From 8dacd50e903267750fe4837c22fbf9e82e4ff10b Mon Sep 17 00:00:00 2001 From: Lindsey Berlin Date: Mon, 17 Aug 2020 12:59:37 -0500 Subject: [PATCH] Update decision tree plot code --- README.md | 60 +++++++++++++++---------------------- index.ipynb | 2 +- index_files/index_11_0.png | Bin 66168 -> 84046 bytes 3 files changed, 25 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 77738e1..9f9fbf6 100644 --- a/README.md +++ b/README.md @@ -21,14 +21,12 @@ In order to prepare data, train, evaluate, and visualize a decision tree, we wil ```python import numpy as np import pandas as pd +import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score -from sklearn.tree import export_graphviz from sklearn.preprocessing import OneHotEncoder -from IPython.display import Image -from sklearn.tree import export_graphviz -from pydotplus import graph_from_dot_data +from sklearn import tree ``` The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame. @@ -71,7 +69,7 @@ df.head() - 0 + 0 sunny hot high @@ -79,7 +77,7 @@ df.head() no - 1 + 1 sunny hot high @@ -87,7 +85,7 @@ df.head() no - 2 + 2 overcast hot high @@ -95,7 +93,7 @@ df.head() yes - 3 + 3 rainy mild high @@ -103,7 +101,7 @@ df.head() yes - 4 + 4 rainy cool normal @@ -122,8 +120,8 @@ Before we do anything we'll want to split our data into **_training_** and **_te ```python -X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']] -y = df.loc[:, 'play'] +X = df[['outlook', 'temp', 'humidity', 'windy']] +y = df[['play']] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) ``` @@ -181,7 +179,7 @@ ohe_df.head() - 0 + 0 0.0 0.0 1.0 @@ -194,7 +192,7 @@ ohe_df.head() 0.0 - 1 + 1 1.0 0.0 0.0 @@ -207,7 +205,7 @@ ohe_df.head() 0.0 - 2 + 2 0.0 0.0 1.0 @@ -220,7 +218,7 @@ ohe_df.head() 1.0 - 3 + 3 0.0 1.0 0.0 @@ -233,7 +231,7 @@ ohe_df.head() 1.0 - 4 + 4 0.0 1.0 0.0 @@ -268,43 +266,33 @@ clf.fit(X_train_ohe, y_train) - DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None, - max_features=None, max_leaf_nodes=None, + DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy', + max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, - min_weight_fraction_leaf=0.0, presort=False, + min_weight_fraction_leaf=0.0, presort='deprecated', random_state=None, splitter='best') ## Plot the decision tree -You can see what rules the tree learned by plotting this decision tree. To do this, you need to use additional packages such as `pytdotplus`. - -> **Note:** If you are run into errors while generating the plot, you probably need to install `python-graphviz` in your machine using `conda install python-graphviz`. +You can see what rules the tree learned by plotting this decision tree, using matplotlib and sklearn's `plot_tree` function. ```python -# Create DOT data -dot_data = export_graphviz(clf, out_file=None, - feature_names=ohe_df.columns, - class_names=np.unique(y).astype('str'), - filled=True, rounded=True, special_characters=True) - -# Draw graph -graph = graph_from_dot_data(dot_data) - -# Show graph -Image(graph.create_png()) +fig, axes = plt.subplots(nrows = 1,ncols = 1, figsize = (3,3), dpi=300) +tree.plot_tree(clf, + feature_names = ohe_df.columns, + class_names=np.unique(y).astype('str'), + filled = True) +plt.show() ``` - - ![png](index_files/index_11_0.png) - ## Evaluate the predictive performance Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, so it doesn't matter which classifier you are dealing with. diff --git a/index.ipynb b/index.ipynb index ef8f71b..f57f4c5 100644 --- a/index.ipynb +++ b/index.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Building Trees using scikit-learn\n", "\n", "## Introduction\n", "\n", "In this lesson, we will cover decision trees (for classification) in Python, using scikit-learn and pandas. The emphasis will be on the basics and understanding the resulting decision tree. Scikit-learn provides a consistent interface for running different classifiers/regressors. For classification tasks, evaluation is performed using the same measures as we have seen before. Let's look at our example from earlier lessons and grow a tree to find our solution. \n", "\n", "## Objectives \n", "\n", "You will be able to:\n", "\n", "- Use scikit-learn to fit a decision tree classification model \n", "- Plot a decision tree using Python \n", "\n", "\n", "## Import necessary modules and data\n", "\n", "In order to prepare data, train, evaluate, and visualize a decision tree, we will make use of several modules in the scikit-learn package. Run the cell below to import everything we'll need for this lesson: "]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import numpy as np \n", "import pandas as pd \n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier \n", "from sklearn.metrics import accuracy_score\n", "from sklearn.tree import export_graphviz\n", "from sklearn.preprocessing import OneHotEncoder\n", "from IPython.display import Image \n", "from sklearn.tree import export_graphviz\n", "from pydotplus import graph_from_dot_data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
outlooktemphumiditywindyplay
0sunnyhothighFalseno
1sunnyhothighTrueno
2overcasthothighFalseyes
3rainymildhighFalseyes
4rainycoolnormalFalseyes
\n", "
"], "text/plain": [" outlook temp humidity windy play\n", "0 sunny hot high False no\n", "1 sunny hot high True no\n", "2 overcast hot high False yes\n", "3 rainy mild high False yes\n", "4 rainy cool normal False yes"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["# Load the dataset\n", "df = pd.read_csv('tennis.csv')\n", "\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Create training and test sets\n", "\n", "Before we do anything we'll want to split our data into **_training_** and **_test_** sets. We'll accomplish this by first splitting the DataFrame into features (`X`) and target (`y`), then passing `X` and `y` to the `train_test_split()` function to split the data so that 70% of it is in the training set, and 30% of it is in the testing set."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']]\n", "y = df.loc[:, 'play']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Encode categorical data as numbers\n", "\n", "Since all of our data is currently categorical (recall that each column is in string format), we need to encode them as numbers. For this, we'll use a handy helper object from sklearn's `preprocessing` module called `OneHotEncoder`."]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
outlook_overcastoutlook_rainyoutlook_sunnytemp_cooltemp_hottemp_mildhumidity_highhumidity_normalwindy_Falsewindy_True
00.00.01.01.00.00.00.01.01.00.0
11.00.00.00.01.00.01.00.01.00.0
20.00.01.00.01.00.01.00.00.01.0
30.01.00.00.00.01.01.00.00.01.0
40.01.00.01.00.00.00.01.01.00.0
\n", "
"], "text/plain": [" outlook_overcast outlook_rainy outlook_sunny temp_cool temp_hot \\\n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 1.0 0.0 0.0 0.0 1.0 \n", "2 0.0 0.0 1.0 0.0 1.0 \n", "3 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 1.0 0.0 1.0 0.0 \n", "\n", " temp_mild humidity_high humidity_normal windy_False windy_True \n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 0.0 1.0 0.0 1.0 0.0 \n", "2 0.0 1.0 0.0 0.0 1.0 \n", "3 1.0 1.0 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 1.0 0.0 "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["# One-hot encode the training data and show the resulting DataFrame with proper column names\n", "ohe = OneHotEncoder()\n", "\n", "ohe.fit(X_train)\n", "X_train_ohe = ohe.transform(X_train).toarray()\n", "\n", "# Creating this DataFrame is not necessary its only to show the result of the ohe\n", "ohe_df = pd.DataFrame(X_train_ohe, columns=ohe.get_feature_names(X_train.columns))\n", "\n", "ohe_df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Train the decision tree \n", "\n", "One awesome feature of scikit-learn is the uniformity of its interfaces for every classifier -- no matter what classifier we're using, we can expect it to have the same important methods such as `.fit()` and `.predict()`. This means that this next part should feel familiar.\n", "\n", "We'll first create an instance of the classifier with any parameter values we have, and then we'll fit our data to the model using `.fit()`. "]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort=False,\n", " random_state=None, splitter='best')"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create the classifier, fit it on the training data and make predictions on the test set\n", "clf = DecisionTreeClassifier(criterion='entropy')\n", "\n", "clf.fit(X_train_ohe, y_train)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Plot the decision tree \n", "\n", "You can see what rules the tree learned by plotting this decision tree. To do this, you need to use additional packages such as `pytdotplus`. \n", "\n", "> **Note:** If you are run into errors while generating the plot, you probably need to install `python-graphviz` in your machine using `conda install python-graphviz`. "]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": [""]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create DOT data\n", "dot_data = export_graphviz(clf, out_file=None, \n", " feature_names=ohe_df.columns, \n", " class_names=np.unique(y).astype('str'), \n", " filled=True, rounded=True, special_characters=True)\n", "\n", "# Draw graph\n", "graph = graph_from_dot_data(dot_data) \n", "\n", "# Show graph\n", "Image(graph.create_png())"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Evaluate the predictive performance\n", "\n", "Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, so it doesn't matter which classifier you are dealing with. "]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Accuracy: 0.6\n"]}], "source": ["X_test_ohe = ohe.transform(X_test)\n", "y_preds = clf.predict(X_test_ohe)\n", "\n", "print('Accuracy: ', accuracy_score(y_test, y_preds))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##\u00a0Summary \n", "\n", "In this lesson, we looked at how to grow a decision tree using `scikit-learn`. We looked at different stages of data processing, training, and evaluation that you would normally come across while growing a tree or training any other such classifier. We shall now move to a lab, where you will be required to build a tree for a given problem, following the steps shown in this lesson. "]}], "metadata": {"kernelspec": {"display_name": "Python 3", "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": "3.7.4"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Building Trees using scikit-learn\n", "\n", "## Introduction\n", "\n", "In this lesson, we will cover decision trees (for classification) in Python, using scikit-learn and pandas. The emphasis will be on the basics and understanding the resulting decision tree. Scikit-learn provides a consistent interface for running different classifiers/regressors. For classification tasks, evaluation is performed using the same measures as we have seen before. Let's look at our example from earlier lessons and grow a tree to find our solution. \n", "\n", "## Objectives \n", "\n", "You will be able to:\n", "\n", "- Use scikit-learn to fit a decision tree classification model \n", "- Plot a decision tree using Python \n", "\n", "\n", "## Import necessary modules and data\n", "\n", "In order to prepare data, train, evaluate, and visualize a decision tree, we will make use of several modules in the scikit-learn package. Run the cell below to import everything we'll need for this lesson: "]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import numpy as np \n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier \n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn import tree"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
outlooktemphumiditywindyplay
0sunnyhothighFalseno
1sunnyhothighTrueno
2overcasthothighFalseyes
3rainymildhighFalseyes
4rainycoolnormalFalseyes
\n", "
"], "text/plain": [" outlook temp humidity windy play\n", "0 sunny hot high False no\n", "1 sunny hot high True no\n", "2 overcast hot high False yes\n", "3 rainy mild high False yes\n", "4 rainy cool normal False yes"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["# Load the dataset\n", "df = pd.read_csv('tennis.csv')\n", "\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Create training and test sets\n", "\n", "Before we do anything we'll want to split our data into **_training_** and **_test_** sets. We'll accomplish this by first splitting the DataFrame into features (`X`) and target (`y`), then passing `X` and `y` to the `train_test_split()` function to split the data so that 70% of it is in the training set, and 30% of it is in the testing set."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["X = df[['outlook', 'temp', 'humidity', 'windy']]\n", "y = df[['play']]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Encode categorical data as numbers\n", "\n", "Since all of our data is currently categorical (recall that each column is in string format), we need to encode them as numbers. For this, we'll use a handy helper object from sklearn's `preprocessing` module called `OneHotEncoder`."]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
outlook_overcastoutlook_rainyoutlook_sunnytemp_cooltemp_hottemp_mildhumidity_highhumidity_normalwindy_Falsewindy_True
00.00.01.01.00.00.00.01.01.00.0
11.00.00.00.01.00.01.00.01.00.0
20.00.01.00.01.00.01.00.00.01.0
30.01.00.00.00.01.01.00.00.01.0
40.01.00.01.00.00.00.01.01.00.0
\n", "
"], "text/plain": [" outlook_overcast outlook_rainy outlook_sunny temp_cool temp_hot \\\n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 1.0 0.0 0.0 0.0 1.0 \n", "2 0.0 0.0 1.0 0.0 1.0 \n", "3 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 1.0 0.0 1.0 0.0 \n", "\n", " temp_mild humidity_high humidity_normal windy_False windy_True \n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 0.0 1.0 0.0 1.0 0.0 \n", "2 0.0 1.0 0.0 0.0 1.0 \n", "3 1.0 1.0 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 1.0 0.0 "]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["# One-hot encode the training data and show the resulting DataFrame with proper column names\n", "ohe = OneHotEncoder()\n", "\n", "ohe.fit(X_train)\n", "X_train_ohe = ohe.transform(X_train).toarray()\n", "\n", "# Creating this DataFrame is not necessary its only to show the result of the ohe\n", "ohe_df = pd.DataFrame(X_train_ohe, columns=ohe.get_feature_names(X_train.columns))\n", "\n", "ohe_df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Train the decision tree \n", "\n", "One awesome feature of scikit-learn is the uniformity of its interfaces for every classifier -- no matter what classifier we're using, we can expect it to have the same important methods such as `.fit()` and `.predict()`. This means that this next part should feel familiar.\n", "\n", "We'll first create an instance of the classifier with any parameter values we have, and then we'll fit our data to the model using `.fit()`. "]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort='deprecated',\n", " random_state=None, splitter='best')"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create the classifier, fit it on the training data and make predictions on the test set\n", "clf = DecisionTreeClassifier(criterion='entropy')\n", "\n", "clf.fit(X_train_ohe, y_train)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Plot the decision tree \n", "\n", "You can see what rules the tree learned by plotting this decision tree, using matplotlib and sklearn's `plot_tree` function."