ps1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Macro 2: Problem Sheet 1\n",
    "\n",
    "### Exercise 1\n",
    "\n",
    "> **Remark 1.** This notebook is structured as follows. First I define all necessary functions in a sort of abstract fashion. Then at the very end I call all functions to produce numerical results and plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from functools import partial\n",
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from joblib import Parallel\n",
    "from joblib import delayed\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = Path(\"data/CNEF_PSID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "##### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compose(f, g):\n",
    "    \"\"\"Composition of two functins.\n",
    "    \n",
    "    h(x) := f(g(x)) for all x\n",
    "    \n",
    "    \"\"\"\n",
    "    h = lambda x: f(g(x))\n",
    "    return h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_yearly_variables(year):\n",
    "    \"\"\"Returns list and dict of relevant columns given year.\n",
    "    \n",
    "    Convert variables names to year specific names. For example instead\n",
    "    of 'x11102' writes 'x1110285' for the year '85. Additionally add\n",
    "    variable name for individual id.\n",
    "    \n",
    "    For details on variable names and more information seek the\n",
    "    codebook: https://www.cnefdata.org/documentation/codebooks\n",
    "    \n",
    "    \n",
    "    Args:\n",
    "        year (int): Year. (Write 85 for 1985.)\n",
    "        \n",
    "    Returns:\n",
    "        as_list (list): List of variable names.\n",
    "        as_dict (dict): Dict of variable name translation to human\n",
    "            readable form.\n",
    "    \n",
    "    \"\"\"\n",
    "    variables = {\n",
    "        \"x11102\": \"household\",\n",
    "        \"i11113\": \"income\",\n",
    "        \"d11105\": \"relationship_to_head\",\n",
    "        \"d11101\": \"age\",\n",
    "        \"d11109\": \"education\",\n",
    "        \"e11101\": \"hours\",\n",
    "    }\n",
    "    as_dict = {f\"{key}{year}\": value for key, value in variables.items()}\n",
    "    as_dict = dict(as_dict, **{\"x11101ll\": \"individual\"})\n",
    "    as_list = [f\"{key}{year}\" for key in variables.keys()]\n",
    "    as_list.append(\"x11101ll\")\n",
    "    return as_dict, as_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_given_year(year):\n",
    "    \"\"\"Load data and assign new columns given year.\n",
    "    \n",
    "    This already does some steps described in part 2 of exercise 1.\n",
    "    \n",
    "    Args:\n",
    "        year (int): Year. (Write 85 for 1985.)\n",
    "        \n",
    "    Returns:\n",
    "        df (pd.DataFrame): Data frame with columss\n",
    "    \n",
    "    \"\"\"\n",
    "    cols_mapper, cols = get_yearly_variables(year)\n",
    "\n",
    "    df = pd.read_stata(DATA_PATH / f\"pequiv{year}.dta\", columns=cols)\n",
    "\n",
    "    df = df.rename(columns=cols_mapper)\n",
    "    df = df.set_index([\"household\", \"individual\"])\n",
    "    df = df.dropna(how=\"all\")\n",
    "    df = df.assign(\n",
    "        **{\n",
    "            \"year\": year,\n",
    "            \"relationship_to_head\": df.relationship_to_head.str.split(\" \").apply(\n",
    "                lambda s: s[0]\n",
    "            ),\n",
    "        }\n",
    "    )\n",
    "    df = df.convert_dtypes()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    \"\"\"Clean data frame.\n",
    "    \n",
    "    This does most steps described in part 2 of exercise 1.\n",
    "    \n",
    "    Args:\n",
    "        df (pd.DataFrame): Frame produced by :func:`load_data_given_year`.\n",
    "        \n",
    "    Returns:\n",
    "        df (pd.DataFrame): Cleaned data frame.\n",
    "    \n",
    "    \"\"\"\n",
    "    TEN_PERCENT_OF_ANNUAL_FULLTIME_HOURS = 312  # (30 h/w * 52 / 10) * 2 (households)\n",
    "\n",
    "    df = df.query(\"relationship_to_head in ['head', 'partner']\")\n",
    "    df = df.assign(\n",
    "        **{\n",
    "            \"is_single\": df.relationship_to_head.groupby(\"household\").transform(\n",
    "                lambda x: set(x) != {\"head\", \"partner\"}\n",
    "            )\n",
    "        }\n",
    "    )\n",
    "    df = df.query(\"is_single == False\")\n",
    "\n",
    "    total = df.groupby(by=\"household\")[[\"hours\"]].sum()\n",
    "\n",
    "    df = df.query(\"relationship_to_head == 'head'\")\n",
    "    df = df.reset_index(level=\"individual\")\n",
    "    df = df.assign(**{\"hours\": total.hours})\n",
    "    df = df.set_index(\"individual\")\n",
    "\n",
    "    df = df.query(\"25 <= age < 56\")\n",
    "    df = df.query(\"hours >= @TEN_PERCENT_OF_ANNUAL_FULLTIME_HOURS\")\n",
    "    df = df.query(\"income > 0\")\n",
    "\n",
    "    df = df.assign(**{\"income\": np.log(df.income)})\n",
    "\n",
    "    df = df.drop([\"relationship_to_head\", \"is_single\", \"hours\"], axis=1)\n",
    "    df = df.set_index(\"age\", append=True)\n",
    "    df.index = df.index.set_names([\"household\", \"age\"])\n",
    "    df = df.dropna(how=\"any\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_clean_data(n_jobs=1, load_from_disc=False):\n",
    "    \"\"\"Load, clean and merge data for years 1980 to 1997.\n",
    "    \n",
    "    Since loading and cleaning the data is time consuming there is\n",
    "    a check if clean data is already available, otherwise use multiple cores.\n",
    "    \n",
    "    Args:\n",
    "        n_jobs (int): Number of cores to use for parallelized data cleaning.\n",
    "        load_from_disc (bool): Should the dataset be loaded from disc if available.\n",
    "            Default False.\n",
    "    \n",
    "    Returns:\n",
    "        df (pd.DataFrame): Cleaned and merged data frame with index ['household', 'year']\n",
    "            and columns 'income', 'age', 'education' and 'work'. Column 'income' is float\n",
    "            while all other columns are category.\n",
    "    \n",
    "    \"\"\"\n",
    "    clean_data_path = DATA_PATH.parent / \"clean_data.csv\"\n",
    "    if load_from_disc and clean_data_path.exists():\n",
    "        df = pd.read_csv(clean_data_path)\n",
    "    else:\n",
    "        _load_and_clean = compose(clean_data, load_data_given_year)\n",
    "        dfs = Parallel(n_jobs, prefer=\"processes\")(\n",
    "            delayed(_load_and_clean)(year) for year in range(80, 98)\n",
    "        )\n",
    "        df = pd.concat(dfs).sort_index().reset_index()\n",
    "\n",
    "    df = df.astype(\n",
    "        {\n",
    "            \"age\": int,\n",
    "            \"household\": int,\n",
    "            \"income\": float,\n",
    "            \"education\": \"category\",\n",
    "            \"year\": int,\n",
    "        }\n",
    "    )\n",
    "    df = df.drop_duplicates(subset=[\"household\", \"age\"], keep=\"first\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dummy Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_dummy_regression(df):\n",
    "    \"\"\"Fit dummy regression on data in df.\n",
    "    \n",
    "    In the formula object C() tells statsmodels to use the variable as\n",
    "    categorical variable.\n",
    "    \n",
    "    \"\"\"\n",
    "    df = df.copy().astype({\"age\": \"category\", \"year\": \"category\"})\n",
    "    model = ols(\"income ~ C(year) + C(age) + C(education)\", data=df)\n",
    "    model = model.fit()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Estimation approaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_quantities_approach_4(df):\n",
    "    \"\"\"Estimate quantities using approach in part 4.\n",
    "    \n",
    "    The correction for year and birthyear here is not *correct*.\n",
    "    \n",
    "    Args:\n",
    "        df (pd.DataFrame): Cleaned data with residuals.\n",
    "    \n",
    "    Returns:\n",
    "        quantities (pd.DataFrame): Estimates of rho, sigma_eps^2 and sigma_mu_tau^2.\n",
    "        var (pd.DataFrame): Sample variances.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def _estimate(var):\n",
    "        _rho = (var.loc[55] - var.loc[40]) / (var.loc[40] - var.loc[25])\n",
    "        rho = _rho ** (1 / 30)\n",
    "        gamma = rho ** 2 * (1 - rho ** 30) / (1 - rho ** 2)\n",
    "\n",
    "        sigma_eps = (var.loc[40] - var.loc[25]) / gamma\n",
    "        sigma_mu_tau = var.loc[25] - sigma_eps\n",
    "        return rho, sigma_eps, sigma_mu_tau\n",
    "\n",
    "    df = df[[\"age\", \"residuals\", \"year\"]]\n",
    "\n",
    "    # create cross-sectional variances\n",
    "    # notation: csvar = cross-sectional variance\n",
    "    data = df.groupby([\"age\", \"year\"])[[\"residuals\"]].var()\n",
    "    data = data.reset_index()\n",
    "    data = data.rename({\"residuals\": \"csvar\"}, axis=1)\n",
    "    data = data.assign(**{\"birthyear\": data.year - data.age})\n",
    "\n",
    "    for correction in [\"year\", \"birthyear\"]:\n",
    "        model = ols(\n",
    "            f\"csvar ~ C({correction})\", data=data.astype({f\"{correction}\": \"category\"})\n",
    "        )\n",
    "        data[f\"csvar-{correction}\"] = data[\"csvar\"] - model.fit().resid * 0.65\n",
    "\n",
    "    var = data[[\"age\", \"csvar\", \"csvar-year\", \"csvar-birthyear\"]]\n",
    "    var = var.rename({\n",
    "            \"csvar\": \"baseline\",\n",
    "            \"csvar-year\": \"year\",\n",
    "            \"csvar-birthyear\": \"birthyear\",\n",
    "        }, axis=1\n",
    "    )\n",
    "    var = var.groupby(\"age\")[[\"baseline\", \"year\", \"birthyear\"]].mean()\n",
    "\n",
    "    index = [\"rho\", \"sigma_epsilon\", \"sigma_mu_tau\"]\n",
    "    columns = [\"baseline\", \"year\", \"birthyear\"]\n",
    "    quantities = pd.DataFrame(columns=columns, index=index)\n",
    "\n",
    "    quantities.loc[:, \"baseline\"] = _estimate(var[\"baseline\"])\n",
    "    quantities.loc[:, \"year\"] = _estimate(var[\"year\"])\n",
    "    quantities.loc[:, \"birthyear\"] = _estimate(var[\"birthyear\"])\n",
    "\n",
    "    quantities = quantities.reset_index().rename({\"index\": \"symbol\"}, axis=1)\n",
    "\n",
    "    return quantities, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_residual_variance(quantities, age):\n",
    "    \"\"\"Predict residual variance.\n",
    "    \n",
    "    Predicts residual variance given `age` using estimates from part 4.\n",
    "    \n",
    "    Args:\n",
    "        part4 (pd.DataFrame): Output from :func:`estimate_quantities_part4`.\n",
    "            Has columns [symbol, baseline, year, birthyear].\n",
    "        age (int or int-array-like): age for which to predict the residual\n",
    "            variance\n",
    "            \n",
    "    Returns:\n",
    "        prediction (pd.DataFrame): Variance estimate with rows corresponding to\n",
    "            `age` and columns corresponding to the columns of `part4`.\n",
    "    \n",
    "    \"\"\"\n",
    "    def _estimate(h, col):\n",
    "        \"\"\"Variance of residual predicted by model.\"\"\"\n",
    "        h = h - 24\n",
    "        rho = part4.loc[0, col]\n",
    "        sigma_e = part4.loc[1, col]\n",
    "        sigma_mu_tau = part4.loc[2, col]\n",
    "        out = sigma_e * (1 - rho ** (2 * h)) / (1 - rho ** 2) + sigma_mu_tau\n",
    "        return out\n",
    "\n",
    "    cols = quantities.columns.drop(\"symbol\")\n",
    "    prediction = pd.DataFrame(columns=cols, index=age)\n",
    "    for h, col in product(age, cols):\n",
    "        prediction.loc[h, col] = _estimate(h, col)\n",
    "\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_part4(quantities, var):\n",
    "    \"\"\"Plot results from part 4.\"\"\"\n",
    "    var = var.add_prefix(\"true-\")\n",
    "    \n",
    "    predicted = predict_residual_variance(quantities, var.index.values)\n",
    "    predicted = predicted.add_prefix(\"pred-\")\n",
    "    \n",
    "    df = pd.concat((var, predicted), axis=1)\n",
    "    df = df.assign(**{\"age\": var.index})\n",
    "    df = df.melt(id_vars=\"age\", var_name=\"type\", value_name=\"value\")\n",
    "    df = df.convert_dtypes()\n",
    "    df[[\"data\", \"type\"]] = df[\"type\"].str.split('-', 1, expand=True)\n",
    "    \n",
    "    fig, ax = plt.subplots(1)\n",
    "    fig.set_size_inches(13, 7)\n",
    "    \n",
    "    palette = {\n",
    "        \"baseline\": \"tab:orange\",\n",
    "        \"year\": \"tab:blue\",\n",
    "        \"birthyear\": \"tab:green\",\n",
    "    }\n",
    "\n",
    "    p = sns.lineplot(\n",
    "        x=\"age\", y=\"value\", hue=\"type\", style=\"data\", data=df, ax=ax, linewidth=2, legend=\"brief\",\n",
    "        palette=palette, style_order=[\"pred\", \"true\"]\n",
    "    )\n",
    "\n",
    "    ax.set(ylabel=\"Cross-sectional variance\", xlabel=\"Age\")\n",
    "    ax.set_frame_on(False)\n",
    "    ax.xaxis.label.set_size(13)\n",
    "    ax.yaxis.label.set_size(13)\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_quantities_approach_5(df, var):\n",
    "    \"\"\"Estimate quantities using approach in part 5.\n",
    "    \n",
    "    Args:\n",
    "        df (pd.DataFrame): Cleaned data with residuals.\n",
    "        var (pd.DataFrame): Variance output from :func:`estimate_quantities_approach_4`.\n",
    "        \n",
    "    Returns:\n",
    "        quantities (pd.DataFrame): Estimated values.\n",
    "    \n",
    "    \"\"\"\n",
    "    forty_year_old_households = df.query(\"age == 40\")[\"household\"]\n",
    "    df = df.query(\"household in @forty_year_old_households\")\n",
    "\n",
    "    # compute (sample) covariances\n",
    "    combinations = [(40, 39), (40, 38), (40, 37)]\n",
    "    cov = pd.DataFrame(index=pd.MultiIndex.from_tuples(combinations))\n",
    "    for comb in combinations:\n",
    "\n",
    "        _df = df[[\"household\", \"age\", \"residuals\"]].query(\"age in @comb\")\n",
    "        idx = _df.groupby(\"household\")[\"age\"].transform(\n",
