diff --git "a/A2/A2_ModelBuilding.ipynb" "b/A2/A2_ModelBuilding.ipynb"
new file mode 100644--- /dev/null
+++ "b/A2/A2_ModelBuilding.ipynb"
@@ -0,0 +1,1212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85356ae0-b730-462e-aab0-4491cbf1b1d4",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0881c073-a413-4082-b7d6-9096f2ad6b1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, LassoCV\n",
+    "from sklearn.feature_selection import SelectFromModel\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score\n",
+    "\n",
+    "import statsmodels.api as sm\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b3464-1bc8-4521-8fad-35a65a82aa6a",
+   "metadata": {},
+   "source": [
+    "# Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "21128113-5b27-4e46-ab40-9a24627580f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "REPO_ROOT = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n",
+    "DATA_DIR = os.path.join(REPO_ROOT, \"Datasets_all\")\n",
+    "\n",
+    "OUT_DIR = Path(\"models\")\n",
+    "OUT_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "RANDOM_STATE = 42\n",
+    "N_SPLITS = 5\n",
+    "\n",
+    "TRAIN_CSV = os.path.join(DATA_DIR, \"A2_dataset_80.csv\")\n",
+    "TEST_CSV  = os.path.join(DATA_DIR, \"A2_dataset_20.csv\")\n",
+    "\n",
+    "TARGET_COL = \"AimoScore\"\n",
+    "DROP_FEATURES = [\"EstimatedScore\"]  # EstimatedScore is excluded from input features as mentioned in slide 16 of lecture 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ad047b-82f5-49a5-8768-ac8144f4e619",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "856adeb4-1bf2-4828-bff1-8ee344e650b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape: (1675, 42)\n",
+      "Test shape : (419, 42)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df = pd.read_csv(TRAIN_CSV)\n",
+    "test_df  = pd.read_csv(TEST_CSV)\n",
+    "\n",
+    "print(\"Train shape:\", train_df.shape)\n",
+    "print(\"Test shape :\", test_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7d4a9dfb-ab3e-4195-9cd1-564e0f691c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['AimoScore', 'No_1_Angle_Deviation', 'No_2_Angle_Deviation',\n",
+      "       'No_3_Angle_Deviation', 'No_4_Angle_Deviation', 'No_5_Angle_Deviation',\n",
+      "       'No_6_Angle_Deviation', 'No_7_Angle_Deviation', 'No_8_Angle_Deviation',\n",
+      "       'No_9_Angle_Deviation', 'No_10_Angle_Deviation',\n",
+      "       'No_11_Angle_Deviation', 'No_12_Angle_Deviation',\n",
+      "       'No_13_Angle_Deviation', 'No_1_NASM_Deviation', 'No_2_NASM_Deviation',\n",
+      "       'No_3_NASM_Deviation', 'No_4_NASM_Deviation', 'No_5_NASM_Deviation',\n",
+      "       'No_6_NASM_Deviation', 'No_7_NASM_Deviation', 'No_8_NASM_Deviation',\n",
+      "       'No_9_NASM_Deviation', 'No_10_NASM_Deviation', 'No_11_NASM_Deviation',\n",
+      "       'No_12_NASM_Deviation', 'No_13_NASM_Deviation', 'No_14_NASM_Deviation',\n",
+      "       'No_15_NASM_Deviation', 'No_16_NASM_Deviation', 'No_17_NASM_Deviation',\n",
+      "       'No_18_NASM_Deviation', 'No_19_NASM_Deviation', 'No_20_NASM_Deviation',\n",
+      "       'No_21_NASM_Deviation', 'No_22_NASM_Deviation', 'No_23_NASM_Deviation',\n",
+      "       'No_24_NASM_Deviation', 'No_25_NASM_Deviation', 'No_1_Time_Deviation',\n",
+      "       'No_2_Time_Deviation', 'EstimatedScore'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AimoScore</th>\n",
+       "      <th>No_1_Angle_Deviation</th>\n",
+       "      <th>No_2_Angle_Deviation</th>\n",
+       "      <th>No_3_Angle_Deviation</th>\n",
+       "      <th>No_4_Angle_Deviation</th>\n",
+       "      <th>No_5_Angle_Deviation</th>\n",
+       "      <th>No_6_Angle_Deviation</th>\n",
+       "      <th>No_7_Angle_Deviation</th>\n",
+       "      <th>No_8_Angle_Deviation</th>\n",
+       "      <th>No_9_Angle_Deviation</th>\n",
+       "      <th>...</th>\n",
+       "      <th>No_19_NASM_Deviation</th>\n",
+       "      <th>No_20_NASM_Deviation</th>\n",
+       "      <th>No_21_NASM_Deviation</th>\n",
+       "      <th>No_22_NASM_Deviation</th>\n",
+       "      <th>No_23_NASM_Deviation</th>\n",
+       "      <th>No_24_NASM_Deviation</th>\n",
+       "      <th>No_25_NASM_Deviation</th>\n",
+       "      <th>No_1_Time_Deviation</th>\n",
+       "      <th>No_2_Time_Deviation</th>\n",
+       "      <th>EstimatedScore</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.653711</td>\n",
+       "      <td>0.361071</td>\n",
+       "      <td>0.561932</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.337159</td>\n",
+       "      <td>0.259684</td>\n",
+       "      <td>0.204687</td>\n",
+       "      <td>0.194165</td>\n",
+       "      <td>0.528455</td>\n",
+       "      <td>0.616451</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.670971</td>\n",
+       "      <td>0.656624</td>\n",
+       "      <td>0.642276</td>\n",
+       "      <td>0.552846</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.380679</td>\n",
+       "      <td>0.256337</td>\n",
+       "      <td>0.264467</td>\n",
+       "      <td>0.110952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.866667</td>\n",
+       "      <td>0.279770</td>\n",
+       "      <td>0.868006</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.188905</td>\n",
+       "      <td>0.331899</td>\n",
+       "      <td>0.142516</td>\n",
+       "      <td>0.456241</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.511239</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.670971</td>\n",
+       "      <td>0.912004</td>\n",
+       "      <td>0.733142</td>\n",
+       "      <td>0.598757</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.794357</td>\n",
+       "      <td>0.148733</td>\n",
+       "      <td>0.151124</td>\n",
+       "      <td>0.176949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.900765</td>\n",
+       "      <td>0.279770</td>\n",
+       "      <td>0.103778</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.225729</td>\n",
+       "      <td>0.523673</td>\n",
+       "      <td>0.842659</td>\n",
+       "      <td>0.838355</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.746055</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.681492</td>\n",
+       "      <td>0.656624</td>\n",
+       "      <td>0.642276</td>\n",
+       "      <td>0.572453</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.308943</td>\n",
+       "      <td>0.148733</td>\n",
+       "      <td>0.151124</td>\n",
+       "      <td>0.312291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.972904</td>\n",
+       "      <td>0.279770</td>\n",
+       "      <td>0.626016</td>\n",
+       "      <td>0.512195</td>\n",
+       "      <td>0.188905</td>\n",
+       "      <td>0.163080</td>\n",
+       "      <td>0.140124</td>\n",
+       "      <td>0.194165</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.156385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.670971</td>\n",
+       "      <td>0.656624</td>\n",
+       "      <td>0.642276</td>\n",
+       "      <td>0.552846</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.308943</td>\n",
+       "      <td>0.352463</td>\n",
+       "      <td>0.197513</td>\n",
+       "      <td>0.008608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.721837</td>\n",
+       "      <td>0.279770</td>\n",
+       "      <td>0.092300</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.698231</td>\n",
+       "      <td>0.585366</td>\n",
+       "      <td>0.797226</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.156385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.714491</td>\n",
+       "      <td>0.656624</td>\n",
+       "      <td>0.839790</td>\n",
+       "      <td>0.772836</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.308943</td>\n",
+       "      <td>0.148733</td>\n",
+       "      <td>0.151124</td>\n",
+       "      <td>0.498326</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   AimoScore  No_1_Angle_Deviation  No_2_Angle_Deviation  \\\n",
+       "0   0.653711              0.361071              0.561932   \n",
+       "1   0.866667              0.279770              0.868006   \n",
+       "2   0.900765              0.279770              0.103778   \n",
+       "3   0.972904              0.279770              0.626016   \n",
+       "4   0.721837              0.279770              0.092300   \n",
+       "\n",
+       "   No_3_Angle_Deviation  No_4_Angle_Deviation  No_5_Angle_Deviation  \\\n",
+       "0              0.346724              0.337159              0.259684   \n",
+       "1              0.346724              0.188905              0.331899   \n",
+       "2              0.346724              0.225729              0.523673   \n",
+       "3              0.512195              0.188905              0.163080   \n",
+       "4              0.346724              0.634146              0.698231   \n",
+       "\n",
+       "   No_6_Angle_Deviation  No_7_Angle_Deviation  No_8_Angle_Deviation  \\\n",
+       "0              0.204687              0.194165              0.528455   \n",
+       "1              0.142516              0.456241              0.521760   \n",
+       "2              0.842659              0.838355              0.521760   \n",
+       "3              0.140124              0.194165              0.521760   \n",
+       "4              0.585366              0.797226              0.521760   \n",
+       "\n",
+       "   No_9_Angle_Deviation  ...  No_19_NASM_Deviation  No_20_NASM_Deviation  \\\n",
+       "0              0.616451  ...              0.670971              0.656624   \n",
+       "1              0.511239  ...              0.670971              0.912004   \n",
+       "2              0.746055  ...              0.681492              0.656624   \n",
+       "3              0.156385  ...              0.670971              0.656624   \n",
+       "4              0.156385  ...              0.714491              0.656624   \n",
+       "\n",
+       "   No_21_NASM_Deviation  No_22_NASM_Deviation  No_23_NASM_Deviation  \\\n",
+       "0              0.642276              0.552846              0.648972   \n",
+       "1              0.733142              0.598757              0.648972   \n",
+       "2              0.642276              0.572453              0.648972   \n",
+       "3              0.642276              0.552846              0.648972   \n",
+       "4              0.839790              0.772836              0.648972   \n",
+       "\n",
+       "   No_24_NASM_Deviation  No_25_NASM_Deviation  No_1_Time_Deviation  \\\n",
+       "0              0.578192              0.380679             0.256337   \n",
+       "1              0.578192              0.794357             0.148733   \n",
+       "2              0.578192              0.308943             0.148733   \n",
+       "3              0.578192              0.308943             0.352463   \n",
+       "4              0.578192              0.308943             0.148733   \n",
+       "\n",
+       "   No_2_Time_Deviation  EstimatedScore  \n",
+       "0             0.264467        0.110952  \n",
+       "1             0.151124        0.176949  \n",
+       "2             0.151124        0.312291  \n",
+       "3             0.197513        0.008608  \n",
+       "4             0.151124        0.498326  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(train_df.columns)\n",
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d0cde5fe-4c9d-4c2c-9f1b-0600b8924676",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing expected cols: []\n",
+      "Unexpected extra cols: []\n",
+      "X_train: (1675, 40) y_train: (1675,)\n",
+      "X_test : (419, 40) y_test : (419,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# USing copy so that we can later make changes/modify without changing the original data\n",
+    "def split_xy(df: pd.DataFrame):\n",
+    "    drop_cols = [TARGET_COL] + [c for c in DROP_FEATURES if c in df.columns]\n",
+    "    X = df.drop(columns=drop_cols).copy()\n",
+    "    y = df[TARGET_COL].astype(float).copy()\n",
+    "    return X, y\n",
+    "\n",
+    "X_train_full, y_train_full = split_xy(train_df)\n",
+    "X_test_full, y_test = split_xy(test_df)\n",
+    "\n",
+    "# Created to apply feature weight vector of slide 19 in correct order\n",
+    "angle_cols = [f\"No_{i}_Angle_Deviation\" for i in range(1, 14)]  # 13\n",
+    "nasm_cols  = [f\"No_{i}_NASM_Deviation\"  for i in range(1, 26)]  # 25\n",
+    "time_cols  = [f\"No_{i}_Time_Deviation\"  for i in range(1, 3)]   # 2\n",
+    "expected_cols = angle_cols + nasm_cols + time_cols              # 40\n",
+    "\n",
+    "missing = [c for c in expected_cols if c not in X_train_full.columns]\n",
+    "extra   = [c for c in X_train_full.columns if c not in expected_cols]\n",
+    "print(\"Missing expected cols:\", missing)\n",
+    "print(\"Unexpected extra cols:\", extra)\n",
+    "\n",
+    "X_train_full = X_train_full[expected_cols].copy()\n",
+    "X_test_full  = X_test_full[expected_cols].copy()\n",
+    "\n",
+    "print(\"X_train:\", X_train_full.shape, \"y_train:\", y_train_full.shape)\n",
+    "print(\"X_test :\", X_test_full.shape,  \"y_test :\", y_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3c8256e-01f0-45ff-85d1-967a83ce5c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "sns.histplot(y_train_full, bins=30, kde=True, ax=axes[0],  edgecolor='black', alpha=0.7)\n",
+    "axes[0].set_xlabel('AimoScore')\n",
+    "axes[0].set_ylabel('Frequency')\n",
+    "axes[0].set_title('Distribution of Target Variable (AimoScore)')\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "sns.boxplot(x=y_train_full)\n",
+    "axes[1].set_ylabel('AimoScore')\n",
+    "axes[1].set_title('Boxplot of Target Variable')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ef3d8a7-67cc-490e-829d-62f82b5ad4d9",
+   "metadata": {},
+   "source": [
+    "# Duplicate removal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c6e1d2e4-ede4-4297-8951-f7382339f45f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After duplicate removal:: X_train: (1675, 35) X_test: (419, 35)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def drop_duplicate_columns(X_train: pd.DataFrame, X_test: pd.DataFrame):\n",
+    "    keep_mask = ~X_train.T.duplicated()\n",
+    "    kept_cols = X_train.columns[keep_mask].tolist()\n",
+    "    return X_train[kept_cols].copy(), X_test[kept_cols].copy(), kept_cols\n",
+    "\n",
+    "X_train, X_test, kept_cols = drop_duplicate_columns(X_train_full, X_test_full)\n",
+    "print(\"After duplicate removal:: X_train:\", X_train.shape, \"X_test:\", X_test.shape)\n",
+    "\n",
+    "# Results show that some of the features were duplicate which were\n",
+    "# removed thus creating 35 columns insetead of 40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3dc1701-69da-4c08-9d4d-e0dae685a719",
+   "metadata": {},
+   "source": [
+    "# Correlation Transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "50af24e0-9c10-4837-a77a-3f1ddcdb1fc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finds similar features that are highly correlated and remove it\n",
+    "class CorrelationFilter(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, threshold=0.99):\n",
+    "        self.threshold = threshold\n",
+    "        self.keep_cols_ = None\n",
+    "\n",
+    "    def fit(self, X, y=None):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        corr = Xdf.corr(numeric_only=True).abs()\n",
+    "        upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))\n",
+    "        to_drop = [col for col in upper.columns if any(upper[col] >= self.threshold)]\n",
+    "        self.keep_cols_ = [c for c in Xdf.columns if c not in to_drop]\n",
+    "        return self\n",
+    "\n",
+    "    # Applies transformation ad return result as pd dataframe\n",
+    "    def transform(self, X):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        return Xdf[self.keep_cols_].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8545e2a6-bb54-440b-8e0c-aa0669a0f3c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "corr = X_train_full.iloc[:, :20].corr() \n",
+    "sns.heatmap(corr, annot=False, cmap='coolwarm', center=0)\n",
+    "plt.title('Feature Correlation Matrix (First 20 features)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcaf3d15-13a6-49c7-b0c3-5921bc6505e0",
+   "metadata": {},
+   "source": [
+    "# Symmetric Constraint Transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dd28622b-e28e-4532-873a-65db76bb5ddd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SymmetryConstraintTransformer(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, pairs):\n",