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, axes = plt.subplots(nrows = 1,ncols = 1, figsize = (3,3), dpi=300)\n", "tree.plot_tree(clf,\n", " feature_names = ohe_df.columns, \n", " class_names=np.unique(y).astype('str'),\n", " filled = True)\n", "plt.show()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Evaluate the predictive performance\n", "\n", "Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, so it doesn't matter which classifier you are dealing with. "]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Accuracy: 0.6\n"]}], "source": ["X_test_ohe = ohe.transform(X_test)\n", "y_preds = clf.predict(X_test_ohe)\n", "\n", "print('Accuracy: ', accuracy_score(y_test, y_preds))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##\u00a0Summary \n", "\n", "In this lesson, we looked at how to grow a decision tree using `scikit-learn`. We looked at different stages of data processing, training, and evaluation that you would normally come across while growing a tree or training any other such classifier. We shall now move to a lab, where you will be required to build a tree for a given problem, following the steps shown in this lesson. "]}], "metadata": {"kernelspec": {"display_name": "Python 3", "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": "3.6.9"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file diff --git a/index_files/index_11_0.png b/index_files/index_11_0.png index a048e3a108a0ec2df4546086788ed1b27dd806cd..189cb8d710540f5439e5938fa499542dafccd9e1 100644 GIT binary patch literal 84046 zcmeFYWmJ@J+b=wdiUJY_ARs7+h=72!Qi6aqQqo8_NH+s2B_iD=N=Wz63`&E5beD8@ z*FNTd@4fH+?Du{?ylcH5-&wkd8LpZ0JdgUt(9TiE z#sFcVWB1P7%Ff(G_nw1+jjf55CEF9$Cv1%OjP2~+J!5A6_w!GzY>b#a5v(r}hGhp1cP7d->d&JNQ0`+)r?cl%2ZU!I(2y5SFNa?Dw!cK(U)D# zMO0u!=o&<=Oc-MAf&6$fTK*yKfn{k;Qjl*`X?cfz?l#Z-nd!uS#B#h|beg{(=E41h z&XwdIHRiGSU5**^_3z}ZC6Sl#pokZ${^vy>6n>Bp!Nd9;&MxK^)c7*VT9{YV|D^8? z_~$a*-Y%F|5MihP{}=iHc27v)e7Q_$K-gGx}xDt9q!~F%1G=kIjKdxu_1yq017GzyQxq7+CR(}i& zv#_Oy&q3N-)Nr>HEns-9q4iDMk5G|+1?y;CK+<_*;Bvtt<$S_a(bKQ7zRXd10@^1< zM5XblbJSn-%FNG4H6F=CT)EocCBCDa4iAfH+=!6K5#Q-!hpiJafw2K6+Q+^(6nT0x z#~5VKK0j7{`H~U~GGl)-f%%?r<0kH-*!$DG=1+2SXBgyvjT`Xh4>CmYoUdQoB4OB- z`?5daS?X79<9~k2?!A>PjWFI`@;&)ec{eL9C|RusKRjElX>RlVW8dzIH|M_}zk!S2 zR8wGfu-D&L_Nf~78Bac|(n?1NPdG=~(gmsPln=zN!4A3|%i9K^)R!lxt?F4<=R2M9 zoK3Vc7Mtcf`*j8?W)T`_vbu?hy{WN>FLpAg+B|cw^HiA?^&L2>DIlj^7!{C5ANkuQ zy0UzjsT{LAGEMZ>oN_Y;dXaEh!ywYDwO0=)^s3&jx__nSxt=S{;neOPebNL$ zUg>=OnI;@OGaR{!dt~W~R_S}MG4gHGoT{qVXu|LO9x)6g->D9GCzETnIYM$iX6N+y z(oh*Ee~N(-V$V`dIw!L8Z$jQZ#LyU@wT;a;hml+nw9L~9WGGMjQoy+JS=sxZ@laZU1qaI+?8LFQsb6MnNcIQ}1Jf<3{{9vVJE}^la z^q9*$P}}Pp#Za|3?AcAZZ=-kjM^L8xBXz`b$uew9_HH57adrODY+gl81YPSvA_R6% zN-4&Q68v~>s+7LPiTd6-x$;*iV`=xA<=`{T>0#5(#y&)N?oh3%dcjuTySBxaap|dj zj=2~;Y?Y@vq+Gd8e-oHJyZe@*K7`%h{p8m?Eu5^iVtBmdy>sHi-WOXeYeK&JT9$fF zM!)S7kD^)k{jsJp8`fA^I+5T$CTxU(Dd$Y72lIw|o^p8Z>!hd}GVdbEE$t7UH)ap# z(}S+Xu|_s_Ryzz>)67Y|-LIGp&)L0ner$~+)6s$8-TckqcBr8lXNqgm7Xx8Ginp7P zV;O$kV$53=ef^&KkxG^va+&L*{jfvHqKe5-i31N+pti{PFE2##>M3gy-_2h~mlYNd z=Ave{+8qZ@&nXX{4CFQb*lAYQctwh4!3b=C>{wXkLmOOVi7P%h+$Hr<|rSY$&tJ;S}V z)L-)v5@dtLck%kh->mfWzr^*;3sz$D7{BZRhkpDjUo)*W!$WjT)@>pf!}g zQF9q*TR(`u*SO2S%KPGa#d!hW^R=S}nvxq4!WKBY1#g~Ujc#)Bl@9CWY1A0~aNOTY zo{rO&uW?YYnP(DQiI9A0q0%5A;Qp&PNuYtr_xWy;OUs-s^tq-uq3S{DpP{$2hpSFb zF8BqOhqE?aeQdQ^6c%<9PQ^c<+Rz>=J6(3`X(X*E}{%=$)+s^yv{OOH+Z zL{UWNQe=W>t<6gdjflA#@9v|~?FY!U0rQj5CIYu8j-saNmA?`R4r4#MPD}!rarSR? zXBJ7{=c|iJYuDT+VKA_^wNfr!=`}uMzd+l%DKh`;HyLYi#a*r9L!uaJ>Mf@pIccR)s@qD9p z+7EJHgl%`LneFq;dZPceYsw?$2}AA1mOOlAZfEAPBTo_p!gQKg@jg7WsC>E_)j3$> zavy?{u*BmsRDt>t^jn|1^B!Y3d903QSE9c6Sav>YudJ3%KxwL?H>jY3yzOXSk7nC? zjuV;vDsWErb&j}i<+xzHs@c-1Wp3*E{tx>>PU$eHY$0u2Ne!X-*dc6DT!KDT0xzIj z65zl`YgaDV=^+9$re%4EY%mW+2StVe8* zR$$lg?AAkqwa#!V{uKSb3OLhS5#+_x>iBIjo4?f*s1#`rXk)fNyWtkyG9W>?4v_E)1iJ(Q_ufj<&@GH_OMyu2lsKQ=*D^%7QHNSxd(@ji2%|x zD%P}k;bm{5610%=h90Xg>mx103n@j5c8e!WqMSyRsxUvEjl7^+j5hZ6A{2q zy(W=0H($|Noxi`8r}E)ceW1`|N`RP)mzm;t`mR-IJEJr1<8Q0Dtl+0n^szJXTB^n?np~}#S}5W4 zn%^%#j~ir{Y00!_!TWGW7(KZ&#Wahgr_PH1n7`nQo^VGVe=^A z7+<&TQ~Ar^3AN=nP0*JZ*mwt!SGCc9{In+)r6NQvT4v*d?oI{p6d>tJMjT39;#La9 z(X6iZf@*s!Mw@ekM5o&IEO?)keuzHNM(XN4DR7kJPA6EWaU4ff?3_;}CxrR=@g70N zzu&w1C!zkMQ=}BB=-^I))6ooV%4|{b!X{;Prt5^OC*Iu5Oug9$Iu^Kge|ysJ&}(%Y z400dS$0-F9g&HjIch5v5w|=Fg3ynva87T$AKayKJ%`+I^UXpy17ZjBwK+>7`%^_}Q z>|We!$R@#w*SH7s>9T`FHKcVckF*M|xQj9A4>KoSSggFyUae7N2t8mAn)mn|`8&4v z$gWT~7Rx(5z8)`TVjDEj4!6;s-*~sjOM8)->Fv|K%LmjUW|8?u)26u^pBr0-F2mEs zPMex!j?R3urMxhRA$**vf>dsb!`9Y#HjE_;M^g5xGqe3^C`w-#`G)6p+iY{~>WI}w zv2RNK%W*3uFLLVwJk@XMMT5e>y=|-d@{N}a1$x-o_VEpghziL0y5;LHFjAf~K>onS zjg6A0%vgFnB0xWsN^v9Ja&YEh`yM`>lO z(C46R84fhn@_3E+u-nzqU8V}avOc4?ONdx_5c0mLrOa`AO6haQtsl~z8v7k@ScHYE z1I&mMU6HSR0_k841OT+_e?|c6N!!4H^C0H2M-1@wKY{d~SeFnx%#7~enq0Mi%QCWt(4!S=~ARj`Nmj9sk#b)SLPHGy}xR6iV9-en>R)+u~fb6=rB+wTY ztelW@l*r={3{9MetR#@8veagCg}!S0_Do!Ph$F>$tFoWUa zz~S!6#YBC_{i^kCAPZWq!T?TGd9<9xb$C3&A1&_B?sR0(>dmgZ-0BRYoVyI==R)Ur z@ejw_$Jl&u9hSa0K9N^h9UA3!UR_Bwzh>@Jsq9Xa(!K}e2X%pnhqsy~e(fx;?&E>e zm0k0nGe1vRrNh)@`Gw(+NPT{O)<(a5yWd#fpjG*QegOaB{7*_fO@t~L#8w1x{$`Qc z2u$Y{sOtZMzEaiTN;5xU8(&w+S#*%6y}=~+k)V-@F5Soy0l~4Q*-6Yuw$BC?{hNyS z68&-b)!c?-y+}g2ZxpJ9Ka4$bjJEmP!ABX`cmx2h57s#84mH@^*fy6?bb zG;~~z2dA}|7USZ)vO0FFL)MD7A?`X(PgVuNsrnoJqd%QFhDzT#eY$7{Kg85wzx&T1 zZ&#WU$pKJW{%q8RNbw+hlU~4zK)sf0Ebh_sTyc3?|_YW8Dh6Y{e z9s&m)w%W`^J%sV`k{G=gj@Tv53--kr&_2$I>c~IDBMoWWjr%`7v#o z6as`mqRnciz#cAqvu0Ho=IwDx&K}(>j^hhIrH&63B4thjA8Klq4qE;9aO(oqxlr+1 zUlQcY-&O74{_3NNf-lV*4OP3#coJ{&th#C(x5*+;P8kkAUD`a0rlsdnMJ@FuUC<;i zbUKlH4`WNdymY{+gD7L_auZsvvVEK0qDTx+e{SP~)cGu%)`9*bL~>3g%W~<3kJ40< zyN0r=p9@TfNfArMt1fwMS8M-n3YQbxwxrb{?&my9|G4-ZCh*o&+w>Ng9|0#Qk8Iy! zyi$u6BN*LwS}S)$K+^m5q14ej*y$2BmvD)@9Vfuo*xpcu0BVD3@uha?b{YZ<{V1n6NyjP(#&7c z%mtn$(jw0#b`9^gtR>=lmJOQyKA(0C(Bn7$!G-r>eB!)aKmghL&04^k-PEiV70dMO zbw{HaJ>TAurRVCPV!@!5e8opcrP!clPBdBmwm(c+$exdGPST z-^iXYV7$!vCyGbQDBISls+zr4mz_;&sQ~y>Nrh=HE3;V3X;afgZv7lI*gttn^S@WM z+kF`U1;9@GIF-Y8WhM0F4J|$WX3g?O^Jyx}bH_M~G%G~0+_p#_!Z{9C9o>h(``AtzFg2skF>;mO9@vLAEO zTj9}eBfQ!Uqej*9CMUkD6v$jqa%`aiJPVCmo z5qE_tNpE4Klf|lcpZB~=mcR|@(-7=bIp&}obDq$;NoF0o=E%A+OZ${WC3FLfr2vCj zNjj!N9{J7LRYu|7qub#b zLxeE7b0!HJW@iBC#F9;_ihpkX5hkT9)g5)SMXQ_~GzlFYU82+6ZOs3GJ)lMi9anKK zODfkl^!3fzzOrx~q((@jZN^2YTZnx_@ogMn+EFs*vwoX>Y|C!SfbC%ZRVwgGj1690 z%Qd#mGo$qjI9DR(je9u()qgjgb|4-BF3Y`0mM80U&q_VOB$im4*is{Cws|c_9|9;* zDAG#jy2+R~v@b%=Q`w6W;SKE4rlsyvHKy&KId%~*B7{`{*6mqoO8UofC63Zxa+`;C1D*87Lz;;=)i?&6=zgHIkwMlq^(+y84<#nf8$vOJoi-Z@z7{3>i+adF@s2hJJ&>r^figzuG1S^W9GQ*kawW3ZHGf$jGk( zfg83{6n(LFwGDkwRa{(-Ul&-~LJ8KLEoOCcI)0rjQ*@lU3)9W#QMaKv0h4ya^i-Kj zON7+mRW}F|-xmO!2V0>eCLCX%%gdYiTjvl# zPtr{~aZ$vtnyjvT+IO&sCq6hbVR7px0fF0RnAZxeH`#r57loiL#K$MlQw@K-=w+~0 zZsp&4pM7Bm2Dr0EI@!K^WiJ?5q&b-*o`vUdW~xj@+J&j2Zgq7KlvH* zBWLBaio{ygW#i*vIupt)nPW?HwP#_`+eRiwztk7id_qGclyihvQJUA{&ghZ^Zc3E@ zd8omU^tm387Q*C<-MLGpThb}EnuTDoDKhY*+S4NWo2Q*F`1VfW?9OC;O~Q&wQ8XON z4&{j>SA?61VDBE_C^GxCggL2$Q>U~} zE(EYdEpxL)tYM)WI~{roKd_zls@ETpDtf`_pT_%q9IYY_6+Y=te`jjBY}Zzw@un_S z+WFCC$%lA#4IH>YU#8cQYfmmF>`9jcB4}#sObRfPeO(#oZWSz@(}pb|e=i@8;rvc} zw|`wrBp>$eCHC(Y>p|Iui|c9f)pXaA_!3LD(+R0a0^TH^u)7@@#M&e2+Lv#TxO#IG zoy+t&^nF|1lUs*LIjAvDcbsbCX1U?Dtg}1UeY}E@FJgJmue4X9rSS>!u*)uDGSnz~ ze{+yKq?pua3OByI+M>Dpg9(412&D$7CNTcKXiGd@J1Wd7*5Gyh1oI!EN&)fum*_C@ z+pXWJ=YH{wo2sze7bTg*noJllfnFzTwj^?fMI3YMp+4$m{goE<6RkoIOWW6FdAB7bjvE_#?u?f*Ok>y7sSBr% z6bNDF2oN{R31HgLU;H=)ne986#R@HEQu~45v(-w4a-FPjk1V6lddscl6eHv3K9ffZMfU+McT8!jziSVT093TmtB%zSe*lx%Z^}caroEMW zW_uJLi&vy72lxw|_S|5a|9rM{yTbh$UTHk#?_)heIs#Ehw&xK`r72@(RAF^>ZE3a~ z(qS7hNFq*KM!LOqYI;^LHxUVWv>t)bh+V+~IhA9AK^T%wx8u?`*}Js#tib(f<3!=7 zIqy4Oef0Xt-8ZK{8=5qY=7&?E+I8hCFb7yDk(JO+2CWa>DF_E1ZA$&CpQh<83~sA< ztz0kKmi}2^CF#kD>kb0*F@ULgq?AKR(h;u@WGS(G6LK3L6a=|U7e2^BX%J6%^7XJL z(A}Sy80OM=&XSu0-6pbVfz0|bQbS(8eI;MF_0ieMUwh6ci5+?Fi8PLhCzwfNWi65M zU>kcbkTQNwB`)&!+xp|NW`#$=KAlzXtMzALB8*T*?FdhXRm60e|qP$^mPt&!&y^3jF87hti4t>PNnXLf? z8gZ#twa>K?F#1^RZ!%wcfGoHHA*v5Zwcs^yFCZdK^@y9j+ute}B(}Xj{CB=HJ-3Mq zsS|*LTChE9Yaeq|=c|}G@XPe1nL~1c&q=5i1#q;+E%(VXiIk2}z5LM$e-O37W2`?F zZ;<0wnjtU?rVim+O|9r2zHx@mUd3ikoSHcxX+nOwnDw#qD8PH#xOEkAPB~ZpQ8rNS&ls2zx>*HP$yvJ4?U(#i@ z5EtKP52Uyr&0LuL6{Da$#hEY9lyz3_whb9_6$rf9qdrntvFtYQv~HfY?zs#ocapZw zvOshq(g{{CV_3*pA@U-?`Dfw#ObPmkJhkKvdn=G_ysNc5uMTfhS2fNc>v%~>R11rVw-2x_;fw!MYcLi#Ga%2HYp>tS^Ce=dA6z7qk>g9ZbC4FP0{O(lrIpV$Z_=?b!Paa}C}=WDwZ)W@4$ z2N9finISwdVFiIVx8h&7GDO(;2`O5j|BJ=4UUUAC09iFp8dH#V773;E&vG2A@+#6T z^yDRAfDab@xgctMf0e8?b7AK9taR#;tbE2<7+wXbbgxfDJaAzkmGIb}5F&0bbCwR$ z(bM~%j`Z_~AMIJF>h(Enu4IIZagCcqQid80Zr{H4w>Oy&QqXui_CVcjo1_s+NG8W> zt-9yg6uGprzhL0gaHen2sriKPwCRPOxS*ahVU zm~VuqS-&~LX3+5%ktB5Lv7&G;rNPb+${B$B}5SJ3&g z;4Iy6@N=sR5TxyZ5wAUZ{pF$NnPcbaGHSi!=Us9Ywqq-Nb9J!{Q~S-G_NLhdws`)k zDB%!tK$DMP9oAF7#ewxgoa?4OQY^e!esWUovdOEi+<_6b(ljTUcebnJfWX{Zu1o;w zfM3fQ=iTX&0m_%@@5!QGugUTIKFJJ-N{SFnnqd&{^8v;wxtWKkoLE^<=?-qcs+oFP zynBw;DS6w1gW*yPPmV4FDsF6PiP62CiILV}l3h>!cm!qfesTNu@4Fm+fS@rQ9K^-+ zRp{WfaU*>Dfa=$huBU6f(o>c`53|t0^{aHa9W+{VaGO!IskG*sV#y3G1OM4&lijUj zjOLkZ&wx?>G2_q={mYm(8xwn-?bvSpvE%r~OG+8-AY*BK;TMiHAEH+&yeNxi^K8AY zK2pL7GtFn+=rogcvihHQ{yjb_GaHBgCgC^Z{Vw3{ZJC5mlcHqg8b#xPK}J>_jIk9% zo$T$!>tBh9hhaW;T;lmOZ^9r7{O2}Ge^2uzHfC~vmKi+ z!B??-vDTyv0e9W?(6z;V0vDBD3VySyYDwt71^)fL0k6_B`?gepVt#-jq4w-?)AFr1 z@j&DHIlDby%f8t`A_!8a@z%-t#@2RcKG`=;p91|8Iec?%Ss(?<=J!&e6&AZ3x&!)@ zTqGI&dfVfI2oK5;8>ra!{ApN>J|}dB7R3@Zhcx|&VdYhF64`l3(_2h$ZUd~2PHO>O zTIHDzshQ!q^n?I$C|V`MjtYs+Ge_onjM}st-_NMd2urb?$ATEl)LD2Ceb^s%>e>e+xXcJdEsdjKc zqTACQW4E?nt+b&5{t~5r$Rk@JrpHs{9OqF~(-)cd`4fkf$AUH4lm_ zRKa(T5Dsyua2{j)fjAI{tpcU4K*a&7$l`g+ z;y^dw1pU1~&n-CDI%sA1q9DF3^A!iP`k&2##3NZ;uAeNE&OSlwx4SF$^5x9yuX4Xwyo(SpZQ=q&Ty;D`ffIFY?spf~XQ~7IxjDC$b013>=n*hx;>7MZW8 zdbYqUp_Fx8I~^-7PJceK94CM&X$YXPzV1(6+krvaAWZbnQ3L^iDh}Ze7+dSL;B$7% z6*<%4i&*N`eM1_9E-%%i548Qv?})g<5;E1DyZ!07Bg*XLz%rW;Itr(8!mYvZyw`S0oo4n@UkyD5*IGP8s~pP@w~A$`hy`?@?y)OcbbEoeRRpv!#w>v0 zug`W^;Y>qCnt6+DZ^Fk)2T7%Bvqv$&eis{+XVmghhx2Y{3;p2>MREWBAeGP6HZ7U$ z&_obP(pr*de^b|&N9Bw5V~=`AZCvDvHI%^7gVFRhnaOqpr)QWboNReJ*-IzXgl;4G z_I~2r221BhK<_C($J$^!65ozw9%A5}*}U|{ucF)OOh*ZP9#=iLO5!knBX@ObbABy{ zAPofYY2BvkYF?GFHgNI;fH;#b&^zKR?XLDDk?(TKdPtZ^n-Z-gCs#)Tx0KtceHPuF zglEOr2&;1;0<9lV#%P2(Gf2umsGH5#8GbhC35_|pd@EJu(3IG{YWv|tN`+OMTEN~- zYzH>0oqbNKtalj_h?l=(UUj#ZOd_yRIeKF!Ey}0D>H=aYiZzU1jt+kXHB3q38VDa{ z#BK$m$!KP-jq}`OEDPN}Z%+q4E|AUo*-LX04@rEfxkA$4vTogCG%Uyw%g4s}PoIgR zR(LYHs%}~ryoq7kQV*~ovVHh&tJV^{UJs(I#<8DYgPtkoXSgJ?_Ejjs5cnniO>J~= zG@yA+&|i3Mdike9S*k-7P_HCkdDk$F8HJk8#S7T*$Pap{iZL$odz-%9q=xDmiXzg zLscAhEEQf9O#%Yab9`?oisEn2hp8gk;LFe#yN?<_TfDZ_f;`m{Ni-@h+AijIH zKZF%jT&jrk-3tx?~uLh#Tvi^uTQ1 z=TEBOPv!fcEd#nUvt#CRkEtS0~z{OTD~9Q*rg6MH{7vdANr1pI*leiF~u0%Neb+SBR8WDl^h{9Ot4 z#=w%D7D=#=n5**y0dcbN+jXKlUm-8N2Ao41+i|kfb8F%a6->>uVNuZm^V}$-=VtRV zQrC9H_RZ&)EdxRCP1CKO0~<|qNwj4AGt!1ur057!?=kTsjA0gIN&*4>9&lL1BLf|A z^)L(cT8?m}GM$N1V2lNPLc)3v#UM2sD}3G`avZLga29VVVG6)!hJ9$!o1`4Y)@tZJ^#XG`%!wNSoD zBt0B8L{p>F*~p?+eZmi_JB%oZTQahZ!enQ)6AL;i&t0M)_I<&mx8~8fN2i{EHH+; zDb2WRnG~i4e3B@0*Qo@Bsw;n>9}^ zS1pb$p`0c8HdPG)9)z8!{awTHK&X~v8GA|9)a_2yCyObs-n5VE3`J;7Vr_Z z^sB0CqR4FZDM*f21qVMDAOMRBNlf(k?pv>Ji2$jj*Keh*9%w)Niw7ZdcN|x+hWZ7_ zf^8Lt3Lt@9g7_DQl5^{^DiNL0Je~jy9wB1hm?Qz-0oXK1BKNPSc^c0;qdNPh0qSDx ze_-AY85_G<*Ak=W>X>iepmD=;^wpwv>T7kEN<$s!c=qxYNc^)tt}#tyYez6KBN%O)=LSN*QY=;T!Dv-Jn;A7rcvnq-KWCQ|KK+A_?tQK^F@!+C? zTGgP~6xg9DC4nbe42I7OY?R$vv9zRxygHvA#k?%l_x3ERP4E;Gj{tG}yZ^Rr&R&LM z-sOm;l6!lt20#}yfs6NA98cu^>XEI!9ckb{FAMrls)b)o#2g`{UNcZ`s;mYN1gg72 zz@I)zBDa%UV$dJ!f|xk*rq`_I2iQnEC&WJTh8ZqN8|#Q1(e)2h$9F`4G}6v65m;oh z0$xyO)cUfQ{~XFKdW9H3Z~1Jxcan3G zD%VdtDFrUQwP$0ftzj}-tH@KAA!zks{|3H*(9j#IkaS=#Niol%V?4r0^dPU$tEO_o zT?8>aD-6(UtvltCzzb`Y(NjbIeBcR9+HYtN!-3&hxX*+<>7uuFduZVI z*CT_~{LVi7Wgcx()lX2MOy5sYBAGj7z3Ta_lrQegA;)zW^mFA%8Pblud~b}UG(C!d zq%(d77Doa1aO^iR{)7cK?TbF$Nid?BV)Crt1A zXT2QII?fs-i$DW-nX4)_T9Sq7@Ic=wnvAaH9?x}zCGCrL=q(XY6~L?oY5)d6SrH`s zJVeT>cpn*#jT>$k4q%$8jTY({NTA}(`c`w4+dz_Rx@R+g{p(J9`ee#nEG>i88E+tP9RAVIy`&|LZXjv! zgGN0d(7svvuF<1sA1PMcXs@VlPYl#C*RBqRsl>@?KG`nt3cK?e@)nc&tWIJgOg6%B zGvfbER)GLFm_coQi=0?`8g3fF$SGMQuUkF@l)w8@N~Si(Hf0n$r|Q}ZMLVXmW&E;V zGcwF1mho(v1R&m3i|wlFHfT83#^3;@Cg|IAe?CNs@&wA(8Z4gn&#MjugRlS~g`8ss zID)>AG4+_eHY1q(Qw#%S?A*3-U85dAJJ^7BFq7Q6B4&Vbf|8hkT#0%7hua~|;NH1< z4L_9QHR^NKqhmVit%>hZvf>q~yIb@`&NB-+X>jS3I2}?M z&(i8;r%MJ{kqBB#Gs#P_7e`j)yQWhw<|E$Kz#Xg6Y^#(>P7* z!A(-1*CZ7G%8sBe^dK04v|4{;UxM`8)EW~F;R7f8Owglmjt`0W;_4+VHT2&zYa zjPQHd9WPR)m_EUCJKxBH)fW)7_XgMKiPxmZLGf6C! z#dv-Rd?P3P^y9TL?{3BLM}UGWZ+j#ocKd?xs@xY$tfj`dgy6j;6{UMClsR|!fcgB7 zcA+mgwXvdc*(FOdAc(?NS zU44AmHD1rSP7$gP24Arkk_FF=%MN*VUiHP_B-JDJIU57JaM0~jU+I~jRsOpHUqoZm zcm^mNUJYA%P|_%H4BS85z6l#@ZVVX2{VCpey!>m;F6!Glp;7krgQ<_oY3f);#kC$P z9iZm8?+voIYdX+NfC1oxNt)ISKb#HD=Z2?$;%aemc7oF4lG<<|AZ1GRFE@^#@B+M{ zfM(KsdPsOxrjN6py*&b!Xl}c`eRyBV*5E7pZYvyn%R9(LU3izwi$tE=7VVYdT8-9Y zu(lkCEgI(`#f*{Io1U?0CYHtjoa7y0wq0t@vO8Csbnh(O^qRlDaQA~rY(XvDYhzhj z7F>nb)c&uH!2`~9#O-ppRNr2zq0`;A_Ri%t#rD*0V_2^BBY0CA0w0}TtjG(E7VUAb zv`|D=QQu~~#;SNY-_LODmPQM@_prYq^9i|ad`SZ@B9JD*a9ZMQkc_$bz{=-pYkaK!NEIVvt`0y`rX z7fbl?T##VnsJ#>$2x#7Jb_He|Y#*Is#9WH0mdMuAzz5I6AKY0mRM8gQA~#%5^D~L{ zWkXX#UBXU>FlC2bzy4XO&&_)#N{arEz!j+;sRxItif`ntBzA2JeC2~N9tsS_qf6>@ z0S{{{ZFmO-mw@=yXQ^YcA@yOsJ9vdcyqc%46J{yut!CQ-#|zX!Uu6WF6fL$363a=N zb|3$ln-hpC7t@AaghGWmZB9(kyA{o>c3?3tb@>F{9ynf5=k3Xa`!-tc!G*ZSG#uD) zHY7?Gm3r7^hms$(@YZ?V<=pGTC;_}%kg;bIl7c;o7GY97@%ll!54NMzoGE`?*}3p! z?7M4Aob~~v4W%mF+Itle@pY*7(}-!*T#uk?5BqnhIlOpWNp?G|Ez2wGc1f@K;VlHJ zFp0JnWVkddus*lldS3tZkMPzrm&->@@g}iQ3u`WfUr8HFR$uc~!KAJ`I6AyaJ^v_L z&`9^h8wKgs^Go%N8pRsAe&u|(g_5T89D@Xdvdan4aejx(Xa;>j4Xe#-z*F!oTktF@ z*Vk(+&7_fcg>1I#o$d8gTTiYHu)SS#cp?tjoD23#fjXAYMxG$Wx!iQ3yjqjferYKt zt$azR{~;uEC?)2yw6ZBjhj$pU_-Y9+V{VPNW{=H?I>)PHQAXHXnMwTG3RtzoPo z(Gz$&WCw(llc*FAwuRO6bJ9w^7uiiPnzwGE3)h(o+&BT<-FhulVXhz{8MNy}=fTkFE-} z_=icPIvem^eL*-bK>dS+pE&c%uDa*7RGwf0KOREO{ttv72|nK@RDf?TO`XyZx8hI* zD2wzaU09M#?fxmB!M7YLW$2; zm%M{SnkDlTygI#1#CsPxGqZi~U;TwG^v^OMM~Gr6wYpGHJ+oENV{Dgdbs@#9b)$=Dk?UXk z@qQ!m=8Niow&wl4s`;04%$Qw*f9pI%y}1AS_Lr;7rQLtOymF%IC3VP;3v&j1_*37B z=prPxTU`j(Kd?nzTwt+z+|xv$*Z6TN)g~w@D#xf}>$+2prr`D!?}RsdYvddj_#^m7 z?*@#>@i`+6_$V-oVKLxA{+a@8+*2AbRp%(jlB{o*2~)GwL;7DGUT1}buQ=z zB6qhnewWs4S~G52de7p|QjfWxvt(+M@Zb4!-T&n`c`CXm8p&R&=eUBr0 zgD<}z#G!rf0>_5Cn^|eQT02az_Abkx=IC2qdY0VySHsz-r0q@9b7Q{6cb@tGHd5VL z9K1n}%U0EKzX_E?HDaDH*1Qa=JBrEqV~V2k7>b{-I$nosOT|{JmPAwuhHhs>SXjWo9RVMA+R9EJ zJ}uh*{jAZN^XPyJrN(1rGA{%PzNV(O$ZCJN_;$6U&dPuh_JmgCcW>6$xfKOtvQIJW z$xej|f3KY+8^Fd(MaPi7t`21|V%DZ(W+vd^7;^h{2krTu)9A@%mSQ7pM0Yn^nGG9k zv1xLK>P}F`N<2lymjqEE8=SEbr{KhxmJXV9(x>*E$9Oxl7fb!3xz+H`-f#R+8D`Is z&3Qa~&4W}Zign@<;}?sre9qe%6oVekzS`)sTbg$*%8%?T^}QyV`4h%h0$+EneNP|C zKfcOD)}Lljr#3S)_m_mQ{v<-3$MNur*Z|W@Vd2vV_mdK50)=_ThW^%gON&??Q}b8) zQoK=l6LViXc`BSa8H_8}4^(}SyKJ#!OLmrhmt4(o-&6AH9H#M62sp;{s7L9V8*emr z+A=LXQQxOBYSN+lHhrV(k6crcIA1wycp6*hz*8sAjC8qyG6yBR-CeEn@fR`2vO=mg zCR~emI^Pu*rP+s7sJOk&(1na2c`287QCgUd{kn)&%Dr1+0ar>?3O(q&9M=>oB}5Q1 z+Bjbe1cWFj2-K@z`dlL^>JYMvayND#XsmGx&$#~d+6@-+4o0D zXDT92ii`s8JfBRauaJc2javsZ8=fZr*@tb4;XkKxpyF$vTwBFyEh@4dEIcReCpNS* z?K;?<2%aa-8%YjUu(Hr;96Zy+)go9ON~HgOM8d0HbADPLIdL{#ueHtwIl2_FuUfjz z@@!a5W4P#s?ajltY@GKqCYKk=E;V}WlXFHIQcRSoSnkI&9arDk9?<-#u6B zyhD7u4u#b;r*8PsHH-iP~!nuM<-SBeUEba0y8z3^f`y2wp9WWQw~^Yz_`_eUwc?1<0TcOv@9$(Lv%B*bN; zo4Qx}UQ|bv)1y83zTmf$PZ^tuK+>h3UObozif}`}y)1g===0iu$gB~xKPLrfte2*8 zjFt1QEG?PF*W9X22{|ouS*C2sFex^c3phx*anAkoRl3;0Xg|fN~WQH zp^u*XrV+F5ve&e+iRse{E0QY?7Q)|$l>#WtYT#7a3T2HVMxt(IC}!G>nBLaRQ0^+3 zEJ@78hx15P6M4gX6f_?-OHSHXTGXnpj{_M?|4-*pCcU$ukY#hZL%aNijLZ|GCz`_4 zA%C(xq(eg$Esgw9jPWPB-Nuu7Fq2Ay^x;P~o*A@cEFxfi)th{# zWtFef)kID%zIUP^d%@GxeeO#AKbSh}sH&dt@1saa3(_Sa-6hKjpqaMo%X?>x-{jT3$le?xp3E@j zlj1qLG~4PND_UOvU}$KV&-w=eIV{xlJoNiyk$wtJVoWddGWC&MHt>Eo_L z6{Odo(Zn_)?n%u{ErMt-d+aY!CMPG^yvhC<3Y68%>izV+-NouYJL+!@R4OxVXd)Q5`F%#2{Fr%c$0E5B9A zRR21usY&qexgDW#K63qzq21b6zYAQBM-6z0ZCAAKM1-!AxqTl#IR+ryED6zmB zVp>t@jNIf*&dtpoQ2i3@>)B%aYL8@fZ1X94&bLwLY)jVTO@fr()7V#P#4NshKOQ^x zp!e1!gesVj#?s$;fluE&;1oI^tMNYfvwH78qMOB<<$>JKov%iUj_&d>*ANCu!}_*n zaML6tddd&dFli(aCkymjI*&@Tvt^Dhn16=KscWY-xNaK-c;Dco?~ak#GAp#)*b-8# zs-PV%DH`!6jQ>f+b7o(O(J#52_($if+oZec+ca_$nV86^6_cF&-q;BRZhpSPoN&65 z>ZXDJ?y863P|!VNRLN74CbneR_h1_?Z)2~sC@8bu$A;6;8lNpsPk_asb!c|Cb$z@E zv(S~~kF^L``s4Q3d3W2RdmaoSnX(@EL{kO*L8w}MzK2d?9-5jwl`%ygzV_EjM#eZ1 z$e<3lb3m(Q*0s!YG)$zH(>rLp$>t zozu?LIx=IO*W}d4(}V>I%YiA$R;U~ke~+A;72QrMIM_;;ahY+5vF(ypj$;V30++gN zub`^F5toTyTUcBQH9A<|AcMI|n7Tdc9KQRU7 z99e)*Z~9XyE-eD+`AH7qDgoVzM%(!-WIC4M2B|6**x8`#SaGpW{>ru_rm7a z)~94i`$h3w732_%qk$9?bM-xwi>Pw<)bs6=5CIZlmuEf(q|l@T9Ug0x_gSTAI| z;0!ItZbal*}w~t?iVu?E)`P2H&!zPcihxi4rF`!3IUjWkNhunA-)=sbQd?OO)bnXtK@o^QvoK|eN z-4N$OA9^WJ@w@7eo&`~ys(A@S>~_al;3t3e*R!&ZaA*6BMvG1anhKkzG7yr~axXRU zi(#sRJ{Rge-8gmoAm`MhF`meSwKqcp>+^03U`EpqzZD|1p9I_8jYn6}&g64mV(ZcX zCmp8Tz#=Zr&(j6DDSKBV_55&sgVAqasS%RXr&-NMs~x>eMF_(&k*`|#Bo7txZmPZa zB>R2*>>lRfPk#vIUG;Hf&zR?SI(n#v#0D2bR8Xx0CdnF)0@xwEIgOT!P<~q^$^lsB}r@}D3xw% zB{!S?7HatJOH&e4fVC<)k`t^pMS60w+>ZT)&o&XY{wbwJJKRYH=d;lohtBkc*K;#l z81FtOs{WH&mhmm!t{XslX~;u_4$o<$i{6uUypAyt z_eL7CRc`NZVVwFUQ{@-3H`l`mlU3(zMaqzLmPa2D5!#WFFIJ(O3Au1jNCvRIqnNbkKUt>`98Q9 zVcVxH;zbE9J|d;xZsAni^7aFl+82$=Xa0wJqE$zDOvL9=FshqDGq|EeOokJm z7eD_Q$|!nP$l5+*O1xmaQqN?o9~tjc{3SlKL>iQ^ma^S2y$RR*+gE+#o+IOv?dPI? z`bimT?9WB|(q2xJjqFX&&K|G*{gLpXbb`p1NIpYq%h4i9}3_RCna@w>Pv zW)bk~-!&Nw_P?^RE9&MS16&ZVV#Mo>LSf!B_-LxU!BoG6i{=X zPx2l2D_RL+7|=2%Q)q2Ri z;U|9;oH*3PGJgDs2xv)=jCrF%rPaq&ofH0n;xasXTQId6k*#E@q{W8^;AWW@eY zUOvu2BQCWSO69$MVuRs>y!S8gkS}?i`X3QTf#Q&B&g0#Hv*>V!8O5u*qBzbFT<4tV zb2e}-{$Wv17bEh;x{Qm!Wzo$Z#(_=>pCWVGD-)!KJXslOB9@TLg*>Dfsxc4oy$;@G z!VY=?+xm_?cTfCs#%I(yPk#KMGEUt#s3zchzX@|llBbKU5LZaC2OjY^E>^8oweMP+ zeXaW%*O#jKQG;=`774RyCB;CY`;R{=CrBtmZ z`jVo?JuYq+@!Fd#_~yimm3BG$U@K!=TVu5$>h?i8TNM<+&>}R|RDCH0IV4hIt@?