    "            lambda x: set(x) == set(comb) and len(x) == 2  # check that both 40 and (e.g.) 37 in data\n",
    "        )\n",
    "        _df = _df.loc[idx, :].set_index([\"household\", \"age\"])\n",
    "\n",
    "        _cov = _df.unstack(level=\"age\").cov().values[0, 1]\n",
    "        cov.loc[comb, \"cov\"] = _cov\n",
    "\n",
    "    # compute quantities\n",
    "    rho = (\n",
    "        (cov.loc[(40, 37),] - cov.loc[(40, 38)])\n",
    "        / (cov.loc[(40, 38),] - cov.loc[(40, 39)])\n",
    "    )[0]\n",
    "\n",
    "    sigma_eps = (\n",
    "        (cov.loc[(40, 37),] - cov.loc[(40, 37),])[0]\n",
    "        * (1 - rho ** 2)\n",
    "        / (rho * (rho - 1) * (rho ** 29 + 1))\n",
    "    )\n",
    "    sigma_mu = cov.loc[(40, 39),][0] - sigma_eps * rho * (1 - rho ** 30) / (\n",
    "        1 - rho ** 2\n",
    "    )\n",
    "    sigma_tau = var.loc[25, \"baseline\"] - sigma_mu - sigma_eps\n",
    "\n",
    "    quantities = (\n",
    "        pd.DataFrame(\n",
    "            [rho, sigma_eps, sigma_mu, sigma_tau],\n",
    "            columns=[\"part5\"],\n",
    "            index=[\"rho\", \"sigma_epsilon\", \"sigma_mu\", \"sigma_tau\",],\n",
    "        )\n",
    "        .reset_index()\n",
    "        .rename({\"index\": \"symbol\"}, axis=1)\n",
    "    )\n",
    "    return quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_rho_approach_iv(df, n_jobs=1, fixed_effects=True):\n",
    "    \"\"\"Estimate rho using an instrumental variable approach.\n",
    "    \n",
    "    Regression equation:\n",
    "        \n",
    "        (1)   x_{ih}* = rho x_{i, h-1}* + error\n",
    "        \n",
    "    Equation (1) has an endogeneity problem, hence we will construct\n",
    "    a valid instrument z_{i, h-1} for x_{i, h-1}* and estimate rho\n",
    "    using 2SLS.\n",
    "    \n",
    "    (!) A minimum requirement for this to work is that for *some* number\n",
    "    of observations x_{ih}* AND x_{i, h-1}* is observed.\n",
    "    \n",
    "    Args:\n",
    "        df (pd.DataFrame): Cleaned data with residuals.\n",
    "        n_jobs (int): Number of cores to use for parallelized data cleaning.\n",
    "        fixed_effects (bool): Use fixed effects correction.\n",
    "        \n",
    "    Returns:\n",
    "    \n",
    "    \"\"\"\n",
    "    # create instruments\n",
    "    def _create_instrument_or_return_nan(household, age, df):\n",
    "        try:\n",
    "            instrument = (\n",
    "                df.loc[(household, age - 1), \"residuals\"]\n",
    "                - df.loc[(household, age - 2), \"residuals\"]\n",
    "            )\n",
    "        except KeyError:\n",
    "            instrument = np.nan\n",
    "\n",
    "        try:\n",
    "            lagged_residual = df.loc[(household, age - 1), \"residuals\"]\n",
    "        except KeyError:\n",
    "            lagged_residual = np.nan\n",
    "\n",
    "        return instrument, lagged_residual\n",
    "\n",
    "    df = df.set_index([\"household\", \"age\"])\n",
    "\n",
    "    to_parallelize = partial(_create_instrument_or_return_nan, **{\"df\": df})\n",
    "    results = Parallel(n_jobs, prefer=\"processes\")(\n",
    "        delayed(to_parallelize)(household, age) for household, age in df.index\n",
    "    )\n",
    "    results = pd.DataFrame(\n",
    "        results, columns=[\"instrument\", \"lagged_residuals\"], index=df.index\n",
    "    )\n",
    "\n",
    "    df = pd.concat((df, results), axis=1)\n",
    "    df = df.dropna(how=\"any\")\n",
    "\n",
    "    # prepare data sets\n",
    "    df = df.reset_index()\n",
    "    df = df.astype({\"household\": \"category\"})\n",
    "\n",
    "    if fixed_effects:\n",
    "        fixed_effect_dummies = pd.get_dummies(df[\"household\"], prefix=\"hh\")\n",
    "        X = pd.concat((df[[\"lagged_residuals\"]], fixed_effect_dummies), axis=1).values\n",
    "        Z = pd.concat((df[[\"instrument\"]], fixed_effect_dummies), axis=1).values\n",
    "    else:\n",
    "        X = df[[\"lagged_residuals\"]].assign(**{\"intercept\": 1}).values\n",
    "        Z = df[[\"instrument\"]].assign(**{\"intercept\": 1}).values\n",
    "        \n",
    "    y = df[[\"residuals\"]].values\n",
    "\n",
    "    # iv regression\n",
    "    beta = np.linalg.inv(Z.T @ X) @ Z.T @ y  # this can be optimized i know\n",
    "\n",
    "    quantity = (\n",
    "        pd.Series(beta[0], index=[\"rho\"], name=\"part6\")\n",
    "        .to_frame()\n",
    "        .reset_index()\n",
    "        .rename({\"index\": \"symbol\"}, axis=1)\n",
    "    )\n",
    "    return quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_parts(part4, part5, part6):\n",
    "    \"\"\"Combine results from part4 to part6 in one data frame.\"\"\"\n",
    "    df = part4.merge(part5, on=\"symbol\", how=\"outer\", suffixes=(\"_part4\", \"_part5\"))\n",
    "    df = df.merge(part6, on=\"symbol\", how=\"outer\", suffixes=(\"_part4\", \"_part5\"))\n",
    "    df = df.convert_dtypes()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_and_clean_data(n_jobs=4, load_from_disc=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Model and use Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fit_dummy_regression(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.assign(**{\"residuals\": model.resid})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4 - Variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "part4, var = estimate_quantities_approach_4(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>symbol</th>\n",
       "      <th>baseline</th>\n",
       "      <th>year</th>\n",
       "      <th>birthyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rho</td>\n",
       "      <td>1.036401</td>\n",
       "      <td>1.036401</td>\n",
       "      <td>1.135712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sigma_epsilon</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sigma_mu_tau</td>\n",
       "      <td>0.185923</td>\n",
       "      <td>0.222065</td>\n",
       "      <td>0.221479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          symbol  baseline      year  birthyear\n",
       "0            rho  1.036401  1.036401   1.135712\n",
       "1  sigma_epsilon  0.000747  0.000261   0.000004\n",
       "2   sigma_mu_tau  0.185923  0.222065   0.221479"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ysgCIj48nKSmJjIwM3N3dcXR05Ny5cxw+fNi6gQshhBB13Z7F8EFT+OdD8x5Hyr+KGqLOjkhYi52dHV27dsXNzQ21Wk2vXr04d+6ccUTCycmJ999/nz59+vDDDz8QERFBo0aNaNeunXUDF0IIIeq6jFjIjANdgXmPY0wkZERCVG+SSFiYXq/nyJEjfPzxx8b7Hn74YWMlpxstXbq0xH0cOnTIbPEJIYQQohTGHhJm6mpdyNETbGwhLw3yc8DW0bzHE6KCZGqTBZ09e5ZBgwbRvXt3QkJCrB2OEEIIIW5HVqJya85mdKD0j5BRCVEDyIiEBYWFhbFp0yZrhyGEEEKIisguTCTMPCIB4OIL6TFKIuHZ0PzHE6ICZERCCCGEEMIUxqlNZh6RAHANAEcv0Gaa/1hCVJCMSAghhBBCmCLrhs7W5nbPd+btni1EFZCfUCGEEEKI8uTngjZDWQRtiW7TkkSIGkBGJIQQQgghyqOxh1euQE6yshja3AwGyE2FAq35O2kLUUGS7lpQTEwMw4cPv+X+1157jbNnz5b4nK+//pqcnBzj1+3btzdbfEIIIYQohUoF9i7g0cAyx4v6Hd4NgbXTLHM8ISpAEolqYO7cuYSFhd1yv06nY9myZcUSCUsqKDBzwx0hhBCipjAYLHs86W4tagBJJCysoKCAl156iYiICJ599llycnJ48MEHOXbsGKCMOHz88ceMGzeORYsWce3aNR5++GEefPBB4z4+/PBDRowYwfjx40lMTCQzM5P+/fuTn58PUOzrS5cu8fjjjzN69Gjuu+8+zp07B8DmzZsZN24co0aN4pFHHiExUSlpt2DBAmbOnMljjz3GSy+9ZOHvjhBCCFFNHVkB84Lgz1ctczwXX+VW+kiIaqzOrpH47dxv/HLmlyrd593hdzMidESZ21y4cIG5c+fSsWNHXnnlFZYvX17s8ezsbMLDw5k2TRnKXL16Nd988w1eXl7Gx9u2bct//vMf3nvvPX766SemTJlC165d2bZtGwMHDmTdunUMHjwYW1tbZs6cyezZswkJCeHIkSPMnj2bZcuW0bFjR3766SdUKhU///wzS5cu5eWXXwbgxIkTLF++HAcHhyr9/gghhBA1VlaistjaEusjoPiIhMFgueMKcRvqbCJhLQEBAXTs2BGAESNG8O233xZ7XK1WM2TIkFKfb2trS79+/QBo1aoVO3fuBGDs2LEsXbqUgQMHsnr1at566y2ysrI4dOiQMSkB0Gq1AMTFxfGf//yHhIQEtFotQUFBxm369+8vSYQQQghxo8IeEpYo/Qpg6wj27pCXBjkp4ORlmeMKcRvqbCIxInREuaMH5qC66R2Fm7+2t7dHrVaX+nxbW1vjc2xsbNDpdAB07NiR2bNns3fvXnQ6HU2aNCEzMxM3NzfWrFlzy37++9//8sgjjzBgwAD27NnDp59+anzM0dGxwq9PCCGEqJWyCrtaW6AZXSEXXyWRyLwmiYSolmSNhIVdvXqVQ4cOAbBu3Trj6ERpnJ2dycrKMmnfo0aNYvr06YwePRoAFxcXgoKCWL9+PQAGg4GoqCgAMjIy8PNThk1//fXXirwUIYQQou6wZFfrQrLgWlRzkkhYWGhoKL/88gsRERGkpaUxYcKEMrcfP348TzzxRLHF1qWJiIggPT29WInZ999/n5UrVzJixAiGDRvGxo0bAZg6dSrTpk3jvvvuw8PDo1KvSQghhKj1sgtHJHwsd0yPYHBvALp8yx1TiNugMhgsXc/MeiIjI2nevLnFjpeTk2PRaUJ//vknmzZt4v333zfbMSz9PbS02v76ais5bzWTnLeaqc6etw9bQdplmHYEPEOsHc1tq7PnrYar7uetzq6RqG3eeusttm/fzuLFi60dihBCCFH7ZCcpt5ac2iRENSeJRC0xc+ZMa4cghBBC1F6vxEB2Mtg5W+6YugJlfYQ+v0aOgojaT9ZICCGEEEKUx0YNLhYejTi/FT5sAWunlbupENYgiYQQQgghRFn0OtDrLX9c6W4tqjlJJIQQQgghynL6L3jLB1ZNtOxxXf2VWyn/KqopSSSEEEIIIcqSnQgGHdjYWva4Tt6gslEWehdoLXtsIUxgsURi+/btDBkyhEGDBpVYWWjjxo1EREQwcuRIRo8ezf79+wGIjY3lwQcf5K677mLYsGF88803lgr5FgsWLOCLL74o9fGNGzdy9uxZC0YkhBBCCLMzNqOzYA8JUNZlFFaJKoxBVIxeZ+0IaiWLVG3S6XTMmTOHr776Cj8/P8aOHUv//v0JCwszbtO9e3cGDBiASqUiKiqK5557jj///BO1Ws3LL79My5YtyczMZMyYMfTs2bPYc6uLjRs3cscdd1TL2IQQQghRQVlWLP3q4qtMbcqMB/dAyx+/ttj4JsQehkFvQf12Vg6m9rBIInH06FEaNmxIcHAwAMOGDWPTpk3FLridnYvKqeXk5KBSqQDw9fXF11dZbOTi4kLjxo2Jj4+32MX6okWL+PXXXwkICMDLy4uWLVvy008/8eOPP5Kfn0/Dhg157733iIyMZPPmzezdu5dFixaxYMECtm/fzi+//FJsO0s2qBNCCCFEFbDWiASAiz9wTBZcV0ZOKuz/CrQZsPENaNAd7njZ2lHVChZJJOLj4/H39zd+7efnx9GjR2/ZbsOGDcyfP5/k5GQ+//zzWx6PiYkhMjKStm3blnvM6OhocnJyit2Xn59/y31lOXnyJL///jsrVqxAp9MxYcIEmjRpQu/evYmIiADg008/ZcWKFUyYMIE+ffrQp08fBg0aBED//v0ZM2bMLdvVZPn5+URGRlo7DLPJzc2t1a+vtpLzVjPJeauZ6uJ5C064iAtwKTmHLAu/dj+VF85ujbgWE0OmvuLHrovnrZB35DJ8tRkUOHihOb+VgqtHOeMboaw/qeaqw3krq7O2RRIJg8Fwy32FIw43GjRoEIMGDWLfvn18/PHHfP3118bHsrKyePbZZ3n11VdxcXEp95ghISG33BcZGXlbIwLHjh1j8ODBeHl5ATBgwABsbW25fPkyzz//PBkZGWRlZdGrVy8cHR3RaDTY2dkZj7F//34WLVp0y3Y1ma2tbbVu1V5Z1b0VvSiZnLeaSc5bzVQnz9u2bAAaNOsA9S382psr60qDK7mbOnneAAryYN0qADRjlsBvz6DJuEpzLz34t7RycOWr7ufNIqmYv78/cXFxxq/j4+ON05VK0rlzZy5dukRycjKgvAv+7LPPEhERweDBg80e741KSnhefvllZs2axdq1a5k6dSpabcmVFGbNmmXSdkIIIYSoxrRZyq011kiIyjnyg7K+xK8VhA2A0P7K/Wc3WTeuWsIiiUTr1q2Jjo7m8uXLaLVa1q1bR//+/Yttc/HiRePIxYkTJ8jPz8fT0xODwcBrr71G48aNefTRRy0RrlHnzp3ZsGEDubm5ZGZmsmXLFkAZHalXrx75+fmsXbvWuL2zszNZWVnGr7Ozs0vcTgghhBA1yLOH4NWr4Frf8sfOz4WEUxB/0vLHrun0evj3E+XzntNApYKw69ef5ySRqAoWmdqk0WiYNWsWEydORKfTMWbMGMLDw1mxYgUAEyZM4K+//mLNmjVoNBocHBz48MMPUalU7N+/nzVr1tCkSRNGjhwJwPTp0+nbt6/Z427ZsiVDhw5l5MiRBAYG0rFjRwCmTZvGuHHjCAwMpEmTJsbkYejQocycOZNvv/2WTz75hClTppS4nRBCCCFqGDvn8rcxhysH4OuhENwVHv/bOjHUVKf+gKSz4N4AWt6t3Ne4H6CCS7uVkSZrnddaQmUoaQFDLWXpeWY5OTk1fk3Ezar7XL3Kqu2vr7aS81YzyXmrmercedMVgD4fbK30/zzpHCzoAJ4hMO1IhXdT584bQOwR2PoONL4Duj5ZdP/ifnD1INz3MzSx7JT521Xdz1v1X64uhBBCCGEtV/bDXH/4erh1ju9yfU1p5jWoO+/9Vo2AtjBhBXSZVPz+sAHK7YVtlo+plrHI1CYhhBBCiBqpsIeEvat1jm/nArZOkJ8N2kzrxVHT6ApAff0y9+bCOe0fgPDBUL+D5eOqZWREQgghhBCiNNZsRgfKRXDhqERGvHViqGmuRcKHLWHXwpIf9wyB4C5FiYaoMEkkhBBCCCFKk5Wo3Fqz9KuLn3KbKYmESf5dAJlxkHyu7O30OshOtkxMtZQkEkIIIYQQpSlMJJysNCIBN6yTkESiXGlX4OhPStfq7lNL3+7sRnivEfz+nMVCq41kTEcIIYQQojTGqU1WHJHwbQHpsdarHFWT7F6oVNlqORq8GpW+nVdjyE2D81uVkQkbtcVCrE1kRKKG2rNnD08++WT5GwohhBCi4qy9RgKg36vwxCZoepf1YqgJclLhwNfK5z2fLXtbr8bg2UhJJq4cNHdktZYkEtWMTqezdghCCCGEKGTQK9NkrJlICNPs/1KpbNWoL9RvX/72hWVgz202b1y1mExtsqCYmBgmTpxI27ZtOXnyJI0aNeLdd99l2LBhjB49mp07d/LAAw/g7u7OggUL0Gq1BAcH8/bbb+Ps7Mz27duZN28enp6etGzZ0tovRwghhKj9Hv1DmfqCqtxNzUabDQlRSlIT1Ml6cVRn+bmwe5Hyec9ppj0ntD/sWwrnNsEdL5kvtlqsziYSv//+O7/99luV7nPEiBEMH152w5oLFy4wd+5cOnbsyCuvvMLy5csBsLe3Z8WKFSQnJ/PMM8/w1Vdf4eTkxOLFi/nqq6944oknmDlzJt988w0NGzbkueeeq9LYhRBCCFEKa8+fTzwFS/qBfxt4aod1Y6mu9AXQ+XG4tEtJEEwR0htsNBCzX5kW5ehhzghrJZnaZGEBAQF07NgRUBKPAwcOADB06FAAjhw5wtmzZ5kwYQIjR47k119/5erVq5w/f56goCBCQkJQqVSMGDHCaq9BCCGEqBN0BUrVJr2Vpx1L+dfy2bvAHS/DQ2tubUBXGgc3CO4KBh1c2G7e+GqpOjsiMXz48HJHD8xBddMPd+HXjo5KJQaDwUDPnj353//+V2y7yMjIW54rhBBCCDNKOgsLu4JPE5i6z3pxFFaMykqQCkMl0eWD2rZizw0bAHkZpicfohgZkbCwq1evcujQIQDWrVtnHJ0o1K5dOw4ePMjFixcByMnJ4cKFCzRu3JiYmBguXbpkfK4QQgghzKiwYpOTt3XjUNsqMRj0kJ1k3ViqG4MBvh4GPz8CGXG3//xe05XpYs0jqjy0ukASCQsLDQ3ll19+ISIigrS0NCZMmFDscS8vL95++22mT59OREQE48eP5/z589jb2zNnzhwmTZrEhAkTqF+/vpVegRBCCFFHZBd2ta4GFZtkelPJLv4Ll/fA+W1g73r7z5eRiEqps1ObrMXGxoY5c+YUu2/z5uJlx7p3786qVatueW6fPn3o06ePWeMTQgghxHXVoat1IRdfuHYSMuLBv7W1o6k+dn6s3HaZBHbOFdtHVpLS6dqlnukLtQUgIxJCCCGEECWrDl2tC8mIxK3iT8KZv0DjCF2eqPh+Tq+HXybBnsVVF1sdISMSFhQUFMTvv/9u7TCEEEIIYYrCEYnqkEj4t4aMWHD0tHYk1ce/nyi37R+o3PSzwlGI6B1QoAWNXeVjqyNkREIIIYQQoiTGEYlqMLWpxzPw8FpoNtTakVQPaTFw7Gel63j3pyu3L7f64NtC6Yp9eU/VxFdHSCIhhBBCCFESGw3YuVSPREIUt3uR0oSu5d3g1ajy+ysclTi3ueztRDGSSAghhBBClGTcV/DqFaUDsrVps+DCDji/1dqRVA+N71CayfV4tmr2Z0wkNlXN/uoIWSMhhBBCCFGW6lAiNPUyfDMcvMPhmf3Wjsb6wgcpH1WlYQ/QOEDsEWVtjIxCmURGJCwoPT2d77//3tphCCGEEKI8ej2kXFRGAqoDF1/ltq5XbdLlK+emqtk6KskEwLktVb//WkoSCQtKT09nxYoVt9yv0+msEI0QQgghSpWVAB+3gY/aWDsShaMnqO0gLx202daOxnoOfQufdYaoP6p+312ehIhPoJH07DKVTG2yoPnz53Pp0iVGjhyJRqPByckJX19fIiMjWbx4MU899ZSxPOwXX3xBdnY2zzzzDJcuXWL27NmkpKTg4ODAW2+9RWhoqJVfjRBCCFGLVaceEqBMr3Lxg7TLkHUN7EKsHZHl6XXw7wJIPg8FOVW//6Z3Vv0+a7k6nUhMmjSpxPsXL1YaksyfP59Tp07d8vjzzz9P06ZNWbt2LWvXrr3leaV5/vnnOXPmDGvWrGHPnj08+eSTrF27luDgYGJiYkp93syZM5k9ezYhISEcOXKE2bNns2zZMlNeohBCCCEqojqVfi3k4qskEpnXwDPE2tFYXtTvShLh0RCaj7R2NII6nkhYW+vWrQkODi5zm6ysLA4dOsS0adOM92m1WnOHJoQQQtRt2UnKbbVKJOpwd2uDAf75SPm8xzOgNtMlbNQ6+OdDaDEKekw1zzFqkTqdSJgyglCWiIgIIiIiKnx8Jycn4+cajQb9DYuH8vLyADAYDLi5ubFmzZoKH0cIIYQQt6m6TW2Cur3gOvofuHoQnLyh3f3mO05+DsTsA1snSSRMIIutLcjZ2ZmsrJKrP3h7e5OUlERKSgparZatW7cC4OLiQlBQEOvXrweUxCIqKspSIQshhBB1U3VMJOq3h6ZDwbW+tSOxvJ0fK7ddngQ7p7K3rYzG/QAVXNpVtxe1m6hOj0hYmqenJx06dGD48OHY29vj41M0XGpra8vTTz/N+PHjCQoKonHjxsbH3n//fd58800WLVpEQUEBQ4cOpVmzZtZ4CUIIIUTdkJWo3Dp5WzeOG3V8RPmoa+KOw9kNyihBlyfMeyxnb6jfDq4egos7q7ZXRS0kiYSFzZ8/v9THHnroIR566KFb7g8ODuaLL74wZ1hCCCGEuJGtIzj7Fq1LENbj5AVdJimJhJOX+Y8X2l9JJM5tlkSiHJJICCGEEELc7K53lY/qJC9TeZdcXwDNhlk7Gstxqw9D37fc8UIHwI75cHaT5Y5ZQ8kaCSGEEEKImiA7CZaPhz9etHYklpOfa/ljBnUGOxdIPAVppZfnF3UwkTAYDNYOocaS750QQog641okZCYoZUerixurNlWnuMwlOxk+bAG/T1ea0VmKxk7pbm3rDAlS4KYsdWpqk4ODA0lJSXh7e6NSqawdTo1iMBhISkrCwcHB2qEIIYQQ5pWfCwu7gY0GXk9QukpXB7aOYO8OeWmQk2KZ9QLWtO8LZRQmJRps1JY99vAPwdFLSSpEqUxOJPbv38+aNWtISEjg//7v/zh+/Dg5OTl07tzZpOdv376duXPnotfrGTdu3C1dpTdu3MjHH3+MjY0NarWaV199lU6dOpn0XFMFBQURExNDQkJChZ5/u/Lz87G1tbXIsSzBwcGBoKAga4chhBBCmFd2YcUmH7CpZpM3XHyVRCLzWu1OJPJzYM//KZ/3nFb2tubg6m/5Y9ZAJiUSa9eu5b///S8RERHs27cPAJVKxSeffMK3335b7vN1Oh1z5szhq6++ws/Pj7Fjx9K/f3/CwsKM23Tv3p0BAwagUqmIioriueee488//zTpuaaytbWlUaNGt/28ioqMjKR58+YWO54QQgghqkBh6dfq1NW6kIsfJJ2BzDjwrcWl4A8vVxK6gHbKNCNr0Osh/piSULoHWieGas6kNPv//u//WLp0Ka+//jo21zPz8PBwzpw5Y9JBjh49SsOGDQkODsbOzo5hw4axaVPxlfDOzs7G6UY5OTnGz015rhBCCCFElanWiUThOolr1o3DnPQ6+HeB8nmv56w3tWzDTPi8Dxz+3jrHrwFMGpG4du0arVu3BjBe4Gs0GvR6vUkHiY+Px9+/aIjIz8+Po0eP3rLdhg0bmD9/PsnJyXz++ee39dybRUdHk5OTY1J85pKbm0tkZKRVYxC3R85ZzSTnrWaS81Yz1YXz5hZ9lEAgrcCOq9Xstfrm2+ENxJ87SrJta5OfV5POm+vlTQSlXEDrHMg5wsFKcbvYBBMMZB/7nYu+EVaJoTqct7Jm15iUSAQHB3Pw4EE6dOhgvO/gwYMmTxMqqdpPSYudBw0axKBBg9i3bx8ff/wxX3/9tcnPvVlISIhJsZmTTG2qeeSc1Uxy3momOW81U504bykbAXCvH4Z7dXutBYPArgC/5j3wa2Z6bDXqvP0zFQC7O6bTvGUr68XRqD78+ypOScdp3qg+OLhbPITqft5Mmto0ZcoUpkyZwsKFC8nPz+fLL7/k+eefZ8qUKSYdxN/fn7i4OOPX8fHx+Pr6lrp9586duXTpEsnJybf9XCGEEEKISimc2uTkbd04StJ6LIz9ApoNtXYk5nPHK+AVCu3ut24cDu4Q3AUMOriw3bqxVFMmJRIDBw5k/vz5HD16lMDAQHbv3s28efPo27evSQdp3bo10dHRXL58Ga1Wy7p16+jfv3+xbS5evGgcfThx4gT5+fl4enqa9FwhhBBCiCpj7wKejcBdKhVazLnNyuJmgPCBMPlfpdyttYUOUG7PbbZuHNWUyeVfe/bsSc+ePSt2EI2GWbNmMXHiRHQ6HWPGjCE8PJwVK1YAMGHCBP766y/WrFmDRqPBwcGBDz/8EJVKVepzhRBC3KRAKzXPhagKfWYoH9WRNgtO/6n8vrebYO1oKk+XD3+9CnsXwx2vwh0vKffbVpO+VaH9Yct/4ewmpQlgdekpUk2YlEgsX76c1q1bGxdcAxw7dozjx48zYYJpP8R9+/a9ZQTjxudOmjSp1P4QJT1XCCHEDba8DTs/gie3Q72m1o5GCGEu2ixY+ZjSLK2mJxKZCfDzw3BxJ6jtwC3A2hHdqn47cPSE1IuQfB68Q60dUbVi0tSmJUuWEBwcXOy+oKAglixZYpaghBBC3KZt70BBLiScsnYkQtR8sUcg+YJShrS6cfIGlQ3kJCujEjXV1UOw+A4liXDxh0fWQYeHrB3VrWzU0OFh6PY0qGtPk+GqYtKIRHp6Oh4eHsXu8/DwIDU11QwhCSGEuG2ejSDlAvhW3+oeQtQYX94J+dnwSgzYu1o7muJs1ODsqzSky0qomY3Sjv4Evz2jvPkR1BnGf1s9RyMKDZpt7QiqLZNGJOrXr8+hQ4eK3XfkyBECAqrxSRdCiLrCYICM69XtXP3L3lYIUTZtlpJEaBzAzsXa0ZTM2JQu3rpxVETUOlj9hJJEdHhIGYmozkmEKJNJIxIPPPAA06ZNY/LkyYSEhBAdHc3nn39e6poGIYQQFpSbCgXXG3D+9BA8+ItVwxGiRjOWfvWpvgtrXfyU25rY3Tp8sFIJqdkw6PRY9f0e3+zcZji+Gjo+CkEdrR1NtWFSInHPPfeg0+n47rvvuHLlCoGBgTzxxBPcd9995o5PCCFEeTKKeu1wbgvk51SPsolC1ESFiYSzj3XjKIsxkaghIxJxx6AgD4I6KesMHlhVcxKIQmc2wqFvldEgSSSMTC7/et9990niIIQQ1VFG7A1fGCDpHPhbsRusEDVZVoJy61zPunGUxbUGjUgcXwW/Pg0ObjBpmzKNqaYlEaCUgd39mVIGdsAsa0dTbZicSABkZmaSlZVV7D4/P78qDUgIIcRtunFEAiDpjCQSQlRUdg0YkQjqolQS8m9d/rbWotfBpjlKWWqA0LuVMqo1VcMeoLZXKnplJVbvnw8LMimROHToEC+//DKXLl0y3mcwGFCpVERGRpotOCGEECao3x4G/xcOLoPE05B41toRCVFzGUckqvGFYtM7lY/qKicFVj4O5zaBSg1D5kHXJ2vmSEQhOydo2B3Ob1U+Wo+1dkTVgkmJxKxZs7jjjjsYN24cjo4y71YIIaoV3+bKh70brH1WGZEQQlSMgzv4tVZKKovbdy0SVkxQylE7esH4b6BRH2tHVTVCByhJxNlNkkhcZ1IiERMTw8svv4yqJmeSQghR2/mEK7eJkkgIUWGdHlM+qjNtNhxfCfm50LUaVdDU5cPy8ZB6SZl2dc/34NnQ2lFVndD+sGGmUsHJYKjZIyxVxKQ+Em3btuX8+fPmjkUIIURFbJkHf89URiQAks4q/+SEELWTQac0dNswq3r9rqttYcSn0OYeeOzv2pVEAPi1VLpwZ8ZBwilrR1MtmDQi0b17d6ZMmcK9996Lj0/xOYMRERFmCUwIIYSJDn0P6THQ+XF48FfwDrN2RELUXDEHwNYBvMNBY2ftaEpm5wK2TkrjvLwMpSKSteSmwd7F0Gu60nW7cV/lozZSqWD05+AeDN6h1o6mWjApkfjxxx8B+Pbbb4vdr1KpJJEQQghr0uuVd8cAXAPAM8Sq4QhR4/30IKRfgeeOgUcDa0dTMpVK6SWRckEpAWutRCLxjLIeIumMMjLS90XrxGFJje+wdgTVikmJxObNm80dhxBCiIrITgJ9gbKoUWNv7WiEqNkMhuKdraszYyIRDz5WGoX84T4lifBtCa3HWScGa8nLBLVd9R21shCT1kgIIYSopgqb0bkGKLcnfoFPOihrJoQQtycvA3R5YOuslPuszlx8lVtrdbfOiFfKTdu7weN/g1cdqnL1+3/g3RCI3m7tSKzOpBGJ3NxcFi1axK5du0hOTsZww8KeTZs2mS04IYQQ5TAmEv7KrUoNyecgIcp6MQlRU9WEHhKFXKzc3TruqHLr3xrsXawTg7U4eIA+H85uhrCB1o7GqkwakZg3bx6bNm1i5MiRJCUl8dhjj2FnZ8eYMWPMHZ8QQoiy3DwiYSwBe9o68QhRkxVOa3KuZ904TGFMJOLK3s5cYo8ot/5trHN8awrtr9yek6n/JiUSW7ZsYdGiRdx///2o1Wruv/9+FixYwJ49e8wdnxBCiLJkFC60vj4i4dUYVDZKHfeCPOvFJURNlF2YSNSAEYkGXaHb09Cgh3WOXzgiEVAHE4ngrsr0t4RISLti7WisyqSpTdnZ2QQHBwNga2uLVqslLCyMY8eOmTU4IYQQ5Wg2TJkr7d9W+VpjDx4NlUWYyeeVjtdCCNPUpKlNjfpYt2N0brpyWxdHJDR20Kg3nP4Tzm+B9g9YOyKrMSmRCAoK4ty5c4SGhtK4cWNWrVqFm5sbrq6u5o5PCCFEWfxbKx838glXEonEM5JICHE7HDwguBvUa2btSKq/h35VFqfbVvNF6eYSOkBJJM5ukkSiPE8++SRXr14lNDSUKVOmMHXqVLRaLW+++aaZwxNCCHHbvMPhzN9KWUYhhOlajlI+aoL8XDjwtXIx33eGdWKwr8NvKBeukzi/BfQ6pRlfHWRSIjF06FDj5z179mTv3r3k5+fj5FRHs1AhhKgufnkK7Jxh4OyiyimFNeVTL1kvLiGEeals4M+XlNve0y17IZt6SemhULg2qy7yDgWfpuAeCDkpNWM6nBmYlEjczNbWFltb26qORQghxO3Q5cORH5TP73y36P6Wo6HpsKI680II01zao3SN9mupJOjVmcYOnLyVppRZieDqZ7ljb3kbjiyHiE+g48OWO251olLBlN1gU7dbspWaSIwaNYpff/0VgMGDB6NSqUrc7q+//jJLYEIIIcqRGQ8YlDKQ6hv+nDt6WCsiIWq23/8D107Ak9shoK21oymfi5+SSGTGWzaRKKzYVNfXYNXxJALKSCQef/xx4+eTJ0+2SDBCCCFug7H0a0DJj2uzQW2rfAghypdt2T4SBoPB+EZtXFYc9627Dy8HL74b+h0OGofyd+DiC9dOWrYpXUHe9YaX10du6rprUXB2I3R4CBzcrB2NxZWaSERERABQUFCAvb09AwcOxM7OzmKBCSGEKMfNzehutGICnPoDHv0TGna3bFxC1ER6fVFDOifzzHfX6XWcST3D/rj9HIg/wMmkk/x+9+/Yqm3xdfIlISeBhJwEDsQfoGdgz/J3aGxKF2+WeEt07SToC8CnSfWf/mUJa5+Fy3vAq5FSjruOKXeNhEaj4bXXXiu24FoIIUQ1cHMzuhsVVlNJPC2JhBCmyE0Fgw7s3ZX1B1XkRNIJ9sXuY3/8fg5eO0iGNqPY4yeTT9K2XltsVDYMbTSUPy78wd64vbeZSFiwu3Xs9WlNdbF/RElCByiJxNlNkkiUpnXr1kRFRdGsmdRVFkKIaqOsEQnvcOVWSsAKYZqsyne11uq0HE88TkO3hng7egPwzp53OJxw2LhNoEsgHf060smvEx39OhLsGmx8LCI0gj8u/MG+uH2mHdCYSFhwalNd7mhdktD+sHWeMr3JYFAWYdchJiUSXbt2ZfLkyYwfP5769etjc8PiksIpUEIIISyscETCrYREorAEbOJZy8UjRE1Wga7WOQU5HE04yoH4A+yP38/RhKPk6fKY3WM2o8NHAzAkZAihHqHG5CHApZQ1TUAH3w5oVBpOJJ0gQ5uBq105fRoadIM+M5RbS5ERieICOygJXepFOLwc2t9v7YgsyqREYtWqVahUKn7++edi96tUKkkkhBDCWro+BY3vgKBOtz7m00S5lREJIUxjTCTKX2j9V/RffHfyO44nHadAX1DssTCPMDQ2RZdXD7Qwveuxk60TLX1aciThCAfjD9I3uG/ZTwjqVPLvvzn5hCvfK0kkFDZqGPQW/DIJ/n4dmtwJzt7WjspiTEokNm/ebO44hBBC3K6ANqVPL/BqDKggJVrpNyGVm4Qom4O7Mk0loF25m266tMk4Xam5V3PjaEMHvw54OnhWKowu/l04knCEvXF7y08krGHUQmtHUP20GQ+Hv4cL25Rk4u5F1o7IYirUkE4IIUQ1Z+sIHsFKB9rkC1CvibUjEqJ6C+2nfJjgvT7vMa3DNFztXHGzq9qSnz0DexKZHEkzLxPWperyYefHSmflIXOrNA5xG1QqGP4hLOwOyecgPxdsTSjfWwuYlEjk5uayaNEidu3aRXJyMgaDwfjYpk2bzBacEEKIUuTnwIp7waMBjFhQ8jbe4UoviexEQBIJIapSoEugWfbb0a8jHf06mraxjQa2vQs6LfR7DeyczBKT0YXtUKBVplNJ48vivENh4gbwa12nGtWZ9ErnzZvHpk2bGDlyJElJSTz22GPY2dkxZswYc8cnhBCiJBlxcH4rnNta+jYTVsCL56BhD0tFJUTNdXEXnNuivLtfho0XNxKVHIXeoLdQYGVQqYoqN2VZoHLT9g/g+zFwcaf5j1UTBbQtSiL0OuvGYiEmJRJbtmxh0aJF3H///ajVau6//34WLFjAnj17zB2fEEKIkhhLv5bQQ6KQxt4ysQhRG2yZC9+OgtgjpW6Sr8/ntX9eY9zacSRkJ5gtlLisOL458Q0/nfqp/I1dfJXbDDM3pTMYikq/ykLrskWtgwUdIeGUtSMxO5MSiezsbIKDlTrHtra2aLVawsLCOHbsmMkH2r59O0OGDGHQoEEsXrz4lsd/++03IiIiiIiI4N577yUqKsr42Ndff82wYcMYPnw406dPJy8vz+TjCiFErWRKIgGQm14n/pkJUWkmVG06mXSS7IJsQtxC8HP2M1soVzKv8MH+D1geubz8jS3V3TotRhmtcfQE9yDzHqumO/0npFyAtc8pHdNrMZMSiaCgIM6dOwdA48aNWbVqFevWrcPVtZz6xtfpdDrmzJnD0qVLWbduHb///jtnzxavbR4UFMR3333H2rVrmTx5MjNnzgQgPj6eZcuWsWrVKn7//Xd0Oh3r1q27ndcohBC1j7Grdek16cmIg3eC4au7LBOTEDWZCYnE3ti9AHT272zWUFr7tMZB7cC5tHMk5iSWvXHhiIS5E4kbRyPqWNO12zZwNjj5wKV/lWpOtZhJicSTTz7J1atXAZgyZQrvvfceL774IlOnTjXpIEePHqVhw4YEBwdjZ2fHsGHDblmk3aFDB9zd3QFo164dcXFF7d51Oh25ubkUFBSQm5uLr6+vSccVQohaq3BEoqRmdIVc/MDOBbKTIDvZMnEJURPpddd/R1Tg6FXqZnvjlESiS0AXs4Zjp7ajvW97APbH7S97Y0t1t46VjtYmc/KCO99WPv/7dcg03zQ4azOpatPQoUONn/fs2ZO9e/eSn5+Pk5Np1QHi4+Px9y8afvfz8+Po0aOlbr9y5Ur69Olj3Paxxx6jX79+2Nvb07NnT3r16lXuMaOjo8nJyTEpPnPJzc0lMjLSqjGI2yPnrGaqi+etfswp3IErGQbSy3jtIc6BOGpPEX1gIzk+rS0XoAnq4nmrDWrjeVPnJtMEAwV27pw5XXITx3x9PgfjDwLgke5h9u9BiDqEXezir8i/aJjbsNTtPDL1BAApMaeIKyOmyp63oDM7cQWu6H3K/JsjrtO0ItivMy7x+0j9+Rliu71Rod1Uh9+35s2bl/qYSYnEwoULGT16tDEZsLW1xdbW9OZGN5aLLaQqZVhs9+7drFy5kuXLlXmBaWlpbNq0iU2bNuHq6sq0adNYs2YNI0eOLPOYISEhJsdnLpGRkWV+80X1I+esZqqT5213NgCBTTsQ2LiM136iNaScIsRFC9Xse1Qnz1stUCvPW/xJADRu/qW+tv1x+9HqtYR5hNGtTTezh6T10bIiZgVn8s6U/f32HAWeznjWb49nGX8LKn3e1l8AILDjXQTWa1rx/dQlfp/Dwu54XFyPR9+noPEdt72L6v77ZtLUpn///ZeBAwfy+OOP88cff6DVam/rIP7+/sWmKsXHx5c4PSkqKorXX3+dhQsX4unpaTx2UFAQXl5e2NraMnjwYA4dOnRbxxdCiFrnrnfhnu/Lr57iE67cJpb8LqsQApPWR+yL2weYf31EoZbeLXG2deZi+kXis8pY/+DfCnr9p0IXqSbT66HjI9DybvAOM99xzCg1N5Un/n6CB/94kKScJMsc1DsU+s5QPt/8X6XyVS1jUiLx3XffsX79etq0acMHH3xA7969mTNnDidOnDDpIK1btyY6OprLly+j1WpZt24d/fv3L7bN1atXeeaZZ3jvvfdo1KiR8f769etz5MgRcnJyMBgM7Nq1i9DQ0Nt4iUIIUQv5t4Lmw5W5uGUp/KefdLbs7YSoy+xdoXlEmT1XutXvxgPNH2BAgwFVfniDwUBadn6x+zQ2Gjr4dgCK1mZYjY2NckE87muwUVs3lgpIykni8b8fZ3fsbg4nHOapjU+Rrk23zMF7TFMSvQk/1MpF6iZNbQIIDg5m2rRpTJs2jV27drF06VLGjh1r0rwtjUbDrFmzmDhxIjqdjjFjxhAeHs6KFSsAmDBhAp999hmpqanMnj0bALVazerVq2nbti1Dhgzh7rvvRqPR0Lx5c+65554KvlwhhKhjZERCiPIFdoB7vitzk/a+7Y0LoCvKYDAQm5bL6fgMzsRncuZaBqfjMzl7LZMgT0f+fK5Pse0fbfUo45uOp5Nfp7J2CpvfUhZbj1hQKy9WKysxJ5HYrFhC3ELQG/REJUfx9Man+XzQ5zjZmrkbuMYOBr5p3mNYkcpQ0gKGUuh0OrZs2cKqVavYsWMHrVu3NiYD4lbVfV6buJWcs5qpzp235POw+kmo3x6Gvlf2ttos+LQz+LWE+36qVhcZde681RJy3spnMBi4mpZLTHI2XRt7A3AtPZf+87eRmVdQ4nMC3B3Y+VJ/bGwq8Dv6TgPITYMXL5Q6Slmp87ZnMeRnQevx4B5YsX1Y2YmkE/g5+aHVaXn4z4eJy4qje0B3Ph3wKXZqO8sEkRIN+7+CAW8UdcAuR3X/fTNpRCIqKopffvmFtWvXotFoGDVqFC+99FK1WNAshBB1TuoliNkLpvzzs3OG6SfNH5MQNdnFf5Xyr0GdSmzy+H3k95xJOcP4puNp4d3CeH9hwqCMMCijDKevZXI2PoMsrQ6NjYqTc+7ETmODj4s9Or0Bb2c7wnxdaOLnShM/F8J8lVtvF3tOx2fw8cYzeLvYMWdkq2Ix6A16bFSlXHy6+CmJRGZ8+dMdK2LfEkg8razDqCGJRGxmLD+e+pFnOzyLjcqGlt4tjY8tGbSEh/98mEPXDnE65TStfFqVsacqotfBNyMg9aKydqLDQ+Y/pgWYlEiMHz+eAQMG8M4779CrVy9sTMyihBBCmIGxGV05Xa2FEKbZ9RlE/Q7jvoGWo255eP2F9RxJOEK/4H7gXfyxIR9uL3GUwdvZjnA/F9Jy8qnnao+NjYrdrw7A3bH0qpc2KhXrjsXi7WzHmxEtsbFR8WPUj3x94msmtZnE3eF3l/xEFz/lQj8zHnyr+N1rbZYyNdJGA74tyt++GriccZmJf03katZVXOxcmNh6YrHHQ9xDWDxoMRnaDMskEaCsLRkwC1Y9Dn/PhCZ3gUvpi/trCpMSiR07dhibxQkhhLCywmZ0piYSuWlwLRLs3cCvZlwICGFRWde7R5dQtSkrP4sTiSdQq9R09OtY7DGVSkWXRl5kawsIvz6yEO7nSrivMsJws7KSCIDQes4EuDsQm5ZLZFw6Leu7U2AoICYzhr1xe8tIJAq7W5uhKV38CcAA9ZqB5tbXVN1Ep0Uz8e+JxGfH06ZeG8Y3HV/idk29ikrYGgwGtsdsp09Qn1LbE1SJVmOUTtfnNsPfr8HoxeY7loWYNLQgSYQQQlQj6YWJRBldrW905Af4cgjs+T/zxSRETWYs/+pzy0MH4w9SYCighXcLXOxcOH4ljT7vbeG9P6MA+PKRzvwwqTtvjWrFg91D6NbYu8QkwhQqlYpeYUoM/5xRkpsu/koX7b1xe0vsywUUdbfOiCv58cqIPaLclldquho4l3qOR/96lPjseDr4dmDxoMW42bmV+7wPD3zI1M1T+eTQJ+YNUKWCYfNB4wBHf4RzW8x7PAuQOUpCCFHTFI5IuJmYSEgJWCHKll36iMTN/SP2XkjmUnI28el5ZgmlV/j1ROKsElOYRxheDl5cy77GxfSLJT/JOCJRRr+Jioo7qtwGVO9E4lTyKR776zEScxLp6t+VRQMX4WzrbNJz2/u2R61Ss/TYUr449oV5A/VqDH1fVD5fNx3yc8x7PDOTREIIIWoa4xoJExMJKQErROkKtMr0P5UaHDxuebiwh0NX/67K1xeSla8bmWFRM9Dz+ojE3gvJ5ObrUKlUxvKvpfaTKByRMMfUptjriUQ1HpG4mH6Rx/9+nOTcZHrW78mnAz69rbKu/Rr0Y26vuahQ8dHBj/gh6gczRgt0fwbqNVcq8O2Yb95jmZkkEkIIUdPc7mJrtyDQOELWNeWCSQhRpHA0wsn7lpKc6dp0IpMj0dhoaOfbDoPBwN5oJZHoYqZEwsfFnhYBbuQV6NkfnQJA14DrSUxpiURwV7jrfaX7dFXS5cO161Xf/FtX7b6rUH2X+nT260zfoL583P9jHDQOt72PYY2HMbP7TADm7pnL2nNrqzrMIho7iPhIWbweNtB8x7EAkxvSCSGEqCYeWAnpV5UEwRQ2Nkq5wfjjkHgWgjqW/xwh6ooyFlofvnYYvUFPu3rtcLJ14uy1DJKztPi62tPQ23yNzHqH+3AyNp3I2HR6hfsYp1Xti9uHwWC4dUGwd6jyYQ7jlyn9DxzKX2tgafn6fGxtbLG1seW9PkpPHVt12QvayzKuyTiytFnMPzCfmTtn4mTrZJZO5gA06AZP7TS5n0R1VWoi0bJlS5NWrh8/frxKAxJCCFGOek2Vj9vhE349kTgtiYQQN7J1gjb3ljjC1zuwN7/f/TuZ2kwA9lwoGo0wZ3Wfx3s14vHejfB1Vd5ZD3ELoZ5jPWxUNiTkJODr5Gu2YxejtoWmd1nmWLdpb+xeZv07i88GfEaoR2ilEogbPdLqETLyM1h8dDE/n/6Z/sH9zXeuC5OIgjylsl79duY5jhmVmkh89dVXloxDCCGEOXlfXyeRJOskhCjGJwxGf17iQyqVioZuDY1fm3t9RCFft+JTc1QqFT9H/IyXQxkJzF+vQfoVuPvzGlGmtTL+vfIvz255ljxdHqvPrGZG5xlVuv+p7aZS37k+w0OHm7ccLCgjYl/eqVQOm7q/xvWWKDWR6NKliyXjEEIIYYrz2+DPV6DpnUpzI1MFdoSwQeBlpukPQtQyebo89AY9jhpH432XkrMB6NLIu7SnVanEzDzyCvQEejji7VjOMY+thMw4GPxfcDdx2mN5fpkMOq3yt8azYfnbW8D2mO08t+U58vX5jAkfw/Odnq/yY6hUKsY0GWP8Oi4rjrS8tGK9J6qMkzd4NFDe5KmBvSVMXiORkpLCsWPHSE5OLnb/qFGjqjomIYQQpUm9CNdO3P4QeNM7lQ8hRHGXdkPKRQjurJTmvG57zHZe3P4i9za9l5e6vATA6sk9iEnJIdDDsbS9VZmf9l3mxVVHGd8piPfGtgUgOz+bwwmH6RbQDRvVTXPrXXyVRCIzvmoSCb1e6fadlw5D5lV+f1Vg08VNvLD9BQr0Bdzb9F5e6frKrd+HKhaXFcdD6x8iT5fHV3d+RWP3xuU/6XYU9pZY2E3pLdH2XgjtX7XHMCOTEol///2XZ555BltbWzIyMnB1dSUjI4OgoCBJJIQQwpJut2KTEKJsh76DQ9/C8I+KJRJ7Y/dSoC/Aw97DeJ9KpSLYy3yLrG/Uor6yuPmfM4nGBdbjfx/PxfSL/DT8J5p7Ny/+BFd/pedDVZWATY1WkggXP3D1q5p9VsKf0X/y8vaX0Rl0PNTiIV7o9IL5px0BXg5eNHZvzM6rO5n09ySW3bWM+i71q/ggjaDvS7BpNvw+HabsAlvzJ6tVwaQ07oMPPuDpp59m9+7dODk5sXv3biZPnsy9995r7viEEELcKOM2u1rf6PJe2LcUMhOqNiYharJSqjYVllrtEqBM9T4Tn0FWXoHFwmoR4Ia3sx1X03I5n5gFQLt67YrFVkxVN6WrRv0jknOTmbVzFjqDjomtJ1osiQCwU9vxYb8P6eDbgfjseCb+PZGEbDP8De3xjFIONuUCbP+g6vdvJiYlEhcvXuThhx8GMLZnnzRpEt988435IhNCCHGryoxIbJoD656H2CNVG5MQNVnW9YvCGxKJxJxEzqedx1HjSCvvVgA8sWw/bWb/zen4DIuEZWOjosf15nT/nFGSnTL7SVR1U7pq1NHay8GL/93xP6a2m8qz7Z+1WBJRyFHjyKcDPqW5V3MuZ1xm0oZJpOVVcU8eta0yKgaw82OlilMNYFIi4ejoiFarBcDDw4OrV6+i1WpJS5PGRkIIYVHpV5XbioxI+EjlJiFuUdiQztnHeNfeWOVCvYNvB2zVtlxLzyU6KRt7jQ2NfZwtFlrv64nEjuuJRGE/iQPxByjQ3zQ6Ykwkas+IxOFrh41vYPcK7MWTbZ+0eBJRyNXOlc8HfU6oeyhnU8/y1IanyMrPqtqDNOgKHR9VkoqEqKrdt5mYlEi0b9+ejRs3AtC3b18mT57Mo48+Svv27c0anBBCiJtUZkSisARs4umqi0eImi6rhETi+jv+hRfuhd2sOzb0RKO2XAOxXuFKTLvPJ5Gv0+Pv7E9Dt4Zk5WdxMulk8Y2remqTlUckvj35LQ+uf5DPDn9WJftLzdbyws9HmP7jYfIKdBXah6eDJ4sHLybQJZDI5EgOXTtUJbEVM/BNeHoPtLy76vdtBiYttn7//ffR6/UAvPjii3z55ZdkZWXx6KOPmjU4IYQQN9AVQNb1aQsuFVj8WDgikSgjEkIAkJ8D2kxQ24F9UefmwkTCOJXIQv0jblbfw5HG9Zw5n5DFkcupdArxorN/Zy6mX2Rv3F7a1LvhIj+oM4xcCD5NKn/gjHglIbF3A4+Qyu/vNqTmpvL1ia/54vgXAPg4+pTzjPIdvpzK098f5EpqDgBO9mr+O6p1hfbl6+TLksFLOJ96nl6BvSod2y0cPZSPQjePPFUzJiUSDg5FjVHs7e2ZPHmy2QISQghRChs1TDuiLJauSBdX7zDlNuls1cYlRE1VOBrh5KOU4QRyCnKo71Kf7Pxsmnk1A4oSCUv1j7jRsNYBnL2WiZ1GGQnp6t+VladXsjd2LxNbTyza0D0I2t9fNQd18YXnjkPa5aLuy2Z2MukkK6JWsP7CevJ0eahQ8Ub3N4r1c6ioXw9d4UpqDi0C3Dh7LZPvdl+ibZAH4zoFV2h/wa7BBLsWPfefK//QPaA7aht1pWM1Sr0MP95Pg3yg5faq228VM7mPxKFDhzh+/DhZWcXngz311FNVHpQQQogSqFRK4yKPBhV7vkcDUNsrlZ/yMsDetWrjE6KmUdtCp8fBrqikq6PGkaWDl6LT61DbqEnN1hIVl4GdxoY2Qe4WD/H5wcWboHXy70S3gG70DuptvoOqVOARrHxYwMydM/n17K/Gr3sF9uKRlo8YR4QqQq83YGOjJIevDG1GgLsDj/ZsxC+HYnhp1TFe+/U4zQPcaBVYuXO67MQy3t//PneH3c0b3d+ommQi+h/49WlIjcbOwfLJ6+0wKZFYsGABn3/+Oc2aNcPRsaiurUqlkkRCCCFqChs1eIfCtZPKqER9Wecm6jhXfxj+vxIfKrwg3BedAkC7IA8cbKvwHecK8nH0YcngJSU/+McMSImGsV9W7o0CXQGoTX6v+bbd3Cm6Tb02bLq4iVHho7in6T00dKtcF+3jV9L4z4+HmT++LW2CPLDXqHmybygA93RuwOHLaazYe4m310fy/cRulTpWS5+WOKgd+OXsL2TlZ/F277exU9tVbGc5KbBhFhxcpnzt05SYtjNoVKkIzcukn5IffviB5cuX06aN9UuACSFEnXVwmVJfvNOj0Os/FdtHm3sgJxkcLP/OqhDVncFgYPWZ1bSp14YwjzBUKhVOdmr6NKln8fURN4qKS+fP43H0CPWhS1lxnNmg9CHIiK9cIrGwK9ho4IHV4B5Y8f3cwGAwsD9+PyuiVrD50mba1GvDsruUC+YRoSMY1mgYTraVa/ZnMBhYsfcyb649gbZAz6ebz7L4oU63bPfmiBa4OWqYckdYpY4H0NGvIwsHLuTZzc/y98W/SdOm8XG/j3G2vY3qXgYDnFgN619SyhGr7aD3C9DrOXLPnK90jOZkUiJhMBho1aqVuWMRQghRlpSLkHoR8nMrvo9ez1VZOELUeJf3QvxxCO4Kfi2JyYzhzV1v4m7vzvZ7tqNCRc8wH3qGVX7Bb2X8dTyejzaeITlLS5dGXqRr0/nt7G+k5KXwTPtnijZ08VMSicx48KngRXJumjJiqbYvqgRVCdn52aw9t5YfTv3A2VRlfZZGpcHXyZc8XR72anvs1fZQycGerLwCXvvlGL8eVkpkT+jSgDciWpS4rb1GzSt3FXUG1+kNqG0qXla2s39nvrrzK57a8BR7Yvfw2F+PsXDAQrwdTZiWlHpJ6e9z5m/l6wY9IOJjqFcFi+YtwKQVNOPGjWPlypXmjkUIIURZKlP6VQhxq8jf4Pf/GC/iCvtHdPbrjI3KcmVey1NYBrawn4Rer+e9fe/x9fGvyS244Y2FqigBG3dMufVtXrGiDtcZDAbm75/PgJ8H8N89/+Vs6ll8HH2Y3HYyf439iw/6fqAkEFXgdHwGIz79h18PX8XJTs1H97Tj7dGtTZqKdjk5mxGf/sOfx+MqFUMzr2Z8e9e3BLsGczLpJA//+TBxWWXsU1cAuz6Dz7oqP38O7hDxCTyyrsYkEWDiiMSRI0f44osv+Pbbb6lXr3gL+S+//NIsgQkhhLhJRqxy61a/4vvITYND34M2C/rOqJq4hKipbqzaxK39I05cTWP3+WT6NvEhzNd6xQnaBrnj6qDhQmIWMSnZBHl60NSrKVHJURxJOFK0KLkqmtLFVrx/hE6vI0+Xh5OtEyqVivjseDLzM+ng24F7m93LwAYDsa1EclKSjSfjmbriILn5epr4ubDw/g63da42RsZz4mo6L/x8hDBfF8J8XSocS7BbMMvuWsbkjZOxUdngaldKHLFH4LdnIfaw8nXLu+HOd8G1AmW9rcykRKJTp0506nTrHDMhhBAWVBUjEgYD/PUKaByh9/MWK+0oRLVkbEZXD4PBwL64fQB08e8CwJ/H41iw+SxxvRvx2rCSp8lYgkZtQ/fG3vx9Mp5/ziRyb5cGdPbvTFRyFHvj9hYlEq5VkEjE3X5H69TcVFafXc2PUT8yrPEwnu3wLABPtX2Kx1o9Ziyjaw7hfi7Y2tgwtEMA/x3VCie721sk/kiPEPZfTGHd0Vie/HY/a6b2wsW+4gvNfRx9+HLIl+Tr843rJPQGvTLCpc2Cre8oIxEGHbgFwbD50PTOCh/P2kz6Tk2dOtXccQghhChP4YiEa0DF9+HoAc71lAV96VcsVt5RiGopK0G5da7HhfQLJOQk4OXgRaiHUuFnjxX7R9ysd7gPf5+MZ8dZJZHo6t+Vb09+q0zHKizAZhyRuFbxAxlHJNqWu2l0WjRfHP/C2PsBlFEdg8GASqWisXvjisdRhguJWXg52+HuaEtDb2fWP9ebQA9HVKrbX+egUql4b0wbzsRncDo+kxk/H2Hh/R0qtK9CN45EpOSm8OSGJ5ni14M7/v1SWeemsoFuU6Dfa2Bf8RGQ6sDklCs2Npa1a9cSFxeHv78/ERERBARU4p+ZEEII0xXkKdWWbDTGaRgV5tNEuYBKOiOJhKjbjCMS3uyL3QMooxEqlYrcfB2HL6cC0DnE00oBFukVrkwt//dsInq9gQ5+HbBR2XA88TjZ+dlKxaPKTm3Kz4WEKEAFfi3L3DQtL42H1j9ESl4KKlT0DuzNhGYT6BnYs1IX4eVZe+QqL686Su/weix6QLngD/KsXLUnZ3sN//dAR0Z+upP1x+P4fPt5nrpeLrayfj3xHZHJkTyXdJI3CpK52681jPgYAjtWyf6tzaQx7f379zN06FA2b95MRkYGmzdvZujQoezfv9/c8QkhhICiaU0u/pWfjlTY4TpROlyLOsxggOyiqU03r484GpOGtkBPM39XPJwq2BegCoV4OxHo4UhKdj4nrqbjaudKC68WFBgKOHTtkLJR/fYw7msYMKtiB0mLUd4h9wkHu7LLly4+upiUvBTa1GvD73f/zsKBC+kd1Ntsi9TzCnTMWnOcZ1YcIkurQ61WkVegr7L9N67nwv/uaQfAe39GsfNsYuV2aDDAoe95ZON8JqWkoVOpmFXPmy97PlxrkggwcUTigw8+4LXXXmPs2LHG+1avXs17773HTz/9ZLbghBBCXOfRAF68oDQsqiyfcOU26Uzl9yVETaXNhIJcZb2QnTN3h9+Nt6M33et3B2DvhSSAsvs2WJBKpWLmcKX/QRN/ZTpMl4AuHE86zp64PfQM7KlUbWp5d8UP4hMGL10s9+9MfFY8y6OWo0LFa11fo4Fbg4of0wSXk7OZ8v1Bjl1Jw05tw8zhzXmgW8MqH/kY1MKPZ/qH8X/bzhGbVoky20nn4Pfn4MJ2VMAzAf3watmfd44v4cNDn5Ccl8b0TtOrVWWwijIpkTh37hyjR48udt/IkSN5++23zRKUEEKIm6hU4OSlfFSW9/VEIlESCVGHGfTQ41nQ6wDoFdiLXoG9jA8XrY+oHokEwJ2tihdaGBE6gjY+bejkX4UFcQr/1pTB18mXT/p9wrHEY7TwNu8i9L9OxPHCz0fIyC0g2MuRz+7rQJsgD7Md77mBTRjepj5N/StQpUuXD/9+AtveU5JUJ28Y8ja0Gc/9KhWenmG8tvM1vjn5DSl5KbzZ401sbaq2ipWlmZRIeHt7c+LECVq3bm2878SJE3h5VZ9fLiGEqNUMBuUffFXwkURCCBzcYfBbJT5UoNNz4KLyrnyXkOp3rWMwGNAbINQj1Lgw3GjtNOV3e9w34FKv5B2UJuEUeDQEW4cyN1OpVPQO6k3voN63GfntiYxN58lvDwAwuIUf749ri7ujeS+81TaqYknEgYspdGjgUf7ox+V9sPZZuHZS+brtBBg8F5yLFuoPbTwUD3sPntv6HL+d+42I0Ai6BXQzx8uwGJMSiYceeohJkyZxzz33EBwcTExMDD/++CNPP/20ueMTQggBsGk2HPoO+s+Ejg9Xbl8eDaHPi1CvadXEJkQN9+7ed8nX5/NIy0cIcg3CAHx0Tzui4jLwdSv7otrSPtp4mu92X2L++Lb0bVKUKOj0OtQ2aqVb97WTSpW320kk9DpYfAfotPDieSXRuonBYGDDxQ30b9AfjU3FS6SWp7DTdPMANx7v1YgAdwce79XIrIu4S/L2+kg+33aeeXe35r6upUzfykmBzXNh31LAAJ4hMPwjCO1X4uY9AnvwxeAviEyOrPFJBJiYSNx33324ubnxyy+/8PfffxMQEMCrr77K8OHDzR2fEEIIgPSrSqWlqmjmpNZA/9cqvx8harIrB+Div+gCO7Hm3BoytBk82upRAGzVNgxu6c/gltWvi3xegZ7EzDz+OZNA3yb12Hp5K/P3z6d3UG9e7PyiUrnp2snbLwGbdA7ys8E9uMQkAuCv6L+YsX0G3QK6sWTwksq/mOvydXoOXUpl2+lrbDudQBM/V/43vh0AM4dbr39Hs+sjE2/8dpxmAa50aHBD9S69Dg59C5vmQHYSqNTQ81nlTRq7sqtIta7Xmtb1imb5/HH+Dzr4dcDfufr9vJXH5HRy+PDhlUoctm/fzty5c9Hr9YwbN45JkyYVe/y3335jyRLlh9LZ2Zk333yTZs2UBibp6em8/vrrnD59GpVKxbx582jfvv0txxBCiFor/apyW0ozunVHY/lh3yXmj2tb7d5BFaJaOr8VNs3hVOeHydBmEOgSSKBLoLWjKlfvMB8WbT3HjjNKVSEnjRPR6dHYx9orG1S0BGzsEeW2lEZ0ebo8Pjr4EQBDQobcbti3uJKaw/bTCWw7lcDOs4lk5BUYH0vJyjf2orCmu9sHcfhSKt/susiU7w6y9ple1HO1V6Yx/fFCUWfqhr1g6HvllswtybbL23h5x8v4O/vz+aDPaeTeqGpfhJmVmkgkJCRQr54yJBYfX/oPo59f+e28dTodc+bM4auvvsLPz4+xY8fSv39/wsLCjNsEBQXx3Xff4e7uzrZt25g5cyY///wzAHPnzqV379588sknaLVacnMrsZJeCCFqImNX65L79zy9/CAAs38/yWf3dSh/f9H/wJEV0KAHtL+/qqIUoubIUqoy7SUbKOpmrdcbGL3oX5r5u/LmiJY42KqtFmJJOoZ44mBrQ1RcBgkZebT1bYudjR2nUk6RmpuKh4uvsuHtJhJx1xOJgJITieWRy7mSeYUwjzDuDrv9ylD5Oj22aqVKUXx6Lj3f2Vzs8dB6zvRt4kvfpvXo2sjL6klEodeGteDE1XT2X0xh6re7+d7/RzRHv1cedAtU1tm0HF3hNWztfNvRpl4bjiQc4eH1D7Nw4EJa+bSqwldgXqUmEkOGDOHgQeUfU9++fW85oYWZYmRkZLkHOXr0KA0bNiQ4WGl8NGzYMDZt2lQskejQoegfX7t27YiLU/5pZmZmsm/fPt555x0A7OzssLOzfj1nIYSwKGMiceuIxOXkbOPnbQJLnpJwi+QLypqL/FxJJETddL2r9d485Z39wv4RZ65lcvhyKvHpudhrql95TnuNmi6NvNl+Wnknf1T7QNr5tmNv3F72xe9jUEW7Wxd2tC5hRCIlN4UlR5VZI893el5Zi2GCC4lZbDulTFfaF53Czpf64+5ki5+bA+2CPfB1tadv03r0Ca9HsFflmsqZi53GhoX3tmHYR5vYcymTd67A6w520OMZ6P18uf02yuNu786SwUt4fuvz7Liyg8f+eoyP+n1Ej/o9qugVmFepicS6deuMn2/atKlSB4mPj8ffv+ifn5+fH0ePHi11+5UrV9KnTx8ALl++jJeXF6+88gpRUVG0bNmS1157DSen6vkDJ4QQVS4vA7QZSr17B49bHt4cpVwwDG7hx5OmdmP1aaLcSi8JUVdlJZAPHMiKAYpGJG7sH1Fd3hW/We8wH7afTmDHGSWR6Ozfmb1xe9kbu5dBrteLKGTGmb5DgwHirl+XlTAisejIIjLyM+hRv0exErk3y8orYPf5JLadTmDb6QQuJmUXe/zg5RT6NVVGTH6Z0qPafn+LObcF3/Uvschg4F5eZ6luGJ0HTWJIt3ZVdghHjSMf9/+YWTtn8fv533l609PM6zWPuxrdVWXHMJdSE4mAgKLh86tXr9K5c+dbttm/fz+BgeXPJzQYDLfcV9oPz+7du1m5ciXLly8HoKCggJMnTzJz5kzatm3Lf//7XxYvXsxzzz1X5jGjo6PJyckpNzZzys3NNWnERlQfcs5qptp+3uzSLxIKaB28ORcVdcvjaw/EAtDKU2/y90GdZ6AJoEs4w+mTJ6uutOxtqO3nrbaqLeetUXIMp+ztyNbnEeAQQPKlZJJJZsMRZUpQAwdttX2dgeo8ALZGxnLypC1+ecooxD+X/uGib0MaAlnXorl0Q/xlnTdNVhzhOSkU2Llz5ko6XC3a7mrOVX469RMqVIz2Gl1sHwaDgQI92KqVvx9zNsex63JR8uBqb0OHAEc6BjrRsb4jXvokIiOTquz7YE62WVfxPfwJbjFbAWjjFsTT3tnsy/HH29HWLD8bD3g/gCHLwLr4dby0/SWuXLlCR+eOVv85bN68eamPmbTY+sknnzROc7rRlClT2Lt3b7nP9/f3N05VAmWEwtfX95btoqKieP3111myZAmenp7G5/r7+9O2bVsA7rzzThYvXlzuMUNCQsrdxtwiIyPL/OaL6kfOWc1U689btNIYy86rwS2vM1tbwNHvowGYcEdbYlKy+XzbecZ1CmJA83LWsP3lhTonmeZBHuBW3wyBl63Wn7daqtactz8yOWyvLFDu1aAXzZs3x2AwELX6CgAje7QgzLcCTcksoJnBgM+WRDycbPFrEEq4Yzhvn3mbmJwYnNt0g8BVOHsE0/yGEs9lnrd4AzTshcbJi+YtildJOnvuLDYqG0aFjWJIx6JF1nq9gSeW7adjiCdT7lCmqg9LcyKHGPo2qUffpvVoG+SB2qYGjDrcKD8H/vkIdn6kNJWzdYY+L2DX/WmeUytT6805kvJ287cJPx7OL2d+wdbTln3J+5jUflL5T7QSkxKJkkYUMjMzTf5Gtm7dmujoaC5fvoyfnx/r1q1j/vz5xba5evUqzzzzDO+99x6NGhWtWK9Xrx7+/v6cP3+exo0bs2vXLkJDTRy6F0KI2iCkJ7wWB9qsWx7acyEZbYGetkHu1HO1Z83hK/x5Io703PzyEwmfcLi8R2leZYVEQgirMRggK4GH9AX0euhvNNfnuV9MyuZaRh5eznaE1nOxcpClU6lUbH6hL24OReWg2/u253TKaWIKMvEJH3h7O/RrAY+uK/GhiNAIOvh1wF5tX+z+34/FsinqGomZecZE4oFuDXmgW8PbO3Z1YTBA5Fr46zVIu6Tc12osDJoD7srsmxuveqMTs/hu90VeHdocmypMllQqFfc1v49jicf4YP8HeNl6MYkamkgMHjwYlUpFXl4eQ4YUL/WVlJREjx6mLQTRaDTMmjWLiRMnotPpGDNmDOHh4axYsQKACRMm8Nlnn5Gamsrs2bMBUKvVrF69GoCZM2fywgsvkJ+fT3BwMG+//fZtv1AhhKjRbB2Vj5vc0aQefzzbm8zrpRPHdw7mww2n+fdcEpGx6TQPcCt9n4WJRNIZaNzXXJELUf3oC6DPDFS56YT6FL0Dv/eCMvrXJaT6ro8odGMSAfBB3w9wsXUxS9w3l8XNK9Dx/l/KNMtSG7XVJAmnYP2LSklgAL9WcNd7yps4JSjQ6Xnwyz1cTs7BzdGWZweEV1kocVlxPLv5WSKTI3GxdeGpRk9V2b7NocxEYvLkyRgMBt58802eeqrohahUKurVq0e3bqZ35Ovbty99+xb/RzVhwgTj53PnzmXu3LklPrd58+bGpEIIIeqcAq3SiK6ECwSVSkWL+kXJgpuDLeM6BfP1v9F8vTOad8eWXMoRAO/r//wSz1Z1xMJUJ36B89tgyNxKV38Rt0FtS0aPp7FT23Hj++zHr6YB0LmRl3Xiuk2p2Vp2nUtiQHM/XO1umIa1ZirEHYN7vgUPEy70j/6kFGDwbwM2SqWqdefXcTD+IFPaTcHb0bvY5t/tvsTl5Bya+LkwpkNQVb4ky8pNh23vwp7/U5JLB3foPxM6Pqo07iyFRm3DWyNb8ejX+/hw42laB7kbF5FXxrGEY0zbMo2EnASCXIL4dMCnaGO1ld6vOZWZSNx9t1InuHHjxrRr184S8QghhLjZ6ifgzN8w5gtoNtR4d2ZeARob1S117h/uEcI3u6L55fAVXryzKd4u9jfvUdFsGHiGQEBbMwYvSpWdDGueUSpyObjDoNnWjqhO+e7kdyw9tpTpnaZzf3OlBPLsES15tGcjXB1M7tdrVfd8vptT8Rn8OKkbXRt7k1uQy5GEIzS5dgLP2MOQHlt+IpGdrPyNsXWCV5QKVjkFOXx44EPis+NpXa81o8JGGTdPy8lnwWal2tvLdzVDo65+JXLLpdfD0R9gwxuQdQ1QQcdHoP8scPYu79kA3NHUl+kDmzB/w2mmrTjE2md60dC74m8GrDu/jlk7Z6HVa+ns35n/9f0fHg4eRMZWzwX/hUw6+ydPnuTYsWPF7jt27JhxapIQQggzyoiD/GxwKD5NadmuaNrP2cBXOy8Uu7+RjzP9m/qiLdCzfM+l0vfrEw4tR4FXzeqkWmvsXawkEQC7PoVrt1bkEmYSe5S9USvR6rX4OxWVp1epVDTyccantOS7mukRplz0/nNW6YXx4vYXmfj3RHY4Xu9ub0pTusKO1n6t4Hp/iO9Ofkd8djzNvJoxInREsc0XbT1HanY+3Rp7Vcm78BZ39RB8OQR+nawkEUFdYNIWiPjY5CSi0NP9whjY3I/03AIe+WofO6+fh9uhN+j55OAnvLzjZbR6LWObjOXzgZ/jUUKp7+rIpERiyZIlxmZyhYKCgliyZIlZghJCCHGDDKW8681drbdEXSMnX4e/m8MtT3msl5IcLNt9EW2B3uwhigroNgX6vw4hvZVpFeueVxZ8CrPLvbSLI7nxqIBO/p0ASMrMI0ers25gt6l3uA8AO84oF7Ad/ToCsFd9/XWYkkjc1D8iMSeRpceWAvBCpxewURVdKl5JzeHL629cvHJX82q/jqSYjDj47RlY3A9i9oKzL4z6P3jsL6jfvkK7tLFR8b972hLu68KFxCzuX7qHjzea3psnOz+b6Vuns+TYEmxUNrzc5WVmdZuFrdq2/CdXEyYlEunp6Xh4eBS7z8PDg9TUVDOEJIQQwshgKOpq7VJUhSk1W8uBiynYqlX0un4xcaMeod409XMlrJ4LSVl5pe9/wxvKP1Z5N9zyHNygzwxlHruTD1z8R5mrLszucEoU+SoVzWw9cLdXusH/b8Np2sz+i5UHYqwcnem6NvLGVq3iaEwqadn5dA3oCsBeXRoGMK279U0drRceXkh2QTZ9g/oa91fIyVbNfV0aMLp9IG2DParuhZhTXgZsmQeftIeDy5RRl+5T4ZkD0G6CcU1IRbk52LL2mV7MGNIUNwcNQ1oV/Z3W6Ut/YyA2M5aH1j/EpkubcLV1ZdGARdzf/P6alZxhYvnX+vXrc+jQIdq3L8rYjhw5UqxpnRBCCDPISQFdHti7gX1ROcrtZxLRG6BbiBeuDre+e6VSqVg1pQcu9uX8mU+IgqsHlVvfZlUdvShJTiqkX1VKbgI4esLgt5SpFrs/gzbjrdIgsC7Zm3EegM4uRaVK915IJl9noJGPk7XCum3O9hraN/Bk74Vkdp1PZHDLJrjbuxObl0aMRk2wKd2tbxiROJtyllVnVqFWqZneafotm3o62/HmiJYltgWodnQFcPAb2PrO9XUQQLPhMOANqNekSg/lYKvm6X5hPNozBCc75W9uRm4+Iz7dyT2dg3mkR0ixtWyHrx1m2pZpJOcm09CtIQv6L6CRe82cYmpSGvbAAw8wbdo0VqxYwa5du1ixYgXPPfcc999/v7njE0KIuq2MaU1AmXOUy00iQFknAUoJWGEZuxfBou6w/f2i+9pOUMpNPrJOkggL2JerTPnp4qk0aEvKzOPMtUzsNTa0DvSwYmS3r3eYMiK5/UwiNiobOvkpU7X2OTiUPyKhzVL6yNhowLcF8w/MR2/QM7bJWBq7Ny62aXx6rvHzav2uucEAUetgYTdYN/36OojO8OifcO/3VZ5E3KgwiQBYfyyOC4lZvLM+ioH/28ZvR65iMBj47dxvPPbXYyTnJtMtoBvfD/2+xiYRYOKIxD333INOp+O7777jypUrBAYG8sQTT3DfffeZOz4hhKjbjIlE0YJQnd7A1lPXE4lmZS92zNYWsPJADJeTs3ltWItbN5ASsJaVk6okEgANbujFpFJB1yetElJdk52fzXFdFjYY6OCrzLTYF50CQIcGnthpalYVol7hPszfcJp/rq+T6OLfhU2XNrHH0YHR5a2RiD8BGKBeMwxqO0aGjiQhO4Ep7aYU22z76QQe/2Yfk+8IY/og812IV1rMfvh7Jlz6V/nasxEMfBNajLR4gj6+czD+7g7M+yOSqLgMnl1xgPf3biXV7m8AJjSbwIzOM7C1qTnrIUpicn2z++67TxIHIYSwtJxU5d3CG0YkDl9OJSU7nwZeToTWKyo3mKnNZGvMVoY3Hm68LyO3gDlrT6I3GHioewjBXjdN2ygckUg8bc5XIQrt+Rzy0pQF1qU0uyLuOOz/AoZ+YKyiI6pOUm4S7fVqdPlZuLorhWSMjehqSP+IG7UJ8mB4mwA6h3ih0xvo4t8FgH1e9TEM/pIyL58dPKDrU+Dih0ql4s5GdzIkZEixEQe93sDb66PI1xlwsqumP4/J52HTHKUvC4CjF/R9CTo9Bho7q4XVp0k9eob58N2eU/zvyJuk2p3AYLAhXP0gE1tMrfFJBNxGIpGRkcHWrVuJj49n4sSJJCQkoNfr8fPzK//JQgghKqb1WGg5GgqKphXk6/R0auhJmyAPVCoVJ5JO8N7e9zh47SAAwa7BtK2n9Ibwc3NgeJsAfj18lWW7om8dlSgckUg6q0wJqM5TFmq63DRlDQQoFzkl0RXADxMg9RL4tYTOEy0XXx0R7BrMl0mZGLISwLkeAHujkwDoWgMTCbWNik/v62D8OtQjlP7B/Wnm3Yx8jyDKvIyu1wTuepcrmVeobzCgUqlumbb06+ErRMamU9/dgUd6hJjlNVRYVpIyRXDfUtDng8YBuk2GXv9RerNUA7FZV/j12ivoHc9ip3Ih+8p9XNQ1u6X/T01lUiJx4sQJHn/8cerVq0dMTAwTJ07k1KlT/PjjjyxYsMDcMQohRN1mYwN2RSMJ3Rp7s3JyD+OCx11XdxmTCIBlJ5Yx/475xq8f69WIXw9f5Yd9l3luYBOcb1w74eyj/MPNTVPmU7vKm0Nms2ex8n1u2Asa9S55G7UGBv8XfnpIeYe1+UhwqWfZOOuCXv9BlRkPTt6k5+Zz8mo6GhsV7Rt4WjuySlOpVHzc/2OTt8/Oz+bBPx4kwDmABQMW4OVQlEzl5uv44K9TADw/uGn1ufjNz1G6Ue/4UBnhQwVt74P+r4F79em0vT9uP9O3TiclL4VG7o34tP+n2BrqcT4hC7frRTJOx2fw79lE7u/WENsa2NzPpIjnzp3LjBkzWLt2LRqN8g+offv2HD582JyxCSGEyElR3qUuQeE7h7uv7gbgpc4vobHRsPHSRmIyikpYtgnyoFNDTzJyC1h1MObmnYDP9TnPsuDafHLTlaZzAHeUMhpRqPkICBuoJB0bZpk/tjokQ5vBD1E/cKHFUBg0B9S2pGRp6R7qTdfGXjhW16k75SjQ6flsy1ke/3pfUcnRX56CBZ0goZRpi7p8WP8SX2+cTkJOAnqDHg97j2KbfP1vNFfTcmke4Mao9oHmfRGm0Ovh8ArldW18U0kiQgfAUzvg7kXVKolYfWY1T2x4gpS8FHoG9uT7od/TwK0BAe6O9AwrKtk9749I3lx7kiEfbufvE3E1oyLWDUxKJM6ePcvo0aOBon9czs7O5OTkmC8yIYQQ8O1o+G89uHIAUBY9fr/nIteuV1DJKcjh4LWDqFAxvPFw7gq5C71Bz/eR3xfbTWGDuq92RqO/ubb5sPkwdT8EF68ZL6rQyV8hN1VZYB1SymhEIZVKqeCktocjyyF6pyUirBMOxB9g7p65vPHvG8b7Gno78/3Ebnz3eM39+deobfhh3yU2RV3j2JU0CvQFrEiL5DVVEvq0yyU/KSGKa/uX8HW88vM1o/OMYs3nUrK0fLZFKcLwyl3NUNtYedrjuc3weR/49SlIjwG/1vDgL/DgavBvbd3YbqDT63hv33u88e8bFOgLeLDFg3za/1Nc7Vxv2dZgMHBflwY08nHmfGIWk749wIQluzl+Jc0KkVeMSYmEp6cnV69eLXbfxYsXZX2EEEKYW0YcGPTGudzf7r7Ia78cZ0OkUo3lUPwh8vX5NPdujoeDBw+3fBhQ3g1L16YbdzO4hR+BHo5cSMxi6+mbSkIGtFUWXdegbqo1TvsH4YFVyrQlU9aheIdC7+t1/Nc9r7x7LCptT+weALpkZ8OxlcUeq9YlTU3QK0z5G/HPmQTUKjVLbTL4zdWFs4knS35C7FEWeLqTo4KBDQbSwa9DsYd/2HeZjNwCeof70KeJFafXxR1T3lD59m6IPwZugUpH6ie3QWh/68VVggxtBk9vfppvT36LxkbD7B6zebHzi2hsSl5JoFKpGNzSn7+e68MbES3wcLJl9/lkIj79h+k/HSY2rfq/YW9SInH33Xczffp09u/fj8Fg4Pjx47z++uuMGzfO3PEJIUTdpddBYflGF3/yCnTsPKuUeCzsH7E7VpnW1C2gGwBNvZrSNaAr2QXZrDq9yrgrjdqGh7o3xMVeQ2xaLiWqYUPqNYpKpUxXCupo+nN6PqeUr0yILCoZKyplX9w+ALqc2Q4n15Cbr+Obf6OJiksv55nVX+/rHe53nElEpVLR1V75G7Ev6ViJ25+6/A9rXJzRoOK5js/d8vikPo35YFxbXrmrudliLlPaFfhlMvxfbzi3SWnKOfDNGzpSV69paJfSL3H/H/ez88pOPOw9WDJoCaPDR5v0XDuNDY/2bMS2F/rxRO9GqFUqVh+8wpiF/7LhbIaZI68ckxKJJ554gq5duzJp0iQyMjJ46KGH6NChAw899JC54xNCiLorKwEMOnDyAY0de84nk63V0czflfoejsCtiQTAwy2UUYnfzv1WbL7tA90asuuV/tzftSHFpF+FRT2VD1G18jLhyI+lrnMpk62DUgI2uFu1e+e1JkrNTeVUyinsVGra5uWBsw+HLqXyxm8neP6nI9YOr9J6hHqjUsHBSylk5RXQ2VVpKLcn48It2xoMBj5I3odBpeLegF40dGt4yzZqGxVjOwbRor6b2WMvJjsZNrwBCzooU/tsNNB1Mjx7WKnGZOto2XhMsCd2D/f9cR8X0i4Q5hHGimEr6OTfyaTn5ubrOHAxmSXbz/Py6qOsPRJLwfXpp1fTcvnqYLI5Q680k6o2qdVqpk+fzvTp00lOTsbLq+aVRxNCiBrnpq7Wm693s+5/vQldVn4WF9IuYGdjR/vrjbUAegb2ZE6PObfUg3curdO1kzdcuz79oSAPNPZV/ELqsH1LlEWhZ/6CsV/e/vPDB0LYACnLWwX2xSujEe3svLE3XADnejW6f8TNPJzsaBPozpGYNPZeSKaLT2tI2MH+vAR0el2xbc+lnGEvubjqDDzV8T/FHjtxNY2rqbkMbO5r2eleOamw6zNl9E17/V34lnfDgFng1bjMp1pLVn4WHx34iB9P/YgBA32D+vJO73dwsXMpcXuDwcCV1BwOXUrl4KUUDl5K5eTVNPJ1xUeD3Rw0tG/gSfsGHjR3rt7Tm0xKJDIyMrC1tcXBwQEPDw9Wr16NRqMhIiKixs8pFEKIaisjTrm93tX65m7WzrbO7Lh3B+dSz+GgcTA+zUZlw93hd5e629PxGXz9bzQvDG6Kl7Odkjh4NICUaEi+AL7NzPN66pq8TPj3eon0dpVo6Fr4fzYvE9Iug6+VpprUcHtj9wLQ2eb6RZ5zPfYeVfpHdAmp+YkEKF2uj8SkseNMIv2aNyEwv4ArthCVEoXNDZNQwgxqVl+J5aJrPdwLe8mgXOi+9ftJdp9PZs7IljzUPcT8QedlwO7/g10LlEploFRi6vfa7U0FtLDtMduZs2sO8dnxaFQaHm/9OJPbTkZ9w5Sr3Hwdx66kcehSCgcvKsnDtYy8YvtRqaCpnysdGnrQPtiTDg09aOzjgs31xe2RkZEWfV23y6REYtKkSbzyyiu0adOGBQsW8PPPP6PRaDh79izTp083d4xCCFE3pV8vcuHqz/mETKKTsnF3tKV9sIdxEweNAy19Wpa6i1PJp7BR2RDuWXSx8PYfkWw5lUCghyNP9wtT7vQOVxKJpDOSSFSV/V9AdhIEdlIujCoj8Qx8M0Lp0jtld7Wc3lHd7Y1TEomuOuWCWmvvzYGLKQB0rgUjEqAsuP5syzn2XEiCzn50yc3lF1sX9sXuo6v6hqpUcUcJzS8g1KtVsedvPZXA7vPJeDjZMrKdmcu9arNg7xLY+THkXJ++E9JbSSAadjfvsSshOTeZd/e+yx8X/gCghXcL5vSYQxPPJsSk5HDwUgqHLqVy6FIKJ2PTbxltcHe0pX0DDzpcH3FoG+xh7ClRE5mUSJw/f56WLZV/VGvXruWrr77CxcWFCRMmSCIhhBDmotMqCwxdA4zTmvo2qYdGbYPBYCA6PZoQt5BSR4bXnF3D6ztfp29QXz4d8Knx/sd6NWLLqQSW7Yrmid6NsdPYKFWbzm5QLlhF5WmzYOcnyud3vFz5qUmeIeDooUxB++cj6PdKJQOsWwwGA5PbTWZ/3H5anfwHgOM5nuTmZxNazxkfl9oxna9jQ0++eayLMsKiyqdLjxn8cmwRe+L20DWwK1n5WXx94mseChqI64gFxmpwADq9gbfXK+9+T+0XhrujmS5u83Ng/5fwz4fKOjBQ1gH1fw0a9THPMauAwWDg9/O/896+90jNS8Vebc/QoEfx0Q1g/u8ZHLq8iYQSRhua+bvSvoEnHRp40L6BJ419nI2jDbWBSYmETqdDrVZz5coV8vPzCQ9X3tlKT6/5VQ6EEKLa6jZZ+dDrGZtbQD1XewKvL7KOyYhhxK8jCPcMZ1XEqhKTid5BvbFX27MtZhvn087T2F2ZZ9wrzIdwXxfOXMtk/fFY5Z1H7+sjE0lnLfbyarX9X0J2IgR2VKo1VZbaFob9D766U7kAazNeKRErTKJSqbgz5E7uDLkT9imLYPcmOQDZdGnkbd3gqpCdxoa+xlKtanq0uJcPvELp7N+Z+AvxfHHsC5YcW0JkUmSxNxcAVh2I4XR8JkGejjzY/dbF15VWkAcHl8GO+UXrvwI7KiMQof2r9Tqgs8mXmPnPbI6nKKNaGm0Tki6NZNlxb+CccTsPJ2XEuEMDTzo09KRNkDuuNXi0wRQmJRJNmjRh4cKFxMbG0rOnUtXj2rVrODk5mTU4IYQQgI0NHk52xaYa7IrdBVDmiISXgxcRoRGsPL2Sb09+yxvdlSZcKpWKx3o14pXVx/jynwuMaFsfVWF3axmRqDxttjJdA6Bv2aMRv5//nV1XdzGz28xi61xK1LA7tLsfDn8Pf8xQ+lJU44uvaqv3dEiLYe/1nLlrLZnWdCODwUBCZh6+rl4MCRkCwIm8Eyw7uQyAJ9o8UWz7HK2O+RtOATBjSFPsNVVYWlWXr/zMbv9AWeMD4N9GSSCaDKl2P8N5BTqiYjM4GpPK4csp7E78jTTH31DZaDHoHMiNH05BWkfsNWpaNnCjTZAHbYPdaRPkQWMf5zq3dtikRGLmzJnMnj0bOzs73n77bQD+/fdfevXqZdbghBCiTku9DI6eFGic0KiLV+suqexrSR5s8SArT69k7bm1PNP+GbwclIumu9sH8t6fURyJSePgpVQ6+lxfQ1FaF1xhupi9yqLR+u0hfFCpm/106ife2v0WwS6NjUlEVn4W2y5vo29wX5xtnW990qA5ELVOqat/cg20HGWmF1G7TN00lUCXQKa2n4rr9YXv9/vG06CeG10b165E4lp6LiM+3YnOYGBvs59QXfwH7v2e7y5/R54ujzuD7qDt769Awx7Q/3UAvtx5gfj0PFoHuhPRpn7VBKIrgGM/wbZ3lfVXAL4t4I5XoHlEtUggCnR6ziZkcvRyGkdiUjkak0ZUnLKuwcYuHoeAVaidL6ECnLQd6OE5kS6tGtImyJ0mfq7Yqk3qolCrmZRING/enB9++KHYfaNGjWLUqFHmiEkIIQTA4jsgO5HXm/3N4bg83ohoSfdQb3R6nbFDb/f6ZS9KbOzemL5BfdkWs40fT/3I5LaTAXCwVTOhSwMWbj3Hlzsv0