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zSK|v%U}UP5Syp6H=c`SiAQQ2yvp4<&zty-SVPUeCl09bijfvI%nJD?=Ly!R}n^n}7u1v;5mm3}XL7oWz(IM#bWqe}Z_E(uo3#>q10tfzdPnqM62DxcRCh3fFNZ*qYK6;h=+{OJPTr+|*ARB{q zUEw)3OFL7x(gx1sVS_{+h^Tgr@sVqz4PAjIWr@{CQgj!=73_i%$>mSS$Yp3a4)p&? zQC~$%!p9MOW`Hn&DeKWgyymyjzh{>| zH(&nRr~Sq{f?+{XdRNV0-ZVs=TrK@zp7%Z5pPa`&u;Zma@IXDcpPq}ajaZx&X*yR!2xSpt6`8eveiafZH zZm4QbTw|1QHZBE618W*L;jmw8B%)t%9fuQ)Y{-U5fxYV`Yxwyy9sIOVL zhNvL`9t}z<8<$)h`&_((i4BpsI19h501Fs){hU1Z63hY?{^t_L0)hN<31NW<{&NXs zfr3yHba>{3LINz3l0^K{P?SF%9y98I4le?wpN)GP6$Hd%#DSvFKs;$lC>IgGjI+71 ztF?pu6Ju9%7S1Q45D)|g5&#Q=Kwt<+kQW4d00KSWx&|}1H@kKU?rd&B1Y&{0Q1|HZ zcoEnq`=P?v2l=mGEcPr!*MG4#FzEkd6BhVC|bkOLjw!{8xJNZ@NfUHXMd_DOZ5K$1U4jZ From e5690aad6ee4e25a4c28b727116511e056755697 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 17 Jul 2024 00:28:25 -0400 Subject: [PATCH 02/44] update ValueError exception when failing to convert to float --- src/treeffuser/_base_tabular_diffusion.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index ef547ad3..7af29735 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -41,19 +41,20 @@ def _check_array(array: ndarray[float]): array = array.reshape(-1, 1) # Cast floats - try: - if not np.issubdtype(array.dtype, np.floating): + if not np.issubdtype(array.dtype, np.floating): + try: array = np.asarray(array, dtype=np.float32) warnings.warn( "Input array is not float; it has been recast to float32.", CastFloat32Warning, stacklevel=2, ) - except ValueError as e: - # Raise the ValueError preserving the original exception context, see B904 from flake8-bugbear - raise ValueError( - "Input array is not float and cannot be converted to float32." - ) from e # + except ValueError as e: + # raise the ValueError preserving the original exception context, see B904 from flake8-bugbear + raise ValueError( + "Input array is not float and cannot be converted to float32." + "Please check if you have encoded the categorical variables as numerical values." + ) from e return array From dc42c93ee98cfecdd85b0ef9e16488e07878bce2 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Sat, 5 Oct 2024 11:20:55 -0400 Subject: [PATCH 03/44] add preliminary m5 notebook --- examples/m5-news-vendor-notebook.ipynb | 2106 ++++++++++++++++++++++++ 1 file changed, 2106 insertions(+) create mode 100644 examples/m5-news-vendor-notebook.ipynb diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb new file mode 100644 index 00000000..40094ac8 --- /dev/null +++ b/examples/m5-news-vendor-notebook.ipynb @@ -0,0 +1,2106 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from functools import partial\n", + "from pathlib import Path\n", + "\n", + "from tqdm import tqdm\n", + "from treeffuser import Treeffuser\n", + "\n", + "path = \"../testbed/src/testbed/data/m5/\"\n", + "# load autoreload extension\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started\n", + "\n", + "To get started, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", + "\n", + "You can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", + "\n", + "```bash\n", + "cd ./m5 # path to folder where you want to save the data\n", + "kaggle competitions download -c m5-forecasting-accuracy\n", + "```\n", + "\n", + "Use your favorite tool to unzip the archive. In Linux/macOS,\n", + "\n", + "```bash\n", + "unzip m5-forecasting-accuracy.zip\n", + "```\n", + "\n", + "We'll be using the following files: `calendar.csv`, `sales_train_validation.csv`, and `sell_prices.csv`.\n", + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# import data\n", + "data_path = \"./m5\" # change with path where you extracted the data archive\n", + "\n", + "calendar_df = pd.read_csv(Path(data_path) / \"calendar.csv\")\n", + "sales_train_df = pd.read_csv(Path(data_path) / \"sales_train_validation.csv\")\n", + "sell_prices_df = pd.read_csv(Path(data_path) / \"sell_prices.csv\")\n", + "\n", + "# add explicit columns for the day, month, year for ease of processing\n", + "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", + "calendar_df[\"day\"] = calendar_df[\"date\"].dt.day\n", + "calendar_df[\"month\"] = calendar_df[\"date\"].dt.month\n", + "calendar_df[\"year\"] = calendar_df[\"date\"].dt.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The data\n", + "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " date wm_yr_wk weekday wday month year d event_name_1 \\\n", + "0 2011-01-29 11101 Saturday 1 1 2011 d_1 NaN \n", + "1 2011-01-30 11101 Sunday 2 1 2011 d_2 NaN \n", + "2 2011-01-31 11101 Monday 3 1 2011 d_3 NaN \n", + "3 2011-02-01 11101 Tuesday 4 2 2011 d_4 NaN \n", + "4 2011-02-02 11101 Wednesday 5 2 2011 d_5 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \n", + "0 NaN NaN NaN 0 0 0 29 \n", + "1 NaN NaN NaN 0 0 0 30 \n", + "2 NaN NaN NaN 0 0 0 31 \n", + "3 NaN NaN NaN 1 1 0 1 \n", + "4 NaN NaN NaN 1 0 1 2 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calendar_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`sales_train_df` contains the number of units sold for an item in each department and store. The sales are grouped by day: for example, the `d_1907` column has the number of units sold on the 1907-th day." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1HOBBIES_1_002_CA_1_validationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CA0000...0000010000
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4HOBBIES_1_005_CA_1_validationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CA0000...2110112224
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5 rows × 1919 columns

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" + ], + "text/plain": [ + " id item_id dept_id cat_id store_id \\\n", + "0 HOBBIES_1_001_CA_1_validation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", + "1 HOBBIES_1_002_CA_1_validation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", + "2 HOBBIES_1_003_CA_1_validation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", + "3 HOBBIES_1_004_CA_1_validation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", + "4 HOBBIES_1_005_CA_1_validation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", + "\n", + " state_id d_1 d_2 d_3 d_4 ... d_1904 d_1905 d_1906 d_1907 d_1908 \\\n", + "0 CA 0 0 0 0 ... 1 3 0 1 1 \n", + "1 CA 0 0 0 0 ... 0 0 0 0 0 \n", + "2 CA 0 0 0 0 ... 2 1 2 1 1 \n", + "3 CA 0 0 0 0 ... 1 0 5 4 1 \n", + "4 CA 0 0 0 0 ... 2 1 1 0 1 \n", + "\n", + " d_1909 d_1910 d_1911 d_1912 d_1913 \n", + "0 1 3 0 1 1 \n", + "1 1 0 0 0 0 \n", + "2 1 0 1 1 1 \n", + "3 0 1 3 7 2 \n", + "4 1 2 2 2 4 \n", + "\n", + "[5 rows x 1919 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sales_train_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To align the sales data with the other DataFrames, we convert `sales_train_df` to a long format. We collapse the daily sales columns `d_{i}` into a single `sales` column, with an additional `day` column indicating the day corresponding to each sales entry." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales\n", + "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", + "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", + "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", + "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", + "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", + " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + " sales_df_long = pd.wide_to_long(\n", + " sales_df_wide.iloc[:100, 1:],\n", + " i=index_vars,\n", + " j=\"day\",\n", + " stubnames=[\"d\"],\n", + " sep=\"_\",\n", + " ).reset_index()\n", + "\n", + " sales_df_long = sales_df_long.rename(columns={\"d\": \"sales\", \"day\": \"d\"})\n", + "\n", + " sales_df_long[\"d\"] = \"d_\" + sales_df_long[\"d\"].astype(\n", + " \"str\"\n", + " ) # restore \"d_{i}\" format for day\n", + " return sales_df_long\n", + "\n", + "\n", + "sales_train_df_long = convert_sales_data_from_wide_to_long(sales_train_df)\n", + "sales_train_df_long.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'number of sales over the entire timespan')" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " sales_train_df_long[\"sales\"],\n", + " bins=np.arange(0, 10 + 1.5) - 0.5,\n", + " range=[0, 10],\n", + " density=True,\n", + ")\n", + "plt.xticks(range(10))\n", + "plt.ylabel(\"relative frequency\")\n", + "plt.title(\"number of sales over the entire timespan\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and test data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset comprises sales data of 100 items over 1,913 days. For simplicity, we select the data from the first 365 days and discard the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_items = 100\n", + "n_days = 1913\n" + ] + } + ], + "source": [ + "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", + "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", + "\n", + "sales_train_df_long[\"day_number\"] = sales_train_df_long[\"d\"].str.extract(\"(\\d+)\").astype(int)\n", + "data = sales_train_df_long[sales_train_df_long[\"day_number\"] <= 365].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the lags of the previous 30 days and merge the sales, calendar, and price data together." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_iddept_idcat_idstore_idstate_iddsalesday_numbersales_lag_1sales_lag_2...yearevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIdaysell_price
0HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14101410.00.0...2011NaNNaNNaNNaN000183.97
1HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14201420.00.0...2011Father's dayCulturalNaNNaN000193.97
2HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14301430.00.0...2011NaNNaNNaNNaN000203.97
3HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14411440.00.0...2011NaNNaNNaNNaN000213.97
4HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14501451.00.0...2011NaNNaNNaNNaN000223.97
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5 rows × 53 columns

\n", + "
" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales \\\n", + "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", + "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", + "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", + "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", + "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", + "\n", + " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", + "0 141 0.0 0.0 ... 2011 NaN \n", + "1 142 0.0 0.0 ... 2011 Father's day \n", + "2 143 0.0 0.0 ... 2011 NaN \n", + "3 144 0.0 0.0 ... 2011 NaN \n", + "4 145 1.0 0.0 ... 2011 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", + "0 NaN NaN NaN 0 0 0 18 \n", + "1 Cultural NaN NaN 0 0 0 19 \n", + "2 NaN NaN NaN 0 0 0 20 \n", + "3 NaN NaN NaN 0 0 0 21 \n", + "4 NaN NaN NaN 0 0 0 22 \n", + "\n", + " sell_price \n", + "0 3.97 \n", + "1 3.97 \n", + "2 3.97 \n", + "3 3.97 \n", + "4 3.97 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_lags = 30\n", + "\n", + "# sort data before computing lags\n", + "data_index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + "data.sort_values(data_index_vars + [\"day_number\"], inplace=True)\n", + "\n", + "for lag in range(1, n_lags + 1):\n", + " data[f\"sales_lag_{lag}\"] = data.groupby(by=data_index_vars)[\"sales\"].shift(lag)\n", + "\n", + "data = data.merge(calendar_df).merge(sell_prices_df)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for posterior predictive checks." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15216, 50)\n", + "(3891, 50)\n" + ] + } + ], + "source": [ + "is_train = data[\"day_number\"] <= 300\n", + "data = data.drop(columns=[\"day_number\", \"day\"])\n", + "\n", + "y_name = \"sales\"\n", + "x_names = [name for name in data.columns if name != y_name]\n", + "\n", + "X_train, y_train = data[is_train][x_names], data[is_train][y_name]\n", + "X_test, y_test = data[~is_train][x_names], data[~is_train][y_name]\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The features of the combined datasets are mostly categorical. We save the column indices in `cat_idx` as they will come in handy later." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_column_names = [\n", + " \"item_id\",\n", + " \"dept_id\",\n", + " \"cat_id\",\n", + " \"store_id\",\n", + " \"state_id\",\n", + " \"d\",\n", + " \"date\",\n", + " \"wm_yr_wk\",\n", + " \"weekday\",\n", + " \"wday\",\n", + " \"month\",\n", + " \"year\",\n", + " \"event_name_1\",\n", + " \"event_type_1\",\n", + " \"event_name_2\",\n", + " \"event_type_2\",\n", + " \"snap_CA\",\n", + " \"snap_TX\",\n", + " \"snap_WI\",\n", + "]\n", + "cat_idx = [data.columns.get_loc(col) for col in cat_column_names]\n", + "cat_idx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Probabilistic predictions with Treeffuser\n", + "\n", + "We regress the sales on the following covariates." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, date, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, sell_price\n" + ] + } + ], + "source": [ + "print(\", \".join(map(str, X_train.columns)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Currently, Treeffuser supports only numpy.ndarray data with numerical values. Therefore, we convert the categorical columns into numerical labels and then convert the train and test data into numpy.ndarray." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "X_train[cat_column_names] = X_train[cat_column_names].apply(\n", + " lambda col: pd.Categorical(col).codes\n", + ")\n", + "\n", + "X_train, y_train = X_train.to_numpy(), y_train.to_numpy()\n", + "X_test, y_test = X_test.to_numpy(), y_test.to_numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to fit the data. We use `cat_idx` to tell Treeffuser which columns are categorical." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:114: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " y = _check_array(y)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", + "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" + ] + }, + { + "data": { + "text/html": [ + "
Treeffuser(extra_lightgbm_params={})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Treeffuser(extra_lightgbm_params={})" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Treeffuser()\n", + "model.fit(X_train, y_train, cat_idx=cat_idx)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Process the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity, we randomly select 10 items. This is controlled by `TOTAL_ITEMS`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "columns_sales_train_validation.head)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The strategy for processing the data is going to be the following. 1) We are going to have X and y where y is the next days sales for a given product. 3) X is made up of 10 previous prices, day of the week, + event types, cat_id, store_id, state_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def proc_train_test(\n", + " sales_train_validation_df: pd.DataFrame,\n", + " calendar_df: pd.DataFrame,\n", + " sell_prices_df: pd.DataFrame,\n", + " context_length: int,\n", + " test_days,\n", + "): # type annotation too long\n", + " \"\"\"\n", + " This function processes the data and returns the training and test data in two ways:\n", + " - undifferentiated: a list of all training and test data (X_train, y_train, X_test, y_test)\n", + " - differentiated: a list of training and test data for each product (X_train_prod, y_train_prod, X_test_prod, y_test_prod)\n", + " where X_train_prod[i] contains a list of all X_train values for the product i with similar grouping for y_train_prod and test\n", + "\n", + " This assumes from the dataframes that\n", + " - sales_train_validation_df:\n", + " - has columns with the format d_1, d_2, ...\n", + " - has columns item_id and store_id\n", + " - calendar_df:\n", + " - wday, month, event_name_1, event_name_2\n", + " - sell_prices_df:\n", + " - item_id, store_id, sell_price\n", + "\n", + " - percentage_omitted: percentage of the data to be omitted from the training data and the test data\n", + " (randomly selected)\n", + "\n", + " Returns:\n", + " - undifferentiated: Tuple of X_train, y_train, X_test, y_test\n", + " - differentiated: Tuple of X_train_prod, y_train_prod, X_test_prod, y_test_prod\n", + " \"\"\"\n", + " np.random.seed(0)\n", + " # First we need to get the training data\n", + " # We will use the first 1913 days as training data and the next\n", + "\n", + " X_train = []\n", + " y_train = []\n", + "\n", + " X_test = []\n", + " y_test = []\n", + "\n", + " # We will also return a second grouping of lists where X_train_prod[i] contains a\n", + " # a list of all X_train values for the product i with similar grouping for y_train_prod and test\n", + " X_train_prod = []\n", + " y_train_prod = []\n", + " X_test_prod = []\n", + " y_test_prod = []\n", + "\n", + " # get all days that start with d_ and look for the maximum\n", + " total_days = max(\n", + " [int(x.split(\"_\")[1]) for x in sales_train_validation_df.columns if \"d_\" in x]\n", + " )\n", + " train_days = total_days - test_days\n", + " print(\"train days\", train_days)\n", + " print(\"test days\", total_days - train_days)\n", + " print(\"total days\", total_days)\n", + "\n", + " # Precompute the required data\n", + " calendar_df_dict = calendar_df.set_index(\"d\").to_dict(orient=\"index\")\n", + " sell_prices_dict = (\n", + " sell_prices_df.groupby([\"item_id\", \"store_id\"])[\"sell_price\"].first().to_dict()\n", + " )\n", + "\n", + " pbar = tqdm(total=len(sales_train_validation_df))\n", + " for _, row in sales_train_validation_df.iterrows():\n", + " item_id = row[\"item_id\"]\n", + " store_id = row[\"store_id\"]\n", + "\n", + " X_train_prod.append([])\n", + " y_train_prod.append([])\n", + " X_test_prod.append([])\n", + " y_test_prod.append([])\n", + "\n", + " pbar.update(1)\n", + "\n", + " valid_size = int((train_days - context_length)\n", + " valid_js = np.random.choice(\n", + " range(1, train_days - context_length), valid_size, replace=False\n", + " )\n", + "\n", + " valid_js = list(valid_js) + list(\n", + " range(train_days - context_length, total_days - context_length)\n", + " )\n", + "\n", + " for j in valid_js:\n", + " x = []\n", + "\n", + " # Add sales values for the previous context_length days\n", + " x.extend(row[f\"d_{j+k}\"] for k in range(context_length))\n", + "\n", + " # Add additional features\n", + " current_day = f\"d_{j+context_length}\"\n", + " calendar_data = calendar_df_dict[current_day]\n", + " x.extend(\n", + " [\n", + " calendar_data[\"wday\"],\n", + " calendar_data[\"month\"],\n", + " store_id,\n", + " calendar_data[\"event_name_1\"],\n", + " calendar_data[\"event_name_2\"],\n", + " sell_prices_dict[(item_id, store_id)],\n", + " item_id,\n", + " j + context_length,\n", + " ]\n", + " )\n", + "\n", + " if j < train_days:\n", + " X_train.append(x)\n", + " y_train.append(row[current_day])\n", + " X_train_prod[-1].append(x)\n", + " y_train_prod[-1].append(row[current_day])\n", + "\n", + " else:\n", + " X_test.append(x)\n", + " y_test.append(row[current_day])\n", + " X_train_prod[-1].append(x)\n", + " y_train_prod[-1].append(row[current_day])\n", + "\n", + " undifferentiated = (X_train, y_train, X_test, y_test)\n", + " differentiated = (X_train_prod, y_train_prod, X_test_prod, y_test_prod)\n", + " return undifferentiated, differentiated" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if PROCESS_FROM_SCRATCH:\n", + " undifferentiated, differentiated = proc_train_test(\n", + " sales_train_validation_df_sub,\n", + " calendar_df,\n", + " sell_prices_df_sub,\n", + " CONTEXT_LENGTH,\n", + " 30,\n", + " 0.99,\n", + " )\n", + " X_train, y_train, X_test, y_test = undifferentiated\n", + " X_train_prod, y_train_prod, X_test_prod, y_test_prod = differentiated" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(X_train), len(y_train), len(X_test), len(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "COL_NAMES = [f\"day_{i}\" for i in range(1, CONTEXT_LENGTH + 1)] + [\n", + " \"wday\",\n", + " \"month\",\n", + " \"store_id\",\n", + " \"event_name_1\",\n", + " \"event_name_2\",\n", + " \"sell_price\",\n", + " \"item_id\",\n", + " \"day\",\n", + "]\n", + "\n", + "CAT_COLS = [\"store_id\", \"event_name_1\", \"event_name_2\", \"item_id\", \"wday\", \"month\"]\n", + "CAT_COLS_IDX = [COL_NAMES.index(col) for col in CAT_COLS]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_df = pd.DataFrame(X_train)\n", + "X_test_df = pd.DataFrame(X_test)\n", + "y_test_df = pd.DataFrame(y_test)\n", + "y_train_df = pd.DataFrame(y_train)\n", + "\n", + "X_train_df.columns = COL_NAMES\n", + "X_test_df.columns = COL_NAMES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Encode the categorical columns as numbers\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "# Get only label of item_id\n", + "X_train_df[\"item_id\"] = X_train_df[\"item_id\"].apply(lambda x: x.split(\"_\")[1])\n", + "X_test_df[\"item_id\"] = X_test_df[\"item_id\"].apply(lambda x: x.split(\"_\")[1])\n", + "\n", + "\n", + "label_encoders = {}\n", + "for col in CAT_COLS:\n", + " le = LabelEncoder()\n", + " X_train_df[col] = le.fit_transform(X_train_df[col])\n", + " X_test_df[col] = le.transform(X_test_df[col])\n", + " label_encoders[col] = le\n", + "\n", + "\n", + "X_train_prod_processed = []\n", + "X_test_prod_processed = []\n", + "for i in range(len(X_train_prod)):\n", + " X_train_prod_processed.append(pd.DataFrame(X_train_prod[i], columns=COL_NAMES))\n", + " X_test_prod_processed.append(pd.DataFrame(X_test_prod[i], columns=COL_NAMES))\n", + " X_train_prod_processed[-1][\"item_id\"] = X_train_prod_processed[-1][\"item_id\"].apply(\n", + " lambda x: x.split(\"_\")[1]\n", + " )\n", + " X_test_prod_processed[-1][\"item_id\"] = X_test_prod_processed[-1][\"item_id\"].apply(\n", + " lambda x: x.split(\"_\")[1]\n", + " )\n", + " for col in CAT_COLS:\n", + " X_train_prod_processed[-1][col] = label_encoders[col].transform(\n", + " X_train_prod_processed[-1][col]\n", + " )\n", + " X_test_prod_processed[-1][col] = label_encoders[col].transform(\n", + " X_test_prod_processed[-1][col]\n", + " )\n", + "\n", + "X_train_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PPC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Standard PPCs\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def max_ppc(\n", + " y_true: Float[Array, \"batch y_dim\"],\n", + " y_samples: Float[Array, \"samples batch y_dim\"],\n", + " number=0,\n", + " name=\"\",\n", + ") -> None:\n", + " # rpeat y_true to match the shape of y_samples\n", + " max_ppc = np.max(y_samples, axis=1)\n", + " true_max = np.max(y_true)\n", + "\n", + " return max_ppc.flatten(), true_max.flatten(), \"max_ppc\"\n", + "\n", + "\n", + "def quantile_ppc(\n", + " y_true: Float[Array, \"batch y_dim\"],\n", + " y_samples: Float[Array, \"samples batch y_dim\"],\n", + " quantile=0.5,\n", + " number=0,\n", + " name=\"\",\n", + ") -> None:\n", + " # rpeat y_true to match the shape of y_samples\n", + " q = np.quantile(y_samples, quantile, axis=1)\n", + " true_q = np.quantile(y_true, quantile)\n", + " return q.flatten(), true_q.flatten(), f\"quantile_ppc_{quantile}\"\n", + "\n", + "\n", + "def zeros(\n", + " y_true: Float[Array, \"batch y_dim\"],\n", + " y_samples: Float[Array, \"samples batch y_dim\"],\n", + " number=0,\n", + " name=\"\",\n", + ") -> None:\n", + " \"Count the number of zeros in the samples\"\n", + " zeros = np.sum(y_samples < 0.1, axis=1)\n", + " true_zeros = np.sum(y_true < 0.1)\n", + "\n", + " return zeros.flatten(), true_zeros.flatten(), \"zeros\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_ppcs(\n", + " y_true: Float[Array, \"batch y_dim\"],\n", + " y_samples: Float[Array, \"samples batch y_dim\"],\n", + " ppcs: List[Callable],\n", + " number=0,\n", + " name=\"\",\n", + ") -> None:\n", + " # plot the distribution of\n", + "\n", + " for ppc in ppcs:\n", + " ppc(y_true, y_samples, number=number, name=name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Complex PPCs\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_model_comparisons(data, y_true, figsize=(12, 8), model_names=None):\n", + " \"\"\"\n", + " Plots model predictions against true values for each day.\n", + "\n", + " :param data: numpy array of shape [models, samples, days] containing model predictions\n", + " :param y_true: array of shape [days] containing the true values\n", + " :param figsize: tuple indicating the size of the figure\n", + " \"\"\"\n", + " sns.set(style=\"whitegrid\")\n", + " models, samples, days = data.shape\n", + "\n", + " # Create a figure and axis object\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + "\n", + " # We will transform the data to a format suitable for seaborn\n", + " # Create a DataFrame with model, day, and sample values\n", + " plot_data = []\n", + " if model_names is None:\n", + " model_names = [f\"Model {i}\" for i in range(models)]\n", + "\n", + " for model_idx in range(models):\n", + " for day_idx in range(days):\n", + " for sample_idx in range(samples):\n", + " plot_data.append(\n", + " {\n", + " \"Day\": day_idx,\n", + " \"Value\": data[model_idx, sample_idx, day_idx],\n", + " \"Model\": model_names[model_idx],\n", + " }\n", + " )\n", + "\n", + " import pandas as pd\n", + "\n", + " plot_data = pd.DataFrame(plot_data)\n", + "\n", + " # Use seaborn to plot the boxplots\n", + " sns.boxplot(x=\"Day\", y=\"Value\", hue=\"Model\", data=plot_data, ax=ax, width=0.6)\n", + "\n", + " # Plot true values\n", + " plt.plot(y_true, \"o\", color=\"red\", label=\"True Values\")\n", + "\n", + " # Setting labels and title\n", + " plt.xticks(ticks=np.arange(days), labels=[f\"Day {i+1}\" for i in range(days)])\n", + " plt.xlabel(\"Days\")\n", + " plt.ylabel(\"Values\")\n", + " plt.title(\"Model Predictions vs. True Values\")\n", + " plt.legend()\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def save_results_to_pkl(results: dict, dir_name, name):\n", + " if not Path(dir_name).exists():\n", + " Path(dir_name).mkdir(parents=True)\n", + "\n", + " path = Path(dir_name) / f\"{name}.pkl\"\n", + " with open(path, \"wb\") as f:\n", + " pkl.dump(results, f)\n", + "\n", + "\n", + "def load_results_from_pkl(dir_name, name):\n", + " path = Path(dir_name) / f\"{name}.pkl\"\n", + " with open(path, \"rb\") as f:\n", + " results = pkl.load(f)\n", + " return results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simple helper function to train a model and plot ppcs\n", + "\n", + "\n", + "def get_ppcs(y_samples, X_test, y_test, ppcs, number=0, name=\"\") -> None:\n", + " \"\"\"\n", + " Returns a dictionary with the samples and the true values for each ppc\n", + " the dictionary a\n", + " \"\"\"\n", + " y_samples = np.array(y_samples)\n", + " y_samples = np.maximum(y_samples, 0)\n", + " y_samples = np.round(y_samples, 0)\n", + "\n", + " ppc_results = {}\n", + " for ppc in ppcs:\n", + " samples, true, name = ppc(y_test, y_samples, number=number, name=name)\n", + " ppc_results[name] = {\"samples\": samples, \"true\": true}\n", + "\n", + " return ppc_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(X_train_df.head())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ab5d2dc11d9c69f7f71f77530143648183ee7097 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 9 Oct 2024 17:20:16 -0400 Subject: [PATCH 04/44] add notebook version of README example --- examples/README_example.ipynb | 162 ++++++++++++++++++++++++++++++++++ 1 file changed, 162 insertions(+) create mode 100644 examples/README_example.ipynb diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb new file mode 100644 index 00000000..742b95c2 --- /dev/null +++ b/examples/README_example.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures patterns in the data.\n", + "\n", + "We first generate the data, then fit the model and plot the samples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from treeffuser import Treeffuser" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We simulate a non-linear, bimodal response of $y$ given $x$, where the two modes follow two different response functions: one is a sine function and the other is a cosine function over $x$." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 0 # fixing the random seed for reproducibility\n", + "n = 5000 # number of data points\n", + "\n", + "rng = np.random.default_rng(seed=seed)\n", + "x = rng.uniform(0, 2 * np.pi, size=n) # x values in the range [0, 2Ï€)\n", + "z = rng.integers(0, 2, size=n) # response function assignments\n", + "\n", + "y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also introduce heteroscedastic, fat-tailed noise from a Laplace distribution, meaning the variability of $y$ increases with $x$ and results in occasional large outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "y += rng.laplace(scale=x / 30, size=n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Treffuser and producing samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting Treeffuser and generating samples takes just three lines of code. Treeffuser adheres to the `sklearn.base.BaseEstimator` class, so fitting amounts to initializing the model and calling the `fit` method, just like any `scikit-learn` estimator. Samples are then generated using the `sample` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = Treeffuser(sde_initialize_from_data=True, seed=seed)\n", + "model.fit(x, y)\n", + "\n", + "y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a scatter plot to visualize both the original data and the samples produced by Treeffuser. The samples closely reflect the underlying response distributions that generated the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x, y, s=1, label=\"observed data\")\n", + "plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label=\"Treeffuser samples\")\n", + "\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$y$\")\n", + "\n", + "legend = plt.legend(loc=\"upper center\", scatterpoints=1, bbox_to_anchor=(0.5, -0.125), ncol=2)\n", + "for legend_handle in legend.legend_handles:\n", + " legend_handle.set_sizes([32]) # change marker size for legend\n", + "\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f3f20f1b7a16d3dcc9af4222488b4c2ba84a3292 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 00:25:56 -0400 Subject: [PATCH 05/44] add __str__ method for printing --- src/treeffuser/samples.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/treeffuser/samples.py b/src/treeffuser/samples.py index 39d6dbed..216f0c48 100644 --- a/src/treeffuser/samples.py +++ b/src/treeffuser/samples.py @@ -320,6 +320,9 @@ def to_numpy(self) -> Float[np.ndarray, "n_samples batch y_dim"]: """ return self._samples + def __str__(self) -> str: + return str(self._samples) + def __getitem__(self, key): """ Prevent the user from removing the first or second dimension of the samples. From 307b8650ab00831c7490778ed1108541d7ab715f Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 00:26:38 -0400 Subject: [PATCH 06/44] add sample statistics --- examples/README_example.ipynb | 76 ++++++++++++++++++++++++++++++++--- 1 file changed, 71 insertions(+), 5 deletions(-) diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb index 742b95c2..c29e4dce 100644 --- a/examples/README_example.ipynb +++ b/examples/README_example.ipynb @@ -4,9 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures patterns in the data.\n", - "\n", - "We first generate the data, then fit the model and plot the samples." + "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures the underlying distribution of the data." ] }, { @@ -55,7 +53,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We also introduce heteroscedastic, fat-tailed noise from a Laplace distribution, meaning the variability of $y$ increases with $x$ and results in occasional large outliers." + "We also introduce heteroscedastic, fat-tailed noise from a Laplace distribution, meaning the variability of $y$ increases with $x$ and may result in large outliers." ] }, { @@ -78,7 +76,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Fitting Treeffuser and generating samples takes just three lines of code. Treeffuser adheres to the `sklearn.base.BaseEstimator` class, so fitting amounts to initializing the model and calling the `fit` method, just like any `scikit-learn` estimator. Samples are then generated using the `sample` method." + "Fitting Treeffuser and generating samples is very simple, as Treeffuser adheres to the `sklearn.base.BaseEstimator` class. Fitting amounts to initializing the model and calling the `fit` method, just like any `scikit-learn` estimator. Samples are then generated using the `sample` method." ] }, { @@ -136,6 +134,74 @@ "\n", "plt.tight_layout()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The samples generated by Treeffuser can be used to compute any downstream estimates of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the samples: [-0.08244012]\n", + "Standard deviation of the samples: [0.96887841] \n" + ] + } + ], + "source": [ + "x = np.array(np.pi).reshape((1, 1))\n", + "y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100\n", + "\n", + "# Estimate downstream quantities of interest\n", + "y_mean = y_samples.mean(axis=0) # conditional mean for each x\n", + "y_std = y_samples.std(axis=0) # conditional std for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we also provide a class Samples that can estimate standard quantities." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the samples: [-0.08244012]\n", + "Standard deviation of the samples: [0.96887841] \n", + "5th and 95th quantiles of the samples: [-1.23079225 1.03337686]\n" + ] + } + ], + "source": [ + "from treeffuser.samples import Samples\n", + "\n", + "y_samples = Samples(y_samples)\n", + "y_mean = y_samples.sample_mean() # same as before\n", + "y_std = y_samples.sample_std() # same as before\n", + "y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95]) # conditional quantiles for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")\n", + "print(f\"5th and 95th quantiles of the samples: {y_quantiles.reshape(-1)}\")" + ] } ], "metadata": { From ebdd4d9d827f7c9496dc372354536f03bede3cb7 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:01:21 -0400 Subject: [PATCH 07/44] delete residual code on ppcs --- examples/m5-news-vendor-notebook.ipynb | 543 ++----------------------- 1 file changed, 23 insertions(+), 520 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index 40094ac8..522bfde4 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -2,31 +2,20 @@ "cells": [ { "cell_type": "code", - "execution_count": 59, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", - "from functools import partial\n", "from pathlib import Path\n", "\n", "from tqdm import tqdm\n", "from treeffuser import Treeffuser\n", "\n", - "path = \"../testbed/src/testbed/data/m5/\"\n", "# load autoreload extension\n", "%load_ext autoreload\n", "%autoreload 2" @@ -65,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -172,7 +161,7 @@ "4 CA_1 HOBBIES_1_001 11329 8.26" ] }, - "execution_count": 61, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -190,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -342,7 +331,7 @@ "4 NaN NaN NaN 1 0 1 2 " ] }, - "execution_count": 62, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -360,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -558,7 +547,7 @@ "[5 rows x 1919 columns]" ] }, - "execution_count": 63, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -576,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -673,7 +662,7 @@ "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" ] }, - "execution_count": 64, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -703,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -712,7 +701,7 @@ "Text(0.5, 1.0, 'number of sales over the entire timespan')" ] }, - "execution_count": 65, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -755,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -784,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -989,7 +978,7 @@ "[5 rows x 53 columns]" ] }, - "execution_count": 67, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1018,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1053,7 +1042,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1062,7 +1051,7 @@ "[0, 1, 2, 3, 4, 5, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]" ] }, - "execution_count": 69, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1104,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1128,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1149,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1585,7 +1574,7 @@ "Treeffuser(extra_lightgbm_params={})" ] }, - "execution_count": 72, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1594,492 +1583,6 @@ "model = Treeffuser()\n", "model.fit(X_train, y_train, cat_idx=cat_idx)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Process the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For simplicity, we randomly select 10 items. This is controlled by `TOTAL_ITEMS`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "columns_sales_train_validation.head)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The strategy for processing the data is going to be the following. 1) We are going to have X and y where y is the next days sales for a given product. 3) X is made up of 10 previous prices, day of the week, + event types, cat_id, store_id, state_id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def proc_train_test(\n", - " sales_train_validation_df: pd.DataFrame,\n", - " calendar_df: pd.DataFrame,\n", - " sell_prices_df: pd.DataFrame,\n", - " context_length: int,\n", - " test_days,\n", - "): # type annotation too long\n", - " \"\"\"\n", - " This function processes the data and returns the training and test data in two ways:\n", - " - undifferentiated: a list of all training and test data (X_train, y_train, X_test, y_test)\n", - " - differentiated: a list of training and test data for each product (X_train_prod, y_train_prod, X_test_prod, y_test_prod)\n", - " where X_train_prod[i] contains a list of all X_train values for the product i with similar grouping for y_train_prod and test\n", - "\n", - " This assumes from the dataframes that\n", - " - sales_train_validation_df:\n", - " - has columns with the format d_1, d_2, ...\n", - " - has columns item_id and store_id\n", - " - calendar_df:\n", - " - wday, month, event_name_1, event_name_2\n", - " - sell_prices_df:\n", - " - item_id, store_id, sell_price\n", - "\n", - " - percentage_omitted: percentage of the data to be omitted from the training data and the test data\n", - " (randomly selected)\n", - "\n", - " Returns:\n", - " - undifferentiated: Tuple of X_train, y_train, X_test, y_test\n", - " - differentiated: Tuple of X_train_prod, y_train_prod, X_test_prod, y_test_prod\n", - " \"\"\"\n", - " np.random.seed(0)\n", - " # First we need to get the training data\n", - " # We will use the first 1913 days as training data and the next\n", - "\n", - " X_train = []\n", - " y_train = []\n", - "\n", - " X_test = []\n", - " y_test = []\n", - "\n", - " # We will also return a second grouping of lists where X_train_prod[i] contains a\n", - " # a list of all X_train values for the product i with similar grouping for y_train_prod and test\n", - " X_train_prod = []\n", - " y_train_prod = []\n", - " X_test_prod = []\n", - " y_test_prod = []\n", - "\n", - " # get all days that start with d_ and look for the maximum\n", - " total_days = max(\n", - " [int(x.split(\"_\")[1]) for x in sales_train_validation_df.columns if \"d_\" in x]\n", - " )\n", - " train_days = total_days - test_days\n", - " print(\"train days\", train_days)\n", - " print(\"test days\", total_days - train_days)\n", - " print(\"total days\", total_days)\n", - "\n", - " # Precompute the required data\n", - " calendar_df_dict = calendar_df.set_index(\"d\").to_dict(orient=\"index\")\n", - " sell_prices_dict = (\n", - " sell_prices_df.groupby([\"item_id\", \"store_id\"])[\"sell_price\"].first().to_dict()\n", - " )\n", - "\n", - " pbar = tqdm(total=len(sales_train_validation_df))\n", - " for _, row in sales_train_validation_df.iterrows():\n", - " item_id = row[\"item_id\"]\n", - " store_id = row[\"store_id\"]\n", - "\n", - " X_train_prod.append([])\n", - " y_train_prod.append([])\n", - " X_test_prod.append([])\n", - " y_test_prod.append([])\n", - "\n", - " pbar.update(1)\n", - "\n", - " valid_size = int((train_days - context_length)\n", - " valid_js = np.random.choice(\n", - " range(1, train_days - context_length), valid_size, replace=False\n", - " )\n", - "\n", - " valid_js = list(valid_js) + list(\n", - " range(train_days - context_length, total_days - context_length)\n", - " )\n", - "\n", - " for j in valid_js:\n", - " x = []\n", - "\n", - " # Add sales values for the previous context_length days\n", - " x.extend(row[f\"d_{j+k}\"] for k in range(context_length))\n", - "\n", - " # Add additional features\n", - " current_day = f\"d_{j+context_length}\"\n", - " calendar_data = calendar_df_dict[current_day]\n", - " x.extend(\n", - " [\n", - " calendar_data[\"wday\"],\n", - " calendar_data[\"month\"],\n", - " store_id,\n", - " calendar_data[\"event_name_1\"],\n", - " calendar_data[\"event_name_2\"],\n", - " sell_prices_dict[(item_id, store_id)],\n", - " item_id,\n", - " j + context_length,\n", - " ]\n", - " )\n", - "\n", - " if j < train_days:\n", - " X_train.append(x)\n", - " y_train.append(row[current_day])\n", - " X_train_prod[-1].append(x)\n", - " y_train_prod[-1].append(row[current_day])\n", - "\n", - " else:\n", - " X_test.append(x)\n", - " y_test.append(row[current_day])\n", - " X_train_prod[-1].append(x)\n", - " y_train_prod[-1].append(row[current_day])\n", - "\n", - " undifferentiated = (X_train, y_train, X_test, y_test)\n", - " differentiated = (X_train_prod, y_train_prod, X_test_prod, y_test_prod)\n", - " return undifferentiated, differentiated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if PROCESS_FROM_SCRATCH:\n", - " undifferentiated, differentiated = proc_train_test(\n", - " sales_train_validation_df_sub,\n", - " calendar_df,\n", - " sell_prices_df_sub,\n", - " CONTEXT_LENGTH,\n", - " 30,\n", - " 0.99,\n", - " )\n", - " X_train, y_train, X_test, y_test = undifferentiated\n", - " X_train_prod, y_train_prod, X_test_prod, y_test_prod = differentiated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(X_train), len(y_train), len(X_test), len(y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "COL_NAMES = [f\"day_{i}\" for i in range(1, CONTEXT_LENGTH + 1)] + [\n", - " \"wday\",\n", - " \"month\",\n", - " \"store_id\",\n", - " \"event_name_1\",\n", - " \"event_name_2\",\n", - " \"sell_price\",\n", - " \"item_id\",\n", - " \"day\",\n", - "]\n", - "\n", - "CAT_COLS = [\"store_id\", \"event_name_1\", \"event_name_2\", \"item_id\", \"wday\", \"month\"]\n", - "CAT_COLS_IDX = [COL_NAMES.index(col) for col in CAT_COLS]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_df = pd.DataFrame(X_train)\n", - "X_test_df = pd.DataFrame(X_test)\n", - "y_test_df = pd.DataFrame(y_test)\n", - "y_train_df = pd.DataFrame(y_train)\n", - "\n", - "X_train_df.columns = COL_NAMES\n", - "X_test_df.columns = COL_NAMES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Encode the categorical columns as numbers\n", - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "# Get only label of item_id\n", - "X_train_df[\"item_id\"] = X_train_df[\"item_id\"].apply(lambda x: x.split(\"_\")[1])\n", - "X_test_df[\"item_id\"] = X_test_df[\"item_id\"].apply(lambda x: x.split(\"_\")[1])\n", - "\n", - "\n", - "label_encoders = {}\n", - "for col in CAT_COLS:\n", - " le = LabelEncoder()\n", - " X_train_df[col] = le.fit_transform(X_train_df[col])\n", - " X_test_df[col] = le.transform(X_test_df[col])\n", - " label_encoders[col] = le\n", - "\n", - "\n", - "X_train_prod_processed = []\n", - "X_test_prod_processed = []\n", - "for i in range(len(X_train_prod)):\n", - " X_train_prod_processed.append(pd.DataFrame(X_train_prod[i], columns=COL_NAMES))\n", - " X_test_prod_processed.append(pd.DataFrame(X_test_prod[i], columns=COL_NAMES))\n", - " X_train_prod_processed[-1][\"item_id\"] = X_train_prod_processed[-1][\"item_id\"].apply(\n", - " lambda x: x.split(\"_\")[1]\n", - " )\n", - " X_test_prod_processed[-1][\"item_id\"] = X_test_prod_processed[-1][\"item_id\"].apply(\n", - " lambda x: x.split(\"_\")[1]\n", - " )\n", - " for col in CAT_COLS:\n", - " X_train_prod_processed[-1][col] = label_encoders[col].transform(\n", - " X_train_prod_processed[-1][col]\n", - " )\n", - " X_test_prod_processed[-1][col] = label_encoders[col].transform(\n", - " X_test_prod_processed[-1][col]\n", - " )\n", - "\n", - "X_train_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PPC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"Standard PPCs\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def max_ppc(\n", - " y_true: Float[Array, \"batch y_dim\"],\n", - " y_samples: Float[Array, \"samples batch y_dim\"],\n", - " number=0,\n", - " name=\"\",\n", - ") -> None:\n", - " # rpeat y_true to match the shape of y_samples\n", - " max_ppc = np.max(y_samples, axis=1)\n", - " true_max = np.max(y_true)\n", - "\n", - " return max_ppc.flatten(), true_max.flatten(), \"max_ppc\"\n", - "\n", - "\n", - "def quantile_ppc(\n", - " y_true: Float[Array, \"batch y_dim\"],\n", - " y_samples: Float[Array, \"samples batch y_dim\"],\n", - " quantile=0.5,\n", - " number=0,\n", - " name=\"\",\n", - ") -> None:\n", - " # rpeat y_true to match the shape of y_samples\n", - " q = np.quantile(y_samples, quantile, axis=1)\n", - " true_q = np.quantile(y_true, quantile)\n", - " return q.flatten(), true_q.flatten(), f\"quantile_ppc_{quantile}\"\n", - "\n", - "\n", - "def zeros(\n", - " y_true: Float[Array, \"batch y_dim\"],\n", - " y_samples: Float[Array, \"samples batch y_dim\"],\n", - " number=0,\n", - " name=\"\",\n", - ") -> None:\n", - " \"Count the number of zeros in the samples\"\n", - " zeros = np.sum(y_samples < 0.1, axis=1)\n", - " true_zeros = np.sum(y_true < 0.1)\n", - "\n", - " return zeros.flatten(), true_zeros.flatten(), \"zeros\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_ppcs(\n", - " y_true: Float[Array, \"batch y_dim\"],\n", - " y_samples: Float[Array, \"samples batch y_dim\"],\n", - " ppcs: List[Callable],\n", - " number=0,\n", - " name=\"\",\n", - ") -> None:\n", - " # plot the distribution of\n", - "\n", - " for ppc in ppcs:\n", - " ppc(y_true, y_samples, number=number, name=name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"Complex PPCs\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_model_comparisons(data, y_true, figsize=(12, 8), model_names=None):\n", - " \"\"\"\n", - " Plots model predictions against true values for each day.\n", - "\n", - " :param data: numpy array of shape [models, samples, days] containing model predictions\n", - " :param y_true: array of shape [days] containing the true values\n", - " :param figsize: tuple indicating the size of the figure\n", - " \"\"\"\n", - " sns.set(style=\"whitegrid\")\n", - " models, samples, days = data.shape\n", - "\n", - " # Create a figure and axis object\n", - " fig, ax = plt.subplots(figsize=figsize)\n", - "\n", - " # We will transform the data to a format suitable for seaborn\n", - " # Create a DataFrame with model, day, and sample values\n", - " plot_data = []\n", - " if model_names is None:\n", - " model_names = [f\"Model {i}\" for i in range(models)]\n", - "\n", - " for model_idx in range(models):\n", - " for day_idx in range(days):\n", - " for sample_idx in range(samples):\n", - " plot_data.append(\n", - " {\n", - " \"Day\": day_idx,\n", - " \"Value\": data[model_idx, sample_idx, day_idx],\n", - " \"Model\": model_names[model_idx],\n", - " }\n", - " )\n", - "\n", - " import pandas as pd\n", - "\n", - " plot_data = pd.DataFrame(plot_data)\n", - "\n", - " # Use seaborn to plot the boxplots\n", - " sns.boxplot(x=\"Day\", y=\"Value\", hue=\"Model\", data=plot_data, ax=ax, width=0.6)\n", - "\n", - " # Plot true values\n", - " plt.plot(y_true, \"o\", color=\"red\", label=\"True Values\")\n", - "\n", - " # Setting labels and title\n", - " plt.xticks(ticks=np.arange(days), labels=[f\"Day {i+1}\" for i in range(days)])\n", - " plt.xlabel(\"Days\")\n", - " plt.ylabel(\"Values\")\n", - " plt.title(\"Model Predictions vs. True Values\")\n", - " plt.legend()\n", - "\n", - " # Show the plot\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def save_results_to_pkl(results: dict, dir_name, name):\n", - " if not Path(dir_name).exists():\n", - " Path(dir_name).mkdir(parents=True)\n", - "\n", - " path = Path(dir_name) / f\"{name}.pkl\"\n", - " with open(path, \"wb\") as f:\n", - " pkl.dump(results, f)\n", - "\n", - "\n", - "def load_results_from_pkl(dir_name, name):\n", - " path = Path(dir_name) / f\"{name}.pkl\"\n", - " with open(path, \"rb\") as f:\n", - " results = pkl.load(f)\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simple helper function to train a model and plot ppcs\n", - "\n", - "\n", - "def get_ppcs(y_samples, X_test, y_test, ppcs, number=0, name=\"\") -> None:\n", - " \"\"\"\n", - " Returns a dictionary with the samples and the true values for each ppc\n", - " the dictionary a\n", - " \"\"\"\n", - " y_samples = np.array(y_samples)\n", - " y_samples = np.maximum(y_samples, 0)\n", - " y_samples = np.round(y_samples, 0)\n", - "\n", - " ppc_results = {}\n", - " for ppc in ppcs:\n", - " samples, true, name = ppc(y_test, y_samples, number=number, name=name)\n", - " ppc_results[name] = {\"samples\": samples, \"true\": true}\n", - "\n", - " return ppc_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(X_train_df.head())" - ] } ], "metadata": { From 638fff0b49c73bebe0252033504d1cd00a637969 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:03:02 -0400 Subject: [PATCH 08/44] update .gitignore with m5 data files for tutorial --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6340d65c..e40f04d2 100644 --- a/.gitignore +++ b/.gitignore @@ -77,6 +77,7 @@ docs/_build # Data files **/raw/** **/data.npy +examples/m5/* # Debug files From 7978870c8f3faf1ea7e0d990814673acc1aea36e Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:24:02 -0400 Subject: [PATCH 09/44] add instructions to upload data on colab --- examples/m5-news-vendor-notebook.ipynb | 64 ++++++++++++++++++++++++-- 1 file changed, 59 insertions(+), 5 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index 522bfde4..ed881700 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -1,5 +1,58 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get started, we first install `treeffuser` and import the relevant libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: treeffuser in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (0.1.3)\n", + "Requirement already satisfied: einops<0.9.0,>=0.8.0 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (0.8.0)\n", + "Requirement already satisfied: jaxtyping<0.3.0,>=0.2.19 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (0.2.34)\n", + "Requirement already satisfied: lightgbm<5.0.0,>=4.3.0 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (4.3.0)\n", + "Requirement already satisfied: ml-collections<0.2.0,>=0.1.1 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (0.1.1)\n", + "Requirement already satisfied: numpy<2.0,>=1.24 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (1.26.4)\n", + "Requirement already satisfied: scikit-learn<2.0.0,>=1.5.0 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (1.5.0)\n", + "Requirement already satisfied: scipy<2.0.0,>=1.13.1 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (1.13.1)\n", + "Requirement already satisfied: tqdm<5.0.0,>=4.66.4 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from treeffuser) (4.66.4)\n", + "Requirement already satisfied: typeguard==2.13.3 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from jaxtyping<0.3.0,>=0.2.19->treeffuser) (2.13.3)\n", + "Requirement already satisfied: absl-py in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from ml-collections<0.2.0,>=0.1.1->treeffuser) (2.1.0)\n", + "Requirement already satisfied: PyYAML in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from ml-collections<0.2.0,>=0.1.1->treeffuser) (6.0.1)\n", + "Requirement already satisfied: six in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from ml-collections<0.2.0,>=0.1.1->treeffuser) (1.16.0)\n", + "Requirement already satisfied: contextlib2 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from ml-collections<0.2.0,>=0.1.1->treeffuser) (21.6.0)\n", + "Requirement already satisfied: joblib>=1.2.0 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from scikit-learn<2.0.0,>=1.5.0->treeffuser) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/agrande/Desktop/treeffuser/.venv/lib/python3.9/site-packages (from scikit-learn<2.0.0,>=1.5.0->treeffuser) (3.5.0)\n" + ] + } + ], + "source": [ + "!pip install treeffuser" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -9,7 +62,6 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import seaborn as sns\n", "\n", "from pathlib import Path\n", "\n", @@ -25,11 +77,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Getting started\n", + "Next, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", "\n", - "To get started, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", + "If you're running this notebook in Colab, manually upload the necessary files (`calendar.csv`, `sales_train_validation.csv`, `sell_prices.csv`) to Colab by clicking the `Files` tab on the left sidebar and selecting `Upload`. Move the files into a new folder named `m5`. Once uploaded, the notebook will be able to read and process the data.\n", "\n", - "You can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", + "If you're running this on your local machine, you can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", "\n", "```bash\n", "cd ./m5 # path to folder where you want to save the data\n", @@ -77,6 +129,8 @@ "metadata": {}, "source": [ "# The data\n", + "\n", + "## Preprocessing\n", "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." ] }, @@ -732,7 +786,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Train and test data" + "## Train and test sets" ] }, { From bb52ab16b5bdc7e9f2b1d91957e1b30b63064491 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:35:46 -0400 Subject: [PATCH 10/44] make titles consistent across notebooks --- examples/README_example.ipynb | 2 ++ examples/m5-news-vendor-notebook.ipynb | 12 +++++++----- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb index c29e4dce..142eaeba 100644 --- a/examples/README_example.ipynb +++ b/examples/README_example.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "# Quick start: Forecasting with synthetic data\n", + "\n", "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures the underlying distribution of the data." ] }, diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index ed881700..8370269f 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "# Forecasting Walmart sales with Treeffuser\n", + "\n", "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle." ] }, @@ -11,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Getting started\n" + "## Getting started\n" ] }, { @@ -128,9 +130,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# The data\n", + "## The data\n", "\n", - "## Preprocessing\n", + "### Preprocessing\n", "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." ] }, @@ -786,7 +788,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Train and test sets" + "### Train and test sets" ] }, { @@ -1140,7 +1142,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Probabilistic predictions with Treeffuser\n", + "## Probabilistic predictions with Treeffuser\n", "\n", "We regress the sales on the following covariates." ] From eae887e5c8c0af89ad371f2acb91acc58ab4cd4b Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:26:31 -0400 Subject: [PATCH 11/44] add pip install treeffuser and clear outputs --- examples/README_example.ipynb | 59 +- examples/m5-news-vendor-notebook.ipynb | 1308 +----------------------- 2 files changed, 43 insertions(+), 1324 deletions(-) diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb index 142eaeba..05fcf404 100644 --- a/examples/README_example.ipynb +++ b/examples/README_example.ipynb @@ -16,12 +16,21 @@ "## Getting started" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first install `treeffuser` and import the relevant libraries." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "!pip install treeffuser\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", @@ -109,20 +118,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RbtD5zQb1x9q8SmcSwS4oWe8ueL42r5LY+i/45dhSjsmjWCLuJkMucn/Ob2aev+xCZ2Zd1uJs8h/PorC62X9G4DnWju0t0/BCFG+/0FyObT6vXIqSc2fR/1Z9WubOrh4IniW//L0/fvtwZCLogkBn8DqXVx9Jmk9O7JNkbNB4nW96fAS3aXYzUtMGiTf2NOiAnMZkVnZ+n6qI67zut6utwJApHaZuv6qoqFz5lKxnXkApWCEsdSGvfNc7dcHvKrtiO+S/AsD/RYQyzFwCISPdg7prcrEWrSPB8BU/Dg3AZu5gdKiBjBdz3UZfj62kAcQf9ZrwcBbbnp7eBM/z+noZLrctzMuxzeeVc9VRPBvOcts9UyzmO3IBVAiDEhBem1dJUtseore9TG79crcXe5ajkAcjiqDCoXyu+B3FizZqKoyd5W5ff/F2OQXVbDlUS5swB4OsYUEv17U2r5Ka9kkUaKaTn9TTK3xWW8YXCNWoU1FRufJJXUYY8O3UZXw7qa9cVA9K1sPJvQAkilqgC7Th7klpenwEda0mjsUshKhRhEYmQlMlfz+USI1Z8Uw8evP4nsbZACbGXg2Ws5jIPXXogvVatxHn2poaaBLHkIjdUrm0nEX/e/Tm8URv+zM3aA8r/WiQpb6it73MDdoSPi+Cmq77AMifWAGV+xTpHQBTMxgiyI26ky2HjDxY/xYJrvZCryW+XDFxX5DGt27Khl5iavvLKr8o5QEHiGrUqaioXNYMKK4lab4SB7ehkhWZ1QNPBmgzKv8fGgMnCyDtbvefXZpwOY3JrLj3fvfv80u2ATagF+PMZ2L0bf/5jtNR5FHKaDXZaDHZ3NtWnRY74QYd0+Mj3J7F3qpe+HoWv/FJCioDNvazZ8VD1ENKdZUjSaQX9PH++Sx4XJ+lZL13fdYoPwuj1GVsWVfMf1neIMLY6a216OzDWYmQ9Xh3/KGnMdZXP/aU/fH3rgwlr7EaU6eionJZM1BF+qId61jZsZqiHet6/M1v7FvSfPjhZvjhZnLiniTD/DI5gXe5Y+KeT6n1G9P16M3jCTfo3Of1h+f3+bbf8+de1fIHofqfPSueYL0Wme4svbV5lbSYbATrtb3H77nwE7s1kHs+UKV/lcuUwcTXJc1nTUM6GaY8v++f53E9qjYkzYfUZWTZdpJ/h0MxnpzH5R49zdZ3XyVXNxeS5vNgdBERYhcERvQw+HrEH1ZsJ7v2BfJn7Se79oUBvUuDqn5xiVCNOhUVlcua3gLmXUZF7uYc2PhjHg35lExtCXcG7h1Y8LWLiu1Eb3uIpLY9yt9L1mP5agvjC5/j+ZRa9wo9d3MOW3+1hNj6LwjWa728Yp5GmEvbyrUl2mmxsyjwMG9HvAkV23k+pZbXg97g+ZRad7uKdqzzNuIGGbDuukfP3TbZneTRW9mwHviZaAeaXDLUJ0CVc8DDUHL1/dzNOb0e/mB0EZnaEh6MLur1mF7ppb/7lv46HruQfPEa9qf8wsurnLFBoxh+Jet7vkPF7/h/lyq2U/XG91j5wu/dY0VvZfiGEur2q4qKymVNb1sfLqMi7MBaECuIiU6CKQv455Ekatq9Y8n6iompyn2LGVIxFq3EM5YZ5OrmMt7+GSFyh3Ju205IXYa1aB2zHfvoKDzC89Of8Yrd8dzCXFt+l1vbCqDFZGNxUD4xjSVsfbeD5JEhjBYPcKbwCCmOe+g0XKNMhJX7lA84PRfAgAPWs2fFkx1VruhtVSwje9b8XsuGAT231nx+Hsh201CKM7ocuOy2tD3CCKzvLmG6o4jCImBxtt/DE7LugZIQpVrJALZuve5HL/09IH05ewtlPpEyaCioZtWBEbSY7iP8gI7ixTgXZK+SZJ5Jy97dNGpLaK3vIMHzHXLGwvZ4l1y1m+2trM6bTnZUOQsrXqVNmElhtZJlm7s5B2vROo7FLFTCMIbAs1ONuqGIGpisonLOuIyKBK0FOmwgA0v/QnpBNRO2/4a7+ALyfgCZj/U0UjzewTUN6WTYW/lInk2L1cZTpbE8P/0ZrEXrmDqsy22sBaQvp6PwCMO1JuJsO71idzyNsOd1tViL1rlrUFqL1pE0MoQmo5ZS23C2N0zgEft+QuV2fsBmlgoFJIzPgKaQ7vOco+q/p87egOLozqKCxFCKM7ocGEoZlIOlR13V/hhAf/K6H4/37O85BdWsLY2lU3iIFpONYn8e4ZL13KAtwaRx8KFjDthhh3Eqa1zf63lOlzfdNe+mLqOlvqO7dnP+T/gWBUTrGzmamU1OQTUJ+9cwR6gg7GQ9v7P+ckg8O9WoG4qoJXhUVM6Z7KhyspPXw2k9mHQQFOle/W8XtxNkboQ9a7pX6b3Ugk2f9ySr86Z7VXPImpUFi7PJ3ZzDwaJ1jOqK4qrOj/lYk8W84e0keKz6cwqqWfWpTIacyC9PvUSW5TjozGAbpRxg+ApMQZgC7KSI9cyLH0HECQeN1lCSxBoCpePsKwzgp7YHWBGbSHYSfhd+vXp6PNT2AUhdRtG6daw05fHR5jlAds+JyNcz4s9T4pR8MbaZWdV+E+nzlju9geqC9Gy4nD2bWYuze/XQ+a27PABPs6/8jlcpPqfcSU2LiXCDjrhwA8+n1HJV7cesaUgnfd5y9/kNwDDdXD7PjyBPSiNcp/P7rlTlvkW4cRdfH2vipzYNKzKTyb7vH6yu2K54uLua0AoC14yN5JpZ8aQ9t401MiDIRAXriQsaGpqJakWJAXDRK0p46GOR8fDgBkfVy6dyBdDXVpTfgt2e7wtAyXqMp46hbyrDEpFMzJgESF3GynXFLDVvJF1TSRBW0IeBNoCqsGv5fvOPehQB96zF6m8F7pIFeT3oDa6Vismzp7I6ZKVXhQPXMX/QvsYi7V60gkydHEn91d8hzX5YOeiq67qNyy0rkVtOYCIAmyzSKoew3nED44Q6dmjnUhw4g7cj3iT6zG7+bUnhSeEh1lzbgLVoHe+ZZ1Ksn+ElXeJVeSB1mfveaBuP8oVjCivtD7Aw8DArYw66J8wBsfHHUPoBFofEFvtM5bqT37uwVQ7U8e3yw9X/hk+EsJjBP7uK7Wx991VCbfVM1NQQOUWp6OA1DkSVKxUfTM0w/ha/fc/zeJdBqBFwx5mufOH3ZJjy+EieQ64jrbtSSS/tT3tuG2mW/XwnoIBb7x7kPH0WDNQOUT11QxHP7Y7+dH18Ub18KlcAvW5FecTIrM1TtviMn7zEiKb9IGjQhMUox1XmUtM1khP2VKrrR3FbYAcJKMHaccYKtIIMw8ZSHL2IuqpSNndOpcbs8X3OrZmn+tFyc00QASnLaakN6d6q8TnmuU2lbJLmkCaeIkxq5j3bXFKqSkFXTLsQyrM1c6kI+w9K97fySmwWaWyhwJ6EgMAmaQ5LxN3M0xzgGqmMp9vsrHGkc521lU3SHGZSyJTCN4ikg2sCinnUAnmmNHeCgrv2pdOgozKXmOETqdJfz0cnUpCBefadhBtLvGUg+jOgnJIvTW1m8tszeT6lFhqNyuR3oaocfMPGt4seZ3chjGZPr9wghIddVOW+xRxpPxaNDktEslK/2fe+bHxBER82RPTa9zzDAWLrv6B137t86JhD0Y6vWLhrHU/YTvNP7QJCxi/0rq/cS/sVDUotLZm969tdCi677NedO3eyePFiYmNjEQSBDz74oN/P5OXlMW3aNPR6PVdffTV/+9vfLng7z5mzLQFzKUrHqKicR3IKqt0aaj22M5wxMncF7nX/raa5C4cs0iwHK/3e+Q60TVvBzxwPEE+dUt4r/xUS9O3oR4xHM3wChMfz16/Dec88k5ukndwR+pVfg8yV5fnwu0UkPrGFh9/tmb33QUcK32/+EbaEb7mlSFxkz4pHkmXypDQO2kYTHKAhRV+vxB8ZIsDcQoYpj0jj5/xe8xpH69qxxMygST+G0RFKQPYmaQ6tcjDDhA6WiLtJn7eczyb9ms+lNL6t3UOE0IGIg3Da+ZX2LbI0xW6Dc0P7JL7f/KPuBIvELMh4mIT7/kHW4mzCDTp2aOfSEuNdAqkq9y2aDm3F+PHvvDJv3VIljcnww83EPLyd1U/+jCzbTmV7LSymhw7fgEqlDYRBjm+Xu6zKhcgg7vOeDDCrut/76vl8XdnT0Pcz9/PdOQXV/PpECi1SMAGyDVPgCK+tV9d9ydXNZas1jdzEn5PTmOzdNj99Lcu2kwWGr7grcC8PRhcxrKOCSLmV2/mMV76b7i5TBkr8aVrJd0j7h6yc03m+7Khyr+OGCpedp66zs5OpU6fyn//5nyxdurTf448dO8aiRYu4//77eeedd9ixYwc/+tGPiImJ4eabb74ILT5LPIM4B7N66i2AWt22ULlMcGmoxYUb/MZ6GUAp5+NcHbdNW0GuM/EgK2m+soovv0vxkN0G+TsySYsehqWtnpCmUjS6QIICNNB2kv+giRjtacLFTm6bGAc+3+f6/rV5ldS2mJCBjsMfU/XG71nTkM4O+1RaTDbqWk04ZNz/rs2rZN+xJrYcqmXRlFgeTTjJqBObCRiRhCEujgWRidC0E9Lupr7sIPkN6dxuziNDPMQ1chkhRit3B2gJ6upg2LAGftc2H6M4gpOO4Wwhg+BjTRRWN/P87ZO5Leq/MH5spqP5DLFiIzE08ULUZ8REXcuciLdY40gnPdMZY+QpxgoeWbA3dY8RzuPWNKSz1HGM8c3lWNqP8NnhOhpuSnZPqM98eJh9x5rQVf1biXUaP9X9jHyf59mUSvPLIBNELufkA7gwcXb+dBHdHi9Pr1Qfc0a/99Xf8+3vmTu/O1c3l6c8y+w50pDke1gi7ia/IZ3V9LwvT5XGUtN1H3GlBsD7+qK3vcoN2hIMnt+buoySY02s60xnhhhFdmAxOvNpxIh472QJuscjULQus4PfVryCbcYhOadedkbdLbfcwi233DLg419//XXGjRvH6tWrAZg4cSK7du3ij3/849A26jw5H1sOAzzHZZdWr3L50suk4W8ic/XLNaNzSTN+BqdLlT8kze8RpO054Sgr6Z+RU1BN7uYcXtSUEWVrVAo+CBomBTYhmztpI4R3GqeR45xMPA2ywupmktr28PuAj5Bl0GtFRtZW8Gs+5UdyLL8T7yKfdBboS3jY8AntZhttKSu4d7eJ64VibvzqNb6dKEJYGcTFKV6LjT/G8tUWGuxBHJ/+DKuzRtL04TqkDgGjMJIY6TTtJpmRooMWh40XRuYR2VTOlySxwz4VzaFaHLJSLH2PXMQ8u55N0p38RP8x12gqiBlmwPjx74hrLubRiDPEzPpZ930vWQ9ln8CJAli0utdJN33ectq3fUGYeJJ6exDvWWdS4Xwuz3x42H1tI2gi3FgDw0P8xjL5NUwGKMlyruPRkEs+GOTi+kJkEHveEy/jzDfJxRVLBj3a2t99zdXNxWqqI0A3F3d0aX/P3Gmwb3nh97xkXoN2q0gaS+g0pBOWvJDV1bPdSROrPi3z+ujzKc6M8pTl1A6/3q3/uOrTMtIsMyEQr7quOY3J/LL+HgA+r4IV4xOxHD+DprGMpuZy9h2u4+OkaHRV/+aPgXv5e+AMch1TuNG2E1vbGXQaEVqqsZ8o4NihAjQxkwcXj3oBueyMusGyZ88evvWtb3n97uabb+a///u/e/2MxWLBYrG4f25ra7tQzXOz8amFLBb3ICJhlbWYZR2hohkBGauspTNgONFtRm/hxMGsEHxfqF4Gl8t9ZatyGdHLQsPfRObql8PN7wNnoKuhZ7xpxXbY/izbJSPvBM/HkPkL959WfVpGmjslzBl1IkuEauxsFWfwnnkmeZVjAJOXV27zwVp+nniSRV1vMUpoQhIEDtiTkUXQYydZPMkScTd59jQWi/kkWkpxyDI1tR+zaMrPuO/I40wQTtJeF0tochZrG6fx0uNbmK8bx9OaICLkVsYXPofx2FVEd1SiQUKnqcEqOyiXR1PomMAmaQ4/af6YEcikiMd5R/drtoffyXbbFDotdubZd3K95hAjhSbiwoNh2Ey46jqC8/6EVrbR0XzG+75HJoLkgM5673voW6IpqhzGykA6ZVF3UuGTMBK97Q3mCKWUS3G0jLqOyMjEHl6O3p7nQD1u5zoeDTlZlYEu0C/gzoq/IvcrMhOVDE/PtvWW8VyynuzUZYrMSC94es6yFtN9zgFciyvu1RXrWRwyg1e+212vOePFXFpMNjLFYqK3/Znc+uVYi9Yp3rhTjVD5EiZxNi90LiHcoKMibDYBKfFK4tPR0zxVGkunxe4+36IpsWABWYZ6wmlxhBDqqGdc6Z+5Q/ycCFsXa6ZEklE+g/fbZpEWcJRhUgeazg50koOx8kmE2lPsfaeaigXJl7y/XfFGXV1dHSNHjvT63ciRI2lra8NkMmEwGHp85re//S3PPffcxWoiALeI+9AiARAo2AkUujtdoGAn0GZEPmaEYzuRAAHoOvgvDslX0zx8JhMDzrDaOJVcxxT0Wg33xVYxreZtADqnr1C8Gb1INvTnJVFRuSD0sXL39dC4+mX96O8QZ3wfQkb03CIqWQ9nviIIiXt1m8GcABsrWds4jRbTGJZodxMo2tAER4POADYTlWEziOis5l7NR+7vfkBXhFHXRbpQTpGczJyaw4QL7VjQ0SXrqQqZRql1CneL23FoAokzt7BIe5iA9OUUH2jE4pD4a3UKYakwUmxFkGVMXe18GPsk5gO/Ym/AC5jQ00EQesFCiNzO4foONEIIEWIHpx1h1EvBhNOOIEKwVkvbtBVoKl8iuOUU08UK9B3reTb1K4q7ougsa6JcGk20ppOYtkrFqGuqJFiwICPgAKre+F63J6GpEjRaCAz3CTpPJtvT01ayXomRS8wia3F29+SMd03Oawbg2RkUHs90RWbylTUeDVQ0+iIlhHgZeBU+bXMZYZ76bX21a6DPrQ9h65zGZIoa0nk0sh5kmfyOTL9xrqs+LeM7cgE3aA/zeRG8Z55JtL6RaxoqwGHhbl0HY4KMBKQrnjvrtpcxaUuw2uuo6brPLYPSne3+MHkn4D3zLJaIu7lec4gxcj3DhA4IjITIRP6oe43XhXQ2OuZyu/A5pY6JXBdYSaDJiICE3SENCWfIFW/UnQ1PPPEEjzzyiPvntrY2xowZc0G/s14axhixYUDHurJbgrByjVCG3FCGDgcvy58giDI19ihiTzW5jcST+5+lqvbj7kG9YrsSD+AnS81XeX4ouJNVrlD6WLn7emi6+2Uj3P5a9+c8DYnUZVBXCo0V4LBh/3wVsuxgiqOETPFWRgpNGA3jKe+ykaKrxRg1m0OnWrlFLAMNnGE3I2hiansF07QComwjRuwAyYqIhB4bBsHCEtO/+HL6S8wvvZO3I94krf5z4uxv8qt99/CZ40kyKGKJuJtNJVChiWOG2IaMoj7/m4AvGC4pnn8HGvZJ46mTItkkzSFVqOJHuh0EaTRggkTRCNShDd3PT0tnEDl6EePa3qXGHopOI9J0aCuJgSFoDSY+t6eSMCwY2uoAZftrtFzASBoYJdUjGj9j67sdBKSfJqvNiNGQzKqOm1l09DTRRUo28VMfmHhhyxH0WpFHbx7vpeI/oG3QyERlSzfyHA0wD8Mhe+lfLvkkeV4ZaEzgICuGnBd6a5unIddXuwb63PoQtl5bfhc17ZMo0Ewn//EsVvv5uNsQrRCgZD2juqL4SeXHTNIZwQHoggkKDmFBewHGqi5yCqr5f2I9pXIsAdOWE1dq6NmPk+bzfICGmi4TU8UqrpHLOCEPZywwdtYPoKmSq9v3catgIkAjEowVhwT7U37BxKq/caLZxD80S4bE4uOKN+pGjRrF6dOnvX53+vRpwsLC/HrpAPR6PXq9/mI0z83Y/3idrnU/xmBrAhkkQEJEg4SM/zTlLllPF4FEC60IgFZQjLixPsZhrNBAZ+1nnHh7H1vGPMqKqAPuFbi/l9glwtjiKqeionKR8esx9uclSF1GVX0Ha44kkR6bTPYD+ZD3O+yfr0KUrMq7I8Btmt1M0tbyWftkRSLEtpsd1ql0OuyMFJoYFxnMsITvoj+wFi0yFllDhxRKviOVqboaYhyn0GNHAELoIuzAWlbawtlhG81CRxChcgcL5Hy2SVO5V/cR08VyZTtUqEeDTJTQzq3CbjbImdwrvI8OB53o+Yv9Vj6X0gBYot2N3t6B3tFBNUkUSskA/LUxnRrJhN2yk2C5lXFCBx+bx1BJKkZzHIliHQHpy4mZMNLt8Xhqg4aV9jHEa2sRRYnTRFFqGc5dhc9BoIUayxgybHmMLjxJgmgkTFtPnjUNk82ByebgqQ8Os8qgY27yCgo3NNNpKXPXsu012aGpEmxdyr/nwqUwaHy45LHFZ1MxpD9cHjEZOFkAaXdD5mM9Dutx7T6SHjmNyRStW8ejIb8jZpihW0t1oEZ9H8LWK2ITFZHhiCKocPTpDXTdo7TXMpDlw0hWgfbAWELv+JOiW9lWg665nP8ndpIsnuJYyEwvj7PndQKkdO5lpXYXMWITQYKFsdRjECx8lLeLoOl3clWMUl1i0ZQY9hcJvG+eSUVpLPl3PEZMyXpmpk4bEtImV7xRN3v2bD7++GOv323fvp3Zs2dfohb1QtJ8gp485u60mtRlaPJfgRN7FNmD219jbV4l3z35HGFCF21yEP/jWMgs8SuihdY+Ty0gI8sycUI9c0/8mZymG5jbCSU1QSzyE7vhKovkyjTyop+akCoqA8FVMzEgfbkSGuCD37i6xmmMbK1ha9FVzI2tJntWPDmNyTx17G5kYOMHh4mt/4KsyndAVsIXrGhpEKOZFWGivHk0m6Q55ElpbmX54DAtVZnZzJkVTwzAhJGwZSW25np2SVN4xP4Af+Q1osQzWOQAtIIDu6RhVGAXCfJJWkYMoz74O8hfv88pYRQGnYhWIyKiFHZd77iB72u3ow0dQb4lkx32qaTZS7hGU0FXRAoVptk870zM2FQyh5FCE1qNyMbA29nQPsl97RoBRum60FgkNEhcLx5mtm0t84SDxNhr2FxY0x1iUbGdtyPe5BNzHMWMJ9ygo9wUygPaDwnATpcUSVy4gYTmEoI1ElqHnQlhdsI7dVjsEmabAxmlJu3mg7XIgEEnulX73VtxPp48Lz08XwYzbvgzaM5mnDmHselCxhZfMoPRZYRbuxTju/gdb6POeb+KjiRR0z6p12tfm1fJSlMekbZiaBMVA8pVMcJl1Pdy75Vr17Ai80myXQaQx/POToLs2gqlzrGnZqKL/Ffg5F7lu1z9qLECAKus5VnHPax2fqa9rpIAczMAXzimkG+eybUepyraoVRV2bF1Llstk/k/7SamayqolEaxU5rCMXkU44Q6jskjSClaR8J3H3KfO2f49e6koR6xiJeYy86o6+jo4Ouvv3b/fOzYMYqLi4mMjGTs2LE88cQT1NTU8H//938A3H///fz5z3/m5z//Of/5n/9Jbm4u69atY8uWLZfqEvrGd0DzULBekTQfKhKpyn2LNQ3p3Bm4l6vba+iSAwjG6v6IHQER2e3ds6LFIFgRgQniSeK6NiIIMuFn9mJ+730CHO3UHvoMa8wMErLuIX3eclbnTffvSj4PNSFVVKxF65juKGLXPgc5w68f0OS2qmoMDvkBAEqdE07RjnWs1uaxSZoDQGrhG9hpx04AGsFKAHaul77E0azD7ohjibjbfb4HQoroSL6dRz61e287LlrNgfVr2NQxA50oUDd2MV/UiGySZrNAyud6zSHqbEFcM2WGUpy8ZD02sZ0f8wHZIYeIvOY7cHwX0afbKW1L4NuGt6hrNCvSJuMi+cfW22lmF0EJd5K/WMkNfGHLV5icHrsnQrbzXfMHzNF+xgyxnPWOGxi5+GlqtgYR62x7kxjOc7dNJnzr68yUDxGACDgn6JL1JLTtI56JrLUu5F7HR3xLLCEQO7IMX9liaEv4IdaidVxrqEVvOkFk9CiKrymC4ncojl7EDytvxGKXMNkcZIrFfEdTwK13PExV7sc0ubz4Wfe47+XavMrubbOk7moabvobN/ozwM5mnDmHscm3RNX5NMIuWTKay9j29NQ5ySmodkt/PBjdQYHGOf5XbFcqNZhblAOT5jM9PoLq0lGYxRD00XHK710VFzyqlvi790U71vGSeSPCx5Bb/2D3gs7z+Xt63wuqe79HrrYBdtFAjnBbd3mwpPk867iHDHueuwqLaz5zlRx7Qv4fgrRNXEs5ncIPCacdLcpi8BH7Axh0IiDwAq8yRyj1SirqMxbxEnPZGXVffvklN954o/tnV+zbD37wA/72t79hNBo5ceKE++/jxo1jy5Yt/PSnP+VPf/oTo0eP5s0337w85Ez8rViT5pOQNF/xoFVsh5JIiruisFfuZLy+mVCNjRNhM4iq/YxQunCg4YwcwRihHlC2ccPoogs94bQTYG9HAGI4Q5cxT1kdxT7Zsy1+6kf6/VdFZQAEpC9n1z6lyHZFP5Oby6v3XPgYgtqPc0oYxa0RJqhwKMKhxhI0EmhEkWF0gCzTIeiJxIIACKLAHqYSQQPXaw65zzuu7RB7Cy20mO/nIc1Gljk+59Pt88iZ/wvWig+zYkkif3O3635urdhO2cZtVHSM5uDo73PN0vvd57Id+oBA2Y6uowKO74KWasbb6/nf4U38saGKeE0dm0rmsKp8Bq3myWxhsldm4EzHAZZodzNSaCLeVAEyTNCIBGHhPu1mQqLu4v/ZFmEXJQRgzn/8iglJ8eTW38neQqiwj2DjLxdxOn4xKzKVd/GTQ4ksEXcyXaxAg4QZHcekUbRds8KdnfiksIl7Q61KmbLid6DlBKM7/kW7+ToCtBoAloi7matRJjWXdp2+rhrDJy8xrOkgusObWJNwDw8yv/eYov5qyQ5Qw2xQ48w5jE2ek3ZGP1VFBsslS0brZUs3p6DaKVHjlP7IuqfbMN/4glJ6y6NSQ2F1M5nUYZckvjwDcRFmYoZP9F/S0vkZl2H8x8C9TLGWAwJ5Reu6pYhcz7/NCGExrGlIZ0P7JMxb32PCp5uIiwgi5pafQ8bDVOW+xerjU7nl+CvcpG0iIHQUXyT+nL+VxjL9WBNr8yqJCg6gpH0SH+tSeXLBRNa4Sv9tfIGiI0lkmPLQa1sJ0mnRyF3cJe4lJsiAtlNAK4hgA71WQ7Bey6a2OQTJGi9JFK/715is6GK66jJfYtTarwPgotd+PVc2/hjKt1Jj1vOebS7f12xnuHOLVgYsshaNIFEhxXG1UEOAICEBdkFHkxBBk11PC6FsDFzK6id/5nVOAsO9ta1UVPqjFy/Mw+8WubXgrh0X2as35MvnMpgifYUkaAjUG5QJxtbl7RVwDrjGT17iRFMXX5LCPAqIlFpYLy7gKxL5nuMDAPYxiZuEfQynhXw5FVkWyBIPMEzswqSLYr7mTWpaTN21H13XsGUl7S0NbLen96jvWpzzBKO/fgcxZCSR0aOwnirCbrei0epoJxTZ1slueQr/ZX2ATLGY2zS7GTd+CmGd1axpSOdWIZ/JlkKaghJpMdmwOyTOEMF88Uu0okBg/LVs/FpikzSHz6U0jr24yP3dGS/msrJjNddrDlEujaZdN9yd9Ze7OYf/JyqZvf8j3UrI5IXoqv7NnYF7ebNlGgvkfL6lLyUs5SZlwVb8Dn9smMGf7EsRUOIRrxeKuStwLwu++xA5jclEb3uIG7QllNpimSBVECRYEMPj4b+7DWYXA/ZyDeEwjkseX3eBcRmtnnVQ3fh5Li5P13LbB4yTTmIQ7YROubVnPVaP87i+447Qr3g05FNqWky0TVvh7anLfwXqj4KlnS4xiK9sMThkmCofRRAE9FfNhrAYVh5JYkP7JDJFZ7+cm8HWnfm8Z57JF3Ia1wvFSqKSM9TCFTaQVfkSmJqpiryeNQ3pPBhdRFtwPHVVpRyLWciM2rdJo5z68Kl8x/QLt9Hd57N31qV9zzyTirDZXmPC+Uat/fpNxjnBlenmklK0jmGiA6sciuww4ZA1dBJIGF20EIokaFBSMkAr2xghn2GUqCRq6ALjyHgxl+nxEeiqknhWs4tQU/Pg69GqfLPpxQtTWN2MQ1b+LaxupqbFpCi211Z4TSKhgTo0XRJaJLqkMN5pn828Ee0c183lqQ3e8TkxQOE7r7DfGs/L3M71QhFL2E2aXMJ48RRfatJJks6QKBsBmemUYxAtzuQkgRO2EKYnRAAwPT6CtOe2AbBx1N9I6KwnUJQxBIj8UfcaK18oJH3ecrJnxZOW/Vvgt8qFVWwn991X6bTbmUUFcalzQYD4rij+/PXrDKeJKQG1GBpO0tTSQoa9lY8Mmdw4dQTDnbIOrokkxCUI22bkW/pSBCuEpS50fw8l63k+ZS7v7csAB8SIzUx3FFFYBNx0PQcCZrDHPp0szUF+Ofoga6oCyDDlcbWthAWyiWPyKFpsZYRFJirxVZmPcezdIjROY/v2kFKsRXuVkmZJ88lmu1O7bjL7bYs4eWITizT7CEAxbOuqSr3iJAe61diXt+NSG1VDTuvuPOPpORyInmB2VDlzoouoN0KgaIPAbi+e63mv+rTMLWg8zvgx2TELySGZ9MzlxMz6GTG+jXBuySuLIYkAWkmljeOGyRw2T1A8dQCVuTwY3YHZJnELBQSk3wlNO8kSD5AacIRNcSsZZ9zLbMcBrhHLeNp+D3ktaXQV/hMCW8AQQUJWd9xdxou51HRdh6YKrhcWcldgFAEJy6HUea29PHtXn3w74i1u0JZAIDRk9owNvhSoRt2ViPNFzAIl8Nu1bXp8F3uPN7HLPsEZADqKqzmFXrQhA4IAgtNvKwLWlhpe4kn4CgqkSRwShyEK4eQcSuTeridIa9jSawaVioqbXrbBfLeh1uZVKllvlfuUA5yxVu1mG+1yECGCmQ6rjXzbWH57PA3pGLjEgj0zMmfJxVhFCSR4VvMWw4RODstXUSCkcet3HyL36GkOFjYBsNsxgWuFrwgXOmhxhPBXx60Ul9cTrNeys7zeXR5oTUM6qwP2ozM1cYvhKE3tFjLsJlbnTfc7EX6cFM2NX/2SUI1NEZVc+hfSNv6YtOAjSuxRWApEJtLiLBGWPm+5u0RZdlJ3ebKcAig6ksSjoceIGZPKtzMe5ttJ6cp92fBfyOZmujAyd9GfWTrrF+RuzqHQmYDiWW5tTXIllH3Bs5pi3tNfz9dBBjY1prNE3E2QYPHKWH3lu+ndYq8b14LhK7DtBLK9tOtyypNZKQvIAO11DG95nzjZrBiUTqNuoFuNfRl/qiD6eaAPj9uD0UVk33FP/5mbrnO0GQk3HsToiGO3bgYL7njIfc4VmYluL569U0LohBTxGKk1R1hx92vKd/TmlU1dRu7hOkptw5mt+Yphcjsms41rvves8vf8V2D4RBIy7mFNyXqoPKL0y9RlBJwoIM7UrCg7ZD5E+4b/IsLczLe1e8izpvEJ13HrqJ6X5Oqf0+Mj2Fmuo1ieQUZhESvt75K/I5PsWT/z215Xn1zjSGf1xBCvsoWXGtWou9JxGnhVb3yPcONBhNBrqWpPZhH7uUXeixY7djSYhUBkZETZToigJF2kiZVocSAiM1M8ggYZs6xlt62cpK83I2Ghs+DvhKpGnUpf9BLL47sKVrSnHEpcpzOjMnrbq8wVjtMl6rDqDOitXdyr+Yglcvf2imcwe3bUNEY6ahTZEnE3w4QOJWtVIxI0/U6lHUdzqHWE86FjDsX6GVxl/xPJwinKpLEU62cAUNNiItygI9yg41r7l9xo20OTPpxIoRWCR9ASFk9+Q7p3NmjSfHc7Oi122hxzMLhicTy0IXOj7uQpV3WG+x5jdcX2bl1I8NpOjt72Kktt9Witp6jSX98tMVSyHswttEjBvG+f5Y5LrB1+PavkEXAA5iYrHscVmYkQtQxOFBBqbuHeFJmM8geokRRNLnSV/LVUwBDrJyjdZYi7qkV4xNSuiE0kf0cms0MsxAwzUK+d7PbU9faMe6OH8edHyHZ6fAQZzjJuqnE3SPx4y11ZrOHGEv+Zpr2cwxhwFYVM5RPNdcy+6S5Iivfypq6eWIG9pByHRqY+YAw6q8RIsUVJali02m9bXFmx05Oeo7C6mer4CBZWPKN4wUrWU1XfQbjxIC0x13nLbLkMrbS7lZjQyERImk/oHX+iKvctThlD+HPg65yMW8TWE4JSdaJkvSLLsmMddwbuZZZjJunjlrt3C24J3MUcbQlp0cO8Ekg8a8e6+mt65vIe9aIvNapR9w3BLVNinsli8QuudpxAI8hY0FIkJSNJMF0sRxAFTjiiGSG2IksygijjQECnrMcJFOz8VPs+DlmgCz3v2a7HcIXHnKicJWcTJ+VhtBQdSaLRPJNp2iIixQ60QaM5YRUJFzpIFk8hCDBzXBTZtR+y8kgSSZ1WRnbsdsedaQQBHDBSaGIiJ4l0epusReu4TlTiv751czbD6r9LYZGG8GuW8+jw8az6tIxwg07JhI0qp+adtwiR2yltG8e4qCnEBBlIyLiH9MZkrNsewqQtweAs7r3nUCI15skYdCLF+hkES1qSc98iQd/u9nA9VRrr7XnynOTA6/9v0JZQZB/FF44pNJ4JJcFDTqS+voPVxqkU69N51MPb6fIuFlY3k/94FjkF1axcV8yPtZG0W0Jp0811T0q3RZgQjV1E2U/xsw8Pu7931adlWOwSIJOlSeTFwL8T6miG4OHumFrFo/gzQIm7TeuvH0QmKh5BP/3B0/jznUizl/6F7Khytr77DG3mmazNQx1n+sP33fPjLV+RqRjladHDlCzu/nAe81GpQJT9FIEG0f0cVn2q6Biu+rSM7O8tQ9tmpL7NTF1zF8O1OrSCrCRcuPqBU8/OZQw2dVow2SQ6LXaKn7lJ+b68DNoLvuavpQLFjqnMs7d6vQM5sU+ydkMlKzKryT6+C9rrlCQlHoOk+Sz9h8zT9j8xS3MI2wmJUmkUqZKWuEil779k3sgUawU2qYYHP52Kxa4kIX0ZOg9tp8AxcS6rnAkkJo2DYR51bIfylrxq1H1D8JQpCaiPoarwOKMDOgiacx8rPk8nzbIfreZjZl4VSXxXE44zjehFGQHwzaQRAA0ykqBlxrhI3nbq/bjd1b1wqWNjVC4yA5GU8Gf4OT/3YHQHWe13UyGPZoZchr3NyEhdAGccI7GHj+Tbw0ToehtKK3jSMBbRUkOo0MV4oZp80tnhmMoOppKlKeaXo0uJdHr/9kgZ2AWZYdfeybdnxQPxynZhxXa2vvsQaRYl6Dl7Vjz87SeMogErIkYpgvjmCjBZoGQ9a8vvIsnszBhEhspcbsHIFia7M+fmdexUPCFXTVW2XduMPJ9S6/bUAf63p12JH/UdvGOcSr4mnU+G/R4OF0HlZ3D7ayTc9w/WOA/PKah2x7+66lp6bmuvNOURqamk1DGF1aWx5N9RTnbyeoicitFSz6j6Jq4XinnmQyWG0WUYAsyTd4K5GXSCEmie+xbf36AZWCC5Zz84UaAkuPTVH5znc99Xj+zYG8VCUgOOUJYyBMaOi5zY0d/Y2ePvvu+ev9i4WfF9jtc9cJ5jXv33CDcqniwX98rvszggl81yFiT9BZLm850Xc0my7HEnM7gN+vxX3AbYqup0r77mRVMlmJu5WdpBsZjA6pCV/FH3Gk2H9tFS38Ha5h91L458t1YrtvOG/BwhQitl0mg+dMzhNs1uIgPs0FTJisy70H8qIsqg1yriXyabQ8mCb/2cE/Jw7jj5G+Zqw/i947v81PYA4Qd0BJcOfU+xatR9Q/BeWcR3p5IDjwZWszZPS0VmNjNnxcPfFuOQRUQkNMhOGVVvBCBU7iS24h3mylO4UXuA+ZpjUJHWPXi4MpoAMh5mbZ7G20MxhDPeVAZGX5NNcVcUwzsFimuC+E1v22a9VIkASEhdxpIvo/lrya2MEeqJltsI0gcRF2BQtkc7KzHolCEsUm7FIXQhAgliHRmOIvJIQwCyFmeT4PzetRtyqTFPpjh8hlsjzrMtcylkmraYZmG7shUMgIwGmRliOdFaEwRGubcf1+Y5A6SdCQ1BurnuMkRAtyck4x4oWY/p6HasJwRW3PRq973wnHArtne3J2k+39+gocZsIi5cS0xYIDQ5oKuRrnX38ZU9xp1B6IrxAbo9Hc7n02mxs0M7l9GhBvLNM7sFU8s+AUMEMQGhDNd9DcJH5FnTAAg36Oi02rE5ZE4Jo5Rg+KTrQIA1R5KoaVfeY1C2qp9xevn8TnaeW7iuib0PFC+i87664pRSl6E/UUCcuYU4V3zfpeQi63P2F1fY4+8XUGoqYfxUMB0mcvxU9xj+/zQfo6ede3T/dh/n9Ryjyr3iNu2yTOHxJlrMikFn0IlEBut7hFIs5jPCxQ5WxhwkIWsaX/7DiFEaRoexmjT202lIV/qz+TpoqYarrnN7erMoQ9bAwdAJVNhmMywlHoMzBi87KR6innXXLn60MZlVn5Zxp7yTWBqIpRENElG08QPDfr5oT8Nid/SoqjIUHRWqUafSw5WcG3UnLV9LTBfLGCvWgwxmWQsCaHHQKRsIFkxokDFJOq7mODrZhsHe5J0Zm/+K4g4XRQiLYUXmk91xM3m/g/w/4fYDqkbdZYnfycY50NsrSwmQzVjPVFBj6zkh9VqFwMPIuf1oDl0BBRgZyQihnVOdYOgsx+KI43MhlQWxMtQUwvAJGG0hRFlOIiHyQFQRFbbZipRB7QvuOsa+sVteg3LqMpoPfcZwoZFhpsNKX854mDNtZmqau9AmzmVsUKN7EeJOaPBYnGQlzfcpfO/tCfn8cJ0ivdCbEeRjLHgGcq+smsujkWZiLMfRdTYyWW5za331lpDg2o4tDp/BmpWPca3zmlceUbLZ9R1NtNlbCENGI4osCjzMylEHSci6h5XrSskw5TFW20KoxupO+Fi0OYflhb8BMxyI+z6rWsfgkOk9kWGQFSJc45HL+6hMmPO747GGgibmRdbn7C/hpMffB1pmbIALa6/3pKkSzC20F/ydMssmUnS1iCjZ4w5NIFVvfI81zuQft8THxhe6+3XGw/z7hMB71pmAUi3lyUWTlL6T9ztq8t7ktG0uq1qXslf4oeLpy1IWRSm6WppkLaNoZZ59J8UhM8ieFU/VGwcJb2mhpewgRQ0iS2311GsiiAuGa6+K5O2GN1lzKJ3aeX1Usogqh3+PgBYLokYPDguaYWP4qDkDh6zo1rkMTxdr8ypJattD9LaXIeqhITGPqUadSg+eKo2lxv4A/9D9mtE0YhM0VEmjGC000C4HEYQFEZk2OYhTjGC6WI4WCZukRedZ98/UBMigC1ZWRzi3fKKWwZZ3wNYJgubcC4CrXDI8jQ7X9t/CCiUeKi7iagpb0qkbvZC4RkOPpILeqhB4TiDRReu4Xt6vaKFJEiGyiFXQEU47f7ct4ZpTfyfS3kVnXSV5WZ+wZ+t73MIuvuQ66lpNhJR/AFKxkqRQsp7syESykyuVPki8u1RQ/o5Msp/8GWXTn6Gr8CXidB3uoOuYpPk9JRg88TDEcpwrfoA/TDtDltMz4JpAGm5K5osPD+OQ8e/d8vRqrc0gu+MM2bN+xMp8kQxTHqu4idXL02j/8Am0HbVcG1QLFdvJnjW/h8Hsei6gTFhbf/Uyx2IWsv94I/9P/AijRkubEM/ntgkkiHXOLaqdxDR+RdnGWp6ylhGqMdGlj+3WBASybDuxUIYgS8TVrOaw/kfkC+m9Ghw5BdXue/LozeP9bw/6wbVg6L5PAzRULgYDNZrOE74xh77eobOO8RrAc3CJE7sN9zuUpBta6jE4RJpkLYfEyZjsEmMtLSQYd5Fhb/XODPepH9twUzLBW9/jzyhJDGvzlDrs2cXvEEs9d+l2Uj3+J+iqviI5OsT9WQMQF5mI8dC/iWtqIc28n5yCRIo8SlsqguQ1mNBjstkxnCwg3Ckd5Ddb3fNedJwGQ7gSJjB+ASz9C+kF1RQ473ds/RdYt71Mbv1ysiaM5O2It2iyVJOirR0yUl+qUafSgxWZiaz6tIx35NtJDfw7gdYmRtNEoGwlVDB1b8c6A+4qpRjihEaCJTPsfxPipimd2xCJXdBRbB1D29HTivijs9xMcfQiklv+B50gojvXAuAqlwzXZOISF61tMdEmzsQeIHPrLQ8T45QluTn3TZoOVGPS1bqzyPryLrm8f8+nL8daWEyIbAZAKzgIwkKY2MkCxy7+bspgmeZzNnTMpmRzDrcKuymTRzGleTvXC5281phOU6CVqW0mYup7xnW5KlLE2VqoeqOIrKx7wDZLmeh66Zc9jBSPCWvthu5EBWvROkUOBLy05567bbJ7kqz/6Fe05+0hdNYPFGkgl7Gw8cdw5iuQJSh+hwejJzPMWEKATSSncTnZCdOwH67G0XGcqty3vDMCPe4hQP4dDk69s5pQuZ2uagf/T2xilngEAZmKwHRqtRO4VVvJyM6P2OOYRKmthbHmKkKd29kah5mM8rt4XldLVv5i6GqiM2QcUsdpImnlMemv/El/H9mzbsIfnskbA90edG0bC9C3F/BKYRChKOci8eJlEEaVuzOy+/I4rs2rxCErHrUVmYnKlvii1dTnvoWprpoEXS1pwSYOtgahTZhLS2c1+Q0+Rr6PEZwdVU52+DtYO5rYckKixvaA83ruRih+h7i0u7k2MJLoii+IaXRm5y79i/scMU2VhDRtVvreDpGKsNlsaJ9EnCYQGuBG2imzjWA8ZzA6YplrKGOsuUVZWPrcB9c1Pp8yl6xEeoQJeBrMW3/1ULcOpG0UCW37SIh3ShQNBQ8yqlGn4ofsWfGszatkS8tkAnX3sDrsbYa11YDkQJLBKotIgkirFMRk8TitcjA1RDNBPoGj4wz73n6ayoXJZLvc7OaZ3FW0DjT1gACRiTxYPoMka4jiWh8iL4PK2eMy0Jo6reTZ0igWZnCrxzZ8nHEPJscodsiT+cyjpqPvlq1LwmLVp2V0Wux80JGC3hHHTLENAQjAToscQr0cRpy2hU3WObxqXQrAH7Svcb3mENcK5QQLVgQ7/IKfkNeVxh2ar1g9saLHgJ2QdQ9b3+0g1FZPuHEXlISQq5uL1VRHgEe2myc9jJTHuyesFZndBl9A+nJFRysykehtD5HkzNx0bUk98+Fh7hA/J9jc4C6u7ppsnk+ZS9aIUug4A2l3kxA3ja3vdvC+eaYiX3LHMopLSrE4JDY2pCtlA32eR9GOdYruX347YXTQIoewSZrDioCPARlZhsYOCw+OLiLhzFdYRYk6KZJ23XBChOO0O4LQiSKnbCEkde6hq7AANHsBiEy5HVKXOfXAWngwuqjPvuG6J57bgzmNyd3Zi3606VpMNsINOoL12itfzmQQMXp9hhD0c2+8DMLkbs1Br+/0MTD9ihMnzScBSMh/BYjEAMSYimk/9TXPOu5xi3L3eb2mZurtBj50zOk2GGdluXVP176Yy1LLcHfGqhepy6B8F+HmZm607WRjzSQAalrMbGASOwxTCQ7V0mmx09JpY4z8BguCuvUWPe+D8jkTT5XGkvX4X/q8fwHpyykscr7bE0Z2t2UIeOhcqEadil/ck0J0EYy/W4mNMx5EsnTSSjBaATowEEU7o4RGRqLUpRAASYY9W98je0olx2IWwrFGhtnr6dIaCBJsVJUdpNOSTrF+Bt+6KRso99qWU7n88IyD8ud904gCJmEYv+Qh0jr3E73tIXLrl3frtdV6TGqxT9JutuGQYfPBWtrEWwEIpx0EgRY5hOgQPTPFUwRqt3Kv5SP0WpEicTLtI4YpgdxNlXw7dRnDjp7GWrSOYyMWklF+l/JdSz0mG+dW0HZnX49MXcZTGzTUdN3nVZvV87o8jRRfY6PnNlg2bPxxj8xN1zGfbp/HXbovCHUWV3dNNk+VxlKb+Q/lOwMTyU6Kp+GmZCpc9zYpnv9Ljqbj8Mc8pt0Cf9up1N4EZZs5dRnZEysUIefhE2mIuZFfn0ghT0ojShfA2NAgjtV38BfHrUw9WcV9hhA6bDauE0vIN00mKH0RQc4SbFEHP+FezUeIkkBTSDxVnQHsb5xGzgYNz6f8gqtqP2ZNQzq2d4sorG7uYVz0tjXYl8fp+ZRarE4R5azF2W5PcJ8JGZczg4jR872fg/HceRmEUb18p4+BmW1+j+zAd8B8N/CY93EuozB1maJD11JPhj3P7zany8NtsUtkaRJZGXM9x2MXUnxgBAF2B6s+LSO2/gt3yMKKzGSit9UTqbX39Jo7tei2vvsq75tnEqjTYLU7SIkdRmOn1d0HXe9tQMpyt1AxKO9tXauJ6fERXiUK+yNrcbZXkuFQnK9Uo07FL9mz4pVyTZX7oCkEfrgZKrajLVnPCGd1isj6o3R1gigr2YESYEJPgTSJ7/EB1oNHuUPeykJtAKOFeixWPU0h8dSdOkaaY3+3bIRnEO0QfElUBo7fCfyq69C2VHPNmBQ+OfUS2uZy9JKNnYUyNeb7u+N0wL2F6ZAhS1PMbZo9/Ms+m7ttvyRTLOZl7atM1JxEDBhLVdC1dJ46xnSxAkGGa1JSyIn9Ld/PUyQLspPiySr5MRi+YqsRarrGuFfmnp4Ht7SD00ORHTWNVa1j3PForuNdZcxWT6wg+3vKAsRvsXfneXJ1c3mqNFbxuoX0zNzMnhUPs97wulWeHrY1O9KpaZ/kPrfvvS2sbmalsJsE0yE47kw4Covpfpc8DIWEpPlkFVQT4TReY7J+zo5GJb5vCbsx2+yEaawESGZu0RRC6k8VgdYjSVwv1ZAknCBRMFLYmsTdtl9AJWSKe2jZu5t/yaPIEPLYVGKlRkpzG7zue2x+j/aCvyualvN/4b4Gt+GW0i1W7CLLtrO7kkWFErv06/YUch1pvWcfukqqnevi8FJk5Z9DjN5Aq3aAHxUEf9/pY2C2F/ydYHOtIjQfN819b3J1c+nqNPLJoURO1hiIPHMnt2t2s0Or6CD63kdPD3enw47deJistv08KGbxgm0JJpvkFbKQvfQvEPUQVblvscbDu+8ipzGZVfJPQA9PumI1/V1vVDmU7PR6np4lCq8dFwnAvmNN3h5Jj/bnOheHniXwhiqqUafSO76rR4+Bx/jKv4nubKRDDsEkhzFaaMCBiIDMMs3nyIAWieFCK3ZERMCABbHjGCmijiXybn5lmUFOQbVXTJIXquTJ5Y3z+RlPVqFvaUFn3kWMrQEbdoxSJJ+I1ynSGRY7OY3jwSkmmh1VzhjzFobTRKp4nHSO8rT9Hu7VfESY6IzptJtY05DOUo5RKY1CGxLNeKdB6E/aIcBDasTTQPOqM+v0UIwz1XG9MJObyl5n5QtzSZ+33P053zJmfidU53mspjpquu5TtnXuWN3r5OSJ52LqwWgo0Ez3PrfPNnX+jkwWCEfQ2VspO93O7ypmcX9kGtd6JGf4OzclIRD7JKGBOnbIc0mLGUakvh2q89EisfXdV1kl/4QW0yQ2MIl3dL/uriHoZIm4m3maA2hw4EBEIwJ2WCF/jOZTSLIsVISCNW8SYj7DbdInfDvvTrcHJbponSIwbBsFFSN7F8stWU9C2z5+ORbKmmd7Jdx4ySQlnyeZkYssV3Ku9JskMdhx1KffvGe7npulHXxqu557Pe7NU+V3UWO+XzmophVII09KIy7YwGxg67tKwlTJsSa+22AlJXYYtS0mZJS+czUnkDtlbiGXP+i+jV4rukMWcnVzeerFXKbHR7Pl+N04ZCjwWZB5btG7Fmv+wjn8Pc/nU2oJO7CWuKAgVu24iZr2SdS1mrwTmI6+Aif3QpuRsOomJktlHD7Q6O2pG4KoRp1K7/Sxeqxp7iJCFjgmjOH/hCXcJO3klBjHbXzGMDqwyDr3sRZZB4IVrSwTINgQBQmdVmSN/Rn0n4rwvWeVIFhfLrPB9ZuMb1xPTkE14VtfYTYHOcEYauypNNpGc++YWhrazPyp/SZmz1tOsYeBNd/xBUnmmYzq3M1sTQlHHXE0E0S0tovb5N1ECB3IMtgFDQEzfsSDZUWE1p7iC8cUVssryU/KYkWmz/avRx3krAlKOa7YFMWD1qPOrIcBeFfROmZIB7GaJFbnTSf/8aweZcyglwnVjyFJUjxL/yHTYrKx49OyvidhD50+z6xgQJEJck402T/crHgY85RkisZGCy9qXqe+NZzco6fJSur93KQuo2jdOp625ZFvyCThvn8ohpI+hNNmPe9ZZoJeCY53yPBXx62cdpZmWzI1Fl3Vv0nRdmIz6dDhQNQFMUHbyb0dHzFVrkAEfqKHyIhSaLIAECp0Kl4Tstiz9T2+56jnkBTLzNRlVOW+RbhxF5aTVcSMcU7GzjEh9+hpJcZxwkLy78tS2ukcF7xkknrbThwsF1mu5ILQj1HTH57vs2H+L/hu3p2KrM6Rf7sliFbEJvLvTTncq/kIAfiL41YCNAKPRxxmzY50Gp0C0us603HIcKimlSlxwyitbeV0/GJEWwv1Z07yT9tczA4HTy6aSNasm4Bsnnoxl0c6VnHz0S+5QXMNP3c8qHh2t73sjk11GXadFnvPrfn8V+DEHkWoe8aPlIvyeJ5Ztp0gVkArPDhiOAB3Bu7l9ab0bm9wWBM4bNDVRGigDrpk5d8hjmrUqZwVbdNWkOd0RwceMuIwwX57PAc197BAzueYPIoF4l6GC218IU1moWYfWmzOYHeJzMBKAsxnlFJOvaWCXwmD6zcE38DjZz48zPXCLKyixFYhg9KQWUr8mTPmKp3uUlSLAg/ztLQWg9RGqKaeOjmCYLoYr6lBHxhCl1mDJMtEC604ECliIjMzHyMhbjtVubDDOFXx9vWRfOHpictKRAmI9jHQXAZgbUE1RRojkcEB3WK9LgayTeZpSC7u+1A3vm0dzCKmqRJsXaRrTqOXzUTRRnjhc0ogt+95PM79YPRb3pUBnPehTDeXitJYHnVKOHQV/pNNjtn8Svtf3BdbxbiKZ5gabiK8o5qThBOk6yBItBNrKqOBqyiUktBrRa6Jj4T6fWBXMpcDcDDO+DFwP//BJqaK5RxkAiTNZ826YjLsrcQ1txDSvBnKdxF6x58gab4isdR1H5rd8Gjd68yoLSUuYhwxThHZfrcTB8tFliu5IHgacmcxjrre51WflhGs13Z7uD0kiLKTYM7BUuKMFWgEgTnp1cqHnV7m72t+REBKPPeXf4DUCHlSGodqWgFYVTWG0Nv+oZQXcyhbsp5G2YrMRG7Z+iUGLNyi/RLTrZPJqn0Bk7YEAiEgJZ6s2vfIvmMZOY3jvSVXXP1BciiyWk2VPZ0GqcugzYixzcwapwxKQlsxY0a2UtP8MXFBQc7SSToQoN1so1BKZqN9UY+kpKGGatSpnBWugNHinCf4le1/QAMBWpFfCg+xzTSVuHADIzOf5t+bcviV9i0EZGxoEMQAdKEjCA0MpUkbhNRxhrBDG9lx2EjjTX/29mBcCYPrN4GK7bxv+B01FhNtKSt4Kk/x7uTJynZMuEFH8eNZ7vizVZ+WuRMhQClDZZBbEZHRakRmUYEeG4G0gqUdg6BllraCENFBvRRN5/QVygeT5pOQNJ/iF3Np8RfXtmWlW0LHb/1Lz7JkHj97TV5P+tfP8+qnA9zeevTm8b3HPvlo3fUW20PGw+7/98qUTYS8w0am24sJEOwM15r61c1KyLoHSkKIjEzsTlRa+hdvY3TjCzRxEKsssY9rmHri70wTy6msj2EvkxhOE2PlWhyiDIEjKeycxGi5jtOxi7kmI1Fpgwymo/+mxhaibK8BSSFWdB0OkkKsQHcZwzTzfh6T/kq4uZmt775Kw03JrMhMdE/ao05sJl48QWFLuiKXczlzIcNLfHTh+so09k1uUhZbDsINilcqqW0P4Vv/SBrXdVdwcJKQdQ/GT+qpae6iTTeXLGdGqNvLvPHHIBUTHR/A58fSuEEsZomoeHzX5hn4w7QzdBX+k/ets8iT0twhEdmpyyD1djj6EUETbnW+B4pO3QIf72O202DzerdcSUPg3srt8d6GxbCqJokN7Urm7OqJIcS0GYlpPQitwJiZkHI7tBlJ0R3mcyGV9Hk94z+HGqpRp3JODP/6fQyyBbOg59a7H6bFc0KKKue2sHfQmZqxo+GvjsUMv+VpFu76DsPqSjHKY4gXzOiwcb1jL9f1ty2lcmnoZfJxTQZvR7xFQmsxMTjg5FomGgzUWEx8FLKMv9cnYbFLPPxuEZ0Wu3uiuF4oZolmN8fkUYwUmqiQ4kAQ0IhwSJhAhNZBkCEEAkPRGiKJu+o6aKokKHUZcT4TYK9xbaZmMET0jAmt2O5WvVdW6B5bsP7O57z+oiNJXokLXt81gO2tPr2IPlp3XjF/bUYlyxC8tLrWbsj1kmL4xFLETw7V8mjCSVZEHejXM5PTmMza8rt4O+JN73vg2bbUZXx9rImPmtKx2CVn/ReBFkL5b9sDZIrFwEfoRZFr7niWhHdeYaZ8iL01IiRtdrc1CEhy/gcQGRQAHYLyL07dsmQlueR3hfcyz76TTdJMij8tc5c9W5tXSV3UYgqNGrdxeL65qGWfLmR4ic+CuLcMWV9hYYAWk41MsZjb2E3d2MXMqP2YyXIZEY4GioNneGWVTo+PZovxv3HIOLPFFXmUnIJq1m7IdS84ElKXMewfMktsu5mrOURQgEbxtlW+BIEtTIoJpaJ5tndIxB0e3rXeDODewiBchmxeJZ3VdlpMPtuzHvWlCzTTSc9cDq4FlEdpS9f7YChZrxiTSUN/flKNOpVzov7q78DX71N/9XdI8yydBLDxBULpoEvQ4JA1xFPHR5tzuENbhYjMeOEkgnOaECUH+7kT45+ms6rjZmWyzbpHOY+aLHFp6WXycVVj2GEbTYIhQtnqaD1JjKWdkQiMaP0zoZobGCfXUV06ijuFr9BrRdqmrUC/7yNmiGU4ELGi45QcTYJQi1Y6yT5pInPk15knH+TBoCISMu7p11iC7knJM0EiVzeXpzZovD0UJesJN+5iqeMYTXWiIh7qYQD1mCCccWyPDpvaM3EBzi5MwOeeugysFbGJrMhUvCXLbR9gLylHOzwZErPI1c1lw7O/4xZ2ETT9TlZkXu9lfLoy+nIak1lx7/39NsE10a9xpLN6Yojb+xe97VVuFAvRV34GwyfwT/Ncch2TCA8Q+Yd8O4L0AQCZYjHBAVraxeFY0pdD0nw+4QBWh8QO7XXc6vyenIJqtzxSQpbzWQZFgqiBzjPkbs7B6kyayJoATwXexZaWyQAskotg4/uKRMvj851b5qO6NcLOM+ci7DtoLlZ4iYcnXTt6Lmx8z/2d0dte5XphJl+Q5uWpW2LbzXXiIXadgLjhQYhNIAi4M8Jd98mVXAAQFRzglvfxlOZxab89enO1uxayq+wXpmbahVDWNKSzYl4iCVH3uLUiH3lum/Nz470lj5wGa1/eR882hht07thQ3wSq47q50Nx9n1ylAb3Gm752jIZgMp9q1KmcE2nZvwV+S5y/PzpfnPKuKKrLS9gkzeZezUdocSABGlfdV8Ag2gEY2bSfpVIn4cYaJd4J1GSJi4R/lfVasnpRnX8wuohwYwktI4ZB1muK8WM8CIKAVRaJEtq4T7sZSdDQJYQSKTWgkSU0p9ZyRjiFFgcaHLQTxHBaCMQOAui1IgAZpjzCjU41edfqvxcvSo/J2NlXrO++6g6s9ixZ1FLfgb6umhRdraIG79u3PAdrJzFhgeQ/7EeO+GzCBHwmdM/25z+exdq8SuydEg6NjDYoklzdXFr3vcv3aCJZPEVhkUD209le92Ew0haex6dnLlcKrju9kY3mmaQGHCFOaoYTe3g2oBJCFUHZtXla5nXs5AZtCXfp9pI8MoSYxhJ2FMqkHRjB3PE3sbp6hlu/7/mUWqKL1rHUVu/9ToPiRbWbsRat4z1nUH2Abi6dFjs36Q7y/zRbmCDW0H7ISn19h1I1w88C43x61wZ7D1301wa/f79Y4SUl64lpO0SMCDS0uyuqVNV3MEMqJiKgkd9G5hFzNJDcqDsJ1sdyOnYxu07Ah4457GoPYL5o4D3rTCqqFQvIszxgx+GPuVXYzUd1czjk6Ja0CTfovO6jWzrISe7R01htdWyWMthinqRkt2Yqi5tOi91b4NtD8qg/z7lviTzP8WxFZqLz7xp3ks05ZU8PwWQ+1ahTuXA4B6004MEXc0lq20OSWIMgCEiygIjkPlSG7vJjwG6mEuQRo+FrUFzUbZJvAhXbid7WbQCBorKu6Eb5UZ3HIybL+WwaTlVgsJmwa8NwGIYjdZwmCDNyYARF1kQyaUKDhP1MOcECWNCiQaZCiqNNF80twl4IHMY1wyP5Q9QZthxSVvWRfgwf32fud8t0y0puFBshEBoyvQVDE5Lmk+DHcHPjOVh7xLGdNzy2gtn4Y553ZuS62v98Si36AyKdwePQA2EH1pIqnqBcGs1eIY0gP9uPg63/6XW8UyvywegOvq/5EWUp8cSdXIu9vhyd+TTPBr5FaFQaOGVUXN6W3KOnOXiqlQ8ds0hjPwsr9nJ7+nIO7n2HR8XPEQsFRtDM14zi69BrFamVkvWYag9TaoslclQ8AbELqSiNpSEzm6Id63ja9i6jxCbSqQCHRJ0cyRpX1Qw/3q3z6V072xqq/bXhQnkABzQOOpMCALjqOkVIvs3IjjOxTHLEMV5zkojmTixNAl3HocZ8Pzkks2LRa1TkVfKtzEQaWN4tfA3E1n/BU9Z1BIQs56qxpYQbSxgdaqDMNptOi500y36lWlDUQ4D/drkSYMINOuLCtV4evnCDzh2q4S5N5lzYRW97iBu0Je6t097KDEJ39RbAHZ+61WOhNyAx5r7u+RBM5lONOpWLworMRMK3/hG9bMMs69BjRQJkBOxolCB5HHShR6sRed86iy92R7CoI5rC6ruYbomgcEN3sGuPQXIIusGHKn4ngpL13ODMLGvIzFYKVxetY1RCCgSN8lohe5XacmWVbfwxOlsrjfIwTtmGMyewni+FMZywhdNoG81y4VN0OLDKWmQEGuUw1jtuIEV/hnX2WdwfXARSGA0WDfKxYuJOt7N6YjKkdm+9+vOi+K7K3ZSsB3ML+pAoFqRlQO0LULFs4NsqPoHmg+lTfU20Pf7mm5HrJMu2E4JOgyzAyWoSguPZ15XOsdELle3V4YPzJPXAX0xfmxFD6xl+aPsnV9W2Y7QHESRp0eAAcwtVuW+xqu6HwFTSpy4nISmepzbkUmN7AICXA15jhlTCnkKZO8QjxAkNONAiySALAk2dVqg5AKdL6bQ6+Nw2gbfr7qT4vpvciRkzPt9CvOYwp6VhtMhB1Eth/N7xXcImfqvXd3wg3rULvQjsrw1eZdsqHOdtjHKNg31W2vDtvxsroTKXeSNC+coYTQDHaZKDKXfE8b5jVg8P275jTV6VQopznuDaiv9BAI4caCQhPhKumsq1GfexolGptvKdgAJu0B72m6zjz5PmWyGjt+e06tMy0izO2tJZ9/SU/KH3mFiXvIvnODcgMeZe7vnaPO8SgUMF1ahTuShkz4pn5Y65WE0Sx+RRzBIV1fA6OYLrxRJM6DklD0cQYLJQxa+0b/G0/R7aSopZKe6munQUmdSRvyOT7Fk/6/niDkE3+FDFr0HcZsQQO5kFGQ8B5VDwEgS0KAadh+FGZS6cKABbF1X1HXx/g4YVmYnE6ubSLtfyL/tsooIDqGvNY4ucQWHANWwc8TcMtW2ATJU0imOacXwiXke+Jp17pfd5VvMWpZ0TYXIWXxSdYrpQjqOzASpPKd/rfJ6epch8Y3fqWk1cLxQTve1liHrI2ztxfBfUHVTavWj1WQmwntP97etvva30XT/XlYK5lcjoUSz44Sb/FSz6w58x5KF3pwSUa3g7IpTw5oPcJpUxzGjiGLHscUzjmDyKb481seNMKE/b/uTMXNS6pSee+uAwMvCBfQ5Jmmpmag5yXDOGCH0Au6yJmKwSE8RqsqTdNOQVEy6aCEFmnFDn1cycgmoSOiyMEyVGiC04BD3lxCuCttXNoPf/jvv2i+dTat2lprwymi9grFx/Hr4ews/naYzyzAz2V9XEnf3qx7t0XDeXjSeqsQoSO7RzMUl2loi7Ea0A43vEzbnOP/zr9wnCggxMlY9AjR4mLoak+azdkEuLycZnoXO5dUKMX0H58K2vkGSdxc7yGe4kmIHeR1CkUbxqS/vQ4xw+8i7uDNqzTHo42y36i4Vq1KlcNNLnLedXn04FoDp5OJsP1rJa+xrhQidRtHNKHs5f7LfykvYNRgmNSr1JAaaJFZjFEByS5NbU6vHiDkE3+FDFr0Fcf0SJmytZrxhDHWdAECEysYd0hstTt+ZIEjXtLm26WGqsK9AIILXDBhSZgDu0B0nQt1MtR2LAylZpJp+PuIfous95WvgT12kOESW0EyRWwNLNhB+9HYPFQrMuBoaHK22p2N5rJp9nbM/CijeUVXjJesUQdQ3mwydCYLiSDduPzEdfuGpXAsxNHu63zqnf+0vvcT69Go+e27MeW79nNaH0s+DxTJh4MAYcxsNEimaa7SE8Yn8Ag07DyvsWwBvfY5ixhABEWjK7S52t+rSMFpONfCGdXwlvESW3ERjYTOjjX9HovO5/SisRLXY6JR2NhBNFC0ZtHI/ePN4rRurfjlsZLdQzQmhGxM648VOIO2UY0PaY6zo8S00NxMt70UI4ehmjzqUdnklC/qqaAIqx5eNdymlMdhqDEeSSxpKUWB478VPiu0oYKTXx87zZuGocW+wSeq3oPn/91d9hWMX/YMCCFgcg9Oif7mxSX0rWM5uDWEWJPFMaac9t4w/TzriN8JzGZO+kGte1OI3THrJAfe3QeO4suO77eYhj7DH3DLFdIkGWZbn/w77ZtLW1MWzYMFpbWwkLC7vUzbliSHtuG2mW/bykfZ0IoYNOOZBWOYgYsRkBmXIpjuFiG5FCOw5NMPox6UpMiGvrbwi8QFcErkHJJZ0xfCK0VCtbbhHXMd9Zpicu3OAVo+KbWOHyGBh0IiabEi/5VthfmW7diyg7cKBhh2MaP3M8wO81rzFPU4iMgFXWckwcy2jOcIAJ2OwSjfrR3Bu6RzHExt/iJR46YL0435g5X6mCQeLykkF3pQXfe9LfZwd6/HnF36Tj8bvedPEePJTIFvNkDDqRyGC9Xw8YdBu7rSYbP9Fs5PuabbSKESTd3e0V/fK5DCZLZRRKSZyWI7lec4hd0hQ6F71Gdq0Sz2fUj+NgiwFHRAJpjVsYIbajCxsBi1b3bKOf6+pefDjb6Rkm4Od5X9BnMoiJ/oK0oy9P3ax4Vr7wezJMeWyS5pAnpaER4B/63zBVLqNKjiEwLpWErHtYua6YDFOe8j6myN11XP/6OjecXEOctp1dEbezoW4490cWce2S+5Tv76NvVeW+xa9PKHV8AV4PeoMFhq8gMYuM8rtY2bGaTG0JkVMWKOeqzFViev1VHXLtHvj7e19/O8v76Jfz8T0DYKB2iOqpU7mw9DKhGD95iTfkLtaykLcdN/GA9kNChS7ChC4EwIFAiGAmnA5ssgabw4E+LEYZpF0rUBhSK6TLll68QpSsZ82RJByyksTiWbUBFG2xORFvUbatnesCKpmcchsPnprvtS36ducMEjWHiRLaaJWD2aGdy6KUWHaUzeUaqYxhQif58hRmUM4ouZ7ZgoUvA9KYHVYJphbahVCe9amX6neLxl8/812Vu7wXfXnr+hjEXZ4L8PbUDYRLumXjzzvhkoXow0t0x/Q4iksN7jJMnvIUnrhiXFtMNv7sWMrVmtNcxyGqct9SMlbprkCzWcig06pkum+S5vDFh4eJnaN4gGPajMRYvoK4UbDgNdo3/Bemlnp2vvMKq4SHaDHZvLcYfbxR3v0i26ucmL/nfUGfySDCQS5G33Dfm4rtsPEFHg2pQm8rZ3SogS8a0nDI8H8sYbEmn6nDuohpU7aJH4zuINxYgoZSLIc6KD5USsWCZHIak/md9UXCNTpaamz8Qfsa49oOQUmk8oXOa19bfpfbQ7jvWBNbDllZNOVnZC2O5IDzXXLVe8VZesyViBPp6dHsbQcmMlEJq4j0c+/6273xM971eO+dz9EVajI9PqKnh36I7RKpRp3KecdrovDVFwIoWU9kUxERsszjAe2MpQ49DmTAIYtokBDAuQUjISNzSophzaFExjsqWKoRsNR3kOhbMUDl3PCY/HMKqllV8h0sdgeLAg9xC7t43zLLHUsFQMl6hhvzGCOb0Jgl0hq2kB0VT/CW1awIuYrwwBNscszh1/J/skDKZ5M0h4qwGeR/N52MF5t5us3ObZrdDLv2uzS0liJ8/T5hgToW2ApAlwRjFvDsEafiu0uI12dxUJX7Vq8iwi56bB/3NchvWal4B/2c52wzI8/1s30xmK07f8f6izWryn2L4cbPmBG4i/w7/kROYx+VMJx4GiY7d9xAtLkRfV01xzfnOLN6ryf76Ww+freILw7V0jTyBkprW3HIcO/uCBZNWYGu5t88GB1KgvMZP+u4hwx7HpukWVg0DjQeOmlAv5Nprm6uUjNWNxd//q8L9UwG0rYL3o5epF+it73KDdoSYmInUxV4Hf9sSGfRlFgKq5uZnXkXC2Y9Bnm/g+J3IDKRhEjAdJhTLSb0soxdklj1aRkvRH5MatdHfGDP5A98m03SHDSiQKiPYsGK2O5+4Yq93Hywlle+m+5zzc7t/CS8ZE9829+jrztL5NFU2fMe9LfV6nmPwL8R7nx+O0oFVlpW89HhOdQ4uitfnK8t3fOJatSpnHe8Joo7/AxuqctoOnWMmuYuJumMBFktoNFTFjCJj9sTmSV+xUThOMEoNSN1SFwt1PCC9EcQBARZBuN+0JvBENF7GRiVs8YVJ5UpFvOY8BbRWhMEwC8tM3j43SIKq5vJjprGMrZiEGRkRDC1MLfxz4wWG9F07UdAZopwhE9180gbMYz8hgDCnPpl83WHmKpRFOu/vThbGbBPzWez9jGwGEGAnNgn2VFSRrhB0cRzK817LA7CjbvIsLeypiHTLaDri0skecuhTLKedHqa/HnknBmzXlUohiIebV+bpxlwAoA/A86fl2hNQzr/Je0jwtwCJevJXvqXfs/tbZgsp33bF0zTlvB50Tpquu5zf+fO8nocMoxpzOflqw66t+G2HKrlesFKuaOD40dP89SGXKYnfItflSsxuHrAZJMorG7u/tJ+JlOXZIZS6aDP5p9/zmKiP68xfn6MyqId6/hPWxWdkgPDVdfx/YIZ1LSbiKtuVrZ9nV482ozehpKtC03kOD4+k8wmaQ5oIa1xC6OEBpaKn/PP4O9RbJlBninNq6oE4CVG/8KWrzDZJObrDsLGDwe9w+I32WUQxnOP++vvs77ncT7HefXfI9xYQoqhk+WWvUwN6YLK493HDCFUo07lvOM1UST5SRNPmk9M0nxioHtVmHY3/1kwgxq7iSO6q/k1rxEkCDRJIUSJ7ehwMEwwIQNd6NnvSGL0+NGQuowt64pZaXqXxu2joVZWjDyn5pdq5J0dGXIR87Q7GSk0ESW0IUtg1MSSZtpPZulu2qQ5vNSSxhRdHDM0HYhISOZWhgvQKgcrn0FgmNDJzfYdBButPBgD36+eTk2LiZsC/sk0sYKDNS1kvJhMmnk/K+07adBaiNTowBDp3tKLCze4NfF8B+CW+g7yG9KVmoy9PGuXSLK7cD343x7zlTEZqni03SWgOpCtO38GnL+g7weji/j0zDzmjWgnNHXZwIwND0Mze9Z8JQO5ZD0BurnElRrcYsQWuwOAW9hFQtsRfjkWyppnMz0+grvLfsNUqYyDhY3UmH8B4M6O9I3fHAhDKUtxIPdwbV4lSW17ujO4z6EPuiqUPK+rJatEqev7YHQRccY6BGDrznymJyn31jNhyvLVFlptGiqkOP5edBWPjT5MorWLmIRxdF37JBV5ldyuO0RLix6dEMbB6EXkP5Tljqv0DdHwjHPcMvpj1jSkc2fgAZoO7cNy6hgxgwif8fs8/RnPvYRQ9DAKfT/bRxvcmpxtRsbXfwXDJkJY1pBc/KmJEgNATZS4wDhfQpcx9oT5j1wv76dVDqEdAxOEE844OxEBGQGZo9IYUp4/TE5BNUlbv8dUykAbSKBs5Uspie+YfjGowONvlJjxAIJ/q974HuHGXZRJccRRT7jYSUPMjdSdOsY14lFa5FCecNzPrMBqvuf4FwGyFQGokaN533ED44Q6BEFmhqaCvfYkBATyDZnOqgSV/K/wK64yHaaY8dxp/gV/DnydORyEkBFEaq2Qdjc5gXedn2fST6JAf7Flg7lv5x2PLeb0ecsvvCajn6DvAQXy9xMs7jpHuEFHsF7rN+nClUxxUBjPI4HPK8/CWeniohva5/keD+Qe5m7OYXzhc0Rru9BPWnROQfeu7/NMQiB1GeS/wt7jTay1LqRY7yMpUrGdmnceIETuYIdjGo/YH2BnwH8xRmxACB4OiTdCZCIncv/KMKGTHY5prA5Z6b4ef9forx0rjySRYcpjrK6FawJOKJ5xT6khj4U+mY8N+tpdY1dLzHVOY6yXpI2z4RJmuqqJEiqXDz4CrGv/Wk7uCYnOkKu4qXOzU6BYQJJFAgUlyDpZrGH307OplyZxk3gSUZQI0IhgVzIS4/SGQa3QL7SO1ZBiAEHcrpXpiPoOwo3VBGg0JESHMKrxNFqLRJTQxn+Km0kYFoK+yYGEiIiDGKGJRE0dP7U+wNaI3zPK0sJobSs/dPwSvV1DOk6V9woHlKxH3xXF61Vv4IhIZF+zwDhHG3RU0VJ2kOz7Huv1WQzKCO8jUcBFn8/fNzvY8z6ep8G91+vx2GJenTfdv4fBs43n2h4/W1K+HhK/be1tG8zZLs+KGV7JDB7sj/0+J09spm7sYvLvVYyCst/9gPiuw1RXlDN+KRdvQj3PupcD8Rpm2XZCoAUCo87ZA+T6voCU7iQEV7+577lttEg2wj0/4HxO9Vd/hy/LDynbrMB6xw3cJe4kLniEW6MyWmuiwR7MVuE6r+txfeea0dvh5f+GtLuZHn8Tda0mjsUshChFxDw9NpnVedN5PqUWKl9SQh48k5eK34GWE8q/mY8Num+vaUgnw95KfkM6qz2eY59hBAP9jiEWP+cP1ahTufT4TAg5jckkOebwa9PfCBbM2NCgx44gdJcV0yAxTahgtFiPXrBSJ0dy2jqSNKGSmNEJ5N8xOGmAobRVc8Hxvd991KVMfHkK0AkOkfaKXWA1Yxd0IEuE046h4TiCYMcq6wkQZFrkED6wz0EGGjssJGph5lWRRNbpu40mD+9LWsl6MHzF1ma4v+s+sizF3CrsVgbkPi7hfBvhfT5/T707l8fjPE/6vV6PxxZzn33zfNVE9TNp+W7R+rbVs5Zmtq+gq7+KGb1IzxyuSeR3tgcIr9Wxwvnxxg4L8aJMQ4eF9n88SxrlaNuMF35iPduMxl6MgwElQ/hu/5+Dod4jE5ju/uA3c9v5nKxiGv9tfcBdlfvPjqW8HXAnfxhzxi0TE3R8F2OBv2RcA5QrcXjObffsWfGKQec0ygrNM7heKGaccS9kKlvK2UnKuZ/KA1J+3u2xdZF2d7enzqNtwIDuQ/q85ezYKnGLbSfFXZNJSxzANml/3zHEtOj6QjXqVC49PhPJisxEore9TKTUigSckcIZLTa4a8PKQJtsoFQeR50cwQyxnPWOG/i+Zjta0QRVnw26CRc0E26o0YeXyvWz2xBIuxvy/wQ2EzrzaSqlWI5KY5ihKQdBkZ0RgEY5lEJpAoci5lPcMQnBZOMvjltp0+1lQcZDPH+0FmvROsVzULITyj5RpAicA7cr9iokfiGrq2e7J5zczTnK59KXk7W427Nzvo1wX8kHr8G7r