|Y zA;f>5m!D^h*ocjkqey3K=hunyKrA;V2j;mc566X753d<*EaTnRKZy}*93uU3hjCpb z3&$49`8dQw�qkF#o%*H&N8DCh;Q`3Rl5gua3nKs>e6r+H!i;-z1$;r*sK^fXHAp z7jT^^^veEp&$KR1ID5Ob&m(^M_*JG_6M=J|kC5K~@G&!OXn=!?9LvWw5qikGYUmdk zi5+NK`7F=UqOW>}qo%V;Q*W)5^zHc=H&+M~d-1g=(qt5;=vPKA_fL{Wjj z{r^9Vl*hW zJh(-To7!qGZt|bRa=KIa43UPFf5%J}$%vXLF*SoCwj{Gvr)6{Q?_#w@5 z@e$+eEhwf+5EiN4?6B053sUg%VwP5;b{d-jG%P@w$NFrV88P!nJ^G&sqjU>QO8g7& zUc(QI#08TDyQ1;1Bb|NP*n7loznG+6&^rCWZ=S5?MTwi~rMOD=fjVFj;}YwFbl zo7d_nztz&_hLxTXy{~O}_xT~>kC3xWlLt!B54XnPdio`j=OsFix$|Hgip@E=9ufP&XZsAQKHXyCu8)dqLy;S1 zIUo~%8%Ud*ts+wK#02O+oYjok_+a?Gx~ub6rI^sqUveK~XXw3$AzPg}Z`E0jGE_yD zhwp>m!rY8)jU*pgQY@@2940TZ*Ubi>Drw()UEji$k-!TY_O}}8sdfI;pe@5xqk6xm z=^R^_$Zt*$#}l)q9hZQGOZ;et$&CC>xf?8ZakT!ajJ3MW4>g>w_8DxqiAp^kDwfGH z<+!*a_jacM(EiQ9Ejcq^y@R?$8I(X&lag6> zPLi#9+tDKRM(%Gum+uD|bOkJ7y4?Gca@TE&gz4hpb`9sT>4@w`^quh^hwCk-Yx`X! zdXG}!_xkmOgMec?x>T(X)|ke{_qyiE%8jJ`?agO0ykr%c_Z%&o_Ot#bOg^n=2??I* z8-@{#R-*&Vl>fak=20A!%lsHWu86lyq!XPN59yCor!{^i7CjNME_E{(UeRYE`PGy2JzqPrkQos0-B;jmD zM>A&pGJ7*^guP%c^k-Zhua}8j+kA51v#Z?!6@+I(qJS#_R*wOG()QAN?uQHKwrvtP z2-=Yv)+qcAZ1-fSt{tvu-d8l6y?EK>#h2A)euhaGn=xc!InL;tR{;0L2BjI&#U!15 z>cC*#T+uOj6kbU}BKyo8A-7vS=(IEM~rw$hx&Gl{y^)EeDf8Nt1q)8X{FEBhwG z4ILObuf3fmyz#rH+REvE>P8L(9|Xd$6PII7`!1bd0`%j46#9E-G3eipw(zwt%Y?7i zgXL0o+rnqUQ*<*LNpGGkT9?Ac%JqLQbuk|*-jGTtD1-op_bN52W69(f(&8czG3371 z?v6H5)WrPSR&8Q7zk?r}nM!xG?J}sI=ZrKe=_?E zLQ%oGGv&hC_SQR<()<~LC&tnYdv4?7m8f+F=EI%kz#UAumd z1Wq`O;@{O9;RDTNG|SGV9u}Hld+fcM{N#ra*8JGbA+1JM^FA%PR{ zebItT+=GGO=1p%Z1^Jv*HKZWcZZ@0iv5D=yS5?oPA$yyzimoSOIJ={Iu`-5>OIIqq z-EhTXJDDK8CrlTW(*%wzP?BL%$oJu;oc1R)usC*h_g>GBG%QwJno5v&=>TcZA2SGf z4}q_=|5_`j%mwb7GLhz^*Po;$eLU~WdVNt$Hq+03574qmKz$iroDc0y{Q*_(kMsSu znENHL+U1uOiy_t|08cTzID;cj&7JiYF>(hQ)JGeOwzH8R9Ct9tY~4|wxH6e_jB4YU*q_Hn*;=nTFwBW%jg*(LV^S9`zojLKuvwYT29g`MY`f`9_vz z8Or9iM!x{n@1y@!IjL#Ze_P2D8{O(2VjkI0_inbr@`&B{#+Z6)T(QohUeuuQ$6H5N8>EuYd4sFjn}^yy7_!`c)Fj1H{?&So&1u+FJ{Nu-Umen9hK@ zmy)~G^k{nDxi$J-iSoFn)p# zykB2fIV@D}4J&Ku3gO1y8cx&&{<(MgPH=0KG&Q#5#croku%`1y75Nlnz0=7WH;@_w zBa^H9N0f>9&Tl*$X0;Pe-*J6|LiGcb(8&bDv-j%r-Ql9akk)VQ1BIx2h-68X>N%SxZpUFL~ zQhCu)Ar=J0)wwLgeCT_S#Gp_pZ$pjQ=E;ktmM}olngyz&3I)7$YSx1*NVQoE8w1oC zqUGBAdc7sVfBo!ImH$r_OR&fs$=8rUk4>*Rf>VZhqZ-8^h=Kt}nHAPhRaZ;mGGFX@^ zpUj6qW{VD`5D*9~MheZ$Pj64N15YBU1%2kN;@~8zIE88|^v&Hr)uh|U&UI#ysB?!G zX;uMFObP7aL6!I_l>^B0UR-TcW1$;WpUFf*+Af{my!Wiy9!aHXJr>gCDjH%bRRcUU zG9j8Vtt3Y>5Dz_HvwTM_B5|s)-Tb@P$;_6}Buw}RnXM2oxakg{8aqRbGxeJtVbJ=oe>CX=KstKK`g1GG{`tjQewOh3Or+NdKTc|@EwmF~ zH4Bv6%@`3~5=sSbp$CP^eaxMq|BZI0efL))z}m`<^rh$xGkN_fuurYZK$z?6*R*AP$7CaM zq)6!_h2?+Q(Rl3l6;mC$`Cj6xh=OX3te!C6f+!)~TI&@I?>$@T?Q%!4#ji)5OQ`%CI2BLKO+OqjtBv{PftpHKR;*cy3r#c!1`A2$R0=KM&m zGBf^mwOghGgN2fL18Tm^WsmKaAPu(b?yovgL|Hm{-igNzyE!lbU|Y; zoOqUNxSWw*TDPJ^n6EB7ICmhCx+v?pLz?Bm2Ov*p&XhxuAYyOz(7-VDNhiPY@X>~j zKHYee{!^#skRRl;Ju>E|E?Bi?J223<)}GJ@P*;1-x%8{a*7uU<2lD7{ybt=Vk(F8{ z&N~|jztPVf%c|FcjeH28%?9N+sh0;Y;1q8SOj<|yKrSh(Vbh?`L(*oucec`G8R@sZ z)u}wif{R{Eia)+~0Gn9aSps5p!9P%K4eD0>*xY8x%+42E4lTYi-J{jkqZ2u(a#?Sf z)zQOxMcVUk=}7Ze-*|jvzF}YVlmJGB_bD0L+2`HK3U*G~P}R6>zi5`?M36mIYRFG0 zs5nuPh~0=V=$fhJk!3sQ^VA_$hj2W1}P#r;RXnQ3VgVp+^%v6mPT{aRr!6ApAbDn|RM2)3+1 z=4ml8J=^tQmCA)CiEaEGyI}wuK?AG#^Q< z&9xVxQ|tlj90;Q+`mT0FnCCOJAk>TVC4=#Pq0Tbi$(lgIEnYy zNhGaN=OXdy-CGbH%yp7;;yU^2{rAxjELbfPskPSc#l$ybqm=YJXoRmW%WZivz$0w% zs#Yb!D>0f*N8e zfI^+dBQ>m0Kh9N=^Wzl^VdV&;ycvYCCVBbUMLGrN`i`?_Jaz?KP(t| zPcB@-;623`#8IxO$JwjRFJz(vokXV%$v@9@{#MQ^S21t6wXzjVYO~C0O^kLn8Jds3 zw>QzPw0$xVl~{j~3E}BXTI{a$M-Et-u9J1H|3cQ5zs=ie2vs<{PX2&+4%lQyPO(&d zyU2i0X~GY@(B_$KZT{-vGd85hx5&_VRq7aMY*_DvNz6+8XfJYJur}8DjjO?luc{6SW zIiq1Y<>#mTT}ht-Le+u13`c1%R_Q|gb)aB7TJA(cwI&R5XC91*uIgE@PmGx7ENPW| z(My3Q`Eg_^0bcY*fIB6sG4^M3RV-j#pCpAc zwjRBo0P1RU1oTXzKkOc7K=LG)Mi|N#vay1iEj+ycdeh6voOWQ^%5vwmFPfKca*H#% z)t!6Rs4J`L%yd`&3|Eq;0u?8X|AxSJkYICo?|Nb1z;LJSER^z=E+pyZELZGLtlZ86 zg8!kGzIzjP~&bhGo4c_2NZ(RZ!Xk^C%bF-y%h%!M)EbnzDi*^Mn)L zBO4^1eCif_)>f2r$KwrT^N1seFXOsgiYUj1u+Im`-gAA<#yPZCfn zS|ORuq$_X!7{wj%gpj2&fmqrpYKk`EAGkgS0EWWm>x?(ql`Rdbjx;A-LfA@Y2)T*JvvV@*jmPUG0mlqaGPe#~!Oz)>%^`dgk)BiQ~8W1^zk zAJ!wZRvjgb_~yNdH}GRWKA4M_xRA!@SJP6?5FMHAT__T)CFgFZ3uTuR0LM4sX(p@E zAYX#72^ywjtRoX+{JCgRxfP~rFrM)7-;;B8CJ5xeYvT%Gb|hwR?L1;ce%sevY$G=- zu%y@TPW?pr7pTA8s5)I9jVMoycjbxuR1K2;*)ofKy&)%gWiSYHV?^Gv)kUM)+Q$IY zuq(q?iwjL&#>YK+l9rb}e^qAfe0(I8JAbwBpzCkcp_=`|!vtk%tsQ$5*g1mu9$9C| z^xNy4)BtMiRSX41i*SBe3E0OtfJD?M`*UTB)nK%`(7(l*yGq`+Pe$Pl#R}ax)MlUG zS95QK5!8l^e)f+YN%JSP=vJB@!Bp4)h4B7NYl(Vy{t(ldm@Dn`T2ynzakAQFtlX*H zW%fv$`}W9R!)Mz{J*6Tb*K6yEuIA;N>}bYXEg!t4Seey(xiGD)%p{IG zJkcQUNTcw}De68aLNwbE(CDc-(3UT#raS1teX$A;T1OhPb8CjE1s%PZb_);={VDHM zF5bR%$=JVr^C+QH8;)9THQibic-oFb+mB-ZH!#IlElJINnR`t_2WT4+z?$%4HM9D1 zmN3SC>HWXISP^OyWo(@36@{FcH!k4nJ)NJ!?5cy|{=xjTCn&V!fOPd*!1J0{!cgpu^+#b5%LuHEuA zaJ>-lnvgx--Em9)KpfO74_Lq?Gs!EJs^uAq<96iA=?*wjJf{Q|0I;DJExY*ZU6O@I zCI&Yqa;Bo~bpJCX-JCZQ9#~5)_L{30Nu*s>+1T1LHZ_u6w2Hp68hm3_7aSS$)R1%k z*N|sc887&P!uxkfHXi^A#%9J6z}u;12Q_+hgw&_`o{Y%l8TN%}hQK~rT8X=VWmX@C_7o*B2{BoTOyC+Lm zLqTDWS27ox{#6*b7d3aR!!8LGlUijl;9%XEspr;BNJy%42pI8=8%ah|x;lqFPLKRfeh_GEQdDWqU<745__thA1 z;)xbYx-5Y-)E>zeV?VFp+06-j1g6Wx$MoSKhqC`?NK?_Rg|UGjdUar!j)m4uL*3Wh z(%^>AV3&l;Bac#+%11uasRw=!rD-gnbuzmLPYF)Pd2+U9!thi}8wB7u zg~`RvL*MH(7}2UpVT!(g{}82JI^j#AD-@;~viCYFB#}J~%&Ru=8!!9toJEg3Ie9?E zY6Rfj-Szs$w};A#3PLUqJYa>C18^r_t(sDXK7U`>GXXUGj-Ieor&JO!jS&$s$NgTL zdJRVSLG<|y)d&RhnL@)CJPftVz~Flj_@L*Um08>c5YX|I_U6Jk*!8@3B$OXtRa#9$ zHpWrOxfRe|p1hQ3x^exfwf~#|GP1T*467M>GFpgtWCEvcOrgDDYmRD9uEsLI4pjCzB8KfirCfb6&`LT7Sxj`pAml){_bp3x`y=K@Rm;xu*yBoRp0|l z#>3TvTs?%Ks){R~&+&DjlEKRIU)nq{EFih%tN*Hr$pM%vt)wJ0NM&_fv&mw&+W;6w z@+a{=9mO(;H3!7MbZ#M%>cI;ECNoO*x6#0;A6M@1R)j@2ApI-ZI-;xAb<5Lci1NRQ z+*>HeU1rr5YbMLw1z&`;`1%LyT zd%!IE_M);%XSI9r)&r%w7+%yW?5S&6*qfVvEsP{>$9yj>BiEx0QQCFRG5_RrxlYwb zRJbp;0^`ZVQYOV|3n_$6;BiHUU4SCzB zC?s8e{d%yr0#48mQAniMH$1!kMBkCodE(oLmH>qFmwog=a_q=7jt7QVP@QDun&Wvl zd#r{H8k7eB>d5ANobZhu6E^o%CsHyqw@}SO#55a_0I-mU>lu07v9Ki(VzpA=03GCC z`m3kI805u(mvb~}Nv&an_fl*kK9ULd0U99aj{WO#Nh7bY@O1k}GBtH>+1BL|!Dw~} z^c*sH0}qZ%sFIt@>t~Fg03Wm{PorVnUBkQvqmx|^gke!hNh>fOM~dBfjSeu?ClgL# z4f)5poW)OqUftvIYQOd~tDN_u+5J*@qEn8Gw4a2+rZ5oip^eXyPv_3REpf z0bYfjhX91q1AfJ}`jImZ5|uIFSosc=SA^0+?|atnLxp|^iH<}L;QvZA=LDX1Di(?$ zHQyO9QpGN(P(g2^XQJDhC(8@Ks0i57xn{T9!ybA-1;FlPKm6DCEVOo*gnG?p7Y&l1 zOmD_c1^s--*T8-4=vbBw$(;tL4Yo%6P??f-&Rnvi)gW;I_CEi1ND(d#0^D=m7i>;T zS|A}W(Z?vG+#dFBl-xkEVJCXhttT26)CWW>Gug?}^ zSRh+x3r}M(6OErv?4DUIlOPc@db1*d!w?eZvUc)BbEUnoo-^1tzmHgn7o_XPZOppd z1>cLa`BWTVLVaF;dps8?N)#Xk5(QXPmh2ACHToNs3uO`ZiT`#2 z+i#7JvnkM~78-Wz&1lmLc%6pWf7@f%O6|iLu@E$r` zO(LARxHz~}JW;xVv*%Y_PlE~}c{=saA!xAoK!TIKb%a>jJeE9J_UEd92ku3Lf;?(1oRg5~wcQTLP)S9Jx{@w)Spgx4VM|_>0;D(Yd?kfZ&V-8YEaA zljz(K9+~6q7UCvmHu=5+hl(Az)hN0o4dn0vDF6*UHD|-s_0Vp*8#1teVHmVXjI^kh z--74_t(7~m#Ek+w2nZ&kbISb{<2CqDBbC-feV@UAMQN@o_R^W`w854M{3v1$q@a1f z;8Sq-FsFS!49?k7FQ4{E_fd|m^(uyJKNPTzadBnNyWNiR)UxIUpDzqFQJBRDApk&g-S!f|zAFUNNUfN9sLJxioHKxc8-V&}dV=R0uvh?9ewBypPR zCtqVo)|+K)%srFgBWhl%0H^PM;Xrp|BD#2Ux3k=7IG?Ml>^Bq~cluc1Z4UtMFVe~F zPra^GDy*LZiP430JPuD??HSHSQ?mjQRSs^yqgX0~UHxfe=Xf&Ul!OpJnSP1_g>YGdO|oWZ zCIL51`{o;2-2)-5Y@1T78K6gcE8M+C7i3Cdsbv2O46^1xq^k`)gH_vT(U4 zZ&IGbi}SZ_8IJa<lU0EsX?R|ZHqCz{}Tw$r)o`Bq!oOD7k#t1;kBquY! z`3x}=pGR2765hXnn;=R7s7&;vS&m^@>HRXnm!cP4>d94q5>ZSMOR9rfJ>#N~w0@CK z#)>I6hgLHt(C}nIPiZf88aiTPKo%O~DyP?GyQKkaoq<^iBn}Xcann|TIqCJ|+^;%% zil4jyH%Jk_1G!H{h_7Ky3ecQiH>L1mFC`DdTS#jg&UO{hyeD0{QgT0hnNEwGG|}fn2_5y zDv?9bTxcuPg5>GjC*v>(1`03Vo-%KtfMO!&RMS6XMCv=j`>YJXNuEIx->Q2=#WC;1 zb{<4FFj7$j>inUA1XfPUq74i+Zqwe9rI-n`UOqU79r1^i`N zyZ5jBF&F$Z)q{&)u%dDOr9<0SN zNM>JXYao0A|7F8t95|o()^ zPup#53Q{q$L~X1D6GW9tMfWAjZ8X!NyOQ?ckURW#DBa8ofJ!W9Bkoev0w9eo)Kf6) z;%yn`XPL6!y0=zZ_a-WfLTG;C3D=z%NGZJn@*5&|!ABFzALkM&!i{QU@e)bmfSMtC~BG~EBKERO(exocqK=7WTojQ9FF&74`l!~lx5fv*2| zBv{y;a*dwCDuExUMdHAG3&%u!scJ!`qSoWH=9#je^nPtfElU_fVy1y)*vGFe%hNjg z`_pXS;=|mVd}xAZe55fOLsH)3LR+=vG>x;wl`%g8lNGlwzii#iGBv_=ggXq)O4rjO zaSl{vt>ecna+B5Pc{f9f;+L${hy4^0-&xZvZGvnU%7L#s!+}o2Q=9Ee$J{f(`9sf7 z&C)W9vz1$bBG>&`_XQGDY<@~E((L$%n?yQWeTNUULs5l;R%e{t5MuG*uQ*qhV^%jV zb|J~pUBt`p|2r}clEwy)F_|6JR&zy2PCDPF6pOO%jJr#mxDIfm&PiQ)Gb9H=k6Ts2{>?j}9DtE{e zudlbkX&AaeD|_gAR~_ML)Rx;avTs{cwBEmSpX%+!|MLR0Dta4pfMc9Mxb;7xMf(fw ztRpy**j2+Dg$D)Yw__2O6)|b|@*DfPyqfWoUz;Smi|KEzAB6mutM$%omNT?3pFBno z;4yN~eK7<>{bJDb^4(?pL-&0aw9#MVRP4iHBZLIl&&B$E9#vrwx|ST5!gU+_ze^@9 zK%rHk_y=|D4TM4k!D&4;TnZ(al{S#HZzYGn(eE!dJYN& z#Lt$0j~;E-M*B|B4o6i>d|}VXtE#zn&pY-HXH1aFExKd#3Lo*`$Y!ew-#Q8d%;31X zr$E$NC0$hqno0s-2GWj;cQ9TzCOIfV-E$@3Z~tlUYjiR0!gTi#OnEzhRJnuh5jO>HD5fpFUxqt-O5Flh-~DV5WxAm3uYOy=6`Y$KiG?tif^ickdK| zd=m?vW#IO$+$+-9-sieT#{-QHMuE&gFsXnyX)olU&yt4;k{8VFaH7({`8PT`N`JB; za^5NMu6LvF5C2#+OsaFQ?)-kkeA@(0lCFmlLV6L-;O_Zr&Bhu?_XV9)v(p~Hd1;;l zft;J69!$o$w53}#fv_Y7cLQLc;u%c+8vNZhXmR}mWLQ_?v;K1tZ{f9MB^5wh=@kA$ zb>B7HyTU!E7LP$sXCsb{ms8PZ5A(GLFEX%YoDpVZOl+;)-(04mt-sv?z7s-FKYs(T zV-}lyJsVbQ`iS0C%yoG;SIGnpW^Hss;(%|u+<3SOn>@3CXYlbW`l;U=cE0GxO1I@> z?vx|=9ue?lCxLM3PI)XZx&3CT>gr#bxXH_udHtU4yfnLw7~CyYije zTL>NKZ+Ye>y+@8W9C}e@KNmN(tdwhXWKPKG!B5M}%)TdPV&W}m&JG!bNN%s>Fx~Dw ze(ulq$69lwmbrh=M%6(b$-9%h9cm=16Vr zBKTP9utOzD#EF?nm~&Na=7+!hMT>ad@v{SyC98fT&c5Kf$o+0{NOgJ4x{C5^p&nAm z6lAm!lc*GA9)~Q7pKT|S!0&U~>M)HdUO-8!grT?92hWekrX1)I72W+H4duG={m?ks zX~4-e#piiaC8l?ZSgq5{9d$3I2z6Gq-%Gd|*AG&9-qvxq^cbsFWni(eg?@d%@MvUW z>oVeuLeZHi>by-Mj;wNw4$Cc0hMe^vK0up-i8L4{GK>t8**{?R9li`3w-j-_5d?S8 z?5sKA1p-{z=OH{4RMg;wOLD&9lk)|%?l6K`cq9S69Pd*qTE$e}oss^Q_OF?=zZ))_ zEbSv*yc5S-m%Kcp#!6wD`(}hQSQgoW6YXwffF8!T~|B%``z**`iF+gjWrk9BKLj81IQjw+Hac0(Mhk%(Fq1}N-V=)7xVj6s!y)|m$BK`Vtdl2#^3G5I`-d>^T&)jfpj$(Oxrus6k4B* z$`f6%zT^aFc#uj@7VAh6F#v0&Sckqk8OLU?b?NM1#Eg|({C99a@8gI0+mUMZ`Ku%U z75!2wmxtR*F+X2TqCi@n?*3+!keow0JJ!LG&Aplc>O(!5iz&`Di?Tv&f2A;dN88Dv zkD+x#%aPs|LVSI(DqLpT?>yF&GwFTrm)rV_D;{h=Abhel?Q63;&;bF}R(6KiHaPfO zWXpT-7BXx;aQK}hl>dXmW52wY3CZF@4I6*H*6V;}v&S%*RG%J*-_iP&$aWsNcJk9l zjK@KUGD4yw7sa!;x0l}a|Fm59(ajf9{?5*6tJOycP$9aSOOyX~C&vQ|rs;GISsath z6#O-HZkCayQK;XhY!kl|R%xgw%CamO|BOUe62eM;3Ta3~Pe7eD+{QNkzw?Y`S#ouZ zYzYu!Wf56RFG}nno%PvA>E&hJ)iO#&`aZo1^|HEy&mu628%V+saz4qWmyzw z`<1D~<%4~cQ>S;=n-1fO2VEtyLV^nzADDaBZ^~_x63$690`D=8hDV;wPjy5>FsRmT zUSM!1uIrb=(-ApEutboQzr56hf1B^;KX`3H=`5V!zQ9gaGCY`kW0vou(}ON&&cmFb zv#0HbPmAZxkw4k;3f?Pfo=kZt>`2&iz2;cx_r8s7zjxi_;rnp9Ume7(1hu%EUaVma ztl8q=#^pt1wIrDu^7qx7l7C#?_|WY3(Am&QpH)y0k?3?D>O6UUG@|XbbNEK(T#`m5 z^=x~sVC2|_^ED?Uv1sFWi~`wF@Ve6NTs@Jb*l_*3TmNRiw5@Hde|zHJqxU1$?QZ9d zGo?dkKTT@GG4et(UC)oLM3N?P8ho5$-^wve-cDaut=QT<{_iUXs+ZPx zo{#{?b=gQluOvBZSx7Zgh{;Fm%m`1gJj)%<-camodEY+NzLObw=l6SXmJX_mK^LmR z%(Sr=np%EQR1i4|A2&CAXiCoi02b?_Ep%esrw9 zm}=hSS4y#{(#m3+Z@9T^KTkASa@}@B?~j#3DdWR8@XqZ8TyC1Hn~;>DE@dq5AkAAV zBtDr~y20l3g;b~FtII>?n{bkruf31wUCpL`NmhX?1f$rfQf|rC(1gp){uqR$1RAWWjSeLbD3 z%dXVQCuchukF*P&uw%4<8KCMd^HU!}J}5eofW@;8eviQ84IL5RpyW@=16{5Hhx0x7 zh8k1RnVKAo_sA6e0o3Q4MQ<2!Un9uTmNqsBv##^^LImeQjly6WQ@DID^D;Yw+ws)xFxoKER5H$-|$8+#uMeZ~tVWDFw+UoD4 z)Rd80UyD;h)ec*U&26|`VXLT#nd0VBDbam`Ke&7fd9jEaCo@R#tN87(2v6@BgXn^d z&Qzqf4@X9tqjr%V0$V&7b(2RwN-HF1f!)EiV-Qo9~LOdDznB z12R*r6S`ciY;Dt0zk-19eJcFy^)$Vm(roRosNMs_bu}gbHRZzk_U~!eyYuU?;Q0yT z3lo?YzB4gWvSYm^Z%_0|T5$`Kh8m=^+R=?ENib+ZFsv!6u#RUpAUpDKX$DNYA*m#G z?Xez9QQWV`i6dg_3!T`Ea)-}79b?JFFUwzVEw)Y5ikizC5*pTGeR!%9)|YORjvgLS}CuKG~kM03*KR+VseI<9lcw&*9f-JyRz&9O>Y>K)LV3>clr=%%yJj zLO%zHw;k+uZ?irewT}>&8}+45u}?Ig7@s4Gs0|* zzIbpc2eQk{^QDgx9IO&fE(@6YVbC8YqKr%=(B5X>fTDr1PX>Zw&3)Eo4xedZYcCaj zZUtSZh>T!3KeHo|>glzrO(lppy&FOpM`G!o!pBjII-mC$*u9MO`2K)qhxVEAp{FSf(H#jgS%S@t|2hEyGw8<0fIw-;O;QEyIXK~ zcNk=Fmu+(Q+k4KQ-G2kq)7@{$Q&mq@_iMQg`7*ZH=CEEDDaBJk_~yLgyi8$vHaha~9{&m9sdJHM)DS2!*l;Wkwg0m?PWe7L;hHOllSi->OK=^JRX~OU-8%(QW z6I-~nE9B}ztfW)_MSU{B{b zv1U3-sllgPccWSvI1))8hK%rWg^Ji*Yob`v0sdDl6%7 zu>r&rnyQQ8q^s*bXes^OcAWGY_YsO8>Cl3PP;-f%Jrvppdt$|hEtcJ|%`Im5OZDC~ zL}91}hfbCB0kOqF*YL#_{Ap~Xr$XO+0f9=b(()O)%=x;f)`R+F=hh89&nKaWLf=#V zRLk-&rI>3l{hk#Ul(L(=t7~z-oLMe@u%r&mmba%QLq>6MfWRwn#=i5z9~(Xy3TeBR zU_S5D4d!=>+xo+zi5}XzZ&kML)6j4wo~EY^SZ(9w%ST9USWku!Eb@%Z7Cm6J-j5q zO=v`990FLj3S4*2Z}cZ_OzoVU**oaoi3EjwiFQ978*Fxr{*$dKCFBqkR`t%+3Ey6I z@y8qcx*f*HnBYkhLqlt^wh^)$caQJe8Xxu76^j>|4RH%_%<&j67SRgd(rfUc67R#MD{|a?_|dWM}e&(M+8q{M00*sc$;cRshMsQGpB8E&iDgXJcMgp@RmuG^ z(7B`_Jztp4A6N`j<@<(u-(Ao6o%ldHNST;KqY4tLnt2{IY2E58ALDIL${wJ{%3v@i zUb<@^kBS#pxP`YMul4)Hc}MphkYG~HZ$*WR?iul>$%&0vpG+{V4@1)DN8#n^!<;`; z8>>v3O#vxO{3=6tzJjSgZF!EJBhvVX7moDB!{JY^PiZGh1v3#NS)DrZsUqHMEOBlp z1Mi=CWO`_}z_M-5ux6sCH#%%ek52UR<2V=0m65F4IDM(-nRb{qTN2N2kF&YLTI(=G z_-!Z(@z7fkTR?djbyaJ#HQ3nLXX}>PPMGdvY1Uuo2RCt5pJaqG5zFhU))SwW6EAMH z&?Hi`tj~Y(K61{eAk6-${bp@wZ9Y{$K@=)DT+I_2PJl92>(<3ec?t4rNo3Zzh8|b> zxu5M{x7l$+R9uh5Gl(M956(5;e*h}FzpYW(lRlJ!g*VpKMju#MVmY6ZA4d8i+%Z)yo@xZkUJ0_Wd8j9MQHRB8}$%P>Tgja~};ZtI*p z(+rw7D;6v1h(+6*CmSaZOZu=ac~JsHM~2)n&H z*wB1+;$pFacYg7Gbz-t*-8cgcJ-v<0;NMrwH*4Q=qMK?37zEC}0|79OeEY)BY;dL! z9oFzMQBnX8w`N20V+k8vDbvt(35ORq9Z7W_4W3wMtXB=~e~hSd zKd|xEXoz~+T-o{Cm~Q7hl}7k(q8hfS%~tp-oCuACuODD_66-us6`C&?5mgy7+;O@p z)1yfovKiiSr{M3BxT%$hE6~s;A7hZz{3wk`hZvh0LNG8<+l+7`6 z#6MkM|4q*hF&|oMeK=Xkl7168O_HZ{;FGR<%jSSW`dz-D64n64eVC4@R%>E!(K0W;i znNqKXqU8I;*A}l3fZ4n!OdT2=Dbs2h2sWUb0a-MM$dMagjo3>F;Kv*7F<^9=?!u2e zy3A*@$fiBL%4@6F{`OBE&f&?2)5h-4<_^Po=^j&ui+43gu4b*)OT8r$N~CVA>l^Kh z`O=p)-yrl8;w>nfdh)Zd3meGBk|-{LaF57@Mk@TZ1nk+e$Hf^)@`3ySfy8Eny-YV5 zp!=4tG=NgwO5=hOI-JlK(XzgUhdowc!!R@Q{6|K012y`NOBa6tQqmO6P0}?}{zWYz zd0lrtIg&cU`0%%PLZUe-yO+M5P|D#EH~xFMxlBoAP|xUMOkJNq7ae(edq=6kbUGqe z$>>OK)y-B@`h4x~*VEuiCIPnJQkxQM#~%?Dkjc7_Xu#kk-d}+sSu`ZV&4rs7k*EqF z5SA=)_cF~9bRb|%=)}&6Q&ChznI0Ipx~U`1S=G{imctN9h}#+Y@-HO@z@F_>8_S>9 zu1UN|r*o2x=NN!d0|p(FqZ0`R2l#qk0wov_*lFK>YJ2w*;4!j(0^hxS5D9uCAMO6~ z0r;Bx#Uo$7FIe09tWMVL72_x{Jw=A3sQ-5rkF5KVA`rOa;}D`Ls%|*4|9t+l`tK}A ziXBq_(qJSqQFv;pmVal#z)6_`0O%N48bSN;l>MJ7N@3NO+S7JvtnRadir2L;PSVvl zuUdTVVnmGyq_`9~0WW0zho)f}Gy~`DuUd9ieLKJpU)*(RLMHIJeUC@1ScDBhpek9c zSODMNe)9$*ol6FdG{+iU%Z_EcO!U~Q5Ev{DqjZr3=c^!#5dj=UQ~7ddb@o+5W_)8h zWkqFR@PRDG1i73dw!??FW|_0oVxVbjDkLdD0E+)gcxD4WHCkzzksHd+85o2z47COk;(k)r3P#PDPxCZ zslj;onz(BdgFwsbPwxbDY~lp0;dvoWm#QGAaAXn87gH?;q$IjeWtcGLLy7ElTi;N! z=^)&wGwUg)$S;c#4aB}Fqg+5;7l0J7=K_ysLdJ!$04c~}O>asm{@L{|CWG>Emnbmo1Hv{2SZMo$rUW->)kMn)qZk@E(Rp~Ixm62zw{Z_>2e{j5bQpN z-NAosQ`#E|k9pZ>LKm`O`SUkG8fH>#a{*2ilSOmno9g>5i|3VDF4&?}>L(@)O$aN} zjdqzE+t`-@JHUEW0Lys=Bc@2?n?5GD=ivJ;dMr5&4EBMMxkze)-IROL1i#}8&LWk( z7Y4P|UMqtHY<{YV?(4ua9@Kbc(n!lGy?)7CuwHDG{|Z}dAHI0j5>2d$+Uo}(r#e91 zE^ACJI|9Odx3-l2t||S}Ef^aWc{Cw(<-%n=SgtD3-GP9)C;LJ#lKg&f{Zl?a`-|XC z)s(P}-!qGrxiy2qtH~cp>`g^*|VO+t~!`^f$3nde*3Qb zFzOHVf0s^3c~L6~A7K6MyczL2v&Mz}RF|0RmsTp=fz_83;jL)^0CgdCH&9|~!f|KY! z%gX#$zMok%PCv}uhw*M)a8#A`z~Ho3RQnwFur}`2H3QTifpS9k zKaGYLX9$I*YbiAaf#hCpp+oJJ)vTq%_pV2PK3&?2m-YsXetYbWL3jEzvtl> z0Zmh(vyuQRh5xsdNb*uysScE{@It;-I}!M zpECp@0{(2L2af6@X7O}Y0f2%3pH)Um3SV%ui0l&gcLJ(ck;oeWSr*#+G35w@=ar;0L z2rrSaFTRBn>;26(^ZMLH4-Lg$=YtFVXH3&3GYQ+3D- zgoMUu!(CsW%=;D)#C0OY<=Bxa^Z(2Oyr=*)&GmyzjyMpI>?!l_r2j{t{s*bRum3+0 zeg09Le*(Y+n95IeG)z)pFm4iWPzdeaR~p^0Be~Uzm^~ry?+we>M=O^wH;!J)4a-VM zWXCVHWA+GY5SJ5xuwY1`tcQOw6AO^HOusUUg!kObF#b{6jEMWRdwxQR=q%XN|9I?4P$o zV-uZIbJJUcTdD|2=}~)XqM2X>(o@?dx)v%gCbPL-w~S-X3ntgMe0!gT9>5BGEZUWyynX*+ z=ftL$-Jbplv~YRHn9kF`RF`fnvI_~b-5<#jyi|Ycp?HyC?)m9A}=;mZ!<$>S&mHK{R&!(V9rt0Z^7PJq&j}5q9 zusvSLqIByn^)=M!ZqKF}TPyh4@nSa+y*DZF0cC91~|1qCH>Dqrw7>n$ll#8Ei}Fp2>BvI|a)o!;*jf%H8aRiX!A0CBvflHtnWg zd-&j3DWKJmy25$I5l|_{l8c&sx~|^iqK?Pio_DLKIn1pa5Bt#)DHpI!w>XK>!32-! z@k%#eXsrSC(0O+AvJgCAtJ~7IlWscRyokjwb%=Ou)<}T6_YX5a3V4p zwU@fL=j`;a{Ec#%oaD2qWW8eqIp6I0?c9p&<0gi@O_~`y!2%jl57<`dqvxW9#+9dr zX+(Y1cuF|GWHwrh{Z)aZsy!Yqww=MM^OcR7e0Rj<*2>?`-j%2suV0F}&RtDORmJke zXz&>}Me+zh7=cZx;!2dtw<$D0bz zr!h5BEebfZ zv3^?`JXaAhr_>kLDxp_ZD<0e4-IoU2UL=j`CpemHJ!=!eG6R7M&fL!TFNf|5rhIAn zU22>p5##}s)Bs|_TrJU8>~kFxr^Xs-B2wRlCwDiyh-1|{_iIyLx*S|Yd1-|Ty!?h2 znO7LfVq0!GErRxNNjTcfhUI`V$QsG?iJC98!8VfO`?bkk+YO_auIqi8L0vhkta6F< z5!8I-BZE6XC}j-JY-kASm@P~xP=v?lQ%P%1PA^JcZNERoT3cS0F{qaBnfWAQ6b# zSx)cn*uH8)l{nPuORJFyZ&ZC}7=3pb#tvh3h}y&U8cRWwj(rmvia6Tfv9&&Ompyv5Q*$xyny}MOiQ39ZGb(eyy$$Yob!3rFMPjX z&d&HIxcKYGJnzHPO)J1y3N++6vk zk$c_~I6vRADKy`a!K^RNMn^@9uHvnT8=9CXhfSs*NCQ|$q$RM&=Zsn6j>_od^q?ZH zkT|&a$swG?r>y{NaWU6Re82Z_2+Bq9>TM3lE}{;7z9u;EN$ChpXJ}m|d!tDE5A9!P zXm=>H6A)=Fq23WK^8>B$O18IHy@>nbCU?X4K-U?%bqp^&GaNfz8Nn1C6Z`qF&~T2g z0dwwQ#hInT$SnnVOjp!>Yp5)}s0p8Xi zzfeT-mB$cUYP8MJ3NOHmd1-HF8xQK2n;-E7j2Q#q;k9_ZQZ_y6s=RKs;$+i##m7ra0GJ>=wDfLU>8kvO9pDTnQSto>JK|`?-?rpK7WyGYfs^TyCwR&2XR^+{ zJN&GHLY2$o_bY3)j>^#$gPx)ODf9U#6@m0T`#wRn`MKq@JD`&VRnJA(RS>mfxC-ll zo2)CUV?uF2ktw!1>TikiQaDFa#Qj&H!c<>&RE$gTIMMvsSxy_<59MjT-%0Rs0arMU z9dD*$lA1EBS~>3X!F?h?wfIEWUl$>zY4ROnMx8fw--%%+gLK!ZdqG?H%WEPG3XWHSITl&4ua~nDi#6myX#*YIV&^c@5fpL zDY14sJ4G0KMVBXApkk8G?~2p-!pA^D>v&($K_GL!lX=$BO;9csoQ`MY)8^`S`ia2d za(T)Z%_j<4D-;z@XLiU~vPw%@p3R^<0;vUwvYeM*3@6defs6d%qRq+SXQMe>m{&>F zpC-L7wL=BuR5adr!`7|NO9kD(kO(xUu|i9OF({GAykT!j%D)<~KhZfRI{YC}x>kIi z^b6B`*EOG0Sr(EiH(;_9f)DX={|$UKZakuhd)y|XpLyz36B)85xL{2M>~maNC7qHQ z)w|#}>C_8zGix4{MME@qf#}A=6|z{?lV-A5IboT3M5JA>GZ5&j33xOPN$Ygbo5jxH zM?S|pPR5ka;?kH5jY8@VW0vZD2PhWHiK?wy^3LM4FXAlPeP-dE`53Q|{^1kjOojl1 z`aQ$venhjP8SA%tc4+N!x+Xwe@l1qzwQ)DNO%!