{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aedb7e8f",
   "metadata": {},
   "source": [
    "# Counterfactual explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a734bee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import trustyai\n",
    "\n",
    "trustyai.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62849953",
   "metadata": {},
   "source": [
    "## Simple example\n",
    "\n",
    "We start by defining our black-box model, typically represented by\n",
    "\n",
    "$$\n",
    "f(\\mathbf{x}) = \\mathbf{y}\n",
    "$$\n",
    "\n",
    "Where $\\mathbf{x}=\\{x_1, x_2, \\dots,x_m\\}$ and $\\mathbf{y}=\\{y_1, y_2, \\dots,y_n\\}$.\n",
    "\n",
    "Our example toy model, in this case, takes an all-numerical input $\\mathbf{x}$ and return a $\\mathbf{y}$ of either `true` or `false` if the sum of the $\\mathbf{x}$ components is within a threshold $\\epsilon$ of a point $\\mathbf{C}$, that is:\n",
    "\n",
    "$$\n",
    "f(\\mathbf{x}, \\epsilon, \\mathbf{C})=\\begin{cases}\n",
    "\\text{true},\\qquad \\text{if}\\ \\mathbf{C}-\\epsilon<\\sum_{i=1}^m x_i <\\mathbf{C}+\\epsilon \\\\\n",
    "\\text{false},\\qquad \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "This model is provided in the `TestUtils` module. We instantiate with a $\\mathbf{C}=500$ and $\\epsilon=1.0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9e9630c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.utils import TestUtils\n",
    "\n",
    "center = 500.0\n",
    "epsilon = 1.0\n",
    "\n",
    "model = TestUtils.getSumThresholdModel(center, epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08943c5",
   "metadata": {},
   "source": [
    "Next we need to define a **goal**.\n",
    "If our model is $f(\\mathbf{x'})=\\mathbf{y'}$ we are then defining our $\\mathbf{y'}$ and the counterfactual result will be the $\\mathbf{x'}$ which satisfies $f(\\mathbf{x'})=\\mathbf{y'}$.\n",
    "\n",
    "We will define our goal as `true`, that is, the sum is withing the vicinity of a (to be defined) point $\\mathbf{C}$. The goal is a list of `Output` which take the following parameters\n",
    "\n",
    "- The feature name\n",
    "- The feature type\n",
    "- The feature value (wrapped in `Value`)\n",
    "- A confidence threshold, which we will leave at zero (no threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac0f09d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import output\n",
    "\n",
    "goal = [output(name=\"inside\", dtype=\"bool\", value=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9107ced8",
   "metadata": {},
   "source": [
    "We will now define our initial features, $\\mathbf{x}$. Each feature can be instantiated by using `FeatureFactory` and in this case we want to use numerical features, so we'll use `FeatureFactory.newNumericalFeature`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee4466b",
   "metadata": {},
   "source": [
    "Finally, we also specify which are the **bounds** for the counterfactual search. Typically this can be set either using domain-specific knowledge or taken from the data. In this case we simply specify an arbitrary (sensible) value, e.g. all the features can vary between `0` and `1000`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73da2506",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from trustyai.model import feature\n",
    "\n",
    "features = [\n",
    "    feature(name=f\"x{i+1}\", value=random.random() * 10.0, dtype=\"number\", domain=(0.0, 1000.0))\n",
    "    for i in range(4)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd4496a5",
   "metadata": {},
   "source": [
    "As we can see, the sum of of the features will not be within $\\epsilon$ (1.0) of $\\mathbf{C}$ (500.0). As such the model prediction will be `false`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c23b579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature x1 has value 9.975980618987379\n",
      "Feature x2 has value 7.350442030401161\n",
      "Feature x3 has value 8.283867308600662\n",
      "Feature x4 has value 8.358105797088806\n",
      "\n",
      "Features sum is 33.96839575507801\n"
     ]
    }
   ],
   "source": [
    "feature_sum = 0.0\n",
    "for f in features:\n",
    "    value = f.value.as_number()\n",
    "    print(f\"Feature {f.name} has value {value}\")\n",
    "    feature_sum += value\n",
    "print(f\"\\nFeatures sum is {feature_sum}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8021162b",
   "metadata": {},
   "source": [
    "We can now instantiate the **explainer** itself.\n",
    "\n",
    "To do so, we will to configure the termination criteria. For this example we will specify that the counterfactual search should only execute a maximum of 10,000 iterations before stopping and returning whatever the best result is so far."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c450a689",
   "metadata": {},
   "source": [
    "We can can now instantiate the explainer itself using `CounterfactualExplainer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ae12616",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\n",
      "SLF4J: Defaulting to no-operation (NOP) logger implementation\n",
      "SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\n"
     ]
    }
   ],
   "source": [
    "from trustyai.explainers import CounterfactualExplainer\n",
    "\n",
    "explainer = CounterfactualExplainer(steps=10_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5107e595",
   "metadata": {},
   "source": [
    "We will now express the counterfactual problem as defined above.\n",
    "\n",
    "- `input` represents our $\\mathbf{x}$ which know gives a prediction of `False`\n",
    "- `outputs` represents our $\\mathbf{y'}$, that is our desired prediction (`True`)\n",
    "- `domain` repreents the boundaries for the counterfactual search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84a42c5",
   "metadata": {},
   "source": [
    "We wrap these quantities in a `CounterfactualPrediction` (the UUID is simply to label the search instance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c52e4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import counterfactual_prediction\n",
    "\n",
    "prediction = counterfactual_prediction(\n",
    "    input_features=features,\n",
    "    outputs=goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b49829e",
   "metadata": {},
   "source": [
    "We now request the counterfactual $\\mathbf{x'}$ which is closest to $\\mathbf{x}$ and which satisfies $f(\\mathbf{x'}, \\epsilon, \\mathbf{C})=\\mathbf{y'}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f479771d",
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6770a8",
   "metadata": {},
   "source": [
    "We can see that the counterfactual $\\mathbf{x'}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c060497",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x1</td>\n",
       "      <td>9.975981</td>\n",
       "      <td>9.975981</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x2</td>\n",
       "      <td>7.868908</td>\n",
       "      <td>7.350442</td>\n",
       "      <td>False</td>\n",
       "      <td>0.518466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x3</td>\n",
       "      <td>8.283867</td>\n",
       "      <td>8.283867</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x4</td>\n",
       "      <td>473.875373</td>\n",
       "      <td>8.358106</td>\n",
       "      <td>False</td>\n",
       "      <td>465.517267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features    proposed  original  constrained  difference\n",
       "0       x1    9.975981  9.975981        False    0.000000\n",
       "1       x2    7.868908  7.350442        False    0.518466\n",
       "2       x3    8.283867  8.283867        False    0.000000\n",
       "3       x4  473.875373  8.358106        False  465.517267"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature sum is 500.00412862965703\n"
     ]
    }
   ],
   "source": [
    "df = explanation.as_dataframe()\n",
    "display(df)\n",
    "print(f\"\\nFeature sum is {df.proposed.sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5844b0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713da382",
   "metadata": {},
   "source": [
    "### Constrained features\n",
    "\n",
    "As we've seen, it is possible to constraint a specific feature $x_i$ by setting the _constraints_ list corresponding element to `True`.\n",
    "\n",
    "In this example, we know want to fix $x_1$ and $x_4$. That is, these features should have the same value in the counterfactual $\\mathbf{x'}$ as in the original $\\mathbf{x}$.\n",
    "\n",
    "By simply **omitting a search domain** to a feature we are constraining it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af59baf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "    feature(name=f\"x1\", value=random.random() * 10.0, dtype=\"number\"),\n",
    "    feature(name=f\"x2\", value=random.random() * 10.0, dtype=\"number\", domain=(0.0, 1000.0)),\n",
    "    feature(name=f\"x3\", value=random.random() * 10.0, dtype=\"number\", domain=(0.0, 1000.0)),\n",
    "    feature(name=f\"x4\", value=random.random() * 10.0, dtype=\"number\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6fec4d",
   "metadata": {},
   "source": [
    "We simply need to wrap the previous quantities with the new constraints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "906d02e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = counterfactual_prediction(\n",
    "    input_features=features, \n",
    "    outputs=goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20da7bb",
   "metadata": {},
   "source": [
    "And request a new counterfactual explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f67e081d",
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2b808b",
   "metadata": {},
   "source": [
    "We can see that $x_1$ and $x_4$ has the same value as the original and the model satisfies the conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d99b80b8",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x1</td>\n",
       "      <td>7.849540</td>\n",
       "      <td>7.849540</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x2</td>\n",
       "      <td>487.848273</td>\n",
       "      <td>9.165100</td>\n",
       "      <td>False</td>\n",
       "      <td>478.683173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x3</td>\n",
       "      <td>2.518644</td>\n",
       "      <td>2.518644</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x4</td>\n",
       "      <td>1.099736</td>\n",
       "      <td>1.099736</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features    proposed  original  constrained  difference\n",
       "0       x1    7.849540  7.849540         True    0.000000\n",
       "1       x2  487.848273  9.165100        False  478.683173\n",
       "2       x3    2.518644  2.518644        False    0.000000\n",
       "3       x4    1.099736  1.099736         True    0.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanation.as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad153c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54e81de",
   "metadata": {},
   "source": [
    "## Using Python models\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "147b1915",
   "metadata": {},
   "source": [
    "We will now show how to use a custom Python model with TrustyAI counterfactual explanations.\n",
    "\n",
    "The model will be an [XGBoost](https://github.com/dmlc/xgboost) one trained with the `credit-bias` dataset (available [here](https://github.com/ruivieira/benchmark-models/tree/main/credit-bias)).\n",
    "\n",
    "For convenience, the model is pre-trained and serialised with `joblib` so that for this example we simply need to deserialised it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6986a0ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.07, max_delta_step=0, max_depth=8,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=200, n_jobs=12, num_parallel_tree=1, random_state=27,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=0.9861206227457426,\n",
      "              seed=27, subsample=1, tree_method='exact', validate_parameters=1,\n",
      "              verbosity=None)\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "xg_model = joblib.load(\"models/credit-bias-xgboost.joblib\")\n",
    "print(xg_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc2144c",
   "metadata": {},
   "source": [
    "This model has as a single **output** a boolean `PaidLoan`, which will predict whether a certain loan applicant will repay the loan in time or not. The model is slightly more complex than the previous examples, with **input** features:\n",
    "\n",
    "|Input feature         | Type    | Note        |\n",
    "|----------------------|---------|-------------|\n",
    "|`NewCreditCustomer`   |boolean  ||\n",
    "|`Amount`              |numerical||\n",
    "|`Interest`            |numerical||\n",
    "|`LoanDuration`        |numerical|In months|\n",
    "|`Education`           |numerical|Level (1, 2, 3..)|\n",
    "|`NrOfDependants`      |numerical|Integer|\n",
    "|`EmploymentDurationCurrentEmployer`|numerical|Integer (years)|\n",
    "|`IncomeFromPrincipalEmployer`|numerical||\n",
    "|`IncomeFromPension`   |numerical||\n",
    "|`IncomeFromFamilyAllowance`|numerical||\n",
    "|`IncomeFromSocialWelfare`|numerical||\n",
    "|`IncomeFromLeavePay`|numerical||\n",
    "|`IncomeFromChildSupport`|numerical||\n",
    "|`IncomeOther`|numerical||\n",
    "|`ExistingLiabilities`|numerical|integer|\n",
    "|`RefinanceLiabilities`|numerical|integer|\n",
    "|`DebtToIncome`|numerical||\n",
    "|`FreeCash`|numerical||\n",
    "|`CreditScoreEeMini`|numerical|integer|\n",
    "|`NoOfPreviousLoansBeforeLoan`|numerical|integer|\n",
    "|`AmountOfPreviousLoansBeforeLoan`|numerical||\n",
    "|`PreviousRepaymentsBeforeLoan`|numerical||\n",
    "|`PreviousEarlyRepaymentsBefoleLoan`|numerical||\n",
    "|`PreviousEarlyRepaymentsCountBeforeLoan`|numerical|integer|\n",
    "|`Council_house`|boolean||\n",
    "|`Homeless`|boolean||\n",
    "|`Joint_ownership`|boolean||\n",
    "|`Joint_tenant`|boolean||\n",
    "|`Living_with_parents`|boolean||\n",
    "|`Mortgage`|boolean||\n",
    "|`Other`|boolean||\n",
    "|`Owner`|boolean||\n",
    "|`Owner_with_encumbrance`|boolean||\n",
    "|`Tenant`|boolean||\n",
    "|`Entrepreneur`|boolean||\n",
    "|`Fully`|boolean||\n",
    "|`Partially`|boolean||\n",
    "|`Retiree`|boolean||\n",
    "|`Self_employed`|boolean||\n",
    "\n",
    "We will start by testing the model with an input we are quite sure (from the original data) that will be predicted as `false`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "550c1ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\n",
    "    [\n",
    "        False,\n",
    "        2125.0,\n",
    "        20.97,\n",
    "        60,\n",
    "        4.0,\n",
    "        0.0,\n",
    "        6.0,\n",
    "        0.0,\n",
    "        301.0,\n",
    "        0.0,\n",
    "        53.0,\n",
    "        0.0,\n",
    "        0.0,\n",
    "        0.0,\n",
    "        8,\n",
    "        6,\n",
    "        26.29,\n",
    "        10.92,\n",
    "        1000.0,\n",
    "        1.0,\n",
    "        500.0,\n",
    "        590.95,\n",
    "        0.0,\n",
    "        0.0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        1,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        0,\n",
    "        1,\n",
    "        0,\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6136a9ce",
   "metadata": {},
   "source": [
    "We can see that this application will be rejected with a probability of $\\sim77\\%$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3789d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.7770493  0.22295067]]\n",
      "Paid loan is predicted as: [False]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(xg_model.predict_proba(np.array(x)))\n",
    "print(f\"Paid loan is predicted as: {xg_model.predict(np.array(x))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfdf2010",
   "metadata": {},
   "source": [
    "We will now prepare the XGBoost model to be used from the TrustyAI counterfactual engine.\n",
    "\n",
    "To do so, we simply need to first create a prediction function which takes:\n",
    "\n",
    "- A list of `PredictionInput` as inputs\n",
    "- A list of `PredictionOutput` as outputs\n",
    "\n",
    "If these two conditions are met, the actual inner working of this method can be anything (including calling a XGBoost Python model for prediction as in our case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "670f8d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.kie.kogito.explainability.model import PredictionInput, PredictionOutput\n",
    "\n",
    "def predict(inputs):\n",
    "    values = [_feature.value.as_obj() for _feature in inputs[0].features]\n",
    "    result = xg_model.predict_proba(np.array([values]))\n",
    "    false_prob, true_prob = result[0]\n",
    "    if false_prob > true_prob:\n",
    "        _prediction = (False, false_prob)\n",
    "    else:\n",
    "        _prediction = (True, true_prob)\n",
    "    _output = output(name=\"PaidLoan\", dtype=\"bool\", value=_prediction[0], score=_prediction[1])\n",
    "    return [PredictionOutput([_output])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb77932",
   "metadata": {},
   "source": [
    "Once the prediction method is created, we wrap in a `PredictionProvider` class.\n",
    "\n",
    "This class takes care of all the JVM's asynchronous plumbing for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4a35f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import Model\n",
    "\n",
    "model = Model(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0874edb",
   "metadata": {},
   "source": [
    "We will now express the previous inputs (`x`) in terms of `Feature`s, so that we might use it for the counterfactual search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69a52cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_feature(name, value, domain=None):\n",
    "    if isinstance(value, bool):\n",
    "        dtype=\"bool\"\n",
    "    else:\n",
    "        dtype=\"number\"\n",
    "    \n",
    "    if not domain:\n",
    "        return feature(name=name, dtype=dtype, value=value)\n",
    "    else:\n",
    "        return feature(name=name, dtype=dtype, value=value, domain=domain)\n",
    "\n",
    "\n",
    "values = [\n",
    "        (\"NewCreditCustomer\", False),\n",
    "        (\"Amount\", 2125.0),\n",
    "        (\"Interest\", 20.97),\n",
    "        (\"LoanDuration\", 60.0),\n",
    "        (\"Education\", 4.0),\n",
    "        (\"NrOfDependants\", 0.0),\n",
    "        (\"EmploymentDurationCurrentEmployer\", 6.0),\n",
    "        (\"IncomeFromPrincipalEmployer\", 0.0),\n",
    "        (\"IncomeFromPension\", 301.0),\n",
    "        (\"IncomeFromFamilyAllowance\", 0.0),\n",
    "        (\"IncomeFromSocialWelfare\", 53.0),\n",
    "        (\"IncomeFromLeavePay\", 0.0),\n",
    "        (\"IncomeFromChildSupport\", 0.0),\n",
    "        (\"IncomeOther\", 0.0),\n",
    "        (\"ExistingLiabilities\", 8.0),\n",
    "        (\"RefinanceLiabilities\", 6.0),\n",
    "        (\"DebtToIncome\", 26.29),\n",
    "        (\"FreeCash\", 10.92),\n",
    "        (\"CreditScoreEeMini\", 1000.0),\n",
    "        (\"NoOfPreviousLoansBeforeLoan\", 1.0),\n",
    "        (\"AmountOfPreviousLoansBeforeLoan\", 500.0),\n",
    "        (\"PreviousRepaymentsBeforeLoan\", 590.95),\n",
    "        (\"PreviousEarlyRepaymentsBefoleLoan\", 0.0),\n",
    "        (\"PreviousEarlyRepaymentsCountBeforeLoan\", 0.0),\n",
    "        (\"Council_house\", False),\n",
    "        (\"Homeless\", False),\n",
    "        (\"Joint_ownership\", False),\n",
    "        (\"Joint_tenant\", False),\n",
    "        (\"Living_with_parents\", False),\n",
    "        (\"Mortgage\", False),\n",
    "        (\"Other\", False),\n",
    "        (\"Owner\", False),\n",
    "        (\"Owner_with_encumbrance\", True),\n",
    "        (\"Tenant\", True),\n",
    "        (\"Entrepreneur\", False),\n",
    "        (\"Fully\", False),\n",
    "        (\"Partially\", False),\n",
    "        (\"Retiree\", True),\n",
    "        (\"Self_employed\", False),\n",
    "]\n",
    "\n",
    "features = [make_feature(p[0], p[1]) for p in values]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c844bcd8",
   "metadata": {},
   "source": [
    "We can confirm now, with the newly created `PredictionProvider` model that this input will lead to a `false` `PaidLoan` prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60a46482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=false, type=boolean, score=0.7835956811904907, name='PaidLoan'}'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predictAsync([PredictionInput(features)]).get()[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e212fc",
   "metadata": {},
   "source": [
    "### Unconstraind basic search\n",
    "\n",
    "To get started we will search for a counterfactual with no constraints at all. This is not a realistic use case, but we will use it as a baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fb48af",
   "metadata": {},
   "source": [
    "We will also create a set of equal bounds for all the features. Again, this is not realistic, but we do it to establish a baseline. Note that boolean features will ignore the bounds anyway, so we can just create a set such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "711b1b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [make_feature(p[0], p[1], (0.0, 10000.0)) for p in values]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb26b042",
   "metadata": {},
   "source": [
    "We want our **goal** to be the model predicting the loan will be paid (`PaidLoad=true`), so we specify it as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "00e04f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "goal = [output(name=\"PaidLoan\", dtype=\"bool\", value=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45b88c0",
   "metadata": {},
   "source": [
    "We now wrap all this context in a `CounterfactualPrediction` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "25603360",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = counterfactual_prediction(\n",
    "    input_features=features, \n",
    "    outputs=goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aecad8d",
   "metadata": {},
   "source": [
    "We are now ready to search for a counterfactual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d32c109",
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10cb34c1",
   "metadata": {},
   "source": [
    "First we will confirm that our counterfactual changes the outcome, by predicting its outcome using the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02a9527c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=true, type=boolean, score=0.5882376432418823, name='PaidLoan'}'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testf = [f.asFeature() for f in explanation._result.entities]\n",
    "model.predictAsync([PredictionInput(testf)]).get()[0].getOutputs()[\n",
    "    0\n",
    "].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcd184e",
   "metadata": {},
   "source": [
    "And indeed it changes. We will now verify _which_ features were changed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cec4ab29",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RefinanceLiabilities</td>\n",
       "      <td>1.230475</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-4.769525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PreviousEarlyRepaymentsCountBeforeLoan</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  features  proposed  original  constrained  \\\n",
       "15                    RefinanceLiabilities  1.230475       6.0        False   \n",
       "23  PreviousEarlyRepaymentsCountBeforeLoan  6.000000       0.0        False   \n",
       "\n",
       "    difference  \n",
       "15   -4.769525  \n",
       "23    6.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = explanation.as_dataframe()\n",