HFCe5h2BNwbmPtV1X6N71C+l9nJpV4s/Xz6Z97a/RYA5y4GMeX7A3x2Xwe2Xt7Kyztexl5tT8/6PRkcMpg7gu8oSiqcfZRymOumw5+vKKVh7V0t87pqqLisOLbFbMPZ1pkZnWcY7x/Q3I8Bzf2sGJl51HO1R28wkJCRx6lUA4bca4zfoPT9srWxZZpvT9ixDCj62byWnotKBa8MbVb5+ft6HRxfDdveKZoq6dNEWSvU4m6wsc7Ft15vIDopi2NX0jhyOY1jV1I5fiWdnPzipXFVNgX4N9xJttPfGNDhYefD691eY0ijKpiiWAuZlEjcaNKkSSxevNgcsQghhChUoIXsRAzYsPlcBtcy8oyLH6OSo0jXphPoEkiwa3C5u3qoxUNsi9nGD1E/8Firx7BXKwtLH+zekMXbzxOdmIVWZ8DOM8Scr6hucauvfJRgxcmfmLdPSSJy44ajS+1FUDMnVCoVrnautPdtz6Frh9h8eTObL2/GzsaOnoHXk4qgO3Dp+Agc+g5SLsC1KAjubMEXVvMUVmvq5NcJTdoVZdqZXwvoPNHKkZmHSqWiV5gPqw9d4R9tU+4uKLpQvq/ZfQSlxChfBLQx3j97ZCse69WIht4ljIKZSq+HyN9g69uQEKXc59lISSBaj7NoJ2ptgZ4z1zI4cTWdk1fTOX4ljcjYdLK0ulu2DfZypE2gB22C3HF2u8JP0fO5kH4egLFNxjK943Rc7SRZL81tJxL79+83RxxCCCFulKn0kDjh0IFrqXkEuDvQPED5Z1a4PqK8aU2FOvt3prlXcyKTI9l6eatxznSAuyN/TOtNuK9L0bxeXT4U5Mq73BWRn6NM4+gyqdQkYsHe71kc+Y6y+bXhzOg+kZHtAnGyUy6y+gT14UCkPz1DM3HyPMnGSxs4dO0QWy5vYcvlLbza9VUmNJsAY5ait3fDxqVeiccRRYz9I/w7Q/J52P8F85xeJCvmGJP6NK7cxXM11StcSSR2ZNZnol7PIKcGnNTrmNR2EvzytLKRf5tiz6nw98FggFN/wJa3If6Ycp97A+g7A9pOUAoFmFG2toDI2HROXE3nxJV0jl9N40x8Jlqd/pZt/d0caBWorGtoE6Ssa/BytiM7P5uPD37MZ0dXYMBAQ7eGvNH9DeVnRpTpthMJg8FQ/kZCCCEq53ozus2qLgDc0bSoy6xxfUR90xIJlUrFjM4zUKGio1/xBk9N/G5IGPYugT9fVipFDf5vZV9B3XPgG6WiUvROmLjhlofnbVvO8gvvolKBa9bdLLn/eVrWdy+2TXRiFp9uOYtOb6B5QENmDf8foX11bLy4kQ0XNzCwwfXpFd6hPPn3E9ip7RjccBD9gvvhZu9+yzHrOoPBYByR6OLfBa4cx2CA1enNSNxzicd7NbJyhObRK8wHgD2pruRpNPzPsSknG0/Ezc4N4o4qGwW04YWfj1Dfw5En+zQuvfN9aQwGOLMBts6Dq4eU+1zrQ5/nof1DSs+TKpaSpVUShqtpnLiqJA0XErMo6dK0kY8zLeq70aq+Oy3ru9GyvhveJZT5/efKP8zZNYfYrFjUKjWPtHyEp9o+VX7xAwFUIJEYMWKEOeIQQghxo+vlETfnKc3h+l/vZg3QxLMJ8VnxdPXvWuJTS1LWO2v5Oj3rj8fhkFSPwfoCSJQSsLctPxd2fqR83uu5EjcZ2qQbK8540trtLr68bwYOtrdO9Wjo7cRH97TjnfVRRMamM2HJbu5q5c+rQ0dxX/Oi7thpeWnsj9tPgaGA7THb0ahs6Fa/B4MbDqZ/g/64S1IBQExmDLFZsbjZudHUqymc2cp5QwCJBQ74uNjTyKf2jUYA+Lo50NTPlVPxGRywaUKPzHjljYjcNGXhs9qeQzm+rDywFwdbGyZ0CTY9kdDrlIX+O/5XNALh7Au9n4eOj4Bt5S/ADQYDsWm5xZKGE1fSuJqWe8u2GhsV4f6utKzvRqv6brQMdKd5gBsu5byelNwU3tv3Hr+f/x2A5l7Nmd1jNs29pXP87bjtRGL27Nnk5eWhUqmws6v6bFOUYcvbkBbD+dajCNbpsPUOlzriwjy0WWBXO//B1hgZcSQZXDmS7Y2dxoaeYUW17l/s/CIvdn6xQrvN1+WzO3Y3vYN6G+/bcDKeZ1ccItTTkYEGFTZJUgL2th1cpiR//q2h6VDj3b8duUq3Rl74ujnQLqAx68f+SpCHZ6m7UalURLStz6AWfizefp5FW8+x/ngcm6KuMbFXI6b0C8PFXoO7vTsbxm1g8673+fv0avY52PPPlX+M7672De7Lh3d8WOdKUd7sxmlNNiobyEpgr15Jzrs28qrV359e4T6cis/gH31remReUO6MUy78Db4tmPen8nv+eK9GBLib0Cm9QAtHf1RG3ZKv905w8YceU6HT42BXsZYAOVodp+MziIpLJzJWuT0Vl0FKdv4t2zraqmke4ErL+u60CnSjZX13wv1cbqtcrcFg4I8Lf/Du3ndJyUvBXm3P0+2e5sEWD6Kxue3L4jrPpO/Yhx9+yIABA2jTpg07d+5kypQp2NjYsGDBAikBa0lR68iPP8a49J2gL6BpgZ6WjQbTqmE/Wvm0IsQtBLUFFzOJWmrXQvj7Nej7kvJRi//RVmsZsWzTt8WAim6NvXGyq/w/OJ1ex+jfRhOdHs3PET/TzEu5oBrUwo8AdwfOpeSy3bYNd6ScUNZKmHluc61RkKdcXIHxdyYtJ5+Zvx7nj+i1+AWcYMP9X+Fk61RmEnEjB1s1zw4IZ1ynIN778xS/HLrCwq3naBbgxoi2yvoLH0cfxvd7h/GXTpB0aSebmg9gg7sn++L2ARgvkq9lX+Ni+kU6+XWq1RfOhdLy0gBwt3fnWKJy4WwckctKYN/1RKJLLewfcaNe4T5sPB6De2YWZMYrd8Yq05o22A9gX1QKXs52PNm3nDcktVlKovzvAki/otzn0VAZeWt7n8kjEHq9gcsp2UTFZRB1PWGIissgOqnkqUkeTrbXpyS5G28b+TijrkRVqdjMWN7a/RY7ruwAlOlub3R/gwZuUq2uokz6z/Trr7/yxBNK85KFCxfywgsv4OLiwocffiiJhKXoCiDxFIlqNfWdA4jOjOGYrZpjMZsgZhMAThonOvh1YOGAhahUKuN6lrrwj0NUocAOYNArlTeSz8OIBaC5dV6pMDO1HZn2/nio9PRrWrSgdumxpTRyb0TvwN7YqW9vVFhto6ZXYC+i06NZdmIZ83rPA8BWbcND3UN4988ovlSN4g79EWX6Q2FvCVG2g8sg4yr4tYKmw9h1LonnfzrMNcMuHOqvJF1lYPOlzQwPHX7buw5wd+TDe9rxYPeGrDoQQ0SbAEB5V/VkbLqyxmLYfLwX9WD8iY2Mn7iR5D7vkqXNMu5j5emVLDqyiCCXIEaGjWRE6Ajqu1RRrwArScxJ5HTyaWIyY5SPjOsfmTFkaDN4ut3TPNX2KWZ1n8WEZhPwcVTWDJCVxB69sk6oticSdzSpx7YZ/SClMbj4woUr0OkxCoK68s7yJCCfZ/uH4VZa5+WcVNi3BHYvguwk5b56zZUpTC3vBnXpl5Bp2fnKyEJ8RrFRhuwSqiZpbFSE+rrQLMCVZv5uNAtwpbm/G35u9lV2/RKfFc+Xx79k5emVaPVaXG1deaHzC9wddrdcI1WSSYlEZmYmLi4uZGdnExUVxTfffINGo2HevHnmjk9cd/LCRvwNBfi7BrF2zHrSs5M4+fsUjl/5lxP29hz38CMuP51Mbabxl+Jc6jke++sxWvi0oJV3K1r5KB/GP6hClKRBN5jwI6x8TBnGTr0M934PTrX7n2610+9VHuoH9+sN5F+vPpKam8onBz9BY6Nh54SdFdrt/c3vZ3nUctZfWM+0DtPwc1bq6E/oEszHm06zXduUs3b1CUs8I4mEKW4YjdD2eon5f51i8fbzqF0P4Vj/J1AZeKb9MxVKIm7UoYEnHRoUjWasOxbL1OWHiGhbn5fvakZgj2eUOH7/D16TthobD4Lyzry/sz8xmTF8dvgzFh5eSJeALowKG8XABgOr3aJSg8FAal6qMTEovG1Xrx13h98NwLrz6/hg/wclPt9J44RWpwXARmWjrI24LiY1lyvUw81ORVO/2l2ZTKVSKaOKPmHX77kCtg78eNWH8ylxNPR24r6uDW99YuY12L0Q9i4FbYZyX2BH6P0CNLmzWB+IfJ2eC4lZRMYqiYIy2pBe4loGAF9Xe5oFuNHc39WYOITWc8FOY57eEjcnEAB3htzJi51fpJ6TVDyrCiYlEh4eHpw7d44zZ87Qtm1bNBoNubkl/5CIqmcwGHj1wHtcDA4i8+JTNFvwD+M7B/Pg2BV02zATdn0K1xJIHDCTtNZ3G58XmRxJSl4KO6/sZOeVoosOPyc/Wvm0on+D/owIlcXzogRN74TH1sPye+HSv7B0ANz38w3/kISlqG1UximLe+L2YMBAe9/2OGpMmNNcgiDXIAY0GMCGixtYEbWC5zo+B4CHkx1jOgTx/Z5LfKW7k7myTsI0qZdAbcdZzz48u9mDk7HnsXU/jGP9nzFgYGq7qUxqM6nKD5uYkYe9xoa1R66y4WQck3qO5Sm3NTjFHYV9X0DXomPe3/x+7m16L3vi9vDr2V/ZdHETe2L3sCd2D3Nt5/LNXd/QxLNJlcdoijxdnrGvyY6YHXx88GNiMmPIys+6Zduc/BxjIhHuEU4X/y4EuQYR6BJIkEsQQa7Kh6e9Z6nvMh9o+DhchC4N3SvfeK0GyNYWsPNsEtnaAprY6cnKzefDDcrv9otDmhW/gE+9BDs/gUPfKiWgARr1hd7Pk9+gFxeTszl9Ip7T8RmcuZbJmfgMLiRmka+7dV6SvcaGpv6uNPMvGmVo5u+Gl7Nl1tbGZ8XzxfEvWHV6lTGBGNRwEE+1fcpqP+u1lUmJxMMPP8yYMWMA+OAD5R2Affv2ERoqC30tYdfVXZzLTcBG50x+bhDHrqTRNthdeVdgyFzOEUT6/7N33vFVFHsffk5N75UUEkINCb2DdKSKKGABC2DDfhXbtbdreb22q2LDroBKExVFBenSewmQAIEU0nty+tn3j0lOEpJAAunMA/vZNrs7mz17znxnfmXLAnqs+Q/+Kq0jYshVUVfRO6g3h7IOcTjrMIeyD3Ek+wjpJemkn0nH19nXISRMNhOJ+YmVem4klyGnNsHW+RA9mZNhU3jDdwGj7YsYmf0L/p+PgRu+g0hpztgYrN+6HTcvf3p1ikRb6khYFvb1QtmsL8SsmFn8dfovfjz+I3d1vwtXnXCSnDMkkoXbz7DMNozHik/gfUlXuUzw7wj37+LsgWMc+SGRoDZxGLx/RMHOvT3vZW6PuQ1y2dlD2jGmaxCv/X6UVQfO8t760yxx/Q9P2OYz5e//oOo5o1IuEI1aw+CQwQwOGUy+KZ/Vp1bzU8JPnC0+S5RXFAB2xc73R7/nyogrG6S3Ns+Yx6Hs8t+jw1mH6RnYk7dHvA2IHvRjuccAcNe5E+4RLsRBqUio+Ps0OHQwg0MH17kOV0+YSEzfIszWyyOUfUJGEXd+s4swVRZL+8WjW7mQ+/zuYVPYSCZ2CxaFMo/B5nfh4I9YbAqnlSCOt7ma40ETiDd6Er+ykFNZf1QrGADCfFyIdowyeNIl2IMIv0vzZbhYygTE0uNLsdiFs/bYiLHM7TFXCogGolZC4tZbb2XYsGFoNBrCw0UW1bCwMF566aUGrZxE8E3cNwAYcq+grbuaZ67tQ1u/8ugIXxqu4DtzW4LVeVyZFMG4+CwGRPmi06gJdQ8l1D3UkYDKrthJzE/kUPYh2noI56J8Uz73rLmHM4VnWDxpca0y5UpaJ/knd+J+bDUa33asLRzE6mP5rGYSKibS05zAmJ1FjHIqoEuwh7QrbUjMxbz6816OK+EsvN2TIR1Fo25bamn+iFomoquJHgE96BHQg/2Z+1mRsIKbom8CoEOgB8M6+rH1ZA47w27hyku7i1ZPVpEJH1c9Go2Wob1i+Lcxm4/iv0VR7Nzb417u6XFPg14/zMeV+TN7M2tQDi/9ephDKQU8xH1856JlsdaNmlzlvZy8uKHLDdzQ5QZyjbmOSDW70nbx2o7XeGPnGwwJHcKU9lMYET6izr44FUkuTOad3e9wOPswKUUpVfafyj/lWO4R0IPvJ31PmEcYnnrPBvmOUalUdAhs3SZNFYkJ8cJbYyLZ5k9O8s8Em3KYE3CMm699lIRDO4nfsoLjyenE20OJV17lFCFYFDWcBk4XA+UjQ2E+LnQK8qBjkDsdAz3oFOROh0D3egkEcamkFafx+cHPWRa/TAqIRqbWTz8yMtKxvH37djQaDX379m2IOkkqcDLvpDBLsmsw5/XnvpH+jI0JrlQm0MOZNl7OnM335tv9+Xy7fzteLlpGd/JjbLcwRnQOcMQrV6vURHlHEeUd5TjeQ++Br7MvB7MO8q91/+K7Cd85eigllxdP7XIl3vw6b+u8mdwjBGedmjVxGWw9kcVeW0f27rbx392bCPV25vFxnZnSK6ypq9wqSU5O4rgSjrvKSL92IuxrUmESyUXJeOg9iPa99Djns2JmMW/9PJYcW8LMLjMdjbbnJsfi4awlyLN52c03N/46mMwTP2zn9p7u3DdtLKhUzB04lGLdrThrnbmnZ8OKiIr0b+fLz/ddwdLdybzxxzE6tg1Cp6mdzbmPc7nfhZvejdFtR7MhaQMbkzeyMXkj3k7eTGw3kWs6XFNjfH2j1cix3GOO0e9cUy4fjfkIAGetM3+e/lMsa5yJ9osmxi+GWP9YYvxiKkXL8dB7EOMfc7F/hgtiyktHWfsizgHtYNhjDXad5oTFZqebRzGb8px45uwVtFF1Jf5ILKd2/4pF0QBVR5jDfV3oFOhBhyB3OgUK4dBcBMO5nCsgVKgYFzmOud3n0tFH+ng1BrX6VNx2223cfffd9O/fn6+//pq3334btVrNv/71L2bPnl2rC23cuJFXXnkFu93Oddddx113VbYZ/fnnn1mwYAEAbm5uvPDCC3Tp0sWx32azMW3aNIKCgvjkk09qeXstn+/ivgPAnN+HUGdXrr2iZ5UyD47uyAOjOnAwJZ8/Dqfxx6E0EjKLWb4/neX701n90FC6BHsCYLTYqiRBUqvUvDb0NWaumkl8bjzPbHmGt4a/JXucLzMOJOexKjccJ8z4t40g2MuZWwZFcsugSIpNVjYnZLE2Lp2/j2aSkmfEdffHEPsM6Jz5cWcSqETSNP9qModK6sa6I6Ln9grXZIcN8/az2wEYEDygXsI8jwofxbw+85jcfnKld71DoLtIrpZ+BHzbge7ifDFaKyVmKy//GsfiHWcAZ3YeiiN3Qj983EROgnl95jVJvdRqFdf3C2dCt2DsdoQT+O6v+NQwEhdnJ2b2b3tBU5MYvxjeHfkuOcYcVp1cxU8JP3E89ziLji5i0dFFPNznYW6LvQ27YmfJ8SUczjrM4ezDJOQmYFWslc5VYC7AU++Jv4s/rw99nY4+HYnyimrSOP1r95/koZ1XcqvHXp4Z1mTVqHcURSGzyMSJjGJOZhVxIqOYE5lFnMwqIjnXgKIIx/s9SidQgBJxXLgqk06+Gjp2iqZjWDCdgjxoH+jWLAXDuaQVp/HZwc9YHr/cISDGR45nbve5dPCRvnyNSa0+LXFxcfTq1QuAJUuW8Pnnn+Pp6cl9991XKyFhs9l46aWX+PLLLwkKCmL69OmMGjWKDh3KH3ZYWBjfffcdXl5ebNiwgWeffZYlS5Y49n/zzTe0b9+eoqKiOt5iyyXPmMcvJ34BwJJzBfdM7IbeufofdZVKRfcwb7qHefPYIE8SPrmJP/NDOajrQWenboAnBrON/q+soUe4N+NighgbE+zodfTQe/DeqPeYuWomf53+i88Ofsad3e9srFuVNAP++/sRAGZr/yK43TuV9rk5aRkXE8y4mGDsBWkc+N90Oicdg292otywkPfXxZOUY0Clgh5h3oyJDmR0dJA0gbpI1p0sBjSM8st1bNuauhW4dLOmMjRqDXNi51S/84txnE09w5/9v2LWpOH1cr3WwP6kPB76YR+nsorRY+Vx7WLajurKxJWTeHfkuwxoM6DJP+8eZaE8F91A6rEdvGkJxWxXsXRXEq9c243Y0AtnvPZ19uWWrrdwc/TNxOXE8VPCT/x26jeGhookhipUzN87n2yjCAmqVqnp4N1BRAYsjRDoqi0f1Z4UNan+b/Qi2HE6HzN6PJ1bZr4ls9XO6WwhEk5kls9PZhZRaLRWe4xGrSJEV0SSWSQYHag6wtNuK2k/8GpcB9/R4qLxSQHR/KiVkLBYLOh0OrKyssjOznaYNGVnZ9fqIgcOHCAiIsLhXzFp0iTWrl1bSUj07t3bsdyzZ0/S0tIc62lpaaxfv567776br776qlbXbA38lPATRpuRAcGD6Rc5jOv61tKMxDOEDnMX0uG7aZDxC3zxBdy8jAPFQZRYbGxOyGJzQhbPrjxMz3BvxseKBmI7/3a8PvR1Hvj7Ad7f+z6dfTszLKwVddtIauSfhCw2ncjFg2LuCT523rwRas9get75ESy6AZK2YftsLHf1/oS1Se78cyKbfUl57EvK480/jxPq7cKoLoE8ObFLi+jlag4YLTb+SReN0RGh5THX7+t5Hz0DezbIO5lalIrVbnWYmVh9OzD51FyyNhXRrVtupbCjlyvfbE3kpV+OYLUrdPa08j/j0ySH6Hks8RhWxcqu9F0MaDOgqatZTt/baHNsNf9zXsCLmvvZn5zP1R9s5tZBkTwytlO54DgPKpWKrn5d6erXlcf6PoauNEGhSqVidsxs1Co1sf6xdPHt0iLMYbenmgEN/X0NTV2V85JTbBYjCmWCIaOIE5lFJOUasNmrd3j2dNbSIdCdqAB32ge4097DRtTZVbQ98jH6kjSmqZ7ntBLMex5fE/jINnByb+S7ujSqExATIicwt8dc2nvLwD9NSa1+2cPDw1mxYgVnzpxh4EDRG5abm4uTU+1MGNLT0wkOLrfrDwoK4sCBAzWWX7p0KcOGlf9Yvvrqqzz22GMUF1cNB1cTiYmJGAxN+2VhNBqJi4u76ON7qXpxT7t7iE7dxcD0GWSYb6MorPa9g+oh/yN882O4Zu7D9tlYgob+l0XXdWN7cglbzxSzO9XgaPS9/vtRburhzc09A7kh7Aa+T/6eR9c/yjvd3sFX37J6LC6FS31mLRFFUXjxt1QA7tb+Cm4BtfgbaNGO+JiwTY/iknuMmTumMmzIa+T06cXeswa2J5ewI6mElDwDqw8mM6OT2tFTqyhKvffatqbntiO5BKNNTTfVSRS7vdJ99VX1JS8pjzzy6u16m7M28/7J9xngO4B5HYRZjj9eTNds4GPb1fzvt/38e3hQvV2vIi3lue1ILuH5taJz65ouHryccS873LN41CUQm6Iwpc0URupHNq97UcIJbzOACWnriY0I4m3VrayMy+erfxL5eW8Sc/v7MTTC7aLeRaPRyADnUtGUA6dzTtdz5eufQpONo3lq9FgI1+U3+bMqMttIKbCQWmAltcBCSqFFzAssFJnt1R6jVkEbDy3hXnrCPHWEeekI99IR5qnHy1l8xzrlncD3+Pd4nv4DdWnYU5N7WxYr/8GMDsW/H3EnkxrzVi+JLFMWK86u4O/Mv7EpNlSoGOw7mOmh0wlzCcN81kzc2Wb03jUAzeF7Mjq6Zr+8WgmJxx57jCeeeAK9Xs+HH34IwIYNG+jWrVutKqBUk/u8pi+vbdu2sXTpUhYtWgTAunXr8PX1JTY2lu3bt9fqelDZObypiIuLO+8f/0IcTM7nSv8oOiath9xjhIe2gbqer+tqWHYHmqO/ErnxISKnf8mAyRN5EGHru/F4Jn8cTufn/am0C2tDdHQUT3V5ivyN+fQI7MHgLoObfKi+MbnUZ9YSWX0ojWNZp/DXmZijXo1r52fxrtXfIBpi14nP17HfiNjwEBFXv0+vcTO4DbDbFQ6m5JNZaKJrV9EQXXMknW+2nWbBrX1w0tafeUFrem6Ljh0CYKR6L0HtxxMUHd0g4qsMvxI/Pkz8kB25O/AI8yDMIwysg7n10GMssF3FljMleIdE0sar/n0lWspza9fBxvaMffSL9GWOy2b+XpPFo4EB2FCYEzuHh3s/3Dy/J/3/Bx8NJjxxKe/c8zB3jOnG0ysOsS8pj9c2ZND2pt5M6NamzqdtKc+tImvj0lE4TQ/VCUIjOhDaCPUvMllJzCrmVFaxmGeLeWJ2CTnF5hqPc3fS0j7ArXR0wY32AWKkIcLPtYqPIwCKAglrYdt8OPF36UYVdJ4IA+/Fya89vB2NHht0uqJFPLuUohS+OPgFyxOWY7VbxQhEuwnM7X75jUA09/etVkJi8ODBbNq0qdK2SZMmMWlS7eweg4ODK5kqpaenExgYWKXc0aNHeeaZZ1iwYAE+PmIofc+ePfz9999s3LgRk8lEUVERjz76qCOfRWtEURTisuN4emUOB5PzWeAFYwACu9b9ZDoXuP4bWDUPdn8FJ9dDl4kAuOq1jI9tw/jYNvxrdEci/YUNpUql4v+G/V/z/GGU1CuKovC/tSI50YNjuuAavkQ42NYWvZvILfHXcyIx4v5F0P0GUKtRq1X0CPd2FDVabLzwy2GScw288PNhXpvavZ7vpnVwQ79wvFI2MsF4FryEOeODfz+IyWbi3wP+7Yj5X18EugYyIXICv5z8hYVxC3mi/xPg35EQVQ7jXY6wyhDLt1tP8/j4Lhc+WSvDbldQq1U46zTMn9kbld3Kuo//yyOB/lhVMDtmdvMVEQCB0dD3Ntj5GfzxFDE3L2f5PYNZvPMMvx08y5Vdy0earDY72lpGemqJ7EjMAaC/+ii4Da238xabrCRmF5OYVUJithANp7OLOZVVQlaRqcbjXHQaIvxcaefvRqS/G+38xDzS35UAd6fafaYsBjjwA2z7CDKPim06V+h5Ewy8B/xKG9x2O8w7yrHEFDp3ar6RjMw2M38n/c2K+BVsTd2KgoIKFRPbTWRu97mVok1Kmg+1NlouLi5mw4YNnD17ljZt2jB8+HDc3NxqdWy3bt1ITEwkKSmJoKAgVq1axVtvvVWpTGpqKg888ABvvPEG7dqVN2QeeeQRHnnkEUCEnf3iiy9atYgA2Juxl1mrZ2FVuuDneidDDOuEzbpPHRp4FVFr4Kp3RYbKrlOqLVImIgB+PZBKdpGZWYMjAfgj8Q/OFJyRztetEJVKxae39OGrfxK5cUgX0F6EWFVrYNwrENwdOo0ViRKrwVmn4eOb+zDto39YvCOJ2FAvbhoQcYl30PqIDfUi9r57gXsBEVrzn9R/MNvNeDt5N8g1Z8XM4peTv7A8fjn39LwHTz/hv3abfTmriGXRjjPcN7IDbk6Xj5/L8fRC/vX9Pt6f0ZMOgSJogKEog5fdNVhRmBV9C/P6zGu+IqKMEU/BgSWipzr+L9SdxnLTgAhm9m/rqPuG45m8+PNhXr4mliEd/Ju4wg3DjlMVhITrtbU+TlEUsovNnMkp4Ux2iZiXLidmF5NRWLNYcNKqifBzJdLPzSEYypaDPGspFqqjMF2Iw12fQ0mpr6pHiMhm3ntWVQdqtRo822DX5YHzhZ3tG5vjucdZEb+CX0/+Sp4pDwC9Ws/YyLHc2e1OKSCaObX6VYiPj2fOnDloNBpCQ0NJSUnh1Vdf5YsvvqBTpwsn+tBqtTz33HPccccdjjCuHTt2ZPHixQDMmDGD+fPnk5eXx4svvgiARqNh+fLll3BrLZdvj3wLgM0Uwp3d9bjsNUNAN9Bcwo+4SgWxU8vXz2yHuJ/hypdEQ7CU09nF/Ov7fdjsCjqNmqFdVTyx8Qlsio12Xu0YEzHm4usgaZaE+7ry7KRo8Rm5FHrcUL6cnwyrn4Sr3gG38oZJbKgXr03txrwf9/PCz4fpHORB38jLxwfnYtiXuQ+z3UwX3y74OjfM36qzb2cGtBnA9rPbWXZ8mYjm5BlK7/yj9GjjzP6zRmYs2MaXs/vhdxmE903JM3Dr5ztIKzDy0fqTvHV9DwBcvEL5aPL3rD31O/f0/lfzFxEAbn4w/HFY/xoUZzg2V6z7N/8kcjKrmJs+287VPUJ45qpoAj1aTx4Ru13BTa/FVQt9Jt4ObSqPhpqtdlLyDKUCoZgzOSWcLhUNSTklFJttNZwZ9Bo1bR1iwZWICqKhjacz6vrM7px2CLZ9CAeXgK3UNKpNTxh0P8RcA5oLO9A3F4rMRfx26jdWxK/gUPYhx/bOPp25tuO1XBV1FV5OzU/0SKqiUqpzYDiHOXPm0KdPH+677z5UKhWKovDhhx+yY8cOvv7668aoZ4vkYuzakguTmbh8EnZFhTblGbaMKcbt9weFucjUT+unYhYD/K8HFKVD12vEeStE6fn6n0Se//kwAP+d3p0Sl795c9ebuGhdWDhxYatO8tLcbRHrk60nsukY5C7yPhxeAX8+B71vheH1kKhp4fUQ/wf4RMLMJRBQucPhxV8O8+WWRAI8nPj1gSsuOflZa3luc7/dhV4N/+5hJDQ0HHwieHf3u3x+6HNmx8zmkb6PNNi1NyZv5L619xHkGsTv035Ht/QOKEjlxOA3mPNrHiHeznxz2wBHXov6oDk+t5xiM9d9/A8nMovpF+nDt7cPwGArwFvvhaqG0bZmj9UMhlzwqN5p3my1s2DTSd5bG4/JasfDWctj4zpz04CIanNPNMfndiHySsyczCwiJc/oEAhlYuFsvoEagiEB4OGsJcLPlQhfN8J9XYnwc6Wtr5hCvF0umJ/jkrDbIeEvYTp6amPpRhV0mQSD7oO2g2rdCdTUz01RFHan72ZFwgr+TPwTo80IgIfOg4lRE7m247V09e3aMgR6I9LUz+1C1KqL+8iRI3z66aeOh6tSqbjrrrv45ptvGrRylyOLji5CwY41vxf3DO6JW85nYkdgPX6IdC4w7TP4/iY48hMYcuCGheAsktbNGhyJ2Wrnld/ieHzZAd65fjSTouJYdXIVD/79IN9f9b3sKWjhFBot3LtwN2arnV8fHEq7tIOQfwasxvq5wNXvifCwZ/fB52Pg+m8hqjzi2FMTo4k7W8C2kznc/d1uvr9rYL06X7dE8g0W1sRlgKLwyvHbwT8MHtjFtrPbgPrLH1ETV4ReQZRXFCfzT7LuzDrGXi86idoDP0WYUYFDRBQaLbUKH9rSKDFbue2rnZzILKZzkAef3doPG0ZmrZ5Fh/wM/hMxBdchD4OuhfXWa/WVRYTdXskEUa9Vc9/IDlzdI4Tnfz7M30czeG7lYZbuTuaVa7rRLazhvu//OpLO/qQ84jMKMVnt9Ajzpk+EDz3beuNZh89YodFCcq6hdCqpNE/KKaGghjwLIKIhhXq7lAuECkIhwtcNL9cm+KybS2D/YuH/kC382NC7Q6+bYcBc8G055j6ZJZmsPLGSnxJ+4nRBeYSvfsH9uLbDtVwZcSXO2hb2Tkkc1EpIeHh4kJKSUikSUkpKSq19JCS1o8hcxJJjywDQFY/g1kERsOS42Hkxjtbno90wmL0KFk4XvRxfTYRbfnKYodw5LAqT1cabfx7nkSUHePuGuznpe5K4nDge3/g480fPb9IMpZJL47NNp8gtsdA3wodIP1cxZA4QHFs/F/AIhjm/wfK74Oiv8N1UmPw/8SMI6DRq5s/szdUfbCGjwERavpEIv8v7+2RTfCY2u8KANmo8cw3gEUyeMY8j2UfQqXX0CuzVoNdXq9Q80vcRLDYLI8JHVNrn66Z3LKflG7lm/hZu7B/Ov0Z3bDW9hxabnXsX7mFfUh6h3i58c3t/PF20PLLhCU7ln0JjNote4SEPN3VVL578ZBEYwS0QJrxeZXe4ryufz+rLH4fTefGXwxxIzhcjFTMu/rNntNhIyCgiIaOI+IxCjqcX8a/RHR2J8b7+J5HNCVmO8uuPZQKik71zkAdvXteD2FCv8wqF5FwD+QbLeevholMTockmwt1GRNf+hDuEghhVqM+RtkuiILXU/+ELMYoE4BkmxEPvW8HFu0mrV1ssdgubkjexIn4Fm1I2YVOEeVigSyBTOkzh2g7XEu4Z3sS1lNQHtWoJXnPNNcydO5c777yTsLAwkpOT+eyzz5g6deqFD5bUmhUJKzDaSrAWt2Nu/ytEj99NSyA3EdwbII57m+5w+5/w7VRIOwgb36z043L/qI6YrXbe+zuBR344wv/d8CzvldzPP6n/8N6e95jXd17910nS4GQVmfhs00kAnpjQRTQE0w6KnUG1C+lcK/RuImJYWUSnlfdB9gkY9Syo1fi5O/HlnH74uztVaqhervx9VNivjwoshlzAow070nagoNAzsGejJPyqlOzOaoaMw2DIg/YjHZu3n8omo9DIu2viScgo4s3relQfkrKF8dTyg6w/lomvm55vbu9PkKczXx76kr9O/4W7Ws876am4dp7c8kYjKmLIE2aMKrWI5hRQ1cdRpVIxPjaYoR39+WBdAnNKg24AnMoqFh0PF2DJriT+OJxOfEYhZ3JKONeAelSXQIeQuLpHCL3aehPq40KBwcLOxFzizhaQmmfgaFoh837cR3qB6YJCwVmnJszHlTAfl9LJlXAfV4I8nbjps+0YLHYWqf+Nr9oLJh6+4D00KooCpzbAzs/h6CoobXQT2hcG3QvRUy7NR7IRScxPZHnCcn458QtZBiEQtSoto9uOZmrHqQwOGSw7IVsZtXqa99xzDzqdjgULFpCWlkZwcDBTp07l9ttvb+j6XTbYFTsL4xYCcHfP2czuWRqhSa0pD+HWEPhEwrQFsGCU+IEZ90ol5+uHr+yEyWbnt4Nn6R/enreC3uLOP+/k76S/mdtjLm66y7sXuSUyf10CxWYbo7oE0i/SF4qzoTAVdG51C/1aG8oiOvm1h1WPwua3oU0P4RgIdArycBQ1WW0k5RjoENiyMq7WB3a7wobSnthRXmfFRo9gUotS0aq1DGozqHHro9g5nLSBbl9PF73Xj8U79k3pGYqHs5YHF+/j1wNnScop4dNb+16yn0tTM6FbMH8fzeDz2f1oH+DO9rPbeXfPuwC8YvEg0mqFLlc1bSUvleBY0au9+yv48xm46ccai7o5aXmiQsjfs/kGrnpvE73a+jAxSseRkmTiM4qITy/keEYhdw9v74jCdiytkDVx6QBo1Coi/EWj3t9dj5uThtPZxTy14iCpeQZS8wyczTNSaKre9Oh4ehEATloVJmtlRaJRq2gf4EbfSF+GtPdjXExwlRC2uxJzMFntdPLV4FtSCG7NKAeBIRf2LRajD2XmS2otRF8j/B/C+zdp9WpLiaWEv07/xfL45ezJ2OPY3s6rHVM7TOWq9lfh79I6o4FJaiEkrFYrX375JbNnz2bu3LmNUafLErVKzVsj3uLXE79yf98paNQa4RSt1jV8T0RIbyEobFbITxLLpahUKv49vgv3DG+Pt6uecPryzsh36B3UW4qIFkhybgkLt51BpYLHxnUWG9PLRiO6VhKRAO/sfoeUohRi/WKJ8Y8hxi/m4nrG+94mPldOXhDSs8ru3GIzc77aSUqeoV6cr1sa+5PzyC42E+bjQgd7qQ2xRxtmx87m+s7XY1Vqtu+ub2x2GzNWzSAuJ46Vzu5EFWeAMb9S2MhRXYJYfu9gbv96J/uT85nywRY+m9XX0cvcEhnVJYhNT4zEVa8lrTiNxzc+jl2xc2fnmYxa/X+g0UPHsU1dzUtn5DNwcJkIhpCwBjrULhLfycxidFo1mxOy2JwAcLbS/q0nsglwdyI1z0BOsZlebb0xmG3kFJs4lVXMyczi857fRachxNvZMaoQ7uvqGFkI83HB11VHUq6B3adz2X06lz1n8jiaVsDx9CKOpxfx28GzTCxNrmey2vh+RxJ9Inz454QIj9o/0A6JgFtAHf9gDUDqXmG+dHAZWA1im0cb6DNHCD3PuicJbGyKLcVsTd3K+qT1rDmzhmKLeL4uWhfGR45nasep9Ajo0WpMHyU1c8EWqlar5ZNPPuHOO2UOgYbkcGo+8enePNLnMTRlTnB7vhW9Rlc8DCOfbLiLq1Qw+zfxRVZNVBKVSoW3qzA9MZhtLFrnjssVFgZEgcFqwGQ14e3s3XD1k9Qb766Jx2yzM6VnCNFthHN9uX9EN04XnOa3U79xS/QtuOvdWZ+0npP5J/kj8Q9ACN4oryhi/GKI9Y9lfOT42j/79qNq3OXurMVZpyaz0HRZOl+vKzNr6hKIqqi0gVbamGgMk6aKaNQauvp1JS4njm/9g3g+uQiyEiCsT6VynYI8+OneIdz93W52JuYy/eN/+N+NvRgXE9yo9b0Ulu1OJjnXwIOjO6BSqXDVazHbzMxbP48cYw6D2gziPm0QoEDUCEdAihaNe4CIzPbXc/DH09BuRI2dVYqiUGCwklZgxGKzc//IDvy0N4XT2UU46bTY7ApFJisWm8KvB87y64Gz1Z5HrYJgL2dCvF1o4+VMqLcLIY7JmRAvF7xddRdsdEb4uRHh58bU3iJRY6HRwv6kfHafzsVWIfv7oZQCR+TBMvp5F4oFtybqGbcY4NBykfshZXf59qgR0O8O6DSh2ZsvpRalsj5pPRuSN7AzbScWe7m5WY+AHkztOJVxkeNkJ+NlRq0+tQMGDGDHjh30798yhtlaGlmGLN786yjr4vJJzTdw7wiRDIqMI2AzNY5zlVdorYot3H6aP4+ksyUhi3duimTB8Wdx17nz6dhP0albXxSX1oTBbGPHqRy0ahXzrqxgG51X2gMeFMtH+z9i1clV5BhyeHrg0/x3+H85lHXIMcXnxpOQl0BCXgIrT6zkitArHEJiwYEFBLgGEOsXSzuvdmJUrTpsVhEdyqnchKnM+Xry+5vZeybvsst8fSanBICRXQJhYxoAJ9WgKThNW4+2jd6rd2vMrSyLX8YvOisPqNX4ZsdXERIAfu5OfHfHAJ5ZcYjle1Nw1bcc8bfuaAaPLzuAza7QL9KHwaWJ2FSoiPGLIcuQxf8N+z80S0s70Vq6WVNFBtyNfeeXZGekkrb2O9LCJpCWb+BsvpG0fCNpBWJ+Nt+IwVJ9DoVCk9mx7OmsrSwMvF0I9XahjZdYD/J0RtcAWbM9nHVc0dGfKzpWFgcuOg1Te4ey53QuidkluDtpGewhTK0aXUhknxCmS3u/A2Oe2ObsBT1vFiO1/h0atz51wGa3cTDrIBuSN7AheQPxueUmjipU9AzoyfDw4YxqO4oor5YTRUpSv9RKSISGhnLvvfcybtw4QkNDUVfotb777rsbrHKXC89ufJ2dto24es3g+r4Vhpkz4sS8PkO/ng+rGU6uB8+QGqP3zBnSjiOpBSzfm8IjPxzAq2MG+eajvLnzTZ4c0ICjJpJLxkWv4a95w9h9OrdyhKSJ/4Xh/yah6Ay/rf4fWrVWJCQDOvl0opNPJ6Z2FIEVTDYTR3OOcijrEAl5CYS6CwFqtBqZv2++IzKHq9aVaL9oYv1iiQ2IJdYvllD3UFS7v4S/nofBD1bJV+Hn7sQnt/Rl+seXX+brd2/sxb8nROPjpoO4LmAz81HKGlbveI6XBr/EtR1rn4m3PojyimJY2DA2Jm/kB0937smKr7Gsk1bDG9O7M2twpMO0yW5XMNvszdYJe8+ZXO5duAebXeGeEe0dIgJAp9Hx9MCnud90P15aV0g/LJyTO09swhrXDbPVTmaRiIZWLgwqC4X0/P9gsavgb4BdNZ7LTa+hTelIQpCnM228nKEkj97R7UrFgnOzCwXcNcSTt6/vCYjgEmqVCt/NpTkYGsO0yWaF46uF+dLJdeXbQ3qJ0YeYqaBv3JHG2lJsKeaf1H/YkLSBTSmbyDHmOPa56dwYHDKYEeEjGBo6FB9nnyasqaS5UCshUZYM48yZM5w5c8axXaVSSSFxiWSUZLAlbQ2o7UyJ6SWSg4GI4uAQEvUc+rUmNr8D61+FXrfAlA+qLaJRq3hjendMNjurDpyFxJloQz9h0dFFdPHt0ugNHkndcNJqGNy+mh45Nz8+3PkKCgrTO04nxD2k+uM1TvQI6EGPgB6VtlvtVh7s/aBj5OJs8Vl2p+9md7oYwnfXubNlxhZUboFgKhC22dUkvusWdvlmvg72KvULuWY+dsXO9h9GANA7qHeT1GdW11lsTN7I954e3JZ1jPPls1apVJX8I95Zc5xN8Vl8emufZpchOSGjkNu+2onBYuO6PmE8XuordDTnKBabhW4BInKZI1fOQwdF9Cr3prett9jsZBWZyCgwkV5gJL3QREaBUSyXbsssNJFdbL7wyVDhozUT7OlEm8AAh0gI9hLzMuFQnUiIi4sjunNg/d9gA+D4TS0uDTHr2oAjEoVpsOcb4cxekCK2aZ0hdjr0uw1Cq47qNQfOZ7IU6h7KiPARDAsbRr+gfuhaUPZsSeNQKyHx7bffNnQ9Lls+2PU1CjaUom48PK1CwqmCVDDlg6tf4zmHdb1aCIm4X2DS2yKJUTVoNWrevaEnFqudP4+Al9M14LeEl7e9THvv9nQPuHxMUloCiqLwyqo4xsUGiyhNFTEVgd3KkZJU/jr9F04aJ+7sXnd/KHe9O7fF3uZYzzZkczj7sENYuGhdUKvU0G4YdrUWkneiNuRVa7Y3tXcYB1Py+XJLIvcs3MOfDw3DpxWHh916IpvOwR6VQuAezz1OrimXNm5taOvRtknq1S+4H13c23K06Ayr8o9S22DfhUYLK/amkJxr4JoPtrBgVl9iQpqHE3ZqnoFbP99BXomFMdGBvDa1GyqVinxTPg+te4iMkgw+ufIT+gX3Kz9IoxWRxhoQm10hu8jkEAPphUIYZBaWC4T0AhPZxaYqoVSrQ6NWEeDuRKCnkxAHns4Ee7lUEgpBns7NdsSoQRh4D3QYXf+RkBQFEjeL0Yejv4K9NDCCb3vodzv0mAGuzaszpKLJ0vqk9STkJTj2qVVqegX2YljYMEaEjaC9d3vpMC05L+cVEllZWezYsYOJE6sO6f7+++/0798fPz+/Bqtca8dgNfDLKZGAblTI9Mo9dxVHIxrrJQ6MhoBoyIwTJk6dao5QotOoeX9mL+7+djfrjvXBS5+KxWMLD697mO+v+p4A16bvvZMINsVn8dnmUyzZnczWJ0fhqq/w2h9ZCSvvZX4n0et9Y+cbCXS99J5GPxc/hoUNq5yXADhrLebZtu0YnX2WGac2QNcp1R7/1MRo4tOLGNE5AO+myCrbSJitdu74WvSOb3tqNIHqYkjdy9acvYDIZt1UP+IqlYpbY2bz1PaX+NZNz7UVnFnPh4ezjp/uG8Lcb3ez+3Qu0z/ayrs39mxyJ+y8EjOzvthBar6RvhE+vD+jN1qNGrti54lNT5BSlEKMX0x5R4jFKJxi2w6sEs2sNiiKcETOLDSJqchUvnzOelaRCXstBIJaBf4eTgR5OhHk4Uygp7NY9nQm0KN07umEn5sTGnUdPjc2K5xYC53G1fk+WwwhPauNGHfRGPLgwA8i90PWMbFNpRG+NP3ugHbDqw1e0lQUmgvZlrON7zZ/x+aUzdJkSVJvnFdILFiwAG9v72r3JSUlsX//fv797383RL0uCz7fuxQrxdiN4Tw1ZULlnRlHxLyx/CPKiJ0K616Bw8vPKyRAmMl8dHMf7vxmF12C7yNeY2R3+m4eXv8wX43/SiadaQbY7Qpv/HEUgHtGtK8sIgDSD7HPSc9GSxYuWhdu63ZbNWepPw5nH2a7ysR+X28GH/uZiBqEhE6j5pvb+qOuS2OoBbIzMYdis43OQR6iI+H4Blh0Pdvaifj9A9sMvMAZGpbxna4hoTiZaR2n1UnQ+Ls7sejOATy5/CDL96Qw99vdPDauM/eOaLreTbVahZ+7HpXKnc9n9cOl1DH8o/0fsSVlC95O3rw94m2cNKWmMCfXw+IbRINw1s+O85isNrKKzJVFQaGJzCJjFZFgtNhrXT9/dz2BHpWFgRAK5dv83PRV8iRcMooCX06A5B1w60oRRUhSPXY7nN4sHKePrBRBIwDcg6HPLOg9q9aBSxqas0Vn2Zuxlz0Ze9iXsY/jucdRKFerZSZLw8OG0zeorzRZklw0523pbdiwge+++67afdOmTWPmzJlSSFwkdsXO0oTFAPT3mUIbb5fKBczFoHVpfCERUyok4n6Fq4wXzOLqrNPw+ax+6DQqcoxvcfNvNzM56uoWKSI+Wn+C99eewmpPdGyr+MU7Z0g7npoonsfnm0/x2m9xFcpV5rYhkTw9Sfi2fLbpJO+tjWfelZ2YNTiyURtSvx06y6GUAoI8nZg1KLJqgbSDZGo0+GjdmB49E1/nhh2CHxMxhknBg1mV9g9PZ2/na5sVTQ0hDyuKiN8PnqXQaOX6fuENWr/Gpiyb9YgupSN4hWcxqWAPooEyoM2ApqoaADq1jof7POxYLzIX4aZzq9Vn2Emr4a3retAx0IM3/jjKf/84RkJGEa9P69YkoX09nXV8Nac/hUYrXqWjXBuSNvDx/o9Rq9TM6/EiZ7OdOZCYRnaxieydcWRZZpOdO4DMT7aSXWQiq8h8wQzLFXHRaQj0dCLA3YkADycCPcTcMbk7E+DhhJ+7vkGiGtUKlQo6jxdCYvVTcPemixqBadYoCiy6Hlx84JqP6n5/+ckicdy+7yA3sXx7u2HQ93boMgmasCFus9uIz4tnb8Ze9qbvZW/mXtKK0yqV0aq1tHdtz/hO46XJkqReuaBpk79/9Y5Jfn5+ZGVlNUilLgf+Sf2HbHMS/s6BvDLupqoFRj4Jwx8vt7dsLPw7QHB3SDsgHGKjLxzyUK8VP4B+Ln78u9vnvLoqgaHBBkLOFUfNnHb+bpRYFKrKAoGtgu2BoihYz2OLUHGXXVEoMFp54Zcj7E3K47Wp3aqODDQAFpudt/48DsC/Rndy9MA6UBRIO8iVRgODxy8Ez8YxPXly2P+xc/EV7Nep+HrnW9w28Inzlj+SWsA9C/eg06iIKs1i21pw5I8oc1wtOMt+JyeM2Ons0xk/l2ZgOmo1w+EV5KXs5DbrSfoE9uHJAU8Kn5cLoFKpuGdEe6IC3Hj4h31kFZnQNELjxWZXyCk2k1Vk5NONp+gR5oVNERF8ygRBWnESSa6vgxoM6Vfy8NcGYGuFs3QSUxpAuRmIVq3C372iGDhHHFTY5ubUQjpUBt4Hu74STuV7voG+c5q6RvWLqQDi/wS9e+1FhNUEx34T+ZxO/I3jd8EzFHreBD1ngm+7Bqvy+SixlHAw66AQDhl72Z+535EQrgwPvQc9A3rSO6g3PQN6Eusfy6n4U0RHN3LnpKTVc95vOZ1OR0ZGBoGBVW2mMzIy0GpbyJdkM6R7QHce6fMI7np3Qrzcqy+k1jRNz1DsVCEkDi+vlZAoQ1EUPlp/mmPphcxcsI07J+TSJSCCPkHNM1JFscnKDzuTmD04ErVaxfjYYD6ZEsaIfpVD36oQDZ+KVjZzhrRj1uDIc8pVWK7QWLrjiijaeLnwxLIDrNyXytGzhXx8Sx/a+Tds0p6lu5M5lVVMO383rusbVrVAfrKIa+7qh5tvVKP54ni5ePOCewz3Go7wwfHvGdp5Kh19OtZYvmuIJ3OGRDqcr1tL5uvErGJOZhXj6aylT0SpTXLhWbxsdqZ5x9A2qplkUbYUwy//4rjWTmJoKPG58RhtRl4Y9ELNuULOYVxMMMvuGUyIt4vDNMdiq73Zj9lqJ6fY7Jiyi00Vls3kls7LtuWVmCuJ+RV7U6qc06XtV2jVBiyFXVEXjCLMxxl/dyf83fX4K7n4xS/Bz12P/4SnxHYPJ/zc9Pi46lufyZ3OGca+BEtmw9//Eb8Bzs3DQb5eKIvYVJscEmmHYO+3wv/BkCu2afRi1KHXzRA1stF/lzNLMh2iYW/GXo7mHHWE2i4j1D2UXoG9HFN77/a1EvsSyaVyXiXQu3dvvvvuO+bNm1dl38KFC+nbt2+DVaw1czq7mG+2JjN32I0EVtcgMhaIqE1+7ZtmuDR2mhgJialtnBaBSqXik5v7MvOzbRwt2Mbru7/Bx8mXj678kBi/mAaq7MWxJSGLJ5YdIDnXgE6r5paBIl9BW299rcwuNGoVGmrXmFCrVUzuEUKXYA/mfrebY+mFXP3+Zt66vgdjG8gB1Wix8e4aMRrxyNhO1ZpNbDm6lO8D/bnfrSOdG3mIe+j0RUzb9jLL4pfx9OanWThp4XkTGj41MZq4swVsO5nTajJfrzsmRiOGdQoot3svTKOzxcILnW8RDZfmgIsPdL+e/nu+5gPPXvyr8CA/JfyE0Wrk1aGv1joRZVkmdUVROJ5eyG1f7WJ0O2d6GZIxWe0UGC1CDBSZyS2pIAyKzBSa6j4y66JTY7DYUQH92vnQtY0Xfm56hyCwaV9lZeKnvDTtRQLdz3Eu/e1xSPwR+j4EPZuHzXuD0/UaaDsIzmyFTW/BlS81dY3qjwuFfjXkwcElwvfh7L7y7UGxIhx69+sbLfKSXbFzKv9UJeGQVJhUqYxapSbaN1qMNgT2pFdAL4LcghqlfhLJuZxXSNx9993cdNNN5OTkMGnSJIKCgkhPT2fVqlX88ssvLFq0qLHq2ap45++9/LQ7n0KjhTemVxNW8NRG+OEm6DgWblrS+BX0bgvDqsb4rw1erjq+u30ANy6wcqZoK7nEc+OvNzIkdAh3druzyUcnCowWXvvtKIt3iHwoXdt40ivcmxJLCT8l/MTa+LU8HPAwsf7VJ+S7FDoGebDyviE8vvQAvx9K465vd/Pf6d25rm/92/0XGCzEhHjh725kYmybKvsVReH9Uys57OZKbw8vOtd7DS6AWsNj/R5j29ltHM05ys60nQwOGVxj8bLM11d/sKXVZL4u848Y1aXCiG/hWTH3aNoIR1Xofyfs+ZrBR/7i45sXc9+mR1mduBqT1cSz/V+jyAS5JWJkILfEQl6JEAG5JZbSbWWT2GexieGCb/Ya+GZv7gUvr1KBm16Ll4sOH1cdfh5OBJVGKQrwcMLXTY+vmx4/N7G84XgGjy45AMDbN/Tg2l7VjMgRzITO71XdrCgijCdA9NUX+xdreahUMO5VWDAStn0EfWaDbyvJVlycKeYVQ6nb7ZC4UYiHuF/KHaedvaDbdUJAtOnRoCO1ReYi4vPiOZ5znOO5xzmWe4z43HhKrCWVyrlqXeke0J3egUI4dA/ojpuuYUe0JZLacl4h0a1bNz766CNeeuklli5dikqlQlEUIiIi+Oijj4iJaV69zC2BQ2dTWVP0IC7hUdwx7LPqC5WFfg1o9OZdVSwXdrg+Fx83PQvvGMz1C0ykZK9E57ODLSlb2JKyhd6Bvbmj2x1cEXpFozt6rTuWwVPLD3I234hOo+JfoztydV9Xlhz/jGUbllFoKQTgtj9u4+drfibYrf4bcx7OOj68qTcLNp3km62nKzci65FAT2e+mN2PIpO1WjOMdUnrOGzOxs+ucGO7yQ1ShwvhZizi9eBR4NuOnucREWWIzNd9mPZR68h8fU3PUFx0GoZ3Km/cbDems97Xm3HmXHo2Yl0URcFosZNnMJNXYiGvxEJ+6XJuiYU8g5Zc/dPkFhrI/T4Tvf5uij0/ZF3yOv46eguG5FtAqX2uDxedGp1GjdFiQwGsNqWSZ9JNA9pyba9QfN30fLj+BEt3J1NkslJkspKSZ3CUU6ng5SmxXNVdJFBctP0MW09m8/tBIciemRRdSUTsy9jHt0e+5YXBL+Ch96i+cql7RDIxjxCRifhyIrQ39JgJh5ZByp5WKCT8IS8J9i0SjtN55Ql2iRohxEOXSaCrX/8+m91GUmESx3KPcTxXiIb43HhSiqqa3AEEugTSK6jcTKmTT6cWGcBEcnlwwU/mkCFD+OOPP0hMTCQnJwdfX18iIyMboWqtkxc3fI5KbSHI04lOgd7VF3KEfm2kjNbVYbPAj7NEop15h8Gphh/dGvB3d+KHO0byf6vbsPLAKPxCdqLx3sKejD3ct/Y+fr1mFW29GicCj6IoPLHsAD/uSgagR5gXd12p4++zH3P1T2sdtqa9Anuht+jp1bZXg4iIMlQqFXcNa8+tgyIdCaHS8kV22h7h3pd8fptdccSQd6/G2dOu2Plgn8hcfufAJ3HpMvOSr3lR7FxAz43/hQF3Q/R1tTokNtSL16d14+Ef9vPF5lNc3ze86aLdXCLT+oQxrU+FnnJF4a+AcH6wZuCdH09PxtT5nIqiUGy2kVdiLhUDFnIrLJdtzzNYyC+xlAsHgwWz9UI+C6UdR9kAAahz7sSl7edo3eMJDj5NkLYfPq46fFz1pZMOn1KfAh+38u3erjrH5z4uLo7o6GhHzoUyERPsJUYaQIzYuDtpHfeSW2Ihv3ReYLRU+ozvTMzhl/2pAMwdHsUdQ8sbwlmGLOatn0emIZMuvl1qTrzoFgBDHxHfec0oD0CjMeYFGPFv8Gm5Ir0KhaURjE6VjkCUyVav8HLH6Xq633xTvkMsHM89zvGc4yTkJWC0GauU1av1tPduTyefTmLy7URH747NI8iCRFJLai1xIyMjpYC4RE7nFBBX/DsqLdzf5/aaCzqS0TVhdAWNDkqyRXbtY6uhe+0aehUJ8HDizet68Ni4zqQXXEn7IB1Lji1h65kEZi04wR1D7YyJ8WBr2kYmRU2qta11XVGpVPi46XHSqnlkbCduG9KO9/a9y5+n/0Sr0jKx3URu6XoLsf6xHDlypFJUi0NZh+jq17VBnNbKGlNmq517F+7mUEoBL1wdw4z+4Rc9WpNRaOTa+f9w2xXtuG1I9aFm/0z8k/jceILdgrmu03WNl/DwXDqMgY3/hYQ1mG1mPt7/MTbFVincaHVc2ysMk8XO+NjgFisiqkWlYpurCxRArH9/knJKyDdYKDAIEVA2FRjLlq3l2yqUO180sfOh16rxcdXh7aLHy1XnWPZ21Yl1JzU+ax/Fx5yCzzVv4NPxVnIsI9mfuYfrO19/ibeuwsNZh4ezjvBzTNEndmvDxG5VzfNAiGZ7hVTPswZHckUHf3zd9YyoMNJjsVt4ZP0jZBoy6RPUh9mxs2uujHdbGP3cpdxOy8ajCWztFQVO/yNM+2KurR9nZkWB5J1w4EfY87XYlncaNE4iiEivm6HdiIsWixabhdMFpyuJhmO5x8goyai2fLBbcLlgKJ0iPCPkSIOkxSM/wY3IC2sXotIW4kYY06JHVl/IaobseEAF/k1s2hQ7FZK2iehNFyEkyggqTaoEMDt2Ntv37eZUVhpPrzjEG9vWYff+gw/2zue22DlM7TgVZ+2lR+TJLjKx5UQ2V/cIId+Uj2/IFm4cl81dQ0Xiv5ldZqJGzY1dbqw0+qBSqRyN780pm7l/7f2MjRzLf4b8B72m9qYbdSU21Is9Z/J4asVB9p7J5eVrYh1Coy588HcCKXkGtp/M5vYrqoYmtNqtzN83H4C7Q0ejN5eAS8Pd13kJ7QtOXpCdQMKZjXx+6HMURWF42HB6B/U+76E39m/rWDZabJhtF9d4bggURcFgsVFgsFJoFA3/AoNVzI1W8kvMLNmdjKezlhBvF4pNNvINFnJM6RQEnEaxOXPTBymUxh2tM656Dd4uOrxc9Xi76PB2FZNXqSjwqbDsXUEs1OrzZugDmzeC5Rh4DCeADnT27eDY/feZv+kZ2LPB85GUcW7Qg57h3vSsZlTvnd3vsCdjD4Eugbw5/M0G67RoVRRliAhO4f1Fo7shsNvh+O+w6W1I2SW2bfsIpnxw8R1pWfFCPBz8sXLOB+8I6HGjGAGtpeO00WokuTCZM4VnSCpMIqkwiTMFZzhTeIazxWexK1VH8Zw1znT06Ugnn06OeSefTng5taIoWBJJBaSQaCTS8w3syFmJ2hlujbml5h7n7AQRMck3CvSujVvJc+k6BX5/QuSTMOSBi3e9nPb9Gb1YfTiNTzee5Ei+P3qXQNJJ47Udr/Hhvo+ZEzuLGzrfgLu+hrC450FRFH49cJbnfz5MvjWFdZkn2ZK+GoPVgFat5YG+t+Pv4k+wWzAP9XnovOdSo8ZJ48Tvp34nsySTd0e+2yA/BnqtmpemxNIz3JunVhxkye5kjpwt4OOb+xDuW/vPwJnsEhZtP4NaBY+Oq16Erjq5isSCRMJ1Xlz9x2uQngTXzK+vW6kbGi1EDYe4n+madZrbY29nwcEFPL35aZZdvQxX3YXvPSXPwNxvdxHmqvBRjFIvfjdWm51Co7DHLxMBhaUioMBgodBYtr3CsrF0uXRbbUcFDqYUOJZ1XgdxBqwlUbg7OeHprMXTRYfXOdO528R6edkGjWY18B7heO1e1bdn3Zl1PLz+Ydp5tmPB2AUEuAZUc4LG5/dTv/PtkW/RqrS8NeIt/F3OEwJ020eiF3vgfRDWPMNWNxqnNoqe/GO/i9+COpq31gpjHiy7Aywl4OIrwqym7IKPh4qAH1c8DNpadHQUZQi/jgM/QOre8u0ebUQUwu43QJvqgzMUW4orCYSKgiG9JL3GS6pQEeoe6hAKnX0708mnE2HuYbUOiyyRtAakkGgk1iZuQ+2cgg4Pbu81reaCzcE/ogyPYIi8AhI3icQ8PevHll6rUXNV9xAmdWvDjlPRfLppJBuS16P3W0c+Kby7510+P/g5M6JncFvsbbWOTpFRYOSZnw6yNnELet8tuHocZU2pL9vgkMHcHH1znXpKB4cO5usJX3PvmnvZlb6LWb/P4sMxHxLiHnIxt31BpvYOI7qNJ3d/t5vDqQVc9f5m3r2xJyM7184h++2/jmG1K0zrHUanoOp/9CM8I+gV2IvriozoOAjB9R+dqk50GANxP0PCWu6+/is2JG/geO5x3t79Ns8MfOaCh+cUmYlPL+KQ1c74/21iWu9QhwN7gdFKkdFaKgrKGv1l2ywUmcS+QqOFwgrLRkvt8xvUhLNOjYezziEGypZ1WjWHU/I5nl5EnwgfZg+OdAiAT7d9yaYSeDbQxk33j7vkOjQI5+nJ7RbQjSivKBLyEpi9ejafjf2MNu7VmyQ1Fgm5CTz/z/MAPNbvMXoG9jz/Afu/F+E/Y6c3eN2aPbHTYPvHQlhtehvGPH/p57QYhI9Ch9Gis8zVF4Y9CjpX6H2r6ET763nY/SWsf1VEUBp4d/XnMhWJ6FoHfoST66BsdEDvIYRP9+sgciioNeSb8knKOlRJLJQt5xhzqj8/oFVpCXEPIdwznLYebcXk2ZYwjzDC3MMadJRaImkpSCHRSOzIWQnAzOgbcdI41VxQ6yRMPkLPb9rRaMROFULi0LJ6ExJlqFQqBkT5MSDKj4SMGD7bNJ4My0EU77XsSt/FkmNLaauezFXdXB3Ow9WhKArL96Tw0q9HMPl8i2vEPkA4sk1uP5mbo2+mg0+HGo8/H118u7Bw4kLuXXsvCXkJ3PTbTcwfPZ+ufg0j9KLbePLz/Vcw74d9rD2awV3f7GL9YyMJvUCW8COpBazcn4peo+ahMTUnd+sZ2JOvx38tQjwCBHerz+rXGaX9KEyKjuITuynKKuH2zk/y1PY7+eHYD7hYehCs706xSTT+i0w2ikwWik02Ck1Wioxi2U2vxWw1cyytkFd/O8qrvx29pDqpVcJJXdjsa/F01uHpIuYepcKg5mVxXFm2dxA+MH8fzWDp7iR+P5TmyJD+0JiODO0oeu3tip3DxkQABvt0uqT6Nwp5Z0TkmyvmOXqM/V38+WLcF8z9ay5xOXHMWj2Lz8d+Trhn4wRVqI5NKZswWA1cFXUVM7rMOH/hvDNCROjcoH0NpqeXEyoVjH8dPhsNW+eLcLAX65BszIedn4sRn+IMca7J/xP7hj5Suezkd4WI2fZh5QzbNiugwIl1YuTh2G8olhLy1GrS9XrS2/YlPaQ76R6BpBuzSU9YSMaBd0gvSa+S9bkierWecI9wh1gI9yide4bTxq2N9GGQSC6AfEMaies7X4/VbmV2tws0xqMni6m5ED0FVj0KJ9dDSU6DJeXpEOjO69N6oCjdUaluZm/GXr7YeoB/LT7MW3+cYmJfM0X6f7ir++209Sy3jz+dl8bjKzaz/ZhoaHdr248CpyRmRs/gus7X1Yutdhv3Nnw94WseWvcQO9N2Mnv1bOaPnk+/4H6XfO7q8HLRseDWvsxfl4CzTnNBEQHw5p/HUBS4aWDbas2hLDYLADqNDpXdVu7QH1T3EM5mq1007k1WSsy20rm1dJuNErPYV2yyUmyyibm5fLmowlRssmKxfQ0m4J0tAOj9RuEU+CdfHPs/ik8+DPba+8yoVVTKaBzdxoNIPzeHMHB31pY29rW4OwkBUL5Nh7uTFle9pl5DE7+z5jgfrT8BCJv+MdFB3DSwrUNEAMTnxpNjNxFktRLZEoTE4pmQfhD8OkC38t57H2cfPhv3GfeuuZf9mfuZtXoWn439jCjvpgkjOid2DpGekQwMGXjhZ3p0lZh3HFPv4T9bLGF9odv1wt9gzfNw3Vd1O74oE7Z/BDs+E4E7AIK7i5HI89FuKLaIwWQZssjITSf94PekH/qBdI2adGykazSkB3mRofXHXPZYrWfgzJlqT+dqtxNusdK2wwTCvSIcIwvhHuEEugbKDNASySUghUQDk1Ns5uEf9nHn0E58MPqDpq5O3XHzg/ajwGYS2UEbOLtn2Y99r8BeDA8L4IBfPKezS/jqyDfoPI7w84mfGBl2JdM6X82fiX/y26nf0BCMh/ODPD85lik9x6Jwd70POXvqPfl4zMc8989z7EzbSbhHw/ayqtUqHhhdeWTh882nGBjlS0xIZT+NnYk5/H00A1e9hvtGlo+8KIqCyWqnxGxjYdy3/HTyB2Z2eJAu9lCKTTGUuIRSfKAAgzmX4lIBUGy2UmKyiXmpSHAIAnNZw79+HZt1agV3rYK7qwvuLk64OV1DshJPse4U/brF0cNjKu7OWtydSidnLW5OWjycxNzdSUvq6ZP07t4VnUbNqaxivt95hr/jMvjpviE4aTUoisKbfx5jROcAeoV7N1gOk5xiMyv3pVBitjmexdU9Qvg7LoPr+oYxpWeoI6xpRTr6dORHwsjK2oPKqwVkUu47G1Y9Ajs/qyQkQLwrn175Kff/fT8703Yy5485fHrlp3T2bbzgEVmGLIcvxMi2tRxdiCtNQtelGXXkNAfGPC8Sth1eAf3nQsSgCx9jLoY1L8Ceb8oTvUVcgf2Kf1EQ1o8ccy45abvINeWSY8ghx5RDrjGXLEMW6SXppBenk2XIxFbRmdmrep8pD70HQa5BBLkFiXnZ5BZEoGsgQSonPN/tjsrJC+5695L/HBKJpDIqRVGaT7iTVkZcXBwrT1v4eH0iIzoH89Wc/uc/wGIQvWJBMU0b+vVcbFbhGNsUl7Yr/HUkjfc3bSXB/As6rz2oVOU/LipUDAwexpN9X6Cd33mcKGtJWVz7mlAUhUxDJoGuwg7fYDXgrHG+pIap2WrHYLZRYhGNd4PZRolZNOzLlg+k5PP1P4lo1CqGdfQn3NfVUTbfYOZkZjE2RcFNr60kBuwKoDLj1uEN1NoiSpJmYSu6tM+WVq1yNOBd9ZpKy+6ljXtXJw3uei2uTlrcnUQZN315w79cEGiqdQ4+mX+SLSlbuCn6plr1Flb33BSl3Pl69+lcpn30DwBdgj2YOaAt1/QKxdP50qP3WG121h/LZOnuZNYeTcdiU3DTa9j5zBhc9doqdamR+QMhMw7u3tzkJmcXxFQEb0eDqaDG+hqtRh5a/xCJ+Yl8Pf5rgtyqhhW90PtWFxRFIbkomY3JG3ln9zs82vdRbuxyY+0OLs6CNzuCSgOPnxC2+ZJy1r0GG16HNj3hznXEHTvmyP9RYC4g15hLjlGIgWxjNrmGHHJ2fkKuOZ8cj0ByXH3IsRnIM+U58vbUBj+rjSCblUC0BOk8CM5PI9BqIUjnQdDQfxPY7YYLB2XIPAbz+4Nve3hwz6X9HVo49fm+SRqP5v7c5IhEA1JosvHdke9x67CJgbHzLnxARhwsu104Wt+7teErWFuaSESAMAUZH9uG8bFT2X16BO9v2MnW7OU4eZxgWvRIbut2a4PaYNvtCkarzdFoN1psGCx6TqVlk28yMP/Iv/HQ+jM26H7MFpGpt8Rsw2ApLVu6XFEgiPVy0VCXuP82u8K6Y5nnKWGqtKbXqnHx345dW4TOEkmMz0DcdWdwzY/HLag9ruHdcdVrcdNrcC0VA676igKhsghwc9Kg16gbPCt5lFcUUV6XZg5TsY5tvJyZOyyKJbuTOZpWyHMrD/Pab0eZ3KMNM/q3pedFjFIcTy9k6e5klu9JIatI/N3VKhjZOYDpfcLRVohPf75z2+w27IodXaHIxoxH0zoo1wond+gxA3Z8AjsWwNXvVSnirHXmvZHvkWvMdYiIWgmqWqIoCqfyT7ErfRe70nexO313pRj+2cbs2p/s2G/CWbf9qMtSRFhsFvLN+RSYCyg0F1JgKihfNhdQ4AqFEV0o8A2m4K87SC/IoORgCbmmXKx2a/Un1QN6N1CKobjcR8FD74Gvsy++zr74OPngq3PDpzATv6wE/NKPE2Q1E2izEah2RRc9GbpfD+2GidwSmcfg5wcgaTv8/BAkrIMJ/z1/7oviLDF3ax5RxCSS1oYUEg3I8iM5KB6bUOvy6BzkfeEDmkMiupooTBcO195tRTKfJqBPhC9f3TqOk5lDWHcsg9GdA9EpWk5lFZc28EXjXUyil99oFcvGCg17o9WGwWwX+xzrolx+sQH7T6kYSsuazpPtV+2cjGvEEVRqC7tTTmNIvrlO9vxlaNUqXPQaRyPeRSeWK23Ta3DVaTiZVcSG41nY7ArhPi5M7hFCv0hfXPQa3PRiJKBs7qrTYLAVM37ZfygwwwcTn2RwyGDhJHvoIAwcAh2qD4nY6Bz4UThiDrgbetxQadeGpA1sSd3CUwOeuujTh3i78OTEaOaN7cSfh9NZtP0MW09m8+OuZH7clcxDYzry0Ji6+SY8/MM+DqeK8K1RAW5c1yecqb1DHTlTaktZtvdrXVU8adaJMJgtgX53CCFxcAlc+VK14aH1Gr1DRBisBh5e/zC3Rt/K4NDBdb6czW7jbPFZwjxENvCDWQe56bebKpXxcvKiT2AfhoYNZWrHqbU/eUapg36XpvluuxSsdivFlmJKLCUUW4opthY71ossReViwFRBGJwjGKrLulwFNZB3pMpmd1T4WMz42uz46D3x63wVPs4+QiiUzisKB51GB8XZIuLS4RUizGzZKIVaCx2uFBGXOk2oGgI9oDPMWS1M6ta8AEdWAiq4/uua611c2vHidukj1hKJpCpSSDQQBUYLv5zZhrpNHoHOoQwPH37hgxyhX5uhkDi5Hv54EiKGVBISNruCqbSxbrLaMFnsmKz2StsqNuSNVjum0mWT1e5o9JftKytnqnis1VapXFnj/uVf4xroZi2V1soa9s460bh30ZVO+gCsmqc4qrwLbgmEdv2cK32fIsAlWJTVaXDRq3HRiWPLevodAkEnBELFCD+14UByHvd8t4ekXAMfrj/B3GEKT06s/jPz3cHvKDAX0CeoD4PalNo295xZ7xG4LpmSHEjdA/F/VhISecY8Ht/4OCXWEnoH9mZ8u/GXdBknrYbJPUKY3COEk5lFfL8ziaW7kxkTXd6juXJfCm19XR2jFDa7wuaELJbsSuLKrkFM6Sl8GG4dFMG+pHyu6xt2SX4XW1O3YrAa0AR2gbCIi8602+gEdIKoEeK7Yd8iGHTveYsvO76MLSlb2HF2B28Nf+uCvgsWm4XD2YfZnb6b3em72ZuxF5VKxeYbN6NWqYn2iybCM4Kuvl3pE9SHPkF9iPKOujjH2fGvwqD7xEhLA2KxWzBajY7JYDOIudUgRECZIKggBsq2F1uLMVgqlLOKfSab6cIXvgBalRZPJ0889B546j3x1FdYPme7p84d2+avaF9wDN+UfegB9O7Q9zbxN/QIrv4ixdmwbyEc/qmyeFBpoP1oiLlGCLkL+eGp1TDgLug8Hv54Csa+XL7Paq6ad0IKCYmkQZFCooH4eksidq+NqIHbut9Sux83x4hE7UKLKoqCxSYa8iarHbPVXmFuq7xssWO22R0NdJPV7pgqlq/YgK9UzhSIyfQ2pmNaTC/9gdEqHHnrYpZTn+g1apx1akfj3lmrwVmvwVmrdqy76DWOMmUNe2edaNg7OdbLtzvrNKQmJRLbpVOpCNDgpL2QGU9/kgsHcu/aezmVf4pNxc/z4cAPK2X7rW+6h3nzywNX8K/v97I5IYuY0OpNMfKMeXxz5BsA7u95f4ObI10SHUaL+Ym/wW4TZgyAt7M3j/Z7lJe2vsR/tv+HPkF96i3RWVSAO09NjObRsZ0dYs5gtvHMT4coNFqJbuPJgHa+rD6URlqB6LHNLjI7hMQN/dpyQz0E7tp2dhsAA694CsKGXfoJG5N+dwohcfz3CwqJmdEzSSpMYtHRRcxbP4/Xhr5GBJXDiSqKwicHPmFX2i72Z+6v0lMe6h5KZkkmQW5B6NQ6fr3214uuuqIoWOwWzDYzJpsJs0aNyZSL2ZDu2GaymTDbzJXWTTYTBqsBg9VQLgpsxvJ1m9EhDiquG61GrEoNZkCXgFqlxk3rhqvOFVedK25aN9x0Yt1d546n0znCoGy5dLun3hMXrUvtvh8sBvhivAiTC+DqBwPugf53gItP1fLF2XD0l6riQa2FqDqIh+rwbgs3fFe+XpQpwloPug/63+X4DqGk1MRNmjZJJA2CFBINQKHRwsfb16MNPY1O5QoF/Vi4/bSjMW8ubbyXLZf14JsTemO2dMe80Q3zpm2OMhWPEw3/8gZ+41La01RS/mOoUoGzVoOTTo2TVo2TVjS+nXVi7qRTi0a+Y71C415bsaEvjnXWlZbRVhYBlfZrNefNK3EpaAr0hNQi3GpFwjzC+HbCtzz494PsydjDrNWzeHvE28KMqIHwddPzzW39ySux4ONWfYSqb458Q5GliMEhg+kb3FdsTD8i8oK0HVRjptcmwa+DaBiUxfIPLc8qPL3jdNaeWcuWlC28sPUFPhj1Qb2KonNzPszo35alu5OJO1tA3FlhuhTh58r03mFM7RNWb9cFyDflczj7MFq1lr5Bfev13I1Cp/Ew43thjlKKoihYFStWu5hsdptj/ebomzHZTCyLX8YTG59gTMAYnLKcGNl2JG5aNyx2CyviV5BanAqI3BThHuGEuocS7BaMs8aZJceXYLFbxGSzlC/bLVjtViw2C2a7uYoAMNvMmO1mx3J99OTXFY1Kg4vWBWetM84aZ5y1zo71MjHgpisXAlW2ac/Zr3O75GAPdULnAu2GYiwuwHnwXOh9i/CDqIhDPKyAU5sqi4f2Y6DrNdBlUv1HADy8AvKTYPW/hRnu1R9AYBfodQu0HQie9fvuSiQSgYza1AB8uy2RV3Y8i85rH+bsYZgyJjbYtbRqlaOBrteoHQ16fYVGvVgW62XL1e7XnSMCtJUb706n1uD052M4t+mK0+zlOGk16DSq5t3TXUcuJTqCyWbi6c1P80fiH9zf837m9phbz7WrG1mGLD4/+DkT202kW0BpVJ0t/4O/nhM9dhP/26T1q8IvD4mMtiOfgeGPVdqVXpzOtT9fS6G5kJcGv8S1Ha+ttP9in5uiKNgVO3bsKIqCglg3WqysiUvnWFo+Qzr40zPcE0VVWrZ0UhQFO3bsdnG8Y5tix6bYHOeqeI2Kx9sVO7vSd/Hx/o/p4BbKgwGDsPtGYfcMxqbYsNvLz1PmkG1TyueKolRarzS327AptvL5Oct2xS4a+RWW7Yodq2J1XKtsm02xCVFQuq9MIFQSC4qt0nJLQavW4mS1oFfs6N2DcdK6oNfocdI4odfoHctOGid0ah1OGqdyIaB1xkVTvlxlXeNcuazWBZ360qOENQeqvG/FWSJE7JGfqoqHqBENJx7O5ehvsGoeFJ4FjR6GPgpXPFzV3OkypblH/5FUT3N/blJINAC7ko8zZ+10UBTC1VfhrHFHq1GjUanQqlVoNCo0ajFpVaXrxhy0p9ajdfVC03mcY7+Y1OI4tQqNGrRqtThWraKsDa9Q+8dY3SM/9/hqy9jMsPkdsFtg0P0ozp7VnPwC562wfu41alOH852n0raK56q0qFQ5d8Wy2dnZ+Pr5nvdvdO71Kq6XhaAMcw9DpVJVLVtaprpzVrdc8R6rO4eC4rg/pezfOcdXOjZlD0pBCkpwN/AKr3JcxXXxv/x8lbZV3H7O/Z/v+LKydsVe+TyKgmIqxF6QAjpnFK8wRyO8rO7FlmLyTHmoUOHr5ItarXY01q1WK2pN+XpFUVDjch3eGUnd0Kg0aNVatGptpWWtSutYLjQXUmgqxMPJgwCXALycvNBpdOjUFaZz1rVq7QXLlG1ziAF1uTCoKBD0aj2ahDWw6HoRvvbuzU39Z2sxxMXFEd02oDy/ROLmc8TDSGG21Hliw4uHczHmi86S3V+J9cCucM2HENKrcevRDGnuDVJJ9TT35yZNmxqAE0V7AQVUkKT8ClbEdCH8AfIhaUGD1u+S8Codxj763fnLtWTSm7oCDYybKxSeEFNzQ68DbFBwusYiCgrZpmpCe16k+blapUaFGFlToxbrKlX5NpXYpkZs16g05dsrbCs7rmybWqUuL1vhvBqVsN3WqDUczDyI0Waku9oNn6IsVMHd0Xi3dRxb8RwV1x11KltXl2/XqrRiu1qDRlU6qc+Zl5UtbeiX7Ss73rGs1jq2VREFFcXBulfR7PgU7aD70V75cq1HKZv8BzLuZzGPvrrp6tCSKEiFY7/RdtdiyNh7jtnSlU0nHiri7AWT/wex0+GXB0UQk58fhJk/gGdI09VLImmlNJqQ2LhxI6+88gp2u53rrruOu+66q9L+n3/+mQULRAPazc2NF154gS5dunD27Fkef/xxsrKyUKvVXH/99cyaNauxqn1RTIiaQK4pl9Npp/Hz9au2zIV+aFWcZ38Nu6o7pqbzVHf9816zrExWvAi55xUKPWdWf81zzn1umUrr5xx+vrLnO++F7qfi/rLt1e1PT08nKCiocrnqjq2hLhW3W2wW5u+bT4m1hCivKKa0n4K73r1SudLmqmNblftX1by/pnMsj1/OzrSdTOs0jf7B/R0NYpXNAsvuQIUC075ApXWqcu5z79WxHRXif+VtlepQ4RxVzlvN/nPnapUa1Yq7IfMY6qvfRxXSs/KxKhX5pny+Pvw1t8fejpeTl6NxHh8fT+dOnR2N+IqNd8DR6K4oDs6938amxFLCnX/dKRK2Gb3QpsfBuAeFKUhLo9v1sP0T2LtQmKbp6h4GudGxWeHY72K5BYZ9bRQURQQBObZKmA2lioRubgBqnQiS0PUa6DKxemfrpqTdULjnH9j0NhSlgXsN0aQkEskl0SimTTabjXHjxvHll18SFBTE9OnTefvtt+nQoTyyzZ49e2jfvj1eXl5s2LCBDz74gCVLlpCRkUFmZiYxMTEUFRUxbdo05s+fX+nY5kqT97Y1BBaD+PHtNK6qk10roL6f2YHMAzzw9wPkGHNw0jhxXafrmBM7x5EZu75JLkxm8orJ2LGz4uoVRHlXSOqWskdENQnoAvdtb5DrXzIFqSK6iqZutuQt+V0z2Uw4zR8EOSfgvh0iVn5LQ1Hg0xHCUf6aj2odXrhJn1viZvhqEvhGwQN7oAlFZbPCboMz20SSvqOrIPdU+T6tC3QYTapnT0JG1hCpSdJsacnfk5czzf25NUrA8gMHDhAREUF4eDh6vZ5Jkyaxdu3aSmV69+6Nl5cIY9mzZ0/S0tIACAwMJCYmBgB3d3eioqJIT29ltieKAutehf0/iC/x5ozOBWKntkoR0RB0D+jOdxO/Y1T4KEw2E9/FfceEZRN4dfurpBWn1fv1Pt7/MVbFylVRV1UWEQDph8Q8KLber1tveIbUWkQkFybzfzv+D1tzf2cugJNaD4Wln4WaYvA3d1Qq6H+nWN7xadPWpbbE/SLm0ZOliDCXQNyv8NO98N8O8NVE2PqBEBGu/tDrZrhxMTx+Em5cSH67q6SIkEgkQCOZNqWnpxMcXP4DGRQUxIEDB2osv3TpUoYNqxpPPTk5mbi4OHr06HHBayYmJmIwGC6uwvWE0WgkLu7CSdM0hmw6bfg/bDoPjuu6tYwfNUVBX3gas2dkU9ekXqntM6srd7e5m/Fe41mespxtudtYfHQxmmINV7epP9vsVEMqP5/4GY1Kwxi3MVXuI+jIBnyBdHUQOQ1wj/WGYsc55yhW10CsLtUnkbIrdh45+AgpxhTshXbG+45vkOfWUCiKwoasDcR4xhCkdqWzpRi71oVjJ1NAldrU1bsoVNoYOug90abu5dTmpRj9Yi54TEO9bxdEUehw8Cd0wCnnbhhb0GenvtAYc3FP3YxHykbc0negrhAO1+weRmHocApDh2Lw6yZyMijACeG71GTPTXJJyOfWMmkOz+18IyKNIiSqs56qyTZ527ZtLF26lEWLFlXaXlxczIMPPshTTz2Fu/uFs49GRkZeVF3rk1oPR51YB4CmTSzRXWuXjK5JURSRlChpG9y3U2S4bSU05BBiNNFM6DOB+Nx4vov7jgf6PYCrzhWARXGLGBo2lHCP8Is+/5cbvkRB4dqO1zKyVzVZg51uheBwgrpOISik+Q6TOsLAjn0Fet9fY7FnvZ7l7jV3syR1Cb29ezMuelzj1fESOZ57nA93fkigSyBrRswHQO0Z0jLe//Nxdhb88z7tMtfAFdMvWLzJhuxtVjA9B6c20m7ItJaTTfxSyT4hzJWO/SbMlypGLgvtK3wdOk9CH9AZP5WK6j38mr+phaR65HNrmTT359Yo357BwcEOUyUQIxSBgVVtxI8ePcozzzzDhx9+iI9P+bCpxWLhwQcfZPLkyYwdO7Yxqty4ODJaN98PSiVUKpFADODw8qatSwuko09HXhz8okNExGXH8dqO15i8YjLPbH6G0+eJWFQTx3OPszpxNTq1jrnda8hfETUcxrzQ/MMgRgwR84Q15y02JHQI13W6DovdwgcnP8BsMzdC5eqHralbARgYMhCV3hUG3A3dLtzwbvb0vR1QiZCgxoKmrk3NaLTQ6yaY+knrFhF2OyTvgjUvwvwB8H5v+OtZOLNVmBB2uBKuegfmHYU718LQR0QSt5YwKi6RSJoFjTIi0a1bNxITE0lKSiIoKIhVq1bx1ltvVSqTmprKAw88wBtvvEG7du0c2xVF4emnnyYqKoo5c+Y0RnUbn4wjYh7YgnojY6+Ffd/BoeUw/An5w3MJeDp5cnX7q1l1chUrT6zkl5O/MKndJO7sfiftvNpd+ATAsuPLUFC4vvP1BLu1UDv7MtqPBFRw+h9hu613rbHoI30f4Z/Uf0gsSmTmqpm8NvQ1Ovp0bLy6XiTbzm4DYGCbgSKj94T/a+Ia1RO+7UTDNHIoVJdnRtLwmArh5AaI/xOO/yEiFpXh7AUdx4mRhw5jwMmj6eopkUhaBY0iJLRaLc899xx33HEHNpuNadOm0bFjRxYvXgzAjBkzmD9/Pnl5ebz44osAaDQali9fzu7du1m5ciWdOnViypQpAMybN4/hw4c3RtUbh5Y2IgHQbji4+ELWMSGEgi5sDy2pnlD3UF654hXu7n43nx36jJ8TfuaXk7/w68lfGd9uPPf1vI8Iz4jznuPxfo8T6x/LoJBB1RdI3Az7F0PnSaIR0Zxx84eQnpC6F05vgY5X1lxU58a7I9/l/j/v51juMW789UZeHvIyE6Oa7z1abBZ2p+8GSoVEa6NvM+/wyTwOy+8UkaUGNG32+XpBUSDruBAO8X/C6a0iaWgZXuEit0OXiWK0r44R0SQSieR8NFoeieHDh1dp/M+YMcOx/Morr/DKK69UOa5v374cO3aswevXZNjtkHlULAe0ICGh0UHXq0X20EPLpJCoB8I9w3lx8Ivc1f0uPj/4OSsSVvD7qd+Z3nH6BYWERq1hcvvJNRc4tQn2fgeufs1fSIDoLU3dK8ybziMkALr4duGN2Df4ueBnfj35K519m3f41P2Z+zFYDXTw7kCAawAcWw35SdB+FPi1b+rq1R+GXCjJaX73dPQXEaa2JXXcnIu5BBI3lYuHvDPl+1RqCB8IHcdAx7EQ3F2OGEskkgZDZrZuavKTwFwE7kHgVpNrWzMlZmqpkFgOo56VP1b1RKh7KM8Neo67ut/F6lOr6RfcDxA92a/ueJUbOt9AF98uABzOPkxmSSbDw4afP7la2kExD+7e0NWvH9qPho3/hYS1Fy4LuGhceGHwC9zV/S5C3EX22mJLMVtStnBlxJVNmniuIiabieXxwq/IMRqxb6HIsDz9i+bX6L5YEtbC9zdB24Fw609NXZvKxP0q5i0tCV32CYj/SwiHxM1QIcoSrn7C36HjlUKQNmVmaYlEclkhhURT4+QBE98Eu7Wpa1J3Iq8At0ARa/zsvubvxNvCCHYLZnbsbMf6Lyd/YenxpSw9vpQR4SO4u/vdvLnrTXal7+LpAU9zY5cbaz5ZepmQ6Nawla4vwvqBkxdkx0NuIvhE1uqwMhEB8MbON1gev5xxkeN4duCzeDl5NUxd64DRamR98noAxkSMERsdOSTaNE2lGoLQ3mJ+ch1kJYB/M0kgmp8ssjPrXEWDuzljMQrTvjLxkHOiwk4VhPYRIw4drhTfva3ZaVwikTRbpJBoalx9yxM5tTTUGugzG4ozQC+d9hqaoaFDubXrrfx47EfWJ61nfdJ6ADz0Huf3CTDkCdMHrTP4tpAeb40W+swCFFBf3NdUj4AerD61mj8S/2Bv+l5eHvIyg0MH1289L4BdsfPX6b/o7NOZSK9IvJy8eLL/k/g6+9InqI8o1NKT0VWHiw90vw72fAM7P4MJrzd1jQRHV4l5h9HndeJvMvLOlJor/QWnNoKlpHyfs7eod8exYsTOPaDJqimRSCRlSCEhuTRGPd3UNbhsCHAN4LF+j3Fb7G18feRrvj/6PQargdtjb8dTf54IOemHxTywq2igtxTGvnxJh0/tOJV+wf14evPT7M3Yy9w1c5nRZQYP93kYF61LPVWyehRFYWvqVt7d8y5xOXFcGXElb494G6CyL4uiQOFZsezeioQEQL87hZDYtxBGPQNOF87/0+CUZbPuch5/osbEYhShWE+sFeKhzF+ujODuwlyp41iR56Elvb8SieSyQH4rNTWrHgWdC1zxsLRrldQKPxc/5vWZx5yYOcTlxF048o/DPyK24SvXzAj3COfLcV/y5eEvmb93PouPLmZr6lZeH/o6Mf4NEyBgf+Z+/rfnf+xM2wlAoEsgg0IGoShKVV+NkmwRYcfZq3n2kF8KbbpD+ABI2g4Hf4S+tzVtfYqzhamQWgudmih5od0uzAxPrIOT64WIsBrL9+s9RPjjjmNFwAHPVmTuJpFIWiVSSDQlNivs+RpsZhj+eFPX5uLJPgHbPgL3wJZ9Hy0MH2cfBofUwlSnpTlaVyRhjXDm7zMHwvtd1Ck0ag13dLuDISFDeHLTk5zIP8GpglP1LiQSchN4b+97rEsSmeo99Z7c3u12ZnSZUfMISNlohEdI9ftbOv3vEkJix2fiGTal03vGEdC5ic+Ri3fjXTcvSfiKnFwv8juUZFXeH9wNokrFQ9uBMjyrRCJpUUgh0ZTknBQiwrtty04MZMiFnQuEs+jQR6XTX3Nj2KPQYRSE9G7qmtSdhL+FaYx74EULiTKi/aL5YfIPrD61mkntJgHCBCm9JL1ekvg9/8/zHMg6gIvWhZujb2Z27Ozzm5xB6/SPqEj01SIgQ8ZhkWAwckjT1aXdUHj8BBRnXbjspWDMF+GWT64XAiI7ofJ+zzBoP0KIh6gRIm+KRCKRtFCkkGhKWmJG6+oI7SPEUN4ZMVTflI0FSVV824mpJdJhNGybL0Ymxrxwyadz0jgxpcMUx/qKhBW8vuN1Hu37KNd1uq5OYWKzDFmkl6QT4ydGNh7o/QBrT69lbo+5+LvUsnHoFQ5XzAOf8+cJabFo9TDyKTESEdKzqWsDWifwCq3fc9oskLxTCIcT6yBlNyi28v1OniLTd/uRQjz4tZehsiUSSatBCommpCVmtK4OlQpiroUt/4PDy6WQkNQfEUNA6yLMswrTwSOoXk9/MOsgBquBl7e9zPqk9bw05KULioBCcyFfHf6Kb498S6BrICumrECn1jGwzcC6Z6oO7AJjnr/4G2gJNIdM12mHRN6FkN6X3ohXFMg8Vj7ikLhZ5AIqQ62F8EFCNLQfKa4pnaQlEkkrRX67NSWtZUQCRHK6Lf+DIyth/P+1zB/Os/sJ2/Qk7Cy1UVYUQBHLNy4qNz9bdCOYCksPUkrLUV72piXlZb+9FkxFwpF20tuNn3Ds8ArxXHrdDP3uaNxr1wc6Z5GvJOEvOPE39JxRr6d/ftDz9A/uz8vbXmZTyiauXXktzw96vjzHQwWMViPfH/2ezw59Rr4pH4B2nu0oNBfi6ywDJdSK4qymMeXZ/DYcWiZy9lxMuO28JOGofXKDEBCFqZX3B3QRZkpRI0VHSks2VZVIJJI60AJbe62I1jIiAdCmh8hRkHMCTm8WP6otCUWBnx/E4+y+6vfbLOXLZ/4RdtA1UbFsyu7ysn//B6778pKrWidSdkPqXug0oXGvW590GCOERMKaehcSABPaTaBXYC+e2/IcW89u5eH1DzOl/RT+3f/fuOvdsdqt/JTwEx/t/4iMkgwA+gT14aHeD9EzsOelXXz3VyLPR7fp4BV2qbfSfCnOhsU3iAb5w4ca16HYaoLjf4rlDlUFYrWUCYfETWLEITex8n63QPEd177Uz8GzlTrLSyQSyQWQQqKpsBhFo1ulAb+OTV2bS0elgtipsPG/IspOSxMScb/A2X1Ynf3QTv8UVGqg1ARCparcwzjj+9JM5BX211T25uVgzINFN4jRmrwzwp+ksUg7JOYtJaN1dXQYLeYn/ga7TSRCrGeC3YL5+MqPWXx0Me/sfoeVJ1YS6BrIg70f5MN9H7Lg4AIAuvh24V+9/8WQkCF18qeokV1fiqzwkVe0biHh6itG8YrSxLsWO7Xxrn1yA5gLIahbzb5CeUlCMCRuFuIh73Tl/U5eEDFYPKf2I8UosvRzkEgkEikkmgy1Fmb/BvlJwnyjNRBTKiQyjoge/pb0Q9thDIx+noxChZAL9VpG1CE7clhfMY+ZKmLpb/sYxr968fWsC4rSOnJI+HUod+ZPPyzyEzQAapWam6JvYlCbQXy0/yPu7C5MYG7ofAPrk9dzZ7c7GRc5DrWqHqOStfaoTWWoVMK07rdHYceCxhUSR0uT0EVfVb6tLsIh8gohxBtAwEokEklLRwqJpkKjhYhBwKCmrkn9EdQV7vmnZfbW6V1h6Dzy4+JoECOFQfcJIbHnGxjxhEhA1tAUpomY9c5eIjpQS0Wlgms+FtF2fCIb/HJR3lH8d/h/HetBbkEsm7ysfkYgKmKzQnEGoAL3+nUib5b0uBHWvChMA9MPQ1DDJASshN0GR38Tyyot/HSfFA4SiURSj0ghIalfGqNxUJ+YSyDrGIT0atjrhPQUISATNwkxMfiBhr0eQHqpWVNQt5Yn7M6liSOB1buIACEiFLuwt78ckpA5eQgflx2filGJye823LXKRhwOLStPALfu5Qp1kcJBIpFI6gMpJJqKn+4TkT+ufLllm51Uh8UgHGM7jhVx25szOz6FNc/DsMdg1DMNe61hjwr76p43Nex1ykg7IOYt2T/iXIz5oHFqHeaAjqzWrdysqSL97hDv3IEf6iUvCCBGHTLiIGkbnCmd8pMql9Hoof1oKRwkEomknpFCoqk4tRHyz8CEN5q6JvXPN9eIH/UbF0OXiU1dm5ox5IqwkABtG8HELGpE4zqhl0UFay1C9fcnRE/29C8g5pqmrs2l4/CPaNO09WhMAjpDu2Hi+2//YvAaUfdzmEtENLIy4ZC0E0znRFErG3HwiRQjEgPvhdAWmNldIpFImjlSSDQFxgIhIjRO4NNCMw6fj05jxY/84eXNW0hs+Z/o4Y4cCu1HNe61c08LB+KGNDm65mMx0uIW0HDXaEzcA0XG4BNrW4eQKCjNRXA5jUgADLhH+IS0HQjniaLsoCiz8mjD2X2lUdMq4BUuztd2IIQPFCG15YiDRCKRNDhSSDQFmcfEPKBTy0zcdiFipsLal+DY78LMSefS1DWqSsFZEUEJhIlFY/oQrLwP9i6E2asa1vZfoxU9wK2FDmPE5yphbcuLClYdYX2FOV1ww0SharZ0mVjewZAfV3mfokB2ApzZCme2i3nOicplVGphmtR2EIQPEOKhNYfOlUgkkmZMK2zFtgBaU0br6vBtJ5yXU/dC/J/QdUpT16gqG98AqwGiJ5eHaG0sPEMBBbZ+0HBCwmIUIYZbk1AN6iYckwtSIPNoy0/kGNKr4Z38mzkqqwGSdpQLh6RtUJJduZDOVbyj4aUjDmH9wNnzwif/chL4RAg/NDe/hrkBiUQiucxpRa2MFkRrymhdEzFThZA4tKz5CYnsE7D7a9GzOerZxr9+vztg87tixCYrAfw71P819i+G1f+GwQ/CqKfr//xNgVotTNAOfC9GJVrz+9MaURTIT4aUXZC8Cw7/ROeC5Krl3INKRxoGQdsBYsSmrlGtshLg9GZIPwiT/1c/9ZdIJBJJFeoxs5Kk1rT2EQmAmGvF/PifYCpq2rqcy6Hlwta+58ymMf1xD4Tu1wMKbPuwYa6RdhCsxtr13LYkypIFJqxp2nrUB+teFVNJTlPXpGEwFcGpTbD5Hfj+JnirC7wbC0tmi9G4gmSqNU5z8hSjEG0HQmifiwuNW5aErtP4yyO0rkQikTQRckSiKcg8KuatuUfVO1z0KiZthxN/Q9erm7pG5Qx7FML6gH8T+g8Muh/2fgv7Fgk7eVff+j2/I4dEK4nYVEb7kYAKTv8jovfoXZu6RhfPjgVgyBEjVC0dux2yjkPyzvIRh4wjIk9GRZy9ILSvME8K7UNiWi6RnjYRhSllj/jcZseLqccN5ceteUEERgjpLcRFQOfzO1PHlWWznlzvtyqRSCSScqSQaAru3wkZR1t2tuHa0PtWEbO9y6SmrkllVKrGj9J0LoFdoMOVkPAX7Pwchj9Wf+e22yGtVEi0phwSAG7+IqynzlXY0rdUIWE1CRGh0oCrf1PXpu4UZwmxUCYcUvaAqaByGZUG2vQoFw5hfcG3vTBRK8Vgi4PoaDE6COLvkn5InC+0T/m5Di4tzQ3xhVjXuYkkjyG9RFjXjuPAyV3sy08RwkTrInJHSCQSiaTBkEKiKXD2Era/rZ1eNzd1DSpzeqsYDep1S/NwQh50nxASOz6FIQ/WX/K+3FNgKQaPENHwbm3MXtXyIzZVTEanbuYWplaTMJWrKBxyE6uW8wwTI31h/YR4aNOj7kJP6yQEREURoShw7cdCXJSNXOSfgdNbxATwaHy5kPiqtOOiw+iWKzQlEomkhdAMWlOXGTbL5Wmze2qTaDRf+VLTXF9R4M+nRUPEaoKBdzdNPSoSNQL6zBEjNhp9/Z037aCYt5ZEdOfS0kUENN9kdFYzZByG1H0iWELqXhEcwm6pXE7nKsyMKgoHzwa6F5WqPCN1GUWZkLpHiIrcROF3BCLJZO4psdzlqoapj0QikUgcSCHR2Pz2GBxdBZPebH7RjBqKkhxYfCOYi8CvI/S+pfHrcPRXISLcApvPSIlKBZPfrf/zprdSs6aKZMSJ0MKx01pmDoGKIxJNhc0i/BgqiYYjYDOfU1Al/InC+pULh4Doph3Vcw+ATuPEVBGbFQY/IMz7Yqc2Td0kEonkMkIKicYmIw6KM4R50+WCqy9MeANW3gu/PQptuguzh8bCZhWJzACGP15uAtGcsNuEzX9Zz+qlYC4BrXPrc7SuyLpXIe5n0LtDv9ubujZ1p7FHJGwWYdZXJhrO7hN+NDbTOQVVQuyX5bgI6SnCrzbHd6Y63ANg7H+auhYSiURy2SCFRGOiKBVySLTi0K/V0esmEcFpz9fw461w13pw8Wmca+9fLCLK+ERC71mNc826kLJHhMT0aw+3rLj0841/VZiQnRsxpzXRYYwQEglrW6iQaMARCZsVso6dIxpKwwGfi2/7csEQ0kuIhtYWMlgikUgkDYYUEo1JQSqY8sHVD9wCmro2jc+EN0Sj5ux+WHEP3Lio4R1NLUZY/5pYHvkMaOvRF6G+8ImE4kzIOw3phyEo5tLP2RycyRuSDqXReE5tFHb9LY3oq8VoRPglBl2wGCEzDs4eEGIh7YBYthqqlvVpVy4YykSDi/elXV8ikUgklzWtvLXRzKg4GtEaHEbris4Zrv8GPhkOx3+HLe/A0Eca9po7P4OCFGHmEzutYa91sbj6Cr+NHZ/C1vlwzSUkqSvKEA6nvu1bt5jwCoOALsJcJ3kH4NfUNaobYX3FVBdKcoRQSDtYPmUeE8kVz8U7orJoaNOj8UYAJRKJRHLZ0IpbGs0QR0brVpyI7kL4RMLUT2HR9bD9U+h/Fzh5NNz1rEbhLzD6+eYdZnPgPSJB2YEfYfRzF2/ycnAJ/PEU9L0drnq7fuvY3OgwRgiJhDUQesOFy7cUFEVEInIIhlLxUJBStaxKLRyhg7uVTyG96j/BoUQikUgk1SCFRGPiGJG4jIUEiEgrV38gzFMaUkSAyGLd+9bmb0rmGyXCwB79VQiK0c9e3HnKQr/Wh3lUc6f9KNj6gfCTaGlCYsXdwsRxxL8h+0TlUYb0Q1WTu4EIuRoUW0E0dBffJTJXgkQikUiaCCkkGhNDrphfbo7W1VExBKzdJhyD6zO/hqKUm4/VRySkxmDwA0JI7Pochs4DvVvdz9FaM1pXR8QQkb047QAaQ3ZT1+b8KIqI1JRxGFL2igAAqGDbR9WbJrkHVR5lCO4uxKZa0+hVl0gkEomkJhpNSGzcuJFXXnkFu93Oddddx1133VVp/88//8yCBQsAcHNz44UXXqBLly61OrbFMPN7MBXVXwbj1kBxFiy9TYirCa/X33lXPwmWEhj5VNPG6q8L4QNEYq+UXbBvEfS/s27HW83C1AfV5SFWdc4w7j/g1Ra7uRmFJzXmi9HHjCOQfkTMM46UdyQ4UISA9usoQiKXiYagbuAR1CRVl0gkEomkLjSKkLDZbLz00kt8+eWXBAUFMX36dEaNGkWHDh0cZcLCwvjuu+/w8vJiw4YNPPvssyxZsqRWx7YoWko89sYiNxFO/wOnNkB4v/pxiM45BTsXiJGOgfe0HCGhUgnn89NboNP4uh+fdUxkIPZtf/l8zvrdAYASF9f417aaRVjhjCMi2laZeMhPqr68sxcExohIScd+E4Lh9j8ubuRJIpFIJJJmQKMIiQMHDhAREUF4eDgAkyZNYu3atZXEQO/evR3LPXv2JC0trdbHtgiKs0Xvo3szt9VvbML6wvjXRKK6lQ8IG/CAzpd2znWvgN0KPWa2PH+ULhPFdDGU+UdcDmZN56Iolc3Z6hO7XYTmzYgTpkllowzZCeJzdi4aJ/EZDooRn7/AGAjqKsK9qlTCof7Yb+DfUYoIiUQikbRoGkVIpKenExxc3iscFBTEgQMHaiy/dOlShg0bdlHHlpGYmIjBUE0s9UbEaDQSV9pT6nf4CwIPfUpW1zlkdpvbpPVqdrgPIaTtWLzO/Inp2+s5NeYLFN3FOZA65R4n6uAS7GodJ8Kvw1rHnuqKz6yp0ZjysTnVPgN64JEN+AEZmiCym8k9NAb+hxYQdfJXEjNfwBDY6+JPpNjRlaShz0/EqeAUTgWJOOWfRF9wCo21pGpxVJjdwzF5RWHy7oDJqz0mryjM7mGgrvDVagVS8oF8AHxP7CcIyLboybiMnlN1NKf3TVJ75HNrmcjn1jJpDs8tOrrmTtlGERKKolTZpqqh53Dbtm0sXbqURYsW1fnYikRGRtatkg1AXFxc+R//UBYA/p3643+eB3LZ0v5L+Gw0TplH6XJ8Pkz7/OJ6lxc+B4C6/5107DuqzodXemZNyeqnhHnWbashtE/tjsntBkXHCOwxlsAOzeAeGoszLmBIJ3Lv63DHmgs719uswqQu86gwB8s8VrocL/xqqsM9SPidBHYVowuBXVEFdMFJ70qdPZ4ShXO1X2Qsfs3hs9aENJv3TVIn5HNrmcjn1jJp7s+tUYREcHCww1QJxChDYGDVH/ujR4/yzDPPsGDBAnx8fOp0bLNHhn49P07ucP23sGAkHFomHI8H1HHkJnELxP8JeveGT3TX0Kg1YDOLBHXTv6jdMYPvF9PlxsinMBz7G5ecI7DoBpj9qzAZsppEaNXMo8KXIfOoEA3ZCeJvWx3uQcIsyb+zmAd0FqZJbvWY8C73lJh7htTfOSUSiUQiaQIaRUh069aNxMREkpKSCAoKYtWqVbz11luVyqSmpvLAAw/wxhtv0K5duzod2+yxmiE7HlCJBoqkegI6wZQPYMkcEfmmLigKrHlBLA9+ANz86716jcqAu2Hbh3D4JxjzIniHN3WNmjUZsXOJ2PUypO6B93oJMZmbWH1oVQCv8HMEQxfx+WuM7M9TP4U/0YrjGgAAFeNJREFUn4GOYxv+WhKJRCKRNCCNIiS0Wi3PPfccd9xxBzabjWnTptGxY0cWL14MwIwZM5g/fz55eXm8+OKLAGg0GpYvX17jsS2KMqdM3yiZPOpCxFwreoADOtX92IH3wHY1DLqv/uvV2HiFir/FwSWw/WMY98r5y2ceE87WYf3AJ6Jx6tiY2G2Qd0a8S9kJwgwpOx6yEqAwlUp3XJQOpAMq8c4FdAH/TqViobNYbuyoVlYTmItFxmlnL7j6/ca9vkQikUgkDYBKqc4JQVIvOOzaDi6FZbdDl6vgxoVNXa2WRfJuCOnZaIm4mpUtYuo++HQ46D1g3hFw9qy57Ib/wrr/wKD7Lyw6mjMlOZWFQnaCEAs5J8Fmqv4YtQ6TeyhOod2FSdPBJUK4j3u1eYhKuw2WzBbmVbeskCZNFWhW75uk1sjn1jKRz61l0tyfm8xs3RhI/4iLY/sn8PsTwt9h9LM1l8tLAq+whgn92ZSE9ITIoZC4CfZ8c37/h/QWFPrVahK5PrITykcVsuOFeDDk1HycRxvw6yAm/44ikZt/B/Bqy8nj8eVftB2vhI1vQvTVjXM/50NR4Jd/QdzP4OQlhJIUEhKJRCJpJUgh0RjoXcGn3eWRbbg+CYwW4mDTmxDeHzqNq1rGaoIvJ4CrH8z8oeUkn6stg+4TQmL7x8JvQlPDK1uWQyIotvHqdj6sZmGKlHNCjCaUTdknRE4GxV79cTo38GtfQSh0FOt+HcDJo3bXjp0mRIRGV3/3c7H89Rzs/Ra0LnDTjxDcTJ6PRCKRSCT1gBQSjcHQR1p+FKGmoN0wGPUsrH0Rlt8JczeCT2TlMjs/F5mEnTzArRUm++s4DrpOgc6Tai5jKhQ9/Bq9sP9vLCxGIQqyzxELOSfFM6lJLKjU4jn6dSgfVSgTDWVJ2y6VMhFhzIefH4RRz4jzNyab34F/3hM5JW74FtoObNzrSyQSiUTSwEghIWneDHkIkneKTMA/3AK3/wU6Z7HPWCBGKwBGP99ofhSNiloN139z/jLpRwBFOBJr9fV7fXOJiH507shCzinITxbXrQ6VGrzbCmfncyefduXPsKFZ/zoc+QlS98Idaxsvs/yuL0ujiKng2k+EuZVEIpFIJK0MKSQamsI0yDgiTE4ulChLUhW1Gq75SDgdpx2A3x8rj3iz9QMoyYbwgdWbPbU2FAXMRVVNfNJKM70Hd7+4cxZnCrGQmwi5p8uXc05CYWrNx6o0lcWCX/vyZe+2oK1zqrb6Z9QzcPofOLsPFt8As35t+Mhp2SdgVekI5KQ3odv0hr2eRCKRSCRNhBQSDU3CWlh5r7Dbrm1iMUllXLxFsrrPrxROxxFDoP1o+OcDsX/MC63P0fpc0g7CyvvAMwxmLKq8L/2QmNfkH2EuLhcIeacri4a80zVncwZQ60Q4Wd/254wstBNioTn4IZwPvRvM/BE+GwMpu2HZHcLMqCFHr/zaw9XviU6Efnc03HUkEolEImlipJBoaDKOiLmM2HRptOkOk96CvQshaoQwabIUQ6fxEDGoqWvX8LgHiehfZw+IHm+/9uX72g0XZl5aJ9jzbblQKBMNxZnnP7eLD3hHCL8Fn0ghHHwihWDwDKvZwbul4BEENy8VQvTYKlj9b5jwRv2LT2O+yBEB0Ovm+j23RCKRSCTNkBbeQmgBlIV+DZBC4pLpdTP0mAGmAtj9NaCC0c81da0aHpsVLAaIGg7xfwnH88BoEfY274xwbLZb4fDy6o/X6MXoQZlQOFc0lDV+WzMBneHGxfDtNbDjU/E3OF843bqSfhi+niw+j31m1995JRKJRCJpxkgh0dDIHBL1i1ojetDnboRTGyAopqlrdOlYTcJxOe9MuTCoKBIKUkGxlZdP2S2mirgHVx5NqCgYPNoIX5PLncghwt9m2e0i0lV9kXMKvr1W+OvE/wW9Z7V+UzuJRCKRSJBCokFRmwuEs6rWpWrYUsmlEdBJTC0BU2GpUEiC/DOVRUJeEhSlXeAEKiEGvMKFuVJROnSaAP1uh+IsyDgMnSdCxOBGuZ0WTbfpQtTXlwAtTBOjHEXpInngtM+liJBIJBLJZYMUEg2IU/5JsRDQuXWGJpWIXAoFKWLKT4GCZCEa8itsM+Wf/xwqDXiFgldb8A4XZkhe4WLZK1xk7S6LgHRinWi4pu6Bdl/D+tfgn/eFWJVConZUFBEJa8A7UuSyqCuGXDESkZsIIb1gxuLGC2srkUgkEkkzQAqJBsQhJGRG65aJzQqFZ0sFQXK5MMhPLhUMKVCSdeHzaJ3BM1QIBO/wUsHQtlwoeLSpvUNz1AgRnSn9EBxcCmmlEZtkxuS6k7AGFl4vnsPta+qWY8JcLI7NOCKSAN60rPaZtyUSiUQiaSVIIdGAWF2DIHqysM2WNC+splKRUCoUSpdDU47ClgIhEorSas7OXIZaCx4hYkTBM1SMHniFlS6HiqhHrr71Z+6iUokkfSfXQWgfWPuS2B7crX7OfzkRPlD83c7ug8U3wqxfap9jYvWTkLxDPN9bVoCbX4NWVSKRSCSS5ogUEg1IUcgQGC3jyDcqiiJMTspEQmFqhXmF5ZLsag/3rLSmEk7MFUXCuWLBPbDxzda6XyemokwhdvQewjxHUjec3CvkmNglomFd/03tnufIp4RJ06S3xGdBIpFIJJLLECkkJC0Hi1E0nAvTxfyc0QSHaLAaLnwulUaYFHm2KZ2HgEcbUgoVQqP7C/HgEQJafcPf18WSflDMg2JkVKaLpWKOiaO/wh9Pw4TXqy+rKGIkS+cMHsEw6+fGratEIpFIJM0MKSQaipIcfI8uBOex0G5YU9emeWMuFtFvitKFKCgTCoWl60XpYr8xr3bn03tUEAihVcQCniHgFlBtz3NBXByhkS0kVO/3pUnPpG3+pRHQGW5cJBynt38k/FcG3Vu13N8vQ+JmmPmDCEEskUgkEslljhQSDcXZ/QTtfx9yd12eQkJRRKbfoozKowiFaRVEQ+ncVFC7c6q1wtTII0jMKwoEzxAxguDZ5vJpWPecCTsXQO9bm7omLZ/IK2DKh7D8Dtg6X/xNndzL9295Dza9JUayUvdB+5FNVlWJRCKRSJoLUkg0FK0xEZ2igLmoVBxkQHFG+XJRetVtNlPtzqtxEuLAow24BwmzEffS9TLR4BEMLr7ShKciE/4P+s6RUcHqi+7XgdUI7UdVFhF7voW/nhXL13wkRYREIpFIJKVIIdFQZBwR84AWICTMxaUiILNcEFQnFIozwVJS+/PqPURIzbJRhJqEgrO3TOJ1Mag1rSOzd3Oi9y3ly1Yz7PoC/nhSrE94A3rc0DT1kkgkEomkGSKFREPRlCMSVrPIb1CcKTIfF5ctZ1a/XBvn5DK0LiJSkXtQ6TwQ3ALLl8u2uwXWPpSmRNLcMOTBlxPKOwRGPAkD5jZplSQSiUQiaW5IIdEQ2O2QeVQs14eQsFnBkCNClhZXEAglNQgE4wUyKZ+L1lk4H7sFVBYI7kFVt+nd5eiBpPWj1gifHID+c2H4E01bH4lEIpFImiFSSDQE+UlgLsLq7IvWzb/yPkUBU6EQBWVTcVaF9Swoyam8rbbRispQacDNv1Qc+INrheUywVBxXe8mxYFEUhEnD7httcgcHt5fvh8SiUQikVSDFBINQcoeABSVFpbMKRcHZcLAZq7jCVUiO7KrP7j6nSMI/KsKBGdv6ZQskVwqejdoO6CpayGRSCQSSbNFComGID8JAJ0hAw4vr7pf5wZufkIUuPpVEAjVbHP1Axfvxs+eLJFIJBKJRCKRnAcpJBqCvreBRkdaejrB7WIqCAR/MbKgc2nqGkokEolEIpFIJJeEFBINgZM7DLyH3Lg4gqNbQPhXiUQikUgkEomkjkhDeolEIpFIJBKJRFJnpJCQSCQSiUQikUgkdUYKCYlEIpFIJBKJRFJnpJCQSCQSiUQikUgkdUYKCYlEIpFIJBKJRFJnpJCQSCQSiUQikUgkdUYKCYlEIpFIJBKJRFJnpJCQSCQSiUQikUgkdUYKCYlEIpFIJBKJRFJnGk1IbNy4kXHjxnHllVfy6aefVtl/4sQJbrjhBmJjY/n8888r7fvqq6+YNGkSV111FfPmzcNkMjVWtSUSiUQikUgkEkk1NIqQsNlsvPTSS3z22WesWrWKX3/9lYSEhEplvL29efrpp7n99tsrbU9PT+ebb75h2bJl/Prrr9hsNlatWtUY1ZZIJBKJRCKRSCQ10ChC4sCBA0RERBAeHo5er2fSpEmsXbu2Uhk/Pz+6d++OVqutcrzNZsNoNGK1WjEajQQGBjZGtSUSiUQikUgkEkkNVG21NwDp6ekEBwc71oOCgjhw4ECtjg0KCuK2225j5MiRODk5MWTIEK644ooLHpeYmIjBYLjoOtcHRqORuLi4Jq2DpG7IZ9Yykc+tZSKfW8tEPreWiXxuLZPm8Nyio6Nr3NcoQkJRlCrbVCpVrY7Nz89n7dq1rF27Fg8PD/71r3+xcuVKpkyZct7jIiMjL6aq9UpcXNx5//iS5od8Zi0T+dxaJvK5tUzkc2uZyOfWMmnuz61RTJuCg4NJS0tzrKenp9faPOmff/4hLCwMX19fdDodY8eOZe/evQ1VVYlEIpFIJBKJRFILGkVIdOvWjcTERJKSkjCbzaxatYpRo0bV6tiQkBD279+PwWBAURS2bt1K+/btG7jG9UNzVpCS6pHPrGUin1vLRD63lol8bi0T+dxaJs39uamU6uyOGoANGzbw6quvYrPZmDZtGvfccw+LFy8GYMaMGWRmZjJt2jSKiopQq9W4urry22+/4e7uznvvvcdvv/2GVqslOjqaV155Bb1e3xjVlkgkEolEIpFIJNXQaEJCIpFIJBKJRCKRtB5kZmuJRCKRSCQSiURSZ6SQkEgkEolEIpFIJHVGCgmJRCKRSCQSiURSZ6SQkEgkEolEIpFIJHVGCgmJRCKRSCQSiURSZxols3Vr5+zZszz++ONkZWWhVqu5/vrrmTVrFu+//z4//vgjvr6+AMybN4/hw4c3cW0lZZhMJm666SbMZjM2m41x48bx4IMPkpeXx8MPP0xKSgqhoaG8++67eHl5NXV1JaXU9Nzk+9b8KQv/HRQUxCeffCLftRbCuc9Nvmstg1GjRuHm5oZarUaj0bB8+XL5zjVzqntmzf19k+Ff64GMjAwyMzOJiYmhqKiIadOmMX/+fH7//XdcXV25/fbbm7qKkmpQFIWSkhLc3NywWCzMnDmTp59+mj///BNvb2/uuusuPv30U/Lz83nssceaurqSUmp6bps2bZLvWzPnyy+/5NChQxQVFfHJJ5/wxhtvyHetBXDuc3v//fflu9YCGDVqFEuXLnU0QAH5zjVzqntmzf19k6ZN9UBgYCAxMTEAuLu7ExUVRXp6ehPXSnIhVCoVbm5uAFitVqxWKyqVirVr13LNNdcAcM0117BmzZomrKXkXGp6bpLmTVpaGuvXr2f69OmObfJda/5U99wkLRf5zknqGykk6pnk5GTi4uLo0aMHAAsXLmTy5Mk8+eST5OfnN3HtJOdis9mYMmUKgwcPZvDgwfTo0YPs7GwCAwMBIRJzcnKauJaSc6nuuYF835ozr776Ko899hhqdfnPjnzXmj/VPTeQ71pL4fbbb2fq1Kn88MMPgHznWgLnPjNo3u+bFBL1SHFxMQ8++CBPPfUU7u7uzJgxg7/++ouVK1cSGBjI66+/3tRVlJyDRqNh5cqVbNiwgQMHDnD8+PGmrpKkFlT33OT71nxZt24dvr6+xMbGNnVVJHWgpucm37WWweLFi1mxYgULFvx/e/cTEtUaxnH8N+liECprojKZjWBKkEUEuUnNyARrWpggFEUEaQQiOUoQuminVvSHoriCiASTzmKECIKSakiKMKVEpI2iZI6QophMNHVaSIfqNnVPzPUc4/sBYThnDjwvDw/yg/c9849u3bql58+f210SfuNnPXP6vBEkEuTjx4+qqqrS/v37VVRUJElas2aNkpKStGzZMpWVlenVq1c2V4l4VqxYoR07digcDsvj8WhyclLSwvmXb/cqwlm+7Rvz5lwvXrxQd3e3CgsLdfr0aT19+lR+v59Zc7h4fWPWloZ169ZJkjwej/bs2aOXL18ycw73s545fd4IEglgGIbOnj2rjIwMHTt2zLz+dVgl6f79+8rMzLSjPMQxNTWl2dlZSVI0GlVPT48yMjJUWFioUCgkSQqFQtq9e7eNVeJH8frGvDlXTU2NHj9+rO7ubl28eFG5ubk6f/48s+Zw8frGrDnf/Py85ubmzM9PnjxRZmYmM+dg8Xrm9Hnj9a8J0Nvbq66uLm3cuFEHDhyQtPB6rjt37mhoaEiSlJ6ernPnztlZJn4wOTmpM2fO6NOnTzIMQ8XFxdq1a5e2bt2q6upqBYNBpaWl6fLly3aXim/E61ttbS3ztsScOHGCWVuCmpubmTWHe/funU6dOiVp4UzZvn37lJeXp82bNzNzDhWvZ07/38brXwEAAABYxtYmAAAAAJYRJAAAAABYRpAAAAAAYBlBAgAAAIBlBAkAAAAAlhEkAAAAAFhGkAAAJNz169eVlZVl/vgVAODvQ5AAACTU58+fFQwGlZqaqkAgYHc5AID/CUECAJBQ4XBYkUhEjY2N6uvr0+vXr817w8PDOnz4sLZt2yafz6e2tjZlZWWZ92OxmG7cuKG9e/dq+/btKi8v18DAgB3LAAD8BkECAJBQt2/f1s6dO1VQUKDs7Gx1dHRIWggJlZWVys7OVk9Pj65du6bOzs7vnr1y5YoePHiglpYWPXv2TKWlpTp+/LhmZmbsWAoA4BcIEgCAhIlEInr06JEOHjwoSSotLVVXV5ei0aj6+/v15s0b+f1+ud1ueb1eHT161HzWMAy1t7errq5OXq9XSUlJKisr09q1a/Xw4UObVgQAiCfZ7gIAAH+PYDColStXqqCgQJLk8/nU3Nysu3fvyu12y+PxyO12m99PT083P09PT2t+fl6VlZVyuVzm9VgspkgksmhrAAD8NwQJAEBCfD1kPTs7q/z8/O+ud3R0yO/3a2pqStFo1AwT4+Pj5vdWrVqllJQUtba2KicnZ9HrBwBYw9YmAEBChMNhTUxMKBAIKBQKmX83b95UX1+fli9frg0bNujChQv68OGDxsbG1NbWZj7vcrl05MgRNTU1aWRkRJL0/v178/A2AMBZXIZhGHYXAQBY+k6ePKnk5GRdvXr1X/fKy8u1adMmHTp0SA0NDRocHJTX65XP59OlS5fMNzPFYjG1t7ers7NTExMTSklJ0ZYtW1RfX6/169cv9pIAAL9AkAAA2CYQCKi1tVX37t2zuxQAgEVsbQIALJre3l6Njo7KMAwNDQ2ppaVFJSUldpcFAPgDHLYGACyat2/fqqamRtPT01q9erWKi4tVUVFhd1kAgD/A1iYAAAAAlrG1CQAAAIBlBAkAAAAAlhEkAAAAAFhGkAAAAABgGUECAAAAgGVfAOauxhB2qYGTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_part4(part4, var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5 - Covariances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "part5 = estimate_quantities_approach_5(df, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>symbol</th>\n",
       "      <th>part5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rho</td>\n",
       "      <td>16.749681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sigma_epsilon</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sigma_mu</td>\n",
       "      <td>0.160546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sigma_tau</td>\n",
       "      <td>0.026124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          symbol      part5\n",
       "0            rho  16.749681\n",
       "1  sigma_epsilon  -0.000000\n",
       "2       sigma_mu   0.160546\n",
       "3      sigma_tau   0.026124"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6 - IV Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "part6 = estimate_rho_approach_iv(df, n_jobs=4, fixed_effects=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>part6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rho</td>\n",
       "      <td>0.240243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol     part6\n",
       "0    rho  0.240243"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combined Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = combine_parts(part4, part5, part6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>baseline</th>\n",
       "      <th>year</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>part5</th>\n",
       "      <th>part6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rho</td>\n",
       "      <td>1.036401</td>\n",
       "      <td>1.036401</td>\n",
       "      <td>1.135712</td>\n",
       "      <td>16.749681</td>\n",
       "      <td>0.240243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sigma_epsilon</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sigma_mu_tau</td>\n",
       "      <td>0.185923</td>\n",
       "      <td>0.222065</td>\n",
       "      <td>0.221479</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sigma_mu</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.160546</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sigma_tau</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.026124</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          symbol  baseline      year  birthyear      part5     part6\n",
       "0            rho  1.036401  1.036401   1.135712  16.749681  0.240243\n",
       "1  sigma_epsilon  0.000747  0.000261   0.000004       -0.0      <NA>\n",
       "2   sigma_mu_tau  0.185923  0.222065   0.221479       <NA>      <NA>\n",
       "3       sigma_mu      <NA>      <NA>       <NA>   0.160546      <NA>\n",
       "4      sigma_tau      <NA>      <NA>       <NA>   0.026124      <NA>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  }
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