-BeeB{qza9|dZgiU{*B)(3Q&7Zy zmGp}l@~A$vbcVi}$Mmt>oDj`gy??e$u%A%i6%V1?*WILXrJc$T{KI>?y~hPp#f!pL zhpBV54<6BFIpQMJ4J$5X*+D59pK5 z-oOCZZGPcF(cKdLlFkRPnJ=ry5k*k9cm3McaK}oI8f@H@I^>P%gzO%?TQj$u;%7Nk zzj!7KIcyfWLugR!?}$En*4k}B-Q{s(2pzp!UQ|87krST8;6;7kceTBGzCKxEHc?1g zK)cR26YY74nHm^DonbC{>WZg8n&jf~6E68l4|3xC8s3FWfagaL@j(IJQb5+}@mVvloxmZRP)6rt1}LqK&wwQuxL+8||If zi7mfp5zAi9Cl`TM_Rl4}r;zwIR?AL3LDEIQ(o2|}jGtBH4_7-vm{xO5x>Q_MQQJr6 zWBp}zc^WO@;$}D<3HWPWtt8t_Zw~Uy)XyKOwZekCyXpu47s7Dac?~PKi0$*m;LQ<& zK;OD`k(7dbMqd>cb6vfVCak8K`XrE`u|uz3@6Te*x7ovc%sxv)h9?RTheyT(^lg1c z(4mpr+4|Pz<6amvZxFf4D1bHZ=8v#(%UuCE?mGupF54kZ-3M!7aSK+ZU$2#FYz%2< z-*$FP`~A7$&bqQ9Bd1JkEa9DSx*6O)OY(>LSMJHqH7El*%g=3t8x)`E8PK{9*vvp6 zdHcLg0WIbmB+Wi}&oRq)FcR&Y?c393Imsh@FLcVqcgyk+A4AVWr0{%q?62HZ_vI&T zlD@5%d~Ex#!w0X=r>$Fp92S$dOGXP9KD~Mm1?a6kD?O=SG*v1LdprT;G}!Xc-P~H& zw)iG=F{}U^BzVIG=r{`(cVlbEX)uQ*LuGSD_Q{;ka@S|zv}Lh>M6zqXD|zHg-=yi# zB7!D)95D3{sXqMru>uNAt(WyRX|xlU!2xMnz=(ZCx9B06XCHx$k@3dHzTCNu-yRF3 z?M4}$p5$Qb_~r2Jy0Vqso0X%4#AK|wVg@MvLhl`^AoWhUVx8v)NbO+|ki~yORyE^m zzd zr~7!^gl<9EO`OrGQ6afC#H}0$6X01rAQd*gkpC>Qgf6XKYlE4UrHI`FH>Uo?bEQ(n zMt`UElXZX{tDrH^dga=QgT7 zo+-sJ)>1Mjv+r_tu`!;+CpU~5a$3*Hd2r+Vf6OMYZR#Qr;v8k7eMuudUZghsqy8Y%`lPtQvEH<|)*JoP)ml`=yj{Mb9Xpz?Yr;ff;#^)Bgle$&j_WQt#eA6!eH_~o)9}{*8Ppqiv!_+ZKo!8FFrDSo;-f08 zt9gs=LnLRUdaF3J>Jgyhq-DNd*7URG+>uuIqG)6+Pp8LP_qh2l9v>r% zmnA(L!qjU$Nkavb!}{)t6ITSzvE>Q7e*L4L&~i+NrICjgRmmp(`swu&{^h^fH=5y^ z{R0eZ8tx=D0@lov_#_)j!*Wf$K8&}&KBu=jaFK#bc1&Cq#1so47Q+?wvXu@fj;*Qh zHWcGx(*{OXeLn0Dh>55&bmof_CpjP6LdpU1>UWM#5T-YeWB7weY@t9AgyI5*Dd2|L z)|RL!8x!bKsXU?aA`6&dZ3#tCec$?y@OThkvXm)dt?K7bq9^9hS>fH{XKOElzGh}U zazO0qwtwxTW>3-ap|EV-X81Q%V=m9;7f&_UHCi8W%2TOyh+2~6iDP+=W(Epjb_`Ft zrysayCAXdIZzU~qCfsn2A>;}xlqGQuK2h^Vf17bO92oTu} z&Q!nQF^a;!6mOLNx&tuPblaJxs0xJDV>Bu9>`R;#X>~21lk2i;MRVzkro{+AoGIuZ zAmCbH8*lJ>S8|E%Ogw2@lKI%|(rCw;t2?ltA%W{+)u!Vc(-@nti!x1yLu|v%b9StW z#_OlelVVM_SG{{i(N$-OV=Hx2IA;@oJP|UQmqsQ0x6H{ECknr0_AI)u$lI;z8Qf&bnl?%Mb1N3E=&vpUzY+ z1oKxn{JxDtnl>3!&SYpcf|(k{b$f|b&U!X;pKtobp?-|OJmrTMAz@S6@T+#Y1G4J#23t&D4T~wyHq=;i$O+;) zUwN-ZOKictd0=JiSd5;Pw=y&FmI)EXK_Dt-dP8>uhdA-Iy19zr7P=8k}I>}u2|f!KBd_~To8aUgSFDn zA1olS8n=9vytKi775fO98pqtJS;X11-bMGY4_;IOstYPJ`+SZpep7_-j>VUHPj3N! z??2@AA7-!{WgJPwOLnHlS;-ii>ZqEhRCV5v5dB5Kwn=ly&UA!xM@wq3rN}bfVypiw z=ymIoTW|zNYA7kbTwBy=JRcKIYb<{0^EtJ}D z+H=^cvfa6F`41WP2==msj;sX=lJ3@ro7IS8yG<7j)deeU><@ibJ<+ma+x$96F7vUf z>25d#o~!}C`$Fe~c$Fe!s5x}G(UpJp=FP2@2ff~dywE>3`k#r_v{)t1G!a0Hg=Cu4 zCH9mD6Tyr#FG0-X5#gRO=Tw0d&oRV+)FUPM=K%#Kn(6>jlCL$tE zVi`%jp=aDn?e+F~u{z!BweO&k#zD2uL_Y7MPmsaTnbHOF_v~mtN&uDZ&NOhll3WFy z^ZNxu07{c0!<=v>sjXuwQ&}w_AmC7oCB03R@#E9R`(mrZg5_pnI9+ejGFpy7_SqM9 zq}4z$-XQBM2qzu#!_m{7p6u}#VOV&ZYh24gJQktX9~A1y-_dsPTu@riMnOp2`R>YJ zlU=A#xouY&g-)w47+mjmKVEzw%!^7ATH^PRiW|uYq>^K7&53xQSDc3yvHG4+%&mBw zh!N@7X!+DnMd+a4R67SZveQ7YNPO`Fy3{eBlxxr%2x6w-WRF1M?kmppg08WYfn~55 zd*)q)#Eu0fr24 z5r>k8%Jw{NEXf)jM%Kp7_U3b?<@85S@NC}XntcwX_U2j~h-kGb)dknOBj4RRd*{|^ z$Efy!m5|zn2_P~F*c==_cGZ@jJnL+oC1UXltQ)4DK8qe2A2sg-RMtr;uB638&EiOU zdVC+Ap@#z}1jG9WD!_1IV+Q~n*R8{dGMd$0stX>peW15oR53{62hcJ4lci|vvBQsJ z*Js}`o$^-2C-kO4Eq-BlU$1{DUnbqd&Tv$dYR84GG)DzQtj^PzHojSwyBn1dB>{vG zWfwL!};fN3K`a??;+Deq7VBlaYKHM|7zGR%yY3wsQ$pG1M<) zkg}S#WY%ptd)+3R+^&hjxniGoPTe_wHfRM@LZ`>0eC00R;XhFG#f?XX=dJSwGv+2Ee%SO#TQZ9rQK(Nq$vBm#zGWQt zvnTxplOXYOD&TY{&bG_wZTgn+%+^NKI%uam+ZeWa;fJDg8p}C~Lahm1`ktJ5)GzG} zjX>I=Wd5Wr%V(QdO*OT@K!4&^LBi+kyNpkU=R(0qnB}^(bmw%FhauEYy9gJS`6Tx` z6wBQ=V35s_C@l}y56W{i~HfK zX%N;G{IMS2mv*DtLb#_%ppMDJLe` zLMBgWHg<~QsBnxR{8ufUL{y@C>WOs`>P`?J;qd%lzbV^EVpH_V*q^Dq)r53}d={J* z#$&2XYWIRYL}*Q${O0}0F!iceTzd}JOHNFg8%ix`Fn!ln9DAW}7lB6kYfeiC zh3l8cv4F{nAcMmBThB@kTuX-lTo6&>N?qeP@RmtM6?@Zc^{e^h8+gt&z|s$j*KB}l zEw<_@(t5Bh|2|07sg$qQ1ItiIT{868FJ#f%)ra0=mWkS6msi8l2h)Yr=PojR2q1av z&kMgV`ekQz)-vmlq)%DTE9MJOd7^8^k90I?d;lCkfV_t(?MZ9cd?ZS# z(LABjY1JE3y+9qjw2TMZ6^#c;*8odCR<5mpW~(_2H1yuhvA_0J3UtBpIi>GfO_Z8$ z-1eEM!Ppi9dLtzIbw#&K!zF2O7#p8&?juP*{T(&kOHSVhGGqbq$9RUep`CW!HbRO^ zSUC|#On2YkPvmnzzhO5ba5C#Y#yBKfF;EZzS)e^FnUeFdw#c;|N#MzIa>(gnRAp3eL^(?T0m`x|30|YDOK6ZCiG5vjEZ~19-Wo5Wto)B8 zWe%>2LWB)ks0zTkcfr3%PSmu!^bGs#5xanjx}e4H1@o7&%fczj;xZ|2fy4uXpCRXa z3C}rQtIdyNKBiiwPnYVq0t3L{3DCm@cxW|*=ED_>p&3q`m1oPE_|bL?G}`19cvK+OFe zk>t3$jH9|1=R9BaN@MFoJL#+@EnEbX7tm5)L5*(lY_V!w2%(HC-hJq7|6S71)B#NK z^Li+&w(7z_MPkF1k7+;vym3U=c+Ev>P9@vK*Z-T>7ZT;Jk870)%}%}&RHsy_J*b8F zdu)UKOSC(E<5#{InuzbEhXO5t|D9#8T2Kn|onO)2-lZk8;ypKp&p+;t0tf%hjYa)j z!gspmlXupk#K6MF#iY~GMo5S|^1+~wSLgOSPx!($t&J_fW8tdIc;4TA<$PfFi-P(% z%>MIh0RJKz;&gw(p&!3iBeUsQkWYR)g4PmNlj_&MF{TS*GBAV&X{brwU5XN2VqR8n zy`Jjq+?f6P?QS?wfY#)9iT5LfESU^$IIjOG) zBlWj`yqUGkNkrvZV#A5$k7Fg9J%4omeB}qa$kMpWTDapIH)D5vfAyjn3f3^T>JogAB0i!v?&b@wJCasD@`V~)s}Q*mm<3KaDAER=zV z&96LDOZG_i2I#EmHjT)i-zG_4-pK`r3w9>{^?O|dxez|iG-EG-H`Z-IEWn)eo!iS! z;l-9dw^0Bdmm^y7a**}%JCG+`4g`aFNpZj18ab?Qy?ZgZvsr-vj+|#CmC;o3J#hL7 zN&#?4HSvvAqJb9%$0f`Uxi8%1`{?q(78>othzg;iNAs`r#&lHfIg4SKUFEZi-<;L( zQReC@u;2Vn@YZgM&22E=4?IFXrTNXk0L(E}SI`;U=s)8C#gS`eCL(#VYG-d}7>~aF zMUqQccNo7?;LRN5W(oyh(y9)txTIR$`!j^-1OnzfwA5=H9M|1Fm$bDt9VN$K2y^ML zfYqt-**1yTxkg-}2#ymhbN(fWNF} zt7I#@8wEq)*UiPBX{|Y$*pJd&(R@J;c@QfmcTXs%{PC)ka0~{(D^4`*3^d@Erm%V9 zW>vGMC7cLYX>nr#sjQ>xcSm)d${$InHH$W-iCn3jfX!S1x&|bN@TWC|sXJRFt^k;_ zsm9ZAJ2xUnZzg(@%TWdypWQhC*=cwp9}{bdf)keb%?((qim{7&cq?9k>SP5=u8?#B z6@Ash$TTm;b)P7|vkPgTFXbK7+iCV1$h`#uYm2v06$HzEOru&F0}yx2 zHm^&Y(^2?aHvq9tHv>0A7l96<=EJ>CHud){=;fhyT^LX(d~I^$gULdFkXxEJYX(nc zB(KOzi||VWEz+#FGwG5WeO{_J)GWziDX+Aw8IVrO*Aq>2E@ z_6g12LZE|0ZArnYx$hOY&h);;)AHW`ef}@?$*qyhVmWIpPieHNXDlAMP$~=g25(9} zD5nhCo(*~(7tn`AoG#{_DeW7Gnw_L-Ac?h%h+?~iQ@Pg;@)k;J2gsQ>$~fqJCy_7p1#0Ej1RT!cNeTE^94$*131@k`R1H#L`@l}uY@WR$txc7)%Hd0;3eqAjqg!9;} z+?$+&^`S<&>NauU{}g$@P|r;BH#j~nRsU-S`Ejj@Rm8{#s*>O4k6vySkqSKxrT%xY z!^CrLoT2L_DW{cehR_D9PR#G-`}#cteSQ_}ExhX`jlq>*@L9UgNcrInB>|u2m_R!T zn_&R4X%)9ap>KYp$JvVBbI6kdQ?SW&FuO**=D0vRnu8%ZW>v@>R{U`FQ!D>e$K$Vb zXk%QOs>lHo+V}*p9pC64W0YDCFBZ+xM$JhtQBkp-htCe{NB6WRKg#;Aj zwxQ+gnG~0wCf4%WzL_|$KB%k{TtyR-Kq;vW<>`6*c!^JH%bkw{p|f$IiV-4dCb*Fq z7`@^)?Le%_`sHnbJT1N<64r=B+!bwTjwRkKRZc?Wq35r$0=c2xi=nnVc~hH z^g}f+UEki7w(U@UW4l#QYqYZ*`{fjVN6R;>C%rhkOpFY>{gmSmjnu*8CE8}2#$TNE zKNXQrnjIJqgQ+zvy}o}{DUH@tZjuUVJCgg@R9>y(1$%6}8)6%~dm~Q&X4Ys~P|n61 zS%AzYJ{-k6tGN1)V4a#Oqkp}JQ~}-wLIOTr0{%Xu&#p4%mMXCQ4xr$-*84-G_W(Vs z?jPybFmr12xckz_f0*`Z{*_+0euw z2WJm9Ec}l~8RPkVE5(Q@?lhRmEUhdV_41vR+t8dd37(}=+R?g`(k{k^%J!~Nh4nuD zL^S4HgB6;NoW_`bJB=5*43d5Z#T1I7O!m*zTWGKA-LvB##f}lj^Sb=1bnBYTOD%UY zp+i|C7c1O;o*wL2i}(i0UF-w@J3WQvMh6$o;p6~_#l$kB!h`I{ry1nB)JMnJk*jjH z?5J#bQ^rxma+Wgh-QJ%Ytn#^An>LockxiIR5%GV_Nd^;T;9=%862{sR;WXtPN;R4|p0Ty0HuL>6yNrtA6$C z**PU8i?#87(Lo`DymqG-|I-`5d*=z`v_pC)ZE6idL(Ofdn)7E%9Cj@gzjH| zHUlF>KMaxh3YbLq4NsKOR{Y7mzi_J5pvY24MnsFFif01}UYR)8lr9oksf0)i2A7_Q z2NzYf3%Nj>Kiuy37kwAW=e$eS`MYDfXke9dvbaLQiZXEHZKZT{VxhwvR&42T&R9%d)Pgap(h%d6xq0TTxvn%q%> zyRVxJtAgd2atcE!cUj-S;$s&GysvH6+Wz{G+Cb})k#sLL!nmU)PWB|fGuteUoLXby0l32&s))Ehu`%iy4UJZA4c<$OI!bgt9|ScIEKVYu80~0t*CN84 z`sV4KdyUNH;vNlT_$iHPHy2de6O(D{%zAO)s~N1M*aUweJ+M)VN|SALrmD zWU$cEM=3vkihlupqEz)=L*lWe9}+w1#m=dB`5vw_uJ=;J%RHWf@okYgm~j~Lk-b;0 zb4gK2nw@~*`BU$lZO)n8@M`@2qdj!8`Vq;oiehe(KtYp@^^LT;53OG`Ypm%-bK_7w zPgKe%eO5~(DmBxN!9NM&~P{Mljj97aKk;n455&pTAaHul{CPCt_@CIab7suUU*j5cf1?|u* z8c(*oPmjqw<;?T}!{hI_aN;+utfhTp;Gt+1%BP=v%lvrCt}gWfxM?z|{QH-wr+>hI zCT)g)d^ko>?y?E(AvQX#Yu5n~Q{93fV4Gu+RT9@&4YnxZnl2v;lx&CSi|ha3_l*;0 z`de%CPDOg>E|w$Tk>p_@e0KhWgSv3Eq&S12HLvL0yGO;3@y#JIyxy%cySN^U!2MQE zN|7vYr_sR>b2M$M4gI>Kb0O|M;b;a; zoP95Ma_pe{@O#BDe?q_gZ-{qO-00@tD-H@#iQlo#@7PdeUTM^t{{5qh1ewx2NXbV# zGP;ZD!av8Vh-gY8i3ond~?caVOpDJ^@d#%Y1aUHJXO%{>E@3a-g$x4@>y# zHQH8IbNp}|i<0oz!6tjVW=d~;i1cX4i)SFOuWv0Q+4gSf`oTkOaoPs64h%ljVM$M= z+lh9iiax9S#t-*%kL$_aJt8eO^LRP{l#7IZ$e0 z9d6pCrjm9B;)%_~t5IL!4F%%d4_t(Fn(E#vK}nJYz_7jkN$t}kABvy{yayh-$X$4Q z?@I8HOn-MO(&kBAtVX7H$8OoiB7Fj`_MO4Oo`}f+d}m$BdXcI4+2IAe{{?ZL!_!>Q zm*V?#LJcQE;q(e6ZYMkH#iul}&o*deUV*25_X;n$AOWJw)?Df3%W?F@*6*Kg4tRIp zELNxd1vX%{8#U8#+?Q~iW2|LL6&tF6YvwS;GQo|T^(qF^rF*`}Dxag(dM*p;c#l3q zD+}qZ){>#yJs!sHw2h~Dp~5|(gO+@r>Qi0MB=||gZc*8xAz*f9Z7MUhdT&CZEb~d@ z8>WF|1YdYeVB~AS9i#vp5<&8-bC}nXl^wp(J0(dy%Ss7jFDVn!aEDQ~^(mitm%LJh;y9Y%^b1Ltxqq@>h3C6p!YrPb7ZH zdcQZsIufesb1vDPPFJ1Cl35L@ohRiVJdQnA7lQJM^Ph^NMqVmhl|3e>Ws~YpEUqf2 zv{mOMr0b@4|8iTMbUWdG(ERl~Jw|LU&-kY9?fQ{Wof(kc=%|S6dyhCl4?=+ag+hqB z2uN_;!%~7mRkJm&BSZOz#)%JT=oly&KBZ;z&Ad7}S=61qBNACU0`h@zUw(`Ys+x<9 zQe!IvFjfYv6>(}!Ybn7VEHDLXCZtE9iTfzM5a629@=zUt=zN%qb0sG$2k3=nBt0U- z8cOz(=m73h+j49?8}d@}LHrlvwC~N~-F;a`TLxRldJffV%6q@I8K24yF_a|U^g5RG zSsGaMCIFkb?nTrPskLE0$C1&5`~-s&5)v)p-C@WL4U4TTxuU!#JJY3PLdrn8WBj^Y zxR5S&j&mOz-9?_J(>aN@3Gkw?M*5earc?mP{4o&9&etmFAFs4mNLOQlk!sAjOzL5g zMi8Nf(r6zhuLlB9+mB=KZ0qS;7fm8&6nseUW}~*VyQFZudYigwoSWpu7HyafTV7p} z?KJwCXtm}?Bnd<;5H{NkcOtd7gwMdhLgmOyIlq(|az%WN@6{(DZm$oEw>esA-+}IV zo?a2=u}+R4bS9FzQip*W@SlZ1S0r@10avq@@iE33Ml(Ap3A;ZbI37pkgbkD;7c&jDyoG|j_B%^+{ zDS;E2zxy}v{rSUvf~JP{IloI%VgP09uwUZcI@6YsmchOY^xe`UNpo`T)b*Hu!8Kq} zWd3Ft004=Oj>r5Rmfdu`(YY$q9LNlR{?775Ps%tS7*)ZNjQb$z?MxNt9H$Ma@|?+(U3L0hw1;t*IP$b*+uW7gwmxTAtfN)(v75) z5}eEgLHRFcXyt({myUPbMLt0{Gnq5d++yM@0x4Q`8@MkbA|Z% zC|b*0!fCp-4^Q**;*Q$zf;uOM5J!Y6a!t&49KRqR4wb=fgyE7QrQ*3Cad8eCTTp>F z(6PTBj_`h zw7vVXQKBAhvDy18@_>+dA$7p`<0#sdrvrk=#|i*gsaj^fhmg*@R>mMpK@N<>f{B}6 z&Q*ZHH`&e9tUq+7JS#K7W#+$}NaWShQW4X0MzVDhax_s?D8AdK@-SO4Qj%P9a)8nt z%de3VSpbikJm0KfQNCABE|ODJ>3={)R&acQNamS%od)esBX4dQ$MzYaPm@sy7gxs9 zX?z(C;No}(FL3MQJ27c7GG=zqa;oNoi%QIQvak;`%o<7viHOXqx}`~tZbf_yEI-Qj zF~o!h;7r6QvHIt!Ue-8F)XjRaGzHTVNE;bh%SXWpAU);(uB+CSljrzgqrfyb`034U zYm4@xR z`JRH9n;75-B!O||qt7WXZPvy5>G?71u}u{9;cg<6dVw##-mVJT=G+o4+D{4Q^Zs_F zDs49Azy`c_>1kQ@HebArKB07whZGPYii5Z`NG9NGb2^EUs&@KoSlB4{xx?Kn4|V3X zy^;IMt$Ab*4aM>?6|mlDxL0r#J^9fA3i$L(XL(HeW_9A%(}=ILE6W>_ddB=fi?5`v z)z%aR{jeEz>2BO~`?^5u{pw_j-NjKxWo7(Y?U=;D5?Vp8|CC7cI^%=bJTq*u9q?K> zPzjUbx7_dRlyNrH_v<|@ONA$SeT`5iDvFc`k7aW=IosH#IFh2_cX%Bx=m?`zp}M(y z@0Gy&_!_-Ug>YgvfJ{Yi?A82b+9e;bjIw8jBnxHu4swk1pjf^A2+iKiD-6YmI442pAF103{Lee5_iJD{J@6`B{)n`NkvFt<5 z8mnEZaG{xO=k5A1A13hgzGoz5m#%lHeX!>RPcu!p&o!==;{md7SR6T3ntPZkvI|a~ z53!r(HduXQba~8(w8FWU)Ko$5y<&|7rk;W43l>gh0Rt8bnPZ)c;61tPci?Kbojk<| zpG!g@UQus@`N7^##&0ThAn-==YHR3c)v>Z9l~pLRQFO!t6gI)<&7K?oo}+DR&7x04 z)sJhl;alIz5#B}DX9tSH`0wTRcPUJSV_mU;cll^ubYEug#C5K*GgMgkSC1sMjj?a& zjgcd63fC4@%;}Z43FYldc@G_3=Rntww_n~jjt(KyGTu*S=pC}_4xOyVxms_V(YQEo z#u!G}+&m2FF{EE!N^g-FbT7qdn~@3-rnPTrJ`1<4wq{b6y0>uX}Te$#0XO! z&;`_mquHl0kYZaWdS=QvrrhA4k_AQT(0NLS=%(sRY71*yiPA*a2pv&&=L3c-EwQjVF^P5$K z$8tjsB-$dO+|!zb6^gQ!FdT^k(-cyNX4>i#6F?WD{{-eaN+DV3kKA2;VA!BRYiEoN zxXX>>Ny9=PM#}&>+-ztC6Pn_>fhTv$o#Rv)^u_iyb>qo^u}J_7k@wM3TLX4e&D|_!0XKO+Jm0_GUnFq}B=Ni4UR%lzgse@KDhhH$SA; zaH|}!;df0}vjBWq{)RH5M^{h@61(2b`q%WcShMl$X9`7@5MvNyB2t(MXL^ZDEL7zq zUX-KBB~-B{td!4@lb4S74e{9?*WaJW_1e|%UwYqK0pJ)DmStPPzJA!l42ac+bqBq= z=RWJ9UXVI2ogOe|3XBjC&kEQXrUS62(pJ-q^5qX!GBk^63gdfH7OK=M_`b*Yb>(5) za95cebl___og1R^_0PuX6y7%2SB%;(@WAl})MuQ@;w;=*YJZ(Cc8pv?@tOktoKsp? z!!m6v-+b;_Q@`ThnQU1Khcg)%^(`~2Py3Wt&|7yVCTOWp_I;EXB!`&3u)FyC$x3!9 z2MaI}ueghS0SAY$4CS89=GkP4gJPI&<|FbL8pDXx&++ma%nq#QHXr|QqNZ`XtkDCiM)ysPFnkTN$QJzW%}#8-;8CG zmlIucNW=2Jq*GllvifXru&CF5@0}2b_L8C4t!MYa_DHe;4-M9Bma9S33`f|WV>$%H6D_U|VWpOajECNF2wDYGc z9NEsYH+qyPO8b;ohd8gt%?OjktqFth4q*^C)9QSW?O+POthMpW0fDnmvo;wu>)sqH zEVibrZz2yDMeXO!%2{+fW*4J#tQzbOvsl#|w$+BBV@yZtSd|SMg0beDw7V!I#3=!} zXF)hU|4abg$oN1Z^OyX3k98`)7g#hX$JK^_{Z@GhPH&204$+i^wV?Ul(r4#R}JUIvlB5&j(u4qDD*esT6dW z=opwcJ&oCK2S1OEOa(rT(OUeux#BDkqt#*rI1C2J_J&P&=xV)^es8wmwd9W}O4zoF4$_k%3xWF1`UWQQ_yU8* zA3q!W=ce#sS30aitLs;bSyk-A=ou#b&t5#62nv7^+!+sGc%ne@Q=QnLS#}dL3E0on zVv{#LtSm6=j0(EqsH^B^7DS+USsjVk4NH#!ETCj%OwmAISbUL>pygyRd$?=52yIaJ zIQuK)d47hn;dAk3{|2jlT;8R}fI{LNHcV3eNb|P1tVErfsER6j{fH8%XLkLY=xkLj z$lO%V?2p5P!Ut@Vbz5ysRRX!3DK_X$o`oDU1Jy;3SGZLv~Z|xsj=h9?F0G{5k(5PssgeTb; z)nnA9y~Tb+#vR*by}o){iu6BP05pwVSQfC@WDj2<;u=U#epghX^I6RCNrsO+?ah(3 zJ;zv?%W=AH8b!G04zndma{f?bQf1!QsS6=n`Rv8Uegp1!83FBu6~%EsDj;J@$Lvr> zzQNd*@%&>4N_f|jTmlt5EdQ5VX0@<|QA|`H*%& z0Wa^A@bfY38kq5%BJCi^5tt2E*Hzd?-TDQ&~Omcs)$P1U}9O?M; zfie__N6h=9KaNvoQZs4k*?Im|eT@DKJL5Ws=(^z{34El7M7kr~j=hZXo~MlA;iW9P1)!as_nBaWdjBfe z5X@;YbzU9G!NU=uc_0`dSmli(*Z-gi+y%rvn_c${mp1>4i8#KHo2=d6s6`L= zau)$ClW6B5CW_y2oWij#!@GJfc6JA6`V==wLNkXCA84nmK9bR1+BEHFcn|F~)BPFP z09OX=gfGiwq{;pKA>Z$ZwAp|gt3ptt{ENBJoDp~fI3QTsp4}-4gZ0HcAH{+b|BL)= zf-kwKUezK#^Wct-i4H8z%Hj4pOq7IkEL3wxRMJxE|JljG_uHGk$1az?cc}P-3@WG7 zRgHH4v3T60&$l1`gej}podMubW3z}ytsL(wbXAeN_3EfHC_O8y$zagXb1zq(ro$w$ zn!ngP9`D2mNk9Yvu~)Vx12;GVWpz2wXX~*g21La#PI6^-wv@HEqQy{=X5kRP2dymT z(Wf9S1Qlm7cG1w_t$ixnE={9nrmx>$?duzaGuZvUR5Az7N5M8nId?&XKoV|`eWnTO z;{gs_`CHRGdHKzs?H!Du&FcJ1-&lUf#398~w}HkZhGLb16lHWAUNZyU{eUef1D4om z4DOc8Bd%8AOh+mTjQ)W!qXf;`1&~3zi)nZO<{bdg|MRYbPs~j-f3WQ9JW~p{oW;Oh zG@ei=Do{zU$Mh&6y^9Vco#_p|wk&0#|4N4v*A-{2gE;$xWroRvG~Wd4vaDYRzt(&Y0doT>BM;)41XQk?ndc5QY0i|H@g z*yotFIZ*rbLqS)jD6g}^C+-y1FuEMDOu2w%hOMuZzRl;=W>2l55oV(~73$SYU?!3tFL<`2_f zbp3%%>qrZ1M({34Sw-!gmI}g1kPGb7dJlFpR&FdiVk(xL7MuNW>9xOceAjACA6)fM zH~w_BCIFiIS7x)89+uLs0n~is5jKVmb?(|*F2f0%wB0k*Z61>57VereUjN!8;07}z z_eA0b`h^I1e|(bzb=<+poXXSczaRl{wEpC>Fjpd!&{Yu38L9V&nlJZ>Dpdgffx6)0 zK#U=?6Yb=Fa%t=v=}dQ0ZV<|7l=i z__Z{P&kuprEu$uC(CzStf4Eq^H}2q-b4lZTchIA?VD~{hQ)r(W|JCft;<8lb$?wTJ zT{T%@eH($H)LJHcv_9J!E+gA-xUA>LT_Xh>DPjSC-*DBWueOZr>0HpJYr7*R`d+uy z_?!;89O438Z5JJ6cWTvk755-#8}+3M>pV2`=x!=Z^UW%AOufTu&Y$iT$4vQi>+d6_Nn3Ipn zd1(0>;t`K$09R1RKJHUZ4^#(K{MR#ot&L>0yy=NEN8Rl0ILWb(P0TfhLZJX;#5V$< zjNgTU=GZnc84fTEz);6SBjce;&1fa0^DJ4A%glKXU6#$vG!Oas; z@a9e%G+1A4<(2tuGfd`n=>h)T?p~BRIU1hail}?+UAE8gTa9_6H+8usyX~zeOpxue z+kdRuCwmx(zgxtfBGvbLFKCASbh4_>D&K~J>l^x}uQS*t!whsL8JUkJNh{ZeCX{K` z;U+hEG*wU=iUX;!G!mqirWk$Wd35r{Gq?9ahZ&dg(iS={kLq`z>(U^}j0VUS+J<2Y z2JR%6Bg)U~Y*xN$Kz=aLeU<9G0jN^*bmZUMJjR{^8p2vWkyHfs+Z&}#+Jyo62L;b2 zbn{EBns<`l4H|V-pMo179#}dE6l+S$;u|_U8a4O$-9)@}@NUiYqqXE*t3S!dF|_Ta z{e{=?hv@i!WHR9bvv4Ix4)_j^aqdDjYEFF=ic zV%z-{hmUYtm8f(BV`T)%l;$qSbPE5 zB(i@DK}q+4Ke?^LjdtcZ&3Rhe+v4w zHopiE>=u2KIvi=sD%R%?@FkUV@qP7EAlW#;0^z83VAgHU_DqZnAc_UG^oTw>1OugS zS%YQwnRQubSwY!uO$ds7@aI)IB5b&Qo`j zvwoWr@@q5h4MhiprYDrR++!WV*yjjCRru;Uu}=++Lc)t0_}jUnfCet|#oeI>i`A{m z)n^Bo_HA>E$>Z}MFH-u9MIEGtUp}`Tn2WO7ZBuN(b#t*|@>nScu5%S&Eg*1UuMEJ= zpsniD7uF*04k)6mzqVKur8Awh#Rg9Id(a~*FSxG0ujWL4d%XsbD{Hyy0@2{>&oV$%z4?hjgArfzU#Dgj9 zAf@^jBC^A1zgVlo1Xawr958q{Gy4PZAX1g~bq7{Wdjp#M*)P&8dAm#ECoThj*UQbHdH%0U!sFMv)V~l)z$~61#5k~`dc;C1D!0x zKbY+IeD%8|kQxw_gc$#Fw<5#~gxvH@o0@+ik-D);S1no>$ zn>$i@jj$Xj?K$40@z?q}`FM*1*aui@AcFl1QfS=+QbkbWX1GZa zaPuD)fl^*b5YCv!3uOziH&=p(gOv17of8@aGQ6&`{4x zRqKG=v3ZBI9JX3z+)um;P%H%qg@*5dGc@{{_kcFX)d&FqYk7lw;HavcK-s3RH$>6 zja^EQhuGwx^deR9&3d}Q^&2SJWX*|RU!ThWH7YuiylZ{tVOh%V)k8M&@5-uN-84hq zTJSEn5dS=*J?)A-%5hM>S*@T)WPkG}M|UX$a6!?aF0Il}U;W6TZIiJ$LF)@-0nb6+ zvrT|&XFsKN%8YnaRHG~%4xi>;)?+p4154ofSFjI8+P&MxWlDTN z0>#`$J`Y3|Jq=o=!U9=ibCrlq*W-rl+8a~Fe@me1ia>cfmw~Fbr#f z!Wa-=L_Jf4<~vz}(l;nxk6BIohgL&4L}+)0-%AOf<5Q)2;!2M6uY$7$3~yB1HBZhT zN4#6!fzrB+Ag+vj2L(`7EZ3Fdo2E4Ustqn5-MLJ~5gV6>p_pJKi`!AnA91nlH#06( z2tvM@bf8tsgll90tl-`oNJd3l{;6xOLz0Z{ccuPL=38JFC*Wa44auiM_WPgdCNmxwM>6jh!BIViq$5B5NIxl@oL4MBbX z>NqxNi`N#Txc(x`tihT`&3)8pt%F}vaj9ecM=BNVGyYpf%1f&ZNDVyPo{lhq@1hZn z#+gE-fC8u=W0j;9-JNr!IIJ7%Ab`F%FX4cY1!U1MQ3$w==b&^8Bmg6Hd8ATZfmH}` zIOh@nP#mVZ_j~-%OoPJ}JJp(@0ohHU0ip(ScwlCu<#PI<6%rsvWP*hUqpd7}C6sI} z*)0>Ma&Q^J%JC0}mM~dNXeQ2d2Ek?X4ZZglD>&2ndfd_>4C=F}Z6Ij)AymkjO&6i% z&-RcVg}O6-J4t@Wyydi%Rg}Wv2aqF>F2I4M!28>p9so$xz3IYCeNcvlBN$9wYnzFg z64Y0ID+~wFc;m$`gE~-I8zyPMCfj#oECe1m_2aZrsa!3=3e7)qVZ=alW)_@xp&9qx z0%EQ@QL=vePVv3LS;gU-yW^><$biArRxl1nYur&6LLj?Q{+2WVa44*ZC!o>4zPs6V z)Kkt@WXUDh$8EEeva$b|UHjV?D9B(i*UIN{(1kT})34J5*oNmq*#tyE$hWXY1n9h1 z|5S6Wefni*ImM4=UqQ%!{-XFfF~I5@&ZJ1;D+#Qd6Fc+VSc8IRU5;eQ7t}IkBgy== zDa>I$pF3I8K3ab41+wkQ8+KHRlJ82a2mIUDZ=T7`^ShA}1^TL!28Us6b}TZ3g?fa! zBn)u7@8BnOU*Nt&$dp9;rg;4AbltE9N_9T_M^*+qZR;+9) z^49riM`S=j>A7@E$Qca~Z-$47%_vLdXhyiW^-;snCQzUUn)nXMKvgZ3uOVjpJYJxB zRND2$l?&^aZd!&s@$7f1IdN)`WW~H54{Lb8L>{^0fg%KgQhFj8y7sq@ezu+0KWHPtZn4 zbUO7)LN+op%3WY(ehN2cPV-$Iq5+DHmAf1m9t1Aj{kN$=OFw%_4*G3gY`6fMI3v0w z+}-dTSX#OxU)8^npq-{o4e6Ndc>(rQs^G5D(KB=u+I>?`+jAUPg`V7j(-D&YGy0#3 z73%V?=RSAm>zs?kO99SaL4WtQrJZLbGuxa;s;8RdUGGmV7NIbPEaeo_$LpA1Immfs z?{DAr*fR;TX{p->DquC)>W|d=k09csK5^a(07Y9mG?U0ZYHrGK8moq3Fg!R|i2CED zjk+_9PC)J>Kfn|$;`Ad18Du1Lm&D4sLvmJxN%6x*Yp;RO1WWW#)?!S9duO{QM8wH| zz~{aV#2wVVVO(Nqf;Xxx-+-{qe`^&c`*MC?k{lJe+^s?0)(r6gv}(uVOiK7TI>}5m z5|9ibDY!qp7nY!SiTA$)-~(=ITcMd8>=6bF5d&VWeX=LEtCe({cMH0^IN7t1WY!#1 zewO4CGT$5v_|vtX0BS^H>LB+$1>1RcJGK$3+#34wBL{&cwFgH8!