+    "        self.pairs = pairs\n",
+    "        self.feature_mapping_ = None\n",
+    "    \n",
+    "    def fit(self, X, y=None):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        self.feature_mapping_ = {}\n",
+    "        self.new_cols_ = []\n",
+    "        self.drop_cols_ = []\n",
+    "        \n",
+    "        for left, right in self.pairs:\n",
+    "            if left in Xdf.columns and right in Xdf.columns:\n",
+    "                new_name = f\"sym_{left}_{right}\"\n",
+    "                self.feature_mapping_[new_name] = (left, right)\n",
+    "                self.new_cols_.append(new_name)\n",
+    "                self.drop_cols_.extend([left, right])\n",
+    "        \n",
+    "        # Keep columns not part of any pair\n",
+    "        self.keep_cols_ = [c for c in Xdf.columns if c not in self.drop_cols_]\n",
+    "        \n",
+    "        return self\n",
+    "    \n",
+    "    def transform(self, X):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        Xout = Xdf[self.keep_cols_].copy()\n",
+    "        \n",
+    "        # Add symmetric combined features\n",
+    "        for new_col, (left, right) in self.feature_mapping_.items():\n",
+    "            if left in Xdf.columns and right in Xdf.columns:\n",
+    "                Xout[new_col] = (Xdf[left] + Xdf[right]) / 2.0\n",
+    "        \n",
+    "        return Xout\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "523ba487-6ef8-423b-a490-f4f64cf9b40f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define symmetry pairs \n",
+    "# FMS symmetric pairs [Slide 17 of lab2]\n",
+    "fms_pairs = [\n",
+    "    (\"No_4_Angle_Deviation\", \"No_6_Angle_Deviation\"),\n",
+    "    (\"No_5_Angle_Deviation\", \"No_7_Angle_Deviation\"),\n",
+    "    (\"No_8_Angle_Deviation\", \"No_11_Angle_Deviation\"),\n",
+    "    (\"No_9_Angle_Deviation\", \"No_12_Angle_Deviation\"),\n",
+    "    (\"No_10_Angle_Deviation\", \"No_13_Angle_Deviation\"),\n",
+    "]\n",
+    "\n",
+    "# NASM symmetry pairs [Slide 18 of lab2]\n",
+    "nasm_pairs = [\n",
+    "    (\"No_1_NASM_Deviation\",  \"No_2_NASM_Deviation\"),\n",
+    "    (\"No_4_NASM_Deviation\",  \"No_5_NASM_Deviation\"),\n",
+    "    (\"No_8_NASM_Deviation\",  \"No_9_NASM_Deviation\"),\n",
+    "    (\"No_11_NASM_Deviation\", \"No_12_NASM_Deviation\"),\n",
+    "    (\"No_13_NASM_Deviation\", \"No_14_NASM_Deviation\"),\n",
+    "    (\"No_15_NASM_Deviation\", \"No_16_NASM_Deviation\"),\n",
+    "    (\"No_18_NASM_Deviation\", \"No_19_NASM_Deviation\"),\n",
+    "]\n",
+    "\n",
+    "sym_pairs = fms_pairs + nasm_pairs  # 12 pairs total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fed88d-1b0c-4b87-9118-6e27fb926638",
+   "metadata": {},
+   "source": [
+    "# Weightd linear regression Transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8796fc88-d6c6-4d7d-a31f-ab34ed5fdbe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureWeightMultiplier(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, full_weight_map):\n",
+    "        self.full_weight_map = full_weight_map\n",
+    "        self.current_weights_sqrt_ = None\n",
+    "\n",
+    "    def fit(self, X, y=None):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        cols = Xdf.columns\n",
+    "        \n",
+    "        weights = []\n",
+    "        for c in cols:\n",
+    "            # 1. Check if it's a standard feature name\n",
+    "            if c in self.full_weight_map:\n",
+    "                weights.append(self.full_weight_map[c])\n",
+    "            # 2. Check if it's a symmetric feature (e.g., sym_No_10_..._No_13_...)\n",
+    "            elif str(c).startswith(\"sym_\"):\n",
+    "                # Extract the original feature name from the new symmetric name\n",
+    "                # (e.g., extracts \"No_10_Angle_Deviation\")\n",
+    "                parts = c.split(\"_\")\n",
+    "                original_feature_name = f\"{parts[1]}_{parts[2]}_{parts[3]}_{parts[4]}\"\n",
+    "                # Use the weight of the original feature (left and right have same weight)\n",
+    "                weights.append(self.full_weight_map.get(original_feature_name, 1.0))\n",
+    "            else:\n",
+    "                weights.append(1.0) # Fallback weight\n",
+    "        \n",
+    "        self.current_weights_sqrt_ = np.sqrt(np.array(weights, dtype=float))\n",
+    "        \n",
+    "        # Validation check\n",
+    "        if len(self.current_weights_sqrt_) != Xdf.shape[1]:\n",
+    "            raise ValueError(f\"Weight alignment failed. X has {Xdf.shape[1]} cols, weights has {len(self.current_weights_sqrt_)}\")\n",
+    "            \n",
+    "        return self\n",
+    "\n",
+    "    def transform(self, X):\n",
+    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
+    "        Xw = Xdf.copy()\n",
+    "        Xw.iloc[:, :] = Xw.values * self.current_weights_sqrt_.reshape(1, -1)\n",
+    "        return Xw\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f5e4388e-34bc-495a-b2f5-290199aed9e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Define weights (13 Angle + 25 NASM + 2 Time)\n",
+    "FMS_WEIGHTS  = [1,1,1,1,1,1,1,2,2,2,2,2,2]  # 13\n",
+    "NASM_WEIGHTS = [1,1,1,2,2] + [1,1,1,1,2,4,4,2,2,2,2,2,1,1,1,2,2,2,2,2]  # 25\n",
+    "TIME_WEIGHTS = [1,1]  # 2\n",
+    "\n",
+    "FEATURE_WEIGHTS_40 = FMS_WEIGHTS + NASM_WEIGHTS + TIME_WEIGHTS\n",
+    "\n",
+    "weight_map = dict(zip(expected_cols, FEATURE_WEIGHTS_40))\n",
+    "weights_kept = [weight_map[c] for c in kept_cols]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09226956-c727-4438-8b0f-c1a3f464a945",
+   "metadata": {},
+   "source": [
+    "# Outlier removal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e641de8f-6ebe-47c1-9469-0e1606432eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_outliers_iqr(X: pd.DataFrame, y: pd.Series, k=1.5):\n",
+    "    q1, q3 = np.percentile(y, [25, 75])\n",
+    "    iqr = q3 - q1\n",
+    "    lo, hi = q1 - k*iqr, q3 + k*iqr\n",
+    "    mask = (y >= lo) & (y <= hi)\n",
+    "    return X.loc[mask].copy(), y.loc[mask].copy()\n",
+    "\n",
+    "\n",
+    "def remove_cooks_distance(X: pd.DataFrame, y: pd.Series, threshold=None):\n",
+    "    # Standardize for numerical stability\n",
+    "    Xs = (X - X.mean()) / (X.std(ddof=0) + 1e-12)\n",
+    "    Xs = sm.add_constant(Xs.values, has_constant=\"add\")\n",
+    "    model = sm.OLS(y.values, Xs).fit()\n",
+    "    infl = model.get_influence()\n",
+    "    cooks = infl.cooks_distance[0]\n",
+    "    n = len(y)\n",
+    "    if threshold is None:\n",
+    "        threshold = 4 / n\n",
+    "    mask = cooks <= threshold\n",
+    "    return X.loc[mask].copy(), y.loc[mask].copy()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b5a72c-0b6e-43ea-b169-ac5c4bea1875",
+   "metadata": {},
+   "source": [
+    "# Define model variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fe47fab7-bab4-4ec4-b5aa-c6e39f45647f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_baseline():\n",
+    "    return Pipeline([\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"lr\", LinearRegression())\n",
+    "    ])\n",
+    "\n",
+    "def make_corr_filter_variant():\n",
+    "    return Pipeline([\n",
+    "        (\"corrfilter\", CorrelationFilter(threshold=0.99)),\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"lr\", LinearRegression())\n",
+    "    ])\n",
+    "\n",
+    "def make_feature_selection_variant():\n",
+    "    return Pipeline([\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"select\", SelectFromModel(LassoCV(cv=5, random_state=RANDOM_STATE), threshold=1e-8)),\n",
+    "        (\"lr\", LinearRegression())\n",
+    "    ])\n",
+    "\n",
+    "def make_combined_features_variant():\n",
+    "    return Pipeline([\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"poly\", PolynomialFeatures(degree=2, include_bias=False)),\n",
+    "        (\"ridge\", Ridge(alpha=1.0))\n",
+    "    ])\n",
+    "\n",
+    "def make_symmetry_variant():\n",
+    "    return Pipeline([\n",
+    "        (\"sym\", SymmetryConstraintTransformer(sym_pairs)),\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"lr\", LinearRegression())\n",
+    "    ])\n",
+    "\n",
+    "def make_weighted_variant():\n",
+    "    return Pipeline([\n",
+    "        (\"w\", FeatureWeightMultiplier(full_weight_map=weight_map)), \n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"lr\", LinearRegression())\n",
+    "    ])\n",
+    "\n",
+    "def make_combine_all():\n",
+    "    return Pipeline([\n",
+    "        (\"corrfilter\", CorrelationFilter(threshold=0.95)),\n",
+    "        (\"sym\", SymmetryConstraintTransformer(sym_pairs)),\n",
+    "        (\"w\", FeatureWeightMultiplier(full_weight_map=weight_map)), \n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"ridge\", Ridge(alpha=0.5))\n",
+    "    ])\n",
+    "\n",
+    "VARIANTS = {\n",
+    "    \"baseline\": make_baseline(),\n",
+    "    \"corr_filter_0.99\": make_corr_filter_variant(),\n",
+    "    \"feature_selection_lasso\": make_feature_selection_variant(),\n",
+    "    \"combined_poly2_ridge\": make_combined_features_variant(),\n",
+    "    \"symmetry_constraint\": make_symmetry_variant(),\n",
+    "    \"weighted_features\": make_weighted_variant(),\n",
+    "    \"combined_final\":make_combine_all(),\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a83529d-736d-41b1-a13d-c176ba571c42",
+   "metadata": {},
+   "source": [
+    "# Evaluation metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a894567b-a035-4969-84c8-c920006d301a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_metrics(y_true, y_pred):\n",
+    "    # Computes metrics (actual score and model's prediction)\n",
+    "    rmse = root_mean_squared_error(y_true, y_pred)\n",
+    "    mae  = mean_absolute_error(y_true, y_pred)\n",
+    "    rsquared   = r2_score(y_true, y_pred)\n",
+    "    corr = np.corrcoef(y_true, y_pred)[0,1] if len(y_true) > 1 else np.nan\n",
+    "    return rmse, mae, rsquared, corr\n",
+    "\n",
+    "# Performs 5-fold cross validation technique\n",
+    "def cv_evaluate_pipeline(name, pipe, X, y, cv):\n",
+    "    rmses, maes, r2s, corrs = [], [], [], []\n",
+    "    for tr_idx, va_idx in cv.split(X):\n",
+    "        X_tr, y_tr = X.iloc[tr_idx].copy(), y.iloc[tr_idx].copy()\n",
+    "        X_va, y_va = X.iloc[va_idx].copy(), y.iloc[va_idx].copy()\n",
+    "\n",
+    "        pipe.fit(X_tr, y_tr)\n",
+    "        pred = pipe.predict(X_va)\n",
+    "        rmse, mae, r2, corr = compute_metrics(y_va, pred)\n",
+    "\n",
+    "        rmses.append(rmse); maes.append(mae); r2s.append(r2); corrs.append(corr)\n",
+    "\n",
+    "    return {\n",
+    "        \"variant\": name,\n",
+    "        \"cv_rmse_mean\": float(np.mean(rmses)),\n",
+    "        \"cv_rmse_std\":  float(np.std(rmses)),\n",
+    "        \"cv_mae_mean\":  float(np.mean(maes)),\n",
+    "        \"cv_r2_mean\":   float(np.mean(r2s)),\n",
+    "        \"cv_corr_mean\": float(np.mean(corrs)),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def cv_evaluate_outlier_leverage_variant(X, y, cv):\n",
+    "    rmses, maes, r2s, corrs = [], [], [], []\n",
+    "    base = make_baseline()\n",
+    "\n",
+    "    for tr_idx, va_idx in cv.split(X):\n",
+    "        X_tr, y_tr = X.iloc[tr_idx].copy(), y.iloc[tr_idx].copy()\n",
+    "        X_va, y_va = X.iloc[va_idx].copy(), y.iloc[va_idx].copy()\n",
+    "\n",
+    "        X_tr2, y_tr2 = remove_outliers_iqr(X_tr, y_tr, k=1.5)\n",
+    "        X_tr3, y_tr3 = remove_cooks_distance(X_tr2, y_tr2, threshold=None)\n",
+    "\n",
+    "        base.fit(X_tr3, y_tr3)\n",
+    "        pred = base.predict(X_va)\n",
+    "\n",
+    "        rmse, mae, r2, corr = compute_metrics(y_va, pred)\n",
+    "        rmses.append(rmse); maes.append(mae); r2s.append(r2); corrs.append(corr)\n",
+    "\n",
+    "    return {\n",
+    "        \"variant\": \"outlier_iqr_plus_cooks\",\n",
+    "        \"cv_rmse_mean\": float(np.mean(rmses)),\n",
+    "        \"cv_rmse_std\":  float(np.std(rmses)),\n",
+    "        \"cv_mae_mean\":  float(np.mean(maes)),\n",
+    "        \"cv_r2_mean\":   float(np.mean(r2s)),\n",
+    "        \"cv_corr_mean\": float(np.mean(corrs)),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6180f838-6fe8-4750-a7c1-6247752bdf36",
+   "metadata": {},
+   "source": [
+    "# Run CV for all variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e0b68fab-01b8-42f1-a04a-c6002a4b02cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RUNNING CROSS-VALIDATION\n",
+      "\n",
+      "Evaluating: baseline\n",
+      "\n",
+      "Evaluating: corr_filter_0.99\n",
+      "\n",
+      "Evaluating: feature_selection_lasso\n",
+      "\n",
+      "Evaluating: combined_poly2_ridge\n",
+      "\n",
+      "Evaluating: symmetry_constraint\n",
+      "\n",
+      "Evaluating: weighted_features\n",
+      "\n",
+      "Evaluating: combined_final\n",
+      "\n",
+      "Evaluating: outlier_iqr_plus_cooks\n",
+      "CROSS-VALIDATION RESULTS\n",
+      "                variant  cv_rmse_mean  cv_rmse_std  cv_mae_mean  cv_r2_mean  cv_corr_mean\n",
+      "feature_selection_lasso      0.156819     0.005442     0.119499    0.558970      0.750414\n",
+      "               baseline      0.156935     0.005503     0.119581    0.558357      0.750007\n",
+      "       corr_filter_0.99      0.156935     0.005503     0.119581    0.558357      0.750007\n",
+      "      weighted_features      0.156935     0.005503     0.119581    0.558357      0.750007\n",
+      " outlier_iqr_plus_cooks      0.158749     0.004739     0.119580    0.548609      0.744360\n",
+      "         combined_final      0.161211     0.007822     0.122830    0.534135      0.734190\n",
+      "    symmetry_constraint      0.161215     0.007823     0.122835    0.534111      0.734179\n",
+      "   combined_poly2_ridge      0.193001     0.010787     0.140305    0.334908      0.700921\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"RUNNING CROSS-VALIDATION\")\n",
+    "\n",
+    "cv = KFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
+    "\n",
+    "results = []\n",
+    "for name, pipe in VARIANTS.items():\n",
+    "    print(f\"\\nEvaluating: {name}\")\n",
+    "    results.append(cv_evaluate_pipeline(name, pipe, X_train, y_train_full, cv))\n",
+    "\n",
+    "print(f\"\\nEvaluating: outlier_iqr_plus_cooks\")\n",
+    "results.append(cv_evaluate_outlier_leverage_variant(X_train, y_train_full, cv))\n",
+    "\n",
+    "results_df = pd.DataFrame(results)\n",
+    "\n",
+    "results_df = results_df.sort_values([\"cv_corr_mean\", \"cv_rmse_mean\", ], \n",
+    "                                     ascending=[False, True]).reset_index(drop=True)\n",
+    "\n",
+    "print(\"CROSS-VALIDATION RESULTS\")\n",
+    "print(results_df.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "280c4f6d-4775-461c-b6a8-fb04c4ba81ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "plt.bar(results_df[\"variant\"], results_df[\"cv_rmse_mean\"], yerr=results_df[\"cv_rmse_std\"], capsize=4)\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.title(\"CV RMSE (mean +/- std) by variant\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a247849-9eaa-4df7-a00c-b07a779cec5f",
+   "metadata": {},
+   "source": [
+    "# Select champion variant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "65ee6037-fcd2-422a-a621-4c5c4abfe3ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CHAMPION VARIANT: feature_selection_lasso\n",
+      "CV Correlation: 0.7504\n",
+      "CV RMSE: 0.1568\n",
+      "CV MAE: 0.1195\n",
+      "CV R²: 0.5590\n"
+     ]
+    }
+   ],
+   "source": [
+    "champion_name = results_df.iloc[0][\"variant\"]\n",
+    "print(f\"CHAMPION VARIANT: {champion_name}\")\n",
+    "print(f\"CV Correlation: {results_df.iloc[0]['cv_corr_mean']:.4f}\")\n",
+    "print(f\"CV RMSE: {results_df.iloc[0]['cv_rmse_mean']:.4f}\")\n",
+    "print(f\"CV MAE: {results_df.iloc[0]['cv_mae_mean']:.4f}\")\n",
+    "print(f\"CV R²: {results_df.iloc[0]['cv_r2_mean']:.4f}\")\n",
+    "\n",
+    "# Get the actual estimator for champion\n",
+    "if champion_name == \"outlier_iqr_plus_cooks\":\n",
+    "    champion_pipe = make_baseline()\n",
+    "    # Clean entire training set once for final model\n",
+    "    X_tr2, y_tr2 = remove_outliers_iqr(X_train, y_train_full, k=1.5)\n",
