{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21e91280",
   "metadata": {},
   "source": [
    "# Discussion 1 - The Circle of (M)Life\n",
    "\n",
    "Welcome to CS 189 / 289A! In this discussion, you will walk through the ML lifecycle using a tiny campus WiFi story:\n",
    "**Learning Problem → Model Design → Optimization → Predict & Evaluate**\n",
    "\n",
    "### Key Learning Objectives:\n",
    "1. Familiarize yourself with all 4 stages of the ML lifecycle.\n",
    "2. Practice using common ML libraries such as `numpy`, `sklearn`, and `matplotlib`.\n",
    "3. Understand how to prepare data and evaluate your ML models!\n",
    "\n",
    "---\n",
    "\n",
    "## Setup\n",
    "\n",
    "Fill in each answer slot between the `ANSWER` markers. Do not delete the marker lines.\n",
    "\n",
    "### Option A — local IDE + uv + local virtual env (recommended)\n",
    "\n",
    "```bash\n",
    "# install uv if you don't have it: https://docs.astral.sh/uv/\n",
    "uv venv                       # creates .venv\n",
    "source .venv/bin/activate     # Windows: .venv\\Scripts\\activate\n",
    "uv pip install -r requirements.txt\n",
    "jupyter lab                   # or: jupyter notebook\n",
    "```\n",
    "\n",
    "### Option B — local IDE + plain venv + pip\n",
    "\n",
    "```bash\n",
    "python -m venv .venv\n",
    "source .venv/bin/activate\n",
    "pip install -r requirements.txt\n",
    "jupyter lab\n",
    "```\n",
    "\n",
    "### Option C — Google Colab\n",
    "\n",
    "- Upload this notebook; the listed packages are preinstalled.\n",
    "- The import cell below enables Colab's custom widget manager when needed.\n",
    "- **JupyterHub note:** ipywidgets 8 + JupyterLab 4 / Notebook 7 render widgets\n",
    "  out of the box; no nbextension step needed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b313329d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score\n",
    "\n",
    "# Colab: enable ipywidgets custom manager when available\n",
    "try:\n",
    "    from google.colab import output as _colab_output\n",
    "    _colab_output.enable_custom_widget_manager()\n",
    "except ImportError:\n",
    "    pass\n",
    "\n",
    "# Consistent class colors everywhere: 0 = no WiFi, 1 = has WiFi\n",
    "CMAP_POINTS = ListedColormap([\"#d95f02\", \"#1b9e77\"])\n",
    "CMAP_REGION = ListedColormap([\"#fdbb84\", \"#a6dba0\"])\n",
    "CLASS_LABELS = {0: \"no WiFi\", 1: \"has WiFi\"}\n",
    "\n",
    "# Set random seeds for reproducible results\n",
    "SEED = 189\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3274ea",
   "metadata": {},
   "source": [
    "## Discussion Context\n",
    "\n",
    "Oski is excited to learn all about machine learning in CS 189/289 this semester! He wants to\n",
    "make sure he has adequate WiFi coverage throughout campus to do his CS 189/289 assignments.\n",
    "He decides to use a maching learning model to predict whether or not different locations around\n",
    "campus have WiFi access.\n",
    "\n",
    "We will represent the campus as a 2D coordinate grid, with the positive Y-axis representing North\n",
    "and the positive X-axis representing West. Let `(0, 0)` represent **Doe Library**.\n",
    "A campus router covers a disk of radius `R_COVERAGE` around Doe. Over the summer,\n",
    "Oski surveyed students around Doe to ask if they can access the WiFi, and records their location.\n",
    "\n",
    "Each point is a student who reported whether they have WiFi (`1`) or not (`0`).\n",
    "The fourth quadrant is Moffitt Library — Oski wasn't able collect any data there while it was\n",
    "under construction during the summer. For now, we'll \"lock\" it away and come back to it in a later\n",
    "question to evaluate the generalizability of our ML models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "455f81f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 1200 points; fraction has_wifi = 0.372\n"
     ]
    }
   ],
   "source": [
    "# Data generation: randomly generate 1200 points within a square bounded by y = - 1.5, y = 1.5, x = -1.5, and x = 1.5\n",
    "N_POINTS = 1200\n",
    "SQUARE_HALF = 1.5          # sample domain is [-1.5, 1.5] x [-1.5, 1.5]\n",
    "R_COVERAGE = 1.0           # WiFi router range around Memorial Glade at (0,0)\n",
    "NOISE_FLIP_PROB = 0.06     # fraction of labels randomly flipped (realistic noise)\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "# Sample uniformly over the square, label by disk membership, then add label noise\n",
    "x = rng.uniform(-SQUARE_HALF, SQUARE_HALF, N_POINTS)\n",
    "y = rng.uniform(-SQUARE_HALF, SQUARE_HALF, N_POINTS)\n",
    "\n",
    "# Any points within a disk of radius R_COVERAGE around Doe are labeled as having WiFi\n",
    "has_wifi = (x**2 + y**2 < R_COVERAGE**2).astype(int)\n",
    "\n",
    "# Add label noise to the data\n",
    "flip = rng.random(N_POINTS) < NOISE_FLIP_PROB\n",
    "has_wifi = np.where(flip, 1 - has_wifi, has_wifi)\n",
    "\n",
    "X = np.column_stack([x, y])\n",
    "y = has_wifi\n",
    "\n",
    "# Mask data from quadrant 4\n",
    "# This represents the data from Moffitt Library, which Oski was not able\n",
    "# to collect during the summer.\n",
    "quad4_mask = (X[:, 0] <= 0) & (X[:, 1] > 0)\n",
    "X_moffitt, y_moffitt = X[quad4_mask], y[quad4_mask]\n",
    "X_rest,        y_rest         = X[~quad4_mask], y[~quad4_mask]\n",
    "\n",
    "print(f\"Generated {N_POINTS} points; fraction has_wifi = {y.mean():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa775a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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cM9YmtHMvuQ37lFVEao9iyiqOrYI7idSfjCedsJbMQw/7UVzZZF33Hz2uKCO1x9D8bWBoyI4MpM5z2EjjqQTh9bzzZ7w7/gqaimx1knHlp7FPXd353l/w73kePezHWjKXjKv/HVNaLvYU1zXLSs9nl/1EkpUeV2zdnukOYDvXMHTUthoMLeYgZoSiGNEwpuwSJElGkmVMmcWxLWUtgmy29ypq/cGvhjEMHUNXwTCQZAVVhpAWxd5L7Oy5c/R7P/cl2j0enn68M/FID+fra9euZf78+fzgq19Ies/QIsiW9KQMXpJi6RRkNanPcDKyG9sCgeCiJ3Dw5fOiC6CpdGz8X/RoOKmtKWMcjplXJIguxFI3XpgEX7Y6MaIhwjWHuw5x/jK+Fnw7nybaeIpoUznhyv1onfG55tzSeLtwzWG82/4MWuzLVA/7aXvlYfSgl8DRTXi3/SkeLhWuOkDbi7Hk+1eWzEAiUeDWFU/DPAROPX1Ftrmhi/hKZhuyuf8OP0bYHxfduE2LYFwQKibJMoojHZM7F9nmSr0Kj4ZRO+qJtlSheptj4Ud9ub4eW6EaaoR3X30DX/n3+zHU8yvRRx99FEmSkvIv3HXXXay95kYkxcKPvv1fPPaT7wGx1fjd934cSZKSfk6dOsXf/vY3vvWtbyGZk1fskmJBcWbHE5Z03nznGfvIl3sVwisQCPpFJEXGKT3sR22rTtE6Nd3FwfYUH+t950l0NYLs6ExfaOioLWdxzr0OS94FwluxJ6mvoUYIVx+MCX4XInXH0bzNzMsp5t/nXcGsrEImp+XygSlLuH3Koj7f01AgmSyYMotiITcmK7IjA1PGwNJ0Gl3O13uzp2yrRVHbqmOrYjWEHmhDba/rU1+7GorFKwPLV61g+9Z3ULQI1s4HmY0bN1JSUsKmTZsS+m3atIkrrliPKauYrMJSsgpKUNILUNLyALj22mupq6tL+Jk0aRJZWVm43W4UZyaSfOHKWEJx5yApCqasEkyZRZjSx2HOmYhsSUxAMlII4RUIBP0iVYytZI7FoPYVx8z1SakNLQVTE5yvuhJpOIkkSZjzJmMumIYpqwRzziTSVicme1DcOSn7K+7c1F+0shxfCc3PLeGLC6/mq0tv4JoJs5AlGc3X2ukUNTLIZhumjHGYs8djcuciyQrhmiMEjm9G61KMocdxrA6g62pO6rR3z9q1a/nc5z7HAw88QHZuHuNnL+dbP/hp/H0jGqTi9AluueUWXC4XaWlpvO9976OhoSFhHJMWJVdXsRoGK1et4MypM2j1tfGdiDfffJMvf/nLCcJbXl7O2bNnWbduHZKscM+n7+O2j3wcxeaOr0ytVisFBQUJP4qisHbtWu677z4kxYwpezxKWgGKOy8msOccyiQJ2eKIre5HcCejK0J4BQJBv3DOvR5Tl+xS7hV3pMwR3B2KO4ec9z6EfdoazHmTcS28hexbv95jH3PuJIC4x60pvQBzflmS05F9+lpMGYkVyKwTF2EpmIJz7nVJTjaO6WtTVgbSg16an/0m9b+6i/pf3UnL899OmblLD/nw7f07HW893uNW+UAw1AjNz3yV5qe+TNuLP6T+sY8SOL45dVvDQI+E0KOxVIySYkZJz48F7gLIJpT0/D7FIf/ud7/D6XSy5fWX+e5Xv8h3fvwIr70Z2y3QdZ13vee9tLa28uabb7JhwwbOnDnD7bffnjCGZLFjRSfPiHLT4rmYzWa2bN2OZLJw5MgRgsEg99xzDy0tLZR3ZrHauHEjNpuNFSv6nqglFZIso9jdKI70MZOs40LG3owEAsGYRra7yf3Ajwmd3ILma46JWt7kfo9jzp1I1vX/0ef27mW3Ez67B83XGjMoJtIvvyfpjE622Ml9/w/xH3iRaGs1lsIZOGfFqhtZCqeT8+7/pu3lHxOu2h9z0LI4MNRIUnhNx+ZfE67YHXthGITO7MDz9u/IWP/JeBvN10rTX/4j5jUL+Hb9jbRVH+p3ecHu8B96lXDl/vMGTaXj9V9iK12W4Kkd2xKujZ/pSqbYqlmxuZGtrs6UkH3PRzx37ly+/vWvo0eCTC7M4pe/+QMb39rKlZevYuPb2zl46DDl5eWUlMQewJ544glmzZrFzp07WbJkCRArmaiHAxgRP06ngyUL5vLWzgN8+BMSmzZtYvXq1VitVlauXMHGN15n0j33smnTJlasWJGyJvo5XnjhhXidaIBrr1zHXx7/VVLo2lhGCK9AIOg3stmKY+YVwzZ+pOEUnq1/QG2uwJw/hbTL7sKcWUTenb8keHILRiSAbfIKTGm5qednd+NednvK9/RgB5qvBVNmMQD+fS9ghP1kXvv5hHah09uT+obObIcLhNe37x9x0T2Hd/tTOOfdAF0zVQ2ASO2x5PmH/agtlQnFGjRvc4IjlaGGYveYnh8T2z5m2zrH3LlzgdhDjOLOo6Agn6bmViSTlRPVTZSUlMRFF2DmzJlkZGRw9OjRuPBKkow5szC2AtdU1l55NU8//QwQO8ddu3Ytmr+V1YvmsPGVf/LhW65m06aNfOxjPcdkr1u3jl/+8pdoIS+arwWnw44e9mKo4aQUn2MVsdUsEAiGDUPXYqLQj1hJLdBByzNfJVwRW92GTm+n5emvYKgRZIsd56wrcS24uVvR7Q3/oQ1JtsDxzeiRxGo5siMjqV1Xm9pWm9TGUCNJYjxQUhWMkEyWpPP0VHGvRspY2O7RoyHU9jqMaBjFUONOWIojHcXqBIsTc/b4PqfMPIdstiHbXFxxxXpOnDhBTU0NmzZt4rIVS9F8LVy2Ygmbt+7g1OmTVFVV91q21el0UlZWRmlBFmWTJjAuP+/8PavJnvVjESG8AoFgWAie3ELDr++h/rG7afj1PQlF73sicOR11Pb6hNWL5mslVL5zaCZmJG5JBpE4Klup6eJA5Vr8bgxdi4XSNJ1B7WjAOf/GhDbWoplJw8uODEx9qKnbF5xzr08ay73s9qQz6ZRi2ItAGpqK6mkk2nyWaGsVaksletgHho6hhlE7ungvd25Tz5gxg6qqKqqqquJvHTlyhPb2dmbOTP48zrFy5UosFgu/+MUvCIVCLJgVCzFbPH8OTa2t/O7PT+N0OFiyYF6P847PP8XDnGHo6NEwmq8Fzd+KoY1sfG5fEVvNAoFgyNG8zbS+9KO4B6vma6X1pR+RXzCtx5Vq8ORW2l79CdHOkCUlLQ9T1ngkSerXqrknHDOviJ+bHlTs/MmajerKxrL7ZRbnTeTfZl+GSVZwzFiH563fEm0uByRki43AoQ04Zq6Pn5U6515H6OzeeAiTZLaRedVnYw490cF/6ct2N7l3/A/BE2+h+ZqxTViY0vNbcWahdtRzPutVrNJQT6jtdRhqbJVvhKMYWjQhnMuIBGLhW13Ovq+88krmzJnDBz/4QR5++GFUVeVTn/oUl19+OYsXd5/H2m63s3z5cn72s5+xatUqFJMZPQIWi4VlC+fzi1//nhVLF2Lp4Xw34bOxOtFDngSbJMmorVXxz0Hzt2HKLE7KXDbaCOEVCARDTqh8Z1x04+gaofKduOZdn7KPHgnS9srDsZAfST6fn9eWhjlnArZJS4dkbo4Z69BDPlr3PM+Tuh3NlY05K7alu6uxgq11RawpmkLozA70kA9L3vkiDJHao4TP7sU2cSEQ2/bNedc3iNSfRPO3Yi2e02u4Tn+RLTacs6/quY3NhUkp7izxJyHb3D2KjR4JxUU3Rmdu6K6/MyPZYUmSJJ5//nk++9nPsmbNGmRZ5tprr+WnD/8YLdARy85ldaZ05Fq3bh2bN29m7dq1KPb0WD5mQ+eyFUvYtOUd1q25rPsc0l1Q3DkYuoYR6UwIIiudSVkuSLlp6Oj+VuQuXu6jjWQYhtF7s39dPB4P6enpdHR0kJbWc8JwgeBSQtd19u7dC8CCBQv6VcEleOJtWv/5/SR71g0PxFM3diV0dh8tf/ta7NohL2prNXokgHX8fPI+/LOEJBlDwemOJr61859J9pUFk/n47Mvw7X6Ojs2/SXo/Y/2ncM69ttfxQ6EQ5eXlTJo0aURKzfUHPRJMTHii67F6uYo5nrIzFg/bt+IMWsiL1tHAOdGTTFZMmUW9xsrqahjd3x7Lm221x7zMeyiFmIr4w4KsEG08lfS+pFh6LWfZ0+9qODRAnPEKBIIhx1a6LOls0pRZiK20+1WrcsEWtGxzYymcgW3iItLX3jvkoguQY3OhpPiSz3fEvlyt4+cnd5Kk1PaLDMlsS8zuJMuxwg0mS+eqUUKyOECLdjvGOQxDR/Oer4AEMScnPdDecz81gh7wxPJu25zIjsx+iy7EqjBJSixUSjIlP+AMpLjEcCOEVyAQDDmSyUzOex/CtehWrCVzcS26lZz3/r8etxHNmUU4ZiR6tCrp+ThnXjksc0y32rlmfKIzUI7NxRXFsTNUc+5E0tfcHZ+zZLbhXvFBgsffpH3T/yXG115kSJKEklmI1FmbV1LMmLKKMWWVdCYYMdCDHURbKuN5rbvD0FJX+EmVu/t8nyjR1mr0YDt62I/mbULzNHTbvq8oabnnE4YQW3n3dtY9GogzXoFAMCwozgzS19zdrz4ZV/871gkLCFcfxJRegHPOtSmzSg0V75uymBmZ4zjYWkO21cnqwjKcF5yNuhbdimPmetS2GlAstDz7tc7i7LFyhGmrP4J7yW3DNr/+cO7UsOvWsK5Gzhe0v6DYgmyyIGcVJxSzV9vrupzrGmje5h6zkkmKCSQFjETx7fGMOehJaq+HfBjO6IDqHZ+/pg1zzgSMSBAkCclsH5UiCL0hhFcgEIwZJFnGMWNtUonB4WROThFzcroP/5Htbiz26bRv/N+46J4jlizjxoS6vqOBFuhA97di6CqS2YEpLRfJZImH1ZxDtqWhpOUliNGF/58qAYWhRRLEuSuSJKO4c9A8jcTPeBVLyjjo+Jh6Ko9vA0PXkBhc7WNJkoe0vOFwIIRXIBAI+oDaXp9kM6Kxij2yZfS8ZvVwAM3beMGcAqjtdSgZ4xJEF0APeZBtbqRuPK8lsy2plKBktvW6alTsachmWyyRh6wgW509ntfKFmfSQ4wkm1KW9BtKIpqKLEmYRrFAAgjhFQgEg6DW1w6SRKEzfbSnMuxYi2efz93ciZKWByYrvn3/RDJZsE9Z2a9iEUOBHvYm2QwtghFKtkPsYYFuhFdxZcdq6J4TX0lBcfctQ5hksqD0MRRItrlQ1KzOqk86khwr6DBc28IRTaUp6CXSeRbtMlvJ6ab+8EgghFcgEPSb9nCAn+3fyGlPEwBl6Xl8bt4VpI3ylutw4lpwE5Gaw4TKdwGxIgDOeTfQ+PgnMDodiTxv/46c9z6UMtXjsNHdytKUevXYk4ObpJgwZY+PpZs0DCSLA6kzjMwwDDRfS2fSiliFKNmZNWDxUlzZyI6M2PayYh5WEWwO+eKiC+CLhrHIJtKto+PxLLyaBQJBv/nziZ1x0QU41dHIUyd3jeKMhh/JZCH71q+R96GfkvOeb1Fw728IHn8zLroQcxrybvvjiM5LtqXRte6uZHGg2Fyd7yXaezv/lCQJ2ersrFl7XiI0Xwt6oC3mwayraP5WdH/boOYuyQqyyTIg0dWjYTR/K1rQg5Ei0cc5NF0nnCJ1ZGAUCyqIFa9AIOg3B1tqkmwHUtguRcy5E+P/H20qT3o/lW04kc1WFHduPG5WsjpRnJkAmNLz0W1uDDWEpFi6zSjVF7qmZ4zZvCiukQ/X0fztaL7zD366vw1TVnHKhB2SJCEjoZOYKypVDPdIIVa8AoGg32TZnLHqNRckvssa4lSJFwOW/ClJNnN+WYqWw4Oha0Rbq9G8jZ3ex3rnSvW8AMlWB4ozK2bvg+iuXbuW++67L8U7YyMsx9A1NH8LT/zlb+RNXRSzaRH0QEfK9rIk8Yvv/5gbVq+N2yQk0kfxWEQIr0Ag6Beav53Lq/cSqdhNqGIP0dZqQOLm0r5VlbmUSLv8ngQPYcWVRdqKD47Y9TV/G0Y0eN6gq51hPUOPYk9OlyinsPWXL3/5y0yfPj3BduzYMSRJ4q677kqwP/7449jsDoKBAO+95XoObXn1/HtP/D6WvarLz2OPPcZXv/wgL736Kk6TFbfZRqEzHesg4oUHi9hqFggE/aLt1Z8ws2oPn5It7DI5kZo8XDF+GnNzx4/21EYca+EMsu78X57f/Q8OBHxk507iemSm9951SDAinaJ7budBkmIF4XWt1zzJ/UV2ZoEkoQdj3tKyPa3HWN2+sm7dOr73ve9RX19PQUGszvDGjRspKSlh06ZNCW03btzI8uXLsTucYGjY7edXrZJiJi0tjePHjyf0SU9Px26343K5Bj3XoUKseAUCQZ/RwwHCZ2Ml8Er1CO+LtPHeSBvjKveM8sxGj1+f3stLEZUak40DbXX8YM+rnOloHrLxdUNnf3M1G6uP0xRMDBGSFBNGNIweCaJHghiRUMzLeZDnl7qu88ADD5CVlUVBQQHf+MY3YmkmnVmYcybwsyeeYf7yNbhcLkpKSvjUpz6Fz+eL9z979iw33XQTmZmZOJ1OZs2axYsvvpjyWqtXr8ZsNieI7KZNm/j0pz9Na2sr5adPoQXa0YIeNm3axLp161DcuQlbzZLZHo83LigoSPix2+184xvfYP78+YP6TIYSIbwCgaDPxBLSJ2/RDXUpvIuFtnCAXY1nE2yaofNG9bEhGT+kRvnOzpf4n32v8btj23hgy9/YVHPifIMudYoNQwdJHnRozu9+9zucTifbt2/n+9//Pv/93//Nhg0b4u/LssxPf/pTDh8+zO9+9zveeOMNHnjggfj7n/70pwmHw2zevJmDBw/yve99r9sVp9PpZMmSJWzcuDFu27RpE+vXr2fliuW89o9n0LxNnDy4k8rKSi5fsxrF7kZx54AkY8os6qyEdPHI2cUzU4FAMOpIJguOOVd3MUo4590wOhMaZSKaikFyZdVQivCVgbCx5nhC2JaBwZ9P7CSoxqoGGdEwssUei4NVzLFczLrGYKu9zp07l69//etMmTKFj3zkIyxevJjXX389/v59993HunXrmDhxIldccQXf/va3eeqpp+LvV1ZWsmrVKubMmUNpaSk33ngja9as6fZ669ati694jxw5QigUYsGCBaxeuoDNW98BYPOWHdhsVpbOjjm0ndtKly2O+INGR0cHLpcr/nNu63qsIc54BQJBv0hfcy+KO5fQya1IVieu+TfGC8P/q5HvSGOiO5sKb0uCfXn+pCEZv8LTkmQLa1Hq/B2UpufEKglJUkJhAUlWBr3inTt3bsLrcePG0dh43mnrtdde46GHHuLYsWN4PB5UVSUUChEIBHA4HHz2M5/mU5/+DK+8+ALr113ObbffwbwF3f+NrF27lu985zvU1dWxadMmVq9ejSzLXLZsIb/67R8A2Lx1O8sXL8DSw3LR7XazZ8/5Y4+uNaR7yjk9kogVr0Ag6BeSLONe9C5y3/8Dct71DWyTFo/2lEaVz8xdx4zMWK5mp9nKe8sWsTi/58LrfWW8OzlG1iKbKOisGRyL100UErkzhncwmM2JxwmSJKHrsSQVFRUV3HjjjcydO5dnnnmG3bt388gjjwAQiUQwNJU7b1nPsW0buOM9N3Hw4AGWLFvOT3/6026vt2rVKiwWCxs3bmTjxo1cfvnlSJLEksWLaW5t48zZSjZv28G61SuQzN2HAcmyTFlZWfyntLSUiKbijYSI6CpVvjY8kWC3/UcKseIVCAT9IlSxG//BV0HXcMy8AvuUlaM9pVElx+7iS4uuIaypmGUZeQgTM1xRPI3t9eVU+s4XO3hv2UIc5ljaR9nqxJRZ1FlmT0e2uZFtw+u9u3v3bnRd50c/+lF8RXlum1kPeYkGG9CjIYryMvnYHe/m4x/5AP/13R/y2K/+j8997nNArIavHvEDMrLVid1uZ9myZWzatIk333yTL37xiwDYsgpZumg+j//paapq6li7elW/6usahkFDwIPamdlKM3RaQn7Msgm7CCcSCAQXA4Hjb9H24g/ir0NndpB+xb/hmnf9KM4qEdXThG/X00RbqrAUTMW9+D3DWtP3HFZl6L9O7SYLX1t6A3uaqmgL+ZmTXUShKyOhjWyxI1tGLudwWVkZ0WiUn/3sZ9x0001s2bKFRx99FADV24LhtPIf3/w+16xdzZRJE2j3B3lzy3amT50KgB4JJNT91WUTpswi1q1bx//8z/8AsHDhwvi9rb3yan70ox/jdDpZftWNSCYzuhpGDweAnrePw5oaF90LCUTDoyq8YqtZIBD0Gd/uZ/tkGy30sJ/mJx/Av/8lItWH8O36G83P/Fcsy9ZFiklWWJo/kWsmzEoS3dFg3rx5/PjHP+Z73/ses2fP5o9//CMPPfRQ7E1DQ5JNaJrOfV99iPlXvoubP/hxppRO4he/iG1Ha97muOhCrDav5m9l3bp1eL1eVq1ahcl0/iFm7dqYffXq1VgsVjR/K2pLJXrIC4aO2l7brTOZ3G0N4dE955WMwbq/XeJ4PB7S09Pp6OggLW3wWVoEgosFXdfZu3cvAAsWLECWZep/fQ/aBV62EKvXWviZp1INMeL4D7xM++u/SLJnv/ub2CYsGNG5hEIhysvLmTRpEjbbpVu16RyGrnXmqY4VtEeNYBhGLJd0ZhGKzY1hGEQbTyX1lRQL5pzez8UNTSXaXAFd8y6n5afMrAVQ5+8gpEXjr2UkCp0ZmJXzCUZ6+l0NhwaIFa9AIOgztsnLk2z2shWjMJPU6N3UoO3OLhg6JFmJF2eQZAXJYke2p2HOK0Oxxbb6JUlCSlGuUDKnLmHYFUMN01V0z9tTk+9wk26xY1VMOE1WCpzpCaI7GogzXoFA0GfSVn4Y3ddK8NRWAGwTF5F++b2jPKvz2CYvx7P1DwnFGySzDdv4kVnt6kEvgaNvoPlakEoWAX0rDD/cGLqGHvRi6NFYlidZ6Yz97VkCDMNAD7TF0kRKErI9DaWHNJGKKxvJ4ojV85VNSQUbABR3TsIZL7Kpzw5TsVrCEhg6hhYFXYfOe+kOWZLJsvVcCnGkEcIrEAj6jGyxkXXjl2Jf4oaO4kgf7SklYM4uIfPqf6fjrd+iBzpQ0vLIWP+pYXOuMjSVUPkuDDWMOW8yLc9+Pb4Vrx55C335p4fluv3B0DXU1ioMLYqhaxjRCJKiIJmsyI50TO7cbvvq/lY0/3mPas0bu7eexFe22KEHZy/Z4sCcPQE97It5YtvT+5xXWlLMyI4MtAvOdSUD9EBHbJwxEKPbF4TwCgSCfjMSXsIDxTHzCuzT1qAHPcjOzGH7MlY9TTQ//RW0jnogJkqS2Ybcua1q6Cp62IcxRFmsBooe7IitDo1YpiuIPTBIihk90I5utncbgqQFU9TgDXT0KLx9mlPIh+ZvAUNHD3hQ0vL6nHZUMlli29WGHkseIiuxkoiRAJJ1bK1su0Oc8QoEgmFFj4QIHHkd395/oHZxzBouJMWE4soa1hWQ950/x0UXYiIVbakEINpUTqT2CIYaIdpWix4NDds8esM4l16ya1hN54pRjwR66Dz0vrd6JBgrYt85H0OPonbU993zXNdAlmN5wy9YKV+Ys7q/jLSPsVjxCgSCYUP1NNL81JdjISRAx1u/JeuGL2GfvGyUZ5aMHg3j2fIEoVPbkK0uXItuwTFzfbftI/WJ5edkmxvV34rqaUDzNSOb7aBFCUai2DsakLLHj8pWqGSxQ8iDJEmJbkmdiT56PB+1u9ED7Yk22+B2O/SwP9loaBjRYJ9WrLLVieZrIdHJKpaIY6AEArGHj64Zu4YLIbwCgWDY8G5/Ki66AGgqHW8+hq106Zg7j2t/7ecEj70JxGJN2175CZLZ3m1mLnPORNSWqvhrkzs3tnXauT0rqWHMNXtossfOwV1WL4p55J2tDMxosi0meLqMoasx0Q0GkBQTJoeMHEq9IjdMTjRTFD3kAyRkmwtFsSN1aa9HI+hhL+gaksWBbHV2+/vVohpaNHl1aoqoyEbfdgY0Wya6vw1Dj22ZK85M9EgUiPbaN+H+DINAIEBjYyMZGRkoI+TtLIRXIBAMG9GmM0k2raNhzJ3H6eEAwRNvJdn9hzZ0K7zu5R8gXLk/LrSYzOR/5BH8B1/Gf2gDss2NtX4nfrONRnUhLSGQhjCdZH8xNBVDi6IHO2LewJIU8whuaIt5FfflQcjTDrQnjasH2hK2a2WLo9tzY0PX0f2tCVvfksmC4u1+u9cwdAw10tnWGhN1w4hlrZINaG/ofe49kJGRMaKVjITwCgSCYcNSMIVoQ2LCBFNmIZJlrNXvNVKfZ6ZIN3gOc1Yx+Xf+kuDJtzGiYWxTVmJKy0PJLCRScyS+peqqegtHXi720lXDNfk+4z/wEr59f0+yZ1z1WaxFMwc0ZsfbvyfUGV52DslkJud93+s2laXqyyBw5DW09kYsBWXYp1yBbInF8kbqjhM4sQV0FVvpUmRHOu0bfhELUQJkRwaZ13weU3regObbFbPZPGIr3XMI4RUIBMOGe+n7CFXsjTshSSYL6Ws/Pua2mWWrE3vZCoInEwXEMav7M16InYE6516XYDNnFpF7x4/x738RLdCOrXQJ9qmXjYl7juhhTMHkUoNWxRhwdi2ftzblmBZUTN2NaSvCtebOJHPw9Ha8//hu/CHIf+y12Gq4c7Uba9RCZNdfcN3wwIDmOxYQwisQCIYNxZVN/kceIVS+Ez3sx1a6NCH2V/O3gST3KR442lpNpOYwpswirMWzBzUvQ9cx1HDCiizjqs8hWV2ETm1FsrpwLbwZx7Tui7f3hCljHOmX3zOoOQ4HjumX49v517inM8QSWlhL5g14TNukxYQrdifYzDkTMaV1Hx/cHb5df0vaeQiV78JaEqsPrEdDaO11RJsrUFxZuJa8d8zFkvcFIbwCgWBYkUzmpHNSLeih7cUfEq7cB5KEvWwFGVffh2xJvULybn8Sz9Y/xl/bJi8j68YHkeT+n5n69r2Ad/uT6IEOLOOmkXHV5zBnlyBbHWRe9Rm46jP9HvNiwZQxjqybv4pn6+9RW6qwFM0ife29vWaw6gnn3OuINlcQOLwBdB1TdgmZ1//HgMbq6kEN57JVdeZprjuGoUWRHRn49vydUOV+8j74kwH9HYwmQngFAsGI07Hx/2KiC2AYBE9uRXHnpEw/qXqa8Gz7U4ItdHo7odPbsE/p37lpuHI/HRv/L/46Unec1r9/m7y7Hh0TW8EjgW3CfGwT5g/ZeJIsk3nlp0lb+SH0sA9zZtHA51a6FN+e5xNsznk3oLZVo3oaY4lAZBOmzGIA1OazhCv3Ypu4aFD3MNJcXI8JAoHgkiB0+p0kW/D09pRto42nUjo+RepP9Pu6wRNbkmxqe12SA5ig/yiO9EGJLoB7xQdjuyOdD0HWkjlk3/p18u/6Xxwz1mLOmYi1ZE7CEcG5urwXExeV8G7evJmbbrqJwsJCJEniueee67XPpk2bWLhwIVarlbKyMh5//PFhn6dAIOgZOcW5XHdpCM05E1OGuphzJvX7ulI3W9lSH9MVCoaXWC7wL1Pwsd9RcO9vyLntOyjODBRXFhlXfgYlPR9JPr9RK5lt2CYuHMUZD4yLSnj9fj/z5s3jkUce6VP78vJybrjhBtatW8e+ffu47777uPfee3nllVeGeaYCgaAnXIvfk2iQJFyL3pWyrSljHK6FtyTYrCVzsU9d3e/rOuZcEz8zjI81YcGgV2qXCnokRPD0dsLVh0Y8jeKFKM4MFHdOgs2cVUzmtfejuGKVjEyZhWTf8l+Dylg1WkjGaH66g0CSJJ599lluvfXWbtt86Utf4p///CeHDh2K297//vfT3t7Oyy+/3KfrDEcRZIHgYkDXdfbu3QvAggULkIfYgSV4ciuBo5uQZAXHnGt6PXeM1B0nXH0IU1YxtklLBuxQE6k/iW/XM6ieRmwT5uNa8r5unbr+lQjXHqX179+OlQAEzHmlZL/7v7stMD9aaEEfRtiLkl6QcC6vR0IYkUBcmIeK4dCAS9q5atu2bVx55ZUJtmuuuYb77ruv2z7hcJhw+HxRZY8nuTqHQCAYPPYpK7vNCmVoKt53/kzg2CYk2YRj7rW4Ft6KZdy0QV/XUjCFrBu/POhxLjXaX/9FXHQBoo1n8O14asD1lrWgBwxjyMJ99Egoltbz5NtgGNjLVpJx9eeQzDY8b/0W//4XMdQI5oIpZF37BfRoCD3QjqVoFrLZOiRzGCouaeGtr68nPz8/wZafn4/H4yEYDGK3J2dVeeihh/jmN785UlMUCAQp8Gx5At/u586/3vxbJNmEa8FN/RpHC3rw73meaFM55vwyXAtu7jaV4VBg6Br+/S8SKt+F4szCtfBmzLn9P4seafSwH7X5bJI9XHu0/2NFQrS/+jDBU9sAsE1cROY1nx90KUnP1icIHt8cfx08uQXZ5sY8bmrC30qk9hg1P303prRYZivZ7ibrhi/FY4HHAhfVGe9I8OCDD9LR0RH/qaqq6r2TQCAYUgKHNvTJ1hOGGqH5yS/h3fFXQuW78L7zF5qf/sqgysf1Rvtrj9Cx6VeEz+4lcOR1mv7yxXipwLGMZHGk3KI1d4bt9Iah6/EzYc/WJ2IZwIxYGs5Q+S46Nv960HMMdckqBhA8tYVQp8CfQ/M0oracjed21oNe2l796aieWXflkhbegoICGhoSk2c3NDSQlpaWcrULYLVaSUtLS/gRCAQji0Hyl2RSPdleCJ5+B7WtJsEWbSon1CXL0lCh+dsJHH0jwWaoEfz7/zks1xtKJEkibfWdCd7jst2Na+l7e+ynB720/vP71P7sPdQ/+kE87/yZ4KlUoWLJtn7P0Zq8UyFbXci2xO9oPeQFJLjAB0DzNCbUTh5tLumt5hUrVvDiiy8m2DZs2MCKFStGaUYCwcWDbujsaqhgX3M1Vt8pZmaP45oJs5jgzh72aztnXYlvz9+TbP1B97eltqfIjjQU6GFfrOpP0vUuDj8Rx4x1mHMmETy5BcnqwDF9HYozo8c+ba/+hNCZHQDoIR/ebX8GObnggGIf/Dmva+EttG/4WaJt0a2Y86cQPLE5ngZTMltRTLmJYUcWO7Ijc9BzGCouKuH1+XycOnU+0L28vJx9+/aRlZXF+PHjefDBB6mpqeGJJ54A4N/+7d/4+c9/zgMPPMDdd9/NG2+8wVNPPcU//zn2n0AFgtHmt0e38dTJ3VT72rAp7bxVd4o3a05y/4IrWZQ3YVivnbb6LpCVmNezYsI55xqcXUKKesM6aTFs/k1i8g1ZwTZheOI+TZlFmLKKUVurE+y2ycuG5XrDgTl3IubciX1qq4cDhMp3Jtklqwsj2JFg6y5UrD84Z1+FbHXiP7QBMHDMXI9j2mUA5Lz3/+Hb+3f0QDuupe8lcPCV8+UaAffy948pz/WLKpxo06ZNrFu3Lsl+55138vjjj3PXXXdRUVHBpk2bEvp8/vOf58iRIxQXF/PVr36Vu+66q8/XFOFEgn9F2sIBPv/mk+zbu4+AGsE+sQBJlsm1u1maP5HvrLh1tKfYJ/yHX8Oz+TfoIR+yI530tR8bcOGDvhBtqaTtxR8Sba5AMllwLriJ9NXJVXguBfRomPpH70gouABgnbgI1/wbYgJp6DhmXoG9bPC7jJq/HXQ1Kb63u7aBI6+hB9qxTV4+qKIaw6EBF5XwjgZCeAX/ipz1tvC1bc+zc/ceVF2LC2+m1UFZRh6/uuLDoz3FPmOoEVRvEyZ3HpLJjOppJHDwZbRAB7bSpdiHYUWqeZuRrK4xtcoaDtpf/yX+Ay8l2LJv+Sq20iVDdg09Gqb9lYcJnoo5bFmKZ5N1/RdRnEOzdax6GvHteoZoSxXWwhm4Fr87ISmHiOMVCAQjQokrkyyrE6fZTEf4vBdwmsXOjMxxfR7HFw2zv7kau8nM3OwiTCnO/3pC03X+WXGQdxrKsSkm1pfMYNW4yf0aQzJZ4pmp1PY6mv78BfSQD4h5SruX3U7ayg/2a8zeuHBVZugaUj/v+2Ihfe3HUFxZBE9sQbI6cS24aUhFF8D7zp8JnjyfYztSfYj2139J9s3/Oeix9aCXpr98Me4PEKk+RKhyH7nv/+GwFs0QwisQCJKQJZlPzV1L3YkzHG6tRZIkcu1upmcW8MFpS/s0xpHWWn6yfyNhLbYVWeBI50uLriGzH3mR/3xyJ69VnY8lPXP4LRRJYnlBaf9uqBPfnufiohu37X4W16J3IQ9xvuZoUwXtb/ySSO1RlPR80lZ9eFi3uYcSzdeK2l6LOXdSjykZJcWEe9ntuJfdPmxzOee8lWAr34FhGIMWx8CxTUlOeNH6k0SqD2EtmTOosXtCCK9AIEjJ5PRc/mPBVTQFvRROK8NhsVKWntunLzvDMHji2Pa46ALU+tt5ofwAH56+vE/X13SdzTUnk+wbq08MWHg1T1PyXNUIerBjSIXX0FRanv9vNG9z7LodDbS99CNMmcVY8gY295Gi4+3f4dv9LOg6ktlGxvpPYp++lnD5LjyV+6m3Z1Iy6woyXSPjJSw7MqCLw5riyBiSFWks9Kjv9qFCCK9AIOgWSZLIc6QxJ6eoX7mag2qU+kDMs9UXCVPtbyekRqj1dzA3p5h5Ob0nZjAw0FLE7kZ1te830AXr+HmEyncl2EwZ41DSCwY8ZioitUfiohvHMAge3zymhTdcdQDfzmfir41oiLYNPyNUsYetJ3fytDWTkCRjOrqNm5bfxu19fIgaDO5F76Kl5nCCd7pr8buHZGzb5OV4tz+ZMLZksWMdP39Ixu+OSzqBhkAgGB3sJjPZNhearnPG00zoXBYhQ+en+9+gpct2bypMssLiFGFLKwa42oVYUfULqxopriwyr72/19VTpO44rS/+kOZnvopv3wsYKeJ1L0RSLKntptT2sUK4cl+STQ/5aNj7D/5izSIkxSRDjQR54chmjrTWDvucbKVLyLntO9inX45t8nIyrrkP14Kbh2RsS14pGVd+BrmzEISSXkD2Tf855McOXRErXoFAMCwsyCnh0UObCWlRzLKCRVbId6ShGTq7Gs5yzYRZvY5x14wVmGSZ7fUVWE0mriyewZUlMwY8J0kxkXXDA6irPowe6MCcX4ak9Pw1GKk/QdNfHwQtttIOV+5Hba0m44p/67aPedw0zHmTiTaePn9tsw3HzPUDnvtIoLjzkmxGNMRZWyZql4cTPRLiUEstM7MKh31e1uLZqO21eLb8gdDpd/Dv/QcZV30GS17/HO1S4Zx9FY4Z62LHDc6sYXWqOocQXoFAMOT84uCb7GysIN1qoyXkQ5FkpmXkY1Ji3r2WXsTuHHaThY/NuoyPzbpsSOdnyhgHGX3zzvbtfSEuuufwH3qVtFUf7tbxSJIkst/1Dbzv/Ilw1UFMmUW4l74PU3p+yvZjBfv0tfj2/QO15XyOevuMdWTXHktqK9vdZA9jwYkLidQdp33Dz+Ovo42naX3+W+Tf/VivD059QVJMKK7hz8h2DiG8AoGgzzQEPGyuOUlYU1laMJGpGclCcqajiZ2NFQBk21w0BX1ENBWfGiFDseM221iWP/Yr9pwjpaONpmJEgtCDx6/iSCfjik8OzRzCfgJHN8YSQkxaMiTlEVMhW2zk3v59/IdeRW2txlo0E/v0tTiOvM7st5/ikBwrr6e4cijMmcDKcSNzXh088XaSTfO1Eqk9MqaqDvUVIbwCgaBPHGqp4Vs7X0SRZCyKwmvVR/nojJVcXjQ1oV3dBbmJZUmiLD2X+oAHh8nC8vxSbimdh8M8ds86m4M+rIoJd2fyC3vZcsJdCiuYC6b0KYPSUKD5Wmn6y3/EnbW8258i/fJ7cPUzhWZfka1O3F1SPDpnX80XJy5h87G3OaNLFOWOZ23xNOwjdGYtWewYmooe7ABZQbanIUkykmV4z2KHCyG8AoGgV96oPs53dr1EW9gPSOTaXRS5Mnj29D7WFE5JOBebkp6HhBSvMGRRTIx3Z/GlhdcyI2tovYeHkuagj0cObqLc04yMxIpxpXx0xkocs69Gba3Gf+AlDDWCZdw0Mq+9f8Tm5dvzXJKHtGfbn3DMviaeGUsPBwie3IIRCWArWxGvRTuUWF2ZXLW4f/WQhwpTZjGRmsMYWsxJTzLZcC64CUt+2ajMZ7AI4RUIBD3iCQf50/GDhOIxuQZNQS9pllhpzYiuYb3gnC3P4ea9ZYt4+tRudAwkJNaXTB/Togvwq8NvU+6JCZyOwZa604xzZnDjxDmkX34P7hUfxIgGhyxVYV/pWtoQwIgE0fwtyJYi1PZ6mv/6ZTRfKxCLw8264UvDkgpztPDueApzwVTU9joMNYRsc4/psKzeEMIrEAh65Ky3BU3ScZmtBDvDgiCWDnJ+bkmC6J7j+omzWV4wiXJPM4XODMY5B18WbjgJRCMcb0+u17q3sZIbJ8YyGMkWG4xC7mVL4QxCZxKrACmuLEydscfeHU/FRRcATaXjzcewlS4dEg9dta0W1dOIpWDqsIfZpEKPhFBbKpEt9gSxjTZXDGi8cPUhwpX7MWUUYJ962aiEeAnhFQgEPZJpc0G4hQJHGkE1gi8aBiDP7uLuGStpCfkIRCMUuzITvuizbE6ybN07H40lLIqCXbEQ1CIJ9rQxUOTAOe9GQhV7iFQfAujMJvXpeP7nVAKkdTRgRIJIgxBKwzBof/WnBI68Hr9u5tWfS4iDHgkksxUlvSCpkL05Z2K3fQw1ih7yIjsT/yY73v5dQoIQ3+7nybn9e8iduzcjhRBegUDQI8WuDBalw57mKsoy8giqEfLtaXx7+c08cXw7uxrPArFczJ+bu45CV8boTngAmGSFaybM5Lkz++I2RZK5dsLAy8kNFbLFRu57v0u49ii6vw3r+HkJYUyW/ClEG04l9DFlFQ96dRo88XZcdOF8FivrxEUjKlSSJJG+5m5aX/x+PKxLdmR0mx/at+8FvO/8GT3oxZRZRMbVn8NaOAPN2xxLhXkB0eYKAodfw7VgZM+uhfAKBIJe+eTsy9ndXMmp9iYKXRmsLCjl1cojcdEFqA908NiRt/na0htHcaYD59bS+eTZ3exsqMBusrC+ZDqT03NHe1pxrIUziNQdx7f37yiuHOxTL0O22HAvu51w5T7U9jqgc0W87hODvl6k6mCSzYgEiTacHLYQHj3sRzJZk2Jz7WXLyf/ILwie3oZstmOfuho5RQxxuPYoHRv/L/5abauh9e/foeDe36C210KKjGMXxiyPFEJ4BQJBryiyzPKC0oTiBAdakp1+znia8UXDuMzWkZzeoInqGs+c2sM79eVYFBNXjjHRBfDu+CueLb+Pv/bt+hu5t38fxZVF3kceIVS+K+bVPGkJst096OspGSmc4SRpyPNaQ+wcue3VnxCpPYpsdeJa8h7cS25LaGPKKEgKc+pK6OTWJJse9BCuOYwlfwqSyYKhJh4nWAqnD/4G+onI1SwQCAZEqvJ+DpMF2xBkEhpp/nxiJy9XHqY9EqAx6OFPJ3awsfr4qM5J87Xg2fpH2jb8DP+h12LJ/C9AbavBfzBWhF5STNjLluOYecWQiC7EYndNXbJ7OedeOyyhSq0v/D8itbHyj3rYj+ftJwimENHe6O7eZZsb2eYifd3H4YLayLbJy7CPQqnGi+9fiEAgGBNcN2E2+5qriOpa3Hb9xDn9LnY/2hiGwVu1yeUH36o9yYyscThMlgE7WVV4WgiqUaZk5Pbrc1E9TTT9+X70cxWedj+H5mvFnF2S0C7aXDmgefUF2eYi9wM/InD4NVRPA9aSediGIUQpcHQTwZNbkEwWZFc2UmchhuDJrdinrOzXWI6ZV+Lb8zx68Hy2MWvJnHi8r3P21dgmLiZccxhTegGWgilDdyP9QAivQCAYEKXpOXxj6U1srD5OQI2wOH8CC3PHj/a0hoSQqvJ27WnOdCbTWDluMh+dsRJFllF1jUMttai6zuzsQmwmc1L/QDTCw/tf50R7AwAZFgf3zV/PxLS+5QP27/tHXHQhlrlJ97dgZBQgKeevZx43Df+hDYQr9iC7snDNvzFplToYZJsL16Jbh2y8rnh3Pk3Hpsdi56+A1FGPpXAGkmxCHkBWKsWVRe7tP8C762+o7bVYi2fj6rI9rbiycEwb2tzf/UUIr0AgGDBFrgw+NL3vq6B36s+wq/EsNsXM+pLpTEobmbSLPSFJEqvGlbGp5vzW8llvC9mdoVA6Bm/XnaLElcni/Al8b/erNAZjaTGdZitfmH8VpemJ9/HPioNx0Q1rKhXeZn66/w1+fNl7+zQnzdPUZY4ypqwSDMPgXHCMdfw81MZyAkdei7cLHnmD3Dv+B1Oq89kxhh7y4X3nL7GVrjML3d+KEQ2heRox50zAOe+6AY1ryiwk86rPDPFshxYhvAKBYMgIqVG21Z+hPuBhWmY+C3JK4nGUz53ZlxCus63+DF9edC1TMob+zLC/3DF1CRZF4Z36ciRi59c59kSv2X3N1VT6WuOiC+CPhvnj8e18dekNCW2PtMU8jOsDHur9HsCgwtPCrw9v4Z5Zq7qdh+ppJNpwEiUzudSepWAqOe//IYGag0RsGWTmTqD+sY8mtNHDfvwHXiR9zd39/ARGHtXTEHd0MudMQjPb0YLtKO48cm77Lubci6eQRn8RwisQCIaEkBrlO7teoqozi9IrlYe5rHAK98xcRVTXePns4YT2mqHzSuXhMSG8FsXEHVOXcsfUpYQ1lc+++RciemIpwHSLnVPtTUl9T3uaYivRCxI15NndHG2to95/frvYJCtsrj3J6sIypmUmV3Xy7vgrnq1/AMPAMAxksxVDi4JhIDvSybz2fja2NfBcQx3+aAWF5Qe5GYUSEkNkErJYjWHMWSXINhd6yIcky5gyCzFlFpK+9t5hq740VhDCKxAIhoRt9WfionuOTdXHsSlmmoJeKr2tZNtcKPJ5gWoPB0d6mt2yq+EsG2uOE9E0SlyZnO5o4ty+rrkzwcY/yg/QcMGKF6DQmZGUmvHGiXN4vSqxhm2BIx1JgpPtDUnCq7bXxUUXYtvfhhoh89r7UdLzseRP4VhHM3/c83K8T62q8VtXMf/pKU/4IrdNWjTIT2JkkEwW0q/4JO2vPoyhxvKAW0vm4pxz7SjPbPgRwisQCIaEhkCiIBmGwamOJp4+tZt0q52OSJC2cICpGfmc06n5uSUpRhp53qk/w6OHNifYzjlOOU1W1pdMZ7w7i3eVzudYWz2Bzi1SRZK5fcripPFK3Fl8edG1fOWd59ENnQyrA6fZgqYbWBUTuqEjS+ejOSO1R+OieyFqRz2OGWsB2H1BshIAJAjmT6WKEJM8dSArOOdei3362sF9GCOIY9plWEvmEq46gOLOwVo4Y7SnNCII4RUIBEPC1Mx8Xq48v53siYQIqVGcnck0xruzqPC04IkEybA6WFFQyrXjZ47WdBPYUHk0yVbhbeXnl78/wVbizuKhFe9ie0M5EV1jad5E8hypY0cX50/g5klz2dlYAUBDwEtAjfDT/W/wnV0vUZqWwxUl07lt8kJMWakfQC4MH7KnqGEs25yMf+9D5EW8yI4MFMfYLkaRCsWRPuJexpqvFf+hV9EDbdhKl2KbOLK7BEJ4BQLBkLAgp4Q1hVPY3BkTqxo6xa5MTHJsZWdVTEzLzOfGiXO4qmQm6daRTUzfE8F4ycPzhNRkG0C61c7VfXxg+NScy9nbXMrbtad4vfoYVtlOhbcFgP3N1aiGjicS4lNzLsc+bQ3B4+dX3Zbi2dgmL4+/vrxwCq+e2k0g0I5kcSJb7czKKmR8WhaQ1Y+7/ddG9TTS9OcvxMO1/Ptfwr3sdtJWfnDE5iCEVyAQDAmSJHH3zFVcN2E29QEPbrOV7+x6CYPzW6gSEqsLp4wp0QVYkjeR58v3JdiW5k8c9LiSJLEwdzzH2urJtDo403G+oL1m6PiiYXY1VBCYvoLM676AfdplROuPY8qegH3KqngFIgDzzqf4+NEX2WhJo1kyMbtoGretHTmxuFTw7/17Qow0gG/3s7gW3ZpQfGI4EcIrEAiGlHHO9Hj93Q9PX86TJ3cR1qJYFTO3T1lMgSMtZb+GgIeXzh6mzt/BtMx8rpswG3uK5BTDwU2T5uCJBHmr9hS6obMwbwIfmjZ0WZrSOqv5XPgQAjGnLYNYrLAkSdgnL0tZwD7SeAbf7ucoAD4Q7nRgO92I1Hgj/Iuciw4VakdDks1QI2i+ViG8AoHg4ueK4mmsKJhEnd/DOGca9m6KjneEg3xr54v4oiEAjrfXc7S1nq8sGVgShf5ikhXunLGCD0xdgkFsW3wouWxcGX84tp2QGiWgRrHICpk2J3aTmfk5xb0WlYjWp84bHak79i/jkDRUWEvmEjq9PcGmuHMwZRaN2ByE8AoEgmHFbrIkZXbqytt1p+Kie46THQ2c7mga0SpBlmEq8PBa9TFkCTKsDgwDIrpKsSuD1ePKuGPq0l77m7InpLSbu7GPBsGTW/HvfxFDi2KffjnOudclhVmNBZxzryNSczhehEG2u8m85vNI8sjVDBLCKxAIRh1/NJzS7uvGfjFwsr0RbzTElPRcNlQewaKYKHJlUOTKAGBOdhH3zlrdp7GsRTOxT11N8MTbcZutdAnWCQuGY+r9JnB8M20v/jD+OlJ7FD3oIW35+3voNTQYuk7gyGuEz+5DcefgnH8jalsNwWNvIikWHHOujhdJgFglp6wbv0y0tRrd34Zl3DSkbnZihgshvAKBYNRZlDeBF88eSrA5zVamZ479nMNdCaoRfrj3NU53NAJgkU20hvy4LInbyYFoJFX3bsm8/os4Zl5BpOEU5rxSbJOWjJkVpX/vC8m2ff8YEeFt3/AzAkdej7/2bP0jkmKOi6n/8AZy3vUNrOPnJfQzZxVDVvGwzy8Voh6vQCAYdSan5/LhacvjMb/59jQ+O3fdkJ+1jgQvnT0cF12IbSt7o2GMLgkyFuT1r5KTEQ0j29NxzbsBe+nSMSO6AHokkMIWTLrnoUb1NBI4+kaCLdpwCtVz/vNH1/DufHpY59FfLr6/aoFAcEmyvmQ6a4qm4IuEyLA6hkxYznQ089SpXVR6W5nozub2qYuZ4O5beb6BcLK9McmWY3MyMS2bs95WFEnmssIpXDd+Vp/HDBx7k443HkUP+5FMFtzLbse9tG+VjkYC+9TVeLf9KdE2ZfWwPxzo/rakjF+GFoEucdmat5mxhBBegUAw6uhhP4Fjb6L7W3FOWoI0REnyfdEwP9z7ajzF45G2Or6/51V+sPI9OFJkghoKxjnTOdpZnegcGTYH/7XkBoJqFLOs9CtMSvO30/bqT0CLFW0w1AieLb/HWjJ3zBQTcC+5DT3YQeDQBgxdxV62kowrPjHs1zXnTUZ2ZKAH2uM22Z6O3CWD11jLXy2EVyAQjCqar5WmJ78Yr0Hr3f4U6Zffg2vhLYMee1fj2ZjoGrFYWVmS8EfD7G2qZFVhWe8DpCCoRgiqUbJsqWM+r58wm92NZ+mInC8A8Z7JCzHLCmaLkrJPT4SrD8RF90JCZ/eOGeGVFBMZ6z5B+pp7wNBHzFlJUkxk3fAAbS/9CM3XAoqJ9HWfQG05S7TxDADWiYtwL79jRObTV4TwCgSCfnOyvZENVUfxR8MszpvA2qKpA95W9O39e1Lhd8/WP+KYfQ2yxTaoeUrEKiDV+tuJaCp2k4USV+aAxjIMgydP7uL16mNEdY0SVxafnL2Gwk4v5XPk2F18Z8WtbK07jTcaZkFOSa/hVD2huFL3VVzDt10+UKRROJO3Fs8m/57HUFuqkF1ZKPZYgpZoSxWSYsaUMfYc9ITwCgSCfnGyvZH/t/tlNCNWB/Zway0NAQ/vn7pkQOOprdVJNiMaQvM2IWcPrnrROEc61d42VEMDYqvVSm8rs7KTC833xpa60wlFIKp8rTxy8E2+syJ5Ze4yW/ucz7k3rEUzsY6fR7hyf9xmyirGPnVkCwuMZSRZwZw7EdXTiGfP8+hBL7ayZSNe/KCvCOEVCAT94pXKw3HRPcfr1cd49+QFA0pAYSmcTujMjgSb7MzElDFuUPMEONxaR2lGDnX+DoJqFLvJTK7dzdt1p7l2/EwkJPY0VXLW20ppeg4Lckq6XbknleUDavxtNAQ85HdJgxlUo+xvrkKWZObnFA86MUf2LV/Ff+hVIrXHMOeMxznnukHvBlxqRNtqaP7zf6CH/QD4D75M2qoP4V76vlGeWTJCeAUCQb9IldQiqmtEdG1AAuOcdyOhij1EqmNxvJLZSsaVnx6SbUubyYTDZIlnv6rxtVPuaea3R7bycsUhnGYLDUFvvP2i3Al8dt66lGO5UwidIsk4upxnVnha+MHeV+NJQTIsDr606Jp4/uqBIJksuObfCPNvHPAYlzq+Xc/GRfcc3p3P4FxwC3IvKTlHGiG8AoGgXyzqrLZzIVPS83vNN9wdssVG7nu/S7j2KLq/DWvJXGSbayimyoqCUl4oP4hfDdMRDtIU9GJTLLjNVmoDHZz1tjIraxxy5yp3d9NZjrXVp0zccVXJDLbVnyGqa3Hb6nFlSYL85xM7EzJxtUcCPH1qT7eC/q9OqGIP3h1PoXkasU5YQNrqO+PntP1B8ySHcRmRIHrQg2weubSjfUEIr0Ag6BfrS6ZTH/CwqeYEmqFTlp7HJ2YP/rxxOJL9Z1gdPLj4Wv5efoDXq46RY3eRb08HKVZvV9M1AtFIQlapWn9HSuEtcWfxX0uuZ0PlUdojQRbklLCueGpSuzNdHMUATnck2wQQaTxNy/Pfgs6HmcChDait1eTe/r1+j2UdP5dw5b4EmymzCMU9MMe2tpCfs95WMoyhl0khvAKBoF/IksyHpy/ntrKFhDWVDKtjxOegGzoVnhacZmvS+WpXil2ZfGrO5RQ7M/jbmb1xu7MzjtesJIb4TM3I63asCe7sXvMrj3dlcbqL+Ja4B+ZJfakTOLQhLrrniNQeJdpSiTm7f5m9XAtuJlJ3PF55SHFlkXnNfQPytv9H+QGePb0XHQPVH+q9Qz8RwisQCAaE3WTptszfcHLW28JP92+kJeQDYEFOCZ+cc3mv58uXF03ljerjtHemN3SarSwvKCXUmeVIQuKGiXMoHmC40TneN2UxP9q7gYgei711mCzcVrZwUGNesuh6N3Yttb0HJJOF7Ju/QrStBj3owZI/ZUB+AjW+dp45vef8VBj6tJdCeAUCwUXFrw6/HRddgL3NVbxaeYQbJ83tsV+61c7Xl93I61XHaAp6mZVVyKpxk2kIeqnytjIxLbvX1XNfmJaZz/dWvZtdDRXIksyS/ImkCQ/klNhnrsN/6JWEtI/mvFLMuZMGPKY5swi61NaN1J/Et/tZNF8L1gkLcC9+d7dJPrr6LwwHQngFAkG/qfW10xD0MiUjb8BOVQOhLRyg2teWZD/YUtur8AJkWh1Jq89CZzqFg/A47u46Vw1RHO+ljLVwBpnXfQHvtj+jeZuwTlhAxrqhTTUZbT5L818fxOhMGxqpPYraXEHWjV9O2b5gCB6+ekMIr0Ag6JWgGsFqMqNIMr8+soW3604BYJYV7py+gtUDTL/YX5wmC3bFQlBLLKmXY0+dvlEw9nFMW4Nj2pphG99/4KW46J4jeGobqqcJU1qyt/PMrHHMyirkcGvtsM1JCK9AIOiW5pCP507vI9CyH5fFzpSMPPY1V8Xfj+oavzu2jfm5JSOy8rUoJm6YOJunLziDsylmrpswe9ivfTGhR0KEyneArmMrXYo8AAc4Q1PRQz4UZ8bQT3AE0cPJJQsxDIywH0gWXkmS+Pz89WxvqKDC00IOZv48xHMSwisQCLrlyRO7aAp6ycKNXw3zQsUB3GYb6VZ7vE1U1zjT0cTcnJEpKn7jpLkUuzLZ3VSJy2xlbdHUITmbHQh6JEjo1FYMNYJt8nIUZ++OWRFN5U8ndrKl7hQmSeHyoim8t2wRijw05dGjLVU0P/NfRBtOoflbkS0Osm/7Lq651/R5DN/ef+Dd/hf0oBdT9ngyr/53LAVTBj23SP0JIvUnMOdMxFo8Mg9L9qmrCB7blGAzZRVjypnQbR+TrLBq3GRWjZuMx+MZ8jkJ4RUIBCmp9bXTdEFWJwCrYqItHEgQXglpxIVvfm4J83MHl8d5sKhttTT99cFYTVhAevPXZN/6NawlPZ81/+XkLjbVHAcgisbLlYexmyzcUjpvSObl2fIEkZrDqG01AOiBdpr++DnMGU9hHd/7NcLVh+jY9Kv4a7WlktZ/fIf8ux/rs5ewHgkRPPk2ur8V68TFWPJKad/4v/j3/TPexj51NZnXf3HYa/baJy8jbfVH8O18Bj3sx1IwlYwBhhkNFUJ4BQJBSmwmM7H6Puc9TrOsTmxKYi3Z0VxxjiaebX+Kiy7E6uR2bHqMvA//tMd+79SfSbJtrTs9ZMIbqT9BtL0uFpIjyUiShB7y4dvzfJ+EN3hqa5JN87USqT2KtWROr/21QAfNT30Jta3zjHTLH3AtvDlBdAGCJ97GMfsqbBMW9O3GBoF7yW24Ft6KEQ0NWVa0wSCEVyAQpCTL5mRGVgFHW88XdbcoJr669AaqvG00Br3MyCxg3ghtMY81oo2nk23NFRi6jtTDtrFJTq7J2zWJx2AwDAMj0IZhGLFVndmO4sxCC3b0qb9sTS1MfRUs/74XzotuJx1bfo9sdSF1ufdow6kREV6IlSyUlNEXXRDCKxAIeuDdpfPZYk/Dm+Yk2+7mugmzKMvIY2pGfrd9moM+njuzjwpvCxPd2dxSOo9cu3vI5rSx+jhv1pxANwxWF5ZxVcmMUdk2NOeXxbdz47bcST2KLsC6omk8X74vyTYUqJ5GNE8DyGbQIjERjgSxjMvDPnlZn8ZwzLoK/95/JBQcsJbM7XNsbbSlMskmKWYMNYxkSXTyMudN7tOYQ4nqaSTaVI4lb/KA00kOlotOeB955BF+8IMfUF9fz7x58/jZz37G0qVLU7Z9/PHH+ehHP5pgs1qthEJDnwJMILgUMSsm1hZPZcGCBch9cP6JaCoP7X6JllDsS7va18bh1loeWvGuzq3r/uOLhtnfXI3dZKY56OVPJ3bG3/vTiR2ENZWb+hDDO9SkrbiDSPUhNF8LAJLZRvraj/Xa79bSedhMJrbUncYkyVxeNJV1xUMjvKEzO5FNVmylS4jUHsWIBMFkwVI8G9eid/dpDFNaLjm3fx/frr+httdiLZmLa/F7+jwHS8FUQie3YugqUuexhCk9H1vZCgIHX423s09ZhXWAq1097EfzNmPKLOpXdirP1j/i3fFULGGHLONe9n7Slr9/QHMYDBeV8D755JPcf//9PProoyxbtoyHH36Ya665huPHj5OXlzq/alpaGsePH4+/Hs0DdYHgUmdPU2VcdM/RFg6wu6mSVeN6X91U+9o41lZPnt3NnOwijrbV8ZP9Gwl3pnWs9LYyzpGesDX7etWxURFeU8Y48u56lNDpdzq9mpf1qaqOJElcN2H2sIRAKY5YIhDZ4sA2cRGGFgVJJn31R/olUObsEjKv+fcBzcGIBok0nkIPtCOZ7TFnpis+iX3qapyzrur0ap7QqxNad3h3PIV3+1MYagTZkUHmVZ/FVrqk136RxjN4tz953qDreLf9CXvZCsw9eDgPBxeV8P74xz/mYx/7WHwV++ijj/LPf/6T3/zmN3z5y6mzkEiSREFBcqURgUAw9IQ0NbVdjfba94XyAwnxudMzCmgN++OiC7HVb0PQk5BPOWr0P6/vUCGbrTimXz5q1++KrXQZpsyi+Ba4pJgxZRVjK+3bNvNgCZ3ZiXf7U5jzyjBCXgwtgpKWi61sJQCWcdOwjBv46j5cfQjPlj/EX+uBdlpf/AEFH3u811jlSM2hbsY8KIS3OyKRCLt37+bBBx+M22RZ5sorr2Tbtm3d9vP5fEyYMAFd11m4cCHf/e53mTVrVrftw+Ew4fD5WprDEcMlEFyqzMgooCnowxMJYlXM5NndOM0WFub1XGmmLRzg2TP7EmyHW2vpiATJsp3PSpVpdeCNJB4VrSgoHfS828MB/nh8B/ubq8mwOrh50twRy8Y1lEgmMznv+3/49jxHtOE05vzJuBbeOqBiAQMhePqd2DwkCalz9a8HOog2nsJSkFxCESBcezSWLrJoNoorq8fxQ+W7kmxGNES4+mCvZ9imLvmbz9tH3jnwohHe5uZmNE0jPz/RqSM/P59jx46l7DNt2jR+85vfMHfuXDo6OvjhD3/IypUrOXz4MMXFqT/shx56iG9+85tDPn+B4FLHMAwePbwZm2KiWdPwRkJENJVPzb6cbXVnKE3PSVnnFqDK24pmJFaqkSUJmcSjoXxHGpPTc9EMA93QWTWujPeVLUI3dPY2VVPrb6csPY8ZWf3b5fr5gU2c6ogVUm8MenjsyNtkWO3Mzk79ZT0UNAd9GBgJjmcRTeVAcw2aoTM3pxj7AM7FFUc66avvHMqp9pnuPJ9lW7JznaGptPz9O4QrdscMionMKz+NY+b6bsfvTpgVZ8+CDWCdsBDr+HmEK/dfYFvQpxCroeaiEd6BsGLFClasWBF/vXLlSmbMmMH//u//8q1vfStlnwcffJD7778//trj8VBSMrqB+gLBxcDRtnrKPc24LTZmZBWg6zpnva384cR2cuyxL+TLCqdwz8xVSX1L3FkokpwgvpIkceOkuWyrPxO3Z1mdfGXJdeTa3fFwGU3X+eGeDRxpOx/2tK5oGnfOWJF0nVQ0Brxx0b2QLXWnh0V4/dEwvzj4ZjwX8NSMfD4zdx1BNcL3dr9Ca6c3sdNs5Qvzr6I0fXQ8bweCdfwC2l75Sex812LHlFmEY+Z6TBnjktoGjrx+XnQBNJX2N/4X2+QV3W4bO2ZcgW/3c2je5vPXnLiwT1m1JEki+9avEzzxFtHG05jzyrBPXT0qfj8XjfDm5OSgKAoNDQ0J9oaGhj6f4ZrNZhYsWMCpU6e6bWO1WrFaR67aikBwqdB1C9inRuiIBLGbz5dfe6v2JFcUT2NSWqKYZFod3Fo6P6EO6rSMAu6csYKbJ81jT1MldpOZpfkT4zWAz31h7m6qTBBdgI01x1lfMr1PtXXlbr54FWloUjh25a+n9iQk4D/R3sAfj2/HgLjoQkyg/3RiB/+15PphmcdQY6gR2jf8FCUtF3QNQ41gqBEy1n08ZftIbfJOpRENEW05i7VwRso+ss1F7vt/iG/fP1DbarEWzcQ597o+z1FSTDhmrIMZ6/rcZzi4aITXYrGwaNEiXn/9dW699VYAdF3n9ddf5zOf+UyfxtA0jYMHD3L99RfHH7JAcDExK7sQi2yKF4APqlFAwm220hEOYmCQZrFR5W1LEl6AmybNZX5OMcfa6sl3pDEnuwhJkshzuLl2Qvd+GanKBJ6z90V4c+yupGo0EhKXFQ4+N3Eq9jVVJdkONNfgMCfXhy33NCfZxiqhMzvQvM3IZjvyBTG/oTM7cc69FoCmoJfXq47REQky1Z7BFEg8TFBMmDIKe7yO4soata30oeKiEV6A+++/nzvvvJPFixezdOlSHn74Yfx+f9zL+SMf+QhFRUU89NBDAPz3f/83y5cvp6ysjPb2dn7wgx9w9uxZ7r333tG8DYHgksRltvKZuWt5/Og2WsN+cmxODEOnwtNKtFOMTbKCTTFhGAZ7m6s43tZAgSONFQWl2ExmStxZlLh7P6+7kNIUIi4hpRT37vj0nLU8fXoP+5qqyLQ6uGHSHKZldp8kJBWGYRDVNSy9ODJl2hy0RxIr5qRb7RQ40mgJ+RLsRRdRZSCjG492o9MrvSHg4Zs7XiDQWaJvq6axMrOUG9vOp9B0L7ktHhJ1KXNRCe/tt99OU1MTX/va16ivr2f+/Pm8/PLLcYerysrKhCD/trY2Pvaxj1FfX09mZiaLFi1i69atzJwpClQLBMPB3Jxifrj6PbSGAqRZbNz7+u+pD5yPDMi0OthYc4KDrbW8VXsybn+9+hj/tfj6ASXZmJdTzLL8SWxvKAdiontL6bx+5Y92mC18ZPpyPjJ9ebdtorrGK5VHONxSS7bNyTXjZ8YfEt6uPcUzp/fQFg4wOS2Xu2as6PYB4saJc/j5gU0YF+TAvmnSXEpcmZxsb8SvxqIqzLLC+6cu7vM9jDa20iXIVmdCxivJZME+JRZKtKHqaFx0ASRFYUfxAt616Dps/las4+d3u8V8IeGaIwSPbgJFwTHrKix5g/dqH2kkwzCM3pv96+LxeEhPT6ejo4O0tH+9RPCCf110XWfv3r0Afc5c1ZX7Nj9Fjb+doBrFabZgN5nRdANFTj5XvXP6ikFlcDrT0Uytv53J6bmMcw79qunnBzayq/Fs/LVVMfPNZTfhj4b59s4XE4Q0y+rkB6ve022pv2Nt9bxVexLDgFXjJjMrO7a96omE2NlQgaprLMmfmBBKdTEQqTtOx5uPxZJk5E4ifc3d8UQZP93/BnuaktNJfnf5rRS6Mvo0fuDoJtpe+Z9Y5ikAxUT2LV/DNmH+EN1BMsOhARfVilcgEFxcFLkyaI8EcF5wfplmscVXdRdSF+hbEv/uKE3PGTYP4KagN0F0AcJalI3Vsax4F4ouxJykTnY0dhs+NT2zIOV7aRYb60umD9GsRx7LuGnkvv8HKd+bk12UJLw5Nle/HpK87/z5vOgCaCreHU8Nq/AOB8PjticQCATAeyYvTCgjaJYV7pqxIqXHcHciNRbwRZMfFCDmeWy94Ey3NeTnWGs9h1pqeaH8IIFoJGW/SwlPJMTmmpO8U3+GcDfnvACXF01h9bgypE53qiyrk0/Oubxf4TyqtynJpqWwjXXEilcgEAwbpek5PLTyXeyoL0fHYGn+RLJtLgJqhN8e2Upj0Itq6FxRPI0FOWM3Xn6iO5scm4vmLs5PC/PGU+LK5JXKIzQGvFR6WwFwmK0caq3hV4ff4t/nd58Q4mLnSGsdP9n/RjytZ5bVyYOLr01ZjUqWZO6dtZp3T16AJxJivDsTuZ8hW7YJCwid2ZloGz8yZQWHErHiFQgEw0qm1cE1E2Zx3YTZZHdmNpqdXYjTbMWqmMmyOjnWVs+rlUdGeabdI0kSn5t3BUXOWHiSTTHz7tIFLMwdT67dzYOLrsVuMmMzmcm1uylNywZgX3M1nsilWw3tj8d3JOTSbg37ef7M/oQ20aZyWp7/NvW/vpfWF39IWsjLxLTsfosuQPq6T1CdPYnHrdn8zJbHm/kzcaz44KDvY6QRK16BQDDivHT2ML5oiJAWodrXhoHBTwJvsGrcZFwW22hPLyXj3Vl8Z8UttIUDOE0WVF3nH+UHOOttYWJaDovyxmNJUdC+O//VA83V/L38AK0hP7OzC3nflMW4zBdP8p6orlHjT46hvjD2WAt00PzX/4x7Ogc9jUTqjpJ/56NIA/Bgr0bh/4oWEQ35QJKpt9jwlR/g47MvG/iNjAJCeAUCQUoaAh6eOPYOZzqamRY4w7vLFrKyD6X9+kJ9wENzyEed/7xDVa2/ncePbuMz80Y3q1BvZFodqLrGd3e/FE/esavxLA6TJZ7G8hxzc4pIt9qTxjjT0czD+15H73TK2lx7kvqAh/9c3PcsTKONWVYodGZQ629PsE9wZ8f/P3jirYTwIgDN00SoYhf2sr6l9LyQ16uPoRl6QkrJbfVnuH3K4pSfc3/wH9qAb89zGGE/tikrSVv5EeRheggUW80CgSAJwzB4eN/rnOloAgyagl5+dfhtTncMjSPLtIx8WkOJSSQsion9LdXdrhDHEvuaqpMyZgXUCGuLplHozMBltrGmcAqfmLUmZf+3607FRfccJ9obqPW1D9eUh4UPTl2KWT6/ys+wOLil9IKiA90m1ejeCasngmqys5qBQUjrvexkj+OeeJv2DT9DbalC87Xi3/sC7a8/Mqgxe0KseAUCQRJnPM3UdwnvMTDYVneGyem5gx7/qpIZPHpoMzW+2BepIsmUuPrvbDNatHVZxZ1jvDuLj85c2Wt/TddT243U9rHKrOxCvr/qPexprMSqmFicNyEhCYp96mo8W/+AHvSiBdqRFAVT9nhsk3ovXJ+KxXkTksK6xruy+pUsJRX+QxuSbMETb6Gv/9Sgxu2Oi+OvXCAQjCgXrmIuxDSAJBqpsJnM/MeCq5iSkc+ktFxmZhXitsRWiaNRLaa/zMkpjofFnENCYm5O36oZrRw3Oan/BHd2v9NljgUyrQ7Wl0xndWFZUuYxxZ2DY94NRJpOo7ZWoXmaQDcwuqTM7CvLC0q5tXR+PEStLD2PT89dO9hbSIwN7ot9kIgVr0AgSGK8O4vS9DxaOZ/wwCQprBnCwgHriqehGTpvVB8nrKmsKCjl1tKRr406EAocaXxk+nKePLmLkBbFrlj4wNQlKcNoUjEtM5+Pz76M58/spyXkY052ER+a1nMh94sRQ9cIHX8Ta+H5NL1GNIh319NkrE1dtagn9GiYm0tmcMPEOUQ0FecQOaM5Zl9JuHJfgs1etjJ2lhz2pO40CITwCgSClHx+/nr+p6qJ0x3NzMwq5JbJ8/uc2q+vXFkygytLes/POxZZVzyNFQWlNAa95Nnd/c4zvaKglBUFF1+e4f6gB9rRfK1J9mhTRf/GiYbpeP0XBI5vBsAxbQ3pV3xyKKYYH08P+fDveR497MNetoq0NR8dsvG7IoRXIBCkxGW2cuOkWJ7dgeZqvtSxmcyMvwi3h0cK2ZGJ4s5JKFwPYMkv69c4ni2/I3B0Y/x14OhGJIudjCv+bUjmCeCadz2ueSNTMlb8SxIIBKNKnb+Db+34J3e/9jt+tHcDjQHvaE/pXw5DjRBtqcJI4TU8GCRZJn3dJxJidk1ZxbgWv6df4wRPbEm2nUy2XSyIFa9AIBg1yj3NfOKNP9IYjJ2j7WmqZGvdaX659oPkOfp2XioYHIEjb9Dx5mPoIR+yzUX65ffimHnFkI1vn7wMy0d/RahiN7LNhW3SEqReahZ3RbY40Lsk65Atjm5aj33EilcgEIwaT5/aTWMwcYV71tvK69VHR2lGY4OorrGx+ji/PrKFVyuPEFIHF6faHWp7PW0bforemYNaD/lo2/BT1I6GIb2O4srCOfsq7GUr+i26AK6FNyfZnAuSbRcLYsUrEAhGjTq/B7okklB1jaZg6jjZfwUMw+DHe1/jaFtd3Lal7jRfXXI9pm7CvAZKqGI3dI0p1nVCFbtH7LyzLzjnXodkthM4/BoAjlnrccwY2xnOekIIr0AgGDWW5U/irbpTRC/IZOQwWVmUN34UZzW6HG2rTxBdgLPeFvY0VbE0f+KQXkvpLPqQZHdkDOl1hgLHjLU4Zqwd7WkMCWKrWSAQjBo3l87jyqLpWDq3H62KmTumLWHlJR5m0xMtXUoPnqPrlvxQYCtdiilnQoLNlDMBW+nSIb+W4DxixSsQCEYNu8nMQ6veRY23jQpfK7OyxpFhvXidZoaCGZnjkJAwumzBz8oaN+TXkhQTubd9F9++fxBtPIM5rxTX/JsGdA4r6Dvi0xUIBL1S62snoEcpTcsZ8nNGgCJ3JkXu1Nue/2rk2F18aNoy/nJyJ1FdQ5Fkbi2dz6S0nGG5nmx3k7bijgH3NwyDSPVBjGgY6/h5SCbLkM0t2lJFtPE05vwyzFnFQzbuaCOEVyAQdEtEU3nq5C5aW/YjyTKZVgefnXsFpemJIqDqGi+dPcy+pircFhvXTpjF9MyCUZr1xc/6kuksL5hEla+NQmcGaWO0RrHmb6P5ma+itsRSi8qOdLLf9Q0seYMvH9nx5q/x7Xk+/tq16FbS19w96HHHAuKMVyAQdMu2ujOcuqAUYFs4wK+PvJ3U7vGj23jm9B5Oe5rY11zFD/a8yqn2xpGc6iWH02xlembBmBVdAM+2P8VFF0APdNDxxqODHjdSfzJBdAF8u58j0nBq0GOPBYTwCgSCbjnjaU6y1fjb2dFQwZa607SFA/iiYbbWnU5oc674geDSJlJ7JNlWd3zA9XbPj3Gsm+tdGvHdYqtZIBB0S6bVwVlvS/y1bhhUedv4+YGNyJKEIsncNnlhUlF3SF20XHBpYcosJlJ3HLW1Gj3sR7Y6sU1cOGjnLHN26nAycxcP7L4SOrMT357n0AId2MuW4176viE9i+4vYsUrEAi6ZdW4yViV83l2W0J+7CYzcmfNXM3Q+Xv5ASa4spP6LhnimFPB2MO14CaiDafQgx2gq+hBD5qvBT08sHq757CUzMU2ObFMom3yMqwlc/s9VrjqAC1//zbhqoOoLZV4tz9F24afD2p+g0WseAUCQbfkOtx8cvYaWnLtBLUoFd4WyrtsPwe1CDdNmsvr1cc42laHw2ThmvGzWDlu8A42FzMhNcrLlYc53tZAviON6ybMIt+RNtrTGlLU9los46aj+VrA0JGdWUgmK6HT7wwq37MkSWTd9J+Ezuwg2nAKc37ZgGOL/QdeTipoHzyxGX3tx5D7WD95qBHCKxAIeiTD5mBdWaws4AsVB5OE1yQpTM8qYHH+BIJqFLMsD0vI0cXGw/tf51hbPQBH2+rY1XiW7yy/hXSrHYDmoI+djRWYZYVl+ZNwj2Enqm4xDCSTBVNG1xjj5KMHgFD5Lnx7/o4e8WMvW4Fr0buQuvlbkSQJ++Rl2LusfPs9RTWcbNR1DH1w59CDQQivQCDoM1cUTWNr3Wlq/e1x262T5+MyW4FYQoy+4I+GefrUHg601JBtc3LTxLnMySkajimPCmc6muOiew5fNMRbdae4ceIcDjRX85P9b6AZsTzJz57Zx4OLrqXYdXHFMtvLVuJ563H08Pnc2rLNhW3y8qS2obP7aHn+W/HVZ7T+JJq3iYwhLGifco7T1hA6szPBZi2Z2226zJFACK9AIOgzDrOFbyy9kZ0NFbSE/MzOLqI0PYewpqIbRp+F92cHNsaFqSXk4+H9r/OVxdcnxQdfrPiiIQwDIrqKSVJQ5NiZuC8SAuAvJ3fFRRdiDyLPndnHPTNXo0hSPIXmWEe2u8l+93/T8dZviTadwZJXRtqajyJbnUlt/fv/mbTlGzj8Gu7lH8Sz9fd4tz+JEfZjm7yUnNseQnFmDMkcHdMvR/M249v9LHrIi610KRnrPzUkYw+Ui+O3KxAIxgwWxcSqwjIgljjj8aPbeKv2JLphsCC3hHtmrsLZuQJORZ2/I2k1qBk6b9WevGSEVzd0TrY3EFAjyJJMviONfIebRXkTMAwjYccAQNV1Xqw4xO7GSkyyzOVFU/nAlCUo8tj3f7UUTCH3vd/ttZ0RCSbb1Ciet39H+xu/QA+0A53xuvWnKPr8P5CHaPvdveQ9uJe8B0PXkcbAZzr6MxAIBBcdLSEf3kiIv5cfYFPNcTRDx8BgT1MlTxx7p8e+upH6/E819JT2i42wpvJ/h9+m0JmBVTGhGzqNAQ9rCqcyJSMPSZIo7ZL+scrbSlCLYmAQ1TVeqzrKy5WHR+kOhgf71NVJNuuEBQQOvxoX3XNEG04SPLZpyOcwFkQXxIpXIBD0g+agj18efJPTniZkJFrDAdIstnh4EcCuxrMYhoF0ge1CilwZTHBnJ8QHA6wsuDS8oE+0xVa6Lkss81REU/GrEU60N3C8rYFpmfl8aNoyfrT3NfxqGN0wCGpRyly5CeO8VXuStlCAukAH0zMLuKpkBrY+buWPRRxzrkHzNePb9wJGJIht0mIyrvwsdY9+MLmxJKN5L93MZ0J4BQJBn/n1kS2c9sRSSOoYNAa9hLVoQpiM3WTpVnTP8e/zruD3x97hYEstWTYnt0yay4ysSyO3c5r1/PaoAVT52vBFw0R1nYd2v8TV42dyx9Sl/HD1bRxsqUFG4v8Ov0XkAi9bVdfY1XiW+oAHgMOttRxpreNLi66h1tfOCxUHaQh4mJ5ZwI2T5mAfxWQQfUWSJNJWfgj38g+ArsUTWLiW3Ea4cm/C+a+Slot1wsIexzM0tdOr+uJ7GBHCKxAI+kRQjSYVaM+zu2kMeBKE95rxM3sdK8vm5N/nrx/yOY4FJrizmZ1dxKGWGto7U2qaZIVsW8zhaEPlUa4onk6BIy1e2L7C28wLFQfjY7SFArjN9oRxD9ee5I2TG/m9L0jY7sacVcJpTxMn2hv5ypLrEtqG1CiKLGMeg2FdkqzABfNKv+wutPZaPG8/gaGrKOkFZK7/FNbi2Sn7G2qUjk2/InD0dQzDwDH1MtKv+OSgzoP1kI9w1QFkZybWwhkDHqevCOEVCAR9wizLOEwWAhekgsyw2pmakU++w01E01g5rpTLi6bG3w+qEco9zeTY3OQ5RidZwWjwubnreK3qGE+f2kNQjZJrd2PqPF80MKjxtVFwwcPKeyYvJNPqZHtDOVbFhJFlcKi1Nv6+EQ0TqT/OtqiPoMkB/jYikQDW4jmc7GjgTEczpek5+KJhfnNkC3ubqojoKqXuXO6YuoQZ2UNfy3eokCSJ7Ju/Qua1X0BtrkDJGIfiSO+2vWfbH/EffDn+OnB0I5LZRsb6gYUlhc7spPXF72NEY/G+luLZZN/ytSFz7EqFEF6BQNAnTLLCNeNn8eyZvXGbIsncPXNlyhKAOxoq+PWRLYS1KBISlxdN5c7py3vdhr4UsCgmrp84mzy7m58f3JjwnoyUVFtXkiTWl0xnfcl0AE62NyYIr+ZrwaarpBla3GZEw2hBL4ojLZ4X+3dHt7GnqZJafweNAQ+HW2rZ1VTBDRPn8uk5l/f42R9preNYWz35djdL8if2OaRJ8zbjP7wBPejBPnk51vHz+tSvK7LFhqVweq/tgieSq2MFj28ekPAamkrbaz+Piy5ApPoQ/v0v4F5yW7/H6ytCeAUCQZ+5pXQeeQ43O+rLsZssrC+ZzuT