#ss_+wpy%*5*laO+a>Bq1Nk~jM z9ld-kcevKMD7g;%J-|pu&(b#7g9&WyjEJXdMKw~2S)B0A`U?;gVR|^g#2Iexz3(fz zC%XHC&WUy4N=u%KgBhAxPyp?IC6NMp0KIrOa>?7F+pYkaQ1ilG3H?vk-#R%WsYvxa za0AeO*XIL0;qEt9DG_-c#XUW>9es$78w%rvOB8Lt|HQhF3}?ezb6q@NxyV~kPZuME zgFt3@ACW=Av&DDw%nMr@MHvzGJoBw8q>AKQAsJ|xEVrqcwmd!Pl+0J327ClC#%Tp+ zWCs7*ihnTyv^%bgk-oYN!Y->UgK=fyKHElK*fJ=}=>OO%6y=PPxL!Q5aZD;NY*ARx z2+-r);=hlA3hDCy^9t52nBstvkEnbn2j~3D9aHF>0(Tl{s*NPet&iKb{AWXTB}e8O zbP9%j3)1_4%-{d#l{p~DGQWWS-6*z`m%KY=m%f{Ym0p;hIYDKd5;lme5VKpdZ_MVU z%-{jqdtr+a()=@>1OCv=vnS4Qve_F{q682~*f~c*prkS$$1>b-wMqrrnL5NN&RdFF zq8+68ASu-ae|-9nLxP@FECWW%jU90VJjRbL9B#8xHLqvo@W|<|JOy> zC289f+1=NLdU(kjbPtzS-B8msT9y;SkPb#tD1n96S_`^Rd)D98U>{#P$WK1I6RP;- z(uJ}os^lI(uoemncHD(9WP*|YOMe=`=H^JzC(Dq0K5_;6KScN8I|ra+yDBHOqIzs1 zD_mGv7u7tqu+_4A6*5VMbw{>y~5Whxh9p z6EzoFNoCJrPu4v~uH1}kz1O?23;%amKxS*br_&XWBhO!VJ%sy|+=3l9N1i)-BxAh? zJyZ(o@Bg`}89W2m*lHi&0C&Q$;SX?k+Syi5*sce$C4&RSdqHD=G0u0PnOBJ2aC5Q) z^ro^xD(CDp85uzDk@$wBMs|Qt)B*w-+6Gr8 z20z~6+Jn}=_U(xutf2~pJN%YvwpDdO0pmG4VTK%J;)Us=D`b-3YKW9P92AiM9kyVq zgeQrL&;}nSJ8*0&D^PyU!Jteksk}Gx0)b#o4dwZ>G5ymn z`-55RM2HV^PG?1=kx4IoWbuh2gYRYJN`gCO@v#ZO;rjM(3MsA#O*OOkIY4vtEhDy- zE^rg%S4FZ8jLTHOAW{6N)&Y}GfsvR=*W|nJtCG)0vQ_%K@$JBB-p^LW5A0Qavmbxz5}z4VhN8^+&jSX#gXWDsoS%8P!E(=}Z>{|m#U3Ic zr;Mk;`I*-`HqpVCyP%+J`?HeRIMsi`_9k$2!!tJIeNpTq|0g#MVC!#w3oazl>dQ== zK9$(~#99z=&Jtdi2*bIubCwW@#3JF#j9uzIu|Q z(g{xM(0izVih~uev4f9YsSe@N#umqO2Nx2u#W*S)*Fo_Ik9NnNoVZ0EGT|tMp^Vb> zjS*I-+IcFeoP!V|3L40lGK79a>z5Q5MwI_4>T{_A;X+?PEyr)skyMt)lXQEbQ0%p` zKJ^2~<>|k95j(__WJmlCm)oZx+4AUnS?GAL4vscESeur(|3%kZV=7z0a0};3%+_${ z#J`+TVBSMYl8EC@0dw2_i@+dAf(u4<(Bjo@_|X(XBwI;|nel(A;KaAZ=HTdQjPAVBK#qoKp{!V%`EVr2WSwH%;#YIXBpd0k+F78GJStd zX~s%QPM?L`Sn?jpWta91W1ed099B_F_)&mecK{nAPc65k9VoCiclnzRuStUbR$@zy@MF6qOUP$R5GQbG2fK@@d}gXDgi8uCVJE6+ z@aR;Z2(Ra2tR}V<_0MJBOm4i~FKa;*bpb~(%(ot+AZ8ZqSf>-S!3Q@;)mSuvZ)W>+ zN)Ljwdp2Dt*SF4zo=EBz?G|j%5r5mtplz8h^dpuawrAIbqE9beL*MQ!`iQ|lY?I*< zn(2rD72^Qg!ypjW9k+LFzVx9hSa%q;aHRzW?n*--77hM;EBN-d78!6_!T%*6`2Wcf z1|^mdfeFvnH%>HEK)b;qaApMsKX*NF0wuhqI!S3nvSMM?dBW!=uOyfJa3+a+&HhIV z;DX8X3bN$K#&*KNaPaz?s954(cmkHiX8T{V^Z$OOqRQ&eW@;s}7?@ui0}Z0U(2;r1 zFTJ#1cxcG^ZZ6d(EG9Pp->==hQN4V43l0s(Enwe+G%!Ais;aGNDIR>&+tZ6@Vga(0 zajyS4P6&(|rY`$${QuWiJv+da+o4|wZ$#)C@MtM$7aV2~GF3GuuNC`zFE@gJN?^p! zUDGBE`Y2yEeaZG%CFa*??Ie8uaGzYH>OsfY+dV1-SF?zFymd(z8m-e^Pwdv*90#-^ zOev$XOXBPw*4G()$mEURen10KJg}`1FuJKcNLQoNGbe$AFJjbNHbK`++$2eFJ0keO zpYnUlU0ErDfRNwWdNxnG#*+-@t|>I(lEw~w=`AyByF&{eiPk06{U51FAHncD^pPrI z#fJb>*biq%=e$oYI~`io)F9N>e+4@FJu0xszu;ibB^4S08C+z!eJs7M;PzDimk8x; zj3l?@tsi$I4&oDH~IXVShN8=B`Fhsu5#5k!F}R?eme7{iIbvuIa{UUNQ-0mmcAr=7Vj8RX1ATIkrgn7x zPjT-2GvBG(+c$O(GWh=4Yz0;5LI``idu5!CFiXJHa~>CFDSAvut}y)ZV#DxYEx;i+ z+7;Ua-srTDoYW*`R(vgP^_b`yEpi{}+2fGOS~8#i$4~ycO4xU_p1uKLrRw?v*O%p& z?1!PJoC$9bYf`blm0Pfzm^6#U&~va=InmhdN3C_HwTyQ~U+C|ZZl#rbpc2#!P>Zmm z!7(0+&ff}u)sDKYB9mS}oYC{% znL>$2>esc3HHpJ^mOKy1bY!+S?Cm!FoRq@E1D&Mk)Zo19gYAt#rvlrJ4HLF1iD5~FiUAe5aP1ViKG>iPg#)`lkG*|t zI^B%Jt=kYOteLPB|HiJHB(T?`f{OE9Ek`tNj~mz`UjDd#@!~#?M8@!-HB3Sm%0Rkh z+)0%(U)P+HG`X552qlBR@T{XwHIi|QCt?KLbIT*9rGH%0hLfIN$>{x@t$k=O@10gq z=x?1FP_S~?6C*>`GmB~{p0RSTw&`#C9R?@Adzj1BC9FWYH8GtE+wSd=?@Pln#oEpX zUU+TV1EH|Svn_bAH5j&W7$|{05IQT1-7|kXAqM-?el}XM&_YS12NT43tLu=X;8ocj z&R*LMxFjdDe>oZbm@>E4MY`Vc*p*I5w7R%ySVUY!LFGvrt*?;Y_zMTlfk59=3YvLc zQ-Li^rJsK_G^^O)%(Fjip6Tt0flEv)cq0@lzU%#>dj-E~XhuXYhnI>7*v!i0?%Pm~ z?|ir2eI3j<>9gHGa2?|R5Mh5bUk%>$&7bD9mY#iBm+<5cT`5w71FY@5yNHCD9*vn{ z2LI-tW*i0UenS#1>|yP6?6lN^{G{26BRLjajB+?CG5W$dGt+XssjoAVG(vRg(pT1H&5{k7xAy#*J8 zv5AMrW0S-j8R_o_LJ8SFlrK`zLNN$JPwNkzbV@TdO7+ekNHj`yE^q43`Ef{?o<5QI zCNzhTtwi_ZASFkJKZdheL>8SQ;HR?e-}@_R-n3Jq{3m*8r*1xjhMqi28&;26-u}`A zWSM`|h7@F4=Q&@+Q>qF@u?TiN-k!e;OQYu->Q~UuUfs-IDakNAi#jqKNwqij{3(^6 zeSAp5!}F8}8>8!kk&&i*T^~Ev54D(N6T^?#OIYY;0M>wmkJO^f@gbJE6sQsr=y zA{$t0=*L<4WqJ;q{jlPXR;yOa%38j(&cZ=e3srK-5F?{^Z2`&ZN7N1c;l&e%^WmTgMSt1JwEaf{i%h5PPDT3~mV^l{g$V5aAUAos(X582eG zmkA!)tX;`Cyfwy2SB`QN3x0x{_KKAJ3PzUho}PGP!ZNEd0|D~e>qFlNQ^ve4I2hfm z+zG5-y6y#D4^-7PR)d{TcR$;AHCzx4)K%C@OM8s0?NO)BFzxxfY6RrqYTXRxtm{Q) zbb|9ICv%{1JZISE-UW7VCeEEh2h>;mgp{j~o?Fb1#C@v3s47lnEo|J_5-!aC zv6$9Xl2m9~*xn8|6#$7taG3h>tc~roRmb98rfNDeO7jXln!30A%w#7^hTfb}ylQ$- zm#o*j7IGfcT=VY_~2yr2EJiLFtNeO+(~}qBBI+}hq_`-G?k34 zS;i?xZVzH3hx$rTt7p2_9#45(um#Urug;VFDL(U6QA-a$0lTIQ3UeJ#5d`nrl2czM z%l<7;%x4_3I5n9(Cs}mQ6LF7OKG^0*KuJHdyi1ri*J@7qJrz%UdCWgkRw;d(@OoTd z6gs0EUfkLmrqRU@)Og5Jfv3kC2>7|Nb^bSo>{_{^*b(s8xw@Z!ZD_}jbh~L=lmuV8 zpq{}k<#HnJ&a6@+WxVlf>T?cI zkyKF5k!w&4-<8uR^EQOb<In`tXZG#T_p4`@!$g2)~A_}D078oS5VyKB7KmT z8|`=p*INhwmUYy9Pd0&nDcw6oUg9`YVz2s5Q+;(bm2hKq6hFs;NtrKYWQXRSN+$~W zoBH?%aS_VNszDD|OQUZ~+G^|NR<=JT*9CU+RYrOGgSZl;u;Qkkldn8&DfSclP(9S* z(nBriq{#`_cS+2qHs|VS`6-NQjhXL()c15<$6Iop*!|+ahfrOB26qw%bBkFp|Yie^ish~2u0fZ-r)?(&kv>4(Nl zBBEPNIRhhRN_qE2n-_y<8Cbw=0`D!>)Lio(oj4df`j~sKYau6db>Rcu3 zO1_#6&PTTYwRLZ_DJ^aN-bBoUjhU#l3HjhBH9+Zdy!)dlDNI8RftTb-&GFM2ntf%} zrD+?&^*MYa;aXDNA;b4`;U1Hmmu7v}aN!=0uRivQzn^0YayBR}u{^y+T)C48Ju_4^ ze9_~!fp0N8^pxl6H1S|jEfxECSY;anH);N-8&s}4g^Ic}ET<;r`BV$%>&VFu`+F>v z>B@B@>;7?)CYUHI=T&w63M>>99q2)b)?}{kS!duru-TZTx>qXgt2g6`@OSUVEfsRg z$`mbk6~_{LV;^`wt!0cndq3yArj=Jz#R2uWy(6ksgJDaZO0n+8kniUXJ&Cs%s8L=) zvrE_H&$XSXowDm(Ym~&7)DIS+;~IFWBhXeVW0iVCGB>%!4jwztQwXAz!SJ@`S_TWe}okL#Sm z$R@*c?xQ{aAjRKfw}B~Z`CGqUuicRg!i_i5UJ8b*9yqCn3yE`r;tiSm>=9BM%Rpyg%v$QN65>>PlPpdNmj-IC1C$+#BY37ZxGwodGRn0!sN;ACyifmjA@mkn z_RFHRt@`tA`*{l1Hs+EuUJ$?QslZvwS~*GerX{=hF?%E!D9Takt-cRe$;$4tDy`j%xshMhIcpbjC}}^ipJV*hBx;-4_oQ`-O-!Y_a8qcr>0&0bU3`jcrMCe@ zm}rKY^Y;!L(a-0pjOw8svSpD*w5K$)Ff76Wpfv%Wali@I-E2dEY%WSkj3_OZFP-yLq zVke!JmE2+Yk*YNg|IA;ZzNRz!XzZEC7&@HV!CWxKwT3Nc`smXt`fcmpSU}Ke(VTHqGo)Q=Nh zQ{o-m{$VJFGUAPgCW)hD?>U!VT0ZJB7>S#-F5sSwzB&U=I}Ub+T+QY3?s1EC zEliHYQTm&<=4zvraq-q#eFIISIsO$j?4rvdNpyO*FPzrh@Jf@yOk5Qakn|=-;;8+3 zd(|vf3->$Mh<4+XbmWH&6iZ}lIa38g9TZ_4qRubNiP*KfGc>eVB;892$GUqFE4{bs zC?gLH7M6`C+E_1Lo{DEW-I-{`ahJYg>8r6>G%>^xE8yx(-#1=uTnbLbZnn3bC##D=@j*((sJ>$MxQ#L zeMYmB;aAcc@KN7PR}xKbMpBF==9&zQgcivK{^NmDq^8#|pL4Fb3wlN|53ho3;AX=t zbjn*2V7x8+-9A#+05P5VdSthgv6LnmjMFaZ;!phz^dt<0U|98^{>8%bUKgfp+w;wF zJhBcwqqo`T-5ia`AW%^oEdSW>rrzB&e5d=?$(sK>HoMjhq`*^A!SvXfQRLf>{k``| z?;a23DRYOqYAy-w@f$<;9*<;sX6LDkuFg>XJ|YM>KzAMPwN*v-BPr#9ynMB9L5-VxH2^9Fb1A z-P&u!|i1wQ2Oe#Tnh!`r*na?@PCwawfgY#t*Ni?#KP{1M+>rq*MWwfxkJ z*Fs%=9_%zZp{Z73nqaKg^x2-^1|GhfTVi6lKXE(BZO!O^iL*CeGdbH5Hy3(kr$O%q z54W)3aQ_T~(+ZQ>G+zAji7GMx^9bh z-6zsgbNNB6YcZ7G2p4$RHa&~h=Ev7>M0}n!d&eAw$Yp|D8O{41E;|=HTJ>?mzUU-H zE?-_>u_Zl!3s=ghcUt~VT8C3QXnxCH-Cpux@U-B0=bPNi-iBYD5NIx=$ zS?FRs)D#DU@b@eeVkg-s;;qLZ+kXyF=m+S~GTzQ(-5FbxSQF8Lq@r(}K){B(`d+EQ zr;z7CDKu(I))1iZr`Ds+wnfI@%0BL_Lm zvR}m0tGw7dE6ZPfWj}M`Djt-mRXvc|>cdh8W@w;`%Kd|a9C8OT9jZ<{A~9ehn;(Ak zv;zuN)r|Qfi_86uvXR?#0%E4LyS?RmwD&P}u1Co559p1oa-!1?_7omb$l=Jy$vgkls9t@Nqri>~ zZPDFsT-n`0l-jtb|1zQ*DfA+~i7p1VrfSQ+Nq5&RqLA1e3_b_nv6=(ldS_xDx3M}k z_pvkMalk;{LDpzA`H6uiqC%Kt%}wK|;i$LApZ_5RfjV6_Ix6hM`ly zKLUevcS&~$NOv=I=g>97z}@3>pL5PVYu$C<-23jnak*H-%%08O-~FlY{sFBc5OVtl zcD8ak(r&B!P#I6~y0vgyV1{w0t|tFBg*_Q4WdNhN*rnhpyCN6hXPk0;z3D}1gD+;b zfS?BimJN%f6w98Qa=hqC^Bi_08h>U~zisv~a%A1X?(E6dG=1)5MM!C3=zj1n{#_h? z>gpOd@M~E9@uhubJz(~uY7gsqPyGKRPy7r(<|S|+#?*Vuk-t4BBTN_q)8C<&+dQkV zIE;Wwbc%5dV*8nj#6}V|uj_<%a*Y#j<~vS{3Tv*Do@&VP70KN;G_c`;ogG^c9Ak-^ zV<3vLz8s@}mLSYpC@X)CaS&UWgDwuIt_Rd6+5FV2P%9~Cm+FJzDE@3JTtX+lqy&og zyl;0O?lPuBstNBlFPhF|2``$Y+pDEOjA{k|MCcL{{biduME7@LQ3uh)m1nA{k5LX|FXn99+*}^UYu4A2bhz>afW6b;w6VuA zu)$`uEQ^5z%Snl~(c`7z%#l!Ca`Lz9l>9*l+7Kn!^BHgosWopdX_<9*fe&ka-5eMGD zk<+Jk4e~ik_v~0z?hNtHv6QPfJ}5Xb6&(*=bbMRhp9#(B_oWcK|5IijZ(pTK*GEka zVgi-rq0=UuoBzI7I%^KOWcg{#gJsHtc(;M**~1HacV*3|_2Qh1TUtidkMJ?V%D~lh zI~{v^M$7&&24%o9f|wC9tGhBo)5Q$BCmjCFViKv~R_lRVpG-vNg#``N655ksS%7(^ zN6>m3H1#OEnyWC(04VY&>eo+Pora!+=p=1eW!68C%s)I{d|@ek{yWRL<*Z6WIm0+A zYO%qE!mWbo){xubJ8@RBVdvJ54h43e5S zD_!hG^Xmh0lXqHlO-$YtB=xu=0co*OW0-VrYg>Eva?7enjmcc+_LRc2c~+eLEP7l8 zI+U|Fy|94kgO&8BWd*o)iMtu8bMGx$7rlT504Wf;_))lpG)i7vrgGiyN4kSWx;!>k z(>LL6q)Mcu@a@ltPU(zUZFw%4si4%65BT1ua~Cua~7W<0QpnXgPZ&3enO*h3j+@lNjykfK%f3hQcl zBEgNPvT4O0k<-J}?@)Xy#~l--=+j5{$F*zcM6<_?%AFKEtUj=9UMI>n{EAH?^z<=E zzGF3~WlvCsdyC6L{8fNCGrY8qWm@gefLl)&=V&R?+Y=*_mDUg$g(W%_Z~v+A@J%>{ z9BYA1)ZWs!pW8i7B7C zeA$itJ-#k8S{X^~tX`V+RpdLo%9`>qEm9So$S^_v(~R`v!S}^`kH!<@e)~@5F)O*X zYbYpl$EBFw4CpuPc!th-tA|rn)o9>0J~|Pz8Y#N&x)s)beBKjyeo|NeQ5AvwcNU;; zoj5U~2^PZadSNHU4!vi9FgA(_W|3r_y(!XFWSW5Op{lfAnje6RCuBP&x)K(8ZO2eUS}OkZ$A5g~ zuGq?l9>pq(?BTqV1(SK-u&Q$@<=+9OixtaRB@l*7&H_!2yF{hIYlW}*?;<#)|o7JM&UHKa;7!4*m+LK=`nO)e%PCE zucNP(MDMJ{`PQ4I5*-ZMQ)szy6ITgBaFl8U$+mAj`&J0KB^T9iAC`Z5#+FQk_)&6_|I^hyowD{yoQ+X{ENHQhVa#{8xgc;P+!HpAd zL5=mR@hesiA~7YW$Rie@s$c|}F}!cPo`ocC=e8(5RP3~nmvtgu ze3`;dZ^d__t#$+HfQs5{D_@DX{bl=-=8QsiY7|paN;%`cKws%CrBpp+t%vG4rCoB9 z#%=vX%2JB>iU2#U9NK%hku~%7z?UB)P=%RV$JdvSW9T=Sq2q%{Q8hf#CT>Fyb5x)f}B4NJq53DA< zpNm!mg^Wh8zZk{4S0A!4#P`p2k5JT}Za`f&A%@{?O+(AU4ba?0)ziC(s{LId???wb zfG*+!YMIn)vAA1o+Irn9FBrW9UxjpTMyP-%Jw}h|qHIdJ^g(u=Q z=4N!*@;~ZXTzQCko3nR$#laC*@l#s$i1ji?B_ca8@;&rEP`iE?WEiE-1M#G{p^)6p zbHMmQ6k87G-nZe>4a6JGFX{;}WvGS(7T^8bYrrsIk!SCa+3g1|$9YFvjwX4M&!4?@ zoe6s*a3ZWBSU#S3-_6^iy1lPSyk5LGCCdSxP5aj&WQz>@5N*k?D=SrTAIk28^mDP> z+kf@P(E@&VWOp-gbXcuYgqKZK!b3=fpSlQQi%-|)|29?m){Pe(Rrg&js(O}G$U`IV z9|;_{Th`89;I^cH-P!1=$@$Q?y5rooD%EZn*t!^}+$LLbOf!padjiABV8cC9lZ$QG zAcE-5MpOOJW-lW)$p)KgUVv?nqs!@--zs@j9TOM=!oq9Ndx7~6kSFN>rs9BjqNu1J zMJ5r1ufCr{l^LTSgg+W4+X8NpVq@UMnhP;^;@IhC@DqQ-Spw{aAN(PTdytPJay^HrUI@RYdQLGWqiC42Xo0+#RF+k1F zv@C^~O9f9lbysxKI36R6FHJDc*WAm`C5>ha^72*-N;sfEBu$qILiD8Q0%)3)f1#Yr zLpXnF#w;OrLo4Bizl-Z>tzb9UP`4uDFl03ATVE_EZ}QlM|Hi#9r-VY(}JD0Wd;;t$u0uABv(SIcqtB2f&0S{&Bb{)ocJWd4j?9S4ZC?o-^$?aFj2A;V( zV*bsNTBvefL;rcbRDM&6;RPzk*Jz0T+v`mHjLP#@0a0WWs-Bssw7(X!5ha0#QPR&nm5QVjXOs`SFu4Yhv+myNC61Kq+B( ze|1AeOU(06$64^U(jGl|$~MPn?yS8b^Jekv+wlpd#d#>_SEiFk;HuWpF`!Po+uD?I z4#<>z6PqV3d?yAa2X?_Bu^zM#uMqbHt=$=XvD}c~5^6A>B6I zq(u9k)nywE@S&9JuY_hs=iVqnN6mEFMgvt`w-|r~L=d?AZGiZdb%=Zl-rxyE2%G1v zN~ULH7pQ^r^QB9;ZQGl+Cf=jTwBCnRkm8rkzHa!k{8su`B`& z?_!gT!1GwVPA{L%;f0%ioLqa6#)c~a{m!A=e#p1%jWhPQZZ3kAOh$EyD9f>jw13Q5 zB;Xz;^1)%l;AkHB2ozrcx5n7kwW=8tub2e`Q#rIjWn@v{Q?_upzk=@s`v~#cM$+VW zTsfNDX+m%j(eAW^G8@bDgaU8B8zLaQ-`e6>Hy(|;{|^0`FrSl&;&J|R(#MZ&fzAi* zl~1#vaRI;9W?*IZFXO{wu_)kU6UOlBuVGd?;itsAGMG#zIV%v}7D_C-u2-$@Oni!T(q&^kD z*8=uQpB0pbrlm)#J=I=~yGnqX;IV;wkPBm`LSk1bGO|0?oLmM z8$0CcF{Gh|mhf8rwCiAnxCs(A`Mny?do2NVqwO9^(M0v3ex~lo#PVV*iO%O z2iQ9WU$DX787ng$dh>~C+le9?2u-AgS3ae?@;R)4vTNaVjQLDxJxn~ZD5qA$ZFcAX z37P}h;8X<;AZn4lE%x)PQk>DSV(`W zMGCzvv1mab_otlFrM<$N3)y;MEoVs2el@yrIOX{#9b)<6?oO8c%)B|o@p^uzJHpq< zHfePO+DFY%9dj@?MG1lx2}ReR=W4U4AbKOa2?oplMKo7;iCF)BqYWH_CIWfQyQ^>* zk>DZ$H31&j;28W1S#r*91Ui32h+h7npFN%7zCFNIq;h1HTXG?usC$7YGl2nFt`|%5 zO~S@a0k!6)FGijGvII7icppF@#?PkWbNRCo&-TOzjChkt_l)I=#v1TB;)ZQ|UofGoHf{-y~`gChkC0jA3&3Pi0 zjno=0ftsu|RXjc2?0>c^EEXrd5ih-?^tq@keXR3G`6+nX$8DP-d}>bLor?jr+6#IK z`WbIVBd(JGcZruX-tnkk|M19bGLhA3rYcd(6gPRSs`q~sE&q`=FKPk5Ll$O7#80Vf zkYvGl&cqY;v1SYpIl+B5Sl9=b4NJRW)%;>t~&sVq;E)}g_{bI;a47qI(&ziNoU&M#CrahrNP5NF-Y%t>dJC}jQ4Ys*BWOcfl#+T>`pz(E>=}1*X zhnQ3OruFwJH1K2mo}kbqx$OwZ5!>{HM4a+*1fCIDNsQec^rebYZXXlP;eDupPf^=(qaJ8e{3Y{btITl94xf z>3C*$_ZSOa3KuO4xiSs&l+P42W4(m{f^^I-R|(gI`)TUc)52U@P?#G|E-r04JHLtc zvkMpZS-L+EU^$&>++nMwc|aM`b_EA>l}6ws1V-)w>UeBUtx6_F+0e;G?;7AH+s5LTBpJgj`M4}R97{LIzaGA zao@jQN&D?bB)ein0&EGF)*M~c@HBXBOYTI^ys|R1fddbkjfOu$ucboP`u$=J=vCG zTIJ{;0Zx_%d3L9t-s$*isY8Aj3x5oUb_}_6MO&dv81~lr7-!E#zM4b0>GV2LQzw7g z#mqQ?*KUGRd4pG7A4m)QrwBgsPZdJd5a(F=SsGOP?U2(i=MCu5C2A+ycyg$+e&eHh5QhYciQqYKgX0TJIf`(8F2-8X^WiRCS$WpQpKLP)FTjT%f0(S^hc&^3 z1{A`r5cBFkSLXiUeMHFNTqh1i1DM=Vk(_MjYLQz4 z40E9eTs{S54lhA^{p0as?71I+TnY!IKezQHM4F97F5e%(8iAwXbrQ(@!KpI{7D)9` zkcaP-aB13;{FK2yICCy@Cwugh)z{Et4IzKzw@CB9BQpje6Mv_ZH30E1>#Gu6>wJ@5 zCtZ3-sE1yQwbx|x#~auA6D_%`GLXLZ8bR**&P|8OQCUZdfaGG`xdX5eUjLql@^4eR zdp$ccNO9iPx))nkIA$g|ps7#pbH|#+iEwH1xbZ zfH(i8ziEBL*Fq#-`^>b@*O1u*=lqz$_1Mm?4i{OjRrDvJreQXmz0Lp28f+6wn4c*j zn>|8cdD*gu*Y2o!!SVhvw!ysYKP+z@6fp(y_7Icaz~URl=Z-Z;w`{Fah=c~15>Xpr^; z6M_0Kl)hiQE8xPiQOE~K4njy(KO!q`u}r@psMQxwIoa(*|BcUo$u{_np{~DQ0DR#8 z@+NB9cj?;h0HIa2-|`A_yiQ27n~bws zT2{c>>8vo!s~}z|OoRFnp4}%jdixC#g<_l6>K`y$-C-(G{#==6t2PL%RJVXY8x5cB zkTF2XE}HJ+fYPs`V$})|H`|RQ-C6qYz~<#XgR2=XCZ1dX6v0sTe#mSl_)v{2H=7ni z!UYf)kvb|pB~_}iQ({3f9ny!xd6vA7i!$aAhK5lw>n}S7g+}4VO<+^a>Be_tJ&TUD z*v0r~)W9(|dbWQQso^VxxwOoWUlF9TBYDmq&8Sme;^_uYizT=k5oZDGVE#ZpKUH)+ zTg%$+Onk!X71KPC<(e(vNCNxNGh(-+P(OwG{2EAm(fs5!f`&ol1o-j4C27BnS*9>K zu>+)RPH+NZoFICJ?H^C&Df>eO@6D&;qKw*wC%~fLEKx4L6Y_fAf*TaU(MNZcQ9=Vk zdX>gE?w?eEfNM=nVT*Q0BOjzC>enM+oB7Dh`?Y?}1U5eBT*Hsac!Q^wONm(g)C8vu zE?hCKjHY$>|M1yRGt7YVt^IqcPxHqKz-O`CYbt_FVoXwPoR+)wul%;hNPlik#TYHY zjt?~@(acS&3#s--*5%H_?E&d|H!I8ME@5&G$$Rh6JV z6^|+M3U*lYma_FS`%*;^>c-s)ytu2HnpMUX1#s+x*FG2hv%d<4_ z@$00SSkiZnQmo9UTGp*Fw+|>~sE@mF9O~-;huzV{<|v$6H+n%SsyHsu<)P@TYUh?O z8A}a4G4#$UjT}Zkqt=M7iYVAilFTHeJIIwX4cGA=1=0hA#r%Y#;qe8f!uO-YonI9i z84aAiGyc~voJSQH#*)OB?+Nl#AA2t0)u%s5IQ^x4udj)1_L77^heqTI>EHAA^Z>1R zF=adaDx<(u%>c_m{5en$Eh`K_OfKi0fehH`Zxk@HMi+8VxjO$2wWpq)&fEY5ct!74 zjyr-9t{Cq6-^6kY^KU4g6+b;=CwjFYhY7s(&qshn#uau^#=38^AWdD1c3|sQi=%a7 z@?wv64dFA3hjgM$4`&lWw?vjpeCuLLr|$d~M`L3qImZ=cc4c1ed(hMJ2RgB0JK|*s zoF1%RCAOADFIvQg!tXojQvv1{N5#--4Y|a=7IMB5L)H3gSyZANklmP1t9FG5h&J=r z{$wohp3MWZZ3HHDw6I~s*I}o*zJFTl4&;)6%`Ws`X)bDw`cn(j5)zL-5=HOF;-F*X6kn(-sUv}69f&*fH`Zm|sxIt?r(9qUk|gN~P9D6r>l- z2sUG$)#Y}}J~P}H>zmjxHBB+?n*bhgBDm3McqFjXEaLkCA~A}d9rCy^Fh;B@UhY1V zL%di-wX;V<1BDwfv^l032PK!!c;8JRlYd|w{VMIQ-mBO3B~Rl=Z{oKaC2CoY<>(s7 zTbgCm7NWIFt8zm4#@8xx*_-#@&w{5Mn2Pb7Mc8}(b_8>cQ5uTe4mb+3I1gbBfgsue;&R-h z6M_)FhkVz9)}8kEx_Ty$hPiJB_KVYsJB^Jo}GpzChf-dCW;7Y98*Sc;i^JW_!rT2u%7_stG)Tg}#i<#T6i^)#pv8Xo-+{AF2-VMYcJ3HB21EjZaAKMUYjXc1{^z1l!z*@@Z zZhYI!GkIGcetW)h{@YwEsk=5E+Wv)~^Ws{TwYf#@oxB2()8{OOC3j8h&G`R;1x9)< z&K2|(tXR9Sf`sj^W5d$uzb5WKKqXqQD%KHbcl=8N?Z_F=L8e9G?HpKSmA96B6K^M7LWpRS2kb1-Oh- z9R}B~-FcdQ)n}%2ReF~5)}hesOeyXgbojF+&zIhxLa&~)XM<*#9k-*0$ZIzmgnrod z{v|Wyd3I^e-SR8DzVb9L7jN*ERJsy{FOWMm%e+Ho zn?4qE5Ob{3!5&ayU3;r-Tr|^L308J@Bv>$-rvUJ^cyJ>lJ4xc)Wn) zO5BIWzb!N~hbN-r=n65{fj`bX+|nI`aQo5syBRAVFj@l$hE=2C=mO6a+Fz_uXzJny zi!HcZ{?~dJwzBd|HgUDO2<@+>LVqt8;qRs|64Q`zRH<>AeH5KH8cltpdCw2_A=M_X zSIy>ZbZNpw*wf?BvS|rM_5_D)7#Cv-$fW^W99L*UuoG;$YLcyf5e+%%w#`Sx*)rKS>lHjT1!C$`<~fkPIAo={E3)JwC>T##0EQkL zg->Z1zD~DIxA0mQ01wB*G`!umQSYpVGh8iGfkubdph7t;{8AALZ1o;{|1Hp9L-tGy zkNcF5b|%2-bMe~gFe^R>;$k)vYK(L(pT(=IzirM1yV0Rf#6vR~0{Tu6N!r<-Ct0Am z%X#SZ4KT&$+W@C!qEBPe#KOYkmVNVD-QyJwvlvjpe?yx|g^yn=dVoY0$RR7X9G-Pu zT}gfV>OB5pQfqw|L`2_UOdRe&5FXWE6iGn7S6a3U4V}00l|27q!T}DnSDcDUD>gja zKvMfu7OgznC2#liBn(|;9KT6s3m(qlUnhToP8rI)Mz%)Z3m>kn$I{Upd+(YifK1yM zw?4bPk=5FwGT~=cuu8bMT92}uuKiejF^l0Q{QlY1A`-npw9ve;i zoW0BO)xS2J(`R;&${F})FA4Z42j}#9-hxkjklm^P*)L2uJ+XJRT*{3y@p#6O(T47 zFuXq?1}`3Lj}Rvz^^KQP0F6RSXIA1Q)S=Mf=v}0p8NUB;!-4-)=H5*r_Zj@sj?lOR z3XLf@25s~sGB?fiZH{}Vy~&vK^=Ka}5Yz37(!XMXuj^hN{tkIYsy@ykTzP;i@p_pW!Fcfy zSIX=W^{n#|dMN8Ah*^GqlMoIvROU{6r=*&{7Oj_WqoW(9dF{shoBI@6;TvTQ6+K~A!NZW~@O>Gx{@01BA;XP2Sf9k4QefaE~n`ei<)@*f} zCG7c4JSCykM)PZHSFwv{Q(~7?Mo3)Kl0}-4K))%wTrSdTKO_i813@tSg&QG2;0#er zquwfS2HXP%51{6rcew2@cdOmZIA|4H+zpvQ!-|9WEn>B6Op2R(BG0KD_^Fr^=kXQs z`RQZ$CtAMfIQtG+>Ir@Uwy#uBhw08zd%ajPuHYZ8oTX2QtMBT;s>vk^OP|61( zhZ~lhRfGgm`Hu82=6lC2xxkLO-4NFl7QB$O_%X5Q%Kh!P@Z>#(mA(oWAWsI>6xm2k zfv%Oq&dIlz4aSSP;aS%F)P-l@eldJ6iPYOS<-#FU$KzkswI_^c+Ap*!J?`L%)hBm8 zil}3GiP$C@cR*&6fi+(|L8RV#TvV3krWgPN9hsoyMVL_ z{mXd$1&V6dhke?pB*B|tyY=>(2q7c=KmY?=>JBa+dt|Yf%L#6VDG$TA?-(lozai#4 zKv!1mGv_UzlMT}BOin*F%Bgfbei0Pvbtzz;$Rhf}Z-ZM~dr~^c@i{&`;pIhm$kDsT z^p1ax@;^EeFrQ#=`*&m@L9nrc={^`TV<5fD_ar5~SW=g>8_bBLjKLd|r}HW{Ntm?3 ze|hoWtDgQQ4E{4TfFY|G)f4cdvUyCg~325%^FnSt-RgMG}TS{{vJ1PQCyD literal 66168 zcmZ6yby$^O&;|N}h=@oENOwqxQin#6PU$Y`?oL7J?iA_nZk2ANyFWS0VH_v8~EOeW$+F0v!tjH z^!V@VM@wD|1d&4GLIR2|se22?nhL!*&`}J!;9<)pY9_6;P%t^NuU5J9UVf44Wbib4 zQ>j+D$}3^Gr!U}x#pIHGUTwErCO4xw!L zzNCK7zR$q@`(eQk6Xr|WL;AnpKI%F_Pf7|3-n@TbQD0x5o9jPQLFx%bbf2D{($oHX zE!%Jy1^AIZ(W|FC2o4UOtFz|!+&sNL&8wYM27fr?<@5T|8&iTikZ!H7-~Rje?>!sz zY?2lKyW;4Zo13-n2onASIG6aS&DvT0l66*xYhlWnhWaET{LyDUl=51R!nQIZZu3+1 zkv{ABpPmZmjOEG9&dsrKvm$IWedMvdy}IJCp4HTrGp+jF>2ZM^TO825nu@a2A3&k) zE}LrNc)ZHr*Vi`?jwEoi7%4q(!0dW?u+SYRR2&b}Q^n@r9~^eam0_~8m#M3W-U zCeOJSv)*rne`oTvFrhy1_xJx6g41&5*N7o5W%Ap;c38){qH>w`;NT#y^&$^g zl+@D*iTZ%o{o?B8#_FN_nc==3YC!u;+2Vw%N|nWoovW*>zP|o{Z`99c{ONkJH&dbC zjdRFbR?6Rc|EJ8lx&59PRf=-S@1ET6yQ32;jBREAUh;e*F4W=R{Y`qE1};pEZO z&K%azjE7iz&*#}QJVQf6Pj@2(y?8RVE-f9MvfNU#GMLx)LkN)*NA*Z?MeD&xRX!Zj z=7?a`Y~hJM(;7F=&B}3&dv|RkZ&>W7Pg&ck!~{{Vu5Ir^b6UKANIag7MwDyh?X+YT zS6@byd7)jH=a(oGn;O*Uw)lT>4dCsijNWaw&8}UdUV9p8Hz8k{rc<7G-PczeK*Xa#2a&wMjw1?uYi>uS5tq5KpUpn48QL# zZ)0;jFm1Ek_v>6nMy9c`(en`w-9YzcT8EaL+{%jW?@3eK^m2|WeK_dvqyJghiYO{*1+$rZkQ8o8cpk?NX~J!4bn|m5Rd*3qNn~(ypSN0n!(-@ex=`UAtdT7aRn;;EL+a zjteMNa6@!S3x46bW;cj7EC~5%SP{&rC+C&UVk|q-k=>;w7LV4OhJ;!W!XIh}9(gNj z$I|4zw_9{Vd#^?Xx!BC-RJ1zHe36_kV8+siw@T}kxS=M`dDr)uZZH==cBl@G^YQA0 z7;4s>Dhg#?U0#t5LQy+#5*Cv9K00H+v{P7_DZSNPk?(frYDzvJEq&DD!OBggtS+oE zJo;Tnadl0hOt@*-=JdIfE1ig90=8(Id+8505pBtc&5^u>%WS3UisF}0vtZ_I7ZzWe zTVxb^PCl=HuDzTS7is`j%lK=o9gY^^?uh)<3x8eL`uKSy?sfeFFdndu+%P2iTV90L`p+d zno0X8D)f#9E&b;8SkF)6H*y&ma1mH-9W@@xOIg--}995H2=49FzHT9}ZEUwHC#e}tu6qz5$3GgYz zTm5p2$6ojFI{)B3%79qKo;EB=v@)D1Ys@}CS!r(;krrsI*Ra*9^g*%k-DzI?B*l@= zYOXz<8=PXiaGN^@-rrLeu23N}4d>Oj(yOH<3`J$7gvM==jTrs?YW$yPX$}Bh3D|Du z-ZZY4kred>Q(=$4a_Cw<*0Ppi?