unsupervised_viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Learning Approach - PCA and tSNE for Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/elabandari/opt/miniconda3/envs/563/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3155: DtypeWarning: Columns (13) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# Read data\n",
    "data = pd.read_csv(\"business-licences-hackathon.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Drop columns not of interest\n",
    "data_to_use = data.drop(\n",
    "    columns=[\n",
    "        \"BusinessType\",\n",
    "        \"ExpiredDate\",\n",
    "        \"Geom\",\n",
    "        \"ExtractDate\",\n",
    "        \"Province\",\n",
    "        \"LicenceNumber\",\n",
    "        \"LicenceRSN\",\n",
    "        \"FOLDERYEAR\",\n",
    "        \"UnitType\",\n",
    "        \"House\",\n",
    "        \"IssuedDate\",\n",
    "        \"Country\",\n",
    "        \"PostalCode\",\n",
    "        \"City\",\n",
    "    ]\n",
    ")\n",
    "# Change business name and trade name to binary\n",
    "data_to_use[\"BusinessTradeName\"] = data_to_use[\"BusinessTradeName\"].notna()\n",
    "data_to_use[\"BusinessName\"] = data_to_use[\"BusinessName\"].notna()\n",
    "data_to_use[\"BusinessSubType\"] = data_to_use[\"BusinessSubType\"].notna()\n",
    "data_to_use[\"Unit\"] = data_to_use[\"Unit\"].notna()\n",
    "data_to_use[\"Street\"] = data_to_use[\"Street\"].notna()\n",
    "\n",
    "# Change labels for Status\n",
    "dict = {\n",
    "    \"Gone Out of Business\": 1,\n",
    "    \"Cancelled\": 2,\n",
    "    \"Inactive\": 3,\n",
    "    \"Pending\": 4,\n",
    "    \"Issued\": 5,\n",
    "}\n",
    "data_to_use[\"Status\"] = data_to_use[\"Status\"].map(dict)\n",
    "\n",
    "# Define Data types\n",
    "categorical = [\"LocalArea\"]\n",
    "numeric = [\"NumberofEmployees\", \"FeePaid\"]\n",
    "pass_feat = [\n",
    "    \"Status\",\n",
    "    \"LicenceRevisionNumber\",\n",
    "    \"BusinessName\",\n",
    "    \"Street\",\n",
    "    \"BusinessTradeName\",\n",
    "    \"BusinessSubType\",\n",
    "    \"Unit\",\n",
    "]\n",
    "\n",
    "# Make column transformer\n",
    "categorical_transformer = make_pipeline(\n",
    "    OneHotEncoder(handle_unknown=\"ignore\"),\n",
    ")\n",
    "numeric_transformer = make_pipeline(SimpleImputer(strategy=\"mean\"), StandardScaler())\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (numeric_transformer, numeric),\n",
    "    (categorical_transformer, categorical),\n",
    "    (\"passthrough\", pass_feat),\n",
    ")\n",
    "\n",
    "# Transform data\n",
    "transformed_data = preprocessor.fit_transform(data_to_use)\n",
    "pd.DataFrame(transformed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use PCA Reduce Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(transformed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.886612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.250834\n",
       "1  0.445484\n",
       "2  0.638054\n",
       "3  0.727520\n",
       "4  0.776677\n",
       "5  0.812589\n",
       "6  0.840610\n",
       "7  0.859262\n",
       "8  0.873932\n",
       "9  0.886612"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine explained variance to reduce dimensionality \n",
    "var_exp = np.cumsum(pca.explained_variance_ratio_)\n",
    "pd.DataFrame(var_exp).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " > The first 6 features explain a good amount of variability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot first two Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting helper function adapted from UBC DSCI 563\n",
    "def plot_pca_clusters(\n",
    "    data,\n",
    "    cluster_labels,\n",
    "    raw_rows=None,\n",
    "    show_labels=False,\n",
    "    size=100,\n",
    "    title=\"PCA visualization\",\n",
    "):\n",
    "    \"\"\"\n",
    "    Carry out dimensionality reduction using PCA and plot 2-dimensional clusters.\n",
    "    Parameters\n",
    "    -----------\n",
    "    data : numpy array\n",
    "        data as a numpy array\n",
    "    cluster_labels : list\n",
    "        cluster labels for each row in the dataset\n",
    "    raw_rows : list\n",
    "        the original raw sentences for labeling datapoints\n",
    "    show_labels : boolean\n",
    "        whether you want to show labels for points or not (default: False)\n",
    "    size : int\n",
    "        size of points in the scatterplot\n",
    "    title : str\n",
    "        title for the visualization plot\n",
    "    Returns\n",
    "    -----------\n",
    "    None. Shows the clusters.\n",
    "    \"\"\"\n",
    "    pca = PCA(n_components=2)\n",
    "    principal_comp = pca.fit_transform(data)\n",
    "    pca_df = pd.DataFrame(data=principal_comp, columns=[\"pca1\", \"pca2\"])\n",
    "    pca_df[\"cluster\"] = cluster_labels\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    plt.title(title)\n",
    "    ax = sns.scatterplot(\n",
    "        x=\"pca1\", y=\"pca2\", hue=\"cluster\", data=pca_df, palette=\"tab10\", s=size\n",
    "    )\n",
    "    x = pca_df[\"pca1\"].tolist()\n",
    "    y = pca_df[\"pca2\"].tolist()\n",
    "    if show_labels:\n",
    "        for i, txt in enumerate(raw_rows):\n",
    "            plt.annotate(\" \".join(txt.split()[:10]), (x[i], y[i]))\n",
    "        ax.legend(loc=\"upper left\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pca_clusters(transformed_data, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reduce dimensionality to Pass to tSNE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components = 6)\n",
    "pca.fit(transformed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = pca.transform(transformed_data)\n",
    "W = pca.components_\n",
    "Z.shape\n",
    "W.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = pd.DataFrame(Z).sample(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit and Plot tSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(random_state=42, perplexity=30)\n",