1i78xRT0+v1JCl1ans1cP0JrDq5UN5n37b2+T2+xpErw9gja934yUtENhWRLSWxhcm0mmzuuKyDY3/A6ROb+YTr+IF9Ew6NfOEmLd+J+2wm73Mx9nuT4Dmbc/nB9ZxAqS1s3fYyufXO98opDZLfMMxt0GkECA3U0WKyecvZOMuMUbKeqvoOZauzvsMtX+NplK0IVMZytyi48xhXW/zK5GQ+5r3tOkgDO3tWPNHb8pnuKKawSoCnN/X/of6+o49nMdRCd1SjTmXIkT0rHqIeon3D12BuwRwyhoKOEUwVKgkSLLTJQfyPYyEJYh2jhCYMgoVxQh31chhRtFHZaWCfR7Cuigd+Vpyek7Svgbfq83Q+koOJoxO9LBEvnKZFCMGABaM4kmZCCZfbMYg2jEIc/xGyj2eXpJDTOJm1eQYaMrMhKV4J1jZ81b0VdKJAmdibKmHpX5ySEzmEHXiKJ8INxEQ9BsRjLVrHdEcRhUWAh1F3wYxwf4P3ALZvz5Wzvh7P9vrEYF0o77NvW/uu1pBMtme78l+BthrQBburhdQ0dxEhy4ii6BZ1dhmIoam38FxtAha7A51GRBe6n3/WzPTSLQQGpjnWH74SF/1VKujLyPc0Ds7Wy+N5rvPgKXo+pZawA2uJCwqi5kAX8dKJ7veqqRJTVzsJolK6zaATeXKRUhXGq2YtQJuRJiGcqpJS9sopxNhTvTzrOYF3sdY8gxWBie6x3OvZMMi+6ampuGFgKgcB6cspLHJq4OHH8PJnwPd1X/sw+oZa6I5q1KkMTZLmE3rHn6BkPeNTl7G/MZmNO9bxHcu/kGRZkT3R1GCJSGZ34xQ2SXNIFUYRKpjZ7JjBm1uODIkXbMjhx2jxnaT3bH2PW8y7+GTrdbSYJ7NXm8RiTSMCMlokksRTVAlj0V89F3vTl7Q2lRGCmR/IHyAbtVASQnbqMrJHvQJHgaiHlcGwzdhdj3XR6h4DvbVoHZOlMoRm3Kt638F5wJyPifRyoI/2Xizvs7/v8b9FW8n7QWYl691uVurxlqx3CxOPSkhhe+c7SlJIpvK8XdI6AFvtk8kX0mkx2SjwnUA9RGLLT3dwg7YEA5z9s+/LS9bX3/wZBwPwuHkZHZ7JIb51l53nGIh3yPeYq2o/Jk4uQ9MiQEQahS3p3e9V6jI+P1zH147hvG/4DdfERyrvbdJ875q1APVHMHUKJMkdjBLO8EfDj5Xscyc9Pf89t+azo8rJTl7vLP3m3X7fTOc9W9/jFnaxh+tIstoHlB2ctTjbaxHoapNbX67NiKn2MJ8frqPhpuSzi8t1MtRCdy5Lo27NmjX8/ve/p66ujqlTp/Lqq69y7bXX+j32b3/7G/fcc4/X7/R6PWaz+WI0VeVc8HiRstlO9sQKjCeDCGgu52vGYIlI5kSziX/ZbyRPSuM23W4MgoVZ4lfM4jn42xqlJBR0b1E1VfZbPuiKxo8R4Dv4u7YuhgtN3BK4i3GOEwhISIigDWSEFkaMT6GqvpqgxhIscgBWREJEC1ZdSPekeHKv8gXOWqxVuW8xzLiLPcdfoWXBn709OCiG2+EDjcSFG4hxts93cO4TT0PubLeuLoNAaC+GSHt9+5Brons+pRY2/hhTqcBKyyk+siVwb9hp6KyHwPBu+aGbXmVB7Qtg3KfoDfpMspliMUvE3eyQ51IcPqNH0oZLWHrNkSQazVbsATKfuSqRDDSD1t+99Gfcu34XmagYW/2dt6+Fgkfd25r2SYoRnNyP3A5+jBQ/bXBJpARvWU1u/XfZ0pDOUscx9ILINbf8XDGuS9ZDxUhImk/DTclct/V7pMulOKo1aMJilFqsR5J4MLpDEYV2Yq7vINz4byJpVYS/PQSD/Xn+e7SzjxjQTovdXT0EYKV9JzM1h3DIEpIG5gilisd3oIu2iu28HfGWUtvYpXc5fCKltlhC7fVs37Gup+DxIBhqoTuXnVH3z3/+k0ceeYTXX3+dmTNn8vLLL3PzzTdTVlbGiBEj/H4mLCyMsrIy98+CIPg9TmXoUpX7FuHGXRA5nqgpCziom0th4T+ZKZdxr+Yjloi7qZJGIcswUmhiuliBvfprtGExygnKt4LDDrKkFKrXOLt+LxIXVyxJSjHvtRsqWZGpbGG5xHk3bZrD1/nB3CI18rU8GlmWmCkUg0ZAI8tocVBvEzkUdC3Xpi5jzbpi/kvexzChE4scgAM7X9ljONqYTHbqMqgrhc4z7ngvV1baJmkWxZ+W9RgIB2XAeeCaDN6OeKu7qsTl5nG7zPH1zLknuo0/xnR0O7c4tBi0FlpHDIOsbi/tUxs0PYXKIxOpeuN7rGlIZ27ytyisbuYBXRGJ7SUEIDI7864eFTHu3R3Bc7c9SXqs8rtfWmbQ0u706PkzknzxkVVybRvnbs7B+u4S75hOl/Hnx3vmVYzeZaw4NeNWxCa6kwTc3+msYPJgdAcFmulMj49gpR8jyl/c8Z6t7/FT25tYvzIR4HttTkOmrusYScIpdu0D26Rf8/Pq6azITOSapHjv9gPZR1+hlSokGVoJJjp1GWs3VFLTPokCzXRWNLqMtSfJ5gXajVqapSDW+CQ1+UuseTCiCNPRPew4ZOSXgswfpvWs7uJ6luEGHXHhBp5PqeWq2o/5xBzHXkS2CdfRabVjkDUsQB74oq1kPQltymIhV7eQ8tMdBEQt5yrLx4QbdymG3hXEZWfU/eEPf+Dee+91e99ef/11tmzZwv/+7//y+OOP+/2MIAiMGjXqYjZT5TzjMgh2NM2luGsGnRY7adZZWEWJkUIT12sOgQMesT/AjZpi4CP0ooi2Kwp75U4m6bQEYVMMOmQIDPee8EvWYzq6feDu+KHAWRqirsHzmQ8PA4o4b2jtIeWPzZCgOcEueQofOuZwr+YjRnMGg6gHGerlMF5vSufapPmkz0vmTzvgzsC9HOyKIsZRy/uWWVTkVZL9uGs1flrxijpFbX9VrRQQDx/otQ3Aq+q6njWO9O6qEkPEgzVUuNDB3L1uQUUm0mT9jL32JIIDtCxwGTvueE6PrTaXUPnGH3fLuFRPd0vgfPSOhfetzgWB0/u2ZnQUxzoP8aFjDs98CH+d00x+8k4vAXJli4++DXyn5yisq5SVtnDyd2SSPetnvcZ0us/nCimo2M7aPA0rTXmEG0uUrUqPpAC/osIeFUwSsu4hPylL0XVzGlH5SVl9ZtNGb8sn1NFOnTWEr31LoeW/QsKZvQwPiqK5S0+VNIpCV+UIlP7g5YFzetZDBAd1DKds+jNkJc33ej6+lYLq6zvc+om90Z2A5ODLf1QT4WggzbGfp0pn90iQ8OxD7gVB4x4SxVQabvozs533siEzG8zvQUu1/5qvvngs8J7aoHFXGcm/wyko3Fe/uAwX+5eVUWe1WiksLOSJJ55w/04URb71rW+xZ8+eXj/X0dFBfHw8kiQxbdo0fvOb35CSktLr8RaLBYvF4v65ra3t/FyAyuCo2K642YFFU+7kqdKVSuWBjtXs0M6FAC0BiBwzTON0eySbpDlkisXcJu7mr45b2cs0fv/1a8yUT3DcNIwxwYGEJl2nVKf3fUmdMSXvmWcqRsnlYNT1tsXYz0C0IjORZz487BGQPpWG+gMcs4+iRE4A4FDEfGjsZIxQzyihGQmRMyhZyC/p34aKa50SBsq2xbUoE0WF58TuHCxzdXOxvqvUnHw6HiqaZ/cff+K6thMFSski32v0uZ61eZVKHNbl8NwuAWcVzD2ICa3XLaimSiID7ASLWhpuelUx3Pr7XOoyLKeOMba5hedTat2SKU2OBzBJkrIgKFkP5VtJc9hJ0UlMF8p42n4PXYUFNHGQOEM5T1nDCKhfDlEjvc/vT9zW2VfjTh0joam7+kifMZ0e24hVuW/RafkhO7RzSYvxLkbvyub3zQD1fD+e2qBhRWZ1T+O4jzCCgPTllO6vR5ZhyyGjuxRaTkE1E6qbSBNkQjV2ZJ2dBLnOq27u2jzFA7exfRLD/uH0nEWVouk8Q9yMHxEXNxL+tphsIPuOh93PzdMA7zMr28+9ihylhF98R1NAS6aHgezsZ9mpy5TFoAufMTn/8azuvrKxl5qvPTyuiUqFk74WEX7w9f4bT1ZxsPXVHln4nseq2a9nQUNDAw6Hg5EjvV/SkSNHcvToUb+fGT9+PP/7v//LlClTaG1tZdWqVcyZM4fS0lJGjx7t9zO//e1vee655857+1UGiUdcVlZYDFmP/4WqN95USj7FDCNheAhUHqG8s5pAsYtj8ihmiV8xXaxgOE3k2dL4RHMdNkliBE2MMtcoBp0/pXZnTEmFP2/DUF2t9bbF2E88mefW1YrMRPblv8Y4WyeJYh2v2pbyuZTGsA4dazTPMEpooksOoFQex1cBU7g3dI+XWKjvgNZDFmTpX3jqxVySzDMhEBY4PRIDvjZPT10v9BV0raJwVsHc5yip4ekNWpB1D56aZv2dL0Y6TYy2Ho4+x97CESy1TeBqTR07Q5WqJMVfRzHB3EUADmQ0DBM6+bZ2N9XEMUU6gqOzgelCJbv2OdhXbuDq9n2K7AY4JXq6usVtXdeXNJ8YzwoeBdVk9xcS4OyXa44k0WKyURw+g4T7vKsg9MgA9aoS8qSXwe1luHic30sSxrnQJepOmsRoZnPQawtxbV4lSZaF3BUYxYIZGezemc+HjplUeNTN9VzYuSVLklPcnvWqsoPEGfegEQUlhCVpfu+Gu19DqmdmcsL4qTA8hFtTl+HuC5GJynPwkLhx4zEmu8rM9Vm3Fbr19kx1XjWHPZ/FQAwvX+9/TUkp06Uyvx5bNfv1IjN79mxmz57t/nnOnDlMnDiRN954g+eff97vZ5544gkeeeQR989tbW2MGTPmgrdVxQfX9obr//Gowed6mduMJHQVIIpWfhCQz3FHFEjdle+2mCdzMi6DpLY9PBhd1KervdcX/jzrRZ03etti9PQAeA6EXrIPyQDsO9bEvxvTWSKa2CTNQUDRAey02onQtKMRJKTQMfzc/oJzK6vcKUvRveXUY0DzuV+KQYFb3uScrs0fQ/X5DCHOKpj7HOMSXd4g91aiTwxar5SsB1MzSA7orCedRmI0pwkWrdw2MQ5mxfPR1sNMkiVkZCqkUbQQSjRNTDB0EmHqZJjGTql1NB865kAjLBFN3RUGkEEX1C1u60nSfL6/QUNS5x6CtzxAbv13e3hm/MlhpBdUU9CX0ezZn53by0sdx2jf9gXPpy/nqdJYd1JJD1mWpX9xLpR+rLx3zoWu9YTA+9ZZ2DQSw2IXKgYruN+3gJR4jIf+ToTURXCAlhWZiW7dx9j05SyaksLmg7UE6jQ9tqnXrCvuTqoYoHBvV6eRGvP9rPKNl/Uju+Mq5aULDCFUbldKhfWRwe0qN+bOpu4tTtF5jmON09BU0e2dHOTC3Nf736bLodCpl9nbsWr261kQHR2NRqPh9OnTXr8/ffr0gGPmdDod6enpfP31170eo9fr0ev159RWlfPAQPTBStaj1WhACKY1ehphxsNYtKEUmCe5DzlU08qmF/1kNzlXvU0NdVR1BtA2bUXPARx6xM4MecPBmQyhrMQ9DC6PwXVt+V3UtJioaTGRKSrJJfdpPyJAFthmm4rNISNrlISiyOAA8le4vGse5ylZ77+gvI8x4M+gOK9bFmpSxIXhHOMSe0x2vTwnz6LvrqoFjL8FTpdCfRl2TRifSvO4NaxSeQfzfsdwmqiQYmkhlL86bmWJuJt5mgPYTDpCg0PAYUEbFEdefRoAn0tp/M+UZmjciTF0MhubxhGft4ug9rjud96jpFnrvt1cJx6iYn8TVbUfeyU+ULK+WzzbWbN0UEZzqlIlRF9XzTRtCQbbKCW+zCdxwcsA9qxqMmYmAAFRy/k8XzFavlP4T5gw0rstG3+MpamICFnmblli1MGDNNVVu/XpCgP+mxvEYu7S7WWBK9bPeY3p85L5eZ5HUoWf5+V6d3N1c7Ga6tjkmNXr9fo+d1eMdKNtNPemyP0aWwMWvHZp2r2Yi0M2UejyTg5y4ef7PPtK4lKzX8+BgIAApk+fzo4dO7j99tsBkCSJHTt28JOf/GRA53A4HJSUlLBw4cIL2FKVi4bHgHHm7aeZIZxAsouME+rcMgibpDnkFExm1adKBvSjN49XXsL8V+D4LsKRmCzryCtapwyMvis6z0HVMyZmCLM2rxKHrKjCr8hMVCasNqMyKaQuY0Vs9/bLbZrdXKOpQJZlYDM/0G0mXOggntMIyNByktzNOWw5ZOTOwL00dlq4LiCA0MhetlwHYAwMdsuiTyNQTYoYkvhOdr15V1x9wVq0ThGohm7vVP4rBAH3XpUAxXugrgVaqpkS0M4hayyCJPEzzbsYArTYHDpCtQ4YPgG6mkg4s4+HNMN41bEUgKtqP6btTAl7LCnEUMP1mgN0FB51G0OuDPuJEVUQDRWNo5FllKx7j8QHL/Fsf+NBf16hpPnsbkymqGEdD0YVdWe5+jN6XbIpnhVDnOfMAhYW/o7HpLcIp7NHW3J1cwmmFLuklFcMrf0CW1AihRZFn27FcCXW70axUMnCTbvbHergLvnlB99396nSWGq67lOyVoO1fqui+N6H9HnLadz+NXfpvoDIHww6ZrM/79hAFxSDZajFz/njsjLqAB555BF+8IMfcM0113Dttdfy8ssv09nZ6c6G/Y//+A/i4uL47W9/C8CvfvUrZs2axdVXX01LSwu///3vqa6u5kc/+tGlvAyV84XHgCHJTyMLAjIwT1/KLfYCJDSMjjDw961a1jg+AOCvm24FspWKiKKIiSAO2scqRad7W9ENVW9QL3U0exRQ3/iCIrZpT6VhQnd279q8SuqiFlN58gTRtDCMDhLEWnQ4wFkwyGHtpHXfu2TIMM56iFGyHqtk9Q5Szn8FTuyBys/g9tf6negGu2XRqxE4VOMdVbyp2E70tldJMs9kbR5+J2iv4vPgZWhJpyrQ21vR6AyY7XqqbKEATBMq0Ip2ZIdIsxxClTWCtuNNpAYYiaSNZZrPedWxFBnFO3SdtZVN0hwWi/kEYUaPFeMnL8EnL2FuMHJEjkPfYuKaoNOY4q5lTUN6z7CNpPk9xbMHqZHYY2vaFz+yKTmxT3pJEQGsjDlIhLETAiN6tGXLkSQ2mH9BuEHHNOt+bpV3k2/PZPXTHrsWUQ91l20rfsd/UlI/726PrNUBkD0rHgr2QEutd2zjAOnPO9bj7+dj4ddHHx5KXHZG3Z133kl9fT1PP/00dXV1pKWlsXXrVnfyxIkTJxBF0X18c3Mz9957L3V1dURERDB9+nR2797NpEmTevsKlcuUQ2N/wJiTq4gRmwi1NyMIIGDn2qsiqS3eyXSxApA5Le9m5QdpTJ48mbSwav7RPpt8+1juMn4MSRnKyXyNt6HqDXJOIK7srKnDuoixHncXUM8pqCbjxVyeT5lLl9XI+9aZFDs9li7P5Ru1CYyUxjJPU0+03MppKZxhYhetUhBdQhBNcggfOuYgCqCRBb52jGS8eMYZ8OyB7ABTk39hUJ+JbrBbFr0agWo8XTdD2cAtWa9kfwY6Yys9cPWFnIJqMvJivTx5rm26avso4qkjjhYSrCc44ZjCDu1c/kPYRIijleFCG3rBRjBmouVWTlkjCEbDesdcwg06gvVa0jOX89mxb/HFoVpe1P0dUZJAhvam08RTS4Qgc0AOJS7cAMMmkpBxD6t7u49+wkDc/XAAC0C//dlfX/ZIGIre9lC3QeGUdHElH/h711zad8p3jGd1nnfWuSur+BfBWaSZt2COmExieLtXmElOQTXR217ts0LHWW8/etSIvSzoow//f/bOPD6q8t7/73NmSSb7ClnASGICEgKJyBoIMYgii/ZSQayx1XvrQnH5tejVLu61aiutShGttt5eoyAIt4JRJCVGJBBASEgIkMQEAiQTsu+T2c75/XFmJjPJJIRNAefzemHMZM45z3nOeZ7n+3yXz+dSwmVn1AE89NBDA4Zb8/LyXH7/y1/+wl/+8pfvoFUefN9Ydt+D5G7x4+Te1YwQGoimEZUgU1vyJZVSGj2iBrVsYbR4glliEcZvd4CqjgUBlcyy7uYa6QQUHoeYAXJDLkXYhLg7W+qZKLdQ3XoVkUkZ/ZjnnyqNokt4mGT28Xt5FV9uT+Nu01HuVm2jRQzmECPQYsZHMAJ+mFFzgDH80vQLx6V+/6NxdJDJ+zZjsLUpwbH45obewcjj1YwUm/HWH1QkoKD/4nSOns4BF45L1YP6feBSNnCTFqMD5iYt7lcs405JwP6sU2YvYWWeQsr7cXULd3R9SKRwmmNyBNPmLuWBL1JoNZtJF4uU6lc5glixHp+Jd1AbPpNNeZU85uRBypwawxt3pvDOC5u5U/o/BCDMzwurKYATJj9UKhHf9m/Z2jZe8WjHMzT0oydxlcVyvk+XSvG+53DO3QUX75+LQVHyosKraamj8aZVrteynSeWDvJ/bHXpb7tKQ+bUGMfcYOysQKvqoeR0J3HjIh1pJva83JmCrXK9z5zy6hdlg+upngnpT5y1h+57xSDv8KUE8cxf8cCDywdPlUbxjnUB+6XRHAi4kVqGsc6cxjWqOrwx4y2YiRP03Kf6lNFyFVhNRGoMJPgaEJDpaavFcNQ2OV0OsAlxN+PPHiGZ9uuWKflIDl6mOKKDdCxLj+PP19XzovZ/mKM+wPKwQharvyJMaCeek8zyrkKFjIhEkNBFt6RlariJmzQKKbFOo3IsRL5eahcZH1D6vcQyEsHaA+au/uTO8XNc2nXBcLHOezkiaTHEZVw6Bq69YtNeXDTAc7IbCYDjXbUjc2oM+U9m8MadKeT/2Moi1VcECl2M1tQD0GbThS3ymsRtz2cTMv9pXvL+JbXhMwdtmm7Ob3hG+zino24kJCwCH43IcfFq1pjmccgUib+5gcLt6wc83u4BzyqoVj6w3d9TpVEuuqfu7tPd3xznACzVBXzz4bPoP3sFSv+F/rNXWF4cx3bTOLQpSxQvXbueYlMU63qm8GL2YZKf20byc9uU9sTPgYBIaDjimMeyCqp55pNDLtdflh5HhqqI0UI1MgIRsYku71Dh9vX8SfUmomDj6SvZABU5jjkFcL0f57zjgeD8TlxucPcOX4L3c1l66jzwoC/su+CJMcHMq9jDLPVh9H7TmN+5GrTw5+vqKdzXTIDcQStKPo6v0APINHebWNt1AwulXPZJCcQIbVx/uVS62gk6TVMI9dWy0rzDoeUIfTxcm14EbyN4hxKbcS/vrPNnsfkTREEk7No06NBjqS5ALZuI0nShMR7jyREllLZMcVlo+4WObJJE280jaA40ERngrWjunqHvLoek48sKl1qKwBA9h+5yspzH8/7qFuVvtRsIV3fTaPHDZ+IdvJRXiQw8rNrEHfIOirJuZ82pOdS0Gngx+zAGs1Ig4C4PM7N2A5lLFkP84w4Pk1aTRkVpFGpNIdd07HUvH+VOq3WICfxDzSO1yjI6axualpMgWqlpNZDdM45sxhFdqiOj6S82WpN48qRkBEly3KtdJSazjwe7X+EUytww/WAp0fo6VKLAMJ8ml3doedh7vZyg5mrHs9xrXMbojt3c7buPT9WpCu2H07WGotwB9L4T55A28F3NHWe8ziXoHfcYdR5cEXDe7b9xp0L0aScEjQ7SURs+k39bqh3VsADjVdWo6Kah08ifem7jT9yGSoCd4bZJM/+NS2ag2tFvknEi6Hw6+F2o3EtVQyd3b1T1n4icJ934OZRcE0Zo6SnShGLKvi3nmDGA+Ig5xBkOIQbFQGs1saMnkJ/umsjdL3Rk01aMTcyARTm9BKn5bwxq3F0SpJ2Xch7a5Qjn/jyP0Lj93ahrM/Qqn/x4MV5AdNJiouPnAArn2jTpGwLkLry//YBlcx9kTV4ltba5ADhzHqbtXwaQsRCKsnIwdBxEBvSv30hNq6GX7shtvlovBssvG+hvLmM69RFqjP4Y6qoJULWCXzTtccsIOqABYPWIHDi2ByQzQToNQg+oRQG1SsRgtmKVlVzZzGducrzPWQXVdBktBOk0vZX/NsRm3As51dBVT1F3KMudeC37cYICJC0m+x+1/Em1i3HGYm6YMKxXxWUomwp378Q5GEbf1dxxxutcgukfHqPOgysCLrtgm/xLSkE1PVvXcUvPTqqyh/O8Oo9QoZ2bVPtQYaXd6oNGVNEo+XKDqoiFwi5OxywkUqMDJ2W4S8mj5E5H0llfkRI/Vh+Jp6ajdyIqyvo10d9+QBNBrPP/T97fZ2L++EL2V7fQbjNwh3c2M1EspLruKkiaquT1uJPhscGFW8yJKgVwUQIZkALG5t1bbU3p3el/H7gEd9qXNfoSzQ6hT90tnPbx7Oypg3KX466u/YwgqQi1KCBJAqKfUizXZbSgFgVSKeQXoYVMDn0AF6UR54XYjVFfV1VKtNwD+n2E0EKwjEJ3tDCzlwQ9aXG/qtUzzhMDbCBc7v/JOcTGzyHW6bsZQJHZdlx2NlhNmEVvXmmfgwyYJZlhAVq6jBbuNn3EHfIOyPs5Wd5LefWLMtoMZmSU0LbbynGfEOg6zalvD1HTM6OXPLivkWb7//njC/n00HRGBOgIdzZmhrJBcmf4nYNh9F0R/p7xOpeadxwQZIWcyoNB0N7eTmBgIG1tbQQEBHzfzfFgMPSZWLY+fysTrYUYZC8ChU60WPBGycOxIrBHupYCaSx3q7YRJHRRLF7L9T951uUcdjbz6CCdQxB7oOtdbCjVaA8zS12CPnQad7f8vN8iklVQze6t67hJ2sE2MY1nWU2Y3IYMnJLDedpyL1/LyTx32zh2b13HbMsOjskRxIp1TA03EWk8BuHXUmX0dwh2912k7H3yls/b3CDup9HiQ8M1t5Ps06QUbxzfafviAJ46O1VDXIZ72bbvCh5P3YWFU39mNSUMaTM0pE1TRY5CvWFooSpkJne3/JzErgIe5UNGqJoJHDYK5jzL8g8OMNuyg83SdJZ672GutkjJ77SRBLs7p6mzmVzpOkWbFti9dR23sJMR14xjeNM+aloNqOPSlHfb+T3p8+70myec5MDMsTcyr+IZpYo0apyS82YzYnolw5zGmfO5899QNkkjp8DVM+go+Cfvdk7joBTLfapPUatEuiYuozZ8JjdsvZEoGuj0jiK57U/MFBSuzk/l6WROjVHInW3ttas6GIMTiBwZy/LiOLJ7xhGk01D0zE1un+mgY+RSGdNXKIZqh3g8dR5cWejjebELcluDYwlr2sdouQotZgTAgoogOnhAvQVvTAiAv7cS5qhq6GT1+iJSZif02625LEK1F9nT02dCddaR7OuRsyNzagxh2/KZKBcTZm1Gq1J26mZEAoUubhV3EZA4T/Hw1VZiOHqYrywqtCl3Etn0EXAtpD7C3RtV1HQYKHATenghsRZT4XoiYhNp/PYIfnInfPsx+Nr2iPdsGfy+LpWwxSW4076s4dSfazbmDilENiRKjJINCpeaLpjVjSnUdBioZQK/0/4Df7rpaG9h7kYV/y19zXSVUtyjTbkTKo9j7Gziy7Wr+Cw+rDc/z04+3nYKSRYoNYezyZbwX9MzjqKgSeRnKhu4SHAvceY810B/z3PJBoL0O0m1tPF48Vja7VWkyC7HxbbvZeW1fr1hTDfnRpag4Si5oXdwn81Ye179HtFiEyqApo8UT2LPz6HoA3a2x/An1ZsMF5oZo6phhL+O5sI9GJyoSex0Mfmd6axc9DjToqopss9zzhqzoBRdON+7Oww0pj0bp+8UHqPOgysLfSaWjIWZMGY4+s9eQcNJfFUSemkYH5nTGCXUkSEewAcjVkQ6ZW+sXY3s+eBZxshVPCrv5ZWtEkXek1y8CC7hkh9fZOPEXXjQtnC61Zu0eQfKzeF0y+MZLjSjEUWapUBOS4EIyKQH1bPI+DRUPOJapm/zCOgDxnP7RpVDN9Fd6CHDvENh//eJIHfiMwQcWEOsrxHCIofWFx5j6orHhQyR5WrSMJnq0CYuISV8JgV5lYT6atl4ehZ3a/P5P/NMaroMbPOeiZcoEnT9EmXs59XQmPcupeZwNh+sBWz5eaHl0HAUWZYQERmtqXeRoOrXZqd5Jaugmle/KCNVjmNFZKeiCGHLK3UxzmxyYPmNKcyPjWJ/tU6hI7FxzLmMkz5jxi69pdWkkZE6HFqrwdCCqXA9VvkBblPvIkLbg0rWKAafoblXNzb9CUJX3s4o6x6afeIIjZ/LH47E09RjcqEmsdPFOBdOuBRV2VMoRk4ZWlX1QGP6MkhxuJRSbM4XHqPOgysL7iaW/DcY1rIPQQZZEqj1n8CqhkWki0VkqA4gAzICvkIPozlJh9SEETWBQiezLTvoarcoOWw9qdBc6aLWYM/fu2gYxKPl1sNRsoFw/Zf8SPDlaauisnIbu5ikq+Ua00l6VH74dNZi6aygvVFPSOx1Sri0ZIOyMKDQFNQYlETzfuFm6Cc5lhE/R1ECqMyFgOsu2YnbAze4iF6UC6mJmV2sJ9VkJadYz8rf9oq8v25ZxMfquxSajbxKpqUvZe7UJxyUI+8HHyRSa2GadJhRQh2fytPJsPG8YezAiJpKawSbpKlk9G1z375x8kC2GsxkM46ilkm9uXV99aHj5xALrCzZAEnJcKe9f2McxL5KXmoaGXYaENs17NJb5MOtnWG8YVOw0GrSiC7VURe6kFy9iojYRCUs3K53MZw+6plCqtWgKEgsepwU27Ua0zMdHGuDPh87Zx64pFCck/FzLl55d++l7bNcTR+1nAuAS6Jo6wLBw1PnwQ8CAgKCACIy0R0HSReV8IWXIGEWNAjIyCgC9j6CkQaCaJP9OCZHcJtql0L8WfQBVOaSYd5B/pMZ383gHyIPW1ZBNcnPbeP+wqtpkX0JFdp5XvMeAL80/4I6sw9eKpEOqwaLLIIsIHXWKwuB7b7QhVA17Ebek28lSKfhhcRa9xxMJRuUcExApCu58KXEkebB0DAUbrHvEblbstj6/K3cr84mXV3iQjPizMFo57Pr601f3ZiCbswcRoX6kq4u4XdXlSrfSVpMhzoEExpa8WeBsKs/L527vqnI4f3gd5nvfQidRqTLaBmQGw6UfLnm4q1U5b7ncmpn3jhT4fp+13H2FGYX1zrmgYyFmeQ/mUFWUwIPdj/A8lO2+SH1EZfxlzJ7Cc9rHuWznnHc/9QfCNr6EC8k1rpQxjh49txxrcXPUVIo7tkyYFGHO/Tj77Ofa9HfyGpK6P+3geCu722fmQrXD873dw5wfpcud3g8dR5c+Uh9RNm9tFaD2UBD5O3cVraLQKGTZgKQgQgaqJVCOMUwx2HDxFamiocJ8/NCN3wcXD3DIXgNnLuX40J4R/Je6ZXYib6OsG2rSDZOYZuUjEm8l99r/4dhNPNXzev8U/gR6rg0Ok610GlV408HTUIwjdfcTpi9sMF2X3dvVFHTY0AlmLm69jNo36tcz3m33Lfa1f53j4fu8sOlkts4AEyF65loLaS65ypGj5/rQq/h7GnK3aLQm2hTlLCrPfSbkr4EpsYQaRtzjuPj5/Cs9V5SLXmMFqqZqK5gmp8ReLz34kmLldzaI/GkFNj0Vks2ENuyk9VBh1jRJbLRmauub19W5GDRH8IiwfZ6f2Jtn9l57qzyWFR2Yl8nzVu7N2x8dCCltW3MHx/Vr1/6hbbt489moGUmLWaNjSR8rjqfKXIxe/ZDVvjM/uodCUMPj54ppD6Yx2so3jAX72UcbkPUdm/lWRlgZ5hzL6RX+fuGx6jz4MpHH4MjuSKH4U37qGiNo/26ZVx77J/IzU3oBDPvWBYAcJ/qU+qlQOKFGnQGM3B9r0HnLlH6bIg0+x53LkZe0QfQeoKOgn+Sb8pnhvQNY9WHec76n6hEkWprGKFiCz4YWSjlYumajNzTQoik4agYx5S7nmWEvS3R15HlvZQ1G5U8pfj23dwq7mJ7/QiFe64vp1TDEcUr4DHiLn9c4sa4vdBJm7JEKQQYAHbjb38hsDDT/SLdroecZx38iSmzl/D4J2P5X/XvUVFLpKnalXA8fo5rsZBNyQGVFxha+K3Xen5lbqVhxO2A4j1bU76UZVFx0KRopmZQiyzC7GEdyjlLNkDZ5zyr2gn+95IyewkZU2OATMWg2ZjrMLgAKl+a7/Z+nT2SHSWfM0r/Gcci5zFK/5lSGd/QSZfxHgAHL+d29Qzyvyij1WBGp1H1eqZCnYzRvnORm0KtwYyf8yVfdpY1zHiyTwVtHz7Bs8JlkNd3oeAx6jz4YcFGYxBpaCEy6RZloagYDtkr8Gtv4nHWESvo0WLBhBoJEck7TDl2ILFtZ8+djXbB5Xt90fe4c5lwku+Cfe9S26Wm1BzOWJUPoUIbz6j+gSAKRMkNNMr+GAlig3UWY0/XM5IetIKJBkJcKA1aGzpZ0/JzB9nrn1S7mKkqpmNYICz6cPC2e+CBEy50wnnGwkz3xlwfY8PZ+HPbBjt3otUMKg2UbCDTRruxafsixqveQ2PtdPAq2j1/vwmORWWqQpu4BEp2YKg9RKk5iuggHZHtxYCJ6MZs4KV+oclFxnCu1/gQNmwksaMnKCHOkDjQBePf08rKayvIQqEHWj0ih/RvP+a0OY13xduHFAp81Wagje/6JxPFCgJONvA3ywLwhpzGFFoNSpX/HtV1FKkn8djNo8m36TZ7qUWnfFmnvOC+Vb5nOTcNhXzZHqJ1945cNP65H9C85THqPPhhwYkaAadQDPNX4pP/BrHHduGNBQAtZvTCMPKEGxhVeQAIJ+fYWJ7FvnjZhLspVybDdn3/c7tDX+/IWU44WQXVrCmYxPvBBwnr2skooY7fy//J7/gHgUIXWtmEYMsQvFX1Jr4+au7nIdQ9EghQTaTCZaefwGxLG3q9H+9Hvstqq8KnlV+VTnJYoMIof6a2e+CBE1xCbM5VnufxzgxopDkZG3bjz56rZlehsLcpM/Q6JkmlxPoZCbFVaDvOO3sJ/qHJ6D//IzUlpQQ3/ISp+n+jls2ITV+jVnuBOQKSFrO9WM/Hpqnc3lLAAo0KRB9lg4WrQTLu27dI+HYLGlGEiERFo/loDl9Z6jgW+QtGtX9GfEMnNxTfyGlzGuHf7iCSBharvqJKTGBF8EFWb08BXDki7W3ODC1nteWfoIEQoQNBgFAfLyrkaTSmZ2I+1gwHa5GBEF8vR9vSEsKdyJzd9G3fuegiGEODhWEvWhj0BzRveYw6D65s9A0n9JHKsiOrKYGwEwIzUSFjQQCsqKi2hjGq8wDjxOO0yb4cb+qCihyCtr7BIvMwgrbWow/pwau5jG5tKILJi4YR80k+mwkkfo4SutlYybL06jNOao4kcGsKyyNhu34Ce4UU6vwOENK+gyopEn+hhw3WWTw2zyYLlPczyH8ds9lCpKWGlXmVdAkpZFvG8Vf1W8S2H1HoGBY9DqScfT974AF9PC0lL16QkJdbI8A+jkPieqk84uf00zi1H/tq20is8m9Q9cD86Cj2b2yhy1jmyCuLSjzNNY3HiBe60Ogr0GFEFlBKp2TJUQn6O+FhWiUzvqhZcG2kUolZEMUy72pXg6QgGzCCrFLaGH0dpSWl+Fsa2He8iVesD5Cv/39E0cBSzQ4arrmdgFOf8IV5JiuGHXTw263411he/aLMIe9lV5SZ3/0eEaJSrW5QB+A1chqjUx9xVOLaDVqVYOOU3PYa8T1T2FE+idnqg0w/+C70TCBsRz7xPVNYk8fAKhIX2BgasjfOw293TvBUv3pwZaNvFZW9mhRcKr4Kt6/H39xAtTyck1IYDXIgFVIUCeIp/LzUtMm+hAptvOz1T8h/g5nyPh5Qb2GmvI8Tzd3kWZI4YfBGK/dQV1V61s08U1WZHc46jimzlxD7wIfkC0qo5er2vfgKRqKFJjpkb+5W55DZs045MP0JSH0Uq3cYTV4jWJYex5+vq+ctn7e5NtIfND7K4uOBB+cBlyrU86iIdq6ifCGxlrd83laqse1/26giK+q3Sp6r0/i2VzE+d9s4MqfGOH6fPz4KlQBWWakmrWk1kCoXOs5rKlxPoNCJUVbTrQ6hHR96NKEIXoGg8Ya6g1CygcduHk10kI5pc5fCor8p1CN9xm3ulizKW8EiaEHtpbQxfg4hETGMV1XzB+0/yVAVUeM/AUHjQ3TSDSRnvoT/j1/nvkSZ2NET+NZ/Mpul6cjgMDzt93e7toBAoZNu2QsrAl7WDqUIrOaAY06z3/c701vIqPwjs9nLi9r/IVUuJNWQR5B+JxR9wCx1CUu99/QaWO4qYc/wfM4WLu/IYNe70JXZQ7y3yx0eT50HVzbc5b2VbFB23k4s6cvDCgnXH0fECjI0yQFslaYwM6iNyRMnKbJXDUfRGLv5prqZYfgRSjuyLDBKPsk36kRORy+kXf+ZktR9NqjI4S+at3lLlYJfzLxBv7omr5JWg7lXx7Eih9/Lq/hYnEqDFECI2I4gwhhOISIrBRXpT/DI2kJuOLyDdHUXIwU9jTgRCBt8BtV59eDyxfdKqjpUL48bj4zzJic/wfaemncAmY6/FW5fT2a0ayV23/Bd5tQYohq+xlT4GtfGziOrKcGhJ7si+CCx7YfZW/4vvjaGM1btR7CPFm1PPW2SD2aTmRbBm2CxmxbJhzJNWr/z271ODvqfpMWYCtcTLLdQJCRw/bWJvXqxGfdC9iFobWCBsIvmLhXofGzuQHqNmHY9zV3KhwIQqNO4EAS/kxNFm9kPg+hDFI14ixJ0NWD56lVk2Urb4a+4wSIxLXIScZWHoLMejUokWuzgZfU/WWeeSeuwQEJGT0DXXKmQEce7D2v3fU52SbPtlgm91bPn814Ndr0LHfr9gRRLeIw6D65sOC0sim7qql79RbsXoSKHWK8O8PXDbOjAJEkE0sloTT3lqX9l8tGH4OQemnVXkdeTwGZpOipRYD75zBCLCRXauY0vGXHf38gquIU1eZXUhp85jOpAyQau6djLAsHAyuppg361X+gi/w1uEXYxVXuQKv/J1Lb3sM+SQIq2hjCpAYvFi5CKHLKLTbQL05Fl2CxNocJZDSMkDn3xv6kpKaVdk6XkJ50vPKGTSwKXBamqkzFjf2eWpffqxtqrM3M1aTz1cq5D6WR5cOHAldhOBsgS87+YLh8luKaJLO8XmDwqhMmjQli9PYXlYfDWiUQWCLvQYaSnx4RGlmmQAyiTYjgmRzBKqENGZtK+Z6ms/Yy4cL9+2rYZtescBsOxyHkYTlipG7GQ6xc92NsmW+5uQ+575DemcM0wf7bq4VjTdWS9nMsLiWlcHdBJc3U1k+WTdItWmiNnsfnhGQ7P2MSYYG6y1uIrmvDzC8Zf9gWNDswGBHM9KhmCzXUIQI/+3+DtDX7DlLy/og/w72nlvkS5fwGUHYMZUk6yZ5ssYwlyMjYHxJnmgcGud6FDvz+QYgmPUefBDwZr8iqJ77HpL6Y+7Frx1XAEwq9FExCJJiSOrTvy+bhHMX4mCR3EWCTK2jX8yvIL5RgJtjOBlerV3Kz6hkLGMIIzLKID0QWExNEaOYP8xpRBJ8msgmoKt6/nL957+OfWSezItvKi6iChWAihDb+OPIxoEBDI6P4Df1a/SXpnCZRsYP74ZWQXQ/PwWTR1mfqpYRzckc9E6Sj7C9cPSh0xZPxAdsWXOi5aNeGFhH2RdVJFyFz0Nydlh3JAUZWo6VAMuvwnM6DCCiV+juNduOrMOwjTf8mj0l460YEgIEmyS6i0pmMsBaqJTBwXzOYS5VJ2I+6YHEGcWEetbgzv9tzOF8LDRAuNiqHUroiprylf6lYuMGujihrzL4hu0rGs773GzyE2fg4rK3LYunYV63qm8HXVSKyygft2BePvfQ/Jxn3cKu5iszSd0to25Vq2eaWuzUCMOIwUjRfRPlpoasNiaKVITiCJFkXXWoAevDjkn0Zzlwlt4hIy0jMpOtVK+Lcf09AdSvJAxtZghpRN9uzTE4nIgK+XemiaveVb4UQBzF/Z/9zurnexNoQ/kGIJj1HnwQ8GygKHi1QO4LZ4otG7mgrbYvjKljn8l2gEYL73IT7rGYeMkoQ8MtgHU4c314f0wKb7XSXE+qKvoeP0e+wDH7JysMZX5BC2bRWLzA2MMp1ittXAcKGZQLkTCypUWFFhpY0gBy/VZmk6yZGBhCQt5o34FN64c+ACCBc+sAuBH8iu+FLHZUGq6kyc21cTFRzjZHlYJwWqXq3SvoS7ASWlxEgnFK66Ox+mvfhLAoUOTkrhfCZNQa+O5i2ft9EmLqHWRsJrD0tnjQrh+S8mATDR/A0vqdYQJHQx3nqKVHkE++QEEOGgcC0L45Tq2WVRTgaz0wZpWXr1mQ3pkg2KSo03BMTPI7u4Fqus/KkiYBr/4zvLQTzsnEeblhBOYkUDIWoLeosPNVK8UulrmscEcQx3iTm0CkEk3LWSX25UUdNtILpUR8ZCsFTuIFRuQV+5A3yaHHOPs8dx0HfFZpBmFFRTNtSNQtJixaAztDjoYs6Ic90QeqIDgMeo8+AHhMzQcoU9PXQx4DR5OZMJ2353Xgz3HptH/eFdpKsPsVDIRzXhJvZXtzAxJpiPqqawPFKLrqee5uKtXB3ZSf6TQwtt5GrSCOguJfrUMYXx3rka15YLtXpEDsmN2aD1Z5a6lkJzBOXSCIYLzQTTgYRApRRJm+CPAGSJP+LqUC/+0vomdVctJPa+AdrSBwPygZ0rfiC7Yg8uIAZ6Z+w5aUmLyY/P6M9zZjMCooNHsb81RdmYxM+hfOIzmArXc2zEPL6t7+BZ1Xv400lVrV8/7VDn8f7ps6sIkrtQIeFnaWGmqplyaQTfSKMJmnSHY5xkxuPWCHImBnb+ve896YC5SYtpbAphR3kDAGkJ4ewob6DLaHEUe6S+nOvIo33jzhSoeBhKNpBVKhBqPsV2dRoVAZMIiJnHf1QrOrgJ8TH9jMvoIB1CC/h7a1hxJJ7H/KqIbNdTeGQ9NR1Kle1gxp1zfqZbTeiBnqlNt3bIG7xz3RB6ogOAx6jz4EqH8+5tsEFvY3p3hAnsnyUtZvKoBHZWpaMxi3xsnEJFdQv5P7byzYcPYbRIrG5cRI9ZYrbFi62nxrH3uW0ADhoCB/osWk+VRrHCHERsc0m/Xaw93BLe8zFQjxU1JrQECZ20yH6ME49jFjQckBL4m3UBX8vJRATqqGk18LHwB67XlEFrOVTE/aAnOA8uIr4rz0ifcWOn9Qja+hdWbE9j/nhFUioyaTGR8XOcjL6ZZD6tcNfN2fYwWrmNDnUgvz+RSI3VwDOfHAJQKsTtknvpTzDimnG0lhfRQBDbpMnEUMdwoZlZ6hJCzdHAmTc/Z8xldLqnNRsVo+3H/oeZU/E17cYp5EnJFG5fz/SDhST3TKBLl9LPQzm74ScE6UtIjgwk9oEnHKd+ZG0hz3xyiMdiT5KfcMCxiY2c9wRVue/xyolEcq1jmWP9mkjTYZaH+VOgmkhyzz5md+4gf3s6mVMfP/t7GuLzu+DftyFXk4bJUIdWk8YQTc4rEh6jzoMrG86G3GA7QHuYoKe112NnO25N+VJqOsayXTcB3wA1maHl1Hywkgk0IYswzrqao0SzRprHV9IEZLMi8TPo5FeRw/vB77HdPEKpROvTJnsuVMOI24mu/iey2YCvbCBW0LNfjqdN9mW4xoBaF81XDcl4a1SOBPJonQ5arFi7m8lZu4rGmxLOPAl7QhcenC2+J8/IsvQ4wra9xiTpICaDxFOlK1wkpfoaH865tDnWmeRax5IuFnGruEsxYHw/gNYTjkrxZJ8mDN5qDlhGcSz+If5Z3kCqXMiKyIOEDtF7tHpEDuE9HzskxM50P2vyKlkeXEhkUwkWrUyRMInlYYUE6Xcy29KGt04ks/ZjqOgt0MgMTWOU2Ik2ap6LrmznoRiscjIRJ7ZAq6IgYTeU7t6oosZqcNGctXtAq95+VzESwwLdtjMztJyIri3UhS484z0Nios01zxVGkVN9wOOcPMPFR6jzoMrG33z5QaaRAYKEzjlzryQWMu1Vf+D6lQ5vvTQgh/IMkHWdibQya1iEM2RszjR3A3gyv1kP2/NAWXx8PIntvO0oq1qq0RzDm/YceiaB/HvqmZE7VYkWaRKjuAd6wJUgsDfxx5nctJiojaqqGk1sL+6pTeBPP8NCqubWWcr9hhSQrMndOHB2eBMYbILtHj3pWXJnBoDoQ+j//yPXNXS6uCvs6NvcYhzLm0KUJBXyS80hVzTYTNgRiuSe3j5K21OWoy+oZMt+gl8ZlNlKAqa1OsNc6qsTZm9xO3YSm7MBhocEmKDwXFPtsKPBUmLWRA/ByoEqnJBr/fjl+a/YT7UjqbySyZ36Rhl9eXvHQt4xepkxOQ8i1RfymOqEUgyxHq1u9C99O0bu+asPScxdvQECPfrt8G0Y5T+MyaKxezXq5QIwLk+24s010yMCaauzeDY3P5Q4THqPLiycTaufHds6jjlzmy6H2NLEQISdXIIJeELmNSwERVWWmRfjskR/LrhcaZFiqALgdBHlMq9j/8LjG1QtlUhI+2qB1GjyInJODQhx3y1mT9aJDZtV8K5Kyw72JqdSpqwF61gBQGiaeI+1ad8qPqRg0S5X2K27T6OFvQWe5wRnsIGD84WZxpbF2jx7hf2sxmLkQHeRBqPOfjr7Og1knJg04tkJi0m88k5Ln9XDKgQxYCJn0NV2UHC9V/Cxkfx//Hr3N3yc2p6DKSLRdym2kWXcDX/99RK6q5ayLLQAw5qj+e/mOA+D81GIWKXEHOHrIJqDDl/YKnmaypH3MY9lTcAt/NYVAKZKPcYm3EvcWtXEWxtQ7CakLrquQaBGJUaTVAhBzon0WW0kFVQTWbbSQRkomjiVnEXIyzVQJBrrnDfvGK7XnVPKyTM7SVmd2OQuxRTnc+zPRdZxCEUcuyvbsEqKz9/yPAYdR788HCuHoSQOGSVN0arRHP8Yub7NCE1tSPIECa08wv1J2iwIJ+WERDQtxk42ObDTVI7IiAZ2/nWeBWxgha1ZFYqwk7aQr5WCymyEYtKIDKskAZ9NRNUR0mViwkSOrEiICLjL3QzSSxjvPc/oeK6fkUdzvfXdzEb9N49hQ0eXGhcoI1CP1oWu0ERfq17xYoBCMad/+7wtEUlkBkPqxtTeFTaS7At/WJZ+m959YsybpcLmKMuwty9D6soknsCSP9/tDZ0kt+YQqq50H0eWvoTZHkvVdrt3ctZaaclWh5WSOjpDm607kRlkQj79mNae2YAihE7Pfg9AvU72V2s52T0fMaePEy00AgyiBodXiOuZ3LqA/javPRr8irJDBqJfLqDGsLYLE1Hi8gCgMpcqho6uXujiveD3yO2fW9vn5RsgM56EERXRRk3RlvGwkyybFXDJCp5jOf0bM9yrhlqLt9lQd/zHcBj1Hnww8MZWNPdGT25W7IYvf9dIoRu1KKKuqpSclOWECflEi00IiLjjRkJkGUQBJnOlnrWme7gKlUVcdpWjllDeNl0B8u0nzFFPKp46uw7+q4GVL4hqMLHEJt6L0Gf/AZtp4Vw2hUiUdQckBIYQT3DVW14m5rYunYV2pTTijKEM/edfeft7v48YVYPvitcoI1Cv03LAPrNDpzJ6LOR6C6yHqM+O4/chjtJmb2E17fD/epsOkpKGdf9FkVJTRR1j6Px26OECO00S37UXbWwl2sOqHr7J715aE68kzRXsrs4jpqecbyYfdhhbKzJq+SPPZuIqi1nhCAgImFFZIM1DQHw1ogsS49j9fYUUi1tbJamklc5kj3ivfxR/TZBQifaEdfDPVsAmBhT2BtyjLwV0diBt24cS0/vQZtyB4wZDvlv0FxdTbxxN6utKYrGs3Mf2nOJnRVlBjDI7QbWU6VRLnmMQ8I5bqaHaqxdFvQ93wE8Rp0HPzycgTXdndFjKlyPn9xJC34cZyTrjFOoKI0i3nIv96k+ZbJ4FDUSBtmLbrwJFbrwCx4Gp+GoHMNrprv4mmQMkoTWKjDl6pDeawbFgNYffEIg9REe+SaMpe1qpooism3S/8w6hRWW5fxF8ybz1Qept+hYZ5rC7fs/opmDtDZ0EmvfeRtaFINxoIIQd/fuKZTw4CLigsqVnclYHMDos7fhhcQ0rg05RnzjUcYJx9hdqCLzzuFkRu+g8XgVEbKZzm/rwVemzjCW10z3cLu2gC+90khJusXlXsb5xqBlH5/r/bjzk98Q0lkBoho0Om4hmWzG0WOWHJ6mZelxkA0gcJpgBAH2WOKJoY6HVJsYrarny+1pXDPMH2yyqvaijvetc7hGVcdtqY847skl5GjcCR11xJkNxOlkJSwd/zeqct9jlHyQ27UFtM7+K/StyO+TS6zcn4pl6b8lM971WZ2XN+xMRMQDwGOsnR08Rp0HPzycgTXd5acN2pQl7Lblk9SGzyR0+3qeDn6XDstJosynqBRG4CMZ+Jc8iwPWWJZ672HuhFSez32HQKELrJBjGU+6WMRceRfNjfWEGKqh4Sgg022SUNWX0dxmILvul7QLCwhUdxJOK+9bb2KVdRHRQToCE+9Ea46mTJNGRWkUn/eoMVkk8htTFPLivgtaX2NtoHv3ePA8uIgYLIR2QQ0+J2+Z/rNXqGl9lvbrlpGxsFcztrLoazLEUiwqM6floN4csZN7CBYs1BFKwzW3E+3ThNY2zn5nnERrh5mCvEqmWvezwpDHp1umE6Q5RLTcQ6S1BqnzNCCDZAFdMD7j7iC6VOfQmn0hsZaM2nWsViVx2hLCZmk6zZGzuLf+JWapSpghlRAgdxPc00iHPpxZPofRmkSskkSqUIxWEGmd+6YLcbqzkaXfYyDEKtElBBISe51jLljd2Ov1K/pCqYbNDC3vPy8M4Vmdl4F1LkTEHpw1PEadBx44YwCjpy85b2ZtBVTuhRHXQsAtPLE/BkmGW8VdVPuOoXH2Kjj6EBFCM622IoqV6jcZLjSTIJ5C6hRABfgOo0oVQ09NMbFCO50t9awNe483m1Iok65imKqVUUId46MD2fzwDPvVyQAyFkJWQRwr8yb1Z9m3Y6jGmqdQwoOLiME8PBdUn9b+vp8oIKz1FOGyRNGBNbAwk2XpcRRuX8891k/AbECt8SV6yZtEx88hdwsESKVEB+mInvcE0fFzeGRtIdn7akmM0joUHZalx5GQ/yajTMVghc9Jw2SV2CxNp9oSwV3iNkzeYYxIXsrVZZ8x1ZrCfL9I3oj4CI4eBauRSf6TWdKgyA2qatv4RJjOcJoZL1aiEcyEq7owpixH1ySzwNCsFFP5JLMg9RFXJRxcjawV228m1eLF9s40isonsSwqjsx4mD8+kjZbGl2rwaz0c8LA88JFy007FyJiD84aHqPOAw/OBX08Yn7GQm44/DvS1YfwsarQNkRCw1EEAQySluWaT1BjpU57NfstKcQP98OrpYh1TeNYLf2YZMs+blPtItarndHtO/mjZi8NUgDl0gg2S9Np6jK5bYbLztldCHWoxpqnUMKDi4gB1Vw4DyNisPc9JA7r16vQWjuI9VXGTubUGGUzdkQAlS+kPuo47qnSKGoMvyHaS8fEb4LJ/ke2Q7aruEbRX40O0pE5NYYV26eQajXwqTydzInRXF0bSH6jltc7FvE6ixBMcKz5E0eFbNtesGj2okaCgGg+6poCQIaqiLt99/F+1ySMuuF4mcoQEEi4Ooa94TPZWrieGylHLQjgM8WlitXl/mXgZAF3x8xn+akVdBkttDoZyRnmHRh0h9GYRIoE2wYwdOB5YbBndd7wzDMXHR6jzgMPzgV9Jqc37kyBvDRq8srxszSQ/M2vsIgyeimEQKELL9mMLMCIYB9GDI+Adj3NPZ2EWk6BRtF77ErP5PUtWTyr0hMhNBMgdvGFHEOR1yQeG8qC584r55lEPbgUMIjH2G1Ibyg5nmd4372P74STewgJi+g9ZoD0BLtm88SYYDYf7OW9UwmQGBXIieZuB3XI/PGRmApVZKYoBhPte1l5rR+fFSZhMFvx1qjI1aTRjZ7N0lQARqpbuT4mBK6ewfKyQgCWh5USrt9Domo/baZgNMOudeTVrtlYySJjONdrdIQNG6k0pu+95r8BJ/co/281EdfzCTDHRWrskbWFaKriWR7WyYKMexX+OwBi3PervdDK0OL2WXlw6cNj1HngwXnAOR9oetlBAjGSLFbigxGD7MXTlnt5Rf0WoFTF9jRWI52uAI0fxpDRbG9OI1Uu5He6HNgr8G85nact9/K4ai3hQjvfWiNAhVvKEpcFryJHoXDoQzbqKYDw4JKAkzE1pBy6oaQNnMkLnfpI/1CfzejLKqhmzcZc3g9+j8im3ZgsdUyMf47OQ5/xgeZTAIqv+hnL0uOoyn2T542JAPhlv44XzYwXT9Fe6wcZ9zra8NuoBF615az96sAwWnsedBQ5fGUaQ0dtE/7Vm0nU1NoqUO+le30BYeYmQmnnm4ZxqOPGkZy9gtUj5lNX1UAgJmg5BsMTqTL6s/pIPCkF1a795hcBAqzrmEZNlwEAXy81Na0GsotrscpjKVBNJD9+CCoQJRuUStiBCq08uOThMeo8+OHhAho6zvlAhVYlIVmjFrmOoxxgDPepPiVI6ARABDTWTiyygNrUhJcphHwhhafNrxPcXIggCNwqatksTSdabMIfA3NVeymzXEPV2//Ty2Bf62bBK9mgcHLFZZxbTp0HHlxM9NE6PWMO3VDSBgbxQver4KzIUTxbQG7oHTyzK5hfiJsI6NnJSYJYZ5rC18W1/Em1i4liOWqVSGroASg5QLj+S55X7+WkHE6CeIp6OQhJFrDoFe1YB2FvUzUdPWassq1iVb3LkUN7vVyGzmikXBrBV0ISc21zj090Ihz/GgkZo0Ui8ltF6zm54+8QFg9NEph7lPBqz2vUdBgosPfb1TOgtdqhWasrqCbaKYy9Jq/SUaTRL7Q90Bx4JroYDy55iN93A84Fq1ev5uqrr8bb25spU6awd+/eQb+/YcMGxowZg7e3N0lJSXz22WffUUs9uCRhN3TsOSrnApu0zguJtUQH6ViWHkfK7CWs9FtB29y/Ev1sBcP9vZgoViCipL0AmGQVBrwVMuLOegA2S9PZLyVwQIrnmBzB8+r3CKAbEZloGvkv1adE67exqGcTa/IqKeoOpaZLoKg7tLc9SYvdc3IN9LkHHnxPWJYe5xgz/WAbV4BiLA3RsMgqqCb15VyyCqqhIoewbQ8T375bIcoFR3UrJ/dgKlyPVYbFqq8IpZ1IbwsVAdN4LPYkV2la6fYfRUNQMiuOxJOrSQPvYILELtQqkXxpPMgyIUI7V3OKqtz3HG3YvXUdf1K96fDOzVQVE0QHBtmLfVICX1vH8451Acci5yntqcghN/QO9pDIHmksH6p+RMM1t4PGV6mgbaqA0HgIugpGTuX94Hf5sf9hJsYEk/pyLlVlB8HcrfDLVeQw/eCvmWrdT1TD10w/+GvuMX/E/2v/E/k/tvY3ngeaA+PnnFW/f19wed4euOCy89R99NFH/OpXv+Ktt95iypQpvPbaa9x8882UlZUxbNiwft/ftWsXd955Jy+99BILFizgww8/5Ec/+hEHDhxg3Lhx38MdePC940JUetomxYw4XEg4nSfPf3ZM4idSDQB1cjCTxHL2SQlcL5ThJZr52jqOyXzDfPUu9suJjJD0TBUPEyq0A4oh2Cb7ACAAXmqFmLRu22tEyz3UVZX2tmcgr4Unp86DSwyD0mIMxbPsxsvkUkGbsIFZ6hLwVvReAXI1aY7q1mM+81BVQUn4Aq6y5uKffBf50VbIfhO8WtkrTGZJrRJW3X5Aw2z1vSyPLGTK6Ans3b8PoQ0kRFolP1bbqYSAW9jJFFUxKlFgh1c6I7x1iJ16YjVdqHsEHjUpFa+3nXgTQ8NhdMCvSm6ntec3gFKIkZyZQe6WRAIOrCE6QMeRkffwVGkU7ze+S6wtdy+1fCI1rQZXIuGSDYTr83hU2kv9geEEySe5WdLipzdBiR9ZTQmuIe8LWe3+PaR4XNCK6SsMl51R9+c//5n77ruPe+9VBt1bb71FdnY2//jHP3jyySf7ff/1119n7ty5PP744wC88MIL5OTk8Ne//pW33nrrO227B5cILoSh425SzHsFij6gUjeOitOdjLaGc1pW+KgAIoQWZoqHCBI6kFAhywJzyWe6WMx0sQR/uYtKKRKLoEbAiAzUqSJYY5rHUu9Q5t75MNfHx5Db4KTB6IEHVxKGYmy4MfxcKmhDF6MDW4hTWfCdq1sxgFU28IeuW5n/5GuAogwR1tpAO3682Z7iuFSqXEiqYQerG9NZGV7BNR17OSJHUyhei0pU6EJA8RzF00yQ0MnkkB5ue1RZbyrf/gmCvowUykgXi8iTkvnEOh2NSWRB0mKMhRbHtZalx0FFDgEH1mC0SLzaeTMFpVH9DLgXNLWYCtdzbNg8UsuX8oKmlox2PSqNN8HmLjTBPrR6zWB7vT+zh3UQkrSYNRsVI+iZT5SQcebUC7jZ+x5SPDySYAPjsjLqTCYT+/fv59e//rXjM1EUufHGG9m9e7fbY3bv3s2vfvUrl89uvvlm/vWvfw14HaPRiNFodPze3t5+fg334MqDO8Ow6ANoPUFM60nCZC+uE7QEiN0MF5o5LYcwSSxDhUSHrKNUvtph7AGMFqrxF7tR+4URKBqhqwtB5UVX8jIqSqMUj4NtgerLmeeBB5cthkqO7Qw3hp+r969/ZWdfI6CvQdBL0DudPCkZAVg4IYrlJ/5ETPchEtVdkPRbjKeO4dXSTXSwD5HGY4pqQ8VwwratIlk+jBqJ8NaDgCItOLn233hhJkpo5FZxF3lSMnlSMkXCJBYA/xBeQNLI7CeRzNpP4Kie8XIZkiij8d5DSuoS1uRVkpK+xKEEkVFyP+gO82mNTE3PSLr3fwS+R5T8vIBI/G19Gety/9U888khrDL9vVvn62n7HjguPSoTA+OyMuoaGxuxWq0MHz7c5fPhw4dz9OhRt8fU1dW5/X5dXd2A13nppZd47rnnzr/BHvywkHwXfL0SwWrCKGtokAMIEdqYIh6hQLqWVtmXYFvRRBAd3Kf6lAJpLABbpSmMkuvIt6SzMlWCfe/SLASRXaxn2eyZLoLgF4x93wMPvm+ci5fHqYL11Q+3AfDYzaMHHQ99jYC+302ZvYTnv5hAm8EMwCL/wzxW/xd0XWWoBCvB3VUARI4YRaQxFwKuhYAMR+hzlroEs1WDBiN1chB/XFtIeula1CorEmBGxWjxBDdpivH3VrM8rBDyO0gRy0GGGKERKmUIv5ZiYTRGq8SmnimsdGe82Iynz4sVo/RzZrAgLtKVpmXT/S5Gmv0cbr1b5+tpuxRTPH7AVf+XlVH3XeHXv/61i3evvb2dkSNHfo8t8uCyQPoTcHwnqpN7sIaM4U916fxd/CMiMpPFMu43r+B59XtECM2MFk4hITJBrERUaciVruNx60N4WVRkeY8mM3wn/sfzeUJ6g1e2Q+ZUJZzjySXx4IrCeXh51uRV0mozwtbkVfaXvuqLQRZ6u9Fn3zQtDy4kTH8AUbAiI+BLD1vXrkKbsoSrAzpZXZNCypglDm1UHbC6GJKtJahVIp2HPmOzPJ3rxTIihCY0WIkXaviD9p+ERcdDwxE6rFo0yHQIvgR6awAzXD2Do2OWKtQoFmUT12+cx88hqymB/JIygnQw7ealrnquTkZaVlMChdvXszyskMyMe8l80k2/XIlqMj/gqv/LyqgLCwtDpVJx+vRpl89Pnz5NRESE22MiIiLO6vsAXl5eeHl5nX+DPbhicEYPWUUOVbnvUXYaJokhfNoWyx7VRAyyF76iEUnQAHBSDqdD9gYgWmjCV+hBowtkwY8e4cv1RSzq2YTXFyIMAxUywWKXsqu3oV8uyQ94R+rBFYDz8PIsS4/j1S/KSJULWRF8EPI7MNQe4qtDdTTelDBwxaf9un3gPMZjQxUOOS9TEzICTQSwrmcKFaVRdBnvodVgZvsXZYoh2JTAmvKlTBwdTFzFM8xSl3C3dh/3tt/H05Z7eUK9jpGcRitY8DM3UF/VTYtvLKE9VfjIVnyFHnzMPViNFury3iVqYjS+XlGDbt4Kt69ntWUT4aouEo7GQOgjblVk1mysZIUhD//aYqpyIfZMxVRXynxyJRqqQ8RlRWmi1WqZOHEi27dvd3wmSRLbt29n2rRpbo+ZNm2ay/cBcnJyBvy+Bx64Q+H29azoXEnh9vWOz+xl9Y+sLWTr2lUE6neSaDmCbO4i1HgKL7XIWs1/0Okdjfe423he/R7JYiX+Qg9bJUUqSESitN2Lq/9u4qfyZqaqjpDCEdCFIF49A82wa4n16lAmWxSPQv6TGcpEb2d/L/v8/OhZPPDgMkTm1BiKnrmJ1eMriW1XaK1KzVH4mxtcxqkDZ6D3sXvBC7evh5IN+KQ+gGpUGppRM/j2+mepCJjGxJhgR4i2zWB2GII1rQb2V7cw986H0YdO4/2uSQB8LSdzRLoKtWAFwIIKL8FMaYcvW3ULqZHDOOSfBqHxmGWRILkNU+F6lqXHEaTTOFQs+mJ5WCHXqyq4hhMKVUvJhl6aj6YEBy3JC4m1RIjNlEsjWN2Y0u88/XAh6J4uBVwm1CwXA5eVpw7gV7/6FT/72c+4/vrrmTx5Mq+99hpdXV2Oatif/vSnREdH89JLLwHw6KOPMmvWLFauXMn8+fNZt24d33zzDX/7298Gu4wHHrhgeVghQfoSksMCHbvZwiPx1HSMpa7NQLswBYtWplqMZIRUw3Z1Go/dPBr4Dc9uX8+zFUrYVY2VKKGBu1U5+Ag9yAi04g+A2tgCokwnXnxonk9WUwJ/0bzJNfV7+bbpbT7qKWJ5WCGxGfcqk9VQ2d+vlN23Bx64Q9JiRU0FiPU14dt5jFj/bcDj7gsxBoDdC748uBAq91LV0MndLb9UvPOh5WSY17G8OA4ZhQpLpjdHzf4zqwmeOX4XVlnRdv3dVaUcMcVTVV9NtLaTY8JIQkx6jskR/LVlHjLziDbr+FjzEqFIdAg+aFOWkBlaTpjwV9b1TOGZTxQj0tljF5txL/y7GlpPQuBISFpM4fr1rDDkkb893ZGukWHegcG7jq8sSaTMHkK1/A/Yw3Wl4LIz6u644w4aGhp4+umnqaurIzk5ma1btzqKIU6cOIEo9jogp0+fzocffsjvfvc7fvOb3xAfH8+//vUvD0edB2eF2Ix7ocSPkJA4xTvWWc+z6hB6vH9GrnU8RepJBCTMs8nyQJBOQ1FeJXd0fchz4mbUWBGQEVDc4wZZg5cmkFazMgQfVm0iXGjDiopD1qt5tWokVtnAW6oUFggGPm1OYYGQR5C+BEr8lMVpqOzvP+D8Eg9+ALBvcCpzCUEAlUhkgJLicMZ332b05WrSWFMa5Qi9UuLH6iPx1HQonrtM3/fB0MItJJNtM+oEIDO0nMzadWT+WKFPSX05F6usaMa+ODyPyPqDxI6cAtHToTKXEV3V+AldTBUP85b8YyRZJtRXy7H6ToJFgWpxJBljhkP2CjLEZgwqK3nmZNcwrN1Q1YVA52n2GiK58x8m3vXPZ4LatvG0I6k/vcsZ+/IizhGeQq+Lj8vOqAN46KGHeOihh9z+LS8vr99nixcvZvFiz87Dg3OA805/0d+UqrKeVhBE/OlkoZhPds84Qny90FT9mz+p8vhUns4BJhHfvpv/1GzGByPdeJFtncLN4jd4CyaixGYEWY2PWkWI1MVIsQEvzNTJIbxjXYBWLWIwS6hFAQG4KtiX/J50ksMCFcPSXt22aAgeZ5vxV9QdSt3zt6JNWaLQonjgwYXGd+0Vtl8vxJZjGhKnKCzYNGYLj8Rzv7qcjpJS2jVZ/d97m9FnMtQR3zOFMV88BTEhkPoIKVEJFNg9dy2toAtmxMhxfFj2e2TgHesCRtYUQOsRxau3UcXEmGC6jBa6jBaqGjoJVUloQXHrmbrx0XgjmAXCVF38r/ACkgzv6BfwNxZwG7sIvP5OKNlAd3sTjRYdB4PmEG3WOVQklqXH9coEhl8LcRm8tT8Gqwzvd03ihonDCHGjddsXZ2NcXUhDzFPodfFxWRp1HnjwncG+02/X9y4eCXMdi4dWk0Z0qSJ5NP3guwTqS9Ai4pcwj3kVe1DLAt2SF29bFvIWPyZNfBBfjCDLWKwWrKJIO/7s8LmJUZ0HGEE9f1S/zfvWOaxiET9jCxPFCsZ1nCR+/FRIure/9+FMC6nts/APfsEouYPdhXh47jy4OPiuvcL5byg5ZSOnwD1bXP60ZmMuNR1jmaHJY4ZYzP7C9S7vvd3oWx7WiXbMPJYWrieZcjgpQMkGMm0bptXbU7hfXUtHm5lYy78JUlVglQVGCg1sIQPCobm6mnjjbvZXT8PXS02rwcw7LOC0vIug0DvIqPwjmLvx8fWFaxcxul1Pz/HdIEMDu1hh+QUBY+fxxsIUqBjOF4U1fGKdzteN8VT+p5Wta5+hvWcKa/JQvIIASYvJPXqa21RrkWRojJhFarlJISMuUTZ9/ZQk7H1zFsbVhTTEPKTBFx8eo84DDwaDfdfbru9drJy8YxkVOWQ0/QUONIMA3wgj+dg4lYrqFt6482HIV1RfV6QuJWDdxwRZ2kEACwKbrdMQJYEqKYJp1gNcI9QQKrQhAo+qN3K3ahsGtCBAtKaT9tJtHCosIdTPi9HDr+1tW8kGDEdzBq76s30nXG2gweLvUaLw4ILCxZMzxJwst96fC+zlsxsQdaEL2a9X9Xvv1+RVEt9lotzaydyM4TCmd7za278mr9LFMJQ6BURRjSQLBMldxKCnyuhPouYQD/EZIcGlHI+ax/K9oeSZFaLh+fsPkTHMn25tCO913cA7Jbfz5+vqubbNQE2rgRwhDasZNFX/hk1ryNWk8TkzuFXcyZSYUCg5wHShGIPKypcx8yA+xdE/prW3Ml0oxstbxQtd06hpNWAqXA+6w0r7y5e6NcjOxrhalh6n0KIEF0KF9byejYc0+OLDY9R54MFgsIcvnBcccFCYNNdVM14uQ40ZAYj1S6CISWC0kNU0mtntPYQ176f1+EHupgs1MshQ5ZPMLssN3OG9h5GdR5kglyHKVsdlVciEC+1YEajiKrpj5lN1tJjhQjMh3ZUQcIsLhcFXh+oUyoWBdtNJi9EC0UmLifbk1XlwAeHiyXlyaDlZbr0/7rx8ZzL0Uh9xHZdO6DUgMoAHeWRtIff9Opv546OYPCqELqOF27UFzFIfUs7hplqyr2EYEZtIe1c12+v9GWY+xf9ZpuKrV7N6vB/Xt+uhYS+xXh0cSYlkeXEc2T3juIWd0HaCFpM3+0wxtEpmniqNYln631mTV8nEmGCiq1scBRomQx1zZSvp6hJCQqMhaTG7DtXxiXUKFdUtLu3TpvRKBi4LV9qqTVyiKF0kLWZZlHvj7WyMq8ypMWTWVkDl3t58Xg8uWXiMOg88GAqcWOzXbMzl/eD3CNLvRG+N5lshktHCCQQBrJ2nae0xky4WEbR1FR2cJlyWCKYDEQkACfibZT4rr62AyiKIuZbumiDU5lbqpBC8MSEjEya0o0JmuKqdeafmUGOZQbpYxC+CCgnvkzfTeFMCFYPtvC9F1ncPrgicS0jN7TH2d9o5Z/RM4dyzeK/tRUxbDtY6/v9L/zQWjIkc0LPYq8QAy266hbmh5Wxdu4r8nkTypFsBCBI0ZEUtxVD6B5ZqKvFvqYaTe/hdyASKuifhk3gHHaWV+NPCf6h3U6SaxAuJtXR8tpJ4y3R6DgvkpxwH2Y+OFi2VUgSH1XEkRwYq+XF9xrezl5PwmazRRrEsPK6PoZZp+28OmQkbIHQx4N6IG1LOnKcq9rKBx6jzwIOB4MZLYPcwrOyZwC108LlqBo/oPkc2nMQqww6rUhl3q7iLmfJ+1KKKTkkHgK9gQCXIfMM4hV4gtNxBxfCBNIex1mJAScDOk5LJ1jzBaPEUemsAXZKFIJ2GG2/OZPLU3zqamFVQ3csY/+N7eyvczuTh8NCceHCBcC4hNXfH2El83w9+18E7dyGNicdiTxJxYgufM4Nt5vGoBJg/PhLMFY7v5G7JwlS4nmOR88hqSnCEHv9oJwWPCWGW+pBSlSoloxLggagqfLNfJ4VqtNZ6kH1AthJpPE7+j63AcL45EIzOKhIuNPHn6+rJaPqIHmEPYSpFF5rKMjqsWnoMHURaa3hHcztFLZNY1hRHZjzsPdZMXZuB9ftOUlrbhlVWuDPnWL8m3p5r59Sfj6wtJLu4lrVh7zFZKlI+HGCc9/Oaupsbvq9NoWeeOmuclVF38uRJj1yWB1cWBpo0KnLo2Pgock8LO4r1tM5VctXsHoZ8YwrZhnE8qt5EXE8pIjIWVEQILbymfZNKawRtsh+RcjNeKhX75QSiqWeY2sCUsYlMmRoDxDg8EbOH+VN+Opwp8kFuYxfNkbN4s+lubmEnnzODVoOZ6CCdIoW06UVHe9fkKYzx4fpCyD4E81e6UDwA7idDD82JB5cY7MbFamsKK6/1GxK33NlgWegBaC1jQkAgpS1TWJYeR0btiy6SWr571zJDLMZwwkq8dTph217jtz56gswVIEPF8atQRU4jMGqeo0AqbNvbTBMPoMOIGitmswmNLgSsRmWctetJppxmQccwoU0p2AhrRiNYCBY6KL7qZxAazbpSgVDrKT6VpwM4DC2AzQdrASiuaXPczwIhnyRrId2ilbyYeS73uuVgLTLwZlMKk68PcXg/czVp/OrAMKBXL7ef1/RSmhsupbZcJjgro27MmDGsWLGCJ598Eh8fn4vVJg88+O4w0KRhI/ZtlXz52DLVkavmrBH51L8OcZe4DZVkwiKItMp+jKCe68UKmkOTebXzAX7rtZ4QuZXinnGsMVzFUnGPwhllR0gcnCggdvQEYkcD+47wH76t7GzZzcaeceTrFBb4+d6F/M7n3/Cv4xiN3XxpK4pYlh5H/vZ05qiOgaFFaXdfDjt38IRTPLjEYDcuUtKXuGqZDgVD8ejY3vXYpMXkx2fYjnOV1Iq3KgZV3VULWar/jOlCMaZOJW1CRqbe6stPj9/FcxPGkf/jcih5kaLYRDq/PYIvPSCDbDWh97qayBGjlGvmv4FaFFD7Dmdvd6RSsNH0ESqVlrFXj+ItrynE7RtJYlQgJ5q7AUhLCGdHeQNdRouiA2vD+OhAh6fu/a5JLBDMbJamu+baVeTwmvZN/s8ynXxSSC2fxsc+LxHZdpAAqZRWw28AeNUmc9bPa3opzQ2XUlsuE5yVUZeTk8Mvf/lL/v73v/Piiy9yzz33XKRmeeDBdwQ7G31dKfrXb+TVzptJmb2EzKTFNDR08rnej9u1Bfgkui4ymVNjePWLMhqsQYTSgSwLeAkmtJjRYCVSa2Dlbx+HTRVQvpW7xBxSvCJpT1nmuug0V4K5W/kJ0NMGPW0sH1ZIgWoiiV17mCvvJFJsIaS5HDMSp6UQ1lmUooj8JzPIDE1G/3k0ZS3dtGvSqC2oZk2eimXpv3UIjveDJ8fOg0sM51UZOUSS4aLuUOrWrkKbclrhrHMaBxNjCskuTmbKVaGKVy8+la07oNQcziztUaKDdPy9/gassuJVnB78HuH6POK8g/BP/zn64n/T0qCnBX/21l/FbV6dxAJcPQNaqwkZmcJcARgzHOgt8Mj+h5LfV1zTRpBOQ6vBjLVsG89Yd1AlRTBaU8+X/mnKvGTbUK7Jq6TRdxYrapKZoznI+8Hv9lamlmxgrraIVLGESkaypn0eNcZuIgUrY9V60sUi8qTkgfvyUpobLqW2XCY4K+3X6dOns2fPHl566SWeeuopJk6cyNdff32x2uaBBxcf8XMgIBKaKghpKSLVkKeEPOLnEPvAhywfDwt8j5Bh3tF7TEUObLqfP19Xz1brZAxokRBok/2okUMxo1LY3kExGr2D8LG2cb1YQYZ5B4+sLSTu19k8srYQkhZTFTCZFUfiydUoGpB4BxI7egL5T2bwX6pPWaDazbUco84aSIvkxwbrLPKkZJdwiVdzGSfMQTxVGuWSI/OdwNYfdn1aDzz4zmHXdbUXWeS94vpO2oy+8G8/ZqK1UKH9oFe/Oaugmv3VLVhlGFmTTXPxVqrKDtJ40yo2+d9F+3XLiBwZS+bUGKKDdLyQWEtzXTUGSQ09LdBcSeQjORyYn81PLb8jhjqC9DuV69o3bicL3Oqqzh8f5fJ7dJCOW9jJDLGYxaqvSFOVsDyskDV5lWQVVJMZWs77we8SVvcVMrBIU6DkINrPm7QYLy8fwmhnIke5TbWLfVF3Q0A0PioLT8eUEh2ks8kY0q8fvm9cSm25HHFOhRI//elPuf3223n55Ze55ZZbmDt3Ln/6058YNWrUhW6fBx5cfNi8dc1tBvI7091X5Dm7/20LREYcDBf3oMNIh+zDv+RZTFcfpTkogsjUR5Tvxs9R8tzy33Ccx74zzy6u5Y0753P3RhU1HQYKSnVkJCRC5WmH5+6qYB2qFiuBQjdarKiQmCvvIU6sI6DhTiAGkhbT2tBJU70/7we/y/GoefzqwDCHGPhQvB/nxRrvyXvx4GJiKKFVu0dn0/3Ku3iiQDGk7H+zjfEA67cYewQiYhMBpdhghSGP7VvTaLaOJ10sIlRq4ogczeF6f+47+hCZEUATGGoPYbLUseymVWQc/QsWyqkSI7EGD8O/XU/ulizWlEYxf3wU+VXpTPMzKlGAq2cAUNnQiXfrPhq6Q0l2GjNv3Pk3Jo8Kcclr+3zrDExWiWNyBInqBnIaU6jpMPDqF2X4W19nulDMAqGNr4RkJZxr3kGuJo2nXs5lYkwYP+0eRorQTKvsyyfW6VQ0JbDsxyuhZINr+NmGV78oo9VgdoR6vxcprz6a2h7ViXPDeVW/3nTTTbS3t7Nq1Sqys7N5+OGHefrpp/Hz87tQ7fPAg4sP24IQCax0/txZgsi+C46fQ64mjY6OGr7db2W5pg5RBpOgYUG0gYjTx2ls0imTorvKsYoc1oa9x1vNKfiNU5KbX0isxVS4XuGX8ldy7OyyR5HznoB1d4HViA8mLAhcI9YQTy3797WwoljP8rBCYjPuJbZkA1TuJTbcD18v96SjA2GorPFujT9P3osHFxND2TQ4jdWqhk621/sze1gHsfZ3Mn4O5L+Bf08d/qKKMJ8mAJaHFRJkU4HJNo/jVvUuEsRT7GYCkdZaTMd3IwPNISkctCSxrmcKRV+UMYZmkgVIiImhyuhP8/GddB+Hmp4HAci3p15U5kJAJFlRv8W38BfMEHs4VlZM8+Q7yYijV85s+3r+4r2Hj7ZPoccs8RPrv0CA7eo0ht/0NClAQV4lXUYL/2eZjlWEzdJ0nrttHBlTY4BMnno5l5pWA3VtBtqFeSz1DkV73RKKDgxz8GZmLvpbr2d9ACP5XBUkzltOzPacl4d1UqCa6FGdOEeclVH31ltvsW/fPvbt28eRI0cQRZFx48bx4IMPMmHCBNatW8fYsWPZtGkT119//cVqswcenDuGsuu3f6ddDw1HHLt+u75jYtdJHqWam9R7UMlWZEHFsKS5DAuNo7P234QKZoZXbwEe7H/ukg1MloqYPDEEFilFEBnmHQoDvHkHNNObY1eRA/lv0KEJRWNtQCPICLIVDTIyApIss6hnE1H6Cr75sJr265Y5FoqBSEcHwlC5xtxO+H2MVg8FgQcXFEPZNDgZfne3/JyaLgP/06Lr55FCFDEK3pTYtGBp6may3INKBUE6DXo5Gq33MVQmCDA38q0QSYvsx6aOm0i5aQkVeZVgtPBXo81oCl1CVkE1C4Q2tqtnEB2k6x1DTu1es7GSRVIEE4UyJFmmdc9a1sQsZBkQtu1hFpkbGGU6RarVwLViNWPEk8hAOF3sZanL2NyR/Q3IsDiygflHH1LUbHxCWD1iHKd6DrHZOo296knceFMmmVNj8C3NdR2zTn1llxFLSwhnf3WL4xrnIuV1Vsagu3nCXSGLB2eNszLqXnzxRaZMmcJPf/pTpk6dysSJE9HpdI6/33///fzhD3/gnnvu4dChQxe8sR54cN5wM6H121nav2MTzLbrvK4+Ek9Nh4EV6p2MVp1ChYwFkS688O/Qw8kCRKBRDmCrkMoid9d3t0A5f1ZzoNdTV7IBTu5BY5XYb40nRtVIJPUIQLes5R3rAu5TfYooS1wlneSVYj0Zv1UkzDLj6eWccqJAGQiZoeVnJCmFIRh/nlCsB0PFUDcAgyTL271DLySmOTY0mU3lTOj6J6GCV2/xgD237qrplFQ3EyOdYM/+jxgvHcFHMDLTuoc/X38HGWag0sQksQJZ7OKQ10R+Jz7CxJhg23Vqubr2M1Y3ptA4e5ViyFiD+UpI5rkF41g9gDHzQmIto/fvIFg0MMlSjk4wsvMEUBLNLHUJJyV/rIKOamsEM4QSAAQgmFaX0Kivl5r3Rxwitr0MrCfhZB1YzaDSkOxfzVW0YpIlSr2mKvRH//MQH/v08Kk5ltnBHUp/9DE2a1oNgOvYzn/y7I2qsyKhdp4n7L8nLXaRYPTg3HDWPHVnwn/913/x1FNPnXODPPDgosLNhLYmr1KZAO0Ti7ORZVOR2F28jjnWPKJUR4kQm6mRQtEJZr6WxjEy2IfJGMHQglUXxuvWe0mbPYC+qtMC1RuuSHCIh1OyQfEKlh1kdWMKjwVO4ERzN+9Y56FF5K/inxCxYkFFvpBCklTFBLGSAKGLR01vc89vTEwZZaveGwojPygLXvYKhRJlsO8xhApFTyjWg6HiAmwA7N6hp0qjyHhSGUOj1q7iOrEcugWqct8j1s7b2HAE4jJov+4O9heuZ7N5GmXSMB5Qb1E2Svs/YoUmjcf8qpC6qtHI8K11OPmLrWxd+wztPVMwFe4hVndY4dFzqE0MEHJ0ur8MAK8e0IVSosrAVF+Bdlg8tFeiixpHAtB8/CAx1PG+ZQ4/13wOyPyveQ6pmkIW+uSzRUolu3VcL49fSBwc3wndiqeuSD2O6uZiNkvTmRgTDPlPw/GdRIoq7gs4De3disyXkxzasvTekKm9Lwu3r1dkwfoY24OGVytyyKzdQOaPF/cSoA8G53liKBttD4aMC64oMWzYMHJzc8/8RQ88+D7gZFQ5T2hVub8mSL+T1oZOYh/4kEe+CSP7H7U8FvsWo/Sf8RNrAwniKa4TytAJRnYxnv+zTOdWcRcf9UxhcmoylGzAP2kxK4e4QLkNV9gmu9VH4tnYMVbJLZkbR2rOH7jFmkuL1ZdQsUOpsrXKjFLXYUUFQCjt/FH9NpyE9hozjQ2dxGbc63Jet7Bx8qELPn9jzENB4MEQkFVQTeGReJaHdSp5b+cYtnfnHdKmLOHA3gZkYFNjipIna6cuateTkTocFm7myee2sc0wnhI5lv9Q72abMIPsjrHM6MljvqoFWYQFAZWQvYIbxCbwxlGUYB8ng25ybN/J1aSRXaxX7jXjXubb72/T/VCpGJokLabV+B6fnkgk15rMJv+7eCGxlsTC9fwksJqgrkosJpl8XQrm2BtJLVdyzjLvecJxueUv51JjUooyoqtbIAIQRWVcJ9+lpHT0Gd/O7d97rJnaVgNpxq8wHC1FBy7PYtDw6tka6O7mib4bbTeGoye148y44EadIAjMmjXrQp/WAw8uOJxDjisaU0i1tJHfmMLKihyWHn6aO9TACRijquEo0XxtHc8xOYIZmqPM9D/NHNPfsZgMaM0iWU0Kt51zQcWAsE1Oq0eEUmcqVQok7LBNdikF1RQ471i/zkXuqqdD5cs+aSwSMrNVBzkmR9Am+3JCDmeiWE6Y0IYVAb0UxurGFMXAPNME2Mcz6YEHFwQDLMJZBdU888khrLKyacmPz+itWoXecOkAC3hfj1Hfxb82fCavqm2qCbNH276v4v1g/17qj/g5/Pm6elr3rGWzNJ1HTct43PsTntGuxiBrqCaK+KtjiASoa8XLL5S58x9W2pJXo3i2k+8iy3upo2LUrtDggG3sPfVyLjUdwQCszH9DqYRPfaTfuIuNn0NGQTXB29ezPLiQ2KYO0B2BwGvZ2pbEx6Yp+Aao2V/d4jbCMDEmjNpWA94akRcSa6EJCBsDPiEQfR2k9xqA7rC/ugUZ+D/LNLwsoitJOr0G9AuJtS6FFv0M9LPFABvtfvCkdgwJHu1XD364cJokUmb/lpV5Ex1eu+vFMlRIHJVH8pUlic3SdHy1alZEHiTWKwRO7gHJTBcayszDeP+LMjKTPh7apGO7brLGB3Tdys7fJsBth7NyRerLuWxRBxJCIwGBYcR3NaMxt/JA0AHC/LT41BoZx3G8sADQJes4KYcDkPzcNgCXROh+O2A3hp9dA1ObskQhafXAg7PFAIvwmrxKrDKoBNwWFQx2rP34wRLy1+RV9srqTY0h9eVc4tt3U9d1DF34KCJt18gw76BRpegt50nJLJRyCRfasAoC/xbHEn/Plv7GZUUO5h1/QSUZ6Cr4J2uYRKvB7LiuMzmwfazZjaHlwYVwYhdIikIF92zpd2+ZU2OYfrCQIP1O9CGjibR58RrHJFBhO2dUw9cEHFhDtI8P5HsrYWVgf/VSZCDE14sM8w4MtYdoNqkJ0daisyvNDIJl6XG8+kUZRShFFn3DqA4Duo8BviavkpoOJwP9POBipPftezepHeddcXsF4qzIhz3w4IqCjbA0V5Pmsjv8vxM6rAhICLTKfvzK8gvypGRuknbgX/s1B0+18o0Uj1H0RYOFqeJhUuVCJbwTfu2ZQ5h2otTkuxyhl4FgX8BeNN4B4xdDcAxelnbaZD8+6plCbMa9hE24BR+1jADIQBu+TBLLeMK4imTjPloNZrKLa10JiQcjDK7IYfT+55hm3esgafXAg7OG/T134/GJDtLx3G3jbFrG9yt/cMr1GuhY+/FBOo2Dh9Hd352rUF9IrOV59XskisfZ3+xN6kaVclxIHF46f5q8RvBE3Ek6ZW/a8KFGCsXf3KiMjfg5ru0q2YBZkuiWvVhnnuloS5BO41I56jzWMqfGkP9khpIKoQsBUYW+vYfk57aR/Nw2xz3YN3Ar9RPIsyTxasdNjmvbz5E5NYYM8w6SKSekuRB9e4+jn5alx/Fj/8O8H/wuRd2hbDWMZZ05ja8sSUo/DjTmbZ9nhpZT9MxNFD1z05nzZp2eTd/+vmCwG/bO0Q/nZ0H/vvYABFmW5e+7EZc62tvbCQwMpK2tjYCAgO+7OR5cYKTa+J2CdBo6esz8r/r3TBKP0i17UybGssc6hlhVPWXmYYwS6tgsTSdPSuYj7z8wUSiniARCImKIbNrNV5YkGm9adcF2jfad6MSYYPZXt7hU39llgwAqnh1PnFyNhIAZFVosSAjo5TCettxLc+QsmrpMvTvaTfdD+VbwDnLNt7ERuJoOf0a7RcQcnKBw5XnCHR4MFWeT+2T3+sRluFQ+nskDYx+z0UG6M1dqbrof4+FsGi0+/IGfk90zTjkuYR1U5lIVMJny051MF4rJNSUiyXCD5hDBSTf3r8asyKEq971+488ZZyoooGQDK2w5s4DjHpznITtcQrpOXHzleR8QJLXyieYW7nvq7X79udUwlge7H0AlKIoV+6tbeD/4XSX83KevB3oG3zuG8B59V566S8EjOFQ7xBN+9eCHiz65bVukVLLlcbY/CngJZqZoqpgolCPLVkbqElnU9SSioPjE/pdbmZJUyfW2HevWtZ2s61E0We0D/3wnA3s4wj7hP1UaRf6TH/YjSfYLGU55M4RILQQIBkxosCISITTzuHod5fW7CJx8JxlTbQtg0mKFOsXQAkUf9GPf1wJhdp6+IYRuPPDAgbPJfRqgWtolxBpa3qvIkvoIxM85O/qMpMV4AdFJi5nWlECR/bjQxehPVlFXc4xS61jwGs+Pfvpw7z24K+Cw5b6tdHMZ57E+oKHplDO73ZaLZ78H53tyG2J26ld15DjU+p3MHtbhtj+1mjSiS3Uu5/p9RyIvDm8gsl3f64Uc5Bl87xhCPvB56QWfBc6VkPn7gMeo8+CHC9skGWfVcpXUSYi/liLvSezpHMt1lKPBgsUiIEsWRGTMVonK/9Q67dSXOmgNsgqqeVV+CLzgsfS4Cy55M+AiZqMjCe1sZj8pVEiTuE+1BQGZbtkLrWAhVqhlhNDA7kIVLMxU2vqFTKp8F78LziFSY4CgGMekntWUwJrypbyQWEtGwI5Lb7L34NJGHyNh0I1N/BzlfdtYybL0Xkm7iTHB1LUZFGqOkjVKDisoxl3JBjKTFpP55OAFFM7XsBsHDv5GAGI42LaKiUIZp8UQGm96szePzPb9qrd/0lsVfway8rMZ62cyRtyOd3u/hsQR274Trp5AUeg8FtnyZhWvnnKvGUDGQuXrUQ1f07Z3LZ9Yp3OwVUek8bDrRs1TsX5GnNUm4nuGJ6fOgx8ukhZTFTCZdzunkWdJ4t3W66htNTBFOIwWCwJwTI5krzSGAula3pNvVXivmr9mpe/7igfBBntytq+X2oW1fXlY4QXJN3Hk1PSsg9fGK4LlQFXue3S0NlBv0fGxaSqTbW3vknW8b72JOjkECRVtsp9Cx+DU1uyecRxs81FCryf3QM6zkPcKYdseJr59N0+VRvXLYfHAgzOiT+7TmfKe3P19f3ULVln5SdJiGDlF+QeueVY22Ctqz5hf1Sev7FjkPHZK4zkYpCT8983RW92YQp4lidWNKW5Pl1VQzda1qzAczVEkxwbJ9XN3rLNwfV9vkN2QcJzL3q/NlYoHPSCSXx0YRqvB7KLb2hcZ5h3M1R1mqfceZQ44Qx6vB/3hnNN4qcPjqfPgysNAuRjOn4MSem1MYaNlLOliEfeptvAzzRaC6MCKSKcYwMnr/punSqPoMlpoNZjxbhRZ6XtI4XVz2u3228ldLMmbog+g9YTyM/0JVuonMNvSxlZhBkVeExEsnyIh8C3R/NW6iBI5llvFXTR5jWBk4XpygWXpM3n1izKMFitbpFTS5T14yUbk+lJEUwez1B3gDY3pTlWvNskywBECu1JwKeTLXMmwV1XajR2H0oltLC5LT+jnBXEZT/ExZDUlULh9PY/5fUGk73ClKMme8F+yAUOpwJ9Up/hUnk5G+iDV2n1Cw9/WdzBchuNNXdRIBp761yFe/aLMkcuWMnuJoyreHdbkVRLfMwW8YW7GvfhuVBHfvpuwbX+F0IcHJO8FbLQuOIzQLqOFIJ3GQRni4vlzR44eEsfv5VV8LE4lT0rGaJF4ZG0h2cW1PBZ7UiEgD4mDdj26qHHMTbW3Z+jV7Gc9NoaaT+nhnLto8Bh1Hlx56DNxK+HGMn4vr2K29hC606VY6stBMvMcWp7QemHAixFCIxICLbI/+6QxfCj8iNULM8lY2Du5paQvgdBkV+MQN+GUixXSSL5LMeiS7wIgX0gh2zKO+d6HKEr6mHdKx1NnDCFfl87fJ7XgvfdTZGC4qZkE8RR79sus8Y7isZtHsyavkuzWccSqFnC3KodmMYgxyXeha65kbtJispqUhPRl6XFk1m7AUl2AVZapMfoPHIpyg7NZGL4PA+tyype5LNBnwc6cGtO/j/PfULzD7Xoy79nSr98d46kiB/7nIcZUNzPKIuFlrgFfL2iqgPw3qDL6E6jfyc2SFp3KyIgAHZ0NMWx9/mG0KUuoDZ/Zp9CoV04M4A7vPYwy9dKayECrwexo59DCpLYNUHwMy9KrCdv2GrPUJf1yUft6JJ1pXZxpWDLMO/oJ2zd/cjuBnRW0VRUS8tg+R0HTbO0hTBaJPCkZL7VIdnEtVhkiTmyBphKwWkBUwehblEY48csN5dnZ2/zMJ4ccz2VQDDWf8iJxznk2aJ7wqwdXIvqU3NsnzI9NU/nKkkRPUzUqyYQKGR+MhAvtRAjNSAgIyPgKBk7LIXzWM84R+nBxv9vDIDAwLcgFhEuYJv0JmL9SCcFU5PDYzaOJDtKxIvIgVOYye1gHK/1WkDJ7CRnmHUxWf8tEsQKAndJ4NlunUdNq4NUvyhxUBO+Ki5lsWsNS4VWFpNSGNXmVNq/DwxASRxEJfGONHzAUNRBcFrPBqFT6fvc7wkWjZLiSMdhz7EtFwXn0sU3/OFkox0st8q3/ZCq6dFhspA2rG1P4ypLEPikBg+zF182BtO5Zy0RrIabC9Y73acvBWuLbd9O9/yNIWkxWUwKpL+eyryMEg+xFNRHcOiGqHz3JmdA3LJc5NYa5dz6MbsycfiHOiTHBqATlpwutiy3U6ugf2/wVm3Gv49xS52kEWcbSXtcbjk1ajG7MHIKm3El0kI7Hbh7N/PFRqAQ4GDSHmh4vLBYzSFaQoWPjo3QUf4r+8z/2e3b2OaYq9z2XZ7csPQ6VgItHERj4+feZe/uGmAf63oWCh+LE46nz4EqEk5csd0sWv+75iM2aaWhUahKG+9GpF9GCQv8hq9AKFsyymm68CRC60KpUVFsjkMHVe9M3ZPAdMZz383I4XTdz0d+Uz/L20VFQxPZ6f5bNse1SKxajbtdTebqDd9rmsEd1HSCQLu5nmfwZU46GkDl1hkNnNmX2Eih50XHuZem/JWzba0wXitm6Az5L+KuDwPhs4BJKczq/uz57IbFWIT12Vtm4yPiuKujOC5dauGqwd99NNaWL523Ti3D1DAiIHBqnY10p6q56rp90K6kFk4g37Wap9x7mpj5MSlMCK/41lpXqN9EJRgfl0HChmQkh3bwQW0t2sZ40Yx5hcjNjqIGSDawpX0pNq4FIdQ2+ahO3RRuIvb4RjKsAyG24g9SXz9HjM4CX3jlP8I07U1zO6/oOxvQ7/tQ1d9FdvoEN1llsss8DtutkVOSQYV4HoYvJnDqHN+5MIfXlXI6b7uF59XuECQZ8ThZATwstki/1Ld1EGl2fnX2OcejKOsmg7T3WTHZxrVK0YsdAz7/Pvfebu5zf4/OgTxnII3c5FTRcLHiMOg+uPDjxOY3e/y5+cidqjcDccREYju6mE8WgkxA5ICeQyHH8BQNNcgA9spYIsYfbRhr4uKWPZ6HvRPYdUQEMlK/nct3mSsw9nYRaTrHSedIHjq1VFiqDWWK26iAvqd8mjDas1Wrqjh8lUmthZXQH1FZASBxVDZ2sPhJPShTMvfNhtq5dpVC1VLe4p2o4g8HhyhI/eJ9lmHeA7rBblY0fNIaygfguDb/B3v3BUg+c78Pdou6GQkQ55jQ0V7IsfalLuDMzXjksf3s6I7x1bG29jq+kCfzMbx9eLQe4uvYzVl7rR3tpKSWmSHZKSfhr0liWHsdT/zrEZmk6WkQWjJ6gSH+114CgwnRCoKb7gX4h+fMJ77nNLRzovvsgOfMlsgoeZJM7g8UplO2c4/vvzbs5KYfTaelkLKDS6PhCmk1c8kyHfq0zDyaAOcamKxsV5+hbl6IVO4Yy91Xk8H7we6y2pihpK3DBNsKF29ezwpBH/vZ0Mqc+7vj8stigXWR4jDoPrjzYJ44TBQSL3TRafLEGx0J7JdUmf6ySTL0cQCv+vGNdwF/VryMiEyE00Sb7UW0JIT7j3v4FDn0nsu+ICmBI+XpJi2lt6KSp3p+PdX+A//mL4g0p+oA0mugWreRJycwX8gkSOpFlaJJ9WWdJ4xrpNNHVzSSIJeySx/Ok/BCtBjMFeZVkPjkHbcpplhauR5s4wGRp7+92/aALk12Dc1n6b8mMH+Bclypn1veNofTLhfYcD2ZoDPTun8mwPNN9uLsHp2My42NsRQMvQsViHvkmjOziWuaPv5HJdz7OZNtp3nlhM3GSllJ9B8111VjlaN6xziNPSibogIaiZ2J49Ysy8gzJFAmTWND8sVL8pAuB8DFoQ5c4eN6ccT75l25zC93cd1ZTglvDse88YM8VfltuZqIouyzmmVNjSMgvZFT7KQSNL3Q0o5VkRgp6asNnwtRMqMhRKt17ppBdnMxzt41z275l6XEU2vRoqbAO/d0q2UBs+17F82dv94UY3xU5PKt6D9QtJIcFnvt5rlB4jDoPrjw4VYcd/mozRlkirGkfHU3HiJFNqASJffJo7jL/DgAVVgDUSIQJ7QSLZvfnvZT5nGykqLGb7ofSYmgHWquhpxWzJDFDLOFh1Sa2CqkMl5oBeMe6gDwpGayQLhZxq7iLzdIUUrWFLPTJd4RA+3nPBtJkbNcPalQMaUG8lPv4+8RQ+uUcFsxBPU/nYiSe6Zgz3Yet7bmaNJ6yF+lM7XOM0zW2HLwNGdhysJY37uzN9VwQUElYSzPD2IVR0vC1MImvpGSXS9mLhexExI7r9+F5c8b5hvcGOj5Xk4bJUEdEd6jD0FqTN3hhgj1XeI04j6XeoUp1K726zdbgWJpVJiKuTiTMcoii6mZXcvSSDcxSl2BQWckzJzs8dg5+QBsyp8aQWVsBlXuhxE/5MHsFxs4mvjxUR+NNCf3amVVQTeGReJaHdRLr/D6e62bAGSUb8KcTfP3w9+pwJVI+T1wJhRYeo86D7x8XIGzkOhjnOKpeg+SdpKm+wYQGg6QmRDSALcnabshYUYFgwYwGNTIaqQf+/ezlaVwkLVaMK1A8dc2VmIq3Eiq0s1j1FTvmPc3yL66n1WDmdc1q/qp5nS+s17PCslwx8ICP+AMpUhnFB5rICp9JZkicoj4RYluI+i7c9n99KWP6YEj5cpda7tjlhHMwiAc1tM/Fq9L3XTkbOD37pzaqhtQu70IrU6z7+Q/1bqjQknv0NN37PyKeeoYjowKaBH98Jt7BC+Hjeg2qihymH3yPQmsKe48FsyZPxcSYZezf2MLEmEJH7qi7qlzoLRo4F2+du2OeKo2ipvsB3qp6W6mc7Usp5KaPlqUn8OoXZRQxiRtvyiSrCdZszOXXPR8xRS6ivL6B03IIb5b54++dxh1+ewhVa7nRKY1DBwT2UZ/oF2rt0+dVue8R0VqHDJSaw3tz/JywJq+Smo6xFKgmDo3Syc1mYEADy3kTeYEVb66ESniPUefB948LEDbqW/VUuH09N5h3UGYeRorGj1ChnR5EemQNFllFgTSWH6l2kSoWUy0NI1xqp0EOYISqiUC6obP+Qt3dd4LeCTCBzHu2uPytpvvXmL79mJKw+azJq+SqEB86atu4Wb0fL8nIzapv2CLZPXXTsVglJBGMVkmZ3BIqFRmxZltFWdJiOF0KlV8qJMjpTwzeONtClNGuB92RwfPlvqPiEw8UDOp5OhevaXOfd+VsYHv2VQ2ddBnvGbgK1d6m/DfI0Z1C29NEoMoI2SsY2Qoj5VoqpQgOqMdyfUwIZaF38FRpFMvSceSEVr39a/xrvybV2sbjxWOxylDXZnD5OdDCfq4L/2BeIPtz0CYuQWeOYG7S4l51Czd9BE5FUjakvpxLfPtuwrXNVMgjAJipKgYrYIBrzCWsHB8CU2Mc3jxtyhIybLRNzve3LD3O7UYZYPX6Ih6V9hIkdpHo1UCKjVfPeSN21h5NNxuIAft5iJvIc8GVUGjhMeo8+P5xAfIs7IPxhcRaTNteY5G5gXHicWaoZJAELIKMv6Dom5oFDaOEOv5lnY5aJfKldxppxjzSxf3IkkCjGMipyNtJHuhil6A3qZ9WplP7kjNfIndLIqa9a1kkfcC07sPIaqiyhJMgnqJL8uI32o+IoZZIqZm3LQtoFHZRrx3B+8HvQsgE5SJOMmI3nK4nigYEGwkyMLBBZv/cdzhofAb34vyQc+q+h/fqgieWn+n5DXaPtmNWH4l3cLa5a1tWQTVh21YxR95FlGzBKogYJQ10NhGt8UI0SYQLbewLuRECuqks+poVxlN8umU6kEnm1BhWN6aQam1jszSdxKhAmrpMDi47+8+BFvZzXfgHMwZdn8MgHrqQPsVSfdoVtu01xqtr0YdOsxGT72CzNB2A5MhAQmzFEb571zJDLGZ/IbCw93rO7bDrTfdtrzn2Rp49ZOLBwELm3vqA23F/1u+Vmw3EGfv5LDcdQwmtXgmFFh6jzoPvH+eTR2Wb7BxakJvux6AuodASgVFWEya0IwBmVAiAFYFKOZLN0nSKvCZx482ZrJwaQ1FWM9qKAjSCxB7r1fz3qTnkD3TNS9CbNCBtCEDJBgJKSkkSTzBRKCNCbEUGTktBWFARKBpQIeClErk6xI+K7ml0pGdyX+2LULkX/cF6Drb5oNWcJiNeWZxOm9NYqtlBtI0EGRhwQbfnC03wMhBpPj24F+eHnFN3Cb5XZwu7brBz9aQLBrtH27NPKaimwHkxd0OIG98zheu1RYTQhohEl+xFpXEEw0eMIlL/b3zpIbkpG3pk/kNQI6u6wAqPf5IMQMrsJTz+ieKhi+4yua/qdnNtcLPwD9EYH1S/ua9aywD0SVUNndzd8nO3/Zs5NUZRsSjZQGzSYlbHzyH5uRRaDWaCdBpiH/gtAIXr/8QiFG+eNmXJWdOD7K9uocaaTHCPlslnMDSdYVe7mD8+yiX/cSCc0cA6y03QxfCwXorwGHUeXPJwO6jyXlGUFbz8ofM01JUqlARBMeg0asZoLejbgwijHQCVbEUQQADKpKu48dZMbsQpN8anCasAIKNWiYPvwi9Bb5JjAsx7RQmL+g5X2mdbDKKDR7G/NYWI2ESusijs8DtOX8WozgMIAgyPGoV//Q58mg+x+pocmhtq+aaklOigq6lpNTBNOkTbvsMOmbE1eXdRHfMQ+wtaWOZd7cKb1RfZxXpSTVY+bYvlvsRR322/XYJe1QFxCb5XZwu3C6fzM3BbCOHKX5Y5dY7rsf/6BRhaoF1PVlMCXUYLRV6TKL4uhozi/0Y2dxFKB/lSCO2nOxmp1lJv0fEJN3BLQCfb6/0JtZ5iszTdEVa1G3FnrOocwAh1mZNqh2aMD2ik2AiWHf/vjgPTyYtZ0zGIYeI0Bu2Ev/O9D/Ezv30sf7aQfCGFj7yziVWVYdYEoa/9jHH7fkOKHMA/tv/MLT2InUD4hcRaMsw7eCExjadKo5R+q9yrfHkgzjmn55pdbMIqQ3Zx7ZCMujPiLDdBQ/GwultrLrc8u8tKUaK5uZm77rqLgIAAgoKC+K//+i86OzsHPSY9PR1BEFz+Pfjgg99Riz24EHDLEm7XQO2sV5jJ204it1ZjOLYLU1cLIYZq1L6hjq+LgvJTQGaOupDMnnWO8776RRkrjsRTH5yC6uqZTLnr2f47cWf2dLuixKVoJBR9AF0NYOroXQziMjgy6me8oP1/HLrmQbhni/Jv1n/z374vUjVvHQKgtnTiL3cT9u3HtO5ZS4x0gv0tOtqvW0abrOQljt7/HJmh5eQ/maHs2J2fywAs88vDCpmjPsBSzdfnXQzjlp1+MLhRN7hkcSm/V0OEW+UI52dgu8enSqNc352+zynvFXhtPOQ8i7W7GatkQd+mfD/ZuI+Xhb8CsMU8ESMaWjXDafIawYTAbrQjUviL5n7+1HMbd7f8nLjkmfhoVUwZFUqQTuPCE7fy2gpi2/cO/H4MoHzgMiedpTpCv/c4aTGMnKL8czbsnc9p67eU2UsGVOawn/eRtYWkvpzLq1+U0Wowcws7Gd2ezxPSOyQb99FqMKNBxsfSSoz+c8KFNkaLp7jDe4/b8WW/V1PheqjMJaPpI/IT1tHuG8NWw1hyNWkD36zTc7WrXcwfHzWkfjojzrLf+6p/uJuv3K01l5vizGXlqbvrrrvQ6/Xk5ORgNpu59957uf/++/nwww8HPe6+++7j+eefd/zu4+NzsZvqwQWE8w7LvpNaPWI+yWQrGqjpT9D9Yiw6GURkKqyRqBAZpuqiTdIRIBrolrSoRRkvzPjRBUUfMDHmJuraDBgtVjYabJVaj7oJw1xOYbHku+go+Cc722MY/fZPiM24V1lEnfNjbDl3hUfiie8yMeaLp7DIHTTJAQQKHciAjKzIJwmRLB8znOayILSdTURSz6kN/80d4koH7YFjshugn2JHT4DGPLC2DF6pdgav2jntmK8A79f3BtvzyNUonhnnXLOzyklyfga2c9q9PS6E2u165V/eK5D/Opi7wHcYhYzBaJXY1Hkzy2YreWNT5IN8uUehIupQ6dgvjea+cTJUHoeADFLGLHGEcDNq1ymUPKERZDUlUNNqoHD7eoWmY5DQ4WC8ii5en/j+ChCDoS9xblZTAmvqfqn0q/069vPZjc1BctXsc2KX0UKrwUxdm4GZQhG3awv40j+Nz80zGC8fIVRo54/qt2iUgmgOiEPqrCeAbiyCim6/URzsDmWFcWUvoa89tSX0Ol5tG8mxyHlUSX40V1eTqDlEnSWJB7sfILpUR8aYAcau07N/Iz6lv4fufDzpQ0zVGDB86ma+cufNu9zy7C4bo+7IkSNs3bqVffv2cf311wOwatUq5s2bx6uvvkpU1MDWv4+PDxEREd9VUz04X7gRBO+bvLucOeQ/+ZJjtyWbFY+tGitlcgxYILZjNyrRSpMcwH9bH1RyatTrGKXtwif5LvYXKEzpXmoV87wPDRyGuZwMg/QnmFswiRWmlYTrv4TsQzB/JcvSewlN7Tl3y8M6Kbd2kkw5kixTRxABiETQxM2qNqyI3BLZCSUbCDGcQEJCkMGvp44V0kq2Hk7lhclXYdr2GrkNS8hwWphzt2Q5Fu3M5kpFVFwXfG7kuQMZAkPBAIvjpYzvLIfnTAuq7XmYDHXUdD9ATasBcFMV2rcKse85nRbfqrd/QpB+J1dHdpL/ZO9mPKspAf8qielCMdqTJQRKPaDygkk/56j3Utf+CH2Y//vf1xwFACoRtkmpaDXRZMTRS1LcR8UkV5NGl9GCTiNyg3kHhqOH0Id2sroxheUN7xFbc0DJ93TK3RtaYcPQkVVQzRLzv0hWlzHN3wQ8PjghcdnnCkXM/JUDvrv24wUgSKfhgagqFte8TbDQxYJoyBrzV17fLvKo8W0ihGYCMPBZ6xQ2S/+hyK3d+TDe8XOY/fZPCNKXOAh9q3LfI0i/kyipBqv8C96ujSXLK4F4426WCnvQpjgRNDvl8WY1JShh7bBCjkfN4ynnHEvngo/mSvSnjuHVXEZrQyexF2lsDti/bub1y82Ac4fLxqjbvXs3QUFBDoMO4MYbb0QUsPEyXAAAnztJREFURfbs2cN//Md/DHjsBx98QFZWFhERESxcuJCnnnpqUG+d0WjEaDQ6fm9vb78wN+HB0DCIZ6wfu3nJBgxHczBLKmQBJEQmimUOLjqAIKGLn/nt4+cd9xFw7TzHbnGZt3NezMe95JpDZc+/RLEsPY787enMUR1TWPJLNvTSH1TkwFE9hF9LbOq9xALky3xT3cxX1jHMFfcQK9ahxkqbEKh42Y7vpFl3Fea20wSIBtrwdVAldO/fzRS5iJ17rWSFv8l0oz+B+p20fisRL00naOtfIH0GRCafueEDGc82GaSM0FIyEhJtRLFnMfFeTp5WvsMcnjP1i+05aDVpqHYpou4qgf5GtfN5YFBlhNWNKaRa2vj0RCJ+a3v54NbkVRJvmY5VhNFSNb7qLoqkeI56L+2/0NYcYKZXJRhlooN8+KgnneyOsRSV6sh48m+KUbzRKV/PNn6fejmXVoOBDFURwTRSao5ibWMKqYY8wvWFUP9vkK0Oua2LQW+xJq+SP1olUEFkgDcwiHxYSBxIViWdwsnD3dfoX5YexzOfHMIqg6+XmmWhB0DfBbIEQGZoOZnXVpBds4BT9XsIFDoZLigE5I03rXJQp8SOnkBHi6IfvaugmkLbs7Ibz/a22mXaMqfGKFQoTnMKSYtZs7GSFYY8gvQlmE53usqtOan9YO6mpns4JyxJ5DemsPKC9bIrBnyOl9m8PlRcNkZdXV0dw4YNc/lMrVYTEhJCXV3dgMf95Cc/ISYmhqioKIqLi3niiScoKytj06ZNAx7z0ksv8dxzz12wtntwlhjEM+bMbl6Vi61sv4ZjcgSLVV8RITQxQmhEFkAWBEyyhiopgk/lVCpfmt//XEPUJL2cYJ/ECfkZVWUHWVkcR37JNh67ebSS1N1wRJmAc56FrnrFGzJmKX/91yGmiodRUUs3XpyQwhCL/01o+xE6Td6cZAQJnKJGDqdOClF0M60CJllSqom/KGO2unchuFXcRRoHoKgagmIGJQq1h7peSEwjYyCvWle9ogHq7m+D4XLytHKeXFln8po540z9Ylv0MoDnwgfxHjqdJ/foabq79HxeHEd+iZLT9eoXZY5jnatOhYO1yMCrX5Tx2M2j+d2/DORJycxWHWSxUOCqfuCMog8ItdRxs6oZS7cPS6x60rRfsa1nJlkFcQMaxfZ+fVBTyDUdNbRGzCBlwhLyt6NsgIx1LpvBQb025xg2XJYex6bti4gMKyQ29V7lOqHlBMmr+Ng0lVe/cCI0Pr4TrEZFumwQ/jbnhH6HOka7nubGOqqqm4lt/A0hhmrmj5wCD+9m78rbSWjfw890+/jlF5P4cssHzBfyifNuJ8rcQahV0Y9+YXwkbbY6CJWgKHC4rfzNXqEUskQmOzzq2cXpJIcFoo2a5yq35qT2Q3Ml7Zo0Vjp537MKqh1evtiMewfv2yE+gwvqfXO65kBybt83vnej7sknn+SVV14Z9DtHjhw55/Pff//9jv9PSkoiMjKS2bNnU1lZSVyc+0nz17/+Nb/61a8cv7e3tzNy5MhzboMHZ4k+OyiXnWloubKTDr+W1TUpZPeMJZtxpItFTOMwHZI3CAL+dDNMaEMUVZxUxZIy242KgTvx8Mscdg6vWeoSdGPg7pafU9NjAMzKIvBj26Tarof6w4AE+95lesBB5nlPIJQuVEggwTXCKSoaRmAWvQkUOjkph7NLHk/tVQt5uzaWVLmQW9hJmTmCW8VdaC0C86+7io59sJB8ZqgOoVWpFG8hMYMmNbskY+sOKx/an0fqIy4hm7M2zi6zZ3tei9AAXrNzkuwaapuczvPUxlxqeh4kXSzi99pVfOmfxnbLBIcRYq86XZNXSXOXEYNZcpzfnti/X3s9N9x0F6Hb1/N08Lv9UyKS76I2710KLPFEWlu4RjjJGKESq0Xm+S8UL3yQTsPEmGBWvPgnh4GQGQqZCRsgZBI0hxCStJjY+Bhb/lgy+s//SE1LN+2hd3BGDYRz9P4q/fi464f5b3AjuwlWNbKcScpnFTnQcBQQIHyMyzX6Gv3O8yNA6kYVE2OeJ731d8xUFSN1CqDqvdzkWx+AfCP+1Q0kG/cxX9zFTFUx5cYRlDOCCLGZ1SNySK7Mxqw5zWRVOZujV7AmT+c4h0vlb0+rkloBUL6VDO8CMpashPjHiaWP3FrNgV7FkUV/U+TYxuQ4tHzX5KkcXj63UZOhPIOLWfHudM015UsvyarY792oW7FiBffcc8+g34mNjSUiIoL6eleWf4vFQnNz81nly02ZMgWAb7/9dkCjzsvLCy8vryGf04MLjD7ehrBtq3q1EBM2QN1B8A4iI6CGVEMem6Xp/CJU2X3nWZLYbJ3O85r3QJYxyzKVQgSB7q5zmYXlhgI7hxfeMDdpMcuilNAO4JrUXZGjaMN21NFh1eBf+zWzrW20qf0xo0ZPKEesMUroxYpDbeJrORn/Wg2tBjMLffKZpT7EeEmFTjCiFUQyzMdpFkvQCj34CCZ6xBC2msahDVWY6weCM6M+5h2uhtuFMsoGm+zdcYVdwnCXd+dWb9NefHAB9TEHgz2UeLtcwGztIRaMiSQrakm/8KKdKsPZGLFrsU6MCaZw+3p+Lf8dL30bVbm45lulP8GXtjy7v2jeRNdRjVEdRL42HWOPFYNZIkinQVP1bx41vk2wvhvyOxxayCTM7U/BET+H2zeqqDEYlMR/N7qvLrjA3l+1KOAliDx282jlg5INYOmBgCjlfXRCXwO7b8VmTauBujYD7YISNtWro1meCITEUfX2T9he789STSVJqjZu1xbwf+ZUsMJmaTq3qXYx1/swusZs6GlFo1IRrTUySv8ZNd0jWZNXyVTr/t5Cj9Q48A5SCtair+v12g1UEGVnLRiAtHxZ+m/J3654+ULO1LcDPYOLOa87XXNZ1KWpPvG9G3Xh4eGEh4ef8XvTpk2jtbWV/fv3M3HiRAByc3ORJMlhqA0FRUVFAERGRp5Tez24eLBP8h/rXiGyvVhZjAIiXbUQQxfDiQKMnU0kmT5Fp1JyHw92j+Babz+OdSheo0C6sKDCikikpYbHP1G42c6UKHu5wznnhfgYMuPpHy6xGzZXTYXKXMq6gzFKgUQIzdQTRr0UxE5NKnHJM1lauJ51PVP4leUXLteJDtKhTVyCvtaPT0/5MELS4zU8Dur24a1Vc8oUQaygp8msYp1pCkUHhlE0yEJ5RkZ9LkABwWCTvTuusEsY7kKMbvU27aS1ue9x90bVOffdGfvemQT8mZugQujlnYuP6d/eihwyazconuP43t+jEtP4r3wDK9V5aFRttMi+rG5MYWVFDns3v81bzSk0RsxyKEB8VDWF5ZFaYjPuZWX8HLY/t83h/VseVkiwvgu8bV4kQ8ugxTpD0ia242w3GoNtKPwjUas0XD8mkevtfes8N53hOn09d3bDuOfwQZBBjL4OFj0Im+4nSL+TmyUtWLvxCgpjwchIFpxcR+X/Z++846Mq0/59nSnpvUAKEEhMKKEkIjWIMYiiiPqiIK7xVX+7uovoFtFVX3XBtlZWV5fFsrtYoqKoqzQpSwxIMOqGhIQISUggYAqQ3iaZdn5/nJnJzGQSEloKz/X5IDJz5pxnzpxznvu5y/f2HM+Iqi2EaVspMESgCZlNglcNua3BVJUWkOWdQkp7Lr/R5jDcsx53Q5FSUHG0CZqqlFBx5KVKmkVAVNfP1ITbFYOuC9Fypfp3MXdkTGZpTRcC1lbsf4PiHZSmr2V1dSLzJ84mJfQ8LWbsjtnp2dpP6HOjrqeMHTuWefPmcc899/Dmm29iMBi4//77WbJkia3ytby8nDlz5vD+++8zdepUSkpK+Oijj7juuusIDg4mLy+PP/zhD8yePZuJEyf28TcSOGN98Je36wi3KihOUJpOz5uwiLQaJbTwTPwfac3+hELTEEZJVVRqIrlV2oW6rZYl2t2sM8xmn/s0AkeMpawonw3mGa57OQ6wsFxPOG3ozt6wsTxMG7Wz8du3hgSKaJEq0csmZhkyuSTnK7RqCXcfFTTDPepNBElN6NWB/LVpHl82X4e2upIkOYMjchi31mzGQA1uKjVxAT7QJBFqrOMG1V6+N04+4+9kNSgS2n5kuXF3h+RCDz7jYITYy2Y4P+yt79mdl/5Mp7y74h185vUS5e2tNMYv7diwp6K1zrjo4mDvDep0bl1VanYTMnQ2sK2Vlo2mcmTusyXnb5Jn4hN9FVs/XsFkUw7XSzoeLE9QPtb4HTeo9rK6OplVlmNZvX1Lk2OIDr5b8dABjJwFfuHdGkkpht1K6L+73sRdnB9nOl1/3S0oSr9R5FtKv+n4/Ok6c9iNw8E4psPQ2Pr0Ciab8siuVAO/gaAYtB4+FOhjCPZ255OWaawsXotvWwUxLdWgMtKuN/ODcRqrfp5rV4AxC6kV0rTPMrGpGA//CAgIIGj0JCrz/kOQyUxtYxvh+ZZ83ZiUTt+x43wsIfX3Tr2ina6VMyoUyl9PQOUekowNPFmwnJS4cEdtxIuIAWPUgVLFev/99zNnzhxUKhU333wzr7/+uu19g8FAYWEhra1Kj083Nzf+85//8Nprr9HS0sLw4cO5+eabeeKJJ/rqKwi6wfrgb4xf2hGCs5Ok0BVIvKTPw32fis+5gc0mJZfuFc0H+BhOocVIGCeZp/6Be1V/JTM1hWUWCRRr1V6n/LyB0m3gXOHsAbAkwlPzCRyXOGny46TZm0tVxajNBlB785l+BjeodjNZVYwGI0ZDFb9UGTn5016mh+pxMxQylSL8aKbO7M0xaQSXJdwAR/ew/2gtG8wzcXc/c51z60P+MY89zNR0SC4AXU6uLicGe6V+54f9ADPwOxnv+esJb9ivLIbsjRLL9+rUeut0OBkh9kaZy3M7QfGgW6ut7Ss1rYnvqTffbTM87DtLPPjUdhLa47lB1VFpmWFOIMOcQICnlqk/bcWXUxQxzPZ+siqXp7Vr8aeZYR4duV6O58XOmPILV0KuVsFZ++ulB31VnbEaoV1JcXQ6R91FBbyHQEu18rflnFmrWe2LTLrsRtGFseiWuJjsHOVvAGpL8FXruXZ8OElFSyhv0hHtMYub+Ia2kCnEuDdR26AjszmZyVGBtjGkqHO5R72ZCZSgxQit1WA2Upn3Hx4/kcz1khuZTcmsmpDQ5Xe0F3p3KISwfge7fFkH+aWeMmER9aeayaxO7CgW6ep8n2P6WxuxAWXUBQUFdSs0PHLkSGS76qXhw4eza9euCzE0QU/pYhK2vzFSpqdgnZTSN6YRl/0UfjTzPxp//FQnUZvNtJvNbGY8N6j2ojU0YESNFiMqCYZJ1bYHgv1klDo9yrFJddzgy6k7LV0ZL5ZihOPa2Xhnr0GLCbPkhirpd2zfPoFoqYhG2QudrKVF8iJeOso0VREazRiYOI/c1mCyi/L5t3EGfmOv47LkROARPvw4h2/zKpgfZ0mxOIMkZutv6BV/K0GGcMdcmy4mtS4rSAdhyB04racxNbhIud57KgfjdJ5c5XE5nNvYuYqHzr7yFkVo15bXZp/4biczktCueNw2yTOpDb8CqbyBudr93KD+js/apzNPtZc49c98a5rILnMCEyP9WVL3PQHmFurNPnzSNo2p1gM6X1/Ov7crj6L1GrKke/QEqzSLSymO4h18ELiW1aZEEpMXO3xfl8xd6XDe1mSU2ORjgM5iyXZaet1dzykLUkkLvZw1GSVUhJaRGhRjK1JYmhzDd1vXEW4s53HzXeRWTSF3xdWEA6uA5c+9zMvqDDbJM3liRAEjThQjmfVISKDSgNlIeV0r6aYEdkkJPDVnPN2JMFvvx5Z2o0VCZp+ioWmtirdInAAd8ku9IXYu0bFz7X6L3glCu6SHz6r+1kZsQBl1gkFAF5NwVzfG8OyXCJdPoZPdONmuQSO54yV1aAhukmcyzM+T/a3B/C+bcTc14z802rYP58nIYbK/gKu5fk8nr52WSr+J3JI1BYNZxyhNFZIkk20ew1BqGaM+pvQYbDkJE1Zy10cy9fpZAKjzKpg6KojU6VFoS//Dy+oMMkuTgcQeJzE7r347+oNC+qETPPl5OpOjAtGWOhUGWOgyDD3APHI95nTfq7fJ493sr9uG9nYFCGlZZVxp2E2AqoV2jT8rD8aSaK/DhnI/xmx5nESpmHivFkY/8LjyxhdfoTt0AI1K4kj4AvYcUxL5ZaCmRc+82x6gNH0tO0/6siwkxxZOt3rQDh+p5Q8GNUuT40i199AFxSh5dfYeRfuiEqtxd5rJPHHOYlZlTHbtTcpfT3TjD6wa6wM9meRPE6pek1HS0WfVzvixfc5V0Y9l/Gsy1HaL2BLls7UlEAFXmXYzS50HQC5THO65ZSE5BFTmM8zXk9XV03go4BThmlbwClJC2RY5ksgCTyZHBdrC8p3uOftcy0fnkpZV5qihaa2KP9PK9nON/bXcw3vmfOgZng3CqBNcWLpYWU6OCqSqQWdrPWVNfB3CKVTIqCUTkVI17bKWAvNI3jFdT4CnlpRrUpk6/XGaN6aRv+8A0T56gjyDukyQ7RSeGYwT/Nli8dq9YsnD8tSq2GSeiZdaxQbDdJ7WrEUCTKhQ65th83KS5NvZzHgAW9goNbiIleq1oKnrCJn20FPm0sh36nBQ1aDDJDsVBlzEdBsG6o2HsrfeVGcZFTuDIlY/HY2HxA7pcj5vGkeW06ItdXoURVtb0MhGfHTlHaHRoBg8vbKYl5AEyb8hLetacrcVEoAljaIG1tT9ig+G/MPSu1XxAu486cs1Zjcy6/wpN9pdP/ZjdPYoWg0j6/e2GnfW91zQbe5qD891V7+X876VBY1J+Y49MX6cqkmtx0g/NRu9rgo37WylSt40kyHUEqmp5y+XnuTJDA3l9TpWfHWAd2ZeR0qoD58cjFV+N/VkmxyNlRQUuRKH6IfzOXEyjGySLhlmmrLeY93xCDzn/h8Aa4pKeEZbQUr+vZ2uPeu5eia+Qsl9PA8pMw5yUFavt0VQ2QEXHY+Arg3bC4ww6gQXli48AJ08OpmvM6wyE9kMJknipDmAIap6vKR2AuQm7lFvIsRjB6ODH4fiIuKyn8JXbqKp2Zcg2a6k/nxqFg1WLL+R4eMc1HkVXOdxgFltezkSvoD/DfMh9L91IEOL5ImkDkCuP8Vc0262MB6NWiJJzuEGw14Kv2ghoq0ePAI78mdO41GyPrxTg4sYpd+iVCI65Ty52XkIrB0J+ht9kWfTqzBQd/dF/noaC7azM6ecb8aF8Ppl1T0XMe5kUCiV2InQKafPeo7+5RmESVdBoNakVOqeaqboRDNXaJrwrO2YKDu8tc+x/GAs5U3jeLYpnidGYPPUzhnShE+lniT/Bj4zuBC9ddakdKVV6Szg3Nvz10NvcK9+L7vc4tM+y5yqSTtaLCpyR0tyPiU1/DpeaUjgvoAcpppzwbCbiuTHbXl0TxZEkPLo27ZnQGpwEaVv/YPV1YkkzlnsMN5OC/IuxmJPaeF+fHUdQsdA1zqVdKNj2YPne0/vRQc5KOQuiz5cefD6UwhWGHWCPiUtq4xXthXylGkXsyxJ8GveeZPbjv+In2QCCSQgWNWEGhk1ZkarfgZAakXRFvMLx49m2mQ3GmQP1G5DCbfPoelhSEXgSHaZ0ht3YdsXTFYVse9YLY2VoZhR04Ka0kvu5IOyIJKMGbbQmMEkc4PGImbaPIwCOZFMU0d1YnekZZXxnw1pLFftJbKtnmneJ5Sk/3ygaKuihzV/FSmxc0+vI3YmxvzZLAC6qRY97w95u964m/MqechzG7z7qqPenvNE1F1oacIiduaU85VpJt/mVfC6+1eO29pp+qUH32rpx/s41OCgk+fQfxU7D4ZlvDkWw2yTOoZbteW0aCLxaSmnoKWJIFM1BXIEl9kbAxkvQuZfQTazLDSFL5vHkW5KoLBuhs1TG51yN+T7MHXCIttr6RvTFKmSxMWkOBtzjZXK5G3FRRjZioNxUHH2+bi9DttZ5WlONXcvT9OFUbk0OYaQ7a9xhSaf6PKjzNe2UtAyFsan2AzAiFPfOsi6WJ8Bw8s34yvnkmRqYFXGZH44UsvmvArmT4xgd9EpLpdyubrwTSh20njsYiyrqxNJMjWwSZ7pEGZ2qVNJNzqWrq7jM7wXHeSg7IvonPfpwlDtTyFYYdQJ+pQ1GSXU6wwcVg8lSQXRpjKuKF+Nr6SzhPgkmmVP0uVEhlLHNNVB1MjIgFmyVFQGxSB5BFLdomKIqp7shlGEW29w53wZEEZdFzhXBluTvcPNOrRGEyM82yiasJhvc+BI+HWk/RzH5OhAni6aRL3OYNvPVmkWYWalt+QROYxbPb7vVi/KetyWdiN/sqjblzKCUr+prD4Yy/yJ4aR4ZEHzSUXcNOF2x2RxV/Qyh8wh9ILrvqXd0k21aE84K8+e5dgpMZAyFijIg0YUw6uriai7EGHsXL4ZF8K3eRXER/jzToHEEq0bvjJKeLSx0qbppz8m2Xp7ApQ3jWOncRLen6tZmlwG0KntkzXv7V7PIm7WfsElUjnusgFzy8+4Se0kSofwUrdTHzbL8bfL/VDJCdN6ET16Ejv4UPEcJXevK6fP+ZTJphyycwCrALa1YELj0RFiO80142Ac3NzN+eshve4ccqbyNHbHI/gByF/PsIP/wcNQQzjNMOED2/d1lnWxaveVmMPRm8w2I2zFVwe4XMrlyp/+jkk9mzmqvcwgv3sJETstOUP0Vawqm+xwvXfSqbTz0KfWWs55bCoOcjOurmOndIBORSvdnJ9OqTnWXEx743/h252+4zltRXaWCKNO0KdYFegvMZ3Ah1ZMpw5xiUpCshUxS6SbL2WDeSZ3+vyI3H4IJBkVoAoYbsv/8lXrCQwdRXZ9dEcJP/QupHKR4zBpxdklezcOh2M/M0zbzLAxQ2HBBlseTXm9jjnq/fzKbSNmGd4xXc8OQwK/c/uIMdJx4lVHMTdpqfy6gf0fv6F4SxbYPbQzX2dMWS0L9WMYrT1JmSaS71HhNflWHiyIoLxJR1aBJyk3r1IMurb6jskdup5AepnX1NJuJKG9oxPHms976Wk7TbVolzh5rWxVjr3xFjp/V2s+EHRMbs4T0WlChK/flsjrtyWS9EI6we0/YzA1w3FLkn7oWBiuCL67BS926O353OaD1OsM1OsMrPjqAG4aFc/JGfhV5LH142aqr47ju8pJzDE2EKmrZ6rmMLLZSDtaCjRjaTOYcRsSy1i3k4rBllXWIT00fDoAuSHzqdqdyRWa/M7FCC4Ms7DoePSHDxIWHQ90dN5Yqd6Dr6kZ/MJJq4nr3I3DCQdDvZtKz/PGmcrTuNiHl8XrqUK2GWKdOpIU7yCl5CVwq6c0cBZ31C23HdNNo+IGeS9z3AqYNMSfF35OAiOcqLkUq0qiK60+q5bc00WT8HZ3YX7Yd3aBTpWxPQp1O6UD9KpoxRnr9RQ61qHNYX+TMbFHGHWCs+dMw1ZW4cxfLGL5p1dwSdtRYtRVaCTQo0Etm6mTfTkih/GS5i0C9c1oPXyhvQm8QxSPjV2uVfiERYSD8lrx0F5NYgJFUd9v3xoivbwg6CrlResDsr7Mof3P5KhAyut1AMyXMkmUikCSOCHvJcOcQCj1SMggyxz2m4qqrpLJ5kPs+cFEWujlykS9eTk0VpCARLj2JEFuRjzHhJMW8Teed86ri00lPeaP+O1bQ7RGT1BoN6r10Ou8pgBPLcV+M2ydOJYml/UunHKm15dl0lgW0kyWenJHlaN1n/b05D7rKl+sN9h9bmlyXEfbphAfxbAbOQuSH3FMXq9YBxMWscJosu3GJEObwcwGlaItt8EwjeKMElqkRDYbxzNfc4BpUXtoPJqNh6zD31DNUsMTRLZ4QguKQW8vPRSTAr/P466ntpPQ7oPRTeZ6p0n2mfjZpMQAExbZXvsgsIxIb5lIrxqgo/MGvnezaqxiQK/53EU3Dif6izem2+rj01yDDiLAiy9VDCiLGPeaDLXN0zrn01xWqtfioa/lpNmXbarZALbw7DTTNI5IYTSaimj0jmKHYRLbmYRUgs2oe2VbIQntPxKw9VUI/q2DlhxGXC+a7Du7DJ/WqTK2RwUTXRh6Z2SIOediWuhPOXTOCKNOcPacYa+9yi0vElSXy9HiIra0PUKSHMVwajhp8ma96QqiVVVsNM/kDu8fCdA3o8JMg0GFCV/q/aYQfXSP8gAYPg3u2qjs9It7RZj1DEkx7AZVMTQAtaMc84qcKgazy+psb22WkwiXazHLSrj1I+2z6HCnWvbjA9PV/KP+FlZPreHb7z9mg3kmudsKSZ3wma1tkyZ0DJEWmYQ1NZfy4g9KS7fhbZuZyn7qK3yAVJ4siGC5IYDo5nyITjwnuXLOOoZWzngC7+0Cx3I+o605YNYqR1eGWFf5Q1YPpv3rlv9Pq4ljzeclLE0u6/n3sT9OxONkqSczPyKc6JKXQFdHaeF+7shKp6XdSL3O4JC8Pn/iUjZbwraxjd9xq8f37G8NZpiXJ8Ftblxllz91c3wklUfa8EJGkiSCfdyJlD0dcqyWJseQeziY0BaJ3HIv/vyCctwMcwLfmy7leqdEdWuCP8CazxVv8mpTouKpsUzsLe1GAjy1JM5ZbPPe9NqIP1POR+FWL56/1vO04qsDcON4Uv06Oi88Ez8bfc6nbDQnkaTbDZo6TuLL4/q7+LZ0OJdL3zE5+2/4omOEppRD5ijc5TaMJbt5zS2fw6ahXKI+AcVuADwrv0GgupoxlCtjXPi2TUvOue+vDXu9RRc9mK2/aVcFFZ2wM/Csn+11IZEL0WoHr20/K8YTRp3g7OlBqKvTKql4B9q6IiTZTE1LOzqDmSPqMNrMWmRZZrrqJ94xXU+GOYFA2Q1f6RTtRjM/GMcRRZUi+hm5+4zGInDEwcsxvAsB2260tFKnzyfphemU1+t4Vft3pmoOo1FJZOpjmK76iek8zRd5C9ki3Y/ObCbAfv9OD8JXHtts+/+vmYXeaLYJvC5Njul5s297upn0zrn35Wz14Lrz+HWVP9RNT1NXHgWXHgvnRHBLO7Wcg59S3jSO1uxPaKIaPAJYXZ1IeZPi3bT2AC6t2MKqvBjSTVX4emhZPGU4qRXvQ0kuU329wNDK1LFBNiMqdXoUfHEv7bU5SLKZk1Iwoxc+7uAls45t09YDRMpttJ8optyo5NoCuGvUtm1d5TBaX0tM7jDe1nyeTr3OQGSA57kx4nvLGS6Au6R4h/JbuZLecEFH6y865Qam5K8Ht1ySOMw6w+WcGuLP0YjrKC6IIN7bjXtPbsKPViQgTn2Cb1Qz2WuWGGKqIUnaz2StB8FuRuU7AnPcDlBgiKAhbBbBTmPr6nyn1cSxpuoPiifOsh9Xzx1XBRXWojtQ2sU5779Xea7Ov1Pm64oDobHSTprFsv8vnutXjgRh1AnOnh6EnqyTiy1nqLESd4xUyUF8ZxrHXzR/Z7RURrDUiCTJDKMGd42KYvcZJCYv5hCLWZOhNKr+zCpjEZzQObwkwqy9xtHLsbFHn+lK1Nk//jY0NUoBi8/P9Yw1FKDCDG1fsFMzyTYRp9VYRGEtpG9Mw2/fGr7wVvFX3Tx8xl/H1FHjWZUxxfYQtmpcpWWVcUdvvE8TFlF6qpnVLsRvHehuxd3T1fj5XFScLn/IRdjVVculNRklxDZ+R8j215TEeedq2IVv24oZbvV1J0s9ma/bLAa2KRlD9FWkHNjC09rPGKZuBt9fkVT3K2L13/Gc6m9saJ/JmgyNzWCwNoV3086mwm7i/culsxkbdISm2hP4Bg7p8mt/zSz0JjNbpSQiA1xL2bgyEpxfs/fSWT97wXOjzvX1kb++Q3qjfF9HIVHyIy43t9dUU85BkePYjmXhq6vjnngZFn7E0Y1pPKl/jY1tScgo2pQqZPSyxLXhzdxR96CtB2+N+zDuGSPbvpsnKNXLPQ4JK10ulht3451dD94nlA3sPu/4mzr257UW3Vn//3TXQ7e4+J2MZpnSo2Vo3vqFreinq237EmHUCS4I1kn/Ic9tGPP3c1gOx2QKIlRq5Bb1LsIlpVrSjIQZCQkJH7mJDwL/weqdijaSg/hlP3N5D1RcTXS9xqGpeKqSz5i/nh/kCKI4jK/UyhB1i63ZumJQ/K3DoABasz9hpnwISZb41+SxMKEa8tc4NCq30ut8lti53PG5uiNHq6vPdCcZ0Y2HxdEw6CKn7QyvUVdGh4NEx4LUbnW07FsuWfc1OSqQ64q/5wqNXbWi08Rka4PVNo3Mx1NIy4ohc6eKZSE5rC6F66W9hOuPgF6G3A+ZHHU1yQV7ma3Ow02jot6Sm0jsXKX/cussIgs8gY6JV5FC+Qch2x/gipZ8m4fH+ZzNmLfEZty/3RvDy4W0hbOX7oLnRp3rRaf977Z5OdQfUwqJujDqOl1PXzyH7tAOdh2oovrqN0i1S7NIyyrD+4ePmaXKo9Vk4iPNTUQH/Yem2hOcMnnzRXUiS+fE8Mo2I7lM4aG5o20e0bSsMtYULWFpRAypdL4P7Mdh/xs8xh6mOVW/d7sQs8NadNduNNHSbiSti89ZPXpJcg7Lw/cTPXpS52p6y++UllXGms/TeSb+VvTHJHyNpxhbuccmdt0f56Ez77ItEPQESy5CanARk6MCqaupQjLriTKXM0Z1nFCpgUipGg0m1Jiok335m3Ehm0wzqDP74FvxLUm6DJtkgg3rxGWdCARnhHWi83bXnPmkZpWH2Ly84yFXks6cIU0cVkUjS274BA6xGRRLPCwGxY6VVD87hlef+CXFxiE0yl4cNoeTrp3d7e+7NDmGyADPXhmhzp9Jyyoj6YV00rLKOjaasAhiUpTwomWScX7vdCHOTuflLK9RV/u2SnTocz512DYtq4zlz71M5c9HlHBcUIySB1S8w2Ff2WV1zLvtASqDZ7D8YKxyDqyGXf56KN6htMHyWa7knaF4OVaNLSa68QeWheSQ6ZlMg88lShP6hNvJLqtjg3km35on4jX5VlKnR9nO8eSoQNu5fya+gr95vMl8jwMsTY4hZ+en+BpOkaePYPnBWErT1zqes+IdpFY8R+bNpjOWerHuy9V1cybX0pngcL1Z87Msv8tZETu3o7I54XYIGKH83QWdrqcJi9ipH8+6tmmKB9Vuf2syShStQtNENphnkiklonMPJUiu44QchCH6KlKnR5G74mpyV1ztsHhY8dUB23FK09dSm7dV+W2d3k/fmMar2r9zs+9PLE2OYdgl42mXPPgv8SysuovPm8ax4qsDjvepq3MKtrEEebtTrzN0vh/tzkG9zsAc4258K76lKeu9Lu9T+yjGltin+Kf5eg77TrUZvVs/fgPdoR39ah4SnjrB+cWSi1B5vJQNlX/gVq0vkgQektFhM8nynzqvaEp1cYzmJPsYT6Ux0EGg0kY/c3kPVLrKM/ntxzk2cdHXb0vsfieWsI2tn6blN2lsDQbKKPGI58WTV1Ju0jHdlM28ETIwntbyAoIMNdyj3sh+cwySBIWmEawqiCBoWDAhLRJf5YF/hOOK+3RhFFfeLefPuPTQWAoLduYX8rDHZ/wveyDjTsXr0Y2HxfkcuqrE7GpsaVllnXTcuts3gFviYrJzcJTusXyn5boM3A2FMGwepYX7CajcQ/2pZqJj59o0x6zVxJ28l648fMU7lJwhO6276AmLSKyJY0FGh87YUo8yVnylI8OQQMA+Ld4FHYUUQIeX/Yvn0LkdQGOUqGaJpcdoOXuZxOdN4wBYNdaHdO1snnwhnc+8XiK8Yb8tl6lX9EBm5kLl0jnLBfU6B6snHqHkR2weuq7Cyp2up9i5PCHJ1JsNSq6rHVYx3sdbLrXlwlpFgzeYZ1JsVyxlPWbOzk+50rCby6XpfEsCS5NjWL1T8frWnPQl+ot7yTkYi0lWfuvrpb2MasxjWYQb0dMfhooaamkn3FgOWlBLdOT/OVX8hmx/g9i2aazJoPvv6OJ7vbKtkA3tSlV2jWEY98TJLucS+32tySih3JRAoUERu17zeXpHB4p+NA8Jo05w7rETjWw/noPW1I5vbR67tL/FX2pFBluyswwYUCNjplXlz5amGJ5TvYavqpUGn1gWGF9yne8icufOCV1Nas0HtvCyei+bDswEujDq7Cca++pYy2/z88oFTJOP8W3TRNJNCUjAlYbd6CoO4BkxnnKDD8NoQEImyrMNN5Oay8yH+Jf0NE0lBtzlNhLM+ZzcfB/pp24jZUHq6ZP8Y3umIN/Vg9+6ir/VYyu+bQ3w4z+6DGV1dQ5dVWI6v2cdW87OT/ld+1sEVrZ0hHRO8/ukLEjtENF1+k72hSSrP81VQqiWQpOUmk9A+h5qZCC18zmwN4JcdVyweoSKdxCy/QGHCdU6xle2FdJg0amzFlI4nOMJi9h1oIp1bdP49quOHqNeltZv1qKGJ19IJ7bxO7RthaAyd3v+O+FcuXguOcNwm8O5Dj6DBWkvCywcrjO77gip0+d2up6saRHO94L1d3WsVB3N09smKZ9z2v6VbYX8yZDBdHUeYe61/CmsgOi2ScwM2c/q6mSWheRA0R5WqveA790Yoq9Sni8mbNeovezJQ3NG275LJwMtf73i7fewdIDoBudnRsf3Gs2qjBnKvp1yL523tz+v1rE4dKCIPf8Lg54ijDpB15xpvoD1AXQsC7WpBUkGT9oZoWoHFEPO+rcMaDBTKQezsv1urpf24ivpUAFBckOnJtKCC8NvgnIY1ZhHvFcLpW8VuOz56Jxcb8sxeXcBlQ06Cg1R6CUldBPgqQXgs/bpaFQS85AZ5tZEgWEMQWFReLafwr3mOBEqIyZdA7VBiXxfm0Ag1cxS5ZGdo4YFqbyyrZB6nYFXthV2me9mDestC8yBjB8758tYcgAj4mfzZIbyknVf1slXpRkKzU1KeLGnWIRTP/Nq4xXT1cyPD3eQQbDfv3ViWBaSQ2BlK3i4rl7tzT1oLSSxkjgnjlUWb1rX29v9nvYLJas0kFV01RrKtYRo7SdU+0lwjmY/C7VfIAFtly7rEJq2+y5uiYv5dm+gQ4/RlOIdpBjWWQyeKFtLK1+ViSbNENYdj+D6v15FuL+nS6kLB+yiA7foummndSacYfWqy24FvaGXkQmH6yz/OaXN3rEsZfHVxcLBGs7sTt4nLasMb3dNl+d0g1nxfs0Iaie88QfIPUC0odXifb0O98ofCdE0sGpcMSx8mLSsoA7DygWpwUWKZ9NyXVhJ185Gb6zCLXFxp3E4L5y6WuR1taA9nUFsf54yH03p8rz1FcKoE3RNDx9gnUJIQTE0Fe3hpxZfJspqRYTWZspBu6yhCS+8aMdDZcDgFshf5V8zZYgvIyrqaXWPwFdtgCm/Os9fUNAVU2/4NeQHEdpYSe1RRQV+VcZkx4dWUIwyUQTZPZAt4qFBJjOjJHceNN4HYAvt5LpP4aqrU6FtHcaqEr6X49n/80juZCMBUjjIMhqfEEZf+0fqa+L4eus6/pcNTArQkZv2GH8y5HFEHcZo+SQUS8p16TSO1OlRSoV1iTKpdFKjt1zXel2VrcVVpzBt8Z8hf70tDNijB7blu4cDq+JHQc1uBxkEh/1bsPYq7WS0WVoqGSsPEEM56pJv4Ka/d30fujD+Or6LJYQ6cpbj9tYxWz7j4KGwntORs0jzWMKYbb9golzIf3Pz2T/8TpaOATftbKzdOKw5TB+E5DC8shA1ZlQ1nwAdRt+r2re4pOkHRoY389SNzzt6YFw8a3aoL2eSTyvl9TrGGfMIqiuGRlXXrais50CnFF2V1+so153jIoi+SvuwM7h7UrFrf52lb5zN6LZvCDHW4N5NG6/TebituXAuw6EoHr/0jT8CsKkxhnviox0qn58siCBWfxdLVN/bwpWdDCu7rhOrMiZ3DlVbfuPP82LY3PZrAvZpyV1Ap4pv26Ku2NS5Aty+a4WLBUIng7jwaweDuKdGY18hjDpB1zg/wOxunLSaOJshl1OdyMK2L4isLIbMJgDc26pIlKuQkNHhBrKMWpJplzX80zSfy4MaGNOUSRX+FE1YwaoFqYonoP6E4hk412ETwZkxchb17b5kVifyTHyFo+eptgRdaxO7dmeypTKH7LI6m9ZdbYOOzOZkboiOYMdPVdTrDCSrclni8T3zgh+A/BIMbc0kmPJZIKXjL7Ww03QpDxrvY77uAAs+foPvzElsbhvPAq9MJjbnY6g+QqS6ncvkQkLdTLbJvbRwP6H1pyDrPXxB8cxZDU07NXobFoMlLDyeyJ+7SJK35Ncpk5hFrBW6f2jbC6dOWOTY7qgrukojsExuRjOgMtm6eTj0pLVvOu5qAZbxolIJ6eYLLRZ5CIvYbGn6WopONNt63RI71+YFffLLA8wctZ9oS0u2nBYVicYG1CoDl0mFfF/2I9zzTzY/9zLLdR+zVUpipzSJyVGBRF92N3z5I7TWwKlDULyDV7bJ3KH/hJHqH6iU/fhXdSKrnLxDzvmHazKUDg9zTd9yhbaEAiKoDUhQPHVdGFRWGZb2wDjC42+i0RLSPadFEP0g7aO3RoQrY8oVp8tFW5NRgklW8txcbZM6PYqZ+wsIqMynfog/LPzIofJZyWUzkitPobomjtRYFweZsIj2n48woq5eed6McZqDLNf5/1LAHM1udsqzgas754NaF3X5Pg4V4LZ9HP8eoyzzn2MS1VfH2b5fp5Br8SIo+QYaK5T72Tk3tQfn7UIjjDpB1zg/wOxunJyDsTzS/gZBlU28oEpHVulRyyYMRzORUaGRO9oFedEOEvwshzJb/1fmqPfzC006RVI0f2u/juC8SlIM93ZMxP0o6XTA05Pwnatt7H7r6JS7WZW/XvE8WfOrLNWStvyovAruU33B6OzdkPwrwiMvVT4zIYGYPCUn6gbVXq7Q/ERp+lp2nvTlfzReBJia8JeaaZB9OCKHKXqFpjJi5Cp8zacwaE34G6vZZwgjyzyOaFUVo0ZPZIRXjYP8xu/MPxDQVkd5xj8I1ehwc/ek0n0kz+bFsF1/E4Yf9Hhqv+bx+eNIrS0BQysJXjXdhvetkxh0kaztjIMMQgnPxN9Kil/4mV3PltyinSd9ud6vxGbMOPSktfdiuFiAGXa/itqso80tGK8xVzmMY1VeDNGGIsabNQxrrITXJnKPPJ2XuRHZck5XeR8AXR33ajYTKDWikkAlm1iq3QTFOyxFDvlEUs88ORPPYhWcKIPoK5V2Yta2ctzCHeodhEoNqFTYKmqt57i8XsfmvEpSIitthrDVu+IWvxhPQ1iP9M5sMizNyaxa+DApQMqC3p/6/o5DekGx6bT39TPxs3myYIZD7ldPiolcHddhQWEtoLE7fnTK3ZSmw9eVPkStXEBq5HzSiGNyVCA5Oz/lWXk3n+mnKzqGToUPpelrWV2dyLKQUC7z+kERF4516llsuYZH/HyE2Np8At20xDy2mYeiL2WpfVFSdx7VCYuoPF7KkZoW1hktress3maHtA5Qjh06pqN1GZ1zU/sbwqgT9Bw7pfmHfI8QaGhGjRnMOtsmKtmEjAmJjpw5leXvH83Kimi+lIl7XRFB4bMorpvB855/hoI8x3ZfzvRDPaABQU9C6K62sTwM07Wz0X/8huLRCYwArZdDuDVuqA/B1W7Mj47g1sLdhHOKpqz38I0rse3T2jrqRNQCPIMjWX0wlqT2DFSaVoJCR/NdfTRHwq8jvnILU8z5mMxgDdffyUYSpSKy5TgKiObygAY+KAviwJzfkGqZoBLnLOavO5UijELDEJawm0hTDUNaTvELUzMtGLlHuwmAL3YuJHVxz0Jo1knMQey2B9dhd4USPSZ2LtGxc4m2/NNqKE6OCrSNzSHh3sUCzGA20y67840+Hu2BKty0J2y6dpn52/mF6ktCqFMmLJOeX6lrudHjG77iSvznPE7u4VpCD3+G2VyNu6SnES/8NSY8JAny19tCxyOOlxJbl48veqhvU45vVzjzUEQc9VsDCKGRkCHDXVYqLgvM6Zg489c7eVd6NnEmzlncbQ7hYMEhvcBFcY0Ny32dEoPDdZiWVcapTU/zsWoX23bMgelvdX0wu6K31NqSDt1ISxcFZ03H3/43hMayeJ7WrMVfaiH9mJnJ455l4/4KVmkyuFydzUTNQTYHBwN2Cyq7sOvq6mRbW7dOWK7zcMu4cveZeFn9d44cDWNr5SnlGo/F5ml32SIvdi636NSU63WoJXjKUg0LkCTnwBefOd7fSb/tLHJvhwi/CgYmthyfMmKkcsLVbrSrvWgxtOMt6R02tVa3Wv/IgB4NwwO9CGjWslOeTUK4P9Epdyttgd59FRo7Pu+yafO5bq9zsdCTHCBX21genk++YFe27ylD8wklnAnKxN74A6vG+pAWsZj1BbO5WbWLbYbLuWfCLbZ9vh6b6CCLkphVRuZOSHY/SXj7UcJHjIH6v1PpPwRdnTs/yrHIZokN5pn8WrMJSZJQSbBQm0V0Yy5JJh1Pb5ukTCI26ZWrqB+1mC8ySkiMv5LIQ08htygN3G9U72WyqgiQCA/JgdiHe3QNOSeIr8koYWbgWqIbf+jYyIWB11U4xrnfZW86GVgnDrCTB7Ek3FsFUh32NWERVaeaWV2dyOz2XUyWc/k+WwZLBTFAiLoFd4wgu4F3KO7GdoaZTrEsIIs04EhhHpGqNsyyOw34sFFKYdmSRZ2qnMOtOUr1ZWDQKTppsXNtHQ5SE26H21e5nBg78v5MttQNV+kezvmGrl6/UPIk/YJe3tfOYr8fq3YRKVWzRPtt98exPndLvoG2ho70AktrstXlSss4q0GzOa+Cl9V7bd73r0wzUR3YwirNXmRk1JgJlhqJOL6RpBfiOq5Zi2e65qSvUiU7obO8D2D77dO1s3myaAnPer3O+PY8LqMQT1M72TnYKsO7M7Yc2xxG2bZfHrhfMZahkxCxlfRgRYjYLXgxKYjwq2Cgkr8e/8o9ihdFMiLLRtwAg+TOSdmPUKkRCWjBHaOstkmXIIGMCo+ASJpjbsK7QMOM5CVET7eTiXBaCVlvRoemzUKX7szoSQ7QaTXYLGX79jlc4PD3ms9LKDcu5G/SQp66brytm4ArbFWar30A9acwt9QAZnzlKnS446nVUH31GwTv/JSRPt64+8/gs/LZ1LTo0avMtgo7gM15FZhk5e/Xb0vsSA4HGn74mK9MM0nyPIaHRxB4D1G8S644jQfOek3aN4e3X2g45Lr1oKoO6Giszmly9eh+4uhKc8/aPH3ZSjMmo8wG83S2f5zDxv0VyEC9hy8SEmCCmCuV1dehTTB8OmsySog1Ked5nE8L3rpSoqgkrSYOIh539IDEzlXy2errqQ+fRbRFAqZ6z1oCDVXU7VnL1quWdHQYsOZSOeXorqn6g3L+rPIQ1n6bJwocf5v89R3VnAm3d65uvhjo5X1t38x+aXIM23bMYYn2W3yn3+nwEYewbHBRR1/Z1lqMrfXkltUS2fAi4Y1KZCVxzmKyLNunZZXhplGzwTSTYYGeNMfdRHFBBK9q/86Ypn2oMYEMNbIf/zbOdLxmrZ7pL+61eSCd7ynA9tuPbvuGWP1dbPJO4spJQzoKMuy0G3tjbDksLvJ9HKu9nc7zg/uGkNA+jf/5cR3L8yo7dzvqY4RRJ+gZExZxOK8AH7kBL3Q275y31E6r7IEJFWrMlJmHkONzBbNbtvGjOY5hmgamjQyCpN/y5Odq1ysnpweU9WZ0aNrcDxKUL0ZSp0dZEvKfU8J99gUsdr/J0uQz6KE5fDo0VdFkUtNu1vCteQISEjulJGYAq8YWQ2EhyIHMnxjOPXsDyTAmAHBDXCgU72BLwMvUNLezf4Tj5PRkQQTlBqXy9j6vHDDLEBZP+qET6D++AbfExVSEXt4x5gpHT7Dz5PaZ10uUt7fy45A7SLIaJ3Yh6u6qAq1Yr+tn4isYWbGFZ5viSTcldHymG8OyUyWr3TaTowKpatDZQrPOzJi3hIe/Gq/kB+6vIFmVyw2qvfyXeEYEedFUewK5uIimNgPjJIm6/G+Y6zuOD+QEFg05xei2zRQTwGf66RTbGaT239WWz2bVGwM+aEviZtUuPm9L4uedn7Jcl0HmzuQO2RU7o3hN0ZLO/WitNJ9UPMSW38Ymdq2rUwpBnKubBR045NVFKNGPinWwZBHEOoZdO1W3xq1XcmhDx4JXELnE8bf267i/fgvhll5U9guYpBfSmWbKZonH90y9QfkNUxagGEqbi6D5JE2aIP5quhu/sVcR6dS/F+i8UHSeLyy/faC+hhvVe/km+llY+DAJ1s9nvAivTYSE20lNfqSTp936fHJeCFkVHG71+J5P2qYpSg6NTh47C0lyDo9o1uIvNWPSyWTuRAmH95OFhTDqBD0irSaOAIKIUR2jFU+85Y6Qq17lTp1ZJkBqISgkjNRJIzj2DRyVw3nC9AAH77oWcJz4u8PR09H/ElEvOnoQ+u5W68rZWLH+u0kJ5XirjHzLZTyjfgCAhPYfCdn+AMxOAs9AaKvHp+hLLpcSuUeziQCakA5KNBQ3EW1uJEajZmbwPsCxv2lVg47LpVxUzZUQNRYmLMLvo5WMNx9if3YNK9oDOyawmx29j2sylB61Y7Y9CV4nGdJaS6As0X7sPZbLQYpxsjgBgM15lZjkwC6rAq0SCqlA6s2/hfzd6Gq+Y6G2iX1uUzpy9TYvV7pydHWe7bYpPdXM6k9zWRaSg7Y6kcslPdcVv0X6xsU8WRDhkAdo/R2sE/YNKqVH6wlTGYbqNgyyB0NaS6g2D6Na8sRfamJu/SdMVAdxeU0O0EKkNphijw49Mef72FU+25H4+7kybyHxEf7cW/MiMzT5JIT4236nnIOxLAtpJnrCIpZGxBCw9VWmmPdTmr6W6Ni5HR58SwVzh7xMXEePUlfVzYIOnPPqrPqD0Okasy8Mmhxlp5toEaAOCptKcd0MGuOjKK3YwuryRKUvq8WD/0z8bPQ5Tj2Frcex/F4/ameTVRDB0lFBrjvVdLFQtJJWE0dwYxSzzVWYZZlsu64WaVllXJnxDyI4hWTtf2t51uQcjKW8aZzNiHPw4hXvIGDr6yw0VTNK/zNJJh2rKmezeqLrvL7l4fsJrGylXRNApptFVNk5ZNuHCKNO4BrrxCsDpd8wtcWTjYYpRKv9iaCGBrzwlVoBCTdzOx+YruYS9QncGiSGZrzIcMnErzUbKZXiAMWou6hyXgYTvQh9W1fAr2wr7NILZjMSNV4gm9Co3bl+9iyuT76atKwyQrb/TZkYasNsk0HmPhMvad4iWGqwdSMxmVS0yB7o0bLe0k7su63rWG7czc7C2Tx14xJCtr9FvKYC/OIhdi6+Hs9Bq4TZbHaUZ3AKF1vFbxMoAp2ZWtmbIlMkAJer85jh0w6bPwBdHctCmslST+7kpbQamJ95vkh4XTaoVIqkiKVq+DP9NLz9LFWA794PjeW0SZ4cyC+gUZvmKNwLFg22OvAMVDxjugwCKvNZFg5FJkWeZFcOlLf+2mbQWj1fqcEwc+SH7DzpS7xbCyea/YlWVeGOgWr82StP5KTbMAL0mXigJ4AmxmrKUaskTGaJNvdgMh/qCDHZvLfFiqHu6t5+/TYllzLphXQ+00/HoDbjH3Ed0XRIlmSpJ5MZm0JqLCzfOZvAtmrcq8qILt7RyTv/5AsdIcTUR4Xnvkd0VQ3q4l5+Jr7ClrKQXeYJt811WIRFT1ik5EADy5+rJEmXoXipxhbbDMf0xMXsylE0DFOgU3cP62/4yrZCW3HCQ9eMdrkAtO9+Yb2XWtqNbDAdxFNq5xr1fwmMr7CNf01GCZ7GWOZpGtjdGEVNVpnt2WN/j4LTXPTFc8xgP4eItPW5zZUSYaHrlmvWAiHfCYtYZTs/XRR29AHCqBO4JvN1DEczkWQzaknmEhl+rakCWQm5tqGlDXfcMOAt6Ziu+okT5iAmG/crulqASlJWNYIBjoNUR2chXvuHnnUF3NJu7JiAb15kq5qmeEfHw6/kGzAbQTbD0T3wRYkS0rztgY5k6M/VTI5aSor0BAFSs11VtUQdPlQTQLRUSYIpn3c2pClVd+pm3FBx/fRHIPgBRSrhYCyJWWXkGOeTZPKmUhPJZ55/JjLQi/DgP0JxkYM3MXV6FLQlwY8HwXsIn56MJ1wu54gcRp06hOv9gap6miRfVlcnsnROjMsJqF5noLxdR7hKDZ6BHd8p9imKncNPkppmk4oo+RjZOZ92bgVmNyEn1sSRuRNm+OqJdm8ievYsfsh2Y11zIp5aNXPHDeW64re4UpWtePcCoohuPEi0rxe19fU0S26YUaGXtNRLAURdMpGUn7/C3VilvO4RTJBXAF83RqEzmtlUN5OUrDJbCLjp899BWz3/PXSSXzXpu+0RvDQ5xtYXNrLAk5QFjqFoa+5S4pzFNG3/lkudPT12++lPCen9Hlfh/G7SWFIMu9F5/oSnUe3YesvFZ271+J5R+jyGeXjChF8rL05YpKTYtP7a9js7e/mtGm8bLRqU4JSyYNne6ol+yHc7yDLpJ68kVoZ71JuQJWhHg2QZszWaszQ5Bu/tGtrMHuj0ZnJ2fkpqpJITGJ10t80g7STnMmERUmkOo5vLqQ+eRnHLDIcWaNaFas7OT0mtKCa3NRhjSQGRx0sJv677c9oXCKPuIsZllandxSnJZlTIICtVrN60Y5IkTEg0mj3xVrVjQkWN7I+7RsVVqgJOmALx9PDFN3YWXpLS/FswOOiqmsz+9cxHU2xhWNsEHBvV8XDPXw8L3yatJg5dgcTt2h14BYUrO3JqObb5uZd5qW01/ARZ5nH8yGhipZ/xltppkd0xhYwhTF8PzcrHblDtJVhqBAmGXaJMGMTOZeFHMvU6Azu3FTI77ioezhvHP7zeYYK+EKkWtn78BnFDfTrn0NSWgLEN9E3c5rMPr+ajHDGHUcIoNjfFoNZLlslpHFge+ExYxJoMJXfU2vu0MX6pLTfUmlcKOCZXJ/2W0vS1ir6XVImXJeHbcQKyjCt/PalBMaSOLYFGDzh1kB9q3Fl8SikCUcsmxcDKSILM/0LLKSpVQ9mvG4fJIxqZEtrNJqaoivCS9FzCMZoPv4dZlqmXvSlhOFqDhF9TOXqjbOsKss/iff0gcC2hbXXUmb15r3kKl0u5LDn4LLwb4lKh3766cHJUoEOLJftwYOrCt5V8OvtinC48N4Ie0IO0ifSNaYqQbuJiUiYswhNLc/oueplar8eEtinMMenY2TSFP3yuZmny46TGRnUOmU5YpBholkVVqmE3eP7EJHcd19bu4WtmcXN8pK214Bd10UQxlq9bJjHHmEGQIQeA6yV3kLBVsWeb49B7DuVKu/kldXqUbRFXc9KXleq1tP/cQL5pJEHta4ku3we1JZ1CscTOJUizHGhkvimd+Y++5vCdrYuJhzy3QUEew0we+MitSHWK4LW9pEt/QBh1FzEuq0wtN//mphiS5B/xl3QOn5GQqZH9qcYPD2polT04LoeSdMUNUFuCXz9JFhWce5w9Jfb5a9b3rXQKxzmFfdZklFDecgOHfC9h1dBiJTfKSah3WUgOkZXFyLLMCTmI2w1P2JL8ZwzRE95yCJDAO5DsxnHsM0dzmaoQf5rRnyjuGJ/+v8zXZLLVmER22XRMMnzQMoUhqsOEqhopaA/l+EkN0b4WDT47bS40HtBYQZDaDSQTkapaQuQG9pw08wfDfQR4agnwVDTydIcO4NlYyQeBvqw22fXKLd4B+Y7n0OqhSre0T1qaHMeaul9R3qYjMsCTzAUpHefJ3pC266uModXWm/XN7I5zHebvQcxjm/k45EemqjXgEcArTVfzees41DowyXP5m8ebBNKCNzokZMxmmQJ5JADvmOaDUTGSN5hnAEqYGrBVAC8Lh2ePxZNhTuAvmr9zmaoYjpd07nrhJGybZB9CnR7VudVcN4Ln3T5XhI5lZ5wNKhchTn3Op0w25VikQDac9txZr8cWz0RyfaYo3mi737PTfR87lzs+V1PepCPLLnc1vLGS69sPcn1MOBhKbK0Fw43u3G+8D0+tGg9PFTN89SDLZDYnc8kQXw5U1OProeUj3bVkGhJ5qCYOahwXPrYq2qJmyo1etJvMBFTuoakuF0NbM7f61jqEYtOyytA1zWCJh5GSkPksc8oJTp0exQ9HajlysIVglRmVz1AOtLgR7d1ObVUZC/UfErL9VOcCnz5CGHUXE04PvmfiK/Dbt4ZoLz2FpmE8/98oMn7YzBqvN7natAdJkjvtQpIgRGqizeyGDPhLTUxWlypeDdHaa1Dj/MB2rZ3WBU6TtTUMM8lXByVHAEhzksqITrkbMpsoPNHEhgZFXiPDnMAucwL/HFWHOfspwqRa1MY2FoUeI6q6ivWmKxglVbGhJpFdXx5ABpZrMrlcnYdalvih/TICPLX4xF3HkcIshsn/5VbtblrcImmqr1ZajYXtsXXOqHSLIqilDllS4aHS0qgeyo+6CPYHzOXmtp9YFpLDqspJfKafTii1TDt1iGHtOuaam6nGIq+Qvx7doR3sOlAFV79B5qMplL71C2or99BKJbH66YRsf41nEhdbDLwO43ipRRi1pd1IWlaZreLWoUAgdi4+7TlI+yuYq93PvOZMNkgzebM2kamTg2zh2qyMEkCmvL6NfxtmcIVvCSqDCb3JTLkcTIDUTIxUiY/7Vm5sfpQMcwISMN/jAE8E/YdGnYEX9XMp9ruCO+omM3l8IJFldQTE34ZbjaUcsquqRbvv4+DJsXT3sGkfOtPTfE6hY9kZZ4PKKcQJ4Ja4mOwcHKRAusNZ381Ze7G7zzwTX0Fp+hZWVycyf+JsUvx2OxRj1Dbo2HBCuc9T1PtZFrKfZyuvIlNKtOXd/fbjaWzYb82jM/DElwdsx1mTUdLROi8oBuLmUaidzea8Sh53/wS5uYJKUxA/NgXxpOo13E4tBqLI2fkpC/V5FJoDyTlSy/L2VWTuTAYW277b5rwKGqXrqVLtJWD0bVSEXk719geYxn6GqE8Qgk5Jc7D0h+1LhFE3mHFevdo/+Mr3kZK7CuR2TC0aMgzTmSCV8rTbWiJM1agtiv5mwIyKWtmHatmfYeoa/FV6Rrjrod0iJYDBsam74KLgbHKcUixhGPzGgl+Ka2PAYgj+vxfSKbd0LUlW5XKnz49sykviff1d3KvZxKgAb8I1rVyjyiFEjuV2wxO246glOBG1gOxKNdvNSdTrDKglmDoqiG9KZzO1fT9DqKa42Z06yQt/XS2FJ4ZwpH0cYa3BGOsLKDXF0qgK4Vq/Mr5umspzhhuINHiSOXYdlPzAtTRxv/k33EIWGI9xyujJOr3Sfsias2Ntp2Z9bVXlJH5hOkKwVMP97luI11RQWeED/IqIU9/CF8+R2xpMSGkBSbKSf5Sz81MlKd1yP6dllZHz6acsC1nLTRHX0awu41npTQJVzcxS5fH58Mdh4eMApMYqRnnMY5sB2GmaxAONMn+KKqC9PJ8YVQVtshaNSsKgtPRAAvw9tTzj/RlBtUWEouKXqnakU5uQZfjngetZuiCVlOkp2Fepn67Kfbopm5n7/wHBd5/eaOtpvpLQsXSJy3vU7lylxM7tnLvZDc4Lu54Uv1m3qfzrVQTXZvOIvIcX8x4g5XFHeaRwwO/jHNR5Fdzp+yMBlT8wx9jAZuN4m7bjBjtJng3mmWSYEwC7gqf85xzSOGyt4l77ADMtRKmMXGrO5xL5Z/b8YCIt9HKWheQwvLIQlWwmynQclUbJVd2//Vti26axJgPmT4xgw35lURlZ4AmUENs2DZ3aRKk5jCWq3Xi11bvMBb3QCKNuMOO0es1tDSa0ReJUazAJxz4EU7vS/cFsZIhUy7XqLDwwOuxCWX+bkYDt8jRSNEUYTGaitXqC9M0gy6Bx77zSFuGQQc9ZVTPbT8LdSBiA4tXzzl6D0WJsjNGX04KJB8y/IUOfwM3NP7GKV3HHwDDpFFqVRBI5/I/mOyqGX8/rx0bSZvg1EQEeJOuzuEG1l8ydySTOWcyxbV/iZy6kHl9eNt7GDaq9bGqcSTylzD78T7QqFfs1McxyK4GWOpZov+WQ7yUkJi+2teka1hrMm6VvERY9HrzCOdUazJLS73GL7zg31nZq/2stDCCGE3IQV6n34eflA6Hjber8+pxPQf0j4w1txMpajJJMpmdiR4gXIHauooBvqYDVn2jml6pTBFt6tAZKLUyp+IAvnthIpmcyO42TALgjpJiJdTtsE2LGkQQ+1D7LKEminFDiJ8ygUTubyAJPW6GHWbZoxEka3DUqJsqFmGWZE/JeVmXM6HQNdHdd2I+ZfB/FO1u0hMntgWS7KMLpMf0sWb2/4PK36M25OofP8fJ6HaHIBEjN3OrxfWfpIyC7rA6TDO81TeFqcxsbzDPw1KptxikoaQHz3HKZKRfzob6UUVIVJ6IWkDp9vlKNDZ3FgxNupy1jFRKgUqnYY5rIV6aZyiLr5rvhyx8xtNTQatKiUavw1NcxxVyI0U2mPjnVNsbNeRW2dJPNeQmMDPBmeks6p6JvIdKuH3VfIoy6gUhPm7Q3VlLppjQ1z8zfzsemTQyVTtF0eDOMjEKuL7MVQUxXHVSKIlwgAcFSI7ertiMZ4FvTRD5uT2bVhOKudaJEOOTiorcPfxcTi8MEZLe/kRVbiKQQSQ150mgOy8MJNFVznccBcj2mKH1DK1sACFfVcrVbPr8wbWKqqpCG8jy+N/2aDBKIbcyy9KRsZpiHJ1OnPwzBK/lhw1u8U5NIhjmBb+UE5k+MYEnhYjxpR1J7c1lkEFQew2CSwVDHsvAcoqc/jLVNV8IX9ypeR68w0iIeJ2T7A1ypysa95CgUD3Vop1ZakUNt5R7u9J3KJ23JzFUfAZNS6bEsREkKd5u4GPL+ixozkgRek2/Fu0DDZ43T0agkJZEdRUts04GZDPP1xC3uJtz3rQFZQxPu5JuiADNz1Pu4rL2QGqNSRHGf+S0CNc2Em+vI0CcA8I7pek7IezkRtYD4hb+h8J03eahlFfsD5rLDfSI/D7sd95+/Yp3hcoxhiVDxAb6eWjKNyQ7Vqz353Zcmx5C5M5mEEH+C7LyzVQ2604o3Cy4QlpaQq6sTuxXh7UTGi4oYdMLtikacBWuINjXiDvzK/84wt2b2twZT3qLrCJlmvg7AM/G38mRBBJntiWzWjydZlcufeQP/U7exNFkRCw+Ivw1TwRF8daf4tWYjJlR8V6kGftPxXHHW44u8lIbAiTTWnQSgasQCimvibIVc6WNW0PTjOkKoYYK6kqIWd44ZJ5DpOZtVluvRanBatfFMMkxvSWee5d7vL+lHwqgbiNgZTOmHTnRUL9m50UvT1xJa+V9M+BBtKOI+1QeMVh1HAkbLZRiPHEONIg9hRkKFbJOLsOqAyRJUMARfmvFGT4MUSJ1vHJltSnsYunv4inDIxcW5NuLt9re6OpGFpiOEqlu4LCqIygYd7nVFLA/fT/SvH6HkrX9gQvEqu0mwPGw/tVUqVLIZP5q5Ub2XDHMCSzyy8De10CD7sL81mOF/vYryeh3BYVG8YfiQ99vKeEe6hamjgogM/5VtgkpvikR/TKLIEMowuYqdlZNYbV9MYW2lZDFSFraHMltrgJZTDpWc6drZpB0r43qpgcy2aax6/GEoTlC2aay0GX4seBh8y5FyP8Qr4XaCfi7gk7an+MrtSqqvfkOpTCzewXXFb7BOnsYfDPcphRW+5VTvqeE9XRL/UN3CNNM+XpLeIlyq4SXNWxTLkQTQjAozw1Wn+MHtN5wigJeNS8iIf9YmSRJ2bCOzVHlQDyv/Nx7ya1huupvPW8ahLgWT/H9EunuS+bhSvar/aQun8r6hcPKKztp6TthaxFmwemftxZIFfYhF4Dq0/hRJxgZ2nhzWUUCEizZi9gu53A+h/pjyt51RZ829TSOOa8LH01a5h0m+NQSYtbS0G/lhw9tMbPoOCRjZ7kvmox+RllXGK9sKucGwl1mqPLJz1KT+qcNjtjyvkt/JbxEsNdIk+dpyAjsUHWaTEkPH/JO/nnD9UUxIBMl1nCrfTObK39jG+GRBBOX6pSSrcrnfvAVfDy2b9DPxib6K3LTHCD38Ga/6TkLlWYmvpOV13bW0eCYqx635pEOuqR84MIRRN5Cwn0hAqV76+A1mmH6gOfsg+JYrel/AzpMR3GjWMkSq4ZeaLfjRajPWANTIyMAp2Q9kGKJqBBSjzoQKDUqoa9jEK219LmMtN+/UnoxVhEMuLs7GiHfl5bPXZIuI448Zk/kg8B/oKr6j3BBBUPgsWx9X/8o9qGQwSCrcopKIHjkJz7aTFNYMp0724agcxl80f+e4NIxRXqOpbm5nvCGfwNpCgpGRKn5CK8ncxDe8rLvRIm77CEReCvnr+TwPNrcpWlzJqlxuYQ/s2AA1xeDhj85gZKd+PE+UycyOC+SKokN4yEYlIVXG9t2e/FxNrFyGpIL5E8Mdvnf6oRPoj33KkZpLSXshnaXJS0j9vTIxhq6MJVw+yV3Sl/xxawyvbEvkbZ+3ucyUQ6vKxIngYErf+ge1VWVEmVsYJVWhN5r5XnMpp2Q/QqQGglRNhHi6k9c2lmhvPUObS9BIJkJp5C9BXxLkXkb6RqUSd27AXKhXvBlW49oq3trJ+JqwiFN53+AjNylhY1dGXU/an/Vwe8F5wnrOGyuVriYegWSaLN0SGjsKWRyqseOcFnIJt3d46iz7rNzyIn9p0/G+xw3MSF7C6p2WdnJt0/B211Ber+NNfSK/VJUD8FHlJHItYdncFVez5p1S9hyDqmHXOQw3cc5i/rpT8W5Hp9xNRk0cSS+k21IGniyIIOVmZd4CbM+Tr/Ig3FSOVoOtnVh6UyR/aVuD2U0mTXUjQWFRBFTu4XppL6vKZvBI22eEyacIbNqNlxradWbmGL3J9ZmiLGK+2N0h19QPrtcBZdQ999xzbN68mdzcXNzc3Kivrz/tZ2RZZsWKFbzzzjvU19eTlJTEmjVriI2NPf8DPhvsH2zQccNZqvKsrl5T4GbcqvcSRjX6jJfRygbMksQ401jcJD1aTPjT2mn3Vo9ciNSIrJKwRl5rJX/2msYzQzpArRTAGOe+q+KBK3DF2Rjxrrx8dvuzJvlTbOK/H5XRbjSzujpRUXMHVD5DkZsbkdXettZSbnVFHDJP4EHjfbzm9nfmSj+iNv+XY7qhxKnqKDIPI1uOI1YqJ0BqwgyoVBLJqlykJti08lUmB7UR3n6Eq83lbGY8WpXEEo/vSVHlQI1eEU42tXNcH8Ao0xFWs4KPCm/ispFBcNQMJj2UfoPOYKQgv4BXfcKR3SqJkY+Rla2itMLXFtp6smgJ5a2/Rl0Kl0uW9mSHlJ7Jpy65Bf/if6KRTTwiv8OfjHfz9/ZEblDp2GCeyZLKLQSYc6k0RXJYGk6YqpY7Aos5WtNCqKYRMxJmJELVLYy+4hbI/RCTZdEmA+bmE1CSjl5XRXnrr2nyNfA/iZEwIQZQjDf7bgJWFK+ImtTI5Yyq3NJ1BWVvvbgidcMlnURzzyWWc17pNpL9+gTcEhezakFqp24JDoUXwU4LueRHHDx05K8nqC6XQBkWqDOZN/0RoKOdXCKKkegTdR3LiqYASl/V5c2rbD2C02riKDfch7oUfO1kWZw9vq98tJ16nQGtSkItWdqc5a/ppH3pH1HGqowStnI/1FdA7ofoG0cyUz6EpJKYMbEEJtxNaTpkVieyNDmGU4dvgcOf0RY+hRj3Jo6eaOJIYxgPt6xi2cofuXmyk1ewjxlQRp1er2fRokXMmDGDf/7znz36zEsvvcTrr7/Oe++9x6hRo3jyySe55ppr+Omnn/Dw8DjPIz4L7B9sAIVfg8aDSreRvGKnO3RJTToeGCy5ccqDWiXLTFUdROoiR65FdgfAS2oHSUKjUtNmligxhfF39R3MmL+E/7HcuGOcRSjFA1dwjugyVAKuFzUTFhEUFoV/5R6uNOwmfWM4Iyu2sFN3KUs8WvCVm2ydKOqN5Wwwz0QCvjTO5BptFu5mI6Ok49TJvmSZx/GGaSHJqlye1qwlXKol0FTLDaq9AFwu78NQq6XQM5p/G2eSrMrlJs1e5s2eDblHMTSewCBr0BjNhHsYcddVocaMygzLyxfygupHtGYdrXoT29rGMYRaxjb9QKk0nN3GiRyRhxBWdYhoS0/apRGKdEm70cQt6iwSpCI4LkH+ehJS3+bVJ05yj3ojwVIjt7hl8bBpGZiUpHFTYCx7a2Q2MJNfqjYxWVWMW/N6jqkCcJcMtOGGm2zEu/mI4klpOQWoaZXUHCeMtkvmE2I8wKRGHTerf3LsZWmZDF1h7Y8b1rIXt6m3dR16dfbinm5haLf9eTVkBhhdiX+fEyznPPdAJfHGn9iW+63ye3a3WDvdQm7CImqPl1Jer7MZ/PY9ol39rqVvvUtAZUePYKUbyYGOfEvnkK8TRrMSgcouqwOnTjZW/cRn4ivw/TkQ1AZIuB23pkgO7KtRusu4MMwSUp8Hnrf9++3nXuZ3aiX0e6m5kG25c0gZXmnLC+zreXFAGXVPPfUUAO+++26Ptpdlmddee40nnniCG2+8EYD333+foUOH8uWXX7JkyZLzNdSzZ8IiqCrAcGADRpMBDWYkmik1hxBhPsRNW6Zg+NpArMWQAxzCqxpkW/RHctq1l6rdUtVqwS+S5fW3s9kwngCNltXdVTWKXDnBOeKVbYXU6ww8uG8IuSuckoydFjVWnTe3xMXoTzTzWds0luR8SoA5l2DjBFaq7mZZSA47CyTCTZ+wwTyTb+UEFkyKYHeRllJTBGOlY6hlM35SC6O1J5mvPcDV5r3sV40lRFvA0XZvNphnMkEqRY0JjWykoMnbJrCbJOXxfUY94Av4sscwhlHGKtSSzNWqE0iWO+3zpnE85uFGCDqa9Qb+YLiPB9RfMNq9ju+N8fxNWsjLmtXEazt60qbGKpNWvc7ANx6zuT7S8sUt91lSYAOmRjU6lS/faGYzd/RQ/rdoCwkUkVtbz9/01/FHzUfEyD8jWxIMN5gVza9W35HE6nIBqFZPIcGwmTCpFm8VjImfDgufhy/uJfxUOqvGFkPQJFozsyjPy+K4qx60xTsg83U+89RxpLWFWOlnsnPUXUtj9FZQ2G77NZ87ihVfzEbeeW2TZjnns4rG4W2uZon2W5eb9cqwjJ1L+O8UqRLAwZjP2ZnLcl2GzSNn5WjEdehPNONm6RGcGlxk61k8J7AJdpRBTTGVx0vZ1PgZS7Tf4jv9TmbHXc3mvAriI/yJbfyOZYE55H4fRejRQ4RoWnHPX0/OwViW6zLwzq6niSPgEYBv5KWkxM4lLfRyfpdRwtKaGFIP3U9k5XcsNB3hi50wc3+OcvwhTUSnKM+ZgMoWZBkCVC0s0X6LsewUJlmmvN2XaGHUnT+OHDlCVVUVV111le01f39/pk2bxnfffdelUdfe3k57e7vt342Njed9rCVPT2KU6SgtshuteOAntaKSZdwkk8OPNF31EzNVP512f1Z9OZWlU6YsqTCgRosJtUoFZjOo1BAyGuauZEZNHLk9eWCIXDnBhcBp8WDTeSuIYOnVb1CcUYJbfBT1FT5kVivdG+7ImMzy9lXM0OSDG+RKU5g6KojXL6tm7/s+HGQEyDIN+OI19VZWG3ajO/QTtXoNkiTTpPLjHvUmxqvL8MBArdmXDWbFSzdUqqXIPAwwE6f6mSLzMKbb3Yd63GiStQTQzGeef6YhbBYhugN8Wz2Mv2j+zmipDF9jNQmmfNw1t/CNZjZD3N15Ly8G8l5kefh+nom/jicLIkhMXkwai8nZ+SkPbXmRcP/XmTp5FtQG8fTBWD5vGkdkWR2vRwXBcQmtpOIG1V6lEEqWaUdLu1FZ7D2t/R3esoZfYGCBOZ29J5pYb76L+923cNnwIJs3LOdgLMtCmpW2fvnrURsaGS43UuIqTy7zdTi6h3CVCoITyW5IJCw6vudVsL1YGDobMlaj4hVLy7KLybjrqYTQ2Ri+vtPvhNwP8bXmxTnRK8OyG53UZSHNBFTmM8zD00Ha5MmCCIfesaXpawmo3MMSDx98G/VYXRTl9TquMe3E21QNuR+S3TYFkww1LXo2jC2msWAvGsMP+EgtnDB489eDsdzq8T2XGPIpZjh1Zi8C2urY+vEbVF8d59jf1e0QGknGXaNiWUgOYRX/4S4MGCq0kO+j5PLm+3QoP8jQkL8R2Qw7T/oS3aszfu4Z1EZdVVUVAEOHDnV4fejQobb3XPH888/bvIIXilGmo0iAj6THB73yorOLDRw9bC4wIVFoHkapehTT5Fx0sjtqD1+GRQxDPXKWchE6qdFTvIPUiueUFi5d9PwTCM41D10zuusJwrJ4sLX6in2KYkuCvuPklkqiXbuyzFJFLuOb6kTqmwy2hO7R6nIOmSI5IQcRME1RhF++s5LFhgJ8TA2U6UII9tEyUncALQZAolYVwA2qvQyVaolT/cy3polsMM/kBnkvo1XHGC0dx4yK/5pHs9N0KUOlWiari9FIVfznRDDfXb2RyPTfMb49G9nix3PXqGg3mvlcN44vmsYhAx9qnyWyspho9yZSHt0IQNIL6bzU9gVD9D8h11l8gHdtJDGrjCxbTpOSP/jX7CjMMow2H2OoqoETJj/Gqsu5X7OFKjmLzxqnc6PbN0SoqrlZ2sV72lt5P+46fldWx9KaGIvHdBw7jZPItRhkzmGzTqjU4BlI+HWPEO5KQqI7epGj62zIWI2Klnbj+QtFngn9KNf4rMK0znlxTvTGsAzZ/gZXaPJtuor2xnw0QL4PnxyMpbypY6zORuPqaqWwosYwjHviZHJbg6kqLeBIxHVoqnIUT13C7TzTpHSocYtfDGMWsTOnnFJzmNJdxjyTjPZxAKwa2c6IxjY2NcxhoukAvoZTbNy6jhYpkQBPLQ/5bIOGWtReQTSOWcrHeZU8xX9QYcYMLD8YS2JEnNKnuHgH1JZQWX6EIAzIKpgzpKl35/s80OdG3aOPPsqLL77Y7TYHDx5kzJgxF2hE8Nhjj/Hggw/a/t3Y2Mjw4cPP6zFbJC985M4FDd1hnzHXIHtSpIqhZfJSniyI4IPAfzCk0R1iUk6vnyPy5AR9QLcThGWStDbfhq5bkVknsaoGHU/duJjo6Q/bjJ9n4iugphJ90Gg0ta1cQT4NFf48uG8ICe16RmmPEahqpk72Ibs9gdHegbachTE+vkRUF3DAEE62OoFNxun4TbiOAJ8ohmf/EQmZVrM7n3ssJHHOYr7Y+SnRPtsor9exrl3pHpGgn0GdycAROYx491OERcfz3KE32KCayffqS3HXqAmWW9BihNZafvtxji2MRIvy/cyyTE5ZLYcsebQdWn5FAEwZGcyLJcPJ0CegluByKZcl2u+ZN0JGV3EAjUqiOvoWpMOfsd44G29fDdlldbaJ30q70WTxmsSR+rv/dITN7EjLKiOnfDbLhvoqHgvr88K5h2tP6eWzx1VOVr+gHz1Dz2uYtoesyVCkfSaYNUR21dc3dq7jIoXOz4TEOR2FFUyPYtkL6ZS3ziKyxpPMJTGQr8yCKSUvgVs9GMIg9m2+GRfCxv0VeGhVzJ0QRmRZnSIYXlFM+Kl07okfRWGxlqjWA6jMX7FZP57IAE/C/T0xNqjIbR3Cg/uGUK8LJEq7gJukXXwpX8HnTePYafESfxC4lvCa7yjVhaGXAhiqqic6xOdCneIu6XOjbvny5dx1113dbhMdfWYOzbCwMABOnDhBeHjHI+rEiRMkJCR0+Tl3d3fc3d3P6Jhniu/t78OOlbTWVfKzwYcYyjGptJw0+xLOSVRAOUPw8gnAS1dJtcmTZjzxDxzCwei7bD0jU6dHKW1Rik0OVUvdIvLkBP0NJymN0/WWdEimtm8s/sW9cOog4TEpvNIUS5Iug8xqRY/tBtVeAqQWNJgJlFqUrhHI4OaFrrWJgpMQFDaTmRbjZV7xDshfQ+WRI3jIbchIlMvB3OrxPVODE0h9/GHgYQ5uTGNJzqe4xUfx4L5EWsxGW0FD6OHPGKVuRpLgqvlKWLNuizd6SUNOlZmNxyqQgYKKBt6Rrrd9xyzjGEZtuo9lW2czY94SJWF883LQ1XFNUDNved4FwK8jShlV+b3iYRszFM/89bhpZ7OsIILJoxc7yJFYJ/6IU9+iz/mUjeYkNteP7zZ/bU1GCeVN48hST3ashnXq4drjEOAZPnvOqpvJ+aAfPUP7w7lZmhxDyPZTBGmMLvv62l8frhZrnd4v3gFfPMcz8YrszjPxFbD5Jdqba6g2eil5cz7BtvP/+m2JvH5bIukb09DnrOGmxMVKO7vijt+pPm8lUcgE0MibXm8RNiwejJArx/G39uvAHW72/YlFvscI9xvL+OArbZ1Wyut1rDYlcqWhic9M03lasxatbKSpeA++5/PE9oA+N+pCQ0MJDQ09L/seNWoUYWFh7Ny502bENTY28v3337N06dLzcsyzIiwer7kribOEM9Ql6QwLHQN+ilbcMLtVzjC7j4Vj6W9nT29y30SenKC/YXk4u5LScMbe4HA2/nJbgwlpkfgqDwyjr+LpoklghNlxodQcHoZRFYCbtw/V0hA2NcYwJ7CJ6NGT2LU7k3Xt08g4ksAjGSUstUgK6SoOcEQXRimjbceY2JQJm4tszbxtfW0Nu3nomsfx2vRXLlfn0V5TRIimlWqjD/5Tb+N/pkeR9EI6scbrbb0sPbQq9EYz8ydGABGk7k9AkuAV9d+5XJ0HRnj4q/HMHPkh0W31NEm+7P+5ng3SfWzTzmGOuYlQ849QcBjG/BUWvs2TLyjFBqB4OzsZXF88B54/EefnQ27dlE75a/ZhPJdeIEv3GqsAc1efdUk3z54BVRRxMTxDuwsxO72XOj0Kgh9wrGC3w/76sP670+LB/vrJX4/u0A70xiqWXv0GKRXroK2eaqMX6wyziVedYt78BzqNS5/zKZNNOWTnoOSG2v1O77OPX5jNxEtHiZMrUVcepLHNwDFDPN+rJ/P4NaNJrfgMCvZDA6T4hZPy6Nu26zIxeTFPbJtEvdnAetMVLFLvYpvhcu4592e+V/S5Udcbjh07Rm1tLceOHcNkMpGbmwvAJZdcgo+P4vYcM2YMzz//PP/zP/+DJEn8/ve/59lnnyU2NtYmaRIREcFNN93Ud1/EFc7uexe9MQWCi4ZeTpJdeSeqSguIlNsIN5VTWvofXpC+ZV3bNLLLZvB6vAwlKhgxnVuKllDeouOFo/DUpPFw9RIyvjwAKB0WqC+E0LHsMk5gnWmarZH41dr9rPJIUwRbreKjEzqkFFInFLFMMxuMUKmJZNk4iJywiEhLLusHgWvZ1OKpOAjVEo/PH9epF2Z5vc5WybrBPBOTrOQarRrrw8qDsfxOeoth0inukr/i3ZM3co3Zm5C2asWTN38VS5PjXBYbWCdUW5FEyt0OBrQrA87lec5fr+hnxqTY9fE9+xDgeZXwuMg5I4PZOkc1VnY27k6jNenM5KhAqhp0TI4K7HbxYGtDFxTDLqOlWCqjRMn/Bgq1s/lg3xCQobomjlQcjUu3xMVk56B4rp0MT/XoqzlRsJspqkNKD3PvIexsDGCDeSZ62WTRxrTcy2Cbk233QPEOpnj8GZOpBoNbAH+Vfq10WupjBpRR96c//Yn33nvP9u/ERCWM8s0335CcnAxAYWEhDQ0Ntm3++Mc/0tLSwr333kt9fT2zZs1i69at/U+jrh+57wWCfkUXmnX2hRRdTU5uiYv5Pltmp2YWy0NyCK/JBw+oTk51EE9dGtERwl3x1QGeunE8N0yKYHNeBW5DYmlqOcK64xFsDLyDgooGbpgUYWmrNZ/0jSOUJG3tbFIAYufaqvbq29cyY97zrMqYYssLAmVSDdj6OjPYz93ePviq9SyMiezUem9pslLIkMsUrromlauAYouXgOlRJGaVsW3HYe6Sv0KrlpjkUcPK2rt5ye0DvHR1yrmKeNzlPlvajbYiiU7hVCyTV3AR5D+nTG5dGdkunl3nIgTYH3LDBitnZDBbf9/Gys4GXC/nr+yyOi6Xcrmu+C1uSlxsSx8CHPJKW7M/oYb9NITPotpS9W7t10rsXFIA74L0LrtcpCxI7ajedirmyS6ro9E8kzCpllGhPoTP/SPf/DcE1YEtfBy8Fordul9c5q9npO4AGsmI0VjJqolju2+deYGQZFl2rVArsNHY2Ii/vz8NDQ34+fldmINaL8CeFDq4oh9VYwkEZ4pSRfeAUkU3xnIdl6QroT6/cJZbJT4CPLsspLBhaTieGzKfZT/P7WQIrnnnTYaWbWSDeSbFfjM69vfFvdTmbSXDOIFN8kyul/aS6Zms9G5FqVQtr9c5jGH5cy+TpMugxn0Y98TLHZOdRaB0eflsalr03KDaq3jwJnLG92paVhk5Oz9lWUgOq6sT+bxpHCnqXJ4YUUCjdxStRRnIMnzhsbDTmAM8tXi7a7r22HTzHHJlUA+okOlFzFn9Tudgbul0X1urSfPX8/+yo0g3JZCsyuUe9SbA8dp1Pn6HiHkFKTWfgK4WPJVuLA7j6+Jz9q3vbHm49te8q+9r0Wqsra7kVLOeWrMPaaoblXzX83Td99QOGVCeuouKs/Xc9aNqLIHgTFmTUUJs2zTwgHn294LFW2Dfk9Smd9WV6rwlmb+qtIDy1lmdvBSjKrcwWZ2HJME3Udd17G/CIupPNZNZnchvPL7nkqYOxXtw7VGyVu19EPiPju4MAMe/B+AhPx3f6dzZYJ5JrjSFZQuvPuNz9N3Wdcwx7mZV5WxmzFvMF18eIN2UwL6qKbwg/Y1kqRgkmfCQHJdjPtNCBlfent54gIQBeJ7pbc/dntLL1AhXv7PLnDvLnPWboAR2VSdwp8+PjNGX8x2THMOa/1kJJ3+CEwW2/D37oii0XtB8onMvVqdxWz9nXeBY0xF0BRJLtG74Wqp2S9PX4l+5h+/yKqmfF2fL8ePUQYJiUlhQtKQjZ3VbYZ9fy8Ko66+c7sbpRasdgWCgohgflnCpVUPRko9G/npbIYX9g9kWgnHO/ZmwiNJTzWxsmUSAp7ZTWM+af+N/2WKyCzokP1IfnUt07FxWgeW4QQRZRXstHrLUm+/uGF/xDlIr1lvyfu52rEKvKoCWk4TLJ7nBrZIRcj2HrnHRscHVvW3xDlQ26Hil+RrmTwwnxbCbB8hipLqcSOqZNv0RW6cO63c6sK+GyABPRYLEgqtJ3aWR5eo5ZBmftRLR/jz2JmTarQEoIg1nTz9Z2Hf5OztfW5Z7ZOqERZTEzlXCn/nruX7CItu9lZZVxrwTxwnGjPnEIXKeSqLx0qVKmNV6j9nrsFq88yTcTprHEpeLCGs6Qm1LO09+eYBVmp8xmJptVburKifxC9MRAqnmi41pQCqpdvPr0ogYnvzyQBdNOS88wqgbqPSi1Y5AMFBx6VFwMeE7JFbXVFLpPoryslritQc6xE9j53LH52rK23REBmhchAwvJ/VPioFVEdqFDppTC6vlugwCKvMVw81V0rhz79T89VByAgw6NJJEpL8nv8sooSn/a0ZVbsEtcTEjK7Yo+Xinmjl66AT6nE85En4doyq3cBVZBJllkozu6HPU4PkTI9zMSHqJEUFegKOoc8r0q7tu3+VEj71slu+XEgMpjzqGZHvjAerWAOwnBsmApp8s7O1zONMsWovgYhHRgzlrTUYJJwxzWardiBsGxpsLybB2PbH7fFpWGWs+L2Er7+HTVkFFxj94RUqkXmfo1EM2dfpcW4s+gE3yTBLC/W0Lty1t45mj2c3l6jyul/eyaSPMHFFg02lMje0YW3/I/xRG3UCln9ywAsEFx8WE7xyC2a8bx7r2ZJZI3zuEbVODiwhr2UhV8AJQyhpcGjNdGSf2E9HS5BgydyodLILs78Pu7k1rZayuFkLH8Er5bMqbdIS1bGSyKo/sHFitvlxR0T/py3WVTxEgNzDu+E+sN11BiFsckYGeZDYnM39iOBh2U2RR2XcbtZjwrsbeA89Xj71s5+jZ060BKJ5vZ08/WdinTo86s1C93X2eblncpIZfRxq3kxh/JWNL3+2y64l13+u8L+c60llnmA1uEBngqVzf+c85PEOshidAyjWpRFvH+Hk6MoqhN8zPk021iVwv7e20kOsP2oBWhFE3UOknN6xAcME5ndEEuGlnU1wQ4Ri2xZI3p8oju1IN/AaAZ+LtWgydBvuJKPPRFKUZucVgWpNRwiulw3ko+jhLg1182GpYgZLzMzSexDGLycoooSp4AdmVatwSF5MYerktHy/kVCuy0USEVMsV6kMcuuYjLpsepYSCAUjtUNm39MvsdDxLP1dXni9nb0mPJqYL8ewRz7dBhasFw2kXEXb3uf7jN5hsyoFKyPyTct+mb1R06Ny6OZ5n8v9ReOpy4nM+5S+JJ5UwLTiIEENno8y+iAIgJTmVqdMfpyirjJodx9Bqj/S+e8oFQhh1AoFgYNHdhG95LwUXgtzgqFtlwV4sGLoPVbqciCwGU1hjOSb5vg5dO+t4nLbDe6iSzB1kb0ilYDUywZJIbu0Kc6IA46kiukrasRqlYcPi4Yt1HZPh5uWKfh50aQh35S1RlPg/xS1xccdEKBDY0ZtCF1cLhtMuIuzuc7fEE53u207Cwl3t2yKu7XB/x84lrSaONZ+XsDS5rNM4rPcFOLYnTJ0eBRUylOh73z3lAiGMuv5Od0nTIpFYMEi4UA9GB90qK70I9bmciCyfq6q5FHUpVI1YAMGRDvtL35iGX34BkQEjCffyhJYTLtsnWUnLKuOVbTJJcgxPBJZSbmldVGyfD2RJCE9prATPg1B9FAytlJ5qpraqjETKUXsFdTwj7PON7ELIrrwl3U2YAgGcZ3Fop/nN1X3raoHm8jnSywUNnMaL6LS/NRklxDZ+R8j215SK3j6ej4VR19/pKmFYJBILBhHOD9jzZuS5WgydbajP8vmlQKfmg5bjDc/LYrhcwYH60YRf90iX7ZOsWBO352h2415XRFD4LIrrZjjmAx3LUvqtho5VNLWCYigt3M+zx+L5peoIRrWEOnSMssMv7rV9584hZNeizc4TplhICuw5r+LQPZjfXBl6Lg21Lrxy3Y3fvmsEXzzX9fPC0hWmtr2MeE1FZxmVPkAYdf2drrwIIpFYMIhwfsCeqRfgtMbgGS6GztjItBxviMoIJpkwbWuPDCNr4vZW4yzcUHFcNbvjTVfSDRYx1RVHx2OyhGnDhxUQnXR3p/ZOrqRInHHp0RQLSYEd9l7rM7o/ulsknOH81pWh5vA8sXi6UycsIvXRrq9jRSD5DUUgGVxf8/nriW78Ac+AkexqmNDRVaYPEUZdf6crL4JIJBYMIpzDmmfqBTitMeg8WVi034DOCvS92a8dDhOc5TifFkgEm35mhKGeYU6GUVfirIowqobt9RNRl4JJ7tDNczXONRklmGRQS4pRFu08Totgsyspkh7hdO76Wy6R4BzTC8/sGS3Cui3eUbM0+XFSY0+zL6cxdqW/2NJu7NCmdKp87e47uRQ+tz9uUAw0VlJeVsu69ispLohwmct7IVH17eEFAoGgM6nTo7oMDXbH0uSYDtkCV8TOddSOy1+vdHk4/n1HZaoT6RvTeKztVeZ7HOi1qK71eJ5z/49VPstpvHSpEiq1myQ6cnIeUCYLF99n/sSI7r+X3bZP3TjeFsJe/tzLlKavVY6X9NtOx+5E8Q4lVOs0DqDTuXP4noLBh9Xo6uK+sOe0950rJixyeT06XFfdXY89HKM1lcHbXfFhLT8YS6nfVOW43ez/mfgKlnh8j1viYtJq4kh6IZ20rDLH49aWgF848VplW6FTJxAIBOeQXutFWXXjrP/vAn3Op0yTc9GoJeZNf6T7/WW8yFbeY5335Xgm/18X43IMay5NjiFk+2tcocnvlJPTm+/jnAeUczCWJF0GoZU5sPkAzF/VbR/pHoWbnMbdXwRXBeeBsy0g6oIOD28cqS6uR4fryupVc+4O08MxKguyT/jaYxYzkpWOEuVN48hSTyYzNqWjzyt0KkRMKXkJ3OrBEEZSRoSDJzJdOxu9rkoJt44ZiicWb97pPIsXAGHUCQSCwUUPwkYdOlQhZFf9QQkhdvFAdlk00BW5H+LbVsESj2+ZZ/Fg9UTugeAHlDBwY6Uy/tMYVPahT8AxDGrxIiwLaWZ1dTJz1UdAV3faJO5uw0241rQTYddBzHlK8TldqDZ1epQl7+25Di04S+qAbVw9HKOrBZnDQqQrozB/PbScAiQIiumkZflkQQTlrb8mYJ8W7wI1z8TPJsXqLezjtCgRfhUIBIOLHoZkyut1bM6r6DKEmJZVRtIL6VSEXs68P22w6bVZX7eFYuxJuB0CRrDOcHnvQpOxc8EvXGlI3oNwl/3E2CkMaglrRafcTeKcxaw03U1p0OWn9bgsTY6h2G8G1Ve/0WXO3oUMt3Z7ngUDlh6Fau3Dmwvf7lnqgAvcEheTrU60LchSg4vIjFunGI3QOR3DyoRF4B0KshlyPySl5hPmef6kaFrafYd2o5nyeh3e2Wug4MuO/Nw+RBh1AoFgcNFFro49PclV68qI6da4SX4Efp+H59z/O2c5Rt2N36o153Asu4lqTUYJnzeN4466X53Wg3C6PMYzyps6C0TO3sDClsP51i+6zoGjh/myTvdCWk0cSUVLSKuJ6/LYrhYAKQtSHRZkPc4TjJ2rpCv4DOkQ8LYbj/U7uGtUJKtyieY4yKbu93mBkGRZ7kKnXGClsbERf39/Ghoa8PPz6+vhCASCC0BX1Z3W15+Jr1BW7q7CvE4h4L6qFD3b4/Zlhauorh1YJL2QzvLmVSRr8gmaOK/b/M0z2Xd5vY7IAE+HDg89fb9H9yx0Tt0o3kFp+lpWVyeSOGexy8rakO0PkKLah5tPkGIInqfwa0/tEOGpEwgEAhd08ihYKuVSg4vIfDRFmRy6WvU7eQQumNfJqZrvTKuIrZyvcfcktHq2YxdcWJYmx5DpmUx9+Kxzrp96Oi/x6d5/bvNPlNfrWPfDse4PlL8e3aEdbP34DeXajJ3LHXW/4vOmcS7vgdTpUcy77QHcxl13Xg263iAKJQQCgQAXniHnVbuzrlZ3lXdO712IStGuqldP+7264XyN+7y2mBL0CUrhzMPncd9dXyene7/NYAZgnpwJJS76MluZsIhdB6pY1zaNYosR56Bx54p+phkrPHUCgUAA6Hb8mY9b7+XUpqeVVbpz/o1zzltXSdbO7xXvILXiOTJvNrkM35yrYoA1GSUUtIdSo9fwToFk22cnb1sv9MfOl7fsQufnCS5uFkyKQC3BiagF3eetxs6l+uo3KPabYVvQWDXuHO4BO494fyvoEZ46gUAgAJZov8XLWM3Nql3cllFC6s1OnrgzXZF3016rxx6rHnjXFL27U3ia2wlu/5lVln128radbYvBc9ADVsih9FMuVH/fC9lHuHgHr7e/zuujsXSN+U23mztfmy491Xb39JqiJf3K6yyMOoFAIAB8p99JU9Z7bDNcrjzEY6POzYTTjRHV4/BmD/quWvXuStPXklmdaNtnJwPqbMNFma8rHTgaK8/rhCwKJfqAC9Xf90L2YLZ2jbH+fy+O1+Xiw+6eXhrRv0S4RfVrDxDVrwKB4Gw4awPlHHg2zpmR9O4CZZIcPg3u2njm+zkNp6toFJwH+rmn7oyuiR72d+7v9NQOEUZdDxBGnUBwkXKOJrn+YKC4HMOZfL8LNPELT53AmfN6TZzNdX0B7ome2iEi/CoQCARdcY7CURe0T2oXE4yrMZSmryWgcg/1p5qJ7un3u0DVfiLvTuDMeb0mzuZev1Bh6x4gjDqBQDBoOOcr+bMtKrBwQQ2ULiYYV2NYXZ1IkrGBzOpEVl2Y0QkEPeJ04t/n3Ftnf6/31vN2jp4T5wIhaSIQCAYN51wstzvZkv5KV+3GnISJARLnLGaVz3IS5yy+wIMUCLrnjNr0nQ4X94AN+3u9F7I/nT7bxwhPnUAgGDRc0DBnf6Wr8KgLD54IcQr6K13dy2d1j/c0TNqPPG+9RRRK9ABRKCEQCAY8F1IbTCDoj/SyErY/FeuI3q8CgUAg6KAfhYgEgj4hdi74hcOpgz0KrV6wns3nEGHUCQQCQT+iv7UdEggGFV3lnLrAVTu7/n5/ivBrDxDhV4FAcKHoD5p2AoHANX11f4rwq0AgEAwkLJV5z8RXnNNm9/3dsyAQnC9cXvvdVcD2AFfeu/6EMOoEAoGgP2CpzEsx7Cbz0ZRzlpg9EPOCBIJzgctrv7dyJU6kBheRGbeO1OCiczTKc4sw6gQCgaA/0Itcn97Q3z0LgoHJQPAAu7z2z/Y+O0uj8HwzoHLqnnvuOTZv3kxubi5ubm7U19ef9jN33XUX7733nsNr11xzDVu3bu3xcUVOnUAgEAgEHVy0uZ99JA00KHu/6vV6Fi1axIwZM/jnP//Z48/NmzePtWvX2v7t7u5+PoYnEAgEAsFFwUUr9H2Beh+fKQPKqHvqqacAePfdd3v1OXd3d8LCws7DiAQCgUAguPjo624kfS0M3NfH74qLIqcuIyODIUOGMHr0aJYuXUpNTU2327e3t9PY2OjwRyAQCAQCQf9gTUYJsY3fEbL9gTOuZD3b4/fHAqRBb9TNmzeP999/n507d/Liiy+ya9curr32WkwmU5efef755/H397f9GT58+AUcsUAgEJxDzlLCQSDojyxNjmGJx/dcock/L0ULpysE6a8FSH1eKPHoo4/y4osvdrvNwYMHGTNmjO3f7777Lr///e97VCjhTGlpKTExMfznP/9hzpw5Lrdpb2+nvb3d9u/GxkaGDx8uCiUEAsHA44t7lWq9mBSlTZhAMFg4j0UL/a0QZMAUSixfvpy77rqr222io6PP2fGio6MJCQnh8OHDXRp17u7uophCIBAMDqzSDedYKkUg6HPOY9FCjwpB+qgStjv63KgLDQ0lNDT0gh3v559/pqamhvDw8At2TIFAIOgz+nm1nkDQH+lRIYhVsw76zT02oHLqjh07Rm5uLseOHcNkMpGbm0tubi7Nzc22bcaMGcO///1vAJqbm3n44YfJysri6NGj7Ny5kxtvvJFLLrmEa665pq++hkAgEAgEgoHOeRIMPxv63FPXG/70pz85CAknJiYC8M0335CcnAxAYWEhDQ0NAKjVavLy8njvvfeor68nIiKCq6++mmeeeUaEVwUCgUAguNg5mxBqP/SC93mhxEBAdJQQCAQCgWAQMkAKiQZMoYRAIBAIBAJBnzDIComEUScQCAT9nP6qXi8QDHj6YQj1bBhQhRICgUBwMdJf1esFAkH/Qhh1AoFA0M/pr+r1AsFA5HTdIvpqX+cCYdQJBAJBPyd1ehSZj6aI0KtAcA44l57v/uZFF0adQCAQCASCi4Zz6fnub150IWnSA4SkiUAgEAgEgr6ip3aI8NQJBALBAKC/5e4IBAOS4h2KNl3xjr4eyXlBGHUCgUAwAOhvuTsCwYDE2q81f31fj+S8IIw6gUAgGAD0t9wdgWBA0g/7tZ5LRE5dDxA5dQKBQCAQCPoKkVMnEAgEAoFAcBEhjDqBQCDozwzyxG6BQHDuEL1fBQKBoD9jTeyGQdWjUiAQnHuEp04gEAj6M4M8sVsguBBcLJJAwlMnEAgE/ZnYucJDJxCcJfaSQIO53Z7w1AkEAoFAIBjUnEtJoP7s9ROSJj1ASJoIBAKBQCAASHohnfJ6HZEBnmQ+mnJBjikkTQQCgUAgEAjOMf1ZCFx46nqA8NQJBAKBQCDoK4SnTiAQCAQCgeAiQhh1AoFAIBAIBIMAYdQJBAKBQCAQDAKEUScQCAQCgUAwCBBGnUAgEAgEAsEgQBh1AoFAIBAIgP4trCs4PcKoEwgEAoFAADi20xIMPIRRJxAIBAKBAOjfwrqC0yPEh3uAEB8WCAQCgUDQVwjxYYFAIBAIBIKLCGHUCQQCgUAgEAwChFEnEAgEAoFAMAgQRp1AIBAIBALBIGDAGHVHjx7ll7/8JaNGjcLT05OYmBhWrFiBXq/v9nNtbW0sW7aM4OBgfHx8uPnmmzlx4sQFGrVAIBAIBALBhWHAGHWHDh3CbDbz1ltvUVBQwKuvvsqbb77J//3f/3X7uT/84Q9s3LiR9evXs2vXLioqKli4cOEFGrVAIBAIBALBhWFAS5q8/PLLrFmzhtLSUpfvNzQ0EBoaykcffcQtt9wCKMbh2LFj+e6775g+fXqPjiMkTQQCgUAgEPQVF4WkSUNDA0FBQV2+n52djcFg4KqrrrK9NmbMGEaMGMF3333X5efa29tpbGx0+CMQCAQCgUDQnxmwRt3hw4d54403+PWvf93lNlVVVbi5uREQEODw+tChQ6mqquryc88//zz+/v62P8OHDz9XwxYIBAKBQCA4L/S5Uffoo48iSVK3fw4dOuTwmfLycubNm8eiRYu45557zvmYHnvsMRoaGmx/jh8/fs6PIRAIBAKB4MKSllVG0gvppGWV9fVQzguavh7A8uXLueuuu7rdJjo62vb/FRUVXHnllcycOZO3336728+FhYWh1+upr6938NadOHGCsLCwLj/n7u6Ou7t7j8YvEAgEAoFgYLAmo4Tyeh1rMkpInR7V18M55/S5URcaGkpoaGiPti0vL+fKK69k8uTJrF27FpWqe0fj5MmT0Wq17Ny5k5tvvhmAwsJCjh07xowZM8567AKBQCAQCAYOS5NjWJNRwtLkmL4eynlhwFS/lpeXk5ycTFRUFO+99x5qtdr2ntXrVl5ezpw5c3j//feZOnUqAEuXLmXLli28++67+Pn58cADDwCwd+/eHh9bVL8KBAKBQCDoK3pqh/S5p66n7Nixg8OHD3P48GGGDRvm8J7VLjUYDBQWFtLa2mp779VXX0WlUnHzzTfT3t7ONddcw9///vcLOnaBQCAQCASC882A8dT1JcJTJxAIBIJzTvEOyF8PExZB7Ny+Ho2gHzPoPHUCgUAgEAwq8tdDSbry/8KoE5wDhFEnEAgEAkFfMGGR498CwVkijDqBQCAQCPqC2LnCQyc4p/S5+LBAIBAIBAKB4OwRRp1AIBAIBALBIEAYdQKBQCAQCASDAGHUCQQCgUAgEAwChFEnEAgEAoFAMAgQRp1AIBAIBALBIEAYdQKBQCAQCASDAGHUCQQCgUAgEAwChFEnEAgEAoFAMAgQRp1AIBAIBALBIEAYdQKBQCAQCASDAGHUCQQCgUAgEAwCNH09gIGALMsANDY29vFIBAKBQCAQXGxY7Q+rPdIVwqjrAU1NTQAMHz68j0ciEAgEAoHgYqWpqQl/f/8u35fk05l9AsxmMxUVFfj6+iJJ0nk5RmNjI8OHD+f48eP4+fmdl2MMVsS5O3PEuTtzxLk7c8S5O3PEuTtzBvK5k2WZpqYmIiIiUKm6zpwTnroeoFKpGDZs2AU5lp+f34C72PoL4tydOeLcnTni3J054tydOeLcnTkD9dx156GzIgolBAKBQCAQCAYBwqgTCAQCgUAgGAQIo66f4O7uzooVK3B3d+/roQw4xLk7c8S5O3PEuTtzxLk7c8S5O3MuhnMnCiUEAoFAIBAIBgHCUycQCAQCgUAwCBBGnUAgEAgEAsEgQBh1AoFAIBAIBIMAYdQJBAKBQCAQDAKEUdcPWL16NSNHjsTDw4Np06bxww8/9PWQBgS7d+9mwYIFREREIEkSX375ZV8PaUDw/PPPM2XKFHx9fRkyZAg33XQThYWFfT2sAcOaNWuYOHGiTcB0xowZfP311309rAHHCy+8gCRJ/P73v+/roQwIVq5ciSRJDn/GjBnT18MaMJSXl5OamkpwcDCenp5MmDCB//73v309rHOOMOr6mE8++YQHH3yQFStWsG/fPiZNmsQ111zDyZMn+3po/Z6WlhYmTZrE6tWr+3ooA4pdu3axbNkysrKy2LFjBwaDgauvvpqWlpa+HtqAYNiwYbzwwgtkZ2fz3//+l5SUFG688UYKCgr6emgDhh9//JG33nqLiRMn9vVQBhTx8fFUVlba/uzZs6evhzQgqKurIykpCa1Wy9dff81PP/3EqlWrCAwM7OuhnXOEpEkfM23aNKZMmcLf/vY3QOkzO3z4cB544AEeffTRPh7dwEGSJP79739z00039fVQBhynTp1iyJAh7Nq1i9mzZ/f1cAYkQUFBvPzyy/zyl7/s66H0e5qbm7n00kv5+9//zrPPPktCQgKvvfZaXw+r37Ny5Uq+/PJLcnNz+3ooA45HH32UzMxMvv32274eynlHeOr6EL1eT3Z2NldddZXtNZVKxVVXXcV3333XhyMTXEw0NDQAimEi6B0mk4l169bR0tLCjBkz+no4A4Jly5Yxf/58h+eeoGcUFxcTERFBdHQ0t99+O8eOHevrIQ0INmzYwGWXXcaiRYsYMmQIiYmJvPPOO309rPOCMOr6kOrqakwmE0OHDnV4fejQoVRVVfXRqAQXE2azmd///vckJSUxfvz4vh7OgCE/Px8fHx/c3d35zW9+w7///W/GjRvX18Pq96xbt459+/bx/PPP9/VQBhzTpk3j3XffZevWraxZs4YjR45w+eWX09TU1NdD6/eUlpayZs0aYmNj2bZtG0uXLuW3v/0t7733Xl8P7Zyj6esBCASCvmPZsmUcOHBA5Ob0ktGjR5Obm0tDQwOfffYZd955J7t27RKGXTccP36c3/3ud+zYsQMPD4++Hs6A49prr7X9/8SJE5k2bRpRUVF8+umnIux/GsxmM5dddhl//vOfAUhMTOTAgQO8+eab3HnnnX08unOL8NT1ISEhIajVak6cOOHw+okTJwgLC+ujUQkuFu6//342bdrEN998w7Bhw/p6OAMKNzc3LrnkEiZPnszzzz/PpEmT+Otf/9rXw+rXZGdnc/LkSS699FI0Gg0ajYZdu3bx+uuvo9FoMJlMfT3EAUVAQABxcXEcPny4r4fS7wkPD++04Bo7duygDF8Lo64PcXNzY/LkyezcudP2mtlsZufOnSI/R3DekGWZ+++/n3//+9+kp6czatSovh7SgMdsNtPe3t7Xw+jXzJkzh/z8fHJzc21/LrvsMm6//XZyc3NRq9V9PcQBRXNzMyUlJYSHh/f1UPo9SUlJnWSbioqKiIqK6qMRnT9E+LWPefDBB7nzzju57LLLmDp1Kq+99hotLS3cfffdfT20fk9zc7PDKvXIkSPk5uYSFBTEiBEj+nBk/Ztly5bx0Ucf8dVXX+Hr62vL3/T398fT07OPR9f/eeyxx7j22msZMWIETU1NfPTRR2RkZLBt27a+Hlq/xtfXt1Pepre3N8HBwSKfswc89NBDLFiwgKioKCoqKlixYgVqtZrbbrutr4fW7/nDH/7AzJkz+fOf/8zixYv54YcfePvtt3n77bf7emjnHlnQ57zxxhvyiBEjZDc3N3nq1KlyVlZWXw9pQPDNN9/IQKc/d955Z18PrV/j6pwB8tq1a/t6aAOC//f//p8cFRUlu7m5yaGhofKcOXPk7du39/WwBiRXXHGF/Lvf/a6vhzEguPXWW+Xw8HDZzc1NjoyMlG+99Vb58OHDfT2sAcPGjRvl8ePHy+7u7vKYMWPkt99+u6+HdF4QOnUCgUAgEAgEgwCRUycQCAQCgUAwCBBGnUAgEAgEAsEgQBh1AoFAIBAIBIMAYdQJBAKBQCAQDAKEUScQCAQCgUAwCBBGnUAgEAgEAsEgQBh1AoFAIBAIBIMAYdQJBAKBQCAQDAKEUScQCAQCgUAwCBBGnUAgEAgEAsEgQBh1AoFAIBAIBIMAYdQJBALBeeLjjz/G09OTyspK22t33303EydOpKGhoQ9HJhAIBiOSLMtyXw9CIBAIBiOyLJOQkMDs2bN54403WLFiBf/617/IysoiMjKyr4cnEAgGGZq+HoBAIBAMViRJ4rnnnuOWW24hLCyMN954g2+//VYYdAKB4LwgPHUCgUBwnrn00kspKChg+/btXHHFFX09HIFAMEgROXUCgUBwHtm6dSuHDh3CZDIxdOjQvh6OQCAYxAhPnUAgEJwn9u3bR3JyMm+99Rbvvvsufn5+rF+/vq+HJRAIBikip04gEAjOA0ePHmX+/Pn83//9H7fddhvR0dHMmDGDffv2cemll/b18AQCwSBEeOoEAoHgHFNbW8vMmTNJTk7mzTfftL0+f/58TCYTW7du7cPRCQSCwYow6gQCgUAgEAgGAaJQQiAQCAQCgWAQIIw6gUAgEAgEgkGAMOoEAoFAIBAIBgHCqBMIBAKBQCAYBAijTiAQCAQCgWAQIIw6gUAgEAgEgkGAMOoEAoFAIBAIBgHCqBMIBAKBQCAYBAijTiAQCAQCgWAQIIw6gUAgEAgEgkGAMOoEAoFAIBAIBgGavh6AQHCmmEwmDAZDXw9DIBAIBh1arRa1Wt3XwxD0EmHUCQYcsixTVVVFfX19Xw9FIBAIBi0BAQGEhYUhSVJfD0XQQ4RRJxhwWA26IUOG4OXlJR44AoFAcA6RZZnW1lZOnjwJQHh4eB+PSNBThFEnGFCYTCabQRccHNzXwxEIBIJBiaenJwAnT55kyJAhIhQ7QBCFEoIBhTWHzsvLq49HIhAIBIMb63NW5C4PHIRRJxiQiJCrQCAQnF/Ec3bgIYw6gUAgEAgEgkGAMOoEgn5ARkYGkiQNmoreM/0+kiTx5ZdfnpcxCfoPsixz7733EhQUhCRJ5ObmunxN0D1Hjx4V50rggDDqBALBgGXlypUkJCT09TAGLJIkdftn5cqV5+W4W7du5d1332XTpk1UVlYyfvx4l68JBILeIapfBRctOr2Jep2eAE83PN0uvsouvV6Pm5tbXw9D4ApjO7Q3g7sPaNzP22EqKytt///JJ5/wpz/9icLCQttrPj4+tv+XZRmTyYRGc/bTRklJCeHh4cycObPb1y40JpMJSZJQqYS/QzAwEVeu4KKjol7H8k9zmfjUNmY8n87Ep7bx0PpcKht05+2Y7e3t/Pa3v2XIkCF4eHgwa9Ysfvzxx07bZWZmMnHiRDw8PJg+fToHDhywvVdWVsaCBQsIDAzE29ub+Ph4tmzZYnv/wIEDXHvttfj4+DB06FDuuOMOqqurbe8nJydz//338/vf/56QkBCuueYafvGLX3Drrbc6jMFgMBASEsL7778PgNls5vnnn2fUqFF4enoyadIkPvvsM4fPbNmyhbi4ODw9Pbnyyis5evToac9JcXExs2fPxsPDg3HjxrFjx45O2zzyyCPExcXh5eVFdHQ0Tz75pK0S79133+Wpp55i//79Ns/Su+++C8Bf/vIXJkyYgLe3N8OHD+e+++6jubn5tGPqc1pq4LvVkHYLfLhI+fPdauX180BYWJjtj7+/P5Ik2f596NAhfH19+frrr5k8eTLu7u7s2bOnR9dDd9fiXXfdxQMPPMCxY8eQJImRI0e6fA1g5MiRvPbaaw77TkhIsHkQZVlm5cqVjBgxAnd3dyIiIvjtb39r27a9vZ2HHnqIyMhIvL29mTZtGhkZGbb33333XQICAtiwYQPjxo3D3d2dY8eOdTpPdXV13H777YSGhuLp6UlsbCxr1661vd/ddQodHuV//etfjBgxAh8fH+677z5MJhMvvfQSYWFhDBkyhOeee87huJIksWbNGq699lo8PT2Jjo7udK6dOd1z4LPPPmPChAl4enoSHBzMVVddRUtLS7f7FAwgZIFgAKHT6eSffvpJ1ul0Z/T58rpW+bJnd8jRj22Wox7ZZPsT/dhm+bJnd8gV9a3neMQKv/3tb+WIiAh5y5YtckFBgXznnXfKgYGBck1NjSzLsvzNN9/IgDx27Fh5+/btcl5ennz99dfLI0eOlPV6vSzLsjx//nx57ty5cl5enlxSUiJv3LhR3rVrlyzLslxXVyeHhobKjz32mHzw4EF537598ty5c+Urr7zSNoYrrrhC9vHxkR9++GH50KFD8qFDh+RNmzbJnp6eclNTk227jRs3yp6ennJjY6Msy7L87LPPymPGjJG3bt0ql5SUyGvXrpXd3d3ljIwMWZZl+dixY7K7u7v84IMPyocOHZLT0tLkoUOHyoBcV1fn8nyYTCZ5/Pjx8pw5c+Tc3Fx5165dcmJiogzI//73v23bPfPMM3JmZqZ85MgRecOGDfLQoUPlF198UZZlWW5tbZWXL18ux8fHy5WVlXJlZaXc2qr8fq+++qqcnp4uHzlyRN65c6c8evRoeenSpefglzyPNFfL8qd3yvLrl8rym7Nl+Z/XKH+/fqnyenP1eT382rVrZX9/f9u/rdfkxIkT5e3bt8uHDx+Wa2pqTns9nO5arK+vl59++ml52LBhcmVlpXzy5EmXr8myLEdFRcmvvvqqwzgnTZokr1ixQpZlWV6/fr3s5+cnb9myRS4rK5O///57+e2337Zt+6tf/UqeOXOmvHv3bvnw4cPyyy+/LLu7u8tFRUW276zVauWZM2fKmZmZ8qFDh+SWlpZO52bZsmVyQkKC/OOPP8pHjhyRd+zYIW/YsMH2fnfXqSzL8ooVK2QfHx/5lltukQsKCuQNGzbIbm5u8jXXXCM/8MAD8qFDh+R//etfMiBnZWXZPgfIwcHB8jvvvCMXFhbKTzzxhKxWq+WffvpJlmVZPnLkiAzIOTk5PTr3FRUVskajkf/yl7/IR44ckfPy8uTVq1c73P/2nO3zVnDhEUadYEBxtg+ZBz/J6WTQ2Rt2yz/NObcDlmW5ublZ1mq18ocffmh7Ta/XyxEREfJLL70ky3LHBLpu3TrbNjU1NbKnp6f8ySefyLIsyxMmTJBXrlzp8hjPPPOMfPXVVzu8dvz4cRmQCwsLZVlWjLrExESHbQwGgxwSEiK///77ttduu+02+dZbb5VlWZbb2tpkLy8vee/evQ6f++UvfynfdtttsizL8mOPPSaPGzfO4f1HHnmkW6Nu27ZtskajkcvLy22vff31152MOmdefvllefLkybZ/r1ixQp40aVKX21tZv369HBwcfNrt+pS9f1MMuPdvkuUPF3f8ef8m5fW9fzuvh+/KqPvyyy9tr/XkeujJtfjqq6/KUVFRDtu4eu10Rt2qVavkuLg428LHnrKyMlmtVjtcY7Isy3PmzJEfe+wx23cG5Nzc3M4nxI4FCxbId999d7fb2OPqOvXy8rItlGRZlq+55hp55MiRsslksr02evRo+fnnn7f9G5B/85vfOOx72rRptgWKs1F3unOfnZ0tA/LRo0d79D2EUTfwEDl1gosGnd7Ehv0VmMyyy/dNZpmvcit45sYJ5zTHrqSkBIPBQFJSku01rVbL1KlTOXjw4P9v715jmti2OID/a2nriEJFiaKpra8iMXoP6lURiYkPikYuEkSvEq1GTUBE0SsfjAlGjRIjqJGYmJz4fgQ0SlBRoBhifAEqEVEUBOojORr0GiVYoFDW/UCYy/CsR+VR1u8Tndmb7pnsaVcna82WtPXx8RH/dnNzg6enp9hm8+bNiIiIQGZmJubPn4+QkBBMnjwZAFBQUIDs7GxJDlTz99fr9QCAqVOnSvY5OTlh2bJluHDhAlatWoXv378jNTUVSUlJAIDS0lJYLBYsWLBA0s9qtcLb2xsA8PLlS8yYMaPd42jLy5cvodFoMGLEiA77JCcn4+jRoygrK0NVVRXq6+vh4uLS4f8GgKysLMTFxeHVq1eorKxEfX09ampqYLFYeuaDq+trgeJ0QDkQkLfIc5QrG7eXZAD/XP9bc+zaMm3aNPFve+aDvXPxVwgNDcWRI0cwZswYBAQEYNGiRQgMDISTkxMKCwths9lavV9tba1kNRqlUileR+2JiIhASEgI8vPz4e/vjyVLlkhy/+yZpzqdDoMGDRJfDxs2DHK5XJK/N2zYMHFpriYtrwsfH592q107O/f+/v6YN28eJk2aBIPBAH9/fyxduhSDBw/u8PhZ78FBHeszvlZbUWdrO6BrUmcjfK22QlAKXTQq+61fvx4GgwFpaWnIzMxEXFwcEhISEBUVhaqqKgQGBuLAgQOt+jVft9HZ2bnV/rCwMMyZMwcVFRUwmUwQBAEBAQEAIOahpaWlYeTIkZJ+KtXvDS4ePnyIsLAw7N69GwaDAa6urkhKSkJCQkKH/d68eYPFixcjIiIC+/btg5ubG+7du4d169bBarX2zKCutgqwfgcU7cw7hdDYpraqy4O65nPGnvlg71y0R79+/UAkvWab56ppNBoUFxcjKysLJpMJGzduxMGDB3Hnzh1UVVVBLpfjyZMnrZa4ah70CILQ6UN2Fy5ciLdv3+LmzZswmUyYN28eIiMjER8fb/c8VSgUktcymazNbQ0NDZ2fmHZ0du7lcjlMJhMePHiAzMxMJCYmYufOncjNzcXo0aP/9vuynoODOtZnqAUlFHJZh4GdQi6DWvi1FaFjx46FUqnE/fv3odVqATR+MT169AjR0dGStjk5ORg1ahSAxuTskpISeHl5ifs1Gg3Cw8MRHh6OHTt24M8//0RUVBSmTJmCK1euQKfT/XB14qxZs6DRaJCcnIxbt24hNDRU/LJpnjw+Z86cNvt7eXnh2rVrrY6jI15eXnj//j0+fPggftG37PPgwQNotVrs3LlT3Pb27VtJG6VSCZvNJtn25MkTNDQ0ICEhQbw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" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales\n", - "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", - "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", - "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", - "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", - "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", @@ -748,30 +191,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'number of sales over the entire timespan')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales \\\n", - "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", - "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", - "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", - "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", - "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", - "\n", - " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", - "0 141 0.0 0.0 ... 2011 NaN \n", - "1 142 0.0 0.0 ... 2011 Father's day \n", - "2 143 0.0 0.0 ... 2011 NaN \n", - "3 144 0.0 0.0 ... 2011 NaN \n", - "4 145 1.0 0.0 ... 2011 NaN \n", - "\n", - " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", - "0 NaN NaN NaN 0 0 0 18 \n", - "1 Cultural NaN NaN 0 0 0 19 \n", - "2 NaN NaN NaN 0 0 0 20 \n", - "3 NaN NaN NaN 0 0 0 21 \n", - "4 NaN NaN NaN 0 0 0 22 \n", - "\n", - " sell_price \n", - "0 3.97 \n", - "1 3.97 \n", - "2 3.97 \n", - "3 3.97 \n", - "4 3.97 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_lags = 30\n", "\n", @@ -1063,18 +269,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(15216, 50)\n", - "(3891, 50)\n" - ] - } - ], + "outputs": [], "source": [ "is_train = data[\"day_number\"] <= 300\n", "data = data.drop(columns=[\"day_number\", \"day\"])\n", @@ -1098,20 +295,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1, 2, 3, 4, 5, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cat_column_names = [\n", " \"item_id\",\n", @@ -1149,17 +335,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, date, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, sell_price\n" - ] - } - ], + "outputs": [], "source": [ "print(\", \".join(map(str, X_train.columns)))" ] @@ -1194,447 +372,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:114: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", - " y = _check_array(y)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", - "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" - ] - }, - { - "data": { - "text/html": [ - "