03KR2gWgkLroQW+VtqjnOnOxCFuUNLNH9xcjCvBIW501gV+NZACQk3j15AVm25DjXC5mSkccHpizhuTP7CWoRshQT7w41Y8Zgi/nCnQMDt9nG1Mx8NF1nT1MlITVKY+fZMEB7OMiuxgr2N09mfm5Jyuv96cQOXq08X2no1aqjPLjo2l6duaJtNTT/5YvoIR8A/n3/JO2yu3AvfneP/QaDZLH3ydYXVE8Dur8tyT7cVZCE8AoEgn6xoqCUFQWlPbYp9zTHRfdCjrTWXbLCu6X2FDsaK7ArsQeSKRl5yJLMZ+au41R7I3WBDqZk5CdsMffENZ25nf9ZcRDNYqe2/iCXhTu4LdzKy5Z0/GY743PGc8+s1ZhlBcMwsCommoLehHHOeZyXe1pSCm9z0MeGykShOettYVv9GdYVTwNA9TTheft3ROqOYsosJm3FHVjGTcO3+7m46J7Du+MpXPNvHLbqP675N9L+2iMJNueCmwY0luLMRjLbMKKhBLspq3jA8+sLQngFAsGgUHWNHQ0VlHtaGO/OZHlBKTl2FxISRpecvXmjlJR+uHn29D6eL98Xf72zsYIvLrw6vgVflpFHWUZev8Y82lrPT/e/ES+5eLJ4Ce0dVdzacZbLcgswr76TrLxJ8faeSIhMq4ODLTVEdA1LpwNTrt1NY8DLC+UHeKv2JCvHlXJr6fx4Pu1af3vS7wlidZcBDF2j5W9fRW2LbX1rniaaa4+Sf+cv0Drqk/oZ4QBaoAO1rQY90IZ1wsIez2z7i3PONUgmC/5DG0DXcMxaj3P21QMaS7bYSFv5QTre/HXcpqTl4lpwy1BNNyVCeAUCwaD4yf43ONhSE3/9Vu0pvrTwGlYXlvFW7cm4Pc+exmWFU0ZjisOKqmsJ27QQy8T18tnDKc+++8prVUcT6hwrjjT2OOdy9we+m7QF3BEO8o0d/6A15CfdYqcjHEQ3YHJ6DhFdwxcNoxoarWE/L1QcJKJr3DE1Vu1nYloOiiQnpLAEKOs8QghXHYiL7jmMaIjAsU1YS+YQrtyf8J6Snk/rC/+PaEPsdy+ZzGRe+wXsU1YO+LPoimPGOhwz1g3JWK6Ft2ApnEnozA5kZyaO6ZenTHk5lAjhFQgEA+Zoa32C6AKcaG9gf0s1d89YydzsIo601pHncHN54VQc5r5tP55sb+QvJ3ZS4W1hUloOH5i6JOVZ8lggqusEtUiS3RsJpWjddy70Hj+HamhEdQ0bicL7Zs0J2sIBJEkix+4ix+4iEI1wW9lCNlafIKCGExyr3qo5FRfeNIuNO6Yu5Y/Ht8eFfnHexHioE1rqKj6GpuJe/G7CNUcJV+wGQHZkYC2cQeDopvPt1Cjtb/wSW+lSpDGag9pSMAVLwcg9FI7NT0EgEFwUNAQ9Ke31fg9SrsSS/IksOfcF3kc8kRA/3LshfkZ8qqORH+3dwA9WvafHHNCjhd1kZkbmuKQY5wV54wc17pL8iUljzsgcl7J8YHOXc1aIFbSYl1PCgZYagr5EEe+6tby+ZDoLc0s43h6rHXyh17V1/HwUVxaar/V8B8WEY9oaJJOFnHd9nWjzWfSgB0vhDFpfeChpLnqgA9XTgDnz0qlANRjGRuJKgUBwUTItIx+J5BCVwWyx7m48m+SYFVAj7G2qGvCYw829s1ZRlh47w1UkmdXjyri2D4lEemJd0VSunzAbqxJb3c7KKuTjs5LzHQPMzk4WNKfJyqS0bC4bV5b03urCZFumzcnyglKybS5+e2QrX9zyDN/f/QrHvS1kv+ubWEvmgmLCnDuJ7Ju/gimzMN7XnDMBa8kcJMWEKfuCBw7DQO2oJ9pcgW/XM6jtdUnX/VdErHgFAsGAGedM57ayhfzt9F40Q0dC4oaJcwZVZUiRUq8H+hP/W+tr5+/lBzo9ifO4ZdK8YS00n21z8V9Lrqct5MeimIZkZS5JEu+bsph3TV6Aqms9poVckjeBK4qns7H6OAYGTrOVT8y6DIti4urxM4kaOhurj6PqGisKSrmtrPt0jA/ve40znmYAmoJeTuxp5L+X3UThbd/u07xdC24heGILWkc9kcbT6IE2TNkTCBx6jdDp7eR98Cco7kujCtVAkQyjm1IhAgA8Hg/p6el0dHSQlta3MACB4FJA13X27o0ly1iwYAFyD5Vd2sIBKr2tFLsyyLa5BnXdQDTCF7c+g/+CpAZus40frHpPn4oEtIcDfOWd5xP6j3dl8c1lN13yyTvaQn5aQn7Gu7MGVNO3wtPCN3b8I8l+3YTZ3D5lcZ/H0SMhfHueo+2lHyE70pEtjvh77mXvI23lh/o9t9FiODRArHgFAsGgybQ6yLQ6em/YBxxmCw8uupanT+2h0tvKBHcW7y1b1OfKPNvqzySILkClr5Xj7Q2D2gK/GMi0OcnsJTlHT3T1bD5HVNdS2rtDttiwlszFlJGcqlIPpPYL+FdCCK9AIOgzLSEfL5QfpMrXxqS0HG6cOId068CyBvVEsSuT+wZYRCGoJifu6MkuOE9pWg4FjnTqAx1xm4TEyl4SpqTCUjAV2ZmZlBnKNnnpoOd5sSOcqwQCQZ8IqlG+s/MlNtYc51RHIxuqjvD/dr+M2s/V0HCzJG9CksOX02xlZtbYLRQwVpAkic/PX8/s7CIUSSbfnsYnZl9G6QBCuSTFRNaNX0ZJj+0ySGYr7hV3YJvU9y3rrgSObqTh8X+j9me30fKPh9B8Lb13GoOIFa9AIOgTOxsqaA37E2x1gQ4OttSyoJscwKNBiTuLj81azdOn9tAa9lPsyuSjM1ZiHaMxpGONfEca/7HgqiEZy1o4g/yP/i9aRwOyI2NQFX/CVQdoe/l/4q9Dp7bR4msm7wM/GoqpjijiL1EgEPSJcwkdNF3HFw1jlhUcZguBLuepY4GV4yazoqCUkBa9KIrEX8pIkoQpY/Bn6xcm5ThHtP4k0ZYqzNlj58GvLwjhFQgEfWJBbgm/Ovw25Z5m9E4nnAyro18OS0da69hccxLN0FldWMa8nOFLRi9JkhDdSwhJTi1XUmfO6YsJccYrEAj6RJbNiUVRUDpDciyKiSybkz1NlX3qv6vhLD/Y8yrvNJxhZ2MF/7PvNTbXnOy9o+CSJtpSRbhyP3ovOyeO2VdCl5A2a8mchEQeFwtixSsQCPpEnb8Dq2JiZtY4NENHkWQkSeJ4eyNX9SFL0z/PHkxKVfhCxUHWFF16hRMEvWNoKm0v/ZDgya0AyFYnmTc8gG3CgpTtLQVTyb75v/Du+CuarxnbhEWkrfrwkM4nVL4LIxLANmkJ8jBW0hLCKxAI+kSOzYVZVojqGibp/PbeOGffSr55IsEkW0cKm+BfA/+hV+OiC6CH/bS98jAF9/y622IKtkmLB+UV3R2ar5Xmp/8zXoVJMlvJuukr2CbMH/JrwUW41fzII48wceJEbDYby5YtY8eOHT22/+tf/8r06dOx2WzMmTOHF198cYRmKhgrNPjbeWvbXzn5zx/ieecvaBfEKAr6jsNs4eZJ8xJs2TYXVxZP71P/+TnJDjDzc4rxREJoeurEDYJLl0jVwSSb7m9DbR35nNzeHU8mlD40omE6Nv4vw5XYsd8r3jvvvJN77rmHNWvWDMd8euTJJ5/k/vvv59FHH2XZsmU8/PDDXHPNNRw/fpy8vOQi01u3buUDH/gADz30EDfeeCN/+tOfuPXWW9mzZw+zZ8/u17V1XUcXXw4XHU+f2sNz259D87ciAetO7+OmgxvIu+PHw7qVdClw4d/8uf/eMGE2U9Jz2d9UTabNwcqCyTjMlj7923h36XwaAx4ONFcDkGtzc6yljs9u+hPpFjvvnbKYVeMmD98N/QviP/gKvt3PoYe82CYvI33N3f2qNWsYBpGawxiRIJaSuchDWB1KSstD1xOFTTKZkRxZI/5dG6o7mTSXSEs1Wsg3LHPpd67mW2+9lRdffJEJEybw0Y9+lDvvvJOiopEp9bRs2TKWLFnCz3/+cyD2ZVBSUsJnP/tZvvzlLye1v/322/H7/bzwwgtx2/Lly5k/fz6PPvpoymuEw2HC4fOH/B6Ph5KSEjZt2oTLNbgctIKRpc7fzi/3vka08VSC/YPhFpQZ6znqysfAYH5OCZMzxmat19FE13WOHz8OwLRp03rM1dwfPOEgQTXKr49uIZJQ61Xik3PWkO8YfD7cQy21HG6txSqbWJw/kWJXxqDHvNgIVe7H89bjCTZL8WwyLr+nT/21kI+OjY+itsbqLctWJ+mX34s5d2Kf+ld5WznaVo/dZGF+TnFSkQot0E7bKw+jX7AD5Zh1Ja75N/Rp/KHE885fCJ3enmCTnVlk3/Jf+P1+1q5dO6S5mvv9L+m5556jpqaGT37ykzz55JNMnDiR6667jqeffppodPhSskUiEXbv3s2VV14Zt8myzJVXXsm2bdtS9tm2bVtCe4Brrrmm2/YADz30EOnp6fGfkpKLKz7sX41I/QnaXv0pzc98lY63f4fqO5+e7qy3FUNL/pvcqTj5Y2MVB1qqOdhSw++Pv8O+zlXYaFDhaeaZU3t46uQujrXVx+2arrOp+gQ/P7CJ/zv0FvvGcFm8/pBmtdMc8nURXQCDo631Kfv0h801J3n61G6Ottaxr7mK3xx5m7Pe5AxHrSE/2+pOs6+pirCautj7xUzozM4kW6TmMFqK2r2pCBzaEBddiJ3Benf8tU99d9RX8OsjW9had5rXq47yi4NvJtUMVhwZZF73Hzjn34B92mrS1318VEQXwDH7KmTHBb4KioJr0S3DVlRjQM5Vubm53H///dx///3s2bOH3/72t3z4wx/G5XLxoQ99iE996lNMmTK0norNzc1omkZ+fn6CPT8/n2PHjqXsU19fn7J9fX33/7gffPBB7r///vjrcyveefPmiepEPXCktY6GgIdpGfkUjtDqItpaTeNb3waLCtkKqOWYyv9G3od/hiRJWFry2S63E64MwQXJ3wOGRva4aSgX/EM75dT46ILU3pT9JaypBKLhPiWr39tUxT/2H8DIVACFer2B/NxSriiexh+P7+CAPQhFmYSAzbQyfdxsVgwgb+5AuHCLrbfqRP3F0pLP6zQl2edMm8uCkhkDHlfVNR7rOEpWWWIR+rNpErfOP//73dFQwZ8PHUSzxO7xcDjMf865nqxBFBgYa7RUFRKiywOlJDFu/vw+HbM0Hv0DkQIHeqAdZAXFkQmSn3Ezp/a4Xa3qGr/2HEv6HVSly1w1M8W/sRWX9eV2hh196UqCJ7fEvJonL8eUHtMOj2foizoMyqu5rq6ODRs2sGHDBhRF4frrr+fgwYPMnDmT73//+3z+858fqnmOGFarFas1+RxDluWELx7DMPDteobA4dfAMHDMWo9ryXtHrOyYHglhRPworuwRuV53aLrOT/a/wYGW8//A3zN5ITdNmjvs1w4ffxPZ0DDk85l59bZq1PrjWItmMjuniHkFE9kX8RFtqgBdZboepiFnEror8/w4msq+lhq+v+dV0q0O5mUXMT+3BIe5/8kXnjuzj5fOHiasRSl2ZfKJWZdR4s7qtv1LlYdBlhJyC79UeYgrSqbxVt0ppC5it6nmJKtSFDEfLs79zXf9+x8ss3OKmJCWQ6WvNW5Ls9hZWVg2qOtoukpQV5M+N58aiY+rGzpPntqFLoHUWfu3NRLkpcrDfHj68gFfe6zhmnM1kfLEVa99ykpMffRClyWDaM0B6DyN1E1W7FNWothcSJJE6MxOQpV7MbnzcMy6ErmzHKQ/GiKgRZN+B00h/5D+DQ01st2Fe+41yfZhmHO/hTcajfL3v/+d3/72t7z66qvMnTuX++67jzvuuCO+Inz22We5++67h1R4c3JyUBSFhoaGBHtDQwMFBakz5xQUFPSrfX/w7XoGz9tPxF97tvwBQ9dJW/7+QY/dE4Zh4NnyBP69/8BQI5jzSsn8/+yddXgbZ7q375kRs5ljx5A4DjM21LQpbrnbhXaxC9/ZPct4Fs6eZTpdZoZut1vacpumSZOmYebYjplJzJr5/pCtWJbMcqAn93X1uqo3o9FIluZ53+d9nt/vhk9dMsm0/Z31cUEX4Mnaw6zOKxtx9RCMhDne04IgCMzNKEA9AfWZ7W4HzxrycQkSlWEfdwf7sCmR2OpWEAQ+Pv9aDuZXUG/voCASYGnRHP7WdJodrVHhhlAkwjl7JyaVhmfqj+EJBTCpdczOyOej89YzJ2Ps9QsHOxt46vyR2ONmdx8/Pbad7666Y9gJmTPoTxizB3zIipLUoi2sXF6GBBNFEAQ+u3gzz9Ydo8bRRYHRxs0lczFNsnhHr9Iw05bLWXt8VmuwQpYrGKAv4E14bqOrL2HsSkZftpy0zR/vL65yoitbjmXNu8f0XEVRCDs6QJBAiabhlXAAyZqDIAg4Xvsd7kNPx453H32OrLf9EElvwabRk6O30OGLXynOTIvPPv5fZtyhPC8vjwcffJDi4mL27dvHgQMH+NCHPhSXht2wYQM2my2V14lGo2Hx4sVs3bo1NibLMlu3bmXlypVJn7Ny5cq44wG2bNky7PHjwXP85YQx74nEsVTjO7Md9/7HUfp1c0Od5+l97rtT/rrJUBSF/Z0NCXt1MgoNrt5hnhUtuvj0rsf56bFt/OToq3z+jSfoGKdH5+GuJh6LSDhFFQpwWqXnz9pMVGn5aApmx46TRJFlOSXcO3M5q6vWojGn89aKJcxOj6rd9AY86CU1GlGKebi6Q376/B7+eHr3uNoJDnYmKjh1+pxxq7qhLMgqRFEUunxuzju6aXL1UWbNRCVKLM+ZnnD8ROzZLldMai33zVjKl5bexHuqVpFtSE2V+fuqVlNsjmaCBARW5JRyY8mFLgaLRkeGLrFQcrr10maPpgJD1Uay7/8JuQ/+CdvGDyNqxmbhKPtdKAEP2sI5qNKLUNmivytBVBFx9+I+8mzc8RFHB97jLwLRSdX7Zq/GOGgSNcOWw43F4+skeTMz7hXvQw89xD333INON7zLhM1mo66ublIXloxPfvKTvOtd72LJkiUsW7aMH/3oR3g8Ht7znvcA8MADD1BQUMC3v/1tAD72sY+xbt06fvjDH3LzzTfzyCOPcODAAX7zm99M/mKGrEbCri6CnTW0/+FBDDOvwbzi7cM2gU8GX81uwq4uwn0tKOEgot6M7HMS6mtBnTb56vKwowPPsReQvQ50pUvRV6xKelyTq5efHtvOmb42Gl192LR6ppnTEYVo2rRoUCp3KA+f2x8nptDj9/CvmkN8ZN76MV/nnvbziBo96pyK6GcR8tNsyCay6b2jpvuNai2fWXQ9PX43T9QcZld7LbWO+P3GsCLT43fT4/eQqR+5ml1RFA53N3Gyt5Vun5s0rRFJjF6DgDDiKu6O0oW8UH+CFrcdUDCpddQ6umn3Orm/cgWiILC3ow6NpGJT4Sw2TWL/8/8K2QYzX1t+Kx1eJzpJneAXLAgCD1Su4GfHtsUM3vMMVm4unnspLndCeENBtjSdps7ZTZE5neuLZiVUDU8GUWdGsmQTcXaisl7IEKqzSgk7OyBJi024ry32/zNsOfzvmns429eOSa2dkK3gm5lxR4b770+dRNd4eetb30pXVxdf+cpXaG9vZ8GCBbz44ouxAqrGxsa4fPyqVat4+OGH+dKXvsQXv/hFKioqeOqpp8bdw5sMQ9VGXHseASDi7ibcXY/KlkfE0YFr32PIAQ+2jR+e9OsMxVezJ9pX11+tqwTcyH4PEUf7pANvuK+Vrn98Crnf+s17aivmZXdjWf1A3HGh7gZ+/sYTtMkKNoONXrUXe8CLXqUmx2Dh5pI5IwarGkdn4pg9cexETwtn+jrIM1hYmlOCZtBEZiA1LRmssSIpAQH9OFxQMnQm1hXOYFd7LVpJjYto2lcSJUxqLUaVFssYbmZ/PrOH7S1n8YdDtHocdPs8VNiykUSBJdnFSVdXA/gjIVSixOz0PBRAI0lEFJmtTWd4x8xlvH/2Gt4/e82Y39NgFEW5aDUH46HT6+LpuqM0unoptWZx2/R5YypEGy8jtSXNzyzkh2vu4Vh3MzqVGkVR2NdRx+z0/ItWHDhRZEXmu4deotbRiS8cQq9Ss7+jnv9Zfmvcb2QyCIKAdf2D0Wxaf0ZLsmRjXnYPgs6MqDXG7hMDaIvi76taScW8KTTBuJK54iQjP/KRj/CRj3wk6b9t3749Yeyee+7hnnvuSfl1mJffB4qM58QWQl3nkax5qAYFPu/JrVjXPZjSVW+w7Syyz4EyKLWryBEktQ5f9RvoShZP6vzuw/9O+DG5Dz2NacldsSpG9+GnaXrtD9Qbou9V0Bgoy6vEGQ6RqTfyhcU3UmrNHPF1Ck1p1Dm748YKhtzs/npmD1ubL1Srv9J8hi8sviF2Y9lQOJM32mqRB2n/LsgsHPcNfIYth7fPWMY/qw/gCHgRBJFppjREQeCu8kWj3sg6vE5eazkHgE6lpsKWRafPjUmt5dbp87h+2sgrVHfQj4KCWorf404mrzhWtjSe4tn64ziDfuZlFvCuypWXTbWuLxzkWwdewB6M7rE2uns51dvGt1behuoiu8xYNDqWZBfz/UMvU+u8kPF4W8VSNhfPHuGZl5bjPa3s66ij3euMTa56A14OdDawKoUCJPqy5eS+5zf4avciao3oylfGBDRs1/8nfS8+hBKKTlb1M9eir9yQstd+s3PFBd7LgbC9DdeBJ4jYWzHOuR7JlEGoI16kQUlSGJOK1xVVOkStCSUcAEVGkNSIhjSUFHiiRlzdCWNKOIjsdcRmuM5df0WvyGgVmYAgogS9RFydWG15LM0uGTXoAtxTvpiHjrwSS/PpJDV3lS2K/Xu718mrzWfjnlPn7GZ3+3nWFcwAoMyaxScXbuK5+hPYA17mZxZye+mCCb3v66dVsb5gBh1eJ6f72nGHAizKmkaJZfQ9vw6vM074X6/SUGxOZ3V+GTeVjJ5ZKTSlkakzxXocZUXBEfAhyzJdPhdZ41TXOtTVyN/PXZBRPdrdzM+PbefLyy5Nf+RQ9nc0xILuAJ0+J0e7W1icHd9+EpFlXmg8yfbms3T6XFTYsrmrbBFV6Xkpu56drdVxQRfgsdpDrM4vn3Sh11RRY++kzXNBdEJRFFrdDupdPRMKvKGeRmSfE01eZdxCYV9HPUe6mrAZc9hYWIlh0OehL1+J9sH5BNtOI5mzrzg/3JFQFIVA4xFkTx/a4oVA6ieEVwPvOIl47HQ98hnk/oq9QNNxBHViwYJh5tqU7/FqC+cgaPWIBgvKoJWpZLCgr5y8hKe2eGFC073Klodki97own2tKKEAamBDyMWLmmiKVw540UpqbhjjKqEqPY/vrLqDve31SILAstzppGkNsX9v8zgSXGwAWjz2uMdzMgrGVXU8EhpJRZE5fcTWn2SUWrNixgGDqRxjylsQBD4ybwO/OrGDZncftY4uzBot+7saONjVyPtnrxnXzXRPe2JtRa2zi06vK2XFS5PB118UOBR/kvG/nd3Ls/XRqmdFUTjW3cKRriY+On8j6/snYJMlWRFgSI7Q6rEzw3b5VuEKCAm/Ec0wfrXDIYcC9D77HQL1BwGQTOmk3/pFNLkz+Gf1AV5oOBE79rWWar6y7Oa49L2oNUw6y3a5IQe89DzxFYLt0SwWkgrVmg+m/HUu36aqyxTvqa2xoDuAEvJhmHMdkikDUWvEOPcGrBs+FH+MohDsqIkT4h4vkjkT24YPosmvQtRbOW3I5OmSNby+/H7c5hxc+x/HffiZCZsAGOfegL5yHfTvC0rmTNJu+GRsn1CVXoTQXxV5XcjJu/3dLA57uC6riK8uu4Vp4whaGToTN5XMYXPx7LigCzDdkoEkJH41Z1gvvxuhSa3lgcoVcW49a/LKE1ZvI1FiyeA7q+5gQ8FMyq1Z5BttQLQ6/OFz+xOC+khokqRrBYSEVPalYnF2ccLfViupmZ8Vv2Lyh0PsaK2mw+saVFmu0O1z86+ag0nbsEbCFw7iCyeqmCXLaqhFKfY3uByZbs1kmjkdaaDOQRApMqUxc5wTBc+hp2JBF6IOPX0v/xhPKMDLjafijw0H2NJ0evIXfxkyuHPBc/TZC0EXIBLGseMPKX/NqyvecSL7XUnH9RWrSLvuo0n/LdTTRO/T3yBsj1b9aUsWk37z5xAnUIVonHcjuorVPHp0Ky/2dSKotRxyO9j65Hf5mLcNjaJwZO+TaFa+jeVlS8dVKCJIKtJv/BTh1fcjex2os0sRBt3IRY0O67r3Y9/6c5Bl5kZ8LM4uJmP9AxN6L8Nh0xp424ylPHx2X2wPd3nO9HEFs4vJNfkVLMgsosbRRY7BQv4YBQqG0hvwIA0Vfgj56fV7xqxfvL5gRsLe98KsooTJzaUiU2/iP+at5x/n9tPlc5FvtPHOmcsT0rphRSaiyATlQfUMQG/Ay67WWj762iPMyyzgg3PWjpgS9oVD/OHULg52NgCwNKeE98xahU6lBqJ/u93tddT2F/wJCNxTvviyTTMDzM8oZEl2MVaHnmAkjEZSMSstjzkZ4zOEDzQdSxgL9zTR19eetIe8xzc2qckrhYi7F/urv8Rftx9Rb8W89G6CbecSjhu60EoFVwPvONGXr8R94Im4MVFvRlMw/H6e/eUfE7a3oUSCKOEQgboDuA88hmXVOyd0DQGVjq0eN0L/zSHc20wIgec1VqolHW5BQjy+nWe6WvnAnGtYMc7eT5UlGyyJbk8QFTHXFM0n2HQUyZSBtnjhlFTObiqaxcLMIh45t59Gdx++cIgTPa3MzZxaQ46B1qBaexf5JhvLckrGJO5h1uhYmDW5fa7plkxO9sZnRMxqHaFImN+c2Emzu49yWzZvmT4P2zCBtNyWzScXbuL5+hPYgz7mZxZy2xArv0vNoqxpLMwswh8Jo+8PgEMxqbXMTs+nxW3H379SDUTCiAhYdXoEAY73tPDw2X18YM7wkoP/rD7A/s762OO9HXWYNTreOXM5EK28/a8lN3Csu4Uev4eq9Lwx+wtfKiRR5LOLN/N6aw0Nrh6mWzJZnVc27t+hZM2FIdZ8otZITlouGToTPUO0lYdu6yhyBPfhp/HX7EHUWzAtvBVt0dQr1qWK3ue+S7A1uoqXPX04tv8WbcmihOMETeonrVcD7zjR5M3EuvFDuHb/HdnnQpWWj+26jw5rlyUHPATbzxHqrifi6i/ikFSIxrQJB157wBunYKT0F6sclIz0t4+ihHzIKDxafZDlOdMnHRwjssyjNQfZ3nIOWZFZkVvKO4vmTWm7ypam0+zviq5UOnxOjve08IkF146rRSEYCfNq81nO9rWTY7Bw/bSqESt8f31iJ3s6zsceb2s+y+cXb74oFbc3FM/mUFcjrf172ZIgckfpAr5z6GXc/dWjje5ezvS1840Vb0FMko6H1O59TxWCIAwbdAf4wOxoQH267ijeUBCVIGJSaykyX+gRP9iVKFoymAP9K924sY6GWOAFEAWRBZOcNF1stJKKa4vG5oM8HKbFd+CvfiOuk6Fp3s3sajjJ8pwSdrXV4gj6EBBYmVuasK/u2P4bPEdfiD321+0n865voC1MjVCGEg7i2PlHvKe3IYgShjnXY1l1f4IU5UQIOztjQTceAZUtL5adRBAwr3gb8ESSYyfO1cA7AUzzb8I4+zpkvxPRmD5i8BHUOuSg90LQBYiECbScQImEJ1SAlWu0kK410tv/gxG1JuSAGxUKcr/mr6CN9o72Bjz4wqEJ6Q4P5tn647zUeDL2eGdrNZIg8u5Zk1cBS0ZYjrCtJT7to6Cwpen0uALvT49t43jPBYeVPe11/M+KtyTtz6139sQFXYj2HB/obBh31mAimNRa/mf5rRzpasYZ8rMgs5CDnQ2xoDtAq8fOyZ62KV/9X2qsWj2fWXQ9H5u/kU6vi/89soUevyfu9zZaStig0iR8fsZJ/hbeLKjTC8l650/wnngZ2e/kKX022z0uqI+moItM6Xx47jqy9eaEyaoc9OE5sSX+hLKM5+jzKQu8jtf/hOfIc0B0m8G9/3FEtQ7z8rdO+tyCqIrWsgxRppP0FtJv/hz+ml1EPH3oSpbg046v4HIsXC2umiCCSo1kyhh1xSeIEurMkqGDSIY0Ql3nkz5nOIIdNbgP/Ztgw2EenL0GszoaPFQZ01ggRFgZdvdfmxZ1WjQ45Rttkw66ALvbE691T5KxVBGW5SS2cSQtkBmOBldPXNAFsAe9vN5ak/T4tmGK0to8qd/jGQ6VKLEkp5iNhTNJ1xnxJ/kMAHxJ7A4ngycU4DcndvLBbX/nkzv/lVBccynRSCoKzWm8tWJpwu/tplFkCDdPq0oydvn26F5sVJYsLKveQXDFO3nNE59abnL30uZxJM0QKeFgTFhjMPIYLQfHgu/UtoQx7+nEsYkgmdLRlQ0xxBAEDHM3I2p0GKquxbz07jF7D4+Xqyvei4Bh1nqC7dXInt6ovZY5C1FnRDKN3vM6gOP1P+Pe/3jscVbxQn546xep99ixavRkqTR01+6htfEcbaI6+iVSaXhgAm4rYXsbEVcX6pwZsaKpZPucEzE2GCs6lZq5GQUJBgxLs4vHfA5HILkIhWMYcYoKa3bSNo2Zacn3uy8GS7KLeer8kbhiF4NKw9wUp5J/e/J1jnRH/X4DkRAPn9uHSaXhcioxWp47Hb1KzWst1UQUmdV5ZSzNKRnxOdcWVWJUa9jZWoMgCKzNr2DZKM+5UunyuTjT106mzsys9PEZwbR5x9bCN4BksKIpmE2w5WTcuH7G6nG97ogkywaOs2VqJNI2fwKXLRf/+f1IxjRMS+5EW5A4UZsKrgbei4Bx3o14TmxBHuT/apy7Gck0thRG2N6WUNAVaDhM+PxeZsy80L+bPWs936xcy+nednzhELMz8kfdRxuMIsvYt/wU7+lXQVEQtUbSbvgkutKlbCycyZ/P7I47fvAeUzASRhLEhKrcyfC+qtX87tTrnOhpRSWKbCicyXWjKEENZoYtB4NKg3dIj+j8YVLVmXoT91Ys5l/VB2NVwRsKZlKVPr5q0VSSZ7TyoTlreaT6AD1+N4WmNB6oXDGuv+touEMBjnY3J4zvaqtlI2OfHE417lCA4z2tdPlc5Bgs5I6x0ntFbum4twoURcFz+Gk8x18CRcZQtRHTkrtTsr84FWxtOsPfzu6NBc/Z6fl8fMG1Y54cT7dkIgliQjXzSL3M6Td+ir6XfkSg6RiCWodxwc0Y5lw/8TcxBOPcG3Dt/Wf82LwbUnZ+UaPDes17sF7znpSdc6wIynjsV/4P4nQ6sVqtOByOOAem8RJx9+I59gIRVzfakoXoZ1wz5sIkX/Ub9D77nYRx05I7sV7z7glf01C8p7fR9+JDcWOizkTug39CUGnY0VLN9pazhOXoauP6aVU4g37+cHoXx7pb0EoqNhXN4q6y1FY6+8JBJEGckA7tiZ4Wfn9qF30BLxpRxa3T543qFdzr93De0U2+yTbh1qBUoygKgUg41gaTSryhIB957R9xLUgA89ILWKdEJ4cLFy685F6qX9v3bJzUqF7S8M2Vt02JHKb74FMJ/Zvm5W/FsuodKTl/IBLmcFcj/kiYhZlFCUYO48EdCvCJnY/G+r0jskK3381MWw6r8kq5blrVmNrJtjad4eFz+2LBd1nOdD4055phi/gGkAMeBEmDkOLvZszz/PQ2BEHCMHczpgUXX4EtVTFgMFdXvBcJyZQ+4R+tOrssaSGAJie1huiBxsS+PtnvJthZizZ/FmsLKlhbUBH37785uTPWAuOPhHi2/hgZOiMbCmem7Lr0qonvUc/JKOAHq++mw+skTWcY9VyKotDo6qXd58So1l42gVcQhCkJugAGtYZlOdMTCsvWF8xAaU6UEb0U1Ng7E/S9fZEgu9pqR51ITQTP8ZcSx068nJLA2+1z860DL8SKI/8uSnx8/rXMHmcf7gCNrt44kZU6ZzfukJ9gJIw96GVPex3fWHHbqLUe1xZVsih7GtX2TnIM5pi14mgM6LinGkEQMC+9G/PSu6fk/JeSq4F3jMhBP55jbxB2dqArXnhR+9VU1hzMy+6NS7voSpeiK49WFLsOPonnyLMoIT/6GddgueY9w7Y3jYSUTBlKFKN9vUnwhAIJfacQ1XgdGnj94RC72mpp9dgpt2WzLLskpWnpkZBEcUxCIrIi89CRrXEFWesLZqa0cltW5FFXEJeC91StwqbVc6CzAbNGx+ZpVSzIKuLwZRJ4/cMUkw0nQTlpkmmtj0NBbCSeOn8kFnQhKlH593P7+NbK21EUhZO9rTS5+5huyaQybfS92nyjNZYm9oSCsSruge2I3oCHN9prx2QpmaY1XNQ98Ii7F3/9QUS9BV3J4imxUr0c+b/xLlNA97++gN4fbQly738c09K7sK5515S/rhIJE2g6iqagiqx3PESw7Szq9EI0hXMRBAHPsRdx7vhj7HjP0edRIqFhVbRGwjjvRrwnXo4zSzDOuxHJlHzmKwkiKkGK6ykG0A358YTkCN8++CINrh4AtjafYX9WPf85f+O4r3Ew/nAIBVK233m4qzmhCnp7y1k2Fs4clxxmMjq8Tv58ejen+9pJ0xq4vXRBQvZgNMJyhJ2tNdQ6usgzWtlQMDMlFesQ7Qu9b8ZS7puxNDYmJ/FcnWrOO7p5pu4oLR4HczPyuaNsISa1lplpuVg1+rjCOAFh1OKqiaKftQHX7ofjxgxVk/u+DlDf/zsYTKsnKhTym5M7OTSoN3ltfgXvrRq5YMmmNfCW6fN58vzh2MrXoNKQpruQXrYPU2g4VQQ7a/EceQ454EFfsQpD5bqEY3w1u+l9/vux6mhVRhGZ93wbST+2dG6w7Sy+2qh4h2HWxpg96JXA1cA7RsL2VtBduMG7Dz6FacGtYy6QmgihvhZ6Hv9yLBBK1lwy7/o6qkErU++prQnP853Zjm3jh8c0e/Se3o6/dg+C1ohx/s1kveNH0eDr6ERbsjCx5H4QOpWatQUVvDrIvk9ASJhZ7+uojwXdAQ51NXLe0TUhg+xgJMxfzuxhd/t5ZEVhcfY03jtr9aSDULO7L+l4k7tvTIE3JEc40NlAj9/D3Iz8WKpOURR+fPTVmDBGb8DDH07vIks/vurTHx99NW5isKutlq8uuwXtm2SV0OF18uU9/+a8sxtfOMj2lrPsbj/PL9a/HbUo8fEF1/Ln07upd/WQrjVyV/kiCk1pOAM+ztg76PQ6mZWeR9k4vlOOgI+z9g6y9eY43WbPnBuodTvIqn0DmxLGMGtjgi/1RCkxZyR813INVs7ZO+KCLsCO1mo2FlaO6pR1W+l88gxWfnfqdeqc3SiAJxSM9atPVlVtPARbz9D12BdjAdVfs5twb3Ncml6RIzi2/TquJSnc04T74JOxBU2wowbP4WeIeO3oypZjnHtDrLjNffgZHNt/G3uu+8CTZL3tB8Nm5y433hy/2EuBHCHsaJvSwOvY/ru41WfE0Y5z559Iv+VzFw5KmrYUYkYHI+Hc9Vdc+/4Ve+w7s53Me74zrj2Vd8xYRobOyMHOBgxqLdcXzUrYq+r0Jte37vC5JhR4/33+KK+3XejFPdDZgE5ST9gwfoDpSW5uAgKlltEre33hIN888ELshvpYzUHuLl/MLSVzaXT3xoKuNxykxW3HGw7y5T3/5n9W3Dqmqukae2fCarzVY2dfRx3X5I9v5Xy58mrzWc7YO4j0r9qCkTBvtJ1nT9t5VuSVMt2SyX8vvxV/OIRWUvHE+SP8v+0Pc7KnFVEQmGZORyOpuKVkHneXJ0r/DWVnazV/Or07Vky0JLuYD89Zxz9rDrCl8TQKWsSyjdxZtpBbxriPfKavnT3tdahEkXX5FUndrm4rnc/J3lb6AlHFObUosXlaFX85u5fzjm60kgqzWotJo0MUBBpdvaMGXlmReaz2EEE5TKkliyZ3L3XObuZmFPD2GcvGNRmZLO5DTyX0+LoPP4152b2xAqyIu4eIO4kzVL+9arCzlu5HP4cyIBfacJhwTwO2jR9GCQcTshGy1477wBPYNn4o4ZyXI5ffZtMVgqDRo86aWjWjoT1yAIEhY4Y51yUcY6i6Ns7cIBlyKID78NNxY0o4hPvQv8d1jZIocnPJXL6y7BY+vfC6pKpSg1d1vnCIRlcv5x3dtLj6CE9g32x/EhnAfR314z7Pqd42Xmg4waneqDzc3IwCVg5pO7m5ZA55Riu9fg/V9s6koh4A25rPJaxinqo9gjsUQN3fexhRZGod3XhCARRFwR708dCRrQmauMnoHuaYrjeRcH29sycWdC+gcKg7fhWoU6nZ1VbLM3VHafM48YaDuEMB6vst/p6rP06v38NIeENB/nJmT1z7zIHOBh6rPcTLjadibTkyCo/XHqbdO7qIys7War5z8EW2t5zllabT/Pe+ZznT155wXJbezLdX3sGDs6/hgcqVfHbhZv5Vc5BaexdtXgc1ji5O9rZxsrcNdzCQdEI4mPOObr538GUOdNbjCvqxanXMTs9jpi2XG4vnjNmuM1VEvPaEMSXoQxmkICYZ0xENtoTj1FnTAfAcfjYWdAfwnNiC7HcT8drjZC4HCPe1JIxdrlxd8Y4R3fSl0HYEiKpW2TZ+GFEz8RaAsaDKKCLUXh03NtRw2jh7E0RCuAcXV60cvfIy+kMIJIzLSX40k6UyLZcbps3m3+ePUm3vREGhwGjj2Ybj2IO+ca9UDUn2dA3jrHz+9YkdcWpcK3JK+dDctXxwzlo2Fc2ixW2nzJpFvtHKn07v5rWWcygoGNVaPjj7moQJRrI0dViJ0OF1UmbNYlZaHrvba2OBRUAgQ2ckJEfY11HPjaMoMFWm5Sbts0y1kMZkCMsR/JHwhJ19FmcX8c/qAzCorUkjqShJUl07YHzgj1worvJ4nbjsLaiVCKe9bSy77j94qaOe/R31GFQarps2i0VZUYereldPUrvF/UkmcAoK5/o6Ru0bfur8kbjHEUXmmbpjSQukdCo1q/t9lh85tx9vOIg/EkTsl3wNyRFCkTBhRabQlJbw/AFO9rTyv0dewRHw0ev30Ov3UGRKJ0NvRKdSXZJtCN30pQRb4pXPNPmzEPUX/KAFSYV13fvoe+kh6K8lkKy5mBbfAYDsTzLRiYSRg14kcxaSJZuIszP+NQquHEWyq4F3jKTf9Gl0/i4ijg40hXPGXAAwGSyr7qf36a/HZn6CWos5SVA1zrsR47wbx3VuyWhDnVuRENh105dM/IJH4L4ZS+kNeAnIYQwqdcx0YFdbLXeVLxqXbd3106r4zcmdcWObi8euOHO2ryNBAnNPx3k2FM5kZloOZdasWGpub3sd21vOxo7zhAL8+uROHlpzT1xfcZk1izfaa+POqZXUsXakj87bQERRaPM40UgqcvTmWFGYagxVzjatgffOWsVfz+7FHwkhCSK3lMyjwnZ57Gk9ff4oLzScxBcJUmbJ4v2z14zb5ee6oioWZR3keG8LoUgYo1rLjLQcVuclts0NTLQMKi3gJhj0E4wEaUYkS5HJrN3DHwJBdqddsJI83dfGx+dfy4KsInINFkSEhN7lUktm0uzCaO9FURR6/d6E8Z5BK+8mVy8vN53GEfCxIKuI9QUViIIY8xZ2h4LoVWo0ikREVii2ZGDT6unyuck2mBPODVEN9YgiY1Rr0EpqApEQHT4nGXojKkFiVX9wv5iYFt1GuLcJ75ntIMuos0tJ2/zxhOMMlevQ5M7Af34fot6CvmI1Qv/fVVe2HP/5/XHHq7NLY3u4adf/J73PfDu28tUUzMa06PapfFsp5WrgHQea7DLIvnhfZF3xArLv/xneM68hiCL6yvUpLR5Iu+GT9D37XULd9SCKGCo3YFx4a8rOPxRFURLMCRQUfKHguALvqrwytJKKbS3niPSLeazJL+8XyZAwjrLiakxSVTowPjMtvqXqRJJ2KU8oQL2rJ07V55r8cvZ11HPWHk0tigi8Y8ayWN+wQa3hM4uuo8fvirsZG1Qalo9RVWl1fjmLsotpdveRY7DEPstWt51/1RykztlDoTmNu8sWjbonmEoOdDTwxPnDsce1zi5+emwb31p5+7jOIwgCP1p7L0+fP8qZvnYKTDZumz4/qbjEpqJZ7OuoJ02rp8Ut4pPDqAGfIBEQZE6qdOzs60CyFsapTW1tPsOCrCLSdUaun1bFi4OMPwqMadxYPJsDnQ20eOykaQ3oVGqWZBePOsERBIGq9LyE9roBj9xmdx9f3/98zF/4WE8zTa5e3jVrJQuzinijvRatpMJFtFtAp1GRrjWgldRJDT0GGPguCYJAmTWTVo8DTyhAuTWbO8sWxlbpSjiE++izBOoPI1myMC26PSF7lioESUXa5o9jueY9KEEfKtvwBYQqWx6mRbcljBtmX0e4pxHPsRdQwiHUOeWk3fip2L9ri+aR8/4/Emw+jqi3oMlLnW7AxeBq4L3MUdnysKy4b0rOrU4rIPv+nxDua0XQGqe8HH9x9rQ4b1SIVnNOxP90cXYxi/t1m3v8br6x/3lqHJ1IgsiqvDLeVbliWCu/6cMUS5UkGc/UmRLGBtLEg9FIKj6/eDOn+9rp8buZnZ6foKikFiU+u2gzj9Uc4ryzi0JTGneWLRzxxjoUvUodFwT84RDfPfRSrM3G3uPlvKOL7666E/M4zjsZhv5NIVr41eq2x/VPN7l6eaT6AHXObgqMadxbsTghoFk0Ot5ZuZzRKLNm8bnFm3mx4STNbjvGgAt9MIBRkdGg8JraQkQQUfWnbvv8Xjp8ThpdfWToTNxbvpj7ZixlXmYhp3rbyNabydAZ+dbBF4koMlpJjT3g413TF/DWGYvH9Dm8q3IlPzq6NVZIN9OWy52lCwHY0ng6FnQH2NFazV3li1iaU8LNzrk8U38Me8CLJAgUm6MGLLdOnzeicMq8zAJeaYqmZTWSihJLBvMzC/nEgk1xx/W9+L/4qnfFHvuqd5H99h+NGBQni2SwwgTvKYIgYF33fswr3o4S9CKZE3+bokaHrnRpkmdf/lwNvFcgAyqfqZJlVKVdHC3iFbmlNLv7eKnxFCE5QqEpjQ/NWTvp9/G7k7uocUT3eyKKzM7WanINFm4umZtwrD3gpcndR7EpgzpXN2L/a6/Nr0i6qllfMIPtLWdjFagDx2YkC8j9q56RyDFY+I9568fz9kbkSHdTgumDNxxkf2cDG0dQD2t297G/owGdSsWq3LJJSRYm218XiPfb9YdDfO/Qy7j6C2yqHR384PAWvrPqjnFlOwYzw5bDDFsOJ3vb8AoQ6rxQ6R5GYF5GAWdFAWfQH2tny9SZeLHhBGf72vnv5bdSlZ4X+5t97+BLhOQIgiCQ3t8De6qvbcyCJ9kGM99ccRsNrl40ohQ36XANsSaE6HfVHQpgUmu5p2IxN5fMpcXTx+nedlyhAAuzikb9Pt1ZupA2jyO20p5mSk8wRgnb2+OCLoAS8OI5/mJKJWenAlFrgAl+PyZKqKcRJRRAnVM+ZX7jVwPvFNPjd+MNBSk0pU36j6iEQzh2/D7Wu2uYvQnr2vddUWov0RabeXjDwZRo7PrCQU73tSWMH+pqSgi8x7qb+emxbbGiGr2k4aaSOczNyB+2rcmq1fPfy25le8s5unwu5mTkszxn+qSvO1XIw0itjyTBvqf9PL8+sTNWufts/XH+a/GNY1L3SsbGwpm83lYTV6y0NKeEtEF/32M9LQnBJxAJsb+jnuuTWPeNh6XZxbweCQFlRBwdKHKEZXnTuWPdu/hL9X4erzmEJIhk6Iw4g36a3X2c6eug2++Ok2pMVrncPoxV5HAIgpCQ5m9y9RKRZRwBPxaNLtbpl2+0xRVsGdQaKmw5VIxgTDCU6BbG9XR4nURkOenfMGmhEiD7Lp7d5ZWAHPDQ8/Q3CTafAECVVkDGbV8GKXGSPVmunDv2FUYwEuY3J3dysLMRBYU8g5WPztsw4ZsbgPONv+I5+nzssefIcwgq7SWZtdY7e3iu/jjdfjez0/O4uWTemBWkdCp1ynSH1aKETlInSApahuzzKorCX8/ujQsOATmEPeAdtZfYqtVzW+n8lFxvqlmYVYRRrcUzqEJdK0X3JZOhKAqPVh+Ms4DzhAI8U3+MD85Zm/Q5o1FkTucLi2/khYYT9AW8zM8sHHMLSyrWE2+fsQx/JMwhBDTmTFbmlvK2yhVoJBUf6S9qO9zZSJffRadvoKdcwRsK8rtTr/OD1XcjiSIzbDkJetUzxyDZOBLP1h3jsdpDUcH/kJ8un4tSayZFpjQ+PDdRzWmi5IxQca3OLkcyZ8ZpAkC0gOkqF3DufjgWdCHanmTf+gs013825a91NfBOES82nOTAoH7TNq+D3516na8su2XC5/SeeS1hzHfmtTEFXn/DEfy1uxF1Zgxzrp9UkVa718m3D75IoD/Yne5tY09bHZ9adB25BsuUpWeSoRIlrptWxTN1R2NjIgKbh9z4PeEgXb5EIY9k8n1XEnqVhs8svJ5/Vh/gvLObIlMab61YMmzq2B8Jx+kED9Ditk/qOkqtmSOm0OdnFiZIPuokNctSkD0wqDV8ZN56/OEQgiAktNCsy6/gcFcjruCFyYlZo0ctSfQFvLR7nRSYbNxbsZgGVw9t/avcDJ2Rt89YNuHrsge8PHn+CGE5gj3gQyOpyNAZubN0IbeXLUj6HFmR2dlaw/GeFmxaA5uKZo3Z/nA4BFEk/dYv0vfi/xLubUZQ6zAtuQP91cAbR6DxSOJY0zHUKdLoHszVwDtFHOtJbOY+7+yO7elMBCHZXtoY+lddB57AufNPsceeI8+Red/3UdnycB98Cn/NbgStEdOit6ArGb2QZEfLOQKREIqi0OTuo8PrZH9HA1uaTzMvo4DPLLo+qZDGVHFX2UJy9Gb2ddSjV6nZVDQrYb/WqNKQqTMltIqM1YHlcqbEksHnFm8e07F6lZpCU1pC3/GMtLGnNyeCVlLxuUWb+WfNAc47uik0pXF3+aJJ7S0PZbgsyoKsIt5ftYYfH30VTziIVaOjwGgDohmTgT3mdJ2Rb668jTN9HSiKwsy0nGEL9MZCi9uOO+SnxtFNRI6gKAqiIHLW3jHsc/58eg+vtZ6LPX6jXxZ0pBXtWNDklJPzrl8QdnYh6syIF6nw7kpCMmcT7mkaMpY5qhjRRLgaeKeIZAUjBpUmwUBgPBjn3xRniDAwNhJKOIhr36NxY3LAg/vgkwiSCs/RF2LjgcYjZNzxNXTFC0Y8p6+/r9gR9NHtc+PrNyuQFYWTvW08dGQrP1771nFV606WNfnlrMkf3iZREATeMXM5Pzu2LSZCkaEz8pYpsJS7HKmxd/JS4yncoQAV1mx6/R68/c4+haY03jJ96lPp+SZbQrXtxWJNfjlV6Xl8bd+zcavum0vmxml8i4I4akHTWCkyp9PhdRGKhPFHQoT7hSK2N5/js4uuT8gM9QW87GyN76v3hoNsbT4zqZX3YFSWiycdeaVhXn4vweZjcYpZ5hVvI/Xr3auBd8q4sXgOR7qb4vYUbyqZO6kZtHnxHQiSBu/JV0AQMMzehGmUwCv73SiBxMb+cG8Loc74HzmKgufoc6MG3qU5JWxrOYs7FCAkRyUIREFAEgRkRcYR9HG0u+my0xBemFXE91ffxeGuJvQqNYuzi980BgMjUevo4tv9bTIQFZJYk1fOouxp6CQ1s9JyL+r2wKUiXWfk6ytuY0drNfaAlwWZhcyZQuUvi0ZHjsFCi9tOuP+zVwkibV4Hrzaf5dqiyrjjXUF/gqAHpNZZyBsKopGkSd2H3qxo82eR9fYf4T3xEko4hH7mNWgL5+B0pr4I7c1/17lElFoz+eqyW9jWfBZfOMSSnOKYXN1YaXHbafc6qbBlx1aPpgU3Y1pw85jPIZnSUWUUJaRQNAVVBFtPJRyvBBOD9FCq0vO4f+YKfnH8NVoFB5IgDiqsiu6xjSbh2BfwsrXpDB1eJ5VpuawrqLgoN4N0nTHhhvdm55Wm0wlSk7vbz7MsZzodXidmtTapmP/FwhX0s6XpNM39HrSbiipjwiOpxqLRcUuSNrOpYkXOdE73thGWZURBQCWKGNQa9nXUJ3wPC002MnSmBO3u+SnYtmn3OvndydepcXRiUGm4sXgOt/4fyfaMB3VGEdZ175/y17kaeKeQQlMa91cOb6s3HIqi8LtTr7OrLSpBKAkiD1SuYF3BjAldR9r1H6f3mW/G3EB005dgWXEfoc5aAg2H447Vz7hmTOe8tqiS5TklfHnv07zeWkuoXxwgQ2ek2Jwx4s3CFfTz9X3PxYp89nfWc7qvjY/M2zCRt3fZ0u1zo1OpJ7ynPxqyInOmL7pfWJmWM2y/qXuIJreiKNQ6u/jOwRfRSNHJzs3Fc7mnYmxCEakkGAnzzQMvxNp2DnU1cqirka8svfmyX4X7wyGerjvK0e4W0nQGbi6em2DzeHf5Yh6pPhArRJQEkUKjLWkHgCiI/Mfcdfzi+Gt0+91IgsjGwkpWjVHZbCR+dmxbbF/fGw7yeO0h8gxWluQkr34fiVBPYzTrpsjoK9ejyRl+i2cq8J3fh+/MDgS1FuPczWhyR74vhh0dyF476uyyMbdeKuEQiOKU7O/C1cB70Qm2nsF95Nl+g+iVGGZfl3CDOdjZGAu6EG20/+uZvSzKmjYhNSJNbgU57/0dwfaziDpLTCoubfMnsL/yU/x1B6KVjgtuwTB3bEU6ACaNjm+tvJ3nG06wrTlaELI8p4Rbp88bcfW6s7UmobL2QGcDLW47BZNot7pc6PA6+cXx12hw9SAJIqvzynhX5UokMXVmYJ1eFz84vIXO/l7MHL2FTy+6jix9oqbvkuziOEtBV8hPMBKOBV2A5xtOsCa/fEIqYpNhf2dDQq9snbObEz2tzM1MbRpYUZSUBvNfndjBke5oJikqfNHGl5beFKeMVmSOtg09df4IsqJg1ehQiRKbhsm6lFqz+P7qu2jzOLBo9SmZtLV6HElNPPZ31icNvMHWM4TtLWgK5sR5f0O0yrf7yf+O2f65Dz9D+s2fQ1+xatLXORaG+vB6T71K5p1fQ1uUuHpXImH6XvoRvrM7gGj2L+3mz6HNn5Vw7ACyz0XfKz+LepSrddEamrm3p/x9XA28F5FAyym6H/sv6N/3DdQfJGxvixk/D5Cs6jGsRDjv7J5w2kmQVGiHuHdIRhsZt30ZJRwEUSLc14r35BZUaYVoC8YmaqBXabirbBF3lY3ufzpAX5I95+i4500ReH9zYmdMKSmiyOxorabAaEtocZoM/zi3LxZ0ATp8Tv5ZfSBp1mBtfgXtXievNJ0mJEcwq/VoLfErLgWFOmf3RQ+8jmH2L+1j2PIYz2v85cwejnQ3YVJrual4zqT/Fj1+dyzoDhBRZF5rqU6QJH2gcjk5BjMHOhowqDVcVzRrxL1lQRBG7Pfv9LoIyuERXYsGo1epERDierej4xfS+WFnJ/7z+3EfeY5wb1N0giIIWK55N+Z+xyAA555H4r12FQXn7r/HAu++jnoOdTZi0erYWFg56VaowSiKEucfDoAcwbXvsaSB13P8pVjQBYi4e+l74YfkvPe3w07A+rb8BH/t3ujrBX249z+OoKS+SPRq4L2IeA4/HQu6sbEjz2FZ8ba4tqBkX1YBYUxfYkVRCHXUIOpMqGxjq84UVBqcex6JM5fWla8k/ebPxQnMp4p5GQVsaYrfX9ZLGsqtl4fTzmRwBv3UOrsSxg93N6U08CabnJ1O4v0K0Rv5WyuWcHvpAo51N/NozUF2tdVi0+jJ0ptjSkrTLsE+7/zMQv5VEy/oIQliSouefnnitZgvriPo4x/V+0nXGVmcPQ1nMKomNVZZyAECkeS1roEhQi4QTSHfWDxnVOvH0fCFQ/zi+PZY9qLQlMbH5m9MmuUYTJrWwJLs4jhNbZUgxSRFvae20rflp0Rc3YQ6axE0BjS5MxEkFc7X/4Jh5jokU/S7EXEkfscijuh38V/VB3mu4XhsfEdLNV9ZevOkRIPiUGRkX6KSWMTTm/TwZH25EWcnYXsr6rTE75ccCuA/vy9h3F+ze/zXOgqpv6teZVgiSSTalJA/wRd3dV5Zwmx2fcGMUXv5Ql31dPzxA3T941N0/PGD9Dz9zehqdhTCjg5ce/4RN+av2Y3//N5RnzsR5mYWcEvJPFRCNNVpVuv48Ny1KVOzGo1j3c38995n+PC2h/nxka10ehOFNSaKTlKhERPns5Zxeje7QwG8oeH/dskmYXmjCNLXObv55fHX6PQ6kQSBVo+dFk80BbmxsHLMK6hUUmCy8c6Zy9FL0YmnUa3lwdlrJqzfPJS+gDepGf1jNQf59OuP8/Gdj/Lp1x/nYGfjuM6bb7QyzZRORJFpcvVxvKeVU71tI0p1TpZ/nz8St2XQ7O7jT6fHFhQ+MOca7ixdSJkliyXZxXxhyQ1MM6ejhIM4tv8OZBk5EC3qUoLeC163coRQ54VtL21RYtuZdtp8fOEgLw+ZTPsjIV5qTCzgnCiCKKEtTsysDWeUICVpnRJUGiSDDQA56EcOXpAxFURpGK2E1NdoXF3xXkT0ZcvjJMkANIVz4gyiISoE8OWlN7O7/TztHgez0vOYN4YVQN9LD8VmnwD+2r24Dz2Fedm9Iz4v1FkDSW4YwfZz6MtXjvq6E+Hu8kXcUDybHr+bAqPtorU3tLjt/Pjoq7Eq38PdTbR6HHx71e0Jqx5n0M/J3lasGv2YW240korrimbFzfwlQWTzGPWI3aEAvz25k2PdLQhEW7feW7U6oe3prrJF/O+RV2LvQyVI3Fm2cMRzv9J0GpnoPmepJRNXKEAwEubTC68b1wpTVmTO9nUiCgIzbNmT3je9tqiS1Xll9Pg9ZOlNcT7Hk0USxIQ0azASYX9nI9PM0YlGb8DDL45v5/ur7xqXfvh/zt/IR3c8Qo/fg0oUydKb2dNRR1V6PmsLUt9KdzyJKM/J3lbCcmTU349alHhL6XzeMkT6NGxvi3naimpDrGdVDvbXYAgCqowL3RiWNe8i1F0fC8aq9EKs6z9AXygQ1zo5QDKVtMlgu/b/0fvcd6I+4oKArnQZ5mVvTXqsacGt+M68hjxIsc606C0gSPQ+/wN81a8DAoaZa7Fe+/8Q+4u13IeejjtPtO7leyl9H1cD70XEuOAWwn2teE5ugUgYTV5lUoNoiCr9rB9HFXPE6yDUVZcw7q8/PGrgVWWWJB1XZ41Nzk9RFHzhUHQvaRw3YZNaO2UVv8Oxu702obWmw+fknL2TykG6vAc6GvjViR2ElejNpNyazacXXjemVfnd5YvIM1o52NmAUa1lY2ElpdbkVoRDefjsPo52NwOgAHs76kjTGrhvRvysfnZGPt9YcRu726PawitzS0fdn/UNEgYQBCHWojZtHOpdHV4nPzz8Smx/Od9o49MLr5u04YVOpZ6S/X2LRseirCJebDyJM+hHLUqoBSlhRR1RZA53NY2r1cyi0WFQaZifWYCAEBOefr2tZtTAqygKJ3paqXZ0km+0siS7OC54huQIW5vOcLK3lUydieunVZGuM9LSbzk4gFWjRxpnmnwwkjUXUWtEDniQTBlEXF3IATeiJvr3NC18S1yBlWS0kf2Ohwi2V4Mio86dgSAIZCpRPfq2IYVyY1kwjAeVJYvst/0wamWq0iS1C4wda8uL9uUef5GI145u+lL05Suwb/1l3N6v9/Q2BJ0R2/oPYLnmvYimDHznXkdU6zEuvJVQ1vDFWBN+Hyk/41WGRRAlbNd+GMuaB1BCgdi+SSoQNQYErSFBLGMsmszqtAJMi94SN9PTFs1DXz56peKJnhb+emYvHT4nmToTb5+5bNz9yheX5BMDcdCEISxH+POZ3bGgC1Dj6OSV5jMj9oC+1nKOp+uO0uf3Mi+zkAcqV4w7IA3W9x48NjTwAuQZraOucgezJLs4wai9Mi13XApjDw8p6mr12Hm0+iAfmjsxg4WLgSSIhCIywUgERQGtRo1GSgxWRvXEeoeHTjbFMUw+/3D6jTiVqnJrNp9fvDkWfH99Ykfcd2FvRx0PzFzBqd62uInjW6bPn1TGQVRrsax9L/ZXfgaAJr8SQaXFuPBWdNOXDltkqcmNn1gIgsAH56zlZ8e20e13IyCwMreUjYVT0zM/VitTlSULy+r748YGLBIjXjsRVxcooBx8Ctv6D4AgIBlsqMxZCDozKksOibv2k+dq4L0EiFojaCdviTcYQaXGvPRunK//5cKYWodpUEXiSFjXvR99xRoCLSdRZRShK1kyamGVM+jnx0dfjaWYuv1ufn5s/Cm7seI5/hKeo8+jhAPoZ67FvOzecVsirsor44WGE3E3r3yjjYpBhV2dXldS/9Rae+ew5z3e3cIfT78Re3ykuwnHMR9fHacphkmtpTcQb5ieKkP79QUz6PG72dJ0hkAkxOz0fN5ftXpc5xjoG44fS17UNdXIAS/IkYStmsH0+j3s72wg32Qln2hGQFYU3KEAWulC9iJLbx73hFEjqViVV5Yg87h2FMW2ZndfwnNqHJ3s72xgZW4pHV5nwgTMGw7S7OnjK8tu5rWWagKRECtySlPScmWccx3awjn46w4gmdLRlS5HkFTIAQ+B1tOo0wpH/IwHKLFk8P3Vd9Ho7sWs1k3JPSAViFojoa46Ql0XnKiCrWdwH32eiKsL9/7HY+O+09vQ3PSllF/D1cD7JkBRFLwnXyHYdg5NQRWC1og6rQDD3M1Jq/eGQ5NfiSZ/7DPUo0MkMSGasjvY2cB1k/RYHYrn5CvYX/l57LFrzyMoQe+4VWbyjVY+tfA6nqg9TIfXyaz0PN5asThu1eAKBbAHfAjQ758a/beRUqEDKd/B1Dm7afM4xtWic2PxbP5+Lr6ycqz7w6MhCAJ3ly/m9tIFhOTIhNShcvRmGt3xVaSTFfAfL0o4iH3rL/Ge2Q6KjK5kMWmbP5E0OLhC/oQ2GlEQWJtfQabeRIOzl2JLOjeXzJ3Q3vL9M5djVGnY31mPTtJw3bRZrMorG/E5rZ7kHr8DDlGuYOKkD8AR9FNszuCBytQbe6hseZgW3hp77Dn2Ao4df0AJBRBUGiyr3olp8e2jnkcQhMvGeET2ufAcf5Gwox1t4Rz0M9chiCLGhW/Bc/KVuGNV1hxc+x5FGaIapoSDuI88T6q5GnjfBDhe/RWeYxfMDgSNHuva944r6E6EgUrUhPEpkPvzHn8pYcxz/GUsa9837lRbVXresEL4T9Qe5um6o4TkCC3uPkxqLaXWLHIMFq4rGj4AqobJDqjHWTR23bQqzBodu9pqUQki6wpmsCCraFznGA2VOHGt3jvLFvLTQUYTKkHi9tIFKby60XHt/SfeU1tjj/11B7Bv+xXpN30m4dhppvSkMoyr88pYmlMy6WvRSCrum7E06VbAcJRZM5P21Q44apVYMrBpDAm9zIsv0hZO2N6O/dVfxQoulXAQx44/oC1eiDpz/EpXlwLZ76bzH5+KtT95T2zBX3+Y9Bs/iWnBzdi3/iJaVApI5iwkYzqyxw5D6j8AFK895dd3NfBe4US8DjwnXo4bU4I+PEeexbbhg1P62vOzCsnSm+N8btO1xmFN2CeDEgknG0zpa/T43TxTdwyALL0Jk1qLI+Bjec503le1esTCqvUFM3i9tSZO5H5uRgGZetO4r2NFbikrUiATOBUsyCrif5a/hd3ttQiCyKpRirrCcoQDnQ00uaI6zIuyi4btmQ3LEdq9TtK1xjjHoKH4kvRVJhuD6Arso/M28OsTO2jzOlCLEpunzR5z0B0ogmpy91FqzYwrwBugw+tkZ2s1vnCIpTklSY8ZTIbOxD3li+P6l9fklccKkVSixEfnb+DXJ3bS6XOiEVXcWDwn5ROw4Qg0Hkna5RBoOHzFBF7vqa1xPcdKJIzzjb8R6qxBk1eJoWoDviGTT0PlOkI9jbGKbUWOEHF1EVbbUn59VwPvFY7scyaIcgDInkSJuFSjFiW+uPgGnqk/Tl2/Cftbps+fkn5cw6z1OAb1EwLoK9enVAKwydUXtwrRq9ToVWpsWsOo76nUmsUnF27i2frj9Pq9zMss4K5xFD5NFa1uOy82nqQv4GVeRgEbCysnLV1ZYLJxd/nous6yIvPDw69wuq8tNrYkuziputax7mZ+f2oXjqAPtShxa8m8hNaXAURdYkpZHKFmosSSwbdX3UG3z41RrU2qkzyYYCSMIAioBJGfHtvGoa4LPb7X5FfwvkH74vXOHr598MWYcMbW5jO8q3IlG/rFKYbjppI5LMspocbRSb7RliBeUmbN4rur7qDL58ai0V20HncAaZiCzOHGL0fCzvhahFBHNXLATaDlJOHeZgStAW3JophWvW76Eqzr3k/E56D32e8Q7m4g2HYGUWMg7By+tmOiXA28Vziq9EJUtjzC9ra48eGaylNNms7IAxMwghgvxoVvQQ768Bx9DiUcxDBzHZa1703paxSb05EEMaHdqMQytj2rORkFKVFc8oWDOIN+svXmSU0s2jwO/mf/c/j7g8LxnhbqnD18YM7YjDAmy5Hu5rigC9EK7Wp7ZyytCtE2p18e34EvEhUMCckRnjh/mApbToLpAIBp0W30tp4eMnb7qNczWvbBHw7x17N72NMebcvLN1o53NWEJIhYtDokQWRnazXTTOk0uHoQBIFmV1+CWtWT54+wrqBiVDWsTL1pxGsSBIFsw+hFTalGW7wQTf4sgoM+Y3VOObrS1HgCXwy00xbgOfwsEE07ywE3CCKiNvp5KwEv2oLZpN/wKRQUJH20TkHUm8l54Ge49j2GY8fvESQ1fn/q65qvBt4rHEEQSLv5s/Q9971o8JVUGOduRj9rY9xxctCHv+YN5KAPfflKJNPlUQAxVgRBwLLiPiwr7puy15BEiRW5pexsrY61hMxOz2d5CvYCx8pjNYd4qfEkITlCrsHKB2ZfM+Ye4KG82nw2FnQH2N1+nnvKF5F2ESpOhysiavM44gLvOXtHLOgO5mh3U9LAq69YRcbtX8Fz7MXoJKxqA4ZZk3e2GpDSBPCEAuxpP09EUdBJKtQeiXJbFp5QiJ8ce5WM/s/vXF8nOQYLVu2FynNn0EcgEkGvujKFAQVBIOPO/8F7cguhjhrUWdMxzLl+3B0ElxJ96TKMC2+NTtTlCIgS6szpcW5DEb+TYPtZIp5edMWLhvQEywjS1GUZrpxP8irDoskuI/vdvyLc14KktyZUd4YdHXQ/+nki7qhwv3PnH0m/5Qvopi+5FJc7KsFImL6Alwyd8aIpWj1ee5jn648TUWQkQWB5znTWFlQwKy0vYdUpB314T28j4upCV7wIbVHy3l53KIBeUo85tXugo4Fn64/FHrd7Hfzs2DZ+sOaucWsJQ/LqWAUFVyhwUQLvTFtO0vHBQRfApk0up2kbQTZSN31Jyr+/ezsuCNC0uO0ICIQiYXSSipAcoc3jxBcOUWK5kBY2aTRRT2ONli6fG3coQIHRhicUGDWlfTkjqrWYFoyvFe5iIYcChPtaUFlzRtxisK1/EPOSOwn1NNH73HdRBqloKXIE/9nX8Rz8d3RAlEi7/mMYZq0HQDttIez625S9h6uBdxIokTD+ugPIfie6kiUpFcQYL4IgoE5P7lzk2vNILOhC1GvS8drvLsvA+2rzWR6rOYg3HMSq0fOuWSunXJDjVG8bz9QdjT1WgINdjdxfuSIx6PpcdP3zM4T7okIU7v2PY17+Viyr3hE7ps7ZzR9OvUFTfz/jHWULY4L0Q1EUhTN9HXT7XRxMIp7RG/BQ7+yd0Kp3UfY09nTEtzll6c0UTUKT2RsK8vC5fRzsbMSgVrN52myun1aFrMjs7ajnTG872QYz6wpmUGHL5vppVWxpPI2CgoDA7aULEoqxis0ZzMso5FhPc2wsTWtgTf7F9XnVSWo8oUC0PS8cRCWKcXurvnCQdJ0prmo/R29BVpzUOXtwBX1oJBUKMl/b9yzfWHEb1mEmFWOh2t7JE7WHafc60EnqmNLb+sKZE3Ypu9Lxnt6GY9tvkAMeBLU22ua06LZhj5dMGUimDDJv/yp9r/yUcE8TosGKKn0aweYLsq7IERyv/RZ9xSoElQZNbgWW1ffj2vsITIGExtXAO0EiPifd//oC4Z5+azBJRfpNn5kybePJMLhRfIBwXyty0I+YInGGVNDo6uWvZ/bECpwcQR+/PP4a/7vmnpSJSCTjRBIN3JAc4ay9IyHoe46/EAu6A7gOPI5xwS1IBithOcKPj7waawVxhfz85cxuikxpCSu9kBzhR0e2crK3lWAkQo29EwSBAqMNq1aHK+inx+/h4XP72FA4k9Wj9IcOZVlOCQ3OubzcdIqQHCHPYOVDc9dOat/4D6d3xcQdfJFoEDaqtZzsaeWN9gvFb6+1VPPfy27h7TOWsaFgJi1uOyWWjGH3ND8ybz3bW85xpq+dXIOF66ZVTUhONBAJ83prDfWuHqZbMlmTVzbm/tzri2bxj+r9CIKAXqXBFw4y3ZKJXqXGFw6xInc6aVpDnMuPJIrcOG02+zrrkA1mDCoNgiDgCvnZ2VYzotLZSHT5XHzv0EuE5AhdPjct7j60kprKtBwOdzfx4TnrWJ47NknXNwsRdw99L/8kVkyqhAI4Xvs92qJ5o8rbavIryXng54Q9dkSVlr6XHko4Rva5CDvaUfdrU5uX3RPVQmg8C198MaXv5WrgnSDug09eCLoAkTCObb9GV7osbh/hckCdUx7TcY64e/r3giXsr/wU67oHkYy2S3uB/Rztbk7obQzJEU72tk5pe02GLnkwyEiSjg33JgZpImEijnYkg5U32s5zuq8NWVFI0xpibTH7OuoTAu8bbbWc7G3FHw5Tbe8gJEfwhIL4w0HStAYcQT9pWgM1jk5qHJ30+T3cMj3Rd3Qk7qlYzC3T5+EJBSbU2jQYXziY1MXnpYaTCaIanb5oi83m4tnkGa2jiohoJBXXT6vi+kmIhciKzPcOvUytI1qFurO1mt1t5/nikhvGNNnYXDwbvUrDztYaMnUm6pw9mDXR4J9rsPDeqtXoVWp6/Z6Y9WNlWi7XFFRwPMn3whlM7jU8Ft5oOx8TpxlwzwpEQrhDAcwaHS82nrykgTfU14LsdaDJnTGle7+hniZ8Z7ZHH0jqpB0c/vqDY9KVdx95FtfeR5G9dhAllHAgznlI0BqQzPG/UUlvQZs3coX6RLgaeCdIqL06YSzi7iXi7kWVxI7qUmJefh+BxqOEOmujq19BRJ1Rju/sTiKubrLe+t1LfYnA8NKI47XUGy8rc0t5qfFUnAbxwsyipAo8mvxZeE9vixsTtUZUmSVU2zv59YkddHij5+nyuSmxpGPTGjAkERU57+juP85FRJERBQGDWoNFo6M34KXQlEb2oGD5QuNJbi6ZO+4V60Bb1GQREBAFgciQHs9Ash5roGuIaMVUc6S7ORZ0B6h2dHC8p4V5Y0zNri2oiBkc2ANeDnY2opEklmQXx1LMX152M60eB5IgkGOwEIiEMaq0eMLx9p4LMye+RTK4sn5olT0Mr24FcKq3lddaqpEVhdV5ZSnt/1XCQXqf/37MLF4ypZN+yxfQTEFw8jccpuffX4f+71ck4AE5EqtAHmAshaL++oM4tv0m9lgJBQjb21APGMQIAtY1775oGcArs+zuMiDZDEs02JCMF9/TdDRUlixy3vULtIVzUWeWoC2ah6SPrkCCracTWpGSIQe8eE68jGv/44T6kqz6UsDynOkJq89SSyazRhEkmCwGtYYvL72JO0sXsiq3jHdVruQ/5q1PfuzsTXFtFYJKE7MUe+r8EdSShEk98ONVaPM40Usa1hYk7lcW9dvSBQfN4lWiSLElA5NaS5pWHxdkvaFg0pvwxUKnUrM8J/F7f+v0eXG6xwMMpw42VfT4kgf6rmHGR8OmNXBtUSXX5FckqLHlG60xqUytpOI/5q2PZUi0kpq7yxcnrcgeKytypsdch6z9E0+1pMLYn35fPIxIzf6Oer5/aAt7O+rY31nPj45uZUdL4iJhorgPPx0LuhBdbPS99KMp8SF2vvG3WNCFqBGMEorPIqgyitBXjK437jv7etxjQVKhSp+GedU7sax9D9kP/BzjvBtSc+Fj4OqKd4KYltyJ//y+aNBSFBAlrOvfP+Ul997T23AffArZ70JfsQrzqvsRx7AXJqg0qLPLCHXXJ/vXEZ8bdnbR/ejniLiiKzTnG38l7YZPYpg5MUcaRVHY0nSaHa3VCAisK6hgU9Es9Co1X1p6Ey83nqLZ3Ue5NZvrp81KmUiGrMic7GkjEAkzOyM/bhVo1uiGFWwYjCCpyLjtSwQ7aog4O9EWzo1VkQ/c4KdbMuj0uXAFA2hVKv5r6Y1J09lr8sp5vbUmasrQn5bMMVhQixLTzOloxPjv0vzMwklVeQci4ZjH84y0HBZmFo37s333rJVYNDr2dzZgVGnYPK2K1fnlaCQVfzi1C38khIDAtYWVF92lqio9uWPNxZgAVKXn8YPVd9Ppc2HV6CcteJFvsvGx+Rt5rOYQAgIZISNqQUIliCzNKRnWlerZ+uO0uO3Yg15EQSRLZ+LZ+uMp8wcONB5LGAv3tRBxdac80xcZUkshCALqrOlYlt9HsP0c6sxijPNuSmpePxQhiRKaIIoYZq5FZZvaiX0yrgbeCSIZ08i442t0/+uLBFpPorLlExnDynEy+Gp20/fiQyiRIOG+VrxnXsN9+Bly3v0rVLbRby6GOdfhPf1qnByctmjeqF8894HHYkEXAFnGueMP6CtWT2g/+7n64zxWeyj2+G9n9xKRZTYXzyZNa+CtFReqrY91N/NozUHaPA5m2HJ458zlE/JttQe8fO/Qy7T2+5kaVBo+sWBTwr7rWNHklENO/Cp2VlounT4nkij272tCVVoehcNUEev6Jxr7O+r5+7l9dHpdGNQa0rQGvrh4La+1VrOnvY6IIjMrLY93z5p44Z4/HOKbB16gqX8v9sXGkwkqTGNhOG3iZTklzM0ooN7ZQ7bBNOy++VRSYLJxb/kSHq89RESRUQkS9/R7I6eSbp8bg0qTIGsp9KeeU8W8zMK4FLkr6EcUhNiqNxmHuhrjtkya3L1oVYm3+VB3A96TW1AiYQyV68dsjiJZE1vEBI0eUZ96owxN4Vz8tXvixnTTouIeglqHOnfGmFyTAIxzb8BzYkvcClo3fcklCbpwNfBOir4tPyHi7kZlyQE5gvONvyNZslPSzJ8Mz/GXQVEItp5BCUfbHny1++j8x6fJfe9vEUfoewTQFlSRfssXcO3/F7K7B23JEqxr3jXq6/pq9hBoPo4SCiDqTKgyisHdi+xzTii1vq3lbMLYq81n2Vw8O26szePgx0dfjaVXT/e18f1DL/ODNXclXflFZJnjPS0EImHmZRbEpQefrjsWC7oQtVn785ndfGPF8K0I4+Wu8kU0uHqod0Vbt7L0Zu4fRdVLJUqszCtjZV4ZHV4n7lCAEnMGkigyIy2Xt89YRliWJ9WWArCnoy4WdAfY2VrNjcVzyE9RYNKr1JNKr6aCm0rmsCa/nBa3nUKTLaXV8M3uPn55fActnj5UgsS1RZXcV7EkpbKlIzHaewnLEQLhxP12cUhGK9B0nO4nvxoLQp5jL5C2+ROxHtbY+extOHb8gWDraVTpRVhWRx2K/OdeRx7UE2teeveIWbeIxw4o475XWNe9j3BvY6yLQJVWgBIJ0fXIBTMM48Jboj66o6DOmk7mXd/AfeBxIq5utCWLMC9767iuJ5VcMYG3t7eXj370ozzzzDOIoshdd93Fj3/8Y0ym4WfW69ev57XXXosb++AHP8ivfvWrSV9PxNNHsPlEwrjv7M4pC7woMhGvPRp0QwGUsB9FUfDX7sV9+OkxqTrpy1egLx+7xGOop4lg8wmUfn9a2e8i1H4O/ZxNiAbbhN5GMJJYmRhOYniwr6M+YU/THvRyqrctoVimL+Dluwdfot0bVUsyqDR8fMG1zOgXcahJ4qXb7O7DHw6lTAfXotHx38tv5byji5AsU2HLGpfwRY7BwtD1xEirm/HQPoyKVLvXkbLAe7lg0eiwDJoAdHpd/PXsHk71tpGpM3FH2YIJVcn/4vhrsclbWInwUuNJikxpF73feDgUonvP/kgIZ9APKJg0ugTTBte+R+NWfigKrj3/iAu8SiRM9+NfJtKvUxxsOUnPE18l+12/IOudP8F74iVkrxNd+XJ0Jcl1u+WAh74XH8Jftx8UBW3JYtJv/BTiGLMhKmsO2Q/8gmDrKRAElEiEnsfjvXE9h5/FULUJTfbof09tQRXagtTalU6UK6a46h3veAcnT55ky5YtPPvss+zYsYMPfGD0mc6DDz5IW1tb7L/vfe97KbkeQVJDklWXoJ66qjjD7GtBiaBEQsghH4qiIEhqlKAH566/TkmBg+/sDiRLDoL6wopLkcMYZ22c8Ew/WT/qytzEMWmY80tJgtm/zx+NBV2Irmj/cuZCmipZejpDZ0I7BXvypdYsZqblTEhtaqqYkZaYIpQEkXLrlSN8PxEUReFHR7dyvKeFiCLT4XPy6xM7qXV0jes8bR5HXMZkgMEGCpcatSixKq+MUmsms/utL8utWWwsupBGVsJB/PUHCXXXE3Z2oPRPbIeaCgSajsaC7uDn+s68hsqShWXVO7Ft+n/DBl0A584/4z+/L7a1Fag/iGPHH8b1ngRRRFs4B23B7JiN31BC7efGdc7LgcvnzjACp0+f5sUXX+R3v/sdy5cvZ82aNfz0pz/lkUceobW1dcTnGgwGcnNzY/9ZLCPvRQQCAZxOZ9x/yRB1JgyV6+MHBQHjvBvH89bGhWHmWtI2f4JoMZSAIGkQNIboYyVCOGnh1OQQJBWCpEJbOBt17gzUWaVop80fViZxLNxVvohNRbPQSmr0kobrp1Vxe5LCppV5pQnVsjl6S9J0ZrIb6cCKFuDW6fMHVRtHg87FTBNeahZmFnFN/oUCG0kQeefM5VguIwGVqaDO2ZMQMBUUdrclisqMhEmtRRJEPKEgjoAvlomZ6la3sdLqtnOyp5W3VixmfcFMrFo9uQYr95YvYX3BDCA6Cel56muEe5ujdnc9jQRbzwBKVCJxMCmYxPvO700YG7pnOx6G69VVj2G1Oxg54EGRL113AFwhqebdu3djs9lYsuRC0c2mTZsQRZG9e/dyxx13DPvcv//97/ztb38jNzeXW2+9lS9/+csYDMPvhX7729/ma1/72piuy7bpP5CsOfhr96CEgwhqPZ5jLyAHvejLlo/9DY4D87J7CDSfiK5wQz4EtQ5VxrRoEJ6C1bZ+1gZc+x9HCfljLUiq9EI0hRMPvGpR4p0zl/POmSN/RhaNns8uup4nag/T5nGQoTNi1uj43cldrM4rY3bGhSrWQpMtYQ8zS2+OrWjzjVa+vfJ29rSfxx8JsyS7OOVFN5czgiDwvqrV3DhtNu0+J+XW7Dd90AWQxGGyJuO0RlSLEr5wiGp7dGUoCSIVtmw2FY2tKGmqCMsRfnViJwf61bQMKg0fmrM2aSFesOkYgabjqNIKUYI+5IAbJegBUZXg3a2dtgDJnBlXVCmoNOjH0ckg6a0J9qSiPvE3F+qqQ5EjqLPLRpwIa6ctQF+xCl/1G7ExQ9W1aHJnjOl6gu3V2F/5GcHOWkSDDeuad2Gcc90Y301quSICb3t7O9nZ8SkxlUpFeno67e3twzwL3v72t1NcXEx+fj7Hjh3jc5/7HGfPnuWJJ54Y9jlf+MIX+OQnPxl77HQ6KSpK3oAuSCosK+5Dk1tBz1P/A4pCqKMa37nXsW74wKRExkNd9VF1LFcXuuKFmBbdjtC/F2m95j0EW04ghwIIgogbka7C+Rh0FlLdRayyZJN519dx7nmEcG8T2sI5WFbfP6UrxVpHF385s4cGVw/ZegtvrViMWpT40ZGtMaP5N9pree+s1bE2ibdMn8/xnlbc/XvRyVa0Zo2O6yahjPRmIN9kI38CVeFTTTAS5lBXI95wiIWZhSkzcSg2Z1BmyYopTUH0u3HNOPdltzSdRi1KFJrScAT9qEURm9ZAwSVWfdvRWhMLuhDdYvntydd56Jp7EgoQB3xlBUmFJn8WctALioJ51TsTqnsFSUXGXV/HueMPBFtO9RdX3Y9qSFVzWI5wzt6BTlJTao1vJzItvYu+F36YMDZAxOug999fJ9ifKlZlFpNx25dRDeP7G3Vi+xyGxqOEus6jyalAWzQ3anjQ39qpLZoX7TgYghIO0f3EVwg0Ho0qVwlC7H1px1jRnUouaeD9/Oc/z3e/O7Jq0unTp0f895EYvAc8d+5c8vLyuPbaa6mtraWsLLnurVarRasdX0GLa//jCakZ9/7HJhx4Q30tdP3zs7GCpmDzCUKdtaTf8nkA1FklZNz1Ddz7H+NVey/P6jPAmo/0+mPcVDKXu8sXJT2vHPTjO7cT2WtHV7oMdWbyJvyhaPJmknnHVyf0XsaLPxzif4+8gicUVQHq9Dn5xfHXyDFYYkF3gH/XHY0F3jyjle+suoO97XX4IiGWZhentLUjGYqi0OZxYFBrRnTSucrI9Pk9fPPAC3T3K139XRD5yLwNLBxFcand6+Qf5/Zxtq+DPKOVu8oWJvghK4rChzNzeNzZxjlRQ7Y1h1unzxu2xWs4zvS1IwjxHrr+SIhuv5usMba0TAWnexNbGF0hPy0ee4LymrZoHghC7F4laqLfWV1x8p5gdVoBGbd9edjXbnD18NDhrTFd8jJLFp9YuCmmsW2oXIeoM+E98QqgYKi6Ns4n3Pn6n2NBFyDc3YBj+2/JeMt/DfuagiCgK16ArngBEN137n78y3HeweYV92FZ+fa45wVaTvYH3f4VuKIQcbRh3/oLcu7/ybCvN1Vc0sD7qU99ine/+90jHlNaWkpubi6dnfEb/eFwmN7eXnJzx96+sHx5NLVZU1MzbOCdCLLXnjAW8doJO7sI9zZGfSA1Bvw1u4j4nOhLl6NKS97sD+A99mIs6A7gq36DsKMjNuPU5s/Cef3HeX7XEwj9AUlG4dn6YyzMKqJs0Owz4nXQt+WnuHb9GUWWUdnykEwZ2DZ++KKqtQzQ7XPzQv0JWjx2ZqTlcGO/Ri7AiZ7WWNCNXb8iU+voSpA9tAe8cY9Nai3XXqTUX7O7j58d206714GAwOq8Mm4snsMj1fs509dOjsHCnWULp0REQlEUTva20ulzMyst94pPmT9bfzwWdCH69/7b2b0syCwcNrMSliN8/9DL9PQ/r87ZzUNHtvLNlbeT2z/hUsIhev79PwQaj3ILRGUB174X0wQENfKMVk72xteT6CXNJd/jTTa5lAQxaR+1ypqDdcMHce74A0o4CJIKy4q3jakiOBl/Or07FnQjXgenmk/wl/2Pck9GDtYNH0CTU46uZPGwBViBxiOJYw2Hx3UN3lOvxgVdANe+f2Gce0OcW5wgqZLep5MZyFwMLmngzcrKIitrdLWTlStXYrfbOXjwIIsXR/+Ir776KrIsx4LpWDhy5AgAeXmpUbJRImHkoBfd9CW4e5vj/k2QNHT88UGQZRRFRgn5YzNM584/kbb548O2HcnDaNzKfhcMSvWc7etIMBWAqM3d4MDb++x38Bx/KXbeUGctCBKOHb/HULVxTMovqcIZ9PH+rX+l0d2LgoJNo+dIdzNfW3YLgiAMW2VcasmkzRvfEnMprdF+dWJHrIpaQWFnaw1bm8/Err/Z3cePjmzlfbNWsySnOGVtQSE5wv8efoXTfdGVjoDAfRVLEnqgryTqnT0JYz1+d8wQIBln+zpiQXeAiCKzu+08d5QtAMB7ZjuBxgt2jygKzl1/QV+5HskwvsnKDdNms6+jnlaPHV84hEGl4W0VS6ekKn48XFtUya62GvoGTUJvLJ49rLOTaf5NGGZcQ6inAVV6UdLPQQmHYttaw+EPh6hzRvd/lVCAUEc1KAo1koZg2xl6nvwqOe/9/Yjax5IlO16Yh+QCHSMR6q5LHJQjhHoa4gKvJq8SUW9F9tkvHCdKY94fTjVXRFXzrFmzuOGGG3jwwQfZt28fu3bt4iMf+Qj33Xcf+fnRlWNLSwuVlZXs27cPgNraWr7+9a9z8OBB6uvrefrpp3nggQdYu3Yt8+aNz+ElGe6jz9P+23fT/qt34j9/AHVuRTSNA0i2PGSfE/or58K9zQRbTiIPrGIVBceOP6AMIy6vS9JnK1lzUGfHr9KHS3ENngWH7W0EW06i9M9MUaK6y4GmI/jOvU7341+Ja4afan5x7DXqXd3IioyiKPQFvOxsOce5/j7b2Rl55A25GRjVWj624FpmD5IEnG7J5P5RirOmil6/h2Z3fNGIJxSIG+vwOjne3cI3D77Ax3c+yqvNiaIhE2FXW20s6EI06D9ac7C/b/PKpNiSKHKfoTONaAs4XImBOOgfkhmZKOHQmKv/w/Z2As0nUMJBMvUmKqxZeENBQnIYUGga8h24FKRpDXxt+Vu4u3wxmwpn8ckFm7i7fPgWHwBRb0ZbOCch6PrOvU77799P60/vovMfnyLYWTvMGaL61Lb+hUTE0xtLX2fJ0Xua7HMRaDg07PMhat7C4ImLIGBZ8bYRnzOUpIFTUqHOjK+AFiQVaZs/hmTNQ9AaEU0ZaPJmYV52b8LT5aAf+9Zf0vbLt9P++/fhPvzsuK5pLFwRxVUQrU7+yEc+wrXXXhsT0PjJTy7k5kOhEGfPnsXrjQYYjUbDK6+8wo9+9CM8Hg9FRUXcddddfOlLXxruJcZMoPU0jlcviHCE+5oRDTZy3vNbQMFX8wbOHX+M/ftA0FP8LuivPJa9DmSvHcmcaHCuL1uOZfU7o9XEQR/qzBLSbvhEQtptZloOczMKOD7IT7ZYb6ayuwa/txtt8YW9XlFrQvY5kEN+lEgQQdKApCbQcgLH9t+Stvnjk/5cxsLxJN639qAPR/9nJAoin128mX+fP0q1vYN8o43bSxeQa7DwmUXX0+VzEZblS5peNag0qEUpZtsG/Tqy/cUs3lCItn7BCoHoKvWvZ/YwL6Ng0tZ8yVaHEUWm2d03rCZxMBKmzesgS2dOkDm8HLi1ZC7Huptj6WZJEHnHzGUjFvBVpuWSrbfEySOqRYmVg4QxVFklcc/xI2BXaclMGzlTosgy9pd/jPfMdlAURL2Zrmse5GBXI9mGC5Pd19tqWFcwY8Kyo6nCotFN2Pd3AM/RF+n8+39GM3N6C0o4SM+TXyP3fb9LmhETBIG7yxfx+1O7oL9fXavIXBu68PcQkhhnDEZXvIDst/0Qz8lXQI5gmLVh3C5H+pnr8J5+7ULaWhCwrL4/qdWpdf0HkKw5+M69jqjWY1xwS1IxIcerv7zgQOZ343rjr+O6prFwxQTe9PR0Hn744WH/vaSkJE5AoqioKEG1KlX4q3cljMleOxFne1T72Bp/AxS0JvC74tp9JHMmojF96GlimJfdi2nR7ch+d1zKZCgfm7+Rve111Dq7yXZ1ULnnr7jCQQDUmSVk3v3NaOVfJEzEa4f+G5Wg1qLOKEYQRHzVuy5a4M032RL2ylSCxJxBq9k0rWFYXeJLWcgygE6l5vqiKp5rOB4bS9MayDdYcYR8uPozG4IgkK6LrgoUFE71to0oVh+SI7zeWkOto4tCUxrrChJdcaaZE78LkiAOW127v6OeP53ZjScUQC1K3Fa6YNI36VSTpjPyrZW3c6irEU8oyKKsolGrmkVB5LOLruef1Qc409dOvtHGHWUL4gKjoepafKe3EWw7yza1mZfUVpT0QtIPv8J7Zq2KFW8FI2Feb6ulztnNNFMaix1N+AZZP8o+F6d2/R2mr0pYaje4ei554J0soZ4mup/8cnQri6hntxzwopHUBJqPD7tHuya/nHyjjT2NJwm+cY7Fvl7S+xXoJGsO2mGKtgajzpqObf2DE752QVKRcefXCDYfJ9zXGr3/DlM/I4gi5sV3YF48fPupEg7iPbdzwtczVq6YwHs5IWiTr1oEbfRmoSmcg6gzE+ptRtRbUFlzESQ1oi56UxBUaqwbPogwSi+hoNIgGtPwnt6Gv/4wKksmxnk3xa2SVaLE6vxyVuWW0vH79xHpD7oAoe563IeeIu3mz+Hc8UdEUzr++oMIogp1emFsRioO836mgrvKFnK6t502r4OIHEEjqXhbxVIMKdoDvVjcU7GYQnMahzobMWt0bCqsRK9S86+aQ+xsrcYV9JNrsMQJgIy22v3Rka1xk5JdbbV8ZdnNsZU0wJq8Mna11cb5zt5euiCplrMr6Oc3J3fGVuYhOcJjNQeZlZYbVwOQSoJtZ6OrBVHCOHvTmAzKIWq+MF4Zx0y9aVj7RgBRrSXz3u9w4sRWXjx3CFFvQdIacAZ9/OrEDh5acw96lZr/PfIKZ/outCVu7Wngw8TfHAt8duSAF3HIhCCZZ/OVRtQ4JX5MCflQAp44o/hklFozKZ27jlDuNJy7Hybc04gmrxLzqndMuVPbAIIgoC2aF63anvzZEAQhSeVMarkaeCeAcfYmPIf+Hbc3qimcgya7jEDraXqf+p9oIVMkhOJ3RQup5t1IsOkYst+Jtnhx0lRIMhxbf4nn+Iuxx54TW8h++0MJKeqIu4eIOzENGWw/h0VvIW3zx0gDvGdeS+ytW5Q6o4ABAi0nCXXUoM6ajqbwgnn7itxSvrT0RrY0ncYdDLCuYAY3XqGFQStzS+NSmwAfmHMN761axdf3P0+D68LfY1Za3oi+wmf62hMyAU3uXvZ31LNqkMSmRlLxxcU3cLSnmS6vi6r0PIqSrIIBTve1x6XDBzja3TwlgddXvYve574X2+/zHH2ejNu/Mmy7ysVAECXOGjIT+lQDkRCn+9rRSaq4oAvQLEickPQsiFzwfi0lzKr8CvYM+hutySu/4le7QNRc3pxB2NEGg72hbfloCsb221RnTR+xDehKQVCp0VduwHvi5Sl9nauBdwJI5kwy3/o93AcejzZtF87BtCTaGO7Y+stoQBaEWHAM29sQ1dq4HraxEHH34DnxUtyY7HXgOfYCltX3x1+TMQ1Rb4kWdQ1iaK+uoXIdgkqL5/hL0X2Vqg0pN3Xo2/JTvCe2xB7rK1aTdvNn44LvRETqrxRUosQXFt/AjtZqGl29lFqzWNMfPLc2nWFfRz1aScWmosqY2cNwJu49/sTCN0kUx9SmlDZMb/Hg8XpnD/+qOdh/nZncW75kXLaL3T43rzSdpjfgpejQ4yxUlAteOHIE155HLmngBbAN4+xk0+oTiuQAJEsODmcTuC4EXtOCm/nQouvZaO+gwdlDiSXzzRF0AcOsDbgPP40mr5KwvRUl6EeVUUjWAz/7PyOpOhjbhg8g6oz4zr2OoDFgLd8AvDjq88bD1cA7QdQZRQn7onIokNRoPth2ZkKvEXH3JNVMHVqCD9G9Duva99L38o9jz5Es2ZgW35lw7GCHIjngQYmEU5YWCrZXxwVdiK6EDI1HY03vg4l4HbgPPUWoqw5NTjmmhbeN2WPzckanUnP9EJWsx2oO8Wz9BSPx4z0t/Of8jSzMKmJWeh4iQoJIyOxhzN3HQoUtm6q0PE4NqoLO1ltikx53KMD3Dr2Et3974mh3M+cd3Xx/9V3oVGocAR/72usBKAvOwqaLD+TdPjdf3fdMrO96R1ChVpPGPcELwWyo0P6lYFVuGS80nIibxMxKy0NWFHa0VHOmtx2zRkeuwYIkiggqNStv/Tzmur1E3D3oiheir1gFwAxbTszx6s2COmt61C70jb8jGdPQFs3Duu5BVJdYletSIag0WK95D+blb0MJB/CEUz/5uBp4U4ig0iBZc4g44p0+VBkTE1FQZ5UiGmwJjd/akuTKVIaqjajzZuKv3Yuot6CvWDNsH12orwX7Sz8m2HYGUWfCtPRuzEsSg/R4CQ3TghDqOp8QeJVwkO5HPxfz2wzUH8Jfu4+sdzyEkMT56UpGVmS2NsVPwBQUtjSeYmFWEek6I++atZK/n91HUA4jCSK3TZ9PqTWx6n08fHzBtbzW366Vb7SxqagyJkSyv6M+FnQHcIX8HO5uIltv5nsHXqS1Ifr33Lq1kxJrFlaNnlV5ZWwsnMnW5jNxYiei3sJeReb6kANrv4mAdtqCSV1/KjCoNXxp6c280nia1n7RlkpbLt888AJhJUKm3kRLf3/uzLQc7ihbyPSc6ZAztv3pNwP6suVTpi8/VYT6WpB9TjS5M1J6v1AUBeeO3+M59iJKOEjAmvrvwdXAm0IEQcB6zXvoff77sb0SUW/BvHxihsuCpCL9ps/Q+8IPomLjoohx7uYRhcrVaQWoRwmgiqLQ+8y3CPc0AVHBDufOP6FKK5j0j0+dm7xqN5l+qq/6jVjQHSDUXY+/7sAVdxMYjYii4I+EEsYHB751BTNYml1Cs6ePXIM1JSYGGknFddOqkmpUy8M40MiKwqPVB2PXaw/4cNo7afe5KbdFdY994SB9/njlMHVGMcFwEJdPwqrIaHJnYLnm3ZN+D6kgTWvgnooL1bkPn9sX84DO1JtI0xrwhUN8dtFmZiaxULzK5YMSDtL73Hfxn98PRLf+0m/9YuweIwd9iJNQFPMefxH3oadjj4MdqbcdvBp4U4y+YhU5D/wcX80bCCot+sp1SPqRNYOVSBjX3kfwntmBoFJjnH8zpvk3AaAtmkvu+35PqKsOyZQxYmvRWAl3N8SC7mD81W+gK12Ga/ffY7M9Q9UGrGvfN2Z1K012Gcb5N+E5+nxszDBrQ9KKQ9mX3Jx9uPErlVaPA1EQWJBZyOHu+M99SU5J3GODWnPRUplLc0r4V83BuAmBUa1lUVYRfz97wdLNHvQiYsA36LhXms5wT8Vi9nRckNwT1BpySpcyb8aDqCT1mCuaLwXBSHzRmSSKmDRaNNLUZ1p84SA7W2to8ziosGWzPGf6uN2S/i/jPvRULOhCdOut76UfYV33PhzbfkO4rwWVLQ/rhg+M6Bc8HL7q3am83KRcDbxTgCotH/PSu8d8vGPnH/EcfubC41d/hSBKGOduBvrdRIZZSU4EYZiiG0Gjx33gCVx7H42NeY6+ALKCbdP/G/P5bRs/hKHqWoId1aizpqPNn5X0ON30pVFj7MErL0mFrmRJ0uOnGmfQjz3gpcBoS8mNsC/g5adHX+V8v7ReiTmTDK2R/Z0NAFyTX84NRZfOLcmi0fGZ/l7YJlcfJZYM7puxBL1Kw3RLJie6ozKoshKVuDMMkhEMymFW5ZZy3tHF9pZzRBSZdK2RD89dh/4KKDpakTud7S3xamK5BislU9we5A+H+Mb+F2jpt8vb1nKWQ11NfGSEtqjLESUcwl+3DyXkRzd92bjrMoKtZy5YqM5Yg6Fy3ZifGycD2k+os5aeJy4YuYTtbfQ+8y1y3vPbcS9WxFEWSqngauC9xCiKklCMBOA58XIs8KYalSUbXflK/DUXZnaCSoNx3o30vvCDhOO9Z7aPK/ACaHIrRp0sqNLySbv+Yzh2/AHZ50Q02LBt+EBKVvXjQVEU/lG9n61NZ4goMmlaAx+cs5bKEdp/xsLfz+6LBV2IFi85gz6KzekIgkCLx85TdUeHdZO6GJRZs/jikhsTxu+rWMJ3Hd30Aha1lqAokT+o2GZZznQEQeD+yhW8Zfp8HEEfhSYbonBlrNwq03J576zVPHn+MH0BL7PS8njXrJVTXsW7t6MuFnQHONBZT4Or57LvCVYUBX/1LnzVu/Gc3IIgqRFECUGtI+O2L425jzbQdJzuJ74S247z1+4l4miPbcl1+VxoJfWwWy2SOXFiJwd9SDpVnMCJEg7hq90Tyx6OFdOCW/DVvAHDSPqmgquB93KgvxAljiT9l8nwnHgZ98GnkP0u9OWrsFzz7jHtb6Tf+ClcBx4nUH8IyZSJackdqDOLkxYpTGWhk6FqI/oZ1xBxdyOZs1JSXR12dkZ7iDNLRnSBGmB/ZwMvN56KPe4LePnF8df43zV3J3iajoeh8pjdfhf+cJgi8wVLuq1NZ7ijdMFll2osMqfzvdV38WjwRSKKglKYwd7OemRFYWVuKfdVXMhKWLX6pAIelztrCypYW1BBWI5M6u88Hjq9rqTjXV73ZR947a/8DO+JLYS66oi4uxFU2mifb8iPfduvyXng52M6j/vQUwn3N/fBp/DO2cwvT+yk3tWDiMCqvDLePWtlwt/GtOROfLW7UQJews4uIq5OJHMm4YA7qsU8KPhOZK9Xk19J5t3fxHPkWWSfEyFnHlfbia4QQj1NCJIKlW1kJyRBEDDM2hgnkgFRubvR8FXvwr7lZ7HHnmMvIPtdpN/82VGfK6g0UUHyIaLkxrk3YN/6i/hrmWLrQEGlHvVzGivOPY/g2vOPaPpaEDAtvBXruveP+Jxj3fHOUrKi0OK2s7ejjtV5YzdMr3V08Wj1QZrdfUy3ZqIWJQKD9kUjsox6yB6iPxIioshIl6FfiU6lZm5/n/HCqoW8p2o1CspFC1IXi4v5fqrS8+KkRiEq+TnjMi/oCtvbYpk5eUB7Phwg4upGZc0h3NOEHAogjkGBLpLEnk8OePhDf9CFqMXp62015BqtCRKn6owist/5E/pe+AGh7gZU6YVIOguB5uMosow6vRBFDqMEvfjO7SLitWOce+OITklD0ebPim2ROZ3OUY4eP1cDb4qJuHvoeeZbMWcUbdE80m/5PGISf8wBrOvfD5KE78xr0ZTv/JswLrhl1NfynHglYcxXvQs54EHUjqx1OxwD/ryeYy+gREIYZq3HtGTs+9WpQva7EbTGcaX+Qj1NuHYP0vNWFNyHnkZfsQZN/vA+vYN1gT2hIHXObsKyzM+PbWdPex0fnbcBzSgrcUfAx/cOvRwLtCd6WvCHw2gkKeaYk6Yzohlyk5+fWTjquS8XLrdV+ZXI7Ix8Nk+bzcuNp1BQUIsSD1SuSEkFe6pRImHcB5/EV7sH2e8h4nUgGayIGj2RWPCN6pKr0vLHFHQB9KXLEpyj5KJ5VLsSlfeOdDUl1RZXWbJRIuE4gSBNXiURdzeqzGICDUcQNQb85/dF/6vdS+Y9375sBEGujF/8FYT91V/HfakCTcdw7vortms/POxzBJUG24YPYtvwwYtxiaNinHdDLABfbAJNx7Bv/SXhvhYkcybWte9FP2PNmJ4bbD2VdDzQcnLEwLuhYAbbm8/iCvppcPUSliPRICmpON7TwstNp0c1FtjfWR+3ugXQqVTcVDwXZ9CHJIiszitjX0c9r7WeIyzLzMnI571Vq8f03t6shOUIBzsbafU4mGHLZnbGxAVDrhTeNmMpm6dV0e51UGLOvCwdowAc234Ty8QpcoRQZy1CTjmSLR/Z50SJhKKFSJIK69r3jfm8piV3Ee5rxXv2NZBlNHkzMW36CLrDWxNa7qwjpYqH7MEKKg3qzBLMi28n3N0Q92/BllMEm4+nSM958lwNvONEURQCjUcI97WgLZiDeoj1WDIPSn/9wSm5FuOcTQSGnFtfsXrCq92LgRzwEGg5iWRMj/XdRVzdeI69QNjZgefoC7Hrj7i66X3hB2RnTUedVjDquRu1ZlolHWURP4MNyVQZRSM+L11n5L+X38KTtUeod/WQYzCTPqjy+3Rv24QdfYrN6SzPvdBWMyMth3srFhOW5cv2hnuxCMsRvnvwZaoHCc6sL5g5rDPVm4l0nZH0URyYLiVy0Ifn1IWMmiBKqLNKiHh6UWeWoCmahyarFP2cTRjKVyW1Nx0OQVKRdsMnsKx7H0oogMoS1Q2/floVT9ddqFiWBJEbRtBx11euI/R6ffzYrA1JNeuBYccvBVcD7zhQImF6/v11Ag2HY2PmlW+LM2+WTBmE7W1xzxvPl3I86CtWY7vuI/3FVW70FSuxrHn3lLxWKvCf30/v899H6bfNU+dXIaq1OHf/HUEQEbQmIo42JGse6vR+z1RZxl+zG/UI7Vm+cJAfHn6FGkcnwfQZ6D09vMffxXQ5iLZo3pjakzJ0Ju6vXMGBzoaEWfdYrAiXZpfwWM2huOea1TrmZxbiD4fwR0LY+oO5RlKheXNtk06IfR31saArKwqBSJitTWe4rmjWuPSir5J6lEgoYUUpGdPRz7gGy4r7kKy5qKyT25eW9BYYtKC9s2whuQYL+zvqMaq1bCysHFG5zbTkTpSgF8/xF1HCIQyV67Gsex/hvhbY9bchL6ZCO+3SaoYP5mrgHQe+c6/HBV0A195/YqjaFJu1mZffS99LP75wgChiXnbPmM4v+93RlV9vM+q8mRhnX4egGtlM2jjneoxzrh/fG7kEKJEwfVt+Egu6iqLg3v8vUJSoBRmgeOwgQMTZ3m+lGP16jraCf67+BDX9NnmanHKCngyelIv5+uxV6MtXjWq/OIBWUnHr9Hn8q+ZCFsGo1o446x7AqtXzmUXX81jNIRrdvZRaMrm3fDFPnj/Cq81nCMkRyixZfGDONeQYpr5P8Eqg1RMVSrEHfDS5+4jIEURB5Inaw3x0fmqNO64yPiS9BW3RPAJNx+LGDVUb0U6bP6lzK+EQEa8dyZyZsOe6Kq8szo1rJIR+0/uhhjGa7FKs696H842/o4T8iHoz1g0fHLMj3MXgauAdB6HOmsRBWSbUXRcLvIaqa5HMWXhPbQNJhXH2JjR5M0c9txz00/mPTxHqOo+o0sHpbdGCgDu/luq3cUkI9zYjey8oUikBD0rQiyLLscAoSBKKHIkG40gIQVIhGtNGlMgE4m3dBJBMafQAjsL5bGk4yXlnF8XmDK6fVoVplAKQm0vmMt2SwaHOJswaHWvzy0c1ZR+gzJrF5xZf6L1+reUcLzWejD2udXbxqxM7+Oqy0QvnxsobbbVsbTpDQA6zIreUm4pnX7Je2mPdzezrqEcjqVhfMINpw9gVDlBuzSIsyzS4elD6RVRkRWF3ey03l8ydtE71VeIJdtRAJIw6b+aYiozSbvgkfVt+SqDhEIJGj2nBrRjnXDepa/AcexHnG39F9rmQLNnYNv3HlLhXmRbdhmH2dUScHajSCsasvHexuBp4x0iwqw7RY0/UARUE1Jnx0ngTMWXue+khPEefi/a3iSrUGdMINBwm2HpmxMKgKwXJkoWg0qDEtImjN1pRb0YZ5GussuYi9s+21VklmJfePeqKN9dgia14BzCqtPz8+PaY7dvR7mb2d9TzteW3jlpFXJWeT9UkXIEGONDZkDBW5+ym1+9Jyf7eG221/Obkztjjx2oO4gkFeGvF6Kn1VLO16Qx/Pbsn9vi1lnN8bvHmEeUv52cWUmzO4ESs31kgz2hBI6k42t08ZYE3LEd4ufE0h7oasWh0bJ42+02tzxzxOuj59//Eij5Vtjwy7vjvUVv4JFM6mXd8NfqbFaVJ9/MHO89jf/WXMaW6iLOT3me+Te6Df5ySuhRRa0C8TGVLr/YHjJHuRz+P98x2wj2NhHoaY+PmZffGVrsQTaF6jr1A95Nfo+/FhwgOKZtPRqinCff+xy40lcthQl11KJEgYdelt1WbDMG2s3hPbUX2OTEvvzc2LmhNiAYb6uxyROPAykhAnVNBznt+Q/bbf0jadR9FMmcRbDtLeAR7uZtL5mIctJJVZJmqkJv6xuPIvguCBW1eB4e6GpOdYkowqxNbRCRBRCeNvH0wVl5tPpswtq35bGz1eLFQFIV/18XL+EUUmWfrjg/zjCiCIPBA5QpmpOVQbM5gVnpuLA0/nIduKvjzmT08WnOAGkcnh7oa+d6hl6h1dE3Z6w3Q6XXxQsMJtjWfxRsKjv6EFOF8429xnRZhexv2V3815ucLKk1KRHT8tXsSbE6VkD+pBOSbnasr3nEgCCLq3BnIPifGhbdiqLoWTXa8obvjtd/iOfxs7LH33E6y7v1uTD6xL+BFI0pxgcJffyBalm8f7NSjIPvdaIsmt59yqVBkmb7nv4+veld0QBCwrH6AzHu+hb9uP5IpA01+Fa7dDxNoOoqgt2Kcuxnzkrtie7uBllP0Pf89Iu5eEAQMleuxXf+fCTeBPKOVb6y4jV2tNbi9Tqbv+wfN7l7C2nSwt6FKK4gpWDkCPqaSiMcenWj4XazLrWKvIBIZpEy2rmBGyqqZQ0nUzUJyBAUFgYvXrxhWZJzBxM+1d1AmYzhmpuWwMHMaZ+0Xtguy9OaYZ3Cq9N0CTwAAjPFJREFU8YQCvNEWb10ZUWReaTpNmTVrmGdNnoOdjfzi+PbYd+HJ80f4ryU3XpT9/mSBLdB08YOdqEtepHgxtJEvN64G3nEiCAKSwYoqrSAh6MoBL97jL8U/IRLGc+RZ3Os/wC+Ov0aNoxNJEFmZWxqTQ5NMGYg6MypbAWFHa7/qkoRl9f1IButFfHepw1+750LQBVAUnG/8ldz3/g7rNe+JDWfc/pWkz1dkmb4XfxgNuv3P957ehiZ/VtIe4zStgVumz8O171Gc9iZMgoSIgoxA2N6KZMlGlNTM61dimgrC9na6/vmZ2F62iSf42Mp3ssuYgzsUYGFWERsLR9/vHysrcqfTMER0YHnO9Iu+x6sWJWal5XG6L76af17G6C1gAJ9YcC3bWs5SY+8i32TjuqJZMc/gVBOIhOMmQgMM9SUeK61uOzWOLvKMViqGMYdQFIV/nNsX97rOoI9n6o7x/tlj61GfDCprLhFHe8LYxcZQuR73gccv/KYBTe6MqOzk/zGuBt4JkszyTAl6UcKJnqsRn4M/ntoV24eMKHJUDs1g4Zbp89CXrcDV32sqWbJQQgE0hXOwrnswZdcr+90Emo4i6q1oCmZPuYJLsD0xDYosE+yoQT+G9qpwXwsRZ2L6z99weERxj3BvdL/QpkR4m7+XJ7VpeBRQ9zZxh16PpXY3yuxNKdGEHor74JNxBWQAWYef4kMP/nnU6vSJcMO02XhCAV5tPksgEmZ5znTur7w0PsbvnrWSnxx9lRaPHYB5GYW8ZfrYsjU6lZobi+dA8ejHTpZ0nZEyS9RTeDBLs0vGfa4na4/w77ojcef4f3PXJfy2/JEw3X53wvMbXb0JY6MR8ToItp5CZc1L0BCIHeOx46/bj6g1opu+FPOKtxJsPXnh3iQImFe+Y9yvPVlEvZnMt34f98EnCPc0ocmfhWnx7ZeNmtTF5GrgnQD6mWuTWt1J5kzUuRUJcmji9KWc7G5NOP5QVyO3TJ+HoNKQdc93cB95hlBXPZrccozzbxlzG8xoDO2f1eTPIuOO/56UWfRoqDOS3EUFAXXGtDE9XzLYQFIl9BIO3k9Phia/Eu/pbQAsiniZ63LS0dNEdlYJGkHAXvcG/vqDZLzlv8Z0HeMh3NeSMCb73cg+x5T0cguCwN3li7mzdCHVjk4iioJGvDQ/6RyDhW+uvJ0mVy9aSU22YXw2cReTD89dx29Pvs5Zezs6Sc1106pYkz92TW6I7tc+PWRfe39nPYe7S1mUFf8d16vU5BtttPZPSgYYLrUdcffi2vtPgu1no0pMy9+KypaH99RW+l75eew3oZ95DWk3fCruPuGvO0Dvs9+JFTGqbHlk3vNtst7xY7yntkIkhH7mupTajI4HlSXrslHou5RcDbxjJP2Wz6P3daLOqRixjy39hk/R++IPCbVXI6jUGOZuxjh3M/odj+KLxKezzIP0WUW9GcvKt6f8uhU5gn3rz2NBFyDYehrP4adjNlyTfo1IGO+prQSajqOy5WKcfzP6GdfgOfEywZYLMo7GBbeMyS0Iop+HacEtuA8+FTdmXHDriM8zVG3CV7svpugl+uzkGa1Ig2bV/tq9BDvPJ2wVTBZNQVVC36PKlodomjrXmb6Alx8e3hKr3s7QmfjUgk3kXyIBiqJRWogmi6Io1Di6kBWFGbbsCa2WMvUmvrDkBryhIGpJQj2BwqF6Vw8KiUVs5x3dCYEX4P6ZK/jx0QuSiDl6C28pjd5HIp4+XLv/QaD1FCpbPsHWU8i+qDB/qPM8/vpDZN33fexbfxk3EfWd3YmubDmG/nY7RVGwb/v1oM6BaCGV68Bj2NZ/AOuad8XGFUWJWnHqzCmb4L/ZOO/o4lhPC5pgEve4SXI18I4RXfFCzJbRiwBUaflkv+2HRNw9CGo9Yr9a0ebiKp46fyR2nIjA5mlTv7cRcXbG7akMEGg9Q6rWJN3/+gK+8/uR9GZAwHvqVbLe8SMy7/oG/vN7Cfc2oymYjbZwzrjOa137XjR5lfjP70cypWOYe0PCijfU04iveheCWoehcgOS0UbmHV8l2H6OiLMTf+PRxH13IOLqgjEEXncowCPn9nOkuxmbVs8tJXOHLfwxLb6DQNNxgi3R3l1RZ8J23UcRBIGwHOFsXweSKDLTlpOy9NrjNYdiQRegx+/mb2f38tnFU+PlfCnp9Xv44eEtsXR2jt7CJxdumnCB0mSK3DL7mgi2nEYJBxD1VlQZRQiSatje5Vnpufxwzd0c7W5Gr9IwNyMflSihKArdT3wlpi0caDpGqOs82sK5CP3V77LXjmv/43EBdYBg88lY4JV9TiKD5DcHGJqB89cfwv7qr4g42pFMGVjXvW/MeujjIdh5nlD7uajE5BXWEvls/XEe6xfSCXpSX5B5NfBOEdKQVc7tpQvI0pnY11mPTlJzbVHliD2OqUI0piNqjchDKkxjkoyTQPa56PrHp3EfeRqAsKRGnTsDXN14T7yMeend6CsmZwKgr1iFvmJV0n/znXud3hd+AHJ0Rure9y8y7/0u6owiNLkzIHcGos6cEHgFtXbMk4BfHNvOqf6iIXfIz69O7MCi0SXt8xU1erLu/Tbe1jOc6m5CyColI7uYFredHx7eEqvyLTDa+PSi60kbpAc9UeLEQ0YYmyr84RAKCvqLIFDwz+oDsaAL0OFz8vC5fXxiwaYpf+3BBDtq0L70Q1aqLOxSm4m4u1FCfhbOvZbFSVa7AxjV2gRVpmDz8XhBfzkCcoSIuyeuAErU6qMm70PacVTpFwrYRL0FyZKVUBuhzrowUZR9Lnqf/TZKKABE9Yt7X/ghOdllE7LmVBQF94HH8Z7YgqIoGKo2Yl52L86df8B96OnYcfrK9aTd8Ikp389VwkG8Z14j3NeCJn8WutJl435NTyjAvwctkqaCq4F3EgTbzxFoPIrKloeubMWoBTur88tZPc69pMkiqrWYV70Tx7Zfx8YkcyamxbdP+tzOvY8QaD8Te6xEQoS669HmV025ILmiKDh2/ikWdCG6n+ra+0/Sb/p0bEw7bT7mZffgOvgkRMLRVeimjwzbsN/hdbKrrZaILDMzLScWdAezo6VmWIGNbp+b79WdotPnhM4WzOf2Y1Jr41prWjx2Hq85lJKK1hyDJaFw52K0qITkCH85s4c32mqRFYWFWUW8r2p1XJtcqkk+yUhc4U013pOvgCxzZ9DOgrCXOklLrr+LddNmjtk6UQ768J58BV/160TcPbGJuqi3giDFG8WLEsa5N6BEwngOPxMbVmVMw1B1YdIhCALWde+n9/nvx1LSkiUrTrLWX38gFnQvXEwEX+0ezIvvGO9HgfvA4zhf/0vssWv3w4SdHfhObo07zndmO4aqjeiKF4z7NcaKEg7S9a8vxK3wDVXXkrb5Y+M6T5fPnbRVL5VcDbwTxPnG33Ht/WfssSZ3Bhl3f3PMnpQXE9OCm9HkzcRfuxfRmIahcl1KlGKCzScQtSYQpWjFpBxGiYRRFBldyeIUXPnwKOEgkSSiGuHepoQxy+r7MS58CxFnB+rMkmHl46rtnXzv0EuxH93TdUdxhwJYxyHm8HjtoWjQ7ccV8nO4q4lyW3yKPFWr0jtKF3DO3hG7ZhGBu8sXpeTcI/Hv80fZ2XrhBneoqxHdWTUfmHNNyl7DFw6xt6MOZ8DH/KxCcgwWHEP6hXPGYGCRcgatOkvlIKVyNAUsJmlTSoYcCtD9z88R6q5HkWVCPY1EvHY02WUIkgpN3gxU6UXInj4kaw7Wte9FZcvDtv5BdNOXEGg6hsqSg75yfYK5u758JTnv/lX0t641oitfFXeMMMzvfiL3g4i7F8+xFxLGvUefR1Al3gdDHdVTGnh953YmpNW9p7ZiWnx7nG/vaOQbrRhVWjzhwOgHT5CrgXcCRNw9uPb/K24s2H4O78ktaHJnIpnSE1LNlxpNTnnMhi9VqGz5hLrqEA3phDprATnqMiSqplz4Q1RrUWeXEuo8HzeuSVJtDiAZrKP2RD91/kjcTFcSRfyRMFaiLWChSASNpGJ1Wjanuho53NuOVaNjTX55zHmo2p5kMqAkzp5zDRZeaDjBnvY6NKKKdQXlLM4uGXf/arktm2+uuJ1dbbWE5QgrcqdPeYETJJfD3NdRn7LAaw94+fr+5+npX80/cf4w6/JnUOvoivXDSoLInWUX33FGP2t91Kd2UABWZ5cmbTFMhu/sDkLd9QAIoogmdwah7gbkoBd1Vinp17wLfcVq5KAPQa2LS5XqiheOqm2ssmRjWpi8CFFXvAhVRhHhngsTVMmUgb5i7NmXQOtpuh7+FKHueiKeXlS2PNTp/7+98w6Po7ra+Dtle1Vvlq1iWe6927iAsU216T20ACENUkiAFAdCCYQEPhISQgkQSCA0Y8BgcDfuVa6yLUuy1dtqe5+Z+/0x0kir3ZV2pVUxmd/z+IG9mnJny5y5957zvh3Wm7RKB9KlGgEQ36P+JNhaE7Gds9bGFXiVDIvvjJ6NV49vj/jbTQRy4I0DIeAD11oNztEcMsUJAILPCcuaJ8Aa0wGKgm78Mpguuv9bXaNmmHU9vBV7IbgtoDUGgAjizYcI8JRugm5C7Ak+vNcBEBKXYIj5wvthWfO4JAvJpo6AoZNFY7w0dKnBBYB0jR6paj221ZWBEnjk+mz4tGwbjir0YM1ZYJOy8VXVCayaeTlSNXrk6M1hU7+TUnLBEV4KGEqahYZV4r9l+0GIOPX8YfkBjNCnYOGwItw9dj6MynC5yWikaw24qnByr6+7N2gjPCAk0l94fVWpFHTb2dd0Fr+ZcRn2N50FIcCcrAIM0ycl7Jyxosoeg+RLH4Jj97vgHc1Q502FaeF3Y96ft4cuX9AqHVQ5Y5G0/KfQjlnU0d4P5X4UwyL1mifh2v8hAvVi4pN+5rVSEmhPCH4P6v56HYT2paSgH8HGM6CVGjB6sWROP20leI8DnmNfS/upC2dBNaJ/Z2JUOePg2vdRaCPNQJkVf2LXrMx8jEnOQmlrPRQBHu8mqI/tyIE3Rtylm+Ha/x+QgBegKHDOJrCGdqUagkBTORRJbQlLhMB9dB2Uw8ZBO3rhoPW5v1Gk5SNp2U/Q/J8HQQQeTFsiFyBqNMcSeIWAF7av/w/eM7sAAOr86Uha9hPQan2P+yqzipFx9z/hryoBrdRAOWxCnx50is2Z2NkQKieYo09CnduGsUlZ8FcfAc/58aEqGSN5H2CtBaXUwqkzY13VcdxaPAsrCybjtLVJKh1jKBo/mLgQKWo9djVUQEEzmJtZgN/uEdfqmrxOtHidIBAD/+HmGrxxYgcemHxRr69jIFg6fCxePLwJHi4ANaOAmlVg6fCxCTt+jdsa1ubhAlAxLK4d2b/LGLGgGTW/15nAymETgb0fhjYyLFTDJ8Ffexy8owmqYROi1n4TgYf31DYE6k6CTR4G7bglcQVpRmeO60GhM64Dn3QEXQBQqEEFfeA9digyiqAbdxEMc24BRTPQjl2MQFtWs2r45JDfJu+2IthcCUVqHhh9YmZoVHnToB2/tCPgMyxMF9zZ6+MblWrMysyHw+HoeeM4kQNvjNi3vAJDu3s5IQAhEAIe0EotBL/4X7rLB+w/e/BbF3h5txWO7W/BX3UYjCkDukmXgU0ZHiZ0EatQhmP7W/CW7ZRe+yr2wb719ZgTImiFCprCxKg1XVc0Deecrah1d9TEjjSloc5tgxBwg3B+BEGBAwU3RcNIBPhcrWjiebxxYifKbE24qmAynpq7EnsaKsEJPGZk5EnJTu1rrwIREBDE98vu98DPc/DzHNyUHyes9XBzAdw/YWGPLkqDSYvXBR/PweLzgBCCxcNG4fK8CQk7/khTOg63hE4dGpUapGl6fiAb6qhHTIZ+6pVwHfoMIAQUq4RxwV2wfvVCh983TcN80fcjem23vP8w3EfWgQgcGG0S3Ee/RvrNf4rZ+k4I+OCr3AvwHNSFs+Ja3xUCbgAU2t3FKAqAUg3d+KVIv+X5kG1VOeOgiiAH6dz3IRw7/93mxMbAOOcmGGZeH7ZdvFAUhaSLfwj91BXgrDXSst9QZOj+socaggCgo9CeNWZAN3UFFKkjQCnUsH7xx7BU//5QKxpsLJ883raeK651BxpOQzvmwpBpJUVqHrQRbhiR8JXvjtIWXyZiIkhSafHE7CtRZm8CJwgoNmdgS+1pAGWg2hShlCBQEgEsEW895wQBAb8H2Tozzjkt+MuRzfjdzMuxfET0Gm2aojEzIw+7GirAEQG+tocWBc0gwHM452wFLwidv25DikaPAx+VH4JRqZamxKtdVpxorcfY5PhLUiKxJHcMSpqrJWlHBc3g9tGitnlPfTvRWo8UtQ4TUnKG7FKPaeF3oZtyJThrHZTphfCW7egIugAgCLBveQ2aonkhgdF9bL0YtNoCn+CxQQj64Dm1DbpxPZdVBa21sHz4K6m2n1bpkHLV72LyDAcA7eiFsOlTwLtaOhppFqYL7opp/6ClOiQLGgIPx453oM6fGVUCM14UKblQpOT2vOEgIgfeGBF8LnBeDyiFGowuGaAoKDNGSiPaQNURMeGiDVprgm7iJYPV3T7hrz4KT+lmUDQL7fglYk0sxPrF9qArwXOgVVqk3fhH+M4dAmvKgKZoXsxP37TGFCbwQQ+iMQRFUSH11XMyC/DFuaOwAKB1SRDcVszk3PBTNFoYJQK0AgalGqltIzGeCNhRX44be0hwuq14NggByu0toEBBSdNQtgUVs0qDSmdLQjyB+4Mz9uaIqk1ltqaEBF6r3wOBCPj1jEtRam2AI+DFuOTsEKW3SGyqOYW3T+6W+jbSlI6Hpi6FaojOHLDGdLDGdBBCEGg4HfZ3EvSJ5XmdRo2ug58AXd573tEYMZs/Es4d74T83gS/G/atryPtxmdj2l+ZOQrJV62Cbd0L4J1NoDVGJF36C6iGxzbb0VXZTWqvOZKwwHs+MDS/kUMQzloHDh7x/+0N0I1fBs3IOdLfTRfdL0oGnjsExpAK3aRLh1xmc3eINbDvwVXyBQL1pWDN2aBVOriPf42UFb8Ry4Oi1LYRgYcyqzjmp+bO6KddBeu6P4e1DRW0CiV+PeMyrK86gdrkbOS2nsUc6zkoDGnYmzMJb9eWhYlHRBtluYJ+HLPUQseqMC4lC9+bsAAZWgP+dXIXWnxuEEJgVmmRotZF9PIdKmRFeTDK1vXtgcnLBfHKsW0oaakBAUGxORPfn7AwpmDuCQbw3ul9IQ8EZ+xN2FZ7GhcncO05kRAuAPu21+E5sQmctQ5CwN2WHdz2/WFYsEmhQjcUq0bnqV7xQAKUGaNiOmegMdwfvHMb4YI9GnoYZ1wL/cRLwTuawCblxGUAEk2kgzUlZqbkfEEOvDGiyCgEG7SD+F2glFpoxl0UMqqjKAraMYtCshLPJyyfPoFA7Qn4q4+AcH4EvA6ocsaDUqjg3PM+1HnToMgcFVaKAIqCdsziXp9XO2aRGOCPrQdAoB17ETQjZ/f9giDWGXrLtgMANEXze1zvEXwueEo3gXe2QJ0/HarciQDEKejri6aHbX8ZIdjtdoRINjIUjXld1IkA4JilFi8e3iyt7Q7TJ+HhacuxaFgxNtWcgqlTVunElGEDUhLUWwpMqZiVkY89jZVSW7E5E1PS+ja993H5IRxq6fhunbI14O1Te/DDiYtCtjvRWo+d9eVgKBoLcopQaEpDrdsmvbedOevsXyGXvmDf/ibch8U6WFprBmerB0fVSsHWMPP6sCx/7egF8J3ZiaClCiACAAqq3InQjIpNIU6Rlh9W/65IL0CwuRK2jX9DoP4UGGMajHNv6/ZeRqu0oHsxQlWNmAJV7sSQka9y2Hio8sXfV7D5LBy73wVnqYIyazSMc2/5Vi7ZyYE3RiiaAWvKACBOQwoRxBvOV4LNlZKZAWk3ciACOGczFMnDJBUqiqKQsnIV7Ftehb+qBIwxA8Y5N/e5PlhdMAPqghl9OkZX/HWlsHy8SjKHcOx4B6nX/D7qqJz32NH87s+km5LrwCcwzLoBxrnR7dMoisLPJi/Be2X7caK1HjwhyNGZcKi5GialRpoaJYTgzdJdIYGhxmXFF2eP4vqi6fjNjMvwddUJNHodGJOUhSW5Q1/X9nvjF2B2ZgHK7c0YpjdjevqIHtdfe6KkJXy6tKQ5tG1H3Rm8emK79PqbujI8MPkiFJrSoKCZMMWh3CGaXAMA3pPbpP+naAbK7LEACIxzb4Fq+OSI31Xd5MsRtFTDc+wr8F4nVFnFSFm5KuZzGufe2mbCIJbgUQoVjHO/A8snj0lT0LyjGdavngebkgtlevhDZF9ov4d4Tm1FsPEMFOmF0I4WrRR5tw0tHzwiydty1loE6kuR/p2XQPXxuzXUkANvLznfRL+7Q+jkXERrTBA8NvFFW/F4ZxUq1pjeL5Z6icax/V8hjkwk6INjx9tIvfaJiNu7Sz4PGwm4DnwM/ZQrxRrlKCSpdbh/wkK8WboLW2pPoczehDJ7E7bUnsKqmVfAqFTDFvBG9GM9YxcTh9K1Btw6SD66vYWiKExJy+3zKLczRqUGzW0BoZ2uqmFrKkPXCAUQfFZ5BL+ecSmuLZyKd8v2SX8bYUjBopzYpmAHg655EBRNg03J7dY1jKIZJF38Q5jm3w4h4GkbDMSOInUEMu74B7xndoLwHDQj54CzVIUbqRAC76lvEh54AYBiFWIiWJdkMO+prWGa8py1Dv6qkn5Xwhto5MAbK500WNUFM6EZlThpvMFGmVkMxpQB3t4IRcoIBLggSMANRp8CVe4EGOfdNthdjBuuTRmoM8GWyvAN2//WXIlgazWIT1xKYM2iQD3nbIKyB1nCFq8LW2tDk2MsPje21p7GFfkTYVSoYVRq4OgidzgY4g9DmcvyxuMvhzeHrNN2LVGydrkxi21i7sWyEeMwLiUbxyx1SFXrMTltWJ9H4f2JbuIlcOx4O7RtwvKY9qU1hm4fCLvdV60PKVOK5GgEANQA5xlEcl8CEK4t/S1ADrwxknHrX6FyVoE1ZfYqiShWhIAPwcbTYAzp0s2/v6FoGikrfgvbxpcQqD0BbfEF0Iy7GNoxC6FIyun5AEMQRVYx/GcPhrRFU7AhhMBXuQ+8vU0/2e8C77FCM3IuFMk91yNbfO6IWb5NbaM3hqZxQ9F0vHZ8u7RdkkqLy/Lis0n8tjM1bTh+OW0ZttScBkcEzMsqDBtRT0wZhgPNoXKVE1I6vqPD9EnnzQONfsa1oBQqeE5sAkBBN2FpzJUQnKMZvrIdAMNCU7wAjKb3xhiKrGIoMotCdI4phRrasRf2+pi9QTNqPhy73w3RBKA1Rqi+ZaNdQA68McMYUqDNiU2Ltbd4K/bCtu55cbqFoqAde5Hk59rfKFJykXb9H0SzA4YdsvWPsWK64E60NFVI0+a0LgnG+XdE3NZfdRhE4EFrzR3T7AIPdeGsmDI284wp0LJKeLo8sY/rlI07L6sQ+YYUHGyugk6hwqyM/IRKLH5bGJ2UidFJmQjwHL6uOoGvq04gRa3D+JQctPhcGJ2UgRafE+ec4tTomKQsXDcAphD9AUVR0E+5EvopV8a1n+9cCVo//b34W4XoCJR67VO9LsdpX3d17n4X/ppjUCTlQD/zelH+dgBhzVlIufwR2L95Q/TwziqGadG9Q9J4pq/IgXeIIAT9HUEXAAiB5/gGqIZPGlD1q3hKA4YyitQRyLjrVfjP7gcAqPNnRK0tJn4XKIqGMqMIgt8DwgdAq/RQJMc22lcxLO4ZdwFePf4NPFwAFChckD0SszJCH9Sy9WZk681h+wtEwOGWWjR6HBidlIk84/lThtZf/P3oVinDudHjwN+PbsWopAyoGBbD9cn47YzLoVMoB8QCcajh2PZPKegCYja+Y9e/+5R7wWiMMC++LxHd6xFCCFwHVsPxzZvgvQ5oiuYh+ZKfgdEnS4mWhBAIbiu8ZdsRaDglViXozAPSv4FADrwJxnf2AFz7V4P32KApnAnDrBtjEpMINleEJRYAYsH5t012cqCgFSpoinous1CNmAJKoQYJ+trE4rUARUFdOCfi9oGmCti3voZgw2ko0vJhnH87pgwbj+cvuB6VjhakqvWSoEZPBAUezx1cj1O2DpvAK/MnDYrrTiIoaa7G6ooSNHudGJucjZtHzUCyOj7LuQaPQwq6nCCgwe0AAYHF50a2zoQqVytO2xq7VQfrC76zB+Dc/R54ZzNUw6fAeMEdcZl39DdBS7gzVLAlvG2o4tj5Dlo/eRyCX0w4DDacgr/mCLK//16H1ntdKSyrf9dRlbDzHaRe/XsoM4sGrd+JJDbXZpmY8Nceh+WTx+GvPgLOUgXn3g9h/eqFmPZlDWltwqdd2ns53RO01sJ99Cv4a471av//JWiVDilX/koq7qd1SUha9mBE2Tkh4IPl498gUHMMhAsgUH9KKsVQMSxGJ2XGFHTPOS1YXV6Cvx3ZguOtdSF/+7zyCFp94Q9hvaXebceJ1jr4Oo2S+oNzTgtePLwJ55wWeLgA9jedxZ8ObQAh4evf3eHulEzj5zlpXZzrVCrUuXY6kQSaymFZ8wQC9afAu1rhObERrZ9GzoQfLJSZ4Tkm/Zl3kkgIIXDu+o8UdNsJ1p+C91RHeZXjmzdDqxL8nrBEtPMZecSbQNxH1oXpNXvLdoD32Ht8YmYMqdBNXC4V1AMAY0yDNsYsx844930ofknb+qLOn47kKx4FNUSl83qCcEFw9nowhvQw4+9EoRo+Cel3vAzBYwetMUStG/RV7pVqIKX+Bf3wlm2Pea1uc80p/KtN2rDGZYPd70GROQNKRjynAIIqZ2vcI8Wu8IKAV45/IwldaFkl7hu/AJNSh4Vt2+J1YXdjJViKxqzMfCTFaBPXmR315RC6JJnVuq2odLSgwJQW83HyDClIVunQ6ndDzbCgKRoCESTPYwDIN/aPqILn+IYwhbZA/SkEm88OGUlD06J7YPnkd9L3kDGmwzj31kHuVYwIfMSZPSLw4NvzKwDJr7gzkUb65yvn5514iBIxHZ4QgI9tpGFa/D2ocifBf64EjCkd2vFL485W5F2tooh6pwcAX+V+eE9vPy9VtbxlO2Hb9HcIHjsohRrGebdFNfnuKxRF9biORFFRJomo2MpWggKPD8sPSqM4LatAi5dHk9eJYW3rvwxFY0Q3ylWEEKw9exTb68tBUxQWZhdh6fCxYQlxO+rLQ9SlPFwArx7fjucvuA6KTg8Wxy11eL5ko2T6vabiMB6auhR5capnUYiWkBdfoh5D0/jxpAvx6vFvUOu2Id+YCi8XkAwZxiRl4YLsvom2RIUIkZujtA8GyswiZNz1GnyV+0ExCqjzpp03uRkUw0I3bglsTeUgAi++3xQFVpcMdUFHLbsyc1SYrnO7Zvy3ATnwJhBRzm1XSJsyZ2zMkmcURUFTNBeaorm97kOwqTyipnKw6QxwngVe3mOHdd2fpEQSEvTBvuVVKHPGQZleMCh9UhfMBKNPDhEcoFW6mL1ZnQFfyFSqWaWF1e8JmQZeWTAZSd2Mdj+pOIw1lSXS63bRiGVd1jxPtIYarguEwOZ3o9ppRYGp4zv537L9cHN+NHud8PMc9Ao1PjxzAD+fcnFM19TO/KxCbKguBd8pSA3TJyG/F8liecYUPDlnJax+D3SsEs6gr811SI8xSZn9lnWvHXNh2MyVIi1/0L5v0aCVGmiLz08tgaRlP4G//hTchz4DETjQCjVUBTOgSOtIRjQtvBstH/8WgscOAGD0yTDOv32wupxw5MCbQDRF82BacBec+z+C4HVAnT8d5ot+MKB9YFNHiGvFXaa8O3+pzxf8VSUh2Zvt+Cr2xnQj5OyN8J3ZBUqpgWbU/Lh8R6NBsUqkXPskHNv/hWDDKbCp+TDOuzXm5JsklRYpaj0sbUpWNEWhwJiK6ekjMDY5G2OSMiNmPndmS+2pCG2nwwJvurZDYKHR40CTxwmBELx2Yjt+OGGRdJ5zzlactjWBb3tgcwZ82FBzMu7Am2tIxgOTLsQnFSVo8rowNjkLNxVN71OQbJ/yTmH0uCC7+8QawgXgObEJwZZKKDKKoB29KO7lFWVWMZIv+wUcu98Db2+EOm9qXKbxRODBO5rA6FNiduhKJEFLNXhnM5TZY0ArRdWv9jr1QM0xsOZsaEYv6rclm1ig1HrxYbX4AhA+CJpVgW+tgbd0E7RjLwIg3q8y7myrSqCobqsS+gsh6Eeg+gj8gfhyFGJBDrwJRj9tJfTTVoIIAih64HPXWGM6DDOugXPvh1KbMmccNKMWDHhf+gqjizzVGYvrk7d8D1rXPiMV4zt2/Qdp1/8hqjtKPCiScpByxSO92peiKNw1Zi5ePLIZ/rYliEJTGu4eOz/mul4uwrQnHyGB6cJhxdhedwYVjhbUu8WRQ5rGgDq3DS8e2Yyn56wERVFgKVoKuu0EeA7VztawY/bExNRhmBhhDbm/IQKPlg9/hUB9+0PJl/Ce3IaUqx+LO/BriubFlA3fFV/lftg2vATeZQGt0sEw91boJ18W93F6AxF4WNc9LyUoUUoNkpb/FJrCWbBt/Bs8R7+StnUf/gKpNzw7aMGXa60Gb28ARTMhuRS+in1S4AUAWqmOeSYp0QTqTsLy6e8heJ1w+hKflCgH3n6iP4JuoKEMzr3vw3f2AIjfIxrOj7sQ+unXhHyBjfO+A3XhbPirj4JNyoG6YMZ5KTKuHDYeyuwxCNSVSm2sOatHuU5CCOxbXwtRwBHcVjj3vI+kZQ/0W39jZVxKNp6ffx1OtNZDr1Sh2JwRV3CYnzUSX1UdD2mL5IhkVmnxu1lX4De7P4Ur6INBoYFJJd5sGzx21LisyDUkY1LaMBy21EhZw0qGRZbOBGenrNKhjq98T6egK+KvKkGg5qjkMtWfCH43Wtc+K2XiCn437Jv/AWX26Jj0jjlbPexbX4O/bVRqnHdrXPrE3pNbQrKCScAL29f/B+baJ0OCLiAmLnlPboFuYvyJm4mA1pgAmglbEqOHkKGFdcNfw5IoE4kceM8TOFs9Wj58FJytXjKjDzaVg7PVQfA6YVp4d8j2ysxRCUtGIAIvJjoQAarcSb3KjuZs9bBtfgX+6sNgzVkwzr01xM84EhRFIeWqx+A+vBaB+pNQpAyHbsqVPT6pk6Avov5spEzJwUKrUGJ6xohe7duu1LS97oyYXJUzClfkRzYiNyrVmJExAvaAJ6SdAgVdmyLQktzR2N90Fo6AHxQAg1LUlh5pSsdxRNbxjYUTrXVYXX4YzV4nxiRn4vqi6b3Klo4Fzt4Qud1W36vAW+e2Y3X5IdS4xPXwqwqmdFsm5q8qCSl/acd3ZnePgZcIAlo+XiVJlgabymH59Emk3/J/EUvaIp6/+mhYm+BzwV95IOL2nLUmpuP2B4zWBN2EpSEVHJRKC/2kywetT50RvE5wlqp+PYcceM8TPMfXgwT9IQ46gt8Fwe+B++hXMF5wZ7+MsjlHEywf/QacTUzUYfQpSLn68ZhvCIB4Y7Gs/p10DM5Sjda1zyDtpj/3uFZLK9UwzLgmrj7TSk24bzC+PVmRLM3gplEzcNOo2KwUL8wpxtba0yGWedPTR0jlSmOTs3F90Qx8VnkEfj6INI0B3x07H8pelp95uQD2NFTi9dKdYNpG8rsaKlDjsuHxWVdEHN0LQT9IwANG1zudZdXwSeGNFNWroOsO+vH0/i+lEX+9x46T1gY8PeeqqO8JrYm8xk9rzT2eL1B7rEMnvB2eg/fkFihiNChhTBF03WkGqoIZoPa8G5YrocwZXJ1w0+LvQZFRBH/lftD6ZOgnXQ42KXtQ+9QOpdKB1iVBcPdPrTggB97zhnbrvrCyBsKDCFxbGUTiA69j+1tSwAQA3mWBfetrSL36sZiPEagvDTkGAEAQ4D25Je5sUd5thXPXu/DXnYAiWbRQi1RfaV58HyxrnpBGIaw5q1u7tW8z2XozfjX9Unx57hisfg8mpuSEJWJdnjcBS4aNhjPoQ6paD4qiIAjxl9DsbqjAG6W7UGlvRpPXhVSNXiqTqna1Rqzpdex4G65Dn4IE/VBkFiFp2U+gSI5vnViZXgjDnJvh3PNfcQqTYWGaf3uv1vT3NZ0Lm2a3+Nw40lIbdZZCmTMOyqzRCNSflNoYQ2psJXxRS9Ri/z3rJl4Cz/ENIQ/m+smXQ5k6AqbF98G26WVp6UU79iKoCwfXhpKiKOjGLRHtAYcYFE3DNP87sH79YliSaqI4bwLvk08+ibVr16KkpARKpRI2m63HfQghWLVqFV599VXYbDbMmzcPf//731FUdP7JjmlGXQD3oc/B6FLAtRWgU6wSlEoPTdH8fhPHCNSeiNB2PMKW0aHoyH2Lt8+EELR89BtpGoizVMFfVYL077wEpsv6kCp3IjLvfh2+s/tBKbVireN5KiCSCPKMKbh/wkIxw7V8Nzzb34IidQQ0oy6QskXVrALqPtSDuoJ+vH5iB4IC31alTNDidcKoVEs1uEKXG5mndAucez+QXgcbytD6+R+Q8Z2/xn1+4+wboRu/FEFLFRRp+b2WeQx2yg0AAMHvheCxwnZqGwTjyoh2fBRFIeXqx+A6uEZcFkkeBv20q2LKpFfmjAOblAPOWttxPFYB7ZjFMfeZ0ZqQfvPzcJ/YAN7ZDPXwKVAXiDMiuvFLoS6YhUD9SbDm7Lhmq/5X0Y69CGxqvugAFQCAdQk9/nlzJwoEArjuuuswZ84cvP766zHt8+yzz+LFF1/EW2+9hfz8fPzmN7/BsmXLcOLECajVicvo4z12cNZaKFJHJKRkJRKq7DEwL/2xKI4hcBACXijSRkA37iKYFt3bL+cEADZ5GHiXpUtbfD9cReYoKNLyEWzuEHOgWAU0nTIYYyHQJsXZGcHvhufUVhimXRW2Pa0xxHXz6g2EC8B9dB0CdSfBpgyHftJlvfZJHQisX/4pJAnHfWQdUq97OiEPJWdsTdJ0dpJai2avCwCBM+CDUalGltaEwi6jXV/57rDjcJYqBK21vbKkZPTJYQ9h8TItfQT+W3YAHOHBO1sQbK6EmgjIq9mDxpMbkHbDsxEN6GmlBsbZN8Z9PjFoPw7H9jelhEjjnJvinnqlNYaIvwNADMyaQR7lnm8o0wvEGTmHI+HHPm8C72OPiVObb775ZkzbE0Lwwgsv4Ne//jVWrFgBAPjXv/6FjIwMfPLJJ7jxxvh/IJFw7vsQjl3/AXgOlEIF04K7+y1bUDduiZhuzwdBsUoQQvrdvs845xa01J/sMKNmWBhjXHdqp912zLHj7bbkqmwYZt8Y941V4CIbYkdKahko2rW52/Ge3Iq0m58f1DrJaAQaz4QEXUCUQ3Qf3wBt8QV9fmhM7/TAoWWVyDemoN5jh06hwpTUXNxcPDPs+0qpIzyk0DRoVWwmE/1BslqHByZdiP+c2oPKc4eQLQRwld8KDUTHHNe+j2Be8v2EnpM1piH50ocSekyZoct5E3jjpbKyEg0NDViypGMNwWQyYdasWdi1a1fUwOv3++H3d9zgHd087QSayuHY/i/pNQn6Ydv0d6jypvablyVFUUDb1OBAeOYqs0cj/TsvwXtyK4jAQ1u8oFdJEIw+uc+lPOrhk0FrjBC8nT4TmoamaHBq/fy1J8Jk7ThrLbynv4FufHziEwNB56lMACA8h2BzJSwf/hr2lFxoixfAeMGd8JXvAu+yQDE8PoekbL0ZszMKsLuxAgBgUmlQnJSJVTMvg6btOxtsPgvf2f1gdMlQF82DftKl8JZuDpFb1Y5ePOhuQBNSc/CEZjFq976JroV4wdbYMl4FrxO+swdAKTX/80sd5wtEEMA7m8HokvpVsONb+01oaBCzBDMyQqeEMjIypL9F4umnn5ZG1z3hP1cS3kgI/FWHwQ7BG29vYY3pMMy8brC7IapGrVyF1s+egrd8NyiahW7KFd0KavAuC3wV+0BrDFDnz+yzpi3naAKtNoJWqsG7WqKcM3L7YKPMHhOiasZZzkHw2sRpU4GH+9h6OA9+CqZt5Crs+i/cqfOhGx97Asy94+djctownLQ2IkNrwMLsUVLQdZWshX3zP6Rt2b0fIPWGZ5B6/R/gOrgGgtsKdf506CYPjbISWmuGypwVlhiozB7T477+6iOhyX1J2Ui99qk+T4NztgbYt70uqlAl5cAw91aoR0zu0zG7wrss8JzcCggcNKMuSIjozPmA71wJbOtfBO9sEQVQ5tzcb7rwg2oL+PDDD4OiqG7/nTx5sucDJZBHHnkEdrtd+lddXR11WybKqDbS+o9MYqAUKgg+BxTphVCk5SNQcwytn/8h4raeU9+g7uVbYV3/F7R+/gya3vkx+Dbt13gJNJ5B41vfR+Pr30XDK7fBses/UOVOAiKMYuIRPhhIWGM6TAvuEsULAPAeGxhjOug2Iw7e0QiupTJkH8+xryEEvDGfg6ZozM4swB1j5uCSEeMlNS4h4INjx79CtuWstXAf+gzKjJFIvuRnSL32CeinrRwyI0OKomBe8gNQSo1ozO51gtaYoJ+yssd9bZteDlkC4ax1cO79b5/6I5blrYKvfA8EvxuBhtOwrHkcwdbE1eS2f88d37wJx4530PivH8B3NnIt8LcJwe9B6+dPg3e2tL12w77lVQQaTvfL+QY18P7sZz9DaWlpt/8KCnonTp6ZKda1NTaGCgA0NjZKf4uESqWC0WgM+RcNzcg5UHQph1HlToByWGQxA5m+4zn2NQgXDHEJ8leVhN18vJX70fjaXQhUlcBfVQLOVg/OWgvX/o/iPicRBLR+/jS4tnOQoB/O3e8hUFeKpGUPglaL65EUq4Rx/u1Dul5YP3UFMu9+HclXPApN0TwoUjrKY4SgL+xBgvDBsOS63sA7GkEiBPChbuCuyp2I1OufBcUoAAoQvHa0vP8LcLbos2aC3yN9VzoTqO/bTTxQeyy8LK+t3jdROHf9J/Rz4jk4vnkrYccfqvhrjkb8fvrK9/TL+Qb10TItLQ1pabH7dMZDfn4+MjMzsXHjRkyePBmAuF67Z88e3H///Qk5B8WwSL3uaXiOfY1gyzkoMkZCN27JgKy9/q8iBHtOsBICPrR++iQEvwtE4EE4LwINp0GIIKl+xUOwqRy8ozms3Ve+B0nLHoCmcDaCrdVgTZn9ltWeSBh9MjQjZ4OfczPs2/4ptdNqPSg6VMCCVunAGqM/qMYKa84CrTGEyfAps2J7SAlaquA99U1bmc2FMTt+JQLXnvcAgQPdlgjG2eph3/Y6Uq78VcTtKaUGjDE9pKYWABSpeX3sSWJsF7sjGEGxKdY17fMZOor9Kq01oT8MIYfGnE4MVFVVobW1FVVVVeB5HiUlJQCAkSNHQq8XRxyjR4/G008/jauuugoUReHBBx/EE088gaKiIqmcKDs7GytXrkxYv2ilBvqpK6TXQUs1BJ8TysxRQ2bKbDAINJ6Br3K/eJMftSBqlq+/5hjcR78C+CA0oxdBM3J2t8fVFl8Qpj3LJuVA0UmWL1B7DBA4ABQEn1jSArQF0F6o0dBak7Q2SogAwW0F4YOicAnEkW5nWUCr3wNHwItcfRLoOEQQBhr9tJWgtWZ4SjeDYlhox14E95F18FeVABCvSz/zKlBsx/eY8Bx8FXvAu21QF8yIOYmQYpUwLb4P1q9ekIQclFnF0E3q2UTAW7YTrV88C7QJejj3fYTU656KSQM5Efhrj4W31YRLNLZDURRMC+9G69pnJT1iWpcEw6zr+9SPaPW+mgTafSqzx8Db5YFBmT02Yccfqqiyx0CZMy5Eo4DRJ0M7ejFcwf9hd6Lf/va3eOutjimPKVPEjMvNmzdj0aJFAIBTp07Bbu9Yw/vFL34Bt9uNe++9FzabDfPnz8e6desSWsMr+N0gPAdaqUHr2mfgqxC9URl9MpKveHRITzv2F66Da2Df2lFr7dr3EVJveDYsU9VXsQ+WT5+Qkn28ZTthWnwv9N0k16hyJ8J80ffh3PMeeFcrlMPGi+twnWYZaF0yAAqUQo32oAuIN3/e0QjCc3E9FLHGdGiK5sFTugWB+lJxdE0xcB/7Gqph46GbsAwAIBABb5Tuwva6MyAgSFHr8IMJi8KUmtoJttYg2FgGRWp+RPWtgUA7ZlGIupKmaC4C9afErObscWg8eUb6m+B1ovmDhyUpTvvWV5G09IGYa6W1xQugyhkPf9Uh0LoUqIZPiml2yLHjX1LQBUQDAOfu96KOOBMNa85GoEtuANtDKZxm5Bxk3P53eMt3gVZqE2JLSdE0Uq5+DI5t/4S/5ihYcw6Mc2/pVb1zNIzzvoNgw2lpSpvWmmFedE/Cjj+USVm5Cu5Da+CvPQE2OQf6qVeJNfnBxNfxUoT0kybWtwSHwwGTyQS73R6y3ku4IGwbX4Lr4BpwtjoQgYBWacEmD5PWH9nkYci4/W+D1fVBQQh40fDK7WG1tYZZ18Mw+yb4KvaCa62GMms0HLveDVPBYvQpyLznjR7PQwgRa6ejZCm3rH4Mzp3vgPc6AD4IsEqoh08GxSqR+b13wLRNLQk+FzylmyF4rFDnz4Qye3Tk8/Ecmt/7OdxHvgTFKMGYMkErNaBVOmTe+xYoVonNNafw1sldIfulqvX447xrwgKMfftbcO3rWG/WTbwE5osSswSSKARBwKFDhwCID7quXf8OUZkCRNGGzO++2eds8WgQQlD3woqwdsaUBdMFt4NwfqjzZ0jr7PEc13VgNdxHvmybbVkI49zbIj6Q+auPwPLJYx16xwyLlCt/DXXe1IjHdh/fAG/pZoBioJu4rFcWg4MJEXj4qw6D8EGoR0wZFF/hoUS0GNAXzpsR71DDufd9OA98gmCDaEUm+FzgKQoQeMl0nmutAe9sGdD1qMGGdzRFFLQItpyD5eNVIXWvvMcGpouIPO+xxXQesZ45+s0+5YpHIPiccB9eC4pVgzVlgGKVUGQWSUGXd7Wi+b2fS5mMzr0fwrjgzojqPxTDgtGnQJlZHNIu+N3gnM1QJOXgmKUubL8Wnwv1HgeydR2j/WDz2ZCgCwDuI1+CTS8AozVDlT02LvWrgRBSAYBAU0VYm+B1gnc295vAPUVRYdaQhPMjUNuRzU4pNUhZ8RuohsUu/O8u+RyOb96UXrv2rwbvc8K84O6wkakqdyLSbn1RDKaEQDN6UVTZReeB1XBs63hw9FeVIGn5T/pdQS2RUDQT9aFCJjHIgbeX+Mr3hCZP0DQIFwDnbAGbMlw0eVZpoy7af5to8brwWeUR0ULNkIyZGhO03q5lO1SY2ITgc4JilaCVHVZx6oKZCekTxSqRdt1TYHRJUmYia85C0sU/lrZxHfpUCrrtOHe9C92E5aCVmrBjKjKKpKWEdmitCaxRLB+LZHnHULSkU9xOoCHUN1ZM+qqA5aNfgzVlgWKVMC/9MbTFC7q9Rm/5HtHEorUGyuwxMC2+L27TiXhQphfA36W0hNYYwRj6J0GyHfOF34Plk8fAu1oBiLMqnX9XJOCF9cs/QVN8AUAEaMZc2OP74DmxsWN/nkOwpRK+swfhPfo1mKRhMMy4OiQ3QZGUA8XcW3vsq/vgp2FtroOfnleBV6b/kQNvL6E1BoB02KxRrFqc+qTQ5hTEwDDjum/9NI0nGMAT+76Arc3vtdzRjMMj5uFHZ9aDbpuaU+aMjViEzyblQJGUI3qpEiKu1174vYT1jWKVSLnyV+BsDRACbijSCkJGhl2VnIA2L19nC+gIIxr9lCvgq9iLYGPbuifDwrz4Pml6csnwMdjZUA5PJxWmi3JHQ9/me9tO5xIeABBcFggeKxijGMAIF4Bt49+gzp8ZNSktaK0VR3xtyTuBulJYVv8OmXe/1m/fOf3UlfCW7+nQy6ZpmBZ+t9+mmdtRpOUj485X4a85AopRwrb5FXCWjjIk3uuAr3I/OEczKJqGq+RzJF/+SMzaxFxrFQSPDYQI8J0rAanYB1/ZDqhGTEHq9U/HtYYqtBmY9NQmk3hOttZjdUUJFDSDG4tmYJihdxaTA4EceHuJfupKcW2wTb6QommwGaOgSMuDbsJSqAvnJFxRZiDgBQFBgY/ZpWZv01kp6LbTpFCjeeUTGO1pBqNLhjJnHPxnD8B1YLW0jeB3izdwngObOgLG2TfFZqHWC1hz5HIYZfaYsDo9WpcUVamHVumQduNz8FcdguCxQTV8SogSUWrQix/byrG18RycGjNmTFiCBUXhnrnK7NHQFC+QdJMFnwu0xgymk6cr8XsQbKmEKopKkvfUN1LQbUfw2OCrKoEmQbMGXaE1BqTf8gJ8FXvBe2xQ50/vN2nUrlCsQhImUaTlgbOcAxF48I5GBFvOQiyy5QBaCQgCnDv/3W3g1Y69CPa2qXMp010QIPhsIIRHsK0UzbHtDaSs+HXM/dQUzQsZTQOAZtT5tcZ7PvLRmYN46sA68G2/iQ/OHMQLF1yHOVkDk/keL3LgjZOd9eXYWH0SPj6IacsfwYx9/wFfcwy0xigmyCz5IRidebC72Ss+P3sUX549BjfnR7E5E3ePnYd0bfhaI+9sgffMLlBKDdyqyBJ4XlYZMlWqypsGzeiFkuZzoOG0tO7K2+ph2/AXsVzCGH3aMsBz2F5fjmpnK/KNqZiTVQAF3VVJN3Z0ky6D/+xBaQqcUqiRtOSH3WY8UzQdUZmKEALL6t/BYK3F5QDgaQK+qQA3fCwUKcPDtk+65GfQjl2MQEMZgs2V8J0JTcoCzYCNZG7e3g8m8oMRxfTvDAvFsNAUze3Xc/SEYfaN8J07hED5HrFWO+AFpdQgUF8KZc54UDQDf/0ptK59FiTog2bUfGjHXhhyDN3ky0EEDu7DX4JW6UCp9Ag2ngFpn8WiKAgeK9zHN4QEXs7RDPuWV+A/dwiMKQPG2TdBM6pDK9y06B4Qzg/vmZ0ARUM7ZjGMs2/q9/eEcAF4Tm4FZzkHZdZoqEfOBUUP3VK2RBIUePz92DYp6AKAnw/ihcOb5MD7bWB3QwVeOf6N9LoWgG/JT3BT0QxA4HtVt0u4ILzluyG4W6HKn57Q0oB42H1qF949tB4AAaNPxSlbA/56ZDMen32ltI0Q9MN18BM4drwtZW6P0KcCw2YDio4bvopRYFJqqJE5RVFIvuRnCEy5Eu4jX4DwQdCKjmlUwgXhK9sB/bSVEfvHCwKeOfg1yu3iuvrm2lPY3VCBh6YujZpY1FPSEa1QIfXaJ8TyGbcVqtwJvS75CNSdCJ+65jl4SjfDNP/2sO0pioI6bxrUedPEMh1LVcj++mkrweiiT5VpxyyCc98HIP6O2QY2KQeq3Im96v/5hCIpB0lLf4zmdx4ArUuG4HVA8NpAuAB4lwUUqwLvbIb39HYAgK9yP3hXCwwzO+poKYqCYdpVMEy7qq387TUEG8s6/s62LQ/wQamNEALLmsfBtaltcZZqtH7xR6QZ0qDMEpPuaJUWyZf9AkLAB1AU6C7LDP0B4Tm0fPgrBOrbcwc+haZoLpIvf7jfzz0UsPu9cERQnap3904ediCQA28cbK4Jl3zbWluGG0ZOB9OLoCvWRT7SsWa27Z8wX3h/v9kKRsN3rgSbN78GnhYDIe9ogiK9EFUQv7xZOhN8FftgXfdneM/sBuF8YAxpUKTmIcnVgtuCdqzV56HV70a6xojbR88OW9dsR5lZBN41C57jG8P/GGUUBwAlLdVS0G3nhLUepdZ6jE0Ozaj1lG6BY+c74B1NUOVOgOnC+6FIDn0QCOlTVnHUv8VO5AAfS7YxrTEg7aY/wXNyi9jn4ZN7XKZg9ClIvfZJOHe/B85SBWX2WBjn3hLTKId3WWDb/Ar8Zw+A0afAMPvG8y75hwT9UlIX0SeLymQBD8BzIIQPm2VwHfgEipzxcB9YDd7dCnXeNBhmXCuKhExdAUpjQqChDJytHhSrEGcUGDYk2S/YXCEF3Y6OEHhKN4d9hxJlC0kIQaDmGHhnE5TDJoRN7XP2JrR89CjcR74CxSjAmrPA6FPgLduJQP2pBH23hza65nIk8UE08KGDn3zj0K0mkQNvHATblIo6wwk8CHpXCu06/HmosTshsG9/E5rRiwbUz9W5579Q8XyIcjdnrQWrT4GaVYBwAVi/eh681wHCiaVCvLNZzGjVJWOCowaLr/gZ3MEA9ApVj8FGnTctTFKPVutDpuy60tRFarCdRo8TYzvNdgcaTsP61fOSKIe/+igsnzyOjDte7tepN2X2GLApuZK4BACAYaEZHVtAo1Va6CddGt850wt7JSJh+ewpBBvE0R1nq4d13fNg9Cnn1Wi5vSabcAFQjAKqnHEQ/G4kX/EI3Ic+R7A5tPSJczSj5cNHQbUJcQQbysBZqqRRoW7MIqTd8Axsm/4BwWMFKAqMPhWmC+6QjkFFUyHrw3JHdxAuCMuax+GvOtzeAZgv/B50Ey8BIQSOb95A6xfPgbPXA1wQlEorXjejAKMxgrPVfesDr3X9X+A5th73sDr8QZOJAKMArTbAqNTgl1OXDXb3oiIH3jiYnVmACkdo+cmM9DywvfzhBZsqw9qI3wPe0Qg6dUSEPfoH3tmEuZwTB1itWIsMcc1oevoIJKm08NeegOBziSVSCrVUpyt4HWB0yVBkFIGmaBhifFigWAVSr30Sjp3vIFB/CorUETDOuQWAWO/LJueGBcmxyeEJTxQojOnS7j21TQq60vXZGxBoOBU1UakzR1pqsKnmFAI8h9mZBViQUxTbNVEUUq96DHZJVSgbxjk3R633BMQpQlAUqDi+P4QLwn30S/irj4FNyoJ+ypXd2iJ2JWiploJuZzylW86rwEtrDEi69CHYNv4NgtsKSqVF0uL7oBu3BLyzOSzw0gp1iPoVAHjP7Aqps9dPXQHGlAnvya2gFGroJiwLCVyKtHwoM0eFOtYwLHTjYrdNjAfPiY0dQRcQH8y3vg7NqAvgq9gD5973IXisoGgWAvEBfg8ojUG8Jq0Jypxx/dKvoUKgqQKeY+tBBAHTakvwit+DzeZ8aFJzcctVv4IpZejaGcqBNw4uzh0DV9AvJVfNysjHbaNjK1mIhDJzJHzlu0PaaLV+wP0vVcMnI/fYenzP14QtCiMcFINJKdm4fpw4AmUMaZJWsSJlBAKNZQARQLFKMMZ0GGffGPc5WVMGki/5GQBxOs2+9VW4D38JCDwYYzqSL30o5KY3wpCCqwqm4NPKw+CJAIaicf3IacjUdqmTjpZ0FEOJzcHmKvzl8GZpBuOEtR42vwdXFkyK6ZoYQyqSL/tFj9sJAR/sm/4Oz+lvQNEMtOOWwLTg7phyBFrX/iGklth7civSbn1REgXpiWhBnqLPv1uBpnAW1HnTwDkawehTpfVUw4zrwNub4Dm5BRB4qHInAIwK/rP7Qw9ACISAN8ToXlM4S8qG9teVwrn3A7BJOVAXzATFsEhe8Rs4dvxLTK4ypsMw60ZJMCfRBBrDH5AIF0Cw5Sy8Zzqy8SmGBcWqQDg/iCCAommYFtwFWqWDbcPf4KvcC1prhn76NdAWX9CnPvFuK3wVe0GrdOJ7Mojlku0WlryjAYLHBhOAlZZTYAUXfBv+CtN3/jpofeuJ8+/XNohQFIWrC6fg6sIpCVEL0k26HN6yXR2OOTQN06J7BvzLbJz3HXCWKhTUn0KBvwVs6gikLP8B2LZAwBrToJuwHO4jX4LWGKWRkXnpg9CNXdzn/npPboH70OfSa97RhNa1zyDjrtdCRr4rCiZhQU4R6lw25BqSw4QpAEA7bgnchz4D6VRLq8wqjklQ/6tzJ8KWDb6qPoEr8icmVBnKvvU1eEo3AxBHve6StaBVehjn3tLtfsHms2ECHryrFZ4TGyOqbUWCTcqGKndiqJgJTUMbh9n9UIJi2LCERIphkbTsAZgWfReEC4LRmeE9szss8CpS86LOSNi3/ROuA59Ir5XZY5B6zRNgtCYkXfyjkG2D1lr4K/eD1pigKZqbsN9vpGx4MCzY5FzQWhMoRglaY4LgtYNWakBYFRTphUi97mnoxiyE5ZPH4asUr5l3tcL65XNgNEaohsf2INkV39kDaP3saem3xZgykXb903HNuCQSRYaog99e0tkOpdKDs1SBd7WGlPsNJeTA20sScSOmVVqk3fQn+M8eAO+yQJU3rdtymv6C0ZqQduMfEWw+KyamdBGaAADThd+DavhE+M6VgDWmQTt+WZjpQW/xVYYbbfPOFgRbzoYpECWptBEVotpRJOUg9Zon4Nz7PjhbPVTDJ8I4p2fFIQBwc+GWg95gAAIhYBIYeNvrd0PaTn/TY+Dl3ZF9cYU4/XKTL38Yju3/gu/cQTG5auZ130ozD1qlA9py/DQjZ8M4/3a4DnwMweuEMmcckpb+OOJ+nK0BroNrQtoCdaXwnt4G7diLQto9JzbC+vWL0vKGc897SL3+mYT8NrTjl8FTuhnBTlKdxtk3gtGaoJ90Gbylm6FILwTXWg3eY4MqswAp1zwObfEC8K5WKehKEAL3sfW9CryEENg2/SPkgZa3N8C55/1B0xhXpORCP+MaWL94DoCYA8LoU8BojKJqYJz63QOJHHgTSLvfRDxBmaJpqAvCRRYGg+4cciiKgqZoXr8Ivkd8KqXpMB3nWFFmj0bKyt/Gvd+M9DzUuEJtA6emDwfTTVKWv/Y4/OdKwBjToRl1QVhSHOECEHyukGvsvE7eua0nlNnjQKm0ISVEAKDOj+/7Q6v1MC/5flz7DCaEEHiOb4C3bCdolQ66yZfFtF7fGc5aB3XhLOinrQThgt0mLwZbq8LyBAAg2Byak0G4gOhn3GlbzloH18FPIpaQxQutVCPtxufgPbNTynZXZowEIP5WU294Fu5Dn4J3W6EumAHdxEs7ZohIFBfZaO09QHwu8PaGsHZ/XaloFONohCp3kjj9PIB+5Kb5t0OZOQot7z8C0IxUDtiesT5UkQNvAhACPtg3vwzPqW2gaBa6icthnH/H/0wBe1/RTbwUnuMb2rxzRbTjLu7TNJH7yJfwnNgM0Ax0E5dDO3phj/tcljceFp8LO+rLIRCC8SnZ+M7oOVG3d+x8B84970uvXQc/QdoNz0o/fufeD+Dc9yFIwAtFah7Myx6EMr0A+imXw7HjnZBjxeJLSyvVSL70F7CtfxG8q1UshZlxba+nDvuK4HW23eyiz0AkAseOf4WYSnjLdiD12iegiiF5SPA60br2D/BXi/65yqxiJF/+CNBN4FWmF4qZyl2UwRRdDDJ4l0V8D7oQbD7bY79ihWLYqJrdyvQCKJc9GPFvjCEVquGTJW/ldrTjLoq4fY/9UOvBmDLA2xulNsJz8J87BK7lLADAfehz6CZdmlDZ11jQjJyDrO+/B/exryB4ndAUzYkocgO0J2R9DSLw0I5eGJexRiKRbQF7IBZLKOvXL8JzfENIm3HerSEF+zLdw9ka4D78OXinBaq8qdCOW9LrJ2fn3g/g2PF2SJt56Y9jzj71cUHwRICuG/ED3m1Dw2t3ht2c292NfBX7YFnz+5C/McZ0ZNz5CkBRcB/5UnS7oRnoJiyLq46W8Bw4a62YjUsx8Bz7CoGGMijS8qGbeEmfAmHQUg3bxpfgObERgiDgjHI0jHNuwozFl4Jue5DkPXZY1/0Z/nOHAIaFdvQimC+8v180mwkXQP3Lt4bNEKhHzkHKFY/0uL91w0vwHP0qpE1TNA/Jl/+y2/2c+z+GY/tb0mhWXTgLyZc/HJKcRngODa/dCaGLV69h1g09LhsMBILXCfvW18RkKK0ZhpnXhk2Vx4O3Yq+oD86LZZVCwAOKZkNHlhSFjLteG5Qls57wnSuB5ZPHOn6zFIWkZQ/2+NuTbQGHKJHW7Dynvulz4PWW7YRz34eiqlXedJjm3x6XXdz5BGvOhGnhdxNyLPfhteFtJZ/HHHhj0anmbHVhQReAVMfr7ZKtDohJY8Gmcigzi6CfdGncdbvtUAwLReoIEELQ8t9fIlB/UjznqW3wnv4GaTc+12sVNcvHv4X39HYIfhcEQuBpERW1+OkzQZtE8QbbhpfEoAuI6lzHN4AxpMI45+ZeXU/3fQpEtJnsmlATja5uSoCYJNQThulXQzNyDvy1x0VFsOwxCDafhWPXvxFsOQdl5igY594K8+L70Lruz1IwUqTmQT813D8YAIKWKtg2vYxA2zGN874DzcjZMV1Hb+B9DqhHzYNxwV0JWXPWFMxE5p2vwFu+B7RKC1/1EXi7CuEQAt7eMCQDr3Pv+6G/WULg3P3eoIjHyIE3AbQX8ndt6wv+mmNoXfuM9MTtOfY1eEcjUq/5fQ97yghd1kAB0ToukShS8yKu1Srb1h6jSU8mMuHDf+6gFHTbCTZVwFe+u1sxkqjHqypB0FIFwd8x5Q9CwDsa4TmxEaY5N4EIAnyVe8P29Z3Z3S+Bl1broRw2HoGaYyHtsToPMfqUMOvHWLNwWXOWVNrHex1o+fBRaTnEa29AoL4UGbe/jMy7XoPv7AHQWhPUedMilmwRnoNl9e+kvnCtNWhd+wzSb/2/yNnLfYAQAvvGv8N9dJ3YwLAwL7o3IYp4jCEV+sltyyI0ExZ4KaUGiozYat8HmhAbV4h2nP66E3DufR/q/Bn9VhYWCXkRMgHoIoxcejuaacdzfGNYgoe/6jC4TmssgwHvdaD1iz+i7i/XoOHVO8KyP4cCmgi1ir0JRN1Bq7TiWlankaW6YIb09KybeElYwpS6cHbUGm3OWoeW1Y+h7q/XoenfD4ZnpEaAdzRHbu8SaGKGpkHaRgSE5yEEvBCCfhCeg9DuQEVRER8q+nMmJmnZg1BmjRZfMCx0E5ZDN+WKmPY1zLxerEFvh6JgmHVDXOcnggD71tcQaCiD4OtY0+UdzfCd3Q9Gnwzd+IuhKZgZtU7aX3Ms/HMR+IizZX3FX7m/I+gCAM/BtuWVDhemBKEZdUGI+QSlUCPp4h8NqOpePKhHTJX+nwgcArXHwTtb4NjxDpreeQCuQ58NWF/kEW8CMMy5BbRKD8/JLaAYJXSTLun79EWCsxIThXXd89L0He9qhX3r66B1ST2atg8kpoX3iFOgp78BRdHQjrsIhn5wiNGOvRCqEVMRqD0GxpgeUpLDmrOQduMf4TqwGpyjCeoRU6JOQRKBR8vqVVLiSrCpApbPnkL6Lf/XrfKVKm+qJGwS1t4LVLmToEwvRKC+FILfCUIICC+KJtAaMwAxu10/dUVochhFQRfl2hIBa0xH2o3PigllClVcRhbqghlIu/4ZuI9vAARe/MxyJ8S8vzhSXQX3sfXgrDUAAMaYIY1SSScThe6INvUfzWWqL/hrj4c38hwC9SehGRk9WTBeqLY1Uv3M68DbG6HMKu61ychAYJx3G4LWGlH72t4IUHTIbINjx9vQjrt4QB4c5MCbACiKgn7ayqjOOr1BO+4iSWShHWXOuAFXteoM77FHXDPzlm5JaOAlhMBzdB285XvAaEzQTb4cyszYp69opRpJy38C85IfipKMvVjvjBVGZ446mlakjkBSlKzTzgRqj4dkiwIAeA7ek1ugmHdbxH0IIWCN6TBf9H3Yt74OEvSBUqhgnHdbr6cuKYZF6nVPwVdVgmCgDJQggFGrwZqz4CvfA9PMawCIo0hGnwLPyW2gWCV0ky6FesSUXp0zHnqd5U4zYHRm0BoTFKl5ce3qLdsBf/VRMLpk0T2KCOAdjWCM6WANqTGXcilzxoFNGR6izU4p1NDE8YDuObEJjt3vgXe1QD1iKswXfk+Su+wMG8XhLFp7X1Ek5cTkqka4AHi3FYwhbVAqPmiNAWnXPYWgpRrW9S8iKLk5tfUv6BswuV458A5RVLkTkbT8p3Du+wCCqxWqvGkwLbpnUPtE0QxA02Gat925CvUG+5ZX4C7pSJDylm1H6vXPSDWMsdIfWbb9QhTxfW/5XvHhixBox14Iw5yb4a86DPu2f4KzVEGRWQTzonuRee9b4FprwCZl93nEwZoyoEwvhCIpB3wwCEW9CxRFgXeEPhhox17UpwzZgcKx89+wf/MGaKWY6e3a/xHSbnwuYsCKRLCtVIZilVBkjATXWgMS8IA1piFl5SrQSk1Mx6EoCqlXPw7HznfgrzkmJlfNuSnMbSga/uqjsH71gvTaV7EXFlcL0m95IWxbTfECuA+vDRHe0I69MOFryfHgOvQZnLv+A8HvBmNMg3nJj3p04OoJwetEoPE0WHN2XAMSRUouNIWzwgIvrTEM2MBGDrxDGO2YRdCOWTTY3ZCg1Xoos8fCfeQLgIhJKrRKB92ExLmACH433F3KPwgXhLvk86g1i+c7ypxxYJOHgWutkdp4ZzOEoE/yLHbu/QC81wFv6SYQTpzeDDaUwfLJY8i4+/W4ZgR6QjV8Mnzlu0UN4Lb10cGqFe4thAvA8vnTsG95VfTKVqjFpB9XK5wHPoZ50b1R9w02n4W/qgSMKSMkUYjRmMDkmACKQtrNz8ccNKX99clR1bJ6wnNyS3g/myoQbDkHRZcRGq1QIfX6Z+At3QzOWgNlznioY0xG6w8C9afEz6EN3tGM1rV/QOZ334j5waUrnhMbYdv4dympVTthGcwXfT/mEkTdpMvhK9/bkZzIsDAt/t6AiW7IgfdbhL+uFLy9EarcCf2in+o7dwj+muMAKAgeG4jfjaRLfw51L9cUIyH4PVJpRmf4LrWS3ybaR0P2b94U14vN2aIDTpdMeee+D8GoQ5OYBJ8Lvsp9CZ3qNy26B5y9HoGmswAANiUXxi5KTP66UgSqj4AxZ0Ezcm6/Tuf3Buf+j+A7vVMqHyFBH7jmCiizx4JrrY2+374P4dj+L+m1InMU1IWz4CtvMyWgKBjn3Bx30I0GIQSC2ypqL3fjUhXVxCLK+04rVP3m6x1srkSw5SwUmaNimmL2VewJayN+D/zVR2LOTu+M4HWGBF0A8Bz9Cur86TEfj1aqkXrDMwhUHwHvboVq+BQwOnPcfektQ+vX8j8K52iC59h6CAE3NCPnxq2mQngOlk+fgP/sQbGBZmC+6PvQjb84of107n4PFAgUyblAspj0E6iJkMjRB1hjGhTphR3GEW305gd6PsEYUpF86c+l141v3AfOVh+yTVRnITa60EdvYI1pSL/1RfjrTyPp+EkokoeB0SVJf3fs+Becez+UXrsyi5B67VOSO9BQwH/2IMAqQTFKEF68QQt+N4jASyVfXeE9djh2/SekLdhwGtrF98Ew/RoEW6uhzBkbU7CJBV/FPtg2vwze0QxGnwzjgruiPkBpxy2B+9jXIXWoqtwJCetLrIQIklAUDDOvg3Fu91rotCZyDTETpb0nAvUnw8o3AcQdyCmKCpvJITwHX+V+8O5WqPOnJ+wBqytyOdEgE7RUoemdH8O5579wH/ocLR88ClfJ5z3v2AlP6aaOoAsAAg/7llcg+N0J7WvXdT4A4CLot/aV5EsfgqJtPZdiFdBNuQLaBE5nnw9oI4h9GGbdEFa2wxjTo8rj9QWKoqDMLIIieVhIO+9sgXP/xyFtwYYyeEs3JbwPfYExpIGiKLCpeR1r6DQLVc7YqNnlnOVcxNmWYHMllNmjoRt/ccICHe+2oXXtM1JJGO9qhXXdn8MettpRZhYhZeVvoRw2HowpA7pJl4jSlwOIv/pIqAoYIXDueR/BTkskhOfgObUNjt3vwV9XCgDQjrkwLDFOmTMOyuzRveoHk5QdsZ01R26PFcHvRvN/forWz56CfdPLaHzjXnhO9M/3Wh7xDjKufR+Fid47d78L3YTlMU/fBbokCQAACfoRbK5MqBapavjksC+iavjkhB2/HTYpG+k3/xm8ywJKoel3LeChiH7GtQAI3Ee/BkCgHXsRDLNuhG78Ujh3vys6N2UWwzD3lgGd5g22Vocn16EjCSkeeLcNjh2iSxJ4Dsph46GffHlCvrP6aVfBV7EXjNYEevhkCF4H9DOvQ3I3a6yK1HxQrEJaQ28n3qS+WPCd3R8+ahME+Mr3RK2OUI+YMiDZ49HoKtbSuV2RPAxC0I+WDx9FsEH0EXbu+g/006+G6YI7kHrDH+E6uBpcay1UOWOhm9L78jNFUg604y+G59h6qY1NGR5SU9wbXIc+C/0et9Vva2+Y2KfjRkIOvAnCX3MMwcYzUKQXxlUnGGnEKHidEAKemM3No/p2Jg0Lb+8Dxvm3I2ipQrDxDACATR3Rr5nWg+XzORSgKAqGmdeHyY4qUkcg+fKHB6lXgCKtQFxX7DIylAQu4sDyye8QaCgTpw4DHnhKN8FzYhOMc2/ps7uPMrMIaTc/D/fRL0H8HmiK5vXoAkZrDDDOvx32ra9LtdHKYeNDbuiC1wnf2f2gFGqo82f0+qGHVkcWHBnKkrBsSuQyG0Vbu7d0sxR023EdWA3dpMvAGtO6TWiLF/OSH0KdPx3+mqNgzTnQjr2w14la7bTf1zoj+FzguiheJQI58CYA67rnQ2puNaMXIvmSn8W0ryp3AgJtUzLtKFLzYg66gOjk4zm+MeRpzTDj2oQnCzC6JKTf/GcEmioAIkCRXjigFmAygw+jNcG04C4xS7XdQKBgBjSjwtXCuiPQcBrBpgrwzhaQdlUsQsA7W+A6sBr6SZfFXPITDUVKbtw3e/2UK6HOmw5/9REwpkyohk+SvuP+6iOwrHlCkgllzVlIvfbJXvVTnTcNbEqupO0NtC0bjEy87WaiUOfPgGrElA6dbgDaMYuljPqg5Vz4ToSAa61OuHYzRVHQjJyTUEEQRXoBfBWhcqi0Stcv67xy4O0j/toTYUIX3pNb4Z+wLKYpM/30axGoOwl/9REAYsmB+eIfxdUHWqVF2k3PwVu2A5y9EeoRU6DMKu55x17S1ZxeZujBuyywb30d/nOHwBjSYJhzU8JuUvrJl0NdMBOBmqNgzNlxe+MC6JCmDHbV0CaAIHS4L/UjhBC4Dn4Cz5F1IAIH7ehFMMy+CWxSNtgI64i2zf8I0ebmbPWiEXwvvI0phkXqtU/Btf8jBBrPQJGWB8P0a4as3CIgeoenrFwFX8UeaalD1WnqW5lZDDe6GJQwLBTphQPc096hn3wFvGU7O0ROKArGhXeD74cSIznw9pGu2bdSe3NFTIGXVqqReu0TCDafheB3QZk1Oq7pK3/NMbgOrAbvErPwDDOvG9IG0EMN3m2Fc98HCDaUgU3Ng2Hmdf2WydiO59Q2eI5+DUIE6Mb1jxCFZc3vJQEFwe9G6+d/QNqNfwTSClBqbYCGVaDIlN7jjAXhgvDXHodb0Qxt4WxpFoU1poPtQ7+VWaPBJmWDd7WAd7ZrTlNg9CmiWEVa/z/cuUs+h2PbG9Jr594PQPggTAvuCttWCPhCRqftBBpP9/r87bMH5xMUTUcdaWqKL4C3bEdI6ZXpgjt75Yx0ytqIPY2VUNIMFmQXIVtv7mPPe4bWGJB+ywvwVeyR7qesOQsOR2xOWPEgB94+0lmftzOdC+8r7M3Y1SB+ieZlj0S2LvyLqEjLi/vcgYYytHz0G6nEINhUDs5ai+TLfhH3sWKhzmXD6ooS1LptKDCm4urCKUhWD11t1p4gPIeWDx4VpQAhJqn5K/cj/fa/9Xm9KBruY1/Dtv6v0utAzTEIfjf0U65M2DkCTRUhqkUAAEJw/PA6/FOdDmfbqC3PkIKfT10KfZQyIN7ZAsvaZyC4LLBVmuDc+hqSr3gkIVnUFEUhZeUqWDe+DOHARxD8HrBJOaDVBpgW3dNva52EEIAQUDQdkpzTjufY+ojBkFKowJqzwrKO45WgPF9oN3SIZ9aBohmkXPkrBOpOgrPXi8IwvXiI/aauDK+f2CG93lhzEr+YugxF5v59IAbEmQhNUf9P98uBt48os4qhHb8UnmNfS23a8RdL02+7Gyrwj2PfgEBcD/u6+gQemroUo8wZAES3H+ee/yJQVwo2aRgMs64PK+GIhvvIl2GesN6yHeBdrXHr2gYaTsO557/gbPVQ5U6AYc4tIevMzoAPTx74Eu6gHwBQ57bhtK0JT81ZAbabwv+hjK9yvxR02+FdFnjLdsTs3RsvroOfRmxLZOClokhQvut0wcl0fKZnnRZ8WnkYN4+aGXF75573Ibgs0mvCBWDf/ApUd7yckLV91pyFtGseQ9o1jyHQcBqcvQGqnPG90mQONlfCsetdcK1VUGaNgXHuLWFBw7n3fbgOroHgd0NdMAtCBJ/f9t9pV6i2acfORvC0Lilup6OhjuBzofXL56TyRNXwSUi+9BdxPQgps0f3ulQIAFaXl4S8Dgo8Pq08jJ9NSawuwWAiB94EkHTxD6GbsBSBxjNQpheGrK9+dHofAi1nIXjsoBgF+KRsrKk4jIemLgUhBJaPfyuNToKNZ+A/dwDpt70UU2JUJINwEALC+ePqP2erR8uHvwJpC6pcaw0CjWeQftOfpG32NFZKQbf9PI1uG45a6jAlLbqDzlCGBCLXOZME1z+HnjOCV3DYOmffUKTlQZFZFJJh6qMZNGhMYYX7p63RbSYDEZZROFs9SMADKsEuNMrMUVFnj3qCd9vQ8sGjUt06Z61DoO4E0m//myQ64j6+IcRRyXdmFyhl+DV0N+2vKZiJjDtebttXA03RvJi1sQkhA56I2OBx4IOyAyizNyFHZ8a1I6ei0NR9kpN9+1shmgCiNvjrYWYfhAvCc3KLWMmRlgfNmAsTIqDCCwJaI/z+LL7++00OBrKARoJQZo6CftKlYUlNdedKwDuaQDg/BL8LwYYyNFrEgvNA7fGwKUHB64TnZGiyVjQiueIoMkbGLfTtObFRCrrtBBvKEOh04/Z3Kh/hrHWifOTZA2jZ+HK/pNsPBKq86eHr4TQDdeHsfjtnpGksTT9ksqZc+WtoxywGrTVBmTkK2Zc/gmRD+E03s5v1N0WEJDrGlAlKObTqqr2ntoaJxXC2evirSjq2ORnueysE3NDPuBaMIRW01gz99KtguuDObs/FGtOhn7oCtEoP28a/wbr+rxEfUKR+OJrQsvp3qPu/lWh49U64j6yLum0iCQo8nj3wFQ40n4Mj4