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homework_churn-in-a-telecom-operator_2026-01.ipynb
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{
"cell_type": "markdown",
"metadata": {
"id": "IWUt4iKZJLIi"
},
"source": [
"# Churn prediction in a telco\n",
"\n",
"Treselle Systems, a data consulting service, [analyzed customer churn data using logistic regression](http://www.treselle.com/blog/customer-churn-logistic-regression-with-r/).\n",
"\n",
"We will use that dataset to do our analysis. The dataset includes information about:\n",
"\n",
"+ Customers who left within the last month: the column is called Churn\n",
"+ Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\n",
"+ Customer account information: how long they've been a customer, contract, payment method, paperless billing, monthly charges, and total charges\n",
"+ Demographic info about customers: gender, age range, and if they have partners and dependents"
]
},
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"%pip install --quiet lifelines scikit-survival"
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.1/222.1 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4eQkkBwbJLIl"
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"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"plt.style.use('seaborn-v0_8-bright')"
],
"execution_count": 2,
"outputs": []
},
{
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"id": "So7R4TFbJLI5"
},
"source": [
"churn_data = pd.read_csv(\n",
"'https://raw.githubusercontent.com/treselle-systems/customer_churn_analysis/master/WA_Fn-UseC_-Telco-Customer-Churn.csv')"
],
"execution_count": 3,
"outputs": []
},
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"source": [
"churn_data.head().T"
],
"execution_count": 4,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" 0 1 2 \\\n",
"customerID 7590-VHVEG 5575-GNVDE 3668-QPYBK \n",
"gender Female Male Male \n",
"SeniorCitizen 0 0 0 \n",
"Partner Yes No No \n",
"Dependents No No No \n",
"tenure 1 34 2 \n",
"PhoneService No Yes Yes \n",
"MultipleLines No phone service No No \n",
"InternetService DSL DSL DSL \n",
"OnlineSecurity No Yes Yes \n",
"OnlineBackup Yes No Yes \n",
"DeviceProtection No Yes No \n",
"TechSupport No No No \n",
"StreamingTV No No No \n",
"StreamingMovies No No No \n",
"Contract Month-to-month One year Month-to-month \n",
"PaperlessBilling Yes No Yes \n",
"PaymentMethod Electronic check Mailed check Mailed check \n",
"MonthlyCharges 29.85 56.95 53.85 \n",
"TotalCharges 29.85 1889.5 108.15 \n",
"Churn No No Yes \n",
"\n",
" 3 4 \n",
"customerID 7795-CFOCW 9237-HQITU \n",
"gender Male Female \n",
"SeniorCitizen 0 0 \n",
"Partner No No \n",
"Dependents No No \n",
"tenure 45 2 \n",
"PhoneService No Yes \n",
"MultipleLines No phone service No \n",
"InternetService DSL Fiber optic \n",
"OnlineSecurity Yes No \n",
"OnlineBackup No No \n",
"DeviceProtection Yes No \n",
"TechSupport Yes No \n",
"StreamingTV No No \n",
"StreamingMovies No No \n",
"Contract One year Month-to-month \n",
"PaperlessBilling No Yes \n",
"PaymentMethod Bank transfer (automatic) Electronic check \n",
"MonthlyCharges 42.3 70.7 \n",
"TotalCharges 1840.75 151.65 \n",
"Churn No Yes "
],
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" <tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>Contract</th>\n",
" <td>Month-to-month</td>\n",
" <td>One year</td>\n",
" <td>Month-to-month</td>\n",
" <td>One year</td>\n",
" <td>Month-to-month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PaperlessBilling</th>\n",
" <td>Yes</td>\n",
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" <td>Bank transfer (automatic)</td>\n",
" <td>Electronic check</td>\n",
" </tr>\n",
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" <th>MonthlyCharges</th>\n",
" <td>29.85</td>\n",
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" <td>42.3</td>\n",
" <td>70.7</td>\n",
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" <th>TotalCharges</th>\n",
" <td>29.85</td>\n",
" <td>1889.5</td>\n",
" <td>108.15</td>\n",
" <td>1840.75</td>\n",
" <td>151.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Churn</th>\n",
" <td>No</td>\n",
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]
},
{
"cell_type": "code",
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"source": [
"N = churn_data.shape[0]\n",
"churned = len(churn_data.query(\"Churn == 'Yes'\"))\n",
"notChurned = len(churn_data.query(\"Churn == 'No'\"))\n",
"\n",
"print(f'customers: {N}\\n')\n",
"print(f\"customers who churned: {churned}\")\n",
"print(f\"customers who haven't churned yet: {notChurned}\\n\")\n",
"print(f'percentage of customers who churned: {100*churned/len(churn_data):.0f}%')\n",
"print(f\"percentage of customers who haven't churned yet: {100*notChurned/len(churn_data):.0f}%\")"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"customers: 7043\n",
"\n",
"customers who churned: 1869\n",
"customers who haven't churned yet: 5174\n",
"\n",
"percentage of customers who churned: 27%\n",
"percentage of customers who haven't churned yet: 73%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"churn_data[\"Churn\"].unique().tolist()"
],
"metadata": {
"id": "GmZRZJMJME4F",
"outputId": "9b7db33f-fd98-4418-b490-443a1b155fdd",
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}
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['No', 'Yes']"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
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"id": "WZgadAo6JLJp"
},
"source": [
"churn_data['Churn'] = (churn_data['Churn'] == 'Yes')"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"churn_data[\"Churn\"].unique().tolist()"
],
"metadata": {
"id": "BxK09LoeMPJw",
"outputId": "4ef465ea-f6cf-470e-fbcc-cb2282aa5945",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[False, True]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
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},
"id": "CEv5C_L7JLJ2"
},
"source": [
"# Drop customerID column\n",
"churn_data = churn_data.drop('customerID', axis=1)\n",
"\n",
"# Drop TotalCharges column: otherwise together with MonthlyCharges you can deduce how many months you have been subscribed\n",
"churn_data = churn_data.drop('TotalCharges', axis=1)"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:18.691230Z",
"start_time": "2020-01-09T22:37:18.307317Z"
},
"scrolled": false,
"id": "Td3MQtORJLKB",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "7e7650f0-f49f-4dfa-80d7-cda5f62c9d41"
},
"source": [
"from lifelines import KaplanMeierFitter\n",
"\n",
"kmf = KaplanMeierFitter()\n",
"kmf.fit(churn_data['tenure'], churn_data['Churn'], label='Estimate for Average Customer')\n",
"fig, ax = plt.subplots(figsize=(10,7))\n",
"kmf.plot(ax=ax)\n",
"ax.set_title('Kaplan-Meier Survival Curve - All Customers')\n",
"ax.set_xlabel('Customer Tenure (Months)')\n",
"ax.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "OaQL73F8JLKQ"
},
"source": [
"data = pd.DataFrame()\n",
"data = kmf.survival_function_\n",
"data['lower'] = kmf.confidence_interval_['Estimate for Average Customer_lower_0.95']\n",
"data['upper'] = kmf.confidence_interval_['Estimate for Average Customer_upper_0.95']"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8iBX5KruJLKK"
},
"source": [
"import altair as alt"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "iexNxw4lJLKd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"outputId": "3696c655-3ce3-4898-b45e-43357fc4411f"
},
"source": [
"label = alt.selection_point(\n",
" encodings=['x'], # limit selection to x-axis value\n",
" on='mouseover', # select on mouseover events\n",
" nearest=True, # select data point nearest the cursor\n",
" empty='none' # empty selection includes no data points\n",