+    "    X_tr3, y_tr3 = remove_cooks_distance(X_tr2, y_tr2, threshold=None)\n",
+    "    champion_pipe.fit(X_tr3, y_tr3)\n",
+    "else:\n",
+    "    champion_pipe = VARIANTS[champion_name]\n",
+    "    champion_pipe.fit(X_train, y_train_full)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "709107ce-69e5-4986-8620-1c47be4bf62f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UQO4oOsvnUEgyik0BFg+MhDL5jecoEs4cUPBHRqR7Ax4DI6KMtHZ133Gf+Dz+UPwyOZDvMwpc3leJum+15LqysrK4z/NA70Io9B3imRWEVpihzAgRp+Yo4iJhRJBZ3/HC6ERkNIaZvZGlhRIFM5rDyywxU5yVCZjl3REsvUQiu/FoESB6KhMFv2Q5LcnqEawGQC9spMVAi+iEnzceAwca3SXaOklPOxDxnknkwIQVDCgImLnPUnGR9KS8E4UHo9wPP/xwm1JOjKJx+jqR1zme68B/d+e9pUHrCtfBgUok2nY4zc5t8zmR9wD/jizPFrnPBxxwgPKghpcZ4+cELTMclPXEdhIrnFnge/vRRx9t9xjtTPQfE4r6SLQov3bdtXrBsd67FMHRosqa316zYMRznrkP0ew9ghAvEpkVhFYoIPjFRGHF6GR4BzBOTXMarTs1RBnxpWijvYCeMgoylhfSyjAlGvpe+eXKxB1+cVGscVsU6x3BKX5GjxmZ5pcba5pSBDN6xql+dv5JJDzHFPSMtHLaNDwarJ0zRmVZyosCi/VXKcB4/qIJvFig2GBUmIkr9OAycYVTruG1XbsrPnk9KfxpVWFEKrJMU7xin35HDj7oweQULveVIoaCgsfBgUB3oF+RiWZcJ68xS59ppbkYuWet196GtgK+11grlsfE42ECV0+8pLw/TzvtNFUaj5F2zTbC0lx8jCWxuM1bb70V11xzjRKkvK+ZqMbrT+8nfcxaneNo8LVa7VYm0NFPTLsM32O8p/oCHiNtOUyI473AZhscFDM6zOWMTnPAwrJbtBnwvcNoO720LJdG/zP3n/B80K/N9xXPA4Ul79uO/Ne0S9E3zPckr6H2ucgZFt47Wv3oeM4z3zt8PRNPaVmgBeHwww/vk3MpDDC6XQdBEAYoq1atCpx33nmBUaNGqVI8LL2z2267qXJbTqcz9Dy+fS666KJ2r2epmfBSTSwpddZZZwUKCgoCGRkZquTSihUr2j0vWiml8PJLkWV2+FqWhYos+3TnnXcG7r777sDw4cNV+aS5c+eqkmHR1hmOx+NRJaNY/slkMqnXs6xS+DFrx8eSOpGwTFh4qbDOYMkirp/7cOutt0Z9/LbbblPb4jGwFBPLR/GYw0v5hB9zJNFKc7FkGctr5eTkBLKzswPHHXecKq/E5/GcdHXOtWsUXmaK15KlmVgSjY91Vqars/2N5KeffgocffTRqhQYzwGP+/jjjw988sknXe5nZ/cTeemll9Q55Xrz8vICp5xySptybtHur3Ci3fsdHdtnn32mlr/yyiuhZb/++qsqXcX3A98XfL9pZe3Cr1dH+xDt/mU5N2570qRJ6n1bWFgYOPjggwM//vhjm+ex/Nnuu++u1ssfPp/HsnLlykBXLFq0SL1/ud9paWmBvffeO/Dtt9/GdN6188DfsRKtdBVLYd1xxx2B7bffXl2/3NxcVaaM712WGCS8R1hmsLS0VJ0L/mb5Nn62hfPGG28EtttuO1Uyq6syXe+9954qVcfzxePneseNGxe4+OKLAxUVFe2eH8t5bm5uDpx88snq/cjtS5kuobvo+L/+FtSCIPQcRkEYVWHiUGcRJkEQBEEYSIhnVhAEQRAEQUhZRMwKgiAIgiAIKYuIWUEQBEEQBCFlEc+sIAiCIAiCkLJIZFYQBEEQBEFIWUTMCoIgCIIgCCmLiFlBEIQBwFNPPaXag4Z3qOopXBfXyXULgiAkKyJmBUEQOoFtjH/3u99hzJgxsFqtqmMVOy+xaxZbiA4E2Pmup219BUEQ+gtpZysIgtAB77zzDo477jhYLJY27Y2//vprXHnllfjll19UC+CBIGZ//vlnXHLJJW2WsxUqBTvb4AqCICQrImYFQRCiwF7yJ554ohJ0n376KYYMGRJ67KKLLsKaNWuU2O0JbMDodDphs9naPcblZrMZen3/TaDRYsBotCAIQjIjNgNBEIQo/P3vf0dzczMef/zxNkJWY9y4cfjTn/6k/u31enHLLbdg7NixKoo7atQo/OUvf4HL5WrzGi4/7LDD8MEHH2DmzJlKxD7yyCP4/PPPlXB88cUXcd1112Ho0KFIS0tDY2Ojet3ChQtx0EEHITs7Wy3fc8898c0333R53d544w0ceuihKC0tVfvF/eN++ny+0HP22msvJco3btyo9oE/3M/OPLMU93PnzkV6ejpycnJw5JFHYvny5W2e89e//lW9lqL/zDPPVM/j/p911lmw2+1yzwmCkDAkMisIghCFt956S/lkd9111y7Pz7nnnounn34axx57LC6//HIlPm+//XYl8P73v/+1ee7KlStx0kknKR/ueeedh4kTJ4Yeo9BkNPaKK65QQpj/pnA8+OCDMWPGDNx4440qUvvkk09in332wVdffYXZs2d3uF8UoRkZGbjsssvUb67rhhtuUCL5zjvvVM+59tpr0dDQgC1btuAf//iHWsbndsTHH3+s9ofnhoKVNoT7779f+YgXLVoUEsIaxx9/PEaPHq3OBx9/7LHHUFRUhDvuuEPuO0EQEkNAEARBaENDQ0OAH49HHnlkl2dm8eLF6rnnnntum+VXXHGFWv7pp5+Glo0cOVIte//999s897PPPlPLx4wZE7Db7aHlfr8/MH78+MCBBx6o/q3B54wePTqw//77h5Y9+eSTah3r169v87xIfve73wXS0tICTqcztOzQQw9V+xYJ18V1ct0aU6dODRQVFQVqampCy5YsWRLQ6/WB008/PbTsxhtvVK89++yz26zzqKOOCuTn50c5k4IgCN1DbAaCIAgRaNP7mZmZXZ6bd999V/1m9DMcRmhJpK+WUcoDDzww6rrOOOOMNv7ZxYsXY/Xq1Tj55JNRU1OD6upq9dPS0oJ9990XX375Jfx+f4f7Fr6upqYm9VraAzjNv2LFirive1lZmdon2gby8vJCy3faaSfsv//+oXMRzgUXXNDmb26fx6KdY0EQhJ4iNgNBEIQIWH5LE4BdQa8pp/7poQ2npKRE+UT5eKSY7YjIxyhkNZHbEbQI5ObmRn2M1RbowaW9IFI88nXxoh1LuDVCY/LkycoLTKFNL63GiBEj2jxP29e6urrQeRYEQegJImYFQRAioMhi0hTLVcUKk51iIVrlgo4e06Ku9LdOnTo16ms68rfW19erRDEey80336ySv1iZgL7VP//5z51GdBOJwWDosJKDIAhCIhAxKwiCEAVWHWAN2fnz52POnDkdniOW7qIwZBSV0UmNiooKJSj5eHehACUUpPvtt19cr2WFBE7nv/baa9hjjz3alBzrrhDXjoVJbJHQtlBQUNAmKisIgtAXiGdWEAQhCldddZUSZqxUQGEarTMYu4Adcsgh6u/IDlr33HOP+s3SWN2FFQwoaO+66y5VJiySqqqqLiOi4RFQNnx46KGH2j2XxxmL7YAlyhghZuUGCnUNRrA//PDD0LkQBEHoSyQyKwiCEAWKSHbGOuGEE1TENbwD2LfffotXXnlFJUKx1iw9rYzialP73333nRJ88+bNw957793t80svLktZsRTW9ttvr2q0sgbt1q1b8dlnn6mILUuIRYMlxehP5b798Y9/VNHXZ599Nur0PkXzSy+9pJLYZs2apawLhx9+eNT10vLA/WG0+pxzzgmV5mINWZbqEgRB6GtEzAqCIHTAEUccgaVLlyoBxwYE//rXv1TzAWbv33333apOLKHgZN1V1nVlXVkmf11zzTWqLmxPYVMDWh1Yg/aBBx5QEVquf+edd1a1ajsiPz8fb7/9tqqqwCQwCttTTz1VVUGIrKZw4YUXqioFrF/LWrO0E3QkZml3eP/999WxsWYtW91SwLNubGfJbYIgCL2FjvW5em3tgiAIgiAIgtCLiGdWEARBEARBSFlEzAqCIAiCIAgpi4hZQRAEQRAEIWURMSsIgiAIgiCkLCJmBUEQBEEQhJRFxKwgCIIgCIKQsgy6OrNsO7lt2zZkZmbG3MJREARBEARB6DtYObapqQmlpaWqgUxnDDoxSyE7fPjw/t4NQRAEQRAEoQs2b96MYcOGdfqcQSdmGZHVTg5bQQqCIAiCIAjJRWNjowo+arqtMwadmNWsBRSyImYFQRAEQRCSl1gsoZIAJgiCIAiCIKQsImYFQRAEQRCElEXErCAIgiAIgpCyDDrPbKzlILxeL3w+X3/vihCGwWCA0WiUkmqCIAiCIIQQMRuB2+1GWVkZ7HZ75ENCEpCWloYhQ4bAbDb3964IgiAIgpAEiJiNaKiwfv16FQFkkV4KJmmskDzRcg40qqqq1DUaP358l0WUBUEQBEEY+IiYDYNiiYKWdc0YARSSC5vNBpPJhI0bN6prZbVa+3uXBEEQBEHoZyS0Fe2kSMQvaZFrIwiCIAhCOCJmBUEQBEEQhJSlX8Xsl19+icMPP1z5U+lNff3117t8zeeff47p06fDYrFg3LhxeOqpp/pkXwVBEARBEITko1/FbEtLC6ZMmYIHH3wwpucz8efQQw/F3nvvjcWLF+OSSy7Bueeeiw8++KDX93UgsNdee6lzlizr/Otf/4qpU6cmdH8EQRAEQRhc9GsC2MEHH6x+YuXhhx/G6NGjcffdd6u/J0+ejK+//hr/+Mc/cOCBB2Iwc+aZZ6K+vj6m6HZfwsj5WWed1W75o48+iiuuuAIXX3xx0h+DIAiCIAjJS0pVM5g/fz7222+/NssoYjuLDLpcLvWj0djY2Kv7KLQnKysLK1eubLMsOztbVSfIyMiQUyYIgiAIwuBIACsvL0dxcXGbZfybAtXhcER9ze23366Ek/bDsluDAVo4Tj/9dCUW2WRAi2aHQ5HP6OjQoUORnp6OnXfeWXmSNWpqanDSSSepx1mqbMcdd8R//vOfuPeFfuiSkpI2PxSy4TYD/vvpp5/GG2+8oZ7Pn/B9EQRBEAQhcbXbKxqd2FDdon7z71QmpSKz3eGaa67BZZddFvqbwrc7gvaee+5RP13B5LQ333yzzbIjjjgCixYt6vK13M/wfe0JV155Jb744gslDouKivCXv/xF7UO4R/UPf/gDfv31V7z44osqCe9///sfDjroICxbtkw1JXA6nZgxYwb+/Oc/q+jqO++8g9NOOw1jx47F7NmzkUgoqpcvX66uz5NPPqmW5eXlJXQbgiAIghAPFHmVTS443D7YzAYUZVpSvpnSppoWfPBLhRKyvkAAuWkmjC7MwJwx+Riel5o19lNKzDKiV1FR0WYZ/6bQYqQvGqx6wJ+eQpG1devWLp8XTSiza1Usr02UBaK5uRmPP/44nnvuOey7775qGaOew4YNCz1n06ZNSjTyN4WsJijff/99tfy2225TEVku06C/lcl2L7/8clxitqGhoY2dgP9mlD0cLuM1ZLSY11kQBEEQ+pPNtXbMX1ejRJ/L64fFqMeogvSUFn0L19Xg8a/Xo7zBCYtJD6vJgEanB9XNbpQ3OHDUtGEpeWwpJWbnzJmDd999t82yjz76SC3vbSiYKe66orCwMOqyWF7LbSSCtWvXqg5ZtA1oMMo5ceLE0N+Mvvp8PkyYMKHNaykm8/Pz1b/5OEUtxSvFONfJx+PtjpaZmdkmMi2NDwRBEIRkF7L/+2kL6uweDMm2wWYywOHxYUVZY0qJvkBYZLm22YXHvlqHLfUOjMxLg9logMfnR73dA5fHp55P8T4s15Zy0ed+FbOMIK5Zs6ZN6S2W3KLwGjFihLIIUEQ988wz6vELLrgADzzwAK666iqcffbZ+PTTT5XQ4vR3b9MTC0Ck7SAZ4Lk3GAz48ccf1e9wtCjqnXfeifvuuw/33nuv8svSV8tkO4raeKB4ZU1gQRAEQUgFAUhRRyE7rjAjJOwyLEaMLczA2qrmlBB9m8Miy04K8fJGbKxxYFxhOqymoPyzGA0ozNSjqsmFFrcX66ualfgtzmrbLt7v96tqQ8lq/+tXMfvDDz+omrEamlg844wzVEmnsrIyNQ2uwbJcFK6XXnqpElmcNn/ssccGfVmuSOhpNZlMWLhwoRoUkLq6OqxatQp77rmn+nvatGkq8lpZWYm5c+dGvT7ffPMNjjzySJx66qmhm5nr2G677dAbmM1mtU+CIAiC0F9QzFEAMiIbKVb5N4UeH48m+pI1suzx+WF3++H1+VHe6ITZZEC6OSgBddAhy2pCk9OLeodHRXEjrZLUZfzNcqiJsG4OKDHLgvudZdBF6+7F1/z000+9vGepDSOr55xzjkoCo2WACWDXXnttm+l92gtOOeUUVfGAlQ4obnmjfvLJJ9hpp51Ucwomgb366qv49ttvkZubqxLg6FHuLTE7atQo5cllGS/uN6tPUJQLgiAIQl9BMUePLK0F0UgzG0NT98keWR5bmI5mlw81zS74/QFkWo1wev2obnIhLd+ghCwxG/WoafFBD51KdAvv1MqqRtu2bVN/X3311aq2f7KRUqW5hNihRYARV7YLZm3e3XffXVUmCIeJXhSzl19+ufLTzps3D99//30omnvdddep6gys5ctBBBOz+Jze4rzzzlP7MXPmTOUzZmRYEARBEPoSijkme9EjGw2726seDxd9yRhZthkNWLSpHvPX1mDplgbU2l2we3wqOtvkpE/WH3oNPbP8mwlurNjAWdJbb71VzZ5rQpaBsUMOOQTJiC6Q6sXF4oQVAxjxY4Z9ZMIVS1HRt0s7g9WanFMHgx25RoIgCEJvQln0yo9bVLIXPbLhVgM+Rs/spCFZOG7GsKT0zG6obsGDn69R0Vinx49smwkmvQ7raoI1ZRmhNeh06hhy0sxweX3KljAsNw3XHToZZk+TshdyplZjn332wfPPP9+n1YY602uRSGRWEARBEAShFQpUlt/KSTMp4coops8fUL/5N5fz8WQUssRq0isbQaPDi8JMi0ryos2Q3tn89KDflTFZ2g3KGhwoq3eqZLZzdh+NVT/Nx5QpU0JClq+7+eab8eGHHyZ12cyUKs0lCIIgCILQ27DsFstvadUAOHVPawGjmalSZ1ZHrc2591bNnW42KtHqdPvgDfgxJMuCDIsJowrTccDkIjz+z78ra4E2Yc8a9C+88EIocTyZETErCIIgCIIQAQUrxV+qdQBzevwqIsv9rGp2qUoFTPBye1nRwIcxhelIsxhx3MzhGF+cGTom1pHXhCy7gbIsarTa+cmIiFlBEARBEIQoaKW4Ugmb2YCCDIv6YaevmhY3mlweGPV6lGRZUZwVFK8UsuHHxqjsggULVDUjdv9MpQZHImajMMhy4lIKuTaCIAiC0DFFmRZVlYAJbNNH5KjSXKwzazLokWExYF11C8YV2rB66Q8o3n330OtYCpPNqCIbKaUCqSO7+wCtpqndbu/vXRE6QLs2Un9WEARBENoTnsBG4UpXBKsW8Df/9jVW4O6LT8K+++7brm5/KgpZIpHZiIuYk5OjumKRtLS0pPfGDKaILIUsrw2vUaq+4QRBEAShvxLYGld8i0f/dhUaG+rV80477TQsXbo0pSwF0RAxG4FWekITtEJyQSGbzOVBBEEQBCHZEtjqm+y485br8Pgj/wo9PmbMGNVpNdWFLBExGwEjsUOGDFGdLjweT/9cFSEqtBZIRFYQBEEQYtc0TZVbcPIJJ2DRokWh5ccddxweffRR1ZRgICBitgMomkQ4CYIgCIKQqrz00kuqVXxTU5P622Kx4N5778Xvfve7AWWjFDErCIIgCIIwwPjrX/+Km266KfT3hAkT8PLLL6sOXwON1DdKCIIgCIIgCG045JBDYDQGY5annnoqfvzxxwEpZIlEZgVBEARBEAYYs2fPxj/+8Q+kp6fjzDPPHFC2gkhEzAqCIAiCIKQwLS0teOCBB3D55ZeHorHkD3/4Q8LKYyZzW18Rs4IgCIIgCCnKsmXLcPzxx2PFihWqHnu4TzYRbK61h+rVurx+Va+WHcbYmIHlv5IB8cwKgiAIgiCkGIyWsrzW7NmzlZAlrFRQXV2dUCH7v5+2YHlZo+oiNio/Xf1mq1wu5+PJgIhZQRAEQRCEFKKxsREnn3wyzj//fDidTrWMyV3ff/89CgoKEiaWGZGts3swrjADGRYjDHqd+j22MAP1do96nM/rb0TMCoIgCEKKQQFR0ehUU7/8nQyCQugb2PxgxowZePHFF0PLLrzwQixYsECV30oU9Mjy/hqSbWvnj+XfxVnWUKvc/kY8s4IgCIKQQqSCh1FIPBywPPjggyrJy+12q2VZWVl4/PHHceyxxyZ8e0z24v1lMxmiPp5mNoaSwvobEbOCIAiCkCJoHkZO/TJiRqHh8PiUh7G8wYGjpg0TQTtAeeyxx3DxxReH/p41a5aKzo4ZM6ZXtseqBRwo8f6itSASu9urHufz+huxGQiCIAhCCpBKHkYh8Zx22mmhpgeXXnopvv76614TsoTltxjx5yAp8p7SbC58nM/rbyQyKwiCIAgpQDweRv5bGFhYrVbVjpaVC4444ohe355Op1PWFYrZtVXN6p6itYARWQrZnDSTejwZ6s1KZFYQBEEQUoBYPIx8PBk8jELPqKmpwQknnBAquaXBBK++ELIa9GDTujJpSBYaHB5sqGlRv/l3MllaJDIrCIIgCClAKnkYhe5D+8BJJ52ELVu2YPny5Vi4cCFsNlu/ndLheWkYlmtL6g5gEpkVBEEQhBQglTyMQvz4/X7cdttt2GuvvZSQJeXl5Vi9enW/n05dq42F9xd/J5OQJRKZFQRBEIQUIJU8jEJ8VFRUqASvjz76KLSMovb5559HaWlpn53OQCCQ1BHYjhAxKwiCIAgpguZh1OrMUnjQWkAPo9SZTU1x9sknn+DUU09VUVjC/bvhhhtw/fXXw2DoO8vI5hSuXyxiVhAEQRBSiFTwMCYDyS7OfD4fbr75Ztxyyy0h20hJSYmKxu6zzz59ui+bU7x+sYhZQRAEQUgxNA+jkLrijG1pb7311pCQPeCAA/Dss8+iqKioX+sX61oHRVr9Ylpa+DgHUMk6YJIEMEEQBEEQBgyp0lyCHbxoJ6CV4Pbbb8d7773X50I23vrFyYpEZgVBEARBGDAka3MJj8ejhKte/1sc8brrrlN1Y6dNm4Zkrl9c2WppSVYkMisIgiAIwoAhGZtLbNq0SVUnuOeee9osp7jtTyEbWb84GqlQv1jErCAIgiAIA4ZkE2dvvvkmpk6dim+//RbXXHMNFixY0K4+MCPF/N0f1oeiAVC/WGwGgiAIgiAMGDRxxmQvemTDrQaaOGMps94WZ263G3/+859x7733hpYNGzYsVG4rWaot6AZA/WIRs4IgCIIgDJjarckgztatW4cTTjgBP/zwQ2jZ0Ucfjccffxw5OTlJV21heIrXLxYxKwiCIAhCTCRLNDGZxdmrr76Kc845B42Njepvs9msvLIXXnihEtDJWgpreArXLxYxKwiCIAhCl/RWNLG3Ir3dFWfd3R+n04nLLrsM//rXv0LLxo0bh5dffrlNkleyVltI5frFImYFQRAEQeiU3oom9nakN15x1pP9YekttqbVOOmkk/DII48gMzNzwJXCSjZEzAqCIAiC0Cm9EU1MFt+oFoldV9WMz1ZUwu3zozQnrd3+zJs6FBYu6yBiS9HKKOzee++NO++8E2effXZUYR9ebYGDgY6qLVhNeuXxTbUp//5AxKwgCIIgCJ2S6GhisvhGtUjs+qpmLNvagAaHF+ML01GQ4Vf7ou3P4s31+Oena1CYYYbbF1Bic0iGHpPzDJi53djQ+qZMmYKNGze2i8bGW22Bz/liVRU21tiT2pucLEidWUEQBEEQ+rR2azK0UNUiw8vLGmE06NV2CzPNKG9yYfGmOtS2uNXz2P62vNGJleVN6nmj8tNhr9iAy08+BMceczTWlte3WW9nQlY7PopSVlWgaG9yeuDzB9Rv/s3Hq5pdWFHehJw0s9oef1P8cn+530JbRMwKgiAIgtCnhfX7u0tXZGTYbNDDHwggw2JCYYYFdo9P2Q78AT/WVjfD4/Mj02qESa/DV++8jNt/dxSqNq/FxhVLcfnV18bd7ECrtsDqCg0ODzbUtKjfE0syUZhpgT8AtV+MDBv0ulCEmMKa+90fzRWSGbEZCIIgCILQp7VbY/WN9laXrsjIsMmoh1GvV6LVYjQgy2pCTYsbZQ1O1DS7kWY2wNnSgufvuBLfffxmaD1Dx07CuN0O7VblgWjVFihSn5m/MSkrHSQzImYFQRAEQejT2q393aUrMjLMqGt+hhnlDU4UZuphNurR5PKo53l8PlSsX4XvHr8RdWUbQ+vY56hTcPIfr8e2Zl+3I8iR1Ra0KgpdeZPtruAgQpLDgoiYFQRBEAShTwvr93aXrq5qxUZGhnXQYWxBBhodHlS1inS9TqesBz9/9Cp++d8D8HuDHlprWgbOveb/MGf/w5XP1WIMJCyCHEvE2uXx4eMVlahrcUtyWCsiZgVBEARB6PPC+r3VpSuWWrHRIsO56WZMG56LNVVNWFvZgkyLAa/ddSWWffV+aN2jJ+2Ii299EMXDRvZKBLmriPWaymY0ONwwGnRRy4cd1cdtcJMFEbOCIAiCIPQLiW6hGmvt2o4iwxSJrBywyxgr9p5YiGeWTQyJ2V0OPxWnXHwNsjPSVUQ2ERHkeCLWrKjAJLEsmwnjizKTpg1uMqALDLKUOPZKzs7ORkNDA7Kysvp7dwRBEARBSACUM6/8GCy1FV67VnuMYo9R1ONmDAs91lUU1+1248gjj8QxJ5+JrMm79lqnskii7Vdumhkba1owIj89qgWhyelRYveMXUcNiOSwePSaRGYFQRCEAUVXfklhYNKdLmXhkeGyiiosX/YTjptxeOj1ZrMZ7733Xp/fV9Ei1kz6em7hJmmDGwURs4IgCMKAIRa/pDAw6W6XMgrStT8vwoknnojKykqUfPoVRk/Yrp1gTZRXOFYit0dbQ3+WM0tmpGmCIAiCMCAI7+gknZMGH93pUub3+3HHHXdgjz32wObNm+FyuXD2eRfgmfkb8PS3G5RtIVk6biW6ccVAQsSsIAiCkPJEdnSSzkmDj3jFHqOwhxxyCK6++mr4fEEBPGK7Gbjgpn9idEFG0rWQ7aoNbk6Ck9FSCRGzgiAIwqDySwoDk3jE3hdffIGpU6figw8+CL12j+N/h1seeQnDhw9P2hayHbXBnTQka9CW5SLimRUEQRAGrV+yu0iSWXLSVe3a0mwLbr75Ztx0003KYkAKi4px5CW3YdZue8FoMiZ9C9lElzMbCIiYFQRBEFKeWDonJSo5RpLMkpvOxN6ZZ56Jp59+OvTcfffdF7ff92+8v96ZUlUC+joZLdkRm4EgCIKQ8vRVcowkmaUGmtjjNedvLWr5u9/9DgaDAXq9HrfccouyGYwYXhp34lg0tPuMUVz+TgZbwmBBIrOCIAhCytNZ56REdWqKTDKTDkypx5w5c/Dggw9i0qRJ2HPPPdWyokx9py1kY2lZK9H6/kUis4IgCMKAoLeTYyTJLLXYsmULrrzyylClAg1GZzUhm4gqARSyry3ajB831MIfAPLSzMi2mZKqEsJARyKzgiAIwoChJ8kxXSV19XWSmdB93nnnHZxxxhmoqalBZmYmbrjhhh4ljnU0EOI9886yMny3vg5GvQ6b6xwwGfTITzdjdEE66uxutU7ek4M5Qau3ETErCIIgDCi6kxwTyzRxXyaZJQupVrXB7Xbj2muvxV133RVa9tRTT+GKK65AWlpawgdCizfX49PlFSoim5Vhgcmoh8frR3mjE40OD8YXZyRVJYSBiohZQRAEYVCjJXXRC8s6tYy8UrBympgeXM2ioCWZ9cRbmUqkmg90w4YNqiXtwoULQ8vmzZuHJ554oksh252BkPJQr61Bs8ur7geDPujctJgMKDTqUdXsQlmDE3npZonW9zLimRUEQRAGLfF0DhtMHZh6s2pDb2T9v/baa6oJgiZkTSYT7rvvPrU8NzcXvQGjrdz/bJsZXoZmw+A9kGU1qcd5jwykaH0yIpFZQRAEYdAST1IX/91db2Uq0ZtVGxId7XU6nSrJ64EHHggtGzNmDF566SXMnDkTvWm9WF3RBJfXh+Isi/q7MFMPHX47HyajTlkNirOtAyZan6yImBUEQRAGLd1J6hroHZjiFfiJtnPEw913391GyB5//PH497//jezsbPQG4WK8tsWNlRXNyLGZwNNU1eRS0VizUQ+314/qZhfSLUbMGT0wovXJjNgMBEEQhEFLeFJXNDpK6uqoKH9/kOhp+1gEPh+Pp2pDPHaOeLjsssuwww47wGKx4OGHH8aLL77Yq0I23Hqx3ZAsDMm2hs45hSzvo5oWV/C+Memxz6QiTB2Rg5SnpQXYvBnwepGMSGRWEARBGLSkelJXbyRp9UbVhkRFezXvcmhfbTa88sorcLlcmDJlSsz709n6o0XcO7JebDckGy6PH2UNDuTYzJg9KhdNTi/qHW51rIfuVJq6UdlAAGhsBOrqWCYCyYyIWUEQBGHQ0hedw3qL3pi27y2Bn4gavcuXL8e5556LJ598EhMmTAgtZzev3h4Y0DoQTYyzUsG0EbmwbNOjjMlgaSbkppkxfWRe6nqofT6goQGor0/aSGwkImYFQRCEQU0qJnX1ZpJWbwj8nkZ7n376aVx44YWw2+3KF7tgwQJYrdaE1cjtamAwa1Reh2KcgnaXMfn4tawRh+44BOOLM1PTQ+3xBKOwjMb6/UglRMwKgiAIg55US+rqrSSt3hL43Y32Njc346KLLsIzzzwTWsb2tFVVVRg+fHhC7BexDAx+3tYIs0HXoRjncopaCtmUa47gcARFbHMzUhURs4IgCILQzc5h/UVftNZNpMDvTrR36dKlOOGEE7BixYrQMtoMWD+2syYI8dovYhkY1Da7kJduUd7YVPRWR/XDUrxSxDqdSHVEzAqCIAhCitFXrXUTKfBjjfZSHD766KP405/+pOrIkoyMDDzyyCM4+eSTE26/iHVgsENpFhweb8p5q9tA+wD9sBSxKeKHjQURs4IgCIKQYsQybT+xJFP9m8IxWWwTXUV7Gxsbcf7556umBxrs7PXyyy9j/PjxvWK/iHVgMKYoA8Py0lLKW93GD8uELgrZ7vhhGcmdPx+48krg4YeBIUOQTIiYFQRBEIQUo6tpe+q4uhY3npm/MWEluxIJRTabCmj/1mr1Llq0SAlXDfpl77rrri6TvXpiv4jHz8vHUslbDZcLqK0NWgq6U3/YbgfefBN49llgzZrgsmnTgL/+FclEvzdNePDBBzFq1Ch1o+6888747rvvOn3+vffei4kTJ6racjR/X3rppaFpCEEQBEEYLGjT9hRaDQ4PNtS0qN9FKuKoQ0WTSxX3H5Wfrn5TrNFLSk9pf8FtP/LlOlzz2jLc/Pav6of/fuSLteqxvfbaC9dee61qfPDqq6+q7l6xCtnuNsHQBga0CnBg0OT0wOcPqN/8O9JCkEwNMzoVoVu2ABs3Ak1N8QvZrVuBv/8d2HNP4MYbfxOy5F//CorkJEIX6GmrkB7AaYTTTz9dde2gkKVQZfHjlStXoqioqN3zX3jhBZx99tl44oknsOuuu2LVqlU488wzceKJJ+Kee+6JaZucwuCbpKGhAVlZWb1wVIIgCILQd4SXoLKa9PhiVRVWlDe18Yxqz6M4o/g9bsawPhdhFKtPfbseizbVw6DTIT/dDLezGY1eE/yBgKrNeuauozAky4yysrJOqxV0BI/xlR+3dBhl7ez4e6MBRZ/T1NT9pC7Kwe+/D0ZhP/44uh3hoIOASy4B9t8f0PduPDQevdavYpYCdtasWaG+yn6/X928F198Ma6++up2z//DH/6giiZ/8sknoWWXX345Fi5ciK+//jqmbYqYFQRBEOIhnnql/Q2nxJ/+doOKxEbzfzLayOjtGbuO6jCxK97j7ez52mN2lxcfLa/AFysr4Q9wat+K8jXL8N4/r8bUg0/G0N2PgV6vwxFTSnH8zOExn99o295S51ARaLbIjZao1VkziVS61iECgd+SuuiNjRcK37ffDorYsMoRIVg54uijgVNPBfbdFzD2jUM1Hr3Wb55Zt9uNH3/8Eddcc01omV6vx3777Yf5NBlHgdHY5557TlkRZs+ejXXr1uHdd9/Faaed1uF22OKOP+EnRxAEQRBiIdWidT0t2RXv8Xb2fKI9Vtvixs9bG2D3+DAs24pF7zyLb1+8H36fF9+8cB+OHLsTLEMm4NdtjTHXxu1s292tkZss5dliEtVap666uuC/46W8HPjPf4AXXwwmh0XCyDgF7DHHAJmZSGb6TcxWV1erwsfFxcVtlvPv8Jpy4bAkB1+3++67qwvt9XpxwQUX4C9/+UuH27n99ttx0003JXz/BUEQhIFNb7WLTdaSXfEeb7Tnc/2LNtTi+/U1MOj1yvZQmpMGs0GPX7Y1oLGuBu89fheqly8Irad47PbIziuAUxeA3e2LqTZuLPtKK0FvRFl7O3rb5YCiJ526AgHgp5+CUdgPPogugnfdFTj9dGCPPQBDz0q79RUpVc3g888/x2233YaHHnpIWRTWrFmj6tDdcsstuP7666O+hpHfyy67rE1ktjs+HEEQBGHw0NN2sf01Xd3dTlva8TKCysiky+NTPtZMa/TjjXZ++Np1Vc2qSsGayma1bPaoXBRk+GE1G2Df9DN+fvQGuBqqQ9udeeTZ2OXYC+AJ6OF0eJBmNnRZGzfWa0Mxm+goa29H6jsT6VVV9Zg3Mg1Djd2IwrrdwLvvBkXszz+3f5xJdkceGRSx48Yh1eg3MVtQUACDwYCKioo2y/l3SUlJ1NdQsNJSwA4gZMcdd0RLS4uqScfsR9oUIrFYLOpHEARBEPqiXWx/WhO602lLO96lm+tRa3djfbUdXr8fRr0e+RlmjC3IaHe8keeHQnbxpjoVWbWY9LAYdfAHdNhc50CD3YXKb17GZ0/eh0AgGEk0pedg/9/fgvEzdkcAATQ0OpVndvKQrmvj9nYr344GJXV2N75cVYV6R88i9R0NdDoS6dl+Nwr9jdi6sRZL7Bko3aEk9oFRVVXQRsCf6t8GESFKS4FTTgGOPRbIyUGq0m9i1mw2Y8aMGSqZa968eaEEMP7NRK9o2O32doKVgpj0Yx6bIAiCMMDorvc0GawJsXbaCmdtZROWbG5Qs8q5aWZVacDjC6C8wYlGhwc7Dc1R50M73vDzw+9fRmQpZAszLUo463R6GPQBWL3NePf+61Cx/PvQtrLHTMXoY/+M3PGj1XMphH1+P8YXZ6qkra5q4/ZFK9/IQYnT48P66ha4fX7MGZ0fsnDEGqmPts7IYzQb9b+JdAr+5kaYGxug93rUObaZ9Oo+GleUgfFFbaPu7Vi2DHjmGeC996Inhc2eHYzC7r13nyV09Sb9egSc/j/jjDMwc+ZMldDF0lyMtJ511lnqcZbtGjp0qPK9ksMPP1yV4Jo2bVrIZsBoLZdrolYQBEEQ+sN72lNrQl922gpnU00Lnl+4GeWNDqRZDGhx+ZBhNaIww6LEaVWTCysqGjEyLy10vOHnx+8PoKbFjWybSa2fXlkgAAR0MFsscNSWq9fwsYNPvxj5c0/A+honappdaDTqlXCeWJwBjx+qNm5Xg4C+aOUbOSjxeP1YXtYEj8+PxVvqMW14LnLTze2iwcGGFbqo57yrgc6sUXlwuzzIhhO2qibo/EEx3mD3qNcyas6oMLBNDUymDc/BkBzbbztN0frRR0ERS19sJGYzcMQRAJPmJ03CQKJfxewJJ5yAqqoq3HDDDSgvL1ct695///1QUtimTZvaRGKvu+46dVPw99atW1FYWKiE7N/+9rd+PApBEARhoNEd72n49DdhRJPix2TQK+9pIqe/YyGWzHyKpOcWbMTGmhZlKWC00GTQqfJdFGQj8tKQaTFiS61DCTjteMPPT47NFDxOm0k9RouBntPmADKzMnHwH/8Pb99zBc665g7sstseWFPVjN3HFWPaiBy1j0NzrPhydXW72rgdDQK66wuOlWiDkhqPS3VVG5JjRU2zG2urmzEjPRc6FUMNRoO5n6/9tBXNTm+7qCv3vbOBzvptdVj7cz2Kqmqo1qFrFeIUssvLGuDw+FXklueaVpGgN9mJ/SeXYIjfwcL9LMYPVFa2PyBqKloJjjsOyMtDtzGZer22bHfp99gyLQUd2QqY8BWO0WjEjTfeqH4EQRAEIZm8p9r0t9MdjLYxWqmJWUYf2YkrfKq+v9FEG/czy2ZSkcKt9Q44PX5VhYBRw20NDiVmjQYddigN1vrk8fMYxhdmoKzejm31DrUuJo3Z66rQ5PKgOC/Y+KiqyQ3bkHE48