    "df[df.difference != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "06ab9efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHp1TTz01l1122Qq9V/PmzcvOO++cu+++O48//nh23HHH7LbbbpkxY0aN9c4555x07949jz/+eE444YT86Ec/Koek5dGvX7+cf/75adasWWbPnp3Zs2fXeA8/bu+9986cOXNy6623ZvLkyenZs2e222678ns1ePDgtG3bNo8++mgmT56cE044IfXq1fvEfS9YsCDvvvtu+WvevHnLPT8AAAAALIvFf+T7df9aVi5dBcvgpptuStOmTbNo0aIsWLAgderUyf/+7/9mwYIFGT16dO6666707ds3SbLeeuvlwQcfzCWXXJKtttoqrVu3TqlUSvPmzT/1EksLFy7MxRdfnA4dOiRJhg0bljPOOKP8+Lbbbltj/d/+9rdp0aJF7rvvvuy6667l5UOHDs1+++2XJBk9enR+9atf5ZFHHsmOO+6YX//612nevHmuuuqq8i/tO3fuXH7uyJEjM2bMmOyxxx5JknXXXTfPPPNMLrnkkgwZMmS537fu3bune/fu5e9/+tOf5rrrrssNN9yQYcOGlZdvvvnmOeGEE8rzTJgwIb/85S8zYMCA5dpf/fr107x585RKpU99rx988ME88sgjmTNnTho0aJAkOffcc3P99dfnr3/9aw477LDMmDEjxx13XNZff/0kSadOnT5132effXZOP/308vc9evTIlClTMj5J5XK9CuCrZJXq6toeAQAAAJbQvn372h7hK0XogGWwzTbb5KKLLsr8+fPzy1/+MnXr1s2ee+6Zp59+Ov/+97+X+IX8hx9+mB49eizXPho3blyOHEmy5pprZs6cOeXv33jjjZxyyikZP3585syZk8rKyvz73/9e4uyIbt26lf+7SZMmadasWXk7U6dOzXe+852lnpkwf/78vPTSSzn44INz6KGHlpcvWrRohW+aPm/evIwaNSo333xzZs+enUWLFuX9999fYubFkejj33/8MlMr27Rp0zJv3rysttpqNZa///775ct4HXPMMTnkkENyxRVXpH///tl7771r/Hz+24knnphjjjmm/H2dOk6Yg6+D6dOn1/YIfE2VSqW0b98+r7zySqoFNXBMwMc4HqAmxwTU9E06JurXr582bdos07pCByyDJk2apGPHjkmSP/zhD+nevXt+//vfZ8MNN0yS3HzzzUscdIvPFFhW/x0fSqVSjf+xGjJkSN56661ccMEFWWedddKgQYP07ds3H3744Wdup6qqKkk+9Ubhiy+19Lvf/S59+vSp8VhFRcVyvZbFRowYkTvvvDPnnntuOnbsmEaNGmWvvfZaYuYv27x587Lmmmtm/PjxSzy2+B4jo0aNyv7775+bb745t956a0aOHJmrrroqu++++1K32aBBg+X+mQNffV/3fzRS+6qrq33O4GMcE/AfjgeoyTEBNX0TjonleX1CByynOnXq5KSTTsoxxxyT559/Pg0aNMiMGTOy1VZbfaH7nTBhQn7zm99k5513TpLMnDkz//znP5drG926dctll12WhQsXLhFEWrVqlbXWWisvv/xyBg8evNJmHjp0aDkOzJs3L6+88soS6z388MNLfN+1a9cV2mf9+vVTWfnpF4vq2bNnXn/99dStW/dTT/Pr3LlzOnfunB//+MfZb7/9Mnbs2E8MHQAAAABA7RA6YAXsvffeOe6443LJJZdkxIgR+fGPf5yqqqpsscUWeeeddzJhwoQ0a9Zshe5r8Uk6deqUK664Ir179867776b44477lPP0FiaYcOG5cILL8y+++6bE088Mc2bN8/DDz+cTTfdNF26dMnpp5+eo48+Os2bN8+OO+6YBQsW5LHHHsvbb79d47JM06dPz9SpU5eYb2kzX3vttdltt91SKpVy6qmnls8u+bgJEybkF7/4RQYNGpQ777wzf/nLX3LzzTcv12tbrH379pk3b17uvvvudO/ePY0bN07jxo1rrNO/f//07ds3gwYNyi9+8Yt07tw5//jHP3LzzTdn9913zwYbbJDjjjsue+21V9Zdd93MmjUrjz76aPbcc88VmgkAAAAA+OIIHbAC6tatm2HDhuUXv/hFpk+fntVXXz1nn312Xn755bRo0SI9e/bMSSedtFL3+fvf/z6HHXZYevbsmXbt2mX06NEZMWLEcm1jtdVWyz333JPjjjsuW221VSoqKrLxxhtn8803T5Iccsghady4cc4555wcd9xxadKkSTbaaKMMHz68xnY+Hj0We+CBB5ZYdt555+Wggw5Kv3790rJly/zkJz/Ju+++u8R6xx57bB577LGcfvrpadasWc4777zssMMOy/XaFuvXr18OP/zw7LPPPnnrrbcycuTIjBo1qsY6pVIpt9xyS04++eQceOCBefPNN9O6detsueWWadWqVSoqKvLWW2/lgAMOyBtvvJGWLVtmjz32qHGzcQAAAADgq6FU/XW/kBdALXq3Z89UPv54bY8BrKC3X3qptkfga6pUKmXdddfN9OnTv/bX1YVl4ZiA/3A8QE2OCajpm3RM1K9fP23btl2mdet8wbMAAAAAAAB8YYQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsIQOAAAAAACgsOrW9gAAX2fv3XhjFixYUNtjQK0rlUpZd911M3369FRXV9f2OAAAAMDXiDM6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwhI6AAAAAACAwqpb2wMAfJ3Vq1cv1dXVtT0G1LpSqZQkqV+/vmMC4piA/+aYgP9wPEBNjgmo6Zt0TNSrV2+Z1y1Vf93fDYBasHDhwuX6H2MAAAAAYMW4dBXAF+D999/P/vvvn3nz5tX2KPCVMG/evPTs2dMxAf+fYwJqckzAfzgeoCbHBNTkmFg6Z3QAfAHefffdNG/ePO+8806aNWtW2+NArXNMQE2OCajJMQH/4XiAmhwTUJNjYumc0QEAAAAAABSW0AEAAAAAABSW0AHwBWjQoEFGjhyZBg0a1PYo8JXgmICaHBNQk2MC/sPxADU5JqAmx8TSuUcHAAAAAABQWM7oAAAAAAAACkvoAAAAAAAACkvoAAAAAAAACkvoAAAAAAAACkvoAAAAAAAACkvoAPgC/PrXv0779u3TsGHD9OnTJ4888khtjwS14v77789uu+2WtdZaK6VSKddff31tjwS15uyzz84mm2ySb33rW1ljjTUyaNCgPPfcc7U9FtSaiy66KN26dUuzZs3SrFmz9O3bN7feemttjwVfGT/72c9SKpUyfPjw2h4FasWoUaNSKpVqfK2//vq1PRbUqtdeey3f+973stpqq6VRo0bZaKON8thjj9X2WF8JQgfASnb11VfnmGOOyciRIzNlypR07949O+ywQ+bMmVPbo8GXbv78+enevXt+/etf1/YoUOvuu+++HHnkkXn44Ydz5513ZuHChdl+++0zf/782h4NakXbtm3zs5/9LJMnT85jjz2WbbfdNgMHDszTTz9d26NBrXv00UdzySWXpFu3brU9CtSqDTbYILNnzy5/Pfjgg7U9EtSat99+O5tvvnnq1auXW2+9Nc8880zGjBmTVVZZpbZH+0ooVVdXV9f2EABfJ3369Mkmm2yS//3f/02SVFVVpV27djnqqKNywgkn1PJ0UHtKpVKuu+66DBo0qLZHga+EN998M2ussUbuu+++bLnllrU9DnwlrLrqqjnnnHNy8MEH1/YoUGvmzZuXnj175je/+U3OPPPMbLzxxjn//PNreyz40o0aNSrXX399pk6dWtujwFfCCSeckAkTJuSBBx6o7VG+kpzRAbASffjhh5k8eXL69+9fXlanTp30798/Dz30UC1OBsBXzTvvvJPko1/swjddZWVlrrrqqsyfPz99+/at7XGgVh155JHZZZddavz/FPBN9cILL2SttdbKeuutl8GDB2fGjBm1PRLUmhtuuCG9e/fO3nvvnTXWWCM9evTI7373u9oe6ytD6ABYif75z3+msrIyrVq1qrG8VatWef3112tpKgC+aqqqqjJ8+PBsvvnm2XDDDWt7HKg1Tz75ZJo2bZoGDRrk8MMPz3XXXZdvf/vbtT0W1JqrrroqU6ZMydlnn13bo0Ct69OnT8aNG5fbbrstF110UaZPn57vfOc7ee+992p7NKgVL7/8ci666KJ06tQpt99+e4444ogcffTRueyyy2p7tK+EurU9AAAAfNMceeSReeqpp1xnmm+8Ll26ZOrUqXnnnXfy17/+NUOGDMl9990ndvCNNHPmzPzoRz/KnXfemYYNG9b2OFDrdtppp/J/d+vWLX369Mk666yTP//5zy5xyDdSVVVVevfundGjRydJevTokaeeeioXX3xxhgwZUsvT1T5ndACsRC1btkxFRUXeeOONGsvfeOONtG7dupamAuCrZNiwYbnpppty7733pm3btrU9DtSq+vXrp2PHjunVq1fOPvvsdO/ePRdccEFtjwW1YvLkyZkzZ0569uyZunXrpm7durnvvvvyq1/9KnXr1k1lZWVtjwi1qkWLFuncuXNefPHF2h4FasWaa665xB+DdO3a1SXd/j+hA2Alql+/fnr16pW77767vKyqqip33323600DfMNVV1dn2LBhue6663LPPfdk3XXXre2R4CunqqoqCxYsqO0xoFZst912efLJJzN16tTyV+/evTN48OBMnTo1FRUVtT0i1Kp58+blpZdeypprrlnbo0Ct2HzzzfPcc8/VWPb8889nnXXWqaWJvlpcugpgJTvmmGMyZMiQ9O7dO5tuumnOP//8zJ8/PwceeGBtjwZfunnz5tX4i6vp06dn6tSpWXXVVbP22mvX4mTw5ft/7d2hSmxRGIbh72AaZJggUxVvQCwWMWgwGKzKRhjtJmXKFAWD2SZM0TRVbO5mFPEyxBHF5AXMaYLYDgeXG5+nrZW+/oZ/f38/o9Eo19fXabfbH7ebOp1OWq1W4XXw/QaDQTY2NjI7O5v39/eMRqPc3t6mruvS06CIdrv95W7T9PR0ZmZm3HPiV+r3+9nc3Mzc3Fyenp5yfHycqampVFVVehoUcXBwkOXl5ZyenmZrayv39/cZDocZDoelp/0IQgfAf7a9vZ3X19ccHR3l+fk5i4uLubm5+XKgHH6Dh4eHrK2tfbwPDw+TJLu7u7m8vCy0Cso4Pz9Pkqyurn76v7i4yN7e3vcPgsJeXl7S6/UyHo/T6XSysLCQuq6zvr5eehoAP8Dj42Oqqsrb21u63W5WVlZyd3eXbrdbehoUsbS0lKurqwwGg5ycnGR+fj5nZ2fZ2dkpPe1H+DOZTCalRwAAAAAAAPwLNzoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDGEjoAAAAAAIDG+gsAeAZ+M6XxcQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf923811",
   "metadata": {},
   "source": [
    "Here we can see the problem with the unconstrained search.\n",
    "\n",
    "Some of the fields that were changed (_e.g._ `IncomeFromSocialWelfare`, `RefinanceLiabilities`, etc) might be difficult to change in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af45ae5",
   "metadata": {},
   "source": [
    "### Constrained search\n",
    "\n",
    "We will now try a more realistic search, which incorporates domain specific knowledge (and common sence).\n",
    "\n",
    "To do so, we will constrain features we feel they shouldn't (or mustn't) change and specify sensible search bounds.\n",
    "We will start with the constraints:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d0f747",
   "metadata": {},
   "source": [
    "The constraints should be self-explanatory, but in essence they were divided into three groups\n",
    "\n",
    "- Attributes you **cannot** or **should** not change (protected), for instance age, education level, etc\n",
    "- Attributes you **can** change, for loan duration, loan amount, etc\n",
    "- Attributes you probably won't be able to change, but might be informative to change. For instance, you might not be able to easily change your income, but you might be interested in how much would it need to be in order to get the prediction as favourable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1d166690",
   "metadata": {},
   "outputs": [],
   "source": [
    "domains = [\n",
    "    None,  # NewCreditCustomer\n",
    "    (0.0, 1000.0),  # Amount\n",
    "    None,  # Interest\n",
    "    (0.0, 120.0),  # LoanDuration\n",
    "    None,  # Education\n",
    "    None,  # NrOfDependants\n",
    "    (0.0, 40.0),  # EmploymentDurationCurrentEmployer\n",
    "    (0.0, 1000.0),  # IncomeFromPrincipalEmployer\n",
    "    (0.0, 1000.0),  # IncomeFromPension\n",
    "    (0.0, 1000.0),  # IncomeFromFamilyAllowance\n",
    "    (0.0, 1000.0),  # IncomeFromSocialWelfare\n",
    "    (0.0, 1000.0),  # IncomeFromLeavePay\n",
    "    (0.0, 1000.0),  # IncomeFromChildSupport\n",
    "    (0.0, 1000.0),  # IncomeOthe\n",
    "    None,  # ExistingLiabilities\n",
    "    None,  # RefinanceLiabilities\n",
    "    (0.0, 100.0),  # DebtToIncome\n",
    "    (0.0, 100.0),  # FreeCash\n",
    "    (0.0, 10000.0),  # CreditScoreEeMini\n",
    "    None,  # NoOfPreviousLoansBeforeLoan\n",
    "    None,  # AmountOfPreviousLoansBeforeLoan\n",
    "    None,  # PreviousRepaymentsBeforeLoan\n",
    "    None,  # PreviousEarlyRepaymentsBefoleLoan\n",
    "    None,  # PreviousEarlyRepaymentsCountBeforeLoan\n",
    "    None,  # Council_house\n",
    "    None,  # Homeless\n",
    "    None,  # Joint_ownership\n",
    "    None,  # Joint_tenant\n",
    "    None,  # Living_with_parents\n",
    "    None,  # Mortgage\n",
    "    None,  # Other\n",
    "    None,  # Owner\n",
    "    None,  # Owner_with_encumbrance\n",
    "    None,  # Tenant\n",
    "    None,  # Entrepreneur\n",
    "    None,  # Fully\n",
    "    None,  # Partially\n",
    "    None,  # Retiree\n",
    "    None,  # Self_employed\n",
    "]\n",
    "\n",
    "features = []\n",
    "for i in range(len(domains)):\n",
    "    features.append(make_feature(values[i][0], values[i][1], domains[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdecdb9c",
   "metadata": {},
   "source": [
    "As before, we wrap this data in a `CounterfactualPrediction`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91cc678b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = counterfactual_prediction(\n",
    "    input_features=features,\n",
    "    outputs=goal\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d265b0d6",
   "metadata": {},
   "source": [
    "And we start a new search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fbce0a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d7a6ca",
   "metadata": {},
   "source": [
    "We test that the counterfactual does change the outcome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f7d56e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=true, type=boolean, score=0.5038489103317261, name='PaidLoan'}'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testf = [f.as_feature() for f in explanation._result.entities]\n",
    "model.predictAsync([PredictionInput(testf)]).get()[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8968d072",
   "metadata": {},
   "source": [
    "And we confirm that no constrained features were changed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bef56708",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LoanDuration</td>\n",
       "      <td>56.719125</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-3.280875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IncomeFromSocialWelfare</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   features   proposed  original  constrained  difference\n",
       "3              LoanDuration  56.719125      60.0        False   -3.280875\n",
       "10  IncomeFromSocialWelfare  60.000000      53.0        False    7.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = explanation.as_dataframe()\n",
    "df[df.difference != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "43fb2097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b413f3",
   "metadata": {},
   "source": [
    "### Minimum counterfactual probabilities\n",
    "\n",
    "We can see that the previous answer is very close to $50\\%$.\n",
    "\n",
    "With TrustyAI we have the possiblity to specify a minimum probability for the result (when the model supports prediction confidences).\n",
    "\n",
    "Let's say we want a result that is at least $75\\%$ confident that the loan will be repaid. We can just encode the **minimum probability** as the last argument of each `Output`. A minimum probability of $0$ (as we've used) simply means that any desired outcome will be accepted, regardless of its probability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e85f2ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "goal = [output(name=\"PaidLoan\", dtype=\"bool\", value=True, score=0.75)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9af0420",
   "metadata": {},
   "source": [
    "We can then re-run the search with all the data as defined previously:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "242718f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = counterfactual_prediction(\n",
    "    input_features=features,\n",
    "    outputs=goal\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e48a29fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea8abca",
   "metadata": {},
   "source": [
    "As previously, we check that the answer is what we are looking for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "04067b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=true, type=boolean, score=0.7572674751281738, name='PaidLoan'}'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testf = [f.as_feature() for f in explanation._result.entities]\n",
    "model.predictAsync([PredictionInput(testf)]).get()[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1f68d8",
   "metadata": {},
   "source": [
    "And we show which features need to be changed for said desired outcome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c39158a2",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LoanDuration</td>\n",
       "      <td>14.604508</td>\n",
       "      <td>60.00</td>\n",
       "      <td>False</td>\n",
       "      <td>-45.395492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FreeCash</td>\n",
       "      <td>10.965166</td>\n",
       "      <td>10.92</td>\n",
       "      <td>False</td>\n",
       "      <td>0.045166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        features   proposed  original  constrained  difference\n",
       "3   LoanDuration  14.604508     60.00        False  -45.395492\n",
       "17      FreeCash  10.965166     10.92        False    0.045166"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = explanation.as_dataframe()\n",
    "df[df.difference != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ebe49d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8e3ee6",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Visualisation\n",
    "\n",
    "Let's try to visualise counterfactuals in action with the following example:\n",
    "\n",
    "We construct three clusters of data and train a model to assign points to a cluster.\n",
    "Our counterfactual question can then be\n",
    "\n",
    "> If we have a point $X$ belonging to a certain cluster $C$, how far would it need to move\n",
    "> in order to belong to a desired cluster $C^'$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "291b3c55",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=2000, n_features=2, random_state=0, centers=[(-5,0), (0, 5), (5, 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6756d5d",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uj6LR7dpz8PGnW3UPQgghxI0iASAhhBDiNnImJxu7quJpsZJUVHhLBoBe27WDMznZWEwmlp6I4dlGFou2u1w8vuprHJrGxviL7H/syTqDSBfy8tiaGMekyM50CWx4EeCsslLKXS4cmkpcQT6qrl/3zmm3Ay+bjXX3PczhjDRGRXTAdBsEzYQQQohbhdQAEkIIIW4jPxkzngB3D8Z27MTAtmGtvZ1qTmZnMeWj97l/+ecUOyrqHNctKBgPiwWLydSkgtSKQlU2jgJ1BhnsLhfzPl/KH/fuZt5ny6hQXQ1eI9I/gNnde+Dr5sZ3h4+S4E8jhPn4MKtbDwI9PFt7K0IIIcS3imQACSGEELeR+/r2r2pdnl5SzO9276Sdjy/fHzkai6l1/+7z653biCvIJ6W4iK9iz9RZZPmlkaPp16Ytfu5ujIro2Oh1zuflMaRde3Td4Nmhw/F1c691nPNSrR/NMHBoKk5Nq7ceTVpxMVsT4xkV0YGogEB+P/kOfj/5jkbvTwghhBCiNUgASAghhLhN/XDLRvYmJ2OzmIkKCGBh776tup9ewSGczs7GwKBLPZk9ZpOJ6V27NXmdh1Z8SbHDgc1s5s2D+4nJzGDJ4CE1avT4ubvz2sQpLDt1gvv79q8zUASg6jqzP/2YcpcTq8nM3seevG0KPwshhBDi20ECQEIIIUQjZZSUcDwrg1ERHeoNGrQ2L6sVk0lBgUa3ds8pK+Or2NP0bdOmSVk4AAfTUimosDM5sjNmk4mfjp3AyIgOBHt6MSisXZPmbIjLR76cmsa+1GSOZ6YzoG0Yw8Ijaoy9u1cf7u7V55pzujSNEqcDVdcxjMoi0BIAEkIIIcStRAJAQgghRCMUOyq4c+kHqLpOW29vNj+0uEnzuDSN59Z9w6H0NH4wcgz39OnXwjuF30++g3ePHiHMx4cZXbs36t5HVn7Fhfw8LCYTyxfeR4/gkEbdvyU+jufXr0YB7u/bnx+PGY/ZZLquHaYuWzp3AR+dOM7OpEQyy0oxAM9LwZp1F87x1uGDzOrWgyWDhzR4Tg+rld9MmMx7x44wv1efZrcLd2kaO5IS6ODnT7eg4GbNJYQQQgjREBIAEkIIIWqxPPY06y6e5/GB0dUyR7LLynDqGhWqSmJRIZquY25CbZ2jGensSUmm3OXitV07rksAyNfNne+OGNWke/Pt9srOViYTRRV1F2yuy8WCPDRdx6XrnM3NadIemqpnSCivTZpKRkkJy07F0C+0LX1D26DpOi9sWIuq61zMz2Nq5y508g9o8LwLe/dtsWN039u4jq2J8eiGwZcL7qVXSGiLzNsYDlXlxQ1riMvP57VJU4lu1/6G70EIIYQQN450ARNCCCGuklJUxE+2bmJLQjyLVy1HN4yqa50DArm7R29CPD35yZhxTQr+AHTyD6g6mtUntE2j7s0pK+NcXi7GFftqaf+YPoth7cN5fNAQhrYPb/T9i3r3ZXBYO7oGBvHjMeNbfoMNEObjw/dGjGZK5y5A5dGwAHd3bCYTJkXBx+bWKvsCOJWTRbnLhQJcyM+retyhqtf1fb3S+rgL7ExK5GJBPj/esvGGrCmEEEKI1iMZQEIIIW5bZ3Ky2ZmUyB1duhLZiEwPq9lEZQNxsJktXFk6WFEUfj1xMr+eOPma8xiGwV8P7ON4Zjo/GDmG3lcEetp4e7PhgUc4n5fHiFpq09TlXF4u8z5bhoHB4v6D8LLZKHU5eTp6WIvWpBkU1o5P7l7U5Pv93T1Y1sD7S51OvKzWGkWa63IiK5Nn1q7C1+bG+3PubvBxLEVRWL7ofjbGXWR4eARBnq3XhvyX4ybxwy0b6BwYxNSoygDV24cP8Kd9ewj39WXVPQ9c9/pSHf38MQAPi4UugUFNmsMwDN45eohDaWm8MHwkfWsJZjpUlVe2bSaztIRfT5jcqKwrIYQQQrQc5Ub9lelK0dHRxuHDh2/4ukIIIb49ih0VjHj3HZyaipfNxuEnnrlmG/T3jx/h93t20Te0Dc8OGca+1BTu7tm7yTVadiUl8tSaVdhVFx38/Nj+8ONNmudKS0/G8Jud23BoGiGenhQ7HOiGwbyevfntpKnNnr8uBXY7j6z8iqyyUv42bSZD24djd7mIzc2hR3BIo4tMX/aTrRv57NQpugcHs3zhfbhZrv23qYdXfMmu5CTMisJ3ho3g+aEjmrT2zSb6nbfIr7DjabXyl6nTqzKXrqfjmRkkFRUyrXPXBr32VzucnsbDK77ErqqEefuwZ/GSGmM+PnGcV3dtx6lpDA+PYOm8hS2wcyGEEELURlGUI4ZhRNd2TY6ACSGEuC3ZXSqaoaMZBnaXC03Xr3nPX/fvw6lpxObmYFZM/Gj0uGYV6PV1c8PAwKwo+LdQNsfUqC6EeHlhM5mrunMZgKppLTJ/XVaei+VcXi7ZZWW8tms7i1cup8/bf+OeLz9j5rIPURvw+tbms9On0DG4mJ/Hy5s3kFteXu36xrgLvHlwX7XHo9u1x8NiwWo20ze0bXOe1k1lRrduuJnNuJnNDGgbdkPWHNA2jDndezYp+AOVneYMKo/XedWRgRbs6YVJUbCZzc0uni2EEEKIppMjYEIIIW5Lbby9+fm4iXx15hSPDRzcoF9wR0ZEsDMpEbNiontw8zsz9W8bxpt3ziQ2J4dFfVqmeLC/uztzuvckv7yc54eOoI23NyUOB3f37E3pdWxN3js0FJOi4GmxEuHnx9aE+MrAk6GTWlJMgd1OiJdXo+edGtWFzQlxqLrO6vNnybOX89HcBQDsTk7iuXWr0Q2DHYkJfLnwPgCeGzKcoe3C8XFza5XiydfLL8ZNYvGAaII9PesMptxseoaE8uadM4nJzKizkPkdnbtgTL2TnLIyFvTqc4N3KIQQQojL5AiYEEKIm0pOWRk7kxMZ1j6ccF+/G7q2puucyMqko78/gR6tVx+mPh+dOM5ru7aj6jp3dOnK3++cxUub1rH6/Hk8rVY2PvDIdattcz4vl9zycroEBjLlo/cpd6nohs7cHr14fcq0BtfwuZJhGHwYc4zf79mJU9OJbteeT+dX1g5avHI525MSAAj29OTg40/XOc+OxAR+vHUTPYKDeWv67CZntFwtqbAQL5uN4FasFySEEEII0VD1HQGTDCAhhBA3DU3XmfnJR5Q6HVhMZvYuXnJDMyHMJhMDw9rdsPWawqwogIKiKFiVypPcG+PicGgqZpPCqewsxnWKvC5rdwsKptulWsGbHnyUC/l5RIe1b1awRVEUHuw/kFKXk4SCAv7virb1PjZbVQHumV271zvPT7dvJqO0hAK7nZ9t38ILw0bQzse3yfsCeO/YEV7fuwuTovDp/HtqLXAMkFVaymPffE2Z08lbM2bTMzikWesKIYQQQlwPEgASQghx03DpGvn2cjTDwGY2KHRU3DJHYW6Uhb37UuJwkG+38+zQYQA8MSiav+zfQ3sfX6Lbta9xz7GMdH63ZyeDw9rz0sjRTcrUAci3l7M/NYXodu0J9fIm1Ktl6rmYFIVnhwyv8fhPx01EudSu/aWRY+qdo3dIKHnl5dhVla/PnmFbYjwHHnuqyc8VYM2Fczg0DYtiYm9KUp0BoI9PHudcbg6aYfDnfXv496y7mrymEEIIIcT1IgEgIYQQNw13i5Wfjp3Au8eOMLdHL9o3M4PjdlLmdLIzOZHeIaE8GT202rXnhg7nycFDMCsKH544zrHMDL4zdDidL7X2fm7dajJKSzidnc3IiA6M7tCx0etrus7MZR9R7HTgZjazZ/ES3C1N6/zVUMGenvx12owGjX1j2ky+OnOKX+zYikvXKayoQNV1rGZzk9d/JnoYz6z9Bl83N6Z3qTsDqU9oG6xmMzYUBt2g4s1CCCGEEI0lASAhhBA3lYf6D+Sh/gNbexs3nQe+/oILeXmgwJYHF9fopnQkI523Dx3gQHoqTk3jdHYWmx9aDFQGUnLLy9AxCPTwaNL6dlUlp7wMzTBwaRqFFRW09b6+AaCs0lL+b+NaTIrCn6dOr7fItM1s5t6+/Sl2Olh57ixPDR7SrOAPwKSozsQ++wIK1JtJdEfnriybt5Ayl5OR4R2atea3SUZJCUtWr6BCVXl7xmy6XApYCiGEEOL6kACQEEIIcQu4kJ9HuerCw2IltaSoWgCo3OXi0ZVf4bjUCt5EZUDksvdmz2P52dP0CgltUtcsl6bx022b8XFzwzAM7undj7bePs1+Ttfyt4P7OJCWigK8eXAfv5ow+Zr3PDl4KE8OHnrNcVdSdZ3U4iLCff2wmEzVrpkaeISssW3bnZqGYRgtVqz6VvTf40eJzc1BNwzeOLCXN++c1dpbEkIIIW5r396fOoQQQohbyK/GT+L1vbsYEd6BAW1qCzZUBirMisI9ffrx1BVBkCBPT54YNKTJa+9MTmRj3EXsqoswbx9eHj22yXNdVqG6+P7G9SQVFfLbSVPpU0t9nQ5+/rhdCmR19PNv9pq10Q2DuZ8t5UJ+Hj2CQ1i+8L4GB32a6mR2Fvd+9RmqrvPvWXcxpkOn67rezap3aOil91ehb2jb1t6OEEIIcduTAJAQQghxC5jXszfzevau9Zqn1cp/58zjq9jT3NWjFyMjWvYYUoSvHwYGHhYLkQEBLTLnqnNn2ZoYT4Wq8uOtm1h1zwM1xjwxKJpwH18UBe7s0q3Bc5/IyiSrtJSJkVGYr8roudof9uzidE42AGeys8m32+tt+a4bBivPxuLUVOb17N2kY2Yrz8ZS7nIB8PGJ49/aANDs7j0J8/HBoWqMauHPrBBCCCFqkgCQEEIIcRsYFh7BsPCI6zJ3t6Bgvph/Dxfy85jauSuqrvODTes5mZ3FL8ZPZFRE44tKR/j6AeBusRDpX3tQyaQozOhWf/v3qx1ITeHRVctRUJjboye/mTilzrEJhQX89/iRqu/HdOhI0DVqJL179BB/2r8XxYDU4mK+N3J0o/YHMLVzF5adjEHHYG6PXo2+/3YypF14a29BCCGE+Nao/89iQgghRCMUVVSwLyW5KrtB3Hxyy8u5+/NlTP3ofc7l5Tb4vt6hbbirRy88rVZ2JCawMe4icQX5/GDThibtY0REB0aER1ChqmSXlaLpepPmudr5/DwMw8Cuulgfd4HR77/D8tjTtY71d3PHYjZjM5kI8fTk37PuqrfYs0NV+fuhAzg1DYeukVFa3KQ9Dm0fzt7HlrD70SVMa0RmkxBCCCFEc0gASAghRIuwu1xM/fi/PLF6BXM/W4phGK29JVGLpSePcyIrk4sF+by+Z1eT5mjv64t++UhYHdk71+LSNLYlJgBwPDOThMKCJs1ztTndezIorB1tvb0pczpJLynhJ1s31To2wMODrxfezytjJ7DyngcwXeO4WE55GRWqVvX9c0NHNHmf/u4e9R41E0IIIYRoaXIETAghRIvIKiul2FGBQ9O4mJ+HU9NuuQ5HhmFQ4nTiY7PVmwlyK+seFHKpQ5hSrfByYmEBxQ4HfUPbXPO59wgO4bP593AhL7fJGSxWs5mh7dpzIjuLEE+vqiNhzeXr5sbSeQvJKStjwofv4qEodKingHTXoCC6BjWs/Xh7H1+mRHVmY/xFFvXqS6cGBr+cmsZru7aTWlzMT8dOoKN/3fsRQgghhLheWuQnc0VR/IH/AH0AA1hsGMa+lphbCCHEraGjnz93dunGhrgLPNRv4C0X/NENg3u/+owjGelM6BTFOzPn3JZBoGlduhLkeTclDicTOkUCcDAtlUdWfoUCPDNkGM8OGV7tHoeqYjObq70efUPb0LeWzl2NsaBXH9JLS5jetVu9n5fM0hL+e/woPUNCmdO9Z4PmDvHyYvW9D3IiK5PxnaKatc/LFEXh79Mb36p8xdkzfH7mFA5VpdTp5NP5i1pkP0IIIYQQjdFSP52/Aaw3DGO+oig2QHKahRDiW0ZRFP58x/TW3kaTpZcUczwzE90w2JYYT4nTia+bW2tv67q4uvDukYw0VE1DNQx2JiVWCwD9asdWPog5Rq+QUL5ccG+LBfZ0w+BHWzeh6jofHD/G/J696RxYeybOU6tXcionGzezmTBvH4a2r77/hMIC7vnyM+wuF/+ZPbfqekc/f946dICfbN3Mw/0HABCbm8Mvxk0iwq/2jKPc8nI8rVY8rdYWeZ4A/u7uKIDFZCLAwx2AQ+mpfHH6FLO792R0h8YX0RZCCCGEaKxm/xSnKIofMBZ4BMAwDCfgbO68QgghxI3U1tuHrkGBXMjLY1BYO3xsNqDyWNi2xAQ0XWdyVOebIiuosMLOy5s34tQ0fj95KqFe3s2ab26PXnxx+hQFFRV8d/ioatc+PhmDAcQXFHAmJ5uBYe2atdZlCtDGy5s8ezkmRSGgnu5bdlVFv1RTqkJVa1z/6sxpcsvLMIB/Hj5YFQBKLipi1fmzODWNtw4frBq/O/k9JkZG8erEKay5cJ6s0lKWDI7m67NneG3XDtwsFpbOW8iykzEA/Hj0OHzqCQaWOBwcSEuhX5u2tb4XU6K68NtJU8koLeG+Pv1xqCoPr/iKClXlm/Nn2ffYk/i71999TAghhBCiuVriz3iRQA7wvqIo/YEjwAuGYZS1wNxCCCHEDWExmVix6AGySksJ8/GpCvR8cuoEr+7aDsALw0ayZPCQ67J+saOCRV9+RmJhAa9OnMK8nr3rHPuvw4fYlhiPbhi8vncXr0+5s+qaYRj8/dB+TmZl8b2Ro+keFHzNtdt6+7D14cdqvTYpMortiQn4u7vTtQFzQeWRsd/v2UWxo4IfjR5HUC3FjhVFYfmi+9gaH0d0u/YEetSdPPzW9Fn8Zf9e+oS2YUwt2TIjIiJ499gRFAUmR3WuejzUywtfNzfKXS4siolipwMAl66zJSGeio3rOJCWiqrrJBYVEJefj0vXMWkav929gyPpaQB4Wa28MnZCrXszDIN5ny8jo7QEq8nMjkcer5E5pigKs684uuZQ1coD80IIIYQQN1BLBIAswCDgecMwDiiK8gbwQ+CnVw5SFGUJsASgQ4cOLbCsEEII0bIsJhPtfX2rPXYxPx+npmEYBhfyG942vbG2JyaQXFSEQ9P48/499QaA2np7YzWZMIAw7+r73ZWcxD8PH8KuuogvzGfzg4sbvAfDMPjt7p2sPBfLowMG8lT0MP4xfTZJRYWEeXvjbmnYsagPY46x7FQMmq7j0nXemDaj1nEhnl4s6tPvmvN1Dgyqt/bOqIiObHrwESpUlS5XHCPzsFrZcP8jfHPhLL/bvbPqcROV77Wn1QpG5XG0CpfKw/0H8tNtm1EUhVBPL0yXgoBel7LBaqMbBvEF+RiAu8Ugu6z0mkcH3SwW/nvX3Xx2+iRzuveU7B8hhBBC3BAtEQBKBVINwzhw6fsvqQwAVWMYxjvAOwDR0dHydy8hhBC3hCcHD+FUdhaqrvHisFHXvqGJ+rVpi6KAh8XC+I6R9Y59sP9A/Nw9cGpqjUCRl82KgYFZUfC2Na6GUXppCR+eOIZT0/jjvj083H8QHlZrVav39JJiMkpLGNi2HSZF4WJ+Hs+sXYWn1cY7M+dUHX/ysFoxKQqGorRoLZ36hNfRRSzAw4MShwOnVtm+fXBYO+b36oPVZGJG1+789cBessvKeHnUGEI8vfjrgb0UVlSwMe4iTwwagpfVyiMDBtW5rtlk4oejxvLPI4eYGtWFzgGBDdrv0PbhNWoZ1Sa7rJQHv/6Sggo7/5g+q0b9JiGEEEKIhlIMo/mxGEVRdgGPG4ZxTlGUXwBehmG8VNf46Oho4/Dhw81eVwghhLid5JSVkVlWSp+Q0AbVGjIMg49PHOd4VibPDhlG1KXgw/qLF4jNyeaBfgMI8fJq8PrlLhdj3n8Hh6bh5+bOrkefqMqCuZCXx12fLQXgrh49eXXiFJ5as5KNcRcxKwpLBg/hpZFjANB0nY9OHKfY4eCxgYPrzaC5EeIL8rn782WUOZ38aep0ZnXvUe26bhj8fs9OjqancTwrE80wcDNbWHPfg1WvKVQ+rx9s3sDh9DReGTOeKZ27VJvnvWNH+NO+PQwKC+Pd2fOwmc3N3vs/Dx/kz/v3oOo6Q9uFSwcxIYQQQtRLUZQjhmFE13atpbqAPQ8svdQBLB54tIXmFUIIIb41Qry8GhWwOZiWym9376BC0ziRlcmmByv/8zutS1emdena6PU9rVbW3f8wh9PTGREeURX8AdidnIhTU9EMg93JiUBlK/hdSZVf9woOrRprNpnqzZo5mZ3F3pQkpnfpToSfHxkllZlHfUPbMr1rtxrjVV1H0/UmdyCLCgjklTHj+fHWTfxo60a6BAbSM+R/+92TksTHJ2Kwqy783d1p6+XN9K7dqwV/APanpbDh4gXKVRcvb95AOx8fQr29CfGsfM/+vH8PdtXFsYwMjmWkMyw8ot59abrOscwMOvj51VnIu3+btlhMJqwmE8OvMV99kosKWfTlp5Q4nfxr5hxGRUjnMSGEEOLbpkUCQIZhHAdqjTAJIYS4vi7XbdmZlMD3RoyukZUgbl/bExOouHS0Kd9ezhPfrOC5IcPo3zasaoym67y8eQMH01P58ehxTOtSM8BypVAv71qDMGsunke7lDUc4euHbhg8Ez2MPiFt8LBaaxxnWn3+LGdycni4/0DaeP8vuJFbXs49X36KS9P5z9EjHHz8KR7/5mvO5eViM5sJ8/au1mnsbG4OC7/8FKem8a8ZcxjXqf7jcXX57MwpXLqOZhhsTYyvFgAK8vDEwMBqMtHRz5+vF91f6xzhPn4YVGYHuXSN+V98gsVkYvW9D9HR35/osPYcTk/DYjYRFXjto2Df27iOTfFxmBRYd//DtR5jGxHRgVX3PEBhRQWDm9GBbdW5s+SUl6MbBm8dOtCgANDh9DSeXrMKf3d3Ppo7n7bePk1eXwghhBCtz9TaGxBCCNE8J7KzWHryOOfz8/i/jWtbezu3FcMwSCosJKeslNmffETft//G6vPnWntbVfalJgOVLdWLHQ62JMTxxDcrqo05lJ7G+osXSC0u5kdbNtY6T1ZpKX/et5uNcRfqXMvE/7KBDqWn8eWZUyiKwrhOkai6zvc2rmNPShJQmZn0g80b+PfRQzy6ajkPr/iSZ9d+Q7HDQbnLiW4YqIZOscOBbhjYXS50w0ChsuX7lb45f5ZSpxOnpvF+zNHGv0iXPNRvIBZFwd1sYUpU9SBpr5BQ/jNrLi+NHMO7s+fWOUdHf38+nrcAMChzuXBoGppuEJOVAcC/Z93Fe3PmsfnBxVVZQfXZl5qMXXVhAGdysusc1yUwiOh27Rt0LLAuw8LDsZnNuFtqPv+6/Hn/HvLs5SQVFrA89kyT1xZCCCHEzaGljoAJIYRoJSGXWmy7mS2E+chf6GtjGAZfxp4mu6yMh/oNwOcaXZoue3HDWjbEXcDDYsGpadhVldf37mJmt+7XeccN81D/gfxwy0bczGZcuo6haXhcVXQ53NcXAwMPi7XOAsWPfbOcc7m5WM1mPpm3sFoG0WVvTJvBwi8/Jb2kGJOiUHEpUFPmdPLYquU4NI21F85x8PGnsbtcKChohkFqUSHnc3MxmxS6BwXznWEjeGbIcFafP8uLw0ZiNpl4e+Yc/rp/DwPahjHiqmNOEyOjeP/4UXTDYF6PXk1+rS7k5WIA/h7utPWuedxqZEQHRkZcu0tpmLcPV5ZPDPf1ZfylrCSr2dygws6XfX/kGH6xfSvdgoIZ3aFTg+9riiHtwtn84KOUu1zVOqXVZ1REB2IyMzCAAbV8JoQQQghxa5EAkBBC3OLa+fjy5YJ7OZaZ0aS6L98Gqy+c4+fbt6DpOmdzc3jzzpnVricWFrA5Po5xHSPpGvS/X443xl3AqWmYUKqCKKMaECS4Ueb17M2UqC7YzGb2piZzOD2NRb37VhsT7uvHynse4ExONpMiO9c6T6nTiWYY2FAoc7lqHRPm48Pyhffxuz07Cfbw5J5q7dv/l5miKApjO3bi2SHDOJWdhZfVypoL50GpbF9fYLfz3rHDuHSdD04c486u3egeFMzbM+bUWHNvSjI/3LyBgW3D+O2kKXTwC2j8i3TJRyePoxkGhRUVHEpLY1JU7a/FtbT19uHl0WP48sxpHhs4uEYXtsZY0KsPC3r1afL9jdXOx7dR45+JHsaI8A74ubnRuYFBIyGEEELcvCQAJIQQt4GeIaHVapqI6kqdTqCyHk6Jw1HtmlPTuOuzpdhdLt44sJf9jz1V1bXq/r79ef/4UTr4+/P29FnkV9gZ2Lb+OiyGYXAiK5M23t43pGbK5WymCZ2imNApqtYxXQKD6s36eHv67Evdq9pVZeDE5efx3Y3r8Hdz4/sjRtPRP4AQLy/+NPXOavcmFBbw2MDBJBcVMiw8AneLBUVReGbIMABcmkZ0u/Z42WzM6Nqd45kZODUdu+rieGZGvc/tZ9s3k1pSTJ69nKMZGXTwC8AwDFKKiwj08MTTauVHWzayOzmJ748YzdyedWcI3dW9J8tOnsDTam12NsujAwbz6IDBzZrjVqAoCoOaUXdICCGEEDcXCQAJIYS47d3dszcX8vLILC3hlbHjq11zaRrlLheqrgMK5aqrKgD0ytgJfH/kaNzMlUGNhpQf/sWOLXx55jSgsOqe+2+JzImeIaH856raN7/ZtYNT2VkA7E5ZBsCj/Qfx03ETqsZcLtCMYaAZBlsS4tgYd4EP7ppfNcZqNrPoimyhvm3aMjw8nANpqbwwdES9++od0obMklJ0jKrX8dVd21l6MgZ3i5XXp9zB6vNnsasqr2zbVG8A6GfjJvLEoCEEeng0uZvY9RJfkM/z61bjabXy1ozZDaofdLNSdZ2CCjvBHp7VahYZhkFMViZBHp5E+NUsdi2EEEKI6+/m+glICCGEuA5sZjM/uyJwcSUvm41XJ0zm4xMxLOrTt8Yv3+4Wa6331WVHUiJ2VcXDYiEmK/OWCADVpmtgIAfTUqoVZV56MqZaACi5qBATCuWaigK4dJ39qSlV141LHbe8rbaqlugWk4l3Z89r0B7+OGUa2xLjifDzp2dwCACrz5/DoWmYFRPZZWUoinKpvtG1j4fdrDWyXt+7i9jcHMyKwgfHj/H9kaNrHbctMZ6MkhLm9ezV6M/ljVChupj1ycckFRVyZ5duvDFtRtW11/fu5oOYoxjAJ3cvon+btq23USGEEOJbSgJAQgghvtXeOrSfP+7bgwI86zms2fN9f8QYXt68ng5+/nXW3LkVvDxqLP3atGXdxfOsu3gBhcqCzFea0CmKiZFRnMrOwsNq5UJ+Hk9HD626/ubBffzryGEMDDr5+XMhP48nBg3hB6PGNGgPVrOZqZ2r17V6fFA0v9uzk0APD6Z37cbIiA6cys5iXMemtYdvCIeqsu7iedr7+jKkXcOLPDdUz+AQdiYlAtA9OLjqcaemoeo6nlYr2xMTeG7tNxhGZZe1v14Krjg1jY9OHEc3DB7qN6DVspvi8vOIycokvaQYVddZff4sf71jelUW0K7kysCozWwmJjNDAkBCCCFEK5AAkBBCiG+1fx89DIAB/O3AvhoBh8aa2a37TdMlrDnMJhMzu/VgZrceFNjt5NvLibyqi5jVbOZvVxXUvtLpnBzsqguTonAhPw/NMHjn6CFeGjm6yS3NHx8UzYP9BmAzm1EUBX93Dzr5/y/750BqCrtTkpjboxdRdXQ9a6wfbN7A5viLGMB/59xdrdNXekkx0PgCy1d6fugIeoe0wcNqrepEdj4vl/lffIJDVXlj2gwKKirQDXBoKmmX1gT45+EDvH34EGBQ4qjg/0bUnj10Pe1KTuTJ1SsxDANvmxsuXWdG1+7V3uOXRo7h+XWraePtxfSut/6/DyGEEOJWJAEgIYQQtw3DMKhQ1apCxA0xObIzX509A8Csbj2u5/ZuWQEeHgR4eDT6vpdGjia5qBB3i4XU4mLKXU76tmnboPem2OHAy2rFbDLVuFZXlktmaQmPrFyOU1NZdjKGw0880+RA05Xi8vOwX/pcJRUVVgWANsVd5DvrVwPw9ztnNbmzmKIoNe7dEHeBcqcTHfgg5hjvz5nHwbRUUouLeHXilKpxJU4nuqFjACUOZ5PWb65jGek4NQ3dMBgSHMxf7phB4FWfl7EdOxHz1HOtsj8hhBBCVJIAkBBC3CISCgvwttoI8bp1C8ReT4Zh8Pg3X7M9MYHxnSL5z6y56IbBe8eOkFNexjNDhuHvXjOI8frUO1nUux/uVgt9Qtu0ws5bzkcxx1hxLpbHBg5maPsIgjw8WiQAArA89jR/3r+HcR078ZsJUxo0b5fAINbd/zAAJQ4H8YUF9LpUy6c+f963h7cPHyDUy5s19z1Y6/sGsOxkDB+fOM59ffvzQL8BOFQNMDCgWu2iq21NiOd4Zgb39OnboMydVydN5YebN9DJ359ZV2R3bYi7gEPTANgYf6HJAaDaTOgUxT8PH0IzdBb27ou7xcpf7pheY9zzQ0dQ7HCgGwbfHTGyxdZvjLt79eHrs7GUOB28MHwkQZ6e120twzB4fv1q1l+8wPyevfnd5Duu21pCCCHE7UYxDOOGLxodHW0cPnz4hq8rhBC3qveOHeH1vbswKQrLpIBqrXLKyxj13juouo5ZMbH3sSVsS4jnFzu2ouo6U6K68I/ps1p7m9dNekkxEz98D6emoQAmFMZFVgbCWkLPf/wVh6bhYbHwyd2L6HcdP4OD33mLggo7nlYrf71jOpOjumAYBpvj4zApChMjoyhxOhjy77dx6TpWk4n9jz1FgIcHn506ybq48ywZNKTqONWVYnOyufuLT3BqGpH+AWx68NEm7zMmM4MHvv4CgI/nLWzUv8s3Duxl9flzPB09lHk9e9c6psThwKlp1zWgcqtJLylmwgfv4dI1TIrC0SXP4uvm1trbEkIIIW4aiqIcMQwjurZrkgEkhBC3gLUXz+PQNCyKwv7UZAkA1SLIw5MewSGcz8ulW1AwQR6e6IYBRmXWQGWb99uXp9WKWVEwKwqaYaBhsC0hngrV1SIdoyIDAkkqLERRIMy7Zjet/akpfHHmFHN79GJ0h47NWmtuj558dOI4nlYrA9u2Q9V1Hvr6Cw6lp2E1m/nhqLEs6t0XD6sVRVVxM1vwsFb+SLOoT18W9ekLQEpRER/EHGVg23bMuJS5U+ZyoQC6YVDmatyRKcMwqmU+9W8bRsxTzwNgakSmVWJhAf88fBCHpvHy5g3M7t4Ty1VH3cqcTmxmMz7NCG7YXS4qVLVJx/cADqen8cq2TXQPCuH1KdOwmc1N3ktLCfH0IszHm5yyMjr4+eNts7X2loQQQohbhgSAhBDiFvBM9DCeWbsKPzc3ZkgB1VqZFIXlC+8jtbiIcF8/TIrC/F59yCwtIae8nO8OH1Vt/KnsLP6wZycDw9rx4rCR7EpO4v3jR5jXozezut96tYD83T34YsG97EhKYMXZMyQUFjKmQ8cmBX90w6DM6awWfPj07kVsT4ynX5u2NY4hlrtcPLryKxyaxtoL5zjw+NPNysp4ZewEnhg0BH93d9wsFr6KPc3BtDR0DDRVZW9qMg/1H8jKRQ+wIymBsR07VXue+1KSOZ2TzdKTx0kpKmKZ5QQRfn70a9OWwWHt+M6wERxMS+WFYQ07MmV3uVj45afE5ubw/RGjeCq6sltcgd3Or3Zuw91s4Sdjxzc4GOHv7o7FZEKh8n0zXxU8Wn3+HN/buLbyuS+4j65BQQ174a4Ql5/H3M+W4dBUfjl+Evf06dfoOV7evIGEwgJSiorYGHeBmTdBjSyr2cy6+x7mfH4ePYKCGxV4E0IIIb7tJAAkhBC3gImRUZx55gUUaLGaLrcji8lUrSOUxWTixasCP5c9u/YbUoqLOJqRwYC2YTy9ZhVOTWNvSjJrL55jc3wcd/XoxetTplXdU2C3czonm0Fh7fC0Nj+rpjaqrvP4qq/Zk5LEE4Oi+cGosQ2+t1dIKL1CQlkyaAh59nJCPBtXL6rM6eRHWzayJSGOClXl4f4D+dm4iQD4urkxu3tPAIoqKpj/xSckFxXy20lTmdalG9Cyn8s23t5VX3tZbSgKla3agKzSUgA6+vvzkP/AavfF5ubw6KrlOLXKI0L6pZ259MpaPYqi8OTgoTw5eCgNdTg9jYTCAnTD4O3DB6sCQH/Yu4s158+hKBDi5cX/jaj9s3Y1f3cPVt7zAAfSUpkUGUVyURHn83IZ07EyYPfhiWO4dB3N5WJj/MUmBYC2JyXi0FRcus6ykzENCgAZhsG/jx7mRHYmLw4bSVRAAJmlJRhAhJ9/o/dwvXhYrZIFKYQQQjRBzdYaQgghbkomRZHgTwvyd3fHrCjoGPi7uWM1VR5vMaGwJSEezTBYcfYM+fZyoDKjYvJH7/HUmpXc/fkyrlcNvfN5uRxMT0UzDP515FCD1tmZlMiw//yTe778jFKnE7PJRKiXd6M/L0tPxrA+7gJ2VcUAPjl1stZx2xLjSS8pxqXr/HX/XjytVl4ZMw5/d3eGh0fgYbFwNCOdVediybfbm/1a3dG5C08MGoJZUXA3mxnbsRNFFRX85+hhtiXGVxubby/Hdakws24YDGjTlpdHjWVwWPsmr98zJBSb2Yy7xcK4jpFVj/vY3DCZFEyKqdFHtaICArm3Tz+cmsaMTz7kxQ1rGPv+f3hh/Wpmd+uO1WTCzWxhYmRUk/Y8MTIKd4sVi8nEQ/0HXvsGYE9KMm8c2MfaC+d5es0q/jZtJr+aMJmP5y6QgIsQQghxG5AMICGEELeFfHs5vm7uNWqp1OU/s+fy+elT9AoJYWBYOz5fcA+rzsUyvmMkP966kczSMtr5+ODn5s6HMcd4ddd2XJfqCJ3Py0XVdayNrImSVlzM0cx0xnToWGdnqw5+/vjY3FCAPqFtGhTE+dWOreSUl1HqdLAx7kKdRYWvJcTTC4vJhKrrmIDpXbvVOq5/2zAURcHdYmFSZGXnq/djjlFYUcHBtDTePnyQtw8fwKXr6IbB0PbhLJu3sEHHdTRd59Vd2zmVnc1Pxo6n/6W28T8YNYZuwUE4XRoLevfh/uWfcyQjHbPJxId3zSe6XWWAZ2R4Bzr4+ZNcVFhZL2j0uKq27U0V7OnJ9ocfI624mO5XdDH73ohRhHh54WY2c1/f/rx//ChbE+J4fuiIBq+ZUFgAVHYts6sq6y5ewN/Nnd4hoZzOyWZvchI9G9A57WqR/gEcfPwpHJrW4ON4bhYzBgamS++th9XK3U38LAkhhBDi5iMBICGEELe8X+3YyscnY2jj5c2a+x5q0C+8IZ5ePDtkWNX3PYNDqn7RXn3vQ5zPy6V7cDBmk4mvz57BpesogNVk4qnoYXUGf949epgvY0/z+MDB3N2rT9XjRRUVTF/2AZpuEOLlxbaHH6v1fm+bjc0PPtrglukAA8PakVlWim4Y9GhksOCr2NP8YvsWugcF88Fd87GYTeSWlzG+UxSd6jj2E+kfwPaHHye7rLTqNQv38SW9uBgDA+cVGTgAxzMzSC8pJtzXD4BihwOHqhLi5UV8QT73L/+CCtXF+7PnkWe38/npU5SrLr67YQ1bH6p8nd47doQ/7duNAfh7uJNcXIRL11F1nYSC/KoAkKIorL3vIVaei6WDn3+zgj8ZJSX4uLnhbbPh6+aOb4h7tetuFgtPDKpsshGbm8Pre3dRoaqcyMoi5qnnasznUFUSCguICgisKqg8vH0EYzp0ZFdSEi5dw6yYsKsq5/PzKjOsDuzlsUG1NvK4JjeLBTdLw3/UG9IunN9Nmsrp7GweGTCoSWsKIYQQ4uYlASAhhBC3vC/OnEbVdQoq7MRkZTCmQ6dmzedhtdK/bVjV90sGRfPChrV422ysWHQ/HeoIjGSWlvD6vt04NY0fbd3EjG7dq4oT55SX4dR0HJpKSnERmq5jriNbycfNrd4jN+8dO8LWhHieGzqc4eERBHp4oBsG4ztGNjpb5Jfbt1LmcnEmN4c9KUnM6taDEoeD3+/ZiQH8cNTYWo83BXt6EnxFe/J/TJ/FqvNnifIPoG+btpzNzWFvajKqrhMVEEjbS53DzuRks+CLT9EMnV+Nn8SJrEyyyipr+jy1ZhX/mjkHHQOb2Uyo5//qAB1MT8WuqphQOJaZgbfNDSgBYE9KEklFRTw8YCAhnl54WK2NKnq8Me4CP9u+hV7Bobw9YzZuFgvvHDnEn/fvwWYyMzmqM9+cP8vEyCjenjGn1kwmr0s1oUyKgk8txaB3JSXy4oY1lLlcdAkIZNW9D2JSFKxmM2/PmIOq63x66gQAk6O6sDH+Ip4WK8PaRzT4ebSE2d17VtV6ulHeO3aE1RfO8Uz0UCZHdal3bGZpCWdychgRHoHHdarDJYQQQtyuJAAkhBDilje/Z2+WnorB392d/m3Crn1DI93ZtTuTorpgMZnqPcbkZbVhvRTU8bbaquoKAXQOCOTePn1ZH3eBp6OH1Rn8uZazuTn8cd/uykyT7Ez2LX6Sd48dQTcMtibGk3ZFps217E1Jxq66AHBqGj2DQwF48+B+vjhzCgAPi4VXxk645lxeNhv39unH/tQU3jt2hF9OmESYtw9pxcWE+fhUHc3bnpiAU1PRDIMvz5yiR0ho1Rx59nL6tw3jnZl3cT4vt9rxo+8MHcGprCw8rFYe6DsAd4uFlKJCNMNgfdxFNF3nYFoqny+4p2Ev5BV+vn0r2WVllDpS2ZmUyJTOXfgy9hROTUMBVpyLBSprLSUVFRJ5RaHxyzr4+fPe7HkcTEthbo/qx6bSiotZ8s0KHJcKUZ/Ly6WooqJae3aLycQD/QZUfb/1ocUkFhbS7zrW3tkYd4G/HtjHpMgo/m/4qFapMZZYWMDre3fh0DSeXbuaM898p85/G3nl5dzx8X/RDIOugUF8vej+G7xbIYQQ4tYmASAhhBC3vJ+Pn8izQ4fj5+bW6Lo8DWVrwLw+bm6svOcBdicnMSmyc7VfZBVF4WfjJlZ11YLKI1KN7ezmZbWBUZlp4mtzw8NqpaOfP1llpXjbbI3q/OXUNGxmM3ZVpXNAIBF+lYEjD6ulKtDVmCyLpMJCFq9ajkvT+PzMSXY+8kTVnABHMtII9fLCw2LFoaksHjCY3+7ZWXV9etfuAIzu0JHRHTpWm7tXSCi7Fy+p+v47Q0cwoVMUp7Oz+M2uHbgMjXKXs8F7vVLf0DaUpDgwMOgcGAjA4wOjeWXrZrxsNjp6epFSVEiAhwftLmUyQWXXrFd3bWdbYgIvDh/JrG498HVzo/TSPoodFfxk62YKK+xVx+Eqn2c3/N2rHye7WqCHJ4EenvWOaa4XN6ylQlVJLCxgepdu9LwiGNdcFaqLJ75ZwbncXH49YTJ3dOla6zhvmxsmRcFiMuHr5lZvgDW1uAhV17GrKrG5OS22VyGEEOLbQgJAQgghbgtXHke6WmZpCT/YvAE3s5k/TJ5WLfOipUUFBBIVEHjNcV+dOcVLmzcA8LdpM5nZrXuD5o/w8+P9OfM4mJ7K3B69MCkKq+55gGOZGfQNbdOomi/jOnbiO8NGEJuTwwvDR1Y9/kz0MDwsVgwMFg8YXOf9RRUVKIpSVXOp1OkAQDMMShyOamNXnz/LDzZvQAGeHzqcRwYMwt1i5d3jR8gqLUFRFO7q0YsSh6NBHbUURaFfm7b0CW1DTnkZZ3Nz+f7I0Q1+7ld6886Z7E5OIjIgoOq9W9i7LzO7deefhw9xPi+HF4aNYGyHTtVe3zM52Xxy6gR2VeUHm9bj0jRe2bYZgN9PuoPY3Bw2xF3AMAwGtA3D182NJwZFMzy8Q5P2CZVFo/Pt5Qxq267ZGTuhXl5klZaiQIv/m9iemMjRjAzsqotf7tjCBzHHOJeXw28mTOHOK4qLB3t68un8e9iXksz0rt3qfU59QtswKbIze1KSeHH4qBbdrxBCCPFtIAEgIYQQraLY4cClaQTVE7hpDpem8WXsaTwsVnYlJ7A3JRmTovDO0UO8PGpsi6xR7HDw9dnTdAkMYlREx2vfcIU3Duyr+vqv+/c0OAAEMCw8gmHhEcTmZPNhzDGmdelaI2OmIRRF4cnBQ2s87max8FR0zcevtDUhnmfWrsKkKPx3zt0MbR9O79A2fHfYSLYnJfDCsJHVxp/KzsKpaeiGwans7KraSO/MvIuvYk+TUlzE02tWYjGZWL7wProFBQOVWTYFFXb83T1qzQ4xKQrfuWqt+rg0DfNVR/ncLBYmRXWuMXZXchLvHjtMhaqSVlLCnV2qd0UL8fICKjtmtffxZW9KMhWqCsD+tBQ6BwRWHn0zYFj7cL4/ckyD91mboxnpPPD1FyjAIwMG8VIz5/tiwb2sv3iB6LB2VTWaWkq3oCDAwNNipY23DyeyMilXXfxm1/ZqASCozMDqG9rmmnOaTSb+dufMFt2nEEII8W0iASAhhBA3XExWJvd99RmaYfD65GnM6t6jxdf4477dfBRzHJTKTktul45wtffxbbE1nlmzisMZaZgUhaVzFzAwrF2D750S1YX3Y44CMLVz/YVva5NVWsr8Lz5BNwzePXaEHY883ug5apNUWMivdm6lnY8vPx07ocbRt9/v3sm/jx0mwN29qtvXmgvnqrptPTF4CE8MHlJj3of7D2J3ShIVqlotOBTg4cHjg6KZ/elHODUNwzDYlZxUFQB6fv1qNly8QLegYL5edH+DjuLV5evYM7y8ZQP+7u6sXPQAYT71Bz08Lf8r7OxdS2HnUC9vVt5zP8cyM5gc2Zns8jL2XAo0PjpgEJH+Afi4ueHUNBZe0RGuqWKyMtF1HaeuszcludnzhXh68eAVdYdaUlRAIGvue4jEwkLCvL2Z9/kyPK1WBoW1fI0uIYQQQjSMBICEEELccFvj46hQVQzg8zOnrksAKKu0FKeuYVYU+oS2YWa37tjMZmZ0bXimzbVklpbg1DQ8LBayy8sade9Px00gpbiQzQnxHM1IR9X1qkLJDVFQYccAHJpG9qUuWi3hh1s2cCAtFZvZTJ+QUBZd0U3LMAzeOXoIAyhxOLCaTJhNJu5qQNeoMB8fVt/7UJ3XHxsYzUub1uNptTE5sjNJhYUcTEth7YXzACQUFJBQWED3S4GhpvjXkYOouk6Jw8HmhLhrBj/GdOzEqxOnEpefx8O1tEWPL8hn2ckYRkZ0IMDDgwAPD/Y99mS1MQtaIPBz2axuPfjs9AlyysqafNztsrSSYg6mpjK6Y8dG1Y1qjE7+AXS6VDB73f0Pk1RUyIhmHH8TQgghRPNIAEgIIcQNN61rN947fgSnpvFw/wHXZY0fjh5LscOBl83K44Oiq+rUtKQ/3TGdX+/YSq+QUCZHVh4h0g2DE1mZtPfxvXREqHa6YbA5IR6AE9lZXMzPo0cjWrh3DwpmyaAhbIq/yHeGjmjeE7lCVmllMMmpaWSUVg8sKYrC0PbhnMjKxNvmxjf3Poivm63qOFdzzOnek6lRXbCazZQ5nYz577/RdB13swWHpqIaOu8cOcQfJt/R5A5qc3r05I0D+zCbTIwIb1h79bt61B3cevDrL8gsLeXT0ydZdc8DdAkMAuCL0yfZkhDPk4OHNCor7FqCPT1Zf/8jzZ6nzOlkxrIPcWk6Pm429jy6pMmvaUN18POng5//dV1DCCGEEPWTAJAQQogbrmdwCIefeAbdMBrVZaox2nr78N6cedccZxgGv9ixlZXnYrm/b/9G1VXp36YtXy68r9pjP9i0nnUXz2NSFNbe93C1LlhXUoDh4REcz8wgyMOTjo385VhRFF4cPpIXhze8/k1DBHh4kFhUiLmOY08f3jWf2NwcogICa73eHJc/C3n2clyahkOrzODystoodTnZcPECC3r1YXgdwRvDMIjNzSHA3aPW411To7rwj0MHcKoqMZkZVQGbpnJqOpd7e7l0HYC4/Dx+vmMrFarK/rQUjj/5XLPWuB6KHBVUqCpOTcOpVf6/x3UOAAkhhBCi9cl/7YUQQrQKN4vlugV/GiOrrJRPT52g2OHgnSOHanSvaqzdKUnYVRXDgFM5WbWOcWoad322lP2pKUyK7MymBx+5KV4LgJ+Nm0gHP38Gtm3H/F69q13LLS/nz/v3cC4vF6/rtN/EwgK+v3EdoV5ehPv68psJU+gaFISHxYKBUW8Np7/s38v8Lz5h0kfvcTq75mu/NTEep6ahGgYfn4ypdY6NcRcY/p9/8ujK5TguFXSuy3tz5jG9azd+MW4iPS9lb1nNZi5HhZpTr+h6aufjy5ODhhDu48srY8ffNJ89IYQQQlxfkgEkhBDiWy3A3QN/dw/KXE4CPTzwbOYvw98bMZqfb99C58BAxnboVOuYs7k5XMzPA2DdxfO8eRN1Nurfpi3bH36s1msvrF/NwUv1gYI8PJkYGdXi6//fxrUcz8oEwNtmY3SHjszo1p3N8XH0Dgklws8Pl6ax6vxZ/N3cmRgZVdU6fEtCZW0pm9nMkYx0el/VWWpCpyjePLAfByr39+1f6/o/376V7PIyStNS2ZGUwNTOXevca9/QNvz9zlnVHuvg58/bM2azOyWJRb36cCo7izZe3oR4eWEYRrNbt7eU744YxXdHSCt1IYQQ4ttEAkBCCCG+1dwsFtbf/zAxWZkMCmvX7FooC3r1uWbh3y6BQQR6eJJbXsboOoJEN6MKVUU3jKqvW9rF/DxOZv0vc6fc5eKb82d5KnpotVo8r+3ewWenT6IAf5g8jfjCfFadO8vYDh1JLCwg1Mu7Rst2qHzdDz7xFA5Vw8/dvdY9dAsMoriiAgOjyUfExnWKZFynSH61Y2vlPhWF+T1789GJ4/Rr05ZP716Em+XG/ghW6nRiVhTJ9mlFumGwJyWJYE+vqowxIYQQ4kaSAJAQQohvvQAPD8Z3irxh63larWx+8FHSS0tqrf2TVFjI0pPHGdo+nMlRjW8R31DlLhdLVq8gLj+f30++g7EdO9U7/q93zOCP+3YRFRDItC51Z8Y0lV1VsZrNaJeCSzazmWGX2stfKbmokApVxaIoHMtMZ+nJGByaRmJhAWeeeaHyGFYd3C3WOotWv7prO/vTUvC0WnlvzjyiAgKb9DxKnU5+uHkDWxLicGga7hYLn5w6gQFcyM8jJiuTobU8r+tlS3wcT69Zhdmk8J9ZcxnVoeMNW/tmp+n6dS+Afdlru3Zc+hwY/HfO3Tf0MyCEEEKA1AASQgghWkxmaQn59vIGjXWzWIj0D8BUy5GgB1d8wbvHjvCd9Ws4n5fbpL3YXa6qbJ26bIq/yLGMdLLKSvnF9i21jnGoKjmXWtxH+PnxxrSZvDBsZK37bq6+oW14edQYJkV25m/TZrLlwcVVXbRWnj3D73bvIKu0lFfGTsDf3R0dWHnuLGZFwd1sJsjTE0szfpn/KvY0Ll3Hpevk2+1NnufTUyfYHF8Z/DGhEO7ry+iIjrhbLLhbLPi7u/GL7Vv4+MRxjGu8Ry1h6akYVEPHoWk8tuprUouLrvuaNztV17n3q8/o+ve/8PPtm2/Imkcz07Grlf8uT+dk35A1hRBCiCtJBpAQQghxSWGFndTiYnqFhDY6wLE89jQ/3roJk6Lw0dz5DA5r3+R9OFStqruUU9Maff8f9+7mn0cO0tHPn5X3PFBnt65ugUFVQaKU4iJiMjPo3zas6np2WSnTl35IidPB09HDWrzjWG0e7j+Ih/sPqvbYgdQUfrx1E05NY39aKisW3V8V4LK7XLw6cQplLheTIjs3q8bOvb378c7RQ/i5uRPdhPfPpWnsTUnGajZhNim4myzc3bM3v54wGU3XOZ+fR7iPL4tXLedYZgZuZjPtfHzrrKWkGwZ/3Lubk9mZ/HDU2Bo1jRpqUa++bE9MAEBR4HhmBiUOB4cz0pnWpSshnl5NmvdWdvFSJhbARydi+NnYidc9E+jHo8fxnfWraePlzV3de177BiGEEKKFSQBICCGEAHLKy5j60fs4NY0JkVE1ivtey8pzsVXBmu2JCfUGgHTD4K1DB0goLOD/ho+ivW/1zlb/mT2Xtw7tZ1RER3qFhPKX/XuIzcnhpZFj6BpUf12a3clJvHvsMLphkFVWyrGMdMbUcbSrZ0goo8I7sjUpHs0weGXbZr6598Gq6wfSUqlQVVy6zudnTt6QAFBtLtcb0g2j6uuH+w/i30cP0cnfnzu6dK3zWFdjvDRqDEsGD8HLZquRSeRQVZ5as5LT2dn8YvxEpnftXuP+59atZk9yErph8KMx47CZTMztWdlJLbGwgHeOHGRg23Zoug6GASjohl7nfnYkJfBBzDHsqovn169m60O1F+euj6brDGnfnp+MHsdv9+yknY8v3YKCmfvZUnTD4L1jR9hWR9Hv21lHP3+CPDzJs5czoG3YDTkGFt2uPXsXP3nd1xFCCCHqIgEgIYQQAjibk4NL17GrKruSkhp9/+IBgzmQloqb2czMbj3qHbv+4nnePnwAh6qRUlTE5wvuqXa9b2gb3p4xB6is3/Kfo0ewqy5SiotYd//Ddc67Of4i31m/BlXXMQFeVit929SfNdItOIitSfEApBRVPxo0PDwCL5sVp6bxQN8B9c7TEJqu86MtGzmYnspPRo9nSueG1Tca27ETzw8dwansLP7vUueqH44ey3eHj8RmNjcq6+f1Pbv48MRxpnftxu8mTa1xr5vFjKbrNQJAu5OTOJSeRrnLxS93bK01ABSTlUG56sLTaqWtl3e157dk9UoSCgvYGHeRP0yexo6kBLoGBTEpsnOdew1w98DAwGIyEeDu0eDneFm5y8XMTz4kpaiIhb37cP6572JSFGJzsjEAh6aRVVba6HlvBx5WKxsfeISkosImF/sWQgghbjUSABJCCCGo/Ot8l4BATudk89yQYY2+f1ynSE4+9TwmRblmNoHVVFmkWFHAaq5/rJfNhoGBCUgsLGTM+//mnzNm13ocKLGwEN0w0AyDQW3b8fG8+dfMjHlkwCA+OXWCcpeLhb37VrsW4unF7keXYFdd+LrV3jWrMQ6mpbLmwnnsqouXt2zAzWLhs9MnWNCrb71FuBVF4anooTUev7KTlmEYbIq/SGJhAdsSExgU1o7s0lIOZ6Tz0zHjmRjVGbvLxb+OHkI3DFaei2XJoGh+t2cnZ3NzeW3iFBRF4fFvvsasKCybt7DacbjOgYEYhoGnxUq/Nm1r3edPx07g59u30DM4pEZBbYvJhAkwgDAfH/4wZdo1X68BbcP4x/RZnM3NYWGvvtccf7XTOVlkl5WhGQZfxZ7h1YlTAegRHMJjAwezJSGeF4eNqPXevSnJnM7JYm6P3gR7ejZ67VuBh9VKD+nGJYQQ4ltEuRHFB68WHR1tHD58+IavK4QQQtwMDMPgk1MnSCwsYMngodf8BXvthfP8asdWsi8VYx4RHsHSeQtrjCt2OPjuhjXklJfzxynT6BYUfM29fHHmFH/et5sh7cL567QZ16W482XJRYXcufQDALoGBhGbW5l1ZTObOfT40/i4uQFwOD2NFzesoY2XN+/Nnldny/YrvX/sCH/ct5sKVcUAbCYzilKZ5RLo4cHhJ55BNwymfPQ+WWWleFlt/Gj0WH6ydTN21UW4rx99Q0NZd/ECAA/1G8Avxk+qtkZSYSFxBfmM7tARWy2dxg6lp7LsRAwzu/eokdmTUlTEu8cOM7BtGHN69GrKy9doZU4n05d9SEZpCXN79OL3k+9o0H2xuTnc/fkyNF2nW1BwtWOBQgghhLi5KYpyxDCM6NquSQaQEEIIcYMpisJ9ffs3ePz0rt14adP6qu9DvWov2uvr5sa7s+c1ai8/27YZh6axJSGOk9lZ9K8ju+Vqmq6jG0a9Ldev1sHPn+WL7ic2Jxs3s4Vn131TNdeVgadXd20nvaSE3PJyVpw7w8P9B3EuLxeb2Uykf0Ctc8cVFODQKotnK1z+H/CwWIgKCEQ3DPakJPHXO6ZT6Kigb2gbMkpLMTDwtFrpFRLCvB692ZIQjwLc2aVbjTU6+vvT0d+/1vXj8/O576vP0QyD9XEX2PnoE9WKK0f4+dUIKF1vXjYbWx5aTIHdXm+QcUt8HK/u2s7AsDAW9OpDicOJgoJL18lrYFc7IYQQQtz8JAAkhBBC3CB7U5L54vRJ5vbsXeOI0LUsHjiItw4dJMTLk1+2YCAhKiCQpKJCFKCdt0+D7jmfl8uCLz7BoWm8M/OuqudSVFHBmgvn6B0SWu341JW6BwXTPSiYfx05iFlR0AyDboHBeF3RqWxg2zDO5+VhYNAzOJRPT53gVzu3AfDW9Nm1Hhd7bugwLubn4dQ0RoRHMDwigmBPL87l5jA5qgs/37aFr8+eQcfgk7sX4e/ugb+7B8sX3kdiYSETI6Owmc3sW/wkJkVpUNbRlZ5YvQLtUla1bhgoXL9MqsawmEyE1BEwvOzlzRvIr7CTXFzE2vPn8bLZuL9vP2KyMnl51NgbtFMhhBBCXG8SABJCCCHqsDUhnviCfBb27tPsGjjlLhePrVqOQ9NYF3eBg48/1ag5vzdiNE8NHoqH1dqoY1qxuTmcyclmSlQXfC8dsbrSp3cvYmdSIn3btKk3UKAbBqvPnyWxsJA9yUmUOJ0AvHvscFUA6JGVX3E2NwdFUfjmngfoXE9x3Xk9erPybCz5dju/nFA9oPXK2AmM7xRFiKcnPUNCeffYESpUFQXYm5JUawCorbcPn85fVOPxnpdqvBxIT6FcdeFusRCbk12V6dQjOKRaHZgylxMfW83X6UxONhfz85jauUutdZVUvbKbl1lReHnU2BoZN+klxWyMu8jw8Iibru5MZEAA9hwXdlXFYWgoqot5PXvz4zHjW3trQgghhGhBEgASQghx2zMMg9UXzlFUUcGCXn2qFQ+uy8G0VJ5f9w2qrrMjKYGP5i5o8HqH09ModjgY3ymyKlijQFVWyJX/W5v0kmIK7HZ6hYRW61J1ZZZMQyQXFTL/82UYwMcnjvP1ovtrjPFxc2NGt5odra728Ynj/G7Pzqo27ABWk4k53XtWfZ9WUoxD0wB4Yf0avl50f51HxEK8vFhbR0czk6JUy5B6Onooh9JScbNYuKdPv2vu9Wrn83JJKazscObn5l6tg1deeTmPrPyK7LIypkR15qvY01hMJpYvvJ+uQZUBrLO5Ocz/4hMU4OuzZxgU1o6tCfF8d/ioqn3+e9Zd/OPQAaLD2vFg/4HV1jcMg7mfLaWowoHFZGLXo08Q4NH4rl5XSispZsXZM0SHtWdYeESz5np/zt1sjr/IwbQ0vj57hjEdOjaofpQQQgghbi0SABJCCHHbW3E2lle2bUI34FxeLr+eMPma91yufeLSdXLLG14HZVPcRV7YsAYFeHTAYL4/cjRQ2XHog7l383XsGWZ371lrNo6m67ywfjXrLl7AajazZNCQqrbntXnv2BE+OXWCB/r15+H+g2pczygpQUHBrrpIKips8HOoTUZJCS5Nr/reYjKx5r6HqrXQfmzgYH6/ZxdQ+Tqfycmu8yhYYwxoG8avJkzm8zMnicvPJyogsEH3XS5w3c7Ht7LlGpV1kq587Veei+V8Xi4uXefz0ydRDQPdMFh74RwvBI0EKgNpJhTKVRdncrI5kJZKhary7NpVnHz6OwB0CwrmjWkzUHWdtw4dIK+8nOeHDcff3QPNMCioqEDVdRSlslh3cwNA9331OeklxVjNZtbd93CdtYkawttm464evbirRy9emzSlWfsSQgghxM1LAkBCCCFueznlZai6jqbrZJWWNuieqVFdWNS7L+fycnll7IQGr3UuLxdV01ENnVM5WVWPa7rOsYwMvG22qmNJV9uelMCm+DgMwKlpbEuMrzMAVGC387s9O1F1nVd37eCu7r1q1K0Z0j6c2d17cCAtlR+Nbl4tlyWDhxBXkE9KcREeViuPDxxcLfgDEOThiUJlq3OoPFrUEvLt5Xx/0zqcmsbB1FSOPvksntb629sD/Hz7FipUlaIKB37ubhTYdZ4bOrzamD6hbaq+Vi/V8HHpOv88cpBTOVlcyM/j5ZFjGR8ZyensbF4YNoIfb92E1WQiqJbCyl+cOcXfD+1H1XWyy8t4886ZWEwm/jx1Ov88fIBZ3XvQ0d+foxnpfHfDWrytNnQMBrQN4zcTJmM2mRr0mhQ7KtAMAytQ6nQ06J6bkWEY5Nnt+Lu7Y2ngcxdCCCFE00gASAghxG3v/r79ic3JpqCigp+Pm9ige8wmEz9r4Ngr3dunHzuSEiisqOCHlwroljqd/DfmKP84uB/NMMgoLeHNO2fVuDfUy7vqyJhJUfju8LqzfzytVjytVpyqipvFinstx9pMisJrk6Y2+jnUJsDDg3dm3VXvmFnderArOYmzOdn8euLkemscpZcUs/bCeYa0D6+189iWhDh2JydxX5/+VKiuqho7FrMJ86XXKKmwEJeu1QhEXdYlIJD4ggIUBb6550GCPT2rHakDGNo+nO+OGMVf9+3Bpet4WKyUqy40w2BnUiIuXecn2zZxdMmzVfd08g/gcHoas7r1qLGmbhiVETADjKpQGMzs1p2ZVxy1+/XObaQUF1V9n1JUxOTIzkS3a8+yUzF09Atgeteancgu++eMOfzt4D7GdYyk9xVBrFvNS5vWs+r8WSJ8/Vh974N4NCCwJ4QQQoimkQCQEEKI256XzcZfps24IWsFeXryxYJ7q77PLitl2tIPKHU6MXQDRflfweCr9Q1tw/tz7uZ8Xi53dulGekkxZU5nrbV/3CwWVt/7ILuSkxjboVOD6ho1xZaEOM7l5rKod99aM16u3tMbDXydF37xKTnlZZhNJrY8uJgwn/91IEssLOC5td/g0DRWnD1DsKdXZWAFsJlMmBSF7YkJPL1mFQC/HD+Rhb371ljj3dnz2Bwfx+gOHestcP3EwGjKnE4SCwvoHRLKH/buRtV1LIqCh8VSo/X8gLZhDKjjaNui3n3JKy8jz27nxWEj61yzX5s2XMjLw666sJrM6BiE+fjwwvo17EtNxmwy4WOzMaaObnHDwiNYWkftn11JibyybTO9QkL46x0zrttnoyWsPBeLZhhklpZyLi+3ztdVCCGEEM138/5EIIQQQtxA8QX5vL53Fz2CQpgc1YVy1Ul0WPsaGSONdTwzA6emoeo6HhYLd/Xoxf/Vk9kzPDyCYe3DmfPZUuLy8whw92DLQ4tr/SU+3NePe5tQFLmhjmWk8/y61ai6zpaEOL5aeF+LzZ1fYcel65hNJoqdDsL4XwBI0/WqlupFDgdFjv8dcSpyOMgsLeXtwwdwaJUFqbclxtcIAKUVFzPjkw+xu1SeHzq8xtGvK5lNJr43orJW07vHjmA1mXDpOp0CAngmehiTo7o0+HlZTCa+U0/g57KfjZ3I2I6R+NhsxBcU0C0omF4hoRRcel0sJhOFjopq96SXFPPLHVuJ9A/gpZFj6jwu9pOtm0gtKSbPXs62xAQmR3XmiW9WsC81macGD+HFej5/N9qc7j1Zdf4sbb296S6Fp4UQQojrSgJAQgghBPD8utXE5uawLSGetw4fwGIy8fzQETwVPbRZ8w4P70CopxfJxUW8MGwESwZfez6npnE6OwsD0O3lpJeW1MhCaYr0kmLsLle97dmvVORwoKCg6joFFfZmr3+lt6bP5m8H9zElqnONX/w7BwbhY7NRUFFR4z4Pi5WN8ReIycoEKjuRLRk0pMa4fanJuDQNl67x5ZlT9QaAAEocDv60bzd2lwu3S53LnhgYzV09evGbndv4+GQMU6K68LdpM5odFITKoNOkyM4ADG3/v0yeP0+9k9/s2k7ngCDu7FL9CNi9X31GSnExAP7uHnV+NrsFBZNnt6MbBp38/Tmbm8PBtBScmsbfDx3ghWEjW+Q5tITXp0zjh6PHEeDu3uD6R0IIIYRoGgkACSGEEICX1YZZUdANAx1wqSoH0lKaHQDydXNjy0OLUXW9zpboV3OzWHig3wCWnYxhbMdOdPTzb9YeAA6lp/Lwiq9QdZ1+oW14a8ZsQr286xx/OjuLVedjGRURQYWmVdUzag5V1/n4xHFKnU4eGziY5fVkFN3fdwBvHz6AZhiYqCqrw68nTCa9tDIIYjGZmN29JwPD2tW4f0yHTnhabTg1jUcH1uyQdrU/7tvNp6dOoCgKjw8czJLBQ/F1c8PucvH+8aMYwKb4iyQXFTWr49a1dA4M4v05d9d6Ld/+vyBcUmFBnXP8Y/osNsfHERUYSI/gEIodDjytVhQUeoeG3jTBHwBFUQi+xtFCIYQQQrQMCQAJIYQQwD9mzOLDmGOEennx8YkYSpyOeoswN4aiKA0O/lz2y/GT+OX4SS2yPsCB1FQcqooBHM3M4KVN6/ngrvl1jn945Vfk2+24WyysuucBugQGoeo6Z3KyySgpIc9ezl09ejWoG9dlS08c5/d7dmIAWWWl/HrC5DrH/t+IUSzo1Qd/d3fKXE5OZGUyPLwDvm5u5JSVkV5Sgqpr/HDUuFrvb+Ptzb7HnsShqrXWULqaWTGhoKBQGYC73Cre3WKhW1AwKcVF+Lq50da77qBZSyh2OFj05ackFBbw6wmTWdCrT9W1H4wcwy92bMXLauP5YSPqnMPNYmHGFQWnfd3c2PzgYi4W5NE3tGbB7YZ48+A+Pog5xl3dezaoK56q68QV5NPRzw93S92fkQrVxbvHjmAzmXlkwKBG/zu50UqdTnYmJdInNJQOLRCYFUIIIW4kCQAJIYQQQIinV1UdmAf7DWzl3bS82d178J9jhyl2ODBBVbexqx1KT6XU6eSKBlZVY5/45mv2p6bg0DTcTGZ2JCXyr5lzGryHMpcLw6jslFXucl1zfISfHwA+bm609a6sEXQsI537v/4C3TD427QZBHh4oOk6bx7cT2JhAS+NHEN7X1+gMkPIUkvwp6iign2pKQxsG0abSwGdh/oNYMXZMzg0lcFXZBQpisLXi+7jZHYWPYJCrntB5Z1JCSQXFeHUNP66f29VAKjU6aRzYBAHH3+aAA+POt+/uvi5uzM4rH2T9lTmdPK3A/vQDIP3jx9l7cXz/P3OWQyqJfPqsvuXf87J7CxCPL3Y+MAjdb5uf9q3h49OHEcBNMNocMbdxfw81l+8wPhOkfRpQhe0D2OO8ebB/UzoFMnvJt/R4Nfz/uWfE5efj6LAlocW15tFJ4QQQtxsJAAkhBBCfAuEeHqx7aHHWH72NKnFxTw7pGZNnA1xF/juhrUoKMzt0RPdMBjboRNRAYEA7EutrCMD4NA1Uq9oY36Zdqmw89VisjIZERFBdlkZZU4HPxpTe+bOtay9eJ4KtbL48+enTzG1c1fWXjzPv48ewqGqpJeW8Pn8e+q83zAMZn/6MXnl5VjNZnY9+gTeNhvr4s5T4nSgGQZvHtzPyIiOVfe4W6wMaRfeoP1puk5sbg4d/Pyrsogao1+btpgU8LBYGN+pU9Wcsz75iJzyMvzc3Nn28GPYbmCmjLvFQltvH3LKynDqGpmlpfxm53aWL6r9CJ9T0ziUngZUdsFLKS6iSx11p8pdLnTDwITSoKAggEvTuPvzTyhzOnn78AH2PfZUo15rwzD49c5taIbB6gvneGTAIHqFhDbo3ov5edhVFQ+LlbTiYgkACSGEuKVIAEgIIYS4ze1OTuTJ1StRdZ3XJk7hsXHRtY47m5uDS9Oq2nL/Z/bcatefiR7Gmwf34e/uQTsfH16bOKXa9e9vXMfys2cY26ET782ZV5VVsfRkDK/u2g7An6ZM486u3WmqWd16sPTEcTTD4P5+/YHKejj2S0EhyzUyOZyaRlpJMbph4Gbo5JWX422zMaBNGFazGRswIrxDnfe7NI3vrF9NTFYmPx87kTu6dK12/cnVK9mXmoyH1crmBx/F392jzrkyS0vwtFrxdXOveqyDnz9bH36MzNJS+lwKSpQ6naQWF6EZBi5No8Bur8pcuhHMJhPf3PsAH5+I4R+H9mNWTPRvU/dRMpvZzD29+/Lp6ZMMDmtfbwHzH4wcg6bruFnMDc7+0QydCtWFjoFmGDhUFRoRAFIUha5BwSQXFWJWFMK8fa590yW/Gj+JP+7bzegOHekvLeuFEELcYiQAJIQQQjRQqdPJIyu/Ir4gn99NmsrUzl2vfVMru5CXx+JVX6PqOlAZjLn7iroyV7qvb392JCVS4nDw0qgxQGUGxx/27KLYUcHZvFzu6d2PX02YVKOQcLnLxddnzwCwPy2F1OKiqhop+1NTqFBVFOBQelqzAkB7U5LQDXAzW6oCC5vi4wAwKwpjO0bWe7+bxcIPRo7mP0ePML1rNzpcOmY2IqIDq+55gCJHBYPa1n20aXdKEruSkyh3ufjpts01AkB7UpJxaCoKChfy8whw9yCrrJS88nL+sGcXw8Mj+MOUaXx84jiv7d6BxWTiywX30iM4pGqOEE8vQjy9qr73c3fnwX4D+fz0SWZ370Golxc3mr+7B88NHc6ETpFkl5cxrpbXOae8jFKnk0j/AF6bNJVfT5h8zc5efu7u/G7yHY3ai7vFyhvTZvDfmGPM79mbkCa8Hp/Pv4d9Kcn0CW1DgEfdQbqr3d2rT53/foQQQoibnQSAhBBCiCscTEvlh5s30DkwkDfvnFmtgO3m+Dhic7Kxqyqv7dpxwwJAiYUFuJkthPk0PFPhsl3JiVXBH4AH+g2oc2yIp1eNzlw/3rKJHUkJaEZlUaDlZ08zv1fvGtkPHhYL/duGcTY3hzBv76qaPQDPDBnG0Yx0rGYTD/VvXn2lr2LP4NQ1TErlkbRO/gFEt2vPxfx8DAyGtK9Z50Y3DL6KPY1L01jQqw9LBg9lyeCa2SZ1HVO6UqR/AIZh4Gm10iPkf0EbTddZsnoFql55RK5nSAiGAbM//RgFBZemohoGy8+eoUdwCN+cP4tT09ANg93JSdUCQLX52bgJ/GzctYsvX2+9Q9vQu5bHY7Iyue+rz9ANg5dGjmHxwMHXta37tC7dmNalW5Pv97bZmNK5SwvuSAghhLj5SQBICCGEuMJPtm4isaiQrLJS1l+8wF09elVd63XpF35Pq5Uh7RtWE6a5Pj99kp9v34KiKLw7ay4jIuo+nlSbi/l5VV/3C23DvJ61/fpeNzeLBUVRUAwDi8mEoijVgjuXKYrCZ3cvIr6wgE5+/tVq1PQMDmHP4iWNWvdcXi4/2LSeNl7e/OWO6ZgUhd/v2YmvmxtmRcHTZmP8pSyUn4wZz4ROUYR6edEtKLjGXB8cP8rr+3aDAWklxbw0ckyj9nJZbG4Oh9JSeX/OPPLtFVU1ei5f25+aUtm2XlH4fP49LDt1AsMwqNBULCYTXAqirblwjscHRfO9jevwttqYEnX9AhG55eU8uXoFxQ4Hb94585qBpqbal5KMS9NQDYN1F8+zeODg67KOEEIIIZpOAkBCCCHEFXoEB5NeUoKBQeSl4seXdQsKZt39D5NaXMywBgSADqWnEl9QwKxuPRrVLv1K6+Mu4NA0FCqPHzU2ALQjKbHq66ZkTPx20hQ6+VcWNA7x9GLAFZ2zrmY1m+leSwCmKX6xfQsns7M4Z87ly9hTlDmdfHr6JLpuMKNrd/409c6qDBOTojC6Q8c658opL0fVdQzDIKesrN51CyvsHExLZVBYe4I9PTmbm4NZMeHv7s78zz9BM3RCPL3Y9egTVfe4NI0Ofv54Wq0oikLP4BAURWFm1+58eeYU6SXFLB4wmD/v34NJUZjepRuzuvVgalQXLCbTdc2U+eRUDCeyMtEMg9/v2cX7c+Zdl3VmdO3Oe8ePUFzhaHAtHyGEEELcWBIAEkIIIa7wp6nT2Rx/kQg/f/rW0l66g59/VW2b+hzPzODhFV8BsDHuAu/Obtov3ksGDeFAagruFivzrshGagjdMMgsLQEqC/MGe3ry94P7uatHT8J9/Ro0h6+be5MzZpoj0j+AE1mZGECErz/JRYUoKJhM4OfuVi1oUuJw8NbhA/jY3HhiUDTWqzpkPRU9hJTiIpyaWlXbqDaqrjNj2YcUORx4WKx8b/hIfrVrOwAvDR+NYRg4NY2csjIMw8Cl69zz1Wccz8zgkf4D2fzgYi7k51V9bvzc3fl60f1V88/s3oNSp7MqSHa9W8oD9AgKuVTcWqn189xSIvz8OPDYUxjQ6Bb1QgghhLgxJAAkhBBCXMFmNjO9liLFZ3KySS8pZnynqMrjPNeQVlyMgoJddZFUVLNdekMND4/g1NPfAahRePlaTIrCD0eN5e0jBxnePoKfbd+CU9P49PQJdj/6vyNZumHwoy0b2ZWcyPdGjObuBh4Tyysv53xeLoPC2rV4MOOX4ycxtH04wZ5ejO7QsTKDB4Myp4vHrjpe9Jtd21hxNhaTYsLTauWRAYOAygLWT69ZRYWq8uadM6va2del3OUiu6wM7VKgZ3tSQlXL+YSiQp6MHsLGuIs8P2Q4iqJwNjeHc7k5AHx44jg/GzeR6HY1axBd1t7HtzkvSZNM6dyFD++aT4nTQbiPL2/s38v4TpHXpYOVoihI6EcIIYS4eUkASAghhLiG45kZ3Lf8c0wo3Nm1G69PmXbNe6Z07sK0xHjO5ebw6wmTm7V+YwM/V3pi8BCeGDyE2NwcticloBsGJQ5HtTEnsjJZff4cdrWys1VtASDDMKpldxRW2Jny8fs4NY0eQSF8ufDeJu+xNlazuVr9pdzyMroFBTO0XXiNDB9Vr9wbGGhXFLx+/9hRTmRlohsGf9m/hzfvnFXvmr5ubnQOCOR8fh6GYTA5qgtHMzMwKyYe6j+ALoFBvDBsZNX4zgGB+Lt7oNvt9AkNZd3F80yO7Fy1v6zSUladj2VQWDsGh9UdGLreotu1R9V1Br/zFqVOB/86eog9jy5pVPcrIYQQQtz6JAAkhBBCXMP5vFwAylUXp7KzGnSPzWzmT1PvvJ7bapSewSF8b/gotibG8/zQEdWutfPxwaSAh8VKVEBAjXuTiwqZ9/kySh1O/jFjFpMiO5NcVIRDVbGrKieyM6uNTy8p5pc7thLs6cXPxk64ZnbQloQ4vrNuNSFeXnwybyHn8/II9fam56WCxRklJdzx8X/RMRjWPrzGcbqfjh2Pl9WKr5sbD17RZax7cHBVMeqewaENep0uB7isZjMGcPDxp+sc62WzsfnBR9kcf5EfbN7A9zeu547OXfjzHdMBuG/556QUF2ExmVh//8MNOjp4veiGQYWqYlz62qGprbYXIYQQQrQOCQAJIYQQ1zCja3dWnT9LcmEhvxg3sbW302SPDYrmsUHRNR4P9fJm9b0PcSo7i3GdImtcX3PhHEUVFWiGwT8PH2RSZGd6BYfga3PDrqpE+PpiGEZVptLPtm1he2ICVrOJroFBVUey6vLX/XuxqyqZpaW8sH4NZ3Jy0DH4aO58Boe152J+HrphUK66OJyexo+2bKR3SCj39+2Poij4u3vwq1qyrO7q0YtQL28qVJUJtTyv2vxm4hRe3ryBTv4BzKzlKODVPKxWKjQNRak87hd76UgYQJ69svi01WSisKKCDleVXapQXRRVOOosqt2SbGYzb8+YzbvHDjO3R69aO7kJIYQQ4vYmASAhhBDiGrxsNj6eu6C1t9FkfzuwjxXnzvDkoKEs6tO31jEd/f3p6O9f67WRER158+B+LIbBzG6VQZFy1UWOvRyA1OJi0ktLaO/ji6rrFDkqUBRQAC+rjcIKO/7udR83mtgpiov5eai6zrHMDDTDwM1sJiYzk0J7BVGBgQwMC+NEViZeVhtfnD7JKouFqIBARl6jK1pt11Vd5/FVX7M/LYVnhwyrlhE1KKwdmx58tN45rzaja3fWXjhHYmEhvxw/qerxf0yfxV/272Vsh041CjBnlpYwfdmHlDmdPB09lBeHj2rUmk0xMTKKiZFR130dIYQQQtycJAAkhBDiW++Hmzew/OwZZl5qL55YVMi2hHjGd4ok0j+AD2KOEZOVyfNDh1+zkHBLKqyw896xI4T7+rGgV59r1gKyu1x8dOI4fm5uLOjdF5OikFpcxFuHD+DUNF7Ztom7evRsdMHm/m3asuORxyl3uqqCRD42N/qGtuFsbg4d/Pxp41WZxfLnfXs4nZ2NoiiM7xTJa7u385Ntm3h55Jhas4+gskbNP48cQjOMqse6BgWz6lwsFwvyMSkKWx5aTIinF9OXfUh2WWmj9n+109lZHEpPxalpvHlwPw/3H8Sf9+3Gw2rlxWEjG/36eFqtvD/n7hqPj4royKiI2tvTH0hLxaFquHSdL86crhYAisnKpNTpYGR4h2bVfxJCCCGEuJIEgIQQQnyrFdjtfBV7Gs0wWHPhHN8dPoq7Pl1Kherir/v38uadM3l97y4qVJVT2VnXzA45mZ1FXnk5Yzt2anY77O9tXMeu5CSsJhO+bu5M69K13vG/2rGNr8+dwaQoKIrCwt598XVzx2YyY1IUfGxuNQooN5SHxcqza74hvjCf30+6g0lRnfliwb0kFRYQ4edf1Rkto7QYp65hUhQcmoZD01B1nY9PxtQZADqUnlZVvNmEgrebjaVzFzDs3X9Soap4WKykFBUR4unFv2bM4e3DB+gT2oYR4RFNei6d/ANwt1gBhX5t2vC73Tv4KvY0iqLgbbPx7JDhTZq3MUZGdMDbZsWpqTx8Rd2iLfFxPL9+NQoKT0cP4bmr6jUJIYQQQjSVBICEEEJ8q/m6udHR35+MklJCvbzwstmwqy5UXQdU9EvdrxSUa7Z/35OSxBPfrEBB4d4+/Xhl7PhG72dHYgK/2rmNgW3DKHE40HQdE1DudF7z3iJHBaquYzGZKL7U6cvXzY1V9z7AgbRUJnSKrApK/ePgfr4+e4bFAwdzX9/+15x7c/xFTudkYVdVfrNrO5OiOmMxmegcGFRt3MujxlJgr8DDauGFYSO596vPUXWd++tZY0GvPnx99gwlTgePDRzM/J598HFz46djJvCn/bsZGd6B/m3aAhDh58drk6YCsCnuIq/u3s7QduH8dtJUzNd4fy7zc3dny0OPEleQT9/Qtvxm5zYq32GwKHXP8fPtm1l68gQTOkXxr5lzmhXgC/H0Ys/iJ7G7XPi4uVU9fionC6emoRsGRzLS67z/Yn4eXlYbYT5Sy0cIIYQQDSMBICGEEN9qZpOJb+55kLO5OXQPDsHTauXVCZP58MRx7uvbnwmRUbw6YTInsrN4fGDtGSyXnc3NRdd1nLpeozPWZXaXC5Oi1HnM6OXNG8guLyOrtJSBbcNQFAUdMJuvHdz4+biJmBQFP3d3Huj3v4BLJ/8AOvn/r7tXWnExfzu4D5eu88q2zUT4+jGmY6d65+5xqSOXp9VKdLt2dY5r6+3Df+/633Go/Y89SbnLVW/L8Qg/P3Y9+kSNx+/t2497+/YDQNN1Vp8/i4fVysROUSiKwstbNlBYUUFueTl39eh1zXpAV/J396hqzf7yqLH4uLnhbrHw6MDBtY53qCofn4jBAHYnJ5JUVEikf82OaY1hMZmqBX8A7u3Tj03xFylxOPn+iNHVrumGwX+PH2Vz/EWOZmRgMil8PHcBg8Lqfj+EEEIIIS6TAJAQQohvPQ+rlYFX/BK9oHdfFvT+X7HkuT17M7dn72vOc3fPXqy7eJ6csjJ+Mnpcjeu7khJ5YvUKzIrCsnkL6d82rMaYyIAASpwOdAxcul7VXSurtLLuzY7EBF7csJYwb28+nreAQA/PqnvbeHvz9+mz6tyfS9ModTrxcXPDuOLxz0+fbFAAaPV9D5FaXMTI8GsHWlRd5+k1q9ibksRzQ4bz9JBh17znSjnlZQS4e1RlXb1xYB/vHjsMwK8mTObunr2J8g8gNjcHwzAI9/WtMYdDVSlxOjEMg5PZWQxtH467xcJTq1dyIC2F7w4fxeKBg/Gy2Xhp5Jh692Mzm+kT2oaL+XkEenjSztsH3TAodTrxvSqIUx/dMPjuhrVsSYjj8YGDaxR/vtyRrTYrz8byx327cVxq5242FA6kpUgASAghhBANIgEgIYQQNw27y0VBhZ12PjV/mb8V+Lt78OWCe+u8vuxUDE5NA2DludhaA0D/mTWXjXEX6R4cjKfVysubNxDo4cG9fSozYf6wdxdFjgoqVBdrL5zngX4DGrS3fHs505d9SL7dzlODh/LbiVP50ZaNmBQaFNwCiPQPwMtqZV9qCkPata+3WPKZnGz2piRhV1X+cmBvowJAP9qyka9iTxPm7cPa+x7Cy2YjoTCfClXFrJhILCgA4L93zWdz/EV6hoTSwc+/2hxZpaVMX/YhJU4HZkXBbDLR3seX306ayr7UFOyqi9f37mLxwME4NY34gny8bTZ+tm0LFpOJ30++g3LVxZrz5xjWPpz+bcP4YsG9nM3NoculY293Lv2AuIJ8FvbqU3Us7VriC/LZGHcRh6by5sH9PDd0xDWPFl7m0is/O4pSeVwt2NOLWd16NOxFFUIIIcS3ngSAhBBC3BRyysqYtvQDylxOHuw3gJ+MGd/aW2px83v2YVtiAiZF4c6u3Wod42WzMbdnr6rvP5t/DwCGYaDqOiMjOpBYWIAB9LtUF6chDqWnUep0ouo6n5w6waEnnmZSVBS6YVTLIqpPTnkZUz56H1XXCXT3wMfdnReHjWBq55rFqTv6+eNmsaAoCr0uHR9rqBVnY1F1nTx7OadzshnaPpwfjBxLRkkJnjYbjw4cBIC3zcZdPXrVOsf+tBQqVBVV11EBNI2L+XlE+PpiNZtQFCt9QtugGwZ3f76MuIJ8rCYT5S4XCgpvHT7AmgvnyC0vx2IysfWhxbT19ql6zX+waT0X8vMA+OLMqQYHgMK8ffC2WTGrCh2vKJ7dEPN69ia9pJjM0lK+N2I0IV5eDb63OXLLy/kw5hjdgoKYKQEnIYQQ4pYlASAhhBA3hSMZ6Tg0FaemsfJs7G0ZAJoU1Zn9jz2JSTE16thQqdPJvM+WEleQz/dHjOLDufMJ8fSqkfVSn+iw9nhZrTg1jYW9+wCVGUuNkVBQgKYb2FWVtNISKC3hhfVriH32xRpj/dzd2fzgo1zIy2NALZlO9ZnXsydfnDlNsKcnvUNCgco6QV8uvK9qjGEY/N/GtXxz7ixzuvfkT3dMrzbHyPAOeFqtuDSNHsHBpBQX81T0EEK8vNl0aV+Dw9pR4nAQm5uDbhg4Aau5smNaiKcXueXlqLqOWTGRVlxMTFYmg8PaE+zpyca4i1VrDWzEESx3i4WvFtxHakkxAxv5ulhMphpHxm6EZ9eu4mhGOjazmUAPz0bVWhJCCCHEzUMCQEIIIW4Kw8PD8Xd3p0Kt3ha7uXTDIL4gn/Y+vnhYrS02b1M1NugCcDAtlfTSEgzg38eO8HQtbcrtLhe/3rmNYoeDn42bQKiXd7XrQZ6e7Hp0CcUOB8GeDcv4udrAtmEMD4/gQFpKVXt3h6bxxoG9vDBsZI3xgR6eDAtv/FqvTpzK/w0fjZ+7e50ZMlllpaw8dxaAFedi+fGY8QRd8bxCvLzYu3gJ5S4Xfu7u1e4N8fQixLMye8bNYmFm1+6sOn+W8Z2iuKNLV6wmE0mFlcfMFGBAm7Y8v241xU4HHhYrux99grk9erHsZAw+bjberqfu0pVKHA5mfPIhGSUlPDpg8C0TSCl1OtGMyqpR5a7KbnSarmNSFJRmdEITQgghxI0lASAhhBA3BX93D3Y+8gR2lwsvm63F5l3yzQr2pCQT4OHOxgcexbsF524swzD49NQJ0kpKeHzQ4AYHg/q2aYPNbEbTDaZEdal1zEcnjrP87Bk0XQeotRi0zWxucvAHKrNj/jN7LgB/3beHt44cRNV1Vp07WxUAunxUzWo2N3r+9RfP8/s9uwj29GRh777cXU9tonKXq+prA7DWEiiyms34mc3sTErk67NnWNS7L8PDI2qM++u0Gfxp6p3V2sj/Yc/OyrbwJhNRAQEczkj7f/buOjrKowvg8G9W40IMd3eHliKlpVClpQI1KtTd3d371d2NulKKFS1S3Iu7xX39ne+PTdOm0U022QTuc05Pk7zvzNxdAtncnbkXn9Z4fD5yXE4eGH4sV/QbQKPw8AprIf3bqoMHyHQ48GnN5PVruGdo6ULhwTJ92xaW79/PBT170yI2tkZzvTzmZJ5ZuIAuiUmMbNOOL9au4YE5M2kSFc0PE86v8hFCIYQQQoSWJICEEELUGyalgpr8AZi7awc+rclxOtmWmVFm4eW68uuWzTw2fw4ew2BzRjpvn3p6lcYlRUQy96LL2JubA8Cjc2fz3V8bOKdbd+4u6jYWY7djUgpMplI7XmrDeT17MXnDWjIKC7my3wDAv8Pl9C8/Y2d2FncMGcqV/QYGNOedM6eT53axKyebtamH0Fpzzr+6sf1by9g4OiUksi0rk/5Nm5Zqp/63HKeTK3/5AZfPx29bt7DiimvK3Alm/k8C6doBg9mXl4vD4+WOIcNIiozks7VrOLNLt+LdVU2iowN6fD1TGhNjs+Px+RjbqUup6xmFhTy3aAGJ4RHcMOioaiXRADampXLTtF9xe73M3LGNWRMvRWvNlswMkiIiiQ8PbBdau0YJvHXK2OLP31i2BJ/WZDgKmbdrZ7l1mIQQQghRv0gCSAghxGHtol59eH/VCjolJNKlqJ5MqDh9/vbdhtY4vJ5K7/+3cKuVG3+bwp7cHFxFncQ+WLWCK/sNoFG4f8eMAnJcLi6sYmewmkiOjGLRpVeW2O2zZN8eDhXko4G3ly8LOAHUKjaWDUX1eLShyXI6AP+uov8eNbKYTPxy7oUcKsincVR0uUeR/v1lpUp+XpF1qYeYsX0bCsW7K5dx61HH1Lj+TmxYGHMuvowsh4OUqKhS1++fPZOZ27diNplIiYqqcoe3/3J6vSjAKPoY4PH5c/h83RrMSvHLuRNpFRdX3YfBiR068vHqVZhNpsO+Bf32rEyunvITFpOJt085nWYxDbNDoRBCCAGSABJCCHGYu2/Ysdw5ZFi1d1ME09hOXdiRlcW+vBzuOHpYQGPTCwvYmZONt+iIV7jFQqw9jGibf+eLSSnGF7WKrytKqRLPa6+UJsVH1ca0/+eo2v68XH7dspkBzZrTq4LOZZ+OO4dvN6xjxvZtNIuJ4cKefXhn+VKeXjifTgmJfH32uUT8a/eO2WSiaXT5v5CnFuQza8d2Hjv2eJbt38+4rl0Js1StDtSa1IP4DAOPYbBk754qjakKm9lcZvIH/EfWKKqrU9aRtqrq06Qptx89lMX79nB9Ub2oadu24vR6CbdYWHnwQI0SQHcNGcb4bj1ICI8gNiyM+36fwXd/bWBc5648NnJUhWNdXi+vLl2My+vjhkFHhfRIZlW8+uditmZmFCcCHxw+MtQhCSGEENUmCSAhhBCHPavZTKajkOnbttKnSVM6JSTW2loFbjeX//wDO7KzeHbUGI5p2Yql+/fi9vk4unlLbjv6mGrNmxIZxcjWbZm+fStj2nXgrK7d6dukSb1IbP0tKdJ/VC2tsIA2cfHFXz/768mkFxZgNpmYPXFSuQmQGLudS/r045I+/Yq/9vqyPzG0ZldONn/u28uI1m2qFIvWmtMnf0a204nZpJh/yeXl1lzam5vDl+vX0q9Js+L5T+/cle83biDdUchtRw+t6lNQJR6fD5fPVyr58eixx9E0OppG4RGc1bV7jda4uHdfLu7dt/jz6wcO5t7fZ5AcGcWI1q1rNLdSirbxjQB/i/ivNqzDaxh8tWEdNw8eUqIY9389t3ABH65egULh8nl5eMRxNYqltvVMacy0bVsA6J6cEuJohBBCiJqRBJAQQogjwvhvvmRfXi4KxayJl9A4KrD6LX/bnpXJUwvm0b5RI2496phStWOmbt3MqkMHcHq9PDz3d64dMIh7f58BwC1HHcOkfyU3AuE1DPo3bUa3pGQu79e/yjtZqkprzU+b/yLT4WBCtx7V7pgWbbeXqMejtSbT4cBjGJhNJnJdruIEUL7bjd1srjCJNaJ1G6Zt24LVZCpuCb8rO5vP165mYPPmHNemXZnjfFqTVliAT2vsmMl2OstNAF34/TfsycnBZjHz4/gL6JCQQFJEJL+ef1G1noOK7MvN5dTJn5DvdvPQ8JGc16NX8bUYexh3DglsZ1hVndOtB2d37R70rl1xYWE0iYomw1FYvCOoIl9tWFvUUUyT63IFNZbacFGvPnRMSMRiMjGwWfNQhyOEEELUiCSAhBBC1Amn18PLSxbj0wY3DDwq6MWeK7MvL7f4CEx6YWG1E0A3T/uVdamH+GPPLronp3BSh04lrndKTAIgwmKld+MmrE09hMvnQ2vN2kMHqx3/B6tW8OLiP9BAltPfhSqYftmyiXtmTcenNRvSUnl21JigzKuU4vWTT+WVPxczum17OiQkAPD52tU8NPd3omw2fppwAc1j/ulUtTb1EN9vXM/odh14/oQTuSJjAM2io4mx+5MLF37/Nfvycvlk7arihM1/WUwmnj5+NG8s+5NTOnai9b92JP1XgceNQVGb8wBrMwXq7RVLyXE60cAHq5aXSADVttpo2W4xmZhy3kTWpx6iW3IKlkqOrv0dgVkpLqjDx15dSimObtEy1GEIIYQQQSEJICGEEHXi9aV/8v7K5Wg0PsPgvmHBTWBU5oUTTuSFRX9wbJu2xTtJqiPGbi/e9RNlK915qkdyCj+MP599ebkMbdmaQwX5LN67B4/PX/OkuhxeD4YGjcbhCX6SItNRiKE1Xp+P9MKCMu/ZmJbK+d9/DcDn486mc2LVnscRrdqwPvUQyw/uZ1NGOp0SEvlo9Uq8hoHD42HOzh3FBY+znU7O/OpzvIbB52vXsGjSlXQpSqr9zen1F9N2eb1cNeVHfIZBvsfN6yedVmKXxrgu3RhXQSv5v71zyum8/OcihrRoVWGNomD4buOGolQTDGtVteNs769czserVzK+ew+u7j+o9oKrpiibjUHNW1Tp3ndPG8erfy5mROs29GvarJYjE0IIIcS/SQJICCFE3VGArp2dCJUZ074jY9p3rPE8L485hU/XrqJ1XDzDWrUu856OCYl0LKoz1Cw6hinnTazxupf3HUCmoxCHx8OlffuW2RmrJsZ368HGtDTSCgvKrcty8/SpZDudANz4269Mu+DiEtcP5edz3ndfkeEo5LWTTmVIi1YAzN+9izeWLcXh9bA9K5PvzzmftvHx7MjKwmo2c0zLVqw4sJ8PVi0no7CwuNC1VxtFx4VKeve0M7h1+lR2ZGexIzvr728rnl+0gC/PmhDwY+/VuAnvnTYu4HEV2Z+XywerVtAtKZlIq41ftmzigp69aBIVxe6cHExKcXGvvpXOk+ty8dQf8/AaBv9bvJCzunYnKSKy0nEZhYU8sWAu0TY7dx0ztNwjgz7D4JbpU/lz3x7uHDKs1lu692/ajA9PP7NW1xBCCCFE2SQBJIQQok5cM2AghjbwGrpGO2FCLT48nOsH1n38EVYr9w8byRlffsbJn3/CkBYtef+0cUFLAoVZrDx1/OgK72kWHc3mjPTij//rp80b2ZObg9cweHHxwuIEUKTNikZjVoooq41rp/7M0n17MZsUb548llaxcZz6xacUeNyYlEIBCsVpHTuTWEZB4Z4pjblx0FHcMXMahvbvKLOZzcXr/c1nGKxLS6VlTCzx4aXr/6QVFDB9+1b6NWlK5//sMqqpK3/5kY3paUVd0fzdxGZs38rvEy9l2rYt9E5pQovY2ErnCbdYiLHbcXg82MxmoouOTua5XLy7chmJEZGc36MXpv98Hzw2fzZTNm/CZDLRJDqKK/sNLHP+5Qf2M2v7Ngq9Hh6YM6vWE0BCCCGECB1JAAkhhKgSt8/H7J3baRkTS5dqHKEKs1iD3k2pvvhx00ZWHtjPpX360TI2rtbW2Zubw5bMDLyGwbxdO/01jcoo1uzx+difl0fzmJhSRapr4s2Tx/LYvDloNPeXcYSvb5OmWJQJq8XEsJati7/er0kznj/hRDakpXJhz96c/93XuHw+wi1Wclz+HUUWkz+BYTWZGNm6LSlRUdwxpPzvl1M6dibGHkauy0mnhETy3G76NG5S4p5rf/2Z+bt3YjObmXHhpaWSSWd9/QWpBfmYlIk5F00iKdK/s8ZrGJXWsqmM2+fD+M/uJZNSJEZEclEVdv78zWo288u5F7Jg9y6OatGyeCfPfbNn8tvWzZhNJiKsVs78z1G3CKsNkzJhQhFRQcHw5jExoPwJxrK64+3PyyXH6azW33khhBBC1C9BSwAppczAMmCf1vqUYM0rhBCifrht+lRm7diORvPlWRPoIS2RAVh18AB3z5qO2+vljz27mXHhJbW2VvOYWDolJLIxLY1hrVoTZin9Y9zj8zHw3TfJcTlJiohk8aQrq71LyGcYfLl+LQ6Phwt69sZusfDwsaWPh2mtmbVjG3azhannTyTH5Sr1/XFi+46cWHQE7/kTTuShOb/TJSmJUW3bo5Ti8zPHM3ndGo5t3ZZBzZpz3dSfOfGzj3n6+NHFdX22Z2WyaO8ejmvTlsZR0eUewfvbH3t24/B6UUqxKSONxIiSO4QOFeTj9vkIsygyHIUkRkRw429T+GXLJka2bss7p55e7efujZNP46UlC+mR3JiOjRKYtn0L53TrWa3EUuOo6FJt4R0eDz6tMWkoLKMm1L1DR9A4Kppom63cQtOG1piU4udzL2RTenqp53PNoYNM+PZLAK7qN7BB79wTQgghRHB3AN0IbARigjinEEKIemJDeioOr4dwi4VtmRkhTQBtzkjHZxj1YleC1zBQgFH08X/N372TFxb9wdCWrbl58NE1OrJlMZn4fvz5ZDocJISHlznXxvS04l01aYUF7MnJoWVcXLXW+2TNKp5ZOB9Da/bn55W56wfgjWV/8trSJYDm4RHHlUpW/FfPlMZ8N/68El/rkphUXHvou43rWbx3Dw6vl/tnz2TaBReT43QydvKneA3Ny0sWsnjSVcWPf2tmBp+vXc2Qlq1KtIW/YeBgnlu0gM4JSfRvUrrg8IsnnMj/lizihHbt6ZSQSK7Lxa9bNwPw+87t9H7rNT4bdzbdK/leX3XwAM8unE//ps25adBRKKVoG9+Il8b8837YsNZVK/hcVY+NPJ4nF8wjKSKS8d16lLoeYbVy/cDB5Y43tOacbyaz5tBBeqU05suzJpQ6Rrb8wH4MQ+M2fMzasU0SQEIIIUQDF5QEkFKqOXAy8DhwSzDmFEIIUb88OuJ47po1nbbx8Yxu1yFkcUzdsolbZ/wGwCNVSDbUtv5Nm3H3McNZfmAf1w4o/Qv39VN/IdflYnNGOse1aUuv/xxTqiqvYfDKkoUs2beXUzt25twevSgrldQ5MQmb2Yzb5yPcYqFpTPXfl8lxOfEZBobW5BQVfy7Lpox0nF4PJqXYkpFR7fX+1i6+ERoIt1iLa/Pkulx4DQOXz0emw8funGyunfozPkOTVpBPptPJ5PVr+fW8ibSMjWPZ/n2c1KETl/cbUOYaDo+H15f9ydbMDE7t2BmlFNF2O12TktmQmoqBJs/t4sNVK3juhBMrjPfaX3/iQH4+qw4e5KjmLRhcxY5YNZEcGcWLo0+q9viMwkLWHDqI1zBYffAgmQ5HqSNyJ7XvyEerVpBWWMCNg46uachCCCGECLFg7QD6H3AHULoipBBCiMPCUS1aMvfiy0IdBov27sVV1Ab8jz27Qp4AArigZ+/iNub/lRgRidPjj7esQsRV9dX6tby2dAkGsOzAflw+H5f26VfqPpvZzJ+TrmL5gf30b9qsRrVsLuvTn/15uRR6vNx1zPBy77tl8BB2ZGdhN5uZ1Ld0TIHq1bgJX541gT05ORzf1r+jp0VsLNcNGMxPm//iyn4D+GDVCv5KS0ejSzxGr2Fw3+8z+HHTX4Dm+/HnF3dk+7flB/azIzsLDby1fCnXDRyMSSm+PftcPlmzimcXLkApOLZ120rjjQ8LJ62wEI0m1m4v/npqQT5zd+1kcLMWFRZ8zne7eXju7zi9Xh4aPpKE/yRiFu3ZzZxdOxjXpVuZdXqqIzEigv5NmrF0/14GNmtOQhnfmylRUcypB3/nhRBCCBEcNU4AKaVOAVK11suVUiMquO8K4AqAli1b1nRZIYQQR6iLevVm1o6teA3NFX3L3t1Rn3wx7hx+3vwXfRo3qVGB6LeW/8nfB8wMrdmbm1PuvTFhYRzbpvLERWUibTaePn5Mpfe1iovjpwkXVGnOfbm5nPfdV+S6nLx1yunF9X12Zmcxa/s2TuvUhaTISHokp5Q6ZnjtwMFcW3SsydAau8UMwHnde7E/P4+RrdvQvlECi/buKT6uuD41tcwEUNekJOxmC4bWjPjX8Syr2cylffoxsk1bNNAmLr7Sx/TB2DP5ZuM6uienFB9L9BkGp37xCfluNxaTiYWXXklkUQev/3pnxVJ+2rQRQ2vCLRaeGfXPc34oP59Lf/oel8/Ll+vXsvKKa4PS+U0pxWfjzibb6SQuLCxo3eSEEEIIUX8FYwfQEOA0pdRJQBgQo5T6VGtd4pWg1vpt4G2A/v3769LTCCGEEJVr1yiBPy69MuBx83ft5LWlizmhXYcyd84Ey7bMDH7ctJGhrVozoGlzkiIjg7Jean5B8cc9klK4pozjZg3Bd3+tZ19eLobWvPLnIj4542z25eZy3Mfvo4FnF85n5ZXXldnd7N/O6tqd5jGxeA2DIS1alkhg3DVkGHfOnEaruDiOa9uuxDiX14vNbKZReARzLprEvrzcMhNErf+T+NmZncWj82bTIiaWe4eOwGo2F19Liozk6v6DStzv9vnIdDjwaY3NbCbH5Sw3ARRrD8NsMmHSmtiwsBLXPIYP0MVzaijz6F91KKVqtCtNCCGEEA1LjRNAWuu7gbsBinYA3fbf5I8QQojQ8xkGd8+azrL9+7hv6AhG/ucX40P5+WQ4CumSmHRY7ga4csqPOL1eVh86xLBWrWnfKCHoa2itOevryeS6nLy7YjlzLp5EcmRUwPOkFuQzZ+cOBjVrQauiAs4PjRjJC4v/YEiLVjx3womlCvaGQpbDwdrUg+zMzuao5i3pkFD5c9q/STNsRcmT4a38O2/WpR7k73eG3IZBWmFBlXZLlVdrZ3T7DoxuX7JOldaae3+fwZfr19IpIZHvxp9HtN1OZ3tSpesA3DlzGsv278NusdAtOYWzKzl6GG618sDwkby/cjnjunSlaXT5tZgu6tWHMIsFp9fL+f/p2NU8JpbHR45i6tbNTOjWk+nbttKncRNSogL/vhJCCCHEkS2YXcCEEELUY4v37eHXLZsp9Hq4Y9Y0lrW9pvjaxvQ0zvrqczRwSe++3H700NAFWkv+Tpj4tEGkteydGDWlAWdRfSKNxuX1BTyHoTWnTf6UXJcLi8nEgkuuIMZuZ3z3nozv3rPK86QXFnLJj9+S6XDw6omn0KdJU8BfTHl/0a6XmiSRDubnccInH5LvcWNSijCLhXkXX0aj8IgKxx3VoiW/njeRArebbkVHvIa3bkPjqCgO5ufTr0lTWsSUXy+nuu6eNZ2vNqwDYFdONmsOHSo+flYVjcLDsZhMKCDOHlbp/QAX9uzNheXUhvo3s8lUbqt2gHFdunFG564c+/F7pBcUYjWbmX/J5USVs6NICCGEEKIsQU0Aaa3nAHOCOacQQojgaBodg8ZfY+S/x1tWHNiPoTUun49ZO7YfdgmgQ/n5uL1eAAzDqLVfnE1K8c6pp/POiqWc3KFThYV/y+Px+cgoLCw+OpTrchLzr8LCVfXdxvVsykjHaxg8vXA+k88cT1phAaM//RCn18vodu15cfTJACzcs5t5u3ZwVtfuVd4ZtT41FZ/2VyUytMZnaLKdzkoTQAApkVEU2DzFn4dZrCysxrG+QPy8+a9/rWcp7i5WVc8cP4Yv1q2hSXR0cWHquuQxDPbm+o/O2bVBemGBJICEEEIIERDZASSEEEeINnHxfHPOefyVlsaodu1LXBvdrgPvrVzGwfx8bj4M2z3HhtmJsYfh9HmJsdmJqKS+TE0c07IVx7RsVe3xdouFR0Ycz9srlnJ65y40j4llU3oa53wzGa+hee+00xncvPJmCt2Sk7GYTFhNZvoX7f7ZkJqK2+fD6fUye+cOAA7k5XHZz9/j9Hr5dM0qou1hHN2iJc8cPxpzBR3EBjdvQeu4eP5KTyMxIpILevaibXyjEvcs27+PVQcPcFqnzsVH4XZlZ3Pa5E9xej3cOWRYrdZj+rdzu/fkg1UraBYdzffjLwg4qRZtt3NFOS3l64LNbObOo4fyzsplnNS+I61qUFBcCCGEEEcmpXXd12Pu37+/XrZsWZ2vK4QQwq/Q48Hp9VRpt8bhIq2ggGUH9jGoWfMG97hPn/wZa1IPAtA0KpoFl15RpXEb01LJcjqZvm0Ln61dzfBWbdifl8umjHRuP/oYruo/iF3Z2Yz57ENcvn+Oq4VbrHx0+pn0b9qs2jHvzM7ipM8/xmcYtIiNZeaFlwLwwaoVPD5vDgaa1nFx/D5xUrXXCFSuy0WUzVYv6icFwuHxsOLgfromJkvRZiGEEEJUSCm1XGvdv6xrsgNICCGOMJsz0jnzq89x+3w8duzxnN2tR6hDqhNJkZGc2L5jqMOolo6JCcUJoNZFRaGroktSMi6vlwu//xoN/LFnF7+efxGtYuOKkyCt4uJ49NjjmbZtC7uys9mflwcKmseUX7S4KnKcThT+o0sZhY7ir6cVFGAUlXyOtgV+tK0mqnOULhhcXi9PLphLakEB9w0bUWFB6P/SWjPuq8/Zk5tDuMXCnIsuK7ebWH2ybP8+7pk1nXaNGvG/0Sdjt8hLTiGEECLU5KexEEIcYWbv3I7L68WrNZPXrz1iEkAN2dPHjaZFdCwFHg+3H31MQGNtZjPdklPYlplBfFg4TaOiS+2AOatrd87q2p0Ct5u5u3bSLSmZxlHRNYq5Z0pjrug3gPm7d3HL4CHFX28eE0OY2YKBpntyCjO3byU+PJx+TcrfbbT60EE+XLmckW3acWqnzjWKq6pcXi/3/D6DndlZPDZyFF0CrBn0b19vWMeX69fi8Rk4vF4+GDuuymO9hsHmjHQ04DM0B/PzaFcLHeyC7d7fZ7A1K5N9eblM3bqF0zt3CXVIQgghxBFPEkBCCHGEOb5NO15bugSX18vEXn1CHY6oAqUU1w86qtpjvz5rAn+lp9G+UUKFOzEibTZO6hCcXVJKKW4cdDQ3/qem1ISiTmZ5bhcH8vK58bdf0WheP+k0RrRuU+ZcF/3wDbkuF79t20r3lBTa/KeIeU2lFRRw7+8z2JeXy9PHj6Z7cgo/b/6LqVs34/R6uWvmNH6ccEG154+y2VFKYTYpYgPchWQ1m7luwGDeW7Wc49u0K1Vnqb5qF9+Ivbk5aALbtSaEEEKI2iMJICGEOMK0a5TA0suuxlOL3bBE/WK3WOjVuEmowwD8ndL+bnl+3ndf4fB6sJpMbM3MKDcBZP7XjiVzkOv3LNi9i0t//BZvUU3Ee3+fwY8TLqBJtH8HVJjZQrMAjmyVZWynzngMH+kFBdVKut581BBuPmpI5TfWIy+OPonftm2hVWwcvevJ954QQghxpJMEkBBCHIHsFguhqYYixD/uPWY4N037lcSICM7q2q3c+z4ddw6frVnFiNZtaBnk7lffbFhXnPwxK1W8w2ZIi1a8dfJY9uTmcEbnrjVaQynF2V271zjW6lqXeogFu3dxYvuOtKqj3Th2i4WxneTYlxBCCFGfSAJICCGECNDe3BwemTublKgo7hs6IigFbrdnZfLKn4vpldKYi3r1QTWwTlVl0VqzaO8eDK0Z0qJlqccUGxZGs+gYEiIisJnLfw67JCbx2MhRtRLjmV27MW3bFgwNl/ftz/UDBxdfG9qqda2sWZeyHA7GfzMZt8/H2yuWsuzyaxpcFzQhhBBCBIckgIQQQogA3fP7DBbu3oXVbKZzYhLnFx1pqokrf/mR7VmZTN+2hc6JSQxu3iIIkYbWl+vX8ui82QDcOWRYqeNPD835nQW7d2Ixm+malMykPv3qPMahLVvz52VXo5Q6LI9EFno8GFrj05oCtxufYWAym0MdlhBCCCFCQBJAQgghRIDiw8Ixm0wopYizhwVlTovJhEKhiz6uCZ9h4PL5iLBagxJbda1PTcXl9aLxH0P6r6TISKxmMyalSIyIqLO4DK1L7IKJDlF7+LrQLCaGe44Zzo+bNnJFvwFYJfkjhBBCHLGULjr3Xpf69++vly1bVufrCiGEqFt/paexYPcuTmjXPui1W2qT1zC4dfpUVhzYz71DhzOmfcnOWAVuN5+tXU1yZCRjO3UJynGtvbk5vLdyOT2TUzijS/n1cCqTVljAKZ9/QqajkLuPGc6lNdxVk+10sPLgAfo2bkpsWGDJrn25uVw55QcMDW+efFqp7wGX18uX69cSHx7OKR061fqxN4/Px4Xff82f+/dxae++3Dfs2FpdLxBew8Cs1GFx9E8IIYQQoaOUWq617l/mNUkACSGEqA05TidDPngbt89HlM3G0suuxlzDnS11Zf7unVw95ScKPR7iw8JZfsU1oQ6pyr7fuIH7Zs/E4fXQPDqGeZdcXu25PD4fwz98l1y3ixi7nbkXXdagd5CsTz3E2d9Mxun1ooCt199SawmXNYcOcves6bSKjeOF0ScSZil/N9YXa9fwwJyZNI6K5ofx55NQjd1QuS4Xs3dup0dySoNpFS+EEEKI4KsoAdQwXokLIYRocAo8bryGgdcwyHf7P24oWsXGobUm3GKhY0JCqMMJyMDmzbGaTdjMZsZ1qVn3qlyXi3RHIYUeD+mFheS5XUGKMjRax8UTFxZGmMXC0WUUpf6vtMICNqalUp03y+79fQYb09OYs2sHv2zeVOG9byxfgk9rMh2FzN21I+C1AM779kvumTWD0yZ/yqH8/GrNUVWFHg8TvvmSAe+8wazt22p1LSGEEEIEj9QAEkIIUSuaRsdw15BhfP/XBi7vOyAonbLqSsvYOH6acAGbMtIZ0bptqMOp0JpDB0kvLGRE6zaYlKJZdAwLL72SHKeTJtHRADi9Ht5fuYIwi4WJvfqUqjGktS4zGZIQEcFFvfrw9YZ1nNO1B43C665OT22ItNmYeeGl7M7Jpn2jihN72zIzGPvlZxhac0GP3twzdHhAa7WJi2d7ViYaaBETW+G9J7XvxEerV2I2mejftFlA6/xtR3Y2Dq+HcIuFA/l5pERFVWueqpi5fStrUw/h8Hp4dN5sjmvbrtbWEkIIIUTwyBEwIYQQooH6Y88uLv/5BxSKCd17cH85NW0emzebT9euRgG3DB7C5f0GFF975c9F/G/xQnqkNObLM8cHnKibv3snWQ4HJ3XoVO3i1R6fj0KPJ+AaQ7Xp6w3reHDOLJxeL23i4pk18dKAxru8XqZs2UTzmFgGNmte4b1aa3ZkZ5EQHlHt5+CnTRt5duEChrVqxaPHjqrVVu+bMtIZ9+VnKKUY1bY9L44+qdbWEkIIIURgKjoC1nDejhVCCCFECZvS0zEMjdvwsvbQwXLvW3PoEB6fD4vJRIHHU+LaW8uWooGtmRmsPHggoPbz07Zt4ZZpvwKw6uBBHhgeeFHltIICTv7iY7IdTm4afDTXDBgU8By14bg2bXlz2Z/szc3lxkFHBTzebrEwrorFvJVSNa7bc1qnLpzWqUuN5qiqTgmJ/HLeRPbm5HBUi5Z1sqYQQgghak4SQEIIIUQDNa5LV37buoXUgnzuHTqizHtWHNjP2tSDaPxJiSv+tfsHYFir1szZtQO72UKnhEQ2pqcB0CUxqdL1d2Vn4zUMPIbBtqyMaj2GRXt3U+D24NUGn61dXW8SQI3CIwLe9ROoX7ds4oE5s+iSmMQ7p55eYaHo+qZNXDxt4uJDHYYQQgghAiAJICGEEA2SoTWzd24nITyC3o2bhDqckIgLC+ersydUeI9JKUxKoYCkiEgirCWTDK+edCqb0tNoFhPLnJ3buWvWdACeGDmK0ztXXER6QvceLNq7mwyHo9zjZ5UZ1KwFYRYLXsPH+G49qjVHQ/XovDlkOhysPHiAebt2ckK7DqEOSQghhBCHMUkACSGEaJCeWjCXz9euwUDz5sljGdaqdZ2un1qQzzlfTya1sIBXxpxSbwvh9m7chEePPZ7lB/Zzed/Sx8FNStElKRmARXv24PR6AX99ocoSQDH2MD4Ye2aN4kuJimLhpVeQ53aTWI325w1Z78aNmb97F1prOiQkAvDdxvUs3LOby/r2p3M5u7BWHtiPyWSiV0rjugxXCCGEEA2cJICEEEI0SGtSD1Ho9WA1mdickV7nCaCpWzdzsCAft8/H/5YsrLcJIIBxXbpVqR7NpX36MXf3DrSGy/oOqPT+YLFbLMXFp3fnZPPuimX0btykyjV0GqqXx5zCor17aB0XR8vYODampXLf7Jk4vV7+2LOLRZOuKjXmi3VreHTebACePO4ExtZR3R8hRM1p7UFnXQeeZRB1M6bIC0IdkhDiCCMJICGEEA3SvUNHcMPUX0iKiOTMECQK+jVphkkpwiwWjmtTf5M/geiQkMDCS68MaQyTfvqe7VmZfLNxPW3i4unTpGmJ62mFBSzeu4eBTZvXaqvzumA1m0skLtW/Onep/3Txcnm9zN21g993bMfp9aKA5fv3SwJIiIbEswrciwEH5D8LkgASQtQxSQAJIYRokHokpzD7okkhW797cgq/T7yULIej3KM6InA+w0ADCvBqo8Q1r2Fwyucfk+92E2axsPDSKwNuW1+WXJeTBbt306dxE5pER1d6f5bDwfzdO+nXpBnNYmJqvP7fOicm8czxo1m0dzeX9O5X4tq1v/7M4n178BkGjSOjCLNauLRP36CtLYSoA+Y2oMxABFiOrJpnQoj6QRJAQgghRDU1joqmcVTlCYP64q/0NG6dPpWEiAhePfEUYuxhoQ6plHdOPZ3Xl/1J3yZNGdC0eYlrhR4PWU4nXsPAaxjkulwk1TABpLXm9C8/I7WgAIvJxLyLL6vwefn7/ozCQswmxfxLLg/q83hKx86c0rFzqa+vT0ul0OMhwmLl4RHHMapd+2qvMWv7Nh6dN5s+TZry7KgxWEymas2TVljAx6tX0ikhscyYhRAlKXMiJE4F71aw1d0xWyGE+JskgIQQQogjxOPz57AxPQ2rycRX69dxWRlFoUOtXaMEnj/hxDKvxdjt3Dz4aD5Zs4qzu3QnKTKy0vnm7tzBrdOn0jwmho/POKtUssbQmt05ORhaYzdbSCsoqDCh49OafXm5xfenFxbWSSLt0WOP48E5s+iWlFLjeld3zZpOhqOQdEchi/bsZmg157t2ys+sPLgfm9lMQngER7VoWaVxa1MP8di82fRITuGeoSMw/ee4mxCHM2VOAXNKqMMQQhyhJAEkhBBCHCE6NEpgxYEDgKZNXHyow6mWq/sP4ur+g6p8/xML5pLpdFDo9TB1y2bGd+9Z4rrZZOLeocN5a9lSRrdvT9v4RhXOZzGZeHD4SN5c9icntu9QZ8/j8W3bc3zb6u/6+bd28Y0o9LjRWtM8Nrba8+R73Pi0/8Begcdd5XE3/vYLO7OzWZeaylHNW9brAupCCCHE4UQSQEIIIUQV7MzO4uk/5tEuPoGbBx+NuZrHZkLp3qEj6N+0GY3CIxjcvEVIYvhty2b+2Lubi3r1oX2jhFpfb1Cz5uzLzcVA0y257HfdL+ndr1TNnYpc2LM3F/bsHaQI6977Y8cxc/tWOiUm1SiB9cqYk3lm4QK6JiYxMoBC6EkRkezPzUWjSYiIqPb6QgghhAiM0lrX+aL9+/fXy5Ytq/N1hRBCiOoa9+VnrD50kDCLhWeOH8PJHTuFOqSQOZCXR7bLSZcAi19vychg7Jef4vR6SYmMYtGk2u84ZmjNn/v2khIV1WB3PR1usp0Ovtmwnk4JidU+fiaEEEKIsimllmutyzznLzuAhBBCiCqItNkwKxO66OMj1ZpDB5nw7ZcAXN1/INcPPKrKY33/6url+0+Hr9piUipku51E2eLCwutl/SkhhBDicNfw9q8LIYQQtURrzePz5zDm0w+ZtWNbiWsvjTmZqwcM5ImRJzD8MNq1MH3bFoa8/xZXT/kRt89X6f3L9u/DZxg4vV5mbt9W6f3/1jkxiUdHHMfYjp35cOyZ1Q1ZCCGEEEJUgxwBE0IIIYqsOniA87/7CofXS5TNxpqrrg91SLVu0LtvklZYQITVystjTmFkm7YV3n8oP59zvplMemEBr550Kse2rvh+IYQQQghRd+QImBBCCFEFSRH+tuJ2s5mmUdEhjqZudE5MIn+/vyNU67i4Su9PiYpi7sWX1X5gh7lp27awMS2V83v0rlI7eyGEEEKImpIdQEIIIeqlnzf/xe3Tf6NJdDTfnH1unXUL2pCWyupDBxndrj2Nwg//DkUur5fZO3fQoVEj2tVBV67q+Hztar7buJ7L+vRnTIeOoQ4nYBvT03jtz8UMbNacib36sOLAfi74/ms8Ph/dkpL5YcIFoQ5RCCGEEIcJ2QEkhBCiwXl5ySLcho9DBfnM3L6V8d171sm6XZOS6ZqUXCdr1Qd2i4Ux7TuEOoxyHczP45F5s3H7fNw4bQpr2rTFbgn9y5d9ebks2LWTY1q1pll0TIX3Xv7T9+zPz2P2zu30SE6hwO1GAT6tyXO76yZgIYQQQhzxQv8KSgghhCjD8W3asS8vFxOKvk2ahTocESLhFitmZcKsDCKsViym0PevcHm9nPL5J7h8XuxmC4snXVlhUspmMWMCNGAzmzmmZSuu6j+Q1QcPctvRx9RZ3EIIIYQ4skkCSAghRL10x5ChjO3chYSIiOLaPOLIExsWxtdnT2Derp2Mbt8Bcz1IABV6PBR43HgNA69h4PB6KkwAfTj2TD5evZJ+TZvRLTkFgOsHHlVX4QohhBBCAFIDSAghhBAiYO+tWMZn61ZzQY/eXNqnX6jDEUIIIYQApAaQEEIIUSc+WLWcd1Ys49SOnblryDCUUqEOSdSSSX37M6lvma+thBBCCCHqpdDvoxZCCCEOAy6vl8fnz+Vgfj4fr17J7pycUId0RNiYlsp1U3/mszWrQh2KEEIIIUS9JjuAhBBCiCCwmc00iYom01GI1WymUXh4qEMKmkKPh2X799EtKZmEiIhQh1PCpJ+/52B+Pr/v2E73lMb0Smkc6pCq5dctm1mbepCJPfvQJDo61OEIIYQQ4jAkCSAhhBAiCJRS/DThAhbs2UW/Jk2JtttDHVLQnPnV5+zJzcFutjD34suIstlCHVIxm9nM3wftbPWgQHR1rDywn9tnTMXl8zFv106mnDcx1CEJIYQQ4jAkCSAhhBAiSOLDwzm1Y+dQhxFUPsNgc0Y6GvAZmgN5eXRISAh1WMU+HHsmn6xexYBmzemSlBzqcKrF4fUCYGiNw+MJcTRCCCGEOFxJAkgIIYQQ5TKbTNwyeAhvr1jGcW3a0r5Ro1CHVELruHjuH35sqMOokaOat+CGQUex6uABbho8JNThCCGEEOIwJW3ghRBCCCEaOK01982ewa9bttAjOZl1aamMbtuBJ44bJd3ohBBCiCNIRW3gG+ZheSGEEEIIUWx3Tg7fbdxAjsvJgj27yXY6+WHTRrZnZVZ5DofHw+qDB3B65RiaEEIIcTiSI2BCCCFEA6C1ZuHe3Rhac0yLVrKrQ5SQFBlJpM2G2evF4/NhMZmwWywkR0ZVabzH5+Okzz8mtaCA5jExTD3/IkzyPSaEEEIcViQBJIQQQjQAX65fy6PzZgNwx5ChXNSrb4gjqr8MrXli/lzWpx3i3qEj6J6cEuqQal2E1cpv51/MmkMH6ZKYxIa0VHqmNK5yN7r0wkL25+XiMQy2Z2WS53IRGxZWy1ELIYQQoi7JETAhhBCiAVifmorL68Pp9bI+NTXU4dRLHp+PLIeD2Tu2M3ndGpbs28sNv/0S6rDqTGJEBCPbtKVJdDTHtW1HUmRklcc2jopiTPsOmJXizC7dJPkjhBBCHIZkB5AQQgjRAFzVfyCrDh3A0JrrBg4OdTj1TpbDwUmff0SGw8GpHTtjoLGaTCRFVD0JciRTSvHSmFN4aUyoIxFCCCFEbZEEkBAi6P7uLig1SoQInmYxMfx87oWhDqPeWrZ/H3luN17DYO7OHbx58lg2Z6RzVtduoQ5NCCGEEKJekCNgQoig2rt5P2c3vozTYi5k9Zz1oQ5HCHGE6Ne0KVE2GxaTiXO6dWdYq9Zc1rc/cWHhoQ5NCCGEEKJekB1AQoigmvHJPHLT89Ba8+UzP9BrhLz7LoSofY3CI5h38eXkuVwkRESEOhwhhBBCiHpHdgAJIYKq73E9sIZZsYVZOWbcoFCHI4Q4gtjM5iMy+ZNeWIjH5wt1GEIIIYSo52QHkBAiqHqN6MaHm17G5XDTvEOTUIcjhBCHtUfmzubTtatIiohk6vkTibFL9y4hhBBClE12AAkhgi6peYIkf4QQog58u3EdXsMgx+Vk1cGDoQ5HCHEY0EYuRs49GDn3oo28UIcjhAgiSQAJIYQQQjRQZ3ftjsVkIi4sjN6NJfEuhKg5nf8yOH4Axw/o/NdCHY4QIojkCJgQQgghRAN137BjuWbAIGLsYVhM8r6eECIIVBRgLvo4MqShCCGCSxJAQgghhBANWKPwI6/wtRCi9qioa9AqGlCoyAtCHY4QIogkASSEEEIIIYQQAgClbKioSaEOQwhRC2SvsBBCCCGEEKJGtFGIkXkFRtpJaM+aUIcjhBCiDJIAEkIIIYQQQtSM8ydwLwLfVnTOQ6GORgghRBkkASSEEEIIIYSoGXMbQAHhYOkY6miEEEKUQWoACSGEEEIIIWpE2QdBow/BdwDCTgh1OEIIIcogCSAhhBBCiDK4fT4+Wr0SgIt69cFmNpe6R2vNA7NnMXfXDm45agind+5a12EKUW8oW99QhyCEEKICkgASQgghhCjDa0sX8/bypQAUut3cOPjoUvesST3Ed3+tx+H1cs/vMyQBJIQQQoh6S2oACVHLXA4XP78xjQXfL0FrHepwhBBCVFG+y42hNYbW5HvcZd7TJCoKpRThFgutYuPqNkAhhBBCiADIDiAhatnL17zLnC8XokyKOz++nqHjBtXJum6XB5vdWidrCSHE4ejGwUeT73FjQnH9wKPKvCc5MoqfJlzAmkOHGNmmTR1HKIQQQghRdZIAEqKWpe1Nx+1yY7NbydifWevr+Xw+7jzhUVbPXs9x5w/lrk9uqPU1hRDicBRjt/P08aMrva9tfCPaxjeqg4iEEA2B1l4w0sGUglIq1OEIIUQxOQImRC274fUr6D2iO8POOooxl46s9fUO7khlw8LNAPz++XychS4AHPkOtq3eic/rq/UYhBBCCCGORFq70Rlj0WnHobOvquReH9qXISUChBB1RnYACVHLmndowrOzHqyz9ZJbJtK4dRKHdqXRcUA77OE2HPkOLu1yE3lZ+XQZ3JFnZ9ZdPEIIIYQQRwzvLvDuBjzgmo3WHpQqfSTfnyg6C7xbwH4cKv7Vuo9VCAH4/z6i81Gmw383rySAhDjMWG1W3lr9HAd3pNK0fWOUUuzbcpC8rHxchW5Wz1mP1lq2JAshhBBCBJulNVjag3cj2EeXmfwBihJFOwAfuKajtReljpxfzbRRAL49YOmAUuZQhyOOYNp3EJ0+FnQeOuomTFFXhDqkWiVHwIQ4DFltVlp0aobZ7P+B2qZHSzoP6oBSinE3nizJHyGEEEKIWqCUFZXwLSp5MSruf+XfaGnlTxZhAfuoIyz5k49OH43OOAeddXj/si0aAPci0E7AC4WTQx1NrTty/qUR4jDi8/r47f3fUSYToy8ZUZzoKY/ZYua5WQ/Jzh8hhBBCiP/Q3q3gWQ/2kShTdI3nU8oEKq6Se2yQ8AMYWWBKqPGaDYp3G+g8wAnuhaGORhzpbEeBCgPtgYhzQx1NrZMdQKLWLJ22inOaXMZNQ++jILcw1OEcVj5/4jveuPlDXr/xfb5+7qcqjwtG8ic3Iw+Xw1XjeWrD1PdmcXnPW/j2f7+EOhQhhBBCNADatx+dcSY65wF05sQ6XVspM8qceOS9OWftApaugIKIS0MdjTjCKXNjVPJ8VPJCTFGXhzqcWicJIFFr3rz5Q7IO5bB15Q4WfLck1OEcVrIOZeP1ePF6fWQdyqmzdX96Yxrjm13B+KZXsG/rgTpbtyoc+Q5euvoddq7bw7t3fUbmwaxQhySEEEKI+s739+sZB/h2hTSUI4VSNkwJX6BSNmKKuT3U4QiBUjaUKS7UYdQJOQImak3XozuRuicdrTXterUOdTiHlYseHk/WoRzMZhPn33dmna37y1vT8bq9KKVY+tsqml3XpM7WrozVbiUyNgJngROL1UJ4VFioQxJCCCFEfWftA2Gn+uuARN0W6miOKErJXgQh6prSWtf5ov3799fLli2r83VF3fL5fKz6fR3JLRNp0alZqMMRQTDl7Rm8ev17hEWG8drSp2jarnGoQyrh0K40lkxZQd9RPWneof4kp4QQQghRktYe8O0DcwvpAiWEEEGklFqute5f5jVJAAkhApGfXYAt3IbNXk5bUyGEEEKICmjtRWeM9bdCt3ZDNZoc8jo4Wmt03uPgnAIRF2OKujKk8QghRHVVlACSfXdCiFL2bt7PxA7XcWG7a9n9174S16LiIutl8id9XwYXtLmGUyLPZ/Evy0MdjhBCCCHK4zsI3p2AGzyrQReEOiLw7fW3gDYyIP9FtHaGOqKg0doIdQhCiHpCEkBCiFK+eu4nDmw/xMEdqXz17I+hDqdK5n29mIwDWbgcbj5++KtQhyOEEEKI8pibgq0/oMA+CmWKCnVE/lbsKgKIAHMTwBbqiErRnnUYqUdhpI5Ae3dWfr+Ri5E2Gn2oK0bBZ7UfoBCi3pMEkBCilB5Du2APt2GPsNH9mM6hDqdKug/tjNliwhZuY8jpA0MdjhBCCCHKZYD9RIi+FxX3YqiDAUCZIlCJU1Bxz6ASfqiXBYp1/tv+HUrGfnT27Wijkk6wrgVgHAIMKHijTmIUQtRv0gXsCGIYBs9c/CrLp6/hoofP4ZQrTwh1SKKeGnXhcFp2bobWms4DO4Q6nCrp2K8dH256mbysAtp0bxnqcIQQQghRDl3wLuS/Dmi0zkVFXRfqkABQ5iQw1+PXx/Yh4JoFeMC7Fp19B6rRW+Xfb+uN//3+MLAfXzcxCiHqtRongJRSLYCPgRRAA29rrV+q6bwi+LYs384f3/+Js8DF6zd9IAmgekZrjdfjxWqrH/V1Og1oH+oQApbYLIHEZgmhDkMIIYQQFTEyAC+gwZcZ6mgaDFPEeAwjHfLfALyV1k5S5qaQ9Dv40sDS8F7XCSGCLxh7G73ArVrrrsBg4FqlVNcgzCuCLLlVEiazibDIMFp3kx0S9Ul2Wg7nt7qaUyIv4MfXfgt1OOXyeX08cPrTnBZzYYOpDSSEEEKI+kVFXQthJ4D9BFT09SGLQ/sy0I5f0L5DIYshUCrySoi4yP/cxT1d+f2mOJS1Q8i7rAkh6oegt4FXSv0IvKq1nlHePdIGPnRSd6exdeVO+hzXnfCo8FCHA8B3L//Kp498Tb9Rvbj7sxswmerfmevaNvPTebx01ds4C12ktEri0x2vhzqkMm1aupXbRj6Es8CFxWZhqvOLUIckhBBCCBEwrb3otOGg8wE7KnkeSoXVwbo+lDLX+jpCiCNXnbWBV0q1BvoAS4I5rwie5JZJHD12QL1J/gC8c/vH5GXms+jnZWxZsSPU4YREj6FdMFvN2MKsjJgwJNThlKtJuxSsNgthkXY69W8X6nCEEEIIIapHO8HIBO3wH6Uycmt3OaMAI/1kf0eu/Ddrda3apLVGe9ajfQdCHYoQohqCVgRaKRUFfAvcpLUu9S+oUuoK4AqAli3l+NHhwOP28Pxlb7Bz3R5ueP1yug7uWK152vZqze6NezFbzDRpkxzQWGehi2kfzCaxWaMG3fkppVUSn+16g9z0PJq0TQl1OOWKaRTN+3+9xM51e+h6VPX+vIUQQgghQk2ZotBRN0PhpxA+DmUO7DVowDzLwLcP0FDwLkRdVe6t2peGLngfLO1Q4WfWq+NbOv9/UPCB/5OEL1DWbiGNRwgRmKAcAVNKWYFfgGla6xcqu1+OgB0e5nz5B89f9gbOAhctOjfj/Q3/q9Y8W1Zs5+vnf2LEhCEcfeqAgMY+cf5L/PH9EpTJxD2f38jRpwU2vi4U5jl45OznSd2Vxh0fXddgumoJIYQQQojg0L4MdPqJ/h1HYSdhqqB+j5FxHnhWAjZU/Cso+7C6C7QSRvpY8G4EbKjoO1GRF4Y6JCHEf9TqETDlT0m/B2ysSvJHHD6SWyWhtcYebqNp+8bVmsMwDO44/hHmTF7IExP+R3ZaTkDj0/dl4nZ60FqTdTC7WjH8bcOiTYxLuJhzW1zJvq3B29Y669N5rJ23gT2b9vPaDe8HbV5Rv7idbhb+tJS9W2RLtBBCiMOH1i60URjqMBos7UvHSBuDTh8FsY+iEr9HxT5VySAXYIACtLsuwiy5vNYYuU9hpB6LUfg12r0aI+dhtGsxKuomUBFgbgphJ9Z5bEKImgnGEbAhwIXAWqXUqqKv3aO1/jUIc4t6rOvgjjw59T72bt5f7bo12tC4HG7/DxpD43YE9kPu5reu4OVr3iW5VSKjJg6vVgx/++LJ78nLKiA/u5Df3vudSU+eX6P5/taiczOUUoRF2mnTs1VQ5hT1z4NnPMu6BRsBeGvVczRtV72kqBBCCFFfaM8adMaFgA/iX0PZa/Za64jk/BV8ewE35L+JSvy+0iEq7kX/UStLR7AfV+shluLbBoWfA07IfQCNFXCiHd+gkudhSllV9zEJIYKixgkgrfUC/PlpcQTqMbQLPYZ2qfZ4s8XMw9/fztfP/8zIc48huWVSQONbdGrGs7MerPb6/zbwpD6snLUWDfQcEbzzzL2P7c4zsx4kY18mR4/1H1Hbs2kfWkPLzs2Cto4Ira0rtuMscBEWaWfv5gOSABJCCNHgaccUwOH/uHCyJICqw9Yb/6GLcKjiUS5laYmKC+HBClMCKDPocDAngi+16IIGbYQuLiFEjQW9DXxVSA0gUV/tWLsLDbTp3rLWCu7N+2YRT1/0KgC3vnc1IyccUyvriLo195tFvH7j+3Qa0J77v7oFq80a6pCEEEKIGtHuFejMiwEDYl/EFD6qdtYxCvy7ZCzta71FuvZu9Rdjtg1BqaD1w6lkzb2gs8DSvdTrS6096LxnwLsbFXMPylI/dotr725/HSL7MPBsQhd+gQo/DRUWgh1JQoiAVFQDqG7+1ROiAdBa88H9k1n88zL6jurFk1PvrZUk0LJpq4qPui37bZUkgA4Tw886iuFnHRXqMIQQQoigUba+kLwQ8KJMcbWyhjby0WkjQOeBKQWS5qCUyZ98cv2BCj8FZWkTnLU869EZ5wIKwsagKijEHEzK0hytk0HngIoredE5BQq/BFzonBxUwuSA59dGJrrgQzC3DLhrmNYedNYkcC+HyGswRV9bFHNLsBR1brYPRtkHBxyXEKL+qXERaCEOF3mZ+SyZsgKtYfXsdWTsz6yVdc644STikmOJS4ph3E0n18oa/7Z5+TamfTibwjxHra8lhBBCiMOLMkXVWvIHAO820LmABuMgeJb726BnXgwFr6EzxhO0EwveTUUfOMCzOjhzVoH2HUCnHoNOPRqj4D8NQVRM0QcWULHVmz/7Vih4B3IfBdfvgQ32rCt6LjxQ8Ea11hdCNByyA0iIIlHxkXTs345tq3bSsksz4hvH1co6bXq04uuD79bK3P+1c/0ebhn2ACiY/tEcnp/9cJ2sK44shmHw0YNfsm3VTi576gJad2sR6pCEEEI0ENrcHP970ob//+am/lbpaP/Xij8Owq5s+2iwfge+naiY+/3razdoF8oUXfP5y+NaCNoJeP3FlSMv/VdMx6Jin0D79qEizqve/MXPEUXrBMDSBgjzP73WvtVbXwjRYEgCSIgiJpOJ/81/lIM7U2ncOhmz2X8G/dCuNAzDoEmblBBHGLhDu9JQJoWzwMX+rQerNYdhGMz6bD5et5cTLhqB2VLx2XxHgZPMA1k0bde41uooifplyZQVfPe/KTgLXBzcmca7a0NYuFIIIUSDoly/obEAbjC3RZn9DTJ0zH3g+BUVdTlKBefQgjJFohI+Lf5c+w6g004DctH20ai4l4L+2kVr7a87hBcwQ8S5JWNSCsJPAe9W8G5EWwcEHIOKfRad/zyY20DYmMDGmuIgaTp4d4K1a0BjhRANjySAhPgXs8VMs/ZNij9f/MtyHj3neQDu/Ph6htXzGi9ej5dDu9Jo3MafwOo/uhfDzj6av5Zs5tqXLq18gjL8+OpU3rvnc9D+hNLFj0wo997stBwu634LjjwHx10wjFvevqq6D6XBykrNYd7Xi+gyuAMd+7ULdTh1IjI2Aq39f3+i4yJDHY4QQoiGxNIJMIEKB9ug4i+bIsZDxPjaXds1Hyg6fuaaBq45EHZscNdwLwHH54AXVGNMkZNK3aLdS9GZk0ApCL8QFXNbQEsoSwtU3P+qHaIyxYKtV7XHCyEaDkkACVGBxb8sw+30ALDwx6X1OgHk9Xi5ut8d7NtykE4D2vHC3Ecwm83c/v41NZo3fV8mXo8PbWjS91VcF2nKWzMozHPgcXqY/+3iIzIBdNuxD3FgxyFMSvHu+hdp3Do51CHVup7DunLnx9eza/1uTrnqhFCHI4QQogFRtv6QMBmMVLANrdvFbUfjP/uk8f9a5A3+GqYo0Bowgzm+7Hs8awEfaA94lgY/BiGEKCJFoMURITczj7vGPMZVfW9n5/rdVR53ypUnEBkbQURMOKdff2KJa1tWbOemY+7jpavfxuf1BTvkgKXtzWDflgN4XB7WL9yEIz+wM+BaazYt3cqB7YdKfH38nacz+JR+9B/dm0sfP7ec0fD75/P5/Mnv8Lo8oODMm06p1uNo6DL2ZeJxekApslNzQh1OnRk6bhAX3H82cUnVK2AphBDiyKWsXVH2EbXeAr7UupbmkDgV7KdA1M1gPz6o82vPGnTOXWDpABFXoOLfLvvG8NPB0h5MiajowHb/VDs2baC9e9HaUyfridDR2gh1CKIekR1Aol7btHQrHz/8Fb2P7c7Zt55W7Xk+evBLlk/3d3u4fvA9/JT7SZXOV7fv04bvMz8EKHH/1pU7uHnY/bgK3WxdtYPeI3sw/OzQ7g5KaZVE16M7sWbOeo4ZN4iI6PCAxn/88Fd8/dzPgOaZmQ/SdXBHAGIaRfPQt7dXOn7nhr143T60hiGnD+SC+8+qzsNo8O7+/EY+uO8L+p/Qi04D2oc6HCGEECIotHags671H2nCgJiHMUWcE+qwasxkaQPxVatdp7VG5z7sb90ecSGm6Bsqvj/nIfBuBsJQEeegzGXXk1SmRqjEHwOMvGZ09lXg+gPMzSHxJ5Sy1+n6om4Y+W9A/v/Qli6ohC9QKrDfD8ThRxJAol578IxnyNifxeo5G+g2pHNxUiJQEVH//GPnKnRj+IxKixn/raxE0VMTX8ZV6AbA5zVIaBJXrbiCyWQy8ezMB3E53IRFBP5DfOnUVbgKXVhtFv5avCXg5/qMG05i3YKNOPKdXPb0BQGvf7gYdFJfBp0kXTSEEEL8w/8OvBulwmphbg2umaBdEHZS0Aoml+L8rSj5U7RjJP9VOAwSQAEx9oPjG8ANBa+jIy9DmSLKv9/S3t/mHg3mVnUVZaW09vnrHQH4DhQVgO4U3DW8e8FIA2tvaQoSSgVvA9r/Z+xeAfYhoY5IhJgcARP1Wnh0OMqkQGvCo6r/oumSx89l0Ml9iUuO4aa3rqhy8qc8ic0SsNotWKxmJj1xHt2P6VKj+YJFKVWt5A/AJY9NIDI2gpTWyYyYcHTA4+OTY3lhziO8sewZmndoUvkAIYQQ4gigjVx0+ij0od4Y+a8Hf/7Cr9DZt6Fz7kEXvBb0+YuZ2/LPrw4msI+ovbUAo/BbjKzr0O5VtbpOQEwJoKJBRYC5GZSR0NPahfasR2s3KvYxVOzjqEYfoOyDQxBw2ZQyQ9hYwORPUlnaBnV+7dmATj8ZnXkxOu+poM4tAmQfAYSBsoO1fvy+IkJLaa3rfNH+/fvrZcuW1fm6ouFJ3Z3Gr+/OosvgjvVqV0VBbiHTP5xD805NGTC6d6jDqRafz1fc6l4IIYQQtUM7Z6GzbwUKQcVjSlkS1PmNvGeh4H1AQ9jJmOKeD+r8/6Y969CejWDpiLL2rLWdHdq7DZ1+OuACFY0pZXmtrFMd2sgE92qw9UeZokte0150+sngOwiWFqiEn2pvR1YQaCMfVGTQ/xx14WR07uOACywdMSX+gvYdQGddCdqFin8NZZFj8nVBawO8W8DctNT3qzh8KaWWa637l3Wt/v6LJASQ3DKJix+ZUK+SPwCRMRGcccNJDSL588Orv3L78Q+zYtba4q999eyPnGg/l8t63EJhnoMD2w/xwytT2bt5fwgjFUIIIQ5D1l7+d9+xQfjJVR6mffsxMs7HyJzkTzqUQ0VOAttgsPZFRd8ShIDLp6zdMUWcjcnWq5aP9Vj/taitWjNo7260czraKAxSTEXhmBqhwo4t+5dpIwN8ewCH/+iXzq3xekbhdxjp4zAKv6rxXP+lTFEl/hy1czZG3nNo756aTRw2GiytQUWjom71z13wEXg3gW8HOvMStHbXbA1RJUqZUNZOkvwRxWQHkBBVtPL3tayavY5RE0cQFRfBoV3pdOjbBpPJn0ddPXc9j5z1PDEJ0Tz7+4MkNm0U4ohh18a9XNP/TtwON2GRdn7O+xSAcYmXkJeZT1hUGHd+fB3PT3oDV6Ebq83C5P1vEx4Z/BoFQgghxJFKG4X+5IC5eZUTJ0b2rf5iwyiInISpjrpDVUZrX51069KuuWjXH/7iyQHuFtG+g+j0Mf7u7tZOmBK+rJ0g/7uu1ujsG8A1HcJOwxT3bM3mM/LQqYPx112yopIXoEzltJKvIe3Zgs44E3CDuTmmpJmVj9GGvzYURqX1pwzHFMi5uegzKyruJVRYcLuuCSH8KtoBJEWghaiCgztTue/Up/A4Pfz6ziw8Lg9ej48hZwzk7k/8HSA+euBLcjPyKMgtZNan8xl/x9igrO12upk9+Q+SWybSZ2SPgMaGR4Wh8BeIjoj5p0jhUacNYO6Xf2C1WWjTvSXOfCdejw+tNc4ClySAhBBCiCBSpgioqFhwWcytgKK6fqbmQY+pOoy8l6HgNbSlMzT6FKXzwNSkVnYDKftwlH148edau8H5C5gaoSqrP+TbA1rj34mzOeixlUcphYp/Ba11lZ4TfwHvGWBkocNOR7l+AyMTIib4uzUpu7/OkMa/E6oWioj/w1n0fwO0o0ojdOHHkPei/xPfXlTUVeXeawo/GaPgI/BuBFSVi2Jr7QHPSjC3RpmTqzRGCFE+SQAJUQUuh3+bqtYaR54DZVK4Cl0sn7a6+J6+o3qyefl20JougzsEbe3nL3uDP374E4BHfryLvsdVPQmU3CKRJ3+7j9Vz1nPc+UOLv37be1dzzm2nktisEZGxkdz4xhV8/8qvnHjZccQnxwYtdiGEEEJUj4q61l+cV1nBfkKow/ErLKo15N0F6aeijTSwHYVq9G6tL61zHwfH9/5P4p5FhY0u/2ZrXwgbCe6lEFX3O6eqnBBz/orOvcefrHJMQXtWAQZ4N6Fin0IpGyR8A67ZYB+OUuFo71509rX+deJeRVlaBCdmaw909G3gXuj/3qsK317ADeiijytZI+FjcM4CSzuUtWqvlXXWteD5EzBB4lSUOeWfa9696KxLQTtR8W+irF2rFrcQRzBJAAlRBa26NOeq5yey6KdlnHHjyXx43xdsX7ubCx88u/ie8+89k/4n9CIqPiqoXbD2bTmIq9CNPdzGwR2pAY/vMbQLPYaWrPqvlKJV139eMIy5dCRjLh1Z5Tm11qydvxF7hJ1O/dsFHNPhrjDPwa4Ne2nXuzU2u7XyAQHQWvPhA5NZM3dDvepAJ4QQIriUMkP4KaEOoyT7CHD+7k9KGemAB9zz0NqDUsH9eVeKbzf+XSo28FVcs1ApMyruxdqNJxiMQ6B9gMffMh0D8IIvo/gWZWkDljbFn+uCt/y1dABd8CYq9vGghWOKnAiRE6t8v4q8Cu3dDvhQUddXfr8KC6gOFgCe5aAL/Z3XvFvh3wmgws+Kvi8MdP5rqPha7IInxGFCEkBCVNGpV43m1Kv87zaVVfxZKUXngcHb+fO3m968ghcuf4MmbVM47vxjgj7/mnkbWPjjnxx/wXDa92lT+QDgmxd+5uMHv0JrzZ2f3MDQcYOCHldD5XK4mNTtZvKz8mnZpRmvLnkqqFvj187fyHf/m4KzwMUj57zAV/vfCdrcQgghREVU7AsQtR2tEiFrEnjXg31MrSd/tJHtPzJk3gOWjhB+Ntq1GHy7IPw0/3Gphih8vL+jmM6E6Aeg8EPwpaNiHyh/jKUr/qOBGlQjtDZC1mlMmRNRjd4P2nzavRLcSyDs5H92NkXdCvnP+Iup2waUXN/W158EQoFNXosKURWSABKiDvm8PlbPWU+Tdik0aZNS+QCgfZ82vL7smVqJJyc9l7tPfBy3w82v78ziu4wPsFhL/rMw75tF/PLWDE65chTDzjoKgPULN+EsdKFMik1/bpEE0L+k7k4nNz0Xt9PD5mXb8Xq8WG3Be2EckxCNNjQWq1mO6wkhhKhTSpnA0h4F6IRvQOehTDG1vq7OvgPcCwArKvp28G5EZ10BGFD4OTT6CGWKq/U4gk2ZIlHxL/3zhSrs5lERE9CmaMi5Fwo/QhsHUTUsNl1bjIIvIP9ZsPb2H9GqoKOb9h1EZ14EeKDwU1TyAgBMkedB5HlljlFhoyBhMmg3yta7Fh6BEIcfSQAdxras2M7BHakMPrVfUH8BFdX35PkvseTXFQC8sfwZmndsGtJ4fF4f2vB3AvR6/UWg/6a1JjcjlycveBmv28va+Rvpe3xPouIimfjgOWxbtZOwSDunXVPBGfw6kJuRxxu3foQtzMpVz00kPCq07wI269CE/qN7s2TKCk67dnTQ/+617taCx6fcw6alWxk1cXjlA4QQQohaoJQCVfvJH8B/BAgD0P4Cxb4D/o9xg/cvdOZFqMQf6yaWEPM/7xFopfzPhWte0NfQ3r3onDtARaHinql+ci3/OdD54FkB7mVgP7qCRQvw/5n6wMitciFtqfsjRGAkAXSY+uvPLdw28iGUUhx12gDu+ezGUIckKNo5U+AiLNLOjrW7Q54AatQ4nrs+uZ5Zn81n7LVjipMVMz+dy0vXvIurwIXZYgIFFqsZi83/T0bbnq34ZFvoz1lrrXn37s+Y/fkClEkRnxzLxY9MCGlMJpOJh7+/o1bX6DWiG71GdKvVNYQQQoj6QsU9jc59BswtIOwUwADHD+D+A38B4n0hjrCO2Qb5j8R5t0BVCzYHQOc946+9gxld8DEq+obqTWTpAp5VRR9XXDNSWdr5i1A7p6Oirq6VznKhZuTcB45vwX4CKu5/h+VjFPWfJIAOU3v+8hfHcxa42L56Z2iDEcWueuEiXr7mHVp3b8mAE/sEbV63y8O7d35KdlouVz43kYQm8VUeO+yso4qPdgFkHMjiuUlv4PP4AAiPDmfsdWMYMnYgYRH2oMUcCJ/Px/Lpa0hoGk+7Xq3x+Xzce/KTrJixhra9WmEym1CKEq3uhRBCCHF4UOZmJY9KAcS/h857HNyL/MfCaon27gDnVLANQdl61do6gVCmyNrd8WRuib/OkEJZmldrCn/79k34d20pqMJRQVPkRRB5UbXWq++0kQ+ObwADXDPBOADm0L4RLI5MkgA6TA09azBzvvqDPZv2c/1rl4U6HFFk+NlHM/zsCra/VtOUt2Yw5e0ZeD0+3E43D31b/RdCJrMJk0nhK/p8+DlHc9FD44MTaDW9fuMHTP9oDtrQPDXtPqIbRbF2/ka01mxbtZNLnzjPfxzt6tAeR2sItq3eieEz6NC3bahDEUIIUY9oowDw1UlNn2BQSqFi7qvVNbQ20BnngM4D3oTkOShTo1pdsz5Q0TeBtYO/85b9+OpNol1AAeD1f24UgrmBFusOBhUBlvbg2wOmRP9/QoRAaErGi1oXFmHn8V/u4eMtr9JruBwVqYjWmh1rd5GXlR/qUKotPDocpRQms4mI6Jr9cI1PjuWxX+7hxMuO44FvbuWG1y5jx9pdXNHrVu4Y9UhInqeNS7bgLHAV/VntJqV1MnFJMdgj7HQ/pjPj7xjL+gV/MTZ2Iu/f90Wdx9dQzJ78BzcefS83D7ufaR/ODnU4Qggh6pj2HUAbeaW/7l6NTj0KnXo0hmNGCCKrr4x/1R8y/DV36hmtDbQvvUQdx5pSyoIKH4sKG1XtY0rKFAXR94K5LUTfhjInBC2+hkgpEyrhW1T8x6iEnyssiC1EbZIdQKJBm/HJXH5773fGXjemxDGmQDx/2RvMmfwHVruVd9Y+T2Kzyn9Aaa2Z8vZM0vdlcNYtpxIVF1mttYPlhIuG43F5yM3IZdyNJ9d4vr7H9aDvcT2KP3/jlo/YsXY3FpuFqe/O4owbT+L7l6dieH2Mu+lkbGE1+yG2d8sBXrnuXVJaJnLdq5dhs5csnHz1Cxfz1AUvk9wykWPPPYawCDvvrn+RfVsO0LpbC/Zs2s+iX5bjdriZ/OT3THzw7FLdzFwOF18//zNWm4VxN518RBZGXzN3PS6nGzSsmr2O0RcfG+qQhBBC1BEj/13I/x8oKyR8i7L8sxNUO6cDTv8njq8gfFRIYqxvlLJA3Kvogvcg/FSUuVmoQypBawOdOQE8a8E2EOI/rFd1ZUyR50Pk+aEOo95Qyg715BihOHJJAkjUSx63B6/HR1iEnZ9e/42DO9OYcOfpxCb+sy05Jz2XFy5/E6/by4bFmxkwpne1OkAt/mU5LocbZTaxedn2KiWAZk/+gzdv/Qivx8uuDXt58JvbAl43mEwmE6dedUKtzd+2R0s2Lt6M1poWnZvx9XM/8dlj36K1Ji+7gMufuqBG8794xZusmbcBW5iNTgPac/IVJV949hjahc92vVHia+GRYbTv3QaA5JaJRMZEYDKbaNa+cankD8CH93/Jj69NRSmF4TM49+5xNYq5IRp308ks/W0VhmFwzu1jQx2OEEKIuuT4HnD767G4F8G/EkAqfAza8SloHyri3NDFWIsM90bIfwFsfVCRVS8yrMKOBWtPwF27AVaHcQg86wEfuJeAzgUVWydL+2sjTQf70Gp34tKe9eCaD2GjUZY2QY5QCFEWSQCJemf3X/u4YfA9uBwuTr1mNL++Mwuv28vuDXt5fMo9xfdZ7VYsVjOG14fNbsVcxi/9FdFak3Uom3PvOoO37/iExq2T6D2ye5XGugpdoDXap3HkOwNatzZtWbGdZy95jeRWSdz7+Y1Ba4l++bMX0m1IZ2ISo+k1vBsbFm3GMDRojTMIj79R43isNitKQVxy4C9cwiPDeG/Di2xfs4vOA9uXeY+r0OVveW8Cl6MevoirAy06NePTHa+HOgwhhBChEDkJcu8DUyTYR5a4pKw9IHmxPwFkigpRgH7aKEBnXwe+PajYZ1C2vjWf07MWMs8CNLgXg7UX2IdUbax7KTpzEmCgYx7HFFE3b6BozxYAlLVD+TeZUsDaHTxr/DuAVN3Ub9Lai84429+6veB1SJofcO0obWSjM84D3FDwLiQvQSlz7QQshCgmCSBR7yz84U+chU58XoMlv64ANNrQeNzeEvdFRIfz4vxHWfLLCoacMbDUsaGKaK2579SnWD59NW17tmRK4Wdl7hopz6iJw9m35QCpezK4/Onq735J35eB2WImPiWu2nP828vXvsuOtbvZt+UAMz+dH7RdQWazmaFnDi7+/Lx7ziA/uwCv28slj9X8ncJb3r2KLoM7kNQ8gaPHDqjWHNHxURXWu7r0ifPw+XxY7VbG33l6NSMVQgghGiZTxDh0+MmApcxftJUKh/pwesj5C7iXA0507iOoxB9qPKUu/Ar4u0aOF1TVk1zaOYfi43HOKVCUANLaic57CVQ4KuqqoNZ0MRw/Q869/nVin8QUXvbxfqVM0OgLMDLBlFCHx798RfWQfKD//jjA5JN2+sf/ey7qPgFk5D4NhR+A7WhU/DuShBKHPUkAiXpn4El9+eyJ71BuL+PvGEvm/iwObE/lkscmlLq3fe82xceAyrJp2TY+e+wb+h7fg9OvO6n4626nm6VTV6A17Fy/l4M702jeoUmVY7RYLVxWw2NP879dzJMXvIxS8OjPd5eouVNdrbo1Z8fa3aA1zQJ4PIEKjwrnxtcvD958kWFBqV1Ukai4SG5+66oK79m0bBthkXZadaley1MhhBCiPlPKHuoQKmdp5/+/CgdL5xpPp717wbef4t43EZMCaueuwseiHV+A9qD+1aJcZ14FnoX+j3VujTqSaa3BvRC0G+wjwP0HxUkn90IoJwEERUkgc912lFLKjo59wZ84CT8DZU4JfA5zY3TM/eD4ASIuDUlRZK0NKHwf/86wZeDdAtaaf88JUZ9JAkjUO217tuLLfW/jcriJr8ZxoH974LSnyDyYzYqZa+gyuBOd+vtfVNjCbAw8qS/Lpq2mdfcWNGmTHIzQAzL7yz/wuDyAf9dTMBJAN71xBX2P60lS8wR6DO1S4/mOJN/+7xc+uO8L0PDgt7cxYEyfUIckhBBC1Hvatx+woMzBeS2lbP0h4TN/0sZ+XMm1jCxQMQHt0tDZN4B3A2CD2McwhZ8WWDzWjpC8DDD8RaH/5v3rn4+dc6EmCSDHN5D7mP+TqOvBNgQcPwNWCJ9Y7Xlrkyl8NISPrtkcEeMhYny1x2vtBLR/91o1KGVCW/v56yiZosHcotqxCNFQSAKoBrTW9arS/uEkIjq8xu3MAewRdpRJgQZ7+D/vLCilePSnu8hOyyU2MRqTyVTjtQJ1+nUn8ueUFZjMJkZfEpxuTBarhZHnHhOUuY40K2aswVXoxmRSrPvjL0kACSGEEJXwH1W6G1AQ/zbKXr2OrP+lrD3AWvKNMSPnAXB8DebmkPAjyhQR4KQKZWpUvXiUieIdRH+LuAgKXiwKbjdaO6qdiMC7BXABGryboPBTwANYUDod6FS9eQ9j2r0MnXmJ/5P4d1D2wRUPKIdq9JH/OTe3RplC29VXiLpQ97/1HiY+f+JbRlvHc82AO3E5XKEOR5Tj6Rn3c85tp3Hfl7fQulvJrL5Sivjk2JAkfwB6DuvKjzkf833Wh3To27byAWXYs3k/P7w6lZyM3CBHVz9prfnt/d/57PFvKcgtDOrcFzxwNnEpsTRuk8KJk46rfIAQQghxpHNOxd8dy4V2zandtRzfAz4w0sC7vsrDVPwrEH4ORN3h31kTJP7jYLFAOJgaA9U/XqciL/UnvCxdUFHXg6kR/vfpNZjigxPwYUY7vsefNHP5d1BVk1JWlLV7yIufC1FXlNa68ruCrH///nrZsmV1vm4wnRZ7IY48J2FRYTz8/R1BOb4j6l5hnoMZH8+lRaem9D2+Z43n+/KZH5jxyVzOvu00Rl9Us109q+es5+07PqHnsC5c8ezEUrvNDu1KY2L76zB8Bha7he/S3g9a16/a5PP5WPLLCho1iaPzwAo6W5Th9y8W8MLlb+Lz+hhyxkDu++LmWopSCCGEEJXR7j/RmZeBsqAafY6qZv0U7d2BznseLB1QUdcX7bgpyci+G5w/grkxKuHnerFbQ/sOgWcF2Aajgpio0b4McHwL1q4ou+zsLot2LUZnXQ5oVPyb8jwJ8S9KqeVa6/5lXZMdQNU0cEwf7BE2bHYrbXu2DHU4opoePecF3r79Yx4Y+zTr/vir1PUtK7bz/cu/knEgq9K5Unen8dGDX7Fr/V5evPwt3C4PO9btZsuK7dWK7fFzX2Tzsm388tYM1szdUOr6/m0HMXwGAF6Xl90b91Vrnbr2+o0f8OQFL3HbsQ+x8ve1Ja75vD4eOvNZzkqZxLQPZpca68hzoLXG8PooyC6oq5CFEEKIOqe1Rnt3oo3Af95pbWBkXYdxsJu/y1EtUbaBqJSVqORl1U7+AOjsW8E1AwreB9fMsteKfQKVNBeVOK1E8kdrTU3e0DYK3sM4NBAj++6A51HmFFTYiUFN/vjnTUBFXVFmUkO7V6Fdc2v0mINBay9G9k0YqcMwHL/V+frKPhiV/If/P0n+CFFlkgCqpnu+uImXFz7Bx1tfIS6pZoWKReik7UnH7fSgTIqM/SWTPGl7M7h52P28c+cn3Dz0/krnioyNwGI1YwuzEh0fyZIpy7l+0N3cPOx+prwzo8KxWmuyDmVjGEbx1xKbJ2C1W9GGJi6l9PdYz2FdiUv2t9yMbxxH216tiq953B6ev+wNbjzmXrau2lFp7H9b9PMyJjS/grtPfLzWjjZuWbEDZ4ELw9DsWr+3xLV1C/5i+fTV5KTl8uqN7+MsLBnDCReP4JQrR3HMuEHc/HbFHb0q4ix08dj4F7jh6HvYsW53tecRQgghaovOvQ+dfio67Tj/jpBA+HaAay7ggcL30NpTo1iM/Hcw0sdhOEq/nlHKXOaOnYCocPy/lmhQZe/sUUqhzEkoZS3+mnYtQh/qhU4djPYG/oab1j7IewZ0tr/9vG9b9eKvI9r5OzpzIjr7BnT+/6o3h3c3uuCjaj1fJbiXgGsOGAch98GazVVNyhSDMsnvYUIEQhJA1WQymWjbsxWRsaHffipK87g9vHPnJzxzyatkHcou9747PrqOLoM7MvK8YzjmjIElruVm5IEGj8tL5sHKdwBFxkby2tKnuPK5i3ht2dOsnb8Rt9ODq9DNyplryx2nteb24x7m3BZXceuIB4uTQE/9dh9XPHMhT894oMy25HO+Wkhepv9dwbzMfP79RtD8bxYze/IfbFi4mecueb3S2P/26vXvkbE/i3ULNjLv68W8eOWbvHjlm9Wqt/PXn1tY+ONSfD5fia9f87+LadahCd2O7sioicNKXGvWoTHKpDBbzbgKXJzb4kpSd6cVX7farFz9wsXc/9WtJLeofsvTGR/PZdHPy9i4eAsvX/NOtecRQgghao1zGv4aJ07wlv86okzmpmAqqk9j6VYiaRIo7d0B+S+Ddx3k3ORvnR1kKv4ViLwSFfs4yl71Oj264B3ACTob7fgp8HWVGcztQEWACgNT5e3Mte8gRt6zaGfd73rBuwnwgHaAJ8DvCUBrNzpjHDrvGXTGmWgjsNd3RuGPGAd7YaSfjjYlABqIAEvHgGMpHVtodzQFg/YdwMh5CKPgi8Pi8YjDk3QBE4elqe/+zg+vTMXn8VGYU8hD391R5n0d+7Xj5YWPl3mtbc9WnHPHWBb/vIyJD1WtRWWLTs1o0akZAKddM4aFPy7F6/Iy4a4zyh2Tk57LugUb8XkNNi7ZQtahHBKaxBOTEM3p159Y5pi5Xy0sroUDYLVb8Lg82Oz+F3gJTRsBGluYlaSWVU+UdOzfjtyMPLSGFbPWMOfLhaAhPDqcq567qMrzrJq9jvtOeRJlUoyaOJwbXru8+FrngR34cNPLZY5LbJbAO2te4PrBd5N1KAdHvpNPH/uWm9+6Mqgd95JbJqJMJuzhNpq0rfzF3r+tnb+RJ877HwnNGvHElHuISYgOWlxCCCFEsYhLoOBlf1LCWmYph3IpFQ6JU8G7Ga2dGNm3o8JPRdmHVT641GTR+N8zthYVJA5+B1xlaoSKvinwgWFjwL3UP0cAiaMSayd8Be4/wdodZar8Z7rOuhK8m9DYoFEKylZ511Bt5IPzN7B2Rlm7VytOwF/M2vU7GDmo6NsCH6/doAsBrz93gxMIoJta/guAA3w7UN6tkPC9v4PZf76vDMdPkPcUWHuh4l6uNAFp5D4Hhe+grf1RjT6sUcIylHT29eBZB8oOlmalnhch6gNJAInDki3M6t8qbFLYwkt3ZchKzeGWYfeTtjeTez67kaPHDih1j1KKiQ+ew8QHz6lWDM07NOHT7ZXvvolNjKHbkM6s/2MTXQZ1IL6M417/tW/rgeLkT1xyDHmZBZzb4kpeXvgErbu1oNeIbjz03R3s33qw1C6bitzz+Y0sm7aa5h2bMOuz+SiTQgERUWGA/92Z/OwCouIiK0zI7Fy/B0NrPAVutqyo+hE0gJRWSUy46wzevOVDfB4fsz6bz4DRvRl6ZvXae5Zl0El9efDb28jYl8nI84cGNPbtOz4hfV8mOel5zPpsPmfccFLQ4hJCCCH+Zoq+Fh11OWCt1psgyhSNtnaHQwMAp3/HSvK84no1Wmt0zv3+2jsR4zFF31L2POZESPjCnyQJGx3UN2QCpX1p6Jy7AIWKfRpTxDlo2yBQdpS5cbXmVKYoCBsZQBAOwAClij6uwpCsy8BTVM8x8TuUpX3ggeKvDUT8R2AcAnPrwMebotAxj0LhJxB+NsrUKLAJ7EPAMQVQYO2BsrQCSxmdbHMf8x+rcy/yHxWrrEZP4XuA9u8y824Ea80bs4TE37t+NEDwd8oJEQySABKHpRMuHoEj30F2ai7n3H5aqesLvl1M6m5//Z8PH5hcZgKoqgzD4PlJb7Bi5houefxcTpg4IqDxSime+/0hsg5lE1fFtvSnXj2atfP/Ii8rH4/LQ3ZqLm4HLJmyorjdff8TesEJvQKKxWqz0m9UT1L3ZDDh7jMIjw5DKcUZN5yE1pp7TnqcFTPX0mNoF56Z+UC5sY66cBh/fP8naXszuPalSwKKAWDcjSez4LslrF2wEQUU5FbtBVYgBozuXa1x3Y7uxM51u9GGpn2fNkGJZc+mfdx+/CN4XB6emHIPnQZU74WhEEL8TWsvYA7pL+ui5pSy1XSGcq9o93JwfuX/pOBNdPiZ/l/oy5rF2hWsXWsYiz/pVJPvSZ3/CrgXFn38Oir2/nJjri0q7jV0/ktg7QW2o6o2yLcHcPprHfkOQjUTQNrIQqefBEY+hI1BxT0b8BymiDMh4sxqra9iHoPw8f5ObOYKdlBbu4Fnpf/jChJV2r0cvH+BrT+4V4OKAnMZCaV6TGsNvr1gTkLFvYQueN1/JM42PNShCVEmSQCJBmH7ml08cd7/iE2K4YSLhvPja9MYee4xnHXLqWXebzKZOOOGk8udr+vRnVAmhT3CxuBT+lUrJq01b976EfO/WUxWajZet4+Xr3k34AQQ+JNAjRpXvYNEdHwUT069F4CZn87l+UlvYgu3MvjU6j2WvznyHVzW/RayU3Pod0IvHvnhzuJruRl5rJi5FsNnsP6Pv8g8kEVis4Qy54mMjeTZWTUrCHjHR9fx9u0fk9I6meMvCGyXTm264tkLGXhiH+JTYmnTIzgvOqe8PYOM/ZmgYfLTP/DgN9XY1i2EEEWM/Lch/3n/L1IJX/t3OIgjklI2aPQB2vEtKuzkkt2qShRbVv5jK7XIyH0SCj9EWwcUHfOpxq8h5qaA9V8f1z1l7YCKfzWwMbHPoHMfB1vfKiWNjMJv/HWX7MNRMY/8kzTzbADDAbjK7ZZWm5Qyga3yNxdV/JvgWgiWdihL6TqWANqzCZ15CWCApSsqYTKYWza4f690zl3gnAKmOFTiFEyxT4Q6JCEqJAkg0SC8ddvH7NqwF7PVzLr5GzEMzc71exhyxkCatAmshgtAu16t+eCvl8lJy6Vd79YBjV308zJ+eWsGvY/txpS3Z+AqdAMQFmmnRedmAccCsG7BRnas28PIc4cEXFj8+AuGc9Sp/bHardjCKn+n0OfzMePjeZhMiuMuGIrZbC6+tnvjPnLS83A7PSyZsqLEuOhGUbTs0oyd6/ZgMpuo7dJ2jVsn88DX9S8RYjKZ6Ht8cLcm9z62Bz+/OQMFDDyx8loCde23939n6vu/c8YNJzLinOrVWBBC1KGCdwANxgF/fZSwY0MdkQghZeuHsv3zBpG/OK3GZOuCEfMoOH6AiIlVPkKlfQfB8Z2/vksV6+5oraHwQ/zdNdaAd3O1dhSpyCuKEj8Kwsp/o6++UfYhqKRfiz83HFOg4AMIPwNT5PmlB+Q+AHjB8S06fDzKVlQ3yNrHX1vGuxUiLq6T2KtDKXvl/+4YqUXH6NxgHPTvMmuIXL8Bbv+uLM8GsFdxV5gQISIJINEgtOvdig2LNqENjSXCjsvhxmwyERkTQOG6/0hqnkBS87J3sJSnMM/Bo2c/j8ftZcXM1ZgtFuwRNhKaxHPpE+czYEzvgOPYumoHd41+DK01sycv4IU5jwQ8RyBJo8lP/cAXT34HQNahbMbfcXrxtTY9W9GqazO2LN/OKVedUGKcUoqI6HAADJ/BnMkLOfvWsndgVcTn9fHmrR+xc/0ernnx4qDtomnIBp/Sj7dWPovH7aVN95Zl3vN3N4m6Ps6RlZrDy9e8g8ftZfPSrQw6uR/hkWF1GoMQIkBho8DxM2ADa49QRyPqEe3dic4YD7oA4t/AFDEeIqrW6KJ4jsyLwbcLsFa5no1SCm0bDO6VYIoBc/V+9itlgvDSR/uDTWsPaA/KVPnrTG3koLNvBiMbFfcsytKukrldkHM74IW8v9Bhx5WRfLP6r+P1J8uKEkDKFAEJvwCeIBwPDDHbEAg/E9wrUTH3hDqa6gu/wF/DyNzYfyxQiHpOEkCiQZj05Pl0O7ozsYnRJLVIZMF3S+hzXA+iG0Xxzp2fsmLmGi594rxq13WpKrPFhMliBrcXs8XCC3MfZsfa3Rx1av9qd4PK2JeJMilcBW5Sd6UHOeIy1tuficftRRV9/G82u5VXlzzl7yhWxm6ivqN6sn31LrTWdBlU9fPr+dkFLPxxKR36tWX13A38/MY0fF6Dpya+wlsrn6vpQzosNO9Y/lb2VbPXcd+pTxEWYefF+Y8Ud5qrC/ZwG2arBZ/PwB5ux2I1Vz5ICBFSKuZxiJwEppQGd5xCBIf2bEY7vkbZh6Hs/zpG7fwFdA5goPPfLnmtypPnAj7AinavBnPr4uNc2nfAnzixlH4zQ8W/509mmFuhTIHtdq5L/iTZWaAL0TFPYYooO+GkjRx03jP+3TietYAXnfskqtG7RW/aGP4286WY/cfvdAEoS/FRPK3d4DsE5uZg7QyeVYC9VBLK/0ZQ7Sd//EkwV1D/DdHa539c5uYocwoq5oGgzR0qppjb0dHXAWFSc000COrvd5XrUv/+/fWyZcvqfN2GRmtNxv5M4pJjsViPjFyd1prdG/fSqEk80fGV/8DZsGgTd57wKM4CF1HxkXyf8WGtx7h5+Tbmfb2IY8YNovPADjWez+fz8er177Np6VauefFiuh/Tpcz79m87yCNnP4/ZYubBb28juUXV27uDv1j1hkWbsYXZ+PSRr1EmxS3vXEVsYkzx9b+LWV/6xHmMurB08TqtNRuXbCEmIZrmHZpUee2r+t7O3s37USjCosLITs0B/LWYXlrwWNDWOVzdfdLjLPttFUopzr1nHJc8OqFO19++Zhd//rqCo08fSMtqHnMUQghRN7TW6NSBRYkeOyppGqqoXo52r0RnXgRoiL4HU+S5gc/vXonOe97fsUkbYOuHqdEHaNdCf4t0gJiHMUWMC96DqkO64D3/48MLlh6YEr8t8z4j5z7/UbhiFlBxgBvw+RM8sc9iCi99VE17d/u7r9mGoKyd0UY+Ov1kMDIgbBQq+g50/stgbuMvGF3wNoSdhClyYi084tK0bx86/QzQ+RDzAKaI4LzuMLJvLapdZEIl/ISytAjKvEKIkpRSy7XW/cu6dmRkFRqopye+wtyvF5HUPIG3Vj93RBy7eP2mD/j13VlYbRbeWvUcKa2SSt3j9XjZvGwbzTs1JT4lDq01VruVxGYBtrKspo792tGxX8XbewNhNpu58fXLK73vk0e+ZvvqXSil+Pq5n7j2pUsDWufV699jxsdz0RpeWfxEqaNGfy3ZwrxvFuEscPHyNe+UmQBSStF1cMeA1gU4sP0QrkI3YZF2vB4vABarudxExkcPfsk3L/wCWvPU9PvpPqRzwGvWpXnfLGLJlBWcfv2JdOgb/O4VQ8cNYs2c9aAUfY+v++McbXu2om1POaonhBANh+efD7W3+ENl6wNJs0A7ytylUxXK1gdi7kBnXAg4wb3Yv4xrPuDy3+SaBg0sAWQ4ZoLrV7AN8xfE1grCzy5/gAoHTPjrEZ0GygqOb/E/BwrQUPAGlJEAUpaWYJn0zxe8m8DIBtzgnI6KexEV+yRaG+hDPf1f96xH24fXTdcz19yiFvdeKPgYgpQAwr2oaN4If/evOkoAae0D50+ABcJO9h8lFOIIJQmgemzOVwvxeXxkHcpmx9rd1frFu6FZ8P0S3A43JpNi4+LNZSaA7jvlSdYv3IQtzMoHf73M87Mf5q8/tzL8nNJF13Iz83A73OV2q2pI2vVuzYJvl6CBtr0q/+FvGAbb1+wiuUUiMQnRrJy1DmeBC3uEjc3LtpVKACW3SkIpRViknZZdSnZsSN+XwedPfEfLLs0Ze+2YgLe43vb+tbx392f0Pb4HoyaO4PPHv6XP8T3ofWz3Mu9fMXMtrkIXZouZhT8urdcJoH1bD/D0xFdwOz0s+mkZ32V8EPQ1TrrseHqN6IYtzBZw3SohhBCHH+3dVdRuujsq4oISP5eVUhD/HrrgQ7CPKpXoUebSr60CZuniry/lWQaRl/nnjTgTXZQAUUVfayi0LxVybgZc4JwJSfNQeFHm8ndbq+hb0KY4IAwVORG8W9CO74Aw/Ak4S9ULVVu7+ZMh3q0Q8e9dWQpM0UXJIfWfzm3Vp41CUBaUsvm7ceXe7+/AFfu4v4CzbYg/oaWNgGtEVSjqVsh7FCwd/GsUMRwzIe9+sHRAxb+FUuHBWxPQBW9C/ltFi6WjIi8J6vxCNCRyBKwee+HyN5j24Ryad2zC68uexh5eu+05Q2Hv5v3kZxfQaUB7lFL8/OY0Xr3+fZq0TeGVxU+UeQzs5IjzcDs9hEXaeXLqveUemfrrzy3cNvIhDJ/mhtcvY8wlI2v50QTfttU72bflAINP7Y/VZmHZtFWYrRb6Hlf5LpDnJr3OnC//wGK18Paa59myfDvPXvIaTdqm8OysB4mKK/0iYv+2g2xZsYMBY3oXF3wGuGno/WxcvBlrmJV7PruRo08bENTHCf46QRabhbAIO6tmr+PeU57E4/RgtVt44td76TWiW9DXDIb92w5yeY9bcDs9RDeK4rv04CeAhBBCiH8z0k8B7xYgDBX/JiqInYe0NgA3SlW+81xrXSL5FKqGBTWlfenotGMBF6gIVPKyarWp195t4NuHNrcB316UbXCVnwuttX9n1n9q/mjvHnBOBftglLXmXUgNx1TIuQ2UHZXwFTrnbvCsBsJQMfejIvy7nrRRCLqwwiRYsBipI8DYD4SjYp9EhZ8U3Plz7ivanQVEXIipIRedFqIK5AhYA3XLO1dz+TMXEhkbgcl0+G1VXD13Pfec9IS/rsndZ3D+vWdy6lWjOfmKUeU+3l0b9nDq1Sfw0+vT6DywA50Gtsfr8fLbB7NZMXM1XQZ2ZNzNJ2M2m1n62yrcTg/a0Mz8dF6DSwBtXbWDm465D6UU/U7oxUPf3s6AMVVvEf7nrytwFbpRkYoty7cz5PSBDDl9YIVjmrZrTNN2ZbSB1Rq0f0NzbfR///2L+Tx7yetY7RZe+uNxeh/bnQ5927D+j00YhmbLiu31KgHkyHfw0UNfoZRi4kPncNenN/Lnr8sZe+2JoQ5NCCHEEaHo6BFAmYWGq0f70tEZp4ORgY6+B1PkhRXe/9/kRnnJDsMxHfJfAPswVPTd9S5BpMyJEP8G2jkNFX5WtZI/AMrSDq1iIP1kfxFpSxu0kQtR12GKqOA4GYBnNTrvSbS1Fyr6ruJjSsrSAqKuKHW79qWCbydY+6CUtepBFn4CeED7wDnDX3Tas8l/zfxP3UV/IqriLmhaG+jCz8FIRUVOQpliqx7Hv9l6gzMb0GAN/okHFXWDv0A5ZlTUVUGfX4iGRBJA9VxVCiE3VBsXb8Hn8eHz+lg+YzXn33smQLnJn+kfz+Hlq99BA/dNvoWjx/p3obxy/bv88uYMDJ/Bop+WExkXyUmXHcews4/i2xd/weVwc9bNgbcrD7V9mw+glMJZ4GL76l0lrs356g9ev/EDOg5oz4Pf3IrVVvoH//n3ncUbN39Ak7YpJDZvxNX9bicmIYZ7J99ETKPAOpbd88VNfProN7Tq0pyjTiszmVwjP78xHa/bW/RnuIw23Vty2VMX8Nj4F4hvHMfxFw6r0jwHd6by7MWvERETzh0fXVdrf38+fvhrfnr1N1D+WkaTnjifoeMG1cpaQgghBPy9Q6QAZYpCxb2OLngXrN1Qtorf3AmIewEYeYAPCt5H24f5d4fYh1f/l3uA3DtAF0LhAf+xKFto2mVrz/qio3HDMYWfUuKash+Dsh9T80U8q0C7Abe/tg9A7gPo8LMqTHz5W8nv89fGsR+DVtH+8WGnlOrEpX2H0OknAgZYB6IavV32nEYeuuATlKUJhJ3uXz/8LPCs8ScO7cP9x6FsP4K5aeCP3/kz5D0DeNHeHaj4VwIbX0TFPgvhC/0d4iytqzVHhfObk1GN3gv6vEI0RJIAEiFz/AVDmfbBbHIz8rj4kcqLyy39bRUuhxuA5TNWFyeA9m46gOEzANCGxuf1AdCqS3O+TX8fw2eUmSCp7446rT99juvBznW7uemtK0tce+3698lOy2X17HWsmLmWQSf1LTV+7LVjOO2a0SiluOfkx9m6cicWq5lf357JhLvOCCiW5BaJ3PJ27b1jcurVJ/DXn1ux2i3FCabuQzozeW/ZL2jK884dn7B2wUZMZhPfv/QrEx86pzbCxWRSRW+8Kszmw293nhBCiPJp7QDvbrC0q/ZOkcDX9KEzJ4JnGdo+ClP8q6jYhyq43w3upWBpjzKnVH0h2wB/a3Jtg7AT/LuBtAHmJqik36r/AMzNwLvH/7PTnBzwcK194Nvhbx9ehaNp5c6TeQnobHD+hrZ2RVlKNm7QRiY652FQRcehqtMC3TYQzI3A58Tf8t0CpiaV73oyJ4ORBlqjjXzIuQ7Q4PgFlfBpyXu9WwHDn1TzLC//8ebcCa65aCwoFQFho0HFgO0YiJiAsnb13xgxHu3dis5/zb9Ly1rFhhO6qOg3Bmhn1caUQSkr2Es3HxFCBJ8kgETIJDZL4IO/Xqry/WffeiorZ67BbDVz2jWji79+zUuX8OT5L5Gbkc+x5x7NiZP+OeplNpsxm4O3NTqYVv6+lucufZ3mHZvy0Pe3l+ryZguz8cgPd5Y5tkO/dqydvwGg0rbcXzz1Pfs2H8AWbkUpRbOOTWscu9aawjwHkTEVbw2uqpHnDmXgiX2x2i01qnXVpG0KtjB/si+ldRCKXJbjwgfPwWw1Yypqyy6EEOLIoLUDnXYiGJlg641q9HHdLOzbX1SnRYNrBtrIrzA5obOu8O9EwQSJv6GqmHRR5maQNNffQt7IRRd+ATjBt7tG4atGn/qLK1t7of51zKiqdNYkcC8HcyIk/lppkWCtHYC1jASdqZyPi8blPQOu6YBCm5ugom9Cu1f46+VYq3YUXZliIXEm4AZfqn+3jX1oGTGWrJ+k4t8Cx49g6QTagcYEFIJvT+lFbAPA2sv/PRF1a/nB6DzAC5jBKED7MiDnJn9s7j/QKcv9haC1F50x3t+6Pv8tSJ5ftR1f4eP8iT0jFRVdQRzlhWfk+OsemcsoPyCEqBVSBFqIEJnU7SZ2b9yHPcLGDa9dzgkXjajyWLfLw4oZa2jZpVnZNXuKrJi5hgfPeAZngYtGTeK48+MbqlRAuiI+r49bRjzAxsVbOGbcIB74KvAf+P/lcrj47qUp2MJsnH7diZgt1Uva+bw+fv9iAWl70pnxyTxadW3OPZ/diC3MVuMYa1t+dgHh0WH1NmEphBACtOcvdMY5gH+3g0r5q05aSvt/QT8DvDvA2hvV6JMKd5QYh/r4f5lXEai4t1D2wI8pa63ReY+CczZEXY+phm3dtdbogjfA8xcq+maUpU0VxxnoQ393Aw1DJXyJspbdAATAKJwMuQ+DKRaV8EOJ5IL2bEIXfgq2YZjCR5Uem/s0FBbttom+FVCQ9wKgUXHPosJGlxoTKK01OucWcP4KYSehYl9AKYXh3Q85d4ApGWKehPxHwbMWFXNvtY/5ae9edN6T/p1T0beBzkOnDsdf7Dq8qNi1Fa1d6EN9Ke5eZh+Oiri4Wt83VY7N8xc6cwJoL0TfhSnyglpbS4gjTUVFoOXsgjiseT1e7jzhUU6JPJ9vXvgpKHO+f98XnN14Eu/f9wUAm5Zu5baRD/HePZ8RSEK1Y/92hEXaQUOrbi0CisFmtzL4lH4VJn8AwqLC0IbGZDbRqHF8jZM/AAd2pLJl+Xa0oVnw7WJcDhcbFm3ixmPu463bP8YwjIDnfO+ez/nk4W94/57P+e6lKQGN3bt5P9cNuos7T3iUgpxCRl04nFmfzWfvpv0sm7aa+d8uCTieuvbGLR9yZtKlXNr5JgrzHKEORwghRHks7eHvTkwRF9RJ8gdAKQsq4XtU4lRUo48rP04UdQeoaLD2B1vpY+JVW1NhinkAU/LsGid/AHDNhoK3wDUNnX1TAHGYILyoNbq1m7+FeEUKPgB8YBSAa27JuaydMMU+iil8FEbBFxiHBmNk31rU+Qx/Yir6doi+039MyvUn4ADcaPeqKsdcISMTnL8B2n8Uzb0Y7ZwGmRPA8ye4foG8hzDFPoYp8cca1XhSluaY4l/DFHM3SllRpkao+Lch/PyiJKJ/17RSdoh9Cax9/XG5ZqGzLvMfJayA1kZAr31LcP/xT62kgnf9xbLxH1/UriX+3UpCiKCTBFAd++Wt6dx+/MMsn7E61KEcETYu3sKGRZtwOdx8+MBXNZ4vJz2Xr579kexU//9z0nN5bPyLrJ6znh9emcryGWtK3L9p6VbWzt9Y5g/H2967hrs/u5GXFz1Bp/7tahxbWboO7sidn9zAhDtP59Gf7wrKnFtX7sDj9gLQoV9bbGE2Hj/3f2xYuIlf3pzOiplrA57TkefE8BkYhg44AfLOnZ+yadk2Vs9Zz4+v+esTtO7WAnuEHbSmaXt/kuzgzlRuHfEg95/2FHlZ+QHHWF3p+zP54ZWpbF25o9x7fn1nJobPIPNQ9v/ZO/Nwm6o/jH/WPvOdJ/M8z6FQmUlRIZSk0pwhDaSRqBQlktJAZYii9BNKlAZCypiQeZ6583jmvX5/rOtwuvN1kdqf5+npDHuvtfY5xz37vPv7fV92rd970dZmYGBgYFA0hDCjxX6KKLMDLWLURZ9bmCsVSnTSQvuildmIFvNx0RKiLiTCnp0kqkEBLVx/R4t8GVHmL7TYuQX7LjluBSwgNKSpYt7bpb8KMglci5Hun9UShRVC+oJzLiT2AP0IiPJgqooIuSvHEFJ60DPeRU97HalnIPVM9PQ30dMn5y2eaFFgqgbYwVQekvsjU55RwlBg4PT8j7GY6FmfI9NeAlOZHJHymqMTIvoDzjqE5BQZpZ6EdC5A+g4i3auQpxojT1+L9AWHlUgp0dPGo8d3Qs/6KvfF2K5XnwkA/RQyeZDaN/khZMoAZEJn5LmviYGBQYlgeABdRI7vO8kHQ2ficXnZvmY332TM/lfGu/+TqFS3PGarGTtQv2WdIu/vzHCy/PM1VK5XgYat6hIS4SAyLpysdBch4XZCI0OILB1BwrFEpC6JiD3bj7/yf7/xxn3vAoL7X72D6+9pR/yRRKo1qoymaZjMJlp2b15yB5sHbXpdXWBC1bZfdzKu32RKV4lj9MJnCI0MzXPbnz5dGYiCb9S2PkIIYspFk3wyBalLokpHFHmND4+7G7/Pjy3Exu1P31KkfSvXqxAQ3ipm+xs9O/txVn+1lvI1ylC3hbpS+OHT5xhET17CPaMKNoj2+/wITZzXv9MhrV8g6UQymklj5u7JxJWPybFNxztbs+yTXwiLCqX2VdVzGcXAwMDA4J+EKMHo9ZJE+o6C/yhYm5/XGqX/lPKusV6D0IqWHJoXwtYSIl9B+nYjQu4t+v6FFLK0sP7oeipkzYLkQcjoDxG2a3IZMER5HSFVpZC9k3rcfwp8BwEv+HarNqk8/JZk5qfKMwcdKdOVoJH1OaAhhQkR9kgux2GCuAXg24f07oK0lwAniLIqmUtEQsTYQh1rUZDSpVrj8EPGu0jHLTn8mIQWDdFTka6lCMetShA7d4zEPur1ERqY6gJukF5wfQvnHqt/X3bcvAvSRiAdPXIIl8JcGRkxHlKHqu30E+oJz2Z1H4dqebTmPG8yMDAoPoYAdAHITM1k7msLCIsOo/ewbgE/E6vDihACoQkcYfaCy3cvc3at38u7j0+n+hWVefy9h4vt63I+RJWKZMbOtzm66zh1ry6gZDgXRvd+ky0rdyCAN38ZTZ1mNZi6eQLbVu+kYeu6mC1mXv3mOZbNXEH1xlWpfdXZSp6d6/bidXnRdcmmH7cwe/SX+Dw+2vVuydMzBpfgUZ4/HwydwcmDp0k6mczPc1bTbdDZHnef18eHT8/m5MHTDJp4H53v78jv324ECSmnUwEYs/h5fpj1CzWaVKVmk8L19J9LRGw4z8x8tFhrv++VO6jZtDohEQ6ad24CqBa5jn1b43F52LpqB1UbVqJc9dLZBtGCslUKNsP8c8VfDL9pLGariYm/jKZG46rFWl/yyRS8bh82h5X0xPRcBaAhUwbQb1RvIktFXJaJdQYGBgYGlw7pXoVMHQ6mcuDdqX6c265HRI0v3nh6OjKhq/phbyqjTKTP85xV6pkACEf3XOpKCrG/lODbA6ZSSqQoCO8WwA2YwPsH5CYAhT4MGe8AAqznXCgzlVcmy541YLuhgCQwHXVVTKrb8kwbvATpz3MvIWxgqQ/mGkjXd+Dfg4h4VYlkBSB9h5EpSmwRUe8jzJVz2WY/+E8qAS8gvFhAiwY9QyWTidyFPWFrmes6pJRKXMQP7s+fZAAA4qdJREFU0q5eM99fqNfv2uCNtRj1OZR29Z4JDelegfT8gQi5XZmNA8LeHum5FXxbEeHD1b7hQ9T7Yml2tt3SwMCgxDAEoAvAu0/MYMXc1WhmEyHhjkBiVVz5GF5fNpI/ftpKh76tL5kA9OWbX7Pw3aXc+MB13D3ytgs2z7h7J3Nk53EObjtMsxua0ObWXL58LwJRpSKJKlWIJINcOLHvFB6nB3uojdOH4qnTrAZRpSJp3fPsiUJUqchcq1ZuGdyFdUv/wOP00LxzU7b8sh13loe1324q9rGUFEd3H+f7mStoel0jrryuEbWb1eTwjmNIXVK1YfCJxI+frmLJxz/icXrJTM3i3pf7YA+x4cp0s2r+Wp6bpQScW4d2vWDr/eKNhfz+zUb6vdibKzsFnwwc3n6UIzuPcU23q3LsN7TtSA7vOIY9zM6MHW9TtUFlHOF2WvUouJ9+wTtL8Lg8eFywbOZyBr11f7HWPmLuUGaP/pJruzejWqMquW4jhCCuQmyxxjcwMDAw+G8j00aDfgr0ZJTw4AJP4cJWpPSDd6syCTbFqbj5lEezK2MA/0HAz/n8ZNBdqyGlv5oveiaareieNjL9Vcj6UgkXsYsQ5vy9E0X448jkR0GLBEfP3LcJfRisTQALwtr07ONCQ8TMREq3Emrymyf0HlX5IzMQYU9wpvIHbIiwhws8LiFsiJipBW53LjLzQyWGZd8Wka8GP+/diky8C4QAew8I6QPCijDXhNivwL0yu7Ir/4h76T+OTLoH9DRE9PsIazNkxBjIfE8JY+FDIKSXMpP+W8qc0GIgdgF4/gB7e6R3DzL5ccCjqotKLcs+fhMi8sWgfbXQByD0gSK9JgYGBoXHEIAuAH6vHykBKfH7gtX/hq3q0rBV3dx3vAhkpmYy7fk5+H1+Phszn5sevo6YsoW4klIMSlcuxamD8UhdElv+wsxxoXlqxmAmP/oxkaUiihyfXqZKKT7eOhFQrWRLp/3Ewb+OcOcLt16IpRaJJ9u9SPLpFL56+1umb5/Eo5Mf4OqbriSuQgw1mwZX8IRFqah3k9lEeEwYNRpXITQyFL9Pp3XP4hsT5seeTfv5YtxCrurcmDrNajL75S9xOz28fOsEFqWejdz1uDw80foFXJluvnhjIfNOfIQjTPkK+P1+9mw8oPrQdUnCsSSuv6ddodfQ/o6WrP/uDwCuLUKrXlpSOkknUqhSvyJCCFre0pyWt1z4Vj8DAwMDg/8olitU3DgSzFXBfwLCC+f7J1OfAfePqLj4xaCnqh/tZwh7pmDPnYJIfx0VRQ6kjUEXGmjhiKhJSigoDK4fCLQFef+EggQga3NEmfxDIIQQkG2wLKUXmTkL8CBC70cIe4HijxrDiggfGvxYxAt5bi/1DPX+mGsU30Dc3BD45pzb54zv3Y7MnENACHT9iHQuUE9GvY2wd4CQvFvgpf+UEhS1SNDKZFf86MiMdxExM5UZ+LmG4ObcL2wBKuUtO+lNz1rImeQ85MXzYTwX6T+JTHoA/MfB0ggR9XqgEsnA4L+EIQBdAB595wFCIxyEx4TRdWDOiMlLiS3ERkRsGM4MFxabhdDIkAs216gvh/Hj7JVUqlue+tcW3X8nMy2Lj55VUZwPj7ub0IgLt9a8aNiqLjHlovnjxy082uJZJv/+WrFagRxhDqZunoCU8qJXfkkp0f16UAueK8sV8PHxuDyYTCau6aoqaNKS0oO8ilr1aMHQqQM5fSSB7o90JjQihE/2vEPSiRTKVlNXfCYNnMr3M5ZTrnoZxnw7nHLVy5zXmoffNJaU06n8vngjL85/CgDNrOEItwdt5/P68bp9SF0GbjuyL2iZTCbueL4n8yd+Q/MuTahcr2hf8u1vb0WDlnUxmbVCi6QnD55mQJOn8Hv93PjQdQx+27iCZWBgYGBwYRGRr4N9NZgrqSqPouD5DaQTCAHvdtXKI0IADcy10MIePP8FmuLAv1vdlknKQwYTMvNTRPjjhRsjbLDyytHKgK3N+a/pb8jMGZAxGZBIPRkRMbzE59C9OyGxF6CDtT0iZkr+a5IuZMrT4D+o2sOsjQHQQu9AZgsr58a0656tkNQHECCsqsXLVAm8mwCB9KxTAlB+c6a9Cu6fALOqHiLbA8jaWqVzJT+q2usiRqI5bi78wTs/zb6hgSNn3LvUM8F/ACl1hOdXsF9f9M9yAcisecqbCAnetcjUUYiYaSU6h4HB5YAhAF0AImLDeeKD/pd6Gblitpj5YNN4/vhpK43bN8DmKPjqRnE5t/2tOMwe/SXfz1gOgD3UxsAJRTcMPF88Lg8bv9+MlOD36ez4fU+xvWCAiy7+nD6SwKNXP09aQjrPzn6MDn1aATBm8XA+H7eQVj2aU6nOWWEk4VgiDzV6MuBVdO/oPjzTaTQZKZk8N+sx3h70ER6nm8fffzgg8iSfTmXptJ/R/TpHdh1n1C3j+Ci78qm4mC0mEEqjKl2lFP0n3MP7T8wgLTGdr9//ju6PdAHUZ+z5Tx9n0XvfcdPDnUhPzkBogvBopQI98GpfHni1b7HXUapi4duydq3fy5i+b+HKcqP7dH5dsM4QgAwMDAwMLjhCWKCAH/Z5EjYE0l5V8fbWVggtBEp9p1qMium/IqUXkMqDxvsnhI+AtNFKlDDXgKwvAIkwFz70QAvpg3TcXuB5lPRuQ6aOBHN1RORrOUyM897RyVkfn6KlkRaatLEEKqE8v+S7KQCuJdkx9i5k2guIuG8CT50r/ARIGXp2fOxopdeodrDk/oAdEXJHwXNq0aifh5oSnMIeVO1tlkZI9y/gWQdkQdorUBQByNIY/EmAjjhjtp2NlC5kwk3ZLYxuJAIyP4LSvxf+/SsEwtIIiRnwApqqcjIw+A9iCED/QWLLRdPp7raXehkFYg+xoWkicPtSoOsSk9mEz+tHCEGLG5tcknUUlzUL15ORnInf5+fTl79k3x8HaNSmHlfffBWN2tTLsf2eTQfQfXrAq6h8jbKc2H8K3a8zadCHJB5LQuqSkIiQgGlzeHQokaUiSD6ZAoDZev5m32/8OIqv3/+eph0bUaVeRVZ++RtS1/H6JKsXrAsIQABtb7uWtrddy8xRnzPxoQ8wWcy8v2EcFWuVy3XsA9sOM2fMfBq1qRc0zhk8Lg9v3PceR3cdZ+iHA6jTvHBXoN647z1O7D+tjN6F4K5/QKufgYGBgYFBfmght+doCRJalDJBzkZ6/lTR4eY6iMhX8k3jkt7tyKQ7lYE0JkCAqTxaqaXqeamD9SoQoQhb6yKtNTfxR0qJdP4P/IcRofcjU0eAbwf49oOtdZ7+PwDSvRL0FLDfhAh9GKmnqPYs23VFWtfZ8dYinXPB1hXN0SnnBpYG4F0LSLAWoorJdKbNzQ6m/MUyKZ2gHz77gLkRoEQPUfq3wh0AqMonczUQEeDoGdymZqqenQwfApaGeQ2R+7iR48HxK5gqq9awc/EfAz0JZdoNqn0tO12sJAUgewdkzDxwfweEI0LvKrGxDQwuJwwByKDESTiehD3ERlhU3lHiheHOEbcGhJ9eF9BgOD/sITbGLBnBko9+5Pp72lG6cqmg5z1uL/PGL8Ln8XHHcz3zFaoy07IY2mYkh3ceY/Db99NtYPGrowpL4w4NMJk1hLCQdCqFeRO+ZsHkpby/YRxV6lXMsX2TDg0oV71MwKuoaoNKWKxmEFCtYSVSTqUipSS6zNmrJmaLmRk732b2y1+SmZrJ3SN7n/e6K9WpEFQ9c91dbfhmyjKcaU7ueC74ZM7r8SIlrPhiDR6XF5sQbF25ndjy0Yy6ZRxHdh5j2MeDaN5FGTyO6v46Jw/G89s3G6jZtFqO9sRf5v3G74s34M7y8Fb/KUz5Y0Kh1ly2WilOHjyNAF5fNvKSen0ZGBgYGBio5KwdoEUhTEXzMQwaJ/U51Trj2w/2dmC/Me9tnV+DzMq+5wd08J8VJoTQwF608x+pZ4BrEZiqI2x/S5ty/6wqmPAgvdtQopNJzZuPv4t0fYdMeUbd8W5FixiBtF4FqcPB8xt6+FNoof0Kv0bpQSY/BLiV7471F4QpuIpYhA8DSy0kdkQ+r2Fge2tziJmufHgK2F66/2b6HfZUodceNKewQ2juoRfCXAlivwbfPrC1KuK4ZrDl4cNoqqYSxTyrwXIN4IGQfgjt/H5H5IZmbQDWBiU+roHB5YQhABkUCV3XmTf+a47tPs49L/fJ0SLz7Yc/8N4TMzCZNd5a9UqukeB+n5+0pAyiSkXkW8prtVly/Ni/FFyZnZSVG19O+Jo5Y78CKXFmuBg08b48x9n041ZO7D+F3+vn09H/K7IAdHT3cXat38c1Xa8kNLJwX4rVGlbms4MfkJGSyZDWLyB1iRDgcXpy3f6MV9Gmn7awZuF6GrWuy/sb3yArLYvazWqw7JNf8Dg93PhQx6D9QiNCGPhm3i16WelOzBYTVnvxruSUr1GWecc/yuGhtP333TzTaTRS1+nx2E3Mf+sbwmPDufrmK1mzcD07f9+DK8vNu49P55PdkwEwW80ITfWXma05/wSe8TWyhVipkEcVUW688PlQfvpsNZXqlDfEHwMDAwODS47MeFe10gDEfoaw5H4uUyCmCqpKAwla/t+Lwn4dMuszQFc/5v37IKyQPj95IFMey041ExAzC2Ftcs6TrjM3wJ+QnVqmgxaLsOYTVOE7ghKovErYAtWuhkft790EFF4AUqUx2tnbuZzfCmEGRy+KYgYgrM2AZgVvaAr2KhSW3M2ZpZSgx6vXRxS9YluYK0MusfOBsV3fAl6wdy/0+Cp17eNL4pNZUkjphzNm246ehT52qWcCPoTRjmZwETEEIIMisWr+Wj595X94XV6O7zvFmyteDnp+2Scr8Lq96D6NDd9tDghAyadTCY1wIKVk4JXPcGLfKTr0bcWznzx2KQ4Dr8fLuH6T2ffnIZ744GGadChaKesZfF4fSKlMiD2+oOeklLgyXYFUqjrNqqOZNGwhVlrcdGWR5kk4nsSgZs+ClJSvUZapm/OvSklPzmDWS/MIjwnjzuG9iIgN55Wvn+OzMfO56vorqHVl3qXEyadTGdntdTwuL8s+WcGCpJkITSClpMv9RfMYkFIyY+TnzHtjITaHjXd+G0OV+vknd+TH308Mls1cjjtLlQyfPpzAt1lz0EwaQgiqNqyERGIPtVG3xdk2rrFLRrBg8hLqX1Ob2lfVyDFHozb1GLtkBCcPnKZ9n5b5rsftdBN/NIkKNcviCHPQdcA/y/TdwMDAwOC/hfRuRaZPVG1W7p9RyUsW8GyCYgpAIuptcH2jfHXOFV9y29baHEqvBOlHmOKKNR+A9CeAd6NK6fIfB9wgHCru/lzsXVRFiv8g2DpB6vPZCynAOiCkD3jWqvFsndFPt1dCl6kq4EOEDc57bdKHzHgPXCvA1hzChqJpDoidjcyaj7B3QWgx6FnzIWs2OPqghRbfj7AwCHM9pLku+PaC5UoQuUe8y5TB4F6hvJhi5xfosSOlB5n6AvgPICJeRljq571t1qeQPl7d8R0pvMH3mWO4TMUfAJn5IWR8oO7oiYiwAQXv492GTLoLpA8Z+SaaI6ctgYHBhcAQgC5jkk4m8/s3G2nUtl6Qke+FRDNpCNSFDc2cM77ytie7MfbOSdhD7bTupQzqZo/+kjlj5hMaGcLTMwaTcCQRv8/P8rmrL5kA9NvXG1i7ZBOuTDcTH57CrL3vFmucPs/0wJnhwuv28sCYOwOP+7w+hrYdxa51e+nUry3PzHyU0pVL8eGWiSx6dwm1rqxepCsdCUcTQUpcmW6O7T0ZeHzv5gP8MGsFx/ac5NieEzz8Rj9adm/Oe49PZ8W8NZhMGhEx4fR47EbqNK/J6IXPBo17YNthtq7cQaueLYgtp64e6X79TEAYfp+fnev38GTbF9H9Or2HdaP/+HsK/fos+fhHPn9tAVJKsjKcrFm0IYcAdGTXMaY9/xnVGlWh34u90bTCx6K2u70lP8z6Bb9Px+PykBKfFjiOGo2r8t76cZzcf4pmnZsE9ilXvQyPvJV7efMZrmhbnyva5n2SA5CRksn9dR8nM9VJyx7NeWHu0Hy3LwwZKZls/GEL9a6pRelKxT9xNjAwMDD47yB9h5FZs8HSRP0A14+rCpbQRyDzAGhxBbYQ5YfQQqEwBsKB7QuXnJkXUrqQ8TcAThBREP0BpI8Bc90c/jxCmIKEBl1PAu+fiLBBBSzSpkQj/0lIfxVwgZ6MiByDcBRgO+D8CjKnAH7I+guyZqLbb0ZETkSLVMbZUs+AtJGAD9JfRTo6Fz7yvhgIYUJGzwbX12C9Jg+/JB+4f1R3fAfBdwgstfIf2LUEXN8BLmTq8xD7P2TyIPBugLAn0M5tF/MfRxksS9CPldCRXSb4T6OO/cztgpGuH88ajjs/B0MAMrhIGALQZYqUMpDupJk0Pj3wPhGx4YXef8faPQgBdVsU8If/b7Tu2YLE1+/i2J4T9H2+J1tWbueDJ2dS7+paDH7nAdrceg2LUmdhMpsCseNLP/4Jn9eP2+khNSGNUpViOZ5dAXSpKF+zLFKX2EJslKoUy9B2IylbtTRDpw4oUpuSPST3dLIju46zf8shpJT8MOsXhk0bhMlkYuYLc/nly9/QTBqayUS73tcWSgiq07wmne5uy/rvN3P/q305tvcEuzfu562Hp+DMcAW2m/DA+3yVMEONJyV+n07y6ZQc450+HM/C975j0WRlyvjpK18yZOoArul6FelJGcrIWBPc98odzHvja3S/DsBXby+h//h72Lx8G/FHE2l3e0ustrzNIHf8vkeVBANIuOqGnIkiY/pOYt/mg2z8YQs1m1ajVY98Srb/RtOOjXh6xmDGP/A+vy/eSGpCOpNWvRJ4vkq9irl6HZUEb9z3Limn0wBltl0Ykk4ms3XVThq3r09UqeBy3zP/phOPJ2Eym/j0wPv5+mj5vD5e7TORrat38vDrd9PlgY55bmtgYGBg8O9FJj+U7bPzBZjKAiaQEmFrU6hKhBzj6Vkg0xCmsiW+1kLN790JZGTfSQRTDbTYLwu1rxZ6N5AzZjwHvoPZP9TPtMTbUb3hOSuDC4XrewiPB5NqI1cx7HaQmYAfqWfkEICk/yR4d4D1apW+dr6kPAS+3WrsuCWIv3kgCWFG2jorEchcHcy5t4kFEWj5s4GpooqA96wHnJD+FtJ2HQgrwlQWEfYw0rcf8CDCzv+i2OWECH8MqZ8CBCI87+qxoH3sNyCzZoD0gePOgncwMCghDAHoMkXXdZJOJOP36VjtFlIT0gotAC35+EfeHzIDgCc+6M/1/fIwZcuF+CMJRJeJotPdbQmLCuXRq58n/kgiR3cd59puzWjepWkOAaXboBv45MV5OMLsNOvchOvubktaovIAulTUbFKNiStHc3TXcRa+u5Qdv+9h94b9NGpdj5seziW5oYhUqFmWUhVjOX04niYdGmIyKTEs6VQKPo8Pi83MiX0n6VdjMAlHE3lm1tmI9twQQvDEB/0BFdV+f70hSF3H4/QGtrHaLVSoqU7WBk26j18Xrcfj9PDV20vo9cTNRMadfb2f6vgyJw+eQipdB48rldfuepuuA2/AbDYFPIL++HkbN9zTjjWLlMBRuV4FNv24hVE9xgGw6ccteVZxHdpxFJNZU23xEkwWE2WqlsaZ6cIRag9sFxLuwGTWkFLiCHcEHk8+lcL67zZzRbv6lK1aOs/Xxmq3omW3qP29De9Csu3XnYHbzW9sWuD2HpeH/o2fwp3lJjQihM8OfRAQSUH9mz6+7yRSl1gdVhJPJOcrAP316y42/rAFV6abD56caQhABgYGBv9VpBfI/kIPfw7h2wWW+vm26+Q5lP8EMqE7yCxkaH+EoysyeSAgENEfIQojGpwnQotDYkJ59FgRWu7tTOeFuboSNPx7AQ3s3RCh9yIstQve13Grqhxy/Qj6CZVYZSoH5wg8QliRWnnw7wakqhiKHBt4XupJKvocHUw1EHHzC7Vs3bsXUgarOSPHo9nOprXh259dUeJQ1Ti5mGCLqHdAJoOICk74ygNhu1pVYPmPgOMWNf6ZFDitHDIhOwo++mOE7WpEzNQ8x5J6FjLjHXDOB1MlRMz07FY9W6HW8k9GaDGI6PeLto+lPpReC9J3YT7jBgZ5YAhAlykmk4lh0x5h7tivaHd7yyK1gG1bvRN3lgcEbF+zq9ACUHpyBv0bP4Xf5yeuQgwzdr5D6cpxpManIXVJbPncS1v7Pt+LrgNvwBFmx2xRH7no0pfe7Kz2VTWofVUNfv92E/s2H0QIcEQ4SE/OIDy6cH+InRlOrA5rQOA5g9Vu5aOtb5JwNIkyVc8mhw35oD+TH/2YuAoxhEQ4SDqZjM/r59PRX+YrAJ3LyYPxICXuLA9Wu4VmnZvQoFUdokpF0qqHOhEIiwrF4/Lg9/kx+U2kxKcFCUAZqZlIHUxmjdDIUDJSMnFlutn7xwHuefF25r/9LdIvaX97S9r3aUWFWuU4svMYrW+9hh8+WYGUykj62O4TgTG9Hi9rFm2gbLXS1GxalSdajiAzLQuz2USTjo1o3rkxd1UZiO6XjF70LM1uaAzAyC+Hsei9pVSpVylgtq3rOoOueoaMlEzMFjNzDk8hJNxBZmomIREhQRVTsRViKFutNPZQO8PnPFGo1/D7Gct574npVG9chde/H5lvelte9B7Wnekj5lC+WhmemfFIgdtnpGSSmZKJz+vH6/biynQFmXmbTCYeeft+5o5dQOueLahcN/9/0xVqlUVoAnuYPV9PJwMDAwODfzcieioy8wOwNEOzdwTyvyAg9XQQZoRw5HzSsx6kB/CCcwFSPw7+Q4BAZk5DRI6+EIeA9J8AhKokMVdERk4C91KwZqdCUfTvaTVugjLntTRA2M76+glhQdpvhMz3AB30o4UTfzin7Sz8caR0g28PmGsqk+dz0c9pBdL/doHKfxSkH3CCb1fhDyhlUPb7AaQMgDKbzj4X8apqAbS2AMtVeaxdgChaK5o4N/FLOJBxi7PT0v5ARbcLpGe1EovyQaY8Dp5VgARfFjLlWfD8ooSq2AUI7dJdGL5UCGGnSK7gBgYlgCEAXcZc369dkap3ztDn2R5sW70TzSS49cluBW6v6+qqUtKJZLwen/rhv/ckuq7z6jfPs3zuaqo3rkr1K/K+KlRYQeVisPzzXzm6+zjdH+lMZFwEwz4eSKPWdUlNSGPC/e8B8Mo3z+eZ/HWGr97+lg+fnkVEXARTNr1BTNngnneL1UK56mWCHitXvQxjl4wA4ND2I2iahtVhpVXP/L80z6XeNbVo3etqNi/fxoNj76Rt75bsWreXKg0qBgQFTdN4atogPhvzFW1vuyZHG9ToBc8wa/SXNO/chM73d+ClXuNJOZ3GgAn3EBoRgtVuweP0sG7JJm64tz21rqweEBk63tWG9d9v5tTBeB7/4OHAmOPvf5/fvl6P7tO57enuuLLcqg1cl0gp2f77HiU8Aks+/CEgAEWXjuS+l4O9BXweH8mnU9F9OrpNkp6UwTuDP+LnOaup0aQq76wZg8Vq4at3vmXKk58EqmYyU7MoDB8//xnODBf7Nh9k+vA5pCdn0HtY93w/w3+n73M9uXVoVyxWc6G8nGLKRtP76Vv4fsZyug26Idcktx6Db6TH4ML5NMRViOXjrRM5sO0ITTsWz8TcwMDAwODyR1hqI6LeKtS2unMZpA4FYYaYuTmrhKwtQQsD3QOh9ygzYed