Treeffuser(extra_lightgbm_params={})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Treeffuser(extra_lightgbm_params={})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model = Treeffuser()\n", "model.fit(X_train, y_train, cat_idx=cat_idx)" From 86fdd892f0cbd68fe595a59b01c69b36de37cce1 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 14:00:03 -0400 Subject: [PATCH 12/44] set n_steps to 50 (like in the testbed) --- src/treeffuser/_base_tabular_diffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 7af29735..24fbf913 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -180,7 +180,7 @@ def sample( X: Float[ndarray, "batch x_dim"], n_samples: int, n_parallel: int = 10, - n_steps: int = 100, + n_steps: int = 50, seed=None, verbose: bool = False, ) -> Float[ndarray, "n_samples batch y_dim"]: From 9db96ca471dc5dd568d6543076a58ede46990045 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 14:23:43 -0400 Subject: [PATCH 13/44] fix progress bar not showing up --- src/treeffuser/_base_tabular_diffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 24fbf913..7ecae652 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -252,7 +252,7 @@ def _sample_without_validation( y_samples = [] x_batched = None - pbar = tqdm(total=n_samples, disable=~verbose) + pbar = tqdm(total=n_samples, disable=not verbose) while n_samples_sampled < n_samples: batch_size_samples = min(n_parallel, n_samples - n_samples_sampled) y_batch = self.sde.sample_from_theoretical_prior( From 4110666d3a49b41b6a49323f6995cba8821323e5 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 14:31:44 -0400 Subject: [PATCH 14/44] support pandas dataframes --- src/treeffuser/_base_tabular_diffusion.py | 152 +++++++++++++++++----- 1 file changed, 117 insertions(+), 35 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 7ecae652..4cb93c4c 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -12,6 +12,7 @@ import einops import numpy as np +import pandas as pd from jaxtyping import Float from numpy import ndarray from sklearn.base import BaseEstimator @@ -79,6 +80,7 @@ def __init__( self.sde_initialize_from_data = sde_initialize_from_data self.score_model = None self._is_fitted = False + self._x_dataframe_columns = None self._x_scaler = None self._x_dim = None self._x_cat_idx = None @@ -97,28 +99,117 @@ def get_new_score_model(self) -> ScoreModel: Return the score model. """ - def _validate_data( + def _preprocess_and_validate_data( self, - X: Optional[ndarray] = None, - y: Optional[ndarray] = None, + X: Optional[Union[ndarray, pd.DataFrame]] = None, + y: Optional[Union[ndarray, pd.Series]] = None, + cat_idx: Optional[List[int]] = None, validate_X: bool = True, validate_y: bool = True, + reset: bool = False, ) -> Tuple[ - Optional[Float[ndarray, "batch x_dim"]], Optional[Float[ndarray, "batch y_dim"]] + Optional[Float[ndarray, "batch x_dim"]], + Optional[Float[ndarray, "batch y_dim"]], + List[int], ]: - """Reshape X and/or y to adhere to the (batch, n_dim) convention, and cast them as flows.""" - if validate_X and X is not None: + """ + If X is a DataFrame, convert it to numpy array and auto-detect categorical columns. + Reshape X and/or y to adhere to the (batch, n_dim) convention + """ + if validate_X: + if X is None: + raise ValueError("Input data `X` cannot be None.") + if isinstance(X, pd.DataFrame): + if cat_idx is not None and cat_idx != []: + raise ValueError( + "Categorical indices `cat_idx` should not be provided when `X` is a DataFrame." + "Instead, ensure that the categorical columns have dtype `category`." + "E.g., X['column_name'] = X['column_name'].astype('category')." + ) + cat_idx = [ + X.columns.get_loc(col) for col in X.select_dtypes("category").columns + ] + # check dtype of columns and convert categorical columns to numerical + X_has_been_copied = False + for c in X.columns: + if not isinstance(X[c].dtype, pd.CategoricalDtype) and not np.issubdtype( + X[c].dtype, np.number + ): + raise ValueError( + f"Dtypes of columns in `X` must be int, float, bool or category. " + f"Column {c} has dtype {X[c].dtype}." + f"Convert the column to a numerical dtype or a category, e.g.," + f"X['{c}'] = X['{c}'].astype('category')." + ) + if isinstance(X[c].dtype, pd.CategoricalDtype): + if X_has_been_copied is False: + # copy the DataFrame to avoid modifying the original + X = X.copy() + X_has_been_copied = True + X[c] = X[c].cat.codes + # at that point, X contains only numerical columns + if reset: + # store the columns of the DataFrame + self._x_dataframe_columns = X.columns + self._x_cat_idx = cat_idx + self._x_dim = X.shape[1] + else: + # check coherence of the input data with the training data + # ensure the columns of the DataFrame are the same as the ones used during training + if not all(col in X.columns for col in self._x_dataframe_columns): + raise ValueError( + "Column names in `X` are not present in the DataFrame but were present during training." + f"{set(self._x_dataframe_columns) - set(X.columns)}" + ) + # reorder the columns of the DataFrame to match the order of the training data + X = X[self._x_dataframe_columns] + X = X.values + + elif isinstance(X, np.ndarray): + if cat_idx is not None: + for idx in cat_idx: + if idx < 0 or idx >= X.shape[1]: + raise ValueError( + f"Invalid indices in `cat_idx`: {idx} is not in " + f"[0, {X.shape[1]}-1] (the shape of X)." + ) + else: + cat_idx = [] + + if reset: + self._x_cat_idx = cat_idx + else: + raise TypeError( + "Input data `X` must be a numpy array or a pandas DataFrame." + f"Reveived {type(X).__name}." + ) + X = _check_array(X) - if validate_y and y is not None: + if validate_y: + if y is None: + raise ValueError("Target data `y` cannot be None.") + if isinstance(y, pd.Series): + y = y.values + elif not isinstance(y, np.ndarray): + raise TypeError( + "Target data `y` must be a numpy array or a pandas Series." + f"Received {type(y).__name}." + ) + + if reset: + # store the original number of dimensions of the response + self._y_original_ndim = y.ndim + self._y_dim = y.shape[1] if y.ndim > 1 else 1 + y = _check_array(y) return X, y def fit( self, - X: Float[ndarray, "batch x_dim"], - y: Float[ndarray, "batch y_dim"], + X: Union[Float[ndarray, "batch x_dim"], pd.DataFrame], + y: Union[Float[ndarray, "batch y_dim"], pd.Series], cat_idx: Optional[List[int]] = None, ): """ @@ -126,12 +217,16 @@ def fit( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). - y : np.ndarray + y : np.ndarray or pd.Series Target data with shape (batch, y_dim). cat_idx : List[int], optional - List of indices of categorical features in X. Default is None. + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. + Default is None. Returns ------- @@ -143,13 +238,6 @@ def fit( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - if cat_idx is not None: - for idx in cat_idx: - if idx < 0 or idx >= X.shape[1]: - raise ValueError( - f"Invalid indices in `cat_idx`: {idx} is not in " - f"[0, {X.shape[1]}-1] (the shape of X)." - ) self.sde = self.get_new_sde() self.score_model = self.get_new_score_model() self._x_scaler = ScalerMixedTypes() @@ -158,14 +246,9 @@ def fit( # store the original number of dimensions of the response # before reshaping the data so as to ensure that predictions # have the same shape as the user-inputted response - self._y_original_ndim = y.ndim - X, y = self._validate_data(X=X, y=y) + X, y = self._preprocess_and_validate_data(X=X, y=y, cat_idx=cat_idx, reset=True) - self._y_dim = y.shape[1] - x_transformed = self._x_scaler.fit_transform( - X, - cat_idx=cat_idx, - ) + x_transformed = self._x_scaler.fit_transform(X, cat_idx=cat_idx) y_transformed = self._y_scaler.fit_transform(y) if self.sde_initialize_from_data: @@ -177,7 +260,7 @@ def fit( def sample( self, - X: Float[ndarray, "batch x_dim"], + X: Union[Float[ndarray, "batch x_dim"], pd.DataFrame], n_samples: int, n_parallel: int = 10, n_steps: int = 50, @@ -189,7 +272,7 @@ def sample( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). n_samples : int Number of samples to draw for each input. @@ -220,10 +303,10 @@ def sample( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation( - X, n_samples, n_parallel, n_steps, seed, verbose + X, n_samples, n_parallel=n_parallel, n_steps=n_steps, seed=seed, verbose=verbose ) # Ensure output aligns with original shape provided by user @@ -295,7 +378,7 @@ def _score_fn(y, t): def predict( self, - X: Float[ndarray, "batch x_dim"], + X: Union[Float[ndarray, "batch x_dim"], pd.DataFrame], tol: float = 1e-3, max_samples: int = 100, verbose: bool = False, @@ -308,7 +391,7 @@ def predict( Parameters ---------- - X : Float[ndarray, "batch x_dim"] + X : Float[ndarray, "batch x_dim"] or pd.DataFrame Input data with shape (batch, x_dim). tol : float, optional Tolerance for the stopping criterion based on the relative change in the mean estimate. Default is 1e-3. @@ -333,11 +416,10 @@ def predict( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - X, _ = self._validate_data(X=X, validate_y=False) - if not self._is_fitted: raise ValueError("The model has not been fitted yet.") + X, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_preds = self._predict_from_sample(X, tol, max_samples, verbose) if self._y_original_ndim == 1: @@ -482,7 +564,7 @@ def compute_nll( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, y = self._validate_data(X=X, y=y) + X, y = self._preprocess_and_validate_data(X=X, y=y) return self._compute_nll_from_sample(X, y, n_samples, bandwidth, verbose) From c197fce806d4bf4583b5bd2809c104c565b8de84 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 14:34:40 -0400 Subject: [PATCH 15/44] add tests for support of pandas dataframes --- tests/test_treeffuser.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/tests/test_treeffuser.py b/tests/test_treeffuser.py index 54c0ebfb..e440d09a 100644 --- a/tests/test_treeffuser.py +++ b/tests/test_treeffuser.py @@ -112,6 +112,36 @@ def test_categorical(): model.fit(X=X, y=y, cat_idx=cat_idx) +def test_dataframe_input(): + """Basic test for DataFrame input support.""" + n = 10**3 + rng = np.random.default_rng(seed=0) + + X_noncat = rng.uniform(low=1, high=2, size=(n, 1)) + X_cat = rng.choice(1, size=(n, 1)) + y = rng.normal(loc=X_noncat + 2 * X_cat, scale=1, size=(n, 1)) + + import pandas as pd + + df = pd.DataFrame({"X_noncat": X_noncat.flatten(), "X_cat": X_cat.flatten()}) + model = Treeffuser() + + # not setting X_cat as categorical + model.fit(X=df, y=y) + assert model._x_cat_idx == [] + assert model._x_dim == 2 + + # setting X_cat as categorical + df["X_cat"] = df["X_cat"].astype("category") + model.fit(X=df, y=y) + assert model._x_cat_idx == [1] + assert model._x_dim == 2 + + # check error is raised if cat_idx is given when X is a DataFrame + with pytest.raises(ValueError, match="`cat_idx` should not be provided when"): + model.fit(X=df, y=y, cat_idx=[1]) + + def fit_and_validate_model( x_train, y_train, x_test, y_test, n_samples=10**2, flatten_x=False, flatten_y=False ): From 39e32647b962e1ea1ce728a9ba130f6d5afbd45a Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 14:40:15 -0400 Subject: [PATCH 16/44] include pandas as dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 41ed97aa..42b1b777 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ tqdm = "^4.66.4" lightgbm = "^4.3.0" ml-collections = "^0.1.1" scikit-learn = "^1.5.0" +pandas = "^1.3.0" [tool.poetry.dev-dependencies] pytest = "^8.2.2" From 4ca58ddd88154e8f5e86ef0495e00debf9b7e0e3 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 15:19:01 -0400 Subject: [PATCH 17/44] update pandas version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 42b1b777..464e71d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ tqdm = "^4.66.4" lightgbm = "^4.3.0" ml-collections = "^0.1.1" scikit-learn = "^1.5.0" -pandas = "^1.3.0" +pandas = "^2.0.0" [tool.poetry.dev-dependencies] pytest = "^8.2.2" From 0ace973c76ca26fbfd448de834d130e3047fb862 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 16:24:56 -0400 Subject: [PATCH 18/44] fix cat_idx handling --- src/treeffuser/_base_tabular_diffusion.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 4cb93c4c..146f0dac 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -204,7 +204,7 @@ def _preprocess_and_validate_data( y = _check_array(y) - return X, y + return X, y, self._x_cat_idx def fit( self, @@ -246,7 +246,9 @@ def fit( # store the original number of dimensions of the response # before reshaping the data so as to ensure that predictions # have the same shape as the user-inputted response - X, y = self._preprocess_and_validate_data(X=X, y=y, cat_idx=cat_idx, reset=True) + X, y, cat_idx = self._preprocess_and_validate_data( + X=X, y=y, cat_idx=cat_idx, reset=True + ) x_transformed = self._x_scaler.fit_transform(X, cat_idx=cat_idx) y_transformed = self._y_scaler.fit_transform(y) @@ -303,7 +305,7 @@ def sample( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._preprocess_and_validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation( X, n_samples, n_parallel=n_parallel, n_steps=n_steps, seed=seed, verbose=verbose @@ -419,7 +421,7 @@ def predict( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._preprocess_and_validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_preds = self._predict_from_sample(X, tol, max_samples, verbose) if self._y_original_ndim == 1: From e2cf6dbefb11d2f07a42b1584ec0cd42f46ef25a Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 16:25:01 -0400 Subject: [PATCH 19/44] update notebook --- examples/m5-news-vendor-notebook.ipynb | 1505 ++++++++++++++++++++++-- 1 file changed, 1427 insertions(+), 78 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index 6152438d..e1f8fecc 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -20,14 +20,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To get started, we first install `treeffuser` and import the relevant libraries." + "To get started, we first install `treeffuser` and import the relevant libraries (if needed)." ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-10T19:30:25.237555Z", + "start_time": "2024-10-10T19:30:23.907554Z" + } + }, "source": [ "!pip install treeffuser" ] @@ -83,18 +86,24 @@ "" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the data" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# import data\n", - "data_path = \"./m5\" # change with path where you extracted the data archive\n", + "data_path = Path(\"./m5\") # change with path where you extracted the data archive\n", "\n", - "calendar_df = pd.read_csv(Path(data_path) / \"calendar.csv\")\n", - "sales_train_df = pd.read_csv(Path(data_path) / \"sales_train_validation.csv\")\n", - "sell_prices_df = pd.read_csv(Path(data_path) / \"sell_prices.csv\")\n", + "calendar_df = pd.read_csv(data_path / \"calendar.csv\")\n", + "sales_train_df = pd.read_csv(data_path / \"sales_train_validation.csv\")\n", + "sell_prices_df = pd.read_csv(data_path / \"sell_prices.csv\")\n", "\n", "# add explicit columns for the day, month, year for ease of processing\n", "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", @@ -115,9 +124,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datewm_yr_wkweekdaywdaymonthyeardevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIday
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12011-01-3011101Sunday212011d_2NaNNaNNaNNaN00030
22011-01-3111101Monday312011d_3NaNNaNNaNNaN00031
32011-02-0111101Tuesday422011d_4NaNNaNNaNNaN1101
42011-02-0211101Wednesday522011d_5NaNNaNNaNNaN1012
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iditem_iddept_idcat_idstore_idstate_idd_1d_2d_3d_4...d_1904d_1905d_1906d_1907d_1908d_1909d_1910d_1911d_1912d_1913
0HOBBIES_1_001_CA_1_validationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CA0000...1301113011
1HOBBIES_1_002_CA_1_validationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CA0000...0000010000
2HOBBIES_1_003_CA_1_validationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CA0000...2121110111
3HOBBIES_1_004_CA_1_validationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CA0000...1054101372
4HOBBIES_1_005_CA_1_validationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CA0000...2110112224
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5 rows × 1919 columns

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" + ], + "text/plain": [ + " id item_id dept_id cat_id store_id \\\n", + "0 HOBBIES_1_001_CA_1_validation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", + "1 HOBBIES_1_002_CA_1_validation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", + "2 HOBBIES_1_003_CA_1_validation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", + "3 HOBBIES_1_004_CA_1_validation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", + "4 HOBBIES_1_005_CA_1_validation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", + "\n", + " state_id d_1 d_2 d_3 d_4 ... d_1904 d_1905 d_1906 d_1907 d_1908 \\\n", + "0 CA 0 0 0 0 ... 1 3 0 1 1 \n", + "1 CA 0 0 0 0 ... 0 0 0 0 0 \n", + "2 CA 0 0 0 0 ... 2 1 2 1 1 \n", + "3 CA 0 0 0 0 ... 1 0 5 4 1 \n", + "4 CA 0 0 0 0 ... 2 1 1 0 1 \n", + "\n", + " d_1909 d_1910 d_1911 d_1912 d_1913 \n", + "0 1 3 0 1 1 \n", + "1 1 0 0 0 0 \n", + "2 1 0 1 1 1 \n", + "3 0 1 3 7 2 \n", + "4 1 2 2 2 4 \n", + "\n", + "[5 rows x 1919 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sales_train_df.head()" ] @@ -163,9 +607,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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item_iddept_idcat_idstore_idstate_iddsales
0HOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_10
1HOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_20
2HOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_30
3HOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_40
4HOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_50
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" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales\n", + "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", + "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", + "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", + "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", + "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", @@ -191,9 +734,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.hist(\n", " sales_train_df_long[\"sales\"],\n", @@ -202,8 +756,8 @@ " density=True,\n", ")\n", "plt.xticks(range(10))\n", - "plt.ylabel(\"relative frequency\")\n", - "plt.title(\"number of sales over the entire timespan\")" + "plt.ylabel(\"Frequency of number of sales\")\n", + "plt.title(\"Number of sales over the entire timespan\");" ] }, { @@ -222,9 +776,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_items = 100\n", + "n_days = 1913\n" + ] + } + ], "source": [ "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", @@ -242,9 +805,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 53 columns

\n", + "
" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales \\\n", + "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", + "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", + "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", + "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", + "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", + "\n", + " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", + "0 141 0.0 0.0 ... 2011 NaN \n", + "1 142 0.0 0.0 ... 2011 Father's day \n", + "2 143 0.0 0.0 ... 2011 NaN \n", + "3 144 0.0 0.0 ... 2011 NaN \n", + "4 145 1.0 0.0 ... 2011 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", + "0 NaN NaN NaN 0 0 0 18 \n", + "1 Cultural NaN NaN 0 0 0 19 \n", + "2 NaN NaN NaN 0 0 0 20 \n", + "3 NaN NaN NaN 0 0 0 21 \n", + "4 NaN NaN NaN 0 0 0 22 \n", + "\n", + " sell_price \n", + "0 3.97 \n", + "1 3.97 \n", + "2 3.97 \n", + "3 3.97 \n", + "4 3.97 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "n_lags = 30\n", "\n", @@ -264,54 +1034,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for posterior predictive checks." + "Treeffuser can handle **categorical columns**, but the dtype of those columns must be set to `category` in the DataFrame." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "is_train = data[\"day_number\"] <= 300\n", - "data = data.drop(columns=[\"day_number\", \"day\"])\n", - "\n", - "y_name = \"sales\"\n", - "x_names = [name for name in data.columns if name != y_name]\n", - "\n", - "X_train, y_train = data[is_train][x_names], data[is_train][y_name]\n", - "X_test, y_test = data[~is_train][x_names], data[~is_train][y_name]\n", - "\n", - "print(X_train.shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The features of the combined datasets are mostly categorical. We save the column indices in `cat_idx` as they will come in handy later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cat_column_names = [\n", + "categorical_columns = [\n", " \"item_id\",\n", " \"dept_id\",\n", " \"cat_id\",\n", " \"store_id\",\n", " \"state_id\",\n", " \"d\",\n", - " \"date\",\n", " \"wm_yr_wk\",\n", " \"weekday\",\n", - " \"wday\",\n", - " \"month\",\n", - " \"year\",\n", " \"event_name_1\",\n", " \"event_type_1\",\n", " \"event_name_2\",\n", @@ -320,8 +1060,41 @@ " \"snap_TX\",\n", " \"snap_WI\",\n", "]\n", - "cat_idx = [data.columns.get_loc(col) for col in cat_column_names]\n", - "cat_idx" + "data[categorical_columns] = data[categorical_columns].astype(\"category\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15216, 50)\n", + "(3891, 50)\n" + ] + } + ], + "source": [ + "is_train = data[\"day_number\"] <= 300\n", + "\n", + "y_name = \"sales\"\n", + "x_names = [name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]]\n", + "\n", + "X_train, y_train, dates_train = data[is_train][x_names], data[is_train][y_name], data[is_train][\"date\"]\n", + "X_test, y_test, dates_test = data[~is_train][x_names], data[~is_train][y_name], data[~is_train][\"date\"]\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" ] }, { @@ -335,55 +1108,631 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" + ] + } + ], "source": [ "print(\", \".join(map(str, X_train.columns)))" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", + "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/Documents/treeffuser-ter/src/treeffuser/_base_tabular_diffusion.py:205: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " y = _check_array(y)\n" + ] + }, + { + "data": { + "text/html": [ + "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Treeffuser(extra_lightgbm_params={}, seed=0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Treeffuser(seed=0)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [12:39<00:00, 7.59s/it] \n" + ] + } + ], + "source": [ + "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Newsvendor model" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Currently, Treeffuser supports only numpy.ndarray data with numerical values. Therefore, we convert the categorical columns into numerical labels and then convert the train and test data into numpy.ndarray." + "We illustrate the practical relevance of accurate probabilistic predictions with an application to inventory management, using the newsvendor model \\citep{arrow1951optimal}. \n", + "\n", + "Assume that every day we decide how many units $q$ of an item to buy. \n", + "We buy at a cost $c$ and sell at a price $p$. \n", + "However, the demand $y$ is random, introducing uncertainty in our decision. \n", + "The goal is to maximize the expected profit:\n", + "$$\\max_{q} p~\\mathbb{E}\\left[\\min(q, y)\\right] - c q.$$\n", + "The optimal solution to the newsvendor problem is to buy $q = F^{-1}\\left( \\frac{p-c}{p} \\right)$ units, where $F^{-1}$ is the quantile function of the distribution of $y$. \n", + "\n", + "Using Treeffuser, we can compute the quantiles from the samples and forecast the optimal quantity of units to buy.\n", + "\n", + "To compute profits, we use the observed prices, assume a margin of $50\\%$ over all products, and assume the actual number of sales of an item correspond to the demand of this item. We let Treeffuser, learn the conditional distribution of the demand of each item, estimate their quantiles, and thus determine the optimal quantity to buy. \n", + "\n", + "We use the held-out data to compute the profit made if Treeffuser was used to forecast the demand of each item and to manage the inventory of each item." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "X_train[cat_column_names] = X_train[cat_column_names].apply(\n", - " lambda col: pd.Categorical(col).codes\n", - ")\n", "\n", - "X_train, y_train = X_train.to_numpy(), y_train.to_numpy()\n", - "X_test, y_test = X_test.to_numpy(), y_test.to_numpy()" + "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", + " \"\"\"\n", + " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", + " $$ U(y, q, p, c) = p * min(y, q) - c * q $$\n", + " \"\"\"\n", + " utility = prices * np.minimum(y_true, quantity_ordered) - stocking_cost * quantity_ordered\n", + " return utility\n", + "\n", + "\n", + "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", + " \"\"\"\n", + " Returns the optimal quantity to order for the newsvendor problem.\n", + " \n", + " It is given theoeretically by:\n", + " $$ q* = argmax_{q} E[U(y, q, p, c)] $$ \n", + " which has a closed form solution,\n", + " $$ q* = F^{-1}( (p - c) / p) $$\n", + " where F is the CDF of the demand distribution\n", + " \"\"\"\n", + " # compute the target quantiles (p - c) / p\n", + " target_quantiles = (prices - stocking_cost) / prices\n", + " target_quantiles = np.maximum(target_quantiles, 0.0)\n", + "\n", + " # compute the empirical quantities corresponding to the target quantiles\n", + " res = []\n", + " for i in range(y_samples.shape[1]):\n", + " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", + " res.append(optimal_quantities)\n", + " optimal_quantities = np.array(res)\n", + " return optimal_quantities\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 16, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "185.07666666666668" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "We are now ready to fit the data. We use `cat_idx` to tell Treeffuser which columns are categorical." + "# we don't know the profit margin of each item, so we assume it is 50%.\n", + "profit_margin = 0.5\n", + "\n", + "prices = X_test[\"sell_price\"].values\n", + "stocking_cost = prices / (1 + profit_margin)\n", + "\n", + "# Compute optimal quantities. \n", + "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", + "\n", + "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", + "optimal_quantities = optimal_quantities.astype(int)\n", + "\n", + "profit = newsvendor_utility(y_test, optimal_quantities, prices, stocking_cost)\n", + "profit.sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "model = Treeffuser()\n", - "model.fit(X_train, y_train, cat_idx=cat_idx)" + "X_test_results = pd.DataFrame({\n", + " \"date\": dates_test,\n", + " \"profit\": profit\n", + "})\n", + "daily_profit = X_test_results.groupby(\"date\").sum().sort_values(\"date\").reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "\n", + "plt.plot(pd.to_datetime(daily_profit[\"date\"]), daily_profit[\"profit\"].cumsum())\n", + "plt.ylabel(\"Cumulated profit\")\n", + "plt.xlabel(\"Date\")\n", + "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", + "plt.gcf().autofmt_xdate()" ] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -397,9 +1746,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 6ecbbfe0a710083dea2c46f2806be404898218dd Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 16:30:26 -0400 Subject: [PATCH 20/44] fix tests --- src/treeffuser/_base_tabular_diffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 146f0dac..6eace448 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -566,7 +566,7 @@ def compute_nll( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, y = self._preprocess_and_validate_data(X=X, y=y) + X, y, _ = self._preprocess_and_validate_data(X=X, y=y) return self._compute_nll_from_sample(X, y, n_samples, bandwidth, verbose) From 325ea3819acaea426a21b3ba887239cd9e210e57 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 14 Oct 2024 16:44:28 -0400 Subject: [PATCH 21/44] fix tests --- src/treeffuser/_base_tabular_diffusion.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 6eace448..b164cb6a 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -113,8 +113,11 @@ def _preprocess_and_validate_data( List[int], ]: """ - If X is a DataFrame, convert it to numpy array and auto-detect categorical columns. - Reshape X and/or y to adhere to the (batch, n_dim) convention + If X is a DataFrame: + - convert it to numpy array and auto-detect categorical columns. + - store the column names at fit time and ensure they are the same at predict time. + If X is a numpy array: + Reshape X and/or y to adhere to the (batch, n_dim) convention """ if validate_X: if X is None: @@ -513,7 +516,7 @@ def predict_distribution( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _, _ = self._validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation(X=X, n_samples=n_samples, verbose=verbose) From 3576425ea0e8f39f51fe017b24bff436a13e7e3c Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Tue, 15 Oct 2024 10:32:50 -0400 Subject: [PATCH 22/44] support DataFrame for y --- src/treeffuser/_base_tabular_diffusion.py | 12 ++++++------ tests/test_treeffuser.py | 5 +++++ 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index b164cb6a..bfd20a9c 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -102,7 +102,7 @@ def get_new_score_model(self) -> ScoreModel: def _preprocess_and_validate_data( self, X: Optional[Union[ndarray, pd.DataFrame]] = None, - y: Optional[Union[ndarray, pd.Series]] = None, + y: Optional[Union[ndarray, pd.Series, pd.DataFrame]] = None, cat_idx: Optional[List[int]] = None, validate_X: bool = True, validate_y: bool = True, @@ -192,11 +192,11 @@ def _preprocess_and_validate_data( if validate_y: if y is None: raise ValueError("Target data `y` cannot be None.") - if isinstance(y, pd.Series): + if isinstance(y, pd.Series) or isinstance(y, pd.DataFrame): y = y.values elif not isinstance(y, np.ndarray): raise TypeError( - "Target data `y` must be a numpy array or a pandas Series." + "Target data `y` must be a numpy array, a pandas Series or a pandas DataFrame." f"Received {type(y).__name}." ) @@ -212,7 +212,7 @@ def _preprocess_and_validate_data( def fit( self, X: Union[Float[ndarray, "batch x_dim"], pd.DataFrame], - y: Union[Float[ndarray, "batch y_dim"], pd.Series], + y: Union[Float[ndarray, "batch y_dim"], pd.Series, pd.DataFrame], cat_idx: Optional[List[int]] = None, ): """ @@ -222,13 +222,13 @@ def fit( ---------- X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). - y : np.ndarray or pd.Series + y : np.ndarray or pd.Series or pd.DataFrame Target data with shape (batch, y_dim). cat_idx : List[int], optional If X is a np.ndarray, list of indices of categorical features in X. If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the categorical columns have dtype `category`, and they will be automatically detected as - categorical features. + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). Default is None. Returns diff --git a/tests/test_treeffuser.py b/tests/test_treeffuser.py index e440d09a..991ebb65 100644 --- a/tests/test_treeffuser.py +++ b/tests/test_treeffuser.py @@ -141,6 +141,11 @@ def test_dataframe_input(): with pytest.raises(ValueError, match="`cat_idx` should not be provided when"): model.fit(X=df, y=y, cat_idx=[1]) + # giving a dataframe for y + y = pd.DataFrame({"a:": y.flatten(), "b": y.flatten()}) + model.fit(X=df, y=y) + assert model._y_dim == 2 + def fit_and_validate_model( x_train, y_train, x_test, y_test, n_samples=10**2, flatten_x=False, flatten_y=False From 460a7fb7c5cb9377b5c1f2795fa15c00f719885a Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Tue, 15 Oct 2024 12:48:09 -0400 Subject: [PATCH 23/44] rename reset argument --- src/treeffuser/_base_tabular_diffusion.py | 45 ++++++++++++++++++----- 1 file changed, 35 insertions(+), 10 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index bfd20a9c..22f77558 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -106,18 +106,43 @@ def _preprocess_and_validate_data( cat_idx: Optional[List[int]] = None, validate_X: bool = True, validate_y: bool = True, - reset: bool = False, + reset_data_schema: bool = False, ) -> Tuple[ Optional[Float[ndarray, "batch x_dim"]], Optional[Float[ndarray, "batch y_dim"]], List[int], ]: """ - If X is a DataFrame: - - convert it to numpy array and auto-detect categorical columns. - - store the column names at fit time and ensure they are the same at predict time. - If X is a numpy array: - Reshape X and/or y to adhere to the (batch, n_dim) convention + Preprocess and validate the input data by methods like `fit`, `predict`, `sample`, ... + (1) It transforms the different possible inputs (pandas, numpy) into numpy arrays. + (2) It checks and convert the shapes to be 2d arrays. (3) It detects categorical columns + in DataFrames. (4) It ensure that the input in `predict` has the same shape/columns as the + input in `fit` (see parameter `reset` below). + + Parameters + ---------- + X : np.ndarray or pd.DataFrame, optional + Input data with shape (batch, x_dim). + y : np.ndarray or pd.Series or pd.DataFrame, optional + Target data with shape (batch, y_dim) or (batch). + cat_idx : List[int], optional + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). + validate_X : bool, optional + If True, validate the input data `X` and raise an error if no `X` is provided. + Default is True. + validate_y : bool, optional + If True, validate the target data `y` and raise an error if no `y` is provided. + Default is True. + reset_data_schema : bool, optional + If True, reset the cached schema of X and y (their shape, the name of the columns, + the categorical indices). + If False, check that X and y have the same shape and columns as the cached properties. + It should be set to True when `_preprocess_and_validate_data` is called from the `fit` + method and False when called from `predict` or `sample`, to ensure that the prediction + data is consistent with the training data. Default is False. """ if validate_X: if X is None: @@ -151,7 +176,7 @@ def _preprocess_and_validate_data( X_has_been_copied = True X[c] = X[c].cat.codes # at that point, X contains only numerical columns - if reset: + if reset_data_schema: # store the columns of the DataFrame self._x_dataframe_columns = X.columns self._x_cat_idx = cat_idx @@ -179,7 +204,7 @@ def _preprocess_and_validate_data( else: cat_idx = [] - if reset: + if reset_data_schema: self._x_cat_idx = cat_idx else: raise TypeError( @@ -200,7 +225,7 @@ def _preprocess_and_validate_data( f"Received {type(y).__name}." ) - if reset: + if reset_data_schema: # store the original number of dimensions of the response self._y_original_ndim = y.ndim self._y_dim = y.shape[1] if y.ndim > 1 else 1 @@ -250,7 +275,7 @@ def fit( # before reshaping the data so as to ensure that predictions # have the same shape as the user-inputted response X, y, cat_idx = self._preprocess_and_validate_data( - X=X, y=y, cat_idx=cat_idx, reset=True + X=X, y=y, cat_idx=cat_idx, reset_data_schema=True ) x_transformed = self._x_scaler.fit_transform(X, cat_idx=cat_idx) From cf424f7dde20d8d5a27c21bd5962b771c9f56443 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Tue, 15 Oct 2024 12:53:34 -0400 Subject: [PATCH 24/44] refactor big function --- src/treeffuser/_base_tabular_diffusion.py | 44 ++++++++++++++--------- 1 file changed, 27 insertions(+), 17 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index 22f77558..0a8f2443 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -65,6 +65,32 @@ def _check_array(array: ndarray[float]): ################################################### +def _ensure_numerical_columns(X: pd.DataFrame): + """ + Convert the categorical columns of a DataFrame to numerical columns. + Raise an error if the columns are not numerical or categorical. + Copy the DataFrame to avoid modifying the original, but only if necessary. + """ + X_has_been_copied = False + for c in X.columns: + if not isinstance(X[c].dtype, pd.CategoricalDtype) and not np.issubdtype( + X[c].dtype, np.number + ): + raise ValueError( + f"Dtypes of columns in `X` must be int, float, bool or category. " + f"Column {c} has dtype {X[c].dtype}." + f"Convert the column to a numerical dtype or a category, e.g.," + f"X['{c}'] = X['{c}'].astype('category')." + ) + if isinstance(X[c].dtype, pd.CategoricalDtype): + if X_has_been_copied is False: + # copy the DataFrame to avoid modifying the original + X = X.copy() + X_has_been_copied = True + X[c] = X[c].cat.codes + return X + + class BaseTabularDiffusion(BaseEstimator, abc.ABC): """ Abstract class for the tabular diffusions. Every particular @@ -158,23 +184,7 @@ def _preprocess_and_validate_data( X.columns.get_loc(col) for col in X.select_dtypes("category").columns ] # check dtype of columns and convert categorical columns to numerical - X_has_been_copied = False - for c in X.columns: - if not isinstance(X[c].dtype, pd.CategoricalDtype) and not np.issubdtype( - X[c].dtype, np.number - ): - raise ValueError( - f"Dtypes of columns in `X` must be int, float, bool or category. " - f"Column {c} has dtype {X[c].dtype}." - f"Convert the column to a numerical dtype or a category, e.g.," - f"X['{c}'] = X['{c}'].astype('category')." - ) - if isinstance(X[c].dtype, pd.CategoricalDtype): - if X_has_been_copied is False: - # copy the DataFrame to avoid modifying the original - X = X.copy() - X_has_been_copied = True - X[c] = X[c].cat.codes + X = _ensure_numerical_columns(X) # at that point, X contains only numerical columns if reset_data_schema: # store the columns of the DataFrame From 1b92d2dfa92f165fd5702ed39c89ca2bb6eb779b Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Tue, 15 Oct 2024 15:52:59 -0400 Subject: [PATCH 25/44] add fancy plot for the newsvendor model --- examples/m5-news-vendor-notebook.ipynb | 352 +++++++++++++++++-------- 1 file changed, 245 insertions(+), 107 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index e1f8fecc..cec02256 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -37,9 +37,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 80, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -95,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -203,7 +212,7 @@ "4 CA_1 HOBBIES_1_001 11329 8.26" ] }, - "execution_count": 3, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -373,7 +382,7 @@ "4 NaN NaN NaN 1 0 1 2 " ] }, - "execution_count": 4, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -589,7 +598,7 @@ "[5 rows x 1919 columns]" ] }, - "execution_count": 5, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -607,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -704,7 +713,7 @@ "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" ] }, - "execution_count": 6, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -734,12 +743,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "Text(0.5, 1.0, 'Number of sales over the entire timespan')" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -757,7 +776,7 @@ ")\n", "plt.xticks(range(10))\n", "plt.ylabel(\"Frequency of number of sales\")\n", - "plt.title(\"Number of sales over the entire timespan\");" + "plt.title(\"Number of sales over the entire timespan\")" ] }, { @@ -776,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -805,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -1010,7 +1029,7 @@ "[5 rows x 53 columns]" ] }, - "execution_count": 9, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1060,7 +1079,7 @@ " \"snap_TX\",\n", " \"snap_WI\",\n", "]\n", - "data[categorical_columns] = data[categorical_columns].astype(\"category\")\n" + "data[categorical_columns] = data[categorical_columns].astype(\"category\")" ] }, { @@ -1072,7 +1091,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -1088,10 +1107,20 @@ "is_train = data[\"day_number\"] <= 300\n", "\n", "y_name = \"sales\"\n", - "x_names = [name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]]\n", + "x_names = [\n", + " name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]\n", + "]\n", "\n", - "X_train, y_train, dates_train = data[is_train][x_names], data[is_train][y_name], data[is_train][\"date\"]\n", - "X_test, y_test, dates_test = data[~is_train][x_names], data[~is_train][y_name], data[~is_train][\"date\"]\n", + "X_train, y_train, dates_train = (\n", + " data[is_train][x_names],\n", + " data[is_train][y_name],\n", + " data[is_train][\"date\"],\n", + ")\n", + "X_test, y_test, dates_test = (\n", + " data[~is_train][x_names],\n", + " data[~is_train][y_name],\n", + " data[~is_train][\"date\"],\n", + ")\n", "\n", "print(X_train.shape)\n", "print(X_test.shape)" @@ -1108,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -1125,9 +1154,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 92, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:243: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " \"\"\"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -1140,18 +1177,10 @@ "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/achille/Documents/treeffuser-ter/src/treeffuser/_base_tabular_diffusion.py:205: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", - " y = _check_array(y)\n" - ] - }, { "data": { "text/html": [ - "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Treeffuser(extra_lightgbm_params={}, seed=0)" ] }, - "execution_count": 13, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -1573,14 +1602,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [12:39<00:00, 7.59s/it] \n" + "100%|██████████| 100/100 [05:00<00:00, 3.00s/it]\n" ] } ], @@ -1617,11 +1646,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ - "\n", "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", " \"\"\"\n", " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", @@ -1634,9 +1662,9 @@ "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", " \"\"\"\n", " Returns the optimal quantity to order for the newsvendor problem.\n", - " \n", + "\n", " It is given theoeretically by:\n", - " $$ q* = argmax_{q} E[U(y, q, p, c)] $$ \n", + " $$ q* = argmax_{q} E[U(y, q, p, c)] $$\n", " which has a closed form solution,\n", " $$ q* = F^{-1}( (p - c) / p) $$\n", " where F is the CDF of the demand distribution\n", @@ -1651,12 +1679,12 @@ " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", " res.append(optimal_quantities)\n", " optimal_quantities = np.array(res)\n", - " return optimal_quantities\n" + " return optimal_quantities" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -1665,7 +1693,7 @@ "185.07666666666668" ] }, - "execution_count": 16, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -1677,7 +1705,7 @@ "prices = X_test[\"sell_price\"].values\n", "stocking_cost = prices / (1 + profit_margin)\n", "\n", - "# Compute optimal quantities. \n", + "# compute optimal quantities\n", "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", "\n", "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", @@ -1687,29 +1715,89 @@ "profit.sum()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the cumulative profit, the average demand and inventory over time in the plot below." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 160, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date profit avg_inventory_weighted avg_demand_weighted\n", + "0 2011-11-25 -2.213333 0.039476 0.784104\n", + "1 2011-11-26 1.600000 0.033068 0.933309\n", + "2 2011-11-27 0.053333 0.046308 0.796895\n", + "3 2011-11-28 5.873333 0.067636 0.789166\n", + "4 2011-11-29 1.580000 0.041286 0.913090\n", + ".. ... ... ... ...\n", + "60 2012-01-24 6.336667 0.138240 0.661504\n", + "61 2012-01-25 7.876667 0.078847 0.530883\n", + "62 2012-01-26 -3.493333 0.051774 0.894360\n", + "63 2012-01-27 -0.710000 0.102579 1.093555\n", + "64 2012-01-28 11.890000 0.116262 1.258714\n", + "\n", + "[65 rows x 4 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/jm/hpk3szdx7lj_nzqt1bfcmxsw0000gq/T/ipykernel_84857/2419160375.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " performance_data.groupby(\"date\", group_keys=False)\n" + ] + } + ], "source": [ - "X_test_results = pd.DataFrame({\n", - " \"date\": dates_test,\n", - " \"profit\": profit\n", - "})\n", - "daily_profit = X_test_results.groupby(\"date\").sum().sort_values(\"date\").reset_index()" + "performance_data = pd.DataFrame(\n", + " {\n", + " \"date\": dates_test,\n", + " \"profit\": profit,\n", + " \"demand\": y_test,\n", + " \"inventory\": optimal_quantities,\n", + " \"price\": prices,\n", + " }\n", + ")\n", + "\n", + "# for each day, compute average demand and Inventory weighted by price\n", + "daily_summary = (\n", + " performance_data.groupby(\"date\", group_keys=False)\n", + " .apply(\n", + " lambda x: pd.Series(\n", + " {\n", + " \"profit\": x[\"profit\"].sum(),\n", + " \"avg_inventory_weighted\": (x[\"inventory\"] * x[\"price\"]).sum()\n", + " / x[\"price\"].sum(), # price-weighted average Inventory\n", + " \"avg_demand_weighted\": (x[\"demand\"] * x[\"price\"]).sum()\n", + " / x[\"price\"].sum(), # price-weighted average demand\n", + " }\n", + " )\n", + " )\n", + " .sort_values(\"date\")\n", + " .reset_index()\n", + ")\n", + "\n", + "print(daily_summary)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 161, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -1717,22 +1805,72 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates\n", + "# dictionary to store color, alpha, linewidth, and linestyle for each line\n", + "line_styles = {\n", + " \"cumulative_profit\": {\"color\": \"teal\", \"alpha\": 1, \"linewidth\": 2.5, \"linestyle\": \"-\"},\n", + " \"inventory\": {\n", + " \"color\": \"blue\",\n", + " \"alpha\": 0.5,\n", + " \"linewidth\": 1.5,\n", + " \"linestyle\": \"--\",\n", + " },\n", + " \"demand\": {\n", + " \"color\": \"orange\",\n", + " \"alpha\": 0.5,\n", + " \"linewidth\": 1.5,\n", + " \"linestyle\": \"--\",\n", + " },\n", + "}\n", + "\n", + "# create figure\n", + "fig, ax1 = plt.subplots(figsize=(10, 6))\n", + "\n", + "# define x-axis\n", + "dates = pd.to_datetime(daily_summary[\"date\"])\n", + "\n", + "# plot cumulative profit\n", + "ax1.plot(\n", + " dates,\n", + " daily_summary[\"profit\"].cumsum(),\n", + " **line_styles[\"cumulative_profit\"],\n", + " label=\"Cumulative Profit\",\n", + ")\n", + "ax1.set_xlabel(\"Date\", fontsize=12)\n", + "ax1.set_ylabel(\"Cumulative Profit ($)\", fontsize=12)\n", + "ax1.grid(True, linestyle=\"--\", alpha=0.6)\n", + "\n", + "# create second y-axis for price-weighted inventory and demand\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(\n", + " dates,\n", + " daily_summary[\"avg_inventory_weighted\"],\n", + " **line_styles[\"inventory\"],\n", + " label=\"Avg Inventory (Price Weighted)\",\n", + ")\n", + "ax2.plot(\n", + " dates,\n", + " daily_summary[\"avg_demand_weighted\"],\n", + " **line_styles[\"demand\"],\n", + " label=\"Avg Demand (Price Weighted)\",\n", + ")\n", + "ax2.set_ylabel(\"Avg Inventory and Demand (Units)\", fontsize=12)\n", + "\n", + "# combine all legends into one\n", + "lines1, labels1 = ax1.get_legend_handles_labels()\n", + "lines2, labels2 = ax2.get_legend_handles_labels()\n", + "ax1.legend(\n", + " lines1 + lines2, labels1 + labels2, loc=\"upper center\", bbox_to_anchor=(0.5, 1.12), ncol=3\n", + ")\n", "\n", + "fig.autofmt_xdate() # rotate x-tick labels\n", "\n", - "plt.plot(pd.to_datetime(daily_profit[\"date\"]), daily_profit[\"profit\"].cumsum())\n", - "plt.ylabel(\"Cumulated profit\")\n", - "plt.xlabel(\"Date\")\n", - "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n", - "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.gcf().autofmt_xdate()" + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1746,7 +1884,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.9.6" } }, "nbformat": 4, From 52307b5531746092dd2fe7964925791d8be0b728 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Tue, 15 Oct 2024 18:52:22 -0400 Subject: [PATCH 26/44] clear outputs --- examples/m5-news-vendor-notebook.ipynb | 1358 +----------------------- 1 file changed, 32 insertions(+), 1326 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index cec02256..9f07b6b3 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -37,18 +37,9 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -133,90 +124,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales\n", - "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", - "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", - "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", - "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", - "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", @@ -743,30 +200,9 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Number of sales over the entire timespan')" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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item_iddept_idcat_idstore_idstate_iddsalesday_numbersales_lag_1sales_lag_2...yearevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIdaysell_price
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1HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14201420.00.0...2011Father's dayCulturalNaNNaN000193.97
2HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14301430.00.0...2011NaNNaNNaNNaN000203.97
3HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14411440.00.0...2011NaNNaNNaNNaN000213.97
4HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14501451.00.0...2011NaNNaNNaNNaN000223.97
\n", - "

5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " item_id dept_id cat_id store_id state_id d sales \\\n", - "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", - "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", - "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", - "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", - "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", - "\n", - " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", - "0 141 0.0 0.0 ... 2011 NaN \n", - "1 142 0.0 0.0 ... 2011 Father's day \n", - "2 143 0.0 0.0 ... 2011 NaN \n", - "3 144 0.0 0.0 ... 2011 NaN \n", - "4 145 1.0 0.0 ... 2011 NaN \n", - "\n", - " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", - "0 NaN NaN NaN 0 0 0 18 \n", - "1 Cultural NaN NaN 0 0 0 19 \n", - "2 NaN NaN NaN 0 0 0 20 \n", - "3 NaN NaN NaN 0 0 0 21 \n", - "4 NaN NaN NaN 0 0 0 22 \n", - "\n", - " sell_price \n", - "0 3.97 \n", - "1 3.97 \n", - "2 3.97 \n", - "3 3.97 \n", - "4 3.97 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_lags = 30\n", "\n", @@ -1091,18 +311,9 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(15216, 50)\n", - "(3891, 50)\n" - ] - } - ], + "outputs": [], "source": [ "is_train = data[\"day_number\"] <= 300\n", "\n", @@ -1137,464 +348,18 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" - ] - } - ], + "outputs": [], "source": [ "print(\", \".join(map(str, X_train.columns)))" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:243: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", - " \"\"\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", - "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", - "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" - ] - }, - { - "data": { - "text/html": [ - "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Treeffuser(extra_lightgbm_params={}, seed=0)" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model = Treeffuser(seed=0)\n", "model.fit(X_train, y_train)" @@ -1602,17 +367,9 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [05:00<00:00, 3.00s/it]\n" - ] - } - ], + "outputs": [], "source": [ "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" ] @@ -1684,20 +441,9 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "185.07666666666668" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# we don't know the profit margin of each item, so we assume it is 50%.\n", "profit_margin = 0.5\n", @@ -1724,38 +470,9 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " date profit avg_inventory_weighted avg_demand_weighted\n", - "0 2011-11-25 -2.213333 0.039476 0.784104\n", - "1 2011-11-26 1.600000 0.033068 0.933309\n", - "2 2011-11-27 0.053333 0.046308 0.796895\n", - "3 2011-11-28 5.873333 0.067636 0.789166\n", - "4 2011-11-29 1.580000 0.041286 0.913090\n", - ".. ... ... ... ...\n", - "60 2012-01-24 6.336667 0.138240 0.661504\n", - "61 2012-01-25 7.876667 0.078847 0.530883\n", - "62 2012-01-26 -3.493333 0.051774 0.894360\n", - "63 2012-01-27 -0.710000 0.102579 1.093555\n", - "64 2012-01-28 11.890000 0.116262 1.258714\n", - "\n", - "[65 rows x 4 columns]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/jm/hpk3szdx7lj_nzqt1bfcmxsw0000gq/T/ipykernel_84857/2419160375.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " performance_data.groupby(\"date\", group_keys=False)\n" - ] - } - ], + "outputs": [], "source": [ "performance_data = pd.DataFrame(\n", " {\n", @@ -1767,7 +484,7 @@ " }\n", ")\n", "\n", - "# for each day, compute average demand and Inventory weighted by price\n", + "# for each day, compute average demand and inventory weighted by price\n", "daily_summary = (\n", " performance_data.groupby(\"date\", group_keys=False)\n", " .apply(\n", @@ -1775,7 +492,7 @@ " {\n", " \"profit\": x[\"profit\"].sum(),\n", " \"avg_inventory_weighted\": (x[\"inventory\"] * x[\"price\"]).sum()\n", - " / x[\"price\"].sum(), # price-weighted average Inventory\n", + " / x[\"price\"].sum(), # price-weighted average inventory\n", " \"avg_demand_weighted\": (x[\"demand\"] * x[\"price\"]).sum()\n", " / x[\"price\"].sum(), # price-weighted average demand\n", " }\n", @@ -1790,20 +507,9 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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aTz/9NFZZGG8aDAaMHTsWbdu2xeHDh1FRUWHVsmutPW6++WZ8+OGHmDt3LjIyMgBA7LNNjZFsbccbb7wRer0ehw8fxuDBgy2Wj+JcqMJJcSoXLlwAIQQpKSkOO+eCBQtEd5YZM2bg1VdfRWZmJpKSkgAA9957L3bv3t0shfM///mP+H9iYiJefPFFrFu3Di+//DL8/PwQGBgIuVyO2NhYq+eYMGECnn/+efz555+igrl27VpMmjQJDMMgJycHq1atQk5ODuLi4gDwa8S2bduGVatW4d3GtKs6tFotli5dirKyMjN3mKFDh5o99KdMmYLbbrtNXKPRqVMnpKenY/HixZg2bRrCw8Ph7+8PpVJptU5RUVEA+EmCxurd0lmxYgUeeOABAMCoUaNQVlaGvXv3YvDgwRg5ciQCAgKwceNGcdCydu1a3HXXXQgKCkJFRQW++eYbrF27Vhxkr1q1SuwfUuA4DqtXrxZdth588EHs2rUL77zzDsLCwjB69Giz6/z888+IjIzEkCFDAPCK5CuvvIKpU6cCAJKSkvD222/j5ZdfNnuhT5s2DZMmTQIAvPvuu1i2bBmOHDmCUaNGiUpVdHS0OLlUWVmJzz77DKtXrxbXO3/11VfYuXMnVqxYgZdeekk891tvvYXhw4eL24888ggef/xxvP/++1CpVPjnn3+QlpaGzZs3W5XDpUuXmlQ48/LysGTJErRu3RrJycmie3r969dnwYIFeOGFF8wUpj59+gDgB6ZHjhxBYWGh6Ga/ZMkSbNq0CT///LPFNVABAQHo27cv9uzZg0mTJmHPnj0YOHAgVCoVbr75ZuzZswft2rXDnj170L9/f6hUKnz//ffgOA5ff/21OLG1atUqhIaGYs+ePQ2Cm0jpY7fffjuefPJJAMCcOXPwwQcfYPfu3UhOThafBxEREWbPgzfffBNLly7FPffcAwBo164d0tPT8cUXX2Dq1KlYu3YtOI7DihUroFar0bVrV+Tm5uKJJ55ocP24uDhcunTJqvxbOp7yrBFISUlBRUUFioqKEBISgnfffRe///47+vfvD4B/hvz555/44osvzBROqe/08ePHm1135cqViIqKQnp6Orp16ybuf/HFF8W1rfPnz0fXrl1x4cIFpKSk4KOPPsKoUaPw8ssvA+DfiQcPHsS2bdus1q+0tBRlZWVNyujYsWNYu3at2TtZq9Xi22+/Fe+b+pw7dw4//vgjdu7ciWHDhonyEhAmcEzHBStXrkR8fDzOnTuHTp06NTjngAEDoFQqzZ4ngwYNwg033IDr16+LE1h79+7FjBkzAAALFy7ElClTxGBGHTt2xLJlyzBo0CB89tlnUKvVZtfYuXMnMjMzsWfPHvE58M4771h8bjbWHiEhIWAYxuxZYssYydZ29Pf3R0hICH2euBGqcFKcCiHE4efs3r27+H9MTAz8/f3NHswxMTE4cuRIs66xfv16LFu2DJmZmdBoNNDr9QgODpZ0jqioKIwYMQJr1qzBLbfcgqysLBw6dEicWUxLS4PBYGjwoqitrW1yndicOXPwn//8BzU1NQgMDMSiRYvMgkbUH2CfOXMGd999t9m+AQMG4MMPPxRno72B116z/l19A5eJ3tKA+jmeHRUoMCMjA0eOHMHGjRsB8NbniRMnYsWKFRg8eDDkcjkmTJiANWvW4MEHH0RlZSU2b94sBna4ePEidDod+vbtK54zJCQEycnJksuSmJhotj6oVatWKCwsFLenTJmCmTNn4tNPP4VKpcKaNWtw//33ixaCkydP4sCBA3jnnXfE3xgMBtTU1KCqqkpcw2N6PwYEBCA4ONjsOvXJzMyETqczWwOlUCjQt29fnDlzxuzY+v147NixeOqpp7Bx40bcf//9WL16NYYMGYLExESr16uurm4wSBJo06YNCCGoqqpCjx498Msvv5hZBBtTVAsLC3H16lWr1reTJ09Co9E0uJerq6uRmZlp9byDBw8Wg4Dt2bNHnI0fNGgQ9uzZg+nTp2PPnj2YOXOmeJ0LFy40WAtWU1Nj8TpS+php2wqDwcbatrKyEpmZmZgxY4ZYPgDQ6/WiF8iZM2fQvXt3szYRFJL6+Pn5ocqS64Er6NrIw4ap97DpLOFhk/y83UUyxZOeNQLC+55hGFy4cAFVVVUNFA+tVotevXqZ7ZP6Tj9//jzmzp2Lw4cP4/r16+A4DgCvoJgqnKbnbdWqFQD+vk1JScGZM2dES69A//79G1U4q6urAcDi8yQtLQ2BgYEwGAzQarW44447sHz5cvH7hIQEq8omAJw4cQIymcxMETfl5MmT2L17t2j5MyUzM9Oiwunv748+ffqICufevXvx0ksvQS6XixNYhBDk5OSIE40nT57Ev//+izVr1ojnIYSA4zhkZWWhc+fOZtfIyMhAfHy8maJo2qdMaaw9LGHLGElKO7r1eUKhCqc3kxodjf3Tp7v1+k3RsWNHMAyDs2fPNnqcMMg1VVB1VvwhFQqF+D/DMGbbwj7hBSScu77ia+3cAB/1b8qUKZg/fz5GjhyJkJAQrFu3DkuXLm20DpaYMmUKnn32WXz88cdYu3YtUlNTkZqaCoCPIieTyXDs2LEGCp+ll4opL730EqZNm4bAwEDExMSIlg0BS+7LvoAF70CXH9sYK1asgF6vN5sBJ4RApVJh+fLlCAkJwZQpUzBo0CAUFhZi586d8PPzw6hRoxxTABOaui/GjBkDQgi2bNmCPn36YP/+/fjggw/E7zUaDebPny9aqkwxHXA1dZ3mUL8fK5VKPPTQQ1i1ahXuuecerF27Fh999FGj54iMjERJSYnF7/bv34/g4GBER0dbDN7R2H0kuOBaQ6PRoFWrVg3ccIHGlxIMGTIE77zzDq5cuYI9e/bgxRdfBMArnF988QUyMzNx+fJl0Xqi0Whwww03mA0QBRob4NqC1LYVonJ+9dVX6Nevn9l39kxqFRcXo3379pJ/5xBkEh4Kzjq2ETzpWSNw5swZBAcHIyIiQoz4vGXLFrRu3drsuPqB9aS+08eMGYOEhAR89dVXiIuLA8dx6NatW4MAefXPC6BZz6aIiAgwDGPxeZKcnIz//ve/kMvliIuLa+DK3tQ72ZbnyZgxY/B///d/Db4TlDdLDBkyBOvXr8fp06dRXV2N3r17A+CfJ7t37wbHcfD39xfvV41Gg8ceewzPPvtsg3O1bWIJVVNIbY/mjJEsUVxc3OxnIsV+qMLpxYSo1U2uoXQ34eHhGDlyJD755BM8++yzDR66paWlCA0NFR8CeXl54uyno8LtR0VFIS8vT9wuLy9HVlaW1eMPHjyIhIQEvP766+K++m4YSqXSYjTL+tx999149NFHsW3bNqxduxYPPfSQ+F2vXr1gMBhQWFhoc3oDgcjISHTo0MHm4zt37my2tgwADhw4gE6dOkkaCCoUCpvq3RLR6/X49ttvsXTp0gZujGPHjsUPP/yAxx9/HDfffDPi4+Oxfv16/O9//8N9990nvoiTkpKgUCjw999/iy/3srIynDt3DrfeeqtDy6tWq3HPPfdgzZo1uHDhApKTk8XBCAD07t0bGRkZkvpZfYRBl2mfad++PZRKJQ4cOICEhAQA/ATQ33//bVNOukceeQTdunXDp59+Cr1eb1EhNqVXr15IT0+3+F27du3sjt4aFBSExMRE7Nq1S7QOmNK7d2/k5+eLaRJs5eabb4ZSqcSnn36Kmpoa3HDDDQB4V91r165h5cqVouutcJ3169cjOjraJi8MR/UxS20bExODuLg4XLx4EVNMF1Sb0LlzZ3z33XeoqakRJy7++usvi8eeOnUK9957r81lail44rOmsLAQa9euxdixY8GyLLp06QKVSoWcnByrVjt7KCoqQkZGBr766ivxvfnnn39KPk/nzp1x+PBhs33W+qGAUqlEly5dkJ6e3kDuSqWyWc/K1NRUcByHvXv3ii61pvTu3Ru//PILEhMTIZfbPnQfMmQIFixYgLVr12LgwIHi+/7WW2/Fl19+CUKI6HorXCc9Pd3muiQnJ+Py5csoKChATEwMAD7eg1QsjalsGSPZ2o6ZmZmoqalpYF2nuA6aFoXidD755BMYDAb07dsXv/zyC86fP48zZ85g2bJloiuVn58fbrrpJixatAhnzpzB3r17zdZRNoehQ4fiu+++w/79+5GWloapU6c2qmR17NgROTk5WLduHTIzM7Fs2TLRbUkgMTERWVlZOHHiBK5fv47a2lqL5woICMDYsWPxxhtv4MyZM+JaN4BfazBlyhQ89NBD2LBhA7KysnDkyBEsXLgQW7ZscUjdBV544QXs2rULb7/9Ns6dO4dvvvkGy5cvF60ntiIMsPPz861ajVoqQnCeGTNmoFu3bmaf8ePHY8WKFeKxkydPxueff46dO3eaDcyDgoIwdepUvPTSS9i9ezdOnz6NGTNmgGXZBlZsRzBlyhRs2bIFK1eubKAgzJ07F99++y3mz5+P06dP48yZM1i3bp2k+zIhIQEMw+C3337DtWvXoNFoEBAQgCeeeAIvvfQStm3bhvT0dMycORNVVVXiOqLG6Ny5M2666SbMmTMHkyZNatIyMHLkSBw6dMgpEyXz5s3D0qVLsWzZMpw/fx7//PMPPv74YwDAsGHD0L9/f4wdOxY7duxAdnY2Dh48iNdffx1Hjx61ek7hWfjxxx9jwIAB4rNKqVSa7RcUhylTpiAyMhJ333039u/fj6ysLOzZswfPPvsscnNzG5zfUX0sOjoafn5+2LZtGwoKCsQcjPPnz8fChQuxbNkynDt3DmlpaVi1apWYU3Py5MlgGAYzZ85Eeno6tm7diiVLljQ4f3Z2Nq5cuWJx8N3ScfezhhCC/Px85OXl4cyZM1i5ciVuvvlmhISEYNGiReL5X3zxRcyaNQvffPMNMjMzxfvjm2++sbvuYWFhiIiIwJdffokLFy7gjz/+wOzZsyWf59lnn8W2bduwZMkSnD9/HsuXL2/UnVZg5MiRdim4TZGYmIipU6fi4YcfxqZNm8T7+McffwQAPPXUUyguLsakSZPw999/IzMzE9u3b8f06dMbfbbdfPPNUKlU+Pjjj80U/759+6KwsBCbN282mzCbM2cODh48iKeffhonTpzA+fPnsXnzZjz99NMWzz98+HC0b98eU6dOxb///osDBw6I7wgpz5PExERoNBrs2rUL169fR1VVlU1jJFvbcf/+/UhKSnKfxwSFKpwU55OUlIR//vkHQ4YMwQsvvIBu3bph+PDh2LVrFz777DPxuJUrV0Kv1+OGG27A888/jwULFjjk+q+++ioGDRqEO++8E3fccQfGjh3b6EPnrrvuwqxZs/D000+jZ8+eOHjwYIOEyOPHj8eoUaMwZMgQREVF4YcffrB6vilTpuDkyZO45ZZbGrikrFq1Cg899BBeeOEFJCcnY+zYsWYzzo6id+/e+PHHH7Fu3Tp069YNc+fOxVtvvWUxN2FjLF26FDt37kR8fDydKazHihUrMGzYMIsRi8ePH4+jR4/i33//BcD3ifT0dLRu3bpBPrf3338f/fv3x5133olhw4ZhwIAB6Ny5s9V1iM1h6NChCA8PR0ZGBiZPnmz23ciRI/Hbb79hx44d6NOnD2666SZ88MEHolXSFlq3bi0GH4qJiREHLYsWLcL48ePx4IMPonfv3rhw4QK2b9+OsLAwm847Y8YMaLVaPPzww00eO3r0aMjlcjGitCOZOnUqPvzwQ3z66afo2rUr7rzzTpw/fx4AP9jaunUrbr31VkyfPh2dOnXC/fffj0uXLomWAGsMGTIEFRUVDaIpDho0CBUVFWYDRH9/f+zbtw9t27bFPffcg86dO2PGjBmoqamxavF0RB+Ty+VYtmwZvvjiC8TFxYlrxB955BF8/fXXWLVqFVJTUzFo0CCsXr0a7dq1A8C7wv36669IS0tDr1698Prrr1t0E/zhhx8wYsQISf2tpeDuZ015eTlatWqF1q1bo3///mJAqOPHj5u5d7799tt44403sHDhQnTu3BmjRo3Cli1bxL5gDyzLYt26dTh27Bi6deuGWbNmYfHixZLPc9NNN+Grr77CRx99hB49emDHjh02TabNmDEDW7duFSdYHMlnn32Ge++9F08++SRSUlIwc+ZMVFZWAuADaB04cAAGgwEjRoxAamoqnn/+eYSGhlqN0A3wniw33XRTg+eJSqUS95s+T7p37469e/fi3LlzuOWWW9CrVy/MnTvXaqAkmUyGTZs2QaPRoE+fPnjkkUdE7zApz5Obb74Zjz/+OCZOnIioqCi89957AJoeI9najj/88IPZunKK62GIM6K6UJxCTU2NGFXMGYNPCoXieVRWVqJ169ZYunSpTRbAlsDbb7+Nn376SRxUN8Unn3yC//73v9i+fbuTS+adeFof02q16NixI9auXdtASaI4D0/rB57Kfffdh969e+PVV191d1E8kgMHDmDgwIG4cOGCR1gUT58+jaFDh+LcuXMWJ2kAOr52BXQNJ4VCoXgQx48fx9mzZ9G3b1+UlZXhrbfeAoAGUYZbIhqNBtnZ2Vi+fLkkD4jHHnsMpaWlqKiosBgcqKXh6X0sJycHr732GlU2nYyn9wNPZfHixfj111/dXQyPYePGjQgMDETHjh1x4cIFPPfccxgwYIBHKJsAHxvk22+/tapsUlwDVTgpFArFw1iyZAkyMjKgVCpxww03YP/+/YiMjHR3sdzO008/jR9++AFjx461yZ1WQC6XmwUBo3h2H+vQoUOzArBQbMeT+4GnkpiYiGeeecbdxfAYKioqMGfOHOTk5CAyMhLDhg2zK6q/s6DrwD0D6lLrRVCTP4VCoVAoFAqF4jjo+Nr50KBBFAqFQqFQKBQKhUJxClTh9EKoUZpCoVAoFAqFQmk+dFztfKjC6UUIedeqqqrcXBIKhUKhUCgUCsX7EcbVwjib4nho0CAvQiaTITQ0FIWFhQD4/GvOSAZPoVAoFAqFQqH4MoQQVFVVobCwEKGhoZDJZO4uks9CgwZ5GYQQ5Ofno7S01N1FoVAoFAqFQqFQvJrQ0FDExsZSI44ToQqnl2IwGKDT6dxdDAqFQqFQKBQKxStRKBTUsukCqMJJoVAoFAqFQqFQKBSnQIMGUSgUCoVCoVAoFArFKVCFk0KhUCgUCoVCoVAoToEqnBQKhUKhUCgUCoVCcQpU4aRQKBQKhUKhUCgUilOgCieFQqFQKBQKhUKhUJwCVTgpFAqFQqFQKBQKheIUqMJJoVAoFAqFQqFQKBSnQBVOCoVCoVAoFAqFQqE4BapwUigUCoVCoVAoFArFKVCFk0KhUCgUCoVCoVAoToEqnBQKhUKhUCgUCoVCcQpU4aRQKBQKhUKhUCgUilOQu7sAvoRer8fx48cRExMDlqW6PIVCoVAoFAqF0lLhOA4FBQXo1asX5PKWq3a13Jo7gePHj6Nv377uLgaFQqFQKBQKhULxEI4cOYI+ffq4uxhugyqcDiQmJgYAcOjQIZSVlSElJQUymczNpaIYDAacPXuWtocEqMykQ2UmHSoz74S2m3SozKRDZSYdKjPPIy8vD3379hV1hJYKVTgdiOBG27p1a/To0QNqtRoMw7i5VBRCCKKjo2l7SIDKTDpUZtKhMvNOaLtJh8pMOlRm0qEy81xa+lK7ll17J6JUKt1dBIoJtD2kQ2UmHSoz6VCZeSe03aRDZSYdKjPpUJlRPBGqcDoBjuOQlpYGjuPcXRQKaHvYA5WZdKjMpENl5p3QdpMOlZl0qMykQ2VG8VSowkmhUCgUCoVCoVAoFKdAFU4KhUKhUCgUCoVCoTgFqnBSKBQKhUKhUCgUCsUpUIXTCbAsi9TU1BYfkcpToO0hHSoz6VCZSYfKzDuh7SYdKjPpUJlJh8rMN9i3bx/GjBmDuLg4MAyDTZs22fzbAwcOQC6Xo2fPnk4rnz3QHukktFqtu4tAMYG2h3SozKRDZSYdKjPvhLabdKjMpENlJh0qM++nsrISPXr0wCeffCLpd6WlpXjooYdw2223Oalk9kMVTifAcRwyMjJolDAPgbaHdKjMpENlJh0qM++Etpt0qMykQ2UmHSoz32D06NFYsGABxo0bJ+l3jz/+OCZPnoz+/fs7qWT2QxVOCoVCoVAoFAqFQnESFRUVKC8vFz+1tbUOPf+qVatw8eJFvPnmmw49r6OgCieFQqFQKBQKhUKhOIkuXbogJCRE/CxcuNBh5z5//jxeeeUVfP/995DL5Q47ryPxzFL5ADKZzN1FoJhA20M6VGbSoTKTDpWZd0LbTTpUZtKhMpMOlZlnkp6ejtatW4vbKpXKIec1GAyYPHky5s+fj06dOjnknM6AIYQQdxfCV8jNzUV8fDwuX76MNm3auLs4FAqFQqFQKBQKxU00VzdgGAYbN27E2LFjLX5fWlqKsLAws4kGjuNACIFMJsOOHTswdOhQe4vvMKhLrRMghKC8vBxUl/cMaHtIh8pMOlRm0qEy805ou0mHykw6VGbSoTJreQQHByMtLQ0nTpwQP48//jiSk5Nx4sQJ9OvXz91FBEAVTqfAcRwuXrxIo4R5CLQ9pENlJh0qM+lQmXkntN2kQ2UmHSoz6VCZ8eSWl7u7CM1Co9GIyiMAZGVl4cSJE8jJyQEAvPrqq3jooYcA8LlXu3XrZvaJjo6GWq1Gt27dEBAQ4K5qmEEVTgqFQqFQKBQKxRdo4dbNjOvXkfDhhxjzww/4s05B8zaOHj2KXr16oVevXgCA2bNno1evXpg7dy4AIC8vT1Q+vQUaNIhCoVAoFAqFQvFmCAFzcTXCSy8BJNXdpXEbiw8eBEcIfjt3Dr+dO4fjjz2GnrGx7i6WJAYPHtyoW/Tq1asb/f28efMwb948xxaqmVALp5NQq9XuLgLFBJvao7oAuLIVKNjj9PJ4A7QPS4fKTDpUZt4JbTfpUJlJh8pMAjUFQNUl+JNiwFDl7tK4hSvl5fj25Elxe2Dbtl6nbPoq1MLpBGQyGVJSUtxdDEodNreHXgMUHQHUUUDMYKeXy5OhfVg6VGbSoTLzTmi7SYfKTDpUZhKpzAHLsIiMjAQYg7tL4xY+/Osv6EzWr74yYIAbS0MxhVo4nQDHcSgqKmrxi7Y9BZvbQxXJ/60tBriW+bAWoH1YOlRm0qEy805ou0mHykw6VGYSUUeCEIKq6ipw+hp3l8bllFRX4/Njx8TtbtHRuL1jRzeWiGIKVTidACEEly9fpmGpPQSb26M0re4HBkBb4vyCeTC0D0vHI2RWWwxwevddXyIeITOKZGi7SYfKTDpUZhIJTAJRhqG8rBzEUOvu0ricz44ehUarFbfnDBgAhmHcWCKKKVThpFAENJnG/2uvu68cFIo9aLKBjGVA9vfuLgmFQqFQ3AGr5P9y2saP8zGqdTp8+Ndf4nZCSAgmdu3qxhJR6kPXcFIoAoZq4/9U4aR4G0KQiMpL7i0HhUKhUFxLTSFACEjiQ8ivOoOogCR3l8ilrDpxAteqjIGSXrz5ZihkMjeWiFIfqnA6iaCgIHcXgWKCTe1hMFnzQBVO2oftwK0yUwTX/Q1xXxnsgPYz74S2m3SozKRDZWYj1/4ESv4Fom5FUHAC0IJcSfUch8UHD4rbkf7+eLgufyXFc6AKpxOQyWRISEhwdzEodchkMrRv377pA00VTn2F8wrkBdgsM4qI22XG1qUPMHhPsAi3y4xiF7TdpENlJh0qMxshhF9SAUAWlIj2rVqWdfOn06eRXVoqbj/bty/8FQr3FYhiEbqG0wlwHIf8/HwaWc1DsKk9CGccqHd6Cmj3oGsK56HQPiwdt8tMWINsqOEHIF6A22VGsQvabtKhMpMOlZmN6EoBXTnAsOBqilCStgpcabq7S+USCCFYdOCAuB2gUOCpvn3dWCKKNajC6QQIIcjPz6eR1TwEm9rDNKKbMtz5hfJwaB+WjttlVnHe+D/nHREK3S4zil3QdpMOlZl0qMxsRFi3798apDoftXmHQaquuLdMLmLbhQv4t6BA3H7shhsQ7ufnxhJRrEEVTgoFALg66yarAFi60JzihZhGJfQit1oKhUKhNIM6d1oEJAAyFf9/C4lS+38m1k0Fy2JW//5uLA2lMegaTgoFABShQNfXeMtQ8XGgLB0I6w6Eprq7ZBSKbZhGWW6BOdgoFAqlRSJYOAMSgcpc/n9O57biuIpDly9j7yVjVPYHundHm+BgN5aI0hjUwukEGIZBeHg4TTjrIdjUHgwDyJSAIgiovca7J1bluq6QHgbtw9Jxu8z0dSHh288A1NHuKYNE3C4zil3QdpMOlZl0qMxsQFcOaEv4MYx/PBiZCn5+fmCI71s4Ta2bDICXbr7ZfYWhNAm1cDoBlmXRpk0bdxeDUgfLsmjbtq3tP1BF8X9bcGoUyTKjuFdmhBgtnMpQrwmJT/uZd0LbTTpUZtKhMrMBmT8f5LC2CJCpwMrUCAkJAYjeLcUhhODH06dRrddjYteu8HNStNgz165hc0aGuH13Sgo6R0U55VoUx0AtnE6A4zjk5OTQyGoegk3tUZEJXN7Eu9OqIvl9LVjhpH1YOm6VmaGGj7QMADLvCZhA+5l3QttNOlRm0qEyswFWDgS1ByL5yKwcI0dZWRk4fXUTP3QOL+3cift/+QXTN29Gt88+w87MTKdc5z2TvJsAMGfAAKdch+I4qMLpBAghKC4uppHVPASb2qMmHyg5AVRmA6oIfp+2DDD4vluKJWgflo5bZWaoMv5/dStQft76sR4E7WfeCW036VCZSYfKTDqEUaC6utotY5cdmZlYeuiQuH2xpAQjvv8eUzZsQGFlpcOuc7msDN//+6+4PTgxETdRr0KPx2cUzn379mHMmDGIi4sDwzDYtGmT2fcMw1j8LF68WDwmMTGxwfeLFi1ycU0obkFwR5SpAbk/IA/gt7VF7isThWIrihCg4xP82s3if4Dqq+4uEYVCoVCcib4SyNthPsEY0A6FkQ+BtJvm0qJcr6rC1HrjboG1aWlIWb4cK48fd8jkwQd//QW9idX7FWrd9Ap8RuGsrKxEjx498Mknn1j8Pi8vz+yzcuVKMAyD8ePHmx331ltvmR33zDPPuKL4FHcjpJEQ3BEFt9qaa+4pD4UiBVYO+MUAQR35bZoWhUKhUHybykvAtYNAwe/GfawcHOvPB0F0EYQQPPrrr8jXaMR9PWJiYBpJoKSmBjP++18M/uYbnL1u/3KloqoqfHnsmLjdMzYWI9q3t/t8FNfhM0GDRo8ejdGjR1v9PjY21mx78+bNGDJkCJKSksz2BwUFNThWKgzDIDY2lkZW8xBsag9R4VTzf1WRQE2BY3JZEQ4oOw0Etuetp14A7cPS8QiZCf2X8w6F0yNkRpEMbTfpUJlJh8qsCYT8m/4J4i53yGzl8ePYePasuN0lKgqHZszAyYICPPrrr0grLBS/23fpEnp8/jleHTgQrw4cCJVcmhryyd9/o1JnTPkyZ8AA2j+8BJ9ROKVQUFCALVu24Jtvvmnw3aJFi/D222+jbdu2mDx5MmbNmgW5lRuitrYWtbXGfHcVFRUA+NmeqKgoEELAcRxYlgXHcWauBAzDgGVZGAwGs3Na28+yLBiGsbgfQINF9db2y2QysVz199cvo7X9Qhm9qU5CexgMBst10lWCBcAxKhCDAYgZAcSOBsOyYIHm1algF9jrB4DA9uASpjisTk3tb247CTIjhNC+Z2OdBJkJx7qsTpXZYKtzwdQUgCMcoKvi+7ED6tRY2R1RpyiTyIL0uecddar/TPWFOjm7nWx5D3lbnVzRTvXHUr5Qp6b221onRpMFEA5MQAIglN2gRZT+KEiuDogfC2Klro6qU8a1a3hu2zZxv4Jlseaee6BkWfRp1QpHZszAB4cP4+19+1Ct5yPnag0GzN+7F+tOncLnd9yBQYmJNrVTpVaLZYcPi9tJoaEYl5zs8fdT/e9bKi1S4fzmm28QFBSEe+65x2z/s88+i969eyM8PBwHDx7Eq6++iry8PLz//vsWz7Nw4ULMnz+/wf4zZ87g0qVLCAoKQkREBNq2bYvc3FwUFxeLx8TGxiI2NhbZ2dmiogoA8fHxiIiIwPnz51FTY7RSJCUlITg4GOnp6WadNzk5GUqlEmlpaWZlSE1NhVarRYZJ2GiZTIbU1FRUVFTg4sWL4n61Wo2UlBSUlJTg8uXL4v6goCC0b98ehYWFyM/PF/eHh4d7VZ1ycnJQUVGBoKAgBAcHW6xTvDYfEWog/1opCi8br+uIOgVqLiLBXw9ZxQWvaafq6mpRZu3bt6d9z4Y6EUJQUVGByMhIdOnSxaV1CtQcRrwqG6qAcFy7dg01JXKUlKV5fDsJMuvcuTOioqLoc89L6pSRkYHr168jKCgIDMP4RJ2c3U6ZmZniM9XPz88n6uTsdiovLxdl1rZtW5+ok6PaieFqEF10CgwhiEqJh7amhq8Tp0XYlV+gUqsQ2+ZOVGiqnVaniKgoTPzxRzOL4+s33YSesbE4e/asWKfRQUG4/cEH8fK+fdhhErU2o6gIQ779FpO6dkWiTIbW/v6Iq/vc2LNng3Zan52Nompj9N2JbdvizOnTHt1OAG/kogAM8cHwXwzDYOPGjRg7dqzF71NSUjB8+HB8/PHHjZ5n5cqVeOyxx6DRaKBSqRp8X9/CeeXKFXTp0gUXL15EaWkpunbtCrlc3mJnJj2lTnq9HqdPn0bXrl0hk8ks1+n8J2C1xeDaTQXxN+b9ckidaovBXlgOMHJwnV8BGNbseHvq5Ox2MhgMoswUCgXtezbUyVRmSqXStXW68ivY0uNg/OPBVeYA6lYgHR5tdp2aKntz6yTIrFu3blAoFPS55yV10mq1Zs9UX6iTs9tJp9M1+R7ytjo5u51Mn6nCWMrb62RKs9qp/CyYnPWAKhJM8jPifoNej6K9zyI6Khqy1FdAZP5Oq9P8vXvx1r594r7BCQnY+eCDkMtkVsu+5t9/8cLOnU1GrW0VGIh2oaFICAlBYmgo2oWGYsH+/cgpLwcARAcEIPPpp8U8nx7bTgByc3ORmJiIy5cvo00Ljqbb4iyc+/fvR0ZGBtavX9/ksf369YNer0d2djaSk5MbfK9SqcwU0fK6G0HohMKLWNhnCZlM5vL9QtnqY62MUvd7Wp1kMpn4vXBMg7J3egIwVIOV+QNs3Xlyfgaq84DEKZCpwu0vo18EwCoATg+ZoQKwcC5PbCfh+sL6CNr3mq6T6f8urROpBcAAqnCwVbkA0QL1fuep7SS8zKWWUep+X+97tpTRkXWq/0xt6nhb9/tyOzX5HqrDm+pkaxntrVP9sZQv1Mkh+6tz+cnroCSg3juaMEowLANwWjDyAKfU6dDly1iwf7+4HapW49tx4yCvu5a1sj/Qowdu79QJc3buxNfHj1s8BgDyNBrkaTQ4mJtr8fvn+/VDoFptV9mb2u/oPmbt+5aGz0SptZUVK1bghhtuQI8ePZo89sSJE2BZFtHR0S4oGcWtsApAEcxH+xSovQ7UFgG1zYhUW3OdjySnCK47J416S3EC+ro8nIHtgZRZQMfH3VseCoVCoTiP2rpAPAEJDb4iDG/1g6G2wXeOoKK2Fg9s3AjOxDr4xZ13Ij4kxKbfh/v54au77sLeadNwS9u2UEsMHBSkVOKJPn0k/YbifnzGwqnRaHDhwgVxOysrCydOnBB9tQHeAvnTTz9h6dKlDX5/6NAhHD58GEOGDEFQUBAOHTqEWbNm4YEHHkBYWJiksjAMg/j4eNEyRHEvdreHKhKozucVTzS0cNtEyXHg2gHjds01INjOc7kQ2oel41aZCXlkFUGA0raXvidA+5l3QttNOlRm0qEya4TEBwBtsTFneB0MwyA0PAoMahwTZd8Cz27bhoslJeL2g927Y0LXrpLPc2tCAvZNnw6OEBRoNMguLUVWaSmySkqM/5eWIqeszCzv5jtDhyK0nnWT4vn4jMJ59OhRDBkyRNyePXs2AGDq1KlYvXo1AGDdunUghGDSpEkNfq9SqbBu3TrMmzcPtbW1aNeuHWbNmiWeRwosyyIiIsK+ilAcTpPtoa8E8nYCikAgdphxv5CLs9b+nFGoqnMHUYUDtcXGWUkPh/Zh6bhVZoY6C6fMO9LuCNB+5p3QdpMOlZl0qMwagWEAVUPZsCyLgKAwfrLcCQrnz+npWH3ihLidGBqK5bff3qxzsgyDVkFBaBUUhP7x8Q2+N3AcrlRUILu0FFH+/uhsEtmc4j34jMI5ePDgBouF6/Poo4/i0Ucftfhd79698ddffzmkLAaDAWfPnkXHjh2p77YHYDAYcP78eevtodMAJSf4mUJHKpyEA6qv8v+3GgWwSkDtHe7ZTcqM0gC3yYwQo0ut3B/I/53fjr2twey3p0H7mXdC2006VGbSoTKTjsFgQGFBEWKCOLAOVjivlJfj0V9/FbdZhsF348Yh2EJQTUciY1m0DQlBWxtddimeic8onJ6GaehkivtptD0Ed0SZn/l+U4WTEHFhvu0XLQA4HSBTA0Edpf/ezdA+LB23yazDY3w/lgUAxcd5q31EP49XOAHaz7wV2m7SoTKTDpWZBbLX8cENo4cA6sgGX1/zG4To5G6AMtBhl+QIwdRNm1Bi0h6vDhyIgW3bNvIrCsVIiwsaRKE0wFD3AJXVWxOgiuCVRH210WVRCoI7rX9rr1M2KV4EwwB+MUBgIj8IEfoxRwdqFAqF4lNwOqDiHFB62hhRv/4hskB+PT/rOJvSR3/9hV1ZWeL2jXFxeHPQIIedn+L7UAsnhcJZUThZBaCKAhgZr3RKtRaJCmdd3qXyDKAyBwjpwiuhFIozEPqxgSqcFAqF4lNUXeaX6yhDAEWoSy55Mj8fr+zaJW77KxRYc889UFA3Z4oEqMLpBFiWRVJSktUcPxTX0mR76K241AJAxyfst04KCqdfncJZmgaUnuLX2Xm4wkn7sHTcJrOaa0D5Gd4FPKSLVymctJ95J7TdpENlJh0qMwtUXuL/BiRYHJuwLIsO0RzY/B2810tI52Zd7mpFBcb88AO0BoO474ORI9HJ04M5EY7PU0rxGKjC6QQYhkFwcLC7i0Gpo8n2sGbh5H9s/4Xb3ssrnYKFU1UXWa3G83Nx0j4sHbfJrPoqkP8HEJjkdQon7WfeCW036VCZSYfKzAKmCqcFGIZBIFsCFB7mxy/NUDgramtxx9q1uFxeLu67KzkZM3v3tvucLkGnAc59DPi3BRInUcXTQ6Ct4AQMBgPS0tJgMJkRoriPJtvD2hpOU5qIgGwRv1ZARB9AXmc5VdcpnLWer3DSPiwdt8nMNEItALDeo3DSfuad0HaTDpWZdKjM6sHpTZbqWFY4DQYDMrMvgyMcwNXafSmdwYD7fvoJJ/LzxX1do6Lwzdixnp8XlVUCre+si59B1RxPgVo4nQR9QHoWjbZH7DAgagDAWLgdaq4BOT/x7hnJTzevECoThdOeqLcuhvZh6bhFZmKU5TqF04ssnADtZ94KbTfpUJlJh8rMhOorvNKpCLSYg1PAwNWtrbQzLQohBE9s2YLtmZnivrigIPxvyhSEqhuZmPcUZEogNNXdpaDUgyqcFAqr4D+WkPsDNYW8csjprB9Xn+t/8bNswcnGYEPKcD4AkUEL6MoAZahDik9p4RjqWTijbgYi+hq3KRQKheL9EAO/REcZ1uiENWHqxikG+xTOBfv2YcXx4+J2oFKJLZMnI57mwaQ0A6pwUiiNIfPnXWL11UBtEeAX2/RvCAEK9/K/6fCoUeFkZfysZE0hbzmlCifFEQgutYKF0wtyb1IoFApFIoFJQIekJpf4cMLEuB0Wzm9OnMDcPXvEbRnD4JcJE9Az1oaxjyegrwKK/+EDJgnxMygeAXVudgIsyyI5OZlGVvMQmmyP/D+Aq9sBbWnD7xjGxBX2um0X1BbzyiYrB9Qx5t+pJZ7LTdA+LB23yUy0cFqIsuzh0H7mndB2kw6VmXSozKzQiHWTZVm0S0rm11lKVDh/v3gRj/z6q9m+r8aMwYj27e0qpluozAbyfwdy/+vuklDqQS2cTkKpVLq7CBQTGm2PkuOArgII6w4gtOH3qkg+f6atSqKYDiWuYWLm2GFAq5GAPMi2c7kR2oel4xaZ1V/DWVsMFB8FWBUQ4/mJuWk/805ou0mHykw6VGZ1GGoAMIBM1eShClWdl4sEhfPfggLcs3499Bwn7ntz0CBM79VLakndSxNRfCnug04bOQGO45CWlgbO5MaluI8m28PQSB5OgFc4Adujy4oKp4Vcm8owQBHs8QGDaB+WjttkFn8fkDSNn+AAAH0lcO0gUHrSteWwA9rPvBPabtLxaZkVHQXK0h1+WptlxumBK1uAyxv4/32R4mNA+v8BeTsaPYzjOKRlXgfX4Un+vWADueXluH3NGlRojQrqtJ498eYgz5+wbEBlNv83INGdpaBYgCqclJYNpze+oKylRREVTokWTrp+gOIK1JH8ehXBpdbLotRSnIhO47sDcIpnoC0FrvwGXPqRj+buDhgWKPobKPm3WalAPBpNNi9fW7yjGAU/blE0fWxZTQ1uX7MGVyoqxH3Dk5Lw5Z13en76k/roq/kYGQC1cHogVOGktGyEQTnD8C6IllBHAX4xgNqGRfOcDqgp4P+3pnAW7AUurefdeCnW4QxAaRpQdcXdJfEuTBVOe/LHUnwDbRlwZgmQ9a27S0LxZUwntoQAZq6GYflUGABg8EGFk3BAVQ7/f2Ciw06rNRhw708/Ia2wUNzXPSYGP0+YAIVM1sgvPZTKS/w7TxXJp46heBR0DSelZSO606qtu7kqw4COT9h2vppC/uWgCOJdZy1RdoqPUht+o00zkC2Swj/5hf8AENIZSJjo3vJ4Kvoqfr2mPBAI783vExROwvETIDK6BqpFIrg4VuZ4Rd5firdiMqmlr3DPQL/oqDEFiC9aOGsKeEVapmoYiNAShAMKdgPQ83Ej2IZDfUIIHv31V/x+8aK4r01wMLZOnoxgVdPrRD0SwZ3WgUo5xXFQC6cTYFkWqampNLKah9Boewizs6yDkhn7twa6zAESJ1sf4IlRb21cE+oG3N6Ha/KN/5eftRxB2MNwi8y0pXyU5YI9xn2MnM/3CgCcZ7vVur2ftQRkKpgpBQ6Atpt0fFZmfq2M6cJ0Goee2maZVZt4wfiihVOTzf/1b8tbcxuBl1l3sNf/5POBW1la8dHhw/jmpHGdf7BKha2TJ6N1sJWJcm9A6AfUndYj8bEnn+eg1dqXcJfiHKy2h2DhtCWlBCG2JVKW+/EvYWsIqVFqCq0f4wG4tQ/rK43/E8Jb8bwAl8tMTInib9zHmEQy9IJ1nPRZ6ST0dS77Yb2bHKTaA2036fiszIR1hXrHLxOxSWamE5Je8MyTjBB51UbLnVanA9g6zxYLkWqLq6sxzyTXpoJlsWHCBKTG2GA99WTaTQPazwACO7i7JM1m3759GDNmDOLi4sAwDDZt2tTo8Rs2bMDw4cMRFRWF4OBg9O/fH9u3b3dNYW2EKpxOgOM4ZGRk+GY0Oi+k0fYIbA90ng20va/xkxT9DZx+F8jb1vwCeYGF0+19WFA4I/vxf4uP8e6hHoxbZCasmaofYdlLAge5vZ/5Mroy/q8yxOGnpu0mHZ+WWWASENaDX37iQGyWmanC6WsutYQAVban+hBkRhhF3Y6GCueiP/9EWa1RTktGjMBtSUkOKa5bYWVAQLxX5qSuT2VlJXr06IFPPvnEpuP37duH4cOHY+vWrTh27BiGDBmCMWPG4Pjx4zb9vqqqCps3b8bLL7+MO++8EzfddBP69++PMWPG4OWXX8bmzZtRWVnZ9Ikaga7hpLRsWBnA2uBCIlPzCk9jkWp15XxYdv+2QMyQpl1qa67RtVXWEBTOsF5AeQY/oCg9BYR7WU4wZyNa6P3N9ydO4V1r5TRwQotFGIRX5gDKCCC4o1uLQ/FRiv8Byk4DIV2BwHauvz7hjJMrgO+51BIDEDkA0JUC6kY8p+rDKgFDZQOFM7e8HB8fOSJudwwPxxM33uigwlIcxejRozF69Gibj//www/Ntt99911s3rwZv/76K3o1kks1LS0NS5cuxYYNG6DRaODn54f4+HiEhYWBEIJz585h165dWLJkCQICAjB+/Hi88MILSE1NlVwnqnBSKLZgS2qUqlx+rYWhBogd2si5Ingl01AD6DU0cFB9CGfiKhoIRPQB8nYCRYeBsJ5UQTdFkJOsnsKpinB9WSieBTHwf8vS+ckyqnBSnIG2mH/3uSsNmF5jTMfS+UXr+bS9FVYORA+043eWXWrn7dmDGr0xVdI7Q4d6Z0Ta+uT8wi8liRoIKEPdXRqLVFRUoLy8XNxWqVRQOSlAE8dxqKioQHh4uNVjJk6ciF9++QU33ngj5s2bh+HDh6NLly6Q1esPBoMB6enp2LFjB37++Wf06tUL9913H3744QdJZaIutU6ifoNR3IvV9ig9BVzdDmguWv5eQFk3gNdXWQ/9bmv+TVYOKMP5v6Yzsx6G2/qwodpo+ZX781ZOVlFnZfbs2WuXy0xvYQ2nl0GflU6i4+NAuwf4/7XFDj89bTfp+KTMxMB7cocHDQJskJlgyVeG8RFyWR+UsURkMplR4TSx+J65dg2rTpwQt2+Mi8O9Xbq4uHROwFDLW9mLjjplvbqj6NKlC0JCQsTPwoULnXatJUuWQKPRYMKECVaPYVkWR48exV9//YXZs2cjNTXV4v0mk8mQmpqKF154AYcOHcLRo/bF1KAWTicgNA7FM2i0PTSZQPFxfsAe2MgaBpmSnzXTlvJWTnnbhscI+SJtmelt/zA/E+uhD0e39mFDrTG0PsPybZP8rMdbgt0iM4OVNZzl5/gQ8QGJQHAn15ZJAvRZ6WTU0fxfbQmf19ZBg3HabtLxWZkJCk3hn3ze5JRZDju1TTIz1PCTkR5q1Wo2tcW8h4IyzKYUV6LMsv7ld5hYOP+zezc4k9zMi267DYwveAxVXeat3Mow6+noPID09HS0bt1a3HaWdXPt2rWYP38+Nm/ejOjoaKvHSbVQCvTs2dOu31KF0wkQQlBeXo6goCDfuJm9HEIIKioqLLeHMDsrsyEtiirSqHAG1FM4OQNQfZX/388GhVMe0PQxbqRRmTkbVTjvGmXyYvR0ZRNwk8xihwHhfRoG66jMBq4d5P/3YIXTrf2sJSAP4r0DOB2/BsxBrta03aTjszIzDUym0zg0LoFNMgvuBHR9BdBkAVe28JMsEX0ccn2P4PpB3nIXfWvjS3XqEGXWahSYViNEBexwbi42nDkjHjcsKck3AgUBXpN/MygoCMFOTjuzbt06PPLII/jpp58wbNgwp15LKp5pXvFyOI7DxYsXfTManRfSaHsIQVdsWffR2DrO2kJ+UCdT+8T6OY/ow5YGGPpKY4h4D8MtMlOG8S/Z+pFIvShKrdv7mS9Sdga4uBq4foifwAGA2iKHnZ62m3R8VmamuX6JwfhOdcSppchMW8JHk68477DrewSiy3CoTYeLMlOE8inYZCoQQvDKrl1mxy267TaHFtOtCHlKW3j+zR9++AHTp0/HDz/8gDvuuEPy70+cONHAcrl9+3bceuut6NevHz766KNmlY8qnJSWjRQLp388ENIZUFvIVSWu32xt2+yuvhLI+RnIXGVuyaNYp+oKcOZ94NKPAKdv+viWDOsdCifFSdQU8IOw2iLj+nMnrOOkUBpEhdU7fh2nTbBC7mHPXucvGYkKpyW2Z2ZiT3a2uD2ha1fcEBfXrGJ5DAat0bssINGtRXEkGo0GJ06cwIm6NbdZWVk4ceIEcnJyAACvvvoqHnroIfH4tWvX4qGHHsLSpUvRr18/5OfnIz8/H2VltscJefnll7F+/XpxOysrC+PGjUNWVhYAYPbs2fjyyy/trhNVOCktGykWztCuQMJEPt9YfTgdn/vJ1kh9rJJf5F55yZgChMJz/TCviBf/Y75fHcu7Iusr+cibFKBwPz+rXz9HqZdYOClOwjQHp+Bx4UALJ4Uiwir46KACugrXXj97LZC1xvge9fDAcpIgxHgvK0Kl/bbyElCwG1zZGbzy++/ibjnLYsGQIY4ro7sR12+G+tQ63qNHj6JXr15iSpPZs2ejV69emDt3LgAgLy9PVD4B4Msvv4Rer8dTTz2FVq1aiZ/nnnvO5muePHkSAwcaIyJ/++23kMlkOH78OA4fPox7770Xn3/+ud11oms4nYRabYPFjOIyrLaHFAtnY0TdDET2N6YiaApWwbtD1hYDtdeMQXI8CLf14ZpC/mVZP4gTKwMibgTy/wCKjgBh3d1TvkZwqcw4HZBf5yYVWk8WQn/mPF/hpM9KJ6A1GaT6teJzA/tZ8MxoBrTdpOOTMuswk/978Vs+2ruDLZyNyowQ/pqc3jgR7EsWTkMV/5xnGEnBcNRqNa+IFe7F/opwnCwoEb97pFcvdIzw/mU/IpyWXzbgH+/ukjiUwYMHgzTi/bZ69Wqz7T179jT7mmVlZYgw6Rtbt27F8OHDERnJLycbPnw4/ve//9l9fmrhdAIymQwpKSm+GQLdC7HaHoQzvpxsVTgJAXTlll9qDMOHhrcVVRT/t+aa7b9xEW7tw8JMtaXASmG9AUbGuzALUYE9BJfLTEiJwpiEwBcL4x0WTvqsdBK6Uv6vMoRfxxXc0aHRG2m7ScfnZSYEdnOghbNJmek1vLLJMMYYC14wyWYzgjutPMjmsYUoM7kfDITDT2nHxe/8FQrMHTTICQV1IyGd+Sj2re9yd0m8nlatWuFMXWCpvLw8HDt2DCNGjBC/12g0YFn71UaqcDoBjuNQVFTke8EBvBTr7cEAnWcDnZ60PVl01rf8OsKKC8Z9xM52VtcpnLWep3C6tQ8bGlE4FYG8azPAWzk9CJfLzGCSg7P+umEvUTjps9IJCJNiAKAIafxYO6HtJh2fl1lAIhDW05iKxwE0KTPR3TTYmIvYUOs7cRHE9Zu238eizBg5jl3NQ0lVufjd8/36oVWQ50d8twuaf7XZ3H333fj444/x7LPPYuzYsVCpVBg3bpz4/cmTJ5HUjMjGVOF0AoQQXL58uVFzOMV1WG0PwU1FHW17PkxhAGcaqbZwH3D2A+C6RAVI5bkKp1v7sJA83FrqmIh+/N/SUx61/tXlMmts/bEyHOj4BNDhMdeUxU7os9IJ6CuNVh953eCy7CxQsNc4gG0mtN2k45My05UDF74GstcB4b2A+LEOTcPUpMyE/qwINQYNIhxAfCSonDqaT30V1svmnwgyq9QDe3MuQcXwsghTq/HSgAHOKql7MGjtn/CnNGDBggW455578N1336GwsBCrV69GTAy/FKO8vBw///yzmcVTKnQNJ4UiBdEqaaJwVuUa10xJwYNdat1KYxZOgI8E7N+aj8RZlQsEJ7uubJ6E3sTCWR9W7vA1exQvwVDFuzcyrHHW/9p+3gVdHe1TgTUobkZfxT+D3RWDwDSCK6vkXStlKoDxkaGtOso45pDIqrQzqNRqoWT45Rav3XILQn1tDfH1A8D1v/gcpVE+pky7gcDAQKxZs8bqd7m5uQgIsD+HPLVwUlouNYXA1e1A8THbf1M/FychQHXdWkJbI9SanouR8QqDQSvtt74KpzPKwprCCfDrNVJmtVxlEzAqnDILCiel5aKOBjq/ACSbRCekkWopzkBw2RfSMHF6+yZf7UVcqxxat44znH9v2JKazIcprq3F5//8CwBQwoA2wcF4um9fN5fKCWiyeRfq5gZ9pAAAHn74YRw+fNjidyzLIiMjA48++qjd56cKp5MI8lU/eS/FYnvUXOMTo5f8a/uJTBVOQvgBnL6atyhZys/ZGDIl0O11oNNT/P8ehlv6sKGGt87IlEYXKUv4xTSukLoJl8pMWMNpbf3x9b+AvB3G9XweCn1WOgnTZQJiLk7HKZy03aTjczITUpDIVIC2BDi1ADi33KFrKBuXGcMrG05aq+x2NNlAdQHA2Rj9vo7vsrNRrOV/o2QMmD94MNRyH7H6CnB642S/D+XfdCerV69GZmam1e+zsrLwzTff2H1+H+uBnoFMJkNCQoK7i0GpQyaToX379g2/ENfASZgdU4bxVklOxwcsEB54fnH2LVq3de2oi7EqM2ejCOKtM4TYPktdW8zPbLsZl8ss/AYgsJ11hbPoCC+b4BSHRih1JG7rZy0N4f6oLXbI6Wi7SccnZWaaVkxe51bL6XhF1AFWpyZl1voO/iMouNcP85PAETc6NHiRWyAEuLSW9/hJfsbopdAEuRUVWHv+PDiDP74u742EsGgs6mEhd7i3U5XLK52KQD5mAcXpXL16FX5+NgbYtABVOJ0Ax3HIz89HdHR0s0IIUxwDx3EoLCxs2B7iy1LCDcSw/OCt5hpv5azK5fdLdaf1cKzKzFXYomwSDsj6DtBk8QFy3Lxm0eUyUwQZUxFYwgsi1bq9n/kiV7bwgciiB/ETEoDDLZy03aTjkzIzVThZBW/pNNTygd8coHDaLDPhfVF2Cqi8zPd7b1c4DdXG5SUSLLhv7N4NrcEAQIYr+hAsHzoWcl/pb6ZUXuL/BiS2eBfq5rB582Zs3rxZ3P7yyy/x+++/NziutLQUv//+O/r06WP3tajC6QQIIcjPz0dUlH2LvSmOxWp7cCYvSymEdudfBorg5iuclTlA3jY+mmTiJPvO4QS8og8zrDFgTtERoM0YtxbH42TGer7C6XEy8wWqLgPV+UCkSRANwTqi09SteWrEXd0GaLtJxydlJrjUCssf5EF8/9JXAIhs9ukly0woB2chT7a3IUbgDbQ5B2daQQG+/9e4RKh/mza4O9lH4xxUZvN/A6g3YXNIT0/HTz/9BABgGAaHDx/GsWPmcU0YhkFAQABuvfVWvP/++3ZfywenPSgUG9Hb4VILANG3AK1G8FFmAxIA/zjAr7V9ZWBkQNVVo2tuS6f4HyBzle0pZiLqAiGU/mtsz5ZC8TGg6G/rida9wMLpVRAOKNjDW9Q9GSE3oWnuPpnKGEmUBg6iOApC+PX2wrNG6GN6jfOvXZ0HnPsEuLzJuE+YSDH4gMIp5hgNtelwQghmbd8O09Wzn/QLB1O41/feAZyen1