    "tsne = tsne.fit_transform(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tsne-2d-one', ylabel='tsne-2d-two'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIg9IoVZE5IDFccx8rc16o0sYxjS7AfHNPn6ZpIvFYjC3PKbR6fHylRovX9lgrd7DMXB6osBWq8crV2u0g5iM7/H5J0f48rljCrYiNxljKGeTlLNJpsoZnhi5c2m/iIgMDoVaEZEDFMcxX7uwxmvzm7SCiNXtDh+fKTFTThHjUO+EtIKIvH/r23WjG3Fto9kvMTYQW7i63qSQ8nBcB99CFFt+/8IKJ0ezvDBT0sm6yPvsDbgiIjK4tOBKROQAzdfavDa/iQWCKCa28Or1Gt2of7/nwGju1hPuqVKaThDhOXBmPEcx09+CJIhjnhjNUW8HdMOItXqX9UaP1xc2ubhcx6oDjoiIiBxCmqkVETlAG40uO1Ez4favM8YW6p2ActbHYjg9UcAYc0uJ5PVqC4vDSM7nC08Os7zdxXcdemHMRrNLO4gZSiVwXcNQJrG7DlczUiIiInLYaKZWROQAVXL+7mrZdMLplw8byKf6s69TpfRueeR0OUM5m8QYw3QpzfPTRSwOjV7Mja0OY0M+06UUMdDohERxxIsnKixWmyxttqi3ugf2OkVERET2i2ZqRUQO0E447ZcgG4ZzSX70yWE+NpEnm07etXGN4zi8dHqEU2M5Xp+vMV1K0+lF5NLw1z91nOubTSrZJF97c5n5eoDnQhzDXy2l8Ty99YuIiMjhoTMbEZEDtDecbjS6VHI+06X0fe0x6zgOs5UsAO+sLJBNwFoz4pf/+ALJhMdGvc3PnZ2kGdZJJDz+zVtLPDs9xLnZ8n6/LBEREZFHRuXHIiIHbCecnpstM1vJ3leg3WtntreQSfIvvjtPN7TEscVxPH7vrWU+/cQIYWQJIlistfbpVYiIiIgcDIVaEZEBtzPba60h4Xnk0wmSnksYRbSCmM1uQGTBdWCylDno4YqIiIg8VAq1IiKHgOM4nBzJkU+5JF0H1/SbUCVdyCZdXAe++MwEZycLBz1UERERkYdKa2pFRA6J87NDfOn5KX7jtQXCCPIpl7/0iVOcqqT5iTPjnJ0sqEmUiIiIHDo6uxEROSQSiQS/8NlZnpsqslBrMVXKcH52CIBXrm3x69+7sXsskUgc8GhFREREHg6FWhGRQySRSPDpU8O7t4Mg4B/9ybXd2VvPhS89P8UvfHZWwVZEREQOBa2pFRE5xF65trUbaAHCCH7jtQVeubZ1sAMTEREReUgUakVEDrGFWms30O4Io/5xERERkcNAoVZE5BCbKmXw3FuPeW7/uIiIiMhhoFArInKI7XRE3gm2O2tqPz5TYH27zbfeXed331jmrcVNoii695OJiIiIPIbUKEpE5BB7f0fk4WySoSz82dUq353f5o/fXSeOoZhO8NPPjPHlc5O4rvvBTywiIiLymFCoFRE55BKJBJ86WaZ9IeSt5TrGQKMb8I131kgnPMCw2Q742jtrPD05xNPHhg56yCIiIiL3TaFWROQImK+2+O71Ta5WW2STLr0oYqsdknBcPNdggSCyrGy1FWpFRERkoGhNrYjIEXBjs8PVagtrwXUMvufQDUJCGwMw5Ds8M5HjerXFH11cpdfrHfCIRfZXr9fjjy6u8j9+66p+50VEBpxmakVEjoBkwgF7888m5PhogUrOx8FyfaVOoZDmX35vkTC2OAZ+/vwMv/Sjx0kmkwc7cJF90Ov1+IffvMqvvXKdMOo3UNPvvIjI4NJMrYjIEXC8kuHkcJZyGhIJn//mDy7xz78zz796fYnTMyVevbpBuxcTx/19bH/tleu8fGXzoIctsi9evrK5G2hBv/MiIoNOoVZE5AgoZ31+5tkxnp8a5l+9vojjeDjG0A0sX7+4xthQhiCOiW1/OjeM+utwRQ6j+WprN9Du0O+8iMjgUqgVETkCjDGcmRgiwOJ7Hpmki+sYABwcoiimF8Z0gpgotnguTJczBzxqkf0xXc7s7t28Q7/zIiKDS6FWROSIMMYwW86STjq4Tv/t31pY2mrw2VPDJF0wBiDm3//4DC+eKB7kcEX2zYsnivz8+ZndYLuzpla/8yIig0mNokREjpDzs0N86fkpfuO1BaLYIbYxX/zYJGk34u988Wk22wEjBZ+PH8urYY4cWslkkl/60eM8N1VkvtpiupzhxRNF/c6LiAwohVoRkSMkkUjwC5+d5bmpIgu1FsPZJFuNHjbhkvNdyrkUALmcyjDlcEsmk3xubvSghyEiIg+BQq2IyBGTSCT49KlhAKy1XFyus1BrY7HUuyGjuSTWWqy1mH49soiIiMhjS6FWROQIM8YwN55nNO9zcXmLXhhzY7PNdifkRDnN3LEhBVsRERF5rCnUiogcccYYojjm3dUWby5t4RhDuxfy3FSJ4UKK4XzqoIcoIiIiclcKtSIiR5y1llev1fjtH9wgisExMFvJ8P2FGs/PDCnUioiIyGNNW/qIiBxR1lqqzR4Xl+tcWNomtv3jsYVrGy3SSY9mLzrYQYqIiIh8AM3UiogcMTth9sLSNquNHo6x1HsRSdfQiyzW9oNtynOYKqYPergiIiIi96RQKyJyhOx0O7620eKNxS0sMFbwyXgO40MpttshYWxJuA6fOllhuqytfUREROTxplArInKE1FoBC7U23TDiZrUx6/UuI4U0GHhqLIEBzhwb4jMnyziOVqmIiIjI402hVkTkCGl2QwB8z8UAFohulhp/bLLEdDnN+FCaUiahrXxERERkIOgSvIjIEZL1+9cyPQdmyhl2Ymsi4TJbyXBmokA5m1SgFRERkYGhmVoRkSOklEkwVUqzUGtTziYopIfIpzxOj+cpKczKEWOtpdYKaHZDsr6nCgURkQGlUCsicoQYY5gbzzNaSOlEXo4cay21Zo/l7Q7tXshWq8d6I2Aok8BzDJOlDHPjef17EBEZMAq1IiJHjDGGcjZJOZs86KGIPDJRFPGn723wp+9tsLrVZqsT4LkO5UyClOcyO5Jls9kjjmNOTxTUJE1EZIAo1IqIiMihFscxv//2Kr/9+hKX1xvEFrAWay3uaJZqFPCNdzeYG8vx1nKdzz7R4QunRxVsRUQGhEKtiIiIHGrz1RbfvrxBrdWl0el3AG8HEZVMkno7otkL6QQRjXbIpV6dbNKlnEsyU8mpPF9EZAAo1IqIiMihtNMI6uLyNqmEQ8J18D2HThjTC2OMAQzEFnphSGxgdbPDBXebyVKa9UbAVCmtdbYiIo851dWIiIjIoWOt5eJyne9dq3J1o83XLq7hGUMpmyDvuxyvpJkqpSikXFa3Wzw1WqBa71JtdekEMebmhlcLtTa1VnDAr0ZERO5FoVZEREQOnVorYKHWJoxhrd5hqtTv+G0sTJfSPDNR4OmJPMOZBE8fK/LW0iadMOLUaJ6Jok+12d19rmY3PMBXIiIiH0TlxyIiInLo7ATRbhjhJ1yODaX5Tq2G77nc2OpQawX86eUqT43m2Gr1eOn0OONDfn+bn3oXRn5Ybpz1dbokIvI407u0iIiIHDo7QdT3XAyGhOsC4DqGOAKMwVoILSxv97hebXN6PE8565NKJhjL+wBMldKUMomDehkiInIfFGpFRETk0CllEkyV0izWWsyUM8xXW0wW08TW4jpJUp5DvdMj4RjKWY/p8hDb7YDJos9nTo0wWc6Q8xPqfiwiMgAUakVEROTQMcYwN55ntJCi2Qn45PEiTyxmeWe5Tq0V0OqGfPn5aWqNLjPlDMbAC9Mlzk4PcW6mpD1qRUQGiEKtiIiIHErGGMrZJOVsEoDpSpb56TbrjS7phEs+4RAZQ6sX4RjDSN6nnE1qZlZEZMAo1IqIiMiR4DgOs5Uss5XsQQ9FREQeItXWiIiIiIiIyMBSqBUREREREZGBpVArIiIiIiIiA0uhVkRERERERAaWQq2IiIiIiIgMLIVaERERERERGVgKtSIiIiIiIjKwFGpFRERERERkYCnUioiIiIiIyMBSqBUREREREZGBpVArIiIiIiIiA0uhVkRERERERAaWQq2IiIiIiIgMLIVaERERERERGVgKtSIiIiIiIjKwFGpFRERERERkYCnUioiIiIiIyMBSqBUREREREZGBpVArIiIiIiIiA0uhVkRERERERAaWd9ADEHnUrLXUmj2Wtzs4xjCS9ylnkxhjDnpoIiIiIiLygBRq5ciI45jr1RY/WNhkeavDQrVJIuHx1Fies5NDzE0UFGxFRERERAaMQq0cCVEU8btvLvN7b65wZb1JtdXlE7MVGtsNmt2QoXSC0aE05WzyoIcqIiIiIiIPQGtq5dCz1vLK1Rq/8doSy9tdqq2AbmD5s6sblHMp1hsBtVZAsxse9FBFREREROQBKdTKoVdrBVyrtgiiGGPAGAjjmCCCXhgT2RgXyPoqXBARERERGTQ6i5dDr9kNyacSJFyHThCR811ia7GxxfccTo3kODGapZRJHPRQRURERETkASnUyqGX9T18Fz59ssw33lkjm/RIuoYffWqUiXySM8eGeG669MiaRFlrd8uds75HKZNQgyoRERERkQ9JoVYOvVImwbFSFoC/9IlpNho9RvM+U8UU48UMpUewnU8URVxcabC81SbpOgRhSIQLwFQpzdx4XsFWRERERORDUKiVQ88Yw9x4ntFC6kBmR6Mo4te/e4NvXFqjE8Rsd3q8NDfK3GiGCJeFWpvRQkqdl0VEREREPgQ1ipIjwRhDOZtkupyh/JBnZuM45tpGk+9eq3Jto0kcx7fcf3GlwTcurRHbfoOqKIavXVyltafZsjovi4iIiIh8OJqpFfkAcRwzX2uz0ehSyflMl9I4jrN739curPHa/CYWMMDz00VeOj2y+zUrW21i238u7+axKIZas8f4UBpQ52UROZx2ll6sbLUZG0rz5EiGG9u9O76fioiIfFgHdiZtjJkG/ntgHIiBX7bW/n1jTBn458Bx4Crw89ba2kGNU462Dwqt87X27n0AFnhtfpNTYzlmK/11vGNDaRwDsYWkZ8gkXbphROlmufFUKa3OyyIyEO6n0d3O1zTaXV6+XONP3lsnjME1MU8fK9ILQmILnutQySU5PZ5nupylkvPVW0BERD6Ug5weCoH/nbX2u8aYPPCqMebfAv9L4A+stX/XGPO3gb8N/GcHOE455KIo4uLyNlfW2/ie4dRIjpnh7F1Cq+Vblzeo5BLMVHKsN7q797H7NbDR6O6G2rmxHJ9/cuRmCbJhKO3xmSfGODuRJ5vx1f1YRAaCtZaLy3UWau3dYzuN7qC/J3ijG1BtdNlo9FhrdPmt79+gnEniew7dAH7jtQW+9Nwx1hoB3722TrMX8sxEgeenS0wUfebGC0yXM5q9FRGRB3JgodZauwQs3fxz3RjzNjAJ/EXgCze/7B8DX0ehVvZJv4nTAr/39hpX1xsYA584XualuWFeOj3Oxi2h1bLR6LHdCXlnpcF6IyDvuxgslh+GUgNUcv7ubdd1+fK5Yzw9WdgtwZsby+G67qN8qSIiH0mtFdwSaAEWam1G8j5r9S4LtTb1bsjl1Toj+RRLmy1Wt7us17tMFNMkXQeDQyeIeX1hk0Y3JOE69OKYP7y4wsmRLG8s1nl+eogvnB5VsBURkfv2WCzkM8YcB14Avg2M3Qy8WGuXjDGjBzk2OdwurjT4k8sbNwOtIeu7XFzeYrTgs9UKKWUThGGI57m0g5jtTohjIJ/qlws3uwFnJgq8vVS/pTx5upS+5fu4rsvTx4Z4+tjQo3+RIiIPwd0a2u0EWoBOELLe6HF5vcFIrt9x3vcc4jhmtdnD98BxDb7ncKyYIpMw1Dsh76016EWWhGdo9kLGh1KcOTakKhYREbkvBx5qjTE54F8A/1tr7fb9foAZY34R+EWAmZmZ/RugHGorW226QYwxhnTCZWmzje+5/MHbqwylPRIOPDddZmO7g3UcHAMfnynhu/2F4DEOzx7L8szkkBqfiMihdreGdrHdU89iod4J2GoH2DhmbiLPW4ubdEOfMIp4aW6UC0ubzNfabLa6/IXnjvG9t1dwcMBCsxPxxuImc2N5Ntshp8fzj2QvcRERGWwHGmqNMQn6gfZ/tNb+y5uHV4wxEzdnaSeA1Ts91lr7y8AvA5w/f/79yxpF7svYUBo/4ZD3PRY32yQ9lxubbXIpjxu1Drm0x1s3NvnZs5NkfY9uaG8G2h+G1mw6STmb3F1DKyJyGJUyCaZK6dvW1I7mfW5sdoD+PoGFdJJqq0sntGzWO/zY6TG6QUx5cojtdo/JYo4oMqw0PNabPWbLWZKeQ7cXYnBoBRH1To9vX26zUu9yZjzP3HhewVZERO7qILsfG+AfAW9ba/9ve+76LeCvAX/35v9/8wCGJ0dAEARstzo8NZoniGIWay1iG3N6okA3jNho9rDASM5nqx3y3EyJVje67YROnYtF5CgwxjA3nme0kLql+zGwG3b9pEfKNXx8pkwviLiE5evvrJLzk5wez9HsBpyseKSTDqWMTzeIcYxho9mjmE5Qa/W3OlvYbJFJJgmC/nvuaCFFMe3ddXs1ERE52g5ypvazwP8c+IEx5rWbx/5z+mH214wxvwBcB/79gxmeHGZBEPCP/uQav/HaAp5jeH6qyE8+M47vGV6+vMHqdpcw6p9seY5DKZsk5yeYLmVuO6HT7IGIHBXGGMrZfnXKXjtht9ENGMv7/MFby8RxTL0T8dRoAWMg5bpshAFvr9TZaPZY2e4wXvB5dnKI6kIPaw0vTBcBqDZD1hohp8ZyADQ6Pb53rXbPPcFFROToOsjux38M3C0N/PijHIscPa9c2+I3XlsgjCCMLK9e32Ss4DNVTFHK+ixsdhgfSlPOeHx8psQT5fRugL3TCZ2IyFG2971xqthvlPfKtSqt65tsdQLyfoKZkoPngHEg4RhcA7lUgtV6l1NjOfKpBFPlNO8sNwhjKKQ8nJtnCY1u9IF7gouIyNF14I2iRA7CQq1FGP3wdhBZVutdfvLpMc5Olvjx06O0eyGzlQwfny0zUkhrRlZE5D44jsOLJ8s4WK5vNFnc7BDFFmsNUWT57MlhFjfbFDJJVrfbpLwUQWTxHJgYShPFsNkOySQcUp7HVClNqxd+4J7gIiJydCnUypE0VcrgudwSbGNrOTmcY25i6JGXF0dRxMWVxl33sf2g+0VEHieO4/DJkxVc1+F712t0Q8t43ifG0gtjKtkk642AdceQ8T2eGs1xopJjKOWRmyjQ6EaM5pKcnihQzia5Xm1h4JZg+/49wUVE5OhSqJUj6fzsEF96fmq3BNlz4UvPT3F+tkgikXho5cXWWmqt4J4hOQgCfv17N/jO1RoJ19AJQj77xChfPncM13WJoohf/+4NvnFpjdiCY+DzT47s3i8i8jhyHIfzx8s8MZqn2Q1JJxxKuSTfn9/CMYZz0wV++ulRjhXTHCummSql2epEd3y/nC6leX66eNua2vfvCS4iIkeTsXbwd8M5f/68feWVVw56GDJggiDglWtbLNRaTJUynJ8dIpF4eJ2MrbVcXK7f1i1579YUYRjym9+7wW98fxFjDNVml1MjBYyJ+Q8/d4qnjw3x1o0t/sHX3yPe80/VMfA3vvAETx8bemjjFRHZb3Ecf+gOxh/lsSIiMviMMa9aa8/f6T7N1MqRlUgk+PSp4X17/loruCXQArtbU5SzSeI45l+/scI/f2WeS6sNHAfmxgq8u7bNs5NFVrbaPH1siJWt9i2BFiC27N4vIjIoHMdhtpL9UOtgP8pjRUTkcNMlTpF90uyG9zw+X2vzypUqjuNgHIhjuLiyTTnrE0SWsaF+Wd3YUHq3A+gOx7B7v4iIiIjIUaZQK7JPsv6dCyF2jm80uiQTDtbGTBczu8HWWssnj5eYu7k/49xYjs8/ObIbbHfW1O7cv1+staxuNvnaW8v8s+9c51vvrhEEwb5+TxERERGRB6XyY5F9UsokmCqlb1tTW8r01+1Wcj6ZhMNUOctCtclTowXiOOZLz03yF56f2G0C5bouXz53jKcnC4+s+7G1lguLVX7nrTV+43v9ZlrZpMu/d36KX/js8Ye69lhERERE5KNQqBXZJ8YY5sbzjBZS9+jmWQaqFDMJekHM+RNlfvZjY3jerf80Xdfl6WNDj2wNba0VcGGttRtoAZq9iF//3iLPTZVuWYt8Px2eRURERET2i0KtyD4yxlDOJu+4RZDjOLx0eoRTY7nHrptnsxuyXu/eso8vQC+ChVpr9/b9dHgWEREREdlPCrUiB+hx7eaZ9T2G8z6eyy3BNunCVCmze/uDOjyLiIiIiOy3g58SEpHHTimT4PRIhi+9MIV3c+luNuny5RcmOT/7wxLoD+rwLCIiIiKy3zRTKyK3McZwerJMJZfi3FSRlUaP2XKa87PFW5pEfVCHZxERERGR/aYzTxEBoNfr8fKVTearLabLGV48UWS0mGW0ePfS6A/q8CwiIiIist8UauXIe3/33oLv8M5ynWu1NjaG8UKS2eEc5Zx/qJof7X3dvhPza6/e4J+/cp0wAs+Fnz8/wy/96HGSybuvjf2gDs8iIiIiIvtNoVaONGstb9/Y4o/fXWe90WU4m8D3XN68sc2bS9tstgOeHM3zmSfK/MipYcaGMrR60W3hLQxDXl/cZrHWYrKU4exk4bZteQ7a3hCbSTpcWW/y3mqTZMKl0e7yW99fJLYGsIQR/Nor13luqsjn5kbv+bz36vAsIiIiIrLfHq+zbpFHbKPe5ldfvs4r16vEMZSyCXzXIZN0qLYCsHBptU4+5ZH0HCaGurhOP8jubF0TRRG/+vI8X31ziSgG14EvPjPBV16cfmyC7d6tdyyWq+sNtlohrmOILIRRxEYzYCidpN3rN3kKI5ivtj7gmUVEREREDpa6H8uR9oPF7d1ACxCE8c31oQZs/1gcQ6Mbslrv0d2zv81CrU2tFfD64vZuoAWIYvjqm0u8vrj9yF5HHMdcXtnmd3+wxL989TrfvbJOGP6wA/HerXca3YjVeo+r1RYJr/8W4DmGdMLBWrv7GM+F6XIGEREREZHH2eMxjSRyQBrdYDe8Aniug+MAGOhX4uI4kEq4pBMu/s7+Njc1uyGLtdZuoN0RxbBYa3FutvzAY4qiiIvL21xabeAZhxPDaeYmhnAch41Gl4Vai2YvYqqY3g2dX397iT94Z4NvvLNKHMN0Oc2fe2aCv/riDJ7n3bLFTjeISLgOngHHgGsglXT5mafH+OZ7G3SCH66pffFE8YHHLyIiIiLyKCnUypE2WcpwciTL5bUmsYV2N+CFmRJpz1DOJHbX1D4zUWCikMJzbsnAZH2PyVIG1+GWYOs6/ed+UFEU8evfnedf/WCZK+stHAOfOl7mk8eLPDOR5+Wr27x6vUZsoZjyeOnMKKdGsvzgRpNvvLNKdHMieb7a5vcvrvLsdJFzs+VbttjxEy5JB4ZzKd5dbdANLY6Bn3l6hM/PjXC92tntfnyvJlEiIiIiIo8DhVo50s5ODvGZk8O4jiEILQnP8MJUgReminz2ydHd7sczw1nW6z0WNju7j93ZuqYwWeCLz0zctqb27GThvscRxzHztTaXV+p89c0V3ljc6pdEG3j5ygYZ3yWZ8HYDLcBmJ+Tly1USrsN2J9gNtADWQi+Id2eL9269k/NdhvMpttsNJoZS9CLLSD5JMuHxzFSZH51TkBURETkIO+cDG40ulZzPsUKSS2stVrbajA2lmRvL4bruBz+RyBGjUCtHmud5/JVPTfOxqaHbOhc/876vreRSjA6lb9u6xvM8vvLiNGenb3+O+xHHMV+7sMZr85ukkg4LtTZB1A/HWNjuhLSDmNV6dzfQ7uhFMXEcU0glcF12g60xkEw4u7PF7996Z7KYJut7dMMYP+GS810MhmY3VBdjERGRAxCGIf/6jRVeuVIlmXDIeZBO+by3uokxHmEU88JMiZ9+ZpSRQkbb54nsoVArR57neZybLX/g+td7bV1zv89xJ/O1Nq/Nb2KBbMLBcxyCKMI1/SuxrgPphMNo3scx3BJsk67DieEcNo5YbYzesqb2J+ZGb5kt3jv+arPHjc0O+feNZW+ZsoiIiDwacRzze2+t8E++fY0o7l+cPl5Oc+GddZ6bLvLtyzVaQcjri1tsdwJemCnyqRMVzdqK3KQzWJEDttHo7q7T7UUx52eLxDZmeauL68KLJyo8M57nyeE0GzOlW9bUvniyzEwlw0wlw/HhPJ87NUKzF3C8nOHsdPGus8V7y5F37JRTi4iIyKM1X2vzxo1tPANjQyl6YUQQWSo5n+sbbVpBiGsclrc6/NGlNRY322y3Q37qmXEcR5uZiCjUihywSs7fabRMsxtTySb5idOjpH2PtOdyYjjDjzw5guu6DA9leX5m6JbuxzsfZifHCpwcu791vO8vR95bTr2XtZZaK7jn14iIiMhHs9Hokvdd8ukEf/reGlEMmaTLC9NFelFE0nVZq3dIJxwmCmk8x+HtpW1Oj+c5PvL+uiuRo0ehVuRDeliBb7qU5vnpIq/NbxJZMBienSpzeiJHLpW85XmH8ymG86mHMv57lVND//VdXK7fNps7N55XsBUREXmIKjkfB1jd7rKzmUIYx4Dl3HSJY8UMnTCkF4S8fHmDY6UMcWwZzaeYLmdUhixHnkKtyIewE/gWay3CGLphxEw5zdmp4gOXATmOw0unR3hiNMuNzQ6phMNsOUM55x9IeLTWUm32mK82ee36JkOZJLmUh8GwUGszWkipmZSIiMhDNF1Kk00lafcinhjOYa1lvJBio9XlD99Z4/Jaf8eCufE8Jys5lusdHMfw8pUqp0ZzfPJkRRec5UhTqBX5EGqtgMVai2oz4Hq1hQUurTQIY/j4bOmBP1iMMXSCmFYvotWLqDa3Hums6M4WAuuNLmEQs1hrc63W4t21JkMpjyfHckyVM+qQLCIisg8cx+GFmSLfem+dThCT9T022z3eWtrmmYki+VSSWivgzRvbfGK2zGqzy2QpDViuVVs8OV7QZ7McaQq1Ih9CsxsSxuwGWuiviX1vtcHJkdwDf7DUWsEtZb7AI5sV3bulUCuIWN5qc7ycIZ/2MAa2OiELmx2KWZ+876lDsoiIyD44PZ7npbkxvnFpjTCO6UUxT40OUWv1cB2DYzwc45DwXJ4aK2CMBQz5VEIXnOXIU7s0kQ+hv8drxPu2jSWZcGl2wwd+vrs95sM81738sLS4RbXZw1p7y5ZCQRQTRJZXrtdwHcNsOYMx/eO9IFKHZBERkX3iui5fPneMv/GFJ/jZZ4/xo6eGaQcBQdQ/23AdKOcSFFLezfMPw8dnSviutuQT0b8AkQ+hlEkwU05zaaWxG2yHc0lyvvuhPlju9piH+SF1t8ZPrV6w+xoSroPnGOIYmt0IzzGcHs8znPN58YkKM2Vt9i4iIrJfXNfl6WNDnB7P8/ULy6xul3n5ygaxhZlymp95ZozpcpqJYpp8KoHvwrFSVhec5chTqBX5EIwxnJ0qEsb9kuNkwiXnu0yXMh/qg+VR7Bt7txLnY8XU7pZC6YTDUNojCGOySZetTkTZ93huakiBVkRE5BFxHIcvnB7nxHCezz01TDuIOTmcYW68gOM4PDmu7fZE9lKoFfmQHMfh47MlTo7kPvIHy/3uG/tR3K2UOee7u1sKWQzDOZ8feXKYJypZYmMYL6QoZZP6wBQREXmEHMfhxGieE6O370N7ry35RI4ihVqRj+CD9no9qOe6k7uVMudSSV46neHUWI6NRpdKzme6lH7grYlERERERA6CQq3IEXGvEmdjDLOVLLOV7AGOUORWvV6Pl69sMl9tMV3O8OKJIsmkZiZERETkVgq1IkfEoyhxFnlYer0e//CbV/m1V64TRuC58PPnZ/ilHz2uYCsiIiK3UH2hyBGyU+I8Xc5Q1jpZeUyFYcgfXFjjDy+sUMmmSDqGMIJfe+U6L1/ZPOjhiYiIyGNGM7UiIvLYCIKAf/LtBV6dr/LWUh1j4anxPFvNHr3IMl9tHfQQRURE5DGjmVoREXksWGv5w4vr/NNXrpFwDFiLNfDOcp1SzsdzYbqcOehhioiIyGNGoVZERB4LtVbAhaUtwgiurjf40SdHdoOtMZafPz/DiyeKBz1MERERecyo/FhEZECEYcjri9ss1lpMljKcnSzgeYfnbbzZDSlnfVwHtjox0OHnzk4QxJYvPjPOj58eUZMoERERuc3hORsSETnEwjDkV1+e56tvLhHF4DrwxWfG+XMfG6PajoitZbyQojTADcCyvsd4wedHTo3yx++ustWJeXNpm798fpaffHrsUAV4EREReXh0hiAiMgBeX9zeDbQAcWz5rddukE56LNTaOAZG8j4nRrKcGS/shltrLbVmj+XtDo4xjOT9x7bzdSmTYLqSA+B4JU212eX0xBA/NjesQCsiIiJ3pbMEEZEBsFhr7QZagNhCM4hYqLVwjCGMLS9fqXJlo8VqvceZ8TxPjeW4uFznO5c3WGn0CKOYJ0ZyPDdZYO7Y0GMXbLWXsoiIiHwYCrUiIgNgspTBddgNtpHtN08qpDy6keW95TrWQhDFdIOIhVqbdNLlwnKdqxtt1hpdLLC01cEzhpGhNJWcf6Cv6U529lIuZ7V2VkRERO6Puh+LiAyAs5MFvvjMBO7Nd+2kC1985hjdICQIY6ztH0+4Dn7CBWCj0WWrHewGWujP8F6vtVirdx/9ixARERHZB5qpFREZAJ7n8ZUXpzk7PdTvflxMk/bgjaUWq/UOxkDB95gqpsj5/VBbyfn9LXH2PI8xkPAcYmvv/I1EREREBoxCrYjIgPA8j3OzZc7NlgGw1jJazLG23WFuPE+9E5JLeRgMU6U006U0H5sa4tJKg81OiDEwW84wlPIYL6QO+NWIiIiIPBwKtSIiA8oYQyXnU8n5zE0UqLWC2xosnZsp4RrDxZUGDpBMOMyN5SlpzaqIiIgcEgq1IiKHwN0aLDmOw/MzJWaHc+ooLCIiIoeSQq2IyCGnjsIiIiJymKn7sYiIiIiIiAwshVoREREREREZWAq1IiIiIiIiMrC0plZE5ABYa6k2u1zdaNELYo4VU0yXMziOrjWKiIiIPAiFWhGRR8xay9tLW/ybH6xweb1BL7JMl9Kcny3yU8+M47ruQQ9RREREZGAo1IqIPGK1VsD357e4vN6g3QvJ+gkWN1tkki7lTIJPPjGiLXdERERE7pNCrYjII9bshmx3AqyNcV2HP31vjSiG+WoLzzU8MZpnuJA+6GGKiIiIDAQt3hIRecSyvkchlSDnJ/jutSpRDMaAxfDK1SqXVpt3fay1lmqjy1s3triwtM1Go4u19hGOXkREROTxct8ztcaYPGCttY19HI+IyKFXyiR4bnqI91YbxPQD7Ww5w4lKlsBaNhpd4ji+rWmUtZYLS9t850qVtUYPA8yUMzw7WWBuoqCS5bvoN+XqsVbvElvLeCFFKZvU35eIiMgh8YGh1hjzLPDfA+X+TbMG/DVr7Rv7PTgRkcPIGMOZiSF+5mMhi7U2kY1IJz3+7HqVhONSySZJJz1eOj1yS7CttQIurDRYa/QAsMD1aotCOsHoUJpyNnlAr+jxZa3l4tI2P1jc5nq1hbUxyYTLmdEMnuux3uwxVcpwfnaIRCJx0MMVERGRD+F+Zmr/IfCfWGu/BmCM+QLwy8Bn9m9YIiKHmzGGF2ZK/PiZFj+4scmfvFsl5Xk8NZYj53u8Nr/JqbEcs5Xs7mOa3ZBuEN3yPBbohhHNbqhQewe1VsC1ans30F7daBNHAY12yG9+fwHHOAylPb70whS/8NlZBVsREZEBdD+hNrsTaAGstV83xmTv9QARkaMujmPma202Gl0qOZ/pUvq2cmLXdfnyuUny6SSNbtxfa+u7YBwssNHo3hJqs76Hn7h1ux8D+J5L1lffvztpdkO6YYQFtjohK9sdvjA3zK988z0wDtiIhOfwG68t8NxUkU+fGj7oIYuIiMgDup+zoMvGmP8S+B9u3v4KcGX/hiQiMtjiOOZrF9Z4bX4TSz94Pj9dvK2cGPrB9vREnjcWt9jb7skAlZx/y9eWMglOj+XYbvVuWVM7W05TygzeDOOjWOua9T18z8UA7V4/3NY7IUFsSHoGay1RbAkjWKi1Htr3FRERkUfnfkLtfwD8n4B/Sf886xvAX9/PQYmIDLL5WpvX5jcxxIRxP0y9eq3KyZEsJ0Zyt339dCnN89PF20LwdOnWbX2MMZyeKDBWSLG83cExhpG8T3kAmx7dttYVGMkl+eSJMqcfYtOrUibBbDnNdjtgq93DGKjkkmQShiDuX0ZwHYPnwlQp81C+p4iIiDxa9xNqS9bav7nvIxEROSQ2Gl0MMWuNgHfX6gShJbIx40Wf6VIKz7v1rddxHF46PcKpsdw9y5WhH2zLOZ/y+2ZxB80ta11vHltr9Liw0mDsIzS9stZSawU0uyFZ3yOfNLR6IQkXvvDUCJ+YGaLa6vLvnpvmX3x3HtdxySQcvvT8FOdnhx7eCxQREZFH5n5C7X9njJkE/gz4I+Cb1tof7O+wREQGVyXnE8bw7lqd7XbIdifAAV5+b4PRXJqfembstsDqOA6zlewta2gPs71rXffqBh++6ZW1lovLdRZqbQCiOOLaRpO3lrZp9WKSnsMnZ4t87okRbmw3ePrY01SbPSaHMjw7mb/tYoOIiIgMhg/8BLfWfs4YkwQ+AXwB+NfGmJy1trzfgxMRGUTTpTSzlSwvX97YDbRz4wU2mwEXlrc5c6xwZMLr3exd67o32PqJD9/0qtYKdgMtwNX1Jv/2rRWMMSzU2jgOxFGM5zhcXWvgeA7rjR6Nbsxavc3zs5WHWvosIiIij8b97FP7I8CP3vyvCPw28M39HZaIyOByHIcXT5R4a2mLUi2J5xjq7YAgtjiO4cpa/a7lxUfF3rWue9fUnh7LfeimV81ueMvt1UaPyxtNjpezJF1DMevTCmLeWtoiiOFP31ohsvDtKxt86YUpMiv1j1T6LCIiIgfjfi6HfwN4Bfivgd+x1vb2d0giIoNvupLlhZkSF5brxLGlE0Y8OZrn1StVwtASxeaO3ZCPCmMMcxMFRgqph9b9+P0zvAnHwTGQdA3D+RQXlraZGEoxPuTzx5fWaQcRmaRHGFm+fXmD2WJG+/2KiIgMoPsJtRXgs8DngL9pjImBb1lr/8t9HZmIyABzHIefe3aclOfy9tIWK/UuS7U2s8NZjLG8Nr/JqbHckS5DNsZQyfm3bV