXp0KFCvFGeIQQQmiPrJkRQhgMz8chXAuMSPDzRw4tnsZRj1lk/jEPRmpj/P3uA2BsYkqfFfsSVdAYASWymuGSJ33ig4tPnDx5kvHjx3P48GGKFSvGmDFjaNmyJUZG8Wc/+wZHsfFhsM7ztLW30srzhJ4/f46Xlxf79+/Hy8uLoKAgMmfOjJOTE/Xr18fZ2ZkcOXJoIbEQQgiQYkYIYUC2+ATz4G1Ugs/3f/QQy0xZsMiYGYB7Z46wvH9bAMo160jLMXMS1X+BjCa0zJewp8iLzzt+/Djjx4/n6NGjlCxZkjFjxtCsWbNPipj/uuQfrtMHpDrlsqSMjbnW29VoNFy4cAFPT0/279/PP//8g6IolC5dOm7UplKlSqRLly5J7T99+pQtW7Zw/fp1GjRoQOPGjZPclhBCGCrZAEAIYTA0iVwRbpPXPq6QAShYpQ4WmT7sQPXmxdNE9x+d3CvSUwlFUTh69Ci1atWiVq1aBAUFsX37di5fvkyLFi2+WsgAlLUxp16uD1PAtLW3WWw7uipk4MNzaSpXrsz48eO5cOECL1++ZM2aNTg4OLB06VJq1KiBtbU1rVq1wt3dnSdPniS47dOnT9OyZUuWLVtGdHQ0gwYNYvDgwbIRgRAizZFiRghhMNRGibuVPb5iHm+e/+8G8f65Y4S9eQ1A5h9yJ7p/40T2n9YpisLhw4epWbMmderU4d27d+zcuZPLly9/czTmY2VtzGlrb4Wlseq7C5rYNTJt7a10Vsh8jo2NDR07dmTNmjW8fPmSCxcu8Msvv/Ds2TN69epF7ty5KV68OMOHD+fIkSNfLUyGDh1Kzpw5Wbt2LStWrGDdunWsXbuWffv2JdvHI4QQKYFMMxNCGIzErpmZ2rAMb188IWP2nJiYW+Dvdx9FUTAxt6Dvai+y5yuU4L5lzUzCKYrCoUOHGD9+PKdPn6ZcuXKMHTuWhg0bJmpL7M+J0MRw9GkoVwMjUUGiNgaIPb9kVlPq5LTEVJ1y3s97/fo1Bw8ejNslLTg4mDdv3nx22tiVK1coU6YMhw4dok6dOnHH7ezs+Pnnnxk4cGByRhdCCL2SbXmEEAYjh4UxVwITfn7tHoO5fmgXLx/e4fXTR2T6ITd5S1agTs+h2NjmT1TfMf/fv/gyRVE4cOAAEyZM4OzZs1SoUIG9e/dSv3797y5iYpmpjaifJwNVclhwJSAC74CIuO26jSBeofvff5upVZS2NqOUtVnc9sspSZYsWWjbti1t27YlJiYGPz+/L65/2bBhA0WLFqV8+fJxx549e0aBAgUIDdXd2iIhhEiJ5DezEMJgJLaYqNCyCxVadtFb/2mFoijs37+fCRMmcP78eSpVqsT+/ftxdnbWWhHzsYwmamr+aEm1HyzwD9fwIiyaF2HRhEbHEB2jYGykwtLYiBwWxuSwMMbGXI1aR1m0zcjIiHz58n3x9YMHD1K7dm0yZMgQd+zx48eEhoZSrly55IgohBAphvxmFkIYDBtzNWZqVZIenPm9zNQqbMxT3jv6+qQoCnv37mXChAlcvHiRqlWr4uXlhaOjo86KmI+pVaq4giWtePr0KUWLFiV2lrhKpeLGjRu8efOGsmXL6jmdEEIkr7Tz018IYfDUqg9Thc69DE+Wp8LHUgGlrc0M5p19XVMUhd27dzNhwgQuXbpE9erV49ZvJFcRk1YFBgZSvHhxfH19432uDx8+TL58+ciSJUu88/9b8BgSjaLEG3ELiY5BE6OgNlKR3kBH3IQQuiHFjBDCoJSyNuPsy/Bk7VP5/37TOkVR2LlzJxMmTMDb25uaNWty5MgRatWqZXA3y4ZIURSyZs1KmTJl8PLyYsSIEVhYWDBv3jxOnjzJsmXL4p0fExPDxYsXadeuHS4uLri4uFCnTp1409NSmrdRmgSthYpdO5fS10IJIXRPihkhhEHJaKKmZFZTrgVGJsvojAookdU0Td8oxcTEsGPHDiZMmMDVq1epXbs2x44do2bNmvqOlqbEFoy9evXiwoULFChQAFtbWwIDAxkwYABOTk6fnJ85c2YaN27M/v37Wbx4MenSpaNatWrUr1+f+vXrU7Ro0RRRiH5tl7qPdy+MiXedwrmX4Zx9GZ4id6kTQuiebM0shDA4kZoYlt4KIjRa0WlBE/s8kp4OmdPkDVJMTAxbt25l4sSJXL9+nbp16zJ27FiqV6+u72gCOHnyJN7e3jg7O1OgQIFvPrfnwYMHcVs/HzlyhPDwcHLlyhU3auPo6EjGjBmTKf3/+AZHsefRO8K+8/9z7P/XhnkzYGdloq14QogUTooZIYRB8g2OYuPDYJ3309beKs3dGGk0GrZs2cLEiRO5efMm9erVY+zYsVStWlXf0YSWREREcOLECTw9Pdm/fz937txBrVZTpUoV6tevj4uLC6VKldL5qM0l/3AOPglN9DODviS2nXq5LCmbjA9EFULojxQzQgiDFXsjpCtOuSyT9Qnx+qbRaNi0aRMTJ07k9u3bODs7M3bsWCpXrqzvaELH/Pz84kZtDh8+TEhICDly5MDZ2Zn69etTr169TzYX+F66/v8rBY0QaYMUM0IIg6ard3bTUiGj0WjYsGEDkyZN4s6dO9SvX5+xY8dSsWJFfUcTehAVFcXp06fZv38/np6eXL9+HSMjIypWrIiLiwv169enbNmy35zW9jUysiqE0BYpZoQQBs83OIq9j959/xoaJQbV+0jaFMmeJm6AoqOjWb9+PZMmTeLevXs0atSIMWPGxHuyvBBPnjzhwIED7N+/n4MHDxIcHIy1tXXcqI2TkxM2NjYJbi/i/9e8fe8amW9J62vehEgrpJgRQqQKX9sN6ZuUGFAZcfPANjZPGkq92rVYtGgRuXLl0k1YPYuOjmbt2rVMmjSJBw8e0KRJE8aMGSMPXBTf9P79e86dOxc3auPt7Y1KpaJcuXJxozYVKlRArf7y7n/7H79L9t0I6+dJudtRCyG+jxQzQohUJaHPqYj9d3R4KHcObmfBr/3p3q41O3bsQKVSYWZmxuTJkxkwYADGxqljF/v379+zZs0aJk+ezMOHD2nWrBljxoyhdOnS+o4mDNTz58/x8vJi//79eHl5ERQURObMmXFycorbJS1Hjhxx57+J1LD4VlCy5+xTNHOa3l5diNRMihkhRKr08RPEQ6NjiI5RMDZSYfmfJ4hfOnaQRg0bcOHCBTZu3MicOXPQaDRx7RQtWpTly5cb9NSr9+/fs3LlSv744w98fX1p0aIFv//+O6VKldJ3NJGKaDQaLly4ELdD2j///IOiKJQqVSpuh7QY+zKcfxWR4FGZU2sXc2nXBt48/5f3kRFYZs5KnhLlqOM2lB8KFk1QGyqgUnZzav5omeSPTQiRckkxI4RI0zQaDXnz5qVRo0bkz5+fX3/9NV4xA1CuXDkuXryop4RJFxUVxYoVK/jjjz949OgRrVq14vfff6dEiRL6jibSAH9/f7y8vPD09OTAgQMEBgXxx5lHKMYJX4+2emhX/r1+iQzW2YiOjMT/0QOUmBjMM2bm133emJgnrEAxU6sYUDwL6hTwgFAhhHbJijghRJqmVqvp3r0769atI1OmTJ8UMk5OTmzatElP6ZImMjKSxYsXU6BAAXr37k3FihW5fv06mzdvlkJGJBsbGxs6duzI6tWrefHiBccvX09UIQPQ7o8l/OZ1gwHrjvDz1tPU6jEYgPC3Qfj7PkhwOxGaDyO1QojUR4oZIUSa16NHD969e8fdu3cB4tbMmJmZ4e7ujp2dnZ4TJkxkZCQLFy6kQIEC9O3blypVqnDjxg02btxIsWLF9B1PpGFGRkak/8E20delMzXj5pG9LOziwuyWVTm2bA4Alpmtsc5rn6i2XoRFJ7p/IUTKJ8WMECLNs7Ozw9HRkePHj5M+fXr69u3L3bt3yZQpE4MGDdJ3vG+KiIhgwYIF2NvbM2DAAGrUqMHNmzdZv349Dg4O+o4nBPChmEjKTUfIa3/+vXGJV773UGJiyJwzLz3/3o6pZfoEt2GEFDNCpFayZkYIIYANGzbQvn17bt++TeHChQHYtGkTbdu2ZefOnTRp0kTPCT8VHh7O0qVLmTp1Ki9evKBjx46MGjWKQoUK6TuaEJ/Y4hPMg7dRSbpWURTevnjK/rnjuea1g+z2hemzYn+iCpoCGU1omc8qSf0LIVIuGZkRQgigWbNmZMmShWXLlsUda926NS4uLvTv35+QkBA9posvLCyMOXPmkC9fPoYMGUK9evW4c+cOq1atkkJGpFiamKS/d6pSqcj0Q664NTMvH97hque2RLUR/R39CyFSLilmhBACMDMzo1OnTqxcuZKoqA/vHqtUKv766y/8/f0ZN26cfgMCoaGhzJw5k3z58vHLL79Qv3597ty5w4oVKyhQoIC+4wnxVWqjxO0kFvrmNZf3bCL6/f9Gc+6ePhT396jwsES1Z5zI/oUQhkGmmQkhxP+7du0aJUuWZOvWrbRo0SLu+JQpU/j999+5dOkSJUuWTPZcoaGhLFy4kBkzZvD69Wu6du3Kb7/9Rr58+ZI9ixBJ5fk4hGuBEfEeYPs1Qc8eM61RWdKZmZMlly0RIcG8ffEUAFPL9AzaeILMP+ZOUFtGQImsZrjkSfi0NCGEYZBiRggh/qNixYpYW1uzd+/euGNRUVGULl2aDBkycPr0adTq5HmSeEhICH/99RczZszgzZs3dO/end9++w1bW9tk6V8IbboSEIHnvwmfrhn+7i07/hjGk5vevAt4iSb6PRmss2NXpgq1ewwmW76CierfJXd6SlmbJTa2ECKFk2JGCCH+4++//6ZPnz48evSIXLlyxR0/deoU1atXZ+HChfTp00enGd69e8eCBQuYOXMmwcHBuLq68uuvv5I3b16d9iuELr0Ii2bF3Td6679boUzksDDWW/9CCN2QNTNCCPEf7dq1w8zMjBUrVsQ7Xq1aNdzc3Pj11195/vy5TvoODg5m8uTJ2NraMm7cONq2bcvDhw9ZtGiRFDLC4NmYqzFT62fdiplahY158oyoCiGSl4zMCCHER7p3786xY8d4+PAhRkb/e8/n9evXFC5cmDp16rBhwwat9ff27VvmzZvH7NmzCQsLo2fPnowYMSLeyJAQqcHxZ6GcexlOct54qIBK2c2p+aNlMvYqhEguMjIjhBAfcXNzw8/PjyNHjsQ7niVLFmbOnMnGjRs5cODAd/fz5s0bxo0bR968efnjjz/o3LkzDx8+ZP78+VLIiFSplLVZshYyAMr/9yuESJ1kZEYIIT6iKAoODg6UKlWK9evXf/Kao6Mjfn5+3LhxA3Nz80S3HxQUxJw5c5g7dy6RkZH07t2b4cOH88MPP2jrQxAixdr/+B3XAiOTpahRASWymlI/T4Zk6E0IoQ8yMiOEEB9RqVS4urqybds2AgMDP3lt0aJFPHnyhEmTJiWq3cDAQEaPHk3evHmZPn06rq6u+Pr6Mnv2bClkRJpRJ6cllsYqdL16RgVYGquok1OmlwmRmkkxI4QQn9GlSxdiYmJYu3btJ68VLFiQ3377jWnTpnHz5s1vthUQEBC3pfLs2bPp1asXvr6+zJw5kxw5cugivhAplqnaiIZ5M+h8ZEYBGubNgKlabnWESM1kmpkQQnxBy5YtuXfvHteuXUOliv8+cmRkJCVKlCBbtmwcP3483kYBsfz9/Zk5cyYLFiwAoH///gwdOhQbG5tkyS9ESnbJP5yDT0J11r5TLkvK2CR+GqgQwrDI2xVCCPEFbm5u3Lhxg4sXL37ymqmpKYsXL+bUqVMsX7483muvXr1i+PDh2NnZ8ddffzFw4ED8/Pz4888/pZAR4v+VtTGnXq4PU8C0NeUsth0pZIRIO2RkRgghvkCj0WBra0uDBg1YsmTJZ8/p0qULe/fu5c6dO8TExDB9+nQWLVqEWq1m4MCB/Pzzz2TNmjWZkwthOHyDo9j76B2h0cp3TT2LXSPTMG8G7KxMtBVPCJHCSTEjhBBfMWbMGObMmcPz58+xtPx0IbG/vz+FChUiR44c+Pn5kS5dOgYNGsTgwYPJkiWLHhILYXgiNDEcfRrK1cBIVJCooib2/JJZTamT01LWyAiRxkgxI4QQX+Hn50e+fPlYtmwZ3bp1i/fas2fPmDZtGn/99RfR0dF06dKFOXPmkDlzZv2EFcLAvY3ScCUgAu+ACCI0H25PjICY/5zz33+bqVWUtjajlLUZGU3UyZxWCJESSDEjxDdoFAX/cA0vwqJ5ERZNSHQMmhgFtZGK9MZG5LAwJoeFMTbmatQqXW82KvShXr16hIeHc+rUKQCePn3Kn3/+ydKlSzE3N2fQoEEcOHCA169fc+3aNUxNTfWcWAjDFv/n7nv2HDxCHls77O1ssZSfu0KI/5BiRogvkHcIRayNGzfSrl07Dh8+zNatW3F3d8fS0pIhQ4YwYMAAMmbMyM2bNylVqhSjR49m7Nix+o4sRKqxe/dumjRpQpYsWXj16hVqtfx8FUL8jxQzQnxE5m6Lj92/f5/ixYvz/v17MmXKxNChQ+nfvz9WVlbxzvvtt9+YOXMm169fp2DBgnpKK0TqoSgKpUqV4tq1awCsWbOGjh076jmVECIlkWJGiP/wDY5iz6N3hMmuOoIP62WmTJnC8uXLUavVqNVqHj169MXdycLCwihevDi2trYcOnTok2fTCCESJ3ZUJla+fPm4d++ejM4IIeLI28ZC/L9L/uFsfBj83YUMfBidCY1W2PgwmEv+4dqIJ5KRr68vPXv2pECBAmzfvp3Jkydz7NgxQkNDOX78+Bevs7CwYOHChRw5coS1a9cmY2IhUh9FURg9enS8B9L6+PiwYcMGPaYSQqQ0MjIjBLp/EnW9XJaUlQe4pXg+Pj5MnjyZVatWkSVLFoYPH07v3r3jtmSuWLEiWbNmZd++fV9tp127dhw5coQ7d+7I9sxCJNHHozIAKpUKOzs7GZ0RQsSRkRmR5vkGR+m0kAE4+CQU3+AonfYhku7Bgwd0796dggULsm/fPqZNm4avry9Dhw6N92wZNzc3PD09+ffff7/a3uzZs4mMjGTEiBG6ji5EqnXw4MFPjimKgq+vL35+fskfSAiRIkkxI9K0CE0Mex69Q9crG1TA3kfviNTEfPNckXzu3btHly5dKFSoEAcOHGDmzJn4+Pjw888/Y2Fh8cn57dq1w8LCghUrVny13R9++IEpU6bg7u4et52zECJxZs6cyb1797h37x6ZM2dmyJAh3Lt3jydPnmBvb6/veEKIFEKKGZGmHX0aqpU1Mt8Su4bmyFPdjgCJhLlz5w6dOnWiSJEiHD58mDlz5uDj48OgQYMwN//ydMAMGTLQpk0bli1bRkzM1wvTXr16UbFiRXr37k1UlIzKCZFY6dKlo0CBAhQoUAC1Wo21tTUFChTgxx9/1Hc0IUQKIsWMSLPeRGq4Ghip80ImlgJcDYzkbZQmmXoUH7t9+zYdOnTAwcGB48ePM2/ePB4+fMiAAQMwMzNLUBtubm74+flx5MiRr56nVqtZsmQJd+7cYdasWdqIL4QQQoiPSDEj0qyrgRGJnl4WEhTArqm/MrVBaUZX+JGJdQrh3qsFr5/4Jeh6FXAlICKxUcV3unnzJu3ataNo0aKcOnWKhQsX8uDBA/r165fgIiZW5cqVKVy4MO7u7t88t2TJkgwePJjx48fj4+OT1PhCCCGE+AIpZkSapFEUvAMiEjUqExoUyMIuLpzd6MG7wFdY57UnfRYbHl//h2D/FwlqQwG8AyLQyCaCyeL69eu0adOG4sWLc+7cORYvXsyDBw/o3bs3pqamSWpTpVLh5ubG9u3bCQwM/Ob548aNw8bGhn79+iGbRwohhBDaJcWMSJP8wzVEaBJ3Y+m1cApBTx+R3b4ww/dcYvDmk/y85RRjjj8gV9HSCW4nQqPgHy5TzXTp2rVrtGrVihIlSnDx4kX+/vtv7t27x08//YSJyfc/xLRz584oisKaNWu+eW769OlZsGABnp6ebN68+bv7FkIIIcT/SDEj0qQXYdGJOl9RFK4f3AlAxuw/sqxva8ZUycvctrW4cXgPxiaJe5c/sf2LhPH29qZ58+aULFkSb29vPDw8uHfvHm5ublopYmJly5aNJk2a4O7unqDRliZNmtCsWTMGDRrE27dvtZZDCCGESOukmBFp0ouw6ER984cGBRAe/AaAe2eOEP7uLeZWGXlx/yYbf+vF9UO7EtyWEVLMaNvly5dp2rQpZcqU4fr16yxfvpw7d+7Qo0cP0qVLp5M+3dzcuHHjBhcvXkzQ+fPmzSMkJIRRo0bpJI8QQgiRFkkxI9KkkOgYEvPElxjN/6aFZbMryLDd/zBs9z9ksysIwNmNHglvCwiNlufNaMM///xD48aNKVu2LLdv32blypXcuXOHbt266ayIiVWvXj1y586doI0AAHLnzs3EiRNZuHAhFy5c0Gk2IYQQIq2QYkakSZqYxK2XscyUFXW6D9OUchQsinE6E4zTmZCjYFEAgp59/YnwH4tOZP8ivgsXLtCwYUPKly/P/fv3WbNmDbdu3aJLly4YGxsnSwa1Wk337t1Zv349ISEhCbqmf//+lCpVil69ehEdLaNzQgghxPeSYkakSWqjxG3KrE6XDrsylQF4cf8Wmvfv0bx/z4v7twCwzpMvUe0ZJ7J/8cG5c+eoX78+FStWxNfXl3Xr1nHz5k06duyYbEXMf3Xv3p3Q0NAEL+w3NjZmyZIlXL16lXnz5uk4nRBCCJH6STEj0qT0xkaJ/uav13ck6nQmvPK5y7TGZZnWuCyvfO5ipFZTq8fgBLdjBFgay3+9xDhz5gzOzs5UrlyZx48fs2HDBq5fv0779u1Rq9V6y2Vra4ujo2OCp5oBlC9fnn79+jFmzBgeP36sw3RCCCFE6id3VCJNymFhnKg1MwB5ipfFbck28pWrSnjwW6KjIslfsSa9lu3Fvny1BLcT8//9i287efIkjo6OVK1alWfPnrFp0yauX79O27Zt9VrE/Jebmxtnzpzh9u3bCb5m0qRJZMyYkYEDB+owmRBCCJH6yR2VSJOSWkzYlqpIz7936K1/Q6coCirVt6fYHT9+nPHjx3P06FFKlCjBli1baN68OUZGKe/9l6ZNm5I1a1Y8PDyYMWNGgq7JmDEjc+fOpXXr1uzYsYNmzZrpNqQQQgiRSqW8OwMhkoGNuRoztX7WrZipVdiYp4xRheQQERHB+fPnCQsL+2ohoygK/v7+1KpVi1q1ahEUFMS2bdvw9vamZcuWKbKQATA1NaVz586sXLmSqKioBF/XsmVLGjRowIABA3j37p0OEwohhBCpV8q8OxBCx9QqFaWtzUjuckYFlMicjtB37wgODubt27e8efOGoKAggoODkzmNboWFhTF48GDs7Oxwc3OjTJkyzJ8//4vnq1QqbGxsyJEjBzt27ODy5cspdjTmY66urgQEBLB79+4EX6NSqfjrr78IDAxk7NixOkwnhBBCpF4p/y5BCB0pZW1Gcm+QrKDQsmJRMmbMSMaMGcmUKROZM2cmS5YsZMyYkb179yZzIt24efMmzZo148aNG2zYsAF3d3datGjB0KFDCQgI+OJ1MTExrF+/nqZNmyZoOlpKUaxYMSpWrJiojQDgwwYCY8eOZe7cuXh7e+sonRBCCJF6STEj0qyMJmpKZjVNttEZFVAiixl5bLJ89nVjY2OKFy+eTGl0Kzg4mODgYBYvXkzNmjWpWLEiEydOJHv27OzcuRP4MK3sY0ZGRgZVxPyXm5sbBw4c4N9/E/fMoSFDhuDg4ECvXr3Q/OfhrEIIIYT4NilmRJpWJ6cllsYqnRc0KsDSWEXdXJYcO3aMH3/8Md5Nu0qlolmzZuTJk0fHSbQvKCiIGTNmxHuqfeXKlVm/fj358+ePO+bv74+pqSk//PADgMEWLV/Stm1bLCwsWL58eaKuS5cuHUuWLOHixYssXrxYR+mEEEKI1EmKGZGmmaqNaJg3g86nmylAw7wZMFUbkTlzZrZv3x5va2FFUdiyZQutWrXi2rVrOk6jHdHR0SxdupTSpUszfPhw1q9fT1hYGPDh47Gzs4v7O8Djx48JCQmhSJEiesusSxkyZKBt27YsW7aMmJjEbfxdpUoVfvrpJ0aOHMmzZ890lFAIIYRIfaSYEWmenZUJ9XJZ6rSPejktsLMyift3hQoVmDlzJvBhalWPHj3w8PDA29ubkiVL0rx58xS/hiIwMJBLly4xePBg/vzzT1auXMmdO3eA+KMusVOndu/eTalSpbCzs0u106nc3Nx49OgRhw8fTvS1f/75J+bm5gwePFj7wYQQQohUSooZIYCyNuZxBY22Jj/FtrPzzxGs/uO3T9aIDBgwIG63rt9//50ePXpw584dli9fzvXr1ylTpgxNmzbl8uXLWkqkXTY2NnTu3JmuXbsyfPhwTE1NWb16NeHh4fHOMzb+8Eydffv20bJlSwDUajUhISFERkYme25dqlSpEkWKFEn0RgAAmTNnZtasWWzevJn9+/frIJ0QQgiR+kgxI8T/K2tjTlt7K62soYldI9PW3ooutcoxd+5cpkyZEv8clYr169dz+/ZtbG1tgQ/rJ7p168adO3dYuXIlt2/fpmzZsjRu3Jh//vnnO1Npl5GREVWrViVz5swA/P777yxfvjxudOa/Tp06xevXr2nSpAkvXrzAzc2N/Pnzc+/eveSOrVMqlQo3Nzd27Njx1V3bvqRDhw44OjrSt2/fuCl7QgghhPgyKWaE+A87KxPcHDJTIqspkPhRmtjzS2Q1padDZuysTOjTpw/jx49n1KhRLF26NN75pqam8RbJxzI2NqZLly7cunWLNWvWcP/+fcqXL0/Dhg3jLbRPCWLXh/Tt2zdudCb2Rjx2OtnRo0exsLDgjz/+IE+ePDx48IDt27enmt3b/qtz584oisKaNWsSfa1KpWLRokU8f/6ciRMn6iCdEEIIkbpIMSPER8zURtTPk4E+RTNTKbs5Zur/lTQf/4cxinedikrZzelTNDP183xY7B/r999/p3///vTu3Ztt27YlOIuxsTEdO3bk5s2brFu3Dl9fXypWrEj9+vU5d+5cEj9C7TIyMiI6Ohr43+jM/fv3AeI2Odi7dy+3b9/m/v37nDlzhmPHjlG5cmW9ZdYlGxsbmjZtiru7+2e3n/6W/PnzM2rUKGbMmMGNGzd0kFAIIYRIPVRKUn7bCpGGaBQF/3ANL8KieREWTWh0DNExCsZGKiyNjchhYUwOC2NszNWov7LdcExMDB07dmTbtm14enpSu3btxGfRaNiyZQsTJkzg1q1bODk5MXbsWKpUqfI9H6JW5ciRg65du9KwYUM2bNhAo0aN4h4SWqxYMX3HSxYHDhzAxcWFc+fOUbFixURfHxkZScmSJcmaNSsnT57EyEjedxJpm42NDUOGDGHkyJH6jiKESGHkN6QQ36BWqchhYUwpazNc8qSnZT4r2ubPSMt8VrjkSU8pazNyWBh/tZCBDyMYK1eupFatWkle2K9Wq2nbti3Xr19n06ZNPHv2jKpVq+Lo6MjJkyeT+iEmiEajISws7IvbDsce79ixI9OnT6dWrVr4+PhQvHhxqlatmmYKGQBHR0dy586dpI0A4MP0w8WLF3PmzBk8PDy0nE4IIYRIPaSYESIZmZiYsHXrVooUKYKLi0vcdKzEMjIyonXr1ly9epUtW7bg7+9PjRo1qFOnDsePH9dq5ujoaNasWUPRokUZPHjwFx92GRgYSOPGjZk9ezYdOnTg1q1beHp6kjt3bq3mMQRqtZoePXqwYcMGQkJCktRGrVq16NatGyNGjODVq1daTiiEEEKkDlLMCJHM0qdPz969e8maNStOTk7f9ZBEIyMjWrZsibe3N9u2bSMoKIhatWpRq1Ytjh49mqQ1G7Gio6NZuXIlDg4OdO7cmYIFC9KrV68vFjMAxYoV4+rVq6xZs4bChQsnue/UoHv37oSGhrJp06YktzF9+nSMjIwYOnSoFpMJIYQQqYcUM0LogbW1NQcOHCA6OhpnZ2eCgoK+qz0jIyOaN2/O5cuX2bFjB8HBwdSpU4eaNWty+PDhRBU179+/Z/ny5RQuXJhu3brh4ODApUuX2LVrF2XLlv3idTY2NkyZMiVV7lCWFHnz5qVevXpJnmoGH75Ppk+fzpo1a5L0IE4hhBAitZNiRgg9yZMnD15eXjx79ozGjRtr5bkiKpWKpk2bxhUf4eHhODo6Ur16dQ4ePPjVoub9+/d4eHhQuHBhevToQYkSJfD29mbHjh2UKVPmu7OlRW5ubpw9e5Zbt24luY1u3bpRo0YN+vTpQ0REhBbTCSGEEIZPihkh9KhIkSLs27cPb29v2rRpw/v377XSrkqlonHjxly4cIG9e/fy/v17nJycqFq1KgcOHIhX1ERFRbF06VIKFiyIm5sbZcqU4erVq2zbto1SpUppJU9a1aRJE7Jmzfpdi/hVKhWLFy/Gz8/vkwevCiGEEGmdFDNC6FnFihXZtm0bXl5euLm5fXG3sKRQqVQ0aNCAc+fOsX//fhRFwcXFhcqVK7Nr1y4WL15MgQIF6NWrFxUqVOD69ets3ryZEiVKaC1DWmZqakqXLl1YtWoVUVFRSW6nSJEiDB8+nD///JO7d+9qMaEQQghh2KSYESIFcHZ2ZuXKlaxevZrhw4d/18L9z1GpVLi4uHDmzBl2796Nv78/TZs2pU+fPtja2nLt2jU2btyYprZPTi6urq4EBASwa9eu72pn1KhR5M6dm969e2v9+0MIIYQwVFLMCJFCtG/fnrlz5zJz5kymT5+u9fYjIiL466+/6N27N35+ftSpU4cyZcpw4sQJunfvzu7du+UmWQeKFi1KpUqVvmsjAABzc3MWLVrEsWPHWL16tZbSCSHEl2kUhRdh0VwJiMDzcQhbfILZ+OAtW3yC8XwcwpWACF6ERaOR3x1Cj4z1HUAI8T8DBgzA39+fESNGYG1tTY8ePb67zfDwcJYuXcrUqVN58eIFHTp0YPTo0RQqVAhFUTh69Cjjx4+nSZMmlC5dmjFjxtC0adOvbsEsEsfNzY2ePXvy+PFj8uTJk+R26tWrR/v27Rk6dCgNGzYka9asWkwphBAfvI3ScCUgAu+ACCI0HwoVI+C/k6CNgCuBH/5uplZR2tqMUtZmZDRRJ3dckcbJyIwQKcz48ePp3bs3PXv2ZMeOHUluJzw8nDlz5mBvb8+QIUNwdHTk9u3brF69mkKFCgEfpp/FPmjz6NGjZMyYkebNm1O6dGm2bdum1fU7aVmbNm2wsLBg+fLl393WrFmzeP/+PcOHD9dCMiGE+J8ITQz7H79j0c0gzr0MjytkIH4h8/G/IzQK516Gs+hmEPsfvyNSI787RPKRYkaIFEalUrFgwQJatGhBu3btOHHiRKKuDwsLY9asWdjZ2fHLL7/g4uLCnTt3WLlyJQULFvzidbEP2jx+/DhZs2alZcuWlC5dmi1btkhR850yZMhAu3btWLZsGRqN5rvaypEjB3/++SfLli3j5MmTWkoohEjrfIOjWHoriGuBkQAkduJY7PnXAiNZeisI3+Ckb3oiRGJIMSNECqRWq1mzZg3VqlWjcePGXLly5ZvXhIaGMmPGDOzs7BgxYgSNGjXi3r17LFu2jPz58ye47xo1anD48GFOnjxJtmzZaN26NSVLlmTTpk1S1HwHV1dXHj9+rJWHX/70009UqlSJXr16fdcuaUIIAXDJP5yND4MJi1YSXcR8TAFCoxU2Pgzmkn+4NuIJ8VVSzAiRQpmamrJ9+3YKFCiAi4sLDx8+/Ox5ISEhTJs2DTs7O0aOHEnTpk25d+8e7u7u5MuXL8n9V6tWjYMHD3L69Gly5sxJ27ZtKV68OBs2bPju0YW0qFKlSjg4OHz3RgAARkZGLFmyhHv37jFjxgwtpBNCpFWX/MM5+CQUSPxozJfEtnPwSagUNELnpJgRIgXLkCED+/fvJ2PGjDg5OfHixYu41969e8eff/6JnZ0do0ePpkWLFjx48IC///4bOzs7rWWoUqUKnp6enD17lrx589K+fXuKFSvGunXrpKhJBJVKhZubGzt27CAgIOC72ytRogRDhgxh4sSJXyx0hRDia3yDo+IKGV05+CRUppwJnZJiRogUzsbGBi8vLyIiInBxceHx48dMnjwZW1tbxo4dS+vWrXnw4AGLFy8mb968OstRqVIl9u3bx/nz57G3t6djx44ULVqUNWvWEB0drbN+U5POnTsDaG1r5bFjx5I9e3b69u0r22oLIRIlQhPDnkfv0PW+lSpg7yPZFEDojhQzQhiAvHnzsnXrVu7du4e9vT3jx4+nffv2PHz4kIULF37Xdr+JVaFCBfbs2cPFixcpWLAgnTt3xsHBgZUrV0pR8w3W1tY0a9YMDw8PrRQflpaWLFiwAC8vLzZu3KiFhEKItOLo01CtrJH5ltg1NEee6nYESKRdUswIkcK9efOG8ePHU79+/bhioXbt2syZM4dcuXLpLVe5cuXYtWsXly5dwsHBgW7dulG4cGGWL1/O+/fv9ZYrpXN1deXmzZucP39eK+01atSIli1bMnjwYN68eaOVNoUQqdubSA1XAyN1XsjEUoCrgZG8jZKpyUL7pJgRIoUKCgpi7Nix2Nra8ueff9KtWzcePXrErl27OHLkCD/99FOKmFpUpkwZduzYgbe3NyVKlKBHjx4ULlwYDw8PKWo+w9HRkTx58mhlI4BYc+fOJSwsjN9++01rbQohUq+rgRE6n172MRVwJSAimXsVaYEUM0KkMK9fv+b333/H1taW6dOn4+rqiq+vL7Nnz+aHH36gfv36rFixguXLlzNy5Eh9x41TqlQptm3bxtWrVylTpgxubm4ULFiQpUuXyvbB/6FWq+nRowcbNmzg3bt3WmkzZ86cTJo0icWLF3Pu3DmttCmESJ00ioJ3QESCR2VOrl7I3z2b8odTUUZXzMnUBqXZNKYfr5/4JapfBfAOiECTAt6EE6mLFDNCpBCBgYGMGjUKW1tbZs2axU8//YSvry8zZ84kR44c8c7t2LEjc+bMYerUqcycOVNPiT+vRIkSbN68mevXr1OhQgV69epFgQIFWLJkiRQ1/6979+6EhYWxadMmrbXZr18/ypQpQ69evWRETAjxRf7hGiI0CS8ozmxwx+/yWczSZ8Qq2w+8efEE7z2bWNyjIREhiXtDJkKj4B8uU82EdkkxI4SeBQQEMHLkSGxtbZk7dy59+vTB19eX6dOnkz179i9eN2jQIH777Td++eUXVq5cmYyJE6ZYsWJs3LiR69evU6VKFfr06UP+/PlZtGgRkZGR+o6nV3ny5MHJyQkPDw+ttalWq1myZAk3btxg7ty5WmtXCJG6vAhL3EYtFZp3YvieywzZdobhu/+haodeALwLeMXDCyd03r8Q3yLFjBB64u/vz4gRI7C1tWXBggX0798fPz8/pk6dSrZs2RLUxqRJk+jZsyeurq7s2bNHx4mTpmjRoqxfv56bN29SvXp1+vfvj729PQsWLCAiIu3On3Z1deXs2bPcvHlTa22WLVuWAQMGMHbsWB49eqS1doUQqceLsOhE3fzVdhtCph/+t9mMbelKcX9Xm5gkqm8jpJgR2ifFjBDJ7OXLl/zyyy/Y2tqyaNEiBg0ahJ+fH1OmTMHa2jpRbalUKhYtWkTTpk1p3bo1p06d0lHq71ekSBHWrl3LrVu3qF27NoMGDcLe3p558+YRHp72nhDdpEkTrK2ttTo6AzBx4kQyZ87MgAEDUsQGEUKIlCUkOoakPvElRqPhwrZVAGTJZUv+CjUSdz0QGi3PmxHaJcWMEMnkxYsXDBkyBDs7O5YuXcrQoUPx8/Nj8uTJZM2aNcntqtVq1q5dS6VKlWjUqBHXrl3TYmrtK1SoEKtXr+b27ds4OjoyZMgQ7O3tmTNnTpoqakxNTenSpQurV6/W6rS7DBkyMG/ePHbv3s2OHTu01q4QInXQxCTtTY6o8FDWDO3K/bNHyWCdjS5z1mBsYprodqKT2L8QXyLFjBA69vz5cwYPHoydnR3Lli1j+PDh+Pn5MWHCBLJkyaKVPszMzNi5cyf58uXDxcUFX19frbSrSwULFmTlypXcuXMHZ2dnfvnlF+zs7Jg1axZhYWH6jpcsXF1dCQgIYNeuXVptt3nz5jRq1IgBAwZobcc0IUTqoDZK/KbM7wJe8rdbU26fOIB1Xnt6L9tL9nyFktS/cRL6F+JrpJgRQkeePn3KwIEDsbOzY+XKlYwcORI/Pz/GjRtH5syZtd6flZUV+/fvx9LSEicnJ16+fKn1PnQhf/78LF++nLt379KoUSNGjBiBnZ0dM2bMIDQ0dT8x2sHBgcqVK2t9qplKpWLBggUEBQXx+++/a7VtIYRhS29slKibv5cP77Cwa32e3r6KbelK9Fmxnyy5bJPUtxFgaSy3nkK75DtKCC178uQJ/fv3J1++fKxZs4bRo0fj5+fHmDFjyJQpk077zp49O15eXoSGhlK/fn2Cg4N12p822dvb4+7uzr1792jatCkjR47Ezs6OadOmERISou94OuPq6oqXl5fWF+znzZuXcePGMX/+fC5fvqzVtoUQhiuHhXGi1sys+aUbb57/C0BkWAgrBrRnYRcXFnZx4eL21YnqO+b/+xdCm6SYEUJLHj9+TN++fbG3t2f9+vWMHTsWPz8/Ro8eTcaMGZMth52dHQcOHMDX15emTZsa3I5hdnZ2/P333zx48IAWLVowevRo7Ozs+PPPP1PllKm2bdtiaWnJ8uXLtd724MGDKVasGL169UKjkWc7CCESX0xE/+f5YM/v3uDfG5fi/rx9+Vzn/QvxLSpFtrsR4rs8evSIKVOmsGzZMqysrPjll1/o168fGTJk0GuuU6dOUa9ePerXr8/mzZtRq9V6zZNUjx8/5s8//8TDw4P06dMzZMgQBgwYgJWVlb6jaU3Pnj3x8vLCx8dH61+nc+fOUaVKFebOncuAAQO02rYQycXGxoYhQ4YwcuRIfUcxeBpFYf7114l6cKa2mKlVDCieBbVK1s0I7ZGRGSGSyNfXl59++okCBQqwbds2Jk2ahJ+fH7/++qveCxmAatWqsXnzZnbt2kWfPn0MdpvePHnysHDhQh4+fEj79u2ZMGECtra2TJw4kbdv3+o7nla4urry+PFjDh06pPW2K1WqRK9evRg1ahRPnz7VevtCCMOiVqkobW1GcpcTKqC0tZkUMkLrpJgRIpF8fHxwdXWlYMGC7Ny5kz/++ANfX1+GDx9O+vTp9R0vnkaNGrFs2TKWLl3K6NGj9R3nu+TKlYsFCxbg4+NDp06dmDx5Mra2towfP543b97oO953qVixIkWLFtX6RgCxpkyZgoWFBYMGDdJJ+0IIw1LK2ozkfntL+f9+hdA2KWaESKAHDx7QvXt3ChYsyL59+5g2bRq+vr788ssvWFpa6jveF3Xp0oUZM2bwxx9/MGfOHH3H+W45c+Zk3rx5+Pj40K1bN/78809sbW0ZO3YsQUFB+o6XJCqVCldXV3bs2IG/v7/W28+UKROzZ89m69at7N27V+vtCyEMS0YTNSWzmibb6IwKKJnVlIwmhjndWaRsUswI8Q3379+na9euFC5cmAMHDjBz5kx8fHz4+eefsbCw0He8BBk6dCjDhw/n559/Zs2aNfqOoxU//vgjs2fPxtfXF1dXV6ZPn46trS2///47r1+/1ne8ROvcuTMAq1cnbneghGrXrh1OTk7069cv1W95LYT4uoiICGr9YIGlsUrnBY0KsDRWUSdnyn3TTxg2KWaE+IK7d+/SuXNnChcuzKFDh5g9ezYPHz5k0KBBmJub6zteov3555/06NGD7t27s2/fPn3H0ZocOXIwc+bMuDVMs2bNwtbWllGjRhEYGKjveAlmbW1N8+bN8fDw0Mn6JpVKxcKFC3n58iUTJkzQevtCiJRHURQePXrE7t27mTRpEq1ateKHH37A3NycNi2a0TBvBp1PN1OAhnkzYKqWW06hG/KdJcRHbt++TceOHXFwcODYsWPMmzePhw8fMmDAAIMsYmKpVCqWLFlCw4YNadWqFWfPntV3JK3Knj0706dPx9fXlz59+jB37lxsbW0ZOXIkAQEB+o6XIK6urty6dYtz587ppH17e3tGjx7NzJkzuXbtmk76EEKkHI0bN8bW1pYmTZowbtw4tm3bxosXL4APP2/srEyol0u3IyZOuSyxszLRaR8ibZNiRoj/d+vWLdq3b0/RokU5efIkCxYs4MGDB/Tr1w8zs9SxaNHY2Jj169dTvnx5GjZsyM2bN/UdSeuyZcvG1KlT8fPzo3///ixYsABbW1tGjBihk/Uo2uTo6EjevHl1thEAwLBhwyhYsCC9evUiJiYxj84TQhiaKlWqxP1do9GgKAoqlYoaNWrQrFkzAMramMcVNNqachbbjlMuS8rYGO6bgMIwSDEj0rwbN27Qpk0bihUrxpkzZ1i0aBH379+nT58+mJqa6jue1pmbm7Nr1y7y5MmDk5OT1p88n1JYW1szZcoU/Pz8GDRoEIsWLcLW1pZffvmFly9f6jveZxkZGdG9e3c2bNigsweEmpiYsHjxYs6dO8fSpUt10ocQImX49ddfKVu2bLxjiqIwceLEeMfK2pjT1t5KK2toYtfItLW3kkJGJAspZkSade3aNVq1akXx4sW5ePEif//9N/fv36dXr16psoj5r4wZM+Lp6YmZmRlOTk4pfsTie2TNmpXJkyfj5+fHkCFDWLp0KXZ2dgwZMiRuukVK0r17d8LCwti4caPO+qhRowY9evTg119/TbGFnRDi+yiKwuzZs7ly5Qrp0qXDyMgItVpNjRo1qFGjxifn21mZ4OaQmRJZTT+s21MSN3IbWwSVyGpKT4fMMrVMJBspZkSac+XKFVq0aEHJkiXx9vbGw8ODe/fu4ebmholJ2vnhmyNHDry8vHj79i0NGjTQ2UhASpElSxYmTpyIn58fw4cPZ9myZdjZ2TF48GCeP3+u73hx8uTJg7Ozs06nmgFMmzYNY2NjhgwZotN+hBDJ7/Xr1zRt2pRffvmFoUOH4unpiaIoaDSar24AYqY2ooQ6mBmNy2H2/B5m6v+N03x8w2gU7zoVlbKb06doZurnkcX+IpkpQqQRly5dUpo2baoAir29vbJ8+XIlKipK37H0ztvbW7GyslLq1q2rRERE6DtOsgkKClLGjRunZMqUSTE1NVUGDBigPHnyRN+xFEVRlM2bNyuAcuPGDZ32s2LFCgVQvLy8dNqPEN/L2tpa+eOPP/QdwyCcPXtWyZMnj5IlSxZl9+7dcccXLVqk/PTTT9+8fvLkyYq5ubny5s0bJTomRnke+l7x9g9X9j96p2x5+FbZcP+NsuXhW2X/o3eKt3+48jz0vRIdE6PLD0mIr5JiRqR6//zzj9K4cWMFUAoUKKCsXLlSef/+vb5jpSjHjh1TTE1NlVatWinR0dH6jpOs3rx5o0ycOFHJnDmzYmJiovTr10/5999/9ZopMjJSsba2Vn7++Wed9hMTE6PUqlVLsbe3V8LCwnTalxDfQ4qZb4uJiVFmzpypGBsbK5UrV1YePXqU6DY0Go2SL18+pWvXrtoPKISOyDigSLUuXLhAo0aNKFeuHPfu3WP16tXcunWLLl26YGxsrO94KUrNmjXZuHEj27Zto1+/fjp5zklKlTFjRkaPHo2fnx9jx45l/fr12Nvb07dvXx4/fqyXTCYmJnTp0oVVq1YRGRmps35UKhWLFi3i8ePH/PHHHzrrRwihW0FBQTRr1oyhQ4cyePBgjh8/Tp48eRLdzvHjx/Hx8cHNzU0HKYXQDSlmRKpz7tw56tevT8WKFXn48CFr167l5s2bdOrUSYqYr2jatClLly5lyZIljBs3Tt9xkp2VlRW//fYbfn5+jB8/nk2bNpE/f3569+6tlx3fXF1dCQwMZOfOnTrtp3DhwowcOZKpU6dy+/ZtnfYlhNC+CxcuULp0aU6ePMmuXbuYPn066dKlS1Jb7u7uFCpUiKpVq2o5pRC6I8WMSDXOnDmDs7MzlStX5vHjx6xfv54bN27QoUMH1Gq1vuMZhB49ejB16lQmTJjA/Pnz9R1HLzJkyMCvv/6Kn58fkyZNYtu2beTPn5+ePXvi6+ubbDkcHByoUqWKzjcCABg5ciS2trb06dMnTY3KCWHIFEVhzpw5VKtWjRw5cuDt7U3jxo2T3F5QUBBbt27F1dUVlUpbT5wRQvekmBEG79SpU9SrV4+qVavy7NkzNm3axPXr12nXrp0UMUkwbNgwhg4dysCBA1m/fr2+4+hN+vTpGT58OL6+vkyZMoVdu3ZRsGBBXF1d8fHxSZYMrq6uHDx4UOcjQ2ZmZixcuJDjx4+zcuVKnfYlhPh+QUFBtGjRgp9//pkBAwZw4sQJ8ubN+11trl27Fo1GQ5cuXbSUUojkIcWMMFgnTpygbt26VK9enVevXrFlyxauXr1K69atMTKSb+2kUqlUTJs2jS5dutClSxcOHDig70h6ZWlpyS+//IKvry9Tp05l7969FCxYkO7du/PgwQOd9t2mTRssLS1Zvny5TvsBcHR0pGPHjvzyyy8EBATovD8hRNJcvHiRMmXKcOzYMXbs2MHMmTO/+7ECiqLg7u5O48aNyZ49u5aSCpE85I5PGJxjx45Ru3ZtatasyevXr9m2bRve3t60bNlSihgtMTIywt3dHRcXF1q0aMH58+f1HUnvLCwsGDJkCD4+PsyYMQNPT08KFy5M165duX//vk76TJ8+Pe3atWPZsmVoNBqd9PFfM2fORKPRMGzYMJ33JYRIHEVRmD9/PlWrViVbtmx4e3vTtGlTrbR9+fJlrl69Kgv/hUGSOz9hEBRF4ciRI9SsWZPatWvz9u1bduzYweXLl2nevLkUMTqQLl06Nm7cSOnSpWnQoIEsDv9/FhYWDB48GB8fH2bPns2hQ4coXLgwnTt35u7du1rvz83NjX///ZeDBw9qve2PZc+enWnTprFixQqOHz+u8/6EEAnz5s0bWrVqxcCBA+nXrx8nT57E1tZWa+27u7uTM2dOnJ2dtdamEMlF7gBFiqYoCocOHaJGjRrUrVuX0NBQdu3axaVLl2jatKksUtQxCwsLdu/eTc6cOXFyctLbVsUpkbm5OQMGDODhw4fMmzePo0eP4uDgQMeOHbVa+FWoUIFixYoly0YA8GGdTtWqVendu7dOt4UWQiTMpUuXKFu2LIcPH2bbtm3Mnj37u6eV/VdYWBjr1q2je/fuss5UGCQpZkSKpCgKXl5eVKtWjXr16hEZGcmePXu4ePEijRs3liImGWXOnBlPT0+MjY1xdnaW9RQfMTMzo1+/fjx8+JAFCxZw8uRJihYtSvv27bl169Z3t69SqXB1dWXnzp34+/trIfHXGRkZsXjxYh48eMD06dN13p8Q4vMURWHBggVUqVKFLFmy4O3tTfPmzbXez5YtWwgODqZ79+5ab1uI5CDFjEhRFEXB09OTKlWq4OzsjEajYd++fZw/f56GDRtKEaMnP/74I15eXrx+/ZqGDRsSEhKi70gpjqmpKX369OH+/fssWrSIM2fOUKxYMdq0acONGze+q+1OnTqhUqlYvXq1ltJ+XbFixRg6dCiTJk3S2XogIcSXvX37ljZt2jBgwAB69+7NqVOnsLOz00lf7u7u1K1bl3z58umkfSF0TYoZkSIoisLevXupVKkS9evXx8jIiAMHDnD27Fnq168vRUwKUKBAAfbv38/t27dp2bIlUVFR+o6UIpmamtKrVy/u37/PkiVLuHjxIsWLF6dVq1Zcu3YtSW1aW1vTrFkz3N3dk+05MGPGjOGHH36gb9++8uwZIZLR5cuXKVu2LF5eXmzZsoW5c+diamqqk77u3bvHyZMnZeG/MGhSzAi9UhSF3bt3U6FCBRo1aoSJiQkHDx7k1KlTODk5SRGTwpQpU4adO3dy7NgxunTpQkxMjL4jpVgmJib07NmTe/fu4e7uzuXLlylZsiQtWrTgypUriW7Pzc2N27dvc/bsWe2H/QwLCwv++usvDh06lKafNyREclEUhYULF1K5cmUyZcrE5cuXadmypU779PDwIHPmzDRr1kyn/QihS1LMCL1QFIWdO3dSrlw5mjRpgoWFBYcPH+bEiRM4OjpKEZOC1a5dm/Xr17N582YGDRok79p/Q7p06XB1deXu3bssX76ca9euUbp0aZo1a8bly5cT3E7dunXJmzdvsm0EANCgQQNat27Nzz//TFBQULL1K0RaExwcTLt27ejXrx8//fQTp0+fxt7eXqd9vn//npUrV9K5c2fMzMx02pcQuiTFjEhWMTExbN++nTJlytCsWTOsrKw4evQox48fp06dOlLEGIgWLVqwePFiFixYwMSJE/UdxyCkS5eObt26cefOHVauXMmtW7coW7YsTZo04dKlS9+83sjIiB49erBx40bevXuXDIk/mDNnDhEREYwcOTLZ+hQiLfH29qZs2bLs37+fTZs2MX/+fJ1NK/uvvXv38vLlS1xdXXXelxC6JMWMSBYxMTFs3bqV0qVL06JFC7JkycLx48c5evQotWrV0nc8kQQ9e/Zk8uTJjB07lkWLFuk7jsEwNjamS5cu3Lp1i9WrV3P37l3KlStHo0aNuHDhwlev7d69O2FhYWzcuDGZ0n7Y/GHy5MksWbIk2aa4CZEWKIrC4sWLqVy5MhkyZODy5cu0bt062fp3d3enfPnylChRItn6FEIXpJhJxTSKwouwaK4EROD5OIQtPsFsfPCWLT7BeD4O4UpABC/CotHocJpQTEwMmzdvpmTJkrRq1Yps2bJx8uRJDh8+TI0aNXTWr0geI0eOZNCgQfTr149NmzbpO45BMTY2plOnTty6dYu1a9fy8OFDKlasSP369Tl37txnr8mdOzfOzs64u7sna9Y+ffpQrlw5fvrpJ96/f5+sfQuRGr17944OHTrQp08fXF1dOXPmDPnz50+2/p8+fcr+/ftl4b9IFaSYSYXeRmk4/iyU+ddfs+LuGzz/DeFaYAQP3kbh++49D95GcS0wAs9/Q1hx9w3zr7/m+LNQ3kZptJZBo9GwYcMGihcvTps2bfjxxx85ffo0Bw8epFq1alrrR+iXSqVi1qxZdOjQgU6dOnHo0CF9RzI4arWaDh06cOPGDdavX8/jx4+pXLkyzs7OnDlz5pPz3dzcOH/+/Hdv95zYjEuWLOHWrVvMnj072foVIjW6evUqZcuWZe/evWzYsIG//vor2desrFixAjMzM9q1a5es/QqhC1LMpCIRmhj2P37HoptBnHsZToTmfyMuH+85FRPvOoVzL8NZdDOI/Y/fEan59g5VX1r0rdFoWLduHcWLF6d9+/bkyZOHs2fPcuDAAapUqZKEj0qkdEZGRixfvhxHR0eaNWvGxYsX9R3JIKnVatq1a8f169fZuHEjT58+pWrVqtSrV49Tp07Fnde4cWNsbGySdSMA+LCT3aBBgxg3bhx+fn7J2rcQqYGiKPz9999UrFgRS0tLLl26RNu2bZM9R0xMDMuWLaNNmzZYWVkle/9CaJsUM6mEb3AUS28FcS0wEoDEThyLPf9aYCRLbwXhG/zlZ4g8f/6cQoUKsWbNmrhj0dHRrFmzhqJFi9KxY0fy5cvH+fPn2b9/P5UqVUpkGmFo0qVLx+bNmylRogQNGjTgzp07+o5ksIyMjGjTpg3Xrl1j8+bNvHz5kurVq1O3bl1OnDiBiYkJXbp0YdWqVURGRiZrtgkTJpA1a1b69+8vu9gJkQjv3r2jU6dO9OrVi+7du3P27FkKFCiglyzHjh3Dx8dHFv6LVEOKmVTgkn84Gx8GExatJLqI+ZgChEYrbHwYzCX/8E9ej46Opk2bNty/f59hw4YRGhrKqlWrcHBwoHPnzhQsWJCLFy+yZ88eKlSo8J1phCGxtLRkz549ZMuWDWdnZ548eaLvSAbNyMiIVq1aceXKFbZt28br16+pWbMmtWvXpnjx4rx+/ZqdO3cma6b06dMzf/589u7dy7Zt25K1byEM1bVr1yhXrhy7du1i/fr1LFq0SK9bIbu7u1OoUCGqVq2qtwxCaJMUMwbukn84B5+EAokfjfmS2HYOPgn9pKAZO3Ysp0+fBuDFixfY2dnRtWtXHBwcuHTpErt27aJcuXJaSiIMTZYsWThw4AAAzs7OvH79Ws+JDJ+RkRHNmzfn8uXL7Nixg7dv39KtWzesrKyYNm1aso+QNGvWjCZNmjBw4ECCg4OTtW8hDImiKLi7u1OxYkXMzMy4dOmS3teovH79mm3btuHm5iaPQhCphhQzBsw3OCqukNGVg09C46acHThwgClTpsS7eXr37h0XLlxgx44dlClTRqdZhGHIlSsXXl5evHr1ioYNGxIaqtvv0bRCpVLRtGnTuDcNMmfOzKVLl6hQoQKHDh1K1qJm/vz5vH37ltGjRydbn0IYkpCQELp06ULPnj3p0qUL586do2DBgvqOxdq1a9FoNHTp0kXfUYTQGilmDFSEJoY9j96h6/dVVMDeR++4+9CHFi1afHLDFBERgbe3t45TCENTqFAh9u/fz40bN2jVqpVs56tFKpWKxo0bc/36dczNzXn27Bn16tWjWrVqeHl5JUtRkydPHiZMmMCCBQv4559/dN6fEIbkxo0blC9fnu3bt7NmzRqWLFmCubm5vmPFjRQ1adKEbNmy6TuOEFojxYyBOvo0VCtrZL4ldg3NjF0nCQsL++w5yf3MC2EYypUrx/bt2zly5AjdunUjJubbu+SJhMuQIQOdOnXCyMiI3bt3o9FocHZ2pkqVKuzfv1/nRc3AgQMpWbIkvXr1Ijo6Wqd9CWEIFEVh2bJlVKhQgXTp0nHp0iU6duyo71hxLl26xLVr12Thv0h1pJgxQG8iNVwNjNR5IRNLAexrNmDWoqVs2LCB9evXs27dOtauXcvatWtZunRpMiURhsbR0ZE1a9awfv16fv75Z9kBS8vc3Nx48uQJxsbGnD17Fk9PT1QqFQ0aNKBSpUrs3btXZ59zY2NjlixZgre3N3/99ZdO+hDCUISGhtKtWzdcXV3p2LEj586do1ChQvqOFY+7uzs5c+bE2dlZ31GE0CpjfQcQiXc1MAIV2lvwnxAqlYoyTdpT80fLZOxVpAatW7cmMDCQPn36kD17dn777Td9R0o1ypcvT/HixXF3d8fFxQVnZ2ecnJw4fPgw48ePp1GjRpQrV44xY8bQqFEjrS/4rVChAn369GH06NG0bNmSXLlyabV9IQzBzZs3ad26NY8ePWL16tV06tRJ35E+ERoayvr16xk0aBBqtVrfcYTQKhmZMTAaRcE7ICLBhUzQs8eMLGPzxT+HFk9LUDsK4B0QgUbeWRdJ0Lt3byZMmMCoUaP4+++/9R0n1VCpVLi6urJz505evXoVd8zR0ZETJ05w+PBhLCwsaNKkCeXKlWPnzp1aH6n5448/SJ8+PQMHDtRqu0IYghUrVlC+fHnUajX//PNPiixkALZs2UJwcDDdu3fXdxQhtE6KGQPjH64hQpPwmxF1OhNyFysb74+Nbf641zNYZ09wWxEaBf9wTaLyChFr9OjR9O/fnz59+rB161Z9x0k1YtfNrF69Ot5xlUpFnTp1OH78OEePHiVDhgw0a9aMMmXKsH37dq2tYcqYMSNz585l+/bt7N69WyttCpHShYaG0r17d7p370779u05f/48RYoU0XesL3J3d8fR0RE7Ozt9RxFC66SYMTAvwhK30NbKJgd9V3nG+2NfoQYA5laZKNWgpU77FyKWSqVi7ty5tGnThg4dOnD06FF9R0oVsmbNSvPmzXF3d//iqEutWrU4duwYx44dI0uWLLRo0YLSpUuzdetWrRQ1rVu3xsXFhf79+8tW3CLVu3XrFhUqVGDTpk2sXLkSDw8PLCws9B3ri+7evcupU6dk4b9ItaSYMTAvwqK/64sW+uY1l3ZtAKBiq26YWqRP8LVGSDEjvo+RkRErV66kVq1aNG3alMuXL+s7Uqrg5ubGnTt3OHv27FfPq1mzJocPH+bkyZNky5aNVq1aUbJkSTZv3vxdRY1KpeKvv/7i1atXjBs3LsntCJHSrVq1ivLlywNw8eJFg3hei4eHB1myZKFZs2b6jiKETkgxY2BComP4nvdRz21ezvuIMIxNTKnSzi1R18YAodGyva74PiYmJmzdupUiRYrg4uLC/fv39R3J4NWpUwdbW9sEb5NerVo1Dh48yOnTp/nxxx9p06YNxYsXZ8OGDWg0SZtKmi9fPsaMGcPs2bO5evVqktoQIqUKCwvD1dWVrl270qZNGy5cuICDg4O+Y33T+/fvWblyJZ07d8bMzEzfcYTQCSlmDIwmJumLd6OjIjm3yQOAUg1aJWq9TFwb39G/ELHSp0/P3r17yZo1K05OTjx79kzfkQyakZERPXr0YOPGjQQHByf4uipVqnDgwAHOnDlDnjx5aN++PcWLF2fdunVJKmqGDh1KoUKF6NWrV5KLIiFSmjt37lCxYkXWr1/P8uXLWb58OZaWhrGz5549e3j16pVMMROpmhQzBkZtlPStVS/v2URIoD8qlYrqnfomqQ3j7+hfiP+ytrbGy8uL6OhonJ2dCQoK0nckg9atWzciIiLYuHFjoq+tXLky+/fv5/z589jZ2dGxY0eKFi3KmjVrEvVATBMTE5YsWcL58+dl1zqRKqxZs4Zy5cqh0Wi4ePEi3bp103ekRHF3d6dChQoUL15c31GE0BkpZgxMemOjJH3RFEXh1JqFABSqVo9s+Qomug0jwNJYvmWE9uTOnRsvLy+eP39O48aNCQsL03ckg5U7d26cnZ0TPNXscypUqMDevXu5cOECBQoUoHPnzjg4OLBq1aoEFzXVqlXDzc2NkSNH8uLFiyRnEUKfwsPD6dmzJ507d6ZFixZcuHCBokWL6jtWojx58gRPT08ZlRGpntyZGpgcFsZJWjNz+8QB/P0eAFCjS78k9R3z//0LoU1FihRh3759XLlyhTZt2vD+/Xt9RzJYbm5uXLhwgevXr39XO+XLl2f37t1cunQJBwcHunbtSpEiRVixYkWCipqpU6diYmLCzz///F05hNCHu3fvUrFiRdasWYOHhwcrV64kffqEb5aTUqxYsQIzMzPatWun7yhC6JQUMwYmqcXEyVV/AZCrWBnsylZJ9v6F+JoKFSqwbds2vLy8cHV11dozUNKaRo0akS1bNjw8PLTSXpkyZdixYwfe3t4UL16c7t27U6hQIZYtW/bVojNLlizMnDmTDRs2cODAAa1kESI5rFu3jrJly/L+/XsuXLhAjx49UKkMb3p1TEwMy5Yto23btlhZWek7jhA6JcWMgbExV2OmTvwP1l4eu5ly2Z9+q5J+Y2GmVmFjrk7y9UJ8jZOTE6tWrWLNmjUMGzZM60+qTwtMTEzo0qULq1evJjIyUmvtlipVim3btnHlyhVKly6Nq6srhQoVwt3dnaioqM9e06lTJ+rUqUPfvn0JDw/XWhYhdCE8PJxevXrRsWNHmjVrxsWLFw16ncnRo0fx9fWVKWYiTZBixsCoVSpKW5uR3O8TqYDS1maoDfAdKmE42rVrx7x585g1axbTpk3TdxyD5OrqyuvXr9mxY4fW2y5ZsiRbtmzh2rVrlCtXjp9++omCBQuyZMmST4oalUrFwoULefLkCZMmTdJ6FiG05d69e1SuXJlVq1bh7u7O6tWrDXJa2X+5u7tTuHBhqlRJ+kwMIQyFFDMGqJS1Gcn9nrXy//0KoWv9+/dnzJgx/Prrr1qbLpWWFC5cmKpVq37XRgDfUrx4cTZt2sT169epVKkSffr0IX/+/CxatCjeiFChQoX47bffmD59Ordu3dJZHiGSasOGDZQtW5bw8HDOnz+Pq6urQU4r+6/AwEC2bduWKj4WIRJCihkDlNFETcmspsk2OqMCSmY1JaOJTDETyWPcuHH07t2bn376SScjDKmdm5sbhw4dwtfXV6f9FC1alA0bNnDjxg2qVatGv379yJ8/P3/99RcREREA/Prrr9jZ2dG7d29ZCyVSjIiICPr06UP79u1p3Lgx//zzDyVKlNB3LK1Yu3YtMTExdOnSRd9RhEgWUswYqDo5LbE0Vum8oFEBlsYq6uQ0jAeEidRBpVKxYMECWrZsSbt27Th+/Li+IxmU1q1bkyFDBpYvX54s/Tk4OLBu3Tpu3bpFrVq1GDhwIPb29syfPx9FUVi8eDEnT55kxYoVyZJHiK+5f/8+lStXZvny5SxZsoS1a9eSIUMGfcfSCkVRcHd3p2nTpmTLlk3fcYRIFlLMGChTtREN82bQ+XQzBWiYNwOmavlWEclLrVazevVqqlevTpMmTbhy5Yq+IxkMS0tL2rdvz/Lly9FoNMnWb+HChVm9ejW3b9/G0dGRwYMHky9fPq5du0aHDh0YNmwY/v7+yZZHiI9t2rSJsmXLEhoayvnz5/npp59S1VSsf/75h+vXr8vCf5GmyB2qAbOzMqFeLt2OmDjlssTOykSnfQjxJaampmzbto2CBQvi4uLCw4cP9R3JYLi5ufHkyRO8vLySve+CBQuycuVK7t69i7OzM0OHDuXgwYOEh4czePDgZM8jREREBP369aNt27Y0bNiQS5cuUbJkSX3H0jp3d3dy5cqFk5OTvqMIkWykmDFwZW3M4woabb23FNuOUy5LytiYa6lVIZImQ4YM7Nu3j4wZM+Lk5MTz58/1HckglCtXjuLFi+t0I4BvyZ8/P8uXL+fu3bs0btyYyMhI1q1bR+/evQkNDdVbLpG2PHz4kCpVquDh4cGiRYtYt25dqplW9l+hoaGsX7+e7t27o1bLGleRdkgxkwqUtTGnrb2VVtbQxK6RaWtvJYWMSDFsbGzw8vIiMjKS+vXr8+bNG31HSvFUKhVubm7s2rWLly9f6jWLvb09Hh4e3Lt3j+zZs7NkyRJsbW2ZNm0aISEhes0mUrfNmzdTpkwZ3r17x9mzZ+ndu3eqmlb2X5s3byYkJIQePXroO4oQyUqKmVTCzsoEN4fMlMhqiqIoKIncNSj2R3uJrKb0dMgsU8tEipM3b14OHDjA48ePadKkiTyIMQE6deoUt/YoJbC3t+fw4cMYGxuTN29eRo8ejZ2dHX/++Sfv3r3TdzyRikRGRtK/f3/atGmDs7Mzly5donTp0vqOpVMeHh44Ojpia2ur7yhCJCspZlIRM7URVTMpzG9VFcXnCmbq/7379PEX2ijedSoqZTenT9HM1M8ji/1FylW0aFH27t3LP//8Q7t27YiOjtZ3pBQtS5YsNG/eHHd3dxQluZ9O9XlFixZl2LBhXL9+HS8vL1q3bs3YsWOxtbXljz/+IDg4WN8RhYHz8fGhatWqLF26lIULF7Jx40asrKz0HUun7ty5w6lTp2Thv0iTVEpK+Q0ntGLp0qX07t2bR48e8UPOnPiHa3gRFs2LsGhCo2OIjlEwNlJhaWxEDgtjclgYY2OuRp1Kh91F6rR//36aNGlC586d8fDwSLXTRrTh8OHDODo6curUKapWrarvOACEhYVRrFgx7OzsOHToEE+ePGHq1KksXboUS0tLhgwZwoABA8iYMaO+o4oUwsbGhiFDhjBy5Mivnrd161Z69OiBtbV13BSztGDYsGEsW7aMZ8+eYWpqqu84QiQreQs+lXF3d8fFxYVcuXKhVqnIYWFMKWszXPKkp2U+K9rmz0jLfFa45ElPKWszclgYSyEjDE79+vVZsWIFy5cv59dff9V3nBStdu3a2NnZ6XUjgI9ZWFiwcOFCjhw5wtq1a8mdOzcLFizAx8eHTp06MWnSJGxtbZkwYYKsjxIJEhkZyaBBg2jVqhVOTk5cvnw5zRQyUVFRrFq1is6dO0shI9IkKWZSkevXr3PhwgXc3Nz0HUUInevYsSNz5sxh2rRpzJgxQ99xUiwjIyN69OjBpk2bUtQULhcXF9q2bcuQIUN4/fo1ADlz5mTevHn4+PjQtWtXpkyZgq2tLePGjSMoKEjPiUVK5evrS/Xq1Vm8eDHz589n06ZNaWpUb8+ePbx69UqmmIk0S4qZVMTDw4Ns2bLRqFEjfUcRIlkMGjSIUaNGMWzYMFauXKnvOClWt27diIiIYMOGDfqOEs/s2bOJjIz8ZHTtxx9/ZM6cOfj4+NCjRw+mTZuGra0tY8aMiSt8hADYvn07pUuXJiAggNOnT9O/f/80N+3Uw8ODihUrUrx4cX1HEUIvpJhJJSIjI1m9ejVdu3YlXbp0+o4jRLKZOHEiPXv2xNXVld27d+s7ToqUK1cuXFxcUtRUM4AffviBKVOmsHTpUk6fPv3Z12fNmoWvry89e/ZkxowZ2NraMnr0aAIDA/WQWKQUUVFR/Pzzz7Ro0YK6dety+fJlypUrp+9Yye7ff//F09NTRmVEmibFTCqxY8cOXr9+LfvLizRHpVKxaNEimjZtSps2bTh16pS+I6VIbm5uXLx4kWvXruk7Sjy9evWiQoUK9OrVi6ioqM+ekz17dmbMmIGfnx+9e/dm9uzZ2NraMnLkSAICApI5sdA3Pz8/qlevzl9//cW8efPYsmULmTJl0ncsvVixYgXm5ua0a9dO31GE0BspZlIJd3d3qlWrRuHChfUdRYhkp1arWbt2LZUrV6ZRo0Yp7oY9JWjUqBHZsmXDw8ND31HiUavVLFmyhDt37jBr1qyvnpstWzamTZuGn58f/fr1Y/78+dja2jJixAj8/f2TKbHQp507d1K6dGlevXrF6dOnGTBgQJqbVhYrJiaGZcuW0aZNGzJkyKDvOELojRQzqYCvry+HDh2Shf8iTTMzM2PHjh3ky5cPZ2dnfH199R0pRUmXLh1du3Zl9erVRERE6DtOPKVKlWLw4MFMmDAhQV83Gxsb/vzzT/z8/Bg0aBALFy7E1taWYcOG8erVq2RILPRh7969NGvWjFq1anH58mXKly+v70h6deTIEfz8/OR3v0jz5DkzqcCYMWOYM2cOz58/x9LSUt9xhNCrly9fUq1aNRRF4fTp02TPnl3fkVKMu3fvUrhwYdavX5/ipqWEhITg4OBAsWLF2Lt3b6LebX/9+jWzZ89m7ty5REdH06dPH4YNG0aOHDl0mFgkl0ePHpE/f35iYmKYNWsWAwcOTLOjMf/Vvn17rl69ys2bN+XzIdI0GZkxcBqNhuXLl9OhQwcpZITgw/oKLy8vwsLCcHFx4e3bt/qOlGIUKlSIatWqpbiNAADSp0/PggUL2L9/P1u2bEnUtVmyZGHixIk8evSIYcOG4eHhgZ2dHT///DPPnz/XUWKRHHbv3k3p0qWJiYmhV69eDBo0SG7cgcDAQLZt24arq6t8PkSaJ8WMgfPy8uLJkyeyk4kQ/2FnZ8eBAwfw8/OjWbNmKW5alT65ublx+PDhFDkNr0mTJjRr1oyBAwcmqQjNnDkz48ePx8/Pj19//ZUVK1aQL18+Bg0axLNnz3SQWOjK+/fvGTZsGE2aNKF69epkzpyZ3Llz6ztWirFmzRoURaFz5876jiKE3kkxY+A8PDwoUaJEmtySUoivKV68OHv27OHcuXN06NCB6OhofUdKEVq1akWGDBlYtmyZvqN81rx58wgJCWHUqFFJbiNTpkyMHTsWPz8/fvvtN1avXk2+fPkYMGAAT5480WJaoQuPHz+mZs2azJkzh5kzZ7Jjxw4ZffgPRVFwd3enSZMmZMuWTd9xhNA7KWYM2KtXr9i5c6cMMwvxBVWrVmXz5s3s2rWLPn36IEsEwdLSkg4dOrB8+XI0Go2+43wid+7cTJw4kYULF3LhwoXvaitjxoz8/vvv+Pn58fvvv7Nu3Trs7e3p27cv//77r5YSC23au3cvpUuX5unTp5w8eZIhQ4bI77ePXLx4kRs3bsjCfyH+nxQzBmzVqlWo1Wo6deqk7yhCpFiNGjVi2bJluLu7M3r0aH3HSRHc3Nx4+vQpBw4c0HeUz+rfvz+lSpWiV69eWhlRs7KyYtSoUfj5+TFu3Dg2bdqEvb09vXv35tGjR1pILL7X+/fvGTFiBI0aNaJq1ap4e3tTqVIlfcdKkTw8PMidOzf16tXTdxQhUgQpZgyUoih4eHjQokULsmTJou84QqRoXbp0YebMmfzxxx/MmTNH33H0rmzZspQoUSJFbgQAYGxszJIlS7h69Srz58/XWrsZMmRg5MiR+Pn5MXHiRLZu3UqBAgX46aef8PPz01o/InH+/fdfatWqxaxZs5gxYwY7d+6U32tfEBISwrp16+jevTtqtVrfcYRIEaSYMVBnzpzhzp07svBfiAQaMmQII0aM4Oeff2bNmjX6jqNXKpUKNzc3du/ezcuXL/Ud57PKly9Pv379+P3333n8+LFW206fPj0jRozAz8+PP/74g507d1KgQAHc3Nzw8fHRal/i6/bt20fp0qX5999/OXHiBEOHDpVpZV+xefNmQkND6d69u76jCJFiSDFjoGK3Hq1du7a+owhhMKZMmUKPHj3o3r07+/bt03ccverYsSNqtZpVq1bpO8oXTZo0CSsrKwYOHKiT9i0tLfnll1/w9fVl6tSp7Nmzh4IFC9KjRw8ePnyokz7FB9HR0fz66680bNiQihUr4u3tTeXKlfUdK8Xz8PDA0dERW1tbfUcRIsWQYsYABQcHs3HjRnr06IGRkXwJhUgolUrFkiVLaNiwIa1ateLMmTP6jqQ3WbJkoUWLFri7u6fYjREyZszIvHnz2LlzJzt37tRZPxYWFgwZMgQfHx9mzJjB/v37KVSoEN26deP+/fs66zetevLkCbVr12bGjBlMmzaN3bt3kzVrVn3HSvFu377N6dOnZeG/EB+RO2EDtGHDBiIiIujWrZu+owhhcIyNjVm/fj3ly5enYcOG3LhxQ9+R9MbNzY179+5x+vRpfUf5opYtW9KgQQMGDBhASEiITvuysLBg8ODB+Pj4MGvWLA4ePEjhwoXp3Lkzd+/eTXK7KbVY1AdPT09Kly6Nr68vx48fZ9iwYfKmXAItW7aMrFmz0rRpU31HESJFkZ8gBsjDw4P69euTK1cufUcRwiCZm5uza9cu8ubNi7Ozc5pd/F2rVi3s7OxS7EYA8GE07a+//iIgIICxY8cmS5/m5uYMHDiQhw8fMnfuXI4ePYqDgwMdO3bkzp07iW5PpVKl+Qe3RkdHM2rUKOrXr0/58uW5cuUKVatW1XcsgxEVFcXKlSvp3Lkzpqam+o4jRIoixYyBuX79OhcuXJCF/0J8p4wZM+Lp6YmZmRlOTk68evVK35GSnZGREa6urmzevJm3b9/qO84X2draMnbsWObMmYO3t3ey9WtmZkb//v15+PAh8+fP58SJEzg4ONC+fXtu3bqVoDbOnz/PTz/9RIMGDejZsycvXrzQceqU5+nTp9SpU4epU6cyZcoU9uzZg7W1tb5jGZTdu3fj7+8vv/uF+AwpZgyMh4cH2bJlo1GjRvqOIoTBy5EjB15eXgQHB9OgQQPevXun70jJrlu3bkRERLBhwwZ9R/mqIUOG4ODgQK9evZL9YZ+mpqb07duXBw8esGjRIs6cOUOxYsVo27btV3eDe/fuHUOGDOHq1at07dqVV69eUbp0aTZv3gykjelnXl5elC5dGh8fH44ePcqvv/4q08qSwMPDg4oVK1KsWDF9RxEixZGfKAYkIiKC1atX07VrV9KlS6fvOEKkCvb29hw4cID79+/TrFkzIiMj9R0pWeXMmZP69eun6KlmAOnSpWPJkiVcvHiRxYsX6yWDqakpvXr14v79+yxZsoRbt25hZmb2xfPv3LnDs2fPmDp1Kl27dmXnzp106tQJDw8PgFS9BXF0dDS///47Li4ulClTBm9vb6pXr67vWAbp33//xdPTUxb+C/EFUswYkB07dvD69WsZZhZCy0qWLMnu3bs5ffo0nTp1SvZ3/vXNzc2Nf/75h6tXr+o7yldVqVKFn376id9++41nz57pLYeJiQk9e/bkypUrWFlZffG8/PnzU6ZMmXjfT+nTp+fVq1epeuvn58+f4+joyB9//MHkyZPZt28fNjY2+o5lsFasWIGFhQVt27bVd5Q0R6MovAiL5kpABJ6PQ9jiE8zGB2/Z4hOM5+MQrgRE8CIsGk0aGGVNyVRKWhjnTiXq1atHZGQkJ06c0HcUIVKlnTt30qJFC3r27MmiRYtS9Tvn//X+/Xty585NmzZtmDdvnr7jfFVQUBCFCxemVq1abNy4Ud9xvmjnzp3MmTOHwMBAbty4QdOmTalWrRqzZs2iUaNGzJ0796ujOobq4MGDdOrUCbVazYYNG6hRo4ZW2rWxsWHIkCGMHDlSK+0ZipiYGPLly0fdunXjRvSE7r2N0nAlIALvgAgiNB9uk42AmP+c899/m6lVlLY2o5S1GRlN1MmcVsjIjIHw9fXl0KFDMiojhA41bdoUd3d3lixZkmw7Z6UE6dKlo2vXrqxZsybF77qVOXNmZs2axaZNm9i/f7++43xWYGAgnTt3pn79+mzZsoUTJ07w4sULTp8+zZIlS5g8eXKqK2Q0Gg1jxozB2dmZUqVKceXKFa0VMmnZ4cOHefTokUwxSyYRmhj2P37HoptBnHsZHlfIQPxC5uN/R2gUzr0MZ9HNIPY/fkek5uOzhS5JMWMgli9fjpWVFa1atdJ3FCFSte7duzN16lQmTpzI/Pnz9R0n2fTo0YOgoCC2b9+u7yjf1KFDBxwdHenbty9hYWH6jvMJDw8PypcvT9++fSlYsCDVqlVj1KhRXLhwgXLlyqW6nbyeP39OvXr1mDx5MhMnTmT//v1ky5ZN37FSBQ8PD4oUKUKlSpX0HSXV8w2OYumtIK4Fflg3mdhpS7HnXwuMZOmtIHyDo7SaT3yZFDMGQKPRsGzZMtq3b4+lpaW+4wiR6g0fPpxffvmFgQMHsn79en3HSRaFChWievXqKX4jAPiwcH7hwoU8f/6ciRMn6jvOJywtLfHx8SEoKCjuWO7cucmSJQtXrlzRXzAdOHz4MKVLl+b27dscPnyYUaNGyW5lWhIYGMj27dtxc3NLM1Ne9eWSfzgbHwYTFq0kuoj5mAKERitsfBjMJf9wbcQT3yA/cQzAgQMHePr0qQwzC5GMYneg6tKlC56envqOkyzc3Nw4cuQIPj4++o7yTQUKFGDUqFHMmDGDGzdu6DtOPI6OjmTPnp0RI0Zw48YNjh49yvz58wkKCqJChQr6jqcVGo2G8ePHU69ePYoXL86VK1eoVauWvmOlKmvWrEFRFDp37qzvKKnaJf9wDj4JBRI/GvMlse0cfBIqBU0ykGLGAHh4eFCyZEnKli2r7yhCpBlGRkYsXboUFxcXWrZsyfnz5/UdSedatWqFlZUVy5Yt03eUBBk+fDj29vb07t2bmJiUM0e9UKFCbNy4kcjISBwdHRkwYAA3btxg8uTJZMmSRd/xvtuLFy9wcnJi/PjxjBs3Dk9PT7Jnz67vWKmKoii4u7vTtGlT2QlOh3yDo+IKGV05+CRUppzpmBQzKdzLly/ZtWsXrq6uMswsRDJLly4dGzdupEyZMjRo0CDBT303VBYWFnTo0IEVK1YQHR2t7zjfZGpqyuLFizl9+nSKK8Dy5s3L1q1buXz5Mu7u7hw7dowuXbrEO0ej0XD9+nUcHR05efKknpImztGjRyldujQ3b97k0KFDjBkzBrVadm/StgsXLnDjxg2ZkaFDEZoY9jx6h67vrFTA3keyKYAuSTGTwq1evRq1Wk3Hjh31HUWINMnCwoJdu3aRM2dOnJ2defz4sb4j6ZSrqytPnz7lwIED+o6SILVq1aJr164MHz6cV69e6TvOJ3788UcqVaqEmZkZHz8JQa1WExoaSkBAADVq1KB27docO3ZMP0G/QaPRMGHCBBwdHXFwcODKlSvUqVNH37FSLQ8PD3Lnzo2jo6O+o6RaR5+GamWNzLfErqE58lS3I0BpmRQzKZiiKDg4ODBmzJhUMTVBCEOVOXNmPD09MTY2xsnJiYCAAH1H0pmyZctSsmRJg9gIINaMGTMwMjJi6NCh+o7yVZ8bXa9UqRKXL19m+/btvH37ltq1a1OrVi2OHj36SfGjL69evcLFxYVx48YxZswYvLy8yJEjh75jpVohISGsX7+eHj16yKiXjryJ1HA1MFLnhUwsBbgaGMnbqLT1QObkIsVMCqZSqahXrx6//fabvqMIkeb9+OOPeHl5ERQURIMGDQgJCdF3JJ1QqVS4ubmxZ88eXrx4oe84CWJtbc306dNZs2YNhw8f1necRDMyMqJZs2ZcunSJnTt38u7dO+rUqUPNmjU5dOiQXouaY8eOUapUKa5du8bBgwcZO3as3GDr2ObNmwkNDaV79+76jpJqXQ2MSNT0Mt9LZ1g+oB2T6hRmZBkbRpax4fyWFYnqUwVcCUjZz/EyVFLMpHDp0qXTdwQhxP8rUKAAnp6e3Llzhxb/x95dR1WV/f8ff15AJRQVQRELxcQEWwdFVATsbgywu7u7x04MRMVuCVuxULHRscBGCQuQvOf3x/zkO35mnAG5954L7Mdas9YI5+z9AuVy3ndXmzYkJGTORZ1du3ZFV1cXT09PuaOkWs+ePalXrx4DBgzQ+oM/f0ahUNCiRQtu3LjB0aNH+fbtG40bN+a3337D399fo0WNUqlk9uzZNGzYkLJly3L79m0aNmyosf6zsk2bNtG4cWOKFSsmd5RMKVmSuBURl6ZRmTeP7vL02nkMcuf55X4l4FZEHMlaMuKamYhiRhAEIQ1sbGw4cuQI58+fx9XVleTkzDdtIG/evLRt2xYPDw+tmer0XxQKBevWrSM0NJT58+fLHSddFAoFzZo1IzAwkBMnTpCcnEyTJk2oU6cOvr6+av87+fDhA87OzkydOpXJkydz8uRJChYsqNY+hT89fPiQy5cvi4X/ahT+LZm45LT9DNk07cD0C8/pvXpPuvqOS5YI/5b5fmfITRQzgiAIaWRvb8+uXbvYu3cvw4YNyzAP/Gnh5ubG48ePCQgIkDtKqpUrV46xY8cyb948/vjjD7njpJtCocDZ2ZkrV67g6+ub8ufatWtz4sQJtfy7u3DhAjY2Nty6dQs/Pz9mzJghppVpkIeHB/ny5aNFixZyR8m0wmLTvlOjUR4TsukbyNa/8O9EMaMloqOjef/+PV++fJE7iiAIqdCmTRvWrVvH6tWrtfIU+vSyt7enRIkSGWojAIBJkyZRpEgR+vfvn2mKTIVCQZMmTbh06RL+/v7o6enRtGlTatSowbFjx1TydSqVSubNm0eDBg0oVaoUt2/fpnHjxipIL6RWQkIC27Ztw9XVlRw5csgdJ9MKi02S7eFXB1HMqIMoZmT25csXFi9eTLFixahZsybOzs60b9+ec+fOkZiYKHc8QRD+RZ8+fZg7dy7Tpk1j7dq1csdRKR0dHdzc3Ni7dy+fP3+WO06qGRgYsGbNGs6dO8f27dvljqNS3zeFuXjxIqdOncLAwIDmzZtTvXp1jhw58stFTXh4OC4uLkyaNIkJEyZw6tQpLCwsVJxe+C9Hjx4lIiICNzc3uaNkatFJSuQ68UUJxCSJ82ZUTRQzMps7dy5bt25l1qxZ/P7777Rs2RIjIyPGjBmDs7MzZ8+elTuiIAj/Yvz48QwfPpxBgwaxZ0/65lNrmx49ehAfH8+uXbvkjpImjo6OdO7cmVGjRhEZGSl3HJVTKBQ0bNiQ8+fPc+bMGXLmzEnLli2xtbXl4MGDKJWpf1gKCAjAxsaGmzdv4uPjw+zZs9HT01NjeuFnNm3aRK1atShfvrzcUTK1ZKW8I7ZJMvefGYliRmaenp7MmDGDgQMH0qpVK8aOHcvixYuZOnUqOXPmpHv37pw5c0bumIIg/IRCoWDJkiV07dqVbt26cfLkSbkjqUyhQoVwcXHBw8ND7ihptnTpUhITExk3bpzcUdRGoVCkHLR57tw58ubNS5s2bbCxsWH//v3/WtQolUrmz5+fMp3w9u3bNGnSRIPphb96+fIlfn5+YuG/BujqpGVTZtXTk7n/zEgUMzL68OEDVlZWf3vn0NTUlObNm3Po0CEqVqzI9u3bM83cb0HIjHR0dNi8eTONGjWidevWXL9+Xe5IKuPm5saNGze4ffu23FHSxNzcnPnz5+Ph4cHFixfljqN29evX58yZM1y4cAEzMzPatWtHlSpV2Lt379+KmoiICJo1a8aECRMYN24cZ86coVChQjIlFwC2bt2KoaEhHTp0kDtKppdTTyfND7/3Tx9jUYvqbOjTKuVjJ9cuYFGL6nhP6p/qdnQAIz3x6K1q4jsqo/z581OjRg0WLVpEQEDAP66RcXNz48KFC3z79k2GhIIgpFa2bNnYu3cvlSpVwtnZmUePHskdSSWaNm1KgQIFMuToTN++falVqxb9+vXLtGcC/S87OztOnTpFQEAA5ubmdOjQgUqVKrF7926Sk5O5dOkSNjY2BAYG4uPjw5w5c8S0MpkplUo2b95Mp06dyJUrl9xxMj1zQ700r5mJj/lK1OtQPr17lfKxmI8RRL0O5cuHd6luR/n/+xdUSxQzMhs2bBjFixenc+fOzJo1i+DgYL58+YJSqSQ+Pp4LFy5QuHBhDA0N5Y4qCMJ/MDIy4tixY5ibm+Po6Mjr16/ljpRu2bJlo2fPnnh5eWW4N1V0dHRYv349jx8/ZvHixXLH0ai6devi7+/P5cuXKVKkCJ06dcLCwoJ69epRrFgxbt++jZOTk9wxBeD06dO8ePFCLPzXkF8pJqq26My8oPB//K/vxsNq71/4d6KYkVnRokU5duwYgwYNYvPmzdjY2ODs7Iy7uzulS5fm8uXLzJgxQ+6YgiCkkomJCX5+figUChwdHTPFAvTevXvz6dMnDh48KHeUNKtUqRIjR45k1qxZPHv2TO44Gle7dm28vLyoW7cuHz58QKlUEhERwfnz5zPlga8Z0aZNm7C2tqZWrVpyR8kSzAx00deVZ92Kvq4CMwNxbpOqiWJGC2TPnp3x48cTGhqKj48PFSpUIDExkQEDBrB161bs7e3ljigIQhoUKlSIkydPEh4eTrNmzYiJiZE7UrqULl2aevXqZcipZgDTpk2jQIECDBo0KMutP7xy5Qo2NjY8evSI48ePExgYSKlSpejWrRvW1tZs376dpCRx7oVcIiIiOHjwIO7u7igUYmG4JugqFNiY6qPp77YCsDHVR1f8PaucKGa0iJ6eHg4ODqxfv57t27czfvx4KlSoIHcsQRB+QenSpfHx8eH+/fu0a9cuw6/ZcHNz48yZMxlydMPIyIhVq1bh5+eX6bbP/hlJkliyZAn16tWjSJEi3Lp1CxcXF6pXr87Ro0e5ceMGZcuWxdXVlXLlyrF161ZR1MjAy8sLgO7du8ucJGupYqqPpt/WkP5/v4LqiWJGEARBTapVq8ahQ4c4c+YMvXr1StP5H9qmXbt2GBsbs3nzZrmj/JJmzZrRpk0bhg0bxqdPn+SOo1ZRUVG0bNmS0aNHM3LkSM6dO0eRIkV+uKZq1aocPnyYoKAgKlSoQK9evShTpgybN28WBzZriCRJbNq0iVatWmFqaip3nCwld3ZdKufLobHRGQVQOV8OcmcXU8zUQRQzWkCSpAz9kCMIws81bNiQHTt2sGvXLkaMGJFhpzkZGhrStWvXDP0O/ooVK4iNjWXixIlyR1Gbq1evYmNjw6VLlzh69CgLFiwgW7ZsP73exsaGgwcPcvv2bapUqYKbmxtlypRh06ZNGX40UdsFBgby4MEDsfBfJg6FjDDSU6i9oFEARnoKHAoZqbmnrEsUM1rg3LlzPH36VO4YgiCoSbt27VizZg0rVqxg7ty5csf5ZW5ubrx9+xZfX1+5o/ySQoUKMXv2bNatW8e1a9fkjqNSkiSxdOlS7OzsKFSoELdu3aJZs2apvr9y5crs37+fu3fvUq1aNfr27Uvp0qXZsGGDKGrUZNOmTRQtWpRGjRrJHSVLyqGrQ9NiudQ+3UwCmhbLRQ5d8citLuI7KzNJkujfv7/YsUwQMrn+/fszc+ZMJk+ezIYNG+SO80tsbW2pUqVKht0IAGDQoEHY2trSr1+/DDvC9L8+fvxIq1atGDVqFMOHD+f8+fMULVr0l9qqWLEie/bs4d69e9SqVYv+/ftTqlQp1q1bR3x8vIqTZ13R0dF4e3vTu3dvdHXF1CO5FDfOTp53wWrtw7GwEcWNs6u1j6xOFDMyu3TpEo8fP8bd3V3uKIIgqNnkyZMZMmQIAwYMYP/+/XLHSTOFQoGbmxtHjx4lLCxM7ji/RFdXl/Xr13Pv3j2WL18ud5x0CwwMxMbGhosXL3LkyBEWLVr0r9PKUqt8+fJ4e3tz//596taty8CBAylZsiRr1qwhLi5OBcmztj179hATE0OvXr3kjpKl+fj4MKRlQ96f+3PbeVVNOfvejmNhI2zNDFTUqvAzopiR2aZNmyhRogT169eXO4ogCGqmUCj4/fff6dixI126dOHMmTNyR0qzrl27oqenx7Zt2+SO8suqVq3K4MGDmTp1Ki9evJA7zi+RJInff/+d3377DXNzc27dukXz5s1V3o+1tTU7d+4kODiY+vXrM2TIEEqWLMnKlStFUZMOmzZtwtHR8ZdH0IT0u3LlCm3btsXFxYVFQ3rR0cpYJWtovq+R6WhlLAoZDRHFjIw+f/7Mnj17cHNzQ0dH/FUIQlago6PD1q1badCgAS1btuTmzZtyR0qTvHnz0q5dOzw8PDLsZgYAs2bNIm/evAwZMiTDfR0fP36kTZs2jBgxgiFDhnDhwgWKFSum1j7Lli2Ll5cXwcHBODg4MHz4cEqUKMHy5cv59u2bWvvObIKDg7ly5YpY+C+jBw8e0LRpU6pWrYq3tzd6enoUN86Ou3VeKuXLAaR9lOb79ZXy5aCPdV4xtUyDxBO0jLy9vYmPj6dHjx5yRxEEQYOyZ8/O/v37KV++PM7Ozjx+/FjuSGni5ubGkydPuHjxotxRfpmxsTErVqzg6NGjHDp0SO44qXb9+nVsbW05d+4chw4dYsmSJWTPrrmHpjJlyuDp6cmjR49wdHRk1KhRlChRgmXLlhEbG6uxHBmZh4cHpqamtGjRQu4oWdKLFy9o0qQJRYoU4ejRoxgY/N/oib6uDs5FczGgfF5qFTBAX/f/Spr/fWD+65/1dRXUKmDAgPJ5cS4qFvtrmkLKaG9JZSLVq1fH3Nyco0ePyh1FEAQZREZGYmdnR2xsLJcvX8bCwkLuSKmiVCopXbo0devWzdDTzSRJokWLFty6dYuHDx+SK1cuuSP9lCRJrFq1ilGjRmFjY8Pu3buxtLSUOxbPnj1jzpw5eHp6YmpqypgxY+jfvz9GRqrdhtbMzIyRI0cyYcIElbaraQkJCRQqVAhXV1eWLFkid5wsJzw8nN9++43ExEQuXbpEwYIF//X6ZEki/FsyYbFJhMUm8UfoS+ISkyhZ3BIjPR3MDfUwN9TDzEAXXYWmTq0R/pcoHWVy584dbty4IRb+C0IWli9fPvz8/FAqlTRp0oSPHz/KHSlVdHR06N27N3v37s3QB1AqFApWrVrFx48fmTp1qtxxfurTp0+0a9eOoUOHMmjQIC5evKgVhQyAlZUVmzdv5vHjxzRv3pzx48dTvHhxFi1aRExMjNzxtM6RI0eIiIgQU8xk8PXrV1xcXPj06RP+/v7/WcgA6CoUmBvqUcVUn4YFczDRuToTHW1okCcJp6I5qWKqj7mhnihkZCaKGZl4eHhQoEABXFxc5I4iCIKMihQpgr+/P+/evaNZs2YZZqpOz549iY+PZ9euXXJHSZdixYoxffp0VqxYQVBQkNxx/ubmzZtUrVqV06dPc+DAAZYtW6bRaWWpVaJECTZu3MiTJ09o3bo1kyZNwtLSkgULFhAdHS13PK2xadMmateujbW1tdxRspT4+Hhat27N48eP8fX1pWTJkmluw9PTk9jYWJRKJcuWLVNDSuFXiWJGBnFxcXh5edGzZ0+VbKEpCELGVrZsWU6cOMGdO3fo0KEDiYmJckf6TxYWFjRt2jRDnznz3fDhwylfvjz9+vUjOTlZ7jjA/00rq1OnDiYmJty6dYvWrVvLHes/WVpasn79ep48eUK7du2YMmUKlpaWzJs3j69fv8odT1YvX77E399fjMpoWHJyMt26dSMgIIDDhw9jY2OT5jYSExOZPn16yp+XLl1KVFSUClMK6SGKGRkcPHiQjx8/0rt3b7mjCIKgJWrUqMHBgwdTHnaUSqXckf6Tm5sbN2/e5Pbt23JHSZds2bKxfv16bt68yZo1a+SOw+fPn+nQoQNDhgyhf//+BAQEULx4cbljpUmxYsVYu3Ytz549o2PHjkyfPh1LS0tmz57Nly9f5I4niy1btmBkZETHjh3ljpJlSJLE4MGDOXDgAN7e3tjb2/9SO56enrx+/Trlz3FxcWJ0RptIgsY5ODhI9erVkzuGIAhayNvbW1IoFNKIESMkpVIpd5x/lZCQIJmbm0uDBw+WO4pK9O/fX8qVK5f0+vVr2TLcvHlTsrKykoyNjaV9+/bJlkPVXr16JQ0ePFjKkSOHlCdPHmnGjBnSp0+fUn2/qampNHfuXDUmVK+kpCSpaNGikru7u9xRspSpU6dKgLRp06ZfbiMhIUEqXLiwpFAoJCDlP0NDQykyMlKFaYVfJUZmNOzZs2ecOXNGLPwXBOEfdezYkZUrV7Js2TIWLlwod5x/lS1bNnr06IGXl1emOGtk3rx5GBoaMnz4cI33LUkSa9asoXbt2uTJk4egoCDatm2r8RzqUrhwYVauXMnz589xdXVl3rx5KeuVMvImEql1+vRpXr58KaaYadCqVauYOXMm8+bNS9f3/eTJk7x+/fpv51HFxsbi7e2d3piCKshdTWU1kyZNkoyNjaWYmBi5owiCoMWmTZuW7ncUNeHx48cSIHl5eckdRSV27twpAdKxY8c01ufnz5+lDh06SIA0ePBgKS4uTmN9y+Xt27fSiBEjJAMDA8nY2FiaMmXKv77LndFHZtq3by+VL19e60dbM4tdu3apbIT7y5cvkoeHh7Ru3TqpVq1aUsmSJaV169ZJ69evl96/f6+ixEJ6iGJGgxITEyULCwtpwIABckcRBEHLKZVKacCAAZKOjo508OBBueP8q/r160sNGjSQO4ZKKJVKqXHjxlKxYsWk6OhotfcXFBQklSxZUsqVK5e0Z88etfenbd69eyeNGjVKMjAwkHLlyiVNmjRJioiI+Nt1GbmYCQ8Pl7JlyyYtXbpU7ihZgp+fn5QtWzape/fuUnJyskrbdnV1lezs7FTappB+YpqZBvn6+vL27VsxzCwIwn9SKBSsXLmSdu3a0alTJ86dOyd3pJ9yc3Pj7NmzPHv2TO4o6aZQKFi7di3v379n5syZautHkiTWrVtH7dq1yZUrF0FBQbRv315t/Wkrc3NzFi9eTGhoKP3792fZsmVYWloyceJEIiIi5I6nEtu3bwege/fuMifJ/K5du0abNm1o3LgxHh4e6OiIx9ysQPwta5CHhwdVqlTB1tZW7iiCIGQAurq6eHp6Ymdnl3JSvTZq27YtuXPnZvPmzXJHUQkrKysmT57M0qVLuXfvnsrb//r1K126dGHAgAG4ublx+fLlXzr3IjPJnz8/CxcuJDQ0lEGDBrFixQosLS0ZP3484eHhcsf7ZZIksWnTJlq3bo2pqanccTK1hw8f4uLiQuXKldm7d684+iILEcWMhoSFhXH06FHc3d1RiJNiBUFIpRw5cnDgwAHKli2Lk5MTT58+lTvS3xgaGtKlSxe2bNlCUlKS3HFUYsyYMZQqVYp+/fqpdJvsO3fuULVqVY4fP463tzerV69GX19fZe1ndGZmZsyfP5/Q0FCGDh3K6tWrsbS0JCYmJkMevnnt2jWCg4PFjAw1e/XqFY6OjlhYWHDs2DEMDQ3ljiRokChmNMTT0xM9PT26dOkidxRBEDKYXLlycfz4cfLmzYujoyPv3r2TO9LfuLu78+7dO3x8fOSOohLZs2dn3bp1XLlyhU2bNqW7PUmS2LBhAzVr1sTIyIibN2+K80b+hampKXPnziU0NJQRI0YQFxfHwoULGTVqFGFhYXLHS7VNmzZRrFgxGjVqJHeUTCsyMhJHR0d0dXXx9fUlb968ckcSNEwUMxrwfZi5Xbt24odMEIRfYmZmhp+fHwkJCTg5OWnddra2trbY2Njg4eEhdxSVqVevHr169WLcuHG8f//+l9v5+vUr3bp1o1+/fvTq1YsrV65QqlQpFSbNvPLly8fs2bMxMTHBzs6OTZs2Ubx4cUaMGKGVRf1fff36FW9vb3r16iXWbqhJdHQ0Li4uREZG4u/vT6FCheSOJMhA/HRpwMWLF3ny5IkYZhYEIV2KFSuGn58fr169okWLFlp3toubmxvHjh3T+ofMtFi0aBF6enqMHDnyl+6/e/cu1apV48iRI+zatYu1a9eKaWW/QKFQ0LhxY0JDQxk3bhxbtmyhRIkSDBs2jLdv38od7x/t2bOH2NhYevXqJXeUTCkhIYG2bdsSHByMj48PpUuXljuSIBNRzGiAh4cHVlZW1K9fX+4ogiBkcOXLl+f48ePcvHmTTp06adUalS5dupAtWzY8PT3ljqIy+fLlY/HixezcuZOTJ0+m+r7vI/I1a9ZEX18/5e9LSJ+8efMyffp0QkNDmTBhAp6enpQoUYIhQ4bw+vVrueP9YNOmTTRp0oSiRYvKHSXTUSqV9OjRg3PnznH48GGqVq0qdyRBRqKYUbPPnz+zd+9eevfuLYaZBUFQidq1a7Nv3z5OnDhBnz59/nYytVzy5s1L27Zt2bRpk9ZkUgVXV1fs7e0ZOHAgcXFx/3l9dHQ0rq6u9OnTB1dXV65evSreNVaxPHnyMHXqVF68eMGUKVPYuXMnVlZWDBo0iFevXskdjwcPHnD16lUxI0MNJEli2LBh7N69m507d+Lg4CB3JEFm4ulazXbt2kV8fDw9e/aUO4ogCJmIs7MzW7duZevWrYwbN07uOCnc3d15+vQpFy5ckDuKynw/e+bFixfMnTv3X6+9f/8+1atX5+DBg3h5ebF+/XoMDAw0lDTrMTY2ZtKkSYSGhjJ9+nR2796NlZUVAwYM4OXLl7Ll8vDwwNTUlBYtWsiWIbOaPXs2q1atYu3atbRt21buOIIWEMWMmm3atImmTZtiYWEhdxRBEDKZrl27snz5chYtWsSiRYvkjgNA/fr1KVmyZKbaCACgbNmyjB8/nvnz5/Pw4cO/fV6SJDZv3kyNGjXIli0bN2/epGvXrjIkzZpy5crFhAkTCAkJYdasWezbt4+SJUvSr18/QkNDNZolPj6e7du34+rqSvbs2TXad2a3du1apk6dyqxZs+jXr5/ccQQtIYoZNbp9+zY3b94Uw8yCIKjN0KFDmTRpEmPHjmXr1q1yx0GhUNC7d2/27t2rdTuupdfEiRMpVqwYAwYM+GEaXUxMDD179sTNzY2uXbty9epVypQpI2PSrCtXrlyMGzeOkJAQ5syZw8GDBylVqhTu7u48f/5cIxmOHDlCRESE+N2vYnv37mXQoEEpr3mC8J0oZlTMy8uLTp064ePjw8aNGzE3N8fFxUXuWIIgZGKzZs2ib9++uLu7c/ToUbnj0KNHDxITE9m1a5fcUVRKX1+ftWvXcv78ebZt2wb8uTaievXq7Nu3j+3bt7Nx40ZxYJ8WyJkzJ2PGjCEkJIT58+dz9OhRSpcuTe/evXn27Jla+960aRN16tTB2tparf1kJadOnaJr16507tyZZcuWicPHhR+IYkbFzp8/z+7du3FxcWHt2rWUKFGCN2/eyB1LEIRMTKFQsGbNGlq2bEmHDh24ePGirHksLCxwcXFRyWGT2qZRo0Z07dqV0aNHs3LlSqpXr46uri43btygW7ducscT/oeRkRGjRo0iJCSERYsW4ePjQ5kyZejZsydPnjxReX8vXrzg5MmTYlRGhW7cuEHr1q1p2LAhW7ZsEZspCX8j/kWomLm5OdmyZQP+nEN99epVSpQoIeZ2CoKgVrq6uuzYsYPatWvTvHlz7t69K2sed3d3goKCuHXrlqw51GHmzJl8/fqVoUOH0rlzZ65du0a5cuXkjiX8C0NDQ0aMGMHz589ZunQp/v7+lC1bFldXVx4/fqyyfrZs2YKRkREdOnRQWZtZ2R9//IGzszPly5dn3759Yg2S8I9EMaNi5ubmP5z7oFQqkSSJPHnyyBdKEIQsQV9fn0OHDmFlZUWTJk00tkbgn7i4uFCwYMFMtxFAcHAwzZs3T/mzq6urmFaWgRgYGDB06FCeP3/O8uXLOXPmDOXKlaNbt248evQoXW0nJyezZcsWOnXqRM6cOVWUOOt68+YNjo6OmJmZcfz4cYyMjOSOJGgpUcyomLm5+d/OV5g2bRrz58+XKZEgCFmJsbExPj4+5MyZE0dHR96/fy9LDj09PXr06IGXlxffvn2TJYOqeXp6Ur16dQBu3rxJnTp16N+/P/Hx8TInE9JKX1+fwYMH8/TpU1auXMn58+extramS5cuBAcH/1Kbp06d4uXLl7i7u6s4bdYTFRWFo6MjkiTh7+9Pvnz55I4kaDFRzKhYgQIFUv7/+9kE06dPF4vVBEHQmPz583Py5EliY2NxcnLi8+fPsuTo3bs3nz9/5sCBA7L0ryqxsbG4ubnRo0cPOnToQGBgIBUqVGD9+vU8ffpUa7bFFtJOX1+fgQMH8vTpU9asWUNAQAAVKlSgY8eO3L9/P01tbdq0iQoVKlCjRg01pc0aYmJiaNasGe/fv8ff35/ChQvLHUnQcqKYUbH8+fMDoKOjw759++jfv7/MiQRByIosLS3x8/MjNDSUli1bpurkelUrVaoU9evXz9AbATx69IiaNWuya9cutmzZkrImAqBChQqMGjWK2bNn8/TpU5mTCumRI0cO+vfvz9OnT1m3bh3Xrl2jYsWKtG/fnnv37v3n/eHh4Rw+fBg3Nzfx5mU6JCYm0r59e+7evcuJEycoW7as3JGEDEAUM6mULEmExSZxOyIO35fR7Hv+hd1PP7Pv+Rd8X0ZzOyKOsNgkihQrRqlSpdi+fTtt2rSRO7YgCFlYxYoVOXbsGIGBgXTu3PmH9Xya4u7uzrlz5zLkw76XlxfVqlUjOTmZ69ev07Nnz79dM3XqVAoWLMjAgQP/NsVYyHiyZ89O3759efz4MRs3buTGjRtUqlSJtm3bcufOnZ/et337dhQKhdjRLh2USiW9evXi1KlTHDx4UIxwCakmipn/8DkhmfNvY1h5L4qtf3zC91U0dyPjePo5gZCviTz9nMDdyDh8X0Wz9Y9PrP8jmo3nbtG0XUe5owuCIFC3bl327t3L0aNH6d+/v8YfuNu2bUvu3LnZvHmzRvtNj2/fvtGnTx+6d+9OmzZtCAwMpHz58v94raGhIatXr+bkyZN4e3trOKmgLtmzZ8fd3Z3Hjx/j4eHB7du3qVKlCq1bt/7bDn2SJOHh4UGrVq0wNTWVKXHGJkkSI0eOZOfOnXh5edG4cWO5IwkZiChmfiIuWYnPy6+sffCRq++/EZf8fw8Ayv+5VvnDfRJX339j7YOP+Lz8Snzy/14tCIKgWU2bNmXz5s14eHho/ORsAwMDunbtytatW2UZGUqrP/74g5o1a+Ll5YWHhwfbtm37z52pXFxcaNeuHcOHD+fjx48aSipoQrZs2ejduzePHj1i69at3L9/H1tbW1q2bMnNmzcBuHr1KsHBwWLhfzrMmzeP5cuXs3r1arGttZBmopj5ByFfEtgY/JG7kX/uUJPW9zG/X383Mp6NwR8J+ZKg0nyCIAhp5erqypIlS5g3bx7Lli3TaN/u7u68e/cOHx8fjfabVjt37qRq1aokJiYSGBhI7969U73+Yfny5Xz79o0JEyaoOaUgh2zZstGjRw8ePnyIp6cnjx49olq1ajRv3pz58+dTrFgxGjZsKHfMDGnjxo1MmjSJ6dOnM2DAALnjCBmQKGb+x83wb+x+9oXYJCnNRcz/koCYJIndz75wMzxzbE0qCELGNXLkSMaPH8/IkSPZvn27xvq1sbHBxsZGazcC+PbtG/369aNr1660atWK69evU7FixTS1YWFhwdy5c1m/fj1XrlxRU1JBbnp6enTv3p3g4GC8vLz4448/OHLkCNmzZ+fGjRtyx8twDhw4QP/+/Rk0aBBTp06VO46QQYli5i9uhn/j5OsYIO2jMT/zvZ2Tr2NEQSMIguzmzp1L79696dWrF8ePH9dYv+7u7hw/fpx3795prM/UePz4MbVr18bT05NNmzaxffv2Xz7wcMCAAVSrVo1+/fqRmJio4qSCNtHV1aVr166MHj0ahUKBJEnUrFkTJycnUcym0tmzZ+ncuTPt27dnxYoVYhc44ZeJYub/C/mSkFLIqMvJ1zFiypkgCLJSKBSsX7+e5s2b0759ey5duqSRfrt06UK2bNnYtm2bRvpLDW9vb6pWrcq3b9+4du1aurfV1dXVZf369Tx48IDff/9ddUEFrbVlyxaaNGnCo0eP8Pb25vXr19SpUwdHR0eN/WxlREFBQbRs2ZL69evj6emJjo54HBV+nfjXw5+L/Y+9+Iq63xNQAMdfiE0BBEGQl56eHrt27aJGjRo0a9YszYcD/oo8efLQrl07PDw8ZN/COC4ujgEDBtC5c2eaN2+esv2uKtja2jJ06FCmTZtGaGioStoUtNP9+/e5evUq7u7u6Orq0rFjR+7evcuePXt49+4dv/32G40aNeLixYtyR9UqT548wcnJibJly3LgwAGyZ88udyQhgxPFDHD2TYxK1sj8l+9raM68Ue8IkCAIwn/R19fn8OHDWFpa0qRJE408eLu7u/P06VMuXLig9r5+5smTJ9SuXZstW7awfv16duzYQa5cuVTax8yZM8mXLx+DBw+WvXAT1MfDwwMzMzOaN2+e8jEdHR3at2/PnTt32LdvH+Hh4dSrVw8HBwfOnz8vY1rt8PbtWxwdHTExMeHEiRO/PKVTEP4qyxczn+KTuRMZr/ZC5jsJuBMZz+eEZA31KAiC8M9y586Nr68vBgYGODo68uHDB7X2V69ePUqWLCnbRgB79uyhatWqxMTEcO3aNfr27auWefq5cuVi5cqVHD9+nAMHDqi8fUF+8fHxbN++HVdX138cWdDR0aFt27bcunWLgwcP8vHjR+zt7bG3t+fs2bNZssj9+PEjTZo0ISkpCX9/f3Emj6AyWb6YuRMZl6bpZQnfYvD5fQaLW9Zgap2iTK9XguUd6nNh26pUvzgpgNsRcb+UVxAEQZUKFCiAv78/X79+xdnZmS9fvqitL4VCgZubG/v27ePTp09q6+d/xcXFMWjQIDp27EjTpk25efMmlStXVmufrVq1okWLFgwdOlSt31NBHocPHyYyMhI3N7d/vU5HR4dWrVoRFBTE4cOH+fr1Kw4ODtSvX5/Tp09nmaImNjaW5s2b8/btW/z8/ChatKjckYRMJEsXM8mSxK2IuDSNyhyeP44LnquIfBWCSaFi6BvlIuxpMD7LZ3Bld+rebZSAWxFxJGeRFzFBELRbiRIl8PX15dmzZ7Ru3Zr4+Hi19dWjRw8SExPZuXOn2vr4q2fPnlGnTh08PDxYu3YtO3fuVPm0sp9ZuXIlnz9/ZsqUKRrpT9AcDw8P6tSpQ7ly5VJ1vUKhoEWLFty4cYOjR4/y7ds3GjVqhJ2dHSdPnszURU1iYiIdOnTg1q1bHD9+HGtra7kjCZlMli5mwr8lE5ectheQ0FvXAChdx4Hhey8y6uBV9HLoA/Dp3etUtxOXLBH+TUw1EwRBO1SuXJmjR49y+fJlunbtSnKyel6fChYsSNOmTTUy1Wzv3r3Y2try9etXrly5Qv/+/TW6/WvRokWZMWMGK1euFGeQZCKhoaGcPHkSd3f3NN+rUCho1qwZgYGBHD9+nMTERBwdHalbty5+fn6ZrqhRKpW4u7vj5+fH/v37qVWrltyRhEwoSxczYbFJab6nuM2fP4iPL5/h9/Z2LGldi6T4OCxtavFbt7SdXPsr/QuCIKiLnZ0du3fv5tChQwwaNEhtD1bu7u7cunWLoKAgtbQfHx/P4MGD6dChA02aNOHmzZvY2Niopa//MmzYMCpVqkS/fv1IShKv+ZnBli1byJkzJ+3bt//lNhQKBS4uLly9ehUfHx8kScLJyYnatWtz4sSJTFHUSJLE2LFj8fT0xNPTEycnJ7kjCZlUli9m0voNaDVpMTbNOgDw/tkjPr9/i2627JiXssbAOE+q29FBFDOCIGifFi1asHHjRtavX6+2E7mdnZ0pWLAgHh4eKm/7+fPn1K1bl40bN7JmzRp2796NsbGxyvtJLT09PTZs2MCtW7dYvXq1bDkE1UhOTmbLli106tRJJTtxKRQKnJycuHz5Mn5+fujq6tK0aVNq1qzJsWPHMnRRs2jRIpYsWcLy5cvp3Lmz3HGETCxLFzPRSUrSeuJLwI513D6+l2JVajDp9EOG7wsgh1FOru7ZjO+KWaluRwnEJInzZgRB0D69evVi4cKFzJ49mxUrVqi8fT09PXr27MmOHTv49u2bytrdv38/NjY2fPz4kStXrjBgwACtOFW8Ro0aDBgwgMmTJ/P6deqnIwva5+TJk7x69eqXppj9G4VCgaOjIwEBAZw6dQp9fX2aN29O9erVOXLkSIYrajZv3sy4ceOYPHkyQ4cOlTuOkMll6WImWZm2F4eEb7GcXDsfSZKo4NCcnHlNKVCiDMUq1wDgWWDazk5ISmP/giAImjJmzBhGjx7NsGHD1LJYv3fv3nz+/Jn9+/enu634+HiGDRtGu3btcHR0JCgoCFtbWxWkVJ25c+eSM2dOhg0bJncUIR08PDyoUKEC1atXV0v7CoWChg0bcv78ec6cOUPOnDlp2bIlVatW5dChQxmiqDl8+DB9+vShX79+zJw5U+44QhaQpYsZXZ20vWOXGPcN5f+f8/zm4Z0/PxYfx/tnjwDIZmCYpvb00ti/IAiCJi1cuJCePXvSo0cPfH19Vdp2yZIlsbe3T/dGACEhIdjZ2bFu3TpWrlzJnj17yJ07t4pSqk7u3Ln5/fffOXDgAEePHpU7jvALPnz4wOHDh3F3d1f7iJ9CoaBBgwacO3eOc+fOkSdPHlq3bo2NjQ0HDhxAqdTOmR0XLlygY8eOtGnThtWrV2vFyKiQ+WXpYiannk6avgFGefNR3LY2ALd99rG4ZQ0WNrMl6nUoALbNOqa6LR3ASC9Lf/sFQdByCoWCjRs34uTkRNu2bbl69apK23d3d+f8+fM8efLkl+4/ePAgNjY2REREcOnSJQYPHqzVD08dOnTAycmJwYMHExMTI3ccIY22b9+OQqGgW7duGu23fv36nDlzhgsXLmBqakrbtm2pUqUK+/bt06qi5vbt2zRv3py6devi5eWFrq6u3JGELCJLP02bG+qlec1M96We1OsxBNNiVnwJf09yYiJFKlSlw+y11O7QO9XtKCWJ5E/vM8SQsSAIWZeenh579uzB1taWpk2bEhwcrLK227RpQ+7cudm8eXOa7ktISGDEiBG0adOGhg0bEhQURLVq1VSWS10UCgWrV6/mw4cPTJ8+Xe44QhpIkoSHhwetW7cmX758smSws7Pj1KlTBAQEYG5uTvv27alUqRK7d+9W21bqqfXs2TOcnJwoVaoUhw4dIkeOHLLmEbKWLF/MpJWBcR6ch01l1MGrzLz8gqnnnjDQ0xcbl3Zpa0ihoG/HNhQtWpQ+ffpw4MABPn/+nOY8giAI6mZgYMDRo0cpVKgQTZo04eXLlyprt1u3bmzdujXV2xaHhoZiZ2fH6tWrWb58Ofv27SNPnjwqyaMJJUqUYOrUqSxbtkylhaGgXleuXOHhw4cqX/j/K+rWrYu/vz+XL1+mcOHCdOrUiYoVK7Jr1y5ZipqwsDAcHR3JnTs3J06c0NihtILwXZYuZswMdNHXlWdKQg4d2Lx8Ee3atSMgIIC2bdtiampK/fr1mTdvHrdv3xajNoIgaI08efLg5+eHnp4ejo6OREREqKRdd3d3wsLCOHHixH9ee/jwYWxsbPjw4QOXLl1i6NChWj2t7GdGjRrFvHnzsLKyEq/zGYSHhweWlpY4ODjIHSVF7dq18fX15erVq1haWtKlSxcqVKjAjh07NFbUfPr0CScnJ+Li4vDz8yN//vwa6VcQ/ipLFzO6CgU2pvpo+lehArA1M6CJoyPLli3j4cOHPH/+nJUrV5InTx7mzJmDjY0NFhYW9OrVi927dxMVFaXhlIIgCD8qWLAgJ0+e5OPHj7i4uPD169d0t1mlShVsbW3/dSOAhIQERo4cSatWrbC3tycoKEhtu0lpQvbs2Rk9ejQ5cuTIkMVYVvPlyxe8vb3p3bs3Ojra99hUs2ZNTpw4wbVr17CysqJbt25YW1uzfft2tR7U+u3bN1q2bMmLFy/w8/PD0tJSbX0Jwr/Rvp9KDatiqo+m3xeT/n+/f1W8eHH69+/P4cOHiYyM5PTp03Tr1o0bN27QqVMnzMzMqFOnDrNmzeLGjRtatehPEISso2TJkvj6+vLo0SPatGlDfHx8utt0d3fnxIkTvH379m+fe/HiBfXq1WPVqlUpu4HlzZs33X3KTRQxGcfu3buJi4ujZ8+eckf5VzVq1ODYsWPcuHGDsmXL4urqSrly5di2bZvKi5qkpCQ6d+7M9evXOX78OBUqVFBp+4KQFlm+mMmdXZfK+XJobHRGAVTOl4Pc2X++y0eOHDlwcHBg0aJF3Lt3j5cvX7J+/XosLCxYvHgx1atXx9zcnO7du7Njxw7Cw8M1lF4QBAFsbGw4cuQIFy9epEePHume0tK5c2eyZcvGtm3bfvj40aNHsbGxISwsjICAAIYNG5aligDxppV28PDwoEmTJhQpUkTuKKlStWpVDh8+TFBQEBUqVKBnz56ULVuWLVu2kJiYmO72JUmiX79+HDt2jH379lGnTh0VpBaEX5flixkAh0JGGOkp1F7QKAAjPQUOhYzSdF+RIkVwd3dn3759REREcP78edzd3bl//z7dunWjQIEC1KhRg2nTpnHlyhXZdzURBCHzs7e3Z9euXezdu5ehQ4ema+1Hnjx5aN++PR4eHiiVShITExkzZgwtWrTAzs6OoKAgatSoocL02