EWptR7PHvwaVn/4g19numb1RmojhMCy5nHY1v8F7iNfwrbx77B89GspYa4vMDSNMUnh96+xyYPnytYfyCPefoSz1aPA1YQypnOmIkGBSwxUQoTRD4AwQY1oaIrmwjj/drgOfAzB64QqdyLMvRBhjyS/JrZ3BOMZ6Xn4uPwQAs4WaXpWTQQU1B+Dde2zSLvpubjPO9gwWhOSr3gU9s3/AGerB2NMg2nB3WBNGf12TuO82yD4XPCc2goQAs2o+TAmOMHGfWw9PCc2gaJpmBbdI8kQXqdNwWvHt0vTqTpWhSvzo4sDGGZeD/qb9RDcNrGBYWFedE9MIzfObYPn6DrwtnqpFrY7LeJI+OtK4T2xGaBpaMdfDGWU7Nio399OD5NUhNEYxbAwzLwOpvnfiatfXfWcPaWbkHrtkxGzu1s/fRLB5koA4jKGbePfwJgy++zM0xNHLbVhI0c/H8TuhgpcMiJ60iejNUNwW0Pa6E5yoQAQqD4Cf9Xh0Lb6U/BV7JWSrnznDsF9+AuQoB/aMQvjSiD8zujZeKFkIxq9YlLTKHMGri7oX+EQztYAx67/INh4Goq0Ahjm3Bzzkl9vkANvP0IEHtf4rXhVnQZLm8j5cD6AK9TiTUCVOwm0xgDB6+zYiaLiWtw3zLgG+mlXAQLX62xmzagL4Dq4JsRQnTGmh2japmsN+NHExXhz/StoADCMD+DqgBVqEHF9ztHU79nA/YE6bypUd7wM4nOBUuv7fTqQYpVIWvYAzBfdD0JIwvWNXQc+gX3bP6XX/uqjIEE/dOMvxrysQgzTm7Gv8Rw0rAJzswqR1I0qGGtMQ/JlDyNQcxSmgmzoRs3tUdTEV7kfts2viDkPDAtFynDQpZvhP3sQyZf/Mubr8JzaBuuXf5K+k+6jXyFlxa8jJnZpRs2HY/e7IaIetMYIVadtdRMvEUdunb7j2jGL406iIwIPVxfJTPAcXPs/hurKX4U0By1VUtDtjPfk1n4PvCTyUnXU9nb0M66F9cvnQjY0TL8mZJtoMrG+qhIxwdNWD0/pFulBy19VAt5lCRODiUaWzoQ/zL0KlY4WKGgGuYZe+jDHCOECaPnwUSmhjLPWwV9zFBl3vNxnm81oyIG3H1EkD0NWSg4ebqrAOVYDBQiGCUGkjhfrdGmFCilX/ga2jX9DsOUsGH0KjPNvjzvDmaJpgO59CZEyswhJy38Kx463wTuaoMwZJ1psdRmhTE7LxarkZDgbD4PyucC7WhAkBIwhNeFC/QMJRVGgBlgxiGKVSHSIJ4TA/s0b4OwNoNUG6abhPvSZZJgxwpCCEYbYFcFohQrq/OnQT54Cugfzcs7eCMtnT4FrrRVnSzg/Ag1lUOVOhLdsB4KWqpg9YZ273wuNEgIP5573IwZe1pyFlMsfgX37m+As1VBmFcO0+L6Qhxp13lQkX/EoXAfXgPhcUBfNgWHGdbG9CZ0gXBCCzxXWzrtbw9qoKD7VA1HuNyElGyalBvZAR/6AgmYwKzOv2/20xReA0RjhPrYe7VKlXd3HVLkTAYoK+Xw4RxNc+z4GrdQgUH8SQtAHVfYY6b7gOrgG+hnXxfxgS1EUCnpYj04U3vI9UtBtR/DY4S3bmXCjmXbkwNtP8G4rrOueR7DxDLjWagxjFFDnT4dx9o3iF7cNZfZopN/2IgS/G5RSO2hKUNrRC6EdvRBEEMRAHgXdpEvhPLgGgYZQfWh/9eGEWtPJxAfhOVg+eQy+swekTHfGlCVq6EZZ0kg03rIdAM+B8J3yBQQOgscORp8M3tkcc+DtqO3tvq0ddcEMqAtmdJvEpCmc1WeXK1qphnLYeARqjoWeP3962LasOStM6QkMC+2EpX3qQywoGRa/mLoM/zm9F2fszRimM+PakdOQotb3uK9q+KRu/ZdZcxZMC+6CY8e/QLggCEWBYlhp9oDwHMAHwdkbJDlJwe8WA/VQVLrjoy21RW5PBHLg7SdsG/4Kf1UJKEYBZZsYgG7CsqhrHbFMaRCeg+/sAZCAB+q86f1S69hd0AXE8ilFah54RyMIHwSjNYM1ZcG5579y4E0gvnMl8FcfBmvMgGb0oh4Vjbynt8NfdRiMLgW8U8wh4O31YAyp0EdIwusLgtcJUBRotR5EEODc+z48JzaCa60G77SAUhsBe6dsaZoGpVBDmRW7wpVqxFT4zuzq0tbzOt9APLgmXfxjtH7+tDiN3LY01HU6lnAB+M4egGb0AjDmLNG7WJ8Kw8xro65VJ5ocvRkPTe2fIK+fugKa0YvAtZwFpdSh+d2fSn9jdEngbF6QTqNtdcEs6d7CO1vgPr4BxO+CeuRcqHLG9ksfY0WdPxOUUhPSX4pVJlSOsity4O0HCBeAr3J/WLv3zC4YZlzbq2Pyrla0fPgrKbGJUqiRcuWvun0y7S8oClB2EggBEDZVI9N77Nv+CdeBT6TXrpLPkHbDs90+nAWbxex4NiUXhPAQXOLUpyp3Aoxzbk5IvwS/G9av/k8yNtcUzQOtT4X7YHtfaXD2BrDmLDCmLPD2BlCsEmxSNpKWPhCXy5R50T2wOBqlrH9l9hgY24wTCCEI1B6H4HNCNXxyv4mdRIM1ZyL91v9D0FoLWqEJqzvmrHVo+ejX0m+C1pqQevXvu11COue0oNppRb4xFTkDoNKUCBitCczwSSCEgDFlgm9b+2XMWSBcAFTb56LKmwrzRaKsZtBSjZb//kLKQncd/BSmC78H/aRL+62fwdYaQOChSI1s8kBrDEhZuQr2La8i2FQORWoeTAvv7lU9eazIgbc/oBnxCapLdjIdoW4wVpx73w8ReyBBH2ybXkbGHX/v9TF7iypvWpjqT394wvYVb8Ve+M8eAKNPgXb8sl5J1w00vLMFrkOhIhucpRqe4xui+sgCHUppFEWLMyyp+QBFIfmyXyZsTdG+7Z/wle+WXntPbwfnbAbbVqZEMSyUWaNB/E6o82aJVm2jF0GdPy2mPjR7nVhdXoJKRwtyDcm4auVjSPe0AjQjTVELfjcsH69CoEGUaqRUWqRc8WjI8s1AEc2b1779rZAHUcFjh33b60i95vcRt3+zdBe21HYs3SwfPg43jureSWkoQVEUki7+ISyfPQXi94CiaOgmLkfSZb8AzapDZuZc+z8KK/1y7vo3dOOXJtzeUvA6Yfn8aWlZQJFeiJQrfxVRjUuVMxbptzw/YPXWcuBNAEFLNWiVVsr4pGgG+smXw7nn/Y6NKAr6ab33oAw0loW1cdZaCH53v2XeRcJffVR8sGCVEAJeUDQDRWYRTAu/2y/n4902eI6vB+8WtVNjDfD2b94MyT51H/4CaTf9OSFPse2ygfHWS8d27LrIfredzMYjoSmaC2/BDPgq9okNFAXDnJsTmmnedeoXAARnM9CpPphWasCmF8T9QBjgOTy9f51UAlPvsaO0tR7PzL0aWkVH0Hbt/1gKuoBYemfb8BLSE6Sm1Vc8p76Ba+8HEIJeMMZ0MBrxYS+al+0pa2NI0AWAdVXHMTerEMP7OZu3twQayuDY8TYCTeXieu/i+6DKnYjM774Bf9Vh0BoDlNljI34eXSU3gTYbVL874Q/Gjh1vh6zFB5vKYdvyKlKueCTqPgP1HZIDbx8IWmvR+tnTopsFRUEzaj6Slj4IwgdAKAa0LgmC1wHVsAnQT786phIC97H18J7cCjAK6CZdAk2BKOenSBkR5nXJGNMHVMzAsfs9OHf9R3pNawxIufLXCTEujwTvbEHTuz+T6grdJWthmHUDjHNv6X4/jz1s1Mi7WuE+vBbGebf1vj8eO1o/fxqB2hMARK/W5MsfjsvCsScU6YWgFKowQRPB50TjG/dB8LugGTkPxgV3hkyxUjSDlBW/gb/6CDhrLZQ542JOZIoVWm0Iy+hlU/PDttOOi988oaSlJqzu1Bn0YX/TOSzI6VjW8Hex0ATEm7ngbh10/2bXoc9g3/IqBL8bgs8BwWMD0gvB6JKlJKOuVDgiJ4yV25uHZODlPXZRba+1GsGWc4DAwbX/I6Td/DwM01ZCM7J78ZlINqhs6oh+mY3ynTsQ1uY/G9pGCIHgtoLWGBM+4u4OWbmqD9i+eqHDQooQeE99A+f+j9Dy4a/g2v2uGDAEHrzbAlXOuB6P59z7Pmzr/wJ/9RH4zx5A65onxExRiGIGIaM1moFpwV0D9oQm+Fxw7fswpI34PaEZmwnGVfJZWDG/68DqiOUcneFdLWFG7UD0+sNYsW99XQq6gGhw4PjmzT4dsyu0SgfzhfeLZvNtsOYs+Mp2iAHG64T76DrY1v8l4v6q3InQTbwk4UEXgFgv3oXky34B/YxrQOuSwOiTYZh9I/S9KNMJRPi8ACAgcGLOxLlDCDSchiI5XJ6U1hhBawZ/GcF1YDUAgE0eBlBiKR7Xts7dvj4t+N1wHlgN64aX4CndjJwoASdXnxSxfbDxlu0A77GJa++C+JmRoA/WL/+EYPPZHvfXT78Gyk4P6rTWjKQ4bVBjhdGHTykznWZn/FWH0fjGfWh49Q40vHYnPCc29ks/IiGPeHuJ4HMhUH8qrN19+AsIHhtIm2YyxSjBWarhLd/VY9ZvNAF9TdE8MaHjOy/Be3o7hIAHmsI5YM2Zveq7v64U9q2vtems5sO04K4e18h4Z3PE9HrOWterPsQCbw/XESZcALyzBXQ3ZRGKlBGgtWZxxNGJvq4D+s+GJ8xFSqLrK9qxF0KVNw2B2uNgTJlwfPNm2BSdt2wnHHv/C9e+1RC8DmjHX4yki3/Ur0/tuonLQWsM8BzfCFAUtOOXSuU5prbAEg3Cc7B/84Z0c9ONWwLj/Duk/k5Oy4WaUcDHB6V9FDSDiUIQDa/fDcFjBwCwKcNBq/UdD18UBeO82wZ0tCL43eA9NrDm7JAHX8EnCuHQKh1UwyaAd7eC1pqQccfLYAypEPweNL/7cylXw3P0K+SMmo8pOdNwqKXDcnB2RgFGxuDE4686DFfJ52KVQ9E86CZe0v8P4oRA8NqBrmYSRICvcl+PGgS0UoO0655CoKkCxO8Sp6R7+dlxtgYEGk9DkTIiYuKUYdb1sKz5fcfSDUVJphaC3w3Lp09KeveCxw7r1y9CkVbYK6e4eJEDby+hFGpQKq2UQEWIAM5aA1J/CoLfBcJzbcIWrFhL2WXk1hVCSMR6S8HfMbqjVTroJizrU78FnwuWTx6T+h1sqoDlk8eRcecr3a5/ssm5oLUm6QbYjnJY+EiecAF4Tm4F11oNZdZoqEfO6dUNQTlsPLxlO0PaGH0y2JSOUQ/hOTi2vwlP6WaAoqGbsAyG2TcjaflPYP3ij9INWlO8ANo+Og7R+pSw0XZ/ZT4yWhM0RXPbThw+McXZ6tDy0SoQr/h5+KsOwV+5H5nfe6dfb76aonmSshrvscN3rgSK1BFgdN2P0Bw734b70GfSa9fBT8WR4DxRrlGvUOEnk5fg36f2oMrVihydGTePmgmy9smQ7xxnqYJu8mVgzVkQvE6oR86BMoKmdH9h3/4W3Ic+BeGCYEwZSL7k55I+u7pwDrwntwAAKFYB1pQB/dQVUjKPp3QzOGstAqCwh9WhhlFiWEUJ7pp2NcpzRqHaZUWBMTUmXWLfuRJYVq+SRCz81UfBOxp71JruK5qieaBUXR56aRa0LilMWrI7+vqZOXa/B+fud6Xr101YDvOS74dso86bhrQb/gjPiQ0gPA/tmEXSspi/qiTcZIYQeMt3y4F3KEMxLAzTr5YcT4ItZyG4W8Emj0CwtRoAAa3SgWr7G+9zwV9XKhrdR7gxUhQFzci58J4KFXPXjJyb0H77KvaEZVsTLgDvmV3QT74s6n4Uw8J88Y9g/eI56Qurzp8O3fjQBwHCBdHywaMhCTCa0QuRfMnPwHsd8Bz7Gry9CarhE6EumtdtkNBNWA5/9VEpqYdW62Fe+iAomoEQ9MNXsQeu/asRqD8lPTU797wPilXCMPN6ZN7zJgL1J0HrU6JmoMaDYeZ1ITKGoKiYZfD6gm78xSGOMYCo890edNvxlu+Gv3I/1AX9nxHrPLAajh1vi1P6DAvj7Bsjvhfe09vhPr4R7oNrQKn1YLRm6W+e0i1S4AWA4qQMPD77SghEAE3R4D12NEQwnw82VcC8+L5+ua7u8JbthGvfR9Jr3t6I1s//gIy7XwNFMzAvugck4BZnQSgKmqL5MMy9tWN7RyMEAH9Xp6OKERPG9rM6lB7dilVL7sTkHly+CCFwl3wums1X7gPhOSl5CwDch7+Ecc6toNjIilmJgNGZkX7L/6HhtTvBWWtAq3Rgk3PBmrNBgn60rn0WjD4FuilX9JuELGetCwm6AOA+ug6a4vlhs1rKzCIoM4u6HgK0OrIGQrT2RCMH3j5gmHk92KRh8JRuRtByDsrssSABD2ilBkLAK9ayMQoQQYDtqxfAmrPElParfhcxmcC8+D4Qzi9qytIMtGMuTLzfJxO5rCOWH6umYCZU97wBf+1xMPqUiEIA3rLtIUEXELVpfeOWwLb+L+DbnIzcR9dBO2EZkpb8IOr5KIZFyhWPIGiphuBuhSJrNGiFSnR8ef9h8M4W+M4dAogARUaRlOTkObEJhpnXg2KVCS0z0RYvAKNL7phqHXdRTGv3fUVTNA/mi38kyh36XVAXzQW//Z0Qr1wAgMAj2Frd74E3aK0V17bbb3w8B8eOd6DOnwFFWkeyVbtzDQDwbguIvR5Iy5fW3qJJKtJtfsK0ShdxloU1Zyf4imJDyhjvBO+yINhUAWVmkZhsuOI34L0OUBQdthyiGj4JpSXrpKALAKBonBUIDrfU9miv6dj2urQcxbVUibNhmaOk4Es4P4jAgUL/BV4AUOWMQe4jm+Eu+QyB+tNQpI5A0FYP++Z/SNt4Sjch7ebn+yX4BupPRhSdDtSVxvx7Vw6bAEV6IYKdnKUYffKAiQDJgTdOiMDDV75b8t5Vj5wDVe5ESYCd54KgWCVoRtFW5kMBAbd0kwk2lcO5+z9iAk0XxCzhX0HwewCaiSqgLwp07AMRBKjzZ/SoatQZdcFMMPoU8J1u2rTGGLMxA63SSZnWnQk0lIGz1sBfezzifq5Dn0pBtx3Psa9hmHldjz9ORUou0Gl62bnr3VDBDiKAs1SBaU/aiNMFJx5Uw8b3WxZ3d+jGXxyiG8s7mmHb0CXLXZ8CVXb/qwD5qw6H3Ph4lwW8qwXNH/wKSRf/UJoi7ywCwhjSwVlrwNkbpcCrnbi82/NQDAvjnFtg2/g3qY1W66GfcU03eyUW3tmCQFM5FGn5oCN5ZFMU6C4P0dGy3NV50+AdOQ+oKQNAAIqGIi0PFMOipbNRSgQIFwixFKR1yRD8LvCOJinwDqSYCK3USDMcvLMFDa/fHfJ3weuE+8iXPa799wY2QoIdIK7/xwpFUUi95vdw7v8IgbqTUKQMF5MEB0izXQ68cUAIQetnT4U8+arypiFl5W+hLpgJX/keMS1dqQUCHrCmTASbK0CxqpD1D38XndeudKfwE2ytgeWjX4NvUyYSVXEeDxlpdHtshQqp1z0Fx85/I9hYBkVaPgxzbul1LTAhBNYv/yRNkfNeB4jfHVrjStOgI5koEAK+F65GnZ9SWWOaaM4e9EpasLrx/a+FO9gkXfJzBBrK4DkuCpkwxnSYFt8HZfbosG0JFwRnrwdjSI/rIS0anT9bztEoZfZzrdVo/fwPSFr+E2jHLA7JT2BMmQBFQfC7oEjNg3b8xdBNvrzHc+kmLociLR/eMztBq/TQjr2oXxWFOuPc+z4cu/4jJudQFLTjLw5N7AKgKV4Y1/d35sI7sPqb9yEEfaBUOlA0DQoUxqV0vxRC+GBIciNryhBHuG3LPqrhk5G09IE4rzAx8G5rxBFou3paolFmFkEzepG0nt4+s+g+9jU4ay10k6+IyfWLVuthmn87/HWlcB9cA+u656EunAn95Cv6PVlPDrxx4K86HDbd5D97AP6qw0ha9iDsW16D98xOaEbNB2NIA63Ww3NiE2ilLsTphzX3fr3Rsf0tKegC7ao4/4yoitO5Jlg/6VJpCpI1ZyH50p/3ug+d8VXsDVmXZjRGBD1W8cfAKgGGhemCO8DokuE9vT1kX1qlgyJ9ZNznVKQXIthyVjyfOQegaJCgD2zqcOjGL4VuypV9uqbzAVqhQta9byJoqxez09MLIq5je09vh23Ty+CdLeA9NqgLpsO8+Ht9GrWrhk+GKnci/NVHpMxzWqUH3bZ+6zq4Btoxi6EunCPaA0IcYbCmTOgmXxb3+qwyq1hKYBoogpZqKX8DAEAIPEe/RtJlv0Sg9hh4RzNUeVPjTnbM0plw89h5+ODMAQQFHgqawbWFU5Gt674cilbpoBo+Gf6qEqlNkTIcxgvugG780m6z/PsbRXqBaILRJdCq8vtPzS5p+U+gHb0AvnMlcB1cDVCAv/KA+O9cCVKvfQKA+FDgryoBo0uGMndiSE4J73Wgde0f4dz5NkCzYJOyEag9Dq61pt9KnNqRA28ccNbI6kFcazXUIyYjadkDSFoW+tTpGXthW0KOAN5ta1sb7L3VVKQSpq5tgteJ1q+eh7d0s5Qs4D97AMmXP9yRKZsggg3h/VGkjIBxwZ1gzdlQZhSB0SeDEALthGXijZiIiWfmZQ/2agRmmHWD5PFJURQUqXlIvuLRMPuy/wUU5iwooiho8W4bWtf9GYLPKYoWCDxclnMI1J5E0rIHoJ9yRa/OSVEUUlaugufkZrR88ChAs2D0qdJNrX1EaFpwFwSfU5SZbEseNM5L/NRjfxCojTwrxTub+pzYtXT4WMzNKkS9244snQn6GD2Zk5Y9COvX/year7BK6CYsh37aVYOu2OU/exCU2giu6ggorQmM1iyK/4y6oN/OSVEU1PnTwdnqQFGhS0v+6iMI1J8C52iC9avnpZp+ZdZopFz9OGilGrzXgYbXvwtf+W6QoB8UTSPYWAYqeww8JzbBNP+Ofp12lgNvHCijrJ8pu0mw0RYvAK0xo/ndn4IEvGD0KWj9/CkkLf8ptMXxfzEVqXkhT70AQqaZvRV7YV37LLzlewCBA63SQ5FVDIqi4dz7AdT50xPqB8pGUeRR500X12bboCgKSUt+AMPM68A7mqBIH9nraU/WnIn02/8O35mdEII+aApnD9j04/mEv6oE4DnwtgbJKhAABK8Nzj3viXWfvZxSo1gFdOOXIlB3Ep7jG0L+1v5wR6u0SLniEXDOFggeGxRpBT26Xw0V2ChZ8IqkYQk5vl6hQlEMtbqdYfTJSL36MdFClFH0u68vEXgEmyrA6JIi6hsDgPvIOjR/8Ah4RxOIwINyNsN03dMwzbs14vaJpmutfjucsxn2zS+HCOkE6k/CfXgt1IWz0PTOj+E7swuC3w3CB8VZSVYhagSo9PBVH0agrhS0UgtuePdqXL3hvAm8Tz75JNauXYuSkhIolUrYbLYe97njjjvw1ltvhbQtW7YM69ati7JH9yjTC2CYeS2c+z6S1hP106/usSbNX3UItFILul3eUeBh3/IKNCPnxH3jM867DS2Np6WSIEqh7nBt4TnYN/4NQtAv3WgFvwucpQqC3wN/zTFw1lrop18F4+yb4rz6yGiK5sF9ZB0CnZKqdJMuCQm6nWGN6QnJdKSVamjHXtjn43ybaX8Y6VqvSDHKhOnjmhbeDRLwwHtmF0DR0BYvgLFTCY1z/8dw7n4PJOgDY0iF+eIfxySd2hs4W4Po+Zs5KqY1vu5QDpsAVd60EIlBVe4EqCL47g40A6HN7q8rhXXts2ISJkVBO2YxzBf/KGTJDACsG/4quRJRFAUIHKxfPgfj3FsSNhL3Vx2G88AnEDw2qAtnwjD9WqkKQ50/E869oYp6lEINRp8q2ld2IdBwWiz9bJuVoRiFuH4e9AKMQryvMwpY1z4r7ePa+VHYcfrKeRN4A4EArrvuOsyZMwevv/56zPstX74cb7zxhvRaperbD9I47zvQjrsYweYKKNIKYhLKDzaWh7UJHjt4lwWsKSOu8yszi5Bxxz/gPf0NQAg0RfOkGyzvaALvagVFUaJOtLsVhBBwrTWgFKq2WjsfnLvehSI5F5oE+LRSDIvUa34PX/luBFtroMoZOyhOMTLhKIdNgDJ7TJuZRtuNpi3RL1H6uLRKh+TLHxYdZyg6JKvWX3s8RFKTd7bAuvYZZHz3jYQkebVDBAG29X+Bp3STtIyRdMnPIprTxwpFUUhZ8Wt4y3Yg2FAGRXoBNKMuGPRp3YGgPWFSqnwgBJ4Tm6DMGg1dl0x0STK3E4LHBq7lbMwJn93hrytFy+pVkvpUsKkcnLUOyZf8DACgzB4N04K74Nj9btuMYjLMF/8IitQ8UAp12EOnImU4fBV7QSvUoFV6CHCBEngxJwUEqvwZkgKZ9H4EvUg0503gfeyxxwAAb775Zlz7qVQqZGb2TloxGqw5Ky5nGkVGYdj0MK019VrUndGaoI+QEUrrU0CrdBD8btFUgQjij4cQMIY0sEkd9Y/eM7sTEngBMfhqRs3HwLqiyvQERVFIueoxqIZPFsX7vTYwxgywxjQkXfzjhJ4r0ijMV74nrE3wuxGoPdanoNgV78ktITq7om/w88j87ht9mo6laEas6xyg2s6hAtdaE1b6BwC+c4fCAq8yswheZ6jRA61LAp0g4xD34S/D3Lq8p7aBX/hd6cFRP20ltBOWg3dbwJoypVG5cd5tsG95VdqPTcqBbsoVEHwOBJsrocgoQtBSBdA20LokpN3wR2iLF6D+bzcmpO/dcd4E3t6yZcsWpKenIykpCRdeeCGeeOIJpKRED3h+vx9+f4czjN0uFu87HI5e90EYeSG8R7eJlm8AQNMwL7gZTne4RGRfIZOugXNb24yAKQ9U6lgIXhs4Vgmfr2O9g+dpsH24JpnziHGXwTTuMgRbzkHwu6HMHAUfw8LXw+cvCAJcLnGk7HA4QMe5PusiKjh9wbB2Fc8gkMDvnvXkXni7nsfXCkXF0YiqRTLdI3A0nEESZjTC0zoounxuiot/iZbK26S1VlpjgnHi1XALCiABn7HDboUvwndI09oClusy+8AYAFcnh6vChVCb8uA7exCMPgmqwjlwBwEy5lJ4yw6Ca60BjLmgU0fDePkvIWQVw+Xn4DMODylZdLXdN0mEkqleQ84z3njjDWIymWLa9t133yVr1qwhR44cIatXryZjxowhM2bMIBzHRd1n1apVBKICuPxP/if/k//J/+R/BAApLy9PUBQjhCIkkWE8Ph5++GE888wz3W5TWlqK0aM7RAHefPNNPPjggzElV3WloqIChYWF2LBhAy66KLJnaNcRr81mw4gRI1BVVQWTyRT3Oc93HA4HcnNzUV1dDaMxcb6z5wvy9f9vXz8gvwf/69dvt9sxfPhwWK1WmM3mhBxzUKeaf/azn+GOO+7odpuCgsQ5jxQUFCA1NRVnzpyJGnhVKlXEBCyTyfQ/+aVrx2g0ytcvX/9gd2NQ+V9/D/7Xrz/epZbuGNTAm5aWhrS0tJ43TBA1NTWwWCzIyoo9MUpGRkZGRiaRnB/V7ACqqqpQUlKCqqoq8DyPkpISlJSUSMkfADB69GisXr0aAOByufDQQw9h9+7dOHv2LDZu3IgVK1Zg5MiRWLasb562MjIyMjIyveW8yWr+7W9/GyKGMWXKFADA5s2bsWjRIgDAqVOnpCxkhmFw5MgRvPXWW7DZbMjOzsbSpUvx+9//Pq5aXpVKhVWrVvW5/vd8Rb5++fr/l68fkN8D+foTf/2DmlwlIyMjIyPzv8Z5M9UsIyMjIyPzbUAOvDIyMjIyMgOIHHhlZGRkZGQGEDnwysjIyMjIDCBy4I3Ak08+iblz50Kr1casVHLHHXeAoqiQf8uXL+95xyFIb66fEILf/va3yMrKgkajwZIlS1BWVta/He0nWltbccstt8BoNMJsNuPuu+8OKVuLxKJFi8I+/+9973sD1OO+8dJLLyEvLw9qtRqzZs3C3r17u93+gw8+wOjRo6FWqzFhwgR88cUXA9TT/iOe9+DNN98M+6zV6sS5LQ0k27ZtwxVXXIHs7GxQFIVPPvmkx322bNmCqVOnQqVSYeTIkXEb1wwl4r3+LVu2hH32FEWhoaEhrvPKgTcC7RaE999/f1z7LV++HPX19dK/d999t5962L/05vqfffZZvPjii3j55ZexZ88e6HQ6LFu2DD6fr+edhxi33HILjh8/jvXr1+Pzzz/Htm3bcO+99/a43z333BPy+T/77LM97jPY/Pe//8VPf/pTrFq1CgcPHsSkSZOwbNkyNDWFu9MAwM6dO3HTTTfh7rvvxqFDh7By5UqsXLkSx44dG+CeJ4543wNAVHHq/FmfO3duAHucONxuNyZNmoSXXnoppu0rKytx2WWXYfHixSgpKcGDDz6I7373u/jqq6/6uaf9Q7zX386pU6dCPv/09Dg9xhOm+vwtJB5Dhttvv52sWLGiX/sz0MR6/YIgkMzMTPLHP/5RarPZbESlUpF33323H3uYeE6cOEEAkH379kltX375JaEoitTW1kbdb+HCheSBBx4YgB4mlpkzZ5If/OAH0mue50l2djZ5+umnI25//fXXk8suuyykbdasWeS+++7r1372J/G+B/HcF84nAJDVq1d3u80vfvELMm7cuJC2G264gSxbtqwfezYwxHL9mzdvJgCI1Wrt07nkEW8CabcgLC4uxv333w+LxTLYXRoQKisr0dDQgCVLlkhtJpMJs2bNwq5duwaxZ/Gza9cumM1mTJ/e4Re7ZMkS0DSNPXvC/WU78+9//xupqakYP348HnnkEXg8ibd9TCSBQAAHDhwI+dxomsaSJUuifm67du0K2R4Ali1bdt59zu305j0ARGW8ESNGIDc3FytWrMDx48cHoruDzrft8+8tkydPRlZWFi6++GLs2LEj7v3PG+Wqoc7y5ctx9dVXIz8/H+Xl5Xj00UdxySWXYNeuXWAYZrC716+0r29kZGSEtGdkZMS99jHYNDQ0hE0bsSyL5OTkbq/l5ptvxogRI5CdnY0jR47gl7/8JU6dOoWPP/64v7vca1paWsDzfMTP7eTJkxH3aWho+FZ8zu305j0oLi7GP//5T0ycOBF2ux3PPfcc5s6di+PHj2PYsGED0e1BI9rn73A44PV6odFoBqlnA0NWVhZefvllTJ8+HX6/H6+99hoWLVqEPXv2YOrUqTEf538m8PbGgjAebrzxRun/J0yYgIkTJ6KwsBBbtmyJ6oQ0kPT39Q91Yr3+3tJ5DXjChAnIysrCRRddhPLychQWFvb6uDJDjzlz5mDOnDnS67lz52LMmDH4xz/+gd///veD2DOZ/qa4uBjFxcXS67lz56K8vBzPP/883n777ZiP8z8TeIeiBeFA0p/Xn5mZCQBobGwMcX5qbGzE5MmTe3XMRBPr9WdmZoYl1XAch9bWVuk6Y2HWrFkAgDNnzgzZwJuamgqGYdDY2BjS3tjYGPVaMzMz49p+qNOb96ArCoUCU6ZMwZkzZ/qji0OKaJ+/0Wj81o92ozFz5kxs3749rn3+ZwLv/7oFYX9ef35+PjIzM7Fx40Yp0DocDuzZsyfuzPD+ItbrnzNnDmw2Gw4cOIBp06YBADZt2gRBEKRgGgslJSUAMGQ+/0golUpMmzYNGzduxMqVKwEAgiBg48aN+OEPfxhxnzlz5mDjxo148MEHpbb169eHjADPJ3rzHnSF53kcPXoUl156aT/2dGgwZ86csPKx8/nzTwQlJSXx/877lJr1LeXcuXPk0KFD5LHHHiN6vZ4cOnSIHDp0iDidTmmb4uJi8vHHHxNCCHE6neTnP/852bVrF6msrCQbNmwgU6dOJUVFRcTn8w3WZfSaeK+fEEL+8Ic/ELPZTNasWUOOHDlCVqxYQfLz84nX6x2MS+gTy5cvJ1OmTCF79uwh27dvJ0VFReSmm26S/l5TU0OKi4vJnj17CCGEnDlzhjz++ONk//79pLKykqxZs4YUFBSQBQsWDNYlxMx7771HVCoVefPNN8mJEyfIvffeS8xmM2loaCCEEHLbbbeRhx9+WNp+x44dhGVZ8txzz5HS0lKyatUqolAoyNGjRwfrEvpMvO/BY489Rr766itSXl5ODhw4QG688UaiVqvJ8ePHB+sSeo3T6ZR+3wDIn//8Z3Lo0CFy7tw5QgghDz/8MLntttuk7SsqKohWqyUPPfQQKS0tJS+99BJhGIasW7dusC6hT8R7/c8//zz55JNPSFlZGTl69Ch54IEHCE3TZMOGDXGdVw68Ebj99tsJgLB/mzdvlrYBQN544w1CCCEej4csXbqUpKWlEYVCQUaMGEHuuece6Yd7vhHv9RMilhT95je/IRkZGUSlUpGLLrqInDp1auA7nwAsFgu56aabiF6vJ0ajkdx5550hDx2VlZUh70dVVRVZsGABSU5OJiqViowcOZI89NBDxG63D9IVxMdf/vIXMnz4cKJUKsnMmTPJ7t27pb8tXLiQ3H777SHbv//++2TUqFFEqVSScePGkbVr1w5wjxNPPO/Bgw8+KG2bkZFBLr30UnLw4MFB6HXfaS+P6fqv/Xpvv/12snDhwrB9Jk+eTJRKJSkoKAi5D5xvxHv9zzzzDCksLCRqtZokJyeTRYsWkU2bNsV9XtkWUEZGRkZGZgCR63hlZGRkZGQGEDnwysjIyMjIDCBy4JWRkZGRkRlA5MArIyMjIyMzgMiBV0ZGRkZGZgCRA6+MjIyMjMwAIgdeGRkZGRmZAUQOvDIyMjIyMgOIHHhlZGRkZGQGEDnwysjIyMjIDCBy4JWRkZGRkRlA5MArIyMTRnNzMzIzM/HUU09JbTt37oRSqcTGjRsHsWcyMuc/skmCjIxMRL744gusXLkSO3fuRHFxMSZPnowVK1bgz3/+82B3TUbmvEYOvDIyMlH5wQ9+gA0bNmD69Ok4evQo9u3bB5VKNdjdkpE5r5EDr4yMTFS8Xi/Gjx+P6upqHDhwABMmTBjsLsnInPfIa7wyMjJRKS8vR11dHQRBwNmzZwe7OzIy3wrkEa+MjExEAoEAZs6cicmTJ6O4uBgvvPACjh49ivT09MHumozMeY0ceGVkZCLy0EMP4cMPP8Thw4eh1+uxcOFCmEwmfP7554PdNRmZ8xp5qllGRiaMLVu24IUXXsDbb78No9EImqbx9ttv45tvvsHf//73we6ejMx5jTzilZGRkZGRGUDkEa+MjIyMjMwAIgdeGRkZGRmZAUQOvDIyMjIyMgOIHHhlZGRkZGQGEDnwysjIyMjIDCBy4JWRkZGRkRlA5MArIyMjIyMzgMiBV0ZGRkZGZgCRA6+MjIyMjMwAIgdeGRkZGRmZAUQOvDIyMjIyMgPI/wMc6zUhpUhWxAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_points(X, y, ax=None, title=None, alpha=0.7, s=18):\n",
    "    \"\"\"Scatter campus points with a fixed class→color legend.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    else:\n",
    "        fig = ax.figure\n",
    "    for label in (0, 1):\n",
    "        mask = y == label\n",
    "        ax.scatter(\n",
    "            X[mask, 0], X[mask, 1],\n",
    "            c=[CMAP_POINTS(label)],\n",
    "            label=CLASS_LABELS[label],\n",
    "            alpha=alpha, s=s, edgecolors=\"none\",\n",
    "        )\n",
    "    ax.set_xlim(-SQUARE_HALF, SQUARE_HALF)\n",
    "    ax.set_ylim(-SQUARE_HALF, SQUARE_HALF)\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"y\")\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "    ax.legend(loc=\"upper right\", frameon=True)\n",
    "\n",
    "    # Draw the x and y axes\n",
    "    ax.hlines(0, -SQUARE_HALF, SQUARE_HALF, color=\"k\", alpha=0.2)\n",
    "    ax.vlines(0, -SQUARE_HALF, SQUARE_HALF, color=\"k\", alpha=0.2)\n",
    "    return ax\n",
    "\n",
    "\n",
    "def add_radial(X):\n",
    "    \"\"\"Augment (x, y) with the radial feature r² = x² + y².\"\"\"\n",
    "    r2 = (X[:, 0]**2 + X[:, 1]**2).reshape(-1, 1)\n",
    "    return np.hstack([X, r2])\n",
    "\n",
    "\n",
    "# Placeholders filled in later sections\n",
    "model_raw = None\n",
    "model_aug = None\n",
    "\n",
    "# Display the generated data\n",
    "plot_points(X_rest, y_rest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96b081f",
   "metadata": {},
   "source": [
    "# Problem 1: Learning Problem\n",
    "\n",
    "In stage 1 of the ML lifecycle, we frame a real-world situation into a question that we can answer with a ML model.\n",
    "\n",
    "This involves asking questions like:\n",
    "* What is the **target** we want to predict?\n",
    "* How would we evaluate success? What loss should we use?\n",
    "* What data do we have on-hand?\n",
    "* How should we engineer the feature (AKA properties of our data) to better suit our task?\n",
    "* How do we split our data into training, validataion, and test sets?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf431f9e",
   "metadata": {},
   "source": [
    "### Problem 1a `[conceptual]`\n",
    "What is the **input** (features)? What is the **target**?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab782e6",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607cf350",
   "metadata": {},
   "source": [
    "### Problem 1b `[conceptual]`\n",
    "Is this a **classification** or **regression** task?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa664acd",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a874a13",
   "metadata": {},
   "source": [
    "### Problem 1c `[conceptual]`\n",
    "What do we want to predict?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613f0508",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50018ef7",
   "metadata": {},
   "source": [
    "### Problem 1d `[conceptual]`\n",
    "Below is a scatter of the **Quadrant 1 - Quadrant 3** survey points (Moffitt Library  / Q4 held out).\n",
    "Could a single straight line separate the two classes? Argue yes or no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d9f8892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "plot_points(X_rest, y_rest, ax=ax, title=\"Surveyed campus (Quad. 1 - Quad. 3)\")\n",
    "\n",
    "# True WiFi router coverage circle for reference\n",
    "theta = np.linspace(0, 2 * np.pi, 300)\n",
    "ax.plot(R_COVERAGE * np.cos(theta), R_COVERAGE * np.sin(theta),\n",
    "        \"k--\", lw=1.5, label=\"true coverage\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55137884",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44689e2",
   "metadata": {},
   "source": [
    "### Problem 1e `[conceptual]`\n",
    "We will treat the data in quadrant 4 as \"hidden\"/\"inaccessible\", then randomly split the rest into **train / val / test**.\n",
    "What is each of the three sets for? Why isn't train + test enough (**hint:** what is the purpose of having a validation split)?\n",
    "\n",
    "| Set | Source | Role |\n",
    "|---|---|---|\n",
    "| `train` | random 60% of Quad1–Q3 | fit the model |\n",
    "| `val` | random 20% of Q1–Q3 | **choose between raw vs augmented model** |\n",
    "| `test` | random 20% of Q1–Q3 | in-distribution test |\n",
    "| `moffitt` (Q4) | all of Q4 | **out-of-distribution** test |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3006aaa8",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5aa34a",
   "metadata": {},
   "source": [
    "### Problem 1f `[code TODO]`\n",
    "\n",
    "Use `sklearn's` `train_test_split` to divide the data we have on-hand (`X_rest`, `y_rest`) into train, validataion, and test sets.\n",
    "\n",