gD6PrNZvLqq6+ioqICFRUVIIRgxYoV4rbwKS8vR15eHn777Td06mR/2iNq4aS0XDg7XGpNYRggblTzyiAEIdJpeIVJbr9/vE9Qc413lbHVYuzfFvCL5S06JceBqJudWz5PonA/Pwvu18qya60wCOR8bLDhLspO8wonAHSf586SWMegNU681HfDazuBj2isDHN9uSi+Scwg/iOsoRTyvlqbBHMktcX8+8J0wliMlusDCqdpyhcb+PbkSezKMk6GjWrfHr2Ys0BBLe+V5UuRXIkBiLoFqMk3jqEozYbjnJvTlFo4nQDDMAgPDwdD/co9AqvtYbDTpZYQPrl65eXmF06mMloiaj0nH6fb+nBTOTjrwzB88BwAKM9wTplsxOUyMzSRFsULLJxe9ax0xSC6uQhWEZm6odtsQFtAHWlfcLN6eFW7eQg+LTOhTsLEl4MsnI3KTEiJYmoBFC2cnvvMsxnTlC9NUFhZidk7dojbKpkMH44cyecmBQDOx9KuyVT8REfCRLp+04vwGYVz3759GDNmDOLi4sAwDDZt2mT2/bRp08AwjNln1Chz61RxcTGmTJmC4OBghIaGYsaMGdBopD84WZZF27ZtfScwgJdjtT3ajAM6PQkESXQR4HRA+mIgc4VjUk6o6oIbeJDC6bY+rJeocAJGd2YhN6qbcKnMOL1JvlLvVTi96llpqsBxOveVozEsudM6Aa9qNw+hRcjMPx4I62n/EpN6NCozId+nqULG+pBLrbiGs+l7edb27SiuNi4peXPQICRHRZkonD4gD4rDYVkWcrkcWq1W3JbJZI1+5M1Ir+MzLrWVlZXo0aMHHn74Ydxzzz0Wjxk1ahRWrVolbqtU5jPAU6ZMQV5eHnbu3AmdTofp06fj0Ucfxdq1ayWVheM45OTkoE2bNr79cvESOI5Dbm5uw/ZQBBrXnEhBpuQDBunKgTPvAx0e5ddx2os6Cqg4D9QU2n8OB2NVZs7GHoVTcKnRV7rVLdmlMhOsmwxrPV9pWC8gKNm+Pu4ibJaZroJfswqGn9l2B4zJ61Kv8UzXVHGQGtrwO105UHKCX4saM7hZl3Hb88GL8UmZ5f6X71cxQ/l3YEhn/uMgGpVZPQtgeW0tWAMQCPiGghXRh19a4t+20cO2XbiAtWlp4nZqdDQmxMeD4ziwgsJp8CELJ2cAKjL4+BkemIvbm5g7dy4YhhGVSGHbWfiMwjl69GiMHj260WNUKhViY2MtfnfmzBls27YNf//9N2688UYAwMcff4zbb78dS5YsQVxcQ4WitrYWtbXGB1tFBe9yZTAYUFpaitjYWMjlcrAsC47jQEySITMMA5ZlYTCYJ/S1tp9lWTAMY3E/0ND32tp+mUwGQojF/fXLaG2/UEZvqZNer0dRURFiY2PFWZrm10kBFgBHOBB5GFD3vV11UkaCEA6oKgCp+52728lgMIgyUygULut7jLYCIBwIo4as7hxN10kGJnYYWGUYDBzEtmisrs7oe6YyUyqVzm0nfRU4wgEyP5C6MjWoE+sPKP35sluQuyc8IwSZtWrVqvF20mrA5P8ByPxBIge657kX1AVsTDE4yECIQuxnHvXcM+ghUwSCkweLzxLxeEMNSN4uEJkKJGIgUOfpY88zov4zlb6fmq6TLe8hb6sT0WQDtddBIvoDBoPD62T6TBXGUkLZmZpigHBg5MH46fRpTP7lF4TIDfhp3B0YFNMFbN05vbbvBXWAwb8urZHJ+MK07JVaLZ747Tfj+QB8OmoUyktKoI+Lg5JVgoCA01ebvRe9uu/VXAW5tB6E9QNJeVF0qfXkOtX/3lOYN29eo9uOxmcUTlvYs2cPoqOjERYWhqFDh2LBggWIiODDxR86dAihoaGisgkAw4YNA8uyOHz4MMaNG9fgfAsXLsT8+fMb7M/IyIBCocDp06cRERGBtm3bIjc3F8XFxqTbsbGxiI2NRXZ2tqioAkB8fDwiIiJw/vx51NQYXeGSkpIQHByM9PR0s86bnJwMpVKJNJMZLgBITU2FVqtFRoZxXZtMJkNqaioqKipw8eJFcb9arUZKSgpKSkpw+bJxXWJQUBDat2+PwsJC5Ofni/vDw8O9qk45OTkoLi7G6dOnERwczNepoACVmRvBMUpU+vdAeESMpDp1NJQiQAEUFxUjN91YHrvqxASj6HoNtOWlKNekeUQ7VVdXizJr3769a/peTg5irl8EQwiq1NeRlNxGQp06IDY0FtmZmW7re4QQFBcX48KFC+jSpYtz20lVjeKiYtRAh+u6NKfVyWI7ObBOgsxKS0sRFRVltZ3Onr+IiMJCEIZBge4kklM6u6lOQ5Cbk4PiMxdsayeXP/dCkdL5RZRcv47LJucJCgpC+3ZtUVlZCY2mAIXav8GxfnY/Iy5cuCA+HxiG8cq+5+p2yszMFGXm5+fnE3WqyM2GjKvCdV0W5IE6vk5Fhbiacw4GWXCz61ReXi7KrG3btsY6VVcj+noGWKKDJqQWM3/9DQZCUKxj8dyOQ/hukLJF9L33T51CdlmZ+P39SUkIKC1FcXExzpw5gx4hKmhrtcjOSEe1H+cVdWqynWJKoa3VIq/KH6WnTnlFnQoKCkABGFJ/OsAHYBgGGzduxNixY8V969atg7+/P9q1a4fMzEy89tprCAwMxKFDhyCTyfDuu+/im2++MevcABAdHY358+fjiSeeaHCd+hbOK1euoEuXLrh48SJKS0vRtWtXauH0gDrp9XqcPn0aXbt2Nc4s66qB9IUAANLlNTAypbQ6VZwDm7MehqhbgOjBLq+TKyycgsxcZuHU14A5/wlgqALp/ApkCpVX9T1TmTndwll+Btyl9YB/AkjSNMt10lUAJcfByuRA1ECP7HuCzLp16waFQmG1nbisdUB5OgCApLwIVhlkseyeUKem9rv1GXHmfUBbCpL0MOAfb3edtFqt2TPVV5579cvoyDrpdLqG7yEvrxM5tQDgdCCdngOUoZBBD3LqXRAQkC6vAayiWXUyfaaaWTgNWjBZKwFdGR46k4Q16ebjtrNPPolOkZF21ckj+h5XDab6KgzyELMorKZlP5aXh/4rV4Kru058cDD+fewx+MvlxvdQ/n9BStPAxY4EIvq5t06O6ns5P4BUnAcXMwKIvMkr6pSbm4vExERcvnwZbdrYmbPdRRgMBmzfvh0XL15ESUlJA3kyDIM33njDrnO3GAvn/fffL/6fmpqK7t27o3379tizZw9uu+02u86pUqnM1oGWl/MBZGQyGVq1aiU+IAFjB6yPTGY5YqAz9zMMY3G/tTJK3e9pdZLL5Q3bg9TWrX+TAwq12fE2XTO0M+A/CzJFEH8eF9fJEo5sJ4ZhRJkJPv1Or5PSH+j6Eh8FuO6aNtdJXw1ociEjeotriFzR90xlJqnsTey3WJbAdmA7zODzuNb7XjxepwWu7eXXuUTf4pF9T5CZsG2tjCxXZbzPSC3ABDd6vFPqVHYGKDkBVhHMR0b2M1+e4WnPPUuwqkh+zZ2+FJAlNnm8tbJYeqZKLbu1/b76frL4HvLmOhEODDHw96XCn38OERaMTAmG0wFcFaAIb7LsjdXJ9JkqHCeTyQCZH5D8FHZkZmJN+vfibwKZWvRVX8GR498jecQs6XXylL6nyQUurYPMvzXQYWaD4wnD4PEtW0RlEwA+uf12hPr7g+M443soehCYiH6QqcIbvCe8su8RDqjKAQMGsuD2XlMna997GkePHsX48eORm5vbQNEUaI7C6SMr16WTlJSEyMhIXLjAu0bFxsaisNA8aIter0dxcbHVdZ/WYFkWsbGxVjszxbVYbA97U6KYogyxqGzaDSH8gngPwK19mLFj0XpNPpC9Bsjf6fjy2IhLZSb354MmNJav1DRKrYc6stgsM9No0EJgKVdTdZlPvVP0N1B0xD1laAzCARkfAxdXW4/SqeKXkKC2qFmXou846ficzEzz+woRnBkGkNcFKXNAapTGZFat0+HJLVvM9qkYPW7xu4TCnN1WB8xeQRMpUT786y8cN3ERva9LF4xJTgZQT2bqKCAg3neC61Tn8QGQ5H6AOsbdpfE5nnzySVRXV2PTpk0oLi4Gx3ENPs1Zj+ojTz7p5ObmisEqAKB///4oLS3FsWPHxGP++OMPcByHfv36WTuNRQwGAzIzMz12oXBLw2J7CAon2wyF05FcPwyceQ/I/93dJQHghX1YcDvSlvApQ9yAx8lM6NvEABD3yKQpbJIZIeY5MIUIva7GNBWKg/IMOhRdBa9IVl6GmA6hPg5SOD2ur3sBPiczYVJDpjKfeBVycTogb21jMlv055/ILCkRt6P8/VFLeO+S8uoKnMjLa/b13UYjKVEulpRg7u7d4naoWo1lJgEzfa6fmVKZzf/1b0vzbzqBf//9F3PmzMGYMWMQGhrq8PP7jMKp0Whw4sQJnDhxAgCQlZWFEydOICcnBxqNBi+99BL++usvZGdnY9euXbj77rvRoUMHjBw5EgDQuXNnjBo1CjNnzsSRI0dw4MABPP3007j//vstRqhtCtMFxxT306A9DHU5q9yUQqMBrIJ3C631nNQoLu/DpWlA5krg2gHpv5UH8hY9QgBtcdPHOwmXyazsDHD9CFDTSO5RVmEcCHpwLs4mZWao4pVmAXdZOD1e4Szl/yqCrQ/GlHUKpwPuEfqOk45PyYzTAjJlQy8hQeF00D1iSWaXMzagJO199FTxSmVsYCC2TpmCWlLnqg+CH0+dcMj13YKV9EaEEDz+22+o1hsnEBcPH47YQPPUV6LMqvOBa4d4zwxfQJPN/w1IdGcpfJY2bdo41TPAZxTOo0ePolevXujVqxcAYPbs2ejVqxfmzp0LmUyGf//9F3fddRc6deqEGTNm4IYbbsD+/fvN1mCuWbMGKSkpuO2223D77bdj4MCB+PLLL91VJYoz8TQLpyqK/1tzzb3lcCe1RUBlDm+llArDGK2cLUGGxceAq1uB6lzrxzCMuVutt2LqTtvpaSC0h3vKYWol1nmgwqmti1ZpxQ0PABDQFuj0FND+EZcUieLD+MUCXV8Dkp8z3y+61DpHuSaE4JsjvyOcqYAcfFCWD0aOxI1xcegZFw8O/GTLxvSTZmscvQorLrVr0tKw0ySK7q0JCXi4bsxrkcocIG87UHLS8WV0B63vAOLHAsHJ7i6JTzJnzhx89dVXYjwaR+MzQYMGDx7cqGa+ffv2Js8RHh6OtWvXOrJYFE9FsHA2Zw2nI1HXKZy6ct5VSaZq/HhPoraId/1hm/k4ESxX9q43UUUCVblAbSNWP19BcCuVNWGhl6kBfZVvKJz+cYA6svFjnUl9C6dJcCuPQFencFpwwxORqQBZlGvKQ2kZ1I9jIBdcap0zKbM2LQ25RTmIlQGlnBrDk5IwsWtXAMDk1O7IO/IT/BgdrleU4M+cHNyakOCUcjgVXcPJo+tVVZhlMo5VyWT48s47wTb2DJLVudZzWicU0g0oQwFlT3eXwmepqKhAYGAgOnTogPvvvx/x8fENAh4xDINZs2bZdX6fUTg9CYZhEB8fL0b3pLgXi+0R3hsI6gAwHnILyNS8K5KuAqi91ngwGBdgcx8uSwcu/QhE9gfiRjbvooLCKWuGwgm4TeF06X2vFyZM/Bs/jq2buPBQhdMmmcn8gdCugDLc+jGuwNTCSThe6fekYBziILURhdNB0HecdFqMzPzjgPBefFCzZlJfZiXV1Zi9YwemK/nnWQ388cntt4vfT+zaFe8dlsGP0UHN6rE2Lc37FE5DjfH5bjJ5NHv7dlyvMq5f/8+ttyI5suEEnJnMWB9TOClO5cUXXxT/X758ucVjqMLpYbAsi4iICHcXg1KHxfaQqT3HuimgiuIVzhr3K5w29+Giv/m/1w85TuFsjoUTcJvC6dL7XrBwyptQONuM5f8qw5xaHHuxSWYB8fyn5jpQsJu36prkX3MZphZOgLfgeJLC2UigETPK0oGK80Bwit2uafQdJx2fk1npKaDkBBDUEYg0CawYmMR/HEB9mb3+xx8oqyqFWsVP/jx28zB0NPm+VVAQEsNjUFJ6CSrGgJ/S07Fs9GgovSQtBQA+1VXb8fxYoM7TaWdmJr7791/xkK5RUXh5wACLPzeTmS8pnCUn+TFCcLIx+BnFoWRlZTn1/FThdAIGgwFnz55Fx44dvSb/ji9jMBhw/vx5z28PdRSguchbON2MzTJrNQI4/4XRdac5NFfhDIgH4u8B1NHNL4sduKyfcQaTCJFNKJx+nh06XpLMdKVAwV6+Tu5QOBPuB7haoOys0SPBk5CpAUVg42s4AX5dV/FxXnG3U+H0mmeqB+FzMqu9DlRccOpklqnMjubl4fOjRxEl4599/uoQPDdwcIPfRKZMxVvbtqOcU0Gvq8aOzEzc2amT08rocFgFEJoqblbpdHjcJP0LA+CrMWOsKtFm/UzwcPEFhbP4KB+BWx5IFU4nkeBkbwCfCRrkadTUeKYLW0ulQXsUH+MHr54UYMa/De/m6yEPU5v6sBBFz6BtaAGSiqGZCqc8AAjrzgezcBMuue8F66ZpUCAvpkmZ6av59ZKCcq13U1oUuT8/uI7qz7sMNmVddjVt7wU6v9i0dclBqVHoO046PiUzMZe1hXgDnI4P/ka4Zl+mpqYGeo7D41u2gAAIZfnrjunWFyp5Q5vJHak3Q8MEQQ9eIVubltbsMriT13btwkWT9C9P9umD/vHxjf5G7Ge+YuEkHB9xFwD8pGeNoHgG1MJJaZmUnOBny9TRxoA97iY01Wxm0+MxaOtck5X8/9oy+4O6EA5gFACj9Sw3RU9EDHjl13TQmsocPneZOhYI9qJZflMyV/BpPOLH8dv6Ks8L2ONNOEjhpLRwrEV6JwQ4vYhPZZQyyyFrij/5+2+cyOcVDgKgbUwndG7T1eKxoWo17ujYERvPngUAbM7IgEarRaDSAV44rqAyh5etXyv89+JVfHT4sPhV66AgvHvbbbafy1eCBtVc4ycxZCqPmZD3FZKSrE9QMgwDtVqNhIQE3H777Zg5c6ZZZg+pUIWT0jLRe1iUWm8kdyNQfZVXNgE+aIm9CifDAp1n84OV5lCdD1Re4icSAts171yeiiIUaD8d4PRNHgpNFr/uMfwG71U49eX8hISQOogYeNdWV9+7+bv4coSm8u7fMj8+QIq3IQRfEixQ9aOMUii2wAlu/fXuQ4bhXbu1ZXxqlGYqnAXV1Xhz715xu5Btjdtvfxqol3tSpCITzyVWIi2zCBd0EajS6fDfjAxMTvWSydzrfwFl6cgPvgXTNh01++rLMWMQLGXALw8C2j1otHR6K9VX+L9+cXSi0cF06dKl0UBmVVVVSEtLw7Zt27By5Urs2bMHwcHBdl2LKpxOgGVZJCUlgWXpi9wTsNgenOAO1ERaCXegr+YHgW5MjdJkHyYcr8wYaniLpL7SmDusOTT3ZVKWDhTuAyJudLnC6bL7Xqa0PQKkMBjkPNOVr0mZGWqNExrKcKM1XV/leoWz6AhfHq4WKDrKK55tx7u2DNYoPwdc/R/vkt/6jsaPFVIYcXo+0JBKevRf+o6Tjs/JrDGXWnkQr3A2MzUKy7L49OJFaLRGC93C225DrDVlEwA0F3GzKgvd/CpwQcdbw9ampXmPwqkrhYFwmLX7MEpqjMtUXuzfH7d37Njkz836GcMAQe2dWVrXUH2V/0vdaR3Ob7/9ZtNxmzZtwoQJE7BgwQK89957dl3LR558ngXDMAgODvb98OdegsX2EF+WHmbhvLQeSP8/oPyMW4vRZB+uzuNlKFMBCROBpGlAcGeXltEiboxU65H3vdC/PTgtSqMyE3JwCq7bwjpOgxvWcQprlIUgKXrn5Bm0C20J/xECbzUGw5hYOYvtupxH9nUPx+dkZs2lFuADuwC8hbMZbD1/Hv+9cEHc7tu6NR694YbGfyRTQcHKMCi+lbhre2amWUoRj0Zbij3Z2fjjinHdZr/WrfGOja60PtfPAKpwegBjx47F9OnTsWHDBrvPQRVOJ2AwGJCWlgaDweDuolBgoT04ndEd0dMsnELSbDcHM2qyD2vqwmcHtAMC2gKBic0LolKeAWSuBAr22H8OwKhwukF+LrvvKy8B148AVVeaPtbDFc4mZSYonIo6Fx6hj9miWDkSwhkDoAgKp655g2mHIuTgbColioCwDkr4nUR8/h1HuOa799fD92RWdz9YmrQVIjg3Y1KmSqfD01u3itssw+DzO+6A7Nwy4NynxjRA9amLzDqkrVHh1HMcfk5Pt7ssLsOgxYXCXOzPyUEZx9cjRKXCunvvtTm1S4N+VnKSd9N1V7C15sIZjO9zb1zC4EP07t0bubm5dv+eutQ6Cd95qfgGZu0hDL5NEyN7CsJA0E7LgyNptA9rLvJ/HeW2WlvMB0uwdcBsDUHh1Ffyrsly104ouOS+LzvL5z2NGgD4t278WA9XOIEmZFZf4Wwzjnc3V9i3hsRuTCMwC5GZPcnCKSiOtq6Xi7uDTyHEKuy+pE++4wjhvUwqs4AOjzo8QIk7ZZZx/Trm792LIKUSS0aMQFAzgn8AADo+YT0KrWDhtHNShhCC57dtQ3aZcULkmb590Ss6HLhWyu+wNllc5+LbLTIE0QG1KKzkJ6fWpqXh8RtvtKs8riK/5Ao2nj2DGiJHLeHvzZV3343E0FBJ5zHrZ/m/8+0QkOB5kbVtgZUBXV4GagqMz16KWygqKoKfn/1jKmrhpLQ8TN1pPc3tRHRF8uDZSE4PVOXw/wcm8evaiv8BCv+0/5zNTYkiIFMaB91ucKt1CYI7qS2DB9bzFc5GERXOOouJOopXApqhKNkFMQnQJOS5NNTYFrjJFQjWHlsnbBSBrpehN8Aw/P1lqOUnwHyEY1ev4uaVK/HDqVP48p9/8MivvzrmxAxrOehUMyycgrL51T//iPvigoLw1pAhxokVuZ/1GAd1zzwZ0WFiV2Mk2/05Ocgps8+i7woMHIcXt/yISp0OpRxfh6f79ME9nZu5VMUXUqOwCj5tnKeN11oQWq0W69evxw1NubQ3ArVwUloeyjCg05PNzxvpDASFy9Uug1KouswPtBVBvEXRUAXk/pd/GUTexAckkYreQQonwJdJW8YrnAGN5yvzSoTJCJkNCqephdMbU4moo4DQroC/m9tReFawct6yIgTd0WuMCqg7ES2coW4thtdTlm5UNKsu8/lWvZw/c3Jwx9q1KK+tFff9ePo0nuvXDzc3kc/RbtQxvOwkrrkjhOClnTux7MgRcR8L4LPbb+ejs5aX8jsbm1gRFFFDLSanpuJjk3OtO3UKLw8YIKlMrmLBvn3IyMtEhwCg1KBG71atsGTEiOafWJSHFyucFKfxj8nEjiWqq6uRkZGBr7/+GqdPn8ZWEzd3qVCF0wmwLIvk5GTfiUbn5TRoD1bOp83wRASFy+BehbPRPiwPrFMslbwCI/PnZyA5HW+RsiPqpcMVzopMoNa16zhddt8LFk5b1h/LA/kUKpYCe3gATcospAv/Eai6ClRk8EFvwnq4ppCA0ZLJKvg+Lw/krYqeoHByemM0UFstnJyej2qrLQYSp0ieJPLZd5wm2/i/gy2c7pDZzsxMjF2/HlW6hpOrs7Zvx6EZM8DaMwmlrwIub+QtjfH3NPzev3XT7v71IITg1V27sPTQIXEfyzBYcccdGJOczO8QLfmh1k/ECgpWDfq1bo12oaHIKuV/tzYtzSMVzt1ZWZi/dy9C2HD8oukCVu6PX+69Fyq59CF6w/GOl1s4c37hJ06jBjokpyvFnBtvvLHJAFOEEERHR2P16tUY0YxJEKpwOgmltyQZbiF4TXuIFs5qfrE8a1ugAGdgVWbqKCBulHGbYfiBbu11PjWKXQpn3YBZ3ki4e1sJ78OnrBDWc7oQl/QzQ10OWZtcamW2p1BxE5JkVpMPFOwFgjq6VuFUhgHJz/I5QAEgZjBvMfaENUWGGt7dTMgNaguMDCg7xbuOakv4e1oiXvNMlYJpVNXa67xi5cB1b66U2eazZzHh55+hNVnPF6hUiilGjly5gvWnTmGSPelC9JVAxXmHRnmfu3s3/u/AAXGbAbDyrrvwgGn5bLHkq6P49bcyNRiGweTUVLyzfz8A4GRBAU4XFqJrtOdMOBdWVmLKhg0gAEo5P5Rq/fDDmPHoEG7He7QOs37mzQonp+OfU4QA0be6uzQ+yapVqxr9Xq1WIyEhATfccAMUiuYtw6AKpxPgOA5paWlITU2FzMbIYhTn0aA9KnP4KKv+rfm8dZ6EzA8I6cxbDYkBgHv6j+Q+rAzlB2jWIgc2hSMtnGrXK5qAC+97KS61Hk6TMtNp+AG/sE5M9ABw8RpnVmY+kRLW07XXbwxFINDhEWm/YRh+LWzVVaC2SLLCaXNfJwQo2AWoW/Gu0Z6OsGZYoOoyEJzskFO7clzwQ1oaHty4EQaTSLt3JydjyYgRSP3sM9ToeYv9K7t2YWxKCvykDiS5OvfcxhROTscHq1EEN2lBf2vvXiyoUwoFvq5TNs1kJuR6bkzhZBVm0UxNFU4A+OHUKSwYOrTR8rgKjhA8tHEj8jTGta4ze/fG/d262X/O+v3MmxXO6vy6ib0g47pgikOZOnWqy67lY/4wFIoNaLKAgt1AmXtzXVqEYfi8lm3G8AFwPI2aa7z86gdLEQYAdqZZACPjP45QOH0ZzmAMAGSr5aXkX6Bwn/2TAe6C0wNnlgCnFpgE+hLSonhwUC1vQczFWeS8a2gu8sHEcn5y3jUciRBV1S+G/1t12X1lsZOv//kHUzZsMFM2J6em4qf77kOH8HC82L+/uD+nrAwf/vWX9IvYksc64yMgY1mTSxve3b8fb+7ZY7bvizvvxMO9LKyfVYTwbaO0PXpwl6go9IiJEbfXpqWBODjljb0sPnAA2zMzxe37Ymvw0YD2jg1GJiqctY0f54lU16X+ovk3fQKqcFJaHra8LCmWKT4KXPwGyNtuvl9wLbRXqUl+Fuj2n+anRREoOQlc3canW/ElGIZfk5lwv+399/ohIP8Pt+d2lYzg3siwxnVZgpLt6jXONYV8eoHi4/y2TsOvE670PoVEREj5UetEhdN0kGvw8AEv4Yyu/aE9+AjcbnDLbw4f/vUXZv76K0zVqZm9e+PbsWOhqLOqzhk4ELGBxqUL7/75Jwo0EqPJCu9QtpHUKkJO6UZSo7x34ABe/+MPs32f3H47HrUWCbPVCD4dS3DHxst3re6Zp+eXH0w2ccvNKi3F4Ss25DB2MgdycszqHqxg8UkPPfxyf3RsQMOIvkC7B/hlJt5G9VX+L1U4ncJjjz2GrKwsyb/LzMzEY489Jvl3VOGktDyENXC2rndyNYTwL3RPjCpnLf+mYOFsjhWNYRwXRbXobz7ZdXWeY87nKTAsvyYzJMVyOgJLCIop52WpUUxzcAr9QnSp1bo2JUlNIW+pKz3Jb1dkAFnfAdeakQrIUVz5DTj7ET/JIgXBSuTMnL+mliRPt7DrK3mlU4i2nfSQZ7lONwIhBG/v3YtZ280nAmfddBO+uPNOyEwCFQUqlVgwZIi4rdFqMXf3bmkXtMWlVkyNYlnh/ODQIcz5/XezfR+NGoUn+/SRVhZLXNvPe3XUXbu+i+ratLTmX6MZFFVVYdIvv5hZoT8fMRBR/gF8VFlHTob7xfJLh5Rhjjunq6iiCqczuXz5MpKTkzF69GisXr0aly9bn0DNzs7G119/jREjRiAlJQW5ubmSr0cVTifAsixSU1N9L4Kfl9KgPTzdwpm7CTi9iLcmugmLfVhXwVvJGAYIqKdwBiQCSVMtRyx0B8KaNBfm4vTY+940NYqH0ajMTBVO8Qcqo6LtytRBQh5Opm4tmpgvV3qeQYdTW8wH/pFKMyycNvd1YmKpsaeMrkSwxMkDbZ/MkYCzng+EEMz5/XfMreeWOvfWW7F0xAiLESin9eyJ7iZupl8fP460ggLbL2rLO1S4R3QN75GPDx/G7B07zPYtHTECz/brZ7bPTGaEmE9gNAZrTI0CAG1DQnBL27bi1+tPn4ae42w7l4O5XlWFUWvW4HK5cb3wg9274/5ObfgNZWizJl499j0kFUOt8f3tTxVOZ7B161bs3r0barUajz76KBITExEdHY2+ffti5MiRGDFiBPr06YPIyEi0b98eTz75JPz9/bF7925s2bJF8vW8vEd6LlqtB1qnWjBm7SFaOD1U4RQsr27OxdmgD2vqXC/UsXw4fFMUgbzV056w5ZqLQOYKIG+nfQW1hOAK5+LUKE6/72uuA9ePGC3NtuDBCifQiMwsKZwMY+JW68J1nGIezrrgKuJg2rq7oMsQAqlIdUcX1nACdlmLberrYT2BtvcBbe7y/EGjfxzQ9TWg/QzjPn2lQ93yHf184AjBU1u3YvHBg2b7Fw8fjvlDhlhNdyBjWSw1SW/AEYIXduywfW2jeD804lJrxcL52d9/49lt28z2LbrtNsw2WVtqiigzTSaQvhC4tL7p8gm5J01cuk3dagsrK/GHHa6EzeVKeTkGrV6No1evivuSIyLw6R13gBHiHzgg8rVZP6st5pcClJ9r9nldiq6MV76VITS2gxMZMGAANm7ciCtXruDrr7/G2LFjoVarkZubiytXrsDPzw/33HMPvv76a+Tm5mLTpk0YOHCgXdeiCqcT4DgOGRkZ4Nw0g0Yxp0F7CK6FnupSK6ZGcZ/CabEPi+60SY69mLaEXwvnSGukqHC6zsLpkvu+Kge4upV3F7YV1nMVzkZlZknhBPi8kcnPACrpqTzsxjQPJ2Bi4ay03eriDAgxyklqPlC5H9D1VaDzC5LzcErq66FdgfDeDdvRE5EpjXIsOgqkLwbyHTMR5oznw4s7duCzo+aeMJ/efjtevPnmJn87LCkJd3bqJG7vvHgR2y5csO3CMYOB1Ln8mkprWLBwrvjnHzxZL3H8giFDMMfKANZMZtpS3pWe2CA/cZLNqHDe26UL5CZWP1e71WYWF2PgqlVIv2acBI0JCMCGiRMRqFTaP3FUjwb9rOoykLsZKDrSrPO6HHU0kPI80PEpd5fELezbtw9jxoxBXFwcGIbBpk2bmvzNnj170Lt3b6hUKnTo0AGrV6+2+XpRUVGYPn06vvzyS+zbtw+nT5/G6dOnsW/fPnz55ZeYPn06opuZTogqnJSWh6e71HqAwtkAQoDKuhnh+us3BcrS7QtO48iUKAKiwlnkXoXA0diTEsXDLZxWEV0c64XD92vFu4NKVJKaheAaWt+llhiMHhPuQF/JK8MM01BOtiBrxELV0lHXDa6qLnvkM2TT2bP4wCTCLMsw+HbsWDwhYQ3k4uHDITOxgr6wY4ftrqYM2/g9KDe3cP54+jRm/vqr2SFvDhqE12+1Mb+iFIWMbWjhjPT3x8j27cXtDWfOoFrnwOA8jXCqsBADV61CdmmpuC8hJAR/PvwwukTVTZxpbcgxag/enBYF8Mxo/S6gsrISPXr0wCeffGLT8VlZWbjjjjswZMgQnDhxAs8//zweeeQRbK+3rtud0DyclJZH4gO8O54rLSRSEAOjeJDCqS3mX4iMDPBva/mYoqO8FVQVLi2vnzMUTmUYX1ZOZ3TN8QUEN1Ipyei9NWhQQAI/qPWLdXdJGrrUsjK+DfRV/DpOKe3hSAQ3PHkQXyZPo3A/UHmJf9b6x3l2pMxrh3gX/LAefN/zi+OfIToN74VhmofVzeSUleHhzZvN9q0bPx73dZWW6zQlMhJP3Hgjlv/9NwDgzPXr+OrYMUlKq1VUkUB4L0AVha3nz2PKhg1m0XNfGzgQbw4aZPv5pChkwkRKvUm2yamp2HL+PACgQqvFmrQ0PNK7t+1lsIPDubkYvWYNSmqMZUmJjMTOBx9Em2ATq78tOUbtwVsVTkIcF0TQCxk9ejRGjx5t8/Gff/452rVrh6VLlwIAOnfujD///BMffPABRo4c6axiSoJaOJ2EsxM7U6Rh1h7qKH5A4akzZx5i4TSTmTIc6PgYED/WutzESLUSc3E6Q+FkWJOgKK5zq3X6fW9PhOXgFCBpGhBzm1OK1FysyiyyH9D2Xv5eNUWTzVvSy846vWwiXL2gQYBnBA4SIr/as3YaACouABe/BfJ2NH1sPWzq61WX+WtcP8S79XmgpVBEcwEo/scY3IiVG9edOigfpyOeDzqDAZN++cVMgXn55pslK5sCbw4ejBCV0dI9d88elNU0MTmVtxPI+dkYRdQS6kigzd3YV9Ua43/80cxy+ly/flgwdKjVNaamiDKTopBZsHACwF3JyfBXKMTtmb/+ivt//hmXy+zMH90Euy5exG3ffmvWVr1btcK+adPMlU0AaDWKD7rn36bZ1zXrZ4LC6elpiUzRVwFnFgNZ39vmQu0lVFRUoLy8XPzU1jquTQ4dOoRhw4aZ7Rs5ciQOHTrksGs0F6pwOgGZTIbU1FSqdHoIXtceHrA+rIHMGIZ3ZWzMQiEMBISBga04Q+EE+GAlXV4CAts3fawDcEk/09th4VSGAIGJHmWhEbBLZpXZfMqDivNOK1cDogYAHR/nc9oJRN/CT8C4M1cjK+cHqOpW9v3eUMt7JVTmSPqZze1mamHi9FZTZHgEltYM+8fzfyXKxxKOej7M3b0bB03SF9zUpg0WDB1q9/ki/f3xholb6/WqKry7f3/jP9JcAEpPNelOfuzqVdy5di1q9MagVNN69sT7I0farGyKMhMmV2wJqhPZH+gwEwg3t9QGKpWY3rOn2b71p08jeflyvLV3r0NdbDefPYvb165Fpck5b01IwB8PPYSoAAvvOv/WQFj3Zq91btDPvNHCWZ3Hv+u0JU6JGO0uunTpgpCQEPGzcOFCh507Pz8fMSaRpwEgJiYG5eXlqK5247IPE1zqUltVVYWdO3fiwIEDSE9Px/Xr18EwDCIjI9G5c2cMGDAAw4YNQ4Clm9GLIISgvLwcQUFBNj1UKc6FEIKKigq+PQxVvOunIhAIt5Jc2t3IA4CQzvxfwvFuXS7GTGa29mFhbY3UfHvOUjiluPU6ALtkJhWDHWs4PRirMuP0/GBWHtBwwCG6nLswSq0ikP+Y4gnuocHJ/MdeBC8Aibk4be7r9dcNa0s9N3iQpTXDgsLpAAunI54POzIzsejAAXE7VK3GD+PHQ9FMJfbpvn3x2dGjyCzhrbsfHj6Mx2+8Ee3CrORuFOMgWF8DfObaNYxZ8w3k+lIooIQOcozv3BlfjRkD1sb6izIL8AMjtI8t1vxGJteWjhiBGr0eK48fF118q/V6vLlnD1YeP46lI0bgns6dm/UM/+7kSUzfvNksz+btHTvi5/vug5+JhdUZNOhnMm9UOK/wf30s/2Z6ejpat24tbqtULWsNvUumDtLS0jBt2jTExsZi3Lhx+OSTT3DhwgUwDANCCM6dO4fly5dj3LhxiI2NxbRp05Dm5sS8zYHjOFy8eJFGqfUQzNpDWwoU7OYtJJ4KqwASJgKt73TbuiwzmdUUApc3AqWnG/+R6FJbKu1iDMsr1TLvnmhyyX0vBg2S4FJrqOFTqVzzHNcaAasyqykAziwFMpY1/JGgbHtSUC1vRUiNoq8C9LbPgtvc1wXFREij5Km5OA1aY1ktWThrCiXJxxLNfT7kVVTgwY0bzfatuOsuJIaGNqtcAKCSy/He8OHittZgwCu7dln/geCeyVoOvJdVUoJh332HuxUH8GzoYbRVlGFE+/ZYc889ZpFim0KUmb6Gn1jxb93syTaVXI6v77oLhx95BDe1MXdfvVRWhnt/+gnDvvsOpwoL7Tr/8iNH8NCmTWbK5v3dumHjxInWlc3aIqDob369czNp0M9E92Kd97inCq7aPqZwBgUFITg4WPw4UuGMjY1FQb1cugUFBQgODoafn2dkZHC6wjlx4kT06tULZ8+exbx583Dy5EmUl5fj7NmzOHToEP766y9kZGSgoqICJ0+exLx585CRkYFevXph0qRJzi4epaVhzxq4lk7FBaDkJFB6svHjBFcnXZm0F1vHx4Fu/zFGhXQU+mp+bVrOL449rzuJHwck3A+oY5o+VsBQy6dSyf/ds9fQmSK4N8oDG34nrnF2oYWz5F9+kqra5IWur+TvDQcMEu2mue0pUxoVLG1R88tTHyFQleDy66kKp+DqK1MaXRAB3qodfQt/37nB00TAwHF4YONGFFYaJ1me6tMH93Tu7LBrjEtJwS1tjQHhfjx92sx1V4QQ49pIC5He8yoqMPy773C1ogIajpflwFbh2DBhAlRyO53q5P5A4iTeTdYWy2NtEXDtIFBywuohfVq3xoGHH8a3Y8ciNtD8OfNHVhZ6fv45nv3f/1DShDtijV6P3PJynMjPx9zdu/HM//5n9v1jN9yA78eNg7IxK3TlJeDKFqDwzyarJhlWxS8vSZzi+HM7i+o6hdPTc/d6EP3798euepNEO3fuRH8r+W3dgdNdalmWxdGjR9Gznt98fQS/89TUVLzwwgs4ceIE/u///s/ZxaO0NDw9JYqA+FJn3R/cyNb8m4og3lpJOD6QihTXOWe4oDIyftABAHGj3RdJ1JHYE1BC6OvEABA9wDjXpcshWMvBCRjb0ZVRnEtO8PeBIhTwq1P2NRf5yYzAdkDSVNeVxZSz7/MeEe0e5CMz24Mqgpd3bZFDApaIcAbecgjw6781Fz1X4TR1p63/LIp1f7CthX/+iT+yssTtnrGxWDKikRyYdsAwDN4fORJ9vvpK3Ddr+3YcmjHD3AWW0xonOuq51BZXV2PE99+LrrkVnBKxgYF4YfhABChd+B6rvc5PNvq3BsJ6Wj2MZRg82KMHxqak4J39+/H+oUPQ1VkGDYTg4yNHsDYtDTN790atwYDrVVW4VlWF63Wfa5WVZms06zNnwAAsvO22pt1zxeBfodLqaQusjM+F6y3oKvjnEcPYvzbdB9BoNLhgkhc3KysLJ06cQHh4ONq2bYtXX30VV65cwbfffgsAePzxx7F8+XK8/PLLePjhh/HHH3/gxx9/xJYtW6xeg2VZu1zHDQaD9ArBBQrnDz/8YNfvevbsafdvPQG12sMVmhaG2B6CwmnFFchjuLQeKD8LtBnjtrWmarWaHzQKFpwAK/k3BRiWj4aqCLJsmXI1MiW/3kdbxg9A5FbSuTgQj7zvWSX/8iaE7/+sZymcFmXWmMIputRW161xdsHKECEPp6ns3B2l1qA1KkrNcTNUhgPIkryOs8m+ztUa+52Q2sZTFU6hDZ28vtSe58P+S5fw5p494naAQoH1994Ltb3Wwka4MS4OD3bvju/+/RcAcOTKFbRftgxDExMxpF07DElMRGt1nbLJyMyiNlfU1mL0mjVmrqihgZF4sHsSAlj7g/Hw7yE9wLK2T0wKbqQ2RmYNUqmwaNgwzOjVC7N37MBv586J3xVVV5utm7WVhbfdhlcGDrTtYAenRPHI95CtCNZNVZT7J9vdyNGjRzFkyBBxe/bs2QCAqVOnYvXq1cjLy0NOjjGYWbt27bBlyxbMmjULH330Edq0aYOvv/660ZQoc+fObaBwbty4EadPn8bIkSORnMzHBzh79ix27NiBbt26YezYsXbXiebhdAIymQwpKSnuLgalDrP2qL+myFORu3edmiizykv82g95gG0urwESlbqqXCBvO79WI872nFM2o4o0KpxSyyYRp9/3+ko+MqQimA8qZSsMw1s59dV8/1cENf0bF2FVZoKLY2MWToB3q60fzMcZNJYWReemyKtCDk6ZutHgLU2iipAcsMumvi73B7rN5S1iXG1dRF8PzX0c2g0I6mQ5sAoh/CC46jIf9dTOdfX2PB+uV1Vh0i+/gDNxnf78zjvRKSLCrjLYwjtDh+Ln9HRU10WWzS4txcoTJ7DyxAkAQP9IJV6PPYc24a0QV1WFqIAA1Oj1uHvdOhy5ckU8T9uQEPzfqIEIKN9v9z0iyuzyBqA8A2g1Egi3IW+mcD9wtimcAh0jIvDrpEn43/nzeH77dpwrku5mHqhUYsnw4Xjsxhtt/5GUCLxNYLGflZ/nPUKCOjo+OJ+jYeS8N5WLg/55GoMHDwZpZMnE6tWrLf7m+PHjNl9j3rx5ZttffvklCgsLcerUKVHZFDhz5gyGDh2KuDj73Zw9QuEsKyvDgQMH4Ofnh5tuusljFrjaC8dxKCoqQlhYGFgJC+QpzoHjOJSUlPDtIazh9HQLp5tzcYoy017gF3oHJjnH7VVbClRedt76KFUkUJHpklycZv3MGfd9bRFw9X+866QUhROo6+/VkgdgzsaqzEQLpwXlmGGBDo/w67Bd5SbNNWLhNNTUWWBc/DoVFE57c3AKRPYHom6W9BOb+zrD8IN/mapR10aPQKa0blHJ/p6fsPGP5900pUI4cFe2oLIkFwEp08Aqmh7jEEIwbdMmXKkwKmvTevbEA927S7++BOJDQrBg6FC8sMNybtZD17W463pHKBgDavctQfeYGKjlcjNlMzogADsffBAx8gKgHHZ7AYj9rLYErKHWfH1tYwjLCOx83o3u2BG3JSVh2eHDeP/QIeRpNAhTqxEVEIBIf3/+4+dnvu3vjyh/f3SOikKw1GAwDnSptXhv5m3n34FJ04BAD1c4g9rzH4rLWbx4MZ5++ukGyiYAdO7cGU8//TTee+89zJw5067zu/QNuXLlSpw7dw6LFi0S96Wnp2P48OHIz88HALRv3x7bt29Hu3ZNuO95MIQQXL58GaEOiB5HaT5m7eEtazjdrHAKMgsLENZv2ng/VucBZWd4pSi8V9PHOyslioBgUXGBwun0+16YLLFHyRL6e/00FW7Gqswac6kFHLvW0BZInYXTVOGUqfmJEmLgB9TOWH/VGKJVpJkKpx0TSS3qHccwvKJZfo63ckpVOAXreOkpVOZdgn9SCWCDwvnBX39hy3ljrtmUyEgsH+0ELxALzO7fH8OTkrDtwgX8kZ2N/Zcuma1V5MCilvDKzL/1ImOGqtXY8cADvBVWCHJkp4VTfA8pS/kdtt5jokut1m63e6VMhhdvvhkv9O8PAyGSoutKgjOYeHQ0816GlXvTG1OjUFxObm4uFI2k7VEoFMjNzbX7/C41vy1atAgajflM1+zZs2EwGPDzzz9j7dq1KC0txWuvvebKYlFaEtG3Au0f5hMsezJuVjgB8C9qYbDUVMAggZoCPppn2Snbjhfq56yUKKrIunJdc875XYm+GTk4BRczD1M4rRLSjXdztDcQjqMRLJymLrUMY3Tndcc6TsHC6QA3PKdQeYlfiy5E3qzK5XMge+K9eHU7kPtf62VrTj7O4n/44E7ChJEN61j/vnIFr/z+u7itlsvx4733ujTwTmpMDF4aMAD/mzIFJXPm4MDDD+PtIUMwJDERKisRV/0VCmydPBk9YuvW7CrDeBdYWyYfrUEMJhNQobb9hjWxMNq4jtMaDMM4T9kEAH0577bNyp038cp6icLJ6ZqdfohiP926dcOnn36KKybeCgK5ubn49NNPkZpqf/5pl1k4hdxA/fr1E/dVVFRg165dWLZsGcaNGwcAuHr1KpYsWeKqYlFaGsqQ5ruguQJPUDgZFqTDowCpsf1FKAwIbM3F6XQLZ53CaajmZ5LdlNfUIRjqFE57LJyxw/mXuaNTzziL2KGNf19+jldgApOAwETnl8dS0CCAj2qqLQN0blQ4HfE8y90MaLL5dZYBCc0/H8C7gJedMaZIunaA344b7Xnrs8rT+Xa0tj5QWP9dmcMrB7ZahQkBio6Yp/Bp4tlYVlODiT//LEZLBYAPR45EaoyEVEgORiGT4eb4eNwcH4//9GwFbfFxnKxQ47/XArE7OxuHr1xBhJ8f1o4fj/7x8SY/DALa3NWsa8u4SoAl/L1n63uClfEKHKevc6v14GVa8kB+Elxf5ZxlK4D3KJwV54FLP/I5VxNpWkRX88EHH2DkyJHo1KkTxo0bhw4dOgAAzp8/j02bNoEQgu+//97u8ztd4RwyZAgYhoFOpwPHcVi6dClWrVoFgA/7azAYsGLFCvz0008AgOLiYuTn52PoUH7AMW3aNDz00EPOLqbDCQrynMAcFC9sD5n7FU5RZlKUQWHwqyuzbWAmWIacpXDKA4DOsy2nO3ACTu1nooXTjsGTPevOXIRdMivPAIqP8S6trlA4k6bzCnt9F9+om/lBnJ8bwvcrw3jXYmFSpTnoKnjLW22RzQpnk+1Wf/mCYK22dTLKVRBinhbFEn5xvFumroJ/ttnq2qm5yLvzy1QgwV2hLP3dGJHUAiXV1Ri7fj2ySo3HTOjaFY/e4J5I5RapyYeyPB19wnuhTyo/TtNzHFiGMU+f4iCC1RygBz+ZKeX8iQ/UKakeEDG9MViFwwPaNbg3BYuvpyucQoRaT28zH2XgwIE4fPgw3njjDWzcuBHVdTlo/fz8MHLkSMyfP9+zLZxCJCWtVouUlBQ8+uijuOOOOwDw2vSlS5fwyy/GxOyHDh3CzJkzRaXUG9eIyGQyJCQ4aJaY0mxkMhnat69bhH79MP83tJtnR2tTBPGBYeRB0mbUHYRMJkP7donSLYLyYL6snJ5XlpuKIOpsCyfDOD3VgYBZP3MGzVnD6aFYlJmhxhgZ2draK1dHcbamUIZ0cc31LREzhP84AlUEUHGBVzhtwKa+blXh9LDUKPrKunV+jPWBLqvg+0DVFd7KaavCWVT3vgnrCZkqEuFh4bwLpQUulZZi9Jo1OHPduN48KSwMX955p1258pyGhdRijbqcGrT8GkV5gOTYCTKZDAmtQoBcVrol3xUTUR6IxXvTWyycVXUKp7/9kVApzaNbt27YuHEjOI7DtWv8EoOoqCiHBEJ0+hrOhIQEJCQkoGPHjkhOTsZ3330HtVoNg8GAzZs3Y/To0eIxCQkJqKioQHx8vLgdEuIF7o/14DgO+fn54ExcYijuw6w9CvfwkT5NXZw8Ebk/kDARaH27y5VNAOC0Fag48jq4zNW8K6qtsDKjgtfITL4Iwzp37YoLcfp935w1nNUFQNHffHh8D8KizMpOA2eWApfWWf+h0F8MHn4fewvKcP6vjQqnTX29vsIpuNvrPE3hFKybAY1PsPnXWaFsXcdZW8y7CAJARF9w8mBoNBpwtQ3znR67ehU3rVhhpmyGqtX46b77EOJpORWFyK+2puK59AOQ8THvBi/1UhyHa6XV4AI7GOXva5Se5p/NFvqFPVi8N71B4RTSDwG8RwHFrbAsi5iYGMTExDgs6r5Lo9T+3//9H+677z4xj0tYWBjefPNNs2N++OEHDB8+XPK59+3bh8WLF+PYsWPIy8vDxo0bxQSlOp0O//nPf7B161ZcvHgRISEhGDZsGBYtWmSWUyYxMRGXLl0yO+/ChQvxyiuvSCoLIQT5+fmIivKwdSotFLE9IiO9J0qtmyEVF1FZXoqAsCrpVk5FKL8eSlvadDTR9g/zLxpnUnkJuP4XP6huJf3ZYitOv+9jhvCpJfzsWMtVmc1PtIR2A4I7OrpkdmNRZk1FqAWMSrcrJo4MtfyAkFUAkf3Mv9NX8pGZGdb2wFqOgHAAGMdNRqnq8jpqbVM4berrXD1LmKmF0w1eG1axpb8BQPgNQEiK7YPhor/5egZ1BFQRIAY9NBoN/EPNA3dtPX8eE376ySwKbEJICP43ZQo6e+IYQuo7VG5/YC1CCK6U+yM8dRJgJVCRVcrP8e7Mge3te2baAyFA+Vne5b/N3bb18aIj/Duq7XhAFe6AIli4N8O688sqPHkNv7aE71usHFB5cDl9nJKSEvzwww+4ePEiSkpKGuQCZRgGK1assOvcLlU4x4wZg7S0NOzcuRMymQx33303YoVoZgBKS0sxePBgTJokfbFwZWUlevTogYcffhj33HOP2XdVVVX4559/8MYbb6BHjx4oKSnBc889h7vuugtHjx41O/att94yyzHjdWv/KNbhao3KjT3r4FwNIcYHcP1gJc6mMosvQkCi9N8qQ4BK2L5Wy9kDT0MNH6zELxaA8xROp+MXY//AyUPToljEFgVAcKk1uMCl1lAF5P9uWeGsvMxbYv1bAx1cqHBWXQYufsuv/Uqa2vzzCbJ2pItyA5faUP5eN2h5mXqKV0NT6zcF1JEAbFwvy+mBkroE7BF9+b/KcBRETkdU8o3iYV8eO4Ynt2yBwWRQ17tVK2yZPBmxgR66jk2qwink0rUzNYrdFP/DK3+t73SNwllbBFw/xEdiBoDgFH6CoilcEW3ar5V71plLobouMqo61ruD+3kx27dvx7333ovKykoEBwcjLKxhlPjmuPe7OFM10LFjR3TsaHmGPTQ0FHPnzrXrvKNHj8ZoKzmqQkJCsHPnTrN9y5cvR9++fZGTk4O2bY2uGkFBQWZKMMWHENeeyF2fpN0esr7jg060vZe3TLkQRiPk37RjEC2sLZO7Zu1kkwhBVWqLPMuy4kq8SuG0QQEQozi7wMIppAayNOkjDKZdnRZFW8ani4CDvAOECThDtePukfqKCSvn21RXzlszPEXhFNyyHbnWm5UD7WcApWlAEB/pEQwLUhe8hRCC//zxB97980+zn93esSPW33svAl2Y/kQygksta6NLrbx59whjOlEsBcHll2teWhSbqSkwKpsAkL+Dt243pjwRzjjB5uo8vp5GFXWndTcvvPACYmNjsWHDhmYFB7KGF4y6nUNZWRkYhmkQlGjRokV4++230bZtW0yePBmzZs2CXG5ZTLW1taitNT7MKir4gRIhBKGhoeA4DgzDgGVZcBxnZpoW9hsM5uvjrO1nWRYMw1jcD6DBWhpr+2UyGQghFvfXL6O1/d5WJ47j+PbQVYIhHFiZ2jvqJFODEA5EWw7Unc8l7VSZB2hLoPLzB+cXD5YQaXWqm6nlOE4st1gn03aqKQBz5Tew/rHg4u50Xp2UYeDAAIZakJpiQBlqWzvVXgdz5TeQ8N5gw3tarKtpncR+xnGObydCgOIjgMwfTEhXsHKFtL7HqkAIB+irQOq+94RnhCAz4RiDwQCmthQgHIgsoJG+5weAgOg0IHq9qCA5pU76GjCEAyADW3du8XjGjy+jTgODSTks1dVYdgc8I2qLIQNAFCHg6p3HrnZiVLxiIPMH0dWAkasafUaY9nWrdUp4CCz0ACMzlrHV7QCrBKuMAqy0h8vfT5EDwUbcBINea/a8slinqlyw5acAdQy4UPPckg3aSREORA2CjGHEOoWGhqJGp8OjW7Zg7SnzXMUze/XCx6NHQ6VQePT7idFVgQVAWJVtfY/1gwwApy0Tnz221okz6NG2cj2QvhNcynNglUES6qQEQMDpqhq0q6W6Nrvv1Zbxz4LgDuCq84Ga6yDX/gIib7LeTroysIQDBwaE8RPL2Zx2qv8eIoSAqy0DqnP5tZyB7T1zvOffFgitBgLaAQaDR7yfml2nujLW/95TuXDhAhYvXuwUZRNwgcL52GOP4ZVXXkG7du0k/S4zMxPvvfcevvjiC4eXqaamBnPmzMGkSZMQHGyc1Xz22WfRu3dvhIeH4+DBg3j11VeRl5eH999/3+J5Fi5ciPnz5zfYn5GRgZiYGJSWliI8PBxt27ZFbm4uiouNi8JjY2MRGxuL7OxsUVEFgPj4eEREROD8+fOoqTFaI5KSkhAcHIz09HSzzpucnAylUom0tDSzMqSmpkKr1SIjI0PcJ5PJkJqaioqKCly8eFHcH8iWoEPX/igprcTly8aACEFBQWjfvj0KCwuRn58v7veGOqnVaqSkpKCkpESsU1XhFcRWlyK8VYxX1Klay6K8oBAazSlorvpbrJPD24m5hOKTq2DQ6aBVxOLSmfNOaydV7SVEVf6LyHji3DpFRKCwzABUF6K45k9oVQlN1+nfE4i5/jUYQsBdOomYIR/Y3Pdqamoc3k6asmuIuf4tAEDRaz4iomKk9T0ocK2gEAZWg2s1aZLaCbB8PzmynYKCgsT7KTj3HFhOi2u6XCR0irVcp04doGw3HekZ2TCkpYmKnjPqpNBeRURpIZTBMoQD5nUienSorkRgYCAuXTyL8iq9eB5nPiNCK08hMQio0ilw3uR4u9vpaj6KtbfxO9MzmryfMjMzUVNTg9K69B2N14mtV6capKYmQFtT4xF9z9hOF5tsJ7/q00hSpUMW3AFpl82HT2I7nT0r9sf6deI0p7Fi506kF6kBGF0cn+ncGdPj45Gbk+P571z2FqR26YQKTQ0u2tD3wpRlSACgKcnDRY3xeFvqVFl6FdHVVSisqYEyrhYRkUE216lzGAMFIcjOzEBFoXE9o7Oee4GVpxDFlSMkMhwF1eFgCn4Fd+1HXAuXISYu0WI7JUQShAG4VqpDnskEhCPaqaKiQqzTlTN/IKxsB7SKGFS1muSi+0li38uuBZAIFNcCSPOo91Nz76eCggJ4Ax07djSrt6NhSP3pAAdz++234/fff8dtt92GiRMn4rbbbkO8aWJgE7Kzs/H777/jxx9/xO7duzFixAhs2bJF8jUZhjELGmSKTqfD+PHjkZubiz179pgpnPVZuXIlHnvsMWg0GqhUDd1H6ls4r1y5gi5duiAri1//1rp1a8hkMo+ZmQQszA5xBjBnF4OFHlz7x0Hq5XTzhNkhyXWqV3aDwYArV66gdVA5ZLk/gw1MANduuufXqWAPSP4fIGG9gdZjzOrU7Fk8rhqGsnN8KoTQbkBQJ35/TT6485+DqGKQh+6IaX8T5HK5tDoRHXDtADhdORA3xmzwZdZOJcfBXPkv2OBO4BImO3VmkstaB5Sng8SOACL7N91OeTvBXKtzdwtoB6b9NIt1rW/hvHLlClq3bg2FBStFs+pUWwTm/HJ+hrrra9L7nrYU5OwHACMH6fq6eV3dbOG8cuUK2rRpA7lcDoO2GsyZhQAA0vkVsAo/93t2aDLBZH8PqGPAJj/VsE5nF4M11MDQ/jGzoBxOtTJlrwVbmQnSegy40J7S61SvjFLvJ51OJ/Z1lmVbjgdOTQHYzC8AVgku5WWztD1inS79CIAFogcBqgixTheLijB//ZtI4k7jaG0ctlQmQ8GyWHnXXZjUzbhkwhfeuWbtVHsdsgufgWOVIJ3nSKoTV5GFyn+XIygyAUzKc9LqVHQIyN8JLqQHH8DHkXWytP/Kr2BLjoOJHQIuciBw4QugpgAkoh+YuNGW26n0JNgr/wXnnwjS7sGm62SjhdP0PUQIAVd+XnyGkQ6Pe879ZGOd6pfRo58RFsqem5uLxMREXL58GW3aNBFI0Y1s3rwZTz31FP78808kJiY6/PxOt3Bu3boVBw4cwJIlS/Doo4/CYDAgIiICiYmJCAsLAyG8dSMrKwslJSWQyWS4/fbbsXv3bgwcONChZdHpdJgwYQIuXbqEP/74o1FlEwD69esHvV6P7OxsJCcnN/hepVKZKaLl5bwvPsMwKCkpQXx8vNjxhL/1kVmJvObM/QzDGPfXXAEIHx2PzfuND3AQam5Ot1Z2j61TvTISQlBaWor4Vh3AdngEYFjvqJM8EAzD8tEeTb6XWnZxf9VVoCKDVzKrr0ImPGjlSiC0M/+/XyuwXV6EgQ3A9bQ0tKp7oEoqO0eAa/shA4C4EYDMuFbLrJ24Gn7AJg+wv05NlUU43i8aqDjLp2QwOcbi8ZU5kBUd4suWMMEs12JTfa+0tFScUHNonUgtXx5FIFB3nKQ+Jvfj+xI4gCFma5il3k9Sym5LO5nKTCZjgOiBfPAapb/F462dp6n99teJ42VftyaswfGKIMBQAxmpthhJ0ynPCEPdu0YZ6rJ2qn+80G4yS/cTZwAu/8Kv34wbDZmsbv2rrgKoqEuPEX6D2/seAODSj4BMDVnsMIs5bs2O92/Fr3c11ECmu94wEIu2FLKKs7wLfOxgsT/sysrCQxs3IkZnQFIAEMrWIESlwqb778dgCwM7r3g/2dpOqjAgvDdYRRD/7Kq3PrjROunLUVNTg2BlmHiczWWUqQEwkEFn833ZrL7HVYu5XFmZHGg9mg/sVXKMf6YpQxqeuy4nK6sOd+izw/SZyvw/e+cdHkXV/fHvzGx203slJCQECL0X6UV6UUCRjt3X+irYexd99Wfv2EUUURQBRQFBQIqCIKEF0giQkN6TrTO/P+7O7G6ybXZnW5jP8+yTzOzszJ1z79y5555zz6EoMEGhpA/j9MJ1PN1HiNqvrgTDaoDgJLSOr+EXfYSb+219729s374dCQkJ6NGjByZNmtSmfweI7N944w2Xzu+VNZwjR47EyJEjUVFRgU2bNmHfvn04deoUzp8/DwCIi4vD3LlzMXz4cMyYMQOJidKHROaVzTNnzmDHjh2Ii4tz+JsjR46ApmmPlMdvaMw3/d90jgROaKVwthsUIYAqgHJ5CbkGJYgcWX8aKFpjuS8kmQQ1iDSLpEdRpA24s+aAVpBBuK6BRKq1FRyEj4jpjeAhwQkgQV8c5Mg0aIBzP5ABY0w/C2XTpxhayF8rA2KnoFVApwVkEEZZf8n6BUwwkDLZuWNrjwHqclJHIR4M9MbZCRoEGIOiVAA6LwUO4jizyJYS5qm+8DPQmAckT3S/3bMaoO4E+T91pmm/thY4v5EESIkd5N41pIDVmcrpTLujKJLqqSEPaCpuq3AKqVCygOAE5JSV4YFt27AlLw8AoFKQAEpZYcCfS25Ar0AbWxg0wIVNxokEJ3NEM0qg4xWuXc+Yy5lTutDO+aBB3gqU1vp9Ft4ZiL+MBI2yVf64oeQ4T6dp8/c8nFV/kU/8cKDDFF+X5pLl7bffFv7ftGmT1WP8XuHkSUhIwPXXX4/rr79e8nM3NjYiz9ipA0BhYSGOHDmC2NhYpKSk4Oqrr8Y///yDTZs2wWAwCP7csbGxUCqV2LdvHw4cOIDx48cjIiIC+/btw/Lly7FkyRKroYHbDQ1GhTN2EFB9iLxIWQO8HpbaoCUdTkw/U+THSx0hEqcECicfcjwkCYgzvgQ9KWdlNFE4dXUAUq0fY/CiwhnZE+jV2/EAqe4EiaCpjAY6GKNecxyJJujLUO18JE3GRYWTopwL0R9I1PwLNJwh+R09qXCGZwKdrzMN2loTfxnpt8KsLxWRHM5AJoq0tbYHsq5gaCLJ56VIXyFEqFVZTnDwuTh1db55z7SGv1c6CE5HXQ1LJ+/J5nMAzNLksDqg5h8AQFlwLzz600/49MgRsGYue7VsMFLCw7Gwb09E+mOOTUcYmknkXToISJ3h+esJKZJcaOdhGUDmUu+NJ/govAqzdDYdptr/jSIUUHhhEtzfFc4WY4TaUDlCrS9p7aYsNe0mSu3Bgwcxfvx4YXvFihUAgGuvvRZPPfUUfvrpJwBA//79LX63Y8cOjBs3DiqVCt988w2eeuopaDQaZGZmYvny5cJ5xEBRFJKTkwVXRL9F32JSRBJHk5xV+iaguZgMsrzJue9JsuSmIiBziaSnFuqjMY/MmIZ18uwAVSqCooilQQorRmQPYolRxQPhGQ4Pd7sNB0UDOGc/F6cwI+yFfHPODmxjB5CXc1A4mXWu+BMo3w3EDSaWHzt49Lnn03+4auH0U9rITNcAgCNtwpElVsoJGUfXCbczKRLZzbPXbw2tANLnSX9e89QoDnDY1m3lalSEEWWF1RGlU4JE926hNyqcQRHOWesAINQ4sdBUbLm/9ii0mkZsv1CDeZt+RpOu1bouisKsbv1xbbcgBDMMubaUqVi8gdgcnMLvtOR+mWBRE4yUrhbh4eGglC5M+gdFeHfyOnMp6b+CbVit9c3kGfPwuNDqsykonDoyeepPXi6sAVAbA/rIKVHaNe1G4Rw3blybxcLmOIqNNHDgQOzfv1+SstA0HRi5PJsKifUmOIFYdCK7AdWHieLnTYWT48g1ATJzLDFCfRT/SWZnU6YEhsKpjCJrCKUgJFnUPbvdhnnLiz2FEzQZPPtLPj6e6F6m/ykFGWRpqhz+zKPPvWDhDHH9HPVnyIRLeJbvB/pG2sisfBdxS0wcAyRPsP9jXvnmZSPjHiIUTodtnbWhmFAUedeoK4gnga/boWBBE6H4haSSATtFkUlbRQj0BgP2/bMOB/L/wfe16W2UzRldu+KliROJC+2p10m/qK29dBTOCxuA2uPE4hd/mdM/o8MzEc4oiWeOv6OKIx9rVP4FlG0nXjMx/ck+jgXKfieTszEDJLP2W302zb0zWK3nXXjFoCknuY6ZYEDpH+8lGc/gR9Mc7QeDwYD8/Hz/z73Du9OGZ5G/EcaZ+obTriVadhXenQIgM5ISX5uvD1bHD9r9qLP1U9xuw8ZcnMJaM2tkLgZ6PWpqf56mfDdw5j3iimkOxwHle6xbyvgBhBMKp0ef+5iBQMZCMjBxlco/gQubLZ83H9NGZoIC4IRlgncv9rSFs/k8GTC2tmjx6FuIMl9/xrPl4GF1numfRSicDts6r5jQVvpa3lqlrXGhkBLDu9QqRFjCGCXQfQXQfTk4JhibTp/G1A+fx/aT+1GjMeCwxjTYH5CcjO3LlmHTokXobkwnwyqiyDvICTn7HQZjVH5n3Y95ePnqxa1zNsSPQr5hGAwqFxROVkeWCVVKY0hwC05HZHdxO7H2AqTtle8BSn6R1Opp9dmkGKDjLCD9KjKJ6k/w76OQDh63/so45pdffsGkSZMQFxcHhUIBhmHafFzFz1pe+8GTuWwkI3EM8ZkPNgY+CO9MOiZNNRlgB8fb/71U8EEbwjOBztd65BINDQ1AqPEF746VyNtwHBm80UFtorc5fw6WBFhRRhF3MCfdadxqw8po8tcY9MEm3nzB6JuAljJA3SonVs1h4OI2soY4+y7L4DC8wqmtdsoVyWPPvSrWfWsQP/j3VhANJ7GQmeDi6ITlR3Cp9bCFsz6XTFbEDyPr91qjLgWKviKeIpFdPVsWACjbAVQeIP130ljpzsv3i07K025bt2cJ8yuF0wULJwAEhaOkoQFL1q/HjqIiBFM6NKuyEESx0HBBSIuMxAuXX45FffqANuvjGhoawPVaBASJVNj8BVuWa0fwyyZcWB/scp/K6kmAKgCIHeLZ9cKaaqD2KHlfWAu6GDcMqDpI2nzlXiBpnOndGBQpuYtrG5lRlH8E6bJGs7x+01/4/vvvcc0116BXr15YsGAB3nvvPSxaRFLWbdiwAV27drWabtJZZIXzUkYZZdkJMSriVstxJDCFN+A4k8IZN8Sz13L1ZelL8j8Cmi8AGYtdH8zqG4Fz68lLrfdj0pbPFqHpQPZ/pY2i6S58jll1hWmfphoo3UL+jx/WNhJpUBSZhGH1ZHDKK9KBCOOfCqcFYhQAb7nUsiRtlP0otRBtvXEZXR3pnxmJlRZBnhJY3gJF4eTLKXKtn4FlccXXX+NQaQkACmouCPvU6YhUqfDi5aPw32HDEBJko724OnHoD5gHgxIDL18xCmfNv0BIJ3HXMce8jKwGoD24/l1dBpTtJIHDrCmctILEACheR+ICxA4EtEbvn0B+p0iBuYVTxqesXLkSQ4cOxZ49e1BTU4P33nsPN9xwAyZMmICioiJcdtllyMx0fbmd13u+4uJiJCQkICTEupWppaUFFRUVSE8PoPQV7Yn0a7zv1pA+D6g/AYR38ex1DAFo4RSsDm4MZvl1lEFR3gsWwCgBxo5FTlNNlGBlDHHz8Qa8wqmpJH85Fjj/A3FxCs8gIdlbQ9HEsqiuIL/z1eCg5ih5LsOzXA8cxA/+WT9VOFmDyT3WGYXTWy61fFoUypbCabTe6FvIxISnFQrz51lKFGGkfUsRxCt+uHEy04rrb2QPIDjZ9no3b5I2m6T3EMnnh/9Gt8ZfMT66AW/WDQNLKXHb4MF4YuxYxIe2r8BeFggutS5aOJ19j2mqgPM/guIo0OwkcdfioWhTgCpWA8CD9cLfF2MnHkFUT6KQNp0DLv5u8ljx1jul6RzpK0M7kqB4/kKHqWTZAh+MS8ZnnDhxAitXrgTDMFAoyHtMpyMTrhkZGbj99tvx0ksvYdmyZS6d3+trODMzM/HDDz/Y/P6nn35yS4P2ByiKQlpamn9HqS3fTdYltR6sebvMFEVcKZInAi3ngcKvgNKtEl+CQlrHjqaXZSBZOKWIxMmvoxSRQsHjbVjfQF4y3lxPyCuculoyCKn4k7yEGRXQcbZtZVzp3DpOj8rs4m9A8fcmC6Ar+KGF00Jm+gbi8UAxzqV/CU4CutzkMTd8AcHCaUORZIJJmQHvWDk9kYMTIAPR7veQtcIOcNjWKYo8V1YtnFFkgsdf0l8xSvJxknqNBo/s2I0kpglhtBaPxe1D7uIxeHPqFLvKpiAzXS3JiVz4pQSF9zKJo4GeDziM2N0Gvq71Tlo4K/cJ+UxTO3VzvU8VcnFqXPu9s/DPvT1FjqJIwEIAqP2XZAUATPEOJMLms1m6BTj7jV+t4QdAsgYkjPSf/uASJjQ0FEol6Qujo6OhUqlQWloqfJ+UlITCwkKXz+91hdNRtFidTgeaDuxYRjRNIy4uzn/vgzUAFXuAkp9tD2C1NYC60rvlMqhJXr2mIklPS9M04mKjQMOYY+hSUzhdsIhI0oarDwHnfrAebKV1kmxvoAgjdc9xQN1xshYOIBYOe7PMYekk76GDF6LHnnuOkyYtih8qnBYy05mt33Q2oXxoR5OLpqfgFU5bFk6KMg00Pa1wsnpAZ7yGD13x/P4d50Fe2L0bZU1NKNaT/nRcpzR01h52+DtBZowSqD8NNBaQd3EgQdHG3JEivYRaewHYQ98E1BwBANCJo9xrZ7wllvW0wunk+yy0o9HlljKtXZT4Obb5bPp7Lk4Zn5OdnY0TJ04I2/3798eXX34JvV4PtVqNNWvWuOV96pW3RX19PYqLi1FcTAaeVVVVwrb55+jRo/jmm2+QkpLijWJ5DIPBgFOnTvlvlNqWC2TGTxFKXJtaU7EXOPUGUL7Tw+UoA879aIqWqzLmr9JUSBqF0WAw4FTuGRgylpHZe1sJ3P0R/gVmkMLCGe30TyRpw40FZB2OtRlVXyicFEUCuwCk3XEscXOK7mv/dwkjSETdqJ52D/PYc89qSFkB99zB/VDhtJCZIpTIOqafr4tlCe9Sa89VVgiK4mGFk58gpIN8ujTAYVsv3wOc/4msP7dGbQ6J2Kmp9lwhHaFvBgpXk3I6+b4pqKnBa8b0aed0kYgODsbwjmkk/oCD5QqCzKgQ0pY4DtC74bEQSDAhJJdx0jhTX2aLqr+IUhqaCoMq1b0+VbBwerjP4597Z9zRUyYBXW83udRK7Klg89n0R4VTXUkCGraUOT5WxuPMmTMHGzZsgEZDJmgeffRR7Ny5E9HR0UhISMDu3bvx0EMPuXx+r6zhfO211/DMM88AIOb+e+65B/fcc4/VYzmOw3PPPeeNYnkUtdp/BnVtaDRLh2LNksD70jfkkRlYT0V3qztOZjJZDRCRRSwVFEPW1OnqJJ35U2u0QFg24EZIZ5/gIwsnIEEb5q9nLRenLxROgLhhGtRkjZm6griISegC65Hnnrdu0kG2A9c4Q1gm0GmBKNdqbyDITBUHpEwW9+Pqf4g3Ruwgz1n8HAUNArwXOIgPtBMU5ZnlDwWfk743Y4nDqMh223rDaeLZENEFQGrb76v+Iu7sISm+y8WpqyfvOEUY0PEKp35y/9at0BoH8mf10ZjUOQgKmnY6XZFarTZaxKPJmnBtject9FJSvpvILXYgqTtnoSggdabj41gdycMLAPEjAIpyr0+lveRSy08IO6NwBkUCQQA6X0fejcHS5xi1KjNe4fS0LMTQcAYo/RWI7g2kX+3r0lzy3HfffbjvvvuE7ZkzZ2Lnzp1Yv349GIbBjBkzMH78eJfP7xWFc/LkyQgPDwfHcXjggQewcOFCDBw40OIYiqIQFhaGQYMGYfDgwd4o1qULb1GMsJH/MDSVWBv0zUDzObLeRmp4t0bAZDmiGTLoVJeTz6UevQ0wBSGQZA1ntNvFEQW/NsWfFE5nBj220LeQGXNvBV7i4YNdueNOCxBF08+UTbepOkBmx8M6ea59J08iimSIFW8QnrghQFQPzwe+YFRAdC9xeSPFoK0hz6uhGYAbiqC9KLWAsW8459tItWJyvgLYWVSE9SdPCtvZqT3Rc8BAQBkp/tlURhsVzlpxv/M19SeJK2hEF3EKp7PUHCHjDmUMeZ5YNz2dksaT9YEeUOosECycIt5nQZHi0/G4gz9aOF1NSyTjNUaPHo3Ro0dLci6vKJzDhw/H8OEkAmRTUxPmzp2LPn2shI6W8Tz6FuJSC5C8m9agaLJmreZfkoPOEwqnpoIEYaEYIKKbaX9wIlE2NRUAutn8uVgYfS0ZnHorV55UKGOIQu5ORMeUqSSPpKdfuq0RcnHWtf2OVzjtRfXzJ3LfJO5/3e4wueV6C97C6UwgnUBGU01cDRXhziv1woSMB1OjOJMfztbkndSEdiRRvT0FEwKglrwn3IFXOG1FM/WH1Cgicr4aWBb3bNkibFMAXp86FVSsi0qXcP+1rv3eV7gTeM+gJQoGo7Kt5PM5p+OHG/sAN5cnhHkp8mnmUtKevP2OFYNfKpweCoAm45d4PS3Kk08+6e1Leh2aptG5c2f/DKjQVEisi8GJ9l+0kdlE4Ww4DWCK9OXgrZsRXSzzZamMg3l1uWSXomkamclBoC/+Sq4XSApncDzQ6Rr3zhGRBUDcgFiSNiwonLVtv6MoMrDwtoXTVfj1cpoqmwqnx557gwQBgwDirlZ/igwa4/zDi8RCZud/JG6Y6fOIFc8ZvJWL81KBb+cO5OmwrTvKeewPCicfpMoJa/GnR47g3zLTOrPr+/fHQJGxJixkxveN/pCLVAyOJhLscXErcZdNHAMkT7B+TOJo4h5vdF/367GUOapY37mGt8KmzPhxll8pnLKF099YvXo1PvnkExQUFKCmpqZNoFeKolBXZ8WI4AQeVzifeeYZUBSFRx99FDRNC2s57UFRFB5//HFPF81jUBSFyEg/fYC0NWTm0JZ1kyc8i1gfNVXkI3XOtDpjJKzWgViCEyV3W6QoCuHBDAAqsCLU+hBJ2jA/a6lvIUqO+cQCb6WRMDiUR1HFkQAoWtupUTz23IdnGoNdiUy23hrOQFKrAEBMf79IQG8hM1cGH97IxVlzFABHPDFsRec0qMmaRLBkss5T6OqJguSpdEWCAm/fwmm3rXMssWYBfq5wOtfe6jUaPPr778J2uFKJ5y+/XPTlLGQWFO0b93x3EVylXeiLnE2NYjax5nafqi4n6beUMaQfvQSwKbOIrsR7xJ+ssLLC6Vc8+OCDeOWVV5CamorBgwcjKkpay7PHRxxPPfUUKIrCgw8+CKVSiaeeesrhbwJd4TQYDMjJyUHPnj3B+FuQmoSRQOxgU+RFWzAqsi6qsYC41SaMkK4M6gryoZi2g7PIbKDnQ5IOqAwGA86eOYGMUBa0DyM7ugzHGV2NlOIDOKkrSZTY4ET7a9BaYTAYcOLECffaMKMiA3SD2uhKZcUy6M+5as1xIhenJDKzhlRrfWgVkbfQnnyf/FuQWY8eYHTOuzgKKLzgUlu6hZy/2+22FU5NJVD0FVkj6ymFk9UBp14j/Wb3e8WnpnAGwcJpX+G029bNI4I6VDhriYLqC8VLaG/2LZzP79qF8ibThMajo0cjOVz8s2Mhs6ieZLI1UPo/gESO5Ywurq5M3NqL5NxSBnA64jJuhtt9akMeUPobiXztKYVTU03yairjgBgHEc+9gE2ZhaaSj7/AsWZu7bJLrT+watUqzJw5Ez/88INHvAo8rnCyLGt3u73itylRAOPspBMzlAkjSTAMR9ZQseibAFU8GXS0fnF5aODB8QOoQLRwnnmXKOhZ15NJADE05gElW4iLosi1X5K04a63kXV2nop07C1UjhVOwM+fe4oiSqdBTT72kpR7EYPBQPoEzkDK6EykRx6FFyycTkWpNRtMc5xnFAlNNTk3rfBcP8YrnE6s4bTZ1gUrmNJ2fx4UQRRnzkAmo3wRII7PzWhngiO/uhqvHzggbGdGR+Oeyy5z+ZKCzAJJ0eTh65XvR8SisGPhLPudTGwnTwQSR1le1p0+1RtpUdRlQNkfRFn2A4UT8PP3EI+uwdhXMoGztOYSYPr06R5zYff4tOLcuXOxe/duYXvXrl2oqKjw9GVlrOEo/1VrIrJIpDhX3GfsEZ5Bgq94MvhFK2h+cOHK2hNfIwwCXRhUu5gSRTKCItsqm7pGIO8joOibwHKpBey61HqM+jPErVOKACO8osJ6cADmCnw+QoXIyQkhT62HLJwcZ/IGoZzIw8kZPDe45dueKs5zCktQBFH+3OnzlTFAr4eBbnfaPoaiSVqI7vf4zp0u6wag96MkXZANzNOgAMDLkyYhWOF7V3SfIKzfVLrW/vgJrtapg9SVRNkEgMjurpfPGrxizL//PYGvIq6LRd9CPNaazvq6JAQmhKTp6jgrMCdg2iEzZ87Enj17PHZ+jyucGzZsQHFxsbA9fvx4bN261dOXlbHG2W+BvA+BxiJfl4R0MIzS+ncV+4Dct4CKvdJdjjO+cDzhhuZp3MnFKUSBi5asOG6jbyTralrOB86LRmkMCKFr9HwS8dZU7gXOrScpityFVzi9fQ+OcMWdFgDCMoAuNwFpV0leJABEgeQnRexZOGmFqW/xVC5O3rquivfM+QHi0dL9HiBZ/BpFAYoyRiJ1UJdhaUS59eU6RjrI5gTHjsJC/HDqlLA9tlMnzO3RQ7prl/wCnH7HlKbM31HFAT0fIF4rriBYOJssJ78r95G/kdkkSJ6UeCMPJ/+8i/HM8AXqUqDgC+DCZl+XhMAogajuJJ6AjF/w1ltv4ezZs7jzzjvxzz//oKKiAtXV1W0+ruLxnj41NRWHDx8WtjmOAxUog0wXoWka2dnZ/hVZjTUATQUkh5aY2WttLVC2g3ykQFNlclGzBacjx6nL7B/nJDRNo0NSLGl3gWjhdEfh5K1iIvMvStaGm88D534Aynaa9gXKjLA5jIokp44balrH1AqPPfcGCdOi+JnCKcjMwA/aRCqcilDiyuap/KLma90pOwonYBpwelzhlDiAmwv45TtOQgwsi3t+/VXYpgC8NmWKW2OXNjLT1pGlEg7c9P0GiiLPm6vuz4pQMrnAcaZnRNdI1j8CVuNEuN3OmEvPwmlTZv6YFkXGrwgLC8OIESPw3nvvYciQIUhOTkZCQkKbj6t43DdkwYIFeOWVV/Dtt98iOjoaAPDQQw9h5cqVNn9DURT+/fdfTxfNoyiVNqx3vqLlPIkcqAgDgp0PHgNdHVmfoAgBEse6Pxtd/B1xDeu00PYifg+kRmE6zgAMjf61aN5ZfGThlKQN6xpJep3QDkDSOLIv0HJw8qRf7fAQjzz3eonSogCmCRc/UTgBo8wMyWTAGZzo6+JYwk+OUZTjvk8RDqDCZK2VGk0l+av0vcIJ2GnrjQVA7TEgNA2IHWD7BC1lJD2WIhyIH+qZQtq8dilwcTsQkmLVmvvx4cM4apYG5cYBAzBAZBoUa1jIzF7aqPYIRZP8mnSQyT296i8SjCi0IxCabvVnbvWpwgTbpWXhtCozf1M4GwvJWCC0o2/WcMu04c4778SqVatw2WWXYdiwYYEXpXblypXo0qULduzYgfLyclAUhbCwMMTF+cdL0xOwLIucnBz06dPHf6LU8m474Z3FuTGGphFlU99CXPrEBq0xR1NNXvQUbT80Nz/o1FRKEoCDZVnk5FX6V32IgXFR4TRoTcqKSAuQZG1YyDdnlrfJz2aEpcIjzz3HmepQCgtnwgjiwhTi/uBZCixkFm59wOmQyv2kTSWMlD6YjnnAIEf9kKctnFovuNTq6oGza4nLY9f/2DzMbltvuQhU/0Osw/YUTm0VUL6LDDi9rXBqqkgEUyveNnVqNR4zS4MSoVTiuQk28kaKoI3MzCP1BgKNRWSCILQjifrqCimTTP8btED13+T/hBFWny+3+1TaC0GD+PeZnwRhsykzf1M4q/4C6k4CHaZ7//mXscratWuxdOlSfPbZZx45v8cVToZhcMstt+CWW24BQMz9jz32GBYtWuTpS8uY02hUOCOyxP2Ookn+ppqjQP1p9xTO+pPkb1iGfWuNMoasiWJ1JE+bnyRU9hmuWjh56yYT7LvovLzCqW8iAwxGaTYjHIAKp0FNUkbwg0VPw+rMUhFIsP44zEWlzp8p30WU8ug+0rdzRTgJbAEnAq7FDiJ9pSe8KDiWnF9T5dn+kGJIvln+mq54tAjBZRzUhS9zcdpJifLcrl2oaDYFoXpszBgkuZAGxSHCZJwPc5GKoaUUqPqbuKe6qnCao60mCqEyWPpgQTyKMBKckFF5Lnp0oLzPhABKet+lIjKHH594ajmEjGiCgoJwmRtRuB3h9XBrhYWFbvkAy7iAvpnkYgSAcJEKJ0ASntccJZHkzGcoxVJ3gvyN6mn/OIoms/gtFwFNhfsDLNaA0OYcoJYDYvv5vqMViyqOpDURm7A5KBLIWOzZ9SuO4JVdg5q8YJiEwLVw1p8huRZDUuxafySFX79JK+wHrQl0Wi4Cqgii4Il9PplQ0sd5IhcnH9jCGcIzpL8+D0WTlBGexnxSw6B2zY1bSIvipMJpPhnlLfTWg1SdqarCG2ZpUDrHxODuYcM8UwZ+mUOguNTyka1dSYnCY9ASKzqtIHmhs/9LLLyeeifTCvLu9CQZS0h7ErNUyRfQZs8Xq/V9ijidMTK5r6JUy7RhwYIF2LhxI2699VaPnN/rCmenTsRCVlhYiF9++QVnz54V9k+bNg2ZmR5Kznsp01hIZveCEx0mubZKRBfyQtBUErdYVxRAbS2ZOacokmrFEaoEMghVV7ifSJ1VI7LxT1DnzxCFM9AISXYthQyjAiK7Sl8esSijgBY1aQPBCaYIxYGmcAoWmSrPzZa3xtydVorraWtIICcmVLy3g6fgOFCFnxIXzOy7xAfFUYQCGng2F+elAkWbJoj0za4pnKyTCicTbFquoa0BQkROqLkDP9hVmN6HTVotrtuwATqzXOGvTJoElafSoAgKdwuRt68VAEc4O5Fgj8o/SUyIuMFA6kzj5HKAezCpYgPjHmjGlPvW1wonqyfxHQBZ4fQj5s+fj7vuugszZszADTfcgPT0dKuu7AMHDnTp/D5JKHXvvffijTfeAMtauinRNI177rkHr7zyii+KJRk0TaNPnz7+E8EvKIK4m7m69ocJJq60jYVAw2lA5YLJnbduhmU4p2iEdCAKrgSdIs1pkZiUCIoJDjzrpo+QtA0HRZMAIbwLTccrySdQcnDyKGOI0mfQEjeqVpM3HnnuVbFAxkLpZNV0Fjj3I3H99AOFk6Zp9OnVDdSpHwFQrg0+PJmLU1dP1q4FRdgOcsZj0BD5cnrHXhxi0dYYE6RHeH6igwkxuY7bwG5bF6OYKGN8pHBautSq9XrMWbsWe8+ZUg+Ny8jA7O7SuXq2kRmjJM83HRQgCqfRU8adcvIKfvUhIGWaw5y7kvSp9adJfx3RzW/WWXoSuzJLmUzGQO5YqaWA9zCgFdLEJpCRhNGjRwMAjhw5gi1btrT5ns8yYjBYj9TvCK8rnP/3f/+H1157DVdffTXuvfde9DDmtTp58iRee+01vPbaa0hNTcXy5cu9XTRJ0Wq1CA72kxdIWLr7a7ciugHqi2RmyhWcdaflSRhOPlJgaIHBYIBCGYA5OHk4jgwAaZXDl7RA3SkykxnWyaV1EpK1YfN1nOYEWnok2hjoQ1NN1tJZ8RaQ/Llngt238JvDr6vjrVB+gLa5EsEAsXa54jbMD1g84VLbXEJyoIalAeE32j9WVwcUrSH3IbXCWfqb9wJsKEKIAmhH4QTstHWxCmdziffdSs3c+XQGA65Ztw5bCwqEr2NDQvD+jBmSp3BrI7Ps/0p6fo8irM11Q1nhA2txHHDmXaDbHQ4ngd3uUy9uJZ5Sna+VXuHU1gDVh4lXhhTrWiXCpsziPeQeLhZzd9pAGwe0Yz799FOPnt/rCueqVatwxRVX4Ntvv7XYP2zYMHzzzTdQq9X44IMPAlrhZFkWubm5gRsV1Rpxg0ln5aqFsONsoP4EEClh4mwnYXXNqKqsQmKnVARs13bqNdJJd72VuNg6Q+WfQNM54o7rQpRaydpw0nggeRKZzQx0lHFE4dRWAciw+EpSmdWfBhrOEAVDyheyn+XhZFkWhaf/RXcVB0rhgrs/YHL79IRLLcenRXGi7QpRalvIxJyU7Z1PieIN1z1+HacdhdNuWxercALeDZzDcUJ+VQMThqU//ICNp08LX0eqVPhtyRJkx0sbDTjgxwXOukrbw3ySLjzT4XhCEpkJkWo9EMugpcwYaTnVbxTOgGhnfNR62Z3Wr7j22ms9en6v+xcWFRVhypQpNr+fMmUKioqKvFeg9k5LKcln6a5LHh3knjtqcDyQOEb8DCNrIB93kGLtia9RGAeBYgbVfKfu6yhwTLBp8G3QAHmriCXI3Xr1Bfz6Qk8ma68+BJz9mkSErD0q7bn9TOEEAIZ3hXV18OFJl1reo8MZyysTQtxeAWmVX44lkxyAZ1Oi8ARFGb0SXJzoyLoR6L6cpNRyRNwwoPs9QIrtMYHkUBTQ416wvR7BTVt2Y+3x48JXoUFB+HnRIgzq0MF75QkUpHCpDTJ7F8V5LhqmBQwfndUDCqchwALgqSvI0ih+/aSvCM8kS0USx/i2HH7OO++8g4yMDAQHB2PYsGH466+/7B7/+uuvIzs7GyEhIUhLS8Py5cuhVrv2ri8tLcW///6Lpibp3mVeVzgTExPx77//2vz+33//laPYSknZDuD0uyTnkRRwnOcSm7emeB1w/AVi6XEHfqZeirQSvkJsLk7WYBaJMdojRXIJfRMJHtVU5LxrsD/hSYWT48jzen4j+T+mPxDVW9pr8IMvP1I4adbN4BFRPYEuN3lGaWFFWDgpyjShJmUuTl0dCfRBK7xjEeh4BVECY/q69ntGRSa5nFHSgyKIcuvltfUcx+G/v27DZ2ZjERXD4KcFCzAy3YupgxryyPu5+DvvXdNVMpcSF+BQN+QTFA50nAWkzSWT0N7AkxZOXnFTBMja0JKfgYLPgaZC35YjKIIsFQnv7Nty+DFr167FihUr8OSTT+Kff/5Bv379MGXKFJSXl1s9fs2aNXjooYfw5JNP4uTJk/j444+xdu1aPPLII6Kuu2HDBnTv3h0dO3bEwIEDccAYtbuyshIDBgzADz/84PI9eV3hnDdvHj766CO8+OKLFppzU1MTXnrpJXz00UeYP3++t4slOX7hysDqyWwW4F7+TB5NNZD7BnDmPTLr7gy6BpJIvPaY+OvxEdU0FeJ/aw6rBkVT4ALawilS4dTXE6WFVrg8+ypZG2b1JFBNwWdmuUEDZEa4NaEdiXu5jTV6LsuMNQAXfiIRHAEgaSwJrCS1Ui6s4dS5vh5bYoJgnBByVZkKiiT14koEbkfwLrXOri1VeEDh5Cc3lLF+FfTML95xLsBxHB7atg3v/P23sE9B0/j+mmtweWfPDoDbyowiHkhq64NIv0IRSly63U1fEztI1GSG2+2Mf+97wsIppPjyL4XTpsz41Cis1nuFkXGJV199FTfffDOuv/569OzZE++//z5CQ0PxySefWD1+7969GDlyJBYtWoSMjAxMnjwZCxcudGgVNWfjxo2YO3cu4uPj8eSTT4Iz84yMj49HamoqPvvsM5fvyetvr2effRZjx47FI488gpiYGGRkZCAjIwMxMTF4+OGHMXbsWDzzzDPeLpakMAzjH/7zzefJwDIoXHwOR2soo03h8puKnftN3QkS7KLqgONjW6MyWrrdfBkzsf2QNPQeMP6yYN4VBLdBJxVOYY1ElEtrACVtwxRD1u82FpnywQaKC1JrQlJIOH8r63VclplBS1xoqw+Tuuo4i6x79UQwBcYs4Icv87MaYRgGGX0mg04cLc2kmNSIcakFTANPKb1AhPWbItPFeBCbbd2gAc7/RIIcObuMo2wnUPy919LavL/9S5TkvIdhwSQiLU1RWDN3LmZ06+bR61qVGR9QTVsTeFG7vYAk7yHagy61/MSSH73P7MqMVzgNPlY4a/4lRgi9/cBk7Y2GhgbU19cLH43GepvUarU4dOgQJk405V6maRoTJ07Evn37rP5mxIgROHTokKBgFhQU4Oeff8b06dOdLt8zzzyDMWPGYM+ePbjjjjvafD98+HAcPnzY6fO1xutRPEJDQ7F9+3Zs2LDBIg/n1KlTMX36dMyaNUvyyHDehuM41NfXIyIiwrf30phP/oZ3lmbwStEknUJtDrFWqWJJsuOQZCCyp3UXGT46baQLURuDE8lfNy2cXFA0GtQMIkIiAjdokFgLJx/1Mci19Zscx6GhoUGaNkxRpByGisBXOO3gsszUpUBjAVFq0ucBkR4c+FI0cWejlZaJwH0Ex3FoQAoikru53s5YHVB1kKzhTJograIuJmgQ4FkLpzfWbwLEK+biNjLhlzbb6iE227q+Caj+h1jBUiY7d72aIyRHb9wQj/cLr+7bhw3/7MD4kGrUsUQR+eSKKzCvVy+PXhewITN+QpDVkfbrr/0iqyOTCEywcTLMO7YKSd5DnlxGoPc/l1q7MvMXC2fpb6Sv6HqrKT7FJUDPnpbj4CeffBJPPfVUm+MqKythMBiQlGRpKEpKSsKpU6esnnvRokWorKzEqFGjwHEc9Ho9br31VlEutceOHcOrr75q8/ukpCSbLr3O4FWFs7m5GUuWLMFVV12FxYsX48orr/Tm5b0Gy7IoKCjwvZVTUDglzLcXP4xYTrU1xvQQ1USpVCWYFM7mC8Z9cUCz0RLqSpoA3sKpqSQuvC6+5PymPtxBrMIpBAyKdulyksssKJoELGi+QLb9dWDlDAYtiVKrCLdw43RZZmGdgLQ5JGJnaEcPFLgVrq7Nc4aWi0DdMSC8CxCe4fBwadoZBZT+Sv5NGCltcLCo3qQfclbZi+lP6jMkRboyRHQjkxHeWu/E6slzamfZhM16E1JniKgDZQxROLU17qfvssP7Bw/i3t9+w8wwYlWoZ1V4d/p0XNu/v8euaY5VmdEKkptSV0/u31/7RUMLCWJG0WRSx0tI0j9E9jA+wx7wEND7X9AguzITAij5UOFk9Sa5uTghHqicOHECqampwrZKJV0+1J07d+KFF17Au+++i2HDhiEvLw933303nn32WTz++ONOnSM0NNRukKCCggLExbn+HHlV4QwNDcW2bdswbdo0b1720kTfRCLUAtIOVEI7At3vJm616otkgKm+CISYRfVrzAcq/rT8jSuRUpXRZKDF6sjL2NUXRv1JBKtPA7pOABPj2jl8jSoBiO4FhKQ6PhYAYgeQOvGXF6HgOlZL/vpLuVzh/I9kQqXDVCDexUiLzedJECu+TUf3cbtYJQ0N+PLff9EvORlTu3Rx+3yiqdwPlBiTRVfsAzIXO+57WAOCdGXk2aSjXbNO0gpiUTNoSb8npcIZkux8GiKA5OsMcyI6qxgiu5KPt1A4TotiE1dSZyhjABR6NDXKF//+i9s2bwYARNBE4bxxyBgsHTLEY9d0GmW0UeGs9c6EkyuYR6gNNA+0kCTy8QQZi4mVU4olS97AHyycfA5OOiiwMwe4QEREBCIjHccqiI+PB8MwKCsrs9hfVlaG5GTr76PHH38cS5cuxU033QQA6NOnD5qamnDLLbfg0UcfBU07NtiMHz8en3/+Oe6555423128eBGrVq3CzJkzHZ7HFl5fwzlq1CibPsgyEtJYSNaEhCR5JpiGIpQMJhNGEBc9c4WSD6zCB/JIGOnaNSjaZFlQu+5WS5XvRnT974C6zPHB/kpYGnG3TBjh3PFBkWSQGuon4f3NLa2MMrAVTncj1aorSKTAwtWShaf/+8IF9Hv/fTy0fTumffUVfjljJ7Jz83niFs+n2nAVTRWgrjRtR3Ql63VV8STY19lvTBZtW+jqEFfzA6gzb7tXFobPxemB1CiXGk7k4bSJKymohFycteKv5wTfnziB6zdsELYjaQ3GdcrA0kGjPXI90Xj4/iVBsFxLZ5FpF6hiiVWeCRC5+JPCGRQZeJMXXkKpVGLQoEHYvn27sI9lWWzfvh3Dhw+3+pvm5uY2SiVv4eacXB/+/PPP4/z58xgyZAg++OADUBSFX3/9FY899hj69OkDjuPw5JNPunhXPljD+fbbb2PKlCl47LHHcOutt6JjRz+d0XOT4GAfz9xEZpMw5pwPIlGGd5bOqhrRhXTq7vj5s2ooFIpLbjbNXSRtw3xqlrB0IOuGwA6QYUfhdEpmNYeNwbwiJVlH+Vt+PuauXYsmnU7Y98TOnZjapYv1dU/lu4H6XBKcSBUr7mKsAWjIJWsmGwuIq3yna8h3qjigx31k5rpoDfm+aDXQ+XrTeuzW6OvJs6lwc/ChCCMDdmMuzqrmZkQFB0PhxKyuXZqKieIVnOycl4ZBS1L+sBpJrNbQN5ElBap4703SCAqnhtS3jUjJVtu6Wwqn9BbO7QUFWLR+PViz/mZWZgrGZnTwzESsA6zKTJVAJob9WWnxYS5rt99D+mbyTIIConpIUSS/x6bMwjoByRN9a5HlI9VfYu60YlmxYgWuvfZaDB48GEOHDsXrr7+OpqYmXH/99QCAZcuWITU1FStXrgQAzJo1C6+++ioGDBgguNQ+/vjjmDVrltPu6NnZ2dizZw/uvvtuPP744+A4Di+//DIAYNy4cUJeUFfxusLZr18/6PV6rFy5EitXroRCoWjjx0xRFOrq6rxdNMlgGAbdu3f3bSHoICBCwrWbviL5crdPQbMaxMfHm3LkBSocSwa/TIj99awcB1TsIR16VE/icigSydswP1jnJ0ACeWZTaVQ4tZYKp1My4zgStRkg7rhuphj4OicH1/74I3Ss5Xq7gyUl2FZQgElZVvoAftAoJoiGtg6o+YcEhOEjsFIUaZMcZ6pPfmKo0wKg8AtiTS38EujyH6vPH8M2k2dT5ebgw2jhbGmpxW2//4gvjx5Fcng4/r75ZnSIcEOxKN9N8gB3vJK4qTvC0EyUbYoh6z/dbeeNhSRHY1gakHWje+dyFnOlglUDdFtF12Zb9yOF82BJCWavXQutwSDsu3PQAExPZ0GB8k5OUzNsyixxFPn4M6yZS60XkeQ9pK0Gzn5LvGykVDi1NSSyuCrOatRyX2FXZqGp5ONLzC2cMjaZP38+Kioq8MQTT+DixYvo378/tmzZIgQSKi4utrBoPvbYY6AoCo899hguXLiAhIQEzJo1C88//7yo6/bq1Qvbtm1DTU0N8vLywLIsOnfujISEBLfvyesK51VXXRXwUWgdwbIsqqqqEBMT45TftIwH4TiwBjXULc0IppTe9yGXkhMvEatD97tNgzRr6JuAi9vJYDfKteiLLMuipqZGujYc0gHo/ZhLyq/fwVs4tXXEUmlMmeGUzNQXyUCFDnI7mNebBw7g7i1bbH6/cs8eaRTO5gskKjVrtKAGhQMxA4HYgbaDUjFKIGMR+V1EF5vWOVZTS57NqHD3nk1FGKpbWrDip3X4vIQomCUNDfi/vXvxf1OmuH5eV/NwcgYiX3cjMAo5OL2YEoWiSRvhU2BZqTubbd3VoEEAWQvH6iXpI3IrKzHtq6/QqDW5Di7q0wdvTB4HKu8kacuMd6NjSt6nehMfudRKIjNPpUVRlwPlu8iyFT9SOP2+nckKp9PceeeduPPOO61+t3PnTotthUKBJ5980i2XV3NiYmIwROI17l4f/bmTNDRQ4DgO586dQ3R0tK+L0j7gOJLmIyhKfKRagxrgWNTX1UMV6OtPmBCicOoa7SucfEoURYRNdzhHSN6G+XrLW0UGsGlX+bcLmT0UoUSR0LeQdZDGgBROyYy3bkZ0cdm6yXEcHt+xA8/v3m2xf1m/fjCwLL7KyQEA7Cgqwv7z53FZ62ULYhXOuuNkgB7aAYgfCUR2d65dKUKJVY5W2rT0cbo68mx2dM+98e+yGmz/5xBKGlMAmM61rbDQrfOKzsNJK8yUtUb3FU7eiu6tlCg8yhhj32mw+rXNtp44GogdRCy8zsKEkEk0RaTL/ZU5F+rrMXn1alQ2m9bzTu3SBZ9eeSVohgF6Pkjq1csT3w77B97t1x8n5H3kUivJe0hIi6Kx9MZwFyFCrX95TtmVmUFrzOvL+c7SGT+cvP/sjWFkvI5Go8Hq1avx22+/IT8/X0it06VLF0ydOhWLFi2CUumeR5bXFE61Wo0NGzagsLAQ8fHxmDFjBlJSJAwdL9M+4Tjg5Cukc8++S3ykWmPURI5SBL51jV+n5ig1ipspUTyGoYVYyyjK+QG8v6KMA/TniUIgJgJivVHhjHTNtUvPsrht0yZ81Cr58v0jRuCliRNxqrISa3JywK9YW7lnDzYsWGB5Elqkwpk8CQjLJNFaxa57M59UYPXEIpAwSlC2Kd4918XZbpbj8PyuXXhtVy5CqH6oZS0HxEfLylDW2IikcBcHhWLzcAJERrzCCTfdkDTGoEyeSOlgj67/ce13dJD4iOQUJdngs7qlBZNXr0ax2ZKc4R074rt586A0X8fkT+8CjgPyPiR9Sdc7XIvo7mnihpDlGWImEvwFfqKZY8mSDkqid4+QgzOAAuBpyoG8j8jYoPs9vimDKlZ87AAZj5KTk4Mrr7wSZ8+eBcdxiIqKQnh4OMrLy/HPP/9g3bp1eP755/HTTz+hRw/X3dK9Ym8vLy9H7969sWjRIjzyyCO45ZZb0LVrV2zbts0bl5cJZCjK9AJWu5Bw1jioZv0gyb3b8C82gwOFk7dw+tui/NKt5C/HeS1xuMeIHQSkTAJUNoLhWENbRxQIigEiu4m+ZItOh3nr1rVRNl+eNAn/mzQJFEWhR0IC5pi9EH7KzcWx1omaxVo4KYpEPHY3yMq59UThLF5rshy6oXDWqdWYs3Ytnti5EzVsCEoMkWjmlMiKsVRetrtj5WRFutQCJosHPyB1FY4zudR6W+EMQJq0WsxYswYnKkwRzXslJGDTokUIc3Nm3qNQFHkWDVpT3+1vMCqiJPijMuwIcw8Lg4RutX5q4bSL4F7swyi1Mn5FY2MjrrjiCpSVleH555/HuXPnUFNTY/H3ueeeQ0lJCWbNmmU3T6cjvDLqe/bZZ1FUVITly5dj06ZNeP311xESEoL//MfFWVQr7Nq1C7NmzUKHDh1AURR+/PFHi+85jsMTTzyBlJQUhISEYOLEiTjTKnVAdXU1Fi9ejMjISERHR+PGG29EY6Nrg4YIdwJVyFiiMloJNC6kRlHGgEtfAEPSVGnL5At4hdNLFk7J23BzsbTn8yWxA0i6n2BLV0e7MlNGkSiunRaIdk2rVasx9auv8OOpU8I+hqLw+ezZuG+EZaqch0dZBiB5cc8ey5Px12YdKJwN+Y7bmhgSRhDLZkM+cP4HgGPBxQ4CGz9cnOIO4Hh5OYasWoWfcnMt9t85ZAj+vfVWhAWZFMStBQWul5lXjMVYOPkBqLspb/RNZIBMUYDS/ywCVtt6+R6g9Dfxaawa8siEROVfLpVFZzDg6nXrsP/8eWFfelQUfl2yBLEhZm7NFftIEKvaHJeu4y42+wcPRuoNdNx+D1GUSdESEyjNEX5s4bQpM1+nRWH1JEd77bHAjlTfjvj0009RXFyMzZs346GHHkJqqqWrdWpqKh5++GFs3LgRhYWFbi2L9IrC+dtvv2HZsmV45ZVXMH36dPz3v//F22+/jaKiIuS2GjC4SlNTE/r164d33nnH6vf/+9//8Oabb+L999/HgQMHEBYWhilTpkCtNnVAixcvxvHjx7F161Zs2rQJu3btwi233CK6LAzDICsry+lQxDIO4BVOVyycTDCYmJ5I7z058OvDWYVTAgunR9pwgLnRchyHotpaNGmdezk7JTNFGLEWiqC0oQFjP/sMu86eFfaFKBT4aeFCLOvXNljF4A4dMKmzKS3R18eOoaDGbCAb0gFImw0kjbd9UX0TUPwtkPumWzlwLQjtSJRtigFqjwMXNkEf1RcJ/ZaACXXe9XTd8eMY9tFHOFNtyiMaE8Ti58mZeGtwJMKUSow1C92+raDA6TxkbRAbNAiQzsLJu9MGRXvfBbTqb7LeusJ6zmybbb0uB6jYC+gbxF1PUw3UHAWaxFujWY7DdRs2YEtenrAvPjQUW5cuRWrrJOstJWTCQyeyfBJgt3/gJwf9NRdn1d8kEF2Ld3NZS/YeYjwQOIifUPIzC6ddmQkKp56kPPI2ujri6XThJ+9fW8YqmzdvxuTJkzFu3Di7x02YMAGTJk3Cxo0bXb6WVxTO4uJijGo16z5q1ChwHIeyMmk6sGnTpuG5557DnDlz2nzHcRxef/11PPbYY7jyyivRt29ffPHFFygpKREsoSdPnsSWLVvw0UcfYdiwYRg1ahTeeustfPPNNygpKRFVFpZlcfHiRbCtUhXIuAifw88VCyfaUX0wziqc7ue58ojMOswgs82JfpJw3Q56lsXC779H5htvoOe77+JMVaucmxxLBl91JoujXZm5qPAcLSvDiE8+wVGzfjImOBjbly3D9K62FVdzKyfLcXj5zz9NXyqjgJj+9nPllu0k1jVlrLQBa8I7A+lXAxSFXYd/wKzXb0TI88+jy5tvYvpXX+GeLVvwzl9/YWt+Popqa2Ewk6WeZfHA1q245rvvLHKOZkRHY+eS+ZgWVgCU/wEAmJiZKXx/vr4eua3rz1lSpgGpM8S5E0f3BtKvcj9ypSoWSJ1JLOneRt9E1ltrrcvNZlt3JUot4LKFj+M4LN+yBWtyTBbLcKUSWxYvRrc4K27IvCLsgxycdvsH4f5rvVomp6nNISmCbLQHTyHZeyh5Mul3pAxU46cutXZlZr60yBdWTn5s4m7eZRnJyMnJcahs8kyYMAE5Oa57h3hl2lSj0bRJRMtv6/V6j1+/sLAQFy9exMSJE4V9UVFRGDZsGPbt24cFCxZg3759iI6OxuDBg4VjJk6cCJqmceDAAauKrEajgUZjmjFraCAvM4PBgNLSUsTGxkKhUICmabAsazHLTlEUaJqGwWA5y2RrP03ToCjK6n4AbToXW/sZhgHHcVb3ty6jrf18Gb12T0GxoDgWlLoCYA1gW43d7d5T3RkYNPWoOFeN2NixYBjGP+7JwX6r96SMBxPdG2xIR3Ctzm9xTx3mALpaUMEpoI3nFntP5m04KChImnsKSwfX4wGwCALMzuVvbY/jONz1889Ye/w4AKC4rg5Xr1uHvTfcgGB+1pjVgTr9DmiKBtfjfrB0sIXMlEqlZRkr94OqPwU6cQTYiGyH9/TXhQt4ae9ebGjlAdIxIgK/LF6M3klJdu9pdFoahqWm4sCFCwCAT44cwaOjRiHFzNXKZj3pqsFVHwTHseCSJgLG7yWrp6ge2NSYjYNFOzAiGCjURSOvhkN+TQ1+MbNSAYCKYdAlNhZdYmNR3tSEfWYukwAwqXNnrJ49G3EqGmwNC4rVAgYtxrdKTr2toADZcXHi+71IU1ohimWda3uqZECVTPYDrvcRdBgQPYDst9G/eawvp1Wk7Lomi2eVP16v1wttnWEY4Z5YXTNxlTY+4073EYpIUBwLWlsDjmXBOlF2AHjxzz/x5l8mN1wlw+CHa67BoA4drN+rrh4sx4Kjw4T78lZf3lpmFvfERIDiWEBdZbfP9tX7iWtVr94aR1gbS7l0TxGmvJS08VpuP0+Zi8Fq6sApE9u0JV+OI1q/h1rfEwUKNDiwejU4ynJts8ffucbnD4oIYQxzKYxhW3/vT1RXVyM5OdmpY5OSklBt5lkkFq/56RQVFeGff/4RtuuMUeTOnDljNXzzwIEDJbv2xYsXAUBImMqTlJQkfHfx4kUkJlquI1IoFIiNjRWOac3KlSvx9NNPt9mfm5uLoKAgHD9+HHFxcUhPT8f58+ctKio5ORnJyckoKioSFFUASEtLQ1xcHM6cOWPh7tu5c2dERkbixIkTFo03OzsbSqWyzaxDnz59oNVqLVyWGYZBnz590NDQgAKzdU3BwcHo3r27sECYJyIiAllZWSgvL7eQQWxsrHfvieOQVFmNpMRYaOpLkFtkmmW1e09dM9By+kvU15ShiR2M48fjERkZ6R/35HI9XY2aqiqcMzuP7XqqRnp6uEv31NLSgurqahw/fhxZWVnS3VOzDgUFpv3+2PbePXwY7x86ZHFPR8vKcNumTVhh5qqaWKNGh7hQNFYXI79UA47jUF1djby8PPTs2dPinmJrtiKcrkZsTG+b93Tu3DlsOX0an5w+jb8qK9GazPBwvDt8OFIUpNt2dE8LzBROrcGAxzZvxj29egEcC6X2HLK7dII2pCtyT5vWsjMMgz4ROdCq1ShtjkJtQQOAHEnrqYTjcPX2PAwJ6ozLQwswWFWCPJ31oDgagwHHKypwvKKtd8NDI0fi6rg4lOTno4TjkFRZiaTEeGiaqsFdvIg4lQpVxgnBbQUFWNa9e2D1e0Z80Zd3DG9BPIDKi+dQarbekb+nvLw8oX+gKIrcU0Q4KsrOg2M5lBnywNEhzt8Tp0dKVQWSEpPQUFeOgrOmMtq6p02lpXjcTNmkADw/cCC6GdfvtqmnpCQk6xpQW1uLC2fOwcDUe7We8vPzBZmFhIRY3FOQ7iLiasqhCDUgviv8ru1VXygCY2hElbYQbLDaa+OI+vp6QWbp6en+9zxVa3Ax/7Tf1FNubq7wHjp58iT69evX5p461NYjMTYCtTXlKC4zLdPwSr+nq0dFRQWa6mNQ35DjvXry8fMklSenJ9BoNAgKcm7JiEKhgNbJJUbWoDiXF7c4Dz8L0BqO49rs5/e5MyNAURR++OEHzJ49GwCwd+9ejBw5EiUlJRapWK655hpQFIW1a9fihRdewOeff95mTWliYiKefvpp3HbbbW2u09rCeeHCBfTs2RMFBQWora1Fr169ZAunVPdU+itoRgnEDgarsHSHsnlPlbvAXdwJQ1AsctSj0Kt3n8C2cMJ79WQwGHD8+HH06tVLOgtnALS9n06fxlXffgtbneKqmTNxff/+5NyFX4JuLgLXcTbYqD4WMrOwcOoaQOW+BoAD3fM+sEy4Rdk5AJvOnMHK3bvxlw33/WlduuCzK65AXGio0/fEchwGfvghjhmVtXClEoV33YWYYBWo48+SvrfH/WBps6AqjQVgzn4FjqLAZt1mER1VinqqamnBkI8+EtJWJDKNGJSYgYiYROTX1OB0VRUaHLzQIpRKfDZ7NuZ07245c3/qVVCGRiDrFrDByVj2449Yc+wYACBSpULl/fe3WUNi9570WnCN+SRgUFgmKJp2ru2xOqCxABSrBh030PU+ou4EEBQBOjQVoBXefZ4a80GfXQNWlQiuy3/aHK/VaoW2Llg4DWqwx1cCALiejwE0I6qPoHJfA61vBJd1E9hgy5Rprcu+JicH127YYPGcvjd9Om4eOND2PRnUoE+9TCycPR8R1uV6qy/X6XQWMrO4J10jqKIvAVUs6IyFYDnOr95P3LEXAFYDruudgCrOqxZOXmZuWTjVZSTisyoBdGiS1XsNhPeTsxZO8/dQm3uq+BMMTYGN6geuVcAjj9/ThU1gq/4GlzBGiCHQHscRrct+/vx5ZGRk4Ny5c+jYOi+2j6FpGs899xymTnUcWPPnn3/Gk08+6bJ+5hUL56effuqNy9iENxeXlZVZKJxlZWXobxw8Jicno7xV+gC9Xm/X3KxSqaBSmfLM1deTGVOGYRAXFyd0kICpAbbG1mJ4T+6nKMrqfltlFLvfI2XvON2038qxbe5J30zcGCkKdMrliGuIdKo+/L6eOBY02wIwoW3WQNA0TdYVNpwGQlJIcmW4dk8URQltmJ8Uau9t72BJCRavX28xiL1l4EB8fPgwDMaX0l1btmBIair6JScDIQlAcxEobXUbmVmUsfYMqavQNCAoUlB6dAYDvjl2DC/++adFKgdzZnfvjodHjcLQVpHjnLknBsBDo0ZhyQ8/AAAatVq8d+gQHh87FlCEkDWarAZMkHENEscC5SRVFRU3FExo28ix7tSTnmWxcP16yxyJXQbh9eHDkZ6WBpom7sxlTU04U1WF01VVOFNdjdPG/4tqa9E7MRGfzZ6N7vHxbWWgDAdamgC2BQzDYHJWlqBw1ms0OFhSguFpac6XndOQFC4UDfR5wuHxQlk4DXDuW/J/TD/X2iRrAC6QSL7osQJgIr37PAWRgSjNaQAr51coFG3ecWDVoCmaRCIOauuqZw2L/ao4QN8ISlcLJqztoIxvH8/u2oUnd+60+O658eNx65Ah9u9JR9bc0UFhQFDbNaae7sutyUwoIxMFdL/TVHYb69t88n7iWFCcjjwHyjChPXijLzfvU/njXLqnmkNA9SEgeQIQlmzzeKfvSVsDVBwGrYol6+FF3JOn97d+D7W5p+QxpIxWz+Dhd66unvQRwbFt+pX2Mo6wtt/W9/7C448/jscff9zhcdaMhGLwisJ57bXXeuMyNsnMzERycjK2b98uKJj19fU4cOCAYLkcPnw4amtrcejQIQwaNAgA8Pvvv4NlWQwbNkzU9Wia9rtZjEuOyr1kQB2SDDq6F9Jj2sECdY4Fjj1H/va833o49qazJJpgVA9B4XQFmqaRnp7uRmEDi+K6Osz6+mu0mK0pv3vYMLw+dSqyYmPxoDFnsFqvx9Xr1uHgzTcjirf+GQNp2JRZ3UnyN4rkx9To9fj48GH8788/cdZM+eJhKAqL+vTBgyNHoleiuHQhrZnfuzce37EDhbW1AIA3DhzAiuHDEcYEk+fDPE0AqwdCO5EInolj3bquNR7cuhW/m+XE7B4fjy/mzEGk2aQdRVFIDg9Hcng4RnfqJO4CQlCtZgDARDP3Z4CkR7GmcNpEyMEp8jXJhJBIvJyBRKp1JXehrpY853QQ0MqjwyswRqu3ocXq11bbuqsBg3iUMSR1ko2gaGq9Hjf99BO+auV6d/ewYXhktBOByAxqQBHqk4BBQAD3qebBZWiV7eM8gGQyYyROi6KuIDmFQ1KsKpy+xK/bmZ4YZVzJuyzjGbxpEPRyrHXP0djYiDyzgBOFhYU4cuSI4Kt9zz334LnnnkPXrl2RmZmJxx9/HB06dBDcbnv06IGpU6fi5ptvxvvvvw+dToc777wTCxYsQIcOHUSVhWVZFBcXo2PHjjZnUGREwnFk8KatBcIcDBr1TUDlAfJ/0niwHIfzRleGgK4PigaYYDKg1jdZVzglSIkCkDZ8/vz5wJeZE9RrNJixZg0umuXcndWtG/5v8mQAwH0jRuDPc+eEnI951dW44aef8N3U/qAA4qoFGzLTNwNNReT/yB44U1WFq9ets4g6y6NiGNw4YADuGzECmTHSRFNU0DQeGDkSt23eDIC4tK765x/cExcMoM5yAMYogdTpQPLlpgGaRKzJycGr+/cL25EqFX6cPx/hQUHS9ZWKUPLXqLCkRkaiR3w8ThrXw24rKMATY0Uo0rzCSYlM50NR5NnU1buucPIpUVRxvonmyISQNsCEEsWXsqwbq21dlQh0X26Sm1g6TAVSZwF0W2tARVMT5qxdiz/N1mcBJBrzcxMmODfrHpYO9HyA3I8PcKpP5TjjRIMfWUSEiQSF+MkXN5HsPSTk4ZQoLYreP1OiAE7ITFdP+sigSO/nEO04l4xRQsSNqWU8hzcNgu1mJHnw4EEMGDAAAwYMAACsWLECAwYMwBNPEFeoBx54AHfddRduueUWDBkyBI2NjdiyZYtF9NyvvvoK3bt3x+WXX47p06dj1KhR+PDDD0WXhV+07YXlsZcOujrg5P8BBZ85HjCU7yGDntBUIKJb+6oPR7k4+bDjfF43F2lXMrODzmDAvHXrcMzMnX5gSgrWXHUVGN7tjaLw2ZVXIsMsuNn6kyfxfo7RWqepAoxrrtrIrOE0aa8hyfixsByDV61qo2xGKJV4cORIFN1zD96ZMUMyZZPnuv79kRxuGhi9sncvdDAqUdZm/CVWNg+XluKmnyzzrq2eMwfZ8fHStrPEsUCXmy3SkZhbOfedP48GjYgBJ2e0druSPzbIzVycxkkMSVPSiEERAvR6GOh+dxtlE7DRP9AMUa6DXSwzE2xV0TpRUYFhH31koWwG0TQ+u/JKvHD55TbdT21i5X68gcO2Xr4bOPEiUL7DuwVzhCICyL4LyLrR65eWrH+QOg+nkBLFywqbEziU2YWfgTMfmDxvvElIEhCZbZoclLmkaDcWznHjxtntlCiKwjPPPINnnnnG5jGxsbFYs2aNJ4on4y5BUcQCY9CSJOH2BjXRvQFNORA/vP3lelKEAaiwrXDyedzctHBeCnAch7t++QW/5ecL+zpGRmLjwoUIV1quQYsJCcF38+ZhxCefQGtcMH/37/sxak5v9EnrBdgKMxQUCUN4F3x4qgK3/73W4qu4kBAsv+wy3DF0KKKDXXRDdIJghQIrLrsMDxjdgi80NGDPBTXGxwJg1WTG+8JmIGkCGRBISGVzM+asXWvhqvzU2LGYlZ0t6XUAAMEJbXZN7NwZbxkjmepZFrvOnsWMbt2cO59g4XThNcm7wbqtcFqP3tuuab4AgAJCO2Brfj6uXrcO9WYTBbEhIfhh/nyMEety7e9QDLHA+VsuTpoJ/HboKQtnkP9ZOB3CGN9tvsjDKXNJ024snDLtHIoyzfZryu0fG5oKZC51aw2j3+IlC+elwKv79uEDs/Qn4UolNi1ciA4R1td4DerQAW+aRXLTscC034pRoUi3aTW5iERcvkuP2/+ut9g/tlMnHLv9djw6ZoxHlU2eWwcPtrjOd7kFJNehQQ1c/B2ozwVKfpb0mnqWxYLvvrNYp3pFdjYJWuQlxmVkgDGbdNpmFkrfIe5YOHlXO52LCqdxXTCUATTQb8gHSn8D6k65fg5NFVD0FVD4Gb7a+yOmffWVhbLZLS4OB266yTVl88LPQMEXQGOh42N9gdLo2eBvCmd7gDH2fVJbOBn/s3A6hPaRwqmuBCr+BOrPOD5Wpl0iK5wegKIoJCcnuxXNScYKKmMAFbX1iJ62aFf1wdhROFm9aYDrpoWzXcnMCutPnsT9W7cK2zRF4durrybRZ+1wy6BBWNK3r7B9oaEBS374AQaWbSOz3WfPYsAHH+CPs2ctzvHAiBHYtmyZhZurp4lQqXDX0KHC9ubKcGzT9SWWuJojZGfKZEmv+dC2bdhuFiQoOy4OX86ZY+ECKWk709YCFXuBqr+FXZEqFYaZBXDbKkbhdDVoEGDmUttg/zhbmK/h9BUlW4C8VUBjW5lZrbfmYiJ/K8c7jSIcrCoBW3JP4NShN5EdZHI/n5CZif033ogusbGunbv5HCmbjyw7Dts6P0morfFamZyi+QIJRFd73OuXlqx/kDpokB9bOB3KzFcKZ/M5oHQrUHXAu9eV8RtkhdMD0DSN5OTkdh9sxevwLnO2LJwlv5BBUitlrF3Vhz0LJ2/dpINMUSZdpF3JrBV/XbiAJa3Sn7w9bRqmde3q8LcUReH9GTPQM4G0xXBKg7PF+/H272sFmVEUhVf37cMta/4PLc2myZFIlQrrr7kGL02aBIUP5PrfYcMQakzwfFYfgwcPVYCrNlp4Y/oSzwCJ+DonB/+3b5+wHaFU4scFCywi0gIStzNtLbGwVe632D3JbB3n8YoKlDY4qQQGJwEdpgNx4qKUAwAiewDpVwGxQxwf2xqOAzrOIddWtXUT9hqaKqJsaNtGUrZab/xgnnHdYt+gB2YfUOCTQg0YsLg6/AQGqy7gpgEDsGXxYsSEuNGv8cq/jyJkOmzrQdHkr76JLB3xF5ovkPWldd5XOCXrH1QJQMdZQPJEaQomrOH0P4XTocx8pXDq5Ai1lzo+GU3W19fjxRdfxJQpUzBgwAD8ZVxjU11djVdffdUi2mwgYjAYkJ+f73JyVBkb2LNwaqqJZaNyv2n9k5F2VR8hyWSNqjXlICga6HY7kLHY7bWr7UpmZpytrcUVrdKfLL/sMtw2xHnFIEypxHfz5iEsKAjdlFVYEHEMfx/9Dr+cPo0jJ0/i6m+/xYO//YI5YcdxT/R+JDKN6JOYiIM334w5PXp44racIj40FLcMHChst1QdQ965f4gFL+lyya5z5OJF3Ng6SNDcuULuTHMkbWf8ZIyh2WJ36/QoTrvVqmKB+KHkeRNLSDIQ3ce1NbEUBURkkWszSsfHewqF7dQoVuvNTYXzbG0tRn36KTaeKcB3jT1xUNMBNDi80ceAD4dGIMgdpYM1mCkJvkmL4rCtK0JMsuOjjfsDrPsTCa4iWf8QFAHEDgIinVy/7YiMxSSIUkiK42O9jEOZ+UzhNE5cyfElLlm8HjTo/PnzGDt2LM6dO4euXbvi1KlTaDSmI4iNjcUHH3yAs2fP4o033vB20SSlwdlZdBnnESycVWQAYR7RsPwPEg00ogsJf9+KdlMfkdnkYw2aAYLdy9toTruRmREDy2LO2rUoazJZh6/MzsbLkyaJPlePhAR8dMUVeGjDRwCAWLoZyzZsQBhF4WxTE7KDqqGkDKhjgzG11wi8N3OmYF30JfeOGIF3/v4bwWjGgohj2FMcha7dZ7uWusMKVVaCBD05diyusBMkSLJ2JqRFabFI5TEsNRXhSiUatWSAta2wEEv79bN1FhkeIRdns9Wv29SbGwrnz2fOYOkPP6C6hSi3HGjs0PbCQ2OnYoSqgPTvQRFA3GDR5wZAXCA5jgTm8WFkUYdtXRkNtFwk1noJ+3K3ENKieDcHJ49fvoeU0X4dJ8GuzHiF09tWdNnC6Xfs2rXLpd+NGTPGpd95XeG8//770dDQgCNHjiAxMRGJrRKbz549G5s2bfJ2sWQCgaAoIGGkMXiQmUOkuhKoPUr+Txrvk6LJ+D+f//svDl+8KGwPSknBV3PnCulPxLKgd2/8fXYUUHAE0Ywa1XWNqDQ6jfRQVoKhaUzqNwPTRs/2m7WwHSMjsaxfP+w+Rtavnq2rw3cVsbja/tJVp2jW6TD3229RVFsr7JvVrZu43JfuwIQQ6yDHkfynxvVVQQyDcRkZ2HT6NABga34+OI5zXCfaGuJOqowyBXQRg64BqD5I1l3HD3V8PE9jIfltWJpr15UKxkyBdwYXFE49y+KJHTuwcs8ei/0dIiKwceFCDExJIZ4rtccs0t2IRnCnDffvyOVhmWRALnFqIrfgI7v6wMIpGRwHNBWSNhrRzev5RP2KkA5A4mjvT2gIFk5Z4fQXxo0bZ/EedOq9CLjsceD1p+63337D8uXL0bNnT1RVVbX5vnPnzjjXKrmzjAwAMlBIsWKNKt9JXiiR2ZKuQ/NbOJa4ubWeqa85Qgaqkdn+MzvuJ7TodHhihym/XbhSiZ8WLkSY0j2XxRcmX4mPv/oK5Q01iKFbUMWGgQaLyyIbsbTXAHTof4XfDXAfGDkSnx4+hN+bM5Gni0Xlj5ugUoa6laqkRafDld98g11mAZK6WQkS5FEomiid+mZilTML6DExM1NQOEsbG3GyslJYh2uTmn+Bsp3EqpY6U3x5GguBsj9M7nxWckxapfoQUbBSJgMJI8RfVyoY2y61VhHpelna0ICF33/fJqjW0NRUrL/mGqRGGgemcUOI/PhI0BxHIgiLiR4cKNaVDlN8XYK2+NClVlIKV5N3Z48VAO1GO9DWkmdUGQvEDpCseF4jtAP5eBvhGZRdav2FHWZjIgDQaDR44IEH0NzcjFtuuQXZxjHBqVOnsGrVKoSFheF///ufy9fzusLZ0tKCBDsver90nxAJRVFIS0vzG6tGu0ZdbgpmYMO62a7qw6AGTrxEBl29H7UcdNUcARqLiKuPmwpnu5IZgDcPHMAFs77lgREjbKY/EYMqKAjXDByDj//cjDiGKJzLsiJxV49+CA2JBkLT3L6G1HSLi8M9l43Aq/t5yy6Lq9etw4YFCzC1i/hUQmq9HnO//dZibWRMcDB+nD8fUQ5SvkjezphQonC2Cqo1KSvLYntrfr5jhVPIw+miK3RUT6D0VzIJ1JBLtp3BHyLUAnbXcFqtN8H10rFisqOwEAu//97CvR0A/jt0KF6ePBlKppVybp52qGwH0HCGrKNzNkooqycu1z5avwkEcJ8qWDi9b3WVTGYURcqvbyH3487qBnUFCaIUkuKXCqdftjODxtSO/H3S5xJibCvvoxUrVkCpVGL//v0INnt3z5o1C3fccQfGjh2LLVu2YJILy5AAHwQN6tmzp12/4R9//BEDBvjfQywGmqYRFxfXLiN8+hyDFmg6S3K+AUD5LqJ8RfUkgTqs0K7qg1ZBeGz1rdZW8fnbJJhBbE8yq2putnDZSw4Px4rhwyU7f3x0Gm4YMAA39+qED2fOxMfjupL1mlE9bObn9DUvT56M6/r3F7a1BgNmf/MNtotJG2L83dXffostZoHeolQqbF26FD0cKXTwQDsTojhbPhs94uMtJhi2FTqRi9GdPJwAcduLHUT+r/rLud9wnCnoma9zcDKhxKpFtZ2XtlpvnW8Aut5mWmtvBZbj8NyuXZj45ZcWymaEUol18+bhjWnT2iqb5uibiXWppRQo+MT5FCIxfYGeDwDp85w73gM43dY5zjQ49wdETCRIjaT9A78G1d1cnHxKFB+uBbaHQ5mxBhJkUe0gn7mkhQoCut5KJol8GQhNxi5fffUVli5daqFs8oSGhmLp0qVYvXq1y+f3+mjonnvuwTfffIOXXnoJdcaE4CzLIi8vD0uXLsW+ffuwfPlybxdLUgwGA06dOtXuInz6BU1ngfxPgdItZDtlChA/DEgaZ/Mn7ao+KMp6ahSOldRlpT3J7IXdu1Fnljz+qbFj3XaltUAVh4TQMCwf0AWjQ0OBeqPyFem7iLSOoCkKH82ahcV9+gj7NAYDZn39Nf4oKnLqHDqDAfO/+w6bz5gSeUcolfh1yRIM6uCcy5bk7azDVKDLLSTKqxkURVlEq91ZVASdo2u6k4eTJ24wmXRoLAJayhweDn0DuS5F+3b9JkBk2OshIHNxm6+s1psyikTltaGgVzY3Y/pXX+HxHTvAcqY1+P2SknDolltwdU8nLMCKUCDrBuLFoakGct8Gzm8ggXacwYdWH6fauqYaOPEicOo17xXMEelXA11uBsI6ef3SkvYPQi5OdxVO/02JAjghM201kPsmUPCp9wpF0cQgEOk49ZiM72hqakJpaanN70tLS9HcbD2InDN4XeFcsmQJnnnmGTz22GPo1o2EqJ46dSqys7PxzTff4IUXXsDs2bO9XSzJUaslSjAsYwnvKspHqg2KADpMc+hC2q7qw5rCqWswReYMksZtrD3IrKi2Fm///bewnR0XhxvNUoNIQmRPYjmJHwG1RgOuy21kOyxD2utIDEPT+Gz2bMwzG+i36PWYsWYN9jpYR69nWSxavx4/njol7AtXKrFlyRIM69hRVDkkbWchKWR9kpX1ZhMzM4X/G7VaHLhwwf65eAunqy61AHEfi+xO/q/+2/6xgMmdVhnj/JpPHyGm3vaeO4cBH3yAX/PzLfbfNGAA9t14I7rGibDmquJISoqwTgBnAKoPA2feBwo+t5oz1J9wKDNFuNH9UO18sCZPo4wmsRF4F2svI1n/wFto27mFE3AgM19FqZXxeyZOnIg33ngD69evb/Pd999/jzfeeAMTJ7qey9YnoboeffRRLF26FN9//z3y8vLAsiyysrIwd+5cdG6VM01GxgI+gp9BA2irLs3gOEK+QXOF0ywCnJ+6cfqCx3fsgNZspnfl5ZdDIbWbcEgS+RgMAEqIy1B0L2mv4SEUNI2v5s6FjmUF5bFJp8O0r77C1qVLMTS1bRAuA8ti2Q8/4LsTJ4R9oUFB2LxoEUak+d+aVZ7LreTjHJXeNoWSgBQWTgCIGwrUnSBBiJIn2g++wrvT+nr9plj0TUDFn0RhMgt0xHEcXtu/Hw9u2wY9ywr7Q4OC8N6MGVjmanqaoAgg63qg+TzJvVx3AtBUWCoBHGeyaBatIes4O0wHgtvmg/UbGCW5B30TycXpIyWvXXKJWDgdwiucnKFtejlPUX8G0JSTSdhLIbBjgPLOO+9gwoQJmDdvHlJSUtDFGNMhPz8fJSUlyMrKwltvveXy+X0WGzo9PT3gXWdlfABFAaoEMtA4/S6QfVfgDc7cxaqF06hw+nFuMG9z5OJFfHX0qLA9vGNHzO7e3Ycl8k+CGAZrr74ac9euFdxj6zUaTFm9GtuXLSOpKYwYWBbXb9iAr48dE/YFKxTYuHAhxnTyvstdG9QVJKBMUAQQ3cfiqw4REeiVkIDjFRUAgK0FBXhq3Djb5xIUTjfzp4Z1IoOs4GSi9Ngb3/lg/SbLcfjh5EmszslBh/BwPDpmjGm9a+FXZGKr0yL7AXp09UDFXjLhZVQ4z9XV4ZZNmyzW9wJA9/h4fDdvHnolSjBZGNqRuHxq68gEJD85wLFA/kckzUjcUKCpiFh0/CmQii2UMaRv19YQi70vYQ0kCjwTDMRd5vdWd7vwazgNblpMA8DCaRfabDkJqwVoL0xq1J8gngjJE2SF049JTU3Fv//+iw8++AC//PILzhojiPfq1Qv3338/br75ZoSEuN5evK5wDh06FAsXLsS8efPQUaTrVaBA0zQ6d+7cLgKu+CXmFgcnrHntrj6sKZwSBgwC2ofMHtq2zTxbK/43aZLnIvc1FoCuOoTu7HHQ1c2+TWfhAkqGwXfXXIMrv/kGvxndHmvVakz68kvsuPZa9E1KAstxuHnjRnxppsSrGAY/LViACWbuqmKQvJ21lAKlvwHhmW0UTgCY2LmzoHAeOH8e9RoNIlU2om/GDiQz8iGm9ajNOh2CFQpxqV4oCsi6yTllJ2EEEJ7llUiOBpbF2uPH8fzu3ThhlAkAfHn0KJ6fMAG3DxkCpuWC1TQzberNLAcnx3H4+PBhrPj1VzRoLd32FvXpgw9mzkS4lGuoAWOuVLO+ryEfaC4hn8p9RAEFfBql1um2rowmE6p8n+5LWA2JyAoA8dIFWnMWSfuHmP5AeIb7kcP5966zEZK9jEOZ0QwZQ7F6onDCCwpnoKQlkkFwcDDuvvtu3H333ZKf2+ujSYZhcO+99yIjIwOjRo3C22+/jYsXnVzwHyBQFIXIyEj/CkvdnogxrsGLH+ZUYI12Vx8hqUB0b2Ix4YkbBnS7HUgcI8klAl1m2wsKLNaLXZGdbd990l3KdoKqO45gBUC1OFgb6KcEKxT4cf58C+WxuqUFE7/4AsfLy3Hbpk349MgR4Tslw+CH+fPbpBwRg+TtTHA3tx7YYJKZW62B47DTXoCkqJ5A4iggOBEcx+GpnTsR9eKLyHrzTZyxkkPaLs7eX1AkCawRkiTu/CLQGQz49PBh9HjnHSxev95C2QSABq0W/92yBcM++ghnG41W3lZRf9vUm1HhrNKymLJ6NW7euNFC2VQxDN6fMQOr58yRXtm0RkQXIGMRmXgQlM0Qn0bIdLqt8+80f1A4hYkEpU+WakjaP0RkkajR7i7DyVhE1hCHOBcYzds4JTPeysl6aR0nv7ZazsF5SeP1HmTfvn0oKirCypUrodFo8N///hdpaWmYMGECPvzwQ1RWVnq7SJJjMBiQk5PTLiJ8+iXRfYBudwApU506vN3VR3Qv4kYW09e0j1GSF6lE7sWBLDOW4/DAtm3CNk1RWHn55Z69qCoOLMeirLwMhrBunr2WBwkJCsJPCxZgtJlyXtHcjEEffogP//lH2KegaXw3bx6mdXUv6qDk7UwRSv62ysPJM6ZTJ4s1vNucSAPDcRwe2rYNT//xB/Qsi6LaWixav95iTaLTNF8gaw59gEavxwcHD6Lb22/jhp9+wpnqarvHHyotxbN//oWfz5xBQ3OtxXet643Tt+BgSQnu374bW1vJdFhqKg7/5z/4DlCkSQAAtqFJREFUz+DB3pvAoiggshvQ+VqSjiF+mNPvC0/hdFvnl0U4m/LFk/gwJQrgp+8hZTQQlmZ/LbYPcUpm3lQ4OQ7QyxbOQOHXX3/FNddcg8GDByMrKwudO3e2+GS5McHsE3+59PR03H///fj777+Rl5eHZ555BjU1Nbj11lvRoUMHTJ3q2xeDFPhVB9neoCiS603E4EWuD/EEqszWHjuGf8xCe9/Qvz96OpET0j1IW+RYDogI7NDvYUolNi9ahOFmSx40Zm2BoSisvfpqzMrOluR6krYz8zycHNfm6wiVyuK+7CqczSXgmkvw2LZf8b+9ey2+OlhSgpW7d4srm7YGyFsFlP5q3XqlrQPKdgJ1p9p+5wbNOh3ePHAAWW++iVs3b0ZRreW1w4KCcN/w4Ti/fDnemjYNEWYWyGZOgb9KLuDqrz/Ht8ePgzOTKV9vhTU1+O/mH7HpzGnU6kznDVYo8PKkSfjzhhucysnqMUKSSSTzGBcDFEmIU21dlUiU5TAPemQ4Cx/RlbHhdu4FJOsfdI1AYwFxV27nOJRZ7EDiveHsOlRdI0lHV77Har9qF1ZjiogrK5x+zcsvv4zp06djz5496NixI8aMGYOxY8dafMaMcd2LzmdBg3g6d+6Mhx9+GA899BA++ugj3Hfffdi6dauviyUj499wLGBoIS8MjgNKfiGBUuKGXdKJlTV6PR79/XdhO0ShwNPjx3v+wrEDgaqDaAnu4tPBmVREqFT4ZfFiTPzySxwsKRH20xSFNVddhbk9/DTHKGO0cHIsGehYS4/SuTN2FxcDAE5WVuJ8fT06RrYdCHFFa7DjTA4+yUkA0Hbt3zO7dmFGt24WQZXsoowhbn0N+UD1QRKx1pyWEqJwhnYAotwPblWv0eCDgwfxyr59KG9qa/GNVKlw19ChuOeyyxAfSuR259ChmNujB1b8+ivWHj+OFpYETGpU12P+d9/h0y5d8Pa0aciIigLLcXjv4EE8tH07hiiKMTYEUHNkSDEiLQ2fXHEFsuP9OCKsvxKWBoQt8nUpCGZrcwOexnzg3A/E3TpziWvn0NYC1YcAZSwQO0DS4nkVsUtvGvNIDnRORxRVMfDutIpQ9wOwyXiUN954AxMmTMDPP/+MoCDp68rnEUH279+PFStWID09HbfeeisAYNEiP+lsZWT8EV09cOxZ4OSrRNk0tABVfwEXt1/yKVHeP3gQhWYWnOWXXWaKuOlJQjuC63Y36iLGef5aXiIqOBi/LVkiKFQMReGL2bNxTS8/TvlCK0wTLjbcaie2So+y3YaVc1v+aewqPgu92WtyUR9TICK9MT2MRq93vnxxQ8nf6n9I0A5zJIpQe7a2Fvf++is6vvoqHti2rY2yGRsSgmfGjcPZe+7BcxMmCMomT4eICHxz9dXYsngxosPIesJQmpgut+Tlofd77+GpP/7Af/buxV1btqBJp0MwRe6Fo1V4bcoU7LruOlnZbA/42KVWUqSIUqupJEGUqg5IU6ZAof40+euK944cMChgqKmpwdVXX+0RZRPwkYXz0KFDWLt2Lb799lucO3cOISEhmDlzJubPn4/p06dDZStqYIBA0zSys7MDOsJne6Ld1QcTanRrMZCXpxChNtz9nIFGAlFmdWo1nt21S9iOCwnBAyNHeu36dHAssruHBpTMHBETEoK9N9yAX/LykB0XJ7lrpEfaGRNGXLj0zVbXNA9NTUWEUikEtdlaUIBr+/e3OObpnTuhK8yDggJ0HCnbBzNn4uaBA6E1GIQcpMcrKvDEjh14adIk58oW0ZWsAdPWAnXHSORMHi2fg9M1Re3A+fN4df9+fH/iBAxW3N4Sw8Jw7/DhuG3wYEQ48Y6d0qULxoXOwx8Hv8b+0yZfWbVej+dauRPvaukEZfxgvD19NrKSMlwqf3tHdFvXNxEXUCvRlr2Gj11qJe0f+Htg3cjDGQApUZySmUFNJqpplWnduy1YA7EO85zfACSNd16BDOtE1lFzgblE51Ji6NChyM3N9dj5va5wZmVloaioCEqlEtOmTcNLL72EWbNmITTUQaMPMJTeiMQn4zTtqj5oBXl5GjTGBOF8BLhoSS8TaDJ7ee9eVLW0CNuPjRmDqGDvzswHmsycQaVQeDR/qeQyS5sLUIxNxU1B0xifmYmfjC/WbQUF4DhOCGjz7B9/4Kk/duLJWBIUSMcxeG/GDNwyaBAA4N3p07Hr7FnBcvjy3r24IjsbI52JgkzRQOxg4OI24pUQ3c+0Fp23cIoI/KVnWfx46hRe278fe8+ds3pMh4gIPDBiBG4eNAihImeuVSnjMfmKCUivrMRtmzdbjeobGhSEFy+fijuGDhWXLuYSxOm2btAAp14jVvCQDr7LNR3dl6QRoX1nBJCsfxAsnO4onEZvAYV/pkThcSizkp+BmqNAymTHKbyazxGZKcKM6YbOkzYZN8S5wjBKso5axu959913MW3aNAwePNgjnqZen4rv2bMnPv/8c5SXl2P9+vWYP39+u1M2WZZFTk4OWFeiGMpITrusDyH9Q5PJwqmULuR4oMmspKEBr+7bJ2xnREfjtsGDvVqGQJOZP+ARmYWlkXWQdtYym6dHKWtqwrHycgDAC7t344mdO6GAqTyvTJmGW83aUkJYGFbNmiVscwCu/fFHNGqdjPgYO5BMGjWXAOYpdDTGCO1OKBf1Gg1e378fXd96C/PWrbOqbPZPTsYXs2ej8O67cfdll4lWNgEIynD3+Hj8vmwZvpg9Gwlm7+uxnTrh6K234q5hw2Rl0wGi2jqjAsKM6Ylq/vVsweyhCAVCU4Fg37hHS9o/SGHh1Pm/hdMpmfHKtzNRahvOkL8RXYAo49r9emkDm8n4B/Pnz4der8fSpUsRFRWFXr16oW/fvhaffv1cD77mdQvnxo0bvX1JGZn2hyIM0FR71MIZSDy1cydazNbSPT9hAlQKn8dEk/FTWq/j3FZQgJ/PnBECTgVRxP1rWpcuGDa0rQXgiuxsXNe/Pz4z5iXNr6nBg1u34p0ZMxxfXBEKRPUGmgpNFhN9iynXpZ01nGWNjXh5716s+ucf1GusD5xndeuGFcOHY2ynTpKmIaEoCkv79cOMbt2wNicHmqoq3DFpEoIUCqBiHwkoEt1P0omvS5qYfmSwX/svcWGUFXr34AMfGTRkSYor8jQEhoXTIWLSoggKZ1cgJAUo3Qo0FhK3XGeCSVUeADg9ENXLlPJHxi+JjY1FXFwcurqZ7swWHh+RFRujAaYb3Y34bUekezJJu4xMoMPw6R/MFc5Lc6B3sqICHx8+LGwPTEnBgt69fVgiGZ/SXEKUOVUCSS9hhey4OKRGROBCQwMAYOWePahobha+V1AspmZ1wbCOnWwG4np9yhRsLyjAuXoSFOPdgwcxu3t3THImT1nKFGJx4c/Nr98MirRqmVXr9Xh9/348v3u3VUtqiEKB6/r3x93DhkkbrEdbC1zYCIASInvGhoTglkGDkJOTY7JqVu0n0SjDs2SFUyois0kb0daRCKHhGd4vQ20OCfoS0Y2kIgtkzN2CWa1r61IDwMLpFM4qnKyeROTV1ZNnWxFC2oG6giiizqwvrvqLLBcISZUVTj9n586dHj2/xxXOjIwMUBSFlpYWKJVKYdsRgZoDUEbGKyjMFE7BpTbaV6XxGRzH4f6tW8GaBUl5aeJE2b3vUqapkMzCx/SzqXBSFIVJWVmChdJc2QSAZydMwWVdabs556KCg/HplVdi4pdfCvuu37ABx26/HdGO1g4rQiy3g1OAbneSQB5mcByH70+exP1bt7bJnwkAKeHhuHPoUPxn0CDEeWppSkO+42Bk7Sl9hr9ABxGrUPU/QM0R3yic1f8Qa1ZQZOArnBQDpEwiiqer0dzbm4XT0XpWWgFkLCBppniZRXYnCmf9KccKJ8eZJsTliahLHo8rnJ988gkoihLC7PLb7RmaptGnT592Fa0ykGmX9RGWRmYngxOB+OGkU5fQwhkoMnv099+x+cwZYXtS585t3CW9RaDIzJ/wiMz4XJz6ZruHTczMFBROc16eNAl3j3AQSMPI5Z07484hQ/D2338DAC40NODuLVvw+ezZzpWVNQANp4k1q9U6uUMlJVj+669CzlBzeiUk4IGRI7Ggd28oGca5a7kCY1SMWT3A6oQ8ehb1xrGmgauscNrEpbYe058offUnAMN07+dY5tc7+ihokKT9A0UBCW5GLe+0ENA1+LXy7ZTMGBEutYClgh7ZnaSGaThD+gV7k1GGFlP6J4UX0pPJSIJOp8OpU6dQV1dndS3wmDEi87ga8bjCed1119ndbq9otVoEezlCpoxt2l19xPS3TKnAJEp+CX+X2Tt//YWVe/YI20E0jZedTU/hIfxdZv6I5DLjw/zz1ggbWJuYePHyy3Gfk8qm8JuJE7ElPx951dUAgC/+/Rdzund3HNmX44D8VUDLRaDTfCEgR2lDAx79/Xd8duQIWttXE8PC8PyECbi+f38w3pjYoJVksMmxxjQKpsBDQr2ZW0naQ75GDyK6rYemAcoY4tLYUuJ9K6cfWK79qk9VRgeEJ5FDmTnjUsvqAX0DaX/mhHQAgiLIJISuzn6QMz4HpyJMspRtMp6DZVk8/PDDePfdd9HcbHvC1lUPVK9Pxd9www04cMB20ty//voLN9xwgxdLJD0syyI3N1eOVuknyPUhHn+X2Q8nT+KuX36x2PfZ7Nnol+y78Ov+LjN/xCMy461yDhK8J4WHY46ZUvj8hAl4cNQo4281RBHk3dXtEKZU4ovZsy3cuG/ZuFFIm2ITijIlUj+7FpqS3/G/nVvQ9a238GkrZVPJMHhw5Eicuesu3DRwoHeUTb6MvDz1Jndfi3pjjXKmgwDag9bWAMeltk5RQPrVQI97feNS62OFU/L+QV1BXIT5tZjtEKdkpoonaU0i7UyKNRUBp94ACj633E9RQNfbgOw7HUfUlt1pA4oXXngBL7/8MpYsWYIvvvgCHMfhxRdfxPvvvy9EqP31119dPr/XFc7PPvsM+fn5Nr8vLCzE559/bvN7GRkZIxwLNOQBFzYB1YcdH99O+LO4GIvWr7cYkP9v4kQs6uPDBOky/oOgcLbYPw5kkmL1nDn466ab8Mjo0aYvmoqBM+8Dxd86dcnhaWl4wMwyWtHcjNs2bwZnZw0oAOijB6JOo0ZOeRne2/w/vLh7B5p0Ootjru7ZEyfvuAMvTpyISJUPXBsFi7GNGW8/sIK1a0JTTXXgDgYtUPglcO4Hu2uTBTjOZL32YR5OSSnZTBSoprPif6utI/lzq/+RvlzeJiQFSJ0BxA+zfQwfnVYZ2/Y7Z9ujYOGMFFc+GZ/w2Wef4ZprrsF7772HqVOnAgAGDRqEm2++GQcOHABFUfjdGMndFfxusVFJSQlCQkIcHygjcymjrgRyngEKVwNVBy+ZvFgnKyow6+uvoTZLgfLfoUNFu0HKtGOE9AdqhwPrSJUKi/v2xZDUVMsvOKPSRznvBvbUuHHok2hybV9/8iRW/fMPDpeWYsOpU3j7r7/w4NatWPj99xj1ySdIf+01BP/vLTz45zF8f/IkatVq1LImpW1AcjL+uO46rJs3D51jYqxd0js4UuBlhdN7GJxcc2eNit0kAFTNvySwliM4PcAZXefaS90K+Sftez9YRVMJlO8Bqmx76LUbOA6oP03+j7CTIoPVWXg+tEGIoC8rnK7wzjvvICMjA8HBwRg2bBj++usvu8fX1tbijjvuQEpKClQqFbp164aff/7Z6eudP38eEyZMAACojJObajV5VpRKJZYsWYIvzYLkicUrTtUbNmzAhg0bhO0PP/wQ27Zta3NcbW0ttm3bhiFDhnijWB6F8WQgBxnRtLv6aD3D6IGUKP4ms9KGBkz76ivUqE2Dhat69MCrU6b4TSAyf5NZICC5zHgFieNI0BNXBsusUeE0W7PoCJVCgS/mzMHQVaugM7qz/WfTJoe/297cGYsjjuK8PhIcaCSHh+OFCROwrF8/77nO2oMJJTLkLNftCPUWkkpc7CC7kjvC5baurgQubCAWx663ic8hqa4EKvaS/2MHAeFOBFbjJxIoyrTmzwdI2j/wqVAcRWe1ht7ohsv4f0oUhzLjOGNAH511d1dtNaCtIZF9bbWVyv1A2e9A7BAS/dcaCaNIzmEftp9AZe3atVixYgXef/99DBs2DK+//jqmTJmC3NxcJCa2jdmh1WoxadIkJCYm4rvvvkNqairOnj2L6Ohop68ZFxeHxkbSzsPDwxEZGYmCggKLY2pqaly+J68onCdOnMC6desAkHD0Bw4cwKFDhyyOoSgKYWFhGDNmDF599VVvFMtjMAyDPrJ7n9/QLuuDCSEDAd6CI/EaCX+TWb1Gg+lr1uBsXZ2wb3R6OlbPnesfg3L4n8wCAY/IjFYAnZeRADaU8wqjBZzRgi7CwgkA/ZOT8eTYsXhsxw6nf5Oni8NH9QOhQSgeHT0aD44ciQhfuM7aotP8NgqOZb0xQEiS98sVYLjV1oPCgZZSEshFfZG4RDoLxwElP5MJg8huQOpMJwscCnS5mQSW8dGEnuT9g2DhdEXhNK7JDvLvlChOyUzfAJx8lSiUfR5v+z3vThvWyXZkZEUEsbjXnwKSJ1pvI0wwEOK7uAqBzKuvvoqbb74Z119/PQDg/fffx+bNm/HJJ5/goYceanP8J598gurqauzdu1fICpKRkSHqmgMGDMDfxojrADB+/Hi8/vrrGDBgAFiWxZtvvol+/fq5fE9eUTgffvhhPPzwwwBIyOaPP/4YixYt8salfQLHcaivr0dERITfWF4uZTiOQ0NDQ/uqD4oikd/44AdB0ZKe3p9kpjUYcNW33+LIxYvCvp4JCdiwYAGCFf4T+c6fZBYoeExmzlhw7OGChZPnwVGj8EteHv48d67NdyEKBdKiopAeFYW0yEjhb1pUFAZ36IBYf1xOYqVe5LYuHrdkxgSTAC+1x4hLrBiFs+4E0FhAJmI6TDPVp76JKJOto5Dy0AxZP+pDJG9nl4CF0ymZ8RZHzmA9tQnvTmsjjzEAIKILUVg1VcTd2I9TxfgLDQ0NqK+vF7ZVKpXgumqOVqvFoUOHBL0JILrTxIkTsW/fPqvn/umnnzB8+HDccccd2LBhAxISErBo0SI8+OCDTnsJ3HLLLfjss8+g0WigUqnw/PPPY8yYMRgzZgw4jkNMTAy+/vprkXdtwuujtUshgiPLsigoKECfPn1kFzs/oN3Wh7nCKbGF019kxnEcbvzpJ2wzc+voEBGBXxYvRoyfDc79RWaBhN/KjHXNwgkACprGliVL8NXRo9CxrIVSGRcS0i4UNIt6ay4CWi4Aoem+iaQaILjd1qP7EYWzNgdInuRcRGCDBijdQv5PGG1SLuvPAOe+B4KTgM7X+cyC6QjJ+wc+bY9LFk5+cte/LZxOyczcxZXVWiqcBq0pqJK99ZuMikzsNZwhVs7WCifHAaW/khQqcUNdmrxrb/Ts2dNi+8knn8RTTz3V5rjKykoYDAYkJVl6jiQlJeHUKevxOgoKCvD7779j8eLF+Pnnn5GXl4fbb78dOp0OTz75pFPlu+KKK3DFFVdYlDc/Px87d+4EwzAYMWIEYmOtBJFyEv8xD8jIyIjDPOedxBZOf+GR7dux+uhRYTtSpcIvixcjPUoOsy5jh/rTgKaCDJiCXchRy7lu4QSAcKUS/xk82KXf+h2NBUDFn0Q5SZnc9vuG00DlASBxtKxwepKILKLs6BqBxnz71icBCojuC9TnAgkjTbuDE4l1q+ksUWBj+rb9qbqcKBOqeCAyW7Lb8CmChdOFoEG8S63CvxVOp6BoomSyemMuzlDL79LnAS3nrUeoNSequ0nhTBxt+Z2hmazzpCgg7jLJbyEQOXHiBFLNAtRZs266CsuySExMxIcffgiGYTBo0CBcuHABL7/8stMKpzWioqJw5ZVXSlJGnyx++uWXXzBp0iTExcVBoVCAYZg2HxkZGUeYeQso/NvNxxXe+esvvPjnn8J2EE3jh/nz0TdJXi8m44Dqg0DpVqD5vGu/D00HEkcB4VnSlisQ0beQ6KbNF6x/L0ep9Q4UDUQZ1+bVHHHuN4ySBHTpequlFUsZBSSOIf9f/M26i2nzOfIMtYc0IDyhaUQeMQPE/5a3cLaXdy1v5WRbRT6mFUSRtLUu05yIbHJM8wVTChQerTHegiJMzs9rJCIiApGRkcLHlsIZHx8PhmFQVlZmsb+srAzJNnKNp6SkoFu3bhb6U48ePXDx4kVotW5Et5YQryuc33//PWbOnImysjIsWLAALMti4cKFWLBgAUJCQtC3b1888cQT3i6W5AQHyy9ff6Jd1kd0XzLLnTLZIy5RvpTZDydP4q5ffrHY9/ns2ZiQmemjEjlHu2xnHsYjMjNPjeIKEVlkwBVlJzH6pYLCeloUod5khdNp3G7rMcaAHfW59tNRcJxlSqDWa/QAIH44oIojFtOynW2/F+rVtwGsJO0fQpKJpdcp63ArOi0CutwEhHaUrjwewimZ2VI4xRAUbpJHfa7ld7wC6oEI+u0dpVKJQYMGYfv27cI+lmWxfft2DB8+3OpvRo4ciby8PItli6dPn0ZKSgqUSv+IEux1hXPlypUYOnQoDh8+jKeffhoAcMMNN+Crr77CsWPHUFpaikw/H1Q6gmEYdO/eXbbU+gnttj7ihgAZi4AE6XNQ+lJm1S0tuG7DBphnUHx50iQs9PMIsO22nXkQj8mMdlPhlDHBGN3tDM2mXeb1xsuYlhVOe0jS1kOSSb+fNse+u3ftUaDgE6Dlou1j+CBCAMktqS63/J63evqwXv2qT1VGEeXKzydWnJaZNYVTXQmU7bDtzWCNuKFkci6ii+V+QeGUc3C6wooVK7Bq1Sp8/vnnOHnyJG677TY0NTUJUWuXLVtmEVTotttuQ3V1Ne6++26cPn0amzdvxgsvvIA77rjDV7fQBq8rnCdOnMCCBQvAMAwUxgiTOh1ZL5ORkYHbb78dL730kuTXzcjIAEVRbT58ZYwbN67Nd7feeqtL12JZFlVVVZdEgKRAQK4P8fhSZm8dOIB6jcnF679Dh+JeG7N6/oTczsTjMZnZsMo5ja6e5KEz+Icrkk/h85rqWwSrmUW9sbKF0xkka+upM4Do3tatlgCZACj9DWg6Z0pvYYuILiT6LccaU6eYTfP5geVa8v6B1QPNJUBTsTTn80OclllUTyBusOWa1PqTQNkfQPkfzl8wug9ZftA62rHO6FIrWzhdYv78+XjllVfwxBNPoH///jhy5Ai2bNkiBBIqLi5GaWmpcHxaWhp+/fVX/P333+jbty/++9//4u6777aaQsVXeD1oUGhoqGDejY6OhkqlshBaUlISCgsLJb/u33//DYPBlLj62LFjmDRpEubNmyfsu/nmm/HMM89YlNUVOI7DuXPnRCVclfEccn2Ix1cya9Bo8MaBA8J2ZnQ0/m/KlICI7Cm3M/F4TGbuWjgv/EwCYaTOJIOySxle4eQMJF0Mo7SsNz9QTAIBr/UPZTtIgBtVPBDvhPdLh6lAUwEJCsUZTJGZWd+71EouM30DkPchWdva6xHnf6etA6r/JkpV7CBpyuIhnJZZ0ri2+/gJiggXXI5bI1s43ebOO+/EnXfeafW7nTt3ttk3fPhw7N+/38Olch2vWzizs7Nx4sQJYbt///748ssvodfroVarsWbNGqSnp0t+3YSEBCQnJwufTZs2ISsrC2PHjhWOCQ0NtTgmMlJ+UGRkvMn7Bw+iRm1SEh4aNQoK2iexzWQCGXctnJwxLYocyp/IgDK651mTp6xweh9dPVC+G6j623J/SylQ9Rf5P3W6c8FalNFA9j3EvdYiPYbRy6Q91SvNR6nVEquus2irgPI9JBpze0XfQgJFAW3dYx1h0JBox5Vmyo6scAYc06ZNw5o1a9DS4uJ70wFet3DOmTMHb775Jl555RWoVCo8+uijuPLKKxEdHQ2KotDU1IRPPvnEo2XQarVYvXo1VqxYYWE5+eqrr7B69WokJydj1qxZePzxx+1aOTUaDTRmrn8NDQ0AiEsDx3EwGAygKAo0TQv7ePj95lZXe/tpmgZFUVb389d0Zj/DMOA4zur+1mW0tT/Q7slgMAj10V7uydP1ZC4zb91Ts1aL/zNLapwaEYGlffpYXNef68lcZu35eZLynniZsSwLhmGkuycEgeJYQNcMzlgfou6J1ZHjOQowXvtSrieKDiapYvQtoJVRln1qxrWgWQ2ooKiAuidH+6WuJ2feQ07fU30hqNKtgDIGdOxgGFgW4DhQ5zYCrAGI7g06vLPz98SEAK3kTumaQYEDaBXYVsd7q57MZSZJPXEK0MboAKy22TQx5eietPVEQaVDwPn5OKL1e8jmPel14AxqMpnEqIC602A4DqwqARwTIa7fa74I6uw6otBHDwStUMLQcR5xqw2KArw4jvDHPqL19/5KQUEBlixZgvDwcMyZMwdLly7F5ZdfLpmHmdcVzvvuuw/33XefsD1z5kzs3LkT69evB8MwmDFjBsaPH+/RMvz444+ora3FddddJ+xbtGgROnXqhA4dOuDo0aN48MEHkZubi/Xr19s8z8qVK4XAR+bk5uYiNDQUx48fR1xcHNLT03H+/HlUV1cLx/BW1KKiIkFRBYgfdlxcHM6cOQO1maWnc+fOiIyMxIkTJywab3Z2NpRKJXJycizK0KdPH2i1WuTmmiKHMQyDPn36oKGhAQUFBcL+4OBgdO/eHTU1NTh37pywPyIiAllZWSgvL8fFi6bgA7GxsQF1T8XFxWhoaMDx48cRGRnZLu7J0/XU0tIiyCwrK8sr9/TyH3+grKlJ+O6+ESNQV10dMPXEcRwaGhqQl5eHnj17ttvnScp74mVWW1uLhIQEye7pTGE9FPohYHVh4E6cEH9PnA5NTU04d6YQGhV7ydcTOJIiIa0lCHGhQF5entA/UBRF7olW4EROTuDck5frKT8/X5BZSEiIe/fE6ZBYVYPI0GaEN51FUZkB+vK/ENXwD1gqCMHJQxELiL6nk4f/QETjXtSHjwCFTPToOhpaOga5ZvfqzXqqr68XZJaeni5JPfWlGRj0Gpw6dhgGJsKpe2qouYiWsnK0BEeirjHHr9se36eePHkS/fr1s3lPjXnfobn4DzSGDUJj2BAk6/YhWQXUGuJRbHZ+p+6pvh4J1Y1gDOVQRf2LmPQhOFN43nhPFW7fE0+g9hGt05v4K7m5ufj777+xevVqfPvtt4IBbtGiRVi8eDH69+/v1vkprvV0wCXAlClToFQqsXHjRpvH/P7777j88suRl5eHrCzrudhaWzgvXLiAnj17oqioCB07klDR/jDjZU4gzg7J99T+70mt06Hb22/jXD1xw4kPDUXR3XcjRKEI2Htqj/V0ydxT/gfgWsrAZiwBwju3j3tqj/V0Kd/ThY2gaw6DihsIQ8pMUEWrgcZ8cMmTQSWMcOmeuKJvSOCYsExwGUtBGyOdtqt6yv0/QN8ENus/ZN2qM/dU8itQ8Se4uGFAylT/uydX2p5wT5cByZNBnXoFNKsGm7EMXFgn8fdUsglU9SEgdjDotCsC73mydk8O9jt7T+fPn0dGRgbOnTsn6Ab+Dsuy2Lp1K1avXo0NGzagqakJPXr0wLJly7Bo0SKX7sPrFk5fc/bsWWzbts2u5RIAhg0bBgB2FU6VSmWRuLXeOFimKAoVFRVITEwUGh7/tzW2Qld7cj9FUVb32yqj2P3+dk8sy6K8vNyp+giUe7KGlPdkLjPencKT97Tm2DFB2QSAFZddhjA7uaP8sZ7MZWavjHLbM12ztcz85p5YHTleEQy0+s2lWE/WrlteXo7EmGDQdUfJOq2Y/gF9T56uJ0FmbryHLPbHDQBqjwB1J8B0mA50XkrSoUT1AYzHiS07lToVaMoHms8CjadINFw79+TperL27na7PphgQN8MBvo2z7bNezI0AxRNUqOYfe+Pba91n2rznphgck8wAGwDQFMApQId3snq2l+H9xTdC6g5DDSeBtSVYKr+AoIT2wRduxT7CFvf+zM0TWPKlCmYMmUKamtr8Z///Afr1q3DQw89hEceeQTjxo3D8uXLMWPGDKfP6XGFMzMzU7T/L0VRyM/P90h5Pv30UyQmJjoU0pEjRwAAKSkpoq/BcRwuXryIhIQEV4ooIzFyfYjHmzIzsCxe3LNH2I5SqXD7kCEev67UyO1MPB6TGceSwY9BDcQNg80UEjZ/LwcNsqDqILF8RfUGYgeY6i0kHLj4OxCcAMT093Up/RrJ23poOomaqq0hEZWj+7hfB8oYIGEUULYTKP4O0DWQvJ9inx+J8Ej/wAdBMmjsH2eO3rjUIyjc/nF+gNMyM8/DqYwBetwPaKqtKptOEZYJMCpA10j63qq/SN7SSz3KdwCzZ88erF69Gt999x2qq6vRu3dvLFu2DEFBQfjkk09wxRVX4NFHH7XI7mEPj/ciY8eOlWzBqbuwLItPP/0U1157rZADFADy8/OxZs0aTJ8+HXFxcTh69CiWL1+OMWPGoG/fvj4ssYxM+2fdiRM4Y7aO4q6hQxEV3I4iI8r4AAq4sJkontF9AFpkpMTYgYC+GVCEeaZ4gYa2GmjIB1SJlvvlHJy+g6KAmL4kb2LtcdLOpSBhJFEW9M1A6a/tT2GIHQxENgOqWOd/o28kf5l21B/w6W5YY65higaC410/H80AEV2B2mNAlTGarxyhNuA4ceIEVq9eja+//hrFxcVITEzEtddei6VLl1qs4bz77rtxyy234J133vEfhfOzzz7z9CWcZtu2bSguLsYNN9xgsV+pVGLbtm14/fXX0dTUhLS0NFx11VV47LHHfFRSGZlLA5bj8Pzu3cJ2aFAQ7r7sMh+WSKZdQFGC6xwMavEDnyTPBq4LOBhjtPbWaVH4lCi0rHD6hJj+JF2Hvh7gONLu3YUOAjpMJxZOwJSXs70QO1D8bzIWEWuvKk768vgK3sJpUEvXdiK7E4WTNXqIBEW5f04Zr9G/f3/k5ORApVLhyiuvxLvvvospU6bYdD0eP348PvroI6fP3856EvtMnjy5zYJigESg+uOPPyS7DkVRiI2N9RvL7qWOXB/i8ZbMNubm4lh5ubB966BBiLeTisifkduZeDwqMybEqHB6JqfYJUWrvKZCvbHGiJCyhdMhHmnryhigy81EeZDyvNG9AXBkosGH/Znf9KlBkQFjrXNaZrzC2XQWyH0diBtKrNvuEJkN9FgBlP5GFM8AkZkMITo6Gh9++CHmzZuHyEjHdXfllVeisLDQ6fN7XeHctWuXU8eNGTPGwyXxHDRNB0wkqksBmqaRnp7u62IEFN6QGdfKuqlkGNw7YoRHr+lJ5HYmHo/KTFirpbZ/XGs4juSPo4N8PuD2GxhLhVOotzJjrAVZ4XSIx9p6SLL05wSkc9F1A4/ITN8EaOvIJIoyRtpz+wFOyywoitRxbQ6RB+9a69bFg8hHV2e8hqxwBgpqtRpz585F165dnVI2ASA0NBSdOnVyfKARryuc48aNc2q2KlASpVqDZVkUFxejY8eONk3RMt6DZVmcP39erg8ReENm2woK8HdJibB944AB6BAR4ZFreQO5nYnHozITFE6RFk5DC3DqdfJ/nycAyAqnoHDqmwGY1RvdDBqQFU4nkPsH8XhEZpX7gfLdQPwwoMM0x8dr68iaVmVMQKxndVpmIUlAx9lAwxkyKRfRVbpC6IwR55WyS22gEBwcjIceeghvvPGGxwx+Xlc4d+zY0WafwWBAUVERPvzwQ7AsixdffNHbxZIUjuNQU1OD1NRUXxdFBqQ+qqur5foQgTdkZm7dZCgKD4x0053Hx8jtTDwelZlglRNp4WR15C/FGNMGyLRewynUW4wcNMhZ5P5BPB6RmVjPB20VUPEnicQcAAqnKJm1nCdyUIQCIR2kKQBrIEo6IK/hDDB69eqFoqIij53f6wrn2LFjbX533XXXYfTo0di5cycmTJjgxVLJyMh4kz+Li/HH2bPC9pK+fZERHe27Asm0P1x2qZVTorSBX8MJEJdjnsSxQNwgQBnt9SLJyLgEbYzO6mxaFD4lSnuMWF13ivyN6CLd5BrNAGlziItuUOB6LF2KPP/881i0aBHGjx+PiRMnSn5+vwoaRNM0FixYgJUrVzodZldGRibwMLduUgAeHjXKd4WRaZ/EDgEie4iPLMlbOH2Ue9AvUUQQ9+LWg1JlDMC4kUpBRsbbCOlAnFQ4dcaUKAr/z8EpCoMGqNxH/g/vLO25Y/pJez4Zr/D2228jNjYWU6ZMQWZmJjIzMxESEmJxDEVR2LBhg0vn97s3anV1NWpra31dDLegKArJycm+j6wmA0CuD1fwpMz+KS3FL3l5wvbVPXsiOz7wB61yOxOPR2UWkgQgSfzvBJda2cIpQFEwX8sqt3XxyDITj0dkRotUOA28hTMwFE6nZWbuwSHl+k2ZgOXo0aOgKArp6ekwGAzIMxun8bjzLHpd4SwuLra6v7a2Frt27cLLL7+M0aNHe7lU0kLTNJKTPRQ5TkY0cn2Ix5MyM7duAsAjAf6888jtTDx+KTPBpdbv5mP9BqHeKg8AoEgaDUVgpjPyFn7Z1v0cj8hMcLUXa+EMDJdap2VG0UC3OwFwAXNvMp7Fk+s3AR8onBkZGTY1ZI7jcNlll+GDDz7wcqmkxWAwID8/HxkZGWAYxtfFueThg1LJ9eE8npLZiYoKrD95Utie2a0b+reTQZjczsTjUZnp6kkERlopLsWD4FIrWzgtKN0KtJQCyRNgUKWgqKgImS3bQbNaICJLVjgdIPcP4vGIzMRaOPWBZeEUJbPgwPcskgkcvK5wfvLJJ20UToqiEBMTg6ysLPTs2dPbRfIIDQ0Nvi6CjBlyfYjHEzJbuWePxfaj7cS6ySO3M/F4TGaaSuD8RiA4UZzCGRRJolHKOeQsabkANBYB2oGAKgUN9fUArSHutnKUWqeQ+wfxSC6zoAggcbRZ5GU1mZSyFTRHH1gWTkBuZzLu8ccff2Dz5s04awzs2KlTJ8yYMcNu0Fdn8LrCed1113n7kjIyMn5AQU0Nvs7JEbYnZGbiso4dfVgimXYN7WKU2pBkIHWm9OUJdIQ0MyQ1CsVpAXAAKJPVSEbG31GEAsmXm7ZLtgB1xwBVAhCcZPwkkr+KMCBjIaBrEB98TEYmwNBqtVi4cCF+/PFHcByHaGPmgNraWvzf//0f5syZg6+//hpBQa55/8hJxmRkZLzCS3v2wGCWUqG9WTdl/AyFpYIk4yatFE6a05JtOkhe7yoTuGirAFZP3MVrjgClvwKFXwInXwFO/R/AhAGhqbIVX6bd8/TTT+OHH37Avffei9LSUlRXV6O6uhoXL17Efffdh/Xr17uVQcQnb4k9e/bgk08+QUFBAWpqasCZ5/UCcbH9999/fVE0SaAoCmlpaXI0Oj9Brg/xSC2zkxUV+PTIEWH7so4dMT4jQ5Jz+wtyOxOPR2XGWzhZHUlGTju5BsygJYGDaKWsSJnDr9HUN4OiKKSmxIGqkN1pnUXuH8TjFZl1vgHQ1gDqMuOnnPzVVhPLvbP9hp8gtzMZV1mzZg2uvfZa/O9//7PYn5iYiJdeegllZWX48ssv8eyzz7p0fq+/TV999VXcf//9CA4ORnZ2NmJjY71dBI9D0zTi4mT3C39Brg/xSCkzjuNwx88/Q8eywr7HRo9udy9EuZ2Jx6MyY8zcPFk1QDu5BqvqAHBxOxA7AOh4pWfKFoiYWThpmkZsZChQKSucziL3D+LxiswoClDFkk9UD9N+VkdcaQMMuZ3JuEppaSmGDRtm8/thw4bhm2++cfn8XnepffnllzFy5EiUlJTgn3/+wY4dO6x+AhmDwYBTp07BYDD4uigykOvDFaSU2ZqcHOwwC7c9tUsXTO/a/vJ+ye1MPB6VGUWblCG9CLdaOQ+ndcwUToPBgIK8E2A5VlY4nUTuH8TjU5nRQUQJDTDkdibjKh07dsTOnTttfv/HH3+goxtxN7yucDY3N2Px4sWIiory9qW9ilotMlCFjEeR60M8UsisVq3Gvb/9JmyrGAZvTZvW7qybPHI7E49HZcYrQ6yIa8h5OK3DhBBrEEcGsg1sDLjMa4HkyT4uWOAg9w/ikWUmHllmMq5w7bXX4ttvv8Wtt96K3NxcGAwGsCyL3Nxc3HbbbVi3bp1bgV+9/kYdP348cswiVcrIyLRfHvv9d5Q1NQnbD48ahS7t0I1exk9JnUX+qkTkm5PzcFonMhvo/TixHBsM4OgQICwDkHNKysjIyAQ8jzzyCPLz8/Hhhx9i1apVoGlik2RZFhzH4dprr8Ujjzzi8vm9rnC+9dZbmDx5Ml555RXccMMN7XINp4yMDHCopATv/v23sN0lNhYPjhrlwxLJXHJEZIn/DW/hlF1qLbGVp1BGRkZGJuBhGAafffYZVqxYgZ9//tkiD+f06dPRt29ft87vdYUzLS0N//nPf3DffffhwQcfRHBwMJhWM6QURaGurs7bRZMMmqbRuXNnYXZAxrfI9SEed2VmYFncunkzzONPvzN9OoIV7ddNUW5n4vFLmQkWzvbbVt2FpmlkJQF0zSEgLJ3kLpWxi1+2dT9Hlpl4ZJnJuEpxcTESEhLQt29fq8plS0sLKioqkJ6e7tL5vf5GfeKJJ/D8888jNTUVgwcPbpdrOSmKQmRkpK+LIWNErg/xuCuzDw8dwsGSEmH7ml69MDnLBWtTACG3M/F4XGbNJYC6FFAlAmFpzv1Gdqm1DscC59aTtCid5iNCXwRUHAFSJskKpxPI/YN4ZJmJR5aZjKtkZmbiyy+/xKJFi6x+/9NPP2HRokUuB6TyusL5/vvvY8aMGfjxxx/b7QyMwWBATk4Oevbs2cZ6K+N9DAYDTpw4IdeHCNyRWVljIx7evl3YDlcq8erk9h9YRG5n4vG4zOqOARV7gYQRziuc4ZkkpYpSTi1gAUUD9bkAq4NB24ALRafRMZIFLUepdQq5fxCPLDPxyDKTcRWO4+x+r9Pp3NLbvK5warVazJgxo90qmzxySGr/Qq4P8bgqs/u3bkWdRiNsPzt+PFIvkRlXuZ2Jx6MyM0vl4TTxl3mmLO0BJoRYgA0tgMEYCZOWFU5nkfsH8cgyE48sMxlnqa+vR21trbBdVVWF4uLiNsfV1tbim2++QUpKisvX8rrWN3PmTOzevdvbl5WRkfECfxQV4cujR4XtvklJuHPoUB+WSOaShre+GeQ0AZKgCCV/9c2gWOOkkmzhlJGRkQlIXnvtNWRmZiIzMxMUReGee+4Rts0/AwYMwM8//4xbb73V5Wt53cL55JNPYv78+bj99ttx4403Ij093arZX45eKyMTWGgNBty2ebPFvvdmzICinXszyPgxriicBg1AMcZP+8wX6zJmFmOa0wIIkRVOGRkZmQBl8uTJCA8PB8dxeOCBB7Bw4UIMHDjQ4hiKohAWFoZBgwZh8ODBLl/L6wpndnY2AODIkSP44IMPbB4XyC4BNE0jOzu73bsNBwpyfYjHFZm9tm8fTlZWCts3DRiAEWlOrptrB8jtTDwel5krLrVn3ge0NUCXm4DQjp4pV6BilCfNaZAYFwmK0ssKp5PI/YN4ZJmJR5aZjBiGDx+O4cOHAwCamppw1VVXoXfv3h65lk+i1FKXwKyxUqn0dRFkzJDrQzxiZHa2thbP7NolbMeFhODFiRM9USy/Rm5n4vGozFyxcHLGKLWUnBalDbwCr28GQxnzlcoKp9PI/YN4ZJmJR5aZjCs8+eSTHj2/19+oTz31lLcv6XVYlkVOTg769OkjRwnzA+T6EI9Ymd29ZQuadTph+3+TJiEuNNSTRfQ75HYmHo/LjA9oI8bCKadFsY0iBKAosAYNcjXDkN21ExheCZWxi9w/iEeWmXhkmcm4Q01NDb7++msUFBSgpqamTeRaiqLw8ccfu3RueQpXRkbGLTbm5mJDbq6wPSItDdf17++7AsnI8CijgPR5RFFyFtZouZMtnG1JHAckTQBYDtqKHCCyO0mXIiMjIyMT0Pz666+4+uqr0dTUhMjISMTExLQ5xh0PVa+/UZ955hmHx1AUhccff9wLpZGRkXGHZp0O/92yRdhmKArvzZgB+hJwm5cJAOggILqX88dzLMAZTL+VsYTmhwyBG2NBRkZGRqYt9957L5KTk7F+/Xr06dNH8vP7lUstRVHgOE5WOGVkAoBzdXV49PffUWSWw+nuYcPQNynJd4WSkXEH3roJyAqnPbS1CGk5DjSogOgevi6NjIyMjIyb5OXl4eWXX/aIsgn4QOFkWdbqvrNnz+Kdd97Brl278Msvv3i7WJJC0zT69OkjRwnzE+T6EI8tmWkNBmzMzcVHhw/j17w8mHv3p0ZE4Klx47xaTn9Cbmfi8YrM6s8A+nogohsQFGH/WM60Dll2qbWCphoo2w66IQ9dQ1tAVbbICqeTyP2DeGSZiUeWmYyrdO3aFQ0NDR47v1+0SJqmkZmZiVdeeQVdu3bFXXfd5esiuY1Wq/V1EWTMkOtDPOYyO1lRgft++w0dX30VV69bhy2tlE0AeGPqVESoVN4tpJ8htzPxeFxmZduA8xsBdbkTB1NAdG8gqqecg9ManAGoPQ4Y1CR1mRyhVhRy/yAeWWbikWUm4wrPPfcc3n33XRQVFXnk/H43hTtmzBg8+OCDvi6GW7Asi9zcXDlKmJ8g14d4WJbF4ePHcYKi8Om//2LvuXM2j+0WF4enx43DVT17erGE/ofczsTjFZnRIlKjKEKB9Ks9U472gDEiLcdxqKqsQkKsCnJLdw65fxCPLDPxyDKTcZXt27cjISEBPXr0wKRJk5CWltamDVEUhTfeeMOl8/udwnnw4EHZFUBGxocU19Xh2T/+wJqjR9FssB4cJEShwDW9euGmgQMxMi3tksitKxOg8FY4VkQuThnrtE6BwlzaHg0yMjIy7YW3335b+H/Tpk1WjwkohfOLL76wur+2tha7du3C+vXrcdNNN3m5VDIyMgBQVFuLYR99hPKmJqvfD+7QATcNGIAFvXsjKlh2p5MJAHiFU+9ELk6OA8DJqT5sQTMAowT0RuVddqmVkZGRaRdYi7EjJV5XOK+77jqb38XHx+Ohhx7CE0884b0CeQjZlcG/kOvDMfUaDWauWdNG2YwJDsaSvn1x44AB6Jec7KPSBQZyOxOPx2XGW+WcsXA2FQIFXwAhKUDX/3i2XIEKEwLo1aBoyuSuLOMUcv8gHllm4pFl1j5455138PLLL+PixYvo168f3nrrLQwdOtTh77755hssXLgQV155JX788UfPF9RJvK5wFhYWttlHURRiYmIQEeEggmCAwDCMx8IKy4hHrg/H6FkWC777DscrKoR9vRIS8Ojo0ZjToweCFX7nfe93yO1MPF6RGSNiDSdrjFIrWzhtw4SApuqQlJgEKMN8XZqAQe4fxCPLTDyyzNoHa9euxYoVK/D+++9j2LBheP311zFlyhTk5uYiMTHR5u+Kiopw3333YfTo0S5fe//+/dixYwfKy8tx++23o2vXrmhubsapU6fQrVs3hIeHu3Rer79VO3Xq1OaTnp7ebpRNgARUqK+vB8e1juMp4wvk+nDMvb/+il/y8oTttMhI/DBnDhb07i0rm04itzPxeEVmgsLphEstn4dTzsFpGyYUHDg0q7LAhWX6ujQBg9w/iEeWmXhkmbUPXn31Vdx88824/vrr0bNnT7z//vsIDQ3FJ598YvM3BoMBixcvxtNPP43OnTuLvqZWq8XcuXMxcuRIPProo3jzzTdxzhgwkqZpTJ482eX1m4CXFE61Wo1bb70Vb731lt3j3nzzTdx2223Q6XR2j/N3WJZFQUGBx/2hZZxDrg/7vPf333jzr7+E7bCgIPw4fz6ayspkmYlAbmfi8YrMIroC6fOAhFGOj+XzcMo5OG3TaQHYno/hdEsfsIooX5cmYJD7B/HIMhOPLDP/paGhAfX19cJHo9FYPU6r1eLQoUOYOHGisI+maUycOBH79u2zef5nnnkGiYmJuPHGG10q3+OPP45NmzbhvffeQ25ursWkRXBwMObNm4cNGza4dG7ASwrnhx9+iM8++wwzZsywe9yMGTPw6aef4qOPPvJGsWRkLnm25ufjrl9+EbYpAF9fdRX6JSX5rlAyMlKiigOie5F1mY7gXWplC6dtGKXsciwjIyMjkp49eyIqKkr4rFy50upxlZWVMBgMSGo1DktKSsLFixet/mbPnj34+OOPsWrVKpfL9/XXX+O2227DLbfcgtjY2Dbf9+jRAwUFBS6f3ytvjW+//RZXXXWVQxNvVlYW5s2bh6+//lryMjz11FOgKMri0717d+F7tVqNO+64A3FxcQgPD8dVV12FsrIyycshI+MvnKqsxLx162Awm8V6edIkzMrO9mGpZGR8CO9SK1s47VP9D4LVZwCDnGBeRkZGxhlOnDiBuro64fPwww9Lct6GhgYsXboUq1atQnx8vMvnKS8vt7v+l2EYNDc3u3x+r7xVc3JysHjxYqeOHTFiBDZu3OiRcvTq1Qvbtm0TthVma9OWL1+OzZs3Y926dYiKisKdd96JuXPn4s8//3TpWsFyygi/Qq4PSyqbmzFzzRrUmbl03DRgAFYMHy5syzITjywz8XhcZgYt0JhHrJcx/ewfy8kWToc05IMq2Yj45mqAnQwgxOFPZAhy/yAeWWbikWXmn0RERCAyMtLhcfHx8WAYpo3Rq6ysDMlWMgXk5+ejqKgIs2bNEvbxLtUKhQK5ubnIyspyeN20tDScOnXK5vd//vknunTp4vA8tvCKhVOr1UKpVDp1rFKptOnX7C4KhQLJycnCh58JqKurw8cff4xXX30VEyZMwKBBg/Dpp59i79692L9/v+jrMAyD7t27y6Gp/QS5PizR6PWYu3Yt8mtqhH3jMjLwzowZoCgKgCwzV5BlJh6vyIxVA2e/Bc5vMObZtIMyFojsBgTL6X9soqkATdFkUCRHqXUauX8Qjywz8cgyC3yUSiUGDRqE7du3C/tYlsX27dsx3MwowNO9e3fk5OTgyJEjwueKK67A+PHjceTIEaSlpTl13UWLFuGDDz6wWCfKjwlXrVqFb7/9FsuWLXP5vrxi4ezQoQOOHTvm1LHHjh1Dhw4dPFKOM2fOoEOHDggODsbw4cOxcuVKpKen49ChQ9DpdBYLdLt374709HTs27cPl112mdXzaTQaC+W4oaEBAKDX61FRUYHo6GgwDAOapsGyrMUCXIqiQNM0DAaDxTlt7adpGhRFWd0PtE3Yams/wzDgOM7q/tZltLWfL2Og3JPBYEBtbS2io6NB03S7uCdX64njONyycSN2FxcLx3WNjcW3V10FhbFjMRgMYFlWkJlCofDrezLf78t6MpdZUFBQu7gnR2V39554mcXExEChUHjonpRgAHCcAayuBWBUtsse0QOI6CHXk717Ag0Da4BarYbSwIHmDIF/T16oJ71e7/A9FGj35Ol6Mu9T+bFUoN+TOZ6op9bvofZwT63LGGj31Pp7Z1ixYgWuvfZaDB48GEOHDsXrr7+OpqYmXH/99QCAZcuWITU1FStXrkRwcDB69+5t8fvo6GgAaLPfHo8++ij279+PMWPGoEePHqAoCsuXL0d1dTXOnz+P6dOnY/ny5aLvhccrCufEiRPxxRdf4OGHH7abP6a8vBxffPEF5s2bJ3kZhg0bhs8++wzZ2dkoLS3F008/jdGjR+PYsWO4ePEilEqlUEE89hboAsDKlSvx9NNPt9l/6tQpBAUFITY2FnFxcUhPT8f58+dRXV0tHMNbWYuKigRFFSAm7bi4OJw5cwZqtSlvXOfOnREZGYkTJ05YNN7s7GwolUrk5ORYlKFPnz7QarXIzc0V9vH5mRoaGiwW/gYHB6N79+6oqakRQiADxPyflZWF8vJyCznExsYG1D0VFxejuroasbGxiIyMbBf35Go9fXrmDL44cUI4JjIoCC8PGIALeXmgze6ppaVFkFlWVpZf35O/1BPHcaiurkZKSgp69uzZLu7J0/XEy6xv375ISEjwzD3RNPpQDLTqZuTmHALLRHj0ntpjPVncE6NCdVU1GhobUHfsGCiKCvx78kI95efnC31qSEhIu7gnT9dT/f+3d9/hURXrH8C/ZzcdUggBAiENCCUQWujSe+/VAnIVFYSr8kO9VkSwoFfUqyKioleKNCly6cWASJUaCASSEEIS0iAhPdkyvz+GbWlkku37fp5nH9izm90575w957xn5szk5mpjFhQUZBfrZOp60uxT/fz80KFDB7tYJ1uvp5qMBzNt2jRkZmbi3XffRVpaGjp27Ih9+/ZpBxJKSkrSJrbG4uLign379mH9+vXYunUrVCoVSkpK0L59eyxbtgxPPfWUtsWzJiRmhsl6EhISEBERgdDQUPz444/o3r17ufecPn0azz77LBISEnD58uVq9TeujZycHAQHB2PFihVwd3fH7Nmzy3Xl7datGwYMGIDly5dX+BllWzhTUlIQHh6OhIQE5OTkoG3btnBycqKrQxZeJ6VSiatXr6Jt27aQy+V2sU41qacd169jytat0LzDSSbDnhkzMDA0tNw6qVQqbcycnZ2tdp3KLrdkPenHzMXFxS7W6VFlr+06aWLWrl07ODs7m26dbnwOpsiDutlzgLuuu6y9bHuPKrtR1wlqKBPWIyFdhZCuT2pbnmx6ncxQTwqF4pHHIVtbJ1PXk/4+VXMuZevrpM8U9VT2OGQP61S2jLa2TsnJyQgJCcGdO3fQtGlTOCqztHA2a9YMmzdvxowZM9CrVy80a9YMERER8PT0RF5eHq5cuYL4+Hh4eHhg48aNJk82Ad7c3LJlS8TFxWHIkCEoLS3VdkPQqOwGXQ1XV1e4uuq6Z+Xm5gLQbYSaA7FmWUUq62dvyuWaspVVWRlFl1vbOsnlcu3rmvfY+jpVpKrl5+/excydO6G/K145ciSGVHADuOY7Nd+vf19ndcte2XJ73/b0/28v61SdMtZmnTQHc9EyCi2XuUFCPuRQAGVeMyhj0lYg9zrQeDhk9btU+NmOWk9674YU+iQK86MN9qmVv98W1sk89VTb45A1rlN1y1jTdSp7LmUP62Tq5ZUdk6pTRmtdJ322tk6VvW5tXnvtNcyYMQOdOnUyyeebbTKtUaNG4fLly3juuedQXFyMHTt2YO3atdixYwcKCwsxZ84cXLp0yWCUJVPKz89HfHw8GjdujMjISDg7OxvcoBsbG4ukpKQKb9CtDk9PT2MVlRiBI9dHQWkpJmzahEKFQrtsYY8emBMZWeXfOXLMaopiJs4sMZM/HLVRVVz1+9QKPjVKLboNOQra1sVRzMRRzMRRzEhNfPXVV+jSpQvCwsLwzjvvlOvmXFtm6VJbkby8POTm5sLLy8ssP45FixZhzJgxCA4ORmpqKhYvXoyLFy8iJiYGDRo0wNy5c7Fnzx78/PPP8PLywoIFCwAAJ06cqPZ3JCcnIzAw0OGbzYl1efePP7D02DHt89EtW2LHtGmQV3J1jxC7c2s9kHcTaDoO8K3i6m3CL0B+AhA4EajX3nzlI4QQYpdsJTfIy8vD9u3bsWnTJhw6dAhKpRKtW7fG9OnTMXXqVLSq5RztFjvj9PT0REBAgNmuxCQnJ2PGjBlo1aoVpk6divr16+PUqVNo0KABAODzzz/H6NGjMWnSJPTt2xf+/v7Ytm1bjb5LrVYjLS2tXB9zYhmOXB+3c3Lwqd5Fk6ZeXtgwceIjk01HjllNUczEmS1mDXoBQVOAuqFVv48p+b80D2eVaFsXRzETRzETRzEjNeXp6YmZM2di9+7dSE9Px+rVq9G0aVMsXboU4eHh6NixIz7++OMaf77DNHFs3LgRqampKCkpQXJycrl7Rd3c3PDNN9/g/v37KCgowLZt26q8f7MqjDGkpaWVu3mZWIYj18frhw6hWKnUPl8+eDA89e47rowjx6ymKGbizBazuqGAT1vAxafq96kfdjuXzDK8gc2ibV0cxUwcxUwcxYwYg4+PD5555hns378fd+/exWeffYZbt27hrbfeqvFn0lGVEDt1PCkJm65e1T7v2bQpZgjMyUSIw9EknNTCSQghxIEpFArs3bsXmzZtwq5du5Cfn4/AwMAafx4lnITYITVjeHnfPoNlXw4fXqs5lAixWaU5QFEqIHevuluttkstHRoJIYQ4FqVSiQMHDmDTpk3YuXMncnNz0bhxY8yePRvTpk1Dr169avzZdFQ1AUmS4OvrSyf3VsIR6+O/Fy/i3N272uczO3RA14CAav+9I8astihm4swWs/xbQPJOwDOs6oTToyng7A3IPUxbHhtH27o4ipk4ipk4ihmpqWeeeQY7duxAdnY2/Pz8MGPGDEyfPh19+/Y1yvZksVFq7ZGtjERF7FteSQlafv010vLzAQB1nJ1xY8ECNKGh0omjenANuL0JqBMINH/G0qUhhBDiIGwlN6hfvz4mTJiAadOmYeDAgUafP9RhBg0yJ7VajaSkJBolzEo4Wn18+Oef2mQTAN7o3Vs42XS0mBkDxUyc2WJW3Xk4SbXQti6OYiaOYiaOYkZqKj09HT/88AOGDBli9GQToITTJBhjuH//Po0SZiUcqT4SsrOx4tQp7fNgb28s7NlT+HMcKWbGQjETZ7aYyd35v6oi036Pg6BtXRzFTBzFTBzFjNSUk5Np77KkhJMQO/LqwYMoVam0zz8dMgTuzjTiJnFw1WnhVJUAVz8EYj4F1MrK30cIIYTYGcYYvvvuO3Tr1g1+fn6Qy+XlHrVJSmnQIELsRFRiIrZdu6Z93icoCJPDwy1YIkKshKaFU63kU59UNO2JWgGoSgFJAUjG705ECCGEWKvXXnsNK1asQMeOHfHkk0+iXr16Rv18SjhNQJIk+Pv70yhhVsIR6kOlVhtMgyIB+KIW06A4QsyMjWImzmwxk7kAkgQwxls5K0o42cM5OCUn/l5SKdrWxVHMxFHMxFHMSE3997//xaRJk7B582aTfD4lnCYgk8ng7+9v6WKQhxyhPn68cAGX0tO1z2d37IjOjRvX+PMcIWbGRjETZ7aYSRIQMJYnnjLXit+jpjk4q4u2dXEUM3EUM3EUM1JTRUVFGDx4sMk+n+7hNAGVSoX4+Hio9O6lI5Zj7/XxoLgYbx85on3u6eKCDwYNqtVn2nvMTIFiJs6sMfPtBPi0BeQuFb+u1rRw0j3Pj0LbujiKmTiKmTiKGampQYMG4ezZsyb7fEo4TSQvL8/SRSB67Lk+lh47hszCQu3zt/r0gX/durX+XHuOmalQzMRZTcyYpoWTEs7qsJp6syEUM3EUM3EUM1ITK1euxKlTp/Dhhx/i3r17Rv98SjgJsWE3793Df06f1j5vVq8eXu7Rw4IlIsRKFd0FHsQAJZUcSDUtnNSllhBCiINp1aoVEhIS8M4776Bhw4aoU6cOvLy8DB7e3t41/nw6shJiw/7vwAEo9CZ4/veQIXA18VxKhNikzONAzlWgyQjAtX7512UuQJ0gwMXX/GUjhBBCLGjSpEkmHWyKzkxNQJIkBAYG0ihhVsJe6+NgfDx23bihfT4gJATjW7c2ymfba8xMiWImzqwxkz1iLs46gUDzf5i+HHaAtnVxFDNxFDNxFDNSUz///LNJP58SThOQyWSoX7+CK+jEIuyxPkqUSry8f7/2uUySajUNSln2GDNTo5iJM2vM5JqEs8g832fHaFsXRzETRzETRzEj1ooSThNQqVS4fv06wsLCIJfTBOKWplKpcPPmTbuqj8VRUYjJzNQ+n9O5M9o3amS0z7fHmJkaxUycWWMmd3/4pZW0cJJqo21dHMVMHMVMHMWMiDh//rzw33Tu3LlG30UJp4kUF9NJjTWxp/r48/ZtfPLXX9rnfh4eWDpggNG/x55iZi4UM3Fmi9mjWjjvnQUyjgHe7YAmw8xTJhtG27o4ipk4ipk4ihmpri5dulS7ZxxjDJIk1XjKHUo4CbEhuSUleGr7djC9Zd+PGYMGdepYrEyE2AT5I+7hVBUBijxAXWq+MhFCCCEW8tNPP5ntuyjhJMSGvLRvH24/eKB9/o+OHY02UBAhdk3TpVZdScJJ06IQQghxILNmzTLbd9GR1QRkMhmaNWsGmYymObUG9lIf265dw88XL2qfh/r44Ivhw03yXfYSM3OimIkza8zcGgJNxwBOnhW/rk04nU1fFhtH27o4ipk4ipk4ihmxVpRwmoAkSfDy8rJ0MchD9lAfafn5eG7XLu1zmSRh7YQJ8HR1Ncn32UPMzI1iJs6sMXP2BHwjK3+dKR8Wig6Lj0LbujiKmTiKmTiKGbFWdAnEBFQqFaKjo2t8Yy0xLluvD8YYnvn9d9wr0g128vpjj+GxoCCTfaetx8wSKGbirCpm1MJZbVZVbzaCYiaOYiaOYkasFSWcJkI/dutiy/Wx+tw57Ll5U/u8o78/3uvf3+Tfa8sxsxSKmTizxiz/FvAgRpdc6tO2cFLCWR20rYujmImjmImjmBFrRAknIVbs5r17WHjggPa5q1yOdRMmwIXm1yJE3O2NwO3NgCK3/GvO3oB7I8C5rvnLRQghhNgxulmFECulVKvx1PbtKFToWmM+HjwYbRs2tGCpCLFhcjdAVVLx1CiNh5q/PIQQQogDoITTBGQyGVq1akWjhFkJW62Pj/78E6dTUrTPB4aG4p/du5vlu201ZpZEMRNn9pjJ3QE84HNukhqjbV0cxUwcxUwcxYzU1LFjx6p8XZIkuLm5oWnTpmjcuLHw51PCaSIuLi6WLgLRY2v1cTYlBUuOHtU+93Z1xc/jxkEmSWYrg63FzBpQzMSZNWZyN/5vRS2cRAht6+IoZuIoZuIoZqQm+vfvD6ma55hhYWFYsmQJpk2bVu3Pp0sgJqBWqxEdHQ21Wm3pohBYvj4Oxsdj6pYtmLd7N366cAFXMjKgqqIshQoFntq+HSrGtMtWjhqFQG9vcxQXgOVjZosoZuLMHjNZFQlnws9A7FdAYap5ymLDaFsXRzETRzETRzEjNbVv3z60b98erVq1wr///W/s2LEDO3bswKeffopWrVqhY8eO2Lp1Kz777DNIkoTHH38cW7durfbnUwsnISZ07PZtjNywAcqHO/9vHy73cHZG58aN0bVJE/4ICEDzevUgSRJeP3gQsffuaT9jWtu2mNGunQVKT4idcXLn/1bUpbY0Gyh9AICVf40QQgixY/v27YObmxtOnz5drpV83rx56N+/P06dOoXly5fjhRdeQJcuXbB8+XJMnjy5Wp9PCSchJnLnwQNM3rxZm2zqK1QocDwpCceTkrTLfNzc0KFRIxy9fVu7rImnJ1aOGlXtbg6EkCpU1cJJ83ASQghxUOvXr8fbb79dYZdsNzc3PPHEE/jggw+wfPlyuLm54cknn8TSpUur/fmUcBJiAsVKJSZt3ozMwkLtMrkkGXSTLSunuNgg2QSAn8eNg6+7u8nKSYhD8Q4H3PwAtwoGPNDOw0mHRUIIIY6loKAA6enplb5+9+5d5Ofna5/7+PhALjBFHx1ZTUAmkyEiIoJGCbMS5q4Pxhjm7d6Ns6m6e8EiGzdG1NNPIzEnB2dTUvB3airOpqbiUno6SiuZpHlBt24Y0ry5WcpcFm3D4ihm4sweszqB/FEWY9TCKYC2dXEUM3EUM3EUM1JTAwcOxBdffIEePXpg9OjRBq/t2rULX375JQYNGqRddvHiRYSEhFT78ynhNJHS0lK4ublZuhjkIXPWx7d//42fLl7UPvfz8MC2adNQ18UF7Ro2RLuGDTG7UycAQIlSieiMDJ6ApqTgbGoqEnNyMLR5cywfPNgs5a0MbcPiKGbirCJmTM2TToBaOKvJKurNxlDMxFHMxFHMSE18/fXXGDBgAMaNG4eAgAA0f9jgER8fj5SUFAQHB+Orr74CABQXFyMpKQnPPvtstT+fLoGYgFqtRmxsLI0SZiXMWR/Hk5Lw0r592udyScKWKVMQVMkIs65OTujSpAle6NIFP44bh8tz5yL3jTewdepUuDtbrqWFtmFxFDNxZo+ZqhjIT+APfUyh+z+1cD4SbeviKGbiKGbiKGakpoKCghAdHY1PP/0Ubdq0wd27d3H37l20adMGn376KaKjoxEcHAyA39O5Z88e/POf/6z259OlXEKMJDk3t9wgQZ8NHYr+Al0OCCEmVJwJJPwCuNQDWr+kW87UgFtD3q1Wqv49KYQQQoi98PDwwMKFC7Fw4UKjfza1cBJiBCUPBwlKLyjQLnuyfXv8s3t3C5aKEGJAXsm0KE4eQMt5PAmlEaEJIYQ4mNdeew0XLlww2edTwmkiIiM3EdMzZX0wxvDinj04k5KiXdbJ3x/fjR5t09OZ0DYsjmImzqwxkz+8r0ldortnk9QIbeviKGbiKGbiKGakJr766it06dIFYWFheOeddxAdHW3Uz3eYhPOjjz5C165d4enpiYYNG2L8+PGIjY01eE///v0hSZLB44UXXhD+LrlcjoiICPrRWwlT18fqc+fwo95Vofru7tg+bRo8LHgPZm3RNiyOYibO7DHTJJyM8aST1Aht6+IoZuIoZuIoZvbjm2++QUhICNzc3NC9e3ecOXOm0vd+//336NOnD+rVq4d69eph8ODBVb6/IhkZGfjpp5/QsmVLfPLJJ+jYsSPatm2LpUuXlsuXasJhEs6jR4/ixRdfxKlTp3Dw4EEoFAoMHToUBXpdIAFgzpw52htl7969i08++UT4uxhjyM3NBaMr6FbBlPXxV1ISFuzdq30ulyRsnjIFwT4+Rv8uc6JtWBzFTJzZYyZz0g0KpN+ttjAFiP0auL3JPOWwcbSti6OYiaOYiaOY2YdNmzZh4cKFWLx4Mc6fP48OHTpg2LBhyMjIqPD9UVFRmDFjBv744w+cPHkSgYGBGDp0KFL0et49iqenJ2bOnIndu3cjPT0dq1evRtOmTbF06VKEh4ejY8eO+Pjjj2u8Tg6TcO7btw9PP/002rZtiw4dOuDnn39GUlISzp07Z/A+Dw8P+Pv7ax9eXl7C36VWq5GQkECjhFkJU9VHal4eJm/ZAoXe5346ZAgGhoYa9XssgbZhcRQzcRaJmaaVU1WsW6YqBkqygNJs85XDhtG2Lo5iJo5iJo5iZh9WrFiBOXPmYPbs2QgPD8eqVavg4eGBNWvWVPj+9evXY968eejYsSNat26NH374AWq1GocPH67R9/v4+OCZZ57B/v37cffuXXz22We4desW3nrrrRqvk8OOUvvgwQMAgK+vr8Hy9evXY926dfD398eYMWPwzjvvwMPDo8LPKCkpQUmJrltWXl4eAP6DZ4xBpVJBkiTIZDLtMg3NcpVKZfCZlS2XyWSQJKnC5ZrvrM5yuVwOxliFy8uWsbLltrZOKpVKWx/GWqdSlQqTN29GWn6+dtmMtm3x0sNBgmy9nvRjRtte9dZJP2b2sk6PKntt10kTM7VaDblcbpZ1kiRXgD0AK82H3P3h/lpZDImpASYDq6T+HLmeKlquv0+1l3UqW0ZjrlN1jkO2tk6mrif9mNnLOukzxTqVPQ7ZwzqVLaOtrZPm9by8POTm5mrf5+rqCldXV5RVWlqKc+fO4Y033jD4zMGDB+PkyZPl3l+RwsJCKBSKcjmOCIVCgb1792LTpk3YtWsX8vPzERgYWOPPc8iEU61W4+WXX8Zjjz2Gdu3aaZc//vjjCA4ORpMmTXD58mW8/vrriI2NxbZt2yr8nI8++ghLliwptzw2NhbOzs64evUq6tevj6CgICQnJ+P+/fva92haUBMTE7WJKgAEBgaifv36uHnzJoqLdVfgmzVrBi8vL8TExBhs3K1atYKLi0u5m3sjIiJQWlpq0O9a07c/Ly8PCQm6eejc3NzQunVrZGdn486dO9rlnp6eaN68OTIyMpCWlqZd7uvra1PrlJSUhPv37+Pq1avw8vIyyjp9EReHk8nJujJ7e2NBSAjy8/Ptop6Kioq0MWvevLldrJOptz3GGO7fv4+4uDiEh4fbxTqZup40McvJyUGDBg3Msk5uxQ0hMV9IyQ/Q0hvIzs5G5s0Y+ORmoMTFCUp5ItXTI9YpLi5Ou3+QJMku1snU9RQfH6+Nmbu7u12sk6nrKTc3VxuzoKAgu1gnU9eTZp967do1dOjQwS7WydbrKT09HQAQHh5usI6LFy/Ge++9h7KysrKgUqnQqFEjg+WNGjXC9evXy72/Iq+//jqaNGmCwYMHV+v9GkqlEgcOHMCmTZuwc+dO5ObmonHjxpg9ezamTZuGXr16CX2ePok5YEfvuXPnYu/evTh+/DiaNm1a6fuOHDmCQYMGIS4uDs2bNy/3etkWzpSUFISHhyMhIQFFRUVo0aIFnJyc6OqQhddJqVQiLi4OLVq0gFwur/U67bpxAxM2b9Y+93V3x5lnnkGIj4/d1JNKpdLGzNnZ2S7WqWwZTdHCqYmZi4uLXazTo8pe23XSxCwsLAzOzs6WW6f75yGl/A54tgQLnkH19Ih1Ki0tNdin2sM6mbqeFArFI49DtrZO5mjh1MRMcy5l6+ukz1QtnPrHIXtYp7JltLV1Sk5ORkhICGJiYhAQEKB9X2UtnKmpqQgICMCJEyfQs2dP7fLXXnsNR48exenTp8v9jb6PP/4Yn3zyCaKiotC+ffsq36vvmWeewY4dO5CdnQ0/Pz9MmjQJ06dPR9++fY0y44LDJZzz58/Hzp07cezYMYQ+4l67goIC1K1bF/v27cOwYcMe+dnJyckIDAzEnTt3qkxkie26V1iItitXaufblEkS9j/5JAY3a2bhkhFCaizrDJC6B/AOB4KnWro0hBBC7IRoblBaWgoPDw9s3boV48eP1y6fNWsWcnJysHPnzkr/9t///jeWLVuGQ4cOoUuXLkLlrF+/PiZMmIBp06Zh4MCBFY50nJ2djXr16gl9robDDBrEGMP8+fOxfft2HDly5JHJJgBcvHgRANC4cWOh71Kr1bh37165KzDEMoxZHwv27tUmmwCwqGdPu0w2aRsWRzETZ5GYlT4A8hOAYr3R/piC/yuz3amMzIm2dXEUM3EUM3EUM9vn4uKCyMhIgwF/1Go+AJB+i2dZn3zyCZYuXYp9+/YJJ5sA7/r7ww8/YMiQIQbJZklJCbZs2YLx48cL50P6HCbhfPHFF7Fu3Tps2LABnp6eSEtLQ1paGoqK+ND48fHxWLp0Kc6dO4fExET8/vvvmDlzJvr27SvUJA3w5PbOnTs0LLWVMFZ9/BYTg1+vXNE+b+PnhyUDBtS2eFaJtmFxFDNxFolZ9nkg4Rfgnt4cZTIXwMUHcKpjvnLYMNrWxVHMxFHMxFHM7MPChQvx/fff47///S+uXbuGuXPnoqCgALNnzwYAzJw502BQoeXLl+Odd97BmjVrEBISos1x8vUGtnwUJyfdsD6MMRw6dAizZ89Go0aNMG3aNJw8eRKPP/54jdfJYQYN+vbbbwEA/fv3N1j+008/4emnn4aLiwsOHTqEL774AgUFBQgMDMSkSZPw9ttvW6C0xNpkFhRg7u7d2udyScJ/x4+Hm5PD/IQIsQ+yCqZFqd+VPwghhBALmzZtGjIzM/Huu+8iLS0NHTt2xL59+7QDCSUlJWnvFQV4jlNaWorJkycbfE5lAxNV5ty5c1i/fj02btyItLQ0SJKE6dOnY/78+ejRo0et7uV0mLPlR13tCQwMxNGjR81UGmJLGGOYt2cPMgsLtctef+wxdNW7+ZsQYiOc3Pm/+gknIYQQYkXmz5+P+fPnV/haVFSUwfPExMQaf09CQgLWr1+P9evX4+bNmwgICMATTzyBbt26Ydq0aZg0aVKVXXmry2ESTnPz9PS0dBGIntrUx+arV7E1Jkb7PKJhQ7zbr58ximXVaBsWRzETZ/aYaVs4i8z7vXaGtnVxFDNxFDNxFDNSXT179sSZM2fg5+eHyZMn44cffkDv3r0B8FsNjYkSThOQy+UIDg62dDHIQ3K5vMJpbaojLT8f8/bs0T53ksnw8/jxcLXzrrS1iZmjopiJs0jMKmrhTDsM5McDfr0An3YV/x3Rom1dHMVMHMVMHMWMiDh9+jRCQ0OxYsUKjBo1yuA+TmNzmEGDzEmtViMtLY1GCbMSNa0Pxhhe+N//cL9I1xLyVp8+6FyLUbpsBW3D4ihm4iwSs4paOEvuAYWpgLKw4r8hBmhbF0cxE0cxE0cxIyK+/vprNG7cGBMmTIC/vz+ef/55/PHHHyYZdIoSThNgjCEtLY1GCbMSNa2P9dHR2Bkbq33e0d8fb/bpY+ziWSXahsVRzMRZJGZyvUGDNN+rpmlRRNC2Lo5iJo5iJo5iRkTMmzcPx48fR3x8PF5++WX8+eefGDRoEAICAvDuu+9CkqRaDRSkjxJOQiqQmpeHBXv3ap87y2T47/jxcKlgIlxCiA1xqgP4DwYCRgF4eFLGlPxfSjgJIYQ4mNDQULz99tuIiYnB2bNnMX36dERFRfFBM+fNw3PPPYf//e9/KC6u+WB7lHASUgZjDM/t2oUcvR/W4n790P7hcNSEEBsmcwIa9gZ8IwHp4SFQ08Ip2fe92YQQolFUBDx4YOlSEGsTGRmJFStW4M6dOzhw4ACGDRuGTZs2YezYsfDz86vx59LR1QQkSYKvr6/RmqFJ7YjWx88XL2L3zZva55GNG+P1h6N2OQrahsVRzMRZTcyohVOI1dSbDaGYiaOYiatuzFQq4LvvgJwcwMcHCA0FQkL4w9vbDAUlVk8mk2Hw4MEYPHgwVq1ahZ07d2LDhg01/jyJUUdvo0lOTkZgYCDu3LmDpk2bWro4pAbuPHiAdt9+i9ySEgCAi1yO8889h7YNG1q4ZIQQoynOBJR5gFsj3sU29is+cFDz2UAdGmGcEGL/7twBfvyx/PJ69XgCOnQo4OZm/nLZG8oNOOpSawJqtRpJSUk0SpiVqG59MMbw7K5d2mQTAN7v398hk03ahsVRzMRZLGbJO4CEX4DCZP5c7sanS5G5mLccNoq2dXEUM3EUM3EiMQsMBN58E3jySaB3byAgAJAkIDsbuHYNcHXVvffiReD2bdOVm9g/6lJrAowxZGdnIyAgwNJFIeD1cf/+/UfWx+pz53BAb6LbHk2bYlGvXqYunlWqbsxszYMHwKFDQLNmQMeO/OBqLPYaM1OyWMzkmrk4H06N0mKOeb/fxtG2Lo5iJo5iJq6qmJWWAr/9BvTrBzRpwpe5uAAtWvAHAJSU8MSyoEB3fGQM+OMPIDcXmD0bCAoy08oQu0ItnMTh5ZWUYMGePZi7e7d2mZuTE34eNw5yGf1E7EVxMbBuHRAdDezezQ+exEHpT41CCCF2TqUCNm8GYmP5vypVxe9zdQVatgQ6ddItU6sBLy+eeG7fzhNXQkTR2TRxaLtiYxG+ciW+PnsW+jczfzBwIFrVYjQuYl1UKmDTJiAzkz/v358GRnBoZVs4CSE2S6HgSdRvv+mm1iU6jAE7dgBxcYCzMzB5MiAyw5tcDjzxBD9mZmcDBw6YrKjEjlHCaQKSJMHf359GVrMSFdVHWn4+pm7ZgrEbNyK5TFPX85GReKl7d3MX06rY0zbMGLBrF3DrFu8+9MIL/H4VjaQk4Pz52p+o2FPMzMViMdNv4VQrgbgfgIT/6qZHIVWibV0cxUxcdWN24AAQEwN4eBj3NglbVDZmjAH79vGePTIZMG0aUJNxa9zcgPHj+f///hvQG8ifkGqhezhNQCaTwd/f39LFIA/p1wdjDD9euIBXDx40mGcTAEJ9fLBq9GgMbd7cEsW0Kva0Df/5Jx/wQJKAKVMA/dUqKgK2bAHy8vh9K6NG8aS0JuwpZuZisZgZJJwK3eBBdA22WmhbF0cxE1edmF27Bpw9y/+vuQ8RANLTefdQHx/Tlc8alY3Z8ePA6dP8/xMmGMZIVGgo0KMHcOoU8PvvwLx5gLt7LQtMHAYdXU1ApVIhPj4eqso6yROz0tTHtYwMDPjvfzFn1y6DZFMmSVjUsyei586lZPMhe9mGc3N5wgkAI0cCYWGGr7u5Ad278yu/ly4B338PZGSIfYemZdQUMcvL462zFy7YZ1cxi21n+l1qNXNwSjL+II9kL/sHc6KYiXtUzHJygJ07+f8fe0y3f1co+IXElSuBc+fsc99ZGf2YXbsGHD7Mlw8fDkRE1P7zBw0C/Pz4senUqdp/nikUFwN64z8SK0EtnCaSl5dn6SKQh0pVKnz+99/44eZNlJQ5cHXy98f3Y8YgUjNkG9Gyh23Yywt4+ml+8OnatfzrksS71wYGAlu38ns8v/+et3R27FjxZzIG3LvH74e5eZM/nzmTv5aXl4cHDwBf39qX/d49YO1aflJ17hy/96Zdu9p/rrWxyHbm0RTwHwy4NdB1o5U5UX88AfawfzA3ipm4ymKmVvN7NouLeRfRgQN1rxUX8+61WVn8gl1MDDB2rOPct6+JWfPmPAn39+ctk8bg7MxbSuPjDW9NsbSSEj4g0tWr/NjMGLBoEd8OiHWghJPYtTMpKXj2998RXabZyt3JCUv698crPXvCiUaitRppafzqdGAgf65p4Rs8GBCZDpUxXe4QEMAfVQkO5vd2btvGD6Q7dvAutqNH8wETSkv5PaCaJDMnR/e3ksS75rq4ALdvu2LnTgkjRgDduomsuaHUVGD9ej40vbc3UL8+EB5e88+zdRkZ/Eq9nx/QqxdQp04tP9CtIX8AQPHDfYPkXMsPJYSYS1QUcOcO7zY7aZLhIDienvxC4+nTfL8RH89bO4cN46OvOsp1JRcXYMYM469vdY6p5lBaaphkKpW61xo04NOgUcJpPSjhJHapoLQUbx85gi9Pn0bZ3jSDmzXDd6NHo1m9ehYpm6kUFfEru3Xr8quQtiQri8/zdfUqTyxfeIF3cz10CLhxgx9MevTg84fpT0ZdkYICPiLt8OG6ucaqo04dPgH2n3/yspSW8jIAPAGNidG9Vy7nSWpYGL8nxs2NX3FPSXGBSgXs2QPk5wMDBogf7BMSgI0b+fc3bszLpD8YhkLBu5H17SuWhNsixvgAFfv385OJ2FjgzBmezBsl8QQMWzgJIVaPMd2I42PHAhUdymUyoGdPvo/euZMnp7//zvfjU6Y8+jhiq+7eBc6dq6PtDWPq6+lKJXD5smUS+dRU3sqt4ecHtG3LHw0aOM6FBVtBR1gTkCQJgYGBNBqdhRyIj8fz//sfEvWboQDUd3fHimHD8FT79matm5wcfhBo1sy4B7nTp3myU78+fx4Tw1sDAZ4A1a3Lr/R6evL/d+7Md8jVYa5tOCcHOHqUD+qjuc+mYUOebLm58YStpAS4fh04cQK4coVfpQ4Pr/hgolAAv/4KJCfzk4wXXhA76EgST+SCg4FGjXR/27w5r8MWLfgJTEhI+cGFJEnC1Kk+iI7m63TsGG+hHTOm+gf9W7d4y6ZKxQdomD69/DZz7BiPw7VrvKy9e1dviHulkl/pv3KFr1+XLtUrkylVtZ2pVPw+rOvX+fNmzfi2kJIC/PUXnyuuxgmnWgUUpwPqYmiHMqAWzkdSqXgL/8WLEtzdQ9G2rSQ0vYIjo/MCcZXFTJKAqVOBxES+n6yKnx8weza/3/DIEX6cqenAcNbu9m1g40YJ9+4F4MwZCb16mfb71GpgzRqe+DEGREaa7rsY4xcenZx4jycACArix7LgYJ5kNmxISaY1o4TTBGQyGeprsgBiNvcKC7HwwAH8culSudeeiIjA58OGoYFRmkQqxxifp8rHR5dkHD3KB30JDeX3+hljh5iYCOzdy1sy//lPnlQqFPy5QsFbOouLecuhRliYLuFMT+dXiJs3r3iUOVNvw/n5PHE6d043AXWrVvw+nEaNdO/z8eFJ182bvNUwO5snIc2b80GA9IuomZQ6OZmv05QpNY91cLDh806deMJe1efJZDL4+dXHgAH83tH//Y/Xe0EBL0t1Wp2bNOEHzXr1gIkT+cG1rK5deRfT2Fhdq/C4cRV3cVKr+bYSHc0TVM1YWffv6xJO/e7H5lbVdiaX84RSLgeGDOGDOwF8W4iPN6yjuDi+/tUeMZEpoIxdjfx8IEn2JEJkbvDysNMmDyPIzuZTB124wH+7PEn3RuPGtes67kjovEBc2ZhpLkpKEn88KtnUfQ7vEdGyJd8P22NScv48sHs3oFLJ0KKFBzp3Nv13ymR8IKLUVJ4MhoYaZ/yCihw7xi8a1K3LzxNkMv6YPds030eMT2LMkcbvMq3k5GQEBgYiMTERRUVFCAsLg5wu/5ocYwybr17FP/ftQ0ZBgcFrgV5eWDlyJFowZpL6YIyfvCcm6h55ecBzz+m6c164oBtJb8QI3YlzTSmVwKpVPJmMjOQtaPrlKSnhJ4V5ebp/8/J49yIvL/6+ffv4zlsm4/dLhoXxh+YKoUqlws2bN022Dd+4AWzYwP/frBk/gDxqbjCFgrdsHT/OY9CrFzB0qO71gwf563I58NRTvBXSnMrG7Pp1PhCRUsnXbebMiq+s659EAbxrtKtr1a2ijPFEc+9entBKEq/fAQN0ie2BA3zkXf2fhKcnH3ioXTueoCUl8c+YPNkweTeXsjFTqXjrtiZxVCj470v/IkRZBQXAF1/wePXowePg5lbxey9f5slqaipDW/X7ABhO3F8EBeqia1e+PVWU5FeXUsm77mlOhBnj5atbt+afaUk5ObzXhP6Ij3XqAK1aqRAbm46XXmoEFxc6xlWHqfep9qhszE6c4D1NRo+ufW8hxqrXQmrt1Gq+r9eMGNumjQrh4TcRHm6e7Ywx4L//5bEMCuL3zhq7G29sLL/NhDF+oblrV9u6aKDJDe7cuYOmNZkE1U5QC6eJFJeZ45GYRnJuLubt3o1dN24YLJcAzO/WDR8MHAgPJydER0cb9Xvz8/n9hfHxPJnTJ5fzEUY1CWenTvxEdPdu/jf63WBr4s8/ebJZty5v+dEnSfxk282t6u6z3t78HofMTN4N5/ZtXjZvb554Dhqk24bv3eOtkDIZ/3zNlUVJ0rVCacTF8alICgqAwkLDh7s7vx8R4N/RtSvvGlvdA76zM9C/P9C+Pb/a2a+f7rUTJ3iyCfDWPnMnmxr6v/vWrXmS+euvfJTAilo41Wqe8Pn48GH9geq10kkSTxqbNeMXDy5f5jEoKdFdgLh/n9eDhwePc7t2/IRA/2Tg2DF+ArdrFzBrlmUO4pqY3b/P78dxdeUXDCSJx6yqZBPgv0VfX95qf/Qo72reowf/fWRk8Is8mvW6fl1zL64ERT031HUvQkhgEW4k1UVaWvW6Jlfm7l1+r29mJjBnDv8NbtvGk+bnnjP9vVTGUlqquzBSpw7vwixJfFuLjOQ9EQAgKCgDcjmvHJWKD87y2GNGuq/WijHGtytfX91vOiWF/wZdXSt+NGjA/6XzAnGamKWk8GOUWs2PoR061Pwz1Wrgp5/4xaGZM/m2bYuKi/lFzbg4/nzAAP4bvHLFfNuZJAHjx/NBmZKSgJMndccyY8jK4vtRxvg5A/WosF2UcBKbpGYMq8+dw2sHDyKvtNTgtTZ+fvhh7Fj0ejjUqSnmPbt4kT8AfpLatClPckJC+P/LJhdduvAujQkJvNvnP/5RsxPQjAzewgfwK32VteQ8Ss+e/JGTw7so3rjB7x988ID/f/hw3Xt//dWwa64+Hx/g5Zd1z/fv1w3mUJZ+K48k8alHasLXlx/gNNRqfiIC8ANu+/Y1+1xTCAriyYa3d/lkTqnk28LVq/y1Vq2qf4+thocH73rbrh1v4e3bV/da7948QWjWrPJEatQofqKQmMi3506dxL7fGBjj371/v65189696seiUSN+r+61a3zkyowM/q9Gz566QUXateOt+E2aAMHFbnBFEXo2L0ZCOm/919RRaSlPFKuTPKlU/CLQsWN8W6xTh19g8fLiv5uiIp4E9+wpEBQLuH6dt5Lk5wMvvqhL+CdO5AmT/sAsZXepUVH8gsfly/y3WZvJ5a2NUsm7DN6+zU+o79zhJ/pPPqlbz7Q0XseVmTpVl6iXluq2E1I9msRKreYXz2q7j5fJ+D7gzh3eOvj887bVYqaRns7PKTRTlYSHl/9tmoOPD7+wt3Mnv0+2RYtHXyisjuJi3rJZUsJvodA/LyG2hxJOYlMYYziVnIzXDx3Cn0lJBq85y2R4o3dvvNmnD1xr0y+uGh57jCdQLi68te5R9+dJEm95W7mS32N44oT4HFaM8ZYolYqfvLRpU/Pya/j48KuGXbvyE+zERL6T1z/4urvzxIYxfsBXq3X/L5vIBAfzE1MPD35C5eGhe5iqW2F6Oi9HZKRhwmUtyp6o79jBT5hOnuQnC3I5P6kXTTb1tWzJt0P9eqtOz5169XiSfuAAf4SF1a6eUlJ4El1UpBuwSv8RGakrI2N8Wzt82Bu5uRJkMn7BZsIE8fnyJImfbLVpw1swz5zhXWObNDHcRsPD9aaXuekGFAHIvoBm+ANgzQHwS/N//MGT4GHDeEtKZSej6em8Pu/e1X3+qFG6ZGLIED4y5h9/8NesdR7Ay5d5KwLA1zUzUzcCcsuWj/77tm15wpqZCaxbx1uYBw+uXfdkS9C/nzk5mf8mUlLKn8S7uBj2bPH35/uekhLdo7hY93/9qRnOneMJeocO/CJEbX731VmfBw8ML3hduWLYRVqzXPNv//78t2otGOO9gzRjI4wda5zksF8//htPS+Pbf21aTC0lOJjHo1EjPqK5JXXsyPcBsbF8vIXa3lupGZMhK4tfvJsypXY9UIjl2djhwDbIZDI0a9YMMlvpQ2Xl1A+TzC1Xr+K3a9dwJze33Hu6BQTgx7Fj0a6CeSJMUR+SxHewIry9+VXAHTv4SUePHmInZJcv6+YdGzXK+FdknZ15wgEAjOli9swz1f+M0aONW6bqaNwYePNNy1+hrs52dvIkH8BH08PbxYUPimSMLl01Xf8ePXh57t7l3XMnT67Z5zDGuwdrWsMLCvjJnIarq+HIuBs3AvHxMhQUNIGbm4QBA/hFmNr8TCVJNyz+I8kf9l0uSgWK0gBnfpatUvHWrKIi/lu9fJl3Uy479cLJk7xlXaXiF2VGjeLfq18PnTrxe7jv3OGxnTat5utmKtnZ/IQe4ANj9ev36MS47Lbu789b8g8e5Mn+qVP8YsqkScZp6TCW3Fxeb0VFummk9P/fsye/nxzgv03NNc06dfjJfVAQf/j7G26n1ZmXULNP3bNHBqWSHwPOneMJfc+e/GJLTX/DRUX8d3fvnu7fe/d4N3WlEvi//9MlkSkpfJusTI8euvdqumpaqsVaJpMhP78Frl6VQS7n21NNe/WU5eEB9OnDf8OHD/MLQtY+nZhmmqjQUN2FirLnIZY6/5Qk3Yjsw4bV/vMY47ceOTnx/aat3gdPdCjhNAFJkuClGZ2F1IiaMfyVlIStMTH47do1pJS9UfIhD2dnfDBwIBZ06wZ5JTtYY9VHURE/MA0cWPPJhDt04Cc2HTqIX/1v145fra5TRzf4j6nY2jZs6WSTl+HRMevenScfsbG8Hp94QmyuUFOQyfiJwvff89aPDh10Fx5ESBJPng8f5i3mBQW6Aasq+vnm5gJKpYRGjVwxaRIfvMqs5A/PXBUPCyfjZ5tyOfDMMzxp+uMPnjitXMlbgnv00CUafHAt3ttgzJiKT4gkiV+E+e473uX3xo3qtRiai0rFuyqWlPBEavTo6iX8FW3rzs68m39YGE/UMzL4NjVhQjUvABiJSsWTqoQE3pIXEaG770uh4AlnZfRvsWzQgHcPDgzk3fhru4/RxGzaNN29brGxfJu4cYMnsY89xstbEcb4byYzkz86ddIlX3/+yXvNVEQu58cNTRIZFmZ4/NIMWqb5V9M6f+MGv53CzY13OfXxqdXq10hWloRjx+pCkvhx19j7iO7dgbNneXxOneIJqLkwprtfujrblkrFL+j9/TdPxJ5/vuJB6Cx57K5b1/CimlrN9y3VHj1cj0zGB3Hr1s0y2x4xPko4TUClUiE6Ohrh4eE0Gp0AlVqNPx8mmduuXcNdPv5+pUaGheHrESMQWtGsz/qfq1IhJiamVvWh6c4aE8OvID/9dI0+BpLET1prQi43X5dRY8TM0VQnZs7O/IB84wZPNK0lp2/ShLeynDjBt3GRhFOt1iUpdevyruPV8cQTQG6uCunpMWjSJByAmbcznwjAIwDITwDy4gFJdziUy/nJf5s2/Hd/6xbvXpmczJMQFxd+surrW74rc1mNGvHf/IkTvKtZaKj1tKRERfHkzM2Ntx5Vt1Gkqm09LAyYN4/fz5WYaNjV79QpfuHOx0f38PKqXVc5zUjh8fH8kZjIT3I13N11CWfdurxe3d35w83N8P/691XWpBdLVfRjFhwsR3Awb4U8fZq3OKal8QRUk3Cmp/P76zMz+TEnM5MnKBoBAfwiAcATEG9v/q/m4eenW65fr82aVa9HRbNmfL+QkgJs3szHHTB2F2mVynAk9dxc/u+AAXybKChQ4f79DERENMJjjxm/xc7ZmQ+Qt20bHxuhc2fz3FubmclHac/O5mWoW5c/WrfWDbjDGG9h1ty6s2sX37YliZezsn2INR27T53i+73Ro/m6VceDB3ydNUWnZNN+UMJpIqYYqMbWlJbyE7Xg4Mq7weQUF+NQQgL23ryJ3TdvIr3MtCZl9QoMxJTwcExs0wZBAjdE1bY+zp/nJ+Kaq27GwBiftqJBg6q7Y2Vm8hNbcx87aBsWV52YyWTVP/iaU//+/ARTpDWqpARYu5Z3lRU9Oa9bl5/oZ2RYaDvzfngTtCKXJ5yy8mdwvr58FEvNoEb6XYQlqfqtlf3788Gh3N35CfYjrpGZhULBywTw+8BE7y+taluvUweYMYMnVPrz8l24wBMpfZLEW98aNtSNYg3wk9WCAt2o2PoPNzfdAFdKJW+B1i+OhwdP7Js3N0yuXF3Lj+xtTmVjVr8+bxUeMIB3r9Uva1KSbjA0DZmM/02DBobJX+fO/P5oY3Jy4vfNffcdHzRp3z7j3TJx5Ag/phYU6FpW9XXrxi9EBAYCkydnIjy8ocl6sURE8NZmSeKDfZk64UxJ4fc5FxXx5woFTzyzs3krt0ZxMbB+veHfurjwWx4etd+xhmO3Ws1v1cjP57dPRETwW4qq6h1WXAz88gt/D3WjtT+UcBKTuHJFN0egpye/v6l1a95V9mJaGvbFxWFvXBxO3rkDVRVTwUoAegcFYXJ4OCa1aYMACzQJZWbygy3Ar4Yaqwvk6dP8c/38ePeYiq5YFhXxOa48PPgJnDWcqBL75OLCu21Xl1IJbNrEW/3u3+e/b2PdX2VWaiX/t4KEE+Anop068Za7Eyd4a5PoPsDFhU874+NjPdOjODvz+y5jYvQGUjIiSSo/IE6nTjx+OTm6h1LJW7bKbjvnz/NuuRXx9tYlnM7OPLlUqXQJZuPG1tHNvrrc3csPIhcQwE/S/fx4gtmgQeUXHk21rj4+vOV7/XrelTMw0DiD62haNgH+e9AMKublxf81HLRObdLEQ5J4b4s6dcyzzRw7xo/rAQE8qdLEIj/f8KJPaSnfjjWvNWjAk80KhqmwSjIZvzUhKopPWRYdzbu5jx5d8YCHajWfFuvePb5PsKXfL6keSjiJSbi48GRTLgcy7ivw9n+ykVUnETE+J5GpzKnyb2WShL7BwZjcpg0mtmmDxhYcMk+p5Pc4KRT8ZKZXL+N9docOfEeclcXve6toyO9Dh/jBxs3NukYOJPatuJhfEOndu+ITXLWad0NLSOC/9SeesMFkU1UMlNwHipL5c6nqw2HdurXr3aDf0mct3Nx465i5lL2dgDF+nMjJKT8SbIcOvHulZmRslUr3/7L3hD3xhP2doDZpwpM9S2vRgg8kFRUF/O9/vBVOdCCo4mLegqfpXt2lC7+45elpvkSvKuZsSZs4kd8bPnCg7h7Mii4ke3vzC9EA3+YlyfJxEuXkxEerbtOGd7HPyOAXKdu1462d+q3JUVG8C7mTEx8LgKYNsj+UcJqATCZDq1atHGaUWsZ41yyFQnfVOSwMaNQtEf9N+gNnTjmB3W4KMAlwbgu0uAn4G166dnNywoCQEIxp2RIT27RBIyMeAWpTHwcP8i5gderwwS+MucN3d+dd2dav593HWrfmIxVqJCbyblYAH5TEnFMMONo2bAz2EjPGgDVr+MmBk1P5Sbw10xTExPBkdPr0R4/QWRmLxiwvDkjayv8vSZW2cBqbQsHvF/P1tcxUDOfP8wtpXbvWfH9mrHqTJN39a2WJTB5vCyfitrx/6NuXD3YWH88HvxJJOAsKeBfS7Gw+9oG/f/V76pg7ZqWlvBdDaCi/FchYUlN1vSJcXcXnkxRZfWvczgICeG+KY8f4vu/aNb5NaZLKmBj+GsDPiSw9xQsxDUo4TcSlouHD7Axj/IrU4cM8KXNz41ey3NyA9dGX8eLZ7WAAEArALwOIbQXk1QWK+OXpMF9fjGjRAiPCwtAvOBjuJhxJoyb1UVLCB3EA+EAhprgKGhbG7705d46P7Dh3Lj8gKZV8kACAv27Mg191OcI2bGz2EDNJ4i35O3bwK/Ft2hi2zh05wrdXSeItMLWd0sViMdOMUuvuD7R43mxfe/EicPQo7yZfdsRQU8vI4AMXKZW8dak2c/naw7ZubrYaM5mM/9bj4vgcwtWVm8vv8c7MrHmLlTljdvQo73V08ybw7LPGuZBx8iS//3vwYPG5t2vKGrczJyfeqtu6NT9f1HQNzsjgvcgAPnCdyPZFbIv1XAKxI2q1GtHR0VCr1ZYuiskkJvJWkA0b+M7D1ZXvLGQyYPu1a3h6xw4Y3JnpmQ+3bpfw2OBifPlcOOIWLMCNBQvwQZ8RGNqshUmTzZrWh6sr8MILvAtMTaaJqK6hQ/m9Mjk5fCRMgF/tu3ePJ7mWGOTCEbZhY7OnmHXowBNJpZJ3o9PcZp2QwKdgAPi9OLW998+iMdMknKois/ZX69yZn2wVFpYfFMaUFAp+YqdU8m6StRm4yp62dXOx9Zh5eIglA9nZwE8/8WTTywuYPdtwUJzqMHfMevXix/2UFD4ORW0wxi/O7d/Pn2sGCTI1a9/OmjTR9YQD+PmjWs2PN5Yc0IuYHrVwEiH37vHudAkJ/LmzM58eQDPU/IH4eEz/7TeDgYBmtGuHpzt2RN/gYLjp9QtVqXhXGycn3o2iujfDa+Z2cnU1/QAcbm6mv+Lm6spbUH/+mXd3a96cdzsB+AiGNndvHLF5mvkjV67kv/XLl3kSGhrK7+dycjL+qJhmJ394I6CquOr3Gftr5Ty2a9bw33vHjrrpLUzp4EHemlC3Lt/f2EI3VGKdCgt5D4jHHqu4901mJh9tNC+Pd5/VDJhl7erU4et05AjvudWmTc1uZWGM9yQ4e5Y/N2frpq25dYsnoZMnW8+AasQ0KOEkVSou5gcXTZc6Z2d+AiqX8xPOPn10g9kcT0rC+I0bUao3+sMLkZFYOWoUpArObjIy+FXQkhI+9Hrv3ror/0VFfGh0TbJ18iRw5gxfrpmcW5L4AcLTk8/9p7l6mpnJE2PNyHeiCdvJk3w9IyPNd1IWEsIPSk2b8qvBQUE8Ea1NlzdCasPXl0/ncegQv0rfogX/vQ0YYOmSGYm2hbMESPwV8B8EuJlnCMigIN7Sef48b0F+/nnTTnsUG8v3n4Dpbg8gjuPYMT6X8N27fNvV356ysnjLZmEhH1l15kzbGvCuZ08+Im9ODh84TeReYoBfSN++nbeQShIfob9LF5MU1S6MHWvpEhBzoYTTjsXF8aGo/fx0j+rM51hYyE9QYmJ4chkaqpsfzcuLjy7WsqXhjf/n797FqA0bUKRUapc9ERGBbypJNgF+Y/iLL/ITrhs3+P0T+lq10iWLmrmq9DGmGzJcf51iYvi9Z7r3SSgoaIC2bSX4+fHEVlN2xgyTyuRk3hKgVvNY1fb+NBH6V0BnzeIDGFArBLGknj35PiQ9nZ9I2tXIgTK9K1G5sUADwTPLWho8GLh+nV94O33auCNg68vN5SNEAvw7WrQwzfcQxzFwIB9AKDOTd9OeOVPXOuXtzQcVKinh5w3mvEfZGJyd+frt2MFvH+jUqfrrwBgfhfXGDX5OMmGC2FRThNgzSjhNQCaTISIiwuKjhCUlAZcuGS6TyXgi5efHT3g086Tl5/ORw65d4/dn6nf/z8/nzzWr07274WfGZGZi6Nq1yC0p0S4b16oVfho3DrJHZExeXnx+yatX+UmXTMa75np46IYMB3i31pAQvtzDg7f+FRXxLjt5eYbddTw8+KhoeXm87CqVBA+PxkhMlHD7Nj+J1vjzT37l39eXPzTr3q4dT7QtRZL4OlqKtWzDtsQeYyaX894DGzbwUSqNPXiVRWMmkwNyF0BV+vC5eUap1fDw4Pcs7dzJByrp2pWf7BYU8ItNCkX5h5OT4aTvRUWPvrXg9m3eK6RxYz6PsDHY47ZuavYUMxcXPofk6tX8mHnkCD+fAPg2PGMGP47W9nYQS8WsfXs+cnxaGm/NrWxUWZWKJ93OzkD9+vy4HRbGu4lOm2aZizv2tJ0R+0IJp4mUlpbCzcI337VsyXeEWVl8p5iVxU9ksrL4Y9gw3Xv//pvPg6Th788HBGnThneLqUxCdjaGrF2Le3p3xA9p1gybJk+GczX7iEkST/CquhLo41P+HhDNcPplh9Du2pU/AF0raFpaKQoKXJGdbfg59+/rWkmTknTfNXo0tS5awzZsa+wxZk2aAP/3f6b7PVg0Zg36AGmH+f8fMQ+nKXTsyPdBkZF8Xw0AP/xQvjeHRv36hgnn+vV8gBNPT37xztubP7y8eC+OVq2AiAi+T/PwMG63XXvc1k3NnmLm58cvRm3ZwsccuHmTD7InSYYXi2vLEjGTyfhgfufP6y6wq9X8vCk1VfdIS+ODcHXvznt+Afzco1Ur/hu0FHvazoj9oITTBNRqNWJjYxEREQG5KW/MeYSmTflDgzHe6qdJOL29da/du8ff26ZN+WkQKpOSm4tBv/yC1Lw87bJegYHYPm0aXM05aWQVJAnw8FCjoOB6hfUxfDi/V/T+fX6Sl5fH77dw9H21tWzDtsSeY2aqZNPiMWvYB8g4CqiVZm/hBHhcy7Y6urjwh7Nz+Yf+PhvgF8oY491mc3P5LQEamoQTAAIDjVtui9ebDbLHmLVty3s+nDrFu93HxPBlxmLJmDVrprulpqgIWLGC9zIoy82t/P7RksmmPW5nxD5YR1ZgZb755ht8+umnSEtLQ4cOHfDVV1+hW7duli5WrUkS3xF6eZW/N3HCBLERwjILCjB47Vok5uRol3Xy98fuxx9HHSucA6oybm68BUczKTMhxIEwxpNNwCIJZ0U0rUTV8dJLPOnMzQUePDD819bunSO2acgQ/jOy50Hu3N11iWXjxrpzhiZN+MV5R+8NRUh1UMJZxqZNm7Bw4UKsWrUK3bt3xxdffIFhw4YhNjYWDas7b4cNEkk2c4qLMWzdOlzPytIua+Pnh/1PPgkfR28aJITYDkWO7v8W6FJbEZGTV0nSjcYdEGC6MhFSGblc153Uns2Zw2/hoVsjCakZ+umUsWLFCsyZMwezZ89GeHg4Vq1aBQ8PD6xZs0boc2y1KwNjDGrGoFSrUaJUokihQH5pKXJLSpBdVIS7eXkYtWEDLqSlaf8m1McHB596Cg2seAhLW60PS6KYiaOYibNozFL36f5vJS2ctoK2dXEUM3HWEjMvL9tJNq0lZoTos45LulaitLQU586dwxtvvKFdJpPJMHjwYJw8ebLc+0tKSlCiNzJrnt69jOHh4QB4f3qZTAa1Wg3GmPZ1SZIgk8mg0puzsqrlMpkMkiRVuFzzPdVZLpfLwRiDSqVCdEYG9sXHY29cHP5OTUWJSgW1Xhmro4mnJw488QT869TRrqul1qmi5ZplmvpQqVTa5RXVh7XVU2XrxMrUkynWSRMzxpjdrJN+GU2xTpqYad5rD+tUVdmNsU7h4eHaqZPMvk4u9fjy+l0BNQOgMso6VbXcVutJfzlguE+1h3UydT0Bjz4O2do6maOeyp5L2cM6PWp5bddJ/zhkL+ukX0ZbW6eyrzsqSjj1ZGVlQaVSoVGjRgbLGzVqhOvXr5d7/0cffYQlS5aUW379+nVkZGTA2dkZvr6+CAoKQnJyMu7fv699j7+/P/z9/ZGYmGiQqAYGBqJ+/fq4efMmiouLtcubNWsGLy8vxMTEGGy8rVq1gouLC6Kjow3KEBERgdLSUsTGxmqXFavVuOvujp0xMdgbF4cMvc+vCV9XV3zVtSvyk5MRnZxskXWSy+WIiIhAXl4eEhIStMvd3NzQunVrZGdn486dO1AoFHB2doanpyeaN2+OjIwMpOm10lpTPVV3nTRMtU6amNnTOpm6nhQKBerWrYs2bdrYzToBpq0nhUKB0NBQ+Pn5mX+d6rmjqLgIabfikZsVTfVUzXW6ceMG8vPz4fxwaF17WCdz1JNmn2pP62TqetLEzJ7WydT1pFAo4Orqivbt29vNOgG2W0/p6emoCdHxZLZs2YJ33nkHiYmJCAsLw/LlyzFy5MgafbcpSKzs5QAHlpqaioCAAJw4cQI99SZrfO2113D06FGcPn3a4P1lWzhTUlIQHh6OhIQE5OTkoG3btnBycrLY1SHGGK5mZGBvXBz2xcfjeFISFGXeV1M+bm44+OST6OTvb9Z1qmj5o654KZVKXL16FW3btoVcLreKK161XSdTX8VTqVTamDk7O9vFOpUto7HXST9mLi4udrFOjyp7bddJE7N27drB2dnZ/OuU/TdYyh6ovcOBwMlGWadHLbfFeiq7vLS01GCfag/rZOp6UigUjzwO2do6mbqe9PepmnMpW18nfaaop7LHIXtYp7JltLV1Sk5ORkhICO7cuYOm+lNHVGHTpk2YOXOmwXgyW7ZsqXQ8mRMnTqBv37746KOPMHr0aGzYsAHLly/H+fPn0a6qOQfNiFo49fj5+UEul5e7GpGeng5/vcRKw9XVFa6urtrnubm5AHQboeZArFlWkcr62tdm+enkZPx88SL2xsXh9oMHFb5fX7N69TC8eXM09vSETJIglyT+r0wGmeb/estc5XIMatYMTSsZ+9sU6/So5Zp4lyWTySCXy7Wva95TWX2Ys54etbyqdaqIsddJ8/2a7o72sE6mWK6/Tvr/t5d1qk4Za7NO+t00zb5OqkK+PPdauUkqqZ6qLkvZfeqj3l/d5fa8j6jtccga16m6ZazpOpU9l7KHdTL18sqOSdUpo7Wukz5bW6fKXq+K/ngyALBq1Srs3r0ba9aswb/+9a9y7//yyy8xfPhwvPrqqwCApUuX4uDBg/j666+xatUq4e83BUo49bi4uCAyMhKHDx/G+PHjAfArFIcPH8b8+fMtWzgBF9LSsOrcuUpfd5HL0T8kBCNatMDIsDCE+fpqkwpCCHEY1MGHEEKIGeTl5WkbpoDyjVYaouPJAMDJkyexcOFCg2XDhg3Djh07jFN4I6CEs4yFCxdi1qxZ6NKlC7p164YvvvgCBQUF2qsM1eVmwelBRrRoUW5ZsLc3RoaFYWRYGAaEhNjUXJnGYMn6sFUUM3EUM3EWjZlfTyD3GuBtxNnqHQRt6+IoZuIoZuIoZtZJfzAnAFi8eDHee++9cu8THU8GANLS0ip8v/69q5ZGCWcZ06ZNQ2ZmJt59912kpaWhY8eO2LdvX7mKrIpcLkfr1q1NWMqqBfv4oKO/P3zd3THyYStmaz8/h23FtHR92CKKmTiKmTiLx8zJHWj5ouW+30ZZvN5sEMVMHMVMHMXMesXExCBAb8Lkilo37RklnBWYP39+rbrQqtVq3Lt3D/Xq1au0j7ip/T1nDuQW+m5ro1arkZ2dbdH6sDUUM3EUM3EUM9tE9SaOYiaOYiaOYma9PD094VXJ2Cf6RMeTAfgIuyLvtwTaGk2AMYY7d+6UGy3LnCjZ1LGG+rA1FDNxFDNxFDPbRPUmjmImjmImjmJm+/THk9HQjCejP4OGvp49exq8HwAOHjxY6fstgVo4CSGEEEIIIcQKPGo8mZkzZyIgIAAfffQRAOCll15Cv3798Nlnn2HUqFHYuHEj/v77b6xevdqSq2GAEk5CCCGEEEIIsQKPGk8mKSnJoMt0r169sGHDBrz99tt48803ERYWhh07dljNHJwAJZwm4+npaekiED1UH+IoZuIoZuIoZraJ6k0cxUwcxUwcxcw+VDWeTFRUVLllU6ZMwZQpU0xcqpqTGHX0Nprk5GQEBgbizp07aNq0qaWLQwghhBBCCLEQyg04GlnGBNRqNdLS0qBWqy1dFAKqj5qgmImjmImjmNkmqjdxFDNxFDNxFDNirSjhNAHGGNLS0miUMCtB9SGOYiaOYiaOYmabqN7EUczEUczEUcyItaKEkxBCCCGEEEKISVDCSQghhBBCCCHEJCjhNAFJkuDr6wtJkixdFAKqj5qgmImjmImjmNkmqjdxFDNxFDNxFDNirWiUWiOikagIIYQQQgghAOUGGtTCaQJqtRpJSUk0SpiVoPoQRzETRzETRzGzTVRv4ihm4ihm4ihmxFpRwmkCjDHcv3+fRgmzElQf4ihm4ihm4ihmtonqTRzFTBzFTBzFjFgrSjgJIYQQQgghhJiEk6ULYE80XRju3r2LBw8eoF69epDL5RYuFVGpVEhPT6f6EEAxE0cxE0cxs01Ub+IoZuIoZuIoZtbn7t27AODw3Zwp4TSi9PR0AEDPnj0tXBJCCCGEEEKINUhPT0dQUJCli2ExNEqtESmVSly4cAEeHh5o164dYmJi4OnpaeliOby8vDyEh4dTfQigmImjmImjmNkmqjdxFDNxFDNxFDPro1arkZ6ejk6dOsHJyXHb+SjhNIHc3Fx4e3vjwYMH8PLysnRxHB7VhziKmTiKmTiKmW2iehNHMRNHMRNHMSPWigYNIoQQQgghhBBiEpRwEkIIIYQQQggxCUo4TcDV1RWLFy+Gq6urpYtCQPVRExQzcRQzcRQz20T1Jo5iJo5iJo5iRqwV3cNJCCGEEEIIIcQkqIWTEEIIIYQQQohJUMJJCCGEEEIIIcQkKOEkhBBCCCGEEGISlHASQgghhBBCCDEJSjgJIYQQQgghhJgEJZyEEFINmZmZUKvVli4GIYTYhby8PEsXgRBiJpRwCsrIyMDGjRvx119/4d69e5YujkPLzs7G7du3AQAqlcrCpbENaWlpePPNN7FixQrs2rULAEAzI1UtNTUVvXv3xgsvvICcnBxLF8cmZGZmYu/evbh8+TIUCoWli0OqKTc3F+np6QBAF1eqKS0tDR988AF++uknnDx5EgDtUx8lNTUVPXv2xKJFi1BaWmrp4tiErKwsnDhxAgkJCZYuCiE1QgmngLfeegvNmzfHd999h6FDh2LOnDm4du2apYvlkD7++GMEBQXhrbfeAgDI5XILl8j6ffTRRwgLC8OlS5ewfft2jBs3DkeOHIEkSXSCVInXXnsNwcHBqF+/Pr766iv4+vpaukhW780330RYWBiWLVuGLl264PXXX0dSUpKli0UeYdmyZWjRogW+/vprAIBMRqcHj7JkyRK0aNECx44dw7///W9MmTIFZ8+epX1qFRYtWoTg4GA0aNAAixcvhouLi6WLZPXeeOMNtGnTBi+//DLatWuHzz//nBo8iM2hI0o1PHjwAHPnzsWhQ4fw+++/Y//+/Vi7di0KCgrw/fffW7p4DqWkpAQvv/wytm3bhj59+uD27dvYvn07ALoiX5UzZ85g8+bNWLduHXbv3o1t27Zh1KhRWLNmDQBAkiQLl9C6FBQUoHHjxli9ejUOHDiAnTt3okmTJtRaV4WsrCzMmjULhw4dwo4dO7B792588cUXOHnyJHbs2GHp4pFK5OfnY968edixYwdCQkLw999/46+//gJALXVV2bt3L3bu3ImtW7di//792LRpE8LCwrBz504AtE8tKysrC02aNMH69esRFRWF33//HU2aNLF0saxaamoqpkyZgkOHDmHr1q3YunUrFi1ahB9++AEnTpywdPEIEUIJZyX0D7T37t2DWq3GokWLMGDAALi4uGDixInw9vZGcXFxufcT49LEljEGV1dXNG/eHHPmzMHy5ctRv359rFu3Drm5uZDJZFQPD5WNw759+5CTk4Nx48YBABo0aABnZ2c8/vjjlf6No1Kr1ahTpw6GDh2KkJAQ9OnTBxcvXsScOXPwz3/+EytWrEBsbKz2vY5Mf5tJSkoCYwzLli1D//794ePjg3nz5gHgXTXLvp9Yjn49uLq6IigoCIsWLcJXX32FrKwsbN++HUVFRdRSp0f/OAQAe/bsAQAMHz4cANCuXTvIZDKMHDmy3N8QwM/PD506dUK7du3w2GOP4cKFC5g/fz7eeustbNiwARkZGZYuolXQ32auXbsGSZLwn//8B/369UNQUBDef/99FBQUaLu+0zZGbAUlnBUoLi7G/fv3tc8bNGiABQsWYMqUKQB0J5menp7a+w/oaqZpFBUVabuOaGL8/PPPY86cOYiIiMCoUaOQkpKCn3/+2YKltC76MdNo3749bt++jQ0bNiA2NhYzZszAnj178OGHH2LkyJGIj4936G1Y85tWKpXaZatWrUJsbCzat2+PMWPGoKSkBJmZmVi9ejXGjh2L4uJih+52WFJSgsLCQu3zkJAQLFiwAEOHDgWgi2mDBg20cXXkbcxaFBcXIz8/X/vcyckJ8+bNw/Tp09G9e3eMGDECf/31F/bt2weA6gwwjJkkSVCr1WjRogUyMzNx8OBBJCcnY9q0afj777+xePFizJ07F9nZ2Q4dO00ipL9P/eyzzxAVFYWePXti3LhxyMzMxIkTJ/D6669j5syZDn8Br7S01GA8ioiICMyfPx89e/YEwPepjDEEBARoY+XI2xixLY57tlSJ999/Hz169MCYMWMwdepUxMfHw9PTE+3atQPAf/Cak8zjx4+jV69eAGjQGlNYvHgxwsPDMXz4cDz55JO4ceMGAMDFxUW7s50yZQpatWqFXbt24ebNm9qTAUdVNmaalrghQ4bglVdewdatW9G1a1ekpaXh4MGDWLhwIfLy8vDUU09pW6EczUcffYQRI0YA4CffMpkMSqUS7u7uWLFiBUpLS7Fp0yb8/PPP2Lp1K7Zs2QK1Wo1XXnkFgGO2ci5duhT9+vXDmDFj8OqrryI1NRW+vr7o2rUrAN1+srCwEKdOnUKXLl0A0NV4S1u8eDE6d+6M4cOH46233sLdu3chSRK8vLy02/H8+fPh6uqKnTt3IjU1FYBj15smZiNGjMBbb72F1NRUyGQyjBgxAgMGDMDnn3+O5s2bIysrC7/99htGjx6NP//8E08//TQAx4zdZ599hmeffRYA36dqtG7dGm+99Rby8/OxZcsWrFu3Dn/88QdWrlyJW7duYcmSJZYqssUtW7YMw4cPx7hx4/DVV1/h3r17aNiwIfr27QtAt0/NyMjAlStXEBERYeESEyKIEcYYYzExMaxfv36sbdu27LfffmOffPIJ69GjB+vRo0e596rVanbjxg0WGBjIoqOjLVBa+/f222+zsLAw9vvvv7PPPvuM9e7dmzVr1ozFxMRo36NSqRhjjP3+++/sscceY//617/KvaZWq81bcAuqKGahoaEGMTt+/Djr3bs3u337tnbZ7du3mSRJ7PLly5YotsXExcWxyZMnswYNGjBJkth3333HGGNMqVQavO/w4cOspKTEYFt6//33WZs2bVhubq5Zy2xply9fZj179mRt27ZlGzZsYK+88gqLjIxkkyZNqvD9J0+eZEFBQSwtLc3MJSVlzZ8/n7Vo0YJt2bKFLVy4kHXo0IF17dqV5eXlad+j2fa///571rlzZ/btt99qX3OkfalGRTHr0qULy8/P175n+/btbNCgQez+/fvaZVFRUczV1ZUlJSVZotgWc/XqVTZmzBhWp04d1qhRI7ZlyxbGmOE+NScnhx07dowpFArtcbqwsJDNmTOHjRo1ihUVFVmk7JZy7tw51qVLF9a2bVv2448/smnTprFOnTqxV155pcL379q1i4WFhbHi4mIzl5SQ2qEWzof27t0Ld3d3HD58GBMnTsSrr76K5cuXIykpCVeuXDF4ryRJuHbtGvz8/LQtnwcPHsTSpUstUXS7olarUVRUhKioKEyfPh1jxozBwoUL8ccff2jvD9OMeMkeXjkeM2YMunfvjr/++gtHjhzB5s2b8eKLLwJwjO4mVcUM4FdONdPHXLt2DQUFBQgKCtL+fXR0NAICAhxuePpLly5BLpdj9erVeOWVV7BkyRKUlJRALpcbtFoOHDgQLi4uBvezRUdHw9/fHy4uLg7TgqFQKLB9+3Y0btwYR48exYwZM7BixQrMnz8fiYmJFY5EGx0djVatWqFRo0YAgKioKKxatcrcRXdojDFkZWXh+PHjePXVVzF58mR89tln2Lp1KxISEvDuu+9qu0Zr9pfPPvssgoODsX//fly4cAG//fYb3n33XUuuhllVFbNbt27h7bff1s4hee3aNTg5OaFevXrav7958yaaNGmiHePBUZw4cQKSJGHNmjUYNmwYvvzyS5SWlhrsU729vdGnTx9tbxK1Wg13d3dcu3YNLi4ucHV1tfBamE9+fj5+/fVXtG7dGn/99Rf+8Y9/YOPGjRg1ahTi4uIqnIbr/Pnz6Nq1qzZOf/31F7Zt22bmkhMijhLOh3r06IH58+ejUaNGBvceyOVy+Pj4lHv/zp070a9fP9y7dw8jRozAyJEjHbJrnbHJZDKUlJQgJiZG2z2vuLgYTk5O+Prrr3Ho0CFERUWBMWZwEHv88cdRVFSE0aNH48knn0SdOnUsuRpmVZ2YHT16FACfPkapVOK7775DXl4e4uPj8dVXX6FHjx5o27atJVfDbDS/7+HDh2PhwoUYP348nnrqKXh5eeG1116r8m8lScK5c+dw9+5dzJw5E66urg5xUQPgcWvbti3mzZuH+vXra397Li4uyMrKMjjh1ti1axcGDBiA9PR0jBw5EoMHD0ZJSYm5i+7QJEmCSqXC5cuXtfsHpVKJFi1a4IsvvsA333yDv//+GwC0CQAAzJs3D1euXMGQIUMwY8YMh5q+4lExW7lyJS5cuAAAKCwsRHFxMXbs2AGlUom4uDhs3rwZ/fv3R/PmzS25Gmaj2adOmzYNixYtwtSpUzFhwgTk5eVhxYoVVf6tTCbDiRMnoFQqMXv2bIfZnwI8bqGhoZg7dy68vb2197t6e3sjNjYWXl5e5f5m//79GDRoEFJSUjBy5Ej069dPe/GDEKtmmYZV66bp5vHbb7+x1q1bl+s2l52dzVq0aMGCg4OZi4sLGzNmDMvKyrJEUe2OptvWkCFD2IQJExhjuvpgjLERI0awQYMGGXQnSU5OZs8//zyTJIn94x//MOja5AiqE7MBAwYwxhhLTExkL7/8MpMkifXu3Zv5+Piw6dOnswcPHpi/4FakoKCArVixgnl5ebHY2FjGmGE3sMTERLZlyxb2wgsvME9PTzZnzhyH6/rFmGG3Ss02tnLlSta7d29WWlpq8N6UlBTm7+/PunbtylxcXNjYsWNpP2kh2dnZrHv37mzBggWMMcN6jIyMZDNmzGCM6eo0MTGRPffcc0ySJDZ79mx279498xfawh4Vs6lTpzLGGLt06RKbPHkyc3Z2ZsOGDWNeXl5sxowZDtfdvqysrCy2cOFC1q5dO5aYmMgYM9yn3rx5k+3Zs4e9+OKLzMvLi82bN88hu4nqx0Tz+3v77be125e+2NhYVq9ePTZixAjm4uLCxo0bR/tUYjOohROV39R/9OhRdOzYEZ6engatl/n5+VCr1fD398fx48fx+++/o379+uYqrl3TdFucMGEC/v77b5w8eRIymQxFRUUAgPfeew9HjhwxGEJ9586dOHr0KE6dOoUff/yxwpYWe1admEVFRSEpKQnBwcH48MMPERUVhblz5+Lo0aP49ddfK7ySas/0f/OMMXh4eGDMmDHo3LkzXn75ZQC8NVjj/v372L9/P+Li4nDo0CGsXr0abm5u5i62RTHGKmx9OHHiBCIjI+Hs7Gywn0xPT0dhYSGcnZ1x7Ngx7Ny5k/aTFuLh4YF+/frh7NmzuHLlCiRJ0nahf/3117Fjxw7t1FIAsHbtWmzfvh2nT5/GmjVr4Ovra8niW8SjYrZr1y48ePAA7du3x1dffYWtW7dizJgxOHr0KDZs2ABPT08Lr4HlMMZQv359jB07Fj4+Pvjoo48AGO5Tb926hTVr1uDq1as4ePAgvvnmG4fqTgtA21NLQ7N/PX/+PDp37qx9j0ZCQgJycnKQm5uLo0ePYseOHbRPJbbDYqmuGeXn57OSkpIKX1MoFJX+Xfv27dn333+vfX79+nXGGG8NuXjxonEL6SAyMzPZrVu3tFd/9a8a69fF1atX2dChQ9mwYcMM/j46Opr5+/uzvXv3mqfAVsBYMduzZ495CmwFqhsz/ecqlYpt2rSJeXt7s927dzPG+OAfWVlZTK1Ws4yMDDOV3jJEYqa5Eq9UKllQUBDbvn279rW4uDjGGGP3799nf/31l4lLTfS338peY4yxI0eOsF69erEXXnjB4D179+5lwcHB7Ny5c6YtqBUxVszOnj1r2oJakerGTP95aWkp+/jjj1mrVq3Yn3/+yRhj2n1CSUmJ3Q+qJBIzzXtycnJY/fr12dGjR7WvaVqIMzIy2IEDB0xVXEJMyu4Tzv/7v/9jERER7NSpU5W+R61WszfffFM7cqdarWbR0dGscePG7Pbt2yw5OZlNmTKFSZLErly5Yq6i2xW1Ws3++c9/spCQENahQwfWsmVLdubMmXLvU6lUbPHixYwxxrZs2cIaNmzIPvzwQ+3OeePGjaxdu3YO0W2WYiauujFTq9Xs7bffNnjOGGNpaWns6aefZs2aNWMjR45kkiTZfdJU05gxxtjRo0dZ06ZNWW5ursF+Mjk52VzFd2j//Oc/2dChQ8stL9v1+T//+Q9jjLHly5ezVq1asR9//FH7+qpVq1inTp0cpos4xUxcdWKmVqvZihUryr0WHR3NJk6cyHr37s1GjBjBJEliV69eNX2hLawmMWOM38rVvHlzxhjT7lMbNGhAI30Tm2e3CeedO3fYlClTWGRkJHNxcWHz5883GP5d48cff2QNGzZkbdq0YTdv3tQu37x5M2vXrh175513mLu7Oxs+fLj2KhMREx0dzfr06cO6d+/Ojh07xvbu3cuGDx/OOnTowAoLC7Xv++GHH1jjxo1Z8+bN2d27d1lRURH7/vvvmbu7O+vZsyd7+umnWZ06ddjrr7/OFAqFXQ/TTzETJxqzli1blvtN3717l40aNYpJksQmTZpkMH2MPaptzL799lvWt29ftnTpUtpPmlFMTAwbOXIkCwoKYpIksXXr1jHGyrekfP/996xRo0asa9eu7MGDB+zu3bvsnXfeYZIksQkTJrDnnnuOeXp6smXLljGVSmXX+weKmTjRmPXo0YOlpKQYvJaWlsYee+wxJkkSmzhxot3vU2sbsw8++IBNnjyZffDBB8zd3Z0NGTLE7luCiWOw24QzOjqavfTSS+zMmTNsw4YNzNnZmR08eNDgPVeuXGGPP/44W716dbm595555hkmSRLr0KED279/vzmLbnc+//xzNmrUKINWj/T0dObi4qLtmnzs2DE2dOhQ9sMPP5Sri71797KPP/6YzZo1ix05csSsZbcUipm42sbs5s2brHv37iw4OFjb/cve1TZmo0ePZpIksXbt2tF+0ox+++039swzz7AjR46wl19+mfn7+5cbtGnXrl2sU6dOFdbbL7/8wl577TU2ceJEdvjwYXMW3WIoZuJqG7NLly6xsLAw1qJFC3b8+HFzFt1iahuzbt26MUmSWJs2bWifSuyK3SWc+hMJ618V6t69Oxs6dChLT083eH9lo6Lt3r2b/fLLL6YrqAPQdOm8efMm27dvn8Frly9fZqGhoezSpUvaZWW7J9nzlePKUMzE1TZmGsXFxeyPP/4wWTmtiTFiplAo2KpVq7RX8InplG0dycrKYjExMYwxxm7dusWaNGnC/vWvfzHGDEe9zM/Pr/Jz7BnFTJyxYqZRWFjIdu7caaLSWgdjxiw/P5+9+eabtE8ldklizPZnLV+zZg0CAwMxZMiQcq+pVCrI5XJcvnwZHTt2xE8//YQnn3zSYGQwYjxV1QWgq48DBw5g1qxZiImJqdaosqySETLtAcVMnKliZs8oZrbp/fffx61bt9CsWTPtHKj6VCoVvv32WyxcuBBxcXEICgqCWq3WjjjriChm4owdM3s+/mjQdkZI9dn0Vv/XX38hMjISzz77LDZu3Ii7d+8CMBxGWi6XQ61Wo3379njyySfx8ccf4/bt2+U+yw7ybouqTl0AumG/Dx8+jG7duqFevXrVir09HrgoZuJMHTN7RDGzTXfu3EFkZCS2bt2KOnXqYOXKlRg+fDi2bt0KQFd/crkc06dPR4cOHfDSSy8BgMOe0FLMxJkqZvZ4/NGg7YwQcTa75efk5GDTpk3o0qULPvjgA0RFRSEqKgpA5Tu6b7/9Frdv38aGDRuQk5OD7du3Y8eOHVX+DXk0kbrQ7GzPnj2LoUOHat9z9uxZxMXFmbXclkQxE0cxE0cxs11HjhyBWq3Gn3/+ia+//hpxcXFo0qQJvvzyS1y6dAmSJEGpVAIA/Pz8sHjxYuzcuRPHjh0DABw4cAA3btyw5CqYHcVMHMVMHMWMkBowcxdeoykpKWGHDx9m58+fZ4wxNnToUDZmzBjtXJll72XT9J3/5JNPmIeHB2vZsiVzc3NjW7ZsMW/B7ZBoXdy6dYsFBQWxK1eusOvXr7MBAwYwNze3KqeusTcUM3EUM3EUM9v13nvvsa5duxrM13f06FE2aNAgNmPGDO0yTR0WFhay6dOns5CQENa9e3fm7u7OTp8+bfZyWxLFTBzFTBzFjBBxNtvC6eLigoEDB6JTp04AgPfeew/nzp3Dvn37UFpaWu7qvVwuR0JCAmJiYlBUVISBAwciMzMTkydPtkTx7Up164I97GZy+fJlKBQKfPnll2jbti38/f2RkZGB7t27W2wdzI1iJo5iJo5iZruKi4vh5OSEjIwM7bK+fftixIgRuHbtGg4dOgRAV3cpKSm4d+8ebt++jYiICKSnp6Nbt24WKbulUMzEUczEUcwIEWezCac+tVqNnj17YuTIkfj1119x/vz5cu/Jy8vD66+/jmPHjuHy5cv49ttvUbduXQuU1r5VVReak9s//vgDaWlpSEhIwJkzZ7BhwwZ4enpaqsgWRzETRzETRzGzDWq1GgAwa9YsnDp1CmfOnDF4ffDgwXB1dcW5c+cA8K7QsbGxePzxx5Gamoro6Gh8//33DlVvFDNxFDNxFDNCasFyjatV03RFqGhIcv1uDPrPU1NTWWhoKPvXv/7FHjx4wBhjLC4uTvue1NRUUxbZbhmrLmJjY7X/bt261ZRFtjiKmTiKmTiKmW2qbGoexgzrbcqUKaxTp04sMzPT4D3du3dnCxYs0D7Pzc3Vzptqryhm4ihm4ihmhJiG1bVwKhQKvPfee/j2228BGI7opbm65OTkBIVCgb/++kv7XKVSoXHjxnj++eexa9cu/PDDDxgyZAhmz56N/Px8ODk5oXHjxuZfIRtm7Lp49tlnkZeXh5YtW2LSpEnmXyEzoJiJo5iJo5jZptLSUrzyyit44oknMHPmTPz555/a1xQKBQBeT6WlpYiLi8O///1vXL9+HZ9//jkePHgAAFAqlXB1dTWYssbT0xMdOnQw78qYCcVMHMVMHMWMEBOzdMarb//+/axTp05MJpOxvn37shs3bjDGyl+9//LLL5mnpyd74403WGFhIWNMd6U/KSmJOTk5MUmS2Pjx48tdfSLVQ3UhjmImjmImjmJmm7Zv384CAwNZ//792X/+8x8WERHBHnvssXItyl9++SXz8PBgy5cvZ4wxtnr1ataiRQs2bNgwtnPnTvbKK6+wxo0bszNnzlhiNcyKYiaOYiaOYkaI6VlVwjl//nz2zDPPsFWrVrG+ffuy119/vdx7XnvtNebr68vWrVtXboTFLVu2MEmSWNeuXbWjMpKaoboQRzETRzETRzGzPXFxcWzixIls8eLF2mUZGRlswIAB7OOPP2aMMVZcXMxeeOEF1rBhQ7Z27VqDCwi7du1iI0eOZD179mRdunRxiFGDKWbiKGbiKGaEmIdVJJyaE6IrV66w6Ohoxhhjr776KuvVqxeLiopijOmmNcnIyNDed1TW2bNn2XfffWeGEtsvqgtxFDNxFDNxFDPbo6mzmJgYtmTJEpaQkMAY090LNnjwYPbss88yxngL9Y0bNwzqrWyrdVpamjmKbVEUM3EUM3EUM0LMy2IJp2YOoooGu2CMnxQNGTKEzZo1S/ueslfqiXFQXYijmImjmImjmNkmTb1pLgBUpKSkhPXq1Yv9+OOP5iqWVaOYiaOYiaOYEWIZZh80aMeOHQgICMCIESOQmJgImUymHeRCX5cuXTBkyBBcv34dv/76q7mL6RCoLsRRzMRRzMRRzGxT2XqTy+VQqVTa19nDefkAPhBJZmYm2rZta4miWg2KmTiKmTiKGSGWZdaEc/369fjwww/Rt29fhIeH4+OPP+aFkBkWQ3NiNW3aNDRt2hSbNm1CdnY2ACA6OhoADHYURBzVhTiKmTiKmTiKmW2qrN7kcrn2PZr5TgHg+PHj2pGBNdLT0wGgwosL9ohiJo5iJo5iRojlmSXh1Jz0tGjRAoMGDcLy5csxduxYREVFISoqyuA9gO7EKigoCOPHj0d2djYWLVqEjh07YvDgwVAqlQY7ClJ9VBfiKGbiKGbiKGa2SbTeNLZv344BAwagXr16uHDhAgYMGIC5c+dCrVaXu7hgbyhm4ihm4ihmhFgRU/bXvXHjRrn7iTQ3ZF+5coWNHTuWjRw5Uvua/ns1/z937hyrX78+kySJzZ07lxUXF5uyyHaL6kIcxUwcxUwcxcw21abeVCoVGzduHPv000/Z/PnzmUwmYzNnzmSlpaXmKbyFUMzEUczEUcwIsT4mSTg3bdrEQkJCWKtWrVi3bt0MbrzW/2GvWbOGhYeHszVr1jDGyg+MsX79eiaXy9mAAQNYfHy8KYpq96guxFHMxFHMxFHMbJMx6i0pKYlJksQkSWK9evViMTEx5lsBC6CYiaOYiaOYEWK9jJ5wHjhwgIWEhLBvvvmG7du3jy1cuJA5Ozuz1atXaycf11xpSk5OZs888wzr2rUry8vLY4wxg6tI165dY7t27TJ2ER0G1YU4ipk4ipk4ipltqm29lZSUMMZ4K8u0adPYwYMHLbMiZkQxE0cxE0cxI8S6GS3h1Fw9WrJkCYuMjDQ4IZo3bx7r0qUL27ZtW7m/+9///se6dOnCFi9ezC5dusRGjx7NkpKSjFUsh0R1IY5iJo5iJo5iZpuMVW+jRo1ymHqjmImjmImjmBFiG4x297NmhK+YmBg0b94czs7OUCgUAIBly5bBzc0NO3fuRFpaGgDdjdoDBgxAt27d8P777yMyMhIKhQINGzY0VrEcEtWFOIqZOIqZOIqZbTJWvSmVSoepN4qZOIqZOIoZITaippnqgQMH2IIFC9jnn3+unUiXMcZWr17NPD09tZPqaq42rV69mrVs2ZJFRUVp35ufn88+//xzJpfLWf/+/dnly5drWhyHRnUhjmImjmImjmJmm6jexFHMxFHMxFHMCLFNwglnamoqGz16NGvYsCF74oknWEREBPP29tb+8GNjY1lAQAB75513GGO6fvGMMebv788+//xz7fOrV6+y7t27s19++aWWq+GYqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIbZNKOEsKChgs2bNYtOmTWMJCQna5d26dWNPP/00Y4yx3NxctmzZMubu7q7tD6/pY9+vXz/27LPPGqvsDo3qQhzFTBzFTBzFzDZRvYmjmImjmImjmBFi+4Tu4fTw8ICrqyuefvpphIaGQqlUAgBGjhyJa9eugTEGT09PPP744+jcuTOmTp2K27dvQ5IkJCUlISMjA+PHjzdFz2CHQ3UhjmImjmImjmJmm6jexFHMxFHMxFHMCLF9EmOMifyBQqGAs7MzAECtVkMmk+GJJ55AnTp1sHr1au37UlJS0L9/fyiVSnTp0gUnTpxA69atsWHDBjRq1Mi4a+GgqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIbZNOOGsSO/evTFnzhzMmjULarUaACCTyRAXF4dz587h9OnT6NChA2bNmlXrApOqUV2Io5iJo5iJo5jZJqo3cRQzcRQzcRQzQmxHrRPOhIQE9OrVC7t370ZkZCQAoLS0FC4uLkYpIKk+qgtxFDNxFDNxFDPbRPUmjmImjmImjmJGiG2p8Tycmjz1+PHjqFu3rvYHv2TJErz00kvIyMgwTgnJI1FdiKOYiaOYiaOY2SaqN3EUM3EUM3EUM0Jsk1NN/1Az2e6ZM2cwadIkHDx4EM899xwKCwuxdu1amkDXjKguxFHMxFHMxFHMbBPVmziKmTiKmTiKGSE2qjZD3BYVFbEWLVowSZKYq6sr+/jjj2vzcaQWqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIban1vdwDhkyBGFhYVixYgXc3NyMlQeTGqC6EEcxE0cxE0cxs01Ub+IoZuIoZuIoZoTYllonnCqVCnK53FjlIbVAdSGOYiaOYiaOYmabqN7EUczEUczEUcwIsS1GmRaFEEIIIYQQQggpq8aj1BJCCCGEEEIIIVWhhJMQQgghhBBCiElQwkkIIYQQQgghxCQo4SSEEEIIIYQQYhKUcBJCCCGEEEIIMQlKOAkhhBBCCCGEmAQlnIQQQgghhBBCTIISTkIIIYQQQgghJkEJJyGEEKLn559/hiRJ2oebmxuaNGmCYcOG4T//+Q/y8vJq9LknTpzAe++9h5ycHOMWmBBCCLFilHASQgghFXj//fexdu1afPvtt1iwYAEA4OWXX0ZERAQuX74s/HknTpzAkiVLKOEkhBDiUJwsXQBCCCHEGo0YMQJdunTRPn/jjTdw5MgRjB49GmPHjsW1a9fg7u5uwRISQggh1o9aOAkhhJBqGjhwIN555x3cvn0b69atAwBcvnwZTz/9NJo1awY3Nzf4+/vjH//4B+7du6f9u/feew+vvvoqACA0NFTbXTcxMVH7nnXr1iEyMhLu7u7w9fXF9OnTcefOHbOuHyGEEGJslHASQgghAp566ikAwIEDBwAABw8eREJCAmbPno2vvvoK06dPx8aNGzFy5EgwxgAAEydOxIwZMwAAn3/+OdauXYu1a9eiQYMGAIAPPvgAM2fORFhYGFasWIGXX34Zhw8fRt++fakLLiGEEJtGXWoJIYQQAU2bNoW3tzfi4+MBAPPmzcP//d//GbynR48emDFjBo4fP44+ffqgffv26Ny5M3799VeMHz8eISEh2vfevn0bixcvxrJly/Dmm29ql0+cOBGdOnXCypUrDZYTQgghtoRaOAkhhBBBdevW1Y5Wq38fZ3FxMbKystCjRw8AwPnz5x/5Wdu2bYNarcbUqVORlZWlffj7+yMsLAx//PGHaVaCEEIIMQNq4SSEEEIE5efno2HDhgCA+/fvY8mSJdi4cSMyMjIM3vfgwYNHftbNmzfBGENYWFiFrzs7O9e+wIQQQoiFUMJJCCGECEhOTsaDBw/QokULAMDUqVNx4sQJvPrqq+jYsSPq1q0LtVqN4cOHQ61WP/Lz1Go1JEnC3r17IZfLy71et25do68DIYQQYi6UcBJCCCEC1q5dCwAYNmwYsrOzcfjwYSxZsgTvvvuu9j03b94s93eSJFX4ec2bNwdjDKGhoWjZsqVpCk0IIYRYCN3DSQghhFTTkSNHsHTpUoSGhuKJJ57QtkhqRqPV+OKLL8r9bZ06dQCg3KizEydOhFwux5IlS8p9DmPMYHoVQgghxNZQCychhBBSgb179+L69etQKpVIT0/HkSNHcPDgQQQHB+P333+Hm5sb3Nzc0LdvX3zyySdQKBQICAjAgQMHcOvWrXKfFxkZCQB46623MH36dDg7O2PMmDFo3rw5li1bhjfeeAOJiYkYP348PD09cevWLWzfvh3PPfccFi1aZO7VJ4QQQoyCEk5CCCGkApousi4uLvD19UVERAS++OILzJ49G56entr3bdiwAQsWLMA333wDxhiGDh2KvXv3okmTJgaf17VrVyxduhSrVq3Cvn37oFarcevWLdSpUwf/+te/0LJlS3z++edYsmQJACAwMBBDhw7F2LFjzbfShBBCiJFJrGz/HUIIIYQQQgghxAjoHk5CCCGEEEIIISZBCSchhBBCCCGEEJOghJMQQgghhBBCiElQwkkIIYQQQgghxCQo4SSEEEIIIYQQYhKUcBJCCCGEEEIIMQlKOAkhhBBCCCGEmAQlnIQQQgghhBBCTIISTkIIIYQQQgghJkEJJyGEEEIIIYQQk6CEkxBCCCGEEEKISVDCSQghhBBCCCHEJP4f4XrYMYDRYhAAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# dictionary to store color, alpha, linewidth, and linestyle for each line\n", "line_styles = {\n", From 2068b7616d792b8d9c7ed2700aeef2ff4f4aca99 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Wed, 16 Oct 2024 16:43:48 -0400 Subject: [PATCH 27/44] add import lightning if needed --- testbed/src/testbed/__main__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/testbed/src/testbed/__main__.py b/testbed/src/testbed/__main__.py index f5c79772..ddab9509 100644 --- a/testbed/src/testbed/__main__.py +++ b/testbed/src/testbed/__main__.py @@ -1,6 +1,8 @@ # noinspection PyUnresolvedReferences import lightgbm as lgb # noqa F401 +# import lightning + import argparse import logging import os From 1e1a270f05970337b566ce64e601131e39be62b6 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Wed, 16 Oct 2024 16:47:23 -0400 Subject: [PATCH 28/44] update some notebooks --- .../inflated/main_inflated.ipynb | 126 ++++++-- .../main_branching_mixture.ipynb | 300 +++++++++++++++--- 2 files changed, 352 insertions(+), 74 deletions(-) diff --git a/testbed/notebooks/paper_notebooks/inflated/main_inflated.ipynb b/testbed/notebooks/paper_notebooks/inflated/main_inflated.ipynb index eb38a6e5..4fc6133e 100644 --- a/testbed/notebooks/paper_notebooks/inflated/main_inflated.ipynb +++ b/testbed/notebooks/paper_notebooks/inflated/main_inflated.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 571, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:57:02.017091Z", + "start_time": "2024-05-17T02:57:01.989665Z" + } + }, "outputs": [], "source": [ "import lightgbm\n", @@ -33,8 +38,13 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 577, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:58:34.880043Z", + "start_time": "2024-05-17T02:58:03.878613Z" + } + }, "outputs": [], "source": [ "rng = CustomRandomGenerator(PCG64(seed=0))\n", @@ -46,7 +56,7 @@ "\n", "shape = 2\n", "scale = 1\n", - "n = 20 * 10**3\n", + "n = 20 * 10**4\n", "\n", "x, y = rng.CIG(p_atom_fn, shape=shape, scale=scale, size=n)\n", "\n", @@ -71,25 +81,26 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 578, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:58:35.002071Z", + "start_time": "2024-05-17T02:58:34.880597Z" + } + }, "outputs": [ { "data": { - "text/plain": [ - "" - ] + "text/plain": "" }, - "execution_count": 4, + "execution_count": 578, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" @@ -108,15 +119,18 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 579, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:58:37.005150Z", + "start_time": "2024-05-17T02:58:35.002346Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" @@ -156,8 +170,13 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 580, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:58:37.977288Z", + "start_time": "2024-05-17T02:58:37.004795Z" + } + }, "outputs": [], "source": [ "X = (np.array([1, 3, 5]) / 6).reshape(-1, 1)\n", @@ -176,15 +195,30 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 581, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T02:58:38.530982Z", + "start_time": "2024-05-17T02:58:37.979383Z" + } + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" @@ -271,6 +305,40 @@ "\n", "g.savefig(\"fig/branching_mixture.pdf\", format=\"pdf\", dpi=600)" ] + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-17T02:58:38.532857Z", + "start_time": "2024-05-17T02:58:38.530417Z" + } + }, + "execution_count": 581 + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-17T02:58:38.534833Z", + "start_time": "2024-05-17T02:58:38.532471Z" + } + }, + "execution_count": 581 + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/testbed/notebooks/paper_notebooks/multimodality/main_branching_mixture.ipynb b/testbed/notebooks/paper_notebooks/multimodality/main_branching_mixture.ipynb index 29d9d194..4591760d 100644 --- a/testbed/notebooks/paper_notebooks/multimodality/main_branching_mixture.ipynb +++ b/testbed/notebooks/paper_notebooks/multimodality/main_branching_mixture.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-18T23:55:56.679752Z", + "start_time": "2024-05-18T23:55:56.675592Z" + } + }, "outputs": [], "source": [ "import lightgbm\n", @@ -19,8 +24,41 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)})\n", + "mpl.rcParams.update(\n", + " {\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.serif\": [\"Times New Roman\"], # [\"Computer Modern\"], #\n", + " \"font.size\": 12,\n", + " }\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-18T23:55:56.841367Z", + "start_time": "2024-05-18T23:55:56.838773Z" + } + }, + "execution_count": 30 + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-18T23:55:59.888689Z", + "start_time": "2024-05-18T23:55:57.090034Z" + } + }, "outputs": [], "source": [ "rng = CustomRandomGenerator(PCG64(seed=0))\n", @@ -30,7 +68,7 @@ "x, y = rng.branching_mixture(scale=scale, size=n)\n", "\n", "# model = tf.LightGBMTreeffuser(sde_initialize_with_data=True)\n", - "model = Treeffuser()\n", + "model = Treeffuser(n_repeats=100)\n", "fit = model.fit(X=x.reshape(-1, 1), y=y.reshape(-1, 1))" ] }, @@ -43,32 +81,36 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 37, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-19T00:07:01.212871Z", + "start_time": "2024-05-19T00:07:00.980728Z" + } + }, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(x, y, s=1)" + "plt.scatter(x, y, s=1, alpha=0.8)\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$y$\")\n", + "color_palette = sns.color_palette(\"tab10\", n_colors=10)\n", + "colors = {\n", + " 0.2: color_palette[0],\n", + " 0.5: color_palette[1],\n", + " 0.8: color_palette[2],\n", + "}\n", + "for xx, c in colors.items():\n", + " plt.axvline(xx, 0, 1, c=c, lw=2, ls=\"--\")\n", + "plt.savefig(\"fig/branching_mixture_data-samples.pdf\", format=\"pdf\", dpi=600)" ] }, { @@ -80,22 +122,25 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T22:11:25.667231Z", + "start_time": "2024-05-17T22:11:20.754803Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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IdR6m76P/zbTPP2F3SCIiZSXaNuKC2Z1ftDucG6pr/grRU/vo3/cGdZ/7dVzVdbbFUu13q0S4FAXNJInkWKyjlfjZ4xguD7V3fsnucG6qblPmBuLwnl+SjoRtjkZEpLz0ffQPAATXf76o+9L55q/CO2cJVipBeNfP7Q4HUIlwsZ+SJJEc6/voZwBUr7nf1qtxY+FfuA7PrEVYyTjh3TvsDkdEpGzEL7QTbT8AhoPghsftDuemDMOgrvkrAPTt3oEZG7Q5IhH7KUkSyaHklXNEjn8CULT3Io1kGAZ1d2WWgIR3v4aVStockYhIeej7ODOLVLXibty1M22O5tYCSzfgnt6AFY/Qv/9Nu8MRsZ2SJJEc6tv1CmDhX7Qez7S5doczJlXL78ZZM430YC8Dh96zOxwRkZKX6r/CwMHM8bR2Y/HeizSSYTio3fArQOZcZplpmyMSsZeSJJEcSccG6d/3BlAas0hZhtNF7R2PAZmlgpalqkEiIpMR/uRVMFN4G5bjm7vE7nDGrHrVvTh8VaR6LxBp22N3OCK2UpIkkiP9e3diJWO4Z8zLNOcrITW3PYzh9pLobid2+qDd4YiIlCwzESO85xcA1BV58Z7Pcnh81Kx7CIDwJ6/YHI2IvZQkieSAZaYJ78qcUGo3/gqGYdgc0fg4/TXUrL4fgL4iqWwkIlKKBg6+gxkdwFVXT2DpHXaHM27B2x8Dw0H05D4Sl7rsDkfENkqSRHIgcmwXqfAlHIEg1avutTucCQkOLbmLHN+t5rIiIhNgWRbhTzOzSME7NmM4nDZHNH7uupkElmT6EIU/edXmaETsoyRJJAeySyuC6x7C4fLYHM3EeGaE8M1bCZZJ/97X7Q5HRKTkxM+eyDSPdbqpWf2A3eFMWLaAQ//+t1QOXCqWkiSRSUr2XiB6ch8ANesetjmayQne9ggA4b2/VGUjEZFxys4iVa1sxhmosTmaifPNX4V7RggrGaP/wNt2hyNiCyVJIpPUv2cnAP6Fa3FPmWVzNJNTtfwuHIEg6f4rRE58anc4IiIlIx0bZHCojUJw/SM2RzM5hmEMXzTr3/tLVT2ViqQkSWQSrHRquOx3zW2ftzmayTNcbmrWZJaIhD99zeZoRERKx8CBt7FSCdwz5uGdu8zucCatetW9GC4Pie4O4mdP2B2OSMEpSRKZhMHju0gP9uKsqqNqyQa7w8mJ4FCyF23bS7K32+ZoRESKX6ZgQ+bCUnD9IyVX4XQ0Tn81VcvvAjItLkQqjZIkkUno//SXANSsfRDD6bI5mtxwT52Nf8FawKJ/zy/tDkdEpOjFu46QvNSF4fZSU6IVTkdTc1vmPtuB1vcw41GboxEpLCVJUvFM8+ZrrW/0eLLnPNFTQwUbbrtxwYZbbb8Y1azPzCb1739TBRxERG4hPDTTUr3yczh8VTZHc2tjPe/5QitxT52DlYwxcOj9QoQmUjTK49K3yCQ4HAbP/2g3XRf6r3usob6Gp75x+6iv69/3JpAp2PDCz7vounB4XK8vZlVL7sDhryE90EP05D4Ci9fbHZKISFEyE1EGD38IQM26B22OZmzGet4zDIOadQ9x5Y3/Sf/enQRvckFQpNwoSRIBui7003amb8zPtyyTgQNvAVCz5kG6Xhvf64ud4XRTveoewrteoX//m0qSRERuYPDwB1jJGO6ps0uqYMNYz3s1ax7gylt/TfzsceIX2vHWN+Y/OJEioOV2IhMQO91KKnwJhzdAYGl5FGz4rGyVu8FjH5OODtgcjYhIcerf/xYA1WseKIuCDZ/lrKqlaug817//TZujESkcJUkiE5A9KVat3ITD7bU3mDzx1C/AM3M+pFMMai26iMh1kj3niXW0AgY1q++zO5y8qc5eNGt9V/epSsVQkiQyTmYiyuCRofXna+63N5g8Mgxj+MSoq4ciItfrP/A2AP4Fa3AFp9scTf4EFq7LNBof7CN6cq/d4YgUhJIkkXEaPPJhSa4/n4iaVfeCw0n87HESl7rsDkdEpGhYlsnA0KqCmrUP2BtMnhlOF9VNnwOuJoYi5U5Jksg4ZWdVqlffX5brz0dyVtUSWJQp2qDZJBGRq2Idh0j1dWN4AwSWbrQ7nLyrWZVZThg5tgszNmhzNCL5pyRJZBySvd3ETpf/+vORsgUcBg68o7XoIiJD+vdnZlSqVzSX7b2pI3lmL8I9vQErlWBgaMm5SDlTkiQyDgMH3wHA17gKV+0Mm6MpjMDi9Th8VaQHrhDrvL4XlIhIpTFTCQaPZhKF6gq5YGYYVy8ODmjJnVQAJUkiY2RZFgOt7wJD9+pUCMPlpmr53QAMHHzX5mhEROwXPfEpVjyCMzgdX2i53eEUTPWqewGDWEcryd5uu8MRySslSSJjlOg+TfJSFzhdBJbdaXc4BZW9YXfwyIdY6aTN0YiI2Ct7way66XMYRuV8lXIFp+NrXAVcXVkhUq4q5zdbZJIGD70HQGDRepy+KpujKSzfvJU4q6dgxgaItO21OxwREduY8QiR47sBqF75OZujKbzsSooB9c+TMqckSWQMMkvtMieE6lX32BxN4RkOJ9UrNwFXr6CKiFSiwaMfYaWTuKc34KlvtDucggssuxMcLpIXO0h0d9gdjkjeKEkSGYP4mWOZUq8eH4HFt9sdji2qmjLJYeTYLsxE1OZoRETsMbzUbuXnyr4NxGicvioCi24DNJsk5U1JksgYDLRmltpVLd1YEaVeR+OdvQjXlFlYqQSDx3bZHY6ISMGlBnqJnjoAXL1XsxJVNw2tLDj0HpZl2RyNSH4oSRK5iboaL+lUisHDLUBlnxQNw6B6aDZpcChpFBGpJIOHW8Ay8c5ZgnvqbLvDsU1gyR0YLg+pnvMkzp+0OxyRvHDZHYBIMav2u4l3tpIe7CWKjz/6aS+m8dbw4+uXz+R3Hl9pX4AFVt30OXrf+zGRk3tJR8I4A0G7QxIRKZjsqoJKvmAG4PD4CSy5g8HDLQwceg/v7EV2hySSc5pJErmF7KzJp7F5HD87QNuZvuE/3VciNkdXWJ7pDXjqF4CZZvDwB3aHIyJSMMneC8TPHAUMqlZssjsc22Ur+w0casGyTJujEck9JUkiN+GwUgweyXRV351otDeYIpGt7jdwSEvuRKRyDA4VKfA3rsJVM8XmaOznX3wbhsdPOnyJeNcxu8MRyTklSSI3UR89hRmPMEAVJ1P1dodTFKpXNAMQ6zhMaqDH5mhERApjYGj2vGroGFjpHC4PVUON1XXRTMpRXpOkcDjMtm3b2L59O8888wzbt2/P5+5Ecm5u5DAAx51LsKi8Uq+jcdXOwDtnCWAROfqR3eGIiORdMlugwHAMJwbCcP+8wcMfYJlpm6MRya28Fm74oz/6I55++mlCoRAAy5YtIxQK0dysqzBS/JykmR05AcBx52KboykuVSvuJn72OAOHPyB4+2a7wxERyavssmv//CacVbU2R1M8/AvW4PBXkx7sJXa6Ff+CNXaHJJIzeZ1J6urqoqWlZfjvwWCQ1tbWfO5SJGeWuc/htuI4q6dy3jHL7nCKStXyuwCIdRwiPdhnczQiIvmVLVRTtfxumyMpLobTRdWyzPlg4IiK+Uh5yWuS9PLLL7NlyxYgs/QuHA5rFklKxjrPaWAoIajAruo3466rxzNrEVgmg1pyJyJlLNnXTfzcCTAcBLTU7jpVKzKJY+ToR8NL7kxTDWal9BWkT1I4HOb555/nhRdeoKmpaVLbikajOYpKxiv73pfTGBiGgd/vv+7nTtKsdncCULXiLmi9OKn9RKPRSXclL7b337P4DhLn2wi3vodreWX0DCm2MahEGgP7VdoYDO5/BwDP3GUkHB4SEftbP0xmDG503httH2M5b1kzFmJ4q0gP9tF3Yh/e0Ar8fj/P/2g3XRf6r3t+rvoL5uK8Otn9j/yvFJ5lWRh5vIid9yQpHA7z6quv5mx77e3tOduWTEw5jYHf72flyusP1kvc5wk4EsQcVfgalgOTS5JOnTqVswNpsbz/DmMKtUC88xBH9u3G8gTsDqlgimUMKpnGwH6VMgY1+97BBfTWhLhw+LDd4VxjImNwo/PeZ43nvBWYvgjvmf2c/fg1SPlYuXIlXRf6aTtz/XLshpnV4455svHlU6X8HhSjZDKJ1+vN2/bzniQFg8HhJXcbNmwAYPPmid/o3djYOKYrIJJ70WiU9vb2shqDG12BWOvOLLU7G1jKSodz0vtZsGBBTmaSiu39v3TkVVIXTzPP2U9gxe12h5N3xTgGlUZjYL9KGoN0/2Uu9p0BDOZ/7os4q+vsDgmY3BiM9cr7eM5bMW+M3jP7CVxuY2bj/HHFM1G5OK9ORiX9HhQrt9ud1+0XZLld1qpVq3jxxRcnlST5/X4Cgcq5Yl2Myn0MHJis8WSW2p0JLM/JNnN5AC2m97+maRM9b50m2babwMbH7Q6nYIppDCqVxsB+lTAGfQffAMAXWk7NzDk2R3O9fI7BeM5b/uUb6XvVjznQg6PnDASW5iWma/ZZJIlJJfweFKt8LrWDPBZuaG1tZcOGDXR2dg7/rKam5pq/ixSjxa4LVDviDJheLvnm2R1OUctWeoq2HyAdvX7tuYhIKbvaQFZV7W7GcLkJLMmsJhhUlTspE3lLkoLBIKFQiNraq/0EDh06xNe//vV87VIkJ9YOVbXbn5iHZeS1AGTJ80ybg2fmfDDTRI7tsjscEZGcSYUvE+86AjBc5lpurHrootng4Q9tXQYnkit5W24XCoV46qmn2L59+3B/pEcffZSnn346X7sUmTQDk7WeDgD2Juaj7ki3VrX8bhLdpxk4/AE1ax+0OxwRkZzItjfwNizDFZxmczTFz7/oNgy3l1RfN4nzp+wOR2TS8npPUnNzs/oiSUlZ6OqmxhFj0PRwPDVLSdIYVK24m553XiJ6aj/p2CBOX1VB92+aFg7Hjdcl3+pxEZHRZJeNVVoD2boa74SOqw63l8Ci2xg88uHQezc3z5GK5FdBCzeIFLtsA9kDyRBmfnstlw3P9AbcM0IkL3YSObaLmjX3F3T/Dodxw34cDfU1PPWN8q+6JyK5lRroIdaRKfddvbyyltpV+90TPq5WLb9rKEn6EKyv5jtUkbxSkiQyxMAaXmq3L1GYEqblomrZnfRe7GTw2McFT5KAG/bjEBGZiMw9lhbe2Ytx1c6wOxxbTOS4Glh8O4bTTfLKWaZ6r9Cmr5lSwnSpXGTIPOclah1RYpabo8nZdodTUqqW3QlAtG0PZjJuczQiIpMzePRjAAJDxzYZG4c3gH/hWgAWp0/YHI3I5ChJEhmyZmgWqTUxlzSTbyBbSTz1C3DVzsBKJYie3Gd3OCIiE2bGI0TbDwBQtWyjzdGUnqqh5YmL0202RyIyOUqSRIasHmogeyAZsjmS0mMYBoGlmS8Tg8c+sjkaEZGJi7TtATOFe9ocPNMb7A6n5ASWbACHk+nWZWY4wnaHIzJhSpJEgCnmFeqdYVKWg0MJVeSZiOySu8jxT7DMtM3RiIhMTLb0d/bCj4yP01+Nf/4qAFYNXXwUKUVKkkSARemTABxLziaOx+ZoSpMvtByHvwYzOkCs45Dd4YiIjJuVShI58Slw9cJPMTIMA7/fj2EUZ3uDbIK5xt1hcyQiE6ckSQRYOJQk7ddSuwkzHM7MMguu3vQsIlJKou0HsBJRnNVT8M5ZbHc4N+Txelm5ciV+v3/Ux03Tytu+s32UbqZqaeZc0Oi6SI0RzVssIvmk2oxS8VLhy8yyLmBacDChJGkyqpZtZGD/Gwwe+5hpj3yraK9yioiMZvDYUFW7pRswjOK9jux0OGzrD3erPkrrl8/kdx5fyQVjJvV00+Tp4sP4krzFI5IvSpKk4kWO7wLgdGoG/dboV+VkbPwL1mC4faTDl0icP4l39iK7QxIRGRPLMof6IxX3Urssu/vD3Wj/DTOrAWhzLqQ+1c0ad4eSJClJxXuZRKRAslcOtdRu8hxuL4FF6wAtuROR0hI/c4z0YG+m18/8JrvDKXknnQsBWOo+h5ekzdGIjJ+SJKlo6dgg0faDABxIzLM5mvKgUuAiUoqGG8guvh3D6bY5mtJ3xZjKxXQNbsNkufus3eGIjJuSJKlo0bZPwUxzxZjCRTNodzhlIbD4dnA4SV7sJHlFJ0YRKX6WZV0t/a0GsrlhGOwfus8326xdpJQoSZKKlj0ptjl170yuZHpkZJaqaMmdiJSC5KVOUj3nMZxuAgtvszucsnEgmVmhsdLdhQPT5mhExkdJklQsM5XIdFYnc4Op5E5gaeam5+z9XiIixSx7Qce/YA0Orwr45Ep7ajr9po+AI8li13m7wxEZFyVJUrFi7QewEjGcNVPpNmbaHU5ZyfbIiHcdIzXQY3M0IiI3N3w/0tLcLLW7VR+hfPYxKiYWDg4kGgBY7em0ORqR8VEJcKlY2ZNi1dKNcEj9fHLJFZyGd84S4mePEzm2i+D6R+wOSURkVKnwJRLn2wBj+ALPZN2sj1C++xgVmwPJeTT7TrDG08nfRXS/l5QOJUlSkSwzPdwfKbB0IxzSbEeuVS3bSPzscQaPfqwkSUSKVvaCmS+0HGdVbc62a3cfo2JxLDmbuOWizhEh5LwMNNgdksiYaLmdVKT42eOkB/uG+mGstDucspRdthJtP4AZj9gcjYjI6LL3TqqqXX6kcHIoORfQkjspLUqSpCKpH0b+eaY34J42F8wUkROf2h2OiMh10tF+YqdbgaGl15IXB7KlwN1KkqR0KEmSiqN+GIVTtWyoyt1RNZYVkeITOb4bLBPPzHm4p8yyO5yydSg5l7RlMNvVS1Xyit3hiIyJkiSpOOqHUTjZJXeRtj1YqaTN0YiIXGt4qd1Q2wLJj6jl5UQqk4TOjh63ORqRsVGSJBVH/TDGZzKlbL1zFuGsnoqViBI9fTDXoYmITJiZjBMd6pVXpVUFebd/aMndnMgxmyMRGRtVt5OKEzmW234Y5W4ypWwNw0Fg6R30f/oLBo9+TGCRZu5EpDhET+3HSiVwBafjqV9gdzhl72AyxK/zMdPiXaQHVfVPip+SJKkoqfAl4ucy/TACS+6wO5ySMZlStlVLN9L/6S+IHN+FZW3FMDSBLSL2Gy7gs2wjhqFeefnWa1bRkZrGPNdlBo/vQl9Bpdjp24pUlMFjmd5I3oZluKrr7A2mQvgbV2F4A6QHeoifPWF3OCIimV55Jz4BVNWukLJL7iJDCapIMVOSJBUlu9RO688Lx3C6h5fZZd9/ERE7xc8cw4yEcfiq8IVW2B1OxTiQnAdkljq6rYTN0YjcnJIkqRjp2CDR4X4YG2yOprJcLQWuJElE7Hdtrzwt+yqU8+laBlx1WOkk88wOu8MRuSklSVIxoic+BTONe0YI99Q5dodTUQKLbgOHi+TlMyQuddkdjohUMMuyRpT+1qqCwjI4G1gKwKL0SZtjEbk5JUlSMQaPZRqaVi3RLFKhObwB/I2rAIgM3RcmImKHa3vlrbM7nIpzzp9JkhrT7TgwbY5G5MaUJEnJG0sfHyuVJDLUD0NXDu2RvTl6cJxJ0q3GV0RkPLLHIF/jalt65dXVeCfVf67UXfbOxREI4iPOYtcFu8MRuSEtxJWSN5Y+PpGTB7ASMZzVU/HOWWRDlBJYugF2vEj8zDFS/T24aqaM6XU3G9/1y2fyO4+vzHWoIlLGsrPZdt2bWu13T6r/XMkzHFQt2UD/vtdZ7engWGq23RGJjEpJkpSFW/XxyV45DCy9Q316bOKqmYp3zhLiZ48TOb6L4PpHxvzaG41vw8zqXIYoImUu1X+F+NnjgJG5cGOjyfSfK3WBZRszSZK7k79jI6A+VVJ89G1Ryp5lmVdLf2upna0Cw0vuVOVORAovO4vknbsEV/XYZrMl9/yNq0ngZoozQoPzit3hiIxKSZKUvfjZE6QHezE8fvzzV9kdTkXL9qeKth/AjEdtjkZEKs2gzUvtJMPh9tLhyPRMWu3ptDkakdEpSZKyl51FCixej+Fy2xxNZXNPm5spv55OETm5x+5wRKSCmPEI0fYDgAr4FIOTzoUArHGrX5IUJyVJUvZ05bB4GIZBYGg2KaLGsiJSQJG2PWCmcE+dg2d6g93hVLxTzkbSlsEcVy/THNcXsBCxm5IkKWt1Zg/JS13gcBFYtN7ucISr94VFTuzGSidtjkZEKsVwA9llmkUqBnHDR1uqHtCSOylOSpKkrC1MnwLAP78Jh6/K5mhGV8r9MibS78M7dwnOqrrM0pfTh/IZnogIAFY6RfTEp4BWFRST/YkQAKu15E6KkEqAS1lbaJ4Einv9eSn3y5hIvw/DcBBYcgf9e3cSOfYxgYVrCxWuiFSoaEcrZjyCs6oW75wldocjQw4mQ/wau1joukiVEWPQ8tkdksgwJUlStmqMKLPNc0DxXzks9X4Z442/atlG+vfuZPDYx0x79PcwDPXIEJH8yd4DGViyAcPhtDkayeoxq+lMTSXkusIqdxcfJRbbHZLIMC23k7LV5O7CALyzF+EKTrM7HBnB17gaw+Mj3X+FxLk2u8MRkTJmWdaIhuLFfcGsEh3ILrnzaMmdFBclSVK2sjeCFvNSu0rlcHkILLoNUGNZEcmvxPmTpPsvY7i9+BtX2x2OfMaBZKZf0jL3OTyomI8UDyVJUpY8JFnmPgtcraYmxSWbvCpJEpF8yh5j/AvX4XB7bY5GPutsuo5L6Wo8Rprl7nN2hyMyTEmSlKXl7rO4DZM+I4h7RsjucGQUgUXrweEkebGT5JWzdocjImUqMtwrTxfMipPBgaSW3EnxUZIkZSm71K7NubBsigIYhoHf7y+bf4/TX41/fhNwteGviEguJXsvkOg+DYaDwOLirRRa6Q4kMkvumtxdGJZpczQiGUqSpOw4MGlydwHQXb2sqPsM3crIPkR+v5+VK1fi9/uveU4p//uGl9wd1ZI7Ecm97CySb94KnIEam6ORGzmVmsGA6aXKkWBaXI1lpTioBLiUnYWubqocCQZML9Fg4037+KxfPpPfeXylDVGOzUT6EJWSqqUbuPza/0us6wj/+j+9QtQIXPN4sY+PiBS37P1IWmpX3EwcHEw2cJe3jTmRY3aHIwIoSZIylF1q15pswDIyk6U36uPTMLO6oLFNVKn3UboRV3A6nlmLSJxvo/rSIQ4mrm3yWCrjIyLFJx3pJ9ZxGFDp71JwIBHiLm8bsyPHsazSXSEh5UPL7aTMWKx2Z5Kk/QkVbCgFVcsyV3izya2ISC5ETuwGy8Qzcz7uunq7w5FbOJqcQ8JyUpXuI3Gh3e5wRJQkSXmZ4+xhmnOAhOXkaHKO3eHIGGSXwSxzn1WPDBHJmexSO80ilYYkLo4MnbfVGkKKgZIkKSvZWaSjydkktZq0JLhnhOg1anEbJivcKgUuIpNnJuNET+4FoGrpnfYGI2O2f6jKXUTFfKQIKEmSspJdspUtJyrFzzAMTjoXArBKS+5EJAei7QewknGcwel4Zi2wOxwZo9bkXCwMEt3tBM3yuw9XSktek6RwOMy2bdvYtm0bTzzxBDt27Mjn7qTCTXEMEHJdwbQMDiYb7A5HxqHNkUmSmtxdOFCPDBGZnMjRjwCoWnJH2fSWqwQRy8clb+Z+4gXmKZujkUqX1/VIzz//PM8++ywAW7ZsYcOGDbz88ss0NTXlc7dSobJL7U6mZjBo+WyOpjCyfZQcjht/CbjV48XgvGMW/aaPGkeMRa4LHE/Nzsl2y+X9EZGxs8z0cIPqquV3TWgbpXzcGMtxr5idDSxlRryDRemTgGYBxT55S5I6Ozvp7OwkHA4TDAYJBoM0Nzfz/e9/n+9973v52q1UsDWeDuDqmuZKUC59lCzDQWuygbu8J1jt6cxZklQu74+IjF2s4xBmtB+HvwbfvIn1WSvl48atjnvF3n/uXGAJa3t2Msc8S8CIEamQi55SfPI6k3Tw4EH6+voIBoMAhEIhDh48mM9dSoXyWxEWuboB2J+snCQpqxz6KO1PhLjLe4I17g5eZgOQu6ug5fD+iMjYDGaX2i3dgOFwTng7pX7cKNX+gBFXHZ6ZjSS622lyn2FXYpHdIUmFyluSFAqF2LVr1zU/a2lpobm5eVLbjUajk3q9TFz2vS+mMTAMA7/fz8L0KRyGRUdqGj1m8Z0AotHoqM3xsvHbtf9byVV8Y/n3H0vOJm65mOKM0OC8Qld62qT3O9n4RnveyP9K4WkM7FfMY2BZJgNHPgTA2XgbkUhk3NsY63Ev38d1u88bdgos20iiu501no4bJkkTPa/lSjH/HlQKy7Lyes9hwWokt7a20tfXx1NPPTWp7bS3t+cmIJmwYhoDv9/PypUrWZQ+AcC+Il1qd+rUqVEPpNn47dr/reQqvrH8+7M9MtZ6Oljt6aQrWrgkabzvTzH9DlQqjYH9inEMnL1nCA70YDk9nIo44fDhcW9jrMe9fB/X7T5v2Klq6UZ63/0blrvP4iY1akuPiZ7Xcq0Yfw8qRTKZxOv15m37BUuSvvOd7/DDH/5weOndRDU2Npb9FZRiFY1GaW9vL6oxMAyDdGyQkNkFFO/9SAsWLLjhFUE7938ruYpvrP/+A4lQJklyd/JqdF1O9j0WY31/ivF3oNJoDOxXzGPQ/+4+BgH/4vXMXrV6QtsY63Ev38d1u88bdvLUNxI2agjSzzL3WQ6Osox+oue1XCnm34NK4Xa787r9giRJzz33HN/97ndzUtXO7/cTCARyEJVMVLGNQf/Bd3Bici5VS7dZa3c4o7L7AFoq+29NziVtGcx19TDNcf0Nx/ky3ven2H4HKpHGwH7FNgaWZXGpbTcAwaZNeY8t38dVu4/bdjIMgzbHQm5L72O1p3PUJKlY3p9i+z2oJPm+YJD3ZrLbt2/n8ccfH06QWlpa8r1LqTCDQ+vPK7FgQ7mJWD7aUvXA1ZLuIiJjkbzYQarnPIbTTWDRbXaHI5M03GRc/fPEJnlNkrIJUTAYpLOzk9bWVlpbW/O5S6kwZiJGtG0PAPsS822ORnLhQCLTSHD1UEl3EZGxGDySqWrnX7gOh6c4ZhkmItvnqNKddcxhwPRS7Yiz2HXB7nCkAuVtuV04HOaJJ5647ueTLdwgMlL05F6sVII+I8iZ9BS7w5EcOJAM8TV2sdDVzdH0gN3hiEiJGDyaWVVQtfxOmyOZnFLvc5QrluFgfyJEs+8Eaz2nOZaj/nkiY5W3JCkYDHL06NF8bV4EuNoPo825iFz21RH79JjVtKem0+i6xJzIMeB+u0MSkSKXvHKORPdpMBwEltxhdzg5Uap9jnJpX3L+UJLUwd9GNmLl/y4RkWH6tEnJstJJBo9/AsAJh5rNlZNsKfe5kSM2RyIipSB7wczfuAqnv8bmaCRXjiVnM2h6qHHEWDjUMF6kUJQkScmKnjqAFY/grJ7Ceccsu8ORHNo7dH/ZjFgH6cHS7XgvIoWRTZKqlpX2Uju5lomDg8nMfaprdZ+qFJiSJClZ15wUK6BvRCW5YtbQkZqGgTU8ziIio0mFLxM/cwwwCCxVklRushfN1npOY6CCFlI4SpKkJFlmmsFjHwO6cliuskvusiXeRURGkz0XeBuW4qpRAZ9yczQ5m6jpps4RZb7rot3hSAVRkiQlKdZ5BDMSxuGvxjev/Kv8VKLs1cNo+wF8VtTmaDIMw8Dv91dEx3uRUnF1VcFdNkci+ZDGObzkbp3ntM3RSCVRkiQlKTu7EFiyEcOZtyKNYqNLZpBedz1YJgvTp2yJ4bO9Svx+PytXrhzu9K5eJiL2Sg/2ETud6b84ntLf+t0tLXuHVhasc58GS2MnhaFvl1JyLMscTpJKvR+G3NyZqmXU9V5gcfo4MLfg+79Zr5KG+hqe+sbtBY9JRK4aPPIhWCbe2Ytw19WP+XXqQ1RajibnELdcTHFGqLfUWFYKQ0mSlJxY5xHSA1dweAMEFqy1OxzJozOB5TT1vkPI7MJvxIla3oLHcKNeJSJiv4HDLQBUrWge92vVh6h0JHHRmmhgvbedxek2u8ORCqHldlJyBodOioFlGzFcbpujkXwacE/DPWMeTkxWubvsDkdEikhqoIdYxyFgYkmSlJbsfaqL0yewtOROCkBJkpQUy0wzePgDAKp1UqwI1cvvBnTDrohca/DIR5mldnOW4K6baXc4kmeHk3NIWE5qrTCJ8/bcpyqVRUmSlJRY52HSg704fNX4F6yxOxwpgKoVmSRpufssPiNhczQiUiwGJ7HUTkpPAjeHkpl7UwePfGBzNFIJlCRJSRk8NHRSXLYRw6mldpXAMyPEFWMKLkNL7kQkI9U/cqmdSn9XiuySu8EjH2jJneSdkiQpGZaZZvDoUFU7XTmsKMedi4FMx3URkcxMgoV37lLctVpqVykOJRpI4SR55RyJbp0PJL+UJEnRy/aziJ1uJT3Yh8Nfg79xtc1RSSGdGEqSVrjP4CVpczQiYrfscis7LpjV1XjVZ8kmcdycdmR6Jg0eev+Gz7vV+Gj8ZCxUAlyKXrafxZLOn7Ia2J+Yx5+88B6gfhaV4rIxjQvpIPXOMKs8nexOLLQ7JBGxSar/CrGOwwBUD92zWEjVfrf6LNnomHMpi8xTDBx6nyn3/xaGYVz3HPW4k1xQkiQl4cz5Ph5JngAHvN0zm7ZUpreF+llUCMNgT6KRzf793O45pSRJpIJlmolbeBuW4QpOty0O9VmyxynnAgy8pHovED97At/cJaM+Tz3uZLK03E5KQsjsotoRp9/0cSI1y+5wxAa74wuATJW7gBGzORoRsUu2qp3aQFSmlOEmsOQOAAYOvWdzNFLOlCRJSViSPg7AvsQ8TH1sK1K3WUtXagpOw2Kdp8PucETEBqnwZWKdmaV2VcsLv9ROikN10z1A5r4ky0zbHI2UK33blKJnpVMsSrcBsCfRaG8wYqvdicxs0nqPGgmKVKJswQZvw3JcwWk2RyN2CSxch8NXRXqgZzhpFsk1JUlS9KKn9uMjs9SuLaVSr5UsmyQvcl2g1ojYG4yIFNxAa2Z5VfXKTTZHInYyXG6qlmX6Y2U/EwWPwTDw+/2jFo6Q8qAkSYreQOu7AOxJzMfSR3ZcxlKqNl1CpVB7zGpOJmfgMOA2b7vd4YhIASWvnCV+9jgYDvXKE6qaMony4JEPsNKFbQ1hmhZ+v5+VK1fi9/tHfVxKn6rbSVEzEzEGj34MoIpmEzDWUrWlVMp2d2IBC90Xud1zik/sDkZECmbgYGbGwL9gDa7qOnuDEdv556/CWVVHerCX6Kn9BBYXrqy3SoxXBiVJUtQix3dhJWP0GUHaU/aVei11typVW0qlbPcm5vPVwC7muS5zOHnF7nBEpAAsyxpeVVC96h6bo5FiYDicVK1oJvzJKwy0vlfQJAlUYrwSaO2SFLWBg5mT4lHnMkDrfgUGLD/HkrMBCA0esjkaESmExLk2klfOYrg8VC290+5wpEhUN30OgMFjH2Mm4zZHI+VGSZIUrXQkTOTkXgCOOpfaG4wUlWyVu4bBQ1iW1n6LlLvsLFJg6QYc3uvvAZHK5J27FFftDKxEjMiJT+0OR8qMkiQpWgOHWsBM45m1kB7HVLvDkSKyPxEiaTkIpi6TuNBudzgikkeWmWbg0PvA1f44IpCpMFc1VOkwm0iL5IqSJClaA63vAFp/LteL4+FQsgFQx3WRchc9fZD0QA8OfzWBRevsDkeKTDZxjp74lHRs0OZopJwoSZKilOy9QLzrKGBQvfJzdocjRWh3PLPkbqD1PdCSO5Gyla1qV7W8GcPptjkaKTaemfNxz5iHlU4yeLjF7nCkjChJkqKULdjgb1yFq6Z0l9qNpU+RTExrsoGE4SUdvkSD2TWhbWh8RIqbmUowePRDYPyrCvS7XZ4+e9w2DIOa1fcBMHDg7XG/fjSl1D9Q8kclwKXoXFvq9V6bo5mcsfYpkvFL4eRM1QoWDOxlefoIb7Nx3Nu42fhobETsFzm+GysewRmcji+0fFyv1e92eRrtuF1luXgCg1jnYf7m79/j61+58QqUcuwfKPmhJEmKTuLCKZKXujCcbqqWlUep11LqQ1RKTletZsHAXhan2/BwGwkmthRntPHR2IjYb2D/mwDUrLoHwxj/4hf9bpevz47t0ZpZrHCfo+rMLuDWy/TLqX+g5IeW20nR6R86KQaW3oHDV2VzNFLMrnjn4poyCw9J1no67A5HRHIoNdBDpG0PANVrHrA5Gil2u+KLAJg3eFCtISQnlCRJUbHSyeH7kWrWPGhzNFL0DIOa1fcDsNHbZm8sIpJTAwffBcvEO3cZnmlz7Q5Hitz+xDxilouqVC/xriN2hyNlQEmSFJXI8d2Y0X6c1VPwL1xrdzhSAqqHbthd7DrPFMeAzdGISC5YljW8qqBmzf32BiMlIYmLvYn5APTvf8veYKQsKEmSotK/7w0g88XXcDhtjkZKgbtuJl2OuTgMuMNzyu5wRCQHEudPkrzYgeHyDDcLFbmV7JK7gcMtOK2UzdFIqVOSJGNmGAZ+vx/DMPKy/ZHrz2u0/lzG4bBzBQAbvG2A1qKLlLrhe1OXbcSpe1NljNpS9UScQax4hIXpk3aHIyVOSZLcUrafgN/vZ+XKlfj9/lEfv9Xrb/X4wMF3htafL8UzvWESEUuxKFQfohPORcQtF/XOMPOdl/K+PxHJHyuVHG4DcbMLZuqDJJ9lYdBRtQqAFWndlySToxLgcks36yfQUF/DU9+4fdKvtyxreKmdZpHKR6H6RCUND/sT89jgPclGbxunIzMmvU0RscfgiU8wowM4a6by31qSdP3kreueM5Zzj1SmjupVLA+3MM/soNYYpM/STKRMjJIkGZMb9QvI1evj59oyvZFcHqq1/rzsFKLfxMfxRWzwnmS95xQ/idxBUoc3kZI0sG+oYMPq++naMzipc49UngH3NHyhFcQ6D3Ont41fxNbYHZKUKC23k6IwMDSLVLXsTvVGkgk5nprFpXQ1AUeSdZ7TdocjIhNwbW+k++0NRkpWzW0PA3CX9ziG7lOVCVKSJLYzUwkGDr0HQPVaLbWTibEw+DC+BIBm7zGboxGRiRjY/+bVe1PVG0kmqGr53cTxMM05yFLXObvDkRKlJElsN3j4A8zYIM7gdPzzV9kdjpSwj+KLSFsGC90XmeXstTscERkHyzIJ79kJQPC2z9scjZQyh9vLEecyAO72Hbc5GilVSpLEdv17fglAcN3D6o0kkxK2ArQmM5UR7/bqxChSSqKnDpDqvYDDG1BvJJm0VlcTAKvdnVQbUZujkVKkJElsNdW8TKzzMBgOatY9ZHc4UgY+GFpyt8HThkPNBEXyJtc98/r3/ALINBN3uL053bZUnkuOGZxOTcNlmGzwqmeSjJ+SJLHVqtRBAAJLN+CqmWpzNFIODifn0JMOUOVIMCdy1NZYJttDTKRYud1uPN6bJzLj+XynBnoYPLYL0FI7yZ3sRbPMygIdb2V8VCNXbOMmNdzsLbj+EZujkXJh4eDDxGIe8+9nQf9e4Hdti2WyPcZEipXL5cLpcOTs892/7w0w03jnLsMzc34uQ5UK9ml8AV8JfEK9M8xCVzcnU/V2hyQlREmS2Ga9px0vCVx19fgXqI+B5M5H8cU86jvAjHgHictnbY1lsj3GRIpZLj7flmXSny3YsF6zSJI7cdzsSTRyl/cEd3uPK0mScdFyO7FNsy9Tpjl42+cxDH0UJXd6zGoOJ+cA0L/3lzZHIyI3Ez25j1Rfd6Zgw4pmu8ORMtMSyyy5u83TTpURszkaKSX6Ziq2mOu8TKPrEmkc1Kx90O5wpAy1xJcCmWU8ThVwECla4aEKp9Wr71fBBsm50+npdKSm4TZMVT2VcVGSJLbYNHSganMuwllVa3M0Uo5ak3MZdNZiRgdYlra3gIOIjC7Vf4WICjZIXhm8E1sOwOe8RzEs0+Z4pFQoSZKC85Lg9qFynAedah4r+WHh4GTNegDWpvajykYixSf86WtgmfhCK/DMnGd3OFKm9iQaGTC9THFGmB3VbJKMjZIkKbi7vG34jBTn07V0OebaHY6UsfbqtRguDzOsSyx0ddsdjkhZMAwDn8836e1YqSThTzO9kYIbHp/09kRuJIVzuBz4wv7dNkcjpUJJkkyqV0tdjXdcrzcwuceXKfv9Tmw55LgZochISaef6lX3AnDv0OeulKjPktxMvj8fN3q93+9n4cKFk9o2wMCh9zEjYZw106haduektydyM+/Hl2JaBjNjp0lc7LA7HCkBeS0B3tnZyUsvvcS8efPYsmVLPnclk3CzXi7rl8/kdx5fecPXVvvd43r9SvcZZjj7iZgedsUXEsrNP0Hkhmo3PE7/3p2scXdQ5xik16yyO6QxU58luZl8fz4mc264Fcuy6Nv1CgC1d2zGcDgnvC2RsegxqzmQDLHW00H4kx3AUrtDkiKXtySppaWFcDjMoUOHqKury9duJEdu1OuiYWZ1Tl9/n+8wkOmCncA9gUhFxsczcz5djrk0mGfY5D3Gz6O32R3SuKjPktxMvj8fkz033Ej8zFES59swXB5q1j08qW2JjNW7sWWs9XTQf+BtPE41LZaby9tyu+bmZjZv3kxNTU2+diElZpazl2Xu85iWwbvxZXaHIxVknyvTrLjZewwXaZujEZHsLFJ10z04A0Gbo5FKcTw1i7B7OlYyxor0YbvDkSKne5KkYO71Zg5I+5MheszJXYUUGY+TjoX0pANUO+Ks95yyOxyRipYKX2bw8AeACjZIoRm01WSWoa5L7cOByoHLjeX1nqR8iEajdodQVgzDwO/3530/PivKhqGy32/HVlz3eDQaxbKuv0m4UPFJebMMB+/Gl/OlwKc84DvEx4lFQG6Khkz2s5uv15er7Dmg0s8F+f585OrYO9r+wy0/AcvE3bCcdM1MIpHIhPevc4eMV0fVam6PtlAbDbPG3cHeZGNe9lNpx2Y7WJaFkccCYCWXJLW3t9sdQlnx+/2sXDnxm2/Hak3qAB4jTWdqKidTM697/NSpU6N+6SlUfFL+WuJLecS/nzmuXla4z3I4mZvy85P97Obr9eWu0s8F+f585OrYe93+kzHq9u7EAK7MXEP34dGXPBXq90cqT9rhJnj7Znrf+zEP+lvZm5xPri6ajVSpx+ZCSiaTeL3evG2/5JKkxsZGXR3KoXxm4FlmMs7a1D4AXo81MdrBaOHChTe8GiiSC1HLwwexpTzgP8SDvtacJUkLFiyY1Gd3sp/9G+2/XEWjUdrb2yv+XJDvz0eujr2f/XwP7PoZ/ekErmkNLLnvC9zoy2mhfn+kMtXe8RiX3nuZ+a7LLHJ105aqz/k+Ku3YbAe3O78FwEouSfL7/QQCAbvDkHHo3/s6fmJcSlezL3FtNZlsn6VcNCYUuZW34iu413eYpe7zzHNeAiafKE30i3quPvuVmijoXDA2dn0+Rvt8W6kk3XteA2DK3V/G5wvgcEwsmdG5QybDWVXLIecK1qQP8qCvlbaB3CdJlXpsLqR8XwwpuSRJSothmfR99A8AvBlbifmZWiHj7bMkMhm9ZhW7EwvY6D3Jg75WTrHWtlj02ZdyNtrne2XqEA8nexigir8/UM2/XDvxLzj6/ZHJ2uO6jVWpg6zydFHv6OWCWWd3SFJklCRJXs2NHCbV100EPx/FF9/wefnqxSHyWW/EmtjoPclaTwfdyR67w9FnX8pa9vNtYPH12k/ACa9HltHZfX2xhsls/7P0+yO30ueo40ByHms9HTzob+V/DW6yOyQpMnltJtva2soHH3xAV1cXAJs3byYUCuVrl1J0LJb2fQjAPtdaksrJpQicS0/hUGIuKz1nWBL+CPi83SGJlL0mdyeznH1ETTct8aU5WOgqMnmvR5tY6+lgg+ckO6Jr1Z5ErpG3b63Nzc00NzezdevWfO1CitxK9xnqkt0Ybh/7nauBuN0hiQCwM7aKlZ4zzB/YTyp82e5wRMqcxWb/fgDejy8lZnlsjkck43R6BkeTs1jmPs/DvoP8OHKX3SFJEVEzWckTi0eHTorB9Y8QN3RzrRSPtlQ9x5P1OEnT+8Hf2x2OSFlb6T5DyHWFuOXijViT3eGIXOO1aObe1Lu8J6g1crMMVMqDkiTJixXuszS6LpEyXNTd/at2hyNynR1DJ8b+PTupsgZsjkakTFkWm/2ZFhDvxZYxaOmCmRSXtlQ9J5L1uAyTh/wH7Q5HioiSJMmDqyfFkzXrcVbV2hyPyPVOpOq55G3ASidZn/zU7nBEytJ8s4P5rsskLCdvxlRtTorTa9HVANztPY43rYtmkqEkSXJu+dAsUsJycjyo9b1SrAwO134OgNXpgwS1zEIkpyzLYmPyYwDejy2j37raNybb50ikGBxLzeZUcgYeIz1U0EdEJcAl5yweG7G0Iu6ssjkekRu76GvEO3cZnDnKQ/5W/j6ywe6QRMpG9NQ+ZlvnSVhOXv/MvUjqcyTFxeC12Br+0P06C/s/JdV/xe6ApAhoJklyaqX7zPAskm7QlaJnGEy59+sAbPIeZYpDyyxEcsKyuPLmXwPQEl96zSzSSNk+R5/9031FM7tSWIeTcziZnIHLStHz3o/tDkeKgJIkyRkDky/6M/d2vPuZpRUixcq/YC1djrm4DXP4XjoRmZy5kSMkzreRwM0vo6vsDkdkDAx+Fl0PQP/e16k1e+0NR2ynJEly5nbPKea4eomYbnbGVtsdjsiYGIZBi7sZgI2ek9Q7eu0NSKTEOTBp6n0bgD2u2xjQBTMpEW2pes77FoKZ5q6U7k2qdEqSJCecVopf8e8FYGdsNRHLa29AIuNw3jGLfYl5OAyLLwT22B2OSEm7y3uC6lQPjkCQT1232R2OyLi0TrkPgGXpY8x16t6kSqYkSXJiTeoAU52D9JoB3okttzsckXH7eWQdpmWwxtNJo+ui3eGIlCQ3qeFlq1M+92skDY/NEYmMT59nFlUrNwHwBb8umlUyJUkyaenYIHekPgHglchakiqaKCXoglnHx4lFAHzJvxsslScWGa/7fYeodUQZdNYSvO0Ru8MRmZCp9/0GaRys9Jxhmeus3eGITZQkyaT1vv+3+IlxLlXLrqEvmSKl6JXIWhKWk0XubuZGDtsdjlS4fPcRyvX2a40In/cfBDJLlgyXO6fbFykU99Q57HeuAeArgV04MMf82rH0AFOPsNKgS/4yKdXJy/R9/HMAfhq9A1N5t5SwPquKndFVPB7Yx+qeNzGTW+wOSSpYvvsI5Xr7Xwx8itdIcTI5g66AehxJafvYvYElycPMdvWxyXuMd+Nju5XgVj3AGupreOobt+c6XMkDJUkyCRZrr/wSzDSnHI0cTs61OyCRSXsj1sRd3hNMJUzvBz8B6u0OSSpYto/QZzXMrC6q7Te6LrLBexLTgpcjG1hoGDmJT8QuccPHK9F1fL3qIx7z72V3opGI5Rvz62/0uyWlQ5f9ZcJWuTupj50Cp4t33PfYHY5ITiRx8dNI5ipf3wc/ocYM2xyRSHEzsPha4GMAPk4spjM93eaIRHLjg/gSzqbqqHIkeEx99CqOkiSZEBdpvhLIFGuou/NL9Dnq7A1IJIf2Judz0TsPK5Xgc6n37Q5HpKht9JxgnusyMcvNzyIq+S3lw8TBy5ENAGzyqiR4pVGSJBPykO8g050DRJ3V1G36qt3hiOSYwb6pnwfDwZL0CVa4z9gdkEhR8ltRvhT4FIAd0TX0q3GslJnjqdnsic/HaVhsqfoArLEXcZDSpiRJxq3e0csj/gMAHJjyEA6PTopSfsKemdRu/AIAXw98iIekzRGJFJ97ku9S7YhzJjWFt2Mr7A5HJC9ejmwgarqZ77rMwv5P7Q5HCkRJkoyLgcVvVH2AyzBpTcylK6CTopSvKfduoc8IMtU5yOOBvXaHI1JUIm17WJ4+imnBS4N3q7qplK2wFeAfousBaOp9m1T4ss0RSSHoiFYBclmPf5P3KAvdF4lZLn4cuRNUwUjKmMPj4y33/QDc5z3CPOclewOSknKrY286j71SxtKrZTKcZoJLr74IwDvxFXSoWIOUuZb4Uk6lpuO2Elz6xZ9NeDvqo1Q6VAK8AuSqF0adY5AvBvYA8LPIenrM3JSgFSlmp53z+SS+gDu8p9hS9QH/Ofw4aZx2hyUlYCzH3nz1QbpVr5bJbn9l77uk+rsJGzX8PLJuwtsRKRUWBtsH7+b/qv05kaMfscQ9hTZmjns76qNUOpQkVYhJ98KwLH4j8AE+I8mp5Azeiy/LcYQixevvI3ew3H2WBlcPm/37+XlUFbxkbG517C2VPkgjLXKdZ3F/puT3m+77SeCe8LZESsm59BSO1t7Nir73uT/5Fi3GFwhbgQltS32Uip+W28mYrEnvZ4XnLAnLyf8avBsLLbOTyjFg+fmbwbsAeNh3kAWubpsjErGHz0jw21XvYwA16x7mtLPR7pBECupI7SY8sxbhJ8ZvVn0AaGlcuVKSJLeUuNjB55KZXjE/jdzOBbPO3oBEbLAvOZ+P4wtxGBa/XfUebithd0giBfe1wMdMdQ4y4Kpj2sPftDsckYKzDCczv/xtUjhZ6TnDJu8xu0OSPFGSJDflsFJ0/+RPcJHmUGKultlJRfu7yEYup6uY7hzg3uS7docjUlDr3O1s9J7EtAw+mfZFHF61f5DK5JneQIu7GYAvB3Yz06Flc+VISZLcVFPPWyS6TxPFx18PNoOW2UkFi1kefjS4CdOCpvQhBg4qUZLKMM3Rn2mkCfwytoorvgabIxKx117nWo4lZ+E1UjxR/TZuUnaHJDmmJKkM5KtU5Fr3aZb07wJgp+chdVIXAdpSs/hlbDUAF1/57yQudtoSRzGUkbVz/4Zh2P7vrxQu0jxR/TYBR5JTqem8Fl1jd0gi9jMM/ufA5wibPua4evn1qo/sjkhyTNXtykA+yrzWO3r5rerMfUi1d32ZU/vmAZpOFgF4NbqW26b0MzPWzqEffJcfOX+NpOG55jmTLbF8K8VQRrbS918pvhb4iJDrCgOmlx8O3KcS+CJDwlaAvxi4l39a80vu9LbRlpwJzLU7LMkRJUllIpdlXr0k+VbN2/iMFBe981jwwDdgn5YViWRZONg1/Ut8qe+vCPZf5s6BHfzF4L2MXI6aqxLOt2J3GdlK33+5W5E6RLPvBKYFfzlwD71mld0hiRSVE6lZvBJdyxcCe/m1qo95N7Hc7pAkR7TcTq5hYPFb1e8zy9lHrxng4xm/iuHQVUORz4o7q6j/6r8kjYP13tM87Dtod0giORXrOsIDybcAeDW6jqOpOfYGJFKkdsZWcygxF4+Rprn7b0n1X7E7JMkBJUlyjS/6P2Wdp4OU5eAHA/cRd+qqociN+BqW8477HgC+GNjDOk+7vQGJ5Egg2cP5H/8HXKQ5kGgYvg9PRK5nYfCXg/dwPl2LP93PhR//e1xW0u6wZJKUJMmwTd6jPORvBeB/DTbTnpphc0Qixe+Aaw1vxVYA8NtV7zHfedHmiEQmx28kaO7+MWYkTLcxg78cuEcNxEVuIWp5eLH/QeIOP/FzbXw+sRNDjWZLmpIkAWB+up1fC3wMwM8j6/gksdDmiERKx08it3Mw0YDbMNla8ybTHNcXEhApBU7SfKv6LYKpyzhrpvIP3i+QwG13WCIl4bJZw4czvgoOF0vME3zZvxuUKJUsJUlCrPMwjydexWFYfBRfxC+0rEJkXCwc/MXAPXSlplDjiPFPa36BPxW2O6yyZBgGfr8fw9DMRq45MPlm9TssdZ8nZbiZ9fV/w6BRmAIkIuXism8eM77wTwB4wH+IR3wHbI5IJkpJUgnIZ6+PKfGznHvpj3GT4nBiDtsH70INY0XGL4Gb/9H/MBfTNUxzDvK5C/+L1ECv3WFNit19hkbbv9/vZ+XKlfh8vpu+dix9pNKT7LNk9/uTSwYm36h6nzWeTpKWgw9m/BreWQvsDkukJNWsvp+3h+5X/ZXAXu7zHs7p9vN9bJMMlQAvAfnogwQw13mZTd2vY5kxOh0N/NnAvep/ITIJ/ZafP+3/PN+ueY2pXOH8//r/4bMesTusCcvXsacQ+79VH6ns6yfTZ8nu9ydXDCy+HviQO7ynSFsGPxi4n+mNjXaHJVLS9rnWMdgX5vHAPr5atYsETj6IL83JtvN9bJMMJUklIpd9kADmOS/xhzU78ZgJvA3L+dml+0gSnWyYIhWvx6zmT/s/z1PTdkJ3B18zXqbTeJCwFbA7tAnJ9bGn0Pu/1esn22fJ7vdnsgzL5Leq3mej9ySmlanQ1Zps4D67AxMpA6/F1uA3kjzgP8RvVH2IlxRWjprN5vvYJlpuV5GWus7yfwR/QZUjwRXPbGZv+TckDY/dYYmUjUtmkPfqfxNn9VSmWVd4MriDqSrmIEXGTCV4PPEqG70nSVsGfzW4ib2JRrvDEikjBj+J3s7r0SYAvlL1Cct738OytNytFChJqjBr3af5g5o38BopjiZn8179b+LwqReSSK71u6cz53f/LX1GkOnOAf55cAeznT12hyUCgMuMc2H7v2OReZKk5eDPB+5nt6qaiuSBwf+OrufnkXUArOx7l8u//HMMy7Q3LLklJUmVwrJ4yHeQb1a/jcsw2ZOYz/f7HyTl8NodmUjZctfV82Pvr3E2VUetI8o/D75Kk7vT7rCkwk1z9HP/+b8k2n5guODIwWTI7rBEypjBL2JreHnwDgDCu17hC4mf4yVhc1xyM0qSKoCZjPNI8hd8KfApDgPeiy3lLwbuUZEGkQKIGFX81/5HOZachc9I8XvVb/Kg7yBouYXYYLHrPP8y+HOCyUs4q6fyd96vciI1y+6wRCrC2/GVfDj9KxguDwvMdv55cAdTHQN2hyU3oCSpzAWSvZz9y++wPH2MtGXwN4N38uPIXVgaepGCiVhe/nv/w7wXW4rDgC8HPmXjpZ9ixgbtDk0qhIHF/b5D/H9qfjl8P+rcb/0HLjpm2h2aSEU5W7Wc2f/ouwwSYI6rl6eCP9MKgyKlb8plbL3nFA+d+3MS59uI4uO/9X+e9+PL7A5LpCKZOPhx5C5+PLiRtGXQEDlM1589Rb15Pi/7G0ufILt7Zdi9/5sZy/tXKqqMGL9f/QZfCXyC07DYHW/knfpv4KqZandoIhXJN2cx271fpyM1jSpHgt+veZNf9X+Ck7TdockIKgFehnxGgq8GdnGntw0s8DYs5wcX7+REqjxO+CKl7L34crrS0/j9KS1U9Xbza/wdAd9a3og1YebwutWt+gQVQ6+MYu4zNNY+S8Vufrqd363dSa0jStJy8PeRDbwfX8p9DrfdoYlUtAFHDX8S3syXAp9yv+8wD/gPsch9gXfN0u2tV26UJJWZNe4Ovlb1EXWOKKZlcLRuE4//o2/T/yfvAqqXL1IM2lMzeGPOt/iNqg8ZPPIBXwzsYZ3nNC8N3k1XelpO91XsvTKKvc9Qscd3I550hO6fvsCXE++AA86na/nhwL2cS0+xOzQRGZLGyd9HNnA8Wc9vVbUwz3WZ34hvp+cdk7pNX8Fw6mKGnbTcrkzUmGH+cfWb/OOat6hzROlO1/Bf+x/hcN09GA4VaBApNkmHj5lf/Zf8wv0wg6aHkOsK/yL4Cr/q/wS/oYpHMjEGJnd6jvP5sy8ycPAdTAzejK7k+b5fUYIkUqQOJufx7/u+xP5ECCcmPe9up+vPniZyap/doVU0zSSVOLcZ4/Ib/5N/FP8HXJ40actgZ2wVv4iuIYUTFXUVKV6GYXDEtYJ3uqfw1cDHrPee5gH/ITZ42zjRfx+WeZvdIUoJWew6z1cCu2hw9YAJ7hnz+KvwnbRc8dsdmojcQtgK8GcD97O5vptfcX9A8mIn5//6WQJLNjDt4d/BPXWO3SFWHCVJJcpLkk2+ozxy5jB9nVFcwLHkLF6ObNDVQpES02/5+YvB+9iV6OLLgd3Mcvax7sov6Pr+QZanmjhFfU7vV5Ly0ui6yKO+faz0nAUgYro5Me0evvyPf58LL7yPllqLlAqD466lhP7wt+h5928I795B5PguIid2U7Pmfuo2fQ33FJXsLxQlSSWmyohxj/co9/oOU+VIZK4WTm/g78LreP3KFMCwO0QRmaBDyQaO9M2h2XuML9UcgCtneYSz3FZbw87YKnbHF5DUYVsALIvoqf38avwnzAtmygenLYP340vZEV3LHQsW6X4GkRLl9Fcz/ZFvEVz/CFde/0siJ3bTv+8N+ve/RfXq+6jd+AW7Q6wIOtuWAMuymJU+x11Vn3Kbpx23YQJwIR2kc+Y9/PrW36VdhRlEyoKJg/fiy/Eu/xzfXHyBM2/+HTOc/fxm1Qd8yb+bD+OLVcq/gnlIssF7kofPvcK5v77EPDLJ0cfxRfwytprLZo3dIYpIjnimNzBry78hduYYPe/8DdGTexjY/yYD+9/ka445vOZewoFkSCsN8qRikiTTtHA4bjzLMpnHb/XaiUqFLzNwuIWBA2/z9cQp8GZ+3pmays7YKvYl5nHvwpAKM4iUoZTDS13zV/njD6qZfeUT7vEeYZpzkIf8h3jAd4iLFxrp3xfGYyUntP1sH6B8HLsKsf1KYmCy1HWeDd6TrPF04DVSkATD7WOvuYSfXF7CFbO4q+2JyNiMduz0zV3K7N/8I2JnjtH30T8weORD5ppn+VbNWfpMP58mGvkkvpCu9K17n43l2Kxjd0bFJEmT7Rdyo9fnutdIsrebyIndDB56n1jn4eGfp3CyO97Ie7FldKSnoWV1IpUhaXh4M9bEW7EVrHSf4R7vUVZ4zlIfa+fiz/6U38NJa/Vc9iXmcSQ5h0HLN6bt5rsPULn0GbKLw0oRadvDfYm3+N26E9Q6osOPdadrODf9Tr78rd/hhf+2iyumVhGIlItbHzsf47f+j2/yt//9RVYkDlLriPKA7zAP+A5zIR3kSu9qYmeCYI3eG7MUeugVi4pJkmDy/ULy0W8kHRsk3nWUyKl9RNv2kLx85prHfaEVVK1o5t+94+DQlXhO9y0ipcPCQWsyRGsyxDRHP782/xJr3afgUhdrPR2s9XRgWtCRns7hxFwCsTRmcvUtt5vvPkCl2meo0AxMZjt7WeS6wFL3eVZ2nuf8S0nWAjhgwPQOXy0+nZ7OfQsbcPiq7A5bRPLkZsdOV3AaH7rv5qXuZaxwn+V2zylWeTqpd4ap73ufsz98n9/Dz8Gq2RxNzuZkaubQbLNxy+3LVXlPkrZt20YoFKKzs5NQKMTmzZvzvcuiZaVTJC+fJX7uBLGuo8TOHCV5sQsYke0bDrxzl1K1/E6qVzTjCk4HIP7uW4CSJBGBy2YNR+qW8yvfeJI//k9/y4zeg6x0n2Guq4dG1yUaXZfgwj7an9/O161pHPZP41RqBmfSU7hs1mBpJtp23vQgkbY93JH8hIeqO1jg6ibgGLF00gJnzVT2RubQ0juTo8nZpNHSahG5Ko2Tg8kQB5MhvINJVns6eGDmZULpDgLxCBu9J9noPQlAr+nnZHImzvASYp1VeCz147uVvCZJzzzzDJs3b6a5uRmAb3/724RCIZqamvK5W9ulY4Okei+Q7L1A6so5Et0dJC6eJnHpLJip657vqqvH37ga/6J1+BvX4NTVQREZA8MwuOSYwUfR9fwsup5aY5AVnrMsd59lhf8SvvQgs7jALP8FHhh6TdxycS5dh3W5gd6PzrEgfZFBp5PL6WpVzssxy0wTNPtY5jrLdGc/Mxz9zHL2Mtd1hWBXjPMvQTOAJ/P8mOXiVGoGbcl6/Itu4/ee+BX+y395m7akrvaKyM3FcfNJYhFVM+7lnt9cx/P/+SXq+o6x0H2Bec7L1DmirPeehp7TnP3LnfwhcLm2mjPpKZxL13EpXcMls4Yqy4llWRiGLqbl9Yy4fft2nn322eG/b9q06bqflQrLMvFYcaY6Bqg2YgQdUWocURqTFpdePUZqsJdU3yVSvRcwYwM33I7h8eOtb8TbsAzf3GV45y7FVV1XuH+IiJStPquKD+NL+DC+hPsWz+HJL4T4s20/IdB/mvmuS8xy9uI1UpmZpoFLXNm5ly8CX6wder3p53K6mj4rQPDKdHreP83K1AU8bgibPiKWl4jpIUEll5a28JDCnwqT6D7N3HQXtZ5L1DoiBI1o5r+OKLPOJDj1H/r5ppmC4GhbAc+0ubT2BjgYrqUtVc/Z9JThKlX3eWbpS4qITIjhdHHG2cA70RqIgpsU81yXWOjq5vZp/TS4ekn3X2aac4BpzgHW0Hn1xTFof+6vcdXNxFUzFWf1VFzVU3AO/XFVT8ERqMHhrcLpryrrVgN5S5JaWloIBq8/M7S0tEx4m4ZlkQ5fIhn3gGVimSaYJpaZBjONZQ393UoP/dzM/NxMg2WyJHWMaZ5+3EYaN+nMf40UM5IuLu04jpVKYKYSWIkYZmwQMx7BjA2Sjkew4lH+EAvqPhNUCsKfXh+rs6oWV1097rp63DPm4ZmZ+eMKztCJT0TyzzBwT5nFEddy2iKzAXBgMsMRZrarl7vnpLh9rsWpIyeoTvcScCSpdUSvFgjoP03PW7t5GHj4M1WlTcsg1eml40+D/EbMorfGSdxykbScJHGRsFzU99Rx5e3j3J48w1xviiROTMvAxEEaB3MHwwweSdOYPoXbHcW0DNI4MHFgWgZT4gaxsyeYaV4g6bz2wpMF1MUdxIceT4x43IGFw7CYHksTPeVhfvo0PvfA0JYtHIaJE5P5A5cJ7+lhTeow8339eMicDzxGCo+RZu5FN+f/Zie/Gj9POhglYCQIGAn8RgKXYcIZ6NoGXwMY7faqoUUDaRxcTNdwcegqbXe6ljOpKSxatZL/83ea+U//+S3a4popEpH8SeKiLTWLttQsEjPn8tRv38G/+k+vEu/uYK7zCvXOMNOd/Uxz9DPVOYgjGSd5sZPkxc5bbttwe3H4qnD4qnH6qjA8PhxuL4bbi+Hy4nB7Mv/v9mZ+7vJguNwYDhc4nZkKzY7Mfw2n6+r/O5zgdI143AGGgWE4IPvHMsHI3zJkw7JuUP5iknbs2MF3vvMddu3addOfjdXq1atJJxPMqLI3Y7Uwhv+YOMDhwOtxXR2w7MDmMBEyDIO+gTip9PVD5XU7qQ649bge1+M5fryYY8vH4wZWNkXBwMLtALcTUskUWOaII19eThklyADDGEr8rp4TLAxMw4HL5STg99IXSRXF+Orx4ti3Htfjxf64y2lQG3BhplNDEw+ZCYfhyQnLzFTOs8zrXltoFweTON0eDhw4kJft5y1J2r59O9u2bWPnzp3DP2tpaeGJJ57g6NGj497eHXfcQSKRYMaMGbkMU0RERERESszFixfxeDx88sknedl+3pbb1dbWXvezcDg86hK8scjXGyAiIiIiIjKSI18bDgaD9PVdu866r69v1ORJRERERESkWOQtSWpubiYcDl/zs3A4PFwOXEREREREpBjlLUkCePTRR6+pZtfS0sKWLVvyuUsREREREZFJyVvhhqznnnuOefPmEQ6HCYVCbN68OZ+7ExERERERmZS8J0kiIiIiIiKlJK/L7UREREREREqNkiQREREREZERlCSJiIiIiIiMoCRJRERERERkBCVJIiIiIiIiIyhJEhERERERGcFldwC3sm3bNkKhEJ2dnePqs9TZ2clLL73E008/necIy994xiAcDrN9+3aCwSCtra00NTWpgXAOTGQM4GoDZ/Unm7zxHouyx6B58+bpd2CcxvteT/Q8ITemz7v9dNy3n77/2M/WPMAqYt/5znes999/f/jv/+yf/TPr4MGDY3rtN7/5Tes73/lOvkKrGOMdg3/2z/6Z1dHRMfz3pUuXXvN6Gb/xjsHIz31fX5+1dOnSMf/eyOjGOwbvv/++9eqrr1rf/OY3rRdffLEQIZaNiXzeJ3qekNHp824/Hfftp+8/9rM7Dyjq5Xbbt2+nubl5+O+bNm0avlJyMy0tLYRCoXyGVjHGOwZdXV20tLQM/z17RUUmbjxj0NnZSWdnJ+FwGMi8/83NzXz/+98vSKzlary/B83NzWzevJmamppChFdWxvteT/Q8ITemz7v9dNy3n77/2M/uPKBol9u1tLQQDAZH/fmtZKfkOjs78xFaxZjIGLz88svD/x8OhwmHw9d8wGV8JjIGBw8epK+vb/h1oVCIgwcP5i3GcjeZY5GMz3jfa41N7uk9tZ+O+/bT9x/7FUMeULQzSdkrIiPV1tbS19d309dt375da0BzZKJjkH3t888/zwsvvEBTU1M+wqsI4x2DUCjErl27rrmC0tLSwqpVq/IWY7mbzO+BjM9432uNTe7pPbWfjvv20/cf+xVDHlC0SVJfXx+1tbXX/CwYDI76pmWFw2Ets8uhiYwBZMbh1VdfzWdoFWOiY5DV2tpKX18fTz31VD7CqwiTHQMZu/G+1xqb3NN7aj8d9+2n7z/2K4Y8oGiX2332jYHMP360qbesV199VbNIOTSRMYDMhzg7Dhs2bABQlZ0JmugYZH3nO9/hhz/84ZifL9eb7BjI2I33vdbY5J7eU/vpuG8/ff+xXzHkAQVNkrZv337Lm9g2b95Mc3MzwWDwuim10bLKrJaWFq39HIN8jsFoVq1axYsvvqiDxAiFGoPnnnuO7373u5ruH0Whfw9kbMb7Xmtsck/vqf103Lefvv/YrxjygIImSePJ7pqbm6+bUrvVTXA7duwY/v/sdOe2bdvYsmWLrqgMyecYtLa28s1vfpOXX355eLqzpqZGN49+Rr5/DyCTBDz++OPDJ0pdRLhWIcZAxm+877XGJvf0ntpPx3376fuP/YohDyjae5IAHn300WuqWGQbpGW1trYOvyHNzc1s3bp1+M/dd99NQ0MDW7duVYI0CeMZg2AwSCgUuibLP3ToEF//+tcLF3AZGs8YZB+HzHh0dnbS2tqqMqSTNN4xkIkb73t9q+fL+Onzbj8d9+2n7z/2szsPMCzLsib3T8iv5557jnnz5g3fjDVy2vK5557j0KFD/OAHP7jmNdu2bRuuo75lyxa2bt1a0JjLzXjGoKWlhdbW1uH+ADU1NZPrdizA2McgHA4Pr4Me6amnntLvwSRN5PfgxRdfJBQK8dhjj7F582YVlhmj8R73b/Z8mRh93u2n47799P3HfnbmAUWfJImIiIiIiBRSUS+3ExERERERKTQlSSIiIiIiIiMoSRIRERERERlBSZKIiIiIiMgISpJERERERERGUJIkIiIiIiIygpIkERERERGREZQkiYiIiIiIjKAkSUREREREZAQlSSIiIiIiIiMoSRIRERERERnBZXcAIiIiE7Vt2zaCwSAA4XCYrVu33vT5LS0tvPTSS2zatIlQKMT777/P6tWr2bx5cyHCFRGREqEkSUREStK2bdsA2LJlC5BJgJ555hmeffbZG74mHA7zwQcf8NprrxEKhdi6dasSJBERuY5hWZZldxAiIiLjtWHDBl5//fXhmSSAZcuWcfTo0Ru+ZseOHTQ3N1/zGhERkc/SPUkiIpI327dv56tf/SrLli1jx44dADzzzDNs2LCBZ555hnA4PKHtdnZ2Eg6HR012WlpaJhWziIiIltuJiEjebNmyhS1btrBhwwZCoRAATU1NbNmyhaampglvt7Ozc9SfB4PBWyZer776KrW1tfT19dHR0cHTTz894ThERKQ8KUkSEZG8e+GFF3jyySf5wQ9+QG1t7XUJ0jPPPDOm7WQTrBvJJj83ez0wnLBt376db3/723zve98b0/5FRKQyKEkSEZG8a25uprm5mSeffJKXX375usdvVmxhPG6WIMHV5CjrscceG172p/uUREQkS/ckiYhIQTQ1NREOh2ltbZ30tj6b7GSFw+EbPgYM3xeVlU2MbrR8T0REKpNmkkREJO86OzsJhUI8++yzPPnkk+zcufOax8e73C4UChEMBoe3O1Jzc/Oorw2Hw8P7zr4me//SzRIrERGpPEqSREQk71566aXhAgnNzc3X9TOayHK73//936elpWX4HqUdO3Zcc79SZ2cnO3bsGG4wGwwG+b3f+71rEqLt27fz6KOPaqmdiIhcQ32SREQkb7Zt28b27dsBhmePvv3tb/Paa68NV76bTJW7bdu2DSc9Bw4cuKZS3Y4dO3j++eevmbUKh8PD8QD09vaqup2IiFxHSZKIiIiIiMgIKtwgIiIiIiIygpIkERERERGREZQkiYiIiIiIjKAkSUREREREZAQlSSIiIiIiIiOUTJ+kO+64g0QiwYwZM+wORUREREREbHTx4kU8Hg+ffPJJXrZfMjNJ8XicVCpldxglxbIs4vE4qvJefDQ2xU3jU7w0NsVLY1PcND7FS2MzMalUing8nrftl8xM0syZMwF4/fXXbY6kdEQiEQ4fPsyKFSsIBAJ2hyMjaGyKm8aneGlsipfGprhpfIqXxmZiHnroobxuv2RmkkRERERERApBSZKIiIiIiMgISpJERERERERGUJIkIiIiIiIygpIkERERERGREZQkiYiIiIiIjKAkSUREREREZAQlSSIiIiIiIiMoSRIRERERERlBSZKIiIiIiMgISpJERERERERGUJIkJc00rQk9JiIiIiJyI67xPDkcDrN9+3aCwSCtra00NTWxZcuWm75m27ZthEIhOjs7CYVCbN68eVIBi4zkcBg8/6PddF3ov+bnDfU1PPWN222KSkRERERK2biSpD/6oz/i6aefJhQKAbBs2TJCoRDNzc2jPv+ZZ55h8+bNw49/+9vfJhQK0dTUNMmwRa7qutBP25k+u8MQERERkTIxruV2XV1dtLS0DP89O6N0I9u3b78mgdq0aRPbt2+fQJgiIiIiIiKFMa6ZpJdffnn4/8PhMOFw+IazSC0tLQSDwVF/LiIiIiIiUqzGlSRlhcNhnn/+eV544YUbLp0Lh8PX/ay2tpa+vokvi7Isi0gkMuHXV5poNHrNf8uNYRj4/f6bPicajWJZxVfAodzHptRpfIqXxqZ4aWyKm8aneGlsJsayLAzDyNv2x50khcNhXn311Vs+r6+vj9ra2mt+FgwGR02exiqRSHD48OEJv75Stbe32x1CXvj9flauXHnT55w6daqoDzrlOjblQuNTvDQ2xUtjU9w0PsVLYzM+iUQCr9ebt+2PO0kKBoPDFe02bNgAMGrFus8mSJBJsEZbgjdWHo+HFStWTPj1lSYajdLe3k5jY+MtZ1xK0ViuHixYsKBoZ5LKeWxKncaneGlsipfGprhpfIqXxmZiPB5PXrc/oeV2WatWreLFF18cNUkKBoPXLa0bbXZpPAzDIBAITPj1lcrv91fs+1bsB5tKHptSoPEpXhqb4qWxKW4an+KlsRmffC61g3FUt2ttbWXDhg10dnYO/6ympuaav4/U3Nx83dK6mxV6EBERERERKQZjTpKCwSChUOiamaBDhw7x9a9/ffjvra2t7NixY/jvjz766DXV7FpaWm7ZfFZERERERMROY15uFwqFeOqpp9i+fftwf6RHH32Up59+evg5r7zyCocOHRpefve9732P5557js7OTsLhMFu2bFEjWRERERERKWrjuiepubn5psvlRiZMN/uZiIiIiIhIsRrzcjsREREREZFKoCRJRERERERkBCVJIiIiIiIiIyhJEhERERERGUFJkoiIiIiIyAhKkkREREREREZQkiRlqa7Gi2laN3z8Zo+JiIiISGUbV58kkVJR7XfjcBg8/6PddF3ov+axhvoanvrG7TZFJiIiIiLFTkmSlLWuC/20nemzOwwRERERKSFabiciIiIiIjKCkiQREREREZERlCSJiIiIiIiMoCRJRERERERkBCVJIiIiIiIiIyhJEhERERERGUFJkoiIiIiIyAhKkkREREREREZQkiQiIiIiIjKCkiQREREREZERlCSJiIiIiNyAaVoTekxKm8vuAETGIh3pJ9HdjuH24Z21AMOpj66IyESlI2ES3aczx9TZCzEcTrtDEhkTy7JIXjlHqu8i7in1uKfMyvs+HQ6D53+0m64L/df8vKG+hqe+cXve9y/20DdNKWpmIsrl1/+S/j07wTIBcFZPZdrDv0t10+dsjk5EpLSY8aFj6r7XwUwDOqZK6Uh0n+biK98nfubo8M9885qY/vgf4Jk2N6/77rrQT9uZvrzuQ4qLlttJ0UpH+jn7F39E/6e/AMvENWUWDn816YErdP/kv3Dlne12hygiUjLSkTBn/+d36N/zCzDTOqZKSYm2H+DMD/91JkFyunBPbwCHi1hHK2f+/P9LrOuI3SFKmdFMkhQlK53iwt/+BxLd7Tir6pj5q/8cf+NqrFSSnvd+TO/7f0fvu3+Dq3oK4LE7XBGRopY5pv5HEhdO4ayqZeZX/gX++auw0kl63h1xTK2ZSvC2z9sdrsg1Ehc7Of83/x4rGce/YA0zvvhtXDVTSIUv0f2TPyHWeZjz2/8dc7/1Hwuy/E4qg2aSpCj1vP93xDoPY3gDzP7G/42/cTUAhsvN1Pt/iyn3/gYAl3/x59SZPXaGKiJS9Hre/fGIY+r/g3/+KgAM59Ax9b7fBDLH1MTFTjtDFbmGZabp/sl/wUrG8M1vYtbX/w2umikAuILTmfWb38E7ZwlmbJDun76ANbQ0X2SylCRJ0UleOUdfy98DMOOxP8AzY951z6n73K/hX7gWK53kvuQ7gKrLiIiMJnnlLL0fDB1Tf+WfjH5M3fQ1/Ituw0oluPyLP8OydEyV4hD+5FUS3adx+Kup/8q/xHC5r3nc4fZS/7WnMDw+4meO0b/vDZsilXKjJEmKzqVf/DlWOol/wRqqVm4a9TmGYTD90d8Dp4v5Zgdr3B0FjlJEpDRcfuOvwEwTWHw71SuaR32OYRhM37wVnC6i7QeInNhd4ChFrpca6OHK2y8BMPWB38ZZVTvq81zB6Uy5dwsAV974K9LR/lGfJzIeSpKkqMS6jhJt+xQcTqY98o8xDOOGz3VPnUPdXV8G4FcCezE0myQico1Y5xEiRz8Cw8HUB//RTZ/rrqunduMXALjy+l9gDVW/E7FL3wc/wUpE8c5eTM26h2763No7Hsc9I4QZ7afv458XKEIpZ0qSpKhkl4TUrL4Pz/SGWz6/7q4vE8fDLGcfTe6ufIcnIlJSelteBqBm7YN4ZoRu+fwpm76Gw19N8vJZIsc+yXd4IjeUjvYT3rMTgCn3/QaGcfOvrIbTxZR7vg5AePermIlo3mOU8qYkSYpG4mInkWO7AIPaoRmiW3H4qjjgytyA/LDvILo3SUQkI3H5zNCyOYO6u8d4TPUGCN72CAB9H/9DHqMTubnwJzuwkjE8MxvxL1w3ptdULbsT15RZmNEB+ve+nt8ApeyNK0kKh8Ns27aNbdu28cQTT7Bjx46bPr+zs5PW1tbh17a0tBAOhycerZS1vl2Z6fHAso1jmkXK2utaR8pysMB9kfnOS/kKT0SkpIR3vQJAYMntuKfOGfPrgnc8luk/03mY2NkT+QpP5IasdJLw7lcBqGv+1ZsuvR/JcDiHl+H3ffwzVbqTSRlXkvT888+zdetWtm7dygsvvMCTTz45nASNZseOHXz1q19l2bJlbNiwgdbWVoLB4KSDlvJjJmIMtL4HQO2Gx8f12ohRxaeJRgDu8uqELiJixiP0738TYPg+o7Fy1UyluilTNCf7RVWkkCLHd5Me7MNZPYWqGxQbuZHqNffj8FWR6rtI9NSBPEUolWDMSVJnZyednZ3DM0HBYJDm5ma+//3v3/R1O3fu5OWXX+bo0aNs3bp1ctFK2Ro88gFWIopryix885rG/foP44sBWO9tx0My1+GJiJSUwSMfYiXjuKfNwTfUE2k8sg1lBw9/iJmI5To8kZvKlvGuWXM/hsM5rtc6XB6qm+4Z2o6W3MnEjWsm6eDBg/T19Q3/PRQK0dV185vlQ6EQTU3j/9IrleXqAfGBMU+rj9SWqudiugafkWStR+XARaSy9R94C4Dq1fdP6JjqbViOa8osrGSMwaMf5jY4kZtI9V8h0rYHgOo1D05oGzVrM5XwIkc/VjlwmTDXWJ8YCoXYtWvXNT9raWmhufnm06DZ+5YOHDjA6tWr2bx58wTCzLAsi0gkMuHXV5poNHrNf4tVqrebWMchMAxcS+4a8xgbhoHf78/+jY/ii/hCYC8bvW0c5N5bvj4ajdrWMLFUxqZSaXyKl8bm1tLhS8ROZ5bCuxZtmPB507e8mYEPXqZvzxs4F2285fM1NsWtVMZn4NOdYJm45ywl5a8jNYHPrxWsxzVjHqmLHfTsfZPA2ocnHM+13zVGN9nvE6UyNsXGsqwJXQQaqzEnSZ/V2tpKX18fTz311A2f09TUxKpVqwgGg2zevJmHH354UjNLiUSCw4cPTzTkitXe3m53CDflPfkBASA5ZR7Hui4AF8b0Or/fz8qVK4f//kliIV8I7GWx6wLH04O3fP2pU6dsPyAV+9hUOo1P8dLY3JivrQU/kJw6n2NnLsKZixPajsM9i1og3tnKkT0fY/lqxvQ6jU1xK/bxqdn/Di6gt24h3ZP4zueduoTAxQ4u732L0565E97OZ79rjCZX3yeKfWyKTSKRwOv15m37E06SvvOd7/DDH/7wpoUYPjvLtHLlSrZv386zzz47oX16PB5WrFgxoddWomg0Snt7O42Njbe8CmKnS3v+Fylg2roHCI1jfD979aDHrOZ0ahrzXZeZEzkK3HfT1y9YsMDWmaRSGJtKpfEpXhqbW7v06V9njqnrHx7XMXU0l4//guS5E8xzhAmsuPlsksamuJXC+KT6urkUPgeGQeM9X8AZqJ34tuZM59LR13H3dLB0/pwJb2ssMxWT/T5RCmNTjDweT163P6Ek6bnnnuO73/3uLWeEtm/fzpYtW4b/HgqF+OCDDyaySyDzQQ0EAhN+faXy+/1F+74le7tJXTgFhoO61ffgmmScexPzme+6zNzI0Vs+txgORMU8NqLxKWYam9Ele7tJdbeD4WDKqk04J/keJVbczZVzJ0ie/JTA3V8a02s0NsWtmMend/8+AHzzmqiZPntyGwvMp3/2IuLn2rA6DhBY/0gOIhxdrr5PFPPYFKN8LrWDCTST3b59O48//vhwgtTS0jLq8zo7O3nmmWfo7Owc/ll/fz8NDWPvfyPlb/BI5oZg37wVuKrrJr29fYn5AEyPnSY92HeLZ4uIlJfIsY8B8IWW46ya+FX4rKpldwIQPd1KOqIb4CW/Bg9nLqRXLb87J9vLbmfwyMQv0EvlGleSlE2IgsHgcKPYkX2SWltbhws1hEIhnnrqKUKh0DWv/4M/+INcxC1lYvDoRwBULbsrJ9u7bNbQmZqKA4vB45/kZJsiIqXi6jH1zpxszz11Np6Z88EyiZzQMVXyJ9V/hfjZ44CRs89v1YpMkhRtP0g6OpCTbUrlGPNyu3A4zBNPPHHdz0cWbnjllVc4dOjQcAW7zZs389xzz1FXV0dnZyfPPvusyoHLsHS0n/iZYwBULd2Qs+0eTDYQcl0h2vYpkLvtiogUs3QkTKzzCACBZbeuRjdWVcvuItF9msGjH1Oz5oGcbVdkpMiJTwHwzlmMq2ZKTrbpnjIL9/QGkpe6iJ7aR/XKTTnZrlSGMSdJwWCQo0dvfp/H008/fc3fQ6HQdT8TyYqe3Jsp8zljHq7aGTnb7qFEA4/59xM5uQ+HY33OtisiUswiQ8dUz8z5uGtn5my7gSW30/PudqLt+7HSSQynO2fbFsmKtGWSpMDi3J63A4vX03epi8iJT5UkybiM+54kkVzJXjXK9QGxMz2NmCOAlYgyxzyX022LiBSr6FADTv+i23K6Xc+sBTirarESseGZKpFcslJJoqcyRRsCi3KcJA1tL9L2KZZl5nTbUt6UJIktLDOduepJ7pMkC4ML/oUAzDfbc7ptEZFiZFnm1WNqjr9kGoYD/8J1AESGEjGRXIp1HsZKxHBW1eGZvTCn2/aFVmB4/JiRMPFzJ3O6bSlvSpLEFvFzbZiRMA5vAN/cZQCY5ug9Bm7085s5718MQGP69MSDFBEpEYlzJzEjYQyPH1/D0pxvPzA0OxU9qSRJci+71M6/6DYMI7dfTQ2ni8DCtQBEh1awiIzFhJvJikxGtP0AAL7G1RjOzMfQ4TB4/ke76bpwtcxsQ30NT33j9nFvv9vXCBhMs64QNCKELfUdEJHylZ1F8jeuzss9Q/4FawGDRHcHqfBlXMFpOd+HVK7oqcx3gmwyk2v+hesYPPIh0fb9TLn363nZh5QfzSSJLWKnDwKZE/pIXRf6aTvTN/xnZMI0HkmnH099IwCL3RcmFauISLEbvp9jaFlcrjkDQbyzF2X2NXSRSyQX0pF+Et3tAPjmr775kyco+10jduY4ZjKel31I+VGSJAVnpZLDN/9+NknKpey2l7hUvEFEypeZjBMbaqfgX5C/Y6qvcRWQaSwrkivRjsxFU/eMUE6ayo/GVVePKzgdzJSKj8iYKUmSgoudOYaVSuCsqsM9bW7e9uMfOqEvdZ/P2z5EROwWP3MM0imc1VNxTZmdt/3452eOqdmVACK5EGsfWlmSp1kkAMMw8GVnk05rJlTGRkmSFFx2qYa/cTWGYeRtP77QSkwMpjsHmOpQp20RKU/R4eXLq/J7TG1YDoaDVF83yd7uvO1HKsvV7wSr8rqf7Paj7UryZWyUJEnBZa9C+ubn94Do8Pq5YNQDsMSl2SQRKU+xoeVvvnlNed2Pw+vHO2fx0D71RVMmL9XfQ/LyGcDI++c3O1MVP9eGGRvM676kPChJkoIyE7Gra+fzfNUIoMvZAMASLbkTkTJkJuPEzh4HwD8/v18yM/vQfUmSO9lk2zNrAU5/dV735QpOwz11Nlgm0Y5Ded2XlAclSVJQsc7DYKZx1c7AVVef9/11OrJJ0jlg/P2WRESK2fD9SDXTcE2Zlff9+YYSsdjpg1iWjqkyOYVaapeVrZ4X1UyojIGSJCmo6PBSu/zej5R1zjGblOWgzhFlhmNi5cRFRIpVdPim96aCHFN9DcvB4SQVvkSqV+0VZHKG76fLY9GGkbLJWEz3JckYKEmSghou/T1vRUH2lzZcnE5NB2ChSyd0ESkvsY6h+5HyfI9nlsPjG74vSUvuZDJS/T2ZRNtw4AstL8g+ffNWApDoPq37kuSWlCRJwVipJIlzbQAFOyACnErNAGCh+2LB9ikikm+Z/kiFux8pyz90g32sU/d1yMTFujIXTT0z5uHwBgqyT1f1lKGl/tbw/dEiN6IkSQomfr4NK53EEQjmtZfHZ51MzQRggUsla0WkfCTOnwQzhXP4i19hZC9yxbv0JVMmLpskFfKi6cj9ZfcvciNKkqRgskvtfA3LC7J2Pis7k1TvDFNlxAq2XxGRfIp1HQXA17CsoMdU79ylACSvnCUdCRdsv1Je4iO+ExRSdn/Z3x+RG1GSJAUzfEIv8FWjiOXjfLoWgEaXltyJSHnIXgn3Niwr6H6d/hrc0xuGYtAXTRk/MxknfuEUAN5QYT+/2SQpfuY4lpku6L6ltChJkoKwLOvq1HqBT+gAp5JD9yVpyZ2IlIHMMTU7k1TYC08AvrmZ43j8jJIkGb/42RNgpnFWT8UVnFHQfbtnNODwBrCSMRIXThd031JalCRJQaR6zmFGwhhON95Ziwq+/1PD9yVpJklESl+q5zxmJAxOF976BQXff3b2SjNJMhFX70cq7FJRAMNw4J2b/fzqviS5MSVJUhDZ+5E8sxdhuNwF33+2eMM81yWclqbXRaS0xYZmcLw2HVOHizecPYGVThV8/1LaYjbdj5Sl4g0yFkqSpCDsuh8p66JZw4DpxW2YzDC15E5EStvIog12cE+dg8NfjZVKEL/QbksMUposyxxepmlbkpSdCe1UkiQ3piRJCmJ4an2uPSd0MIar3M02z9kUg4hIbsSzSZJNx1TDMK7el6Sr8TIOyUtnMGODGG4vnvpGW2LwzlkChoN0/2VSfVqGL6NTkiR5Z8YGSV46A9h31ROu3pc0y7pgWwwiIpNlxiMkujsAhu+tsINXpZRlAuJnMw2QvbMXYThdtsTg8PiGE7TY2RO2xCDFT0mS5F38/EnAwlU7E2dVrW1xdKSmAVBvKkkSkdIVO3McsHDVzcRVM8W2OHwq3iATEMsmSXOW2BqHd85i4GrSJvJZSpIk7+JDV2m8cwpf1W6kzvQ0TAuCVj/pwT5bYxERmajhK/FDTV3t4p298OqSpYEeW2OR0nH1O8FiW+Pwzs4mSZpJktEpSZK8K5arRjHLQ7eZmcnSQVFESlX83NCXzNn2fsl0ePy4p88FdEyVsTFTCRLdmd5EdidJvqHvJPHzbWoqK6NSkiR5VyxXjeDqkruYptdFpETFz7YB4CuCY+rw1fhzSpLk1hIX2jNNZKtqC95E9rPc0+diuH1YiRjJy2dsjUWKk5IkyatUfw/p/stgOPDOWmh3OJxOTQd0QheR0pQKXyY9cAUMBx4bmsh+1tUlS202RyKl4GrRhsUFbyL7WYbDmVkyimZCZXRKkiSvssmIe3oDDo/f5migI5sknT2BZVk2RyMiMj7ZY6pnRgMOj8/maK7OZsXPHdcxVW6pmFaWwMjiDUqS5HpKkiSvsleNimFZCMCZ9BTSODCj/aR6VeVORErL8JdMm+9HyvLUzwenCzM6QFr9ZuQWriZJ9t6jnJWNQ2XAZTTjKlAfDofZvn07AC0tLWzZsoXNmzff9DXbtm0jFArR2dlJKBS65fOlvBTLDcZZaZxcNKYzy+omfq4N95RZdockIjJm8XOZZW3Fckw1nG68MxuJnztB8sJJwL42D1Lc0rFBklfOApkeScUg+3uU6G7HTCVwuDw2RyTFZFwzSc8//zxbt25l69atvPDCCzz55JO0trbe8PnPPPMMTU1NbN68ma1bt/LKK6/c9PlSXizLGl6nXixXjQC6HfWAeiOISGmxLOtqklQks/NwNZZMkiQyuuxFU1ddPc5A0OZoMly1M3AEgmCmM0UlREYYc5LU2dlJZ2cn4XAYgGAwSHNzM9///vdv+Jrt27fT3Nw8/PdNmzYNz0RJ+Uv1nMeMDWA43XhmzrM7nGHnh5MkTa+LSOnIHlNxuorqmJqdFUieV5IkN1Zs9yMBGIZxtRS4vhPIZ4xrJungwYP09V1twhkKhejq6hr1uS0tLQSD118paGlpGWeIUqqyBxzPrAUYznGt7MyrC9kk6fxJ9UYQkZIxvHy5fgGG021zNFdlVwqkutvBMu0NRorWcGW7IkqSYGSFRq0ukWuN+ZtrKBRi165d1/yspaXlmpmikbIzTiPV1tZek2SNl2VZRCKRCb++0kSj0Wv+W2gDHYcBcM5ovOW4GYaB33/j6nfRaPS6ykm3es2N9BhTMDx+rESUcOdx3DMKf0XW7rGRm9P4FK9KHpuBjiMAOGfe+phaSJZ/SqbfTDKGY+BSRY5NKbD7dyd2ZmimZmqoqD6/TAsBED1zfNS4xvJdY7TvKONh99iUKsuy8lpKfsKX91tbW+nr6+Opp54a9fG+vj5qa6+9gTMYDI6aPI1VIpHg8OHDE359pWpvb7dlvzWnWnEBF00fZ24xbn6/n5UrV97w8VOnTl138LjVa27IMPDOXkTs9EE69r5PomFw/NvIEbvGRsZG41O8KnFsqk8dxA1cTN/6mFpo1dUzcfd04Oo7V5FjU0rsGB8j1k/dYA+WYdB2JQbh4vn8GokUdUC65xxH9u/Bcl9bWn8s3zVG+44yEfrdGZ9EIoHX683b9iecJH3nO9/hhz/84ahL6oDrEiTIzC7d6Plj4fF4WLFixYRfX2mi0Sjt7e00NjZOaMZlMiwzzYVfZkpsz1//OVxTZt/0+be6ErBgwYJRZ5ImyjtnMbHTB5luRKm14TNl59jIrWl8ileljo1lpune2Y0FzL9tE65pc+0O6Rrhi6uI7O7A2XeOxs99uaLGplTY+bsTO/EJvYB7WgMrVq8t6L7H4uInM0iHL7Kg1o13/rXfCcbyXWO07yjjUanHtcnyePJbjXBCSdJzzz3Hd7/7XZqamm74nGAweN3SutFml8bDMAwCgcCEX1+p/H5/wd+3RPdpSCcxvAFq5izAMCbXkivXB43sjcbmpQ5bP1N2jI2MncaneFXa2CS6T2OlEhgePzUNiyZ9TM01c95yIrtfwdV3ruLGptTYMT6xK2cy+567pCg/G765ixkMX8ToPUtgxcZxvz5X31H0uzM++VxqBxNoJrt9+3Yef/zx4QTpRoUYmpubr1taFw6Hb3gPk5SX+PlTAHjrG4vuZA7gnbUQgHj3aax0yuZoRERuLjbcRHZhcR5Th27Gd/Zf0DFVrpO4kPlO4KlfYHMkoxv+TqAKjTLCuI602YQoGAzS2dlJa2vrNX2PWltb2bFjx/DfH3300WuSqGwDWil/2QONZ+jAU2xcdfU4vAFIp0hcGr1Co4hIsUgMHVO9s4qjCednuWpnYnirMCyT1GUdU+Va2e8E3tnF+Z0g+10loSRJRhjzcrtwOMwTTzxx3c9HFm545ZVXOHToEJs3bwbge9/7Hs8999xwf6UtW7bcdImelI/sVSNvkV41MgwDz6wFxE63kjh/Em99o90hiYjc0PDsfJFeeDIMA/fMRhKdrSS726FxAkV1pCylBnpJD/QABp6Z8+0OZ1TZ7yrJK+cw45HMRVSpeGNOkoLBIEePHr3pc55++ukx/UzKm2WZxIc6VxfrCR0yscVOtxI/f5KatQ/aHY6IyKgsM02iux3I9J0rVu6Z80l0tmb6JYkMyV40dU+bg8NTnEUJnFW1OIPTSYcvEb9wCv88XdCXCdyTJHIrqZ4LWPEIhsuDe3pxVWAayaM1yCJSApJXzmEl4xhuL+6pN68UaifX0Ix8svu0vYFIUbm6/L54E3wA71B8iaFZWxElSZJz8ewNmjPmYTicNkdzY9lZrsSFdiwzbXM0IiKjG77pfWZjUR9T3TMaAUhe7NAxVYYlzhf38vssFW+Qz1KSJDmXKPKiDVnuqbOHusTHSV45Z3c4IiKjGr7pvcivxDun1GM5PZBKkLx81u5wpEhkL5wW8/J70OoSuZ6SJMm5qzcYF/cJ3XA48QwtD9FBUUSKVfZKfLGWT84yDAep4ExAx1TJMGODpHrOA6Ww3C6TJCUvncFMxm2ORoqBkiTJKcuyir7890helf0UkSJmWdaIK/HF/SUTIB2cBeiYKhnZIk6u2hk4/TUF2adpWjd8LH2Tx4xAHc6qOrBMErqvThhHdTuRsUj3X8GMhMFw4Jk5z+5wbin7pUNXPUWkGKXDlzCjA+Bw4plR/MfUdE0mSYrr5ndhRNGGAs6COhwGz/9oN10X+q/5+frlM/mdx1eO+ljD/5+9Pw+P67zzA9/v2WrBUtg3EkWCpESKhGjLsuQFarsX2y3Ycadj9aSZO8502rnN9sz0bfuZO9LdJtY8UefeyVx6Jjee+zw3biVxT5LuNtKJe7Nl2pbdbi+QZe0LSFESSRAFgMSOKiy1nOW9f1SdQoEoALWdOudUfT/P48ciavsBp8457+9dfu9AOx7/7PsRGDyB5PVXkL59A6Gjp+sWM3kTkySqKbvHU+sdhqwGXI7mcDtzkG9CCMuTO9kTUfOyk41AbxSSqrkczeHMjlyStMBrKhXsmVjnUdDZhQ1cn4vv+tlwf9u+j9mCgyeRvP4KR0IJAKfbUY3t7Arv/al2ABDoHYakaBDpbRjri26HQ0S0i1/KJ9vM1h6A11TKySf5PmkTsMIdFWKSRDXll6INNklR8zuA86JIRF7jVk98xWQFWm8UAK+pzc7S09CXZwF4v/y3zU7mMksxCEN3ORpyG5MkqqmMz3o9gYKLIm/oROQxOx1P/uiJBwA11/HEa2pzyyzOAMKC0toBpb3b7XBKonb0QQ61AZaBzFLM7XDIZUySqGbM7Q0YiWUA/uk1Ali8gYi8ydyKw9xYASAh0D/idjgl03Kx8pra3DIFRRskSXI5mtJIksQ2AeUxSaKasaeFqF2DkIMtLkdTuuCu4g37lwclIqqnfCGc7kHIwXD+5weVOD7osXrZSZJ4TW1mTpaud/J7ztklZGN1O6oZv+wKfzet/xggK7C2EzA3VqBGet0OiYho301k9ytxbJcxdpvaOwxIcu6augo10uN2SOSCjINFGw4r810NFm8gG5Mkqhm71ygw4J+58wAgqwEEeqPILE4jffsGkyQi8oSdnvi919SDyhi7TVIDCPQNI7M4g/SdG0ySmpAwjfyGrE5Nvz+ozHc18iNJi7cgLBOSrFT9nuRPnG5HNZPx6UgSULhf0nWXIyEiytrpiffzNZW98c1IX5mDMHVIwRaoXQNuh1MWrXsQUiAEYWTy1fmoOTFJopqwMknoK7cB+KsKk81O7DLcJZ6IPMBKJ6GvzgPwVyEcW5DrOppafvr9wIjvNhSWJDl/zqXZJmhq/vrmkmdlFm4BEFDauqG0drgdTtnyc5AXeEEkIvdlFqcBAEq7z6+pTJKaUnqf9XR+YY/esk3Q3JgkUU34tWiDLTBwHIAEc2MV5pY35/kTUfPw4/5IhbIly3lNbVaZA9bT+YE9ksTZJc2NSRLVhH1B9OPceQCQA2Fo3UMA2PNJRO7ze0+8HAxD6x4EwN74ZiOEVZDk+/P7GyiYXSKE5XI05BYmSVQTfu/1BHYSvAxv6ETkMj8XwrEF2BvflIy1BYhMEpIagNY77HY4FQn0DgOKCpHehrG+6HY45BImSVQ1YejILMUA+HckCdi9qSwRkVuEoSOz3AjXVHvxO0fnm4l9vAN9x3xbPltSVAT6jgNgm6CZMUmiqmWWYoBlQA61QY30uR1OxQIDvKETkfuy11QTctjn11S7wh1H55uK36ff23aq3rJN0KyYJFHV0gs700IkSXI5msrZF0Rj7Q6s9LbL0RBRs8pfUwd8fk3NdTzpq7dhpZMuR0P10gjT74HCjlMm+c2KSRJVzc8bHhZSWiJQIr0AgPTCtLvBEFHTaphramsHlPZuADslzamxCSF2ptv5tOiILTjEkdBmxySJqtYovUZAYdlPDq8TkTvy19SBxrmmsje+OZgbq7C2E4AkI9B/zO1wqhLoPw5IMsytdRgba26HQy5gkkRVEZaZ7yH0e68RwA3kiMhdu66pPh9JAgquqex4agr2vVPrHYasBV2OpjqyFoTWcwQAO06bFZMkqoq+ehtCT0PSQvl9hvzMHg1jyVoicsPONTXIayr5zk7pev+PggIFVW/ZcdqUmCRRVfJz5weO+7bUZ6F8NZvlWVhGxuVoiKjZ5CuD9Y80xDU1v//ccgzC0F2Ohpzm901k78aqt82NSRJVxb5wBBtgqh0AKO09kMPtgGVCX5xxOxwiajLpBthEtpAa6YMcagMsM7+fHjWuTIMUbbDtlAHnSFIzYpJEVUnn90NojKF1SZI4vE5ErmmUynY2SZIK1nqyN76RmdsbMBLLAIDgwIi7wdSInewZ8UUERcrlaKjeykqSYrEYLl26hImJiZKeOzU1BQBIJBKYnJxEIpGoLEpyhWWJAx8TQuRv6I3S6wkUTA9hzxER1ZEQIt85U0llu8724IHXbfOQa7pT2BvfHOypomrXIORQa9Xv5+R3slRKuA1qRz8AoM9adjkaqje11CfaSc6VK1fQ2dl56PMvX76ML3/5y/l/P/744xgbG6soSHKHLEv48h+/hNmFjV0/Hx5ox+OffT/09UVYqU1AVhHoi7oUZe0FOQeZiFxgJpZhJTcBWanomtoW1va9bj94Xz9+61PnDrymO8VO+FgGvLHVevr9Yd/legkMnoARX0SftQSgvW6fS+4rOUmyE5xnnnmm5Dd/9tlnkUgkMDo6Wn5k5AmzCxu4Phcv+lh+WkhfFJKi1TMsR9lTBzOLtyAssyEWTxOR9+U34eyNQlIrv6YWu24P97ft+5jT8qPzi9O8pjYwJ6bfH/Rdrpfg4ElsX3sefWIJQGMsLaDSlJwkVSIabZzRBdqr0RYY27TuQUiBEEQmBX1lvqFGyYjIu9INth7JpnUPQdKCEHoa+uptBHqH3Q6JHJBp0DaBPTKWHUmiZuJoknT58mUAwBtvvIHz589jfHzcyY+jOss0WNEGmyTJCPSPID37FtJ3bjBJIqK6sK+pjbLHjE2SFQT6jyM99zYyCzeZJDUgK5OEvnIbQON9f+1Oiy6xBg0GdGebzuQhjh3p0dFR3H///YhEIhgfH8fHP/5xRKPRqqbeCSGwvb1dwygbWzKZ3PX/5ZAkCeFw+MDnpG5fBwCIzqGqj8thn5dKpSDE7kWckiQhFApV/JnJZHLPe9rvq/QeA2bfwvbcO1Dv+cCux4u9ppLPLvx/8hYeH+9q5GOzc009su81tZRrc8WfX+Q6ayvlunfQsVF6o8Dc29iMvQ3l5EMHvk8trrG0l5PnTmbubQACcmsX0pIGONwmcErRdoESgtLaCWyt44iyhltmX2mvK/NzC/+fSiOEgCRJjr2/Y0nS3UUazp07h4mJCTz11FMVv2cmk8HVq1erDa3pTE9Pl/2acDiMc+f2XxhpbK7D2lqHAHB9JQnEqzsu+32eXa2pmmRoPzdv3txzQdI0DefOjaJ1+F5sv/pdmMuxXRdqwzBx5coUdL02myJWcmyofnh8vKvRjo2U3kTn5lr2mrqaBBLFr6mHXZsrcdh1ttzrXrFjEzCCaAWQuHUVvVoAqlp8XVKtr7G0lxPnTvDWi2gBkGrpqUk7zYnveSmKtQvC4TC6Bk4geeMVDKurRZOkYq+rRKNd15yWyWQQDAYde3/HkqSJiQlcuHAh/+9oNIrnnnuuqvcMBAI4e/ZstaE1jWQyienpaYyMjJTdI3NYZp4v9dl9BGfPv7fiGA/7vFKqNVXqxIkTRUenVFXB119K4WMA1m+9g//3//I3gCTlK0Dde++9Vfd0VnNsyHk8Pt7VqMcmPf061gCoXUMHXlOd6DU96DpbznXvoGOjd4ewMvUMtM0lKIpc9WdR+Zw8d+KxnyAJoOvk/ThWg3aak6MDB9mvXZBcyCVJymrJrytHo17XnBYIBBx9f0eSpFgshieffBJjY2P54g0bGxsYHq5uHrIkSWhpaalFiE0lHA7X/O9mF20ID52qyzFxosLNQReiq2tB/KKQEZLSWLszh1WrvaTXVRIDv9PexePjXY12bNJr8wCA0JH6XFOLOajyXTnXvWLHRkRPY0VWIFKbMBPLNfssKp8T587qcgwA0BY97evzcr/vnplblzSsFk+SavWdbbTrmtOcTqbL2kz2IFNTU/lCDdFoFI8//viu6naTk5P4/Oc/X6uPI5c12q7wd7MkBbfNTgDYt+eIiKhWdiqDNdaid5ukagj0ZtsE3C+psQhDR2ZpBkDjtgns8/KIsgYZlsvRUL2UtZns1NQUnnvuOczOzgIAxsfH84nQM888gytXruQr2I2Pj+PSpUvo7OxELBbDU089xf2SGkitN43zolmjG1F1FcPqKl7Xj7sdDhE1sGa4pgYGTyCzOJ37XQfdDodqJLMUAywTcrgNamTvep1GoHb2I40AglIGg0oc82aX2yFRHZS1mezY2BguXrxY9PEnnnhi17+j0eien1FjCIg0jPWF7H83aK8RAMya3QA4kkREzjJTW01xTQ0OnsDm63+TW9PKJKlRFCb4bq0lcpokyViWe3HUmsdRZZVJUpOo2XQ7ah72hmpqRx+UcPshz97NsvyzGHfW7AGw/xxkIiLg4OuaecBj9uvyhXA6+vPXVD9dK0sVyI2ScbpdY2mEPRPtCo8HWZT6AQBRdaUeIZEHcEcsKlufyCZJlVwQnapU54R5oxOWADrkJNqlJIAOt0MiIg867Lp2UCU3YKcnvnAUyU/XylIFB0YAAObGCsIh7gfTKPIjST4eBS2lku6S3AuYwFHOLmkaTJKobPZIUqVz552oVOeEDDQsWh0YVOIYVleR4fQQItrHQde1gyq5ATuFcO4u2uCXa2Wp5GALtO4h6Ku3c/cRdjz5nbBMZBZvAfD3SJLtoHNuSc6utxpW1yBBQKAxpxbSDk63o7L120lSA1wQDzNrcF0SETmrEXriS2VPubM728jf9NXbEHoakhaC1j3kdjiOWpW6kREKQpKOXnnj8BeQ7zFJorJoMNAl1gA09gJjW754A+cgE5EDLD0NfSW7R1JgoPE7nuxE0J62Tf6Wnyo6MAJJauwmpZDk/NYgR7lWuSk09jeaai67R4CA0toBpa3xq7vMcSSJiByUWbwFCAtKayfU9sa/pnIkqbHsTBVt/E5TgLNLmg2TJCqLXektMHCyYUt9FrJHknqVTQRE2uVoiKjRpG/bRRsafxQJ2Jmm3SnWEYTucjRUrXSDb4J8t3zVWyZJTYFJEpXFvjA0S6/RtghixWwFwJ5PIqq9TBOtRwKQnYXQ3g0JnLLkd0KInfLfDbwJcqH8SJK6CkAcWjq8EUv5NxNWt6Oy5EeSmqTXCADmzG70KFucQ09ENZdeKF7ZrpEFB05ge2MVR5VV3DAG3A6HKmTEF2GltgBZRaBv2O1w6uK22QlTSGiXU+iQkgeWDi8s80/+xCSJSibDwpCSLdrQLL2eABAzevCeQIwjSURUU8LUkVmcAdBcHU+BwRPYfvelbKcbZzH7lr0eKdB/DJKiuRxNfehQsWB24Ii6vmuj+cPK/JM/cbodlWxQiUOTLKQRgNrZ73Y4dTOXW5fUzySJiGoosxQDLANyqA1qR5/b4dRNMFfFj+s6/C2/HqlJptrZ8lVvFVa9bXRMkqhk9i7TS3Jfw5f6LGTPQe4Sa7B0dnsSUW3kyycPnmiKQji2wFC2UT2oxKHAdDkaqlTaHklqolFQoKDqLdfUNbzmaelS1aK5vYKWpObp8QSAuAhjwwpBhshPjSEiqlazlU+2qZE+pBCEKlkYUtbdDocq1GxFR2yx3EjSUY6ENjwmSVSyaG5oeUk+OElqvGouUn40yb4pEBFVy+6JDzbBJrKFJEnK30c45c6fjI1VmFvrgCQj0H/c7XDqyp6C36NsIWBuuxwNOYmFG6gkMiwcVbNFGxbkg9cj7Vfp5cH7+vFbnzrnWIxOmjW7cRbz+UpURETVkISFzOI0gOx0u2azJPchas1mpyxl3I6GypW+fR0AEOgbhhwIuRxNfaVEAMtmG3qVTXRkFt0OhxzEJIlKMqDEEZQMpISKNenwXeGLVXoZ7m9zKjzH2Qs17ekxRETV6BJrEHoakhaC1j3kdjh1tyhlO9vsadzkL+nb7wIAAoP3uByJO2bNbvQqm+jMLLgdCjmI0+2oJPZUu5jRAzTRAmNbfrrd4i0I03A5GiLyu34r2wMdHDwBSVZcjqb+7BkJR5VVFm/wIXskKTh0yuVI3DFr9AAAOjN3XI6EnMQkiUpyTC1IkprQitWONALZfU2WYm6HQ0Q+N2Ble6CDR5qzJz4udWDbCkCTLAyyeIOvCCGaPkmyizd0Zm67HAk5iUkSlcSeEjFjNmeSJCBhMdfzaU8zICKq1IA9kjTUnEkSJAmx3P3kGKfc+YqRWIK1nQBkFcGBEbfDcYXdYdxurMFMbbkcDTmFSRIdSoGZL3U5Y/S6HE31OtuDFVXgi4eOAADS88WTpMar6kdETlBgolcsA2jenngAmMk1NI9xU05POeheZlk7o0iB/mOQVK1eYXnKlghh2cyus2bHaeNi4QY61KCyDk2ysG0FsGK1odPtgKrUFtYqqsC32XIU2AZiV97A//PaD3c9NjzQjsc/+36nQiaiBjKkrEOFCTnUCrVr0O1wXJNPktRllyOhQvvdH+37XKbJp9rZZowe9CqbSM9fB9DtdjjkACZJdKj8eiSzB0DjFG0otwLfWjBbgSqSWcTM6gp0nj5EVAF7+nJw6BSkJiyEY5sxszMThpR1aGBBHC8pdn+0Nft6JNuM2YsHcSs3kvQBt8MhB3C6HR3Kngox06RFG2xJJQKltQMyRH7PKCKich1T7Kl2TboeKWfdasGGFYIiCRxReE31AxZt2GG3ifabgk/+xySJDhVt8sp2eZKUb9TYjRwionIdy48kNXeSBEiccuczxtodWKktSIqGQN8xt8NxVczogYAEc2MFLYLFGxoRkyQ6kCKMfA9fs1a2K2Q3argBIhFVQoOBoVzJ6+CR5u6JB3aKAbHCnT/kizYMjEBSmnvKeQYaElq2XWRXq6TGwiSJDtQrlqFIAhtWCGtWq9vhuM5u1PCGTkSVOKquQZEEttACpd37HU+HVQOttqrnDMuA+4oTU+38XBl2PZBdq9yf2/eMGktzdwPQoexd4bNT7Zp3gbHNHknql+MIIoM0Ai5HRER+Es1N1V2U+31RtOGgaqC1qOppT+Pul+PQRKaq9yLnOZEkFft+HVRp1kvWgkM4vvVGbnPos26HQzXGJIkOZA8hc6pdltLagYTUjgg2EFVX8a7RvOV7iah89ojJQm5zar84qNpZNTZEGGtmC7qUbfRbSzV/f6odSVhI37GTpNqup7v7+3VQpVkvWcuNJGXbSgLsTG4snG5HBxrIDSE3fdGGAgvSAAAuNCai8u0kSQMuR+IddilwTlnyti6xDpFJQdKC0HqPuh2OJ8QD/YCsIowUuuVNt8OhGmOSRPuyUlvoFqsAgFu5xbW00wPMOfREVI4gdPTL2d7yRZ+NJDnJrnA3ILj43csGrTsAsqNIkqy4HI03WJKK4MBxAGwTNCImSbSv1Py7kAAsm23YEGG3w/GMxVwPMMuAE1E5ouoKZAlYNVuRlFrcDscz7E44uxFO3pRPko7e63Ik3sKtQRpXWWuSYrEYvv71r+PYsWO4cOHCoc9/+umnEY1GEYvFEI1GMT4+XnGgVH/pubcBcBTpbotyPywB9ChbaJOS2GQCSUQlOKFm19xM85q6y4zRC0tIiGADxsYa1PYut0OiIuwkKXT0tMuReEvwyD3Ay9/BcY4kNZySR5ImJycxNTWFK1euIJFIHPr8J598EqOjoxgfH8fFixfxzDPPYGpqqqpgqb5SuSRp2uhzORJvyUgBLFodAIDjXJdERCUaySdJjX1NlSSprLLOaWi4bXZm/zt33yFvCUJHj8htgnyESVKh4JGd/RNlWC5HQ7VU8kjS2NgYAOCZZ54p6fkTExN46qmn8v9+5JFH9vyMvEsIgfQ8k6T93NT7MKjEcUJdwpQedTscIvI8sStJavTSsvuVDd+vtPO00Yuj6hpS82+j9b4P1itMKtExdRkSALWjjyN9d9F6h5FGAEEpgyPKGmZZDbhhOLImaXJyEpFIpOjPyR+MtduwkpswoGDO5AXxbnbiaDd6iIgO0itvoE1OwxAyZs1ut8OpC7usc+H/Fle3iz7XvqamZ6/VM0Qq0Uhu1kSQU+32kCQZd+TsdiBsEzQWRzqzik3H6+joQDxe3R4LQghsbxe/wNJeyWRy1/+X9dqb2amRi3I/TBSvYpNMJiHE7ikVkiQhHG78NTo3czf0Y3cNrxf7mxRTzbEh5/H4eJfXjk2p1zy78RQzenZdUxvhOmr/DvYxSafTZcefT5JuX8f25gbA6mk1V8q5s99373ju+xs6errk+1wp/PZd389teRDHrRmcUJfwk/R9ux4r5e/lteuaXwghHN2U25EkKR6Po6OjY9fPIpFISWuZDpLJZHD16tWq3qMZTU9Pl/2a9msvQwVw54C9PG7evLnnhA6Hwzh3zvu7ZFdr0erAthVAi5wdXgeyo23F/iYHqeTYUP3w+HiXV45Nqdc8uyd+2txdtKERrqN3/w7z8/Po7Ows6z2WrAhSCCJkpHHrtZ9hO9Qco21uOOjcKf7dE7tGkm6UeZ87iN++6/u5I2c3lS02klROu8Ar1zW/yGQyCAaDjr2/I0nS3QkSkB1dKjYFrxyBQABnz56t6j2aSTKZxPT0NEZGRvbtqdkvA19++U+gA/kh5GJOnDhRtAe0GQhIuGX04mxgHifUJczjJIDif5NiSjk25B4eH+/y2rEp9ZqXX4+k717j2QjXUft3sI/NkSNHyn4PAQl35AGMWDPok5No4b2+5iptE/TIm2iXUzAhIzhwAid7zH3vc+WOMPntu76fO/IALAH0Kpt7qt6W0i7w2nXNLwKBgKPv70iSFIlE9kytKza6VC5JktDSwr0lyhUOh/f9u1mWgCzvvkhZehr60gwA4I40COxTraXZT+Rpow9nA/MYUZcwn/tZuX+Tg44NuY/Hx7v8dGxUoedGnHem6toa4Tp69+9Qac/uHXkII9YMrMWbvjm2flTuuWMn+KvqAISsIhTSij6vWHuiWWSkIBbMTgyp6xhRl/Cmfiz/WDnnuJ+ua17gdJLtSJI0Nja2Z2pdIpHIV8gj7yhWgeiIOYf/QlhQ2rqxabQBqG6aZKO6WVC8gSVJiGg/A9YiFElg3WpBXLS6HY5n2TMXUize4Cn2Vhfx8NF9qxYOD7Tj8c++343wPOOm0YshdR0n7kqSyL9qVt1uamoKly9fzv/70Ucf3VXNbnJysqQNaKn+7q5ApK1PA8jtqt0gQ+FOuGX25ofXw4IFRYiouEHrNoC9o0i0m70G1lhfgLlVXaEnqp38SFLwKIDiVQvvTpqa0bTRD4AV7hpJySNJ9mayzz33HGZnZwEA4+PjiEaze8Q888wzuHLlCsbHxwEAX/nKV3Dp0iXEYjEkEglcuHABo6OjDvwKVGv2As3Q0dPAjMvBeFhKBHDH7MQRdR1DuUYQEdHdhqw7AIBbRu8hz2xuGSkIrXcY+vIsUnNvo/X0w26H1PQ0GBhWVgEAq8Hy15o1k/2q3pJ/lbWZ7NjYGC5evFj08SeeeKKkn5HX7Wx4GDx6LwD2iBxk2ujLJUl33A6FiDxICIHB3PWBI0mHCx09DX15FmkmSZ4wrK5CkQQ2rBC2lerWlTe6JSuCLSuAVjmDo8oq7Kq35F+ObCZL/tUjb6JDTsKCjODQPW6H43n23h6DZY4k2XtDNEplHyIqzlhfQAuS2U1kjR63w/G84NEzAIDUHNclecEpdQEAcMPo5/T7Q2Sr3nKj+UbCJIl2OakuAgDWgkOQteIVijrbg7Cs2mwk53d2z/CAtQhh6vs+7+6/l703RDgc5t+SqIHZRQhmzW4Y+2zMTTtCw9kkKT33zp5r6kHXSl5HnWG3Ca7r/S5H4g92m+AEk6SG4Eh1O/Ivu9doORjd9zltYW3fCjcP3teP3/qU/zeGK1Xh8Hr6zjRCR+8t+jxWBCJqTqmZKwCAG2xklkTrHYYcboeV3ED6zs3s2tgcXkfrS4LAiVySdMPoz+0GSAeZzq07HFGX8FOXY6HqMUmiXU5q2QviSnD40OfaFW4KDfe3ORKXVwlIuGn04/7ALFKxq/smSUDxvxcRNbZU7CoA4Lox4HIk/iBJEkLR+7D99gtIzVzZlSQBvI7W05CyjhZZR0qomDO7mSSV4JbRB1NI6FG20Gax4p/fcbod5bVJSQwo2T2RSkmSKOt6ruyn3WNMRAQAQXML+socAOAGizaULHQsOxuB11R3nczNLJk2+mCxuViSNDTMmt0AgKPW/CHPJq/jt57y7LnH80YndMX/u8DXy3U920Ocil2FECz7SURZPensdhnLUg+2RcjlaPwjFM0lSbPXeE11kd0m4FTR8thtgiPWnMuRULWYJFGePdXuhsELYjliZg8y0GClNqEvxdwOh4g8oieVvR7My9xfphzBwROQtBCvqW4SAqdybQJOFS3Pu7m/F0eS/I9JEuWdUnlBrIQFGXfkQQBAktNDiCinN51t4M81SZJUq60NJFlBaDi7Fik5c7UWoVGZ2sUGOuVtmELiJshlumH0wxJAt1iDsbnudjhUBSZJBAAIQM9tfsaRpErMyUcBlD+H/rBy6ixrS+Qd5ZyPQWTQmcmu6ZhX9iZJjbCVwt2/g721QShU/dTC/JS7GDueak2SpEO/e/YoSMzsgV5CjS/ey3YkRRC3zexGsgd1nDbT38SvWN2OAGRr+iuSwIrZinWr1e1wfMfuKU7NXIEQouSe1IPKqbOsLZG3FDtX99v24IS2BAkCaucAttJtAHZXZGuErRT2+x1qEX/o2FkAQGrmalnXVCrNYd+9o7n1NKXuj8R72W7XjX4cVdeQjl3FV18J8m/iU0ySCABwWrsNAHjXGHQ5En9akAcgKRrMrXUYa7ehdZc3vYZlbYn84e5zdb9tD+zpy6FjZ4F3Sn+/g97Tq0r9m5QjeOReQFZhbq7CWLsDrXuo6vek3Q767g1b2aIj5bYJeC/Luq4P4KOha0jNXMHsxin+TXyK0+0IAHCPlp0W8rbOJKkSpqQimNsjKXmL00OImt29uY6n8LFRlyPxJ1kL5vedS9560+Vomou+vogOkYAppHylNiqPvbY7s3gLQZFyORqqFJMkQkCkcUxZAQC8wySpYvk59DNTLkdCRG4KIpO/poZG7nc5Gv8KjZwHACSn33A5kuaSyiWlM0Yv0tBcjsafNkQYG2o3AMEqdz7GJIlw1JqHLAksmBHEBdcjVSp8IndDv/k6hOCCTKJmdY+2CEUS2FQ7oXWwEE6lwgVJEq+p9WMnpW9z+n1VlkLHAQDDJsvY+xWTJMKwmZt7zFGkqoSOnoGkBmBurUNfrv6iyGpB5BZ+76pjr/FcCo24G4jPhY7eC0kLwtpOQF+acTuc5iBEfnpjrdoEjVDJsRKL4RMAgGMWkyS/YuEGwnDuBGavUXUkVUPo2Fkkb7yG5M3XEeg7VtX7sVoQuYXfu+qcVrNJ0iKTpKpIioZQ9CySN15FcvoNBPqPux1Sw2szVmFurMKEjJtGX23eswEqOVZiKXgMgIRusYYOaRtx0eJ2SFQmJklNztxOoE9k586/ywWaVQuPvCefJHV84NM1eU9WCyI38HtXmTYpiSPqOoCd6TZUufDI+XySVKtrKu2vL3ULAHBbHippf6RyNEIlx3LoShjBoZNI376Oe7XbeDFzyu2QqEycbtfkkreyRQbmjU5sirDL0fhf+MR7AADJmSkI03A5GiKqt3u1OwCAWaMLGYU9x9XKr0uauQJhmS5H0/j6UtlpjbPysMuRNAa7TXAmNwWX/IVJUpNL3nwdAPAOp9rVRGBgBHK4HSKTQvr2u26HQ0R1Zk+14zW1NgIDI5BDbRDpbaRvX3c7nIYmwUJfahoAEGOSVBPhkWySlF2n2HzrsvyOSVITE0Igef1lAMBbenmbn1JxkiQjnCv5ayegRNQsBE7nRpLe1rn5aS1IsoLQ8exeU7ymOiuqrCJoJSEHW3BHZpJfC8HhMzCgoFNOol9O7Ps8SZIQDochSVIdo6PDMElqYvrKHIzEMgwoXI9UQ3bPEW/oRP5TTRWuPnkDvcomDCFzE84aajn5AABg+/or7gbS4M5qcwCyU8SExOZhLchaEPNytsPkjLazX9LdFf/C4TDOnTuHcDi77KEZqwF6EQs3NDH7hjMvH0GGG8bVjD0HOTX3NqzUFuQQ954i8otqqnCdyzUybxj93ISzhlpOvQ8AkJ57G8HgR1yOpnGdzTXiwyffB9xyOZgGEpOjOGbN4qw2jx+nzwJg9Vq/YJLUxJI3sknSLZkVmGpJ6xqE1nME+so8tm++jrazH3Y7JCIqQ6VVuM4GsnvOXdGPOhJXs1I7+qD1DkNfnsUxM4Yr4Aa9tdYipXFcXc7+96kHgL95092AGsi0MoJHjOdwr3YHGoxdVQNZRdTbOJ7apCw9jdStKwCAWwqTpFpruSfbC7T97ksuR0JE9aAKHfeoCwCAKxkmSbXWcupBAMBxi0McTjit3YYsCcS1XqiRXrfDaSgrUg/WzBYEJBP3aAtuh0NlYJLUpFK3piBMHUqkF6tSl9vhNBz7hp68/gqEsFyOhoicNmzNQpMsrJqtWLA63A6n4dhT7o6btyCxSljN2euRFsInXY6kAUlSfnT5nDbrcjBUDiZJTWo7N9Wu5eQDAKup1Fzo2FlIgRDMrXVk7tx0OxwictiImR3hyDaGeE2ttVD0LCQthFZs46iy6nY4DUbk1yMthJgkOeGKni2pnl23yCTfL5gkNYG7S0oKIbD9brb0d/jUAy5E1PgkRUP4xHsBcModUS15sVSuECI/Dewq1yM5QlI1hE9kN5a1Rz2oNo4qa+iQk0gLFSuhqNvhNKS39UEYQkavsokBmWuQ/IJJUoPTNA2BYHDXz/TlWRhrdwBFRcuJB9wJrMHcXc4T2JlyZyekRFS+u8+rwlK5lZbJrXV5XX1lDh0iAUPIeFvn/jJOsa+p55gk5R30XS71e35eiwEArulDsCTW83JCBhreMbLbApwL8PvrFzwbGpyqqlBkeVeZyYf0FzAGYLX1JE4Gw+4G2CCKlfNsFSn8HwGk5t+FucWeI6JKOFEmt5oy38Vsv/0CAOBdY4DbKTjILogzoi6hXUpiQ/D+VYvz43xgBgDweuYYWmoeIdmuZoZxVruNc9ocWDvQH5gkNYnCMpO/HnkHUIFp9RQecjmuRnN3Oc9YpBtRdRVb77wIQHEvMCIfc6JMbqVlvovZuvY8gGwjk5yjRnpwR+rHIBZxfyCG59Kn3Q7JE6o5P7rlTQyra7CEhCn9KB6ucWy04019GI/hBZxSF3DNTLodDpWg7Ol2Tz/9NC5fvpz//4PEYjFMTU0BABKJBCYnJ5FIJCqLlGqiQ9rCcXUFlgBut9zjdjgNz240bb31M5cjISInhI0E0vPvQAB4I8P1HE67rpwCALxXm3E5ksZgT7W7bvRjW4RcjqaxrVjtmDO6oEgCQ8l33A6HSlBWkvTkk09idHQU4+PjuHjxIp555pl8ElTM5cuX8dhjj+HMmTN4+OGHMTU1hUgkUnXQVLnzgewFcdroQ1qprNeUSvdaLklK3nwdAZF2ORoiqrWh7bcBALflISQEJys5zU6S7tXuICRlXI7G/+ypdkzw68NuExzdvuZyJFSKspKkiYkJjI2N5f/9yCOPYGJi4sDXPPvss/jGN76Ba9eu4eLFi5VFSTVjJ0lv6Lwg1sOC1YmE1gNYBk6Y026HQ0Q1diSZTZKuyyydXA/rchfumB1QJQuj3HOmLHdXhgyJJE6piwDYJqiX1/TjAID+5E1Y6W2Xo6HDlJwkTU5OFh0FmpycPPB10WgUo6Oj5UdGNReWMrhXvQMAeINz5+tmvuUMAOCU+a7LkRA1j1pXsCumRUqhN5Xtib+RG+GgyhWrElqMPY35PblRkMNeV4/vgtcU+5vYlSFDoey0uhFzGrIkMGt0YdVqdyPMpnPH7MCCGYECs+LtQfb7Pjfj99xpJRduKLaWqKOjA/H4wYsF7XVLb7zxBs6fP4/x8fEyQ9whhMD2NjPvUiWTuxcGvkebgSIJ3DY6sWRx2mO9zLXch/vikzhuzSAAvabVr5LJJITghbHW7HPn7nOoGdi9zQep1/eu0ljs19Wygl0x92uzkCEQ6D+O+EYHAFaxrEaxKqG2wuP2eiaKXw2/gbPaPDQYB77OrvLWiNfKg86PUv6Wp83suhh2mtaThNcyx/Gr4Tdya5X3L5VRzrWtkb/nBxFCOLpnXslJUjweR0dHx66fRSKRAwsxjI6O4v7770ckEsH4+Dg+/vGPVzWylMlkcPXq1Ype26wKL6DvD94EALyUGXEpmuYU1/qhdg4A6ws4q83nh9tr4ebNm03ZkK+X6elpt0OoO7u3+SD1+t5VGov9ulpWsCvmfYHsBrKtZz4EvFizt216hx23mNmDFbMVPcpWbsrd8X1fZ2vEa2Up58d+f0tzK45jVnYk7qXMCcdipL1eyxzDr4bfwPb1V6AqD+z7vHKvbfu9ppFlMhkE79oLtJZKTpLuTpCA7OjSQYUYCtcvAcC5c+cwMTGBp556qowQdwQCAZw9e7ai1zajZDKJhYUFAECL2MpPtXuZF8T6kiS03vchxH/2l3gweLOmSdKJEyeaqteoXpLJJKanpzEyMnLoSEajKaVXrl7fu0pjcbJn0dYmJXFGmwcAtI7+AvDi245/JtkkvJw5gU+E38RDwRt4B48c+opGvFZW8z3feus5yBCYMXo4s6TOZs1ubKmdaNXXcQLTuIb+os+r5NrWiN/zgwQCAUffv+QkKRKJ7JlaV2x0qdDExAQuXLiQ/3c0GsVzzz1XQZhZkiShpYXVgypxr/kuZElg2ujFCuce113b6EcQ/9lf4n5tFmEpjaSoTc9HszXg6y0cDvOaU4SXvnduxfK+wDQUSWA1MISTPUcAMEmqp5dySdJZbR7TJew546XvrBdsTv0EAPAyZ5a4QEKs5RzuS0ziPvMtXN4nSarkO9ts33OnO8RKLtwwNja2Z2pdIpHYM1pki8ViePLJJxGLxfI/29jYwPDwcIWhUjVOG9kb+EtpjiK5ITAwgmWpB6pk4YHcFB0i8q+HctOXY60sTOSG22YX5owuqJKF4e233A7HV8JGHKnYVQgAL6dH3A6nKc203Q8AOG7dQpvUPNPj/KasEuCPPvrormp2k5OTu0aKpqam8oUaotEoHn/8cUSj0V3P//znP19tzFQmfe0OhsQdWELCK+w1coUkSXhLuQ8A8HDghsvREPlfqZXQnNAnJzCiLsMUEmZba1MEgsr3Ym7qeHRr//0aD3PQd8h0oGKeFyqTDW9l13bPyUcRF611+1zasan1IDh0D2QIPBiY3vWYm9c22q3k6XYA8JWvfAWXLl1CLBZDIpHAhQsXdhVheOaZZ3DlypV8Bbvx8XFcunQJnZ2diMVieOqpp1gO3AWbb/4YAPCOMYgN0VxDsV5yTT2NMf2nOKUtokfe4LRHoiqUWgnNCe8PZjs6rulDSCtsZLrlpfQJ/Fr4ZfSmY9DjixW9x2HfoYMq5tXq86p5v/IJHNt6EwBwTTldp8+kYtrO/yLSt9/Fw8Eb+FF6Z729m9c22q2sJAkAnnjiiZIfi0ajBz6f6kBY2Hjt+wCAn6e52aGbtqQ2vGMM4Yx2Gw8FbuA7qfe6HRKR7zldwW4PIfBQIDvV7sXMSTj4SXSIuGjFdWMA92oL2HzjRwB6K3qfg75DB1XMq5QT71mq48oyOvQlSGoA7yr3AEi7EgcBbeceweJ3v4Zj6goG5HUsWJ27Hq/7tY32KGu6HfmPvHgdRnwJKQTxWqZ2VdWoMi/kEtUPBK9DAofTifxm2JpFn7KBlNDwRiZ6+AvIUc+n7wEAbLz6LCRhuRyN9304mN0bqfXsh5GWQi5H09yU1g7MyNk9qj4QvO5yNFQMk6QGp0xnN+94SzkDvfyBQ6qx1zLHkLQ09Cqb+fLBROQf543sVKUX0idrujE0VebVzHFk5BCM+FJ+3x8qLogMHgxOAwDaH/i4u8EQAGBKzU6d+1DwXSgwXY6G7sYkqYGZW3HIt7NVf6ZUrgXzggw0/DxzCgDwC8FrLkdDROUwNtZw0squR5pMcz2HF+hQcav1PICdBJaKe39wGkHJQELtQSjKPSe94KZ8AutWC9rkNCvfehCTpAaWuvJjSMJC8OhprMiVzdWm2vtpKtu4GtXmEDbcmZdOROXbeO37UGDhht6HebPL7XAo52b7+wAAI9Y0OqQtl6PxLnuq3XT7A3XZcJkOJyQ53yZgx6n3MEnykXJKlQrLRPLNvwHAYXWvWbA68bY+CFkSOLHxqtvhEFEphIWNV58FAPyUo0iesqn1IHRsFDIEPhx6Z9djbpRTrvXn1eL9jinLOKauwBAyZlrvr0FUVCs/S98DU0g4qS3hiLLqdjhUgItUfKScUqX3mO/gU5lF6EoL2kZ/AfjBc26ETPv4SeoMTmt3MLL5KoShux0OER1iKPkujPgSkgixCI4HRR78VaRmpjAWfAfPJs/DgALAnXLKtf68Wrzfr4Sye0m9nBlBRmkpOwZyTkK04LXMMTwYvIVfCF7DAs67HRLlMEnymdJKlQr8euQFQAXebXsfzmhBFyKlg7yhR7FuhdGJbWxO/Rgc1CXyMoHT8WxH05Q6yiI4HtR634ewIbWhQ97Ew8HreO6u0b56l1Ou9edV83498gbeG8gWtfhBahQcB/Wen6Tvw4PBW3g4eAPfMzll1CvYMmtAJ9VFHFdXkBEKbrTXa4M6KocFGX+byi6cXX/uz1m6lsjDTqmL6MnMQ1I0vKpyfzMvkhQVr6jZtUm/EpqCBF5Tbb8YugpZEriSOYLbXEvnSdeNftwyehCQTJxKvOh2OJTDJKkB2cPqL6RPcTd4D/tp6gwycgj6yjxO5SpmEZH3fCyUrZrW9p5fxrbEa6pXTSnnsGUF0K9s4LwWczscTwiKFD4UfBcA8DcpVrn1LgnfS2an2Z3ceAlWiqNJXsAkqcEMKWs4H5iFJYAfplji08vS0HA9N9L3kP4iwM1liTxnSFnDaGAOAhI6P/R33Q6HDqBLAfwkfQYA8LHwFHhNBd5rvIagZGDW6MLbxqDb4dAB3tSjuG10ICDSSLz8HbfDITBJ8pxqq9h8KvwqAOA1/TgWrY4aREROut7+ECQtiH6xhPs8vLnsQd/LeleOovLU89g14vfk0dDrAIC5ljPQuodcjoYO86PUfcgIBSPqMs46dE2tdcU8pyrwmdsJPGi8AgD4Xuo8AJb99jIBCc+mspUH15//a6iCRZ3cxtWnHlNNFZsB6w7eE4jBEhK+vc15836QUVoQed8nEP/5N/Hp8Cu4ph+B8OCNbL/v5fBAOx7/LNe9eVk9j12jfU+OKct4X/AWLAG81fEIPup2QHSoTRHGj1Nn8LHwFfxa+GU8Lx6u+WfUumKeUxX41p/7cwSgI2Z0syKjT7ycOYHHlDfRuh3HA+pruIZ73Q6pqTFJ8qBKq9h8WP8ZAOCFzEksWJ1OhEYO6Bx7DEs//w6i6ioeDNzES5mTbodUVLHvJflDPY9d43xPBH6t5WUAwAuZU0gE+l2Oh0r1bOo8Phx8B0fVNUS3pgDUPlECvFXB7m4hYwOJFy8DAL6VfJ8nO99oLwsyrnR+FA+v/DUeMl7EN6UotkTI7bCaFqfbNYjkzddxzIrBEDIuJzmK5CdKawdeVLO97J8OvwIVpssREdF92jxOa3dgCBnf5jXVV7ZFEN/PTVs6t/6jptyL7mz8JxBGBnPyEK7qR9wOh8oQax1FYPAkAtAxHn7N7XCaGpOkBiAJE8vf/TcAgMn0vVi1nNv7gZzxqvoA1q0WdCtb+GjoqtvhEDU1WZj49fBLAIAfp+7DGq+pvvO3qbOIW2G0mnHEX/iW2+HU1bCygpHNVwEAz6kfBtci+YwkoedjvwUAeCT4NvrkhMsBNS8mSQ3g3sTPoS/PYhthPJN8wO1wqAKGpOFb2w8AAB4Nv4EOieU/idzygPEajqjr2LIC+G5uRIL8RYeKb21n901a+/F/RLvVHA1NCRYutP4MEoC20Y9gXjnqdkhUgfDIedyUj0ORBH6j9XmwUqM7mCT5XJe8ifviPwUA/ER7BEkRdDkiqtQLmVO4qfchJOn4zdbnAVH7i2IjVh+j+nGqCpeX6OsL+KDxPADgL7cfwjbXA/jW85lTWApGIfQ0fkn/IZqhoTkWfAfH1BXoUhDdH/9HbodDVfiR9lHoQsZZ7TYeDnAvRTewcIOvCfz9luehCh2hY+fw1uJ9AJqjt6wRCUj4060P4//S8U3cH5jF9vZV1HrBcaNVH6P6cqoKl2cIgeVvPw0NBt7RB/B85pTbEVFVJLzS80n86p1/ixPWLTyg3cKr+ojbQTmmQ9rCp8PZYiNTnR/FmbYulyOiasTlTlxOvhe/1vIKPtPyAteWuYAjST72C8FrGA3MwYSC3vHfBSTOO/a7BasT30m+BwDwntXvwdhcr/ln2BWUCv93d4OX6CDFvkOLq9tuh1W1kc1XkbzxCgwomNjiWo5GsKn1oHPsMwCAv9/6fMNOZZZg4b9q+wlaZB23jB7caH/Q7ZCoBn6QGsWs0YVWOYO/79AME9ofkySfGlLW8PdaXgQAvNn1ywj0RV2OiGrl+6lRzBldCFnbWPyLfwFJWG6HRNTwjihreO/aswCA59QPYcmKuBwR1UrXI7+BRakPbXIav9X2Y8hovGvqx0Nv4l5tAWmh4t9vfgSQ2LxrBBZk/OnWGEwh4YHADE5uvux2SE2FZ5EPBaDjH7X+GJpkYSpzFNfbH3I7JKohEwr+aPOj0KUAUrfezK+PICJnBKDjt9v+FoowED71IF5R3+d2SFRDkqrh24FxpISKe7RFPBp+3e2QamrInMcnc6Wi/2zrg0zwG8ys2YO/2s5Oh3/P6rNIzb3jckTNg0mSz0jCwj9q+zGG1HUkrBD+ZGuM0+wa0KLVgVd6PgkA+IDxIu7XYi5HRNSYJFj4h20/xYCSQFJpR//f/X1eUxtQXO7MTaEEfjX0Oh7Qpt0NqEb01dv4dOZbUCSBl9IjeMGjm5FTdX6YPovXMscgw8LiN76MsPD/9GY/YJLkMx/Rf4L7A7PICAX/euOXsSnCbodEDpltPYfI+8cBAP+o7UcYURddjoio0Qg81vIC3huYgSFkPN/796C0sBe+Ub2cOYEfpc5AloB/2PYT9KT83fmkmUncmfh/IYwUZowefJ3r6BqYhD/ZGsOm2gUjsYxfT/8Vgsi4HVTDY5LkI2s//c94wMwOqf+HzV/ALbPP5YjIaT2f+BxuyiMISCY+3/YDDClrAA4uxdzoJZoP0qx/k0p+v3qW8z7ss9w6Ph8PvYmPhq7BEsC/3/wFrIaGXYmD6ucb2w/j9UwUmmThw0v/CemFabdDqkhIymBs6c+gr85jQ2rD0xu/jAw0t8MiB6VEAD/t/03ILRH0iyX8TvsPocAs6bVevP76AUuA+4LA2fUfYe2H2f2Q/nL7QbymH3c5JqoHSVHx7cA4xjf+M05qS/i99u/hX218DG3ho0VLMTd7Ke9m/ZtUUpZ7v3LeTpTyPqh0uBvHRwiBD+rP44MtrwAA/mL7Ibyqj+AX6xoFuUFAxr/b/Ah+r/17OKEt4fZ/+B8xID6J6/DPrIwWKY3/pv1Z9KRXIIda8Vfi15AQTJCawZbWjaF/8E9w89/+Dzit3cHvtv8A/3bjlw59nZeuv37CJMnjZFj49ZaXcDZ+FQDwE3UMP0jd43JUVE+GpOHpzV/Bf9v+PUTVVfx+5Lt4IRkB8FC+FDPtaNa/SbHfe7i/rezXlfKaSnni2AgLK9/7Gj5o/BwA8K3tB/C3aZ/v70Rl0aHiq5sfwxODP0ZPag6fwV9gRf1FXDO8vw9Nl7yJi21/g6PqGtJyGCc/+0+x8ie3ADTfNa9ZBYdO4ZuBT+PvpP8a92m38XuR7+K74jOHvs4T11+f4XQ7D2uRUviv27+PXwplE6SeT3wOL2vM+JvRtgjif0v8Kt7WBxGSdDyyOIH4z7/JPROIytAqpfDI4gQSL3wLAPCftj6A76be43JU5IakCOAn/f8A4RPvRQA6/uv27+PjoTcgwbvX1NPqbTwe+RaOqmtIWCH8aOCzCA6ecDsscsGsMoz/b+JXsWkFcVxdwW+m/yOr3jmASZJHnVbn8UTkWzij3UZaqPhZ72fQ8YFPux0WuSiNAL668TG8nB6BjGxv+N/JPIMWKeV2aESed496B09EvomB1DQkLYhva4/ix+n73A6LXGTKAQz+5v8dU8pZyJLAr7W8gottP/DchrOWkcGH9Un8N+3Pok1OY8bowf+a+BQ2AlyX3MxmzF78y8Q4ls02dIgE5v/dP8H6838NYZW2TokOxyTJY8ytOD6W+T5+L/IsupUtLJnt+BeJT2K+lTdzAgwo+N+3PoJXuz4BKCpOWTfwP3T8JT4UeMfTPaB+I0kSwuEwZJmXSL9rlVL4L1t/it+PfBddyjY21G4c/e1/jnfU026HRh4gqRq+H/g4/nTzw9CFjNHAHP4fnX+JU4kXIEzD7fDQl5zG3NP/PR42XoIsCTyfPoWvJB7FmuXctFjyj0WrA19OfBrvyqcAy8Dqs3+E+f/9f0D69nW3Q2sIXJPkEVZqC+s//ybiz/8VRs3syMCPUmfw19sPIgMNvJ3TDgk3Ig/h73zmE3jta/8zeuUV/B/ansMvGNfwbuAjME0TiqK4HWRVLEtAlouXsjUtAWWfx4qxK6uV837hcBjnzp2DeUDVn4PiOOjzKn2sUR12fCplprbwQf1neG/nqwhJOiwBTKZPY/XeT+O9/ccA3Kjp55G//SxzL26ZvfjNlp/hpLaE9649i9i/eh3njPO4iSFYde1TFjipLuKT4ddwevEOdACbaMXXNx7GG/qxOsZBfpAUATwT+CSe+qUMVv7mPyA9/w7mvvZ/Q9voL6Dzkd9AoNeZqp3NcC8rO0l6+umnEY1GEYvFEI1GMT4+XtPnN5vM0gwSL30HG2/8ECKTTY4WpT78afxB3DAG3A2OPC04dApfD17A0ZWf45MtryKqriIa/0vM/5uX8YJ5H57fHkFaCuWf70TVMqccVq2tnEpuB1VWK+X9yn3ssGpBrDK0WynHpxxDyhreu/omZv63/w8+aCQBCZg1uvBn2x/CtNGHX5SDtQyfGshtswtf2RjHh4Pv4NfbX0dofREfx/fxgc4Qnk/fg+fT92DJcm4frZBI4pHgNYwF38awmt3uwYSCrocexb96cxhXVzm1mvYhSYi8/1G0nP4AVr7/R9ia+gk23/wRNt/8MVpOP4TIA5+AJKyafmQz3MvKSpKefPJJjI+PY2xsDADwhS98AdFoFKOjozV5fjMQlon07etI3ngVm1efg740k39M6x1G10f/Ab7yTAo3jISLUZJfWJKCH6bP4YXMSfxS6Cp+ueUasBTDA4hhVMh4Wx/ClD6MNzPDWHSwapkTDqrWVkklt0rfr9zHSsEqQ3tVWp1PEhaOKcs4p83hvYFbOKKuAxuAALAideMvE+fxun4MgptsUgkEJEymTyN03yP43OllxL7/Z4jI2/hE+E18Ivwm7pgdSKzdh61rFlpEdWuXVJiIqis4HZ/B/H/4K/xO6grk1uzodUYoeDF9EvFTn8DvPfoxZKZ+CIBJEh1Mbe/CwN/775D+4K9j7Sd/hu23f47tt1/A9tsv4B+jBa+1HMGUHsU1fQh6DSaTNfq9rKy/0MTEBJ566qn8vx955JE9P6vm+Y1GmAaM+CLSC7eQWZhG+s51pGJX8yNGAABZRcs9D6LjoU8iNHIekiQB3/6hazGTP22JEL6VfB/S934Mv31uA9e+9xfoxQpGA3MYDczhN1ufx+ZcJxb/4jzea0iQ1SAWzQgSooWNR/INBSb6lA0MKus4u34dt//02/h8agqBDj3/HFNIuNN6Gu//exfwlW+s4rrODicqnykH0Pmhv4unftqK8NIUPhx8B2e02xhU4hhMPI+F//Q8fgfAWkcLFq0IVsw2rFptOLqxgM03kxgxbyKobUOGgAwLLVIGp+MzWPneG/h0egrtHcvokTchSwJYB1Lr2UXis0YXXkifws8zp7AtgvhFtcPdPwT5UnDoJAb//v8VmaUYEq8+i803fojW5CbGQu9iLPQuDCFjzuzCeuYoNt4AtJ5haN1DUEKtbofuKSUnSZOTk4hE9g4zT05O1uT5XiQsE8I0IIwMhKEX/L8OYWZgpZOwUpswk5uwkpswkxuwkhsw4kvQ40swN1aBIsObcrgNoWOjaL33IbSc/gCUsL96+Mm7dCWMjoc/gj/5URjbC7dwvzaL0UAMx5VltBnr2Jz6MX4RwC/mTs20ULFstsNIt2Pxr96A0tYJpbUDSjgCORCCFAhl/18LocOKo11KwoQECzJMIUMWJoSwIEkscEAHE0JAESaC0KFKJhRYUCUTbfoK0gvTGDRvI6itIiTpCEsZhKUMzqy9hYU//xF+I30DwY4NdMlbUKTcOrE4kIwDAQDbVgDvGgN4PXMMU/owHh45iY+ceC8g/dDNX5kagCUpeF0/jtf14whLGdynzeGXBhO4N7yGzNIMupRtdCnbgL2X6yqw+Jffwd8FgPa73mwdiP8cOAkAuWWjG1YIibbjeM8vPIL/5W8NvLJap1+MmkKgL4reT3wOPb/8D/Ev/sWfoDtxDaPaLHqULRxXV3DcXMHSX72ef77cEoHWOZBrC+TaA62dUEJtu9oD2fZBEGGxjRYpDVPIsCDBhNxQW5NIQpT221y+fBlf+tKX8MILLxz4s0qff5jz58/D1HX0tQXKfKUo+p+HPrdmJEBRISkqIKuQVC373/s9W5IQ30zDMHfHEtQUtLVoTfeYV+Lwy2P7/VyCQEgVCCkCmXQakjAhQ9S4Il5uREqqwciUJEFYYk90kpQ9R6wiBRXsx+5+nQRAkvd5v9zrhNh79pf7mFTwX4f9CbJX3WK/Q+V/u/0u5ZIkHfBY8fvZfj8Hsr/7ft+agx+rzXdNQIIJBZKsQAsGsJUBMtbuJN0L51wzP+aVOOrxmDB1yLAgCwsyBBQZUGXAMK38SWRfaWVZgaapSOoChsh2NlmQPfu78Tvkn8dURUJHW/DA+0Dha2RYUGBCk0wEZJGt5FjTNUtSwf/VYraKtOv/bEsbaSiahjfeeKMGn7FXySNJ8XgcHR27h30jkQgSieJTGcp9/mGCwSAykgS1o/H3Beho239hcbM+5pU4/PLYQa8JcTidfE4r+O/2A+oweOWca9bHvBKHs48Vf/yg+qKtYSfiaMzHvBKHXx47qKPtoPfzKzW9hECg3MGTMt6/1CfenfAAQCKRKDqlrpLnH+bFF1+s6HVERERERETlKHkhQSQSQTy+u4JFsdGiSp9PRERERETkBSUnSWNjY3umyiUSiXx572qfT0RERERE5AVllaR69NFHd1Wnm5ycxIULF/L/npqawuXLl0t+PhERERERkdeUXN3OdunSJRw7dgyJRALRaBTj4+O7Hrty5Qq+9rWvlfR8IiIiIiIiryk7SSIiIiIiImpk3AGSiIiIiIioAJMkIiIiIiKiAkySiIiIiIiICjBJIiIiIiIiKsAkiYiIiIiIqACTJCIiIiIiogJMkoiIiIiIiAqobgdAtff0008jGo0iFouVtIFvLBbD17/+dZw/fx4AMDY2hkgkUo9Qm1K5x8dmH6cnnnjC4QibVznHJpFIYGJiAgAwOTmJCxcucLPsGin3HKn0nKLK8DzxLt5fvIttMx8S1FC+9KUviZ/+9Kf5f//+7/++ePPNN/d9/szMjPjMZz6z6/l/+Id/6GiMzazc41Pot3/7t8WXvvQlp0JreuUem8JjEY/HxenTp0s+lrS/So5DpecUlY/niXfx/uJdbJv5E6fbNZiJiQmMjY3l//3II4/ke/GKuXTpEi5cuJD/9+c///ld/6baKvf42CYnJxGNRp0MremVc2xisRhisRgSiQQAIBKJYGxsDF/96lfrEmsjK/ccqfScosrwPPEu3l+8i20zf2KS1EAmJyeLDsVOTk7u+5rvfOc7u07c0dFRDuc6pJLjY7OH58kZlRybN998E/F4PP/vaDSK2dlZR+JrFuUeh2rOKSofzxPv4v3Fu9g28y+uSWogdm9doY6Ojl03qEJTU1MAshfIqamp/PPYW+GMco+PbWJiAhcuXMDTTz/tVGhNr9xjE41G8cILL+z62eTk5K6bGpWv3ONQ6TlFleF54l28v3gX22b+xSSpgcTjcXR0dOz6WSQSKXqCAtkTEMiewPYCws997nPo6OjgwloHlHt8gOyxYQ+f8yo5NoXsG9njjz/uRHhNo9zjUO1xo/LwPPEu3l+8i20z/+J0uwZy90kIZE+yw4ZoR0dH8/89NjaGP/zDP6x5bFTZ8fn2t7/NXtc6qPTcsX3pS1/CH/3RH3E6RJXKPQ7VHjcqD88T7+L9xbvYNvMvjiR53MTERH7odT/j4+P50pB3D98W68Gw2T1Id/ck2b0YdDgnjw+npVTHyWNT6NKlS/iDP/iDXTc0qky5x6Ga40bl43niXby/eBfbZv7FJMnjypmDOjY2tmf4NpFI7HshtG9WhYs219fXOfxeBiePDwBcvnw5/9/f/va3AWT3Wrhw4QJ7Yw/h9LEBsonYpz71qfy5xIZHdco9DpUeN6oMzxPv4v3Fu9g28y9Ot2swjz766K6KKfbmfbapqaldF8bf+Z3f2fX8K1eu4Hd/93frE2wTKuf4jI2N4eLFi/n/ffjDH8bw8DAuXrzIG5gDyj137OdGIpH8AtvDRq7ocOUeh8OeT7XF88S7eH/xLrbN/EkSQgi3g6DaunTpEo4dO5ZflFm40O/SpUu4cuUKvva1r+36GQB0dnYiEomwgeGwco8PkO3ds/dUuHDhAi5evFjXmJtFqccmkUjg4Ycf3vP6xx9/nMemBiq5hu33fKo9nifexfuLd7Ft5j9MkoiIiIiIiApwuh0REREREVEBJklEREREREQFmCQREREREREVYJJERERERERUgEkSERERERFRASZJREREREREBZgkERERERERFWCSREREREREVIBJEhERERERUQEmSURERERERAVUtwMgIiKq1NNPP41IJAIASCQSuHjx4qGvmZiYQCKRQCQSwczMDD7/+c/n34OIiAgAJCGEcDsIIiKicj399NMAkE+MJicncfnyZTz11FMHvubChQu7Eqt/8k/+Cb7yla84HzAREfkGp9sREZEv/eEf/iEuXLiQ//fY2BgmJiYOfM3k5OSuUaNIJIKNjQ3HYiQiIn9ikkRERI6ZmJjAY489hjNnzuDy5csAgCeffBIPP/wwnnzySSQSiYreNxaL5afM3W1ycnLf17W3t+Nzn/tc/nNjsRii0WhFMRARUePimiQiInLMhQsXcOHCBTz88MP5ZGR0dBQXLlzA6Ohoxe8bi8WK/jwSiRyYeP2zf/bP8Nhjj+Hhhx/G7/zO7+DYsWMHTs8jIqLmxCSJiIgc9y//5b/EF7/4RXzta19DR0fHngTpySefLOl97ARrPx0dHYjH4/s+HolEcPHiRfz0pz/Fv/7X/xpjY2P45Cc/ycINRES0C5MkIiJy3NjYGMbGxvDFL34R3/jGN/Y8XqvRnIMSJAC4dOkSHnnkEXzlK19BLBbDF7/4RTz22GN49tlna/L5RETUGLgmiYiI6mJ0dBSJRAJTU1NVv9d+64gSicS+j8ViMWxsbGBsbCz/Ht/4xjcQiUTy66WIiIgAjiQREVEd2AUSnnrqKXzxi1/cM3JT7nS7aDSKSCRStPCCnQQVi6G9vX3Pzw+avkdERM2JSRIRETnu61//Op544gkA2STmySef3DXFrpLpdr/7u7+LycnJfJJz+fLlXQlPLBbD5cuX8/sojY2N4emnn95TFW9qaorFG4iIaBduJktERI55+umn83sX2aNHX/jCF/Cd73wnX/mumip3Tz/9dH4k6Y033sgnYkA2afryl7+8a9QqkUjgq1/9Kjo7O/OV8Ao3lyUiIgKYJBEREREREe3Cwg1EREREREQFmCQREREREREVYJJERERERERUgEkSERERERFRASZJREREREREBZgkERERERERFfDNZrIPPfQQMpkM+vr63A6FiIiIiIhctLS0hEAggBdffNGR9/fNSFI6nYau6+C2Tu4RQiCdTvMYuIR/f/fxGLiPx8B9PAbu4zFwH4+B+wzDQDqdduz9fTOS1N/fj3Q6jW9+85toaWlxO5ymtL29jatXr+Ls2bM8Bi7g3999PAbu4zFwH4+B+3gM3Mdj4L5f+ZVfgSRJjr2/b0aSiIiIiIiI6oFJEhERERERUQEmSURERERERAWYJBERERERERVgkkRERERERFSASRIREREREVEBJklEREREREQFmCQREREREREVYJJERERERERUgEkSERERERFRASZJREQNwLJEVY8TERHRDtXtAIiIqHqyLOHLf/wSZhc29jw2PNCOxz/7fheiIiIi8icmSUREDWJ2YQPX5+Juh0FEROR7nG5HRERERERUgEkSERERERFRASZJREREREREBZgkERERERERFWCSREREREREVIBJEhERERERUQEmSURERERERAWYJBERERERERVgkkRERERERFSASRIREREREVEBJklEREREREQFmCQREREREREVYJJERERERERUgEkSERERERFRASZJREREREREBZgkERERERERFWCSREREREREVEAt58mJRAITExMAgMnJSVy4cAHj4+MHvubpp59GNBpFLBZDNBo99PlERERERERuKitJ+vKXv4ynnnoKAHDhwgU8/PDD+MY3voHR0dGiz3/yyScxPj6OsbExAMAXvvAFRKPRfZ9PRERERETktpKn28ViMcRiMSQSCQBAJBLB2NgYvvrVr+77momJiXyCBACPPPJIfiSKiIiIiIjIi8pak/Tmm28iHo/n/x2NRjE7O1v0uZOTk4hEIkV/TkRERERE5FUlT7eLRqN44YUXdv1scnJy10hRIXvEqVBHR8euJKsSyWSyqtc3C2EaSN96AyK9jeDx85Bb9ias5bL/9jwG7uDf331ePQaSJCEcDuf/HZbSOKvNIyMUXNWP5n+eSqUghCj6Hvv93Gu8egyaCY+B+w46Bsb6AjJz16B2DEA7ehqSJNU7vKZQq/NAGDrSM29CZFIIHDsHpaWjFuE1BSGEo9/vstYkFZqamkI8Hsfjjz9e9PF4PI6Ojt0HOhKJFE2eyjE9PV3V65uBnIyj7aX/CGVzCQAg1CA23/PrMPrvqcn78xi4i39/93ntGITDYZw7dw4AcK96G/+47W/RImcAAItmO37ScgGWJRAKhYq+3jBMXLkyBV3X6xZztbx2DJoRj4H77j4GwZs/Q/jaD2A3GzN992Lrgc8ASsXNPTpENeeBvL2Otpf/LN9es9QgtmrYXmt0uq4jGAw69v4VnzVf+tKX8Ed/9EdFp9QB2JMgAdnRpf2eX6qRkZFdPaa0m9DTWPnT/xHG5hKkUBvkUCvM9QW0v/4X6LnwJLT+4xW/dzKZxPT0NI+BS/j3d59Xj4Hdk9ZlreJC+w8QkEwsm20ISTr6lQ38avw/A8Yv4ct/dgWzCxu7Xjs80I7HP/t+3Hvvvb4YTfLqMWgmPAbuK3YMkld+gvi1HwAA1L7jMFbnEFh6B5GZH6HzU7/nZrgNqdrzwNJTWP3Tf7qrvYZ8e+1L0PpHah90g9E0zdH3ryhJunTpEv7gD/7gwCp1kUhkz9S6YqNL5QqHw2hpaanqPRrZ6t/8OYyVOShtXTj6uX8OpbUTd/7snyN5/RVsfP9rOPqP/zkkqbrtsXgM3MW/v/u8eAyEEPh45vsISCbe1gfxrzY+hlYpjf9z5Bl0YQ2rP/xTzC6cxPW54lOe/dbY9eIxaDY8Bu6zj4G5FcfCD/89AKDzkd9A9y/9l0jemsLtP/mnSF37GcT5j6L1zAddjrYxVXoeZNtrs1BaO3H0H//PUNq6cOc//k+59tof4ejn/idIsuJAxI3D6amkZbeWJyYm8KlPfSqfIO1XiGFsbGzP1LpEIrHvGiaqnrGxhvjzfw0A6B2/CDXSC0lR0f9rvw8p2ILMnevYmvqpy1ESkRO23/45hsQdpIWK/7D5CzChICFa8Kdb2Wtu4qXvoN2qbrozEXnT2k/+E0R6G4HBk+j66AUAQPj4KDo/9OsAgNUf/gmEZboZIhUwNlZ32muf/N1se01W0Pfp/xNku712lYXO3FZWkmQnRJFIBLFYDFNTU5iamso/PjU1hcuXL+f//eijj+5KouwNaMkZ8Re+CWHqCA6fQcvpD+R/rrR25C+U68/9hS+m1BBRedYn/xwA8Leps4iLnV7Na8YRLIZGAMvAA8ar7gRHRI4xkxvYePVZAED3r/zDXaMPnR/+e5BDrdCXZ7H19s/dCpHuEn/hW7n22n272mtqWyc67Pba5J+zveaykqfbJRIJfO5zn9vz88LCDc888wyuXLmC8fFxAMBXvvIVXLp0Kb+/0oULF7iRrEOEoWPj1e8DADo//Jk9Q5CR9z+K9clvILM4jVTsKsLHzrkRJhE5ID3/LtLz78CEjL9N3bfn8bcjH0R/ahrnzKsI4BwycHYeNxHVz8ar34cwMggMnEB45D27HpNDrYg8mL3/b7z8XbTd92GXoiSbMI2d9tqHfr1Ie2081167hfTsNYSie6/pVB8lJ0mRSATXrl078DlPPPFEST+j8lmWgCzvP/dy89rPYSU3oLR3o+WeB/c8roTb0Xr2EWy+/gNsvPY3TJKIGsjGa9nF2u8o92BT7F1btBg6AbVrEFi7g/cEZvBi5lS9QyQiBwghsPH63wDIdoYWW6PR/r5PYH3yz5G8+Tr09UVonf31DpMKJG++lm2vtXai5d7373lcCbfl22uJV59lkuQi1oT0CVmW8OU/fmlPZSogW53qvwo9BwBoP/9L+y70a3/vL2Pz9R9g663nID75u5BU9iYT+Z0wDWy+lT3/31LOFn+SJKH9/C9i7UcTeCh4g0kSUYMwlm5BX56FpAbQdrb4mm+tsx+hY+eQmpnC1rXn0fnBX6tzlFRoc+onAIDWs2OHtte23/45hGlAYgl3V1RX5ozqanZhA9fn4nv+d/vOGravvwIAB1avCUXvg9LaCZFJIjkzte/ziMg/kremYG0noLR2ICYP7/u8ttGPAABOq3cQljL1Co+IHJS+/jIAIHzqfdkS0vtovS/bNti+9nxd4qLiLD2NrWvZtWFt939k3+eFhs9Aae2AldpCauZKvcKjuzBJagBRKwahp6C0dyMwtH8PsSTJaLn3IQDA9tsv1Cs8InLQdq6R1HLP+yEOKO+vdQ9hVeqCIgmcUefrFR4ROSh141UAQGvu3r6f1lxxgFTsLRib6w5HRfvZfvclCD0FtbMfwSP37vs8SVbQcu/DAMCCGy5iktQATpo3AGQvgofVjM8nSe+8yKopRA0gafckF1mLeLdpZQQAcC4w52RIRFQHUmoDxuJNAED41MHnv9rRh+DQKQAC2++wk9QtyYJZP4e11+zEduvtF9hecwmTJJ+TIHDCnAaAXWUk9xM+8R5IagBGYhmZxVsOR0dETtLX7kBfmQdkBS13VbUqZloeAQCc0+YggTddIj/Tlq4DAIJH7oXa1nno81ty0/E5k8QdQghs33gNABA+8d5Dnx86cR6SFoKZWEbmzk2nw6MimCT53JCyjlZsQ9JCCB8/vGKdrAXzJ+f2uy85HR4ROcheixgaPnPgegTbvDyElNDQLqcQVVacDo+IHKQtvQtgZ4bIYVpOvg8AkIxd5cayLtBX5mBurEBSNISKVBi2rN0dV7IaQPjEeQDA9s3X6hIj7cZyGT53Sr0DAAhFz0BSSqtWFz7xHmy/80J2MeAjv+FkeETkIHvqRsshU21slqTgLX0IDwRmcJ82jxmz18nwiMghwrKgrmZng4RPPlDSawIDxyGHWmGltpC+cxOhI/c4GCHdLXnzdQDZIlqyFtzzeLEqxg8YIXwUQGpmChj7TL1CpRyOJPncvdoCACB07P6SX2P3YKRm32JvEpFPCctEMnYVQGlTN2zv6IMAgFO5awcR+Y+xEoNspCFpIQQHT5T0GklWEIrm7v+33nQyPCoiefPwqXZ3VzH+2XIHgGzBDbbX6o9Jko9JEDilZhs6pUy1swX6opBDrRCZFNKc50rkS5mlGER6G1IghMDA8ZJfd8PIbiQ5oi5BhuVUeETkoMzc2wAA7ci9++61U0zo+CgAIMkkqa6EZSJ5K7v1SjmdWvNmJ1IIQmSSbK+5gEmSjw0q62iT09Ch5qrWlCbbm5TddJL194n8KRV7CwAQOnqmrEbSbbMT25aGkGTgiLLmVHhE5CB9Nnv+B46eKet14ePZWScprkuqq8xSDCKThBQIl9WpJSBjXj4CIDfljuqKSZKP2aNIt+Whktcj2fJT7njSEflSajY71S4Uva+s1wnImM6NJp1SF2seFxE5SwiBzNw1AEDgaHnnf3ZdUlt2JsntG06ER0WkZ7PHK3S0vJE/AJiTjwIAUrfYXqs3Jkk+djLXwJnL9TKUIz8vOXYVQnDKDZHf5EeScqPC5bCn3J3QmCQR+Y2xdhvWdhxCUqCVuB7JJkkyQsPZ0af0/DtOhEdFpHLTI4NHT5f92lkllyTNvsX9kuqMSZKPHVOzJXwX5MGyXxscPAFJDcBKbUFfvV3r0IjIQUZ8CWZiGZDkA3dt38/1/EjSAsCbLpGv2B0kRucQJDWw67G7y0jfzbIEgrmqdkyS6ic9Z48klTc9EgBWpB5IigYrtQVjje21emIJcJ9qkdLoU7JlIhfk/rJfLykqAgMnkJ67hvT8uwj0HK11iETkELtXMjBwAnIgVPbrZ4xemEJCRE6hXWwc/gIi8oz07ewmsmbH3vt2sTLStuGBdjz+2ffnO1aYJNWHuZ3Id0ZXMpJkSQoCgyeQnnsb6fnr0LrLnz1EleFIkk8dU5cBAItmO9JS+Y0kAAgeyRZ7SN9+t2ZxEZHz7HM2dLT8USQAMKDgttkJAOgXnHJH5Cd2kmR0FJ9FcncZaft/duIUHMqOJOmrt2Em2UniNLtTS+s5CiXcVtF72McsxcS2rpgk+dQxJTvVbsaofDNI+6SzL7hE5A/2ORsYPFnxe8wYPQCAfotJEpFfCNNAZmEaAGBGhip6D6WlHWpXNsHi/d956blsYlPJKJItP0WSndp1xSTJp+yRpOqSpOxIUubOTZYCJfIJIaz8fhnllP6/W8zMXjv6raWaxEVEzssszUCYOqRgC6yWrorfJ5SfcsdGt9PSd7JVBKu5XttJUubOTQjTqElcdDgmSb4kcDyXJN0yK0+StJ4jkIItEEYGmaVYrYIjIgcZa3eym8gqGgK9wxW/T8zoBpAdSWLFJCJ/sEd+tIETgCRV/D75kYncVDByTmYh16lVxci/1j0EOd9em6lVaHQIJkk+1CFtIyKnYAoJc0blPUmSJOdPWvYmEfmDvbdJYGAEklJ57Z15swuGkBFGCkaco0lEfrArSapCPknKjUqTM4zNNZibawAkBPpL30T2bpIk50ei2F6rHyZJPjSsrgEAFswO6FUWKNy5UHJeMpEf2OdqNb2SAGAWFG/gugQif9hJkqo7/wN92Qa7ubkKcztRdVxUXCaXhGo9RyqqRFookNsTK7N4q+q4qDRMknzoiLIKINsTXK3gwAgAILPAk47ID/IjSUPVNZKAneINGXaSEHmeMI38VCu1ilEJAJCD4XzxBrsQBNVeugZT7WyB/pHce05X/V5UGiZJPnQ0N5I0V4MkyR7+zSzdghBW1e9HRM4RQhTMb698EbBtzsyuS+JNl8j79NXbgGlACoSgRCpfj2yzO0l5/jvHHkmyR4Gqke/UXmR7rV6YJPnQ0dxI0lxu4XU1tJ6jgKJCZFIw1lkKmMjLzI0VWKktQJKrKtpgs0ejM4tcCEzkdfYoUqDvGCSp+uabPTKRWZyu+r2ouHxluyrXkAGF7bUk22t1wiTJZwLQ0StnN3+rxXQ7SVYQ6I0C4JQ7Iq+z56JrvUchqVrV72evSTI3VripJJHH2ed/oO9YTd4vkJ9uP12T96PdrPQ2jPUFALUZSZIUle21OmOS5DNDyjpkCUhYIWyIcE3eM1AwhEtE3pVeqG0jKSUCiEsRABxNIvI6+xytpkpaofz0reVZCEOvyXvSDntrFaW9B0q4vSbvGRjIHvs0R//qorrSaFR3tVyPZLMvuDzpiLxNz023+d7bJl78X3+Y//mD9/Xjtz51rqL3XJF60CESyCzeQvj4aC3CJCIHZJZynST9x1CLnc2USC/kUCus1BYyy7MI1mC0g3bUeuQPKJgiydG/uuBIks/Y65Hma7AeyRa0izdwJInI0+xG0ptrLbg+F8//b3F1u+L3XJZzFe54/hN5lpXeWYdil++uliRJBeuSeP7Xmj2SFOiP1uw9d4o3TNfsPWl/TJJ8ZlBZB1Cb9Ug2eyTJWLsDK52s2fsSUe0IU0dmeR4AMJ9bS1QLK1IuSeIu7kSelVnOTd1q64LSUpupWwAQ6Ivuen+qncJCG7WSb6+tL8LKsL3mNCZJPjOoxAEAd8yOmr2n0toBpbUTQHZuMhF5j74yD1gG0ghgzWqt2fsuy9lSwpnFGZaVJfKo/NSt/to1uAFAyxUC0HnvrzknkiSlJQK5JbuOVF+Zr9n7UnFMknwkLJJok9OwBLBQwyQJALRcOWF9hRdKIi+yF22vyD0ApJq977rUCUnRIHRuA0DkVfmiDTWaamcL9GXv/ewgrS1zKw5rOwFAyrevaiVf4Y6jf45jkuQj3VZ2PdKq1Qa9xjU37D1XeKEk8ia7V3JFqt16RAAQkgytZwgAoC/P1fS9iag2Cos21JLd4DbWFmDp6Zq+dzOzr9dq1wBkLVjT97bbaxz9cx6TJB/pEtkkqdajSEDBSBJPOiJPyqxkE5hVubZJEgBoPblOEo4kE3mOEMKxkSS5JQI53A5AcPpWDTkx1c6msVO7bpgk+Yg9klTL9Ug2jiQReZueS5LWpNoVbbGxk4TIu8zNdVjJDUCSofUerel7S5LEkQkH7CS1tU+SeLzqh0mSj3TnR5I6a/7ediPJWF+EZWRq/v5EVDlhGtBXbwMAVms83Q4o7CThdDsir8ksZxvcWtdgzaduATvFG7jGpXbsTi17zVct5YttrC2wveYwJkk+0m1lN5J1YiRJae2EHGoFhMUhdyKP0dcXAMuEpIWwKbXV/P21nmzvtL4yCyFqsU0lEdWKvVaw1qNINhZvqD17erR9ba0lpW2nvWbkOs/IGUySfMJMbaENWwCABav2SZIkSfl1CXYPCBF5gz2tQus5Aki1q2xny76vDCu1BXNrvebvT0SV01ezHZdONLiBwum2HEmqBXN7I1fZDtC6j9T8/Qvba0xsncUkySfsRtK61YKUCDjyGfkpN0u8UBJ5SX7qRo1LydpkNQC1sz/7WbzpEnlK/vx3KEkK2B2kq3cgDN2Rz2gm+mr2eCmRXsiBkCOfwfZafTBJ8gm7t8CJqXY27pVE5E32WiGnepKBnQYY1yUReYvT57/S3g0p2JKdbs/pW1Wzj5dTSS0AaH1sr9UDkySfsHt3nSj/bePibSJvyk+3c2hNQva9Od2WyGusTBLmxgqA3LRYBxRWuGPxhurpDq5HsgU43a4umCT5hH3huuNAZTvbTiNpHsIyHfscIiqdECK/CNi+MTqhsHgDEXmDvpId2ZFbIlDC7Y59DrcBqZ16JEk7I0m32V5zEJMkn7Cr2zg53U7t6IWkBQHLgL624NjnEFHpzI1ViEwyu0dK96Bjn8NGEpH3OL0eyZYvK82RpKrtrCF17pipkV5IWijXXrvj2Oc0OyZJPmDpaRjxRQDOTreTJHmnN5kNJSJPyPdKdg1CUjTHPsceSTY3VmGltx37HCIqXWbFrmzpbJIUyI8kc01SNYSh5zuZnTxmu9prS2yvOYVJkg8YuRMuhSC2RO03kisUYPEGIk/J1GE9EgAooVYorZ25z+S6JCIvqMfUrez7D2U/b+02hLAc/axGpq/dAYQFKRCG0tbl6GcFcmvU9DUmtk5hkuQD9gkQlzoA1H6PlEJad+5CyQo3RJ5Qr0YSwAqXRF5jb+7u9HQ7taMfkBUIPQ1zY83Rz2pkhdMjJQf2tCtk78Fkf0eo9pgk+YA933Rddm6qnY1JEpG3ZBzeI6nQzpQb3nSJ3CYsM38vdnokWVJUaJ0DAHY2r6XyZfKdWs5UIiyUH/3j8XIMkyQfsJOkuNTp+GdpXUySiLwk30hyYOf2u6nsJCHyDCOxDGFkAEWF2tHn+OflO0nZSVIx+29Xl5F/ttccV1aSFIvFcOnSJUxMTJT03KmpKQBAIpHA5OQkEolEZVE2OcMeSZLqN5Jkbq1z8TaRyywjAzOR2yOly7nKdjaOJBN5h13VVusegiQrjn+ePfrBkYnK5adHOzzyB9zVXkttOf55zajkJGlychJTU1O4cuVKScnO5cuX8dhjj+HMmTN4+OGHMTU1hUgkUlWwzUpftUeSnE+S5FArlNaOXZ9LRO4w1hcBCEjBFsgtzl8/80nS2h0IIRz/PCLan52sOL0eyZZf48JOkors2tOu2/ljJhcU2+Exc4Za6hPHxsYAAM8880zJb/7ss88ikUhgdHS0/MgIQLacpJFYBgCsy50AdMc/U+0agrkVh746j+DQScc/j4iKy0+16xp0fBEwAGid/YAkQ+gpmJtrUNu7Hf9MIiouv76lDlNts5/DNS7VsLYTEOltABJUB/e0K6R1D8HcWoe+dhvBI/fU5TObiaNrkqLRKBOkKunxxVw5yRCSCNflMznlhsgb7PWI9ZhqBwCSouXXPvD8J3KPZYn89h/2PbnwMSc6TfIjSWsLEKZR8/dvdPb1WmnvhqwG6vKZOxXueL12QskjSZW4fPkyAOCNN97A+fPnMT4+7uTHNSRj1W4kDQEJ53uSgcILJU86IjcZdU6SgGyDzFhfgL56G+Hj7OQicoMsS1iO3UIrgP/fd29j/vs/BAAMD7Tj8c++35HPVNq7IGlBCD0NfX0xvw8PlSbfqVWnUSSA68ic5liSNDo6ivvvvx+RSATj4+P4+Mc/XpORpWQyWaMI/WFrYQZArpF0yFKwZDJZk3UEojU7xSa9NIvt7Z3iDfbfvtmOgVfw7+++eh+DVG7httXajWQyiXDYudFk+/ohRbIjScnFGajb3ivewvPAfTwGzpIkCaGAirARBwC8tiAjIeK7npNOpwHsPgaSJJV0jTioraB0DsBYmsHm7ZsIhTsr/A2aw93nwfZiDAAgtfftajuVopRjV+y4Wbn2Wmp5tuzPbARCODOqanMsSbLXMNnOnTuHiYkJPPXUU1W97/T0dFWv95vwzasIIdczcevg5968ebMmNy0lsY0IgPTKPK5evbrn8WY7Bl7Dv7/76nUMIkuzUADMxVPQbt7EuXPnHPss+/oRTAEtANZi72C2yPnvFTwP3Mdj4IxwOIx7BzshQyAjFCTE3sbz/Hx25KDwGITD4ZKuEQe1FVqVFgQAzL/1GtLpUEXxNxv7GLTcehtBACsZCfNlXjtLOXbFjpu8uY0OZEuPX71yBajD2lUv0XUdwWDQsfd3LEmamJjAhQsX8v+ORqN47rnnqn7fkZERR3tTvWb1rW8iA0AtYbrNiRMnajKSZOknsDj5byDrSZw5EYUcagOQ7cWYnp5uumPgFfz7u6+ex0CYBha+k+09PvneDzheRMG+fqRDGay99T20GNs4dvaso59ZCZ4H7uMxcJYkSdDnso3sZbMdwN6G75EjR3D9+vVdx6DUHvWD2gobq6exdect9AQEOjx4/nvJ3efBymv/ETqAI/fej9Dp8v52pRy7YsdNGPdg4Sf/GpKRxunjR/PViZuFpmmOvr8jSVIsFsOTTz6JsbExRKNRAMDGxgaGh6vfMT4cDqOlpaXq9/GL5fgiAHvTsKUDn1u7m1ULVtq7YW6sQk2uI9Tdv+dzmukYeA3//u6rxzHQV29ni7aoAbT1HXG8up19/dCGRrAGwIwvIhwOQZK8uec4zwP38Rg4J55b37JkFS/9HwqF8n//cu/9Bz3fHDiOLQAischjWyL7OCzm2mutg8cRdOBvt99xUzv7YKwvQk2uIdw3VPQ5jcrp+2LN7n5TU1P5Qg3RaBSPP/54PkECsvssff7zn6/VxzUFYRow4tnEyKmF25ZVvDfJrqaTWeZiQCI3FC4Crkf5b5va2Q/ICoSRgbmxWrfPJaId9vm/bLbt+nlnexCWJRAKhXDu3LmyEiT7tfuxLMFCABUyU1uwtrMLx+tZaAdg6XYnlTySZG8m+9xzz2F2dhYAMD4+nk+EnnnmGVy5ciVfwW58fByXLl1CZ2cnYrEYnnrqKZYDL5ORWAYsE5IagNLe5chnyLKEL//xS5hd2Nj181/JAPcDMNZZ4Y7IDXYJbrWzvjdcSVagdfZDX70NffU21EhvXT+fiHYqWy7fNZLUFtb2vW8/eF8/futT+69rOei1dtU8u8FtbqzCyqQgB7guqRT28VJaOyAH6zsFVes+guSN17htgwPK2kx2bGwMFy9eLPr4E088sevf0Wh0z8+oPPlGUtego1NeZhc2cH1ud+WcY6EQ7m/hXilEbnGjnKxN7RrKJ0nhkfN1/3yiZmdvwZFdk7RXsfv2cH9b0eeW8lqbEm6HHGqDldqEsb6AQP/xMqJuXvb1upT147WW37aF7bWa8+ZkcwJQ/40kCy3lLsw86YjcsbNHUv3nmHNDaSL3CMuEvp5d37JsFU+SnKR1DQDYaYPQ4XQ3r9e542XweNUckyQPc7MnecnMDvHrq7drUjGPiMrjZicJkyQi9xgbK4BpwISMNav+xRPs0RAmSaXTV927Xu8crwW212qMSZKHGbkGihs9EytWOwQAkUnC3MoOy9ubndVzETlRM8r2JC8AAFQXOkny0zfWmCQR1ZuRa3DHpQiEC800jUlS2Yx1Fzu1OvoBSYbQUzC31uv++Y2MSZKHudmTbEDBppwdTTJyDSV7szO7ms5BVXKIqHJ2TzIUFWp7T90/Pz+StHYHwjLr/vlEzcy+98cld/a8sdscnL5VOnskyY01SZKqQY1k7xPG2kLdP7+RObaZLFXH7Z5kANgOdKM9lcBff+s5/HR794lnV8Ihotqze5K1zn5IslL3z1cjPYCiAqYBI7ECrbP/8BcRUU3sJEmdrnz+znRbJkmlsPQUzM3sdgluLI8AsomtEV+CvnYboeh9rsTQiJgkeZTbPckAsKl2YwDTEPE7uL7oTgxEzcjNRcBArgx41yD05Vnoq7eZJBHVkX3+r8vujCTZ2w4YiWUIU4ekaK7E4Rdmbj9LOdQKJVz/QhtAbgRr+g1OkawxTrfzqJ2e5AFXepIBYFPL7s3UYRUvFUpEznCznKzNTtBYvIGovgyXp9spbZ2QtCAgLOjrS67E4CdmbtaPG0sjbFxH5gwmSR7l5nok25aaTZI6xbprMRA1Iy+c/zvrkpgkEdWLEML1NUmSJLGsdBnsJMndTi17HRnXJNUSkySPshsmarc7020AYFPrBgB0iDgAFmkgqhe7WIrm4vmfv+lyJImobszNdQg9DUgyElLEtTjUrp3iLXQwI7enlVvTowGWbXcKkySPcrPmvm1b7QAgIQAdbVLKtTiImokQwhPnv10whjddovqxS0mrkV5YkjtT7QFO3yqHGben2w24FoP92VZyA2Zqy7U4Gg2TJI/ywnQbS1LzZSV7lQ3X4iBqJubmGoSRASQZakefa3Hk1yStL7AMOFGd2GsA3aqSZmMZ8NLl1yS5OPIvB8JQWjsB8JjVEpMkDxLCyn/J3TzpgJ3pfn0ykySieshPte3og6S4V4BUjfQAcq4M+MaKa3EQNRMvFG0BOJJUMsuAmbs+un3MOOWu9pgkeZC5ketJlhVXe5KBnQslR5KI6sNeeOt2T3K2DHi29LfB/VKI6sILs0iAggY3R5IPJG/HASEgaaH8SI5bmNjWHpMkD8r3JHX0uVb+22afdH1ywtU4iJpFfrqNi4uAbRoXbxPVleGVJIkjySWRk2sAsmuCJElyNRZWJKw9JkkeZE+38VIjiSNJRPXhlek2hTGwDDhRfbi9kbRNkpX8JtIsK70/ZSubJHnhes1OrdpjkuRBO+uRPHDS5WLo5ZokorrwynSbwhh0NpKIHGcmN2DlKpOpLlZKs+U7SbgNwL7k7dxIUgnrxy3L2a1U7O8Mk6TacW9VMO3LC+V/bWpn9qRrlTNokdLYFkGXIyJqXIUbSXrh/Ldv/AZHkogcZ9/7lbZuyJr791qtexDJ69l1SVScYk+36zw8qZVlCV/+45cwu7C30/nB+/rxW586V1Us9kiSubEKS0974jvkd0ySPMgrw+0AIAdC2EQr2rCFXnkDMyZPOiKnWMkNiPQ2AMkTPcn5kaTVOxBCuD7nnqiReWkWCVB4/rOTZD/yVukjSQAwu7CB63PxPT8f7m+rPpZwG+RgC6z0Noz1BQT6jlX9ns2O0+08JtuTnCsB7JELZVzuAMB1SUROsztIlEgPZDXgcjTZ4jGQZAgjA3Nzze1wiBqal0aRgcK9kjiSVIywLMjJdQDeOGaSJEHNr0viMasFJkkeY27FITIpQJKhdfS7HQ4AYF3KJUlcl0TkqJ3Kdu7fcAFAUlSoucXbLN5A5CwvFW0Bdu+7I4Sz62n8yNxYgSQsQNGgRHrcDgfAToU7Xq9rg0mSx9jD7WqkF5KquRxNVjyXJPUpLANO5CSv9SQDu6fcEZFzdirbeuP81zr6syPJegrm1t4pYs3OzK3VUjr6IEneaE5z9K+2vHFUKS9/kfTIVDsAiEudADiSROQ0r+yRUmjnpsskichJhofWIwOApGrZ/ZLA878YO0lSPTLrB+C2DbXGJMlj7N5arwy3A8C6bI8kMUkicpI93c4r6xEB3nSJ6sFKJ/OjNZoHirbYNJ7/+zLiuZGkEirb1Qu3bagtJkke46WNZG32dLt2OYUgdJejIWpcdqldL53/nG5H5Dx7qq3cEoEcanU5mh0qNyjdl7m+CMCbSZIRX4IwDZej8T8mSR7jxek2GSmIDSsEgBXuiJxiprZgbWfX/XmqJ7l7p5HExdtEzvDiekSgsBAAk6S75afbeShJUtq7IKkBwDJhJJbdDsf3mCR5iBBip7qVh6bbAMCyla3hz3VJRM6wO0iU1k7IgbDL0ezIVreTIDLJfBJHRLXlxQ5SYGdUm4UAdhNCwIh7byRJkuSCiqRMbKvFJMlDrOQmrPQ2AG/1TADAshkBwAp3RE7J9ySXuClhvchqIL94m+sSiJzhtfLfNpUlpYsyN9cAIwMhSVDavVH+22YntpwiXT0mSR5iX4SU9h7IWtDlaHZbNtsBcCSJyCn5og0emmpnU7t50yVyktfKf9vseKzkJszkpsvReId9vKxQByRFdTma3ewpksY6r9fVYpLkITs9yd66SALAkpVLkrgmicgRusfK/xZihSsiZ9nVyLyWJMmBEJTWTgAsA17I7jCyWrp2/dyy3F+3yWIbteOt9LfJGavebSTZI0ksA07kDK+uSQAKkyTedIlqzdLTMHOL7D15/ncPwdxah752B8Ej97gdjifY12uzdXeSJMsSvvzHL2F2YXdb6cH7+vFbnzpXl9hYbKN2mCR5iFeH24GdkaROeRsaWFaSqNa8Wt0KKFi8zel2RDVn5EpJS8EWyC0Rl6PZS+0aBGJX2eguYP8trHDXnsdmFzZwfS6+62fD/W11iQvYWddqrC1ACAuSxEljleJfzkPyCzc9ON1uWwSxbQUAcModUa1ZmVR2ITC8t3Ab2JkCzEYSUe3lO0g6ByBJksvR7MWR5L3ySVLr3iTJbWqkF5BkCCMDc2PN7XB8jUmSh3h5TQIgYTk3mtTD4g1ENZXfSDLcBiVcvx7HUtnVNq3UJswkz3+iWvLyemSgYINSJkkActu12NPtWryXJEmKCrWjDwAT22oxSfIIr24kWYjrkoicYXi6gyS3eLutG8DOAnMiqg2vn/8qR5J2sZIbEOltAFLR6XZewNH/2mCS5BHVbCTZ2R48sKJKraqt5CvccSSJqKa8vB7JZt90DVa4I6qpnT2S3OsgPagdYV+XzM01WJlUPcPypPzIf1sX4LHy37adTYCZJFXDm0e3CVWzkWRbWNu3osrwQDse/+z7axIjR5KInOHVjSQLaV2DSM1c4V5JRDXmhan2h7Uj/otwG6zkJoz1BQT6j7sUpTfk97Tr9OasH6Bw9I+dWtVgkuQROxtJVt5IKlZRpZaWOZJE5Aivr0kAuPcGkROEaeSr23lhJHm/doTWOYh08l3oq3eYJNkzfzycJOWLbbBTqyqcbucRfphus2RmS5N2yVtQhOlyNESNw1i1y/97c00CULj3BnsmiWrFSCwDwoKkBqC0e3N9C7BTdVdfZ6M7vzyio9/lSPZnz0rS1+5ACPc3uPUrJkkeYVQx3a5eNkQIaaFClgTaRcLtcIgagmVkYCRWAHi7k4Rz3Ilqb2cWyYCn97PROjkyYctPj/bwSJLa2Q9Agsgk80XBqHzePSObjL7q3Y1kd0hYyq1L6hTr7oZC1CCyU20EpEDYkxtJ2uyRJHMrDiu97XI0RI3BD7NIgILCLRxJ8sV0O1kNQI30AODofzWYJHmAlUnC3FoHUPuF24dVvivXSm5dUgdHkohqorCDxI2NJEu5RliWgBxqzSdxXJdEVB37nDP8kiTZaxL3GUmqR4VdL7AKtmtROr073Q4A1O6DjxkdjoUbPMDed0RuiUAJtdb0vQ+qWPPgff34rU+dK+v97JGkDmu9ViESNTVjPXv+u1W04aBrBLC7QqbWNYT0dgL62h0EB0/WO1SihmGfcw/MTOEkgL94eQNvvP5DAJXdm51mlyc3EssQpg5J0XY9Xo8Ku15gt9eU1o6yt2upN61rEKnpNziSVAUmSR5gf4Gd7EkqVrFmuL+t7PexK9x1Cueq6BE1E90jRRtKqY6pdQ8iPXeNPZNENTC7sIExfRVQgKsrKq4b2fOvknuz05TWTkhaCEJPQV9fQqDnyJ7nOF1h1wvs9pqXt2uwadwEuGqcbucBxqo/htuBnQp3HUySiGrCCxtJlsq+RnFDWaIaEAI9uS017M3avUqSpPy6xGY+/72wp1Wp8sV22KlVMSZJHuCnk84eSYqIBITFMuBE1fLT+a9xrySimmkTm9AkC6aQsG7Vdqq9E3Y2KF1wORL3+GUNGbAzhZvX68oxSfKA/PCthzeStMWtFuhChgILRnzJ7XCIfM1rG0keRuUGhUQ1Y09bX7HaYPmgObYzfYsjSb64Xueq71mpTZjJvetN6XDePyubgO6j6XYCEpZzxRvYO0FUnWIbSXq5EpTdM2lursLKpFyOhsjfOnJbadj3VK/bmW7b2CNJB12D8/taeXhPS5scCEFp6wbAjq1KlVW4IRaL4etf/zqOHTuGCxcuHPr8p59+GtFoFLFYDNFoFOPj4xUH2qgsPQ1zw95I0vsnHZCdcjeEODeVJKpSsY0ka1mNstaUcDvkUBus1CaM9QUE+o+7Gg+Rn9kjSUuWd/dHK2Rvdt/oI0n7XYNVoeO/Ta0ByCaMae/2Z+Vp3YMwN1ezJQC04wAAQuJJREFU7bWj97odju+UnCRNTk4ikUjgypUr6OzsPPT5Tz75JMbHxzE2NgYA+MIXvoBoNIrR0dGKg21EdvlfOdQKOey9ijbF2OuSOJJEVJ39KtvVqhqlE7SuQaRvvwt99Q6TJKIq2FtpLPlkJMkuLqOvL0JYJiRZcTki5xS7Bh9RVoEOQA63Qwm3Adve31Rb6xpEauZKwye2Til5ut3Y2BjGx8fR3l7ayTwxMZFPkADgkUcewcTERPkRNrh8T3KnOxtJViI/3Y7Dt0RVyZf/98HUDZvazXUJRLVgjyQt+2QkSW3vARQVMA0YuRkwzaQvV4nQD0sjbCqL7VTFkTVJk5OTiET2nvSTk5NOfJyv5XuSfVC0wWaXAWcjiag6OyNJ/jn/ufcGUfWEsPJbafhlJEmSFWid/QCas6x0n5IA4K9OrXyFuyY8XrXgyGayiURiz886OjoQj1e/t04ymaz6PbwkuRjL/kd7376/myRJCIVCdYzqYPZ0O2NtAdvbWwD8MQLmd/b3o9HOAT+p9THIrGSTJLO1G9vb25AkCeGwN3dxTyaTEEJAtPYAANLL89h2YboJzwP38RhUR5IkaPo2NBgwhYRVyxtTaQ+TTCYhR/qBlXlsLcxADNwDACVdt+zrh9cd9Lv05keShpBMJvecB05fvyv5G5rhTgCAvurO9dppQghHZ2E5kiTF43F0dHTs+lkkEimaPJVrenq66vfwkrb5G9AArKSBHi0AVfX+HN81qxUmZCimjukrryMpBdwOqak02jngRzU5BpaFzvgiJAA3lzcgNq8iHA7j3Dl3izPs5+bNm0gmk1DWk4gASC7N4urVq67Fw/PAfTwGlQmHwzjRagEAVn1S/hvIXgNgaQgBWLxxFUktO6JSynXLvn543UG/S5+SS5K6h3b9PvZ54PT1u6K/oZFGFwAruYGrr78CaN7pcK8FXdcRDAYde39HkqS7EyQgO7pUbApeuUZGRjzb01qJxZ9swgJw9L4HoKqKZ6taFbIgIyFF0CXWcaQ9gED0rNshNYVkMonp6emGOwf8pJbHwFhfwLKwADWAMw88DEmSPb0u8cSJExBCwNw6gqXn/x2UVAL33XsPJFWraxw8D9zHY1AdSZKgv/UTAP6ZagdkrwGba/dh49YL6FAMjJzN3vtLuW7Z1w+vO+h36c1Nt1O7BnGiexjb29u7zgOnr9+V/g0XJyOwthO4Z6AL2sBI7QNzkaY5e/9xJEmKRCJ7ptYVG12qRDgcRktLS9Xv4wWWnoa1uQoAaBsaAeDtqlaF4lIHusQ65O3VhjkeftFI54Bf1eIYbM/vlJJtbfXeOX43uzEswmEsB0IQmRS0zAYCkWHX4uF54C4eg8pt59b0+aX8N5A93mLgGDYAiMRSWcfe78l0ADo65ewojtY9BCUczics9ToPKv0bat1DSG8noCTX0NLinQ73WnA6MXVkjHdsbGzP1LpEIrGr2h0hv8+QHGqD0uKf3iQgmyQBXLxNVKn8zu0+WgQM5NZTsGISUVXsoi1+GkkCsqMoAKCvLfhiZKhWepVNAEASoWz5bx/h9bpyNUuSpqamcPny5fy/H3300V3V7CYnJ0vagLaZ7FS281cjCQDW5WySxA1liSrj5/PfrnDH85+oMkauOqxfyn/btM4+QJIh9BTMrXW3w6mbPjnb8W93EPtJviIpK9yVrazNZKempvDcc89hdnYWADA+Po5oNAoAeOaZZ3DlyhWMj48DAL7yla/g0qVLiMViSCQSuHDhAjeSvYufG0n5kSSedEQV2W8jWT/YKSvLbQCIyiWElb93+m0kSVI0qB29MNYXYazdgdrW5XZIddGbK9qwLne6G0gF7Ou1wW1bylZykjQ2NoaxsTFcvHix6ONPPPFEST+jHX5uJK1LnQCyw7dOl2AkakQ7G8n6Z48km8q9kogqZm6sQRgZWPBH+e/O9iAsS0CWs/d5rWsQxvoi9LU7CDVJ4SZ7JGndgyNJhcemGKWD1+tKOVK4gUpjN5JUHzaSNqTIriH3ZulNIqoFYRow1hcB+LOThHPciSpn3/sTUsQX5b/bwhpkWcpX3/2ljIX3APjWt5/Hz56VPFeB1wn2SFI810HsJYXH5m7DA+347x47AwAwN9dgZVKQA41VBtxJTJJc5OeRJFNSoEZ6YcSba8idqBaM+CIgLEhaEEp7t9vhlC2/Jml9EcI0ICm8lRCVyr73e3FU4iB29d1oKIT3tADq1iKub8U9WYG31vpy5b/t9dheU6wysk0Jt0EOt8FKbkJfu4Ngg5UBd5L3uzAalJVJwtzMlQD24ZokgOsSiCq100Ey6Mupqkp7FyQ1AAgLRnzJ7XCIfMUegV334KhEKZbMbLGJfiVxyDMbQ2H5b78eM7sznsV2ysMkySX2ok053O67cpI2rksgqox9ztjnkN9Ikszzn6hCdidJ3IdFAABgMZck9SkbABq/DLhd/nvLCiAt+XOqmsbrdUWYJLlkZ9G2P0eRAJ50RJXyc2VL205ZWY4kE5XDrjLmt+l2thWrDaaQEJQMdEjbbofjuN5c0QY/bfx7N5XX64owSXJJIzWSOHxLVJ6GOP+72UlCVK7C8t9+nbplQcaylS1d3gxT7vpyRRuW7yrXLkkSwuGwL6ZM83pdGSZJLvFz0QZbvsLV6u2m2nmbqFp+SpLs8r93Kzz/iag0dvlvSDI2JH/tkVSomdYl7Ywkte+6HobDYZw7dw7hcNjN8ErC63VlWJLIJX5qJO1H7RoAAFjpbVjJTSgt/r3gE9WLMPV8sQM/dJLcXf7XNmwu4DEAxjp7JolKld/6o7MfVkpxOZrKLRYkSasux+I0eyRpyYzsez0E4OlS6HZb09xYgaWnIWtBlyPyByZJLjEaYE2SnCtfbG6sQl+7zSSJqAT6+lKu/HcIio9K599dYnZVlvFYJ6CvLUJYJiTZvw0+onoprGwJH3fqL+bW5/TJjZ8k2aNlS2YEdpmtYiW3vVwKXWmJ7JQBX5lHcPCE2yH5AqfbucBKb8Pcyp5cfk6SAG4qSVQuw+flv23rVgtMyIBlwEisuB0OkS/oK/MAAK3niMuRVKdZptsFkUFHrvz3oo8LNwCA1n0UAKCvzrsciX8wSXKB3ZOktHZADra4HE118sUbVpkkEZWiESpbAoCAjLiUbTSweAtRafSVOQA7DVa/sqfbdcubkITpcjTOsZPAhBVCSgRcjqY6Wk8uScp9B+lwTJJcsLNHir8bSUBBWUmuSyAqSWY510jyeU8yAMRz1bk4kkxUGrsXP+Dz8z8hwkgJFYok0Gqsux2OY+wkadH0Z7n2QvZ3LsMkqWRMklyQH273eU8yUFBWkiNJRCXRVxsnSbL3ebFHx4hof8LUoa8tANjp1fcvKT/lrl3336qkYhU7ixlQsksj7JEzP7PvOXYblA7Hwg0usIc6A73DLkdSvZ2ykuyZICqFnh9J8v/5H5c7AJOdJESl0NcWskVbAiEo7d1uh1O1RTOCqLqKNt1/axJLrVDXnyv/7ff1SEDhdLt5CCF8vSa2XpgkuSCTn5Ps/55ku2fCSm7C3E5AafH/hYTIKVZqC+bWOgD/T7cBCqfbcSSJ6DA7s0iONEQD1U4c2gz/jSQBpVWos6fbLTTAdDutawCQZAg9BXNjFWqkx+2QPI/T7epMCGtn4Wav34fbs2XA1Y4+AFwMSHSYTK6RpLR1+b5oCwCs5ZIkY/UOhNW4i7eJasFej9QIU22BnQp3bT6cblcKCQJ9+TVJ/u8AlhQtX2yL7bXSMEmqM3NjFUJPA7ICrXPA7XBqwh7CtRekE1FxjbQeCQA2pHZIirZrg1wiKs6+RwZ8XtnOtujjNUml6JS3EJBMGELGquXdPZDKobF4Q1mYJNVZvrJV1yAkpTFmO7KsJFFpdtYjNUYjSUjyzk13edblaIi8Ld9J0gCzSICdJClkbcFKbbkcTe3Z65GWrXZYDdJcZvGG8jTGUfeR/FS7BmkkAUCASRJRSXbK/zbG+d/ZHoR6wPlfagUpomZQuCapEaQRQNwKA9jZ/7GR9DdQZTtbvlObxbZK0hhDGT6yU9muMRpJwE6vGIdviQ6WabBOkrawhkDPUWwD+PEPX8T3J3cWNw8PtOPxz77fveCIPMTc3oCVzFZSa4TtP2xLZgQdchKZ1cYbmehvoPVItnynNpdHlIRJUp3pDVTZzmY3+Iz1RVhGBrLq712piZwgLDPf29ooa5KAna0MwqklXF+KH/JsouZk99wrkV7IgZDL0dTOotmOe7SF3CjZoNvh1NRAA20ka7PbnkZiGZaehqwFXY7I2zjdrs7yPckNsEeSTWntzFbqEhYM7pdCVJQRXwJMA5IagBrpdTucmrE7SbKbLnJ6HVEx9lS7Rij9X2jRym0o3YAzSfrlbKfPQgPskWSTWyKQw9kiFI04RbLWmCTVkZVOwtzIVoFppJEkSZJ2Ktw14IWSqBZ2RpEHIcmKy9HUjtZzBAJAq5xBq5R2OxwiT2q0qbY2e/8gfaWxCreoQkeXsg2gsabbSZIErZvryEvFJKmO7EXbSmsnlHBjlJO02euSeNIRFdeojSRZCyIhZRsRgwqn2xEV04hT7QHgTi5JyqzMQxKWy9HUTpdYAwBsWkFsi8aZHgkUVrhje+0wTJLqqFEbSQAQ6MlOH+RJR1TcTmWrxjv/7U1l+5kkERWVL//fQEWbAGDNaoMhqYBpoEM0zvnfbWVn/dwxO90NpEqd7cE9VUZ3KhLPswLpIVi4oY529khprJ4kgBvKEh1Gz+0j1Ijn/5rUjRHMcCSJqAjLyEBfy67XDfQeczma2hKQsKH1oitzB91iFUBjrLfsEXaS5O+iDW1hDbIs4ct//BJmF7LVFU+aq/g0gNg776BfltwN0OM4klRHjbhHkq1wup0Q7JkgKiSEQGY5BgAI9EVdjqb2VuUuADsLnYloh74yDwgLcqgNSlun2+HU3IbWA2Bn9KURNMpIkm12YQPX5+K4PhfH64vZ8ZHWzArba4dgklRH+UZSIyZJnQOArEDoKZgbK26HQ+Qp5tY6rOQmIMkN2UmyJmWTJLtkLhHt0HP3fq13GJLUeD33CS07etSdW8fTCLobZCSpmGWrHaaQEIAOM7HsdjiexiSpToSp58stBvoba7gdACRFzW+Ql1mKuRwNkbfouXNC7exvyH0p1uRuAECXvAkNhsvREHlLZik71bYRR5EBYEPrA9A4I0mWns6vr2qUkaRCJpR8xT621w7GJKlO9JXbgGVCCrZAae9xOxxHBHqzNwB7xIyIshp5qh0AJBHClhWALLF4A9Hd8ud/A+2PWChhT7cTq5Dg/wp3+socJABbVgAbDVbZznY7l/xllmbcDcTjmCTVyc5FMtqQw+0AEOjLjpBlFpkkERWye+vsjoSGI0n5m+6Qsu5qKEReY48kaw3aSbKldkJSNKgw0S1vuR1O1TK5IjvZa5r77bViFeqqZY+QsVP7YKxuVyeZxWy23qg9yQCg9Wd/N509E0S75JOkvsabamu7Y3biHm2RSRJRAWHoDVvZLi+31jKzOI1BZR0rVrvbEVXFTmoXPDLVrliFOtuD9/Xjtz51ruz3zI8ksVP7QEyS6qTRp9sABSNJyzEIYUGSOFBJJITYWbjdwOf/vJkt3sAkiWhHZmUuV9mutSEr29m0vuFckhTHlO7v65zdXrvtsaINdoW6QsP9bRW9lz2SpLO9diD+VerEHl1p5EaS1jUISdEg9DSM9UW3wyHyBHNzDVZqK9fb2nh7JNk43Y5or/z+aA081R7YmUo82ADnf8ZjI0lOWLbaYUCBMDJsrx2ASVIdWHoa+toCgMaebiPJys6msqyYQgRg51zQugYhqwGXo3GOnSR1K1sISRl3gyHyiJ31iI1ZtMFm/35+31BatvR80nC7gZMkC3J+6wa21/bHJKkOdHu4PdwGpbXT7XAcZZc350lHlNUMU+0AICmCWLdaADRGbzJRLTTDVHsguwcUAAwocUjw7wal7cYqICykEGzYyna2FTlblZAV7vbHJKkOChdtN/JwO7BzI8gs3jrwebWu1ELkVTvnf2M3kgDgttEJgFPuqDkcdh+zrIL1iI1a2TJH6x6CAQVByUCPvHH4CzyqXc9urroqd8MLle2ctCJl97fT2am9LxZuqAM7S2/Y8r8FtNx0Qn05VrQSCwAMD7Tj8c++v96hEbmiGSrb2ebNTpzFPI4oa+Asd2p0+1UcA7L3uf/+wnugr+Yq2zV4J4kkK1iVutEvlnBEWcOyFXE7pIp0ZJYA7CQQjWyVI0mHYpJUBzt7JDR+Iyk/krQyh/nEOq7Pb7ocEZF7hBC79khrdLcLKty96m4oRHVRrOKYLbMcy1W2a4PS1lXnyOpvSe5Fv7mEo+oaXtePux1ORToy2fXjy3Kvy5E4z04EMytzEJYJSVZcjsh7ON2uDpppuo3a0QdJCwGmgQ7h7wWcRNUyE8sQ6W1AVqD1DLkdjuN2VbgTnFJLzS2zMA0ACAyMNPxUewBYySUWR5Q1lyOpXIeeHQNfkvpcjsR5CSkCSQsCpgF99bbb4XgSkySHWZkkjHj2pGuGJEmS5Pzv2SNWXY6GyF1pu5HUOwxJ0dwNpg4WzA5YAmiT02jBttvhELkqXZAkNYNlyd9JUquUQtjMzn6xixo0NEnKz3Bgsa3imCQ5zC5goLR1Q2nx5xzdcuWTJGvZ5UiI3LXTk3zC3UDqRIeKZasdANBjrbgcDZG7MovTAIBgvz+nnpVrOZdY9CqbCMJ/2wAcVbIdu2rXIHSpcbdrKBTIfTftexXtxiTJYek7NwE0T08SsHPS9TFJoiaXXmi+899el9QjDk+SDqoOxgqY5GtCILOQ7SRtlk6SlBTObwNwRF13N5gKHFWzI2CBJklqgZ17UyZ3r6LdWLjBYXZ2HhxsjoskAARyv2ufWHI5EiJ35c//JkqS5s1OvBcz6C2hk2S/6mCsgEl+1yY2YaU3AVlp+I1kC80ZXegMbOOIsoabRr/b4ZTFniYYHDgBNEnBt+DgSQA7HXq0G5Mkh+VHkpooSQrmes3axSZapRS2GnxDNqJirNQWjPVspaRmGkmaNbIVk/qs0jpJDqoORuRXfSLbSRDoPQpJbfz1iLZ5swujmMNRH65LsmPOXq+3XI2lXrKjZhLMjVWYW3EorR1uh+QpnG7nIGEayCxlh9uDTTLcDgBysAVq1yAAYFhh8QZqTuncekQ10gsl3O5yNPUza2bXJfSIVViG/9YlENVCb66ToFmm2tnmctNt/Va8QYGJASXbWRMYaJ7pdnIwDK07215Lc13SHkySHKSvzAGmASnYArXTX8PO1bKTwqMqkyRqTs1WtMG2brVg0wpChoC+2CRzVojuYq/JDfSPuBtInc0buSRJXYME/6wrHFDiUCULGTkENdL45b8L2fcorkvaq+wk6emnn8bly5fz/3+QWCyGqakpAEAikcDk5CQSiURlkfqQPdUu2H8cktRc+ag9vZAjSdSsMk1YtCFLwqyZnXKXvnPD5ViI3NFrT7drolEJAFiyItCFjKBkoEfeOPwFHmFPtYtrfU2xp1UhO0niuqS9ymq5P/nkkxgdHcX4+DguXryIZ555Jp8EFXP58mU89thjOHPmDB5++GFMTU0hEmmOMthAQSOpidYj2eyRpGGOJFGTSjdh0QabvS7J7igiaiZB6OjMbaYebLKRJAtyvsKln9Yl2bNe4oEBlyOpP7uwWIbX6z3KSpImJiYwNjaW//cjjzyCiYmJA1/z7LPP4hvf+AauXbuGixcvVhalT9lZeTOtR7LlK9zJCQSguxwNUX1l1yNmp5o130gS8iNJnL5BzWgoV0paaetuyoXwsVwnyTF1GZ3tQV+U+s+PJAWaa2kEsHOP0lfmYWVS7gbjMSVXt5ucnCw6CjQ5OXng66LRaPlRNQAhRNOuSQAAta0LW2hBq7SNI+oapn1WCpSoGs28HhEAZo1s8YbM4i0Iy4QkKy5HRFQ/9jTzZptqZ5sxe/EI3sExdQWJsOb5Uv8SBIaV7L5u64FBl6OpP7WtC0prJ8ytdWSWZhA6etrtkDyj5CSp2Fqijo4OxOMHl2611y298cYbOH/+PMbHx8sMcbdkMlnV6+vFiC/BSm0BsgKjtQfm9vaBz5ckCeFwuE7R1U4ymYQQu3uC7N9lSe5Dq3ULw8pq0SSp2Gtpf/Z33y/nQCMq9RgkZ64BANTeKJLJ0nvm/HoduNuy1Y4MNASMDDbmrkPt2btPTCm/a7FrBM8D9/EYHPz9PaZmG9zBoXsOvEc2qplcJ0lUWcGbud/9oFL/9W4L3P3375MTaJF1ZISChNZbtzi8IplMQuk7BnNrHZsz12B1+WdfLyGEo2vISk6S4vE4Ojp2DxtHIpEDCzGMjo7i/vvvRyQSwfj4OD7+8Y8jGo1idHS04oCnp6crfm09aQvX0AbAaO3FW2+/c+jzw+Ewzp0753xgNXbz5s09N0r7d1mS+zCSS5JKfS0dzi/nQCM77BiEr76IEIANNYLFq1dLfl+/XgfuJiBhSe7DUWse81MvYKtv7wLuUn7Xg64RPA/c18zH4KDv73E1W7QhdOTeA++RjeqO2YmMUBCWdbQZh69Lrndb4O6/v53UzhrdEFLzjXrfvHkTQm5DGMDS269iO3DE7ZBKpus6gsGgY+9fcpJ0d4IEZEeXDirEULh+CQDOnTuHiYkJPPXUU2WEuNvIyIgvemA2Vt7AFoD2Y2cwfPbsoc/3azWVEydOFO0lA4AlOVtGc7/iDcVeS/tLJpOYnp72zTnQiEo9Biuvfh06gIHRhxE+c/j5b/PrdaCYRakPRzGPLrGNY0WugaX8rsWuETwP3MdjsP/3N4gM+uVc0YYj9+CEHNj3HtmoLMiYM7pxQltCV/r2oc+vd1vg7r//sVxSO2M23ygSkP37JzPvx/qNSbRm1nC8hDarV2iasxs1l5wkRSKRPVPrio0uFZqYmMCFCxfy/45Go3juuecqCHNHOBxGS0tLVe9RD+tL0wCA1mNnfRFvpQ66QS5I2Sl2R5Q1aDCg3/V1a9aba7X8cg40soOOgTB06LmiDZGRUWhNeqyW5D7ABMylWxV/X4tdIyRJgqZpPA88gMdgr6i6ClkCElI7lNYONOtdbsbsySZJmcOTJLfbAvbI34zRgzZXI3FHOByGdvJ+rAMwlmIIaQpkzbnRmVpyusOh5Op2Y2Nje6bWJRKJPaNFtlgshieffBKxWCz/s42NDQwP+2euY6WEZSI9/y4AIHjXAjivVHKp1mEVawBgQ2pHwgpBkQRLgVPTSC9MA6YBuSUCtbP5ysnaFuVsJ0n6zk0IyyzrtQddX7JTZUYPvDn6oZoWNSa7wb0aGGrq79qMkR2VKSVJckopf38FJo7mlgTcMppzJAkAlPYeKG3dgLCQ4f52eSWPJAHAo48+isn/f3t3GhzHeR54/N89N44BSIInMOANiuBlXpZEWbdsQXScbLzesBxZjpyU10ltFH8IVdnUlrRV8m6+MBsfKWdjaTeSU3ZMancly3FEWpYtURIhUaR4iAR4gRcu4iABzACYu7v3w2CGAAEMZgYz6DmeX5XLIrpn5pme7rffp/t9n25uTiRGzc3N7N27N7G8paWFjo4Ompqa8Hg87N27d0J1u+bmZr7//e9nKfT8FbnVhREOoNic2BdOrO43XZWXbXct4uu7C2eMckWSijWJ76IoXIsuZLO9gxXWfq5KhTtRAkLdsTmIjqVrin5YTTIDynwURxlGyE+4rz3xLI5UJGtf4hWxIkmeLJDv1bRE8YoP3Rp21RbN+T4T8eINVeHetC+SZEsq23+pZQibojOq27mpV5oRZl5QFAVH7Vr8F44S7LqI01M4Q+5yKa0k6Qc/+AH79u2jo6MDn8/Hnj17JhRhePPNN2ltbU1UsGtqamLfvn1UV1fT0dHBCy+8MKuiDYUi2HURAMey1VOWvp2qykvdosK8yTvTd7kerWGzvYPllptzHZoQpognSc7atSZHYjJFwbl0LYGrpwl1XUgrSYpLVhErl68VIlPxc92gIzb5vZjO9+no190EdBsuNUK4v2PmF+TITNs/MdROWwCU7kUtiBUa8V84Sqhr5mJjpSKtJAng2WefTXmZx+NJun6xiu9gjmUl3kkCrkVjxRtWWPtNjkSIuZEYaivHP47aWJIU7LqEe/vsHv8gRL5zK37mWfzoBgzZS3eoLcQqXHZoC2hQewjdaCOD7uacSBRtKOGhdnGOsQt7wW5JkuJSnpMkUpe4krxMHsjVHl2AbijMs/ipUpI/K0qIQqcFRogMdAOx4Xalzlm7DoBQ1wWTIxEi9+KlpHu1aqJqYUx8z6X4kLv4haN8tHzsIbKSJIFj6WpQVDTfTaLDMo8cJEnKOj0cSNxadpT6cBsgjI1urRqA5XI3SRS52BVTsM5bgqWsdMe3x8XbwMjADTT/9M/UE6IYxO9KXNcWmBxJfrg+NpIk2JmfF0lsRpjFlthQvHhCV8pUuysxj16G3MVIkpRloRuXwdCxuGuwVs43O5y8EG8o42N/zTJTpZtSrkQksiN+YnEmGWpXSvuZxVWJbUFsbkZ8rqYQxWq5DN2a4MrYuT/S347DCJoczWRL9B5UxWBAK8dnlF4p+6mqiMaHiQe7Y+11KZ2vppKfg0QLWCqdpFJzLVrDfVxkhclJ0nSVbkCqXonsSFS2Wzb9ULtk+2ExVr1y1K4jcqubUOcFytfuMDscIXJCRU/Mvb0aXYj0AGDEcDFsnUdldJAleg+tzDM7pAmW6bHy5FdKtPLuVFVEG6M6jwEtR4/x8Y3Gku8XSZKUZYnKdrUyHyku/uyBeutNVHRTY5GKVyJXDEMn2HkeAGfdXUnXnW4/LMaqV87aBkY+fUcmA4uitswyiFOJ4tdt3NCqJUkac8vhoTI6yDK9G/IuSYrNHy3VJClu/Plo1FLBY1WwUOulq0f6SjLcLosMwxjXSVpncjT5o0+vwq/bsSsayyyDZocjRE6E+9rRg6Modif2DMpdF6v4BaNQ9yXTnpciRK6ttvYBcC26CEO6Vgm3HHXA7bs2+cLQoizRewC4HC3tSoTj9Wqx/ppDibLQkHnkciRnUeRmB7rfh2Jz4Fi6yuxw8oaBwtWxsclrbL0mRyNEbgTbW4HYBZKpno9WquwLPSh2F0Y4SLjvutnhCJETK22xJOlyid+VuNMtZyxJWqz3YiF/LpKEeq5iI8qobqdXqzI7nLxhoCT24Vqty+RozCdJUhYlOkm1DSgWm8nR5Je2sSs1a6w9JkciRG4EO84B4PQU15yi2VJUS2L4YbyNFKK4GKwau5NU6kO37jRinY9a5saKliiRng+CnbH2+kp0EUaJP0T2TvE7a7VjwxFLmSRJWRSIJ0n10km6U1tkCUDsRGKUdrUUUXwMw7h9kaR+vcnR5B/X8g0ABK6fNTkSIbKvRh2mSg0QNVSpbHcnRcFVHzv+1+bRRdJ4ey1J7WSXI7EkaZnejWGYO4/cbJIkZcnETtIGk6PJP53afIKGjXI1TI1hbpU7IbItOtiDNjoEFmuihKq4zTmWJAXbz5X8SVcUn/gw8uvRGqLIUNs7uVZsBGCtLT+SJMXQCVxvAeDS2AVccVusv2bFSYhwX7vZ4ZhKkqQsiQ7eQBsZHOskTV/+t1TpqFyJxOYl1eoyzlUUl8C1M0Cs9L9qtZscTf5xLF2NYneiB0cI98q8JFFcGqyxogQXo9LhnopzxSYAVlr7sBE1ORqoDt/ACPkJ4qBTk+dZ3klH5Wokdoet1IdIS5KUJYFrsWEkzmVrUW0Ok6PJT/FxrnUyGVAUmcC1TwFwrdxsciT5SVEtOD2xYYgy5E4UFcOgYewOycXIUpODyU+2+csYoRybcvtZUmZaFLwGQKdaJ5UIpxGfRx6/AFiqZO/IEv+VUwC4Vn3G1DjyWfwEUqd3SilgUTQMQ0+cSCRJmp5reWzITbDET7qiuCwwblGpBgkZ1sQzAcVEiqLQYYlVucuHIXcLA7G72fGYxGQXxvprgetnS7q/JklSFhi6RnDs6qh0kqbXoc1nVLfjIExIHiwpikS45xp6YATF7sKxVIbaTse1ItY2BtpbMLSIydEIkR0evQOAy5FFaDIfaVqdqgeAu2zmVkyzEWVBqBOAjrGYxGSd2nwCODFC/pLur0mSlAWhG5fRg6OoznIcS1ebHU7eMlC5EFkGwGjbqWnX0/Xk1e+SLZ/ptUJkg6IoiX0tcPU0ELtTolisgOyHU7EvWYGlvAojHCTYecGUGKorHdJ+iKzyaLEO98WoDLVLpt0SS0g8lltUKAHT4lhtiz2vyVK5gCGl2rQ48p2BmrjT5r9y2uRozGM1O4BiEIgPtVuxWR4iOYPzkaVsc1wjeO00f/vTtXT2Dk9YXre4kr1Pbk/6Hqqq8Lc//SSj1wqRLfH9cEf7YeqBN6+5+PTv3pX9cBqKouJa9RlGzhzGf/lkYvjdXKpw2aT9EFmjR8PU6WNJksxHSmpUqaAjOh+PdYD1tm6Ohc25oLzBFvu9ytZsg/PyfKRk2tV6GrQ2AldOwwN7zA7HFJIkZUE8y3at2mJyJPnv/NidpFB3G/2Ofi53hzJ6n87eYS53ebMZmhBp6+25xTKtCxQ40j+PPl32yWTKVm1l5MxhApdPwSNPmRaHtB8iG4LXW7ARZUgvo0ubZ3Y4ea81UovHOkCjrcukJMkYlyRth/OjJsRQONrVegBC3ZfQgqNYnOUmRzT3ZLjdLGl+H6GuiwCUSdGGGXmNcny2GjB06rXSrr8vCl+93o5V0enXKunT3WaHk/diczYVwn3XiA4Pmh2OELPib/sEgNZwLSB3JWbSGqkFYvOSVOb+eWlLLF4WWEbRFKvMH0/BiFqJraYODJ3A5ZNmh2MKSZJmyd/2CRg69sUrsVYtNDucgnDDFZvcvkq/anIkQszOSu0aAGcjdUgnaWaW8qrEvM14B1OIQmQYBv5LsX24ZazzL5K7Hq1hVLdTpoZZae2b88+P30Xqdy6XR7WkqLxhJwCjFz82ORJzSJI0S6MXjwFQtnaHyZEUjhuutQCs0K6ZcjVJiGwwdI0VY0lSS1hKyaaqbOyk65/lSVdRJCk1i6IouFyukv4NIjc7iXr7iGKR+UgpMlBpicTayi32uR9JstEWq0TY45ICW6kqWxtrrwOXT5ZkVVJJkjIQr4CkR8OxCW3czralOtLMBhzLsJRX4yDMGqv5z0wQIhOh7jbKCBDQbYkHJYvbpqskV77uswD4r55GD6Vf5Sr+vk6nc9YxZtNsqnIWGrvDQWNjIy6Xa9KyYvqeycQvkHaptYSxmRxN4TgVXg7AFvt1MOZuX6lWR1ll60c3oNvVMGefW+gctWuxlFehh/wE28+ZHc6ck8INGYhXR7J0n+H3IkFGKOev/qWduiVDUh0pFYpK2dodDJ96m032Di5Gl5kdkRBpG73wEQDnIrXocr1pkmkryRkGTynVzNOG8F9Jf5x7sgp1ANvuWsTXdzfOJvSMJIup2CrnWVS15CsEjp5rBqDNIncl0nE+soygYaNaDbBEn7uLpJ+xxR4geyW6mKC1cs4+t9ApikrZmh0Mn/4Noxc/Lrm5XJIkZaizd5i7h1vBASeDdVwe8EEJDz1IV1nDToZPvc1mezuv+XdiSCdTFBDDMBhtjXWSTo5dGRVTm6qS3CeuWh5zDTF64SiwNWvvC1C3qCKj98uGUqqaV0rf9U6RwR7CvVdBUblsWQ2EzQ6pYGhYOBuuY4fjKmu1tjn73K2Oa0CsvZYHtaSnbN1nY0nS+Y9Y8PlvlNSjbqRnmiGLEWWzLTam9kR4hbnBFKCylVsI4qBaDbDahAmcQsxG5MZlor6bhLFxTiZtp+3TcKy0rP/ScaxG6Y1zF4UtfhfJtWIjQWXykEORXPzC0lrtEoau5fzz5qkjrLDeRDcUTstFrbSVrdqC6ixHGxkk2N5qdjhzSpKkDC3Xr+NSIwxqZVyNLjI7nIKjWG1jV+Bgm12q3InCErwYG2p3xbKSiNyQT9t1rYYRazVGOMgqTY5/UVhGzn0IQPld95ocSWE6F6llVLdTwSiBq5/m/PN22q8A0BZdzLAhSW26FIuN8nX3ADDSesTkaOaWJEkZatBiz0Y6GV6BIaV/M3LBEps8+Rn7dSzk/mqSEFlh6AQuHAXgkmWtycEUKoWO8g0ArNMumByLEKkL97cT7rkCqoXydXebHU5B0rDwSXglAMOfvpPbDzMM7nbEhvUdDcn8sUxVbPgcAKPnPyypKneSJGVAC44mrn7GD3SRvi61Fq/uolwNs97WZXY4QqTEevMq+uggqquSdlWGbmQqniQt169TrgRNjkaI1MQ79WVrtmEprzI5msJ1NBR7XqL/wsdogZGcfU6d3kmNZQS/bpOhdrPgXL4BS3k1emAEf9sJs8OZM5IkZWDk7HtY0eiOVtOpzU/8fbqSt2JqhqLySSiWZN47dqWnGLdhsu+Tj9+10OKdSba/j6PzFAAVGx9AUyZPYC3GfTgXRmwLcCxdjYqRGA5jplR+N01+V1NleizPpjz7+GWGFmXkzHsAVG5+JOl7iuQ6tfkMWBdiaBFGzrw77XrJjrlU2tkNWmwOzYmwDI1O1/g2UVEtVGx6AIDhU78BCrM/kC7ZY9JkGAbDJ98G4MPQWhg31C5fS9Pmsw9Da3nE1UqjrYtqdbQot+F03ydfy+UWWrwzyeb30f0+bH2XAKjc8gicvTZpnWT7cCHuv7lUueVRQjcus8t5kXdD68HEocuptj3yu5on02N5NuXZx792pXaVL4WH8OPitTYXT63L/LsIhevubcwf+BXX33mdl98vw1AmXrdPdsyl0n5HfbdYM1ZBL9ZfE+m4s02s1t18HRhpO8EPf/wO/+mPHjY7xJyTJClNoRuXCfddI4qF4+FVU66Tj6Vp81WfXsWlyGLW2nq5x95GgNg8pWLbhoVWLrfQ4p1Jtr5PoPV9FEPHtngljsUrgGtpfWah7r+5UrHxAboPvcxii4811l7aokvMDmnGtkd+V3NleizPpg2Iv/axihNgh6OBlQwOhjJ6L3Fbe/lGtvuPUBEcwjXYwplI/YTlyY65VHiPv4kFnbbIYjq1BVmJuRTd3v4quyoXs8bWS3XPMaD4kyQZbpcm3/GDALRZ1uA3HCZHUxyaQ7HE6F7nJRRDCjiI/GToGqOnfg1AmQy1yQrV4eK8JXY5/nMOKeAg8tci1UujvRvdgPdDcgspGzTVjnvb5wF42Jnd0tJ6KMDwibcA+G1Q7vJmS/PYHblN0bMlUcBBkqQ0RH23GGl5H4BT1i0mR1M8Tofr8eouqlU/ntHSqsEvCsfohaPow7fQbS5cd91ndjhF44x1EwCb7e3MV3M3gVuI2bjfeR6Alkgdt/RKk6MpHu7tT6ChstrWR4P1Rtbe1/fJQfSQnwGlmtZIXdbet9SdCi/Hq7uoYJSRluIvBy5JUhq8x98EXcNZv4E+dbHZ4RQNDQvvBe8CYK3vKIZR/JMBRWExDAPv0X8FIFS/HcVmNzmi4nFLreF8ZCkWxeBhZ4vZ4QgxidMIcLfjMgDvBdebHE1xsboXcMayEYAvlp0EZn/+1wLDDDW/DsBx6w55TEsWje+veY++UfT9NUmSUqQFRvCN3bqtuud3TY6m+BwJrSNoWKmK9BMoofKSs5GsssxMVbhyVZWmkCrjzVTNbPyywLVPCXVdBIuVUP22uQivpLwdiHWS7nG0UaEETI5GFJpcV5TcGj2FQ4nSGZ3HxTyYN1dsjtt2EDKsrLDeZLOtY9bvN9T8OnrIj33Rci5YZGhkth0JNRDGRrivncDl4u6vSeGGFHk/egMj5Me2sJ6yNduA98wOqagEDDtHgg086mpl4L39YOw2O6S8N1MFtUyrOeUipnysjJesmtn4eA3DYPDwfgDKNj3CoEMm6WfbpegSrkVrWGG9ySPOFrysMTskUUByWVFS8w+zJXoagIOBLZhZgbFY+ZVyDgfX8wXXGb5c9jHnvUsJY8vovcL9HXiP/RsA8x/+GsYvfNkMVQABw8FZ60a2RU8y8O7PcK3eiqIU5z0XSZJSEB0ZvH3QPfSHRbszmO03wY08UNYGPVdYa2vjMovMDinvJau0ZVaFukKrjDdTvIHLJwh1XUSx2in/7JegPXvj5kWcwq8Cm/lW5W95wHmet6MPmR2QKEC5qDw49NHPsROhMzqPsxHPrN5LTO/XgY1ss1+lxjLCF8tO8bp/Z9rvYega/b/8IWhRytZsx7V6K3A4+8EKjlu3s109T7j3KqPnP6Ji/S6zQ8oJ6e2nYPDwfoxICEdtA2Vrd5gdTtEaNZxcct8NwL3RD7Egle6EuQwtwq23fwyAe0cTlvJqcwMqYq2RWi5GlmBTdDYMScdGmC8y0I33418C8GZgK3IXKXfC2Pg//tj5/wHHORptnWm/x1Dz64S6L6E4yqh54lsoivxeuRJUXFTfHZt6MvDOT9GjYZMjyg1JkmYQ7LrI8KnYw2PnP/KUHHQ5dsn9WSzl1VQbXh6VSdzCZN5jbxK51YWlvIrq+75idjhFTuENf2yIY/1oC4F2qXQpzHXr16+AFuW6Wk9LpNbscIre+UgtHwQbUBV4qvwDyiMDKb929MLHDB7+GQA1n/8GVrc8FynXqu7+HSwV84gO9uD98Odmh5MTkiQlYWhRbh58EYCKzQ/hqpda+7mmqXYWfP4bAHzB9SkLVRlPHKcoCi6XSxL1ORIZ6mPw/VeB2Nh2i7NcfoMc69QW0ByMzUe6+eb/xGJETY5IlKqR1iP42z4B1cJ7tvuRu0hz4zX/Tq5GayhTw9zf+zMigz0zvsZ/5TR9b3wPAPf2Jiq3yHPs5oLqKGPBY08DMHTkNSIDxTcUXZKkJAbff5Vw71VUZwULHvm62eGUjPLG+7iu1mNTdJ6s+AAV3eyQZlVJLluf6XK5aGxsxOVy5eTz8tlcb/95FTb6fvEDjHAQp2c9FZsfAm7/Bk6nM+ufKWJ+EdhO0FJO5FY390SPmh2O6Waq2mZGpcpcvW+u2tJkpqqMFx0Z5Oahl2LL7/19BtX5cx5XqdKw8E/DD9GruSnTfHT/+L/g0dqnXNcwdHwn3qLnwN9gREK4Vm1NXGQVuRU/bsob78O1cguGFqHvje9jaLELW/lWzTZTUrhhGoHrZxk68hoANU/8RyzlVSZHVDoUReG3tofZE/gXVlpvstt1il8GzC27nGkludlUVpruM2f7voVorrf/tvBxQv3niCh2Xunbie+7E6tZltr2n0sBw8HJ+U3c2///2B49wXFbaT8MMlk7YEalytnu+2a0pcncWRlPMXR+L/wL6vVhRlxLWXn/V+BE8T80M5/4jDL+3vc4zy56l6rRfn6fN1hRsZyPQmvojs7Dqmg0RHvo/ue3CHVeAKB8/b0s+t1vo1ikWzsXxh83Qze28iTnoPsSr/7tPjrqvpB31WwzJXvTFCKDPfS+9j8Ag8otj1DReJ/ZIZWcYdXN/tFd/HHlYT7vOkunNp9T4RWmxpRJJbnZVlbK1fsWorna/htt7TR6Y0nRO9YHOHkDILsVs0RyN8oacO94At/xg3yt/AO+69tNv+42OyzT5FOlymzs+2a0panG9Luu49S7OggZVj6s/iKbLZmVohazM2y4eHfJ13lqUQu+Tw6x1X6drfbrt1eIQKgTFJuDeQ9+laqdu1FUi3kBl6jO3mEu39BRbPfwjcr32Bk9zs2uGqA4kiQZbncHze+j58DfoPt92JesYsEX/sTskErW6chy3h17uvnXyj9glbXX5IhEsVtu6efrFR+gEBvbft663uyQStaCR/+IHmUx5WqYP618m0p5yKzIsfsd53jUFSsY8i8juxi2LzQ5otKmqXZqmr7JTx1f5UiwgZtaBbqhEDFUBpR5VN//B3j+9AdU3/0lSZBMdiqyItFf+3zk10VTeEeSpHG0US/dP/mvsWpWlQtY8gd/jWqXuQdm+rl/O2fCddgUnW9V/oaa4NRjk4WYreWWfv6s8m0cSpRe5woZ224yxWrjXx2/Q79WSY1lhD93v0WV4jc7LFGkNkbP8pXyYwAcDGzmVGSFuQGJhFtqDa/67+E73i/zl4NPsnfwSX7i/BrzH9iD1V1jdnhiTLy/ZkWjZ/9/J9Be+BWKJUkaE+5vp+uVvybS346lYh5L//B5rJUyWdNsBio/HnmAC5ElOJUo9/UdYKTlA7PDEkVms+06f+5+C5caoS2ymI8W/nsZ254HAkoZ/zj8KINaGUssXr7tPog73Gd2WKKYGAYD7/yURyLvAPDbQCOHAltMDkpMR0dFKg3mp3h/rV31YESC9PzsvzHS8r7ZYc1KySdJhmEwfPq3dL3y10SHerFWLWLZUy9gryndicL5JoKVF4cf5Uy4DosRpe/n3+XB8GHsRLL2GVNVOCpWmX7XXG4js7a9Hg1zf/h9/qTyMHZF41x4GT8afgRNtZsSj5jspu7m+8NN9GmVLLCM8lDPj/GdeAuMwjleZzp2SqWSXL5xK37u69vPUHOsSNOhwGbeCGxHOuH5r5TO2YUkgpV/tf8OZWu2Y0TD9P38e9z81f9CDwdTfg8z2srppH2p9KWXXsLj8dDR0YHH46GpqSmr68+lUO81Bn7zzwSungbAuXwDi7+8F0tZ6U4QzldRLPzvkYf4sxWXWef7kC3ap9RWXeGXga2cCq/AmOVJ7c4KR+MVWyWzTL9rstfN9NqZzH0lP4Ol/kt0vfQKW7XYsx3eDa7nDf/2sSuVIp8M6hV81/cET5V/QKO9m5sHf8SX1VoOWLbQruX/cJtkx46ZFepKtXqmFY3POS7Q5DqNKxhBsdr5lfIABweWmR2aSFEuz0didjTFyuL/8FcMHv4ZQ82v4zt+EH/bCeY/9FXKG+9DUZKfY81oK6eTVpL0/PPP09TUxK5duwD4i7/4CzweDxs2bMjK+nPBMAwCVz/Fd/xN/Jc+AQywWJn/4Fepksl/ec1ApWXeQzy4+1EuHfgeNZYRnq54n+7oGd4JNuIzNs36M3JVzSkfZfpdC7mSnxWNrfZrPORspa5/kAgwShk/Gb6npMtMFwK/4eTFkUf549XdbBn5gLpIF39Z1UVruJbDobu4EFmKkecJrhlV6vKxkpxZypUguxyXeMB5Drcau7I9YF/K5m/8Z87/cxt3VrEU+a/U9uFCoagW5j/8NZzLN9L/y38gOtRL38+/h735ddw7v0hF4y5U+/TPfDSroued0kqSDhw4wAsvvJD493333Tfpb7NZP1e0wDChzov4L59g9OIxtOFbiWXl6+9l/sNfwzZvyZzGJDJXtnorP3E8Sf3AUR52trDMOsSTFc2Eg8foff0srpVbcK3chNW9EEWRYRMlzTCIDPWxLnqez5VfotHeiVOJPewuqlipufdL/OOJRZwbkMpphcBAoc39Wb74h1/hV//4A9ZFz9No76LR3oVXd3E6XI/dvwEtsM7sUEUeUNCpCvfiPfYm/y70K+qqu1CV2HCdQa2MQ8EtOOofZEdNHdBmbrBCFKGyVZ/B86ffx/vxvzH00RuE+65z89/+gVtv/RNla7dTtuozOJdvxFqVn/21lJOk5uZm3O7Jw9Cam5uzsn4mDEPHiIQxIiG0wDCa34fu9xEdGSI6eIPIwA3Ct7qIDvZMeJ1id1G5+WHcO5qwL6jNWjxi7kQUO28FN/N+aB33OS5yt6ONRZZhRluPMNoae/Cf6qzAvmg59oUerO4aLJXzqdM6GVE1goaNEDZChjXvrz6LqanoWPUQ0ZFB5uu3UK39VKhBqpQACy0+Fll81HcN0/HDUR4HcMReN6iV8UFoHTQ8yLcfvp/wyXcBSZIKia16EW/bH+P/3rqLh5ytbLNfo0oN8IDzAvRf4PrfvcY3lAraK6ro0arw6uV4DRcLgjqRgWWUGaPYiRDGisw/KUQGFnTsShQ7UarCPfgvn2R99ByrnLdYaPGx2OJlicWL80aEWzegHkCB9ugCDgfXcyK8Ah2VB/OwYyZEMVHtLuZ97iu4tz3O8KlfM3z6t0QGbkzury1ejr0m1l9bF+1FsSoM605C2AgaNhRDn/PYFcNIbfbroUOHeO655zh27FjSv2W6/kw2bdqEFgmzsGKsp2MYQBoTuFQritWGYnWgWG0wi4ZRURS8IyGi2sTPd9gsVJTZplw20/JMl5XS+8603K7qlNt09EgYtCip7h+x+UwKihLbrYwJf4/93hZVIaobkyaLJ1uWWG5RiGrGpMWKAlaLMu38cwWSv+9sYlIVNF2fMqbYsqnjVVUFfYpl0y83xr6LgqomP+ZiEzKnjldRbk/YVJj4/zNT0BQLUcNCRLGhERtSW0zHhlnHXP68r4GNKFYjilXRUA1t0udNx0DBQIm1AvH9F7gzecrkmIu3IapCTtoBFAWrOv2RoMA0x/n0bUA8pkzaAUUBNYVz61THeuw4V9D1qTtCihL7PsbYuT/VM7iBgmq1E9JVQoZ10pzDfNu/8zGmQnvffIyp0N53Nq+1WhSqKhwkSy+MaAQjGsaIptdfi/XdlcR/9w+HsNhsnDlzJrXXpynlO0ler5eqqqoJf3O73fh8vqysPxOHw0FYUbBW5cfD3ariyVqay2bzWnnf1JarOXq0VbLZajPNZMt0ptts3jeXry00VhI3kiYppmPDrGMuH983V/KxHcj0tbmKNx+Vjf1vOvm2f+djTIX2vvkYU6G972xem2z4nGKzg232VWStoX7s9txVo005Sboz4QHw+XxTDqnLZP2ZHD9+PKPXCSGEEEIIIUQ6Up6M4Xa78XonVpqY6m5RpusLIYQQQgghRD5IOUnatWvXpKFyPp8vUd57tusLIYQQQgghRD5Iq6zX448/PqE6XXNzM3v27En8u6WlhUOHDqW8vhBCCCGEEELkm5Sr28Xt27eP+vp6fD4fHo+HpqamCctaW1t5+eWXU1pfCCGEEEIIIfJN2kmSEEIIIYQQQhQzeYqmEEIIIYQQQowjSZIQQgghhBBCjCNJkhBCCCGEEEKMI0mSEEIIIYQQQowjSZIQQgghhBBCjCNJkhBCCCGEEEKMI0mSEEIIIYQQQoxjNTuAO3V0dLB//37q6+vZs2fPjOu/9NJLeDweOjo6pnxY7UzLxWTpbLMDBw6wceNG3G73hL97PB4g9nv6fD42bNiAz+fj7NmzU64vJkrnN0hlG8txkL50tpnP5+PAgQMANDc3s2fPngnry3Ews3T3UWn7s0v2d/NJu28+6f+YK+9yACOPHDlyxDh48KDx9NNPGy+++OKM6z/33HPGkSNHEv9+5plnjLNnz6a8XEyW7jZ79NFHjYaGhkn/a29vNwzDMF588cUJf0/ldy116f4GM21jOQ7Sl+42e+655xL/7fV6jYaGhgnry3GQXCbbW9r+7JH93XzS7ptP+j/mysccIK+SpLhnnnkmpQ3U0NAw4d/79++f0HjPtFxMlu42u/N3am9vN/bv3z9heXt7uzTOacjkN0i2jeU4SF8626y9vd14+umnDa/Xm/jb008/bTzzzDOJf8txkFy6+6i0/dkl+7v5pN03n/R/8kM+5QB5N9wuVc3NzVPesmxubk5puZgsk212563L/fv38+yzz074W/zWs5hZpvvtdNtYjoP0ZbLNzp49i9frTbzO4/Fw9uzZCevIcTC1dLe3tP3ZJfu7+aTdN5/0fwrLXJ0HCjZJ8vl8k/5WVVWF1+tNabmYLJNtNr4BeOmll/jWt741aZ1Dhw4BcObMGTZt2iTjopPIdL+dbhvLcZC+dLeZx+Ph2LFjE/7W3NzMrl27JvxNjoOppbu9pe3PLtnfzSftvvmk/1NY5uo8ULBJktfrpaqqasLf3G53YsPMtFxMNptt5vP56OjomJS5b9iwITFRsampicceewyPx8OGDRuyGnuxyOQ3SLaN5ThI32y3WUtLC16vl7179yb+JsfB9NLd3tL2Z5fs7+aTdt980v8pLHN1HijYEuB3fnmI7ajxnXSm5WKy2WyzH/3oR1NeIdm1a9eE1zc2NiaqIonJMvkNkm1jOQ7SN9tt9txzz/HKK69MWF+Og+mlu72l7c8u2d/NJ+2++aT/U1jm6jyQ0ztJBw4coKWlJek6TU1Nk27Tp8Ltdk+6bTY+c5xpealI5zeYzTZ79dVX2b1795SfP76Mo8fj4cMPP0wx+uKQ698g2TaW4yBmro6Dffv28Z3vfGfSlUI5DqaX7vaWtj+7ZH83n7T75pP+T2GZq/NATpOkVGqcZ2rXrl2Tbpv5fL5EwjXT8lKRzm+Q6Tbz+Xz4fL5JExQ7Ojp4/vnn2bVrV2LZ8PAwdXV1KcdUDHL5G8y0jeU4iJmL4+DAgQPs3r070WGMz9OQ4yC5dLe3tP3ZJfu7+aTdN5/0fwrLXJ0HCmq4XUtLS2ISHMDjjz8+oVJF/KF2qS4Xk820ze78DSDWGEzF4/Gwd+/eCY1Hc3PzlJMbxW3p/AapbGM5DtKX7nEQX9ftdtPR0UFLS0vizpUcBzNLd3tL259dsr+bT9p980n/J7+ZcR5QDMMwZhFzVjU3N9PS0sKLL76Ix+PhiSeeoKmpKbGT7du3j9bWVl5++eXEa/bt20d9fX0ik79zXOhMy8VkybbZVL9BS0sL3/72t3n77bcnvVf86cnV1dV0dHRkPLyy1KTzG6SyjeU4SF+qv4HP52Pnzp2TXr93716++c1vAnIcpCLddkfa/uyS/d180u6bT/o/5snHHCCvkiQhhBBCCCGEMFtBDbcTQgghhBBCiFyTJEkIIYQQQgghxpEkSQghhBBCCCHGkSRJCCGEEEIIIcaRJEkIIYQQQgghxpEkSQghhBBCCCHGkSRJCCGEEEIIIcaRJEkIIYQQQgghxpEkSQghhBBCCCHGkSRJCCGEEEIIIcaRJEkIIYQQQgghxvn/VrvaLpvUhBMAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "X = (np.array([1, 3, 5]) / 6).reshape(-1, 1)\n", + "X = (np.array([0.2, 0.5, 0.8])).reshape(-1, 1)\n", "bandwidhts = [0.1, 0.09, 0.045]\n", "y_lims = [(-0.4, 0.4), (-0.75, 0.75), (-1, 1)]\n", "\n", @@ -125,8 +170,13 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T22:11:33.895307Z", + "start_time": "2024-05-17T22:11:33.891492Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -148,11 +198,16 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T22:11:58.211252Z", + "start_time": "2024-05-17T22:11:46.092189Z" + } + }, "outputs": [], "source": [ - "X = (np.array([1, 3, 5]) / 6).reshape(-1, 1)\n", + "X = (np.array([2, 5, 8]) / 10).reshape(-1, 1)\n", "\n", "n_samples = 3 * 10**3\n", "x_vals = []\n", @@ -168,28 +223,47 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-17T22:11:59.922416Z", + "start_time": "2024-05-17T22:11:58.214691Z" + } + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, { "data": { - "image/png": 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QGklAhIhSZNeK0+XVFjJrPAjWPJzTP5WULpe+VNuXE8gbqc3qefjVpO572HY8ql1e8Fmw2BO+O4+/u3XK5Y2Ygl1vBfngcS2xFEIknCQgQkQpsmvF5Q1A9R+1G+Z9GpeSZ0z3ARBQrHTNCdUx6DGlg/YzWrcHGNqCBGiLB1rs0HAATm43NhYhsoQkIEIMgeJ2dq/DYvTBE+ic9zntyuFXtEm90sGOx7QF9cqXw+jZxsZiL+oekrt1rbGxCJElJAERYgjy9zwO/i4YMx/KlxkdDoHS6doCa2oQ3k+DA6jPDdse0a4vu9PYWHTLV2mX+/4KbfXGxiJEFpAERIh+9Dec1qz6yN/5O+1OF38z6XN/9OvifwJA3fEYp8+eO7/YMpXsrYTOBiicAHMTvPJtPyILUuudXTiL58Kki7Vi1G2/MyQmIbJJkhatECI9OF3ecHFiIKjyg8pd4dseuGkhAJd0vYm58wwUjNFqB1LFtKth5CyUxoNs+vOD/K1A61J46NZFFOcZHFskVYX3fqVdX74KzFZDwvD4A6x+anf494duXUTx8m9A7Xuw/Xdw2XchZ4QhsQmRDaQFRIgIkYWmfRWTKmqAT3SEpg1fdidYcpIc4QBMJrj4HwH4WMfTWNUUbPkAOPAinNsHtoKUqJ/pYfYNUDpNmxl122+NjkaIjCYJiBAxuKTrLcr9JwjmFMHSrxsdzvku+Cz+ERMoCzaysvNvRkdzvmAQ3vgv7fryVZBbbGg45zFb4IofaNff/QV4OoyNR4gMJgmIENEK+rm5XZtttGPJP6bewRPAkkPHsu8B8KmOv5AT7DI4oJ46d1bCuX0EbSNoXbTK6HD6tuAWKK2ArmZ4/9dGRyNExpIERIgojdj9KOMC9bSZiui68I6krfcSjciCyvbZt3LGPI6iYGt3d1Eq8HWR8/f7AHgq55N0mgsNDqgfZgtc+S/a9bcfgtZTxsYjRIaSBESIKBQHmijesgaA9SO+hNtkT9p6L9GInOEzaLLwROHXAPh4x0bMLQ5DYwt7+yEsbbU0mUbyQn4KFe+G9JimfdontPlJfC6o+pHRoQmRkSQBEWIwqsqXW9di8nVwxDqL1/OuNTqiQW21X8qunMXY8FH8+t1a7YWRGg7Cuz8H4LGib+Ax5RobTx96TNPuC8J1awBFWyzvcJXR4QmRcSQBEWIQV3RVcbH7LVTFzO+K/glVMRsd0uAUhT8UfROPkkNO3Tvw3i8NCcPp8lLf6MS34asQ8NI1+cNstV8KnD8Ph9HdWJFMikJ93iw6FmktScFn/gE6Gw2OSojMIgmIEAOY6DvOV1v/DwDn8h9wzDbD4Iiid8YygccKvwGA+tqPaax5MymTkkVO4Nbe5aPm99/A2rCXNlMRTVf/NDxxW2SLQyp0Y0XSY/uHsx/npGUSps5zUPlV8Kfo0GYh0pAkIEL0w9xxhh8234tddbPXtoi2Jd80OqSYvZ53LZ3TP44S9JGz8fN4zhxM+D4j51Ip2P6/rHS9RBCFXxX/M4H8MQnffzz5lBx+UfJDgtY8OPYmvPBdWS1XiDiRBESIPpT5zzHmr7cwMtDAKUs5Pyv9VzClQddLb4pC08qfccQ6ixFqOyOfvhUaDiVl19d1PEPJe/cD8FjhN9hpN37NnKGotVbQcv06UMyw88/w0mrja2qEyACSgAjRyyzPXn7ceBdW51EazKP579L76DSl75TcqjWPB0r/g1OWcswd9fD7j8ChVxK3Q7+b21t/xZfbfgPAcwW38EqBMeu9xIuv4hqcV2ujoNi6FjZ+GdxtxgYlRJqTBEQInbuVwrd/zL813U1ZsBFv6Ux+NPKnNFjGGh3ZsLWbi/n3sv/BO3axNs34E7fC89+NS2FlZM1H497XGPnk9Vzb+RwALStW88SIrw57H0bz+AN8o2YeD5f8ENVkhf3Pwa9WaGvayGypQgyJJCAiqzk73Zw7upP25/+FwM8XUbDjV5gJ8nbuVZy55QWazaOMDjFu2s1FNN78NKwI1bLs+AM8fAFUfg12b9QSkyFwuTrZ+NjDNP/qWkZW3oitsYY2UxH/Xfpj2pZ+J3VWC46D93KvpPHmv0LxJGg7Ba/cAz+bB6/9GNrPGh2eEGlFVsMV2cPTDmdr4OweOLMXzu6l8Ow+TD5X+C6+4mk8ZPoSH9iX84CtwMBgE8NkzaV+xb3YJlxF4Vv/ga1hD+yt1H4UM0xaAdOvhpIp2mq/BWPBlk+r20+X14fJ24657RS2jpPgPIHt7C7G1m/jOwE3AAFMuBZ8ke+fu442czGfNfbpJkRgwlLqv/AWefsrKaj+NZaWo/D2T2Hz/8LEpdrPuAu0/2HxJLAXgcmSUYmYEPEQcwLicDhwOp0JCEUMWdtprV/a0wGooSp9tbtaP1y139ffI+8fcZ8+70fPv9PP4/vdbu/b+7r/QNvtdf/zttfP8wpvs28+rDis09liv5yPXP5JXnvjGHCQvbuCNNceDt8v8vdUui2W++7YEeTh17XrivpV7l7kp6RxB5zYDC3HoP4t2PJWv/+r/jiVYqrty9lmv5gvlVzM8Q8OA2cNf76JuK37f2hFUb/JD+c0U3SwEk7vg5NvAf38/0xWLRExW7Xr5ojfI6+bLDD1Cpj36YH/6UKkqOLiYioqKga9n6Kq0Y8pa2xsZMyYMQSlAlwIIYQQfTCbzZw5c4aRI0cOeL+YEhCQFhAhhBBC9C8hLSBCCCGEEPEgo2CEEEIIkXSSgAghhBAi6SQBEUIIIUTSSQIihBBCiKSTBEQIIYQQSScJiBBCCCGSThIQIYQQQiSdJCBCCCGESDpJQIQQQgiRdJKACCGEECLpJAERQgghRNJJAiKEEEKIpLMYHYDIEMfegp1PgK0AVvwDlE0zOiKRydrq4d1fgKsR5t8Es64zOqL0oqqw/zmoeQ4Kx8Ml34aCUUZHJRJkzZo1FBcXA+B0Orn77rsHvH9VVRVr167lmmuuoaKigk2bNrF06VJuvvnmuMYlq+GK4at+HJ77p+7fbSPg9hdg/CLDQhIZrNkBv/sIdDZ0/+3qH8HldxkXU7p5/Sfw1oPdvxdOgK++AsXlxsUkEmLNmjUA4aSjqqqKjRs3snbt2n4fU1lZyR133IHT6aSiooLVq1dz5513xj02SUDE8JzbD7+5HII+mH8ztNZB3ftQOg2+8TbY8o2OUGSSYADWXQlndsPouTBxKVQ/BijwlZdg8sVGR5j6Dr0KT9yiXb/oq1rrZdMRmHyZduKgKMbGJ+KqpKSEY8eOhVtAABRFYaBDf2VlJStXruzxmESQGhAxPJt+pCUfMz4KN/0WPrdeO5tqPgrv959hCzEkH/xJSz7sRfCFp+ETv4BFnwdUePmHWteC6F/AB6/8i3Z9+T/ADT+Dz1eCJRdOvAP7njY2vgxVWVlJZWUlq1atwuFwsG7dOlavXo3D4Ujofh0OB06ns89EoqqqKqH7joYkIGLoTlXD4VfAZIFr79fOnHJL4Op/027f/AvwuoyNUWSOYADeeUi7/qHVUDhOu37Nj8GaB6d3whHjv1RT2oEXoOkw5JXBVaFEpHQqXPod7fq7D0sSF2fr1q1j5cqV4foJvTujurqa6urqhO67vwSnuLgYp9M54GM3bNhAZWVlOFlKBClCFUO3/ffa5bwbexadLrgF3vgvcJ6Ammdg0ecMCU9kmMOboOW41vqx5Cvdf88fqXUlvPdL2PoIzLjGsBBT3tZHtMuLvgb2wu6/L7tTSz5O74IT78KUy4yJLwOVlpaGWyAcDgerVq0CYNOmTQM+Tr/fYJYsWRJzfUZpaSnNzc393r548WIAKioqAC2JuuWWW9i4cWNM+xmMJCBiaNytsPcp7fpFX+l5m8kMi78Er98HOx6TBETExwePa5cXfhFseT1vW/IVLQE5sgnaTne3johuDYe05EIxn/+ZzS+DhbdA9R9h118kAYmjyJEj27dvj/ogPlCR6HANlHxAd+Khu/XWW1m1alW/3TlDJV0wYmgOvgQ+F5TNgEl9FP5d+AVQTFC3BZx1yY9PZBZ3m9YCAnDBZ8+/feR0KF8BahD2ViY3tnRR84x2Of1qbehtbwtvC93vefC5kxZWttC7QxJd2BmpdyKh00e39KeysudnKLIFJ56kBUQMzf7ntct5n+67an7EWC0xOfEuHHgRVnwjufGJzHLoFQh4oGw6jJnX933m36QlvAdehEu+ldz40kHNs9rl3E/1ffukS7QC8rZTWi3NnBuSFlo2qKqqYuXKlf3+3ls8umAqKiooLi7G4XCcl3D0t2+n08ktt9zC0aNHw4/R60UGSlqGQhIQETtPR3ex35yP93+/2TdoCcj+5yUBEcOjn733l/ACzL4eXvoB1G6BjgaZWCtS4xE4u1crGO9v0jaTCeZ8At7/NRx6WRKQOKisrGTt2rVs2rSJTZs2UVpaCmgH9MGKQOPVBXPPPfdQVVUVTlIqKyt7JCwOh4PKysrwPCHFxcXcfffdPZKNdevWcfPNN8e99Ua6YETsjr8NfjcUT4axC/q/n/4FVrsZXAP3OQrRr4APHH/Xrs8e4KBYNBHGXQCo2gFUdDv8inY55TLIK+3/fnoB7+FNMhomDioqKrjmmmuorKzkkUe0AuDKykqqqqriPqtof+6++26cTmd4KPC2bdt6JDfV1dXnJTv33HMPa9asCf80NTXFvQAVZCIyMRQvrYb3f6ONPLjhZwPf9/+WQ8MBuPWPMPeTyYlPZJYTm+EP10HeSPj+Ye1MvT9v3A9v/rfWUnLLo0kLMeX9+RY4/Cpccx9c+u3+7+dzw5qpWn3Xqrdh3MLkxSiyjrSAiNjpZ6MVVw5+X/0++mOEiNXR17XLig8NnHxA9/vt2FsQDCY0rLTh98Lxd7Xr064a+L5WO0z9kHb98KuJjUtkPUlARGza6rUWDRSYcvng95cERAzX0Te0y2kfHvy+E5aANR9cTXCuJrFxpYuT28DXqbUgje6ngDeSnqQcfyexcYmsJwmIiI3jTe1y/KI++5KdLi/1zq7wj3P0Mm3egWYHtJxIbqwi/XU5oT40W2TFIGfvABZb93owx95KWFhpxRFK4CquHLwFCWDypdpl3Vat/kaIBJEERMTm+NvaZT/dLy5vgLs27Az/uJQ87awU5IxKxO7kNm1uj5KpUDQhusfoXQjH3kxcXOlE736p+FB09x89V1tSwdepzYwqRIJIAiJiU/e+djnpkugfo5+R6o8VIlq1W7TLSSuif4w+i2fte1IH4vd2tyD1NWFgX0ym7s+3nDSIBJIERESvs0lbthtg4kXRP658uXYpCYiIlf6e0d9D0Ri7QFuczt0KjYcSE1e6OLNHGzKfW6JN4hatKaFumBPvJiYuIZAERMTi5DbtcuTMgecS6E0/eDQcgK6W+MclMlPAB6d2aNdjaQExW2G8tpgWJ7fGP650EpnA9TeBW18mX9L9+GxvRRIJIwmIiF74y2xZbI/LHwmlodVy67bFNyaRuc7s0eajsBfByFmxPbZ8qXZZl+0JSKgLK9bP7Jj5YLFrrUjNR+MflxBIAiJiobeATOz+Mus96iUQ7GdeO/0MVrphRLQiz96jGb0RSX+PZnMCoqrdz788hhYk0FqRxl2gXddboYSIM0lARHQC/u4vooj++N6jXoL9Tayrn4FJAiKiNZT6D93EUAtI48Hs7fZrrYP209r6L+MvjP3xE0J1Xie3xzcuIUIkARHRObs3ojl8ZuyP1w8I9TulT1lEp/4D7TKWgmddwSgoDS2mla0HUL31Y+xCsOXF/viJoeHzp7L0/ycSThIQER29+2XCRbE3h4PWh2/JBW+79CmLwXW1QMtx7breFRArPek9VR2XkNKOnsBNWDy0x+vz95zZq60RI0ScSQIioqNPSDSUplwAs6V7YSv9i1GI/pzZo10WT8ap5nfPrOvyRr+NcYu0y9M74x1dejizW7vU/w+xKp6sTd8e9HVvS4g4kgREREdPQIZ6NgrdyYskIGIwEe+3yDojlzcw4MMii6LbS0PrntTvTGysqUhVh/+ZVZTu7q9s7cYSCSUJiBic3wPn9mvXxy8a+nYkARHRGuLBMzJZ6SiZByjQXg/tZ+MfYypzntCG0JqsMGr20LejF6LKSBiRABajAxBp4FyN1gybWwJF5TE91KQo1Du7ACgonU8haAeXYABM5vjHKjJDOAFZ1OPPke8nAKvZhC/QXdQcOQxcteVrBdONB7VumBEfTWTEqUX//42Zqy3QN1T6CYd0wYgEkAREDC7yYBDLbIqAxx9g9VPal9dDNy+g0FYA3g5tiuzRc+IcqMgIng5oPKxdH7cQ/BE3RbyfAB64aeF5v/cwfpGWgNTvhJlZmIAMp8sUtBE0oL0e3k6w5Q9ve0JEkC4YMTi9D324X2Ymc/c2pBtG9OfsPkCFEeOgYPTwtqV3+2VbIeppvQB1mJ/ZEWOgYCygaqNhhIgjSUBEnyKL+fyndmp/HE79h04/IGTr0EgxuHidvUN3F042Jbyq2p1wDXUETCR99Jr+uggRJ9IFI/qkF/OZVT9/aqjR/hiPA0JEIarT5Q2PasizmSnOG0Zftcgc8UxAxi5AK0Q9rRWijhgz/G2muvYz0NkAiglGzx3+9sZdAIdfhTOSgIj4khYQMaAJ/lqUgIegrZB6ZWzsczH0pp+Rnd2Hy+2JenilyCLxTEByCmBUaCG7bOmG0f9/I2cNbQbU3vTXQVpARJxJAiIGNNV3BIAapnLXxl3DTxZKK8A2AvxdWJoPxylKkTF8bmgIDfmORwIC2dcNE88EDroLUc8d0IbkCxEnkoCIAVV4tSThuHX6sLdlUhTq2zx4RmkTRJnPytA+0cu5Ggj6Ia8MCifEZ5t67dLpLHm/xTsBKZ4E9mJtKL4+H5AQcSAJiBiQ3gLiiEMC4vFrdSVVznEAWM9Kk67oJfLgGeOQ735lWxfCmTiNgNEpihSiioSQBET0y6QGmOx3AHDMNvwERHcslMzYGrLkjFREL95n7xAqRAXaTkJnY/y2m4o6m6C1TruuP+940F8PmZBMxJEkIKJf4/115KgegtZ8zpjj1BwOOKwzALA17ENRpfhUREhEApIzAsqm99x+ptJHqpRWgL0wftsNL+yX4f8/kVSSgIh+6d0v3lHzUZX4vVVOWybgVuyY/F2M95+M23ZFmgv4QpOQwdn82eF5aCKnVx+ybOmGSUQCB92FqGf2assoCBEHkoCIfk31HQW0BCSeVMXMcWtFaB9H4rptkcYaDkJoyPd3XmkJD9EOqpKARC1RCUjZNLDmg7+re5p8IYZJEhDRrynhFpA49iWHHAt1w1RIAiJ0oYOnb/T8+BWg6sIJyM74bjfVJCoBMZlh7Pye+xBimCQBEX1TVSb7tALUeLeAQHch6lSfnE2JkHACEv+EN9yF0HIculriv/1U4G6DZu0zy9g4JyAghagi7iQBEX0yt9WSr3bixYqvZEbct68Xok7xHUVRg4PcW2SFcAKycJA7DkFeqTafBcCZPfHffirQn1fhRMgvi9tm9XWhWopCq1dLC4iIE0lARJ+s57QvszrrFDBb4779eks5QYudXLWLsYFTcd++SDPBQPgA6ktAixuQ+XUgCep+0deFun9nTmg/u7UF74QYJklARJ+sDdrS28et0867zaQowx6hEFTM+EZqM6JO9UodSNZrOgq+TrDk4i+J35wzPWT6UNJE1X+EnLJMQjXbwNOqdWUJMUySgIg+6S0gfSUg+oymwx2h4AkVt0ohavbRm/X1n84TO7Qbxi7QCh4TQRKQYQkoFvxlWjdM89Ftw1+YUmQ9i9EBiNQ0UAISL95QsaEUomYfvVlf99uxoesJOnhq2w7VljQeBk+HtlJupvC6oPGgdn1cAmpoQtyj5mM9t4u33qziyQ/KeejWRRTHYcFdkZ2kBUScr/0sZtc5gijUWioSthvvKO2LcqrvCEghalbTE96EJiAFo2HEeECFs3sTtx8jnN2nfYbyRsKIcQnbjT4kXx+iH9kdK60hIlaSgIjzhYbZnbZMxGOyJ2w3vtKZeLGSp7owt55I2H5EilPVcM1RIs/ete1naCGqPr/J+EXxn0Mlgp6AaCcNao/uWJdXZkgVsZEERJwv9OWcyO4XAMxWaq1TAbCek7kFstWYwGlMnlYw22DUnMTuLGMTEL3+Y1FCd+MbOZsAJoqCrZQEmxK6L5H5JAER5wu1gBxLdAJC94yo1rOSgGQrvTmfMfPAYkvszvQEpH5nYveTbFEUoEYW/g61u0S15HLKos2nIssoiOGSBESc77SWDCS8BYTuGVFt0gKStcKjoBJZ/6HT99FwAHxdid9fMvg9cG6/dn2A/6Fe+Dvc7hL9MzsltFaUEEMlCYjoyd0KLccAOG5N0HwMERyhfVjP7aG+xSUFbVkofCad4O4DAArHa4WaagDO1iR+f8lwrgaCPrAXd8/2Ogy9h0j3nuvneHgZBWkBEcMjw3BFT2e0YkD/iAl0mAoTvrs66xT8WLB4nNz/l1dosIwFkOF92UJVmeoNDcOOcwuIPkJDl2czU5xn0/Zz9DWtcHPikrju0xCR3S9xKEDtPUT6gZt6FgbrXbNTfUfoHPbeRDaTBET0FKr/8I2aD+7E7y6gWKm1TqHCd4QK3+FwAiIyj9PlDTf962fVowJnGaG2o5osKGPmxXV/Hn+A1U91d+2Fk9pwApIhhaih5+EetZDmvhKuPvROzqxmE76ANhR+sNmNT1i1ofkjAw10dUkhqhg6SUBET6H6D9/oBVCbnF0es86gwneEqb4jvJ97eXJ2KpIu8sxaP6vWC1D9ZbNp6AgCQ5/eP2qZNhIm9DxcZfN6tFwM1IrYOzl74KaF4d97t3j01mXK57R5AuMCp7A17AVKhhW+yF5SAyJ6qv8AAN+oBCyJ3g9HuE9ZZkTNNnoBqnvUgrhM7x8VPQE5VwP+NK81CvjC3aa9VxGOnCRsOOs29SVcPN6QoSsLi6SQBER083SEp3P2jV2UtN0eCycgR2WVzSyjFzJ6k5jwUjIF7EUQ8GqjYdJZ4yEIeMA2gkDxlB43RU4SFu/ETh8hZzsnCYgYOklARLfTu7TpnAsnEMwfk7Td1lmnoposFAZbKQs0JG2/wmCqGm718o5O8AyokRQlc7ph9PlMxi0EJXlf5+EWkMZ9SdunyDySgIhu9dXa5fgLk7pbn2LDVzoTgArphskapcFGioKtBDDhG5ngGVB7y5QEJMEr4PZHbwGxOh3kBmUsjBgaSUBEt1OhBGTC4qTvWj8DlrkFssdUr/Zan7RMQrXkJnfn+pwj6Z6AnNqhXSb5pKHdXESDeTQgE5KJoZMERHQLf5klPwHxjJIEJNtM92n1Rg7rzKTsL7Ios614rvbHM3sg4E/K/uPO7wkPm2dC8uczcYSWUZgWeh2FiJUkIELT2QTO0Iq0ST6bAvCO1lfZPCyFqFlihlcrAD1im52U/UUWZXYUTAZbAfi7oClNu/3O7NUKaXNLobQi6bs/HHrd9NdRiFhJAiIA6Di2FQB/cQX1npzEz8XQi2/kXAKYKA46ZZXNbBAMUOE7BHQfyJJKMcHY0MibdO2GObVdu5x4UVxmQI3VEatWtzPDu19OGsSQSAIiAAiG6j+2eCYnZy6GXiJX2ZRC1PQV7Yqr1pbD5Kku3IqdOsvkJEYYIQ1Xxo38/7qOva/9ccJFhsTisE5HVcyUBpspCzYaEoNIb5KACABsZ3cCcNQ6y7AYwvOBeI/06K+XhenSR7Qrruac0RLeo9aZqIo5WeH1pNc66S0JaSDy/9t6+D3tjwatZ+M12fGO1Gpppnv3GxKDSG+SgAhQVaz6AcGWnILAvuhFbVN9R3r01w9n6XCRmmyh91uy6j96MykKZ4u1FhC1fifO1lZD4hiqgmAb4wL12i8GFKDqvGO1JE7qQMRQSAIioNmB2dWID2u4FcIIx2RK9qyRc1ab8v+w1ZgExOMP8J2Xm2kxlaAEffhPVhsSx1BN92ojT/wl0yDXuLVYPGO15EcSEDEUkoAIqN0CaC0QPqXv1TOT4YR1GgFMlAabMbefNCwOkVj2oAtrkz4CxrguPxSFQzatC8FWv824OIZgeuiAr7dAGMUT2v9U32FtRI4QMZAERECdloAczInvcuix8pjs4VYQe/1WQ2MRiTPddxAFlUbzKJzmMkNjOagnIKe3pVXN0UxvDQDescZ1vwD4i6bSrozAhg9rg0zLLmIjCYiAWq2aXv8yNtIBmzY0MufUFoMjEYky26Ot3rrfNt/gSOCQTUu6rfXbuGv9B2lRc2RW/cz0hRKQiSuMDUZRwsOobafTp5hXpAZJQLKdqzm8Au4hq/EJiH5Qyql/P/y33suKp8MZquh/Ofg5Xm0FVT3ZNNIx6zS82DC7WxgXSI9uv6m+I9hVD22mQvylxhWN6w6EPrO2k+8ZHIlINxajAxAGq9MO9L6S6bSbiwwOprsbyNZymBE2J+3mYjz+AKuf2h2+z0O3LqI4z6gIRbR6v24P3LQQi+oNFyzuT4EEJKBYOWqbyRzvXmZ5azhtKTc6pEHNDidw85mYxBVw+1OTsxDawXZqCwSDYDI+JpEe5J2S7U68C4B3/DKDA9F0mAqpC01INtsrfcqZZpr3EDa8BHJHUm+ZaHQ4QHfXo941lOrmhOI8kAJdWADHrDNwK3bM7mbOOXZKS6WImiQg2c7xJgDe8ssMDqSb3jQ/25seB4RsFjkzZ2Q3S3/mhF5T94QVhkwf3pcamzYfyHzvztSfUjwYCH8uUqEFCSCgWDgQqqV58bkNMn+PiJokINmssym8mqYnpRIQ7cxOrxUQqStyZs5opvDXuw8845cnI7yoHLDNQzXZGBloYFzglNHhDMjSdIB8tROXkscJa0WPOptkr98USU+G5np3D3JPIbpJApLNjr+lXY6aQzB/tLGxRKjJWQjAFN9RRgTSa4ZKMYCAJ9yt5p5wscHBdPOa7HjGaeupzPfsNDaYQeTUap/Zg7Z5BBVzjxmDk71+UyT9MzvHuwdFDRoWh0gvkoBks1D3CxUfMjaOXlrMZXjL5mBCZb73A6PDEXFir9+GXXXTYirBVzbH6HB66Cq/HID5ng9SetRVzom/A7A7x9gJyHpzWGcStORSGGyj3H/c6HBEmpAEJJsdCyUgU1MrAQHomqTFdIF7h8GRiHix1/4dgN05S1Km/kPnDnVBzvPswuP19uhWSplaBq8rPD/OrhxjJyDrLaBYcE+8FIBFbpkPRERHEpBs1XICmh2gmGDKpUZHcx73pCsBWOipTv3CQBGV3NDZe6odPAG8oy/ApeRRoHZga0jR4ucTm1ECHhrNo6hPweHCXZM/DMAiT3pNay+MIwlItjr0inZZvhzsxs//0Zt7/DI8Sg6lwabwuiEifZUEmrA11RBEYU+KdR8AYLKwN2cRALknXkv47nqPHoqqm+dIFRBK4FKsBQnAHUpAZnn3kRfsMDgakQ4kAclWh17SLmddZ2wc/bHYqbFphW25tW8YHIwYroUerSvNYZ2REhPe9aXaro3MyXW8kvB99R49FFU3TygB2Z2CLUgA/qJJnLRMwkyQBZ70Wl1YGEMSkGzkboNjb2vXZ6ZoAgJ8YF8KQK7jZYMjEcN1kVubpntnzlKDI+lfdc5ygijkNOyhNNBgdDg9NRyEpsOoJmvKFaBG0l/fC93SDSMGJwlINjr6OgR9UFoBI2cYHU2/ttkvAcB+ejslgSaDoxFDZQ+6uCBUmLg1N/XqjXRt5mIOW7XROUvcxi2G2Lt7pqHdQ9sHTwPgLr+cLlO+YbENRj9pWOzZCkG/wdGIVCcJSDY68KJ2OfO6lOxL1rWYR3LIqq20eZF7s8HRpL/IA1syh5Ze6NmGDR++4gpqLVOTtt+h2G7X5ifRW2yMcH73jJ/GbZXabdNSt8UStAnJ2kxFFAZbsZ2Uz6wYmCQg2cbTAQde0K7P+7SxsURha642PHJ517vhv0XO05BKczSkusgD23CGlkYmMtHMvrms6x1t/9OuT+mEF2BbrtbqNt+zk8KA09hgQsxtdVT4jhDEhKviWqPDGVBQMbM11HKZe+h5g6MRqU4SkGxz8G/gc0HJVJh4kdHRDGqrXWuyn+vdTVGgGaDH7I8pM0dDFolMZAabfTMn6OZCz1btcdNvSEZ4w3LGMgHP6EWYCXJx15tGhwNA/qFnANhvm08wt8zYYKKwJfcKAOxHXoSAz+BoRCqTBCTb7N6gXS68NeXPRgHOWcbhHrsEE0GucCV+eKSIr+Xut7GrHk6bx+MdlRqLpw2mc/aNAFze9brBkQCqSsGBjQC8k/thg4OJTo1tIU5TCWZ3MxySAnLRP0lAsknHOa0AFWDBrTE3pRulc+5nAbiy6xWZlMwAsa54G+lK16sAvJl3TVokvACdMz5JABPTfQcZ568b8L6JrquZ7juIteUIHiWHLbmXx337iRBUzPw97xrtlx2PGhqLSG2SgGST6j+CGoAJS2Dk9Jia0o3UOeMTuJUcJvhPMtO3v8dtqbxuR6aIdcVbncV5jLnePQRReDtvZYKjjJ9g3ih25Wjdk9d0/m3A+8arrqY/V7m0FoSt9ktTevRLb2/khWpVjrwGLSeGNvGayHgWowMQSRLwwbbfadeX3WlsLDFSbQVssV/BlV2b+Ejn88Bnwrd5/AFWP9W9BPhDty6iOM+AIMV5RuzS3m+7ci6iyTzK4Ghi82r+x1ns2cqVrldo9XYCuYDW4hGZaAzUGtT7vnk2M8V5tqhjGBFo5XKX1mL5Wt71MT4DY521jMc96UPYa9+E6sdwLfkBd23YGb5dPqcCpAUke+x/HtrrIX90Wox+6e3l/E8AcHHXm5jbBm4WF8bLD7ZTUPMXAF4sSL/3266cJZw2TyBPdZG7f0P477G0BsUy22lkS56e1Fzt+hs2vHhGL+SAbV78nlySuBZ8QbtS/Tj43cYGI1KSJCDZQFXh3Ye16xd9FSw5xsYzBMdtM9htuxAzQQo/WGt0OGIQKztfxOTv4oRlKnttFxodTsxUxcQr+R8HoGDHr6lvaktorVTkyK6gqmILuvlo53MAtF9wR9rUz0TyTrsO/4gJ0HkO+74njQ5HpCBJQLLBgRfh9E6w5sOyO4yOZsieG3ELAAU1T1AsM6MOy0C1M8MpOgXIDXZyQ8dTALxQcFNaHjxBq2NwmoqxtNXy/B//Z9AWj97/0+EkKx/tfJ6SYAvnzGPonPHxIW/HSB7VxOOK1nJZuPVhbEFpBRE9SQKS6QJ+eP0n2vUV/wD5I42NZxj22i7kkHU2Jr+bW9ofNzqctBZ5xt27e2CoRae6j3dsZITajrdkBu/mXhXv0JPGY7LzbMFtANzc/mdyBjmA9v6f9v6/9dXN0hfF08onO9YDsHHEF8Ecfd1Iqnk9/zoazKOxdJ7mhs6njA5HpBhJQDLd1rXQsB9yS+CSfzI6muFRFP5UpBXQXuV6lQrvIYMDEr1Zmg/z8VDrh/PiHxJUzAZHNDxV+dfjLyynLNjITR1/Hta2enez9Kdk8/0UqB3UWSbxThoncAA+xcYThV8D4BMdGygJNBockUglkoBksqaj8MZ/addX/oeWhKS5Q7a5dM78FCaC/IPzf6S4LYUoapCyN+7Gio8PcpbSleLThkfDp+TQfIXWgnh9x9PYzu0e5BHDM9uzlxF7/wjAH4q+iZrmCRzAe/YrcI9dgl31cIfzYZnLR4RJApKpfG7YeDt4O2DypXDhF42OKG6ar7gPp6mEcn8tJe/+xOhwUtZwazlidWPHE9jr38et2Pld0T+lbe1Hb11Tr+F9+2VYCDDypTvJD7YnZD9FgWa+3XI/AK/nfZSanAsSsp+kUxSaP/w/eLGy2LONazufNToikSIkAclEwSA8+004sxtyS+HGR8CUOS91MLeMtcXfBaBw9+9Z2fmisQGlqOHWcsRiRddb3NL+JwB+X/RNGi1jErYvI6wr/g7nzGOwttXy7Zb7sajxnUhL8bn4XstPKA024S2ZwR8LV8V1+0bzlc0Kd8V8qW0dOceqDI5IpILMOSoJAJztHbg23gF7K1FNFjo+vhaKJmi3JfmMOJE+sC9nwwitVefrrf/LNZ3aypuyUu7QRFsg2ZflXW/xrZb/BqBt4Vd4S5+GO4N0mkbwUOm9BC12LvBUc1fzT+I2qiM32MnoF77MbG8NLiWPho/9Drcp82bpejn/k/w99xpMBCl94etQIy0h2U4SkEzSeJjcJ28ib38lAUz8b+E/0za+e/2IZJ4RJ8PTBZ+j7YKvA/C11v/j9tZf4XV3yEq5QxBtgWQki+ql+L3/5nst/4WZIG/lXk3L5T9OcKTGOW6dTsMNj+HFxmLPVn7ceBeW5sPD2uZk31H+s+Hb2E++i0vJ4/6yn+AvmR6niFOMovBI8XfYbl+BEnDDhi/Dmw+CX04UspUkIJmgsxH+9gP41QpyTm3BpeTxQOmP2Zx31bDObFOeotBy+X9QWfB5AK7tfI7xf/oQV7g2YVU9BgeXwYIBlnW9w4Pn/oGi7b8A4G/5n+I3xXeBKf2LJgfiLr+c/yr7T1pNRUzxOxj/l5V8oXUdZf5zMW3H4nTwNecvuL/hW4wPnMJfMI6flP03h21zExR5aggoFn5aci+dF3wVUOGNn+D7v0to/eBZ6pvbpeUyy8haMOko4INz+7XJxY68BodeAX8XAO6Kj/CvHbdy2jIR6LlWygM3LTQq4sRRFCoLv8gR2yy+3vq/jGw/yT/yU77c+ht47SaY+1EoXwb5o84rioxcq8NqNuELBMO3xbpuR7xFxmZ0LKgqxYEmpviOcoFnOxMe285dHfUA+PNG88ucr7Ml9wrj4kuyAzkLuGfUL/m68xcs9mzjhs6nuaHzaTwbl/DpznmcsE7jtGU8pq4J5AY7sao+CoLt5B47x21tf2OeZxcTHt/PhND23rNfzsTP/ArH304Z+rySRVXMNF5+H4+eKOULbY9Q1HKYome/RMBUyubcq7jkqo/BjBVQOD5jCplF3yQBSTcv3IX6wZ9QAr3O8MddANfcR3PpMk5HLPqULXbal3GX7REenvo+vq2/Z3TgLOz5o/YDBO0lqAVjMecVQ04h2AuxKvlsdXThUvK4dmE5f9tzliAmVEXhxsUTIXcIB/0hdmu5fH58/u4uI1WFVz7QDkg3Lp4wcCz97DO/y8e1HSfDv4/YtZVrQ4mDfl0BzPgp2vY6t7bVYVF9lLxZyDda6igKOikONjPxd05+09Vz5tl2ZQSb8j/GRZ/7d7a8cHxIzzmdNZtHsab0xzx8URONr6xhjncPOWd2cBs7uu/0W/hD5INeAH1VHBWFD3Iu4rmCWzmQs4AHcsuA7EhAAFC0FZJ32FfwqfYn+VjgNUrdzdpkZS9o88gEcssI5o+F/JFYR4yCvDKw5YPJAmar1tpmsoDJql2WToXpKyVpSSMxJyAOhwOn05mAUERU3noJWl10kcspSzl11iks+tAntX7jOoXAiWqaa7v7pffuCoZ/j7yejNuM2P/mmZfzC/dopvkO8dmRx2g/voPRgbOYaATOnwRpUujy+CFYEPF3x4Hz7pp0+iDMoweHvo3INq/jB7t/j7wO4DgIU0LXjxyBQkAFWkI/QRQazaM5bq1g+kUf5aEDhfh9Nr5zwJHV77ftM2bwsOtrFAac/OOU0zj2vc84/ymKAy3k0l2k2oUdS9E49rhHccw6ncuuuo6fbWmFToCDGfm/iea2ZuCXXI75yi/y2st/ZaZvPxfaz2ByHsPMOSC2ri1ueUxLRIShiouLqaioGPR+iqpGf8rW2NjImDFjCAaDg99ZCCGEEFnHbDZz5swZRo4ceOmPmBIQkBYQIYQQQvQvIS0gQgghhBDxIMNwhRBCCJF0koAIIYQQIukkARFCCCFE0kkCIoQQQoikkwRECCGEEEknCYgQQgghkk4SECGEEEIknSQgQgghhEg6SUCEEEIIkXSSgAghhBAi6SQBEUIIIUTSSQIihBBCiKSzGB2AEEIIIRJnzZo1FBcXA+B0Orn77rsHfcy6detwOp0UFxdz9OhR7rnnnh7b2LBhAwBHjx7F4XDwyCOPhG+PlqyGG8Hp8uLyBsK/59nMFOfZDIxICCGEGLo1a9YAhJOOqqoqNm7cyNq1awd8zJ133tkj4bjjjjvYuHEjAKtWrWL16tVUVFSEf3c4HGzatCmm2KQLJoLLG+CuDTvDP5HJiBBCCJFu7r//fu68887w7ytXrmTdunUDPmbTpk09WjOKi4txOp3h3x0OB5WVleHfp02bxvbt22OOTbpghBBCiATSD9abNm1i9erVVFVVcfToUVatWhVuRUgEh8MR7kbpraqqipUrV/b5uOLiYq655ho2btxIcXExDoejR5y9Wzq2bdvW77YGIi0gQgghRIKsW7eOlStXcvPNNwOwevVq7rzzTqqrq6murk7ovh0OR59/792i0dsjjzyCw+GgpKQknDD112VTWVmJ0+nkkUceiTk+aQERQgghEqS0tDTcAuFwOFi1ahVwfitCb/r9BrNkyZIeXSzRxtTc3Nzv7cXFxaxevZpNmzaxZs0aVq5cya233tqjJUUvRHU6ndxyyy0xF6CCJCBCCCFEwugtHwDbt28PF3IOZqAi0eEaKPkArZVG74JxOBzccsstLFmyhKNHj4bvU1xcHE581q1bR0lJCceOHYspEZEuGCGEECLB9O6QobQUDFV/9SVOp7Pf2/S6Eb2mo6Kigh07dlBcXBzublm9enWPLpyVK1fidDqpqqqKKT5pARFCCCESrHfR50BFoBCfLpiKioo+i0iBfvftcDj6TJL0eBwOB2vWrGHVqlU9hulC7MmVJCBCCCFEAlRWVrJ27Vo2bdrEpk2bKC0tBbQD9kBFoBC/Lph77rmHqqqqcJJSWVnZI2HRh9Tq84SsXLmSBx544LzRMzt27AjHdPfdd/dIaNavX8/ixYtjHgkjE5FFqHd2cdeGneHfH7p1EeOLc40LSAghRNqqrq6mqqqKiooKVq5cGa6tgJ61IYm2Zs2acMKwbds2HnjggfBtlZWVrF69ukd9h9Pp5P7776esrCw8Yqb3xGSRc4kcPXqUBx54QGZC7UvkDKcDzW4qCYgQQgiRHFlRhBo5w6nMbiqEEEIYLysSECGEEEKklqwrQjUpCvXOrvDvsuCcEEIIkXxZl4B4/AFWP7U7/PtDty6iOM/AgIQQQogsJF0wQgghhEg6SUCEEEIIkXSSgAghhBAi6SQBEUIIIUTSZV0R6oACXq7reIb5ng9oNpdhbv4XKF5gdFQikx19A6ofg2AALvgMzP6Y0RGJTOashc2/hJbjMGk5rPhHsMpki1FTVdj3NOx5CvJK4MIvwqQVRkeVtiQB0fnclD3zOb7c9k74T8En3oAvVMKUywwMTGSszb+EV/+1+/f9z8Gl34Vr/sOwkER6i5z1GXpNM1C/Ex7/FHS1aL8ffgVqnoUvPw/2oqTHmnZUFf72fdj22+6/ffBn+PjPYcntRkWV1qQLRvfKPeTUvYNLyeOJEV+hxrYAk78Lnvw8tJ8xOjqRaY6+3p18XPhF7UwU4N2fw55Kw8IS6S1y1uceMz+7W2HDl7TkY9wiuOY+yCuD07vgmX/UDq5iYNt+G0o+FLjk2zDvRkCFF+6CE+8ZHV1akgQEoPZ92P57VBR+XvKvPDfiNu4v+098oxeC20nXc9/H6fIaHaXIFH4PPPcd7fqSr8AnfwnX3g8fWq397cV/hi6nYeGJDPTafeA8AcWT4cvPwaXfhs9XgskCB16A/c8bHWFqaz8DVaGWyY/cp/3c/HtYcAuoAXjhexDwGRtjGpIERFWh6t8BcM37DLvtSwDwKTYaPvxTgijkHn4e38kPDAxSZJTtf4DWWoIFYzm94l7qnV3UO7twLv0ujJoNbids/oXRUYpM0XIcdjyqXf/kL7u7WyYs1rr8AN74T60OSfTtrQfB2w4TlsCKb2p/UxS4bg3klkLDfti93tgY01DWJyC2U1ugdjNY7LSv+EGP23yj5vFu7pUAFGx92IDoRMYJ+OCdnwHQtvyf+d5fD3U3l/sVuPrftPu9vw487QYGKjLGuw9D0AfTPgxTr+h526Xf1hKShgPSCtKfziat1gPg6h+BKeKwmVcKl31Xu/7OzyEYTHZ0aS3rE5C8PX/Uriy8jeCI8efd/mzBbQDYj74MbfXJDE1kogMvQscZyB+Na+5t598+63oom6Gdbe16MvnxiYyieDtg9wbtl8u+d/4d7EWw7E4AfO//trs1Trqcu23/Hfi7tNqZ3gkcwEVf1f6PTYfhSFXSw0tnWZ2AFAac5B5+Qfvloq/2eZ+T1inst81HUQNQ/XgSoxMZSa+gX/wlMPexCKKiwLI7Qvf9nRQHimHJPfA0eDu0pHbK5X3fafGXAAVr7duseeLFnsWr2S4YhA9C3/sXf1P7fPaWMwIu+Kx2feefkhdbBsjqBOSyrtdQgj4YvxjGL+r3flV512tXdj0hBwQxdM46OP42oAw8bO+Cz4A5R+tXPrs3WdGJDJS37wntykVf6fvgCVA8CWZcA8CVrleSFFmaqNuizZ1iGwFzPt7//S78gnZ58CVwNScntgyQ1QnIxV1va1cWfW7A+223X0LQkqsVc9VLMaoYoppntcvJl0Bxef/3sxfhnbYSgPbtT0qTuBiSUf4z2M7uBMWkjdYYSOgMfkXX23KSFUnvBp37yYEnbBu7AMYuhIAX9v01ObFlgKxNQMr855jhO4CKAnM+MeB9PSY7nqnaGYK8ucSQ6e+deZ8e9K4dMz4FgPuDDdy1/gNpEhf9crq84dqNQLA7eVjmfle7MvlSKBg98EZmfpSgJZexgdNM8R1JYLRpJODvPmlYeOvg959/o3Z54IXExZRhsjYBWe7WZjz1TlgOI8YMev+umaEkZd8zcoYgYueshVPbIYqEF8A9dSVdSi6jAueo8B1KfHwibUVOPhaM+G5a3hWa1XnuJwffiC0fz5QPA7DC/XYiwkw/dVu0IfG5pdHNhj071EVz7C2ZxydKWZuA6GcH7hk3RHV/99SrwZoHrbVwdl8iQxOZ6MCL2uXkS/pNeE2K0n0ma7KzO0ebk2axe2uyohQZojTQwEzffq2Fd3Z033FdM7QD6FK3zOoJaPUcADM/Cibz4PcfOV2bxyfoh8OvJja2DJGVCUh+sJ2Z3v0AuCs+Gt2DLLndQ7DkzSVipQ/Pm3Vdv3fx+HueyVbblwFwoUcSEBGbC0NJq2/cEigcF9VjPJOvIoCJCf46zK0nEhle6lNVOPg37foAn9nz6IWq+5+Lf0wZKCsTkPmenZgIctIyiUDhAMWAvU3XCgNlrLeIia8Ljoeaw/X3UBR25iwliMI032FMHbIekYjeBZ4dALinXB31Y1R7EQdt8wDIOf56QuJKG42HodmhDZWf9uHoH6cnK443ZWr2KGRlAnKBZzsAu0JN3FELDVWjdov08YnoHX8X/G4onKA10Uap1VzCUessAOzHX0tUdCLDmFU/8z07AfBMvrLf+0UWr+oFrDtzLgLAfvz1Hrdl3Sisw6HhyFMu0+b5iNa4RVrNiKcNTu1ISGiZJPsSEFXlArf2xtgV+rBFrWQKjJypLT7keCP+sYmM43R56dj3MgCdk67C2RXbWdFOu/YezTnxZtxjE5lphvcAeaqLNlOhtqBmP3qvnBtUVXbalwJgq3uH1eu3nr+qbrbQWyxjaf0ArVZk2lXa9aNZ3ooUhaxLQKzNBykLNuJRctifsyD2Dcz4iHYp3TCiH5Fnlu1uP217tL7k35yaEvMX+V7bIgByTr5LfUtn9p6RiqgtDLXw7sm5MLriyQi1lqk0m8ow+buY5c3SSfCCATgRKsSdfGnsj9eTliPSajmYrEtAck9oLRc1toX4lD6mwh5MxZXa5TEZqib6FnlmqbTWMj5wigAm9uZc2HOkS3Dw4dxHbLNwK3ZMXU38/IlnsveMVERNr//YHWsXM4CisDdnEQDzPLvjGFUaObsXPK3a7Kdj+29B6ldFqAWkvlpmRR1E1iUgOSc3A7An9CGL2aQVoJjBeQJasrxSXAzKfkp7vx21zqLLlH/eSJfBBBQr+21aS53ery9Ef0xuJ1NDE4kNKQEB9uVcAMA8z664xZVWjocmcJu0HMyW2B9fFKr1UoOhpRdEf7IqAVHUAPbT2vC0GtsQMlvQCpImLNauy5tLDMJ+SmvKrRlKd1+IfkYqCYgYTM7prZhQqTdPoMVc1qPFLdpuO/27scJ3iJxgVyLDTU0nImaQHSp9ygY9mRF9yqoEZIrPgcnbjkvJ44S1YhgbCq0qKd0wYhA5p7YAsH+oCS/dZ6RzvHswq/64xCUyU/j9Fkp4I1vc3L7geaNe+tJgGYu/sBwLAWZ7s2zSxWCwOwGJZvbTXvT6r+ZRWjFvQI4RA8qqBGSOV+vTPGCbh6rEVpzVw9RQAnJcFm4S/Svzn8PaVksAEwdtc4e8nROWCgL2EnLVLqbJtOxiAPb69wHC3XaRIpORwboA3RMuAWBetrW6NeyHrhZt1uvxF8b8cL3+64fbtaG75oaacB1I72HPUkieZQnIXO8eYHhnowCUrwCTFdpOaZPVCNGHuaGE95h1Bm5T3pC3oyqm8AFhbrYWBopB5QS7sJ3T3h99JSCxcE/Uuh/mebOsDkTvMilfBmbrkDfTZi6mzjIJgOaaN8Ij4iITQCkkz6IERFGDzPZow8qGNPw2ki0PJobmEJE6ENGPOaGEt2aYBwMAz4TlAMzO1qGRYlAzvftR1AAN5tE0WQZZ/XYQ7olawjvVd5TcYGc8wksPJ0Lzf0yOvfulN72WZssbz0VddJ5tsiYBKfcfp0DtIGjN45h1+vA3qNeB6BPWCNHLHE+oxW24CS/gHq8lIDO9NSiqnDlls75mMIXuLubhtn4ABArGc9o8HhPBcCKd8VQVTmij1pgyjALUkP05WgIirZb9y5oERH8TeMYtJaAMYWhVb3qB0vF3pQ5EnKck0Mi4QD2qYuKAbf6wt+crm4tLySNPdTHZJ91+2ayvGUwB5oRax/bH4f0GUBM+gGZJAtJ4CDobwGKHCUMbwhxJTwQn+Y+RH2wf9vYyUdYkIHoW7x5/cXw2OHGpVgfSXg8tx+KzTZEx9C9t76j5dJnyh79Bkzm8UNicbBuZIAZlVb1M9x4E4EAcWtyg+wCqt6xkPL01e+JSsOQMe3Ot5hJ8JdMxoYa7/yMNZYh0psmOBERVw2cHngkr4rNNW153lixjvUUv+pe2J14JL9roLZA6EHG+ad6DWPHhzxvNafOEuGxT70KY6juK4smCM/h4zP/Ri3uC9vmf10cSFzkqKVsLUrMiAbE0H6Qw2IpHycEzZlH8Nqz3E56QBET0FG5xi1fCC+GunNnePdLtJ3rQ32+e8ctBUeKyzSbzKM6ax2IiiK1+a1y2mbLiXP+h0z//WbuuziCyIgGxndRmozxsnQPmIaz/0h89U5YWEBHB1NnABP9JgijaASFOjtpm4sVKUbAVc8vRuG1XpD+9hTeeCS90d8PYQjP6ZqxmB7Sf1o4PE5fGbbOe8drrobUitcVtu5kiKxKQ7tkB41OcFVa+XFsXprUWnLXx3bZIW7Z67f1WZ5lC0F4ct+36FRtHbLMByAlNOCUEAR8zvTVA9wEvXvQRXDknMzwB0VuxJywBa27cNhsoGMcZ8zhMBMk5vT1u280UmZ+AqCq2U/3PDjgsOQUwfpF2XVpBREj4/RanYsBIeh1Ixp+RiqjZGvZiV920KyPwlc2K67b1uSys53aBN4PnAzke//oPnX7csYdOTES3jExAIsfJn609iLnzDH4sHLHG98MJdL9hT8h8IEKjt7jFY/htbwfCTeLSAiI0+oHtQM58UOL7ld5gGUuDeTRK0A91GfyeC6//EnsCEnm86Wt9nQOhlvccOWk4T0YmIJHj5J/+63oAHNYZeE32+O8scj4QkZUiv4BOnz2LpUEbJqu3VsTTIdscgpiwtNVB68m4b1+kH/3AFq/5P3oLtxxn6qSLLSegtQ5MFq1bPUaRx5u+ZjvVW5Fyzu3CFnQPO9xMkpEJSCR9yOKBfuo/Isdi97c65IAmrdDOOlqOQVv9cEIVaSryC+ix9etRUDljHofTXBb3fblNeRyzTtN+0av2RdZS1CA5p7cB3a1j8dadgGToSZbe+jH+QrDFYc6eXhrMY2gyjUQJ+pnhOxD37aezzE9APPrZaN8JSORY7CHN1W8vgrEZ/gEVUZsdno0yMQcDiHgvSwKS9cr9xzF7WulScjmuJ6Zxps+Iyqkd4HUlZB+GCtd/XJKY7SsKNaF6sDnZMqtslDI6ASkKNDMucAoVZVjLoQ9KX7hI6kCy3uzQfAz9tbjFg96kK/PPCP2AdtA2l6BiTsg+zprHEcgfC0EfnNyWkH0YKo4L0PXnQHhWWUlAImV0AqKfjfrK5tBpGpG4HU2R+UCENh32NO9hIDEFqLpwctN4CDrOJWw/IvXNDR3QEtnihqLgmRia0TfTkt7WU9ByXOtGnxTfIcyR9NdnhvcAFjU7p13vS1YkIO7xyxK7o0kXAwo0HYb2s4ndl0hZkdNhnzWPS9h+Ok0j8I2co/0i3TDZS1XDLW4JTUAAb2hK8Yw7ydITqrELwV6YsN3UWyYSyB2JDS/TvIcStp90k9kJSKj+I27rv/QnrxTGhEY8ZNoZgoianvDGczrs/ngmZOgZqYjaeH8dRcFWgmY7DtuMhO7Lq7eAnNwGvgwayaGP7JkSffdL5Ki3qAcvKEp4lto5Mi17WMYmIHnBDib7tWXL4zkddr8my7ow2a7HehwJFj4gSAtI1tLfb95xS/ArcVxiog/+kumQPxoCHq0YNVMMYQG6yFFvsQxe0L8XsmZ14ShkbAIy01uDKTQcMpA/Ztjbixyu2+fyyVIHktVMaoCZ3v0AuJORgOhTbp/dB67mhO9PpJ7w+i/JOMFSlLRcfDOyteK87+y209B0BFBgcvxWre6PvjLuLG8NJrXn6reDHl8yVMYmIPEeDhk5XLfP5ZP1DLphP3Q2xWWfIn1M9jnIVbvoVPLxlc1O+P6C+aNg5ExAhVqZYTHTnTfbpqqGR8DoB7aECy++mT6j/SJbK877ztYTqXELIbck4bH4ymbToRRgV91M8R3pcdugx5cMlbEJyBzPwBOQxV3+SBgVOvDUSrN4ttET3oO2eWBKzHDISCZFoXOc1griPvJ2wvcnjNV7ts3RgTOUBRvxY8E75sLkBKHXSdRtBX8GnKEfe0u7nHJ5cvanmMKzI8+V4bhAhiYgiq+Tab6DQGKHQ55nsnTDZKt53l1A4kcj6Dz+AL+v00bamGrl/ZZt9PqPo9aZqNa85Ox01GzIKwN/F9RXJ2efiTSEAtTh2h+a1G22TEgGZGgCYju1FQsBGsyJHQ55nimyMF1WCvqZ69EKy/blXJC03eqzK1ob9oK7NWn7FcbTu18SseJyvxSle7bQNOqG6VNbPTQfDc3/kaQuLLrX65nt3QdqMGn7TVUZmYDk1GlN0nttixI+HLIHfSa9M3uhqyV5+xWGsp7bTZ7qokMp6F6nJQlazCM5Yx6Hoga1ZnGRNeYkaf6P84RnfU7zVjc9gRq7EHKLB7zrkIbd9rdb63S6lFwK1A6sjfuHvJ1MkZEJiK1Oe3Ml82wUgBFjoGw6WmHgluTuWxgmp1Z7v9XkLERN0HTY/cn4lUrFecxtdYwJnCGAiUO2Ocndud7KW/s+BHzJ3Xc8HQ/VTUXR/TLUYbd9CSrm8LIg9no5RmReAtLVgvWcdnawL2dR8vefhpXiYnhyTuoJ76Kk7zu8UJheUCcyXm6d9lofts2myxT/1VsHNHoe2IvB1wmndyV33/Gkfz9PvSLpu9bXhcmpfz/p+041mZeAHH8HBZVTlnJaErAc+qCmZEgTpYiOz43tlNb9sdeW5BY3YG9OaARE/QcyH0iWsNdqCcheW5JGv0QymdL/JKv1JDQ7Er7+S39qQgmI/dR7MIzWlEyQeQnIMf3DuciY/esfztO7wN1mTAwieU5uQwm4aTGVcMoyKem7bzGXheYdUcHx96TvXySXogaxh1rcducsNiaINJyQrIejr2uX4xeDvSjpu3fYZuBWcjB3NTHJfyzp+08lmZeAON4EYG+y6z90RROgZIpW4VwnTWwZ75j2ftuXsyi5Bc8RPJM/pF3Rv1hFxprsO4rZ3YJLyeOobZZBQeh1IFsgmIYTZh2p0i6nrzRk937FRk2otfQCdwZNaz8EmZWAOOug8SCqYqLGqAQEuivFpV8+84UO+vsM6H7ReSZdGYrljaxv0s10Czw7Aa0ZP6BYjAli7ALIKQJPG5xJs3VNAv7ulkKDEhCAXfaLALjAs92wGFJBZiUgRzYB4B13EZ2mEcbFUXFlKJ4q42IQidfRAKe0CZl22pcaFoZ3wnIw50DbSWg8bFgcIvEWerQz5nDtT5JErlXidAe65wNJt1a3Uzu0OXPsxTCh/y6s86a+j7OdOVoCMtu7D3vQFfftp4vMSkAOvQqAZ8rVxsYx/WpQzHCuBlpOGBuLSJwjVYCKb9R8YwqeQ1RrXvdiWul2QBBRyw12hheg08+gkyVyrRKXNwAzrtFuCH3npg39pHDahwdcMqH31PfxdtYyHl/RFCz4w7MoZ6OMSUCcbW0EQ01rrskGJyB5pd3V1YdeMTYWkTiHtS9f91TjmnLDpn1Yuwy1AorMs9BTjQU/vuIKTlsmGhvMzI9qlye3ps3imyZFwXtQ+8y2jL/c8BVnu0Jdp9lcB5IxCUjA8Q4mfxfNpjI8ZUmenKcvM6/VLg+9ZGwcIjECfjj6GgBuo1vcoPv9duwtGX2VoRa7taL2rinXGBwJUDQRxizQiu3TpKvZ134Oy1mtteFfdo/G7Qt2dysZkIy4J18FwCLPtqyt3cqYBCTnuHYw+MC+1LDRCD3oB4Tj74Cn3dhYRPyd3Kr1JeeW4htr0HDISCNnarPwBrxpc0AQMQgGuNCzDQDX1BRIQKC7FeTQy8bGEaXcY5swoXLcUkGLuaxHt1JkMpKouo/e3BMvxYuV0YGzWJsOJHx/qSgzEhBVxe7QmtZ25hhXDNjDyBlQWqEdEI6+YXQ0It4O/k27nH71gH3JSaMoMOt67boem8gY1jPVFAZb6VTy8YxLke84/STryGtpMS17nkNrjd6ae+l5t0UmI4mq++hNteaF53LJO5qdn9nMSEBO78TSVotHyWF3zhKjo9EoSkQ3THqcIYgoqSrse1a7PufjxsYSafYN2uWhV8FvbP+2iK/wCZb9IjBbDY4mZMJiyBsJntaUnxXVHnSRG5pBdpv9EoOj6bYtlAzpyVG2yYwEZN8zAHyQsxSPyW5sLJH0BOTgS2lxhiCidKoaWmvBmg/TU6Q5HGDiRZA/WjsgnEjtA4KIgaqSe/g5ALan0METkxlmf0y7vu+vxsYyiAs821ECHk6bx1NnmWJ0OGE77CsIYMLWWMNo/2mjw0m69E9AVBVqngHgvdzkLyw0oMmXQv4o6GqWabIzyb6ntctZ14Itz9hYIpnMMOs67XooKRcZoL4aS+sJ3EoO1TnLjY6mp/k3apf7n0vpk6wVXVpCvs1+SWrUCIZ0mArZb9MWlFzmzr6ThvRPQE7vgpbjBC257MxZZnQ0PZktMPdT2vU9lYaGIuIkGOw+uM+70dBQ+jT/Ju1y3zPgcxsaioiTvVrCW52zIrVaeEGb9TlvJHS1hJclSDW5wU6WuLcA8F7uhwyO5nxbci8H4HJX9s3hk/4JyJ6NAHimXp16H06ABTdrlwdeAF+XsbGI4Tu1XZtx1FZg6FTOkSJnqawvuYjgiPFaN4zUHqW/YDDcvbE5BQ+e2knWJ7Xre54yNpZ+LHe/gw0v3pIZHLNONzqc87yXewWqycZk/zEm+RxGh5NU6Z2A+L2w60kAXHNuTeque0xNPNAY8onLoKgcvB1w4MXkBSgSY+cT2uWs68GaGglvjwr+jbvpnBVqmdm93tjAxJDpU4E37XkV2k4RtBUmffbTqC0MfffWPJOSc9Bc7tKmaOicfXNKdb/oOk0jcFV8BIAPubJrIsH0TkAOvQSuRigYi2fKh5O66/OmJu6PyQQXfFa7Xv1YcoITieHpCLe4dcz9bFLnDIhF15xQq9vhV9NmlkrRkz4V+KGXfglAx6wb8Sk2g6PqR/lyKJsBPld3fVSKMLedZJ5XWzCvc9anDY6mf52ztc/spV1vYFLTcIXhIUrvBKT6j9rlos+ByaCVIaOx+IuAos1S2XTU6GjEUO19SmvJKp1G29gVSZ0zIBb+stkwbhEE/fDBH40ORwzRiICTpe73AOiY93mDoxmAosDiL2nXq1Pr/Za3R4tnr20RgREGT18/gK5JV9FqKqI46GSx+/3oW9jTXPomIC0ntAlwAC78grGxDKZ4Une9wI4/GBuLGLodj2qXS25PyabcHpav0i7fX5fSoxNE/650bcKCnyPWmfhGzTM6nLAeNUf6AfKCz2ongad2QP1Ow2KLXMW2vrGFvD1/AuCV/BSar6cvZit/z9Nmlr2u85noW9jTXNokID3eWM4u3O/8H6BCxZVQNs3o8Aa39Gva5Y7HoMtpaChiCE5shvpqMNu0FrdUN/8mbU6Q9nqoedboaESs/B6u63wGgKr8jxkbSy+9Zw11+4LU+wtwzfiEdof3/s+w2CJXsX32T7/A7G6mwTyaHfYVhsUUrVfzP04AE/O8u7E27A3/vc+EL0OkTQIS+ca698m3se1+XLvhkm8ZGxhRvkFmfBRGzQFPG2x7JPlBiuF5+6fa5YVfgPyRxsYSDUsOLP26dv29/8vaxa7SVe7BpygNNtFsKuOd3KuMDmdAekLy46ZQnHufAmedoTEpapCPdWj1KJvybiCopMByCYNoMo/ifbs2JLdwZ/cxonfCl0ktImmTgET6WMdTmHwuGD0Xphm/EmlUbxCTCS7/Z+36ll+DtzO5QYqhq/9AW+BNMcMl3zY6muhd9FUw52gtN8ffNjoaEa1gkILtvwLgxYIb8adq8Wkvx20z8JRfBmqgO2E3yDL3O0zyHydoK+S1/OsMjSUWLxZoI9jyDz7NOP9Jg6NJvLRLQIoDTXysMzTt71X/kvp98ZHmfRpKpoCrCbb9zuhoRLT+/t/a5YKboXSqsbHEomAUnoVad5H31R9T3+IasPk2spuzod2Tsc2+KW/3k1hbjtChFPBaXvocPAHaV3xfu1L9R2g8bEgMihrg5vY/A9C26Ot0mkYYEsdQHLXNojpnWeg5PG50OAmXdgnITe1PkKN68I5bQv3Yq1N2KGSfzBa4PPQBfetBaD9rbDxiQE6Xl6adL8Khl1FNEa9diovsEmxc/B08Sg6209uofOznPVrnetdVtbv9Ea14/oxt9k1p3k547ccAPDPiNtymFJrqPwreCStg5nVaK8hr/2FIDCtdL1HuP0GHUkDbojsMiWE4niy8HYBLu95kqteYJC5ZUjoBifyCDARVKrwHudqlrRrYfPG/ctfGXSk7FDJS5PNwzrwZxl+o1YJs+jejQxMDcHV14X7hbu36oq9Tby1Pi4Q3skvQnzeavxZ8BoAvtq3D7G3vM+FI9c9QNnC6vHS8ch+0n8ZfWM4r+Z80OqSYmRSFc8tXoyom2P88HTXJnVjL5GrktrZHAdhY+CXUnKKk7j8eaq0VdIQmE7yz9efacPoMldIJSGThadDvZpXzZ5gI8m7ulXgmpH5Vsy7yebj8wMd+Ciiw+0k4nF0z36WTEe+tYYL/JE5TCU1Lv5e2B+sXCm6i3jyBkmAL+X+/Ny2fQzbwHX+fvB2/AaD5ivtSd+KxAXj8Ab79mptX8m4AwP7y9+Iy6q/HSVx/3YGqStFrd1OgdnDMMo1Neak1eigWLZf9iA6lgKm+oxRW/9rocBImpROQSKVv3stk/3HaTEU8WvgNo8MZUO9RMeedLU9YAsvu1K4/fSe0Zn6xUdo5+joF27XhhL8r+idUW/r0I/fmV2z8tvjbBFEoqHmSqzpljZiU09lEySvfxITKW7lX0zX1I0ZHNCxPjrids+axWNrq4NlvDnsUVuRJnNsX7Ls+acuvyT36N3xYeaT4O2kx8qU/wbxR/LFIm8un+P3/YYa3xuCIEiMtEpCPdD7HiH1/IojCL4t/QLu52OiQBtR7VEyfZ5ofuQ/GXQBdzbD+i+BpT36gom/n9sPG21FQ2ZR3PdtyLzU6omGrybmADSO02Sq/0vp/TPceMDgiEeb3woYvYmk9wVnzOB4rSu0TrGi4TXk8XHIPqsmqLcRZ9aO4DQXvax6Sxt2vom66F4DHi+7AYZsZl30Z6a3clbxnvxwl6OOu5vsyclRMyicgV3f+ja+2akPSNoz4MrtTdUGmWFly4JZHIbdUGya59kPacE9hrDN74PFPg7sV77il4bOQTPBswW24pn4EGz7uafpXbGeqo35stkwNnUxOl5fTDU24H78VTrxL0DaCNaX/nlajNgbisM2i9cOhEWTvPqwVpSai2+/4W4z46xdQgn46Z3ySV/NSfNbTaCkKa4u/h7dsLiXBFv5f4w8Z46/PqInJUjcB8XsofOtH3NH6CwDaFn6VZwpuMzio4evx5jGNo/2WDVA4EZqPwm+v0SaNCgaNDjMrde58muDvr4X20/jKZtNww6P4lByjw4obVTHR+JFfcsA2j3y1kzFP38yHXK9G9dhsmRo6mTznjtC57lrsJ97AreTQcP1vOWWdbHRYceWa/3m47kHtl3d+Bs/8g7ao4yB6j9Dqs+hbVbmm8wXGPPs5clQP1TlLabzm5+k1NcMg3KY8zn7qSeoskygLNvLjxu+hOF7PmBFqKZWAOF1e6ltcNO18Ed+vL6egei0AG0Z8kZYr7suIN1bv5sP20vnwjbdhzsch6INX/gWeuBU6GowONW30/rKK+Yyg5Tj85bPkP/MVTN4O9tkWssr6E/z2koTEayTVVsD9pT+hOmcZpoCbf3A+xD83/wfjfbVGh5Y9vC5492FG/flqpvsO0aEU8J9l9+Muv9zoyBJj+Z1w3YPayJhdf8H/fxfT/P5fcHa6+31IZM1HX93YM701/L+mH/K11l+iBH28Z7+cn5Xeq028l2GCeSP5z7L7OWadRlGwlTHPfpZvtqyhOJD+K12nzhKyvi6Urb/F884fGO/XVowN5I7k5/ZvsC33MpZnQPLRF5OiUO/NhY+so7j8z+S9/v/gyCb45RKY8wmYtAImLsOZNwmXr/tDmGczU5w3/Cp5p8sbzqDjtc1k07+sdA/duojiwaZPcLfCgRdh11/g2NuAimqy8kzeTTw14nNpM/vkUHhMuTxY+u/8evLfKdjyU5a632Op+z1cz6/kUtdidtqXDvh4vRVPl67vm4QL+KD5GDQe6vnTcBC8HZiAfbaF/Kr4+zRZRhsdbUKE3yuzvog5bzrmZ+5kZFstpS99A//m++GC22DsfBg5E0ortK7pvqhBJvqOM8e7l7Eb/5UfN+4AwKPk0HnpD3n46IqMOEHtj9Ncxo/Kfsrn2n/PRzuf5/Ku11nR9TY77MuxH74dyudA4XjILUmr/0PqJCBV/07R+7+hCHArObyWdz0LP3sf2/6WeYU3kTz+AKuf2g3AQ7d+gbxpl8JTX4dz++CDx7UfoDCnmGPM4Kh1Ji3mMm66fBEUF2srUEb+mHv9bjJ3X1fMtHZ56fJ1N9kFgio/fn4fAP/xifkQ6OsLoO/mz0HvF8194rAtc5ubUf4zAJgIYmuy03CmC8XvxqJ6CHa1YXadw9TZgLX1GJazu7G0Hu+xJfekK2m5/N9Z/0b/Z2WZRFVMtC79Lj85WsGt7X9kmXszecer+BZVAPj+OJXveCbSaB5Nk3kUeYdPsMDdhMdkR62Hn75+Ar9iRUXhXz82FwrtgBLx5addb3X76fIGQn9XsJpN+IIqoJBrM1OUa9Pu2x1Z6ELt5/cB7hOXx/WzncG27e3QZv6MTDSaHf3O4eAvLKdt2V38ZN8srWUgQ0V+vz1w03L+bdQ6Ptb5NDd0PEVe6wl4a033nRWTNlN02QyKckbyjZZzFAadlASbmfhIE//jcWr3awUfVt7Ju4pnCj7DXRdeC47dSX9uyeY12Xm06B+54PpVtD3zA2b5aljhfgdefKf7ThY7jBgLI8ZD4TgYMQ4KJ0DhONpto3BZS0Exo1rs2EvGGX7iEHMC4nA4cDqd8Y/EOxW3aTlVzrFU25fh8hXwnUO1NNdqM8Ht3RUMXx/s91S6LbbtwB5UmLMGa0k1Oed2YW6owdp0ECXYBDRRwRYAjh+A49H9Zwell1nW74T6OG0z2b4Scb12V3SP8RdM5LXgInbmLKXFUcZ3prhS5n2TrPf77tMednMbo/wf4s5xDjoPvs7YwBk4fYQcjjABmADUHQR9UvDTe+DzEf/HU7vg1AD/56xmyYXiciieDMWTaLWP5/f7AjT4xvAt7yya6jLvPTXQbadP1vJbLuKPwQX8YMYZLGeqsbTVYW47icnvgvrDgHb/wtA2WkI/XqzUWqYyes6l/O+pGbT7C6Gtnb27dmb8/y3yturpM3jYdSfjAqdY0vU+K3JPoLgaMXvagE7gaOhnYJ3zPk/+ZYkpsi8uLqaiomLQ+ymqGn1ZcmNjI2PGjCEoRZJCCCGE6IPZbObMmTOMHDnwyuExJSCQwBYQIYQQQqS9hLSACCGEEELEQ+ZWPwkhhBAiZUkCIoQQQoikkwRECCGEEEknCYgQQgghkk4SECGEEEIknSQgQgghhEg6SUCEEEIIkXSSgAghhBAi6SQBEUIIIUTSSQIihBBCiKSLeTXcWNTV1fHkk08yadIkbrvttkHv/8gjj1BeXk5dXR3l5eVce+21Md1upFhiW79+PfPnz6ewsLDH38vLywHt/9bW1sa8efNoa2tj7969fd7fKLE812ieSyq/rhBbfG1tbaxfvx6AzZs3c9ttt/W4fyq9trH+37Pl85lOr2FfsunzmU3fuxl5PFUT5N1331Vfeukl9fbbb1fXrVs36P3vvfde9d133w3//q1vfUvdu3dv1LcbKdbYrr76anXmzJnn/dTW1qqqqqrr1q3r8fdo/n/JEutzHey5pPLrqqqxx3fvvfeGr7e2tqozZ87scf9UeW2H8ryy5fOZLq9hX7Lp85lN37uZejxNWAKi+9a3vhXVP2zmzJk9fn/yySd7fBEMdruRYo2t9/+jtrZWffLJJ3vcXltbmzIf9EhDea4DPZdUfl1VNbb4amtr1dtvv11tbW0N/+32229Xv/Wtb4V/T5XXNtb/e7Z8PtPpNexLNn0+s+l7V5dpx9OEdsFEa/PmzX02c23evDmq2400lNh6N3U9+eST/OAHP+jxN71ZMJUM9XXo77mk8usKQ4tv7969tLa2hh9XXl7O3r17e9zH6Nc21ueVbZ/PdHgN+5JNn89s+t6NVTp9XlMiAWlrazvvb0VFRbS2tkZ1u5GGElvkm/yRRx5h1apV593n5ZdfBmDPnj0sWLAgJfpdh/o69PdcUvl1hdjjKy8vZ9u2bT3+tnnzZi655JIefzP6tY31eWXT5zNdXsO+ZNPnM5u+d2OVTp/XlEhAWltbKSoq6vG3wsLC8D9qsNuNNJzY2traqKurOy8bnTdvXrj46dprr2XlypWUl5czb968uMYeq6E814GeSyq/rjD8992+fftobW3l+9//fvhvqfDaxvq8svXzCan7GvYlmz6f2fS9G6t0+rymxDDc3v8M0N4k+htksNuNNJzY1q5d22eGfckll/R4/Ny5c8NV+UYaynMd6Lmk8usKw4/v3nvv5dFHH+1x/1R4bWN9Xtn6+YTUfQ37kk2fz2z63o1VOn1eo24BWb9+Pfv27RvwPtdee+15TZXRKCwsPK/5JzJLG+z2eIvluQ4ntg0bNnD99df3uf/IYVbl5eW89957UUYfm0Q/14GeS7JfVz2eZLy2Dz74IPfdd995Z0/JfG37E+vzSrXPZywy9TXsSyZ8PqOV7t+7iZROn9eoE5Boxh0P1SWXXHJe809bW1s4mRns9niL5bkONba2tjba2trOK3qqq6vj3/7t37jkkkvCt7W3tzNx4sSoY4pFIp/rYM8l2a8rJOe1Xb9+Pddff334wKXXECT7te1PrM8r1T6fscjU17AvmfD5jFa6f+8mUjp9Xg3rgtm3b1+44Afgox/9aI8qXH0CoGhvN9JgsfV+rqC94ftSXl7O97///R4fkM2bN/dZMGWEWJ5rNM8llV9XiP211e9bWFhIXV0d+/btC7e4pNJrG+vzyqbPZ7q8hn3Jps9nNn3vDiZdP6+KqqpqIja8efNm9u3bx7p16ygvL+e6667j2muvDb/ADz74IDU1NfzhD38IP+bBBx9k0qRJ4Qy1dz/dYLcbaaDY+nqu+/bt4zvf+Q5VVVXnbUuf8a64uJi6urohd20lSizPNZrnksqvK0T/fNva2li6dOl5j//+97/PHXfcAaTWaxvrezYbPp/p9hr2JZs+n9nyvZupx9OEJSBCCCGEEP1JiVEwQgghhMgukoAIIYQQIukkARFCCCFE0kkCIoQQQoikkwRECCGEEEknCYgQQgghkk4SECGEEEIknSQgQgghhEg6SUCEEEIIkXSSgAghhBAi6SQBEULEbPPmzbz88susX78+/Le2tjZuvPFGA6MSQqQTSUCEEDGpq6ujqKiIefPm8cgjj4T/vnnzZoqKigyMTAiRTiQBEULEpK6ujnnz5vHyyy/3WC108+bNKbN6qBAi9UkCIoSIiZ5kvPTSSz2W8N67dy/z5s0zKiwhRJpRVFVVjQ5CCJFe2traWLp0KQcPHgz/bdasWT1+F0KIgUgLiBAiZnV1dZSXl4d/37x5s7R+CCFiIgmIECJmhYWFFBYWhn9/8sknufjiiw2MSAiRbixGByCESD/l5eXMnz8/PAy3pqaG66+/3uCohBDpRGpAhBDDtnTpUrZt22Z0GEKINCJdMEKImC1dupS2tjYA1q9fz3XXXWdwREKIdCNdMEKImLS1tXHnnXdSWFhIW1sb69ev5+mnnzY6LCFEmpEERAgRE70A9eWXX6auro5HH33U6JCEEGlIakCEEEIIkXRSAyKEEEKIpJMERAghhBBJJwmIEEIIIZJOEhAhhBBCJJ0kIEIIIYRIOklAhBBCCJF0koAIIYQQIukkARFCCCFE0kkCIoQQQoik+/+2dENcd91axgAAAABJRU5ErkJggg==", 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "colors = {\"blue\": \"#1f77b4\", \"orange\": \"#ff7f0e\"}\n", - "histogram_color = colors[\"blue\"]\n", - "density_color = colors[\"orange\"]\n", + "color_palette = sns.color_palette(\"tab10\", n_colors=10)\n", + "colors = {\n", + " 0.2: color_palette[0],\n", + " 0.5: color_palette[1],\n", + " 0.8: color_palette[2],\n", + "}\n", + "histogram_color = color_palette[6]\n", "binwidth = 0.015\n", "\n", "# Initialize the FacetGrid object\n", - "g = sns.FacetGrid(df, row=\"x\", aspect=4, height=1.5, palette=None, sharey=False)\n", + "g = sns.FacetGrid(df, row=\"x\", aspect=3.5, height=1.1, palette=colors, sharey=False, hue=\"x\", )\n", "\n", "# Draw histograms\n", "g.map(\n", @@ -212,7 +286,7 @@ " density_true = branching_mixture_density(x_val, scale=scale)\n", " density_true = density_true(y)\n", " sorted_indices = np.argsort(y)\n", - " plt.plot(y[sorted_indices], density_true[sorted_indices], color=density_color)\n", + " plt.plot(y[sorted_indices], density_true[sorted_indices], color=kwargs[\"color\"])\n", "\n", "\n", "g.map_dataframe(plot_curve)\n", @@ -252,6 +326,142 @@ "\n", "g.savefig(\"fig/branching_mixture.pdf\", format=\"pdf\", dpi=600)" ] + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/achille/miniconda3/envs/treefuse/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = {\"blue\": \"#1f77b4\", \"orange\": \"#ff7f0e\"}\n", + "\n", + "color_palette = sns.color_palette(\"tab10\", n_colors=10)\n", + "colors = {\n", + " 0.2: color_palette[0],\n", + " 0.5: color_palette[1],\n", + " 0.8: color_palette[2],\n", + "}\n", + "histogram_color = \"#555555\"\n", + "density_color = colors[0.5]\n", + "\n", + "\n", + "binwidth = 0.04\n", + "\n", + "# Initialize the FacetGrid object\n", + "g = sns.FacetGrid(df, row=\"x\", aspect=3, height=1.1, palette=colors, sharey=False, hue=\"x\", )\n", + "# Draw histograms\n", + "\n", + "\n", + "# Define a function to plot the curve on top of the histogram\n", + "def plot_curve(**kwargs):\n", + " data = kwargs[\"data\"]\n", + " color = '#%02x%02x%02x' % tuple([int(c*255) for c in kwargs[\"color\"]])\n", + " darker_color = '#%02x%02x%02x' % tuple([int(c*200) for c in kwargs[\"color\"]])\n", + " y = data[\"y\"].values\n", + " x_val = data[\"x\"].iloc[0]\n", + " density_true = branching_mixture_density(x_val, scale=scale)\n", + " # y = np.linspace(x_val * X_SHIFT_COEFFICIENT, 8, 1000)\n", + " density_true = density_true(y)\n", + " # density_true = np.where(density_true == 0, None, density_true)\n", + " sorted_indices = np.argsort(y)\n", + " # remove indices where y is nan or none or denisty is nan\n", + " sorted_indices = sorted_indices[~np.isnan(y[sorted_indices])]\n", + " sorted_indices = [i for i in sorted_indices if not np.isnan(density_true[i])]\n", + " plt.plot(y[sorted_indices], density_true[sorted_indices], color=color, lw=3, alpha=0.9)\n", + " # fill under the curve\n", + " # plt.fill_between(y[sorted_indices], density_true[sorted_indices].astype(float), color=color, alpha=0.1, zorder=-1)\n", + "\n", + "\n", + "g.map_dataframe(plot_curve)\n", + "g.map(\n", + " sns.histplot,\n", + " \"y\",\n", + " # bins=bins+0.1,\n", + " binwidth=binwidth,\n", + " kde=False,\n", + " stat=\"density\",\n", + " color=histogram_color,\n", + " alpha=0.2,\n", + " # make edge darker\n", + " edgecolor=\"black\",\n", + " linewidth=0.5,\n", + ")\n", + "\n", + "\n", + "# Color horizontal axis in black\n", + "g.refline(y=0, linewidth=1, linestyle=\"-\", color=\"black\", clip_on=False)\n", + "g.set(xlabel=\"$y$\")\n", + "g.set(xlim=[-1.05, 1.5])\n", + "# g.set(ylim=[0, 3])\n", + "\n", + "\n", + "# Define and use a simple function to label the plot in axes coordinates\n", + "def label(**kwargs):\n", + " ax = plt.gca()\n", + " data = kwargs[\"data\"]\n", + " x_val = data[\"x\"].iloc[0]\n", + " darker_color = '#%02x%02x%02x' % tuple([int(c*200) for c in kwargs[\"color\"]])\n", + " ax.text(\n", + " 1,\n", + " 0.5,\n", + " f\"$\\\\mathbf{{x={x_val}}}$\",\n", + " color=darker_color,\n", + " fontweight=\"bold\",\n", + " ha=\"right\",\n", + " va=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + "\n", + "\n", + "g.map_dataframe(label)\n", + "# Set the subplots to overlap\n", + "g.figure.subplots_adjust(hspace=0.1)\n", + "\n", + "\n", + "# Remove axes details that don't play well with overlap\n", + "g.set_titles(\"\")\n", + "g.set(yticks=[], ylabel=\"\")\n", + "\n", + "# Remove vertical lines\n", + "g.despine(bottom=True, left=False)\n", + "plt.tight_layout()\n", + "g.savefig(\"fig/branching_mixture.pdf\", format=\"pdf\", dpi=600, bbox_inches=\"tight\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-17T22:12:46.429691Z", + "start_time": "2024-05-17T22:12:45.977779Z" + } + }, + "execution_count": 27 + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } } ], "metadata": { From 90f4411c744b3db8031906a06eba3a27498ea200 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:22:50 -0400 Subject: [PATCH 29/44] fix pandas deprecation warning on apply --- examples/m5-news-vendor-notebook.ipynb | 69 +++++++++++++++++++------- 1 file changed, 51 insertions(+), 18 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index 9f07b6b3..df6800ba 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -470,9 +470,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date profit avg_inventory_weighted avg_demand_weighted\n", + "0 2011-11-25 -2.213333 0.039476 0.784104\n", + "1 2011-11-26 1.600000 0.033068 0.933309\n", + "2 2011-11-27 0.053333 0.046308 0.796895\n", + "3 2011-11-28 5.873333 0.067636 0.789166\n", + "4 2011-11-29 1.580000 0.041286 0.913090\n", + ".. ... ... ... ...\n", + "60 2012-01-24 6.336667 0.138240 0.661504\n", + "61 2012-01-25 7.876667 0.078847 0.530883\n", + "62 2012-01-26 -3.493333 0.051774 0.894360\n", + "63 2012-01-27 -0.710000 0.102579 1.093555\n", + "64 2012-01-28 11.890000 0.116262 1.258714\n", + "\n", + "[65 rows x 4 columns]\n" + ] + } + ], "source": [ "performance_data = pd.DataFrame(\n", " {\n", @@ -486,19 +507,20 @@ "\n", "# for each day, compute average demand and inventory weighted by price\n", "daily_summary = (\n", - " performance_data.groupby(\"date\", group_keys=False)\n", - " .apply(\n", - " lambda x: pd.Series(\n", - " {\n", - " \"profit\": x[\"profit\"].sum(),\n", - " \"avg_inventory_weighted\": (x[\"inventory\"] * x[\"price\"]).sum()\n", - " / x[\"price\"].sum(), # price-weighted average inventory\n", - " \"avg_demand_weighted\": (x[\"demand\"] * x[\"price\"]).sum()\n", - " / x[\"price\"].sum(), # price-weighted average demand\n", - " }\n", - " )\n", + " performance_data.groupby(\"date\")\n", + " .agg(\n", + " profit=(\"profit\", \"sum\"),\n", + " avg_inventory_weighted=(\n", + " \"inventory\",\n", + " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", + " / performance_data.loc[x.index, \"price\"].sum(),\n", + " ),\n", + " avg_demand_weighted=(\n", + " \"demand\",\n", + " lambda x: (x * performance_data.loc[x.index, \"price\"]).sum()\n", + " / performance_data.loc[x.index, \"price\"].sum(),\n", + " ),\n", " )\n", - " .sort_values(\"date\")\n", " .reset_index()\n", ")\n", "\n", @@ -507,9 +529,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aTz/9NFZZGG8aDAaMHTsWbdu2xeHDh1FRUWHVsmutPW6++WZ8+OGHmDt3LjIyMgBA7LNNjZFsbccbb7wRer0ehw8fxuDBgy2Wj+JcqMJJcSoXLlwAIQQpKSkOO+eCBQtEd5YZM2bg1VdfRWZmJpKSkgAA9957L3bv3t0shfM///mP+H9iYiJefPFFrFu3Di+//DL8/PwQGBgIuVyO2NhYq+eYMGECnn/+efz555+igrl27VpMmjQJDMMgJycHq1atQk5ODuLi4gDwa8S2bduGVatW4d3GtKs6tFotli5dirKyMjN3mKFDh5o99KdMmYLbbrtNXKPRqVMnpKenY/HixZg2bRrCw8Ph7+8PpVJptU5RUVEA+EmCxurd0lmxYgUeeOABAMCoUaNQVlaGvXv3YvDgwRg5ciQCAgKwceNGcdCydu1a3HXXXQgKCkJFRQW++eYbrF27Vhxkr1q1SuwfUuA4DqtXrxZdth588EHs2rUL77zzDsLCwjB69Giz6/z888+IjIzEkCFDAPCK5CuvvIKpU6cCAJKSkvD222/j5ZdfNnuhT5s2DZMmTQIAvPvuu1i2bBmOHDmCUaNGiUpVdHS0OLlUWVmJzz77DKtXrxbXO3/11VfYuXMnVqxYgZdeekk891tvvYXhw4eL24888ggef/xxvP/++1CpVPjnn3+QlpaGzZs3W5XDpUuXmlQ48/LysGTJErRu3RrJycmie3r969dnwYIFeOGFF8wUpj59+gDgB6ZHjhxBYWGh6Ga/ZMkSbNq0CT///LPFNVABAQHo27cv9uzZg0mTJmHPnj0YOHAgVCoVbr75ZuzZswft2rXDnj170L9/f6hUKnz//ffgOA5ff/21OLG1atUqhIaGYs+ePQ2Cm0jpY7fffjuefPJJAMCcOXPwwQcfYPfu3UhOThafBxEREWbPgzfffBNLly7FPffcAwBo164d0tPT8cUXX2Dq1KlYu3YtOI7DihUroFar0bVrV+Tm5uKJJ55ocP24uDhcunTJqvxbOp7yrBFISUlBRUUFioqKEBISgnfffRe///47+vfvD4B/hvz555/44osvzBROqe/08ePHm1135cqViIqKQnp6Orp16ybuf/HFF8W1rfPnz0fXrl1x4cIFpKSk4KOPPsKoUaPw8ssvA+DfiQcPHsS2bdus1q+0tBRlZWVNyujYsWNYu3at2TtZq9Xi22+/Fe+b+pw7dw4//vgjdu7ciWHDhonyEhAmcEzHBStXrkR8fDzOnTuHTp06NTjngAEDoFQqzZ4ngwYNwg033IDr16+LE1h79+7FjBkzAAALFy7ElClTxGBGHTt2xLJlyzBo0CB89tlnUKvVZtfYuXMnMjMzsWfPHvE58M4771h8bjbWHiEhIWAYxuxZYssYydZ29Pf3R0hICH2euBGqcFKcCiHE4efs3r27+H9MTAz8/f3NHswxMTE4cuRIs66xfv16LFu2DJmZmdBoNNDr9QgODpZ0jqioKIwYMQJr1qzBLbfcgqysLBw6dEicWUxLS4PBYGjwoqitrW1yndicOXPwn//8BzU1NQgMDMSiRYvMgkbUH2CfOXMGd999t9m+AQMG4MMPPxRno72B116z/l19A5eJ3tKA+jmeHRUoMCMjA0eOHMHGjRsB8NbniRMnYsWKFRg8eDDkcjkmTJiANWvW4MEHH0RlZSU2b94sBna4ePEidDod+vbtK54zJCQEycnJksuSmJhotj6oVatWKCwsFLenTJmCmTNn4tNPP4VKpcKaNWtw//33ixaCkydP4sCBA3jnnXfE3xgMBtTU1KCqqkpcw2N6PwYEBCA4ONjsOvXJzMyETqczWwOlUCjQt29fnDlzxuzY+v147NixeOqpp7Bx40bcf//9WL16NYYMGYLExESr16uurm4wSBJo06YNCCGoqqpCjx498Msvv5hZBBtTVAsLC3H16lWr1reTJ09Co9E0uJerq6uRmZlp9byDBw8Wg4Dt2bNHnI0fNGgQ9uzZg+nTp2PPnj2YOXOmeJ0LFy40WAtWU1Nj8TpS+php2wqDwcbatrKyEpmZmZgxY4ZYPgDQ6/WiF8iZM2fQvXt3szYRFJL6+Pn5ocqS64Er6NrIw4ap97DpLOFhk/y83UUyxZOeNQLC+55hGFy4cAFVVVUNFA+tVotevXqZ7ZP6Tj9//jzmzp2Lw4cP4/r16+A4DgCvoJgqnKbnbdWqFQD+vk1JScGZM2dES69A//79G1U4q6urAcDi8yQtLQ2BgYEwGAzQarW44447sHz5cvH7hIQEq8omAJw4cQIymcxMETfl5MmT2L17t2j5MyUzM9Oiwunv748+ffqICufevXvx0ksvQS6XixNYhBDk5OSIE40nT57Ev//+izVr1ojnIYSA4zhkZWWhc+fOZtfIyMhAfHy8maJo2qdMaaw9LGHLGElKO7r1eUKhCqc3kxodjf3Tp7v1+k3RsWNHMAyDs2fPNnqcMMg1VVB1VvwhFQqF+D/DMGbbwj7hBSScu77ia+3cAB/1b8qUKZg/fz5GjhyJkJAQrFu3DkuXLm20DpaYMmUKnn32WXz88cdYu3YtUlNTkZqaCoCPIieTyXDs2LEGCp+ll4opL730EqZNm4bAwEDExMSIlg0BS+7LvoAF70CXH9sYK1asgF6vN5sBJ4RApVJh+fLlCAkJwZQpUzBo0CAUFhZi586d8PPzw6hRoxxTABOaui/GjBkDQgi2bNmCPn36YP/+/fjggw/E7zUaDebPny9aqkwxHXA1dZ3mUL8fK5VKPPTQQ1i1ahXuuecerF27Fh999FGj54iMjERJSYnF7/bv34/g4GBER0dbDN7R2H0kuOBaQ6PRoFWrVg3ccIHGlxIMGTIE77zzDq5cuYI9e/bgxRdfBMArnF988QUyMzNx+fJl0Xqi0Whwww03mA0QBRob4NqC1LYVonJ+9dVX6Nevn9l39kxqFRcXo3379pJ/5xBkEh4Kzjq2ETzpWSNw5swZBAcHIyIiQoz4vGXLFrRu3drsuPqB9aS+08eMGYOEhAR89dVXiIuLA8dx6NatW4MAefXPC6BZz6aIiAgwDGPxeZKcnIz//ve/kMvliIuLa+DK3tQ72ZbnyZgxY/B///d/Db4TlDdLDBkyBOvXr8fp06dRXV2N3r17A+CfJ7t37wbHcfD39xfvV41Gg8ceewzPPvtsg3O1bWIJVVNIbY/mjJEsUVxc3OxnIsV+qMLpxYSo1U2uoXQ34eHhGDlyJD755BM8++yzDR66paWlCA0NFR8CeXl54uyno8LtR0VFIS8vT9wuLy9HVlaW1eMPHjyIhIQEvP766+K++m4YSqXSYjTL+tx999149NFHsW3bNqxduxYPPfSQ+F2vXr1gMBhQWFhoc3oDgcjISHTo0MHm4zt37my2tgwADhw4gE6dOkkaCCoUCpvq3RLR6/X49ttvsXTp0gZujGPHjsUPP/yAxx9/HDfffDPi4+Oxfv16/O9//8N9990nvoiTkpKgUCjw999/iy/3srIynDt3DrfeeqtDy6tWq3HPPfdgzZo1uHDhApKTk8XBCAD07t0bGRkZkvpZfYRBl2mfad++PZRKJQ4cOICEhAQA/ATQ33//bVNOukceeQTdunXDp59+Cr1eb1EhNqVXr15IT0+3+F27du3sjt4aFBSExMRE7Nq1S7QOmNK7d2/k5+eLaRJs5eabb4ZSqcSnn36Kmpoa3HDDDQB4V91r165h5cqVouutcJ3169cjOjraJi8MR/UxS20bExODuLg4XLx4EVNMF1Sb0LlzZ3z33XeoqakRJy7++usvi8eeOnUK9957r81lail44rOmsLAQa9euxdixY8GyLLp06QKVSoWcnByrVjt7KCoqQkZGBr766ivxvfnnn39KPk/nzp1x+PBhs33W+qGAUqlEly5dkJ6e3kDuSqWyWc/K1NRUcByHvXv3ii61pvTu3Ru//PILEhMTIZfbPnQfMmQIFixYgLVr12LgwIHi+/7WW2/Fl19+CUKI6HorXCc9Pd3muiQnJ+Py5csoKChATEwMAD7eg1QsjalsGSPZ2o6ZmZmoqalpYF2nuA6aFoXidD755BMYDAb07dsXv/zyC86fP48zZ85g2bJloiuVn58fbrrpJixatAhnzpzB3r17zdZRNoehQ4fiu+++w/79+5GWloapU6c2qmR17NgROTk5WLduHTIzM7Fs2TLRbUkgMTERWVlZOHHiBK5fv47a2lqL5woICMDYsWPxxhtv4MyZM+JaN4BfazBlyhQ89NBD2LBhA7KysnDkyBEsXLgQW7ZscUjdBV544QXs2rULb7/9Ns6dO4dvvvkGy5cvF60ntiIMsPPz861ajVoqQnCeGTNmoFu3bmaf8ePHY8WKFeKxkydPxueff46dO3eaDcyDgoIwdepUvPTSS9i9ezdOnz6NGTNmgGXZBlZsRzBlyhRs2bIFK1eubKAgzJ07F99++y3mz5+P06dP48yZM1i3bp2k+zIhIQEMw+C3337DtWvXoNFoEBAQgCeeeAIvvfQStm3bhvT0dMycORNVVVXiOqLG6Ny5M2666SbMmTMHkyZNatIyMHLkSBw6dMgpEyXz5s3D0qVLsWzZMpw/fx7//PMPPv74YwDAsGHD0L9/f4wdOxY7duxAdnY2Dh48iNdffx1Hjx61ek7hWfjxxx9jwIAB4rNKqVSa7RcUhylTpiAyMhJ333039u/fj6ysLOzZswfPPvsscnNzG5zfUX0sOjoafn5+2LZtGwoKCsQcjPPnz8fChQuxbNkynDt3DmlpaVi1apWYU3Py5MlgGAYzZ85Eeno6tm7diiVLljQ4f3Z2Nq5cuWJx8N3ScfezhhCC/Px85OXl4cyZM1i5ciVuvvlmhISEYNGiReL5X3zxRcyaNQvffPMNMjMzxfvjm2++sbvuYWFhiIiIwJdffokLFy7gjz/+wOzZsyWf59lnn8W2bduwZMkSnD9/HsuXL2/UnVZg5MiRdim4TZGYmIipU6fi4YcfxqZNm8T7+McffwQAPPXUUyguLsakSZPw999/IzMzE9u3b8f06dMbfbbdfPPNUKlU+Pjjj80U/759+6KwsBCbN282mzCbM2cODh48iKeffhonTpzA+fPnsXnzZjz99NMWzz98+HC0b98eU6dOxb///osDBw6I7wgpz5PExERoNBrs2rUL169fR1VVlU1jJFvbcf/+/UhKSnKfxwSFKpwU55OUlIR//vkHQ4YMwQsvvIBu3bph+PDh2LVrFz777DPxuJUrV0Kv1+OGG27A888/jwULFjjk+q+++ioGDRqEO++8E3fccQfGjh3b6EPnrrvuwqxZs/D000+jZ8+eOHjwYIOEyOPHj8eoUaMwZMgQREVF4YcffrB6vilTpuDkyZO45ZZbGrikrFq1Cg899BBeeOEFJCcnY+zYsWYzzo6id+/e+PHHH7Fu3Tp069YNc+fOxVtvvWUxN2FjLF26FDt37kR8fDydKazHihUrMGzYMIsRi8ePH4+jR4/i33//BcD3ifT0dLRu3bpBPrf3338f/fv3x5133olhw4ZhwIAB6Ny5s9V1iM1h6NChCA8PR0ZGBiZPnmz23ciRI/Hbb79hx44d6NOnD2666SZ88MEHolXSFlq3bi0GH4qJiREHLYsWLcL48ePx4IMPonfv3rhw4QK2b9+OsLAwm847Y8YMaLVaPPzww00eO3r0aMjlcjGitCOZOnUqPvzwQ3z66afo2rUr7rzzTpw/fx4AP9jaunUrbr31VkyfPh2dOnXC/fffj0uXLomWAGsMGTIEFRUVDaIpDho0CBUVFWYDRH9/f+zbtw9t27bFPffcg86dO2PGjBmoqamxavF0RB+Ty+VYtmwZvvjiC8TFxYlrxB955BF8/fXXWLVqFVJTUzFo0CCsXr0a7dq1A8C7wv36669IS0tDr1698Prrr1t0E/zhhx8wYsQISf2tpeDuZ015eTlatWqF1q1bo3///mJAqOPHj5u5d7799tt44403sHDhQnTu3BmjRo3Cli1bxL5gDyzLYt26dTh27Bi6deuGWbNmYfHixZLPc9NNN+Grr77CRx99hB49emDHjh02TabNmDEDW7duFSdYHMlnn32Ge++9F08++SRSUlIwc+ZMVFZWAuADaB04cAAGgwEjRoxAamoqnn/+eYSGhlqN0A3wniw33XRTg+eJSqUS95s+T7p37469e/fi3LlzuOWWW9CrVy/MnTvXaqAkmUyGTZs2QaPRoE+fPnjkkUdE7zApz5Obb74Zjz/+OCZOnIioqCi89957AJoeI9najj/88IPZunKK62GIM6K6UJxCTU2NGFXMGYNPCoXieVRWVqJ169ZYunSpTRbAlsDbb7+Nn376SRxUN8Unn3yC//73v9i+fbuTS+adeFof02q16NixI9auXdtASaI4D0/rB57Kfffdh969e+PVV191d1E8kgMHDmDgwIG4cOGCR1gUT58+jaFDh+LcuXMWJ2kAOr52BXQNJ4VCoXgQx48fx9mzZ9G3b1+UlZXhrbfeAoAGUYZbIhqNBtnZ2Vi+fLkkD4jHHnsMpaWlqKiosBgcqKXh6X0sJycHr732GlU2nYyn9wNPZfHixfj111/dXQyPYePGjQgMDETHjh1x4cIFPPfccxgwYIBHKJsAHxvk22+/tapsUlwDVTgpFArFw1iyZAkyMjKgVCpxww03YP/+/YiMjHR3sdzO008/jR9++AFjx461yZ1WQC6XmwUBo3h2H+vQoUOzArBQbMeT+4GnkpiYiGeeecbdxfAYKioqMGfOHOTk5CAyMhLDhg2zK6q/s6DrwD0D6lLrRVCTP4VCoVAoFAqF4jjo+Nr50KBBFAqFQqFQKBQKhUJxClTh9EKoUZpCoVAoFAqFQmk+dFztfKjC6UUIedeqqqrcXBIKhUKhUCgUCsX7EcbVwjib4nho0CAvQiaTITQ0FIWFhQD4/GvOSAZPoVAoFAqFQqH4MoQQVFVVobCwEKGhoZDJZO4uks9CgwZ5GYQQ5Ofno7S01N1FoVAoFAqFQqFQvJrQ0FDExsZSI44ToQqnl2IwGKDT6dxdDAqFQqFQKBQKxStRKBTUsukCqMJJoVAoFAqFQqFQKBSnQIMGUSgUCoVCoVAoFArFKVCFk0KhUCgUCoVCoVAoToEqnBQKhUKhUCgUCoVCcQpU4aRQKBQKhUKhUCgUilOgCieFQqFQKBQKhUKhUJwCVTgpFAqFQqFQKBQKheIUqMJJoVAoFAqFQqFQKBSnQBVOCoVCoVAoFAqFQqE4BapwUigUCoVCoVAoFArFKVCFk0KhUCgUCoVCoVAoToEqnBQKhUKhUCgUCoVCcQpU4aRQKBQKhUKhUCgUilOQu7sAvoRer8fx48cRExMDlqW6PIVCoVAoFAqF0lLhOA4FBQXo1asX5PKWq3a13Jo7gePHj6Nv377uLgaFQqFQKBQKhULxEI4cOYI+ffq4uxhugyqcDiQmJgYAcOjQIZSVlSElJQUymczNpaIYDAacPXuWtocEqMykQ2UmHSoz74S2m3SozKRDZSYdKjPPIy8vD3379hV1hJYKVTgdiOBG27p1a/To0QNqtRoMw7i5VBRCCKKjo2l7SIDKTDpUZtKhMvNOaLtJh8pMOlRm0qEy81xa+lK7ll17J6JUKt1dBIoJtD2kQ2UmHSoz6VCZeSe03aRDZSYdKjPpUJlRPBGqcDoBjuOQlpYGjuPcXRQKaHvYA5WZdKjMpENl5p3QdpMOlZl0qMykQ2VG8VSowkmhUCgUCoVCoVAoFKdAFU4KhUKhUCgUCoVCoTgFqnBSKBQKhUKhUCgUCsUpUIXTCbAsi9TU1BYfkcpToO0hHSoz6VCZSYfKzDuh7SYdKjPpUJlJh8rMN9i3bx/GjBmDuLg4MAyDTZs22fzbAwcOQC6Xo2fPnk4rnz3QHukktFqtu4tAMYG2h3SozKRDZSYdKjPvhLabdKjMpENlJh0qM++nsrISPXr0wCeffCLpd6WlpXjooYdw2223Oalk9kMVTifAcRwyMjJolDAPgbaHdKjMpENlJh0qM++Etpt0qMykQ2UmHSoz32D06NFYsGABxo0bJ+l3jz/+OCZPnoz+/fs7qWT2QxVOCoVCoVAoFAqFQnESFRUVKC8vFz+1tbUOPf+qVatw8eJFvPnmmw49r6OgCieFQqFQKBQKhUKhOIkuXbogJCRE/CxcuNBh5z5//jxeeeUVfP/995DL5Q47ryPxzFL5ADKZzN1FoJhA20M6VGbSoTKTDpWZd0LbTTpUZtKhMpMOlZlnkp6ejtatW4vbKpXKIec1GAyYPHky5s+fj06dOjnknM6AIYQQdxfCV8jNzUV8fDwuX76MNm3auLs4FAqFQqFQKBQKxU00VzdgGAYbN27E2LFjLX5fWlqKsLAws4kGjuNACIFMJsOOHTswdOhQe4vvMKhLrRMghKC8vBxUl/cMaHtIh8pMOlRm0qEy805ou0mHykw6VGbSoTJreQQHByMtLQ0nTpwQP48//jiSk5Nx4sQJ9OvXz91FBEAVTqfAcRwuXrxIo4R5CLQ9pENlJh0qM+lQmXkntN2kQ2UmHSoz6VCZ8eSWl7u7CM1Co9GIyiMAZGVl4cSJE8jJyQEAvPrqq3jooYcA8LlXu3XrZvaJjo6GWq1Gt27dEBAQ4K5qmEEVTgqFQqFQKBQKxRdo4dbNjOvXkfDhhxjzww/4s05B8zaOHj2KXr16oVevXgCA2bNno1evXpg7dy4AIC8vT1Q+vQUaNIhCoVAoFAqFQvFmCAFzcTXCSy8BJNXdpXEbiw8eBEcIfjt3Dr+dO4fjjz2GnrGx7i6WJAYPHtyoW/Tq1asb/f28efMwb948xxaqmVALp5NQq9XuLgLFBJvao7oAuLIVKNjj9PJ4A7QPS4fKTDpUZt4JbTfpUJlJh8pMAjUFQNUl+JNiwFDl7tK4hSvl5fj25Elxe2Dbtl6nbPoq1MLpBGQyGVJSUtxdDEodNreHXgMUHQHUUUDMYKeXy5OhfVg6VGbSoTLzTmi7SYfKTDpUZhKpzAHLsIiMjAQYg7tL4xY+/Osv6EzWr74yYIAbS0MxhVo4nQDHcSgqKmrxi7Y9BZvbQxXJ/60tBriW+bAWoH1YOlRm0qEy805ou0mHykw6VGYSUUeCEIKq6ipw+hp3l8bllFRX4/Njx8TtbtHRuL1jRzeWiGIKVTidACEEly9fpmGpPQSb26M0re4HBkBb4vyCeTC0D0vHI2RWWwxwevddXyIeITOKZGi7SYfKTDpUZhIJTAJRhqG8rBzEUOvu0ricz44ehUarFbfnDBgAhmHcWCKKKVThpFAENJnG/2uvu68cFIo9aLKBjGVA9vfuLgmFQqFQ3AGr5P9y2saP8zGqdTp8+Ndf4nZCSAgmdu3qxhJR6kPXcFIoAoZq4/9U4aR4G0KQiMpL7i0HhUKhUFxLTSFACEjiQ8ivOoOogCR3l8ilrDpxAteqjIGSXrz5ZihkMjeWiFIfqnA6iaCgIHcXgWKCTe1hMFnzQBVO2oftwK0yUwTX/Q1xXxnsgPYz74S2m3SozKRDZWYj1/4ESv4Fom5FUHAC0IJcSfUch8UHD4rbkf7+eLgufyXFc6AKpxOQyWRISEhwdzEodchkMrRv377pA00VTn2F8wrkBdgsM4qI22XG1qUPMHhPsAi3y4xiF7TdpENlJh0qMxshhF9SAUAWlIj2rVqWdfOn06eRXVoqbj/bty/8FQr3FYhiEbqG0wlwHIf8/HwaWc1DsKk9CGccqHd6Cmj3oGsK56HQPiwdt8tMWINsqOEHIF6A22VGsQvabtKhMpMOlZmN6EoBXTnAsOBqilCStgpcabq7S+USCCFYdOCAuB2gUOCpvn3dWCKKNajC6QQIIcjPz6eR1TwEm9rDNKKbMtz5hfJwaB+WjttlVnHe+D/nHREK3S4zil3QdpMOlZl0qMxsRFi3798apDoftXmHQaquuLdMLmLbhQv4t6BA3H7shhsQ7ufnxhJRrEEVTgoFALg66yarAFi60JzihZhGJfQit1oKhUKhNIM6d1oEJAAyFf9/C4lS+38m1k0Fy2JW//5uLA2lMegaTgoFABShQNfXeMtQ8XGgLB0I6w6Eprq7ZBSKbZhGWW6BOdgoFAqlRSJYOAMSgcpc/n9O57biuIpDly9j7yVjVPYHundHm+BgN5aI0hjUwukEGIZBeHg4TTjrIdjUHgwDyJSAIgiovca7J1bluq6QHgbtw9Jxu8z0dSHh288A1NHuKYNE3C4zil3QdpMOlZl0qMxsQFcOaEv4MYx/PBiZCn5+fmCI71s4Ta2bDICXbr7ZfYWhNAm1cDoBlmXRpk0bdxeDUgfLsmjbtq3tP1BF8X9bcGoUyTKjuFdmhBgtnMpQrwmJT/uZd0LbTTpUZtKhMrMBmT8f5LC2CJCpwMrUCAkJAYjeLcUhhODH06dRrddjYteu8HNStNgz165hc0aGuH13Sgo6R0U55VoUx0AtnE6A4zjk5OTQyGoegk3tUZEJXN7Eu9OqIvl9LVjhpH1YOm6VmaGGj7QMADLvCZhA+5l3QttNOlRm0qEyswFWDgS1ByL5yKwcI0dZWRk4fXUTP3QOL+3cift/+QXTN29Gt88+w87MTKdc5z2TvJsAMGfAAKdch+I4qMLpBAghKC4uppHVPASb2qMmHyg5AVRmA6oIfp+2DDD4vluKJWgflo5bZWaoMv5/dStQft76sR4E7WfeCW036VCZSYfKTDqEUaC6utotY5cdmZlYeuiQuH2xpAQjvv8eUzZsQGFlpcOuc7msDN//+6+4PTgxETdRr0KPx2cUzn379mHMmDGIi4sDwzDYtGmT2fcMw1j8LF68WDwmMTGxwfeLFi1ycU0obkFwR5SpAbk/IA/gt7VF7isThWIrihCg4xP82s3if4Dqq+4uEYVCoVCcib4SyNthPsEY0A6FkQ+BtJvm0qJcr6rC1HrjboG1aWlIWb4cK48fd8jkwQd//QW9idX7FWrd9Ap8RuGsrKxEjx498Mknn1j8Pi8vz+yzcuVKMAyD8ePHmx331ltvmR33zDPPuKL4FHcjpJEQ3BEFt9qaa+4pD4UiBVYO+MUAQR35bZoWhUKhUHybykvAtYNAwe/GfawcHOvPB0F0EYQQPPrrr8jXaMR9PWJiYBpJoKSmBjP++18M/uYbnL1u/3KloqoqfHnsmLjdMzYWI9q3t/t8FNfhM0GDRo8ejdGjR1v9PjY21mx78+bNGDJkCJKSksz2BwUFNThWKgzDIDY2lkZW8xBsag9R4VTzf1WRQE2BY3JZEQ4oOw0Etuetp14A7cPS8QiZCf2X8w6F0yNkRpEMbTfpUJlJh8qsCYT8m/4J4i53yGzl8ePYePasuN0lKgqHZszAyYICPPrrr0grLBS/23fpEnp8/jleHTgQrw4cCJVcmhryyd9/o1JnTPkyZ8AA2j+8BJ9ROKVQUFCALVu24Jtvvmnw3aJFi/D222+jbdu2mDx5MmbNmgW5lRuitrYWtbXGfHcVFRUA+NmeqKgoEELAcRxYlgXHcWauBAzDgGVZGAwGs3Na28+yLBiGsbgfQINF9db2y2QysVz199cvo7X9Qhm9qU5CexgMBst10lWCBcAxKhCDAYgZAcSOBsOyYIHm1algF9jrB4DA9uASpjisTk3tb247CTIjhNC+Z2OdBJkJx7qsTpXZYKtzwdQUgCMcoKvi+7ED6tRY2R1RpyiTyIL0uecddar/TPWFOjm7nWx5D3lbnVzRTvXHUr5Qp6b221onRpMFEA5MQAIglN2gRZT+KEiuDogfC2Klro6qU8a1a3hu2zZxv4Jlseaee6BkWfRp1QpHZszAB4cP4+19+1Ct5yPnag0GzN+7F+tOncLnd9yBQYmJNrVTpVaLZYcPi9tJoaEYl5zs8fdT/e9bKi1S4fzmm28QFBSEe+65x2z/s88+i969eyM8PBwHDx7Eq6++iry8PLz//vsWz7Nw4ULMnz+/wf4zZ87g0qVLCAoKQkREBNq2bYvc3FwUFxeLx8TGxiI2NhbZ2dmiogoA8fHxiIiIwPnz51FTY7RSJCUlITg4GOnp6WadNzk5GUqlEmlpaWZlSE1NhVarRYZJ2GiZTIbU1FRUVFTg4sWL4n61Wo2UlBSUlJTg8uXL4v6goCC0b98ehYWFyM/PF/eHh4d7VZ1ycnJQUVGBoKAgBAcHW6xTvDYfEWog/1opCi8br+uIOgVqLiLBXw9ZxQWvaafq6mpRZu3bt6d9z4Y6EUJQUVGByMhIdOnSxaV1CtQcRrwqG6qAcFy7dg01JXKUlKV5fDsJMuvcuTOioqLoc89L6pSRkYHr168jKCgIDMP4RJ2c3U6ZmZniM9XPz88n6uTsdiovLxdl1rZtW5+ok6PaieFqEF10CgwhiEqJh7amhq8Tp0XYlV+gUqsQ2+ZOVGiqnVaniKgoTPzxRzOL4+s33YSesbE4e/asWKfRQUG4/cEH8fK+fdhhErU2o6gIQ779FpO6dkWiTIbW/v6Iq/vc2LNng3Zan52Nompj9N2JbdvizOnTHt1OAG/kogAM8cHwXwzDYOPGjRg7dqzF71NSUjB8+HB8/PHHjZ5n5cqVeOyxx6DRaKBSqRp8X9/CeeXKFXTp0gUXL15EaWkpunbtCrlc3mJnJj2lTnq9HqdPn0bXrl0hk8ks1+n8J2C1xeDaTQXxN+b9ckidaovBXlgOMHJwnV8BGNbseHvq5Ox2MhgMoswUCgXtezbUyVRmSqXStXW68ivY0uNg/OPBVeYA6lYgHR5tdp2aKntz6yTIrFu3blAoFPS55yV10mq1Zs9UX6iTs9tJp9M1+R7ytjo5u51Mn6nCWMrb62RKs9qp/CyYnPWAKhJM8jPifoNej6K9zyI6Khqy1FdAZP5Oq9P8vXvx1r594r7BCQnY+eCDkMtkVsu+5t9/8cLOnU1GrW0VGIh2oaFICAlBYmgo2oWGYsH+/cgpLwcARAcEIPPpp8U8nx7bTgByc3ORmJiIy5cvo00Ljqbb4iyc+/fvR0ZGBtavX9/ksf369YNer0d2djaSk5MbfK9SqcwU0fK6G0HohMKLWNhnCZlM5vL9QtnqY62MUvd7Wp1kMpn4vXBMg7J3egIwVIOV+QNs3Xlyfgaq84DEKZCpwu0vo18EwCoATg+ZoQKwcC5PbCfh+sL6CNr3mq6T6f8urROpBcAAqnCwVbkA0QL1fuep7SS8zKWWUep+X+97tpTRkXWq/0xt6nhb9/tyOzX5HqrDm+pkaxntrVP9sZQv1Mkh+6tz+cnroCSg3juaMEowLANwWjDyAKfU6dDly1iwf7+4HapW49tx4yCvu5a1sj/Qowdu79QJc3buxNfHj1s8BgDyNBrkaTQ4mJtr8fvn+/VDoFptV9mb2u/oPmbt+5aGz0SptZUVK1bghhtuQI8ePZo89sSJE2BZFtHR0S4oGcWtsApAEcxH+xSovQ7UFgG1zYhUW3OdjySnCK47J416S3EC+ro8nIHtgZRZQMfH3VseCoVCoTiP2rpAPAEJDb4iDG/1g6G2wXeOoKK2Fg9s3AjOxDr4xZ13Ij4kxKbfh/v54au77sLeadNwS9u2UEsMHBSkVOKJPn0k/YbifnzGwqnRaHDhwgVxOysrCydOnBB9tQHeAvnTTz9h6dKlDX5/6NAhHD58GEOGDEFQUBAOHTqEWbNm4YEHHkBYWJiksjAMg/j4eNEyRHEvdreHKhKozucVTzS0cNtEyXHg2gHjds01INjOc7kQ2oel41aZCXlkFUGA0raXvidA+5l3QttNOlRm0qEya4TEBwBtsTFneB0MwyA0PAoMahwTZd8Cz27bhoslJeL2g927Y0LXrpLPc2tCAvZNnw6OEBRoNMguLUVWaSmySkqM/5eWIqeszCzv5jtDhyK0nnWT4vn4jMJ59OhRDBkyRNyePXs2AGDq1KlYvXo1AGDdunUghGDSpEkNfq9SqbBu3TrMmzcPtbW1aNeuHWbNmiWeRwosyyIiIsK+ilAcTpPtoa8E8nYCikAgdphxv5CLs9b+nFGoqnMHUYUDtcXGWUkPh/Zh6bhVZoY6C6fMO9LuCNB+5p3QdpMOlZl0qMwagWEAVUPZsCyLgKAwfrLcCQrnz+npWH3ihLidGBqK5bff3qxzsgyDVkFBaBUUhP7x8Q2+N3AcrlRUILu0FFH+/uhsEtmc4j34jMI5ePDgBouF6/Poo4/i0Ucftfhd79698ddffzmkLAaDAWfPnkXHjh2p77YHYDAYcP78eevtodMAJSf4mUJHKpyEA6qv8v+3GgWwSkDtHe7ZTcqM0gC3yYwQo0ut3B/I/53fjr2twey3p0H7mXdC2006VGbSoTKTjsFgQGFBEWKCOLAOVjivlJfj0V9/FbdZhsF348Yh2EJQTUciY1m0DQlBWxtddimeic8onJ6GaehkivtptD0Ed0SZn/l+U4WTEHFhvu0XLQA4HSBTA0Edpf/ezdA+LB23yazDY3w/lgUAxcd5q31EP49XOAHaz7wV2m7SoTKTDpWZBbLX8cENo4cA6sgGX1/zG4To5G6AMtBhl+QIwdRNm1Bi0h6vDhyIgW3bNvIrCsVIiwsaRKE0wFD3AJXVWxOgiuCVRH210WVRCoI7rX9rr1M2KV4EwwB+MUBgIj8IEfoxRwdqFAqF4lNwOqDiHFB62hhRv/4hskB+PT/rOJvSR3/9hV1ZWeL2jXFxeHPQIIedn+L7UAsnhcJZUThZBaCKAhgZr3RKtRaJCmdd3qXyDKAyBwjpwiuhFIozEPqxgSqcFAqF4lNUXeaX6yhDAEWoSy55Mj8fr+zaJW77KxRYc889UFA3Z4oEqMLpBFiWRVJSktUcPxTX0mR76K241AJAxyfst04KCqdfncJZmgaUnuLX2Xm4wkn7sHTcJrOaa0D5Gd4FPKSLVymctJ95J7TdpENlJh0qMwtUXuL/BiRYHJuwLIsO0RzY/B2810tI52Zd7mpFBcb88AO0BoO474ORI9HJ04M5EY7PU0rxGKjC6QQYhkFwcLC7i0Gpo8n2sGbh5H9s/4Xb3ssrnYKFU1UXWa3G83Nx0j4sHbfJrPoqkP8HEJjkdQon7WfeCW036VCZSYfKzAKmCqcFGIZBIFsCFB7mxy/NUDgramtxx9q1uFxeLu67KzkZM3v3tvucLkGnAc59DPi3BRInUcXTQ6Ct4AQMBgPS0tJgMJkRoriPJtvD2hpOU5qIgGwRv1ZARB9AXmc5VdcpnLWer3DSPiwdt8nMNEItALDeo3DSfuad0HaTDpWZdKjM6sHpTZbqWFY4DQYDMrMvgyMcwNXafSmdwYD7fvoJJ/LzxX1do6Lwzdixnp8XlVUCre+si59B1RxPgVo4nQR9QHoWjbZH7DAgagDAWLgdaq4BOT/x7hnJTzevECoThdOeqLcuhvZh6bhFZmKU5TqF04ssnADtZ94KbTfpUJlJh8rMhOorvNKpCLSYg1PAwNWtrbQzLQohBE9s2YLtmZnivrigIPxvyhSEqhuZmPcUZEogNNXdpaDUgyqcFAqr4D+WkPsDNYW8csjprB9Xn+t/8bNswcnGYEPKcD4AkUEL6MoAZahDik9p4RjqWTijbgYi+hq3KRQKheL9EAO/REcZ1uiENWHqxikG+xTOBfv2YcXx4+J2oFKJLZMnI57mwaQ0A6pwUiiNIfPnXWL11UBtEeAX2/RvCAEK9/K/6fCoUeFkZfysZE0hbzmlCifFEQgutYKF0wtyb1IoFApFIoFJQIekJpf4cMLEuB0Wzm9OnMDcPXvEbRnD4JcJE9Az1oaxjyegrwKK/+EDJgnxMygeAXVudgIsyyI5OZlGVvMQmmyP/D+Aq9sBbWnD7xjGxBX2um0X1BbzyiYrB9Qx5t+pJZ7LTdA+LB23yUy0cFqIsuzh0H7mndB2kw6VmXSozKzQiHWTZVm0S0rm11lKVDh/v3gRj/z6q9m+r8aMwYj27e0qpluozAbyfwdy/+vuklDqQS2cTkKpVLq7CBQTGm2PkuOArgII6w4gtOH3qkg+f6atSqKYDiWuYWLm2GFAq5GAPMi2c7kR2oel4xaZ1V/DWVsMFB8FWBUQ4/mJuWk/805ou0mHykw6VGZ1GGoAMIBM1eShClWdl4sEhfPfggLcs3499Bwn7ntz0CBM79VLakndSxNRfCnug04bOQGO45CWlgbO5MaluI8m28PQSB5OgFc4Adujy4oKp4Vcm8owQBHs8QGDaB+WjttkFn8fkDSNn+AAAH0lcO0gUHrSteWwA9rPvBPabtLxaZkVHQXK0h1+WptlxumBK1uAyxv4/32R4mNA+v8BeTsaPYzjOKRlXgfX4Un+vWADueXluH3NGlRojQrqtJ498eYgz5+wbEBlNv83INGdpaBYgCqclJYNpze+oKylRREVTokWTrp+gOIK1JH8ehXBpdbLotRSnIhO47sDcIpnoC0FrvwGXPqRj+buDhgWKPobKPm3WalAPBpNNi9fW7yjGAU/blE0fWxZTQ1uX7MGVyoqxH3Dk5Lw5Z13en76k/roq/kYGQC1cHogVOGktGyEQTnD8C6IllBHAX4xgNqGRfOcDqgp4P+3pnAW7AUurefdeCnW4QxAaRpQdcXdJfEuTBVOe/LHUnwDbRlwZgmQ9a27S0LxZUwntoQAZq6GYflUGABg8EGFk3BAVQ7/f2Ciw06rNRhw708/Ia2wUNzXPSYGP0+YAIVM1sgvPZTKS/w7TxXJp46heBR0DSelZSO606qtu7kqw4COT9h2vppC/uWgCOJdZy1RdoqPUht+o00zkC2Swj/5hf8AENIZSJjo3vJ4Kvoqfr2mPBAI783vExROwvETIDK6BqpFIrg4VuZ4Rd5firdiMqmlr3DPQL/oqDEFiC9aOGsKeEVapmoYiNAShAMKdgPQ83Ej2IZDfUIIHv31V/x+8aK4r01wMLZOnoxgVdPrRD0SwZ3WgUo5xXFQC6cTYFkWqampNLKah9Boewizs6yDkhn7twa6zAESJ1sf4IlRb21cE+oG3N6Ha/KN/5eftRxB2MNwi8y0pXyU5YI9xn2MnM/3CgCcZ7vVur2ftQRkKpgpBQ6Atpt0fFZmfq2M6cJ0Goee2maZVZt4wfiihVOTzf/1b8tbcxuBl1l3sNf/5POBW1la8dHhw/jmpHGdf7BKha2TJ6N1sJWJcm9A6AfUndYj8bEnn+eg1dqXcJfiHKy2h2DhtCWlBCG2JVKW+/EvYWsIqVFqCq0f4wG4tQ/rK43/E8Jb8bwAl8tMTInib9zHmEQy9IJ1nPRZ6ST0dS77Yb2bHKTaA2036fiszIR1hXrHLxOxSWamE5Je8MyTjBB51UbLnVanA9g6zxYLkWqLq6sxzyTXpoJlsWHCBKTG2GA99WTaTQPazwACO7i7JM1m3759GDNmDOLi4sAwDDZt2tTo8Rs2bMDw4cMRFRWF4OBg9O/fH9u3b3dNYW2EKpxOgOM4ZGRk+GY0Oi+k0fYIbA90ng20va/xkxT9DZx+F8jb1vwCeYGF0+19WFA4I/vxf4uP8e6hHoxbZCasmaofYdlLAge5vZ/5Mroy/q8yxOGnpu0mHZ+WWWASENaDX37iQGyWmanC6WsutYQAVban+hBkRhhF3Y6GCueiP/9EWa1RTktGjMBtSUkOKa5bYWVAQLxX5qSuT2VlJXr06IFPPvnEpuP37duH4cOHY+vWrTh27BiGDBmCMWPG4Pjx4zb9vqqqCps3b8bLL7+MO++8EzfddBP69++PMWPG4OWXX8bmzZtRWVnZ9Ikaga7hpLRsWBnA2uBCIlPzCk9jkWp15XxYdv+2QMyQpl1qa67RtVXWEBTOsF5AeQY/oCg9BYR7WU4wZyNa6P3N9ydO4V1r5TRwQotFGIRX5gDKCCC4o1uLQ/FRiv8Byk4DIV2BwHauvz7hjJMrgO+51BIDEDkA0JUC6kY8p+rDKgFDZQOFM7e8HB8fOSJudwwPxxM33uigwlIcxejRozF69Gibj//www/Ntt99911s3rwZv/76K3o1kks1LS0NS5cuxYYNG6DRaODn54f4+HiEhYWBEIJz585h165dWLJkCQICAjB+/Hi88MILSE1NlVwnqnBSKLZgS2qUqlx+rYWhBogd2si5Ingl01AD6DU0cFB9CGfiKhoIRPQB8nYCRYeBsJ5UQTdFkJOsnsKpinB9WSieBTHwf8vS+ckyqnBSnIG2mH/3uSsNmF5jTMfS+UXr+bS9FVYORA+043eWXWrn7dmDGr0xVdI7Q4d6Z0Ta+uT8wi8liRoIKEPdXRqLVFRUoLy8XNxWqVRQOSlAE8dxqKioQHh4uNVjJk6ciF9++QU33ngj5s2bh+HDh6NLly6Q1esPBoMB6enp2LFjB37++Wf06tUL9913H3744QdJZaIutU6ifoNR3IvV9ig9BVzdDmguWv5eQFk3gNdXWQ/9bmv+TVYOKMP5v6Yzsx6G2/qwodpo+ZX781ZOVlFnZfbs2WuXy0xvYQ2nl0GflU6i4+NAuwf4/7XFDj89bTfp+KTMxMB7cocHDQJskJlgyVeG8RFyWR+UsURkMplR4TSx+J65dg2rTpwQt2+Mi8O9Xbq4uHROwFDLW9mLjjplvbqj6NKlC0JCQsTPwoULnXatJUuWQKPRYMKECVaPYVkWR48exV9//YXZs2cjNTXV4v0mk8mQmpqKF154AYcOHcLRo/bF1KAWTicgNA7FM2i0PTSZQPFxfsAe2MgaBpmSnzXTlvJWTnnbhscI+SJtmelt/zA/E+uhD0e39mFDrTG0PsPybZP8rMdbgt0iM4OVNZzl5/gQ8QGJQHAn15ZJAvRZ6WTU0fxfbQmf19ZBg3HabtLxWZkJCk3hn3ze5JRZDju1TTIz1PCTkR5q1Wo2tcW8h4IyzKYUV6LMsv7ld5hYOP+zezc4k9zMi267DYwveAxVXeat3Mow6+noPID09HS0bt1a3HaWdXPt2rWYP38+Nm/ejOjoaKvHSbVQCvTs2dOu31KF0wkQQlBeXo6goCDfuJm9HEIIKioqLLeHMDsrsyEtiirSqHAG1FM4OQNQfZX/388GhVMe0PQxbqRRmTkbVTjvGmXyYvR0ZRNwk8xihwHhfRoG66jMBq4d5P/3YIXTrf2sJSAP4r0DOB2/BsxBrta03aTjszIzDUym0zg0LoFNMgvuBHR9BdBkAVe28JMsEX0ccn2P4PpB3nIXfWvjS3XqEGXWahSYViNEBexwbi42nDkjHjcsKck3AgUBXpN/MygoCMFOTjuzbt06PPLII/jpp58wbNgwp15LKp5pXvFyOI7DxYsXfTManRfSaHsIQVdsWffR2DrO2kJ+UCdT+8T6OY/ow5YGGPpKY4h4D8MtMlOG8S/Z+pFIvShKrdv7mS9Sdga4uBq4foifwAGA2iKHnZ62m3R8VmamuX6JwfhOdcSppchMW8JHk68477DrewSiy3CoTYeLMlOE8inYZCoQQvDKrl1mxy267TaHFtOtCHlKW3j+zR9++AHTp0/HDz/8gDvuuEPy70+cONHAcrl9+3bceuut6NevHz766KNmlY8qnJSWjRQLp388ENIZUFvIVSWu32xt2+yuvhLI+RnIXGVuyaNYp+oKcOZ94NKPAKdv+viWDOsdCifFSdQU8IOw2iLj+nMnrOOkUBpEhdU7fh2nTbBC7mHPXucvGYkKpyW2Z2ZiT3a2uD2ha1fcEBfXrGJ5DAat0bssINGtRXEkGo0GJ06cwIm6NbdZWVk4ceIEcnJyAACvvvoqHnroIfH4tWvX4qGHHsLSpUvRr18/5OfnIz8/H2VltscJefnll7F+/XpxOysrC+PGjUNWVhYAYPbs2fjyyy/trhNVOCktGykWztCuQMJEPt9YfTgdn/vJ1kh9rJJf5F55yZgChMJz/TCviBf/Y75fHcu7Iusr+cibFKBwPz+rXz9HqZdYOClOwjQHp+Bx4UALJ4Uiwir46KACugrXXj97LZC1xvge9fDAcpIgxHgvK0Kl/bbyElCwG1zZGbzy++/ibjnLYsGQIY4ro7sR12+G+tQ63qNHj6JXr15iSpPZs2ejV69emDt3LgAgLy9PVD4B4Msvv4Rer8dTTz2FVq1aiZ/nnnvO5muePHkSAwcaIyJ/++23kMlkOH78OA4fPox7770Xn3/+ud11oms4nYRabYPFjOIyrLaHFAtnY0TdDET2N6YiaApWwbtD1hYDtdeMQXI8CLf14ZpC/mVZP4gTKwMibgTy/wCKjgBh3d1TvkZwqcw4HZBf5yYVWk8WQn/mPF/hpM9KJ6A1GaT6teJzA/tZ8MxoBrTdpOOTMuswk/978Vs+2ruDLZyNyowQ/pqc3jgR7EsWTkMV/5xnGEnBcNRqNa+IFe7F/opwnCwoEb97pFcvdIzw/mU/IpyWXzbgH+/ukjiUwYMHgzTi/bZ69Wqz7T179jT7mmVlZYgw6Rtbt27F8OHDERnJLycbPnw4/ve//9l9fmrhdAIymQwpKSm+GQLdC7HaHoQzvpxsVTgJAXTlll9qDMOHhrcVVRT/t+aa7b9xEW7tw8JMtaXASmG9AUbGuzALUYE9BJfLTEiJwpiEwBcL4x0WTvqsdBK6Uv6vMoRfxxXc0aHRG2m7ScfnZSYEdnOghbNJmek1vLLJMMYYC14wyWYzgjutPMjmsYUoM7kfDITDT2nHxe/8FQrMHTTICQV1IyGd+Sj2re9yd0m8nlatWuFMXWCpvLw8HDt2DCNGjBC/12g0YFn71UaqcDoBjuNQVFTke8EBvBTr7cEAnWcDnZ60PVl01rf8OsKKC8Z9xM52VtcpnLWep3C6tQ8bGlE4FYG8azPAWzk9CJfLzGCSg7P+umEvUTjps9IJCJNiAKAIafxYO6HtJh2fl1lAIhDW05iKxwE0KTPR3TTYmIvYUOs7cRHE9Zu238eizBg5jl3NQ0lVufjd8/36oVWQ50d8twuaf7XZ3H333fj444/x7LPPYuzYsVCpVBg3bpz4/cmTJ5HUjMjGVOF0AoQQXL58uVFzOMV1WG0PwU1FHW17PkxhAGcaqbZwH3D2A+C6RAVI5bkKp1v7sJA83FrqmIh+/N/SUx61/tXlMmts/bEyHOj4BNDhMdeUxU7os9IJ6CuNVh953eCy7CxQsNc4gG0mtN2k45My05UDF74GstcB4b2A+LEOTcPUpMyE/qwINQYNIhxAfCSonDqaT30V1svmnwgyq9QDe3MuQcXwsghTq/HSgAHOKql7MGjtn/CnNGDBggW455578N1336GwsBCrV69GTAy/FKO8vBw///yzmcVTKnQNJ4UiBdEqaaJwVuUa10xJwYNdat1KYxZOgI8E7N+aj8RZlQsEJ7uubJ6E3sTCWR9W7vA1exQvwVDFuzcyrHHW/9p+3gVdHe1TgTUobkZfxT+D3RWDwDSCK6vkXStlKoDxkaGtOso45pDIqrQzqNRqoWT45Rav3XILQn1tDfH1A8D1v/gcpVE+pky7gcDAQKxZs8bqd7m5uQgIsD+HPLVwUlouNYXA1e1A8THbf1M/FychQHXdWkJbI9SanouR8QqDQSvtt74KpzPKwprCCfDrNVJmtVxlEzAqnDILCiel5aKOBjq/ACSbRCekkWopzkBw2RfSMHF6+yZf7UVcqxxat44znH9v2JKazIcprq3F5//8CwBQwoA2wcF4um9fN5fKCWiyeRfq5gZ9pAAAHn74YRw+fNjidyzLIiMjA48++qjd56cKp5MI8lU/eS/FYnvUXOMTo5f8a/uJTBVOQvgBnL6atyhZys/ZGDIl0O11oNNT/P8ehlv6sKGGt87IlEYXKUv4xTSukLoJl8pMWMNpbf3x9b+AvB3G9XweCn1WOgnTZQJiLk7HKZy03aTjczITUpDIVIC2BDi1ADi33KFrKBuXGcMrG05aq+x2NNlAdQHA2Rj9vo7vsrNRrOV/o2QMmD94MNRyH7H6CnB642S/D+XfdCerV69GZmam1e+zsrLwzTff2H1+H+uBnoFMJkNCQoK7i0GpQyaToX379g2/ENfASZgdU4bxVklOxwcsEB54fnH2LVq3de2oi7EqM2ejCOKtM4TYPktdW8zPbLsZl8ss/AYgsJ11hbPoCC+b4BSHRih1JG7rZy0N4f6oLXbI6Wi7SccnZWaaVkxe51bL6XhF1AFWpyZl1voO/iMouNcP85PAETc6NHiRWyAEuLSW9/hJfsbopdAEuRUVWHv+PDiDP74u742EsGgs6mEhd7i3U5XLK52KQD5mAcXpXL16FX5+NgbYtABVOJ0Ax3HIz89HdHR0s0IIUxwDx3EoLCxs2B7iy1LCDcSw/OCt5hpv5azK5fdLdaf1cKzKzFXYomwSDsj6DtBk8QFy3Lxm0eUyUwQZUxFYwgsi1bq9n/kiV7bwgciiB/ETEoDDLZy03aTjkzIzVThZBW/pNNTygd8coHDaLDPhfVF2Cqi8zPd7b1c4DdXG5SUSLLhv7N4NrcEAQIYr+hAsHzoWcl/pb6ZUXuL/BiS2eBfq5rB582Zs3rxZ3P7yyy/x+++/NziutLQUv//+O/r06WP3tajC6QQIIcjPz0dUlH2LvSmOxWp7cCYvSymEdudfBorg5iuclTlA3jY+mmTiJPvO4QS8og8zrDFgTtERoM0YtxbH42TGer7C6XEy8wWqLgPV+UCkSRANwTqi09SteWrEXd0GaLtJxydlJrjUCssf5EF8/9JXAIhs9ukly0woB2chT7a3IUbgDbQ5B2daQQG+/9e4RKh/mza4O9lH4xxUZvN/A6g3YXNIT0/HTz/9BABgGAaHDx/GsWPmcU0YhkFAQABuvfVWvP/++3ZfywenPSgUG9Hb4VILANG3AK1G8FFmAxIA/zjAr7V9ZWBkQNVVo2tuS6f4HyBzle0pZiLqAiGU/mtsz5ZC8TGg6G/rida9wMLpVRAOKNjDW9Q9GSE3oWnuPpnKGEmUBg6iOApC+PX2wrNG6GN6jfOvXZ0HnPsEuLzJuE+YSDH4gMIp5hgNtelwQghmbd8O09Wzn/QLB1O41/feAZyen1gD6PrNZvLqq6+ioqICFRUVIIRgxYoV4rbwKS8vR15eHn777Td06mR/2iNq4aS0XDg7XGpNYRggblTzyiAEIdJpeIVJbr9/vE9Qc413lbHVYuzfFvCL5S06JceBqJudWz5PonA/Pwvu18qya60wCOR8bLDhLspO8wonAHSf586SWMegNU681HfDazuBj2isDHN9uSi+Scwg/iOsoRTyvlqbBHMktcX8+8J0wliMlusDCqdpyhcb+PbkSezKMk6GjWrfHr2Ys0BBLe+V5UuRXIkBiLoFqMk3jqEozYbjnJvTlFo4nQDDMAgPDwdD/co9AqvtYbDTpZYQPrl65eXmF06mMloiaj0nH6fb+nBTOTjrwzB88BwAKM9wTplsxOUyMzSRFsULLJxe9ax0xSC6uQhWEZm6odtsQFtAHWlfcLN6eFW7eQg+LTOhTsLEl4MsnI3KTEiJYmoBFC2cnvvMsxnTlC9NUFhZidk7dojbKpkMH44cyecmBQDOx9KuyVT8REfCRLp+04vwGYVz3759GDNmDOLi4sAwDDZt2mT2/bRp08AwjNln1Chz61RxcTGmTJmC4OBghIaGYsaMGdBopD84WZZF27ZtfScwgJdjtT3ajAM6PQkESXQR4HRA+mIgc4VjUk6o6oIbeJDC6bY+rJeocAJGd2YhN6qbcKnMOL1JvlLvVTi96llpqsBxOveVozEsudM6Aa9qNw+hRcjMPx4I62n/EpN6NCozId+nqULG+pBLrbiGs+l7edb27SiuNi4peXPQICRHRZkonD4gD4rDYVkWcrkcWq1W3JbJZI1+5M1Ir+MzLrWVlZXo0aMHHn74Ydxzzz0Wjxk1ahRWrVolbqtU5jPAU6ZMQV5eHnbu3AmdTofp06fj0Ucfxdq1ayWVheM45OTkoE2bNr79cvESOI5Dbm5uw/ZQBBrXnEhBpuQDBunKgTPvAx0e5ddx2os6Cqg4D9QU2n8OB2NVZs7GHoVTcKnRV7rVLdmlMhOsmwxrPV9pWC8gKNm+Pu4ibJaZroJfswqGn9l2B4zJ61Kv8UzXVHGQGtrwO105UHKCX4saM7hZl3Hb88GL8UmZ5f6X71cxQ/l3YEhn/uMgGpVZPQtgeW0tWAMQCPiGghXRh19a4t+20cO2XbiAtWlp4nZqdDQmxMeD4ziwgsJp8CELJ2cAKjL4+BkemIvbm5g7dy4YhhGVSGHbWfiMwjl69GiMHj260WNUKhViY2MtfnfmzBls27YNf//9N2688UYAwMcff4zbb78dS5YsQVxcQ4WitrYWtbXGB1tFBe9yZTAYUFpaitjYWMjlcrAsC47jQEySITMMA5ZlYTCYJ/S1tp9lWTAMY3E/0ND32tp+mUwGQojF/fXLaG2/UEZvqZNer0dRURFiY2PFWZrm10kBFgBHOBB5GFD3vV11UkaCEA6oKgCp+52728lgMIgyUygULut7jLYCIBwIo4as7hxN10kGJnYYWGUYDBzEtmisrs7oe6YyUyqVzm0nfRU4wgEyP5C6MjWoE+sPKP35sluQuyc8IwSZtWrVqvF20mrA5P8ByPxBIge657kX1AVsTDE4yECIQuxnHvXcM+ghUwSCkweLzxLxeEMNSN4uEJkKJGIgUOfpY88zov4zlb6fmq6TLe8hb6sT0WQDtddBIvoDBoPD62T6TBXGUkLZmZpigHBg5MH46fRpTP7lF4TIDfhp3B0YFNMFbN05vbbvBXWAwb8urZHJ+MK07JVaLZ747Tfj+QB8OmoUyktKoI+Lg5JVgoCA01ebvRe9uu/VXAW5tB6E9QNJeVF0qfXkOtX/3lOYN29eo9uOxmcUTlvYs2cPoqOjERYWhqFDh2LBggWIiODDxR86dAihoaGisgkAw4YNA8uyOHz4MMaNG9fgfAsXLsT8+fMb7M/IyIBCocDp06cRERGBtm3bIjc3F8XFxqTbsbGxiI2NRXZ2tqioAkB8fDwiIiJw/vx51NQYXeGSkpIQHByM9PR0s86bnJwMpVKJNJMZLgBITU2FVqtFRoZxXZtMJkNqaioqKipw8eJFcb9arUZKSgpKSkpw+bJxXWJQUBDat2+PwsJC5Ofni/vDw8O9qk45OTkoLi7G6dOnERwczNepoACVmRvBMUpU+vdAeESMpDp1NJQiQAEUFxUjN91YHrvqxASj6HoNtOWlKNekeUQ7VVdXizJr3769a/peTg5irl8EQwiq1NeRlNxGQp06IDY0FtmZmW7re4QQFBcX48KFC+jSpYtz20lVjeKiYtRAh+u6NKfVyWI7ObBOgsxKS0sRFRVltZ3Onr+IiMJCEIZBge4kklM6u6lOQ5Cbk4PiMxdsayeXP/dCkdL5RZRcv47LJucJCgpC+3ZtUVlZCY2mAIXav8GxfnY/Iy5cuCA+HxiG8cq+5+p2yszMFGXm5+fnE3WqyM2GjKvCdV0W5IE6vk5Fhbiacw4GWXCz61ReXi7KrG3btsY6VVcj+noGWKKDJqQWM3/9DQZCUKxj8dyOQ/hukLJF9L33T51CdlmZ+P39SUkIKC1FcXExzpw5gx4hKmhrtcjOSEe1H+cVdWqynWJKoa3VIq/KH6WnTnlFnQoKCkABGFJ/OsAHYBgGGzduxNixY8V969atg7+/P9q1a4fMzEy89tprCAwMxKFDhyCTyfDuu+/im2++MevcABAdHY358+fjiSeeaHCd+hbOK1euoEuXLrh48SJKS0vRtWtXauH0gDrp9XqcPn0aXbt2Nc4s66qB9IUAANLlNTAypbQ6VZwDm7MehqhbgOjBLq+TKyycgsxcZuHU14A5/wlgqALp/ApkCpVX9T1TmTndwll+Btyl9YB/AkjSNMt10lUAJcfByuRA1ECP7HuCzLp16waFQmG1nbisdUB5OgCApLwIVhlkseyeUKem9rv1GXHmfUBbCpL0MOAfb3edtFqt2TPVV5579cvoyDrpdLqG7yEvrxM5tQDgdCCdngOUoZBBD3LqXRAQkC6vAayiWXUyfaaaWTgNWjBZKwFdGR46k4Q16ebjtrNPPolOkZF21ckj+h5XDab6KgzyELMorKZlP5aXh/4rV4Kru058cDD+fewx+MvlxvdQ/n9BStPAxY4EIvq5t06O6ns5P4BUnAcXMwKIvMkr6pSbm4vExERcvnwZbdrYmbPdRRgMBmzfvh0XL15ESUlJA3kyDIM33njDrnO3GAvn/fffL/6fmpqK7t27o3379tizZw9uu+02u86pUqnM1oGWl/MBZGQyGVq1aiU+IAFjB6yPTGY5YqAz9zMMY3G/tTJK3e9pdZLL5Q3bg9TWrX+TAwq12fE2XTO0M+A/CzJFEH8eF9fJEo5sJ4ZhRJkJPv1Or5PSH+j6Eh8FuO6aNtdJXw1ociEjeotriFzR90xlJqnsTey3WJbAdmA7zODzuNb7XjxepwWu7eXXuUTf4pF9T5CZsG2tjCxXZbzPSC3ABDd6vFPqVHYGKDkBVhHMR0b2M1+e4WnPPUuwqkh+zZ2+FJAlNnm8tbJYeqZKLbu1/b76frL4HvLmOhEODDHw96XCn38OERaMTAmG0wFcFaAIb7LsjdXJ9JkqHCeTyQCZH5D8FHZkZmJN+vfibwKZWvRVX8GR498jecQs6XXylL6nyQUurYPMvzXQYWaD4wnD4PEtW0RlEwA+uf12hPr7g+M443soehCYiH6QqcIbvCe8su8RDqjKAQMGsuD2XlMna997GkePHsX48eORm5vbQNEUaI7C6SMr16WTlJSEyMhIXLjAu0bFxsaisNA8aIter0dxcbHVdZ/WYFkWsbGxVjszxbVYbA97U6KYogyxqGzaDSH8gngPwK19mLFj0XpNPpC9Bsjf6fjy2IhLZSb354MmNJav1DRKrYc6stgsM9No0EJgKVdTdZlPvVP0N1B0xD1laAzCARkfAxdXW4/SqeKXkKC2qFmXou846ficzEzz+woRnBkGkNcFKXNAapTGZFat0+HJLVvM9qkYPW7xu4TCnN1WB8xeQRMpUT786y8cN3ERva9LF4xJTgZQT2bqKCAg3neC61Tn8QGQ5H6AOsbdpfE5nnzySVRXV2PTpk0oLi4Gx3ENPs1Zj+ojTz7p5ObmisEqAKB///4oLS3FsWPHxGP++OMPcByHfv36WTuNRQwGAzIzMz12oXBLw2J7CAon2wyF05FcPwyceQ/I/93dJQHghX1YcDvSlvApQ9yAx8lM6NvEABD3yKQpbJIZIeY5MIUIva7GNBWKg/IMOhRdBa9IVl6GmA6hPg5SOD2ur3sBPiczYVJDpjKfeBVycTogb21jMlv055/ILCkRt6P8/VFLeO+S8uoKnMjLa/b13UYjKVEulpRg7u7d4naoWo1lJgEzfa6fmVKZzf/1b0vzbzqBf//9F3PmzMGYMWMQGhrq8PP7jMKp0Whw4sQJnDhxAgCQlZWFEydOICcnBxqNBi+99BL++usvZGdnY9euXbj77rvRoUMHjBw5EgDQuXNnjBo1CjNnzsSRI0dw4MABPP3007j//vstRqhtCtMFxxT306A9DHU5q9yUQqMBrIJ3C631nNQoLu/DpWlA5krg2gHpv5UH8hY9QgBtcdPHOwmXyazsDHD9CFDTSO5RVmEcCHpwLs4mZWao4pVmAXdZOD1e4Szl/yqCrQ/GlHUKpwPuEfqOk45PyYzTAjJlQy8hQeF00D1iSWaXMzagJO199FTxSmVsYCC2TpmCWlLnqg+CH0+dcMj13YKV9EaEEDz+22+o1hsnEBcPH47YQPPUV6LMqvOBa4d4zwxfQJPN/w1IdGcpfJY2bdo41TPAZxTOo0ePolevXujVqxcAYPbs2ejVqxfmzp0LmUyGf//9F3fddRc6deqEGTNm4IYbbsD+/fvN1mCuWbMGKSkpuO2223D77bdj4MCB+PLLL91VJYoz8TQLpyqK/1tzzb3lcCe1RUBlDm+llArDGK2cLUGGxceAq1uB6lzrxzCMuVutt2LqTtvpaSC0h3vKYWol1nmgwqmti1ZpxQ0PABDQFuj0FND+EZcUieLD+MUCXV8Dkp8z3y+61DpHuSaE4JsjvyOcqYAcfFCWD0aOxI1xcegZFw8O/GTLxvSTZmscvQorLrVr0tKw0ySK7q0JCXi4bsxrkcocIG87UHLS8WV0B63vAOLHAsHJ7i6JTzJnzhx89dVXYjwaR+MzQYMGDx7cqGa+ffv2Js8RHh6OtWvXOrJYFE9FsHA2Zw2nI1HXKZy6ct5VSaZq/HhPoraId/1hm/k4ESxX9q43UUUCVblAbSNWP19BcCuVNWGhl6kBfZVvKJz+cYA6svFjnUl9C6dJcCuPQFencFpwwxORqQBZlGvKQ2kZ1I9jIBdcap0zKbM2LQ25RTmIlQGlnBrDk5IwsWtXAMDk1O7IO/IT/BgdrleU4M+cHNyakOCUcjgVXcPJo+tVVZhlMo5VyWT48s47wTb2DJLVudZzWicU0g0oQwFlT3eXwmepqKhAYGAgOnTogPvvvx/x8fENAh4xDINZs2bZdX6fUTg9CYZhEB8fL0b3pLgXi+0R3hsI6gAwHnILyNS8K5KuAqi91ngwGBdgcx8uSwcu/QhE9gfiRjbvooLCKWuGwgm4TeF06X2vFyZM/Bs/jq2buPBQhdMmmcn8gdCugDLc+jGuwNTCSThe6fekYBziILURhdNB0HecdFqMzPzjgPBefFCzZlJfZiXV1Zi9YwemK/nnWQ388cntt4vfT+zaFe8dlsGP0UHN6rE2Lc37FE5DjfH5bjJ5NHv7dlyvMq5f/8+ttyI5suEEnJnMWB9TOClO5cUXXxT/X758ucVjqMLpYbAsi4iICHcXg1KHxfaQqT3HuimgiuIVzhr3K5w29+Giv/m/1w85TuFsjoUTcJvC6dL7XrBwyptQONuM5f8qw5xaHHuxSWYB8fyn5jpQsJu36prkX3MZphZOgLfgeJLC2UigETPK0oGK80Bwit2uafQdJx2fk1npKaDkBBDUEYg0CawYmMR/HEB9mb3+xx8oqyqFWsVP/jx28zB0NPm+VVAQEsNjUFJ6CSrGgJ/S07Fs9GgovSQtBQA+1VXb8fxYoM7TaWdmJr7791/xkK5RUXh5wACLPzeTmS8pnCUn+TFCcLIx+BnFoWRlZTn1/FThdAIGgwFnz55Fx44dvSb/ji9jMBhw/vx5z28PdRSguchbON2MzTJrNQI4/4XRdac5NFfhDIgH4u8B1NHNL4sduKyfcQaTCJFNKJx+nh06XpLMdKVAwV6+Tu5QOBPuB7haoOys0SPBk5CpAUVg42s4AX5dV/FxXnG3U+H0mmeqB+FzMqu9DlRccOpklqnMjubl4fOjRxEl4599/uoQPDdwcIPfRKZMxVvbtqOcU0Gvq8aOzEzc2amT08rocFgFEJoqblbpdHjcJP0LA+CrMWOsKtFm/UzwcPEFhbP4KB+BWx5IFU4nkeBkbwCfCRrkadTUeKYLW0ulQXsUH+MHr54UYMa/De/m6yEPU5v6sBBFz6BtaAGSiqGZCqc8AAjrzgezcBMuue8F66ZpUCAvpkmZ6av59ZKCcq13U1oUuT8/uI7qz7sMNmVddjVt7wU6v9i0dclBqVHoO046PiUzMZe1hXgDnI4P/ka4Zl+mpqYGeo7D41u2gAAIZfnrjunWFyp5Q5vJHak3Q8MEQQ9eIVubltbsMriT13btwkWT9C9P9umD/vHxjf5G7Ge+YuEkHB9xFwD8pGeNoHgG1MJJaZmUnOBny9TRxoA97iY01Wxm0+MxaOtck5X8/9oy+4O6EA5gFACj9Sw3RU9EDHjl13TQmsocPneZOhYI9qJZflMyV/BpPOLH8dv6Ks8L2ONNOEjhpLRwrEV6JwQ4vYhPZZQyyyFrij/5+2+cyOcVDgKgbUwndG7T1eKxoWo17ujYERvPngUAbM7IgEarRaDSAV44rqAyh5etXyv89+JVfHT4sPhV66AgvHvbbbafy1eCBtVc4ycxZCqPmZD3FZKSrE9QMgwDtVqNhIQE3H777Zg5c6ZZZg+pUIWT0jLRe1iUWm8kdyNQfZVXNgE+aIm9CifDAp1n84OV5lCdD1Re4icSAts171yeiiIUaD8d4PRNHgpNFr/uMfwG71U49eX8hISQOogYeNdWV9+7+bv4coSm8u7fMj8+QIq3IQRfEixQ9aOMUii2wAlu/fXuQ4bhXbu1ZXxqlGYqnAXV1Xhz715xu5Btjdtvfxqol3tSpCITzyVWIi2zCBd0EajS6fDfjAxMTvWSydzrfwFl6cgPvgXTNh01++rLMWMQLGXALw8C2j1otHR6K9VX+L9+cXSi0cF06dKl0UBmVVVVSEtLw7Zt27By5Urs2bMHwcHBdl2LKpxOgGVZJCUlgWXpi9wTsNgenOAO1ERaCXegr+YHgW5MjdJkHyYcr8wYaniLpL7SmDusOTT3ZVKWDhTuAyJudLnC6bL7Xqa0PQKkMBjkPNOVr0mZGWqNExrKcKM1XV/leoWz6AhfHq4WKDrKK55tx7u2DNYoPwdc/R/vkt/6jsaPFVIYcXo+0JBKevRf+o6Tjs/JrDGXWnkQr3A2MzUKy7L49OJFaLRGC93C225DrDVlEwA0F3GzKgvd/CpwQcdbw9ampXmPwqkrhYFwmLX7MEpqjMtUXuzfH7d37Njkz836GcMAQe2dWVrXUH2V/0vdaR3Ob7/9ZtNxmzZtwoQJE7BgwQK89957dl3LR558ngXDMAgODvb98OdegsX2EF+WHmbhvLQeSP8/oPyMW4vRZB+uzuNlKFMBCROBpGlAcGeXltEiboxU65H3vdC/PTgtSqMyE3JwCq7bwjpOgxvWcQprlIUgKXrn5Bm0C20J/xECbzUGw5hYOYvtupxH9nUPx+dkZs2lFuADuwC8hbMZbD1/Hv+9cEHc7tu6NR694YbGfyRTQcHKMCi+lbhre2amWUoRj0Zbij3Z2fjjinHdZr/WrfGOja60PtfPAKpwegBjx47F9OnTsWHDBrvPQRVOJ2AwGJCWlgaDweDuolBgoT04ndEd0dMsnELSbDcHM2qyD2vqwmcHtAMC2gKBic0LolKeAWSuBAr22H8OwKhwukF+LrvvKy8B148AVVeaPtbDFc4mZSYonIo6Fx6hj9miWDkSwhkDoAgKp655g2mHIuTgbColioCwDkr4nUR8/h1HuOa799fD92RWdz9YmrQVIjg3Y1KmSqfD01u3itssw+DzO+6A7Nwy4NynxjRA9amLzDqkrVHh1HMcfk5Pt7ssLsOgxYXCXOzPyUEZx9cjRKXCunvvtTm1S4N+VnKSd9N1V7C15sIZjO9zb1zC4EP07t0bubm5dv+eutQ6Cd95qfgGZu0hDL5NEyN7CsJA0E7LgyNptA9rLvJ/HeW2WlvMB0uwdcBsDUHh1Ffyrsly104ouOS+LzvL5z2NGgD4t278WA9XOIEmZFZf4Wwzjnc3V9i3hsRuTCMwC5GZPcnCKSiOtq6Xi7uDTyHEKuy+pE++4wjhvUwqs4AOjzo8QIk7ZZZx/Trm792LIKUSS0aMQFAzgn8AADo+YT0KrWDhtHNShhCC57dtQ3aZcULkmb590Ss6HLhWyu+wNllc5+LbLTIE0QG1KKzkJ6fWpqXh8RtvtKs8riK/5Ao2nj2DGiJHLeHvzZV3343E0FBJ5zHrZ/m/8+0QkOB5kbVtgZUBXV4GagqMz16KWygqKoKfn/1jKmrhpLQ8TN1pPc3tRHRF8uDZSE4PVOXw/wcm8evaiv8BCv+0/5zNTYkiIFMaB91ucKt1CYI7qS2DB9bzFc5GERXOOouJOopXApqhKNkFMQnQJOS5NNTYFrjJFQjWHlsnbBSBrpehN8Aw/P1lqOUnwHyEY1ev4uaVK/HDqVP48p9/8MivvzrmxAxrOehUMyycgrL51T//iPvigoLw1pAhxokVuZ/1GAd1zzwZ0WFiV2Mk2/05Ocgps8+i7woMHIcXt/yISp0OpRxfh6f79ME9nZu5VMUXUqOwCj5tnKeN11oQWq0W69evxw1NubQ3ArVwUloeyjCg05PNzxvpDASFy9Uug1KouswPtBVBvEXRUAXk/pd/GUTexAckkYreQQonwJdJW8YrnAGN5yvzSoTJCJkNCqephdMbU4moo4DQroC/m9tReFawct6yIgTd0WuMCqg7ES2coW4thtdTlm5UNKsu8/lWvZw/c3Jwx9q1KK+tFff9ePo0nuvXDzc3kc/RbtQxvOwkrrkjhOClnTux7MgRcR8L4LPbb+ejs5aX8jsbm1gRFFFDLSanpuJjk3OtO3UKLw8YIKlMrmLBvn3IyMtEhwCg1KBG71atsGTEiOafWJSHFyucFKfxj8nEjiWqq6uRkZGBr7/+GqdPn8ZWEzd3qVCF0wmwLIvk5GTfiUbn5TRoD1bOp83wRASFy+BehbPRPiwPrFMslbwCI/PnZyA5HW+RsiPqpcMVzopMoNa16zhddt8LFk5b1h/LA/kUKpYCe3gATcospAv/Eai6ClRk8EFvwnq4ppCA0ZLJKvg+Lw/krYqeoHByemM0UFstnJyej2qrLQYSp0ieJPLZd5wm2/i/gy2c7pDZzsxMjF2/HlW6hpOrs7Zvx6EZM8DaMwmlrwIub+QtjfH3NPzev3XT7v71IITg1V27sPTQIXEfyzBYcccdGJOczO8QLfmh1k/ECgpWDfq1bo12oaHIKuV/tzYtzSMVzt1ZWZi/dy9C2HD8oukCVu6PX+69Fyq59CF6w/GOl1s4c37hJ06jBjokpyvFnBtvvLHJAFOEEERHR2P16tUY0YxJEKpwOgmltyQZbiF4TXuIFs5qfrE8a1ugAGdgVWbqKCBulHGbYfiBbu11PjWKXQpn3YBZ3ki4e1sJ78OnrBDWc7oQl/QzQ10OWZtcamW2p1BxE5JkVpMPFOwFgjq6VuFUhgHJz/I5QAEgZjBvMfaENUWGGt7dTMgNaguMDCg7xbuOakv4e1oiXvNMlYJpVNXa67xi5cB1b66U2eazZzHh55+hNVnPF6hUiilGjly5gvWnTmGSPelC9JVAxXmHRnmfu3s3/u/AAXGbAbDyrrvwgGn5bLHkq6P49bcyNRiGweTUVLyzfz8A4GRBAU4XFqJrtOdMOBdWVmLKhg0gAEo5P5Rq/fDDmPHoEG7He7QOs37mzQonp+OfU4QA0be6uzQ+yapVqxr9Xq1WIyEhATfccAMUiuYtw6AKpxPgOA5paWlITU2FzMbIYhTn0aA9KnP4KKv+rfm8dZ6EzA8I6cxbDYkBgHv6j+Q+rAzlB2jWIgc2hSMtnGrXK5qAC+97KS61Hk6TMtNp+AG/sE5M9ABw8RpnVmY+kRLW07XXbwxFINDhEWm/YRh+LWzVVaC2SLLCaXNfJwQo2AWoW/Gu0Z6OsGZYoOoyEJzskFO7clzwQ1oaHty4EQaTSLt3JydjyYgRSP3sM9ToeYv9K7t2YWxKCvykDiS5OvfcxhROTscHq1EEN2lBf2vvXiyoUwoFvq5TNs1kJuR6bkzhZBVm0UxNFU4A+OHUKSwYOrTR8rgKjhA8tHEj8jTGta4ze/fG/d262X/O+v3MmxXO6vy6ib0g47pgikOZOnWqy67lY/4wFIoNaLKAgt1AmXtzXVqEYfi8lm3G8AFwPI2aa7z86gdLEQYAdqZZACPjP45QOH0ZzmAMAGSr5aXkX6Bwn/2TAe6C0wNnlgCnFpgE+hLSonhwUC1vQczFWeS8a2gu8sHEcn5y3jUciRBV1S+G/1t12X1lsZOv//kHUzZsMFM2J6em4qf77kOH8HC82L+/uD+nrAwf/vWX9IvYksc64yMgY1mTSxve3b8fb+7ZY7bvizvvxMO9LKyfVYTwbaO0PXpwl6go9IiJEbfXpqWBODjljb0sPnAA2zMzxe37Ymvw0YD2jg1GJiqctY0f54lU16X+ovk3fQKqcFJaHra8LCmWKT4KXPwGyNtuvl9wLbRXqUl+Fuj2n+anRREoOQlc3canW/ElGIZfk5lwv+399/ohIP8Pt+d2lYzg3siwxnVZgpLt6jXONYV8eoHi4/y2TsOvE670PoVEREj5UetEhdN0kGvw8AEv4Yyu/aE9+AjcbnDLbw4f/vUXZv76K0zVqZm9e+PbsWOhqLOqzhk4ELGBxqUL7/75Jwo0EqPJCu9QtpHUKkJO6UZSo7x34ABe/+MPs32f3H47HrUWCbPVCD4dS3DHxst3re6Zp+eXH0w2ccvNKi3F4Ss25DB2MgdycszqHqxg8UkPPfxyf3RsQMOIvkC7B/hlJt5G9VX+L1U4ncJjjz2GrKwsyb/LzMzEY489Jvl3VOGktDyENXC2rndyNYTwL3RPjCpnLf+mYOFsjhWNYRwXRbXobz7ZdXWeY87nKTAsvyYzJMVyOgJLCIop52WpUUxzcAr9QnSp1bo2JUlNIW+pKz3Jb1dkAFnfAdeakQrIUVz5DTj7ET/JIgXBSuTMnL+mliRPt7DrK3mlU4i2nfSQZ7lONwIhBG/v3YtZ280nAmfddBO+uPNOyEwCFQUqlVgwZIi4rdFqMXf3bmkXtMWlVkyNYlnh/ODQIcz5/XezfR+NGoUn+/SRVhZLXNvPe3XUXbu+i+ratLTmX6MZFFVVYdIvv5hZoT8fMRBR/gF8VFlHTob7xfJLh5Rhjjunq6iiCqczuXz5MpKTkzF69GisXr0aly9bn0DNzs7G119/jREjRiAlJQW5ubmSr0cVTifAsixSU1N9L4Kfl9KgPTzdwpm7CTi9iLcmugmLfVhXwVvJGAYIqKdwBiQCSVMtRyx0B8KaNBfm4vTY+940NYqH0ajMTBVO8Qcqo6LtytRBQh5Opm4tmpgvV3qeQYdTW8wH/pFKMyycNvd1YmKpsaeMrkSwxMkDbZ/MkYCzng+EEMz5/XfMreeWOvfWW7F0xAiLESin9eyJ7iZupl8fP460ggLbL2rLO1S4R3QN75GPDx/G7B07zPYtHTECz/brZ7bPTGaEmE9gNAZrTI0CAG1DQnBL27bi1+tPn4ae42w7l4O5XlWFUWvW4HK5cb3wg9274/5ObfgNZWizJl499j0kFUOt8f3tTxVOZ7B161bs3r0barUajz76KBITExEdHY2+ffti5MiRGDFiBPr06YPIyEi0b98eTz75JPz9/bF7925s2bJF8vW8vEd6LlqtB1qnWjBm7SFaOD1U4RQsr27OxdmgD2vqXC/UsXw4fFMUgbzV056w5ZqLQOYKIG+nfQW1hOAK5+LUKE6/72uuA9ePGC3NtuDBCifQiMwsKZwMY+JW68J1nGIezrrgKuJg2rq7oMsQAqlIdUcX1nACdlmLberrYT2BtvcBbe7y/EGjfxzQ9TWg/QzjPn2lQ93yHf184AjBU1u3YvHBg2b7Fw8fjvlDhlhNdyBjWSw1SW/AEYIXduywfW2jeD804lJrxcL52d9/49lt28z2LbrtNsw2WVtqiigzTSaQvhC4tL7p8gm5J01cuk3dagsrK/GHHa6EzeVKeTkGrV6No1evivuSIyLw6R13gBHiHzgg8rVZP6st5pcClJ9r9nldiq6MV76VITS2gxMZMGAANm7ciCtXruDrr7/G2LFjoVarkZubiytXrsDPzw/33HMPvv76a+Tm5mLTpk0YOHCgXdeiCqcT4DgOGRkZ4Nw0g0Yxp0F7CK6FnupSK6ZGcZ/CabEPi+60SY69mLaEXwvnSGukqHC6zsLpkvu+Kge4upV3F7YV1nMVzkZlZknhBPi8kcnPACrpqTzsxjQPJ2Bi4ay03eriDAgxyklqPlC5H9D1VaDzC5LzcErq66FdgfDeDdvRE5EpjXIsOgqkLwbyHTMR5oznw4s7duCzo+aeMJ/efjtevPnmJn87LCkJd3bqJG7vvHgR2y5csO3CMYOB1Ln8mkprWLBwrvjnHzxZL3H8giFDMMfKANZMZtpS3pWe2CA/cZLNqHDe26UL5CZWP1e71WYWF2PgqlVIv2acBI0JCMCGiRMRqFTaP3FUjwb9rOoykLsZKDrSrPO6HHU0kPI80PEpd5fELezbtw9jxoxBXFwcGIbBpk2bmvzNnj170Lt3b6hUKnTo0AGrV6+2+XpRUVGYPn06vvzyS+zbtw+nT5/G6dOnsW/fPnz55ZeYPn06opuZTogqnJSWh6e71HqAwtkAQoDKuhnh+us3BcrS7QtO48iUKAKiwlnkXoXA0diTEsXDLZxWEV0c64XD92vFu4NKVJKaheAaWt+llhiMHhPuQF/JK8MM01BOtiBrxELV0lHXDa6qLnvkM2TT2bP4wCTCLMsw+HbsWDwhYQ3k4uHDITOxgr6wY4ftrqYM2/g9KDe3cP54+jRm/vqr2SFvDhqE12+1Mb+iFIWMbWjhjPT3x8j27cXtDWfOoFrnwOA8jXCqsBADV61CdmmpuC8hJAR/PvwwukTVTZxpbcgxag/enBYF8Mxo/S6gsrISPXr0wCeffGLT8VlZWbjjjjswZMgQnDhxAs8//zweeeQRbK+3rtud0DyclJZH4gO8O54rLSRSEAOjeJDCqS3mX4iMDPBva/mYoqO8FVQVLi2vnzMUTmUYX1ZOZ3TN8QUEN1Ipyei9NWhQQAI/qPWLdXdJGrrUsjK+DfRV/DpOKe3hSAQ3PHkQXyZPo3A/UHmJf9b6x3l2pMxrh3gX/LAefN/zi+OfIToN74VhmofVzeSUleHhzZvN9q0bPx73dZWW6zQlMhJP3Hgjlv/9NwDgzPXr+OrYMUlKq1VUkUB4L0AVha3nz2PKhg1m0XNfGzgQbw4aZPv5pChkwkRKvUm2yamp2HL+PACgQqvFmrQ0PNK7t+1lsIPDubkYvWYNSmqMZUmJjMTOBx9Em2ATq78tOUbtwVsVTkIcF0TQCxk9ejRGjx5t8/Gff/452rVrh6VLlwIAOnfujD///BMffPABRo4c6axiSoJaOJ2EsxM7U6Rh1h7qKH5A4akzZx5i4TSTmTIc6PgYED/WutzESLUSc3E6Q+FkWJOgKK5zq3X6fW9PhOXgFCBpGhBzm1OK1FysyiyyH9D2Xv5eNUWTzVvSy846vWwiXL2gQYBnBA4SIr/as3YaACouABe/BfJ2NH1sPWzq61WX+WtcP8S79XmgpVBEcwEo/scY3IiVG9edOigfpyOeDzqDAZN++cVMgXn55pslK5sCbw4ejBCV0dI9d88elNU0MTmVtxPI+dkYRdQS6kigzd3YV9Ua43/80cxy+ly/flgwdKjVNaamiDKTopBZsHACwF3JyfBXKMTtmb/+ivt//hmXy+zMH90Euy5exG3ffmvWVr1btcK+adPMlU0AaDWKD7rn36bZ1zXrZ4LC6elpiUzRVwFnFgNZ39vmQu0lVFRUoLy8XPzU1jquTQ4dOoRhw4aZ7Rs5ciQOHTrksGs0F6pwOgGZTIbU1FSqdHoIXtceHrA+rIHMGIZ3ZWzMQiEMBISBga04Q+EE+GAlXV4CAts3fawDcEk/09th4VSGAIGJHmWhEbBLZpXZfMqDivNOK1cDogYAHR/nc9oJRN/CT8C4M1cjK+cHqOpW9v3eUMt7JVTmSPqZze1mamHi9FZTZHgEltYM+8fzfyXKxxKOej7M3b0bB03SF9zUpg0WDB1q9/ki/f3xholb6/WqKry7f3/jP9JcAEpPNelOfuzqVdy5di1q9MagVNN69sT7I0farGyKMhMmV2wJqhPZH+gwEwg3t9QGKpWY3rOn2b71p08jeflyvLV3r0NdbDefPYvb165Fpck5b01IwB8PPYSoAAvvOv/WQFj3Zq91btDPvNHCWZ3Hv+u0JU6JGO0uunTpgpCQEPGzcOFCh507Pz8fMSaRpwEgJiYG5eXlqK5247IPE1zqUltVVYWdO3fiwIEDSE9Px/Xr18EwDCIjI9G5c2cMGDAAw4YNQ4Clm9GLIISgvLwcQUFBNj1UKc6FEIKKigq+PQxVvOunIhAIt5Jc2t3IA4CQzvxfwvFuXS7GTGa29mFhbY3UfHvOUjiluPU6ALtkJhWDHWs4PRirMuP0/GBWHtBwwCG6nLswSq0ikP+Y4gnuocHJ/MdeBC8Aibk4be7r9dcNa0s9N3iQpTXDgsLpAAunI54POzIzsejAAXE7VK3GD+PHQ9FMJfbpvn3x2dGjyCzhrbsfHj6Mx2+8Ee3CrORuFOMgWF8DfObaNYxZ8w3k+lIooIQOcozv3BlfjRkD1sb6izIL8AMjtI8t1vxGJteWjhiBGr0eK48fF118q/V6vLlnD1YeP46lI0bgns6dm/UM/+7kSUzfvNksz+btHTvi5/vug5+JhdUZNOhnMm9UOK/wf30s/2Z6ejpat24tbqtULWsNvUumDtLS0jBt2jTExsZi3Lhx+OSTT3DhwgUwDANCCM6dO4fly5dj3LhxiI2NxbRp05Dm5sS8zYHjOFy8eJFGqfUQzNpDWwoU7OYtJJ4KqwASJgKt73TbuiwzmdUUApc3AqWnG/+R6FJbKu1iDMsr1TLvnmhyyX0vBg2S4FJrqOFTqVzzHNcaAasyqykAziwFMpY1/JGgbHtSUC1vRUiNoq8C9LbPgtvc1wXFREij5Km5OA1aY1ktWThrCiXJxxLNfT7kVVTgwY0bzfatuOsuJIaGNqtcAKCSy/He8OHittZgwCu7dln/geCeyVoOvJdVUoJh332HuxUH8GzoYbRVlGFE+/ZYc889ZpFim0KUmb6Gn1jxb93syTaVXI6v77oLhx95BDe1MXdfvVRWhnt/+gnDvvsOpwoL7Tr/8iNH8NCmTWbK5v3dumHjxInWlc3aIqDob369czNp0M9E92Kd97inCq7aPqZwBgUFITg4WPw4UuGMjY1FQb1cugUFBQgODoafn2dkZHC6wjlx4kT06tULZ8+exbx583Dy5EmUl5fj7NmzOHToEP766y9kZGSgoqICJ0+exLx585CRkYFevXph0qRJzi4epaVhzxq4lk7FBaDkJFB6svHjBFcnXZm0F1vHx4Fu/zFGhXQU+mp+bVrOL449rzuJHwck3A+oY5o+VsBQy6dSyf/ds9fQmSK4N8oDG34nrnF2oYWz5F9+kqra5IWur+TvDQcMEu2mue0pUxoVLG1R88tTHyFQleDy66kKp+DqK1MaXRAB3qodfQt/37nB00TAwHF4YONGFFYaJ1me6tMH93Tu7LBrjEtJwS1tjQHhfjx92sx1V4QQ49pIC5He8yoqMPy773C1ogIajpflwFbh2DBhAlRyO53q5P5A4iTeTdYWy2NtEXDtIFBywuohfVq3xoGHH8a3Y8ciNtD8OfNHVhZ6fv45nv3f/1DShDtijV6P3PJynMjPx9zdu/HM//5n9v1jN9yA78eNg7IxK3TlJeDKFqDwzyarJhlWxS8vSZzi+HM7i+o6hdPTc/d6EP3798euepNEO3fuRH8r+W3dgdNdalmWxdGjR9Gznt98fQS/89TUVLzwwgs4ceIE/u///s/ZxaO0NDw9JYqA+FJn3R/cyNb8m4og3lpJOD6QihTXOWe4oDIyftABAHGj3RdJ1JHYE1BC6OvEABA9wDjXpcshWMvBCRjb0ZVRnEtO8PeBIhTwq1P2NRf5yYzAdkDSVNeVxZSz7/MeEe0e5CMz24Mqgpd3bZFDApaIcAbecgjw6781Fz1X4TR1p63/LIp1f7CthX/+iT+yssTtnrGxWDKikRyYdsAwDN4fORJ9vvpK3Ddr+3YcmjHD3AWW0xonOuq51BZXV2PE99+LrrkVnBKxgYF4YfhABChd+B6rvc5PNvq3BsJ6Wj2MZRg82KMHxqak4J39+/H+oUPQ1VkGDYTg4yNHsDYtDTN790atwYDrVVW4VlWF63Wfa5WVZms06zNnwAAsvO22pt1zxeBfodLqaQusjM+F6y3oKvjnEcPYvzbdB9BoNLhgkhc3KysLJ06cQHh4ONq2bYtXX30VV65cwbfffgsAePzxx7F8+XK8/PLLePjhh/HHH3/gxx9/xJYtW6xeg2VZu1zHDQaD9ArBBQrnDz/8YNfvevbsafdvPQG12sMVmhaG2B6CwmnFFchjuLQeKD8LtBnjtrWmarWaHzQKFpwAK/k3BRiWj4aqCLJsmXI1MiW/3kdbxg9A5FbSuTgQj7zvWSX/8iaE7/+sZymcFmXWmMIputRW161xdsHKECEPp6ns3B2l1qA1KkrNcTNUhgPIkryOs8m+ztUa+52Q2sZTFU6hDZ28vtSe58P+S5fw5p494naAQoH1994Ltb3Wwka4MS4OD3bvju/+/RcAcOTKFbRftgxDExMxpF07DElMRGt1nbLJyMyiNlfU1mL0mjVmrqihgZF4sHsSAlj7g/Hw7yE9wLK2T0wKbqQ2RmYNUqmwaNgwzOjVC7N37MBv586J3xVVV5utm7WVhbfdhlcGDrTtYAenRPHI95CtCNZNVZT7J9vdyNGjRzFkyBBxe/bs2QCAqVOnYvXq1cjLy0NOjjGYWbt27bBlyxbMmjULH330Edq0aYOvv/660ZQoc+fObaBwbty4EadPn8bIkSORnMzHBzh79ix27NiBbt26YezYsXbXiebhdAIymQwpKSnuLgalDrP2qL+myFORu3edmiizykv82g95gG0urwESlbqqXCBvO79WI872nFM2o4o0KpxSyyYRp9/3+ko+MqQimA8qZSsMw1s59dV8/1cENf0bF2FVZoKLY2MWToB3q60fzMcZNJYWReemyKtCDk6ZutHgLU2iipAcsMumvi73B7rN5S1iXG1dRF8PzX0c2g0I6mQ5sAoh/CC46jIf9dTOdfX2PB+uV1Vh0i+/gDNxnf78zjvRKSLCrjLYwjtDh+Ln9HRU10WWzS4txcoTJ7DyxAkAQP9IJV6PPYc24a0QV1WFqIAA1Oj1uHvdOhy5ckU8T9uQEPzfqIEIKN9v9z0iyuzyBqA8A2g1Egi3IW+mcD9wtimcAh0jIvDrpEn43/nzeH77dpwrku5mHqhUYsnw4Xjsxhtt/5GUCLxNYLGflZ/nPUKCOjo+OJ+jYeS8N5WLg/55GoMHDwZpZMnE6tWrLf7m+PHjNl9j3rx5ZttffvklCgsLcerUKVHZFDhz5gyGDh2KuDj73Zw9QuEsKyvDgQMH4Ofnh5tuusljFrjaC8dxKCoqQlhYGFgJC+QpzoHjOJSUlPDtIazh9HQLp5tzcYoy017gF3oHJjnH7VVbClRedt76KFUkUJHpklycZv3MGfd9bRFw9X+866QUhROo6+/VkgdgzsaqzEQLpwXlmGGBDo/w67Bd5SbNNWLhNNTUWWBc/DoVFE57c3AKRPYHom6W9BOb+zrD8IN/mapR10aPQKa0blHJ/p6fsPGP5900pUI4cFe2oLIkFwEp08Aqmh7jEEIwbdMmXKkwKmvTevbEA927S7++BOJDQrBg6FC8sMNybtZD17W463pHKBgDavctQfeYGKjlcjNlMzogADsffBAx8gKgHHZ7AYj9rLYErKHWfH1tYwjLCOx83o3u2BG3JSVh2eHDeP/QIeRpNAhTqxEVEIBIf3/+4+dnvu3vjyh/f3SOikKw1GAwDnSptXhv5m3n34FJ04BAD1c4g9rzH4rLWbx4MZ5++ukGyiYAdO7cGU8//TTee+89zJw5067zu/QNuXLlSpw7dw6LFi0S96Wnp2P48OHIz88HALRv3x7bt29Hu3ZNuO95MIQQXL58GaEOiB5HaT5m7eEtazjdrHAKMgsLENZv2ng/VucBZWd4pSi8V9PHOyslioBgUXGBwun0+16YLLFHyRL6e/00FW7Gqswac6kFHLvW0BZInYXTVOGUqfmJEmLgB9TOWH/VGKJVpJkKpx0TSS3qHccwvKJZfo63ckpVOAXreOkpVOZdgn9SCWCDwvnBX39hy3ljrtmUyEgsH+0ELxALzO7fH8OTkrDtwgX8kZ2N/Zcuma1V5MCilvDKzL/1ImOGqtXY8cADvBVWCHJkp4VTfA8pS/kdtt5jokut1m63e6VMhhdvvhkv9O8PAyGSoutKgjOYeHQ0816GlXvTG1OjUFxObm4uFI2k7VEoFMjNzbX7/C41vy1atAgajflM1+zZs2EwGPDzzz9j7dq1KC0txWuvvebKYlFaEtG3Au0f5hMsezJuVjgB8C9qYbDUVMAggZoCPppn2Snbjhfq56yUKKrIunJdc875XYm+GTk4BRczD1M4rRLSjXdztDcQjqMRLJymLrUMY3Tndcc6TsHC6QA3PKdQeYlfiy5E3qzK5XMge+K9eHU7kPtf62VrTj7O4n/44E7ChJEN61j/vnIFr/z+u7itlsvx4733ujTwTmpMDF4aMAD/mzIFJXPm4MDDD+PtIUMwJDERKisRV/0VCmydPBk9YuvW7CrDeBdYWyYfrUEMJhNQobb9hjWxMNq4jtMaDMM4T9kEAH0577bNyp038cp6icLJ6ZqdfohiP926dcOnn36KKybeCgK5ubn49NNPkZpqf/5pl1k4hdxA/fr1E/dVVFRg165dWLZsGcaNGwcAuHr1KpYsWeKqYlFaGsqQ5ruguQJPUDgZFqTDowCpsf1FKAwIbM3F6XQLZ53CaajmZ5LdlNfUIRjqFE57LJyxw/mXuaNTzziL2KGNf19+jldgApOAwETnl8dS0CCAj2qqLQN0blQ4HfE8y90MaLL5dZYBCc0/H8C7gJedMaZIunaA344b7Xnrs8rT+Xa0tj5QWP9dmcMrB7ZahQkBio6Yp/Bp4tlYVlODiT//LEZLBYAPR45EaoyEVEgORiGT4eb4eNwcH4//9GwFbfFxnKxQ47/XArE7OxuHr1xBhJ8f1o4fj/7x8SY/DALa3NWsa8u4SoAl/L1n63uClfEKHKevc6v14GVa8kB+Elxf5ZxlK4D3KJwV54FLP/I5VxNpWkRX88EHH2DkyJHo1KkTxo0bhw4dOgAAzp8/j02bNoEQgu+//97u8ztd4RwyZAgYhoFOpwPHcVi6dClWrVoFgA/7azAYsGLFCvz0008AgOLiYuTn52PoUH7AMW3aNDz00EPOLqbDCQrynMAcFC9sD5n7FU5RZlKUQWHwqyuzbWAmWIacpXDKA4DOsy2nO3ACTu1nooXTjsGTPevOXIRdMivPAIqP8S6trlA4k6bzCnt9F9+om/lBnJ8bwvcrw3jXYmFSpTnoKnjLW22RzQpnk+1Wf/mCYK22dTLKVRBinhbFEn5xvFumroJ/ttnq2qm5yLvzy1QgwV2hLP3dGJHUAiXV1Ri7fj2ySo3HTOjaFY/e4J5I5RapyYeyPB19wnuhTyo/TtNzHFiGMU+f4iCC1RygBz+ZKeX8iQ/UKakeEDG9MViFwwPaNbg3BYuvpyucQoRaT28zH2XgwIE4fPgw3njjDWzcuBHVdTlo/fz8MHLkSMyfP9+zLZxCJCWtVouUlBQ8+uijuOOOOwDw2vSlS5fwyy/GxOyHDh3CzJkzRaXUG9eIyGQyJCQ4aJaY0mxkMhnat69bhH79MP83tJtnR2tTBPGBYeRB0mbUHYRMJkP7donSLYLyYL6snJ5XlpuKIOpsCyfDOD3VgYBZP3MGzVnD6aFYlJmhxhgZ2draK1dHcbamUIZ0cc31LREzhP84AlUEUHGBVzhtwKa+blXh9LDUKPrKunV+jPWBLqvg+0DVFd7KaavCWVT3vgnrCZkqEuFh4bwLpQUulZZi9Jo1OHPduN48KSwMX955p1258pyGhdRijbqcGrT8GkV5gOTYCTKZDAmtQoBcVrol3xUTUR6IxXvTWyycVXUKp7/9kVApzaNbt27YuHEjOI7DtWv8EoOoqCiHBEJ0+hrOhIQEJCQkoGPHjkhOTsZ3330HtVoNg8GAzZs3Y/To0eIxCQkJqKioQHx8vLgdEuIF7o/14DgO+fn54ExcYijuw6w9CvfwkT5NXZw8Ebk/kDARaH27y5VNAOC0Fag48jq4zNW8K6qtsDKjgtfITL4Iwzp37YoLcfp935w1nNUFQNHffHh8D8KizMpOA2eWApfWWf+h0F8MHn4fewvKcP6vjQqnTX29vsIpuNvrPE3hFKybAY1PsPnXWaFsXcdZW8y7CAJARF9w8mBoNBpwtQ3znR67ehU3rVhhpmyGqtX46b77EOJpORWFyK+2puK59AOQ8THvBi/1UhyHa6XV4AI7GOXva5Se5p/NFvqFPVi8N71B4RTSDwG8RwHFrbAsi5iYGMTExDgs6r5Lo9T+3//9H+677z4xj0tYWBjefPNNs2N++OEHDB8+XPK59+3bh8WLF+PYsWPIy8vDxo0bxQSlOp0O//nPf7B161ZcvHgRISEhGDZsGBYtWmSWUyYxMRGXLl0yO+/ChQvxyiuvSCoLIQT5+fmIivKwdSotFLE9IiO9J0qtmyEVF1FZXoqAsCrpVk5FKL8eSlvadDTR9g/zLxpnUnkJuP4XP6huJf3ZYitOv+9jhvCpJfzsWMtVmc1PtIR2A4I7OrpkdmNRZk1FqAWMSrcrJo4MtfyAkFUAkf3Mv9NX8pGZGdb2wFqOgHAAGMdNRqnq8jpqbVM4berrXD1LmKmF0w1eG1axpb8BQPgNQEiK7YPhor/5egZ1BFQRIAY9NBoN/EPNA3dtPX8eE376ySwKbEJICP43ZQo6e+IYQuo7VG5/YC1CCK6U+yM8dRJgJVCRVcrP8e7Mge3te2baAyFA+Vne5b/N3bb18aIj/Duq7XhAFe6AIli4N8O688sqPHkNv7aE71usHFB5cDl9nJKSEvzwww+4ePEiSkpKGuQCZRgGK1assOvcLlU4x4wZg7S0NOzcuRMymQx33303YoVoZgBKS0sxePBgTJokfbFwZWUlevTogYcffhj33HOP2XdVVVX4559/8MYbb6BHjx4oKSnBc889h7vuugtHjx41O/att94yyzHjdWv/KNbhao3KjT3r4FwNIcYHcP1gJc6mMosvQkCi9N8qQ4BK2L5Wy9kDT0MNH6zELxaA8xROp+MXY//AyUPToljEFgVAcKk1uMCl1lAF5P9uWeGsvMxbYv1bAx1cqHBWXQYufsuv/Uqa2vzzCbJ2pItyA5faUP5eN2h5mXqKV0NT6zcF1JEAbFwvy+mBkroE7BF9+b/KcBRETkdU8o3iYV8eO4Ynt2yBwWRQ17tVK2yZPBmxgR66jk2qwink0rUzNYrdFP/DK3+t73SNwllbBFw/xEdiBoDgFH6CoilcEW3ar5V71plLobouMqo61ruD+3kx27dvx7333ovKykoEBwcjLKxhlPjmuPe7OFM10LFjR3TsaHmGPTQ0FHPnzrXrvKNHj8ZoKzmqQkJCsHPnTrN9y5cvR9++fZGTk4O2bY2uGkFBQWZKMMWHENeeyF2fpN0esr7jg060vZe3TLkQRiPk37RjEC2sLZO7Zu1kkwhBVWqLPMuy4kq8SuG0QQEQozi7wMIppAayNOkjDKZdnRZFW8ani4CDvAOECThDtePukfqKCSvn21RXzlszPEXhFNyyHbnWm5UD7WcApWlAEB/pEQwLUhe8hRCC//zxB97980+zn93esSPW33svAl2Y/kQygksta6NLrbx59whjOlEsBcHll2teWhSbqSkwKpsAkL+Dt243pjwRzjjB5uo8vp5GFXWndTcvvPACYmNjsWHDhmYFB7KGF4y6nUNZWRkYhmkQlGjRokV4++230bZtW0yePBmzZs2CXG5ZTLW1taitNT7MKir4gRIhBKGhoeA4DgzDgGVZcBxnZpoW9hsM5uvjrO1nWRYMw1jcD6DBWhpr+2UyGQghFvfXL6O1/d5WJ47j+PbQVYIhHFiZ2jvqJFODEA5EWw7Unc8l7VSZB2hLoPLzB+cXD5YQaXWqm6nlOE4st1gn03aqKQBz5Tew/rHg4u50Xp2UYeDAAIZakJpiQBlqWzvVXgdz5TeQ8N5gw3tarKtpncR+xnGObydCgOIjgMwfTEhXsHKFtL7HqkAIB+irQOq+94RnhCAz4RiDwQCmthQgHIgsoJG+5weAgOg0IHq9qCA5pU76GjCEAyADW3du8XjGjy+jTgODSTks1dVYdgc8I2qLIQNAFCHg6p3HrnZiVLxiIPMH0dWAkasafUaY9nWrdUp4CCz0ACMzlrHV7QCrBKuMAqy0h8vfT5EDwUbcBINea/a8slinqlyw5acAdQy4UPPckg3aSREORA2CjGHEOoWGhqJGp8OjW7Zg7SnzXMUze/XCx6NHQ6VQePT7idFVgQVAWJVtfY/1gwwApy0Tnz221okz6NG2cj2QvhNcynNglUES6qQEQMDpqhq0q6W6Nrvv1Zbxz4LgDuCq84Ga6yDX/gIib7LeTroysIQDBwaE8RPL2Zx2qv8eIoSAqy0DqnP5tZyB7T1zvOffFgitBgLaAQaDR7yfml2nujLW/95TuXDhAhYvXuwUZRNwgcL52GOP4ZVXXkG7du0k/S4zMxPvvfcevvjiC4eXqaamBnPmzMGkSZMQHGyc1Xz22WfRu3dvhIeH4+DBg3j11VeRl5eH999/3+J5Fi5ciPnz5zfYn5GRgZiYGJSWliI8PBxt27ZFbm4uiouNi8JjY2MRGxuL7OxsUVEFgPj4eEREROD8+fOoqTFaI5KSkhAcHIz09HSzzpucnAylUom0tDSzMqSmpkKr1SIjI0PcJ5PJkJqaioqKCly8eFHcH8iWoEPX/igprcTly8aACEFBQWjfvj0KCwuRn58v7veGOqnVaqSkpKCkpESsU1XhFcRWlyK8VYxX1Klay6K8oBAazSlorvpbrJPD24m5hOKTq2DQ6aBVxOLSmfNOaydV7SVEVf6LyHji3DpFRKCwzABUF6K45k9oVQlN1+nfE4i5/jUYQsBdOomYIR/Y3Pdqamoc3k6asmuIuf4tAEDRaz4iomKk9T0ocK2gEAZWg2s1aZLaCbB8PzmynYKCgsT7KTj3HFhOi2u6XCR0irVcp04doGw3HekZ2TCkpYmKnjPqpNBeRURpIZTBMoQD5nUienSorkRgYCAuXTyL8iq9eB5nPiNCK08hMQio0ilw3uR4u9vpaj6KtbfxO9MzmryfMjMzUVNTg9K69B2N14mtV6capKYmQFtT4xF9z9hOF5tsJ7/q00hSpUMW3AFpl82HT2I7nT0r9sf6deI0p7Fi506kF6kBGF0cn+ncGdPj45Gbk+P571z2FqR26YQKTQ0u2tD3wpRlSACgKcnDRY3xeFvqVFl6FdHVVSisqYEyrhYRkUE216lzGAMFIcjOzEBFoXE9o7Oee4GVpxDFlSMkMhwF1eFgCn4Fd+1HXAuXISYu0WI7JUQShAG4VqpDnskEhCPaqaKiQqzTlTN/IKxsB7SKGFS1muSi+0li38uuBZAIFNcCSPOo91Nz76eCggJ4Ax07djSrt6NhSP3pAAdz++234/fff8dtt92GiRMn4rbbbkO8aWJgE7Kzs/H777/jxx9/xO7duzFixAhs2bJF8jUZhjELGmSKTqfD+PHjkZubiz179pgpnPVZuXIlHnvsMWg0GqhUDd1H6ls4r1y5gi5duiAri1//1rp1a8hkMo+ZmQQszA5xBjBnF4OFHlz7x0Hq5XTzhNkhyXWqV3aDwYArV66gdVA5ZLk/gw1MANduuufXqWAPSP4fIGG9gdZjzOrU7Fk8rhqGsnN8KoTQbkBQJ35/TT6485+DqGKQh+6IaX8T5HK5tDoRHXDtADhdORA3xmzwZdZOJcfBXPkv2OBO4BImO3VmkstaB5Sng8SOACL7N91OeTvBXKtzdwtoB6b9NIt1rW/hvHLlClq3bg2FBStFs+pUWwTm/HJ+hrrra9L7nrYU5OwHACMH6fq6eV3dbOG8cuUK2rRpA7lcDoO2GsyZhQAA0vkVsAo/93t2aDLBZH8PqGPAJj/VsE5nF4M11MDQ/jGzoBxOtTJlrwVbmQnSegy40J7S61SvjFLvJ51OJ/Z1lmVbjgdOTQHYzC8AVgku5WWztD1inS79CIAFogcBqgixTheLijB//ZtI4k7jaG0ctlQmQ8GyWHnXXZjUzbhkwhfeuWbtVHsdsgufgWOVIJ3nSKoTV5GFyn+XIygyAUzKc9LqVHQIyN8JLqQHH8DHkXWytP/Kr2BLjoOJHQIuciBw4QugpgAkoh+YuNGW26n0JNgr/wXnnwjS7sGm62SjhdP0PUQIAVd+XnyGkQ6Pe879ZGOd6pfRo58RFsqem5uLxMREXL58GW3aNBFI0Y1s3rwZTz31FP78808kJiY6/PxOt3Bu3boVBw4cwJIlS/Doo4/CYDAgIiICiYmJCAsLAyG8dSMrKwslJSWQyWS4/fbbsXv3bgwcONChZdHpdJgwYQIuXbqEP/74o1FlEwD69esHvV6P7OxsJCcnN/hepVKZKaLl5bwvPsMwKCkpQXx8vNjxhL/1kVmJvObM/QzDGPfXXAEIHx2PzfuND3AQam5Ot1Z2j61TvTISQlBaWor4Vh3AdngEYFjvqJM8EAzD8tEeTb6XWnZxf9VVoCKDVzKrr0ImPGjlSiC0M/+/XyuwXV6EgQ3A9bQ0tKp7oEoqO0eAa/shA4C4EYDMuFbLrJ24Gn7AJg+wv05NlUU43i8aqDjLp2QwOcbi8ZU5kBUd4suWMMEs12JTfa+0tFScUHNonUgtXx5FIFB3nKQ+Jvfj+xI4gCFma5il3k9Sym5LO5nKTCZjgOiBfPAapb/F462dp6n99teJ42VftyaswfGKIMBQAxmpthhJ0ynPCEPdu0YZ6rJ2qn+80G4yS/cTZwAu/8Kv34wbDZmsbv2rrgKoqEuPEX6D2/seAODSj4BMDVnsMIs5bs2O92/Fr3c11ECmu94wEIu2FLKKs7wLfOxgsT/sysrCQxs3IkZnQFIAEMrWIESlwqb778dgCwM7r3g/2dpOqjAgvDdYRRD/7Kq3PrjROunLUVNTg2BlmHiczWWUqQEwkEFn833ZrL7HVYu5XFmZHGg9mg/sVXKMf6YpQxqeuy4nK6sOd+izw/SZyvw/e+cdHkXV/fHvzGx203slJCQECL0X6UV6UUCRjt3X+irYexd99Wfv2EUUURQBRQFBQIqCIKEF0giQkN6TrTO/P+7O7G6ybXZnW5jP8+yTzOzszJ1z79y5555zz6EoMEGhpA/j9MJ1PN1HiNqvrgTDaoDgJLSOr+EXfYSb+219729s374dCQkJ6NGjByZNmtSmfweI7N944w2Xzu+VNZwjR47EyJEjUVFRgU2bNmHfvn04deoUzp8/DwCIi4vD3LlzMXz4cMyYMQOJidKHROaVzTNnzmDHjh2Ii4tz+JsjR46ApmmPlMdvaMw3/d90jgROaKVwthsUIYAqgHJ5CbkGJYgcWX8aKFpjuS8kmQQ1iDSLpEdRpA24s+aAVpBBuK6BRKq1FRyEj4jpjeAhwQkgQV8c5Mg0aIBzP5ABY0w/C2XTpxhayF8rA2KnoFVApwVkEEZZf8n6BUwwkDLZuWNrjwHqclJHIR4M9MbZCRoEGIOiVAA6LwUO4jizyJYS5qm+8DPQmAckT3S/3bMaoO4E+T91pmm/thY4v5EESIkd5N41pIDVmcrpTLujKJLqqSEPaCpuq3AKqVCygOAE5JSV4YFt27AlLw8AoFKQAEpZYcCfS25Ar0AbWxg0wIVNxokEJ3NEM0qg4xWuXc+Yy5lTutDO+aBB3gqU1vp9Ft4ZiL+MBI2yVf64oeQ4T6dp8/c8nFV/kU/8cKDDFF+X5pLl7bffFv7ftGmT1WP8XuHkSUhIwPXXX4/rr79e8nM3NjYiz9ipA0BhYSGOHDmC2NhYpKSk4Oqrr8Y///yDTZs2wWAwCP7csbGxUCqV2LdvHw4cOIDx48cjIiIC+/btw/Lly7FkyRKroYHbDQ1GhTN2EFB9iLxIWQO8HpbaoCUdTkw/U+THSx0hEqcECicfcjwkCYgzvgQ9KWdlNFE4dXUAUq0fY/CiwhnZE+jV2/EAqe4EiaCpjAY6GKNecxyJJujLUO18JE3GRYWTopwL0R9I1PwLNJwh+R09qXCGZwKdrzMN2loTfxnpt8KsLxWRHM5AJoq0tbYHsq5gaCLJ56VIXyFEqFVZTnDwuTh1db55z7SGv1c6CE5HXQ1LJ+/J5nMAzNLksDqg5h8AQFlwLzz600/49MgRsGYue7VsMFLCw7Gwb09E+mOOTUcYmknkXToISJ3h+esJKZJcaOdhGUDmUu+NJ/govAqzdDYdptr/jSIUUHhhEtzfFc4WY4TaUDlCrS9p7aYsNe0mSu3Bgwcxfvx4YXvFihUAgGuvvRZPPfUUfvrpJwBA//79LX63Y8cOjBs3DiqVCt988w2eeuopaDQaZGZmYvny5cJ5xEBRFJKTkwVXRL9F32JSRBJHk5xV+iaguZgMsrzJue9JsuSmIiBziaSnFuqjMY/MmIZ18uwAVSqCooilQQorRmQPYolRxQPhGQ4Pd7sNB0UDOGc/F6cwI+yFfHPODmxjB5CXc1A4mXWu+BMo3w3EDSaWHzt49Lnn03+4auH0U9rITNcAgCNtwpElVsoJGUfXCbczKRLZzbPXbw2tANLnSX9e89QoDnDY1m3lalSEEWWF1RGlU4JE926hNyqcQRHOWesAINQ4sdBUbLm/9ii0mkZsv1CDeZt+RpOu1bouisKsbv1xbbcgBDMMubaUqVi8gdgcnMLvtOR+mWBRE4yUrhbh4eGglC5M+gdFeHfyOnMp6b+CbVit9c3kGfPwuNDqsykonDoyeepPXi6sAVAbA/rIKVHaNe1G4Rw3blybxcLmOIqNNHDgQOzfv1+SstA0HRi5PJsKifUmOIFYdCK7AdWHieLnTYWT48g1ATJzLDFCfRT/SWZnU6YEhsKpjCJrCKUgJFnUPbvdhnnLiz2FEzQZPPtLPj6e6F6m/ykFGWRpqhz+zKPPvWDhDHH9HPVnyIRLeJbvB/pG2sisfBdxS0wcAyRPsP9jXvnmZSPjHiIUTodtnbWhmFAUedeoK4gnga/boWBBE6H4haSSATtFkUlbRQj0BgP2/bMOB/L/wfe16W2UzRldu+KliROJC+2p10m/qK29dBTOCxuA2uPE4hd/mdM/o8MzEc4oiWeOv6OKIx9rVP4FlG0nXjMx/ck+jgXKfieTszEDJLP2W302zb0zWK3nXXjFoCknuY6ZYEDpH+8lGc/gR9Mc7QeDwYD8/Hz/z73Du9OGZ5G/EcaZ+obTriVadhXenQIgM5ISX5uvD1bHD9r9qLP1U9xuw8ZcnMJaM2tkLgZ6PWpqf56mfDdw5j3iimkOxwHle6xbyvgBhBMKp0ef+5iBQMZCMjBxlco/gQubLZ83H9NGZoIC4IRlgncv9rSFs/k8GTC2tmjx6FuIMl9/xrPl4GF1numfRSicDts6r5jQVvpa3lqlrXGhkBLDu9QqRFjCGCXQfQXQfTk4JhibTp/G1A+fx/aT+1GjMeCwxjTYH5CcjO3LlmHTokXobkwnwyqiyDvICTn7HQZjVH5n3Y95ePnqxa1zNsSPQr5hGAwqFxROVkeWCVVKY0hwC05HZHdxO7H2AqTtle8BSn6R1Opp9dmkGKDjLCD9KjKJ6k/w76OQDh63/so45pdffsGkSZMQFxcHhUIBhmHafFzFz1pe+8GTuWwkI3EM8ZkPNgY+CO9MOiZNNRlgB8fb/71U8EEbwjOBztd65BINDQ1AqPEF746VyNtwHBm80UFtorc5fw6WBFhRRhF3MCfdadxqw8po8tcY9MEm3nzB6JuAljJA3SonVs1h4OI2soY4+y7L4DC8wqmtdsoVyWPPvSrWfWsQP/j3VhANJ7GQmeDi6ITlR3Cp9bCFsz6XTFbEDyPr91qjLgWKviKeIpFdPVsWACjbAVQeIP130ljpzsv3i07K025bt2cJ8yuF0wULJwAEhaOkoQFL1q/HjqIiBFM6NKuyEESx0HBBSIuMxAuXX45FffqANuvjGhoawPVaBASJVNj8BVuWa0fwyyZcWB/scp/K6kmAKgCIHeLZ9cKaaqD2KHlfWAu6GDcMqDpI2nzlXiBpnOndGBQpuYtrG5lRlH8E6bJGs7x+01/4/vvvcc0116BXr15YsGAB3nvvPSxaRFLWbdiwAV27drWabtJZZIXzUkYZZdkJMSriVstxJDCFN+A4k8IZN8Sz13L1ZelL8j8Cmi8AGYtdH8zqG4Fz68lLrfdj0pbPFqHpQPZ/pY2i6S58jll1hWmfphoo3UL+jx/WNhJpUBSZhGH1ZHDKK9KBCOOfCqcFYhQAb7nUsiRtlP0otRBtvXEZXR3pnxmJlRZBnhJY3gJF4eTLKXKtn4FlccXXX+NQaQkACmouCPvU6YhUqfDi5aPw32HDEBJko724OnHoD5gHgxIDL18xCmfNv0BIJ3HXMce8jKwGoD24/l1dBpTtJIHDrCmctILEACheR+ICxA4EtEbvn0B+p0iBuYVTxqesXLkSQ4cOxZ49e1BTU4P33nsPN9xwAyZMmICioiJcdtllyMx0fbmd13u+4uJiJCQkICTEupWppaUFFRUVSE8PoPQV7Yn0a7zv1pA+D6g/AYR38ex1DAFo4RSsDm4MZvl1lEFR3gsWwCgBxo5FTlNNlGBlDHHz8Qa8wqmpJH85Fjj/A3FxCs8gIdlbQ9HEsqiuIL/z1eCg5ih5LsOzXA8cxA/+WT9VOFmDyT3WGYXTWy61fFoUypbCabTe6FvIxISnFQrz51lKFGGkfUsRxCt+uHEy04rrb2QPIDjZ9no3b5I2m6T3EMnnh/9Gt8ZfMT66AW/WDQNLKXHb4MF4YuxYxIe2r8BeFggutS5aOJ19j2mqgPM/guIo0OwkcdfioWhTgCpWA8CD9cLfF2MnHkFUT6KQNp0DLv5u8ljx1jul6RzpK0M7kqB4/kKHqWTZAh+MS8ZnnDhxAitXrgTDMFAoyHtMpyMTrhkZGbj99tvx0ksvYdmyZS6d3+trODMzM/HDDz/Y/P6nn35yS4P2ByiKQlpamn9HqS3fTdYltR6sebvMFEVcKZInAi3ngcKvgNKtEl+CQlrHjqaXZSBZOKWIxMmvoxSRQsHjbVjfQF4y3lxPyCuculoyCKn4k7yEGRXQcbZtZVzp3DpOj8rs4m9A8fcmC6Ar+KGF00Jm+gbi8UAxzqV/CU4CutzkMTd8AcHCaUORZIJJmQHvWDk9kYMTIAPR7veQtcIOcNjWKYo8V1YtnFFkgsdf0l8xSvJxknqNBo/s2I0kpglhtBaPxe1D7uIxeHPqFLvKpiAzXS3JiVz4pQSF9zKJo4GeDziM2N0Gvq71Tlo4K/cJ+UxTO3VzvU8VcnFqXPu9s/DPvT1FjqJIwEIAqP2XZAUATPEOJMLms1m6BTj7jV+t4QdAsgYkjPSf/uASJjQ0FEol6Qujo6OhUqlQWloqfJ+UlITCwkKXz+91hdNRtFidTgeaDuxYRjRNIy4uzn/vgzUAFXuAkp9tD2C1NYC60rvlMqhJXr2mIklPS9M04mKjQMOYY+hSUzhdsIhI0oarDwHnfrAebKV1kmxvoAgjdc9xQN1xshYOIBYOe7PMYekk76GDF6LHnnuOkyYtih8qnBYy05mt33Q2oXxoR5OLpqfgFU5bFk6KMg00Pa1wsnpAZ7yGD13x/P4d50Fe2L0bZU1NKNaT/nRcpzR01h52+DtBZowSqD8NNBaQd3EgQdHG3JEivYRaewHYQ98E1BwBANCJo9xrZ7wllvW0wunk+yy0o9HlljKtXZT4Obb5bPp7Lk4Zn5OdnY0TJ04I2/3798eXX34JvV4PtVqNNWvWuOV96pW3RX19PYqLi1FcTAaeVVVVwrb55+jRo/jmm2+QkpLijWJ5DIPBgFOnTvlvlNqWC2TGTxFKXJtaU7EXOPUGUL7Tw+UoA879aIqWqzLmr9JUSBqF0WAw4FTuGRgylpHZe1sJ3P0R/gVmkMLCGe30TyRpw40FZB2OtRlVXyicFEUCuwCk3XEscXOK7mv/dwkjSETdqJ52D/PYc89qSFkB99zB/VDhtJCZIpTIOqafr4tlCe9Sa89VVgiK4mGFk58gpIN8ujTAYVsv3wOc/4msP7dGbQ6J2Kmp9lwhHaFvBgpXk3I6+b4pqKnBa8b0aed0kYgODsbwjmkk/oCD5QqCzKgQ0pY4DtC74bEQSDAhJJdx0jhTX2aLqr+IUhqaCoMq1b0+VbBwerjP4597Z9zRUyYBXW83udRK7Klg89n0R4VTXUkCGraUOT5WxuPMmTMHGzZsgEZDJmgeffRR7Ny5E9HR0UhISMDu3bvx0EMPuXx+r6zhfO211/DMM88AIOb+e+65B/fcc4/VYzmOw3PPPeeNYnkUtdp/BnVtaDRLh2LNksD70jfkkRlYT0V3qztOZjJZDRCRRSwVFEPW1OnqJJ35U2u0QFg24EZIZ5/gIwsnIEEb5q9nLRenLxROgLhhGtRkjZm6griISegC65Hnnrdu0kG2A9c4Q1gm0GmBKNdqbyDITBUHpEwW9+Pqf4g3Ruwgz1n8HAUNArwXOIgPtBMU5ZnlDwWfk743Y4nDqMh223rDaeLZENEFQGrb76v+Iu7sISm+y8WpqyfvOEUY0PEKp35y/9at0BoH8mf10ZjUOQgKmnY6XZFarTZaxKPJmnBtject9FJSvpvILXYgqTtnoSggdabj41gdycMLAPEjAIpyr0+lveRSy08IO6NwBkUCQQA6X0fejcHS5xi1KjNe4fS0LMTQcAYo/RWI7g2kX+3r0lzy3HfffbjvvvuE7ZkzZ2Lnzp1Yv349GIbBjBkzMH78eJfP7xWFc/LkyQgPDwfHcXjggQewcOFCDBw40OIYiqIQFhaGQYMGYfDgwd4o1qULb1GMsJH/MDSVWBv0zUDzObLeRmp4t0bAZDmiGTLoVJeTz6UevQ0wBSGQZA1ntNvFEQW/NsWfFE5nBj220LeQGXNvBV7i4YNdueNOCxBF08+UTbepOkBmx8M6ea59J08iimSIFW8QnrghQFQPzwe+YFRAdC9xeSPFoK0hz6uhGYAbiqC9KLWAsW8459tItWJyvgLYWVSE9SdPCtvZqT3Rc8BAQBkp/tlURhsVzlpxv/M19SeJK2hEF3EKp7PUHCHjDmUMeZ5YNz2dksaT9YEeUOosECycIt5nQZHi0/G4gz9aOF1NSyTjNUaPHo3Ro0dLci6vKJzDhw/H8OEkAmRTUxPmzp2LPn2shI6W8Tz6FuJSC5C8m9agaLJmreZfkoPOEwqnpoIEYaEYIKKbaX9wIlE2NRUAutn8uVgYfS0ZnHorV55UKGOIQu5ORMeUqSSPpKdfuq0RcnHWtf2OVzjtRfXzJ3LfJO5/3e4wueV6C97C6UwgnUBGU01cDRXhziv1woSMB1OjOJMfztbkndSEdiRRvT0FEwKglrwn3IFXOG1FM/WH1Cgicr4aWBb3bNkibFMAXp86FVSsi0qXcP+1rv3eV7gTeM+gJQoGo7Kt5PM5p+OHG/sAN5cnhHkp8mnmUtKevP2OFYNfKpweCoAm45d4PS3Kk08+6e1Leh2aptG5c2f/DKjQVEisi8GJ9l+0kdlE4Ww4DWCK9OXgrZsRXSzzZamMg3l1uWSXomkamclBoC/+Sq4XSApncDzQ6Rr3zhGRBUDcgFiSNiwonLVtv6MoMrDwtoXTVfj1cpoqmwqnx557gwQBgwDirlZ/igwa4/zDi8RCZud/JG6Y6fOIFc8ZvJWL81KBb+cO5OmwrTvKeewPCicfpMoJa/GnR47g3zLTOrPr+/fHQJGxJixkxveN/pCLVAyOJhLscXErcZdNHAMkT7B+TOJo4h5vdF/367GUOapY37mGt8KmzPhxll8pnLKF099YvXo1PvnkExQUFKCmpqZNoFeKolBXZ8WI4AQeVzifeeYZUBSFRx99FDRNC2s57UFRFB5//HFPF81jUBSFyEg/fYC0NWTm0JZ1kyc8i1gfNVXkI3XOtDpjJKzWgViCEyV3W6QoCuHBDAAqsCLU+hBJ2jA/a6lvIUqO+cQCb6WRMDiUR1HFkQAoWtupUTz23IdnGoNdiUy23hrOQFKrAEBMf79IQG8hM1cGH97IxVlzFABHPDFsRec0qMmaRLBkss5T6OqJguSpdEWCAm/fwmm3rXMssWYBfq5wOtfe6jUaPPr778J2uFKJ5y+/XPTlLGQWFO0b93x3EVylXeiLnE2NYjax5nafqi4n6beUMaQfvQSwKbOIrsR7xJ+ssLLC6Vc8+OCDeOWVV5CamorBgwcjKkpay7PHRxxPPfUUKIrCgw8+CKVSiaeeesrhbwJd4TQYDMjJyUHPnj3B+FuQmoSRQOxgU+RFWzAqsi6qsYC41SaMkK4M6gryoZi2g7PIbKDnQ5IOqAwGA86eOYGMUBa0DyM7ugzHGV2NlOIDOKkrSZTY4ET7a9BaYTAYcOLECffaMKMiA3SD2uhKZcUy6M+5as1xIhenJDKzhlRrfWgVkbfQnnyf/FuQWY8eYHTOuzgKKLzgUlu6hZy/2+22FU5NJVD0FVkj6ymFk9UBp14j/Wb3e8WnpnAGwcJpX+G029bNI4I6VDhriYLqC8VLaG/2LZzP79qF8ibThMajo0cjOVz8s2Mhs6ieZLI1UPo/gESO5Ywurq5M3NqL5NxSBnA64jJuhtt9akMeUPobiXztKYVTU03yairjgBgHEc+9gE2ZhaaSj7/AsWZu7bJLrT+watUqzJw5Ez/88INHvAo8rnCyLGt3u73itylRAOPspBMzlAkjSTAMR9ZQseibAFU8GXS0fnF5aODB8QOoQLRwnnmXKOhZ15NJADE05gElW4iLosi1X5K04a63kXV2nop07C1UjhVOwM+fe4oiSqdBTT72kpR7EYPBQPoEzkDK6EykRx6FFyycTkWpNRtMc5xnFAlNNTk3rfBcP8YrnE6s4bTZ1gUrmNJ2fx4UQRRnzkAmo3wRII7PzWhngiO/uhqvHzggbGdGR+Oeyy5z+ZKCzAJJ0eTh65XvR8SisGPhLPudTGwnTwQSR1le1p0+1RtpUdRlQNkfRFn2A4UT8PP3EI+uwdhXMoGztOYSYPr06R5zYff4tOLcuXOxe/duYXvXrl2oqKjw9GVlrOEo/1VrIrJIpDhX3GfsEZ5Bgq94MvhFK2h+cOHK2hNfIwwCXRhUu5gSRTKCItsqm7pGIO8joOibwHKpBey61HqM+jPErVOKACO8osJ6cADmCnw+QoXIyQkhT62HLJwcZ/IGoZzIw8kZPDe45dueKs5zCktQBFH+3OnzlTFAr4eBbnfaPoaiSVqI7vf4zp0u6wag96MkXZANzNOgAMDLkyYhWOF7V3SfIKzfVLrW/vgJrtapg9SVRNkEgMjurpfPGrxizL//PYGvIq6LRd9CPNaazvq6JAQmhKTp6jgrMCdg2iEzZ87Enj17PHZ+jyucGzZsQHFxsbA9fvx4bN261dOXlbHG2W+BvA+BxiJfl4R0MIzS+ncV+4Dct4CKvdJdjjO+cDzhhuZp3MnFKUSBi5asOG6jbyTralrOB86LRmkMCKFr9HwS8dZU7gXOrScpityFVzi9fQ+OcMWdFgDCMoAuNwFpV0leJABEgeQnRexZOGmFqW/xVC5O3rquivfM+QHi0dL9HiBZ/BpFAYoyRiJ1UJdhaUS59eU6RjrI5gTHjsJC/HDqlLA9tlMnzO3RQ7prl/wCnH7HlKbM31HFAT0fIF4rriBYOJssJ78r95G/kdkkSJ6UeCMPJ/+8i/HM8AXqUqDgC+DCZl+XhMAogajuJJ6AjF/w1ltv4ezZs7jzzjvxzz//oKKiAtXV1W0+ruLxnj41NRWHDx8WtjmOAxUog0wXoWka2dnZ/hVZjTUATQUkh5aY2WttLVC2g3ykQFNlclGzBacjx6nL7B/nJDRNo0NSLGl3gWjhdEfh5K1iIvMvStaGm88D534Aynaa9gXKjLA5jIokp44balrH1AqPPfcGCdOi+JnCKcjMwA/aRCqcilDiyuap/KLma90pOwonYBpwelzhlDiAmwv45TtOQgwsi3t+/VXYpgC8NmWKW2OXNjLT1pGlEg7c9P0GiiLPm6vuz4pQMrnAcaZnRNdI1j8CVuNEuN3OmEvPwmlTZv6YFkXGrwgLC8OIESPw3nvvYciQIUhOTkZCQkKbj6t43DdkwYIFeOWVV/Dtt98iOjoaAPDQQw9h5cqVNn9DURT+/fdfTxfNoyiVNqx3vqLlPIkcqAgDgp0PHgNdHVmfoAgBEse6Pxtd/B1xDeu00PYifg+kRmE6zgAMjf61aN5ZfGThlKQN6xpJep3QDkDSOLIv0HJw8qRf7fAQjzz3eonSogCmCRc/UTgBo8wMyWTAGZzo6+JYwk+OUZTjvk8RDqDCZK2VGk0l+av0vcIJ2GnrjQVA7TEgNA2IHWD7BC1lJD2WIhyIH+qZQtq8dilwcTsQkmLVmvvx4cM4apYG5cYBAzBAZBoUa1jIzF7aqPYIRZP8mnSQyT296i8SjCi0IxCabvVnbvWpwgTbpWXhtCozf1M4GwvJWCC0o2/WcMu04c4778SqVatw2WWXYdiwYYEXpXblypXo0qULduzYgfLyclAUhbCwMMTF+cdL0xOwLIucnBz06dPHf6LU8m474Z3FuTGGphFlU99CXPrEBq0xR1NNXvQUbT80Nz/o1FRKEoCDZVnk5FX6V32IgXFR4TRoTcqKSAuQZG1YyDdnlrfJz2aEpcIjzz3HmepQCgtnwgjiwhTi/uBZCixkFm59wOmQyv2kTSWMlD6YjnnAIEf9kKctnFovuNTq6oGza4nLY9f/2DzMbltvuQhU/0Osw/YUTm0VUL6LDDi9rXBqqkgEUyveNnVqNR4zS4MSoVTiuQk28kaKoI3MzCP1BgKNRWSCILQjifrqCimTTP8btED13+T/hBFWny+3+1TaC0GD+PeZnwRhsykzf1M4q/4C6k4CHaZ7//mXscratWuxdOlSfPbZZx45v8cVToZhcMstt+CWW24BQMz9jz32GBYtWuTpS8uY02hUOCOyxP2Ookn+ppqjQP1p9xTO+pPkb1iGfWuNMoasiWJ1JE+bnyRU9hmuWjh56yYT7LvovLzCqW8iAwxGaTYjHIAKp0FNUkbwg0VPw+rMUhFIsP44zEWlzp8p30WU8ug+0rdzRTgJbAEnAq7FDiJ9pSe8KDiWnF9T5dn+kGJIvln+mq54tAjBZRzUhS9zcdpJifLcrl2oaDYFoXpszBgkuZAGxSHCZJwPc5GKoaUUqPqbuKe6qnCao60mCqEyWPpgQTyKMBKckFF5Lnp0oLzPhABKet+lIjKHH594ajmEjGiCgoJwmRtRuB3h9XBrhYWFbvkAy7iAvpnkYgSAcJEKJ0ASntccJZHkzGcoxVJ3gvyN6mn/OIoms/gtFwFNhfsDLNaA0OYcoJYDYvv5vqMViyqOpDURm7A5KBLIWOzZ9SuO4JVdg5q8YJiEwLVw1p8huRZDUuxafySFX79JK+wHrQl0Wi4Cqgii4Il9PplQ0sd5IhcnH9jCGcIzpL8+D0WTlBGexnxSw6B2zY1bSIvipMJpPhnlLfTWg1SdqarCG2ZpUDrHxODuYcM8UwZ+mUOguNTyka1dSYnCY9ASKzqtIHmhs/9LLLyeeifTCvLu9CQZS0h7ErNUyRfQZs8Xq/V9ijidMTK5r6JUy7RhwYIF2LhxI2699VaPnN/rCmenTsRCVlhYiF9++QVnz54V9k+bNg2ZmR5Kznsp01hIZveCEx0mubZKRBfyQtBUErdYVxRAbS2ZOacokmrFEaoEMghVV7ifSJ1VI7LxT1DnzxCFM9AISXYthQyjAiK7Sl8esSijgBY1aQPBCaYIxYGmcAoWmSrPzZa3xtydVorraWtIICcmVLy3g6fgOFCFnxIXzOy7xAfFUYQCGng2F+elAkWbJoj0za4pnKyTCicTbFquoa0BQkROqLkDP9hVmN6HTVotrtuwATqzXOGvTJoElafSoAgKdwuRt68VAEc4O5Fgj8o/SUyIuMFA6kzj5HKAezCpYgPjHmjGlPvW1wonqyfxHQBZ4fQj5s+fj7vuugszZszADTfcgPT0dKuu7AMHDnTp/D5JKHXvvffijTfeAMtauinRNI177rkHr7zyii+KJRk0TaNPnz7+E8EvKIK4m7m69ocJJq60jYVAw2lA5YLJnbduhmU4p2iEdCAKrgSdIs1pkZiUCIoJDjzrpo+QtA0HRZMAIbwLTccrySdQcnDyKGOI0mfQEjeqVpM3HnnuVbFAxkLpZNV0Fjj3I3H99AOFk6Zp9OnVDdSpHwFQrg0+PJmLU1dP1q4FRdgOcsZj0BD5cnrHXhxi0dYYE6RHeH6igwkxuY7bwG5bF6OYKGN8pHBautSq9XrMWbsWe8+ZUg+Ny8jA7O7SuXq2kRmjJM83HRQgCqfRU8adcvIKfvUhIGWaw5y7kvSp9adJfx3RzW/WWXoSuzJLmUzGQO5YqaWA9zCgFdLEJpCRhNGjRwMAjhw5gi1btrT5ns8yYjBYj9TvCK8rnP/3f/+H1157DVdffTXuvfde9DDmtTp58iRee+01vPbaa0hNTcXy5cu9XTRJ0Wq1CA72kxdIWLr7a7ciugHqi2RmyhWcdaflSRhOPlJgaIHBYIBCGYA5OHk4jgwAaZXDl7RA3SkykxnWyaV1EpK1YfN1nOYEWnok2hjoQ1NN1tJZ8RaQ/Llngt238JvDr6vjrVB+gLa5EsEAsXa54jbMD1g84VLbXEJyoIalAeE32j9WVwcUrSH3IbXCWfqb9wJsKEKIAmhH4QTstHWxCmdziffdSs3c+XQGA65Ztw5bCwqEr2NDQvD+jBmSp3BrI7Ps/0p6fo8irM11Q1nhA2txHHDmXaDbHQ4ngd3uUy9uJZ5Sna+VXuHU1gDVh4lXhhTrWiXCpsziPeQeLhZzd9pAGwe0Yz799FOPnt/rCueqVatwxRVX4Ntvv7XYP2zYMHzzzTdQq9X44IMPAlrhZFkWubm5gRsV1Rpxg0ln5aqFsONsoP4EEClh4mwnYXXNqKqsQmKnVARs13bqNdJJd72VuNg6Q+WfQNM54o7rQpRaydpw0nggeRKZzQx0lHFE4dRWAciw+EpSmdWfBhrOEAVDyheyn+XhZFkWhaf/RXcVB0rhgrs/YHL79IRLLcenRXGi7QpRalvIxJyU7Z1PieIN1z1+HacdhdNuWxercALeDZzDcUJ+VQMThqU//ICNp08LX0eqVPhtyRJkx0sbDTjgxwXOukrbw3ySLjzT4XhCEpkJkWo9EMugpcwYaTnVbxTOgGhnfNR62Z3Wr7j22ms9en6v+xcWFRVhypQpNr+fMmUKioqKvFeg9k5LKcln6a5LHh3knjtqcDyQOEb8DCNrIB93kGLtia9RGAeBYgbVfKfu6yhwTLBp8G3QAHmriCXI3Xr1Bfz6Qk8ma68+BJz9mkSErD0q7bn9TOEEAIZ3hXV18OFJl1reo8MZyysTQtxeAWmVX44lkxyAZ1Oi8ARFGb0SXJzoyLoR6L6cpNRyRNwwoPs9QIrtMYHkUBTQ416wvR7BTVt2Y+3x48JXoUFB+HnRIgzq0MF75QkUpHCpDTJ7F8V5LhqmBQwfndUDCqchwALgqSvI0ih+/aSvCM8kS0USx/i2HH7OO++8g4yMDAQHB2PYsGH466+/7B7/+uuvIzs7GyEhIUhLS8Py5cuhVrv2ri8tLcW///6Lpibp3mVeVzgTExPx77//2vz+33//laPYSknZDuD0uyTnkRRwnOcSm7emeB1w/AVi6XEHfqZeirQSvkJsLk7WYBaJMdojRXIJfRMJHtVU5LxrsD/hSYWT48jzen4j+T+mPxDVW9pr8IMvP1I4adbN4BFRPYEuN3lGaWFFWDgpyjShJmUuTl0dCfRBK7xjEeh4BVECY/q69ntGRSa5nFHSgyKIcuvltfUcx+G/v27DZ2ZjERXD4KcFCzAy3YupgxryyPu5+DvvXdNVMpcSF+BQN+QTFA50nAWkzSWT0N7AkxZOXnFTBMja0JKfgYLPgaZC35YjKIIsFQnv7Nty+DFr167FihUr8OSTT+Kff/5Bv379MGXKFJSXl1s9fs2aNXjooYfw5JNP4uTJk/j444+xdu1aPPLII6Kuu2HDBnTv3h0dO3bEwIEDccAYtbuyshIDBgzADz/84PI9eV3hnDdvHj766CO8+OKLFppzU1MTXnrpJXz00UeYP3++t4slOX7hysDqyWwW4F7+TB5NNZD7BnDmPTLr7gy6BpJIvPaY+OvxEdU0FeJ/aw6rBkVT4ALawilS4dTXE6WFVrg8+ypZG2b1JFBNwWdmuUEDZEa4NaEdiXu5jTV6LsuMNQAXfiIRHAEgaSwJrCS1Ui6s4dS5vh5bYoJgnBByVZkKiiT14koEbkfwLrXOri1VeEDh5Cc3lLF+FfTML95xLsBxHB7atg3v/P23sE9B0/j+mmtweWfPDoDbyowiHkhq64NIv0IRSly63U1fEztI1GSG2+2Mf+97wsIppPjyL4XTpsz41Cis1nuFkXGJV199FTfffDOuv/569OzZE++//z5CQ0PxySefWD1+7969GDlyJBYtWoSMjAxMnjwZCxcudGgVNWfjxo2YO3cu4uPj8eSTT4Iz84yMj49HamoqPvvsM5fvyetvr2effRZjx47FI488gpiYGGRkZCAjIwMxMTF4+OGHMXbsWDzzzDPeLpakMAzjH/7zzefJwDIoXHwOR2soo03h8puKnftN3QkS7KLqgONjW6MyWrrdfBkzsf2QNPQeMP6yYN4VBLdBJxVOYY1ElEtrACVtwxRD1u82FpnywQaKC1JrQlJIOH8r63VclplBS1xoqw+Tuuo4i6x79UQwBcYs4Icv87MaYRgGGX0mg04cLc2kmNSIcakFTANPKb1AhPWbItPFeBCbbd2gAc7/RIIcObuMo2wnUPy919LavL/9S5TkvIdhwSQiLU1RWDN3LmZ06+bR61qVGR9QTVsTeFG7vYAk7yHagy61/MSSH73P7MqMVzgNPlY4a/4lRgi9/cBk7Y2GhgbU19cLH43GepvUarU4dOgQJk405V6maRoTJ07Evn37rP5mxIgROHTokKBgFhQU4Oeff8b06dOdLt8zzzyDMWPGYM+ePbjjjjvafD98+HAcPnzY6fO1xutRPEJDQ7F9+3Zs2LDBIg/n1KlTMX36dMyaNUvyyHDehuM41NfXIyIiwrf30phP/oZ3lmbwStEknUJtDrFWqWJJsuOQZCCyp3UXGT46baQLURuDE8lfNy2cXFA0GtQMIkIiAjdokFgLJx/1Mci19Zscx6GhoUGaNkxRpByGisBXOO3gsszUpUBjAVFq0ucBkR4c+FI0cWejlZaJwH0Ex3FoQAoikru53s5YHVB1kKzhTJograIuJmgQ4FkLpzfWbwLEK+biNjLhlzbb6iE227q+Caj+h1jBUiY7d72aIyRHb9wQj/cLr+7bhw3/7MD4kGrUsUQR+eSKKzCvVy+PXhewITN+QpDVkfbrr/0iqyOTCEywcTLMO7YKSd5DnlxGoPc/l1q7MvMXC2fpb6Sv6HqrKT7FJUDPnpbj4CeffBJPPfVUm+MqKythMBiQlGRpKEpKSsKpU6esnnvRokWorKzEqFGjwHEc9Ho9br31VlEutceOHcOrr75q8/ukpCSbLr3O4FWFs7m5GUuWLMFVV12FxYsX48orr/Tm5b0Gy7IoKCjwvZVTUDglzLcXP4xYTrU1xvQQ1USpVCWYFM7mC8Z9cUCz0RLqSpoA3sKpqSQuvC6+5PymPtxBrMIpBAyKdulyksssKJoELGi+QLb9dWDlDAYtiVKrCLdw43RZZmGdgLQ5JGJnaEcPFLgVrq7Nc4aWi0DdMSC8CxCe4fBwadoZBZT+Sv5NGCltcLCo3qQfclbZi+lP6jMkRboyRHQjkxHeWu/E6slzamfZhM16E1JniKgDZQxROLU17qfvssP7Bw/i3t9+w8wwYlWoZ1V4d/p0XNu/v8euaY5VmdEKkptSV0/u31/7RUMLCWJG0WRSx0tI0j9E9jA+wx7wEND7X9AguzITAij5UOFk9Sa5uTghHqicOHECqampwrZKJV0+1J07d+KFF17Au+++i2HDhiEvLw933303nn32WTz++ONOnSM0NNRukKCCggLExbn+HHlV4QwNDcW2bdswbdo0b1720kTfRCLUAtIOVEI7At3vJm616otkgKm+CISYRfVrzAcq/rT8jSuRUpXRZKDF6sjL2NUXRv1JBKtPA7pOABPj2jl8jSoBiO4FhKQ6PhYAYgeQOvGXF6HgOlZL/vpLuVzh/I9kQqXDVCDexUiLzedJECu+TUf3cbtYJQ0N+PLff9EvORlTu3Rx+3yiqdwPlBiTRVfsAzIXO+57WAOCdGXk2aSjXbNO0gpiUTNoSb8npcIZkux8GiKA5OsMcyI6qxgiu5KPt1A4TotiE1dSZyhjABR6NDXKF//+i9s2bwYARNBE4bxxyBgsHTLEY9d0GmW0UeGs9c6EkyuYR6gNNA+0kCTy8QQZi4mVU4olS97AHyycfA5OOiiwMwe4QEREBCIjHccqiI+PB8MwKCsrs9hfVlaG5GTr76PHH38cS5cuxU033QQA6NOnD5qamnDLLbfg0UcfBU07NtiMHz8en3/+Oe6555423128eBGrVq3CzJkzHZ7HFl5fwzlq1CibPsgyEtJYSNaEhCR5JpiGIpQMJhNGEBc9c4WSD6zCB/JIGOnaNSjaZFlQu+5WS5XvRnT974C6zPHB/kpYGnG3TBjh3PFBkWSQGuon4f3NLa2MMrAVTncj1aorSKTAwtWShaf/+8IF9Hv/fTy0fTumffUVfjljJ7Jz83niFs+n2nAVTRWgrjRtR3Ql63VV8STY19lvTBZtW+jqEFfzA6gzb7tXFobPxemB1CiXGk7k4bSJKymohFycteKv5wTfnziB6zdsELYjaQ3GdcrA0kGjPXI90Xj4/iVBsFxLZ5FpF6hiiVWeCRC5+JPCGRQZeJMXXkKpVGLQoEHYvn27sI9lWWzfvh3Dhw+3+pvm5uY2SiVv4eacXB/+/PPP4/z58xgyZAg++OADUBSFX3/9FY899hj69OkDjuPw5JNPunhXPljD+fbbb2PKlCl47LHHcOutt6JjRz+d0XOT4GAfz9xEZpMw5pwPIlGGd5bOqhrRhXTq7vj5s2ooFIpLbjbNXSRtw3xqlrB0IOuGwA6QYUfhdEpmNYeNwbwiJVlH+Vt+PuauXYsmnU7Y98TOnZjapYv1dU/lu4H6XBKcSBUr7mKsAWjIJWsmGwuIq3yna8h3qjigx31k5rpoDfm+aDXQ+XrTeuzW6OvJs6lwc/ChCCMDdmMuzqrmZkQFB0PhxKyuXZqKieIVnOycl4ZBS1L+sBpJrNbQN5ElBap4703SCAqnhtS3jUjJVtu6Wwqn9BbO7QUFWLR+PViz/mZWZgrGZnTwzESsA6zKTJVAJob9WWnxYS5rt99D+mbyTIIConpIUSS/x6bMwjoByRN9a5HlI9VfYu60YlmxYgWuvfZaDB48GEOHDsXrr7+OpqYmXH/99QCAZcuWITU1FStXrgQAzJo1C6+++ioGDBgguNQ+/vjjmDVrltPu6NnZ2dizZw/uvvtuPP744+A4Di+//DIAYNy4cUJeUFfxusLZr18/6PV6rFy5EitXroRCoWjjx0xRFOrq6rxdNMlgGAbdu3f3bSHoICBCwrWbviL5crdPQbMaxMfHm3LkBSocSwa/TIj99awcB1TsIR16VE/icigSydswP1jnJ0ACeWZTaVQ4tZYKp1My4zgStRkg7rhuphj4OicH1/74I3Ss5Xq7gyUl2FZQgElZVvoAftAoJoiGtg6o+YcEhOEjsFIUaZMcZ6pPfmKo0wKg8AtiTS38EujyH6vPH8M2k2dT5ebgw2jhbGmpxW2//4gvjx5Fcng4/r75ZnSIcEOxKN9N8gB3vJK4qTvC0EyUbYoh6z/dbeeNhSRHY1gakHWje+dyFnOlglUDdFtF12Zb9yOF82BJCWavXQutwSDsu3PQAExPZ0GB8k5OUzNsyixxFPn4M6yZS60XkeQ9pK0Gzn5LvGykVDi1NSSyuCrOatRyX2FXZqGp5ONLzC2cMjaZP38+Kioq8MQTT+DixYvo378/tmzZIgQSKi4utrBoPvbYY6AoCo899hguXLiAhIQEzJo1C88//7yo6/bq1Qvbtm1DTU0N8vLywLIsOnfujISEBLfvyesK51VXXRXwUWgdwbIsqqqqEBMT45TftIwH4TiwBjXULc0IppTe9yGXkhMvEatD97tNgzRr6JuAi9vJYDfKteiLLMuipqZGujYc0gHo/ZhLyq/fwVs4tXXEUmlMmeGUzNQXyUCFDnI7mNebBw7g7i1bbH6/cs8eaRTO5gskKjVrtKAGhQMxA4HYgbaDUjFKIGMR+V1EF5vWOVZTS57NqHD3nk1FGKpbWrDip3X4vIQomCUNDfi/vXvxf1OmuH5eV/NwcgYiX3cjMAo5OL2YEoWiSRvhU2BZqTubbd3VoEEAWQvH6iXpI3IrKzHtq6/QqDW5Di7q0wdvTB4HKu8kacuMd6NjSt6nehMfudRKIjNPpUVRlwPlu8iyFT9SOP2+nckKp9PceeeduPPOO61+t3PnTotthUKBJ5980i2XV3NiYmIwROI17l4f/bmTNDRQ4DgO586dQ3R0tK+L0j7gOJLmIyhKfKRagxrgWNTX1UMV6OtPmBCicOoa7SucfEoURYRNdzhHSN6G+XrLW0UGsGlX+bcLmT0UoUSR0LeQdZDGgBROyYy3bkZ0cdm6yXEcHt+xA8/v3m2xf1m/fjCwLL7KyQEA7Cgqwv7z53FZ62ULYhXOuuNkgB7aAYgfCUR2d65dKUKJVY5W2rT0cbo68mx2dM+98e+yGmz/5xBKGlMAmM61rbDQrfOKzsNJK8yUtUb3FU7eiu6tlCg8yhhj32mw+rXNtp44GogdRCy8zsKEkEk0RaTL/ZU5F+rrMXn1alQ2m9bzTu3SBZ9eeSVohgF6Pkjq1csT3w77B97t1x8n5H3kUivJe0hIi6Kx9MZwFyFCrX95TtmVmUFrzOvL+c7SGT+cvP/sjWFkvI5Go8Hq1avx22+/IT8/X0it06VLF0ydOhWLFi2CUumeR5bXFE61Wo0NGzagsLAQ8fHxmDFjBlJSJAwdL9M+4Tjg5Cukc8++S3ykWmPURI5SBL51jV+n5ig1ipspUTyGoYVYyyjK+QG8v6KMA/TniUIgJgJivVHhjHTNtUvPsrht0yZ81Cr58v0jRuCliRNxqrISa3JywK9YW7lnDzYsWGB5Elqkwpk8CQjLJNFaxa57M59UYPXEIpAwSlC2Kd4918XZbpbj8PyuXXhtVy5CqH6oZS0HxEfLylDW2IikcBcHhWLzcAJERrzCCTfdkDTGoEyeSOlgj67/ce13dJD4iOQUJdngs7qlBZNXr0ax2ZKc4R074rt586A0X8fkT+8CjgPyPiR9Sdc7XIvo7mnihpDlGWImEvwFfqKZY8mSDkqid4+QgzOAAuBpyoG8j8jYoPs9vimDKlZ87AAZj5KTk4Mrr7wSZ8+eBcdxiIqKQnh4OMrLy/HPP/9g3bp1eP755/HTTz+hRw/X3dK9Ym8vLy9H7969sWjRIjzyyCO45ZZb0LVrV2zbts0bl5cJZCjK9AJWu5Bw1jioZv0gyb3b8C82gwOFk7dw+tui/NKt5C/HeS1xuMeIHQSkTAJUNoLhWENbRxQIigEiu4m+ZItOh3nr1rVRNl+eNAn/mzQJFEWhR0IC5pi9EH7KzcWx1omaxVo4KYpEPHY3yMq59UThLF5rshy6oXDWqdWYs3Ytnti5EzVsCEoMkWjmlMiKsVRetrtj5WRFutQCJosHPyB1FY4zudR6W+EMQJq0WsxYswYnKkwRzXslJGDTokUIc3Nm3qNQFHkWDVpT3+1vMCqiJPijMuwIcw8Lg4RutX5q4bSL4F7swyi1Mn5FY2MjrrjiCpSVleH555/HuXPnUFNTY/H3ueeeQ0lJCWbNmmU3T6cjvDLqe/bZZ1FUVITly5dj06ZNeP311xESEoL//MfFWVQr7Nq1C7NmzUKHDh1AURR+/PFHi+85jsMTTzyBlJQUhISEYOLEiTjTKnVAdXU1Fi9ejMjISERHR+PGG29EY6Nrg4YIdwJVyFiiMloJNC6kRlHGgEtfAEPSVGnL5At4hdNLFk7J23BzsbTn8yWxA0i6n2BLV0e7MlNGkSiunRaIdk2rVasx9auv8OOpU8I+hqLw+ezZuG+EZaqch0dZBiB5cc8ey5Px12YdKJwN+Y7bmhgSRhDLZkM+cP4HgGPBxQ4CGz9cnOIO4Hh5OYasWoWfcnMt9t85ZAj+vfVWhAWZFMStBQWul5lXjMVYOPkBqLspb/RNZIBMUYDS/ywCVtt6+R6g9Dfxaawa8siEROVfLpVFZzDg6nXrsP/8eWFfelQUfl2yBLEhZm7NFftIEKvaHJeu4y42+wcPRuoNdNx+D1GUSdESEyjNEX5s4bQpM1+nRWH1JEd77bHAjlTfjvj0009RXFyMzZs346GHHkJqqqWrdWpqKh5++GFs3LgRhYWFbi2L9IrC+dtvv2HZsmV45ZVXMH36dPz3v//F22+/jaKiIuS2GjC4SlNTE/r164d33nnH6vf/+9//8Oabb+L999/HgQMHEBYWhilTpkCtNnVAixcvxvHjx7F161Zs2rQJu3btwi233CK6LAzDICsry+lQxDIO4BVOVyycTDCYmJ5I7z058OvDWYVTAgunR9pwgLnRchyHotpaNGmdezk7JTNFGLEWiqC0oQFjP/sMu86eFfaFKBT4aeFCLOvXNljF4A4dMKmzKS3R18eOoaDGbCAb0gFImw0kjbd9UX0TUPwtkPumWzlwLQjtSJRtigFqjwMXNkEf1RcJ/ZaACXXe9XTd8eMY9tFHOFNtyiMaE8Ti58mZeGtwJMKUSow1C92+raDA6TxkbRAbNAiQzsLJu9MGRXvfBbTqb7LeusJ6zmybbb0uB6jYC+gbxF1PUw3UHAWaxFujWY7DdRs2YEtenrAvPjQUW5cuRWrrJOstJWTCQyeyfBJgt3/gJwf9NRdn1d8kEF2Ld3NZS/YeYjwQOIifUPIzC6ddmQkKp56kPPI2ujri6XThJ+9fW8YqmzdvxuTJkzFu3Di7x02YMAGTJk3Cxo0bXb6WVxTO4uJijGo16z5q1ChwHIeyMmk6sGnTpuG5557DnDlz2nzHcRxef/11PPbYY7jyyivRt29ffPHFFygpKREsoSdPnsSWLVvw0UcfYdiwYRg1ahTeeustfPPNNygpKRFVFpZlcfHiRbCtUhXIuAifw88VCyfaUX0wziqc7ue58ojMOswgs82JfpJw3Q56lsXC779H5htvoOe77+JMVaucmxxLBl91JoujXZm5qPAcLSvDiE8+wVGzfjImOBjbly3D9K62FVdzKyfLcXj5zz9NXyqjgJj+9nPllu0k1jVlrLQBa8I7A+lXAxSFXYd/wKzXb0TI88+jy5tvYvpXX+GeLVvwzl9/YWt+Popqa2Ewk6WeZfHA1q245rvvLHKOZkRHY+eS+ZgWVgCU/wEAmJiZKXx/vr4eua3rz1lSpgGpM8S5E0f3BtKvcj9ypSoWSJ1JLOneRt9E1ltrrcvNZlt3JUot4LKFj+M4LN+yBWtyTBbLcKUSWxYvRrc4K27IvCLsgxycdvsH4f5rvVomp6nNISmCbLQHTyHZeyh5Mul3pAxU46cutXZlZr60yBdWTn5s4m7eZRnJyMnJcahs8kyYMAE5Oa57h3hl2lSj0bRJRMtv6/V6j1+/sLAQFy9exMSJE4V9UVFRGDZsGPbt24cFCxZg3759iI6OxuDBg4VjJk6cCJqmceDAAauKrEajgUZjmjFraCAvM4PBgNLSUsTGxkKhUICmabAsazHLTlEUaJqGwWA5y2RrP03ToCjK6n4AbToXW/sZhgHHcVb3ty6jrf18Gb12T0GxoDgWlLoCYA1gW43d7d5T3RkYNPWoOFeN2NixYBjGP+7JwX6r96SMBxPdG2xIR3Ctzm9xTx3mALpaUMEpoI3nFntP5m04KChImnsKSwfX4wGwCALMzuVvbY/jONz1889Ye/w4AKC4rg5Xr1uHvTfcgGB+1pjVgTr9DmiKBtfjfrB0sIXMlEqlZRkr94OqPwU6cQTYiGyH9/TXhQt4ae9ebGjlAdIxIgK/LF6M3klJdu9pdFoahqWm4sCFCwCAT44cwaOjRiHFzNXKZj3pqsFVHwTHseCSJgLG7yWrp6ge2NSYjYNFOzAiGCjURSOvhkN+TQ1+MbNSAYCKYdAlNhZdYmNR3tSEfWYukwAwqXNnrJ49G3EqGmwNC4rVAgYtxrdKTr2toADZcXHi+71IU1ohimWda3uqZECVTPYDrvcRdBgQPYDst9G/eawvp1Wk7Lomi2eVP16v1wttnWEY4Z5YXTNxlTY+4073EYpIUBwLWlsDjmXBOlF2AHjxzz/x5l8mN1wlw+CHa67BoA4drN+rrh4sx4Kjw4T78lZf3lpmFvfERIDiWEBdZbfP9tX7iWtVr94aR1gbS7l0TxGmvJS08VpuP0+Zi8Fq6sApE9u0JV+OI1q/h1rfEwUKNDiwejU4ynJts8ffucbnD4oIYQxzKYxhW3/vT1RXVyM5OdmpY5OSklBt5lkkFq/56RQVFeGff/4RtuuMUeTOnDljNXzzwIEDJbv2xYsXAUBImMqTlJQkfHfx4kUkJlquI1IoFIiNjRWOac3KlSvx9NNPt9mfm5uLoKAgHD9+HHFxcUhPT8f58+ctKio5ORnJyckoKioSFFUASEtLQ1xcHM6cOWPh7tu5c2dERkbixIkTFo03OzsbSqWyzaxDnz59oNVqLVyWGYZBnz590NDQgAKzdU3BwcHo3r27sECYJyIiAllZWSgvL7eQQWxsrHfvieOQVFmNpMRYaOpLkFtkmmW1e09dM9By+kvU15ShiR2M48fjERkZ6R/35HI9XY2aqiqcMzuP7XqqRnp6uEv31NLSgurqahw/fhxZWVnS3VOzDgUFpv3+2PbePXwY7x86ZHFPR8vKcNumTVhh5qqaWKNGh7hQNFYXI79UA47jUF1djby8PPTs2dPinmJrtiKcrkZsTG+b93Tu3DlsOX0an5w+jb8qK9GazPBwvDt8OFIUpNt2dE8LzBROrcGAxzZvxj29egEcC6X2HLK7dII2pCtyT5vWsjMMgz4ROdCq1ShtjkJtQQOAHEnrqYTjcPX2PAwJ6ozLQwswWFWCPJ31oDgagwHHKypwvKKtd8NDI0fi6rg4lOTno4TjkFRZiaTEeGiaqsFdvIg4lQpVxgnBbQUFWNa9e2D1e0Z80Zd3DG9BPIDKi+dQarbekb+nvLw8oX+gKIrcU0Q4KsrOg2M5lBnywNEhzt8Tp0dKVQWSEpPQUFeOgrOmMtq6p02lpXjcTNmkADw/cCC6GdfvtqmnpCQk6xpQW1uLC2fOwcDUe7We8vPzBZmFhIRY3FOQ7iLiasqhCDUgviv8ru1VXygCY2hElbYQbLDaa+OI+vp6QWbp6en+9zxVa3Ax/7Tf1FNubq7wHjp58iT69evX5p461NYjMTYCtTXlKC4zLdPwSr+nq0dFRQWa6mNQ35DjvXry8fMklSenJ9BoNAgKcm7JiEKhgNbJJUbWoDiXF7c4Dz8L0BqO49rs5/e5MyNAURR++OEHzJ49GwCwd+9ejBw5EiUlJRapWK655hpQFIW1a9fihRdewOeff95mTWliYiKefvpp3HbbbW2u09rCeeHCBfTs2RMFBQWora1Fr169ZAunVPdU+itoRgnEDgarsHSHsnlPlbvAXdwJQ1AsctSj0Kt3n8C2cMJ79WQwGHD8+HH06tVLOgtnALS9n06fxlXffgtbneKqmTNxff/+5NyFX4JuLgLXcTbYqD4WMrOwcOoaQOW+BoAD3fM+sEy4Rdk5AJvOnMHK3bvxlw33/WlduuCzK65AXGio0/fEchwGfvghjhmVtXClEoV33YWYYBWo48+SvrfH/WBps6AqjQVgzn4FjqLAZt1mER1VinqqamnBkI8+EtJWJDKNGJSYgYiYROTX1OB0VRUaHLzQIpRKfDZ7NuZ07245c3/qVVCGRiDrFrDByVj2449Yc+wYACBSpULl/fe3WUNi9570WnCN+SRgUFgmKJp2ru2xOqCxABSrBh030PU+ou4EEBQBOjQVoBXefZ4a80GfXQNWlQiuy3/aHK/VaoW2Llg4DWqwx1cCALiejwE0I6qPoHJfA61vBJd1E9hgy5Rprcu+JicH127YYPGcvjd9Om4eOND2PRnUoE+9TCycPR8R1uV6qy/X6XQWMrO4J10jqKIvAVUs6IyFYDnOr95P3LEXAFYDruudgCrOqxZOXmZuWTjVZSTisyoBdGiS1XsNhPeTsxZO8/dQm3uq+BMMTYGN6geuVcAjj9/ThU1gq/4GlzBGiCHQHscRrct+/vx5ZGRk4Ny5c+jYOi+2j6FpGs899xymTnUcWPPnn3/Gk08+6bJ+5hUL56effuqNy9iENxeXlZVZKJxlZWXobxw8Jicno7xV+gC9Xm/X3KxSqaBSmfLM1deTGVOGYRAXFyd0kICpAbbG1mJ4T+6nKMrqfltlFLvfI2XvON2038qxbe5J30zcGCkKdMrliGuIdKo+/L6eOBY02wIwoW3WQNA0TdYVNpwGQlJIcmW4dk8URQltmJ8Uau9t72BJCRavX28xiL1l4EB8fPgwDMaX0l1btmBIair6JScDIQlAcxEobXUbmVmUsfYMqavQNCAoUlB6dAYDvjl2DC/++adFKgdzZnfvjodHjcLQVpHjnLknBsBDo0ZhyQ8/AAAatVq8d+gQHh87FlCEkDWarAZMkHENEscC5SRVFRU3FExo28ix7tSTnmWxcP16yxyJXQbh9eHDkZ6WBpom7sxlTU04U1WF01VVOFNdjdPG/4tqa9E7MRGfzZ6N7vHxbWWgDAdamgC2BQzDYHJWlqBw1ms0OFhSguFpac6XndOQFC4UDfR5wuHxQlk4DXDuW/J/TD/X2iRrAC6QSL7osQJgIr37PAWRgSjNaQAr51coFG3ecWDVoCmaRCIOauuqZw2L/ao4QN8ISlcLJqztoIxvH8/u2oUnd+60+O658eNx65Ah9u9JR9bc0UFhQFDbNaae7sutyUwoIxMFdL/TVHYb69t88n7iWFCcjjwHyjChPXijLzfvU/njXLqnmkNA9SEgeQIQlmzzeKfvSVsDVBwGrYol6+FF3JOn97d+D7W5p+QxpIxWz+Dhd66unvQRwbFt+pX2Mo6wtt/W9/7C448/jscff9zhcdaMhGLwisJ57bXXeuMyNsnMzERycjK2b98uKJj19fU4cOCAYLkcPnw4amtrcejQIQwaNAgA8Pvvv4NlWQwbNkzU9Wia9rtZjEuOyr1kQB2SDDq6F9Jj2sECdY4Fjj1H/va833o49qazJJpgVA9B4XQFmqaRnp7uRmEDi+K6Osz6+mu0mK0pv3vYMLw+dSqyYmPxoDFnsFqvx9Xr1uHgzTcjirf+GQNp2JRZ3UnyN4rkx9To9fj48GH8788/cdZM+eJhKAqL+vTBgyNHoleiuHQhrZnfuzce37EDhbW1AIA3DhzAiuHDEcYEk+fDPE0AqwdCO5EInolj3bquNR7cuhW/m+XE7B4fjy/mzEGk2aQdRVFIDg9Hcng4RnfqJO4CQlCtZgDARDP3Z4CkR7GmcNpEyMEp8jXJhJBIvJyBRKp1JXehrpY853QQ0MqjwyswRqu3ocXq11bbuqsBg3iUMSR1ko2gaGq9Hjf99BO+auV6d/ewYXhktBOByAxqQBHqk4BBQAD3qebBZWiV7eM8gGQyYyROi6KuIDmFQ1KsKpy+xK/bmZ4YZVzJuyzjGbxpEPRyrHXP0djYiDyzgBOFhYU4cuSI4Kt9zz334LnnnkPXrl2RmZmJxx9/HB06dBDcbnv06IGpU6fi5ptvxvvvvw+dToc777wTCxYsQIcOHUSVhWVZFBcXo2PHjjZnUGREwnFk8KatBcIcDBr1TUDlAfJ/0niwHIfzRleGgK4PigaYYDKg1jdZVzglSIkCkDZ8/vz5wJeZE9RrNJixZg0umuXcndWtG/5v8mQAwH0jRuDPc+eEnI951dW44aef8N3U/qAA4qoFGzLTNwNNReT/yB44U1WFq9ets4g6y6NiGNw4YADuGzECmTHSRFNU0DQeGDkSt23eDIC4tK765x/cExcMoM5yAMYogdTpQPLlpgGaRKzJycGr+/cL25EqFX6cPx/hQUHS9ZWKUPLXqLCkRkaiR3w8ThrXw24rKMATY0Uo0rzCSYlM50NR5NnU1buucPIpUVRxvonmyISQNsCEEsWXsqwbq21dlQh0X26Sm1g6TAVSZwF0W2tARVMT5qxdiz/N1mcBJBrzcxMmODfrHpYO9HyA3I8PcKpP5TjjRIMfWUSEiQSF+MkXN5HsPSTk4ZQoLYreP1OiAE7ITFdP+sigSO/nEO04l4xRQsSNqWU8hzcNgu1mJHnw4EEMGDAAAwYMAACsWLECAwYMwBNPEFeoBx54AHfddRduueUWDBkyBI2NjdiyZYtF9NyvvvoK3bt3x+WXX47p06dj1KhR+PDDD0WXhV+07YXlsZcOujrg5P8BBZ85HjCU7yGDntBUIKJb+6oPR7k4+bDjfF43F2lXMrODzmDAvHXrcMzMnX5gSgrWXHUVGN7tjaLw2ZVXIsMsuNn6kyfxfo7RWqepAoxrrtrIrOE0aa8hyfixsByDV61qo2xGKJV4cORIFN1zD96ZMUMyZZPnuv79kRxuGhi9sncvdDAqUdZm/CVWNg+XluKmnyzzrq2eMwfZ8fHStrPEsUCXmy3SkZhbOfedP48GjYgBJ2e0druSPzbIzVycxkkMSVPSiEERAvR6GOh+dxtlE7DRP9AMUa6DXSwzE2xV0TpRUYFhH31koWwG0TQ+u/JKvHD55TbdT21i5X68gcO2Xr4bOPEiUL7DuwVzhCICyL4LyLrR65eWrH+QOg+nkBLFywqbEziU2YWfgTMfmDxvvElIEhCZbZoclLmkaDcWznHjxtntlCiKwjPPPINnnnnG5jGxsbFYs2aNJ4on4y5BUcQCY9CSJOH2BjXRvQFNORA/vP3lelKEAaiwrXDyedzctHBeCnAch7t++QW/5ecL+zpGRmLjwoUIV1quQYsJCcF38+ZhxCefQGtcMH/37/sxak5v9EnrBdgKMxQUCUN4F3x4qgK3/73W4qu4kBAsv+wy3DF0KKKDXXRDdIJghQIrLrsMDxjdgi80NGDPBTXGxwJg1WTG+8JmIGkCGRBISGVzM+asXWvhqvzU2LGYlZ0t6XUAAMEJbXZN7NwZbxkjmepZFrvOnsWMbt2cO59g4XThNcm7wbqtcFqP3tuuab4AgAJCO2Brfj6uXrcO9WYTBbEhIfhh/nyMEety7e9QDLHA+VsuTpoJ/HboKQtnkP9ZOB3CGN9tvsjDKXNJ024snDLtHIoyzfZryu0fG5oKZC51aw2j3+IlC+elwKv79uEDs/Qn4UolNi1ciA4R1td4DerQAW+aRXLTscC034pRoUi3aTW5iERcvkuP2/+ut9g/tlMnHLv9djw6ZoxHlU2eWwcPtrjOd7kFJNehQQ1c/B2ozwVKfpb0mnqWxYLvvrNYp3pFdjYJWuQlxmVkgDGbdNpmFkrfIe5YOHlXO52LCqdxXTCUATTQb8gHSn8D6k65fg5NFVD0FVD4Gb7a+yOmffWVhbLZLS4OB266yTVl88LPQMEXQGOh42N9gdLo2eBvCmd7gDH2fVJbOBn/s3A6hPaRwqmuBCr+BOrPOD5Wpl0iK5wegKIoJCcnuxXNScYKKmMAFbX1iJ62aFf1wdhROFm9aYDrpoWzXcnMCutPnsT9W7cK2zRF4durrybRZ+1wy6BBWNK3r7B9oaEBS374AQaWbSOz3WfPYsAHH+CPs2ctzvHAiBHYtmyZhZurp4lQqXDX0KHC9ubKcGzT9SWWuJojZGfKZEmv+dC2bdhuFiQoOy4OX86ZY+ECKWk709YCFXuBqr+FXZEqFYaZBXDbKkbhdDVoEGDmUttg/zhbmK/h9BUlW4C8VUBjW5lZrbfmYiJ/K8c7jSIcrCoBW3JP4NShN5EdZHI/n5CZif033ogusbGunbv5HCmbjyw7Dts6P0morfFamZyi+QIJRFd73OuXlqx/kDpokB9bOB3KzFcKZ/M5oHQrUHXAu9eV8RtkhdMD0DSN5OTkdh9sxevwLnO2LJwlv5BBUitlrF3Vhz0LJ2/dpINMUSZdpF3JrBV/XbiAJa3Sn7w9bRqmde3q8LcUReH9GTPQM4G0xXBKg7PF+/H272sFmVEUhVf37cMta/4PLc2myZFIlQrrr7kGL02aBIUP5PrfYcMQakzwfFYfgwcPVYCrNlp4Y/oSzwCJ+DonB/+3b5+wHaFU4scFCywi0gIStzNtLbGwVe632D3JbB3n8YoKlDY4qQQGJwEdpgNx4qKUAwAiewDpVwGxQxwf2xqOAzrOIddWtXUT9hqaKqJsaNtGUrZab/xgnnHdYt+gB2YfUOCTQg0YsLg6/AQGqy7gpgEDsGXxYsSEuNGv8cq/jyJkOmzrQdHkr76JLB3xF5ovkPWldd5XOCXrH1QJQMdZQPJEaQomrOH0P4XTocx8pXDq5Ai1lzo+GU3W19fjxRdfxJQpUzBgwAD8ZVxjU11djVdffdUi2mwgYjAYkJ+f73JyVBkb2LNwaqqJZaNyv2n9k5F2VR8hyWSNqjXlICga6HY7kLHY7bWr7UpmZpytrcUVrdKfLL/sMtw2xHnFIEypxHfz5iEsKAjdlFVYEHEMfx/9Dr+cPo0jJ0/i6m+/xYO//YI5YcdxT/R+JDKN6JOYiIM334w5PXp44racIj40FLcMHChst1QdQ965f4gFL+lyya5z5OJF3Ng6SNDcuULuTHMkbWf8ZIyh2WJ36/QoTrvVqmKB+KHkeRNLSDIQ3ce1NbEUBURkkWszSsfHewqF7dQoVuvNTYXzbG0tRn36KTaeKcB3jT1xUNMBNDi80ceAD4dGIMgdpYM1mCkJvkmL4rCtK0JMsuOjjfsDrPsTCa4iWf8QFAHEDgIinVy/7YiMxSSIUkiK42O9jEOZ+UzhNE5cyfElLlm8HjTo/PnzGDt2LM6dO4euXbvi1KlTaDSmI4iNjcUHH3yAs2fP4o033vB20SSlwdlZdBnnESycVWQAYR7RsPwPEg00ogsJf9+KdlMfkdnkYw2aAYLdy9toTruRmREDy2LO2rUoazJZh6/MzsbLkyaJPlePhAR8dMUVeGjDRwCAWLoZyzZsQBhF4WxTE7KDqqGkDKhjgzG11wi8N3OmYF30JfeOGIF3/v4bwWjGgohj2FMcha7dZ7uWusMKVVaCBD05diyusBMkSLJ2JqRFabFI5TEsNRXhSiUatWSAta2wEEv79bN1FhkeIRdns9Wv29SbGwrnz2fOYOkPP6C6hSi3HGjs0PbCQ2OnYoSqgPTvQRFA3GDR5wZAXCA5jgTm8WFkUYdtXRkNtFwk1noJ+3K3ENKieDcHJ49fvoeU0X4dJ8GuzHiF09tWdNnC6Xfs2rXLpd+NGTPGpd95XeG8//770dDQgCNHjiAxMRGJrRKbz549G5s2bfJ2sWQCgaAoIGGkMXiQmUOkuhKoPUr+Txrvk6LJ+D+f//svDl+8KGwPSknBV3PnCulPxLKgd2/8fXYUUHAE0Ywa1XWNqDQ6jfRQVoKhaUzqNwPTRs/2m7WwHSMjsaxfP+w+Rtavnq2rw3cVsbja/tJVp2jW6TD3229RVFsr7JvVrZu43JfuwIQQ6yDHkfynxvVVQQyDcRkZ2HT6NABga34+OI5zXCfaGuJOqowyBXQRg64BqD5I1l3HD3V8PE9jIfltWJpr15UKxkyBdwYXFE49y+KJHTuwcs8ei/0dIiKwceFCDExJIZ4rtccs0t2IRnCnDffvyOVhmWRALnFqIrfgI7v6wMIpGRwHNBWSNhrRzev5RP2KkA5A4mjvT2gIFk5Z4fQXxo0bZ/EedOq9CLjsceD1p+63337D8uXL0bNnT1RVVbX5vnPnzjjXKrmzjAwAMlBIsWKNKt9JXiiR2ZKuQ/NbOJa4ubWeqa85Qgaqkdn+MzvuJ7TodHhihym/XbhSiZ8WLkSY0j2XxRcmX4mPv/oK5Q01iKFbUMWGgQaLyyIbsbTXAHTof4XfDXAfGDkSnx4+hN+bM5Gni0Xlj5ugUoa6laqkRafDld98g11mAZK6WQkS5FEomiid+mZilTML6DExM1NQOEsbG3GyslJYh2uTmn+Bsp3EqpY6U3x5GguBsj9M7nxWckxapfoQUbBSJgMJI8RfVyoY2y61VhHpelna0ICF33/fJqjW0NRUrL/mGqRGGgemcUOI/PhI0BxHIgiLiR4cKNaVDlN8XYK2+NClVlIKV5N3Z48VAO1GO9DWkmdUGQvEDpCseF4jtAP5eBvhGZRdav2FHWZjIgDQaDR44IEH0NzcjFtuuQXZxjHBqVOnsGrVKoSFheF///ufy9fzusLZ0tKCBDsver90nxAJRVFIS0vzG6tGu0ZdbgpmYMO62a7qw6AGTrxEBl29H7UcdNUcARqLiKuPmwpnu5IZgDcPHMAFs77lgREjbKY/EYMqKAjXDByDj//cjDiGKJzLsiJxV49+CA2JBkLT3L6G1HSLi8M9l43Aq/t5yy6Lq9etw4YFCzC1i/hUQmq9HnO//dZibWRMcDB+nD8fUQ5SvkjezphQonC2Cqo1KSvLYntrfr5jhVPIw+miK3RUT6D0VzIJ1JBLtp3BHyLUAnbXcFqtN8H10rFisqOwEAu//97CvR0A/jt0KF6ePBlKppVybp52qGwH0HCGrKNzNkooqycu1z5avwkEcJ8qWDi9b3WVTGYURcqvbyH3487qBnUFCaIUkuKXCqdftjODxtSO/H3S5xJibCvvoxUrVkCpVGL//v0INnt3z5o1C3fccQfGjh2LLVu2YJILy5AAHwQN6tmzp12/4R9//BEDBvjfQywGmqYRFxfXLiN8+hyDFmg6S3K+AUD5LqJ8RfUkgTqs0K7qg1ZBeGz1rdZW8fnbJJhBbE8yq2putnDZSw4Px4rhwyU7f3x0Gm4YMAA39+qED2fOxMfjupL1mlE9bObn9DUvT56M6/r3F7a1BgNmf/MNtotJG2L83dXffostZoHeolQqbF26FD0cKXTwQDsTojhbPhs94uMtJhi2FTqRi9GdPJwAcduLHUT+r/rLud9wnCnoma9zcDKhxKpFtZ2XtlpvnW8Aut5mWmtvBZbj8NyuXZj45ZcWymaEUol18+bhjWnT2iqb5uibiXWppRQo+MT5FCIxfYGeDwDp85w73gM43dY5zjQ49wdETCRIjaT9A78G1d1cnHxKFB+uBbaHQ5mxBhJkUe0gn7mkhQoCut5KJol8GQhNxi5fffUVli5daqFs8oSGhmLp0qVYvXq1y+f3+mjonnvuwTfffIOXXnoJdcaE4CzLIi8vD0uXLsW+ffuwfPlybxdLUgwGA06dOtXuInz6BU1ngfxPgdItZDtlChA/DEgaZ/Mn7ao+KMp6ahSOldRlpT3J7IXdu1Fnljz+qbFj3XaltUAVh4TQMCwf0AWjQ0OBeqPyFem7iLSOoCkKH82ahcV9+gj7NAYDZn39Nf4oKnLqHDqDAfO/+w6bz5gSeUcolfh1yRIM6uCcy5bk7azDVKDLLSTKqxkURVlEq91ZVASdo2u6k4eTJ24wmXRoLAJayhweDn0DuS5F+3b9JkBk2OshIHNxm6+s1psyikTltaGgVzY3Y/pXX+HxHTvAcqY1+P2SknDolltwdU8nLMCKUCDrBuLFoakGct8Gzm8ggXacwYdWH6fauqYaOPEicOo17xXMEelXA11uBsI6ef3SkvYPQi5OdxVO/02JAjghM201kPsmUPCp9wpF0cQgEOk49ZiM72hqakJpaanN70tLS9HcbD2InDN4XeFcsmQJnnnmGTz22GPo1o2EqJ46dSqys7PxzTff4IUXXsDs2bO9XSzJUaslSjAsYwnvKspHqg2KADpMc+hC2q7qw5rCqWswReYMksZtrD3IrKi2Fm///bewnR0XhxvNUoNIQmRPYjmJHwG1RgOuy21kOyxD2utIDEPT+Gz2bMwzG+i36PWYsWYN9jpYR69nWSxavx4/njol7AtXKrFlyRIM69hRVDkkbWchKWR9kpX1ZhMzM4X/G7VaHLhwwf65eAunqy61AHEfi+xO/q/+2/6xgMmdVhnj/JpPHyGm3vaeO4cBH3yAX/PzLfbfNGAA9t14I7rGibDmquJISoqwTgBnAKoPA2feBwo+t5oz1J9wKDNFuNH9UO18sCZPo4wmsRF4F2svI1n/wFto27mFE3AgM19FqZXxeyZOnIg33ngD69evb/Pd999/jzfeeAMTJ7qey9YnoboeffRRLF26FN9//z3y8vLAsiyysrIwd+5cdG6VM01GxgI+gp9BA2irLs3gOEK+QXOF0ywCnJ+6cfqCx3fsgNZspnfl5ZdDIbWbcEgS+RgMAEqIy1B0L2mv4SEUNI2v5s6FjmUF5bFJp8O0r77C1qVLMTS1bRAuA8ti2Q8/4LsTJ4R9oUFB2LxoEUak+d+aVZ7LreTjHJXeNoWSgBQWTgCIGwrUnSBBiJIn2g++wrvT+nr9plj0TUDFn0RhMgt0xHEcXtu/Hw9u2wY9ywr7Q4OC8N6MGVjmanqaoAgg63qg+TzJvVx3AtBUWCoBHGeyaBatIes4O0wHgtvmg/UbGCW5B30TycXpIyWvXXKJWDgdwiucnKFtejlPUX8G0JSTSdhLIbBjgPLOO+9gwoQJmDdvHlJSUtDFGNMhPz8fJSUlyMrKwltvveXy+X0WGzo9PT3gXWdlfABFAaoEMtA4/S6QfVfgDc7cxaqF06hw+nFuMG9z5OJFfHX0qLA9vGNHzO7e3Ycl8k+CGAZrr74ac9euFdxj6zUaTFm9GtuXLSOpKYwYWBbXb9iAr48dE/YFKxTYuHAhxnTyvstdG9QVJKBMUAQQ3cfiqw4REeiVkIDjFRUAgK0FBXhq3Djb5xIUTjfzp4Z1IoOs4GSi9Ngb3/lg/SbLcfjh5EmszslBh/BwPDpmjGm9a+FXZGKr0yL7AXp09UDFXjLhZVQ4z9XV4ZZNmyzW9wJA9/h4fDdvHnolSjBZGNqRuHxq68gEJD85wLFA/kckzUjcUKCpiFh0/CmQii2UMaRv19YQi70vYQ0kCjwTDMRd5vdWd7vwazgNblpMA8DCaRfabDkJqwVoL0xq1J8gngjJE2SF049JTU3Fv//+iw8++AC//PILzhojiPfq1Qv3338/br75ZoSEuN5evK5wDh06FAsXLsS8efPQUaTrVaBA0zQ6d+7cLgKu+CXmFgcnrHntrj6sKZwSBgwC2ofMHtq2zTxbK/43aZLnIvc1FoCuOoTu7HHQ1c2+TWfhAkqGwXfXXIMrv/kGvxndHmvVakz68kvsuPZa9E1KAstxuHnjRnxppsSrGAY/LViACWbuqmKQvJ21lAKlvwHhmW0UTgCY2LmzoHAeOH8e9RoNIlU2om/GDiQz8iGm9ajNOh2CFQpxqV4oCsi6yTllJ2EEEJ7llUiOBpbF2uPH8fzu3ThhlAkAfHn0KJ6fMAG3DxkCpuWC1TQzberNLAcnx3H4+PBhrPj1VzRoLd32FvXpgw9mzkS4lGuoAWOuVLO+ryEfaC4hn8p9RAEFfBql1um2rowmE6p8n+5LWA2JyAoA8dIFWnMWSfuHmP5AeIb7kcP5966zEZK9jEOZ0QwZQ7F6onDCCwpnoKQlkkFwcDDuvvtu3H333ZKf2+ujSYZhcO+99yIjIwOjRo3C22+/jYsXnVzwHyBQFIXIyEj/CkvdnogxrsGLH+ZUYI12Vx8hqUB0b2Ix4YkbBnS7HUgcI8klAl1m2wsKLNaLXZGdbd990l3KdoKqO45gBUC1OFgb6KcEKxT4cf58C+WxuqUFE7/4AsfLy3Hbpk349MgR4Tslw+CH+fPbpBwRg+TtTHA3tx7YYJKZW62B47DTXoCkqJ5A4iggOBEcx+GpnTsR9eKLyHrzTZyxkkPaLs7eX1AkCawRkiTu/CLQGQz49PBh9HjnHSxev95C2QSABq0W/92yBcM++ghnG41W3lZRf9vUm1HhrNKymLJ6NW7euNFC2VQxDN6fMQOr58yRXtm0RkQXIGMRmXgQlM0Qn0bIdLqt8+80f1A4hYkEpU+WakjaP0RkkajR7i7DyVhE1hCHOBcYzds4JTPeysl6aR0nv7ZazsF5SeP1HmTfvn0oKirCypUrodFo8N///hdpaWmYMGECPvzwQ1RWVnq7SJJjMBiQk5PTLiJ8+iXRfYBudwApU506vN3VR3Qv4kYW09e0j1GSF6lE7sWBLDOW4/DAtm3CNk1RWHn55Z69qCoOLMeirLwMhrBunr2WBwkJCsJPCxZgtJlyXtHcjEEffogP//lH2KegaXw3bx6mdXUv6qDk7UwRSv62ysPJM6ZTJ4s1vNucSAPDcRwe2rYNT//xB/Qsi6LaWixav95iTaLTNF8gaw59gEavxwcHD6Lb22/jhp9+wpnqarvHHyotxbN//oWfz5xBQ3OtxXet643Tt+BgSQnu374bW1vJdFhqKg7/5z/4DlCkSQAAtqFJREFUz+DB3pvAoiggshvQ+VqSjiF+mNPvC0/hdFvnl0U4m/LFk/gwJQrgp+8hZTQQlmZ/LbYPcUpm3lQ4OQ7QyxbOQOHXX3/FNddcg8GDByMrKwudO3e2+GS5McHsE3+59PR03H///fj777+Rl5eHZ555BjU1Nbj11lvRoUMHTJ3q2xeDFPhVB9neoCiS603E4EWuD/EEqszWHjuGf8xCe9/Qvz96OpET0j1IW+RYDogI7NDvYUolNi9ahOFmSx40Zm2BoSisvfpqzMrOluR6krYz8zycHNfm6wiVyuK+7CqczSXgmkvw2LZf8b+9ey2+OlhSgpW7d4srm7YGyFsFlP5q3XqlrQPKdgJ1p9p+5wbNOh3ePHAAWW++iVs3b0ZRreW1w4KCcN/w4Ti/fDnemjYNEWYWyGZOgb9KLuDqrz/Ht8ePgzOTKV9vhTU1+O/mH7HpzGnU6kznDVYo8PKkSfjzhhucysnqMUKSSSTzGBcDFEmIU21dlUiU5TAPemQ4Cx/RlbHhdu4FJOsfdI1AYwFxV27nOJRZ7EDiveHsOlRdI0lHV77Har9qF1ZjiogrK5x+zcsvv4zp06djz5496NixI8aMGYOxY8dafMaMcd2LzmdBg3g6d+6Mhx9+GA899BA++ugj3Hfffdi6dauviyUj499wLGBoIS8MjgNKfiGBUuKGXdKJlTV6PR79/XdhO0ShwNPjx3v+wrEDgaqDaAnu4tPBmVREqFT4ZfFiTPzySxwsKRH20xSFNVddhbk9/DTHKGO0cHIsGehYS4/SuTN2FxcDAE5WVuJ8fT06RrYdCHFFa7DjTA4+yUkA0Hbt3zO7dmFGt24WQZXsoowhbn0N+UD1QRKx1pyWEqJwhnYAotwPblWv0eCDgwfxyr59KG9qa/GNVKlw19ChuOeyyxAfSuR259ChmNujB1b8+ivWHj+OFpYETGpU12P+d9/h0y5d8Pa0aciIigLLcXjv4EE8tH07hiiKMTYEUHNkSDEiLQ2fXHEFsuP9OCKsvxKWBoQt8nUpCGZrcwOexnzg3A/E3TpziWvn0NYC1YcAZSwQO0DS4nkVsUtvGvNIDnRORxRVMfDutIpQ9wOwyXiUN954AxMmTMDPP/+MoCDp68rnEUH279+PFStWID09HbfeeisAYNEiP+lsZWT8EV09cOxZ4OSrRNk0tABVfwEXt1/yKVHeP3gQhWYWnOWXXWaKuOlJQjuC63Y36iLGef5aXiIqOBi/LVkiKFQMReGL2bNxTS8/TvlCK0wTLjbcaie2So+y3YaVc1v+aewqPgu92WtyUR9TICK9MT2MRq93vnxxQ8nf6n9I0A5zJIpQe7a2Fvf++is6vvoqHti2rY2yGRsSgmfGjcPZe+7BcxMmCMomT4eICHxz9dXYsngxosPIesJQmpgut+Tlofd77+GpP/7Af/buxV1btqBJp0MwRe6Fo1V4bcoU7LruOlnZbA/42KVWUqSIUqupJEGUqg5IU6ZAof40+euK944cMChgqKmpwdVXX+0RZRPwkYXz0KFDWLt2Lb799lucO3cOISEhmDlzJubPn4/p06dDZStqYIBA0zSys7MDOsJne6Ld1QcTanRrMZCXpxChNtz9nIFGAlFmdWo1nt21S9iOCwnBAyNHeu36dHAssruHBpTMHBETEoK9N9yAX/LykB0XJ7lrpEfaGRNGXLj0zVbXNA9NTUWEUikEtdlaUIBr+/e3OObpnTuhK8yDggJ0HCnbBzNn4uaBA6E1GIQcpMcrKvDEjh14adIk58oW0ZWsAdPWAnXHSORMHi2fg9M1Re3A+fN4df9+fH/iBAxW3N4Sw8Jw7/DhuG3wYEQ48Y6d0qULxoXOwx8Hv8b+0yZfWbVej+dauRPvaukEZfxgvD19NrKSMlwqf3tHdFvXNxEXUCvRlr2Gj11qJe0f+Htg3cjDGQApUZySmUFNJqpplWnduy1YA7EO85zfACSNd16BDOtE1lFzgblE51Ji6NChyM3N9dj5va5wZmVloaioCEqlEtOmTcNLL72EWbNmITTUQaMPMJTeiMQn4zTtqj5oBXl5GjTGBOF8BLhoSS8TaDJ7ee9eVLW0CNuPjRmDqGDvzswHmsycQaVQeDR/qeQyS5sLUIxNxU1B0xifmYmfjC/WbQUF4DhOCGjz7B9/4Kk/duLJWBIUSMcxeG/GDNwyaBAA4N3p07Hr7FnBcvjy3r24IjsbI52JgkzRQOxg4OI24pUQ3c+0Fp23cIoI/KVnWfx46hRe278fe8+ds3pMh4gIPDBiBG4eNAihImeuVSnjMfmKCUivrMRtmzdbjeobGhSEFy+fijuGDhWXLuYSxOm2btAAp14jVvCQDr7LNR3dl6QRoX1nBJCsfxAsnO4onEZvAYV/pkThcSizkp+BmqNAymTHKbyazxGZKcKM6YbOkzYZN8S5wjBKso5axu959913MW3aNAwePNgjnqZen4rv2bMnPv/8c5SXl2P9+vWYP39+u1M2WZZFTk4OWFeiGMpITrusDyH9Q5PJwqmULuR4oMmspKEBr+7bJ2xnREfjtsGDvVqGQJOZP+ARmYWlkXWQdtYym6dHKWtqwrHycgDAC7t344mdO6GAqTyvTJmGW83aUkJYGFbNmiVscwCu/fFHNGqdjPgYO5BMGjWXAOYpdDTGCO1OKBf1Gg1e378fXd96C/PWrbOqbPZPTsYXs2ej8O67cfdll4lWNgEIynD3+Hj8vmwZvpg9Gwlm7+uxnTrh6K234q5hw2Rl0wGi2jqjAsKM6Ylq/vVsweyhCAVCU4Fg37hHS9o/SGHh1Pm/hdMpmfHKtzNRahvOkL8RXYAo49r9emkDm8n4B/Pnz4der8fSpUsRFRWFXr16oW/fvhaffv1cD77mdQvnxo0bvX1JGZn2hyIM0FR71MIZSDy1cydazNbSPT9hAlQKn8dEk/FTWq/j3FZQgJ/PnBECTgVRxP1rWpcuGDa0rQXgiuxsXNe/Pz4z5iXNr6nBg1u34p0ZMxxfXBEKRPUGmgpNFhN9iynXpZ01nGWNjXh5716s+ucf1GusD5xndeuGFcOHY2ynTpKmIaEoCkv79cOMbt2wNicHmqoq3DFpEoIUCqBiHwkoEt1P0omvS5qYfmSwX/svcWGUFXr34AMfGTRkSYor8jQEhoXTIWLSoggKZ1cgJAUo3Qo0FhK3XGeCSVUeADg9ENXLlPJHxi+JjY1FXFwcurqZ7swWHh+RFRujAaYb3Y34bUekezJJu4xMoMPw6R/MFc5Lc6B3sqICHx8+LGwPTEnBgt69fVgiGZ/SXEKUOVUCSS9hhey4OKRGROBCQwMAYOWePahobha+V1AspmZ1wbCOnWwG4np9yhRsLyjAuXoSFOPdgwcxu3t3THImT1nKFGJx4c/Nr98MirRqmVXr9Xh9/348v3u3VUtqiEKB6/r3x93DhkkbrEdbC1zYCIASInvGhoTglkGDkJOTY7JqVu0n0SjDs2SFUyois0kb0daRCKHhGd4vQ20OCfoS0Y2kIgtkzN2CWa1r61IDwMLpFM4qnKyeROTV1ZNnWxFC2oG6giiizqwvrvqLLBcISZUVTj9n586dHj2/xxXOjIwMUBSFlpYWKJVKYdsRgZoDUEbGKyjMFE7BpTbaV6XxGRzH4f6tW8GaBUl5aeJE2b3vUqapkMzCx/SzqXBSFIVJWVmChdJc2QSAZydMwWVdabs556KCg/HplVdi4pdfCvuu37ABx26/HdGO1g4rQiy3g1OAbneSQB5mcByH70+exP1bt7bJnwkAKeHhuHPoUPxn0CDEeWppSkO+42Bk7Sl9hr9ABxGrUPU/QM0R3yic1f8Qa1ZQZOArnBQDpEwiiqer0dzbm4XT0XpWWgFkLCBppniZRXYnCmf9KccKJ8eZJsTliahLHo8rnJ988gkoihLC7PLb7RmaptGnT592Fa0ykGmX9RGWRmYngxOB+OGkU5fQwhkoMnv099+x+cwZYXtS585t3CW9RaDIzJ/wiMz4XJz6ZruHTczMFBROc16eNAl3j3AQSMPI5Z07484hQ/D2338DAC40NODuLVvw+ezZzpWVNQANp4k1q9U6uUMlJVj+669CzlBzeiUk4IGRI7Ggd28oGca5a7kCY1SMWT3A6oQ8ehb1xrGmgauscNrEpbYe058offUnAMN07+dY5tc7+ihokKT9A0UBCW5GLe+0ENA1+LXy7ZTMGBEutYClgh7ZnaSGaThD+gV7k1GGFlP6J4UX0pPJSIJOp8OpU6dQV1dndS3wmDEi87ga8bjCed1119ndbq9otVoEezlCpoxt2l19xPS3TKnAJEp+CX+X2Tt//YWVe/YI20E0jZedTU/hIfxdZv6I5DLjw/zz1ggbWJuYePHyy3Gfk8qm8JuJE7ElPx951dUAgC/+/Rdzund3HNmX44D8VUDLRaDTfCEgR2lDAx79/Xd8duQIWttXE8PC8PyECbi+f38w3pjYoJVksMmxxjQKpsBDQr2ZW0naQ75GDyK6rYemAcoY4tLYUuJ9K6cfWK79qk9VRgeEJ5FDmTnjUsvqAX0DaX/mhHQAgiLIJISuzn6QMz4HpyJMspRtMp6DZVk8/PDDePfdd9HcbHvC1lUPVK9Pxd9www04cMB20ty//voLN9xwgxdLJD0syyI3N1eOVuknyPUhHn+X2Q8nT+KuX36x2PfZ7Nnol+y78Ov+LjN/xCMy461yDhK8J4WHY46ZUvj8hAl4cNQo4281RBHk3dXtEKZU4ovZsy3cuG/ZuFFIm2ITijIlUj+7FpqS3/G/nVvQ9a238GkrZVPJMHhw5Eicuesu3DRwoHeUTb6MvDz1Jndfi3pjjXKmgwDag9bWAMeltk5RQPrVQI97feNS62OFU/L+QV1BXIT5tZjtEKdkpoonaU0i7UyKNRUBp94ACj633E9RQNfbgOw7HUfUlt1pA4oXXngBL7/8MpYsWYIvvvgCHMfhxRdfxPvvvy9EqP31119dPr/XFc7PPvsM+fn5Nr8vLCzE559/bvN7GRkZIxwLNOQBFzYB1YcdH99O+LO4GIvWr7cYkP9v4kQs6uPDBOky/oOgcLbYPw5kkmL1nDn466ab8Mjo0aYvmoqBM+8Dxd86dcnhaWl4wMwyWtHcjNs2bwZnZw0oAOijB6JOo0ZOeRne2/w/vLh7B5p0Ootjru7ZEyfvuAMvTpyISJUPXBsFi7GNGW8/sIK1a0JTTXXgDgYtUPglcO4Hu2uTBTjOZL32YR5OSSnZTBSoprPif6utI/lzq/+RvlzeJiQFSJ0BxA+zfQwfnVYZ2/Y7Z9ujYOGMFFc+GZ/w2Wef4ZprrsF7772HqVOnAgAGDRqEm2++GQcOHABFUfjdGMndFfxusVFJSQlCQkIcHygjcymjrgRyngEKVwNVBy+ZvFgnKyow6+uvoTZLgfLfoUNFu0HKtGOE9AdqhwPrSJUKi/v2xZDUVMsvOKPSRznvBvbUuHHok2hybV9/8iRW/fMPDpeWYsOpU3j7r7/w4NatWPj99xj1ySdIf+01BP/vLTz45zF8f/IkatVq1LImpW1AcjL+uO46rJs3D51jYqxd0js4UuBlhdN7GJxcc2eNit0kAFTNvySwliM4PcAZXefaS90K+Sftez9YRVMJlO8Bqmx76LUbOA6oP03+j7CTIoPVWXg+tEGIoC8rnK7wzjvvICMjA8HBwRg2bBj++usvu8fX1tbijjvuQEpKClQqFbp164aff/7Z6eudP38eEyZMAACojJObajV5VpRKJZYsWYIvzYLkicUrTtUbNmzAhg0bhO0PP/wQ27Zta3NcbW0ttm3bhiFDhnijWB6F8WQgBxnRtLv6aD3D6IGUKP4ms9KGBkz76ivUqE2Dhat69MCrU6b4TSAyf5NZICC5zHgFieNI0BNXBsusUeE0W7PoCJVCgS/mzMHQVaugM7qz/WfTJoe/297cGYsjjuK8PhIcaCSHh+OFCROwrF8/77nO2oMJJTLkLNftCPUWkkpc7CC7kjvC5baurgQubCAWx663ic8hqa4EKvaS/2MHAeFOBFbjJxIoyrTmzwdI2j/wqVAcRWe1ht7ohsv4f0oUhzLjOGNAH511d1dtNaCtIZF9bbWVyv1A2e9A7BAS/dcaCaNIzmEftp9AZe3atVixYgXef/99DBs2DK+//jqmTJmC3NxcJCa2jdmh1WoxadIkJCYm4rvvvkNqairOnj2L6Ohop68ZFxeHxkbSzsPDwxEZGYmCggKLY2pqaly+J68onCdOnMC6desAkHD0Bw4cwKFDhyyOoSgKYWFhGDNmDF599VVvFMtjMAyDPrJ7n9/QLuuDCSEDAd6CI/EaCX+TWb1Gg+lr1uBsXZ2wb3R6OlbPnesfg3L4n8wCAY/IjFYAnZeRADaU8wqjBZzRgi7CwgkA/ZOT8eTYsXhsxw6nf5Oni8NH9QOhQSgeHT0aD44ciQhfuM7aotP8NgqOZb0xQEiS98sVYLjV1oPCgZZSEshFfZG4RDoLxwElP5MJg8huQOpMJwscCnS5mQSW8dGEnuT9g2DhdEXhNK7JDvLvlChOyUzfAJx8lSiUfR5v+z3vThvWyXZkZEUEsbjXnwKSJ1pvI0wwEOK7uAqBzKuvvoqbb74Z119/PQDg/fffx+bNm/HJJ5/goYceanP8J598gurqauzdu1fICpKRkSHqmgMGDMDfxojrADB+/Hi8/vrrGDBgAFiWxZtvvol+/fq5fE9eUTgffvhhPPzwwwBIyOaPP/4YixYt8salfQLHcaivr0dERITfWF4uZTiOQ0NDQ/uqD4oikd/44AdB0ZKe3p9kpjUYcNW33+LIxYvCvp4JCdiwYAGCFf4T+c6fZBYoeExmzlhw7OGChZPnwVGj8EteHv48d67NdyEKBdKiopAeFYW0yEjhb1pUFAZ36IBYf1xOYqVe5LYuHrdkxgSTAC+1x4hLrBiFs+4E0FhAJmI6TDPVp76JKJOto5Dy0AxZP+pDJG9nl4CF0ymZ8RZHzmA9tQnvTmsjjzEAIKILUVg1VcTd2I9TxfgLDQ0NqK+vF7ZVKpXgumqOVqvFoUOHBL0JILrTxIkTsW/fPqvn/umnnzB8+HDccccd2LBhAxISErBo0SI8+OCDTnsJ3HLLLfjss8+g0WigUqnw/PPPY8yYMRgzZgw4jkNMTAy+/vprkXdtwuujtUshgiPLsigoKECfPn1kFzs/oN3Wh7nCKbGF019kxnEcbvzpJ2wzc+voEBGBXxYvRoyfDc79RWaBhN/KjHXNwgkACprGliVL8NXRo9CxrIVSGRcS0i4UNIt6ay4CWi4Aoem+iaQaILjd1qP7EYWzNgdInuRcRGCDBijdQv5PGG1SLuvPAOe+B4KTgM7X+cyC6QjJ+wc+bY9LFk5+cte/LZxOyczcxZXVWiqcBq0pqJK99ZuMikzsNZwhVs7WCifHAaW/khQqcUNdmrxrb/Ts2dNi+8knn8RTTz3V5rjKykoYDAYkJVl6jiQlJeHUKevxOgoKCvD7779j8eLF+Pnnn5GXl4fbb78dOp0OTz75pFPlu+KKK3DFFVdYlDc/Px87d+4EwzAYMWIEYmOtBJFyEv8xD8jIyIjDPOedxBZOf+GR7dux+uhRYTtSpcIvixcjPUoOsy5jh/rTgKaCDJiCXchRy7lu4QSAcKUS/xk82KXf+h2NBUDFn0Q5SZnc9vuG00DlASBxtKxwepKILKLs6BqBxnz71icBCojuC9TnAgkjTbuDE4l1q+ksUWBj+rb9qbqcKBOqeCAyW7Lb8CmChdOFoEG8S63CvxVOp6BoomSyemMuzlDL79LnAS3nrUeoNSequ0nhTBxt+Z2hmazzpCgg7jLJbyEQOXHiBFLNAtRZs266CsuySExMxIcffgiGYTBo0CBcuHABL7/8stMKpzWioqJw5ZVXSlJGnyx++uWXXzBp0iTExcVBoVCAYZg2HxkZGUeYeQso/NvNxxXe+esvvPjnn8J2EE3jh/nz0TdJXi8m44Dqg0DpVqD5vGu/D00HEkcB4VnSlisQ0beQ6KbNF6x/L0ep9Q4UDUQZ1+bVHHHuN4ySBHTpequlFUsZBSSOIf9f/M26i2nzOfIMtYc0IDyhaUQeMQPE/5a3cLaXdy1v5WRbRT6mFUSRtLUu05yIbHJM8wVTChQerTHegiJMzs9rJCIiApGRkcLHlsIZHx8PhmFQVlZmsb+srAzJNnKNp6SkoFu3bhb6U48ePXDx4kVotW5Et5YQryuc33//PWbOnImysjIsWLAALMti4cKFWLBgAUJCQtC3b1888cQT3i6W5AQHyy9ff6Jd1kd0XzLLnTLZIy5RvpTZDydP4q5ffrHY9/ns2ZiQmemjEjlHu2xnHsYjMjNPjeIKEVlkwBVlJzH6pYLCeloUod5khdNp3G7rMcaAHfW59tNRcJxlSqDWa/QAIH44oIojFtOynW2/F+rVtwGsJO0fQpKJpdcp63ArOi0CutwEhHaUrjwewimZ2VI4xRAUbpJHfa7ld7wC6oEI+u0dpVKJQYMGYfv27cI+lmWxfft2DB8+3OpvRo4ciby8PItli6dPn0ZKSgqUSv+IEux1hXPlypUYOnQoDh8+jKeffhoAcMMNN+Crr77CsWPHUFpaikw/H1Q6gmEYdO/eXbbU+gnttj7ihgAZi4AE6XNQ+lJm1S0tuG7DBphnUHx50iQs9PMIsO22nXkQj8mMdlPhlDHBGN3tDM2mXeb1xsuYlhVOe0jS1kOSSb+fNse+u3ftUaDgE6Dlou1j+CBCAMktqS63/J63evqwXv2qT1VGEeXKzydWnJaZNYVTXQmU7bDtzWCNuKFkci6ii+V+QeGUc3C6wooVK7Bq1Sp8/vnnOHnyJG677TY0NTUJUWuXLVtmEVTotttuQ3V1Ne6++26cPn0amzdvxgsvvIA77rjDV7fQBq8rnCdOnMCCBQvAMAwUxgiTOh1ZL5ORkYHbb78dL730kuTXzcjIAEVRbT58ZYwbN67Nd7feeqtL12JZFlVVVZdEgKRAQK4P8fhSZm8dOIB6jcnF679Dh+JeG7N6/oTczsTjMZnZsMo5ja6e5KEz+Icrkk/h85rqWwSrmUW9sbKF0xkka+upM4Do3tatlgCZACj9DWg6Z0pvYYuILiT6LccaU6eYTfP5geVa8v6B1QPNJUBTsTTn80OclllUTyBusOWa1PqTQNkfQPkfzl8wug9ZftA62rHO6FIrWzhdYv78+XjllVfwxBNPoH///jhy5Ai2bNkiBBIqLi5GaWmpcHxaWhp+/fVX/P333+jbty/++9//4u6777aaQsVXeD1oUGhoqGDejY6OhkqlshBaUlISCgsLJb/u33//DYPBlLj62LFjmDRpEubNmyfsu/nmm/HMM89YlNUVOI7DuXPnRCVclfEccn2Ix1cya9Bo8MaBA8J2ZnQ0/m/KlICI7Cm3M/F4TGbuWjgv/EwCYaTOJIOySxle4eQMJF0Mo7SsNz9QTAIBr/UPZTtIgBtVPBDvhPdLh6lAUwEJCsUZTJGZWd+71EouM30DkPchWdva6xHnf6etA6r/JkpV7CBpyuIhnJZZ0ri2+/gJiggXXI5bI1s43ebOO+/EnXfeafW7nTt3ttk3fPhw7N+/38Olch2vWzizs7Nx4sQJYbt///748ssvodfroVarsWbNGqSnp0t+3YSEBCQnJwufTZs2ISsrC2PHjhWOCQ0NtTgmMlJ+UGRkvMn7Bw+iRm1SEh4aNQoK2iexzWQCGXctnJwxLYocyp/IgDK651mTp6xweh9dPVC+G6j623J/SylQ9Rf5P3W6c8FalNFA9j3EvdYiPYbRy6Q91SvNR6nVEquus2irgPI9JBpze0XfQgJFAW3dYx1h0JBox5Vmyo6scAYc06ZNw5o1a9DS4uJ70wFet3DOmTMHb775Jl555RWoVCo8+uijuPLKKxEdHQ2KotDU1IRPPvnEo2XQarVYvXo1VqxYYWE5+eqrr7B69WokJydj1qxZePzxx+1aOTUaDTRmrn8NDQ0AiEsDx3EwGAygKAo0TQv7ePj95lZXe/tpmgZFUVb389d0Zj/DMOA4zur+1mW0tT/Q7slgMAj10V7uydP1ZC4zb91Ts1aL/zNLapwaEYGlffpYXNef68lcZu35eZLynniZsSwLhmGkuycEgeJYQNcMzlgfou6J1ZHjOQowXvtSrieKDiapYvQtoJVRln1qxrWgWQ2ooKiAuidH+6WuJ2feQ07fU30hqNKtgDIGdOxgGFgW4DhQ5zYCrAGI7g06vLPz98SEAK3kTumaQYEDaBXYVsd7q57MZSZJPXEK0MboAKy22TQx5eietPVEQaVDwPn5OKL1e8jmPel14AxqMpnEqIC602A4DqwqARwTIa7fa74I6uw6otBHDwStUMLQcR5xqw2KArw4jvDHPqL19/5KQUEBlixZgvDwcMyZMwdLly7F5ZdfLpmHmdcVzvvuuw/33XefsD1z5kzs3LkT69evB8MwmDFjBsaPH+/RMvz444+ora3FddddJ+xbtGgROnXqhA4dOuDo0aN48MEHkZubi/Xr19s8z8qVK4XAR+bk5uYiNDQUx48fR1xcHNLT03H+/HlUV1cLx/BW1KKiIkFRBYgfdlxcHM6cOQO1maWnc+fOiIyMxIkTJywab3Z2NpRKJXJycizK0KdPH2i1WuTmmiKHMQyDPn36oKGhAQUFBcL+4OBgdO/eHTU1NTh37pywPyIiAllZWSgvL8fFi6bgA7GxsQF1T8XFxWhoaMDx48cRGRnZLu7J0/XU0tIiyCwrK8sr9/TyH3+grKlJ+O6+ESNQV10dMPXEcRwaGhqQl5eHnj17ttvnScp74mVWW1uLhIQEye7pTGE9FPohYHVh4E6cEH9PnA5NTU04d6YQGhV7ydcTOJIiIa0lCHGhQF5entA/UBRF7olW4EROTuDck5frKT8/X5BZSEiIe/fE6ZBYVYPI0GaEN51FUZkB+vK/ENXwD1gqCMHJQxELiL6nk4f/QETjXtSHjwCFTPToOhpaOga5ZvfqzXqqr68XZJaeni5JPfWlGRj0Gpw6dhgGJsKpe2qouYiWsnK0BEeirjHHr9se36eePHkS/fr1s3lPjXnfobn4DzSGDUJj2BAk6/YhWQXUGuJRbHZ+p+6pvh4J1Y1gDOVQRf2LmPQhOFN43nhPFW7fE0+g9hGt05v4K7m5ufj777+xevVqfPvtt4IBbtGiRVi8eDH69+/v1vkprvV0wCXAlClToFQqsXHjRpvH/P7777j88suRl5eHrCzrudhaWzgvXLiAnj17oqioCB07klDR/jDjZU4gzg7J99T+70mt06Hb22/jXD1xw4kPDUXR3XcjRKEI2Htqj/V0ydxT/gfgWsrAZiwBwju3j3tqj/V0Kd/ThY2gaw6DihsIQ8pMUEWrgcZ8cMmTQSWMcOmeuKJvSOCYsExwGUtBGyOdtqt6yv0/QN8ENus/ZN2qM/dU8itQ8Se4uGFAylT/uydX2p5wT5cByZNBnXoFNKsGm7EMXFgn8fdUsglU9SEgdjDotCsC73mydk8O9jt7T+fPn0dGRgbOnTsn6Ab+Dsuy2Lp1K1avXo0NGzagqakJPXr0wLJly7Bo0SKX7sPrFk5fc/bsWWzbts2u5RIAhg0bBgB2FU6VSmWRuLXeOFimKAoVFRVITEwUGh7/tzW2Qld7cj9FUVb32yqj2P3+dk8sy6K8vNyp+giUe7KGlPdkLjPencKT97Tm2DFB2QSAFZddhjA7uaP8sZ7MZWavjHLbM12ztcz85p5YHTleEQy0+s2lWE/WrlteXo7EmGDQdUfJOq2Y/gF9T56uJ0FmbryHLPbHDQBqjwB1J8B0mA50XkrSoUT1AYzHiS07lToVaMoHms8CjadINFw79+TperL27na7PphgQN8MBvo2z7bNezI0AxRNUqOYfe+Pba91n2rznphgck8wAGwDQFMApQId3snq2l+H9xTdC6g5DDSeBtSVYKr+AoIT2wRduxT7CFvf+zM0TWPKlCmYMmUKamtr8Z///Afr1q3DQw89hEceeQTjxo3D8uXLMWPGDKfP6XGFMzMzU7T/L0VRyM/P90h5Pv30UyQmJjoU0pEjRwAAKSkpoq/BcRwuXryIhIQEV4ooIzFyfYjHmzIzsCxe3LNH2I5SqXD7kCEev67UyO1MPB6TGceSwY9BDcQNg80UEjZ/LwcNsqDqILF8RfUGYgeY6i0kHLj4OxCcAMT093Up/RrJ23poOomaqq0hEZWj+7hfB8oYIGEUULYTKP4O0DWQvJ9inx+J8Ej/wAdBMmjsH2eO3rjUIyjc/nF+gNMyM8/DqYwBetwPaKqtKptOEZYJMCpA10j63qq/SN7SSz3KdwCzZ88erF69Gt999x2qq6vRu3dvLFu2DEFBQfjkk09wxRVX4NFHH7XI7mEPj/ciY8eOlWzBqbuwLItPP/0U1157rZADFADy8/OxZs0aTJ8+HXFxcTh69CiWL1+OMWPGoG/fvj4ssYxM+2fdiRM4Y7aO4q6hQxEV3I4iI8r4AAq4sJkontF9AFpkpMTYgYC+GVCEeaZ4gYa2GmjIB1SJlvvlHJy+g6KAmL4kb2LtcdLOpSBhJFEW9M1A6a/tT2GIHQxENgOqWOd/o28kf5l21B/w6W5YY65higaC410/H80AEV2B2mNAlTGarxyhNuA4ceIEVq9eja+//hrFxcVITEzEtddei6VLl1qs4bz77rtxyy234J133vEfhfOzzz7z9CWcZtu2bSguLsYNN9xgsV+pVGLbtm14/fXX0dTUhLS0NFx11VV47LHHfFRSGZlLA5bj8Pzu3cJ2aFAQ7r7sMh+WSKZdQFGC6xwMavEDnyTPBq4LOBhjtPbWaVH4lCi0rHD6hJj+JF2Hvh7gONLu3YUOAjpMJxZOwJSXs70QO1D8bzIWEWuvKk768vgK3sJpUEvXdiK7E4WTNXqIBEW5f04Zr9G/f3/k5ORApVLhyiuvxLvvvospU6bYdD0eP348PvroI6fP3856EvtMnjy5zYJigESg+uOPPyS7DkVRiI2N9RvL7qWOXB/i8ZbMNubm4lh5ubB966BBiLeTisifkduZeDwqMybEqHB6JqfYJUWrvKZCvbHGiJCyhdMhHmnryhigy81EeZDyvNG9AXBkosGH/Znf9KlBkQFjrXNaZrzC2XQWyH0diBtKrNvuEJkN9FgBlP5GFM8AkZkMITo6Gh9++CHmzZuHyEjHdXfllVeisLDQ6fN7XeHctWuXU8eNGTPGwyXxHDRNB0wkqksBmqaRnp7u62IEFN6QGdfKuqlkGNw7YoRHr+lJ5HYmHo/KTFirpbZ/XGs4juSPo4N8PuD2GxhLhVOotzJjrAVZ4XSIx9p6SLL05wSkc9F1A4/ITN8EaOvIJIoyRtpz+wFOyywoitRxbQ6RB+9a69bFg8hHV2e8hqxwBgpqtRpz585F165dnVI2ASA0NBSdOnVyfKARryuc48aNc2q2KlASpVqDZVkUFxejY8eONk3RMt6DZVmcP39erg8ReENm2woK8HdJibB944AB6BAR4ZFreQO5nYnHozITFE6RFk5DC3DqdfJ/nycAyAqnoHDqmwGY1RvdDBqQFU4nkPsH8XhEZpX7gfLdQPwwoMM0x8dr68iaVmVMQKxndVpmIUlAx9lAwxkyKRfRVbpC6IwR55WyS22gEBwcjIceeghvvPGGxwx+Xlc4d+zY0WafwWBAUVERPvzwQ7AsixdffNHbxZIUjuNQU1OD1NRUXxdFBqQ+qqur5foQgTdkZm7dZCgKD4x0053Hx8jtTDwelZlglRNp4WR15C/FGNMGyLRewynUW4wcNMhZ5P5BPB6RmVjPB20VUPEnicQcAAqnKJm1nCdyUIQCIR2kKQBrIEo6IK/hDDB69eqFoqIij53f6wrn2LFjbX533XXXYfTo0di5cycmTJjgxVLJyMh4kz+Li/HH2bPC9pK+fZERHe27Asm0P1x2qZVTorSBX8MJEJdjnsSxQNwgQBnt9SLJyLgEbYzO6mxaFD4lSnuMWF13ivyN6CLd5BrNAGlziItuUOB6LF2KPP/881i0aBHGjx+PiRMnSn5+vwoaRNM0FixYgJUrVzodZldGRibwMLduUgAeHjXKd4WRaZ/EDgEie4iPLMlbOH2Ue9AvUUQQ9+LWg1JlDMC4kUpBRsbbCOlAnFQ4dcaUKAr/z8EpCoMGqNxH/g/vLO25Y/pJez4Zr/D2228jNjYWU6ZMQWZmJjIzMxESEmJxDEVR2LBhg0vn97s3anV1NWpra31dDLegKArJycm+j6wmA0CuD1fwpMz+KS3FL3l5wvbVPXsiOz7wB61yOxOPR2UWkgQgSfzvBJda2cIpQFEwX8sqt3XxyDITj0dkRotUOA28hTMwFE6nZWbuwSHl+k2ZgOXo0aOgKArp6ekwGAzIMxun8bjzLHpd4SwuLra6v7a2Frt27cLLL7+M0aNHe7lU0kLTNJKTPRQ5TkY0cn2Ix5MyM7duAsAjAf6888jtTDx+KTPBpdbv5mP9BqHeKg8AoEgaDUVgpjPyFn7Z1v0cj8hMcLUXa+EMDJdap2VG0UC3OwFwAXNvMp7Fk+s3AR8onBkZGTY1ZI7jcNlll+GDDz7wcqmkxWAwID8/HxkZGWAYxtfFueThg1LJ9eE8npLZiYoKrD95Utie2a0b+reTQZjczsTjUZnp6kkERlopLsWD4FIrWzgtKN0KtJQCyRNgUKWgqKgImS3bQbNaICJLVjgdIPcP4vGIzMRaOPWBZeEUJbPgwPcskgkcvK5wfvLJJ20UToqiEBMTg6ysLPTs2dPbRfIIDQ0Nvi6CjBlyfYjHEzJbuWePxfaj7cS6ySO3M/F4TGaaSuD8RiA4UZzCGRRJolHKOeQsabkANBYB2oGAKgUN9fUArSHutnKUWqeQ+wfxSC6zoAggcbRZ5GU1mZSyFTRHH1gWTkBuZzLu8ccff2Dz5s04awzs2KlTJ8yYMcNu0Fdn8LrCed1113n7kjIyMn5AQU0Nvs7JEbYnZGbiso4dfVgimXYN7WKU2pBkIHWm9OUJdIQ0MyQ1CsVpAXAAKJPVSEbG31GEAsmXm7ZLtgB1xwBVAhCcZPwkkr+KMCBjIaBrEB98TEYmwNBqtVi4cCF+/PFHcByHaGPmgNraWvzf//0f5syZg6+//hpBQa55/8hJxmRkZLzCS3v2wGCWUqG9WTdl/AyFpYIk4yatFE6a05JtOkhe7yoTuGirAFZP3MVrjgClvwKFXwInXwFO/R/AhAGhqbIVX6bd8/TTT+OHH37Avffei9LSUlRXV6O6uhoXL17Efffdh/Xr17uVQcQnb4k9e/bgk08+QUFBAWpqasCZ5/UCcbH9999/fVE0SaAoCmlpaXI0Oj9Brg/xSC2zkxUV+PTIEWH7so4dMT4jQ5Jz+wtyOxOPR2XGWzhZHUlGTju5BsygJYGDaKWsSJnDr9HUN4OiKKSmxIGqkN1pnUXuH8TjFZl1vgHQ1gDqMuOnnPzVVhPLvbP9hp8gtzMZV1mzZg2uvfZa/O9//7PYn5iYiJdeegllZWX48ssv8eyzz7p0fq+/TV999VXcf//9CA4ORnZ2NmJjY71dBI9D0zTi4mT3C39Brg/xSCkzjuNwx88/Q8eywr7HRo9udy9EuZ2Jx6MyY8zcPFk1QDu5BqvqAHBxOxA7AOh4pWfKFoiYWThpmkZsZChQKSucziL3D+LxiswoClDFkk9UD9N+VkdcaQMMuZ3JuEppaSmGDRtm8/thw4bhm2++cfn8XnepffnllzFy5EiUlJTgn3/+wY4dO6x+AhmDwYBTp07BYDD4uigykOvDFaSU2ZqcHOwwC7c9tUsXTO/a/vJ+ye1MPB6VGUWblCG9CLdaOQ+ndcwUToPBgIK8E2A5VlY4nUTuH8TjU5nRQUQJDTDkdibjKh07dsTOnTttfv/HH3+goxtxN7yucDY3N2Px4sWIiory9qW9ilotMlCFjEeR60M8UsisVq3Gvb/9JmyrGAZvTZvW7qybPHI7E49HZcYrQ6yIa8h5OK3DhBBrEEcGsg1sDLjMa4HkyT4uWOAg9w/ikWUmHllmMq5w7bXX4ttvv8Wtt96K3NxcGAwGsCyL3Nxc3HbbbVi3bp1bgV+9/kYdP348cswiVcrIyLRfHvv9d5Q1NQnbD48ahS7t0I1exk9JnUX+qkTkm5PzcFonMhvo/TixHBsM4OgQICwDkHNKysjIyAQ8jzzyCPLz8/Hhhx9i1apVoGlik2RZFhzH4dprr8Ujjzzi8vm9rnC+9dZbmDx5Ml555RXccMMN7XINp4yMDHCopATv/v23sN0lNhYPjhrlwxLJXHJEZIn/DW/hlF1qLbGVp1BGRkZGJuBhGAafffYZVqxYgZ9//tkiD+f06dPRt29ft87vdYUzLS0N//nPf3DffffhwQcfRHBwMJhWM6QURaGurs7bRZMMmqbRuXNnYXZAxrfI9SEed2VmYFncunkzzONPvzN9OoIV7ddNUW5n4vFLmQkWzvbbVt2FpmlkJQF0zSEgLJ3kLpWxi1+2dT9Hlpl4ZJnJuEpxcTESEhLQt29fq8plS0sLKioqkJ6e7tL5vf5GfeKJJ/D8888jNTUVgwcPbpdrOSmKQmRkpK+LIWNErg/xuCuzDw8dwsGSEmH7ml69MDnLBWtTACG3M/F4XGbNJYC6FFAlAmFpzv1Gdqm1DscC59aTtCid5iNCXwRUHAFSJskKpxPI/YN4ZJmJR5aZjKtkZmbiyy+/xKJFi6x+/9NPP2HRokUuB6TyusL5/vvvY8aMGfjxxx/b7QyMwWBATk4Oevbs2cZ6K+N9DAYDTpw4IdeHCNyRWVljIx7evl3YDlcq8erk9h9YRG5n4vG4zOqOARV7gYQRziuc4ZkkpYpSTi1gAUUD9bkAq4NB24ALRafRMZIFLUepdQq5fxCPLDPxyDKTcRWO4+x+r9Pp3NLbvK5warVazJgxo90qmzxySGr/Qq4P8bgqs/u3bkWdRiNsPzt+PFIvkRlXuZ2Jx6MyM0vl4TTxl3mmLO0BJoRYgA0tgMEYCZOWFU5nkfsH8cgyE48sMxlnqa+vR21trbBdVVWF4uLiNsfV1tbim2++QUpKisvX8rrWN3PmTOzevdvbl5WRkfECfxQV4cujR4XtvklJuHPoUB+WSOaShre+GeQ0AZKgCCV/9c2gWOOkkmzhlJGRkQlIXnvtNWRmZiIzMxMUReGee+4Rts0/AwYMwM8//4xbb73V5Wt53cL55JNPYv78+bj99ttx4403Ij093arZX45eKyMTWGgNBty2ebPFvvdmzICinXszyPgxriicBg1AMcZP+8wX6zJmFmOa0wIIkRVOGRkZmQBl8uTJCA8PB8dxeOCBB7Bw4UIMHDjQ4hiKohAWFoZBgwZh8ODBLl/L6wpndnY2AODIkSP44IMPbB4XyC4BNE0jOzu73bsNBwpyfYjHFZm9tm8fTlZWCts3DRiAEWlOrptrB8jtTDwel5krLrVn3ge0NUCXm4DQjp4pV6BilCfNaZAYFwmK0ssKp5PI/YN4ZJmJR5aZjBiGDx+O4cOHAwCamppw1VVXoXfv3h65lk+i1FKXwKyxUqn0dRFkzJDrQzxiZHa2thbP7NolbMeFhODFiRM9USy/Rm5n4vGozFyxcHLGKLWUnBalDbwCr28GQxnzlcoKp9PI/YN4ZJmJR5aZjCs8+eSTHj2/19+oTz31lLcv6XVYlkVOTg769OkjRwnzA+T6EI9Ymd29ZQuadTph+3+TJiEuNNSTRfQ75HYmHo/LjA9oI8bCKadFsY0iBKAosAYNcjXDkN21ExheCZWxi9w/iEeWmXhkmcm4Q01NDb7++msUFBSgpqamTeRaiqLw8ccfu3RueQpXRkbGLTbm5mJDbq6wPSItDdf17++7AsnI8CijgPR5RFFyFtZouZMtnG1JHAckTQBYDtqKHCCyO0mXIiMjIyMT0Pz666+4+uqr0dTUhMjISMTExLQ5xh0PVa+/UZ955hmHx1AUhccff9wLpZGRkXGHZp0O/92yRdhmKArvzZgB+hJwm5cJAOggILqX88dzLMAZTL+VsYTmhwyBG2NBRkZGRqYt9957L5KTk7F+/Xr06dNH8vP7lUstRVHgOE5WOGVkAoBzdXV49PffUWSWw+nuYcPQNynJd4WSkXEH3roJyAqnPbS1CGk5DjSogOgevi6NjIyMjIyb5OXl4eWXX/aIsgn4QOFkWdbqvrNnz+Kdd97Brl278Msvv3i7WJJC0zT69OkjRwnzE+T6EI8tmWkNBmzMzcVHhw/j17w8mHv3p0ZE4Klx47xaTn9Cbmfi8YrM6s8A+nogohsQFGH/WM60Dll2qbWCphoo2w66IQ9dQ1tAVbbICqeTyP2DeGSZiUeWmYyrdO3aFQ0NDR47v1+0SJqmkZmZiVdeeQVdu3bFXXfd5esiuY1Wq/V1EWTMkOtDPOYyO1lRgft++w0dX30VV69bhy2tlE0AeGPqVESoVN4tpJ8htzPxeFxmZduA8xsBdbkTB1NAdG8gqqecg9ManAGoPQ4Y1CR1mRyhVhRy/yAeWWbikWUm4wrPPfcc3n33XRQVFXnk/H43hTtmzBg8+OCDvi6GW7Asi9zcXDlKmJ8g14d4WJbF4ePHcYKi8Om//2LvuXM2j+0WF4enx43DVT17erGE/ofczsTjFZnRIlKjKEKB9Ks9U472gDEiLcdxqKqsQkKsCnJLdw65fxCPLDPxyDKTcZXt27cjISEBPXr0wKRJk5CWltamDVEUhTfeeMOl8/udwnnw4EHZFUBGxocU19Xh2T/+wJqjR9FssB4cJEShwDW9euGmgQMxMi3tksitKxOg8FY4VkQuThnrtE6BwlzaHg0yMjIy7YW3335b+H/Tpk1WjwkohfOLL76wur+2tha7du3C+vXrcdNNN3m5VDIyMgBQVFuLYR99hPKmJqvfD+7QATcNGIAFvXsjKlh2p5MJAHiFU+9ELk6OA8DJqT5sQTMAowT0RuVddqmVkZGRaRdYi7EjJV5XOK+77jqb38XHx+Ohhx7CE0884b0CeQjZlcG/kOvDMfUaDWauWdNG2YwJDsaSvn1x44AB6Jec7KPSBQZyOxOPx2XGW+WcsXA2FQIFXwAhKUDX/3i2XIEKEwLo1aBoyuSuLOMUcv8gHllm4pFl1j5455138PLLL+PixYvo168f3nrrLQwdOtTh77755hssXLgQV155JX788UfPF9RJvK5wFhYWttlHURRiYmIQEeEggmCAwDCMx8IKy4hHrg/H6FkWC777DscrKoR9vRIS8Ojo0ZjToweCFX7nfe93yO1MPF6RGSNiDSdrjFIrWzhtw4SApuqQlJgEKMN8XZqAQe4fxCPLTDyyzNoHa9euxYoVK/D+++9j2LBheP311zFlyhTk5uYiMTHR5u+Kiopw3333YfTo0S5fe//+/dixYwfKy8tx++23o2vXrmhubsapU6fQrVs3hIeHu3Rer79VO3Xq1OaTnp7ebpRNgARUqK+vB8e1juMp4wvk+nDMvb/+il/y8oTttMhI/DBnDhb07i0rm04itzPxeEVmgsLphEstn4dTzsFpGyYUHDg0q7LAhWX6ujQBg9w/iEeWmXhkmbUPXn31Vdx88824/vrr0bNnT7z//vsIDQ3FJ598YvM3BoMBixcvxtNPP43OnTuLvqZWq8XcuXMxcuRIPProo3jzzTdxzhgwkqZpTJ482eX1m4CXFE61Wo1bb70Vb731lt3j3nzzTdx2223Q6XR2j/N3WJZFQUGBx/2hZZxDrg/7vPf333jzr7+E7bCgIPw4fz6ayspkmYlAbmfi8YrMIroC6fOAhFGOj+XzcMo5OG3TaQHYno/hdEsfsIooX5cmYJD7B/HIMhOPLDP/paGhAfX19cJHo9FYPU6r1eLQoUOYOHGisI+maUycOBH79u2zef5nnnkGiYmJuPHGG10q3+OPP45NmzbhvffeQ25ursWkRXBwMObNm4cNGza4dG7ASwrnhx9+iM8++wwzZsywe9yMGTPw6aef4qOPPvJGsWRkLnm25ufjrl9+EbYpAF9fdRX6JSX5rlAyMlKiigOie5F1mY7gXWplC6dtGKXsciwjIyMjkp49eyIqKkr4rFy50upxlZWVMBgMSGo1DktKSsLFixet/mbPnj34+OOPsWrVKpfL9/XXX+O2227DLbfcgtjY2Dbf9+jRAwUFBS6f3ytvjW+//RZXXXWVQxNvVlYW5s2bh6+//lryMjz11FOgKMri0717d+F7tVqNO+64A3FxcQgPD8dVV12FsrIyycshI+MvnKqsxLx162Awm8V6edIkzMrO9mGpZGR8CO9SK1s47VP9D4LVZwCDnGBeRkZGxhlOnDiBuro64fPwww9Lct6GhgYsXboUq1atQnx8vMvnKS8vt7v+l2EYNDc3u3x+r7xVc3JysHjxYqeOHTFiBDZu3OiRcvTq1Qvbtm0TthVma9OWL1+OzZs3Y926dYiKisKdd96JuXPn4s8//3TpWsFyygi/Qq4PSyqbmzFzzRrUmbl03DRgAFYMHy5syzITjywz8XhcZgYt0JhHrJcx/ewfy8kWToc05IMq2Yj45mqAnQwgxOFPZAhy/yAeWWbikWXmn0RERCAyMtLhcfHx8WAYpo3Rq6ysDMlWMgXk5+ejqKgIs2bNEvbxLtUKhQK5ubnIyspyeN20tDScOnXK5vd//vknunTp4vA8tvCKhVOr1UKpVDp1rFKptOnX7C4KhQLJycnCh58JqKurw8cff4xXX30VEyZMwKBBg/Dpp59i79692L9/v+jrMAyD7t27y6Gp/QS5PizR6PWYu3Yt8mtqhH3jMjLwzowZoCgKgCwzV5BlJh6vyIxVA2e/Bc5vMObZtIMyFojsBgTL6X9soqkATdFkUCRHqXUauX8Qjywz8cgyC3yUSiUGDRqE7du3C/tYlsX27dsx3MwowNO9e3fk5OTgyJEjwueKK67A+PHjceTIEaSlpTl13UWLFuGDDz6wWCfKjwlXrVqFb7/9FsuWLXP5vrxi4ezQoQOOHTvm1LHHjh1Dhw4dPFKOM2fOoEOHDggODsbw4cOxcuVKpKen49ChQ9DpdBYLdLt374709HTs27cPl112mdXzaTQaC+W4oaEBAKDX61FRUYHo6GgwDAOapsGyrMUCXIqiQNM0DAaDxTlt7adpGhRFWd0PtE3Yams/wzDgOM7q/tZltLWfL2Og3JPBYEBtbS2io6NB03S7uCdX64njONyycSN2FxcLx3WNjcW3V10FhbFjMRgMYFlWkJlCofDrezLf78t6MpdZUFBQu7gnR2V39554mcXExEChUHjonpRgAHCcAayuBWBUtsse0QOI6CHXk717Ag0Da4BarYbSwIHmDIF/T16oJ71e7/A9FGj35Ol6Mu9T+bFUoN+TOZ6op9bvofZwT63LGGj31Pp7Z1ixYgWuvfZaDB48GEOHDsXrr7+OpqYmXH/99QCAZcuWITU1FStXrkRwcDB69+5t8fvo6GgAaLPfHo8++ij279+PMWPGoEePHqAoCsuXL0d1dTXOnz+P6dOnY/ny5aLvhccrCufEiRPxxRdf4OGHH7abP6a8vBxffPEF5s2bJ3kZhg0bhs8++wzZ2dkoLS3F008/jdGjR+PYsWO4ePEilEqlUEE89hboAsDKlSvx9NNPt9l/6tQpBAUFITY2FnFxcUhPT8f58+dRXV0tHMNbWYuKigRFFSAm7bi4OJw5cwZqtSlvXOfOnREZGYkTJ05YNN7s7GwolUrk5ORYlKFPnz7QarXIzc0V9vH5mRoaGiwW/gYHB6N79+6oqakRQiADxPyflZWF8vJyCznExsYG1D0VFxejuroasbGxiIyMbBf35Go9fXrmDL44cUI4JjIoCC8PGIALeXmgze6ppaVFkFlWVpZf35O/1BPHcaiurkZKSgp69uzZLu7J0/XEy6xv375ISEjwzD3RNPpQDLTqZuTmHALLRHj0ntpjPVncE6NCdVU1GhobUHfsGCiKCvx78kI95efnC31qSEhIu7gnT9dT/f+3d9/hURXrH8C/ZzcdUggBAiENCCUQWujSe+/VAnIVFYSr8kO9VkSwoFfUqyKioleKNCly6cWASJUaCASSEEIS0iAhPdkyvz+GbWlkku37fp5nH9izm90575w957xn5szk5mpjFhQUZBfrZOp60uxT/fz80KFDB7tYJ1uvp5qMBzNt2jRkZmbi3XffRVpaGjp27Ih9+/ZpBxJKSkrSJrbG4uLign379mH9+vXYunUrVCoVSkpK0L59eyxbtgxPPfWUtsWzJiRmhsl6EhISEBERgdDQUPz444/o3r17ufecPn0azz77LBISEnD58uVq9TeujZycHAQHB2PFihVwd3fH7Nmzy3Xl7datGwYMGIDly5dX+BllWzhTUlIQHh6OhIQE5OTkoG3btnBycqKrQxZeJ6VSiatXr6Jt27aQy+V2sU41qacd169jytat0LzDSSbDnhkzMDA0tNw6qVQqbcycnZ2tdp3KLrdkPenHzMXFxS7W6VFlr+06aWLWrl07ODs7m26dbnwOpsiDutlzgLuuu6y9bHuPKrtR1wlqKBPWIyFdhZCuT2pbnmx6ncxQTwqF4pHHIVtbJ1PXk/4+VXMuZevrpM8U9VT2OGQP61S2jLa2TsnJyQgJCcGdO3fQtGlTOCqztHA2a9YMmzdvxowZM9CrVy80a9YMERER8PT0RF5eHq5cuYL4+Hh4eHhg48aNJk82Ad7c3LJlS8TFxWHIkCEoLS3VdkPQqOwGXQ1XV1e4uuq6Z+Xm5gLQbYSaA7FmWUUq62dvyuWaspVVWRlFl1vbOsnlcu3rmvfY+jpVpKrl5+/excydO6G/K145ciSGVHADuOY7Nd+vf19ndcte2XJ73/b0/28v61SdMtZmnTQHc9EyCi2XuUFCPuRQAGVeMyhj0lYg9zrQeDhk9btU+NmOWk9674YU+iQK86MN9qmVv98W1sk89VTb45A1rlN1y1jTdSp7LmUP62Tq5ZUdk6pTRmtdJ322tk6VvW5tXnvtNcyYMQOdOnUyyeebbTKtUaNG4fLly3juuedQXFyMHTt2YO3atdixYwcKCwsxZ84cXLp0yWCUJVPKz89HfHw8GjdujMjISDg7OxvcoBsbG4ukpKQKb9CtDk9PT2MVlRiBI9dHQWkpJmzahEKFQrtsYY8emBMZWeXfOXLMaopiJs4sMZM/HLVRVVz1+9QKPjVKLboNOQra1sVRzMRRzMRRzEhNfPXVV+jSpQvCwsLwzjvvlOvmXFtm6VJbkby8POTm5sLLy8ssP45FixZhzJgxCA4ORmpqKhYvXoyLFy8iJiYGDRo0wNy5c7Fnzx78/PPP8PLywoIFCwAAJ06cqPZ3JCcnIzAw0OGbzYl1efePP7D02DHt89EtW2LHtGmQV3J1jxC7c2s9kHcTaDoO8K3i6m3CL0B+AhA4EajX3nzlI4QQYpdsJTfIy8vD9u3bsWnTJhw6dAhKpRKtW7fG9OnTMXXqVLSq5RztFjvj9PT0REBAgNmuxCQnJ2PGjBlo1aoVpk6divr16+PUqVNo0KABAODzzz/H6NGjMWnSJPTt2xf+/v7Ytm1bjb5LrVYjLS2tXB9zYhmOXB+3c3Lwqd5Fk6ZeXtgwceIjk01HjllNUczEmS1mDXoBQVOAuqFVv48p+b80D2eVaFsXRzETRzETRzEjNeXp6YmZM2di9+7dSE9Px+rVq9G0aVMsXboU4eHh6NixIz7++OMaf77DNHFs3LgRqampKCkpQXJycrl7Rd3c3PDNN9/g/v37KCgowLZt26q8f7MqjDGkpaWVu3mZWIYj18frhw6hWKnUPl8+eDA89e47rowjx6ymKGbizBazuqGAT1vAxafq96kfdjuXzDK8gc2ibV0cxUwcxUwcxYwYg4+PD5555hns378fd+/exWeffYZbt27hrbfeqvFn0lGVEDt1PCkJm65e1T7v2bQpZgjMyUSIw9EknNTCSQghxIEpFArs3bsXmzZtwq5du5Cfn4/AwMAafx4lnITYITVjeHnfPoNlXw4fXqs5lAixWaU5QFEqIHevuluttkstHRoJIYQ4FqVSiQMHDmDTpk3YuXMncnNz0bhxY8yePRvTpk1Dr169avzZdFQ1AUmS4OvrSyf3VsIR6+O/Fy/i3N272uczO3RA14CAav+9I8astihm4swWs/xbQPJOwDOs6oTToyng7A3IPUxbHhtH27o4ipk4ipk4ihmpqWeeeQY7duxAdnY2/Pz8MGPGDEyfPh19+/Y1yvZksVFq7ZGtjERF7FteSQlafv010vLzAQB1nJ1xY8ECNKGh0omjenANuL0JqBMINH/G0qUhhBDiIGwlN6hfvz4mTJiAadOmYeDAgUafP9RhBg0yJ7VajaSkJBolzEo4Wn18+Oef2mQTAN7o3Vs42XS0mBkDxUyc2WJW3Xk4SbXQti6OYiaOYiaOYkZqKj09HT/88AOGDBli9GQToITTJBhjuH//Po0SZiUcqT4SsrOx4tQp7fNgb28s7NlT+HMcKWbGQjETZ7aYyd35v6oi036Pg6BtXRzFTBzFTBzFjNSUk5Np77KkhJMQO/LqwYMoVam0zz8dMgTuzjTiJnFw1WnhVJUAVz8EYj4F1MrK30cIIYTYGcYYvvvuO3Tr1g1+fn6Qy+XlHrVJSmnQIELsRFRiIrZdu6Z93icoCJPDwy1YIkKshKaFU63kU59UNO2JWgGoSgFJAUjG705ECCGEWKvXXnsNK1asQMeOHfHkk0+iXr16Rv18SjhNQJIk+Pv70yhhVsIR6kOlVhtMgyIB+KIW06A4QsyMjWImzmwxk7kAkgQwxls5K0o42cM5OCUn/l5SKdrWxVHMxFHMxFHMSE3997//xaRJk7B582aTfD4lnCYgk8ng7+9v6WKQhxyhPn68cAGX0tO1z2d37IjOjRvX+PMcIWbGRjETZ7aYSRIQMJYnnjLXit+jpjk4q4u2dXEUM3EUM3EUM1JTRUVFGDx4sMk+n+7hNAGVSoX4+Hio9O6lI5Zj7/XxoLgYbx85on3u6eKCDwYNqtVn2nvMTIFiJs6sMfPtBPi0BeQuFb+u1rRw0j3Pj0LbujiKmTiKmTiKGampQYMG4ezZsyb7fEo4TSQvL8/SRSB67Lk+lh47hszCQu3zt/r0gX/durX+XHuOmalQzMRZTcyYpoWTEs7qsJp6syEUM3EUM3EUM1ITK1euxKlTp/Dhhx/i3r17Rv98SjgJsWE3793Df06f1j5vVq8eXu7Rw4IlIsRKFd0FHsQAJZUcSDUtnNSllhBCiINp1aoVEhIS8M4776Bhw4aoU6cOvLy8DB7e3t41/nw6shJiw/7vwAEo9CZ4/veQIXA18VxKhNikzONAzlWgyQjAtX7512UuQJ0gwMXX/GUjhBBCLGjSpEkmHWyKzkxNQJIkBAYG0ihhVsJe6+NgfDx23bihfT4gJATjW7c2ymfba8xMiWImzqwxkz1iLs46gUDzf5i+HHaAtnVxFDNxFDNxFDNSUz///LNJP58SThOQyWSoX7+CK+jEIuyxPkqUSry8f7/2uUySajUNSln2GDNTo5iJM2vM5JqEs8g832fHaFsXRzETRzETRzEj1ooSThNQqVS4fv06wsLCIJfTBOKWplKpcPPmTbuqj8VRUYjJzNQ+n9O5M9o3amS0z7fHmJkaxUycWWMmd3/4pZW0cJJqo21dHMVMHMVMHMWMiDh//rzw33Tu3LlG30UJp4kUF9NJjTWxp/r48/ZtfPLXX9rnfh4eWDpggNG/x55iZi4UM3Fmi9mjWjjvnQUyjgHe7YAmw8xTJhtG27o4ipk4ipk4ihmpri5dulS7ZxxjDJIk1XjKHUo4CbEhuSUleGr7djC9Zd+PGYMGdepYrEyE2AT5I+7hVBUBijxAXWq+MhFCCCEW8tNPP5ntuyjhJMSGvLRvH24/eKB9/o+OHY02UBAhdk3TpVZdScJJ06IQQghxILNmzTLbd9GR1QRkMhmaNWsGmYymObUG9lIf265dw88XL2qfh/r44Ivhw03yXfYSM3OimIkza8zcGgJNxwBOnhW/rk04nU1fFhtH27o4ipk4ipk4ihmxVpRwmoAkSfDy8rJ0MchD9lAfafn5eG7XLu1zmSRh7YQJ8HR1Ncn32UPMzI1iJs6sMXP2BHwjK3+dKR8Wig6Lj0LbujiKmTiKmTiKGbFWdAnEBFQqFaKjo2t8Yy0xLluvD8YYnvn9d9wr0g128vpjj+GxoCCTfaetx8wSKGbirCpm1MJZbVZVbzaCYiaOYiaOYkasFSWcJkI/dutiy/Wx+tw57Ll5U/u8o78/3uvf3+Tfa8sxsxSKmTizxiz/FvAgRpdc6tO2cFLCWR20rYujmImjmImjmBFrRAknIVbs5r17WHjggPa5q1yOdRMmwIXm1yJE3O2NwO3NgCK3/GvO3oB7I8C5rvnLRQghhNgxulmFECulVKvx1PbtKFToWmM+HjwYbRs2tGCpCLFhcjdAVVLx1CiNh5q/PIQQQogDoITTBGQyGVq1akWjhFkJW62Pj/78E6dTUrTPB4aG4p/du5vlu201ZpZEMRNn9pjJ3QE84HNukhqjbV0cxUwcxUwcxYzU1LFjx6p8XZIkuLm5oWnTpmjcuLHw51PCaSIuLi6WLgLRY2v1cTYlBUuOHtU+93Z1xc/jxkEmSWYrg63FzBpQzMSZNWZyN/5vRS2cRAht6+IoZuIoZuIoZqQm+vfvD6ma55hhYWFYsmQJpk2bVu3Pp0sgJqBWqxEdHQ21Wm3pohBYvj4Oxsdj6pYtmLd7N366cAFXMjKgqqIshQoFntq+HSrGtMtWjhqFQG9vcxQXgOVjZosoZuLMHjNZFQlnws9A7FdAYap5ymLDaFsXRzETRzETRzEjNbVv3z60b98erVq1wr///W/s2LEDO3bswKeffopWrVqhY8eO2Lp1Kz777DNIkoTHH38cW7durfbnUwsnISZ07PZtjNywAcqHO/9vHy73cHZG58aN0bVJE/4ICEDzevUgSRJeP3gQsffuaT9jWtu2mNGunQVKT4idcXLn/1bUpbY0Gyh9AICVf40QQgixY/v27YObmxtOnz5drpV83rx56N+/P06dOoXly5fjhRdeQJcuXbB8+XJMnjy5Wp9PCSchJnLnwQNM3rxZm2zqK1QocDwpCceTkrTLfNzc0KFRIxy9fVu7rImnJ1aOGlXtbg6EkCpU1cJJ83ASQghxUOvXr8fbb79dYZdsNzc3PPHEE/jggw+wfPlyuLm54cknn8TSpUur/fmUcBJiAsVKJSZt3ozMwkLtMrkkGXSTLSunuNgg2QSAn8eNg6+7u8nKSYhD8Q4H3PwAtwoGPNDOw0mHRUIIIY6loKAA6enplb5+9+5d5Ofna5/7+PhALjBFHx1ZTUAmkyEiIoJGCbMS5q4Pxhjm7d6Ns6m6e8EiGzdG1NNPIzEnB2dTUvB3airOpqbiUno6SiuZpHlBt24Y0ry5WcpcFm3D4ihm4sweszqB/FEWY9TCKYC2dXEUM3EUM3EUM1JTAwcOxBdffIEePXpg9OjRBq/t2rULX375JQYNGqRddvHiRYSEhFT78ynhNJHS0lK4ublZuhjkIXPWx7d//42fLl7UPvfz8MC2adNQ18UF7Ro2RLuGDTG7UycAQIlSieiMDJ6ApqTgbGoqEnNyMLR5cywfPNgs5a0MbcPiKGbirCJmTM2TToBaOKvJKurNxlDMxFHMxFHMSE18/fXXGDBgAMaNG4eAgAA0f9jgER8fj5SUFAQHB+Orr74CABQXFyMpKQnPPvtstT+fLoGYgFqtRmxsLI0SZiXMWR/Hk5Lw0r592udyScKWKVMQVMkIs65OTujSpAle6NIFP44bh8tz5yL3jTewdepUuDtbrqWFtmFxFDNxZo+ZqhjIT+APfUyh+z+1cD4SbeviKGbiKGbiKGakpoKCghAdHY1PP/0Ubdq0wd27d3H37l20adMGn376KaKjoxEcHAyA39O5Z88e/POf/6z259OlXEKMJDk3t9wgQZ8NHYr+Al0OCCEmVJwJJPwCuNQDWr+kW87UgFtD3q1Wqv49KYQQQoi98PDwwMKFC7Fw4UKjfza1cBJiBCUPBwlKLyjQLnuyfXv8s3t3C5aKEGJAXsm0KE4eQMt5PAmlEaEJIYQ4mNdeew0XLlww2edTwmkiIiM3EdMzZX0wxvDinj04k5KiXdbJ3x/fjR5t09OZ0DYsjmImzqwxkz+8r0ldortnk9QIbeviKGbiKGbiKGakJr766it06dIFYWFheOeddxAdHW3Uz3eYhPOjjz5C165d4enpiYYNG2L8+PGIjY01eE///v0hSZLB44UXXhD+LrlcjoiICPrRWwlT18fqc+fwo95Vofru7tg+bRo8LHgPZm3RNiyOYibO7DHTJJyM8aST1Aht6+IoZuIoZuIoZvbjm2++QUhICNzc3NC9e3ecOXOm0vd+//336NOnD+rVq4d69eph8ODBVb6/IhkZGfjpp5/QsmVLfPLJJ+jYsSPatm2LpUuXlsuXasJhEs6jR4/ixRdfxKlTp3Dw4EEoFAoMHToUBXpdIAFgzpw52htl7969i08++UT4uxhjyM3NBaMr6FbBlPXxV1ISFuzdq30ulyRsnjIFwT4+Rv8uc6JtWBzFTJzZYyZz0g0KpN+ttjAFiP0auL3JPOWwcbSti6OYiaOYiaOY2YdNmzZh4cKFWLx4Mc6fP48OHTpg2LBhyMjIqPD9UVFRmDFjBv744w+cPHkSgYGBGDp0KFL0et49iqenJ2bOnIndu3cjPT0dq1evRtOmTbF06VKEh4ejY8eO+Pjjj2u8Tg6TcO7btw9PP/002rZtiw4dOuDnn39GUlISzp07Z/A+Dw8P+Pv7ax9eXl7C36VWq5GQkECjhFkJU9VHal4eJm/ZAoXe5346ZAgGhoYa9XssgbZhcRQzcRaJmaaVU1WsW6YqBkqygNJs85XDhtG2Lo5iJo5iJo5iZh9WrFiBOXPmYPbs2QgPD8eqVavg4eGBNWvWVPj+9evXY968eejYsSNat26NH374AWq1GocPH67R9/v4+OCZZ57B/v37cffuXXz22We4desW3nrrrRqvk8OOUvvgwQMAgK+vr8Hy9evXY926dfD398eYMWPwzjvvwMPDo8LPKCkpQUmJrltWXl4eAP6DZ4xBpVJBkiTIZDLtMg3NcpVKZfCZlS2XyWSQJKnC5ZrvrM5yuVwOxliFy8uWsbLltrZOKpVKWx/GWqdSlQqTN29GWn6+dtmMtm3x0sNBgmy9nvRjRtte9dZJP2b2sk6PKntt10kTM7VaDblcbpZ1kiRXgD0AK82H3P3h/lpZDImpASYDq6T+HLmeKlquv0+1l3UqW0ZjrlN1jkO2tk6mrif9mNnLOukzxTqVPQ7ZwzqVLaOtrZPm9by8POTm5mrf5+rqCldXV5RVWlqKc+fO4Y033jD4zMGDB+PkyZPl3l+RwsJCKBSKcjmOCIVCgb1792LTpk3YtWsX8vPzERgYWOPPc8iEU61W4+WXX8Zjjz2Gdu3aaZc//vjjCA4ORpMmTXD58mW8/vrriI2NxbZt2yr8nI8++ghLliwptzw2NhbOzs64evUq6tevj6CgICQnJ+P+/fva92haUBMTE7WJKgAEBgaifv36uHnzJoqLdVfgmzVrBi8vL8TExBhs3K1atYKLi0u5m3sjIiJQWlpq0O9a07c/Ly8PCQm6eejc3NzQunVrZGdn486dO9rlnp6eaN68OTIyMpCWlqZd7uvra1PrlJSUhPv37+Pq1avw8vIyyjp9EReHk8nJujJ7e2NBSAjy8/Ptop6Kioq0MWvevLldrJOptz3GGO7fv4+4uDiEh4fbxTqZup40McvJyUGDBg3Msk5uxQ0hMV9IyQ/Q0hvIzs5G5s0Y+ORmoMTFCUp5ItXTI9YpLi5Ou3+QJMku1snU9RQfH6+Nmbu7u12sk6nrKTc3VxuzoKAgu1gnU9eTZp967do1dOjQwS7WydbrKT09HQAQHh5usI6LFy/Ge++9h7KysrKgUqnQqFEjg+WNGjXC9evXy72/Iq+//jqaNGmCwYMHV+v9GkqlEgcOHMCmTZuwc+dO5ObmonHjxpg9ezamTZuGXr16CX2ePok5YEfvuXPnYu/evTh+/DiaNm1a6fuOHDmCQYMGIS4uDs2bNy/3etkWzpSUFISHhyMhIQFFRUVo0aIFnJyc6OqQhddJqVQiLi4OLVq0gFwur/U67bpxAxM2b9Y+93V3x5lnnkGIj4/d1JNKpdLGzNnZ2S7WqWwZTdHCqYmZi4uLXazTo8pe23XSxCwsLAzOzs6WW6f75yGl/A54tgQLnkH19Ih1Ki0tNdin2sM6mbqeFArFI49DtrZO5mjh1MRMcy5l6+ukz1QtnPrHIXtYp7JltLV1Sk5ORkhICGJiYhAQEKB9X2UtnKmpqQgICMCJEyfQs2dP7fLXXnsNR48exenTp8v9jb6PP/4Yn3zyCaKiotC+ffsq36vvmWeewY4dO5CdnQ0/Pz9MmjQJ06dPR9++fY0y44LDJZzz58/Hzp07cezYMYQ+4l67goIC1K1bF/v27cOwYcMe+dnJyckIDAzEnTt3qkxkie26V1iItitXaufblEkS9j/5JAY3a2bhkhFCaizrDJC6B/AOB4KnWro0hBBC7IRoblBaWgoPDw9s3boV48eP1y6fNWsWcnJysHPnzkr/9t///jeWLVuGQ4cOoUuXLkLlrF+/PiZMmIBp06Zh4MCBFY50nJ2djXr16gl9robDDBrEGMP8+fOxfft2HDly5JHJJgBcvHgRANC4cWOh71Kr1bh37165KzDEMoxZHwv27tUmmwCwqGdPu0w2aRsWRzETZ5GYlT4A8hOAYr3R/piC/yuz3amMzIm2dXEUM3EUM3EUM9vn4uKCyMhIgwF/1Go+AJB+i2dZn3zyCZYuXYp9+/YJJ5sA7/r7ww8/YMiQIQbJZklJCbZs2YLx48cL50P6HCbhfPHFF7Fu3Tps2LABnp6eSEtLQ1paGoqK+ND48fHxWLp0Kc6dO4fExET8/vvvmDlzJvr27SvUJA3w5PbOnTs0LLWVMFZ9/BYTg1+vXNE+b+PnhyUDBtS2eFaJtmFxFDNxFolZ9nkg4Rfgnt4cZTIXwMUHcKpjvnLYMNrWxVHMxFHMxFHM7MPChQvx/fff47///S+uXbuGuXPnoqCgALNnzwYAzJw502BQoeXLl+Odd97BmjVrEBISos1x8vUGtnwUJyfdsD6MMRw6dAizZ89Go0aNMG3aNJw8eRKPP/54jdfJYQYN+vbbbwEA/fv3N1j+008/4emnn4aLiwsOHTqEL774AgUFBQgMDMSkSZPw9ttvW6C0xNpkFhRg7u7d2udyScJ/x4+Hm5PD/IQIsQ+yCqZFqd+VPwghhBALmzZtGjIzM/Huu+8iLS0NHTt2xL59+7QDCSUlJWnvFQV4jlNaWorJkycbfE5lAxNV5ty5c1i/fj02btyItLQ0SJKE6dOnY/78+ejRo0et7uV0mLPlR13tCQwMxNGjR81UGmJLGGOYt2cPMgsLtctef+wxdNW7+ZsQYiOc3Pm/+gknIYQQYkXmz5+P+fPnV/haVFSUwfPExMQaf09CQgLWr1+P9evX4+bNmwgICMATTzyBbt26Ydq0aZg0aVKVXXmry2ESTnPz9PS0dBGIntrUx+arV7E1Jkb7PKJhQ7zbr58ximXVaBsWRzETZ/aYaVs4i8z7vXaGtnVxFDNxFDNxFDNSXT179sSZM2fg5+eHyZMn44cffkDv3r0B8FsNjYkSThOQy+UIDg62dDHIQ3K5vMJpbaojLT8f8/bs0T53ksnw8/jxcLXzrrS1iZmjopiJs0jMKmrhTDsM5McDfr0An3YV/x3Rom1dHMVMHMVMHMWMiDh9+jRCQ0OxYsUKjBo1yuA+TmNzmEGDzEmtViMtLY1GCbMSNa0Pxhhe+N//cL9I1xLyVp8+6FyLUbpsBW3D4ihm4iwSs4paOEvuAYWpgLKw4r8hBmhbF0cxE0cxE0cxIyK+/vprNG7cGBMmTIC/vz+ef/55/PHHHyYZdIoSThNgjCEtLY1GCbMSNa2P9dHR2Bkbq33e0d8fb/bpY+ziWSXahsVRzMRZJGZyvUGDNN+rpmlRRNC2Lo5iJo5iJo5iRkTMmzcPx48fR3x8PF5++WX8+eefGDRoEAICAvDuu+9CkqRaDRSkjxJOQiqQmpeHBXv3ap87y2T47/jxcKlgIlxCiA1xqgP4DwYCRgF4eFLGlPxfSjgJIYQ4mNDQULz99tuIiYnB2bNnMX36dERFRfFBM+fNw3PPPYf//e9/KC6u+WB7lHASUgZjDM/t2oUcvR/W4n790P7hcNSEEBsmcwIa9gZ8IwHp4SFQ08Ip2fe92YQQolFUBDx4YOlSEGsTGRmJFStW4M6dOzhw4ACGDRuGTZs2YezYsfDz86vx59LR1QQkSYKvr6/RmqFJ7YjWx88XL2L3zZva55GNG+P1h6N2OQrahsVRzMRZTcyohVOI1dSbDaGYiaOYiatuzFQq4LvvgJwcwMcHCA0FQkL4w9vbDAUlVk8mk2Hw4MEYPHgwVq1ahZ07d2LDhg01/jyJUUdvo0lOTkZgYCDu3LmDpk2bWro4pAbuPHiAdt9+i9ySEgCAi1yO8889h7YNG1q4ZIQQoynOBJR5gFsj3sU29is+cFDz2UAdGmGcEGL/7twBfvyx/PJ69XgCOnQo4OZm/nLZG8oNOOpSawJqtRpJSUk0SpiVqG59MMbw7K5d2mQTAN7v398hk03ahsVRzMRZLGbJO4CEX4DCZP5c7sanS5G5mLccNoq2dXEUM3EUM3EiMQsMBN58E3jySaB3byAgAJAkIDsbuHYNcHXVvffiReD2bdOVm9g/6lJrAowxZGdnIyAgwNJFIeD1cf/+/UfWx+pz53BAb6LbHk2bYlGvXqYunlWqbsxszYMHwKFDQLNmQMeO/OBqLPYaM1OyWMzkmrk4H06N0mKOeb/fxtG2Lo5iJo5iJq6qmJWWAr/9BvTrBzRpwpe5uAAtWvAHAJSU8MSyoEB3fGQM+OMPIDcXmD0bCAoy08oQu0ItnMTh5ZWUYMGePZi7e7d2mZuTE34eNw5yGf1E7EVxMbBuHRAdDezezQ+exEHpT41CCCF2TqUCNm8GYmP5vypVxe9zdQVatgQ6ddItU6sBLy+eeG7fzhNXQkTR2TRxaLtiYxG+ciW+PnsW+jczfzBwIFrVYjQuYl1UKmDTJiAzkz/v358GRnBoZVs4CSE2S6HgSdRvv+mm1iU6jAE7dgBxcYCzMzB5MiAyw5tcDjzxBD9mZmcDBw6YrKjEjlHCaQKSJMHf359GVrMSFdVHWn4+pm7ZgrEbNyK5TFPX85GReKl7d3MX06rY0zbMGLBrF3DrFu8+9MIL/H4VjaQk4Pz52p+o2FPMzMViMdNv4VQrgbgfgIT/6qZHIVWibV0cxUxcdWN24AAQEwN4eBj3NglbVDZmjAH79vGePTIZMG0aUJNxa9zcgPHj+f///hvQG8ifkGqhezhNQCaTwd/f39LFIA/p1wdjDD9euIBXDx40mGcTAEJ9fLBq9GgMbd7cEsW0Kva0Df/5Jx/wQJKAKVMA/dUqKgK2bAHy8vh9K6NG8aS0JuwpZuZisZgZJJwK3eBBdA22WmhbF0cxE1edmF27Bpw9y/+vuQ8RANLTefdQHx/Tlc8alY3Z8ePA6dP8/xMmGMZIVGgo0KMHcOoU8PvvwLx5gLt7LQtMHAYdXU1ApVIhPj4eqso6yROz0tTHtYwMDPjvfzFn1y6DZFMmSVjUsyei586lZPMhe9mGc3N5wgkAI0cCYWGGr7u5Ad278yu/ly4B338PZGSIfYemZdQUMcvL462zFy7YZ1cxi21n+l1qNXNwSjL+II9kL/sHc6KYiXtUzHJygJ07+f8fe0y3f1co+IXElSuBc+fsc99ZGf2YXbsGHD7Mlw8fDkRE1P7zBw0C/Pz4senUqdp/nikUFwN64z8SK0EtnCaSl5dn6SKQh0pVKnz+99/44eZNlJQ5cHXy98f3Y8YgUjNkG9Gyh23Yywt4+ml+8OnatfzrksS71wYGAlu38ns8v/+et3R27FjxZzIG3LvH74e5eZM/nzmTv5aXl4cHDwBf39qX/d49YO1aflJ17hy/96Zdu9p/rrWxyHbm0RTwHwy4NdB1o5U5UX88AfawfzA3ipm4ymKmVvN7NouLeRfRgQN1rxUX8+61WVn8gl1MDDB2rOPct6+JWfPmPAn39+ctk8bg7MxbSuPjDW9NsbSSEj4g0tWr/NjMGLBoEd8OiHWghJPYtTMpKXj2998RXabZyt3JCUv698crPXvCiUaitRppafzqdGAgf65p4Rs8GBCZDpUxXe4QEMAfVQkO5vd2btvGD6Q7dvAutqNH8wETSkv5PaCaJDMnR/e3ksS75rq4ALdvu2LnTgkjRgDduomsuaHUVGD9ej40vbc3UL8+EB5e88+zdRkZ/Eq9nx/QqxdQp04tP9CtIX8AQPHDfYPkXMsPJYSYS1QUcOcO7zY7aZLhIDienvxC4+nTfL8RH89bO4cN46OvOsp1JRcXYMYM469vdY6p5lBaaphkKpW61xo04NOgUcJpPSjhJHapoLQUbx85gi9Pn0bZ3jSDmzXDd6NHo1m9ehYpm6kUFfEru3Xr8quQtiQri8/zdfUqTyxfeIF3cz10CLhxgx9MevTg84fpT0ZdkYICPiLt8OG6ucaqo04dPgH2n3/yspSW8jIAPAGNidG9Vy7nSWpYGL8nxs2NX3FPSXGBSgXs2QPk5wMDBogf7BMSgI0b+fc3bszLpD8YhkLBu5H17SuWhNsixvgAFfv385OJ2FjgzBmezBsl8QQMWzgJIVaPMd2I42PHAhUdymUyoGdPvo/euZMnp7//zvfjU6Y8+jhiq+7eBc6dq6PtDWPq6+lKJXD5smUS+dRU3sqt4ecHtG3LHw0aOM6FBVtBR1gTkCQJgYGBNBqdhRyIj8fz//sfEvWboQDUd3fHimHD8FT79matm5wcfhBo1sy4B7nTp3myU78+fx4Tw1sDAZ4A1a3Lr/R6evL/d+7Md8jVYa5tOCcHOHqUD+qjuc+mYUOebLm58YStpAS4fh04cQK4coVfpQ4Pr/hgolAAv/4KJCfzk4wXXhA76EgST+SCg4FGjXR/27w5r8MWLfgJTEhI+cGFJEnC1Kk+iI7m63TsGG+hHTOm+gf9W7d4y6ZKxQdomD69/DZz7BiPw7VrvKy9e1dviHulkl/pv3KFr1+XLtUrkylVtZ2pVPw+rOvX+fNmzfi2kJIC/PUXnyuuxgmnWgUUpwPqYmiHMqAWzkdSqXgL/8WLEtzdQ9G2rSQ0vYIjo/MCcZXFTJKAqVOBxES+n6yKnx8weza/3/DIEX6cqenAcNbu9m1g40YJ9+4F4MwZCb16mfb71GpgzRqe+DEGREaa7rsY4xcenZx4jycACArix7LgYJ5kNmxISaY1o4TTBGQyGeprsgBiNvcKC7HwwAH8culSudeeiIjA58OGoYFRmkQqxxifp8rHR5dkHD3KB30JDeX3+hljh5iYCOzdy1sy//lPnlQqFPy5QsFbOouLecuhRliYLuFMT+dXiJs3r3iUOVNvw/n5PHE6d043AXWrVvw+nEaNdO/z8eFJ182bvNUwO5snIc2b80GA9IuomZQ6OZmv05QpNY91cLDh806deMJe1efJZDL4+dXHgAH83tH//Y/Xe0EBL0t1Wp2bNOEHzXr1gIkT+cG1rK5deRfT2Fhdq/C4cRV3cVKr+bYSHc0TVM1YWffv6xJO/e7H5lbVdiaX84RSLgeGDOGDOwF8W4iPN6yjuDi+/tUeMZEpoIxdjfx8IEn2JEJkbvDysNMmDyPIzuZTB124wH+7PEn3RuPGtes67kjovEBc2ZhpLkpKEn88KtnUfQ7vEdGyJd8P22NScv48sHs3oFLJ0KKFBzp3Nv13ymR8IKLUVJ4MhoYaZ/yCihw7xi8a1K3LzxNkMv6YPds030eMT2LMkcbvMq3k5GQEBgYiMTERRUVFCAsLg5wu/5ocYwybr17FP/ftQ0ZBgcFrgV5eWDlyJFowZpL6YIyfvCcm6h55ecBzz+m6c164oBtJb8QI3YlzTSmVwKpVPJmMjOQtaPrlKSnhJ4V5ebp/8/J49yIvL/6+ffv4zlsm4/dLhoXxh+YKoUqlws2bN022Dd+4AWzYwP/frBk/gDxqbjCFgrdsHT/OY9CrFzB0qO71gwf563I58NRTvBXSnMrG7Pp1PhCRUsnXbebMiq+s659EAbxrtKtr1a2ijPFEc+9entBKEq/fAQN0ie2BA3zkXf2fhKcnH3ioXTueoCUl8c+YPNkweTeXsjFTqXjrtiZxVCj470v/IkRZBQXAF1/wePXowePg5lbxey9f5slqaipDW/X7ABhO3F8EBeqia1e+PVWU5FeXUsm77mlOhBnj5atbt+afaUk5ObzXhP6Ij3XqAK1aqRAbm46XXmoEFxc6xlWHqfep9qhszE6c4D1NRo+ufW8hxqrXQmrt1Gq+r9eMGNumjQrh4TcRHm6e7Ywx4L//5bEMCuL3zhq7G29sLL/NhDF+oblrV9u6aKDJDe7cuYOmNZkE1U5QC6eJFJeZ45GYRnJuLubt3o1dN24YLJcAzO/WDR8MHAgPJydER0cb9Xvz8/n9hfHxPJnTJ5fzEUY1CWenTvxEdPdu/jf63WBr4s8/ebJZty5v+dEnSfxk282t6u6z3t78HofMTN4N5/ZtXjZvb554Dhqk24bv3eOtkDIZ/3zNlUVJ0rVCacTF8alICgqAwkLDh7s7vx8R4N/RtSvvGlvdA76zM9C/P9C+Pb/a2a+f7rUTJ3iyCfDWPnMnmxr6v/vWrXmS+euvfJTAilo41Wqe8Pn48GH9geq10kkSTxqbNeMXDy5f5jEoKdFdgLh/n9eDhwePc7t2/IRA/2Tg2DF+ArdrFzBrlmUO4pqY3b/P78dxdeUXDCSJx6yqZBPgv0VfX95qf/Qo72reowf/fWRk8Is8mvW6fl1zL64ERT031HUvQkhgEW4k1UVaWvW6Jlfm7l1+r29mJjBnDv8NbtvGk+bnnjP9vVTGUlqquzBSpw7vwixJfFuLjOQ9EQAgKCgDcjmvHJWKD87y2GNGuq/WijHGtytfX91vOiWF/wZdXSt+NGjA/6XzAnGamKWk8GOUWs2PoR061Pwz1Wrgp5/4xaGZM/m2bYuKi/lFzbg4/nzAAP4bvHLFfNuZJAHjx/NBmZKSgJMndccyY8jK4vtRxvg5A/WosF2UcBKbpGYMq8+dw2sHDyKvtNTgtTZ+fvhh7Fj0ejjUqSnmPbt4kT8AfpLatClPckJC+P/LJhdduvAujQkJvNvnP/5RsxPQjAzewgfwK32VteQ8Ss+e/JGTw7so3rjB7x988ID/f/hw3Xt//dWwa64+Hx/g5Zd1z/fv1w3mUJZ+K48k8alHasLXlx/gNNRqfiIC8ANu+/Y1+1xTCAriyYa3d/lkTqnk28LVq/y1Vq2qf4+thocH73rbrh1v4e3bV/da7948QWjWrPJEatQofqKQmMi3506dxL7fGBjj371/v65189696seiUSN+r+61a3zkyowM/q9Gz566QUXateOt+E2aAMHFbnBFEXo2L0ZCOm/919RRaSlPFKuTPKlU/CLQsWN8W6xTh19g8fLiv5uiIp4E9+wpEBQLuH6dt5Lk5wMvvqhL+CdO5AmT/sAsZXepUVH8gsfly/y3WZvJ5a2NUsm7DN6+zU+o79zhJ/pPPqlbz7Q0XseVmTpVl6iXluq2E1I9msRKreYXz2q7j5fJ+D7gzh3eOvj887bVYqaRns7PKTRTlYSHl/9tmoOPD7+wt3Mnv0+2RYtHXyisjuJi3rJZUsJvodA/LyG2hxJOYlMYYziVnIzXDx3Cn0lJBq85y2R4o3dvvNmnD1xr0y+uGh57jCdQLi68te5R9+dJEm95W7mS32N44oT4HFaM8ZYolYqfvLRpU/Pya/j48KuGXbvyE+zERL6T1z/4urvzxIYxfsBXq3X/L5vIBAfzE1MPD35C5eGhe5iqW2F6Oi9HZKRhwmUtyp6o79jBT5hOnuQnC3I5P6kXTTb1tWzJt0P9eqtOz5169XiSfuAAf4SF1a6eUlJ4El1UpBuwSv8RGakrI2N8Wzt82Bu5uRJkMn7BZsIE8fnyJImfbLVpw1swz5zhXWObNDHcRsPD9aaXuekGFAHIvoBm+ANgzQHwS/N//MGT4GHDeEtKZSej6em8Pu/e1X3+qFG6ZGLIED4y5h9/8NesdR7Ay5d5KwLA1zUzUzcCcsuWj/77tm15wpqZCaxbx1uYBw+uXfdkS9C/nzk5mf8mUlLKn8S7uBj2bPH35/uekhLdo7hY93/9qRnOneMJeocO/CJEbX731VmfBw8ML3hduWLYRVqzXPNv//78t2otGOO9gzRjI4wda5zksF8//htPS+Pbf21aTC0lOJjHo1EjPqK5JXXsyPcBsbF8vIXa3lupGZMhK4tfvJsypXY9UIjl2djhwDbIZDI0a9YMMlvpQ2Xl1A+TzC1Xr+K3a9dwJze33Hu6BQTgx7Fj0a6CeSJMUR+SxHewIry9+VXAHTv4SUePHmInZJcv6+YdGzXK+FdknZ15wgEAjOli9swz1f+M0aONW6bqaNwYePNNy1+hrs52dvIkH8BH08PbxYUPimSMLl01Xf8ePXh57t7l3XMnT67Z5zDGuwdrWsMLCvjJnIarq+HIuBs3AvHxMhQUNIGbm4QBA/hFmNr8TCVJNyz+I8kf9l0uSgWK0gBnfpatUvHWrKIi/lu9fJl3Uy479cLJk7xlXaXiF2VGjeLfq18PnTrxe7jv3OGxnTat5utmKtnZ/IQe4ANj9ev36MS47Lbu789b8g8e5Mn+qVP8YsqkScZp6TCW3Fxeb0VFummk9P/fsye/nxzgv03NNc06dfjJfVAQf/j7G26n1ZmXULNP3bNHBqWSHwPOneMJfc+e/GJLTX/DRUX8d3fvnu7fe/d4N3WlEvi//9MlkSkpfJusTI8euvdqumpaqsVaJpMhP78Frl6VQS7n21NNe/WU5eEB9OnDf8OHD/MLQtY+nZhmmqjQUN2FirLnIZY6/5Qk3Yjsw4bV/vMY47ceOTnx/aat3gdPdCjhNAFJkuClGZ2F1IiaMfyVlIStMTH47do1pJS9UfIhD2dnfDBwIBZ06wZ5JTtYY9VHURE/MA0cWPPJhDt04Cc2HTqIX/1v145fra5TRzf4j6nY2jZs6WSTl+HRMevenScfsbG8Hp94QmyuUFOQyfiJwvff89aPDh10Fx5ESBJPng8f5i3mBQW6Aasq+vnm5gJKpYRGjVwxaRIfvMqs5A/PXBUPCyfjZ5tyOfDMMzxp+uMPnjitXMlbgnv00CUafHAt3ttgzJiKT4gkiV+E+e473uX3xo3qtRiai0rFuyqWlPBEavTo6iX8FW3rzs68m39YGE/UMzL4NjVhQjUvABiJSsWTqoQE3pIXEaG770uh4AlnZfRvsWzQgHcPDgzk3fhru4/RxGzaNN29brGxfJu4cYMnsY89xstbEcb4byYzkz86ddIlX3/+yXvNVEQu58cNTRIZFmZ4/NIMWqb5V9M6f+MGv53CzY13OfXxqdXq10hWloRjx+pCkvhx19j7iO7dgbNneXxOneIJqLkwprtfujrblkrFL+j9/TdPxJ5/vuJB6Cx57K5b1/CimlrN9y3VHj1cj0zGB3Hr1s0y2x4xPko4TUClUiE6Ohrh4eE0Gp0AlVqNPx8mmduuXcNdPv5+pUaGheHrESMQWtGsz/qfq1IhJiamVvWh6c4aE8OvID/9dI0+BpLET1prQi43X5dRY8TM0VQnZs7O/IB84wZPNK0lp2/ShLeynDjBt3GRhFOt1iUpdevyruPV8cQTQG6uCunpMWjSJByAmbcznwjAIwDITwDy4gFJdziUy/nJf5s2/Hd/6xbvXpmczJMQFxd+surrW74rc1mNGvHf/IkTvKtZaKj1tKRERfHkzM2Ntx5Vt1Gkqm09LAyYN4/fz5WYaNjV79QpfuHOx0f38PKqXVc5zUjh8fH8kZjIT3I13N11CWfdurxe3d35w83N8P/691XWpBdLVfRjFhwsR3Awb4U8fZq3OKal8QRUk3Cmp/P76zMz+TEnM5MnKBoBAfwiAcATEG9v/q/m4eenW65fr82aVa9HRbNmfL+QkgJs3szHHTB2F2mVynAk9dxc/u+AAXybKChQ4f79DERENMJjjxm/xc7ZmQ+Qt20bHxuhc2fz3FubmclHac/O5mWoW5c/WrfWDbjDGG9h1ty6s2sX37YliZezsn2INR27T53i+73Ro/m6VceDB3ydNUWnZNN+UMJpIqYYqMbWlJbyE7Xg4Mq7weQUF+NQQgL23ryJ3TdvIr3MtCZl9QoMxJTwcExs0wZBAjdE1bY+zp/nJ+Kaq27GwBiftqJBg6q7Y2Vm8hNbcx87aBsWV52YyWTVP/iaU//+/ARTpDWqpARYu5Z3lRU9Oa9bl5/oZ2RYaDvzfngTtCKXJ5yy8mdwvr58FEvNoEb6XYQlqfqtlf3788Gh3N35CfYjrpGZhULBywTw+8BE7y+taluvUweYMYMnVPrz8l24wBMpfZLEW98aNtSNYg3wk9WCAt2o2PoPNzfdAFdKJW+B1i+OhwdP7Js3N0yuXF3Lj+xtTmVjVr8+bxUeMIB3r9Uva1KSbjA0DZmM/02DBobJX+fO/P5oY3Jy4vfNffcdHzRp3z7j3TJx5Ag/phYU6FpW9XXrxi9EBAYCkydnIjy8ocl6sURE8NZmSeKDfZk64UxJ4fc5FxXx5woFTzyzs3krt0ZxMbB+veHfurjwWx4etd+xhmO3Ws1v1cjP57dPRETwW4qq6h1WXAz88gt/D3WjtT+UcBKTuHJFN0egpye/v6l1a95V9mJaGvbFxWFvXBxO3rkDVRVTwUoAegcFYXJ4OCa1aYMACzQJZWbygy3Ar4Yaqwvk6dP8c/38ePeYiq5YFhXxOa48PPgJnDWcqBL75OLCu21Xl1IJbNrEW/3u3+e/b2PdX2VWaiX/t4KEE+Anop068Za7Eyd4a5PoPsDFhU874+NjPdOjODvz+y5jYvQGUjIiSSo/IE6nTjx+OTm6h1LJW7bKbjvnz/NuuRXx9tYlnM7OPLlUqXQJZuPG1tHNvrrc3csPIhcQwE/S/fx4gtmgQeUXHk21rj4+vOV7/XrelTMw0DiD62haNgH+e9AMKublxf81HLRObdLEQ5J4b4s6dcyzzRw7xo/rAQE8qdLEIj/f8KJPaSnfjjWvNWjAk80KhqmwSjIZvzUhKopPWRYdzbu5jx5d8YCHajWfFuvePb5PsKXfL6keSjiJSbi48GRTLgcy7ivw9n+ykVUnETE+J5GpzKnyb2WShL7BwZjcpg0mtmmDxhYcMk+p5Pc4KRT8ZKZXL+N9docOfEeclcXve6toyO9Dh/jBxs3NukYOJPatuJhfEOndu+ITXLWad0NLSOC/9SeesMFkU1UMlNwHipL5c6nqw2HdurXr3aDf0mct3Nx465i5lL2dgDF+nMjJKT8SbIcOvHulZmRslUr3/7L3hD3xhP2doDZpwpM9S2vRgg8kFRUF/O9/vBVOdCCo4mLegqfpXt2lC7+45elpvkSvKuZsSZs4kd8bPnCg7h7Mii4ke3vzC9EA3+YlyfJxEuXkxEerbtOGd7HPyOAXKdu1462d+q3JUVG8C7mTEx8LgKYNsj+UcJqATCZDq1atHGaUWsZ41yyFQnfVOSwMaNQtEf9N+gNnTjmB3W4KMAlwbgu0uAn4G166dnNywoCQEIxp2RIT27RBIyMeAWpTHwcP8i5gderwwS+MucN3d+dd2dav593HWrfmIxVqJCbyblYAH5TEnFMMONo2bAz2EjPGgDVr+MmBk1P5Sbw10xTExPBkdPr0R4/QWRmLxiwvDkjayv8vSZW2cBqbQsHvF/P1tcxUDOfP8wtpXbvWfH9mrHqTJN39a2WJTB5vCyfitrx/6NuXD3YWH88HvxJJOAsKeBfS7Gw+9oG/f/V76pg7ZqWlvBdDaCi/FchYUlN1vSJcXcXnkxRZfWvczgICeG+KY8f4vu/aNb5NaZLKmBj+GsDPiSw9xQsxDUo4TcSlouHD7Axj/IrU4cM8KXNz41ey3NyA9dGX8eLZ7WAAEArALwOIbQXk1QWK+OXpMF9fjGjRAiPCwtAvOBjuJhxJoyb1UVLCB3EA+EAhprgKGhbG7705d46P7Dh3Lj8gKZV8kACAv27Mg191OcI2bGz2EDNJ4i35O3bwK/Ft2hi2zh05wrdXSeItMLWd0sViMdOMUuvuD7R43mxfe/EicPQo7yZfdsRQU8vI4AMXKZW8dak2c/naw7ZubrYaM5mM/9bj4vgcwtWVm8vv8c7MrHmLlTljdvQo73V08ybw7LPGuZBx8iS//3vwYPG5t2vKGrczJyfeqtu6NT9f1HQNzsjgvcgAPnCdyPZFbIv1XAKxI2q1GtHR0VCr1ZYuiskkJvJWkA0b+M7D1ZXvLGQyYPu1a3h6xw4Y3JnpmQ+3bpfw2OBifPlcOOIWLMCNBQvwQZ8RGNqshUmTzZrWh6sr8MILvAtMTaaJqK6hQ/m9Mjk5fCRMgF/tu3ePJ7mWGOTCEbZhY7OnmHXowBNJpZJ3o9PcZp2QwKdgAPi9OLW998+iMdMknKois/ZX69yZn2wVFpYfFMaUFAp+YqdU8m6StRm4yp62dXOx9Zh5eIglA9nZwE8/8WTTywuYPdtwUJzqMHfMevXix/2UFD4ORW0wxi/O7d/Pn2sGCTI1a9/OmjTR9YQD+PmjWs2PN5Yc0IuYHrVwEiH37vHudAkJ/LmzM58eQDPU/IH4eEz/7TeDgYBmtGuHpzt2RN/gYLjp9QtVqXhXGycn3o2iujfDa+Z2cnU1/QAcbm6mv+Lm6spbUH/+mXd3a96cdzsB+AiGNndvHLF5mvkjV67kv/XLl3kSGhrK7+dycjL+qJhmJ394I6CquOr3Gftr5Ty2a9bw33vHjrrpLUzp4EHemlC3Lt/f2EI3VGKdCgt5D4jHHqu4901mJh9tNC+Pd5/VDJhl7erU4et05AjvudWmTc1uZWGM9yQ4e5Y/N2frpq25dYsnoZMnW8+AasQ0KOEkVSou5gcXTZc6Z2d+AiqX8xPOPn10g9kcT0rC+I0bUao3+sMLkZFYOWoUpArObjIy+FXQkhI+9Hrv3ror/0VFfGh0TbJ18iRw5gxfrpmcW5L4AcLTk8/9p7l6mpnJE2PNyHeiCdvJk3w9IyPNd1IWEsIPSk2b8qvBQUE8Ea1NlzdCasPXl0/ncegQv0rfogX/vQ0YYOmSGYm2hbMESPwV8B8EuJlnCMigIN7Sef48b0F+/nnTTnsUG8v3n4Dpbg8gjuPYMT6X8N27fNvV356ysnjLZmEhH1l15kzbGvCuZ08+Im9ODh84TeReYoBfSN++nbeQShIfob9LF5MU1S6MHWvpEhBzoYTTjsXF8aGo/fx0j+rM51hYyE9QYmJ4chkaqpsfzcuLjy7WsqXhjf/n797FqA0bUKRUapc9ERGBbypJNgF+Y/iLL/ITrhs3+P0T+lq10iWLmrmq9DGmGzJcf51iYvi9Z7r3SSgoaIC2bSX4+fHEVlN2xgyTyuRk3hKgVvNY1fb+NBH6V0BnzeIDGFArBLGknj35PiQ9nZ9I2tXIgTK9K1G5sUADwTPLWho8GLh+nV94O33auCNg68vN5SNEAvw7WrQwzfcQxzFwIB9AKDOTd9OeOVPXOuXtzQcVKinh5w3mvEfZGJyd+frt2MFvH+jUqfrrwBgfhfXGDX5OMmGC2FRThNgzSjhNQCaTISIiwuKjhCUlAZcuGS6TyXgi5efHT3g086Tl5/ORw65d4/dn6nf/z8/nzzWr07274WfGZGZi6Nq1yC0p0S4b16oVfho3DrJHZExeXnx+yatX+UmXTMa75np46IYMB3i31pAQvtzDg7f+FRXxLjt5eYbddTw8+KhoeXm87CqVBA+PxkhMlHD7Nj+J1vjzT37l39eXPzTr3q4dT7QtRZL4OlqKtWzDtsQeYyaX894DGzbwUSqNPXiVRWMmkwNyF0BV+vC5eUap1fDw4Pcs7dzJByrp2pWf7BYU8ItNCkX5h5OT4aTvRUWPvrXg9m3eK6RxYz6PsDHY47ZuavYUMxcXPofk6tX8mHnkCD+fAPg2PGMGP47W9nYQS8WsfXs+cnxaGm/NrWxUWZWKJ93OzkD9+vy4HRbGu4lOm2aZizv2tJ0R+0IJp4mUlpbCzcI337VsyXeEWVl8p5iVxU9ksrL4Y9gw3Xv//pvPg6Th788HBGnThneLqUxCdjaGrF2Le3p3xA9p1gybJk+GczX7iEkST/CquhLo41P+HhDNcPplh9Du2pU/AF0raFpaKQoKXJGdbfg59+/rWkmTknTfNXo0tS5awzZsa+wxZk2aAP/3f6b7PVg0Zg36AGmH+f8fMQ+nKXTsyPdBkZF8Xw0AP/xQvjeHRv36hgnn+vV8gBNPT37xztubP7y8eC+OVq2AiAi+T/PwMG63XXvc1k3NnmLm58cvRm3ZwsccuHmTD7InSYYXi2vLEjGTyfhgfufP6y6wq9X8vCk1VfdIS+ODcHXvznt+Afzco1Ur/hu0FHvazoj9oITTBNRqNWJjYxEREQG5KW/MeYSmTflDgzHe6qdJOL29da/du8ff26ZN+WkQKpOSm4tBv/yC1Lw87bJegYHYPm0aXM05aWQVJAnw8FCjoOB6hfUxfDi/V/T+fX6Sl5fH77dw9H21tWzDtsSeY2aqZNPiMWvYB8g4CqiVZm/hBHhcy7Y6urjwh7Nz+Yf+PhvgF8oY491mc3P5LQEamoQTAAIDjVtui9ebDbLHmLVty3s+nDrFu93HxPBlxmLJmDVrprulpqgIWLGC9zIoy82t/P7RksmmPW5nxD5YR1ZgZb755ht8+umnSEtLQ4cOHfDVV1+hW7duli5WrUkS3xF6eZW/N3HCBLERwjILCjB47Vok5uRol3Xy98fuxx9HHSucA6oybm68BUczKTMhxIEwxpNNwCIJZ0U0rUTV8dJLPOnMzQUePDD819bunSO2acgQ/jOy50Hu3N11iWXjxrpzhiZN+MV5R+8NRUh1UMJZxqZNm7Bw4UKsWrUK3bt3xxdffIFhw4YhNjYWDas7b4cNEkk2c4qLMWzdOlzPytIua+Pnh/1PPgkfR28aJITYDkWO7v8W6FJbEZGTV0nSjcYdEGC6MhFSGblc153Uns2Zw2/hoVsjCakZ+umUsWLFCsyZMwezZ89GeHg4Vq1aBQ8PD6xZs0boc2y1KwNjDGrGoFSrUaJUokihQH5pKXJLSpBdVIS7eXkYtWEDLqSlaf8m1McHB596Cg2seAhLW60PS6KYiaOYibNozFL36f5vJS2ctoK2dXEUM3HWEjMvL9tJNq0lZoTos45LulaitLQU586dwxtvvKFdJpPJMHjwYJw8ebLc+0tKSlCiNzJrnt69jOHh4QB4f3qZTAa1Wg3GmPZ1SZIgk8mg0puzsqrlMpkMkiRVuFzzPdVZLpfLwRiDSqVCdEYG9sXHY29cHP5OTUWJSgW1Xhmro4mnJw488QT869TRrqul1qmi5ZplmvpQqVTa5RXVh7XVU2XrxMrUkynWSRMzxpjdrJN+GU2xTpqYad5rD+tUVdmNsU7h4eHaqZPMvk4u9fjy+l0BNQOgMso6VbXcVutJfzlguE+1h3UydT0Bjz4O2do6maOeyp5L2cM6PWp5bddJ/zhkL+ukX0ZbW6eyrzsqSjj1ZGVlQaVSoVGjRgbLGzVqhOvXr5d7/0cffYQlS5aUW379+nVkZGTA2dkZvr6+CAoKQnJyMu7fv699j7+/P/z9/ZGYmGiQqAYGBqJ+/fq4efMmiouLtcubNWsGLy8vxMTEGGy8rVq1gouLC6Kjow3KEBERgdLSUsTGxmqXFavVuOvujp0xMdgbF4cMvc+vCV9XV3zVtSvyk5MRnZxskXWSy+WIiIhAXl4eEhIStMvd3NzQunVrZGdn486dO1AoFHB2doanpyeaN2+OjIwMpOm10lpTPVV3nTRMtU6amNnTOpm6nhQKBerWrYs2bdrYzToBpq0nhUKB0NBQ+Pn5mX+d6rmjqLgIabfikZsVTfVUzXW6ceMG8vPz4fxwaF17WCdz1JNmn2pP62TqetLEzJ7WydT1pFAo4Orqivbt29vNOgG2W0/p6emoCdHxZLZs2YJ33nkHiYmJCAsLw/LlyzFy5MgafbcpSKzs5QAHlpqaioCAAJw4cQI99SZrfO2113D06FGcPn3a4P1lWzhTUlIQHh6OhIQE5OTkoG3btnBycrLY1SHGGK5mZGBvXBz2xcfjeFISFGXeV1M+bm44+OST6OTvb9Z1qmj5o654KZVKXL16FW3btoVcLreKK161XSdTX8VTqVTamDk7O9vFOpUto7HXST9mLi4udrFOjyp7bddJE7N27drB2dnZ/OuU/TdYyh6ovcOBwMlGWadHLbfFeiq7vLS01GCfag/rZOp6UigUjzwO2do6mbqe9PepmnMpW18nfaaop7LHIXtYp7JltLV1Sk5ORkhICO7cuYOm+lNHVGHTpk2YOXOmwXgyW7ZsqXQ8mRMnTqBv37746KOPMHr0aGzYsAHLly/H+fPn0a6qOQfNiFo49fj5+UEul5e7GpGeng5/vcRKw9XVFa6urtrnubm5AHQboeZArFlWkcr62tdm+enkZPx88SL2xsXh9oMHFb5fX7N69TC8eXM09vSETJIglyT+r0wGmeb/estc5XIMatYMTSsZ+9sU6/So5Zp4lyWTySCXy7Wva95TWX2Ys54etbyqdaqIsddJ8/2a7o72sE6mWK6/Tvr/t5d1qk4Za7NO+t00zb5OqkK+PPdauUkqqZ6qLkvZfeqj3l/d5fa8j6jtccga16m6ZazpOpU9l7KHdTL18sqOSdUpo7Wukz5bW6fKXq+K/ngyALBq1Srs3r0ba9aswb/+9a9y7//yyy8xfPhwvPrqqwCApUuX4uDBg/j666+xatUq4e83BUo49bi4uCAyMhKHDx/G+PHjAfArFIcPH8b8+fMtWzgBF9LSsOrcuUpfd5HL0T8kBCNatMDIsDCE+fpqkwpCCHEY1MGHEEKIGeTl5WkbpoDyjVYaouPJAMDJkyexcOFCg2XDhg3Djh07jFN4I6CEs4yFCxdi1qxZ6NKlC7p164YvvvgCBQUF2qsM1eVmwelBRrRoUW5ZsLc3RoaFYWRYGAaEhNjUXJnGYMn6sFUUM3EUM3EWjZlfTyD3GuBtxNnqHQRt6+IoZuIoZuIoZtZJfzAnAFi8eDHee++9cu8THU8GANLS0ip8v/69q5ZGCWcZ06ZNQ2ZmJt59912kpaWhY8eO2LdvX7mKrIpcLkfr1q1NWMqqBfv4oKO/P3zd3THyYStmaz8/h23FtHR92CKKmTiKmTiLx8zJHWj5ouW+30ZZvN5sEMVMHMVMHMXMesXExCBAb8Lkilo37RklnBWYP39+rbrQqtVq3Lt3D/Xq1au0j7ip/T1nDuQW+m5ro1arkZ2dbdH6sDUUM3EUM3EUM9tE9SaOYiaOYiaOYma9PD094VXJ2Cf6RMeTAfgIuyLvtwTaGk2AMYY7d+6UGy3LnCjZ1LGG+rA1FDNxFDNxFDPbRPUmjmImjmImjmJm+/THk9HQjCejP4OGvp49exq8HwAOHjxY6fstgVo4CSGEEEIIIcQKPGo8mZkzZyIgIAAfffQRAOCll15Cv3798Nlnn2HUqFHYuHEj/v77b6xevdqSq2GAEk5CCCGEEEIIsQKPGk8mKSnJoMt0r169sGHDBrz99tt48803ERYWhh07dljNHJwAJZwm4+npaekiED1UH+IoZuIoZuIoZraJ6k0cxUwcxUwcxcw+VDWeTFRUVLllU6ZMwZQpU0xcqpqTGHX0Nprk5GQEBgbizp07aNq0qaWLQwghhBBCCLEQyg04GlnGBNRqNdLS0qBWqy1dFAKqj5qgmImjmImjmNkmqjdxFDNxFDNxFDNirSjhNAHGGNLS0miUMCtB9SGOYiaOYiaOYmabqN7EUczEUczEUcyItaKEkxBCCCGEEEKISVDCSQghhBBCCCHEJCjhNAFJkuDr6wtJkixdFAKqj5qgmImjmImjmNkmqjdxFDNxFDNxFDNirWiUWiOikagIIYQQQgghAOUGGtTCaQJqtRpJSUk0SpiVoPoQRzETRzETRzGzTVRv4ihm4ihm4ihmxFpRwmkCjDHcv3+fRgmzElQf4ihm4ihm4ihmtonqTRzFTBzFTBzFjFgrSjgJIYQQQgghhJiEk6ULYE80XRju3r2LBw8eoF69epDL5RYuFVGpVEhPT6f6EEAxE0cxE0cxs01Ub+IoZuIoZuIoZtbn7t27AODw3Zwp4TSi9PR0AEDPnj0tXBJCCCGEEEKINUhPT0dQUJCli2ExNEqtESmVSly4cAEeHh5o164dYmJi4OnpaeliOby8vDyEh4dTfQigmImjmImjmNkmqjdxFDNxFDNxFDPro1arkZ6ejk6dOsHJyXHb+SjhNIHc3Fx4e3vjwYMH8PLysnRxHB7VhziKmTiKmTiKmW2iehNHMRNHMRNHMSPWigYNIoQQQgghhBBiEpRwEkIIIYQQQggxCUo4TcDV1RWLFy+Gq6urpYtCQPVRExQzcRQzcRQz20T1Jo5iJo5iJo5iRqwV3cNJCCGEEEIIIcQkqIWTEEIIIYQQQohJUMJJCCGEEEIIIcQkKOEkhBBCCCGEEGISlHASQgghhBBCCDEJSjgJIYQQQgghhJgEJZyEEFINmZmZUKvVli4GIYTYhby8PEsXgRBiJpRwCsrIyMDGjRvx119/4d69e5YujkPLzs7G7du3AQAqlcrCpbENaWlpePPNN7FixQrs2rULAEAzI1UtNTUVvXv3xgsvvICcnBxLF8cmZGZmYu/evbh8+TIUCoWli0OqKTc3F+np6QBAF1eqKS0tDR988AF++uknnDx5EgDtUx8lNTUVPXv2xKJFi1BaWmrp4tiErKwsnDhxAgkJCZYuCiE1QgmngLfeegvNmzfHd999h6FDh2LOnDm4du2apYvlkD7++GMEBQXhrbfeAgDI5XILl8j6ffTRRwgLC8OlS5ewfft2jBs3DkeOHIEkSXSCVInXXnsNwcHBqF+/Pr766iv4+vpaukhW780330RYWBiWLVuGLl264PXXX0dSUpKli0UeYdmyZWjRogW+/vprAIBMRqcHj7JkyRK0aNECx44dw7///W9MmTIFZ8+epX1qFRYtWoTg4GA0aNAAixcvhouLi6WLZPXeeOMNtGnTBi+//DLatWuHzz//nBo8iM2hI0o1PHjwAHPnzsWhQ4fw+++/Y//+/Vi7di0KCgrw/fffW7p4DqWkpAQvv/wytm3bhj59+uD27dvYvn07ALoiX5UzZ85g8+bNWLduHXbv3o1t27Zh1KhRWLNmDQBAkiQLl9C6FBQUoHHjxli9ejUOHDiAnTt3okmTJtRaV4WsrCzMmjULhw4dwo4dO7B792588cUXOHnyJHbs2GHp4pFK5OfnY968edixYwdCQkLw999/46+//gJALXVV2bt3L3bu3ImtW7di//792LRpE8LCwrBz504AtE8tKysrC02aNMH69esRFRWF33//HU2aNLF0saxaamoqpkyZgkOHDmHr1q3YunUrFi1ahB9++AEnTpywdPEIEUIJZyX0D7T37t2DWq3GokWLMGDAALi4uGDixInw9vZGcXFxufcT49LEljEGV1dXNG/eHHPmzMHy5ctRv359rFu3Drm5uZDJZFQPD5WNw759+5CTk4Nx48YBABo0aABnZ2c8/vjjlf6No1Kr1ahTpw6GDh2KkJAQ9OnTBxcvXsScOXPwz3/+EytWrEBsbKz2vY5Mf5tJSkoCYwzLli1D//794ePjg3nz5gHgXTXLvp9Yjn49uLq6IigoCIsWLcJXX32FrKwsbN++HUVFRdRSp0f/OAQAe/bsAQAMHz4cANCuXTvIZDKMHDmy3N8QwM/PD506dUK7du3w2GOP4cKFC5g/fz7eeustbNiwARkZGZYuolXQ32auXbsGSZLwn//8B/369UNQUBDef/99FBQUaLu+0zZGbAUlnBUoLi7G/fv3tc8bNGiABQsWYMqUKQB0J5menp7a+w/oaqZpFBUVabuOaGL8/PPPY86cOYiIiMCoUaOQkpKCn3/+2YKltC76MdNo3749bt++jQ0bNiA2NhYzZszAnj178OGHH2LkyJGIj4936G1Y85tWKpXaZatWrUJsbCzat2+PMWPGoKSkBJmZmVi9ejXGjh2L4uJih+52WFJSgsLCQu3zkJAQLFiwAEOHDgWgi2mDBg20cXXkbcxaFBcXIz8/X/vcyckJ8+bNw/Tp09G9e3eMGDECf/31F/bt2weA6gwwjJkkSVCr1WjRogUyMzNx8OBBJCcnY9q0afj777+xePFizJ07F9nZ2Q4dO00ipL9P/eyzzxAVFYWePXti3LhxyMzMxIkTJ/D6669j5syZDn8Br7S01GA8ioiICMyfPx89e/YEwPepjDEEBARoY+XI2xixLY57tlSJ999/Hz169MCYMWMwdepUxMfHw9PTE+3atQPAf/Cak8zjx4+jV69eAGjQGlNYvHgxwsPDMXz4cDz55JO4ceMGAMDFxUW7s50yZQpatWqFXbt24ebNm9qTAUdVNmaalrghQ4bglVdewdatW9G1a1ekpaXh4MGDWLhwIfLy8vDUU09pW6EczUcffYQRI0YA4CffMpkMSqUS7u7uWLFiBUpLS7Fp0yb8/PPP2Lp1K7Zs2QK1Wo1XXnkFgGO2ci5duhT9+vXDmDFj8OqrryI1NRW+vr7o2rUrAN1+srCwEKdOnUKXLl0A0NV4S1u8eDE6d+6M4cOH46233sLdu3chSRK8vLy02/H8+fPh6uqKnTt3IjU1FYBj15smZiNGjMBbb72F1NRUyGQyjBgxAgMGDMDnn3+O5s2bIysrC7/99htGjx6NP//8E08//TQAx4zdZ599hmeffRYA36dqtG7dGm+99Rby8/OxZcsWrFu3Dn/88QdWrlyJW7duYcmSJZYqssUtW7YMw4cPx7hx4/DVV1/h3r17aNiwIfr27QtAt0/NyMjAlStXEBERYeESEyKIEcYYYzExMaxfv36sbdu27LfffmOffPIJ69GjB+vRo0e596rVanbjxg0WGBjIoqOjLVBa+/f222+zsLAw9vvvv7PPPvuM9e7dmzVr1ozFxMRo36NSqRhjjP3+++/sscceY//617/KvaZWq81bcAuqKGahoaEGMTt+/Djr3bs3u337tnbZ7du3mSRJ7PLly5YotsXExcWxyZMnswYNGjBJkth3333HGGNMqVQavO/w4cOspKTEYFt6//33WZs2bVhubq5Zy2xply9fZj179mRt27ZlGzZsYK+88gqLjIxkkyZNqvD9J0+eZEFBQSwtLc3MJSVlzZ8/n7Vo0YJt2bKFLVy4kHXo0IF17dqV5eXlad+j2fa///571rlzZ/btt99qX3OkfalGRTHr0qULy8/P175n+/btbNCgQez+/fvaZVFRUczV1ZUlJSVZotgWc/XqVTZmzBhWp04d1qhRI7ZlyxbGmOE+NScnhx07dowpFArtcbqwsJDNmTOHjRo1ihUVFVmk7JZy7tw51qVLF9a2bVv2448/smnTprFOnTqxV155pcL379q1i4WFhbHi4mIzl5SQ2qEWzof27t0Ld3d3HD58GBMnTsSrr76K5cuXIykpCVeuXDF4ryRJuHbtGvz8/LQtnwcPHsTSpUstUXS7olarUVRUhKioKEyfPh1jxozBwoUL8ccff2jvD9OMeMkeXjkeM2YMunfvjr/++gtHjhzB5s2b8eKLLwJwjO4mVcUM4FdONdPHXLt2DQUFBQgKCtL+fXR0NAICAhxuePpLly5BLpdj9erVeOWVV7BkyRKUlJRALpcbtFoOHDgQLi4uBvezRUdHw9/fHy4uLg7TgqFQKLB9+3Y0btwYR48exYwZM7BixQrMnz8fiYmJFY5EGx0djVatWqFRo0YAgKioKKxatcrcRXdojDFkZWXh+PHjePXVVzF58mR89tln2Lp1KxISEvDuu+9qu0Zr9pfPPvssgoODsX//fly4cAG//fYb3n33XUuuhllVFbNbt27h7bff1s4hee3aNTg5OaFevXrav7958yaaNGmiHePBUZw4cQKSJGHNmjUYNmwYvvzyS5SWlhrsU729vdGnTx9tbxK1Wg13d3dcu3YNLi4ucHV1tfBamE9+fj5+/fVXtG7dGn/99Rf+8Y9/YOPGjRg1ahTi4uIqnIbr/Pnz6Nq1qzZOf/31F7Zt22bmkhMijhLOh3r06IH58+ejUaNGBvceyOVy+Pj4lHv/zp070a9fP9y7dw8jRozAyJEjHbJrnbHJZDKUlJQgJiZG2z2vuLgYTk5O+Prrr3Ho0CFERUWBMWZwEHv88cdRVFSE0aNH48knn0SdOnUsuRpmVZ2YHT16FACfPkapVOK7775DXl4e4uPj8dVXX6FHjx5o27atJVfDbDS/7+HDh2PhwoUYP348nnrqKXh5eeG1116r8m8lScK5c+dw9+5dzJw5E66urg5xUQPgcWvbti3mzZuH+vXra397Li4uyMrKMjjh1ti1axcGDBiA9PR0jBw5EoMHD0ZJSYm5i+7QJEmCSqXC5cuXtfsHpVKJFi1a4IsvvsA333yDv//+GwC0CQAAzJs3D1euXMGQIUMwY8YMh5q+4lExW7lyJS5cuAAAKCwsRHFxMXbs2AGlUom4uDhs3rwZ/fv3R/PmzS25Gmaj2adOmzYNixYtwtSpUzFhwgTk5eVhxYoVVf6tTCbDiRMnoFQqMXv2bIfZnwI8bqGhoZg7dy68vb2197t6e3sjNjYWXl5e5f5m//79GDRoEFJSUjBy5Ej069dPe/GDEKtmmYZV66bp5vHbb7+x1q1bl+s2l52dzVq0aMGCg4OZi4sLGzNmDMvKyrJEUe2OptvWkCFD2IQJExhjuvpgjLERI0awQYMGGXQnSU5OZs8//zyTJIn94x//MOja5AiqE7MBAwYwxhhLTExkL7/8MpMkifXu3Zv5+Piw6dOnswcPHpi/4FakoKCArVixgnl5ebHY2FjGmGE3sMTERLZlyxb2wgsvME9PTzZnzhyH6/rFmGG3Ss02tnLlSta7d29WWlpq8N6UlBTm7+/PunbtylxcXNjYsWNpP2kh2dnZrHv37mzBggWMMcN6jIyMZDNmzGCM6eo0MTGRPffcc0ySJDZ79mx279498xfawh4Vs6lTpzLGGLt06RKbPHkyc3Z2ZsOGDWNeXl5sxowZDtfdvqysrCy2cOFC1q5dO5aYmMgYM9yn3rx5k+3Zs4e9+OKLzMvLi82bN88hu4nqx0Tz+3v77be125e+2NhYVq9ePTZixAjm4uLCxo0bR/tUYjOohROV39R/9OhRdOzYEZ6engatl/n5+VCr1fD398fx48fx+++/o379+uYqrl3TdFucMGEC/v77b5w8eRIymQxFRUUAgPfeew9HjhwxGEJ9586dOHr0KE6dOoUff/yxwpYWe1admEVFRSEpKQnBwcH48MMPERUVhblz5+Lo0aP49ddfK7ySas/0f/OMMXh4eGDMmDHo3LkzXn75ZQC8NVjj/v372L9/P+Li4nDo0CGsXr0abm5u5i62RTHGKmx9OHHiBCIjI+Hs7Gywn0xPT0dhYSGcnZ1x7Ngx7Ny5k/aTFuLh4YF+/frh7NmzuHLlCiRJ0nahf/3117Fjxw7t1FIAsHbtWmzfvh2nT5/GmjVr4Ovra8niW8SjYrZr1y48ePAA7du3x1dffYWtW7dizJgxOHr0KDZs2ABPT08Lr4HlMMZQv359jB07Fj4+Pvjoo48AGO5Tb926hTVr1uDq1as4ePAgvvnmG4fqTgtA21NLQ7N/PX/+PDp37qx9j0ZCQgJycnKQm5uLo0ePYseOHbRPJbbDYqmuGeXn57OSkpIKX1MoFJX+Xfv27dn333+vfX79+nXGGG8NuXjxonEL6SAyMzPZrVu3tFd/9a8a69fF1atX2dChQ9mwYcMM/j46Opr5+/uzvXv3mqfAVsBYMduzZ495CmwFqhsz/ecqlYpt2rSJeXt7s927dzPG+OAfWVlZTK1Ws4yMDDOV3jJEYqa5Eq9UKllQUBDbvn279rW4uDjGGGP3799nf/31l4lLTfS338peY4yxI0eOsF69erEXXnjB4D179+5lwcHB7Ny5c6YtqBUxVszOnj1r2oJakerGTP95aWkp+/jjj1mrVq3Yn3/+yRhj2n1CSUmJ3Q+qJBIzzXtycnJY/fr12dGjR7WvaVqIMzIy2IEDB0xVXEJMyu4Tzv/7v/9jERER7NSpU5W+R61WszfffFM7cqdarWbR0dGscePG7Pbt2yw5OZlNmTKFSZLErly5Yq6i2xW1Ws3++c9/spCQENahQwfWsmVLdubMmXLvU6lUbPHixYwxxrZs2cIaNmzIPvzwQ+3OeePGjaxdu3YO0W2WYiauujFTq9Xs7bffNnjOGGNpaWns6aefZs2aNWMjR45kkiTZfdJU05gxxtjRo0dZ06ZNWW5ursF+Mjk52VzFd2j//Oc/2dChQ8stL9v1+T//+Q9jjLHly5ezVq1asR9//FH7+qpVq1inTp0cpos4xUxcdWKmVqvZihUryr0WHR3NJk6cyHr37s1GjBjBJEliV69eNX2hLawmMWOM38rVvHlzxhjT7lMbNGhAI30Tm2e3CeedO3fYlClTWGRkJHNxcWHz5883GP5d48cff2QNGzZkbdq0YTdv3tQu37x5M2vXrh175513mLu7Oxs+fLj2KhMREx0dzfr06cO6d+/Ojh07xvbu3cuGDx/OOnTowAoLC7Xv++GHH1jjxo1Z8+bN2d27d1lRURH7/vvvmbu7O+vZsyd7+umnWZ06ddjrr7/OFAqFXQ/TTzETJxqzli1blvtN3717l40aNYpJksQmTZpkMH2MPaptzL799lvWt29ftnTpUtpPmlFMTAwbOXIkCwoKYpIksXXr1jHGyrekfP/996xRo0asa9eu7MGDB+zu3bvsnXfeYZIksQkTJrDnnnuOeXp6smXLljGVSmXX+weKmTjRmPXo0YOlpKQYvJaWlsYee+wxJkkSmzhxot3vU2sbsw8++IBNnjyZffDBB8zd3Z0NGTLE7luCiWOw24QzOjqavfTSS+zMmTNsw4YNzNnZmR08eNDgPVeuXGGPP/44W716dbm595555hkmSRLr0KED279/vzmLbnc+//xzNmrUKINWj/T0dObi4qLtmnzs2DE2dOhQ9sMPP5Sri71797KPP/6YzZo1ix05csSsZbcUipm42sbs5s2brHv37iw4OFjb/cve1TZmo0ePZpIksXbt2tF+0ox+++039swzz7AjR46wl19+mfn7+5cbtGnXrl2sU6dOFdbbL7/8wl577TU2ceJEdvjwYXMW3WIoZuJqG7NLly6xsLAw1qJFC3b8+HFzFt1iahuzbt26MUmSWJs2bWifSuyK3SWc+hMJ618V6t69Oxs6dChLT083eH9lo6Lt3r2b/fLLL6YrqAPQdOm8efMm27dvn8Frly9fZqGhoezSpUvaZWW7J9nzlePKUMzE1TZmGsXFxeyPP/4wWTmtiTFiplAo2KpVq7RX8InplG0dycrKYjExMYwxxm7dusWaNGnC/vWvfzHGDEe9zM/Pr/Jz7BnFTJyxYqZRWFjIdu7caaLSWgdjxiw/P5+9+eabtE8ldklizPZnLV+zZg0CAwMxZMiQcq+pVCrI5XJcvnwZHTt2xE8//YQnn3zSYGQwYjxV1QWgq48DBw5g1qxZiImJqdaosqySETLtAcVMnKliZs8oZrbp/fffx61bt9CsWTPtHKj6VCoVvv32WyxcuBBxcXEICgqCWq3WjjjriChm4owdM3s+/mjQdkZI9dn0Vv/XX38hMjISzz77LDZu3Ii7d+8CMBxGWi6XQ61Wo3379njyySfx8ccf4/bt2+U+yw7ybouqTl0AumG/Dx8+jG7duqFevXrVir09HrgoZuJMHTN7RDGzTXfu3EFkZCS2bt2KOnXqYOXKlRg+fDi2bt0KQFd/crkc06dPR4cOHfDSSy8BgMOe0FLMxJkqZvZ4/NGg7YwQcTa75efk5GDTpk3o0qULPvjgA0RFRSEqKgpA5Tu6b7/9Frdv38aGDRuQk5OD7du3Y8eOHVX+DXk0kbrQ7GzPnj2LoUOHat9z9uxZxMXFmbXclkQxE0cxE0cxs11HjhyBWq3Gn3/+ia+//hpxcXFo0qQJvvzyS1y6dAmSJEGpVAIA/Pz8sHjxYuzcuRPHjh0DABw4cAA3btyw5CqYHcVMHMVMHMWMkBowcxdeoykpKWGHDx9m58+fZ4wxNnToUDZmzBjtXJll72XT9J3/5JNPmIeHB2vZsiVzc3NjW7ZsMW/B7ZBoXdy6dYsFBQWxK1eusOvXr7MBAwYwNze3KqeusTcUM3EUM3EUM9v13nvvsa5duxrM13f06FE2aNAgNmPGDO0yTR0WFhay6dOns5CQENa9e3fm7u7OTp8+bfZyWxLFTBzFTBzFjBBxNtvC6eLigoEDB6JTp04AgPfeew/nzp3Dvn37UFpaWu7qvVwuR0JCAmJiYlBUVISBAwciMzMTkydPtkTx7Up164I97GZy+fJlKBQKfPnll2jbti38/f2RkZGB7t27W2wdzI1iJo5iJo5iZruKi4vh5OSEjIwM7bK+fftixIgRuHbtGg4dOgRAV3cpKSm4d+8ebt++jYiICKSnp6Nbt24WKbulUMzEUczEUcwIEWezCac+tVqNnj17YuTIkfj1119x/vz5cu/Jy8vD66+/jmPHjuHy5cv49ttvUbduXQuU1r5VVReak9s//vgDaWlpSEhIwJkzZ7BhwwZ4enpaqsgWRzETRzETRzGzDWq1GgAwa9YsnDp1CmfOnDF4ffDgwXB1dcW5c+cA8K7QsbGxePzxx5Gamoro6Gh8//33DlVvFDNxFDNxFDNCasFyjatV03RFqGhIcv1uDPrPU1NTWWhoKPvXv/7FHjx4wBhjLC4uTvue1NRUUxbZbhmrLmJjY7X/bt261ZRFtjiKmTiKmTiKmW2qbGoexgzrbcqUKaxTp04sMzPT4D3du3dnCxYs0D7Pzc3Vzptqryhm4ihm4ihmhJiG1bVwKhQKvPfee/j2228BGI7opbm65OTkBIVCgb/++kv7XKVSoXHjxnj++eexa9cu/PDDDxgyZAhmz56N/Px8ODk5oXHjxuZfIRtm7Lp49tlnkZeXh5YtW2LSpEnmXyEzoJiJo5iJo5jZptLSUrzyyit44oknMHPmTPz555/a1xQKBQBeT6WlpYiLi8O///1vXL9+HZ9//jkePHgAAFAqlXB1dTWYssbT0xMdOnQw78qYCcVMHMVMHMWMEBOzdMarb//+/axTp05MJpOxvn37shs3bjDGyl+9//LLL5mnpyd74403WGFhIWNMd6U/KSmJOTk5MUmS2Pjx48tdfSLVQ3UhjmImjmImjmJmm7Zv384CAwNZ//792X/+8x8WERHBHnvssXItyl9++SXz8PBgy5cvZ4wxtnr1ataiRQs2bNgwtnPnTvbKK6+wxo0bszNnzlhiNcyKYiaOYiaOYkaI6VlVwjl//nz2zDPPsFWrVrG+ffuy119/vdx7XnvtNebr68vWrVtXboTFLVu2MEmSWNeuXbWjMpKaoboQRzETRzETRzGzPXFxcWzixIls8eLF2mUZGRlswIAB7OOPP2aMMVZcXMxeeOEF1rBhQ7Z27VqDCwi7du1iI0eOZD179mRdunRxiFGDKWbiKGbiKGaEmIdVJJyaE6IrV66w6Ohoxhhjr776KuvVqxeLiopijOmmNcnIyNDed1TW2bNn2XfffWeGEtsvqgtxFDNxFDNxFDPbo6mzmJgYtmTJEpaQkMAY090LNnjwYPbss88yxngL9Y0bNwzqrWyrdVpamjmKbVEUM3EUM3EUM0LMy2IJp2YOoooGu2CMnxQNGTKEzZo1S/ueslfqiXFQXYijmImjmImjmNkmTb1pLgBUpKSkhPXq1Yv9+OOP5iqWVaOYiaOYiaOYEWIZZh80aMeOHQgICMCIESOQmJgImUymHeRCX5cuXTBkyBBcv34dv/76q7mL6RCoLsRRzMRRzMRRzGxT2XqTy+VQqVTa19nDefkAPhBJZmYm2rZta4miWg2KmTiKmTiKGSGWZdaEc/369fjwww/Rt29fhIeH4+OPP+aFkBkWQ3NiNW3aNDRt2hSbNm1CdnY2ACA6OhoADHYURBzVhTiKmTiKmTiKmW2qrN7kcrn2PZr5TgHg+PHj2pGBNdLT0wGgwosL9ohiJo5iJo5iRojlmSXh1Jz0tGjRAoMGDcLy5csxduxYREVFISoqyuA9gO7EKigoCOPHj0d2djYWLVqEjh07YvDgwVAqlQY7ClJ9VBfiKGbiKGbiKGa2SbTeNLZv344BAwagXr16uHDhAgYMGIC5c+dCrVaXu7hgbyhm4ihm4ihmhFgRU/bXvXHjRrn7iTQ3ZF+5coWNHTuWjRw5Uvua/ns1/z937hyrX78+kySJzZ07lxUXF5uyyHaL6kIcxUwcxUwcxcw21abeVCoVGzduHPv000/Z/PnzmUwmYzNnzmSlpaXmKbyFUMzEUczEUcwIsT4mSTg3bdrEQkJCWKtWrVi3bt0MbrzW/2GvWbOGhYeHszVr1jDGyg+MsX79eiaXy9mAAQNYfHy8KYpq96guxFHMxFHMxFHMbJMx6i0pKYlJksQkSWK9evViMTEx5lsBC6CYiaOYiaOYEWK9jJ5wHjhwgIWEhLBvvvmG7du3jy1cuJA5Ozuz1atXaycf11xpSk5OZs888wzr2rUry8vLY4wxg6tI165dY7t27TJ2ER0G1YU4ipk4ipk4ipltqm29lZSUMMZ4K8u0adPYwYMHLbMiZkQxE0cxE0cxI8S6GS3h1Fw9WrJkCYuMjDQ4IZo3bx7r0qUL27ZtW7m/+9///se6dOnCFi9ezC5dusRGjx7NkpKSjFUsh0R1IY5iJo5iJo5iZpuMVW+jRo1ymHqjmImjmImjmBFiG4x297NmhK+YmBg0b94czs7OUCgUAIBly5bBzc0NO3fuRFpaGgDdjdoDBgxAt27d8P777yMyMhIKhQINGzY0VrEcEtWFOIqZOIqZOIqZbTJWvSmVSoepN4qZOIqZOIoZITaippnqgQMH2IIFC9jnn3+unUiXMcZWr17NPD09tZPqaq42rV69mrVs2ZJFRUVp35ufn88+//xzJpfLWf/+/dnly5drWhyHRnUhjmImjmImjmJmm6jexFHMxFHMxFHMCLFNwglnamoqGz16NGvYsCF74oknWEREBPP29tb+8GNjY1lAQAB75513GGO6fvGMMebv788+//xz7fOrV6+y7t27s19++aWWq+GYqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIbZNKOEsKChgs2bNYtOmTWMJCQna5d26dWNPP/00Y4yx3NxctmzZMubu7q7tD6/pY9+vXz/27LPPGqvsDo3qQhzFTBzFTBzFzDZRvYmjmImjmImjmBFi+4Tu4fTw8ICrqyuefvpphIaGQqlUAgBGjhyJa9eugTEGT09PPP744+jcuTOmTp2K27dvQ5IkJCUlISMjA+PHjzdFz2CHQ3UhjmImjmImjmJmm6jexFHMxFHMxFHMCLF9EmOMifyBQqGAs7MzAECtVkMmk+GJJ55AnTp1sHr1au37UlJS0L9/fyiVSnTp0gUnTpxA69atsWHDBjRq1Mi4a+GgqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIbZNOOGsSO/evTFnzhzMmjULarUaACCTyRAXF4dz587h9OnT6NChA2bNmlXrApOqUV2Io5iJo5iJo5jZJqo3cRQzcRQzcRQzQmxHrRPOhIQE9OrVC7t370ZkZCQAoLS0FC4uLkYpIKk+qgtxFDNxFDNxFDPbRPUmjmImjmImjmJGiG2p8Tycmjz1+PHjqFu3rvYHv2TJErz00kvIyMgwTgnJI1FdiKOYiaOYiaOY2SaqN3EUM3EUM3EUM0Jsk1NN/1Az2e6ZM2cwadIkHDx4EM899xwKCwuxdu1amkDXjKguxFHMxFHMxFHMbBPVmziKmTiKmTiKGSE2qjZD3BYVFbEWLVowSZKYq6sr+/jjj2vzcaQWqC7EUczEUczEUcxsE9WbOIqZOIqZOIoZIban1vdwDhkyBGFhYVixYgXc3NyMlQeTGqC6EEcxE0cxE0cxs01Ub+IoZuIoZuIoZoTYllonnCqVCnK53FjlIbVAdSGOYiaOYiaOYmabqN7EUczEUczEUcwIsS1GmRaFEEIIIYQQQggpq8aj1BJCCCGEEEIIIVWhhJMQQgghhBBCiElQwkkIIYQQQgghxCQo4SSEEEIIIYQQYhKUcBJCCCGEEEIIMQlKOAkhhBBCCCGEmAQlnIQQQgghhBBCTIISTkIIIYQQQgghJkEJJyGEEKLn559/hiRJ2oebmxuaNGmCYcOG4T//+Q/y8vJq9LknTpzAe++9h5ycHOMWmBBCCLFilHASQgghFXj//fexdu1afPvtt1iwYAEA4OWXX0ZERAQuX74s/HknTpzAkiVLKOEkhBDiUJwsXQBCCCHEGo0YMQJdunTRPn/jjTdw5MgRjB49GmPHjsW1a9fg7u5uwRISQggh1o9aOAkhhJBqGjhwIN555x3cvn0b69atAwBcvnwZTz/9NJo1awY3Nzf4+/vjH//4B+7du6f9u/feew+vvvoqACA0NFTbXTcxMVH7nnXr1iEyMhLu7u7w9fXF9OnTcefOHbOuHyGEEGJslHASQgghAp566ikAwIEDBwAABw8eREJCAmbPno2vvvoK06dPx8aNGzFy5EgwxgAAEydOxIwZMwAAn3/+OdauXYu1a9eiQYMGAIAPPvgAM2fORFhYGFasWIGXX34Zhw8fRt++fakLLiGEEJtGXWoJIYQQAU2bNoW3tzfi4+MBAPPmzcP//d//GbynR48emDFjBo4fP44+ffqgffv26Ny5M3799VeMHz8eISEh2vfevn0bixcvxrJly/Dmm29ql0+cOBGdOnXCypUrDZYTQgghtoRaOAkhhBBBdevW1Y5Wq38fZ3FxMbKystCjRw8AwPnz5x/5Wdu2bYNarcbUqVORlZWlffj7+yMsLAx//PGHaVaCEEIIMQNq4SSEEEIE5efno2HDhgCA+/fvY8mSJdi4cSMyMjIM3vfgwYNHftbNmzfBGENYWFiFrzs7O9e+wIQQQoiFUMJJCCGECEhOTsaDBw/QokULAMDUqVNx4sQJvPrqq+jYsSPq1q0LtVqN4cOHQ61WP/Lz1Go1JEnC3r17IZfLy71et25do68DIYQQYi6UcBJCCCEC1q5dCwAYNmwYsrOzcfjwYSxZsgTvvvuu9j03b94s93eSJFX4ec2bNwdjDKGhoWjZsqVpCk0IIYRYCN3DSQghhFTTkSNHsHTpUoSGhuKJJ57QtkhqRqPV+OKLL8r9bZ06dQCg3KizEydOhFwux5IlS8p9DmPMYHoVQgghxNZQCychhBBSgb179+L69etQKpVIT0/HkSNHcPDgQQQHB+P333+Hm5sb3Nzc0LdvX3zyySdQKBQICAjAgQMHcOvWrXKfFxkZCQB46623MH36dDg7O2PMmDFo3rw5li1bhjfeeAOJiYkYP348PD09cevWLWzfvh3PPfccFi1aZO7VJ4QQQoyCEk5CCCGkApousi4uLvD19UVERAS++OILzJ49G56entr3bdiwAQsWLMA333wDxhiGDh2KvXv3okmTJgaf17VrVyxduhSrVq3Cvn37oFarcevWLdSpUwf/+te/0LJlS3z++edYsmQJACAwMBBDhw7F2LFjzbfShBBCiJFJrGz/HUIIIYQQQgghxAjoHk5CCCGEEEIIISZBCSchhBBCCCGEEJOghJMQQgghhBBCiElQwkkIIYQQQgghxCQo4SSEEEIIIYQQYhKUcBJCCCGEEEIIMQlKOAkhhBBCCCGEmAQlnIQQQgghhBBCTIISTkIIIYQQQgghJkEJJyGEEEIIIYQQk6CEkxBCCCGEEEKISVDCSQghhBBCCCHEJP4f4XrYMYDRYhAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# dictionary to store color, alpha, linewidth, and linestyle for each line\n", "line_styles = {\n", From 581b039182481ee1cf863ca1850d6315b46387d1 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:26:58 -0400 Subject: [PATCH 30/44] mask output of pip install treeffuser --- examples/m5-news-vendor-notebook.ipynb | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index df6800ba..e4e1f6cb 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -24,22 +24,29 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-10T19:30:25.237555Z", - "start_time": "2024-10-10T19:30:23.907554Z" - } - }, + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], "source": [ + "%%capture\n", "!pip install treeffuser" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 172, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", From 1e77c43cbb36e9fe7d4b0ee32b858d5028531999 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:35:56 -0400 Subject: [PATCH 31/44] add anon output --- examples/m5-news-vendor-notebook.ipynb | 1307 +++++++++++++++++++++++- 1 file changed, 1276 insertions(+), 31 deletions(-) diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index e4e1f6cb..f232420f 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 175, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -131,9 +131,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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3HOBBIES_1_004_CA_1_validationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CA0000...1054101372
4HOBBIES_1_005_CA_1_validationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CA0000...2110112224
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5 rows × 1919 columns

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\n", + "
" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales\n", + "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", + "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", + "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", + "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", + "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", @@ -207,9 +741,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 182, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Number of sales over the entire timespan')" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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item_iddept_idcat_idstore_idstate_iddsalesday_numbersales_lag_1sales_lag_2...yearevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIdaysell_price
0HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14101410.00.0...2011NaNNaNNaNNaN000183.97
1HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14201420.00.0...2011Father's dayCulturalNaNNaN000193.97
2HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14301430.00.0...2011NaNNaNNaNNaN000203.97
3HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14411440.00.0...2011NaNNaNNaNNaN000213.97
4HOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_14501451.00.0...2011NaNNaNNaNNaN000223.97
\n", + "

5 rows × 53 columns

\n", + "
" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales \\\n", + "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", + "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", + "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", + "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", + "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", + "\n", + " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", + "0 141 0.0 0.0 ... 2011 NaN \n", + "1 142 0.0 0.0 ... 2011 Father's day \n", + "2 143 0.0 0.0 ... 2011 NaN \n", + "3 144 0.0 0.0 ... 2011 NaN \n", + "4 145 1.0 0.0 ... 2011 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", + "0 NaN NaN NaN 0 0 0 18 \n", + "1 Cultural NaN NaN 0 0 0 19 \n", + "2 NaN NaN NaN 0 0 0 20 \n", + "3 NaN NaN NaN 0 0 0 21 \n", + "4 NaN NaN NaN 0 0 0 22 \n", + "\n", + " sell_price \n", + "0 3.97 \n", + "1 3.97 \n", + "2 3.97 \n", + "3 3.97 \n", + "4 3.97 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "n_lags = 30\n", "\n", @@ -285,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 185, "metadata": {}, "outputs": [], "source": [ @@ -318,9 +1089,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 186, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15216, 50)\n", + "(3891, 50)\n" + ] + } + ], "source": [ "is_train = data[\"day_number\"] <= 300\n", "\n", @@ -355,18 +1135,464 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 187, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" + ] + } + ], "source": [ "print(\", \".join(map(str, X_train.columns)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 188, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:234: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " Returns\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", + "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" + ] + }, + { + "data": { + "text/html": [ + "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Treeffuser(extra_lightgbm_params={}, seed=0)" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model = Treeffuser(seed=0)\n", "model.fit(X_train, y_train)" @@ -374,9 +1600,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 189, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [04:43<00:00, 2.84s/it]\n" + ] + } + ], "source": [ "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" ] @@ -410,7 +1644,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ @@ -448,9 +1682,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 191, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "185.07666666666668" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we don't know the profit margin of each item, so we assume it is 50%.\n", "profit_margin = 0.5\n", @@ -477,7 +1722,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 192, "metadata": {}, "outputs": [ { @@ -536,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 193, "metadata": {}, "outputs": [ { From 43717778624823dfbcc6d2b027ece039a0329e81 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:42:08 -0400 Subject: [PATCH 32/44] remove CastFloat32Warning for cleaner output --- src/treeffuser/_base_tabular_diffusion.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index cd5fa9c2..bda05032 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -14,7 +14,6 @@ from tqdm import tqdm from treeffuser._score_models import ScoreModel -from treeffuser._warnings import CastFloat32Warning from treeffuser._warnings import ConvergenceWarning from treeffuser.scaler import ScalerMixedTypes from treeffuser.sde import DiffusionSDE @@ -39,11 +38,6 @@ def _check_array(array: ndarray[float]): if not np.issubdtype(array.dtype, np.floating): try: array = np.asarray(array, dtype=np.float32) - warnings.warn( - "Input array is not float; it has been recast to float32.", - CastFloat32Warning, - stacklevel=2, - ) except ValueError as e: # raise the ValueError preserving the original exception context, see B904 from flake8-bugbear raise ValueError( From effc5a5ea8d178981aface198d4264e8fca8c08b Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:43:12 -0400 Subject: [PATCH 33/44] add output to readme notebook --- examples/README_example.ipynb | 75 ++++++++++++++++++++++---- examples/m5-news-vendor-notebook.ipynb | 2 +- 2 files changed, 65 insertions(+), 12 deletions(-) diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb index 05fcf404..4df475a9 100644 --- a/examples/README_example.ipynb +++ b/examples/README_example.ipynb @@ -25,10 +25,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "!pip install treeffuser\n", "\n", "import matplotlib.pyplot as plt\n", @@ -46,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -92,9 +93,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 1.29it/s]\n" + ] + } + ], "source": [ "model = Treeffuser(sde_initialize_from_data=True, seed=seed)\n", "model.fit(x, y)\n", @@ -118,9 +127,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.scatter(x, y, s=1, label=\"observed data\")\n", "plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label=\"Treeffuser samples\")\n", @@ -144,9 +164,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 439.38it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the samples: [-0.02825952]\n", + "Standard deviation of the samples: [0.99196348] \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "x = np.array(np.pi).reshape((1, 1))\n", "y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100\n", @@ -168,9 +211,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of the samples: [-0.02825952]\n", + "Standard deviation of the samples: [0.99196348] \n", + "5th and 95th quantiles of the samples: [-1.24177638 1.14385214]\n" + ] + } + ], "source": [ "from treeffuser.samples import Samples\n", "\n", diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index f232420f..78c60080 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To get started, we first install `treeffuser` and import the relevant libraries (if needed)." + "To get started, we first install `treeffuser` and import the relevant libraries." ] }, { From 469c9ecb0d68447c13456f2f6da71e7a1e5443c2 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 16 Oct 2024 17:57:34 -0400 Subject: [PATCH 34/44] remove CastFloat32Warning from _warnings too --- src/treeffuser/_warnings.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/treeffuser/_warnings.py b/src/treeffuser/_warnings.py index 0772ac28..6365c12d 100644 --- a/src/treeffuser/_warnings.py +++ b/src/treeffuser/_warnings.py @@ -1,6 +1,2 @@ class ConvergenceWarning(Warning): pass - - -class CastFloat32Warning(Warning): - pass From 54e9078659481c1bdb7a7de3564f22e3257a2ecd Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Tue, 22 Oct 2024 19:57:56 -0400 Subject: [PATCH 35/44] update output after restarting kernel --- examples/README_example.ipynb | 28 ++--- examples/m5-news-vendor-notebook.ipynb | 168 +++++++++++-------------- 2 files changed, 86 insertions(+), 110 deletions(-) diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb index 4df475a9..b48ee079 100644 --- a/examples/README_example.ipynb +++ b/examples/README_example.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -93,14 +93,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 1.29it/s]\n" + "100%|██████████| 1/1 [00:01<00:00, 1.02s/it]\n" ] } ], @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -164,22 +164,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:00<00:00, 439.38it/s]" + "100%|██████████| 100/100 [00:00<00:00, 435.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Mean of the samples: [-0.02825952]\n", - "Standard deviation of the samples: [0.99196348] \n" + "Mean of the samples: [-0.05467086]\n", + "Standard deviation of the samples: [0.99499814] \n" ] }, { @@ -211,16 +211,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean of the samples: [-0.02825952]\n", - "Standard deviation of the samples: [0.99196348] \n", - "5th and 95th quantiles of the samples: [-1.24177638 1.14385214]\n" + "Mean of the samples: [-0.05467086]\n", + "Standard deviation of the samples: [0.99499814] \n", + "5th and 95th quantiles of the samples: [-1.25918297 1.06402459]\n" ] } ], diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb index 78c60080..39eb21ec 100644 --- a/examples/m5-news-vendor-notebook.ipynb +++ b/examples/m5-news-vendor-notebook.ipynb @@ -25,29 +25,13 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%%capture\n", - "!pip install treeffuser" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ + "!pip install treeffuser\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", @@ -102,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -210,7 +194,7 @@ "4 CA_1 HOBBIES_1_001 11329 8.26" ] }, - "execution_count": 178, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -228,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -380,7 +364,7 @@ "4 NaN NaN NaN 1 0 1 2 " ] }, - "execution_count": 179, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -596,7 +580,7 @@ "[5 rows x 1919 columns]" ] }, - "execution_count": 180, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -614,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -711,7 +695,7 @@ "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" ] }, - "execution_count": 181, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -741,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -750,7 +734,7 @@ "Text(0.5, 1.0, 'Number of sales over the entire timespan')" ] }, - "execution_count": 182, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -793,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -822,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1027,7 +1011,7 @@ "[5 rows x 53 columns]" ] }, - "execution_count": 184, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1056,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1089,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1135,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1152,17 +1136,9 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/agrande/Desktop/treeffuser/src/treeffuser/_base_tabular_diffusion.py:234: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", - " Returns\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1178,7 +1154,7 @@ { "data": { "text/html": [ - "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
Treeffuser(extra_lightgbm_params={}, seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Treeffuser(extra_lightgbm_params={}, seed=0)" ] }, - "execution_count": 188, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1600,14 +1576,14 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [04:43<00:00, 2.84s/it]\n" + "100%|██████████| 100/100 [05:55<00:00, 3.55s/it]\n" ] } ], @@ -1644,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1682,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1691,7 +1667,7 @@ "185.07666666666668" ] }, - "execution_count": 191, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1722,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1781,7 +1757,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 18, "metadata": {}, "outputs": [ { From ab2d54350f577c68ada05ee9e8b3875f14f5ab67 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 23 Oct 2024 12:35:14 -0400 Subject: [PATCH 36/44] add homepage to pyproject.toml --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 1557a56d..0bf72176 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ authors = [ license = "MIT" readme = "README.rst" packages = [{include = "treeffuser", from= "src"}] +homepage = "https://blei-lab.github.io/treeffuser/" repository = "https://github.com/blei-lab/treeffuser" include = [ "pyproject.toml", From d3632ca43ce7307ef2c050a5e063de3d7b79fdcc Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 23 Oct 2024 12:51:12 -0400 Subject: [PATCH 37/44] add homepage badge --- README.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 38fb5ae5..27eb5d34 100644 --- a/README.rst +++ b/README.rst @@ -7,9 +7,6 @@ Treeffuser GitHub repo stars - - PyPI version - PyPI - Downloads @@ -18,6 +15,12 @@ Treeffuser Documentation + + + PyPI version + + + Website

From 3e9e0512d978862b0cb65e3ed043753693bf08a1 Mon Sep 17 00:00:00 2001 From: aagrande <47125106+aagrande@users.noreply.github.com> Date: Wed, 23 Oct 2024 12:56:43 -0400 Subject: [PATCH 38/44] reorder badges --- README.rst | 29 ++++++++++++++++++----------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/README.rst b/README.rst index 27eb5d34..e23200ab 100644 --- a/README.rst +++ b/README.rst @@ -4,23 +4,30 @@ Treeffuser .. raw:: html - - GitHub repo stars + + + PyPI version - - PyPI - Downloads + + License - - arXiv + + + GitHub repo stars - - Documentation - - - PyPI version + + PyPI - Downloads + Website + + + Documentation + + + + arXiv

From c2165a705815c7d2b3047bb8a3ee763e6b15b4d9 Mon Sep 17 00:00:00 2001 From: Nicolas Beltran-Velez <61052993+velezbeltran@users.noreply.github.com> Date: Fri, 25 Oct 2024 11:14:13 -0400 Subject: [PATCH 39/44] Time benchmarking (#110) * added requirements txt * added script for measuring the execution time of the model * added kin8mn preprocess * fixed script and is ready to run * added functionality to save runs for ease of plotting * speed benchmark saved values * minor changes * ran pre-commit * ran pre-commit * add requirements --- .../paper_notebooks/speed_benchmark.py | 323 ++++++++++++++++++ testbed/src/testbed/data/links.json | 2 +- .../src/testbed/data/uci/kin8nm/preprocess.py | 32 ++ .../src/testbed/data/uci/kin8nm/raw/data.zip | Bin 0 -> 407056 bytes 4 files changed, 356 insertions(+), 1 deletion(-) create mode 100644 testbed/notebooks/paper_notebooks/speed_benchmark.py create mode 100644 testbed/src/testbed/data/uci/kin8nm/preprocess.py create mode 100644 testbed/src/testbed/data/uci/kin8nm/raw/data.zip diff --git a/testbed/notebooks/paper_notebooks/speed_benchmark.py b/testbed/notebooks/paper_notebooks/speed_benchmark.py new file mode 100644 index 00000000..74c36130 --- /dev/null +++ b/testbed/notebooks/paper_notebooks/speed_benchmark.py @@ -0,0 +1,323 @@ +""" +This notebook contains the code neccesary for generating plots and tables needed +for the run-time analysis of the algorithm. +""" + +from testbed.data.utils import get_data # noqa E402 +from testbed.data.utils import list_data # noqa E402 +from testbed.models.treeffuser import Treeffuser # noqa E402 + + +from dataclasses import dataclass +from argparse import ArgumentParser +from typing import List +import numpy as np +from numpy import ndarray +import matplotlib.pyplot as plt +import seaborn as sns +import os + +import time +from jaxtyping import Float +import pandas as pd +import pickle + +from matplotlib import rcParams + + +# make plots pretty +sns.set(style="whitegrid") +# sns set font for presentations and such +sns.set_context("talk") + +# Set the font to Arial +rcParams["font.family"] = "sans-serif" +rcParams["font.sans-serif"] = ["Arial"] # Use Arial if available + + +N_ATTEMPTS = 5 +FILE_NAME_TRAIN_TIMES = "train_times.pdf" +FILE_NAME_SAMPLE_TIMES = "sample_times.csv" +FILE_NAME_M5_SUBSET = "m5_subset.pdf" +FILE_NAME_DATAPOINT_PER_DATASET = "datapoint_per_dataset.pkl" +FILE_NAME_DATAPOINT_PER_FRACTION = "datapoint_per_fraction.pkl" +ATTEMPT_LOAD_DATAPOINTS = True + +N_SUBSETS = 10 + + +# Simply to match the paper (we could run on all but it would take too long) + +NAMES_TO_PLOT = { + "kin8nm": "kin8nm", + "m5_subset": "m5", + "bike": "bike", + "energy": "energy", + "kin8nm": "kin8nm", + "movies": "movies", + "naval": "naval", + "news": "news", + "power": "power", + "superconductor": "superc", + "wine": "wine", + "yacht": "yacht", +} + + +@dataclass +class _Datapoint: + """ + Class representing a single datapoint to plot. Just for convenience + and for cleaner code. + """ + + # name of the dataset + dataset_name: str + # dataset shape used for fitting the model + dataset_shape: tuple[int, int] + # Number of samples taken (different from n_samples which should be 1) + n_samples: int + # mean and std of the time taken to fit the model + fit_time_mean: float + fit_time_std: float + # mean and std of the time taken to sample from the mode2 + sample_time_mean: float + sample_time_std: float + + +def parse_args(): + parser = ArgumentParser() + msg = "Where to save the outputs of the notebook" + parser.add_argument("--out-dir", type=str, default="output_speed", help=msg) + return parser.parse_args() + + +def make_datapoint_from_dataset( + dataset_name: str, + x: Float[ndarray, "batch x_dim"], + y: Float[ndarray, "batch y_dim"], +) -> _Datapoint: + """ + Create a _Datapoint object from a dataset by fitting the model + N_ATTEMPTS times and taking the mean and std of the time taken + to fit and sample from the model. + """ + + t_fits = [] + t_samples = [] + n_sampled = None + + for _ in range(N_ATTEMPTS): + t_start = time.time() + model = Treeffuser() + model.fit(x, y) + t_fit = time.time() - t_start + print(f"Time taken to fit model on {dataset_name}: {t_fit}") + + x_to_sample = np.concatenate([x, x, x, x, x, x]) + x_to_sample = x_to_sample[:1000] + n_sampled = len(x_to_sample) + + t_start = time.time() + _ = model.sample(x_to_sample, n_samples=1) + t_sample = time.time() - t_start + print(f"Time taken to sample from model on {dataset_name}: {t_sample}") + + t_fits.append(t_fit) + t_samples.append(t_sample) + + t_fit_mean = np.mean(t_fits) + t_fit_std = np.std(t_fits) / np.sqrt(N_ATTEMPTS) + t_sample_mean = np.mean(t_samples) + t_sample_std = np.std(t_samples) / np.sqrt(N_ATTEMPTS) + + datapoint = _Datapoint( + dataset_name=dataset_name, + dataset_shape=x.shape, + n_samples=n_sampled, + fit_time_mean=t_fit_mean, + fit_time_std=t_fit_std, + sample_time_mean=t_sample_mean, + sample_time_std=t_sample_std, + ) + return datapoint + + +def compute_fit_and_sample_time(dataset_name: str) -> _Datapoint: + """ + This function computes the time taken to fit and + sample from treeffuser on a given dataset. + + Parameters + ---------- + dataset_name : str + The name of the dataset to use. + + Returns + ------- + _Datapoint + A _Datapoint object containing the results of the computation. + See the _Datapoint class for more information. + """ + data = get_data(dataset_name) + x, y = data["x"], data["y"] + # convert to float + x = x.astype(np.float32) + y = y.astype(np.float32) + + print(f"Running on dataset: {dataset_name}") + print(f"Shape of x: {x.shape}") + return make_datapoint_from_dataset(dataset_name, x, y) + + +def plot_train_times(datapoints: List[_Datapoint], save_pth: str, annotate=True): + # array with color for each datapoint + colors = sns.color_palette("tab10", n_colors=len(datapoints)) + # Extract data for plotting + x = [dp.dataset_shape[0] for dp in datapoints] + y = [dp.fit_time_mean for dp in datapoints] + yerr = [dp.fit_time_std for dp in datapoints] + labels = [f"{NAMES_TO_PLOT[dp.dataset_name]}\n{dp.dataset_shape}" for dp in datapoints] + + # Create scatter plot + plt.figure(figsize=(15, 9)) + for i in range(len(datapoints)): + plt.errorbar( + x[i], + y[i], + yerr=yerr[i], + fmt="o", + capsize=5, + capthick=1, + ecolor="red", + color=colors[i], + label=labels[i], + ) + + # Annotate each point with the dataset name and shape + if annotate: + for i, label in enumerate(labels): + # make the text not too small + plt.annotate( + label, + (x[i], y[i]), + textcoords="offset points", + xytext=(5, 13), + ha="center", + fontsize=18, + ) + else: + # plot legend but put it + plt.legend(loc="lower right", fontsize=18, ncols=2) + + # Add titles and labels + plt.xlabel("Number of Samples in Dataset") + plt.ylabel("Mean Training Time (seconds)") + plt.grid(True) + plt.tight_layout() + # set x lim + plt.xlim(0, 1.2 * max(x)) + plt.ylim(0, 1.2 * max(y)) + + # Make the font of everything larger + plt.tick_params(axis="both", which="major", labelsize=18) + plt.tick_params(axis="both", which="minor", labelsize=16) + plt.xlabel("Number of Samples in Dataset", fontsize=25) + plt.ylabel("Mean Training Time (seconds)", fontsize=25) + # plt.title("Training Times vs. Dataset Size", fontsize=28) + + # tight layout + plt.tight_layout() + + # Save plot + plt.savefig(save_pth) + # save also png + plt.savefig(save_pth.replace(".pdf", ".png"), dpi=200) + + +def make_table_for_sample_times(datapoints: List[_Datapoint], save_pth: str): + + df = pd.DataFrame( + { + "Dataset": [dp.dataset_name for dp in datapoints], + "Time per sample (seconds)": [ + dp.sample_time_mean / dp.n_samples for dp in datapoints + ], + } + ) + df.to_csv(save_pth, index=False, sep=",") + df.to_latex(save_pth.replace(".csv", ".tex"), index=False) + + +def create_a_datapoint_per_dataset( + dataset_names: List[str] = None, +) -> List[_Datapoint]: + + # remove m5 subset / not uciml datasets + datapoints = [] + for dataset_name in dataset_names: + dp = compute_fit_and_sample_time(dataset_name) + datapoints.append(dp) + return datapoints + + +def create_a_datapoint_per_fraction_of_dataset( + dataset_name: str, n_fractions: int +) -> List[_Datapoint]: + dataset = get_data(dataset_name) + x, y = dataset["x"], dataset["y"] + + x = x.astype(np.float32) + y = y.astype(np.float32) + datapoints = [] + fractions = np.linspace(0.1, 1, n_fractions) + + for fraction in fractions: + subset = int(len(x) * fraction) + x_subset = x[:subset] + y_subset = y[:subset] + dp = make_datapoint_from_dataset(dataset_name, x_subset, y_subset) + datapoints.append(dp) + + return datapoints + + +def get_pkls(pkl_name: str): + try: + with open(pkl_name, "rb") as f: + return pickle.load(f) + except FileNotFoundError: + return None + + +def save_pkls(pkl_name: str, data): + with open(pkl_name, "wb") as f: + pickle.dump(data, f) + + +if __name__ == "__main__": + args = parse_args() + if not os.path.exists(args.out_dir): + os.makedirs(args.out_dir) + + dataset_names = list(list_data().keys()) + dataset_names = [name for name in dataset_names if name in NAMES_TO_PLOT.keys()] + + datapoints = get_pkls(os.path.join(args.out_dir, FILE_NAME_DATAPOINT_PER_DATASET)) + if datapoints is None or not ATTEMPT_LOAD_DATAPOINTS: + datapoints = create_a_datapoint_per_dataset(dataset_names) + save_pkls(os.path.join(args.out_dir, FILE_NAME_DATAPOINT_PER_DATASET), datapoints) + + plot_train_times( + datapoints, os.path.join(args.out_dir, FILE_NAME_TRAIN_TIMES), annotate=False + ) + make_table_for_sample_times(datapoints, os.path.join(args.out_dir, FILE_NAME_SAMPLE_TIMES)) + + datapoints = get_pkls(os.path.join(args.out_dir, FILE_NAME_DATAPOINT_PER_FRACTION)) + if datapoints is None or not ATTEMPT_LOAD_DATAPOINTS: + datapoints = create_a_datapoint_per_fraction_of_dataset("m5_subset", N_SUBSETS) + save_pkls(os.path.join(args.out_dir, FILE_NAME_DATAPOINT_PER_FRACTION), datapoints) + + plot_train_times( + datapoints, os.path.join(args.out_dir, FILE_NAME_M5_SUBSET), annotate=True + ) diff --git a/testbed/src/testbed/data/links.json b/testbed/src/testbed/data/links.json index bac4a9c0..8d1bc051 100644 --- a/testbed/src/testbed/data/links.json +++ b/testbed/src/testbed/data/links.json @@ -13,5 +13,5 @@ "news": "https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip", "power": "https://archive.ics.uci.edu/static/public/294/combined+cycle+power+plant.zip", "superconductor": "https://archive.ics.uci.edu/static/public/464/superconductivty+data.zip", - "wave": "https://archive.ics.uci.edu/static/public/494/wave+energy+converters.zip" + "wave": "https://archive.ics.uci.edu/static/public/494/wave+energy+converters.zip", } diff --git a/testbed/src/testbed/data/uci/kin8nm/preprocess.py b/testbed/src/testbed/data/uci/kin8nm/preprocess.py new file mode 100644 index 00000000..ae52cb69 --- /dev/null +++ b/testbed/src/testbed/data/uci/kin8nm/preprocess.py @@ -0,0 +1,32 @@ +import argparse +import zipfile +from pathlib import Path + +import numpy as np +import pandas as pd + + +def main(path_raw_dataset_dir: Path): + # Process the raw data file named temp.csv + raw_data_path = path_raw_dataset_dir / "data.zip" + # unzip the data + with zipfile.ZipFile(raw_data_path, "r") as zip_ref: + zip_ref.extractall(path_raw_dataset_dir) + raw_data_path = path_raw_dataset_dir / "data.csv" + + df = pd.read_csv(raw_data_path) + y = df["y"].values + x = df.drop(columns=["y"]).values + categorical = [] + + # Save preprocessed data + np.save( + path_raw_dataset_dir.parent / "data.npy", {"x": x, "y": y, "categorical": categorical} + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("path", type=Path) + args = parser.parse_args() + main(args.path) diff --git a/testbed/src/testbed/data/uci/kin8nm/raw/data.zip b/testbed/src/testbed/data/uci/kin8nm/raw/data.zip new file mode 100644 index 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36e55ae087d91e1e0c65fed6be71e0e88cf3b6cd Mon Sep 17 00:00:00 2001 From: Nicolas Beltran-Velez <61052993+velezbeltran@users.noreply.github.com> Date: Fri, 25 Oct 2024 12:08:29 -0400 Subject: [PATCH 40/44] Nicolas/readme website (#112) * attempt at a nicer display * another attempt * Update README.rst * fixed pre-commit --- README.rst | 51 +++++++++++++++++++++++++++++---------------------- 1 file changed, 29 insertions(+), 22 deletions(-) diff --git a/README.rst b/README.rst index e23200ab..85baee0e 100644 --- a/README.rst +++ b/README.rst @@ -4,33 +4,32 @@ Treeffuser .. raw:: html - - - PyPI version + + + PyPI version - - License + + License - - - GitHub repo stars + + + GitHub repo stars - - PyPI - Downloads + + PyPI - Downloads - - - Website + + + Website - - Documentation - - - - arXiv -
-
+ + Documentation + + + arXiv +
+
Treeffuser is an easy-to-use package for **probabilistic prediction on tabular data with tree-based diffusion models**. It estimates distributions of the form ``p(y|x)`` where ``x`` is a feature vector and ``y`` is a target vector. @@ -38,7 +37,15 @@ Treeffuser can model conditional distributions ``p(y|x)`` that are arbitrarily c It is designed to adhere closely to the scikit-learn API and require minimal user tuning. -Treeffuser is detailed in the paper: `Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees `_. +.. raw:: html + +

+
+ Installation ============ From 8233660bb7c5cf41ada123ac4b4f9fcbc8c1d337 Mon Sep 17 00:00:00 2001 From: Achille Nazaret <32526896+ANazaret@users.noreply.github.com> Date: Fri, 25 Oct 2024 13:39:41 -0400 Subject: [PATCH 41/44] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 85baee0e..202e46d7 100644 --- a/README.rst +++ b/README.rst @@ -42,7 +42,7 @@ It is designed to adhere closely to the scikit-learn API and require minimal use
From fb2e23d2889ac27a2971c13f362ab3cf6797146c Mon Sep 17 00:00:00 2001 From: Achille Nazaret <32526896+ANazaret@users.noreply.github.com> Date: Thu, 31 Oct 2024 10:59:38 -0400 Subject: [PATCH 42/44] Delete NOTICE.rst (#113) --- NOTICE.rst | 10 ---------- 1 file changed, 10 deletions(-) delete mode 100644 NOTICE.rst diff --git a/NOTICE.rst b/NOTICE.rst deleted file mode 100644 index 190d6df2..00000000 --- a/NOTICE.rst +++ /dev/null @@ -1,10 +0,0 @@ -Notice -======================= - -A large portion of this codebase is inspired by the song SDE -implementation of diffusions found in [this repository](https://github.com/yang-song/score_sde_pytorch). -That codebase is subject to the Apache License 2.0. - -Throughout were code has been taken from the above repository, the original code is marked with a comment. -If the code is copied from that repo it should be noted that the original code is subject to the Apache License 2.0 -and the user should refer to the original repository for the license. From 180fa053c2cb73e2bb34fa4f4390bf4efb5b5ca3 Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Sun, 3 Nov 2024 23:16:19 -0500 Subject: [PATCH 43/44] fix readme (with correct repo url) --- README.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 202e46d7..ad53b39e 100644 --- a/README.rst +++ b/README.rst @@ -60,7 +60,9 @@ You can also install the development version with: .. code-block:: bash - pip install git+https://github.com/blei-lab/tree-diffuser.git@main + pip install git+https://github.com/blei-lab/treeffuser.git@main + +The GitHub repository is located at `https://github.com/blei-lab/treeffuser `_. Usage Example ============= From be5c61d6f98c32c5cdce20d76be20024ce33633e Mon Sep 17 00:00:00 2001 From: Achille Nazaret Date: Mon, 4 Nov 2024 12:40:34 -0500 Subject: [PATCH 44/44] update readme --- README.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/README.rst b/README.rst index ad53b39e..89b2a6c5 100644 --- a/README.rst +++ b/README.rst @@ -41,6 +41,7 @@ It is designed to adhere closely to the scikit-learn API and require minimal use