m/x4sWMHj2aUaNGpWo9kaAe9+7d49q1a1qx8D+tbGxsOHjwILdu3aJy5cr07t2bMmXK4OHhka6iZsKECWzevJmtW7fi4uKiwsSC8GtEMQPk0NWhabFcap9uJgFNi+Uih+6vf9uzZctGvXr1mDt3Lrdu3eLt27ds3ryZEiVKsHLlSurUqYOZmRmdOnVi27ZthIWFqe4LEARB+IvWrVuzfv161qxZk+6Tvt3d3Xn27Bl79+6lfv36/P777yxZsoRDhw5hYmKiosTaz8PDg/z58/P777/z6tUrLly4wPDhw1m6dKnc0bIkDw8PzMzMaNasmdxRflmVKlXYv38/d+7coWrVqri7u1OqVCk2bNhAQkJCmtpasmQJCxYsYNmyZRo/b0cQfkYhia1UUtwM/8bJ1+o7yMyxsBG2ZgZqaz8pKYnr16/j4+ODr68vN27cQJIkbGxscHJywtnZmVq1apEtWza1ZRAEIeuZN28eEydOZPXq1QwcOPCX2pAkicKFCxMREYG5uTm7d++mVq1aKk6qvaKjo+nSpQt+fn4MGzaMPn36ULRoUXLkyMH+/fvp06cPISEh5M6dW+6osjAzM2PkyJFMmDBBY33Gx8djYWFB7969WbRokcb6Vbf79+8za9Ys9u7dS5EiRZgwYQK9evX6z7Nhtm3bRs+ePZkwYQJz587VUFrt0qNHD0JCQrhw4YLcUYS/ECMzf1HVzIDGhf+cAqaqKWff21F3IQN/Hm5Xu3ZtZs6cSWBgIO/fv8fLywtra2s2btxIvXr1MDMzo127dnh4ePD69Wu15hEEIWsYP348I0aMYPDgwezZsyfN9ycmJjJ+/Hjevn1LUlISZ8+ezVKFTGxsLAMHDuTFixccP36c+fPnU6pUqZSHy3LlylGoUCFevHghc9Ks5dChQ0RFReHm5iZ3FJWqUKECu3fv5v79+9SpU4eBAwdSqlQp1qxZ89MNPY4dO4abmxvu7u7MmTNHw4kF4d+JYuZ/VDUzoKOVsUrW0HxfI9PRyljthcw/MTMzo2vXrnh5efH+/XsCAwMZNWoUb9++pW/fvhQpUoRKlSoxduxYzp49m+bhZkEQBPhzZ67FixfTtWtXunXrxsmTJ1N976tXr7C3t2fp0qVMmzYNAB8fH3VF1UoPHz7k2LFjjBs3joYNG/5t+98dO3bw5s0bcYaHhnl4eFC3bl3Kli0rdxS1sLa2ZteuXTx48AA7OzuGDBmClZUVq1atIi4uLuW6gIAA2rdvT4sWLVi7dm2W2oRDyBhEMfMPihtnx906L5Xy/fmuWFp/bL9fXylfDvpY56W4cXaV5vsVOjo6VK9enSlTpnD58mXCw8Px9vbG1tYWT09PHBwcyJcvH61atWLdunXiHUBBENJER0eHzZs307hxY1q3bk1gYOB/3nPixAlsbGxS1oZMnz6d5s2b/+uZM5nRw4cPKVu2LG3btv3hQfHdu3csXLiQffv2MX/+fMzNzWVMmbWEhIRw8uTJDLnwP63KlSvHjh07CA4OxsHBgWHDhmFlZcWKFSsIDAykWbNm1KxZk507d6Knpyd3XEH4G1HM/IS+rg7ORXMxoHxeahUwQF/3/37B/O83TeeH+xTUKmDAgPJ5cS6avsX+6mRiYkLHjh3ZunUrb9++JSgoiIkTJxIVFcXgwYOxtLSkXLlyjBw5En9//x/epREEQfgn2bJlY+/evVSuXBkXFxcePXr0j9clJSUxfvx4mjZtSs2aNbl16xa1a9cG/twI4Pbt2wQFBWkyuqwKFy7MgwcPCAkJSfnYsWPHmD59Ojt37qRbt25af8ZJZrNlyxZy5cpF+/bt5Y6iMWXKlMHT05NHjx7RuHFjRowYQe3atcmZMye7du1CX1//vxsRBBmIDQBSKVmSCP+WTFhsEmGxScQkKUlSSujpKDDS08HcUA9zQz3MDHTRzeBDsJ8/f+bUqVP4+vri4+PDmzdvMDAwoEGDBjg7O+Pk5ETJkiXljikIgpaKioqiXr16fPnyhcuXL1O4cOGUz71+/ZrOnTtz5coV5s2bx6hRo36YVpWUlETRokVp1aoVa9askSO+LNq2bcvjx48pUqQIERER6OrqoqOjw7Jly7LcttT/RJMbACQnJ2NpaYmLiwvr169Xe3/a6P3799SsWZOoqChiYmIwMzNjzJgx9O/fHyOjtB0vkZmIDQC0kyhmhH8lSRIPHjxI2SHt4sWLJCYmUrJkyZQd0uzt7TE0NJQ7qiAIWuTNmzfUrVsXQ0NDLl68SL58+fD19aV79+7kyJGD3bt3U7du3X+8d9KkSaxevZq3b99mmdeW2NhYjh8/zt69e7GysqJmzZo0bdpU7D75/2mymPHx8cHFxYVr165lyULyy5cv2Nvb8+7dOy5dugTA3Llz2bZtGyYmJowePZqBAwdmyaJGFDPaSRQzQpp8/fqVs2fP4uPjg4+PDy9evCBHjhzUr18/ZdSmTJkyYoGgIAg8fvyYunXrUqJECerVq8fixYtxdnbG09MTU1PTn9737NkzSpYsiaenJ927d9dgYkFbabKYadeuHY8fP+bOnTtZ7ndZXFwczs7O3Lp1iwsXLlCpUqWUz4WGhjJ37ly2bNlCnjx5GD16NIMGDSJnzpwyJtYsUcxoJ+1c0CForVy5cqXsaBISEsLDhw+ZN28e8Of2rOXKlaNEiRIMGDCAI0eOEB0dLXNiQRDkUrp0aTw9Pblx4wZLlixh9uzZHDt27F8LGQArKysaNGiQ5TYCEOT34cMHDh8+jLu7e5YrZJKTk+nSpQtXr17l6NGjPxQyAJaWlmzYsIGnT5/Stm1bpkyZgqWlJfPmzePr168ypRYEUcwI6aBQKChbtiwjRozAz8+PyMhIjh8/TrNmzTh16hQtW7bExMSEhg0bsmjRIu7fv48YCBSErMPf358ePXpgYmKCrq4uDx48SPW97u7uXLhwgcePH6sxoXZLTk4mLi6OiIgIuaNkGZ6enujo6NC1a1e5o2iUJEkpb0Lu2bMHOzu7n15brFgx1q1bx9OnT+nYsSPTp0/H0tKSOXPm8OXLFw2mFoQ/iWJGUBkjIyNcXFxYuXIlT5484cmTJyxduhR9fX2mTZtGxYoVKVq0KH369GH//v18/vxZ7siCIKhBUlISU6ZMwcnJCVtbW4KDg9m1axfe3t4MHz48VW9qtG7dmjx58rB582YNJNZejo6OdOrUSbwRpAGSJOHh4UGbNm3Ily+f3HE0avLkyWzcuBEPDw+aN2+eqnuKFi3K6tWrefbsGV26dGHWrFlYWloya9Ys8ftd0CxJEDTg27dvkp+fnzRixAipbNmyEiDp6upKdnZ20ty5c6Vbt25JSqVS7piCIKTT27dvpfr160s6OjrS3LlzpeTk5JTPrVu3TgKkWbNmpaqtwYMHS+bm5lJCQoK64mo9Hx8fCZC8vLzkjiIrU1NTae7cuWrtIyAgQAKkkydPqrUfbbNs2TIJkBYtWpSudl6/fi0NHTpUypEjh5QnTx5p+vTp0sePH1UTUku4urpKdnZ2cscQ/ocoZgRZhISESGvXrpVatmwp5cyZUwIkc3NzqWfPnpK3t7cUGRkpd0RBENLI399fyp8/v1SwYEHp/Pnz/3jNrFmzJEBat27df7Z369YtCZAOHTqk6qgZSocOHSQzM7Ms/bqoiWKmV69ekqWl5Q8FeGbn5eUlAdKYMWNU1ubbt2+l4cOHS/r6+lLu3LmlqVOnSlFRUSprX06imNFOopgRZBcfHy+dPn1aGjNmjFShQgUJkHR0dKTatWtLM2fOlAIDA7PULxdByGiSkpKkKVOmSAqFQnJ0dJTev3//02uVSqU0ZMgQSaFQSHv37v3PtqtWrSo1a9ZMlXEznLdv30rGxsZSnz595I4iG3UXM58/f5YMDQ1TPWqYGZw4cULS09OTevXqpZaZEe/evZNGjhwpGRgYSMbGxtLkyZMzfEEuihntJIoZQeu8evVK2rhxo9S2bVvJ2NhYAiRTU1Opa9eukpeXl/Thwwe5IwqC8P+9fftWatCggaSjoyPNnj07VW88JCcnS507d5ayZ88unT59+l+vXbt2raSjoyO9fv1aVZEzpNWrV0uAFBAQIHcUWai7mFm/fr2ko6MjvXr1Sm19aJPLly9LBgYGUosWLaTExES19hUWFiaNHj1aMjQ0lHLmzClNmDBBCg8PV2uf6iKKGe0kzpkRtFpiYiJXr15NObTz1q1bKBQKqlWrlnJoZ40aNdDV1ZU7qiBkOadPn6Zr164oFAp27dqFvb19qu9NSEigRYsWXLp0iXPnzlG1atV/vO7z588ULFiQyZMnM3HiRBUlz3iSk5OpU6cOsbGxBAUFZbnDNNV9zkzNmjUxNTXl+PHjamlfmzx48AA7OzsqVKiAn58fBgYGGun3w4cPLFmyhNWrVwMwePBgRo0ahZmZmUb6VwVxzox2EruZCVotW7Zs2NnZMXfuXIKCgnj79i1btmyhRIkSrFq1ijp16mBmZkanTp3Ytm0bYWFhckcWhEwvOTmZGTNm0LhxYypWrMjt27fTVMgAZM+enf3791O+fHmcnZ1/ugVz7ty5ad++PZs3b0apVKogfcakq6vL+vXrefjwIUuXLpU7TqZy7949AgMDcXNzkzuK2r148QJHR0eKFCnCkSNHNFbIAOTPn58FCxYQGhrK0KFDWb16NcWLF2fs2LF8+PBBYzmEzEcUM0KGUrBgQXr06IG3tzfh4eFcvnyZIUOG8Pz5c3r16kXBggWxtbVl4sSJXLx4kcTERLkjC0KmEhYWhqOjIzNmzGD69On4+vpSoECBX2rLyMiI48ePY2pqiqOjI2/evPnH69zc3Hj27BmXLl1KT/QMr0qVKgwbNowZM2YQEhIid5xMw8PDg/z589OsWTO5o6hVeHg4jo6O6Ovr4+vrS548eWTJYWpqyty5cwkNDWX48OGsW7eO4sWLM3r0aN6/fy9LJiFjE8WMkGHp6upSu3ZtZsyYQWBgIO/fv8fLy4vy5cuzadMm6tWrh6mpKe3atWPTpk28fv1a7siCkKGdPXsWGxsbHjx4wKlTp5g6dWq6p3jmy5cPPz8/lEolTZo0ISoq6m/X2NnZUbduXYoXL56uvjKDGTNmYGpqyqBBg8TZMyoQFxfH9u3b6dGjB9mzZ5c7jtp8/foVFxcXPn/+jL+/PwULFpQ7Evny5WP27NmEhoYyatQoNm7cSPHixRk5cqSYZSGkiShmhEzDzMyMrl27sn37dsLCwggMDGT06NG8ffuWfv36UaRIESpWrMjYsWM5c+YMCQkJckcWhAxBkiTu3LlDo0aNsLa25vbt2zg4OKis/SJFiuDv709YWBjNmzcnNjb2h88rFApOnDjxyyNAmUnOnDlZtWoVPj4+7Nu3T+44Gd6hQ4eIioqid+/eckdRm/j4eFq3bs3jx4/x8fHByspK7kg/MDExYebMmYSGhjJ27Fg2b95M8eLFGT58OG/fvpU7npABiA0AhCwhKiqKkydP4uvri6+vL2FhYeTMmRMHBwecnZ1xcnLC0tJS7piCoNUOHz5Ms2bN1LbhRmBgIA4ODtSvX59Dhw79sMhdkiQUCoVa+s2IWrduzbVr13j48CG5c+eWO47aqWsDgMaNGxMXF8fFixdV2q62SE5OplOnThw9ehQ/Pz/q168vd6T/9OnTJ1asWMGyZcv49u0bffv2Zdy4cRQqVEjuaGIDAC0lRmaELMHExISOHTuyZcsW3rx5w61bt5g4cSKfPn1i8ODBFC9enHLlyjFixAj8/f2Ji4uTO7IgaMT9+/dZsGAB8+fP59GjRyQlJf3jdZIk0bJlS7XuHFijRg0OHjzIyZMn6d279w8L/kUh86MVK1bw9etXJk+eLHeUDCskJIRTp05l2oX/kiQxePBgDhw4gLe3d4YoZADy5MnD1KlTCQ0NZfLkyXh5eVGiRAkGDRrEq1ev5I4naCFRzAhZjo6ODlWqVGHChAmcP3+eyMhI9u/fz2+//cbevXtp0qQJJiYmNG3alJUrV/L06VO5IwuCWixfvpzffvuNS5cusWbNGlq3bs3du3f/8VpNFRONGzfGy8uL2NhYpD/PQtNIvxlNkSJFmDlzJqtXr+b69etyx8mQtmzZQq5cuWjfvr3cUdRi+vTprFu3jg0bNtCqVSu546RZ7ty5mTx5MqGhoUybNg1vb29KlizJgAEDePnypdzxBC0ippkJwl9IksSDBw/w9fXFx8cnZUe0kiVLppxrY29vj6GhodxRBSFdpkyZwrZt29i4cSOOjo7Anw8Pixcvpm/fvjKnI6WQ0dER77n9TFJSEjVq1ECSJK5fv46enp7ckdRG1dPMkpOTKVasGM2aNWPdunUqaVObrFy5kqFDhzJv3jzGjx8vdxyV+Pr1K6tXr2bx4sV8+fKFXr16MWHCBI1OERfTzLST+C0hCH+hUCioUKECo0eP5vTp00RFRXH48GEaNWrE0aNHadq0KSYmJjRp0oRly5bx6NEj8c6xkOHExMSgVCrZuHEjTZo0QaFQoFAocHBwoFixYjx//lz2bc0VCsV/FjIPHjzgwoULXLt27afT4zIzPT091q9fz507d1i5cqXccTIUPz8/3rx5kymnmO3atYuhQ4cycuRIxo0bJ3cclcmVKxfjx48nNDSUOXPmcPDgQUqVKkWfPn3EVuVZnBiZEYRUkiSJP/74I2XU5vz588THx2NpaYmTkxNOTk44ODiQK1cuuaMKwn8KCwsjd+7cKYfmNWvWDH9/fypVqpSyq9i0adMwNzeXOek/c3d35+rVq4SEhFCyZEmMjY0z7SLu/zJkyBC2bNnCw4cPKVKkiNxx1ELVIzNt27bl6dOn3L59O1Otx/Lz86NZs2Z07tyZrVu3ZuqRzZiYGNatW8fChQuJiorC1dWVSZMmUaJECbX1KUZmtFPm/VcuCCqmUCgoW7Ysw4cPx8/Pj6ioKI4fP07z5s05deoUrVq1Il++fDg4OLBo0SLu3bsnRm0ErWVubo6BgQFKpZLFixfz/v17AgMD8ff3Z/r06Vy5coWTJ0/KHfNvIiMjqVOnDidOnGDkyJFcvHiRo0ePkpSURLdu3eSOJ4vZs2djbGzM0KFD5Y6SIbx//54jR47g5uaWqQqZa9eu0aZNG5o0aYKHh0emLmTgz0N3R40aRUhICAsXLuTEiROULl2aXr16ibWuWUzm/pcuCGpkaGiIi4sLK1as4MmTJzx58oSlS5diaGjItGnTqFSpEkWKFMHd3Z39+/fz+fNnuSMLwt/o6OgwYMAArly5QpUqVTAxMcHNzY23b99q3SLbhIQEHBwciI6OxtfXlx49emBra0vRokXp2bMn4eHhsk+Pk0Pu3LlZvnw5hw4d4vDhw3LH0Xrbt29HV1c3UxW/Dx8+xMXFhSpVqrBnz54ftjXP7AwNDRkxYgTPnz9nyZIl+Pn5UaZMGVxdXXn8+LHc8QQNEMWMIKhIyZIlGTx4MMeOHSMqKgp/f386dOjA5cuXadeuHfny5aNevXrMmzePW7duiVEbQWsYGhr+sHj8yZMnlC5dmjp16siY6kdJSUl0794dpVKJt7c3lSpV+mGb6KCgIF6+fElycrKMKeXTrl07nJ2dGTJkCNHR0XLH0VqSJLFp0ybatGmDiYmJ3HFU4tWrVzg6OmJhYcGxY8ey7AY1BgYGDBs2jOfPn/P7779z+vRpypUrR7du3Xj06JHc8QQ1EsWMIKiBvr4+jRs3ZunSpQQHBxMSEsKqVaswMTFh7ty52NraYmFhQc+ePdm9ezdRUVFyRxayiH8qov861SY0NJT+/ftjaGhIlSpVNJjs371//57g4GCGDRuGtbX1D587fPgwFy5cYMiQIejr68uUUF4KhYLVq1cTERHBtGnT5I6jtS5dusQff/yRaRb+R0RE4OjoiK6uLn5+fuTNm1fuSLLT19dnyJAhPHv2jBUrVnD+/Hmsra3p0qULDx8+lDueoAaimBEEDbC0tKR///4cOnSIyMhIzpw5Q/fu3QkKCqJTp06YmZlRp04dZs6cyfXr1384LFAQVOXu3bs//bd1+fJlxowZg52dHXnz5sXf31+rHow+fPhAZGQkNWvW/OHjJ06cYNOmTZQsWTJTTRv6FcWLF2fatGksX76c27dvyx1HK3l4eFC8eHEaNGggd5R0i46OpmnTpkRGRuLv74+FhYXckbSKvr4+gwYN4unTp6xZs4aAgADKly9Pp06dePDggdzxBBUSxYwgaFj27Nlp0KABCxcu5O7du7x69YoNGzZgYWHBkiVLqFGjBgUKFKBbt254eXkRHh4ud2Qhg1MqlSxcuBBbW1s2bNjwj9dUr16dmJgYFixYwP79+zWc8L/lypWLbNmy/bCwd82aNfz+++/ExMSwY8cOjI2NZUyoHUaOHEm5cuXo169flp1y9zNfvnxhz5499O7dO8Mvjk9ISKBt27Y8fPgQHx8fSpcuLXckrZUjRw769+/P06dPWbduHVevXqVixYp06NCBe/fuyR1PUAVJEAStkZCQIF24cEGaOHGiZGNjIwGSQqGQqlWrJk2ZMkW6fPmylJSUJHdMIQOJiIiQmjZtKgHS+PHjpcTExJ9eq+3/tlasWCEZGxtLTZs2lYoVKyaVLVtW6tOnj/Tt2ze5o2mVS5cuSYC0evVquaOojKmpqTR37tx0tbF+/XpJR0dHevXqlYpSySM5OVnq2LGjlD17dun06dNyx8lw4uPjpY0bN0qWlpYSILVt21a6c+dOqu51dXWV7Ozs1JxQSCtxzowgaLGwsDD8/Pzw9fXF39+fqKgo8ubNi6OjY8rZNtp6DoggvytXrtCxY0diY2Px9PTExcVF7kjpdvjwYZ49e8aHDx9o27YtVatWzfDvsqtD37592b17Nw8fPswU049Ucc5MjRo1yJ8/P8eOHVNhMs2SJImhQ4eyZs0a9uzZQ9u2beWOlGElJiayfft25syZw/Pnz2nVqhVTp07Fxsbmp/eIc2a0kyhmBCGDSE5O5vr16/j4+ODj48ONGzeQJIkqVarg7OyMk5MTtWvXzlJbcgr/TJIkli5dyvjx46lRowbe3t6Z9jBF4Z9FRUVRrlw57O3t2b17t9xx0i29xczdu3epXLkyBw4coHXr1ipOpzkzZ85k2rRprF+/nr59+8odJ1NITExkx44dzJ49m2fPntGiRQumTZuGra3t365VZTGTLEmEf0smLDaJsNgkopOUJCsldHUU5NTTwdxQD3NDPcwMdNHNROchqYMoZgQhgwoPD8ff3x9fX1/8/PwIDw/H2NiYRo0apRQ3hQsXljumoGFRUVH07NmTo0ePMnbsWGbPnp0lC9yQkBBMTEzInTu33FFks2PHDrp164aPjw9OTk5yx0mX9BYzw4YNw9vbm9evX2fYn4e1a9cycOBAZs+ezaRJk+SOk+kkJSWxc+dOZs+ezZMnT2jWrBnTpk2jWrVqKdeoopj5nJDM7Yg4bkXEEZf85yO4DvDXrVn++md9XQU2pvpUMdUnd3ZdhL8TxYwgZAJKpZKgoCB8fHzw9fXl6tWrKJVKKlSogJOTE87OztStW5ccOXLIHVVQo6tXr9KxY0eio6PZtm0bzZo1kzuSRt29e5cNGzZw+PBhjIyMMDAwoE2bNvTt25cCBQrIHU/jJEnC0dGRZ8+ecf/+/Qx9/kh6ipm4uDgsLCxwd3dn4cKFakinfnv37qVjx44MGTKE33///Yft1AXVSkpKwtvbm9mzZ/PHH3/g4uLCtGnTqFGjRrqKmbhkJWffxHAnMh4FkJaH7+/XV86XA4dCRuTQFVNr/0oUM4KQCUVFRXHq1KmU4iYsLAwjIyMaNmyYUtxYWlrKHVNQEUmSWLZsGePGjaN69ep4e3tTtGhRuWNpTGRkJF26dOHkyZPY2NjQunVrSpYsyc2bN7l27RqWlpZ4enrKHVMWT548oWLFiowcOZK5c+fKHeeXpaeY8fb2pnPnzjx69IgyZcqoIZ16nTp1ChcXF9q3b8/27dvFGjENSU5OZvfu3cyaNYtHjx7h5OSEQqEgOjo6zcVMyJcEjr34SmySlKYi5n8pACM9BU2L5aK4cfZ0tJS5iGJGEDI5SZK4c+cOvr6++Pj4cOnSJZKTkylbtmxKYVOvXr0se9hgRvfx40d69uzJkSNHGD16NHPnzs2w02h+xdevX2nTpg3ZsmVj2LBh1K1bFyMjo5R3rq9cuYKjoyNRUVFZ6vvyVzNnzmTWrFncvn2b8uXLyx3nl6SnmGnUqBEJCQkZctH29evXadCgAXZ2dhw+fJjs2cUDrKYlJyezb98+Zs6cSXBwMHny5OH48ePUqVMnVfffDP/GydcxaR6N+Znv7TQubERVMwMVtJjxifJeEDI5hUJBlSpVGD9+POfPnycyMpL9+/fz22+/sW/fPpo0aYKJiQlNmzZl5cqVP5zjIWi3wMBAbGxsuHjxIkeOHGHRokVZ7oF94cKFPHz4kEWLFuHo6EjOnDl/mIKTkJBA0aJFefLkiYwp5TVu3DisrKzo169fljuQ9/nz55w+fRo3Nze5o6TZo0ePcHFxoWLFiuzbt08UMjLR1dWlY8eO3Lt3j3r16pGQkEDdunVp3LgxAQEB/3rv90IGVFPI/LWdk69juBn+TUWtZmyimBGELCZ37ty0adOGjRs38vLlS+7du8fMmTOJi4tj1KhRlCpVipIlSzJkyBCOHz9ObGys3JGF/yFJEr///ju//fYb5ubm3Lp1i+bNm8sdSxaXL19m/PjxlC9f/m/rCJ49e8bIkSMpWLAgZcuWlSmh/HLkyMG6deu4dOkSmzdvljvOL3F0dKRkyZJpvm/Lli0YGxvTrl07NaRSn9evX9OkSRPy58/P8ePHMTIykjtSlqejo4OjoyO9evXi7NmzFCpUiAkTJjBs2DBu3779t+tDviSkFDLqcvJ1DCFfEtTaR0YgppkJgpAiOjqaM2fOpExJCw0NJUeOHNSrVy9lh7SyZcuKxacy+vjxI7179+bQoUOMHDmSefPmZel3bLt37050dDQ7duzA0NCQr1+/8vDhQwICAjhx4gRxcXFs2bKFUqVKyR1Vdt+nIz569Ij8+fPLHUftkpOTKVasGM2aNWPdunVyx0m1qKgo7OzsiI6O5tKlS2JXygwoLlnJxuCP6V4j81++r6HpY503S28KIIoZQRD+kSRJPH78OGUTgXPnzhEfH0+xYsVS1to4ODiQK1cuuaNmGdevX6dDhw58+vSJrVu30rJlS7kjye7hw4e0bduWXLlyUbhwYXR0dHj//j2RkZE0bNiQUaNGUaxYMbljaoWIiAjKlClD06ZNs8SGCCdOnKBp06Zcv379h+11tVlMTAyNGjXi6dOnBAQEZMgNCwQ48eIr96Li1VrIfKcAKuXLgXPRrPu7WBQzgiCkSmxsLOfPn08pbp48eUK2bNn47bffUkZtKlSoIEZt1ECSJFatWsWoUaOwsbFh9+7dYje6v3j69CnLly8nKioKAwMDGjdujLOzM8bGxnJH0zqbN2/Gzc2N06dP4+DgIHcctWrTpg3Pnz/n1q1bGeJ1KTExkRYtWnDx4kXOnj1L9erV5Y4k/IJP8cmsC/6o8X4HlM+bZc+hEcWMIAi/5NmzZymFzZkzZ/j27RuFChVKGbVp2LAhefLkkTtmhvfp0yfc3Nw4cOAAw4cPZ8GCBVl6WtmvkiQJSZJQKBQZ4sFWXSRJon79+oSFhXH37t1Mu4thTEwMefLkYenSpQwZMkTuOP9JqVTi6urKnj17OHHiBI0aNZI7kvCLzr+N4er7b6kelbnjd5AL21byIeQJ2XLoY1X9N5yGTiVfkeKp7lMB1CpgQH2LrLm2ShQzgiCkW1xcHBcvXkwpbh4+fIiuri516tRJKW4qV64szkdIo5s3b9KhQwciIyPZsmULrVu3ljtShhUbG8ucOXOIi4tj8eLFWbqgCQ4OpkqVKkycOJHp06fLHUctHjx4QNWqVXn79i0mJiZyx/lXkiQxYsQIVqxYgbe3Nx06dJA7kvCLkiWJlfeiiEtO3aP19UNeHJg5AoC8hYoR+zmK+Oiv5DQxY6j3WXKZpv6wX31dBUMqmqCbBV/bxJOFIAjppq+vT+PGjVm6dCnBwcGEhISwevVqTExMmDdvHra2tlhYWNCzZ0+8vb2JioqSO7JW+z6trE6dOpiYmHDr1i1RyKSBUqnkf9+nMzQ0pGzZsixdupSJEyfKlEw7WFtbM3bsWObNm8cff/whdxyVkySJZ8+e0aZNG60vZADmzZvH8uXLWb16tShkMrjwb8mpLmSSEhPwWzEbgAoNmzH26A1G7r9MDqOcREeFc3bz72nqOy5ZIvxbclojZwqimBEEQeUsLS3p168fhw4dIjIykjNnztCjRw+CgoLo3LkzZmZm1K5dm5kzZxIYGJjlzr74N58/f6ZDhw4MGTKE/v37ExAQQPHiqZ9uIEBYWBgTJkwgMTHxh493796dpUuXMn/+fJYuXSpTOu0wadIkihQpwsCBA/9W+GmjGzdu0LVrVywtLcmRIwcGBgaUL1+e8ePH8+nTp79d/+XLF9zd3TUfNI02btzIpEmTmD59OgMGDJA7jpBOYbFJqb729YNbxHyKBKB8wz+31jc2M6dIxT83q3h8+Yxa+89MRDEjCIJaZc+enQYNGrBgwQLu3r3L69ev2bBhA4ULF2bp0qXUrFmTAgUK0K1bN7y8vAgPD5c7smyCgoKoWrUq/v7+7Nu3j+XLl5MjRw65Y2U4ERERLFiwgGPHjv3tcyNGjGD8+PGMGjWK7du3y5BOOxgYGLBmzRrOnDmDl5eX3HH+1YkTJ6hbty6vX7/G0dGRGjVqoFAocHJy4sqVK9jY2PD27duU65VKJTlz5sTe3l6+0Klw4MAB+vfvz6BBg5g6darccQQVCItNSvWD9ef3//dvNmde0//7fxOzPz8f9iZNfesgihlBEASNKFSoEG5ubuzdu5fw8HAuXrxI3759CQ4Opnv37hQoUIDq1aszdepULl++TFJS5n9xliSJNWvWULt2bfLkyUNQUBBt27aVO1aGValSJapXr46Hh8c/fn7u3Lm4ubnRq1cvjh8/ruF02sPR0ZFOnToxcuRIIiMj5Y7zU+PHj2f69OmcP3+eDRs2cPHiRUaNGsWrV684f/48DRs2ZNy4cSnXS5KElZWVVq/RO3v2LJ07d6Z9+/asWLEiS6/hyihiYmLYsmULkyZNYtWqVf/4MxOdpCTd8wx+caRUCcQkZc1ZDtr7ky4IQqb3fWvnOXPmEBQUxLt379iyZQslS5Zk9erV1K1bl/z589OxY0e2bt3Ku3fv5I6scl++fKFTp04MGjSIvn37cunSJaysrOSOleG5ubnh4+PDmzd/f3dToVCwbt06mjdvTvv27bl06ZIMCbXDsmXLSExM/KEY0DZPnz7921qSdu3acf78eQAGDBiAj4/PD5/X5p+hoKAgWrZsib29PZ6enlpddGVl1tbWKes7X716RYUKFRgxYgQnT55k2rRpWFtbExIS8sM9ycrUFyK5C1ik/H/0x4i//X9u80JpzpyUhv4zE/ETJAiC1jA3N6dHjx7s2rWLDx8+cOXKFYYMGUJoaCi9e/fGwsICGxsbJkyYwIULF/62JiKjuX37NlWrVsXHx4c9e/awcuVKMa1MRTp37oy+vj5bt279x8/r6emxa9cuatSoQbNmzbh3755mA2oJc3Nz5s+fj4eHBxcvXpQ7zj8qXrw4165d++FjgYGBFCr058OeqakpsbGxKZ9TKBQYGhpqNGNqPXnyBCcnJ8qVK8f+/fvFNuta7NGjRykzAyZMmICFhQUvXrwgMDCQFy9eUKlSJSZNmvTDPbo6qR9hK1zeBsM8f25Q8eD0UQC+hIfx6t4NAErXSfs5UHpp6D8zEVszC4KQIYSHh3Py5El8fHzw8/MjPDwcY2NjGjVqlLL9c+HCheWOmSqSJLFhwwaGDRuGtbU1e/bsoWTJknLHynR69uzJhQsXePr06U/f/f78+TP29va8f/+ey5cvZ8nDSJVKJXXr1uXLly/cunVL6x6wN2/ezLBhw3B3d6dMmTI8fPiQjRs3smLFCtzd3Tl16hSjRo3izp07wJ9fjzaOdrx9+5a6deuir6/PxYsXMTU1/e+bBNno6OgQFhZG/vz5sbKyYt26dTRu3Djl85cvX6ZTp068fPky5WO+L6O5GxmX6qlmgfs9OThnFPDj1sxGefIxdPc5jM3MU58XqJRPH6eiOVN9T2YhihlBEDIcpVJJUFAQvr6++Pj4cPXqVZRKJRUqVEgpbOrWrauVoxxfv36lb9++eHt7M3DgQJYsWZJpDy6UW0BAAHZ2dpw6dYqGDRv+9Lr3799Tt25ddHR0CAgIIH/+/BpMqR3u3r2Lra0ts2bNYsKECXLH+Rtvb29WrVrFixcvKFq0KP3796d79+4AhISE8PHjR2xtbQFSDkfVJh8/fqRevXp8+vSJS5cuUbRoUbkjCf9BR0eH9+/fY2ZmRqFChfDz86NChQopn3/x4gVly5bl27dvKR+7HRGH76voNPVz68Q+Lm5fTXjIE/Sy58Cqhh1OQ6dgWjTtUyWdiuSkimnW+30iihlBEDK8jx8/cvLkSXx9ffH19eXdu3cYGRnRsGHDlOJGG95xv3PnDu3btycsLIyNGzfSsWNHuSNlapIkUa5cOWxtbdm5c+e/Xvv8+XPq1q2LhYUFZ8+exdjYWEMptceYMWNYtWoVDx48oESJEnLHyTRiY2NxdHTk4cOHXLx4EWtra7kjCamgo6NDhQoV0NPT48mTJ2zduvWHjVkuXLhAly5deP36dcrHwmKT2PrHJxnS/qlnmTyYG+rJ1r9cRDEjCEKmIkkSd+/excfHBx8fn5Qd0cqUKYOzszNOTk7Ur19fo6MhkiSxceNGhg4dSrly5dizZw+lSpXSWP9Z2aJFi5gyZUqqToK/e/cu9erVw9bWlhMnTmS5EbOYmBisra0pV64cPj4+Wje6kRElJibSunVrzp49y+nTp6lVq5bckYRUmjFjxg9/rlWrFk2aNEn585gxY3j9+jW7du1K+ViyJLHyXlSqD85UJX1dBUMqmqCbBX9uRTEjCEKm9vnzZ06fPp0yJe3169cYGBhgb2+fUtyos7D4+vUr/fv3Z+fOnfTv359ly5ZluYdkOb1//z7lTKMhQ4b85/UXL17E0dGRpk2bsnv3bnR1dTWQUnscO3aM5s2b4+3tLUYO00mpVNKrVy927tzJsWPHfngQFjKv829juPr+G5p8uFYAtQoYUN/CSIO9ag9RzAiCkGVIkkRwcDA+Pj74+vqm7IhmZWWVMh3N3t4eIyPV/EK4e/cu7du35+3bt2zcuJFOnTqppF0hbdq0acPTp0+5c+dOqkYbjh49SuvWrXFzc2PdunVZboSibdu2XL58mYcPH5InTx6542RIkiQxevRoli1bxo4dO+jcubPckQQN+ZyQzNoHHzXe74DyecmdPWu9+fKdKGYEQciyoqOjOXv2bMqUtNDQUHLkyEG9evVSipuyZcum+WFWkiQ8PDwYMmQIpUuXZu/evZQuXVpNX4XwX06cOEHTpk0JDAykevXqqbpn69at9OrVi8mTJzNr1iw1J9Qur1+/ply5cri6urJ69Wq546SaNi38X7BgAePHj2fFihWpGhEUMhefl1+5GxmvkdEZBVApXw6ci+bSQG/aSRQzgiAI/Pkg9Pjx45TpaOfOnSM+Pp5ixYqlFDYODg7kyvXvvzCio6MZMGAAXl5e9O3bl99//x0DAwMNfRXCP0lOTqZYsWI0a9aMdevWpfq+RYsWMXbsWJYvX87QoUPVmFD7LF++nBEjRnDlyhVq1qwpdxy6dOlC69atad++/Q8fT05Opnfv3lSuXJmRI0fKlO5HHh4euLu7M2XKFGbOnCl3HEGFZs+ezbt37/j48eMPWzLr6OhQs2ZNFixYgI6ODvHJSjYGfyQmSVJrQaMAjPQU9LHOSw5d7duOXFNEMSMIgvAPYmNjOX/+fMqUtCdPnpAtWzZ+++23lOKmQoUKP7wTfP/+fdq3b8+rV69Yv349Xbt2lfErEP5qypQpLF++PGWnu9QaO3YsixYtwsvLK0v9fSYnJ1OjRg2Sk5O5ceMGenry7pBkZmbGyJEj/7Zt9PHjx2nWrBk3btygatWqMqX7P4cOHaJt27b06dOHtWvXas1IkZB+SqWSBg0acOPGjR8OadXV1aVAgQLcu3fvh01GQr4ksPvZF7Xn6mhlTHFj7TobStOybhknCILwLwwNDXF2dmbFihU8fvyYp0+fsmzZMgwNDZkxYwaVKlWiSJEiuLu7s3fvXlatWkWNGjXIli0bN2/ezFIPvhlB7969+fr1K/v27UvTfQsWLKBnz5707NkTX19fNaXTPrq6umzYsIF79+6xfPlyueP81KZNm6hcuXLKGTNyOn/+PJ06daJNmzasXr1aFDKZRHJyMt7e3lSsWJELFy6QI0cOLl26RO3atQFQKBQcOHDgb7slFjfOTuPC6l2Q71jYKMsXMiBGZgRBENIsLi6Oixcv4uvry/Hjx/njjz8AKFCgAP3796dFixZUqVJFK08hz8oaNWpEfHw8Fy9eTNN9SUlJtG3bllOnTnHq1KmUh5isYNiwYWzatImHDx/KetDjP43MhIWFUaRIEZYtW8bgwYNlywZw+/Zt6tevT7Vq1Thx4oRWHtgrpM33Imb27Nk8evQIZ2dn4M+pxBcuXODMmTM0bNiQZcuWMXz48J+2czP8Gydfx6AAlUw5+96OY2EjbM3EFGYQIzOCIAhppq+vT+PGjenduzc6OjoYGBjQq1cvateuzZIlS6hatSoWFhb06NEDb29vIiMj5Y4sAO7u7gQEBPDo0aM03aenp4e3tze2trY0bdqUBw8eqCmh9pk1axZ58uTRykXsnp6e6Orqyj4K+uzZs5Qt3g8dOiQKmQwuKSmJ7du3Y21tTbdu3bCysuLatWucOHECMzOzlOscHBx4/fr1vxYyAFXNDOhoZYyRnoL0jtV9XyPT0cpYFDJ/IYoZQRCEX7B161aqV6+Orq4uN2/eZPPmzRw8eJDIyEjOnj1Ljx49uH37Np07dyZ//vzUrl2bmTNnEhgYiFKplDt+ltSqVStMTEzYvHlzmu81MDDg6NGjFClShCZNmvyw+DczMzY2ZsWKFRw5coRDhw7JHSfF9x0D27ZtS968eWXL8e7dOxwdHcmdOzc+Pj7/uUGIoL2SkpLYtm0b1tbWuLq6UqZMGa5fv86xY8eoUaPGP95TqFChVLVd3Dg77tZ5qZTvz0I3rUXN9+sr5ctBH+u8YmrZ/xDFjCAIQhrExMTQq1cvevXqRefOnbl27RrlypVL+Xz27Nmxt7dnwYIF3Llzh9evX7Nx48aUgxtr1qxJgQIF6Nq1K15eXnz48EHGryZr0dfXp1u3bmzbto3ExMQ0358nTx58fX3Jnj07jo6OhIeHqyGl9mnTpg1NmzZl8ODBfP36Ve44AAQEBPD48WPc3d1ly/Dp0yecnZ2Ji4vD39//h3fthYwjMTGRLVu2ULZsWXr27Im1tTU3b97kyJEjVKtWTWX96Ovq4Fw0FwPK56VWAQP0df+vpPnfh3GdH+5TUKuAAQPK58W5aK4svWvZz4g1M4IgCKkUHBxM+/btCQ0NZe3atbi6uqbp/qSkJK5evZqyQ1pQUBAKhYKqVaum7JBWo0YN2XeOyszu3r1L5cqVOXDgAK1bt/6lNp4+fUrdunUpWrQoZ86cyRLvxoeGhmJtbU2/fv1YtmyZxvv/3zUzPXr0ICAggCdPnsiyNu3bt280adKE+/fvc/HiRcqXL6/xDEL6JCYm4unpyZw5cwgJCaF169ZMnTqVKlWq/PSeHj16EBISwoULF9Ldf7IkEf4tmbDYJMJik4hJUpKklNDTUWCkp4O5oR7mhnqYGeiiKzaT+HeSIAiC8J+2bdsmGRoaStbW1tKDBw9U0ua7d++kbdu2SZ06dZJMTEwkQMqbN6/UoUMHafPmzdLbt29V0o/wo+rVq0suLi7paiMoKEgyNjaWGjVqJMXFxakomXZbuHChpKOjI928eVPjfZuamkpz586VJEmSPn36JBkYGEhz5szReA5JkqTExESpRYsWkoGBgXTp0iVZMgi/Lj4+XtqwYYNkaWkpAVK7du2kO3fupOpeV1dXyc7OTs0JhbQSY1WCIAj/IjY2Fjc3N3r06EGHDh0IDAzE2tpaJW2bm5vj6urKrl27+PDhA1euXGHo0KGEhobi5uaGhYUFVapUYcKECZw/f/6XpkYJf+fu7o6vry+vX7/+5TZsbGw4fPgwFy9epHv37iQnJ6swoXYaPnw45cuXp1+/frJ+vbt27SI+Pp6ePXtqvG9Jkujbty8nTpxg37591KlTR+MZhF+TkJDA+vXrKVWqFP369aNGjRrcu3ePvXv3UqlSJbnjCekgihlBEISfePToETVr1mTXrl1s2bKFLVu2pOnAxbTQ1dWlVq1aTJ8+nWvXrvHhwwd27NhBpUqV8PDwwN7eHlNTU9q2bcvGjRt59eqVWnJkBZ06dUJfX5+tW7emqx17e3u8vb3Zv38/Q4cORcrks7azZcvG+vXruXnzJmvXrpUtx6ZNm3BxccHCwkLjfY8fPz7ltcDFxUXj/QtpFx8fz9q1aylZsiQDBgygTp063Lt3j927d1OhQgW54wkqIIoZQRCEf+Dl5UW1atVITk7m+vXrGn8X2NTUlC5duuDp6UlYWBg3btxgzJgxhIWF0b9/f4oWLUqFChUYM2YMp0+fJj4+XqP5MjJjY2M6dOiAh4dHuneWa9WqFRs2bGDNmjXMmDFDRQm1V+3atenbty8TJ07k7du3Gu//9u3b3Lx5U5aF/4sXL2bhwoUsW7aMbt26abx/IW3i4uJYtWoVVlZWDB48GDs7Ox48eMCuXbvEGqfMRu55boIgCNokNjZWcnd3lwCpe/fu0tevX+WO9DdRUVHS7t27pV69ekkFCxaUAMnIyEhq3ry5tGbNGun58+dyR9R6AQEBEiCdOnVKJe3NmzdPAqRVq1appD1tFhUVJeXPn19q166dxvr8vmZm8ODBUoECBaSEhASN9S1JkrR161YJkCZMmKDRfoW0i42NlZYvXy5ZWFhIOjo6Urdu3aRHjx6ppG2xZkY7iWJGEATh/3v06JFUsWJFSV9fX/Lw8JCUSqXckf6TUqmUbt++Lc2bN0+qX7++pKenJwFSmTJlpGHDhkm+vr5SbGys3DG1jlKplMqUKSN16tRJZe2NGDFCUigUkre3t0ra1GY7d+6UAOn48eMa6c/U1FSaMWOGlCdPHmncuHEa6fO7I0eOSLq6upK7u3uGeE3IqmJjY6Vly5ZJBQsWlHR1dSVXV1fpjz/+UGkfopjRTmJrZkEQBGDnzp307duXIkWKsGfPHipWrCh3pF/y5csXTp8+jY+PDz4+Prx+/RoDAwPs7e1Ttn8uVapUuvp48+YN+/bt4969e7i4uNC8eXOyZcumoq9AcxYvXsykSZN4+/Yt+fLlS3d7SqWSnj174u3tzbFjx3B0dFRBSu0kSRJNmjThyZMnPHjwAENDQ7X2Z2ZmhoODA3v27OHx48fp/jecWgEBATRu3BhnZ2f27Nkjtk3XQrGxsaxbt46FCxcSERGBq6srEydOpGTJkirvS5VbMwsqJHMxJQiCIKvY2Fipb9++EiB17dpVK6eV/SqlUindv39fWrx4sdSwYUMpe/bsEiBZWVlJgwYNko4ePSpFR0enqc2AgACpZs2aUqVKlaQePXpIhQsXlgYOHCglJyer6atQn/fv30t6enrS8uXLVdZmQkKC5OLiIhkZGUnXrl1TWbva6MmTJ1KOHDk0MlJiamoqlShRQqpXr57a+/ruzp07Uu7cuSV7e3vp27dvGutXSJ3o6Ghp0aJFUv78+SU9PT3Jzc1NevbsmVr7FCMz2kkUM4IgZFl//PGHVLlyZUlfX1/atGlTpp9C8vXrV+nIkSPSwIEDpeLFi0uAlD17dqlRo0bSkiVLpAcPHvzn96BmzZpSmzZtpHv37kmSJEkXLlyQcufOLR09elQTX4LKtWnTRqpYsaJK/+5jYmKkOnXqSPny5ZOCg4NV1q42mjVrlqSnpyfdvXtXrf3kzZtXAiRPT0+19vPd8+fPJXNzc8nGxkb6/PmzRvoUUufr16/SggULJDMzM0lPT0/q06ePxtYJimJGO4lpZoIgZEne3t706dMHCwuLLHnOgCRJPH78GF9fX3x8fDh37hzx8fGULVuW+/fvo6ur+7d7bt++ja2tLadOncLBwSHl48WLF2fEiBEMHTpUk1+CSvj4+ODi4kJgYCDVq1dXWbsfP36kXr16fP78mUuXLlGkSBGVta1N4uPjqVKlCnnz5iUgIAAdHfVskmpoaIhSqSQqKkrtU9rev3/Pb7/9Bvw5zaxAgQJq7U9Ina9fv7Jq1SqWLFnCly9f6N27NxMmTKBYsWIayyCmmWknsTWzIAhZSlxcHAMGDKBz5840b96cGzduZLlCBkChUFCmTBmGDRuGr68vUVFRnDhxgpEjR/5jIQN/FoDly5f/4aH/7du3lCpVipiYGE1FVylHR0cKFy7Mpk2bVNpu3rx58fX1RUdHhyZNmhAZGanS9rVFjhw5WL9+PVeuXFH59/C7pKSklKJJ3YXM58+fcXZ2Jjo6Gn9/f1HIaIEvX74wZ84cLC0tmT59Oh06dODp06esW7dOo4WMoL1EMSMIQpbx5MkTateuzZYtW1i/fj07duwgV65ccsfSCoaGhjg7O9OnT5+fXnPy5EkaNGjww/fs5cuXxMTEUK1aNU3EVDldXV169erFrl27VF6QFSpUCH9/fyIiImjatCnR0dEqbV9b1KtXj169ejFu3Djev3+v8vZ9fX1RKpVq/zcWFxdHq1ateP78OX5+fhQvXlyt/Qn/7vPnz8yaNQtLS0tmzpxJ586defbsGWvWrKFo0aJyxxO0iChmBEHIEvbs2UPVqlWJiYnh2rVr9O3bF4VCIXesDOXNmzeUL18e6c/1lgDcv3+fT58+UbVqVZnT/brevXsTHR3N3r17Vd526dKl8fHx4cGDB7Rr146EhASV96ENFi5ciK6uLqNGjVJ525s2bUJXV5dChQqpvO3vkpOT6dKlC1evXuXo0aNZcrRWW3z69IkZM2ZgaWnJnDlz6N69O8+fP2fVqlUULlxY7niCFhLFjCAImVpcXByDBg2iY8eOuLi4cOPGDSpXrix3rAwnMjKSihUrEhISgkKhSCkET58+TYkSJTAxMZE54a+ztLSkYcOGapsmVbVqVQ4fPszZs2fp2bMnSqVSLf3IydTUlMWLF7Njxw5OnTqlsnbDwsI4duwYBgYGKmvzf0mSRP/+/Tly5Ah79uzBzs5ObX0JP/fx40emTZuGpaUl8+fPp2fPnoSEhLB8+XK1FrJCxieKGUEQMq1nz55Rp04dPDw8WLt2Lbt27cLY2FjuWBmOJEnky5cPW1tb/P39+fjxI/Hx8SxatIiLFy8yZMiQH65XKpXcu3ePGTNmEBgYSHJyskzJU8/d3Z1Lly7x6NEjtbTv4ODAzp078fb2Zvjw4WTGvXd69OhB/fr1GTBgAHFxcSppc9u2bejp6ZEjRw6VtPdPJk+ezKZNm/Dw8KB58+Zq60f4Z1FRUUyZMgVLS0sWLVqEm5sbISEhLFu2jIIFC8odT8gARDEjCEKmtHfvXmxtbfn69StXrlyhf//+YlrZL/r+fevXrx+5c+emVKlS1K1blzVr1jBkyJB/PBzy/v37LFu2jJo1a1KgQAG6du3K9u3b+fDhg6bjp0qrVq0wMTHBw8NDbX20bduWtWvXsnLlSubMmaO2fuSiUChYt24dL168YO7cueluT5IkPDw8aNeundp+dn///Xfmzp3L4sWL6dGjh1r6EP5ZZGQkkyZNwtLSkqVLl9K3b19CQkJYsmQJ5ubmcscTMhLZNoUWBEFQg7i4OGnQoEESILVv316cEaEGFy5ckJYvXy49evToXw/LTExMlC5evChNmjRJsrW1lQAJkKpWrSpNnjxZCggIkBITEzWY/N8NGzZMMjMzk+Lj49Xaz6xZsyRAWrt2rVr7kcuUKVOkbNmySQ8fPkxXO+fPn5cA6ezZs5Kpqak0d+5cFSX80/bt2yVAGjt2rErbFf5deHi4NH78eClnzpySkZGRNHbsWOn9+/dyx0oVcc6MdhLFjCAImcazZ8+kqlWrStmzZ5fWrFmT6Q/BzGjCwsKkbdu2SZ07d5ZMTEwkQMqTJ4/Uvn17afPmzdKbN29kzXf37l0JkPbv36/WfpRKpTR06FBJoVBIe/fuVWtfcvj27ZtUsmRJqX79+un6GezevbtkZWUlKZVKlRczx48fl/T09KRevXqJ1wkNef/+vTRmzBjJyMhIypkzpzR+/HgpPDxc7lhpIooZ7SSmmQmCkCns378fGxsbPn78yJUrVxgwYICYVqZlChQogKurKzt37uTDhw9cvXqVYcOG8eLFC9zc3ChUqBBVqlRh/PjxnD9/nsTERI3mq1ixIjVq1FDbRgDfKRQKli1bRufOnenatSunT59Wa3+apq+vz9q1azl//jyenp6/1ManT5/Yt28fbm5uKv85vnz5Mu3atcPFxYUNGzaI1wk1e//+PaNHj6Z48eKsW7eOYcOGERoayrx58zA1NZU7npAJiGJGEIQMLT4+nmHDhtGuXTscHR0JCgrC1tZW7ljCf9DV1aVmzZpMnz6da9eu8eHDB3bs2EGlSpXYvHkz9vb25MuXjzZt2rBx40ZevXqlkVzu7u74+fmpvT8dHR22bNmCg4MDrVq14saNG2rtT9MaNWpEly5dGDVqFBEREWm+f9euXcTHx6t8Hcv9+/dp2rQp1apVw9vbGz09PZW2L/yfsLAwRo4cSfHixdm4cSOjRo0iNDSUOXPmkC9fPrnjCZmIKGYEQciwQkJCsLOzY926daxcuZI9e/aQO3duuWMJv8DU1JQuXbrg6elJWFgYN27cYOzYsXz48IH+/ftTtGhRKlSowOjRozl9+jTx8fFqydGxY0f09fXZunWrWtr/q+zZs7Nv3z7Kly+Ps7Mzjx8/VnufmrR06VKSk5MZO3Zsmu/18PCgadOmWFhYqCzPixcvaNKkCcWKFePIkSNq3e45K3v79i3Dhw+nePHibN68mbFjxxIaGsrMmTMz9BbugvYSxYwgCBnSwYMHsbGxISIigkuXLjF48GAxXSST0NHRoWrVqkyePJmAgAAiIiLYs2cPNWrUYOfOnTRq1Ih8+fLRokUL1qxZQ0hIiMr6NjY2pmPHjmzevFkj58EYGRlx/PhxzMzMcHR05M2bN2rvU1MKFCjAggUL2LJlCxcuXEj1fbdu3eLmzZu4u7urLEt4eDiOjo7o6+vj6+tLnjx5VNa28Kc3b94wdOhQSpQowbZt2xg/fjyhoaFMnz6dvHnzyh1PyMREMSMIQoaSkJDAiBEjaNOmDQ0bNiQoKIhq1arJHUtQo7x589K+fXs2b97MmzdvuH37NlOmTOHLly8MGzaMEiVKUKZMGYYPH46vry/fvn1LV3/u7u6EhoZy5swZFX0F/y5fvnz4+/ujVCpp0qQJUVFRGulXE9zd3alTpw79+/cnISEhVfd4eHhgbm6Oi4uLSjJ8/foVZ2dnPn/+jL+/v9j2V8Vev37N4MGDKVGiBF5eXkyePJnQ0FCmTZsmikZBI0QxIwhChhEaGoqdnR2rV69m+fLl7Nu3T/yyzGIUCgWVK1dm3LhxnDt3jsjISA4cOIC9vT0HDhzA2dkZExMTnJ2dWbFiBY8fP07zAZW1a9embNmyat8I4K8KFy6Mv78/YWFhNGvWjNjYWI31rU46OjqsW7eOJ0+esGjRov+8/tu3b+zYsYOePXuqZD1LfHw8rVq14smTJ/j6+mJlZZXuNoU/vXz5koEDB2JlZcWuXbuYNm0aoaGhTJ48WUz3FTRKFDOCIGQIhw8fxsbGhg8fPnDp0iWGDh0qppUJGBsb07p1a9avX8+LFy+4f/8+s2fPJjExkTFjxlCmTBlKlizJ4MGDOXbsGDExMf/ZpkKhwN3dnYMHDxIZGamBr+JPZcuW5cSJE9y9e5d27dppfDc3dalYsSITJ05k7dq1PH369F+vPXDgAJ8+faJ3797p7jc5OZlu3bpx6dIljhw5QpUqVdLdpvDn2qP+/ftTsmRJ9uzZw4wZMwgNDWXixIkYGxvLHU/IgkQxIwiCVktISGDkyJG0atUKe3t7goKCqF69utyxBC2kUCgoX748o0aN4tSpU0RGRnL06FGcnJw4ceIEzZs3x8TEhMaNG7N06VKCg4N/OmrTvXt3JEnCy8tLo19DjRo1OHjwIKdOnaJ3794aWbejCdOnT+f169dYWlr+60iZh4cH9evXp1SpUunqT5IkBg0axIEDB/D29qZ+/frpak/4c8OVPn36ULJkSQ4cOMDs2bMJDQ1l/Pjx5MqVS+54QhYmihlBELTWixcvqFevHqtWreL333/nwIEDYiGpkGo5c+akWbNmrF69mmfPnvHHH3+wcOFC9PT0mDRpEuXLl8fS0pJ+/fpx6NAhvnz5knJv/vz5adGiBZs2bUrzNLX0aty4MV5eXuzYsYNRo0ZpvH91+D6Kqqen99Ov5+nTp5w9exY3N7d09zdt2jTWr1/Pxo0badWqVbrby8qeP3+Om5sbpUuX5siRI8ybN4+QkBDGjh1Lzpw55Y4nCIgN1gVB0EpHjx6lR48eGBsbExAQQI0aNeSOJGRgCoWC0qVLU7p0aYYNG8a3b984f/48Pj4++Pr6smHDBvT09Pjtt99wcnLC2dkZNzc3XFxcuH79usb//XXo0IHIyEgGDhxI/vz5mTBhgkb7VycdnX9+H/XOnTtYWFjQtm3bdLW/cuVKZs2axfz581UyXS2revr0KXPmzGH79u2YmpqyYMEC+vfvj6GhodzRBOEHCikzvOWjAcmSRPi3ZMJikwiLTSI6SUmyUkJXR0FOPR3MDfUwN9TDzEAXXTGPXxB+WWJiIhMnTmTx4sW0aNGCLVu2iLMJBLV7/vw5vr6++Pj4cObMGWJjY7GwsODz58/UqlVLts0mZsyYwfTp09m4caNKtyrWNt8fRRITE8mePfvfPm9mZsbIkSP/s6jbtWsXXbp0YeTIkSxevFisq/sFT548Yfbs2ezYsYP8+fMzbtw4+vTpI4oYoEePHoSEhKRpq3FB/cTIzH/4nJDM7Yg4bkXEEZf854utDvDXWcw6wO3/v0ZUX1eBjak+VUz1yZ1dV9NxBSFDe/nyJZ06deL69essWbKEESNGiIcRQSNKlCjBwIEDGThwIHFxcQQEBODr64unpyenT5/G1NSUWrVq4ezsjJOTEzY2Nj8dYVClqVOnEh4eTr9+/ciXLx+tW7dWe59y+P5z/k+FTGr5+fnh6uqKq6srixYtEq8dafTHH38we/Zsdu7cibm5OcuWLcPd3V0cLipoPTEy8xNxyUrOvonhTmQ8CiAt36Tv11fOlwOHQkbk0BVLkwThvxw/fhxXV1dy5szJ7t27qVWrltyRBIHQ0FCKFy+Oq6srX7584dSpU0RHR5M/f36aNGmCs7Mzjo6O5MuXT20ZwdSRHAAAuOhJREFUlEolXbp04dChQ/j6+mJvb6+2vrTVwYMH+fjx40+njV27dg0HBwcaNGjAwYMHyZYtm4YTZlwPHz5k9uzZeHt7U7BgQSZMmICbmxv6+vpyR9M6YmRGO4li5h+EfEng2IuvxCZJaSpi/pcCMNJT0LRYLoob//q7TYKQmSUmJjJ58mQWLlxI8+bN2bp1q5hWJmgVR0dHYmJiuHTpEgkJCVy+fDllStrdu3dRKBTUqFEjZdSmWrVq6OqqdmQ+ISGBZs2acfXqVc6fP4+NjY1K29d2iYmJ6Orq/uNoWHBwMHZ2dpQrVw5/f38xHSqVHjx4wKxZs9izZw+FCxdmwoQJ9O7dmxw5csgdTWuJYkY7iWLmf9wM/8bJ1zFpHo35me/tNC5sRFUzMVQrCH/16tUrOnXqRGBgIPPnz2fkyJFiaoigdfbs2UPHjh0JDg6mXLlyP3zu7du3+Pr64uvri7+/P58/fyZfvnw4OjqmjNoUKFBAJTmio6NxcHDgxYsXBAQEpHv7YrlER0cTExODgYFBus8lefnyJXXr1iVPnjxcuHBB7HaYCvfv32fmzJns27ePIkWKMHHiRHr27CmKmFQQxYx2EsXMX3wvZNRFFDSC8H9OnDiBq6srhoaG7N69m9q1a8sdSRD+UXx8PIUKFaJnz54sXrz4p9clJSVx7dq1lB3Sbt68CUDVqlVTdkirWbNmuk62j4iI4LfffiM+Pp5Lly5hYWHxy21p2pcvX9iwYQPz5s0jV65cFCpUCAsLCwYNGkTdunXTPDUsIiICOzs74uLiMtz3Qg53795l5syZ7N+/H0tLSyZOnEiPHj3StU4pqxHFjHYSxcz/F/Ilgd3Pvvz3henU0cpYTDkTsrSkpCQmT57MggULcHFxwdPTU63rDQRBFUaMGMGOHTt4/fp1qh/+3r9/j7+/Pz4+Pvj5+REVFUWePHlo3LgxTk5OODk5/dID+PfRiLx583LhwgVZdln7FePHj+fYsWMMHDgQCwsLHj9+THBwMA8ePCB37txMmjSJBg0apKqt6OhoGjZsSEhICJcuXcqwo1SacPv2bWbOnMnBgwcpUaIEkyZNonv37mJd0S8QxYx2EsUMfy723xj8Md1rZP7L9zU0fazzik0BhCzp9evXdO7cmStXrjBv3jxGjRqlkR2hBCG97t+/T8WKFdm3b98vnYOSnJzMjRs3UkZtAgMDkSSJSpUq4ezsjLOzM3Xq1En1A+b3dSLW1tb4+flliHUiFhYWrFy58ofvX0REBFeuXMHDw4MbN27g6emJg4PDv7ajVCpxcnLi6tWrnDt3DltbW3VHz5CCgoKYOXMmhw8fxsrKismTJ9O1a1dRxKSDKGa0k3iKAM6+iVF7IQN/rp2JSZI480Z9U9kEQVv5+vpiY2NDSEgI58+fZ8yYMaKQETKMChUqULNmTTw8PH7pfl1dXWrWrMn06dO5evUqHz58YOfOnVSuXJktW7Zgb29Pvnz5aNOmDRs2bODly5f/2p61tTXHjx8nKCiIjh07kpiY+Eu5NOXDhw9YWVkRGRn5w8dNTU1p3rw5hw4domLFimzfvp1/e49VkiS2b9/O+fPnOXz4sChk/sGNGzdo0aIFVatWJTg4mG3btvHo0SN69uwpChkhU8ryTxKf4pO5Exmv9kLmOwm4ExnP54RkDfUoCPJKSkpi0qRJODs7U716dW7fvk3dunXljiUIaebu7o6vry+vXr1Kd1umpqZ07twZT09P3r17x40bNxg3bhwfPnxgwIABFCtWjPLlyzN69GhOnTpFfHz839qoVasW+/fvx9fXlz59+vxrESC3/PnzU6NGDRYtWkRAQMA/Fl9ubm5cuHCBb9++/bQdSZIYN24cu3btSvWUtKwiMDCQpk2bUr16dR4/fsz27dsJDg7G1dU1Xeu0BEHbZfli5k5kHGnZOyk+NpqTaxewtE1tptQuwlzHChyaO4ZvXz6lug0FcDsiLq1RBSHDefPmDQ4ODixYsIB58+Zx7NgxTE1N5Y4lCL+kY8eOGBoasmXLFpW2q6OjQ9WqVZk0aRIBAQFERESwZ88eatWqxc6dO2ncuDEmJiY0b96cNWvW8Pz585R7nZyc8PT0ZNu2bYwdO1aluVRt2LBhFC9enM6dOzNr1iyCg4P58uULSqWS+Ph4Lly4QOHChX86ZS4pKYlDhw4xc+ZM2rRpo+H02uvq1aspm0s8f/6cHTt28ODBA7p16yaKGCFLyNJrZpIliZX3oohLTv23YGPfVjy/cQkdXV3ylyjLx7cviI+JppB1FQZs9UE3lS8c+roKhlQ0QVdsQytkUv7+/nTr1o3s2bOza9cu7Ozs5I4kCOnm5ubG6dOnef78uUamSUqSxL1791LW2gQEBJCUlETp0qVTdkirX78+GzduZNiwYSxcuJAxY8aoPdevSkhIYOnSpaxatYrw8HCqVatGmTJlOH36NGZmZixevPhfDwX19vamU6dOmgusxS5fvsyMGTPw9/fH2tqaKVOm0L59e5WfcST8H7FmRjtl6WImLDaJrX98SvX175//we/tfgOgxbj51O7oRsTLZyxp9edJ5R3nrKOKc+oXhvYskwdzQ/GuiZC5JCUlMWPGDObMmYOjoyPbt2/HzMxM7liCoBJXrlyhTp06+Pv707hxY433/+XLF06fPp1yaOerV6/Q19fH3t4epVKJv78/Hh4e9O7dW+PZ0iIpKYkLFy6we/duYmNjKV++PM2aNaNChQo/vScyMhITE5MsfxZVQEAAM2bM4NSpU1SoUIEpU6bQrl07sQZRA0Qxo52y9L/8sNikNF0vKZUp/6/4/y8aCsX/fQufXjuv1v4FQdu9e/eORo0aMXfuXObMmcOJEydEISNkKrVq1aJcuXK/vBFAehkbG9O6dWvWr1/PixcvePDgAbNnzyYpKYlz584Bf44eubi4cOzYMWJitHPDGT09PRwcHFi/fj3bt29n/Pjx/1rIJCUl8fDhwyxdyFy4cIGGDRtiZ2fHhw8f2LdvH3fu3KFDhw6ikBGytCz9rz8sNilN34D8xUtToOSfpz8fXTiBFZ3sWdm1Ycrnv3x4l+q2dBDFjJC5nDx5kipVqvD48WPOnj3LhAkTxC9YIdNRKBS4u7tz8OBBIiIiZM9ibW3NqFGjOHnyJJGRkRw6dAgrKyt8fX1p3rw5JiYmNG7cmCVLlhAcHKzVmwT8Gz09Pe7evSt3DFmcO3eOBg0aUL9+faKiojhw4AC3bt2ibdu24jVWEMjixUx0khLlf1+WQkdXl14rvani3A7DPCZEvXmBZZVamBS2/PPzeqnf8lAJxCSlpXdB0E7JyclMnTqVJk2aUKVKFW7fvk29evXkjiUIatO9e3ckScLLy0vuKD/ImTMnLVu25MGDB9jb22NkZMTw4cPR09Nj8uTJlC9fHktLS/r168fBgwf58kX9B0WrgiRJXLx4kc+fP8sdRWMkSeLMmTPUr1+f/8fefQdS/f1/AH9ee0tG22hHS0PaKa4tJUlS4ba39t71ae+hXCQaUorsdkqb9iQppZCG7d57fn/0ze/Tp1S4975dzuOvuvf9Puepwft1zzIzM8Pnz59x8uRJ3LlzB4MGDaJFDEX9S61esMEXVPwTKvV6DeGyek/Z70uLi7DGwggAoK3fvEJt8SrRP0VVJ+/evYObmxsuXryIlStX0tEYqlbQ1tbGwIEDweVyMW3atGo39UleXh6nTp2CmZkZDhw4gKtXr6JBgwa4ePFi2Vqbffv2QUZGBj179oS1tTWsrKzQvn17kX8tBQUFUFBQAIC//l7BYrEQGBiIpk2bijJatUAIwdmzZ7F8+XIkJCSgc+fOCA8Ph52dXbX7d0ZR1UWtfuqQlqr4N4aMx3dRnJ8HABDw+YjeugxFed8+3WrPdqxQWzKV6J+iqouzZ8/C2NgYjx8/xtmzZ7Fw4UJayFC1BofDwYMHD3Djxg2mo/ySqqoqoqKioK6uDjabjc+fP8PKygpbt27F06dPkZKSgm3btkFVVRUrVqxAx44d0ahRI3h6euLYsWPIzc0VSa5Vq1ahQ4cO4PP/7qw1Ho+H0tJSREZGiiRPdUEIQVxcHHr16gULCwsUFxfj9OnTuHnzJuzt7WkhQ1G/UaufPFRkpCr8B3Dr1CGsMm+DrUP7YI2FERKPflsE2nP4ODRp+/cnEUsBUJap1X/8lITi8/lYvnw5LCws0K5dOyQnJ/92K1WKqonMzc3RpEkTxjYC+Bs6OjqIjY1FYWEhrKysfpim1bRpU0ycOBERERH4+PEj4uPjMXz4cFy/fh1Dhw6FtrY2evXqhVWrVuH27dsQCKo+LZrH48Hf3x99+/b95Un0hJAf+iGEgBCCiRMnoqSkpMr9V0eEEERHR6NHjx6wtLQEn89HVFQUrl+/DltbW1rEUNRfqNVP0/WVZCq0ZgYAmrTthLqN9PDxzSuUFBWgUZsOGLxkC+xmrapQO4L/9U9RkiQzMxNsNhvLly/HsmXLEBMTg3r16jEdi6LETlpaGp6enjh8+DDy8vKYjlMufX19xMbG4tWrV3BwcEBR0c8HNsvLy8Pc3BwbN27Ew4cP8erVK+zevRs6OjpYv349unTpggYNGmDkyJE4fPhwpTc+iIqKQmZmJry8vH56r7S0FPv378e9e/fKChoWi4UnT57A19f3l7klGSEEkZGRMDU1hY2NDVgsFmJiYpCYmAhra2taxFBUBdT6YqaiOtm5YEZoAlZcfYUVV9MxOfgMujqOEFv/FMWU8+fPw9jYGA8fPsSZM2ewZMkSejgbVat5eHggPz8fISEhTEf5rbZt2yIyMhI3b96Eq6sreLzf76Spq6uLsWPH4sSJE8jOzsaFCxfg4eGBe/fuYfjw4dDR0YGpqSmWLVuG69ev//WUMS6Xi06dOsHY2PiH1wUCAUaPHo3JkycjOzv7h+mq7dq1g5eXFwoKCvD169eKf/HVDCEEERERMDExgZ2dHeTk5BAfH48rV67A0tKSFjEUVQm1upjRVpSGgjQz3zgUpFnQVqQPglT1x+fzsWLFCpibm8PQ0BDJycno378/07EoinF6enqwsLCo1lPNvuvRowdCQ0MRERGB8ePH//UWzXJycujbty/++ecfJCcnIyMjA1wuF7q6uti2bRtMTU1Rr149DB8+HIGBgXj//v0v23n37h0iIyN/GpUhhGDGjBk4fPgwgoODYW5u/tO969atAwCJXjdDCMGpU6fQpUsXODg4QElJCWfPnsWlS5dgbm5OixiKqoJaXcxIs1gw1lKAuL+FsAAYaylAmn7zoqq5Dx8+wMrKCsuWLcOSJUsQFxeH+vXrMx2LoqoNDoeDq1ev4vHjx0xH+SMbGxsEBASAy+ViwYIFlWqjYcOG8PDwQEhICLKyspCQkIDx48fj2bNnGDVqFOrXr4/OnTtj0aJFSEhIKBsFOnDgAGRlZTF8+PAf2luzZg22b9+OXbt2wdnZ+Zd9ampqQllZGffu3UNcXFylcjNFIBAgLCwMnTp1gqOjI9TU1HD+/HlcvHgR/fv3p0UMRQkBi0jqCVpC8rmEjz0PRbNry+9MMNKAuhwdmaGqrwsXLmD48OHg8/k4dOgQBgwY8OebKKqWKS4uRqNGjTB69Ghs3LiR6Th/ZevWrZgxYwY2bdoEb29vobX7/v17xMXFITo6GnFxccjJyYG6ujosLCxw8eJF9OnTB6GhoWXX79u3D+PGjcPy5cuxZMmS37atpaUFdXV1sFgs3L9/H4qKikLLLQrfi5gVK1bg3r176N+/P5YuXUrP4JJwo0aNwsuXL3Hp0iWmo1D/UqtHZgBAXU4aHTTlxTY6wwLQQVOeFjJUtSUQCLBq1SoMGDAArVu3RnJyMi1kKKoc8vLyGDlyJA4cOCAxO25Nnz4d8+fPx8yZMxEYGCi0duvVqwd3d3ccOnQI79+/x7Vr1zBjxgw8fPgQWVlZOH78ODp06IB58+Zh+fLlGD9+PCZPnozFixf/sW0Wi4WBAwfi9evXWL16tdAyC5tAIEBISAg6dOiAIUOGQEdHB5cvX8bZs2dpIUNRIlLrixkA6N9IGcoyLJEXNCwAyjIs9G+kLOKeKKpyPnz4AGtrayxZsgSLFi1CfHw8GjRowHQsiqrWvLy8kJ2djfDwcKaj/LXVq1eDw+HA09NTJGtRpKWl0a1bNyxduhSdO3eGgYEBgoOD0bFjR/j4+GDZsmWQlpbG69evsX//fqSnp/+xTW1tbcybNw/r16+vdtP6+Hw+jhw5gnbt2sHFxQUNGzbElStXEB8fj169ejEdj6JqNFrMAJCXloKtnipEPd+OALDVU4W8NP1jp6qfS5cuwdjYGElJSYiNjcXy5cvpbmUU9ReMjIxgamoqERsBfMdisbBnzx44ODjA2dkZV65cEUk/nz59QmhoKMaOHYvhw4dj6tSpKC0thampKRYvXoycnBxMnDgRenp6MDIywsyZM3HmzBkUFxf/sr358+dDX1+/QpsYiNL3abjt2rWDq6srdHV1kZiYiNjYWPTo0YPpeBRVK9Cn6v8xUJODRWPRjpgMaKgIAzU5kfZBURUlEAiwdu1amJmZoUWLFkhOToaFhQXTsShKonA4HMTGxv7VCEN1ISMjg0OHDpVtE3z//n2h93Ho0CGUlpZi1KhReP78OaytrWFkZIT4+HgsWbIEly9fRlZWFkJCQmBqaoojR47AwsICdevWhb29PXbt2oXU1NSy9hQUFLB3715cunQJAQEBQs/7t3g8HoKCgmBkZAQ3NzcYGBjg+vXriI6OhqmpKWO5KKo2osXMv3TWViwraIQ15ex7O9f9N6NH4zqYOnUqHj58KKTWKapqsrKyYGNjg4ULF2L+/Pk4c+YMGjZsyHQsipI4Q4cOhZKSEqMP2JWhoKCAU6dOQV9fH5aWlkhLSxNq+76+vrCzswMhBGw2G5qamoiMjISKikrZNRoaGnB2dgaXy8WbN29w9+5dLFmyBHl5eZg+fTqaNWuG3NxcREREIDo6Gt27d8eIESMwe/bsSh/gWVk8Hg+BgYEwNDSEu7s7WrZsiZs3byIyMhImJiZizUJR1De0mPmPztqKcGmmJpQ1NN/XyLg0U4NGXiZ4PB527tyJtm3bonPnzvDx8cHnz5+FEZuiKiwhIQHGxsa4ffs2oqOjsWrVKsjI0INcKaoyVFVVMWzYMHC53L8+RLK6UFdXR0xMDJSUlGBhYYEPHz4Ipd07d+4gKSkJLi4usLS0BI/HQ2xsLLS0tMq9h8VioX379pg7dy7Onz+PnJwchIWFQVZWFo8ePYKNjQ3q1q2LjIwMFBYWYuzYsWKZbsbj8RAQEIA2bdpg1KhRMDQ0xO3btxEeHo4uXbqIvH+KospHi5lfMFCTA8dQA+015QFUfJRG8L8fZO015THGUONbexwOAJR9001KSsKECRNQr149JCQkCC07Rf2JQCDAP//8g379+qFp06ZITk6GpaUl07EoSuJ5eXkhPT0dZ8+eZTpKhdWrVw9xcXHIy8uDtbU1vnz5UuU2uVwu6tevj507d+Lt27eIi4uDrq5uhdpQU1ODo6MjVFRUMGfOHDx8+BCrV6+GtLQ0iouLERYWhoYNG2LSpEmIiIhAXl5elXP/W2lpKfz8/NCqVSt4eHigXbt2SEpKwsmTJ9GpUyeh9kVRVOXQYqYcCtJSsNZVxQQjDZjWU4SC9P+XNP/9Q/v376UFPFwM2I7eJAPWuv+/2L9bt25QVVUtu44QAikpKWhoaKBJkyYi/Eoo6v9lZ2fD3t4e8+fPx9y5c3Hu3Dk0atSI6VgUVSOYmprC0NBQojYC+LemTZsiNjYWKSkpcHR0RFFRUaXbKiwsRHBwMJSVlZGcnIyoqCi0adOmSvlYLBYMDQ3h7e2N+Ph45Obmok2bNigqKkJ0dDQcHBygqakJc3NzbNq0CQ8fPqz0qE1JSQn279+Pli1bwsvLC8bGxkhOTsaJEyfQsWPHKn0dFEUJFy1m/kBdThp9GypjSru6GN2qDqyaqKC9pgJaqMvBQFUWLdTl0F5TAVZNVDC6VR1Mba+JB2EBOMTd90M70tLSsLa2/mF3qDp16uD69evQ09MT95dF1UJXrlyBsbFx2SLV1atX02llFCVELBYLHA4HYWFhYl/LISzt27dHREQEEhMT4ebmVukpc8eOHcPnz5/x6tUrnDhxAt26dRNy0m9T+44dO4a8vDyMHj0az549w4YNGyArK4tFixahbdu20NPTw9ixYxEWFvZXo00lJSXw8fFBy5YtMW7cOHTt2hX37t1DaGgoOnToIPSvgaIoISCU0C1YsICoq6uTgoKCH14PCAggAIi0tDRp0KABAUB8fX0ZSknVFnw+n6xbt45IS0uTXr16kdevXzMdiaJqrKysLCIrK0u2bNnCdJQqCQ8PJ9LS0mTs2LFEIBBU6F6BQEAaN25MAJBDhw4JJY+WlhZZs2bNL9+bN28ekZeXJ8+ePSt7raCggERHR5Np06aRli1bEgBERkaG9O3bl6xdu5YkJyf/8HUVFRWR3bt3kyZNmhAWi0VcXFzIgwcPhJKdqjlGjhxJevfuzXQM6j9oMSMCL168IADIwYMHf3g9MzOTyMjIkAEDBpAvX76QCRMmECkpKRIWFsZMUKrGy87OJra2tgQAmTdvHiktLWU6EkXVeM7OzsTIyKjCRUB18/0DuIULF1bovlmzZhEAxN3dXWhZflfM5OfnEwMDAzJgwIBy/8xTUlLIrl27iL29PVFSUiIASIMGDYi7uzvx9PQkDRs2JFJSUmT48OHk4cOHQstN1Sy0mKmeaDEjImZmZqRv374/vf7y5UtSUlJCCCGEx+ORoUOHEnl5eXLhwgUxJ6RquqtXr5ImTZoQTU1NEhkZyXQciqo1YmJiCACSmJjIdJQq27BhAwFAtm7d+lfX+/r6EgBEQUHhp9kJVfG7YoYQQqKioggAEhQU9Me2ioqKSFRUFDEzMyMyMjIE3860JsbGxmTlypXk1q1bhM/nCy07VXPQYqZ6omtmRITD4eDixYt4/vz5D6/r6+tDVlYWwLd1NIGBgejduzccHByQlJTERFSqhiGEYNOmTejTpw+aNGmCpKQk2NjYMB2LomoNc3Nz6OrqSuxGAP82a9YszJ49G9OnT0dwcPBvrz158iTGjBkDJSUleHp6QlFRUUwpAWtrawwdOhTe3t7Izc0t97rCwkLs3bsXXl5euHjxIoYPH47z589j37590NfXx/r169GlSxc0aNAAI0eOxKFDhyR2/RNF1RpMV1M1VWFhIdHQ0CDz5s3747VfvnwhXbp0ITo6OuT58+diSEfVVDk5OcTe3p4AIHPmzCkbBaQoSryWLVtGVFRUyNevX5mOUmUCgYB4eHgQGRkZEhUV9ctrLly4QOTl5UmPHj0IAJKUlCTUDH8amSGEkLdv3xI1NTUyduzYn97Lz88nmzZtIvXq1SPS0tJk9OjRv/x5W1JSQi5cuEDmzp1LOnToQAAQFotFTExMyNKlS0liYiLh8XhC+7ooyUJHZqonOjIjIgoKCnBzc0NAQAB4PN5vr1VVVUVUVBQ0NDRgaWmJd+/eiSklVZNcu3YNxsbGuHLlCiIiIrBu3bqyUUCKosTLw8MD+fn5CAkJYTpKlbFYLOzbtw82NjZwcnJCYmLiD+8nJSXBwcEBvXr1goaGBjp37szI9sUNGjTAmjVrsG/fPly9ehUAkJ+fj40bN8LAwABz586Fra0tnj59Cn9/fzRv3vynNmRlZdG3b1/8888/SE5ORkZGBrhcLvT09LBt2zZ0794dOjo6cHV1RWBgIN6/fy/uL5OiqP+gxYwIcTgcZGZmIioq6o/XamtrIzY2FsXFxbCyssKnT59EH5CqEQgh2Lx5M3r37o1GjRohKSkJdnZ2TMeiqFpNV1cXbDYbvr6+TEcRChkZGRw5cgRdunSBra0tHj58CAB48eIFrKys0KJFC+zevRvR0dHw8vJiLOf48ePRtWtXjBkzBmvXroWBgQHmz58PBwcHPHv2DFwuF82aNfvr9ho2bAgPDw+EhIQgKysLCQkJmDhxIp4/f45Ro0ahfv366Ny5MxYuXIiEhIQ/fnhJUZTw0WJGhDp06IDOnTv/9Q8zPT09xMbG4vXr13BwcEBhYaGIE1KSLjc3F46Ojpg5cyamT5+OixcvVviEbYqiRMPLywuJiYl49OgR01GEQlFREeHh4WjSpAksLS1x8+ZNWFpaok6dOoiOjsbx48chLy8PV1dXxjIWFBTA1NQUjx49wqJFizBo0CA8f/4c+/fvh4GBQZXalpGRQc+ePbFy5UrcunUL79+/R2BgIFq3bg0fHx/07t0bWlpacHZ2hp+fHzIyMoT0VVEU9Tu0mBExDoeDqKgovH379q+uNzIyQmRkJG7fvo1hw4bRT3moct24cQPGxsa4fPkywsPDyw6LoyiqenBwcICWllaN2Ajguzp16iAmJgaysrLo1asX8vPzERcXB01NTXC5XAwZMgR16tQRe64vX75gzZo10NfXh4+PD9q3bw85OTnMmzcP+vr6IulTR0cH7u7uCA4Oxvv373H9+nXMmDEDb968AYfDQePGjdGhQwfMnTsXFy5cQElJiUhyUFRtR4sZEXN1dYWcnBwOHDjw1/d0794doaGhiIqKwpgxY0AIEWFCStIQQrB161b06tUL9evXR1JSEuzt7ZmORVHUf8jLy2PkyJEIDAysUQ+yderUgZaWFkpLS6GlpYW6devi0qVLSElJAYfDEWuWz58/Y9WqVdDX18fy5cvh6uqKFy9e4MqVK9DW1sbkyZPF8jNUWloaJiYmWLp0KRITE5GVlYVDhw6hY8eOCAgIgJmZGTQ1NTFo0CD4+PggPT1d5JkoqragxYyIqaurw9nZGVwuFwKB4K/vs7a2RkBAAAICAjBv3jwRJqQkSW5uLgYPHowZM2ZgypQpuHTpEvT09JiORVFUOby8vJCdnY3w8HCmowgFj8fDsGHD8PDhQ/j5+eH169dlD+gtWrRA7969xZLj06dPWLFiBfT19bFq1SqMGDECqamp2LlzJ5o0aQIVFRXs2LEDUVFROH78uFgy/ZumpiZcXV1x4MABvHv3Drdv38a8efOQnZ2NSZMmQU9PD4aGhpg5cybi4+NRXFws9owUVWMwu5la7XDp0iUCgJw/f77C927bto0AIBs2bBB+MEqi3Lhxg+jr65M6deqQkydPMh2Hoqi/1L17d2Jpacl0jCr71RbN37dklpKS+uPWyVXxfWvmjx8/kqVLlxJ1dXWioKBApk2bRjIyMsq9b+DAgaRBgwbk06dPIstWUbm5ueTYsWPE09OTNGzYkAAgSkpKxNbWluzcuZO8ePGC6YhUOejWzNUTLWbEQCAQkJYtWxI3N7dK3b9w4UICgPj7+ws3GCURBAIB2b59O5GVlSUmJibk5cuXTEeiKKoCfH19CYvFImlpaUxHqZI5c+YQACQoKOiH18eMGUMAkFGjRhGBQCCSvjU1NYmZmRlRU1MjioqKZMaMGeTt27d/vC89PZ0oKyuTyZMniyRXVQkEAnL37l2ybt060q9fPyIjI0MAkBYtWpCpU6eSqKgoUlBQwHRM6n9oMVM90WJGTNatW0cUFBTIx48fK3yvQCAgY8eOJdLS0iQ8PFwE6ajqKjc3lwwePJgAINOnTyfFxcVMR6IoqoK+fv1KVFRUyLJly5iOUmkbNmwgAMjWrVt/eq9jx46kY8eOBABZunSpUPvNzs4mCxYsICwWi8jIyJCZM2eSzMzMCrWxadMmwmKxyI0bN4SaTRQ+f/5MwsLCyLhx44iuri4BQBQUFIilpSXZunUrefLkicgKRurPaDFTPdFiRkzevXtHpKWlyc6dOyt1P4/HI4MHDyYKCgrk0qVLQk5HVUe3bt0iTZs2Jerq6uTEiRNMx6Eoqgo4HA7R1dWVyNPjMzMziZqaGlmwYMFP792+fZsAIBEREWTt2rUEANmxY0eV+8zKyiLz5s0jKioqRElJiSgqKv6y/79RWlpKOnbsSIyNjUlpaWmVs4mLQCAgjx49Ips2bSLm5uZETk6OACAGBgZkwoQJJDw8nHz9+pXpmLUKLWaqJ1rMiJGjoyPp2LFjpe8vLCwkZmZmRF1dndy9e1eIyajqRCAQkB07dhA5OTnSpUsXkpqaynQkiqKqKDExkQAgsbGxTEepsNLSUvLmzZtfjghMmDCBNGzYkJSWlhKBQEC8vb0Ji8Uihw8frlRfHz58IHPmzCHKyspERUWFzJs3j3z48KFszUxlXb9+nbBYLLJly5ZKt8G0vLw8EhERQSZNmkSaNm1KABA5OTkyYMAAsmHDBvLgwQM6aiNitJipnmgxI0anT58mAMjt27cr3cbnz59Jp06dSP369UlKSooQ01HVwadPn8iQIUMIADJ16lRSVFTEdCSKooRAIBAQIyMj4uzszHSUSvnVQ3J+fv5PIzZ8Pp+4u7sTWVnZChVumZmZZNasWURJSYmoqKiQBQsWkKysrLL3q1rMEELIpEmTiIqKCklPT69SO9WBQCAgz549I9u2bSPW1tZEQUGBACBNmjQhY8aMIcePHyefP39mOmaNQ4uZ6oluzSxGlpaWaNiwIXx9fSvdhpqaGqKjo6GiogI2m433798LMSHFpDt37qBz586Ii4tDaGgotm3bBnl5eaZjURQlBCwWC15eXjh58iSysrKYjlMmNDQUCxcuhJOTE0JDQ/H169dfXsdisX567fjx4/jy5Qs8PT3LXpOSkgKXywWbzcbgwYNx/fr13/afmZmJmTNnwsDAAPv27YO3tzdevXqF1atXQ0tLq2pf3H+sXr0aqqqqmDp1qlDbZQKLxUKLFi0wdepUREVF4ePHj4iJicHgwYNx6dIlODk5QVNTE3379sU///yD5ORkemYdVWPRYkaMZGRk4OHhgUOHDqGgoKDS7ejo6CA+Ph4FBQWwsrLC58+fhZiSEjdCCHbv3o3u3bujTp06uHPnDpycnJiORVGUkLm7uwMAgoKCGE7yTUBAAEaPHo1Xr14hOzsbc+fOhZOTE9LS0v7qfl9fX5iZmaFZs2Y/vC4rK4uQkBB06NABtra2ePz48U/3vnv3DjNmzICBgQG4XC5mz56NtLQ0rFy5EnXr1hXGl/cTdXV1bN26FSdPnqwx5/58p6ioCEtLS2zduhVPnjxBSkoKtm/fDnV1daxatQrGxsZo1KgRPD09ERISgtzcXKYjU5TwMD00VNukpKQQACQwMLDKbd27d4/UqVOH9O3blxQWFgohHSVunz9/JkOHDiUAyOTJk+m0Moqq4YYOHUoMDQ0ZX9vw/v170rFjR+Lr61v2WmRkJHFzcyOGhobk3r17v71fIBAQW1tbEhwcXO41Hz9+JG3btiVNmjQpm9r15s0bMnXqVKKgoEDq1KlDli1bRnJzc/+YVxjTzL7ntrKyIk2aNKk1i+eLiorImTNnyMyZM4mRkREBQKSkpEiPHj3IypUryc2bNwmfz2c6pkSg08yqJ1rMMKB///6kT58+QmkrISGBKCgoEEdHR4napYUiJCkpiTRv3pyoqqqSkJAQpuNQFCUGsbGxBABJTExkNMfbt29Jw4YNyZEjR354PTk5mXA4HNK9e3fy5MmTcu/n8/mkpKTkjx/AZGRkED09PdK8eXPi5eVF5OXliYaGBlmxYkWFDrIUVjFDCCGpqalEUVGRzJw5UyjtSZpXr16Rffv2kUGDBhFVVVUCgGhra5MRI0aQ4ODgH9YqUT+ixUz1RKeZMYDD4eDSpUt49uxZldvq2bMnQkNDERERgQkTJtA5sRKAEAIfHx+YmppCVVUVd+7cgbOzM9OxKIoSA3Nzc+jp6VVp7aQwqKmpwcjICHfv3kVRUVHZ6x06dMDEiRMhEAiwbdu2cu+XkpKClJTUH9f18fl89OjRAy9evMCBAwcwd+5cpKWlYfHixVBXVxfa11MRBgYGWLJkCbZu3Yq7d+8ykoFJurq6GDNmDE6cOIGcnBxcuHABXl5euH//Ptzc3KCjo4Nu3bph6dKluHbtGvh8PtORKeq3aDHDgEGDBkFDQwN+fn5Cac/W1hZ+fn7w9fXFwoULhdImJRpfv37F8OHDMX78eHh5eeHq1ato3rw507EoihITKSkpeHh44MiRI+UuthcHZWVl9OrVC3v37sXZs2d/eM/Y2Bhjx47FwYMH8fbt23LbkJaWLve9V69eYfz48WjWrBni4uIwadIkKCgo4Nq1a1BQUBDa11FZM2fOROvWrTFu3Lha/bAuKyuLvn37Yu3atUhOTkZGRga4XC709fWxfft2dO/eHTo6OnB1dcWBAwfopkNU9cT00FBtNWXKFFK/fn1SUlIitDY3bdpEAJDNmzcLrU1KeJKTk0mLFi2IqqrqT1M7KIqqPV69ekVYLNYP61WYMnLkSKKmpkZCQ0N/WHsZExNDunTpQj58+FCh9l6+fEnGjBlDZGVliZaWFlm3bl3Z2pSzZ88SOTk5MmzYsAqv0RDmNLPvEhISCACya9cuobZbU5SWlpIrV66QRYsWkS5duhAABADp1KkTWbBgAbl8+XKtm95Op5lVT7SYYUhycjIBQE6ePCnUdufNmye0DQYo4RAIBMTHx4fIy8uTjh07kmfPnjEdiaIohllZWRFTU1OmYxBCCJk4cSKRlpYm8+fPJxEREeTBgwekf//+xNLS8q/bSElJIV5eXkRGRobo6OiQDRs2kLy8vJ+uCw0NJVJSUmTy5MkV2gRBFMUMIYRwOByipqZG3r59K/S2a5r379+TgwcPkuHDhxNNTU0CgKirqxMnJyfi6+tL3rx5w3REkaPFTPVEixkGdenShdjZ2Qm1TYFAQDw9PYm0tDQ5ffq0UNumKu7Lly9k+PDhBAAZP3483XWOoihCCCHHjh0jAMiDBw+YjkIIIWTPnj2kc+fORFNTkxgZGZHevXv/1ejJ+/fviYeHB5GWlib16tUjmzZtIvn5+b+9x8fHhwAgK1as+Ot8oipmcnJyiLa2NnFxcRF62zUZj8cj169fJ8uWLSOmpqaExWIRAKRdu3Zkzpw55Ny5c6S4uJjpmEJHi5nqiUUIXTHOFB8fH0ycOBHp6elo1KiR0Nrl8XhwdnZGbGwszpw5gx49egitberv3bt3D87Oznj79i327dsHV1dXpiNRFFVNlJSUoFGjRnB3d8fmzZuZjgPg29kvX79+BZ/PR7NmzSAnJ1futYQQ3LhxAz179oS2tjbmzp2LsWPHQklJ6a/6Wr16NRYtWoQ9e/Zg/Pjxf7xeW1sb3t7emD9//l9/PX8rKCgI7u7uiImJgaWlpdDbrw1ycnIQHx+P6OhoxMTE4MOHD1BRUcGAAQNgbW0NKysr6OnpMR2zwviEIKuQj8wCHjILeLh57wEKi0vQybgjVGSkUF9JBvWVZKCtKA3pXxwsS4kHLWYY9PnzZzRo0ACLFi3CggULhNp2UVERrKyscPfuXVy+fBlt27YVavtU+Qgh4HK5mDJlClq2bIljx46hZcuWTMeiKKqamTlzJg4cOICMjIw/7gomTIQQCASC3y7g/x0+n4+LFy/Cy8sLM2bMwJgxY6CoqFjhDNOnT8eOHTsQEhKCIUOG/PZ6URYzhBBYWFggNTUVDx48+OuCjPo1gUCA5ORkxMTEIDo6GomJieDz+WjTpg2srKxgbW2N3r17V4uNIMrzuYSP5OwiJGUXoYj/7TFZCoDgX9f8+/cK0iwYaymgo5YC1OUq9/+Kqjy6mxmD1NXVMXToUPj5+UEgEPz5hgpQUFDAqVOnoKenB0tLy78+0Zmqmry8PIwcORJjxozByJEjce3aNVrIUBT1S15eXsjJyRHrafRXr17FkCFDIBAIKrWVP4/Hw+PHj/H8+XM8fvwYU6dOrXAhAwAsFgtbtmyBq6sr3NzcftpRTZxYLBZ2796NjIwMrFq1irEcNYWUlBQ6deqEBQsW4PLly8jOzsaxY8fQo0cPHD16FGw2G5qamrCzs8POnTuRkpLCdOQyRXwBotO/Ys/DXFx7X1hWyAA/FjL//X0Rn+Da+0LseZiL6PSvKOYL95mO+j06MsOwy5cvo0+fPjh37hzMzMyE3n5mZiZ69eoFKSkpJCQkQEdHR+h9UN88ePAAzs7OeP36NXx8fODm5sZ0JIqiqrmePXtCRUUFsbGxIu/rwYMH6N27N9q3b4+4uLgKjwaVlpYiOzsbdevWFdpIUklJCQYOHIiEhAScP38eXbp0+eV1ohyZ+W758uVYtWoVkpOTYWRkJLJ+ajNCCB48eIDo6GhER0cjISEBPB4PzZs3L5uO1q9fP0ZGx15+KcHpV19RwCOoyoMxC4CyDAu2eqowUCt/qiYlPLSYYRghBK1bt0aXLl0QHBwskj5SU1PRs2dPNGrUCOfPn4eqqqpI+qmtCCHw9/fH5MmT0bx5cxw7dgytWrViOhZFURLAz88PHA4HL1++FOmagrS0tLL1LRcvXqzwgZV8Ph9ZWVmQkpIS+odi+fn5MDc3x4sXL5CQkPDL75/iKGaKi4vRvn176Ojo4OLFi5CSopNXRO3r1684e/Zs2ZS09PR0yMvLo1+/fmVT0lq2bAmWiNej3M4qRPybfLCAKhUy331vx6KxMjprV3zkkqoY+j+VYSwWC3v37sWyZctE1kfTpk0RExODFy9ewNHREcXFxSLrq7bJz8/H6NGj4eXlBTc3N1y7do0WMhRF/bWhQ4dCWVkZ/v7+Iuvjw4cPYLPZUFBQQExMTIULGUIICgsLsWbNGpGM7isrKyMyMhI6Ojpgs9nIyMgQeh9/Q15eHnv37kVCQoJI/z6o/6eqqgpHR0fs3bsXaWlpePToEdasWQOBQIC5c+eidevWaNq0KSZOnIjw8HDk5eUJPcP3QgYQTiHz73bi3+TjdlahkFqlykNHZqoB8m2LbJF/CnT58mWw2WzY2tri6NGjlV78SX3z8OFDODs749WrV/Dx8cGIESOYjkRRlAQaM2YMYmNj8fLlS6F/X/769SvMzMzw5s0bXLlyBc2aNfvldYWFhZCTk/tl/wKBAD179sSiRYtga2sr1Hz/9ubNG/Ts2ROqqqq4dOkS6tatW/aeOEZmvhs1ahQiIiLw5MkTOjWbQfn5+bhw4ULZlLTU1FTIysqid+/eZVPSjIyMyh21uXDhAt6+fYvhw4eX28fLLyU4mvJFVF9CGZdmanTKmQjRkZlqgMViiWU4u3fv3jh69ChOnjyJSZMmVWrxJ/VNQEAAunbtCmlpady6dYsWMhRFVRqHw8Hr169x5swZobZbXFwMR0dHPH/+HDExMb8sZJKSkuDo6Ih69erh4sWLAPDTz4atW7ciPT1d5NsWN27cGHFxcXj//j3s7OyQn58v0v7Ks3HjRrBYLMyaNYuR/qlvlJWVYWtrW7ZJwLNnz7Bp0ybIy8tj8eLFaNeuHXR1dTFmzBicOHECnz9//uH+6dOnw83NDfPmzfvl804RX4DTr75C1BsqswBEvqKbAogSLWZqGQcHB+zfvx8+Pj5YunQp03EkTn5+Pjw8PODh4QFXV1dcv34dbdq0YToWRVESzMTEBG3btoWvr6/Q2uTz+RgxYgSuXLmC8PBwdOzY8Yf3b9++DQcHB3Tq1AkPHz7Ejh070KdPHwD44ZPugoICLF++HB4eHpCRkRFavvK0atUKUVFRZed0lZaWirzP/9LW1sb69etx8OBBnDt3Tuz9U7/WokULTJkyBVFRUfj48SNiYmLg5OSEy5cvw8nJCVpaWujbty/Wrl2Ls2fP4u7duwCAdevWwcvLCzwe74f2zmfkV3mx/98gAPJ5BOcymCnOawNazNRCHh4eWL9+PVauXInt27czHUdiPHr0CCYmJggJCcGBAwfA5XLpeQQURVUZi8WCl5cXTp06haysrCq3RwjBpEmTcOLECRw9ehR9+/Yte+/mzZuws7NDly5d8PTpUwQGBuLx48cYNWrUL4uV0NBQfPnyBZ6enlXO9be6du2KkydP4syZM/D09BT60QV/w8PDA7169cKECRPoOtNqSFFREZaWlti6dSuePHmC1NRUbN++Herq6li9ejXMzc1/uD4gIACDBw9GYeG39Sufivm4m1Ms8kLmOwLgbk4xPpfwxdRj7UKLGQnw8uXLn4ZPq2r27NmYNWsWpk2bhkOHDgm17ZooMDAQXbt2BfDtYWDkyJEMJ6IoqiYZMWIEWCwWDh48WOW2li5dCh8fH+zfvx8DBw4EAFy/fh02NjYwMTFBSkoKgoOD8ejRI7i7u/92xMXX1xf9+/dH06ZNq5yrIszNzREUFITg4GDMnDlTrH0D385K8fHxwcuXL/HPP/+IvX+qYgwMDDBhwgSEh4cjJycHZmZmP4wwEkIQERGB1q1b49OnT7ibU1Tu9LKXt6/Cf8owrOrfGvM7aWN+J21cDw346bp3zx4ieLYHVvVvjUUmDbHWsh0OzfUqNyMLQHJ2UdW+UOqXaDFTTSUnJ2Py5Mlo0qQJrK2t0a9fP6xcuRLv378XWh/r16/HqFGjMGrUKMTExAit3ZqkoKAAXl5eGDVqFJydnXHjxg0YGhoyHYuiqBpGS0sLjo6O8PX1rdJ6xu3bt2PlypVYt24dPD09kZiYCCsrK5iamuLVq1c4fPgwHjx4gOHDh/9xs4Fnz57h8uXL4HA4lc5TFUOHDsWuXbuwdetWFBQUiL1/Q0NDzJ49G2vWrMGzZ8/E3j9VOTIyMrhz507Z/6N/F+vp6em4dDkBSdlF5Y7KZDy5hxfXL0JRvU65faQlXcOe0dZ4cPY0eLwS6DRrBVkFRTy6UP6zFAGQlF0EPl2vLHSinwBL/RVCCFgsFm7cuIF58+bhwoULMDY2xrhx49C8eXPcvn0b8fHxeP78OQIDA4XSJ4vFgq+vL3JycuDk5ISzZ8/C1NRUKG3XBE+ePIGzszNSUlLg7++P0aNHMx2JoqgajMPhgM1m49q1a+jevXuF7z906BCmTZuGmTNnokePHmCz2YiPj4eRkRGOHj2KIUOGVGizGS6XCw0NDQwaNKjCWYRlwoQJ+PDhA5YtW4abN2+Kvf9FixbhyJEjmDBhAs6cOSPy806oqvvy5Qvy8vKgqKiI9u3bo2vXrujYsSOMjY1hZGSEXL40Hj39VO79xrZD0c1pFPI+ZmG9Xeef3ieE4MRKb5QWFaKj9RAMXrwZsgrfzpIpzv/91tFFfIKsQj7qK9HHb2GiWzNXI+np6fDw8IC8vDymTZuGnj17QllZueybZ2JiIthsNj5+/AhZWVmh9VtYWAg2m41Hjx7h8uXLdOQBQFBQEMaPHw9dXV0cO3aMngZNUZTICQQCNG3aFAMGDACXy63QvTExMbC3t8eAAQNQWlqKc+fOoV27dliyZAkGDx5c4R0zS0tL0aRJEwwdOpTxtZWEECgpKaG4uBihoaEYPHiwWPuPjY2FlZUVAgMD4e7uLta+qcr5+vUrlJWVf/nvPjm7CDGv/3xeTe7b9LJixnHBBnQbMhoA8O7ZA2wfZgYA6GDthLSkayjK+4JGrTvAZvoyNDLs8Nt2rZqooKOWQgW/Iup36DSzamT//v14+vQpNmzYADabDRUVlR8+BSopKYGuri6eP38u1H4VFRURERGBRo0awdLSEunp6UJtX5IUFhZizJgxcHd3x+DBg3Hjxg1ayFAUJRZSUlLw9PTE0aNH8fXr17++79q1a3B0dIS6ujpiY2ORk5OD48ePIzk5ucKjMd9FRkbi/fv3jE0x+zcWiwUVFRW0bdsWrq6uOH/+vFj7t7S0hIuLC2bOnImPHz+KtW+qclRVVcv9d59ZwKvSw29W2ouyX9+NPg65/43KpN5KwL6xA5H7tvxnKKn/9U8JFy1mqpGrV69i3rx5vzwEKiUlBd7e3mjQoAFat24t9L7r1KmD2NhYyMjIgM1mIzs7W+h9VHdPnz5Ft27dEBQUBC6XiwMHDkBFRYXpWBRF1SIeHh4oKCjA0aNH/3gtIQR+fn7o3bs3iouL0bhxY4SFheHOnTuVGo35N19fX3Tp0gXt27evdBvCNnToUPTr1w8DBw7EnTt3xNr3li1bUFJSgrlz54q1X0r48ngCVGV/PAH//3ck6+LoBu8TiZh6+DykpKVRUpCP2+FHyr8XQD6PnjcjbLSYqUYaNmyIs2fPli10/Pr1K27cuIHNmzdj3LhxUFRUxJ49e0R2wGaDBg0QHx+P3Nxc2NjYIC/vz8OwNcWhQ4fQuXNnlJaW4saNG/D09KRzoymKErsmTZrA0tLyt2fOEEJw5swZdOvWDV5eXpCVlUVwcHDZAZhV/RmRkZGB6OjoajEq82/S0tI4fvw42rRpAysrK6HPUvidBg0aYO3atfD19UVCQoLY+qWEjy+o2uoKNZ36Zb9ubGgMAKjbSA/KGpoAgNx3v5/dwqti/9TPaDFTjSxYsABPnz6FmZkZnJyc4OnpiVmzZoHL5cLQ0BDBwcFo0aKFSDM0b94cMTExePLkCQYPHlzj99cvLCzEuHHj4ObmBkdHR9y8eRPt2rVjOhZFUbUYh8PB9evX8eDBgx9eJ4QgLi4OvXr1goWFBe7fvw8dHR08f/4cw4cPF9oHMAEBAZCXl8ewYcOE0p4wqaioIDIyEpqammCz2Xj79q3Y+h43bhy6deuG8ePHo6SkRGz9UsIlLVW1/ydNjDpBXkUVAJDxOBkAkPv2NfJzcwAAWk1+v425TBX7p35Gi5lqpE2bNggPD4eJiQkUFBSgrq6OSZMmITExEdu3b4eenp5YchgbGyM8PBwXL17EqFGjwOfXzEOenj17hu7duyMwMBC+vr44ePAgnVZGURTj7O3toa2tXbYJACEEMTEx6NGjBywtLVFSUoIWLVpAVVUVCQkJaNSokdD6FggE8PPzw9ChQ6Guri60doVJS0sLsbGx4PF4sLKyQm5urlj6/X72zJMnT7Bp0yax9EkJn4qM1G8ffh+cPY0NDl2xb4xj2Wvxe9Zhg0NXHFk4HrIKijAfOwcAcDMsCJsH98D2Yf0g4POhqqWDrk7ln0MnBUBZhj56Cxv9E61mmjdvjh07diA4OBi+vr5wcXGBmpqa2HP069cPR44cwbFjxzBt2rQqnXtQHR05cgSdO3dGYWEhrl+/Di8vLzqtjKKoakFOTg4jR45EYGAgTp06BVNTU1hbW4PFYiEiIgJ16tRBZmYmYmJihD5af+HCBaSmpla7KWb/pauri9jYWGRkZMDe3l5s59B06NAB06dPx4oVK5CamiqWPinhqq8k89s1M8X5X/HxTRo+vXtd9lp+bjY+vknDlw/vAAC9RozH4CVbUK95G+S+TYe8sgqMbYdiUtAZqGholdu24H/9U8JFt2amfsvX1xdjxozBsmXLsHTpUqbjVFlRURFmzJiBvXv3wtXVFT4+PlBVVWU6FkVRVBlCCPbu3YuJEycCAHr27ImlS5fCzMwMI0aMQFhYGGJiYmBmZib0vt3c3HD79m08fvy4Wn3Ao62tDW9vb8yfP/+H169du4YBAwagf//+OHHihFCPLShPXl4eDA0NYWRkhKioqGr150T9WWYBDwG/OWdG1Ea3qkMLGiGjIzPVXGlpKaP9czgcrFmzBsuWLcPu3bsZzVJVz58/R/fu3eHv7w8fHx8EBwfTQoaiqGqDEIJTp06hS5cumDhxItTU1NCpUydcvnwZ5ubmmD59Oo4dO4bDhw+LpJD5+PEjjh8/LlEj1aampjhx4gRiYmLA4XAgEIh+pygVFRXs2rULMTExOHbsmMj7o4RLW1EaCtLM/PtWkGZBW1Gakb5rMlrMVGOEEMTGxmLNmjWM5pg3bx5mzJiByZMnIyQkhNEslRUSEoLOnTsjPz8f169fx9ixYyXmhzVFUZLny5cvuHHjBj5//vzHawUCAcLCwtCpUyc4OjpCVVUV586dw5YtW5CUlIRXr15hxYoV2LVrF/bu3SuyQyODg4PB5/MxcmT5c/6rI0tLSwQGBuLgwYNi2zrZ3t4egwYNwrRp0/7q75iqPqRZLBhrKUDcTwAsAMZaCpCmzx5CR4uZaozFYuHx48dYsWIFowd1sVgsbNy4EW5ubhgxYgTi4+MZy1JRRUVFmDRpElxcXGBjY4Nbt26hQ4ffn85LURRVWcXFxZg2bRoaNmyI0aNHo3379jh16lS5IwaXLl2CsbExBg8eDA0NDVy4cAEXLlyAmZkZhg4dChUVFUycOBHLli3D6tWrMWbMGJHkJoTA19cX9vb2qFevnkj6ECVXV1ds27YNGzduxPr168XS5/bt25GXl4cFCxaIpT9KeDpqKUDcayzI//qlhI8WM9XcyJEjwefzERwczGgOKSkp+Pn5wcLCAoMGDcLNmzcZzfM3UlJS0KNHD3C5XOzZsweHDx9mZDMFiqJqj5CQECQkJCAyMhKhoaFgs9mYPXs2Tp069dO1PB4PLBYL2trauHTpEs6dO4e+ffuWva+iogITExNER0djypQpP60XEabbt2/j3r171X7h/+9MmTIFixcvxty5c+Hn5yfy/ho3boyVK1diz549uHHjhsj7o4RHXU4aHTTlxTY6wwLQQVMe6nJ0ipko0A0AJMDgwYORkpKC5ORkxqdGFRQUwMLCAk+fPkVCQgJat27NaJ7yHDt2DBwOBzo6OggJCYGxsTHTkSiKqsEIIWCxWBgyZAgKCgoQFRVV9t6kSZNw48YNXLp0CYqKin/dZnx8PKytrcHn8xEZGQkbGxtRRAcATJgwAREREXj16hWkpavfA1d5GwD8FyEEEyZMwP79+xEWFgYHBweR5uLxeDAxMQEhBDdv3oSMDF3YLSmK+QLsf5SLfB4R6SgNC4CyDAtjDDUgL03HEESB/qlKAA6Hg3v37uH27dtMR4GSkhIiIiJQv359sNlsvHnzhulIPyguLsbkyZMxdOhQWFpa4vbt27SQoShK5FgsFgoLC5Gfn//TVFZXV1coKSkhICDgr9u7efMmBg0aBAsLCxgZGYl0pCE/Px+HDh2Ch4dHtSxkKoLFYmHXrl0YPHgwhg4dikuXLom0PxkZGezbtw/37t3D9u3bRdoXJVzy0lKw1VMV+XQzAsBWT5UWMiJE/2QlgKWlJRo1agRfX1+mowAA6tati9jYWEhJSYHNZiMnJ4fpSACA1NRU9OzZE/v378fu3btx9OhROq2MoiixUVRULJsy9uXLl7LXTU1N0aNHDxw6dAjFxcV/bOfJkyewtrZG+/btERoaijFjxiA8PBwfPnwQSe7Q0FB8+fIFnp6eImlf3KSlpREUFISePXvC3t4ed+/eFWl/Xbp0waRJk7BkyRKkp6eLtC9KuAzU5GDRWFmkfbAbK8NATU6kfdR2tJiRANLS0vDw8MDhw4eRn5/PdBwAQKNGjRAXF4esrCzY2dkxnuv48eMwNjZGbm4uEhMTMWHCBMan5FEUVftMmzYNiYmJuHXrFoBv055kZGTQqVMnZGdn4+XLl7+9/82bN2Cz2ahXrx5Onz4NZWVljBgxAiwWCwcPHhRJZi6XiwEDBsDAwEAk7TNBXl4eJ0+eRIsWLWBpaYmUlBSR9rdq1Sqoq6tj6tSpIu2HEr7O2oplBY2wnhq+t8NurIxO2n8/tZSqHFrMSAhPT098+fIFoaGhTEcp07JlS8TExODBgwdwcnJCSUmJ2DN83zloyJAhYLPZuHPnDjp16iT2HBRFUQDQuXNnmJmZYe3atcjOzi77UMXU1BRPnz4Fn88v997Pnz+DzWaDxWIhNjYWdevWBQBoampi0KBB4HK5EPYy16dPn+Ly5csSvfC/PKqqqoiOjoa6ujrYbDYyMzNF1peamhq2bduGU6dO4eTJkyLrhxKNztqKcGmmBmUZVpULmu9rZFyaqdFCRkxoMSMhDAwMMGDAAHC5XKaj/KBz5844deoUzp8/j9GjR4vlwLLvXr58id69e2Pv3r3YsWMHQkJCoK6uLrb+KYqifmXbtm1ISEjAnj17yqaGRUZGwsTEpKxA+S9CCJ4+fYqsrCzExcWhcePGP7zP4XDw+PFjJCYmCjWrn58fNDQ04OjoKNR2qwttbW3ExcWhqKgIVlZWIj0TxsnJCTY2NpgyZQq+fv0qsn4o0TBQkwPHUAPtNeUBVHyU5vv17TXlMcZQg04tEyNazEgQDoeDy5cv4+nTp0xH+UH//v0RHByMI0eOYPr06UL/5PBXwsLCYGxsjOzsbFy5cgWTJ0+m08ooihIrHo/3y+93bdu2xapVqxASEoIBAwbAyckJ3t7ecHBwQIMGDX7ZFovFgomJCc6dO4dWrVr99H7//v2hr68v1LWTpaWlCAgIgLu7OxQUau75F3p6eoiLi0N6ejocHBxQWFgokn6+bz6Qk5ODpUuXiqQPSrQUpKVgrauKCUYaMK2nCAXp/3+u+O8Ds9QP97FgWk8RE4w0YK1LF/uLG/3TliCOjo6oW7dutRudAYAhQ4Zgz5492LFjB9asWSOyfkpKSjBjxgwMHjwYAwYMwJ07d9ClSxeR9UdRFPVfJSUl2LdvHwwNDZGZmfnLEenp06fj6NGj8PT0hK6uLm7cuPFXhyu2a9ful69LSUnB09MTR48e/WFzgao4ffo0Pnz4AC8vL6G0V50ZGRnh9OnTuHnzJlxdXcHj8UTSj76+PpYtW4Zt27YhKSlJJH1QoqcuJ42+DZUxpV1djG5VB1ZNVNBeUwEt1OVgoCqLFupyaK+pAKsmKhjdqg6mtKuLvg2V6TkyTCGURJk6dSrR0dEhJSUlTEf5pZUrVxIAZO/evUJv++XLl8TExITIysqSbdu2EYFAIPQ+KIqiylNUVET27NlDdHV1CYvFIi4uLiQjI+OX14ri+1N6ejqRkpIi+/btE0p7tra2pGvXrkJpS9S0tLTImjVrqtxOZGQkkZGRIZ6eniL7GVJSUkLatWtHunbtSng8nkj6oCjq/9GRGQnj5eWFDx8+4PTp00xH+aWFCxdiypQpmDBhglA3Kzh16hSMjY3x4cMHXLlyBVOnTqXTyiiKEouioiLs3r0bzZs3x8SJE9GzZ088ePAAR44cQcOGDX95jyi+PzVp0gSWlpZCGZ3PyMhAdHR0jVz4/zs2Njbw9/eHn5/fHw/grCxZWVns3bsXN2/exJ49e0TSB0VR/48WMxKmffv26Nq1a7WcagZ8+wG+detWDBs2DG5ubjh37lyV2ispKYG3tzccHR3Rr18/3LlzB127dhVSWoqiqPIVFRVhx44daN68OaZMmYK+ffvi0aNHOHToEAwNDRnJxOFwcP36ddy/f79K7QQEBEBBQQHDhg0TUjLJMWLECGzZsgXr1q3Dpk2bRNJHjx49MHbsWCxYsABv374VSR8URX1DixkJxOFwEB0djTdv3jAd5ZekpKQQEBAAMzMzDBw4ELdv365UO69evUKfPn2wc+dObN26FSdOnICGhoaQ01IURf2osLAQ27ZtQ9OmTTF9+nT0798fjx8/RlBQEFq3bi3UvkgFN0yxs7ODtrZ2lT7QEggE4HK5GDp0aK09WHj69OmYP38+Zs2ahcDAQJH08c8//0BRURHTp08XSfsURX1DixkJNGzYMCgoKCAgIIDpKOWSk5PD8ePHYWRkBGtrazx79qxC90dERMDY2BiZmZlISEjAtGnT6LQyiqJEqqCgAFu2bEHTpk0xc+ZMsNlsPHnyBIGBgWjZsqVQ+/p+3kxFv6/Jyclh1KhROHjwIIqLiyvV9/nz5/Hy5ctaN8Xsv1avXg0OhwNPT0+RTN3W0NDAli1bcOzYMURHRwu9fYqivqHFjARSU1PD0KFD4efnJ9ZzXSpKWVkZkZGR0NLSApvNRkZGxh/vKS0txezZs+Hg4IDevXvjzp07MDExEUNaiqJqq/z8fGzatAlNmzbF7NmzYWNjg6dPnyIgIAAtWrQQal+lpaUAqramxsvLCx8/fqz04YxcLhetWrVCjx49Kp2hJmCxWNizZw8cHBzg7OyMhIQEoffh6uoKc3NzTJw4EQUFBUJvn6IoWsxILA6Hg5cvX+L8+fNMR/ktTU1NxMbGQiAQwNLSEh8/fiz32vT0dPTt2xdbt27Fpk2bcPLkyXIPmKMoiqqq/Px8bNiwAQYGBpg3bx7s7e3x/PlzcLlcNGvWTKh9fZ9OlpCQgIKCAkhJVf7Hb+vWrdGzZ89KTTX7+PEjTpw4AQ6HQ0e7AcjIyODQoUMwNTWFvb19ldci/df3gundu3dYsWKFUNumKOobWsxIqB49eqBVq1ZCPUBNVJo0aYK4uDhkZmbC3t7+l59ORUZGwtjYGBkZGbh8+TK8vb3pD1qKokQiLy8P69atg76+PhYuXIhBgwbh+fPn2L9/PwwMDETSZ2xsLAYOHIh27dpBSUmpyu1xOBzEx8fj5cuXFbovODgYfD4fI0eOrHKGmkJBQQGnTp2CgYEBLC0tK/xn+ifNmzfHwoULsWnTJjx48ECobVMURYsZicViscDhcHDixAnk5OQwHeePWrdujaioKNy9exfOzs5lUy1KS0sxd+5c2NnZoWfPnkhKSoKpqSnDaSmKqom+fPmCNWvWQF9fH4sXL8aQIUPw/Plz+Pj4QF9fXyR9FhYWwt7eHmPGjMHOnTuhpaUllHadnZ2hqqoKf3//v76HEIL9+/fDwcEBOjo6QslRU6ipqSE6OhrKyspgs9n48OGDUNufM2cOmjVrhnHjxlXr6eEUJYloMSPBRo4cCYFAgODgYKaj/BUTExOEhYUhPj4enp6eePXqFfr164fNmzdj48aNOHXqFJ1WRlGU0H358gWrVq2CgYEBli9fDhcXF6SkpGDPnj3Q09MTWb8FBQUwNzdHYmIi4uLi0KRJE6G1raysDFdXV/j7+5dtJvAnt27dwv3792v9wv/y1KtXD3FxccjLy4OVlRW+fPkitLbl5eXh4+ODq1evVtujFShKUtFiRoLp6OjAwcEBvr6+Fd7ekykWFhY4ePAggoOD0bp1a6Snp+PSpUuYOXMmnVZGUZRQff78GStWrICenh5WrVqF4cOHIyUlBbt27RJqYfErpaWlcHZ2xt27dxEVFYU2bdoIvQ8Oh4M3b94gLi7ur67ncrlo3Lgx2Gy20LPUFAYGBoiNjUVqaiocHR1RVFQktLb79u2L0aNHY+7cuUIf+aGo2owWMxKOw+Hg/v37uHXrFtNR/gqPx0NSUhIIISgqKoKHhwe6d+/OdCyKomqQT58+YdmyZdDT08PatWsxcuRIpKamYseOHWjcuLHI+xcIBPD09ER8fDxOnDghsh0Zu3Tpgnbt2v3VJ/35+fk4dOgQPDw8IC0tLZI8NUX79u0RERGBxMREuLm5/fXI19/YsGEDpKSkMHPmTKG1SVG1HS1mJBybzUbjxo0lYiOAN2/ewMzMDBs3bsT69euxZMkSrFy5UiKyUxRV/X38+BFLliyBnp4e1q9fD09PT6SmpmLbtm1o2LChWDIQQjBr1iwEBwcjMDBQpKMg39dOnjp16o+f9IeGhuLr16/w8PAQWZ6apHfv3ggJCcGpU6cwYcIEoc1+0NLSwoYNGxAUFIQzZ84IpU2KqvUIJfEWL15MVFVVSV5eHtNRyhUdHU20tLRIo0aNSEJCAiGEEIFAQCZOnEikpKTIiRMnGE5IUZSkys7OJgsXLiSqqqpEUVGRzJw5k7x7946RLGvXriUAyI4dO8TSX05ODpGXlycbNmz47XW9evUi5ubmYskkClpaWmTNmjVi7zcgIIAAIAsXLhRamwKBgPTp04e0aNGCFBYWCq1diqqtaDFTA6SmphIAxN/fn+koPyktLSULFiwgAIi1tTXJysr64X0ej0eGDh1K5OXlyfnz55kJSVGURMrKyiLz588nKioqRElJicyePZu8f/+esTz79+8nAMiSJUvE2u+wYcNI69atiUAg+OX7jx8/JgDIkSNHxJpLmJgqZgghZOPGjQQA2bp1q9DafPToEZGVlRX7vxWKqoloMVNDmJubExMTE7J27VrStGlT4uXlxXQk8ubNG9K7d28iLS1N1q5dS/h8/i+vKyoqIubm5kRVVZXcuXNHzCkpipI0Hz58IHPnziXKyspEWVmZzJ07l3z48IHRTGFhYURKSoqMHz++3KJCVM6cOUMAlI16/9fs2bNJ3bp1SVFRkVhzCROTxQwhhMyZM4cAIEFBQUJrc9GiRUROTo48efJEaG1SVG1EixkJV1paSsLDw0mnTp0IACIlJUUAkIEDBzKaKzY2lmhra5NGjRqRS5cu/fH6L1++kK5duxIdHR3y/PlzMSSkKErSvH//nsyePZsoKSkRFRUVMn/+/J9Ge5lw/vx5Ii8vT5ydnQmPxxN7/3w+nxgYGBAPD4+f3ispKSE6Ojpk2rRpYs8lTEwXMwKBgHh4eBAZGRkSFRUllDYLCgpIs2bNSL9+/cReAFNUTUI3AJBwHh4ecHBwwN27dwF820VHVlYW9evXZyQPj8fD4sWLYWVlhU6dOiEpKQm9e/f+432qqqqIjIyEhoYG2Gw23r17J4a0FEVJgszMTMycORP6+vrYu3cvZsyYgbS0NKxZs0Zoh1BWVlJSEhwcHNC7d28cPHiQkZ3CpKSk4OnpiaNHj/50Nsrp06fx4cMHeHl5iT1XTcJisbBv3z7Y2NjAyckJiYmJVW5TUVERe/bswYULFxAYGCiElBRVO9FiRsKNGDEC8vLyP+y0QghhpJh59+4dzM3NsWbNGqxevRpRUVHQ1tb+6/u1tbURFxeHkpISWFlZ4dOnT6ILS1FUtffu3TvMmDEDBgYG8PX1xaxZs5CWloZVq1ZBU1OT6Xh48eIFrKys0KpVK5w4cQLy8vKMZRk9ejSKiopw5MiRH1739fWFiYkJ2rVrx1CymkNGRgZHjhxBly5dYGtri4cPH1a5TQsLC7i6umLWrFnIyckRQkqKqn1oMSPhLC0tceHCBaiqqkJK6ttfJ4/HE3sxEx8fj44dO+LZs2c4f/485s+fX5anInR1dREXF4fXr1/DwcEBhYWFIkhLUVR19vbtW0ybNg1NmzaFv78/5s6di7S0NKxYsQJ169ZlOh6Ab4UWm81GnTp1EBUVBVVVVUbzNG7cGFZWVj+cOfPmzRvExMSAw+EwmKxmUVRURHh4OHR1dWFpaYlXr15Vuc3NmzejtLQUc+bMEUJCiqp9aDFTA5iamiIxMRH16tUre01cxQyfz8eSJUtgaWmJjh07Ijk5GX369KlSm4aGhoiKisLt27fh4uICHo8npLQURVVnb968wZQpU9C0aVMEBgZi/vz5SEtLw7Jly6ChocF0vDKfPn2ClZUVSkpKEBcXV6ERaFHicDi4ceMG7t+/DwAICAiAgoICXFxcGE5Ws9SpUwcxMTGQl5cHm81GVlZWldqrX78+1q1bBz8/P1y+fFlIKSmq9qDFTA3Rpk0b3Lhxo6ygUVRUFHmf7969g4WFBVavXo2VK1ciOjoaOjo6Qmnb1NQUx48fR3R0NMaMGSO0A8soiqp+Xr9+jUmTJqFZs2Y4dOgQFi9ejFevXmHJkiWoU6cO0/F+UFhYCAcHB7x+/RqxsbHQ09NjOlIZOzs76OjogMvlQiAQgMvlwsXFBWpqakxHq3Hq16+PuLg4fP78GTY2Nvj69WuV2hszZgy6d++OcePGoaSkREgpKap2kGE6ACU8jRs3RlJSEry9vdG3b1/wCUFWIR+ZBTxkFvCQxxOALyCQlmJBRUYK9ZVkUF9JBtqK0pBmsSrU19mzZ+Hm5gYWi4WzZ8+iX79+Qv96rKyscODAAbi5uUFbWxvr168Xeh8URTEnPT0da9euBZfLhZqaGpYtW4bJkyczPmWrPDweDy4uLrh16xbOnj0LIyMjpiP9QFZWFqNGjQKXy4WlpSXS0tLown8RatasGWJiYtC3b18MGjQIkZGRlV43JSUlhb1796JTp07YsGEDFi5cKOS0FFVzsQj9yLvG+VzCR3J2EZKyi1DE//bXKwVA8K9r/v17BWkWjLUU0FFLAepyv9+Jh8/nY9WqVVi+fDkGDBiAoKCgH6a3icL27dsxbdo0rF+/HrNnzxZpXxRFiV5aWhrWrl0Lf39/qKurY9asWZg4cWK1LWKAbxureHp6IigoCOHh4bC2tmY60i89ffoUrVu3Rvfu3ZGbm4tHjx6BVcEPq6ojbW1teHt7Y/78+UxH+cnFixdhaWkJe3t7HDlypEo72s2ZMwc7duzAgwcP0KxZMyGmpKiai04zq0GK+AJEp3/Fnoe5uPa+sKyQAX4sZP77+yI+wbX3hdjzMBfR6V9RzP/v1d9kZmaCzWZj+fLlWLZsGWJiYkReyADA1KlTsWjRIsyZMwcBAQEi74+iKNFITU0Fh8NBixYtEBYWhtWrV+Ply5eYO3dutS5kAGDu3LkICAhAQEBAtS1kAKBVq1YwNTXFtWvXwOFwakQhU9317dsXR44cwYkTJzB58uQqTYteunQp6tWrh4kTJ9Lp1RT1l+g0sxri5ZcSnH71FQW8b9/8Kvot8Pv193KKkfK5BLZ6qjBQkyt7//z58xg+fDgIIThz5gz69+8vnOB/acWKFfjw4QM4HA7q1q0LBwcHsfZPUVTlpaSkYPXq1QgMDISmpib++ecfjB8/HsrKykxH+ysbNmzAhg0bsHXrVri5uTEd54+aNWuGa9euVXkzFurvOTo6Yv/+/fDy8oKOjg6WL19eqXaUlZWxc+dO2Nvb4+jRoxg2bJiQk1JUzUNHZmqA21mFOJryBQU8UuEi5r8IgHwewdGUL7idVQg+n4+VK1fC3NwchoaGSE5OFnshA3w7sGz37t1wdHSEi4sL3fGFoiTAixcvMHr0aLRq1QrR0dHYsGEDXr58iZkzZ0pMIRMQEIA5c+Zg4cKFmDZtGtNx/ogQguTkZMjIyCAiIoLpOLWKp6cn/vnnH6xYsQI7d+6sdDt2dnZwcnLC9OnT6XlrFPUXaDEj4W5nFSL+TT6Aio/GlOd7O/Fv8sFZvglLly7FkiVLEBcXx8hhnN9JS0sjODgYPXr0gL29Pe7evctYFoqiyvfs2TOMHDkSrVq1QlxcHDZt2oTU1FTMmDEDSkpKTMf7a+Hh4eBwOBgzZgxWrlzJdJy/cuvWLTx8+BAWFhbw8/MDn89nOlKtMmfOHHh7e2Pq1Kk4fPhwpdvZtm0bCgoKsGDBAiGmo6iaiRYzEuzll5KyQkZUWg3yROj5RCxdurRKixqFRV5eHmFhYWjWrBmsrKyQmprKdCSKov7nyZMnGDFiBNq0aYNz585h27ZtSE1NxbRp08SyXbwwXb58GS4uLhg4cCD27NkjMWtPfH190bhxYyxduhQZGRmIjY1lOlKtwmKxsGHDBri7u2PkyJGV/vNv1KgRVq1ahb179+LatWtCTklRNQstZiRUEV+A06++QtQ/XlkA3tZtXu6mAExQU1NDdHQ0VFVVwWazkZmZyXQkiqrVHj9+jOHDh8PQ0BAXL17Ejh078OLFC0yePBkKCgpMx6uwu3fvwt7eHt27d0dwcHC1+CDnb+Tn5+Pw4cPw9PSEiYkJ2rdvDy6Xy3SsWkdKSgq+vr6wtLSEk5MTrl+/Xql2Jk2ahE6dOmHcuHEoLS0VckqKqjloMSOhzmfkC2WNzJ98X0NzLkO0I0AVpaOjg7i4OBQUFMDa2hqfP39mOhJF1ToPHz7EsGHDYGRkhISEBOzevRsvXrzAxIkTJbKIAb7tuGZlZYWmTZvi5MmTEvV1HDt2DHl5efDw8ACLxQKHw0F4eDjev3/PdLRaR1ZWFiEhIejYsSNsbGzw+PHjCrchLS0NHx8fPHjwANu2bRNBSoqqGWgxI4E+FfNxN6dY5IXMdwTA3ZxifC6pXnOv9fX1ERcXh7S0NAwcOBBFRUVMR6KoWuH+/ftwdnZG27Ztce3aNezduxcvXrzA+PHjK31oYHXw/v17sNlsqKioIDo6GmpqakxHqhBfX1+Ym5tDX18fAODm5gZpaWkcPHiQ2WC1lJKSEiIiItCwYUOw2Wy8fv26wm107twZU6ZMwdKlS/Hq1SsRpKQoyUeLGQl0N6eo3OllL29fhf+UYVjVvzXmd9LG/E7auB4a8MM1/NJSnPHZgPX2XbDIpCHWWrXH6Y2LUFyQV26fLADJ2dWvWGjbti1Onz6NGzduwNXVFTwej+lIFFVj3b17F05OTmjfvj1u3bqF/fv349mzZxg7dizk5OT+3EA19vnzZ1hZWaGgoABxcXFiOUNLmJ48eYIrV67Ay8ur7LW6deti8ODB8PX1pWeWMERDQwOxsbGQkZEBm81GTk5OhdtYuXIlNDQ0qnyGDUXVVLSYkTB8QpCUXVTuqEzGk3t4cf0iFNXrlNtG6PJpOOuzHp/evUbdxnrI/5iNK4d8cGCaGwSCX6+NIQCSsovAr4bfSHv27Iljx44hIiIC48ePp9/sKUrIkpKSMGjQIHTs2BHJycngcrl49uwZOByOxBcxAFBUVISBAwciLS0NsbGxMDAwYDpShXG5XNStWxeOjo4/vM7hcPD06VNcuXKFmWAUGjZsiLi4OOTk5MDGxgZ5eeV/cPgrqqqq2L59O06fPo2wsDARpaQoyUWLGQmTVchHEb/8h3Vj26FYdikVnrtCfvl+xuO7SI46BgCwm7Ua3icS4bbBH8C3UZ1H56PKbbuIT5BVWL2mmn1na2sLf39/cLlcLFy4kOk4FFUj3L59GwMHDkSnTp3w4MEDBAQE4MmTJ/D09ISsrCzT8YSCx+PB1dUV169fx+nTp9GuXTumI1VYSUkJDhw4gJEjR/40za9fv34wMDCgGwEwrEWLFoiJicHjx4/h5OSEkpKSCt0/aNAg2NnZYerUqfj69auIUlKUZKLFjITJLPj9NCrlOnUhq1D+FqjPrpwt+3XbAXYAgFa9LSAj/22R67Or56rUP5Pc3d2xefNmrF27Flu2bGE6DkVJrFu3bsHe3h5dunTBkydPEBgYiMePH2PUqFE1pogBvh0wOX78eERERODYsWPo2bMn05Eq5fTp08jKyvphitl3UlJS8PLyQkhICL58+cJAOuq7Tp064dSpU7hw4QJGjRpV7kyIX2GxWNi5cydyc3OxePFiEaakKMlDixkJk1nAq9Jf2qf3b8t+rVJXG8C3H3bKdep+ez/zTbn3SqF6FzMAMGPGDMybNw/e3t500StFVdCNGzdga2uLrl274vnz5wgKCsKjR4/g7u4OGRkZpuMJ3cKFC8HlcuHn5wc7Ozum41Sar68vunXrhrZt2/7y/dGjR6OoqAhHjhwRczLqv8zMzHD48GGEhIRg2rRpFZoWraenh+XLl2PHjh24c+eOCFNSlGShxYyEyeMJIIoTX/7mG6oAQD6v+pw3U541a9bAy8sLHh4eiIyMZDoORVV7iYmJsLKyQrdu3fDy5UscOnQIDx8+LNsNqybasmUL1q5di02bNmHkyJFMx6m0169fIzY29pejMt81atQI1tbW8PX1FWMyqjyDBw/G3r17sXPnTqxatapC906bNg1t27bF2LFjwedXz2nfFCVutJiRMHxB1Ra316nXsOzXeR+zAAACgQAFn3O/vV+/8W/v51Wxf3FgsVjYu3cv7O3t4ezsTBe+UlQ5rly5AjabjR49euDNmzc4cuQI7t+/D1dX1xpbxADAwYMH4e3tjblz58Lb25vpOFUSEBAARUVFDBs27LfXcTgc3Lx5E/fu3RNTMup3xowZg9WrV2PJkiXYs2fPX98nKysLHx8f3LlzB7t27RJhQoqSHLSYkTDSUuVtyvx3WvYYUPbrB2dPAwCeXo4Hr7jof+/3/+39MlXsX1xkZGRw+PBhmJiYwM7ODg8ePGA6EkVVG5cvX4a5uTl69eqFd+/eISQkBPfu3YOLi0uNLmIAIDIyEh4eHvD09MTatWuZjlMlAoEAfn5+cHFxgaqq6m+vtbW1Rb169ehGANXI/PnzMW3aNEyaNAnHjh376/tMTU0xbtw4LFq0CBkZGSJMSFGSgRYzEkZFRuq3f2kPzp7GBoeu2DfGsey1+D3rsMGhK44sHI9Ghh3QwWowAOD0xoXYPLgHgmd7AAD0jU1haGZTbttSAJRlJOefjIKCAk6dOgV9fX1YWloiLS2N6UgUxaiLFy+if//+6NOnD7KyshAaGoq7d+/C2dkZUlKS83+7sq5cuQJnZ2fY2dnBx8cHLJZkfDhTnnPnziEtLe23U8y+k5WVxahRo3Dw4EF6wHA1wWKxsHnzZgwfPhxubm44c+bMX9+7du1aKCkpYdq0aSJMSFGSoeb/9Kph6ivJ/HbNTHH+V3x8k4ZP7/7/pOH83Gx8fJOGLx/eAQCcl+9E/zGzUKd+Y3x8kwZlDU30cB2D0dsP//aBRvC//iWJuro6YmJioKioCDabjQ8fPjAdiaLEihCC8+fPo1+/fujXrx9yc3MRFhaGpKQkODk51YoiBgAePHgAOzs7dO3aFYcPH64RGxr4+vqiTZs26N69+19d7+npidzcXJw8eVK0wai/JiUlBX9/f5ibm2PQoEG4devWX91Xp04dbNmyBcePH6drQ6laj0XoCYMSJbOAh4Cnnxjrf3SrOhJX0ABAamoqevbsiYYNG+L8+fNQU1NjOhJFiRQhBOfOncPy5ctx+fJldOrUCUuXLoW9vb3Ej0hUVFpaGnr27AltbW1cvHgR6urqTEeqspycHDRs2BBr1qzBzJkz//q+Pn36QE5OrkKjANWBtrY2vL29MX/+fKajiER+fj7Mzc3x4sULJCQkoFWrVn+8hxACKysrPH36FA8fPoSysrIYklJU9VM7PpKrQbQVpaEgzcyDiII0C9qKkjmfvmnTpoiNjUVKSgoGDRpEp1lQNRYhBPHx8ejduzfMzc1RWFiIiIgI3Lp1Cw4ODrWukPnw4QPYbDYUFBQQExNTIwoZAAgKCgIhBO7u7hW6j8Ph4OzZs0hNTRVRMqoylJWVERkZCR0dHbDZ7L9aC8NisbB79268f/8eK1asEENKiqqeaDEjYaRZLBhrKUDcjyMsAMZaCpCW4Aeh9u3bIyIiAlevXsWIESPotpZUjUIIQWxsLHr27Ak2m43S0lJERkbixo0bsLOzq3VFDAB8+fIF1tbW+PLlC+Li4lC/fn2mIwkFIQS+vr4YOHAgdHR0KnTvkCFDoKamBn9/fxGloyqrbt26iI2NBQCw2Wx8/Pjxj/c0a9YMixYtwqZNm+hOdVStRYsZCdRRSwHinhtI/tevpOvduzdCQkJw8uRJTJ48uUIHllFUdUQIQXR0NLp37w4rK6uy31+7dg02Nja1sogBgKKiIgwaNAgvXrxAbGwsmjVrxnQkobl58yYePHjwVwv//0tJSQnDhw+Hv78//UCnGmrcuDHi4uLw4cMH2NnZIT8//4/3zJ49Gy1btsS4ceMgEFT/s+AoSthoMSOB1OWk0UFTXmyjMywAHTTloS4nmVPM/sve3h7+/v4SfeI3RRFCcPr0aXTr1g02NjaQlpZGbGwsrl69Cisrq1pbxAAAn8/HiBEjcOXKFURERKBDhw5MRxIqX19fNGnSBBYWFpW638vLCxkZGWWjAFT10qpVK0RHR+P+/ftwdnZGaWnpb6+Xk5PD3r17ce3aNezfv19MKSmq+qDFjITq30gZyjIskRc0LADKMiz0b1SzFha6u7vD2tq6Vj/wUZKJEILw8HB07doV9vb2kJeXR3x8PBISEsBms2v9v2lCCCZNmoSwsDAcPXoUffr0YTqSUOXl5eHw4cPw8PCo9JlAnTt3RocOHeDr6yvkdJSwdOnSBWFhYThz5gw8PDz+OOLSp08feHp6Yt68eXj//r2YUlJU9UCLGQklLy0FWz1VkU83IwBs9VQhL13z/qn8zZa0Dx8+xKVLl3D9+nXweDwxpKKoXyOE4OTJk+jcuTMGDhwIFRUVnDt3DpcuXYK5uXmtL2K+W7JkCXx8fMrWlNQ0x44dQ35+Pjw8PCrdBovFAofDQUREBH3wrcbMzc0RHByMQ4cOwdvb+4/TotevXw8ZGRl4e3uLKSFFVQ817wm1FjFQk4NFY9GOmLAbK8NATU6kfVRXHA4HLi4usLa2xtixY2FmZsZ0JKoWEggEOHHiBIyNjTFo0CCoq6vj/PnzuHDhAszMzGgR8y/bt2/HqlWrsG7duio97Fdnvr6+sLCwgL6+fpXacXNzg7S0NAIDA4UTjBIJZ2dn7Nq1C9u2bcPatWt/e62mpiY2btyIQ4cOIS4uTkwJKYp5tJiRcJ21FcsKGmE90nxvh91YGZ20FYXUquTIyclBjx49EBUVBW9vb1y+fBkRERHg8XgYMWIE0/GoWkIgECA0NBQdO3aEk5MTtLS0cPHixbIDMKkfHTp0CNOmTcOsWbMwZ84cpuOIxOPHj3H16tVKLfz/Lw0NDTg5OcHX15duhFLNTZgwAcuXL8fChQv/uCZm5MiR6NevHyZOnIjCwkIxJaQoZtFipgborK0Il2ZqQllD832NjEsztVpZyJSUlKB///7Iy8tDTEwMRo0ahU6dOkFXVxejR49GVlbWHxdjUlRVCAQChISEoEOHDnB2dkb9+vWRkJCAM2fO1Lj1H8Ly/f/qqFGjsG7dOqbjiAyXy4WmpqbQps95eXnh2bNnuHLlilDao0Rn8eLFmDx5MsaPH48TJ06Uex2LxcKePXuQnp6ONWvWiDEhRTGHFjM1hIGaHDiGGmivKQ+g4qM0369vrymPMYYatXJqGY/Hg7u7OwQCAY4cOYL27dv/sMD2zp07SE9Pp9uZUiLB5/Nx+PBhtGvXDi4uLmjUqBGuXr2KuLg49OzZk+l41da1a9fg5OQEKysr7N+//6/WwkmikpISBAYGwt3dHfLy8kJps1+/fmjatCndCEACsFgsbNu2DUOHDoWrqyvOnz9f7rWtW7fGvHnzsG7dOjx+/FiMKSmKGTXzu34tpSAtBWtdVUww0oBpPUUoSP9/SfPfv2ipH+5jwbSeIiYYacBat2Yu9v8b79+/x6NHjzBt2jQYGhr+8N6pU6dw6dIlTJkyBQoKkn/eDlV98Pl8BAcHo23bthg+fDj09PSQmJiImJgYdO/enel41dqjR49ga2uLTp064ejRo5CVlWU6kshEREQgKytLKFPMvpOSkoKXlxeOHTuGz58/C61dSjSkpKRw4MAB9OvXDwMHDsSdO3fKvXbBggXQ09PD+PHj6TRCqsarnU+tNZy6nDT6NlTGlHZ1MbpVHVg1UUF7TQW0UJeDgaosWqjLob2mAqyaqGB0qzqY0q4u+jZUrjHnyFTWhw8fkJOTg27duv3welRUFHx9fdG8eXO6ZoYSGh6Ph4MHD8LQ0BAjRoxAs2bNcP36dURFRcHU1JTpeNVeeno62Gw2GjVqhPDwcCgpKTEdSaR8fX1hamqKtm3bCrXdUaNGoaioCEeOHBFqu5RoyMnJ4fjx42jTpg2srKzw/PnzX16noKCAPXv24NKlSwgICBBvSIoSM1rM1GDSLBbqK8mgo5YCrHRV4NRUDS7N1eHUVA1WuiroqKWA+koykKa7IQEAVFVVISsrixcvXpS9tnv3bmzduhX5+fkIDg6GmpoagwmpmoDH4+HAgQMwNDTEyJEj0apVK9y8eROnT5+GiYkJ0/EkQnZ2NthsNmRlZRETEwMNDQ2mI4nU69evERsbK9RRme8aNWoEGxsbOtVMgqioqCAyMhKamppgs9l4+/btL68zNzeHm5sbZs+ejezsbDGnpCjxocUMRf1P8+bNMWvWLIwePRp2dnbQ19fHjh07oK+vj6ioKFrIUFVSWloKf39/tG7dGqNHj4aRkRHu3LmD8PBwdOnShel4EuPr16+wsbFBbm4u4uLi0LBhQ6YjiZy/vz+UlJTg4uIikva9vLxw69Yt3L17VyTtU8KnpaWF2NhY8Hg8WFpaIjc395fXbdq0CXw+H7NnzxZzQooSH1rMUNS/TJkyBYGBgejfvz+GDRuGwMBA7N27l66ToSqttLQUXC4XrVq1gqenJzp06ICkpCSEhYXB2NiY6XgSpbi4GIMHD8aTJ08QExODFi1aMB1J5AQCAfz8/ODi4gJVVVWR9GFra4t69eqBy+WKpH1KNHR1dREXF4e3b9/C3t4eBQUFP11Tr149rFu3DgEBAbh48SIDKSlK9FiErgyjKIoSupKSEhw4cABr1qxBWloahgwZgsWLF6N9+/ZMR5NIfD4fw4cPx8mTJxEbG1trztqJj48Hm83G1atXRbohxLx587Bv3z68ffu2Wn54o62tDW9vb8yfP5/pKNXO9evX0b9/f5iZmSEsLOynjTAEAgF69+6Njx8/Ijk5WWi74VFUdUFHZijqL3yv+S9fvsxwEqq6Ky4uxt69e9GiRQuMGzcOJiYmuH//Po4dO0YLmUoihGDq1KkIDQ3F4cOHa00hA3xb+G9oaCjyTSE8PT2Rm5uLsLAwkfZDCV+3bt1w4sQJxMXFgcPhQCAQ/PC+lJQUfHx88OLFC2zYsIGhlBQlOrSYoai/FBQUhL59++Lo0aNMR6GqoeLiYuzevRstWrTAxIkT0aNHD9y/fx9Hjx4V+g5Utc2KFSuwe/du7N27F4MHD2Y6jthkZ2fj5MmT8PLyAkvEG7W0bNkSffr0oRsBSChLS0scOHAABw8exJw5c37ajrlt27aYOXMmVq1aVe4OaBQlqWgxQ1F/gcViYfjw4RgxYgTc3d0RFxfHdCSqmigqKsLOnTvRrFkzTJkyBb1798bDhw9x+PBhGBkZMR1P4u3evRvLli3DmjVrMGbMGKbjiFVQUBAIIXB3dxdLf15eXjh37hxSU1PF0h8lXK6urti+fTs2bdr0yxGYJUuWoEGDBpg4cSI9e4aqUWgxQ1F/SUpKClwuFxYWFhg8eDBu3LjBdCSKQYWFhdi+fTuaNWuGadOmwczMDI8ePUJwcDDatGnDdLwa4ejRo5g8eTKmT5+OefPmMR1HrAgh4HK5cHR0hLa2tlj6HDJkCNTU1ODn5yeW/ijhmzx5MpYsWYK5c+f+9PeopKSEXbt24cyZMzh8+DBDCSlK+GgxQ1EVICsri2PHjqFDhw6wsbHBkydPmI5EiVlhYSG2bt2Kpk2bwtvbGxYWFnj8+DEOHjyIVq1aMR2vxoiLi4O7uzvc3NywadMmkU+zqm5u3LiBBw8eiORsmfIoKSnBzc0NAQEB4PF4YuuXEq5ly5Zh/PjxGDNmDE6ePPnDezY2NnB2dsaMGTPK3c6ZoiQNLWYoqoKUlJRw+vRpNGjQAGw2G69fv2Y6EiUGBQUF2Lx5MwwMDDBr1ixYW1vjyZMnCAgIQMuWLZmOV6PcuHEDgwcPhrm5Ofz8/CAlVft+VPn6+kJXVxfm5uZi7dfLywsZGRmIjY0Va7+U8LBYLOzcuRODBw/GsGHDcOnSpR/e37p1K4qKiujOcFSNUft+QlCUEGhoaCAmJgZSUlKwtLRETk4O05EoEcnPz8fGjRthYGCAuXPnws7ODs+ePYOfnx+aN2/OdLwa58mTJ7CxsUH79u1x7Nixn7aZrQ3y8vJw5MgReHh4QFpaWqx9d+rUCR07dqQbAUg4aWlpBAUFoVevXrC3t0dycnLZew0bNsTq1avh4+ODq1evMheSooSEFjMUVUmNGjVCXFwcsrOzYWtri/z8fKYjUUKUl5eHdevWQV9fH/Pnz8fAgQPx7Nkz+Pr6omnTpkzHq5Fev34NNpuN+vXr4/Tp01BWVmY6EiNCQkKQn58PDw8PsffNYrHg5eWF06dPIzMzU+z9U8IjLy+PsLAwtGjRAlZWVkhJSSl7b8KECejSpQvGjRuH0tJSBlNSVNXRYoaiqqBly5aIjo7Gw4cP4eTkhJKSEqYjUVX09etXrF27Fvr6+li8eDGcnJzw4sUL7Nu3DwYGBkzHq7FycnJgaWkJFouF2NhY1K1bl+lIjOFyuWCz2dDT02Okfzc3N0hLSyMwMJCR/inhUVVVRXR0NNTV1cFms8sK1KdPn6Jfv3549OgRtmzZwnBKiqoaWsxQVBV17twZp06dwvnz5zF69OifDiyjJMOXL1+wevVq6OvrY9myZRg6dChevHiBvXv3MvZQWVvk5+fD1tYWWVlZiI+PR6NGjZiOxJjHjx/j6tWrYl34/18aGhoYMmQIuFwu3cK3BtDW1kZcXByKiopgZWWFmJgYmJqaYuPGjRgxYgSWLVuGtLQ0pmNSVKXRYoaihKB///44dOgQjhw5gunTp9MHAAny+fNnrFy5Evr6+li5ciWGDx+OlJQU7N69G7q6ukzHq/FKSkrg5OSEhw8fIjo6utZvpsDlcqGlpQUHBwdGc3h5eeHZs2dISEhgNAclHHp6eoiLi0NKSgpsbGyQn58PKSkptGrVCpqampg0aRL9uUVJLFrMUJSQODk5Yc+ePdixYwdWr17NdBzqDz59+oTly5dDX18fq1evhru7O1JSUrBjxw40btyY6Xi1gkAgwOjRo3H+/HmcPHkSXbp0YToSo0pKSnDgwAG4u7tDXl6e0Sx9+/ZFs2bN6EYANcjt27dRUFAAQggEAgEIIThz5gx27NiBqKgoHD9+nOmIFFUpMkwHoKiaZNy4ccjKysLixYuhra2NcePGMR2J+o/c3Fxs3boV27ZtQ3FxMcaPH485c+agQYMGTEerVQghmD59Oo4cOYKQkBAMGDCA6UiMCw8PR3Z2NqNTzL6TkpKCp6cnVq1ahe3bt0NdXZ3pSFQVxMfHY9SoUT+8RgjB5cuXER4eDgcHB0ybNg1sNhtqampl1/AJQVYhH5kFPGQW8JDHE4AvIJCWYkFFRgr1lWRQX0kG2orSkK5lZ0FR1QctZihKyBYuXIisrCxMmDABmpqaGDJkCNORKAAfP37Eli1bsH37dpSWlmLChAmYPXs26tevz3S0Wmn16tXYsWMH9uzZQ/+P/A+Xy0X37t1hZGTEdBQAwOjRo7F48WIcPnwY48ePZzoOVQWtWrWCnZ0doqKiAKBsbSePx8P58+exY8cOGBoaYtGiRdi+fTs+l/CRnF2EpOwiFPG/TT+TAvDvFaFSAJL/dyqBgjQLxloK6KilAHU58W4nTlF0mhlFCRmLxcKWLVswbNgwuLm54ezZs0xHqtVycnKwcOFC6OvrY/PmzRg7dixevnyJTZs20UKGIT4+Pli8eDFWrFhBH5L/Jz09HbGxsdViVOa7hg0bwtbWFlwul+koVBXp6uoiIiICGRkZ+Oeff344I2vz5s3Q1dXF6tWrERJ2EtHpX7HnYS6uvS8sK2SAHwuZ//6+iE9w7X0h9jzMRXT6VxTz6UY4lPjQYoaiREBKSgoBAQEwMzODo6Mjbt++zXSkWicrKwvz5s2Dvr4+tm3bhgkTJuDly5fYsGED6tWrx3S8Wis0NBQTJkzAlClTsGjRIqbjVBsBAQFQVlaGi4sL01F+4OXlhVu3bv1w6CIluerXr4/Zs2fj2bNnuHr1Knr06FG26YaDxwQsiLyDeznFAICKbgfw/fp7OcXY/ygXL7/Qowoo8WARun0FRYlMfn4+BgwYgNTUVCQkJNT6nZrE4cOHD9i4cSN2794NFouFyZMnY+bMmdDS0mI6Wq139uxZ2NjYwMnJCUFBQZCSop+nAQCfz0fTpk1hYWFR7Rbcl5aWQldXF0OGDMGOHTsYy6GtrQ1vb2/Mnz+fsQw12e2sQsS/yQcLFS9ifuV7OxaNldFZW1EILVJU+ehPEooSIWVlZURGRkJLSwtsNhsZGRlMR6qx3r9/j1mzZsHAwAB79+7F9OnTkZaWhrVr19JCphq4ffs2HB0dYWZmhoCAAFrI/MvZs2eRnp4ODofDdJSfyMrKYtSoUQgKCkJhYSHTcSgR+F7IAMIpZP7dTvybfNzOov9uKNGiP00oSsQ0NTURFxcHgUAAS0tLfPz4kelINUpmZia8vb1hYGCA/fv3Y+bMmUhLS8OqVaugqanJdDwKwLNnz2BtbQ0jIyMcP34ccnJyTEeqVrhcLoyMjNCtWzemo/ySl5cXPn36hLCwMKajUEL28ktJWSEjKvFv8umUM0qkaDFDUWLQuHFjxMXFITMzE3Z2digoKGA6ksR7+/Ytpk+fDgMDA/j5+WHOnDlIS0vDihUrULduXabjUf+TkZEBNpsNLS0tREZGQllZmelI1Up2djbCwsLg5eUFVjXd2rZFixbo27cv3QighiniC3D61VeI+l8dC0DkK7opACU6tJihKDFp3bo1oqKicO/ePTg7O6O0tJTpSBIpIyMDU6dORdOmTXHgwAHMnz8faWlpWLZsGTQ0NJiOR/3Lx48fYWlpCYFAgNjYWDpS9gtBQUEAAHd3d4aT/J6XlxfOnTuHlJQUpqNQQnI+Ix8FPCK0qWXlIQDyeQTnMkQ7AkTVXrSYoSgxMjExQVhYGOLj4+Hp6Vm21z/1Z2/evMHkyZPRtGlTBAUFYdGiRUhLS8OSJUtQp04dpuNR/1FQUAA7OztkZmYiLi4OTZo0YTpStUMIga+vLxwdHav9ui4nJyeoq6vDz8+P6SiUEHwq5uNuTrHIC5nvCIC7OcX4XMIXU49UbUIPzaQoMbOwsEBQUBCGDRsGLS0tbN68udpOL6kO0tPT8c8//4DL5UJFRQVLly7F5MmTfzilmqpeSktL4ezsjHv37uHcuXNo3bo105GqpevXr+Phw4fYvHkz01H+SElJCcOHD0dAQACWL18OGRn6+CDJ7uYU/XLnspe3r+JCwHZkPExG/qdvJ2I6LtiAbkNGl12zb8xAvLx99ac29TqaYLxfZLl9sgAkZxehb0M61ZQSLvrdiKIYMHToUGRnZ2PSpEmoV68e5s2bx3SkaufVq1dYs2YN/P39oaamhuXLl2PSpElQVVVlOhr1GwKBAJ6enoiPj0dkZCRMTEyYjlRtcblc6OnpwdzcnOkof4XD4WDPnj2IiYmBnZ0d03GoSuITgqTsol+OymQ8uYcX1y+ibiO9smKmPHUb60O5zv9PHa3X9PcfWhAASdlF6NVACdL0AzxKiGgxQ1EMmThxIrKysjB//nxoaWlVy21ZmfDy5UusWbMGAQEB0NDQwKpVqzBx4kSoqKgwHY36A0IIZs6cieDgYBw+fBgWFhZMR6q28vLycOTIEcyaNUtitqnu1KkTjI2NweVyaTEjwbIK+Sji/3qCmbHtUHRzGoW8j1lYb9f5t+3053ijs4Nrhfou4hNkFfJRX4k+flLCQ/81URSDlixZgqysLIwbNw6ampoYNGgQ05EYk5qaitWrVyMwMBB169bF2rVrMWHCBLr7lQRZt24dtm7dip07d1a7k+yrm5CQEOTn58PDw4PpKBXi5eWFadOmITMzE/Xr12c6DlUJmQW8ct9TrvP3O0Ge3rQYYatnQU2nAZqb9IHFxHlQ1dT5q/5pMUMJk2R8HERRNRSLxcL27dvh7OwMV1dXXLhwgelIYvfixQt4eHigZcuWiIqKwvr16/Hy5UvMmjWLFjISxNfXF/Pnz8fSpUsxadIkpuNUe76+vmCz2dDV1WU6SoUMHz4csrKyOHDgANNRqErKLOBV+eFPVkER6joNoKyhidyMV7gZdhB7RtugpPD3O5ZJ4ffFFEVVBi1mKIphUlJSCAwMRJ8+feDg4ICkpCSmI4nF8+fPMWrUKLRu3RqxsbHYtGkTUlNTMWPGDCgpKTEdj6qAsLAwjBs3DhMmTMDSpUuZjlPtPXr0CImJiRI5tVRDQwNOTk7gcrkgRFx7YVHClMcToCr7aNrNXIklF55j+rHLmBd9F/08pgEAcjNe4eG5qN/eKwCQz6O7eFLCRYsZiqoG5OTkcOLECbRu3RpWVlZ48eIF05FE5smTJ3B3d0fr1q1x5swZbNmyBSkpKZg2bRoUFRWZjkdV0IULF+Dq6oohQ4Zgx44ddGe+v8DlcqGlpQUHBwemo1QKh8PB8+fPcfnyZaajUJXAF1StCG3Yuj1k5OQBfJtd0MHaqey9T5lv/ng/r4r9U9R/0WKGoqoJFRUVREVFQUNDA2w2G+/evWM6klA9fvwYw4cPh6GhIS5cuIDt27cjJSUFU6ZMoUWMhEpKSoKDgwN69+6NwMBASEtLMx2p2ispKUFgYCBGjhwJOTk5puNUSt++fdG8eXNwuVymo1CVIC1V+Q8c8j5m4XLQHhTn55W9di/uZNmvNRr+edqkTBX6p6hfocUMRVUjWlpaiIuLQ0lJCSwtLfHp0yemI1XZw4cPMWzYMBgZGSEhIQG7du3CixcvMGnSJCgoKDAdj6qkFy9ewMrKCq1bt8aJEycgLy/PdCSJEB4ejuzsbHh5eTEdpdJYLBY8PT1x7NixGvE9qrZRkZEq9+HvwdnT2ODQFfvGOJa9Fr9nHTY4dMWRheNRWlSIqM1LsMKsBTYP7oF/bDrivO+3c5J0DFrCqL/tb/uWAqAsQx89KeGi/6IoqprR1dVFXFwcMjIyYG9vj8LCQqYjVcr9+/cxdOhQtGvXDomJidizZw+eP3+OCRMm0AdfCffu3Tuw2WxoaGggMjKSnv1TAb6+vujevTsMDQ2ZjlIlo0aNQklJCQ4fPsx0FKqC6ivJlLtmpjj/Kz6+ScOnd6/LXsvPzcbHN2n48uEdlDU0YeY1Aw1btUNebjYKPn2Etn4L9PWYivH+kZCV//0HVIL/9U9RwsQidAUfRVVL165dw4ABAzBgwACcOHFCYk7cvnfvHlasWIHjx49DX18fCxculOgpNdSPPn36hD59+uDjx4+4cuUK9PT0mI4kMV69egUDAwP4+vrC09OT6ThV5uDggLdv3+LWrVsi70tbWxve3t6YP3++yPuq6TILeAh4+omx/ke3qkMLGkqo6MgMRVVTpqamOH78OKKjo8HhcKr9zkHJyckYPHgwOnTogKSkJHC5XDx79gwcDocWMjVEYWEh7O3t8ebNG8TGxtJCpoICAgKgrKyMoUOHMh1FKDgcDm7fvo3k5GSmo1AVoK0oDQVpZtatKEizoK1I19ZRwkWLGYqqxqysrBAYGIgDBw5gzpw5TMf5pTt37sDR0RHGxsa4d+8e/P398eTJE3h6ekJWVpbpeJSQ8Hg8uLi44M6dO4iMjISRkRHTkSQKn8+Hn58fhg0bBhUVFabjCIWNjQ0aNGhANwKQMNIsFoy1FCDucoYFwFhLAdJ0x0NKyGgxQ1HVnKurK7Zt24aNGzdiw4YNTMcpc+vWLdjb26Nz58549OgRDhw4gCdPnmD06NG0iKlhCCHgcDiIjo5GaGgounfvznQkiXP27Fmkp6dL5Nky5ZGRkcGoUaMQFBQksWv7aquOWgoQ91g/+V+