diff --git a/Health Insurance Cross Sell Prediction/Dataset/README.md b/Health Insurance Cross Sell Prediction/Dataset/README.md new file mode 100644 index 000000000..6893268eb --- /dev/null +++ b/Health Insurance Cross Sell Prediction/Dataset/README.md @@ -0,0 +1,29 @@ +# Health Insurance Cross Sell Prediction Dataset +## Overview +This dataset contains information related to health insurance policyholders, focusing on predicting the probability of a policyholder responding to a cross-selling campaign for vehicle insurance. + +## File +- train.csv: Contains the training data with features and the target variable (Response). +- test.csv: Contains the test data for which predictions need to be made. + +## Column Description + +- **id**: Unique identifier for each entry. +- **Gender**: Gender of the policyholder. +- **Age**: Age of the policyholder. +- **Driving_License**: Whether the policyholder has a valid driving license (0 - No, 1 - Yes). +- **Region_Code**: Code for the region of the policyholder. +- **Previously_Insured**: Whether the policyholder already has vehicle insurance (0 - No, 1 - Yes). +- **Vehicle_Age**: Age of the vehicle. +- **Vehicle_Damage**: Whether the vehicle has been damaged in the past (false - No, true - Yes). +- **Annual_Premium**: Amount of the annual premium. +- **Policy_Sales_Channel**: Code for the channel through which the policy was purchased. +- **Vintage**: Number of days the policyholder has been associated with the company. +- **Response**: Whether the policyholder responded positively to the cross-selling campaign (0 - No, 1 - Yes). + +## Summary +- File Size: 662 MB +- Number of Records: 11.5 Million + +## Dataset +This dataset can be accessed from [Kaggle](https://www.kaggle.com/competitions/playground-series-s4e7/data?select=train.csv) \ No newline at end of file diff --git a/Health Insurance Cross Sell Prediction/Dataset/dataset_review.png b/Health Insurance Cross Sell Prediction/Dataset/dataset_review.png new file mode 100644 index 000000000..e6ce5c1b5 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/Dataset/dataset_review.png differ diff --git a/Health Insurance Cross Sell Prediction/Dataset/dataset_view.png b/Health Insurance Cross Sell Prediction/Dataset/dataset_view.png new file mode 100644 index 000000000..705bd2004 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/Dataset/dataset_view.png differ diff --git a/Health Insurance Cross Sell Prediction/Model/README.md b/Health Insurance Cross Sell Prediction/Model/README.md new file mode 100644 index 000000000..6379063a3 --- /dev/null +++ b/Health Insurance Cross Sell Prediction/Model/README.md @@ -0,0 +1,80 @@ +# Health Insurance Cross Sell Prediction-Model + +## 📝 Description +This folder contains the pre-trained machine learning models and scripts used for predicting whether a customer will buy a vehicle insurance policy. The goal is to automatically categorize customers based on the likelihood of purchasing the insurance, helping to target potential buyers effectively. + +## 📂 Contents +- Health_Insurance_Cross_Sell_Prediction.ipynb: Jupyter Notebook containing the complete process of data preprocessing, model training, evaluation, and visualization. +- model.pkl: Pre-trained model used for prediction. +- scaler.pkl: Pre-Fitted Scaler for data fitting. +- README.md: This document. + +## 🎯 Goal +The goal of this prediction project is to enhance understanding of customer behavior by organizing and analyzing data on various features. By automatically classifying customers based on their likelihood of purchasing insurance, the project aims to provide insights into potential buyers and improve targeting strategies. + +## 🧮 What I Did +In this prediction project, various models were evaluated to find the most effective one for classifying customers. The models evaluated include: + +- Logistic Regression +``` +Description: A linear model used for binary classification. It estimates probabilities using a logistic function. +Performance: Achieved an accuracy of 88%. +``` + +- XGBoost +``` +Description: An implementation of gradient boosted decision trees designed for speed and performance. +Performance: Achieved an accuracy of 88%. +``` + +- Naive Bayes +``` +Description: A probabilistic classifier based on Bayes' theorem with strong independence assumptions. +Performance: Achieved an accuracy of 64%. +``` +- LightGBM +``` +Description: A Light Gradient Boosting Machine (LightGBM) was trained for high performance and efficiency with large datasets. +Performance: Achieved an accuracy of 88%. +``` +- Neural Network +``` +Description: A basic feedforward neural network used for classification tasks. +Performance: Achieved an accuracy of 88%. +``` +- Ridge Classifier +``` +Description: A linear classifier with L2 regularization to avoid overfitting. +Performance: Achieved an accuracy of 88%. +``` +- Stochastic Gradient Descent (SGD) Classifier +``` +Description: A linear classifier optimized using stochastic gradient descent. +Performance: Achieved an accuracy of 88%. +``` + +## Data Preprocessing and Feature Engineering +- Data Cleaning: Normalized data, removed missing values, and handled duplicates. +- Feature Engineering: Created new features such as interaction terms, and performed encoding for categorical variables. +- Data Scaling: Standardized numerical features to ensure consistent scaling. + + +## Model Performance Analysis +- Training and Validation: Evaluated models based on accuracy, precision, recall, and F1 score to select the best-performing model. +Best Model +- The best-performing model, LightGBM, has been saved as model.pkl and is ready for deployment. + +## 📈 Performance of the Models Based on Accuracy Scores +- Logistic Regression: Accuracy: 88% +- XGBoost: Accuracy: 88% +- Naive Bayes: Accuracy: 64% +- LightGBM: Accuracy: 88% +- Neural Network: Accuracy: 88% +- Ridge Classifier: Accuracy: 88% +- SGD Classifier: Accuracy: 88% + +## 📢 Conclusion +The Health Insurance Cross Sell Prediction project demonstrates the effectiveness of machine learning models, particularly LightGBM, in accurately predicting customer behavior. The models help in organizing and prioritizing customer data, providing valuable insights for stakeholders. + +## ✒️ Your Signature +Tanuj Saxena [LinkedIn](https://www.linkedin.com/in/tanuj-saxena-970271252/) \ No newline at end of file diff --git a/Health Insurance Cross Sell Prediction/Model/health-insurance-cross-sell-prediction.ipynb b/Health Insurance Cross Sell Prediction/Model/health-insurance-cross-sell-prediction.ipynb new file mode 100644 index 000000000..885eabf53 --- /dev/null +++ b/Health Insurance Cross Sell Prediction/Model/health-insurance-cross-sell-prediction.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":73291,"databundleVersionId":8930475,"sourceType":"competition"}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-10T14:42:37.623454Z","iopub.execute_input":"2024-07-10T14:42:37.623924Z","iopub.status.idle":"2024-07-10T14:42:38.102752Z","shell.execute_reply.started":"2024-07-10T14:42:37.623888Z","shell.execute_reply":"2024-07-10T14:42:38.101644Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/playground-series-s4e7/sample_submission.csv\n/kaggle/input/playground-series-s4e7/train.csv\n/kaggle/input/playground-series-s4e7/test.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/playground-series-s4e7/train.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:42:38.104934Z","iopub.execute_input":"2024-07-10T14:42:38.105434Z","iopub.status.idle":"2024-07-10T14:43:08.012674Z","shell.execute_reply.started":"2024-07-10T14:42:38.105400Z","shell.execute_reply":"2024-07-10T14:43:08.011260Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":" id Gender Age Driving_License Region_Code Previously_Insured \\\n0 0 Male 21 1 35.0 0 \n1 1 Male 43 1 28.0 0 \n2 2 Female 25 1 14.0 1 \n3 3 Female 35 1 1.0 0 \n4 4 Female 36 1 15.0 1 \n\n Vehicle_Age Vehicle_Damage Annual_Premium Policy_Sales_Channel Vintage \\\n0 1-2 Year Yes 65101.0 124.0 187 \n1 > 2 Years Yes 58911.0 26.0 288 \n2 < 1 Year No 38043.0 152.0 254 \n3 1-2 Year Yes 2630.0 156.0 76 \n4 1-2 Year No 31951.0 152.0 294 \n\n Response \n0 0 \n1 1 \n2 0 \n3 0 \n4 0 ","text/html":"
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idGenderAgeDriving_LicenseRegion_CodePreviously_InsuredVehicle_AgeVehicle_DamageAnnual_PremiumPolicy_Sales_ChannelVintageResponse
00Male21135.001-2 YearYes65101.0124.01870
11Male43128.00> 2 YearsYes58911.026.02881
22Female25114.01< 1 YearNo38043.0152.02540
33Female3511.001-2 YearYes2630.0156.0760
44Female36115.011-2 YearNo31951.0152.02940
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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:43:08.014671Z","iopub.execute_input":"2024-07-10T14:43:08.015900Z","iopub.status.idle":"2024-07-10T14:43:08.698826Z","shell.execute_reply.started":"2024-07-10T14:43:08.015861Z","shell.execute_reply":"2024-07-10T14:43:08.697562Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:43:08.700428Z","iopub.execute_input":"2024-07-10T14:43:08.700813Z","iopub.status.idle":"2024-07-10T14:43:08.716096Z","shell.execute_reply.started":"2024-07-10T14:43:08.700764Z","shell.execute_reply":"2024-07-10T14:43:08.714700Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"\nRangeIndex: 11504798 entries, 0 to 11504797\nData columns (total 12 columns):\n # Column Dtype \n--- ------ ----- \n 0 id int64 \n 1 Gender object \n 2 Age int64 \n 3 Driving_License int64 \n 4 Region_Code float64\n 5 Previously_Insured int64 \n 6 Vehicle_Age object \n 7 Vehicle_Damage object \n 8 Annual_Premium float64\n 9 Policy_Sales_Channel float64\n 10 Vintage int64 \n 11 Response int64 \ndtypes: float64(3), int64(6), object(3)\nmemory usage: 1.0+ GB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:43:08.719023Z","iopub.execute_input":"2024-07-10T14:43:08.719604Z","iopub.status.idle":"2024-07-10T14:43:12.556442Z","shell.execute_reply.started":"2024-07-10T14:43:08.719563Z","shell.execute_reply":"2024-07-10T14:43:12.555213Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":" id Age Driving_License Region_Code \\\ncount 1.150480e+07 1.150480e+07 1.150480e+07 1.150480e+07 \nmean 5.752398e+06 3.838356e+01 9.980220e-01 2.641869e+01 \nstd 3.321149e+06 1.499346e+01 4.443120e-02 1.299159e+01 \nmin 0.000000e+00 2.000000e+01 0.000000e+00 0.000000e+00 \n25% 2.876199e+06 2.400000e+01 1.000000e+00 1.500000e+01 \n50% 5.752398e+06 3.600000e+01 1.000000e+00 2.800000e+01 \n75% 8.628598e+06 4.900000e+01 1.000000e+00 3.500000e+01 \nmax 1.150480e+07 8.500000e+01 1.000000e+00 5.200000e+01 \n\n Previously_Insured Annual_Premium Policy_Sales_Channel Vintage \\\ncount 1.150480e+07 1.150480e+07 1.150480e+07 1.150480e+07 \nmean 4.629966e-01 3.046137e+04 1.124254e+02 1.638977e+02 \nstd 4.986289e-01 1.645475e+04 5.403571e+01 7.997953e+01 \nmin 0.000000e+00 2.630000e+03 1.000000e+00 1.000000e+01 \n25% 0.000000e+00 2.527700e+04 2.900000e+01 9.900000e+01 \n50% 0.000000e+00 3.182400e+04 1.510000e+02 1.660000e+02 \n75% 1.000000e+00 3.945100e+04 1.520000e+02 2.320000e+02 \nmax 1.000000e+00 5.401650e+05 1.630000e+02 2.990000e+02 \n\n Response \ncount 1.150480e+07 \nmean 1.229973e-01 \nstd 3.284341e-01 \nmin 0.000000e+00 \n25% 0.000000e+00 \n50% 0.000000e+00 \n75% 0.000000e+00 \nmax 1.000000e+00 ","text/html":"
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idAgeDriving_LicenseRegion_CodePreviously_InsuredAnnual_PremiumPolicy_Sales_ChannelVintageResponse
count1.150480e+071.150480e+071.150480e+071.150480e+071.150480e+071.150480e+071.150480e+071.150480e+071.150480e+07
mean5.752398e+063.838356e+019.980220e-012.641869e+014.629966e-013.046137e+041.124254e+021.638977e+021.229973e-01
std3.321149e+061.499346e+014.443120e-021.299159e+014.986289e-011.645475e+045.403571e+017.997953e+013.284341e-01
min0.000000e+002.000000e+010.000000e+000.000000e+000.000000e+002.630000e+031.000000e+001.000000e+010.000000e+00
25%2.876199e+062.400000e+011.000000e+001.500000e+010.000000e+002.527700e+042.900000e+019.900000e+010.000000e+00
50%5.752398e+063.600000e+011.000000e+002.800000e+010.000000e+003.182400e+041.510000e+021.660000e+020.000000e+00
75%8.628598e+064.900000e+011.000000e+003.500000e+011.000000e+003.945100e+041.520000e+022.320000e+020.000000e+00
max1.150480e+078.500000e+011.000000e+005.200000e+011.000000e+005.401650e+051.630000e+022.990000e+021.000000e+00
\n
"},"metadata":{}}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:43:12.558185Z","iopub.execute_input":"2024-07-10T14:43:12.558602Z","iopub.status.idle":"2024-07-10T14:43:16.334794Z","shell.execute_reply.started":"2024-07-10T14:43:12.558550Z","shell.execute_reply":"2024-07-10T14:43:16.333398Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"id 0\nGender 0\nAge 0\nDriving_License 0\nRegion_Code 0\nPreviously_Insured 0\nVehicle_Age 0\nVehicle_Damage 0\nAnnual_Premium 0\nPolicy_Sales_Channel 0\nVintage 0\nResponse 0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.histplot(df['Age'], bins=30, kde=True, color='blue')\nplt.title('Distribution of Age')\nplt.xlabel('Age')\nplt.ylabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:43:16.335984Z","iopub.execute_input":"2024-07-10T14:43:16.336304Z","iopub.status.idle":"2024-07-10T14:44:15.287179Z","shell.execute_reply.started":"2024-07-10T14:43:16.336277Z","shell.execute_reply":"2024-07-10T14:44:15.285519Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(6, 4))\nsns.countplot(x='Response', data=df, palette='viridis')\nplt.title('Distribution of Response')\nplt.xlabel('Response')\nplt.ylabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:15.289170Z","iopub.execute_input":"2024-07-10T14:44:15.289655Z","iopub.status.idle":"2024-07-10T14:44:16.479110Z","shell.execute_reply.started":"2024-07-10T14:44:15.289610Z","shell.execute_reply":"2024-07-10T14:44:16.477869Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"gender = df['Gender'].value_counts()\nplt.pie(gender, labels=['Male', 'Female'], autopct='%1.1f%%')\nplt.title(\"Percentage of Gender\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:16.480840Z","iopub.execute_input":"2024-07-10T14:44:16.481298Z","iopub.status.idle":"2024-07-10T14:44:18.702120Z","shell.execute_reply.started":"2024-07-10T14:44:16.481256Z","shell.execute_reply":"2024-07-10T14:44:18.700523Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"sns.countplot(data=df,x='Response',hue=\"Gender\")\nplt.title('Target Variable')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:18.704168Z","iopub.execute_input":"2024-07-10T14:44:18.705643Z","iopub.status.idle":"2024-07-10T14:44:29.828017Z","shell.execute_reply.started":"2024-07-10T14:44:18.705583Z","shell.execute_reply":"2024-07-10T14:44:29.826422Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"df['Gender'] = df['Gender'].replace(['Female', 'Male'], [0, 1])\ndf['Vehicle_Age'] = df['Vehicle_Age'].replace(['1-2 Year', '< 1 Year', '> 2 Years'], [0, 1, 2])\ndf['Vehicle_Damage'] = df['Vehicle_Damage'].replace(['No', 'Yes'], [0, 1])","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:29.832448Z","iopub.execute_input":"2024-07-10T14:44:29.832857Z","iopub.status.idle":"2024-07-10T14:44:57.639556Z","shell.execute_reply.started":"2024-07-10T14:44:29.832825Z","shell.execute_reply":"2024-07-10T14:44:57.638104Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_655/878367157.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df['Gender'] = df['Gender'].replace(['Female', 'Male'], [0, 1])\n/tmp/ipykernel_655/878367157.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df['Vehicle_Age'] = df['Vehicle_Age'].replace(['1-2 Year', '< 1 Year', '> 2 Years'], [0, 1, 2])\n/tmp/ipykernel_655/878367157.py:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df['Vehicle_Damage'] = df['Vehicle_Damage'].replace(['No', 'Yes'], [0, 1])\n","output_type":"stream"}]},{"cell_type":"code","source":"df.drop('id',axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:57.641027Z","iopub.execute_input":"2024-07-10T14:44:57.641414Z","iopub.status.idle":"2024-07-10T14:44:58.091029Z","shell.execute_reply.started":"2024-07-10T14:44:57.641382Z","shell.execute_reply":"2024-07-10T14:44:58.089641Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":" Gender Age Driving_License Region_Code Previously_Insured \\\n0 1 21 1 35.0 0 \n1 1 43 1 28.0 0 \n2 0 25 1 14.0 1 \n3 0 35 1 1.0 0 \n4 0 36 1 15.0 1 \n... ... ... ... ... ... \n11504793 1 48 1 6.0 0 \n11504794 0 26 1 36.0 0 \n11504795 0 29 1 32.0 1 \n11504796 0 51 1 28.0 0 \n11504797 1 25 1 28.0 1 \n\n Vehicle_Age Vehicle_Damage Annual_Premium Policy_Sales_Channel \\\n0 0 1 65101.0 124.0 \n1 2 1 58911.0 26.0 \n2 1 0 38043.0 152.0 \n3 0 1 2630.0 156.0 \n4 0 0 31951.0 152.0 \n... ... ... ... ... \n11504793 0 1 27412.0 26.0 \n11504794 1 1 29509.0 152.0 \n11504795 1 0 2630.0 152.0 \n11504796 0 1 48443.0 26.0 \n11504797 1 0 32855.0 152.0 \n\n Vintage Response \n0 187 0 \n1 288 1 \n2 254 0 \n3 76 0 \n4 294 0 \n... ... ... \n11504793 218 0 \n11504794 115 1 \n11504795 189 0 \n11504796 274 1 \n11504797 189 0 \n\n[11504798 rows x 11 columns]","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 \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 \n \n \n \n \n
GenderAgeDriving_LicenseRegion_CodePreviously_InsuredVehicle_AgeVehicle_DamageAnnual_PremiumPolicy_Sales_ChannelVintageResponse
0121135.000165101.0124.01870
1143128.002158911.026.02881
2025114.011038043.0152.02540
303511.00012630.0156.0760
4036115.010031951.0152.02940
....................................
1150479314816.000127412.026.02180
11504794026136.001129509.0152.01151
11504795029132.01102630.0152.01890
11504796051128.000148443.026.02741
11504797125128.011032855.0152.01890
\n

11504798 rows × 11 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"numeric_cols = df.select_dtypes(include=np.number).columns\nplt.figure(figsize=(12, 8))\nsns.heatmap(df[numeric_cols].corr(), annot=True, cmap='PiYG')\nplt.title('Correlation Matrix')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:44:58.092727Z","iopub.execute_input":"2024-07-10T14:44:58.093235Z","iopub.status.idle":"2024-07-10T14:45:09.616626Z","shell.execute_reply.started":"2024-07-10T14:44:58.093191Z","shell.execute_reply":"2024-07-10T14:45:09.615239Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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YGB5MmTRy0sT548vHz5kqioKMLCwkhISEg1zvXr1zMlD6mRhgYhhBBCCCGEEOILycgwie+NNDQIIYQQQgghhPiuaXynr0GwtLTk2bNnamHPnj3D2NgYfX19NDU10dTUTDWOpaXlF8vXd1rdQgghhBBCCCHE983Hx4cjR46ohR06dAgfHx8AdHR0KFasmFqcxMREjhw5oorzJUiPBiGEEEIIIYQQ3zWFQiO7s5AhERER3L59W/X33bt3OX/+PLly5cLOzg5fX18eP37MunXrAOjevTuLFi1i6NChdOzYkaNHj7J161b27dun2sbAgQNp164dxYsXp2TJksybN4/Xr1/ToUOHL1YOaWgQQgghhBBCCCG+AqdPn6ZSpUqqv99OItmuXTv8/f15+vQpDx48UK3Pnz8/+/btY8CAAcyfP5+8efOycuVKqlevrorTrFkzgoKCGDNmDIGBgRQuXJj9+/enmCAyM2kolUrlF9u6EJ9JXm8phBBCCCHE1+Nbfb1loR422bbvK0sfZ9u+s4v0aBBCCCGEEEII8V3T+DZGTnw3ZDJIIYQQQgghhBBCZBrp0SCEEEIIIYQQ4rumkEfsWUqqWwghhBBCCCGEEJlGejQIIYQQQgghhPiuaXwjr7f8XkiPBiGEEEIIIYQQQmQaaWgQQgghhBBCCCFEppGhE0IIIYQQQgghvmsyGWTWkuoWQgghhBBCCCFEppEeDUIIIYQQQgghvmsa8og9S0l1CyGEEEIIIYQQItNIQ4MQQgghhBBCCCEyjTQ0iExRsWJF+vfvn+Z6e3t75s2bl2X5EUIIIYQQQoi3FBoa2bb8F0lDg8gUO3fuZOLEidmdjc9W3Mmbpb3X8sfMs1xf8YTKhWtkd5ZSWLxyGXWbNaJEpTKUrlaRzn16cPHKpXTTnD53ll6D+1OpbnXcfYpx5PffMi0/J8+epkm7lhQpX4qajeuze99PKfLr7lNMbanbrNFH7ycyMpLJs6ZTuV5NilUoTb0Wjdmyc/sH063fHECdZo0oVqE0levXYvq82cTExHz0/t934/Yt2nbvRNEKPlSuX4vVG9aqrd+976cU5S5aweez9jl++hTcfYqxfnNAuvEyq85Tc+DIIeo2a0TRCj40bNWUP/7+U239yIljU+y7W//eGd7+pu1bqdawDkUr+NCiU1suXbn8WflRKpUsWr6UinWqUaxCaTr36cH9hw/U4rx48YJhY0fiXbk8PlUrMHryBCIjI1XrY2JiGDlxLA1bNcWrbEn6DhuYIh8nz55OUW53n2IEhwR/s+U+efY0fYYOpGKdapSoVIYf27Zg74Ff1Laxfc9O2nbvROlqFVXXow/l/Vurh7v379GhV1fK16pK0Qo+1PixHguWLSEuPu6Ty5mRfKXmQ/UTExPDpJnTKFP9B0r8UJb+vkMIDg1Ri/M08Ck9BvWleMXSlK9VhVkL5xEfH69aHxQcxNAxI6jdtCEepYszbe6sVPPyKdfWr7nch44dpXPfnpSrWRnvyuVp1aU9f/3zt9o2PvVe+jUe14+fPkn1mnXhcvL3ic+9j33Nn/eXPM7h046VvQd+oVGb5hSvWJqKdaoxatJ4wl+EfzDdh3yoLj73/iVEWqShQWSKXLlykSNHjuzOxmfT1zXg+qMrTAgYkd1ZSZO9rR0jBg1j54YtrPNbhbWVFV379SI0LCzNNFHRUbg4OTNy0LBMzcujJ4/pNagfJYsVZ/u6TbRp1pKxUyel+HJWwMGRY3sPqJZ1y1Z99L5mLJjDn//8zdRxE/lp83baNGvJlDkz+O1/v6eZZt+BX5m7dCE9Onbhp83bmTBiNPuPHGS+3+KP3v+7Il5H0LVfL6wsrdi6ZgODevdjycplbNu9Uy2ekaGhWrkP7tr7yfs8fOwoF69cwsI8d4biZ0adv+/cxQsMHTuShnUbsG1tAD+Ur0jfYYO4dee2WryypUqr7XvGhCkZ2v6vhw8yY8EcenTqyjb/jbg4OdNtQG9CQkM/OT+rN6xl47bNjBk6goBVa9HX16db/95qXxSHjRvF7bv/smLBYhbPmseZ82cZN22San1CYiK6urq0atqcUsVLpluGvVt2qpU9l2mub7bc5y9exNmxAHOnzGTH+s00qF2XERPGcuzPP1RxTp09Q62q1Vm9aBkblq/BMk8euvbvxbPnzz9Y7m+lHrS0tKhXsw7L5y9m7+adDOs/iO17drF4xbKPLuPH5OtT6mf6/Nkc++sP5kyehv+SFQQFB9F/+BDV+oSEBHoO6kdcXDwblq9h8ujx7PnlZxat8FPFiY2Lw9TUlK7tO+FSwDnVvHzqtfVrLveZc2cpXdKbJbMXsNV/AyWKFqfXkAFcu3FdFedT7qVf63H91soFS9WuWQVdXdXWf8597Gv+vL/kcQ4ff6ycvXCeERPG0qhufXYHbGPO5OlcvnaFsVNTfmYfIyN18dan3L++NRqK7Fv+i/6jxRaZ7d2hE8+fP6du3bro6+uTP39+Nm7cmL2Z+wj/u/wb83fP4PC5/dmdlTTVrl4Tn5Le2NrkpYCDI0P7DSTi9Wtu3r6VZppyPmXo260nVSr+kGac2NhYZi6Yyw91a1CiUhladGrLybOn083L1l07sLG2YUjfgTja56dlk2ZUrVSZde89cdfU1MTczFy1mJqYflyhgfOXLlK/Vh1KFi2OjZU1TRo0wqWAE5euXkk3TREPL2pXr4mNlTVlvH2oVbU6l64mPx1JTExkxdrVVG9Ul2IVStOoTXMOHj2cbl72HviVuLg4Jo0cSwEHR2pVrU6rps1Zt2mDWjwNDQ21cpvnMvvocgM8e/6cqXNmMn3cJLS0MvayoA/V+ctXrxgzZYLq6V3H3t24futmutvcsHUTZbx96Ni6LY72+enTrScFXVwJ2L5VLZ6OjrbavnMaG2coz+s2baBxvYY0rFMPx/wOjBk6Aj1dPXbt3fNJ+VEqlazfEkDX9p34oXxFXAo4MWXMeJ4HB3Hkj2MA3Ll3lz//+ZvxvqPxLORBUa8ijBg4lF8PH+R5UBAABvr6jBk6gsb1G2Fulv5nmMs0l1rZFRl4affXWu6u7TvSp1tPinh6YZfXljbNWlKmlA+HjyU/mZs+fjLNf2yKq7MLDvb5Ge87msREJf+cPvnBcn8r9WBrk5eGderh6uSMtZUVlcpVoE71mpy9cO6jy5jRfH1K/byKeMXOn/cwtO9AvIuXpJCrGxNHjuX8pQuqp9R/n/yHO/fuMm3cRFydXSjnU4beXXqwecdW4uKSemjYWFnjO2AI9WvVwcjIKNW8ZOTa+q2Ve/iAwXRs3Q6PgoXIZ2tH/x69yWdrp9awlpF76cfm/31ZdVy/ZZIzp9o1S1tLW239p97HvvbP+0sd52997LFy4fJFrK2saN20BXmtbSjqVYQmDRpx+b3vONt/2kXd5j9StIIPdZs1YvOOrWlskQzXxVufcv8SIj1yBIlM1759ex4+fMhvv/3G9u3bWbJkCc8/4emW+LC4uDi27d5JDiMjXJycPmtbk2dP58LlS8ycOJUd6zdT7YcqdB/