m+vYOSofHUuc9NMOHjHIaFIK9e3scYeswe2u77gWInmyeU15I/XG0CW1aQEbZPTq/5Nyuod6jV89tiizHZR17njC6P6fA1OJ8xNDRhnb1CDH9YHrmxyqkGENtigkM1NNykhzGh5AUV6ALAv+Rnep+9D4JtPoGOCVyTTpwejsGxwQCHaXVhnln7ajAwkK/0uZgVBEAQh2aA4YyRs1sg8/LytEbUtri69p5xWpqD7vqIJPl9ATbszWskp6vJGJ6qaXW2m6vsbTbSV5thQ3exW5bOG56ap/WxxeVXVJ0ZoizOtGFtoVRUJ2F0r3O+ZZTViZEG6sgcs+uYzLHvhNuSVjsTV9/8HBqMJa6qasLayRYlltoiNdu6644Htji84VqLtDyPrHJDwOvJ3k8uvrivxB/xYsrVeid0dh2aHZpTbRJbX1ijfrbZOY0uzErEGd7AOvs5kUIMKemGdXi821dqVR5bWAt6HFLL8e0SWBaULvsTo915D/vKl7XfeZAIOOSToh91hh26fAxWBZaMCiljaE5IcEbOCIAjCoKOz0lmRSTpmgw55GRYVmRxTmNGh97Qwwwyn16+8oEzS0euCosZiMqDAqFPlqhhF5POSAU20UWBrQq0gw4xiJnFZDMomwOdk24wYlpcWNZOfUccME9D81dP44cmH1HrLVtXjq/8+gf1OvkBFGncZY8XeEwsxpij6uevIA8sqB4x+1re44fUFo8Xx+oK7UyIt2v7wNbz2jJ6WNTiVcGX1BXZUW1/drITtzFF57ZLUtcgyo9c6vx++2jpkuZuh93rbPI9Cl/fZ8Pw0lGRb8dPmehXV5fmmtWC4zo09F32BSZ++hbTqtlWgFIWFwIknBn8KCtBtWP2JApZNEpLUUhANEbOCIAjCoKKrrlXRknQ4jexwezEsN61DMVTV7IbVqEdBhkVFOjkFzaia2+tHo9OD/AyLepzPS4YuU5poowCnUKOY/WVrI/xKxkJFUpncNa44QxXbp7CK9Htme+tx91UXYfOKJaH1Tp69F8bteZSK6k6OIVIazQNb1xL0pVY3udSUflGWBV+sqsKuYwvarKszX3B3S6R15MnNSzdjyvAcLFxXA5NRr6L1FqMBI/LSVYtezSsdSboeyGyuRwlcqK9wIo/bjhDcrM3La8BBBbdHQcsBUf62+dj5o3cxbv6nMLZGccOpHDUBtnPOQua8w7ofQeW+ULxSxLLebAoiYlYQBEEYNHSWUU7/J8VJd6sRMPrHKOzMkXnYUNsS9FW6PKpeK8XJqLx0NDiDiVX9RXikkpHOkflpWFnepMpx6VorXAaTmgJKhKdbjWr5ljoHRuSntznuH778EP++9Qq0NDaov00mE/7v//4PJ5/ze+W7jTUSGumBtRoNqnpCo4MlvoDibCsmlWSq/aQnNpbyZj0pkdaZJ5e1ZmeNzsUe44tUNQMeI88py4lFRpb1bpeyErjqGpDtcGP62AL8sCloIeCAx2oyqIgshWyWzaiqE6hz5fNB9/nnGP/ss5gwf367/fMbDNi28x6Yv9uhSJ89Q/mPw8VxzFD8ZmcH7QQpXhFKxKwgCIIwKOiqdNayLfXK+7nL2PxuFePXIp30ls4YmaumyDn9zCgePZfMcrd4e1YyqidEi1TS80rhymijy+dXTQuaXT4luofmGDB7VB421tqxpb4FE0qCDQ1qG1vw30f+ji9eezq07tziYXjimecw74A9u7Vvmgf227XVeP/nciVamezEaDYjloyKctuxlDdLRIm0eDy53F54JNfocqr6sEanXT22rcmpjmFyaZYSwLQQbK2zK48srQV8TJXZMniBp54CnnsO2Ly5tU7Cbzgzs7Fxv8OwYq/DsNmcqQTw7iNy40t043OZyMUorC16JDkVETErCIIgDAq66hyVnWbGr+VN8PoC3SrGHzk9rWW6J6pkVE/oKFLJ6KPd7YcvEFA+0DpHMJI8uiBddf6i+KLIXVvdojy/9Y0teOGGs1CzYXlo3TvsdgCOveQWzNllpx7tIwXinoZC/LqtERNLspRXlIMA7VrF2t0rUR3CYq3VqyK5o/NQu60K1b9sQ7FFpxK6WqJEXVkXllF6CllGZRmdza/YAt0/HwX+9z92h2q3H57xE7B8/6Pwww5z4NCbggI4N619ndlBEoWNhohZQRAEIeWJJdGnq6x5TXw2Oz1q2j3eYvy9XTKqu3QVqfxpUx0M+uC+0/upRZK1GqpFmVYYdDr8sKFWVWgonTBViVm90YRpx/0Rw3abh+LC/ISIdNoTjAY9hubY1D51p7tXIjuEdVmrV5V8aMDwhjocWWLATx5z9KhrmOjkOgvSTMDXC4MNDr76qv16mXzFklqnnw7TjBnYkZ1nwwVwq7c2Ji9sdvaAisJGQ8SsIAiCkNLEmujTVecogx5KJNAXGZnoFWtktTdLRnWXriKVjBQy8spEsGhJTBTjRr0O+lZxu/MJF8PZVIudDjkdpqLRSmAxQSwRJKK7V190CAOrEdTVKSGrBC0QPeoaKTqbm4E33gCefRZYv779eik8jz8eOPlkoLQ0tJhroM82JswDOwobDRGzgiAIQsoST6JPV52jKPp2HpOvasX2JLIaTyvZvqCrSCUjr+lmoyofVdLalEBj64Y1mP/DEmRvt5vq2EVPMRPbZp39V2VHYNewkkwrGp3eTqftYy2RlYjuXix9RnvESs3u0dpmN551dIjLBdTWBkVpFAGvoq7RROemTUEv7H//G3xtJOPHB2vDHn5496KousEThY2GiFlBEAQhqYhV+MSb6BOLDeBQZoYDPY6sxtJKtrvHHS9dRSq5fFR+Wqglq3ZePn37VTx/9/Vqv467+WkMmTRb1ZuNTGxjYHJDTUuH0/bxlMjqqVVD29bG6hasq27Bqopmdf0nlWTBYtJ33+7R0hKMxEbxtHYIxS6rEdBKwI6mkeKX299nn2CXrl12GfQVCXqCiFlBEAQhaYhH+HQn0SdWG0BfRVY1AbuusjnUacztC8RcEzUWYol27jQ8B7uMzsOC9bVYtaUKL/3zZvz82Ruh533+4kPIGTYW2w/JVlHPcOxuj/KHNru86pyGn6/ulMjqrlUjfFssI8aIM0t8bal1qBq62w3JUscZ8zml+GxsDIrYaO1iO4KC9803g1aCNWvaP84I6rHHAqecAgwfjrgZ5FHYaIiYFQRBEJKCeIVPdxN9YrEBdCey2l3hvnRzPX4ta1RVFIbl2TCpOEuV99Jq34bXNO2OqI412snz0li2HtdcdTLWrV4Zev3cQ4/DxKP+GBxgeHyYPiIvJGgphlnlgDHHd5ZuayPENXHcnRJZ8Vo1okXpuZ3dMgtU1y7u48j8dBw7fWi7Ll3t8PmCXtj6+qA3Nla2bgWefx549dXg6yMZPToYhZ03D0hPR9wMQi9srIiYFQRBEPqd7tQG7UmiT1+I1ViEe22LG7V2NywGPYoyTai3e7B0az2mDc9VFRVY/3Xx5gZVKosJRd2N1vK8zR1XiPnra5Q3lpUCuD4t2snHH3/8cVx88cVwOBzqNRZbGs666m+Ye/DRaj/9m+pQ1uDAL2UN2GVMvhKZFInM3i/NtSE33dJmALKmogl2t69ds4VYS2TFc406itKzIkO2zYwJxZkqsa/T7mseTzAKy2hsa1JXTNHbH34IWgk+/jj66+bODfphd989/haxEoWNCRGzgiAIg4Te8mQmgu5YBhKRLNTfwr0404r11XbkpJlV9zD+VDW5lKBlpJYVBlj2ls0DWAc2lg5WnVk3VOWB1sz4OWPzMXV4Dpqbm3HaaafheUYVWxk2dhL+dNtDKB05Vv3NpgXTRuTCsk2PsgYnlpc1qjqwjMhSyFJ8Ryv5xel9NlvoTomseO7XHpXj0pK6mprUNjutRhD+mrffDorYFSuibDANOPpo4NRTgxHZeJEobFyImBUEQRgEQrO7fer7iu6IkWSt6xqPcOe0vcfnh8lmCkUSMy1GrK9uQabVhNJsq4rc+v0BZKTF3sEqlmYJX62uUqL2rOOPwieffBJ6zayDT8DvrroJtgg/JgUtI7K0RDBRjgKc1gJGZDsr+UVx3lHJr44i5/Her92K0tPbShHbmtRVVu8IdeeiXYI+4KGRzQkqKoAXXgBeeikYxY2EHlgK2GOOCfpa40HrzkUrAcWwEDMiZgVBEPqZ3haaPelT31d01zKQjHVd4xHuFKmMuLI6AKOyhNHOFpcPQ7Ks8PgCqgQWKwfE28EqVuvGjTfeiM8//xzp6em4874HUV8yEz69Kfq+e3xK1I4vzlTHQdHXnZJfnUXOu3O/xhWlb2oKClGnM/QcCtmPlpejweFV+817iNdoXVUzqpscOMRXiYLXXgI+/DC6j3bXXYN+2D33jN/PajIFBSx/xAvbLUTMCoIg9CO9LTQT0ae+L+iJZSDZ6rrGI9xZ2opT2ZyOLzTq1T5r0WezSY9Gp0dFOPm8eDtYxWrd2HPXWXjiiSew2267YcyYMXjlxy0xXQdt4BBvya/OIufdvV+7jNLbjNg13wDdhg1Bb2wY3CYjshSyI/N+a5aRofNh/C/fYOhbr6Jg4+r2J9hqBY48MihiWSc2HrgNJoHl5EgUNgGImBUEQegn+kJoJqpPfbR9T6R47KlloL8Tunoi3NnylBn3Vc0uJVpbGIU26VHX4lHHPbYgI9RaNp4OVtGsG2t/XYJP//c8zrnm/9qI4tOZoNRKrNch3pJfXUXO+ZpftjViyaZ65GW0byfc1f0aLUpv1QWwk82H2bkBlHqiNCsAlEeW1gJGZLkNS30tRn70FkZ++AasDVGsBOzMxQ5dxx0XFKPxYDT+FoXlv4WEIGdSEAShn+gtodlbfep72xaRipaBRAn3nYblYEV5I7bUsdIAUJRhgcGgw9RhOW3qusaT2BYeAU43G/D+i4/jPw/+H3xeD4qGjsA+J/6uR9aNeEp+8aezwY92T1HILt5Sr6wMhRkWJfT571jv11CUvqYJrspqpLnsyE/rfKDFZC/aJYo3rsa4919D6befQe9rbyVwTZsBy9lnBhsdxCtEGYWlgKUnVkg4ImYFQRD6id4Qmr3dp763bRGpZhnoLpGCkffByPw0TBuZix1Ks1Qm/ZerqlTyF/2y3Uls0yKni1ZuwocP/xU/ff1x6LElC77A5ANPxXbDcmO2brBNLEtbhTdGiEf4djQgC7+nGJGleDUZdMp6wYj11BG5IUHb5f1qt0NXV4diduzi7W5sG+FtV63ArEPWpx/i2MefQvHa5e1W5zOZsHHOvvhpr8Ow55F7wBKtVW1H0P+qRWHpixV6DRGzgiAI/USihWY0Elm+qq/8t6lkGegJFHpDc6z4tawJ9Xa3qg6w3ZDMUFF/VhroSZSa59FYtQqPXHoa6qvKQssPOOl87HLc75GXaY3ZukHB+eqirR1G47s7AIm8p+imKKy3oLzBiYIMM6qb3SoJKzctVz0/6v3KWq9aUhdLZnVAeLUCfX09dvrmA6R/+Q6yaquRFfFcR14BNhw4Dxv3ORRrvCYVIaa3OSZYBYL2A0ZhB9ggLFkRMSsIgtBP9EWd1ESWr+oLW8RgIppdg2WvwqfmuysS/X4//v73v+O6666Djx2tONOdlYujLr0dO+6yZ1y2kFij8d255tHuKXqEGZGlkOU5oZeYzRrYgKHN/RpHpy6tWoFp1Srs+827GPntJzBEJIKp542ZjHWHHIPqXfaAI6BDdbMLWTajKs/V6XnnAISluChiLclV13gwIGJWEAShn+irOqldTQVTMHF7XQmmvrBFDBZiFYjdiVJXVlaqhK4PPvggtGyPPfbAfY88gZyCkh5FThMdjY92T9EjzCYMa6ubVY1adh9jMtyUETlBAZ5h5EEGhSyjsl0dg8eDba+8iX3feBlDV//c7nGfwYiy3faG5awz8FPmsGCd2WaPqjPLiGybOrPRKhrQRkAhG293LyFhiJgVBEHoR/oq6amjKB8TjliKKZZkrr6wRQyGrme9LRDvuOOOkJDl66+//nr1Y+xG9nxvR+M7uqcoaGek5yq7QW2zGyftPALb55qgYxS2OnpVgnbwua++Cv+zz2FG+W82C43GjBx8O3t/fDtjP7hz83D6yJE4uDSr6w5gfRSFTcZ7N1kRMSsIgtDP9FXSU2SUL95krlRrH5usXc96WyDefPPNeP/991FbW4vnnnsO++67b7f3NTxyGkAATU4v3F4f3N4AzEYdDHo2F/B1Oxrf2T3F7hEtLi9m5Buxvbceui0d+2HbsHo18OyzwBtvqMYIkUOrtSWj8c60/fHTdrvAlmZBusWE+mYXPllZoUR0MkRhk/XeTVZEzAqCICQBfZ301J3oYCq1j+2rrmfdiZ4l2q7h9XrbRF3Zyev1119HVlYWiouL0RO0yCk9p+VNTmytc6Cm2Q2XzweLQY8smwnZNhPqWtxKbMVLR/dUi8OFik0VyPG0YFJGPuDUd55MRf/sF18AzzwDzJ/f/mG9Acu23xlvTtkXK0vGqlbBRj/Q7PKhxe1XTRVcbr9KEGOTitA11KKwFLEUs31AKnTsSzZEzAqCIAxCuhsdTIVasL09ja8J2HWVzfh5WyNqW1yqTmm06Fk0sZtIu8aHH36Iiy66CO+99x7GjRsXWj4+3o5UHcD9zbIa8cEv5ervJpdPJZelmQ1weXzKBqDXAV+urkRBa6mueAm/pzZWNKKsogr2qlro/X5Y0834Zm01NtTao3tXWcXgv/8FnnsO2Ly53boDubnA8cfjX0N2xo9OI/wBIM1oCOlidgludHiRYdajNMeq/LK0GRTkZwUFbFZWn3phU6VjX7IhYlYQBGEQ0pPoYLLXgu3NaXxt+nfp5npVecDrC2BYng2TirNgNRvaRM9ItKlidsTqqV2D0dgbbrgBt99+u/r7hBNOwLfffgtLb3g4dTrO+KPJ4YHHDxXF9PkBj86vGjywBmy9w9sjkTU804Rhw81Y3uLAJ5takG4zYnh+GqxGgzp3LM9V3ezE/pNLgoJ23Trg+eeB115TtWUjqR42Gr/seyQcBxyMEUNy4fm1HBlVLahoolVBh2aPD06fHz4eiE6HRqcXDU4fHLY0tBSVomBYPvoDqRjSPUTMCoIgDEJ6Gh1M5lqwvVV1QZv+ZXY9mxlwmr0o04R6uwdLt9arDHwtevbO0m3KS0qRF22qeOfR3bdrbN68GSeddBK++eab0LKSkhI4HA4lZhk57ah2bbzwPLFMFjuULVhXo3yznJZnNDbLZlZRW5c3oO6hbg0QWBeW9WEZYfX71TpYa3ZiSWbo+BkFHpGXhk3Vzdj85gco+fJt6L76qt2q/Do91k3dBZsOPRaN20+ByxdAVaMTqxsrVOR8+9Is1K+pRp3draogGA16pJuN0FstqDBY8ZknAxMKc2DNit8ukSikYkj3EDErCIIwCEm1ZK546I2qC+HTv8WZVqyvtiuhaDEZ1A9LSLGU1IyRuUrMUfjx3FEERpsq3lhrx7ypQ7FgfW1cdo23334bZ5xxhkruIvTKMjp72WWXKcG6cF2Nqk7Bbbi9fpiNerXN42YMw85j8mP2+2qPra5oUuK9ONOiMvvpNeVUvUGvg9WoVxHbmhYXDDod7K2JYDH5iB0OgMfATl2tcHqf0/xFmWGeVTbScjow/PP3sdd7/0NWWRQrQXY21u11CL6atT/yxo9Sr+WVTTNAieBV5U3K0zsi16Y8vrwvMtMscFvS4M7Igt1ght/nQ53LB6fXrzqd9RepWDEkGRAxKwiCMAhJpWSuZBDq4dO/9Ip6fH6YbMEWpTrokGU1qcQoZvv7/MGWqRNLsjq1Oew5oVCJzFjsGm63G9dccw3uueee0LKRI0fixRdfxC677KL+ppC97+NVqHMwcei36/nrtgbcV2fHn/abEBK0nWXLE+0xCtmVFU1KrPsoYnU6FSnV4Lkw6vXwBYKeYUY9v9tQ23EWPsUrRSzFbAQsh6V5j0la+VaM+uB1jPj0PZgcv4neEPQFn3YaavY5EJ8ur1GR4mjne2iuDdUtbqypalZtZf35GdhsSYPFbFZ22GaHF0aDTiV+UaCzZW9/zToM5EFmbyJiVhAEYZCSCslcySLUw6d//f4ATAY9PBRrrVYGRkCbXB61jOWkSGaUyFqkzSEWu8b69etx4okn4rvvvgstmzdvHp544gnkMsGpteMXI7IUsuOLMqDXBQVhltWsInxrKpvV47NG5WJrvbNNtnxQwLnw3boaLNpYizSTAS1uH3LTLcoD2+BwY2NNCwIBHbw+P4bnBps50HLQ6PSgOMuCZpdXHceXq6pQr8R0mLViWwNqt1XhiBE2lDJc2gGs62rWA9mLf8Dkj15H8aL50EU0RQjodHDvsScsZ50JUMTrdHCyyUGYCI7EZjEhIz8bzbYMbKj1Ko+v3+VBg8OjBLTZaMC4ogxsPyQbDU5Pvzb9GMiDzN5ExKwgCMIgJtmTuZJFqIdP/2ZajWrKvbzRiUKjXp0rTukzQskIX73DrR7nv6MR71Tx6tWrQ0LWbDbjrrvuwh/+8Ic214ge2TWVTchLM8HuZmJWQIlUPofClqKI4uiXbY1Yzmn31mx5/mYUkJFk1o9dX9UCfyCgooOb6xxKtNuMBuTYzKhsdsHr9KOi0QGb2ajOBR/ncTMpjD5UCtlQFr7fjzxnM4o9Ddhc1YjFngwM2aEk+r3lcCD/rddx4mNPIXPLhnYPe2zp+GXOfqg/+njsud+MVntDsLkB29ya9Do12AiPGvsNRngyMlFrsMKcFcB+I3JRsXAjnB4/0ixGJbYZUR9dmK7uh2anFxZv/0/hD9RBZm8iYlYQBGGQk8zJXH0l1LvyeUZO/7LNKROjGNGkuGWUjxE/roNRyVEFGSqSxqhoT6eKDzjgAFx99dV45ZVX8NJLL2HGjBntnrOqogmVjS4l5gJwQa/TId1iRGEGmwIY1fKt9Q58v6EWG2vsKM2xKSG7eFOdEoOsFUt7hMPrU9FlHgejlSa9XkUreQw8l1WNQbsBRSuPbWReGnYanoPxhRn4aHmFOna9zwtzYwNMLc3QBfxq/woyLL+VvcoIO+6tW4NVCV59FbqGBmRGHFfTkGFYuf88/DhtT9hyM1U1A5YDYz1Y1XbWF1BCttbuURaHyUOy4LOlw53J32nqfJdXNavzvc+kQuXv/WljnTp+RmR57VSUOcmm8AfqILO3EDErCIIgDGqhHku3pWjTv0zuWlHeqFoCMwqbl2ZWYoqlt+iffXPpVizbUq/WxS5TnU0Vh4vpuqoyTJk4pk0FAnb1omeWjRCi7f+PG+vg9vlhgx4ZZhO8/oAS24xcFqRbUNXiVFUXvllTowQ4xTe3RSFb2CreampcDK7Cag56YCk8mUBVaLSo1zA6W5JlxWE7liIn3Yx0s0FFOCmyKJB9LXbkuRthcdmjWgi0SKrayA8/BBscfPyxiuBGUjl1Nn7c8zCsmzgNZpNBWRtYZ5Z8tLwcDQ6vShTjteI1q3H5sd6tR60vA2Oy8oJT805Pm/PN87nr2AK1jOeiOMugktnsrrbPSxbBOFAHmb2BiFlBEARh0PaLj6fbUuT0L0XUyPw0TBuZix1Ks1S0lglRWoUCdpeqbnKp2qYUjIxIRpsqDhfT33/8Jt586CacfNGfcfM1V4SeZzKZ1E9HVRYYnaTQpCizGA1q+j9Tb1QlxFiii/s1LC8N00bkKEG7vrpFRVdHta6f/25yeRGUzzrlm+W0u8vjV0KU0/Fc94TiTIwvyWwrspqakF5RhoK6CvhsZiDKND1FrM3vQfZ7bwEv/wdYvrz9xUhLA44+GjjlFBSOHo1ZLW7s6PGp7dO2Qd77OShkGRHmfee12qBLz8SIMWmo3dIAul15vB1NzcsU/sBExKwgCIIwKPvFd6fbEv+9x/gCjGlt3cq/Kez4OM/P64u3hoQxf+weL9ZXNcNmMmL/7YoxdfhvpbrCxXRFXSO+eOpOfPPOy2r5s/feisKxO+Hi4w/o9NxStLGBA0Ury2UxOssoaYbViJzWMlTNTo8S07uMyUduuhlDcqxqn+iR5esZpWXiGJsicJkhoGepV+Wd9amoqQEmo05FeouzrcFpeC5vaADq6wGPBwXGAIbmpqnmBozmhh+jpaYSJW+8hp2+fR+Wxob2BzF8OHDqqcAxxwRbxyo5HbQmhFPd7FLWgsIsGzxZOcoP6zdpZbQCqhpBTbNLHSf3QYsaRw62ZAp/4CFiVhAEQRiU/eLj7bbUmZinqI0mjDMtJuw4NEcJ49WVzUrMRorplStW4I17rsTWdatCj83Z/3CY8oZ22VWL4pGdyFhNgT5Q1bygpkUJVJYJo8hLt5owZ2wBRhdkqNeMLchQzQQ8vgC2NTiUX9bILggUnmYjAv4AyhqdKhrL7TKqSiFJ7+2c4dnQVVcHhWyYPYDPow2AXbo21dpRQNG8YSVGvftfDPvuKxj8USoEzJmjSmthr70AQ9dJVw69CXWZebAOL4afrcdaYQ1Z1vhl+TCWEmOptCkjcjq1DMgU/sBCxKwgCIIwKPvFx9NtqSsxP3dcYdwtdDlt/+p/nsPrD94CjytYd9VgtmKXU6/CrAOPQnZm5213eV1+3taoRCk7kdFeUJhpQEGmGQ12L7bU2VWJrXGF6RjTKmQJo7OzR+dhQ41dldRiBNagNyA7LWhjsJn0KG9wqUSwZpdHVStIhxf7Fhox1VcH1P22fc0HSysAI6P7j8tHxcv/Q+mbr6Jow2/iPITVChx5ZFDEsk5sV/BcZmQAubkwuwGUb4DD60dGq5ilkP1pczCJzWrSqyS8vAxzSg+yhPgRMSsIQo8ZaD5KYXD0i4+125LFqMPby8rV9D3FPBOfeNxtxPz6mqAvNMYWus3Nzbjw/PPxP/pHW8kbNhYH/+kOZJaMUhn79H5yqr2juqdcX22zC8PzbCqhiWKW+6WDXnUnYx1YluLSsvbDMRkMKMgww+sLqMQu2hBYAYGdyVg7loKXNoVJGXoYmxow1BbA/sNL1PQ/Kat3tKkokN1Sj9kLP8LYT97GkJrq9jtbWgqcfDJw3HFAzm/R6Q4xGoHs7OAP/82KEpZAm4oS3BlGZClkeSzVzW7lG+YPSdVBlhA/ImYFQegRA8VHKYI8efrFd3QtEn2NYum2VJRlxdtLy/Duz+UqQknBxGQkJnsxCqiJ+W31wchqLG1I16xZg8MOOwwrV64MPb79PkdhrzOugtEcFGKFmXpsrXMEqwuYojcD4HmgkJxUnIWlW+pVxQFaA2g5YN1bN+upmo0wRal3S28sn7PjsCxkWEzBOrM+nzq2LIsBGW4H9NXVyIBVJVHRQjAkxxYSslpFgYlVGzHpo9cxdP7nMHg97Xdy9uxgFHaffUKitFNstqDYZTQ2ysApvKIEbQ+0FvD88LowaW1M2HVM1UGWED8iZgVBwGD3UQ4UQZ5K/eI7EqYdXQtmrzNqmMhr1FW3JfpNuT1OZbPrV1G2WdlE2SyByVBTR+QqQcvXMPmKUVSuq6s2pNZAoWpPS4yWNEw/5Srsst8RbYV528ZXnV4Xq9mgPKK/bmtUJcH89MmaDSq6ymOiIGcrV147tqNl2a3KRqcSg+MKMzE0z6b8tR63F+muFuS5W1Bb70O9yYrDppSqjmLavvFYFq+vRtFXn+Kwr95B3qpf2u2Xz2SC/ogjoKOInTw56r63sSiYTcgvLYCOItbSeY3X8GoESzbVK48srwGjsdoAo7cGWULyImJWEIRB7aMcKII8lfrFdyZYF66vaXctvl9fi9cXbUFprg0TirMSeo06KtXEafelWxpUDVnaDFhii9PZQ7KtoSltJl/lpuUq8UvP6Jyx+fhqdVWXbUizs7NV84Pzf38RZp5xHXyZQ9pFVTnVn2UzqW2xY1Vn14Xnx+sPttGlkGUtAgpdNkqYzcz+XBteXbQVizfXq3VzG6zGMH1ELpxeH3ReHwpczTA3NULn96lrymPlNQ0Xsqithf25FzD3+ReQUV/Tbn8ceQVYve/hWDLnABy85/btqhEgwqKwqdmLRks6kGXGKJ8bc0w+DI/hFtKqEWw3JFhzlx5ZitnIz5nuDLKE1ETErCAIg9ZHOVAEebL3i2/TEKDFjS9XV6Le4W0jWJdva8DHv5Yjy2ZWU9rautItBiXUWAeVgiWqXzXGa9RRNDiyVBM7Sb23rExN82eotq1eJQLrHHYV+SzJtqAg06Iii1oXMAo/ViqggIsUxub6Ddhl1IQ2gnvWrFl475Mv8Mz8jepvemS5vqbWhCsea3FWcP86EmN8jAMACn11frKtKLFZ1XXZVGNXnlebUY/vNtQiw2LA7uMKQpFZluvyuV1Ib25C7bYmVX3AaDKEKhdk2Yy/XYcVKxBgg4O33kJ6a0Q5nNqJ22P9wUejbPYe8OoNaKx3BJsjRKGs0YW3N7WgQpeJotE5GNLNgQn3a/vSLBWR5mujXWvemxNLMtW/eT3Ezz9wETErCEJK+ChTSZAPRP9td4vNh0dhKXBYrJ/CkLVANcsCf7Ob048b62FklnrYqeL0d21LUKjV2IPlphixjPcadWUl0dbFa0fxR2FpNAC1DpeqFsCOV4GWgBK22+oD8Hh5bU1KTI/ITwuJ+XBhbHd58cKTj+Cm667BOzvvjM8++wzGMN8ot6dFvKePyFFNFjw+v2p4QPG5rrql04g395XWC0asKX55fijEKYYnFWdCb9Dhi9XVal3jizJD96De5YTJ7UDZtkrkZ1iQlZ+mPL88ZrNBp6brpw3JwJDvvgKefRb47rvwS6LwGYzYOHtPbDn8WDSMmxRa7nR51ToYqW6D1YpAVha+qm/ANos+IYPHrgZZXAUHTxwwiH1oYCNiVhCEpPFRDgRBPpD9t/EWm4+0cFCoLS9rUpHWJZvrQ55TwvarPFcUq0qwWoOCleWh+PxMq1kJNa4j3msUj5VEG+CUZlvx89YGNcXP6fpmtxcB6JSVVQnaRh+yrX4cPmUIDtphSJtry/Nh9jlwwfln4/XXX1fLvv76azzyyCO46KKLoooxCleKMVYh4HuHf3cV8db2ldYLRqyV77VVDLN6Ab29X6+pVhFZrsPgsMPcWAdnYwvsvuBxsVHCfpOLQvVkbY4W5L37BnQvvABs3dpum87sXMyffQDenjwXliHF2G5INrLDxDWjusq7mmZCdYsHdosNlsJ8FBVkBfe3trxHg8fIgeLQHKsqi/btumqsqWxSCXP0AnPwQysIu6+JfWjg0yMx63Q6YWXNOEEQBh2J9lEOBEE+GPy3sRabj2bhYHcmvR4oybCqKKDmOeVjFGBMZKKgooDVMBn1KtLIa8HffF481yheK4k2wKEY00Q2k4w4NU+vqcUYFJsUtZyyZ0Q18pouWLAAJ554IjZuDFoIyOWXX47zzjuv3f71pL1q+GCM+65FrDW4f24KVHsz0luq0dRox7o6uzoX7OzFUlyEvuBp7hqAVoI33wQcwcoM4dSNmYj1hxyDsjl7os6jg6WsQVUSWGNswpThOartrWZPGFKUjTfKvFjt0MPld8KyqQyjChoxPNfWo8Fj5ECRLXqdHOx4g9tucLI8mR7Dc9OUf5jXiNecz2MCHwW+2IcGJnGLWb/fj7/97W94+OGHUVFRgVWrVmHMmDG4/vrrMWrUKJxzzjm9s6eCIAwoH+VAE+QD0X/bE7tENAuHJkwpEBl5VR7RVtsAhUamxYgGuwfGsFJSXJ6XbsLK8iY1dR5eLzWWaxSvlUQb4LCZAK8dO2kxSYpRTCUOfUyxYqtVs0q0WrihDtNH5ql18fvx7rvvxl/+8hd4vey+BeTl5eHpp59Wpbg6orvtVTsbjOl8Ppga6jCivhzWzCw0GQxYXt4Ah8evzrPRYITD6cbQJd+h8PnbgOWL260/YDBg3fTdsP7gY+DYfsdQqaxsE1REdo2hSQlaDkrodR4xqgQlI4rxVVlQMDNZLnxAt6aiSQnL7gweIweKTrcPP1Q0KXsEy5Plp5tU0h4T19jVrKqJ7X2DQp2VJjgI0kqqpYKfX+hlMXvrrbeqN+bf//73NqPMHXbYAffee6+IWUEYRPQkqjTQBPlASIhLpF0imoWDQjQ/w6ySnfi7yeVvYxtgshenh3mOGDXUrgUFMJOZDAadEpnxXKN4rSTaAGfRxlpVn5UiiPtCAe7x++BpFeKl2Ta1rKLBGdxfVxPOOOMMvPfee6F177777njhhRcwfPjwLs9Xd9qrRhuM6bwemBsbYGpuRHVNC0bmWFWUstzpVEKWtg6rw46dvvkE0796BwW1Fe3WG8jJgePoY7F6n8PxUS3Uuo0R55fdwhiRXVXrxK6zx2PM2FIUZgerJnQ0oKMNgJHUsgZHm8e7GphEDhRp4F1Rzs5nfuUHppUgEOCAKPizurJJWVJUJN1kwOiCdHi9gVBJtZ2G5ah7Ipn9/EIvi9lnnnkG//73v7HvvvviggsuCC2fMmUKVqxYEe/qBEFIcbobVRpognwgJMQl0i4RLWrI3lRjCzKUoCirdyqxSKHY5PQoIcN1Hjs6P1RPVrsWs0bn4dgZw9otj+Uahe8HxbLylXr9KkpMcR0ZDdQGOIz4/bqtqbW7lEl5MSkGc4wGjMy3qX9TfDLqV15ZjUP32gVbWz2mXMc111yDm266qU3CV28OxjZsrcEIOJHpdSmrRlmzC9k2I/YYX4D5a2uxqbYR4+xV2O29DzDth89gdbOOblu8Eyai+biTsGC7Odhs96Gh1qMS9jiAYD1ard0tr6TXloZakw26HCPGTBiuzgWvYWcDuhL6pmvtMOn1cQ0eeb3Xt9bJpeWD77Ma1SzBoHyxWRaTattLqwOvpc8XUEI3N82klnm8AfXcQqNeVZ6gEB6Zn5bUfn4hPuJ+l/HNOm7cuHbLOb3i8UTp/iEIwoCnO1GlgSbIB0JCXCLtEh1ZOFjIf+qwHNX+1WzQo6YlKErChens0XlRr0VHyzsjshYrKyPwN6O9tC/wN8VyZDSQ+2TUBzt6ba4L2gwKMyxqKptClt2mOI2uRFpRAY466ig88MADKCwsxHPPPYcDDjgAfcFwsx/HFgawuL5JtZZt9AV+q0jArl1ZFqR/twBzX3wK269pbyXw6/Qon7UbFsw9DKMP2APLyhrQ0OhR1SVYfoxCliXKWIFi0rA8pBXlw5OeCb/BgK1VzZg0JD107mIZ0DFSuvfEQmyud8Q8MKGNYdnWhqCVI8BKEkEfc1GmWf1NrzUHHfQBO72ArzWZkM+Dya+WA0FfMQcwyiM8MrdX/fwDsaLJgBKz2223Hb766iuMHDmyzfJXX30V06ZNS+S+CYIgpIwgHwgJcYm0S3Rm4ai1uzF7VB72mFCI3DRz6Mue8DxpAoDRs/B96M41ilaLlZURuB/04dK+wKivtp3wqPQB25cg3VKtKgsw2tfsCjYyGJpjU1PXnMrWrumdd96prvO1116LIUOGoFdhmLihAairAzwelFp1GLJDyW8dtUwG5MMD3RuvAc89h3Hr1rVbhcOWjvkz9sGPux6M7HGj1DJvVYtqUcsmC3a3H81On7JTNOlMWOk1oT6QhV0yspXAr6htbhdJjXVAN6YoA7uMzY9J7PF6fLaiUu1XYaYZGRaziuRvrac3NhhdZtteRvgNrX5sllLj9rnM5QmoZRS9FOT0QNOTvUNpVq+Jy4Fc0WTAiNkbbrhB+YIYoWU09rXXXlP9pWk/ePvtt3tnLwVBEJKcgZAQ1127RGfNCGK1cPSWAOiqFit9uHycUV8SGZXeeUyBiiBTCHO/ctJN8G1Zgi9/qMTcg44MXVNW9mFktldhUll9fVDI+traVbgPquPW5s3A888zwgQ0NbVbRWXJcPww9zD8PH1PuM1s/OBCRUWTik7X292wGvXKXlHLEmlmG9yZ2TAXWpHp86KsyYVfyxqV7zbaNYxnQBfLwESbJWDS3fjCdJQ3ucAxDwcUFLZ8r9Gy0ODxqWtrMenh9/iUeM1JM8LCQsFq5pitc12t0Xgz8tKCiWC9wWCoaDIgxOyRRx6Jt956CzfffDPS09OVuJ0+fbpatv/++/fOXgqCIKQAyZ4QF8vUZ7x2ia5EaCwWjlgEQHdtIF3VYuU0unatSHhUmufLqNdhVGFGsKlAYws+e+4BrP7wGVgsVlxw1D5qv8Kjyb0ynex0BkUsxSmjspFw2YIFwQYHn37a7jkBnQ4bd9oZn8w6AL+M2A4ZFpPyDKsavr4A/LoAijMt2FhjR5UbqDWlwzw0B2aW/GI00+mB1aRXx3rYTkMwvjgz6nEmekAXqvmbk4aCDLb49Yba/lK4c4BR3+JRXmiuk15sbivNrIfLG0BJlgnTRuSqRhw8VkZkuc7JvTRDMhArmgxIMctSI7fddhvOPvtsfPTRR723V4IgCClKsibExRr5jCe6FmsUqrMoXCwC4J1lZSqZh2Ir3qhtV7VYIyPN2nPpyaRXk1P3FL+ehip889hfUbZyUfB5Tgeee+px1JiLemc6mYK0uTloJaCYjXpwDtViVonYVavaP56RgabDjsTr2++NzPFjYHF6UdhaZ7bF7VXT8rRdUNhml+RjdTlQZ2Ijgt/ElqU1cWprvUPt0riiYGmrvhjQhV873g9ssqFdE/qei9KtMOlpI0lXVRIobjlgGVOQoY7PZjaqQYsS1AgK6txenCEZaBVNBqyYZVYmS3KdfvrpvbdHgiAIKU