/FCVstJihKAkwdepULFq0CHPmzIG/vz+jWW7cuAFbW1t07doVz58/x8GDB/Ho0SOMHDlSYtb1UBUzZ84cHDhwAAEBAbC2tmY6jkTy9fWFkZERTExMmI4iVJ6envj06RNOnDjBdBSqAtTlpNFBU15sozMsAB005aEuR6eYUcJHixmKkhArVqzAuHHjMGbMGISHh4u9/2vXrsHa2hrdunVDamoqgoOD8fDhQ4wYMYIWMTXYhg0bsHHjRmzbtg1ubm5Mx5FIWVlZOHnyJDgcTo07VLRFixbo27cvnWomgfo3UoayDEvkBQ0LgLIMC/0bKYu4J6q2osUMRUkIFouFXbt2wdHRES4uLrh06ZJY+r169SosLS3RvXt3pKen4/Dhw3jw4AGGDx9OD0ms4fz9/TFnzhwsXLgQU6dOZTqOxAoKCgKLxcKIESOYjiISHA4H58+fR0pKCtNRqAqQl5aCrZ6qyKebEQC2eqqQl6aPnJRo0H9ZFCVBpKWlERwcjB49esDBwQF3794VWV8JCQmwsLBAz5498fbtWxw9ehT379/HsGHDaBFTC4SHh2PMmDEYO3YsVq5cyXQciUUIga+vLxwdHaGlpcV0HJFwcnKCuro6/Pz8mI5CVZCBmhwsGot2xITdWBkGanRHS0p0aDFDURJGXl4eJ0+eRPPmzWFlZYXU1FShtn/p0iUMGDAAvXv3xocPHxAaGoq7d+9i6NChkJKi3zJqg0uXLsHFxQUDBw7E7t27a9zUKHG6fv06Hj16VKMW/v+XoqIi3Nzc4O/vDx6Px3QcqoI6ayuWFTTC+p/+vR12Y2V00lYUUqsU9Wv0yYSiJJCqqiqioqKgpqYGNpuNzMzMKrd54cIFmJmZoW/fvvj48SNOnDiBpKQkODk50SKmFrl79y4cHBzQvXt3BAcH01G4KvL19YWenh4GDBjAdBSR4nA4ePfuHaKjo5mOQlVCZ21FuDRTE8oamu9rZFyaqdFChhIL+oRCURJKR0cHsbGxKCwshJWVFT5//lzhNgghOHfuHPr27QszMzN8/vwZJ0+exJ07dzBo0CBaxNQyqampsLKyQrNmzXDy5EkoKNBtVKvi69evOHLkCDw9PWv8/yVjY2MYGxvTjQAkmIGaHDiGGmivKQ+g4qM0369vrymPMYYadGoZJTY1+7srRdVw+vr6iI2NxatXr+Dg4ICioqK/uo8QgjNnzqBPnz4YMGAA8vPzER4ejtu3b2PgwIF0WlEtlJmZCTabDRUVFURHR0NNTY3pSBIvJCQEBQUF8PDwYDqKWHA4HJw+fRrv3r1jOgpVSQrSUrDWVcUEIw2Y1lOEgvT//yz47wOj1A/3sWBaTxETjDRgrUsX+1PiRf+1UZSEa9u2LSIjI3Hz5k24urr+ds46IQSxsbHo1asXLCwsUFxcjNOnT+PmzZuwt7enRUwt9fnzZ1hZWaGgoADx8fHQ0dFhOlKN4OvrC0tLSzRp0oTpKGIxfPhwyMrKIjAwkOkoVBWpy0mjb0NlTGlXF6Nb1YFVExW011RAC3U5GKjKooW6HNprKsCqiQpGt6qDKe3qom9DZXqODMUIejgERdUAPXr0QGhoKBwcHDB+/Hjs37//h8KEEIKYmBgsX74c169fR7du3RAVFQUrKytawNRyRUVFcHBwwKtXr3Dp0iXo6+szHalGePjwIa5du4bQ0FCmo4hNnTp1MGTIEPj6+mLOnDn0e0sNIM1iob6SDOor0cdFqvqiIzMUVUPY2NjA398fXC4XCxYsAPCtiImMjISpqSlsbGwgLS2N2NhYJCYmwtramj5s1HI8Hg+urq64efMmTp8+jXbt2jEdqcbgcrnQ1taGvb0901HEisPh4MWLF2I7B4uiKIqW2hRVg7i7uyM7Oxve3t748OED7t27h1u3bqFXr16Ij4/HgAEDaAFDAfhW6I4fPx4RERE4deoUevbsyXSkGqO4uBiBgYEYPXo05ORq1yLoPn36oHnz5uByuejbty/TcSiKqgXoyAxF1SCEEDRt2hT16tWDn58fvn79irNnz+LSpUswNzenhQxVZsGCBeByufDz84OtrS3TcWqU8PBw5OTkwMvLi+koYsdiseDl5YVjx47h06dPTMehKKoWoMUMRdUAAoEAYWFh6NSpExwdHdG6dWvY2trixYsXKCwspEUM9YPNmzfjn3/+waZNmzBy5Eim49Q4vr6+6NGjB9q0acN0FEaMGjUKpaWlOHz4MNNRKIqqBWgxQ1ESTCAQIDQ0FMbGxhg8eDDq1q2Lixcv4sKFCzh58iQcHBzg7OyMK1euMB2VqiYCAwMxc+ZMzJs3D97e3kzHqXFevXqF+Ph4cDgcpqMwpkGDBrC1tYWvry/TUSiKqgVoMUNREkggECAkJAQdOnSAs7MzdHR0cPnyZZw9exZ9+vQBAMjIyODQoUMwMTGBnZ0d7t+/z3BqimmRkZHw9PSEp6cn1qxZw3ScGsnf3x/KyspwdnZmOgqjOBwO7ty5g6SkJKajUBRVw9FihqIkCJ/Px5EjR9CuXTu4uLigYcOGuHLlCuLj49GrV6+frldQUMCpU6egr68PS0tLpKWliT80VS1cuXIFzs7OsLe3h4+PD516KAJ8Ph9+fn5wdXWFiooK03EYZW1tjQYNGoDL5TIdhaKoGo4WMxT1B3xCkFnAQ3J2EWLS8xCa+gVHX3xGaOoXxKTnITm7CJkFPPAJEV0GPh+HDh1Cu3bt4OrqCl1dXSQmJiI2NhY9evT47b3q6uqIiYmBkpISLCws8OHDB5HlpKqn+/fvw87ODiYmJjh8+DBkZOhGlqJw5swZvH79ulZPMftORkYGo0ePRlBQEAoLC5mOQ1FUDcYiRIRPYBQlwT6X8JGcXYSk7CIU8b/9N5ECIPjXNf/+vYI0C8ZaCuiopSC0U5B5PB6OHDmCVatW4enTp7CxscHSpUthYmJS4bZSU1PRs2dPNGzYEOfPn4eamppQMlLVW1paGnr06IF69erhwoULUFdXZzpSjeXs7IwnT57g3r17dOQLwIsXL9CiRQsEBQXBzc2tSm1pa2vD29sb8+fPF1I6iqJqCjoyQ1H/UcQXIDr9K/Y8zMW194VlhQzwYyHz398X8QmuvS/Enoe5iE7/imL+f6/+ezweD4GBgTA0NIS7uztatmyJmzdvIjIyslKFDAA0bdoUsbGxSElJgaOjI4qKiiqdj5IMHz58gIWFBZSUlBATE0MLGRHKysrCqVOnwOFwaCHzP82bN0e/fv3oRgAURYkULWYo6l9efinB/ke5uJdTDACo6LDl9+vv5RRj/6NcvPxSUqH7eTweAgIC0KZNG4waNQqGhoa4ffs2wsPD0aVLlwqm+Vn79u0RERGBxMREuLm5gc/nV7lNqnr68uULrK2tkZeXh7i4ONSrV4/pSDXawYMHwWKxMGLECKajVCscDgcXLlzAixcvmI5CUVQNRYsZivqf21mFOJryBQU8UuEi5r8IgHwewdGUL7id9ef54qWlpfDz80OrVq3g4eGBdu3aISkpCSdPnkSnTp2qmOZHvXv3RkhICE6dOoWJEyeCzjSteYqKiuDo6IiUlBTExMSgadOmTEeq0Qgh8PX1xaBBg6Cpqcl0nGpl8ODBqFOnDvz8/JiOQlFUDUWLGYrCt0Im/k0+gIqPxpTnezvxb/LLLWhKSkqwf/9+tGzZEl5eXjA2NkZycjJOnDiBjh07CinJz+zt7cHlcrFv3z4sXrxYZP1Q4sfn8+Hm5obExERERESgQ4cOTEeq8a5du4bHjx/Thf+/oKioCDc3NwQEBIDH4zEdh6KoGogWM1St9/JLSVkhIyrxb/J/mHJWUlICHx8ftGzZEuPGjUPXrl1x7949hIaGiu3hc9SoUdiwYQNWr16Nbdu2iaVPSrQIIZg4cSJOnTqFo0ePonfv3kxHqhV8fX3xf+3de0BUdf4+8GcuXEcMuQuI1yzxp2KwaV6RjWEQvpl3rUwS08oot2+taXYxrdbWb665piuQhquh6dqaOogYYibiani3NAUBEUEQgeE6M+f3hzFpCeLCcObMPK//Zuac83mPyDDP+dy6deuGsLAwsUuxSDNnzsTVq1eh1WrFLoWIrBDDDNm0WoMROy9XwtzTdWUAdl2uREV1LVavXo1evXrhxRdfxODBg3Hq1Cls2bIF/fr1M3MVv/f666/jjTfewNy5c7Fx48Z2b5/a1ttvv421a9ciPj4eTzzxhNjl2ITKykps3rwZM2bMgFzOP6l3ExQUhEceeYQLARCRWfCTl2xa+hVdm8yRuRcBQFWDEXM+/QIvv/wyhg8fjtOnTyM5ORl9+/Y1c+vNW7p0KZ577jnExMTwzqmErVixAh988AE+/vhjPPfcc2KXYzM2b96M6upqxMTEiF2KRZs5cyZ27dqFq1evil0KEVkZhhmyWeV1BpworTN7kDGRyRCoHoejp3/Exo0bERgY2F4tN0smk2Ht2rUYPXo0xo8fj8zMTLFLovu0ceNGzJ0719TTRu0nISEBGo0GXbp0EbsUizZ16lTY29vjiy++ELsUIrIyDDNks06U1jY5vCzn2CGsi5uCJWEPY/4jnpj/iCeytq6/45isrV9gzYwovDOkq+mY4pwLzbYpl8lQ8YBv27yBNqRUKpGcnIyQkBBERUXhzJkzYpdELaTVahETE4OYmBh8/PHHYpdjU06fPo2srCxO/G8BV1dXTJgwAYmJiVxBkYjaFMMM2SSDICD7em2TvTJXfjyJn7My4PSAa5PXOH9oHwp/PA1Vp5YvxSoAyL5eC4MF/jF3cnLCjh070KVLF0RERODy5ctil0T3kJmZifHjx0Oj0SA+Pp6bNbazxMREeHp6Ijo6WuxSJGHmzJn4+eefceDAAbFLISIrwjBDNqmkxoBaQ9OBYmDUJLx34BJmrNrS5DFj5n+M9767hMdn39+wnlqDgJIay9ys0tXVFSkpKXBwcIBarUZJSYnYJVETzpw5g6ioKAQHB2PLli1QKpVil2RT6urqsGHDBkyfPh329vZilyMJw4cPx4MPPsiFAIioTTHMkE0qqm5+vwOVqxvsHJ2aPaajpw/kCoVZ2hdT586dkZqaips3b2L06NGorKwUuyT6jcuXLyMiIgL+/v745ptv4OTU/P9Vanv//ve/UVpaitjYWLFLkQyZTIbY2Fhs3boV5eXlYpdDRFaCYYZsUlG1XrT//HJYdpgBgJ49e0Kr1eL8+fMYO3Ys6urqxC6JflFSUgK1Wg07Ozvs2bMHrq6uYpdkkxISEjB06FA8/PDDYpciKdOnT0dDQwM2bdokdilEZCUYZsgmVemNMIrUthGATi9W6y03cOBA7NixAwcPHsS0adNgMFjm0DhbUllZidGjR6O8vBx79+5F586dxS7JJuXm5iItLY0T//8LPj4+iI6O5lAzImozDDNkkwxGcSfg60Vuv6VGjhyJ5ORkbNu2DXFxcVyFSER1dXUYO3YsfvrpJ6SkpKBXr15il2Sz1q1bhw4dOmDixIlilyJJM2fORHZ2Nn744QexSyEiK8AwQzZJIRd31SelyO3fjyeffBJr167F6tWrsWjRIrHLsUkGgwHTpk3DwYMHsWPHDgwcOFDskmyWwWDAunXrMHXqVKhUKrHLkSSNRoPOnTsjMTFR7FKIyApw+RuySR2UcsiBJoeand63E9oVi2C8bWjV3tVLcSBpFbr0C8aUD9ZAu+J9nN73DeqrdaZj1s2ZBLlSiSFTn8fQqbPuem05AJXy9/cRdDodLly4gMDAQItbHSk2NhYlJSWYP38+PD09MWfOHLFLshmCICAuLg7btm3D1q1bERoaKnZJNm3v3r3Iz8/nELNWUCqVeO6557Bq1SosW7aMC1gQUauwZ4Zsko+zstk5M3W6SpQV5KL8ar7pOd2N6ygryEVF8VUAQFVZMcoKclFV9uvyxeVFBSgryEXNzfImr20E4KyvRkpKCpYuXYrJkyejZ8+ecHFxwcCBA/Hll1+28t2Zx7x58/Daa68hLi4OycnJYpdjMxYtWoTVq1fjH//4B8aOHSt2OTYvISEB/fr1Q0hIiNilSNqMGTNw8+ZNbNu2TexSiEjiZAIHwZMNKqrWY/1P5aK1v+qZcBScPQ6ZTAaZTAaj8ddolZ2djaCgINFqa47RaERMTAySk5Oxc+dOqNVqsUuyaqtWrcLLL7+MDz/8EPPnzxe7HJtXXFwMf39/LFu2DK+88orY5UheWFgYjEYj9u/ff89jPT098dprr/H3gIh+hz0zZJM8nRRwVIgzb8VRIUPM+CcA3BpCdHuQcXZ2hsFguOM5SyKXy5GYmAi1Wo1x48YhKytL7JKsVnJyMuLi4vCnP/0Jb775ptjlEIANGzZAJpPh6aefFrsUqzBz5kxkZGTgwoULYpdCRBLGMEM2SSGTYaCHI9o7zsgADPRwxOL3F2H79u2ws7ODXH7r11Amk6G+vh4hISHw9fXF9OnTkZycjNLS0nausnl2dnbYsmULBgwYgKioKJw7d07skqxOamoqnn32WTz99NNYtmwZZDLpLBhhrQRBQGJiIsaNGwd3d3exy7EKY8eOhaurKz7//HOxSyEiCWOYIZsV5OGI9h5jKfzSLnBrlbC0tDQ4OTlBLpdDEAR88cUXSE9Px/Tp03H8+HFMnToVXl5eeOyxx7Bo0SIcOXLEIvZ7cXZ2xs6dO9G5c2dEREQgPz//3idRi2RlZWHcuHEIDw/H559/bgq7JK7MzEycO3eOE//bkJOTE5555hmsX78eer1lbyRMRJaLfyXJZj1gr8AAd4d2652RARjg7oAH7BWm50aMGIHvv/8enTp1glwuh0ajQWhoKJYuXYoTJ06goKAA8fHx8Pf3x/LlyzFo0CD4+Pjg6aefxj//+U8UFxe3U/W/16lTJ+zZswdyuRwREREW14MkRefOnUNUVBQGDBiAr776CnZ2dmKXRL9ISEhA9+7dMWrUKLFLsSozZ85EUVERdu/eLXYpRCRRXACAbFqdwYj4szeg0wtm7aWRAVApZXg+sBMcFL+/h5CXl4dz584hIiKiyWvo9XocPnwYKSkp0Gq1pg3ngoODERkZCY1Gg0GDBkGpbN8V18+fP49hw4ahR48eSEtLQ4cOHdq1fWuRn5+PoUOHomPHjjhw4ADc3NzELol+UVFRgc6dO2P+/PlYuHCh2OVYncahtRs2bMCXX34JV1dXTJky5Y5juAAAETWFYYZsXk5FPTZfrDB7O5N7dkT3jm23f0xRURFSU1Oh1WqRmpqKsrIyuLq6Qq1WQ6PRmDamaw/Hjh3DqFGjMGTIEOzYscPi9smxdKWlpRg+fDiqq6vx/fffw8/PT+yS6Dbx8fF44YUXcPnyZfj7+4tdjlURBAGvv/46PvnkE9jb26O+vh6BgYE4c+bMHccxzBBRUzjMjGxe9472CPc3707ean9VmwYZAPDx8cGzzz6LL7/8EsXFxcjMzMSrr76K3NxcxMbGwtfXF0FBQZg/fz4yMjLQ0NDQpu3fLjg4GF9//TXS09MRExNjsauxWaKqqipERUWhpKQEqampDDIWKDExERqNhkGmje3evRu9evXCJ598AgCor68HAPj6+opZFhFJDMMMEYBgTydToGmrOTSN11H7q/CIp3l3uFYoFBg8eDDee+89ZGVlobi4GBs3bkT//v2RmJiI0NBQuLu7Y9y4cYiPjzfLhP2wsDBs2rQJmzdvxquvvgp2+t5bfX09JkyYgDNnziAlJQW9e/cWuyT6jVOnTiErK4sT/83gxIkTuHTp0h3PKRSKdutRJiLr0L6D64ksWLCnE9wcFNh1ubLVc2ga58hEdXVp8x6ZlvDw8MBTTz2Fp556CkajEdnZ2dBqtUhJScELL7wAo9GIvn37QqPRIDIyEsOGDYODg0Or2x0/fjxWr16N2bNnw8vLC2+//XYbvBvr1LgBaXp6OrRaLYKDg8Uuie4iMTERXl5eiI6OFrsUq/Pmm2+iuroaS5YsMT0nk8ng4+MjYlVEJDUMM0S36d7RHjMDOyH9ig4nSusgA+4r1DQe39/dAWF+qrtO9m9vcrkcwcHBCA4OxsKFC3Hjxg2kpaVBq9Vi06ZN+L//+z+oVCqEhYWZwk337t3/6/ZmzZqFkpISLFy4EJ6ennjhhRfa8N1YB0EQ8OqrryI5ORlbtmxBWFiY2CXRXdTV1WHDhg2IjY3lynJmIJPJsHjxYvj4+CAuLg6CIECv1zPMENF9YZgh+g1HhRyRAS4Y4uOM49drkX29FrWGW5FGDuD22SC3P3ZU3NqIM8jD8Y7lly1Np06dMHHiREycOBGCIODkyZOmFdJeffVVzJkzB71790ZkZCQiIyMxYsQIODnd3zC5BQsWoLi4GC+99BI8PDwwYcIEM70baVqyZAn+/ve/Y82aNfy3sWBff/01ysrKMGPGDLFLsWpz5syBl5cXpkyZAqPRCBcXF7FLIiIJ4WpmRPdgEASU1BhQVK1HUbUeOr0ReqMApVwGlVIOH2clfJyV8HRSQCHxndorKiqwb98+05C0/Px8ODk5ITQ01NRr06tXrxbtSG80GjFt2jRs3boVu3fvxh//+Md2eAeWb82aNXjxxRexePFiLvNr4dRqNWpqavDdd9+JXYpN2LZtG55++mns2rULoWFhd3zubt+dgh4PPoie3bujg5V97hJR6zDMENFdCYKAs2fPmnptvvvuO9TX16NHjx6mfW1GjRoFlarpleDq6+sxZswYHDx4EOnp6QgJCWnHd2B5tm7dikmTJuHll1/GihUrWhQKSRw5OTno0aMH1q9fj+nTp4tdjs24WW9ookdcQOOyKlLsESci82GYIaIWqaqqQnp6uinc5OTkwN7eHiNGjDCFmz59+vzuC7pOp8Pjjz+On3/+Gd9//73Nrti1b98+jB49GuPHj8c///lPyOXiz6eipr3zzjv429/+hqtXrzYb2Klt1BqMrZ6rOMCC5ioSUfthmCGi+yYIAi5cuGAajrZ//37U1tYiICDANBwtLCwMHTt2BACUlZVh+PDhqKqqwqFDh2xuL5WjR49i1KhRGDp0KDcVlQCDwYBu3bohKioKa9asEbscq5dTUY+dlytRLfFVJIlIHAwzRNRqNTU1yMjIMIWb8+fPQ6lUYtiwYaZw06lTJwwbNgwuLi44cOAA3NzcxC67XZw/fx5Dhw5Fz549sW/fPt7llwCtVovRo0fjyJEj+MMf/iB2OVbtWEkN9hbo7rs3pimN1wn3VyHYzPt7EZFlYJghojZ36dIl03C0b7/9FtXV1fD19cVjjz2G1NRUPPzww0hPT7f6L/ZXrlzBkCFDoFKp8N1338Hd3V3skqgFJkyYgAsXLuD48eOc12RGjUHGXBhoiGwDwwwRmVVtbS0OHjxoCjdnz54FcGuJ6Llz5yI6OhpBQUFWN4ekrKwMI0aMQEVFBb7//nt06dJF7JKoBYqLi+Hn54dPPvkEcXFxYpdjtXIq6rH5YoXZ25ncsyOHnBFZOYYZImpXeXl5WL58OVasWAGlUomGhgZ4e3sjIiICGo0GarVa8j0YOp0O4eHhOH/+PA4ePIiHH35Y7JKohZYtW4aFCxeisLDQZoZCtrdagxHxZ2+0eo7MvTTOoXk+sBMXBSCyYgwzRCSKLVu2YPLkyRg/fjx69uyJlJQUnDx5EjKZDI8++qhphbSQkBAoFNJZcrWhoQFjxozBgQMH8O233+LRRx8VuyRqIUEQEBgYiIEDB2LTpk1il2O1tHmVOFlaZ9Yg00gGoL+7AyIDuBEnkbXirQoiEsWkSZPw2WefYdu2bXB1dcWJEydw5coVJCQkICAgAMuXL8fgwYPh7e2Np556Chs2bMC1a9fELrtZRqMRM2bMQFpaGrZv384gIzGHDh3Cjz/+iNjYWLFLsVrldQacaKcgA9xaDOBEaR1u1hvaqUUiam/smSEiUS1atAjvvfce4uPjMXPmTNPzer0eWVlZphXSjh07BgAIDg42rZA2aNAgKJXKVrV/5MgRuLm5oVevXq26jiAIeO2117BixQokJydj0qRJrboetb8ZM2Zg//79+Pnnn61uDpelyCjU4fC1mnYLM8Ct3pnB3k4Y6WvdC44Q2SqGGSISlSAIiIuLw+rVq/HVV19h3Lhxdz3u2rVrSE1NhVarxZ49e1BWVgZXV1eEh4cjMjISERER8PX1va+2a2pqEB0djfT0dHTs2BGhoaGYMmUKxo0bd997wXz00UdYsGABVq1ahZdeeum+ziXxVVRUoHPnzliwYAHeeustscuxSgZBwMpTZag13P1rR86xQ9i//lNcOXMcuvJSAMCTC/6KQRNifndsna4Kn04dhbKC3GaPa+SokCGunxsUXJ2OyOrw1hMRiUomk+HTTz/FxIkTMXXqVKSnp9/1OG9vb0ybNg2bNm1CcXExDh8+jLlz5yIvLw+xsbHw8/NDUFAQ3nzzTWRkZKChoeGebRcUFKCmpgafffYZ0tLS4OnpiTfeeANvv/02gFtBqyUSEhKwYMECvPvuuwwyErV582bU1tYiJiZG7FKsVkmNockgAwBXfjyJn7My4PSA6z2v9e+l80xBpiVqDQJKajjUjMgaMcwQkejkcjmSkpIQGhqKMWPG4Icffmj2eIVCgUGDBuHdd9/F4cOHUVxcjE2bNqF///5Yt24dQkND4e7ujnHjxuHcuXNNhpJDhw4hNzcXgwcPRkhICOLj45Gfn4/3338fAFq0x8i//vUvzJ49Gy+99BLefffd+3/zZBESEhIQGRkJPz8/sUuxWkXV+mZfHxg1Ce8duIQZq7Y0e9zJ1K+RvXML+oWPadP2iUiaGGaIyCLY29tj27Zt6NOnDzQaDS5cuNDicz08PDB16lQkJSXh6tWrOHr0KObNm4fi4mK4urreNZQIgoCSkhJ06tQJ0dHRUKvV0Gq1AAAHB4cWtZueno6pU6di4sSJ+PTTT7nBokSdOnUKR44c4cR/Myuq1jf7pUPl6gY7x+Y3uSwvuoLtH7wOvz4DoJ4zv8Vty8EwQ2StGGaIyGJ06NABu3btgru7O9RqNQoLC+/7GnK5HMHBwXjrrbdw8OBBdO7cucljJ02ahG+++QapqakICAjA4sWLcenSpRa188MPP2DMmDEYOXIkkpKSJLV8NN0pMTERXl5eiI6OFrsUq1alN8LYivONRiO2vP0SjPoGTP7wH1Ao7Vp+LgCdvjWtE5GlYpghIovi4eGBPXv2QK/XQ6PR4MaNG2ZpRyaTISAgAD169ECfPn3w4YcfoqCgAGvXrgXQ/HyZCxcuQKPR4OGHH8a2bdvue7EAshx1dXXYsGEDYmJiYGfX8i/HdP8MxtatN3Ro01rkHDuE6Dc+gGfXnvd9vr6V7RORZWKYISKLExAQgD179uDKlSv4n//5H1RXV5u9TS8vL/Ts2RM3b95EXV1dk0PGCgsLoVar4ebmht27d8PFhZvxSdnXX3+NsrIyzJgxQ+xSrJ5C3rphmFcvnAYA7PzrW3h3aFcsnzDc9NrOZQuxOmZ0s+crW9k+EVkmhhkiskiBgYHYtWsXsrOzMXny5BatTtZSVVVVSE5ONj2WyWSoqalBSUkJvLy87jpnJiMjAwUFBdBoNGhoaEBqaio8PDzarCYSR0JCAoYPH46HHnpI7FKsXgelvE2+dNTXVKO+phoNtb/e5NDX193x+LfkAFRKfuUhskat222OiMiMBg8ejH/961+Ijo7G888/j88//7xNNjM8ffo0lixZglOnTuHJJ59EUVERli9fDp1OB7Va/bvjS0tLERoaCplMBoVCgT/96U+oqqqCIAic9C9hOTk5SEtLw/r168UuxSb4OCtxvLTp10/v2wntikUwGn5dQnnv6qU4kLQKXfoFY8oHazBx0d9Nr90ozMPH0cEA7r3PjPGX9onI+vA2BRFZtIiICCQlJSEpKQnz5s1rk2sOGDAA8+fPR2ZmJp544gksXLgQ7u7uWLNmDYYOHXrHsQ0NDdi7dy8GDRoEpVKJkJAQrFy5En379kW3bt0we/ZsbN++HRUVFW1SG7WfdevWoWPHjpgwYYLYpdiEe4WJOl0lygpyUX413/Sc7sZ1lBXkoqL4qtnbJyJpkgkt3RWOiEhEK1euxCuvvIKlS5fiz3/+c5teOzc3F926dWvy9UmTJuGrr77CypUr8fLLL6OmpgYZGRlISUmBVqvF+fPnoVQqMXToUERGRiIyMhL9+vVjr40FMxgM6NatG6Kjo7F69Wqxy7EJBkHAylNlzW6caS6OChni+rlBwd9JIqvDMENEkvHOO+9g8eLFSExMvOeEbYNwa8fvomo9iqr1qNIbYTAKUMhl6KCUw8dZCR9nJTydFM1+wTEYDPDw8EBFRQWcnJywc+dOhIaG3nHMpUuXkJKSgpSUFOzbtw/V1dXw9fWFRqOBRqNBeHg4XF1d2+BfgNqKVqvF6NGj8Z///AchISFil2MzMgp1OHytBu35xUMGYLC3E0b6qtqxVSJqLwwzRCQZgiDgxRdfRHx8PLZv344nnnjid8fcrDfg+PVaZF+vNd0BlgN37G9x+2NHhQwDPRwR5OGIB+zv3CvGaDQiKysLQ4YMMT3Xr18/nDx5sska6+rq8N1335l6bc6ePQuFQoHBgwcjMjISGo0GAwcObJO5P/TfGz9+PC5evIjs7Gz2oLWjm/UGrD5jnuXWm/Ni306/+/0mIuvAMENEkmIwGDBlyhTTZpcjRowAANQajEi/osOJ0jrIgPu689t4/AB3B4T5qeCguBU09Ho9Fi1ahCVLlsDDwwOxsbGYNWsWevTo0eJr5+XlmXpt0tLSUFlZCS8vL0RERCAyMhLh4eFcFa2dXbt2Df7+/vjkk08QFxcndjk2R5tXiZOlde3SOyMD0N/dAZEBXEKdyFoxzBCR5NTV1WH06NE4evQoDhw4gI7d+2Dn5UpU64VWfUGSAVApZYjq6oLuHW9thPm///u/GDVqFDQaDZTK1k0grq+vR2ZmJrRaLVJSUnDixAnIZDI8+uij0Gg0iIyMREhICBQK3kE2p2XLlmHhwoUoLCyEm5ub2OXYnDqDEfFnb0DXyt/Xe2n8fX4+sJPpBgURWR+GGSKSpMrKSowaNQoej4xA6IsL7rs3pimN1wn3UyHI3d6swaKwsBB79uyBVqvF3r17UV5eDnd3d6jVakRGRkKtVsPb29ts7duSvLw8+Pr6QqFQoE+fPggODsbGjRvFLstm5VTUY/NF868AOLlnR9ONCSKyTgwzRCRZGTklyCw333yHcH8Vgj2dzHb92+n1emRlZZnm2hw7dgwAEBwcbOq1aVwemu5PSUkJvL29TcP7kpKSsG/fPoSFhYldmk07VlKDvQU6s11f7a/CI+30+0tE4mGYISJJsvY7u8XFxaZem9TUVJSWlsLV1RXh4eGmVdJ8fX3bvS4punnzpmk1OZlMBkEQMGLECLzwwguYPHkyF2MQUWOgaeueVQYZItvBMENEklP7y5j71s6RuRdLGXNvMBhw9OhRU6/NkSNHIAgC+vfvb9rXZsiQIbCzsxOtRksmCAIcHR1RX19veq4x1OzevRuRkZEiVkc5FfXYdbmy1XNo7jbnjYisH8MMEUmOra+GVFpaitTUVNMqacXFxXBxccHjjz9u6rUJCAgQu0yL4ufnh8LCwjuemz59OuLj4xkCLYA5ViMkItvAMENEklJeZ8Cas9ynopHRaMTx48dNK6RlZmbCYDAgMDDQtK/N8OHD4eDgIHapogoJCTHNQwKAefPm4aOPPuIeMxamLfeJIiLbwDBDRJLCHcSbd+PGDaSlpZl6bQoLC+Hs7IywsDBTuLmffXKsxahRo7B//34AwPLlyzF37lxR66HmGQQBJTUGFFXrUVSth05vhN4oQCmXQaWUw8dZCR9nJTydFFAwkBLZNIYZIpIMgyBg5aky0x3b38o5dgj713+KK2eOQ1deCgB4csFfMWhCDACgTleF1M8+Qm72YZQXFaC+phoPePuiv/pJjJweBwdVhybbdlTIENfPTVJfnARBwKlTp0y9NgcPHoRer0fv3r1NK6SNHDkSTk7WP1F6yJAhyMzMRFJSEqZNmyZ2OURE1EYYZohIMoqq9Vj/U3mTrx/cuAbaFYvg5tcV1y9fBHBnmLlRmIePo4OhtHeAZ7deqCguMoWe3kP/iOdWJjfbfsxDrvBxlu7SyBUVFfj222+h1Wqh1WqRn58PR0dHhIaGmnptHnzwQUkNvfrtHfwqvREGowCFXIYOt93Bv1mQg7zLuQgPDxe7ZCIiakMMM0QkGcev1yIlv6rJ13XlZbB3dEJVWQk+jg4GcGeYqbx+Ddm7t2LQ+OlwUHVAQ10t4meNRf6powCAd/ZfgFNH1yavr+nSAUEejm32fsQkCALOnTtnWiHtwIEDqK+vR48ePUy9NqNGjYJKZZlD6zi3goiIAEC6txiJyOYUVet/94X1dipXt2bPd/Hwxohn55ge2zk4wr9vEPJPHYVMLodc0fRHovyX9q2FTCZDYGAgAgMD8dprr0Gn0yE9Pd3Ua/PZZ5/B3t4eI0aMMIWbPn36iN5r09yqV7/9f2G84zwBh6/VIPNaDVe9IiKyIvwkJyLJqNIbmwwy/9X1ykpwZt9OAED/iLHNzpkxAtDp27J1y6JSqRAdHY1Vq1bh4sWL+Omnn/DXv/4VSqUSCxcuRN++fdGtWzfMnj0bX3/9NSoqzL9h6W/lVNQj/uwNnCytA3D/myw2Hn+ytA7xZ28gp6K+2eOJiMjysWeGiCTDYGy7UbGl+TlYFzcFFSVF6Br0KMYuWHbPc/Rt2L4lk8lk6N27N3r37o1XXnkFNTU1yMjIMA1JW7t2LZRKJYYNG2bqtenXr59Ze23acqd4AYBOL2DzxQqE+6sQzJ3iiYgkiz0zRCQZCnnbfFm+fOI/WD09EqV5l9BnRARmrPqq2V6ZRso2al9qnJycoNFo8Le//Q0//fQTLl68iBUrVsDFxQXvv/8+BgwYAH9/f8TGxuKrr75CeXl5m7bfGGSA1geZRo3X2Vugw7GSmja6KhERtTeGGSKSjA5Keas/tE6l7UDCC+OgKy/FY1Nm4plPkmDv5HzP8+QAVEp+ZAJAjx498NJLL2HHjh0oKytDWloapk6diqysLEyaNAkeHh4YNmwYPvjgAxw7dgxG438/PC+not4UZMxlb4GOQ86IiCSKq5kRkWTcazWz0/t2QrtiEYwGA8qv5gMAVJ084KDqgC79gjF67nv4i6Y/BEGAws4evg/1u+P8MfOXwq/PgCavb02rmZlLXl6eacPOtLQ0VFZWwsvLCxEREYiMjIRarYa7u3uLrlVrMCL+7A1U6wWzbpIqA6BSyvB8YCcuCkBEJDGcM0NEknGvPV7qdJUoK8i94zndjevQ3biOB7x9YWioR+P9G0NDPfJPH/vN+U0HpZa0T0BAQABmzZqFWbNmob6+HpmZmaZNOzds2ACZTIZHH33UtK9NSEgIFIq7L5WcfkVn9iAD/DqH5tsrOkQGuJi5NSIiakvsmSEiyTAIAlaeKjPtK9KeHBUyxPVzg0JCG0pamsLCQuzZswdarRZ79+5FeXk53N3doVarTb023t7eAIDyOgPWnL3R7jW+2LcT96EhIpIQhhkikpSMQh0OX6sx+93628kADPZ2wkhfy9xAUor0ej2ysrJMK6QdO3arlyw4OBgajQaDnpmDszV2d/055xw7hP3rP8WVM8ehKy8FcOfmqACw9b1XkJt9GJXXr0EQABcPLzw07HE8PvvPcH6g011r4s+ZiEh6ODiYiCQlyMOxXYMMcGsYEufKtC2lUomhQ4di8eLFOHr0KK5du4akpCT07t0ba+MTcPqmocmf85UfT+LnrAw4PeDa5PXPZqTAaDTCs9uDULm6oawgF5nJCUheMLvJcwQA2ddrYeA9PiIiyWDPDBFJjjavEidL69ol1MgA9Hd34FyKdnRV14Avzt9s8nVdeRnsHZ1QVVaCj6ODAfy+Z6ahrhZ2Dr8G0H/MiEbu8SzYO6uw6GBus+3HPOTK+VFERBLBT2sikpwwPxUu3qyHrp1WuQrz47Cj9nStxtDs6ypXt3tew87BEamffYQLh/ejqrTEtLpdt6BB9zy3qFrPMENEJBEcZkZEkuOgkCOqq0u7rHIV1dWFy/W2s6JqfZv8cSrNu4SC0z+YgkyvQSPx1NLEZs+R/9I+ERFJA/9CE5Ekde9oj3B/8/aYqP1V6N7R3qxt0O9V6Y3477fZ/NXUv8RjSVYh4r78Ft69+uDnrAz8+y/zmj3HCECnb4vWiYioPTDMEJFkBXs6mQJNWy2Y3Hgdtb8Kj3g6tdFV6X4YjG3X56aws4PvQ/3wh7HPAACyd21ByeWLzZ6jb8P2iYjIvDgomIgkLdjTCW4OCuy6XNnqOTSNc2SiurqwR0ZECnnromn+mWw01FSjR8hQAIC+oR4Xsw6YXm+o0TV7vrKV7RMRUfvhamZEZBVqDUakX9HhRGkdZMB9hZrG4we4OyDMT8U5MiJLyavCydLaJoeand63E9oVi2A0GEzzYVSdPOCg6oAu/YLx4KCR2PreK3Dq6ApXHz+UXytEzc1bG3B2fuj/4eWN+yCX3/1nLAfQ390RmoAOZnhnRETU1tgzQ0RWwVEhR2SAC4b4OOP49VpkX69FreFWpJEDd3wxvv2xo0KGgR6OCPJw5M7vFsLHWYnjpU2/XqerRFlB7h3P6W5ch+7GdTzg7QvvXn3Qe0gYrp4/g+JL5yFTKODVvTceGhaOUbFzmwwywK3/F1zJjIhIOtgzQ0RWySAIKKkxoKhaj6JqPXR6I/RGAUq5DCqlHD7OSvg4K+HppIBCxmFFlqSoWo/1P5WL1j73mSEikg5+WhORVVLIZKbAQtLi6aSAo0Jm6llrT44KGTyd2ENHRCQVHBhOREQWRSG7NfSvvfvLZAAGejiyp46ISEIYZoiIyOIEeTiafVPU3xJ+aZeIiKSDYYaIiCzOA/YKDHB3aLfeGRlurWbHRSCIiKSFYYaIiCxSmJ8KKqXM7IGmcX+hMD+VmVsiIqK2xjBDREQWyUEhR1RXF7MPNxMARHV14f5CREQSxE9uIiKyWN072iPc37w9Jmp/Fbp3tDdrG0REZB4MM0REZNGCPZ1Mgaathpw1Xkftr8Ijnk5tdFUiImpv3DSTiIgkIaeiHrsuV0KnF1o19KxxjkxUVxf2yBARSRzDDBERSUatwYj0KzqcKK2DDLivUNN4/AB3B4T5qThHhojICjDMEBGR5NysN+D49VpkX69FreHWnzE5AONtx9z+2FFxayPOIA9HLr9MRGRFGGaIiEiyDIKAkhoDiqr1KKrWQ6c3Qm8UoJTLoFLK4eOshI+zEp5OCihk7bVrDRERtReGGSIiIiIikiQOGCYiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIklimCEiIiIiIkn6/445e6hr1t/aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "instancia = ATSPInstance(\"br17.atsp\")\n",
    "#instancia.print_instance()\n",
    "x = solve_atsp(instancia)\n",
    "plot_atsp_solution(instancia, x)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}