")\n",
"\n",
"base = alt.Chart(data.reset_index()).encode(\n",
" x=alt.X('timeline:Q', scale=alt.Scale(zero=False), axis=alt.Axis(title=\"Customer tenure (months)\"))\n",
")\n",
"\n",
"line = base.mark_line(point=False).encode(\n",
" y=alt.Y('Estimate for Average Customer', scale=alt.Scale(zero=False), axis=alt.Axis(title='Customer survival probability')),\n",
" color=alt.value('blue'),\n",
" tooltip = ['timeline', 'Estimate for Average Customer']\n",
")\n",
"\n",
"band = alt.Chart(data.reset_index()).mark_area(\n",
" opacity=0.5\n",
").encode(\n",
" x=alt.X('timeline:Q', scale=alt.Scale(zero=False)),\n",
" y='lower:Q',\n",
" y2='upper:Q'\n",
")\n",
"\n",
"alt.layer(\n",
" line, # base line chart\n",
" band,\n",
" alt.Chart().mark_rule(color='#aaa').encode(\n",
" x = alt.X('timeline:Q', scale=alt.Scale(zero=False), sort=None)\n",
" ).transform_filter(label),\n",
" # add circle marks for selected time points, hide unselected points\n",
" base.mark_circle(size=80).encode(\n",
" y=alt.Y('Estimate for Average Customer', scale=alt.Scale(zero=False), axis=alt.Axis(title='Customer survival probability')),\n",
" opacity=alt.condition(label, alt.value(1), alt.value(0))\n",
" ).add_params(label),\n",
" # add white stroked text to provide a legible background for labels\n",
" base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(\n",
" text='Estimate for Average Customer:Q'\n",
" ).transform_filter(label),\n",
" # add text labels for stock prices\n",
" base.mark_text(align='left', dx=5, dy=-5).encode(\n",
" text='Estimate for Average Customer:Q'\n",
" ).transform_filter(label),\n",
"\n",
" data=data.reset_index()\n",
").properties(\n",
" title=f'Kaplan-Meier Survival Curve - All Customers',\n",
" width=600\n",
")"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
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],
"text/plain": [
"alt.LayerChart(...)"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"from lifelines import KaplanMeierFitter\n",
"kmf = KaplanMeierFitter()\n",
"fig, ax = plt.subplots(figsize=(10,7))\n",
"for r in churn_data['gender'].unique():\n",
" ix = churn_data['gender'] == r\n",
" kmf.fit(churn_data['tenure'].loc[ix], churn_data['Churn'].loc[ix], label=r)\n",
" # kmf.survival_function_.plot(ax=ax)\n",
" kmf.plot(ax=ax)\n",
"# kmf.fit(churn_data['tenure'], churn_data['Churn'], label='Estimate for Average Customer')\n",
"# kmf.plot(ax=ax)\n",
"ax.set_title('Kaplan-Meier Survival Curve - All Customers')\n",
"ax.set_xlabel('Customer Tenure (Months)')\n",
"ax.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"id": "gDmUO9iWQbnH",
"outputId": "52f738e9-782d-40fe-e527-16203e085da9"
},
"execution_count": 19,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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vf/0r4Ta9r776SlLdD8rdunVr8vGnP/1JkUgk4WvUP3f48OHNnvvCCy80C3HIy8tr6ANxYsiQIZo/f742btyod999V1OmTNGWLVt04oknNvRNpKP+69/YSSedJI/H01AAWZalJ554QocffriKi4sTXisvL08nnHCCnnrqKUUiEUnS/PnzFY1GmxROkvTXv/5V/fv3VyAQ0Lbbbqtu3brp2WefTfj1Taa4uFhbtmxJ+XlOtfQ1GjBggHbdddcmReK8efPUtWtXDR8+XJK0bt06bdq0Sffdd1+z74n6XrBk4R5Ov7/rvfDCC1q3bp2GDh2qpUuXaunSpVqxYoV+/etf69FHH025IGlJ/Z+/21/vMWPGaP/999f48ePVvXt3nXLKKXr88ccdzbm+H+9Xv/pVs8d23XXXZv16gUCgWTFTUlKiHXbYoVnfVUlJScO/N1Ld3/Pnn3++2Z/niBEjJDX/82zpe2fSpEkqKirS0KFDtfPOO+uiiy5q1kMJwF2k6gFwrF+/fvrPf/6jQw89VIcddpjeeOONJqtPmzZt0sEHH6zi4mJdd9116tu3rwKBgD744ANNmjTJlR+4pLqepTfffFOXX365Bg4cqKKiItXW1mrUqFEtvkai1Drrp36o448/Xv/3f//XcP9ZZ53VYjDC0UcfLb/fr7POOkuRSKTJb5Ubq5/DzTffrIEDB7Y4pqioKOlzH3roIW233XbNHs/La/rPtt/vTyuZzefzaciQIRoyZIh22WUXjRs3Tk888YSuueaaZj/01ftlwEdjv1xJkeoK4wMPPFCPP/64rrzySr399ttauXKlo2S6U045Rffee6+ee+45HXvssXr88ce16667asCAAQ1jHn74YY0dO1bHHnusLr/8cpWWljb0pjRe5XJq11131eLFi7Vq1aqkq6r1kn2dWvqea+lrJNX9sD9jxgytX79enTp10r///W/99re/bfizrv+eOP3003XWWWe1eI3+/fsnnKfT7+969atKib6//+///k+//vWvEz7fiV133VVSXYhCor8jjTn9ngwGg3r11Ve1cOFCPfvss3r++ec1b948DR8+XC+88IKrCZaJrmX3741U92d62GGH6YorrmhxbP0vkOq19L2z22676csvv9Qzzzyj559/Xk8++aTuuusuTZs2LeUeTgDOUDgBSMnQoUP1r3/9S6NHj9Zhhx2m1157reG3rq+88op+/PFHzZ8/v6HZWlKTpujGvv/++2bR1UuWLJGkhLHHGzdu1IIFCzR9+nRNmzat4f76lZp0zJo1q8lvg3v06NHiuGAwqGOPPVYPP/ywDj/8cHXt2rXFcX379pVU91v1+t8gO1X/3NLS0pSfm67BgwdLkn744QdJP6/I/bIZvqWEPDtjxozR7373O3355ZeaN2+eCgoKdNRRR9k+76CDDtL222+vefPm6YADDtDLL7+sqVOnNhnzj3/8Q+Xl5Zo/f36TH6yvueaalOcpSUcddZQeffRRPfzww5oyZYrt+C5dujT7Gkl1X6fy8nLHrztmzBhNnz5dTz75pLp3767KysqGAAxJ6tatmzp16qR4PJ7W94TT72+pLgDmqaee0pgxY3TiiSc2e/ySSy7R3//+960unA4//HB5vV49/PDDjgIikn2tf8nj8ejQQw/VoYceqtmzZ+uGG27Q1KlTtXDhQo0YMSJhEVa/7fbLL79sWO2r9+WXX7p61lrfvn1VVVW11X/HCwsLNWbMGI0ZM0amaer444/XjBkzNGXKlK06Xw5Ay9iqByBlhx56qB599FEtXbpUo0aNauhXqP9Na+PfrJqmqbvuuqvF68RiMd17771Nxt57773q1q2bBg0a1OJzWnoNSVuVLDZo0CCNGDGi4aNfv34Jx1522WW65pprdPXVVye9Xt++fXXLLbc0pPA1tm7duoTPHTlypIqLi3XDDTcoGo2m9Fw7CxcubDF18D//+Y+kn7coFRcXq2vXrs36LBL9OSZzwgknyOv16tFHH21IYnRyxpPH49GJJ56op59+Wg899JBisVizbXotfS+88847euutt1KepySdeOKJ2nPPPTVjxowWr7Fly5YmxVvfvn319ttvN0n/e+aZZ7Rq1aqUXne33XbTnnvuqXnz5mnevHnafvvtm/ziwev1NpwV1lIiot33RCrf3//85z9VXV2tiy66SCeeeGKzjyOPPFJPPvlkwxbKdJWVlencc8/VCy+8oDvuuKPZ47W1tZo1a1bDAbh9+/bV5s2b9fHHHzeM+eGHH/TPf/6zyfM2bNjQ7Fr1K1r1c67//vtlITZ48GCVlpbqnnvuafL+nnvuOX3++efNjl7YGieffLLeeust/fe//2322KZNmxSLxWyv8cvjF3w+n/r16yfLslr8twPA1mPFCUBajjvuON1///06++yzdfTRR+v555/XsGHD1KVLF5111lm65JJLZBiGHnrooRZ/WJfqfvN944036uuvv9Yuu+yiefPm6cMPP9R9992X8FDU4uJiHXTQQbrpppsUjUbVs2dPvfDCCwlXtdw2YMCAJtvFWuLxePSnP/1Jhx9+uHbffXeNGzdOPXv21HfffaeFCxequLhYTz/9dIvPLS4u1t13360zzjhDe++9t0455RR169ZNK1eu1LPPPqv9999fc+fOTWvuF198sWpqanTcccdp1113lWmaevPNNzVv3jz17t27ydlJ48eP1x//+EeNHz9egwcP1quvvtqwGpiK0tJS/frXv9bs2bO1ZcuWZsVPMmPGjNEdd9yha665Rnvuuad22223Jo8feeSRmj9/vo477jiNHj1aK1as0D333KN+/fq1WLDayc/P1/z58zVixAgddNBBOvnkk7X//vsrPz9fn376qR555BF16dKl4Syn8ePH6x//+IdGjRqlk08+WcuWLdPDDz/csGqYijFjxmjatGkKBAI655xzmm2//OMf/6iFCxdqn3320bnnnqt+/fppw4YN+uCDD/TSSy+1WDCk4+9//7u23XZbDRs2rMXHjz76aN1///169tlndfzxx2/Va82aNUvLli3TJZdcovnz5+vII49Uly5dtHLlSj3xxBP64osvGlbeTjnlFE2aNEnHHXecLrnkEtXU1Ojuu+/WLrvs0iQY47rrrtOrr76q0aNHq1evXlq7dq3uuusu7bDDDg1nc/Xt21edO3fWPffco06dOqmwsFD77LOP+vTpoxtvvFHjxo3TwQcfrN/+9rdas2aNbrvtNvXu3VuXXnrpVr3fxi6//HL9+9//1pFHHqmxY8dq0KBBqq6u1v/+9z/94x//0Ndff51wRbveb37zG2233Xbaf//91b17d33++eeaO3euRo8e3aohJ0CHlqU0PwBtSH3k8HvvvdfssVtuucWSZB155JFWNBq13njjDWvfffe1gsGg1aNHj4bIa0nWwoULG5538MEHW7vvvrv1/vvvW/vtt58VCASsXr16WXPnzm1y/Zaisb/99lvruOOOszp37myVlJRYJ510kvX9998njMr+ZTRxSxHKieinOPJkEr3O4sWLreOPP97adtttLb/fb/Xq1cs6+eSTrQULFtjOZeHChdbIkSOtkpISKxAIWH379rXGjh1rvf/++w1jzjrrLKuwsND2PdR77rnnrLPPPtvaddddraKiIsvn81k77bSTdfHFF1tr1qxpMrampsY655xzrJKSEqtTp07WySefbK1du9bx17ix+++/35JkderUqUnMc+P30VLUdG1trVVWVmZJsv7f//t/LT5+ww03WL169bL8fr+11157Wc8880yL1/vlvJPZuHGjNW3aNGvPPfe0CgoKrEAgYO2xxx7WlClTrB9++KHJ2FmzZlk9e/a0/H6/tf/++1vvv/9+wjjyJ554IuFrfvXVV5YkS5L1+uuvtzhmzZo11kUXXWSVlZVZ+fn51nbbbWcdeuih1n333efofdlZs2aNlZeXZ51xxhkJx9TU1FgFBQXWcccdZ1lW+nHk9WKxmPWnP/3JOvDAA62SkhIrPz/f6tWrlzVu3LhmUeUvvPCCtccee1g+n8/61a9+ZT388MPN4sgXLFhgHXPMMVaPHj0sn89n9ejRw/rtb3/bLPb7qaeesvr162fl5eU1+/dl3rx51l577WX5/X5rm222sU477TTr22+/bfL8RH/36v9d+6VevXpZo0ePbnLfli1brClTplg77bST5fP5rK5du1rDhg2zbrnllmbHMdx8883NrnnvvfdaBx10UMO/L3379rUuv/xya/PmzS1/sQFsNcOyEvwqGABa0SGHHKL169e3ymGsAAAAbqPHCQAAAABsUDgBAAAAgA0KJwAAAACwQY8TAAAAANhgxQkAAAAAbFA4AQAAAICNDncAbm1trb7//nt16tRJhmFkezoAAAAAssSyLG3ZskU9evRodvj4L3W4wun7779XWVlZtqcBAAAAIEesWrVKO+ywQ9IxHa5w6tSpk6S6L05xcXGWZwMAAAAgWyorK1VWVtZQIyTT4Qqn+u15xcXFFE4AAAAAHLXwEA4BAAAAADYonAAAAADABoUTAAAAANjocD1OAAAAQCZYlqVYLKZ4PJ7tqXRo+fn58nq9W30dCicAAADAZaZp6ocfflBNTU22p9LhGYahHXbYQUVFRVt1HQonAAAAwEW1tbVasWKFvF6vevToIZ/P5yi1De6zLEvr1q3Tt99+q5133nmrVp4onAAAAAAXmaap2tpalZWVqaCgINvT6fC6deumr7/+WtFodKsKJ8IhAAAAgFbg8fCjdi5wa7WPP00AAAAAsEHhBAAAAAA2KJwAAAAA5KzevXtrzpw52Z4GhRMAAACAOmPHjpVhGM0+li5dmu2pZR2pegAAAAAajBo1Sn/5y1+a3NetW7cszSZ3sOIEAAAAtDLLkqpDmf+wrNTn6vf7