QJ91ujhcuX0zxhLeMtw8XLl9UC3vw8AGV6lanxo/1GDZ2JE8Dn350/gp7ePLbn3/w7PlzlEolJ8+c4t7DB5QuWSrdNFdvXFN1u3z4+BF//P0X5XzKquKsWLeGn37dx5ihvuwO2Erb5i0ZPn40p86eSbvcly5RvEgRtLWTv5CV8fbh7oP7vHj5UhUWGRVF1Ya1qVy/Fn2GDuT2v3c+utyJiYn4ThhN+1ZtKODgmOF0H6rzgSOHERoWxtI5C9nqvwE3Z1c69+nOixcv0tzmhcsX8SnhrRZWOpXP+9TZM5SvVYU6zRoxYcaUDHX9jIuL4+qN65QqkXw8KRQKSpUoqdaV92Py8+jJY4JDQtTi5DDKgWdBd1WcC5cuYpwjB+5uBVVxSpUoiUKh+OCQpNQ0btuCinWq0blvT85eOP/B+N9auSMiItJtOIqOjiY+Pj7DjUtvfUv18ODhQ/7852+KFyn2UWV8KyP5el9G6ufq9WvEx8dT6p3tOtjnx8rSkguXksvr5FhA7cdimVI+RLx+/VHXp4xcW7/1cicmJvI68jU5jXN+qDrS9C0c172HDqB8rSq06dYx1R6Cn3of+9Y+79R8ynH+qbzcPQl89ow//v4TpVJJcGgIh44eoVzpMqo4ew/8wuIVfvTt3oufNm2nb4/eLFzux559P6e53Y+pi4+9f32LFIrsW/6LMvZoTIgMunnzJr/++isnT56kRIkSAKxatQo3N7cPpo2JiUnRnS4xQYlC8785gUp6jv35B0PGjCA6OprcZuYsn7/kk3oJvPU08Cm79/3MoV37sMid1DW/Q6u2/PXPcXbt/Yn+PVIfYx8cEoJZLvWudWa5chHx+jXR0dHo6enhWcidSaPGYZ/PnuDgIJasWkHbHp3ZvWErhoaGGc7jiIFDGTdtEpXr10RLUxMNhYJxw0dRvEjRNNPUrl6TsBfhtOneCZRK4hMSaNrwR7q27wgk9eJYuXY1KxYspbCHJ5D09PLshfNs272DEkVT/yERHBpMXiub98pt9mZdCDmNjbG3s2fCiDG4FHDiVUQE/gHrad21A7sDtmFpkSfD5V613h9NTU1aN22R4TQfqvOzF85x+epl/vjlMDo6OgAM6TuAo38c4+BvR2jSIPX5HFL7vM1z5SI4JHlsbJlSpalS8QdsrKx5+PgR8/0W031AXzauWIOmpmaaeQ4LDychIUFVj2+Z5TLj7v17n5Sft/+mdoy+G+f97qFaWlrkNDZOMeY3PbnNzBkzdASF3AoSGxvLjp9207FXVwJWraWgS9rXv2+p3PsPH+TytauMHTYyzfLMWbKA3LnNU/xQ+pBvoR5adenAtZvXiY2NpUn9RvTu0v2jyvhuvj+Ur/dlpH6CQ0LQ1tbG+L1hjGamZqqyBIemfs1+uy6jPnRtTc23Vm7/gPVERkZRvXLVNMv0IV/zcW2gr8+QvgMo4umFhoaCw8eO0HfYIBZMn02lchUAPus+9q193qn5lOP8UxX1Ksz0cZMYPNqX2JgY4hMSqFi2PCMHJw+9WLxiGUP6DKDqm14Sea1t+Pfuv2zdvZP6teumut2M1MWn3r+E+BBpaBCZ6tq1a2hpaVGsWPIPNFdXV0xMTD6YdurUqYwfP14tzKyIEebFvv25Hz7V3gO/MH568vh2vzkLKVa4CCWLlWDH2k2EvQhn+55dDB41nICVa1PcTDLq5p3bJCQkULtZQ7XwuNhYcuZMeppT4ofkFvw61WsxdljG5rEo55PcGu9SwAmPQh5Ua1ib/UcO8WO9BqmmSa3cF69c4uKVyyyaMRcrKyvOnDvL5NnTsTDPjU/J1H/UnDx7mhVr1zBqyHA8C7rz4NFDps2bhd/qFXTv2IUHjx4SFR1Nl3491csdF4ebswsA9Vs24cmb3gDFvIrgN3dhhspd2MNT1XgBUNjTk3rNG7Nt1w76dOuZapr3y71k1nw2bN3MNv+NaHzEjMUfqvMbt24RGRVFmRrqXTpjYmJ4+PgRTwOfUq9lE1V4l7YdM/zFqlbV6qr/OxdwwrmAEzUb1+fU2TNqT6m+N/nz2ZM/n73q7yKeXjx6/Ih1mwOYNvbbnyj35JlTjJ48nnHDR6XZs2blujX8eugga5YsR1dXN4tz+OXNmjSVyMhIbty6yexF8/EPWE/H1u0+mC618/pb96FrK3zb5d534FeWrlrOgulzPvm++rUzNTGlXYvWqr89ChbieVAwazauUzU0fMx97Fv+vNOSkeM8s9y5+y/T5s2ie4culCnlQ3BwELMWzWfC9KlMHDmGyKgoHj5+xJgpExj77hxCCQkYGSYN/eg+oA9n3gzpsra0Yk/Atgzt+3u/f4nsIw0N4qvh6+vLwIED1cKK93fJptx8HSqVrYBnQQ/V3297Gxjo62Nna4udrS1e7h7UatKAnT/vpku7T2tlj4yKQlNTk61rNqCpUH/qbGCgD8COtZtUYW97IpibmaWY0CokNBQjQ0P09PRS3Zdxjhzks8vHg0cP08xPauXu1Kc786fNokKZckDSD+jrt27gH7A+zYaGRcuXUrdGLRrXS2pAcS7gRFR0NOOnTaJr+05ERiXNwL1k1nzy5LZQS6utkzQsYunsBarZmd/+eDLPZU5ImPpTkZA3TwbSGr+qraWNm7MLDx4/ynC5Dxw9RGhYKFUb1laFJSQkMHPhXNZvCcjwpFzv13lkVCS5zcxZs3h5irg5chiRwyiH2uf9tht8ap93cGhouvMW2NrkxdTEhAePHqbb0GBqYoKmpqaqHt8KCQ3B3Mw81TQfys/bf0NCQ8n9ziSaIaGhuDg7q+KEhqlvIz4+nhcvX37ynBpvuRcsxLmL59ON8y2U+9TZM/QaMoCh/QZRv1adVPO0ZuM6Vq33Z8WCpbgU+PhhXN9CPVjlsQTAMb8DCYmJjJ82iXYtWqfbUwdSntexcbEfzNf7MlI/5mZmxMXF8fLVK7WnvSFhIaqymOcySzGvzds6/Jjj/UPXVoVC8c2W+5dDBxg7dSKzJ09P896SUd/Ccf0uz0LuHD/1T5rr07uPfaufd3oycpxnlhXr1lDEw4uOrdsCSd9x9PX1adu9M3279UDjzb7G+Y5Sq2cAhWbSuvG+o1U9g9/O5/SpdZGR+9e36L86KWN2keoWmcrV1ZX4+HjOnEke337jxg3Cw8M/mFZXVxdjY2O15b8+bMLQ0FDVoGBna5vmj/dEZSKxcZ/+qjU3ZxcSEhIIDQtT25+dra3q5v5u2NsnPF7unpx4b9K34yf/wcvdM8U+3oqMjOTho0fkNk/9SxakLHd8Qjzx8fEpbuqaCk0SlYlpbic6OjqVNEl/K5VKHO0d0NHR4emzwBTlfvujwtrKShWWxyKpMcLLw4PT586pvd7u75MnyG+XL82x6QkJCdy6c5vcaXy5TK3cTRo0Yuf6zWxfG6BaLMxz06FVG5bNW5Tmdt73fp27ubgSHBqCppZminKbmpiipaWlFva2V4uXu2eKSf6OnzyR7ucd+PwZ4S9epPt5A2hra1PQxZUTp0+pwhITEzlx+hRe7h6ppvlQfvJa22BuZqYWJ+J1BBevXlbF8fLw5OWrV1y5fk0V58SZUyQmJuJZKPX9ZtT1WzfT/DHx1tde7pNnT9NzcD8G9uyb5pCa1RvWsmzNSvzmLlIbE/4xvvZ6eF9iYiLx8fHpXn/eev+8dszv8MF8vS8j9VPQ1Q0tLS21a/Ld+/d4GhiIl0dyeW/dua32Q/b4yRMYGRrimN/hg2V560PX1m+13L8c3M/oSeOZMWGKqlH7c3xrx/X1WzfSvUeldx/7Fj/vD8nIcZ5ZoqOjVY0JbynePPhRKpMaBSzMc/Po8eMU9+281klDOfNYWKjCrK2sgE+vi4zcv4T4EOnRIDKVi4sLNWrUoFu3bixduhQtLS369++Pvr5+dmctQwx0DbCzyK/6O6+5La62hXjxOpynoY+zMWdJIqOiWO6/ikrlKpDbzJywF+Fs2r6V50FBVP+hiipep97dqVyhEi2bNEtKFxmp1oPg8ZMnXL95g5zGxlhZWmFvl4/a1WsyYsIYBvcdgJuzC2FhYfxz+iTOBZzS/MLVtOGPbNq+hdmL5tOwTj1OnjnFgaOHWTJrnirOzAVzqVi2PNZWVjwPCmLxymVoaiqoVbVGhsttZGhE8SLFmL1oPrq6ulhbWnH63Bl++nUfQ/oNUMXzHT8Gi9y5GdCzDwAVypZn3aaNuDq74FkoqdvjwuVLqVC2PJqamhgaGtK+ZRtmzJ+NMjGRIl6FiXgdwbmLFzAyMExzzGPtajVYumoFYyZPpFObdtz69w4bt25iaL9BqjhLVy3H090Du7y2vIp4xZqN63kSGJjmcJHUmOQ0wSSniVqYlpYW5rnM1bo5vv95f6jOfUp44+XuQd9hgxjYqy/2dvl4HhTEH3//SeUKldL8sdi6aQs69OyCf8B6ypcuy6+HD3Ll+lXGDU8asx8ZGcmSVcupWqky5mZmPHz0iDmL52OX15Yy3h9+93rbFq0ZOXEshVzdcC/kzobNAURFR9GgTj0g5ef7ofxoaGjQpllLlvuvIp+tHTZW1ixasRQL89xULl8RAEf7/JQtVZpxUycyZugI4uLjmTJ7BjWrVFP1IIKkbq1xcXG8ePmS15GvuX7zBgCub4bYrN8cgI21NQXyOxITG8OOn3Zz8swpls/78GvQvtZynzxzil6D+9OqaQuqVvpB9U51bS1tVePTqvX+LFrhx4zxk7GxslLFMdA3wMDA4INl/xbqYe+BX9DS1MKpgBM62tpcuXaV+UsXUb1KtRQz9GdERvIFKc/rD9VPDqMcNKpbnxkL5pDT2BhDQyOmzJ6Bl7un6kda6ZKlcLTPj++E0Qzs1Y+QkGAWLl9C8x+bquZrAVTHd2RUJGHhYVy/eQNtbW3VD5MPXVu/xXLvO/ArIyeOZfiAwXgWclcdy7q6uuQwSnpy/qF7aWq+1uN6z76f0dbWVl3DDh/7jV17f2K872hV3j/nPva1f97wZY7ztz50rMxdspDnQUFMHTsBgIplyzFu6iQ279xGGW8fgoKDmT5/Nh4FC6k+s55dujFtzkyMjIwoW6o0sbGxXLl+jZevXqoNg3lXRuric+5f3xqF4r/9ADOrSUODyHRr1qyhc+fOVKhQgTx58jBp0iRGjx794YRfAfd8XqwbskP1t2+zpDkjdv29Bd81A9JKlmU0FQru3r/HT7/sJexFOCY5c+LuVoi1S1eqjZt++PgRYe/M9H/5+lU69uqm+nvGgjkA1K9Vh8mjk8o4adRYlq1ZxawFc3kW9BxTExM8C3mk+1Qnr7UNi2fPZ8a8OWzYuok8FhaM9x1FmVKlVXGeBT1n6NgRhL94QS4TU4p4FWbjCn9ymX7c5JWzJk5h3tJFDB87ihcvX2JtaUnf7j1p1rCxKs7TZ4FqN5Fu7TuhoaHBwmVLeB4UhKmpCRXLlKdv916qOH269sDUxISV69bw8MljjHPkwM3ZNd1hKDmMcrB8/mImz5pG0w6tMc1pQveOXdSe+L589Ypx0yYRHBKCcQ5jCrq6smH56o96mpJR73/eH6pzDQ0Nls5ewPxlixk9aTyh4WGYm5lRrHDRdMcjF/H0Yvr4ySxcvpT5fovJZ2vHgumzcXIsACTNDn7zzi1++nUvL1+9wsI8N6W9S9G7aw+1L3dpqVmlGmFhYSxa6UdwSAiuTs74zV2o6t75/uf7ofwAdGzdjqioKMZNm8yriFcU9SyM39yFanMITB83icmzp9Opbw8UGhpUqViZEQOT38kO0GNgX9VcHQCN27UE4PLxpN5bcfFxzFwwl+dBQejp6eFcoAArFyyhZLES32y59/yyl6joaFauW8PKdWtU4cWLFMN/SdKwmy07txMXF8eAEUPV66tTV3p17sbH+FrrQVNTi9Ub1nLv4QOUSiXWlla0aNyUts1bfVT53pWRfL1/Xn+ofgCG9RuEQkNBf9+hxMXFUtrbh9FDhr9TFk0Wz5rPxJlTad2lPfr6+tSrWSfFxJZvj29ImuV/38H9WFtaqYZrZeTa+q2Ve9ueXcQnJDBp1nQmzZquCn/3PpmRe+n7vtbjGsBvzUqeBj5FU1OT/PnsmTVxKtXeeWjxufexr/nzhi93nMOHj5XgkGCePgtUrW9Qux6vIyPZtH0rsxbMJUeOHJQsVoKBPfsm57deQ/R19VizcT2zF81HX08fZ8cCtG6W9mTRGamLz7l/CZEeDWVm9/0RIhO5drHO7ixki0vTb2Z3FoQQQgghhEhBO5dRdmfhk5QeaZdt+/57ctqvi/9eSY8GIYQQQgghhBDftY94gZfIBDIZpBBCCCGEEEIIITKN9GgQQgghhBBCCPFdy8Q3kooMkOoWQgghhBBCCCFEppEeDUIIIYQQQgghvmsa8nrLLCU9GoQQQgghhBBCCJFppKFBCCGEEEIIIYQQmUaGTgghhBBCCCGE+K7JZJBZS6pbCCGEEEIIIYQQmUZ6NAghhBBCCCGE+K5pyCP2LCXVLYQQQgghhBBCiEwjDQ1CCCGEEEIIIYTINDJ0QgghhBBCCCHEd02hoZHdWfhPkR4NQgghhBBCCCGEyDTSo0EIIYQQQgghxHdNJoPMWlLdQgghhBBCCCGEyDTS0CCEEEIIIYQQQohMI0MnhBBCCCGEEEJ81xTyiD1LSXULIYQQQgghhBAi00iPBiGEEEIIIYQQ3zUNhbzeMitJQ4P4ql2afjO7s5AtPIY5Z3cWssWJBWezOwvZwiDCMLuzkD00/5s3fM3XCdmdhWyhjPtvljvGWie7s5AttKL+o+d3WEx2ZyFbJOb8bx7niXqa2Z0FIb5a0tAghBBCCCGEEOK7JnM0ZC2pbiGEEEIIIYQQQmQaaWgQQgghhBBCCCFEppGhE0IIIYQQQgghvmsa/82pY7KN9GgQQgghhBBCCCFEppEeDUIIIYQQQgghvmsKeb1llpIeDUIIIYQQQgghhMg00tAghBBCCCGEEEKITCNDJ4QQQgghhBBCfNc05BF7lpLqFkIIIYQQQgghRKaRHg1CCCGEEEIIIb5rMhlk1pIeDUIIIYQQQgghhMg00qNBCCGEEEIIIcR3TaEhPRqykvRoEEIIIYQQQgghRKaRhgYhhBBCCCGEEEJkGhk6IYQQQgghhBDiuyaTQWYt6dEghBBCCCGEEEKITCM9GoQQQgghhBBCfNekR0PWkh4N4qNVrFiR/v37Z3c2hBBCCCGEEEJ8haSh4RsVGBhIv379KFCgAHp6euTJk4cyZcqwdOlSIiMjszt7WWLxymXUbdaIEpXKULpaRTr36cHFK5fSTXP63Fl6De5PpbrVcfcpxpHff8u0/Jw8e5om7VpSpHwpajauz+59P6XIr7tPMbWlbrNGmbb/z1HcyZulvdfyx8yzXF/xhMqFa2R3lj7Lts27qF+zGWVLVqVD6+5cuXQtzbh3bt9l2KDR1K/ZjJKFK7Bpw7ZU4z1/FsSYEZOoUqEu5byr0qJxe65euf6lipAhSqWSRSv9qFi/OsV+KEPnfj25//DBB9Nt2rGVao3rUvSH0rTo0o5LVy+rrd+2Zyfte3fFu1oF3MsW5+WrVym2cfXGdTr374lPjYqUqVWZcdMnZ8q1R6lUsmj5UirWqUaxCqXp3KdHxsq0fSvVGtahaAUfWnRqy6Ur6mWKiYlh0sxplKn+AyV+KEt/3yEEh4aoxXka+JQeg/pSvGJpyteqwqyF84iPj1etDwoOYuiYEdRu2hCP0sWZNndWinzc/vcO/X2HUK1hHdx9irF+c8An1kRKSqWSBf7LKNe0JoVrlaPDkF7ce/Thutm4ZxuVW9XHq2ZZmvXuwMXrV9TWB4UGM3TaWMo1qUHROuVp1L0NB/84qhbn7qP79Bo9GJ9GVSlerxKt+nXhxPnTmVa2j6FUKlm4bjnlW9ahSP0KdPTtw73HD9NNc/rSOXqOHUyFVnUpWNOHw3//rrY+Lj6e2asWU79HK4o1qESFVnUZPms8z0OCvmRR0rVl81Zq1ayDdwkf2rRqy+VLl9ONf+jgIRrWb4R3CR+a/NiU//3vT7X1ISEhjBk9lqpVquPjXZpePXpz/37y8fPk8ROKeBVLdTl08NAXKWNqNm3fQvUGtSlWvhQtO6Y8l9934Mgh6jZrRLHypWjYqil//K1e7sO/HaFr356UrVYJj1JFuX7zRoptbNu9gw49ulDqh3J4lCqa6jUvq32vx7lSqWTRiqVUrFuNYhVL07lvBq/xO7ZSrVEdilb0oUXntinuWzExMUyaNY0yNX6gROWy9B+R8hr/z+mTtOragZJVylGhTjXmLF6gdo1//PQJ7qWLpVguXE7/u+Wn2LxlMzVq16R4qZK0bNuaSx/Yx8FDB6nXqAHFS5WkUdPG/O/P/6mtHzV2NJ5FC6st3Xv1VItTo3bNFHFWrVmd6WUT4i1paPgG/fvvvxQpUoSDBw8yZcoUzp07x/Hjxxk6dCh79+7l8OHD2Z3FdCUkJJCYmPjZ27G3tWPEoGHs3LCFdX6rsLayomu/XoSGhaWZJio6ChcnZ0YOGvbZ+3/XoyeP6TWoHyWLFWf7uk20adaSsVMn8dc/f6vFK+DgyLG9B1TLumWrMjUfn0pf14Drj64wIWBEdmflsx06cJR5sxfTuVs71m1agZOzI317DiY0NPXjIiY6Ghsba3r164qZea5U47x8+You7XujpaXJ/EUz2LxzHf0G9sLYOMeXLMoHrd64lo3bNzNmsC8By/3R19ej28A+xMTEpJnm1yMHmbFoLj06dGHbqg24FHCm28A+hISFquJEx0RT1rs0Xdp0SHUbz4OD6Ny/J3Z5bQlY7o/f7AXcvneHkVPGfX6ZNqxl47bNjBk6goBVa9HX16db/97pl+nwQWYsmEOPTl3Z5r8RFydnug3oTUhocpmmz5/Nsb/+YM7kafgvWUFQcBD9hw9RrU9ISKDnoH7ExcWzYfkaJo8ez55ffmbRCj9VnNi4OExNTenavhMuBZxTzUtUdDR5rW3o37MP5mZmn10f71q5ZR0bdm1hXL/hbFm0GgM9fboM70tMbNp188tvh5juN49ebTqzw28dLg5OdBneV+3zHj59PPce3mfxxNnsWb6JqmUrMmDSCK7eSv5B1mPkQOITEvCftYTtS9bi4uhEj1EDCQoNztQyZsSqbRvY8NM2xvYZyuZ5q9DX06frqP7p1kNkdDQuDk6M7jko1fXRMdFcvXOD7i06sH2RPwtGTeXuowf0Gj/0SxUjXQf2H2T2rDl069aVgM0bcXZxpmeP3oSGhKYa//z5C/gOH0mDhg3YtCWAipUqMrD/IG7fug0k/bgb0H8Qjx49Zt68OWzaEoCVlRXdu/UgKjIKgDyWeTh05IDa0r1HNwwMDChTtkyWlHv/oQPMnD+H7p27snVtAM5OTnTr30vtXFYr98ULDBszgkZ167NtbQA/lK9Iv6EDuXXntipOVHQURbwKM6BX3zT3Gx0dTRmf0nRu3zHTy/SpvtfjXHWNHzKCgJVr0dfTp9uADF7jO3Zl25qNSfet96/xC95c4ydNw3/xCoKCgujvm3yNv37rJj0G9aVsKR+2+wcwa+JUfvvzd+YuXZhifysXLOXYzwdUS0FX10ytg/0HDjBzzmy6d+3GloBNuDg5071Xz7SP8wvnGTbCl4b1G7A1YDM/VKxEv4EDuHX7tlq8MqXLcPTgYdUyY+q0FNvq1aOnWpwWzVtkatm+dgoNRbYt/0X/zVJ/43r27ImWlhanT5+madOmuLm54eDgQP369dm3bx9169YFIDw8nM6dO5M7d26MjY354YcfuHDhgmo748aNo3Dhwqxfvx57e3ty5sxJ8+bNefVOS/7r169p27YtRkZGWFlZMXv27BT5iYmJYfDgwdjY2GBoaIi3tzfHjh1Trff398fExISffvqJggULoqury4MHH269/pDa1WviU9IbW5u8FHBwZGi/gUS8fs3N27fSTFPOpwx9u/WkSsUf0owTGxvLzAVz+aFuDUpUKkOLTm05eTb9J3dbd+3AxtqGIX0H4mifn5ZNmlG1UmXWvfc0U1NTE3Mzc9ViamL6cYX+Qv53+Tfm757B4XP7szsrny1g/VYaNKpD3Qa1cHC0Z/ioQejp6fHz7l9SjV/Q3Y2+A3tQrUZldLR1Uo2zbk0AFpa5GTPBl0IebtjYWFGqdAny2tp8yaKkS6lUsn7bJrq27cQP5SriUsCJKaMm8DwkiCP/O5ZmunWbN9K4bgMa1q6HY34HxgzxRU9Pj117k3vgtGnaks5t2uNZyD3Vbfz+1//Q0tJi1MBh5Lezx8OtEGMGj+DQsaM8eJT+E7cPlmlLAF3bd+KH8m/KNGY8z4ODOPJHOmXatIHG9RrSsM6bMg0dgZ6uHrv27gHgVcQrdv68h6F9B+JdvCSFXN2YOHIs5y9dUD2p+vvkP9y5d5dp4ybi6uxCOZ8y9O7Sg807thIXFweAjZU1vgOGUL9WHYyMjFLNi0fBQgzu059aVauneTx9at2s27mZ7q06UrlMBVwcnJg2bBzPQ4I5/NfvaaZbuyOAJrUa0KhGXQrkc2Bc/+Ho6eqxc//Pqjjnr1ykVYOmeLoWwtbahh6tO5HD0Igrt5J6AoW9COf+44d0adEWFwcn7PPaMahzL6Kio7l1999MK2NGKJVK1u3eQrfm7ansUx6X/AWYNngMz0OCOfL3H2mmK1/Ch37tulGlTMVU1+cwNGLVlAXULF+F/Hnz4eXmzqgeg7hy6zpPngd+odKkbcP6DTRq1JD6Derh6OjAyFEj0NPTY/fuPanG37RxE6VL+9CufVscHPLTq3dP3Nxc2bx5KwAP7j/g0sVLjBzpSyH3Qtjb2zNilC8x0TH8uj/puq+pqYm5ubna8tvRY1StVhUDA4MsKfe6TRv5sX5DGtapn3QuDxuJvl7yufy+DVsCKFPKhw6t2+GQ34E+3XpS0MWVTdu3qOLUrVmHHp26UqqEd5r7bdO8FZ3bdsCrkEeml+lTfK/HuVKpZP3WT7jGb87gNb5P2tf4/UcO4uzoRI+OXbHLa0uJIsUY1Ksfm3ds4/Xr12r7MzHOqfZdTVtLO1PrYd3G9fzYsBEN6jfA0cGR0SNHoa+nx+49u1ONvzEggDI+penQrj0ODg707tkLN1c3Nm/ZrBZPR0db7fw1NjZOsS0DAwO1OAb6+plaNiHeJQ0N35iQkBAOHjxIr169MDQ0TDWOhkbSRCdNmjTh+fPn/Prrr5w5c4aiRYtSuXJlQt9pMb1z5w67d+9m79697N27l99//51p05JbQIcMGcLvv//Onj17OHjwIMeOHePs2bNq++vduzfHjx9n8+bNXLx4kSZNmlCjRg1u3Ur+wR8ZGcn06dNZuXIlV65cwcLCIjOrhbi4OLbt3kkOIyNcnJw+a1uTZ0/nwuVLzJw4lR3rN1Pthyp0H9An3a59Fy5fpFTxkmphZbx9uHD5olrYg4cPqFS3OjV+rMewsSN5Gvj0s/Iq1MXFxXH92k1KeBdThSkUCkp4F+PSxSvppEzf/37/C7eCrgwfPIbqlerTulkndu/4+cMJv6BHTx4THBKCT4nk4y6HkRGeBd3T7OYZFxfH1ZvXKVU8+Qu3QqGgVPGSXLhyMdU0qYmNi0VbWxuFIvkWoqerC8DZi+c/siTJksuUnL8cRjnelCn1/MXFxXH1xnVKvVMPCoWCUiVKqurh6vVrxMfHq/3QcLDPj5WlJRcuJW33wqWLODkWwDxXci+EMqV8iHj9mtv/3vnkMmWWR0+fEBwagk/R9z5vt0JcuJr65x0bF8eVm9fxKVpCFaZQKPApWoLz76QpXMiTX48dIvzlCxITE9n320Fi42Ip6ZV0HpkY5yS/bT72HPyFyKgo4hPi2bJ3F2YmuSjknLlP+j7kUeATgsNC8CmSXKYchkZ4uhTk/PX0u9h/rFeREWhoaGBsmLU9l+Li4rh27TrepdSPae9SJbl4MfXP+uLFi3iXUv8h7VPah4sXk47v2LhYAHR0kxu/FAoFOjo6nD93PtVtXr16jRs3btCgYf3PKU6GJZ3L19TO06Rz2Vt1nr7vwuVLKRoQSpfySTP+t+J7Pc5V1/jin3CNL/4J1/g8lqrtxsXGoqur3virq6tLTGwMV26oD6/sPWwA5WtVoU33jvz2v7Qbcj9F0vl9jVLe6se5t7c3Fy6mcZxfuoi393vHuY9PivinT5+mQuVK1G1Yn4lTJhMeHp5iW6v911CuUgWatmjGmrX+akNH/gsUCo1sW/6L5K0T35jbt2+jVCpxcXFRCzc3Nyc6OhqAXr16UbduXU6ePMnz58/RffMDYNasWezevZvt27fTtWtXABITE/H39ydHjqQbTJs2bThy5AiTJ08mIiKCVatWsWHDBipXrgzA2rVryZs3r2q/Dx48YM2aNTx48ABra2sABg8ezP79+1mzZg1TpkwBki6sS5YswcvLK82yxcTEpOg6p4iJU+U/Ncf+/IMhY0YQHR1NbjNzls9f8lm9BJ4GPmX3vp85tGsfFrlzA9ChVVv++uc4u/b+RP8evVNNFxwSglku9W73ZrlyEfH6NdHR0ejp6eFZyJ1Jo8Zhn8+e4OAglqxaQdsendm9YWuajUbi44SHvSAhIYFcZurHQC4zU+7f+/ReNI8fPWXntj20bN2EDp1bc/XydWbPWICWtjZ16mXPfBZvx56amap3zzczzZViXOpbYS/CSUhISPVYvXv/Xob37V20BDMXzmV1wDraNGlBZFQUc/2Sup8GhXx6V/rgkBBVft7P39t17wsLf1um9+ohl5mqTMEhIWhra2OcQ/2LtJmpmaqugkNTP4ffrstuwWFvP2/1PJqb5CIojfyFvwgnITEhRRoz01zcfXhf9ffc0VMYOHEEPo2qoqWpiZ6uHgvHzSCfjS2Q1Hi9esYieo8dQvF6FVFoKMhlasryqfPJmSPlE7Mv6W09mKdSprfrMkNMbAxzVi+hVoWqGGXx9TksLPzNdey9Y9rMjHt376WaJjg4hFxm79WJWS5CgpPqxN7eHksrSxYuWMSo0SPR19dnw/qNPHv2jOCg1M/Z3bt2k98hP4ULp33fzkzJ53Iqx+u9e6mmCQ4JTnnum5qleb34Vnyvx7nqvpXaNT6t+1ZGrvGhaVzjcyUfC6W9fVi/dRO/HNxP9cpVCQ4NwW/1iqT0wUnngIG+PkP6DKCIpxcaGgoOHztC3+GDWDBtNpXKVfi8wqvKE5Z2edI6zoODMUvlehD8zv22TOkyVP6hMjbWNjx69JAFixbRs08v1vuvQ1NTE4CWLVri5upKTuOcnL94gfkLFxAcHMyQQYMzpWxCvE8aGr4TJ0+eJDExkVatWhETE8OFCxeIiIhIcWGKiorizp3kp3P29vaqRgYAKysrnj9/DiT1doiNjVVrRc2VK5daI8elS5dISEjA2Vl9vHJMTIzavnV0dPD09Ey3DFOnTmX8+PFqYaOG+jJm2Aj2HviF8dOnqML95iykWOEilCxWgh1rNxH2Ipzte3YxeNRwAlauTXETy6ibd26TkJBA7WYN1cLjYmPJmTMnACV+KKsKr1O9FmOHZWxeg3I+yWNcXQo44VHIg2oNa7P/yCF+rNfgk/IrskZiYiJuBV3o2Tepgc7F1Zk7d+6yc/ueLGto2HvwV8bPTD4HlsyYlyX7TU0BB0cmjxzPjEVzmb9sMQqFglaNm2OWywyFRsZb7d8/r5fMmv8lsvtN+vnIfsbNnar6e+nkuV9sXwvW+PHqdQSrZyzCNKcJR/76nQETR7Bh7nKcHQqgVCqZuGAmuUxysWHucnR1ddn+yx56jh7E1sX+WJiZf7G8/Xz0AOMWTlf97Tc+5QScmS0uPp6BU0ahVCoZ2zt75mjIbNra2syeM4vx4yZQoVwlNDU18fYuSZmyZVAqlSniR0dH8+uv++nSpXM25Pa/53s9zvce+IXxM76Oa3wZbx8G9erHhJlT8J04Bh1tbbq178yZC+fQeNM7z9TElHYtWqvSeBQsxPPgYNYErMu0hoYvpWb15O8izk5OODs5U6teHU6dPq3qPdG2dZvkOM7OaGtpM3HKJPr16YuOTuYN9fua/Vd7FmQXaWj4xhQoUAANDQ1u3FCfNdnBwQEA/TdjrSIiIrCyslKbK+EtExMT1f+1tdXHnWloaHzURI0RERFoampy5swZVYvpW++OYdbX11cN6UiLr68vAwcOVAtTvE4aG12pbAU8CyaPnXzb28BAXx87W1vsbG3xcvegVpMG7Px5N13afdqETpFRUWhqarJ1zQY0FerlMTBIqtsdazepwt72RDA3M0sxiU9IaChGhobo6emlui/jHDnIZ5fvs8a0C3UmpjnR1NQkNER94sfQkLA0J3rMCPPcZuR3tFcLs8+fj98Opz1WNrNVKlsez4LJcybExiZ1hQ4JCyG3efIPvZCw0DQnKjTNaYKmpmaqx+rHTlxYu1oNalerQXBoCAZ6+qChwbotG8lrnffDid94/7x+2707JDSU3Oa51fLn4pxGmUzelkn9aVhIaAjmb34Am5uZERcXx8tXr9SeeIWEhaiGSpjnMuPS1SvvbSNUtS6r/eBTDk/XQqq/VXUTFqr2wz44PBQ3x9TrxiSnCZoKTbWJH99uw/xNT5gHTx6xcc82flq5CSd7RwBcHZ05fek8AT9tY1x/X/45d4pjJ/7kxK7DGBkmXdcL9XPl77Mn2XNwH11atMu8gr/nh1Jl8XQtqPo79s18GcFhoeTOpX7cu6ZRDx8j6cfXSJ48D2TNtEVZ3psBwNTU5M117L1jOiQEM/PUG3XMzc1STBQZEhKKmXnysVuwoBtbtm7i1atXxMXFkyuXKW1ataVgoYLvb47Dh44QHRVNnbp1MqFEGZN8Lqc8Xt9/aPKWuZl5ynM/LCTTJ2L90r7X47xS2Qp4vjPvheq+ldo13ukTrvFv6sY8VxrX+FD1Y6Fdi9a0bd6KoOBgjI1z8PjpU+b5LSKvddrzLXkWcuf4qX8+otTpMzUx/eA9633m5uaEpHI9SCs+QN68eTE1MeXhw4dqwzTe5eHhTnx8PI+fPCG/vf3HFUSIDJA5Gr4xZmZmVK1alUWLFqWYvOZdRYsWJTAwEC0tLQoUKKC2mKfxReV9jo6OaGtrc+LECVVYWFgYN2/eVP1dpEgREhISeP78eYr9WFpaflTZdHV1MTY2VlveDpswNDRUNSjY2dqm+eM9UZmoukF/CjdnFxISEggNC1Pbn52treqC/m7Y254TXu6enDh9Um1bx0/+g5d72r04IiMjefjokdqPRPF5tLW1cXVz5tTJM6qwxMRETp88i4dnoXRSps/Tyz3F0IsH9x9haZXnk7f5sQwNDLHLa6taHPM7YG5mxj+nT6niRLyO4OLVy3i5pz6hmba2NgWdXTlxJvlYTUxM5MSZU3gVSr/HUVrMc5lhYGDA/iMH0dXRUZtf4YNleu+8Ti5Tcv6Sy5R6/rS1tSno4sqJd+ohMTGRE6dPqeqhoKsbWlpaaufo3fv3eBoYiJdH0na9PDy5dee22o+c4ydPYGRoiGN+hwyXKbMYGhiSz8ZWtRTI54B5LjP+Offe533tCl4FU/+8dbS1KeTsyj9n1evmn3OnKfwmzdshd+/PiK2pUJCYmPSkO/rNkDYNhXochYYGicrPf4NQegwNDMlnbataCtjlx9zUjH/eebVmxOvXXLxxlcKuqU9emlFvf3zdf/KIVVMWYGKc83Oz/0m0tbVxc3PlxAn1z+3kiVN4eqb+WXt6enLyhPo96J9/TqTakzBHjhzkymXK/fsPuHr1GhUrpnxSu3v3HipUrECuXFk3YXHSuezGiVPq16d/Tp1Unafv83L3UIsPSedtWvG/Vt/rcW5omNZ96xOu8Wc+4Rr/LDDFdjU0NLDInRs9XT1+PbQfyzx5KOiS9lwz12/eIHcm9tpKOr/dOHHyvfvwyZN4pdHz18vDUy0+wD8n/kkzPkDgs2eEvwjHPHfaeb9x4wYKheKTewEL8SHSo+EbtGTJEsqUKUPx4sUZN24cnp6eKBQKTp06xfXr1ylWrBhVqlTBx8eHBg0aMGPGDJydnXny5An79u2jYcOGFC9e/IP7MTIyolOnTgwZMgQzMzMsLCwYOXKk2gRwzs7OtGrVirZt2zJ79myKFClCUFAQR44cwdPTk9q1a3+ROoiMimK5/yoqlatAbjNzwl6Es2n7Vp4HBVH9hyqqeJ16d6dyhUq0bNIsKV1kpFoPgsdPnnD95g1yGhtjZWmFvV0+alevyYgJYxjcdwBuzi6EhYXxz+mTOBdwokKZcqnmp2nDH9m0fQuzF82nYZ16nDxzigNHD7Nk1jxVnJkL5lKxbHmsrax4HhTE4pXL0NRUUKtq9ozxf5eBrgF2FvlVf+c1t8XVthAvXofzNPRxNubs47Vs05Txo6fiVtCVQu6ubN64naioKOrUrwnA2FGTsbDITa83wyDi4uK4e+de0v/j4wh6HszN67fQN9DH1i7p6XzL1k3o1L4Xa1aup0q1Sly5fI3dO35mxOjsG9eooaFBmyYtWL52FflsbbGxsmHRyqVYmOWmcrmKqnid+vWgcvmKtPwx6Rxo27wVIyePo5BrQdzdCrFhawBRUVE0qF1XlSY4JJjg0BAePH4EwK1/b2NoYIBVHktyvvlSGrBjC4XdvTDQ1+f4qRPMXjKf/t37pBgj+9FlataS5f6ryGdrh42VNYtWLMXCPDeVy79TpvfO67YtWjNy4lgKubrhXsidDZsDiIqOokGdekDSZGON6tZnxoI55DQ2xtDQiCmzZ+Dl7qn6olq6ZCkc7fPjO2E0A3v1IyQkmIXLl9D8x6ZqXUqv30zqTRYZFUlYeBjXb95AW1tb1RgRFxfHnTdvYoiLj+NZ0HOu37yBgb4Bdra2n1U3bRs1x2/javLZ2JLX0poF/n5YmJlTpUzyD8UOQ3pSpUxFWjVoCkC7H1viO2M87i5ueLgUYt3OzURFR9GwRtKT6vx29tjZ2DJ23lSGduuHiXFOjvz1O3+fPcnSSXMAKFzQA2OjHPhOH0/PNp2Shk7s28PjwCdU8M6a1x6q1UODZizb7J9UD3msWLB+BRZm5lQuXT65Hob3pkrpCrSq1wSA11GRPHjySLX+8bMnXLtzk5w5jLG2sCQuPp7+k0dw7fYNloyfRUJiomrui5w5jNHRztxZ5z+kdZvWjBk9loKF3HB3dydgQ9J5Wr9B0jE9auQYLCxy07dfHwBatGpBl05dWLd2PeXKl+XA/oNcvXKV0aNHqrZ56OAhTE1NsbSy5Nat28ycMYuKlSriU9pHbd8PHjzk7JmzLFy8IMvK+1bbFq2SzmW3gngULMT6LW/O5dpJ5R4xfjQWuS3o3zOp3K2btaRDjy6s3biecmXKsv/QAa5cu8rY4aNU23zx4gVPnwXyPDgIgHtvxvWbm5mpHiAEhwQTHBKi+n5w684tDA0Mk655ObO+wel7Pc41NDRo07Tlm/uWHTbW1ixanso1vs+ba3zjt/et1oyc9OYaX9CdDVsycI2fo36NB1i9cR1lS/mg0FBw+PejrFzvz+yJ01Q9cvf88jPaWtq4OicNET78+2/s2vcT44ePztR6aNuqDaPGjqZgwYJ4FHJnQ8DGpPtwvaSJV0eMHkUeCwv69Ul6JWurli3p2KUza9evo3zZcvx6YD9Xrl5lzKgxQNJ326XL/KhSuQrm5mY8fPiIufPnYWdrSxmf0gBcuHCBi5cvUbJECQwNDLlw8QIzZs+idq1aqb6d4nv1McM7xeeThoZvkKOjI+fOnWPKlCn4+vry6NEjdHV1KViwIIMHD6Znz55oaGjwyy+/MHLkSDp06EBQUBCWlpaUL1+ePHky/hR25syZREREULduXXLkyMGgQYN48eKFWpw1a9YwadIkBg0axOPHjzE3N6dUqVLUqfPlulxqKhTcvX+Pn37ZS9iLcExy5sTdrRBrl66kgIOjKt7Dx48IexGu+vvy9at07NVN9feMBUlfpOvXqsPk0UnzQ0waNZZla1Yxa8FcngU9x9TEBM9CHmk2MgDktbZh8ez5zJg3hw1bN5HHwoLxvqMoU6q0Ks6zoOcMHTuC8BcvyGViShGvwmxc4U8u0+x/xaV7Pi/WDdmh+tu3WVJd7Pp7C75rBmRXtj5J1eo/EBYWzvKlqwkJDsXZpQDzl8zE7M1Eac+ePld7ehv0PJjWzZPHIW9Yt5kN6zZTtFhh/FYljSct6O7GjDmTWLJgOauWr8PaxpKBQ3pTo3bVrC3cezq2akdUdDTjZkzhVcQrinoUxm/2ArUJVB8+fkTYOzNP16xcjbDwMBat9CM4NATXAs74zV6oNjxgy+4dLF2zQvV3u15dAJg0YiwNaiU1SFy6eoXFq5YTGRVJfjt7xgwZQb0an9+w2LF1O6Kiohg3bXJSmTwL4zd3YcoyvXNe16xSjbCwN2UKCcHVyRm/ueplGtZvEAoNBf19hxIXF0tpbx9GDxmuWq+pqcniWfOZOHMqrbu0R19fn3o169C7S3e1/DVu11L1/6vXr7Hv4H6sLa04uGsvAM+Dg9Ti+Aesxz9gPcWLFMN/yfLPqpvOzdoSFR3N2LlTeBkRQVF3L5ZPm4+uTnLdPHjyWK1ualWqStiLMBb4Lyc4LAQ3R2eWT52vGjqhraXFsslzmbNyMT1HDSIyOhI767xMHTpW1YhgmtOEFVPnM2/1UtoP7kl8QgIF8uVn0YRZmdKN+2N1atKaqOgoxi6YxquICIoW8mT5xLlq9fDw6WPCXibfq67cuk77Yb1Uf09fnvQjukGVWkwZNJrnIUH89s//AGjUq63a/vynL6akZ9EvWaQUqtdIOqaXLvEjJDgEFxdnFi9ZqBpCEBgYqDbWuHBhL6ZMncziRUtZtHAxdnZ2zJk3mwJOBVRxgoKCmT1rblKX69zm1KlTm67duqTY957de8iTxwIfn1JfvqDvqVG1OqHhYSxesfTNueyC39xFqu7vTwMD0Xjn+l3Y04tpEyazaNkS5vstIp+tHfNnzMHJMbncv/3vd0ZPGqf6e8hoXwB6dOpKzzfn99ad21m6Kvn8bN896Z4wcdQ41Y/ZrPa9HucdW7cjKjqKcdPfucbPSeUa/+59q8qb+9aKN/ctJ2f85rx3je/75ho/4p1r/ODh7+6aP4//xYq1q4iNjcPFyYmF0+eozZ8F4Oe/kqeBT9HU1CR/PntmTZhKtXceYGWGGtWrExYWxpKlSwkOCcbFxYWli5a8c34/VT+/vQozbfIUFi5ZzIJFC7Gzs2P+nLk4FUg6zhUKBbdu3eKnvT/z6tUrLHLnxqeUD7179lI1lGvr6LD/wAH8lvkRGxeHjbUNbVq1Vpu3QXxdFi9ezMyZMwkMDMTLy4uFCxdSsmTJVONWrFiR339P+YaUWrVqsW/fPgDat2/P2rVr1dZXr16d/fu/3KvtNZSpzQIkxFciLjQiu7OQLTyGZf2X96/BiQVnPxzpO2QQ8R9964jmf/PJgubrhOzOQrZQxv03yx1j/d+YZO19WlH/0fM7LObDkb5DiTn/m8d5op7mhyN9h3QN9bM7C5+k3drUh59lhbXtUn89cWq2bNlC27Zt8fPzw9vbm3nz5rFt2zZu3LiBhYVFivihoaGqOVAgaQ4PLy8vVq5cSfv27YGkhoZnz56xZs0aVTxdXV1Mv+ADT+nRIIQQQgghhBBCfCExMTHExKg3ROrq6qr15nlrzpw5dOnShQ4dOgDg5+fHvn37WL16NcOHD08RP9d782xs3rwZAwMDmjRpkmJ/HzuH3ueQySCFEEIIIYQQQogvZOrUqeTMmVNtmTp1aop4sbGxnDlzhipVkofsKBQKqlSpwvHjxzO0r1WrVtG8eXPV2/HeOnbsGBYWFri4uNCjR48UbzPJbNKjQQghhBBCCCHEd+3duS+ymq+vLwMHDlQLS603Q3BwMAkJCSnm1MuTJw/Xr1//4H5OnjzJ5cuXWbVqlVp4jRo1aNSoEfnz5+fOnTuMGDGCmjVrcvz4cdWEqJlNGhqEEEIIIYQQQogvJK1hEplt1apVeHh4pJg4snnz5qr/e3h44OnpiaOjI8eOHaNy5cpfJC8ydEIIIYQQQgghxHdNoaGRbUtGmZubo6mpybNnz9TCnz179sH5FV6/fs3mzZvp1KnTB/fj4OCAubk5t2/fznDePpY0NAghhBBCCCGEENlMR0eHYsWKceTIEVVYYmIiR44cwcfHJ92027ZtIyYmhtatW39wP48ePSIkJAQrK6vPznNaZOiEEEIIIYQQQojvWnbO0fAxBg4cSLt27ShevDglS5Zk3rx5vH79WvUWirZt22JjY5NiMslVq1bRoEEDzMzM1MIjIiIYP348P/74I5aWlty5c4ehQ4dSoEABqlev/sXKIQ0NQgghhBBCCCHEV6BZs2YEBQUxZswYAgMDKVy4MPv371dNEPngwQMUCvWBCTdu3ODPP//k4MGDKbanqanJxYsXWbt2LeHh4VhbW1OtWjUmTpz4ReeN0FAqlcovtnUhPlNcaER2ZyFbeAxzzu4sZIsTC85mdxayhUGE4YcjfY80v40nC5lN83VCdmchWyjj/pvljrHWye4sZAutqP/o+R0Wk91ZyBaJOf+bx3mi3peZrf9rp2uon91Z+CRdAgpn275XtDyfbfvOLtKjQQghhBBCCCHEd+39XgDiy5LaFkIIIYQQQgghRKaRHg1CCCGEEEIIIb5rGh/xmknx+aRHgxBCCCGEEEIIITKNNDQIIYQQQgghhBAi08jQCSGEEEIIIYQQ3zWFQoZOZCXp0SCEEEIIIYQQQohMIz0ahBBCCCGEEEJ816RHQ9aSHg1CCCGEEEIIIYTINNKjQQghhBBCCCHEd02hIc/Ys5LUthBCCCGEEEIIITKN9GgQ4it0YsHZ7M5CtvDuWzS7s5Atroy9lt1ZyBYJxtrZnYVs8fpqYHZnIVs8WHEsu7OQLXL4u2d3FrJFHh2v7M5CtrjQxj+7s5AtPPZ0ze4sZIs4zZjszkK20EU/u7MgvgHS0CCEEEIIIYQQ4rsmk0FmLRk6IYQQQgghhBBCiEwjPRqEEEIIIYQQQnzXFBrSoyErSY8GIYQQQgghhBBCZBppaBBCCCGEEEIIIUSmkaETQgghhBBCCCG+azIZZNaSHg1CCCGEEEIIIYTINNKjQQghhBBCCCHEd02hkGfsWUlqWwghhBBCCCGEEJlGejQIIYQQQgghhPiuyests5b0aBBCCCGEEEIIIUSmkYYGIYQQQgghhBBCZBoZOiGEEEIIIYQQ4rsmr7fMWtKjQQghhBBCCCGEEJlGejQIIYQQQgghhPiuSY+GrCU9GoQQQgghhBBCCJFppKFBCCGEEEIIIYQQmUaGTgghhBBCCCGE+K4pNOQZe1aS2hZCCCGEEEIIIUSmkR4NQgghhBBCCCG+azIZZNaShgaRquPHj1O2bFlq1KjBvn37sjs7qYqMjGTukoUc/eMY4S9eYGNtTasmzWnWqHG66dZvDmDLru08DQzExMSEapUq079Hb3R1dT8rPzdu32LyrGlcvnYVUxNTWjVpRsfW7VTrd+/7iVGTxqul0dHR4ezvxz9rv+nZtnkXG9ZuJiQkFCdnRwYP60chD7dU4965fZflS1dz/epNnj4NZMDg3rRo3SRFvOfPglg0fxl//3WCmOho8traMHr8cAoWcv1i5fhSijt506l6Twrl88DCxJJeizty5Pz+7M5WhimVShb5L2fbL7t5FRFBEXdPxvQbhn1eu3TTBezexuqtGwgODcHF0YmRfQbj6VoIgMeBT6jaqkGq6eaMmUKNClUAOH72JAvXLOPm3Tvo6+nRoFpt+nXqgZbml7+tbNq6Bf8NawkOCcHFyRnfIcPwKOSeZvwDhw+xyG8JT54+wc7WjgF9+lK+TDnV+iXL/fj14AGePQtES1ubgq5u9O3ZG093DwBOnTlNx+5dUs+L/wbcCxXK3AJmkFKpZNkvG9h1/AARUa/xyu/G8Ka9sLOwSTPNmoNb+e3i39x79ghdbR0887vRp14H7PPkVcUJfhnK/N2rOXnjHK9joshnkZeO1ZpRuXCZrChWhuRuVhTTKq5oGugQeeMZT5f/RWzgy7TjNy2KRdOiamExj8O53W97qvHtRlYnRxFbHkw/xKtT9zM1759qz9bDbFv3K6EhL3B0sqXX0Na4ujumGveXncc4tO8v7t15BICTmz0dezVWix8W8oIVC7Zy5p/LvH4ViUdRF3oNbU1eO8ssKU9aNm/ZjP+6pPPb2dkZ36HD8HhzLqbm4KGDLFq6hCdPnmBnZ8eAvv0oVzb5/B41djQ//fyzWprSPqXxW7xELeyP//2B34rl3Lp1Cx0dHYoXK8b8OfMytWwfy6pzaczruqOZQ4+Ii495OOsIMY/C047f0QerTj5qYdH3Q7na0l/1t45NTvL2qoChpzUKHU1e/nOPh3N/Iz4s8guVIn1KpZLFK/3Y/vMuXr2KoIinF6MH+5LPNv372KYdW1kTsC7pPlbAiREDhuJRMPk+sG3PTvYd2s+1G9d5Hfmav/cfwzhHDrVt9B46gOu3bxAaFoZxjhyUKu7NwB59scid+4uUNT1bN29j3dqNhASH4OTsxNDhg3D3SP3ecuf2v/gtWca1azd4+uQpg4b0p2XrFmpxVq/y57cjx7h39z66urp4Fvagb//e2Nvny4riCAHI0AmRhlWrVtGnTx/++OMPnjx5kt3ZSdWMBXP485+/mTpuIj9t3k6bZi2ZMmcGv/3v9zTT7DvwK3OXLqRHxy78tHk7E0aMZv+Rg8z3W/xZeYl4HUHXfr2wsrRi65oNDOrdjyUrl7Ft9061eEaGhhzbe0C1HNy197P2m55DB44yb/ZiOndrx7pNK3BydqRvz8GEhoalGj8mOhobG2t69euKmXmuVOO8fPmKLu17o6WlyfxFM9i8cx39BvbC2DhHqvG/dvq6Blx/dIUJASOyOyufZNXmdWzYtYWx/YezedFq9PX06Tq8LzGxMWmm+fW3Q0z3m0fPtp3Z7rcOV0cnug7rS0hYKACWufPw+7Zf1Jbe7bpioG9AuZKlAbh+5ybdRwygbAkfdixbz5zRU/jt+P+Ys+LzzqOM2H/wADPnzaZ7525sXR+As5Mz3fr0JCQ0NNX45y+cZ9goXxrVb8C2DZv4oUJF+g0eyK3bt1Vx8tnlY8SQYezYtI11K9ZgY21Nt949CX1TJ4U9vfjt10Nqy4/1G2JjbUOhggW/eJnTsvbwdjb/8TO+TXvhP3AOejp69Fk6mpi42DTTnL19iSblarNm4GwW95pEfEI8vZeMIiomWhVn7Po53H/+mNldx7B5+GIqeZXGd800rj+8kxXF+iDzBp6Y1SrE0+V/8u+In0iMiSff6BpoaGummy76QSg3Om9ULXdH/ZxqPLM67qD8Ejn/dMcOnmDZnE207lqfpRvH4+Bsi2/vWYSFpt64cuHMdSpVL8XMZcOZv2Y0ufPkYnivWQQ/TzqmlUolYwfNJ/DxcybM6cfSgAnksTJjWI8ZREWlff340vYfOMDMObPp3rUbWwI24eLkTPdeHzi/R/jSsH4DtgZs5oeKleg3cIDa+Q1QpnQZjh48rFpmTJ2mtv7QkcOMGD2KBvXqs23zVtat8adWjZpfrJwZkadVCXI3LsyDmUe40SWAxOg4CsxphIZO+sd51L/BXKzrp1pu9NisWqfQ08Jp7o+Aklt9t3Oj+xY0tDVxnNEAsulB7+qNa9m4fTNjhowgYMVa9PX06TawNzEx6dzHDh9kxsI59OjYlW2rN+JSwJluA3ur7mMA0dHRlPX2oUvbDmlup2TR4syeMJ29m3Yyd/JMHj5+xIBRQzO1fBlxcP8h5syaT9dundi4eS3OLgXo3aMfoSGpH/fR0dHY5LWhT9+emJmbpRrn7OlzNGnWGP/1q1iybAHx8fH06t6XqMioL1kUIdRIQ4NIISIigi1bttCjRw9q166Nv7+/2vqffvoJJycn9PT0qFSpEmvXrkVDQ4Pw8HBVnD///JNy5cqhr6+Pra0tffv25fXr15maz/OXLlK/Vh1KFi2OjZU1TRo0wqWAE5euXkk3TREPL2pXr4mNlTVlvH2oVbU6l65eVsVJTExkxdrVVG9Ul2IVStOoTXMOHj2cbl72HviVuLg4Jo0cSwEHR2pVrU6rps1Zt2mDWjwNDQ3MzcyTl1yp3yAyQ8D6rTRoVIe6DWrh4GjP8FGD0NPT4+fdv6Qav6C7G30H9qBajcroaOukGmfdmgAsLHMzZoIvhTzcsLGxolTpEuS1TfsJ6tfsf5d/Y/7uGRw+9+30YnhLqVSybudmurXuSOUyFXBxdGLasHE8Dw7myJ9pN7b5bw+gSa0GNKpRlwL2DoztPxw9XT127k/60aWpqUnuXOZqy+G/jlGjQmUM9Q0A+PW3w7g4FKBn287ks7GlhFdRBnXpw6Y923kdmbnn+fvWBWzgxwaNaFivPo4OjozxHYm+nh67ftqdavwNmzdRxqc0Hdq0wyG/A3169KKgqxubtiV/+a5doyY+3qWwzZuXAo6ODOk/iIjXEdy8dQsAbW1tzM3NVUtOk5z89scxGtSth4ZG9nw7VyqVbPp9D52qNaOipw9ONvmZ0GYQQS9COXYx7V5SC3tOpK53VRyt8uFs48C4VgMJDAvi2sPkH2YX716jWfm6uOdzIa+5FZ2rNyeHviHXH95Oc7tZKVdtd4J2nOfVqQfE3A/l8cJjaJkakKNk+k/qlAlK4sOjVEvCq5Q/ZPTsc2FW14MnS/74Qrn/NDs27KdmwwrUqFeefA429BvRHl09HQ7sST2fvpO7U69pZQq45MMuvzUDR3dCqUzk3MmrADx+8Ixrl+7Q17cdLoUcsLW3oq9vO2JjYvlt/5frZfch6zau58eGjWhQvwGODo6MHjkKfT09du/ZnWr8jQEBSed3u/Y4ODjQu2cv3Fzd2Lxls1o8HR31c9jY2Fi1Lj4+nukzZzCw/wCaNm6Cfb58ODo4Ur1a9S9Z1A+yaFqEwLUnePHnHaLuBHNv4n60zY0wKVcg3XTKhETiQyNVS8KL5EZEQ08bdCyNuTfpANH/BhP9bzD3Ju3HwDUPOYql34PgS1AqlazfGkDXdp34oVxFXAo4MWX0eJ4HB3Hkf8fSTLduywYa121Iw9r1cMzvwJghI9DT1WPX3j2qOG2ataRzmw54Fkq7N0zb5q3wcvfA2tKKIh5edG7dnotXLhEXH5eJpfywDes30bBRfeo1qIuDowMjRg1HT0+PPbtTbwwt5F6Q/gP7Ur1mNXR0Uv++tmjpfOrVr4NjAQecXZwZP2EMgU8DuXbt+pcsyldPoaGZbct/kTQ0iBS2bt2Kq6srLi4utG7dmtWrV6NUJj3euXv3Lo0bN6ZBgwZcuHCBbt26MXLkSLX0d+7coUaNGvz4449cvHiRLVu28Oeff9K7d+9MzWdhD09++/MPnj1/jlKp5OSZU9x7+IDSJUulm+bqjWtcupLUsPDw8SP++PsvyvmUVcVZsW4NP/26jzFDfdkdsJW2zVsyfPxoTp09k+Z2L1y6RPEiRdDW1laFlfH24e6D+7x4mfzEKTIqiqoNa1O5fi36DB3I7X+/zBPCuLg4rl+7SQnvYqowhUJBCe9iXLqYdkPMh/zv979wK+jK8MFjqF6pPq2bdWL3jtRvhOLLevT0CcGhIfgULakKy2FkhKdbIc5fvZRqmti4OK7evE6poiVUYQqFAp+iJdJMc+XmNa7fvsmPteq/s53YFI1Rurq6xMTGcOXml/sSExcXx9Xr1yhV0lsVplAoKFXSmwuXLqaa5sKli5Qq4a0WVrqUT5rx4+Li2L5rJzmMjHBxdk41zrE/fif8xQsa1K2f6vqs8DgkkJCXYZR0KawKM9I3xD2fC5fuZfwziIhOahgyNjBShXnmd+PQuT948foViYmJHDjzOzHxsRRzSvsLe1bRtsiBtqkBry8+VoUlRsYRdSsIA2eLdNPqWhnjvLwFToubYtOvItrmhmrrNXQ0selXiacr/yI+/Ot56hcXF8/N6/coWjK5G7VCoaBoyUJcvZSxxp+Y6Bji4xPIYZz0OcfFJv2Q0tFJvmcpFAq0dbS5fP5WJuY+4+Li4rh27RqlvNXPb29vby5cTPv89vZ+7/z28UkR//Tp01SoXIm6DeszccpktQcj165f4/nz5yg0NGjaohk/VKtCj969UvSKyEo61jnRNjfi1ekHqrDE17G8vhqIobtVuml185rivqcrhbZ2xH5sTbTzJPc4VGhrghKUcQmqMGVsAiQqMfLM+gcGj548JjgkBJ/iyZ9hDqMceBZ058LltK/RV29cp1SJ5HufQqGgVPGSXLic+n0sI168fMHeg79S2MMTbS3tDyfIJEnf165TspR6eUqWKsGli59envdFREQAqDWyCfGlyRwNIoVVq1bRunVrAGrUqMGLFy/4/fffqVixIsuWLcPFxYWZM2cC4OLiwuXLl5k8ebIq/dSpU2nVqhX9+/cHwMnJiQULFlChQgWWLl2Knp5eqvuNiYlJ0VVOEROX5twJIwYOZdy0SVSuXxMtTU00FArGDR9F8SJFU40PULt6TcJehNOmeydQKolPSKBpwx/p2r4jALGxsaxcu5oVC5ZS2MMTAFubvJy9cJ5tu3dQomixVLcbHBpMXiv1m7TZm94KwaEh5DQ2xt7OngkjxuBSwIlXERH4B6ynddcO7A7YhqVFnjTz/CnCw16QkJBALjNTtfBcZqbcv/cgjVQf9vjRU3Zu20PL1k3o0Lk1Vy9fZ/aMBWhpa1OnXo3Pzbb4CMFhIQCYm6oPczEzzaVa977wF+EkJCakmubfh6mPQ9/x60842OWnSCFPVVjZEqVYv3Mz+44eoEaFKgSHhrB0/UoAgkKDP7lMHxIWHkZCQgJmud7Lfy4z7t67l2qa4JBgzMxSxg8OUa+j3//3B0NGDic6Oprc5uYsX+SHqYn6+fPWzj27KV3KB8s8mXvefoyQl0lDoMxyvHeO5zBRrfuQxMREZu9cjpdDQQpY26vCp3UYjq//dCr7NkdToYmeji6zOo3CNrd1puX/U2mZ6gOkaAiIfxGFlolBmumibj3n8eI/iHnyAi0TfSyaFsV+Yh3uDNhJYnTSj27L9qWIuvGcV6c+/Rr5JbwIf0ViQiKmZjnVwk3NcvLw3tMMbWPlgq2YmZtQ1DtpqI+tvRUWlmasWrSN/iM7oKevy46NBwh6FkpocHhmFyFDks9v9Z5+6Z7fwcGYmb0X38yM4JDk61CZ0mWo/ENlbKxtePToIQsWLaJnn16s91+HpqYmjx4nNVotXbaMwYMGYWNlzdoN6+jUtTM/79pDzpzq9Z4VtHMlHctxoerzJsSHvkbbzDC1JAC8vvqU+5P3E/MgDC0zQ6w6+uC8pBnX2qwlMTKO11eekhgdh03Pcjz2+xMNDbDuUQ4NLQVa6Wz3SwkOTboOp7ym50pxjX4rLDw87ePkwb2PzsOcJQvYtGMLUdHReBXyYPHMeR+9jc8RHvamPO/fp8xyce9u5swPk5iYyKwZc/Eq7EkBp9TndfmvUCjkGXtWkoYGoebGjRucPHmSXbt2AaClpUWzZs1YtWoVFStW5MaNG5QoUUItTcmSJdX+vnDhAhcvXmTjxo2qMKVSSWJiInfv3sXNLfXJCKdOncr48eqTJY4a6suYYSPYe+AXxk+fogr3m7OQi1cucfHKZRbNmIuVlRVnzp1l8uzpWJjnxqek9/ubB+Dk2dOsWLuGUUOG41nQnQePHjJt3iz8Vq+ge8cuPHj0kKjoaLr066mWLi4uDjdnFwDqt2zCk8CkL3fFvIrgN3dhmvX5rsIenqrGC4DCnp7Ua96Ybbt20Kdbz3RSfj0SExNxK+hCz75dAXBxdebOnbvs3L5HGhq+sJ8P72fc3Kmqv/2mzP3i+4yOiWbfkQN0b91JLbxM8VIM7tqH8fOmMXzqOHR0tOneuhNnLp3/Zt9RXaJ4CbZv3ExYeDg7du9k8IihbFyzPsUX4MBnz/j7n+PMmjo9S/P366nfmLJlkerved3GffY2p29byp2n91nZb6Za+NJf1vMqKoIlvSZjYmTMsYv/MNx/Giv7zVBrkMgKOcs5YtU1ucfZg6kHPmk7Eeceqf4fcx/u3wrCeWlzjEvnJ/zoTXIUt8PQw5p/h+z67Dx/bTav2cuxgyeYtXw4OrpJPZG0tLUYO6sPsyesplGlnig0k3pIlCjjCcqvbIKKz1SzevK9ydnJCWcnZ2rVq8Op06cp5e1NYmIiAF06daJq5aTJbieOm0DVGtU5eOgQTRqnP8F0ZjCt5ordkCqqv+8M2f1J23n5z73kP+4EE3k1EPcdnTH9wYWQvZeJD4/i39F7sRtcmdyNi0CiktDD14m8/ixLPve9B35h/Mzk73JLZs7/4vv8kA4t29CoTn2eBD5l6Zrl+E4cw5KZ87NtWNyXMG3KTO7c+ZdV/suyOyviP0YaGoSaVatWER8fj7V18pMrpVKJrq4uixYtSidlsoiICLp160bfvn1TrLOzS3sMoK+vLwMHDlQLU7xOetJUqWwFPAsmd9u1yJ2bTn26M3/aLCq8mT3epYAT12/dwD9gfZoNDYuWL6VujVo0rtcQAOcCTkRFRzN+2iS6tu9EZFTS04Mls+aTJ7d6N1ztN11Ml85OmlQHUPW2MM9lTsh7T5FD3rTUpzUPg7aWNm7OLjx4/CjV9Z/DxDQnmpqahIaoP9kMDQlLc6LHjDDPbUZ+R3u1MPv8+fjt8Nc1nvl79EPpcni6JXedjn0z4V9wWCi5zcxV4SFhobg6pt7l3ySnCZoKTYLD1CeYCgkLTfU4PfjHUaJioqlfrVaKde2btKJd45YEhQRjnCMHjwOfMnfl4hQ9ezKTqYkpmpqaKSaGCwkNSfFU8y1zM3NCQlLGN38vvoG+Pna2dtjZ2uHl4UntRvXYtWcXnTuoN7Ls/nkPJjlzUrF8hUwoUcaV9/DG3d5F9XfsmzHEIa/CMM+ZfE6HvgrHOa/DB7c3fdtS/rxykuX9ppPHNPn4eRT0lK1/7GWL7xIcrZLmPHC2ceD8ncts/d9eRjTL3CFwH/Lq1AOibiX/+NfQSmrI0jLRV+vVoJVTn+h7qT8BTU1iZCyxT1+gY5nUjdjQ3RqdPMa4rm2rFs92cGUirz/j3tjse/tSTpMcKDQVhIW8UAsPC3mBqXn6T9u3rfuFzf77mL50KA5O6vdfZ7f8LNs0kdevIomLj8fE1Jg+bcfjVDB/ppchI5LP75T3UvN3rnHvMjc3J+S9J98hIWnHB8ibNy+mJqY8fPiQUt7e5DZPesOAg0Pyk14dHR1s8trwNDBjPUY+14s/73D9SqDq77cTPmrnMiA+JHneG61chkTdep7h7SZExBD9MAzdvCaqsFcn73Ol6Wo0c+pBgpKEiBg8fupGzJEXaW8ok1QqW0FtzoTY2KT7WEhoqOpzePu3i1Pq9zFTE5O0j5NcaX/uaTE1McXUxBR7u3w42OenSsNaXLhyicLunh9OnAlMTN+U5/37VEgo5p/xfe2t6VNm8ucff7Ji9TLyZGMvvK+F5n90roTs8m0+ehJfRHx8POvWrWP27NmcP39etVy4cAFra2s2bdqEi4sLp0+fVkt36tQptb+LFi3K1atXKVCgQIolrUlrIOlHu7Gxsdry9oe8oaEhdra2qiU+IZ74+PgUXaA0FZokKhPT3Ed0dHQqaZL+ViqVONo7oKOjw9NngWr7s7O1xSpP0iu/rK2sVGF5LJIaI7w8PDh97pzaBEJ/nzxBfrt85ExjPFxCQgK37txW+5GYWbS1tXF1c+bUyeR5JRITEzl98iwenp/+Kj5PL/cUQy8e3H+EpZXcvL40QwND8tnYqpYC+Rwwz2XGP2eTz7+I1xFcvHaFwgVTH0uvo61NQWdX/jmXnCYxMZF/zp1ONc2OX3/iB5/y5EpjCIGGhgYW5rnR09Xjl6MHsbTIQ0Enl1TjZgbtN6+ePHHqhHr+T53EyyP1L4VeHp6cOHVSLez4iX/SjJ+8XSWxceoTgimVSnb//BN1a9XJ0jG8AIZ6BtjmtlYtDpZ2mBmbcurmBVWciKhILt+/gYd92q+aVSqVTN+2lGMXj7O09xRszNRfZRgdlzR8TfHe0zyFQhNlOtfWLyUxOo7YwJeqJeZROHFhkRh6JDdoKfS10XfKTeTNjP8AU+hpoZ0nh6qxInj3Be4M2smdwbtUC0Dg2hM8Xpz25KpZQVtbC2dXe86duqoKS0xM5NypqxT0SHtiwC1r97Fh5U9MWTQIl3QaDwxzGGBiasyjB4HcvHaX0hWKZGr+M0pbWxs3NzdOnEw+XxMTEzlx8iRenumc3yfVz+9/TvyTZnxI6pUU/iIc89xJ996Cbm7o6Ohw7/49VZy4uDiePHmCtVX68yFklsTIOGIeh6uW6LshxAVHqE3QqDDQwbCgJa8vZ7zxQ6Gvja6NCXHBKSfpTXgRTUJEDEZFbdEyNeDFn1/+rTKGhobY5bVVLY75HTA3M+OfM8mfYcTrCC5evYxXGj/0tbW1KejiyonT6vexE2dO4ZXOa1AzQvmmd8vbBpCskPR9zZVTJ9TLc+rEKTw8P708SqWS6VNm8tvR3/FbsRibvNk/9E3890iPBqGyd+9ewsLC6NSpU4oxiT/++COrVq1i69atzJkzh2HDhtGpUyfOnz+veivF225mw4YNo1SpUvTu3ZvOnTtjaGjI1atXOXToUIZ7RXyIkaERxYsUY/ai+ejq6mJtacXpc2f46dd9DOk3QBXPd/wYLHLnZkDPPgBUKFuedZs24ursgmehpKETC5cvpULZ8mhqamJoaEj7lm2YMX82ysREingVJuJ1BOcuXsDIwJD6teummp/a1WqwdNUKxkyeSKc27bj17x02bt3E0H6DVHGWrlqOp7sHdnlteRXxijUb1/MkMJAf6zXIlDp5X8s2TRk/eipuBV0p5O7K5o3biYqKok79pFd2jR01GQuL3PR6MwwiLi6Ou3fuJf0/Po6g58HcvH4LfQN9bO3yJm2zdRM6te/FmpXrqVKtElcuX2P3jp8ZMXrwFynDl2aga4CdRfIX8LzmtrjaFuLF63Cehj5OJ2X209DQoG2j5izbuJp8eW3Ja2nNgjV+WJibU7ls8tP2DoN7UqVsRVo1aApA+8Yt8Z0+HndnNzxcC7Fux2aioqNoWL2O2vbvP37I6Yvn8JsyL9X9r9qynnIlfNBQaHD4f8dYsXktc0ZPQVPzyz4taNuyNSPHj6GQW0E8CrmzflMAUVFRqokZR4wdhUVuC/r3TupR1bp5Czp068LaDesoV7Yc+w8e4Mq1q4wdMRpImqB1xeqVVCxfgdzm5oSFh7N521aeBz2nWuWqavs+ceokj588plGDhl+0jBmhoaFBiwr1WXVgM7a5rbExs2TpvvXkzpmLip4+qng9Fo2goqcPzconXbumb1vC/jO/M7vzaAz09Al+mfQUzUjPED0dXezz5MU2tzVTtiyiX4NOmBgYc+zScU7cOMfcrmOzpazvC913mdw/Fib26Qtin7/Conkx4sMieXUyeTxzvrE1eXXiPqH7k36c52lbklenHxAXFIFWLgMsmhaDRKXqx9XbN1G8Ly4ogrjnEVlTsHT82LoGM8auwNktPy7uDuwKOEB0VAzV6yX16Js+ZhnmuU3p1CfpPN/sv491fjvxndwdSytz1bwL+gZ66BskzZP0+6GTmJjmwMLSjLu3H7Fk1kZKVyxGcZ/sm/Szbas2jBo7moIFk87vDQEbk87vem/O79GjyGNhQb8+Sed3q5Yt6dilM2vXr6N82XL8emA/V65eZcyoMQBERkaydJkfVSpXwdzcjIcPHzF3/jzsbG0p45P0ul4jIyOa/NiYJX5LscyTBysra/zXrQWgWtVq2VALSZ5vPYdlO29iHoUR8+Ql1l1KExccQfj/kiepLDC/MS/+uE3QjvMA2PQqz4u//iU28CXa5oZYdS6NMiGRsMPJE8TmqlWI6PuhxIdHYlTImrz9K/J8yxliHmRsbpfMpKGhQZumLVm+dhX58tphY23NohVLsTDPTeVyFVXxOvXtTuXylWjZuBkAbZu1ZuTksRRydcO9oDsbtgYQFR1Fg9r1VGmCQ4IJDgnhwaOHANy6cxtDAwOsLC3JaZyTi1cucfnaVYp6FsbY2JiHjx+ycIUftjZ5s6w3w1ut27Rg7OgJuBVyw929IAEbNhMVFU29Bkn35TEjx5HbIjd9+vUCkr6v/Xvnrur/z58HceP6TQwM9LG1swWShkvs//UAc+bNxMDQkODgpB4gRkaGac6VJkRmk4YGobJq1SqqVKmS6sRHP/74IzNmzODVq1ds376dQYMGMX/+fHx8fBg5ciQ9evRQ9T7w9PTk999/Z+TIkZQrVy6pp4CjI82aNcvU/M6aOIV5SxcxfOwoXrx8ibWlJX2796RZw+TxlE+fBaJQJD+Z69a+ExoaGixctoTnQUGYmppQsUx5+nbvpYrTp2sPTE1MWLluDQ+fPMY4Rw7cnF3p0q5jmnnJYZSD5fMXM3nWNJp2aI1pThO6d+xCkwaNVHFevnrFuGmTCA4JwTiHMQVdXdmwfDWO+T/czflTVK3+A2Fh4SxfupqQ4FCcXQowf8lM1YRDz54+VxtPH/Q8mNbNO6v+3rBuMxvWbaZoscL4rUoaR1nQ3Y0ZcyaxZMFyVi1fh7WNJQOH9KZGbfUfZN8K93xerBuyQ/W3b7OkOUJ2/b0F3zUD0kr21ejUvC1R0dGMnTOFVxERFPXwYvnU+ejqJE+g+vDJY8JehKv+rlmpKqEvwljov5zgsBBcHZ1ZNm1+iqETO3/9mTy5LShTPPVhSH+e/JvlG9cQGxeHi6MTiybMorx36S9SznfVqFad0PAwFi9bSnBICK7OLvgtWKwaCvE0MBCNd47rwl6FmTZpCouWLmb+kkXks7Vj/qw5OBVIehKsqVBw9949ftr3M2Hh4ZjkzEmhgoVYu3w1BRzVJ83a+dNuCnt64WCfPV3L39euSmOiY6OZsnkhr6JeU9ihIAt6TET3nTeCPAp+SnhE8ptvtv+Z9HrbbguHq21rbKv+1PWuipamFvO7jWPhz/4MXD6ByJgobM2tGddqIGULqc/Pk12Cd19EQ1cLq25l0TTUIfL6M+5P2q82k75OHmM0jZO/TGubGZK3fyU0c+iR8DKayOuB/DviJxJeRqe2i69OxWrehIe9ZK3fTsJCXuDobMeUhYNVE0Q+DwxVO+73bj9KXFw8E4aqN+636dqAtt2SGspCg8NZNncTYSEvyGVuQtXaZWjVJfvepAJQo3p1wsLCWLJ0KcEhwbi4uLB00RLV0KjAwKdq9/TCXoWZNnkKC5csZsGihdjZ2TF/zlzV+a1QKLh16xY/7f2ZV69eYZE7Nz6lfOjds5daD8uB/QegqaXFiNGjiImJwcPdnZXLlmfrDP3PNp5Coa+N3dCqaBrpEnHxMbcH7Ux6S8QbujY50cqpr/pb28II+/G10DLWIz48ioiLj7nRbZNaI5qenSk23cuiaaxH7NOXBK49wfMtZ7O0bO/q2KodUVFRjJsxmVcRryjqWRi/2QvVJgJ/+PiR+n2sSjXCwsNYtNKP4NAQXJ2c8Zu9UO0+tmX3DpauXq76u12vpO83k0aMpUHteujp6XH496MsXrWMqOgocpuZU8bbh24Tp6Xb+/ZLqFajKmFh4fgtWU5IcAjOLs4sXDLvneP+GRqKd7+vBdGyWRvV3+vXbmT92o0UK16U5auWArB9a9J3m66deqjta+yE0dSrr/5g4b9EoZChE1lJQ6n8zmb9EVlu8uTJ+Pn58fDhw0zfdlxo9j9Jyg6R+v/Ncnv3TfuNId+zK2OvZXcWskWCcdYOP/haxBx/kt1ZyBYPVhzL7ixkixz+7tmdhWyRR8Mru7OQLa5UW5rdWcgWHnu6ZncWskWMUXx2ZyFbGOmZZHcWPsn0/6XeMzkrDCv333sdvPRoEB9tyZIllChRAjMzM/766y9mzpxJ795ZO0GYEEIIIYQQQmSUQiaDzFLS0CA+2q1bt5g0aRKhoaHY2dkxaNAgfH19sztbQgghhBBCCCG+AtLQID7a3LlzmTt3bnZnQwghhBBCCCHEV0gaGoQQQgghhBBCfNfef8W9+LKktoUQQgghhBBCCJFppEeDEEIIIYQQQojvmqZMBpmlpEeDEEIIIYQQQgghMo30aBBCCCGEEEII8V1TKKRHQ1aSHg1CCCGEEEIIIYTINNLQIIQQQgghhBBCiEwjQyeEEEIIIYQQQnzXFBryjD0rSW0LIYQQQgghhBAi00iPBiGEEEIIIYQQ3zWZDDJrSY8GIYQQQgghhBBCZBppaBBCCCGEEEIIIUSmkaETQgghhBBCCCG+a5oaMnQiK0mPBiGEEEIIIYQQQmQa6dEghBBCCCGEEOK7Jq+3zFpS20IIIYQQQgghhMg0GkqlUpndmRAiLXGhEdmdheyR8N88LRUxidmdhWxRaLxbdmchW1yafjO7s5AtlBoa2Z2F7KH4b5ZbI+6/eV3jv/lxk6jz33yGp/iPHuf/1eu5jqlhdmfhk6w63yXb9t2p8Ips23d2+W9eDYUQQgghhBBCCPFFSEODEEIIIYQQQgghMo1MBimEEEIIIYQQ4rumkNdbZinp0SCEEEIIIYQQQohMIz0ahBBCCCGEEEJ81zQV0qMhK0mPBiGEEEIIIYQQQmQaaWgQQgghhBBCCCFEppGhE0IIIYQQQgghvmsKDXnGnpWktoUQQgghhBBCCJFppEeDEEIIIYQQQojvmkImg8xS0qNBCCGEEEIIIYT4SixevBh7e3v09PTw9vbm5MmTacb19/dHQ0NDbdHT01OLo1QqGTNmDFZWVujr61OlShVu3br1RcsgDQ1CCCGEEEIIIb5rCg3NbFs+xpYtWxg4cCBjx47l7NmzeHl5Ub16dZ4/f55mGmNjY54+fapa7t+/r7Z+xowZLFiwAD8/P06cOIGhoSHVq1cnOjr6k+oyI6ShQQghhBBCCCGE+EJiYmJ4+fKl2hITE5Nq3Dlz5tClSxc6dOhAwYIF8fPzw8DAgNWrV6e5fQ0NDSwtLVVLnjx5VOuUSiXz5s1j1KhR1K9fH09PT9atW8eTJ0/YvXt3ZhdVRRoahBBCCCGEEEKIL2Tq1KnkzJlTbZk6dWqKeLGxsZw5c4YqVaqowhQKBVWqVOH48eNpbj8iIoJ8+fJha2tL/fr1uXLlimrd3bt3CQwMVNtmzpw58fb2Tnebn0smgxRCCCGEEEII8V1TKLLvGbuvry8DBw5UC9PV1U0RLzg4mISEBLUeCQB58uTh+vXrqW7bxcWF1atX4+npyYsXL5g1axalS5fmypUr5M2bl8DAQNU23t/m23VfgjQ0CCGEEEIIIYQQX4iurm6qDQuZwcfHBx8fH9XfpUuXxs3NjWXLljFx4sQvss+MkIYGIYQQQgghhBDfNc2PnJQxO5ibm6OpqcmzZ8/Uwp89e4alpWWGtqGtrU2RIkW4ffs2gCrds2fPsLKyUttm4cKFMyfjqZA5GoQQQgghhBBCiGymo6NDsWLFOHLkiCosMTGRI0eOqPVaSE9CQgKXLl1SNSrkz58fS0tLtW2+fPmSEydOZHibn0J6NAghhBBCCCGEEF+BgQMH0q5dO4oXL07JkiWZN28er1+/pkOHDgC0bdsWGxsb1WSSEyZMoFSpUhQoUIDw8HBmzpzJ/fv36dy5M5D0Ror+/fszadIknJycyJ8/P6NHj8ba2poGDRp8sXJIQwNw7NgxKlWqRFhYGCYmJh+Mf+/ePfLnz8+5c+e+aHeTj2Fvb0///v3p379/dmdFCCGEEEIIIb4qim9g6ARAs2bNCAoKYsyYMQQGBlK4cGH279+vmszxwYMHahNbhoWF0aVLFwIDAzE1NaVYsWL8/fffFCxYUBVn6NChvH79mq5duxIeHk7ZsmXZv38/enp6X6wcGkqlUvnFtp6F2rdvz9q1awHQ0tIiV65ceHp60qJFC9q3b5/uLKOxsbGEhoaSJ08eNDQ0PrivhIQEgoKCMDc3R0sra9pqxo0bx+7duzl//nyq64OCgjA0NMTAwCBL8pNV4kIjMhRv/PQpbNu9g2H9BtGmecs04y1euYylq5arheW3y8fPW3Z+Vj4BDhw5xKLlS3kc+JR8eW0Z0Ksv5UuXVa0fOXEse37Zq5amjLcPy+YtSrmxhE87LZVKJYtXLWP7z7t49SqCIh5ejB48nHy2dumm27RjK2s2rSc4NAQXRydGDBiCR0F31fpte3ay79B+rt28wevI1/z9628Y58ihto2rN64zZ+kCrly/ikKhSdUKPzC0z4CPOiYVMYkfV+A3lEoli/yXs+2X3byKiKCIuydj+g3DPm/65Q7YvY3VWzeoyj2yz2A8XQsB8DjwCVVbNUg13ZwxU6hRIekVQcfPnmThmmXcvHsHfT09GlSrTb9OPdDSzPi1odB4twzHzQzFnbzpVL0nhfJ5YGFiSa/FHTlyfn+W5gHg0vSbGYqnVCpZvMKP7T+9Oa49vRg91PfDx/X2razZuC7p8y3gxIiBQ/EolHxcx8TEMHPBXH49fJDYuFjKePswashwzHOZqeJMmTOD8xcvcOvfOzjY52fHuk1q+7h7/x4TZkzhzt27RLyOwMI8N7Wq1aBHpy5oa2mnXp4M3GdSL88W/De8LY8zvoPUy/O+t9ekJ0+fYGdrl+KadPi3I2zdtYOr16/x4uULtq3bhKuzS+p5VirpMaAPf/3zN/Omz6ZyhUofXwDFJ5Z72xb8N6wlOCQEFydnfAcPS7/chw+xaNmS5HL37kv5MuUAiIuPY+HSJfzv7z95/PgRRkZGlCrhTf/efbHIbaHaxtXr15i7aD5Xrl5BodCkyg+VGdp/0CfdYzXiPv26tnilX/L13NOL0YMzcNzv2MqagHeO+wFDU7+e37iedD3ffyzF9Rzg97//h9+aFdy8fRtdXR2KFy7KgmlzMl6ADH7cqnK+e34PyWA53z+/C753fi987/wenHx+7973E6Mmj09127/vPYRZrlycvXCOOUsWcvf+PaKjo7G2tKRJgx9p27xVmvlK1Pm0Ucmbt27Bf33Sce7s5IzvkGF4uKd9nB88fIhFS985zvv0pVzZcqnGnThlEtt27mDIwMG0aZmc9z4D+nHj5k1Cw0IxzmFMqZLe9O+rfi5klCKDx3l2Xs+fBj5lwsypnDpzGgMDA+rVrEP/Hr1V3+VT+54G4JjfgT0B21IvTwau55l97X5bhzv27OJVxCsKe3gxeugI8tkl1+HV69eYu3gBV669uYZV+oGh/ZKvYTdu3WTVujWcvXCe8BfhWFta0bRRY1o3S/s79Lt0TA0zFO9rs+vWyGzbd0Onydm27+zyXc3RUKNGDZ4+fcq9e/f49ddfqVSpEv369aNOnTrEx8enmiYuLg4dHR0sLS0z1MgAoKmpiaWlZZY1MmRE7ty5v7tGhow6fOwoF69cwsI8d4biF3Bw/D979x0XxdEGcPx3FAEBRUCjICjSFBXsiL2LHUs0irH33nvvvcWCvQJ2E/WNPZpEjTX2Fmvs0lUUEOHeP8CDgzsEpRh9vu9nP3m9m92dZ3d2dpmbmeXY3gOqZcPy1Z+dhwuXLzFs/GiaNvJi23o/alSpRr/hg7l9945aukrlK6jte9akaZ+978TW+K7Hd/tmxg0Zid+KdRgZGdJ9UF+ioqK0rrPvyEFmLZ5Pz45d2bZ6E84OTnQf1Jfg0BBVmsioSCq5V6Drjx01biMgKJAuA3phm98GvxXr8Jm7iDsP7jJ62oR0jU+b1Zs3sGnXFsYPGMHmxWswMjSi24h+RL1LIe6jh5jps4Be7bqw3WcDhe0d6Ta8nyruvLm/4/dtv6otfdp3I7tRdiqXqwDAzbv/0GPUQCqV9WDH8o3MGzuNo3/9ybyVSzIl7k9lZJCdm4+vMclvVFZnJVXWbFqP77bNjBs2Cr/V6zEyMqL7gD4pl+vDB5m1aB49O3dj2zpfnB2d6D6wD8EhCeV65sK5HDvxB/OmzmDd0pUEBgUyYMTQZNtq2rAJnjXraNyPnp4ejes1ZMXCJezdvJPhAwaz/ZddLFm5/PMDT2T/oQPMXjiPHl26sXW9H06OjnQf0FstnsQuXr7E8HGjaNaoiapO6j9skFqdFBEZQUm3Egzs3e+j+9+42TfV98j0tP/QAWYvmEuPLt3ZusEPJ0cnuvfrlULcFxk+diTNGnuxbaM/NapWo//QhLgjIyO5cesG3Tt1ZctGf+bPnMuDh//Sd/AA1TYCAgPo2qcHtvlt8F27EZ9FS7h77y5jJo3LjJBVVPX50FH4rVyPkaER3Qelotz/NI+enbqxbY1vfH3eR70+j4ykkrsHXdtprs8BDh09wshJ4/Cq35gd6/3ZuGwN9Wt7pmt8H6iu76Gj8FsVH+fAVF7fnbqxbW18nEmv70Xx1/eUGaxbspLAwEAGjEy4vj1r1eHYngNqS0V3D8qULI2FuTkARoZGtGnekvVLV7LbfzvdOnThpxVL2fbz5/84kdj+gweYPX8uPbp2Z8smP5ydnOjRN4Vyfukiw0ePpGkTL7b6+lOjWjX6DxnE7Tt3kqU9cvQ3Ll+9Qp7cyZ+RypUpy+wZM9m9YxfzZs3m0ZNHDB6evA5MT1lVn8fExNBrcH+io9+zacVapo6dyC+/7mHxSh9VmhEDh6g9ox3+5Vdy5shJnRq1PjnejKi712xcj99Wf8YOH4Xvqg/HsLfqGAYEBtK1X8+4Omz1BnwWLObuvXuMmTxetY3rN69jnsuc6ROmsMtvG107dGbh0sX4bdv8ybH+F+jo6GbZ8i36qhoaDAwMyJs3L9bW1pQqVYpRo0bxyy+/sG/fPtatWwfEjVFZtmwZjRs3xtjYmKlTp3Ls2DEUCgVhYWG8evUKIyMj9u3bp7btXbt2YWpqytu3b3nw4AEKhULVu+DD+keOHKFMmTJkz56dChUqcOvWLbVtTJkyhTx58mBqakqXLl0YMWJEug29KFiwIAsWLFD9OywsjO7du/Pdd99haGhIsWLF2Ls3oZX2+PHjVK5cGSMjI2xsbOjXrx9v3rxR2960adPo1KkTpqam2NrasmJFQk+Ad+/e0adPH/Lly4ehoSEFChRQjRP6sP8uXbqQO3ducuTIQY0aNbh06VK6xJrYi4AAps+bzcwJU1Ld8KOrq4ulhaVqyWWWS+37V69fM27aJCrXq4l7zSp06tOdm7dT/uV101Z/Krp70KltO+wL2tG3ey9cnAvjt32rWrps2fTV9p0zR460BZwCpVLJxm3+dGvXmRqVq+Hs4Mi0MZMICA7kyJ/HtK63YbMvLRp50bRBY+ztCjFu6EgMDQ3ZtXe3Ks2PLdvQ5ccOuGppgf/9xJ/o6ekxZtBw7GwLUrxIUcYNGcWhY7/x8PGjdItRE6VSyYadm+nethM1K1bF2d6RGcMnEBAUxJHjv2tdb912P76v70Uzz0Y4FCzE+AEjMDQwZOf+PUBcOcltbqm2HD5xDM+qNTE2imvU23f0MM6FHOjVrgsFrG0o61aKwV374v/Ldt68faN131ntz6tHWfjzLA5fyPxeDGmlVCrZuMWPbh06U6NKfLkeN5GAoECO/HFM63ob/DfRonFTmjaML9fDRmFoYMiuvb8A8Dr8NTv3/MKwfoNwL1OOooWLMHn0eC5eucSlq1dU2xk1aBitW7Qkv7W1xv3YWOenacPGFHZ0wipfPqpXrkrDuvX4+9KFdD0OG/x9ad6kKU0bNomLZ/hojAwT4klq0xY/Kpb3oGPb9hSyK6Sqk/y3b1GlaVSvIT07d6N8WfcU933zn1us99vE5DHjU0yXETb4baK5VzOaNmqCfSF7xo2Ij3vPzxrTb9rsT8XyFej4Y3zcPXrjUrgI/lvjHp5NTUxZudgHz9p1sCtQELfirowaOoLrN2/w7PkzAH4/HlefjR42ErsCBSnmUpSxI0Zz6LcjPHz0MFPiViqVbNzqR7f2ierzsfHl/s9jWtfbsGUTLRo1TVSfq5d7gB9btaHLjx1xLVpc4zbev3/PjIVzGNy7P62atqCgbQHs7QppbWz7HKo403p9b07l9d1X+/VtaGCodj/W0dHl9PmzNGvYRLWfIs6FqV/HE4dC9ljns6KRZ30quHtwPr2vb9+4cu7VOK6cjx0ZV85/3v2zxvS+m/2p6FGBju3iynmfnr0pUrgIm7eq/5H4IiCA6bNnMn3yNI3PSD96t8WtuCtW+awo4VaCTu07cvnKFaLfR6drfB9kZX1+8swp7j64z4wJkyns5Exlj4r06dqTzTu2Eh0dF6+pialambh24zqvXr+iaYPGnxxzetfdSqWSTVv86NaxS9wxdHRi2vhJBAYF8lv8Mfz9xB/o6eoxeuiIhDps+CgOHU2ow5o28mLEoKGULVUaG+v8NKrXgCYNG3Hk2G+fHKsQSX1VDQ2a1KhRAzc3N3buTGh9njBhAk2bNuXKlSt06tRJLX2OHDlo2LAhfn5+ap/7+vri5eWVYq+B0aNHM3fuXM6dO4eenp7atn19fZk6dSozZ87k/Pnz2NrasmzZsnSKUl1sbCz16tXjxIkTbNq0ievXrzNjxgx0deNa0+7evYunpyfNmzfn8uXLbNmyhePHj9OnTx+17cydO5cyZcpw4cIFevXqRc+ePVWNJ4sWLWL37t1s3bqVW7du4evrS8GCBVXrfv/99wQEBLBv3z7Onz9PqVKlqFmzJiFaWnA/Nc6Rk8bSwftHHArZp3q9h48eUr1RXTybN2b4+NGqh8sPBo0eTkhoKMvm/cTWdZso4lSYLn178PLlS63bvHT1Mh5JHtYruHtw6epltc/O/n2eKvVr0bBVMybNmkbYy7BU5/tjHj99QlBwMB5ly6k+MzUxwdWlmNofTolFR0dz/Z+blC+TkHcdHR3KlynHpWuXNa6jybvod+jr66sNUTKMf1fw35cvpjGStHn87ClBIcF4lEoSd5GiXLyuOe53H+IuVVb1mY6ODh6lympd59o/N7h55x+a1094CH0X/Y5s+tnU0hkYGBD1Lopr/9z8nLBEvIRynVBGTU1M48u15jIaHR3N9Vs3KZ/oWtDR0aF82XKqa+H6zRu8f/9e7Y/sQgXtyJc3L5eupL7sJ/Xw0SOOnzpJmZKlP3kbScXFc0Mtr3HxuGvN66WrV5I1IFQo75Hm2CIiIxg+bhSjh47A0sIy7Zn/DNHR0Vy/mca4r1ymfLm0xf06/DUKhQJTk7jhA+/evUNfT0t9dunip4aTJqpyXyYdyn2ZclrvAZrc+OcmLwID0NHRoUWHNlRrXIceg/ty+17yX8s/12fFWeYTru/v8mrd7u59ezEyNKROjZpa83vj1k0uXrlMmZKl0hRnSqKjo7lx8wbl3dXLuXs5dy5d1lLOL1/GPWk591Av57GxsYwaN4YOP7bHwf7jz0gvX77k1/37KOHqpnXY1+fKyvr80pXLONo7qA2lqFjeg/A3b7hz767Gfe/c8wvly5bDKtHrANMiI+ruuGMYpJbG1MSU4kWLqdK8exet/ZkshTosPDycnDlypi3I/xgdhU6WLd+ibyLqwoUL8+DBA9W/27RpQ8eOHSlUqBC2tsnHhHl7e/Pzzz/z9u1bIO71H//73//w9tY+Jg9g6tSpVK1aFRcXF0aMGMHJkyeJjIwE4KeffqJz58507NgRJycnxo0bR/Himn9N+FyHDx/mzJkz7Ny5k9q1a1OoUCEaNmxIvXr1AJg+fTre3t4MGDAAR0dHKlSowKJFi9iwYYMqvwD169enV69eODg4MHz4cCwtLTl69CgQNwmJo6MjlSpVokCBAlSqVInWrVsDcb0lzpw5w7Zt2yhTpgyOjo7MmTMHMzMztm/frjXfUVFRvHr1Sm1JqSvd6o3r0NXVpW3L1qk+Nq5FizFlzAR85i9m7NARPH76lHY9u6h6c/x96QJXr19l3tSZFCviQgEbW4b2G4ipiSkHjx7Rut2g4GBVV8sPLM3NCQoOVv27YvkKTBs3iVWLljGwV1/OXfibHgP7ERMTk+r8pyQoJG5fFrks1D63yGWu+i6p0JdhxMTEJMu7RZK8f4x7qbIEBwexxm8D0dHRvHz1ivk+PwEQGByUljDSLCg0Lp+WuZLEkMtc9V1SYS/DiImN0byOlmO1Y99uCtnaUbKoq+qzSmXLc/H6Ff732wFiYmJ4ERjAso2rAAgMydi4vxUfymFaymho2IdyneRaMLcgKL48BgUHo6+vn2xsukUuC61lICXeXTtSqqoH9Vt6UdqtJH269kjzNrRJiCd5eQ3WcgyCgoOSx5/LIk3XNcCsBXMpUdyNGlWqpWm99BAaFqqlfrL4SNzJ02s7p1FRUcxfvIh6dTwxMTEBwL1MOYKDg1m7cb2qPluwZFHc9oMCPzesVFHV5+lV7tNQHz16+gSApauX0719Z5bMWkgOU1M69unGy1faG9w/RYpxartvpSLOoBAt17e59mtg595fqF/bE0OD5BOj1WxSj5JVy9Oq84+0bv49LRo3TV2AqZBSOdeWV63lPFH6NevXoqeri/cPKT8jzV+0kHKVPKhcsxrPnj9j4dz5nxjJx2VlfR4Ukvw57cO/NZW1gMBAjp86SfNGXqmMLqW8p1/dHaz1GCYcD/cyZePqsE2J6rClP6m2r8nFy5c4cPgQLbyapTFKIbT7JhoalEql2tjSMmXKpJi+fv366Ovrs3t3XNfxHTt2kCNHDmrVSnmMlqtrwh8gH95bGhAQAMCtW7coV66cWvqk/04vFy9eJH/+/Dg5OWn8/tKlS6xbtw4TExPVUrduXWJjY7l//74qXeJ4FAoFefPmVcXToUMHLl68iLOzM/369ePgwYNq2w8PD8fCwkJtH/fv3+fuXc2txhDXAJIzZ061ZeaCuQDsPfArZWtUUi1n/z7Ppq2bmTpmYprGDVf2qEjdmrVxdnCkYvkKLJu3iNevX7P/yCEAbt2+zduICCp61lDb35NnT3n05DHPnj9T+3zFujWp3nf92nWpXrkqTg6O1KxanSVzFnD1xjXO/n0+1dtIbO/BfZStXVm1aJuHJDM4FLJn6uiJrN/sS5lalajWpC7W+ayxMLdAJ53Hde85vJ/SDaqqlsyIOzIqkv8dOUDzeurdJyuWKc+Qbn2ZuGAGJTwrUb9DC6q4VwT4ZluvP1fSaz0ry3VazJkynW3rfJk1cSp/nDzOOr+NWZ2lz3b0j985c+4swwcOyeqsZIjo99EMGTUMlErGDk+Yr8TB3p4p4yex3ncjZat4UL1eLayt4uozRQZd13sP/ErZWpVUS1aWe2Vs3KR+3dp3pnb1mhQtXIQpoyagUCg48Nvhz9r23gO/UrZmJdXypVzfF69c5t6D+zTT8kfl+mWr2LJmI+OGjmTjFn9+PfhlDzu7fuM6vpv9mTzh489IHdq1Y6vvZpYvXoauji6jx48lveaJ/6/W5wC//LoHUxOTT5v4Nos5FLJnyriJrPfbRNlqFajeoDbWVlZa67Dbd+/Qb9hAenTuRgV3jyzIsfhafTmzGWagGzduYGdnp/q3sXHKM6Vmy5aNFi1a4Ofnxw8//ICfnx+tWrX66BwA+voJXc0+VOyxsZ822/TnMDIySvH78PBwunfvTr9+yScAS9zDI3E8EBfTh3hKlSrF/fv32bdvH4cPH6Zly5bUqlWL7du3Ex4eTr58+Th27Fiy7af0+tCRI0cyaNAgtc903sSNm6teqSquLgk9QA78doiQ0BBqN22g+iwmJobZP81n4xY/Du5KPmuwJjlMTSlgW0A1j