(?9FN*kM+a$ab3)N&GuYIBDg63oaeQn-|bzNSYQA_xD1RmgyuRGs%csvnq>_(AmITe>ZlS z(!X`XZ-TgQ7G6N_Y8`do>5A|%B?-CNcyNE|slC(=jQolMNvT&KkHA+Erv_)XHZtF| zSSyZHWtWj<*DB*F5zWQD1-HC0ZW@sIf<*z&$H+%gQ|2jD*e^Mk-oQbVl~HOGP@-(V z*7oGg4xd7WxY3_?Gso|zgyIy*sR?nI!Kt01^xn7bY9tK0hE+L#n<-k`z zS4wo%qLe(-TU#ElD(}BLE@9;-Fb_gLS=q!LWj1oagHfdabHXj-=9%IR)k#Fi)R6IB zoW{wv3eQcKnrr_^1-5B}+Gf#8)8~q{eNw)}`x5a(+ z3iA1(I;EcgPacgxPOw46Zt!RPQ}^<>D`85cVxc`_WpeIGLC#=fJvg7q$<3wDx93=p z!GxE4G<8AtuVw5o3qp_@MA|&9TBaMCX)jd-W3N+{7!Z^Zmxg+_7>5RZ9!8Fs62y&P>0^SJ5A?R_3k-NV6!sr zFNL=|9&>ld_}+R3m?#&bh^6~y;H5{Zp3QAR==T>-lcd-yllqt4zf=r|lssA8#Lc#% ztf@I)R)k0ke$;NK)}bRmV^-J?*HYO+8u7DhggL*xmuw@90Xy>lw=u#_9gM zGfIoAnuK*W(F%0cm^S}U>SC590Oaua{h~@JlBB%c4?hFHPRN4eB`wJQVoatxhW?() zHAx(U#LZP5p58V)X|V6(lx$nSs`F|oNXLqivQBmk(l9TK=O9&%Cl1HXFHFv2VjQV{ zP?VR^r8=|ds`NJv{#RC~KF~>K!dQGlB?jL% zhLYcb4uVgG!mZats89ZS$F=#B!_>?9HWBa9;?=z~Kq4-#EZDGf^q3B+trL_;$f)zA z?!|n58(r5{4OKB&>sU06ev1jF3mx@Y;!m`8rhkLZ_ z*a80W6&rmYRji5TZEWWRq2=`z`>S1ALtfM*De9SHoUoGbE;hvim}Q#TaraPXRf_RCz!Ur zJ?lqgyHHmL1ig=%MD$)g;Bd-L8 zeBSKtlG9qN?tHi2xYH-w>I~>!!d}zJX*~wQHjLKxahs3t+{`SWC9T5XZXky>pi6b| zZ&48k(eYW6?W$-;z%bJx1}DeHxF}b;=ln`(A(HPqB8hqL#u1T-|0=N~j~?>5ZmC|FGER@~YevtSYgovYqUtEiX)wK)=!8&^7s@zKT;yWN4+zOVmAXNQ?jZj+s}(p4W>i5)w{3;w~871 zWcb^9SBwYpP`SN5_dTt~Qg4#`dP87g@6rfy@ol`u<}bKgJWg7sR^Gyk>-Y^@B+4bH z8-8vZ@fa%H% zt!lrhQFbKe`8$E6)nP>)1;@+3I=7pz9}IImYu=#K5?#muU^ zKmOt_exjC~5Eq3mJXS?Y(U8gE{WXX!hnOJZM__z~p^StkxP6Fqatg;V$JXYwwv0XK z4buQNdd%-icBtImmfj@7)ZEh2)NJSenLuR5Gb$@Ly5JMkA(rRfjyg)i9UWeJrUDlY zgZ34J-~Un7w|sl&E5bO~-&_$VtDbh|@Lw;sR7^eRrlT$jW&HB1P!#@ymDy+Yy@aYE zdp@34YRb1SAwYG}7+LAB;y$yRUym!jb>Zo!N)Rq9ZBT$0yTK>CSRG0Vt5VlU88l*~ zsI=$dt$Vy6Gln7~Riy8UiLuu|ZimKYt*>L~mJ?DDsS=4vf3)8bksuOF9*U#-27RdN zj`5u|fqG24MD!tl}d!WTJ%B2?d&kuWVsITkih=Aum z^65D*@K>h6;fLD`_hN(nYBulmnRmO}GoJdETY5RQ5~o#p3=7QmYjA|9qSR5SjI5G3 z_@ONcxo(t`ppq&yvNks_*<46dUGx8>>KP3qH?ZxfZyAv;4b6MgcC@QSoYw)cFHyt5y8P6y!J!fqu zt?UP>!m6<$)jAbTZEUDAM!zarUsN_#W+wjYx1VO8+ekT2)*=EgV>j%mH(4|Usp0;MUnWc=!Fr=cW^iNQ=rdfhHa8YxbRgA?D?h%d4j zLfVclF`q^^AVC$=D}C;$;>3`{%0902Q|oBC zUqa&|5m^U5abt`F5?R%OeROh%3bZ-daFFUC9Adv*aDS0D3eye)rNr~WCHy9ZXP5!q zOuRP7cexF9^Xz;_(=yaiy#}LKc#`~l=dquQt8;JdcIT@*zBS3 z`XMgG$T$dn;EnTrygWuGe})U82kw1P3LkZ=U`ex!VTs6)-Ju8iMZ3+(w} z{1mU(W9%rG1>1cv!<2o*ZibEPPFYB>05tKqjb<_5=xu#>;DNa#j$}`{#{CUF8l1QgH`KTY{5RICA@%T7GxIi>lG!%CSy^xblQ_?fIvW8XM7G zPE?8h$bE<^rv!Ii8j*H>MlRj>ma?&4emJ{ndVRs}htJ-^f5*RASXoT7*jN8rpNSQx z00{r*P&+iGHos^I4)Xb$$ps0ucv`i&;pV(&KEi{<3QU}C*b(r$KCK?S`Vz-szxGAr z8BBAj#q6Y#kx-KdXiRDjodrx zfQ~e_s-4Z@>iIjLG3*7~Gg}ea;h@K)R$Zf0;6}@jk1*Ftp?G_{-J|Ghh?0rVSRd+m zdG0e7WWVq8FmrAfiClpIR7>SCSy@>e@m0Z?|KdT*YDjCw3`rGUBz{VRT*buVmYZEq zDCOtoZWDad@3`^x`P{!{g>XBPynJHsB^Lz9ak(SROkWE3?p_0Lz>7|_*M&$7QgvEh zozUvHYAkKrZX|}*#~stcU6@tnPy)KSiH;k+J?5sS7D4QnivZqj`0TCI)$%I=bVd70 z^DxJnnKu<6QswEAL#K$deiRVTXiK|_J4x_-5sQ`k(?u5`XHgrOFf}^ov7+YbXt)_+ zeV2(D9lXP8eK=(o8#?ucGN#wnG>EDp3GnNuolW-|@IJwr&3B>en_Y;{pDMlQjN>c2 zexAQc$*lN;`=GSK-u5yV2Sxh4=c4@S>e;Q$$%#3>nuAMzyIrj3H^2fo2xWlwqn_hO zet?3B9+S!PNF;!c99#eFJ=X>;1_Ivi5$}C(Rz3_{s|VCgY5l*kSwiRrNFRf4BQ|fY zUZiC`Q3%Hrh~$4cON1_fZV)LDMe;pEl$iI-&1v|vbUKdP7J%J( z?Yla4Yp=5~A6op3oQ9Y#kHrX-pYn%?kABgCcOQeQ*zbXW>Tua< z2E58q)x5qncW375RQmgLGy48m(bvgCs+lZSn11gsUn_r;UoxYX#_p>hfxWDmAe?V* z;ow`1zNHBhZf&%P_;z@dr&^HkT9+|rny^eZuLCi{9J!=_o zmELhaIh)yKzmmk4fHkv9`4=rFD?5&yL8ZYoB%@P#2rn%LqWXZo*qlxxEN^IQc~ct2 z+u7=LzQLQQ;-)McH7(IStBNkxHpBV^kKV|i6p0AzJZy3^m+2uwBvGA-+vd4BMx3(8 zazzU*y|k3f^)7)m&lj6<7;4j#v`UNqp#q9lZyFi0^Ddr-A)%IVt+=(W^$d#APH z*!*{}!!lJk7Em?vwKeOc#{4lPlSt-O*`6B6CSf^uZPci#yi@{Cl>vaSRe(GSBBi2Z z<>pjY*20Ath*GfQHf-U*QuFE3Yqeu|_h!Pr=PCpDHBuYvLZ-y#NEdaQcN3*X ziMinjI&l(IN=oR^n(g^`56n%ylx>~dK5D%@-&D9G$Bbo^svwUa7d>WRfJSC#rA#vv z0;4nglatQ$b0-%H<66oDgZ{^Ocyriq0ny`cX(l1Lpq{?%3h18dSdXpKL0iVcbobod z3y}`_Vz2KPnZ0*b)09`Xprxd|?!2&epH*~7qMgLyy)brq>x%F-g6B~t zseHwGAq$p}^}ic+`1~Ef+3>h)&=sY$}rpk(005S%BQqfXV zj{Ha+b;hv&My&}bAv9F2u!MGGIeG2TSD783!1b}-xPNzGkGYO}EzNI~+^?rZ&wZa;}Dw^RR2UHs3~ zp1@CEI=wwm3Mg|w&{$hB2&pN@oG~t5g8d%HhvN6!{%$lL+!;NCmRGTCrrkor{Kh~H z;AxLiCK7+SY^Q~j*)dX5Xjudz_PjqTI|^5ap}!e>b!bRMwv>v({R_ zNtu)8KPhzf_RB6LzJB046=j9WD{Z9!#le-M=sEg^2^+iP>?8vSr~?ef{rLw%?(*c|d(8&7&?S^!jnI)1vL-myzYy?mFF{p}+? z*~w|qvx$vPpex4KV`LkDS`~pH#d9fk9T)%myX%Q3W%uTw=S4lFGdDkuemZp7dRq(X z)B^fC7Q4d9_)Qi9<9ms~e{j5tLQb7rpThkFc%551yV79oXz_Q~>y8JB zIXj~xYy`R4URzPw^fC<%Pz~sv;dXqQf+HVKW0&sg!`+FOh{yUoV?YHRa_$)JnoWI# zEH8_+eSB!jx(#qB(c5erV?f7mH=5mr{3dGI+YsXOT zSl)qT0dx`DhRp!u>q~Enn+b|riI0|!+BXr;!;Aj)IshE(+^oe=e4JI+P(>=%`o0qK zNI@)sA=y0s{$~n31T-p?iy@V8BZ4jjz zV?OM(7Jd&3y8k`uyJw%Wg}Bf*y+wjNEI4g@e;%8oLCXlTP(wiw5UWBBEE()T?+Tv2 zf|BXN1yxG;P_1>l512{wBVqULFrwd13>5DeNr2(9Mvj-5>;mXT_&;4 z|G0<(X%vsOYk|HoH-^*oI%}yF^6B}R2kCyU^RYWSFR6Mvxx-Y}02%@&`05PYE=kPD zf2bIqJlwJdHk!W!1ZQ8hbG$A$@`zRLdV! zp1l{rL_&MIe=-)6-w&st5|J&n|Np5|6w;B4&}P)C3$vvE(pZ^dGeTF#8@!IIsK*V@ zA?8A^w3ZOxp012i>v+QJ3R-S0Ddp$pcQ|**P^`ntCJ%03($EhHg3eX0x_(B?ok!#(!#$U-S6j8a0@A-TLsElY z+mA%hs_hZAW{QjI%9nrVdxi}XuTMqE+G$9@KfDc5hJ4aOQjX1ei=1nA?hfx?Ajq@ zpI-BA&h*m@uJQ{YYN_^%v7~_V;M_9xpI0Zx+!tx&Py^;hA99+bi=$9I2&6i8=H&gpUQu67vAsa z&1I}hR^C^*dm-xpggpSp5VW4%p|e;aWcu~XgzveQ)7#$rn&Z8-?+Yj1CoKezZ~m$X zDxv{70tu1I3ZXibp+DrLhZ56I9|?$Z}c7 zP%1F8Dvs*dd3Evg)o`=CZ>-4?R#)oxI$r|#v)}ZGNn&Pn{_mmdPRLSw@T!M^t*N7D zRf7hqKGybM!ksXn7v>51GdMSqlT*o>p3T=a78aX;n6a4PPTNVie}(N!IzB?=+$I); z%v$^uw zX;+#aGTfudmj1D@lxU+hG`AnaeH5jTDtW`r8YDpvBpB`3IBH6=u^F;H|K-Gk_#cF( zR{dSQ*!LHlIodX7O}LP*#ja0Fw!(dMm|x7Vd~}5Em%yjBog_4ehOa=Rzu^`K%BA%a`Y}kg-Po`N%xn*!8v!yni zI|SU06R70eqUD!i_9>ftX_lXql!YGAYlvfijK5=D!@>;lx+R1%ms&|NpQig%H}@#bL#8%Q!~M4(Gg;`ol2Rv@Um>b2^y z+wHQvtDKc$WJF-K?es~xhQ^P<_AEarL~6qgn6LL+Lhz2G056Dj+Sy5n6#+dbP!hCZX5mEytI215?8YC% zasLR`2JPu5J}nF5x3V5aoJ|+5Y-IBPg;Cj z51-?N6X0xrNp`TjU_2fVwqwk-^C!dh$sZ4iwU7C#>T((|@(XqBj-R7^P;6Oo)U_0L zv-m9NF{OpEKFsa_3g79OneN=|!xj>eH|G7CpvBct@ElOo%t{HOp(O}0Se_lmzMe}R zi@kYt^g{fN=?PNsr$x!FPlufzF0P;@;z)C_qc&T|FOhd+*d{We&-upI>cybdLUV)7 zil9+9h$FUaOlLQ@#`yS);=ur}RQCXf{sw7#JNeaxd(4N(Hs2P9-@X13BRCJ&SYx82 z=-HS4zrAB#Nk~X&)LQU)|B)UW(bUwAn^{3o21ld|egW9VmgVJZe6`yJzkYswKJWV* zY(q-}lh^Xj^U%ojw2778HGQ4#RLSKTeJ(}Xj8)dm_$Qd#@9JxA83*>?EP<5%ZQOOH zLu9bJG;U{RZXASdf{%>0fUenAw0Li>>hF+!FV_T9E^#+avA%h(uK!VoH++3x^ z&h6^xSI=?G)}5oGlycAv2H6v`&G!DhyvuRr*i?>{3Zp^%|0eycX=$vaY;2DF!-n4U z_JUEBFbg8KmyCfX-t`&znY4$1I?FOy8B=u+>Xa|nH#KEbp2A86A|KwJVZos4+FCx3 ztE15kdY#|=YsU=H$aEg$(H7o6?IoF$P(F06Aa3|z2aR9Q>`h64Z55Y*1|Adrz}otH zGMjt+X;+wjSE!zoCemnxzwl}Jnu5aEz`$Nt7{NgJQvv@p0jDfjtc!~aD=X{XLQ}%bmotPYF&>wr<-rt~ z{^!q$L@#_%xc`QyaGso=Dk&*l?$6bE-Z~j|kA5^@p6TfL>gTt%(B%BzXW_=yC+T+v zV}JSbh2Q(Z;O4ch_1A!al*GiV+gsW1JV{`1wC9~KCJd{ivvYfUd!g0S{o(Ff>++#$ zF!4PF6&2Onx8WaNBhSf^_5APwZ&TIKNQjLU5*B{h*h{$&3Q7eHjoa1E5PFr;z4DKC zb|-nV>0CVE!#4i@on9d`dw6)5n3xDX6N=P>r#kp@adW==Mj^M^{c>n@G9$8Y<=FG?mhun39s0m$$XPe#ZU@0grBEY7TB>~@y}=MZxQe+5$|8`l8%myEbr_z0xy)7mIhYs zX188sprhN_*_mBqL3sA8Os7p7m|aA~XtCKffl0^8+}wPrq@u$1@!=lCwuOZS9UWbf zN?EQNoQ9fOgVh3;rKP2}O_S+27k6O643oMe`^1relqy#y}G(e%LI%9 zJfD(^sv;q_157S;gHhrBegZ5kKOzvQMI|M`oS=;cW@dpTuA`HFvx7#=1Ni{coDb88 zpl^Oaqj@GMFFy*RrKP!#OVTMZG4|j>-7oh3jE#+rjFf{HFi34}SLwF}XCTRNad3QE zl-JUF34y@zyynFCIy3V6^260`SzC2YO~6mC-nT^jUOaE!Y)_VGEVv~E1O!x9SErmn|}U0sy7TmH7_S;9|U?#%<0KSze1kO#ONp;0|SCjWhFDD3&xFe$SEnS zb$`J#GL|fQvm#vEqZDu-{P;n7^McE0Fu8Mhm|yo;cd^a;5rh}Gebg_G(OU8f3K}(L z6N@eGM1+K3O8m8}q_}wRP#U+ekWhZY5h0&Pz31@e=H|_fo2%=!ABYGZ7jVR*qm_2t zx0)Q@4|gl=0od5sa36$)Hx3q>V(Y-ECdS86_L;bPY&!NTl3NXojYS{Zsi~<^ZN$Kx z0IVAy9`kQrUH#0*$ETvAvPZ?<<+(kYGjt;=A_D(&w#NJo8OJVq!8i zHHCwN)7D;GUpHV@S}iRt#lgl_l9RhWTKNckNl|e;6psa3o+wh8E-2M$LxljWgoTI8 zNK5nDt|AZ+q{PKxgLsJ?4kLYHAVy_nw$$eR3=$O;rSAHqqr*f&Q3WEMy6fkwmoHxe zj{+0T28ft2Fb;Ni^&SR;U6hD@`g8-O*yZ*0s@}0AR3KqfSOuHBpM1Nxji!or=v#x>Q|9UUD;1BokJTis$P;G8@>JWroKZFykz8!pkPQ&m-6U0cK3 z(@|H)fAuOhGV((M_gh9rfOVEjRi&kGd3f&rWQiUww$RhjnJr~BHvW4az{|@E+#GK> z37Ds8e{Y+TlvGeq5cc{#Jal$;)?-i!BHpm!nbGyh1~f80E<_yz@M?WyqoKb3A8G+h z%4LbH9AAy)$&R7@$j=WIdp5)#2*zY59A z%R4wYB&0bX%qJx!T>}pc!DS-mbvpwH1KgI|X^)nPDettj#r^W+=*VKRx#2LASn9~- zE(nX}?*87-*Z03X$=1#axN%BKN@Js2!UDjN!=<*evNCHElfWAEpbmg?Y%mzV`$ch; zGst$XZf?Y!b_N`>LM$*aMOx!%YfBa-_FpEAC1Z<7u_Y%b2MLXG=j|zMQ17i8w-t9N17QjmvShBj_IQ{b{4qeB^el%kT5(jU}0+dOB)1aZcdIKKOiF^fq~7gr~Y=YAmAr- zq@+jZyHgi=jUazs9xg=?akE2uXvmqFnRB(4z@V>dvq0J25&*cfPez8Gz5VHEj-;QT zA4qt7o;MC4sQ1tT5pg>+0igN&+Y7)&Kr)c|&g1%bgp8iP=z@TZ4Nw*k7ZZg_CMpzW z;A64S(K%d>NdH}LhVRTN#_fxH*ZJ5umqd_i029z{Lx1z80Fc##`39_qlx}Gw7vAqF zDFiUs<&mcC$8?bV-)Y`NklrJRQpY5n!`5VhWug-j@`{Q=+E->tz*8{S7yBB3RE39! z1J-bh+S%C&((l{1Z$DOYs4+!}QF?lMzIpRTM@Q!$W%nZn9ZRGA*&2}Uq{PMbY`Di( zHbLaY$Hxl@2>h*J^gY7_@1Wr1R1+250?{Oy@Yc|Y!TT+Ue_vnUzkf~ogv6PXQq$5H zX=u9Vf1(@M2n01*gHmH-eI10|kA^u1khwr){2m*#|C1>U9Ra2SG7$h@Uhjw0nGCWX zUEfXH%P-5z%PM8sPZ1F_i>1^3{kuTAcG{n1vs>@Ezr6$x&@9{310Xf({swfE#`lnKHV0L$93i-j2vaYR{3$;R@su9FE8v&j$cj>Sgz%L8!B z3kwTLL_O)H4Gq_OGgXL)h>VPk0IXx5N>gG2y96~zBS7YK z2DSwx1~mc`3E^J@LIcP$!*Q{(fSy~=)k+Hr`p#6`{Q>w6@PBhKWoo5;4%i;#r!Nu6 z0*Fkp_3lFx6LBFS=D>FbPz}&YF@u1g@7hSeNsK5e!bVHTQJL61Ze!RyM4t5q^{Vo7IcIRG%72914Bb`F){QYA&GvlVHRfQR?pim z@YqwwPeck55)x6-?%IDZc8RsE;dBtPNkAq+AvekYN(RUx$UUbQ7hMlS-@blrb~*mt z-u}dJpE&WoeiwL73XmzJuioilSz9qkql~n+EbD-gf{bRrIq;s96$K4#sn#+jCnu+- zhOKH1zc@5B6oe8bIr;CtK4b_O^Zom%83919zxYeM2H+yEu8s^Lzys13_wCz8X9zCX z)j+tpO@DuXRb{1wq@=m2>3cS|m(QQ;ISC;#bNrhe_{Y)I2%z##3Kt%)*=kdec>t72 zy|lE6iH;5q41CAN_7rmH4kHcy=sd6W;^j*~OFV9!;J^Ir?QQUWe0WK~{)xYiFcg$4 zzJ7jyZiEB}o0*v*o{Rzy0egbMrU1_i3$q0CTP`cdE+7{O3k&n|{!2@8eJo_{2om#J zO&-@LARU|Hm?`|-*=cn>6_Jv%@X6Z2Dk&)e+#3{v@>dB=3@**d(Wzha(a~tIF5D#~ z`d{I!<6^9nTuGvdP+~S@Hd6C)^Vj_d42iMdELIyD8v36itOpX1Tx^Puj2zK>&jSBN z_w(n~-CbZ3?jyN#Z5x|%P_6a$_Bvs8@$z|(%!@_Dq$R&S(;Yve zADlD%{P|P&m+$q#K{qf^649&CL*SiLQc_ZU-Vah^f!%9FL_~m>1r3J0)&x{DEmfJ< z<;W(uq2l(Urq50uJg>IRxKyXtB%{grrtFNv_A^M*pqAd&p|y1mOHXerEDUaZ8qjVz zUA{7q#NtsIV?wT#$dwmPQ@&L&38JS=I2GYKu zo*vmCu{;Oi!PW7apF>D=M~6^R5Y|&y&K9QnUhyCn@Xv?W2>!)4a|`qHzqhVWe?1Hk zZVxOs=@IIV)88Dq@X*um0w#xOItlRayP0_rDb>lrkXmd_Kp|dCSK9>_9u9t`TMZV- zHa9QaIA(xDBLIpH$OCYOU%!rbCW=t=5Ff7b2?+9JQxGi~(S`|^H#YZX#I;)<$H(WH z%`07p7r!5|tm$`LJ{>E*a&>icLX_$&+dE1O-}1D#Eg!C^_yR>$vCSTHKiC$CBzgW3~+t?y_{?U05MAwZ#w_J$^GR ziZ^pDB|Be`)SyD#>~j8~pd_7E@%^H!vx*_~jB8hlJSxnHQ|@r!DO4%H%9jvceaQ1i z=rOTBdB4dSRBB3v*ate|A zgRYYdiHp$q`4HEE(%|dFamauo4bbg>*Ai}iI|ST4kf22 zOmE);%BQNJV81(A(!zs{rP5?MQ>msVL=;IC3f2V9Tyw#Vg%18cHCdveZ39 z(ASQfJ3CX-G9`XSGgX|#0<**qoUj6FAjq#L4Gv#Ndhg29x|xJ&vU%F-5W>N&k=BN z6g(GVnqZ%|;C%jmja7gVd)*!@2&j)*`99xh9p1kijL>P*;%-zBxOrr8Qoi5UR+N9( zv&*w}QV}!QtAdd%d54>sSP*lo&pCu1RL;~gFjS+h6Qm@|YBoJCZ_G7kV&Xl2&(ba@ zs-=8tbAS8#M`5nhQWAlloUI|Yl1Nr{{9xtw0b$>2C35=D$oE`3Sv3>+bB8?MStW&m zhL#XIHfA|~p7)`!u7PIs15((dTHsjO52)iew}k8im6R&UWH^?9MA+oNlHso!Pu@Di)7w|6xV2@^pS+v;tE zUE!O3Tk&nfI3=bf@8|@~bbWmcyabQAEk`Bk1;46i5Gf_I(`K@kg++&{a;^*E;(01A z|6$t;3MFjAaP8L9y}|AhdcunMy}L|zCU|4n_lHHr_s6@J1@u}j4Gn}AG)T{nS{@1a zJ&p#-=6%~WOt;i6oVeYGVK%L4ai#utA~IhR>U2Jvbp^O@!I75b8?0sy=4`~B=1H%v zZ6e>i;xxZM(0^+VWB5K{2mxCU`w6DEqN0WcTbfhwa=80&*(N9{Dk)Dfd_SHW`l%`( zE`B#-yE-%!6NO$+*b8<%yrnBbpBimln+d z;4X1Rsl>bowK1PI+keW_FjC1FcIeExF7jxKnYYYFP0K3IrGSjg3&Dzz`%kSRq{x^g&!rIKWtD zc&MnfM5QiS=W$8jUrxOc7QQd9l6^TZC@U!~qrM7CQd(OyDy_SQq2Av2?0A>tq+*_9 zL~&uT0HsM{$k$DwT9Y41tyHtlYC*)RJb_|E=RJ*X7pzDo&8ljFsd?K zl_&LDB|awE_|aN>b5*x4o#QR_dn)=ebaU5>&8$dhNhrn&h0t&l$KyI)?4F7I;@L+2 z>FA7J#9KLbep4&cjjff!(xNO*ae|<)o}<&*ti^_dcX(8CUct=K*h*{>cZ(5|%31S| zm}<}ajpo?vs#}qeElFf;)jC6Ky!(@eoSb+UW+$C2Zj;x4++qxu9?X=-o(<%T{ogDA zHF)IE&4~{hG4NIse-GssXS|F)i3bSRHpLip+IIi2*sx)r+eMW(xKN%~ni2KD)F-8L zDe1y-&)2goco@$8{{0_2UnPnt+0#Q6i{E~Zf}JF&!+q2&3*QvfPfsSK3rx3Lhdpnc zYBc2KF(H$}p0Pvr-tpxx)}IvYB~=v(lccoYSw@54>zSGcD7c>~dB_QpR}hZA}ysFl=Mm3#8oZd0A&vIOg)d1r81+0gKfD#}(|Cj6rKs}+v} zXY+(ZD;ZPPO2CPP|*DJnl7(nR_nn zaxu~`sl&&N^?p6x#eJlX+1(+)v2@HjD=RH|;uD&BccH@YrpN;eQ$SZwtxET16)&bn z340r1xC`)OY^65dxo-J#$hv#!w3KLZNW@@5Q4WF(Ip1VOseABf=JArWAR(t&%O0i# z=LY|7Y8u{l_<)!ub66h|5<)XK&3@XSf9Mik>Ew-tuV1vXdcUIomo9I@(|z-ww1$rJ z_w&JpfkJ!kW_(Y5_DH_!OG8%-0WW)smdGAe=<0~df9Q%sRFvB!ruQ+IGJfgOE<@%R z$KUPqZ(qJIWY99!Rg@Gpw@lh}7}{jq*!PH7Mk0WjW23WXrrFNtN2qIZvqTs36>b8* zce$G;8CI8d+a7wHHyanS_7ia9;uBPqo=Cq5IDyT6qFX9+#1E` zF2J(7ZDvobkQj6Og?cFoa*?HF_C;E!5CYt?iWQurzOWJ-j}kzUEDNcQ*g1 z*Pllvq!&kV@o|3+{ZQT#EiHqxZgxht%!JbX&N1yDAg3D}~K~dYygUWG#D1^N;#jso7VHVUm|tn(B(`2VNVC8@Izu3#Yb;To_R7MmEpF zWA2%?ImN`}l)VFMT+GkkpLrKY+?rRu#ZUeyFO$`Iw&bzn{ZLyM38jZ6{TZqW$|@MO7TagenQDTUsIwb`A(eGvNO^I+{4)-yXkF4cy{ zT`Qu6Ci0e|%AuY1o?DojXN3}3uLkT6~h5lZ%ea19TS&7LI%B?jt zg~w~jrVLNUu}T1{!jk={CHEb^(_9%aE{<#E?j0tbEPC1RjySuv^B&53=B$P(FOB(6!?(U8` zKC_-#?}wRTDQhWl?^FBi{X6?y_XN(v3s!RS@72UDB#5k>RxuGJF$e{&6$}xS-o1;m zxFHVzDb!6m(ZCUtpld-tJ@Ir>GWrX780R#^R>~KcR!e2mJPkd^Bvq~ zbsPpnBxMEnIKQ2rdV)`7pql50z-ZD${bl=;f1E_$BrniqIVJeuj{^)5Z7vW>43 z6z|A1**@&ew~#82qy=`aA?Y-0T|F4ofep$0Z6(Gr7tA-9Yp;fC5h3kBu%~ja`qvct z7WrA;)&=wfR+ut3h2WPE!=;-X9;8FEboiTph zG0r@;oR<4@D$}m+Y_YpNt(7l6nqWl9KD*p_o}0%l%xPI*ytFvS)E4EeiAvGh@Zx(f zn<;OI5}o@e*{pa+rdl(5hfBY4+3g#h=38H`c0&kC_DjCYqBm&M=tO;@UD#0-&};13 z=6vKxgy8Ab5IDCf%|}qXA7gXXoG6>TO=&0_vsQrhu2~>b@Yljg0=^UG?nO(^N{?w@ zV|}DQV~}(&b|F#8=_*2d*}D!f8oqsxS?+IREtz$TZ;Z-a5}a|!eBT=xlXU!d>8)Em z8!fKAT2-$BNLV0v?Y$13y&N>s*Gf%`y^1)NIWbVLuB2R`g;mGhG%UQFuB&pH$x*3mwzvii1f_Nq?tiV=cf1dluN_v4LY~eO zURQ-{HWz+-fxI@y_6l!f`g#BtUn-wnvvQt`0`~32lK0Yzgn3a?N-OoqiKp47#8cH7 zWK=df^=?=9sQe`FCx^50 zna=Md(Ug}wXU3D@6BLvY1?A755A28TeCTlNE#b5gNr^p7*B(PYddSo3i0`f&|^23PNzAg)}}j8 zKLS`{fFN!kMhUk&y1-o8kb}228=jcT5K~bF|D`zrz5}f7KF-6q-|8@stGN&`1Y0ZG zRq%h`ev&R8x>f#iT~O01Z1uzp(Zx_9+9qaA*!~?xSs69}_0`K-UP8%kJVofLK}^%0 z10oxDf7QDOQ^Liszm1@#%06HK<)iHwn{rHGRrd)Y8~9opN*dZ#f~>}n&&$*H+J@PDK~oJ*=rQWlaQR18ZQH4taDd`CR=$g0t>_^WYHEZD;?%W5A?{_3lh zF_jd}v9if${X~h4CCk7bb5@`naP%KZ!s03vu4h3 z#@zV4wFhqC&C?GpVH;Cn8?mzzgTkt+BC_10+W(BtIdKwEk_SrN2nRCGYi@5m#M;Ea z8^zWdI_DWzOciLbichJf-W&9zI_V@Z*g>hkeU)9vBw{|`KBv|M34&rEA0GNiv9|() z5Ib$rmsgv5XG z2R0`-tl6rtrw6a93O0%5wQpT{N)QO6rgyq68IlK?b%m;J9y0=#id#G`v0akL1Jt)* z)T7T^)AQ$lZt6H5!u<>^*@&l1KV!X}cm)Xy_0!UXhp3y2drYLj)-}`A6%3AQA8mVY zX?xr4r9}tF`PgKuZCeYwG(^`2>+%MHBjv^wG#53sx$K(_S}{V8a1v#Ni2)vy+GHC#3Hn`HhgiyOY5hgq)MkU@n_AH+ZYM!fb8Y? zSlg4I<6kWA-PATEUf1EZ)v^zvYnTt%Yp3s0;W*oMUxzeRu)T-;=9T_n$uAEXRn}Qb z8$PltihR0sBP(~l@H{W7ZPnYOO51WUy`f4sv+9Wl3iIz@~?mTNU$u|rQ zr4`3JTQ9umW)=l7-+E%H4v8tF=rXTf4jAAxJt3phvuFI;5Zx`*b>-U|lHwn`7?9JV zPYN#WfwG^&7|5s(^+P9h5ZLl2A=wpd2#|mXlf(dzB%uc}^>QMl`X4A)?a=1|oa&$0IlVfInV=;_I9> z9CQwkd8t4J6(?7WMk%705;r7Tk#V>FmZ~_WKYN1mFI*1UCB=`NH<0d%SD{)j#9x!T z-atVfgitLB*Ryuq%piFAde9IyF23#_{EH`ab8lhoQ3o+*FZNvbytJaQ%8AS@^S*aA ztrLcq>?66qjl7kl-+CCRnS-MPZcEE}JFQc}5OZ^hu~a_Y({eR%@J=-Qdl@p&_sxqC0NZu)^IJ`nmGYr#h?Fpr^AZoJQSqQ)VNa{yFM6VRC`i zyAQv225Sm#4cXOFUezi~{AAZ9GYJaOz!sp{iPigCD~cWn8sqPj#Z zn3gxzK+Q%VX0xvBE+kg6;A7O^Mo|^Vu&gQW<1OA%l9gV;%u6Urb^!T0{fjd{M9;~i z|ASKm-^E{fZ_>p%^-LV4Ggh9d16A!&33N`8>zqnS67Ky)zsG2mtxscSBQi#1gMZw{%`SCyPwwov++4C0iE!LuDpBJ>m&3E%HwS4B-kH(6VnbgA%#vf5matg}j zOtzJ`^Du|FL#^sf?w0OaRcVjr#Js~7kahfgjyOG@Y@~}9?>ge~ZMeOd^Xjf*%{JEK zCP+#beG}SC{|eaP_M$I@iHpxrCvz0FEm5wV#3@@xiAlmhz%BR^sEm%_a2XmJ>K)9T z+8r#(0z}^e48Ag!;;W3|Y-J)|j%bYAFA!VT9QC$6_ehP~aFGvH-u8h@RXoc@eZkBt zm-S`7&s|pSx`wT7u~%mwr*n98YdbEV4q3pw@B0w3iy-l>AC!T<_vloskv~^FA_3ak zkK~))Yi&W8?&h|$b;dJR7U3)%M2Mi-9nyreK}ZayBy2`a+Q z+fv@DT$b;`Mh&6Dp{Qg4_Xcan$dMy1;hEgVX>M;wujJsj+>G8v8@UlW zbuI6&kH)A(M&QogVGpVp_Ro_koR%W0E96Yk#A#;2>6R*A($BP9Q+oHym3g)dLjF!P z1N`E}gb*g7_14e`fBTWKd@)|%j%en?9`EqNWuHX%7d6k%bu{&&wDu~Qnhg&DBXznE z)y56e@?)(?n+KcD9}ZjS37pSq9+N{){T3^VFTWrt3DJIHI*AD?$)B(+HMy)FN4-nt zOl)RXy1NRpq-HMavhHC6ipb@R*$_2YeJF6EEX?o3K}Y4T$1zmelSX2;%iG8*XpYwU%*dUG! zlTV+|ANH_#6X{8G#N?vUdCUz4qQC4VQ5lIMZ44fAK~~A`0-i8Yv%2)sbyS-jd&k5G zVUd16xyUcg$(|ey#o5?oHrqLJ;-S1FwEXCbK=jm|QuJrgPlpGV@fsm(I}!rP91qis zC!HD2;y0mt{fB^sCUqFCD@05Zdwk*kIwXSx)?JdsA@_ovA!XBH2l1zDmG_!r5V1yN z{c;MEzHToPbIvw=;U^ImS{-H+G{~WPjqc^22-d7tZ7!!TFXstvFhWVkycWCM(wOsg zWnBEZ@%Zl?Yas2cc#cEO{UtpGw?vTOaEZ#QA|+YdBi{)j;{1mhTI)VsXg`EfhO!v+ zNiq9(+uXo|{7^|Kyvg{nTyFG%&2o#0sAU%mW&i5ugH);1p1u>`lN^lz2oSFs3BE7$T#ocuI@e#zFmB4U->drR;cl)P+ zqgzM%8#=1KJa>B|tbD|f-VGXT+thmdhtQ>7R*ozKx)elMgXg?472MXows^k>>tnJpkJyp+Ox3ZyBS#}lw9m3I?Uk z<$TkW)lphK==j8!_rI0bd!c@D-V>UB?v$(Zco^39@p*78KfaRqbs?iN5ow5a);Y5Z zWl3I^0dKI{E;bqQAeTmt?(rXNyXen4{R~pWwz`3=ALD*&XC)f%3|@vQ+|{6 zeGY%S=WB(!7o8amCa1%S(wB+_#Oi~e`9B#9mT+MG;UP~YO_|FLflJvTo0JWd0+oOH zUw=Dz^{YYpP2vext>+FsFXJV=L#cvoM4`^<^oyIOqG)W{a=VeDiaT6y!gP5O2U20}E5-F!z3G?U=V-;?nViQ(sCFjid{8s*jZ z5p8c`1d4QvMT=@lB9foiARRlp%k1jutvl`6>e-2%o?uctp(_x2MfRpCw#NDxiIT+K zk+RU!GfxbZ7$7tp1u>B>;1HoTcEEW{)ghAp*V%m( zdG{D6sV02I1Tq14JMGr(Zv?cg^(x##?mu4LiOY4gF%TgIRqYy@j)f>pVEdxr|ADoJ zjYz#Xg73Efedd=M>xx!%FLvBSQH4e{_3q*|mz^trUFS;h_&Fq-+1_Z^xXJI-MRgVS z+kTE^3w&BlZwcUMX$18tRDob!9s4%eOrl3iEA8S+&d3=}rcf*rtz=P&zTshzClb;a z4~c%X<*lLn>)7JqQT&>WroK;w0L2RJhsq)%2jqi@52eH%b*{-PNUHx%@l8e~p%~=b z_HW|P{usJ)SfFl|f=}I7`%K0THHg$SLN)T~Q}NHz(FRsZcM8Yj1n@{V;cy~c`M45# zw_<(!s}$^4j^qe`R$i_etSisos{Ii1l(NP$EubhSV^OUP1P=D~7zrwi?MAzBI*m-={Pk?