3gFTmwhcA3fkDpD4J+JBaKVXFZGuHTH8Z3CuQWV8g4hYUa2yZ/KASaDBD7DyE5WxKp3DcgnR+DjITETqwaONKDzLpHpUcFvYomiWX7+GQByFzEmCFvxsimxuCvSO41xQtJU2cI5KI4Mp/zXEjOIrm9SSlDq6FoGdCSJ8CTaEBhPcPpGcj4EQlnkUiHLcUPJn/CARcJlE+QujgT1TCo/26vPY0MDAoQUTAn+Mi0qxZM7lhw4aLPq9B0dm5bo+K2ZaSN34cxY+zV7J6wTr6Du9Z6B+q/yQ2/vAnL/Ycj8/jo1Hbeoz/8cXAc+PuncyPs1cC0OWBDgz7WFV1+Lw+Xuz5BttW7eS+V++g52M3AXB39Uc4dTAeW4iNJ95/mOvvKboYl3w6ldT4NKrUr1js9IMhrV9g358HsYXYmLnrnTzbhhKOJTK07ShOHTxNtUaVeXvN2ByVL8mnU7mv1mNkpStTOnuojW/SPy3UOh5q9CSH/joCqHj1sKhQSleOZf+Ww/g8PsJjwvA4Pei6ZNSXw7ima+5Xp86w6L3v+N/Eb+h0d1v6vdibzuY+ANgcVt75bSy6rjO4+XMBfyLNrPHh5gmFShl7tc9E1i7ZhMxOcPO4vMSWj+bzox8W6ljPl5X/+40JD35A+eplmLD8pXxbvQwMDAwMDEoKPel+8Pyq7oQ+ghY+JMc2UvpBupT5MyA9fwAawtr4wqwpZZhKGQtgQ8R+hUzsxZkKE1Fme7Fiy/XTbVTSl3Agoj4ssEolL6Segsz8CLQyiJC7wbsRmfwwyCzAjlZ2S+77+RNAC1OVHiWA1JOQaeMALyL8WYSpTIH75Dte1jxk2quAhJA+aBEv5DN3JriXKwkndbh60NICEfNRoc5hpecPZOqLoMVCxHPg/AKy5oEIQ8R9izDFntexGBgYnEUIsVFK2Sy35y7vhkuDC843HyzDmeHClelm8ZQfeOzdh/ji2IcXVfxJOJ7Esk9WcPpw/HmPlZXmRAiVbvX3apEuD3TE6rBidVjpfJ+KO89IyWT8/e+x6cetZKU7mT58TmD7G+5tj9lqxmIz07j92StoaUnpzBk7n1/mrcl1DX+t2cWWldsBVf1StUGl84q+3LF2D65MN65MN8f3nQxUbP2dua8v4OSB00gJB7cf5c/l23Jsc2DLoaD9bxtWcIXYGZ54/2FishO4fB4fqQlpPPnRIEwmDaEJwqJC+eL4h3xx/MMCxR+AWwZ3Yfa+97j35T5omsa13ZphtpqJKBXBq30m8sHQmchzriR1uKN1oSPmn//sCcYuGcHoRc8FXnvNdPH+HE4bPgdnupOje47z29eGGG5gYGBgcJFw9AGsShCxX5/rJkKYAuIPgLA2vWDij5Q+kJKzP0nMyk/HXANs7QAbhA4olvgDIKImg6UFWK5Bpk9SFVDFWWfqCMicAekTVNKWuaZaG1YQVvSke5D+RLWtey3S+RVSuhGmuBITf0B5zWhR49CiJp63+AMg9USU15IX/PHKp0f6c982+SFk2ghIGwGRryEiRiKi3yn0OaywNkUr9TVa7Aw0Sx1E+EhE3HeI0ssN8cfA4CJitIBdZmz84U/mjf+a9n1acuODF75UsnWvq1nxxa+B2xcbn9fHwKZP48p0YbVZmXt0CjZH8frAAVr1bEHPTTex78+D1L+6NjNGfs6mH7fQa8jNdOjTiq8SpgNgc9jQdZ0xfSex6cc/0f0Si80clJp2z4u3c9ND1xEaFRpkajy27yQ2r/gLs8VMaFRooNUJYNmsFbzzyEeAoP/4fnQf1LnYx3KGfqN6M/f1BdRpXoNnb3gFr8vLsOmP0KbX1QHPJVCJWJpJQ/frmEwmajSpmmOsBq3qULluBfZuPsh9r/Sh73O9CrWGXev3snPtHuo0rxEQNBq2rku1RpXpOvAGju4+zsCJ9+ba9lRYXl74DPFHEhjZfRz7txzi9OEEbnqwE4d3HqVJ+wbc9cJthR7LZDbRqE09AJ6eMZjNy7fR4/Gbir22onLV9VeQdCIZgNrNDP8eAwMDA4OLg+bogrS1BMwlkzxVRKSeBK6fwdoUYa6BzJwF7m852xokVCqW0BDR76p9pAfpj0eYSuU5bl4IaxOImoiM7wh4IHUL0r4JIYp4Lim9Z9covSrqvtQPyJShqqLKsx6Z9jLSf+hsy5n7V0TUmzmHkl7wHwNTxZy+QeeBlE7AVKg2rjOIkHuQvgMg08BcBXn6KtBKQdzCnLH1vn3ZJtAOZXpsu/m81iuEAPOFSWo1MDDIG6MF7DJC13W6hd2Nx+XFarfw8V9vUa7a+av/BRF/VF3RKFXx4qvzmamZ3FrqQfw+P2armTmHPiC6TNR5j3tX1UGcPpwQuG+2mFiY8kmQuDT+/vf48dOV6H4ds81MtwE38PAbd2Ox5h17DjC4xbPs3rAfW4iNp6c/Qrvbz5oOvvPoxyyesgwpJZ3ubptnglZx+Pi5T/nijUWB+3EVY5mx8+1Am5eUkt8Wb+DYrhN0uqct0aWjAJVONfbOSRzecYyhHw6kUZt6hYqmP0PC8STuq/0Yfq8fe6gdr8cLUgkrpw7FM+vlLwHJ3SNv445ne573cb7e7x1+XbgOqUte//4FGrZWQs6fK/5i3dJNXH9Pe6o2KFwl0KVCSsmOtXuIqxBD6Upxl3o5BgYGBgYGuSKljkx7BTzrEBHPImxtC7Wf7vwB0p5RIkfMbIQWpR6Pv0ElaAmBiPtRCSjedcE7m+ujxS1U8+spyISuoCdDSN98W5TyPAY9DRnfRiVniVBE6bVFFl6k/zQyY6JqAQt7LLC/nvEuZHyEEoe8qGqaM8fRCO1v6V5S+pCJPcF3QCW1xXxxXlXggXHdvyCTBytz75jPEJYGRR5DP90K9HgQIYjI1xD24Gp/3fkDpL8G1mbq+WJWZV0MpPQh014E73YIH4WwNkSI/M/fDQz+TRgtYP8ShBDYQ23ZDv4Cm6PwCv/5UKpi7CURfwBCI0N5+I27KVutNP1e7F0i4g+cFbXgzOtqD6qWAVj77SZVLWNW7Uf9x/fj9OEEju4+nu/Yz81+nKtvvpJbBnem9a3BVVO9h3Wjct0KVKxdnjuHF666prBc2ekKLPazX24JRxNZ/vnqwH0hBC27Naf3U90D4g/Aqvlr2bBsC0d2HeetASq+M/5oIl9N+pa9mw8UOG9WmhMpwef143F5eHPFy0z69VXa3d6SU4cT8Hq8+Ny+IMHtfBg2bRDDPh7E+J9fCog/iSeSGX7zWOaN/5qhbUZyPsL2qUPxPNhgCHdXf4T9Ww5xZNcxxt07mW+mfF8i6wf1XtS/prYh/hgYGBgY/LPxbgDnV+Dfg0x5uvD7ZbwJMhN8h1Rc+hn8xwCXavvSE8DWFrACZhBlQYtFhD9zzvybQWagUskWFusQhBaBiJmLCHsCETuv0OKPlC6k62d0zzZl+OxcAua6QfuL0EcQUa9D1EQgFBXppIGpNiLyFQB05xL05MeVcbIer+La8YB3i6q8KQFk5mdqTJmFdH5bvEHstwAWwJqr4bfmuB6t9M9oUW/kK/7orp/R4zuipwxV1U6XAvfP4FoMvr8g+U7kqUbomXOQ3q3oiX3R00bn2epmYPBvx2gBu4wQQjBp9av8PGc1V11/RY7UqeKSlpTOC11fJ/F4EsPnDKFByzolMm5JceuQrtw6pGuJjtnl/g4snfYzoKoxrrrhCkzm4C+zO1+4lanDPqFK/Uo8Pf0R1i39gzF9JwHw5EcDue7ONhzfd5IT+0/RpEPDwP6V6lTg1W+ez3XectXKMO6HUSx4+1v2bNxPpTr5R30XFmemi0kDP1SClcWE3+sHAc40Z4H7VqhVFqTEFmKjaoNKSCl5/JrhpCWmo5lNzN7/HtGlI4P2STiexFMdXiQ1Pp1R/xvGfaP7sPzzX6nTrAYms4maTaoBcPfI2zh54DRISb9RvUvkWC1WC+37tAp6zOc54yEAHpe3UBVMzkwXqfFplKlSKmjbRe99x9Fdx9F1yaevfMmBrYc5uucEq+avpWqDyoHWMYPLh6XTfuLDZ2ZT/9ravPTV0wVW8RkYGBgYZKOVQVW32FVcd6H3i8guhnEjzdXPJl1HjoOMiWBpijTVQYTWBXMtENbguPEzWK4EEaWqdxx9in8c/hNI/wmE9CE9m5FpI1V0e+S4PFumZHL/bJHmTIS7BzLeDkraEkID+40IQMbNQzq/RdhaI6xN1Rj+45D6rHod3L9A6Y0qpt2zBmydglO9zgfHrWpMtDy9nQpCi3gGGdoPtCiEcBR/LWkjldDlTwLHmmwvp9yR0gX+E2Cqol7LksJUNvu80Azo6r/Mj5BZFvAfVJVB1muhmK+VgcHljCEAXWZUqlOBe18+jy/AXPjp01Xs/WM/XrePqU99wjtrxpbo+P9EnvxoEO3vaMXI7uPwOD2s/N/vOUSDXo/fRM/Hbgw8tv67zXicKsZ87bebqNm0GoObP4cQcE3XZoyYO6RQc7/cazy7NuzDYrMQUy6aJh0asv333fy6YC2RcRHccF97okpFFjzQORz66wjJp1Lwe/1omiCqdCQRceFsWbUDe5idmx7qlOe+dVvU4s0VL3N83yla9WyBlJLUxHR8Hh+arnNw22GiOzYK2ufnz1Zx8mA8fq+fT178gkmrXmXxlGV8P3MFP8z+hSmb3qBstTJEl45kzOLcxbCSpEyVUgyZOoBf5q3htie7oWn5n0Qknkjm4UZP4sp00/2Rzgx8897Ac/WuroXFZgEhaNi6Loe2Hwt8Bkzm8z85WfjeUhZPWcYtg7vQbeD5e0AZFMzUp2aRmZrFlpU72LpyB1d2uuJSL8nAwMCgxJC+I8isTxHWKxH24n+vSOlBpj4L3p2IiJcQtqsR5ioQOxe828DepfCDec9UENsQekrgYWGuqYyHXT+CNh4R8SzYO+Q5jNAioNSPKqpdK/jcSM+aD1mzwHE7Wuhd6rh8h5EpQwA30vU9aDHg3we+w2D7Hhx5BF54t2enfJ2xB7CDiEA6v0Hkso8wV0eE/721/5zzBmFSBtsxM5DSVaLm0JqjM9LWHLAgtHDVApUxGfwnEeFPFtowWpjKnf9izDXBkwHoYKqc52ZSz0Am3KTa+2xtENHvn//c2QjLFRAzDenZAJkfKhHPfhP4toD/OCBBK11i8xkYXE4YApAB1RtXQdM07KE26l1T+1Iv56JR/9o6xJaNIuF4Ms27NMm1YuTMY1tX7SDldCqhkSFomuDWoV05uO0IQoAr083233cVel5XlhvdryMEuLPcHN19nKc7vITH7QUByz5Zwcfb3irSsVS/ogplqpbmyM5j3HBve4Z9PIi+lQbw64J1bPh+MzWbVqP2VTUC20spWbNoPe4sN+36tKRO85rUaV4z8PyDr93Jh0/NRvfpvD3oI2bueudvr11tyBbMymb7UCWeSMbr9mKymHiw4ZOERYbw/oY3KFOlaKaNaYnpJB5PomrDykXqi7++Xzuu75f3VaZz2b5mF16PF6/by89zVgcJQG1uvYYyVUvhcXpo2Loe13ZrzpcTv6He1bWof62qjpv61Cd89fYSrrr+Cl755jlMpsL1waclpjN12Cx8Hh/vPzGDDne0NiLgLwJ1W9Tkr992IwRUqlsyVXcGBgYG/xRk8oPgP4zMmguxlRCW+gXvhPL3wbVY/Th2dAf3ctU6I53ItOGIUj8BKD+ZInjKSN8BsHUA9xIQDrCckyDm3ZxdmeEC9y/AswWOJ4QZRMHij9QzVPUJPkgfi7R3yU6XOjcd1Q/matmtaBJM+XgGho+EjNdVFVL4cEh5HHxbkKkvqCoZW5uC124qC9EfIF0/IUJ6B85rSlL8Ccx1rmmzc5FKLcOD1BMRMR+X+Hx5riN6ijL6NtdGmKvlvaFvH8hUwA3uFSW/DmtzhLU5MuQu0JPAVAVkumolNNe4YMl2Bgb/dAwB6F+MlJIxd07i1wXruLl/Jx5958Fct2vcrgFvrxlD0skUrrr+v3Nl3B5iY9qOSSQcS6Js1byvAriy3DzX5VU8Tg9Wu4V5Jz4iNDKUyvUqULtZDQ79dYRBE+8r9Lwj5z3JzJGfU6FWORp3aMCejQcI1EZLSDiaVORjsdqtfPjnBLLSnAFBwWwxq3ElOfyNvp+xnHcfnw5IDu04yv2v9A16vkHLulgdFtxZHtxZ7lznNFnM+H0e1i5Whu7Pf/oEs16eR+KxZFIT0nBlulm3ZBPdipB0dupQPAMaP4XX6yM8OhShCZ6ePrjEKzYad2hAZGwEHmcivYbkTP86VywrV70Mj7/7UOC+3+/nf28tBglbftnO4R3HqNYw7ytc52ILsWJzWNE0gdVhxXqRfLyKyo61e5gzZj7NOjfmlsE3FrzDP5zRXz/Hll+2U7lehUvmZ2ZgYGBwwZBulMjhR6aOhPAhhRInZNYsSJ+o7viPIuydssUZO5jy+eGeD3rWF5D2KiAg4jWE/brgtDH7DZA5C/QTEDa00ONK6QXvVjBXDxhK50BYQYRkJ1WpqHsAYa6KjBwL7uWI0PtVy5lriWo7sjbJc07h6A62q0GLQwgzOpKzSWCZhV67sLVG2FoXevtzkdKvYufRwN4t3zYp6dmITH5YHXfImXN+Tb0WFxEhHOAITgjLtTXfUg9M1cG3HULuzHdMKX3KM0orU2TTbKFFqJZEUC13ofcUaX8Dg38bhgD0L+bkwdOsWbgen8fH1+9/z4Ov3RUUV34uNRpXpcZ/UAi3WC0FJqlJKQP+MufcxBFq583lLxdpPiklaYkZlK1Wmi8nfM03Hyyjdc8WlK1WmtT4dEIi7LTtfS0PNRxKyx4tuP+VO4K+6NYu2cR3037ixoc60eLGpuz78yCfj1tI044NuemhTkHVJK99/wILJy+ldOU4Xrp1PCFhDl799nniysdwfN9JfB4fuq5zbO9JNi/fxvQRc7ny+kZcc/NVTH70Y6rUr0hMuWjuefH2HMcRWz4GIcDqsFImWzy7tnszNny/mVVfrUUzaZitZupfW4cfZv9CjcZVqX5FlQJfn53r9uL3+/E4PSRmt9u998R0pv01Kd/9PC4Pa5f8QeW65alSv+AEsIiYcGbtexePyxtISSssJpOJ+tfWZt/mQ4RHh1K2WuFLiG0OG+9vGMfGZX/SrEsTrLZ/phfNyG6vkZqQzh8/b6VBy7rUbFq8HwL/FKw2C81u+A/+gTMwMAhC+vYhU58DrRQi8g2EFnapl1QiiOgpyPSJ4FkNvq3I5EFQ5s+CU5r8pwAfIEE/oSqHYj9VJsXFbSVzLQPcgADvJtDCkLZWAZ8docUgSi0p1FBSSnB/D9KFzPoKfH8CNii1LFcRSAgrxH6lKplsrQPCk/RsBt9eROgAhCXb59JRcBCHTHlMVUSZKkPcIkTURGT6eNXiZLuhUMeQ7/juX9Xn0VQFET0l18+jzPwIMrJbo/zxiLCHcmwT2DbjQ2WYLTNBT4TwZ9T7GvowuusnSH0atHKI2Nk5I97P91ikSkHLzU9JTxsNWZ8hra0R0R8GPpeB9yuP/c6O7VJJcP6jYGmMiP2iRNduYPBfw4iB/xfjcXu5v87jpCWmU656GaZunlAiUZPFISU+lcM7jlHvmlqXpQHr5uXb+G76z3Tq1+68fkhOG/4ZCycvxZXlVpXHZo0zZTpte7dk+GdPcJO9L16PD1uIlcm/jaVaIyWcZKZm0rvsQ3jdPiw2C/87PY0H6w8h4VgSthArE38ZHVS5coaR3V/n98Ub0UwafZ/vyX2j7yAtMZ3X+72DM8PFs7MeY3Dz50hLTMceYiM0KpTE40kg4JG37qPn4zfnGBNgz6b97N6wjza3XUNETDhbVm5nxM1jcWW6CY0MYe7RqTzX+VX2bT4IwNTN46lQM//e8oyUTB5vOYJje04ghMBiM1OpbgWO7jrOFe3q89KCZzCbc57Qjuj6Glt++QspJe+tH0eVehWL9L4AZKZl8cdPW6nboiZxFfKvEvF6vOz/8xCV6lYgJPw8jBL/odxd/RFOH07AYrMwZdMbJWZWbmBgYHAp0ZPuyzbKtSLCh6lqkH8J0p+AjO8IuEGEI0qvK9BUV+rJyNThIL2IyLEI0/l7okj3WmWeLGwgXSA0sFyJFjO9yGPpmZ9D+hlfSg+gq+qWqA8R5prZ7V0FrEdPQZ5uC7hAhGW/Lvlf/5beLcjMueA6E+FuR8R+gbCUbAiEHn+j8iLCrjyXQnKKUnrqi+CcBwhw9EWLHJn3eFlfQNoYAET0B0Gm2nr8zeDfA9gQESMQIXeU2HFIf4KKttcTIHwUWujZqnIpvchTZ9oHbYi4BQjzWcsB6duLTJ8MlisQoQ/k+jtFerchE28j0MoX/SmarUWJrd/A4N9IfjHwJVIBJIToArwNmICPpZSvl8S4BueH1Wbho60TObD1MDWbVr2k4s/9dZ7A5/VR/9o6jFuW95dXcdm9cR8rv/yNKztdwYJ3luDKcvP0jMEFxmw7M5y8+fAUUk6lMvTDAXkKFE06NKRJh4bnvc5NP27FlelGM2lIKbHYLOh+HV2X2EPU1Q9riBWvx4fX7SM89uzVIM2kBcyNNU0gNJFvm9cZ6l9bm83LtyF1GRCIImLDGbtkRGCbqNIRZKU7kVLidWdHdkrYs3F/nsdS68rq1LqyeuB+qUqxSAm2EBsVapXDEWrn2J4TuLPc2ENtnDqUkOvrm5XuZOLDH5CelMHQDwcyffskpJQc2n6UUwdP82Kv8fi9ftZ+u4mnOrzIWytfyfFZ3v/nQVyZap5ju08USwB6/NrhnD6cgMli4tP97+frzWOxWoI8k/5tjP/xRZZ8/BON2tQzxB8DA4N/D6YqwKbs2/+uv23CFAcxM5HulQhH10IlKgktGsKGgBZRIuIPgLBdDWU2g2cVMvlxZaLs/at4g+nHUBVKOphqgf8AmOtDymNImYEMG4wWNjj/MaSXgHAgPQT7AeWyudRV5LvMImDgbK4K5pwX2M4bS+NsQ2I/Uj8K0pkjfUuEPaaSxNAQ4fkfqxbSB2m9CrAhzH+rhra1gayjgARLCds9eFaCngr4IetjOEcAEsKCtDQF7w7QosEUfH4mkx8F/37wrABLXcgtCc5cG/WT1QOYEEVovzMwMMjJeVcACVXHtxu4HjgKrAf6Sim357WPUQFUOKSUbF6+DYvNQsNWdS/1corN1lU7GH7TGFyZbmwhNhZnfFqi47uy3PQu86ASVswaQgikX6fd7S0ZPmdIvvvOf2sx04Z/htfjo1nnJgyccA8bvv+Ta7s3o3yNsoHt1ixaz1sDplC1QWVe+eY5rHYLkwZMZf33m7nv5TvofH/OBIuMlEy+GL+I6NKRVG1YicyULOzhNsb0mURMuShGL3qOuIoxfDv1BzJSMrn9qe6YrWZudtyJlGCxmZm6eULQD/Dtv+1i+Rdr6HBHK+pfU5uje06wcPJSajSuQlzFWBq2rpujze/M5ygk3EGd5jU5efA0EbHhQZUrSSeT+XnOaupeXYttq3fyyYtfIAS89NUztLixaaHfi/1bDrFn035a9WhBWFQovy/eyJRhn9CgVV2e/GhArmbJX7yxkJmjvsDv89O8SxMatKxDVOkoujzQAU3TGNziOXZv2AeA0AT/Oz2NiJjwoDF+X7yRdwZ/RK0rq/PCF0OLXGWm6zpdrHcgdYnNYeXdda9TtUHBrWQGBgYGBpcPUnqV6bEWg8gnmvrfhPSfRibdDXoCIurtIG8gPWNKoL1IxMzO0xRX+k+C61uwNCu0ca6UHmTK4ypGPXwEmiP3auJ8x9CTkSnPAi4V124qh3R+rXyOcIJWHq30igLH0bO+BtdCROi9Bb7vUurI01eeTf+Kna+qjUoyojwwlw+Z9SmkTwA0sF6LFjO1xOdRc0n1XpjiEMUQP6V0gXTl2nonfUeQibcogS30QbTwoWfndM5H+o+CtSXC0jDYDwrQE3qAbydgRcRMR1hzLVhA9+yC9HFgbYwIe+yCvB8GBv8mLnQFUAtgr5Ryf/ZknwO3AHkKQAaFY/6kxcwc+QUgGTp1INfdVbCh3z+RetfUokHLOvy1Zhf3ji7ZCHsA3a/j9/rVHSlVJYzFRJlCeLPEVYhBM5mw2jViykbx2DXD8Xp8fDZmPl+e/BhTdrvR+0NmkHI6jZ2Ze1i7eCNlq5fh57m/4s5y8/bgj3IVgCYN+pBfv1oL2dUqZouJirXL4/P6SDiWjMmkYXfYuHVI18A+UkqqXVGF4/tO4Qi1E/c3w9r619YJJFEBVKxVjvtfvYN7ajyKx+2lfPUyDJs2iJMHThNXMYaXeo7HYrMw7odRVKxVjukj5vC/iYuxOix8uHkCpSurhK6YstHc9qSKNG3Yqi5NOjTAHmovsghS/YoqQV4/13S9imu6XpXvPrHlYwLx6ttW72Tjsi2YrSYsVjPX39OOCctfpP8Vwzh1MJ6o0pHYcjFOLsw8+aFpGgPfvJe5ry+gZfdmVKlf9AoiAwMDA4N/NkJYwNHzUi/j4uJakp145UWmTwo2h3b/BLgAC3g3QF4CUNKd2X5BJij1HcJUvsBphbCqNKjzQGjRiJgPgx+0XgNaCOieAo2Dz6CFdIeQ7vluIz1/Il1LEfYbEdEzkVlzEfYbEJYLl44rhBm00khMgFNVOF2wuUSe7+8Z9KyvwbsBEXp/UHqX9B1QLVjShYwYBZgh63MIuRst5BZVbVTqF9CTEeZzQjFcS5FpowGf8mCKfjfnuqKnIDM/AUuDPMUfAM1aB2KL3kZoYGCQk5IQgCoAR865fxS4ugTG/c+za/0+3FluNE1jz6b9l60AZLaYef37km/7OkNIuIMX5j3J4qk/cOODHfH7/LgzPXTq17bAfdv2vhaTxURqfBpX3dCY5XNX4/P4cKY78Xp8AQGo3jW1SU1IR0pJ1YaViIgNRzMJ7KG2oEqhc/G6vEhdggCp67g8Pg7vOKqMh0Nt7Fi7h3LVgw2ohRC8/esYdq3bS/XGVfI07T6X+COJuLLceJweDmw9zNA2I9FMGuExYSSfSkUIweIpyxj45r389NkqFdVu1ti6aifX3ZV7RHvdFrUKnLcgju87SUZKZq6+ROdy3V1tyEjJYMqwWWSlOQHljZSZlgWAI9RBnWY1OX04kYzkTL7+YBm9s8WqkqTXEzfT64ngK5R+n59tv+6kQs2yBfoCXQx8Xh+TBn3IoW1HeOy9hwp8bQ0MDAwM/uNYmqIcGkxgaYSU/rMmvGGPIZMfAy0G7PlU6OjJgFeNoaeCqbyqpvIdAHMVhChamML5IEylodRK1S6VneykO79WyWOWetkmw0Vbj9SzVNsXTmTWHETp39Cixl2A1eeCvRO4fwTfLkTEaPTUkeD8Cuw3q6qni2TfIL1/QdoLgAvpWgXhj4AWCbbrwb1S+TnhhazZ6n3HC2nbkfbrEFoYQgsHLVxV/einQYvLrqIC0PNMTROmsoiIZ4u+Xj0D/IfAXKdAT6fiokJgnCAcl8xGw8DgQnDR6ueEEP2FEBuEEBvi4+Mv1rSXNf1G9aZy3QpUa1SZXk/kjKr+t7Lxhz+Z/OjH7NmUt//M32nZvTljvx1O2aqlmfjQFN4Z/BHbVu8scD8hBK17Xs3N/a+nbNXSDJhwL7Wuqs5TMwYHpUM9OvkBBr11L1M2vUGV+pWILhPF1D8m8NS0R5i4cnRgO5/XhzPTBcCQKf3p1K8t3Qd3plHb+lRvXIU7nu+JyaxRqlIczbs0yXVN9hAbjds3IDy6cOkkletVoHWvqwmNDKFVzxYITeDKdOP36dgcVix2C1e0qw9A76e6KXEoOoyrbsi9B/zUoXgyUgrurz59OJ6HGz1JvxqD2b/lUNBzW1Zup/8Vw3iy3Sjmvr4g33GEEDTr3DTQHma2mrjp4U7c9HCnwDYhESGYTBpCEzlEsUM7jvJgg6EMbvEcCceTClx3URjTdxIvdH2NB+oP4eTB0yU6dnFYNX8tv3y+hp3r9jL+vveCnstIyeSbKcvY/vvuIo/rzHDy2dj5LJ76A7qevz+CgYGBwYVA6plI13Kk/9SlXsq/Cuk/pTxsMIHzfyomPBtha4tW9k+00ssRptwvZgGIqHeUX03YIISlHlJKZGJfZOKtyITuSOm58Ady7nqEJSD+AJD+OsgU8KxHpjyvIsMLQPr2If1nfo/ogP+c2zm/B6X/GHrSAPTUUUjpLvRapfQgZd7fq0JY0aImosV9q3yGnP8DvKrlTr+Y/xbOOW55DJn2MjLlKbUOWzsQdsAM9j7ZsfJmZfR9TnqXlBKZ/CAyvgMy8Tak/WYIuQtsncHaHv1Uc/Sk+4v0+uWG1DOQCZ2RiXcik/NORTuvOaSOTL4febopMuURLkVokoHBhaIkJNNjwLl9IhWzHwtCSvkh8CEoD6ASmPdfT+W6FZi2fdKlXkaRyEp3suKLNVRtUDGoVamwJJ5IZtQt4/C4vPwweyULk2cGjI8Lw/czV+DMUALMoneX0rh9gwL2COaWwV24ZXCXoMdOH0mgf+NheN0+ujzQkccmPwhAueplgip4ju09waNXP48rw8WwaYPodHc7npquDPv8fr9Kx4oI4e4XbgtcSTi+7ySTBk4lukwUQ6YOKFTFz9/RNI3nZz8OKD+kV26fyNHdx3lq2iM4wu1YrGZiykXz++KNdLyzDTc91Amz1Zzr6zpvwiJmjvwCs8XE5LWv5Wum/PX733N4xzF0XWfWy/N4af7Tgef++nUXXq8P3aez4fvN9H0u/5L7irXK8cwnj7L224006dCA+KNJJBxNDFRXDXrrXspUiSM0KpQbH+oYtO/MkZ9zeMdRNJPGoneX8uDYuwD1mh/ecYxy1csUOer9DFtX7cg2l7ZzYOthylYtGZPMwqLrOrpfDxh8l6oYg5QSq91C6SrB1Vsjuo5l76YDCCGYvPY1qjWsnNuQuTL50Wks//xXTCYNs9VMl1xaGksKKSVJJ1OIKhURqLAzMDAwkEl3gP8IYIZSPyG0yEu9pMseKT2QOhRVvZON51eklIHzECkleH4HLRphyd1vUtjaBLeOySzwbQWkai/znwRz4b9zzgcpJTJ9LLhXQdhTaI5OYGmi4trxgXsZOK+FkN55jqG8j94DNIidi7DUh6jJSOcXCMftqprl7/Omvgie1YBFmRKH3l3gWvWseZA2CrAhI0aghdye/w4iXEXO+0+CKQ60i1d5LCyNss2a12Y/4gEsoJ9CmLtC6TXZVUAC6VqkItkjXwqOb5dO8PwKSPDtQejxgeoe/dQ1IFPB+we414D9PM4z/AdAzwCc4Fmb52bSswGZ+gyYKiCiPkBohbuwqhZ8Gjzr1bG4l6u1i6jir9nA4B9ESVQArQdqCSGqCfVX4A7g6xIY1+Ay5MWeb/DeE9N5ptNo9m4uei+z7teDbhdVcW/VozlWuwWLzUKHvq0BFdn9XOdX6B7Rj6/eWVLkNe34fQ9+rx+P08OvC/L+ovnt6w24Mt34vH4WnDNPWlI6/aoP5ta4+5n18rygMtLJj37MHz9vY9X8tSz96Kcir+3v2ENsjFn8PJ/snkyjNvWo2aQaFWqVo3/jpxh75yT6XzEMoYk8RbUfP12J1+3F4/Yy44W5pCdnAKqy6e/vRZ0WtbDYzNhDbTRoGSz2derXlrJVShMWHco9LxZwwpNNu97X8tDrd/HO4I+Z9dI8Hm85IjCnI8zBXS/cRo9Hb8xhJF37qurYQ21YbGZqNK4aeHz4jWN47OrneaDeE4GqrKLy0Ot3ERLuoNaV1biyU6NijVFcju87Se8yD9ItvB+rvlKfu4at6/HKN88xcOJ9vPD5kKDtTx9KwOPyIjRB4vHkXMfc/tsuln/+K16PN+jxrLQsdL8fKSXOdOcFOZ4zjLt3MndXHcQD9YYU+30xMDD4dyGl+sGIzFJGsv4Tl3pJ/xJMIELU/8+c8ofcFXQeIjPeQqYMRCbejnT/VqhRhRYK9lvUHa0UMvkB5R9zMfD+CVnzVHJU2jNqPVFvKxEocJzB5wnSn4Ce9RV68mD0lGfAtRRwA37wrEd3LkGmvaD2teVht6BFo66bC8jFCDlXMqehqmqckPYy0vNHvpsLYULELUTEzEDEfq08q84DKaXyNvIdLdT2IvQBwAZYQasBto7g6JO9NpsSZZ0LlGmzTILMWX8bwAG29qh2w3rBSXvWJtmfReCcGPhiYa6bnWSmQciDeW4m08YoocrzpzJ/Lwpaqex1WtVcwhCkDf49nHcFkJTSJ4R4FPge9Rd3upSymHmPBpcjB7YeYs+mA7Tq0ZyT+0/hcXqwh9o4fTiBmk2qFTzAOZSqGMuzsx5j5Ze/0eOxnD/2C6Jpx0Z8evAD/D4/ceVjANj+227+WrMLV6abGSPm0OvxwrfTpSWmc2THUUIjQ3A7PfR5tgegqkuO7z1JmaqlsdrUF3TzLk2Y9dI8BNDlgbNVKltX7iAjORO/T+fr978PEkRKVYrDarcihCC2fHSB61m9YC1Hdh7n5gGdciRhASSfSmHmqC+IqxDDncN7YTKbyEpzknwyGb9Px+v2kpGSRXTp3L/Ibn+qO+Pvfz8Qu/5izzdof3tL3ntiBqUqxvLe+teJjFNl1216XU3pyqNxZ7lp1KZe0DilKsYyct6TJJ5IptoVhb8q6Mp0I3WJ7tfJSnMGXaU8l1OH4jl9OIEGrepwx3M9qXlldewhtsA6pJT88dM2pJSkJaZzbPcJajYt2mcRoPN9HahSvyKje7/JkNYjGbtkONFlooo8TnFYNX8tmalO/D4/X05YRJteylqtacdGNO2YU4x6bvbjfPDkTOpfWztXsWrLyu0Mv3EMCMG6pZt49pPHAs899t7DWOxWokpFcPOA6y/cQQErvliD3+sn6WQyB7Yepv41F85g08DA4PJACIEMfw4yPlA/IM3G34WSQAgTxP4PXN+DrTWYa+f0S/FsUJUbWMC3HWzXFmpsLeoNdM+9kNQXcEHa8wUaLZcIptIgAGmDbDNqISwQ/QEyYwpoZcBxS2BzKV3IhK4g01CR8mawdgD2ZnsfdYaE7qqFzL1GVZTYWueYVkS8rGLKtbj8/ZLOxdEDMt46s/A8fXCC5hEOsF5ZuPELQGZMhMwZgA8Z8RJayB35z23vAKW+A+kPNnM+F3MNlMjmUK9H0NoFRE0BmQwiKiipS0RNBs86MFctVgpZ8DwWROzsPM8RA1gagG8/IIssOql/O/PBf1xVEBkeQAb/IkrENUtKuQQoemmFwWXPiQOneOzaEYDk6/e/45lPHuPdx6dR66rqXH1T8b7A2t52LW1vK9wJSG78XdyoXLcCJrMp10qVghh1yzh2rd+LZtKYtOqVQFvb813G8NevO4mrEMtHW9/EardSpX4lPj/2Ie4sd5BIUL9lHWwhNrweHzfcExw/+ug7D1DryupElYqgda/8vdM3L9/G6/3ewefxs+qr3+k64Aba92kZFOc+8eEprF/6B2armVIVY+nyQEciYsO57cluLPnoR264v0Oe4g9Ap7vbYQ+1Me6ed3Flukk6kcIXbyxC9+ukJqTx02erqHdNbeq2qIkQgjrNcjch/vTV/zH7pXnouiQkwsHM3ZPznfcM5WuUZcCEe/hl3m/c+NB1fDBkJpXqlqfboM6BL9/DO48xuLkqKW53e0ta3NiU04cTgjyDhBDc8mgXFr67lNpX1aBqw+JHuk8bPof4I4kknUjh+xnLuaOAdrbious6P326Cq/HR+f72nPV9Vcwe/SXaCaN6+4q2NC8cfsGTNk0Ps/nj+4+gQQ8WW4O/nUk6LnYctGMmDPkPI+gcFzfrx3LPllBqYqxQYlxBgYG/2200Psg9L5LvYx/HcJcBcL65/18+NMqrl2LBXuPoo1tKoMUGkg7mMrleF56dyPTXlI/+iP+1i6UB9K7Q7WV2drlWgEjTOUh5gvwbgX72QsWQotGRDyfc0A9BWQ6SvwBFbfeGBH97tk2OEt98G4GJJhy/14SWgiE5l1tkhta2CB0S0NVKWNtCtZWRdr/vHGtQLVyAWmjkfbuOWLYIdvvJut/oMcjQu9FmPJulRK21hDzCegJqkLo788LASIml8ctYCvZ4y9IlBERL6s1msqqNr8ij2++aK2NBgYXE3EpTK2aNWsmN2zYcNHnNSh5tqzczoibx+LKdBMWHcqCxJmXekm5knwqhcM7j1H/2tpYrIUvqb239mMc33sSe6iNFz4fytU3X0VWupNbIu8BwGKz8P6GcQXGpXtcHtIS088rSWrV/N954z4lzAgBVruVBq3rMu6chLWXer3B74s3YbaaePy9h7nh3vZFnsfv9/PhU7PYvXE/Ayfexy/zfmXRu9+hmU34PT6VjmYxIXWdhq3rMm7ZqIBHzRkGt3iO3Rv2AWC2mhnz7XCuvC7/FqpD24+Qmeak3tW1EELwZLtR/LVmJxablRFzh3BtNxUP+vOcVbzVfyquLDdRpSNxZbjwen3YHFY69G3NE+8/HDgp8Pv9Ra4i+zszX/yc+RMXI3XJSwueodkN+ceoFpev3v6W6SPmICX0HtaN+0bfQVpSOq4MF6Ur557WVhScmS7G9p3E8X2neHrGIyWS9FZc0hLTCY0KOe/3xsDA4L+LdP+KzJwG9pvRQm691MspEaSehkwdqRKuIl9ViVf/cKRvvxJPbB0QWnAls55wa7ZXkB0R+RLC0Svvcfynke6VkPYyCA1sN6BF5X1RoyjoaePAuRAsDVWVi+P2oEooKd0q+cq3C/RERFj/fE2xg9d9Etw/gfVqxPm2NhURZS4t8hRCdNd3kPJ49j0rovS63AUg5yL1ucMH9s5oUW/l2KbE1uzbr6LhTRUREaPyFAWl74jydbK1uuivq4HBvwEhxEYpZbPcnrswuXkGly2uLDffz1hO6cpxgR/c+dGwdV2uu6sNm5f/Rf83+l2EFRaP6DJROVp3MlIyWf75r9RoUjXPNpQRc4fw4dOzqd2sOs1vbArAj7NXopk0dL+O1WGhUp3yBc5vtVuLJf543F52/LabKg0q0rJHc7o/0plNP27l8M6juJ0eTuwLToh48qNBzBn7FXEVYujUL7hqREqJ1+3Fas//CpzJZGLQW/cH7te+qjo397+etYs3MmWY6vf2e1VaxtZVO9m9cX+O1++el27nxZ5v4Pf6MZlNbF6+laYdG+Z5krJh2Z+81PMNEIJ+o26jzzM98Pv8IFWlt9/nD2x7TbdmVG24hCO7jtO619V8P/Nn/F4/WV4n3079gbpX16LLfR0Cx5IfHreX3xatp0Ktcnm2iF11fWN+W7SB+i1rc9X1uSenlQSJx5Pwef1IXZJ4TKWZRcSE59rqVxwcoXZe+fq5EhnrfImILZljMjAw+G+iEnoGAm6V/GRtgTAXv9LzUiLda5Wfjb0bMvNjcP8A6Mj08YgSEkCKtB7PeqTzK4S9G8LWssDthbk6mKvn/qSpLPiykym1MrlvA0jfYWRid5BuQILUwbMVPf1NkF4VV+9aDJkfg707WvhjeY51Bj31JXB+AbaOiKjJ+UaNC2EDzYrM+hCkG+nbhoidV+AcKg3tNtBTQZig1HKQXhVPbml6weLJAaR7BTL5UdDCIWZerp9/zd4FGT0D6VyAcNyaq/ijBssCJKBnmysXMLeehkyfAMKOCH8SIQofYCJTh4N3E2BXvk0ht+XcRvqQibeqlrkMK5RaVTQDZ4MgpJ6u0tzMtREl1F5ocHljCEAGQUy4/z1++2YDQhOMnDeswDYuTdMYMmXARVpdyVKY1KTaV9Vgws8vBT0WWSoCi82C7te5tluzC5pk9Eyn0ez78yAWq5mZu9/h4XH98Pv8TOw/lb1/7A8kkp0hIjacgW/em2Mcv9/P09e9zNaVO+h0d1uenVXwydMZhBBUqFmO6+5uy5yxX5GakB703F+/7mTSgKnc3P/6QILa1TddyXfuz+kZex8ZyZl8NWkJLbs3p9KZdry/pXLt+H03Xo8X3S/54+dt9HmmByPmDmHWy19SuV4FWvVoEdg2JNzB5N9fA8DldPPdtL+ZZ+uFr2oce+ckNi77E6lL2vVpya51e7nnpduDWhBH3/YmKadTObb3JJ3ubpdvG+HxfSdxOz1FSuA6Q59ne3B83ym8Hh/3j+lb5P0NDAwM/jsIFUt9Jn5cFC/p8VIjPX9mx7JLcC0D23UETs3zEUyKNIeern6oF8JQWEonMukBwI10fgOlf8s1EauwiMjx4PwKzBUR+bX/+LZn+xBlf39rsWC9Itu/RqrYcOfngB8ypyIdPRHmvFNKpXSCc64az/2LMgIuqJVH+gPTI/1I32HwH1MG5RkTwNoCEf7s3y5k6aAnAT6QJuTpNmqNWMDWFhH9bv5zFoD0x6uENmsLhOns50F61iOThwAe0JOVqXVYf6R0QdbnICLA0QMhNIStVf6vPYDjVvAdVIlf4QVfKJLpb4BzPuq98SIiXzz7nO+QWrOtfdCaA2ilgewLkXmmnPlAZmT/n+zbhgBUXGTyQPBuAQTEzssz7c/gv4MhABkEEX8sEY/Li81hJelE7klCBfHXml280mci0aUjGbt0RKG8Xy4Fpw7G43F5sYfaSDyeTNmqpbDYLIF2pmN7T7DgnaU0al2XdrefvQrW9rZrIDvK+saHriv0fCcOnGLy4I+JLR/Do5MfwOYo+IR117o9+Lx+CLVxYt8pImLCMZlNPD39kSId6/G9J9m5bi+gkr4ata3PTUVYO0BkXARfnprGttU7WPP1Bg79dYQ+z/XkuetH4/P6mTLsEzrc0SqouiO6dCSuTDdIycr5vzN/4mIsVjNvrXqFWleevWLY+f4O/Dx3NZkpmdzzkjLJLl25FE9Ny/84LVYzJotJvUZAy1uaBxlwF8TBrYdxZbqx2i38PGcVPo+fcfdMDhKAImLDSE/KACkJiw7Nc6wNy/7kxZ5vIIABb95Dt4GdC70OUNU+L/7vqRyPb/9tF2/c/x7lq5dh5LwncYQ5ctnbwMDA4L+DEAJiP0c6v0bYWl8WrVK5op8EIVS8tv8YIuRO0MLUfcf5t7XpmTMh/Q0lCMQtDLQ16c4fIOsjVU2Tb5z5+dlEKN+cguPSsf7NdNlUA0znVrSYlFigp4EwI4UNmfoC6EmIiJGIHP5DdjDXA98BMMWoSqSCsLWH8MdVGp3tJmRCt+z3xg+4wb8P7B3BevaClBAmZOQbkDEF/HtQ4g+cSRfLD+ndinQtB9t1CP8BMJVBWJuffV56VVWUnqXEztIrVaUSIFOeBLLOvjZW5SEp08YqwQ0NgZ5rdU1uCGEN+CdJfwJ64h0gsxBRk1SFV9C6t4Bz8dljdX6JjHhBvRZ6OjKxh3rNtLeh1OogI2gAEfkaOP8HpnKqHS/X9diREa9B1nRw3FrodjyDPPAfRaXeOdTfHAwB6L+OIQAZBPHkR4N455GPKFe9DNfdXbDxbG5Me/4zEo8lkXo6lR9n/ULvpy5CKkQxeP7TJwKpSfGHE+jV7TUc4Q7eW/865aqVYfiNYzix/zTfTfspqEVICBEkCJ0hIyWTN+57l4zkTJ6eMZhy1YOvfLw96CM2/bBFxZU3qUqPR28EIDMti3cfnYbb6eGxdx/k14XreH/ITKo1qsydL9zKvDcW0aRjo2KlWJ2hTNXSRMaGk5DdWjTt+c+KLACBOvZGberTqI0y0/O4vdjD7LidHqw2C7aQ4PayCctfYvncXyldOZZX+ryF1CVup4fVX60NEoBKV4pjxo63C5z/xP5TpCWmU7tZDYQQmEwmXv/uBRa+9x3tbruWNrdek+e+y2at4N3HplH9iqq8/v0L2ENsDJk6gLcGTKV8jTJsXbUDzaRRqmJc0H7jfhjFj7N+oe7VtahSL+8rjpuXb8Pr8iKlZN2SP4osAOXF5EencWz3CRKOJLL88zXFet8MDAwM/m0Icw1E+NBLvYzzw3Yd2LuCbycifJT6sezoUXLjZ81GVVFkqYSrkF5I6YXUIYAXvNuR9g6BVCYhHBD9MdI5H+G4BaFFlNxa8kG6vgMRBzKeM4KGCOuPxAf4EKGPKCNr90olwLiWIJ0LAR8y1Y+ImRo0nhII56m4clPNIJ8ZqSchk+5ThseRkxC2qwP7iGyTZ+lcqKQvmYWqVrGBlLlWrGiOruDoip7YJzse3QtYIHxY3serpyMT7wLckDkFiQWQEP3B2Wod6VStZfhA+kBPB1P2hUNTOWVuDRD9McKa7U2oJ3Mm6UzqKRQnt0pmzQLvn4AfmT4xRxWTTJ/AWfHpDDoq5Swt+/g9oHuz1xJ8Xii0UAjNWakeNIeUSlCy34QoyX8PFwk9az5kvKkqtyLfVIlilxARNQGZNgYsjcDa5pKuxeCfgSEAGQRRpV5F3lz+8nmN0bB1XfZs2o/UJbWuyqMv/B/AualJA5o+hc/rx5XpZu3iTfR47Ea8Hr8q/RXg8/oKGA0Wvfcd65f+gd+nM2XYJ7y84Jmg56PLRmG2mRGaFlQVNW/8IlZ8sQap6zjC7az7dhNet5cD2w6TfCqFdre3ZOiHA3L1s5FSsmPtHuIqxFC6UhxSSr6Zsoy9m/bTd3gvylVTIpTVZmH8Ty8y4MqnEUJQ9+qSMdSz2iy8v34c65b+QbPOjXNUNcWUjebWoV3ZtWEfZrMJr0e9jq16tshtuCCO7DrG1pU7uKbbVcSUjWb777t55rqXQQh6P9Wde7MrhRq2rodm0ti9cT9piel5+st8/NxnONNd7Nt8gE0/bKHlLc1p0qEhn+yeDMCBbYfZvmYXLXsEry2ufEyhkr+6PNCRnz5diTvLHbR9akIaL/UcT2piOiPmDqFG46oFjnUu1a+owtHdx5G6pHLdgv2mDAwMDAwuD4QwIyLHXLgJHLdDxjuqRc525gKJCURodluNCUSwL4ywXR0QRS4GUk+CtBcBr1pP5OvKf0hofxP4QgMVLdIbiYoiN6sKn1wQwgqWXHz7nF+Dbx/gRWZMQNi+zLmNrRNY5qq2qPDnETJdGUibc08+BRAx2cKJuSZCy31NZw/agxJNJKqSxgfYlHcQSgASWgQy7AnI+kyZVpvOXpwS0R+B8xuw1ENYrzr7eMQIZJoftChE6F35ryGv4zDXVoKUsOaIeQeUb4/nT8AL5rrZHkAW9PRJynPJcgXoiRD6UIGpb9J/HLAGHRsA7hXI1BGAD+nZlEPg+8eT/ooSD13LIWQTnFPZdSkQ1uaIuIWXdA0G/yyMFDCDEkdKyZ8r/iIyLpxqjS6PmOeF7y5h6lOzsdosvLvuNSrVqcCBrYeY89oCGrdvQNf+Z6NGpZSs/motfp+ftr2vRdNUeetPn63irQFTAUnXgTcwcELwFQ5XlpslH/3IkZ3HWL1wHU07NuLZWY+y6N3vmDZ8DkhJj8duJOF4MmsWrMPldIMEe6iNkfOG0SLbhPpc3nn0Y5bNXK58jH5/jaSTKYzs/joep4fI0hHM3vtuUMvQ6cPxHN55nMbt6xcpDe18kVIy6+V5/L54I/1G9aZl9/y/DNMS07m72iPofp3w2HDmHPqA+W8tZtrwOfg8PupeXYvJv40FVCz8I82eQfdLqjaoyPsb3sh1zFfveIu1325ECMHUPycExLELzeevL+CTF7/A5/VzZacrGLdsZME7nYPP62PNovWUrhx3SZO7DAwMDAwuP6Q/AbSwIKNe6TsMru9UwpKlQcnPKZ3gPw2mygVGdUs9CxnfShlACzui9O8FCwdSgmuBqngJuVNVLhV2bZ4NyudICHDcjRbxdKH3LUn0rK/BNR+s7cH1FWjlEFFvqQqZooyju5SAJn2IiOdziinFQLp/UxVItg453j8pJXjWKJEp+7Mj9RTk6ZacqT4SpVYhTPkHn+hZiyBtBKAhYj4Gc33lHWRpBJ7fkKmjADdYmqDFfnHex3Qx0RPvUNVgCETcd7l7IRkYXGDySwEzBCCDy47MtCzG3/8eqfFpDJv2CHEVYvhx9krKVit9XhHdyadSsIfaCvRYWfTeUj5+9jMkcOfwXtw5XMWaSin57esNJBxPYt8fB7DYLTz42l04QoPTEbqF98OV6cIeauOukbex8J1vEULj5gHX0+eZWzCZTezesI/3h8xk/5aDIOGZTx5l6tOzCYsMYcyS4cSVj0FKSdewu/E4PQhNMOzjQZSqFMfzXV5F9+sgoOuAG3ji/YfzPR6P20taQhqx5WMKPFG7WBzdc4IBjYfhcXkxmTW+dc4h5XQaT3V4ieRTKYz6chhXdlJX9v5c8RcvdHsNV6aKhf/y5Me5jun3+9m+ZjflqpcuViIbqPf43cens2bhOu4c3otugwpu8/p98UZevWMiQgi6D+7Cw68Xwg+hGMQfTeT9ITOILhvFwAn3FJj2ZmBgYGBQPKR3F+BHWOpf6qWUONK3F5n+JpjrIcIezeHhUuD+eioy4Sbl12PvjBY1oeB9vHvAvQLsHfOtsjkfpHutSlmz36CSkPQksDS7JOc9Uk8B/wlVQXOe8+sJPcH3l7pjaYYWO+f8F1hEpPQi4zuoBDEtBFFqRYEinp7UHzwr1J2Qh9Rt33EQZohbBplTwHcQETECYb48LiafQUqnark018nXrNzA4EJixMAb/KtY8uGPrP12Ez6vj/eHzMDmsLJu6R8IAa98/RxNOzYq1rjRZaL4ffFG3nt8OnWa1+C5Tx8PGEKfy7G9J/F4vKDD8b0nAo8LIWh5S3MmP/oxyz5ZAUIQGhnC/a8EJzpVa1SJA1uPoOs605//DCkBAR6nJ1CVU7dFLV7//gVW/u93ql9RmZmjPuf0oXgSTBpLP/6JfqN6kxKfhtftBUDqknrX1OL04UTVpw4IRCCuPS8yUjJ5uNGTpMSn0fm+9iWa6JZwPIlX+7yF3+fnhc+HUqZKqULvW7FWOW5/+hZ++fI37niuByaTidhy0czYmdMn6Ip29ek64Hr+/GU7D4/LW1wxmUw0alOvWMdyhkPbj/L99J9xOz28+/h0bh5wfaACLC+u6XoV45aNIi0xnatvvnDxm+888hHrlvyB2Wqicp0K9Hjsxgs2l4GBgcF/Fd35HaSqFm8ZMQqtkEa7lwsyZSj4dqkqD0tDZXxcmP08m8G3C6mVVvHduMH1Q6H2FZZaYLlwFa5SSmRyf8CpqkssjUGLRkTWUQbZgPQngudXsDbPxVT6POdOfRpc36tqpdD7kAldlVeOvSsiamzQtqAHPGOklMjMKeBeiQh7Qpk9ezeAFocwZ/tC+o+cnUxPUqlchUh8y3WtehJIT5FNl4WwQNzX4NkA1mYFij+Aeh08v6lWM0d3ZfiMDtIO7qUgMxHhj+UQf6Q/EbTwQs1xqRDCAfbL169RencjU4eCiEREv1twS6PBZUfRZH0Dg38ApSvHYTJr2BxWylUvrdK8nB78Pp1D24/k2H7NovU80vxZ5oydD0DSyWQeajiUW0s9wObl24K2fWvAVE4ePM3aJZvYuOzPXOfv+3wvmrRvQN2ra1K/VV3SEtM5tvcEaYkqHt3qsCI0DU0T2Bw5v6DG/TCK52Y/RpcHOgbyNYQQ1Gl+1pdn1fzfmTdhEZXrlWfiw1PYv+UwNocVi9VM7WxfpahSETRoWQeTxUT9lnWoWLs8G5ZtRs+OQS9TtRT9x/fL97XcvWEfGalZ+Dw+fp6zOt9ti8oX4xay4/dd7F6/l9mjz/bYJxxLxO10F7j/vS/3Yfr2SdxwT/t8txNCMGDCvby/flyxxb/CElMuCrPVjD3URvkaZQoUf87QoGUdru3WrNDbF4ewmDA0s4bQBKFRIQXvYGBgYGBQdDxrUYk6LiWS/NsQoShDX5l9u2Ckdxcy6R5k2quQMRVMFQABIfmfg1xUhB0QgA+8G8H9MzJzGgBS+pGJNyNTn0bGd0T3Hy+5ef1HVLsdbsj6BOnZpEydcYH7x8Bm0n8cGd8KeaoRuvN79aDvL8j4ALwbkSmPItPHI5MfQibcgvT8obYJfwVwABr4j6v2tmIgPX8iT7dHxndSJsZFRGgxCPsNhRYLhK0losxmROn1aJa6EP48iBglnKS9ptLFkvohpR7YR08bh4xvg4zviNSLl1RsUDAyfbxKw/NuRmZ+fqmXY3ABMCqADC472va+FqvDSlpiOtfd1Ybta3bx1HUvo/t1/jdxMT0evSmwrZSSMX3fwuPycmj7UVr1aMH67zZzbO9JfB4f00fM5Z01Zw0YqzaoRFZaFlJKytXI/QpIdOlIXvnmee6uOogpQ2cy9clP8Hl9mMwmJq99jftG9yE0MgSzxcytQ2/Osb8j1E6rHi2ofkUVfv9mAxnJmQx86z5aZZsPb1u9g3H3Tsbr8fHNB8tIT8wAlHHyncN7BdKzpJTcOeJWNJNG044N0TSNLvd3ZNnMFXjdXp795FHCovI+eUtNSGPv5gOERjjwOD3c3L8Tp48kYA+xERLhYEzfSexcu4eBb91Hu3Ni0fMjMy2L0be9yalD8bTs0RyrzQoCqtRXJbAfPTubr95eQmiEgw+3vElM2WgAdqzdg8/jo1Gbeqz9diM/zVnNzf070bhdyXsTnA8RMeFM3TyBnWv3cOX1uZhLXkIef+8hqtavSGSpSDoVM8HPwMDAwCB/RGg/pHs5Kp0q/xbryxER9R4y61OEpXbhzaD1EyhxxQX6CUSpFYAHPBvQ099EOG67pG08KhVsjkoO8x07K7xoZ7xZPKolDAA/ZH0B4UPRMz+FjIlguRIR/UGOyhqpZ4BrMZhrBZkxB2EqA1q0SsgyVQRrW9XmJAGZgfTuQFjqqQqhM6lfmVPB0TlQnQRmNYZ7pfLmwQzerWBtihZyI9JSKTtVzAne/OPn80I6FwEudSdrHoTcqiqS/MfAVPqCVNycm46lhd4Lofci/SeRrjPC2N9sSpz/Q6WiZYBnM+QRI29wnljqgWcdIBGWkgmNMfhnYQhABiXKif2nyEp3FjnpqCgIIbi229mWxqgyUVhsFjxOD/FHEtF1PVBpIYQgqnQkyadSEEIQFh1G3atrYTJpaA4rTToGCwwvL3yG37/ZQJUGlahct0Kea8hIziA9KQPfOS1WJovOll+2U6XeDdz9QsEl4eWql+Gzgx/keDwr3YUQAt2nqyqiECtSQoubmhJTLpoRN4/FYjMTXTaKH2evROqSsUtHcEXb+kTGhRNXIYa0hHREAdUmz97wCod3HEPTBJN/G8OGZX/Sr8ZgzGYzD427mw3fb8aV6ebtgR8WWgD6+bNVbFu9A4/Ly5/L/+KFL4bi9+lc2129Xz/MWonP48Pt8vDXr7toc+s1/DxnFRP7TwEEd73Qi9kv/w+v28uvC9ayIPkTrLaLZ1ZdGMpUKUXpynF4XJ4SH9vj9jKu3zsc2HqYJz7oT+P2hRfAHKF2+jzTo8TXVFzijyayc91eruzUiNAIoyLJwMDg34EwV0eUXnGpl3HBEKZYRPgThd5ez5yhBBNzXcCJCB+BEALpO4VMHgB4kFkLEGVyrzKWehYyfRxIpzIx1qLznU/qWci0F0FPRkS+HIiwL/C4zDUQ4cOQ0gvOhaoiyK4u0gnhQFquBu86QANzI6TrJ0gfjxJVNoD3DxVBf+5akgdmR6YLiP0cYamvKlZc3yKlR6VlyRSI/gQhE8HSEOlcRkDYEBbw7VY/uK3XoH6WaeDIXpe5MsRMRzq/A82hhBkA/EhLg7Mx7+a6YG2qqtPyESWlexXSsw7h6K3GPvO4dzc4z0lD821Fz5wG3u1KmNJiIe5bhBZWqNf6fBCmssjIieBehgi5K9iDynG7ahUTEep480F6NiBTXwBzdUTUxCATdIP8EWFDwNIYtAjEJU4wM7gwGAKQQYnx54q/GHGz6mW+f0xfbh3S9aLMW6lOeXo+diOr5q/lzuG9crTZvP3rq6z83+9c0bY+seWiiS0XTb8Xb2f6iDksnvIDceVjiYgNo23va7GH2Gjfp1WBc8aUjabXkK58OeFrzhip+71+Ktc/f7O35l2acPvTt7Bn437uH9OXhKOJmCxmrryuERMefJ8Ny/5ECEFkqXBcmW4sNgv7Nh/kirb1+WHWLxzeeQy/18/04Z/x5orRec6TcCwJr9uLPdRGekoWn7w4D92v4/F5OLb3OLpPld1mJGfy3fSf6fJAwT4AletXRAiBPdRGzabVuPrm4CtitzzahdkvzyMsKiwgbuxYuwePy4vUJbs37OeMH6KmaRTWG1FKyayX5rFm0XruHtWbNr0uXISt1+NlSOsX2LNxPzcXwmS7KPz29QbWLf0DV6abif2nBCLqLzfSktJ5uNGT+H1+ylQpxcfb3rrUSzIwMDAwyAPpPw3oRfZ+kXoSpE9ARbibEaXXIrRw9ZxvH5B9oUQm5TUEMnM6OOcDfqQwISJfy39S51xwLQV8yLRXIHICMvl+FdkeMRbNcX2+uwthgZDeOR+P+QQ8K0FEI