6yJcTFM/UZa3SNJCIK1ZUAoBD6dHmF8qiOLcqMe9o21kgzI4/MjGf9UpZ2YtITxVS2zYStKxbio39dD2dzg3qNwWDAldfeiOLdj8PyssbETiczWYlRWP601qptx7ZtADt0vfJK8HmRjBoFnHYaMG8eXDDBuWgLzF6/qkaQlZaFFpcPXp8feosZ7owsVOssMJaWwvtLM+DztmknrND1z4Au8trRIsAmG1p03eUNHsdhU4YqEUtvMysVVDa61HXl9VywrkYl7TGS29szJAOposmAtxmwJNcXX3yhGiQIgiAIyU13pj5jia51VYYp1ihUZwKA+17W4FRWAEYLNTEaj2COJdJclGXFF6uq1P5uqGnGxhoHLEYdRuZa8f3L9+PHt58OvSYtrxh/vuMhTJwyEyvKmxI3nex2B4VpY2NQ0LY/GcCPPwa7dHFmNNpz5s4Niti5c5W1gBFMh9uLbJsZFU1OlQin/L052XBnZsFrsWIzKxKUZKl9Z/UC7i4bIXAqn3VamTTV6GTim1EJQrb37asBXdQ6uq3RdV47nmfek9uXZqkKBb9sa1D3+dDcNHU/UdCypJfNYsD+k4sxdUROr86QDJSKJoNCzB588MG4+uqrsWzZMsyYMUP5ZsM54ogjErl/giAIQj9MfXYVXUtUFKojARBAQHVy2ljTospiUVjFuu+Rz+ss0qyJN/6b52j70hxsqnWgunwLfnjg/9C48dfQukZMnYszr74DPmsmft3WqERTj6eTaRWorQVaWqI/7nIB77wTtBL8+tu+hEhLA446CjjlFGDsWLWorN6BnzbXq1Jd7I7l9vpQ5fCh2qTD0FFDYEuzBo+/6reKBDy/qvRYugXlTU7UNLvR5PKopClaEUoyrSpC25dCrKezBGygsOOwHPXa1VXNSsz2JgOlosmgELMXXnih+n3PPfe0e4wXzheRYSkIgiD0Hz0RnfxM5xev9jh/a4I2UVGoaAKgrsWNtdXN2FxjV1FSNi1YVdGsHmed2lj2PZyOIs0TSzLVtthBShNBbLdamG7CkvtuRFPZevV6vcGIuadcgmNPPx/ZaWYs3RKc2u+RkGcyV5gflmKnTWmt5jro/vMf4KWXgs+LZNiwYBT2mGOAzMzQYgrZj5aXq1JWrBVrTE9DozUd2xp9qHZ64Wt0w+LwtYu0c/vadZg+IgfNTq8ShyTHZkRls7vXEqc6I1GzBIzQ/rKtUd2rveVjHygVTQaFmGU5LkEQBCE16Ino7CxpjFHbREShIgWA1WjAiopGNDqCvshgC1xrsBWp04Npw3NDgjaeadtokWbu5zPzN7YRQarKQZoFe511Nd6+/fdIyyvBOTfch9k7z1IdzFjjlJFiEvc5jagPqxEeSc1dvRxTP38TeT9+DV204NCcOQDzVvbck+bdiNUH1HrqnX6UjiiBNysbLpMZvAJTcwKqnWxpbhr2m1SENIuxjaALvw5LNjeoBCp6Uymu2UWL0dmDti9JiBCLt6FAT2cJuP/LtgbvKTZY6M26r8le0WSg0nt99gRBEIR+p7tTn7EkjSUqCqUJgG/XVuP9n8vV6znlnZceLN7P6e6CDLNK6GHEdkZ6Ln0IcU/bRvo4NZEeLoKUeE43wzNqJxz0pzuQPnoKxm0/SglZ7XxtV5qlnsumCzGdU4pSLakrQqBSyH6ybDMKv/kcR3/5DvLXRmkLb7WyLiZw6qnAhAlRj43bXVXvxg92E9JHjoIrp33DCbaTZSSaQjaa/YHXYefR+fh56zqUNbjUeVFtYDMtKsFq4foalObYeiTIultPuDMPbmcDNlal+H5jrSrblZdhVtHq3q77mqwVTQYy3RKzTAC76667sHz58lBXsCuvvBJzaT4XBEEQkobuTH3GmjR23IxhCYtC8bl7GgqVF3ViSZbqzEVhyX1YvKlOCVmum/7W8gYnWlzemAVzR5FATQR99cGbWPr1h/jDLfdDr9ergvoUP+4Ju6oapXp9MCKrna9dxxao9fLvTs8po6+MwjKpK0p92EBVFZrufxxHvfc60hvbWwka8wqx7ZCjMfEPZ0GXm9vRBcZWrwELanxYVAksrvMhz92IwgaXOg5WAIjV/hBqMJFjUxFwNh/Q6vGSntZJ7a2GAh0N2Hg86yqbUNPsUpYSRpc5KOmLuq/JVtFkoBO3mGXP6bPOOgtHH300/vjHP6pl33zzjapy8NRTT+Hkk0/ujf0UBEGIm4HeHz3W44t36jOepLFERqGYKU/xyMoFBn3w9RRjWn1RFsxnpK222Y0pI3JiEsydRQLzrcBHj96Kj197Xj139KQdcfhpF6htThmeg4XralQN1toWni9Du/PV0TndtcSGYY46oLqDpK6ffw4mdL3zDiaE2Q00aibvhPUHH421O+2CRo8fBaY0BOVzGCYTkJ2NzT4T/re0TAlERh657yaDLmjLcHjUucuL0ZYR3qQgmn2iJ3VSe7OhQEcDtopGB9ZUtagqDOMKM5WQDX+N1H0dxGL2b3/7m6o1e+mll4aWUdQyIeyWW24RMSsIQlIw0Pujx3t88YjOeJPGEhWF6mi6WKsvWtbgQF2LByftPEKVY+pK9HQWCVyy7Ge8dteVWPHrL6Hnb1izUtUt5XPY8nbW6FzsMb5IeXSjna8259TlRZqzBYVeO3T22vY7Q9HKkloUsYuCjRfC995nMmHrbvsqEds4erxaZvEH4HY6lG81BCsI5eSo30og/rglJBC5wsJ6i4pca7YMDgJ47khXtozerJPa2w0Fog3YWDOYJcVmjsxrkziYiOMRUlzMrlu3Docffni75SzJ9Ze//CVR+yUIgtBtBnp/9O4eX6yis7/qZXbm7w1u16cisrEI2c4igWU/fIgn/n6tEorEarPhzCtuxqg5h6pp9nisEkzSKnY3A40N7fywCpbdYnMDNjkoL2/3cEtOPtbufwTKDjwC7qy2paMoYs0GHawWM5CXpyKxKiLbiUAcW5CBBrtbtd9lhy927hqSHfSJdmXL6M3r3hcNBSIHbKxP/M7SbSrpKxpS93UQi9nhw4fjk08+wbhx49os//jjj9VjgiAI/clA74/eF8fXX/Uyu+vvjRZtjib0nA47nr7rBnz5ziuh10+YNBmvvfqKyv2IyyrBklr0w7LlbBQ/LFasCEZh2W6WtWIjmTYNgVNPxZdDp2BtnRMjMtPaRGp5XOVuYMT4UcjfaSKgb1tntzOBSJ8rKxHwx+0LViDae1IRDt1xSKfivDeve4f1hAMBtZ+MhDMqzk5sPSF8wMZ1/1rWGNPxDHRL0kAnbjF7+eWXK1vB4sWLseuuu4Y8s/TL3nfffb2xj4IgCDEz0Puj98Xx9We9zHj8vZ1ZLXz+QBuht2nNCtx/3UXYtmFN6PXT9z8Kzz76MLYbWaT+jul8UbxSxLLZQSSMzH76abBL13fftX+cUdWDDgqW1tppJyVep9Y7UOUsx6Zau/J2Wi0mNJhs2OI3IXNkOmbtOAy6KEI2mkBkpYKfNtepCPbowgzV/Yu+2ZIsi6qf25/XPZpQpv+ZNggmaFU3u0Kd2Jhgl4iZk1iPh93DBrIlaTAQt5j9/e9/j5KSEtx99914+eWX1bLJkyfjpZdewpEsHSIIgtCPDPT+6H11fP1ZLzMWf29XVou54wrbCL23/vN4SMgaLDbMOvlKTJp7KFz4bdq+Q1hfnRUJWFqLbWcjYe3YV18Fnn8e2Lq1/eMFBcCJJwZ/CgvbPDQkx4b9J5fgh0on1roMaDLbYDEZMD4GMRUuEMcUpquyZRSyLKXF0mWNvgBGF2SoJgjrqltiitj31nWPFJZc58qKZiW2SXG2FZOKs1S5MwrNRFmBujoeMpAtSYOFbpXmOuqoo9SPIAhCsjHQ+6P35fH1Rb3MjqZ3O/P3xmK1WFXZhJH5aUocsTBC+h5nwfbdAuiNJuxw2g2wFAxX5ba+XF2JgkxLdMHCpC0KWIrVaA2D1qwJWgneeCN6pHb77YNR2EMOAcztE5CUdSAzE0NGjsRhZnOn57mj86QJxJ+3NqCs3qkSntgkgA0m0kwGVZ6L5cbiidj31nWPVk+Y0Wief3p9maTF40y0Faij4yGvhCXQDTRL0mAibjH7/fffqy5gO++8c5vlCxcuhMFgwMyZMxO5f4IgCHEx0Puj9/Xx9Wa9zO5WnOAx/rK1QXXianJ5VR1UreyStr+rt1bj0Omjsaq8CR/+WgGHx4DZF9wBc1Y+XH6jsiGYDXpsqXO2FywUppofNhKK2i++CFoJvv22/ePsynXggcFWs9OmqTqwUe0GrEjAhK5WCwGf1dF57uo8USC+tWQbfi1rUuthObGSLGubOrPxRux767qH1xOeVJKp2gNHu36JtgJFO55Y2uCmsiVpMBG3mL3oootw1VVXtROzW7duxR133KFErSAIQn+RaN9fsiWGDJT+792tyMDXvbV0m2rbmm4xwGwwID/D3Cay9+1bL+C/j9+HuR9+BqvZBOZAZVqM0FmHIKADcm1GVbqKU/Js27q+qhmVjU4U61qbHERL2KKw/e9/WWwd2LSp/eMUp7QRnHQSUFIS/aDDymol+jwdPX2ouv42szHUcCL8HkimGQmtnjDr2Wr1hPvDCjTQLUmDibjF7K+//orp06e3Wz5t2jT1mCAIQn+TKN9fstaqTfX+792tyKAJu611DqSbjciyGFV3LtZVpfdyYq4Br/zjWnz/+fvq+X8470wces2jqjQVI5T+AJR4shr1ar1GvR7Ndhfc3hZ4VvuArChWgPXrg15YClm7vf3jEycGrQSHHRZsOxstUpuVFRSxYWW1En2eOKjZfmi2ErmZVltSz0j0tlUm1gHoQLckDSbiFrMWiwUVFRUYM2ZMm+VlZWUwGrtlwRUEQUg4PfX9JXut2lTt/06h8cu2RizZVK86VoWWI1iiyeP1I91iDEZLw6Z3w4XdjkOz4fEFVJerwgyLSnhavmQRXnn8BjRWbQutc9Yuc+ALBNS5Meh0ypagYfC4kd/cAFNVLdLz0mDVh7WLZamtr78O+mFpKYiE1oD99gtaCWbNim4loLClgM3MjP54L1SuSJWIfW9ZZfjanzbW4aMVlSivdyghymg9KztEG+QNdEvSYCJu9XnAAQfgmmuuwRtvvIFs+n1Af3y9apiw//7798Y+CoIgdIvu+v76u1ZtrJGlVOv/rkW6KWQXb6lX0VKK0fx0M2rsbtQ0u+H1+6FvPVaWbdKOL1zYMaGJflBGYyubnNjw+ctY+PL9CLQ2LUjLzMY///UoDjv8cDz1zXq0+Oyot3tQaNTD4nLA1tIEs9sJp8cLj8eHYblpah/Q0gK8/nrQSrBuXfsDYIT1uOMAtm0fNqz949xvLQprsfTYqhLLNDjF65LN9Uqo56SZceSUUizcUJfUEfvesMrw3np+wQZ88EsFGp1emI169X7le5Rlv6INQAeKZUfohpi96667sMcee2DkyJHKWkBYc7a4uBjPchQrCIKQpMQqKvqzVm1fWhv60g8cHulmRJZC1mTQYWNNCxZvrlNCjOfSZDCh2eVBdZMbn62oVEKTxx0p7Pj68dnAI7dejfWLvgxtZ/R20/DCf/6DXXaaqI6PUbmaRgf0HjuwYRusBsBo1MPp9Sl7AktC7WlzQHfHHcHyWk1N7XeeTYIYhT3iCCAtLaaErkRcz66mwVeUNeCHTXX4YWOdslBQwHGwdez0odhzQmFSR+wTaZXhOX7ym/X4eHkFnG4/irOCpclaPD6sqWyGyxNcV7QBaKpbdoRuitmhQ4di6dKleP7557FkyRLYbDacddZZOOmkk2CK0w9EHnzwQdx5550oLy/HlClTcP/992P27NkdPp9R4GuvvRavvfYaamtrlai+9957cQhLnwiCIHRAPKLC7vKqgu7Mdvf7A+2SaXorMaQvrQ19LZrDI91MXC+stygxyUgsjzErEKwuoCUIjStMh8fnDwmQSGG3csn3eOD6i1FbWRbazq5Hn40XH/kHhhdkqb91Xi92y/LD4apBuccOhxFocftQ73DD7fFhTtUqHPPZZ8he8HX7Ll683nvtFfTDzpkT3SrQRUJXT69nZ9PgjFp/srJS1QCg8EpvjSj+uq0B/6yz40/7TcDOrXVUk5VEWGVUwt/aaizbUg+vL4CCTLNKCkRrVQeWKGOUNTvN1M66ksj9EPqXbplc09PTcf755/d442y0cNlll+Hhhx9W1REoSg888ECsXLkSRUXBjizhuN1uZWXgY6+++qoS1hs3bkQOP0wEQRA6IB5Rwed+vLwSKyuasLaqRU3fcgo6vMxRbySG9KW1Idr5sHu8WLSxFsu3NeKIqaWYOjwnYV/m0SLdrD5Q1cjOTx51jptdPjQ4PKr9qqqPWpSpIrdatCxS2DlamkNCNjMnD0f+6TYcftghGJqXgYqKOriramBzO1CabsYBk4pU9YMttXbYG5ux/Q9fY9bX7yBny4b2O5uRARx9NHDqqcDIkd1O6ErE9exoGrzZ6cbnKyuVeJs6LBsZ1uB9mWU1q/UzGsn6qbNG5SpLRkf719/iLRH7wNezzJfXH1DilVUSNFjuK81kVPc2B6fZNlOHA9BUs+wI3RSzq1atUlHR8KjpJ598gltvvRUtLS2YN2+e8s3Gwz333IPzzjtPRXYJRe0777yDJ554AldffXW753M5o7HffvttKAo8atSouLYpCMLgIh5RwbaWFHn84mMGPH/bTHqVaER/5tQRuchNM/VKYkhfWRuinQ+2QWX3qOoml/KtbqhpwUE7lCSsrWg07ycTcyaWZKKswamSv1pcXlUqi9dBGziwFqwmdiKF3djpu+PQU36HVT//hCMu+T+MGFaKMSYf3n33O5SV18HtY6RXh6G5aZg2PAcH5/nheOtVWP73XxgaG9rvJL9LaCWYNy8oaCOhB5YClkI2BsGVqOsZbRq8kZFlnx/jCzNDQlZDrws2SOA5Yt3ZHYYGc1v6u0pHpHBle90F62t7vA9cH+8bVrUw6XXqnuG/NQwGHVhxjc9hEqBUJhjkYvbPf/4zdtxxx5CYXb9+PQ4//HDMnTsXO+20E26//XakpaXhkksuiWl9jLL++OOPKplMgyPI/fbbD/Pnz4/6mjfffBNz5sxRtW6ZgFZYWIiTTz5Z7RsbNkTD5XKpH41GtiQUBGHQEKuooEDVRN74okwUZlhVn3smk7BGKacrOYVLPx6FWKITQ/qq5mXk+aCQ5XHyy56RK6vJoKoKMCs8UW1FO/J+svPT0ByriqqxOgEjiaU5v12n8Ag47W38DgoXdjOP/T12Px6YlK7HKEMzli7egAaHF0WZVvU6CibHtwvg/vJtYNG3SIvWxWv33YNWgrlz2/ldlWhlNQKK2Ghlt/roekZOgy/bWq+ikfmZ5uizp5bguuvt7qSo0hEpnnldmLiXZTNhQnFWj/aB9wYj+7SoWM0GdR8bLYzJBu8hn49DJcDr82NUYbpUJhigRJ9/iMIPP/yAgw8+OPQ3PbMTJkzABx98gPvuu09ZBJ566qmYN1xdXQ2fz6cSx8Lh3/TPRmPdunXKXsDXvfvuu7j++utx9913q+hwR1Bks+qC9jN8+PCY91EQhNQnFlHBxxmVDRd5FKzThueiJNsKp9evBFdZoxOluWm98oUfLviikShrQ/j54Nc8I7IUACxvZTEalJilpqOoZAUAihBG1XqCZhGgUAlfF73ITAars7sxLMfaRshqpZFG5Frx0N23Y+rUqWr2jqJuj/EF2Hd0NuYNteCcYXocMdSEbTXNSsiOzEtDBnwY+eUHOOiGC3HMPVdj5A9fQxcuZJnEdcopCLz7LqrufRDLxk7Bsm2NqGpyBvePrWcLCwGWoGQDhDiFbDzX02rShzpR8XdH51obePE8ji7IgJnWELc36nMZ5WYyGJPqOovKc2DBurvaLEWirnc08by8rFHtD1sM19rd2FxnV55pNqtg4wo2wOjOPvDe2q40S0VdzXq9is6yKxz91v4Ay7154FQVK2w4cLti8cEO9sgsxeewsFIkn332mYrMauy11164/PLL0ZuwjS79sv/+979VJHbGjBmq8xgTyG688caor2Hkl77c8MisCFpBGDzEWhidRJsKn5GeqyKVTrcPFU1O7DepqFemYvuq5mX4+eCXPW0FjMhqkSy316+aCZiNBhRnGaJOhcfrdeysBBK3xY5VnA5udnlDy/lcR3017vzb77Bo4TdqPZdeeik8WaNg9NsQsNuVjWBrjg25aWZ13oa4GjHxoxcx6uO3YWmsb7cfvqFDYWAU9uijUeY34fNVVViyeJVKCAtAB1tuNnaYPBwHzh6O4blpvX49+ZwvVlVhY409rqn27YZkqnVypoD3NK0FGv6AX617u9Js9bz+rNIRzdLCGQ7ecyy/tr66RQ0Qh2bbVJSePup494HrpB1mVUUTFm2qVwMyY8CvkjiZ7MejnFCSiXPnjsGI/Ng7rwkDVMzm5eWpxggUghSVjNSGi0TaBuIZzRUUFChBygYM4fDvkg5aAQ4ZMkR5ZcMtBZMnT1aRXG7fzNF0lCYP/BEEYXASq0hk5Caa6KXIy7JS7EGJprQogjgR9FXNy/DzQRHJagIsh6WdD4qNkiyripqy3FPkVHh3/ZYdlUCaNToPx84Yho219tByTkOv+OFrvPfAtbA31gXPD21oh5yEQIMX+dk+WHJsKpLK8l3561Zgr4XvY/aK72Dwt4+EVm0/Dd/PPRQ7nTYPwwoyUVbvwGs/bVFT9QGjEbaSYjjT0rHF4cfGtQ0o827AmbuO6tGgpavrycerml2oaHLFPd1PS95xM4bhvjq7SvbiumktYERW3Ss2k3o8MvmL15FRSkYta5pdKmFKVepoHcgkukpHNPFMX3ZZgyMUEfa1dmXTurjtNDRH3Vfx7APP05m7jkZhZhkWrqtBDb3uZqAoy6qsK8fPHC5CdoAT86cyI6+33HILHnroIbzyyitK0HKZBlvZxpOMReHJyCqTyJg8RrhO/v2HP/wh6mt22203vPDCC+p52puUiWkUudGErCAIQqwiUZvC7c9uQH1R8zL8fGyrd4Cz7y4vI1jBqJmqJNB6/HaXp421IR6/ZbTobWclkGaPzlPLV22rx3XXX48vX30stM95uQXY7+g/oGDoBOSlW5RHsqnRjuyPPsDF37yDkVvXtjtOr8mMrXvsj/UHH4Py4uFqunm21az2a9GmOqxo8KElvwjZhXkI6HTgVR1qDahp7xXljarc0/G5w3s0eOjoejLxjeeQ91RkUuKYwnT8vLUBby3ZhqOnD1X3ZbR9YNktlt9i1QLe11w3rQWMyFLIRivLRX80o6HLy5qUlYRR8fwMs4qIchYi0VU6Ii0+tLVsbXDA5wcyLYZgO2EmZun1KMw0oarJhRUVjcoqEu8+8Fz/bo8xmDe1VN2nPL9M1uRyqVIw8IlZzP7tb39TZbFY15WR0X/+85+qRJcGGybss88+cW2ckd0zzjgDM2fOVIll9N2yMoJW3eD0009X5bfoeyW///3v8cADD+BPf/oTLr74YqxevRq33XYb/vjHP8a1XaF3SYaSL4LQHZGYDN2AElnzsqP3onY+KNgqGl2qZFVBhkVFZLVKApECvquqEGuqmvH+z+XYb3IR6h0eNe3b0fR5NHHB9bnqK3Heicdg5ZIfQstnTt8F5557BdbW+lWjg9qNZZj582cY/v6byGgKRm3DacgpwFezDsCaPQ7CmAlBa1x1rV0dV36mFdU6M35ENqpzrCqCGX5eKeizbWZVIoxR286mumP9nIt2PfnaZ+ZvbDfdr1WVKKt3qkoEPP/bD83ucCBDwcrII5/LZC96UmktiFaOiwLvy9WVykbCaHxJRjDxTouITh2Wo7ysiRywRVp8aNexO73ITTcpn7bVyOoLwcgs/2Oi5ZZah/Kqd2cfeC6ZSLi+xt6nlRqEFBKzjLouX74cv/zyi6oiUFpa2ubxm266qY2nNhZOOOEEVFVV4YYbblBWARr833///VBS2KZNm9q8KWlxYMIZfVOsoEChS2HLagZCctAfJV8EIVEiMVm6AcVb8zKasGJCW2fvRf4w8jihKBNvLt0Kh8unHk+3mFQUM1LAa0lK0fyWTNrh44s312PJlnoV3eQU9pShORiVnx7T9PnCBQtw8MGHoK4+KFAZNDn95PNx5KHHqooSJcsWYe+F72PHJd/A6Guf+LRmxCR8PecQ/Dx5JoxmkyrRlNnkhMPjR0aGFTtNGwfdqCFoqXWgKRD003IfI2F0E7qAElsdTXXH+zkXeT2114X7s8OrSmTZOPVPMWjs8rzxOzJa+a1wtIFIvcOLXcbkq/a3nIqnfYaRWYrn+etrMHtUXkIHbJEWHw+FdCCAkkyrSgBj4l9+ukV5n2l/4MyA0aDDDqVZ3dqH/qjUICQHcZm/jEaj6tIVjY6WdwUtBR3ZCj7//PN2y1iaa8GCBd3altC7yAeJkOzEIhJTrRtQNGGVZTUqPyY9r519qfOYpo3MVck32jqqmt1RBXxHVSE0EcbkLSOjqyqxjLaxAFZVNikvJ6ewO2wU4PWytSMmWizIsNmUmC0sKMZVl96ASWMmomThl9j1nVdRuOqXdsfuMZiwdNruWLjrIdhQPEp1Dctvcav6wIwOV/uNGL/dcMzaYQSGtR6HVspJvZ7nK+J4GLlEQKeeE22qOxGfc5ERy8iqEi6PXwltRo4zrbYeN8wI965ye6yXzA5iFLRNLnqmdaq01R4TChMq9iItPrwXmPjlRyD0b3rQGRGm5YCedM4KjCmKUuc3iZqOCMlH72QyCIMO+SARBhKp0g1oU00LnluwCTUtLlXWimWPKJC+XFWlhNE+E4tCyWydfanHIuCjCbAmhwdLttarKW52SWtwepWoLcywwGzSKw8kRRorQrTLljcFlIhFc7NqJZuTno7H/u9OXHv/w/jjiedi5qKvMeqev8JWU9XuuBsyc/HRlH3w8ZS94MnKUYJyaJoJJTlWlOTYsNVvQhlsOHT3sdg+IsqnlXJaW9msqhgUGX/zpPKYGhxuVXSfz4mc6k7U51xkxJKlpLSqEiyKGp6El4gqA5HtmeklnTEyWKWDyWAUlbyHKCYTTfhsB9vJkuomtxp4jC7MgMmgV/tg1OtU7dnJpdndshj0daUGIbkQMSskBPkgEYS+F7L//HQNVpU3IcNqQHWzW00ZF2daVM1NigMm+zDSpX25d/al3pWADxdgeWlmrKtpUVUBNtU61DRxXYsHQ3JsqtwXo4paFQiKNIom/pvJZfUVNXCv24DXvv4Yu0yZgtKwWuP752RgcnYmhlxzHoye9gX/Vw0Zh49nHoDyXeai2ulXYtrt9CgxVlqYCVdOATxp6aiqdWCHIVnthGy0Uk5b6x2hNsUUfD6/H9NH5qnnRL42UZ9zkRFLiky3zweLX48qh6tNEl5PqwzE0p6ZthLWF+6t7ljhgyVGhFmBggKWlo5wX3pPmpH0VdMRITkRMSskBPkgGZgMhGS+gXAM0QTKcws2YmV5EwozzciwmJQ4YDJPeb0TLp9PeRHVNDKFJCN+PfxS1wQYt/nJigqVgc5Wv6yUSAHLZCLW4tUbdKHpe4qVJpcHPqcLFmcT3DW1yGxowo233YMnX3kJe86ejU8efxyGL74AnnkGuu++Q2RbG5/BiJ+23wWf73wQto0Yj3S2QvWyZakn2KHLmo7mgjw0l46AU69DRa2jy2S9yFJObFdMKPJ2Hp2HQ3cqjTrdnuiuXlrE8petDWhx+ZS9gW2Uw4Um6W6VAc0S0R/tmSPRxD5/huWmJdyXHms9aWlnOzARMSskBPkgGXh0leSSCiJxICYkalPdFKqchqaQZWSSxeILM/XYWmdHs9OrxCyz1ilyE/WlzugaPZ0UbYz8tnh8dAioqCunxdnJiX5Pbfo+0GJHQUM98qzNMJkNWLJ6DZ576u9YvyFYSuuL777Da3vuieNqa9tty5Wdi5/3OBjzZ+6Pn71m1cRgel66So5q9vhht6Sj1mTDNocf66pb1DXmvsUqisJLOTFZTju+jkph9cbnnBaxZEez137aik3VLSqZKzzxubtl4cItEf3Rnrmvfel91XRESGExy57YscIqA8LgQz5IBhZdJbnsPDo/VOQ+WUUij+G1RZuxrZ7TlxY1Nc5M6VTPbNamuumRpbUgPImJU/sFGVY0OZtR0ehAjs2sPImJ+lLnthnRY6IQRSynxlmzlJ7ZdKsRRoMedT4nspwt8FeXsZsOirKtYBXX195/Fy/950E4nUHhyEn4fwI4NlLIbr896zLCfPDBGOkBzKyFu6JSFcC3pdvgyswC0jORpteDVy/LEfSaHj4lGE2NRxTxeSXZNvXTX59z2j4cvlOpes9RmCeiLFykJUJrz0wPM60fWntmJgAetH1Jn78XEu1L76umI0IKi1mWzOINwDdrVzeCzyd+lMGIfJAMHLpKclm8uQ4/b21EaY4VpTlpSVn+hsfwzrIyfLe+TkUQN9c5lKjjNPLognRVEihVM5u1qW4WlufxcMq4kB7V1uPg1D773LPUEctTMaOIvxPxpa5tmyKBtUEBE3Yo1auIX21dCwp9Dtjq6zEs24IyvxceHRDwefDgw3fi628+DK1nEoCXAeyoLaBX4YADgNNOA6ZPV/YBrr3AEpz63+DUYZldh5FDCtsJSIo21mJlQlNvX8ve/JxLdFm4aJaIvmzP3B8kS2k9IUnF7Pr160P//umnn3DFFVfgyiuvVGWyyPz583H33Xfj73//e+/tqdCGZJzilQ+SgUFnSS7M9q5t8WBLnR3jizKUaKLsSLbyN6x3+unyClUiKivDohKSGMHUvILjizNSNrNZm+p2ev3KW8njYRkuTvUrj6rTA68P2GlYrooiBh+PXm6ru9sOn2YvMPixu9WFsrqa4GeSxwuTwYa9JxUhULkeV95wFVbUVIfWcQaABzg44h85OSw4Dpx8MhDZxpxT7dnZ0OXkYGqhB+t/2tInEbeuPlsT/TkXub1jpw9V16unn+18LRPzWCGACWbhrWv7oj1zf5FqpfWExBDTXcyuXxrHHXec6v51yCGHtLEWsKHB9ddfH2pNKwxOH6B8kKQ+ndUT/Xlbg8qKZi3O7zfUYVijM9QKM1nK36jI8toaVSKKApuJSoRT8YxgUviVNThVgk0qZjZHTnWH1wylD5J+2Qklmbhwr7Hq/dhbvsSJaYCluREGtwtpeiC3NBOmCiYw2XBwphPNz92Pya+9Bnvra/nJ9FCrmMWECcpKgMMPB6wR9wlbk1PkZmUFBa36XDH1ScQt1s/W8M85lr1qcftUYhoHE7HMYMayPf7uCaz3S1GsVbswGQyh1rUcAAxkD2mqlNYTEkfcQ7Jly5Zh9OjR7ZZz2a+//pqo/RJSuDGBfJCkNtGib1phfAomwggPo7JaK0x68Shok6H8DbfPL2q2JaUvMMwyqu5NRqWCj5tSMrM52lT31OE5KgLH65FXasapu4zEiPygGEqoLzEQwK75BthXlKNqq1O1wGVJJ1oaqpucmLLpZ8z99j1Yv/0aea3C9V8AdgDwkk6H0XvsjYaTT0HWHrtCF9lyle3Rc3OBtLR+GSjH+9nK7apB3ca6bgUWevOznOt+ffFWNZeSnWaC1+dX74Nt9Q5V+5fJesPzbOIhFQavmJ08eTJuv/12PPbYYzBzBM2OKW63WsbHhN5DGhMMLvrLShIZ+eN8ZKg7UYYZlY0u9cWrlXsKL4yfDOVveL7o5yzOtqh9ZYZ/0IEZxGTUKQFenG1N2ahUR1Pds8fk984MjccTbHDQ0IBhfj8OnFCAnzbXq8oJjTUN2O77z3DY528hY+umNi+7B0CJ2Yxj9zsMS+Yejk9zCuH3BpD/3WZMG5GDycNyocvODkZiTb+VD+vrgXJ3Plt7IkZj3d7QHGvcloPwdXOQyd9a5J5VLxrsHlWLePdxBcpLzYFdZ+tNRkubIPRYzD788MM4/PDDMWzYsFDlAlY74M391ltvxbs6IQ6kMcHgoT+tJNFaUFKwWk361uL3xmD/+sBvkU5mRytvZpOr36cu+YXLaGFplk1NuXOfND8pI2nVzS51THNGp3Zmc3cjlZHihAOUDgVTS0tQxPJ3GGyOUNJUDcfrL8L6+mvQNTfjEUbsAZzS+hzPqNGom3c8xk7aBZ8ETLAa9GhodqkKDIsqnfiwBpiDXByal47hMQjZRBJ5Dvh3PM0QehpYiOWzfOnmejQ4PGpWpKPPgGhCM3LdtNPkpv3W7au6yYWNtS14Y/E2VX2is8+WZLa0CUKPxOzs2bOxbt06PP/881ixYoVadsIJJ+Dkk09GOqeJhF5DGhMMDpLBShIe+VuyqV4VW+eXIvdn+9Js9eWmJR2x3JW9xYs1lc2qnWp/l78JjyxPHZajOlWpLlQuj+qMZTHpMXdcAaaOyEGqE2+kMlKc0FfJRDKrUa88xUqs5Nmwa6EJw3RuVVqrDazHtWAB8Oyz0H36KdICATQAOA/AK3QKMMdi+6loPOBYrBs/BVvqnfBUuTG20Iz1tXbU6y0wDC1CptWqkvG+Xl0Nl9eHo6cP7zNxFE2gUYRykMP7OxqR9pmeBha6+ixnrd5fyxrV+35iSVabz4Cyejv2GF+krB0/b2tEbYsLbl8gJDSH59rarVsNOm0mJYw319vVfk0ako2hObYOP1sS8TkkUV2hr+hWGiNF6/nnn5/4vRE6RRoTDHySyUqiRf62G5Kl/s7LMCuvHbebw3amrVOXFLL0pk4qycJBOyS2XmV3vgzDI8u1djcmFmfCWxhQrU9ZzJ9fzOzwlMpR2e4QKU5YmumHiiYl4vIzLNh5aCay3E2o+Gk9PrXqsf/kEhWBVTgcAGfenn0WWLUqtM4fGMwAsK71b8Zv7540HSfO2AU5Pj/WVttBOfxFtRe+rAIMLcwKWT7Y1MHh9qKs3tln93RHAm1dVZMSn/QAsztVJJH2mZ4GFjr7LGfFkBUVjfD4AuozQHucv1l9gB3L5q+thcvnh88XwLA8GyYVZ8FqNiihuaaiSQ1SItfN9dIO1OjwojDDghybSdlxon22kJ5+DklUV0h6Mfvss8/ikUceURFaluVitYN//OMfGDNmDI488sjE76WgkMYEA59ks5Jwm+xvP2VEjvqi1NCmLmkt4BfbxCFZOGe3UW06F/WUnnwZRnpKtddPH5k3KKdIIwdJ1JMryhvVIGRytgGOqko0NZZjxNAs5ORasanWrjyxJfY66F54AXjllaDdQFtfa8ODK2knaF2Wnp6BE0/5I47Ydz9139S49WjKzoMuKwtbypuQGwhaUzT7crDVLROUzH1yT3c2UGTXLdYi5tR+aba1yw5cPQ0s0NpBYcprMK4oQ0VNNZHP99SWWodK0ApvQ8zZkSWb69U26+0eldjFWs/899Kt9cofS6G5prJJRdvLGhxtjpM2A1oM+CcHL0zi7OizhfTkcygZZpeEwUXcYvZf//oXbrjhBlxyySW49dZbQ00ScnNzce+994qY7UWkMcHAJxmtJJ3dd7QajMhPUx2EEi1ke/plKGXiOh4kNTrcsFfWYrTXjrSAD66AD3UOP1pcFGcGTNq6GmMe+y+weD474bQ5r+zXdXZeHt4I69w1ffsdcOypl2PM8OHw2dLgzsyGU2eCx1UD+ALKm0mB5Qy7t+lfNur1yqrCCHpv39OdDRT1Oj2mDM3BDxtr8fPWBowuzOi0lm1PAgvaIG1jTQvWV7dgdUVzKLpKCwztOrTu8G9tvVwnZ0KYhMmBZHmjC8MsRlhNRmUPCSVhjsxV3cQ8tXaY9Po279f6Frey2zDxkfWJI89B5GdLdz+Hkml2SRg8xC1m77//fjz66KOqnuz//d//hZbPnDlTNVMQehdpTDCwSVYrSW/ed9ESkhL1ZShl4iI6d+n8sNTVwFZVi/T6GljTzKrbFgvqu1rsGPHlB9jhkzeQvX51+5OZlob5u+2GExcvxqaqqtDiK84+G78/6/d4fb0d3uFD4LMEBVwmAqqu6aaaFpj0OlUeKtiRLHjNWROXthWWjOqLe7qrgSItFaNa0jGiIF0lXnV2j3c3sBA+SGPptKJMq7IUMBLLsmq09NCuw32kbUCDUVVaelhOjvWTSXgLYy0Jk89LNwcF7t4TC7G53hF6v/L8F2VZMKkkUwnirj5buvs5FMvs0i9bGzCG/t442w8LQsLELLuBTZs2rd1yi8WCloiMV6F3kIjTwCWZrSS9cd9FsxJw+pVRK37ZJ4PVYiBg87qQ31ANU4MHJrMeXje7hPlVW98iez12WfAhdv7uI2S0/GYlCTFsGHDqqXAfcQROOOYYbG4Vsvk5OXj6rrtw6FFHoUJngbd5M1pgCHb2ahVZLNDPUlCslkAfp04XUIlLFLJpJoNqLRxMRur9ezqWgSI9s0dPG6rus67u8XgHeNEiltyP3TIK0FgatOuMLEjH2buOxH9/2tbmM4BVCPhjtBnhaPapGs+qmzDCLRse1eXOjqDQHFOUgV3G5ofer6xG8sWqKqwsb2rX2CHaZ0t3P4c6GzQwAW11VRPWVbUES/21ft4NRuuP0M9ils0RFi9e3KYrGHn//felzmwfIhGngUmyW0kSed91aCUob1TTr4xaRRMdydCYISUytf1+oLFReV2LXC6MsgawbEsLfH4/KppcsC3/BQct+hC7r/0RRn/7c1m9wzTk//5c6PbeGzAYwFje0//3f9j3zDOx28yZeOHpp2EuHYUNHr8SSqxkQaEULn7YSGPa8Bw0OIJT3Ftr7cojy3toSLZViem+uqdjHShy32Ldl3gGeB1FLPlvnpPxxZlK7FW3eNp9BrA+LPeRyXLZNiMK0i1qQGAxGkLNG2jZoD0hXGhGvl93HVugHo/ls6W7n0MdDRq0xiv0+KabjRiVn65mBMRHK/SLmL3ssstw0UUXwel0qjfXd999h//85z+hRgqCIPSMwWAl6cxXx4QY+gg5/cqoVeQXZjI0ZkjqTO2wBgdK0LYKJhbg/2zpFoz56Ruc8MMHGFu2rv1LjSb8MmtvbDrkKMw6YA4CWZY2nbr23n9/fPT22xgzc3fM39SADfM3ho5fJTHpdO3ED/2wc8cXqqoSLJNW0eBUWfTa1P4OpVlxt4FNpoFirAO8ePzwvJfCPwMYzWbtZL3erxK9aA1ZvKlOedaZyEVbBK0DfH1uJ8cRz2dLdz+Hog0atEoKLS4vTAa2PLaq883HxEcr9IuYPffcc2Gz2XDdddfBbrer+rKlpaW47777cOKJJyZkpwRhsDPQrSSd+eooipgQQx8hp18ZteqJ1SLREdRYktP65dp10OCABKqqYHv0cdz47uvIaKpr93hVZh4+nb4fFk7fB0Ujh+CUWUPx+AtP4oeff8ZrDz0EPdvMtnbpmmDJjnr8PHYeIo+1I8+pdi3WVTYHa6Q2u/Dx8kpYVlf3yWCgPweK8frhIz8DGMX+clWVGhxQPO80LEfNYmypc6iIbF6aGZNjOI54Plu68zkUbdDAqhmMKvN3ttXUJgFNrENCv5XmOuWUU9QPxWxzczOKiooSsjOCIAwOK0lnUSp6LZnJzYQYfhly+rW7EbRER1BjydR+Z+k2Nb2+scYe2ian4CcUZyo/cFeCIC7xHWYlaNfggPzyS7A27NtvYwIjthFsGD0ZP8w9FIvGz0SLDxhXlA5nSwNOvfQifPndQvWce954A1dceWXMx89jnzdtKJwef7v916bEv99Y229lm/proNgdP7z2GcDHuZ+zR+eFBgFslMD7atrIXBXdpkCM9Thi/Wzp7kAwctDAagstbq9K+hpX1D4BLVmsQ8IgErP77LMPXnvtNeTk5CAtLU39kMbGRlXh4NNPP+2N/RQEYQDRVZSKJYqY2c2EGHrtuhNB6yqCOm/qUJX1Hc8XdVeZ2lajAZ+uqFSJTWOLMtU2y+odeHPJNpWcQzHDJKMetw+lcKWApZBttRKE8HqBjz4CnnkGWLQouG/hDxuM+Hn6Hvhx7qEoHzY2eD0CAbjsbmxauwwPP/p3NDcGa8qy3JqH64vx+CmQKOL5b+53spZt6o+BYk8qIITfE2ZGYdMtQQFbFLuAjZdo96IalBVlqgFLV++Z8EED1/XWkm0ozbEh09q+dXEyWIeEQSZmP//8c7ijRADoof3qq68StV+CIAxgYolS7TQ8B8dOH6oy4eONDHUlmhZvrsc/P12jyoCFtwLtSih3FlGmL3Bbo0OVTmKbUG6LQnxVZRP8fj4arK3K8krdbh9qCQB1dYDd3n7nuJzNDdjkoKys3cMt2XlYsfdheHvyHvDn5sJs/O0YXG4vvvziNfz4ycvq3BHax1544QXsueeeMR1/LBG2ZGsK0tfEa3Po6J5gQwSHx4theWldvh+6E12Ntl1u863F2+D2+dUgk13EunrPaNeU21xX3aLuZb4vkqlKizDIxOzSpUtD//71119RXl4e+puNE1jNYOjQoYnfQ6FfSPpMbSGlr32sUSpGBrsjajoTTcymLm90qt9DJhaqLkqxTnN3FlFmjc+KBheybWYlFLWkF5YgKsqywuVhKSwPqBXjaR86jv7hTeVY+lUVhk1qnxCHFSuA554D3nwTcAW7N7Vh6lQETjsNX5buhDX1LpgcHlS3uJHHpCu9AeVOB155/DZUrVkSeslBBx2EZ555BoWFhTEffywRtt5sCpJMn1md7UusNodERLG7Y7OJtl12H1tV3gRfIACWCmaZMHYgi9UakuxVWoRBJGanTp2qbjT+0GoQCZPC2FBBSH2SOlNbGDDXvjeTcToSTZrA5Jcxs8DNBn2H/emjfbF2FlF2s36qw4PxxRlq3arQfXOw0D19wKFaoD5/TO1D9W4XzE2NMLU0Y4TPg21VHtQMz1I2BdWV67PPgKefBr77rv0JMJmoSIHTTwd22knZDKbWO1C1vBx2lw9+swVr9enYsn4ZvnzsJrhbGoLb1Bvw5+v/iltv+EvUjm49rYPcW01BkukzK5Z9icXm0NModne76EVuN7z7GMvl8ZjqWqIPyjoTo4OhSouQAmKWzRJ4U48ZM0aV4wofsZvNZpUEZjCI3yXVkZ7ag5e+uPaRESt+AR43Y1iPI2qR62Xd02iiSROYaWYDfH7AxPZTcQiEziJM2xocqpg9fYGq0L3XD6/fD5Mh6BHUaoFq24zaPtSoh7GlGebmRhhcztB2WZaJHaBcNXXAS+8Azz8PbN3a/kTk5wOsKsOfiMTcIblp2HfWOMyv86Op0omaWjtWzv8gJGTzi0vx6FPP4qiD2gcrEhVh642mIMn0mZXIfelJFLsnUd3I7YZ3H+Nzwxs06KzxWUMGepUWIQXErNYkwR+ZbCAMGJIlOUPo/2uvfYkxikhvXGWTs8fXvreiZx0lqmRZjUpAhIsmfgF7fD64vVBigxHUeKe5O4owTRuRi1EFGahsDNbgZkF4ileeQ0aAtfat2jbbRCF9PmQ7GmHcVAlbFO1i3rAOe7/3GkoXfsoEhfZP2H77YBT2kEMYXWj7GCOs2dlAbi6GGo04JhDA3CYX7C4vKnd/DCcdujcmTJyE5595CvkUw13Qkwhboqebk+kzK9H7EksUm8lg9GjzOoQLw55EdSO3q3UfM9kiBmXG6IOywVylRUihBDA2RyguLsbZZ5/dZvkTTzyBqqoq/PnPf07k/gl9yGBPzhjMhF97fhlzWpHRGPUlZtAj3WyAx1ePPScUduva91b0rKP1shMVb+HIIv5MXml2+pTfL7zWZbzT3B1FmFjzk/ujbTM33aTq5bJQfLrZGNqmFoXcLs+MopY6oKkJ4/Qu1VQgXUvq8ftRtHghRr/7XxQt/bHdPgQ4E3bAAdCddhowfboqpN8Go1EJWCVkWy0DlZWVahZNu4ajCzMw/5uv1bJ4xF5PImyJnG7ur8+saJ7YRO9LV1HsNZVNCECnSsFFJjH6/IFuR3Ujt8v3P384EDSbWgdl2R0MygQhVcTsI488ojJcI9l+++1V0wQRs6lLbyZnCKlx7Z1uH5ZuqVf+OE4rMhrDLzEWbGfSFIvdxysKeit6Fst6i7IsqiMSy0Xx3mUka0JJJtiTiMsj1xfPNHe0CFOkUGNEls2uDDqd8tLynDY53Kgvr8YwvxO7FedC1xSsHsC2r9XNTpRtrcbMRZ9j4sdvIKO8vZXAmZ6JX3Y/CCv3OQw5Y0eq1w0JP29Wa1DEZmSEBK7L5cKVV16J5557DosWLcKoUaNCT2dwojv0JMLWXTEcKSIZXe7rz6yOZhiG59oSui+dRbEpZLfWOVCam4bcdEu7wSE7rnXXmxy5XV4Xvlc4UFODMr6/CjKUDzzyPZNMSXjC4CJuMcsqBkOGDGm3nB7asijlYITUobeSM4Tkh9eUQo8tZClkC8O+hFiLNStgQlmDUxVs32VsfFnHvRU9i2W97EQ1b2qp+lv7gnV5fHh98dZey6pu17mptTzXlrJ6VK7dhEy3HTtmW1VbUrZz1RhSX4Fj3n0a5rfegMnRvvRW3bBRWLTXEajb50AY02wweP0qgk4BvP/kEgwZWgDk5TEbt83r1qxZgxNOOEGJWMKgA8sompgk1o/EK4ajiUjWO+X17KvPrM5mGNZUNCV8X6JFsfk+ZUSWQpYDmWiDuFUVTcpqwxmK7niT2w3KjMFBmV6vU3Vm2aWvyelp856h2E2WJDwR1YOPuMXs8OHD8c0332D06NFtlnMZ6xIKqUtvJGcIqQGvaV6GBd+sqVGlqtpcewTQ5GJNSxtqW1xxi87eivjHul52ooos4N/VNHdPvwxDQi0QwCizD1N1QI1BB6cnE1ZTDvLTzcH1MSX822+DDQ6++ALprTVeNQK0B+yzDxbufii+LxqHkfnpsLTuB5PYRuSnY7VDh298GTimNCjaw3nppZdw3nnnoampSf1tsVhw5plnwkgLQgrRkYjcVseIu1PdB1PDhF1vfGZ1NRPAaKnT61f1WMMf7+m+RA6O6JGltcBo0KuSWfSucsqfkdLwxhX7Ty5W2+zuoC1aO12KZK57Q01Lm/cMSaYkvGQR1ULfEfcnGj8YL7nkEng8nlCJrk8++QRXXXUVLr/88t7YR6GPkFqAg/vas6PQe8vKUO/wIKe1lBSTPeiRo3Bii9kGpydu0cnKAl6fH1vrHcixmYJfvGFfot2NnvVkJqGzae6EfBmyaxY7dDU0qOQuHq0qqRXaOTvw+uvB+rBr17Z/fVYWcOyx0J1yCqpzCrFs0RYU2VoFMBNxDUZ4MrLgychEhsePdQ2eNoMMh8OhPqf//e9/h1Y5YcIEvPzyy5gyZQpSic5EJFujUtwxAr+mqlkl2PVW/dKuZgJKsm3w1Nph0usTHvUPj2LPX1uNZVsb1DJ/IKCSsfIzzGrqn5FqbRDHf/fUmxy+3VFIVwOGyPcMeeXHLUmRhJdMlS2EJBez9F3V1NTgwgsvDHUCs1qtyit7zTXX9MY+Cn2I1AIcvDA5iS1ka+1utLh8qvwOvyiZ7MEvSqNBB4s3PtHJL5dv11ZjU61dZflT0OVnWNS22J+9JxGrns4kRJvm7vGXocMRFLHNzcGoa7sTsjnYoYudulqjpW0YOxZgQteRRwKtrcKddfZQgo/PbIE7MxvetPSQHzZNp28T2V6xYgWOP/54LFu2LLTaU045Bf/617+QmZkZOj+p4m3sSkRS0PK6larkxe61Pk7UTAAtOXtPLMTmekev1FLlcX62ohINDi8KM83IsJhVkmZ5g1PVOKZ1Rb1PWwdxvL8TWQor2nuG77NkSBxOpsoWQgqIWd4Ed9xxB66//nosX75cNUsYP368mr4SBgZSC3Bwwi85tpBdXtao/u31BctLqazlANSXQTyiM1wYTi7JUtqr0eFFS61dRX8nFmcocdDdiFXSlHliucLGxqCIjdLqW4nahQuDVoJPP20ncgNsRrPXXsHSWnPmtKtKYDUbgcwM1OQVwpbV1i4RGYGmrYCVZuyt7W75+fzggw8qa4G2z6k2DRuriNxvchHSLMZeE+ixzgSMKcpQvvJEDxa0+5MVOcYXpqO8yQW+FS1GAwoz9ahqcmFNVRNy0syYHPY+7e1SWMmSOCzVeAY33TZOZWRkYNasWYndGyFpkFqAg49wccgvRk0cNjvjF4fRhCGzoNl9q7rJhYqGYC3Wg3Yowa5jC7otovq1zBNbx1LAMsIarf42o7Rvvx0UsatWtXvYbbVh5W4HoGbecZg4e4c2yWDh9WHzR+eg0FcejEBnBjqNQKelpYWELCvMUNzydypPw8YqIilke1O0xTMT0Bufn9r9WZqThoIM2n+8qGp2IctqUpYgnoO1lS3YZYy1T1vDJkvicLKIaiGJxezRRx+Np556CllZWerfnfHaa68lat8EQehjEiUOowlDevhmpOeqZgz1LW715ce6tfQa9nSfEzGVGtOXYaMTTnbhavAGxWo0WNWFVoKXXw6K3Qjqikqx/qCjsG3vg9FisqKqyYlNy8uDFQkoaFllICcnVB+WRxFrBPrwww/HpZdeiorqOlx369+Rl5OphJZW2zYVp2GTJTG1v3MKwu9PXrOpI3JD9aBpCdLrdKrKAG0OfTkgSZbrkyyiWkhiMZudnR26QflvQRAGLokQhx0JQ2ZcM5KUbjaqjGhWGkgEiYiEdfZlqPe44amuRXFtPdLr/FSAbV9M68CPPwLPPgt89JFK+oqkasoszN/9EGD3udC1tv6m5BiRl6Y8xT9WOnDopNHQMfkrxhJN7k2LMe/EeSHxwsjr7BP/hE21Dry6tAoWY03IQsDoXbzexmTw1va3iEyWnILI+5Oe89y04OCQvlmX16eaJdDmkEi6ugeS5foki6gWkljMPvnkk1H/LQjCwKSn4jAVoyTtvgz5AWlvhrm5CXqnA9W1dpW4xrJaIeiRfeedoIj95Zf2K2US17x5qDv6eLxeZ0aWzYy0ViGr4bOlwzqmCL/6DZgFM4pjGGRU1tThhiv/hNf/+wpGmB7B+eef36WFYNaovLimYZPJW5tMial9nVOgiUk2iODsBsuRMemN29O1RmP5nHg97bEQ6z2QDNcnWUS10D+kVrFBQRC6RbwRtp5G5FIxSqJ9GVbWNKJszSYM1XtU9NPp8aGa3kSb8bci9ZWVwIsvBn9qatqvbNgw4NRTgWOOUWW2WliRoHqb+oJv3Ro86RlwZ2XDbzLDwvajNS1d+vm47a1rflVNENgMgfzpT3/CoYceivll7NTmwdiCdDS7fKi3u1Ub0jEF6VhX3aIaXvB4YhlgJKO3NpkSU/sqpyBSTLIpA+vqshwZBW1virV474FkuD7JIKqFJBaz06ZNi/mG1LrMCIKQHMQbYUtERC4loyTNzRhur8cxuV781OzH1joXan0BJQAZkVVtYzeuAm55Bnj//WAt2Uh22SVYWmvvvYGwCKzVFOyw5gjoYMrOU/VhA2GPxxKp5iCAlQlYz1sri8g8hsceewzGzHxsWLZBCY5Fm+qVj5JTzxSzjCQXZ1lQ2+xCXrpFFfXvbIBRmGHGq4u29pq3ticDpcGUmNqRmOR7knV1+TirOPSGWOuuvzoZrk8yiGohScXsvHnzQv92Op146KGHsN1222EOy8gAWLBgAX755RdVe1YQhI6/tElnH7KJ9ijGG11JZEQuJaIkFKRsbMCfVnHKJCzW1qUgZFTWCj/yv/oUulueA5Ysab8OliVkXVhGYidObPMQryfXY9cZYRo+FKucOozLyoo7Ul1fX49zzjmnTYLtzJkzVbWCMWPGqPPL6HF1sxtOtw/ZNhNMNhM8Xj/KG52qFFpBhhl7jC/8//bOA8ypMnvj7ySZTO+FGYahg2ChK4Io1sVFUWxIURQV29rL2sWy/kVlrevq2lARKRZQEUHECqJYQBQQpNfpvSYzyf85X0jIZDIzSSaZtPfnE4fce3NzS5L73vOd8x7UGhtavcEorDL4zDc0kFIXApnWxKQ0LpAGEeKra7Uj87ZYC3abq0AQ1SQAxeyMGTNs/77qqqtw00034dFHH222zF4xBCeEOL1oS26bFAqJpY6zC7m3L/TuRld8Ue0esFGS6mqLgJW/TpobyPal11dZ0gjmzQMKC5uvIzsbmDwZuOgiICWl2eyDZbX4qcSIvwyRqNGYUW+MsAwRGxrdGiL+8ccfMXHiROzatcs2TRwLZs6cCb1eb+uyJnZqUgyUk3z4/EjkLkOnUd3X5Pz2yIhDl9TYVm8wrJ8/b1scBWLqQqDSZrexxGjVIMJXdmS0uSIhnzP73nvv4eeff242/ZJLLlGRgjfeeMNb20ZIUOLsoi1Du8s35inhNKJnOrqnxTW5kA/vkYYfdxZ79ULvbnTFV9EYf0ZJmkS6tUCmqQ4R0uDAaGz5RZs2WbxhxSPW2XLDhllSCU4/HdA5+QnVaLDfFIkPyxpQYtSpSG+mh0PEH3/8MS644AI0HIoap6SkKJvEc845p+Xtj2j9eVs3GL4o3gtWWzB/4W8xGYwFnCS8cVvMSkeZ1atXq65f9sg0aWtLSDjj7KIt06TdZPSh4p+8yjrkpMbYLuQyZCi9zeOjtOhzqErZ3Qu9s/QEdy+I/r6AehtrpHvfviIlYOONtSpqqXJfHRsUiFj84guLiBWLLUfE+3XcOEsqgV0TgmbLpKTAnJCA1esOoKTe3O4h4lGjRqFz587Ys2cPRo4ciXnz5qFr167NlhOLs3S1HqgIrdVI39Ag5vpGVbwmrYStVmit3WD4ongv2IetOxp/i8lgLOAk4Y3bYvaWW27Bddddpwq9jjvuONswmERkpcUtIeGMs4u2DP1K3mRyjF66wqK4yqCmieCQZeRitX5vGUb1TvfoQt9SekKfjHi3Loj+voB6k72FlVi6+k/UFpUiJ0aLqCgN6rWRymS+qKrucIOC0lIZbrI0OZBmB45kZACTJgETJwJpac7fTG7iJc0gIUE9lcYK3hoiTk1Nxfz581WE9pFHHkGkCGYnyDnJiI9CRlyUullSn7F6I3Qajcr/zUqIVhFaV86dL4r3fH2jFAh+uKEkJoOygJOENW6L2bvvvlsVHDz33HN455131LT+/fsr/9kJEyb4YhsJCRqcXbSlqlxVlkvOrIjbeqMqzLGijYhQETT56+6FvrU8xINlNUiM1qkLkisXRH9fQL1CXR3MpaX4fc1fMBRUomdqrG0/YvVaW4OCbat+RdYPnyHi448tbWkdMA8ciMoJk1Bx0qmIjo1WjgDNzk58vEXExsR4RbiZTCY888wzmDJlCrKysmzTpdDWWmzbEvbnbkjXZGXNJZ+xSJ1GRfx3FFa7de7aW7znKC4lp9dXN0qhWFQWCGIyKAo4CWmPz6yIVgpXQprjLLop9kjyEHEhkVmJlonIsNJoNqshYfnrjJYu9K7kIWYm6lRluysXxEC4gLqLEk3ltagrKUNsTSXSI6Gi4PtLqpGZEN10W02NyPplDYYv+QCdNq9vvjKJeo4Zg6LzL8baxFzsF2/YjYXKUisnJdaSnpASa2kzK+1mDxVfOeJJhDs/Px+XXnopVqxYgaVLl+Lzzz+H1qG5QmvYnzsRrnLukmP16r3kuSfnztPiPWfisltarNMbKzPMqKg1YltBFfplWWzB3CGUi8oCQUwGbAEnId4Qs2IT8/7772PHjh2444471FCYpB106tQJOTk5nqySkJDAWXQzIVqnIntSBCbI8LZMs4oxMUCXZavqjDAnRrscEXUlD1GKjc44shP+Kqhy6YIYCBdQV9l7sBS/btiJ/P2FMBobbaIzOykahkazrUGBrroKXb9aiu7LFiOuwEkqgaQPSBrBxIk4qE/Ais15KJcbgYRotQ4RZNuLa7AvIhp/79ETuRmWdIKWcDfC/eWXX6pobF5ennr+1Vdf4bvvvsPJJ5/s1vHwxblzt3ivJXG5Ja9S5fPK+qw3SvVGE/7Mr8C+klrotBFqWfG3dXVbw6GoLBDEJG2uSEiK2Q0bNuD0009HUlKSsooRqy4Rs+J/KEUKb0sBBSFhSkvRTTGt31lssYGS/EWTSSJ0RiVsUmIjceZRWcrNwJ2IqKvD2Smxelw0tIvLF8RAuIC2iBy4ykoc2HUQX63fjfLaBovojNOrYyE5sXuKq2FoaIR+zy4c+cVidPnmc+jq65qtqqFff+imXQ6MHauirCKO1v2Rp9bZ7VB6gkkXCU16ElJj47G9qBprdpehS3pTgeqIqxFuSSuQPFixOZT3FiS9YO7cuW4LWcdzJ++zr9Ry85STbLlBEoHry3Pp2khBlPq8/76vHJsOVsDYaEZuagz6dUpEtF7rVkQ1XIrKKCYJ8YGYve2223D55ZfjySefRMKhggdh7NixmCyei4SEOS1FyMYclWXzmd1VXN0satY5OcatqJo7w9nuXhAD7gJaV2fxha2shLmxEeu35TcRnbac2ORo4LvvcPTKj9BzS/NUApNGg+2DRqpUguMvOF3ZaFlR6QmlNUocm6JjUJ+YjMYYy3GXd3BHHLUVJdXWleG08yfjm2++sb3mjDPOUMGAiNjkdglPEbHW9y2URgqVlpzgjIQo5Wjgq3xSV0cKzh2Yrb4D8rkV0Sv+y9bl3Ymohpr7BiGkA8XsTz/9hP/973/Npkt6gXWYjJBwp6XoptBSxNPdiGhIFGy5EIVFWVmTIi170WndZ11NNbp8sxw9li1C/MF9zVZliE/EtpPHYu2IMdDkZCs3gwg7IStIt68qfSxic7LQ6FDU5Yk4aul8Ll++XOXHFhUVqeUkN1ais5On34hvd5ViV9EujwuZ7If5Y3RaFFdZGilYh/hFzPoqn9RVcbm/rA6l1QYckZXY7CbMnYhqMLhvhJrLgr/gcSReF7NRUVGoENNxB7Zu3YoMsbEhhLQa3WztAu1ORDQYC7ZcQoSrCFgRsiJoHRDRac2JjTu4D92XLULu18sQWVvTbNnKrj2w7uRx2DTsZGhjog8Xctn7zMrxSUpCZFo2jKX7Ua2JRLyTzfJEHDmeT0nT+vvf/94kCCDWW92OHNLuQib7Yf5eGXH4dU+Z8pXNSYmRSisVpRW/Y3E72CEpE17OJ3VVXAruRlSdiZlAv5lzxWXBXyItmMRhKLpVkAAQs9J5RvK8Fi5cqJ7LF0ByZe+66y7VqYYQ0nF4UvQTkBcyaxRWUgkkpaAVpPlEjz/XYdDXnyD7t7WIcHCBMEdEYPfA45Fy7ZVIHH0CjqwxoqexEdGRWovFlnVfxS1AXAnkodUi02z2uTgaMGAApk+fjldffRVnnXWW6uaVlpammma0t5DJfphfrLnEa1acLCIkSSICytdYotoyzxf5pK6KS9kPdyKqrYmZQL2Zc8VlQfCHSAsmcRjKbhXEz2L23//+Ny688EJkZmaitrYWo0ePVukF4oP42GOPeXnzCCFt0Vp6gqNwrTc24oedJYFzIZMorAhYGe1xEoVtQk0NsHgx0t55B2O3b2822xgbhz2njMWq48cgpkd3jOiVCmONsamAtevUpSy27KZ3VKRbPLqHDx+OK664Qq1L1u2NQib7Yf6yGgMaTCZEag83WRD7N+Vx3GhStl3ezid19fjJdFdvGlwRMx3hvuHODaArhXCfbjiA+oZGlNU2dKhICyZxGA5uFcSPYlZcDMQLUdrX/vbbb6iqqsKQIUOUwwEhxD84S09wjMCIkC2orFMFN307JXp8IWt3ZFciqdZc2DaisIp9+4C5c4H331ei1/GdyjvnYueZ52PbiNOwvdqsBFtaWQ0+/q2+qUdsVkqTTl2+trcyGAyqycywYcOaFMdKS/Arr7zS64VM9sP84mMsfsYiXKN0lvVKYw7lcazV+Cyf1NXj54roFVwRM+LU4Y5bh68jma4Uwv2wo1ht44AuyR0m0oJNHIaLWwXxg5g1Go3qh3j9+vU44YQT1IMQEng4RmCkA5NcQKXSPdtkETZyEbO/kH2/vQijtRkqz7IlQdCuIUqD4XAUtrGxbcG7di0gVn9ffuk8d/aEE7Hh1HPxW25/GGSf6htRWW9AfFQkOifH2jxi/6w0YWdRBM7pnY7chLbFqKuFeK2JevHgvvjii/Hzzz8jPj5eCdq+ffu2u5Cptfe0H+bvmRGHtHi9ypHNSNConNmKOqNqpas6ghW51xHMHVw5fq6IXncj1r4QNJ5EMtu6OWkwmVW6hxTAdaRICzZxSLcK4jMxK33Bu3btisa2LkSEEL/hLAIjQqa6vlG1cxVbJPFjTYlNUfPkIUJi2R952HSgAjqtxqlI9WiIUkRpVZVFxEqaQFtIpPaTTywiduvW5vPj4oDzzwcuuQTR3bvjWLMZPasNqDU0YM2OEug0EeiWFoeICA2McfEwJyahky7S7ahTW4V4rYn6H79cqiKv1kJZidCuW7euRTHraq6pRNYlt7alGwnHLmDiZyzpBvsP+c1KRF78jkXItpYy4Y2calcKGdsSvR0tZhz3W7qReRLJbOvmpLLWqP4mOJnni/0KVnEYDG4VJIjTDO677z7ce++9mDNnjmqWQAgJLJxFYKSVruRQ6nWRSIyOUJEhsWwSgVNSbcCW/CqLaMpKUFFNR5EqF2y3LuzuRGGFgweBd98FpLBU0g8c6dYNuPRS4LzzgPjDXgNWu6ki0cs1BqSnxMOQnApjfALMh9rBuusR2xYtifrfdxfihUfvxqqP37Ut27t3byxYsEClYrUn11T8dBev39/mjYRjxFOOjbU+Lj3ekjvcWspERxcHtSZ6O1LMONvvlDg9dhdVo6u6OXI9ktnWzUlZrUHlcUvXM2f4SqQFmzgMdLcKEuRi9j//+Q+2bduGzp07o1u3boiTSIkd0taWEOI/nEVg7HMo7QuB5KIgUdqKWqMSPkmxemg1Ec1E6kl90tseoiysQuGBImSa612LworKkt8LicKuWOFc9I4aBUydCpx4YpMGB832WROJksR0RHfJgFmr8VnUqaW8w8r8PXj7vuux56/NtmXHX3ARHnnqOWSmpajXtRbdbG3Y/fgeqapoz9UbCceIp6SYCK2ljwRicVBHiZkWW/AerFBRbPEzdib+WvpMtXVzIu/RPT1e/VvW21EiLdjEYchaD5LAELPnnnsuPzyEBDDOIjAJ0TpbDqX821oIJNFZMdYX0hOi1Dxn0aee6XEtDlFGNBiRUl0O4/4CGFLrgRQXcmeXLrWI2I0bnexADDB+vCUS26tX6+uSm+nUVOiNEYjI24VayQV2Ima9FXVyFvVevXwx3njiXtTVVKvnOn0UJt08A0edMh6LN5YiSlfuUnSzpWF3T3Id3e3gFojFQR0hZtra7635VfgzvwInJKRbLM5c/Ey1lRMsiIDuSJEWjOLQmwWZJLRxW8w+9NBDvtkSQnxAQHqq+hhnERj5r1d6vBqKl0hU97Q41f51f1ktiqrq0SkpWs13vGBbo09CE4FsNkNXW4PIqgro6mpRXd+AqAiz8nJtkYICYP58y6O4uPn8nByVCwvxqxbbrJaQ8yeOBJLmpNdb9jna9x6xzqLeNdWVmPvcv2xCtlPXXjjqkgeRMXAAUuKi3I5uOhOhHZHr6CiYzTCrGx1JT5Govr+Kg3wtZlq7UZAUHBHv+0pq1chFUozls+bqZ6qtnGB/iLRgFIfudkYk4YnLYra6uhp33HEHPv74Y1XQcNppp+GFF15g1y8SsASTOXhHRGAkRy81LgpajQapkg9YUoOGRhMyE6PRr1OiyhF0xBp9kouJHLst+0qQGm1CZE01NI0Ntgu7COKeGfEqF7AZGzZYorDLloklSvP5xx0HXHYZcMoplkYGLSFpBomJFhGr0/kl6uQY9Y6NS8B1M57BE7dcimPPOA99z7sJZQYNemfG26Li7Y1uWt9T9kUyMyQ9RKLqEkWX9Xgj6mwvmKXV7PaiKtV0QfKsJYqfEhcJvVbjl+IgX4qZ1m4UVH5xViLyKuqwraAKfTsluP2Zai1C7i+RFozi0N2RBhJ+uCxmH3jgAVX0NWXKFERHR2PevHm4+uqrsWjRIt9uISEeEGj5fx1NSxGYY3ukqhzMKDkeh/Ipv9laiC15lc3yOu2jT520jRgVXY+68jzk5TWo/FqJwkprWRGyiTE65eVqe72kEnz+OTBnDrB+ffMNjIqSdoKWSGy/fq3vjAhX6dIl0dpWxK67USdPovZS4Z6TqMP24lolUOXzVd/pKJw1421EZfbAn0U1yEqKVvZL3rI+ku2SKOF3WwtVPrPRZFZiVm4ceqTHobTG0O6os1UwHyyrxdaCStQYGlX3MGm6IOJZopMSr91eUGVbviMFkK/ETFtFUVGRGhyZnagcMuQ4ezuS6S+RRnFIwlbMimidPXs2LrroIvV86tSpOP7449HQ0ACdQ5SEhAbBOkQfiPl//sDVCMzIXulKtDpGNAtKq9HJXIdR2ghEHKhFZz1wRv8srNtbhv2lNcoRQZoSSERWNSVIjrGkDyxYYHEmKCxsvlFZWcCUKcCFF1oirK0RHW0RsZJS4OJ5cnWfPYnaV1ZW4rrrrkN+cSnOvv1prN9biryKejQ0mJDeta+ywZLiOolk/ranDIO6pqgIeHvTAcQbuLCyHtWGBltUXfZmT0kNdhZXY0jX5HZHneUYdUuLxce/HYDJZFbRemvKiURkG02SdmDEnB92qfMtNzKhMMrhSlHUgNxkXDgkB4VVhqD7LSQkXHBZhe7bt69Jk4ShQ4cq39kDBw4o71kSWoI0mIfog80c3Je46vdpH9EsLq5AUl0VhkabMbhLErLjDv9MiGCVyKMIWYnKiqhR7WI3bwaemAMsWWKJyjoydKglCnvGGZZ2sq0h1lvSqUsKwXzwefckai/dDidMmICth7xvR44aDXPfMSivMSI+WgsJxGYlx0Cv0yI5RofiaiM27CtTIl+iqp6mA1hvzExmM07r1wk7iqttw/+S89xoNiMjPkoJ+PYg2yfD6JIjKzHleqPF9UKaa0jXuPJaA+L0OtQ3mJEWH6Uiw6EwyuFqeopGY8kbDsWAQ7AGLQjxSMyapM+3w0VIIrJsoBC4eCpIg32IPtjMwQOB3JQYdOmdgKKYWtRXRSA6MtkiUp1c1KzermhoAL74wpJK8PPPzVcqvxdnn20RsUcf3foGWPNhRcS2JXbb8XmXC/fqbYX4Y18FEmIsHbUket9S1F7m/+9//8Mtt9yC+npLIVxCQgJycjqjNF6P7CMyVORSiqQSorT49q8ilV8p4vNgeZ2KZsp3yNN0APsbM9nGlHh9k8IsOTvltUav3JilxOrV8RIBK999sW/Tyfk3A8kxeuSkxKCkxqAit/GxoTPKEYxFUd4KOARz0IIQj8Ss/KhL0Zd9SkFNTQ3GjRsH/aGKYoE+swHazlSnQWFVPdbuKMa2/Epccnw3ZQYeikP0wWYO7lekIEuaFFRUIKKxERmiI9uy1pLl33vPkkpw4EDz+RkZwKRJwMSJQJrFhqi9+bBt4eoN2DtrduH5L7cp9wWJQErHMCl8G9kzDQNyU5pE7aPN9Zg+fTrek309hDQ/kCYIuuRszPlhNzonR6s8VkGaT1TVNSgxKF8NcQiTlIP2pAM43pjJ0H9i9GGxL8P/3roxk++D8hqOibQVmkn0/ff95Soqa2w0WyzddJqQG+UItqIobwQcgj1oQYhHYnbGjBlOPWdJ4OEoSOWv/EDJ0LBcoMQ7saTGiJtO7d1M0IbCEH2wmYP7ZchPWsyKKHWluYGVLVuAd94BPv7Y0nbWkYEDLd6wY8bYLLNaRArAJArrRj5sS7h6A7Zi40E8u9IiZK1CU9IDJB91+cZ89ZqjcpLV8f/hx7W47dpp2LFjh+19brrpJjz55JOIiopSnyH7GyZr8wkRgdJF7WCFDM0b1fz2pAN05I2Zs++NeBBLMZtOJ/82qBQTey/iUBrlCJaiKG8EHEIhaEGI18QsCcxcJHtBKj9W6/eUHq5OjolUF7+teZV454c9uHREtyZ336EwRB+M5uAdMuQnHbakxaw8nFlktfSar76ypBL88IPzyOqZZ1q6dImYbQtpciAiNtZ7ER9XbsB+21OKFZvyUVPXoLRzo8mSOiVIZLXG2IjvdxSrtrFrP5mDR2f/G8ZDxyg5OVkVv46XRg4tCD/VfKLaoL5j+kiNip7mpsSif3aCyqP1NB2gpRsz+W0R71P5fB+RnahcFnzxvdEcSrU4WGb53jh6EXOUo+PxRsAhFIIWhNhDGwIf4c9cJKsgldQCuQiKkM2wE9IJ0ZGob2hEcXV9s7vvUBmiD7Y8OJ8O+dXWWqKwEo2V0KEDIlaaFXRVVgIffGCJxO7b13yd4kQgaQTy6NSp9feXz5Y1H7atiK0HtHUDJp/V9fvKVL6n3II5HoJGGT7XAsWV9fh1bxlKdm2yCVlxbJk/f75q3d2a8JOCKEkvkM+YRHolGnt05ySbd6+n6QDOBKYUZ/2ZV6FcDsQ7WM7Z+7/u98rn2vF7Y/1MaDQmDOqS3MSLONBHOUIVbwQcQiFoQYg9FLM+wN+5SFZBKjmy1miR/d23XHQjtVp0To5pdvcdzEP0wZoH55MhP4k6iiAVEXuocMkZ4itqtdoyNJqRUbAPw1d/htxvl0MjItiRI4+0RGHHjrWkCrSG5MBKLqyI2Hbkw7ZFWzdgIi7l0WAJxDZDtK3kg0phk0QiX/3f/zD+b5tVJPaxxx5Tha/ORlnshd/G/eXKOkvITrZ0U7MXfnITKA0qxJPX3c+hvM/4QTlYvikff+wrw/bCahUbzU2LVc0uovVar/62OH5vpHDt262F6mZA8mWDYZQjlPFGwCFUghaEWKGY9TKBkItkFaRS7KW6BcUcLhiRNpUVdUaV+ybR2t3FNU3uvoN1iD6Y8+C8OuQntliHCrqUoG0FEbIrNuehvNqAgTt/xxErFqPTbz81X1CE6OmnW0SsWGy1de7FjUAErAjZVnL2vHWT0dYN2IFS+Yw3HN6dCKhcWRGxZrMJDaUHEZmao6Yd2z0FR3bPwrp16xAnKREujLLId/mkPun48Nf92FNSjaNzkqCJsBRJCSVV9Vizs1i5Hiz9/aDbHq3y/j/sLFHrOVher77TPTLi0T87EWlxlhsKb/+22H9vuiNOFYYFyyhHqOONgEMoBS0IEShmvUwg5CJZBam4Fkixl1x4JLVAIrIiZGUIVCJHIiSc3X0H2xB9sNPuIT8ZN5cUAsmFdbGgSy5YG7YeQLelH2Hod58i/mDzVAJDfCIiJ12MiMmTgc6d216pRGol/UCKujowBaetGzCzimMe/i42HkozaKwpR9GSp2E4uAXZ055HTFImjslJVPPshawroyxZSTEYN7CzWnZHYbVtG+SGYc2OIiXqB/ZMVutwZ5TG/v3lhlg6UuVEx6h8WfvGDL7+bQmWUY5wwBsBh1ALWhBCMetlAiUXSS4+Yr8lrgVS7CU5spJaIBFZEbLyYyU/Yi3dfYfCxStYzMA9HvKT9AGJwMpDCrVcZfdu1M1+C6cs+hD6uuapBBW5PfDn38bjj8En4pwRvS2esk6w5trWavXQZ6YjMyu1zePrqxSc1m7AxCtVWvZG1DeqaKxQt+d3FH3yFBqrStTzok9mode0WdDapUO4O8rSfBvqlLCV7/zxPdNUs4HWXu/s+Nq/v1h/iX9tfJQe8pWVNCJxUEiJTVGv9/VvS0eOcgTLd9dfeCPgwKAFCTsx+/zzz7u8QrGvcZcXX3wRTz31FPLy8jBw4EC88MILOO6449p8nRRmTJo0SVmELV68GIFAIOUiie2W2G+Ja4EUe0mOrKQWyAVCLqRt3X0HwxB9KJiBuzXkZ82FlSisM3uslpDo7fffW1wJvv4aMQ5VUOaICOQPHYkdY89H8VGDVfSytqxWFQA5QyKOPxcZ8JcxEtXaRkQdLET3/TWtHl9fp+C0dAO28UAFUmKlK5cBZlMjytcsRPnqeYDZkoahiUtG0gmTldDNTtS3a5TFfhvkM/jJbwfU905GRlx5vT2O7y/5qmInJmkGUTqtckyQfRInBekyFip5jsH03fUn3gg4hELQghCXxewzzzzj0tGSL4C7YlYMyG+77Ta8/PLLGD58OJ599lmMGTMGW7ZsQWZmZouv27VrF+644w6ceOKJCCQCLRdJBK3Yb1kvDpIjG+opA/4uwHMXl4b8cuIRUVBgEbJt5MI2QdIOPvrI4kqwbVuz2YbYOOw99SzsPHM8ajOzbdPrDA3QH6qUd9hY7G/QYnGxFkWNschOiUGGi8e3I1JwnN2AHZmdoKZt2bkfRUueQt3uDbZ50d0GIP3sO6GNT4HZZMJfBdXISY1v1yiLdRtkuk5rKZhy5/VWHN9f/F3T4vXIK69DRoJGtZuVLl0ibq2/LUdkJah/y3EMRmESbN9df+ONgEMwBy0IcUvM7ty5E77i6aefVl12pk2bpp6LqP3000/xxhtv4O6773b6GmmhO2XKFDz88MP47rvvUCYFLwFCIOYihdPddyAU4HmC0yE/DXBMAjA81YzOlYXurVDstObOBd5/35KG4IC5Z09sPO1crD5yJLI7N/08yjGUqvueGfHKpsvWbjYpCebkZKz+LQ9FDRFuH19/peBoNBrEFGzCwdl3oLHm0G9FhAZJoyYj6fiLEKHRIlIjBWER+HlPKbqnx6vvSHSkpl2jLO0dpXF8vfi7SoqQ5MuKO4PME/cFSSGS4y7HWz73b6/ZHZQRzWD97hJCgjhn1mAwKJHbq1evJi1u3V3HL7/8gnvuuafJhef000/HmjVrWnzdI488oqK2V155pRKzrSH91K091YUKJxd2bxOIuUjhcvcdCAV47b7pKK5EfWExYutqkBarc/3CLakDa9daUglWrnQewT35ZNWlK+KEE5BWXoeYzXmq3arkxUoUVtIKRMgmxugwODcZEeJMIO1m5aHRoKCizuPj668UnCeeeAJvPHCPEkuCNj4V6ePuRHTXY9Rz8R2QPTGazPj8j3zkV9Srbl3d0mKRGK1TN6aejLK0d5TG2evF7mtwbgq2FVZie0G1Si8QD1tZVnJoZZ3BGtGUbReLMylQraxvUJFoa4OGQP/uEkL8i9sqtKamBjfeeCPeeust9Xzr1q3o2bOnmpaTk9NiNNUZRUVFKsraycF0XZ7/+eefTl+zatUqvP7661i/fr1L7/H444+rCG5HE07R0EAiUArwPKKqChFlZegkqQGy+XFN8yxbRHJnP/nEImKl5awjUpl//vnAJZcA3bvbJmcnx+CM/lk2n1nJv5TUAonIDuqViewenS3NDuw+s+05vv5KwTniiCNsQja251B0Ovs2mGOSlEySgLMmQnxmofZdRLwM2yfFRmJLXqXaddlOT0ZZ2jtK09LrpVFCcqwex/eMxilHZKBHRpwqcMuvrA/aiKbKL95wQH0W46K00Gu1KqXC3q83oL+7hJDgErMSRf3tt9/w9ddf40xpY3kIiaY+9NBDbolZd6msrMSll16KV199Fenp6S5vr+Tk2kdmc3Nz0RGESzQ0kAikAjyXaGg43GJW/u0OBw8C774LLFxo8ZZ1RLpWiYAVIRtvyQO1RwSeFBWJHVXvzHjERGoQnRiP9K7ZiHCyfHuPr79ScKT5wbU33IwfDjSgsu8YNJgPe8DaN1KIj9ap1rMHSuvQJTkWPTPilBtBZmIUUmIjVb65u6Ms7R2lEQF6Yu8M5VN7oKxWtd6VCHp/u9fLsZNtC8bRCPs82f2ltYjT65AYpYNGE6FygyWlQiLRImgD7rtLCAleMSuuAVK0JW0e7X84jzrqKGzfvt2tdYkgFSuc/Pz8JtPleVZWVrPlZf1S+DVu3DjbNGt/dUl1kKIxSXuwJyoqSj1IeBBoBXhOkShhdbVFwMpfd1/766/A228DK1Y4t+QaNUqlEuCkkyyhRxc6fyEhHp26ZWN4Vg4i4mN9dnx9nYIjbWjfe+895XJiv23/fPBfuGn+OmzJrwAO3TPY+zlII4Vao+nw8RAhmRqDrIRoJajGD+qs1ufJKIunozT2Vf1WVwlJBxnRKw2DJAXk0OuDeTTCPk/2mJwk1Yktr6JOpXmI84rkBm8vqsKQ2OTA+O4SQkJDzBYWFjp1GaiurnY7oqLX6zF06FCsXLlSRU+s4lSe33DDDc2W79evH37//fcm0+6//34VsX3uuec6LOJKApdALMCzYTRaBKzkbbsbhZXOXkuXWkTsxo3N58fEAOeeaxGxvXu71vmrrhFJWemISU1FjVmDTSW1OLBuX6v5ld44vr5KwZEb3YkTJ+LHH39EbW2tyqm3sr2gSonClg676Nd6o8WHNi5Kp4a6JTJYVmNQArLOaFIivqNGaVqq6pfj/t1fhWqbrOfI26MRHenxap/jLvUSkuKiCtwkbzs6Uu3PwbI6/KEtVxZnNPInhHhFzA4bNky5DUiOrGD9kXvttdcwYsQId1enUgAuu+wytV7xlhVrLhHGVneDqVOnqlxcyX2Njo7G0Ucf3eT1yVKYAjSbTsKXgCrAs0ZhJQ3Axe5cTRA7rvnzLY/i4ubzc3IsqQQXXGBpH9vm5pjx64EK5Oni0eXobBnSUNMlqcDV/EpvHF9vp+AsWrQIV1xxhc3Z5Pbbb8eFF16IJHFgMJvx8+4S1Bga0ZqpmbSzNTaYkBarV61ntRERKKyqg8lkRmWdscPsrtyt6vfmaERHe7w6RpWlm5l0NZNmEJLDLeej2tCArqlxqsOaP4vY2MiBkBASs//3f/+Hv//979i0aRMaGhpURFT+/f333+Obb75xewMuvvhiFe198MEHVdOEQYMGYdmyZbaisD179qg7dkKCqgCvPVFYYcMGSxR22TLLuhyRpiJTpwKnngrYda1qlchIFGpjsDHKhOTUKJuQ9SS/0u/H9xDiVHLnnXeqRitWpCBVUqFEyAqyjVIlb21j2xIyu9FsRn2jCTskimsyqYYE4m7wxqodSI2P7hC7K3cdObw1GuEPj1dnUWURtNLVTI59aY1BpVicPyRHtQz2F2zkQEiIidlRo0YpJ4GZM2fimGOOweeff44hQ4YoKy157gmSUuAsrUCQQrPWePPNNxGo8E7ev3R4AV57cmEFEa3Ll1tcCZy5dUjut+SLSypBv36urzc6GkhJARISUCMRt8YSr+RXunp8ffU92LZtm7oZ/lVyiA8xYcIEvPLKKzYhK9TUN+BAmWvd0uoNjWqYWwSWuBqI7ZVEB/eV1qJHegKi9Vq3xJ0n++5JDmx7o+X+8nhtKaosf8WaS1oCH5WT5NfCNTZyICTw8cggVoqsxFGAhNedPMV5C8chWoMIicB6GoUtKbGkEcybZ0krcERGKaZMAS66CEhNdX29sbGW5eWvn9wefPU9kFbWV199tcqXF6TIU0aJZJqj2Ko+JA7tsS7hGKyV4yJ+XNWaCGXLJekGCXE61VBhV3E1hnZLcVncebrvnp6j9kTL/eXPHNA57mzkQEhoiVl3Gg0kii9lmBOKd/KhKM7bdRwKq2CurEJCfTW6x0aoBgPi2+oWmzdbUgmWLLEUeDkyZIglCnvGGSpFwGUSEiyRWInI+tHtwVffA+kSeN1119me9+3bFwsXLsTAgQOdLh8n4lBafNkhItaZPEqIiUR6QhQaGkyqeYREZtN1euV5KjmcMvQtjQraEnet7bsU4J3UNwMpsXqngrM958jT0Qh/OiIEVI57CDVhISSccEnMSpGVq3fG0gQhnAnFloyhKM49icKWVhuwavN+1BaXIVdjRIwGqNeYsKOwDkVVdaoBQZuCViK30p1LROzPPzefL6L17LMtRV3uFDXKZ0luJEXE6vV+j4T58ntw0UUX4bHHHsO+fftwySWX4KWXXkJ8C764Qqx6zzjsKqqB/DpFHBKz5paEr05rcTyLsERn5RV6nQZV9Q0q9aAtcdfavqfG6pVn7Pq9ZeiRHqc8Yx1vCv0RrfS3P3Og5GA7Esy2Z4SEEy6J2a+++qqJ/Y00Rrj88stt7gWSLysdwcRxINwJtTv5QBTnHZXuYB+FNVVUomBfPiJqa5XHZ3ystSuRFl1TY1VLWPFtzUqyFOQ0Q6rspbmBNDmQZgeOZGQAkyZJRaQYMLu+kVIcaW0362Jb6Y6IhPnye5CWloZ58+bhr7/+Ur9DbZ17+XwM656Gn3aXqXxYKQRTnb3sXAzk31adVl3fAJPZrERtvF4LQ6MJVXUN0Gk0iNRq2hR3Le273Ayt31eGeqNJdRpOi4tSTSuc3RR2dLQyEPyZA7HJjL9FPiHENVy6+o0ePdr270ceeQRPP/20MiW3cs4556jiLym8EJutcCbU7uQDTZx3VLqDvM9Ha3egtqgUudoGNBobkF9Tqyrc5YLfPztJtTy1Hgfx/bS2hJV/29i61VLQ9fHHlrazjgwYYHElGDOm1YhqM0S4ShRWCp08cPvwdSTMW98Dsem77777VCc/+7bXUogqD1eQfTrz6Cx8+1cR/thfpqrjxW5LRKxlPlTqQHZStDoOKTGR0Go0KKioQ3mdEQ2mRpTVGFUkVYqS2hJ3zvbdDLMy/xd7sOzkaJRUG5Rgbu2msCOjlYGeu+ovAkHkE0J8UAAmUVjJWXNEfGKvuuoqhDuhdicfSOK8Q9IdzGaYKyqw/vs/Yd5XhCNSY9UFrMxghiYCSImPQlmtUW1LYkyi7eImw8UiZFWnJhmjFhcOSSX44QfnQlTEq4jYQYPs3tpsW4esLy1O31w8iKPBIWeCQ2PgARkJ88b3QBqkiDvBn3/+qez/xLLPU5u+rmlxuPHU3njhy7+wU3XUMqlwrF4ToUSlzB/YJVndJNUYG5EYrUVqQhRK64yorG1EZoIG3dPiVKpBW+LO2b5Lrm1xlQFJMZEwNpibRHlbuynsyGhlIOeu+gtXRb4gzwMpRYKQcMJtMStdtsTJ4Mknn2wyXZomsANX6N3JB4o493m6Q329zRe2uKIWeXklyEw4nDKgExN9jUYVBMXrdSitNaK6vhHx0ZZjIgI0vr4ayQvmAu/NB/bta/4e4iwgaQQyqmEXZXTWXlavjUBOSuzhwjJxJBARG+d5F6pg+R7IfPk9uemmm1B3KJotN9EiaNvTHGV4zzQVfV22MQ+bD1So9IHU2EjUGE3qRkUir8mx+sOG/Y0mROs0SEuPRe+MOBWljWpoW9w523ex95Kovk6jU+vOSoxWUd5AG7EJ1NxVf9KWyBfe+2Vf2BfHEhJUYvaZZ57BBRdcgM8++wzDhw9X09auXavy1z744AOEO6E2XBco4twn6Q7iCytOHSJi7VIARJiKoJQLlhVpb5oSG6nabMo5bDRYxImat283ui1+D0euXQmdk1QCc//+qJowGeWnnoHo+FhLxNVZe9naBiWg5X0lGi6i6kCjFmNG5SK3sxuWXEH8PRDnlGuuuUZZb1mRRiriVtCnT592b5dEYKef2FNtg3jHCiJkpUWsdTslJ1r8TaWd7dGdEzFleFdE63Uuiztn+66RCLAJyKuoQ1J0pGrbar+OQBqxCcTcVX/TksiXz1C4FscSEtRiduzYsUq4/ve//1XDf8K4ceNw7bXXMjIb4MN1nhROBYo492q6g10UVikMB2SIXyKj8n5S4CVEIAK5KbGoqjeisKIekRFm5Pz2E/p/sRjZvztxJZDh8DPOQNF5F2NtWg/sL6uFYXMx9NqSJhFXOScSkRUh2+1QSoO8my41CandErGttB5rDlSjS3ZK0NwAefo9kOYHklawfft227R//OMfmDVrlmpl7S1EgFi3SW5cJNquhGSkFmU1BhQcupE5rmeax99Xx32vb5DUEQ2MDcDA3GTV5SqYR2zChdZ+MwOxOJaQcMWjpgldunRRbW1J8AzXWQundhRU4mB5vfohlojrBUNy0C09vtUfcNkPf4vzdqc7iGgVg32HKKwzJHIqglMio+JUYD1nUvB1TJIOUd99ilE/LEdGsRNXAnEVkOYGkyfjYGyKJeJaVN0s4mq18pJqdkktkPnQ6mCIT4AhIQnmQy1qOyVGBJX7hSffA/m8/ec//8Edd9wBwyG/Xeng9frrr6tRIF/lXcfotCipMSC/vF65HMTqLQL29L4ZKnLa3u+r476Lm8G3fxWoFq1i9RXMIzbhQFvFpoFWHEtIOOORmC0rK1MXms1i+g7gqKOOwhVXXNGkhSQJnOE66wV888FK7CutURdyyeGTH+ovNufjutG9MG5QTps/4BcN7eIzcd5W1NjjdAcRriJgRcg6icI6Q9YtkVMRnGK5Je4EqcV56PbZh+jxzTLo6yzD003o29fS4EDazcYcirj+kecQcW1u5XVMTiLqoEFiegaqxCfWocApUHIpffk9kJbVkh9r5dhjj1VpBj179vTqdthH0tJi9comS9wFREimx+tVCsBve8pUVL5LyuFz5q19l8+vNGQItBEb4lmxqUT0A6U4lpBwx20x+/PPP2PMmDGIiYnBcccdp6aJVZeYmH/++ecYIl2LSMBgvYCLkN2SV6F+kBOjI6GPjYTB2KjyAp9duRUZCVEqGumP/C9X7LbcSncQ0WrNhZWUAg+QFIAz+nXCnqUrkbV4Ibr+/hMiJMfWHnmvU0+1iNjjj2/iLiBFPtaIq7OojQjkPVUN6J6SgdqcaJTHRCHeSaV+IOVS+opTTjkF06ZNw+zZs3Hbbbcpv2q9OzZlLmKNpIkX8Ja8SiVk5XMvKSSC+L7WGhpwsKzOZ8PDgTZiQ5rjavrASX3SA6I4lhDigZi99dZbla+sOBroDpm0NzQ0KFuuW265Bd9++y2PawAhF01JLZCIrPzoZsRHQZoYNTSaodNq0SkxCgWVBsz5YTdO7JPe4flf7thttZmDGasB8vMtQtZReLpDTY3yhc2eMwfZ27Y1ny+2WBdeCEyZIvYeTlfhrIjMSkN0DEzpSSisNiEmNRndK81+L7DrSGS/HD9DL7zwAiZOnIi//e1vPs+7ls++1SbLKmQFGfqvrDcjKVbv0+HhQBmxIc5xNX1AxGwgFMcSQjyMzNoLWUH+/c9//lN5zZLAQi7gkiMrYlEKm8RSSjoQWQWFXMDF7/Kv/Co1BN6n02HvVF/nf3lSQNEssqWLQKa5HhEVhUCRZ1FYG/v3A3PnAu+/b4nqOiLD3hKFPffcNi2ymheRRcAYF6fyYU36KFSKzZPOqFqtBkKBXUdRVFSkunZNnTpVFXtZiYuL86mQtc+7rlKNEEyI1FqaXlgxNIh1lkaNXEguLYeHwxNXi03FqzicvruEhJSYTUxMxJ49e9CvX78m0/fu3YsEiViRgEIu4CIaJdppaIDK85KgpWSPamBGQ2Ojel6jiUBlXWOH5n95WkCh5skodE0lUFrZviisvPannyxdur74wnlerXTAkwYHI0e63G3LWkS2ragGCZ1SUR2bCF2UHgl6nXpP+6iN7I+/C+w6gu+++051Dty/f78awRk6dCh69erVYe9vzbv+dXcJtOL92igpLZbPu3xHKuqMyv9VehlweDh8cafYVH6XwuG7S0jIidmLL74YV155pbLLGSkXdwCrV6/GnXfe2aTFLQkMLBfwWHy1pQANJrPtgUP6Tzw25cc3Vm9CfLS2Q/O/3Lbb8kIubJPCsCVLLCL2kMVc0zePBaSSXlIJevRwe/URkZHI7JGDjypLcHCvAVGRpSpaK0b5cXodclNjmkRtQjmX0mQyYebMmXjwwQfRKN3RJHIdHY0DBw50qJi15l2Lr+/e0loUVtar1rLSkUuEbGykVjVOkHPA4eHwxd1i01D+7hISsmJWRKx8SWWYUHJlhcjISFx33XXqgkUCCzlXo/tm4PXVu1ArLTzVgPdhGiUqazQh0QT0zYxXQ2Ydlf/lcgTE3GDJhXXDkaBF8vKAefMAMeUvK2s+v2tX4JJLgPPPt+TGuosULqWkYG+DDt/v3I+EOD20kVrVzlTSO8prapGVFIULezQvpgvFXMr8/HxceumlWLFihW3aySefjLlz56Jz584dvj2WvOsc5ebxzV+F2HqwEqnxenXcpTuY2GZxeDi88cRbOxS/u4SEtJiVKuPnnntOVRxbzc0luhIrkSwSkCTHRALmwyJQgrLyM2w/OF9tMOLI7ETUNZR1WP5XqxEQkwmleUU4Os6MzJKIJk4BHqUSrFsHvP028PnnwKHoYBNklEHyYSWl4JDHq1uIqb+0q42Pt+QC/2Ipahucm6Jmi5iVYW2dJkJ1l9pdUoPjeqSGdPRm5cqVmDJlihK0guzrjBkzcP/990PryTH2UsHhDztL0Gg2o1tanIrOyimI1Eao7ePwMAnkxjeEEC/6zAoiXo855hhPX046kA37y2FsBHSHdJNV1spT7aFp9Q1m1aq1I3/AnUVA4iNMaCgtRUV+MTpHaTC0R5bngk8M+JcutYjYjRubz4+JsRRziYjt3duz95BCMBGxsq5WcoET5YbiENLaNJTN1GXE5pFHHsG//vUvJeyFrKwsvPvuu8qGy184Omd0To5FjbEBOwurEBOpwxlHdlKtbEP5BoO4DtMHCAlBMStNEVzhjTfeaM/2EB9QUm2EpMlK9Emv08IEswrURmikCCwChoZGGM2W5Tr6B1xFQAbl4Kc/9iBvz16U1dQoF4BeaYdbvrpNYeHhVILi4ubzc3IsubBir+VJow85FpKCkJICREX5tvVuEFJcXIyXX37ZJmTFpWDOnDnIzMz02za15JyREBWJY3KS1c3UXwVVSswSYoXpA4SEmJh988030a1bNwwePNh2kSLBgRQdyfC2XL9leFUr/9ZGiKRV7gbyg607tFyH/oAbjaqYK7e8HF2yNChOSEOdMVkVSokbgNsCesMGS0HXZ59Z1u2INPkQVwJpdODJMLdsj4hfEbGRTW2dvNp6N8jp1KmTEq/jxo1TEVqx7dO46ALhK9h6lBBCQheXxawUeM2bNw87d+5U3XouueQSpMrwKgl4RvVOUzmvEpWKPNQgywyTMoyXNAOJyqbERqrlfI7cCFVXW4qvpDmBQ1cstxHRKnmwImIlL9YRiZxKi1lJJXCwk3MZEb5WEeuCCPa49W6QYjQaUVdX18SaT7oESk59bgtNJTqacI+WE0JIKONyuOTFF1/EwYMHVZTlk08+URcpMT1fvnw5I7UBTnZyLE49IgORGvHWNKthfLEhkr/yXKbLfFnOZ4joLCoCduwADhxoImQ9oqQEeOklS5T1ttuaC9lOnSzTv/4aeOwxz4SsNAbJyLBYc6WnuxzNteYCyw2EDF9LgwSJgMtfeR5K1fLiOT169GhcdtllzX4HAkXIOkbLnRHq0XJCCAllIswe5gzs3r1bpR68/fbbquBj48aNiI+PR6BTUVGBpKQklJeXqwYQ4YIUvzz+2Wb8tKsEtfUmlTcr+bIxURoc2z0V9/y9v/crdOWjVVVl8YVtr3i1snmzpaBLPGKlwMuRwYMtqQRnnNFqKoAr9lqQz0c7BKccc2sxnUQFRSxJxDZUqqE/+ugjNUpTWlqqnj///PO48cYbEYjIz9x7v+xrMVouNxkSLb9oaJeQuMkghJBgxx295rGbgeTAyY++XAisRujEP8g5aKtgS8STCNZVfxVg9bZiVNQ2IDFGhxN6p2FUn8wWxZV1WHxfaa16LsVhkk/b6gX/UC6senjjsyF+xitXWlIJpFuXIyJazzrL4g/bHocNO3stbxCq1dAGg0GN0IhFn5Xu3btj+PDhCCXvUEIIISEYma2vr8eHH36oHAtWrVqFs88+W0VmzjzzTL8XeIRrZNbd6J8rwtd+3Z9uOIAfd5aguNoSBZXCrOE903DWMdlN1++LKKzk1b73HvDuu5bUBEckBWDiRMtD0gA8Rey1JBJLr+Q2kTzYiRMn4ueff7ZNO//88/H6668jOTnwnQBCPVpOCCGhgk8is9dffz3mz5+v8uDEpkuKwdLbIyCI130zpbhFcgJlKFUiUOIZ62mXKVn3m9/vsvSx12hUdyTpslBSY8DyjXkorKzD5SN7IDdeZxGw0mbWzSisCGsRyXXGxqYOBlu3Au+8I+PYlrazjgwYYEklGDPGkhLgKVKwJJFYJ/ZapDkLFy7E9OnT1Q+MtYHKM888o4pDgyWiGarRckIICWdcjsxK5LVr167Kmqu1H36J3AYyoRKZteYAbj5Y0cQ30xs5gPL6hT/vxce/HYDJZEampBUcaoKrIrsVtYgz1OKcHvEY18czk/mDZbVYt7cM+0trYGg0IyrChEE7NuCYlR8h6ue1zouxzjzTImIHDoTHyLbKeRcR62lObZghOfGSCyvesVb69OmDBQsWqN8DQgghJCgis1OnTmX0IoBwxzdTIk/uRKJk2U0HKpSFV3Ks3iZktQ1GRNdUIqGyEtW1ddi1pxbFnWPdttQSIbticx7KaxuQo2lAn9XL0WPZIsQX5jVfWETnhAnA5MkWhwJPkTQYq72WCGPiMtJ6VhohWJk8ebIStvZWXIQQQkhQNE0ggYOrvpk7CqrwzdZCt3IEZd3V4rcZYbHtiqqpQkxtFSIN9Wq+SWNGDSJQazSpFAF3kMiuRGQ1O3fi7DXLkPvNcujqm6cSmPv3R4REYaWwqz1pAGKnJbmc8vCkUQJRNz6vvvoqNm3ahFtvvVWlGXFYnhBCSKDAEFWQ4kqXqXpjI77aUgijyeRyTq113YkRJlSWlyKpwohoh9o+Y4NqiIuYSI3KdXUZkwkVK77EMf97A7kbf2k22xyhwb5hJ2D9yeMw4uIxSE9oRxcyib5KFFaisUFSnBgoVFdXY+vWrU1SCGSoZ/369dAxqk0IISTAoJgNUtrqMiVita7BBJ22EX0yE2zzRfjK8pJTK1XdUgxje62kT1dWIrO0FENRjvK6KlSZzYiKj2qSM1tZb4Q2IgK9MuNV0VabiMvB4sXKWitp1y4kOcw2xCVg9+lnY/eYc1GVmon9ZbUY3GDy7MBIHqykJrTTIzaUcMfBQvyipRlKfn6+Eq9dunSxzQtnIevOMSSEENKxhO/VKchpyzczUqtBtM6MzsmxbebUdoqKOOxIYJI2t8CQrinYVVyDTfvLkF9eh6RYS7FUeY0RJrMZR3ZOVMu0ekHfs8fiDfvBB5YWtg5U5HbHzr9fgP0nno7GKEsUtq6+QXUmcyvia/WIlUgs8zg9sqISsTZ79mzccMMNqK21eApfffXVWLp0KcId2nkRQkhgQzEbxIgYkVQBq1gRYSpiRVwMclNi8MXmgpZzanUaVSFo2LELiG0+DJ+dHIPzB+cgNTYSv+0rR1GVJV82OUaPgbnJOLmvtL+Nab5iie6uWWPp0iWtZB3MMswREcgfNhJrRo6FdsTxiLBLARBBJe/TM8PFiK9Aj9h2W7dVVlYqe625c+faXjtgwAA8/fTTCHc8sb8jhBDSsVDMBvnQZEu+mUrY/lXULKdWY6iHvrIC9aVlSKk1IMYkF2LnBVYiVice1xWnH1mPvHJLkVZWUrRyL2i2H9Io4eOPLZHYbduar0wiphdeiIgpU2BOSEfl5jxUlNaqdUkUVgrJRMhKV7LBuW3Yfck8WZ9EYukR2+JnT25yRITZW7c5ppkU7d6imiBIjqyVa6+9VgnZmBgnNythhKvHsEmqDgkrmH5CSGBAMRsCQ5POGiE0yalNi4W+thqRVZXQGurVD/CByjqXIqCy7oyEaPVwyv79gET03n/fkqrgSI8ewKWXAuPHW6KoIpIBnNE/y+YzK40TJLVAtkeErNOIr0B7La9Zt8nnY96br+Gz155Qnf0Esdp67bXXVM4scc/+zpVGJCS0YPoJIYEDxayPhyYlktnQaEZVnVF10xKP1fOH+H5oUuXU5sSjfNc+lPy+BxlxkYiM1KLanQhoS0jqgLQzlVSCL75QebbNGD3aImJPOMGpm4AIVjk2TjuAOSKFR1Z7LToTeMW6bf7TD+CbT+bbng8dOlQ1QejVq5drbxAGuGp/J8uR8ILpJ4QEFhSzPhyaTIvVY0teJYqrDGgwmZQDwN7SWhWlvWZ0L98MTR5yJEBZGXLr6nB211isizC4FwFtCYngLVliEbF//tl8fmwscMEFwJQplohsG8j+t9pwQVrVSioBnQm8bt12xLBRNjF7yy23YObMmYhiyoZbx1CKLWW+LEfCB6afEBJ4UMz6aGgyRqfF+n1lqDE0IikmEpHaSBgbTSisrMeXfxZgeI80DO6W4r03NhiUgLU6EngUAW2J/Hzg3XeBBQuA0tLm87t2BS65BDj/fO+4CUiupthrHUpLIN63bssZfDLOm3YDpp5zGsZLCghx+xiKa4gUW8pyJHxg+gkhgQfFrJeRIUcRjSU1BiVkMxIOe7RG6bTITo5WXbnW7CzGoK4eDvPbR2HFw1VE7CE7JY8ioC2te/16SxT288+Bhobmy4wcKX2OgZNO8k53rfh4i4gVmy3iNeu233fsx64fl+OsiVfYrNuSYyPx3KwnWInfDvs7OYYyn8Vf4QXTTwgJPChmvYwMOTaazMgvr1cXO6uQtWJsMCMxJlJ5t3pcOCJRWKsvbKOX8/Vk3Z99ZhGxf/zRfL4IzXPPteTD9unT/vcTMS9pBJJOIGkFxLtOF4a9eOS2qSjOPwCDJgrHjzlfRRN9UYgYbvZ3PIbhCdNPCAk8KGa9jAw5ikD9eVcp0uObijMZmqyoM6r5Wk2Ee4Uj1iisiFixwfI2hYXA/PmWR1FR8/mdO1tyYS+80FKI1V4kkmst6vJGVJc0wWQy4cknn8T999+PxkM3PGs/+B+eu+8GdEk73BGOeG5/x2MYnjD9hJDAg2LWF0OTvdLw9ZYCHKyoQ0KUDpoIwGQG6hoaERepQ3ZStFrOpcIRo9EiYOXh7Sis8PvvliisRGPlvRw57jhLFPbUUy2uAu1F2s1KFDYpie1mfeRjWVBQgKlTp2L58uW2aSeddBLeffdd5KQntv8chiHO7O9IeML0E0ICD4pZHzAoNxlDuiZjxZ8FKp2gwWSGThuBjPgoHJUdp+x+Wi0ckSistH+VXFhfRGFFtEoerDQ4WLeu+XwZ7h83ziJi+/f3znuy3axXfSxbihR+/fXXmDx5Mg4ePKjWIdMkOvvggw9C542bEUII008ICTB4dfMB+0prYTSZVVTWrAcSonUqraCqrgG/7C7BkG6pzgtHpMjK6kjgrOCqvZSUWBwJxJmgoKD5/E6dgMmTATHNl0Isb8B2s173sRS7NyksrKg12kRu15RorF30Gp576nGVYmA5nZ1Ui9rTTjvNO+eSEGKD6SeEBA4Usz7yIJS0gtP6dcLOompliyU+s+JVKcVhIkQksmbDGoWVv75g82ZLKoF4xEqBlyODB1tcCc44w5IG0F7YbtZnPpapsXqs/DNfVdWf1DdD/RWR+9LTM/Htwv/Z1nH66afjnXfeUYKWEOIbmH5CSGBAMetDD0IlPuL0qKxrUB6zkVrphGVWEbWC0mp0wiFXAme5qu1FIrtffmkRsT/91Hy+iNaxYy2pBMcc4533ZLtZn/pYmmHGjuJqaDUa6DQRKhtFIv7yOZt4+dVY98UiVJUV4ZGHH8Y999wDLQvrCCGEhAEUsz72IBQxIlZcNmpqUJufB+Nf9YB9dNZbSIT3/feBuXOBAweaz09PByZNAi6+GMjI8M57st1sh/hYyk2RdJNLi9ejqt5yg2QlKTUd1zz0PKrqjZh+9YUUsoQQQsIGitkO8CCMaGxEZFUlIqsrUFtdh3ijEdGRad5947/+shR0ffQRUFfXfL5EXyWV4MwzvefnKtFdya1lu1mvOhZER2qctlE1NphUukplUQG+n/sMBt73OBCfZZs/YNjx2FVc7Z7lGyGEEBLkUMx2gAehvqIM+spyJVqKqurRMyNetZRtN2LV9c03llSCNWucR0zHjLGI2IEDvWeFFRVlEbHeaF0b5jhzLOiWFovEaJ3qPGXfRjVSp8HBDaux+o1HYKiuwNwn/4k7n34TGknvkKC/oUG93iXLN0IIISREoJjtAA9CncmM6voGJWQTY3QYnNvONraVlcAHHwDvvAPs3dt8vvi4ShqBOBN4swAoJsYiYsWhgPjMsUDcCuTjIZ8R62dIH2HCB/+dia8WvmF7/f5d21BalI+0zGx1oyQtVlu1fAsDPPHlJYQQEtxQzHZAC8yq0hok1hlVRFaEbHayh7myO3ZYBOyiRc79Z8UTVqKwZ51liZ56CxGvImJFzBKfOxZINFZEbGZiFFJiI7Fu41a88/ht2L/1d9vr+x1/Kqbf9xSS09JQWWdUQlbaJzu1fAsTWvPl9VbrXoplQggJPChmO8CDsP5ANGJrKlVqgdtCQzxDV62ypBJ8913z+TLELJZaImKHDvVuV634eCAtzbvCmLTqWGBv91Nea0TknrV46ZbrUCGuF6qfhR73Pfx/OOK0i7C7uEblyIpok4isN0VbKPnyyiiJ3Fy299h0hFgmhBDiPhSzgUpVlaWYS4q6du5sPl/awV50ETBlCtC5s/fe1+oRK5FYbxWKEZcdC6zozA348MVH8eOSd23TevbsiYULF2Lo0KGMELoZ5Zb5cnPpadS6I8QyIYQQz6CY9RH2URxtcRESayuRkxLbdpqB5MBKKoHYa4mgdaRPH4s3rLSbjfXixVMu8iKQJd/WG40TiNuuF/b89N3KJkJ2woQJeOWVV5Ak54hm7W5HuWW+LCf/DkSxTAghxHMoZn2AYxQn2RQLM+qxo7AKRVV1OKN/VlNBK+73P/xgSSX46ivLc3vkAnnKKRYRO2KEd1MJJE0hOdkiYmmy71fXC3vx1HnQaJx01gX48YsleP755zF9+nQKJQ+j3NIlzVoUFohimRBCSPugmPUyzqI42poI6PVadE2NxZ6SGqzbW4aspGhEiB/sxx9bUgnEJ9ZZzuqFF1pSCbp29e6Gim2XCFiJ9B2ydiL+c71Ij9UhITZa2WtZi7n+99//wlg+A8d4q0NbmEa522tZ5muxTAghpH1QzHqZtqI46fFRKNu2C7WfvIXYxR9a2tk60r27JQo7frxF0HoTyYMVEctGBwHjevHeyh/w6E3XYfTE6zBk9JkOxVyZ/t7MoI9yt9eyzNdimRBCSPugmPUyLUZxzGakbt6AIUs/RPZPq6AxH25FauOkkywidtQo70dLxVZLRKy3xTFpF19+8h4euP561NTUYOl/H8Jtk87Escd0YUpBO72dJVpqH+Vuj2WZr8UyIYSQ9kEx62Ucozia+jp0+/wj9Fk0F0m7tjV/gRRxnX++JZWgZ09vb45FvIozQTRz+QKJqqoq/OMf/8Dbkid9iG5dc5EcraGQ9YK3s4yQeMuyzNdimRBCSPugmPUy9lGc0ZtWY/jMexBdVtJsOXNuLiIuuQS44ALvt4WVi6qkEUgklvZaAceGDRuUO8GWLVts06TA67nnnkOMQ2MKmvR75u3s7Q5gvhTLhBBC2gfFrJexj+JsjknHaAche+CowYiedjlSx57hffcAOhMENCJMxV7r5ptvRn19vZoWHx+vpk2aNMljk34K3ubuAsEmlgkhhHgOxawPsEVxkmKwZ8BxyP7zN2w7/lQUnjcBfUYORqqn7WxbQkSxRGHFYovOBAFJRUUFrr76aixYsMA2bfDgwep5H/EO9tCkn12pQkcsE0II8QyKWR9hjeIUz34N+YZGZOi16OdJO9vWkOYGVnstRocCXsx+8cUXtuc33HADnnrqKUQ7yWV21aRfllu8fj+7UhFCCAlrKGZ9iLLiGnIMUFgIlJZ6b8VRURYRK7m2FLFBQZcuXfDWW2/hkksuwWuvvYYLJFe6HSb9OwurUF5jZFcqQgghYQ/FbDAhUTxxJqC9VsBTWloKjUZjaz8rnHXWWdi5cyeSJR2knSb9O4uqUVHbgG7pcexKRQghJKxh66dgQOy7unSxdAGjkA14fvjhB5UPe9VVV6lUAHvaErKO9m7OEEsobUQEGs3mVgWvCGJ2pSKEEBLqUMwGMiJcRcCKkBVBSwIak8mEWbNm4cQTT8Tu3bvx/vvv49VXX/XY3k0KvRzFsNWkv3tGHFJiI50KXjPMKKisQ52hEVX1DUX3q/IAACYFSURBVM3WQQghhIQSTDMINCRHUnJhJZ2AHrFBQ1FRES677DIsXbrUNm3UqFEYO3asT0z6xxzZCT/sLGnWlaq02oBthZXYXlCNxJhIfLrhADYdrKAXKiGEkJCFYjZQEEstya+Uwi4dT0sw8e2332Ly5MnYv3+/ei7C8p577sHDDz8MnYfn0hWTfnkfe8FbbzThp90lKK6qR1p8FI7tlopovbaZnRchhBASSlA1+RurR6wIWW83USA+pbGxEY8//jhmzJihUgyEzMxMzJkzB3/72998btJvL3jF3eCPAxUoqzGiX6cE9MxMQGqcXi1nb+cl66PJPyGEkFCCYtZf0CM2qKmqqsL48eOxcuVK27RTTjkFc+fORXZ2doeZ9FsF78YDFcrdYFCuHlmJ0U0Eq3Ud1ggvTf8JIYSEEiwA84e9loidHj0sHbvoExuUxMXFITExUf1bLLgkpWDFihVeFbKuImJVGipISkFmQlMha4XuBoQQQkIVRmY7AhEXcXGWdAK6EoQEIhhff/115Ofn47HHHsPJJ5/s1+2xt/MSYeuIFI/JfFmOEEIICSUoZjuCtDRGYIMcKe4Su62RI0fapqWkpGDVqlUBkYNqtfNydDewt/OS4jFZjhBCCAklAiLN4MUXX0T37t1Vn/rhw4dj7dq1LS4rvp3i4ylCQh6nn356q8sHBAEgdojniN3WoEGDVI7sgQMHmswLBCFrb+cltl1S7FVZZ0Sjyaz+ynOZLvMDZXsJIYSQkBGzCxYswG233aYqwn/99VcMHDgQY8aMQUFBgdPlv/76a0yaNAlfffUV1qxZg9zcXFU5brVFIsRbGI1G/POf/1RtaMVHtrCwEHfeeWfAHmCru4FEYMtrjdhVXK3+ynPachFCCAlVIsx+bg8kkdhjjz0W//nPf9RzsTgSgXrjjTfi7rvvdskeSSK08vqpU6e2uXxFRQWSkpJQXl5uK+AhxBFJKZg4caJqTWtl3LhxmD17NtIkbSSAka+0uBbU1Deg2tCIOL0WsVG6JrZehBBCSCDjjl7za86swWDAL7/8ogzmrUhluKQOSNTVFWpqalQELVU6Zjmhvr5ePewPDiGtsXjxYkybNg1lZWXqeWRkJJ588kncfPPNQSEGZRsNDdJAoVTZcdU3mFTxl+TUWhsuEEIIIaGCX9MMZOhWIqudOnVqMl2e5+XlubSOu+66C507d1YC2Bliai/K3vqQqC8hzpCbnltuuQXnnXeeTcj26NEDq1evVtODQcgKe0tqsGjdPmw+WIHkWD26p8Wpv1IcJtNlPiGEEBIq+D1ntj3MnDkT8+fPx6JFi1TxmDMk6ishautj7969Hb6dJPCRofmxY8fiueees0278MILVR63pMEE035Ip6/SGiN6Z8Qrmy6txuJDKy4H0iFM5vs5u4gQQggJDTGbnp4OrVarvDrtkedZWVmtvnbWrFlKzH7++ecYMGBAi8tFRUWpXAv7ByGOSNT1mmuusX1m/vvf/2LhwoVIlsYWQYTkykpqQXZS87a1jp3ACCGEkFDAr2JWr9dj6NChTVqCSgGYPB8xYkSLr5P8xUcffRTLli3DsGHDOmhrSagzYcIE9bmSoq/rrrsuaNIK7Kk1NKoc2ZhI580R2AmMEEJIqOH3NAOx5RLv2LfeegubN29WIqK6uloV4AjiUGBfIPbEE0/ggQcewBtvvKG8aSW3Vh5VVVUINKxm9RIJk78c2g0ctmzZojp3OXL//fcrT9lgxb4TmDPsO4Hx80kIISQU8HsHsIsvvlj5dz744INKlIqQkIirtShsz549yuHAyksvvaRcECSf0R7xqX3ooYcQKEiRjeQmspo88JgzZ47tpqlbt2645JJLECq42gms3tiI937Zx88nIYSQoMfvPrMdTUf4zFqryaUIR3IXZchXImV55bWqExMN7P2DiNcbbrgBb775pm3acccdp2zg7G+Ygh3r50+KvSRHVlILJCIrQlY+f8N7pOHHnZYiMX4+CSGEBLteC50reIDAavLA5I8//lCuBPZC9oorrsCXX34ZUkK2rU5g4wflYHdJDd0OCCGEhAx+TzMINdypJpd/E9/fXLz++uuqo1xdXZ2aFhcXh5dffjmk0gucCdouKTHqcyZFYZIjKykI/HySjuxCZ//ZC8aCSkJIcEAx64dqcuuPPPEtlZWVym5r3rx5tmkDBw7EggULcMQRR4T84bfePNnDzyfxNawXIIR0NKE1vhpk1eTEt9x+++1NhKwUfYntVjgI2UD/fNJJITRh9zlCiD9gZNZP1eSyXHvgMF7biGfsJ598gpqaGrz22mu46KKLEO501OezNRi5C496Aetny9p9bnthlZov6S9MOSCEeBOKWS8jP9IjeqYp5wL58XZWTS7z2/NjTjHgGmLv9uGHH6q/PXv29Ph4hxId8fn0xOlDxLVsE50+ghfmYxNC/AXTDDq4mry9F2sO4zln7dq1OOmkk1BUVNRkunSSo5DtuM9na9DpI7Rh9zlCiL9gZLaDq8nbE/HiMJ7zY/LMM8/grrvuQkNDAy6//HKVWsBhzI7/fLYFI3ehjX0+tqQWOMJ6AUKIr6CY7eBq8vZAMdCU4uJiJV6XLFlim1ZSUqIMlpOTk7123EMVb38+24JOCqFNIORjE0LCE6YZBBEcxjvM6tWrVetjeyEr0dlvvvmGQjZACRQnBeLbfGzJu5Z87Mo6IxpNZvVXnreUj01nC0JIe2FkNojgMB5gMpnwxBNP4IEHHkBjo0UUpaenY86cOTjzzDP9fYpIKzByFz752OJaYG0OIzcoEpEVIeuYj81iVkKIN6CYDSLCXQwUFBTg0ksvxeeff26bNnr0aLz77rvo3LmzX7eNBL6TAgmsfGw6WxBCvAXTDMJgGC9UWLZsmU3Iyj4++OCD+OKLLyhkgwh/OSkQ/+Rjy823/HWWWmDvSSsFY1pNhM2TtqzGqObLcoQQ0haMzIb4MF4oIVHZ5cuX48svv8TcuXNx6qmn+nuTSJA4KZDAgsWshBBvQjEbhISLGKisrERCQoLtuezfyy+/rDp6SSMEErx0tJMCCSzobEEI8SZMMwjRYbxgRyKwvXv3xuLFi5tMF3FLIUtIcENnC0KIN6GYJQGFND645557lDOBFHxNmzYNu3fv9vdmEUJ8UMwqxYCOebHWYlaZH6rFrIQQ78I0AxIw7N27F5MmTVIeslZOOOEExMXF+XW7CCHehc4WhBBvwshsGBHI5uTSglaaIFiFrE6nw6xZs/Dxxx8rH1lCSGhBZwtCiLdgZDZMCFRzcoPBoNIKnn76adu0bt26Yf78+Tj++OP9tl2EEN8TLsWshBDfQjEbBgSqObnkwk6YMAFr1661TTvvvPPw+uuvIyUlpcO3hxDS8dDZghDSXphmEOIEsjm5XMT++usv9W+9Xo8XXngBH3zwAYUsIYQQQlyGYjbEccecvKPp2rUr3nrrLWXB9f333+OGG27g8CIhhBBC3IJiNsRxxZxc5styvkaisBUVFU2mjRs3Dhs3bsTQoUN9/v6EEEIICT0oZkOcQDEnf/fddzFkyBBcffXVzVIaJMWAEEIIIcQTKGZDHH+bk0vr2auuugpTpkxBVVUVFixYoIQtIYQQQog3oJtBiONPc/JNmzYptwJJI7By2WWX4dxzz/X6exFCCCEkPGFkNgzoaHNyifjOnj0bw4YNswnZ2NhYvPnmm+oRHx/v1fcjhBBCSPjCyGyY0FHm5JJKcP3112POnDm2aUcffTQWLlyI/v37e/W9CCGEEEIoZsMIX5uTFxQU4MQTT8TWrVtt06ZPn47nnnsOMTExPntfQgghhIQvTDMgXiMjIwP9+vVT/5ZUAin0euWVV8JayFqL7MTLV/76ozkFIYQQEsowMku8GvmVXNlp06Zh1qxZ6NOnT1gfXWkjLN3VRMiKl69YoIlzhBTc+aN9MCGEEBKKUMwSj/nll19QXV2Nk046yTYtNTUVH330UdgfVRGyi9btU22EpfuaNK0Qr98/D1YoZwlfFN4RQggh4QjTDIjbyFD5888/jxEjRijrrby8PB5Fh+MjEVkRsr0z4hEfpYNWE6H+9sqIR1mNUc1nygEhhBDSfihmiVuUlJTgvPPOw8033wyj0Yj8/Hw88cQTPIp2iGOEpBZIRNbRLcJahCfzZTlCCCGEtA+KWeIya9asweDBg5ukEdxxxx148skneRTtEOszyZGV1AJnSNMKmS/LEUIIIaR9UMySNjGZTEqwiu3Wnj171LS0tDQsWbIETz31FCIjI3kU7RAPXyn2khxZZ0j3NZkvyxFCCCGkfVDMklYpLCzE2WefjbvuuguNjRZxNmrUKKxfvx5nnXUWj54TpBmFuBZIoZdjXqzVqkvmy3KEEEIIaR8Us6RFGhoaVDT2s88+s+V73nffffjqq6/QpUsXHrkWkOMk9lvJsZHYXliFyjojGk1m9Veey3SZ7+3ua4QQQkg4QjFLWkSn0+HBBx9U/87MzMTy5cvxr3/9S00nrSO2W2K/1S87EeW1RuwqrlZ/5TltuQghhBDvQVVCWmXy5Mkq1UAsuLKzs3m03BS0XVJilGuBFHtJjqykFjAiSwghhHiPCHOYmV1WVFQgKSkJ5eXlSExM9PfmBBRffPEFVq5ciccff9zfm0IIIYSQMKbCDb3GyCxRubEPP/wwHnvsMVWgNGDAAEyaNIlHhhBCCCEBD3Nmw5x9+/bh1FNPVbmw1iD94sWL/b1ZhBBCCCEuQTEbxixduhSDBg3Cd999p55rtVrVzWvevHn+3jRCCCGEEJdgmkEYIm1o7733XsyaNcs2LTc3F/Pnz8fIkSP9um2EEEIIIe5AMRtm7Nq1CxMnTsSPP/5om3bOOedg9uzZSE1N9eu2EUIIIYS4C9MMwozbb7/dJmSlDe2zzz6rcmQpZAkhhBASjDAyG2a8+OKLWLVqFeLi4rBgwQIce+yx/t4kQgghhBCPoZgNcUwmEzSawwH4rKws1Z62Z8+eSE5O9uu2EUIIIYS0F6YZhDASeR04cCBKSkqaTB8yZEjQCVmxDcuvqMOuomr1N8x6fRBCCCGkBRiZDUFqa2txyy234JVXXlHPr7jiCixatCho26juLanBmh3FSsjWN5gQpdOge3ocRvRMUy1jCSGEEBK+UMyGGH/++ScmTJiA33//3TYtPj4eBoMBUVFRCEYhu2jdPpTWGJGdFIOYSC1qjY3482AF8sprcd7gLhS0hBBCSBjDNIMQ4u2338bQoUNtQjYmJgZvvPEG5syZE5RCVlIJJCIrQrZ3Rjzio3TQaiLU314Z8SirMar5TDkghBBCwhdGZkOA6upq3HDDDXjzzTdt04488kgsXLgQRx11FIKVgsp6lVogEVnHFAl53ikxWs2X5eTfhBBCCAk/GJkNciQKO2zYsCZCVnJkf/rpp6AWskKtoVHlyEpqgTNi9To1X5YjhBBCSHhCMRvkrFmzRuXJCuIdKykFr7/+OmJjg78wKkavVcVekiPrjBpDg5ovyxFCCCEkPGGaQZAzffp0fPnll0rQSlpB3759ESpkJkQp1wIp9pIcWftUA6tVV7/sRLUcIYQQQsITitkg4+DBg8jOzrY9F4H36quvqta00dGhlTcq+yb2W+JasL2wSuXFSmqBRGRFyCbHRqr5wWo5RgghhJD2wzSDIEEikdKKtkePHliyZEmTeQkJCSEnZK2Ij6zYb0kEtrzWiF3F1eqvPKctFyGEEEIYmQ0CysrKcOWVV+LDDz9Uzy+77DL89ttv6NKlC8IBEbRdUmKUa4EUe0mOrKQWMCJLCCGEEIrZAOfHH3/ExIkTsWvXLtu0qVOnIjMzE+GE1YqLEEIIIcQephkEcFrBv//9b4waNcomZFNSUvDRRx/hmWeegV6v9/cmEkIIIYT4HUZmfSxIPRkaLy4uxuWXX94kN3bEiBGYP38+unbt6stNJoQQQggJKihmfcTekhrValU6VImxv/ihis2UVN9LDmhL/PzzzzjvvPOwb98+27S7774bjzzyiHIsIIQQQgghh6GY9ZGQXbRuH0prjKoVq3SwEuN/8UsVm6nWqvAzMjJQVVWl/p2enq6aIJx55pm+2ExCCCGEkKCHObM+SC2QiKwI2d4Z8YiP0kGriVB/xfi/rMao5styzujWrRtmz56Nk08+WTkWUMgSQgghhLQMxayXkRxZSS2QiKxjfqy1Il/my3LCt99+i8rKyibLjR8/XnX16ty5s7c3jxBCCCEkpKCY9TJS7CU5spJa4AzpYCXzq2oNmDFjhorAXnfddc0itfRQJYQQQghpG4pZLyOuBVLsJTmyzpBWrPXlRZh60ThV1CUidu7cufj000+9vSmEEEIIISEPC8C8jNhviWuBFHtJjqx9hFWE6+qvV+Kj5+5FRWmxmqbRaPDoo49i7Nix3t4UQgghhJCQh2LWy4h4FfstcS3YXlilcmQltaCyphbzXpqFVR+8bls2JycH8+bNw4knnujtzSCEEEIICQsoZn2A2G6J/ZbVZ/avHbvw/qw7sXvTOtsyZ511Ft58801lv0UIIYQQQjyDYtaHgrZLSgx+XL8Jf588AWWlJZYDrtNh5syZuPXWW1WKASGEEEII8RyqKR+nHBw3sD9OGDnC5iG7atUq3H777RSyhBBCCCGhImZffPFFdO/eHdHR0Rg+fDjWrl3b6vLvvfce+vXrp5Y/5phjsHTpUgQqEn2VdIJrrrkG69atU/tHCCGEEEJCRMwuWLAAt912m/Jc/fXXXzFw4ECMGTMGBQUFTpf//vvvMWnSJFx55ZVKHEqDAXn88ccfCFQkL/bll19GSkqKvzeFEEIIISSkiDC31Fe1g5BI5bHHHov//Oc/6rnJZEJubi5uvPFG3H333c2Wv/jii1FdXY0lS5bYph1//PEYNGiQEoxtUVFRgaSkJJSXlyMxMdHLe0MIIYQQQtqLO3rNr5FZg8GAX375BaeffvrhDdJo1PM1a9Y4fY1Mt19ekEhuS8vX19erA2L/IIQQQgghoYFfxWxRUREaGxvRqVOnJtPleV5entPXyHR3ln/88ceVsrc+JOpLCCGEEEJCA7/nzPqae+65R4WorY+9e/f6e5MIIYQQQkgo+MxKYZRWq0V+fn6T6fI8KyvL6WtkujvLR0VFqQchhBBCCAk9/BqZ1ev1GDp0KFauXGmbJgVg8nzECIs3qyMy3X55YcWKFS0uTwghhBBCQhe/dwATW67LLrsMw4YNw3HHHYdnn31WuRVMmzZNzZ86dSpycnJU7qtw8803Y/To0fj3v/+tWsLOnz8fP//8M1555RU/7wkhhBBCCAk7MStWW4WFhXjwwQdVEZdYbC1btsxW5LVnz54m3bJGjhyJd999F/fffz/uvfde9OnTB4sXL8bRRx/tx70ghBBCCCFh6TPb0dBnlhBCCCEksAkan1lCCCGEEELaA8UsIYQQQggJWihmCSGEEEJI0EIxSwghhBBCghaKWUIIIYQQErRQzBJCCCGEkKCFYpYQQgghhAQtFLOEEEIIISRooZglhBBCCCFBi9/b2XY01oZn0lmCEEIIIYQEHlad5kqj2rATs5WVlepvbm6uvzeFEEIIIYS0odukrW1rRJhdkbwhhMlkwoEDB5CQkICIiAiv30WISN67d2+bfYRJYMNzGTrwXIYWPJ+hA89laFHhZQ0k8lSEbOfOnaHRtJ4VG3aRWTkgXbp08el7yEmkmA0NeC5DB57L0ILnM3TguQwtEr2ogdqKyFphARghhBBCCAlaKGYJIYQQQkjQQjHrRaKiojBjxgz1lwQ3PJehA89laMHzGTrwXIYWUX7UQGFXAEYIIYQQQkIHRmYJIYQQQkjQQjFLCCGEEEKCFopZQgghhBAStFDMEkIIIYSQoIVi1k1efPFFdO/eHdHR0Rg+fDjWrl3b6vLvvfce+vXrp5Y/5phjsHTp0vacL+Knc/nqq6/ixBNPREpKinqcfvrpbZ57ErjfSyvz589XnQDHjx/v820kvjufZWVl+Mc//oHs7GxVSd23b1/+1gbpuXz22WdxxBFHICYmRnWTuvXWW1FXV9dh20uc8+2332LcuHGqG5f8Zi5evBht8fXXX2PIkCHqO9m7d2+8+eab8BniZkBcY/78+Wa9Xm9+4403zBs3bjRPnz7dnJycbM7Pz3e6/OrVq81ardb85JNPmjdt2mS+//77zZGRkebff/+dhzzIzuXkyZPNL774onndunXmzZs3my+//HJzUlKSed++fR2+7aR959LKzp07zTk5OeYTTzzRfO655/KwBun5rK+vNw8bNsw8duxY86pVq9R5/frrr83r16/v8G0n7TuXc+fONUdFRam/ch6XL19uzs7ONt966608tH5m6dKl5vvuu8/84YcfigOWedGiRa0uv2PHDnNsbKz5tttuU/rnhRdeUHpo2bJlPtk+ilk3OO6448z/+Mc/bM8bGxvNnTt3Nj/++ONOl58wYYL5rLPOajJt+PDh5muuucbT80X8dC4daWhoMCckJJjfeustnpMgPJdy/kaOHGl+7bXXzJdddhnFbBCfz5deesncs2dPs8Fg6MCtJL44l7Lsqaee2mSaiKETTjiBBzyAgAti9p///Kf5qKOOajLt4osvNo8ZM8Yn28Q0AxcxGAz45Zdf1PCyFY1Go56vWbPG6Wtkuv3ywpgxY1pcngTuuXSkpqYGRqMRqampPtxS4qtz+cgjjyAzMxNXXnklD3KQn8+PP/4YI0aMUGkGnTp1wtFHH43/+7//Q2NjYwduOfHGuRw5cqR6jTUVYceOHSpdZOzYsTzAQcaaDtY/Op+sNQQpKipSP47yY2mPPP/zzz+dviYvL8/p8jKdBNe5dOSuu+5SuUOOX1YS+Ody1apVeP3117F+/foO2kriy/MpgufLL7/ElClTlPDZtm0brr/+enWzKd2ISPCcy8mTJ6vXjRo1SkaN0dDQgGuvvRb33ntvB2018RYt6Z+KigrU1taqnGhvwsgsIW4yc+ZMVTi0aNEiVdRAgofKykpceumlqqAvPT3d35tDvIDJZFJR9ldeeQVDhw7FxRdfjPvuuw8vv/wyj2+QIQVDElX/73//i19//RUffvghPv30Uzz66KP+3jQS4DAy6yJy4dNqtcjPz28yXZ5nZWU5fY1Md2d5Erjn0sqsWbOUmP3iiy8wYMAAH28p8fa53L59O3bt2qWqcu3FkKDT6bBlyxb06tWLBz6IvpviYBAZGaleZ6V///4qMiRD3Xq93ufbTbxzLh944AF1s3nVVVep5+IAVF1djauvvlrdoEiaAgkOslrQP4mJiV6Pygr8ZLiI/CDKXf/KlSubXATlueRrOUOm2y8vrFixosXlSeCeS+HJJ59UEYJly5Zh2LBhHbS1xJvnUmzyfv/9d5ViYH2cc845OOWUU9S/xQqIBNd384QTTlCpBdabEmHr1q1K5FLIBte5lFoER8FqvUmx1B2RYGFER+sfn5SVhbDNiNiGvPnmm8pq4uqrr1Y2I3l5eWr+pZdear777rubWHPpdDrzrFmzlJ3TjBkzaM0VpOdy5syZymLm/fffNx88eND2qKys9ONeEE/OpSN0Mwju87lnzx7lLHLDDTeYt2zZYl6yZIk5MzPT/K9//cuPe0E8OZdyjZRzOW/ePGXt9Pnnn5t79eqlnIGIf6msrFTWlPIQ6fj000+rf+/evVvNl/Mo59PRmuvOO+9U+kesLWnNFUCIV1rXrl2VsBHbkR9++ME2b/To0erCaM/ChQvNffv2VcuLTcWnn37qh60m7T2X3bp1U19gx4f8+JLg+17aQzEb/Ofz+++/V7aHIpzEpuuxxx5T9mskuM6l0Wg0P/TQQ0rARkdHm3Nzc83XX3+9ubS01E9bT6x89dVXTq+B1vMnf+V8Or5m0KBB6tzL93L27NlmXxEh//NNzJcQQgghhBDfwpxZQgghhBAStFDMEkIIIYSQoIVilhBCCCGEBC0Us4QQQgghJGihmCWEEEIIIUELxSwhhBBCCAlaKGYJIYQQQkjQQjFLCCE+IiIiAosXL+bxJYQQH0IxSwgJetasWaN6uJ911lluv7Z79+549tln4Q8uv/xyJXgdH2eeeSYCAdm+8ePHt7lcYWEhrrvuOnTt2hVRUVHIysrCmDFjsHr16g7ZTkJIeKPz9wYQQkh7ef3113HjjTeqvwcOHEDnzp2D5qCKcJ09e3aTaSII/UljY6MS1a5ywQUXwGAw4K233kLPnj2Rn5+PlStXori42GfbKO+n1+t9tn5CSPDAyCwhJKipqqrCggULVGRQIrNvvvlms2U++eQTHHvssYiOjkZ6ejrOO+88Nf3kk0/G7t27ceutt9qiosJDDz2EQYMGNVmHRG8limvlp59+whlnnKHWl5SUhNGjR+PXX391e/utkUz7R0pKipr39ddfK8H23Xff2ZZ/8sknkZmZqQSjdR9uuOEG9ZDtkO154IEHYN+pvL6+HnfccQdycnIQFxeH4cOHq3VbkWOWnJyMjz/+GEceeaTapiuuuEKJ048++sh2bOxfY6WsrExt3xNPPIFTTjkF3bp1w3HHHYd77rkH55xzTpPlrrnmGnTq1Emdh6OPPhpLliyxzf/ggw9w1FFHqfeW4/zvf/+7yfvItEcffRRTp05FYmIirr76ajV91apVOPHEExETE4Pc3FzcdNNNqK6udvs8EEKCF4pZQkhQs3DhQvTr1w9HHHEELrnkErzxxhtNhNynn36qxOvYsWOxbt06FTEUsSV8+OGH6NKlCx555BEcPHhQPVylsrISl112mRJTP/zwA/r06aPeQ6Z7CxGqt9xyCy699FKUl5er7Reh+tprrylRaEVEp06nw9q1a/Hcc8/h6aefVstYEaErqRjz58/Hhg0bcNFFF6mI8F9//WVbpqamRglSed3GjRvx/PPPY8KECWo567EZOXJks22Mj49XD8kNFtHsDJPJhL///e8q7eCdd97Bpk2bMHPmTJUaIvzyyy/qvSZOnIjff/9d3UzIfjremMyaNQsDBw60HYft27er7ZPIsOyX3NTI+ZD9JYSEEWZCCAliRo4caX722WfVv41Gozk9Pd381Vdf2eaPGDHCPGXKlBZf361bN/MzzzzTZNqMGTPMAwcObDJNlpFlW6KxsdGckJBg/uSTT2zT5Cd20aJFLb7msssuM2u1WnNcXFyTx2OPPWZbpr6+3jxo0CDzhAkTzEceeaR5+vTpTdYxevRoc//+/c0mk8k27a677lLThN27d6v32L9/f5PXnXbaaeZ77rlH/Xv27NlqW9evX99s+84991xzW7z//vvmlJQUc3R0tDofst7ffvvNNn/58uVmjUZj3rJli9PXT5482XzGGWc0mXbnnXeq/bUix378+PFNlrnyyivNV199dZNp3333nXqv2traNrebEBIaMDJLCAlatmzZoqKRkyZNUs8lOnnxxRer3Fkr69evx2mnneb195Zh/unTp6uIrAzvy9C3pDzs2bPHrfXI0Lxso/3j2muvtc2XNIO5c+eqYfi6ujo888wzzdZx/PHHN8lxHTFihIq6Su6rRDrlb9++fW1RVHl88803KrJp/z4DBgzw6FhIZFRylSVNQSKlko4wZMgQW2RV9kki4LINzti8eTNOOOGEJtPkuXUfrAwbNqzJMr/99pt6D/v9ksIziQTv3LnTo30hhAQfLAAjhAQtIlobGhqaFHxJQFTyLv/zn/8okSm5lO6i0WiapCoIRqOxyXNJMZACJxnWlzxReU8RkVKY5A6Sw9q7d+9Wl/n+++/V35KSEvWQ17iKCGwZzpehfOuwvhURf1bkOLlT9OWI5MFKDrE8JAXgqquuwowZM5QjgifnwBmO+y37Jnm4kifriDgrEELCA0ZmCSFBiYjYt99+WxUK2Uc1JVon4nbevHlqOYk2Sp5sS0hE0j76J2RkZCAvL6+JoJV12yP5nyKiJE/WWrhUVFTk9f2U6KkUqL366quqcEtEtEQe7fnxxx+bPLfm8Ip4HTx4sNq/goICJZrtH1Js1hrOjo2rSCGZtRBLzsG+ffuwdetWp8v279+/mY2XPJdIrqMAt0eiv5J/67hf8qDTASHhA8UsISQokUr40tJSXHnllaoy3v4hw97WVAOJDoqwlb8ynC3D7lLoZF8l/+2332L//v02MSqFV+KdKs4BIiZffPFFfPbZZ03eX8TinDlz1DpFTE6ZMsWjCKQUTYlwtn9Yt0OEpBS1ydD5tGnTlIWXFDo5VvpLasNtt92m0i5kX1944QXcfPPNap4IQtk2cQGQgjcZfpfUjMcff1wVx7WGHBt5P1mvbJNjdFqQ6PSpp56qCrtkWVn/e++9p47dueeeq5YRp4eTTjpJnZcVK1aoZeR4Llu2TM2//fbb1Q2HuBWI4JWCNomsiwNDa9x1110qai0FX3KzIWkJ4r7AAjBCwgx/J+0SQognnH322eaxY8c6nffjjz+qgiZrEdIHH3ygiqj0er0qEDv//PNty65Zs8Y8YMAAc1RUlHqNlZdeesmcm5urCrKmTp2qirLsC8B+/fVX87Bhw1TRU58+fczvvfdes2IyVwrAZBnHxxFHHKHmP/zww+bs7GxzUVGR7TWyL7If1mItKQC7/vrrzddee605MTFRFWLde++9TQrCDAaD+cEHHzR3797dHBkZqdZ53nnnmTds2GArAEtKSmq2fQUFBaowKz4+Xm2XfWGdlbq6OvPdd99tHjJkiFpHbGys2v7777/fXFNTY1uuuLjYPG3aNHNaWpo6ZkcffbR5yZIlTYrIpOBLtq9r167mp556qs1CPWHt2rW2bZRzJefSvoCOEBL6RMj//C2oCSGEeIZEkcUT119dzAghxN8wzYAQQgghhAQtFLOEEEIIISRoYZoBIYQQQggJWhiZJYQQQgghQQvFLCGEEEIICVooZgkhhBBCSNBCMUsIIYQQQoIWillCCCGEEBK0UMwSQgghhJCghWKWEEIIIYQELRSzhBBCCCEkaKGYJYQQQgghCFb+H701wLBRenNkAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "y_pred = champion_pipe.predict(X_test)\n",
+    "sns.regplot(x=y_test, y=y_pred, scatter_kws={'alpha':0.4}, line_kws={'color':'red'})\n",
+    "\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2, label='Ideal Fit')\n",
+    "plt.title(f'Champion variant Performance on Test Set\\nCorrelation')\n",
+    "plt.xlabel('Actual Expert Score')\n",
+    "plt.ylabel('Model Predicted Score')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f24e9f6-8cfc-4822-bc8a-04151f164a72",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Final evaluation on testset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b7379136-2c36-4e7c-bbe9-39b9028a80a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TEST SET EVALUATION\n",
+      "TEST RMSE: 0.1493\n",
+      "TEST MAE : 0.1140\n",
+      "TEST R²  : 0.5891\n",
+      "TEST Corr: 0.7676\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"TEST SET EVALUATION\")\n",
+    "\n",
+    "test_pred = champion_pipe.predict(X_test)\n",
+    "test_rmse, test_mae, test_r2, test_corr = compute_metrics(y_test, test_pred)\n",
+    "\n",
+    "print(f\"TEST RMSE: {test_rmse:.4f}\")\n",
+    "print(f\"TEST MAE : {test_mae:.4f}\")\n",
+    "print(f\"TEST R²  : {test_r2:.4f}\")\n",
+    "print(f\"TEST Corr: {test_corr:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7278b39c-1012-4a53-8db7-eafda9b2d0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_16644\\1925586135.py:11: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x='Importance', y='Feature', data=importance_df, palette='magma')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "final_step = champion_pipe.steps[-1][1]\n",
+    "coeffs = np.abs(final_step.coef_)\n",
+    "\n",
+    "importance_df = pd.DataFrame({\n",
+    "    'Feature': champion_pipe[:-1].get_feature_names_out(),\n",
+    "    'Importance': coeffs\n",
+    "}).sort_values(by='Importance', ascending=False).head(10)\n",
+    "\n",
+    "sns.barplot(x='Importance', y='Feature', data=importance_df, palette='magma')\n",
+    "plt.title('Top 10 Most Influential Movement Features')\n",
+    "plt.xlabel('Absolute Coefficient (Weight)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d5fd3da-f5dc-4f27-821c-72a593eaf726",
+   "metadata": {},
+   "source": [
+    "# Save champion variant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "105af544-de53-4de5-8677-5dd9651487de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved as Pickle: models\\champion_model_final.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "artifact = {\n",
+    "    \"model\": champion_pipe,\n",
+    "    \"feature_columns\": kept_cols,\n",
+    "    \"target\": TARGET_COL,\n",
+    "    \"dropped\": DROP_FEATURES,\n",
+    "    \"train_metrics\": {\n",
+    "        \"cv_rmse_mean\": float(results_df.iloc[0]['cv_rmse_mean']),\n",
+    "        \"cv_r2_mean\": float(results_df.iloc[0]['cv_r2_mean']),\n",
+    "        \"cv_corr_mean\": float(results_df.iloc[0]['cv_corr_mean']),\n",
+    "    },\n",
+    "    \"test_metrics\": {\n",
+    "        \"rmse\": float(test_rmse),\n",
+    "        \"mae\": float(test_mae),\n",
+    "        \"r2\": float(test_r2),\n",
+    "        \"correlation\": float(test_corr)\n",
+    "    },\n",
+    "    \"overfitting_gap\": {\n",
+    "        \"r2_gap\": float(results_df.iloc[0]['cv_r2_mean'] - test_r2),\n",
+    "    }\n",
+    "}\n",
+    "out_path = OUT_DIR/\"champion_model_final.pkl\"\n",
+    "with open(out_path, \"wb\") as f:\n",
+    "    pickle.dump(artifact, f)\n",
+    "\n",
+    "print(f\"Saved as Pickle: {out_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df577c8e-468d-4918-b1e1-4999299b0476",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}