td122zX58Hq9euqpp7T33nsrEAiovLxc06dPVywWa3ieYRi69957deSRR6qgoEC77bab3nrrLS1dulSHHHKICgsLNWzYMC1btqzhOcuWLdMxxxyj7t27q6ioSEOGDNFLL72UdH6bNm3S+PHj1a1bNxUXF2v48OH66KOPUn+jKWLFCQAAAGhlNWGp6PDMv27Vc1JhcOuv89prr+nMM8/U7bffrgMPPFDLli3TeeedJ0m65pprGsZdf/31mj17tmbPnq1Jkybp1FNPVXl5uaZMmaIdd9xRZ599tiZMmKDnnnuubn5VVTriiCM0Y8YM+f1+/e1vf9NRRx2lL7/8UjvuuGOLcznppJMUDAb13HPPqaSkRPfee68OPfRQLVmyRNtss83Wv9kEKJwAAAAANHjmmWdUVFTU8Pnhhx+ujRs3avLkyTrrrLMkSeXl5br++ut1xRVXNCmcxo0bp5NPPlmSNGnSJO233366+uqrNXLkSEnS73//e40bN65h/IABAzRgwICGz6+//nr985//1L///W9NmDCh2dxef/11vfvuu1q7dq38fr8k6ZZbbtG//vUv/eMf/2go5loDhRMAAADQygoCdas/2XjdVP3617/W3Xff3fB5YWGh+vfvrzfeeEMzZsxouD8ejyscDqumpkYFBQWSpP79+zc83r17d0nSnnvu2eS+cDisyspKFRcXq6qqStdee62effZZ/fDDD4rFYgqFQlq5cmWLc/voo49UVVWlbbfdtsn9oVCoyRbA1pDVwunVV1/VzTffrEWLFumHH37QP//5Tx177LFJn/PKK6+ooqJCn376qcrKynTVVVdp7NixGZkvAAAAkA7DcGfLXCYUFhZqp512anJfVVWVpk+fruOPP77Z+EDg5+osPz+/4bZhGAnvq62tlSRddtllevHFF3XLLbdop512UjAY1IknnijTNFucW1VVlbbffnu98sorzR7r3LmzszeYpqwWTtXV1RowYIDOPvvsFv8QfmnFihUaPXq0LrjgAv3973/XggULNH78eG2//fYNy38AAAAA3LX33nvryy+/bFZQba033nhDY8eO1XHHHSeprjD6+uuvk85j9erVysvLU+/evV2di52sFk6HH364Dj/ceZfcPffcoz59+mjWrFmSpN12202vv/66br311jZZONXWWlofDjsa2zUQkMdjtPKMAAAAgOamTZumI488UjvuuKNOPPFEeTweffTRR/rkk0/0//7f/0v7ujvvvLPmz5+vo446SoZh6Oqrr25YjWrJiBEjtN9+++nYY4/VTTfdpF122UXff/+9nn32WR133HEaPHhw2nOx06Z6nN566y2NGDGiyX0jR47UxIkTEz4nEokoEok0fF5ZWdla00vZ+nBYPcIj7AdKKvhiT20aeDfFEwAAADJu5MiReuaZZ3TdddfpxhtvVH5+vnbddVeNHz9+q647e/ZsnX322Ro2bJi6du2qSZMmJf153TAM/ec//9HUqVM1btw4rVu3Ttttt50OOuighp6q1mJYVjrp7u4zDMO2x2mXXXbRuHHjNGXKlIb7/vOf/2j06NGqqalRMNh84+i1116r6dOnN7t/8+bNKi4udmXu6VpbE3JcOEnS6+Z87btd635DAAAAYOuEw2GtWLFCffr0adL/g+xI9udRWVmpkpISR7VBm1pxSseUKVNUUVHR8HllZaXKysqyOKOfdQ0E9L1e0jdVW7Ql2nID3EYzojElZ0qSauLRTE4PAAAAwE/aVOG03Xbbac2aNU3uW7NmjYqLi1tcbZLqTj6uz3jPNR6PodKCoEoLEkesrK0JSc7aoAAAAAC0Ek+2J5CK/fbbTwsWLGhy34svvqj99tsvSzMCAAAA0BFktXCqqqrShx9+qA8//FBSXdz4hx9+2HDg1ZQpU3TmmWc2jL/gggu0fPlyXXHFFfriiy9011136fHHH9ell16ajekDAAAA6CCyWji9//772muvvbTXXntJkioqKrTXXntp2rRpkqQffvihyanBffr00bPPPqsXX3xRAwYM0KxZs/SnP/2pTUaRAwAAAGg7strjdMghhyhZqN+DDz7Y4nMWL17cirMCAAAAgKbaVI8TAAAAAGQDhRMAAAAA2KBwAgAAAAAbFE4AAAAAXPf111/LMIyGBO22jsIJAAAAgCRp7NixMgxDF1xwQbPHLrroIhmGobFjx2Z+YjmAwgkAAABAg7KyMj322GMKhUIN94XDYT3yyCPacccdsziz7KJwAgAAAFqZZVmqtkIZ/0h29E8ie++9t8rKyjR//vyG++bPn68dd9yx4fxVSXr++ed1wAEHqHPnztp222115JFHatmyZUmv/cknn+jwww9XUVGRunfvrjPOOEPr169PeY7ZkNVznAAAAICOoEZhlWwckfHX3dzlJRUqmPLzzj77bP3lL3/RaaedJkl64IEHNG7cOL3yyisNY6qrq1VRUaH+/furqqpK06ZN03HHHacPP/xQHk/z9ZlNmzZp+PDhGj9+vG699VaFQiFNmjRJJ598sl5++eW032OmUDgBAAAAaOL000/XlClT9M0330iS3njjDT322GNNCqcTTjihyXMeeOABdevWTZ999pn22GOPZtecO3eu9tprL91www1NnlNWVqYlS5Zol112aZ034xIKJwAAAKCVFSigzV1eysrrpqNbt24aPXq0HnzwQVmWpdGjR6tr165Nxnz11VeaNm2a3nnnHa1fv161tbWSpJUrV7ZYOH300UdauHChioqKmj22bNkyCicAAACgozMMI60tc9l09tlna8KECZKkO++8s9njRx11lHr16qX7779fPXr0UG1trfbYYw+Zptni9aqqqnTUUUfpxhtvbPbY9ttv7+7kWwGFEwAAAIBmRo0aJdM0ZRiGRo4c2eSxH3/8UV9++aXuv/9+HXjggZKk119/Pen19t57bz355JPq3bu38vLaXhlCqh4AAACAZrxerz7//HN99tln8nq9TR7r0qWLtt12W913331aunSpXn75ZVVUVCS93kUXXaQNGzbot7/9rd577z0tW7ZM//3vfzVu3DjF4/HWfCuuoHACAAAA0KLi4mIVFxc3u9/j8eixxx7TokWLtMcee+jSSy/VzTffnPRaPXr00BtvvKF4PK7f/OY32nPPPTVx4kR17ty5xRS+XGNY6YS7t2GVlZUqKSnR5s2bW/wmyDVra0LqEa6LrnwhNE/De+6Q5RkBAAAgmXA4rBUrVqhPnz4KBNILZ4B7kv15pFIb5H5pBwAAAABZRuEEAAAAADYonAAAAADABoUTAAAAANigcAIAAABaQQfLYMtZbv05UDgBAAAALsrPz5ck1dTUZHkmkCTTNCWp2VlUqWp7R/YCAAAAOczr9apz585au3atJKmgoECGYWR5Vh1TbW2t1q1bp4KCAuXlbV3pQ+EEAAAAuGy77baTpIbiCdnj8Xi04447bnXxSuEEAAAAuMwwDG2//fYqLS1VNBrN9nQ6NJ/PJ49n6zuUKJwAAACAVuL1ere6twa5gXAIAAAAALBB4QQAAAAANiicAAAAAMAGhRMAAAAA2KBwAgAAAAAbFE4AAAAAYIPCCQAAAABsUDgBAAAAgA0KJwAAAACwQeEEAAAAADYonAAAAADABoUTAAAAANigcAIAAAAAGxROAAAAAGCDwgkAAAAAbFA4AQAAAIANCicAAAAAsEHhBAAAAAA28rI9ATgXCktbapKPyfNKQX9m5gMAAAB0FBRObcgnX0tbvk4+pigoHTqI4gkAAABwE4VTG2LlRZTnr074eCQmbQjnKRanagIAAADcROHUhnxXtEQ1RcsTPm6aUm04qFHWEHUSxRMAAADgFgqnNsRr5amgtjDh45ZlqtITUkwxicIJAAAAcA2FUxvis3wKWIGEj5uWJEUzNh8AAACgoyCOHAAAAABssOLUhkRNryJ5iWtdM+qRRSkMAAAAuI7CqQ2Zc9PuUjjxVj1J2m77kCbNzdCEAAAAgA6CwinHFQQkhetue/9ypO34tZ/3V03kZqmodecFAAAAdCRs7MpxhUZAg43dHY83dvtYofpKCwAAAIArWHHKcYZh6Mn82Zqx5P+0TV6hOuW3HDNeZcY0s2xyhmcHAAAAdAwUTm2AYRjKt3zyWT75rZYLJ5NUCAAAAKDV8NM2AAAAANigcAIAAAAAGxROAAAAAGCDwgkAAAAAbBAO0YaYcSliJXjMzOxcAAAAgI6EwqkNyPNKfp9UEzEVjbU8pjoabbi9KWJqi1Wd/JrKU9BoOaEPAAAAQFMUTm1AJ3+e9i0PqjIekhRtcUxl+OeK6m3rI31vJv+jLVJQh/qGUDwBAAAADlA4tQFBw6/DC4YopgTLTZLWeUOqiNTdzleeio3ChGMjlqkqhX66HoUTAAAAYIfCqY2oWxlKXOSEjEY5H6ZPRiSQcGytJZmeqORzcYIAAABAO0bh1A4t+17auDbx4xFDsoLSqJ2lTonrKwAAAAA/oXBqh/IMqTDJDrzaWmmLKcXimZsTAAAA0JZROLVDvvy6FL5EIlEpSbsUAAAAgF/gAFwAAAAAsMGKUzsU9ZiKGJGEj5uGKUsJTtIFAAAA0AyFUzt0R59rbcdsHy7TJGtY608GAAAAaAfYqtdOFCgg6/P+jsf/EFilkMKtOCMAAACg/WDFqZ0wDEO1U++S/GFNnLZI2xTmtzjONEzNLJuc4dkBAAAAbRuFU7tiSJGg5kw9IPEQf0jeR+tuWrQ5AQAAAI6wVa+dKAhIQ/qldjBTyGylyQAAAADtDCtO7YRhSP+8KawZH7+tbfIK1Sk/0OK4qmhUM3+6HZapLVZ10uvmKU9BI8lpugAAAEAHQOHUjhiGlO+vlS+vVv782hbHmMbP978d/0jLzeTfAkUK6lDfEIonAAAAdGgUTh1YkQpUbLQcIiFJEctUlUKKKSaJwgkAAAAdF4VTBxY0AipIUjhJUsSKZmg2AAAAQO4iHAIAAAAAbLDi1A5FDVNho+XHIsbPK0jhqBRKcgZuyJJMjySfu/MDAAAA2hoKp3YkT3ny1wYVNUKq8bS8xa7GE2u4vXiFqc55SXqcDMkKSqN2ljq1HNIHAAAAdAgUTu1I0PBrjy1DVBCPqSDBkU6bzFDD7YCvVoVJWpxqa6UtphRL7XgoAAAAoN2hcGpnfJZfHtMvWQkGRH9uazP8ppQfSXyxqCkrmuhCAAAAQMdB4dSO5HmloqBUFZLMBGF4W37eqadZO1xre83tw2WaZA1zZ4IAAABAG0Xh1I4E/dKhg5JvrasKB/S7z/vL2O1jR9f8IbBKIYUlFbkzSQAAAKANonBqZ4I259R6DEO1U++S/GFd/v/e1bZFLUfmmYap6aVXSpJCMrXFqk54zTzlKWhwQC4AAADaLwqnDsmQIkH5LL/8ln3W+JvxxfrUTDyuSEEd6htC8QQAAIB2i8KpAzNNryKRls9ANo2f7y9SoYqNlguniGWqSiHFFJNE4QQAAID2icKpA7v5qiGJH/SH5H207mZQfhUkWU2KWAmSKAAAAIB2ouXlBrRbBQFpSL/UDmaKmHybAAAAoGNjxamDMQzpnzeFNePjt7VNXqE65QdaHFcVjWpmhucGAAAA5CoKpw7IMKR8f618ebXy59e2OMY0Wr4fAAAA6IjYgwUAAAAANiicAAAAAMAGW/U6sKhhKmy0/FjE+Dkpb3PUVCDU8ta9GstUzGNJ9sdBAQAAAG0WhVMHlKc8+WuDihoh1XhajhKv8cQabk8ITJZiLQ6TJPWoKdOovGHqFHR7pgAAAEBuoHDqgIKGX3tsGaKCeEwFCZLJQxFLt33eX8ZuH9te73v/Kq2NV6rASrB89ZM85SmY5DwoAAAAIFdROHVQPsuvAsuvAqvlxw1Lqp16l+QPa+pN76qTr/lePNMwNb30SknSm/HF+tRMvl+vSEEd6htC8QQAAIA2J+vhEHfeead69+6tQCCgffbZR++++27CsdFoVNddd5369u2rQCCgAQMG6Pnnn8/gbDsaQ4oE5bP88rfw4bN+LpSKVKhiI/GHX/mqUkixZHv+AAAAgByV1cJp3rx5qqio0DXXXKMPPvhAAwYM0MiRI7V27doWx1911VW69957dccdd+izzz7TBRdcoOOOO06LFy/O8Mzbh3BUqgm3/BGKpHatAsOvAiOQ8MNvkB4BAACAtiurhdPs2bN17rnnaty4cerXr5/uueceFRQU6IEHHmhx/EMPPaQrr7xSRxxxhMrLy3XhhRfqiCOO0KxZszI887YtzysVBSUzKlXWJP6oF2ORCAAAAB1c1nqcTNPUokWLNGXKlIb7PB6PRowYobfeeqvF50QiEQUCgSb3BYNBvf766wlfJxKJKBL5efmksrJyK2fe9gX90qGDpFiCYAhJqg7/fLu25STytNRY9ktZhEgAAAAg12StcFq/fr3i8bi6d+/e5P7u3bvriy++aPE5I0eO1OzZs3XQQQepb9++WrBggebPn694PHEFMHPmTE2fPt3VubcHQZu6xJM8IC9lXnlUZYW0MLrIdiwhEgAAAMg1WQ+HSMVtt92mnXfeWbvuuqt8Pp8mTJigcePGyeNJ/DamTJmizZs3N3ysWrUqgzNuH0zTq0jE0+zDjDj/9vEbPpV5SpMGSBAiAQAAgFyVtRWnrl27yuv1as2aNU3uX7NmjbbbbrsWn9OtWzf961//Ujgc1o8//qgePXpo8uTJKi8vT/g6fr9ffj8rF1vj5quGtPyAPyTvo3U3Q1EpYLNK5fX6VJBv/3oRq+VDeQEAAIBsyVrh5PP5NGjQIC1YsEDHHnusJKm2tlYLFizQhAkTkj43EAioZ8+eikajevLJJ3XyySdnYMYdS0FAGtIvrvc+8zoa//7nHhXZFEVBvzRoF8nnoHgCAAAAcklWD8CtqKjQWWedpcGDB2vo0KGaM2eOqqurNW7cOEnSmWeeqZ49e2rmzJmSpHfeeUffffedBg4cqO+++07XXnutamtrdcUVV2TzbbRLhiH986awZnz8trbJK1Sn/ECzMVXRqGb+dNsXMJWfnzhFwoxLNRGf4nFDonACAABAG5PVwmnMmDFat26dpk2bptWrV2vgwIF6/vnnGwIjVq5c2aR/KRwO66qrrtLy5ctVVFSkI444Qg899JA6d+6cpXfQvhmGlO+vlZEfltVCUWR5ft5Sd0v5ZNvr7RAq137WREkuJ08AAAAArcywLMvK9iQyqbKyUiUlJdq8ebOKi4uzPZ2ctrY6ols+e0+eQEi+Fs6vjUQM3bLlSRm7fez4mk/k3aIuSSL9aqywKq1qjfYNUyejMJ1pAwAAAI6kUhtkdcUJuS1o+LXHliEqiMdU0ELiezgi1U49VPKHNfWmd9WppepKkmmYml56ZSvPFgAAAGg9FE5Iymf5VWD5VdDCuqRRf18kKJ/ll99quXACAAAA2joKJ2TU5pgphRI/XmOZinks1eRHbK+VpzwOyQUAAEBGUDgho8brStmdbbtdqKckye9LHiJRpKAO9Q2heAIAAECro3BCq/NZPvU2y/W1b7mj8at93ymv1qdiI/HWv4hlqkohxRSTROEEAACA1kXhBFeYEa8ilifh4+N/qJDhDydNIm8cIlFg+FVgs5IUsaJJHwcAAADcQuEEV8yYNMR2TJ++Vbq4YokMjnECAABAG0PhBFvhBAs7liX9qldcX37jdXSdFcuKZJoe+f3ND9MFAAAAchmFExLK80