9NeQfr3Zq8/u73KVC3nYP5jqHZAB/hPIYUNmT4Z3GdEnjPtSxIRPRnp3Q5pI1GVNhqYr1CR86CqgEovBz0jSJzBXAtcD54THQ9gQwjbOcdkRsTMzPe4pe8AMnkw4EY6vwkWMX17CHYF8UHWV9mx9B6QqSrVypqrn2z+80oXMvUl0E8hIl4qVCWY5rgecnkfRfgTSO9m5TXl+hkR0ivveVNHgn+/aouL74wERPRkhOWfVb39T0QI7bL2MDIoGMMDyKDE2LJqOx63F7fTw9rFGwu1T1piOqcOxZfI/FLq6Of0CqvHJF9O+JrPX1/Ipp+2BB5f/vlqdL9OZmoW7w+dwYQH3+fz1xcUab6Hx90dqGwBMFtMOEJt/PLlb8QfTQw8Pnv0l9xb+zEWT12WYwy/38+CyUv45KUvyEp3Aqpqqd+o3oxe9CzVGlameZemXHmd8rYpVSkWi82M2WqiXe9riasYS40mVbnurjYA1G5WA7PZhD3URqN2+aeTjJg7hNpXVafboM7UbFI1SGy5oV87bh54vbqKJyU/zPqlUK9J43YNeHPFyzw76zEef++hHM/fNeJW5ifM4NMD7xERq04Sew25mcp1K1ChZlnue+UOXvnmeW7u34k3fnqx0FH1B/86wpcTvmb/lkO8dldOo+jM1EzSkzMKNVZBHNh6mEPbjyElfPvhD5RkkmL5GmWQusQWYqNKvcs3OSLxWBI+jw9Xppsju46X6GtkYGBgYFBySPevyPjrkPHXK4Pr3LbRM5DOJSo+/lxECAgHYAMRBueKEviB7O9wEZ73ArRQVPuYSY1REFoc6ueLBbTS4P4JvLuVSJEx7pw1pyGzvkB6txY8JupHr7C1R1gbKxNsmaXmCH86O8r7bJCF7vxapVg5eqn4ckd3JGZkwi3gXoKKQ/eT3eeV7T+kfH4CbVZaDFrs7KDYd6HFBIs/oFq+pD97PBNYO6g1WRoW6rjOjuM6cyP72M7B3hGsTZQHjwhT84Q+ACF3ABpo5cFSzJZ853xwfQueNci0UcUb4wye35QvkkyD9Nfz39ZcCyXe+UA/BfoJZPp/52KUnvYG+qlm6GljCt7Y4D+HUQFkkC9+vz+7EqPgUozr+7Xju+nLyUzJ5O5ROa+u/J09m/YztO0odF1n4Jv30v1vkdrODCdvPvgByadTGfbxIMrn4clzdPdxFryzFI/Lw6QBH3LDve0xmVRf8cmDp1k89Qc8Li/TnptDj0dvxGq30vn+Dnz09GyklPh1Ha9LcnzvyaBx1y39g+9nLuemh67jqutzj5cfOe9Jfpz9C1tXbqf1rdcy5o5JJJ1MxmQ2MfjtB4irGMvc177C6/Yx+bFp3HBv+0A8t5SSyYM/5vsZy5HAqYPxPDPz0Xxfs7tfuI0ylUthtprpeGdrHpkUbPbXpENDJq99jfSkjAITr5p2bMR768+eLD3y9gMs/mAZ3R65gdrNamIPc7Bs5grcTg+3Di18NVed5jWDDK3/zt/bgZbNXIGuS+4edRtV6lWkSr2KAcEr8UQyi95dSvUrquRbmRURG47QBBa7hdiyweXjf/7yF8NvHIOU8NJXT9PixvzLhguiUt0KRJWOIOlkCs2ub1yiEbK1rqzOm7+M5tju47S+gFVMF5qqDSvT8a42/L54I3cO73lJYnYNDAwMDApG+a1kBzO4loKjS85tku5SFTZCQNwPATFECDvELlDpWba2wT4xtjaqrcm3A8JfPDuWe40SIGwdVaWBiAFTFbA2zW7R0lVqlvsXCBuIFhp8MUma60PYQISIUJU8vsNKP5IOsJxt0ZLJD4F3p7oTtxBhrl74FyViBKRPAlsrREjfIK8a6V4FqU8DEjwrEbHzEJYrkBnvI/HlMpgJwrJb6s4YNwNIP3raa4jwp/JN7RKmssjw4eBcoDyE8CFsBVeq5xjHUg8Z/kx2otggtQQ9BUQIQjhUBRTKEBu8gZYpGTYUhCO4FasoaHGoN8h6judSMTHXVGOJELDmn6oqoiaAaxlST4T0N9VnNy+fpssMqacgk/qB/yhEvJGj6k3qKZA1A+Vn9RkydADCFHdJ1mrwz0RciiuzzZo1kxs2bLjo8xoUjVXzf2fsnZMIjwnj3bWvUbpy4WO8C8Pn4xYy84W5+P06FWuXZ9LqV4iMiwg8/7+J3zB9xBy8Hh/NuzRl7LfDc4zh9/vJTM3i7mqD0f1+okpFMnv/e4EfnM4MJ3dVfQSv20tkqQhm7zv7XMLxJDwuL28P/BCf18dzsx+nVMVYQMWj317uYbxuLxabhfnx03CEOXI9jqx0Jy90e51je46TfDIVKaUSI2xmQGAyaeh+nZDIED4/OjXQorZ02k+8M/gjfB7lI9T2tmsYOW9Yib2+JYHf78fv0y+YD8/R3ccZ0OQpPC4vJrPGt845AfEO4LFrnmf3xv1YbGbGLlE+R3mxf8shtq3eSeteLQLm0gBvD/qQxVNVFG37Pq0YMXfIea/b4/IQfzSRctULnwRmYGBgYGDwT0N6tyOT7gbpQ8RMy9XzQz95BapyxYGImaWqZIozl3MxMnU4ICDsIbB1hsRuqEoZC6LMVvDtRibezpk2KVFmR+C8TbpXIpOzL5SFD0ULvV897jsC/uMqwj1bqNBPtwQ9QYkXUR8gbC2LteYcx5D1RXYliwQEIvoThO0apP8EMule8J9QoofjFvBsBMetCEdXZMrj4P4V1S53pjrIDPYuiLBHEOb8zXalazkydYiqCDLXQYv7Jvh56UVmfgp4EaH3BrWI5YaeOV2174kwRNwihKlcsV+TfNctJbi/B388hNymItLPZzz/CfAdyH6vC3duKr3bQU8Ha4uLckFK+vaC6wewtUNY8q/GL9b4Wf9Dpo0GXGCqjlYquHJPSh8yvpPynxJhiFI/XxATb4N/NkKIjVLKXPs2jQoggzyZ+/oCfF4/6cmZrP5qHb2G5Ey0Oh/a3Ho188YvIj0pg5MHTzOg8VN8evB9zBb1sYyrEINmMmGza5SpHKxcSyl5odtrrFvyB6UqxTJp9Svs++MgzW9sEvTH3RGm0qZ2/LabJh0bBj0XV15FVY5bNjLH2oR2djtNE3kaKh/YeojPxy1k57rdeF0+okpHopk0MlIy8DiVv86gSfchEDS/sWmQWHD6cEIgsj00KoSBb95bqNfN4/Iw97UF+H1++g7vhSP0whnbmUymIEGmpImIDcdkNmF1CCJjw3OIKc5MN7pfRwiBKzP/6PjqV1Sh+hU5e8s79WvHD7N+QUrJjQ9dR/zRRKSUlK5U9Ksh237dyfczfqZ9n1Z5VoUZGBgYGBhcLghLfSi9AZBBlS5BRLwEGZPA2hosqkJXSh2Z/iZ4/0CEP1soUUj69hAQQLw7s6tDzlyI9gECTOWzW8k0MFUJOm+Tnk2oaiUJzqXoriVgaaSMp82Vgo8rcjwy/TWwXJVtsFxCOG5RxsjeLWDvCVZVrStM5RClcrb6q3X/oaqkcAJWVank/V0ds2sx0vU9xHyKyM/YWDhASlSrXM5gBZn6ArgWAALpT0BEBl801Z1LIWsWOG5DC7kVsuaq+aVLCVMhBYeXFAchBNhzVpXlhpRelW5mro7QonIfz1QOiihWXQgRJi+k9CoBU2ZB5hQotTrgi1ViWK5QFU3SAblUgwlhhrhFyrjc0sQQfwxyYAhA/0FOH0ng1MF46resne+P+w53tOLQX0cQmkbjDiUfx12hZjk+2jaRuyoPxOfxkRKfRla6k4gY9Yey3e0tMVvNpCWkc/297YL2TUtMZ91SVUobfySR1QvWcs+o23OdJ658DG1uLdqX/5pF6/H7/Ggmjfot65CVloU9JPhqysmDpxnU7Fn82UlgthArV998JU9Ne4RfF67jvSemU7dFTa7v1y4gap1LryE38+NnKzl1IB6P08OPn66k7/N5G9qd4fNxC/nijUWAxOP2MnBC4YSjS0FWupP1S/+g5pXVqFAz5xd2RGw47657na0rt3Nt92Znr/JJyfbfdvPga3fx/YyfqXVldZp3aZLrHPu3HOKlXuOxhdgY8+3zOYSdBi3rMD9hOlLC5p+3cV/txwAYPmcIrXq0yG3IXPG4vTx3wyu4nR5+nrOauUenBj6rBgYGBgYGlysFtfdoIb3g74a7nt8g61PAiUx5olDJaCLkHqRnPchMRPgwwIzEDnjB2l6dA4gIiFuiWscszf62f2+k61sVl+7bCbiU/4+1TY5IcGFrhbAtLnBNRUUIOyJmetBjUk9XopClHiI3rxxzFdRPrhC1TfRkZOJt4D+QvYEOvu35JlsJ2zUQORbp24UIuSfnBu4fzqwG/HvVqJnTIeszJVRlvg/4wLsFaWsPIX2zK4BsUELVUeeLatvbDNig1LI8RaB/Nj6QbkAHeeZ2yZ4rCkttiFsK/tMqrSu3bbRIsLUv0XkN/j0YAtB/jKO7jzPoqmcAaHvbtTw9Y3Ce2/Ye1p1WPVoQEuEgqlTkBVlPbNlobn+mB0un/UTX/tcH/aAWQtC6Z+4+KOExYYRHh5KelIkQIldx4Xz46u1v0f3KUHrzz9uY8MD7jF0yImibhGNJAfEH4LYnu9PvRXUFpVWPFgWKC+HRYTS7oQnfTfsJIChSPj90nyodlhL8vrOm116Pl1OHEihf45/TlvRMp5c5tP0oCMGMHZOIqxCbY5vKdStQuW5wjOunr/xPiVxSMubb4fnGoX/y4hec2H8KoQm+fv97HnrtLnxeHx8//xknD5xmwIR7KFdN9Z3/unAtHpcXgNUL1hZJAEJKghpmi9g9e/CvI7gyXdRtUatoOxoYGBgYGPzT0KI507qFlvO7PTeEKRYROyf4wVI/qrap7MoitV0pMOW0HRCmCohSP6Cf7qDaWwDwg6l0sQ7hfJC+QyBsyqMn+cGA35CMXaCEMffPEHIPWtiDCC0G4r5Tke/WZqo9K2oSMvlh0JOUYbH9ZtUu5foW9Hhw9EFowZU+wtEVQR6ejJZm4Fmlboc9qkSp9AmADzLfRRlySxBmEDa00AeQjp4glAm1nnS/qmoKfxYtJPcLqkVFz/gIMqeB/UZExChlxiy9Oaq1Ang2Ah5lGeQ7UGDU+9+R0g8yDaFFF7xxjn0l4DrvFjUhHCrGPusT1f53gbx3hKm8qpYzMCgGhgD0H2PvH+pqgyvTzebl2wrcPi/j5ZLkgVf78sCrffNNCspIyeTkwdNUa1QZk8mEpmnM2vce895YRJX6lejYt/V5r2Pz8m1sW72TG+5tR+f7O3Bgy2H8Pj9CA82UU1Cpf21tajSpyr7NBwmNDKHHY12K3C710Gt3opkEVruVa7pdxXczltOye7NAQlZu3PF8LzxuH36vn3tH9wFUdUr/K4Zx+kgCDVvV5Y0fzjNpoYQ4sPUwHpcXi83MyYPxuQpAubH55624s9yYLCZ2rN2TrwBUv2UdNv24BanLgPn0z3NWs3jKMjxOL+lJGby5/GUAug7szC9f/g5S0v2RwpUkn8Fqt/La0hEsnfYTHfu2zvc9+jtrl2zild5vgoAHxvSl1xOFN9U2MDAwMPhvIf/f3n3HR1FtARz/3dmaXglIR0CQIggooiAWUBFs2EWxK3bsBXsHxIIN5Ym9IFhRVFARQQEp0qQjvUN62Tr3/TEhEtKT3SyE8/183ucl2Zl7z4Z1d3Lm3nN8C9EZ1wBOVMpHKHsrtPah8z+xivNGXVj2dq1aohztIOlNayVO1HnVH8eWVqkEjjZzrHbljg5YNXQA7BD/SOmrbsoaJ7gdjIRy/9DXOmhtqfLPhdgHMKL6FMaQCzoX7Z0O2U8CAXTMTRDcBHisbVqeqVDwsTVQ7gi0+zSUvQnKVs/qApb3P7S7H8pxJCptZrF5zfwvIPthrALTS1BJL1b6eamk18D7G9haoBytre1UKh50Dta2OxPsHVAJT6KMwm5rKh6dNxZ8C6z/UQA5IyAECSCtfZA7ypq3YCLa0RGyrWLgOv4ZjOizS54UeyvkvgaOLoX/zlWZrwC9+zwIbkBHnYeKvQ2deRvoICpxdNlJJwr/vdOvBP9ctLsfRuLLVZp7f0bU6RB1esUHChEhkgA6xBx7Zhead2jKxuWbuW745ZEOp8iYu9/ny5e/p9NJ7Xn+p4ex2f+7sMnclcW17e7EW+Dl2H5deHSCVSg5NiGGa565rFLjb127nVnfzqPb6Z1o1q7kh8Dm1dsYNuA5/F5fUTv48+8aQGxiDBk7MrnsoZJbswzDYMyCkaRvzyA2KbZahZJjEmK47dXr2LMtg6uOuA1taj4f+Q3jlr1c5jnuaBc3jLii2M+2rd3Ork278Xv8/P3LEvw+f1EL9UXT/+Gl68fQpG0jHh5/J66o8gsDVteUD35j+axVXHD3WTRqdRiefG/RCqWgP0jjNtadim9e/5GvX/uBM687lQvvLuUCALj66Ut54oJRJKTG0Xdw71KP2euie86m7bGtcEe7ihJA0fFRgMJmN4hN/K/FaptuLfk64z2Aaq2SOurEduUWoi7LP3+swO/1Y5qav39ZKgkgIYQQZdJ574DOtb7OH4+KH2a1sM7/EI0BugBVWAA5bDEU3pQrr2iucp1Qag2SGs0b3IbOuAXwoxJfQ9mboc189O5+ViFfe2tU0lh03v/AeRxG9Plo7QHsVu2TfcfyTkdn3glGGir5Y3T+B9aKFBUFqZNQtjJucvrmgmcyUADZD0JUH3RgPXrPQNA+UIdhJVWAvLcgYTjkjrG6TNn3u0YoLFSszXSrcxM+yHsX0v4qufUu/0OKkluB5SV/N1qjsx+2urVFD8KI+69xiFJOcJ+GDm5HB9ah7C0g9Wt0zvPgmQIEwEhGOfbpEOv5DnLfKHwu2qot5KhZp9T/OKyuZcHdoJzW6qK93ea8P0EpCSAjdgjEDqnedP6VYG4DglDwNTqYVTgn6NxXUYkjyj43uLlw65kGzw9o/VyNVwLVNdq/Gp11J6gEVNKr1qo2cdCSBNAhJjouildnPRvpMIoxTZMvX/4erTXL56xi/T+baNmpedHje1eRePN9zJuysMrjB/wBbjn2ATx5Xt5/bDyfbXmb6Ljib+wFOQVWPTUTvAU+AL586Tt+8H5W4fjJDaq+1HR/Gdsz0VrjLfCxY/3OKp/f+IiGtO56OMv+XMmpl59YlPwBeOWmsWxZs53dWzOY+eVfnDqoV43j3d+y2asYffNYfAV+Fv72D+8ufwXDUNiddoKBIHaXA4fDRl52Pm/e+R7BQJBxwz6lzxW9SUorub2wQ88jmbD9f5WaWylFp97F7/6dcO6x3PHm9ezYsItzb+0HWLWvYhKiS7Shz9qdjd1hIyYhhnA58/o+TJ8wC0+uh8sfCU+hRSGEEHWE6xRrNQegXIUrnHUmYALaqoETRjqwBr1nEOCDpHdQFbTcDuncue9Y9XDQ6NzXUIkjIbgFzGzAA4ElYG+JSngGnfsW5vbO1s9VLKR8gbL/1wxC57xoJdKCPiuhU/CV9Zy0DXzzIKrkzRgd3IJmb9IrCuwtrC99f4L2W+fj2+cMO8rVCxVlJTTM3HFga27tjou7478k0966MJhWJy+CwH4JIFuDwsSPgqhSbpAFt0DBN9b8eWPRMTcV2yamfQvQ6VdZv7u4+zFiLof4p9HaBJ2Jih+234D7JPecx6JihoCz1KZBVaaUgpQvwTfHqlNjZqA932F1KQtD8tJ+BBj1rG5w7v5F//1YwVRw49PWEGxNIbgRHJ2B8DVXOVjpnBHWFkbs6LzPUHE3RzokUQOSABIRZxgGR53UjhVzVhOXFEvDlvWLPd6uxxE0a9eI1QvWcckDVV9mHPAHKcj1FNXr8eR5SiSAWnc5nCufuJhZk+azduE6zKBJ6y6HV/9JlWPtovVsX7eT7v27FBWHbtm5OQNuPI3Z380v2tZVFTa7jZd+fwqfx1dihc/hHZuya9NutKlpfESY2nwWdjPTaLRprfpxup2M/OUxfvl4Br0v7EFMQgwBf4CYhGg8+V6cLjtRsVX/kN2wbBOefB9turUs8xilFH2v+G/l0KfPf8WHT3yOw+ng9XnDadza+j1MnzCL4YNHY9gMRv7yOEd2D099ngbN03h/1athGVsIIUTdYkSfh3Z2BuxFW1dU3L1oXQDKjYq5Lqzz6/zPQWdYX+eNq9UEEI62UOAEFOzt3mRvCa6e4J0O0Vehsx+Hgs/3Dxq8P4P9Wutb/9LCpIsDMMDZGbjSqotjxIGzR/HTzVx0znAo+NI6PvZOlC0FXKdaB7hOAvWKVdg37lawdwTP9yj3yUU1Z7Tvb8h9BSgAoz5G1H/dc5XtMHT8o1DwLSrm2lJbmKv4Z9C5L1orlkr7N7algpEAOg+MNFD/XUNprdGenynqkub5CWIuRxmxqKTRpf+u3QOsQsLBbVYrelvxbfpa+9AZt0FgKcQ9jBHVr/jj/uVWpynXaaXWulFGPLj7FsaeBmlzrZ+HoRW7MqKtOktmOhj10OmXWB3FUBBV/m4BpRxW16zgVrA1rpVW8Qcde1vw/QVolKPs629xcFDl1V0Jl27duul58+bV+ryipPycAh49Zzhb1mzjvvdu5ehTOlZ8UhgEA0HWLd1Io1YNiIotfdml1rrab8o/fzSdL1+ezBnXnFxh7ZfsPTlsWLaZtt1bFVtJEwor563l7t6PogzF8eccw4Mf3VHiGJ/Hx9OXvMT6pRu58+0hxf5NfF4/SlEirnlTFvHJs19wwrnHcv7Q4ne0/D4/f34zj4Yt64ctqQXw/dipLPtzFZc8cC5N2jQq87idm3Yz78eFHN2nY1Fx5sr664e/eeKCF1AKrnn2Mgbe3r/U49686z0mvfkTx599DMM+u5Nr29/JphVbcLod3PTS1Qy40bogeaj/s8wt7CZ3yf3ncu1zg6oUjxBCCFHXaO8MdEbhHf74JzGiq1/jp3rzF7ZId55Q4rpP6yB6xz7bmLBbx6ooVPLnKEcbAMwdx4FOB5yQ+CaG21r9rM08K4m2Xw0lM/0G8P2OtUoHcJ+NkfjCfnMHAH+Z24O0f4XVAhwf2Jph1PupWs/fmstEZ94M3t8h+kqM+PsL488E/1KrvffeWj6AmXGrVXgaq1W8SnrL2qJXA9ozzdr2o/PBSMVI+/O/x4Lb0btPt5bO2xpg1Jtazki1T5tZUPAtONqinMdEOpyDntam9foy4lHOKjRQERGjlJqvtS51Sd+B0SpIRMzvE2axYs5qdm9O54073q21eT35Xh49ZzjXtr+TZbNXYbPbaNW5RZnJH6jZHYM+l/fmjXnDK1X4Nz4ljo69jmTVvH8Zc88HrJq/ttrz7m/j8s2grCLcqxesK/WYP76ey4Kfl7Dt3528MuTtop8v+u0fzku6koHJV7Pir9UABINBHj77eR4842mW/L6cccM+ZcPyzcXGczgd9L6wR1iSP8FgkNG3jOW24x6kRYem3PvuLeUmfwDSmqRy5vV9qpz8AVg6czl+r7Ud8O+fl5R6jLfAy1evTMbvDfDnpHlsXbudC+85C5vdRnR8NMcN+O9O5jk3n47Daa1E6n1R+W1QTdPk2zd/4t1HPiU3M6/KsQshhBAHA+XqhUr9AZU6KSzJH7NgEuaewZgFU8qY/ziUq2cZ130K1N6t426IHwnJ36LqzSxK/liHOaxjMVB2qwah9i9D5zyL9vxaSlA7KEr+4EJFX1HiEKXs5daGUY62qMSXIeb6Eq3iq0oHllsrnghA/juFySdQRqL1u9kn+WP9cV5Y5wcHJE+ocfIHAPvhFNUGsre3Ci2b6dZjZjpoDXghuKPKQ+vgFnRgY81jLIMyElAxV1Qp+WPmf42ZcSvatyBscR2slDJQ7j6S/KkjJAF0iGvRsSkA7hgXbY5tVWvz/vbZHyz4eTEbl29m9M1ja23eysrNzOP+vk/yxYuTuPukx/B5/SEZt9f5x9GxVzvqN6/H7a+XXN67c9NuZk+aR8AfwBXlxOvxM37E12itmTRmCj6PH0++lynv/wbAhn82s/DX/xIhCkpsbwunvyb/zdQPprPirzU8f0X1tjgtnLaU23o8xLuPfFpuJziAftedSoPmaSTVT2DQw+eXeozT7aTFUU1xx7hISI0ntVEy/a45lW+zP2D81reLdSLr3r8rX6a/x8Rd42h1dIty55726R+8fe8HfD7iG168fkzVn6gQQghRDdozDZ33jrWqoZYoe2OUvXmlj9f+lWjPT5hmnpVoMfNLP87MgKwHwD8bsu60VuRUhbnN2gIFgNfaFpX3Krrg++LxJ78HUZejEl9E2a0tKzp9MBRMgKy7SiQfVMJwq5W6+0JIm49ydqpaXOzdhvUT5H+Ezh9f5fOL8f5BUUFoFVeiwPW+lDKs7VzYwN48ZFt0lL0ZKuVbVMKLEHcfeucJ6J29MPM+BvuREHOVVXsnYVSlx9TBXZj5X6B3nYHe3R+zYFJIYq0pHdgA2Y+Adwo649oKr0eFOJhJDaBDXJtjWvHqnOfYuWEX3c7oXOPxfp84iwU/L+bc286kefuyWy7urUXjjnGVe1xVLJu1kvceHU/nk9tz6YMDa7RiyAyamIV1bYIBEzNoVnBG5bijXTz3w/5F+P7z+MCRrP17PXanjej4KHZv3sOHT02keYemnDqoF39+MxelrEQSQIMWaUTHRaM1JKTGM+zTodRrbCU4lv6xgvRtGZxw7rHFuqqFUkrDJLSpcbod1GtSuRbv+3vqohfJ3pPD+qUbObZfF9of36bMYw9rUZ9xy19m4/ItZdYzUkox+s9nWL1gHS06Ni2qieR0O0s93h1dua5o3gKfVXszqPHkeyt1jhBCCFET2jcPnXkHEATvdFTyB5EOqYT/tj4pUE609oKRDPV+tNrWF+MAZbdq6ShnUZescsc308HMsjpbGfWtlSmB9eBoD5lDQe8B7wy08yirS5d/MUQNwEh4pNLPQTnaolI+qcrTLim4qbB7mBfy3kLH3oJSLnRwBzr7Sav9fPwjleowpWz10LiAILj6VHi8kfgi2nwUVHzJ7mI1oOzNwN4M8j6wWrvjh4LxqJhBqLi7IO6uSo+lffPQ6ddYY+xNbnl+hqizqhST9q+EwGpwnxq6bl3FEmyhLf8gxIFGEkCCFh2a0qJD0xqPs2H5ZoZf+Rq+Ah+zJ83nsy1vl3lsh55HMuKXx9ixfhc9B5ZcTvjv4g2kb8+kS5+OlW7X/fjAkWTsyGLZrFV0PrkD7XqUnUioSHxKHA99cgdT3vuNs246rdJJgsryef08dMbTrPhrDdc8N4iBt58JQMAXwNQmhs2B3WHDsCnQGofLQZdTO/LJxjcxDIP4lDjAWu1jJUQ206rL4UWt6Bf8vJhHzx2OUoq/f1nCHW/eENL49zqia0uemfwQ65duqnZ3saQGiRTkFKBNTUK9+HKP1VpzxwnD2PDPZuo1SWXsklFFhbT35Ypy0eGEttWKpyynXdmbrWu2sXtLOtcNvzykYwshhBClMvdgre/1Wy21D0TBdVgxFhR2uMKKO7gZ7MVXlysjFpI/QXt+RblPs9qXl0P7V6LTLwJtomNvsVqFp3xldcSyNUHvOhW0AjzoPZdQtI2rYCIq9dvicye/h877EFwno+ylX/dq/2p09jCwNUQlVLEduK2eVWDatIHtMMB6bjr7afD+AtjRthao2OsrHEo7e0P8w9bv0b8cM+MmVPyTKFu90o8P7rQ6iDmOQaPQuW8BBir2RlQ5XbC0Dpaoh1T8cR8E1qKdx1tFp7UJ0dW7BtLemRQVqcYOyoWKuapqYwTWovdcCCgomIBKfr9asexP2RpB0hto7++oqIukELSo0yQBJEJGm/81zjTNilfMtDvuCNodd0SJny/+fRkP9XsGZSj6XXcqN79UuXaRsUmxZO3OAa2JSYiu+IQK9DyvOz3P617jcUqzdOYKVs5bi7fAx/uPflaUAHp04j18+MQENi7fXFgvSHHJA+fR5VSrEHRivZIt02MTY0okuzau2IIZ1Pi9Pv5dvCEsz2GvTr3bl2jDXhUjf36UXz+ZSZtjWxV15wKrPfuo697EDJrc/b+bSKqfSH5OAWsWrMM0NTvW72T3lnQaNE8LxdOokN1h57rnJfEjhBCiFrn6QPRFEFiJinsw0tGUznUKuHpAYCXY2oBvGjiPAdt/W6u11uj8D8G/FBV7M0bcbZUb2zcHdBAobOUeO8Tq2rR3e1rye+jMeyCwGPBgVbcwS61LoxwdUInDy51OZz8G/oXgXw7OryC6/A5SxcZXUZA6Gfz/WEWa9yYRjCSsP7kMlJEIgJn/DeS9BVH9MWJvKTaOmfcR5DxnJVzc/QuTRxqdOwqV8HzJmM1M9O4zgYD1O3f2gHxrpZjGRMUNLeWcXHT6xRBYg469B6OUpJTWQfSegRDYCLbGUG8GigDKiKv076TY7yfqLHT+R1Z3tsQ3Ua6SRb4rFNxEUbIxsKZacZQZn6sXylW9m5lCHEykBpAImebtmzD07Rvpc8WJPFvONqeK/LtoA6Zp4snzsmzWqkqfN2LqIwx+/CKe/OZ+mrUre1vZ4t+XcVHD6xnS9V4yd1V9P30wGKzyOftr3r4xhs3A5rARnxJHbmYe/3vwY+7r8wRtjmlJfk4BPo8fp9NRolhy+vYMlsxYTjBQdhx9B/fmqN7taNquMbeMvqZGsf42/g/ee/QzMnaGp/ZAUv1Ezr9zQIkVOx8/PZG/Ji9g3o8Lef8xq91rTHw0x59rFfTr0Kst9ZuVfifsQBHwBxh+5atc1/EuFk3/p8TjpmnyxAUv0D/6MsYNq+HScyGEEHWOUjaM+GEYyR+gHEdWfEIEKOXCSBqDUW8aRvIYVP0VGMnjiq8s8c2CnFHg+QadcUvZg+1Dm3lozxSs7UI2iLn5v8e8M9F574KRhIq7E3ABbnD0AHsHVOJL1XsytibWOCgwSt9qXh6rSPMJKCPmv5/FP4SKuxsV/xhEnW8VdM5+AIJrIHeMVX9mXwWfAX5rO5uZgZU8coAq45onuNU6VudDYAVWMei9NWyKXytqra1VPb651ioqNOS+ivYvLjmuzoLAWsADwfUonVvt5A+AsrdEpc1F1V+I4S6ryHcFnCeA+xSrXXvCM1U6VWuzqJi2EIcyWQEkQqrPoBPpM+jEGo1xyqCe/PrpDHZvSWfIC4MrfV5qoxQGDSu9MPC+/vfAR2RszyQ3PZdfP57JwKGltxLfl9aa2d/N59s3fmLeTwvp0KstI39+rNTtR/ubNv4PXr99HG2OacljX9yL0+UgLjmWEy88np8/nM6uzXsYdf0Y5nw3H7/Xz1v3fMBjX9zD6JvH0qRto6KEB8Durelc1+5OgsEgR5/akSe/vr/UOWPio3n+x4crjK0iC6ct5YVr38DvDbBo2j+8NOOpGo9ZWfUap2J3Wr/fvfWFvAVeFk1bhivayb+LNxIMBCv1bxApc75fwMwv5+DJ8zL8itH0u64PHXq25ehTrBVdm1Zs4a8f/sbn8fPp819x5RMXh61ekxBCCFEbSv3DXtmxkhIKytlyVIzne6ueD0GwH4kRZXVy1b6FhW3qg+D5GSPlY2vlDV6UvWYNTVTCU+DshjbSwHVctcfR/tXo3FfBeTQq+ipUzH+r2bVWYKSCmWn9Loz9VndHDYKcp0C5IPYelOtkwAdRZVzj2tuC+1TwzoTYm1FRF6HRgIGKuem/ec18dPqF1sqZqMFYW9QKAC96z+VWzSZbw31+GUngPt1aeeU61Yq5hqzXRvWvc5RyVCu5Z20duxi0B5LelJU+4pB24P7lJA5Z8clxjP7z2ZCN5/P6rXo6hbWEjurdnnVLNqJNTeuulWuNPvWD6bxy89v4CqxuYKvn/8v6pZsq7BwF8Mbt48jalc3i6cv4+5cldD+zC8P6P8vi35cT9AexOWw4nDZc0U5sdgO7w87Y+z7inFv7cekDxduvrl+6iWAwiCfPy5Lfl1f6d2CaJptXbaNekxSiYvYvyFi2/JwClFKYQZP8nAK2rNlGfEoccUmxFZ9cQ+ffNYDEtARM06TPFVZSMT+7gII8DwFfgIAviLfAd0AngOo3r4c2Na5oJ1m7c/joqQnYnQ7eWvgCjVsfRlqzesTER2MYiiZtG0nyRwghRJ2knMei4x8r3AJWsgtqqeyF12gqyuo6tde+dZHMXdYh9uo3FNHBnVZbc3sblHJiOjpBxjVocyc6ejBGfMkbamb2k1DwDURdghF/b8kxM2+C4EarlbujPezTPlspBSkTwTsNnN1QRiLa/w868zariHPSWIjqD8pt1UhyNCs3fqWMEkkRFV/KSnz/QggUrvrxTEDVm4HeeQLW1jmshNQ+CSClFCrxZbR+sdzC0loH0Xnvg84E1xkoI65G/x7hoAu+B50DaHTe/yQBJA5pB+5fTkKEwA/jfuGVIW+TUC+e1+cOJ7VhMtc+exnHnN6ZxLT4creK7Wvr2u0E/VZdI8NmFWFuVEYXqv0dcUxLFk9fBkDTto0A+OfPVQT91rLctKap3PzK1fgK/Pz9yxJeumEMuZl5fPjEBE4d1Iu0Jqksm7WKxwaOwAyYNGnTiE0rt3D105dU+vfw7KBXmPXtPGLio3ln2UuVTuAcN6ArF9x9NusWryepQSI3HHU3NoedN+cPp1Grsp+/1poxd7/PnO/mM/jJiznlkp6VjnUvwzDoO7h3sZ8l1U9k8OMX8f3bUzlryGnExNe81lM4tercghemPc6GZZsZc/f7+Dx+lAJPrnWxFRXjZtzyl1m/dCNHdAtN21YhhBDiQKJ1ATp7BJCPinsAZSShtRed/TgEt6Hin7C6Te1HObtB8odW63fXqf894DrJqovk/wcV/1DNYvOvslbFaA3Rg62uVnsuBgpbzed/bBVj3vec4HbIHw/4IX8cOvZGlLF/EwsXVqUNXfj1/hN7sbZ1JVrf5oyyimZjQ+d/jhFX+jY5rQMQXA+2phUX0PbOAZ0HrpOsBI6jHRjRYAbB1dfazpX0KjpnNLhOLp5k24dSBtrMheCGwiTZfn8+FkyA3Jet30fe22gckDga5T653PhqQnt/s7bHuQdYNaEqoFy90Hn/AzS4B4QtLiEOBlIDSNTYtn938MK1b/D1q5PR2tpz/PevS7iv75N8+8aPEY3t8xHfEAyY5Gbm89fkvwHrjkank9qXmvzJ2JnFrs17iv0sLyuPbf/uJDEtgZadmnH2zafz5Df3V3olzeNf3svD4+/i7cWjOOxwq57P1U9dXLREOn1bBkumLyetSSp9rjiRuOQ4nFFOHC47sYkxTPngN4b2epjMHVlk78mhcZuGTMr5iLNvPqPSv4fZk+bjK/BRkFvAv4sqVxR61qR5PDdoNAmpsTQ5sjF/fb8An8ePGQhWuPpo7cL1fP/2VLas2c4L17xR6Tgro0mbhqRvy+DDJyey4q/VIR17f8FAkMfPH8l5yVfx9es/VGuMtse25vSrTuapb+6nS5+juOrJS4qtHItNjKFDzyPLbFMvhBCi7jLzPsDcfQFmQeiul7Q2MfPew8wZiTbDU7+vSvHkjbOSBAXfonMKizAXfAEF34FvFjq77HbtytkJ5T6j2B/5RXWRUj5BOTrULDj/AquzFR7w/opVM8f73+POHiXPMZKtws4qGmwNQMWUOEQlj4Xoq1CJw1HOTsUe02Y2es+56Own0HvOs66dzfTCR01wlN7FVmuNTh+M3n0eeveAwrbspdOeH9AZ16Oz7kTnvmbFZCSiUn9GpU5CJYywfubqjZH6BUbcraVu3dM6gOn7B73rNPSey9AZ15YymQ8r0WUW/s9rJWjCRHt+RGfcgc56DJ3zXKXOUc6jUfWmoer9iBF9YdhiE+JgICuABP8u3sCUD36jx4BudDqp6t2cHj9/JOsWb+S3KCdN2jaia99OPHrOcDx5Xv75cyWdT+lYtPKltp16+Yl88syX2O02Op3Urtxjl85czgOnP42pNXe9fSMnX9KTF294i9mT5pGXmQcK8rPz2bRqGz+9O42P1r9BfHLFxfAcTgfdz+xS7GcX3HU2f/2wkKUzrURKUn1r/7fNZuPN+cP5a/LfdDq5PdFxUUx6cwratBJrSim69OlY5d/DwKH9+ey5r2jYqgFtu1e8Pz5jZxZPXfQifq+f38b/AYAryokyFPEpcXTv36Xc85MaJKKUwhXlJC3EhZp/eOdX/N4AEGDml3Noe2zrkI6/r5Vz1zB/yiI8eV7G3vsh597Sr9pjdeh5JMOnlH2RK4QQ4tCig1shZwTgg6y70O5TKlzVUSmeryHnRSCADmxEJb1a8zFrQsVgbdmy/ZcsMVKw7kM7wahf9rnh5uoLtncguB2cPdHpgyHqMvAvAdcJqNhbS5yilBNSvwP/InB0KbWNurI1RMU/UPqcZiZoP+AF04fOfQMCywofdBSvw1OMD/zzAQ3BbVYRZ3vppQi0fzVWIemg1R5+b1xGNBjNyxh/vzG0RqcPAv9SigpL++aUPDD6EgjutGoL+eaAcqCiL7XOz7rfqiEUdQFGwuPFxw/8CyoWZatiN9fglsJ4/LB/Ae1yKFtK1eYRoo6SBNAhzjRN7jzxEfKzC5j0xk98tO4NkuonVmkMwzBQyvpoN2zWorLouCh8BT4U4I4pZelrLbn84Qvoc/mJxCXFEJNQ8g7NvuZ8vwBvgXU35ddP/yAmIYbfJ/yJJ8+6E+Rw2fHke9Gmxul2kL0nt1IJoLI8OuFufnp3Gk3aNqJVl8PxeXw43U5SG6Vw5vV9io4bMKQvK+euQZsam91g65rtVZ7rmqcvZfBjF2Kz2yrVdcEwFPsfZmrNpJwPcbqdFY6RclgSr88dzvLZq+hxVrcqx1ueATf25e+fF2PYbfS6oJQ7cyF0WMsG2Ow23DEuWnZqHta5hBBCHGJUtFUEWNtBxVLZy3JtZqLzxoLRABU9qKg+i9Z+QBcmF3Th12WvEikxbmAtumCy1Q7b2bmqz6ZMKvpyqyeVzv+vGLLrNGv3U3AHRHBFhrKloOpNtTqO7TwW8IN/MSptBspILvs8IxFcvct8vNw57U3RMdeC5ztwHG21g6fwuspWv8ykjlIutPtc8HwFzqPB1rTsOaIHoX2zQOei4u6uVpzoPKtu0N7i3SiILlm/SSknKv6ekqcHt1uFvPFDwWfouLuKtsqZee8WJikVJH9YYpVUuaIutrqYmemoUuozCSHKJwmgQ5w2NQHffy0RA/6qtzh//Mt7GT/ia1p2bl7U3eilGU/xy8cz6HxyB9Ka1LxrQE00aF65OwunDOrFpDFT8Hn9nD+0P4lpCVYB3ygnKY2SGXBjX+KSY/l85LecdPHxNG5d9fag+4pNjOH8Owcw96eFDEy5GsNmMGra47Q5pvgKndOvPJk1C9bx3VtTQWvycwqqNV95xZK/fm0yP7zzKwPv6M/pV51MQmo8T016kBkTZ5FUP4Hlf63h/KEDcEWVn8wL+AOs+XsdjY9oSNO2jcKy8qvHWd2YuGschs3AHR3e5GJSWgJjl77In9/M5ZgzOod1LiGEEIcWZSRC8mfg+9NaieJfiA6sBHf/UmrK/EdnDbMKCGO3OkhFnY32LbJWr6Ah6W2IuQbMnajYuyoVi9ZmYZekHCu5lPZbuQmQcsfyzra2HblOwoi9DqXsqJirij93pawOUwcK5bC6bulgYcey8G7LNuKGQtxQzJzRQGGSztETlfxmuavAjMThaP1UhSvFlC0FlfJpjWJURizadQZ4fwDncZA4DsOoQrMKIxlsaVatHqN+YZKzkOdHrK12dvD9BVVIACkjFpU0pvJxCCGKUXtrttSmbt266Xnz5tX6vKJ0C6ct5ctXvuekS06oVrHeusQ0zcKVNtYH3PI5q9nwzyZ6X9SDqNioKo9XkFvAx09/gcPl4NKHBuJ0lSxU99h5I/jzm7kADLzjTG566eoSx+Rl5/P2PR8QDJoMGXUlsYnFVzPlZuaRsSOTxkc0rNQKn32lb89gUPObCfgC2Bw2vkp/r0qdwvZ1X98nWT57Fe5oF++uHF0iToA92zLISc+lefsDq0NEWcbe/xHfvP4DSilGz3qWFh3KvuMWLpm7snjr3g+JTYjmuucHVZiIE0IIcXDR/pXoPRcCGhwdMMr5491Mvw58MwEnKv5RVPQFmFkPQ8Hn1gHuszASR1Vtfh1A7+iMlYxwoupNKWcrUvnMHV0LOy65USmfoRzlb8GPFK2DgFF03aQDa8Az1Sqa7Ci9IHKombtOs4o6Y4OEFwtX3SyH2FsxbEnWv0vmneCfB7H3YkQPrJW49qW1F6Wqd92hzVwIrABHe5T67zpae39DZ9wGRjwqeTzK3jhU4QohAKXUfK11qdswZAWQoPPJHeh8cg2L6NURhmEUK41+ZPfWHNm9+jVmxg37lO/fmgpKYXPYuPzhC0occ/pVJzP3x4UYhipzS1NMfDR3vj2k1Md2btzFDZ3vwe/x0/+Gvtz8cskEUnmiYt04XQ6UAneMG4ez+NvCjC/nMPLq12jQPI0Xpj1e7ra3JTOWF60o27Rya4nf3dpF67njhIfRWnPJ/edyxaPhW/a9Zc02nr3sZaJio3h4/J0k1kuo1jizv5uHN9+HM8rJ8lmrIpIAemPoe0yf8Cc2m416TVK46J5zaj0GIYQ4lOngdvD+Bs7jUPbmoZ/A3AHKAJ0Pwa3lHqoSnkHnvGgVII461/qZux+64BtAo6rR5UgpOyS9js4dB1EDqp38sQaLt54HgCr7mkFrj1XPxda81Do64WQWfAdZ91mrVFImomwNUPZWENsK7ZuHmX4juE/BiL44vIGoaKwLTwcE1kLeaOvnnh+h/h/gmw++GdbvM+dpCEECSPsXA/ZKJ+aqm/wBa7UOzpJ/gyrXSVB/cZVvWkaaNjMguAns7Wv9NStEqEgXsIPUstmreOriF/nlkxmRDkWUQ2vN3jV2ZtAs9ZjjzzmGz7a8xfitb9PhhLZVnmPZLKulvM/jZ8YXs6t8flRsFK/99RzXD7+C1+c+j91h58MnJzAw9WpeuXks7zz4MQU5Hrau2c6f35S/cu/SB8/D7rDRtntrWncpuYd9yYzlmEETX4GPP77+q8qxVsU7D37Cqvn/snTmcr4eXb0OXgBXPnExTreDtCYpHH/uMSGMsPKi4txWrS1DVXt1lhBCiOrROmh1a8p+xvp/M7dy55lZVuKoMpwngPscsLdDJY4s91Blq4+ROBwj7s6iltzKdQIqbTqq3m/Vbr+tXL0xUt6vcZcklfIxKvZ2VPI7KHvJ1b5aa7SZj951Bnr3ueiMG2o0X2m0mY6Z/Sxm3rtoXcr1V94YIABmFnh+Ln5uxg3gmwbZz1irgkIRT3A35u7zMHeejPYvKfq5ShoDMdehEodTtBUMQO9C+xaBvRmgrERRGW3aq8LMH4/eczl6zyWYBd/XeLyaOOiSP8Fd6F190XuuQGcOjXQ4QlSbrAA6CGmtefCMp8nPLmD2pHm0PbYVjVrVrB6NCI9rnrkMM2CSsSOTvlecWOZxNSkm3aXvUSTVT2T7+p1c/MC51RqjSZtGNGlj1evxFnj58MkJaFPz07vTOOni49mzNR00tDmmZbnjDH7sIgY/dlGZj/cc2J3xI78mfWtm2GvqNGvfhLk//I0GmtSgFtGJF/TgxDAXm67IkFFXktY4heiEaM68oU/FJwghhAihgNW5iSBoXbi9KbbcM7R/GTr9UtBBdNyDGDGDyj1eKRsq4YlqRad1EHQWqCSUEfk/qpWtIcTeVOpjZsFkyLrXWnljZgJe8M1Eax3ShIDOegi80wGHNVfUfitn3f0hd4NVhNvZfb8nEGVtxQIgNFuudf7nhZ2+NDp7JCrlA2sqWwNUnFVAWTt6Qt6nQJb1fcaVqLQFqJRJEFgFruNrHohvDuABFPjmQVT/mo95qAisAh0ACsD3R6SjEaLaJAF0kNpbo2b/r8WBJSrWzd/TlrJ7SzqLjnmAD9e9QUx8dEjniE+O4/3Vr2IGzUq9FrJ2Z5O1O6fMAs0Ol4PDWtQnY0cmzignt71+HQOGnEZqo+QaF/RObZhMsyObkLkjm69GT6Zr3050PrkDWmtevH4MM7+aw/l3Dih1q1xVXfHoBRx+VDPcMS6OOb1zscfycwqIinUfNHefomLcDArB76QyPnp6Ip8++yWdTmrPU98+IO8vQohDnlIudPyTkDcOos5F2Spx0837R2E3rgB4voEKEkDVpbUPved8qwW3+3RU4sthmSdkcl8D/Fbyx5ZmbaeJujT0n8c6sM/XJRucGLE3od39rBo0+xW7VskfoQu+QDl7lLqCqVoCK2HvmnBV+kpewxaHmToJdvfFKpBceLi9MYSoRo6KGYL2LYBSCnOLCji7gqM1+P+B2FsjHY0Q1SYJoIOQUopR0x7n+7E/c2y/LpXucnUo+vWzmbzzwMd0PqUjd/9viFXjp5ZsXr2NoD/AltXb0KbGjHKyZ2tGUQJow7JN5Od4aHtsqwovfPJzCnjk7OfZumY79757C136HFXscaVUpf5Q37RyC7cc8wDBQJAL7j6Lq5+6tMQxhmHwxrznWTJjBW2ObUVUjJt2xx1R4jjTNFm9YB0Lpy2lQbN6nHhhj0pdwGXuzCLgC2C3u8jalQ3AltXb+PWTGfg8fj547HMuuvecUgtmV4VhGPQa2L3Ez58fPJpfP5nJEd1a8vKMp8rtjnYo+vipiQT8QRb/vpzVC/6l7bHVr4ElhBB1hRF9YdXalbtPg7y3rdot0deGL7DAvxDYAATBMxmtXyxqC39AcveBvM2gFCpxDNibVdjRqjpUwnPo3FfAdljJ1T97jymjlpOytyhalRO6gGxYlTdUuVu5DHsDdOJotHcKKuqSYv+W2syHgs+sFU3uc6qVNFOOI1Bpv1U9/nJorcE7BYI7IfqCYsWe6xKl3KiUCZEOQ4gak798DlItOjbj1tFhvKCoI16+4S0Kcj38PuFPzrzuVNof36bS525Zs41/F22g2xmdK1V3JRgM8usnMwFwup2MuPI1UHDypT2ZP2URPc87liZtrKKK86Ys4vHzRoBSXPHYhVx8b/lFfWd8MZuVc9fgzffx+h3jeOeflyv9PPa1ZMYKqwaPx8/vE2aVmgACiEmI4bgBXcsd69lBrzBj4mzMoIkryglK0fvCirdK3f/Bbbx553s0b9+EnoUJmpSGSUTFujFNTcAX4MbO9/D6X88THRfai4hgMMgvH1l1s9Yt2cimlVsjUtT5QNauRxtWzV+LM8pJo9aytVQIIapD2ZtB2mwgGJYERxH74WBvAoF14Dr1gE3+aB1E540D7YGkd1H2Zihb6n7HaKu7mYpHVaEteGmUrR4q4ekajRFKKu4+tJkJKgoVe035x7pPLrWOk85+HDyTAQOFHaKqXuw7LLxT0Zn3ASb4F1dYw6o0WmsrWaqiD5rV2UIcrCQBJA5qWbuzmT9lEe1PaEv9ZvVKPN6kbSM2LrfuNKU1rfz2pV2b9zCky32gNS06NmX0n89WeM7EUZP48EnrzkCzdk3weaxifoahmLjjnWLHLp+9Cr/PjxnULPxlSakJIE++l2H9n2X90o1ceM/ZoMEd46rRioweZ3Xl42cmsmdLOpcNO7/a4wD89f2CosLWwaBV56gyWnRoyoipjxb7WVRsFGOXvsTglrcQ8AXYvSWdpTNXcGy/o2sU4/5sNhvH9Duahb8uIa1JqiQ4SvH8lIdZOXctTY9sRFxS+TUuhBBClM3qEhTebbRKOSHlWzDTwajZNu2wKvgScl8FAhDcjkoaXeIQnfsK5L0LaEh8pdrFrA9EytYAlTyuZoOY6UAAcICZEYqwQsPcDZiAz+pmVw7tnQ3BdeA+G2XEWD/TGp1xnZX8c54ISW+jlEKb2WBmhW4bnhACkARQrZnxxWw+ff4rTr7kBC68++xIh1MnaK255ZgHyNqdjc1u4+P1bxCTEFPsmJG/PMZfkxfQqsvh1GucUumxt6/bCVrjyfOy4Z/NlTpn56Y9+AtboB/WIo0d63ehteb8oSXv0Jx+9cn8+slMsnZnU69JKhuWb6bZkcX3d/81eQGr5q3Fk+fl2zd+4rW/nmfHhl10O63iu2I+j4+8rHyS6icW+3lS/UQ++vcNtNalboeb/d18fhv/B2fddHqFq6Uuuvcc3n9sPFFxUZxw7jH0u/aUCuMqT1JaAr0vOp5pn87EFeXkiG6H12i8sjw96QF2btxNaqNk2f5VCofTUa1udEIIIcqmgzvQGTeCzkUlvopy1Lyj015K2cBW8ibYgWXfTlwla/IA4F8MFAA2CKwADuwEkA7uRuc8ByoBFX8fqozaPqGiEp5AZz8FRj2ILrvhRq2LGgi+xWBuQ8WXXchc+xYUdnzT4PkFlfw/6wFzN/hmWT/3zQQzHa1z0HvOA+1HxwzBiJOaO0KEitJaV3xUiHXr1k3Pm1d+O+m6JBgMMiB6EAF/EKfbwdglL9KwZYNIh3XQC/gD9I8ehBk0cbodvLVoFI1DtKIjGAwy8qrXWfTbP1z7/CD6DCq7g9deGTuzeOGaNwC4Z9zNJKUlUJDn4eOnJrJ7azqXPjiwWJInOz2Hy5oOwefxEx0XxRe7xhWr47N51VZu6nofAMcN6MawT4dWKvbdW/ZwY+d7yc8pYNDD51e6oPKebRkMbnkLPo8fV7STbzI/qLCukGmaIa2rpLVmy5rtxMRHkbEzi43LN+OKcnHcgK4HzZLgMXe/z9ev/sAxZ3Tmia/vq9W6U0IIIQ5MZs4rkPcmYILzZIzktyIdUrVp7UOnXw3+vyH2VozYmytxTgCdNwaCu1Bxd5QovAyFndMybweViEp6C2X778admfextYoo5hqMWu5cpb0z0Z5fUNEXohzt/ospcyh4fgJsEHsnRmzVSzOYuW9DwXiIvgKjjhdl1gWT0VkPAgVga4lR7wd0cCfavxxyXoLgGnC0RSV/DgUT0NlPA16wtcCo91OkwxfioKKUmq+17lbaY3L7uxYYhkFcciw5GXkowyA6vm4WR6uOjB2ZZOzIokXHplX+A9/usHPzy1czfsQ3nHRxD+o3SyUYDGKz1Xy5tc1m44EPby/6ftr4P/jjyzmcd0f/opUxBXke3NGuoriT0hJ45rsHi43zwGlPsWzWKsBa0fPl7veKHvN5/JhBE21qvAU+gvt18Wp8REPeXjyKbWt30Omk9pWOfeG0f/B5fAR8AX743y+VTgDt++s3g5odG3ZVmKgMdXJDKUVSWjzXHDmUzN3ZoK2uZLe8cjX9rj01pHOFQ8Af4IuXvgNgwS9L2LRiC83aydJlIYQ41ClHezRWvTxqWN8m4vxLwL8UCEDua5hmJir6cpS97Jp6StlRFXROUo52qHo/l/i5Dm6HnOcAH2Tdi3b3QanQtGeviLVy6ybAi/Z8A2nz/quzpGLZW9h573amqo29B3JfAfyQMxzTPQDDdgBv46spd19rhU9gFSr+UbSZjt7dDwiCvRUqaTLYGqGUgXadDOoVq5NezPWRjlyIOkVuTdcCpRSvzXmO654bxCt/PE1ivYRIh3RA2LRyC1e2vo3bj3+It+75oFpjnHPLGXyy4U269u3EOQlXMjDlav5dvCGkcW5fv5MXrn6d6RNm8cDpT6G15smLRnFO/GDuOeVxTNMs89wta7YXfe3zBIo9ltowmTvevIEjexxBr/O7s+i3f0qcf1iL+nTpc1SVWnEffWpH3DFubA4bZ998eqXPS26QxONf3ktqo2SUobjx6HvZsWFXpc8PlXVLN5GfU4AZMDGDJn6vn82rtoV0Dr/Pz85Nuwn1Cki7w07b7q1xx7iIT46lvnToE0IIASh3H1Tyh6jEN1ExN0U6nJqxtyxsZe4CgpD/Pjrj6vDNp6JB2QE7qBhq9/71Ptd4OkBRK3dAxT1ktQOPux+iqtAhbi8jBlQU1vMJwu6+6MDamgZcjNYaM+dlzN0D0d4ZIR27qpRyYCQ8i5EyEeU4CoKbrd+pzgf/MnTmzeicZ9HaROe+DnoP2NugoqR0hhChJAmgWpLWtB7n3zmAlp2aRzqUA8by2asxTRNvvo9Z39ZsS+CXr0zG7/WTn13Ar5+E9gPOsP33n4nNbqMg18PML+agtWb57FXsWF92kuTed28hoV489Ron89yPw0o8fvpVJ5OTnsv0z//kiYEjWf/PphrHm3JYEp9uGsPEHe9w8X3nVuncY844muw9OfgKrBVEG1dsqXE8VXVE18NpWrhVLqVhEp1Oas8Fd58VsvELcgu4uu0dXNn6NoYPfjVk4+714vQnGPHzY7yz7GXc0bVzh1IIIcSBTzk7oVzHHzRbmsuijERUvSkQ/zjgBDRoXxjni0clj0fF3Y1K+aKwuHboac80dO5baDP9v7lth0HCCHD1QyWPKza3MqIxYm/CiLm8WjEp5Ualfmu1qgfQXvBOq/HzKCaw1CqsHViKzrzDmkabIb8BVi329uDqDSoesENgFRRMBP98a1scGgLrC+tBCSFCRbaAiVpnmia/ffYHAX+A1EbJ7Niwmyseq9w2pbKcOqgnf/+yBGWoCtuX7zVh1LdMHDWJky45gSGjrizzgiytSSqPf3kvc76fT7/r+hAV66ZDr7asmLOaRq0Po16TsotLdz+zS4kOYPvLz8rHDGpMU3Nf3ydpeVQzHv3inkq1ni+L3WEnNrHq/3nv2ZaB32+tVAr4ArTu0qLaMVSX0+3k9b+eJxgIVmnlU2WtXbSBrF3ZBHwBa1XXPlv9QsHhdHBk9+p3ajuQFOR5yM3Iq1IBdSGEEHWfMhJQ0edjag/45qBih6DNPKsLlK1FmddUWvusP/Rth6OM6DLH11oXG0M52oCj/OYUNaF9fxcmSALgnYZK+azoMSOqH0T1C8u8ytYQHXMzZD8K2NAqGvZ77jWbIAlr1ZITjHpo72yrELNyQvInKMcRoZmnOqEpW1E3OHP3eRBYC1qDkQauU8D7O9iSwdYqYjEKURdJEWhRLT6vHwCny1Hlc8eP+JoPn5wIwFVPXswFd4VmdcfurenYHbZKbbHzef2cFXs5ZtDE4XbwztKXOOzw+pWeKxgMsmP9LtKapta4k9TyOav54LHxrP77X7J25eCKdnHHm9fT94reVR7r38Ub2LM1na6ndapWfR6fx8dlzW7CW+AlJj6Gj9e/EZIkTPaeHB4842l2b01n2Kd3ctSJ7So+KUw8+V5uOeZ+Nq/axpnX9+GON2RveWl2bd7DjZ3uwZPv4cJ7zubqpy6NdEhCCCEOUNrMQO86E3QuuM/ASBxZ8hit0XsGQuBfsKWgUn+02tjvx8yfANmPga0ZKmU8yogPf/yeaeisoaALrARWypeg3GFbbbQ/M38iZD9p1YiKuR6jgnpJVaF9i8C/CNz90NkP/7fKKPpajPj7QzZPTWgzEzw/gqMDytEBrU1ri5itQamvESFE+corAi1bwESVLZu1koEpV3Fe0pUs/n1Zlc/f9u9O/F4/fp+f7et3hiyu1IbJla6v5HDaqdckBXeMC3eUi4R6Vbu4sNlsNGzZICRtxI/s3prnfnyYrn074Y5xgdY0a9e44hP3s3Tmcm497kEePXcEIwu7kVWV0+1k7OJR3Pfurby1cGTIVuD8+slM1i3dSPq2TN6+78OQjFld7mgXY5e8yBe7xknypxxLfl+G3+fH7w0w9YPpkQ5HCCHEgcy/zEqe4AVvyULOFg8ElgEFENwJwe2lH5b7GhAAc6u1CqQ2uHpD1OXgPB6cvdA7u6F3nVxsO1hYBTcDfut36C9ZExIKE2jaX+WhlbMTKmYwylYPXP2waje5UK6TahJxjWmt0cE9aG1a2wqjL0E5OgCglIGyN5XkjxBhIFvARJX9OO5XvPnWXu/v355a5dUcgx+/kO3rd6KUYtCw88s8Ljczz7oRklD1zgoVMU2T0bOeYemMFbQ7vg3RcZHvzHbvu7cw5/sFHHZ4fQ4/qlmVz1+1YB1+j3Vh8OvHMxj65vW4oqpegyapfiK9zj+uyueVp9XRzTEMA3eMi44927L0jxXkZeVzbL+jI1ILwTAMYhND/7oKt4I8Dw6nPSSJx4oc3ecoYhJi8Hn8DBw6IOzzCSGEOIg5jgZ7IwisgeirSj1EqSh01MVQ8Dm4eoGtjJtdrlOg4AvAAEflO6Zp7UNnDrUSKNFXW/PYDkMlvoIyYss9VykDFX8vAObOE4Ag6GzwzQF3eLZ/FZs/+nK0708w81Bxd5d4XGsPes8lEFiGjr4S5ewKji4oW9WaTRjR56JdxwCOKp9bWVp70Bk3gn8lxD+FEdW39OMybwLvdLC3hZQJKCV/lgpRG2QLmKiyBT8v5pGzn0cDj028h+5ndgn5HPOmLOLRc4ajFDzz/UN0PrlDyMZet2QDQ3s9gt8X4OlvH6BLn6NCNnZ1mabJq7e+w4KfF3Pdc4OqlYDZvmEnVxx+C2irWPWnm8aQVD8x9MFW0/p/NpGxI5PczHyGDx6NUooL7j6bKx+/KNKhHRSmjf+DEYNfxRXt4tXZz9KkTaOwzxkMBvF5/DWqRyWEEOLQYP1NEUCpsssDaK3RvvkoW1qZbeO11hBYCbY0lJFc+fk9v6Cz7ipcieQA/IALFXcfKuaKSo9jZo+E/PdAxaBSvwtboqQqtG8uOuN6q2MWWJ3RcKLSpqNU5G9i7kt7fkJn3gcUgHEYRlrJVcRae9E7OhZ+50SlTkLZa7/upBB1lWwBEyHVpc9RfLrpLT7dOCYsyR+Anz+cjt/rx+fxh7yr188f/U5+dgF+j58vX/m+Suf+9cPfTHxxEjkZuSGN6feJs/j+ralsXbOdEVe/Vq0xGjRLY8ioK2nQIo0rn7go5MmfXz+byV29H+XXT6v379G8fROOPqUj65ZswO8N4Mnzsnp+aNud1mVfj55MwB/Ek+fhj6/n1sqcNptNkj9CCCEqRSlVbvIHQOcMh8xr0bsHoP1Lyh7H0bZKyR8A7M2tIsJEgZFa2GJdgf3wKg1jxN+LqvezlVw5AJI/ANjbgIrB2r6FlQjSmej8byIaVqnsrUABRJdYwaW1DzP3NXTum+DsBdit48taDVYO7V+BuedizMz7reLiQohKkbV2olriU+LCOv4Z15zCjC9mg1L0HXxSSMc+bkA3vn7tR7SpOeWynqUes/j3ZYwf8Q09zzuWfteeCsDSP1bw5AUvYAZN5nw/n5G/PB6ymH4b/2dRS06nu/r7nc8fOoDzq7BdJ317BlM/+J02x7Qsd5VVXlYeI696nYAvwPI5qznmjKOJSyp/OXVZzhpyGvN+XEhuVj7XPjeoWmMcis649hRWz/8Xm9POsf2OjnQ4QgghRNX5/ixcoeMC/xJwdKzwlMpS9paQ+iUE1qKdPVG+38FIs7ZLVXWsva3Zy6D9q60aRc6eISkUrQP/WsWZXb1R9pJdr5QRD6lTwdyGznkbvF9ZD+S+BDGX1Hj+UFL2lpDyLQQ3gLNHscd03tuQ+xagIepSVL3nwUiu1u9QZz1ktbn3LwdXd4gaGKJnIETdJgkgcUDqfHIHJu4ah1IKd3TV69js9cETnzN57M907HUk1w+/nLSm9ejY60g+Xv8GAV+A1EYl21trrRnW/1k8eV4W/rqEjr2OpEGLNDK2Z6IMhb8gQPr2zCrHsnn1Nu4+6TG8+V6e/u5BOpzQtuixjr2OZO6PCzGDJoNrsCVKa82Kv9YQnxJLo1blX7wA3H/aU2xauRW73cbrc5+nWbsmpR5nd9pxuOyYAROH046jGt3f9kqqn8joWc9W+/xDVb9rTuW4/l1xRbsOiJpVQgghRFWpuHvRmXeDrUFYausoeyuwt7IWoJQzvg5uQWcMAe2FxNcxHK0rPYf2L0HvGQTKAPeZqISaXdNo7UXvuQC0xyqAnfZnqdu6lBENRkuIuwXt+xG0AkebGs0dLsreDOyl1LPUQay29BoIWoWpy6DNHFCusgtB2xpBYLX1tVH5Tr5CHOokASQOWDXderJ7azqfPPslQX+Q38b/ybwpi/hs81u4olwVdgtzRjnx5HkxgyYLf/uH14+6m6hYN70uOI6dG3Yz5MUrqxzPj+N+JX17Bmj47PmveHrSg0WPDbyjP83bN8HusNPppPZVHnuvD574nAkvTAKtGT71UdofX/6FQdbuHIL+IA6nnZz0sre1uaJcvDLzaf78dh49zupWo6ScqL5w1nTKy8rjnz9X0fbYVmFf4SeEEOLQpFy9UPX/qtI5WgfQmXdYBZljh2LEXF7jOHTuu1adIYA9F6DTZqKMSn72+QvP0/ngW1jjWNA+KxFFwErqaE/h9rXSKXtTSPnKSn64etd8+sBaKxmGQiWNtZI3YaJib0DrfCCAir2zzOPM/M8g+0mr1lHKl6XWi1KJI6Dga7A1RrlOCFvMUJiMCm4Ae1spVi0OevIKFnVWbGIMUTFucjPzAPDkeZk1aT6bVmzh9KtOIq1p6XcdlFI0bNmA7N05BINBJo6aRMAXID87n6ZtG3Pfu7dWK56jT+nAV6MnA9DjrOI1uZRSdO1b+U4XZZk/ZTHefC92p53ls1dVmAB69PO7GDfsUzqd3J72+6xIKk2Ljs1o0TF8FwUicoLBIDd1uY/MXdm4Y9189O/rNdqKKIQQQoSMfzH4Zlpbx3KGQwgSQDjaQ8HebwLgXwSu0ssClOA+Awq+guAGVPywSk+ptS6186ky4tDxT0PeWMBE546BuPvK3Ral7IdXubZRmXHlvm0lN1DovHGohCdCMm5plIpCxT9Y8YH5H2ElxLzg/R3sJf/NlYqC6EtDH+R+tJmN3nW6lfBzdkUljwv7nEKEkySARJ3ljnbx9uJR/O+Bj/nnjxX0HHgsL1z9Oj6vn18+nsF7K0eXee6mlVsA0CbEJsXgcDkwbAbdTqt+kqZr3078b8mL+Dy+Mrda1dTVT1/CUxe9SGJaQpn1jfbVoeeRvDj9ybDEUlU5Gbn88vEMWnZqTsdeR0Y6nEOKN9/Hjo27MYMmAV+AzF3ZpDVJjXRYQgghBNiaATasosKhuT4wos/D9P4C3p9BpYCj8h1hlRGLSvm42M90cCuYmWA/stQkj1kwFbKGoo1UVMrnKNt/W5a09w+UzkEThOA6yN8Gzm7gLr19esg5u4DnR0CDIzzNXaos6mLIeR6UE1y9IhtL4F8gHyiwVqEJcZCTBJCo0+o1TuHBj24HYMVfq/nuraloU5OXnV/ueV1OPYo/vv4LpRQX33sObY5piTvWTXxyzbbGHHZ4ePcoH31KR77c/W5Y5wiXh896jtUL1mEoxSt/PkPLTs0jFovf5+fhAc+zfPYqrnn2Ms69NfR1Cg4k0XFRXHz/uXz7xo+ccmlP6jUuWRtLCCGECBWtTQgsA1vDCrt9KVsKpP4AgVXgPDZkMRhJr6GDu8FIqLB7WXm0bxE6vbDNfOwNqNjiK8W1mQNZDwF+q4hzwVeo2CH/nZtxE2CCcmG1r9dgJKP9y8D/D7hPQxkJhWNlgYoquy5ONRjRF6PtRwAGylm5G51aa3TW3eCZAtEXYcQ/GrJ4AIyYK9BR/UFFo1SEu5E62oG9A/gXQMwNkY2ljtOBDeise0DFoRJHoYykSIdUJ0kCSBwyGh/RkNOuPInNq7Zx1VOld0xYPmc1W1Zv466xN9K171EkH5ZUYrvWweCrVyfzybNf0uOsbtz51o2l3o0Kh2AgyEdPT2T3lnSuefrSKtWs2b05Hb/HjzvGRfr2TFrWfEdctS37cxXLZq3Ek+dl3LBPQp4AMk0TwzBCOmZNXfP0pVzzdPiXUgshhBA6634reaBskDoZZWtQ7vHKVh9sob+JpmwhWO3qXwAEAT94psE+CSAdWI/ecx6Qt8+k+yRvzJ1YPdN9oBqgYi8F2+Fg1EPvLuzqmv8JKvUrzLyPIOfZwro4X6HsoVtNrpxV7C4a3GL9++GD/I/RsXejjJiQxQNUmBisLUo5S6z4EuGhc4ZbWz6xofM+QsXdFumQ6iRJAImQMk2TfxdtoF6TFBJS4yMdTpGCPA/Xtr+T/Ox8Gh/RkCO7l+z2sOKv1dx7yuOgFN1O78TjX9xb63GGgmmajLnrfcygya8fz+C828+kRYembF61FW+BL6wra6a8/xsTRn5rbSPakcVT3z5Q7vEFeR7AKvj94Ee3M+bu9znyuCPo2rfyS7HDoUnbhtjsNtwx7grrKFWF1ppnB73Cb5/9wQnnHctjE++pteScEEIIURatNUDtfSZ5Z2AV4Ym2VrlUkAA6EJm5b0Lum+DsbHWh0umouDuKHaO9swoLPBdSaah925W7TrHalweWo+KHoRwdrfN8c0Epq+5RcLN1bP4nFNXF8c0A+2XhfYLlsaWBkQpmBtgbW0kpIWrKdjjwB6BR9uYRDqbukgSQCKlR141h+ud/YLfbeXvJqHLriJimyaQ3fyJzZxYX3H02MfHh+/DYtWkPuRm5+Dx+1vy9jmAgiN1R/OW/de0OUApvvpeNy7eELZZwMwyDJm0asmPjLmw2G6mNkpn700IeHzgSBVw/4nLOuSU8W5rsDrt1I8tQ2J3lv70smv4PD535LGjNs5OH0emk9rw25/mwxFVVyQ2SeHfFK2xcsSWkCaCc9Fx+nzALgDnfzWfPtgxSGx4Yd7iEEEIcmrRvHjrjOsAFKR9brdTDLfY2azWLrTk4j6vRUNrMQGfeCzoflTACZW8cmhgrkvsqEADfIlTKh6jS6ggFt1nHADhPQSW9Xqy4s1I2VMJjJc9zdAX3QPDNRsXdZ/0s+lLIec7aKuasZMHqMFHKCanfQWClVSQ5/WK06ySM2JsjGpc4uKm4O606X0YMOGve4U6UThJAIqTm/rAAb74PFaNYPf/fchNAU96fztj7PiIYMNm6dgcPfnRHmcfWVOMjDqN7/67M/m4+591+ZonkD0DPgd3546s5rP9nE3e+dWPYYilLMBgEwGYru+tDZb3y5zP8/csS2hzTirikWBZPX4bf60ebmrk/LgxbAujUy3uRnZ7Dnq3pXPLAeWUet3trOiOvfh1fgQ+An96bRqeT2oclpupKqp8Y8rbrsUkxNO/QhC2rtlG/eRpJaQkhHb8sm1dtJX17Jh17lV6cUgghxKFL571jdTgiH53/BSr+/vDNpX3o9GvAvxDi7sSIua7mY+a9B74/sTpovYBKfLnGY1aKo7NVxwhnYaHq0oLLBQq3fNsalNvZa19KGSUSQ1ZdnAGFdXFc1Q47VJQRC86u6B1drOfpX4F2nYRytIt0aOIgpZQNovpHOow6TxJAIqSuePRC3hj6Lg1bNuDoUzuWe6yvwIfWGtM08eZ7yz22pgzD4NEJd5d7jNPl4JHPyz8mXJbPWc19fZ5Aaxgx9RHa9ajZqpOY+Gh6nte96Pt+157Cr5/OxJPr4bKHBpZzZs0YhsH5QwdUeNxrt/6PnRt2FX3f/cwDpOtEmBmGwWtznmPTiq00bmNtMwu3FX+t5p6TH0cZijOv78NNL14V9jmFEEIcRFx9wDsTAFXZVujV5V8IgaWAz1pBE4IEELYmFBVPtjWt+XiVpJLfs9rH21sVFWkucUzsLejgFkCjYmtez+SALIprJEOw8DpeHTjlH4QQpZMEkAips246nbNuOr1Sx/a77hS2rdtB5s4srh9+eZgjO7BNHjsVT5714TlpzJQqJ4B8Hh82u63MhELDlg14/sdhADRp06hmwYZAbFIsheUGsNlt5GbmlXqcaZooperUqhWH08HhR5VxpzAMVs//F6013jwfi6b9U2vzCiGEODgY0eejnV1BOVC2MF8j2FsBDlBR4AhNRy8VdT4YCVa9HHftrR5QygnOY8o/xpaCSn6rWuPrwEZ09mNg1EMlPIFSUdUaJ9xU8kfg+R4cXWpv+50QotoOrDY04pDicDq4ceRg7n//NpIbHIB3NGpRr/N74HQ7cLod9Dj7GH79dCb/Lt5QqXN/nziLcxIGc0H9a9m8elupx0yfMIshXe5jSJf7mF5Yg6Y8WbuzWTpzOX6fv0rPo7JuGX0NnU/pgM1uw+Gy0/HEksuFF/yyhLNir+D8etewccXBW5Mp0npfdDzN2jUmITWe60cc2olWIYQQpVP25uFP/mB1dlL1pqCS3kMlvRmaMZVCufuios6u9BarA42ZOxZzR3fMrEeLCnLr7MetrW2eyZA/IaLxlUfZGqBirq16JzEhRETICiAhDgDH9juaD9a+DsCIK19j2ayVaK15fe5wmh1Z/t2UL176joA/SEFOATO/nMMl959b4pi5P/5dVG9n7o9/0/vCHmWOl70nh6vb3oHf6+fI41ozYmopxQlrKCrGzcifH2Pzqq3Ep8QRnxJX4pgJL3yDz+PD5/Xxy0e/c7W0KK+W+JQ43pg3ItJhCCGEEEDhNibnoX3jb19a+yH3BUBDwVcQczXYWxS2vXcChtVx6wCitUbnjgb/QlTcvVL3R4iDiKwAEqIM2XtyKMgtqLX5Ug5LIuWwJDYs34wnz4tSim1rd1R43mlXnYTDZcfhctDt9E6lHnPe7WeSUC+ehNR4zrv9zHLH27RyK36vH0+el6UzV1bruVRW4yMalpr8AWvlirUqykm30zuHNQ4hhBBCiMiwFxaRjra2xhn1AFDxj6Hi7kclPg/u8DTvqDbfH5A3Dnx/oDNrXttICFF7ZAWQEKX46b1pvDzkbexOO6P/fIYWHWqvqODdY4cw+paxxCbFUZnSN/2v70v3/l1xR7uITYwp9ZiWnZozccc7lZq/zTEtaX98G5bMXMEVj15QldBD6oyrT6HTSe1xup2kHCZ3CoUQQoi6QmuNzn4UvD9DzBCMmCsjHVLEKKUg5QvwzQHHUVZ3LUApN8SEduu29i1C574BrhMwYgZXfyAjHtCADVRiiKITQtQGtXefaW3q1q2bnjdvXq3PK0Rl3XHCMJbNWoXNZnDV05eWuq0qnIb1f5a/f12KYVM88/1DdOp9YLVIF0IIIYSoLu1fjd5zPuABDFT9ZSglGxPCzdzZA8w9gBuV8gnK0aHaY2nPNAisgKgLUbayt6hprUHnFSW2hBDhp5Sar7XuVtpj8k4ratWc7+dzVZvbGXnNGwSDwUiHU6Zzbj0Du9OOK8ZFj7NL/W8npHIyclm7aD2maQKwa8se/F4/oEjflhmyefJzCggGyv69L/59GU9cMJLpn/9ZqfF8Hh8PnP4UF6Rdw2+f/xGqMIUQQghRl9nqg3IC0WBrccAnf7SZj5k+GHPnCWjv9KqdqzVm9pOYO0/EzB8fpggrSUVT9OefctdsKPfJqNibKkj++NB7zkPv7IqZ9VCN5hNChIasABK16sIG15K5Mxt3jIsnvrqPLn2OinRIZcrPKcDutON0OcI6z55tGVzX/k78Pj89B3bngQ9uZ83Cdbx+2ziatW/Mra9ei91R892anw3/ivce+YyEegmM+XskSWkJxR4PBoOcEz8Yb4EPp9vBuytHk9ak/KKDf347l+cGvYInz0vyYYmM3zK21ONM08QwDuyLOyGEEELUHh3cDv7F4Dz+gF8dogsmobMeBgrA1hSj3s+VP9e/Cr3nAqzVTrbC1U6V2OMfBjqwCV0wAeXsinL1Dv98/mXoPZdgPXeFqr8iYs9diEOJrAASB4xGrQ/DFe1Ca0hrVi/S4ZQrOi4q7MkfgDV/ryPgD+DN9/HX5L8BaNW5BS/NeIqhY24MSfIH4JvXfiQYMMnPzmfhr0tLPK6Uwuaw7f0Gu6PiVqrN2lkdytwxLtoe25oVf63m0+e+Ytu//xWv/nL0ZM5wXsK17YeSl50fkucihBBCiIObsjVAuU874JM/ANhbY9W8iQZH6Q03wOroZeZ9gJn3Hlpb3Vex1QNltwo825pGNAGi7E0w4u6qleQPAPbDwZYGOMF1iiR/hDgASBFoUauenTyMP77+i5admtO49WGRDueAcFTvdjRqfRjrlmzksmEDwzbP6VefzPgR3+B0O+l44pElHjcMg1G/PcFP7/1Gj7O6kdyg4sLLjVodxtuLRrF59TaatWvMNUfeQcAb4KvR3zN+61iUUnz63JdoU7Nz427+/mUJPc/rHo6nJ4QQQtR5OrgNVCzKKL2DpggP5WgLqV9BcBM4e5Z5nM4bC7lvWl+bmai4oVbb+5Rvwb8QXL1qKeIDg1JuSJ0MwZ1gaxTpcIQQyBYwIcJGa00wEKz0Ch6tddjvjOzemk5cUgyuKFfIx968ehs3drobn8ePw+Xgu7yPMAyDl4e8xc8f/o7dZWfs4hep1zgl5HMLIYQQdZ2Z9x7kvADKjkqZiLK3inRIYj9m9vOQ/yGgIeoSjIRHa3V+rYMQXA+2xigV+ms9IcTBobwtYLICSIgwyN6Tw83H3M+uTXu4+eWrOeeWMyo8pzaWxaY2TA7b2I1bH8Z1wy/n9wmzuPTBgUU1f+548wbOve1MUhomEZd0ECzzFkIIIQ5EBV8BPmsnkvdPkATQAUfF3oLWGaBNVNwdtT6/zrgWfPOtbVep31srcIQQYh+yAkiIMPj105m8dMMYPHle6jVO4ZONYyIdkhBCCCEOYmb+N5D9IKg4VMoXKHvjSIckwkRrDb7pYOaC+wyUqvievdZB9I69W/xd1mvEcUR4AxVCHJBkBZAQtazDCW2w2W043Q56X9Qj0uEIIYQQ4iBnRJ+DjjoDsKNUxY0axEHM8z06a5j1tf8fVPz9FZ6ilA0ddSEUTABHB6sAsxBC7KdGCSCl1EjgLMAHrAWu1lpnhiAuIdi8aiu5mXm0OaZVWLdHZe7K4pvXf6RJm0accmnZhf2qIq1pPT5e/wYZO7Np1KpBSMYMp4kvTuKPr/9i0MMX0O20srtbCCGEECJypK7LoUEHNwF+IAjBdZU+z0h4Bh3/GEo5wxabEOLgVtMVQFOBB7XWAaXUcOBBoOIUtRAVWDT9H4ad+SwoxWUPncdlD50ftrmevHAUS2csB6XI2JHJ+UMHhGTcmIQYYhJiQjJWOG1Ytol3H/kMX4GPJwaOZFLuR5EOSQghhBDikKWiL0P75oPOQsU9ULVzJfkjhCiHUZOTtdZTtNaBwm9nA7IZWYTE8lmrCPgDePO9zJ+6OKxzpW/PRGvQpuajpyaGda7KWvP3Oi5tciNXtr6Vbf/uCOtcUXFRKMBmM4hNPvATVkIIIYQQ1aG1l0jUP60qZSRgJP8PI2UCyt480uEIIeqQGiWA9nMN8EMIxxOHsD5XnMhhhzcgPiWOK5+4OKxz3TTqSgybgTIUqY0OjBblHz/zBbu3pLNt3U6+ffOnsM6V1iSVET8/ylVPXcLLM54O61xCCCGEEJFgZo9E7zgKvecstC6IdDhCCBERFW4BU0r9DJRWxGSY1vqbwmOGAQHg43LGuQG4AaBp06bVClYcOlIbpfDuildqZa7u/bsyfMojrJy7lr6DT6yVOSty9CkdmPvjQtCajj2PrPD4mmrXow3terQJ+zy1afHvy/jfAx9xVO/2XPvsZWGtIyWEEEKIA1z+h4CG4GbwLwHnsWGf0sx9Cwq+gZjrMKIHln1c/njwTkfF3IhySi1GIUT41LgNvFLqKuBG4FStdX5lzpE28EJUbOXcNThcDg4/qlmkQzkoXdTwejK2Z+KOcfH0dw/SqXf7sM5XkFvAKzeNJTs9l6FvXk9a03phnU8IIYQQlWdm3gWeqWAkoFIno4z4sM6ng1vQu07H6pVjR9VfWGp9Hu1fid5zAeAFEjAazLV+rj2ARqmosMYphKh7ymsDX6MtYEqpM4D7gLMrm/wRQlROm2NaSfKnBpIPS8ThtKNNTWK98F7kAXz7xk9MnzCL+T8t5I073wv7fEIIIYSoPJUwCpX6Hare1LAnf6wJ40A5ABcYCZS18UJjx0oSAeShtYn2LULv6I7ecQza+0f4YxVCHDJq2gXsNcAFTC3cXjFbaz2kxlEJIUQNPf/jw/z68Uxadz2cZu2ahH2+pPqJ2OwGhqFIPiwp7PMJIYQQovKUUmCvvRtryoiHlC/ANwdcJ6NU6ffdlZGIRgEa0OiCCZD3MWDVKdL5n6FcJ9Ra3EKIuq1GCSCtdatQBSKEEKGUWC+BgUP719p8fQf3xu6wkZuZzxnXnlJr8wohhBDiwKTsh4P98PIPMpLBeTz4/gR7O8h+Bms7mAIcqKiyawcJIURV1XQFkBBCCKw7i6dc1ivSYQghhBAHFO2dg/bNQEWdh7K3jHQ4BxylFCS9AzoX7V8JGdcCGoyGqNSvUUZircShdeHqo+AGVPQ1KNuB0RlXCBFakgASQgghhBBChJwObkdnXA940fkTIG22dOUshVLKqhnk7IqOuwt881Gxt9Ra8gcA76+Fq498aP9SVPL7tTe3EKLW1KgItBCi8jat3MINne7m9h4PsWdbRqTDEUIIIYQIL+1jb20bdEFoh9YaM+9dzIxb0f7lIR07UpRSGDFXYiSNRjna1O7k2rv3i32+FkLUNZIAEqIGfB4fj5z9PIOa38TcH/8u99j3HvmMdUs2snLeWr4aPbmWIhRCCCGEiAxlbwrxj4HzJFTS2NCu/vHPh5yXwDsFnXFz6MY9VLnPgNjrwd0flfBCpKMRQoSJJICEqIE53y9g4bSl7Ny4m1duGlvusS07N8cd48Lhckh7d1EnaK15Y+i7XNjgWj557stIhyOEEOIAZERfgJH8NsrVPbQDK/feGUBFVWsIrX3o4M7QxXQQU8rAiL0VI3EUyt440uEIIcJEagAJUQON2zREa407xkXLzs3LPfbSBwfSslNzXNEuOp/coXYCFCKMdmzYxXdvTcHvDfD+I58x8I7+uKNdkQ5LCCEOalpr0Dmg4qReTjmUowMkvoD2LUTFDKry+drMQu8eAGY6OuoijITHwhClEEIcWCQBJEQNtOjQlNfmPM+mlVvp3r9Luccqpejev2stRSZE+CXUiycqNgqbw09CahxOtyPSIQkhxEFNaxOdfjn4F4CrNySOkSRQOZT7NJT7tOqd7F9iJdrwg+cbkASQEOIQIAkgIWqoefsmNG/fJNJhhNX0CbPY9u8OBtzYl9jEmEiHIw4QUTFuxi4ZxbJZq+h0UnsMQ3YVCyFEjZjbwL8IMME7HXSu1R1KhJ6jExjJEPRC1EWRjkYIIWqFJICEEOWa++PfjLz6dYL+AIunL+PZyQ9FOiRxAElukETP80Jc10EIIQ5VRgOwt4bAanAcDSo20hHVWcqIg9SpoHNRRkKkwzko6cAG0D6Uo3WkQxFCVJIkgIQQ5crJyAMg4A+SnZ4T4WiEEEKIukspG6R8AeYOMBrI9q8wU8oGSpI/1aG9v6Ezbre+jn8YI1pWUQlxMJAEkBCiXL0v7MGqeWvYtHIbQ0YNjnQ4QgghRJ2mlA1sDSMdhhDl0t5ZgBfQ1nZFSQAJcVCQBJAQolw2u40ho66KdBhCCCGEEOIAoaIvRnu+B+1FxdwQ6XCEEJUkCSAhhBBCCCGEEJWm7Iej0mZGOgwhRBVJyxYhhBBCCCGEEEKIOk4SQEIIIYQQQgghhBB1nCSAhBBCCCGEEEIIIeo4SQAJIYQQQgghhBBC1HGSABJCCCGEEEIIIYSo4yQBJISo837+aDqDW9/KG3e+i9Y60uEIIYQQByytNTq4B60DkQ5FCCFEiEkCSAhR57143Ri2rd3B5LE/8+/iDZEORwghhDhg6awH0bt6onefidYFkQ5HCCFECEkCSAhR5zU4PA1XtAvDMEhukBjpcIQQQogDl+dbIAjmDvCvjHQ0QgghQsge6QCEECLcXp75NHO+W8CRx7UmqX5ipMMRQgghDlzus60kkFEfHG0iHY0QQogQUpGoh9GtWzc9b968Wp9XCHHwWzZ7FQumLuaki4+n8RENIx2OEEIIUadorcFMByMBpeResRBCHGyUUvO11t1Ke0ze1YUQB42MnVncd+oT+L1+vhr9PRN3jkMpFemwhBBCiDpDKQW2lEiHIYQQIgykBpAQ4qDhzfdimhrT1BTkejFNM9IhCSGEEEIIIcRBQRJAQoiDRoPmadwy+mo6ndSex7+8F5vNFumQhBBCCCEOeTq4BzP9Ksw9g9DBrZEORwhRBqkBJIQ4qGmt+fDJCcz9cSFXP30pXU7tGOmQhBBCCCEOKWb2cMh/D9DgPhsjcUSkQxLikFVeDSBZASSEqJENyzez5u91EZt/1by1fD7yW1bMWc1TF46KWBxCCCGEEIcsW1PACbjA3izS0QghyiBFoIUQ1Tb7u/k8dfGLKOCGFwZz9k2n13oM8SlxoDV2p43EtIRan18IIYQQ4lCnoi+xiodrP7j7RTocIUQZJAEkhKi2xb8vw+/xo7VmwdTFEUkAHXZ4fYZPfZRls1ZxymU9a31+IYQQQohDnVIK3KdFOgwhRAUkASREDaxZuI6/Jv/NCecdS7MjG0c6nFrX/4Y+/D5xFt58H5c+NDBicbQ/vg3tj28TsfmFEEIIIYQQ4kAnRaCFqKa8rDwuaXwjPo+fqFg3E3e+g90hOVUhhBBCCCGEEJEhRaCFCAOfx0/QH8QMmnjzvQT8wUiHJIQQQgghhBBClEoSQEJUU1L9RG5/83o69GzLQ58MxR3tinRIQgghhBBCCCFEqWQLmBBCCCGEEEIIIUQdIFvAhBBCCCGEEEIIIQ5hkgASQgghhBBCCCGEqOMkASSEEEIIIYQQQghRx0kCSAghhBBCCCGEEKKOkwSQEBFmmmakQxBCCCGEEEIIUcdJAkiICBp7/4ec7riYO3oOw+/zRzocIYQQQgghhBB1lCSAhIigL176HjT8u2gDaxeuj3Q4QgghhBBCCCHqKEkACRFBXU87CleUk+j4aJq0bRTpcIQQQgghhBBC1FH2SAcgxKHsyW/uZ+OyzTRokUZUbFSkwxFCCCGEEEIIUUdJAkiICLLZbLTo2CzSYQghhBBCCCGEqONkC5gQQgghhBBCCCFEHScJICGEEEIIIYQQQog6ThJAQgghhBBCCCGEEHWcJICEEEIIIYQQQggh6jhJAAkhhBBCCCGEEELUcZIAEkIIIYQQQgghhKjjJAEkhBBCCCGEEEIIUcdJAkgIIYQQQgghhBCijpMEkBBCCCGEEEIIIUQdJwkgIYQQQgghhBBCiDpOEkBCCCGEEEKIErRnKmbO62gzPdKhCCGECAF7pAMQQgghhBBCHFi0bx46827Aj/bNQKV8FumQhBBC1JCsABJCCCGEEEIUZ+aAUkAQzKxIRyOEECIEZAWQEEIIIYQQojhXb4i+Cvz/oOLui3Q0QgghQkASQEIIIYQQQohilDJQcXdGOgwhhBAhJFvAhBBCCCGEEEIIIeo4SQAJIYQQQgghhBBC1HGSABJCCCGEEEIIIYSo4yQBJIQQQgghhBBCCFHHSQJICCGEEEIIIYQQoo6TBJAQQgghhBBCCCFEHScJICGEEEIIIYQQQog6ThJAQgghhBBCCCGEEHWcJICEEEIIIYQQQggh6jhJAAkhhBBCCCGEEELUcZIAEkIIIYQQQgghhKjjJAEkhBBCCCGEEEIIUcdJAkgIIYQQQgghhBCijpMEkBBCCCGEEEIIIUQdJwkgIYQQQgghhBBCiDpOEkBCCCGEEEIIIYQQdZwkgIQQQgghhBBCCCHqOEkACSGEEEIIIYQQQtRxkgASQgghhBBCCCGEqOMkASSEEEIIIYQQQghRx0kCSAghhBBCCCGEEKKOkwSQEEIIIYQQQgghRB2ntNa1P6lSu4ANtT7xgSsV2B3pIESdJq8xEW7yGhPhJq8xEW7yGhPhJq8xEW7yGhMAzbTW9Up7ICIJIFGcUmqe1rpbpOMQdZe8xkS4yWtMhJu8xkS4yWtMhJu8xkS4yWtMVES2gAkhhBBCCCGEEELUcZIAEkIIIYQQQgghhKjjJAF0YHg70gGIOk9eYyLc5DUmwk1eYyLc5DUmwk1eYyLc5DUmyiU1gIQQQgghhBBCCCHqOFkBJIQQQgghhBBCCFHHSQIoApRS45VSCwv/t14ptbCM49YrpZYUHjevlsMUBzGl1ONKqS37vM7OLOO4M5RSK5VSa5RSD9R2nOLgpZQaqZRaoZRarJT6SimVWMZx8j4mKq2i9ySllKvwM3SNUmqOUqp5BMIUBymlVBOl1DSl1DKl1D9KqTtKOeYkpVTWPp+fj0YiVnHwquhzT1lGF76PLVZKdYlEnOLgpJRqs8/700KlVLZSauh+x8j7mCiTPdIBHIq01hfv/VopNQrIKufwk7XWu8MflaiDXtJav1DWg0opG/A60BfYDMxVSn2rtV5WWwGKg9pU4EGtdUApNRx4ELi/jGPlfUxUqJLvSdcCGVrrVkqpS4DhwMUlRxOiVAHgbq31AqVUHDBfKTW1lM+9GVrrARGIT9Qd5X3u9QNaF/6vO/Bm4f8LUSGt9UqgMxR9bm4BvirlUHkfE6WSFUARpJRSwEXAp5GORRySjgXWaK3/1Vr7gM+AcyIckzhIaK2naK0Dhd/OBhpHMh5RJ1TmPekc4P3CrycCpxZ+lgpRIa31Nq31gsKvc4DlQKPIRiUOQecAH2jLbCBRKXVYpIMSB6VTgbVa6w2RDkQcPCQBFFm9gB1a69VlPK6BKUqp+UqpG2oxLlE33Fq4tHicUiqplMcbAZv2+X4zciEsquca4IcyHpP3MVFZlXlPKjqmMAGZBaTUSnSiTincPng0MKeUh3sopRYppX5QSrWv3chEHVDR555cf4lQuYSyFxLI+5golWwBCxOl1M9Ag1IeGqa1/qbw60spf/VPT631FqVUGjBVKbVCa/17qGMVB6fyXmNYy4mfwroIeQoYhfVHuhCVVpn3MaXUMKxtFR+XMYy8jwkhDihKqVjgC2Co1jp7v4cXAM201rmF9fO+xtqqI0RlyeeeCDullBM4G2sL/v7kfUyUSRJAYaK17lPe40opOzAQ6FrOGFsK/3+nUuorrOXx8gEigIpfY3sppcYC35Xy0BagyT7fNy78mRBApd7HrgIGAKdqrXUZY8j7mKisyrwn7T1mc+HnaAKwp3bCE3WBUsqBlfz5WGv95f6P75sQ0lpPVkq9oZRKlTpmorIq8bkn118iFPoBC7TWO/Z/QN7HRHlkC1jk9AFWaK03l/agUiqmsEAhSqkY4DRgaS3GJw5i++0lP4/SXztzgdZKqRaFdxEuAb6tjfjEwU8pdQZwH3C21jq/jGPkfUxURWXek74Friz8+gLg17KSj0Lsr7Be1DvAcq31i2Uc02BvXSml1LFY18qSZBSVUsnPvW+BwYXdwI4DsrTW22o5VHHwK3MnibyPifLICqDIKbFnUynVEPif1vpMoD7wVeF/u3bgE631j7UepThYjVBKdcbaArYeuBGKv8YKuzfdCvwE2IBxWut/IhSvOPi8BriwlrcDzNZaD5H3MVFdZb0nKaWeBOZprb/F+uP9Q6XUGiAd67NUiMo6AbgCWKKUWlj4s4eApgBa6zFYicWblFIBoAC4RJKMogpK/dxTSg2BotfYZOBMYA2QD1wdoVjFQaowudiXwuv7wp/t+xqT9zFRJiWvBSGEEEIIIYQQQoi6TbaACSGEEEIIIYQQQtRxkgASQgghhBBCCCGEqOMkASSEEEIIIYQQQghRx0kCSAghhBBCCCGEEKKOkwSQEEIIIYQQQgghRB0nCSAhhBBCCCGEEEKIOk4SQEIIIYQQQgghhBB1nCSAhBBCCCGEEEIIIeq4/wPLUjopCj3GVAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(X[:,0], X[:,1], c=y, s=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b99fc0",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We now train a KNN model in order to classify points and assign them to a cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6c4ef294",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61396083",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "If we take a point such as `(2.5, -1)` it is clear this point will belong to cluster `2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "20db0e93",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "ORIGINAL = [2.5, -1.0]\n",
    "plt.scatter(X[:,0], X[:,1], c=y, s=5)\n",
    "plt.scatter(ORIGINAL[0], ORIGINAL[1], c=\"red\", s=50)\n",
    "plt.plot([ORIGINAL[0], 5], [ORIGINAL[1], 0], c=\"red\")\n",
    "plt.plot([ORIGINAL[0], 0], [ORIGINAL[1], 5], c=\"grey\", linestyle=\"dashed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "450d78fe",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "knn.predict_proba(np.array([[2.5, -1.0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78254968",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can now create our prediction function. I will simply take the `x` and `y` and return the cluster classification,\n",
    "along with a probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2b7eb6b8",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def knn_classify(inputs):\n",
    "    values = [_feature.value.as_number() for _feature in inputs[0].features]\n",
    "    result = knn.predict_proba(np.array([values]))\n",
    "    index = np.argmax(result, axis=1)[0]\n",
    "    _output = output(name=\"cluster\", dtype=\"number\", value=index, score=result[0][index])\n",
    "    return [PredictionOutput([_output])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3251fe25",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We wrap this function in the `Model` wrapper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0553afb3",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from trustyai.model import Model\n",
    "\n",
    "knn_model = Model(knn_classify)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d582d36",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can test it and confirm it works as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6ed2a16",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=2, type=number, score=1.0, name='cluster'}'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [feature(\"x\", \"number\", 2.5, (-10, 10)), feature(\"y\", \"number\", -1.0, (-10, 10))]\n",
    "knn_model.predictAsync([PredictionInput(features)]).get()[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62769fce",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We now define our goal. We want it to belong to cluster `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f07ba3d7",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "goal = [output(name=\"cluster\", dtype=\"number\", value=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0b4e41",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We pass this data to the explainer along with the search boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "63f8ab79",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "prediction = counterfactual_prediction(\n",
    "    input_features=features,\n",
    "    outputs=goal,\n",
    ")\n",
    "explanation = explainer.explain(prediction, knn_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1709b11",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The counterfactual we get is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "69b0b868",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.501921601686025, 2.6401536249215436]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = [f.as_feature().value.as_number() for f in explanation._result.entities]\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad3e53c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can confirm this point belongs to cluster `1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e60c63e7",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=1, type=number, score=1.0, name='cluster'}'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testf = [feature(\"x\", \"number\", result[0]), feature(\"y\", \"number\", result[1])]\n",
    "knn_model.predictAsync([PredictionInput(testf)]).get()[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2518f039",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "And visually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7f0dd574",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(X[:,0], X[:,1], c=y, s=5)\n",
    "plt.scatter(ORIGINAL[0], ORIGINAL[1], c=\"red\", s=50)\n",
    "plt.scatter(result[0], result[1], c=\"green\", s=50)\n",
    "plt.plot([result[0], 5], [result[1], 0], c=\"grey\", linestyle=\"dashed\")\n",
    "plt.plot([result[0], 0], [result[1], 5], c=\"green\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "740b5f9f",
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>proposed</th>\n",
       "      <th>original</th>\n",
       "      <th>constrained</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>2.501922</td>\n",
       "      <td>2.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y</td>\n",
       "      <td>2.640154</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.640154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  features  proposed  original  constrained  difference\n",
       "0        x  2.501922       2.5        False    0.001922\n",
       "1        y  2.640154      -1.0        False    3.640154"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanation.as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "77d3fc41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cvnLlyvj+978fjRs3joKCgrj33nsr+te3RaIiAAAAADu9u+++O2rXrh0vvvhi/PrXv45bb701/vCHP5TcfvPNN8dBBx0UM2bMiCuuuCLmzJkTgwcPjpNPPjlmz54dV111VYwZMyYmTpxY6rw33XRTHHTQQTFz5sy45JJL4mc/+1k8/fS/LtEuLi6Ok08+OT799NN46qmn4rHHHovCwsI444wzSo4fOnRotGzZMl566aWYPn16XHLJJVGnTp2IiHj//ffj//7f/xsnnnhizJo1K+6+++6YNm1aDB8+vOT4H/zgB7FkyZKYPHly3HvvvXHnnXfGRx99tAN/k//iMxUBAAAA2Om1atUqrr/++sjlctG+fft48803Y9y4cTFs2LCIiOjdu3epWDdkyJA46qijYsSIERERsf/++8c777wTN910U/znf/5nyX49e/aMSy+9tGSf6dOnx7hx46Jv377x7LPPxhtvvBHvvPNOtG7dOiIifv/730eXLl1i1qxZ0a1bt1i8eHH89Kc/jYKCgoiI2G+//UrOff3118epp54aF154YcltN9xwQ/Tr1y/GjRsXixcvjsmTJ8eLL74Y3bp1i4iIO++8Mw4++OAd9Fv8/3mnIgAAAAA7ve7du0culyv5uUePHrFgwYIoKiqKiIhDDjmk1P7vvvtu9OzZs9S2nj17ljpm03m+qkePHvHOO++UnKNVq1YlQTEiomPHjtG4ceOSfS666KI477zz4thjj43rr78+3n///ZJ9X3/99Zg0aVLsvvvuJX9OOOGEKC4ujoULF8Y777wTtWvXLjV7QUFBNG7cuDy/oiSiIgAAAADfervuumuV3O/IkSNj7ty5ceyxx8bUqVOjS5cu8cgjj0RExJo1a+Kcc86Jl19+ueTPK6+8Em+++Wbsu+++VTLvJi5/BgAAAGCn98orr5T6eebMmbHffvtFXl7eFvcvKCiI6dOnl9o2ffr02H///Usd8/LLL2923g4dOpScY8mSJbF48eKSdyu+/fbbsWrVqujYsWPJMfvvv3/sv//+cdFFF8WZZ54ZEydOjIEDB0bnzp3j7bffjnbt2m11xo0bN8acOXNKLn9+7733YtWqVWX4jWwf71QEAAAAYKe3ePHiuOyyy+K9996L++67L26//fY4//zzt7r/8OHDY8qUKXHttdfG/PnzY9KkSXH77beX+tzFiH+FxhtuuCHmz58fd9xxRzz44INxwQUXRETE0UcfHQceeGAMHTo05s6dG6+88koMGzYs/u3f/i26du0aa9eujeHDh8dzzz0XixYtimnTpsXs2bNLouQll1wSM2bMiOHDh8err74aCxYsiEcffbRkhvbt20f//v3jggsuiJdffjnmzJkTP/rRj6J+/fo75Hf4Vd6pCAAAAEC5LT3y6e0+R35+fqxbt64Cptm6wYMHx9q1a+OII46IvLy8OP/88+Occ87Z6v5dunSJe+65J66++uq47rrronnz5vHzn/+81Je0RET85Cc/iTlz5sQ111wTDRs2jF/96lfRr1+/iIjI5XJx//33x8UXXxx9+/aNWrVqRf/+/ePGG2+MiIi8vLz45JNPYtiwYbFy5cpo2rRpDBw4MEaOHBkREZ06dYqnnnoqRo0aFUcffXRkWRb77rtvnHTSSSX3/9vf/jbOO++86NevX+y5554xevToGDNmTEX/+jYjKgIAAACw06tTp078+te/jnHjxm1223vvvbfFY0488cQ48cQTt3nehg0bxj333LPV2/fee+/4y1/+ssXb6tatG5MmTdrm+bt16xaPPfbYVm9v3rx5PPTQQ6W2DR48eJvnrAgufwYAAAAAkoiKAAAAAEASlz8DAAAAsFN76qmndsh5t3bZ9LeBdyoCAAAAAElERQAAAAAgiagIAAAAACQRFQEAAACAJKIiAAAAAJBEVAQAAAAAkoiKAAAAAPA1v/jFL6J79+5Jx/Tr1y8uueSSKp+jMtSu6gEAAAAAqLnq1atXqff35ZdfVsr9/PSnP40f//jHScfcd999UadOnR00UfUiKgIAAADA/8qyLIqKiqJBgwbRoEGDpGObNGmyg6aqflz+DAAAAMBObd26dXHxxRdH69ato1GjRnHUUUfFrFmzIiLiueeei3r16sXkyZOjZ8+e0bBhw3jppZc2u+x448aNcfHFF0ezZs1ir732iiuvvDKGDRsWJ598csk+X7/8uX379vHLX/4yzj333GjatGnst99+cdddd5Wa7corr4wDDzwwdtttt+jQoUOMHj06NmzYsIN/I9tPVAQAAABgpzZixIh4+OGH46677ooZM2ZEu3bt4oQTTohPP/20ZJ+rrroqfvGLX8S8efOiU6dOm53j17/+dfzpT3+K3/72tzFlypT4/PPP49FHH/3G+77llluia9euMXPmzPjhD38YF110Ubz33nsltzdo0CB+97vfxdy5c+OGG26I8ePHx6233loxD3wHEhUBAAAA2Gl98cUX8dvf/jauvfbaGDBgQHTs2DFuv/32qF+/fkyYMKFkv5///OfRt2/faNeu3RYvY7799tvj0ksvjYEDB0ZBQUHcfPPN0ahRo2+8/wEDBsQPf/jDaNeuXVxyySXRtGnTeO6550puv+KKK6Jnz57Rpk2bOO6442L48OHxwAMPVMhj35F8piIAAAAAO60PPvggNmzYED179izZVqdOnejWrVu888470bVr14iIOOSQQ7Z6jtWrV8eKFSuiW7duJdvy8vLikEMOieLi4m3e/1ff9ZjL5aJZs2axcuXKkm33339/3HbbbVFYWBhr1qyJjRs3RsOGDZMfZ2XzTkUAAAAAvvV23XXXHXLer38bdC6XKwmRM2bMiKFDh8YxxxwTDz74YMycOTP+67/+K9avX79DZqlIoiIAAAAAO61999036tatG9OnTy/ZtmHDhpg9e3Z07NixTOdo1KhRNGvWLGbPnl2yraioKObOnbtds82YMSP23nvvuPzyy6Nr166x3377xYcffrhd56wsLn8GAAAAYKe16667xrnnnhsjRoyIJk2aROvWreOGG26If/7znzF06NB47bXXynSe8847L66//vpo165dFBQUxG9+85tYtWpV5HK5cs+23377xeLFi+PPf/5zdO3aNZ588sn4n//5n3KfrzKJigAAAACU25dffrnd58jPz49169ZVwDRbNnbs2CguLo6zzz47/vGPf8QhhxwSjz76aOy2225lPscll1wSK1asiGHDhkVeXl4MGzYs+vXrF7Vqlf9C4OOPPz4uuuii+OlPfxrr1q2LY489Nq644ooYO3Zsuc9ZWXJZlmVVPURFW7JkSY249jxVLpeLtm3bRmFhYeyEf21UI9YalcVaozJYZ1QWa43KYq1RWaw1tmTjxo2xzz77VPh5d3RU3BGKi4vj4IMPjn//93+P0aNHV/U4yRYtWhS1a2/+fsO6detGq1atvvF471QEAAAAgG+waNGiePrpp+PII4+MdevWxe233x4LFy6MU089tapHqxKiIgAAAAB8g1q1asWkSZPiiiuuiCzL4oADDojHH388OnToUNWjVQlREQAAAAC+QevWrWPq1KlVPUa1Uf5PkgQAAAAAvpVERQAAAAAgiagIAAAAQJkUFxdHUVFRVY/BdioqKori4uLtOoeoCAAAAECZ5OXlxZIlS4TFGqyoqCiWLFkSeXl523UeX9QCAAAAQJnk5eVFUVFRfPjhh1GrVsW9V61+/fqxdu3aCjsfW1dcXBx5eXmiIgAAAACVpyKC1Fflcrlo3bp1FBYWRpZlFXZediyXPwMAAAAASURFAAAAACCJqAgAAAAAJBEVAQAAAIAkoiIAAAAAkERUBAAAAACSiIoAAAAAQBJREQAAAABIIioCAAAAAElERQAAAAAgiagIAAAAACQRFQEAAACAJKIiAAAAAJBEVAQAAAAAkoiKAAAAAEASUREAAAAASCIqAgAAAABJREUAAAAAIImoCAAAAAAkERUBAAAAgCSiIgAAAACQRFQEAAAAAJKIigAAAABAElERAAAAAEgiKgIAAAAASURFAAAAACCJqAgAAAAAJBEVAQAAAIAkoiIAAAAAkERUBAAAAACS1K7qAUiTy+WqegSACpVlWVWPAAAAQCLvVAQAAAAAkoiKAAAAAEASUREAAAAASCIqAgAAAABJREUAAAAAIImoCAAAAAAkERUBAAAAgCSiIgAAAACQRFQEAAAAAJKIigAAAABAElERAAAAAEgiKgIAAAAASURFAAAAACCJqAgAAAAAJBEVAQAAAIAkoiIAAAAAkERUBAAAAACSiIoAAAAAQBJREQAAAABIIioCAAAAAElERQAAAAAgiagIAAAAACQRFQEAAACAJKIiAAAAAJBEVAQAAAAAkoiKAAAAAEASUREAAAAASCIqAgAAAABJREUAAAAAIImoCAAAAAAkERUBAAAAgCSiIgAAAACQRFQEAAAAAJKIigAAAABAElERAAAAAEgiKgIAAAAASURFAAAAACCJqAgAAAAAJBEVAQAAAIAkoiIAAAAAkERUBAAAAACSiIoAAAAAQBJREQAAAABIIioCAAAAAElERQAAAAAgiagIAAAAACQRFQEAAACAJKIiAAAAAJBEVAQAAAAAkoiKAAAAAEASUREAAAAASCIqAgAAAABJREUAAAAAIImoCAAAAAAkERUBAAAAgCSiIgAAAACQRFQEAAAAAJKIigAAAABAElERAAAAAEgiKgIAAAAASURFAAAAACCJqAgAAAAAJBEVAQAAAIAkoiIAAAAAkERUBAAAAACSiIoAAAAAQBJREQAAAABIIioCAAAAAElERQAAAAAgiagIAAAAACQRFQEAAACAJKIiAAAAAJBEVAQAAAAAkoiKAAAAAEASUREAAAAASCIqAgAAAABJREUAAAAAIImoCAAAAAAkERUBAAAAgCSiIgAAAACQRFQEAAAAAJKIigAAAABAkmoTFSdOnBi77757rFu3rtT2QYMGxZlnnllFUwEAAAA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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e615e762",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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