    "60% of the available data should be in the train set, 20% of the available data should be in the validation set,\n",
    "and the remaining 20% should be in the in-distribution test set.\n",
    "\n",
    "\n",
    "Print the size of all 3 sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e5e4561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Use sklearn's train_test_split to split the data into train, val, and test sets.\n",
    "# Hint: first split with test_size=0.40, then split the temp half/half\n",
    "# === ANSWER id=L6 ===\n",
    "X_train, X_temp, y_train, y_temp = ...\n",
    "X_val, X_test, y_val, y_test = ...\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18af554d",
   "metadata": {},
   "source": [
    "# Problem 2: Model Design\n",
    "\n",
    "We'll use a **logistic regression** model. We'll cover logistic regression in more detail in a couple of lectures!\n",
    "For now, all you need to know is that it is a *linear classifier that\n",
    "outputs a probability* of the positive class. You call `.fit` to learn weights/parameters of the model\n",
    "and `.predict` / `.predict_proba` to score new points. (The details of how the best model paremeters are found\n",
    "will be explained later in CS 189/289.)\n",
    "\n",
    "We'll compare 2 sets of features (AKA properties we can derive from our data):\n",
    "- **Raw:** $[x, y]$\n",
    "- **Augmented:** $[x, y, x^2 + y^2]$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db9f67e",
   "metadata": {},
   "source": [
    "### Problem 2a `[code TODO]`\n",
    "Fit `model_raw` on raw $[x, y]$ training features. Then run the interactive\n",
    "decision-boundary plot in **Raw** mode.\n",
    "\n",
    "**Follow-up `[conceptual]`:** What *shape* is the boundary?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c63a48af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: fit LogisticRegression on the raw (x, y) training features\n",
    "# === ANSWER id=2a ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a58a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Staff helper — do not edit. Decision-boundary plot + feature-mode toggle\n",
    "def plot_decision_boundary(feature_mode, threshold=0.5, show_moffitt=False):\n",
    "    \"\"\"Decision regions for the already-fit raw / augmented models.\"\"\"\n",
    "    global model_raw, model_aug\n",
    "\n",
    "    if feature_mode == \"aug\":\n",
    "        if model_aug is None:\n",
    "            print(\"model_aug is not fitted yet — complete the Optimization section first.\")\n",
    "            return\n",
    "        model = model_aug\n",
    "        mode_name = \"Augmented (x, y, r²)\"\n",
    "    else:\n",
    "        if model_raw is None:\n",
    "            print(\"model_raw is not fitted yet.\")\n",
    "            return\n",
    "        model = model_raw\n",
    "        mode_name = \"Raw (x, y)\"\n",
    "\n",
    "    # Meshgrid over the campus square\n",
    "    grid_res = 300\n",
    "    xx = np.linspace(-SQUARE_HALF, SQUARE_HALF, grid_res)\n",
    "    yy = np.linspace(-SQUARE_HALF, SQUARE_HALF, grid_res)\n",
    "    XX, YY = np.meshgrid(xx, yy)\n",
    "    grid_X = np.column_stack([XX.ravel(), YY.ravel()])\n",
    "\n",
    "    grid_feats = add_radial(grid_X) if feature_mode == \"aug\" else grid_X\n",
    "    probs = model.predict_proba(grid_feats)[:, 1]\n",
    "    preds = (probs >= threshold).astype(int).reshape(XX.shape)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    ax.contourf(XX, YY, preds, levels=[-0.5, 0.5, 1.5], cmap=CMAP_REGION, alpha=0.35)\n",
    "\n",
    "    plot_points(X_train, y_train, ax=ax, alpha=0.65, s=14)\n",
    "\n",
    "    if show_moffitt:\n",
    "        ax.scatter(\n",
    "            X_moffitt[:, 0], X_moffitt[:, 1],\n",
    "            c=[CMAP_POINTS(int(lbl)) for lbl in y_moffitt],\n",
    "            s=22, marker=\"D\", edgecolors=\"k\", linewidths=0.4,\n",
    "            label=\"Q4 construction\", alpha=0.9,\n",
    "        )\n",
    "\n",
    "    theta = np.linspace(0, 2 * np.pi, 300)\n",
    "    ax.plot(R_COVERAGE * np.cos(theta), R_COVERAGE * np.sin(theta),\n",
    "            \"k--\", lw=1.5, label=\"true coverage\")\n",
    "\n",
    "    # Live accuracy on the relevant evaluation set\n",
    "    if show_moffitt:\n",
    "        eval_X, eval_y, eval_name = X_moffitt, y_moffitt, \"Q4\"\n",
    "    else:\n",
    "        eval_X, eval_y, eval_name = X_val, y_val, \"val\"\n",
    "    eval_feats = add_radial(eval_X) if feature_mode == \"aug\" else eval_X\n",
    "    eval_probs = model.predict_proba(eval_feats)[:, 1]\n",
    "    eval_preds = (eval_probs >= threshold).astype(int)\n",
    "    acc = accuracy_score(eval_y, eval_preds)\n",
    "\n",
    "    ax.set_title(f\"{mode_name} | threshold={threshold:.2f} | {eval_name} acc={acc:.3f}\")\n",
    "    ax.set_xlim(-SQUARE_HALF, SQUARE_HALF)\n",
    "    ax.set_ylim(-SQUARE_HALF, SQUARE_HALF)\n",
    "    ax.legend(loc=\"upper right\", fontsize=8)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# --- Interactive widget (Model Design: Raw mode; Augmented works after Optimization) ---\n",
    "_toggle_md = widgets.ToggleButtons(\n",
    "    options=[(\"Raw (x, y)\", \"raw\"), (\"Augmented (x, y, r²)\", \"aug\")],\n",
    "    description=\"Features:\",\n",
    "    value=\"raw\",\n",
    ")\n",
    "\n",
    "def _plot_md(feature_mode):\n",
    "    plot_decision_boundary(feature_mode, threshold=0.5, show_moffitt=False)\n",
    "\n",
    "_widget_md = widgets.interactive(_plot_md, feature_mode=_toggle_md)\n",
    "display(_widget_md)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffad5acd",
   "metadata": {},
   "source": [
    "**Static fallback** (if interactive widgets don't render in your notebook environment):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "533110a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Static fallback\n",
    "plot_decision_boundary(\"raw\", threshold=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9cffd8",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f33f6ebd",
   "metadata": {},
   "source": [
    "### Problem 2b `[conceptual]`\n",
    "Why does a linear boundary do poorly here?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89ef133",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ed2050",
   "metadata": {},
   "source": [
    "### Problem 2c `[conceptual]`\n",
    "How can we add a single new feature given the data we already have that we can pass as an input into our classifier\n",
    "model and improve its ability to predict whether a student has WiFi or not?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af9353d",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3725578a",
   "metadata": {},
   "source": [
    "# Problem 3: Optimization\n",
    "\n",
    "Now we will fit a logistic regression model to both feature representations (raw $[x, y]$ and augmented $[x, y, x^2 + y^2]$ ) and look at how well they match the **training** labels.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b17215",
   "metadata": {},
   "source": [
    "### Problem 3a `[code TODO]`\n",
    "Fit both models: `model_raw` on $[x, y]$, `model_aug` on `add_radial(...)`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae3bc94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: fit model_raw and model_aug\n",
    "# === ANSWER id=3a ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b01a07d",
   "metadata": {},
   "source": [
    "### Problem 3b `[code TODO]`\n",
    "Report **training** accuracy for both models. Which fits the training data better?\n",
    "\n",
    "**How to compute it.** For each fitted model:\n",
    "1. Call `model.predict(X)` to get predicted labels for the points in `X`.\n",
    "2. Pass the true labels and those predictions into `accuracy_score(y_true, y_pred)`.\n",
    "\n",
    "- **`model.predict(X)`** — runs the fitted classifier on feature matrix `X` and returns a predicted class label (0 or 1) for each row.\n",
    "- **`accuracy_score(y_true, y_pred)`** — returns the fraction of examples where `y_pred` matches `y_true` (correct predictions / total examples).\n",
    "\n",
    "Remember: for the augmented model, pass the *augmented* features into `predict` (e.g. `add_radial(X_train)`), not raw `(x, y)`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4518994b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: training accuracy for raw and augmented\n",
    "# === ANSWER id=3b ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f7bb3b",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7234d25",
   "metadata": {},
   "source": [
    "### Problem 3c `[conceptual]`\n",
    "If a model hit **100% training accuracy**, would that guarantee real-world\n",
    "quality? Which set would you check next?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1efb13",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f4aef4",
   "metadata": {},
   "source": [
    "# Problem 4 Predict & Evaluate\n",
    "\n",
    "Finally, in this stage of the ML lifecycle we'll use the validation set to choose a model, the test set\n",
    "to measure how well our chosen model those, and calculate precision and recall.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71558d03",
   "metadata": {},
   "source": [
    "### Problem 4a `[code TODO]` + `[conceptual]`\n",
    "Compute **validation** accuracy for both models. Which model do you pick, and\n",
    "why is **val** (not train, not test) the right set for that decision?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85d13cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: validation accuracy for both models\n",
    "# === ANSWER id=4a ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a5a4ac",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bde3f5",
   "metadata": {},
   "source": [
    "### Problem 4b `[code TODO]` + `[conceptual]`\n",
    "For the chosen (augmented) model, compute a `confusion_matrix` on the **test**\n",
    "set. Label all four cells in WiFi terms\n",
    "(true \"has WiFi,\" false \"has WiFi,\" etc.).\n",
    "\n",
    "**Steps:.**\n",
    "1. Get predictions with `model.predict(X_test)` (use augmented features for the augmented model).\n",
    "2. Call `confusion_matrix(y_true, y_pred)` — with default settings, rows are the **true** label and columns are the **predicted** label, ordered `[0, 1]`:\n",
    "\n",
    "With rows = true label and columns = predicted label, the confusion matrix will look like:\n",
    "\n",
    "|  | Model predicted 0 (no WiFi) | Model predicted 1 (has WiFi) |\n",
    "|---|---|---|\n",
    "| **True 0 (no WiFi)** | TN — correctly \"no WiFi\" | FP — false \"has WiFi\" |\n",
    "| **True 1 (has WiFi)** | FN — false \"no WiFi\" | TP — true \"has WiFi\" |\n",
    "\n",
    "3. **Also compute TN, FP, FN, TP independently** from `y_test` and your predictions (don't just unpack the matrix). Count:\n",
    "   - **TN** — true = 0 and pred = 0 (correctly \"no WiFi\")\n",
    "   - **FP** — true = 0 and pred = 1 (false \"has WiFi\")\n",
    "   - **FN** — true = 1 and pred = 0 (false \"no WiFi\")\n",
    "   - **TP** — true = 1 and pred = 1 (true \"has WiFi\")\n",
    "\n",
    "Check that your hand counts match `confusion_matrix(...).ravel()` (order: TN, FP, FN, TP)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c138c585",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: confusion matrix for model_aug on the test set\n",
    "# === ANSWER id=P2 ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdb2d1b",
   "metadata": {},
   "source": [
    "### Problem 4c`[conceptual + code]`\n",
    "Write accuracy as a formula over confusion-matrix cells, then compute it and\n",
    "confirm it matches `accuracy_score`.\n",
    "\n",
    "$$\\mathrm{accuracy} = \\frac{TP + TN}{TP + TN + FP + FN}$$\n",
    "\n",
    "**Steps:**\n",
    "1. Using the TN, FP, FN, TP counts from Problem 4b, plug into the formula above to get accuracy from the confusion matrix.\n",
    "2. Separately call `accuracy_score(y_true, y_pred)` — it takes the true labels and your model's predictions and returns the fraction of examples that match (correct / total), which is the same quantity as the formula.\n",
    "\n",
    "Confirm the two numbers agree (e.g. with `np.isclose`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2baa34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: accuracy from cm_aug cells; compare to accuracy_score\n",
    "\n",
    "tn, fp, fn, tp = cm_aug.ravel()\n",
    "acc_from_cm = ...\n",
    "acc_from_sklearn = ...\n",
    "# === ANSWER id=4c ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n",
    "\n",
    "print(f\"accuracy from CM cells: {acc_from_cm:.4f}\")\n",
    "print(f\"accuracy_score:         {acc_from_sklearn:.4f}\")\n",
    "print(f\"match: {np.isclose(acc_from_cm, acc_from_sklearn)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e29dc1",
   "metadata": {},
   "source": [
    "### Problem 4d `[conceptual + code]` — precision & recall\n",
    "\n",
    "> The campus wants to publish a coverage map. A **false \"has WiFi\"** sends a\n",
    "> student to a dead zone to fail their submission; a **false \"no WiFi\"** just\n",
    "> makes them needlessly avoid a good spot.\n",
    "\n",
    "Which metric matters more here, and why? Compute both `precision_score` and\n",
    "`recall_score` for the augmented model on the test set.\n",
    "(Precision / recall of the **\"has WiFi\"** / positive class.)\n",
    "\n",
    "**What the metrics mean** (positive class = \"has WiFi\" = 1):\n",
    "\n",
    "- **Precision** $= \\dfrac{TP}{TP + FP}$ — of the spots we *predict* have WiFi, how many actually do?\n",
    "- **Recall** $= \\dfrac{TP}{TP + FN}$ — of the spots that *truly* have WiFi, how many did we catch?\n",
    "\n",
    "**How to compute with sklearn.** Reuse your test predictions from earlier (`y_test_pred_aug`), then call:\n",
    "\n",
    "- `precision_score(y_true, y_pred)` — returns precision of the positive class\n",
    "- `recall_score(y_true, y_pred)` — returns recall of the positive class\n",
    "\n",
    "Both take `(y_true, y_pred)` in that order, same as `accuracy_score`. You can also check them against your hand-counted TP / FP / FN from the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a01aa6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: precision and recall for model_aug on the test set\n",
    "# === ANSWER id=4di ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438f0ea6",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91086e96",
   "metadata": {},
   "source": [
    "### Problem 4e `[conceptual]`\n",
    "Re-run the interactive plot below in **Augmented** mode (use the threshold\n",
    "slider — raising it trades recall for precision).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39476602",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interactive widget with decision threshold (Predict & Evaluate)\n",
    "_toggle_pe = widgets.ToggleButtons(\n",
    "    options=[(\"Raw (x, y)\", \"raw\"), (\"Augmented (x, y, r²)\", \"aug\")],\n",
    "    description=\"Features:\",\n",
    "    value=\"aug\",\n",
    ")\n",
    "_slider_pe = widgets.FloatSlider(\n",
    "    value=0.5, min=0.0, max=1.0, step=0.05,\n",
    "    description=\"threshold\", continuous_update=False,\n",
    ")\n",
    "\n",
    "def _plot_pe(feature_mode, threshold):\n",
    "    plot_decision_boundary(feature_mode, threshold=threshold, show_moffitt=False)\n",
    "\n",
    "_widget_pe = widgets.interactive(\n",
    "    _plot_pe,\n",
    "    feature_mode=_toggle_pe,\n",
    "    threshold=_slider_pe,\n",
    ")\n",
    "display(_widget_pe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d438509",
   "metadata": {},
   "source": [
    "**Static fallbacks** (if widgets don't render):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f15e4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Static fallback — Raw and Augmented\n",
    "plot_decision_boundary(\"raw\", threshold=0.5)\n",
    "plot_decision_boundary(\"aug\", threshold=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254bbcc8",
   "metadata": {},
   "source": [
    "# Problem 4 (continued): Generalizing to Hidden Test Data (Moffitt)\n",
    "\n",
    "Remember: Moffitt (quadrant 4) was locked away at the start — Oski never surveyed\n",
    "there, so **none** of our train / val / in-distribution test points came from Q4.\n",
    "But now that Moffitt has reopened, Oski was able to resume his surveying in that region!\n",
    "We now unlock that held-out region as an **out-of-distribution** test set.\n",
    "\n",
    "The WiFi rule is the same (coverage is still a disk around Doe), but the *locations*\n",
    "are new. This asks whether the model learned a rule that generalizes, or just memorized\n",
    "patterns specific to Q1–Q3.\n",
    "\n",
    "**Do not refit.** Reuse `model_raw` and `model_aug` exactly as trained earlier, and\n",
    "evaluate them on the Moffitt points (`X_moffitt`, `y_moffitt`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e6e59e",
   "metadata": {},
   "source": [
    "### Problem 4f `[conceptual]` — predict first\n",
    "Will the augmented model do well or badly on Q4? Justify *before* running code.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4f2832",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad7747d",
   "metadata": {},
   "source": [
    "### Problem 4g `[code TODO]`\n",
    "Evaluate `model_aug` on quadrant 4 (`add_radial(X_construction)`); report accuracy and\n",
    "confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d4d2a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: evaluate model_aug on the construction zone\n",
    "# === ANSWER id=4g ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad63095",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visual: see Moffitt's hidden points land inside/outside the learned circle\n",
    "plot_decision_boundary(\"aug\", threshold=0.5, show_moffitt=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca079b6",
   "metadata": {},
   "source": [
    "### Problem 4h `[conceptual]`\n",
    "The augmented model does about as well as on the random test set. Why can it generalize to a\n",
    "region it never saw?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d98839",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb54f91",
   "metadata": {},
   "source": [
    "### Problem 4i `[code TODO + conceptual]`\n",
    "Evaluate `model_raw` on Q4. Why is it even more lost out there than on the\n",
    "random test set?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f75cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: evaluate model_raw on Q4\n",
    "# === ANSWER id=4i ===\n",
    "pass  # TODO: implement\n",
    "# === END ANSWER ===\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24940c5e",
   "metadata": {},
   "source": [
    "### Problem 4j `[conceptual]`\n",
    "Describe a situation where a model that **aced the random test set** would\n",
    "*fail* on quadrant 4.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb832ada",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b82f88",
   "metadata": {},
   "source": [
    "### Problem 4k `[conceptual]`\n",
    "Random split vs. spatial hold-out — what does each simulate about deploying a\n",
    "model in the real world?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b6b398",
   "metadata": {},
   "source": [
    "*Write your answer here.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f89c9f",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "You just walked through an entire ML lifecycle:\n",
    "\n",
    "1. **Learning problem** — features, target, train/val/test (+ a spatial hold-out).\n",
    "2. **Model design** — logistic regression as a black box; raw vs radial features.\n",
    "3. **Optimization** — `.fit()` matches training labels.\n",
    "4. **Predict & evaluate** — choose on val, report on test, read the confusion matrix.\n",
    "\n",
    "Congrats!\n"
   ]
  }
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