+GTpx?O0l1mw=oALFeN#vh(Bs3Hz%!OQrqB*ZQpb2H<9|yxn z#gJQ)O1Oec+h$WGur%^;tC~<-I6*aH}*2> zEg5!oD5SW_O2y#I8J0tzIfD83uvsN~wTLf8$(o+qwm)Az4c_cS4)T}~YeY)deaJw~tSOux6l-9mMtb-KX zG7BWF_{UomG3kc1D&B)X!t-H_elq6|*4%n@Yaf>P%t>&Lmcv+d{r=UryzHOM z_IKcE)#rbTnwfMN;@!gyg{(l5>9&s>+Z@_HVV9KJwOSb3-)$1P_qkZ2klxj|XBFm< zn)fC`SC*36r49Y`9U=5qSIrxE@=yB6i-@oj*6U~-8N{esNNwZj;}Vnq@t*LU%_0HvxP$bj?gbaayiN*R0~$j3^S<<=BH~t3X##C(iXbU z276+^r=OVAzSJyHpcknpt3~4%9puqw{JT6LJ^b*Dsg!_5tadbLO zL*iGWh!X6M+Od}Br?z4}tTQl9Q0TTNs77pN^?uqo*nWimZH7lu{v(OpWF9l8o}EHR z9!6<1+`TNlwFWU^w^`S9P(&uNi9@bPM=X8 zpFH}9U)AieMW*vO_obvoowT`TU9S#xa)6F8B$>&BAmC{aILe)q z!zgfa_^Il|u9flWdM1b}UX$y=&do_sgGJqqOVk&yTpNn0y{_(C$gM zyJ{YutV%vg<2*e}oDRRYKI=EbxVGA~CFiCz={u}xxckodC6K&tp!orf4df`D(tlsl z7-N2o@S|nLmK7H&D{HS|p`<2gjLwNhnT&J2?s%_g_!eKhs9@@NWF>0X#AR=lq@-I` z1%&r^vw78#*p4`@m-uLKs5ZyC9J^Q_r5Uk!{yuE5QrSX%RknUUou+<{+(#O zol%CfJU=H}KGvoQz5Y`DvwcX*9Bm9On-LRo%1%ec#Q@Bvhft-_>m7H=#Tqg|N-|l^ zx(DG^Ct)Gx%n<~n3X1nK%`sow%QfiG9~f)N7guVrDBZ7XDjGMgSSWZ>aPh^blew4~ zfzOZBElS8Y+VYsD`ofwgW6igQJyOS-s-2iK)H0riwu& z8c+|8d}o?Q7<=sYM+s3p{jTRx2#hbrQ`&S^mK!Z=e#q_JjKOyx$hamD*oYQ#3t|-zbz??kb%O zJAvdzb<)@uJ5;!fc$!%l?^Y0z@;H$YDP_wgNI4c6BCp;fz62t3hEI5!WrRWVhHJQO zm0h+#LnLMnqDD2|Ohl|nRhCmLEYGiSm}<@_-Zt9W#%*w%xysz>Zo+vP%B;W0OA95~ znkm*OLBJBBSwT1Epy7|Fay2)$KXV&N{eCFGtX#uzWFtLV-iPxsV-CT=KnG4SLA?Tz zoc}ObuRG)Hp8ve_sre{1bMQuGLOZkn`8=C!A8T4ZB_VpVmC`WIYSsLrV8S7k;0XpR z1w{6>Eh{IT?^UVu;eX8O;N-P&WL>XU!)e+S-ogZ!&$Dw~UUzsas>uDhEL)0g5TomwD`iC90hCYY#m45`p)KeDG)c6zcv8Aus#%92*ZYwuVB~p~*spe_2WOB=8eKH#K{P**8E=Xl0z)GBg|*pwvfG_|Gl@FL$in zk_|M6k(;c4ji4JEL~n|uo;_^VTVrBZ+r!||nFCAVKihOfJffk;xyAx%h91QXzt3*|oW+zb-hO`_3l?8d76v-4i|7&gwl+ ze9(TJe$dBVO`HSrFOKToAzGApYui5u78NI$>wZ&AY37m4g)~(L_8b0AI+M8JQ8~Tv zzxye|<{4qLI7KKV8u=n%hOIzl4h0d@PS79Y-8tA850FhHfsz?u%!$J}o=R)RR!Fma z%d5fWf7tZm?)2c|=xJ_TTbbkMCIY3DpE^RX~9)z&)2}PxS%e<)WA@6Brm~LXo z>dWct2e#0+_Fv>v7<=Ar)raumq0r#YA66O(Y2gpB)+mJ}jil3AJe~$Jf$pawDiV+H z-9gSXH0&nIm)|s$Lc9`;jSey0fkUe&p5lDGYRJSrETuQandYY+9JoI$v*d;in_tW) znIo5ahOf!hFjCeRJ4Du{t2lDW+>goe15p+wt^5&VLW`XE5*$Q4?#~_KqLmUD7EuZD zSx4q#l{~o+0^Yt>PLVJ)q1H0u7qImi1~nOzU}Sw4k&)1nk;tgSe%*oV2W^eM0N;;% zy#v#t3v=AuZqja8a8(xK&XrGc7u4iV)ReD#A5BH^+yez^;y{Lonq$8D>LfM60|_fejuI(3c=@25o1TcCdJ;zH3prTY@7K?z!i{9;BBr%S)fOQ4By0=uHMo` z%L44XJG)LMVNJhrU=s?6f&)=cZRKljHbH2A_oIC3VWen4idqV83a2L2=`7{fBvIC> zVwB;piu-6ML{QsCS)8BWo~;al@TIyqel?F9LUzFyht7FNsJp6s$PrIg8jW$u-||wj z*lq&Jo;G<~oCJOa#;gD-?|#gSX&w*Jn^}gGjf-!};8zf_Z{26bQ;TeOkQXm?4*>mc}B||0`kspBiS}V_H2J12Fb39$?S~wsR=f75x!9*adSzYOn|rG1=QskWlV7IRCkg~g=A3wA$%EHy+R z(L}JSmj}#%f1VUK7V7_f#-NGY1-j<-1b-L)tJTM&U;TSN%u|YiS2Z24Z=Nu4X&O8N zMS8|e9R;{cjTn1qQ?}b%Jl}Hap;k13le&9sEmh5&pCxR@vL^yqNdteyp}ww;^m%BX zR9?Id7JU77F}R1obv2RkkfzW&`Rxj^*PH*P}sH zx;MWtMeH`|*T&FjoSY9dg~MDsm8cKY6f8&s+#*RNPtdRe)4#DqihfO><`3>uz$>*@ zOUy^jZ{GWx_y~6|u~&q(^z9EUk;*eX^EBAP6_*czd@4go1DO#M@tVQAx2(SH`r-M$ z2cF=^_K9DUdtLz<4S86wgeH%}>yI_BzA8vHv{|vXeH%6Z93i;*guCN0i~D!D=McTz zSO)X#%tLgvoa{qAD*w1*jreC`X0cd>ZEpvE@Ow-<)6AF3uzAm=HQ{g zpIB!wjE))Bd`OCaiWI|KEGFfZYf2Ty8ed4wja8gqv4qGfMq8}nL;LB>d&Cr0PV&-Y zBn^DJn*7qk8Pv+aS$MIJFklX{ zzZcf!J&#Y_#61R+HSzVXKqVm|aX*?Ho6CWixbd__+leDbr&&KDjZ6g|Y6xlVOe0ik zoQ~^WCPs+pm#LM^EL0RYn(BjgDf$zcp%Dm4+=!J$Xg37k*x+>>`N9uMZF%8fK6L^L z@pwHit7T85zJA&+b4G6WD>(@*qi8Rg!EgWdcdQ@3;#T5cr?-ZRJ*9qjiH?mnEm?q^ z4xBtxVv9H+ghpDc=`HtvyMcm(R+)ENJ*KmgsT#_kHc3^_G8%kkTsvl{1Z<_D6IkM}7aLzPMM zj4e4lI~H!zVluCtl8(N>)S|$3d~LvoL#3;1;}+5$-YCU|aCsjgK!C1pa`ms*UjM}G ze>MSKNA=I)2lX3Pp44I;Sd665>A&1}gLw5o=9MSM$@61N#zyRj@7fzyD){rp&>R+i zMW&U|rY%t^0>Mb=6ehFHZ&*<+7YJdvZa&pj3@5XEZw>~AXe|RKiI94A$LFy0-z%v3 zZ2#8@vpIKr-4qt%U?5lMsLB24gZetZ@`VPu(sk>`Yizo+R%^%_XQ88vJ>)B#%mvJG z_zVU(A(dUVE*`YUbl&Q?*ibon2@h@h?E+QYf?p`cyyaeoxo2T<>j{+Gy0CDGP?@{B zH-E}-`Wxr%CXbd|t7cYKc$SJiN|m^NBF{zFd3mVYBQJvB>eXszo$htJ*HHy4L^TzY z`4$taNm?r=DWSo|oHghJ_iKEvBljpQe16NuRHwsihwW`bGcovHpw1pe#HLo)9zM>W zJj7bjT#!adi%Ro)U$r);OKS6>PXATljs>cYfIVGvd8r$zf0zq55M_g|k5ghY9xfxY z>P}{GK9Uf=nOv0Q_Bc4JHAt$Z$*QutvyALT2$LYtk84c*4D#pT+~TZ=DbXK7V<<=I zBnXR=BoI7qU+3=23W1x9mSegiT1qn*$fjZ5T~ewhqmB4i)h_=eT%6{|?EEqYgF{&e z=;;ijb}erCfYr15Ad?%~S z8Gx1z{;)UNvVD2G2#1gAwMruqA?p3Rj)^>7ELZ#>d7r~lcjU84hgN*7oL0iI7t77U zM3EmNJh2tb1kb&Kv%XSsy(7Fp(aspqGJ6nY#EPCLsKY@J-AMN9VqPN|LV|Jim*l+2 zslsTk#C@WpK|-oBOh8;h+_jNoXB)%VaL1XR{twknsYVyiXO}BcRQ>GSyS3|-yvxDl zfDd9~Az)XjnY2I`*I=}#j9fYzp8LpI|$> zD{Rijm?u=xr&e+^1G*dKLULGxR>ix8D{%Lw&qNH%_1I?Eh%htV+JoPt_(a#lJmOJ0taBb|Nt&el!5kE8U zEW8ih(%pbzejz4Oyoy_NQ8FfuPH_6Xua6GhYgz)GA3J3W6e#ae3FXycsFNwVeby+?jLqc0sgXWL- zZKB$8Q8N!PFYo`Cp##0r|IIIJp5`g9ma(ttF~D#tmJEu&hJdL5S{M{K9H`r4 zW&d5_|E20i6{G2%_@KMYyT4q;Q=ptGAyLR^69rC?ds5xb z!rc7zS0(R2P?Z67y$E7!m~SA-xu!}>FOTx=mpyut5l;pq@&6HA4IWRo&CjGaxO zqqDo#;6YfCa#AjP!FcYMDN(fd_tRB-8i9X(^{Pq{Y zRh`nzc0Jn|5WJ2$fS>C+f_Xh=UQn|RZkPV9Izhi!Q0aE|gBZkYDlrF9NrQtQTZ=07 zwomAvDn8k0+PV7md}BW}<`N>`3NIBqrvL>m;$&w1yK0KYr1}STLVxi!8`;uPCH>@u zSLDI6y;wqr$R1tM+B><*6i`?V)GUnNF_v(F%OTK|Qns8Bpz7vl4^ZdVrLM%P6G; zj0DbD#wv9CpsN@C-xDNAH2Ne54f{xAh;N5Nd@LUMg56m|eX+%J{bYbNml*$uT~*GO zE@X9_814U*m?X&-63g`nJ;gS4zLrI~4-_uNWVKF@KFK%o)0#K!6e&l9jq@RP1^R>S z7nQ8HZ{Jo|J5BbM%zUoUT;=k$-{mF1!u zmK=PZf%GY&qR;}ONRwarsxXu^TKtvzH+Jx7W)ra~8c~hj2v1T(DEI0v+^t>-;gfAe z0dnx<8k#h)FOf8q<#dcJv-Pg?K%)rAQg_X4zRqaT<84d&f?E1&7`-khXMI~rdYGnH z>5w!Q9lAsacok#p17pPFRDXTG@KNb=Et&$G<^w?#tKTexQj{Ui6y-8i3y!Fd4yHsxBoHPcX#JMgx= zVZ8l(zM$7{j86Qo0)&W|sM)J+NVUmjr^rns$!8(|J9eiA~ zWrOvfGuc0ndWElw5!b)C>lrCYa<|CtPySG%!#1VYpGa8dLK1=5N6!hOnX3R&Gqb&_ zUoz6EOk%@b+BGwq+c#pGhX12jBU4;#=v7>dty2OM>h;M2DbAbJ@YB9oow2j3FM)dC zU`-P75>yjK$&nT(Hhm?|=zx41@zq<4vD1=@c%?fR4(~u38A}vbTvUv;zn;ub$1YAT z7IH+nqptzdy5ZI|Ayq+3a9B{=NTCd4`{%be6*mi744cKH?aDXVESnB8TS7tUrvq_- z2molQ`Y%odlxCEU$5VqmAz{ZNKEX4-n?Vm@ggg)4mvYJ<)%LuoM(MgUfwK*5p5qiYkL)|wpw6okDscRp9wclj$#@p??5G1=?i_)Y%RQ(+Uy7|^f zOXBaqmf?cR_caz7f6fM1uIJ;71xAx0Z)g3~jxR~hH74VObVbEz<>e#D-fn>X18z)* zIoncUu^iddhWO&hZ2W{mF!yaEn42Ixac?oAOR1VO{0=OVFE&F@NVr%3%&9W8&|t0& zjtgQWT(nNyw3`nqthyHi8?Q^$`i{e+?c<&HvP)k^?+jg=I(*j1!%I1jXh5+<1@`6EpC*^*dv{wA(sY1wvD(!Fs>?k?M?u+~V~ccSxMw~eBT3+W`+0Erc25M;_Z!Afzw83IsPF^*Iqgc@ z1$eGwX+ZT52trcR(0HkHKN>mmViqZv!=-C#fp20$O;8Kj8;RV3c18NkFnQ${R8fI< zrRPe3k3aTz`8mU9b#jd0uN)eL*{J))ScYrg*=A}v4jf{IhD-2xZJ@?{Rb_Ib0%Nkf=E+uIw=4>GhFERmu_ zJf3$Q8%Mv?kUGH$m=IeGL{YBz>GdC^ruyKScLAnr2jdF?@b3eWagW#6?Yp*2x5E_P zDtD59gfhHribiLaL$QIDge@)kO+Z<}#FMwXv+$?D<{maMGc!{v1E0NP%x>rUh}dY0 zKuicVS*v)PF=a4r23OPT_B9051C=*fR@Ig$K_~zEnx7ti39`koI&2(z8}94iJm`PQ zKjHntD@an=Pve!wZb#S+AV^uiw3*hM?#~)%yjTPlHeCGKk#I9I(ir+HR;L}!~7u5-pEb>{3)=?eAmrc=8)s)rz()BXD}-9AtQoH2I0L)F{A2cL@wnoNUD?-s8;tMq5i04IheapXG+oh_j{ z20*R+%-}@cH9K~17h7z_OAT2cw=b@^z$TSq_jzuv_u>o2JkwziVuAu-9#WZ%#-qFK z$Qcd02R~CJOvG(J>S4+D6AK5mdbqE?O;6v9I?EQF7EeTy!4ERAFGM!ndm>z)UVhN~ zNRzYmtyX*<71V_d#P{R!Hv8Bb>zv?dbsZt)i0Lw<1ko&VIG$YtUW`!Y$ahoxY;+gS z*HOLT<+>)TtN6JX96z|@Sj3Fbot}+NFj#MX_Wcp6{sIRYiCTvoyzMVYAeG6LpHpf7 zx0SH)lNyWGkYQea*U<-lCeL?w++It|qrq(DCL<(Lsa+4R1dCxx>ld(9Sq=fE&C~x` zH8R!XeBM8{(%z2qJ4z@}AMVnS-(^%O3;gPgYH#oG?@l2OGld<+ae2sZZeIMyB9y~; zjl^p#TmHkVq;!zOytp`+wF9jK84`{i$~(I0?7S3ki{xfr-m|9eNM^8Xu~?2yPcQ%06R47+=%LaSl=#Xu5E}e37@)1dedtt&9sq-bLHKwGfM08W zzBiX@&;kBRXW+YBSu)@u(CcV}onNWj5q9+eV2#h??b*%dDE=YKK37$JO{2yn_G?V`NwufU;E9WAjxbjD}+2!|pn|N8xZ! zPEIbjT5-C41s2xZZ2(LQ;sApP6SHY|1PT-etu`RkZ~+`vJvJ&a7%u#{et;1|uOkeu zMF!TSrU9&_bOr~2UY&wPw0tiAYW@Kcfr!NK}<8~b(P^v^?b5399us~MNZb1XQ9&Z71 zPXSEtc?MvWDNnNG_P1A1Ma9K!o_FJc6T*OPhKhnR%2LKopic6i?*IgggOd}V`^!C+ zFEy{)ySs@22?byc*PWrqjQJ?71sUZ45D>t*z`MEucq()uiNplVRgowhKzN%TA4db- z%>oB>X=DA1?ICMxYjB;jsHkZDI={cLpn#5%@nEKW1yG#2f#vV~{#nkasksRDHo)GT z!2y>OO+Yk@5xe6TXB0y4Gsp^6IMlD9lw> zUVa|*0zjnc>+Am;h9j`bQKUpQV#QG~KSuzM$x28_82=^;1^u7>6!Xc7e(eC021W(E z_`JpYN&R2j;QoCLZ9Y%YkB*Ivt*fU8pcCUniT-(+t88@E95K^4dH6rJfLIm~5CC@B z&;?XM4M=G{YR<2B62x$wiPRc2I;~&czt7FdA+Xj#Da8Wv@BjW9l(O;Pjt;OO5Z13@ z#v1v+)JC1C%|{F1la@XP^Bdqck&uxwz;I2!1M^W(j57vUP7-GZP=q9Lyk7SK-n?+W zV8jE+Ks%*9qQO_cAi02v0e~c2Zs&r)esy|aKgy>gaSA@n?ZK3dg99vdbYg(T`ezDS z^}29yIEbng6%_%=OlL8&+7aOTz}k$&<)yI}K`BN5XY8vu@!2*XpMOt>efKT`Obx)( zCM6{eTfh)3hPFz;6BKxe2hXNRFxLacc!_Zb|E&eEvhun)*8101UZAh|q5my5FL7cx z#1u&$`_15{rU$TgRU;HBt^kck|MKk^NWH8BepfpbLhE7jc)Eh650HU4Q9c0FRispm z_WnKaI`Q%GT}PjDg#4#P;Pg83&}8Y!U$MEJ=OrX0R8>`FWvw*1oY0vK0KOxiKWxYX zvtSr3urc5@Xayj?HYI>ofc9%>EC7f8aDV?A;@^Q6BMR2^`ZdDXIG-;T{s-_}0EGhU z0Hcnu6cN_n?qHglkT4hE^d|N$_xH_}Jw!!=UA+XVJx7({`EBV6p=oK$ooNzARRLV7 zS-%^AsNhKA`tAAzfBo}k_%yvJr7(_cAt)$B33znn+AXO7?5L!Ko*`Yn5Z1dPAucW} zPwa@P+8}y@A(=@mr zy4YGR4zQ2_*ETpbbg=A6iWai#iPnJ)#?9}y)dCAaOg;E)$2lH4HaU5DbA!unPvndc z4TBUU;EVO2Z){fXfq+(+Hr_ZdTRNSs5Fv02C?VT)DzY;5;219PA#xoBqfyMT15L4H6|n zyB<0O*F*t88em1SIO0CAgFWNnduoS?)^nHGTh4Gj)LV6354MsZmE-t_eJoSmJ0iJ0XH0ra!t;$kpy*dEbd z0f-tH8TX5ADOp=letv#CIs5~{w_`vniO-Y$00^5Na|;ol7<&L3ZV?9_0jRUq@d*k2 zfB(*44Z}gdjuCBkImyh;HMrm;Ap*=W0Q4$LqDhMXfTeaxx2vD~(}FBFJGiqZ_#|5r zmauC_Evf_Q8xoozA9GDnH61NC4FSuogR**TVai=?sfO`z`kecSne3{tZRg5Lo6SnA z(*htzedj;Bz6O{yYwz*^-ws{PHgB)L(^ga9W$m9RAJ_&HRg~Y?DFkm5vNH_sbmZI6 zo-EAHW^#J`1kQi`S`;CnaK0FdqSz01M;{C%Qe)+PvpX02jw|2Tlm`I~x%-m)7 zG7&9@z>-}6A`{k{)V7`VVNUPwis=y%Lx7UneH4S2eeey=8n`=KPf|h@IDgo8?w7kU zVA|9-qlMqz-pb|nZlWhxHeJcJIjGn*`J<3Fk|bi$cswk&ReSYL7wqo-O`?hEH?-rN zg8SFNjfFZJrq2;AMu6S?OFCn{Z`}H>tG9PyevY!>z`A8mNN(yw9VgtGf9D#kjLy%W zCY!ZR+Rncmc!P%;O6Y?^u@^`y|@z4TO?___U zVm>Afsana;G&(vO1>C>FLjiQHm1egI@hW0Kmpne$X*!zj{W*&tf{YxDczM98hvVFL zcfVbDSS^^g78ig!cd*XqW>iDt6$sC(xPV8=KD@KGMYoa!ZZ!8OI}4bUG1Yt;ji!(5 znB{Ci_Sd#Zl2Mbe8A`e5RrWyq#B1IG&~zE#F97WltB<6;22P#{&Y8i?!U78nws@{H zz%$n#al%TejSK#Yq5q-7P5Xsx;A!E5KcT8@F~x9M+Wv{t!lHNIxBf7|4jU;;`TYJo zAOFGcFuZzp#g&UsTJHN*Y>dh#LwB-}^f|?T8jFqf>l+~uGhIl0{n`#T!B+3HsN0LC z-v#xRp%m6psqiJ=0_g}v#iPmJo^OBDI`p`*-fpe|p`Vh{L=Vl}pD56ncIKfUlp`@| z*88=-y$xPIar!gqUgP;UvJgxLue(gMma-#l=UcZYdA_;AGsy4LcB3U}t=n`z(ZuB6 z?kn99RnsNfOXnvIIqgGGBynzF-`VkhLy(hmrD*7}Cw6l{Q%oimLsWbAV$1|P!CGc3 zN@^YUfd{;p@$G<_+?2-tnt8d{n+nt9fnkRh5YO_jL` zOP5k2Sj-H(uvC649<6OIW0TJ9_4yiZt!L)u`8PH1S9F$X`-Wx5NB1n3d(~BDUS%uyTh?^TT?c6m5t%XAyGb>Wd z^M?>sx7 zLPNey?q=nGchhU7Rjj_W3X~Cl#J=2`%LpA`NAEqhJ()Tj-TS@}lkI{-xKr9GJp5Pk z>Hg1>`>bH5ToZ7_xBe=t8d1r*KCu3+>$BL`?ON)|D%zT}CW}zr)f};K1^z=>zXpRU%`G40ZHIvnU8g?yI$rPwkQumv2P|kY!$_*NT(2%0N1VO&#>=>^lSn2Vb`R^sTm7H768+0Hk$& zgR!3yRsbfY@wnVyGW$rYh+C8`H6#96f#w>AJ1E3`tCimz7g4cd6v4Dt}=< z##W_n-zqiX-6JP8<4JPNUh)iL$Uh$iYBd64Sf4z!KjkX!J-D1ZTC6l1*FLS+CCN=< zaBSf)hHg79Gkx{yxjH=G9?FtW*l7JLqVwkqCN3R3mE3Bd+-g$MeFa_OJHO23b))h8 zJX9FocF7?-1KymcyY|PuVls>--(qgh&=e3U&i>%!+_4i^WS?d<*$Y3{e#+m&Ioah_ z<2PF@FPas`Kn`WAE-#>j()EgOFVlP^Tk`ljo9Q>Y81#OSaVN=_78Ia7zb#RLi6Am1 z$(2@qV~0P@cbV@dFMUHAA1dyjFntXd_6SIfbI2jyLHtFUT!vCS5K4Ng!fU8O4!L4< zRES3XWvcPc^#1W3$FNNBlY`Y#WqyF|(XpnSriPjT7b)ql8G5ln;4D+vb9(&fs2%Yn zv>yC~JsGyJOv#V+ja}6w$266;YHjuLps>)GUMqmx8cIdgr`JhK2d_qD18z=dptzA> zus@OaATY6vgx$t*lHD2Gm-0olL3Gf>9>@(M$g;YjOKUcs0Aav`1xb=q<9dJGEmo+c z?ZM)`PA^s7pWg67LXeMzNzG3WGA>!lHC@qKM)5r6a39`#D6H&U`2tH$PyUX>d+g14Q0(tR=4~p zcLhujdF^@JWx#kSES2XZ4~30jF(z0Dj$eB|7*5V>st!6l`+GAT`-lE5d{c4Rk8&`E zCzE2zK|@-uZu#_5s;D$~C|%-M7xISyQ?7)EE4=*7$LErMLj#%e+f4h^)a66nc}@?m zs_6g-%rOu%!pP8^=QT_Rk_oJxpAvV@r%jk~LRt^CYXhQrA)UY64Xq__upy9q&gT`? z*45ZS^&FSkXc{Nykin73yKLn**V@VDZEDJ2cAv_IfYRgMehE=$scOs=1wO&U*&)}l zC+8Kp>*yqzTAvN?H9f+E;|*LW#q8UZ*c%FVJ{l-s>->=KVTO+yO3p9JBvN$WT-7H( z{5e_ayYJStcjpowkxd50bZ%YAur z=KfyARFIw(6BEXtGxgAA*eRl?QOy639`fa9U`m4W7t^zPShAOOMNcA_VJK-{RKf`JI4&E-Gm3cV^$eE3re zl)pj(%$H+s)rNDd_ew+3OKv^C-3_~}IEy&{P6-IHKs_d0Cu8~p5a~FFJZ-Y34+mRo zWFV>fHCjJfYNFu~7oTT0D&^GTOS4lP72Xp3`FgMx5wAHf`QfPH=>w1m<}pd%S) zUX&cG!f>rMdA*@0HwW7~?>TSA_-iO3F~0Q?J}uV_Ili8hjQSzu{O+RGuSKj(tJoJf z67#{y`Nb-BO(Ub}6h-l_me=JwsUs;oR>>NCn&Ma5JQ{{lnvfGWSCU;B(G1}&V>67V z?%{*kB9+zxkq1S3h9fhIoXoq2vsLgIa4dltz7Dm5hYav`xHRLh0&P9MGrGIf^ae6& zJ{a_SP#n=#dx^d8$<^yvPg%TC%XlEILU{@*{mHCN7^i67VIuKeim}}Hl;Og-@Q7q) z<83u%>eif`nV1VjchB2TkRAKVPvw=j2R7@wbxmTZY5pxgmoyWf+|PFzX4_iRmHNpi zU*bzozr(_E9(inDn3P6?l2rU|gL4lIQ{sI1NXA>Uc15dTvEr4Mv^5&Iy3f7VTy#i> z<^~}`KV5(~r*GbFb{(4E8q@&B%(pGI1-Mz)a#GU$-ePZZIPi6`-zGG1K5d73tAxJL z;*nRJD97bNf@o>-eZP#KLt;G8JG|c!H`1$|*o+CriOYd`gNMxKcAwWY;R;5oJFct~ zs!~e{i1zo=@j7qL7aNMi1a+&68uTI1IH_IX51$|U!QGE3@C+Zb)evT;7p?pO%4Ir& zsR4Iy>af#A+0=^9WbFF@+x4T9?m8{xPqad8{c>DXC@chwVQfp)*Ej^N-7V-xY*&xl z9*4EpRqL^B!1>ND(FJ>8x63U3N%nGG#2Hm*V$ z!T7*s|IMkOU|5F{Pie0wuIm{ibBNzi-t09w6>U>xQ2$OLc~f}_hWU!oTm}}CL!1`; znM$*Mt*hi^ydCg@F+`wZI5BU~yj3is;X_W`3wU%xYI>1%ag~NiYyl?<5+N>{P zrR2s*#ynVLalJGVGqAyaB9Fq4c{;i4b&a1DY|m1og9Mhf8YmTIiCC$kY(o9j!c!v9 z9s@=?^rWQF0MWxBj}i*dW*N z+ey`;1Oej3IB-5tpe?ewFGq@3PwAT-#rt}QE-$lAiitrnI;fvK2SXW~TEbB_AE7Wz zZF4Sbs^VBs*eJ?rEE2|>kcy1ST7*&}!st>lh8T5Oc$?nk-8!?Vlcg6v6yI zRl@Zc8L`4O|Gw)3zH3Z)tcuHE+iOUWQl|86R?|&HO}WgFvW&X!6duEMq;8e|Ew=vKA42;IXF4H&weNBs&W`h0_x_ z&ee)!aVTpltbT>K2!W*VdC?W*VwtsP(DkG__2u~}6&kL0f3F97Byf-6o)eEE7J~Dh zY|oGKsH{OA>sa+}`z06S=_yV7!rQQ^YDd!On^~WiN7dYf%bFbxJeI=O9D7FPFg-@y z7$@R5?`w5~G&&J@?+;B?MvEcbn=!1z{9UqYXpr!8n(Lt4+r)1;a0=GKizq|M`bD~V zWK4|};cw$k&SZFLzPX8Pz_xZ4C-&^zZdNAn;S?IN^-J~%7AcSI-7F$PWMhgPLT5#V z&>*%0dmQGLfLH=j?`xVxO>SMx*Rt$JTsIQbz8PS#QqY$WKzoNDHlH2uvNPNH}{e}_;@wWQcWis2DJNuvNs? zh#TU&@CjZO+?#qy(Vhj;S5Ah9k*|xyQ}ZB?h(Qa-vqT}S=X@^Wn z|D%P%mw?w0!%6szsPeb|4!Yb7B`+WcT$7w@J=L;kXJ*}OiqQru5zKN?FT6J%; zJlUhWd)`hbmv-*#eEQwx+S(9T)60)&>YYf4WeSRlq#|2os9$Bjz(N>dWZKtN>4i+w zGqS`frsBVryP7xIay*h%2=zVK;y`w&r|%w8$KxQ+#WA^k)BOa|#OM(3OS!S0&tIgX zFNldzAX>s33~_%=)JAy8kza+Ax(m?rCn4Fea4?p7u$MivMjGz*83EaPWoz({RbKyc z2B~-mgfmIYIIYh=Ih_=OMJbV-G<&-8^Q{qf6rB*-moOwAAq2>4Q92{~@A!%8FCc}1 z#A3Hq>ALE=A0SwHlkOJpPS}0vsDYj}dM}sq+&7^Tevn3^;*@?czNg@6>af6&?Vzxp zs}%@6S3u04#U*))hWXq?5gc5#?d|ntC{d?`o$CXXRn|M{p z)^&KSnF9ri4JGm|B^=6`&eK zEM7up_Xq6YtQD#68HwAgN<<1blWZ8-3C7mvC#{^EEQ;ozxom!Sdcf zqNdl|pMri-QeoE(Pd~nrR-4RoJFYicz$=t=y-I30rxm$RvF4Q-K9UX%)EPDHkFW1A zDj{HZk>kRA%l|z=TNG|_`lBmu)7rBePD6QUbt2rVl;TsK%p%*LJ04r>kT9_wV)#aZ@`&d0wCw9Lzfjn!jC)ePQs$@%N!0Rr&j@ zzbIP9Gm@HaPo_<)H$D43RtuQa=DWeM71=~wBs)tJAWLM`a#%2yM$#}BW9(F+fG)ZB zHP-)O)orf@TyjQi>KP(_C)P&DeT+Y+D*tD{)ItNjxdQ_QaWPoO)u}4LtowUS--y>X z3BsvM2Jqi9Vxb!9NVW0`-Kh?lI@}ikq(M44BVa z;k9mpuR`|x?Ymos+&vFaA121%Q{Ad~XDTTWCe43|4qtW{(N>p+fshw1H`oYnOK5B# zJ6^x?{!w^g9D_pfh#(f6r_FQblkf&~R=M<`6g08eOcCUul3QD+U^f^rJ^Pp~5Kw+& z3wc?Mqfn^m^$exS8RshKGogBD*xqi6REV};f1c$!uFWoHg|y5eyhAgZ#l`c6r65kP zy%sF0P*=%v$S^4=a+sM`zs?U(zg|YCN%#8;F*wF%{e{inecf{z$k2-P{WI3~?dhvT zH``;}7uM3lJlh`daV6LnF3tQRKi!Sf6r}S$N8H_(6dB!R|9PDtqW;L<%FNi1C{FCS zWm4L#r6@v-JMesld%vHconS-#<)5BYHnAPA<5Uxczx zu0Z;v0;u|j_E_xEqN`S)aYNd*dGl#$bWKdpX~6gobS(9GCu{W-`)=-l%Q4eSp&!K}sg(J`Er#yA$mmP2(QUSiug(-0j zI&%qKhF(|;wJIY^NbL#ljA#O<RQ=_*)bbm<(K z&dO@tXwz=uswg(#gUSonyM;SSkvc!!FgeC zJ)(v?>em1*qbG{ckl!TKt~AVaAgQS%d2eg^On<9piaUIxuRnMy!FuqhdsfxLc8U9l zL_XKfz&1@?FevTsWRKryFk?zWqh8b)zW4o}x0szizgFXQ%OS4~-Q<>$WH16vf8XTlq#B#^+IXWASp36#Z;4$}}Lo^c6lBsosf!j|M5?U^2^# z7TVw9VBn^C`i=I`jNB^g?H{~18U@2kVC=oKOGn8}X&;*Z&Cp|Dnv6B%b5VIhyvZju z*Y2&|vQ?_EV=;qrL0T~1{NZf;j=LJ^Cz?~iYFtgDpi)Rp!5(dVfGiXg5zzcI^V1)8 z>aDYaqc+JPf9{|4o`Gb=e{#4e!sBcCaT%$LqdwbD7sy(52nUI^OijKM0jD7F#i1Cf zNXb4g5+?jJsptKE+T%dZo#pLm`%x8InIJJLO6<*HpgDz8sGNBfJR$-l3>!hT#o;TZUK5eoO>%N!Kik3%$cfXAkdjO=2Yl<>Y|oNaH> zN!G%I2ogI>DZRM3F&p%&7nx`2l`FJW{VR75nY;V>&Eog#ryAd(yDxdY3uu%!HLefq zf#SF6r^8Z8SrssmZv@A?m@Lse6lfrSRcGmDUHRuJF#>Nk4HfGVtFOd@BQ+OAL~%C_ zW1my1{>cA{LWIoTnMgk0T(s-DKa!LXZ6BSk>?3A8ld{Y2{=Z&;n>m&x9l(%2Q$HRC z6`}Y%*9dl*!6?#?UE^%w2^M)#K{qn8>k%T`&B>u7__>s(GY+V-YgU(dh=bx(8#TW= z@!Oy)r=*Aa%Gf3HCaFT$%W~J-y3vwC1t#hwzqeQ=@gI9_z=m%59~7Ytduq+iUO+2bIdK2jA5U~)KZ3sR-Pig=$NZ|nI6V&Y8Cm}=lSifq;W9P&`Z0Gd=a)dg|o(EYF22!o9=8% zr=0WZ%=!94Stq4}?q~N_HpQ)_W_Aa|sd`xzFD7Hv?#9j@HulDI%(dXm*uz63gzCex2=#A=e(Jq&L|kI+u6-xjO9>C~mYeEC|r0DRrwf zZU!C~p-7Mw2vva>oVQC00cW-6^6t=b4x1aSw_#6lV0B7ij%d(lQo4E1y@zA-W!^>{ z?2EVN_TOw0uJ_Z_trM#W|Jb2HboM6mqU4@LayTcSKPvH`KE=e%bevU)=S!+pKCu=C zchDNL&A??vF8b5H7Ny-%33SsiYfv{E+0pg06gsU-qI@T&>>7*j9CxeDUsgq+@9Olk zPZKGmX-f-bWPlpgo?&{Cv0q(19d3n{V^h&U{3D|}ee3!}50*%^x+3DVE6`>}|~(8P@R4YY&<40EjNg~gwv zA*qxUOf*!&ttHIWj(=6>*?g!K5N)Na4X0P6nsK7p>I#}8!sN{4rdp0|)9t9O=x;1? zJd*|!Jjvt5NTd$ZOA5Xqh|*p)B}A%sKD)${K?H4SYRQ!7(Oqac`-b8j_7yy35s!@o zJwFqVOe9-&XY&Np#BX6<8fCqSjz6`GDF3U$@ZtB8&M)G=&s^1CPYY?aK1|B4Rx<3w zt5+#2=Toqkiw6NgWvQG?x<}5xl%3cm{DKWj`u+2#3Ma-2>zkA~dwnLZdcmqyw*OYlWCT&f5H`8dp7>Cksyi+@{a}_&D%3VoUhq-8?HMdX46xi z`K|_`1zK#gdUFs32-a~S-~HE%{mAfbKKl-W<~8B=s8AOwpOg{!+G?@6){~-G#E$6n z!kBPLVhemj=t8)*mdBkXYn&j~Uh3V@^=luZNBsr(rgWq)q%e@z;#9YtP$$M|z>yw7 zEjFv8y(xTc&^=@BmQd7aa50!DymW_DZJpR(^k#Vo?w)`q_t0o4+-Ss8xsZnNeC>=n z6UtsKw#)HB9Z(4>q(+~GC$k=)ydp`Po4d^2o==jaITLBZsR}Qwe!yu>@ZawyJNn(4 z`c4IfdF7JU@+Rz?8>}#&`LvE(wJ*Mgx>TQZik#v{?mNzXO;O_4`A};3Gfg3~7|#VtYyG%n z@B+q@!01+!ki^UqUK-pPat21=^%Sv~VO4LC)cGm%^(pfRv|t(Yj}0~Zz2YaQC7r+@ zXM2Y-@|Vn1-v{a?Ff9(=uL-LAZly)ICYke6i2}#57S! z7$U#=h3Nm<{i~YRI55o)ugMen985Py3uBhK-G+j*Rq(?V6si#hn&P{*@FBCtkx%S|f-#T>LwllfaJQ@Z?P9 z!zFRYFCja|aY?!jeONQeQLBQU(adlwFY1%ZnX(DvUzt%)M=9AtBucLZ{codeJ2qU? zM_CarOZC=#p@HRE;#ygsSZmt*M9+K8ix<;8r{`HlLQO^*o849?=_S~suLO+T>2vel zpAW8XHO;BT1AAyy3ywzBktl80;Gj6Wt?