30YZUyCaZK6d0S5OlSih97aoyFWovLa3UCGzNZStPtRWx3IjynX/psTH8s7SjWfr8iIiID6H7W1G4aYy4D08AU8Blg8DZEFBHZo9fr8fKVTearLabLGV48USSZfLgzdJ7n8VPPjJFKOvzxpQ3OTqdwjCWy/ZnIjUb3SIfah80Yw9x4ntFCimY35IWZIcYLSb5zZYvAWrpBRNozFLNJHGJSiQSOgUouTSbh8OTIhy99FhERkYNzP2tq3wMu0l9H+/8G/rpKkEVkkPV6Pf7hN6/ya69cJ4zAc+Hnz8/wSz96/KEHW8dxODGc49Wr/T1odwKtgYc2Qym3stbS7IbUO5bnZspMFLPk0x7XNlosbrborjX5c2cnuLLeZCyfJuu7/MiTw3ziRElNokRERAbQ/dRZ/XVr7R/tPWCM+ay19k/2aUwiIvvq5Subu4EWIIzg1165znNTRT43N/rQv990Kc3z00Vem+8HWwM8P11kupR+6N/rKLPWcnFpmx8sbt/SfOoTJ0r8+OlRvn2lSjrp0g4i5sbz/IXnjpFMeAzfY19gERERefzdT6j9e8C59x37b+9wTERkIMxXW7uBdkcY9Y/vB8dxeOn0CKfGcmw0ulQUovZFrRVwrdreDbQAa40eF1eafP7JYf7C85Msb3dwjGEk71P+CE2pRERE5PFx11BrjPk0/fWzI8aY/2TPXQXA3e+BiYjsl+lyBs/llmDruf3j+8VxHGYrWa2h3UfNbkg3jG7Z9xagG0Q0exHT5QxllXyLiIgcOveaJkgCOfrBN7/nv23g39v/oYmI7I8XTxT5+fMzeDcvz+2sqX3xRPFAxyUfTdb38D2X98+9+glXXY1FREQOsbt+yltrvwF8wxjz31lrrwEYY8attcuPbHQiIvsgmUzySz96nOemivva/VgerVImwWw5zXY7uGVN7ekxdTUWERE5zO5nS59re27+DlpLKyKHQDKZ3JemUA+DtZZaK6DZDcn6HqVMQms/74MxhrmJAiOFFGv1LrG1jBdSlLR2VkRE5FB70HosnRWIiOyjnQ6+16ptumGE77nMltPMTRQUzN4nDENeX9xmsdZispTh7GQBz/Oo5HxtlyQiInKEPGio/f/syyhERASAarN3y5Y0BthuB4wUUgpqe4RhyK++PM9X31wiisF14IvPTPCVF6fxPK2fFREROUru2ijKGFN+/3/AP9vzZxERecjW6t1btqSxwPVqi7V69yCH9dh5fXF7N9ACRDF89c0lXl/cPtiBicgjY62l2uwxX21Rbfaw9v29z0XkqLjX5exXYXeiYAao3fxzEbgOnNjvwYmIHDWxtbdtSWNvHpcfWqy1dgPtjijuHz83q+uuIoedtZaLy3UWau3dY1OlNHPjeS3VEDmC7jpTa609Ya09Cfwu8OettcPW2grwc8C/fFQDFBE5SsYLKUZyt3ZhHsklGS+kDmhEB+tuMzGTpQzu+z7BXKd/XEQOv1oruCXQAizU2tRawQGNSEQO0v0sPPqEtfY/2rlhrf2qMeb/so9jEhE5skrZJJ88UebCSoNuEOEn3P6WNNmjtd2QtZZas8eF5Tr1TojngMXszsScnSzwxWcmbltTe3aycNBDF5FHoNkN73q8fI/3S3WXFzmc7ifUrhtj/gvgV+lXwX0F2NjXUYmIHFHGGE5PFBgbSh/Zk66dssK3l+u8t9ror4EpZyhnEyzU2owWUpSzSb7y4jRnp4du634sIodf1r/zv/W7HYfbS5YNlkouSTmbxGJwgGwqceTec0UOg/v59P/LwP8R+HX6ofaPbh4TEZF9YIyhnE3ec7bhMNspK+wGEfDDZlmF9BCu88OZGM/zODdb1hpakSOolEkwVUrftqa2lEnc9TF7S5YNlmoz4Pvzm2R8j2Y32r14NlnK3HNtrmZ7RR4/HxhqrbVV4G89grGIiIjslhX6CXf3mAW6YUQm6d1zJkZEBt/9hEZjDHPjeUYLqdu+7m6P31uyHMb9i2WxtVzfaOF5DhdX6nzieOmWipA7jW1nL/FeGOK5LkNpj9PjBUrZpMKtyAG565mBMcYF/lfAFPBVa+2f7rnvv7DW/lePYHwiInLE7ITWnO8ynEuy3uhhAN9zP3AmRkQG24N0Nd6paillEruzsJmky+p2h8XNzm2Pz/oeBksYw1a7R2xj6u2QtUaPpNfvPLdQazFTzrC81b5jqK42e7yxuMX1aotuFPcDcM5nZbvLmYmCui/vsziOma+2uLHZIekZSpkkvTAmBsbyfv/iRS/SDPoRdK/L3f8QyADfAf5bY8w3rLX/yc37/h1AoVZERB66vWWFk6U0Q5kko7kkpycKlDUTInKo3a2r8T1nTveE4Ci2LG+1OVZKYzC3PL6Y9gDDG4ub+AmHqxstcikX37u5h6WBdhBxdaNJbA2u0w/Ge0P12naHt27U6UYRl1YbWAvdXsxsJXPPccpHF8cxX7+wynevb3K12qTZDqkUfCbySRKui5908V2HyXIGs6exoD4zbnVYy+fvFWo/aa09C2CM+X8A/y9jzL+kv5528F+5iMiAOawfRO93r7JCETncHrSr8d4QbLHUWj3eW28QW0s64ZJKuqQTLlfXG1RyPpaYk6M5giDCnSxwYWmLcs5nsdbmeCVLtd7lidH8zY7rfXvDajOIWG10yfou8c0v2OoEdG5unP1B3Zflw5uvtXl7uc7VaotuYImA1xc2aY3keH66yOuL24wVUgxlffK+91AuMhyGz91bXsPNSoaF3UqGmEwyQSphSCU8cr5LLpUcyNd5r1C7+xtgrQ2BXzTG/B+APwRy+z0wERH5odtL8nY+iBwqOZ/pUhrHuevW4wPnqDfLEjmqsr6HxdLoRrvbmuV8965r6XdCsMWyWGvT6ARcXmuwXu9SyfpkPIetbsDceAHHGLJJl1I2ifU9kp5DqxviOQ6FlIcLeK5HxnNxiAliQyeMiGJY3WpTyiTIJF0KKQ8MOAZiC+mki3+zfFlr/vfPRqNLL4yxFsI4JraWOIZuENPqRVgLQRTTDSLyN38OH+Uiw4OUwj+ufvgamtS7EdV6l0Iqge851Hs9Gp2I69Umw7k0VzcazJRznJ0qMF3ODtTrhHuH2leMMT9jrf03Owestf9nY8wN4B/s/9BERGTHrSV5Me+uNXlzcZvpcgbXGJ6fLvLS6ZFDFWxF5Ogppj0cDJdXG/2SYOD56eLN0uHb7YTIRjdivdFlrd4hl/TYboekEg6bnZgwslxc2SbrJ7ARvPhEGeNAJwipNgNqrR71TkC10eOF6SKdMGKk69MKIt5ZaRBGsLrdwQIjeZ/nZ4Z4+8Y2T47mWNpuM1PKUEwnteZ/H+xdQ9vtBeT8frm47yXYbPVwHPATDpmkizGQcJ1bmgze70WGOI6Zr7XZaHR3LxRvtkMWay2iuN+o0PdcFmutgSox7587NHl3rckbi1tstQM2m11+4sw43V7I//S9BSaLWbbaVZ4YzXNto8FEMYUxzkC9TrhHqLXWfuUux38F+JV9G5GIiNxmb0levRvxxuL27lVpx3P5wfwGCddyY6tHznc5WUkxN1HEdd17PKuIyMF6f3kn1gKWj00O3QwSDvVuwDsrDUYLqd3QuLeccqqY4vsLW7SDmPVGj5FckkI6wXqzy5X1FinP4czEEIUUNMKIZi9kqx2yWu+wWu8QRDGdMGYk73Ot1iJYt/SimKTnMFvOkEs61LshF1YajBZ8hrM+w/kUURQxW8lQzCR5YbrITCUzUDNbj7u9a2iXt5tkEh7VVo+k53Ftrc6T4wWmSmlGcz69IOTZyQK+65Dz+59793uRIQxDfvsHy/zpu+v4CZdiyuP5mTJPjqRZ3Ozw3loDz3XIJBxmylmanWAgwp61/fXlS1tdvnutRieI2Gz1WKsH/PG76zw1lsVaQxjHdKKYq+tNnp4sUO8EjOZTA1dK/0A1EsaYX7bW/uJ+DUZERO5s79XmRifE2v4MRsJ1cIm4WuvwyvUrXFpt4hrLF5+d4NmlOn/+3LSCrYg8lu5U3plJusSA6xjSSZfFWpv1Rg/HGBZqbaaKKSzc0t14spjixZNlgigmk3BZ2W7z2vwmowWfOLaEsWW+2mRsqEwl5zJeSOM6XVIJl/dWm4RxTBRDDFxeazJTyeI5Do1OSK0VcHZqiE5g6QYRK9tdLPDkaI61eo/Ndo/FWpvXvE022z3KOZ+cnxjINYmPm501tAubLfJ+kj+8uALAbCnNC7MVLq1t80QlT60ZcOZYgc/PDeM67gN1P47jmN9/e41/+p3rRHH/c3W0kGS7HdDqlfjGOyukPBdMv0Qd4JMnH/+90Xf+bc1X2yxttVjaahPG4ACphENkLdVWQDGTJJVwsTGEFoIwJp/qXwgYtFL6Bx3t+X0ZhYiI3NPejsC5lIdjYDjr4xroRPDda1VK2RRb7R7phMNbS1uUskm+c2WDT50cVlmyiDwWdmdmOwH1bsh7q3V8r3/hrRPGBFFML4xIJTxqrR6Nbohn2P2aa9U2rT1rJqEfcM/NFDk9UWBle5Xkza/NJFwquSRxtPPNYaaSpZRJ0Oz1Z4HL2QTYfjVMGFuSnks26RJEMbF1AUMQ9jtC+QkXxxgsBgss3yxJdg1sNHq8t9rk5GiOvO8N3NrLx9HOGtpM0uPSap2bvbhI+wn++L01RvNpurGllEne/L2wzFZ8Pihy7i01BriwtLX73LG1XFxqsDnk4yddukHMdidiNJek3gmZrWQYhB/pzpIl17GM5FJ0gphGNyTve4wVfGIbk3ZdJotprlYbnBjOstHsMlPOMJIbzFL6Bw21q/syChERuae9HYEbnR5pz+GtG9vEGOrtgNlKju/P10gnHJ4cK/DqtRrtXsxbN7Zp9azW24rIgbDWUmv2WN7uYLBstgKWqm3aseXqeoO05+IYy3YnJptycbAM59NcWWtSbQfUmj0+PlMi4VhiDL0wZKsV3NJEytDfm/TcTBHPgWsbTaI4YrsVMjeWpxtEJD2Hs1NDnKhkGSmkWNjs4DmGF2ZKXFqpU8x6dIKIfCpB0oFM0iPvx1hjSHgOZd/j9FiOkbzPQq1NN4x2uyMnPIelrQ6phLvbpEjb+3x0lZxP0uvPKnqOgzH9jtTWgrWGGEsq0f9cs/RD8Gwle8/njOOYr11Y47X5TSyQS0Ipm+CZyTwZz+V7V2tstLoMZTxiC+1eRC7lMZTpdwROu3B5rcE7S9scK/YvXDyO1VA7S5bq3ZhmJ+CLH5vgT99dI5fy2Gz1+NTxYZq9HptdyxMjeSpZj7/0ySmenyySz/gDWWlw36HWGJO11v7Mfg5GRETubm9H4KlShjPHhthodGl0Qv6b379IL7I8O17gtfka1kLCcTDAa/ObnBrLfeCHvYjIw2Ktpdrs8fbSNm8v9RvUrG538RwYG0rzp+9t4DmGhGsYyacIwpiUnyHCcK3a5JnJIYaaPXK+R7XZZaKYxnMsvcCyUGuTutkMaDSfZCzvs1rv0OyGTJcyHBtKkfRcLixtk0165FMe05UsZ27udQ3sVr5UskmGjlfI+/3QdHGpwcJWh5RnyKcSnBrNMTGUYnwoTWnPY69ttDD0w1Q64dC5Ga72NikatDWJj5vpUpoz43mstVzbaDBZStPqRZQyCW5stZgspkjf/Hs39EPwB5mvtXcDbcaDRi/mV775LsmES7Mb8DMfm2Cr2+HTJyu8fWObSs6n1uoRxTHtbsiVTsjvX1ynF8Ucr2T45GyFL5079tgF253S4W4Q0Ykg6Vi+MDeKMf1/c5mEw7FiiXTSIwamimmmy5mBvvj9gaHWGPMZ+o2hcsCMMeY54Jestf/xfg9ORETuzHEcZitZZitZwjDkneUJlusdrLVYC88cG6IXRZSz/n1fwRYR+TB2Soob3QBrwbGWlUaPjUaXP7taY3mrQyGdYLsT4hhYb25jMPTCmITr8d5ak9PjeYIoppBK4BhwjWGq3G+8tN7o0Q0j8FxaQchTYznmq20cA2vbHZY2O3juNmCYKadwcFioNUl4/fWVx4dzfPpk+Zbgcae9sAGeGh9ird4ltpbxQmo3yO6Uc2Z9j6duztjmfYeVRg+spRPE5FPebpMiGLw1iY8bx3H4wulRnhjJMlPJsrTZIuG5xFHMczMl1rbbxDi7HbKnS+kPfM6NRheLpR3ElLI+//TPruC5LmFkKaR9/vTddX7p8ye5uLRJOe9zo9bmydEcp0Zz3Nhs8+3LG/QiSyGV4OpGi4TrcGaywNPHhvb/L+QB7CxZqt+csQ1iw2wxRSGdoBPGPDddZKZ8uBqb3c+/tv878NPAbwFYa79vjPncvo5KRETum+d5/C8+PcPYkM/SVpdCOkG7G1LJZxhKe/d9BVtE5EHtNqSptVistdlo9JgqpVnabJNPe/Sifpnu8lb/drsbEWGJrcViyCT6W7Fkkx4nhrOkEy6X15r4Xr+seLKUZiiT5KmxPLG1OE6/2c1QOkk3DLmwVKcTRGTdfii9uNKgE0QU0kmcoL9Q8geLW8xN5G+5sHe3vbArOZ9Kzt8N6gvVFiv1LrVWD4PB0l/DOZZLkky4YPuzYdb2n9PG/TFOljIDtybxceQ4DrMjeWaGc7d0yR5KuSxsdm7ZgueDZhnjOAZr2Wz2iKxlo9HfCirhOpSzSWIsYeTQ6MTUOuCamBMjWc7NFOlFllo7pHtzffV2J6CS9Qkiy8pW+7ELtbtLlvI+Y3mfeifEc8BieGose+gCLdxn+bG1dv59Lzy629eKiMijl0wm+dmzk8xvNMmnPS7cqJNPeRjj3PcVbBGRB7Uzg9nfJ7YHwHtrjf7FtSBmZ+Gp6zjEsWUonaDa6jI+lGJpq0PW9xgrpJitZBjJ9y++PT9dBOzNfWoNZ8bzzI3nqbUCbmzebM7kABiC2OK4Pwwz7aC/L20QxbvNpe5WrXLbDDOQTSUopj3eWWmwUGsTxZY3Freo5JJMFlMsbnZ4fX6Lp48VeOvGNpVsAgvU2j1ubLZhIs+ZYwWeGsv1Q+77tiwaxLWKj4M7XYTYqVa6l92fcafHhaUG37u2QbXZ4/JGg794dhLfg6Tn4jhgY4PvWSZLScaKI7S7/Vn+yWKKC8sNcn6/SWNsb67rxZJwDWNDj+fnqzGGcs7n008kj8Tv4P2E2vmbJcjWGJME/ibw9v4OS0REHtTOFe3pSva2TeQHeZ2MiDy+dhrSdIMfznd4rkMQ9mdJZysZwqjJdidgKJ1kOOPxzESB5e02p8fylLMJnhjLc7KSpRXEZH2PYtpjsx3edhK+tws89Dsij+aTbLV/uI93OuHQwZJwd9ZaWuKbwbra7O0+151mmGfKGcrZBJVckrVGf2Z2pyHUeqNH0nN2g3u9E2CB1UaXtXqXVq//etcbPd5c3ObkcI5SNnnblkXqivzo7N0yarXe4VvvrTOaT5FJ9i/2Lm7W+SufOsFvfG+B9XqPhAM/8+wEy1sdiimfp8YLnJspstnuz3J+7FiOjUaXd1bqGAMnh7O8MFVibix30C/1nu5WlXDY3E+o/Y+Avw9MAgvA7wH/6/0cFIAx5mdufl8X+BVr7d/d7+8pInIY7F1vKyKyn3bWje5tkOQZmClluFptUcn5TJUyJBzDRDFFJunhALExGMMte7pW9jzvnU7C93aBb3ZDMkmX6XKKP7tSuxlCYW4sh4PDtWoTi6XZiRgr+lSbXarNYDdU3mmG+Xq1RSFd4MJyv4R5KJMkk3B2G0LVO0F/HEA+lcDQf/xWOyTh9htNJTyHtUa/2zM399bdS12RH52dnzH0f3atbsz3av39izdqHUZyPh+f9vn589N0wgjfc5mv1vn+Yo8fP5NhsxWw2Q4pZRJMljIA/Pmz46w1KqSTDmfG8sxNFB67JlFH1QeGWmvtOvBXH8FYdhljXOD/Cfwk/SD9Z8aY37LWvvUoxyEiIiIid7czezpfazGcS7LR6DFdzlLKejwxNkI5598SXD+q9886lbNJxgtplrc7OMYwkvcppj0WNjss1tqs1DvkfZd+cfEPQ+WdZpgtloVai3YQs90OWG/0GM0nmS6nma+2yacSNDoRM+UMvgsz5Qxv39jCc/oBfbacIQhjDOAYs/s93k9dkR+NvX//SddhuxvgOJBwHayFjWaXwBquVdvkUi6XVlu7X9/ohOT9xO7P6k6Nxe72+6yS84NxP92PR4D/EDi+9+uttf/B/g2LTwLvWmsv3xzDPwP+IqBQKyIiIvKY2Dt7Ojd+69rUR3Eyv7NusPy+ZnizlSyOMbR6t7eB2QkbcOsMcyeI+2tyw5gnRnI3y1Z7nBrN8uNnRihnk1SbPTYaPWIM5WyCn3hmjKtrTWrtgF4YE9t+2B3J+3d97eqK/Gjs/XtOJRxmyhkWai3yKZdumOTpiTzT5RTztSSRZXdG3hjIpbxbnuN+S3j3ljzvUMn5o3E//6p+E/gm8Ps8ugZRk8D8ntsLwKce0fcWERERkfv0uK7Zu1t43Jk9e/8M88RQino7YLKUoZL1KGaSdMOIp8byu6FkunzrLFwx7VHOJLlWbdO9WcI6W07fth/ujqlSWl2RH5G9a7BTyQTHhnyemyriOpDxPfK+y1NjBVo9y/fna4zkfNabXZ6ZLJD33Q/1s9pb8rxDJeePxv2E2oy19j/b95Hc6k6XMuwtX2DMLwK/CDAzM/MoxiQiIiIiA+L9jaXgh6HyTjPMzU7AfK2D50CM6YefpMdoIbU7y3anAD83UWB0KH3HctMHKVuVh2vvz7jRDThRSbPR6GFvxoypUppyzuel0yOcGus3gUolXHK+Sy6V/FA/K5WcH5z7CbW/bYz5c9ba39n30fzQAjC95/YUcGPvF1hrfxn4ZYDz58/fEnhFRORgRFHExZUGK1ttxobSzI3l1ERDRA7E+xtLvT9Uvj+gWmtxHOeBZ1bvNVP9uM5iHxV7//6nS5k7rnU1xjy05or3qg6Q/XU/f8N/C/jPjTFdIOBmybm1trCP4/oz4EljzAlgEfifAX9lH7+fiIh8RFEU8evfvcE3Lq2BjYksPD9d4keeKDE3MaRwKyKP3IOEyg8KwTI4rLXUmr1bGojt/B7s5wWGe1UH3EkURVxc2uZarT/Op0azzA5ntQ3fh3A/3Y/zj2Ig7/ueoTHmfwP8Lv0tff6/1to3H/U4RETk/l1caewG2vVmwLVqk9cXNlnZ7nBmqcmXzx1TsBWRx5pmVgeftZYLS9t850p1d6unmXKGZycLzE0U9vUixYNcGImiiN/67gJvLTd4c3mbhGsYySX5mWfG+bEz4wq2D+h+uh9/FnjNWts0xnwFOAf8PWvt9f0c2M1y50dZ8iwiIh/Bylab2EIU098jMu53F2z1Qr5xaY2nJws8fWzooIcpIiKHWK0VcGGlwdrN/YctO3sQJxgdSu/7BYv7vTBycWmLN5fr/P++O08YgePAx44V+c6VGk+OD2mv+Qd0P5cA/gHQMsY8B/ynwDXgf9jXUYnILmst1WaP+WqLarOHtbcvIb/T1+weW2/w1o0tXr1W5ep6g9WtJt96d53ffWOZtxY3iaJH1dRcDruxoTSOgU4QYeP+sZ1GK7Hth14REZH91OyGt+w/DP1g2w2juzZyOghXqy0urTZIeR6FVAKD4Y0bm7TDmI1G96CHN3DuZ01tZK21xpi/CPx9a+0/Msb8tf0emIjcfb+zJ0ezLGx22Gh0qWQSrDZ7XF5tkrzZtW+qmMYANzZbXK92ePV6jXzKI+MZHMfljRubRBYmiyk+PlPmpz82RiWX0roh+UjmxnJ8/skR/vDiCsbpXzX97KlhNlv9tUJjQ+mDHqKIiBxyWd+7Zf9h6DcE8j33sWnYFMcxK9td1ptdttoBAMVMknYQEkcxlfftuywf7H5+stvGmL8DfAX4nDHGvc/HichHdKf9zuarTa5vtPjB4haOgYRreGtpm+GcT8JzmSqmaHRCMkkPbMyr1zaILDR7IWHk8O3Lqzw5lqMTxry5tM2NrQ69KOJTJyucmRhSsJUPzXVdvnzuGKcncnz7cpUbWx02Wx16keHzT44wN5Y76CGKiMghV8okOD2WY7vVu2VN7Wz58dkj+PpGi5XNNu1eyGw5y3trDTZbPU6NZvnE8RLTJV0EflD3E04vAl3gF6y1y8aYGUBF3iKPwJ3KZNYaPVa2Olgg4RkurjS4tNrAWnAch3Y34PhwlnImSa3dY36zw+p2Bz/hMpJL4jgOroHVrS4YiELLlfUWm82A0bzPcF5vpPLhua7Lx6ZKnJkoaGsfERF55IwxnJ4oMFZI3db9+HG5cH9lvcGby3WeOTbEheVtXpgpYgx88WNj/OzZCTWJ+hDuJ9Set9b+4s4Na+11Y0xrH8ckIjfdqUym0QlIeA5RENPoRgRhjAF6YUw6aVjZ7mGMIeEa1updVrfaWAwGQxRBo9MjlRgC06bW6rHR6JJIOOR8j1eu1vjpj6kMWT4613V5+tiQGkOJiMgjZ4yhnPMp3yzjtdbecY/a/RbHMfO1dn+5WM5nupTGcRwcx6EXWmrNgLOTJbpRTMpzODWSJZF4PGaTB81dQ60x5m8A/zFw0hjz+p678sCf7PfAROTO+52dOTbEq1eqABgD9XZ/ZtZzDLGFRi/giZEKsY2p1dt8YW6UK+sNXMclCAJ+7PQY3TDEdaCYTvDESI6lzf4b7lq9Q60VUEx7d3wTFnlU7nYiICIi8iDu1p9kbjyPMWZfAm8cx1xfb/CtK1UuLDfI+i6eMTw3XeKl0yPMltM8O1nkB4ubXKu2cQ08O1lkuqxlOh/WvWZq/wnwVeC/Bv72nuN1a211X0clIsCd9zsbSrn0gpjX5jeJIstUOU3ScRgvpmj2Is5M5KnkkqRcmK/6fP3CCtlUEsdEPD9VYKaUwvU8mr2Ypa0Or1zd4NRYgZLnAIZGp8f3rtV4bX4TS7+5wvPTRV46PaJQIY9EHMd8/e0bvL3SYqsd3Lz4kuHMsSK5VPKRXWEXEZHBd6f+JAu1NqOFFKVM4p6B94PcKRBba/nahRVeX9jmt19fJMZhYshnbjTH9+drnBrLMVPJ8pknSgxlPNpBRDrh8sxEnhlt4/Oh3TXUWmu3gC3gLz+64YjI+91pv7OXTo9waizHeqNLGEZstwPaoSXhOqzXO2R8j1Y34NJqnYTnsbTZxjFgseRSCb43v8FGIwADGT/J0laHzzxRYTTv0+hGu4EW+m3wX5vf5NRYTnumySNxbbXO1y9V+eobS4Rxf1ugnzwzRi+wpFPJBzrhEBGRo+1u2/jsHL9b4L3bPrNRFHFxpcHyVpuka+j0Qlphv7vybDlNKunyncs1rtWatHoxELNQi8kmPZ4cS7DR6DJbyfKF02M8MVZQRdJDoi7GIgPIcRxmK1lmK9lbrhJmki6r2x0WN/tlxGEEed9jOO+TcA1Lmx3qnYjNVkg+7dHohGRTHlEUM1lMM1vJ0gpC3r8TroXdN2GR/fbWarMfaCMwjqHdi/idN5Y4O1UknfrgEw4REZEdd9vGJ+t79wy8d/qMiaKIX//uDb5xaY1eGNMKAk5WcpQzHqF12G4HDOc8NjsBnuPgOBDHEIQx7V5IuGe7nr3ncvLR6XKAyIDbmcmdLmeo5HxOTxQ4N1tibqzAcC6J6xraQcx2O6QVhORSHsZAsxsxNpTi9FieZ6eG+MwTFeYmCv39at//PUB7pskjs90OiOL+n63tX2KJImjsOfm424mIiIjIXjv9SfaaKvW397lX4L2TiysNvnFpjdiC48DyVpeXr2yA42KB69UW2H6zzlqrx9MTQzhOvwdKwnM4N1PUdj37RDO1IofMTsj95IkSi7UWv/vmCtVWD9cxfGK2BFHEx2dK/Nm1Ku1ehMnBp08M89x0EWMM06U0z08Xb1tTqzdheVSOV9KMFVKsbHfAGIyBsbzPyJ4LK3c74RAREdnrTv1Jdnoz3Kkh507gvZOVrTbxzXI2a/v/RRaanZBkoh9sC+kET43mCaKYhVqbF2ZKVDJJvjA3wueeUn+S/aKzApFDynVdvnxukqePFZivtcHG1NsB12sdKr7l3z03RSWX5Mx4gbnx/O4eoo7j7K7Z1ToPOQjnZ0t86flj/PYPluiFFs+BHz8zxlDKEHHvEw4REZH3u1N/kp3jdwu8dzI2lMYxEFtIJxz8hEMYxWRTHkFkGcklOT6SJZt0GS34NDohjmM4Wclwdqakc6l9ZHZKuwbZ+fPn7SuvvHLQwxB5rFlrqTZ7rNW7xNYyXkhReow2IhfZKwgCXrm2xUKtxVQpzRPDGbqxeaT7C4qIHFVRFHFxuc58rUMh5fHkaJZKXvvY711TC2BtzHQpw4lKhkQywemxHKcnCgAHsi/uYWeMedVae/6O9ynUioiIiIhIHMdcWdnmj9/d4Ps3tugEMb0w5IXpMj82N8LpyeKRD2c73Y9XttqMFVKMFnzagVV4fQTuFWpVfiwiIiIicsTFcczXLyzz3eub/Js3l7m60Sbne4wXfJrdNcbzKcaKGcpHvHGk67o8fWyIp48NHfRQZA8VdouIiIiIHHHztTZv3GhwdaPN9WqbKIbtdsiNzTa1VsiNepsLy3UOQ5WnHD4KtSIiIiIiR5i1lsVam612AIbdrf3im/9Za3GMod4JqbWCAxypyJ0p1IqIiIiIHFHWWi4u11mpdwDLZrPL6fECCbcfbhMGzk4VGc/5eI72CZfHk9bUioiIiIgcUbVWwEKtTd53OTaUotULubbe5MxEnkzS40Qly+mxLJWcR4jRPuHyWNJvpYiIiIjIEfXDmVeH2eEsI3mfc1MlrDG4jiWbdPA9lxBX+4TLY0uhVkRERETkiLp15tUh4yfJ+HButkQpk9B+qzIQFGpFRERERI6oUibBVCnNQq29e2xnRtYYQzmbpJxNHuAIRT6YQq2IiIiIyBFljGFuPM9oIaUZWRlYCrUiIiIiIkeYZmRl0GlLHxERERERERlYCrUiIiIiIiIysBRqRUREREREZGAp1IqIiIiIiMjAUqgVERERERGRgaVQKyIiIiIiIgNLW/qIiIgcEWEY8vriNou1FpOlDGcnC3ieTgVERGSw6ZNMRETkCAjDkF99eZ6vvrlEFIPrwBefmeArL04r2IqIyEBT+bGIiMgR8Pri9m6gBYhi+OqbS7y+uH2wAxMREfmIFGpFRESOgMVaazfQ7oji/nEREZFBpnqjfWatpdYKaHZDsr5HKZPAGEMURVxcabCy1WZsKM3cWA7XdQ96uCIicgjEccz1aovLaw08x2G2nGaymMZ1uCXYug5MljIHN1AREZGHQKF2H1lrubhcZ7HWIoyhG0bMlNIcG/L53bfW+Oa7a1gLQRTxI0+O8JkTJcaLWUrZJMaYgx6+iIgMoDiO+cMLK/zma0ssb3VwHDg/U+LF2TxffGaMr765csua2rOThYMesoiIyEeiULuPaq2AxVqLajPgerWFY2LeXNxiqpTi995eYasVEMXQC2L+8Z9eoRNEWLvO89NFjpczZNPJ3ZldERGRe9mpDLq+0eAP315ldbuD6xgia/newialbIKfnBvh7HRJ3Y9FRORQ0SfZPmp2Q8KYm4HWstWO+NbldebGC1xea1LJpmj3erSDmCi2LG+2Wai1WW90OT1aoJJPMlPOMDdRULAVEZG72qkMWqi1qTa7XK+2CGPLdqeHtWAMNDohG52Qc7MVzs2WD3rIIiIiD41C7T7K+h7dMMICsTW8vbxNHEPK6/fnWt5qM1rwaXS7gCWd9HBdlwvLdarNgJlyhq12yEghRTmbvOPaXBERkVorYKHWBiCfSuAnXC5vNMn7CSJrcQx0wphUQh/7IiJy+OjTbR+VMglmymkurTRoBxGOgWzSZWmrxWdPDfOt99axFhIOvHRmnGq9yY3NFidHcxRSLp5jWKy1WNvusFbv7p6wAEyV0syN5xVsRUSEZjcEwGDxjOWTx8uEUcxaowvAJ4+XySRccr4aEoqIyOGjULuPjDGcnSoSxvD2jS3eW63T6oZUmwGdbp3zsyWmSxl6Ucwfvr3MU+MFJotprq03WTQuk+WA45UczSCk1gxvee6FWpvRmzO4IiJytGV9D4Pd7eFgbcxYIcUnT5QpZxN0g5hM0sPafqmyLoiKiMhholC7zxzH4eOzJTKeYWGzzbcvd3EcgzUO1WbAylaVEyM5To4OUckmubTWoBdZUr7BWths9nDucvLR7IYKtSIiQimToJJL8tr8Fpb+RdXxQopao0sviJnf7FDOJAgiy7luyOljQwq2IiJyaCjUPgLGGDY7Ea1Oj6lSlrVGh3I2yXev1ihkkmRTXa5utHBNjqmhDNlkgji2DKUTnBrL4XsuENz2vFlfPz4REel/zpRzPidHc3SDCD/hYmzM6wtb/ODGFvVuxGLNUu8GGANjQ2nKOf+ghy0iIvJQKBU9IqmEQyaV5DvXVvsb31tDDDQ6AdmkhwE811DwPYYLKYIo5sRwlpG8z3ghhWPMbWtqS5nEQb0cERF5zOT8BHnfI+97YCMWNwMuLG+TTrg0OgFhZOmGDWYrGZa3Owq1IiJyaCjUPiKz5QyVbJLZSpar602avZDJYorxoTSOsTw5lieXdJksZ6i2AkaLaUbyPtOlDKVsklI2yWghpe7HIiJyR6VMgqlSmoVqg9fmt3l3rc7SVpdaq8vJkTztXkirFxLH3HVZi4iIyCBSqH1EyjmfT8yW2Gh0OTGcxRhodfplYKOFDGBJJRxefKJMxk9gTP+q+97wWs4mtYZWRGTAWGsfyZZsxhjmxvO0eiHXa8uUMkkySYd6x+HS6jbPHiviujCc9xnJa5ZWREQOD4XaR8QYw3MzJSIM7602SHoOW60ejuOQ8hwSCZfTYzlOTxQ0AysickhYa7m4XGe+2mSt0aPeCXhiJMfcSJZsxn/oAdcYw1arRxTDZitgupQhjiG0lmPFFDPlLKfHs7pAKiIih4pC7SO00wn55Eiuf8U+6WKBVi9SSbGIyCG00ejw8rurvL3S5Hq1hQP84YUVfu7ZSSaLPsdK2Ye+5/jYUBrHAI6h3YuYHc4QxpZPH68wO5rl7FRJnzUiInKoKNQ+YsaY28qIS3HMtY0Wr89XwRhOlDNMV7I4jnOAIxURkY8iiiJ+741lvnWlxvfmN4ljKKQT5H2H7y/UKOfGWKy1Hvqe43NjOT7/5AjfuLRGOukRRJbPPznC506PMJxPKdCKiMiho1B7wOI45msXVviDt9e4tFYn4cBz0yWOlzN88mSFWYVbEZGBdHG5zhs3tqk1e3R6EemkSzcMKaZ8UgmHd1frTA1laLZ7DzXUuq7Ll88d4+nJAitbbcaG0syN5XBd96F9DxERkceJQu0Bm6+1efXaJpfW6tjYYo3L//TqPGcmilxYrvPCbInPnhqmnPV1dV1EZIDM1zq8u9bkiZEcS1ttVho9EsbwwnSRi8vb9CLLYq3N6JDPVCX7UN/jXdfl6WNDPH1s6KE9p4iIyONKofaAbTS6tHoRcQwZ3+OtG1t4xpBOOHxvfpPl7Q5brYDRfIq5iTwz5YxmbkVEBkAh5fU7Hzc7fGK2zNvL24wVUlxY2ibte1xbb9LoRQylEzx9rEAllzroIYuIiAwkhdoDVsn5ZG42jAojSxzDeDnFfK1N3vewGL7xzhqphMv3FzLMTRT4kScqlHOauRUReZw9OZblxRNl3lqq89rCJtPlLDPlDM1exGazR85PkE7AtY0WF5brzJRjNRAUERH5EBRqD9h0Kc3HZ4tUmz1ubLXxXJgoplncbFNIe1xea3BqJEe7FzNfa3O91ia2ljPjhYfeMVNERB6eSi7FF58dZ6ac5VtXNkh6hqzvst0JwDEkXIeE6xBby/WNFq1exI1am0zSo5xNYDFMldJ6rxcREfkACrUHzHEcXjo9xonhHG/eqLG0NcxCtUmt2cNzDeNDKYyB7W5AIe3RiyyNTshCrf3QO2aKiMjDY4zhzLEiY0MZjpXSrDZ6dHsBzxwrUu8GOBg8F3K+Rzrp0uiErNS7hFGbF2ZKJF30Xi8iInIfFGofA47jcHI0z4mRHBuNLgu1JteqbbZbAd+5ukEvhJGcTxhbjIFcqv9ja3ZDneiIiDzGjDFUcj6fOTVMrRXQ6PSoZH3eXWvQCWIAsimXXNLh7eUGN2otMr7HxeVtpotphtIul5Y2iXGIsUwV00yrt4KIiMgtFGofI8YYhvMphvMpnpu2VBtdCpkkF5e2Wai1iYGPTRbI+/1tGbK+fnwiIoNg7x7lU6UMT9UKbDS6pBIuNzZb1Lsx9U5IwnN4b61Oqxfy+sImz08X6QQRr16v4RqH8SGfHz8zxs88M64tekRERG5SKnpMGWOo5FP8+bMTPDc1xMWlOq0wuhloHaZKaUqZxEEPU0REHpDjOMxWssxWslhrcYzh+/ObFNIeF1e2KaR8ao0ejmu4ttHCGOj0Ylq9gG4Y8zs/WGY46/PJkxWttRUREUGh9rHnOA7Hh3PMVrLUWgHNbqiOmCIih4QxhrnxPOmky7cvb3BqNM/yZptUMsGl5TqdIKLVCxnOp1nbbpHyHMYLPm/c2CLhOTw3NaQZWxEROfIUagfE3tI1ERE5PIwxzJQz1Jpd3rqxzVAmyXsrdXKpBBvNLt0gZr7a5jMny2R9j+9cqTKc8/nu9RovPTXKl89NKtiKiMiRpk4TIiJyIKy1VJs95qstqs0e1tqDHtKBMcZwdqrIjz89RjGToJRLsrzZJpfwmCpmaHZDkgmX1xa26AQxNzbbLG52+fo7a1xcrh/08EVERA6UZmpFROQjs9Y+0BIJay0Xl+ss1Nq7x476nqyO4/DZJyokHcNms8foUArXwOpWm3MzJWYrWV6b36IbRozkfMYLSXpRzLVqmzPHho7s35uIiIhCrYiIfCR7A6q1MeuNHkOZBMfLGfK+RzadvC3k1lrBLYEWtCcr9IPtJ06U2Wj1eHtpGy/pYVyHfCpBoxMQ25hsMkEl53N5rQnA5dUGF5frR/qCgIiIHG0KtSIi8pHUWgHztRb1dsDbS9tc3WjRiyKmSxmeHMlzrOiTTSU5PZ6nlE1ijKHZDe/4XNp/ux9sf+rMKO1OxFvLWyxtJdhsBeQSLp89Ncx8tc31m12RnxrNM1FM6YKAiIgcaQq1IiLykTS6AQvVFtc3Wrx8dQMHg+vAeCHFt65u8PR4gSCKWal3OTOeZ248f9d9trX/dp/neXzp3DHOLOd5a2mbRidksphiqh0wU8ryzmqDnO9xvJImn+5v76YLAiIiclTp7EFERD6SOLZ89/pmfx/VwBLHEfmUSzGdYKPZI4wigl5MtxfsziiWMgmmSunb1tRq/+0fcl2XpyeLnDk2tLteOZN0aXQCiperJBMuOd/F0C851gUBERE5qvQJKCIiH8lGs0s26RBbl+12l7zv8syxMn92ZYPNdsRms8NMJU++laCST+3OKM6N5xktpLT/9gd4/5Zu5WySdhDrgoCIiMhNCrUiInJPvV6Pl69sMl9tMV3O8OKJIsnkD8tce4El6Tm4PcNPPT2G9/9v7+5jJLnv+86/f1XVVf38OE87Ow+75HJ3uRQlmqLoB0qKGcmWk/jkS4xLZFxyMhKfbZyC5HAXJJYdILgDDARnIM7hYiNRHg7OxbHhwPJZsePYkuwzbUkURVEUKXK55C73aWYf5qF7pp8fqup3f/TMaHa5S3Kf2NM7nxcgqLu6uvpXo9L2fOb3re/PMXz+W8sYY5jIBvQjOHlpk0MTaZq9aGdGUetv3x5jjP4gICIisotCrYiI3FS/3+df/dk5fuv5C4QReC789ScW+JmPHNoJtrPFJKWUz8krTQ6VU9S7IeVsEs8xdAcRq6st5ssZWr2IqayvGcW7QH8QEBER+S5n1AMQEZG969mzGzuBNhu4PHqwyHeWNvjiyRUGgwEA8+U07ztYYL6UwnUdotjiGFhpdAkjSyZIkE95HMgHzJVSmlEUERGRu0oztSIiclMXq+2dQLs4keFLJ68SxrDW6nG+2uGnnlrE933+4sNTJAOP1y5t8s3zXZ5cLPPs2XW6gxjPgScPlfFcB+Mo0IqIiMjdpVArIiI3NV9O47nw0HRuGGgjcBxwjMNvPHeeo1NZPnZiBtd1+fCRCU7M5JgqpDhzpc7TxybpRTEPTGSZySVJJT2ygUqPRURE5O5SqBURkZv6vsNF/voTC3xnaYMwHgbaEwdyrDW6hBGcr7aptQeUMz7GGCq5JP/N+w9wYa7AqcsN2mFELnABRx16RURE5J5QqBURkZvyfZ+f+cghvnhyhbVWD8c4rDW6dEOL58JULthZomeb4zgcmsiyWMnsrK+qDr0iIiJyryjUiojI2/J9nx8+Mc35aoffeO78ThfkH398nrzPzhI911OHXhEREXkvKNSKiMg7SiQS/NRTixydynK+2mYqF5D3YbqUV0mxiIiIjJRCrYiIvCu+7/OxEzMqKRYREZE9RaFWRETeNZUUi4iIyF7jjHoAIiIiIiIiIrdLoVZERERERETGlkKtiIiIiIiIjC2FWhERERERERlbCrUiIiIiIiIythRqRUREREREZGwp1IqIiIiIiMjYUqgVERERERGRsaVQKyIiIiIiImNLoVZERERERETGlkKtiIiIiIiIjC2FWhERERERERlbCrUiIiIiIiIythRqRUREREREZGwp1IqIiIiIiMjYUqgVERERERGRsaVQKyIiIiIiImNLoVZERERERETGlkKtiIiIiIiIjC2FWhERERERERlbCrUiIiIiIiIythRqRUREREREZGwp1IqIiIiIiMjYGkmoNcb8kjHmNWPMS8aY3zHGFHe99lljzGljzCljzCdGMT4REREREREZD6Oaqf0i8D5r7fuB14HPAhhjTgCfAh4BfgT4VWOMO6IxioiIiIiIyB43klBrrf0ja2249fRZYG7r8Y8Bv2mt7VlrzwKngSdHMUYRERERERHZ+/bCPbV/G/iDrccHgYu7Xlva2iYiIiIiIiLyFt69OrAx5kvAzA1e+gVr7e9u7fMLQAj8+vbbbrC/vcnxfxr4aYCFhYU7Hq+IiIiIiIiMn3sWaq21H3+7140xnwZ+FPiYtXY7uC4B87t2mwMu3eT4nwM+B/DEE0/cMPiKiIiIiIjI/W1U3Y9/BPhHwCette1dL30B+JQxJjDGHAYeAp4bxRhFRERERERk77tnM7Xv4F8AAfBFYwzAs9ban7XWvmKM+S3gVYZlyZ+x1kYjGqOIiIiIiIjscSMJtdbaI2/z2i8Cv/geDkdERERERETG1F7ofiwiIiIiIiJyWxRqRUREREREZGwp1IqIiIiIiMjYUqgVERERERGRsaVQKyIiIiIiImNLoVZERERERETGlkKtiIiIiIiIjC2FWhERERERERlbCrUiIiIiIiIythRqRUREREREZGx5ox6AiNwfrLXU2gNavZBM4FFKJzDGjHpYAMRxzMVah/Vmj0o2YL6UwnH0Nz0RERGR+4FCrYjcsV6vxx+/vs7ZtTbTGZ9yxuFAOc+xmdzIg20cx/zJa6u8eHEDCxjgsfkiTx+fVLAVERERuQ8o1IrIHel2u3zuzy/wH549S2jBM/Djj8/zJDCVT1LO+CMd38VaZyfQAljgxYsbHJnOsljJjHJoIiIyZvZyVZLIfqZQKyK3LYoifv+VVf79V8/S6EcYIJlw+e0XLnJk4mFavXDkoXa92dsJtNvs1naFWhGR/e1mITWOYy5W21za6BJjKacTTOaSrDZ6LG90d94/V0rdVlVSFEWcutrk6maH6UKKY9NZXNe926cnsm8o1IrIbbHW8vzZKmfWGoR2WNYbW+gOIhzH5WqrTyZ47/+JsdZSbfVZbfSIrSXpOXgGwl3J1gCVbPCej01ERPYOay2nrjRYqnV2ts2VUjw0leFPT63yrQs1Tl5p0uwNODSR5pEDeRxjyAQJumFMkHC5WGvfclVSFEX8zgsX+dM31mn3Q9K+x194qMJffXxewVbkNinUishtWW/2eGl5ExfDIAxxHQcbWyzDEuTDE2lK6cR7OiZrLacu13l5uc6FahsLTGQSzJVSXNro4LgOgzDmkdkCc8Xkezo2ERHZW2rtwTWBFuBirU27H/L8hRqnV5o0un081+GFCxusNfrkkglmCkl81yGyMJH1OTYzuKVQ+9qlTf7zy5f5znKdOAbHgXq3z8MzeR6ZL9/t0xTZF9QlRURuy1K1zcVam6+8cZUf+545oijGcwyFpMvf/L7D/MWjlff8PqNae8D5amcn0AKstQY4juHR+SK+a5guJDHG8sZKC2uvL0wWEZH9otULr3lusVyutTl1pUG7HxLHFteB1UYPA3T6Eb0w4o2VBglv+P223uxzq18lp1ZaLFdbPH10io88NMnTR6dYrrY4tdK6S2cmsv9oplZEbkut3efMSotKLsPJSzU+/dQhOv2Q982W+OT7pwiCe1fea62l1upzpd7FMYbJXEA549PqhfTCaCvQWjqDmEEUs9LskXAND0zmdo6xVOvsiUZWIiIyGtffItPqhlyodql3+6w2e5xeabBYydAPY5r9kMBzSPoOsYVqq0cq4XGokrnlGSIXeGy+zMvLG4QxeM7wuQqPRW6fQq2I3JZC2sfzHFq9kIOlHGfX2mQDjyNTaZLJe1faa63ltct1njtbZbXZxwAL5TSPHswzlU8SeC4Gy2qzx3pzQBTH2NgymfZJ+xbLd2eP90IjKxERGY1iyqOYTnBmpYmfcGl1B1xtdHCxhGHMbCHNWrNH0nfwXZ+JbMB6o0ds4HsWisQRGAzpW+wfUUi5XKh2OLvW3lk1oJDyKaQUa0Vul8qPReS2LJbTfP8DZbLJBK1+BBiOTmdZrGTv6eeuN7s8f67Kcq1DwjV4JuZCtc35agcLLJSSFNIJljfaNHsDMoFLOnB57XKDKL62RmwUjaxERGT0rLW8frXJZrtPMe3jGsglPQLPcKne49TVBhudAYulNE8fnWRxIsXFapur9R6B67JcbRPZ4Qzurd5oM4gNK40e2ZRHPumRTXmsNHoMYi0NJHK79BudiNyWcjbgLzw0yXQhRbMbkk16HJ/KUr5HXYXjOObcWosvv7bCs2fWuLzZJbKW7z1coZL26IUR7a37ozwzDN3WGqZyAcbGXOmEtPoRueTwb3lzpdR73shKRET2hlp7wHKtTRhDL4wIPJfYgmMcLm10GcSW9WaPwDO4DniuQznjE3gutXaPjU6CR+dLTOeTtPoRt9LeKR24FDMJEp5DGFkCzyGb9Egn9Wu5yO3S/3tE5LYYYzg+W2C6mL7ni9BHUcSXT67y0vIGJy9vcnqlwXwlw0Z7wIsXa/zQwzMEnksYW555Y43TK01evLgBDGdjP3SoRCWTYLGSZjKXvKdjFRGRva/VHVBtDXYaCxrgcDnNI7N5XrhQw3ESeAaySY9mL2K12QMMV+sdDk/maPdjMr6LwdxS1Y+1lozncGQqx0tLG7iOwXMN758tcGz63lY6idzPFGpF5LYZYyhn/Ht6X6q1lq++WeU/v3SJRnfAqSt1jkzlePXSBgfLGa5udnFdw2Ix4Nxak6+fXacfxqR9j3q3T6sXUu8OmJ8tsljJaH1aEREhhms65VvgbLXN00fL/PAjM1RbPdYafS5vdsj6Lo/M5jm32ubDD03x6qVNFuYKZHzvlqt+au0B652Q7ztUZDIT0OiF5AKPp49PUslqqTmR26VQKyJ7Wq094OSlTaLY4hpDwnN5ZXmTB6fyZJMungO+Y+iEEadXmqw1e3R6Mfm0Ry7pUUglWKxkODKZVlMoEREBwBiYzPnUuyGDMCbhOeSTHhO5JI8vFPmzN9bpDjoU0z6FpEe11acTxVhref9ckY+fmOKDh8qUM/4tVf20eiEWQymT5LFUcqf0+UAhpeohkTugUCsie1qzN6AXWlabXXqDiEzg0u6H9KOIKHI4PlPgymaHr5+D0yubvO9ggVcv1fEcg+e6nDhQ4IGJDMcOFPQLg4iIAJDxPZKJYRfifhTjuw5TuYBMkGCukOLJw2UygUMcw0a7T606YCIbsFhO8+hckY8/PIXr3nq34u1SZcvwXt20P3yeSarHg8idUKgVkT3NWugPQqaySU5eaYCNKacTPH1sCtdYzq218ZwEnmPJJ30uVNscKCRZqXd5aCbPQjHJoUpGs7QiIrLDAJ1+RDLhkkwMw2mnH9HohSxt9kh6DocrOS5UW2STCT56ZJLZcorHDhaYr2RwnNtbQKSUTjBXSrFU6+xsU+NCkTunUCsie5a1llZ3QDrp4bmWDy4U6YYhh8ppTBQSuy6ljM/hSoZmf8BXzqxTaw0ophIsTGSod3o8PHuQYwfymqUVEZEdrX7EbClFPu3TG0QE3jCknl1t0uhFZAOXciZBPlWkF0Ycnc5xbCZ3x98lxhiOzeSYyifveZNFkf1E69SKyJ4UxzHfPF/jleU6V2ptpnJJEq5DFFteOL9BtRuz2R7QDWP+6LWrvLi0yUarTzbwMI5hudZmKp/GOo5+WRARkWtkAg+DIRd4VLI+m50Bb6626IaWN1eaLNc6xIDrGNK+x1Q+ede+S7abLM6X07d8T66I3JhCrYjsOdZaXlra4E9PrbK00aGcS/HmWotvnq9ytd7nQDHNa1fqNHoRq/U+UWy5VOswU0zS7IWkfZdyxqcQeEyo27GIiFxnuwwYoNmLWG/2WSinCVxYKKdZb/Zp9iJA5cEi40DlxyKy59TaAy5UO1ggsuDYmEomoNYOqWR9XBeChEc3jPAThoQX0OwNeGAiR70T4nsO5YzPIweLzG/90iIiIrJtdxnwubUm6cSwm36M2So7LjCZ8zk0kVV5sMgYUKgVkT2n1QsJPBfDcO1A13XxPUMu6VHJBExkA1r9iGzg0R3EhLHlYDFD1nd5YrHMbDHJE4cr/MAD5dtu5iEiIve37TJgyFJt1XatWTvsTHxoIqsmgyJjQqFWRPactO/iEDNbTHJ5o4NrLFOZgOl8in4Y4xh49GCBwHWotvqcXW/zwESGtO8yV0rzA0cqlDOB/rIuIiLvSB2JRcafQq2I7CnWWlY2O7y53uaVS3VcY8gGHk8dKfPBxRLrrZDYWqZzw9Da7A1o9iK6g5BKNsl8KaXZWRERedfUkVhk/CnUisieUmsPuFDr0uyGTOaSDKKYhOvQGVhc1+P4gfQ1+5fVCEpERO7Qdimyyo1FxpOmM0RkT2n1QnphhMWQSrjkkwlSCZdeGNPqhaMenoiIiIjsMQq1IrKnZAJvp0nUbkHCJROouERERERErqVQKyJ7SimdYLGcYqGc3gm2k1mf49NZNe0QERERkbfQtIeI7CnGGI4dyDOZT7La6BFby0w+SSnjq2mHiIiIiLyFQq2I7DnGGCrZgIqaQImIiIjIO1D5sYiIiIiIiIwthVoREREREREZWwq1IiIiIiIiMrYUakVERERERGRsKdSKiIiIiIjI2FKoFRERERERkbGlUCsiIiIiIiJjS6FWRERERERExpY36gGIiIiI7DfWWqrNHuerbbqDmNlikoVyGsfRfIOIyK1SqBURuUWdTodnTte4UGuzUErz0SMlUqnUqIclImPAWkut1efk5U1OXanz7aVNugPLgUKSjzxU4enj0wq2IiK3SKFWROQWdDodfuWZc/zaV8/Sj8B34dM/cJjPfPSQgq2IvC1rLaeuNDh5pcF3ljf41rka04Uk7X7IqasNXAwPTOY4PJkd9VBFRMaK/hQoInILnjld2wm0AP0Ifu2rZ3nmdG20AxORPa/WHrBU69AbRDQ7Axq9kFcub9LuR5xbb3Fqtc53ljew1o56qCIiY0WhVkTkFlyotXcC7bZ+NNwuIvJ2Wr0QgCDhYhzoRzGNboi1FgN0BzFrzR619mC0AxURGTMqPxYRuQULpTS+yzXB1neH20VEAOI45mKtw3qzRyXjkwk8OoOY2FoslmzgMpkNmCkkafUGOMYwk09ysJjEMYZWL6Sc8Ud9GiIiY0OhVkTkFnz0SIlP/8Dht9xT+9EjpVEPTURGLI5jLqy3+PrZKm+sNEl5ho1OxGI5zWIlhesYHBxiLA9MZKl3Qh6ZLRCFMb04xjOQTw1DsIiIvHv6V1NE5BakUik+89FDPDpbUPdjEdkRRRFffPUqJy/X+eqZdTwXZotpVhtdLm+2sVRI+wmmcgkemsnjOIZDlTSvXG7wxkqTTCLBfDHJ8ZkcpXRi1KcjIjJWFGpFRG5RKpXiE48qxIrIkLWWr5+t8fsvXyETuDS7w/Lhr76xxmQuYCIX8OLSBr7rMFdMkQ4S/MCRCeZLaRYmsjzR6BFby0w+SSnjY4wZ9SmJiIwVhVoRERGR22St5UK1zemrm5TSHknPZSLv8+ZKk4G1FDM+3zhXZa6UIeO7JByHq/UutfaAcsankg2oZINRn4aIyFhTqBURERF5l6y11NoDWr2QtO+yUu/y8tIG56td/uTUClPZgJl8ksBzCRLQ7IZM5VJstHt4bsDleoduGKkZlIjIXaRQKyIiIvIuxHHMCxc2OLPSxE+4ZBKGlUYfi2W10eGx+QJX6n2ChMtMIeCBiRydQcj59Ta5lMdiOc1ao8tyrUvGd0d9OiIi9w2FWhEREZF3YK3lpaUN/vz1VTzPIY5iAt/hlUt10gmXlcYA34XBIObcWotjBwoEjmG9H9OPYiJref1qncVKhoVyCjvqExIRuY8o1IqIiIi8g2qrz8Vqm8haLqw2Sfsub661yAUJiimXaruHjeHwRJo4hgvVJn/tA3PUzq5RTCVwHUNoYSKTpJL2aPcjKqM+KRGR+4RCrYiIiMjbsNby2uU659bbPHd2HccYjh3IkwtckgmD50AlE7DW7BEkXJarHUoZnwsbLdJ+gocmA2IsSd8l4UA7RGvRiojcRfoXVUREROQGrLVUW33Or7e4WOvgO1BIebQHMd84V6XVDZnIBRyZynF8GpqlFMWUh7UGx7FkggRvrrUppHzyqQSOMVhgoZzSWrQiIneRQq2IiIjIdeI45qWlGi8vNzh1uc6VzQ75dIJKNmB5qU4UWw5PZmj2QlYbPY5O5+iHMRdrbTAwX8rQ7UcslDMMoojJrE/a93hwKsv754pai1ZE5C5SqBURERHZxVrLCxc2+Pqb6zx3rkarF5L0HOq1NouVLPlUgk4/JuO7HC5nmS4GPL5YIuO7fO3NKpVmj0EY048h4Rjed7DMYiXNTGE4Q6tAKyJydynUioiIiOxSaw84s9Kk0Yto9SIM0OhGFNIegzBmKheQSyYopDwcY5jKp3YCa7sf8dzZKquDPgY4WEpzuJLm2IG8wqyIyD2iUCsiIiKyS6sX4idcHAOOgdgafA8KyQTltM9iOc16u0+MYSLrc3w6uzMDe/xAnul8kiv1Lo4xTOYCyhlfgVZE5B5SqBURERHZJRN4ZAOXYirBoUqac+ttLIapbJKHZ7N86FCZTmiJrWUmn6S0K7QaYyhnA8rZYMRnISKyfyjUioiIyL4RRRGnrja5utlhupDi2HQW13Wv2aeUTjBfSoO1OA4cKKSYzAVM53zmyxkWJrKaeRUR2UMUakVERGRfCMOQz79wiT8/s4ZrDLG1fOTIJH/18dlrgq0xhmMzOabySY4dyGMtOEAmmVCjJxGRPUihVkRERO5b1lpq7QGt7oBXL9f5/LeWCGMwBiazAX92epUTB/OcmC1c8z5jDOWMTznjj2jkIiLybinUioiIyH3HWku11ee1y3VWmn3SCYevnF5jEFkcx2AtrDZ7zBVTXN3svCXUiojI+FCoFRER2ed2ZjN7IZnAG9sS2ziOuVhtc3mjw2YnpN0fcKXeozuIyac80oFLszeglA4IrcVaiKxlupAa9dBFROQOKNSKiIjsY9ZaTl1pcLHWptULGQxiKlmfDywUKWeCPR9urbVUmz3OrzU5ebXOcrXLpc0OJ680mMwGlDIJDlWy9MIYG1k+MFfkfLUNgOvAhx+c4Nh0dsRnISIid0KhVkREZB+rtQdcrA1nN6/We8PAZ+HMWpMPH5nk+IH8ng221lpeu7TJM2+s8trVBkvVDliodfrEsWW12SO0loTb4chUlqlCilTC4dGDBdr9iKMzOT5ypPKW7sciIjJeFGpFRET2sVYvpNmLqHdDzlfbWDvcvtLo89rVJtOF1J5qlrS7VDq2llMrDV5artPph0SxpR/FVFt9immfOAbfdRhElt4gZraY4oOLRcrZgGygTsYiIvcLhVoREZF9LBN49AcRgzDeCbQGSLgOg0HESr27Z+613S6VXqp1AFhv9qh3B0SxJeG6uE6Iu9XZGAyRhazvMV9K8fhiiUcPFihlfAVZEZH7jEKtiIjIPlZKJ3hwKstaszcMg3a41I3vQD+KOXl5k15ocYHDUxkeXyjhOM5IxlprD3YCLYCfcGnUOiR9h5XNLoWUR7tnODKZo9kfcLCQ5sSBPN97pMIHRzhuERG5txRqRURE9jFjDI8vFPEcSyGV4PJmF8fAVD5JvTvglUubrDX7WGtZuJohCmM+9EBlJAGx1QuveZ4NXCrZBKlEjmYnotMfsDiR5qHJLAdLaebKKQ4U0pqdFRG5zynUioiI7HOO4/CB+TLz5SyrjR6xtXQHEf/lpctc2ujS7IXUuwOWNzokE4Z+bHnqyMR7HmwzwbW/thgMs6U0RyczfO+DFeqdPvlkgsVKhnJ273duFhGRu0OhVkRERDDGUMkGVLIBAK9e2qTVD+mFMfXugDiGen/ASr3PH7x8mVzS4wPzpXcVHK21VFv9ncCc9l1cY8gkb61ZUymdYK6UuqYEeb6UZmEiy6ICrIjIvqVQKyIiIm8xk08ymU1yZrWFYwztcMB0Lkl3EJFwE1yodlioZK/pjLy7M3Had2h2Q9ZafcIwYqna4Vy1zXqzT9JzOTGbo5TxOVhKc2wm966CrTGGYzM5pvLJPdO8SkRERk+hVkRERN6ilPH5yNEJWt2Q19ca5Poek9mAQRiTCVwCz6XVC3dC7e7OxNbGvLxUJ7bDJXbWmj3K6QDXsWx2Q+qEpNZdcimfpVqHqXzyXS8bZIyhnPH31DJDIiIyWgq1IiIi8hbbDaQGUcTBS0levdKgH4Zk0z5Hp3N4zrX3uO7uTLza7PPmWgsMzJdTDCLLy8sbfPBQCQDLsLNyL4xI+9414VhERORWKdSKiIjIW1hreeNqk2Y3YiofEPgere6AxUoaz3U4WEpTSid29t/dmbjRHWABLFgLnmOIgX44XAjXAL7rEHgu8NYGUCIiIrdC3yIiIiLyFtVWn1eWN7lU79IZRGR8lwO5JPOVDDOF1FvuZd0dTHPJBAbADJfdqfsu07lgax1Zj6TncriSxnPgYCl1TTgWERG5VQq1IiIi8harmx1OXmny8vIGkQXXwKMHi5yYzd+wVHh3Z+LJrM8DExliaxmElkrG58NHKjxQyRAbc9vdj0VERG5EoVZERGSfiuOYi7UO680elWzAfCm1s/ZsrRPuBFqAyMLLyxt87OGpGx7r+s7Ejy+WaHZD1lv9txxbRETkblKoFRER2WeG68b2+OrpdS5U2/TDmNjCY/NFnj4+ieM4xFiChEu7H+28L0i4xNibHvf6zsSVLCxO3PPTERGRfU6hVkREZB/ZXnrnWxdqfOX0OhhYLKfxHMOLFzc4Mp1lsZLhYDHFgxMZlja6hHGM5zjMFZMcLKZGfQoiIiLXUKgVERHZB6y11Fp9zq23eHlpk3p3gMWCNZyvtjk+k6M7iFlv9lisZFgop/nwQxO8cGGDfhTjuw6PLxRZKKdHfSoiIiLXUKgVERG5j1lrWW90ee5clW9f3MQ4sFRtM5lN0uqGZJIe1hoGYYwBKtkAAMdxePr4FEemcze851ZERGSvUKgVERG5D23PzJ68vMmZ1SZfOrnC5c0ui+U07UGEY2C2mGK12SdIGHzP4fhMnvnSd8uLHcdhsZJhsZIZ4ZmIiIi8PYVaERGR+4S1llp7QKvT51ytw5mrDS5udDi90iCZcPEcw6WNDnOlNN3QcqDgcniyyMFSmkcO5JkvpzUTKyIiY0ehVkREZEwNuxj3WW30iK2lP4iotnpstEJeWKoRW+j0IwJ3GGYr2YArm11q7QEfXCxybCbP44slFsrpnbVid4JxLyQTeFpHVkRE9jyFWhERkTGyvbbsWrNLpxuxXOuw1u6z2RkQhjGPHMhzZr3JqSsNYgtRHJNLesyXkkQxGAPGWGaLaR6eyb0l0J660mCp1tn5vLlSimMzOQVbERHZsxRqRUTkntiZ8esOiBmGqWyQ0MzfHYjjmD95bZVvX6yx1hpweqXOYjlDZGOKKZ9LGx18z+GN1QabnQGphEcy4bLe7DORzTGdT/JAJcvDczmeemCCSi55zf8WtfbgmkALsFTrMJVP7qw9KyIistco1IqIyB27vgx2Oh+wWu+xvNGh2hpwsdoim/LIJxMcmcrxPfMFNruRSlzfwfWlwI1OnxcvbhBZuLLZoR/BS8sbfN/hCmerLZKuyyCOafcjJnMBq40epWyKfCrB9z84wXwpxYFiioWb3Dvb6oU3HEerFyrUiojInqVQKyIid8Ray6nLdV5ernOh2sYCucClkvHJpxJcrLZodCPeXGszXUiystllpd5hEFuSnofrWIrpgOlcQCa5v2ZyrbVUmz3OV9tstvuU0j5z5fTOsjrXlwJHscViGUQxruPgGohj6EUxWPBcB7BM5ZNc2exyqJJlMu9zqJLmYw9PUckm33Y8meDGvxbcbLuIiMheoG8pERG5I7X2gPPVzk6g9R2IbMxGp08vjMglPc6vN3GMIeUBxvCFb1/iYClNOuGSSXp0e3Ueni3gOua+v4dzZ/a11eXUaotXr9RZb/Y5t9oil0rwocUy33u4xFQh9ZZS4NhauoMY33MIPEMmcIljS+A6JFyXhXKK2UKK+WLMUraDMfDARJbH5ouUM8E7jq2UTjBXSr3lntpSOnHXfw4iIiJ3i0KtiIjcNmstK/Uum50+QcKBOCaMYl6/2uUbZ9dIuC6FtMfxmTyd3oBz1Q6bnT7tXkwy4UI24Ox6m+MzOXphRNr3du7hLKUT910X3u1GTJdrTV5fafN7L1/i8kYXjOUjR6aoNnt8e3mDqUKS+AbnGrjw6ME8Z1aaTGaTGANPHCpTSnp85OgkE5kE660BAAdKaXJJj+MzOUoZ/1397IwxHJvJMZVP3lc/dxERub8p1IqIyG3ZDmgnrzQ4t97mYrXNidk8lxtdnj+3DsZhEFvWmn3OrDY5VM5Q7w3I+AnCqM9mJyThOmAtgzAm8NydY7e6A1bq3Zt24b0Xy868F0vZbDdi6oXwx6dW6IeWQWQxBv7s9AqfODFDvRvR6A5wbvDZMQ5PHSnx2EKJ9WaPZMIlG7hkk/7ObOqdnoMxhnLG1z20IiIyNkYaao0x/wD4JWDSWru2te2zwN8BIuDvWWv/cIRDFBGRLdeHPqxlqdYhG7hMZX06vYjz6y1cAxOZgHzaI+G4tAchucCjmPaYKQSs1HuU0hku1ztstgcYYLGUwmDp9EM812W12eV8tUs2cDEMQ9lyrU3Kd3GAq40etXZ/57UblSxvj7fZG2AtGCyxhW4vpB0OZ4pn8klKGR9rLS9c2ODMShM/4ZILHCayAeVscFc7Nm83Yqq1hmP3HMP2YaMIuoOYhGvIJRNM5oblwtcH+0o2YMIYFiuZG36GAqmIiOw3Iwu1xph54IeAC7u2nQA+BTwCzAJfMsYctdZGoxmliIjAjdcvTfsuFovBcLCcppD2qXd71JoDImu5vNFjudamkPE5WPB59GCBc+tN6t2Yi7UWhypZrI15bK5EZGNeWd6g0Y1pDwacmC1QbfYoZgIOllI4QLU14Nkz6xTTPt9Z3qScSVBM+/TCmEYvZCo3DKG7x3ux2uLsWouVRo/JnM+gH9GLLOdrHQqBx9GZHA9MZfCM4Wtnqlgg6ztsJhxOXW1wsJgiH3jMljJ35T7f7YZLpYwPWNK+y1QuoNrqY4xlKh8wk0txuJzaCacqBRYREXl7o5yp/WXgHwK/u2vbjwG/aa3tAWeNMaeBJ4GvjWB8IiKyZff6pRZLsxdRaw6X7ylmhsEy7XtkfMPaZpeHpnN8+eQVrDEMBhEPTef5xvkq86U0SxtNpnIpwjjmyUNlojim1gmJcbi40cL3DC9e3GC2kGK92aOQ9kknXC5U2zwwlaUXRlgsb1xtUsz4O2W607mA739weO9otdXn/HqLM6stzq40Obfept7t84NHJ1hp9iglEziO4fnzNU5dbeI7hnLWJ45jzqy3+PPTa/iux4NTGT78wATAXVmrdbsR0+Vakx88NsX/d2qF2a0ldp48XObJxQJzlRyVbLATXjXzKiIi8vZGEmqNMZ8Elq21377uL84HgWd3PV/a2iYiIiO0XTZrsSzXOqw1+/gOpHyPl5Y2KWV9DIaj01kOlDI0+zEfOlTBWovjGOIYPGfY6TjjJ3AdB98ztHrDQpxUAPXuAN8brp06iCyugdBCbxDhGqhkfbKBSxwPy3Q3uyGZpEfguRig0Q2ptQeU0gleu1znG+dqvH61wdXNDrOlNK3+gFon5DvLDU7M5lneaBPGhn7YwPdcFsspJrIBL17YIIogNjGrjf5W46aZu7JW6+5GTEcmezx5uMxGu89MIcWx6Syu677zQUREROQa9yzUGmO+BMzc4KVfAH4e+OEbve0G2+xNjv/TwE8DLCws3OYoRUTk3dgum232ItaafQBc12Gz06eQ8alkfIqZAN9zMMaQTfrUuw1iC8bAbCFFync5kE/R7ERsdkOMgYTnUEwliKJ4a43VIQsEvsuDpRRHpnJkAo/Xrza27kO1HCgkuVrvknAdDLBQTuM53w3fK80+gyjGYoksXFhvc2QqzWqjR2QtyYTDaqNPdxBzsJTaCsURvhsO79M1ECRcPGcYsBvdwV1bq3V3I6b5u3JEERGR/e2ehVpr7cdvtN0Y8yhwGNiepZ0DXjDGPMlwZnb3d/wccOkmx/8c8DmAJ5544obBV0RE7o7tstlvX9wAhn+BLKZ8uoMIB0PK98gFHgbLQjnNIIx4eCbHa1cbTGQCAsfh6aOT5FPDDr3LG10OFJJUsj7HprI0BxFrrT75pEejF7JYTtMfxCyU0zx8IA9Apx+xVOtgMVSyPj/40CR+wuB7Hp4DFkMm8Gj1QrKBy4FCkksbHQLPoR/FzBRSfOPNKocraYwxuAYCzyGdcHAcQyphyKc8kgmH2WAYdI0xJFzDYjmttVpFRET2qPe8/Nha+zIwtf3cGHMOeMJau2aM+QLwH40x/4xho6iHgOfe6zGKiMi1tstmU75L6uLGVsmv5Uq9CwxnNWEYLI8fyPPwgTxXNjtsdkIsltliivlSis1uxPGtbsQOkEkOOwtba0k4hvPVNrGFcBBzeCrL++eKO/eWXrN+qu8Ol/zZ6G597rAz8HbwNBgenMzgGDizkqA9iJnK+Tw6X2AmF1DK+HiO4dVLdfKp4f2188UUs/kk2WSCMysNeiFUMgk+enSCJw6V1KBJRERkj9pT69Raa18xxvwW8CoQAp9R52MRkb3BGMNCOb0zY7pd9tvuD2dGYRgsy5lhs6btTsS7lTPuDe9LNcbwgfkSC5XsTTv9Xr9+ainjM1VIvWX/7VnlpVqHw5NZJnNJckmPuUKSxiBioz3AAbJBggOFFP0wwvdcZnJJHpnNMZEPuFDt0B3EzBaTLJTTOI7zljGLiIjI3mCsHf/K3SeeeMI+//zzox6GiMi+sHu92rQ/bNLU6kdvCaLWWtabPZZqbVr9kJTngrFUssNZ291B8Zo1cH0XC7RvcMzbGePuY1w/drBcrfdwjGEyF+wEchEREdlbjDHftNY+caPX9tRMrYiI7H3Xz5gClK/bx1rLqUubPHu2xjfPr3Ol3scxlg8dKuO7Do8cLPL08Ukcx7lmDVyL5VKtQ9r3KGcSWAxzpdQtrxF7ozHebHslm7ytn4OIiIjsDaqnEhGRu67WHnC22uGbF2o0+hEb7T7V9oAXLm6Q9F1evLjBxa11b3evgdvsRaw2+1yotgnj4bGWah1q7cGoTkVERET2OIVaERG561q9kEZ3QGyhP4ixgLXD5XHa/QgLrDd7O/tu6w2GbRQs0Auja44nIiIiciMKtSIictdlAo9cMoFjwE84W8vjQMI1O/fhVrYaSe1e/3W7i7IBAs+95ngiIiIiN6JQKyIid10pneBwOcUHF0rkfJdi2qecTvD4fJFuP+Kx+SLzpdTOvnNbj7OBy2TWZ6Gcxtv6htq9VI+IiIjI9fSnbxERueuMMRybLTCRT/LYQuFtux9vr4G7vQbtBxdKd9z9WERERPYPhVoREbknjDFM5JJM5N65u/ANuxLfy8GJiIjIfUPlxyIiIiIiIjK2FGpFRERERERkbCnUioiIiIiIyNhSqBUREREREZGxpVArIiIiIiIiY0uhVkRERERERMaWQq2IiIiIiIiMLYVaERERERERGVsKtSIiIiIiIjK2FGpFRERERERkbCnUioiIiIiIyNhSqBUREREREZGxpVArIiIiIiIiY0uhVkRERERERMaWQq2IiIiIiIiMLYVaERERERERGVsKtSIiIiIiIjK2FGpFRERERERkbCnUioiIiIiIyNhSqBUREREREZGxZay1ox7DHTPGrALnRz0OuakJYG3UgxC5CV2fslfp2pS9TNen7GW6Pu9Pi9bayRu9cF+EWtnbjDHPW2ufGPU4RG5E16fsVbo2ZS/T9Sl7ma7P/UflxyIiIiIiIjK2FGpFRERERERkbCnUynvhc6MegMjb0PUpe5WuTdnLdH3KXqbrc5/RPbUiIiIiIiIytjRTKyIiIiIiImNLoVbuKWPMPzDGWGPMxK5tnzXGnDbGnDLGfGKU45P9yRjzS8aY14wxLxljfscYU9z1mq5PGTljzI9sXYOnjTE/N+rxyP5ljJk3xvyJMeakMeYVY8zf39peNsZ80RjzxtZ/l0Y9Vtm/jDGuMeZbxpjf23qu63OfUaiVe8YYMw/8EHBh17YTwKeAR4AfAX7VGOOOZoSyj30ReJ+19v3A68BnQden7A1b19yvAH8JOAH8xNa1KTIKIfC/WmsfBr4P+MzW9fhzwJettQ8BX956LjIqfx84ueu5rs99RqFW7qVfBv4hsPvG7R8DftNa27PWngVOA0+OYnCyf1lr/8haG249fRaY23qs61P2gieB09baN621feA3GV6bIu85a+1la+0LW48bDIPDQYbX5K9t7fZrwH87kgHKvmeMmQP+CvBvdm3W9bnPKNTKPWGM+SSwbK399nUvHQQu7nq+tLVNZFT+NvAHW491fcpeoOtQ9iRjzCHge4CvA9PW2sswDL7A1AiHJvvbP2c4iRLv2qbrc5/xRj0AGV/GmC8BMzd46ReAnwd++EZvu8E2teCWu+7trk9r7e9u7fMLDEvrfn37bTfYX9envNd0HcqeY4zJAr8N/M/W2roxN7pMRd5bxpgfBVastd80xvzgiIcjI6RQK7fNWvvxG203xjwKHAa+vfWlNwe8YIx5kuGMw/yu3eeAS/d4qLIP3ez63GaM+TTwo8DH7HfXNtP1KXuBrkPZU4wxCYaB9tettZ/f2nzVGHPAWnvZGHMAWBndCGUfewr4pDHmLwNJIG+M+Q/o+tx3VH4sd5219mVr7ZS19pC19hDDX9Aet9ZeAb4AfMoYExhjDgMPAc+NcLiyDxljfgT4R8AnrbXtXS/p+pS94BvAQ8aYw8YYn2Hzsi+MeEyyT5nhX6f/LXDSWvvPdr30BeDTW48/Dfzuez02EWvtZ621c1u/b34K+GNr7d9E1+e+o5laeU9Za18xxvwW8CrDss/PWGujEQ9L9p9/AQTAF7eqCZ611v6srk/ZC6y1oTHm7wJ/CLjAv7PWvjLiYcn+9RTwt4CXjTEvbm37eeCfAr9ljPk7DFc5+O9GMzyRG9L1uc+Y71bdiYiIiIiIiIwXlR+LiIiIiIjI2FKoFRERERERkbGlUCsiIiIiIiJjS6FWRERERERExpZCrYiIiIiIiIwthVoREZE7YIwpGmP+p3v8Gb9ujDlljPmOMebfGWMSN9nvnDFm4l6ORUREZK9RqBUREbkzReCehlrg14HjwKNACvipe/x5IiIiY0OhVkRE5M78U+BBY8yLxph/bYx5Zuvxd4wxHwEwxjSNMb9ojPm2MeZZY8z01vZJY8xvG2O+sfWfp270Adba/2K3AM8Bc1vvrxhj/sgY8y1jzL8CzI3eb4xJGmP+b2PMy1v7Pr21/SeNMZ83xvxXY8wbxpj/Y9d7ftgY8zVjzAvGmP9kjMnezR+aiIjI3aJQKyIicmd+DjhjrX0MeA34w63HHwBe3NonAzxrrf0A8AzwP25t/z+BX7bWfgj4ceDfvN0HbZUd/y3gv25t+ifAn1trvwf4ArBwk7d+BsBa+yjwE8CvGWOSW689BvwNhrPAf8MYM79VwvyPgY9bax8Hngf+l3f6QYiIiIyCN+oBiIiI3Ee+AWzf8/r/Wmtf3NreB35v6/E3gR/aevxx4IQxOxOseWNMzlrbuMnxfxV4xlr7Z1vPPwr8NQBr7e8bY2o3ed+Hgf9ra7/XjDHngaNbr33ZWrsJYIx5FVhkWFJ9AvjK1th84GvvePYiIiIjoFArIiJyl1hrnzHGfBT4K8D/Y4z5JWvtvwcGW6XDABHf/f51gO+31nZ2H8cY84fANPC8tfantrb9E2AS+JnrP/b6cRhjPsN3Z4P/MjcpS97S2/V4e2wG+KK19ife7nxFRET2ApUfi4iI3JkGkAMwxiwCK9bafw38W+Dxd3jvHwF/d/uJMeYxAGvtJ6y1j+0KtD8FfAL4CWttvOv9zwD//dY+fwkobb3/V7be/5i19tJ1+x1lWKZ86m3G9SzwlDHmyNZ70lvvExER2XM0UysiInIHrLXrxpivGGO+w/De2ZYxZgA0gf/hHd7+94BfMca8xPA7+RngZ2+w378EzgNf2yoH/ry19n8H/jfgN4wxLwB/Cly4yef8KvAvjTEvAyHwk9ba3q6y5+vPadUY85Nbxw62Nv9j4PV3OB8REZH3nPluNZSIiIiIiIjIeFH5sYiIiIiIiIwthVoREREREREZWwq1IiIiIiIiMrYUakVERERERGRsKdSKiIiIiIjI2FKoFRERERERkbGlUCsiIiIiIiJjS6FWRERERERExtb/D2IUqcC2sFCHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z[\"tsne-2d-one\"] = tsne[:, 0]\n",
    "Z[\"tsne-2d-two\"] = tsne[:, 1]\n",
    "plt.figure(figsize=(16, 10))\n",
    "sns.scatterplot(\n",
    "    x=\"tsne-2d-one\",\n",
    "    y=\"tsne-2d-two\",\n",
    "    palette=sns.color_palette(\"hls\", 10),\n",
    "    data=Z,\n",
    "    legend=\"full\",\n",
    "    alpha=0.3,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:563]",
   "language": "python",
   "name": "conda-env-563-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}