8DbiLfktrBk7ZIVydKamppgamLKjvUJs85/mGPB0sIi2cQ+QSEhWFqot0onZmOdn1xmZjx8/EitS2BqVa9UBddEM2y/e/cOgODQYHJbJnRxDg4NwdlBc6NTrpxm6OrqJst78EfyrkmDOp40qONJUEgw2Q2NQKFgwxZf8lvlT9N2PqZGhcq4Fimq+ve76Li4g0JDyG2hHndhe81xm+U0Q1dHl6DQJHGHhqh1q/zg4B+/EREVSZM69ZN91+F7b9q3aENgcBA5TE158vwZ81ctIX8+zWP6RcqSXusfzm9wSAi5E034GhwSgrOWxtRcZh/KtfovRsEhwaru/5YWFkRHR/Pq9Wu1X8GCQ4M1loGPyfddXiBudvKY2FgmzphC+9ZtVUPWPkdCPMnLq4WW69TSwjJ5/KHBabquz5w/w6Mnj6lQu6ra54NGDqWUW0nWLluZ6m19ilxmubTUT8EfiTt5+qTnNPp9NENGDufps2esXrpC1Zvhgwae9WjgWY+g4GCyG8XXZ36byG+dvvXZB9UrVVWbM0FVn2sq946fUO7NUz/s5UM9al8w4bkpW7Zs5Ley5tmL56nejiaZFaeluZbrO0TzNbBjz88UdnSmaGHNbwDKbxVXnzvZOxIcEsLSNSuoXyd9JsdMqZxru161lvP49OcvXCAkJIS6DRPuWTExMcxdMA9ff1/27/lVbf+5zHJRsEAB7OzsqNPAk8tXLuPm6vbZsX1J9bmluQVXrl9Lso0Q1XeJKZVKdu3dTSPPBsmehdMiI+ruD+slP4bBFHZMeGtQg7r1aFA3SR3m75tsvqG79+/RpU8PWjRpRvdOXT451v8K3W90Usas8tX/5Pbbb79x5coVmjdvnqb1vL292b9/P9euXeO333776LCJj3F2dubs2bNqnyX9d3pxdXXl8ePH/POP5gkMS5UqxfXr13FwcEi2ZMuWTeM6muTIkYNWrVqxcuVKtmzZwo4dOwgJCaFUqVI8f/4cPT29ZNu3tNT+sGNgYECOHDnUFoP4MWXGxsbY2tiolu+9mrFz42a2r/dTLXksc9PR+0fNr4vU4u3btzx6/Fj1h3kR58IEhQSjq6ertj9bGxtymeVCT09P7bOcOePGsrkVc+XUuTNq2/7rzGncirkm2+cHzwNeEPbypVqjQFoYZzfGNr+NarG3K4SlhQWnziWUq/A34Vy+fhW3YpqH6ejr6+PiVJjT5xPyHhsby+nzZ3Erqj3vKbE0tyB79uzsP3IQg2zZks1d8bmMsxtTwNpGtTgUKISluQWn/k4S941rlHDRHHe2+LhPXUhYJzY2llMXzmlcZ8e+3dTwqIJ5kolDP1AoFOSxzI2hgSG//naQvHm+w8VR82sCRcqSXusJ5TqhjCaUa81lVF9fHxfnwpw+p35+T587q7oWXAoXQU9Pj9OJtnv/3wc8e/4ct+KfVvYT7+v9+/fEKtOnoTkuniKcPqt+nZ46e0ZrXt2KFVdLD/F1Uhpi69yuIzs2bWHbBn/VAjCs/2Amj52Q9kDSSF9fH5fCRTh99rTqs9jYWE6dSyHu4q7J4z59Si39h0aGh48esnKJT4oN4JYWcfXZgUMH4uoz9/KfF5QWxsZa6vPz6VDuz5/Veg/QxKVwEbJly8b9h/+qPot+H82TZ8+wyvtpY9U/0Brnp1zf5z/h+n7xPNl23759y4HfDtGsURNSI1YZq2ogSQ/6+voUKVyE02fUy/nps2dwc9VSzl2Tl/NTicp5o/oN2O6/la2+m1VLnty56fBjO5b9tFRrXpTxdda7d+kzGeSXVJ+7FXfl9t07an/0/3XmNCbGxtjbFVLb59kL53n4+FGqy4Q2GVF357eyxtLCUi1N+Jtwrly7qnGbqjrscHwdVi6hDrtz7y6denWjSf2G9OvZJ9m6Qnyur6pHQ1RUFM+fP48bK/3iBfv372f69Ok0bNiQdu3apWlbVapUIW/evHh7e2NnZ4e7++f9sdS3b1+6du1KmTJlqFChAlu2bOHy5csUKlTo4yvHi4iIUL3p4gNTU1Psk0zyU7VqVapUqULz5s2ZN28eDg4O3Lx5E4VCgaenJ8OHD6d8+fL06dOHLl26YGxszPXr1zl06BCLF6fuj/R58+aRL18+SpYsiY6ODtu2bSNv3ryYmZlRq1YtPDw88PLyYtasWTg5OfH06VP+97//0bRp048OXUkNs5xmmOU0U/tMT08PS3NL7AoUVH3WuU8PalatTpvvWwEwe9F8qlWqglW+fAQEBrJk1XJ0dXVUr+3yKOuOW7Hi9Bs+mEG9+1HQtgABgYH8cfI4NatWp1gRF435aduyNR17dWWd30aqVKjEvsMHuXbzOhNGxL2v9+3btyxdvYLa1WtiaWHBo8ePmbdkIbb5baiYTt3UFAoFP37fmhXrV1PAxgbrfNYsXrWMPBa5qVm5WsIx6d+TmlWq0aZ53DFp94M3o6dOoGhhF4oVKcqmrX5ERETg1aCRap2g4CCCQoJ5+OQxALfv3cE4e3byfZdXNXGQ344tlCjmRnYjI/46e5q5SxcyoEdfje9nT08KhYJ2zX5gue8aCuS3IX9eKxat9SGPpSU1KyX8EttxSC9qVaqGt1dLADq0aMPImRMp5lSE4oWLsmHHZiIiI2hat6Ha9v998ohzly/gM22Bxv2v3rKRymU9UOgoOPznMVZuXs+8sdPS5ZfsjJLdIDu2eRJ+rcxvaUNhm6K8fBPGs5AnWZiz5BQKBT+2asOKdaspYGOLdT4rFq9cRh7L3NRMNG9A0mu9Xeu2jJ48nqKFi1CsaDE2bfYjIjICr4Zxw19MTUxp1qgJsxbNI2eOHBgbmzBt7izcirmq/VH28NEj3ka8JSg4mKioKG7+Ezchrr1dIfT19dl74Ff0dPVwdHAkm74+125cZ+GyxdStVSddJ1Rr19o7Lp4iLhR3KcrGLfHxxM+GPmriWPLkzsOAXn0BaNuqDR17dmW970YqV6zE/kMHuHbjOuNHjFFt8+XLlzx78ZyA+HkHHvz7AIh7ME0883pSefPmVf3Cm9HatWnL6Inj4uIuWoyNm+Prp/g3A4waP4Y8efIwIP4VnW1/aE3H7l1Z77uByhUrs/9gfNyjxgJxfzAPGjGUGzdvsmTeQmJjYgkKihuznDNnTtWvl35bN1PC1Y3sRtn568wp5i1awIA+GV+ffaBQKPixZZu4+jy/LdZWicp94vq8Xw9qVqlOmxbx5b5VW0ZPjS/3LsXi6vNE5QTi6/PgYFVPvtt34+vzvHH1uYmxCS2bNGfp6uXkzfMdVnnzsdZvAwB1qn/6a/4+GqdNfJwrNFzffeOv7w9x/tCW0VMSxbklFdf3vOTXN8S94jnmfQwN6ybvsea/Yyv5vsureq44d/Fv1vltwvv7H9L1OLTzbsuYCeNwcYkr55v84st5/B+6o8aN4bs8eejfJ66ce//Qmk7durJ+0waqVKrMvgMHuHb9OuPiy7mZmVmyBjQ9PT0sLCyxi5+4+/LVK1y7do2SJUqSI4cpjx4/Zsmypdjkt9HawPG5srI+r1CuPPYF7Rg5aSyDevcnODiIn1Ys5YfmLZP9yLZzzy+4Fi2Go73DZ8ec3nW3QqGgbas2LF+3CttE10xuy9xqc+n4bdtMieJuZM8eX4f9tJABvRLqsNt379ClT3cquHvQrk1b1dwNOjq6mOfS/KPK10BH8eU+m32NvqqGhv3795MvXz709PTIlSsXbm5uLFq0iPbt26vNvJoaCoWC1q1bM2vWLMaN+/x3Z3t7e3Pv3j2GDBlCZGQkLVu2pEOHDpw5c+bjK8f7559/KFmypNpnNWvW5PDh5GMmd+zYwZAhQ2jdujVv3rzBwcGBGTNmAHE9Hn7//XdGjx5N5cqVUSqV2Nvb06pVq1TnxdTUlFmzZnH79m10dXUpW7Ysv/76q+o4//rrr4wePZqOHTsSGBhI3rx5qVKlCt99912q95EeHj15TGiiNzu8CAxg2PhRhL18iblZLkq6lcB35TpVpapQKFg2dxELly9h7JSJhISFYmlhQekSpZJNvJNYSVc3Zk6cyk8rlrHQZwkFbGxZNHOu6ialo6PDP3dvs3vfXl69fk0ey9xUcC9Pn24909SL5GM6ebcnIjKSCbOm8Tr8NaWKl8Bn7iJVzxDVMQlLOCb1atYhNCyUxat8CAoJprCDEz5zf1LrSrjl5x0sW5vQTbp9764ATBk1Hq/6cQ0SV65fY8nqFbyNeIudbUHGDR1FY8+EoS0ZqfMP7YiIjGT8vGm8Dg+nVHE3VkxfiEG2RHE/faJWFupVr03Iy1B+WreCoNBgCts7sXzGwmRdKHfu28N3ufNQsYzmxsbjZ06ywnct76KjcbZ3ZPGkOVRxr5AhcaaXYgXc2DB0h+rfI1tNBGDXyS2MXDswq7KlVae27YmIiGDCjKlx5dq1BD7zf0perhOf31p1CA2NL9fBwRR2dMJnvnq5Ht5/MDoKHQaMHEZ09DsquHswdugItX2Pmz6ZcxcS5lFp0b4NAAd27sE6nxW6unqs2bSeB48eolQqscqbj9YtWtLuh8/rBZeUZ+26hISFsmTlsvh4nPGZv1jVnfbZ8+dqY29LuLoxY9JUFi9fykKfxRSwsWXhrHlqD85H//ydsVMmqP49dOxIAHp27kavdJzM8nN41q5LSGgoS1bEx+3kjM/CJQlxv3iOQidx3CWYMXkai32WsHBpfNyzE+IOCAjk2B9xr71t0Vb9j8U1y1ZStnRcQ/jVa1dZusInrj4rUJBxI0fTqL56I2RG6+QdX+5nJSr3c1NR7hPX545a6vM1CcMD2/eO6y49ZdR41R8/g/v0R1dPl5GTxxEVFUVxl2KsWeSTrq9kVsXZtj0RkRFMmJkoznka4kx83/oQ58pEcc5Lcn33i7++RyW6voeoX98Q90dlrWrVNTYixcbGsmDZYp48e4Kuri421vkZ2KsvLb3S1kv2Yzzr1CU0NJSlPnHl3NnJmWU/LVF1k3/+/Lnac2wJtxLMmDqNn5YuYdGSxdja2LJwzjwcHVL/h7GhoSGHj/7G0hU+REREYGlpSUWPCszu3DVdn0uSyqr6XFdXlyVzFjJ59nTadu2AkZERjes1TDZx7+vw1xw+eoQR6TQ3TUbU3Z1+jLtmJs6Ywuvw15R0LYHPgsVqx/Dq9WssXbk8oQ4bMYpG9RLqsEO/HSYkNJS9+39l7/6EoTRWefNx4Of/pUvsQiiU6TXji0iz2rVrkzdvXjZu/O9PGpZRokPCszoLWSPm27wsdaIyf06TL0HRiZrHBX/trszUPLzra6dM5wlS/zN0vs24FdHfZr3Gt3m6ic321Y9K1kjnGy3n32p9ni1XyvPdfan2/zsty/btWWDUxxN9Zb6qHg1fsrdv3+Lj40PdunXR1dXF39+fw4cPc+jQoazOmhBCCCGEEEJ81eSNYJlLGhoyiUKh4Ndff2Xq1KlERkbi7OzMjh07VK/MTDrjdWL79u2jcuXKmZVVIYQQQgghhBDik0lDQyYxMjLSOJfCB0kneUzM2lpekyeEEEIIIYQQn0omg8xc0tDwhXBIwwQ+QgghhBBCCCHEl0oaGoQQQgghhBBCfNWkR0PmkhkxhBBCCCGEEEIIkW6koUEIIYQQQgghhBDpRoZOCCGEEEIIIYT4qilk6ESmkh4NQgghhBBCCCGESDfSo0EIIYQQQgghxFdNJoPMXNKjQQghhBBCCCGEEOlGGhqEEEIIIYQQQgiRbmTohBBCCCGEEEKIr5oOMnQiM0mPBiGEEEIIIYQQQqQb6dEghBBCCCGEEOKrJpNBZi7p0SCEEEIIIYQQQoh0Iw0NQgghhBBCCCGESDcydEIIIYQQQgghxFdNhk5kLunRIIQQQgghhBBCiHQjPRqEEEIIIYQQQnzVFNKjIVNJQ4MQXyJdRVbnIEvE5NDP6ixkiSsz/8nqLGSJ4sOdsjoLWeLKtFtZnQWRiZTZvs0HW0VMbFZnIUvovo3J6ixkCaXet/ncwjcathCpIQ0NQgghhBBCCCG+ajp8mw2/WUXmaBBCCCGEEEIIIUS6kYYGIYQQQgghhBBCpBsZOiGEEEIIIYQQ4qumo5Df2DOTHG0hhBBCCCGEEEKkG+nRIIQQQgghhBDiq6Yjr7fMVNKjQQghhBBCCCGEEOlGGhqEEEIIIYQQQgiRbmTohBBCCCGEEEKIr5oMnchc0qNBCCGEEEIIIYQQ6UZ6NAghhBBCCCGE+KoppEdDppIeDUIIIYQQQgghhEg30qNBCCGEEEIIIcRXTQfp0ZCZpEeDEEIIIYQQQggh0o00NAghhBBCCCGEECLdyNAJIYQQQgghhBBfNXm9ZeaSHg1CCCGEEEIIIYRIN9KjQQghhBBCCCHEV016NGQu6dGQRdatW4eZmVlWZyPdKBQKfv7556zOhhBCCCGEEEKILCYNDRp06NABhUKBQqFAX18fOzs7hg0bRmRkZLrto1WrVvzzzz/ptr3U2rFjB9WqVSNnzpyYmJjg6urKpEmTCAkJyfS8fCr/7Vup07Qhpap60LpzO65cu5pi+gNHDtGoVTNKVfWgqXdL/jh5XO17pVLJ4hXLqNawDqWrVqBL3578++ihWpqXL18yfPxo3GtWwaN2VcZOncTbt29V30dFRTF68niaerfErVI5+g0flCwfZ/4+RzGP0smWoOCgVMWdmnxq8rHjFRUVxZTZM6hYtwZla1RiwMihBIUEq6V59vwZPQf3o0y1ClSpX4s5Py3g/fv3qu8DgwIZNm4UDVo2pXiFMsyYPydZPu7cu8uAkUOp07QhxTxKs3GzX6riTg3/rVuo27g+pSu606bDjx8vE4cP0ahFU0pXdKfpD9/zx4k/1b5fusKHRi2aUq6yBxVqVKFLr+5cvnpF9f3Z8+coXrakxuXqtWvpFldSWVkGps2bRcsO3pSsUp7m7Von28f9fx/QsXc3qtSvTamqHng2b8yi5UuJfh/9eUGnozKO7izrs54/Zv/NzZVPqVnCM6uzlCZKpZLFq3yo1qQupWtUpEv/Xqk7/zu2UqdFI0rVqEDrru25cj3h/L989ZJp82fRsHUzSteoSK1mDZi2YDavw8NVaW7e/oeh40dRs1kDSteoSCPvFmzc6p8hMWqSVXEnFvYyjJpN61OsUhlevX6dbrGlmP9tW6jbpD6lK7nTpmMq67Xvm1K6kjtNW6vXa9Hvo5n300Katv6eclU8qFG/NqPGjyEgMCDZdv44/idtOv5ImcrlqVCzCv2GDEz32BL7kuu1JauWa7xvl61e8fOC1kCpVPLTah+qetWlVK2KdB6YunLut3MrtVs2omStCvzQvT2Xr6sfhwmzp+L5QxNK1apIpUa16DNyEPf+faCW5sqNa3Qa0JPy9avhUb86XQf34eadjHlGVSqVLF65jGqN6lC6WgW69Evl+d6xlTrNGlKqmgetu7RTu54h/nzPmUFFzxqUrVmJAaOSn+9iFUonW349dED1/Zm/z2lMk9rntLTw376Ful4NKF2lPG06pf5ZtnSV8hqfZQ8fPUK3fr2oVKc6xcuX4uY/t5JtY+KMKdRr3pgyVT2o4lmDvkMHcu/B/XSNS4jEpKFBC09PT549e8a9e/eYP38+y5cvZ/z48em2fSMjI/LkyZNu20uN0aNH06pVK8qWLcu+ffu4evUqc+fO5dKlS2zcuDFT8/Kp9h0+yKxF8+jZuRvb1vni7OhE94F9CNbSUHLh8iWGjR9N00ZebFvvR40q1eg3fDC3795RpVmzaT2+2zYzbtgo/Favx8jIiO4D+hAVFaVKM3zCGO7cv8fKRUtYMmcB5y/+zYQZU1Tfx8TGYmBggHfLHyhfplyKMezdspNjew+oFvNc5qmKPTX5/JTjNXPhXI6d+IN5U2ewbulKAoMCGTBiaEJsMTH0Gtyf6Oj3bFqxlqljJ/LLr3tYvNJHleZddDS5cuWiW4fOODs4acxLRGQk+a2sGdCrL5YWFqmKOTX2HzzA7AVz6dGlO1s3+uHk6ET3vr20lomLly4yfMxImjXxYtsmf2pUrUb/IYO4fSehTBSwLcCoocPZ4b+NDSvXYm1lRfc+vQgJjdtmCVc3ju47pLY0b9IUaytrirq4pFtsSWVVGfigacMmeNaso3E/enp6NK7XkBULl7B3806GDxjM9l92sWTl8s8PPJ0YGWTn5uNrTPIbldVZ+SRrfNfju30z44aMxG/FOoyMDOk+qG/K5//IQWYtnk/Pjl3ZtnoTzg5OdB/Ul+D4shwQFEhAUCBDeg9g18YtTB09gROn/mLcjEmqbVy/dQPzXObMGDuJnzduoVu7Tixcvhi/HVsyPGbIurgTGzdjMk72DhkSnyb7DyWq1zbE12v9UqjXLl9k+NiRNGvsxbaN8fXa0EGqe11kZCQ3bt2ge6eubNnoz/yZc3nw8F/6Dh6gtp1Dvx1m5IQxeDVszPZNW9i4ci3169bL0Fi/5HqtY5sf1e7Xx/YewN6uEHVq1Pr8wJNY7bce3x2bGT94JP7L12FkaEi3Iako50vm06tDV7atii/nQxLKOYCLcxGmjBjPno3bWDFnMUqlkq6DexMTEwPAm7dv6T60H/m+y4u/zzo2LlmFcfbsdBvSl+hEPyikF9X5HjoKv1XrMTI0ovvAVJ7vTt3YttY3Ls6k53tR/PmeMoN1S1YSGBjIgJHJz/eU0eM5tueAaqlZpVqyNHs371RLk9rntNTaf+gAsxfOo0eXbmxd74eToyPdB/RO4fq+xPBxo2jWqInqWbb/sEFqz7IRkRGUdCvBwN79tO7XpXARJo8Zzy/+O/BZsASUSrr3TygL3wIdhW6WLd8iaWjQwsDAgLx582JjY4OXlxe1atXi0KFDAMTGxjJ9+nTs7OwwMjLCzc2N7du3q62/e/duHB0dMTQ0pHr16qxfvx6FQkFYWBigeejEsmXLsLe3J1u2bDg7Oyf741+hULBq1SqaNm1K9uzZcXR0ZPfu3amK58yZM0ybNo25c+cye/ZsKlSoQMGCBalduzY7duygffv2qc7H7du3qVKlCoaGhri4uKiOS2KPHj2iZcuWmJmZYW5uTpMmTXjw4EGq8pqSDf6baNG4KU0bNsberhDjho3C0MCQXXt/0Zh+01Z/Krp70KltO+wL2tG3ey9cnAvjt30rENeyvnGLH906dKZGlWo4OzgybdxEAoICOfLHMQDuPrjP8VMnmThyLK5Fi1PKrSSjBg1j3+GDBAQGApDdyIhxw0bRokmzj/4RbZ7LHEsLS9Wio/PxyzA1+fyU4/U6/DU79/zCsH6DcC9TjqKFizB59HguXrnEpfhf8E+eOcXdB/eZMWEyhZ2cqexRkT5de7J5x1aio+N+rbbOZ8XIgUNpUr8hJiYmGvNS3KUoQ/oOoH7tumTTz/bRmFNrg98mmns1o2njJtgXsmfcyNEYGRqya/fPGtNv2uxPRY8KdPyxPYXsCtG3Z29cChfBf9tmVZoGnvXwcC+PTf78ONjbM3TAYMLfhPPP7dsA6OvrY2lpqVpymuXk6B/H8GrUGIVCkW6xJZaVZQBg1KBhtG7RkvzW1hr3Y2Odn6YNG1PY0QmrfPmoXrkqDevW4+9LF9L1OHyOP68eZeHPszh8YX9WZyXNlEolG7f5061dZ2pUjj//YyYREBzIkT+PaV1vw2ZfWjTyommD+PM/dCSGhobs2ht373As5MCCqbOpVqkKttb5cS9dln7denHsxJ+qXkvNGjZh5IAhlC1ZGhvr/DSqWx+v+o05/PvRrzruDzbv2s6r16/p0PrHDIw0Sf4/1GuN4uu1EfH12p6fNabftNmfiuUT1Ws94uu1rXH1mqmJKSsX++BZuw52BQriVtyVUUNHcP3mDZ49fwbA+/fvmTFvNoP7DqBl8+8pWKAA9oXs8ayt+Y/w9PCl12vZs2dXu18HhYRw9/49mjXySs/DoCrn3X+ML+f2jkwfHV/Ojx/Tut76rb60aOhF0/qNcShYiPGD48r5zv8lPBu2bNyMMiVKYZ3PChfnwvTr2ovnAS94En/e7z98wMtXL+nTqTt2tgVxsLOnV4duBIcE8zQ+TbrGufUTzvfmVJ7vvimfb4i7FhKfUwMDg2T7+5TntLTY4O9L8yZNadqwSVw8w+Ovb23Pslv8qFjeg45t46/v+GdZ/+0Jjb2N6jWkZ+dulC/rrnW/33s1p0zJ0lhbWeFSuAh9uvfi+YvnPH32NF3jE+IDaWhIhatXr3Ly5EmyZYv742j69Ols2LABHx8frl27xsCBA2nbti2///47APfv36dFixZ4eXlx6dIlunfvzujRo1Pcx65du+jfvz+DBw/m6tWrdO/enY4dO3L0qPqD3MSJE2nZsiWXL1+mfv36eHt7p2rYg6+vLyYmJvTq1Uvj9x8aPT6Wj9jYWJo1a0a2bNk4ffo0Pj4+DB8+XG1b0dHR1K1bF1NTU/78809OnDiBiYkJnp6evHv37qN51SY6Oprrt25SvmxCjwEdHR3Kly2X7EbywaWrl/FIUulWcPfg0tXLADx++oSg4GC1NKYmpri6FFOluXTlMjlMTSlWJOGX6vJly6Gjo8Pla5r3m5IW7VpTrWEduvTrxd+XLqZqndTkM6nUHK/rN2/w/v17tRtToYJ25Mubl0tXEuJ3tHfA0jyhAaVieQ/C37zhzr27qY47I0RHR3P95g3Kl0vIv46ODuXLuavyn9SlK5eT3YgrlPfQmj46Oprtu3ZiamKCs5Pm3hrH/vidsJcv8WrU5BMj+bisLAOf4uGjRxw/dZIyJUt/8jZEgoTzn3AuTU1M4s+/5nooOjqa6//cpHyZJNdHmXJcuqb93L5+E46JsTF6etrni379JpycOXJ8QiRpk9Vx371/D591K5k+ZlKGNSImparXyibJf9mP1GvlUl+vQdwfZwqFAlMTUwBu3LpJQEAACh0dvm/7A9Xr1aZH/95qv5qmt/9avbZz988UtC1A6RIlP3kbmjx+9oSgkGC1HpGmJia4FtFezt/Fl3OPpOW8tPZy/jYigl2/7iZ/Pmvy5vkOADvbApjlzMnO//3Cu+hoIqMi2fG/XyhUwA7rvPnSMcpE57vMJ5zvMp9wvr/Lm2y7U+fOpFK9GvzQuR079/6CUqlMts8W7VtTrVEduvTvxd+XL35OyFriSeP1ffVKmp5bUuNtRAQ//2831lbW5P0u7ydv579GodDNsuVbJG+d0GLv3r2YmJjw/v17oqKi0NHRYfHixURFRTFt2jQOHz6Mh4cHAIUKFeL48eMsX76cqlWrsnz5cpydnZk9ezYAzs7OXL16lalTp2rd35w5c+jQoYOqIWDQoEGcOnWKOXPmUL16dVW6Dh060Lp13DjCadOmsWjRIs6cOYOnZ8pjjW/fvk2hQoXQ19dPMd3H8nH48GFu3rzJgQMHsLKyUuWjXr2ErpVbtmwhNjaWVatWqR7M1q5di5mZGceOHaNOHc2/jkRFRSXrOqcTFa1qbQ4NCyMmJgYLc/UeAxbmFtxPMt7wg6DgYCzM1bu8WZqbExQcrPo+bhvqaSySpEnabU5PT4+cOXIkG/+XktwWlowbNoqiRVx49+4dO3b/TKfe3fBbvR4X5yIprpuafCaVmuMVFByMvr4+OUxN1dPkslDFFhSS/Bh++Hda4s8IoWGh8TEmzZ8F97X0oAkKDsLCInn6pMfx9z//YOjoEURGRpLb0pIVi33IZZZL4zZ3/vIzFcp7kPe77z49mI/IyjKQFt5dO3Ljn5u8e/eO75s0o0/XHmnehkjuw7mwyJXkXOYy13qeQl+Gabk+zLXWmaFhYSxft4oWjZpqzcuFK5c4cOQgS2YvTEMEnyYr43737h1DJ4xmcK/+5Mubl0dPH39GJKmXYr2m9V4XpDG9tmMUFRXF/MWLqFfHU9UL7fGTuPiWrfRh6IDBWOWzYr3vRjr16Mre7T+TM2fOz4xMU77/G/UaxB2zvQf20aVdh09aPyUfYrVMWs7NtZfzsA/lPJeGcv7wgdpn/ru2MddnEREREdjZFmDlvCVki38eNM5uzLqFy+k7egg+G1YDUCC/DSvmLE6xsfFTqK5nTedb2/WcmvMdouV8J7m39+nag3Kly2JkYMjJM6eYMmcGb9++pW3LuOdq1XNaYRfeRSd6Tlv18ee01EqIJ8kxyGWe8nNL0vhzJX9uSY3N27cyb8lCIiIiKFigICsXLf3o3wZCfCrp0aBF9erVuXjxIqdPn6Z9+/Z07NiR5s2bc+fOHd6+fUvt2rUxMTFRLRs2bODu3bhfd2/dukXZsmXVtleuXMrj9m/cuEHFiuqTC1WsWJEbN26ofebq6qr6/8bGxuTIkYOAgOSTOSWlqcX2U/Jx48YNbGxsVI0MgKrB5YNLly5x584dTE1NVcfH3NycyMhI1THSZPr06eTMmVNtmblgbqry/V9gV6AgLZs2p2jhIpR0dWPKmPGUKO7GBg2TIu498Ctla1RSLUm78oqMV7ZMWbb7bmbj6nVU9KjAkFHDNI6ffP7iBSdP/UWzJl7puv//ahmYM2U629b5MmviVP44eZx1fv+N+V++NHsP7qNs7cqqJTPOf/ibcHoN7Y99wUL06txdY5rb9+7Qb+RgenbsSsVy5dM9D19S3AuWL6ZQwYI0qls/w/OQmaLfRzNk1DBQKhk7PGG+ktjYuOeErh27ULtGLYoWcWHKuIkoFHET0aWH/2q9BnDk96O8ffuGxvUbfva29h7cR5m6lVXL+5iMPQ4Na9djxypf1i9aQYH8tgweP0L1w05kVCRjZ06mZDE3/JatZdOS1TjY2dNzeH8ioz5vEvS9B36lbM1KqiWrz3ePjl0p5VqCIs6F6fxjBzp5t2NtonuUXYGCtPSKf04r7saU0dqf0/6rGnjWY9t6f9YuW0lBG1sGjx6e4vwYXxsddLNs+RZJjwYtjI2NcXCIm/hpzZo1uLm5sXr1aooVKwbA//73P6yTjOnTNM4rvSVtdVQoFMTGxn50PScnJ44fP050dHSGt1yGh4dTunRpfH19k32XO3dureuNHDmSQYPU39ag8yZhxvpcZmbo6uoSnKTVOzgkGEsLS43btLSwSPbHYVBIiGoehQ//DQ4JIbdlQt6CQ0JU3eQtLSxUkwB+8P79e16+eqU2nOBTFHMpygUN3fKqV6qKq0tx1b/fRb/7aD6TSs3xsrSwIDo6mlevX6v9EhAcGqyKzdLcgivXryXZRojqu6yUyyxXfIzq5yc4JBgLLXNlWFpYEhycPH3SuTWyGxlha2OLrY0tbsVdadCsMbt+2UWXjp3V0v285xfMcuakWpWq6RBRgi+pDKRFvvgumPZ2hYiJjWXijCm0b90WXd1v8yb7qapXqoKrSzHVvz8MOwsODSa3ZUJ9FxwaonUC1lw5zbRcHyHJyvubt2/oPrgfxtmNWThtNvoafsm8e/8enfv3okWjpnTv0OWTY0vJlxT36fPnuH3vDm7H4rosf2iwr9ywFl3bdaKPlsaYz/XJ9ZqG9Emv4ej30QwZOZynz56xeukKtTl1Phxfe7tCqs+yZctGfuv8PH/+/LNi+uC/Wq8B7Nj9M1UqVk6X+171SlUonqicR8cfh6Ck5TwkhMJayrnZh3IeqqGcJ8mjqYkJpiYmFLCxxbVocSo0qM7hP4/SoJYn/zu0n6fPn+G3bK1qLoJZ46ZSoUF1fjv+O/Vr1v2MOKviWjTR+X6Xwvl2/ITzbR5/vs21nG8N9/bEirsUw2ftKt69e6caHp1UMZeiXEjlMNfUSIgnyXkLDfnI9Z0k/tCUY9PG1MQUUxNTCtja4lbMlYq1q3Lk96PUr/PfeguT+G+QHg2poKOjw6hRoxgzZgwuLi4YGBjw8OFDHBwc1BYbGxsgbqjEuXPn1LZx9uzZFPdRpEgRTpw4ofbZiRMncEmnGezbtGlDeHg4S5cu1fj9h0kqP5aPIkWK8OjRI549S5gg6NSpU2rpS5Uqxe3bt8mTJ0+yY5RS10sDAwNy5MihtiRuvNHX18fFuTCnzyUcy9jYWE6fO4tbseKaNolbMVdOnTuj9tlfZ07jViyuZ0h+K2ssLSzU0oS/Cefy9auqNG7FXXn1+jXXbib0Ljl9/iyxsbFqN9BPcfP2PxobSYyNjbG1sVEt9naFPprPpFJzvFwKF0FPT4/TibZ7/98HPHv+HLfiCfHfvntH7ab415nTmBgbqz2QZgV9fX1cChfh9NnTqs9iY2M5dfaMKv9JuRV35fTZJGXi9Cmt6RO2q+RdtPqrGpVKJT/v2U2j+g3R10vfBrwvqQx8qtjYWN6/f0+s8uONoUKdcXZjbPPbqJaE859wLhPOv+Z6SF9fHxenwpw+n3BuY2NjOX3+LG5FE85t+Jtwug3sg76eHj/NnKex0fzOvbt07NeDJvUa0L9773SMVN2XFPf8qbPYsc6P7Wt92b7Wl4nDxwCwfslKWjf7Pj3DTp5/TfXauc+r1z40Mjx89JCVS3ySTUjtUrgI2bJl40Gi4RnR76N58uwp+fKlz1j9/2q99vjpE878fY5m6TQPj3F2Ywrkt1Et9gULYWluwenzScr5De3lPFt8OT+VtJz/rV7Ok1EqUSoT7meRUZGqV7p/oKNQgEKh6uXyyXEaa7ueP+F8n/+E8/3iudbtQtwzWA7THFobGT6ksbTU/GPWp4iLp4ja9frR55ZixZNf32dOf/Y9WqlUolTyWfOnCZES6dGQSt9//z1Dhw5l+fLlDBkyhIEDBxIbG0ulSpV4+fIlJ06cIEeOHLRv357u3bszb948hg8fTufOnbl48SLr1q0D0DqZ1NChQ2nZsiUlS5akVq1a7Nmzh507d3L48OF0yb+7uzvDhg1j8ODBPHnyhKZNm2JlZcWdO3fw8fGhUqVK9O/f/6P5qFWrFk5OTrRv357Zs2fz6tWrZBNdent7M3v2bJo0acKkSZPInz8///77Lzt37mTYsGHkz5//k+No17otoyePp2jhIhQrWoxNm/2IiIzAq2FjAEZOHEee3LkZ2KsvAG1btqZjr66s89tIlQqV2Hf4INduXmfCiLg8KxQKfmzVhhXrVlPAxhbrfFYsXrmMPJa5Va88si9oR6XyFZgwfTLjho0i+v17ps2dRb1adciTqIfG3fv3iI6O5uWrV7x5+0b1DuPCTs4AbNzsh7WVFQ529kS9i2LH7p85c/4sKxYs+WjcqcknQOc+PahZtTptvm+VquNlamJKs0ZNmLVoHjlz5MDY2IRpc2fhVsxVdQOvUK489gXtGDlpLIN69yc4OIifVizlh+Yt1W7OH+J9G/GW0LBQbv5zC319fVVjRHR0NHfv34v7/++jeREYwM1/bpHdKDu28Y10n6Jdm7aMnjiOokVcKF60GBv9/YiIiFBNzDhq/Bjy5M7DgD5xr3xq+0NrOnbvyvpNG6hcqTL7Dx7g2o3rjB81Nj7/Eaxcs4pqVaqS29KS0LAwNm/bSkBgAHVq1lbb9+mzZ3jy9AnNvLSPZ08vWVkGIG5yx7cRbwkKDiYqKkp1vu3t4uZ+2XvgV/R09XB0cCSbvj7Xblxn4bLF1K1VJ90bYT5VdoPs2OaxU/07v6UNhW2K8vJNGM9CnmRhzj5OoVDw4/etWbF+NQVsbLDOZ83iVcvIY5GbmpWrqdJ17t+TmlWq0aZ5/Pn/wZvRUydQtLALxYoUZdPW+OujQSMg4Y/tiKhIFo6bzJs34bx5Ew4k/LJ++94dOvfrSQX38rRv5a16p7yOji7muTTPW/I1xG1rrX6vCo1vkC9UwC7ZWPD0lqxe2xyf/4aJ6rU8eRjQO0m95ruByhWT12vR76MZNGIoN27eZMm8hcTGxBIUFHcec+bMib6+PiYmJrRs1oIlK33I+11e8uXLx7qN6wGS1X3p5Uuv1z7YtecXcltYUtlDfWhpuh6H71uzfMNqbPPbkD+fNT+tji/nlRKOQ6cBPalZuRre8eW8fUtvRk2fQFFnF4oXKcrGbXHlpGn9uHL+6Olj9v92iAply5PLLBcvAl6wyncdBgaGVCkfF4tHmfLMWbaIyfNn4t2sFUplLKt816Gnq4t7yTLpH2fLNvHXsy3WVlYsXqHhfPeNP98tPlzPbRk9Jf58uxRj05ZUnO956uf72PE/CAoJxq1ocQwMDDh55hSrNqyhfZuEt8ls3OKHdT4rHArZExUVxY49qX9OS4t2rb3jym8RF4q7FGXjh3gaxMUzauLYuOeWD8+yrdrQsWdX1vtupHLFSuw/FH99jxij2ubLly959uI5AUFxb0P70GBoaWGBpYUlj5485sDhg3i4l8fcLBcvAgJYvWEtBgYGVK5QKV3j+5LpKOQ39swkDQ2ppKenR58+fZg1axb3798nd+7cTJ8+nXv37mFmZkapUqUYNSpurKOdnR3bt29n8ODBLFy4EA8PD0aPHk3Pnj21Dq/w8vJi4cKFzJkzh/79+2NnZ8fatWupVq1ausUwc+ZMSpcuzZIlS/Dx8SE2NhZ7e3tatGiher3lx/Kho6PDrl276Ny5M+XKlaNgwYIsWrRIbTLK7Nmz88cffzB8+HCaNWvG69evsba2pmbNmuT4zFnK69WqQ2hoKItX+RAUHExhRyd85v+k6ib47MVzdHQSGnNKuroxc+JUflqxjIU+SyhgY8uimXNxTPQ+9E5t2xMREcGEGVN5Hf6aUq4l8Jn/k9q5mjlhClPnzqRzv57oKBTUqlaTUYPU38/cc1A/tVdBtWjfBoCrf50H4h70Zi+aT0BgIIaGhjg5OLBq0VLKlVafz0Ob1OTz0ZPHhL4MS/XxAhjefzA6Ch0GjBxGdPQ7Krh7MHboCNX3urq6LJmzkMmzp9O2aweMjIxoXK9hskn+PsQLcTNA/+/gfqzy5uPgrr1A3HvrE6dZ57eRdX4bKVOyNOuWrkjVMdDEs05dQsJCWbJ8WVyMTs74LFqi6lL47PlzFIluLCXcSjBjyjQWL1vCwqWLKWBjy8I583CMHyqlq6PD/QcP2P2/PYSGhWGWMydFXYqyfsUaHOzt1fa9c/fPlHB1o1BBOzJDVpUBgHHTJ3PuwnnVvz+cywM792CdzwpdXT3WbFrPg0cPUSqVWOXNR+sWLWn3g3cGHY20K1bAjQ1Dd6j+PbLVRAB2ndzCyLUDsypbqdbJuz0RkZFMmDUt7vwXL4HP3EXJz3/8H8QA9WrWITQs/vyHBFPYwQmfuQnn//qtm1y+fhWA+q281PZ3YNturPNZcfDoEULCQtl7YB97D+xTfW+VNx8Ht+/JuIDjZVXcWcmzdl1CQkNZsiJRvbYwUb324jmKRK/cK+FaghmTp7HYJ1G9Nnue6l4XEBDIsT/i3ozVou0Pavtas2wlZUvH/UE5qN8AdHV1GTlhDFFRURQvWozVS1Zk6BtGvuR6DeJ+bf751700adAoQ4eAdW4TX87nJJTz5XOSlPOnjwlLfBxq1iEkLJTFaxLK+fI5CcfBIJsB5y9dYOM2f16+foVlLgtKu5XEd+lq1SSShQoUZMn0eSxdtxLvXh1RKHQo4ujM8tk/qQ3jSC+d2rYnIjKCCTMTne95Gs534uu5Vvz1vDI+TkcnfOYlOd/94s/3qETne0jC+dbT02Pzjm3MWjQPpVKJbX4bhvYbRIvGCT8UREdHM/unJM9pC1P/nJZanrXjn1tWxl/fjs74zF+s/bnF1Y0Zk6ayePlSFvrEX9+z5qk9yx7983fGTpmg+vfQsSMB6Nm5G7269ogrCxcvsHGzH69ev8LC3ILSJUqxceXaZBNTCpFeFMrUzhIoPsvUqVPx8fHh0aNHWZ2V/5TokPCszoLIREq9b7OlWfH+2xxaUHy45jG5X7sr025ldRZEJlJm+zbnJ1HEfJv1miL623ysVuplzutfvzRKnW8z7my5jLM6C5/kwesTH0+UQQqaZkyPqC+Z9GjIIEuXLqVs2bJYWFhw4sQJZs+eTZ8+fbI6W0IIIYQQQgghRIb6Nn8+zAS3b9+mSZMmuLi4MHnyZAYPHsyECRMybH89evRQe91m4qVHD3mPvRBCCCGEEEKIzCFDJ74SAQEBvHr1SuN3OXLkIE+ePJmco/QhQye+LTJ04tsiQyfEt0CGTnxbZOjEt0WGTvy3PAw/9fFEGcTWpHyW7TuryNCJr0SePHn+s40JQgghhBBCCCG+HtLQIIQQQgghhBDiq6bg2+xhllW+zX7KQgghhBBCCCGEyBDSo0EIIYQQQgghxFdNRyE9GjKT9GgQQgghhBBCCCFEupGGBiGEEEIIIYQQQqQbGTohhBBCCCGEEOKrJkMnMpf0aBBCCCGEEEIIIUS6kYYGIYQQQgghhBBfNQU6Wbak1ZIlSyhYsCCGhoa4u7tz5swZrWlXrlxJ5cqVyZUrF7ly5aJWrVrJ0nfo0AGFQqG2eHp6pjlfaSENDUIIIYQQQgghxBdgy5YtDBo0iPHjx/P333/j5uZG3bp1CQgI0Jj+2LFjtG7dmqNHj/LXX39hY2NDnTp1ePLkiVo6T09Pnj17plr8/f0zNA5paBBCCCGEEEIIIb4A8+bNo2vXrnTs2BEXFxd8fHzInj07a9as0Zje19eXXr16UaJECQoXLsyqVauIjY3lyJEjaukMDAzImzevasmVK1eGxiENDUIIIYQQQgghvnKKLFuioqJ49eqV2hIVFZUsh+/eveP8+fPUqlVL9ZmOjg61atXir7/+SlWUb9++JTo6GnNzc7XPjx07Rp48eXB2dqZnz54EBwenanufShoahBBCCCGEEEKIDDJ9+nRy5syptkyfPj1ZuqCgIGJiYvjuu+/UPv/uu+94/vx5qvY1fPhwrKys1BorPD092bBhA0eOHGHmzJn8/vvv1KtXj5iYmM8LLAXyekshhBBCCCGEEF+1T5mUMb2MHDmSQYMGqX1mYGCQ7vuZMWMGmzdv5tixYxgaGqo+/+GHH1T/v3jx4ri6umJvb8+xY8eoWbNmuucDpEeDEEIIIYQQQgiRYQwMDMiRI4faoqmhwdLSEl1dXV68eKH2+YsXL8ibN2+K+5gzZw4zZszg4MGDuLq6ppi2UKFCWFpacufOnbQHk0rS0CCEEEIIIYQQ4qumyML/pVa2bNkoXbq02kSOHyZ29PDw0LrerFmzmDx5Mvv376dMmTIf3c/jx48JDg4mX758qc5bWklDgxBCCCGEEEII8QUYNGgQK1euZP369dy4cYOePXvy5s0bOnbsCEC7du0YOXKkKv3MmTMZO3Ysa9asoWDBgjx//pznz58THh4OQHh4OEOHDuXUqVM8ePCAI0eO0KRJExwcHKhbt26GxSFzNAjxBdJ9k3ETs3zJ3lxP3SQ3XxuDchnXmvwluzLtVlZnIUsUH+Wc1VnIEicXnczqLGSJ9caDszoLWaJ38IaszkKWCDV9lNVZyBJm72yzOgtZ4tXuG1mdhSxh2e7jv5iLT9eqVSsCAwMZN24cz58/p0SJEuzfv181QeTDhw/R0UnoL7Bs2TLevXtHixYt1LYzfvx4JkyYgK6uLpcvX2b9+vWEhYVhZWVFnTp1mDx5cobME/GBNDQIIYQQQgghhPjK/Xc68/fp04c+ffpo/O7YsWNq/37w4EGK2zIyMuLAgQPplLPU++8cbSGEEEIIIYQQQnzxpEeDEEIIIYQQQoivWlomZRSfT3o0CCGEEEIIIYQQIt1IQ4MQQgghhBBCCCHSjQydEEIIIYQQQgjxVVMo5Df2zCRHWwghhBBCCCGEEOlGejQIIYQQQgghhPjKyWSQmUl6NAghhBBCCCGEECLdSI8GIYQQQgghhBBfNYX8xp6p5GgLIYQQQgghhBAi3UhDgxBCCCGEEEIIIdKNDJ0QQgghhBBCCPFVU8hkkJlKejQIIYQQQgghhBAi3UiPBiGEEEIIIYQQXzn5jT0zydEWQgghhBBCCCFEupGGBiGEEEIIIYQQQqQbGTohhBBCCCGEEOKrJpNBZi7p0SCEEEIIIYQQQoh08001NKxbtw4zM7NM29+DBw9QKBRcvHgx0/b5pcrsYy+EEEIIIYQQHyjQybLlW5RlQyc6dOjA+vXrAdDX18fW1pZ27doxatQo9PQyJlutWrWifv36GbLtzFCwYEEGDBjAgAEDsjorWcp/+1bW+m4gKCQYZwdHRg0aRvGixbSmP3DkEItXLOPJ82cUyG/DwN79qFKhkup7pVLJkpU+bN+9i9evwynp6sbYYSMpYGOrSvPy5UumzZvFseN/oqOjoFa1mowcOITs2bMDcObvc2zc7MeV61d58+YNtja2dPT+kYZ1E8rb9l92snvf/7hz7y4ALs5F6N+jd4p5TyulUslP61ew7defeR0eTsmirozvP5yC+W1TXM/3l22s2bqJoJBgCts7MrrPEFwLF1V9HxgSxOwVP/HX+dO8iXhLwfwF6NGmI3Wq1FCluf/4X+Ys/4m/r10i+v17nO0c6NexO+4lyqRbfKmlVCpZ/usmdv11gPCIN7jZFWFEy97Y5rHWus7ag1s5evkkD148xkA/G652RejbuCMFv8uvShP0KoSFP6/hzK0LvImKoECe/HSq04qaJSpmRljJ+G/fwrpNH64FJ0YOTt218PTZU2xtbJNdC4ePHmHrrh1cv3mDl69esm2DP4WdnDVuS6lU0nNgX06cOsmCmXOpWbV6usenjVKpZMnq5WzfE3/NFndj7JARatesJv47trLWf2Pc8bJ3ZNTAoRR3iTteL1+9ZMnq5Zw8c4pnL16Qy8yMGlWq0bdLT0xNTAC4efsfVm9ax99XLhEWFoZVvny0bNKcH1u2zvCYP0cZR3c61+1F0QLFyWOWl95LOnHk4v6sztYn2755N77rtxMSHIKDUyEGDe9F0eKFNaa9d+cBK5dt4Ob1Ozx/9oL+Q7rzQ9tmamnevHnLiiXr+ePoSUJCwnBytmfgsJ64FNNc9rOSx8QfKN6lFgZm2Xl64hZHeq0g7M4zrekVOjqUn9CSIt5VMM5rRvjTUK6vP8rpKds1pq+5rBuu3etybOAaLiz8X0aFoZLeddiH+/mOX3bxOvw1JYq7MXbYKArYxtUNZ8+fo1PvbprzsmYjxVyKsnSlD8tWr0j2vZGhIWeOnfzMiFNv55Zf8V+/i5DgMOydCjJgeFdcijlpTLt750EO7D3KvTsPAXAuYk+3vm21pp8zZRm/7DhA3yGdaOndOMNiSA3/bVtY57uBoOBgnB1TWQaWJykDFZPcx3Ymuo9tTH4fe/T4EXMWLeDCpQu8exdNRY8KjBw8DEsLiwyL82OUSiWr/tjBngtHeR31Btf8Tgyp1wkb87xa19l1/jC7/j7Ms7BAAOxy56djpaZ4OJRQpemzcQoXHt5QW69JyRoMq985Q+IQIrEsbV7x9PTk2bNn3L59m8GDBzNhwgRmz56dLN27d+/SZX9GRkbkyZMnXbb1LYqJiSE2NjZL87Dv8EFmLZpHz87d2LbOF2dHJ7oP7ENwSIjG9BcuX2LY+NE0beTFtvV+1KhSjX7DB3P77h1VmjWb1uO7bTPjho3Cb/V6jIyM6D6gD1FRUao0wyeM4c79e6xctIQlcxZw/uLfTJgxRfX9xcuXcbJ3YP602ezYuBmvBo0YNWk8x47/oUpz9u/z1K9dlzWLl7NpxVryfvcd3Qb05kVAQLodn1VbNrBp1xYm9B/BlsVryG5oRNcR/Yh6F6V1nV+PHmKmzwJ6/9iFHT4bcC7kSNcR/QgOTTimI2ZO5MGjf1kyeS6/rPCndqVqDJwyiuu3b6nS9Bw9iPcxMaybs5TtS9fjbO9IzzGDCAwJSrf4Umv94e1s/mMPI1v2Zt2geRhmM6TvsrFERWuvS/6+c4XvKzdg7aC5LOk9hfcx7+mzdAwRUZGqNOM3zuPfgCfM7TaOzSOWUN2tAiPXzuDmo7uZEZaa/YcOMHvhPHp06cbW9X44OTrSfUBvrdfCxcuXGD5uFM0aNVFdC/2HDVK7FiIiIyjpVoKBvft9dP8bN/uiUGTNWMc1vuvx3b6ZcUNG4rdiHUZGhnQf1Fftmk1q35GDzFo8n54du7Jt9SacHZzoPqivqpwHBAUSEBTIkN4D2LVxC1NHT+DEqb8YN2OSahvXb93APJc5M8ZO4ueNW+jWrhMLly/Gb8eWDI/5cxgZZOfm42tM8huV1Vn5bIcPHGPR3BV07u7NOv8lODoVYmCv0YSEhGlMHxkZhZV1Pnr174SFpbnGNNMnzufsqb8ZN2UYm7b54O5Rmn49RhDwIvPrrpSUGeZFib71OdxzOf7lRxL9JpJm+8eia6CvfZ3hXrj1qMvRvqtY79Kf4yM2UmZo3HaSsvcqR153J8KfBGdkGCoZUYet2bgev63+jB0+Ct9VH+7nvVV1QwlXN47+76Da0rxxU6ytrClaxAWADt7tkqWxtytE7Rq1M/6gxDty4DiL566hQ/cfWOU3DwenggzuNZFQLeX84rmr1PKszKKVk/FZP5M8eS0Z3HMCgQHJz+Ufv53i2pVbWObWfD1kJlUZ6BxfBhwc6d7/I2VgbHwZ2KDlPhYRfx/ro/k+9jYigm79eqNQwKoly9mwcg3R0dH0HTIgS59xff/ay/azBxharyMrO0zCUN+AQf4ziHqv/bklt6k5Par/wJrOU1ndaQqlCxRlxLZ53At8rJaucYnq7O6/RLX0rvllN46Lr0eWNjQYGBiQN29eChQoQM+ePalVqxa7d++mQ4cOeHl5MXXqVKysrHB2jmuJfPToES1btsTMzAxzc3OaNGnCgwcPADh48CCGhoaEhYWp7aN///7UqBH3q6um7vvLli3D3t6ebNmy4ezszMaNG1XfaRr6EBYWhkKh4NixYwCEhobi7e1N7ty5MTIywtHRkbVr1yaLValU4uDgwJw5c9Q+v3jxIgqFgjt37iRb52MUCgWrVq2iadOmZM+eHUdHR3bv3q36PqW8HTt2DIVCoXa8PuTlwzH9cLx2796Ni4sLBgYGPHz4kKioKIYMGYK1tTXGxsa4u7urjscH69atw9bWluzZs9O0aVOCg9PnwWWD/yZaNG5K04aNsbcrxLhhozA0MGTX3l80pt+01Z+K7h50atsO+4J29O3eCxfnwvht3wrEnZeNW/zo1qEzNapUw9nBkWnjJhIQFMiRP+JiuvvgPsdPnWTiyLG4Fi1OKbeSjBo0jH2HDxIQGNeK3K1DJ/p270VJVzds89vwY6s2VCzvweFjR1V5mTlxKj80b0lhJ2cKFbRj4sixxMYqOXXuTLocG6VSyYadm+nh3YmaFaviXMiRGcMnEBAcxOETv2tdb/0OP76v70Uzz0Y4FCjEhAEjMDQwZOf+Pao0F69dxturJa6Fi2JjZU3Ptp0xNTbh2u24VvLQl2H8++QRXVu3w7mQIwXz2zK4S28iIiO5ff9eusSXWkqlEv/ff6FznVZUc/XA0dqOST8OJvBlCMcu/6V1vZ96TaaRe23s8xXAyboQE7wH8Tw0kBuPEq7Ny/dv0KpKI4oVcCa/ZT661P0BUyNjbj5K+/X7uTb4+9K8SVOaNmwSdy0MH42RYQrXwhY/Kpb3oGPb9hSyK6S6Fvy3J/yR3KheQ3p27kb5su4p7vvmP7dY77eJyWPGp2tMqaFUKtm4zZ9u7TpTo3L8NTtmEgHBgRz585jW9TZs9qVFIy+aNoivO4aOxNDQkF174+pMx0IOLJg6m2qVqmBrnR/30mXp160Xx078yfv37wFo1rAJIwcMoWzJ0thY56dR3fp41W/M4d+Pat3vl+DPq0dZ+PMsDl/47/Zi+MB/404aN/OkoVdd7OwLMGxMPwwMDdj78wGN6V2KOdN3UFdqe1ZDXz/5H+SRkVEcO3Kc3gO6ULJ0cWxsrenS80fy21ixa9vejA4nTUr1b8iZqdu5t/ssQVf+ZX/7nzC2yoW9Vzmt61h5OHN391nu//o3r/4N5PaOU/x78BJ5yzqopTO2Mqf6oi7sb7uQmOiYjA4FSP86TKlUsmmLH906dom7nzs6MW38JAKDAvkt/n6ur6+PpYWlasmZMydH/zyGV8PGqobT7Nmzq6UJDgnh7v17NGvcJDMOCwBbNv1Co2Z1aNCkJnb2NgwZ3RNDQwP+9/MRjenHTRtE05b1cXQuRAG7/Awf15tYpZLzpy+rpQsMCGbBzJWMmzYIPT3dzAglRaoy0KgJ9oUKMW5EfBnY85Ey8GN8GegRXwa2JbqP1W9Izy7a72MXL13k6bOnTBk7EScHR5wcHJk6fiLXblzn9LmzGRLnxyiVSrae2U/7Sl5Udi6Dw3e2jG3ck6DXYfx567zW9So5laKCQwlszPNia5GP7tVbYpTNkGtP1J9JDPQNsDAxUy3GBtkzOqQvmCILl2/PFzVgxMjISNV74ciRI9y6dYtDhw6xd+9eoqOjqVu3Lqampvz555+cOHECExMTPD09effuHTVr1sTMzIwdO3aothcTE8OWLVvw9vbWuL9du3bRv39/Bg8ezNWrV+nevTsdO3bk6NHUPzSOHTuW69evs2/fPm7cuMGyZcuwtLRMlk6hUNCpU6dkjRBr166lSpUqODg4JFsnNSZOnEjLli25fPky9evXx9vbm5D4luDU5i0lb9++ZebMmaxatYpr166RJ08e+vTpw19//cXmzZu5fPky33//PZ6enty+fRuA06dP07lzZ/r06cPFixepXr06U6ZM+ciePi46Oprrt25SvmzCQ5WOjg7ly5bj0tUrGte5dPUyHkluNhXcPbh0Ne7m+/jpE4KCg9XSmJqY4upSTJXm0pXL5DA1pVj8rx0A5cuWQ0dHh8vXNO8XIDw8nJw5cmj9PjIykvfv36eYJi0eP3tKUEgwHqUSjo+piQmuRYpy6brmfL6LjubaPzfxKFVW9ZmOjg4epcpyMdE6JYq6su/YIcJevSQ2Npb/HT3Iu+h3lHMrDYBZjpzY2RTgl4O/8jYigvcx79mydxcWZuYUddLcpTmjPAl+TvCrUMo5l1B9ZmJkTLECzlx5cDPV2wmPfANAjuwmqs9c7Ypw6MIfvHzzmtjYWA6c/52o9+8o7Vg83fKfGnHXwg21B6m4a8GdS1cua1zn0tUryR68KpT30Jpem4jICIaPG8XooSOwtEhbfZIeEq7ZJOXcpZjWeiA6Oprr/9ykfJkkx6tMOS5d0x7/6zfhmBgbpzic7/WblK9zkX6io6O5deM2Zd1LqT7T0dGhrHtJrl6+/knbjImJISYmlmwG2dQ+NzAw4NKFa5+V3/SU0+47jPPl4uHhhPL67tVbnp++jZWH9iEeT/+6hU2N4pg55gPA0rUAVpUK82D/hYRECgWeG/pxfs4vBF9/lGExJJYRdVhc3RCklsbUxJTiRYtp3eaxP/4g7OVLvBpqHz6w45ddFLQtQOkSpbSmSU/R0dH8c+Mupd1dVZ/p6OhQxt2Na5dvpbBmgqjId7x/H4NpzoT7V2xsLFPGLKB1ey/s7FMeZpYZoqOjuX7zBuXLpaEMXPn8+9i76HcoFAqyZUu45g2yGaCjo8OFSxdSWDPjPA0LJPhNGGUKJgxZNTHMjou1PVef3E7VNmJiYzl87S8io6MoZq3+N8WhayeoP687bVcMZ9nRzURGa+/9J0R6+iJeb6lUKjly5AgHDhygb9++BAYGYmxszKpVq1QVwaZNm4iNjWXVqlWqVue1a9diZmbGsWPHqFOnDj/88AN+fn507hw37ujIkSOEhYXRvHlzjfudM2cOHTp0oFevXgAMGjSIU6dOMWfOHKpXT91444cPH1KyZEnKlIkbh16wYEGtaTt06MC4ceM4c+YM5cqVIzo6Gj8/v2S9HNKiQ4cOtG4d1wVq2rRpLFq0iDNnzuDp6ZmmvGkTHR3N0qVLcXNzA+LiXbt2LQ8fPsTKygqAIUOGsH//ftauXcu0adNYuHAhnp6eDBs2DAAnJydOnjzJ/v0p/5oWFRWVrOuzTlQ0BgYGAISGhRETE4OFufoYOgtzC+7/+0DjNoOCg7EwV+8eaGluTlB8D4sP/02axiJJGvNc6t/r6emRM0cOgkI099TYf/ggV29cZ/zw0drCZd7SReTObZmsIeRTBYXGx5Ikr5Zm5gRqyWfYyzBiYmOSrWORy5z7j/5V/Xv+2GkMmjwKj2a10dPVxdDAkJ8mzKKAtQ0Q15C2ZtZi+owfSpnG1dBR6GCeKxcrpi8kp2nm/hEW/Co0LgbTXGqfm5uaqb77mNjYWObuXIFbIRccrAqqPp/RcQQj182k5sgf0NXRxTCbAXM6j8Emt1W65T81Eq4FDectvkdSUkHBQcmvnVwWqnKeWrMWzKVEcTdqVKmWpvXSy4drziJX0ljMtV6PoS+1HC9zc611R2hYGMvXraJFo6Za83LhyiUOHDnIktkL0xCB+FRhoa+IiYnF3MJM7XNzi1z8++DT/kA2Ns5OMdcirF3hR0E7W8wtzDi0/xhXL98gv03mXtcpyZ7XDIC3L8LUPn/74iXZvzPTut7ZGbswyJGdDjcWERsTi46uDifG+HHT709VmrLDvVC+j+HCooyfk+GDjKjDgrXezy0ICtY8DGbnnp+p4O5B3jzfafw+KiqK/x3cR+cfO3wspHTzMvR1XDk3N1P7PJdFTv598FjzSkksW7gey9y5KOPupvrMd+1OdHV1aNG6YXpm95NpLQMp1Msay4B52u5jrsVcMTI0Yv7ihfTr1QelEhYsWURMTAyBQVkzXCrkTRgA5sY51T43N85JcHhYiuveDXhI93UTePc+GqNshkxrMRC73AlzS9UuWoG8OS2xNDXjTsAjlv3mz8PgZ0xvMTC9w/hP+FYnZcwqWXq09+7di4mJCYaGhtSrV49WrVoxYcIEAIoXL67W2njp0iXu3LmDqakpJiYmmJiYYG5uTmRkJHfvxo2P9vb25tixYzx9+hQAX19fGjRooPVtBzdu3KBiRfVJ3CpWrMiNGzc0ptekZ8+ebN68mRIlSjBs2DBOntQ+UZCVlRUNGjRgzZo1AOzZs4eoqCi+//77VO8vKVfXhBZvY2NjcuTIQUD8mP+05E2bbNmyqe3jypUrxMTE4OTkpDoPJiYm/P7776rzcOPGDdzd1f949vDw+Oi+pk+fTs6cOdWWmQvmpjnPWe3M+bOMnTqRCSPG4FDIXmOaVRvWsu/QQRbOmKtqSEmrPUf2U7phVdUSHd+9OyMsWuvD6zfhrJm1mG1L19OhRRsGTh7FP/fiuucplUomL5qNuZk5m+avYMuStdSsUJVeYwcToOUBL73sO3uUykOaq5b3MZ/f7XfmtmXcffYv09oPV/t82a8beR0RztLeU9k4dAHe1ZsyYt0M7jx98Nn7/C84+sfvnDl3luEDh2TaPvce3EfZ2pVVy/sMLOcfhL8Jp9fQ/tgXLESvzt01prl97w79Rg6mZ8euVCxXPsPzJDLO+KnDUKKkcZ02VC3XkK1+P1PbsxoKnazr6lq4TWV6v9qkWnT0P62bu1PLChRuU5lfvRfgW3ooBzospvTgJri0qwZAnlKFKNmvAQc6Lk7H3P83PA94wcnTf9GskZfWNEd+P8rbN29pXL9R5mXsM21as4MjB44zde5IDOJ76ty6foft/nsZNbF/ls2t86Uwz5WLudNmcuz4n7hXq0SFmlV4/fo1RZwLo6OTOX8WHbh6glqzOqmWz3lusbWwYl2XaazoOAmv0jWZuseH+4nmaGhSqgbu9q7Y57GlbrGKjG3ckz9uneNx6Iv0CEWIFGVpj4bq1auzbNkysmXLhpWVlVr3VGNjY7W04eHhlC5dGl9f32TbyZ07NwBly5bF3t6ezZs307NnT3bt2sW6des+OX8fKhylUqn6LDo6Wi1NvXr1+Pfff/n11185dOgQNWvWpHfv3lp7KXTp0oUff/yR+fPns3btWlq1aqV6c8GnSDrmVKFQqCazSSlvqYkN4oazJL4phYeHo6ury/nz59HVVX/wMTExSbp6mowcOZJBgwapfabzJiFPuczM0NXVJTjJr5bBIcFau3BbWlgkm1QoKCRENbPwh/8Gh4SQ2zJ3om2G4OzkpEoTEqq+jffv3/Py1Sssk7Ssn/37PL2HDmRY/8E0qa/5V4O1vhtYvXEdKxctw9nBUWOa1KjhUVntzRDv4ic6DA4NIU+i4xEUFkIRe80zT5vlNENXR1dt4scP27CM/8X44dPH+P6yjd2r/HEsGNdwUtjeiXNXLuK3exsTBozk1IWzHDt9nNO7DmNiHFcOivYvzMm/z/DLwf/RtXX7T47zY6oUd6dYwYSuw+/ex5WZ4NehWOZM+KUk5HUYTvkLfXR7M7ct4/i1M6zoP5PvciUcx8eBz9j6x162jFyKfb4CADhZF+Li3ats/XMvo1r1Sa+QPirhWkh+3iy0zJodN9Y4ybUTGpymWbbPnD/DoyePqVC7qtrng0YOpZRbSdYuW5nqbaVW9UpVcHVJmIH8w/C64NBgcicaChYcGoKzg+ZyniunluOVqC744M3bN3Qf3A/j7MYsnDYbfQ3DJu7ev0fn/r1o0agp3Tt0+eTYRNqY5cqBrq4OIcFhap+HBIdiYZlL80qpkN/GimWr5xAREcmb8DdY5rZgzLCpWFvn+8wcf7q7u8/y7HRCl2m9+Akfs39nxpvnYarPs3+Xk8BLD7Rup8qsdpyduYt/tpwAIPjqQ0wLWFJ2RDOubziGdeUiZM+Tky7/Lleto6OnS5U57SnZvyFrCvVM38DiZUQdZqH1fh5MYcfkw0t+3rsbs5w5qValitZ87ty9iyqVKmfq2why5jKNK+dJJn4MDX6JhUXK5dx/w8/4rt3BfJ9JODgVVH1+6cJ1QkNe0qJ+Qn0VExPLknnr2Oa7h22/pn/d/TFay0BISLJeCx9oLAMhabuPQdxwi307dxMaFoqurh45TE2pVq82+a20v5kqPVVyLEXRLgk/RL2LiWtAD3nzEstEvTFD3rzE8bsCKW5LX1eP/PFvpiicz46bT++x7ewBrW+VcLGK2++TkBfkz6W5J8/XTPGNzpWQVbK0R4OxsTEODg7Y2tp+9JWWpUqV4vbt2+TJkwcHBwe1JWfOhK5G3t7e+Pr6smfPHnR0dGjQoIHWbRYpUoQTJ06ofXbixAlcXOLG4n9owHj2LOHVUYknhvwgd+7ctG/fnk2bNrFgwQJWrEj+WqQP6tevj7GxMcuWLWP//v106tQpxbg/l7a8pTa2pEqWLElMTAwBAQHJzkPevHEVXZEiRTh9+rTaeqdOnfrotg0MDMiRI4fakvjXfn19fVycC6tN1hMbG8vpc2dxK6Z5jLxbMddkky3+deY0bsXiemnkt7LG0sJCLU34m3AuX7+qSuNW3JVXr19z7WZCT5fT588SGxuLa9GE/Z75+xy9hvRnUK9+fO+l/vq0D9ZsWs/ytavwmb9Ybc6HT2Gc3ZgC1jaqxaFAISzNLTh1IeH4hL8J5/KNa7i5aD4+2fT1KepUmFN/qx/TUxfOUSJ+ncjIuLcu6CjUqwtdHR1iY+MaqiLjh7wokvwaoKNQEKvM2FmcjQ2zY5PbSrUUymuLRY5cnP3nkipNeMRbrv57i+IFtc8XoVQqmbltGccu/8WyPtOwtlB/pdSHMY06SX4N0tHRRZnBMSYVdy0U4fTZhHIbGxvLqbNncCvuqnEdt2LF1dJD/LWgJb0mndt1ZMemLWzb4K9aAIb1H8zksRPSHkgqGGc3xja/jWqxtysUf80mKefXr2qtB/T19XFxKszp8+rH6/T5s7gVTYg//E043Qb2QV9Pj59mztPY2+jOvbt07NeDJvUa0L9773SMVHyMvr4+zkUcOXcmYRx1bGws585cpJjr59WnAEZGhljmtuDVq9ecPnmeytU+3hMvo0SHR/Ly7nPVEnz9EW+ehWJTM6GMZzM1Iq+7I0//0j5uXy+7AcpYpdpnyphYVW+NGxt/Z6PbIDaVHKxawp8Ec37ObnZ5Ts6Y4MiYOizufm6plib8TThXrl1Ntk2lUsnPe3fTqF5D9PU0v7Xj8dMnnDl/jmaNMm8SSIg7Nk5F7NUmcoyNjeX8mcsUddU+H4fvup2sX7mVOUvGU7io+hj9ug2qsW7rAtZsnq9aLHOb07qdF3OXTsioUFKkr6+PS+E0loHixTmt6ZkuDfexxHKZ5SKHqSmnz50hJDSEalWqfnyldGBsYER+87yqxc7SGgtjM84/SJgX5k3UW64/uUsx67T9IBWrVPIuJvkPhx/cfhE3LNbCxOyT8i5EWnwRczSkhre3N7Nnz6ZJkyZMmjSJ/Pnz8++//7Jz506GDRtG/vz5VekmTJjA1KlTadGiRYrd0ocOHUrLli0pWbIktWrVYs+ePezcuZPDhw8Dcb/mly9fnhkzZmBnZ0dAQABjxoxR28a4ceMoXbo0RYsWJSoqir1791KkSBGt+9TV1aVDhw6MHDkSR0fHVA0p+FQp5c3BwQEbGxvVsfrnn3+YO/fjwxScnJzw9vamXbt2zJ07l5IlSxIYGMiRI0dwdXWlQYMG9OvXj4oVKzJnzhyaNGnCgQMHPjo/Q2q1a92W0ZPHU7RwEYoVLcamzX5EREaoJnIaOXEceXLnZmCvvgC0bdmajr26ss5vI1UqVGLf4YNcu3mdCSPi5k5QKBT82KoNK9atpoCNLdb5rFi8chl5LHNTM34Mun1BOyqVr8CE6ZMZN2wU0e/fM23uLOrVqkOe+AabM+fP0nvIALxbtqZ29Rqq8aD6evqqhrDVG9exeKUPsyZOxTpfPlWa7EbZP6tXywcKhYJ2zX7Ax3cNBaxtyJ/XikXrfMhjYUmtigk3z45De1GrYjW8vVoC0L55G0bOmkgx5yIUdy7Khp2biYiMoKlnXI8MO9uC2FrbMH7BdIZ1749ZjpwcOfE7J/8+w7Ip8wAo4VKcHCamjJw5kV4/dsbAwIDt//uFJ8+fUtW9YvLMZiCFQkHrqk1YfWAzNrmtsLbIy7L/bSR3TnOquSZcbz0Xj6KaqwetqsR1iZ25bSn7z//O3C5jyW5oRNCruF9ZTAyNMcxmQMHv8mOT24ppWxbT36szZtlzcOzKX5y+dYH53TL/7QvtWnvHXQtFXCjuUpSNW+KvhQZx18KoiWPJkzsPAz5cC63a0LFnV9b7bqRyxUrsP3SAazeuM35EQp328uVLnr14TkBQ3NtUHsSPk7W0sFCbhT2pvHnzZtovQQqFgh+/b82K9aspYGODdT5rFq9aRh6L3NSsXE2VrnP/ntSsUo02zVsB0O4Hb0ZPnUDRwi4UK1KUTVv9iIiIwKtB3Pn/0MgQERXJwnGTefMmnDdvwoG4h1FdXV1u37tD5349qeBenvatvFXXsI6OLua5Pv0X9YyW3SA7tnnsVP/Ob2lDYZuivHwTxrOQJ1mYs7Rr/WMzJo+dQ2EXJ4oWc2az7y4iIyJp2KQOABPHzCJ3Hkt69YtrxI+Ojub+3YcAvH8fTWBAMP/cvItRdkNsbOPK7KmT51AqlRQoaMPjh09YPH8VBexsVNv8Uvy9cC/uo1sQdvsZL+8HUGFSa948DeXuzwl/eDU/NJ47P5/h0pJ9ANzbc45yo5rz+mEgwdcekbukHaUGNuLa2t8AiAwJJzIkXG0/MdExvHkeSug/TzM0nvSuwxQKBW1btWH5ulXY2thibWXF4hXLyG2ZO9mcMqfPneHJ0yc0a+ylNX+79vxCbktLKnlk7j0MoFXbJkwbt5DCLg4UKebINr89REREUr9JTQCmjFmAZR4LevT7EYibf2H1Mj/GTRtEXqs8BAfFzUdklN2Q7NmNyGmWg5xm6vMl6enpYm5phm3BzKm7NWnX2pvRkxKVgSTPdKMmxJeB3onKQA8NZWCkhvtYoOb7GMSd20IF7TDPlYuLVy4zc94cfmztjV2BgpkXfCIKhYKW5TxZf+Jn8pvnxcosNyt/346lqRmVnUur0vXznUYVpzK0KBtXNy07uhkPeze+y2HJ23cRHLx2kgv/3mBe67hhn49DX3Do6kk8HEqQ08iEOwEPWXRoEyVsC+PwXdZPCCq+fv+Zhobs2bPzxx9/MHz4cJo1a8br16+xtramZs2a5Eg047eDgwPlypXjzJkzLFiwIMVtenl5sXDhQubMmUP//v2xs7Nj7dq1VKtWTZVmzZo1dO7cmdKlS+Ps7MysWbOoUyfh4SNbtmyMHDmSBw8eYGRkROXKldm8eXOK++3cuTPTpk2jY8eOn3QsUiulvOnr6+Pv70/Pnj1xdXWlbNmyTJkyJVXzRaxdu5YpU6YwePBgnjx5gqWlJeXLl6dhw7g/TMuXL8/KlSsZP34848aNo1atWowZM4bJkz//15F6teoQGhrK4lU+BAUHU9jRCZ/5P6mGMDx78RydRONqS7q6MXPiVH5asYyFPksoYGPLoplzcbRPaO3v1LY9ERERTJgxldfhrynlWgKf+T+pNVLNnDCFqXNn0rlfT3QUCmpVq8moQUNV3//y614iIiNZtWEtqzYkvFmkTMnSrFsa14tky87tREdHM3DUMLWYenbuRu8umseBp1WXVu2IiIxk/PxpvAoPp1QxN1bMWIhBtoRYHj59QujLMNW/61evTejLUBatW0FQaDBF7J1YMX2hauiEvp4ey6fOZ96qJfQaM5i3kW+xtcrP9GHjVY0IuXKasXL6QhasWUaHIb14HxODQwE7Fk+aQ2EtwzYyUvtaLYh8F8m0zT/xOuINJQq5sKjnZAz0E+Z9eRz0jLDwV6p/bz/+KwDdfxqhtq3x3gNo5F4bPV09FnafwE971jFoxSTeRkVgY2nFBO9BVCpalszmWbsuIWGhLFm5LP5acMZn/mJVF9Jnz5+jSNQLpYSrGzMmTWXx8qUs9FlMARtbFs6ap3YtHP3zd8ZOmaD699CxI4G4Mtqra4/MCSwVOnm3JyIykgmzpsVds8VL4DN3kdo1++jJY0ITvb63Xs06hIbF1x0hwRR2cMJnbkLdcf3WTS5fvwpA/VZeavs7sG031vmsOHj0CCFhoew9sI+9B/apvrfKm4+D2/fwpSpWwI0NQxPeyDSy1UQAdp3cwsi1/60JwWrVrUZo6EtWLdtAcFAojs6FmL90KubxXcpfPAtU630VFBBM+x96qf7tt2E7fhu2U7K0K0tXzwYg/PUbfH5aS8CLIHLkNKVazYr06NMRPf0v6xHp3Kyf0Tc2pNbyHhiYGfP0+E121ptMTFTCr5c57fNiZGmq+vfRfquoMLk1NZZ0I3ueHIQ/DeXKikOcmrQtK0JQkxF1WKcf2xMRGcHEGVN4Hf6akq4l8FmwONmPTjv3/EKJ4m4UKmiHJrGxsfzyvz00qd8o2TDRzFCzbiXCQl+yepk/IcGhODjbMWfJeNVEqC+eB6rNIfLztn1ER79n7NBZatvp2L0VnXq0zsysp4mqDKyILwNOzvgsSFQGXjxX6ylZwtWNGZOnsthnKQuXpXAfmzxB9e+hY+LvY10S7mMPHv7LwqWLefnqJdb5rOjasTPtWmt+Q11m8fZoSER0FLN+XU145FtcbZyY+8NwDPQSnluehL7gZcRr1b/D3rxi8m4fgsPDMDbIjkMeG+a1Hk65QnE9n/R19Tj34Cpbz+4n8l0UeXKYU61wWTpU8srs8L4gMhlkZlIoEw/SF5nizz//pGbNmjx69Ijvvvv2xkelRXSSX1q+FbpvMuc95l+aN9cDszoLWcKgXNaNBc9KiveZO+TkS1F8lPbuz1+zk4vSPiHx12C98eCszkKW6B28IauzkCXCDB5mdRayhNm7b/MX8le7Uz+B/NfEsl2ZrM7CJ3kVkXXPmTmMcn880Vfmy2qu/8pFRUURGBjIhAkT+P7776WRQQghhBBCCCEygbzeMnPJ0c5E/v7+FChQgLCwMGbNUu/e5uvrq/a6yMRL0aJFtWxRCCGEEEIIIYT4skiPhkzUoUMHOnTooPG7xo0b4+7urvG7pK+wFEIIIYQQQgghvlTS0PCFMDU1xdTU9OMJhRBCCCGEEEKkiQLFxxOJdCNDJ4QQQgghhBBCCJFupEeDEEIIIYQQQoivm0J+Y89McrSFEEIIIYQQQgiRbqRHgxBCCCGEEEKIr5rM0ZC5pEeDEEIIIYQQQggh0o00NAghhBBCCCGEECLdyNAJIYQQQgghhBBfNYX8xp6p5GgLIYQQQgghhBAi3UiPBiGEEEIIIYQQXzmZDDIzSY8GIYQQQgghhBBCpBtpaBBCCCGEEEIIIUS6kaETQgghhBBCCCG+ajIZZOaSoy2EEEIIIYQQQoh0Iz0ahBBCCCGEEEJ81RQyGWSmkh4NQgghhBBCCCGESDfSo0GIL5AyOiars5AlHq48ltVZyBKO5dtkdRZEJjq56GRWZyFLVOhXIauzkCUunj2X1VnIGjrf5i+Hd199m9d3aQPbrM5CljCvbJ/VWRBp8m3WS1lFejQIIYQQQgghhBAi3UhDgxBCCCGEEEIIIdKNDJ0QQgghhBBCCPF1U2Z1Br4t0qNBCCGEEEIIIYQQ6UZ6NAghhBBCCCGE+KoplNKlITNJjwYhhBBCCCGEEEKkG2loEEIIIYQQQgghRLqRoRNCCCGEEEIIIb5uMnIiU0mPBiGEEEIIIYQQQqQb6dEghBBCCCGEEOLrJj0aMpX0aBBCCCGEEEIIIUS6kR4NQgghhBBCCCG+bvJ6y0wlPRqEEEIIIYQQQgiRbqShQQghhBBCCCGEEOlGhk4IIYQQQgghhPiqKWTkRKaSHg1CCCGEEEIIIYRIN9KjQQghhBBCCCHE1016NGQq6dEghBBCCCGEEEKIdCMNDUIIIYQQQgghhEg30tCQyQoWLMiCBQu0fv/gwQMUCgUXL15M1fY6dOiAl5dXuuRNCCGEEEIIIb5KSmXWLd8gaWhIg0aNGuHp6anxuz///BOFQsHly5c/ax82NjY8e/aMYsWKfdZ2PlfdunXR1dXl7NmzWZqP1PDfvpU6TRtSqqoHrTu348q1qymmP3DkEI1aNaNUVQ+aerfkj5PH1b5XKpUsXrGMag3rULpqBbr07cm/jx6qpXn58iXDx4/GvWYVPGpXZezUSbx9+1b1/f1/H9Cxdzeq1K9NqaoeeDZvzKLlS4l+H51+gaeCUqnkpw0rqNKmISWbVKXTyL48ePIoxXXOXblAr/FDqOrdCJd6Hhw++bva99Hv3zN39RKa9PSmtFd1qno3YsSciQQEB2ZkKGmWu1UpnFa2oYhvBwqMq0e2vDlSTt+yFEW3d1FbHBa20JrednRdim7vgmnZAumd9VTz37aFuk3qU7qSO206/vjxsn/4EI2+b0rpSu40bf09f5z4U/Vd9Pto5v20kKatv6dcFQ9q1K/NqPFjCAgMUNvG9Zs36NqnBxVqVKZSrWpMmDZZrexnBqVSyeJVPlRrUpfSNSrSpX+vZNeoJv47tlKnRSNK1ahA667tuXI94Xi9fPWSafNn0bB1M0rXqEitZg2YtmA2r8PDNW4r7GUYNZvWp1ilMrx6/TrdYkuL7Zt307ReO6qWa0jntv24duWm1rT37jxg5OBJNK3XDo8Sddm8aWeyNG/evGX+rGU0rfcjVd0b0bXdAK5fvZWRIWSoMo7uLOuznj9m/83NlU+pWULz/fu/QqlUeQTCqQABAABJREFUsmTbGmr2ak659nXpNnUw/z57nOI6529cou/sUdTq1QK3NtX57ezxZGmCX4Yw1mcGtXq1wL2DJz1nDPvodjNTetZzAEtX+NDo+6aUq+JBhZpV6NK7O5evXsnIENLN4V1/M7iVD11qz2VSj43cu/EsVeudOnKDDlVnsXB08us+q/lv30JdrwaUrlKeNp1S/wxXukp5jc9wh48eoVu/XlSqU53i5Utx8x/tdZhSqaTHgD4UL1+KI78fTZd4PpVSqWTRhhVUbt2AEo2r0nFEHx48Sfm+dvbKBXqOH0yVNg0p4lk+2fMawMHjR+k8qh/lv69DEc/y3Lj7T0aFIEQy0tCQBp07d+bQoUM8fpz8Brx27VrKlCmDq6vrZ+1DV1eXvHnzoqeXdfN0Pnz4kJMnT9KnTx/WrFmTZflIjX2HDzJr0Tx6du7GtnW+ODs60X1gH4JDQjSmv3D5EsPGj6ZpIy+2rfejRpVq9Bs+mNt376jSrNm0Ht9tmxk3bBR+q9djZGRE9wF9iIqKUqUZPmEMd+7fY+WiJSyZs4DzF/9mwowpqu/19PRoXK8hKxYuYe/mnQwfMJjtv+xiycrlGXcwNFi9bRObdm9jfN9hbF6wGiNDI7qNGUDUuyit67yNjMS5kCNjew3W+H1kVCTX796iR+uObF+8jkVjpnP/8UN6TxyWUWGkmaWXKxb1i/JsxXHujdpNbNR7Coz1RKGvm+J6kQ9DuNXFV7XcH7NHYzqLhsWyfEKh/YcOMHvBXHp06c7WDX44OTrRvV8vrWX/4uWLDB87kmaNvdi20Z8aVavRf+ggVdmPjIzkxq0bdO/UlS0b/Zk/cy4PHv5L38EDVNsICAyga58e2Oa3wXftRnwWLeHuvbuMmTQuM0JWWeO7Ht/tmxk3ZCR+K9ZhZGRI90F91a7RpPYdOcisxfPp2bEr21ZvwtnBie6D+hIcGne8AoICCQgKZEjvAezauIWpoydw4tRfjJsxSeP2xs2YjJO9Q4bElxqHDxxj0dwVdO7uzTr/JTg6FWJgr9GEhIRpTB8ZGYWVdT569e+EhaW5xjTTJ87n7Km/GTdlGJu2+eDuUZp+PUYQ8CIoAyPJOEYG2bn5+BqT/EZldVbSxdo9m/E/sJMxnQayafJSjAwN6TljGFHv3mldJyIqEucC9ozs2F/j90qlkgFzx/I44BkLBk9hy7QV5LP8ju7Th/A2MiKjQkm19K7nAArYFmDU0OHs8N/GhhVrsc5nRfe+vQgJ1bzNL8Xp326weclRvNpXZOLK9tjY52bOkK28Cn2T4nqBz16yZdlRnFzzZ1JOU2//oQPMXjiPHl26sXW9H06OjnQf0DuF83uJ4eNG0axRE9UzXP9h6uc3IjKCkm4lGNi730f3v3GzLwqFIt3i+Ryrtm1k0y9bmdBvOFsWrCK7oRFdR6f8vBYRGYGznSNjew9JIU0kpYq6MbhT74zI9n+PMguXb5A0NKRBw4YNyZ07N+vWrVP7PDw8nG3bttG5c2eOHz9O5cqVMTIywsbGhn79+vHmjfpN4O3bt3Tq1AlTU1NsbW1ZsWKF6jtNQyeuXbtGw4YNyZEjB6amplSuXJm7d+9qzGNsbCzTp0/Hzs4OIyMj3Nzc2L59e5riXLt2LQ0bNqRnz574+/sTEaH+sPH69Wu8vb0xNjYmX758zJ8/n2rVqjFgwABVmqioKIYMGYK1tTXGxsa4u7tz7NixNOUjNTb4b6JF46Y0bdgYe7tCjBs2CkMDQ3bt/UVj+k1b/ano7kGntu2wL2hH3+69cHEujN/2rUDcQ9fGLX5069CZGlWq4ezgyLRxEwkICuTIH3H5v/vgPsdPnWTiyLG4Fi1OKbeSjBo0jH2HDxIQGPervo11fpo2bExhRyes8uWjeuWqNKxbj78vXUj3Y6CNUqlkw89b6P5DB2p6VMHZzoEZQ8YREBzEkZN/aF2vSlkP+rfvTq2K1TR+b2pswuppi6hXpRZ2+QvgVqQYY3oO5trtmzwNeJ5B0aSNeYNiBO64yOuzD4n6N4QnPx1DL1d2TMul3PtAGaPkfViEaol5nfwGb1jQHItGxXm6VPsxzAwb/DbR3KsZTRs1wb6QPeNGjMbI0JBde37WmH7TZn8qlq9Axx/bU8iuEH179MalcBH8t24GwNTElJWLffCsXQe7AgVxK+7KqKEjuH7zBs+ex/1q9vvxP9HT02P0sJHYFShIMZeijB0xmkO/HeFhKnoUpAelUsnGbf50a9eZGpXjr9ExkwgIDuTIn8e0rrdhsy8tGnnRtEF8XTF0JIaGhuzauxsAx0IOLJg6m2qVqmBrnR/30mXp160Xx078yfv379W2tXnXdl69fk2H1j9mYKQp89+4k8bNPGnoVRc7+wIMG9MPA0MD9v58QGN6l2LO9B3Uldqe1dDX10/2fWRkFMeOHKf3gC6ULF0cG1truvT8kfw2Vuzatjejw8kQf149ysKfZ3H4wv6szspnUyqV+O7fTlevH6lephJOtvZM6TmSwLAgfjuXvJfCB5VKuNOnZWdqlq2s8ft/nz/m8p3rjO40gGL2hSloZcuYTgOJfBfF/r9+y6hwUi296zmABp718ChXHhvr/DjY2zN0wGDC34Tzz+3bmRTVpzmw9RxVG7pSuX5xrAta0n5wXbIZ6vPHr9p7Y8TGxLJ8yl68OlYit5VZ5mU2lTb4+9K8SVOaNmwSVy8Pjz+/2p7htvhRsbwHHdvGn9/4Zzj/7VtUaRrVa0jPzt0oX9Y9xX3f/OcW6/02MXnM+HSN6VMolUo27NpCj9Yd457XCjkyY+h4AoKDOJzi81oFBnToQW0tz2sATWrVo7d3ZyqULJsBORciZdLQkAZ6enq0a9eOdevWoUw01mbbtm3ExMTg4eGBp6cnzZs35/Lly2zZsoXjx4/Tp08fte3MnTuXMmXKcOHCBXr16kXPnj25dUtz164nT55QpUoVDAwM+O233zh//jydOnVK9uD7wfTp09mwYQM+Pj5cu3aNgQMH0rZtW37/PXl3Kk2USiVr166lbdu2FC5cGAcHh2QNFYMGDeLEiRPs3r2bQ4cO8eeff/L333+rpenTpw9//fUXmzdv5vLly3z//fd4enpyOx1v5NHR0Vy/dZPyZcupPtPR0aF82XJc0tIN8tLVy3gkuflUcPfg0tW4IS+Pnz4hKDhYLY2piSmuLsVUaS5duUwOU1OKFXFRpSlfthw6OjpcvqZ5vw8fPeL4qZOUKVn604L9BI+fPyUoNBiPRDcXU2MTXJ1duHgz5a6JafX6bTgKhYIcxqbput1PoZ/HFP1c2Xlz+Ynqs9i30UTcDiS7U54U1zXIlwOnFa1xXNIS6/7V0Lc0VvtekU0X6/7VebbqBO/Dsu7XvujoaK7fvKH2IBVX9t25dEXz8K1LVy5TvlySsl/eQ2t6gNfhr1EoFJiaxJ3Xd+/eoa+nj45Owq3D0MAAgL8vXfzUcNIk4RpNuO5NTUzir1HN1190dDTX/7lJ+TJJjleZcly6lkL8b8IxMTZW62F29/49fNatZPqYSVn2S1h0dDS3btymrHsp1Wc6OjqUdS/J1cvXP2mbMTExxMTEks0gm9rnBgYGXLpw7bPyKz7fk4BnBIWF4F4s4R5imt2E4vZFuHz7089PdHTccD4D/YTzrqOjQzY9fS7cytrhBJlRz0VHR7P9552Ympjg7OSUfplPZ++jY3jwz3NcShdUfaajo6Bo6QLcvfZU63q/rD9JDrPsVG3web1tM0LcM1waz+/VK8kaED52H9MkIjKC4eNGMXroCCwtLNOe+XSm9XmtcFEu3fhvDOsR6W/JkiUULFgQQ0ND3N3dOXPmTIrpt23bRuHChTE0NKR48eL8+uuvat8rlUrGjRtHvnz5MDIyolatWun6d5km0tCQRp06deLu3btqf7ivXbuW5s2b89NPP+Ht7c2AAQNwdHSkQoUKLFq0iA0bNhAZGalKX79+fXr16oWDgwPDhw/H0tKSo0c1jw1bsmQJOXPmZPPmzZQpUwYnJyc6duyIs7NzsrRRUVFMmzaNNWvWULduXQoVKkSHDh1o27Yty5enrsv+4cOHefv2LXXr1gWgbdu2rF69WvX969evWb9+PXPmzKFmzZoUK1aMtWvXEhMTo0rz8OFD1q5dy7Zt26hcuTL29vYMGTKESpUqsXbtWq37joqK4tWrV2pLSl2hQ8PCiImJwcLcQu1zC3MLgoI1d/UNCg7Gwly927CluTlBwcGq7+O2oZ7GIkka81zq3+vp6ZEzRw6CQoLVPvfu2pFSVT2o39KL0m4l6dO1h9Z40ltQaFxeLJPk1SKXueq79BD1Lop5a5ZSv2ptTIyNP75CBtPLZQSQrCHg/csI9Myya10v4nYAT5b8wb9TD/B0xQmy5TGl4OSG6Bgm/Pqbt0N5Im4F8Pps5vx6r01oWGh82U9aTi0IDtZ8boOCgzSmT1pmP4iKimL+4kXUq+OJiYkJAO5lyhEcHMzajeuJjo7m5atXLFiyKG77QZkzR8eH/FrkSnLd5zLXGkvoyzAtxyvhuk62TlgYy9etokWjpqrP3r17x9AJoxncqz/58ub9nDA+S1joK2JiYjG3MFP73NwiF8FBoZ+0TWPj7BRzLcLaFX4EBgQTExPD/v8d4erlGwQHfdldyr8FQS/jzoFFzlxqn1vkzKX67lMUtLIln+V3LNq8klfhr4l+H82a3f68CAkkMB3vE58iI+u53//8g3JVK1C6kjsb/TexYrEPuczUj+2X5PXLt8TGKMmZS/0eliOXMS9DNA+d+OfyY/749TIdh9bNjCymWcIzXPJnlJTPb9K630JrPa7NrAVzKVHcjRpVqqVpvYzy4ZnMwizJ86mZeZZfhyJrbNmyhUGDBjF+/Hj+/vtv3NzcqFu3LgEBARrTnzx5ktatW9O5c2cuXLiAl5cXXl5eXL2a8MPirFmzWLRoET4+Ppw+fZr/s3ffYVEcfQDHv4CI9CoiKCJIEWl27L33aDTG3nuv2HvX2HvDXmOJJho1akxif+09GntBjqJIkXLvH4cHR1FU8Cy/z/Pso7c7Ozez7Mzuzc7MGhsbU6NGDY3fqJlNGhrek4eHB6VLl1bPXfDvv/9y7NgxOnTowIULF1i9ejUmJibqpUaNGiQkJPDff/+p40g+j4OOjg52dnbpnjjnz5+nXLlyaXZ1Tenff/8lMjKSatWqaaRhzZo16Q61SGnlypU0a9ZM/QSvefPm/P333+r979y5Q2xsLCVKJD1NNDc312j4uHTpEvHx8bi5uWmk4+jRo29Nx+TJkzE3N9dYps6emaF0f65mTJjM1tXrmTZ2In/+8xerN6zNsu/65Y/9FG1UWb2k1+slM8XGxdF/0giUSiWje2pnjgbzci54rG2jXnT0Pqxaizj3kBfH/yPmXgivLjzi3sT96BkZYFY6PwCmxRwx9rbn6erjmZn8z1JsXCwDhw0GpZKRQ5LGtxdwcWHC6HEErl9L8fKlqFSrKg72DlhbWaOjkzWXkz2//0bxauXUy6c4ryNeRdB9UB9cnJzp3qGLev3sJfNxdnKiXo3aWZ4GbRg9cTBKlNSv/iMVStRly4adVKtZER3dz2MM87dk718H8G9XS73ExWfNea+fLRuz+o7l3tOHlOtcn5Jta3L66jnK+pbU6Ln0tSlerDjb1m1i7fLVlPEvzcCAwenOC/AlioqMYenEvbQbWBPTtzSwf4sO/3mUU2dOM6Rf+vMaZLVf/thH0YaV1EvsJ7iuiS/LrFmz6NSpE+3atcPT05PFixdjZGSU7tx5c+bMoWbNmgwaNIiCBQsyfvx4ihQpwvz58wFVb4bZs2czYsQIGjRogI+PD2vWrOHx48fs3Lkzy/KhvRkHv2AdOnSgV69eLFiwgFWrVuHi4kKFChWIiIigS5cu9O6degIaR0dH9f9TNhro6OiQkJCQ5ncZGhpmOF0RibOj7927FwcHB41tBondm98mJCSEHTt2EBsby6JFi9Tr4+PjWblyJRMnTsxwOvT09Dh79ix6epqT7715MpqWgIAA+vfvr7FO91X6b2mwtLBAT08PRYonFYoQRbpd4WysrVPdTASHhGBjba3eroojhJw2OZPFGaLuVmljbZ1q0qi4uDjCX7zAJkVLe+5cqqeeLvmdiU9IYOyUCbRp3jLVcckMlf3L4uORNJzjdWKX2ODQEHJaJR0PRWgIHi4f30VU1cgwnMdBT1k1Zb7WejO8PH2fqFs71J91sqlujrNZGGr0ashmbkj03Yw/GUiIfM3rJ+Hqt1UYe9mTPZcZHoGtNcLlHViFyOvPuDt678dk471YWlgmnvua56EiRIG1tXWa+9hY26QZPuU5GxsXy8CAITx+8oQVC5emKrN1ataiTs1aBCsUGBkago4OazasI49D1kw0VqlseXw8k97C8zpx4jtFqIKcNprntXuBtM9rS3OLdI5XUtl/41XkK7oM6I2xkTFzJk1HP9mwiZNnz3Drzr/4HlF13X0zhK5c3ap0at2enskaJbKShaUZenq6hCjCNNaHKEKxtvnwp7J58tqzaMUMoqKieRXxCpuc1owYPBEHh9wfmWLxvioWLYN3gWT1eVzieR8eSs5kvXkU4aG45/u4SUk9nd3ZMnk5LyMjiI2Lw8rMghYju1HIOXXPyU8pK+s5I0NDHPM64pjXEV9vH+o0rs+O3Tvo2LZD5mYik5iaG6Grp0N4qOYbfl6EvsLcKvW1N+hRGMFPw5k9bLt6nTJBVV+1rzydKWs7Yuug3R4cSfdwKf5eoSHv+PumuOcLVaSqx9/m1NlTPHj0kNLVKmis7x8wiCK+hVm1aFmG4/pQlf3L4eNRSP359WvV/ZoiLATbZPevwWEhFHR2zfL0fEt0tPiayZiYmFQ9tQ0MDFL9Rnv9+jVnz54lICBAvU5XV5eqVaty/HjaD7uOHz+e6jdUjRo11I0I//33H0+fPqVq1arq7ebm5pQsWZLjx4/zww8/fEzW0vX1NldnoaZNm6Krq8uGDRtYs2YN7du3R0dHhyJFinD16lUKFCiQasmePfu7I06Dj48Px44dU4+jfBtPT08MDAy4f/9+qu/PmzfvO/dfv349efLk4cKFC5w/f169zJw5k9WrVxMfH4+zszP6+voar70MDw/n5s2k1+UULlyY+Ph4goKCUqXD7i3djQ0MDDAzM9NY3tZAoq+vj6e7ByfPJKUlISGBk2dO4+vlneY+vl4+nDijOcbp+KmT+HqpepnksXfAxtpaI0zEqwguXr2sDuPr7cOLly+5cv2aOszJs6dJSEjAp1Da3/smbXFxcSQo025U+ljGRsbks8+rXgo45sfG0poT588ky8srLt64ip/Hx70+9U0jw73HD1kxaS4WZuYfm/wPlhAdy+unL9RLzMMwYkMjMfZOamzTNdTH0DUnkTfT7jmUFt0c2dDPZapurAjeeYHbA37m9sAd6gXgaeBJHi3I2BwomUVfXx9Pj4KcPH1SvS4hIYETZ07h6532WFxfbx9Onk5x7p88oRH+TSPD/Qf3WbZgMRYWFummwcbaGiMjI/Yf2I9B9uyUKun/cZlKh7GRMY558qoXl/zOiWU0qdwnldG0y5++vj6ebh6cPJuU/4SEBE6ePY1voaT8R7yKoHO/nuhny8a8qbNS1T8/TZzG9tUb2LZqPdtWrWfskBEABC5YRvPvvs/MbL+Vvr4+7gVdOXMqaXLZhIQEzpw6j5eP51v2zBhDwxzY5LTmxYuXnPznLOUqlvroOMX7MTY0wtHOQb24ODhhY2HFyStJ8yFFRL7i0u1r+LgWektMGWdqZIKVmQX3njzk6p2bVCxaJlPi/VBZVc+lJSFBqf6x9znKpq+Hk5sdV8/eU69LSFBy9X/3cClknyp8bkdrJqxqx7jlbdWLX5kCeBR2ZNzytljZvv11z5+C6h6uoMbfKyEhgROn3/L39fJO/fc9dfKdf9/kOrRux/Z1m9m6ZqN6ARjcZwDjR455/4x8gFT3a/ne3K8lv6694uL1K/gWTP++UnxZ0uq5PXny5FThgoODiY+PJ1euXBrrc+XKxdOnaU+6/vTp07eGf/Pv+8SZGaRHwwcwMTGhWbNmBAQE8OLFC9q2bQvAkCFD8Pf3p2fPnnTs2BFjY2OuXr3KgQMH1F1X3lfPnj2ZN28eP/zwAwEBAZibm3PixAlKlCiRap4GU1NTBg4cSL9+/UhISKBs2bKEh4fz999/Y2ZmRps2bd76XStWrKBJkyZ4eWn+CM2bNy8BAQHs27ePOnXq0KZNGwYNGoSVlRW2traMHj0aXV1d9cRobm5utGjRgtatWzNz5kwKFy7M8+fPOXToED4+PtSpU+eDjkVaWjdvyfDxoynkURCvQl6s27SBqOgoGtatD0DA2FHY5sxJv+69AGjZtDntundi9Ya1lC9dlt8O/s6V61cZM3Q4oOpd0qrZjyxdvYJ8eR1xyG3P/GWLsLXJSZXEsXwuTvkp61+aMZPHM2rwMGLj4pg0cxq1qlbHNqeqF8Se/b+STS8brgVcya6vz5VrV5mzaD41qlZHP9u7h8FkBh0dHVo3bMaSTavJ55CXPLlyM3ftMmytbahSurw6XLuhPalaugIt6qt+KL2KiuT+46RXuD569phrt29ibmqGva0dsXFx9J04jGv/3mDh2BnEJyTwPPEJg7mpGdkzMMwnq4XsvUzOxn68fhLO66CX2P5QlLjQSF6eSrpJyze6Fi9P3iNkn2ryvFytS/DyzH1in0eQzcoI26ZFIUFJ+F+q4T5v3kSRUuzzCGKDIj5NxpJp/WNLho8dRaGCnngX8mLtpg1ERUXRsG4DAIaNHoGtrS19E1/x1fKH5rTr0onA9WsoV6Yc+37fz5VrVxk9bKQqH3Gx9B86iGvXr7Ng1hwS4hMIDlbNdWJubq7uibVhyyb8fHwxMjTi+KkTzJo7m749e2Fm+mkmAtXR0aHV981ZGriCfHnz4pDbgfnLF2FrnZMq5Sqqw3Xo040q5SvyY+NmquP1QwuGTxxDIQ9PvAoWYt2WxONVpx6Q1MgQFRPNnFHjefUqglevVH/XN09WHVP02ggNCwPAOV/+T5b/N5q3+o7xI2fg4elGIS93Nq3fQXRUNHUbVAdg7Ihp5LS1oXvv9oBq4rX/bqvmFomLi+V5kIKb129jaJSDvI6qRrkT/5xBqVSSzykvD+8/Yv5Py8mXP686zi+NkYERjrb51Z/z2OTFI28hwl+F8STk0Vv2/Pzo6OjQomYTlu1YSz47Bxxy5mbB1pXktLChcrGy6nCdJvancrFyNK+hmlskMjqK+0+T8vro+ROu3/0XcxNTctuobjh/P3EESzMLclvbcuvBHaatmU+lYmUo7aP9Weozu56LjIpi2arlVCxXgZw2NoSGhbFp2xaCngdRvUo1reUzI2o0Lcayyb+S38MOZ4/c/L7tDDFRsZSrpfohunTiXixzmvB95wpkN8hGHuecGvsbmeQASLVem1o3b6G6hyvoibdnIdZuTryHq6O6hxs2diS2OW3p++YertmPtOvWicD1aylXpiz7DiT+fYeOUMcZHh7Ok2dPCUqcN+juvbuAqnHcxtpGvaRkZ2dHHnuHVOs/BR0dHVo3asbijavJZ5+XPHb2zF2zFFtrG6q+x/3aw6ea92sAYS/DeRL0jKDEucv+e6i6D7KxtCanVcZ7gnw1tPiaybR6bmekx/mXTBoaPlCHDh1YsWIFtWvXxt5e1Zrs4+PD0aNHGT58OOXKlUOpVOLi4kKzZs0++Husra35448/GDRoEBUqVEBPTw8/Pz/KlEn7ScP48ePJmTMnkydP5s6dO1hYWFCkSBGGDXv7e8TPnj3LhQsXWLYsdZcxc3NzqlSpwooVK6hTpw6zZs2ia9eu6lduDh48mAcPHpAjRw71PqtWrWLChAkMGDCAR48eYWNjg7+/P3Xr1v3gY5GWWlWrExoayvzliwlWKPBwdWPxT/PU3SSfPHuKbrLxxYV9fJk6diLzli5izuIF5MvryNypM3F1Sep62r5lG6KiohgzZSIvI15SxMePxT/N06gMpo6ZwMSZU+nQuxu6OjpUrViFYf0Hqbfr6WVj5bpA7j64j1KpxN4uN82bNKX1Dy0yNf/v0uH7lkRFRzF67hReRkRQpJAPS8f/hEH2pLw8ePKI0Bfh6s9Xbl2n7ZCk9y1PXaqa7K9h1dpMGjCSIMVzDp84BsB3PTSHEayeuoASPkXQtuCdF9ExyEbuLmXRM85O5PVn3JuwD2Vs0qSl2XOZoWeWdM7qWxuTp28l9ExzEP8imsjrT7kzbDfxL7JukpyPUbNaDUJCQ1mwdJHq3HdzZ/GcBeoupE+ePUUn2RhrPx8/poyfxPzFC5izcD758joyZ/os9bkfFPScI3+qemY0aanZhW7lomUUL1oMgMtXLrNw6WIioyLJn8+JUQHDqVc7c8v1u7Rv0Yao6GjGTJukKqPefiyeOVejjD549FDdEABQq0p1QsMS64oQBR4F3Fg8M6muuHrjOhevqiZNqt2socb37d+6G4fcqZ8aalPVGhUJDQ1n+aI1KIJDcXV35qeFE7GyVnWHfvbkObrJ5s0IDlLQ5ofu6s8b1mxjw5ptFC7qw8IV0wGIePmKxfNWEfQsGDNzUypWKUPXnu3Ipv9l3ip45fNlzaCkruMBzcYCsOOfzQSs6qetZH2wdvV+ICominHLZ/IyMoLCbt4sHDoVg2Q9Jh8+e0zYy2T1+Z0bdJyQlNcZ6xYCUL98DcZ3HQrA8zAFM9YtVA/LqFu2Ol2+096rW5PL7HpOT1eX/+7eZffeXwgNC8PC3JxCnoUIXLqSAi4uWsljRpWsXJCXYVHsWPkX4SGvcCxgy4Dp36uHTiiCXnxx86nUrFaDkLBQFixL/Pu6urP4p/lJf9+nTzXm//Hz8WXKuInMX7KQOYsT/77TZmncwx0+dpSRE8aoPw8aqep+3q1DZ7p/wgm531fH71sRFR3N6LlTePHmfm3CbI37tfuPHxIaHqb+fOXmNdpo3K/NAVT3a5MHjgLg8PFjDJs1QR1mwGRVo1uPFh3o2apTVmZJpJDWMIm02NjYoKenx7NnzzTWP3v2LN2e4XZ2dm8N/+bfZ8+ekTt3bo0wfn5+75ON96KjVGpxsIr4Krx69QoHBwdmzpxJhw6ZO74xNuTTPyn+HOiGvdZ2ErTi+uCftZ0ErXBd8aO2k6AVOq/j3x3oK/TS5NucRbx079LaToJWnO965t2BvkK6BSy0nQStOBu1UdtJ0IqiBlkzxvtzl+0bvV/Tzf/5vqXlbWIV2vtdoW+d/jx1KZUsWZISJUowb948QDWsyNHRkZ49ezJ06NBU4Zs1a0ZkZCS//PKLel3p0qXx8fFh8eLFqoee9vYMHDiQAQMGAPDixQtsbW1ZvXp1ls3R8GU+phBade7cOa5fv06JEiUIDw9n3LhxADRo0EDLKRNCCCGEEEKINHwhj9f79+9PmzZtKFasGCVKlGD27Nm8evWKdu3aAdC6dWscHBzUczz06dOHChUqMHPmTOrUqcOmTZs4c+YMS5cuBVTDc/r27cuECRNwdXUlf/78jBw5Ent7exo2bJhl+ZCGhm9I165dWbduXZrbWrZsyeLFizMc14wZM7hx4wbZs2enaNGiHDt2DBubtN/0IIQQQgghhBDi3Zo1a8bz588ZNWoUT58+xc/Pj3379qknc7x//77GK4hLly7Nhg0bGDFiBMOGDcPV1ZWdO3dqzLs3ePBgXr16RefOnQkLC6Ns2bLs27dPY+h7ZpOhE9+QoKAgXrx4keY2MzMzbG1tP3GK3k2GTnxbZOjEt0WGTnxbZOjEt0WGTnxbZOjEt+WLHTrx/KXWvls/56edNPpzID0aviG2trafZWOCEEIIIYQQQoivhzQ0CCGEEEIIIYT4qulIP/5PSvfdQYQQQgghhBBCCCEyRhoahBBCCCGEEEIIkWlk6IQQQgghhBBCiK+bDJ34pKRHgxBCCCGEEEIIITKN9GgQQgghhBBCCPF1kx4Nn5T0aBBCCCGEEEIIIUSmkYYGIYQQQgghhBBCZBoZOiGEEEIIIYQQ4uumlLETn5L0aBBCCCGEEEIIIUSmkR4NQgghhBBCCCG+ajrSoeGTkh4NQgghhBBCCCGEyDTS0CCEEEIIIYQQQohMIw0NQgghhBBCCCGEyDTS0CCEEEIIIYQQQohMI5NBCiGEEEIIIYT4usnrLT8paWgQ4jMUY59d20nQCtPVXtpOglboRCdoOwlaocyup+0kaEWg8QBtJ0Erzp8+o+0kaIXf4mLaToJWXJx2S9tJ0Aqf61W0nQSt0Cn0bV7HLpoe1HYStMKP77WdBPEFkIYGIYQQQgghhBBfN+nQ8EnJHA1CCCGEEEIIIYTINNLQIIQQQgghhBBCiEwjQyeEEEIIIYQQQnzVdGToxCclPRqEEEIIIYQQQgiRaaRHgxBCCCGEEEKIr5u83vKTkh4NQgghhBBCCCGEyDTS0CCEEEIIIYQQQohMI0MnhBBCCCGEEEJ83WTkxCclPRqEEEIIIYQQQgiRaaRHgxBCCCGEEEKIr1uCdGn4lKRHgxBCCCGEEEIIITKN9GgQQgghhBBCCPFVU8rrLT8p6dEghBBCCCGEEEKITCMNDUIIIYQQQgghhMg0MnRCCCGEEEIIIcTXLUHbCfi2SI8GIYQQQgghhBBCZBrp0SCEEEIIIYQQ4qumlNdbflLfXI8GJycnZs+ene72u3fvoqOjw/nz5zMUX9u2bWnYsGGmpE0IIYQQQgghhPjSfVE9GurVq0dsbCz79u1Lte3YsWOUL1+eCxcu4OPj88HfkTdvXp48eYKNjc3HJPWD3L17l/z586s/m5iY4OjoSMWKFenbty+urq6fPE2fI6VSyYJli9m2ewcvX0ZQ2MeXkYMDyJfX8a37bdy2hVXr1xAcosC9gCvD+g/Gu5CXentMTAzT5/7Ebwd/53Xsa8qULMWIQUOxsbJWh3ny9Anjpk/m9NkzGBkZUb9WXfp260m2bKqi9Dz4OdPn/sSV69e4//ABLb7/gaH9BqZKy9pNG9i8YxtPnj7FwsKC6pWq0LdbTwwMDD7q2GzetIXAwDUoghW4ubkyZOhgvLy90g1/4PcDLFywiMePn+DomJfefXtTrlxZ9XaFQsGc2XM5fvwEES9fUqRIEQYPHUy+fKpj/fjRY+rUrpdm3NOmT6Fa9WoflZ8PtWvLQbau+Y0QRTgurnnpMbglHl4uaYb99ecjHNj7N3dvPwTAtaAT7Xs00Qgfqghn2dwtnD1xmVcvI/Eu4k6PwS3J42j3SfKTHqVSyYLli9n2S7KyMDADZWH7FlZtSFYW+g3G2zPpPNm662f2HtjHtRvXeRX5in/2HcHM1DRVPEf/OcbiVcu4+e+/GBhkp5hfEeZOmZXp+UyV/q2bWb0ukGCFAndXNwIGDtEoyyntP3iA+UsW8vjJYxzzOtKvZ2/KlykHQGxcLPMWLeTYP3/x6NFDTExM8C9ekr49e2Ob01Yjnj//OsbiFUu5+e8tsmfPTrHCRZk746cszeu7lBr7A94dq2JgYcTjv29wqPtSwv59km54HV1d/Mc0pWCL8hjbWRDxOJSrgYc5OWFbmuGrLOqMT5caHOm3knNz9mZVNt6LUqlk4bZV/Hx4Ly9fReDn5sXw9v3IlztPuvucvXaB1Xs2c+2/mzwPU/BTv/FULl5WI4wiPITZG5dy/OIZXkZGUMTDh6Fter813s9RMdeSdKjRnUL5vLG1sKPHgvYcOp/6vulztXHbZlave1M/uREwYPDby/ehA8xfuiipfPfoTfnSSX/bg4cPsWXHdq5ev0b4i3C2rtmIh5u7Rhxjp0zgxOlTPA9+jpGhIb7evvTr0Rtnp/wpv+6TUSqVLP5lDTuO7eNlVAS+Lp4M+7E3jrkc0t1n5W+b+OPc39x9+gCD7Nnxdfak93cdcLLLm2b8veaN4J8rZ5jZbTSV/EpnZXbSpVQqWbBiSdJ1zNuXkQOHZuw6tnGt6jxxcWVYv0FpX8du3lBdx347nOo6dvXGdWYtmsuV61fR1dWjWoXKDO7VDyMjoyzJ69vs336CXzb8RVhIBPkK2NGuX10KeKZd95w8coWda47y9FEI8XHx2OWxpm7zMpSvWVgj3MO7QWxY+DtXz/9HQnwCDk62DJjYHBs7i0+QIyG+sB4NHTp04MCBAzx8+DDVtlWrVlGsWLGPamQA0NPTw87OTv3DURsOHjzIkydPuHDhApMmTeLatWv4+vpy6NAhraXpc7JyXSDrt25i1OBhbFgRiKGhIV369iQmJibdfX47+DvT5s6iW4fObF29HndXN7r064kiJEQdZuqcmRz5+09mTZzC6oXLeB78nL5DB6m3x8fH031AH2Jj41i3dBUTR45l16+/MH/ZYnWY17GxWFpa0rltB9wLuKWZlr37f+OnRfPo1r4TuzdtY9ywkew79DtzFi/4qOOyf9/vzJwxiy5dOrNh03rc3N3o3q0nIYqQNMOfP3+BgKHDadioIRs3b6BipYr07zuAf2/9C6gu/v36DuDhw0fMnj2LjZs3kDt3brp26UZUZBQAuexyceDQfo2la7cuGBkZUaZsmY/Kz4c68vtJlszaSMvODVi0fizObnkJ6DmD0JAXaYa/cPY6lWr4M33JUOasGknOXFYM7TGD4CDVcVMqlYweMIenj4IYN6sPizaMI1dua4Z0m0ZUVPrn3Kewcn0g67dtYtSgYWxYFohhDkO69M9AWZg3i27tO7N15XrcC7jRpX9PFKFJ50l0dDRlS5aiU+t26cZz4PAhAsaNomHt+mwP3MjaRSupXa1mpuYvLfsO7Gf67Jl07diFLWs24ObqRpfe3TXKcnLnL55nyMgAvqvfkK1rN1K5QkX6DOrPrduq8zw6OpprN67RpX0nNq/dyE9TZ3L3/j16DeirEc+BPw4SMGYEDevWZ9u6zaxdtoraNWpldXbfqtjghvj1qs3BbkvY6B9A7Ktovts3Ej0D/fT3GdIQ3641ONxrOYGeffhr6FqKDVLFk5JLwxLYlXQj4pEiK7Px3lb9somN+39mRPt+rBu/EMMcOeg2ZTAxr1+nu09UTDTu+VwIaNcnze1KpZK+M0fyMOgJswdMYPOkpeS2yUWXyQOJjI7KqqxkCUMDI64/vMK4DcO0nZT3tu/AfqbPmUXXjp3ZErgBN1dXuvTt8ZbyfYEho4bxXb0GbA3cQOXyFekzOKl8A0RFR1HY149+PXqn+72eHgUZP2I0uzZuZ/HsBaBU0qVPD+Lj4zM9jxkVuH8LG//YxbAWvQgcOgdDgxz0mDuMmNj0z/OzNy/StGI9AofOZlGfycTFx9N9zjCiYqJThV1/aAc6OjpZmYUMUV/HBgawYelqDA1z0KV/r7dfxw79zrT5P9GtXSe2rliXeB3rpXkdi4mmbMnSdGqV9nUsKPg5Hft2xzFPXjYsXc3imXP59+5thk8ak9lZfKd/Dl5izbzfaNy+ElNWdidfATsm9V9NeGhEmuFNzAxp1KYi45d0ZlpgTyrWKcKiSTs4f/KWOszThwpGd1uGfT4bRs/vwLTAnjRuWxF9gy/qGXPmUyq1t3yDvqiGhrp165IzZ05Wr16tsT4iIoKtW7fSoUMH/vrrL8qVK4ehoSF58+ald+/evHr1SiN8ZGQk7du3x9TUFEdHR5YuXareltbQiStXrlC3bl3MzMwwNTWlXLly3L59O800JiQkMHnyZPLnz4+hoSG+vr5s25b2k6L0WFtbY2dnh7OzMw0aNODgwYOULFmSDh06qC96t2/fpkGDBuTKlQsTExOKFy/OwYMHNeJxcnJiwoQJtG7dGhMTE/Lly8fu3bt5/vw5DRo0wMTEBB8fH86cOaPeR6FQ0Lx5cxwcHDAyMsLb25uNGzdqxPvy5UtatGiBsbExuXPn5qefflL3ungjJiaGgQMH4uDggLGxMSVLluTIkSPvdRzSolQqWbt5A53bdqBy+Yq4F3Bl0qixBAU/59Cf6ce/ZuM6mtRvRKO69XHJ78yowcPIYZCDHXt2qfIU8ZKff9nF4N79KVmsBIU8CjJ++GjOX7rAhcuXAPjn1Alu3/2PKWPG4+HmTrlSZejZqRubtm8hNjYWAIfc9gT0G0SD2nUxMTFJMy3nL12ksLcvdWrUwiG3PWVKlqJ2tRpcunr5o47NurXr+O67RjRoWB8XF2eGjxhGjhw52LlzV5rhN67fSOnSpWjTtjXOzvnp0bM7BQt6sGnTFgDu37vPpYuXGD48gEJehXBycmLYiABiomP4LbFXkZ6eHjY2NhrL4T+OUK16Na08EQDYvm4ftRpVoGb98uRzdqDPsLYY5MjO/l1/phk+YGJX6jetQgH3fDjmt6f/yA4olQmcO3UVgEf3n3Ht0m16B7TBvZAzeZ1y0zugDa9jXnN43/FPmTUNSqWStVs20LlNByqXSywLIxPLwrEj6e63ZvM6mtRrRKM6iWVhkGZZAGjV7Ec6tmqHTyHvNOOIi4tjypwZDOjRh2aNmuDkmA+X/M7UrFI9k3OZRvo3rKNxw+9oVK8BLs4ujBo6HMMcOdjxy840w6/btJEy/qVp16oNzvmd6dW1B54eBdm4ZRMApiamLJu/mJrVqpM/nxO+3j4MGzSUq9ev8eTpk6T8zprOgF59adr4e5zy5cPF2YWa1bI+v29TpE9dTk3cxp3dpwm+dI99beZhbG+JS8MS6e5jX8qd27tP89+v/+PFvefc2n6Ce79fwK54AY1wxvZWVJrbkX0t5xAfq70fWykplUrW79tGp4atqFSsLG6OLkzoFsDzsGD+OPNXuvuV9StJz6YdqFK8XJrb7z19yMV/rzK8fV+8XDxwsndkRPt+RL+OYd/xP7IqO1ni2OXDzNk5jYPnvpxeDG+s2biexg0a0ahuA1X9NCSxfO9J+zq2bvMGyviXol3LxPLdpTue7h5s3LZZHaZerbp069AZ/+Il0/3e7xs2pljhojjY2+PpUZCeXbrz9NlTHj95nOl5zAilUsmGQzvpWLs5Ff1K45bHmXHtBvM8TMGR8/+ku9+CPpOoX7o6LvZOuOV1YWzbATwNCeLqvVsa4W48uM26A9sZ3bp/VmflrZRKJWu3bqRz62TXsRHjCFK84zq2aT1N6jVMdh0LIEeOHOzYs1sdplXTH+nYqi0+6fSGOfr3MbJly8aI/kPI7+iEd8FCjBo4jANH/uD+wweZnNO327v5b6rUK0alOkXJk9+WjoPqk91An8N7zqYZvlARZ0pU8CSPky12eayp3bQ0ji65uHHhnjrMpqUHKVzKjZY9apLfzR67PNYUK1cQc8u0702FyApfVENDtmzZaN26NatXr0aZrGVo69atxMfHU6pUKWrWrEnjxo25ePEimzdv5q+//qJnz54a8cycOZNixYpx7tw5unfvTrdu3bhx40aa3/no0SPKly+PgYEBf/zxB2fPnqV9+/bExcWlGX7y5MmsWbOGxYsXc+XKFfr160fLli05evToB+dbV1eXPn36cO/ePc6eVVU6ERER1K5dm0OHDnHu3Dlq1qxJvXr1uH//vsa+P/30E2XKlOHcuXPUqVOHVq1a0bp1a1q2bMn//vc/XFxcaN26tfp4RkdHU7RoUfbu3cvly5fp3LkzrVq14tSpU+o4+/fvz99//83u3bs5cOAAx44d43//+5/G9/bs2ZPjx4+zadMmLl68yPfff0/NmjW5dUvzYve+Hj5+RLBCQalkNwymJqb4eHpx4fLFNPeJjY3l6o3r+BdPuvnW1dXFv3gJdSPC1evXiIuL07gRcXbKT247Oy5cUsV74dJFXF0KaAylKONfiohXr/j3TtoNT2nx8/bh6o1rXLqialh48Oghf/7zN+VKlX3HnumLjY3l2rXrlPTXzGNJ/xJcvHgpzX0uXrxISX/NG69SpUtx8aIqv68Tn5pkN8iuEWf27Nk5f+58mnFevXqNGzdu0LBRgw/Oy8eIjY3j5vW7FClRSL1OV1eXIiUKcfXSv2/ZM0lMdAxxcfGYmqkuxrGvVY1I2bMnPSXW1dVFP7s+l89/3Pn8MdRloVgmlIViSWUhI67dvM6z50Ho6urSpO2PVKxfna4DenHrTsaO8YeKjY3l6vVrGuVUVZZLqstpShcuXcS/hOZ5Xtq/VLrhQdXwqKOjg6mJqpvttRvXCQoKQkdXl+9b/kClWtXo2qeHxlPTT808fy6Mc1ty/2BSPl6/iOTpyVvYl3JPd7/Hx2+Qt7I3Fq65AbDxyYd9WQ/u7juXFEhHh5prenN2xi4UVz/tDfe7PAp6QnBYCCW9iqrXmRqZ4O1SkIu3rnxwvG8aiw30U9R32fQ5dyPjZUN8OFX99J7l+/KlVA0I7yrf7xIZFcXOvbtxsHfALpd2hsc9Cn5K8IsQShYsol5namiMV34PLt65luF4XkapHrSZGycNGYh6Hc2wFVMY2rwHNuZWmZfoD5B0T5d0TTI1MUm8jqVd7mJjY7l68zr+xVKcJ8VKcOFKxv/ur2Nfo6+vj65u0k+hHInDV/938fx75uTDxcXGcefGY7yLJw3X1NXVxbuYC7cuv7v+VSqVXDpzmyf3gyno5wSoHnqe++cGufPaMLHfajrVmczwTos5/efVrMrGF0OZoNTa8i36ohoaANq3b8/t27c1frivWrWKxo0bM2/ePFq0aKGez6B06dLMnTuXNWvWEB2d1G2sdu3adO/enQIFCjBkyBDVU9jDh9P8vgULFmBubs6mTZsoVqwYbm5utGvXDnf31DdyMTExTJo0iZUrV1KjRg2cnZ1p27YtLVu2ZMmSJR+Vbw8PD0DV4wLA19eXLl264OXlhaurK+PHj8fFxYXdu3dr7Fe7dm26dOmCq6sro0aN4sWLFxQvXpzvv/8eNzc3hgwZwrVr13j27BkADg4ODBw4ED8/P5ydnenVqxc1a9ZkyxbVU+6XL18SGBjIjBkzqFKlCl5eXqxatUqje+H9+/dZtWoVW7dupVy5cri4uDBw4EDKli3LqlWr0s1jTEwML1680FhSdp0LVqi68FpbaV4cra2s1NtSCg0LIz4+HutkDQSqfawJVgSr49XX1081fs/a0prgEFW8wSGKNL/3zbaMqlOjFj06daVV1w74lS1BrSYNKF6kKJ3bts9wHCmFhqryaGWdIo/W1iiCg9PcJzhYgZV1ivxYW6EIVuXFyckJu9x2zJs7nxcvXhAbG8uqlat59uwZwc/TjnPnjp3kd86Pn5/vB+flY4SHvSQhPgFLa3ON9ZbW5oQGh2cojuVzt2BtY0GRkp4A5HXKja2dNSvmb+Xli1fExsaxafVenj8LISQ4LLOzkGFvzrlMKwshaf9N0/Lg8SMAFq5YQpc2HVgwbQ5mpqa069mZ8BcZO84fIjQsNDH9KfNsjSKdPAcrgtMMn16ZjYmJ4af5c6lVvaa6V9LDR6rheouWLaZz+47MnzUHM1Mz2nftRHh41uX3bYwSx9hGPgvTWB/5LByjXBbp7nd6yg5ubv6bttfm0jtmMy3/N4Nzc/ZwfcMxdZjiQxqijIvn3NzPY06G5ILDVV2jrc0tNdZbm1uqt30IJ3tHctvkYu6mZbyIeElsXCwrd2/kWchznod+XkNHvlZJ9VOK8mpp9Y7ynaI+s7ROtw58m03btlCiUhlKVirDX8f/Ydnchejrpz8MKSspXqjOZSszC4311mYWGT7PExISmLFlMX4uhSjg4KReP3PLEnydPamopTkZklNfxyxT/g2t0q2jQ8PTOU/ecu1LS8kixVEoglm5YQ2xsbGEv3jBT4vnAfBckfHr4cd6ERZJQnwC5laaPQ3MrUwIC0l76ARAZEQ0rauOo0WF0UwdtJZ2/eriU0LVM+1F6Cuio16za92f+JV0ZfhPbSleviAzh23k6rn/sjQ/QiT3xTU0eHh4ULp0aVauXAnAv//+y7Fjx+jQoQMXLlxg9erVmJiYqJcaNWqQkJDAf/8lFazk8zjo6OhgZ2dHUFBQmt93/vx5ypUrl6GLzb///ktkZCTVqlXTSMOaNWvSHWqRUW96HLwZTxcREcHAgQMpWLAgFhYWmJiYcO3atVQ9GpLnNVeuXAB4e3unWvcm//Hx8YwfPx5vb2+srKwwMTFh//796njv3LlDbGwsJUoktT6bm5trNLxcunSJ+Ph43NzcNI7D0aNH33ocJk+ejLm5ucbSfUAfilcuq17S60nyJTn1vzMsC1zFiEFD2bJ6PbMnT+fPf/5i8cpl2k6aBn19fWbOmsG9e/epUK4SpUqW4czpM5QpWwYd3dRVR3R0NL/9to+GDbXTmyEzbFq1hyO/n2TMzN7qnhzZ9LMxekYvHt5/xneVulO3TCcunLlG8TI+6Op+uvGte/b/SvGqZdWLNsuCMiEBgM5tOlCtUhUKeRRkwrAx6OjosP+Pg+/Y+/MVGxfLwGGDQalk5JCk8e0JiU8iOrXrSLXKVSlU0JMJo8aio6OaiO5T8PixHD1erFMvuvp6HxSPW9PSePxYjl9bzGZ90UHsbzufogMa4Nm6IgC2RZwp3LsO+9vNz8TUf7i9fx3Av10t9RIXnzXnvX62bMzqO5Z7Tx9SrnN9Sratyemr5yjrW1Ljiaf4etWpWYutgRtZtWgZTnkdGTB8yFvnCchMv578gzK9G6iXuEyYG2LKxvncfnyPyZ0C1OuOXjjO6RvnGdi060fH/yH2/P4bxauVUy/avI4VcHZh4vCxBG5aT7GqZanYoAYOuR2wtrJG9zOYu+JdchhlZ9rqHkxa3o1mnauyZt5vXPnfHSDpmlWsXEHq/FAGJ7fcNGxVgSKl3Tmw89Tbov36JSi1t3yDvsgZQTp06ECvXr1YsGABq1atwsXFhQoVKhAREUGXLl3o3Tv1hD+Ojkmz16ZsNNDR0SEh8cY5JUNDwwynKyJC1fK4d+9eHBw0ZwX+2LcJXLum6ir35q0UAwcO5MCBA8yYMYMCBQpgaGhIkyZNeJ1iMqzkeX3TSJHWujf5nz59OnPmzGH27Nl4e3tjbGxM3759U8X7NhEREejp6XH27Fn09DRvhtObtwAgICCA/v01xwtGPQ8jIiJpjo033fkVISHktMmpXq8ICcHdLe3JFy0tLNDT00ORonVcEaLAxlr1dhEba2tiY2N58fKlRq8GRahCPVTCxsqaS1evpIgjRL0to+YvXUS9mrVpUr8RAG4FXImKjmbslAl0btvhg25qLS1VeQxJ0ZqvUCiwTucNKjY21qkmilQoQrC2ScqLp2dBNm/ZyMuXL4mNjcPKypJWLVrjWcgzVXwHDxwiOiqauvXqvnf6M4u5hSm6erqEKjSfMocqwrG0MU9nL5Wta35l0+q9TF00GGdXzdmu3QrmZ8nG8bx6GUlsXBwWlmb0aj0WV89PNyN5pbIVNOZMeFMm0ywLrh9QFqwy/qadnInlxiXZjOzZs2cnj70DT549zXA878vSwjIx/SnO2xAF1tZpl0Eba5s0w6css7FxsQwMGMLjJ09YsXCpRl2VM7EMueR3Vq/Lnj07eRzy8PRp1uU3udu7T/Mk2URf2RInfDTKZcGrp2Hq9Ua5zHl+4W668ZSf1prTU1W9GgAUl+9jms+G4kO/4+qaIziUK4iRrTkd7yX1wtPNpkf5GW0o3KcuK527ZW7G3qFi0TJ4F0iqb17HJZ734aHkTPYUVBEeinu+Aqn2fx+ezu5smbycl5ERxMbFYWVmQYuR3SjknP5QFJF5kuqnFOU1NOQd5TtFfRaqwCad8G9jamKKqYkp+Rwd8fXyoUy1Chw6epja1bN+ktsKvv545U86z2LjVEN5Ql6EkdM82Xn+Igz3vGm/QSm5KRvnc+zSSZYPnEkuy6Trw6nr53n4/AkV+n2nEX7Q4vEUdvVi2YDpH5uVt6pUtjw+yd4Mob6OhSrU9azqc0i6E2pbmqdznoSEvPffvU71mtSpXpPgEAVGOQxBR4c1m9eTx/7TvWnGzMIIXT1dwlP0XggPicDCKv17Zl1dXezyqPLr5JabR3efs3PtnxQq4oyZhRF6ero4OOXU2MfBKSfXL95LKzohssQX2UzftGlTdHV12bBhA2vWrKF9+/bo6OhQpEgRrl69SoECBVIt2bNnf3fEafDx8eHYsWPq8Ztv4+npiYGBAffv30/1/Xnzpn61UEYlJCQwd+5c8ufPT+HCqlfX/P3337Rt25ZGjRrh7e2NnZ2deljFx/j7779p0KABLVu2xNfXF2dnZ27evKne7uzsjL6+PqdPn1avCw8P1whTuHBh4uPjCQoKSnUc7OzSH+9oYGCAmZmZxmJlaYVj3rzqxSW/MzbW1pw4k9QiG/EqgotXL+PrlfYbR/T19fF09+DkmaQ0JyQkcPLMaXy9VD/cPD0Kki1bNk4mi/e/e3d58vQpvt6qeH29fbh1+1+Ni9vxUycxMTbW+AHyLtHR0akaE/QSPys/cFZafX19Chb04ORJzTyeOnkaH5+0J/Tz8fHh1EnNlu0TJ06m+eYWU1NTrKwsuXfvPlevXqNixQqpwuzcuYsKFStgZWWZatunoq+fDTcPJ86dThqHmJCQwLnTV/H0Tv9HyObAvaxbvptJ8wfg/pbGA2NTIywszXh4/yk3r/1H6QqF0w2b2YyNjXHMk1e9qMvC2UwoC2eTykJGeHoUJHv27Px3P+mGJTYulkdPnmBvl/sDcpcx+vr6eHoU5OTpk+p1CQkJnDhzSl1OU/L19uHkac3z/PjJExrh3zQy3H9wn2ULFmNhYaER/k1+7967q7HPoyePyZ076/KbXGxENOG3n6oXxdUHvHoSSt4qSX+37KaG2JV05fHxtOccAshmZJBqrKgyPgGdxN4519YeZa1vf9YVHqBeIh4pODtjNztqjs+azL2FsaERjnYO6sXFwQkbCytOXkmaFygi8hWXbl/Dx7XQW2LKOFMjE6zMLLj35CFX79ykYlHtvEHnW6OqnwpqlNeEhAROnH5L+fbyTl2+T51MN3xGKZVKlEre6yHLxzDOYYSjrYN6cc6dDxszK05dT5o7JSLqFZf/u46Pc8F041EqlUzZOJ/D5/9hSb9pONho3nO1q9mMzSMXs3HEIvUCMKBpF8a0GZA1mUvG2Cid61iya1LSdSzta5K+vj6ebh6cPKt5npw8exrfQh/2d7exssbIyIh9h37HIHt2jXnAslo2/Ww4u9tz6cwd9bqEhAQun72Dq1fGfzsolUriYuPUcboUdODJfc0hIE8eBJNTXm0pPqEvskeDiYkJzZo1IyAggBcvXtC2bVsAhgwZgr+/Pz179qRjx44YGxtz9epVDhw4wPz5H9YNtGfPnsybN48ffviBgIAAzM3NOXHiBCVKlEg1T4OpqSkDBw6kX79+JCQkULZsWcLDw/n7778xMzOjTZs2GfpOhULB06dPiYyM5PLly8yePZtTp06xd+9edQ8BV1dXfv75Z+rVq4eOjg4jR45Mt1fG+3B1dWXbtm38888/WFpaMmvWLJ49e4anp6c6j23atGHQoEFYWVlha2vL6NGj0dXVVfeOcHNzo0WLFrRu3ZqZM2dSuHBhnj9/zqFDh/Dx8aFOnTofnD4dHR1aNfuRpatXkC+vIw657Zm/bBG2NjmpUr6iOlyHnl2pUqESP37fDIDWzVsyfPxoCnkUxKuQF+s2bSAqOoqGdeur8mViynf1GjBt7izMzcwwNjZh0sxp+Hr5qC92pUv44+KUn4BxI+nfow8KRTDzli7kh8ZNNRqyrt9U3eRHRkUSGhbK9Zs30NfXVzdGVChbnjUb1+Ph5o5PIS/uP3zAvKWLqFC2fKoeIO+jZauWjBo5Gs9CBfHy8mLDug1ERUXRoKEqjyOGj8LWNie9+/QCoHmL5nTq0Ik1gWspV74s+/f9ztUrVxk5crg6zgO/H8DS0hK73HbcuvUv06fNoGKlipQqXUrju+/ff8D/zv6PeQvmfnD6M0vjljWZNnoZbgXz4+7lzI4N+4mOiqFGfdVs81NHLcEmpyUdejUFYNPqvaxZ/DMBE7til9tGPe+CoVEODI1yAHD0wCksLE2xtbPmv38fsnDGekpXLEqxUhn/cZ7ZdHR0aNX0R5YGriBfHkcc7JOVhXIV1eE69O5KlfKV+LFJYllo1pLhExPLgqcX67YkloU69dX7BCuCCVYo1DNv37r9L8ZGRuS2s8PczBwTYxOaNmjMwhVLsLPNhb1dblZtWANA9UpVszTfrX9syfCxoyhU0BPvQl6s3aQ6zxvWVQ3ZGTZ6BLa2tvRNfJVdyx+a065LJwLXr6FcmXLs+30/V65dZfSwkYCqwaD/0EFcu36dBbPmkBCfQHDivCbm5ubo6+tjYmJC0++asGDZYuxy2ZE7d25Wrw1U5bdKtSzN79v8b84eSg5vQtitJ4T/F0Tpcc159TiU28m6xjY+MJp/d57iwoLfALjzyxlKDGvMy/vPUVx5QM7C+SnSrx5XVqnerBAdEkF0iidr8bHxvHoaSuhN7czAn5yOjg4tajZh2Y615LNzwCFnbhZsXUlOCxsqF0uaULfTxP5ULlaO5jVUPccio6O4//SRevuj50+4fvdfzE1MyW2jGkL4+4kjWJpZkNvallsP7jBtzXwqFStDaZ/inzaTH8nIwAhH26QG0zw2efHIW4jwV2E8CXn0lj21r3XzFqprdUFPvD0LsXazZv00bOxIbHPa0re76jrWstmPtOvWicD1aylXpiz7DiSW76Ej1HGGh4fz5NlTgoKfA6gbDG2srbGxtuHBo4fsP/g7pUr6Y2VhybOgIFasWYWBgQHlSn/4JM0fQ0dHhx+rNGT5rxtxtHXA3saORbsCyWlhrTG3QpdZQ6hUuDQ/VFLVf1M2zue3U4f5qfsYjHIYqudzMDE0Jkd2A2zMrdKcANLOyjZVo8SnoKOjQ6vvm6uuY3nz4pDbgfnLF2FrneI61qcbVcpX5MfGidexH1owfOIYCnl44lWwkOo6FhVFwzr11PsEK4IJDlFwP3GOnVt3Eq9juVTXMYAN2zfj5+WLkaEhx0+fZObCOfTt2ivVfF1ZrU6zMiycuB0XD3tcPPPw65Z/iIl+TcU6qklv54/fhpWNGT92U73paMeao7h4OJDLwYrY2DjOHb/JsX3n6TAw6Tpe78dyzB61mYJ+ThQq4sz5E7c4+/cNRs/78PnAvgYf+kBPfJgvsqEBVMMnVqxYQe3atbG3twdUT2iPHj3K8OHDKVeuHEqlEhcXF5o1a/bB32Ntbc0ff/zBoEGDqFChAnp6evj5+VGmTNpPOMaPH0/OnDmZPHkyd+7cwcLCgiJFijBsWMbfZ121qupG3cjIiHz58lGpUiWWLl1KgQJJT2RnzZpF+/btKV26NDY2NgwZMoQXL158cD7fGDFiBHfu3KFGjRoYGRnRuXNnGjZsqDHh2axZs+jatav6lZ+DBw/mwYMH5MiRQx1m1apVTJgwgQEDBvDo0SNsbGzw9/enbt2P71bfvmUboqKiGDNlIi8jXlLEx4/FP83TGJ7y4NFDQsPD1J9rVa1OaGgo85cvJlihwMPVjcU/zdPoPj2kzwB0dXTpGzCY2NjXlC5ZipGDhqq36+npsWDGHMZPn0zLTm0xNDSkfq269OykOdaxSZsf1f+/ev0ae3/fh71dbn7fsQeALm07oKOjw7wlCwl6/hxLSwsqlilP7649Puq41KipyuOihYtRBCtwd3djwcJ56i6nT58+1ZhTwM/Pl0mTJ7Jg/iLmz1uAo6Mjs2bPpIBr0nn2/HkwM2f8hEKhwCanDXXr1qFzl06pvnvXzl3kymVLqVL+H5WHzFCxeknCQl8QuPhnQhXhuLg5MmneQPUEkUFPQ9DRSepRsmfbH8TGxjFusGZjZKvODWndRfUjJSQ4jCU/bSRUEY6VjQXV6pShRSftz0XRvkViWZiWrCzMzEBZCEssCyGJZWGmZlnYvHM7i1Ymvfa3TY+OAEwYNlp9wz+gZx/0sukRMH4UMTExeHt6sXLuYszNzLI0zzWr1SAkNJQFSxepyrKbO4vnLFB3mX3y7KnGHCJ+Pn5MGT+J+YsXMGfhfPLldWTO9Fm4uqjO86Cg5xz5UzW5cJOWP2h818pFyyhetBgA/Xv3RU9Pj4AxI1T5LeTFigVLszy/b3Nm2k70jXNQdUlXDCyMefzXdX6uNZ74mKQeeOYudhjaJN00H+69nNLjm1N5QWeMbM2IeBzKpaUHODFuqzay8EHa1fuBqJgoxi2fycvICAq7ebNw6FQMkjX4Pnz2mLCXSdetK3du0HFCP/XnGesWAlC/fA3Gd1XV88/DFMxYt1A9LKNu2ep0+a7VJ8pV5vHK58uaQdvVnwOajQVgxz+bCVjVL73dPgs1q9UgJCyUBcsSy7erO4t/mp9Uvp8+1ai//Xx8mTJuIvOXLGTO4sTyPS2pfAMcPnaUkRPGqD8PGqmas6Bbh85079QVg+wGnD1/jrWbNvDi5Qusrawp6leEtctWpZpw8FNqU6MpUa+jmbBuDi8jI/ArUIj5vSdqvBnlYfATwiKS7v22HlXdZ3SaOUgjrjFtBlC/tHZfx5ue9i3aEBUdzZhpk1TXMW8/Fs+cm/o6Fham/lyrSorrWIF0rmOrkua+atNDde8yYdhoGtZWNUhcunqFBSuWEhkVSX5HJ0YNGkb9mh/+MOxDla7qzYuwV2xZfoiwkAicXHMTMLONeuiE4lmYxrwRMdGvWTHzFxRB4WQ30Mchnw09R31P6apJDz9KVPCk06D67Fz7J6t+2ou9ow39JzbHw9fpU2dPfMN0lNK0Iz7Sq1evcHBwYObMmXTo0CFT4459y4y7X7NYI22nQDuC4y5rOwlakTs67fd8f+2U+l/k6L2PtsDyy/vxmhm6nZ6n7SRohd/iYtpOglZcnKa91/9qU+yF59pOglZkL/T+82J8Da7o7NN2ErTCz+Z7bSfhg7y++Exr353dJ5fWvltbvtgeDUJ7zp07x/Xr1ylRogTh4eGMGzcOgAYNtP+EVwghhBBCCCGEdn2bj5O0pGvXrhqve0y+dO2qnVcNfagZM2bg6+tL1apVefXqFceOHcMmnbcbCCGEEEIIIYT4dkiPhk9o3LhxDBw4MM1tZloc5/u+ChcuzNmzZ7WdDCGEEEIIIYTIEJkx4NOShoZPyNbWFltbW20nQwghhBBCCCGEyDLS0CCEEEIIIYQQ4uuWID0aPiWZo0EIIYQQQgghhBCZRno0CCGEEEIIIYT4qimlR8MnJT0ahBBCCCGEEEIIkWmkoUEIIYQQQgghhBCZRoZOCCGEEEIIIYT4usnrLT8p6dEghBBCCCGEEEKITCM9GoQQQgghhBBCfNVkMshPS3o0CCGEEEIIIYQQItNIQ4MQQgghhBBCCCEyjQydEEIIIYQQQgjxdUvQdgK+LdKjQQghhBBCCCGEEJlGejQIIYQQQgghhPiqKeX1lp+U9GgQQgghhBBCCCFEppEeDUIIIYQQQgghvm7yestPSno0CCGEEEIIIYQQItPoKGWwiviMxYZEaDsJWqHU0dF2ErRCmf3bbPvUjY7XdhK049s8zb/Z8o3uN5rvb/QJms9gV20nQSsuD7qk7SRoRULOHNpOgnbofZv1WnYzI20n4YNE/n1fa99tVMZRa9+tLTJ0QgghhBBCCCHE1+0bbfjVlm/z8aEQQgghhBBCCCGyhPRoEEIIIYQQQgjxVZMZAz4t6dEghBBCCCGEEEKITCMNDUIIIYQQQgghxBcmJCSEFi1aYGZmhoWFBR06dCAiIv3J9ENCQujVqxfu7u4YGhri6OhI7969CQ8P1wino6OTatm0adN7pU2GTgghhBBCCCGE+LolaDsBma9FixY8efKEAwcOEBsbS7t27ejcuTMbNmxIM/zjx495/PgxM2bMwNPTk3v37tG1a1ceP37Mtm3bNMKuWrWKmjVrqj9bWFi8V9qkoUEIIYQQQgghhPiCXLt2jX379nH69GmKFSsGwLx586hduzYzZszA3t4+1T5eXl5s375d/dnFxYWJEyfSsmVL4uLiyJYtqXnAwsICOzu7D06fDJ0QQgghhBBCCPFVUyYotbbExMTw4sULjSUmJuaj8nP8+HEsLCzUjQwAVatWRVdXl5MnT2Y4nvDwcMzMzDQaGQB69OiBjY0NJUqUYOXKle89maY0NAghhBBCCCGEEFlk8uTJmJubayyTJ0/+qDifPn2Kra2txrps2bJhZWXF06dPMxRHcHAw48ePp3Pnzhrrx40bx5YtWzhw4ACNGzeme/fuzJs3773SJ0MnhBBCCCGEEEJ83bT4esuAgAD69++vsc7AwCDNsEOHDmXq1Klvje/atWsfnaYXL15Qp04dPD09GTNmjMa2kSNHqv9fuHBhXr16xfTp0+ndu3eG45eGBiGEEEIIIYQQIosYGBik27CQ0oABA2jbtu1bwzg7O2NnZ0dQUJDG+ri4OEJCQt45t8LLly+pWbMmpqam7NixA319/beGL1myJOPHjycmJibD+ZCGBiGEEEIIIYQQ4jOQM2dOcubM+c5wpUqVIiwsjLNnz1K0aFEA/vjjDxISEihZsmS6+7148YIaNWpgYGDA7t27yZEjxzu/6/z581haWma4kQGkoUEIIYQQQgghxFdOmaC9oRNZoWDBgtSsWZNOnTqxePFiYmNj6dmzJz/88IP6jROPHj2iSpUqrFmzhhIlSvDixQuqV69OZGQk69atU09MCaoGDj09PX755ReePXuGv78/OXLk4MCBA0yaNImBAwe+V/qkoUEIIYQQQgghhPjCrF+/np49e1KlShV0dXVp3Lgxc+fOVW+PjY3lxo0bREZGAvC///1P/UaKAgUKaMT133//4eTkhL6+PgsWLKBfv34olUoKFCjArFmz6NSp03ulTUf5vu+pEOITig2J0HYStEKpo6PtJGiFMvu3+SIc3eh4bSdBO77N0/ybLd/ofqP5/sqeoGWUz2BXbSdBKy4PuqTtJGhFQs53d73+Kul9m/VadjMjbSfhg7zc/6/Wvtu0RoF3B/rKfJt39UIIIYQQQgghhMgS0tAghBBCCCGEEEKITCNzNAghhBBCCCGE+KrJjAGflvRoEEIIIYQQQgghRKaRhoYssHr1aiwsLLSdjA/i5OTE7NmztZ0MIYQQQgghhMg8CUrtLd+gL66h4fjx4+jp6VGnTh1tJyVTrF69Gh0dHXR0dNDV1SVPnjy0a9eOoKAgraTn9OnTdO7cWSvfnVFKpZL5SxdRsW51ilYoTcde3bj34P4799u4bQvVG9WlSIVSNO/QmktXLmtsj4mJYcL0KZSpUZnilcvSN2AQwSEKjTBPnj6h24DeFKtYmvK1qzJj3mzi4uLU2w8c+YOOvbtTrlYVSlYpT4tObfn7xD8acZw59z96DOxLpXo18CpVlENHD3/E0UiZx83UaFiHouX9+bF96jymtP/QAeo1+46i5f1p1KIpf/7zl8b2g4cP0bl3d8pWr4S3fxGu37yRKo6tO7fTrlsn/CuXw9u/CC9evsy0/GTUps2bqFmnFsX8S/Bj65Zcuvz2Wb9/P/A79b9rSDH/EnzXtAnH/jqmsX3E6JH4FPHTWLr26J4qnj+P/cmPrVtSvFRJylQoR5/+fTMzW6kolUrmL1tExXrVKVqxNB17Z/Dc376F6t/VpUjFUjTv2JpLV9M492dMoUzNyhSvUpa+wzTP/Z17d+NVumiaiyIkBID/XThHyy7tKVOzMkUrlqbeD9+xZtP6zMu3lsr8pFnTaNq2BYXL+9O4dfNU37Fg+RK8ShVNtRSvVOaj8pzZZfnNMaxUpzrFKpSiY8+u3LufdAxPnz2Dt3+RNJfLV68AsHDZ4jS3l6hY+qPy+r42bt1MjQa1KVq2JD+2a/XuY3PwAPW+b0TRsiVp1Px7/vxbs7wvXLqYet83okT5UpSuUp6OPbpw8R11yKegjfp87JQJ1Gpcn2IVSlG+ZmV6DerHnbv/ZWq+skox15Is6hnIn9P/x/Vlj6niV1PbSfooSqWSeeuWUaF1PYo0rkiHEb259/jBW/c5c/kc3ccNomKb+hSqV5pDx4+mCrNgw3Lqdv2BYk0qU+qHGnQY0ZuLN65kVTY0fOp6DaDXwL5Ua1CbouX9qVSnOgFjRhD0/Ll6+3/37tK+e2cq1KpK0fL+1PyuHnMXLyA2LjbzMp6GjVs2U6N+bYqWKcmPbTNYjzVpRNEyJWn0Qzr1WJNGlChXitKVy9Oxu2Y99ujxY0aNH0PNBnUoVtafWg3rsWDJImJjszaf4tv2xTU0rFixgl69evHnn3/y+PFjbScnU5iZmfHkyRMePnzIsmXL+O2332jVqlWaYePj40lISMiytOTMmRMjo8/7lTUr1wWyfusmRg0exoYVgRgaGtKlb09iYmLS3ee3g78zbe4sunXozNbV63F3daNLv57qH0kAU+fM5MjffzJr4hRWL1zG8+Dn9B06SL09Pj6e7gP6EBsbx7qlq5g4ciy7fv2F+csWq8OcPfc/SpcoycKZc9myeh3FixSjx6B+XLtxXR0mKjoKd1c3hg8YkqnHZd+B/UyfM4uuHTuzJXADbq6udOnbQyOPyZ2/eIEho4bxXb0GbA3cQOXyFekzuD+3bie9+icqOorCvn7069E73e+Njo6mTKnSdGzbPlPzk1H79u9n+qyZdO3chc0bNuLu6kbXHt3Tz/eF8wwZFkCjBg3ZsmETlStWok//ftz6V/OVR2VKl+GP3w+ql2mTp2hsP3DoIMNGjqBh/QZs3bSFNatWU7tmrSzLJyQ79wcNY8PyQAxzGNKlXwbP/fad2bpqPe4F0jj35yae+xOmsHrBMp4/f07fgKRzv2bV6hz5Zb/GUqZkKYoVLoq1lRUAhjkM+bFxUwIXLmP3xm10btuReUsXsnXnz5mX709c5t9oVLcBNatUT/N72v3YiiN79mssLvmdqV656gfnNyvK8sq1gWzYspGRQ4axfvmbY9hDfQz9fHw5vPd3jaVx/UY42DtQqKAnAG1btE4VxiW/M9UqV/vgvL6vfQf2M332TLp27MKWNRtwc3WjS++3lPeL5xkyMoDv6jdk69qNVK5QkT6DNI9NPsd8DBs0hO0bt7Jm6SocctvTpVd3QkLTjvNT0FZ97ulRkPEjRrNr43YWz14ASiVd+vQgPv7zfwWvoYER1x9eYdyGYdpOSqZYsX0d6/dsZXT3QWycsRzDHDnoPKofMa/Tr/eioqNxz1+AEV0HpBsmn70jw7sOYMf8tayduggH29x0GtWXkPDQrMiGmjbqNYDiRYsxY+IUftn8Mz9Nns6DRw/pPyypns+WLRv1atdh6dyF/LL5Z4b0Hcj2XTtYmOzeLtOPxe/J6rG1ifVYr3fct4wI4LsGDdm6LrEeG9hf475Fox5btgoHe3u69Eyqx/67+x8JCUpGBYxgx6ZtDO43gC0/b2POgnlZlk8hvqiGhoiICDZv3ky3bt2oU6cOq1evVm87cuQIOjo6HDp0iGLFimFkZETp0qW5cSOpxX7MmDH4+fmxdu1anJycMDc354cffuBlsiewaQ0d8PPzY8yYMerPs2bNwtvbG2NjY/LmzUv37t2JiIj44Hzp6OhgZ2eHvb09tWrVonfv3hw8eJCoqCj1MIzdu3fj6emJgYEB9+/fJyYmhoEDB+Lg4ICxsTElS5bkyJEj6jjf7Ldnzx7c3d0xMjKiSZMmREZGEhgYiJOTE5aWlvTu3VvjBiJ5/u/evYuOjg7nz59Xbw8LC0NHR0f9XW+O+/79+ylcuDCGhoZUrlyZoKAgfvvtNwoWLIiZmRk//vgjkZGRH3yM3lAqlazdvIHObTtQuXxF3Au4MmnUWIKCn3PozyPp7rdm4zqa1G9Eo7r1ccnvzKjBw8hhkIMde3YB8DLiJT//sovBvftTslgJCnkUZPzw0Zy/dIELiS3C/5w6we27/zFlzHg83NwpV6oMPTt1Y9P2LeoW4aH9BtK+ZRu8PQuRL68jfbv1JF9eR4789ac6LeVKlaF3l+5UrVj5o4+HZh7X07hBIxrVbaDK45DhGOZIymNK6zZvoIx/Kdq1bINzfmd6demOp7sHG7dtVoepV6su3Tp0xr94yXS/t9UPLejYuh2+hbwzNT8ZtWb9Who3+o6GDRri4uzCyOEjMMyRg527dqYZfv2GDZQpVZp2bdri7OxMz+49KOhRkE2bN2mEy55dHxsbG/ViZmam3hYXF8fU6dPo37cfTZt8j1O+fLg4u1Cjeo0sy6dSqWTtlg849zdl8Nzvlf65n8MgBzbWNupFV1ePk2dP813dBurvKejuQe3qNSng7IJDbnvq1axN6ZKlOHvh3MfnW0tlHmBY/8E0b9KUPA4OaX6PkZGRxrEJDgnh9n93+K5eww/Oc2aXZaVSybrNG+jcrqPqGLq6MWn0OJ4HP+ePxGOor6+vkQ9zc3MOHztCw7r10dHRSTOvijd5rd8gzXRlhTUb1tG44Xc0qtcAF2cXRg1NPDa/7Ewz/LpNGynjX5p2rRKPTdceeHoUZOOWpPJep2YtSpXwJ69DHgq4uDCo7wAiXkVw89atT5Sr1LRVn3/fsDHFChfFwd4eT4+C9OzSnafPnvL4yef/YOfY5cPM2TmNg+f2aTspH02pVLJ29xa6NG1LZf/yuOcvwOR+owgKCebQiT/T3a9csVL0adWFqqUqpBumbsXqlPIrTl47Bwrkc2Zwx95ERL7i5t3bWZEVNW3UawCtm7fE18sH+9z2+Pn40qFVOy5evqTusZDXIQ+N6jbA3dUN+9z2VCpfgTo1avG/8x937XrrsXhTj9VPrMcCEo/F7p1pH4tNG1X3LW/qsW6J9djWFPVYSX/y5km7HitbugwTRo+ltH8p8ubJQ6UKFWnbsjUHD/+RZfn8HCnjE7S2fIu+qIaGLVu24OHhgbu7Oy1btmTlypWpZg8dPnw4M2fO5MyZM2TLlo327TWfst6+fZudO3eyZ88e9uzZw9GjR5kyRfNJ5bvo6uoyd+5crly5QmBgIH/88QeDBw/+6Py9YWhoSEJCgrpLfmRkJFOnTmX58uVcuXIFW1tbevbsyfHjx9m0aRMXL17k+++/p2bNmtxKdmMUGRnJ3Llz2bRpE/v27ePIkSM0atSIX3/9lV9//ZW1a9eyZMkStm3b9tFpHjNmDPPnz+eff/7hwYMHNG3alNmzZ7Nhwwb27t3L77//zrx5H99q+vDxI4IVCkolu1EyNTHFx9OLC5cvprlPbGwsV29cx794CfU6XV1d/IuXUP+guHr9GnFxcRo3YM5O+cltZ8eFS6p4L1y6iKtLAWysrNVhyviXIuLVK/69k/YFOiEhgVeRrzA3M//wTGeAKo/XNNKvymNJdfpTunD5UqobztL+pdIN/zmKjY3l2rVr+JfUzHfJkiW5cDGdfF+6SMmSKfJdqlSq8GfOnKFClUrUa9SA8ZMmEhYWpt527fo1goKC0NXRoWnzZlSuXpVuPXuk6hWRmdTnfrEPOPeLfcC5n8su3Xh3/7YHwxw5qF65SrrpvXbjOucvXaRY4SLvlc+UtFnmP8TPu3fi5JiPon6FP2j/rCjLqmMYrBHG1MQU70Je6cZ55M8/CQsPp2Hd+ummdfuuHYl5/bi/cUbFxsZy9fp7HptLF/EvkfF6LjY2lm07f8bUxAR3N7fMS/x7+Fzq88ioKHbu3Y2DvQN2uew+OB7x/h4+e0xwqAJ/v2LqdabGJvi4eXLh+tu72L+P17GxbN23C1NjE9ydCmRavCl9LvVaeHg4e/f/ip+3L/rZ9NMMc//Bff4+8Q9FCxd9rzxmlLoeK5HiWJR4Rz32HuU7NjaWbTveXY+9jIjA3Nws3e1CfKwv6vWWK1asoGXLlgDUrFmT8PBwjh49SsWKFdVhJk6cSIUKqpbcoUOHUqdOHaKjo8mRIweg+uG3evVqTE1NAWjVqhWHDh1i4sSJGU5H37591f93cnJiwoQJdO3alYULF35kDuHWrVssXryYYsWKqdMYGxvLwoUL8fX1BeD+/fusWrWK+/fvY29vD8DAgQPZt28fq1atYtKkSer9Fi1ahIuLCwBNmjRh7dq1PHv2DBMTEzw9PalUqRKHDx+mWbNmH5XuCRMmUKaMakxyhw4dCAgI4Pbt2zg7O6u/+/DhwwwZkv5wgZiYmFRdoXVjYjEwMFB/Dlaoxk+/6a79hrWVlXpbSqFhYcTHx2OdrIFAtY81/927q45XX18fs8Rjrg5jaa0esx0cokjze99sS8vqDWuJjIyiRpWs7VqclMcU6bO04r+7d9PcJ1gRnPqYWFqnexw/R6Fhoen/bdPLd3Aw1tYpwltbE6wIVn8uU7oMVSpXwcHegYcPHzB3/ny69+rB2tVr0NPT4+GjRwAsWrKEgQMG4JDbnsB1a+jQuSO/7NiFuXnmNyy9OcfSPPfTOf8ydO6HpHPuW6V/Lvy8Zxe1q9Ukh0GOVNuqNKhFSOLfpXuHzjSp3yhD+UuPNsv8+4qJiWHP/t/o2LrtB+0PWVOWFekeQ83zPrmff9lJ6ZKlsLPNleb2mJgY9v7+Gx1atX1XljJNUnlPnY83f9eUVMcmjXyn+BsfPfYng0YMJTo6mpw2NiydvxhLC8tMTX9Gabs+37RtC7MWzCEqKgqnfE4sm7sQff20f5SJrBGc2N3dxiLFOWBhpd72MY6c+puB00cRHRNNTktrlo2bjaW5xUfHmx5t12uz5s9h07bNREVH4+PlzYKZc1J9X8tObbl24zqvX7+mScPv6Nm523vlMaPeWo+97VhYp5XPNOqx4Rmrx+4/uM/GzZsY0Kffh2fmC6T8Ridl1JYvpkfDjRs3OHXqFM2bqybjypYtG82aNWPFihUa4Xx8fNT/z507N4DGxIpOTk7qH/BvwrzvxIsHDx6kSpUqODg4YGpqSqtWrVAoFB88NCA8PBwTExOMjIxwd3cnV65crF+fNIla9uzZNfJ16dIl4uPjcXNzw8TERL0cPXqU27eTnqwbGRmpGxkAcuXKhZOTEyYmJhrrMmPiyeTpy5UrF0ZGRupGhox+z+TJkzE3N9dYug/oQ/HKZdVL8okXP3d79//GohVLmTlhSqoLivi81apRk0oVKuLm6krlSpWZP2cul69c4fSZMwDqeVI6dehAtSpV8fT0ZPyYceigw+8HDmRKGvbs/5XiVcqql8/l3D9/6SJ37v6X7tCAwEXL2bxyLaMGBbB280Z+/f39ujHv2f/rF1vmDx09TGTkK+rXrqvtpHyUp0HP+Ofk8bcO/zh09DCRryKpX7vep0tYFiperDjb1m1i7fLVlPEvzcCAwemOl/7a1alZi62BG1m1aBlOeR0ZMHzIW+dDER9vz5H9FPu+inrJ6nqvhE8Rts8JZP20JZQt6s+AqSNRhH2953u7lq3ZsmYjS+YsRE9Xj2FjR6XqET1jwhS2BG5g6rhJ/Pn3X6xev0ZLqf1wxYsVZ9v6TaxdsZoypUozcFja9dizoCC69u5J9apVadLoOy2kVHwrvpgeDStWrCAuLk79BB9U47MMDAyYP3++el3yVvc340qTT56YslVeR0dHY7uurm6qyif5jKx3796lbt26dOvWjYkTJ2JlZcVff/1Fhw4deP369QdNpGhqasr//vc/dHV1yZ07N4aGhhrbDQ0N1XkB1VwVenp6nD17Fj09PY2wyRsR0srru/KfnK6uqh0q+fFIb3balMf9fb7njYCAAPr376+xLup5GBERr9SfX8e+BkAREkJOm5zq9YqQkHS7h1laWKCnp4cixRMsRYgCG2sbAGysrYmNjeXFy5caTzgVoQr1UAkbK2suXb2SIo4Q9bbkfj2wn9GTxzNz4lRKlUh/PGxmScqj5gVFERqS6un9G6ox1imOSagCm3TCf44sLSzf+bdNycbGRv0kRB1ekX54gDx58mBpYcmDBw/wL1lSfe45Oyc15GXPnh2HPA48efrkQ7OjoVLZCvgkm/fi9eu3nPuuH3DuWyWe+1bpnPshaZ8L23/ZiYerO4U8Cqb5nXnsVXMZuLm4oggJYeHKpdSunvHZ3yuVrYCPZ7J8a7HMv6/tu3dSvky5D95fM+2ZV5bf7Jf6GCrwcHVPFd/OPbuxMDenYvny6abz5907KF+23CetL5LKe4pjE6J4x7FJHT7l38jI0BDHvI445nXE19uHOo3rs2P3Djq27ZC5mcgAbdfnpiammJqYks/REV8vH8pUq8Cho4ffqxyL91OpRFm83QqpP8cm1nvBYSHktEq6NinCQvBwdv3o7zPKYUg++zzks8+Dr4cXtTo35ecDe+j0feuPjjst2q7XLC0ssbSwxMkxH87581Otfi0uXL6In7evOsyb4UEu+Z2Jj49n3JSJtPmxVar77I/1wfWYIo16zPod9dh39dmxawcd2yXVY0HPg+jQrRN+Pj6MHjYyk3L15fhW50rQli+iR0NcXBxr1qxh5syZnD9/Xr1cuHABe3t7Nm7cmGnflTNnTp48Sfqh8OLFC/77L+nVTmfPniUhIYGZM2fi7++Pm5vbR7/9QldXlwIFCuDs7JyqkSEthQsXJj4+nqCgIAoUKKCx2Nll3jjKnDlVFXfy45F8YsjMZmBggJmZmcZiZWmFY9686sUlvzM21tacOHNKvV/EqwguXr2Mr5dPmvHq6+vj6e7ByTOn1esSEhI4eeY0vl6qHzSeHgXJli0bJ5PF+9+9uzx5+hRfb1W8vt4+3Lr9r8bF4fipk5gYG+OSP6n3xq+/72PkhLFMGzeJCmXKZc7BeQdVHgty8nRS+hMSEjhx+pQ6/Sn5enlrhAdVftIL/znS19enYMGCnDylme+Tp07h65NOvr19NMIDnDh5It3wAE+fPSMsPAybnKobPs+CBcmePTt3k3XXjo2N5fHjx9gn9qT6WMbGxjjmyatePurcP/sB5/6zp6nijYyMZP8fB/iuXsYm/0tQJqgbSDLK2Nj4synz7+Ph40ec+t+ZDB+b9GRFWc5j74CNtY1GmIhXEVy6cjlVnEqlkp17dlOvVt10xzA/fPyIU2c/Pq/vS19fH0+Pgpw8fVK9LiEhgRNn3nJsvH1SH5uTJ975N05IUPL6tXZe+/Y51edKpRKlkvcux+L9GBsZq3/457PPg4tjfmwsrTl54Yw6TETkKy7evIqvh1emf79SmaBu1M0K2q7XklMmPvSKfUv5ViqVxMXFkaDM/B+l6dZjbzsWH1OPJXtA+CwoiPZdO6neLjNqrPqBohBZ5Yvo0bBnzx5CQ0Pp0KFDqrHPjRs3ZsWKFUyfPj1Tvqty5cqsXr2aevXqYWFhwahRozRaMwsUKEBsbCzz5s2jXr16/P333yxenHWvwEmLm5sbLVq0oHXr1sycOZPChQvz/PlzDh06hI+PD3Xq1MmU7zE0NMTf358pU6aQP39+goKCGDFiRKbE/aF0dHRo1exHlq5eQb68jjjktmf+skXY2uSkSvmK6nAdenalSoVK/Pi9au6J1s1bMnz8aAp5FMSrkBfrNm0gKjpKPdGZqYkp39VrwLS5szA3M8PY2IRJM6fh6+Wj/mFSuoQ/Lk75CRg3kv49+qBQBDNv6UJ+aNyU7NmzA6rhEsPHj2Zov4H4FPJSjxM0MDDA1ET11DQyMpL7D5Pehf3o8WOu37yBuZkZue0+/Edq6+YtVHks6Im3ZyHWbk7MYx1VHoeNHYltTlv6du8FQMtmP9KuWycC16+lXJmy7DuwnyvXrjJ6aNLfODw8nCfPnhIUrHrn9Jsf1jbW1uonw8GKYIIVCnWebt2+hbGRMblz2WXJXAWp8t2iFSNGj8TT0xPvQl6s27CeqKgoGibOhD9s5Ahy2drSp5fqlW4tfvyR9p06Erh2DeXLluO3/fu4cvUqo0aMAlR/n0VLFlO1SlVsbKx58OAhP82ZjWPevJQpVRpQ9Rz6vnETFi5ehF2uXOTObc/qNYEAVK+W9msQP5aOjg6tmv7I0sDEc9/envlL0zj3eyWe+00Sz/0fWjJ8QuK57+nFus0ZOPdnaZ77b/x26Hfi4+KpW6N2qvRt3L6F3LnsyJ/PCYAz5//H6g3raPH9Dx+fby2VeYD7Dx4QGRVJsEJBTEwM12+q3mTkkt9Zo+fWjl92kdPahnKlynxUflVpz9yyrKOjQ8tmP7Jk9XIck507OW1yUjnZMQQ4eeYUjx4/4rv6DdNN345fdpHTxoaymZDX99X6x5YMHztKdWwKebF20wZVeU98A8qw0SOwtbWlb+IrHFv+0Jx2XToRuH4N5cqUY9/viccm8UleZFQUy1Ytp2K5CuS0sSE0LIxN27YQ9DyI6lk8t85b86mF+vzBo4fsP/g7pUr6Y2VhybOgIFasWYWBgQHlSpf9tAfgAxgZGOFom1/9OY9NXjzyFiL8VRhPQh5pMWXvT0dHh1b1m7JkcyCO9nnJk8ueeeuWYmtlQxX/pJ5G7Yf3okqpCrSo2wSAV1GR3H/yUL394bMnXLtzE3MTM+xt7YiMjmLplkAqlShLTitrQl+Es3Hvdp4pgqlRJnPfhJWSNuq1i5cvcfnaFYr4FsbM1JQHjx4yf8ki8ubJo/6Rvmffr2TLlg03lwLoZ8/OlWtXmbNoHjWqVku3sfWjj0XKemxjYj1WL1k9ltOWvj1T1GPr1lCubDr12MrlVCyfrB7bqlmPqRoZOpLbLjcD+vQnNDTpdaY2Nun36BTiY3wRDQ0rVqygatWqaf5oady4MdOmTeNiOjPMv6+AgAD+++8/6tati7m5OePHj9fo0eDr68usWbOYOnUqAQEBlC9fnsmTJ9O6ddZ0N0vPqlWrmDBhAgMGDODRo0fY2Njg7+9P3bqZOzZ45cqVdOjQgaJFi+Lu7s60adOoXj1rfkhlVPuWbYiKimLMlIm8jHhJER8/Fv80T2PSyAePHhIaHqb+XKtqdUJDQ5m/fDHBCgUerm4s/mmeRvfZIX0GoKujS9+AwcTGvqZ0yVKMHDRUvV1PT48FM+YwfvpkWnZqi6GhIfVr1aVnp67qMFt37SAuPp4JM6YyYcZU9foGtesyceRYAC5fv0r7Hl3U26bNnZUqzIeoWa0GIWGhLFi2KDGP7iz+ab66a92Tp0/R0Ulqvfbz8WXKuInMX7KQOYvnky+vI3OmzcLVJWnm6cPHjjJywhj150EjAwDo1qEz3RPzveXnbSxasVQdpm3XjgCMHzHmrTPWZ5aaNWoQGhrKwkWLCFYE4+7uzqL5C9VdEJ8+fYKubtLQIz9fP6ZMnMS8hQuYO38ejo6OzJn1E64FVPnW1dXl1q1b7N7zCy9fvsQ2Z05K+ZeiZ/ce6gYlgP59+6GXLRvDRo4gJiYGby8vli9ZqvEazMzWvmUboqKjGDM12bk/K41zP9kbMmpVrU5oWCjzly0mOCTx3J+V4tzvnXjuD0t27g8cSko//7KLqhUrpZpAEVRPZGYvms+jJ4/Q09Mjr0Me+nXvRdOGjTMn31oo8wCjJo/nzLmz6s9N2vwIwP6ff8Eht7067zt/3UODOvUypZttVpTl9q1U587YKRN4GfGSwj5+LJ49X+MYgupv7Ofti7NTftKSkJDArr2/0KB25uT1fdWsVoOQ0FAWLE08Nm7uLJ6zIOnYPHuKjm7yY+PHlPGTmL94AXMWJh6b6UnHRk9Xl//u3mX33l8IDQvDwtycQp6FCFy6kgLJ5jj61LRRnxtkN+Ds+XOs3bSBFy9fYG1lTVG/IqxdtuqLmGfIK58vawZtV38OaKa6nu74ZzMBq768Ce86NG5JVHQ0Y+ZP5eWrCIp4+rBk7CwMsier954+IuxFmPrzlX+v025YT/XnaSvmAtCgcm0m9RuhOt8f3mPXoV8JfRGOhZk5Xq4erJmykAL5knpmZgVt1Gs5cuTg0JE/WLhsCVHRUeS0tqGMf2k6t5uqvp5n09Nj5drV3HtwH6VSib1dbpo3aUarH1pk3bGonngsliSrx+YuSP9Y+PoxZcIk5i9KVo/NmKW+b8lIPXb85AnuP3jA/QcPqFpH81Xcl05n3as8PzvvGMYtMpeOMuWEBEJ8RmJDIrSdBK1QJpuT41uizP5tduPTjY7XdhK049s8zb/Z8o3uN5rvb3SWc5/BHz+XwJfo8qBL2k6CViTkTP0Wom+C3rdZr2U3e/856T4Hodu0Vz4tm3i/O9BX5ovo0SCEEEIIIYQQQnwoZfy32fCrLd/m48NPqFChQhqvoEy+JH+FpRBCCCGEEEII8TWQHg1Z7Ndff033lZC5cuX6xKkRQgghhBBCCCGyljQ0ZLF8+fJpOwlCCCGEEEII8U1TymSQn5QMnRBCCCGEEEIIIUSmkR4NQgghhBBCCCG+asp46dHwKUmPBiGEEEIIIYQQQmQa6dEghBBCCCGEEOLrJnM0fFLSo0EIIYQQQgghhBCZRhoahBBCCCGEEEIIkWlk6IQQQgghhBBCiK+aMkGp7SR8U6RHgxBCCCGEEEIIITKN9GgQQgghhBBCCPFVk9dbflrSo0EIIYQQQgghhBCZRhoahBBCCCGEEEIIkWlk6IQQQgghhBBCiK+aMkGGTnxK0qNBCCGEEEIIIYQQmUZ6NAghhBBCCCGE+LrJZJCflPRoEEIIIYQQQgghRKaRHg1CfIb0QmO0nQStuNBqtbaToBWF9nXVdhK0Qi8yXttJ0IpQ0wfaToJW3H7xj7aToBU+16toOwlacXnQJW0nQSu8pntrOwlacXHaLW0nQSt0HkRoOwnaUchI2yn4IMoEpbaT8E2RHg1CCCGEEEIIIYTINNLQIIQQQgghhBBCiEwjQyeEEEIIIYQQQnzVlDIZ5CclPRqEEEIIIYQQQgiRaaRHgxBCCCGEEEKIr5oyQXo0fErSo0EIIYQQQgghhBCZRhoahBBCCCGEEEIIkWlk6IQQQgghhBBCiK9bvFLbKfimSI8GIYQQQgghhBBCZBrp0SCEEEIIIYQQ4qsmk0F+WtKjQQghhBBCCCGEEJlGejQIIYQQQgghhPiqKeOlR8OnJD0ahBBCCCGEEEIIkWmkoUEIIYQQQgghhBCZRoZOCCGEEEIIIYT4qslkkJ+W9GgQQgghhBBCCCFEppEeDUIIIYQQQgghvm7xSm2n4JsiPRqEEEIIIYQQQgiRaaShQQghhBBCCCGEEJnmkzU0rF69GgsLC/XnMWPG4Ofn96m+/qOlTL+23b17Fx0dHc6fP6/tpGRIxYoV6du3r7aTIYQQQgghhPgGKRMStLZ8i95rjoa2bdsSGBgIgL6+Po6OjrRu3Zphw4aRLdv7TfcwcOBAevXq9V77fKjnz58zatQo9u7dy7Nnz7C0tMTX15dRo0ZRpkyZT5KG9/Xvv/8yceJEDhw4wPPnz7G3t8ff358BAwZQrFgxbSdPqzZu28Kq9WsIDlHgXsCVYf0H413IK93w+w8dYP7SRTx6+oR8efLSr0dvypcuq96uVCpZsGwx23bv4OXLCAr7+DJycAD58jqqw4SHhzNp1jSO/HUMXV0dqlasQkC/gRgZGQHw6MljanxXL9V3r1+2Gl8vbwB27t3NiAljNbZnz56d/x09/lHH422USiXz1y5j677dvHz1ksKePozqORgnh7zp7nPm0jlWblvPlX9v8DwkmLkjp1C1dAX19ti4OOYGLuHPM//w8MljTIxNKFW4GP3bdcfWOmeW5eV95e5YGpt6XuiZ5iDi4iMezDhEzMOw9MO3L0XuDqU01kXfC+Hqj6vVn7M7mJOnRwWMfezRza7HixN3efDTYeJCI7MoF2+3actmVq8NJFihwM3VjYBBQ/D2Sr8s/H7wAPMXLeTxk8c45nWkX6/elCtbLs2w4ydNYOvP2xnUfyCtfmyhXt+rXx9u3LxJSGgIZqZm+JcoSd/evbHNaZvp+UuPUqlk/solbPtlBy8jIijs7cuo/kM1ymxaNvy8hVWb1qrqDhdXhvUZhI9n0vEaM30iJ86eIig4GCNDQ/y8fOjftTfO+ZzUYS5du8JPS+Zz9eY1dNDBq2AhBnTrjUcBt6zKbrp+3vwrGwN3EKIIw8XNib5DOuHplXY6dv/8O/v3HObOv/cBcC/oQudeLdMNP2PCInZt30+vge1p2qJ+luUhsxzc8T9+23SK8JBXOLrY0rJPVZwL5n7nficOXWPxuF8oXLYAfSZ+9wlS+uGUSiWLf1nDjmP7eBkVga+LJ8N+7I1jLod091n52yb+OPc3d58+wCB7dnydPen9XQec7FJfA5RKJb3mjeCfK2eY2W00lfxKZ2V2MkypVDJ//XK2/Z54HSvow6jug8hn/5br2OVzrPx5A1dvJ17Hhk2mSqkKGmEWbFjOb38e5GlwEPrZ9PEs4E6fVl3wcS+U1VnKVMVcS9KhRncK5fPG1sKOHgvac+j8Pm0nK8M2btvM6nVv7uncCBiQsXs69XUsnXu67bt28DLiJX7evowcPIx8jknXh6vXr/HTgrlcuXYFXV09qlaqzOA+A9T3dNqgVCpZsGkF2w78wsvICAp7eDOy84C3n+dXzrNq10bVeR6qYM6QiVQpWV4jzPB5E9l1WPN8KONXgiWjZmZJPoRI7r17NNSsWZMnT55w69YtBgwYwJgxY5g+ffp7f7GJiQnW1tbvvd+HaNy4MefOnSMwMJCbN2+ye/duKlasiEKh+CTf/77OnDlD0aJFuXnzJkuWLOHq1avs2LEDDw8PBgwYoO3kadVvB39n2txZdOvQma2r1+Pu6kaXfj1RhISkGf7cxQsMHj2cRvUasjVwA5XLV6T3kAHcuv2vOszKdYGs37qJUYOHsWFFIIaGhnTp25OYmBh1mCFjRvDvf3dYNncBC2bM5uz5/zFmyoRU37d87iKO7NmvXjw9PDS2mxgba2z/fceeTDoyaVuxdR3rdm9ldK/BbJq9AsMchnQe0ZeY1zHp7hMZHY27sysju6d9rkXHRHP19g26Nm/HtvmrmTtiMv89vE+PsYOzKhvvLVeL4uRs4sf96Ye40WkDCdGxFJj1HTrZ9d66X9SdYC7WW6xebnTbpN6mmyMbrj81BpTc6r2NG103o6Ovh8u0hqCTtflJy77f9zP9p5l07dSFzes24O7mRtde3dMtC+cvnGfI8AAaNWjIlvUbqVyxIn0G9ufWv/+mCnvo8B9cvHwJ25ypG45KFCvO9ClT2b19B7OmTefBowcMGDIo0/P3Nis2BLJ++yZGDwhg45LVGObIQeeBvTTKbEq/HfqdaQt+onvbTmxdvg73Am50GdgLRWjS8fJ0L8iEoaP5Ze1Wls6Yj1KppNOAHsTHxwPwKjKSLoN6kzuXHRsXr2btguUYGxnReWAvYuPisjzfyR3a/xfzZ66kbZcfWL5hFgXcnBjQfSyhIWFphj9/5jJVa5Zj7rLxLA6ciq2dDQO6jeF5UOrr4J9/nODKpRvY5LTK4lxkjpN/XGPTgsM0bFOGscvakNclJzMGbuFF6Ku37vf8STibFx3GzSfPJ0rpxwncv4WNf+xiWIteBA6dg6FBDnrMHUZM7Ot09zl78yJNK9YjcOhsFvWZTFx8PN3nDCMqJjpV2PWHdqCjo4XK7B1WbF/H+j1bGd19EBtnLFeV91H93nodi4qOxj1/AUZ0Tf+eKZ+9I8O7DmDH/LWsnboIB9vcdBrVl5Dw0KzIRpYxNDDi+sMrjNswTNtJeW/7Duxn+pxZdO3YmS2BG3BzdaVL3x7pX8cuXmDIqGF8V6+B+p6uz+D+mvd0awPZsGUjI4cMY/3yN/d0PdTXh6Dnz+nUuxuOefKyfsUaFs+ez+07dxgxfvQnyXN6Vu7YwPq92xnVdSAbpizB0MCQLuMHvP08j4nG3akAwzv1f2vcZQuX5MiKneplWv8xmZv4L4gyPkFry7fovRsaDAwMsLOzI1++fHTr1o2qVauye/duQkNDad26NZaWlhgZGVGrVi1u3bqVbjxpDZ1YuXIlhQoVwsDAgNy5c9OzZ08A2rdvT926dTXCxsbGYmtry4oVK96a3rCwMI4dO8bUqVOpVKkS+fLlo0SJEgQEBFC/ftJTmlmzZuHt7Y2xsTF58+ale/fuREREvDXuXbt2UaRIEXLkyIGzszNjx44lLvFmU6lUMmbMGBwdHTEwMMDe3p7evXu/Nb43+7Vt2xZXV1eOHTtGnTp1cHFxwc/Pj9GjR7Nr1y6N8Hfu3KFSpUoYGRnh6+vL8eNJT8cVCgXNmzfHwcEBIyMjvL292bhxo8b+FStWpHfv3gwePBgrKyvs7OwYM2aMRhgdHR2WL19Oo0aNMDIywtXVld27d2uEuXz5MrVq1cLExIRcuXLRqlUrgoOD35nf97Vm4zqa1G9Eo7r1ccnvzKjBw8hhkIMde3alGX7dlo2UKVmK9i1b4+KUn15duuPp7sGGbVsA1fFeu3kDndt2oHL5irgXcGXSqLEEBT/n0J9HALh99z/+OvEPYwNG4lPImyK+hRnWfzC/HfydoOfPNb7PwtwcG2sb9aKfTV9ju46OjsZ2G6usa2xTKpWs2bmZLj+0pUqp8rjnL8CUgaMIUgRz6J8/092vfPFS9GnThaplKqa53dTYhBWT5lKrfFXy58mHb0EvRnQbwJVb13kc9DSLcvN+bJsW5mngScL/uk3U7WDujt+Hvo0JFuUKvHU/ZXwCcSGR6iU+POlm3NjHgex2ZtydsJ/oO8FE3wnm7oR9GHnkwrTo25+kZ4U169fRuOF3NKzfABdnF0YGDMcwRw527t6ZZvj1mzZSplRp2rVug3N+Z3p260FBj4Js2rJJI9yzoCAmT5/K5PGT0uyp1qpFS3y9fbDPbY+frx/t27Tj4qVLxMbFZkU2U1EqlazdupEurTpQuVxF3F1cmTx8HEGK5xz660i6+wVuWU+Tug1pVLs+BZycGT0ggBw5cvDz3qS6rGn97yjmVwSH3PZ4unvQu1N3ngY949HTJwD8d/8u4S/C6dm+C/kdnSiQ34XubTujCFHwODHMp7J53S7qfVedOg2qkN8lLwOHdyNHDgP27jyUZvhRk/rTqGltXN2dyZc/D0NG9SBBqeTsyYsa4Z4HKZg9dRmjJvUnW7a3N8x9LvZvOUOFuj6Uq+2Ng5MNbQbUIHsOff789VK6+yTEJ7Bkwh4atitLTnuLT5fYD6RUKtlwaCcdazenol9p3PI4M67dYJ6HKThy/p9091vQZxL1S1fHxd4Jt7wujG07gKchQVy9p3l/duPBbdYd2M7o1m//wfKpKZVK1u7eQpembansr7qOTe43iqCQYA6dSP86Vq5YKfq06kLVFL0YkqtbsTql/IqT186BAvmcGdyxNxGRr7h593ZWZCXLHLt8mDk7p3Hw3JfTi+GNNRvX07hBIxrVbaC6pxuiuo6le0+3eQNl/EvRrqXqOvbmnm7jts2A6nxZt3kDndt1VN3TuboxafQ4ngc/54/Ee7qjf/9JNr1sDB80lPz5nPDyLMTIIcM4cPgQ9x/c/1RZ16BUKlm7Zwudm7SmcolyuDsVYFLv4QSFKDh06li6+5Ur4k/vHztR1b98umEAsuvrY2NprV7MTUwzOwtCpOmj52gwNDTk9evXtG3bljNnzrB7926OHz+OUqmkdu3axMZm7OZz0aJF9OjRg86dO3Pp0iV2795NgQKqHwUdO3Zk3759PHmSdCO3Z88eIiMjadas2VvjNTExwcTEhJ07d771aZeuri5z587lypUrBAYG8scffzB4cPpPaI8dO0br1q3p06cPV69eZcmSJaxevZqJEycCsH37dn766SeWLFnCrVu32LlzJ97e3u88DufPn+fKlSsMGDAAXd3Uf56U80QMHz6cgQMHcv78edzc3GjevLm6sSM6OpqiRYuyd+9eLl++TOfOnWnVqhWnTp3SiCMwMBBjY2NOnjzJtGnTGDduHAcOHNAIM3bsWJo2bcrFixepXbs2LVq0ICSxxTksLIzKlStTuHBhzpw5w759+3j27BlNmzZ9Z37fR2xsLFdvXMe/eAn1Ol1dXfyLl+DC5bRvKC9cvkip4iU11pUuWYoLl1U31w8fPyJYodAIY2piio+nlzrMhUsXMTM1xaugpzqMf/ES6OrqcvGK5vf2HNyP8rWr0qpLew4fO5oqPZFRUVRrVIcqDWrTa3B//r2TdTc0D58+JjhUQanCxdXrTI1N8HH35Pz1y5n6XS8jI9DR0cHMWPsXr+z25ujbmPDyTNINQ8Kr17y6+hRjr7d3pTbIY4nXrs4U2tIep9G10M+VlB9dfT1QgjI2Xr1O+ToeEpSY+KTfdTkrxMbGcu36NfxLJp23urq6lCxRkgsXL6a5z4WLFylZIkVZKFWKC5eSwickJDBs1AjatmpDAReXd6YjPDycX/f9hp+Pb6pGtazy8MkjgkMU+BdLqgdMTUzwKeiVbj3wOjaWqzevU6qY5vHyL1qCC1fSPl6RUVHs+HU3eXI7YGebC4D8jvmwMDfn5727eB0bS3RMNNv37sI5X34c7N7dTT+zxMbGcvPabYqW9FGv09XVpVhJX65cvJGhOGKiXxMXF4+puYl6XUJCAhNGzKZ5m4bkd/n0jWcfIi42nrs3n+JZ1Em9TldXh0JF83H7yuN099sV+A9mFkZUqOOTbpjPyaPgpwS/CKFkwSLqdaaGxnjl9+DinWsZjudllKqXh3myujrqdTTDVkxhaPMe2Jh/Xr1YHj5TXcf8/ZKGjJoam+Dj5smFTLyOvY6NZeu+XZgam+Du9PYGaZE5VPd01/AvnqJeLl5S47qU3IXLlzTCA5T2T7qOqe7pgjXCmJqY4l3ISx3m9etY9PX1Ne6xcxgYAPC/C+czJW/v6+GzJwSHhVDKN8V57lqQCzeufHT8py+fp3zbetTt+SPjlswg7GX4R8f5pZI5Gj6tD25oUCqVHDx4kP379+Po6Mju3btZvnw55cqVw9fXl/Xr1/Po0SN27tyZofgmTJjAgAED6NOnD25ubhQvXlw9eWDp0qVxd3dn7dq16vCrVq3i+++/x8TEJJ0YVbJly8bq1asJDAzEwsKCMmXKMGzYMC6muBnv27cvlSpVwsnJicqVKzNhwgS2bNmSbrxjx45l6NChtGnTBmdnZ6pVq8b48eNZsmQJAPfv38fOzo6qVavi6OhIiRIl6NSp0zuPw5teIB4putynZ+DAgdSpUwc3NzfGjh3LvXv3+DexK7SDgwMDBw7Ez88PZ2dnevXqRc2aNVPly8fHh9GjR+Pq6krr1q0pVqwYhw5pPhVr27YtzZs3p0CBAkyaNImIiAh1g8X8+fMpXLgwkyZNwsPDg8KFC7Ny5UoOHz7MzZs3M5QPgJiYGF68eKGxJG8cCg0LIz4+HusUvQCsrawJVqTdeyJYocDaSvPGycbKiuDEYTNv/k0ZxjpFGCtLze3ZsmXD3MyM4BBVGCNDQwb17sesiVNZMGMORXz96D1kgEZjg5OjE+OGjWLe1FlMGT2ehIQEWnZux9OgZxk+Ru8jOFSVNpsUabe2tFJvywwxr2OYtXIhtStUw8TYONPi/VD6VqoxlrEhmvMmxIW8Qt86/fS9uvqEexP3cbv/z9yfcYjsuc1xW9gMXSPVD+hXV56QEB2LQ/dy6BhkQzdHNhx6lkcnmy7Z3hJvVggNC00sCynPW2v1eZtSsCL4neFXBq4im54eLX5o/tbv/2nuHEqULUW5KhV58vQJc2b+9IE5eX9v0mtjmbIesFKXx5TCwhPrjpRlIY19Nu7YSrEa5Sheoxx/nfyHZbMWkF1fdQ4YGxmzes4SfjnwG0WrlaF4jfL8feoflkyf+97zFH2M8NCXxMcnYGVlobHe0tochSJj3b4XzQnEJqclxUr6qtetX/Uzenq6NGle9y17fl5ehkeSEK/E3FJzbLWZpTHhIWkPnbh58SF//nqRdoNqfIokZgrFC1XDvpWZhcZ6azMLgsPT7maeUkJCAjO2LMbPpRAFHJzU62duWYKvsycVP5M5GZILThzaZGORouxaWKm3fYwjp/6m2PdVKNK4Imt2bWLZuNlYmlt8dLzi3ZLu6VLfo6Q3tFl1HUtR91smXccU6d7TJd0nlixWHIVCwap1gcTGxhL+4gWzF85Tx68NwWGJ6Ta31FifGed5mcIlmdR7OMvHzqZfq66cuXKeruMHqYcECpGV3ruhYc+ePZiYmJAjRw5q1apFs2bNaNu2LdmyZaNksqdr1tbWuLu7c+3au1vag4KCePz4MVWqVEk3TMeOHVm1ahUAz54947fffqN9+/YZSnPjxo15/Pgxu3fvpmbNmhw5coQiRYqwevVqdZiDBw9SpUoVHBwcMDU1pVWrVigUCiIj057k7cKFC4wbN07dY8LExIROnTrx5MkTIiMj+f7774mKisLZ2ZlOnTqxY8cOdU+Dt1EqlRnK0xs+PklPY3LnVj1RCwoKAiA+Pp7x48fj7e2NlZUVJiYm7N+/n/v376cbx5t43sSRVhhjY2PMzMzUYS5cuMDhw4c1jsWbhpLbtzP+xH7y5MmYm5trLFNnfxmT1VhaWNKmeUt8Cnnj7VmIft17U7dGbVatX6MO4+ftQ4PadfFwc6d4kaLMnjIdSwtLtu7Ynilp+OWP/RRtVFm9ZOR8+1ixcXH0nzQCpVLJ6J7amaPBsroHvgd6qhedD+zu/eLEXcIO3yLqdjAvT93j9sAdZDMxwLKyOwBxYVHcGbkH8zLO+B3she/+nuiZGBB5/Rm8Z7n9HF29dpX1mzYyfszYd47Tbtu6NVvWb2LJ/EXo6eoxfPTI9667MmrP779RrEY59RIXn7Xndd1qtdi+fD2Bc5eSL48jA0YPVTd4RsdEM3LqeAp7+bJh0SrWLVhBgfwudBvSh+g0xrx/rtat3M6h/X8xcWYABgbZAbhx9V+2bdzDsLF9Pstx+pklKjKGpRP30m5gTUwttDfx27v8evIPyvRuoF7iMuFHwZSN87n9+B6TOwWo1x29cJzTN84zsGnXj44/M+w5sp9i31dRL1l9HSvhU4TtcwJZP20JZYv6M2DqSBRhH9+AIT5fBZxdmDBqLIEb1lG8Ymkq1amGg7091lbW6Oh8mpfx7Tn6O8V/rK5esvK6VrtsVSqVKItbPheqlCzPgmHTuPzvNU5fOZdl3ynEG+/9CKZSpUosWrSI7NmzY29vT7Zs2VKN139fhoaG7wzTunVrhg4dyvHjx/nnn3/Inz8/5cqlPVt6WnLkyEG1atWoVq0aI0eOpGPHjowePZq2bdty9+5d6tatS7du3Zg4cSJWVlb89ddfdOjQgdevX6c5C21ERARjx47lu+9Sz1KdI0cO8ubNy40bNzh48CAHDhyge/fuTJ8+naNHj6Kvn34XYzc31Qzg169fp3Dhwu/MV/K43twcJiR2z5k+fTpz5sxh9uzZ6vkn+vbty+vXr9ON4008CSm6+LwtTEREBPXq1WPq1Kmp0vem8SMjAgIC6N9fc3yo7qukoTeWFhbo6emhSPEEUhGiwMbaJs04baytU00qFBwSgk3iRKRv/lWEhJDTJmniO0VICO6Jfwsba2tCUrQox8XFEf7ixVvnWPAp5MXx0yfS3a6fTZ+Cbu7cf/Qw3TDvo7J/WXw8koZ3vE4cthQcGkJOq6TjowgNwcPl42fIVzUyDOdx0FNWTZmvtd4M4X/d5vqVpLkh3kz4qG9lRJwi6YlmNitjom4Fpdo/PfERMUQ/CMUgj4V63ctT97jSdCV65jkgXkl8RAzeu7sQc+jTdkO0tLBMLAua56WqLKR9TtpY27w1/Nlz5wgJCaFG3drq7fHx8cycPYv1G9ez75dfNb7f0sISp3z5yJ8/P9Xr1OTipYv4+viS2SqVLY93sjdDxCZOfBccqiCnTbLzOiQk3Tc/WJgn1h2hKfMfkqoMm5qYYGpiQr68jvgU8qZ0nUocPHaYOlVrsvfAPh4/fcKGRavU3W6njZpI6TqV+OOvo9Su8mmekJtbmqKnp0tIiokfQxXhWFtbpr1Too1rdrJ+1XZ+WjyOAm5O6vUXzl0lNCScJrU7qtfFxyewYNZqtq7/ha2/LsvMLGQaU3MjdPV0CE/x5pcXoa8wt0pdJwU9CiP4aTizhyU18CoTVI1k7StPZ8rajtg6vP0YfgoVfP3xyu+u/vxmDpSQF2HkNE86ZxUvwnDP++5hTlM2zufYpZMsHziTXJZJ17pT18/z8PkTKvTTvJcZtHg8hV29WDbg/Sf7/hiVSpTF2y3prQ/q8h6W4joWFoKHs+tHf59RDkPy2echn30efD28qNW5KT8f2EOn71t/dNzi7ZLu6VLUy6Eh6U4Wr7qOpbgHDE26jlmne0+nwMM1qTzVqVGLOjVqEaxQYGRoCDo6rNm4njwOn2YYZKUSZfFxS32/pggPTXWeu+f/+PM8ubx29liamXP/ySP8fb7Bt9jFf/kPhr4k793QYGxsrJ474Y2CBQsSFxfHyZMnKV1a1fVOoVBw48YNPD0904pGg6mpKU5OThw6dIhKlSqlGcba2pqGDRuyatUqjh8/Trt27d436Ro8PT3VwzrOnj1LQkICM2fOVN88vm3YBECRIkW4ceNGqmORnKGhIfXq1aNevXr06NEDDw8PLl26RJEiRdLdx8/PD09PT2bOnEmzZs1SzdMQFhaWap6G9Pz99980aNCAli1bAqoGiJs3b2bob/I+ihQpwvbt23Fycvqo7sMGBgYYJI6TeyM2LmlCTn19fTzdPTh55jRVKqjOk4SEBE6eOU3zJmnPB+Hr5cOJM6do9cOP6nXHT53E10vVQyOPvQM21tacOHMKDzfVRSjiVQQXr16m6XdNVHF4+/Di5UuuXL9GIY+CAJw8e5qEhAR8CqU/78b1WzfImU4DCKh+xN26/S/lkr2W6WMYGxljbJR0Y61UKrGxtObE+TMUTGxYiHj1ios3rvJDnY97jdubRoZ7jx+yesp8LMzMPyq+j5EQGUtMZJjGutjgCEyLOhJ1SzVZp65Rdow97QjecSHD8eoa6mPgYEHIvtS9st5MEmlSJC/ZLI0I/+vTTh6mr69PQY+CnDx1ksoVk5WF06do3jTteWt8fXw4efqUxqsqT5w8ga+3qizUq10H/xRzOHTr1Z26tevQoF6DdNOiVKoaHF+/zprJINM8r62sOXn2NAVdk5XZa5dp1rBxmnFk19fH082DE2dPUaVcRSDxeP3vNM0bvWUuGaUSpVKpvgmMjolGR0dH44m/ro4O6OiQkPDpbl709fVxK+jC2ZMXKV/JH1Dl5+ypi3zXrHa6+61f/TNrV2xj5oLReBTSvHbVqFNRYxgFwIDuY6lRpyK1G6Tf21Dbsunr4eRmx9Wz9yhaTnVDnpCg5Or/7lGlUeprbW5Hayas0rx/2L7iGNGRr2nRqwpWtmafJN3vYpzDCOMcSQ85lEolNmZWnLp+Tt2wEBH1isv/Xef7CukPdVEqlUzdtIDD5/9hWf/pONjYaWxvV7MZjcrW0ljXdFwXBjTtQnkf/0zMUcakdx07eeEMBZ0Tr2ORr7h48yrNajfK9O9XKhN4/Za3eIjMo7qnK8jJ06c07ulOnD5F8+/TuY55eauuYz8kXceOnzqpvo6p7ulsOHla857u0pXLNPvu+1TxvWmg2PHLTgyyZ6dUiU9zzhsbGmFsmKJ8/5+9+46K4moDOPwDRLrSRAFRQVBEAXvvEnsvsVfsvffeYy+oWMGGPSbRz1hjNFFji2JvGIMFlSIqgoqw3x/o6gpYIjC6+z7n7Dns7Ozw3juzs7N37n2vpTV/nTuN++uGhZjYZ5y7fpnvazZM0/99P+Ih0U+fkM0q9RtlQqSVNBlU6ubmRoMGDejSpQtLly7FwsKC4cOH4+joSIMGqV+gvmv8+PF0794dOzs7atWqxdOnTzly5Ah9+vRRr9O5c2fq1q1LQkIC7du3/6TtRkZG0qxZMzp16oSXlxcWFhacOnWKGTNmqGNzdXUlPj6ehQsXUq9ePY4cOYK/v/8Htzt27Fjq1q1Lrly5aNq0Kfr6+gQHB3PhwgUmT55MYGAgCQkJlCpVClNTU9atW4eJiQm5c+f+4Hb19PQICAjAx8eHChUqMGrUKNzd3YmJiWHHjh3s3buXQ4eSJxlMiZubG1u3buXo0aNYWVkxZ84cHjx4kOYNDb169WL58uW0bNlSPXvFjRs32LhxIytWrMDAIO0yl7dr2YZRk8ZR0L0AhQoWYt3GIOKex9GwbtIMIiMmjMUuWzYG9Ew6btp835