pFQakqJJkJiqcrzgrrNe/bKissVKE30OIY0/Ro2qT+rTLHGov0PQAAALQ+CickFPRLhw6SYi0cfltvbVj6aEWtrPywrPyWV5IsI3HvU7q88qjKCmlhdJHtWNL3AAAAsLUonJBU0KbWCFl58tcGFTVCqvG0vCxlen4unGIy5XfwbReOSaFwshE+dfeUypugWKtH+h4AAADcQOGErRI0/NpjyxAVxGMqSLAyFW60my5uOOtv+mip5LNsXtvv06BdJF9+8nGk7wEAAGBrUThhq/ksvwosvwoSFDqGTQHUkvyAqfwkzzPjUk3Ep3jckGwKJwAAAGBrUTgho5Kd92Q26oWaucOVttfaIVSu/ayJSno4FAAAAOACCidkVPLznix5ZvSXsdvHjq71bXC5IjJVQO8SAAAAWhmFE1pdwCf16xPXZyvsznsyVDv1Lskf1nU3fixfgvOeTMPU9FL7FanG7GLLiSwHAABAMhROaHWGIc26JKZ3jHcU8oQSjotGvJp9+SFSJCiPZchvbX0h4zS2nMhyAAAAJEPhhIwIyK99NUjx2ljCMeFaafZPt52m79nxGz6VeUoVV+LrEVkOAAAAOxROcEXYQeK31+NXgS9xYZJO+p79eU+S1+tTAZHlAAAA2AoUTtgqeV6pKChVhSTTpvaoCkll3SS/z73Xf3+5KZszcFXs82nwLobteU8AAABAIhRO2CpBv3ToICmW4PDbejVhaeFiKe7ODrwGt5U7iy0fGJsoXz6x5QAAAEgPhRO2WrAV2oKSnfdkKaBe4XJ9E1ju6FrElgMAAGBrUTghJyU/70mS/qZeu/6oCy/5KuH5t+nElgMAAAAtaflX+kAW1J/35Iyhb77oKkWC8lv+Fj98lovNVAAAAOjQ0lpxikQieuedd/TNN9+opqZG3bp101577aU+ffq4PT90IIYh3XRJWK9531ZBbaH8VqDFcabp0bRJ/TM8OwAAAHRkKRVOb7zxhm677TY9/fTTikajKikpUTAY1IYNGxSJRFReXq7zzjtPF1xwgTp16tRac0Y7ZhiSz18rX22t/JbLSRI2aqyI7Zg85XFILgAAQAfkuHA6+uij9cEHH+jUU0/VCy+8oMGDBysYDDY8vnz5cr322mt69NFHNXv2bP3tb3/TYYcd1iqTRttld95TjSHF8qWo10w4xjRS32EalqlQgnOiolZUW2prtDC6yPY6RQrqUN8QiicAAIAOxnHhNHr0aD355JPKz2/5MJzy8nKVl5frrLPO0meffaYffvjBtUmi7XN63pNp5KkqFpS6hBTNa3mg6fm5cIrJlN/Bt/EZsSulqsSP72r01o0Fv5NhJI4sj1imqhRSTDGJhD4AAIAOxXHhdP755zu+aL9+/dSvX7+0JoT2yfl5T37pwyEq2BJTQcstTgo32lEXNxJv5/NZPvWKlOsbv31s+RfW14rHPCr2JS+IIpbNkhkAAADapa2OI//kk0/0f//3f4rH49p///01aNAgN+aFdsjpeU8+y68Cy6+CBFvrjEb3JzvvSZLOXV2hmDcsK8G1TMPUzB3qIsvjTgP9AAAA0OFsVeF055136rrrrtPBBx+saDSqq6++WldccYWmTp3q1vyApOzPe5L69K3SxRVL1NIuvEjinXkJESIBAADQ8aRUOK1atUplZWUNn8+dO1effvqpunbtKkl66623dPTRR1M4oVXVn/f02Qqvo/ErlhXJND3y+7cupc8rj6qsECESAAAAHVBKhdOIESP0u9/9TpdccokMw9C2226r559/XieddJJM09RLL72kbt26tdZcAUmtd95TOCaFwslG+NTdUypvfvICjBAJAACA9ielwum9997T5MmTtc8+++i+++7TfffdpzPOOENnnnmmDMPQbrvtpr/+9a+tNVegQf15T4YVlpXgvCcrxdjyj5ZKvgS9UPWCfp8G7SL5Wg6XbECIBAAAQPuSUuFUXFysu+66S2+++abGjh2r4cOH67XXXlM8Hlc8Hlfnzp1baZpAU17lKVgbVMgTUtRwJ7a80J+8cDLjUijyU4iETeEEAACA9iWtcIhhw4bp/fff18yZM7XXXntp9uzZGj16tNtzQwdmd1Cu5Nfu5hDl+WKJr+Ewtryezyf5k604mVI08csBAACgHUupcIrFYrrvvvv0+eefa8CAAbryyis1ZswYXXDBBXrwwQc1d+5cde/evbXmig7A6UG5klQV8qusm19+X8uPGzbb7lob6XsAAADtR0qF0znnnKP33ntPRx99tP7yl7/o448/1u23366XX35Zf/7zn7Xffvvp8ssv14UXXtha80U75/ygXGnhYim+dUF5TZiGafO4ZMknKXmGOel7AAAA7U9KhdNTTz2lt956S7vttptqamq055576vbbb5dUV1QdddRRmjhxIoUTtorTg3JTkeigXLNRgMT00ittr7NDqFz7WROVrHjyGz6VeUoVF+l7AAAA7UVKhVP37t31wgsvqG/fvnr55Ze17bbbNnm8tLRUjzzyiKsTBNyQ+KBcS54Z/WXs9rGj63wbXK7NMVNGOHmh4/X6VOAgQIL0PQAAgLYhpcJp7ty5Ou2001RRUaHtt99ejz/+eGvNC9hqzg7KNVQ79S7JH9Z1N34sX4JDck3DbFiRchZbLkex5QAAAGgbUiqcDjvsMK1Zs0br16/noFvkPMOQZl0S0zvGOwp5Qi2OiUa8mn35IVIkKI9lyG/Zb5nLD5jKt4ktr4n4FI8bxJYDAAC0EynHkRuGQdGENiMgv/bVIMVrW84RD9dKs3+67SSyXJJm7uBOLxQAAADajubd8gmMGjVKb7/9tu24LVu26MYbb9Sdd965VRMDnAhH6xL2kn0o4leBVdjiR9AqdPQ6Psun3ma543l9G1yuiJKn9NWrsSLaYlUn/Qg5iDYHAABA63G84nTSSSfphBNOUElJiY466igNHjxYPXr0UCAQ0MaNG/XZZ5/p9ddf13/+8x+NHj1aN998c2vOGx1cauc9SWXdlPC8p3qJkvfqjf+hQoY/nHQRqXEvlB1iywEAANoOx4XTOeeco9NPP11PPPGE5s2bp/vuu0+bN2+WVLd9r1+/fho5cqTee+897bbbbq02YUBqnfOeEifv/axP3ypdXLFEhgs78IgtBwAAaDtS6nHy+/06/fTTdfrpp0uSNm/erFAopG233Vb5+XTBI7PcOO/JWfLez1YsK5JpeuRPkL7XWDgmhcLJx6QSW15js10vT3msSAEAALSSlMMhGispKVFJSYlbcwEyzjCkmy4J6zXv2yqoLZTfCrQ4zjQ9mjapf0rXdiu23OmWPrbzAQAAtJ6tKpyA9sAwJJ+/Vr7aWvktZ8l6TrgVW+5kSx/b+QAAAFoXhRPQStyMLfcbNskWqtvOBwAAgNbhOI4cgL3WjC0HAABA9rDiBPwkaiQuYEzj598xmGby3ze4HVsOAACA7Eu7cNq0aZP+8Y9/aNmyZbr88su1zTbb6IMPPlD37t3Vs2dPN+cItCqv8hSsDSrkCSlqtLzdzfT8XCw5CYlwM7Y8FXbJexLpewAAAOlIq3D6+OOPNWLECJWUlOjrr7/Wueeeq2222Ubz58/XypUr9be//c3teQJbJZy0/cev3c0hyvPFEo6wLOmpPlF9scJZ7H4qseVu4DBdAACA1pVW4VRRUaGxY8fqpptuUqdOnRruP+KII3Tqqae6Njlga+V5paKgVBWSzCTFU1XIr7JufvmTZDDcckm1XvO+6npsubPznpJHlnOYLgAAQOtKq3B67733dO+99za7v2fPnlq9evVWTwpwS9AvHTpIisUTj6kJSwsXS3GbxaHWii1/f7mpfJvLFft8GryLYVs8OeHkMF2JLX0AAACNpVU4+f1+VVZWNrt/yZIl6tat21ZPCnBT0OWf/Z2GSDh1W7mz2PKBsYny5W9d0xRb+gAAANKTVhz50Ucfreuuu07RaN3eJ8MwtHLlSk2aNEknnHCCqxMEckV9iETUiKrGU53wo14sScx4tmLL67f0FRuFST/8ym+0pQ8AAABprTjNmjVLJ554okpLSxUKhXTwwQdr9erV2m+//TRjxgy35wjkBL/l196RIYonKSbCjXbAxY3E++8MGbpow0SZSVavpKax5WGZClmJxwbkk+Egxi+VLX0AAACok1bhVFJSohdffFFvvPGGPvroI1VVVWnvvffWiBEj3J4fkDHJk/fqeD1+FfgSb10zGhU2ZsSriJV8Udfn8zuOLD8jdqVUlfjxfka5biuc6Kh4AgAAQGq26gDc/fffX/vvv79bcwGywmnynlQ3pqybkqbv1ZsxaYjtGLvznnyWT70i5frGv9z2Wp9Zy7Ulaqo4SWGXKkIkAAAA6qRVOF1yySXaaaeddMkllzS5f+7cuVq6dKnmzJnjxtyAjHCSvCc5S98L+KR+feL6bIXX0WvbnfdkyNCEjRNVEzdVm+B1TcPUzB3qtvPFbd6DU4RIAAAANJVW4fTkk0/q3//+d7P7hw0bpj/+8Y8UTmhz3EreMwzppkvCes37tmvnPRkyVOj1SwlqsUgr7MzjXCgAAICm0iqcfvzxR5WUlDS7v7i4WOvXr9/qSQFtWf15T4YVlpXgvCcrjdjyTCNEAgAA4Gdp/fS200476fnnn292/3PPPafycucRy0B75GZsearqkvciST8sK0k0HwAAAFqU1opTRUWFJkyYoHXr1mn48OGSpAULFmjWrFls00OH52ZsearskvckaXdvueYE3U3fswuRIEACAAC0dWkVTmeffbYikYhmzJih66+/XpLUu3dv3X333TrzzDNdnSCQa7ITW16bPHkvXK5vAvbJe5L0aXy5wjIVdKEnyWmIBAESAACgrTOsrdy3s27dOgWDQRUVFbk1p1ZVWVmpkpISbd68WcXFxdmeDtqQUERasKguktyOXWx5KCIdMdn5a9vFlodNS5tMU/vsKgVazqNQ2DJ1UnVd+t7TRbe4VsRELDNpiETEMhVRVKN9w9TJKHTlNQEAANyQSm2wVec4SVK3bt229hJAm5DrseU+yy/FJCXaNdfoVyRhy76vKiCfo+18TkIkCJAAAABtXVqF05o1a3TZZZdpwYIFWrt2bbNm87hbh8kAOSZXY8s9nrpVrMVfJR5jGpJ2qrtdv/KUTGv0QgEAALRVaRVOY8eO1cqVK3X11Vdr++235wcrIA1uxpbn50mlnZXwkFxJKpBPZeFyrcpCLxQAAEBbl1bh9Prrr+u1117TwIEDXZ4O0HHUx5aHPCFFjZa3spmenwunmEz5k/yVzbf922zo/LUTU+qFcpNd8p5E+h4AAMhdaRVOZWVlnAUDbKVUY8tDpqG8JEevJUveq1ffCxUwpGCGFoqdJu9JpO8BAIDclVbhNGfOHE2ePFn33nuvevfu7fKUgI7Db/mlJFvhGseWz5g0JOm17JL30uFGiITf8KnMU5o0eU+qS9+rUkgxxZTsawIAAJANaRVOY8aMUU1Njfr27auCggLl5+c3eXzDhg2uTA5oy5yd95Q4slxKLX3PLnnP6dzCjYo1t0IknCTvSaTvAQCA3JX2ihOAluV5paJg3VlOpk0dYHfek2FIsy6J6R3jHYU8LR8gFY14NfvyQyTZ90E5Sd+z5NMOO5Tr22B2QiTohQIAALkorcLprLPOcnseQLvh5nlPkhSQX/tqkOK1LfdChWul2T/djhvJL+YkfU8ydPbqiaqJZzZEgl4oAACQy7b6ANxwOCzTbNoHYXfqLtDeuXXeU71kvVBGijkt9ul7kkxDsVhmQyTohQIAALksrcKpurpakyZN0uOPP64ff/yx2eMcgAt0LHYhEnYBEvVS6YViSx8AAMiktAqnK664QgsXLtTdd9+tM844Q3feeae+++473XvvvfrjH//o9hwB5Di7LXtOAiScYksfAADIhrQKp6efflp/+9vfdMghh2jcuHE68MADtdNOO6lXr176+9//rtNOO83teQJwIGqYCtv0OXktj/LlbGUnWfqeZfm0m1Guzy37EAk3AyTY0gcAALIhrcJpw4YNKi8vl1TXz1QfP37AAQfowgsvdG92AFJSY9YqarScvlcv5q9W93hp0uLJSfqeZOhYTdSJAVN77ST94lQCSe4HSNQj3hwAAGRaWoVTeXm5VqxYoR133FG77rqrHn/8cQ0dOlRPP/20Onfu7PIUgfbNjfOe6tXHkidT1nejLvzD52qhzmngLH1PMuOGomG/8mszFyIBAACQDWkVTuPGjdNHH32kgw8+WJMnT9ZRRx2luXPnKhqNavbs2fYXAODaeU8Bn7RHH+mTFc5ed9WyLoqaHgVtCjFn6XtStOWUdAAAgHYlrcLp0ksvbbg9YsQIffHFF1q0aJF22mkn9e/f37XJAe2ZW+c9GYZ0+8VSOHmwncKmdPy0utuZ6oUKW41v20xQztP3UkH6HgAAcMNWn+MkSb169VKvXr3cuBTQobh13pNhpHatqBFVjSd5QREyQiqNbV0vlGlI2qnutpNeJ9L3AABArkq7cFqwYIEWLFigtWvXqvYXjRAPPPDAVk8MQOsZGtpXgSQLThEjosWBRYobtcpPcsCuXS9UgXwqC5drVcA+eU8ifQ8AAOSutAqn6dOn67rrrtPgwYO1/fbbu761BkDrClqFCiYpiFKRvBfK0IXrJ+r7zaYCSWqSqMfUbeV1K1LRqGz7r5wifQ8AALglrcLpnnvu0YMPPqgzzjjD7fkASMAufc9p8p7koB/KkKyAs2vZ8eUZ6lniT5rQZzb63Ut13Ey6yiVlpxeKPigAADq2tAon0zQ1bNgwt+cCoAVO0/eSJe/9Un1IRGKFKus7WOMv+0iyqU+chEjYJvQ1eo0zYldKVcmHZ6MXij4oAAA6trQKp/Hjx+uRRx7R1Vdf7fZ8APyCk/Q9u+Q9Kb3Y8ppoXDF/ddJxTkIk7Pgsn3qFy/VNjvZC0QcFAAAcF04VFRUNt2tra3XffffppZdeUv/+/ZWf3/QoTc5yAtzlRvpeOrHlboVI2M5Nhs5PsRdqS9RU0lN85XxLn5NeKPqgAADo2BwXTosXL27y+cCBAyVJn3zySZP7CYoAcleqseVuhkjYSbUX6tTIlZLNEU1ubukDAAAdm+PCaeHCha05DwA5KJMhEpJ9L5RP2dvSBwAAOraUepzi8bg+/fRT7bzzzgoGg00eC4VC+uqrr7THHnvI4/G4OkkA2eFWiISTAAkn0tnS52a8OQAA6LhSqnAeeughnX322fL5mv8Ukp+fr7PPPluPPPKIa5MDkHn1IRJO1YdI1HiqE36szVurqGyWrxyq39LXxZf4o3P+z/9GxZOEaqSqxopoi1Wd9CNkE2sOAADappRWnP785z/rsssuk9frbX6hvDxdccUVmjt3rk4//XTXJgggs9wOkXArQKKxVOLN3eA0slwithwAgPYqpcLpyy+/1L777pvw8SFDhujzzz/f6kkBSI/dIbmSs4NyczlEIhucRJZLxJYDANCepVQ4VVdXq7KyMuHjW7ZsUU1NzVZPCkBqnB6SK6V2UK5TyVan3A6QSNXmmCmFko8p8Prk9yVfpnISWS4RWw4AQHuVUuG08847680331T//v1bfPz111/XzjvvnNIE7rzzTt18881avXq1BgwYoDvuuENDhw5NOH7OnDm6++67tXLlSnXt2lUnnniiZs6cqUAgiz+ZAVnm5JBcydlBuelIHiJRFyBx4R+ysxo9XldKseRjdqwq190lE22LJ6dqHPQ55SmP7XwAALQhKRVOp556qq666ioNGzasWfH00Ucfadq0abriiiscX2/evHmqqKjQPffco3322Udz5szRyJEj9eWXX6q0tLTZ+EceeUSTJ0/WAw88oGHDhmnJkiUaO3asDMPg0F10eG4ckpuK+hCJT1bYj121rIs2VVmK+pI3TgXyJZ/DlZ1kfJZPvc1yfe1zFlu+MrBcGyKmOtcm/iJ6vZLP5sBdeqEAAGi/DMuyHHcmRKNR/eY3v9Hrr7+uESNGaNddd5UkffHFF3rppZe0//7768UXX1R+vs1PFz/ZZ599NGTIEM2dO1eSVFtbq7KyMl188cWaPHlys/ETJkzQ559/rgULFjTc94c//EHvvPOOXn/9dUevWVlZqZKSEm3evFnFxcWOngO0F1tqpGffkooLpAIXFmkty2abnukk0vxnPfv+qEsqvnKleLJkyTSSF2qmYWp6aV1s+WVLb5HPSlzEBP3SoF3si6eIZTrqhYooqtG+YepkFCa/IAAAaDWp1AYprTjl5+frhRde0K233qpHHnlEr776qizL0i677KIZM2Zo4sSJjosm0zS1aNEiTZkypeE+j8ejESNG6K233mrxOcOGDdPDDz+sd999V0OHDtXy5cv1n//8R2eccUbC14lEIopEft42k6xHC0Bq7EIkUlmVkqTvlm2rcPQrtXDiQepzkyF/kkLol/IDZsLUPzMu1UR8qg4btvHmXq9PBQ7+GYxYUbb0AQDQhqRUOEl1xdMVV1yR0pa8lqxfv17xeFzdu3dvcn/37t31xRdftPicU089VevXr9cBBxwgy7IUi8V0wQUX6Morr0z4OjNnztT06dO3aq4A0pNOtHm2zNwh8b8jktSjulz6aqIMm6xzJytTbOkDAKDtSblwyqZXXnlFN9xwg+666y7ts88+Wrp0qX7/+9/r+uuv19VXX93ic6ZMmaKKioqGzysrK1VWVpapKQM5ya3YcidSjTavNuOSzWG52eiF+r5wuXwBM+l2PjMuhSI/HbqbpHAi3hwAgLYna4VT165d5fV6tWbNmib3r1mzRtttt12Lz7n66qt1xhlnaPz48ZKkPffcU9XV1TrvvPM0depUeTyeZs/x+/3y+/mBA5CyH1vuxE1X7G87xq1eKEOGLtowMWkvVOM+KJ9P8ifrCjWlqE2CX71U4s3ttvSxnQ8AgNaXtcLJ5/Np0KBBWrBggY499lhJdeEQCxYs0IQJE1p8Tk1NTbPiyOv1SpJSyLgAOqxsx5Yn0pZ6oTLJ6Za+IgW1b/4e8hvJm6sosAAASF9Wt+pVVFTorLPO0uDBgzV06FDNmTNH1dXVGjdunCTpzDPPVM+ePTVz5kxJ0lFHHaXZs2drr732atiqd/XVV+uoo45qKKAAJJfp2HIn0umFMiNeRazmq8yN+Xy1Mtw5mikrnGzpi1oxrbY26KXoe7bXo18KAID0ZbVwGjNmjNatW6dp06Zp9erVGjhwoJ5//vmGwIiVK1c2WWG66qqrZBiGrrrqKn333Xfq1q2bjjrqKM2YMSNbbwGAS1LthZoxaYjtmN59K3VJxdKMF0+OesgcnAslOdjSZ0gBy0e/FAAArczxOU6NAxbs5PJhtJzjBNhz+7wnt1iWNOGOWn22IvlKU2P/79Z3VehP/3dEESOiqd0vkyRds/YG+azEhUwsJm3c6FOB375Sc3oulFtqrLAqrWrOjgIAoJFWOcdp8eLFjsYZbXlfDIAmMpm+54RhSHMv9qgyGlFciVMYwqZ02tV1xUHccK9Rqz4kIpleXct17prkseVO0/daA2dHAQCQHseF08KFC1tzHgBySC6n7xmGVOLzK9l2s5CLWTGpRJZL0jf+5TL8ZvLAiRTS99zC2VEAAGydNnWOE4DMyNX0vXRsbYiEk8hyqWlsuVNu9kLZSfXsqEqr+qd+qMRYmQIAdCRpF07vv/++Hn/8ca1cuVKm2fQHivnz52/1xABkVy6m76XDSYhEn75VurhiSdLiyc3Ico+nbqve4q/sx7rZC+Xk7CivPPqxtpKVKQAAfiGtwumxxx7TmWeeqZEjR+qFF17Qb37zGy1ZskRr1qzRcccd5/YcASAlAZ/Ur09cn61wdkzBimVFqqrKk8+XeDUmlWhzu9Up5UvdOvtk1Sa/YDZ6oVJdmSKlDwDQUaRVON1www269dZbddFFF6lTp0667bbb1KdPH51//vnafvvt3Z4jgBxnt+UskwESUl0f1E2XhPWa923lW/nKT5CEZ0a8DStS0yb1T3pNu1Wpxpxs2ettluuiDclDJOp7oWy/vi5t56vnZGVKkiJWlLAJAECHkVbhtGzZMo0ePVqS5PP5VF1dLcMwdOmll2r48OGaPn26q5MEkJuchkhkOkBCkvKMPJXk+xXyhBRTyz/cGwGprO9GrVrWxfZ6K5YVyTQ98vtbXolJNUTia99yVXmqksebe6WaiE+Lv0perWU62lwibAIA0PGkVTh16dJFW7ZskST17NlTn3zyifbcc09t2rRJNTU1rk4QQO5yEiJRHyBRHbEPkXBzZcpv+bV3ZEjS2HJJ2uviiN71LFBBbaH8VvNDq0zT07AaZZrJQybG/1Ahwx9W0kWkRiESbsSbZyvanC19AICOJq3C6aCDDtKLL76oPffcUyeddJJ+//vf6+WXX9aLL76oQw891O05AshhdiES2Yw2rwt0sJmgIfn8tfLV1spvJS8C7LbzSfZb+lyPN89CtHm9VLb0AQDQ1qVVOM2dO1fhcFiSNHXqVOXn5+vNN9/UCSecoKuuusrVCQJo29pKtHk0QaCD5Zd69a3UN8uSnyZez25LXzrx5snGmoZkyadw1L75yu1eKAAAOpK0Cqdtttmm4bbH49HkyZNdmxCA9ieVaHNHZxu5uKXPqzwFa4MKeUKKGi2/+BmXv6PKqKnSWDflKUHQRKMtfXZSjTe329LXo7pc+somaELZ6YUCAKC9SKtwGjFihE4//XQdf/zxKi529ltYAEgmW1v6nPRCRYyIFgcWyZsXk9/KzLnhqWzp+75wuRSsSpgeKNX1QtVEfIrHjYz2QgEA0F6k9RPA7rvvrilTpuh3v/udRo8erdNPP11HHHGE8vP5vzGA9GRzS5+jXqgMc7Klr/F2vpk72AdN7BAq117RiUqaXqHW2dJnF1tOZDkAINelVTjddtttuvXWW/XSSy/pkUce0Zlnnimv16sTTzxRp512mg4++GC35wmgA0hlS18us0vfk5wdqGu3pS/VoIlvg8v17lJTPpttgm5u6XMaW05kOQAg1xmWZVlbe5FwOKynn35aM2bM0P/+9z/F4za/Ms6iyspKlZSUaPPmzWwzBNqgLTXSs2/V/VAfsPnB3q1eqBqjWm8H31RBbaECLUSWS1Ik4tHkSwc6vmYqB+omY8lKKWjimlW3JC2czHhdSt9+/aRgy281ZRHLTBpbHrFMRRTVr/MHqcCmcGJlCgDgplRqg63erL969Wo99thjevjhh/Xxxx9r6NChW3tJAEgom/Hmyfh8terTt0orlhU5Gm+XvudUqkET8ptSsl+XmZIV88luO18q7GLLvfLox9pKx4fp7pu/h/xG8qqZAgsA4La0CqfKyko9+eSTeuSRR/TKK6+ovLxcp512mubNm6e+ffu6PUcAaJDNXqhEkeWSJEM69w8fqzaSlzB5T0otfa81ODl0d4dQufazJsrN4ikZp4fpRq2YVlsb9FL0PdtrsvUPAOC2tAqn7t27q0uXLhozZoxmzpypwYMHuz0vAEgo071QTiLL64UKQyqNlSo/SfFUz61eKNtrpNELtTZWpZJQ8vdQ4PXJ73OnuHJ0mK4hBSyfbYEVsUxVKaSYYsq10A8AQNuVVuH073//W4ceeqg8Hvv/6QNAW+ckslz6ObY8btQq30H3qJOVJzd6odI5dHe8rpTN29WOVeW6u2Sia8WTE44KLEkRy8GBYAAApCCtwumwww5zex4A0CrcOlDXrcjyXO6FSnVlamVguWripvys6gAAOgDHhdPee++tBQsWqEuXLtprr71kJPn15wcffODK5AAgXbkaImEY0sUVS2y36TXuhbIb68Z2Pim9lamwTIVsVtcC8iX9f0ZrsTs7SiJEAgDgnOPC6ZhjjpHf72+4nY3/CQKAUzkbIiFJhuT1exz1QUn2W/rcijavm1pqKX1nxK6UqpKP2d1brjnBiRn7/4bTs6MkQiQAAM45LpyuueaahtvXXntta8wFAFyVSoiE3ZY+J9v5UgqRMJKHSKSypc+t7XxO+SyfeoXL9U3A2Za+T+PLFZapYIa29DlN6SNEAgCQirR6nMaPH6/TTz9dhxxyiMvTAYDMcrqlz8l2PjdDJJxs6ctWtLkhQ+evn6jvN5sKJKk3oh5Tt5XXbemLRqVgBrZC1kslRIItfQAAJ9IqnNatW6dRo0apW7duOuWUU3T66adrwIABbs8NAFqdky199dv5qiP2W/q8Hr8KfM5+wHZzS1+mos0brpVnqGeJX7VJvh5mo9eK22yZzAa29AEAUpFW4fTUU09p48aNeuKJJ/TII49o9uzZ2nXXXXXaaafp1FNPVe/evV2eJgC0HrstfW4HTbi5pa9epqLNG8u3+z9Io9cJx6RQOPFQr1fy5bsyLcfY0gcASEVahZMkdenSReedd57OO+88ffvtt3r00Uf1wAMPaNq0aYrFbA7/AIA2xO2gCbe29GUr2jwdHy2VfEnS94J+adAu2SmenHCypY/tfADQvqVdONWLRqN6//339c477+jrr79W9+7d3ZgXAOSUVIImnEjlXKiEW/oM6dw/fKzaSJ7ykqxIZasXqrH8gJmwn8uMSzURn+JxQ8pw4eSE0y19bOcDgPYt7cJp4cKFeuSRR/Tkk0+qtrZWxx9/vJ555hkNHz7czfkBQIfldEtfqNDZdj4p871Q9WbucGXSx3cIlWs/a6Ka7O/LEU629LGdDwDav7QKp549e2rDhg0aNWqU7rvvPh111FENZzwBANzhZEtf/Xa+sCesuNXyD/am8XOxlMleKJ/lU2+zXF/77GPLvw0u12ZVybCSF3/ZOkzXyZY+EvoAoH1Lq3C69tprddJJJ6lz584uTwcA0Jjdlj4nq1JWQCrru1GrlnVx9Jpu9UIZMnTRhokyk6QHmoap6aV1q1FODtPtZ5TrtsLMHabrFAl9AND+pVw4RaNRXXjhhdpvv/0onACgBXaH6UrODtR1wmnQxF4XR/SuZ4EKagvltwItjmmNXihDxk/FX8t8lk+9IuX6xu/sMN3PrOVaF61Sp/zcWpkioQ8A2r+UC6f8/HztuOOOiufioRwAkEVux5Y75ShowpB8/lr5amvlT7ClLxsMGZqwcaJq4qbNmVBmQ5/UqZErJZsdcbt7yzUnmNmVKQ7dBYD2La2telOnTtWVV16phx56SNtss43bcwKANsnt2PLWkOzQ3ca9UJkMkTBkqNDrl7yJx1jyqVe4XN8EnK1MfRpfrrBMBXNsVYctfQDQdqVVOM2dO1dLly5Vjx491KtXLxUWFjZ5/IMPPnBlcgDQ1rgdW+4WJ71Qpic7IRJOGDJ07tqJ2mSaGrCTFEjwf6+wzLpeqRzFlj4AaLvSKpyOPfZYl6cBAB1PrvVCWZb0VJ+ovljh7DClTB+o6/UYiof9+vyrxGNMQ9JOdbe3RE3bc6GykdKXypY+AEDuSKtwuuaaa9yeBwB0GLncC3XrJRG9Y7ypkCeUcEw04tXsyw+RJMVkyr/1Z6k7kp8nlXZW0l6o/EY1UK72QqWCXigAyB2Z+b8dAKBBLvdCBeTXvhqkeG3ilalwrTT7p9txI7ONWvk2/9fy0QsFAGglaRVOHo8n6W/nSNwDgORS6YWy29Ln1na+enYrU4b18+1qMy4pceCEJAXyJZ/D7Wlbi14oAEBrSatw+uc//9nk82g0qsWLF+uvf/2rpk+f7srEAKCjc7qlz83tfKm66Yr9bcf07PujLqn4KmPFU6q9UOuqTHWy+b9hgdcnv49eKADoyNIqnI455phm95144onafffdNW/ePJ1zzjlbPTEA6OicbOnLynY+n7RHH+mTFc7Gf7dsW4WjX8mXocIu1V6os3WlbM4P1o5V5bq7ZGLGiyen7Hqh6IMCgK3nao/Tvvvuq/POO8/NSwJAh5aL8eaGId1+sRROvkNPYVM6flrdbTPiVcRKfDaUW2dC1XPSC9XbLNfXPme9UCsDy1UTN+XPse1wTnuh6IMCgK3nWuEUCoV0++23q2fPnm5dEgDgUCajzaW64imVom7GpCFJH8/kmVBSXS/URRsmykxyILAkmYap6aV1vVBhmQpZicdmK9rcrheqvg+q0qr+qRcqMVamACCxtAqnLl26NPmfg2VZ2rJliwoKCvTwww+7NjkAQHLZijZ3IuCT+vWJ67MVXtuxmT4TSqornuqCMJw5I3alVJX48WxFm9v1Qnnl0Y+1lST0AcBWSqtwmjNnTpPPPR6PunXrpn322UddunRxY14AAAdyOdrcMKSbLgnrNe/bKqgtlN8KNBtjmh5Nm9Q/c5NKkc9yHm+eq9HmJPQBgDvSKpzOOusst+cBAEhTLvZB1TMMyeevlWGFZVnNf3C3jJ/7nkwzcQ9UPbd7oewYMnT++on6frOpQIKvc9Rj6rbyuu18W6KmlJ/8mtna0ucECX0AkFhKhdP69etVXV2tXr16Ndz36aef6pZbblF1dbWOPfZYnXrqqa5PEgDgjkz3QnmVp2BtUCFPSFGj+Yubnp+LJScrT5nuhZIkX56hniX+hCl9ZqO5nBq5UkoecJe1LX0AgK2TUuF08cUXq0ePHpo1a5Ykae3atTrwwAPVo0cP9e3bV2PHjlU8HtcZZ5zRKpMFAKQnW71QfsuvvSNDFE8QSmBZ0lN9ovpihc0yzU+y0QslJU/p88n5dj4pd7f01bOLNpcIkQDQMaVUOL399tt68MEHGz7/29/+pm222UYffvih8vLydMstt+jOO++kcAKAHJPNXqi6AIbEP2Tfckm1XvO+qnwrX/lWy9WaGfHaJvNliyFD566dqE2mqQE7SYEE/2cNy6wLmMhRTqPNJUIkAHRMKRVOq1evVu/evRs+f/nll3X88ccrL6/uMkcffbRmzpzp6gQBAO7I1V6oPCNPJfl+hTwhxRLsc4s12tJXY1Q16Y1qidfyKF8Zig+U5PUYiof9+vyrxGNMQ9JOdbdzsRcq1RAJ4s0BdDQpFU7FxcXatGlTQ4/Tu+++q3POOafhccMwFInYL/EDAFDPbjufJIUb/a+lKhpTxJN86Szmr1b3eGnGiqf8PKm0sxL2QUlSfhvohXISIkG8OYCOKqXCad9999Xtt9+u+++/X/Pnz9eWLVs0fPjwhseXLFmisrIy1ycJAMisTIdI2G3nMxodPDv78kNsr1fWd6Mu/MPndos6rkrWByW1n14oVqYAdFQpFU7XX3+9Dj30UD388MOKxWK68sorm5zb9Nhjj+nggw92fZIAgMzI1QN1Az5pjz7SJyucjV+1rIuipkfBzO3Ws9VeeqEkVqYAdEwpFU79+/fX559/rjfeeEPbbbed9tlnnyaPn3LKKerXr5+rEwQAZE6uHqhrGNLtF0thM/m4sCkdPy0zc0pHe+iFcoqDdwG0NykfgNu1a1cdc8wxLT42evTorZ4QACC7cjVEwjBSm1u1GZeUvNIK5Es+h4fDuqG99EI5xcG7ANqTlAsnAADagpuu2N92TGnZZl186ZfKt/kB3+erde3Q3Y7SCwUA7Q2FEwAgbZkOkbCTai/U2lUlurpiqO24Pn2rdHHFEteKp2TaUy9UKuwO3iVAAkC2UTgBAFKWqyESTnuhJGnCHXEt/87r6LorlhXJND3y+zPT1OV2L1Su9kFJzg/eJUACQLalXDjFYjE98sgjGjlypLp3794acwIA5LhcDZGQnPdC3X5ZWK9531a+la98q+Wqzox4NWPSEJdnaM/tXqhc74OyC5EgQAJALki5cMrLy9MFF1ygzz//vDXmAwBoI1IJarDb0pfJ7Xz18ow8leT7FfKEFEtQdcQ8np9vy5Q/gxs13OyF+jS+XJusKgVsDgTO1sqUkxAJAiQAZFta/wcYOnSoPvzwQ/Xq1cvt+QAA2hGnW/oyuZ2vnt/ya+/IEMWTHM4ablRPxY0MLps54KQXqnEf1EnV9v1QubwyJdn3QUn0QgFoPWkVTr/73e9UUVGhVatWadCgQSosLGzyeP/+/V2ZHACgbXOypS8b2/nq+S2/km39Mqyfb9vFm2c62lyy74Wy5NMOO5Tr22DbTuhz2gcl1fVC7Zu/h/xG8gOwKLAApMqwLMuyH9aUp9HWhYYLGYYsy5JhGIrHbTa9Z1FlZaVKSkq0efNmFRcXZ3s6ANDhbamRnn1LKi6QCgLZnk1ToYh0xGRnY3v2/VGXVHyV8eIpGkveCxWJW6qJm9pnVymQ4OsbtsyGFamni27JyYIiYpm2h+lGrZjWWZtUZARtr0fYBAAptdogrRWnFSsc5rwCANCGpRJv/t2ybRWOfiVfhnu17HqhZBqKxfwKGFIwN3fgOeLoMF1DClg+2wKLsAkA6UircKK3CQDgtlw7E0pyFm8eNqXjp9Xdjhqmwja9UF7Lo3ybkIZsClv2We65HG/uqMASYRMAUpd2PNBDDz2ke+65RytWrNBbb72lXr16ac6cOerTp4+OOeYYN+cIAGjHcvVMqHpO480lqcasVdQIJR0T81ere7w048VTssI03GjTfnsIkQCA1pBW4XT33Xdr2rRpmjhxombMmNHQ09S5c2fNmTOHwgkA4FgunwmVqtmXH2I7pqzvRl34h8+TnVfrKo+nrldrcZLDdNMJkcjleHMAaA1pFU533HGH7r//fh177LH64x//2HD/4MGDddlll7k2OQBAx5DKmVC5JpU+KElatayLoqZHwQwtODk5TFcydPbqiSmFSLSHlSnizQGkIu1wiL322qvZ/X6/X9XV1Vs9KQAA2gonfVBSdnuhbAMkJEchEgH5tLu3XJ/G2/bKVKrx5qTvAZDSLJz69OnT4gG4zz//vHbbbTdXJgYAQEtyNUQilVWzXO6FSsYwDM0JTlQ4yXlWUu6vTPkNn8o8paTvAUhJWoVTRUWFLrroIoXDYVmWpXfffVePPvqoZs6cqT/96U9uzxEAgJwPkUhFLvZC1bMvTA15vX75kkwsnZWpTB+8m0r6Hlv6AEhpFk7jx49XMBjUVVddpZqaGp166qnq0aOHbrvtNp1yyiluzxEAgDYfIpHrvVBOQiTqBf1Sv16SL+FPEYZmGhMV85vKT1JgNV6ZykVs6QPQWNpx5KeddppOO+001dTUqKqqSqWlpW7OCwCAZtpyiESu90I5C5GoK1w3VEqLlthd0VDQ79egXZR0dSqXsaUPQGNpF071CgoKVFBQ4MZcAABwDb1QqXMSIuFXXSFkV2CZ8boVrOqwFE+wStj4/Ci7g3ezFW3OgboA6qVVOP3444+aNm2aFi5cqLVr16r2F/96btiwwZXJAQCQKnqhWp+TAssTkyqrk2/9Mw1JO9Xdttuyl+vR5gDav7QKpzPOOENLly7VOeeco+7du/OPGAAgZ6TaC1Udse+HyuTKVK73QjnlZOtfgXwqC5drVcA+RCIbARKpsguRIEACaNvSKpxee+01vf766xowYIDb8wEAYKs52Q6XqytTrdEL5eaZUKmwX5kydP7aidpkmhqwkxRoYXxYps6I5W6AhOQ8RIIACaBtS6tw2nXXXRUKJd9zDQBALsvllD63e6Fy8Uyoel6PoXjYr88TbOlrvJ1vS9SU3Z7EbPRCOQmRIEACaPvSKpzuuusuTZ48WdOmTdMee+yh/F9kjRYXF7syOQAAWlNbTulrzK4XKltnQjlht6Uvv1ENdGrkSsnmSKW+nh00p2Ci7eu6XWA5CZEgQAJo29IqnDp37qzKykoNHz68yf2WZckwDMUTxecAAABXpNILtWpZF9VEYzJ84aTjcnFLn08+9QqX6xsHfVCStKz2Wx1VdZntuGyFTXCYLtB2pVU4nXbaacrPz9cjjzxCOAQAoEOwizfPRrS5XS9U0z6oqGo8yX9oDxkhlcZya0ufIUPn2vRBSZIlaXJsjpbrW0fXzXTYBIfpAm1fWoXTJ598osWLF+tXv/qV2/MBACCnOA2RyEa0eSq9UEND+yqQpE8rYkS0OLBIcaNW+Vbicdlg1wdVb4yuUF7A1F47SfkJ9iWGLdM2+rw1cJgu0PalVTgNHjxYq1atonACALR7TkIkshEgkaqgVahgjhVETjmJNpckM24oGvYrv1YK5uBmGA7TBdq2tAqniy++WL///e91+eWXa88992wWDtG/f39XJgcAQC5oDyEStvHmhmQFMjOXdDg5dFemFI21+lQAdFBpFU5jxoyRJJ199tkN9xmGQTgEAKBDs+uDkjLfC1WvvtcpsUKV9R2s8Zd9JNms1mQrRAIAsimtwmnFCofHmQMA0AHk6mG6qSTvSfXpe3HF/NVJx+ViiERjyQrYsNX4ts0ynLJzLhSA3JRW4dSrVy+35wEAQJuVq4fpOknek5qm7zkNkQh7wopbyd9IplemPB4pFJEWJwmRaHygrpOQCGLLAdRLq3CSpGXLlmnOnDn6/PPPJUn9+vXT73//e/Xt29e1yQEA0Fbkah9UKsl7kmRECmUkCZHwGHkK5AcV9oYUNZIvr2V6ZcpJiESBfCoLl2uVw3OhiC0HUC+twum///2vjj76aA0cOFD777+/JOmNN97Q7rvvrqefflqHHXaYq5MEAACZYd8L5VffnsN04yWhpK1QESOiz4ozvzJlHyJh6PyfzoXaZ1cpkCAQo3FseSa39BFbDuSutAqnyZMn69JLL9Uf//jHZvdPmjSJwgkAgCRyLUQi1V6oZd95dOKkQptRherVd6jOuPxdRT25tTJlyJDP8ksxSYl2xDVadcv0lr5UYsvZ0gdkjmFZVsqnOgQCAf3vf//Tzjvv3OT+JUuWqH///gqHw65N0G2VlZUqKSnR5s2bVVxcnO3pAAA6kFBEWrCoLiDCTqYP1LUs+14oSbrkDmnpd86v+/frqxVI8h7qV6YKrUIFMpSHHo1Jazcl38JoydJDO8zRt0FnW/ok6emiWzJWoEQsU6tq16rICNqOZUsfkFgqtUFaK07dunXThx9+2Kxw+vDDD1VaWprOJQEAaPdyNURCct4Ldd8fUgubOO1q+5Wpsr6DdeEfPnc0Tzc4O1DX0NmrJ6om7nxLXyaxpQ/IvLQKp3PPPVfnnXeeli9frmHDhkmq63G68cYbVVFR4eoEAQBoT1IJasi1LX2SswIrnRj0qOlRMIPvw9mBuoZiMb8ChhTMwUTyVLb0Adh6aRVOV199tTp16qRZs2ZpypQpkqQePXro2muv1SWXXOLqBAEA6Ghy9Vwop9KJQa8245ISPyGQL/kcFgpuc3ou1KawqWTNCgVen/y+7FRgdr1Q9EEB9tLqcWpsy5YtkqROnTq5MqHWRo8TAKAtCEWcb+krLpAKMtMe5KpQRDpisrOxPfv+qEsqvspo8eSkF8o0Irplp8scXW/HcLnuLpmY0eLJaS8UfVDoqFKpDTzpvMDw4cO1adMmSXUFU33RVFlZqeHDh6dzSQAA0EjQL3UqSP7RFoulxuq39Dnx3bJtHW1ddFN9L1ShP/FHZ59PvcLljq63MrBcGyKmQmEl/bBbZUxFfS9UsVGY8MOv/EZ9UAASSWur3iuvvCLTbL6cHg6H9dprr231pAAAQPvnZEtf4+182eDkXKgJmybKNBK/CdMwNb20LkDio6WSz2avT9AvDdpF8uWnNtdEnPRC0QcF2EupcPr4448bbn/22WdavXp1w+fxeFzPP/+8evbs6d7sAACALbuVmEwHSKTCaZpfLjNkyG85exP5AVP5SQonMy7VRHyKxw3JpcLJKc6EApJLqXAaOHCgDMOQYRgtbskLBoO64447XJscAABIzGmIRC4GSKTDLkBCym6IhBMzd7CPLt8hVK79rImSMtML5ZVHVVZIC6OLbMfSC4WOLKXCacWKFbIsS+Xl5Xr33XfVrVu3hsd8Pp9KS0vl9XpTnsSdd96pm2++WatXr9aAAQN0xx13aOjQoS2OPeSQQ/R///d/ze4/4ogj9Oyzz6b82gAAtFVOzoXKxplQreWmK/a3HdOz74+64A+fKs9I/iOO1/IoX5kpsHyWT73Ncn3tc3aY7rfB5docM2WEkxcnXq872/k4EwpwJqXCqVevXpKk2uQnxqVk3rx5qqio0D333KN99tlHc+bM0ciRI/Xll1+2eJju/Pnzm/RX/fjjjxowYIBOOukk1+YEAEBb0da3udlJ9Uyo75Ztq6pYVD5/8m1nISOk0lhpRoonQ4Yu2pC8D0rKbi8UZ0IB9tIKh/jrX/+qrl27avTo0ZKkK664Qvfdd5/69eunRx99tKHAcmL27Nk699xzNW7cOEnSPffco2effVYPPPCAJk9unlG6zTbbNPn8scceU0FBQcLCKRKJKBL5+R/PyspKx3MDAADZlc6ZUEND+yqQ5He8ESOixYFFihu1SfuN3JRKH5RUl9iXrHAy43Vx7vG46IUCMiStwumGG27Q3XffLUl66623NHfuXM2ZM0fPPPOMLr30Us2fP9/RdUzT1KJFixoO0ZUkj8ejESNG6K233nJ0jT//+c865ZRTVFhY2OLjM2fO1PTp0x1dCwAA5J5UAySCVqGCGSqIWovPJ/mTvQez7pypTKIXCh1dWoXTqlWrtNNOO0mS/vWvf+nEE0/Ueeedp/3331+HHHKI4+usX79e8Xhc3bt3b3J/9+7d9cUXX9g+/91339Unn3yiP//5zwnHTJkyRRUVFQ2fV1ZWqqyszPEcAQBoD5ycgZTL6XupsF2dMiSrjZ+BlQ30QqGjS6twKioq0o8//qgdd9xRL7zwQkNhEggEFAqFXJ1gMn/+85+15557JgySkCS/3y+/n7+0AICOyWnyntR+0vfsz30qVFnfwbrwD59nYjppse+Hkiz5lKnkvXr0QqEjS6twOuywwzR+/HjttddeWrJkiY444ghJ0qeffqrevXs7vk7Xrl3l9Xq1Zs2aJvevWbNG2223XdLnVldX67HHHtN1112X8vwBAOgonCTvSW0/fS/VEIlVy7ooanoUzNEisT4kIplMx5YDHV1ahdOdd96pq666SqtWrdKTTz6pbbfdVpK0aNEi/fa3v3V8HZ/Pp0GDBmnBggU69thjJdUl9i1YsEATJkxI+twnnnhCkUhEp59+ejpvAQCADqO9J+9J6YVIRA1TYSN5pUhsefoIkUB7k1bh1LlzZ82dO7fZ/emEMFRUVOiss87S4MGDNXToUM2ZM0fV1dUNKXtnnnmmevbsqZkzZzZ53p///Gcde+yxDUUbAADYem25FyrVEIkas1ZRI3mLQcxfre7x3I0tf3+5qXybVcJin0+DdzEyVjwRIoH2Kq3C6dVXX036+EEHHeT4WmPGjNG6des0bdo0rV69WgMHDtTzzz/fEBixcuVKeTyeJs/58ssv9frrr+uFF15IffIAAKCZjtgLNfvyQ2zHlPXdqAv/8HnGEr9TjS2/rdzZlr5dwxMVjCfe0ufmqhQhEmivDMuyUg7s/GUhI0mG8fNfxnjcZiN1FlVWVqqkpESbN29WcXFxtqcDAEDOCEWc90L58qWAzQ/aubgyZVnSJXc474WSpKtue1WFvuS/a87klj5Llu7cZo7jLX2SdNnSW+RLUpC5eZiuUzVWWJVWtUb7hqmT0fKxMkBrS6U2SGvFaePGjU0+j0ajWrx4sa6++mrNmDEjnUsCAIAsc7LNra2vTKXXCxVVjSd5v07ICKk0lrtb+pIdqJvNw3SBtiStwqmkpKTZfYcddph8Pp8qKiq0aJH9nlYAAND2pJrSVx2xT+rL9MpUqr1Q/Tfvq0CS+UWMiD4rXqS4Uav8DB28m+qWvqQH6mbhMF2gLUqrcEqke/fu+vLLL928JAAAyDEdYWWqsdOutttGVqjuZfvoworPZKh5O0M9n69WRpaSw5OtTtWfCRWO2k+uNRL6SN9DW5FW4fTxxx83+dyyLP3www/64x//qIEDB7oxLwAA0Ia19fOjUj0Xas2qYl176b5Jx/TcoUYXVyyxPXapNQosu3OhelSXS19NlGEzOTd7oUjfQ1uTVuE0cOBAGYahX+ZK7LvvvnrggQdcmRgAAGjbUtkOZxeDno3tfE56oSRpwh1xLf/Oazvuu28LNLlioO24Pn2rdHHFkq0unlI5F+r7wuXyBcykARJu90KRvoe2Jq3CacWKpr9+8Xg86tatmwKBgCuTAgAAHYPTLX3Z2M7ntBfqzstiesd4RyFPgjOhLEMP3jJYa1Y5S/NdsaxIpumR3791y3BOQiQaB0gk7YOSWqUXym84+wONWFHbLX1s50NrS6tw6tWrl9vzAAAAHZCTLX25up2vXkB+7atBitcmriqG/iGidz0LVFBbKL/V8i+aTdOjaZP6uzq3VEMknHB0SLKLvVBOt/SxnQ+tLaXC6eWXX9aECRP09ttvN8s537x5s4YNG6Z77rlHBx54oKuTBAAA7VcqW/pyVV1xkuSNGJLPXytfba38ln0FaJqJQybqud0LZRdvHvNKNRGfFn9l/6Ju9kI52dLHdj5kQkqF05w5c3Tuuee2eDhUSUmJzj//fM2ePZvCCQAAYCs4WXlyqxeqnl2AhCT16lquc9ckD5FojXOhnGzpc7KdT2JLH9KXUuH00Ucf6cYbb0z4+G9+8xvdcsstWz0pAACAX3K0RSzDIRKpiiZZ1bH8Uq++lfpmWeZ6oVIJkJCkb/zLZfjN5Nv/snAuFAl9yISUCqc1a9YoPz/xrw7y8vK0bt26rZ4UAABAvfZwJpRXeQrWBhXyhBQ1Er+JMy5/R5VRU6WxbspTy2/CzV4oJwESUtMQCfuxmT8XioQ+ZEJKhVPPnj31ySefaKeddmrx8Y8//ljbb7+9KxMDAACQ2v6ZUFJdD9TekSGKK/lSTMSIaHFgkbx5MfmttDK8UpZqgISTLX2pnAvVr5fks3mrTgosNxP6JLb0obmU/kYeccQRuvrqqzVq1Khm0eOhUEjXXHONjjzySFcnCAAA0CECJHJYqlv6nJwLFYtLGyqlRUvsr+dW2ESqW/r2zd9DfiP5i1JgdRwpFU5XXXWV5s+fr1122UUTJkzQr371K0nSF198oTvvvFPxeFxTp05tlYkCAAAgO9LZ0md3LpRfdYVQrc0KoZthE0639EWtmFZbG/RS9D3ba9Iz1XGkVDh1795db775pi688EJNmTJFllX3t8EwDI0cOVJ33nmnunfv3ioTBQAAcKI9hEjkotY4EyrfyU+iP4VN2P25Ou2XcrSlz5ACls9xz1SlVf1T31RirEy1fSlvnu3Vq5f+85//