%zQXV9|~13J}B4A$z~Fn;3Omu9$;&lcCm z-5lOxL3EgU6GT)Qayvh;p>d=9&U#8u%*D*$trn9t=;}miVEC08BTo9R4I28lKL$$n z&df#sbEPps%96E-?)}8v_FE}(NYI00)-l%hc1%b{r=}M;WTGS1LY09Ak?lxhj5qcy zefV!#42=1^+4t(yDtHZJaLwfK-ZGaq&W!|gt-ne8ofJMqWNh`6PdgS_3~w9ah_{fL z*nrj(koV~To~iST$PpEhK_14*amwQP)^b<+BCRG#MMx8OcJU(l(kE=(JI0IMpG)626bKEz`yvx!jJ6?3z_X!u^%dmGj4QA;(ypJsJvWi`paolFXUTX zhb=~5a&Jg3Sj_kM9-_zp;>w*bxoz@@sa+zl2=l|k!{SxcZ5)+Cv^C7XASpeuF!%2> zb8KM<&yoOvb|{hu_QPM>jf8E@JCq5g>endl7u(@w4M(2s@^%*s&+_Sf+!qTAWiyb_ zzH{QA$(jq-5?1PKi&C#P_^JE*7B81h#?#$r7??mP81`iZL}Q!|Q&(xvDtmuB7qR?E z&+5@pkOm7gp;3lpApAAyPrZN79p)Py>UBDHWyA{j+EQ4nH4_i5F~&LcL^o` z#C1CmKI1gJolQaYUao)YYnPMJS`V!ofniY;^Y7o9H6s;1z>lLI6n}%_PKpsIJfgjn zx?;uk5=1fH{0pS%WF#1YFhr~uhU${BLOsQhzZ{lw&j@yu%s7y@370>&FS=9#(u%G_ z6?}w}nliBmdzdH=uQA6}Q{7K=i2VW2=Xf5ji*H^Y+wRKpk{*JQ<95{c|5hLYTNov8 zg*Qw47lf()mK2D=S}JUUQh=FXQdUY4t1~Kc>M!c8;J&JPVC`6%trzIgf=yid&2fnv zG^B^C#u0gnNSLyeg7w78N5jGfEh}$StiE}_(5kB5{ysvrpMWe}6hibK5ts&IaVo^5 zxEqFnafo?Vq(F(C?Km1bFTCbTi^~Gu$O@sLt`_qz{?T?Zxb!~k$E1;v1zmlN;*4?Z zXU#|B72A?v1=n*#Tgcxua2vmaYnh{2g0%2x(Rj3RB}CL1?NjdCTRnQX7t1Zs5I^~V zLMDYfvhBf6|J!|?*NQk9#U=6nTEt;GQymQK7}X#+m(r?MBP|Sh?H+XA_^vz^)#-f9 zcH_@Bza1jYFNltDE7p{l%9SjMLzo^yF9xqmm?~bh8Ztm=wvpp|NhY~K-EQ{XTRYEK zk74ybh1~ra9YhSv{!33WA-CGxoCe+F=J#H@dpp)Gw{rD5>)l<>%>CX$26j)eD! zBM@(LR*d)psAyLMUDZBe*5O>&=)$!629*N?3`%Kzg8U6qi$G@GykaYL_D`ELvSA<+ zp-g&5h|dx|{SpChYIo7fX1nYXnawe`3^IF!P4=-_Z)|Uf#ozi2OD4u{(&ZFZjSnuP zLeiIS2FQ=RadTi=7u&GkUBZ$bIf`pb9NJazc~M$r(uHe7pRU{`9sPCmFg@_$Z?zQL z7S9=;hmR&_OFXX% zM_(}`4gKkv0X&7H#zE@tZg+m7T#r$apH9m~reziTRpvw}5(_X6JHvpcw3>NV#lj~h zt{yP7hcT8m$5SA{wt_!p@$DODoz0zbht}6!p&}57_~L7mN;=!^iptK1{)!4tm-z~q zzYWdZ0w}&kjbZO9`Io;h@TQrYK^Gm@HnPbYFoV0*DHZPokEMXT!?reRBPY!_+CK-F zLQ?z2K{S@*GLZOjJE*`v`jeg4IDQ z-(%uN)&hzL?MTrPQbqCe)cGeQ@PnDl3WFJn8gzBgjs=X=JkYLzqmsmq^H0!Wp#i@5 z_j^5!DABbA<0LauJsmg!UW@6ZhJ4LxXc46JwjGu4mp55 zs3~LGw>4VpToipPS>!ImJ3J!3N!}y3Yaf!;1P)Yuc+vB-zXn2~{Vwn>jzus%7w*r4 z?-S+Qe6Bh4@h&MzUo;!bhny5Lp3X#XZSX%WRLV;(70Tl0@#EwL(iXluqxPAo_qwFF zEa-uFm(oVowtUWFvPg8BqBfn>+jTr*5-k=A%+jbZegyWa|Hm$G%?`{|W4%1bMQ@60wUPl`m*t(8Z=Wl=@|;J%7Tzk~8L3Y7>DXy9!?TF36p z>WYha@wr0VeM#Q=!Fz+}cTDBs=PDl813^i&Y+ka4cN5oC;ni$;8bNZOYq~5x)l5s6 zrzl6{l#t%+Fth#7JAh~2q4(!l7-Mj+ukMkG z5R&cq%nhf!6W5tjyJmM(5XvbZ{reWYrIZe>L5Ai!A$mnzZo9_mK-k!<$yS{?F$I+iHBvf=R zs(vNDD9%)s&^Grc@#puru({2K9*^ae>_X%5AgMQ^55&GfLq(!bEy${1^GAwECj$Lr zSem51Vu$8=kK|RPRe!AWBwj(%L~Tj9S40!$yRgWP;KzX;mKh-gJZ6ZC#npfmC9#Ox z?X*8S;c?XcD4z6*h5X>|NjscmMJ4h*m|E<3j&)dNOGBUGj zN*kJB;I9aWdeZt0q`vFn^k_!S1S{*3+*kWL7Lb6za1YMfkGHiJRuVU_3`+mH8!jeM zX9nqB8sx7r=I##nD~lJIAX62^sU$w>%NG+w7S`(_EIDPc3o_dnfUfEmvofAfaKtu) z?GC`Yan+gH%ntbGziF`qGu;JZ$EW@}JzvNT-;J9Q^t+N{UNgDGXt|Pb6XGW7rI*9f zGnHlB?1@gL%VJVRiwf%bXHJ7jD&D?Ow-wR`mKtOU~OERORtUcE4&tIq9uK zeJ%y!m7E?S=D(}JJSnT3&Lp3*9gn_*0JAlbBGE0>@_4*dp~2%Tt>{o{i4Ee0c3Ysl z3C=oX+($^=p}q#Pe#XWQ)RZN|bRX=uO!SgwP|E%|wyKEazw@c@?Ru2V|AqDjzp?K4 zboed<|FTq52vh;gmT2dT*;~w|TrE1oR~}Uvg_K7uG^10Z>JN@-RHMxBrZQ=tExwY5 z`56w}@UT}=JZzGrtY2rkb)FuEQUB7G1G7R~N5`ve?Zy1vOAb2FJAMptvFoehc33VJ z?$kV!i8KrNYfu56Cj*26ffKs~{mE3=kg)j2=e7H<9Aq|9y@Edi)u2oqhv))!8o>T7 z->(obJD^U>C-!)_oJ)>;LgdzTMPJz}cSfLdU$+^{rl_BMDLz57KhKUUEA>KZNvb+=2|h zSPH+$khT<&rzfmJZhRpHAW=DUH%cD=8Dlv8(<&~;N$cpM(P}liEdY+X1lkZTU2JEM zmtP>^@n{zc+;ia0!$N=|$r2&Fd-T{pVP%Y(PZN0__J9x8`g?wJ@boY^d6!t%xDgn| zKTSYo8CfAB-uCb(*hM~g8#v;|H6=vvUV}IR`)mk}QK=3|`}Nz%+Jva+j3hy376a;{ z>(V|LwugRaA8SI*io^RBB(>ec$SP3Pz9EIibkP7ovrfW zV$Rv`HaYiDX=cpVyd>I#6<-_@9~hG+j{gq!|KN0);+yj<9*PvRq?;RQCcn~2yfMenv+@{8ME;rbH9Q_4exMR zgjZ@(O=SivjGV-TNq+6 zh6FS;xqEas**}4-)TN3PazYa=!-M6?Pa075*ujdwz~{k-5G3BEhjrG7uri^QsnTMI znQ0V9zPl184$JKRmDT;L4<8J82m14+_+mZqFs5C@Xx*g1;h~%^YN6g&^y9W>mQUuV&?W4G}eS*_OiXz>$K7L%IH*!E)R0 zwHax@Ya04?hI5_04ujN69S46@u`HY_MU>l$QYV$UKUa;XKddd%GQBb`+}MQ&Eash!n}8XQ;oXQn^XbWPYUON{qDgXL6_@$RSU}u}&D(B-mje31WifM=79$iQavt{Vpfcp-p~X zDu9MyQJ*47hH4G{*1o|ju_S4f7`%K>te+Usyf}qw`SW7dk7R#q-f@@5KD(xU*af?f z@5!T1SKvvdW<;pbHT;395+>BrI<>P%oY!U`qkN#L9$QBZ7jkfcts$&Fqy818M~*5S z0s-k{_qAv)Z1WebjlA6}wckt(eza$lGfRqy<$y#UV+XmYKirOCThQNVI|;b%dBsA% zzdOUO@?^Mig0b^Uf0Y>NiOOb$QJ^18%-OFlX$Siv{kp28HD<^m`ULH_do==Mq$u7pE%aJBr1&(pC&vsmdT8XE)>*wz0&_b>O$%_)eEB46m+<_ufF z)u+;~F??IMJ(x*IeOGP?mKh=~tSf!8f$1l?k-v8-cdHO6%1$w|T|Yh7cNTmmg^`^* z3zvscEti8@!@WR>tc8IT78M=m>b3eAR-(iimTOoPdk&&Y=$JXd&Lso>Gz-ftS2yU+ zzIBejcaGnVRyuyu3n~#F)Ugd#r7xf4<_eg#M5taVM{%)N8TfU?l-nJTOTe_si?E=-ln8D3^hH-Z7A@^NaBAhxhAFoa<(dqlCQ-# z`z{LyvlAujl;yK2zIXhGIr}+)NXN1mn>9XO}(FV#}b2> zBi`K3>fqsSuMtLNg>m-Be~X<232IQKC~iq$uBk!w5v@fH^P+Um8mbaje&3~M+%| zgcO%YSTm3f04DQOZs*eGzdO6(w_wpVtYYWP;jU0kEf71>$xVB%+IahHq7b32g&(IW zO@_*4$6RVi5xo7yezje~rmd~5$@Nmc4*>zLeN8jR4Jj$J*8Du20Mo{0LY8+wbjFnKw3*={%cnd+7U_a`}nYZh8^-<>J2tH8pd4t0E84jh=yemGrwCk8q#;X)PUqq#F2%L0=VtBK zweA>s#Ft9eN7{R9yza{-zGY9pjaV@qONPXxMwwVk_Wx88vHvbd?6-mynP6sK;c23h zT0Bl(dI13i#!^C?gBa%D!aN!LldnL0tD~n^em}yqED^(rW15;THq_UY`{NNN?iC6? zBLjn+6Rnhlx`i{iN7hotR9c7x0c{8?g-g)e`E}%g%*fblp8q8i7%xkdyZ>jAEK*5! z918~&J>i!cVpF2Bt@U6aX);zOWtXK(;Wt+Uyd*i(Ow@uNpZ)J?qG{F8K76Qqn3Yq& zC`vF5Oz<#|a37@A@MP+{e|1bI5uJNAXuzmO94_OxN5;m33fWk!O`gD*I`F5@${fdD z$GBf+uidP{J_1c7oL4x14_F8DR&URqW>OIgAZLdRo=3cwIIo0PIJ%^t0tJ6^WWKUMd(bP7t z$10Ug3S+Yh>i!Lt(Sn%q^cN5vT8i%#?C!lJx>0=ra+TR{PhBAgOzsC0p9;_ z3h@M`?*|p-{#`yxmgC-f+xU8;n=3kGnWqWZp1Y9v0Q~SOZSfX^!JEO`4Pyh{9U?bdI(}Oe>5>I9(5% z6(%Xls;h^^q-1YRaba0I{@|ffH}ISR)&zMt%SFtnQhuteQgx!gDtmFNpdBU$Vdc4# zrnk;XujV;R{|PfRL~}!9Ce432!QiK<_m6N<(n|u~+g+ay9}aAArdY9R3y!;ww|T4c z^foV|1H}(FGP@Tfv5Sr<-+K!$R(;Nrmr3Gna#*O=Cd`sP4fG$RQ*jcs4DLNRqw-{E z;T2!1w_M1Z@KiKw%5R{Q7$KmeO5u-R8${~PuXq#H?T3u;r`mw5EAh@yZ1En<`g9PZ z%EAX({Aich77ynS_hDH**bGiDA(1if*YU#Nz$?g3Uq&T_!BvepUD*2zo?Wa6h@pNg z&d8BiFg%PI@4lya{<@``jCWi7B$pSHo;dg;u`s&D2{X8MzjJ2BX=gNT!1IK_h1tMX z<mCVLaLH6bn*mk>kbY+S$czN%~w4Z>4BC9fzc3+Pl|5 zVWEeE6jQR}ePnb}J+>MLH;312$X#&UN6VfXZDpm%87At>v(qjQB&4B#E46Hu^A;q& zyYWuPS9>woD%w^(Bj#tIJCs+lLhB%!;{5P2qx+Qq%|R z(-f?DNr5*D>NB8y3A;pAT3oH0rjU{ZG`OZED#yJTva& zlogIj*R65B;X3D#0+&c_E#;WUa_8L$1-BhT&OLuG?JVY@{?$L~c&mL~~B<)wN(UuBqAf*}wfL zNa-E!9~wlL`%;C^^A(RK?SQfH#cj>1!)ZgF}*s z2RoI^PMS;yOO~9VhE}OfO+!P->wdE{mhorQsjsh(tWhsaH1jC+UB0hyPQ@H&NJz-z z*L&9UV|zHy0$@l+M`bZFF&%p3>Cev3!T9)@673Zn<6sR;z!>kKs9~a$ zSrVAW#@pbUem)ll*=(ih#M9$_M0mKpz5OJzu>sf%0Lus&pYsB&aW6#3)bVJs&T^p|pppo5M9afB9ex*H zd@UHbKAI-*mF{{w#Y@?V2uT+}xzJ*m&(JA`lp%ca2QN|sC>n>0b>}^VUd(K4Do(UW z0NJ8Yqvi-ahmDQB7m_suGLkRG%5Tm`_-@^w>K*nc@kKk-8lAxcPr*rnPYM5J4PJ(& zq_h~15D*d)^6>Bg8~6>}DVE`TycpqoCM74&2P}iP@L17fv2KLH4xtRqzbd(;{H zx$=GU{SlX!*K=$v02RtUQdRU;r9B$U@GUAj1XP;>6a_vWsvpKb5T!UKW6%4;n(8nB z|0zmpwcL1ncexL+J(QeiEs>Z(hI_8l)O_>QNhPgy1zay;QGV* z5(gaN9tAV=1$bkTA8?VloQ~W92oFG8nZg%c_$MTbwiYh;o)CP%uC)&P(cpKG{P#Op zKWY4kh?vNdhyi>R!35Ayb8~Y~PQDSN24mvBxkDm;XSO~V$5b>HJ^HEIY6%AyccK0l zFBIb+AtqssB(N9_dI5$Nc$n$*ux6#%^^%&JI)Fbdmn(W2=|55c(2|a?bWZEt!F$M= zx3i5dcCM~>0I3S>q2SejdyrLDHZnG51L$4=?FYE5MqLXF_gg~=2nYyo;RS$Z)cbeT zvasL_tLL}P&CTFNb+@ab-r~mg_A~fT^XWpX7N5*4MJ^HN9@K5@N#Ep!$faPVg zTAYQqKtK|Zk+BAtCxD~ebam6}^-YU_V*JcZ)ADc@n3lGhE)+P4@5iZDVT9zvKUZNK z3Ml)3zd>kYV*>=#vJX%W0EWf0m|q<&B}7DI9I2|4nQ#!j7;|@aaRD%GRK&zTB*_c7 zFjy4S)J(0d`5g8XnUAa>|2UT!{dl>l!SQf@IEhORRK?(buI}%<-JGmmT-XEIx%*54 zv`QQns|8kQk9k4$FL-auJ3>~A*)S3ja3nPUaW(lWklICnu4-sVcBK!%KLBbSfCu_s zUj3dr>JG?j3bzYz$x+RzC@D`aFCW)K`6h8y2mYOnA9P&Q z0mC8OL9kuFZl>lZNDQhoQ$j}lZDk>^# ztpg?e*ReZ=j*ClkpP@<{ElLXDgaBxmgOd{qBBH#C4iqj;*71);EIHhK5F!;8glId;5P@u&m5RDj3^`{|kwg`@eP#MHxlK$VFEuW85Ne zy#M~yK}Sb-5Skh+2_1yZf0V4;VG;sVhQrSc{OLk)!^ucz3j&$CiF2cBRR}v!8Rse(J z#y0!})4>5H{hzkJGN`U#>+&EWxCID=;2PXD1b24}?(VJug1fuBLvSZRaCdjN;0|;0 z-ZxbMlPGqYyw{2k?#K8+S3U8 zi?bh2R^UXNAxJ%Yr+^A$Jht$1wcz1AEBA2wU1JB`Q&&eP zg-rTFx~i<0*kA3(m$;&Faz{or=7|}Jq_uQao3eQ2kg>qM=a<;wx!E_5w;zvfh2Zi? z4KNwclukW)XQ>!w)4*`*QO2F#AmSvh{aBCJTtx@!v{^4VRJfIMfhWOL>eoRfgog0y z1FLS!KWmUIWqeLwlF>G=8v8OA ziOYVw?qHJU?lOL{-QS)>hqtQkGVW^eso6@#p&q)@b41gF!tJEV&Sd;0>?$?4H$sl_ zBi;98PKUz7g@nCr6^Kh}HbyeLZn&ThT-KSZql97UMvc!N(yH#sm7h!_wWkWnIqG*> zCE+rAM728X>nrEuuaB!b*=&@88>B!9$or5uj?61s@+wjFR-Ej^6tl%!Pbc@*>`-3U zzZ->3Lf|9$aNGNLo8<)hKeVTeyTYZ9%_{3(hN!(0vMc8?z@w8YFFQM1kV4c?{iDZu zoR!mLFnn}y!u{?{mBZRGbqIwjC}ZX{l`kFl(Fo_jh4fN65 zaEZx2h# zy5-fn-;61)4KSM@c3mJ=DSD+k43BX%$NaMEp7E(;)XOdAlqH(k*4jwMex#$B%+BW1x_aot4$^~g z+q%!2)=xcyszVsrxPM=Onyzu#>f>&mlKn+yZ3gr**E6G<@4vZZ<9S%q%h>bpg7_O7 z1&j5td?k}FxPi%6p_dl_*@49{>o>S{<>^S@DayD}0U$bf7Oz`1e|14UCF9`l3tAdy zD-HP**Mm02WD49{xuvJc7Yr+}ZTVs6mk!OJsjEmG9b@Dgajgx(Q5;)XY^>E+>Kgjq zyD=rmt$&v8k!za1t%rz{RJ6%^uxb==NrLe`nq!-`r#mgy|7{*1{DMntV~a~n20{JnEV(-gsMYo+jb zZ6c9mIzOeBhK=B^B{c*s^9X+?g3rAQo{yueSr!*}D6Os`qZ_jXKBYh93 ztV*r8^5Ot&YqHv2)G?aG3%2x&ZmO`(f%k1gi~G45Eg zjgzX~vLlgxRT<6y{u@5xNUy@P_1kp_SlpR*Yxq{jn?&zVifInj8Z@2H$Z_xF-#QZm z-cP3TUr!dygp~=HhCVH-Cob}E3WYT7S%jjq3%pxtaSD@~o;YRuTojcZjN9R+wsu@L zg6xI(BwIEl8@0%u^t97dCn&68D1ZMexFW9<{@3+mc+pYn4vcmvZ_Q`mK!i;xk?0nw z0qPeqNec@Rsr&iK$-XxWk0L-B;e*>@q(sJ^$!wdrn2gCs`Ug)V-p-`z`nm0(Yo#k} z(!)eNVD@vAIw=*&>CvrFXFbrx&_EsGqlxOSy3|}sNOFFo97`U?XK6{qrl+1$?bm;F zLbp7yu>i@ZE}>??HO!xDQPUdL$P0Dl& zZsAAO(HHhTS5(y{qFBufno84|nqV63SyvOD4_G%-K`XoS+YntMQPu#6r4M#@RUCv0dB@Esf|nDiMPXlhmMyyM{Nhw*^rWuG-)s`}G|1qn!9 zhddf+SZ@gD@jE!SB2i4TAPAnPL|*@K$LavJ;b6QhQXbF}F^!*|W^Y))n`(;TkZ@Tc zB?A^vQ&Kn(X#l_?DfD-?1zht1y9a2VphTy#^7io&&z;ZQxI~)3?_s;;tbA~SR<#aR zR;3VtIYg!`H#Ya>IHf9U7|TEVE3w+&cURXjG$kk#yNdHR>Eqmh>fTWy(wH_$`j_t2 znMtt^$LYyBoD-2=hNb=)-vo(&NZ-YkkZx7Hlle@J2_&|pjBwz#-wFTWpzy+NA8$QX zM0n_otkyU%M+ClQGK|t6Z3V4xwucA0%*^J;o(W^Q-?8_(uSHQo`)ZhYgHTX!n_tK7 zsd-$-&J52-x4Qb?WPoj5acxAyqa!dtpOhp-J=;uz&ymC!P z{(ZUiyn(ARxB4%B56#qP+a;xaUH04M4(a_!*QXGrmc{$otbI*bp+c>y%NbaHw4_pl z@5g4N6!#3_r$F{s+T5?K0#*B_W% zL%-)_w}T)(OxHq3a^+X4vRgELJqnRcxPz=NnRLohy(e5Ii7;jJB0tYufV((AX65RYT|d0#COP&L?6_^7_F?H5GS7LR03{TLn;7vILrvQ&rbp%G#j9YRV;Oo zu6E&|OIp6~9o^ka=Gbm81d)j7aPbI2l9ir}wMiGk03r9U@+$F|RpN2|Xo&3Zavr~@ zrt=+{U!K{bzstb^l`2cMc>m2^Zwx7Vo7a4x1c#%38?0Vs_8GGa+EN=`E`hEEVg%r^8dO z0VYL$nI->bia+JcWdZ6LdmcYMT?=bHV-Sdc%&3g&Nw3Fq!Rjc|U? zpUVP-H1v=FykZq&hJ`?`86=;XS;_BA_>zq))?!z)f?@?&KnXoYfK;s0k!2vR<$xR8 z6Q^|Ag4$AH{tT^DDij2cLi#q3sXBavjDg-LL!t@Pse&+(69;_Tm9HC(am?kP*pn#Si1$FPzf}<6b zqagr95>0>*Rs6BN-ytg#6~h=aU@Zq%gUQNc#=XK^yYhrbmJ@Nc$U?(8#;QB;0jvyG z8?k#R4-jT>n5*q;5TXJeoM6#K;_l9$COLxd0SlhT*TLH1G zjhR>?WF17P|0ezShjG8Mp%IbgHNgXxH#i_(0{sv7ah7Y(=oZ|W5SH2zD2+GYF-X{0 zu5p`L^m)1!*O_rL{$Kl`Wl$R-zqtE9?WgL#soDSYK7#kaaZBqRVTyJ{2M0_&1dTks zpR##A+wsV|aMEF=O@}pvr)L5RpGmD~LcF92V;3T!3TU5CNWEp4G7KVKl5t((0G1Kn z^-~txj9xGMug8NIQ~7tG=P~OO8*{TrUSN}sX8+3p5`%U@1jP~*|sAT8%BL|q&OdMbbI-S`ujXYy1QNYL{{Oi6Q z0f8h}shEgpTsE0>^M9iFt{BH;jUe<9AS!u;Xdx>_tw@Z+Vx&ArP}Xn z-f_Sk`BTY)+o+(op9u9+NR78wf3%{Ppt9fNj@@WVEJ`2#4#p6<+UTa#K$kiQCkzGv zNe_ii?C~;IlA!j%)gv~Z$EcPMyP35O34Dw#3Go|fg{rbc{p)Rv5YwB$$Wk{JoBQ?q z$)9g+n~_da#|95>#%*CCg&E(rg-|~Ng){AO12S1LwIx*wvTywmg$`qVG##HCM|#4g zDew?SB&fHZMo$9TCBSysRwT6#^&0H6Q+|&j0brhjS?}OALQO8k-N=G}01w>h$sq}#-I7 zqTydp(Qa`x1Ifh^2w1StztQM}YGgvsgACiYz?5rUTy7$X>k52-9T&0aB4P~UMboC(OS7zgL?TyRdeh6|uhAjUAAdcL?NQIFcb6#HV?pQE$GN%aC^I%v{$6+gllaHRL9`C~ z_MM@62h&r|!s%b`klyj-6lY96Y1Jl;7CoTUbuXfk|N69a zj&Xhlm>2_VZT~Pe`uE&9#?Pny&h~;c$_@b~We+zjAs!GF(Sn)Nl&pXJ4vYXVajT=C zTsSCRdu%JSTjCEXl1x>eQcAv3mT$<{Avw09G+sY8hbY-vr3wB}|MK(sVys@3Tpq#N z$LGMLqTM6CLSiiNKS{1^8ZsLDMpbq5ChzqSV*}M_?2+`3`?;^_Yf~(i!7#QDf|=i? z6GZ&}^DyDl;ukyCb+=QV5e&U=-4sVrW}s*1=W=)B9x3nQ7#!F|i+1ur|6*XwsiGk9 z6C8;g%GOco$kWr6f2As>(WIladd8!s%qrz7x;i?p5~_U>2NfU9hQ;#T+}VaXhi;%F zlwNLDln^BYZY$H(>yItjH%5**n3p2lCYil&-xBZkTCy1=r)$G*!fDkKVnO4MWt7{( z<)Gio-S6W|Xl{kggiYvS@c9q9r+#Vh&RM#_E2!z7HTo&lni+V;?AYQuN3kcOkcwSu zc1C&g1o-N`{To4TK1v0dohn@4la8@A%J4W<36s&}<8n0{@~?R75JZVD%^%cJqt!;L zudv`{E?WCjVE?68nD75 zvV)(@_Ir<}ApN}+2M|xeY3e&kvtYG~yO{WS&&ZA*A~xXsx!CAR`J%k^Hw{2&!P=H9 z%>3)s`f|3qZBXkbBRpIotn}3@=|hI1oyU(4K%2~-H1CJe{1|fQ$@$rAXi^YjBn<{R z-oBnVOs5}T{x11MrUAjfNTp{~eIc=|bat=y3aosc2q zW2UnAfpTFQriF^^SQzx$Err?OyJy}%^GyCDv}jV#`-=R+Hz4372r zPyh`0U!%5Pk$wn30o!_E;V|RcWssVDCq;u>Z}qC=4#b>s{$(uw z!IYPJ4%9)gbHh|ah%{HYO~2P43DLT?(@zvsr#FfNGw}jQs3nM3c4gDnC-CcE)AcOJ zj~1dKzXeB-S$7T2T$-}55cZVe97e1G)qB%s#FN!dIB!u)8 zx>wJM$V-iXrxPAR@SlMc{B=-NqPG1;?ews2yb`8ccnEx6-WTr z$gX=0WMyW|+D_al6& zw9ns1F+^FvAar1V;5Ft1srhVQz>LF;|6#I`dwc(Wd&hT-v#gi(MKJ#67#T6$Vu6vU zH`mj1$nNrCKjEnzfFsEo0tq!pG0d^=d`jD>abD&Zj?97c7I z{+evXd~~FJIB_RLrD^wO`*evHQ|=g|bW>~Td+18hK1^^0-h^X;hEMII4U>5~Y3GK; z!ko`k7L7h!2~xK8GtP~A^tjnh*G`G)qmp@ne-Vo++5g{G4&oDQy+48KDxacZ%Y^+T zQae%}pb4(vzRk9+gttD`zR22TWia8l5Z)X)gWlm&B;TQ51o)3n4xIm3L-+b1QI(dw zB91!&lnDU-EuO#Q_Jvj`PklkAErk-i;!cXRu}UgoBMU49vJM2YSK!+Bg1`Z(iL&n; zK3=n~y^<=+#-TTrw>ozJ>G0*ki@JgMpO;Iq=j6XD{Rv!@X&7KI$K3bh2ke)5n$~wu zh~GiR&rc&$rpqVGH^twfTVTQJ=-{{cwG>fSr(KliG#(;2XaRH;ezu&Az$V2U^Lh;n zD~nb0Fy}u@Mi^zvMf>4acu(9JTs%%f85$KDWfgM~<|T7nQ*%hd-}z7t5l~?@1z^Po zGVh+Y7qKDRXaYvp#xGDj&b`jP;yj-BZaX~A`)x*OEauqi#Foi~S#0iHy3xwWD^SB2 zN8P1e7+t(`%fQ!B%y|Qs&==tbrZKMWy%=nT%fGu9b8Pn&CyBrVd;>Y{tegj35rHZd zB`PJ2x(w7~m>ea1rdszbWHFj|EaqC$P4iCFOjU^zyBTRWu3- zkuRMJ@}M{D~3g!VC6WK;J)Nf z{>`n)+`N-W^hW0(;31bFn}KtZ+BxxDl78uNVoF|e&^(xsOL2=;ur3VpGVWJ-hp z5=Iep>QY8@6V5f`I?av(+HY+zGP-ljr{I5J$8qf8=Tf!8VHcKk6qR($m&CGAqO%9_ zFLLs7y(m3ALkYXW`Ae*ESPj%+>j3wj+B-`GREb`ykib_grz_@78ZI|!S4_-cOt}hFbzwha zCWu!s)KjPYwpBH75MecY4|a)g4?s{iQ?=n74OvzP-A^C0YHNk-vOMG;BMF&^UHDIAI1f?m+&9b!XHYQzd^m|6RaO2 zVEX)0Ej|m1hh|fD_AcTbF-W>lcyarySLHC^pKhDdK5CJSy$6^wa5%P+8f~~42I&-w z9rqxJvANUd7pANseJLR_lhs4yoEmaPhfvYW$}@NnC35 z;d-<6gzr_>P`<2Rh*YOVd8dEz^Za|qJO$)eI!l3}O{_t#Jw=dw8`D06Z+{Ojz0S$K zQ@2NU2iQ1)!yLLxosSk&hD(By+l3-mzy|#X$gEf5x7Cr$bk@v82w!mDoTK_Yp+w|U z?p4rRjmwvQZQ`eh>n-pR%$)*fwISXoAyN-(+2HwT>`V#cd-%F-x=q|e`EHcjjs-qM zjnz!@vBVxBa1npI@ydR+PoeK(_dz`!MKTS(g#3?M@UC8GCn+ciGO zR4kP`9>AHFLQlFd2yd3wt|V%jRFDPE{rNL2TKpM5L_#qUEZ)2*pnhhi?OAF%gzgnq zcR8_cA>-^-9Nf~vr>Lc?nQMVjHe}!J2jR;vuT$+Na#jCfMQ{7(Ai@okxVVVM%g^@q ziyWyVEXCF->nsXM3^?GZ)H5h)yJ67RY}g9neT)^;N>qww9+&2dKCPJ&PUMR9PE9cj z{9&qE^WTk^={v&``8F~GA%%k1bM=I*=c>YCO-!-bcT>ncns#AyjUqp@hUC|clP`q3 zL!S38^j3c6{ft%dT52Toa5(Zfbl7Yeh^y*9PbD~QauU^8l9s4A9E{ZDB6*+z29n%G zaKb_4YDbUHGs5h;P0YU0J1MQeqyd{uh!NuSHdG*8UYZN;2UqOSSu~PXf}pd=vK$29 z?1yj62Wt8%GX>=!J1_4b^t?2tSB+7{(&OGu@HrOBS$FGhni2sVd8v-*kWQpgvqVuh zt)FWJ3f}=}MLi*uF|SfUv{^X58#K7XI{g3aUIp^by3;Q&koQEH#O`JmKEeFmD)MzG z6*nPN5rr?Jsj*rS7>Zy8F;rdGixPuMGOc)Bw>YYmK075E3B^pI?Dn^^hm7rsfj44 z4oY?4IF0+c%arsEqAP`rjN#rY&XLN^8uD$Rf?T-u@S4X>fs);C92#i;a}aADD^zWN+Zpwv8e zlVHmIEmj$MkECs+kv)L=hNr* zVrWUnh9QiD2dU;9?IGhKLcr{+tNByuu2hatZ&^-4t)7`NmEED#cN>il}H^0*; zmp|k@ComG%d~ZCK$YA&P%OVgckF0BzB^V&O+ncd@D0p0rk9JXPy`?WtMSot}j7{j5 zP%R4hn;fsDn$HTq+O4`JhM%e{3NC3BSn|;%L+cv?=Ct4L_f?Us;qJ@gfJRGlbz} zTZnEL=%)3i@G4ZZYBG5=@!myUum>h01X1)V=d=wSeI^^ldowcEZh=+`nr_cMIlOmJXtvD*f z++Lw6O2Guu_rvhE749$79q7d!%Lb z!zsL~4E$7DHBkqmA3_$v*_-9pPQKCxT?Ve?>0M*_MsSlc`r0b9if>=SsaT^HlXyU( zrwpAA371%unzz``oCWQV`-5xhTbfAqwI3heVv zi!uFj`6xiBkn5^Y0MX-Xdi02*khcQL01z-T1@{;?buN4c(TBf{et-0uVMjqHrw4zJ z(o9|k9}ZIfcDPOC3T@{0W>!846+wagZqOaablB^zTu~h#l{n zyR=#5hvdNxO!SYB&s@iecACKj@mP5sXD7(&V_(_@hW;YlhDai7R>Seuhh~=siFw~Q z*6wJ`=)Cq|e64MXKbK^uN;8=E6eLy*IZbN+xZu0x?QeYbaP-Vo-yPl;(l*C~xwQLg zqt9QkX1HkAe5TYPwN!|5t4At&Z<&e~n58Q$H|sE$Rg$eZP*w!}cIA09`Ux{ZDi!tY z<>Dkq`#K0}z1H}YcU*O~LeT;-Q7vblTNZ}rDc~2yW6OKcjC#y!c+dIsja_tEj?U9Q z=I#Dt9TFxP&am>XoAc;ri7$OY>s7-;8-K!4Jlx{WgUv=cDMER5wyH~msv+}~jG8ao zXe>gswU<3lp?{u&qrh9D_n@!-AtYb(C^@JU9CGbn(sn*BQ zO}>}n5{#{~kXUMjpM%x_Sx_jR%1wIi<84?$=%UA&c*5g;(3QXE!TP$H1g~=7{N7x# z@%a}@i#a-J^a^n}qPWe7b4j?dgd1287JXpDAUPX@BBtd292h61#{od|T+OXYmmqcG z?}(5svXLe#Xh;R_rN_0QP3LN_2R53({w|I5%(f`czY=UI`z;IA*r$w8uwI;k0nyLo|Ly%sUt0+#9)F1T32(eKy$X~*Jjz3~0cH~-3 zP?J5STboW}nu#^K%tjAyL$Eh|zwuqU;QPld!ShOZUYr%IyYTo}l^5>8?sHiJ0pcI; z{1S*b+kXmtab+IVZ4(JCa|%XBH4k;2dByu=b3Q*<=bRbx8>UHXdzPNy(ZFj_VZ zZ>Nq>E9tltN6|=0QRl>YXb4p5ESD`a6SDMkBP&{{w7Sxtj+kjvlhFI}qQz;LN?J?l zvbF+arn00Bcx4RHRt zj`_w5$l`53%Bd{Y$bw*HSKs@lrY9$|Gp*r`AV}Yq%g9r@v^@(7;1?`8eRU#7yn;S? z-eR`VeNe!dCIOdCyZoY3TEb+sSN7}HkBMfB*0N8RTOzHlE7fwb0~l|1)Z5&9_;e3?_Ii@_VCp zI^-89W0W z3H0t`X`k~s0l7M-j>;1A5fe9naD2FQp>wW$o=@}(nK8?Tg2e92@b1$TQUwaqy5T31 zoo3eV@k;W_OAIG<9Pq@s+)~_{bDsFa{(VKq>)9Yo9);~zlIm&GcEtb37x-GqhE5GN zHEe?Ou8}R$3%eRPn!X0N+d^YiM$PJq#aQ}EiBT*>AmICBqWY%8)&s!s#wV{FMrX5% z4Gd7lR=wm17%&Km@)vXmZ2AoiCk$OH?nY2Sd27NXw2_k;-o)!}m1Z(O4SRg@f4Ec5 zD<{i(p<2AvJhkz`#iZ568C~e_RPu3ZU(p>TBY<2}pmzn>}h@Z+=NnT2rD+s{yS5efojGmY0 z^w#d~F`oZ@5-`BWeZ*+WzK^p>9TmFcmFV7%!8 zg3Au19T}OK?W>UYzIWx@p0KXQU^nMLlz`5f)lyKbOFMy12z8EAFsY>@oB6+@ce^$u zoFv6ZP==ojhX)(nR8^)FM(vrbCEYtI!1u_rc5>WlSUpY^~A zS;`|&WcXz9NnCzw4wuxPcZ84a`wBg0M?vvlZ7HenqO%Y^wV}AdmX@s%qZ*$fx>J+e zb<+iVe*3EGVu!5Uq&V1dP+Enl!N!8NQ`yJP$ESa4e#Xd13Ex}Cj-K1~cKwme!s2+i zporJQW>o_PEPesvqLv#>R!YrQee%}qECNKoB$@F(Nw+w!dT8B#+0Bdkc61eu8b!g+ z&+o{^;QoUL3+i%$4z#MfAX;6IQL%~nNdXH@jM)8#}o67S8K5;r*<*&&57yR>-iJzBg zT0QhObNh5oS9>7kUEmQHoQjbABF{5$lH(OuP$0Cr`_Jv`cEj}gm zd;=L1lVM^7Q#ZJ5_z!2A=he`iJAK}H#uHvpPu#`bp?%rPYn?Q$-U@d6o+H(IZ>!BD ztgQ9tJ<$>cxa{R`x2+^_{A*#N52L|12-nV@z8JZZbt1#Npp=Z~;sbhL^+GZp_uH}I zKQrLK3FH?w*owhgP#G{Ca=?FzDOC;twGQelPeMB_$=qF~yxdZqP@HjfZ;w`UUds{1NLM q&_UuI49-6{?!PV(7}v%&eCCaf&Tyzl$Fl^$A8}zBp-KT=zyAfq6eQOG