aOPGjVq6dKksy9LOO++sLl26tMb8AAAAHGkPIRKpysZ5T5nm8dStOC3+Kvk4N8+OkpwVWF559GNtJWl+HUTaXYddunTRkCFD3JwLAABA2tpDiES9ZLHlpvFzsZSN856cstvWJ9X1TtkFSOTnSaWdk2/pa42zo5wgza9jyUxcCwAAQAa09RAJJ7HlVkAq67tRq5Y52+2zYlmRqqry5PMl/uG+NValnKTv9TbLddEG+/Q92y19WTg7ql4qaX5o2yicAABAh5SLvVBOY8v3ujiidz0LVFBbKL8VaHFM4/Oe7Fam3FqVSjV972vfcpmGzYG6KXD0Z+rS2VHoeCicAABAh5LrvVCOYssNyeevlWGFZVktryTl+aVefSv1zbJi29dcsaxIpumR3791+xfTSd9zg9M+KMn9Xih0HBROAACgQ2kPvVBOtvRJ0hmXv6PKqKnSWDflqXn113hVyi2tkb5nx0kflPRzL1R1+Kd+qCRaY2XK7uBdkvdyG4UTAADocNp6L5TTLX0RI6LFgUXy5sXkt9r3j31Oos09MamyOvMrU04P3iV5L7e1779BAAAA7ZSjLX0pyFa0uVvpe06kujLlVkqfk/Q9kvdyH4UTAABAEk4CB5zI9UN3sxVt7mb6nhNuHroruXvwLsl7uY3CCQAAoAWphEg4kc1DdxOdC2WlECAhuRcike30PTuETaAlFE4AAAAtcBoi4UR90ER1xD5sws2VKSchEnYBElLTEAk3tvSlk75nN9at7XxS9rb0IbdROAEAACTgVohEtiLQnYRIpBog4daWvlTT9+y29Lm5nU9KbUsfOgYKJwAAgFaWzQh0N0IkfL5a9elbpRXLihyNz8aWvkxv52uMg3c7BgonAACADGjLEeiGIV1cscR2m57b50I52dLn9mG6qaAXqmOhcAIAAMgxjlYwXE7pSxQg0cCQvH6P8hP0QbWWVLb0ZTLaXKIXqqOhcAIAAMgR2eiFchIgUS9khFQaK3VUPGXjXKhMR5tL7sWbhyzJ9EgZrkuRAgonAACAHJGNXignARLSzyEScaNW+Zb9dZ1s2eu5Q40urlgiuxomWYGV69HmkrMtfRFDsoLSqJ2lToGMTQ0poHACAADIIdnohXIjQEJKPUTiu28LNLlioO24ZCl9rRFtLmV+S19trbTFlLbEIwraFKZ5ylPQaMNNc20UhRMAAAAcS9oLZUjn/uFj1UbyEp4JJUmypDtm76Lvvi1w9Jp2KX1uR5tLUo/oDrpow0TbcU4LLLstfdGoR+uskF6LL1LApq4rUlCH+oZQPGUYhRMAAABspdQLVWjfC/WHKV+klNJnN9auXyrVLX3f53+rqd0vsx3nVoGVb/m0jVmqTqpVMMn7iFimqhRSTDG5sUoI5yicAAAAYMvtXijDUErnPNn1TNkduut0S58k3bnNHH2f/62jeTktsJyEUuRbPhUYSlo4SVLEchC7CNdROAEAAMARt3qhnEqlZ8rJobtOt/Rd+uMVrhdYTkMp7KLoSd/LHgonAACANiob5z1lkpODd90+dFdyt8ByekCv08N0Sd/LHgonAACANiYb5z2lyvZAXUley/5A3VS29GX67KhUQymScXqYbn36nl1kPdxH4QQAANDGpHreU3Uk+ZlPbq5KtdaBuk44WXmy64XKJieH6Uaikk2bGVoJhRMAAEAb5OS8J6crU26uSrXWgbqJpHp21IplRaqqypPPl3xpx82VqdYQikhbbOaX583OuWDtFYUTAABAO+VkZap+VSrZilSqMhki4aQPSmraC9WWV6Y8HilkSi/9LyKb2k/FgTwdsbef4sklFE4AAADtWEf4odlJH1Q6K1N2KX2psguScHKYrt/rUWFJSCvii5K/VlzKiwZ1YGyIgv4O8E2QARROAAAAaPfSWZlym126nqOznuRTD5Uqnpe8oKu2TFV6OCjXTRROAAAAaPfR5lLqh+66kdLns3zqbZbra99y22s5PespXz7bvjDTkiQOynUThRMAAEAHlu1oc7diy1uDG71QhgxdtGFi0m16Ts96QnZROAEAAHRgqUabuxUikc3Y8mRaoxfKzfOeUkX6nnsonAAAADq4bPzQnOnYcqdyoRfKDfXpe69+JAVtit2iYF3xTPGUHIUTAAAAHHOzFyqV2HK7LX1ubufLRi+U2/LypK7Fks8bUbIciUhM2hDOU2W1P+mqI6tSFE4AAABwIFu9UE639GVyO98v5eK5UF7Lo2h+SF+WJI8tj8Wkqo1B6cMh8iXZTsiqFIUTAAAAHMhWL5STLX2Z3s4n5ca5UMnky6fSWKniRvLXi3pNqUtIBVtiKkhQOIWjdcWw3Z99e0fhBAAAAEeytdqQypa+TGkLvVBOYsslKZoXVUFAKkgy1m6VsSOgcAIAAEC7kOloc7d6oUzDvkcK2UfhBAAAANdl8kDdXI02/6WEK0/+kLyP/nQ7Q1sNkToKJwAAALgm1RCJbiVSvs1PpHYFVq5Gm0up90JVRWOyq+l8lk+GMhjRB0kUTgAAAHCR0xCJiCm9/Vld8RSy2WHnJKUvF/ugJGe9UFXRqGb+dHtm2WTba/Y2y3XRhokUTxlG4QQAAABXOQmR6FSQnZQ+Kfd6oSzlyfq8v4zdPnZ0va99y2Ua5k/FIjKFwgkAAABZkUpKn13PlJN+qVR7oTrHO8tr8+OyGwWWIUO1U++S/GFdd+PH8iUoskzD1PTSK7fqtbZGTdh+THs+KJfCCQAAADnLac+U0+18TnqhokZUn/k+UcgTkhRJOta9AsuQIsG6DyvB6lSW0ve8nrqv78LF9mPb80G5FE4AAADIWU56plLZzueoF8pSqxRYTtL8kp751IrpexEjyfz9UmlpnvLiyb9u9QflVlbbb8FsiytTFE4AAADIadn4AdvNAssuzS/V5D2p7kwoV6LcLY9CRkiLA4uSjgv6g9o7MiRpX5XXI22obL8rUxROAAAAQJrcSPNzkrwnNU3fc0u+fCqNlSpuJF6uixqmQp7QTwVi4vfq99Vtl7Rb+atfmbJblco1FE4AAABoFzJ56K7b7JL3JMlsVNyYpkcRK3mh5fPVynCQWJ4vn+25VnZhGvWcfm3tzvjKRRROAAAAaNNSPXTXLkSiLfh/V+9RFySRRJ++Vbq4Yomj4gn2KJwAAADQpjk9dLc+RKI6Yr+dLBdXpny+RpMO2GeDr1hWWNcLZbOSBWconAAAANDmOQkZaPMrU41Wjrx/OdJ2uPV5f1k6rxUn1LFQOAEAAKBDSHVlykm8eSqihpn0cbuznnyWT73Ncn3tW+7o9YzdPlZ0pamA8lOaJ1pG4QQAAIAOIxvx117lKVgbVMgTShqyYHfWkyFDF22YKNOmAKsyY5pZNnmr5ozmKJwAAACAFriV0ue3/LbnPdmd9VTPkJH0LCVJMm3S9pAeCicAAACgkdbohXLjvKd0uBlb3tFROAEAAACNZLsXyk3ElruHwgkAAAD4hWz0QrmlSWy5AyuWFamqKi/h80zDIytQt53Qjld5tlsJ2yoKJwAAAGAruNUL5ZpGK0dX3fS+fFbLL2yanp9WpAKaNql/0kv27PujdPki21WpYG1Qe0eGtMviicIJAAAASENbOBfKLl3P+2jdeU+1U+9Sk4rrF75btq3yw53kS3KYbtQwFfKEfgrBoHACAAAAoNzthUrnvKfr5rwrXwurRKbpaViN8lsB+a3kbyJZ3HpbR+EEAAAApCkXe6GcnvdkGqaml14pSfL5a22Loo6OwgkAAADIgGS9UDWGFMuX5NIRTE7Oe0JqKJwAAACAVuSkFyrkkdZbUkGRJG9GpweHKJwAAACAVuSkF2ptWPpwhVRbKwqnHEXhBAAAALQyu16oLVbdf6OGqbBN5LfX8ihfGY7nA4UTAAAAkG15ypO/NqioEVKNJ3kyXcgIqTRWSvGUYRROAAAAQJYFDb/22DJEBfGYCpJs6YsYES0OLFLcqFW+lbn5SXXR5EkfNzyyAnVzTKbGkEwjT23trCcKJwAAACAH+Cy/PKZfSlIQxQ0plifX0vdSUX+eUzI9+/4oXb5IRpLthqZXqvUGNcoaok5tqHiicAIAAACyzEnynpT59D2fr1Z9+lZpxbIiR+O/W7at8sOd5PMnPhPKskxVekKKKaa2tOpE4QQAAABkmZPkPan10vcSHpZrSOf/4X+yIgEZSryMZJqehhUpI1IgI8lhulbUI8umjysXUTgBAAAAOcAueU/6OX3PbdNLr0z6eG+zXBdtmJi0eKrnZEvf9r03atJcx9PLCVnYHQkAAAAg23yWT73Nckdjv/YtT7wqpZ+39Dn1w9ddFEqeIZFzWHECAAAA2hgzLkVsVp88Hik/yU/7hgxdtGFi0oLINEzb1ShJMgzp4ool9sl7jbb0tTUUTgAAAEAbkeeV/D4pGpKqbQqnUEQq7WxfPPktdwIaDEPyJwmFaOsonAAAAIA2IuiX9ugjFdZKBUnajcJRafFXP4VIwBUUTgAAAEAb4suXPDJlJSmcai0pangk+TI2r/aOwgkAAABoI/KUpyIFVaWQIlbiSO+wpA2+kAqNUvkpnlxB4QQAAAC0EUHDr0N9Q346PDaxtbGIFscXqTaPvXpuoXACAAAA2pCg4ZeUPNBhS2am0qFwjhMAAAAA2GDFCQAAAGin3DjvCXX4EgEAAADtjNvnPYHCCQAAAGh3Uj3vKRxt+cwn02h827R9XZ/lk6EkL9iGZb1wuvPOO3XzzTdr9erVGjBggO644w4NHTo04fhNmzZp6tSpmj9/vjZs2KBevXppzpw5OuKIIzI4awAAACC3+fKloFH3kYjXW1dkhSJStIWgvsaF0/TSK21fs7dZros2TGyXxVNWC6d58+apoqJC99xzj/bZZx/NmTNHI0eO1JdffqnS0tJm403T1GGHHabS0lL94x//UM+ePfXNN9+oc+fOmZ88AAAAkOMils0qUZ60x84e5dW2fNaTZfn0dKxcX2q5o9f72rdcpmHKb7Wc+ufz1WrqTW9rQ6xaQf++jq6ZK7JaOM2ePVvnnnuuxo0bJ0m655579Oyzz+qBBx7Q5MmTm41/4IEHtGHDBr355pvKz8+XJPXu3TuTUwYAAAByntODciWpyhtSWX6p/EZLxZOhm0MT9eoXpgr9ki/BWbqmYTpakTIMyeevVb63VkYbW5TKWuFkmqYWLVqkKVOmNNzn8Xg0YsQIvfXWWy0+59///rf2228/XXTRRXrqqafUrVs3nXrqqZo0aZK8Xm+Lz4lEIopEIg2fV1ZWuvtGAAAAgBzj9KDcGiuihdFFiivxQbmGYchn+eWzJL9N0ER7lrXCaf369YrH4+revXuT+7t3764vvviixecsX75cL7/8sk477TT95z//0dKlS/W73/1O0WhU11xzTYvPmTlzpqZPn+76/AEAAIBc5uSgXDjXpg7Ara2tVWlpqe677z4NGjRIY8aM0dSpU3XPPfckfM6UKVO0efPmho9Vq1ZlcMYAAAAA2oOsrTh17dpVXq9Xa9asaXL/mjVrtN1227X4nO233175+flNtuXttttuWr16tUzTlK+FTZd+v19+P5U2AAAAgPRlbcXJ5/Np0KBBWrBgQcN9tbW1WrBggfbbb78Wn7P//vtr6dKlqm0UMr9kyRJtv/32LRZNAAAAAOCGrG7Vq6io0P3336+//vWv+vzzz3XhhRequrq6IWXvzDPPbBIeceGFF2rDhg36/e9/ryVLlujZZ5/VDTfcoIsuuihbbwEAAABAB5DVOPIxY8Zo3bp1mjZtmlavXq2BAwfq+eefbwiMWLlypTyen2u7srIy/fe//9Wll16q/v37q2fPnvr973+vSZMmZestAAAAAOgAslo4SdKECRM0YcKEFh975ZVXmt2333776e23327lWQEAAADAz7JeOAEAAADIfWZckpngMaPx7QSDGj1uqe0dCEXhBAAAACAhr1cK+qVQRIomOE+3ceE0vfRK22tuHy7TJGuYSzPMDAonAAAAAAn58qVBu0jxeOIxluXT07Fyfanljq75Q2CVQgpLKnJnkhlA4QQAAAB0cBEr+fY6b55Hwfxkx/8Yujk0Ua9+YSo/T/J5Wx5lGqZm7mC/IpWLKJwAAACADipPeSpSUFUKKWJFE46rskIq85TKbyQunvLyDJX4/HVb+hJcKtpoS184ea2WcyicAAAAgA4qaPh1qG+IYkrQvCSpxopoYXSR4qpNei0nW/rCllT/UvE2lg9B4QQAAAB0YEHDL8nvyrV8+ZLykwywJFW58lIZ57EfAgAAAAAdG4UTAAAAANigcAIAAAAAGxROAAAAAGCDwgkAAAAAbFA4AQAAAIAN4sgBAAAAZERAPt1nzdCS9dUKdg1kezopoXACAAAAkBGGYcgvn3yKypCR7emkhK16AAAAAGCDwgkAAAAAbLBVDwAAAICtiGXajvHKI7/hy8BsMo/CCQAAAEBCecpTkYKqUkgRK5p0bJUVUpmnNGnx5PPW/TeQ7+YsWx+FEwAAAICEgoZfh/qGKKZY0nE1VkQLo4sUV23ScYUF0k49pM5Fbs6y9VE4AQAAAEgqaPgl+V27nq8N7uYjHAIAAAAAbFA4AQAAAIANCicAAAAAsEHhBAAAAAA2KJwAAAAAwAaFEwAAAADYoHACAAAAABsUTgAAAABgg8IJAAAAAGxQOAEAAACAjbxsTwAAAABA+xGxzKSPh6xIhmbiLgonAAAAAFstT3kqUlBVCiliRZOOLVJQIctUJ6MwQ7PbehROAAAAALZa0PDrUN8QxRRLOm5t7Uatql2ruOIZmpk7KJwAAAAAuCJo+CX5k46pMkKZmYzLCIcAAAAAABsUTgAAAABgg8IJAAAAAGxQOAEAAACADQonAAAAALBB4QQAAAAANiicAAAAAMAGhRMAAAAA2KBwAgAAAAAbFE4AAAAAMiYgX5P/thV52Z4AAAAAgI6ji6dYQ41+KjAC2Z5KSlhxAgAAAJBRba1okiicAAAAAMAWhRMA4P+3d+dBVV13HMC/j+0BIouCLGFHUUS2gBKkSgy06FiKiVUzoRUl1kahYDBG00Yxi4FJNFFbQhYjONEISScaoxVF1OdSZTdooC4IYiOIVgiLuPFO/3C44wvo04S8+wLfz8yb4Z5z7rm/e39eH785j/uIiIhICxZOREREREREWrBwIiIiIiIi0oKFExERERERkRYsnIiIiIiIiLRg4URERERERKQFCyciIiIiIiItWDgRERERERFpwcKJiIiIiIhICxZOREREREREWrBwIiIiIiIi0oKFExERERERkRYsnIiIiIiIiLRg4URERERERKQFCyciIiIiIiItWDgRERERERFpwcKJiIiIiIhICxZOREREREREWrBwIiIiIiIi0oKFExERERERkRZGcgega0IIAEBra6vMkRARERERkZy6a4LuGuFBBlzh1NbWBgBwcXGRORIiIiIiItIHbW1tsLKyeuAYhXiY8qofUavVuHTpEgYPHgyFQiF3OGhtbYWLiwsuXrwIS0tLucMZkJgD/cA86AfmQT8wD/qBedAPzIP8+nMOhBBoa2uDk5MTDAwe/FdMA27FycDAAM7OznKH0YOlpWW/+4f4S8Mc6AfmQT8wD/qBedAPzIN+YB7k119zoG2lqRsfDkFERERERKQFCyciIiIiIiItWDjJTKlUIi0tDUqlUu5QBizmQD8wD/qBedAPzIN+YB70A/MgP+bgrgH3cAgiIiIiIqJHxRUnIiIiIiIiLVg4ERERERERacHCiYiIiIiISAsWTkRERERERFqwcJJRZmYm3N3dYWpqitDQUBQXF8sdUr926NAhxMTEwMnJCQqFAtu3b9foF0JgxYoVcHR0hJmZGaKionD27Fl5gu2n0tPTMXbsWAwePBjDhg3DtGnTcPr0aY0xN27cQGJiIoYOHQoLCwtMnz4dly9flini/ikrKwv+/v7SFxmGhYVh9+7dUj9zII+MjAwoFAosWrRIamMufn4rV66EQqHQeI0aNUrqZw5057vvvsMf/vAHDB06FGZmZvDz80NpaanUz/fpn5+7u3uP+0GhUCAxMREA7wcWTjLJy8tDamoq0tLSUF5ejoCAAERHR6OpqUnu0Pqtjo4OBAQEIDMzs9f+t99+G+vXr8cHH3yAoqIiDBo0CNHR0bhx44aOI+2/VCoVEhMTcfz4cRQUFOD27dv4zW9+g46ODmnMiy++iK+//hpffPEFVCoVLl26hGeeeUbGqPsfZ2dnZGRkoKysDKWlpXjqqacQGxuLb7/9FgBzIIeSkhJ8+OGH8Pf312hnLnTD19cXDQ0N0uvIkSNSH3OgG83NzQgPD4exsTF2796NqqoqrFmzBjY2NtIYvk///EpKSjTuhYKCAgDAjBkzAPB+gCBZjBs3TiQmJkrbXV1dwsnJSaSnp8sY1cABQGzbtk3aVqvVwsHBQbzzzjtSW0tLi1AqlWLr1q0yRDgwNDU1CQBCpVIJIe5ec2NjY/HFF19IY6qrqwUAcezYMbnCHBBsbGzEhg0bmAMZtLW1iREjRoiCggIREREhUlJShBC8H3QlLS1NBAQE9NrHHOjO0qVLxa9+9av79vN9Wh4pKSnCy8tLqNVq3g9CCK44yeDWrVsoKytDVFSU1GZgYICoqCgcO3ZMxsgGrtraWjQ2NmrkxMrKCqGhoczJz+j7778HAAwZMgQAUFZWhtu3b2vkYdSoUXB1dWUefiZdXV3Izc1FR0cHwsLCmAMZJCYmYurUqRrXHOD9oEtnz56Fk5MTPD09ERcXh/r6egDMgS7t2LEDISEhmDFjBoYNG4agoCB8/PHHUj/fp3Xv1q1b2Lx5MxISEqBQKHg/gB/Vk8XVq1fR1dUFe3t7jXZ7e3s0NjbKFNXA1n3dmRPdUavVWLRoEcLDwzFmzBgAd/NgYmICa2trjbHMQ987efIkLCwsoFQq8cILL2Dbtm0YPXo0c6Bjubm5KC8vR3p6eo8+5kI3QkNDkZOTg/z8fGRlZaG2thYTJkxAW1sbc6BD58+fR1ZWFkaMGIE9e/ZgwYIFSE5OxqZNmwDwfVoO27dvR0tLC+bMmQOA/ycBgJHcARDRwJSYmIhTp05p/C0B6c7IkSNx4sQJfP/99/jnP/+J+Ph4qFQqucMaUC5evIiUlBQUFBTA1NRU7nAGrClTpkg/+/v7IzQ0FG5ubvj8889hZmYmY2QDi1qtRkhICN566y0AQFBQEE6dOoUPPvgA8fHxMkc3MH3yySeYMmUKnJyc5A5Fb3DFSQa2trYwNDTs8RSSy5cvw8HBQaaoBrbu686c6EZSUhJ27tyJAwcOwNnZWWp3cHDArVu30NLSojGeeeh7JiYmGD58OIKDg5Geno6AgACsW7eOOdChsrIyNDU14fHHH4eRkRGMjIygUqmwfv16GBkZwd7enrmQgbW1Nby9vXHu3DneDzrk6OiI0aNHa7T5+PhIH5vk+7RuXbhwAfv27cO8efOkNt4PLJxkYWJiguDgYBQWFkptarUahYWFCAsLkzGygcvDwwMODg4aOWltbUVRURFz0oeEEEhKSsK2bduwf/9+eHh4aPQHBwfD2NhYIw+nT59GfX098/AzU6vVuHnzJnOgQ5GRkTh58iROnDghvUJCQhAXFyf9zFzoXnt7O2pqauDo6Mj7QYfCw8N7fD3FmTNn4ObmBoDv07qWnZ2NYcOGYerUqVIb7wfwqXpyyc3NFUqlUuTk5Iiqqioxf/58YW1tLRobG+UOrd9qa2sTFRUVoqKiQgAQ7777rqioqBAXLlwQQgiRkZEhrK2txVdffSUqKytFbGys8PDwEJ2dnTJH3n8sWLBAWFlZiYMHD4qGhgbpdf36dWnMCy+8IFxdXcX+/ftFaWmpCAsLE2FhYTJG3f8sW7ZMqFQqUVtbKyorK8WyZcuEQqEQe/fuFUIwB3K696l6QjAXurB48WJx8OBBUVtbK44ePSqioqKEra2taGpqEkIwB7pSXFwsjIyMxKpVq8TZs2fFli1bhLm5udi8ebM0hu/TutHV1SVcXV3F0qVLe/QN9PuBhZOM/v73vwtXV1dhYmIixo0bJ44fPy53SP3agQMHBIAer/j4eCHE3UedLl++XNjb2wulUikiIyPF6dOn5Q26n+nt+gMQ2dnZ0pjOzk6xcOFCYWNjI8zNzcXTTz8tGhoa5Au6H0pISBBubm7CxMRE2NnZicjISKloEoI5kNMPCyfm4uc3a9Ys4ejoKExMTMRjjz0mZs2aJc6dOyf1Mwe68/XXX4sxY8YIpVIpRo0aJT766CONfr5P68aePXsEgF6v7UC/HxRCCCHLUhcREREREdEvBP/GiYiIiIiISAsWTkRERERERFqwcCIiIiIiItKChRMREREREZEWLJyIiIiIiIi0YOFERERERESkBQsnIiIiIiIiLVg4ERERERERacHCiYiISEYTJ07EZ599JncYPRw8eBAKhQItLS0/av/8/HwEBgZCrVb3bWBERDJh4UREpIcaGxvxl7/8BZ6enlAqlXBxcUFMTAwKCwv7ZP66ujooFAqcOHGiT+bThe5f5B/0OnjwoNxhPpIdO3bg8uXLePbZZ6U2d3d3KBQK5Obm9hjv6+sLhUKBnJycPo3jySefxKJFi/p0zsmTJ8PY2Bhbtmzp03mJiOTCwomISM/U1dUhODgY+/fvxzvvvIOTJ08iPz8fkyZNQmJiotzh6czt27c1tsePH4+GhgbpNXPmTEyePFmjbfz48TqP89atWz963/Xr12Pu3LkwMNB8O3ZxcUF2drZG2/Hjx9HY2IhBgwb96OPp2pw5c7B+/Xq5wyAi6hMsnIiI9MzChQuhUChQXFyM6dOnw9vbG76+vkhNTcXx48cB9L5i1NLSorHq0tzcjLi4ONjZ2cHMzAwjRoyQfhn38PAAAAQFBUGhUODJJ58EAKjVarz++utwdnaGUqlEYGAg8vPzpWN0H/fzzz/HhAkTYGZmhrFjx+LMmTMoKSlBSEgILCwsMGXKFFy5ckXjvDZs2AAfHx+Ymppi1KhReP/993vMm5eXh4iICJiamvZYqTAxMYGDg4P0MjMzg1KplLZtbGzw17/+FY899hgGDRqE0NBQjRWonJwcWFtbY8+ePfDx8YGFhYVUeHXrbeVl2rRpmDNnjrTt7u6ON954A7Nnz4alpSXmz58PADhy5Ih0TVxcXJCcnIyOjo775vnKlSvYv38/YmJievTFxcVBpVLh4sWLUtvGjRsRFxcHIyMjjbH19fWIjY2FhYUFLC0tMXPmTFy+fFnqX7lyJQIDA/Hpp5/C3d0dVlZWePbZZ9HW1gbgbnGjUqmwbt06aeWurq5O2r+srAwhISEwNzfH+PHjcfr0aanvm2++waRJkzB48GBYWloiODgYpaWlUn9MTAxKS0tRU1Nz3+tARPRLwcKJiEiPXLt2Dfn5+UhMTOx1ZcHa2vqh51q+fDmqqqqwe/duVFdXIysrC7a2tgCA4uJiAMC+ffvQ0NCAL7/8EgCwbt06rFmzBqtXr0ZlZSWio6Pxu9/9DmfPntWYOy0tDa+++irKy8thZGSE5557Di+//DLWrVuHw4cP49y5c1ixYoU0fsuWLVixYgVWrVqF6upqvPXWW1i+fDk2bdqkMe+yZcuQkpKC6upqREdHP/S5AkBSUhKOHTuG3NxcVFZWYsaMGZg8ebJG7NevX8fq1avx6aef4tChQ6ivr8dLL730SMcBgNWrVyMgIAAVFRVYvnw5ampqMHnyZEyfPh2VlZXIy8vDkSNHkJSUdN85jhw5AnNzc/j4+PTos7e3R3R0tHR9rl+/jry8PCQkJGiMU6vViI2NxbVr16BSqVBQUIDz589j1qxZGuNqamqwfft27Ny5Ezt37oRKpUJGRgaAuzkPCwvDn/70J2nlzsXFRdr3b3/7G9asWYPS0lIYGRlpxBAXFwdnZ2eUlJSgrKwMy5Ytg7GxsdTv6uoKe3t7HD58+BGuLhGRnhJERKQ3ioqKBADx5ZdfPnBcbW2tACAqKiqktubmZgFAHDhwQAghRExMjJg7d+5D7y+EEE5OTmLVqlUabWPHjhULFy7U2G/Dhg1S/9atWwUAUVhYKLWlp6eLkSNHStteXl7is88+05j3jTfeEGFhYRrzrl279oHnfa/4+HgRGxsrhBDiwoULwtDQUHz33XcaYyIjI8Urr7wihBAiOztbABDnzp2T+jMzM4W9vb20HRERIVJSUjTmiI2NFfHx8dK2m5ubmDZtmsaY559/XsyfP1+j7fDhw8LAwEB0dnb2Gv97770nPD09e7S7ubmJ9957T2zfvl14eXkJtVotNm3aJIKCgoQQQlhZWYns7GwhhBB79+4VhoaGor6+Xtr/22+/FQBEcXGxEEKItLQ0YW5uLlpbW6UxS5YsEaGhoQ887wMHDggAYt++fVLbrl27BADpnAYPHixycnJ6Pb9uQUFBYuXKlQ8cQ0T0S8AVJyIiPSKE6LO5FixYgNzcXAQGBuLll1/Gv//97weOb21txaVLlxAeHq7RHh4ejurqao02f39/6Wd7e3sAgJ+fn0ZbU1MTAKCjowM1NTV4/vnnYWFhIb3efPPNHh/hCgkJefQTBXDy5El0dXXB29tb4xgqlUrjGObm5vDy8pK2HR0dpTgfxQ/j/Oabb5CTk6Nx7OjoaKjVatTW1vY6R2dnJ0xNTe97jKlTp6K9vR2HDh3Cxo0be6w2AUB1dTVcXFw0VohGjx4Na2trjZy5u7tj8ODB0vajnPe9uXZ0dAQAad/U1FTMmzcPUVFRyMjI6PUjeWZmZrh+/fpDHYuISJ8ZaR9CRES6MmLECCgUCvznP/954LjuhwncW2j98GEKU6ZMwYULF/Cvf/0LBQUFiIyMRGJiIlavXv2T47z341gKhaLXtu7HULe3twMAPv74Y4SGhmrMY2hoqLH9Yx980N7eDkNDQ5SVlfWY08LCote4u+O89xoaGBj0KF5/eF17i7O9vR1//vOfkZyc3GOsq6trrzHb2tqiubn5PmcEGBkZ4Y9//CPS0tJQVFSEbdu23XesNr2d98M+Jry3XHfvu3LlSjz33HPYtWsXdu/ejbS0NOTm5uLpp5+W9rl27Rrs7Ox+dOxERPqCK05ERHpkyJAhiI6ORmZmZq8PFuj+Tp3uX0TvfbBBb48Wt7OzQ3x8PDZv3oy1a9fio48+AnD3QQsA0NXVJY21tLSEk5MTjh49qjHH0aNHMXr06B99Tvb29nBycsL58+cxfPhwjVf3Qyp+qqCgIHR1daGpqanHMRwcHB56Hjs7O41r2tXVhVOnTmnd7/HHH0dVVVWPYw8fPly61r3F3NjY+MDiKSEhASqVCrGxsbCxsenR7+Pjg4sXL2o8RKKqqgotLS2PlDMTExONfwuPwtvbGy+++CL27t2LZ555RuNpgDdu3EBNTQ2CgoJ+1NxERPqEK05ERHomMzMT4eHhGDduHF5//XX4+/vjzp07KCgoQFZWFqqrq2FmZoYnnngCGRkZ8PDwQFNTE1599VWNeVasWIHg4GD4+vri5s2b2Llzp/QggmHDhsHMzAz5+flwdnaGqakprKyssGTJEqSlpcHLywuBgYHIzs7GiRMnfvJ38bz22mtITk6GlZUVJk+ejJs3b6K0tBTNzc1ITU39SXMDd395j4uLw+zZs7FmzRoEBQXhypUrKCwshL+/P6ZOnfpQ8zz11FNITU3Frl274OXlhXffffehvgB26dKleOKJJ5CUlIR58+Zh0KBBqKqqQkFBAf7xj3/0uk9QUBBsbW1x9OhR/Pa3v+11jI+PD65evQpzc/Ne+6OiouDn54e4uDisXbsWd+7cwcKFCxEREfFIH3t0d3dHUVER6urqYGFhgSFDhmjdp7OzE0uWLMHvf/97eHh44L///S9KSkowffp0aczx48ehVCoRFhb20LEQEekrrjgREekZT09PlJeXY9KkSVi8eDHGjBmDX//61ygsLERWVpY0buPGjbhz5w6Cg4OxaNEivPnmmxrzmJiY4JVXXoG/vz8mTpwIQ0ND6UtVjYyMsH79enz44YdwcnJCbGwsACA5ORmpqalYvHgx/Pz8kJ+fjx07dmDEiBE/6ZzmzZuHDRs2IDs7G35+foiIiEBOTk6frTgBQHZ2NmbPno3Fixdj5MiRmDZtGkpKSu77UbneJCQkID4+HrNnz0ZERAQ8PT0xadIkrfv5+/tDpVLhzJkzmDBhAoKCgrBixQo4OTnddx9DQ0PMnTtXa1E6dOhQmJmZ9dqnUCjw1VdfwcbGBhMnTkRUVBQ8PT2Rl5enNeZ7vfTSSzA0NMTo0aNhZ2eH+vp6rfsYGhrif//7H2bPng1vb2/MnDkTU6ZMwWuvvSaN2bp1K+Li4u5b+BER/ZIoRF/+JTIRERE9tMbGRvj6+qK8vBxubm5yh9Onrl69ipEjR6K0tLRPC2QiIrlwxYmIiEgmDg4O+OSTTx5qheeXpq6uDu+//z6LJiLqN7jiREREREREpAVXnIiIiIiIiLRg4URERERERKQFCyciIiIiIiItWDgRERERERFpwcKJiIiIiIhICxZOREREREREWrBwIiIiIiIi0oKFExERERERkRYsnIiIiIiIiLT4P+eT85kqDQIpAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Exercise 1\n",
"\n",
"Find interesting features that separate the Kaplan-Meier Survival Curve"
],
"metadata": {
"id": "p7iYfGtPLDb1"
}
},
{
"cell_type": "markdown",
"source": [
"# Exercise 2\n",
"\n",
"Consider only these features: gender, SeniorCitizen, InternetService, Contract"
],
"metadata": {
"id": "OQbZkfE-W0Vc"
}
},
{
"cell_type": "markdown",
"source": [
"https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.get_x_y.html"
],
"metadata": {
"id": "ckDU5maLWByj"
}
},
{
"cell_type": "markdown",
"source": [
"Example:\n",
"```python\n",
"from sksurv.datasets import get_x_y\n",
"X, y = get_x_y(data, attr_labels=[\"Churn\", \"tenure\"], pos_label=True)\n",
"```"
],
"metadata": {
"id": "316E_eyLWEIJ"
}
}
]
}
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