KOPbsQGLSWimXL8+v+vVy8conxw0cBSfXdtnkrlgWuJLdTLhztHfBbvgQ722xUq1gZgLx5nClfuizjp01i7NCRxL96xdTZM6jlU109/d/P/9uBoaGh+ott/+8H2b7zFyaMGKOOfcnKZXgV8iRXTieexjwlYP1a7t2/T5P6DdOsft6lp6dHu4bNWboxkNyOTuTMbs+Ctcuxs7GlWtm3GYo7Du+NT9lKtK6f9CX8LC6W0Htve1ncfXCPyyHXyGqRBQe7HMS/ekX/KSO5fOMqiyfMIiExkfDXdxiyWmRRj2dX0sPNZ8jRvhQv7jzixb0nOHQpS3xEDNF/vJ0Cy3V+Ux4fvkH4trMAOPaqyOMjN3l5/wmGtmbYdy6LKiGRR/uvqN9jXbsgz/+N4lV0LOYFHcjZvzIPN53mRWjGT4fWrnUbRo8fi4eHB54FC7EuKIi4uDgavm4UGDl2NNnt7OjXO2m2m9YtWtKpaxdWr1tDxfIV+HXPHi5eusTYkUnHqKWlZbJGzEyZMmFjY4tznjwAnLtwnosXL1KkcBGyZLHg9p07LFqyGKecTnh7ZUxSPT09Pdo2a8nSNSvJldOJnPaOLFy5BDubbFQrX1m9Xqf+PahWoTKtmyRdsLb/vjUjp42nYH4PPAsUZO2WpPpqVLseALfv3WH3b/soW6I0VpZWPHj4gBXrAzEyMqZi6XIAlClemllLFjBp7g+0btwclSqRFesDyWRgQKkiGXtnqHmbBkwdOx93D1cKFHJjS9AO4uKeqxsFJo+eh62dDd37tgWS8i+sXBLE2KkDyeFgR2RE0jFrYmqMqakJWS2zkNVS80d2pkwGWNtakitPxiY7/Vw1vi/O8mm7cHbPgYu7PXu3nuJFXDwVaiU1BC+b8j+sspnTrGslMhtlIqeL5rStpubGAMmWf0309PRoVa0hK3ZtIJedIw62OVjy82qyWdpo5FboNmcYVYqUpUWVpPPA9A1+/HriIHN7jsfU2ESdz8HcxAzjzEbYZrVOMQFkDmu7ZI0SStDT06Nt/e9Zumk1uRycyJndgYXrlmFnbUu1dzLtdxrVh2plKtG6btINgmdxsYSGvf0eu/MgjMs3r5HVPOl7LPZ5HMs2r6ZKyfJks7bh0ZPHbPjfNh5ERlCjXNUML+eXMDUyJZeds/p5Tlsn3J0K8vhZNGFRdxWM7OPatWyddE1XwANPj4Ks3fT6mq5O0jXdyAljsMtmR/8313TNW9GxRxdWr19LhXLl2b1vDxcvX2Lc8NFA0vHSpnkrlgauIJdTLhwdHPBbtoRsttmo+vqaDiBoy0YKe3pjamrKsRN/MWfhfPr37EMWC2USWuvp6dG27vcs27qa3PY5ccxuj9+GFdhZ21Ct5Nve277j+lGtVEVa1U5qVI+NiyX0/tt9fPdhGFf+uU5W8yzYZ8tObFwsizcH8F3pythaWXP7/l3mrFlCrhyOlCtSMlkcukBXkzIqJc2yVwUEBNCvXz/q1q3Ly5cvqVixIrt27frgMIF3tW/fnufPnzN37lwGDx6Mra0tTZs21VjHx8cHe3t7ChYsiIODwydt19zcnFKlSjF37lxCQkKIj4/HycmJLl26MHJk0pzD3t7ezJkzhx9++IERI0ZQsWJFpk2bRrt2qXedq1GjBjt37mTixIn88MMPST8y3d3p3Dmp26mlpSXTp09n4MCBJCQk4OnpyY4dO1LtDvaukiVLcurUKaZMmUKXLl2IiIjA3t6esmXLMm/evE8qN8Do0aO5efMmNWrUwNTUlK5du9KwYUMeP07bbt4ODg4cOXKEYcOGUb16dV68eEHu3LmpWbNmijNnfIlaPtV59OgRfiv8iYiMxN0tH/5zF6q7P4c9uI++/tu7jUW8vPlhwhQWLlvCfP9F5HbKxYIfZuOW9+3dvE5t2hMXF8f46VN4GvOUol6F8Z+7UKN3xQ/jJzNl9g/49u2Bvp4ePpWrMXLgEI3Y/ANWEHY/DAMDA5xz52HWpGlUr+qjfv3J06eMnz6ZiMhIslhkwcPdnXXLVpHX2SVN6+hdvs3aEPc8jnELpvM0JoaiBb1YNmkuRpnflu122F0ePXl7TFy8foUOw3q9LfuyBQA09KnN1EFjeBgZzsG/kqZbatxL8zMS+MMiSnql3mMnozxYfxJ9E0NyDf0OA3MjYs7d5cagH5NmiXjNyDErmbK+HbZlaGdOngm1yZTFmFfRccScu8vVbht4FR2nXsc4lxWO3ctjkMWYl2FPuL/6OA83/Z2hZXujZvUaPHr0iMX+S4iIjCR/vvwsWbhIfY65f/++xuevsHdhpk+ZysLFi1iwyI9cTrmYP2sObh/olfU+Y2Nj9h/8jcXL/ImLi8PW1pZyZcoy07cLmTNnTvMypsa3VXvinj9n/KypSZ9Zz8IsnbVA4zN7+94doh9Hq5/XqladqOhH+K3yJyIqEnfXfCyd9fbcYZTZiNPBZ1i7ZQOPnz7B1sqGYt5FWL94pTqJpEvuPCyaNofFgctp3bMjenr6FHDLz9KZCzWGcWSEajXKE/3oMSuXbCAq8hGu+Z2ZtWgc1jaWADy4H47eO+fCn7b8Snz8K8YMmaGxnY7dmtOp+4eTf37tSlUtwNPoOLav+pPHUc/I5WrHoJnN1EMnIh8+0aiLb1X7Gt8T9/I5k9fN52lsDIVdC+LXdwpGhm8/e3ciwoiOeaJ+vuXQTgC6zNb8vhrffhD1y1bPmMC/kG+TNkmfd78fePoshqIeXiydMEfze+z+XaKfRKufX7xxhY4je6ufz1iZ9D3WoGptpg4YjYG+Pv/c+ZefD+zi0ZPHWGbJSiE3d9ZMX4xr7vT7Tk4PhXJ7s2bI26FAI5pPAGD70U2MCBigVFifpOZ3NYiKfsSi5UteX9Plx3+un7qnQdj9+xp5Ewp7eTN94hT8li5mvr8fuZ1yMX/GHM1rurbtiXsex4Tpk3ka85QiXoXxn+en8f1w4dJFFi9fSmxcLM658zB2+Ejq1VI2CW6nRq2IexHHeP+ZScd5AU/8x8x67zi/p3G9diHkKp3G9lU/nxHgB0CDKjWZ0mcU+voGXPs3hF8O7uZJbAx2VraULVyC3i07k9kw476zhe7SU6VXBq90EBMTg6OjIwEBASnmRhDaJz4q5uMraSH9aN3suhncNlDpEBRRcPfXkYgtoxnE6mbW6yiLtMnL8q0JeXJU6RAU4XXl6x16kp6MHJSf7lgJhWZ+fCpzbXRuxnWlQ1CE3r2UZ7fRdoYF7ZQO4T/5Z/h2xf638/S0H+71tcu4+bi+QGJiIhEREcyePRtLS0vq16+vdEhCCCGEEEIIIb4RqlcydCIjfRMNDaGhoTg7O5MzZ04CAwM1Eg6GhoZ+MOfApUuXyJUrV0aE+Un++OMPatWqlerrMTG6eQdfCCGEEEIIIYR2+CYaGvLkyZPqHO0ODg6cPXs21fd+ai6HjFK8ePEPxiuEEEIIIYQQIm2pEqRHQ0b6JhoaPiRTpkwfnGLya2NiYvJNxSuEEEIIIYQQQnyOb76hQQghhBBCCCGE+BDJ0ZCx0nbuQSGEEEIIIYQQQug0aWgQQgghhBBCCCG+MVFRUbRu3ZosWbJgaWmJr6/vRycXqFy5Mnp6ehqP7t01p1oPDQ2lTp06mJqaYmdnx5AhQ3j16tVnxSZDJ4QQQgghhBBCaDVtTAbZunVrwsLC2LdvH/Hx8XTs2JGuXbsSFBT0wfd16dKFiRMnqp+bmpqq/05ISKBOnTrkyJGDo0ePEhYWRrt27TA0NGTq1KmfHJs0NAghhBBCCCGEEN+Qy5cvs3v3bk6ePEnx4sUBWLhwIbVr12bWrFkfnH3R1NSUHDlypPja3r17uXTpEvv37yd79uwULlyYSZMmMWzYMMaPH0/mzJk/KT4ZOiGEEEIIIYQQQqupXiUq9njx4gVPnjzReLx48eKLynPs2DEsLS3VjQwAPj4+6Ovrc/z48Q++d/369dja2lKoUCFGjBhBbGysxnY9PT3Jnj27elmNGjV48uQJFy9e/OT4pKFBCCGEEEIIIYRIJ9OmTSNr1qwaj2nTpn3RNu/fv4+dnZ3GskyZMmFtbc39+/dTfV+rVq1Yt24dBw8eZMSIEaxdu5Y2bdpobPfdRgZA/fxD232fDJ0QQgghhBBCCCHSyYgRIxg4cKDGMiMjoxTXHT58OD/88MMHt3f58uX/HEvXrl3Vf3t6emJvb0+1atUICQkhb968/3m775OGBiGEEEIIIYQQWk31SrlkkEZGRqk2LLxv0KBBdOjQ4YPruLi4kCNHDh4+fKix/NWrV0RFRaWafyElpUqVAuDGjRvkzZuXHDlycOLECY11Hjx4APBZ25WGBiGEEEIIIYQQ4iuQLVs2smXL9tH1ypQpQ3R0NKdPn6ZYsWIA/PbbbyQmJqobDz7F2bNnAbC3t1dvd8qUKTx8+FA9NGPfvn1kyZIFDw+PT96u5GgQQgghhBBCCKHVVAkqxR7poUCBAtSsWZMuXbpw4sQJjhw5Qu/evWnRooV6xom7d+/i7u6u7qEQEhLCpEmTOH36NLdu3eKXX36hXbt2VKxYES8vLwCqV6+Oh4cHbdu2JTg4mD179jB69Gh69er1yb0yQBoahBBCCCGEEEKIb8769etxd3enWrVq1K5dm/Lly7Ns2TL16/Hx8Vy9elU9q0TmzJnZv38/1atXx93dnUGDBtGkSRN27Nihfo+BgQE7d+7EwMCAMmXK0KZNG9q1a8fEiRM/KzYZOiGEEEIIIYQQQnxjrK2tCQoKSvX1PHnyoFK97VHh5OTEoUOHPrrd3Llzs2vXri+KTRoahBBCCCGEEEJoNSWTQeoiGTohhBBCCCGEEEKINCM9GoQQQgghhBBCaDVVgvRoyEh6qncHbQjxlYmPilE6BGXo6qdSV8utq33LdHR/qwz0lA5BGTq6v/V0tKuuKpOunth0k9dQN6VDUMT5adeUDkERhrbmSofwn1xqsUqx/+2xsZNi/1sp0qNBCCGEEEIIIYRWkxwNGUuam4UQQgghhBBCCJFmpKFBCCGEEEIIIYQQaUaGTgghhBBCCCGE0GoydCJjSY8GIYQQQgghhBBCpBnp0SCEEEIIIYQQQqvJ9JYZS3o0CCGEEEIIIYQQIs1IQ4MQQgghhBBCCCHSjAydEEIIIYQQQgih1SQZZMaSHg1CCCGEEEIIIYRIM9KjQQghhBBCCCGEVpMeDRlLejQIIYQQQgghhBAizUiPBiGEEEIIIYQQWk2mt8xY0qNBCCGEEEIIIYQQaUYaGoQQQgghhBBCCJFmZOiEEEIIIYQQQgitJskgM5b0aBBCCCGEEEIIIUSakR4NQgghhBBCCCG0miSDzFjSo0FLjB8/nsKFCysdhhBCCCGEEEIIHScNDd+AevXqUbNmzRRf++OPP9DT06Nx48YcOHDgs7abJ08e5s2blwYRZiyVSoXfsiVUrludYpXK0rlPD/69HfrR923YupnqjepStFIZWvq24/zFCxqvv3jxgskzp1OuRlVKVC1P/xFDiIiK1Fgn7H4YPQb1pXjlslSs7cOshfN49eqV+vXwiHCGjh1Jne8b4Vm2ONPnzkoxlrUbg6jbvDHFKpWlWoPa/DBvNi9evPhvdbF8CZXrVadY5bJ07vuJdbFtM9Ub16Vo5TK07NyO85dSqItZ0ylXsyolqpWn/8jkdfHXqRO07tqRkj4VqFS3OnMWLdCoi7th9yhUtliyR/CF859dzo9R10P96hSrUpbO/T6jHprUpWiVMrTskrwetvz8Ix16d6XUdxUpVK4YT54+TbaN3kMH4NO4NkWrlKFy/eoMnziGh+HhaVa2d33Nx/6oSeMoVKZYskeDVs3SptwKHecpHcO79u1Rv37i71MprhMRGfHF5U5Wni2bqNGwDsUqlKZVp+T78X17Duyj3veNKVahNI1afc/hI39qvL7/4AG69ulJ+e+q4FmqKFeuXU22jdt3btNv6CAq1qhK6SoVGDRyGBGRkcnWS08btr4ud8XPKHfzxhSrWJpGrb/n8NEUyt23J+WrV8GzdMrlfkOlUtG9f288SxflwKGDaVKeT6VSqfBb4U/lBjUoVrUcnfv1/PTjvmk9ilYtS8su7VM/r1WvRKHyxVM8r126eoXO/XtSpmZlytWuxvgfphAbG5tmZVPHmsb79s05skqd6hSvVIbOvbvzb6hmnfUZ3J/vGtSmWMXSVKlTnRHjR2ucs//59xadenalUi0filUsTc3G9Vjgv4j4V/FpV/AUKFEXl65cpkufHpT1qUj56lUYP21Suuzn9FDcrRRLeq/m8My/ubL8HtUKp3yd/LVS8rqlepO6FCpXTOOxYm1AmpVNiDekoeEb4Ovry759+7hz506y1wICAihevDheXl7Y2NgoEF3GW7VuNeu3bGTs0JEErVyNiYkJ3fr3/uAP9V/372XGgjn08O3KlsD15HfLR7cBvYmMilKv88P82fx+5DBzpkwncPFywiPC6T98iPr1hIQEeg7qR3z8K9YtC2DKmAn8vGsHfsv91eu8jI/HysqKrh18ye+aL8VY/rfnV+YuWUiPTl34ZeNWJo4cw+4De5nvv+i/18WQkQStWI2JsQndBnxiXXTqypaA9eR3TaEuFryui8nTCVy0nPDwcPqPeFsXV65fo8egvpQvXYatgUHMmjSNg38eYu6Shcn+34oFS/h9xx71w8Pd/bPL+TGr1q9m/dbX9bD8dT0M/IR6WPi6Hla9roeBvYl89LYenj9/TvlSZejSrmOq2ylZtDizJ/7Azg0/MnfKTG7fvcOA0UPTtHxvfM3H/vABg/l95x71Y//Pu8iaJSvVq/qkXbkz+Dh/Y/KocRrHcLWKlZOts3PjjxrrWFtZf3G537V73x5mzp9Dd9+ubF4dRD5XN7r166VRnnedPRfMsDEjaVyvAVvWBFG1YmX6DR3I9ZAb6nXi4uIo4l2YAb37priN2Lg4uvbthZ4erFi0lDXLVxEfH0+fwf1JTMyY7qfqcnd+XW43N7r1/0i5x74u9+pUyv38dbl7pVzud63duB49Pb00K8/nUJ/XBo8gaFkgJibGdBvY58PH/YG9zPCbS4+OXdiyct3r81ofzfPai+eUL1WWLm1TPq89jAinc/+e5MrpRNCyQPxnL+DGrRBGTR2fpuVLj327au1qgjZvYMywkaxf8eYc2UujzkoUK86sKdPZselH5k5LOmcPHPn2c58pUybq1a7DsgWL2bHpR4b1H8y2n7ez+J3zXVpToi4ehofTpW8PcuV0Yv3KNfjP8yPk5k1GTxqXbuVMSyZGply5c5GJQSOVDuU/UfK6BaB35+78/sse9aNV0xZpVravmepVomIPXSQNDd+AunXrki1bNgIDAzWWx8TEsGXLFnx9fZMNnejQoQMNGzZk1qxZ2NvbY2NjQ69evYiPT2qRr1y5Mv/++y8DBgxAT09PfSEVGRlJy5YtcXR0xNTUFE9PTzZs2KDxf58+fUrr1q0xMzPD3t6euXPnUrlyZfr3769e58WLFwwePBhHR0fMzMwoVaoUv//++xfXhUqlYu2mILp28KVqxcrkd3Vj6tgJPIwI58Dh1Le/ZsM6mtZvRKO69cnr7MLYoSMxNjJm+86fk8oU85Qfd/zM0L4DKVW8JAXdCzBp1DjOng9W34E/euIvQm79w/Txk3DPl58KZcrRu0sPNm7brK5XR3sHRgwYQoPadTE3N08xlrPnz1HE05s6NWrhaO9AuVJlqP1djWSt0p9UF5v/Q11s/MS66JN6Xew+sJd8ed3o0akruXI6UaJIMQb16sfGbVt49uyZxv+zzJIVWxtb9cMwk+FnlfOT66G9L1UrvK6HMa/r4Y8P1MOmdTSt14hGdV7XwxDNegBo27wVndt2xKugZ6rbadeiNd6FPHHIYU8RT286t+nAuYvn0/zu19d+7FuYW2js54uXL/Hk6RMa1an/5eVW6Dh/4/2yGRkZJft/1lbWGuvo66ft1+uaDetp0qARjeo1IK+LC2OHj8LE2JjtO35Ocf11m4IoV7oMHdu2x8XZhT7de+KR350NWzap16lXuy49OneldIlSKW7jbPBZ7oXdY/KYCeRzdSOfqxtTxk3g4uVLHD91Mk3Llxp1ues2SNqPw16Xe+dHyt3mdbm7vS731nfKXasuPXxTL/cbV65dZXXQOiaNzvgfXiqVirVbNtC13TvntdETeRj5kfPaxvU0rdfwnfPaCIyNjdm+8xf1Om2/b0Xnth3wKlgoxW0cOvIHmTJlYvTAYTjnyoNngYKMHTySfb//Ruid22lWxrTetyqVinWbgujasXPSucItH1PHTSQ8Ipzf3jlXtGvZBu9CXjjYO1DYyxvfth05d+HtOdvJMSeN6jYgv1s+HOwdqFKxEnVq1OLvs2fSrOxfQ10cOnKYTAaZGDVkOM6581DIoyBjho1k38EDhH7CnXWl/XHhIPN/msH+M7uVDuWzKX3dAmBmaqbxnWVqYpJGpRPiLWlo+AZkypSJdu3aERgYiEqlUi/fsmULCQkJtGzZMsX3HTx4kJCQEA4ePMjq1asJDAxUN1b8+OOP5MyZk4kTJxIWFkZYWBiQ1BJarFgx/ve//3HhwgW6du1K27ZtOXHihHq7AwcO5MiRI/zyyy/s27ePP/74g7///lvjf/fu3Ztjx46xceNGzp07R7NmzahZsybXr1//orq4c+8uEZGRlHnnAtHC3AIvj0IEXziX4nvi4+O5dPUKpUuUVC/T19endImS6h8Ul65c5tWrVxoXni55nLHPkYPg80nbDT5/Dre8rthav+05Uq50GWKePePGzZBPLkNhTy8uXb2s7hZ5++4dDh89QoUy5T95G/BOXRT/D3VR/D/URfYc6u3Gv3yJkVFmjW0bGRnx4uULLl69rLG897ABVKztQ9vunTj4x6HPKuOn+KJ6eP+YKF7yi4Z2PH7ymJ17f6Wwp1eaN6h8a8f+jzt+pnSJkjjY2//3QqPscf7GlNk/UL5WVVr4tuPHnT9rnIffaNq+JZXrVadzv578fe7slxQ55fJcuUzpkm9jTSpPKfU+el/w+fPJfkiXLV0m1fVT8jL+JXp6emTO/PazbpTZCH19fc4Ep9+PrjeS9uNljXJ8tNwXvrzckNTrYdjYkYwaMhxbG9vPD/4Lvf28vz2GLczNXx/3KZ+j4uPjuXTtCqWLv1dfxUsSfPHz9ruhoaFGY5nx68a1tDq202PfJtVZhMY6FuYWeBYslOo2Hz9+zP/27KKwp3eq5+zQ26Ec+esoxYoU+6wyfiql6uLly/jU93Pw2TQpm0jZ13DdsmJdIOVqVaVph1asWr9GYyikNpMeDRlLGhq+EZ06dSIkJIRDh97+UAsICKBJkyZkzZo1xfdYWVnh5+eHu7s7devWpU6dOuo8DtbW1hgYGGBhYUGOHDnIkSMHAI6OjgwePJjChQvj4uJCnz59qFmzJps3bwaSejOsXr2aWbNmUa1aNQoVKkRAQAAJCQnq/xsaGkpAQABbtmyhQoUK5M2bl8GDB1O+fHkCAlIfA/bixQuePHmi8Xi/C9mbscE21prdkm2srVMdN/woOpqEhARsrG3ee4+Nehx1RGQkhoaGZLGw0FzHykY9ZjsiKjLF//vmtU9Vp0YtenXpTtvuvhQuX5JaTRtQomgxunbo9MnbePd/plgXqcTzwbqIilBvN8W6sLZR13HZUmU4e/4cu/buJiEhgQfhD/FftTzp/RFJ2zE1MWFInwHMmfwDi2bNp6hXYfoOH5TmjQ0frIf/ckxEff7Y+jmLF1CiWjnK1arK/Qf3WTh9zmdv42O+pWP/YXg4f/51lCb1Gn5i6VKn5HEO0LtLd2ZNns7yeYv5rnJVJs+azvotG9WvZ7OxZezQkcydOpO5U2eSwy47nXp15dJ7DW5f4m15ktdBZCp1EBEZkcp+//RzlVchL0yMTZjrN5+453HExsUxa8FcEhISCI9I+xwU70u13FbWRKZSjhTLbfV55QaYMW82hT29qZrCMJmMoD7urd4vyweO+8epHyefU/5SRUsQGRnBqqA1xMfH8/jJE+b6Jw2LC0+j3CPpsW8jUz1H2iTLmTLHbz4lK5elfI0qhD24z4KZyc/Zbbp0oFjF0tRp1pCihYvQu2uPzyvkJ1KqLkoVL0FkZCQB61ar9/O8xQvV2xfpR+nrltbNWjBzwlRWLVxKswaNWbF2FXMWL/isbQjxKaSh4Rvh7u5O2bJlWbVqFQA3btzgjz/+wNfXN9X3FCxYEAMDA/Vze3t7Hj58+MH/k5CQwKRJk/D09MTa2hpzc3P27NlD6OsEQjdv3iQ+Pp6SJd+2qGbNmpX8+fOrn58/f56EhATy5cuHubm5+nHo0CFCQlK/8z9t2jSyZs2q8eg5qB8lqpZXP7ShxfXE36dYvjqA0UOGszlwPfOmzeTw0T/VP9RTs3PPLkpUK69+KFkX5UqVYVCvfkycOZWilctQt3kjKpQpB4De67sjVpZWtG/ZBq+Cnnh6FGRAz77UrVGbgKA1X/S/d+7ZRQmf8urH13BMdGzVli0BQSybuwh9A31GTBqb4l3vz7Fzz65v9tj/edcOLMzNqVapyme/92s6zgG6d+xCUa/CFMjvjm/bDnRq3Y6AoLXq151z5+H7hk0o6F6AIp7eTB41jsKe3qzZGKRg1GnD2sqK2VN/4Pc//6BU5fKUrVaRp0+fUiC/e5oPDfmaHDx8iBOnTjJswOAM+5879/5Kie8qqB9KHveuLnmZMmoCqzeup7hPeSo3qIGjvSM21jboK5SvIq11bNOOzWs2sHT+Ygz0DRg5Ifk5e9bk6WxeHcQPE6dy+MifBK7/su+ur42rS14mj53A6qB1lKhclip1vsPRwQEbaxv09LT3862Er+26pX2LNpQsWpz8rm40b9SUwb0HELR1Iy9fvlQ0roygSlAp9tBFmZQOQHw6X19f+vTpw6JFiwgICCBv3rxUqlQp1fUNDTW7Aerp6X00gdfMmTOZP38+8+bNw9PTEzMzM/r37/9ZJ5+YmBgMDAw4ffq0RkMHkGreAoARI0YwcOBAjWVx4dHExLwd8/8yPimOyKgostlmUy+PjIoif76Uky9aWVpiYGCQ7M5fZFSkukusrY0N8fHxPHn6VOMOZ+SjSHV3cVtrG85fuvjeNqLUr30qv2VLqFezNk3rNwIgn6sbcc+fM2H6ZLp28E31Ar5K+UoaY+7e7JMU68LtP9SFta26LCnWRVQktu8kHG3fsg3tWrQmPCKCLFksuBsWxjx/P3I6OKZadq+ChTh28q9UX/8UGVUPn8PK0gorSyvy5MqNSx5nfBrVJvjieQoX8vrsbb1RpXwlvDzeKec3cuyrVCq27/yFejXrJDsHfYqv7Th/n6dHIfwDVvDy5UuNIQXvKuRRkDNp2PX4bXk0E8NFRkUlu7v1hq2NbSr7/fOSBpctXYZff/yFR9GPMDDIRBYLCyrX+u6Dn/O0kmq5H0Wlmvw4xXI/+rxynzh9gtt371D2O83v14EjhlDUuwgBSz7cKPxfVClfES+PtzkT1Mf9o0iy2b49J0U+iko10bBV1tSPk8/d73Wq16RO9ZpEREViamwCenqs2bSenA45P2s7qUmPffvmfcnPFZG4u+XXeJ/GOdvZme/q1yL4wjkKe3qr18mRPamnZ15nFxISEpg4fQrtW7VNdl3zpZSsizo1alGnRi0iIiOTxujr6bFmw3pyOqb/51uXfI3XLe/y8ijEq4QE7obdwzl3ni/alhDvkibLb8j333+Pvr4+QUFBrFmzhk6dOn1RNuzMmTNrDHkAOHLkCA0aNKBNmzZ4e3vj4uLCtWvX1K+7uLhgaGjIyZNvE4E9fvxYY50iRYqQkJDAw4cPcXV11Xi8GaKREiMjI7JkyaLxsLayJpeTk/qR19kFWxsb/jr1NmdEzLMYzl26gHcqP+oMDQ3xyO+ukbwsMTGR46dO4l0o6cTv4V6ATJkycfyd7f7z7y3C7t/H2zNpu96eXlwPuaFxMXDsxHHMzczI6+ySarne9/z582SNCQavn3/oLriZmRm5cjqpH19UF6f/Q108uJ9su3p6ethly4axkTG/7ttNjuzZ8cif+qwSV65dJdsXjndOtR5Op8ExcfptPfxXqteNeV96Z8DMzOybPPZPnjlN6J3bNK7X4L+X+ys7zt915fo1slhkSbWR4c06trZpN67f0NAQD/cCHD/5NtbExET+OnlCvY/e5+3pqVE2SNpnqa3/MVaWVmSxsOD4qRNEPYqicsXUG7nTStJ+/MxyF/LUWB8+v9y+7Tqybd0mtqzZoH4ADO03iEljxn9+QT6BmWlqx/3bY/jtcZ/yOcrQ0BCPfO4cP61ZX8dPn8S74H/b77bWNpiamrL7wF6MMmfWyBHzJdJj3+Z0cMTWxlZjnZhnMZy/eOGD+//NOTv+ZeoJfFUqFa9evSJRlfbjrL+GurC1SdrPe/bvSdrPJUunRdHEa1/7dcuV61fR19dP89mShJAeDd8Qc3NzmjdvzogRI3jy5AkdOnT4ou3lyZOHw4cP06JFC4yMjLC1tcXNzY2tW7dy9OhRrKysmDNnDg8ePMDDwwMACwsL2rdvz5AhQ7C2tsbOzo5x48ahr6+vbvTIly8frVu3pl27dsyePZsiRYoQHh7OgQMH8PLyok6dOv85Zj09Pdo2b8WywJXkdsqFo70DfsuXYGebTWPKOd/e3alWqQqtmjUHkrJMj5o0joLuBShUsBDrNgYR9zyOhnWTMuJbmFvQuF4DZiyYQ9YsWTAzM2fq7Bl4F/JSn8DLlixN3jzOjJg4hoG9+hEZGcHCZYtp0eR7jR8db+Zkj42L5VH0I65cu4qhoaH6B1ml8hVZs2E97vny41WwEKF3brNw2RIqla/4WXdK9PT0aPt9K5atfl0XDg74LUuhLvq8roumr+uiRRtGTX5dFx6FWLfpE+pijmZdAKxav4bypcugr6fP/kO/sWJtILMnTVeX4eddOzDMZIh7vqS7J/sPHWT7/35hwvAxn1zGz66HnK/r4c0xUeGdeujbnWoV36mH5m0YNeWdetj8uh7emSUhIjKCiMhIdab16yE3MDM1xT5HDrJmycq5i+e5cPkSRb0KkyVLFm7fvc3C5f44Oeb8ot4MqZbzKz/2ISkJpFfBQrjldU27cit0nP/+52EioiLxLuiJkZERR0/8xYo1q2jfqq36/67dFISjvQOuLnl58eIF23b8xInTJ1k27/Onq/2Qdi1bM2riOAoW8MDToyBr39uPI8ePwS6bHf179QGgTfNWdOzehdXr11KhXHl279vDxcuXGDditHqbjx8/JuzBfR6GhwNw699bQNKPjjc9Xrbv+BmXPM5YW1lx9vw5fpgzi7YtW2fYXa92LVsnHb9vyr1J83M6csLrcvd8p9w9Uij38BTKHZFyud883pcjR44M6ckBr4/7Zi1fH/dOONo74rdiCXY2753X+vWgWsXKtGry5rhvzagp4yno7kGhAgWTzmtxcTSsU0/9nojICCKiIgm9mzRl9vWbr89r2ZPOawBB2zZRuJA3piYmHDt5nNmL59O/e59kOU2+RFrvWz09Pdo0b8XSwBXkeudckc02mzrXxrkL57lw+SJFvYuQxcKC23fv4Ld0CU45c6p/gO/cvYtMmTKRL68rhpkzc/HyJeYvWUgNn+/SPMmvknUBELRlI4U9vTE1NeXYib+Ys3A+/Xum7X5OL6ZGpuSyc1Y/z2nrhLtTQR4/iyYs6q6CkX2cktctZy+c4/zFC5QoWhwzU1OCL5xjxoI51K1ei6xZsmRoPShBV5MyKkUaGr4xvr6+rFy5ktq1a+Pg4PBF25o4cSLdunUjb96kC2SVSsXo0aO5efMmNWrUwNTUlK5du9KwYUMeP36sft+cOXPo3r07devWJUuWLAwdOpTbt29jbGysXicgIIDJkyczaNAg7t69i62tLaVLl6Zu3bpfFDNApzbtiYuLY/z0KTyNeUpRr8L4z12oMeXc7bt3ePQ4Wv28lk91Hj16hN8KfyIiI3F3y4f/3IUa3b6H9RuEvp4+/UcMJT7+JWVLlWHMkOHq1w0MDFg0az6TZk6jTZcOmJiYUL9WXXp36a4RX9P2rdR/X7pymf/t3Y1DDnv2bt8JQLcOvujp6bFw6WIehodjZWVJ5XIV6du913+ri+dxjP/hnbqYk0JdRL9XF9GP8FvuT0TU67qY815d9H1dFyPfqYvBw9/91/x57AjLV6/k5ct48ru5sfCHOeo8DW/4B64g7H4YBgYGOOfOw6yJ06he1eezy/nRemj9+piY8U49zP6EYyL69THxph5ma9bDpp+2sWTVMvXz9r06AzB55Dga1qmPsbEx+w/9xqKVS4l7Hkc2G1vKlSpDt0nTP3jH+z+X8ys/9p/GPGX/wQMMT+Ox7Uod55kyZWLjti3MWDAHlUpFrpxODOk7UD3sCZIygc9cOJeH4eEYGxuTz9WVFfMXU7JYiTStg5rf1SAq+hGLli1J2o/58uM/z0/dVTrswX11fhSAwl7eTJ80BT//xcxf4kdup1zMnzFHowHo4B+HGDNpvPr5kNEjAOjRuSs9X+/bW6H/Mn+xH4+fPMbR3oEuHX1p17J1mpbtQ9TlXv663G758Z/7Trnv39cYT17Yy5vpE6fgt3Qx8/0/UO7J49XPh4x5XW7ft+X+GnRq3Z64588ZP2Nq0nHvWRj/2Qs+fNxXe++85prKeS3g7fCP9r26AK/Pa7WTGiTOX7rIopXLiI2LxTlXHsYOGUn9mv/9JkFK0mPfdmqbdK6YMH0yT2OeUsSrMP7z/NR1ZmxszIHff2Px8nfO2aXL0rXjD+pzdiYDA1atDeTf26GoVCocctjTsmlz2rZIv+NeiboAuHDpIouXL03az7nzMHb4SOrV+vLrtIxQKLc3a4ZsUz8f0XwCANuPbmJEwAClwvpkSl23ZDY05Nf9e1i8aikvX8bj6OBA2+ataN+iTfoXWugcPdWXZiwTOu/Zs2c4Ojoye/bsDyan/C/io2LSdHvfDF39VOpquXV1EJuO7m+VgXYk1PtsOrq/9XT0Dpoqk66e2HST11A3pUNQxPlp1z6+khYytE0959rX7HiBKYr971KXRyn2v5UiPRrEZztz5gxXrlyhZMmSPH78mIkTJwLQoMF/G5MthBBCCCGEEEJ7SEOD+E9mzZrF1atXyZw5M8WKFeOPP/5I0+RnQgghhBBCCCG+TdLQID5bkSJFOH36tNJhCCGEEEIIIcQnkWSQGUsG0AkhhBBCCCGEECLNSI8GIYQQQgghhBBaTXo0ZCzp0SCEEEIIIYQQQog0Iz0ahBBCCCGEEEJoNVWC9GjISNKjQQghhBBCCCGEEGlGGhqEEEIIIYQQQgiRZmTohBBCCCGEEEIIrSZDJzKW9GgQQgghhBBCCCFEmpEeDUIIIYQQQgghtFqiSno0ZCTp0SCEEEIIIYQQQog0Iw0NQgghhBBCCCGESDMydEIIIYQQQgghhFZLVKmUDkGnSI8GIYQQQgghhBBCpBnp0SCEEEIIIYQQQqslSDLIDCU9GoQQQgghhBBCCJFmpEeDEEIIIYQQQgitJtNbZizp0SCEEEIIIYQQQog0Iz0ahPgKJRobKB2CIuINXigdgiIyxxkqHYIy9JQOQBlPfrmsdAiKsK6QV+kQFHHOYr/SISjCw6CO0iEoQu92jNIhKOL8tGtKh6AIzxH5lA5BEVeW31M6BPENkIYGIYQQQgghhBBaTaa3zFgydEIIIYQQQgghhBBpRno0CCGEEEIIIYTQapIMMmNJjwYhhBBCCCGEEEKkGWloEEIIIYQQQgghRJqRoRNCCCGEEEIIIbSaDJ3IWNKjQQghhBBCCCGEEGlGejQIIYQQQgghhNBqMr1lxpIeDUIIIYQQQgghhEgz0qNBCCGEEEIIIYRWkxwNGUt6NAghhBBCCCGEECLNSEODEEIIIYQQQggh0owMnRBCCCGEEEIIodUSZOhEhpIeDUIIIYQQQgghhEgz0qNBCCGEEEIIIYRWk+ktM5b0aBBCCCGEEEIIIUSakYYGIYQQQgghhBBCpBkZOiGEEEIIIYQQQqslSjLIDCU9GoQQQgghhBBCiG9MVFQUrVu3JkuWLFhaWuLr60tMTEyq69+6dQs9Pb0UH1u2bFGvl9LrGzdu/KzYpEeDEEIIIYQQQgitpo09Glq3bk1YWBj79u0jPj6ejh070rVrV4KCglJc38nJibCwMI1ly5YtY+bMmdSqVUtjeUBAADVr1lQ/t7S0/KzYpKFBAR06dGD16tUAZMqUiZw5c9KsWTMmTpyIsbGxwtF9G06d+ZuA9Wu4dPUy4RERzJ8+i2qVqnzwPTv37GLVujWE3g7F3Nyc8qXLMbhPPyyzWn5RLGH3w5g4cxonT5/C1NSU+rXq0r9HbzJlSvp4nfj7FJ16dUv2vt937sHWxvaL/vf7Nm7aSOCa1URERpIvXz5GDB2GZyHPVNffu28vfksWc+/ePXLlysWAvv2oUL6C+vXR48bwy44dGu8pW6Ys/osWq5/XrFOLe++dsPr16Ytvx05pVKrPt3njFtasXk9kRCRu+dwYOnwQhTwLprhuyI2b+C9eyuXLVwm7F8agIf1p1aalxjqrVgZy8MDv3PrnX4yMjPAq7Enf/r3Jkyd3RhQHgA1bNxG4bg0RUZHkd83HiEFD8SxYKNX19xzYh9+yJdwLu0cup1wM6NWXimXLq19XqVQsWu7Ptp+38zTmKYU9vRkzdCS5c+VSr3PpymXmLlrAxcsX0dc3wKdKVYb2G4SpqSkAV69fY+WaAP4OPkv042gcctjzfeOmtGneKv0qgrSvi/0HD7B5+zYuXbnM4yeP2bJmA+758mtsY8L0yfx18gThEeGYmpjg7enNgF59ccnjnG7l/BiVSsWKw9vYceYgT188wytnPgbX6oSTdY5U37P99H62/72fsOhwAJyz5aRj+UaUcS2sXqf32smcCb2s8b4GRaoytLZvupTjc6lUKhauXc6WX3/m6bMYinh4Mq7PUPI45kr1PSfPn2HV1nVcvH6V8KgIFo79AZ+ylTTW2fvnQTbt2s7F61d4/PQJPy5aQ4G8+dK7OJ9sz7a/2BH0J9FRMeR2zUHHAXVx9ciZ4rrHf7/IT2sOcf9uFAmvEsiR04a6LctRsWYRjfXu3HpI0OK9XDr7D4kJiTjmsWPQlJbY5rDMgBKlbMPmTQSuS/oey++WjxFDhn34871/H37+i99+vvv0pWK5t99ji5f58+vePTx4cJ9MhoZ4uBegb8/eeL3+brx77x5LVy7jxKmTRERGks02G3Vr1aZrp84YGhqme3lTo1KpWLRxJVv37eBpbAxF3D0Z03UQuR2cUn3PqYtnCfh5A5dCrhL+KJL5w6ZQrVRFjXVGLZzCzwd3aywrV7gkS8fOTpdyfIxKpWLRCn+27tjO06cxFPHyZszgEeR2Sv3zDLBh22YCgt58D7gxcsBQPD3eHidbfv6R/+3bzeWrV3gW+4yju38ni4WFxjaqN6nLvfua1y/9u/emc9uOaVfANFbcrRS+NXpSMLcndpY56LWoEwfO7v74G4XiXrx4wYsXLzSWGRkZYWRk9J+3efnyZXbv3s3JkycpXrw4AAsXLqR27drMmjULBweHZO8xMDAgRw7N64Tt27fz/fffY25urrHc0tIy2bqfQ4ZOKKRmzZqEhYVx8+ZN5s6dy9KlSxk3bpzSYX0z4p7Hkd8tH6MGDfuk9f8OPsvIieNoXK8BPwVtYc6UH7hw+SLjpk3+ojgSEhLoOagf8fGvWLcsgCljJvDzrh34LfdPtu7OTT/y+8496oe1lfUX/e/37d6zh5lzZtO9azc2BW0gv1s+uvfqSWRUVIrrnw0+y7CRI2jUoCGbgzZStXIV+g0cwPUbNzTWK1e2HL/t3a9+zJg2Pdm2evXoqbFOyxYtk62TUfbu3secWfPp2s2X9RtXky+/K7179CMqMuV6eP78OY45HenTtyc2tjYprvP3qTM0a96UwLUrWbx0Aa9evaJX977ExcalZ1HUdu/bw8z5c+jeuSubVweRz82Nbv17pb5vzwUzbOxIGtdrwJbVQVStWJl+QwdyPeTtvl21djVBmzcwZthI1q9YjYmJCd3691J/CT4MD6dL3x7kyunE+pVr8J/nR8jNm4ye9PY8denKJaytrJk2fjLbg7bQpYMv8xf7EbTl87rWKV0Xcc/jKOJdmAG9+qb6fz3cCzBp9Dh+3rAN/3mLQKWiW79eJCQkpHkZP9X6YzvZenIPQ2p1ZHmHiRgbGjFww3RevHqZ6nuyWVjTvUoLVvlOYWWnyRTLXZDhW+ZwM/yOxnr1C1fhl36L1I9e1ZT7TL9vxZa1rPt5M+P7DmPTvBWYGpvQZVR/Xrx8kep74p7Hkd/ZjTG9Bn9gnecULejNoE690iPsL3J0/3nWLPyVJp2qMH1VT3K75mDqwEAeP0q5e6x5FhMata/MpKVdmbG6N5XrFGXJ1O2cPX5dvc79O5GM67Ech9y2jPPzZcbq3jTpUBlDI+XuQe3eu4eZ82bTvXM3Nq8NIp9bPrr1+cj32OgRNG7QkC3rNlC1UmX6DR6o8T2WO1duRg4ZxrYNW1izPABHBwe69e5J1KOkbf5z6x8SE1WMHTGa7Ru3MnTAIDb/uJX5ixZmSJlTs2p7EOv/t42x3QcTNH0pJkYmdJs06MPH+Yvn5M/jyqguAz+47fJFSvH7yp/UjxkDx6dt8J9h1frVrN+6kbFDRhK0fDUmxiZ0G9g72Q+yd/26fy8zFs6hR6eubFm1nvyu+eg2sDeRj94eJ8+fP6d8qTJ0affhRoPenbvz+y971I9WTVukWdnSg4mRKVfuXGRi0EilQxGfadq0aWTNmlXjMW3atC/a5rFjx7C0tFQ3MgD4+Pigr6/P8ePHP2kbp0+f5uzZs/j6Jr+Z0KtXL2xtbSlZsiSrVq1C9ZnTg0pDg0KMjIzIkSMHTk5ONGzYEB8fH/bt2wdAYmIi06ZNw9nZGRMTE7y9vdm6dav6vY8ePaJ169Zky5YNExMT3NzcCAgIAN6Ou9m4cSNly5bF2NiYQoUKcejQIY3/f+jQIUqWLImRkRH29vYMHz6cV69eqV+vXLkyffv2ZejQoVhbW5MjRw7Gjx+vfl2lUjF+/Hhy5cqFkZERDg4O9O379iL9xYsXDB48GEdHR8zMzChVqhS///57mtVfhTLl6NutJz6Vq37S+sEXzuFgb0+b71uS08GRot5FaNawMRcuXdRYb+sv26nXoglFK5WhXvPGbNy2+YPbPXriL0Ju/cP08ZNwz5efCmXK0btLDzZu20x8fLzGutZW1tja2Kof+vpp+/Fbs34tTRo1pmGDhuR1ycuYUaMxMTbmp59/SnH99UFBlCtTlo7tO+Di4kLvnr0o4F6AjZs0fyRmzmyIra2t+pElS5Zk2zI1NdVYx9TEJE3L9jnWrd1Ao8YNqN+wHi55XRg5ejjGxsb8/NOOFNcvWMiD/gP7UqNWdTJnzpziOn5L5lO/QV3yurqQL38+Jkwcy/2w+1y+fCU9i6K2ZsN6mjRoRKO6Dcjr7MLYYaMwMTZm+86fU1x/3aYgypUuQ8c27XFxdqFPt5545Hdnw9ZNQNLnd92mILp27EzVipXJ75aPqeMmEh4Rzm+Hfwfg0JHDZDLIxKghw3HOnYdCHgUZM2wk+w4eIPR2KACN6jVk+MAhlChaDCfHnNSrVYcGdetx4Pffvpm6AKhXqy49fLtSukSpVP9vs4ZNKF6kGI4ODni4F6B3t57cf3Cfe2H30ryMn0KlUrH5xG7al29IhfzFcc2eizH1exDxNJo/rp5O9X3l8xWlrGthnKxzkMvGnm5VvsckszEX72o2MBoZGmFjbql+mBmZpneRPolKpWLN9k10b9mRamUqkt/FjelDxvEwMoL9Rw+n+r6KJcrSv0N3vitXOdV1GvjUoldrX8oWKZEOkX+Z/206QrV6xalSpxg5ne3oPKQ+mY0MObgz5X1dsKgLJSt5kDOPHTly2lD7+7Lkypudq8H/qtfZuGw/Rcrko02vmjjncyBHThuKVyhAVivzFLeZEdYEraNJw8Y0qt+AvC55GTvi9ef7l59SXH/dxg1J32NtX3++e/TCw70AG95p7KxTsxZlSpXGKWdOXPPmZUj/QcQ8i+Ha9aRGl/JlyzF53ATKli6DU86cVKlUmQ5t2rH/YPqdxz5GpVKxdudmujZtR9WSFcifx5WpfUfxMCqSAyf+SPV9FYqWpm+rLviUrpjqOgCZDQ2xtbJRP7KaW3xw/fSiUqlYuzmIru19qVqhMvld3Zg6ZgIPI8I58Mfvqb5vzaZ1NK3XiEZ16id9DwwZibGR5vdA2+at6Ny2I14FU+/VCWBmaqZxbabk9cun+OPCQeb/NIP9Z6QXw3+RqFIp9hgxYgSPHz/WeIwYMeKLynP//n3s7Ow0lmXKlAlra2vu37//SdtYuXIlBQoUoGzZshrLJ06cyObNm9m3bx9NmjShZ8+eLFz4eQ2w0tDwFbhw4QJHjx5V/8iZNm0aa9aswd/fn4sXLzJgwADatGmjbiwYM2YMly5d4tdff+Xy5cssWbIEW1vNLvhDhgxh0KBBnDlzhjJlylCvXj0iIyMBuHv3LrVr16ZEiRIEBwezZMkSVq5cyeTJmnf3V69ejZmZGcePH2fGjBlMnDhR3Riybds2dU+M69ev89NPP+Hp+fZk3rt3b44dO8bGjRs5d+4czZo1o2bNmly/fh0leBfy4v6DBxw++icqlYqIqEj2/XaACmXLqdfZuWcXi5b707d7L37ZsJW+PXqzcJk/P/8v5R+oAMHnz+GW1xVb67d3wsuVLkPMs2fcuBmisW7Tdi2pXLc6nfv25O/gs2lavvj4eC5fvkzpUm9/KOnr61OqVCmCz51LNfZSpTR/WJUtUybZ+qdOnaJStSrUa9SASVOnEB0dnWxbqwIDqFClEt+3bE7A6kCNRquMFB8fz5XLVyhZuqR6mb6+PiVLl+D8ufNp9n/eJNlJqdElrcXHx3Pp6mWNH8H6+vqULlGK4POp7NsL55P9aC5buox6/Tv37hIRGaGxjoW5BZ4FC6nXefkyHkNDQ40GMePX3fs+dPzGxMSQNUvWzyvkJ0qPuvgvYuPi+Ol/v+Do4EiO7P+9S+GXuBcdTuSzaIrneTskyNzYFA/HvFy4+2nn2YTERPZfPMbz+BcUcnTVeG3fxSPUntONNsuGseTgRp7Hp353MSPduX+PiEeRlHmnMcDCzBwv94IEX067z/jX5FX8K25evYdnibzqZfr6+ngWz8v1C7c/+n6VSsX5UyGEhUZQoHAeIOmGxpmjV7F3smXKgEC61JnGqC7+nDx8Kb2K8VHx8fFcunKZ0iXf+3yX/MDn+/y5z/p8x8fHs3X7j1iYm5M/X+rDYp7GxJA1a/qf31Nz50EYEdFRlPF+e5fSwswcL7cCBF+9+IF3fpqTF85SsUM96vZuxcSls4h++viLt/lfJH0XRVKmuOZ3kZdHIYIvpL4PL129QukSmt/zpYuXJPjC558DVqwLpFytqjTt0IpV69codv0itJ+RkRFZsmTReKQ2bGL48OGpJmx887hy5ctvdsXFxREUFJRib4YxY8ZQrlw5ihQpwrBhwxg6dCgzZ878rO1LjgaF7Ny5E3Nzc169esWLFy/Q19fHz8+PFy9eMHXqVPbv30+ZMmUAcHFx4c8//2Tp0qVUqlSJ0NBQihQpou4mkydPnmTb7927N02aNAFgyZIl7N69m5UrVzJ06FAWL16Mk5MTfn5+6Onp4e7uzr179xg2bBhjx45V/7Dw8vJSD+dwc3PDz8+PAwcO8N133xEaGkqOHDnw8fHB0NCQXLlyUbJk0kk/NDSUgIAAQkND1WODBg8ezO7duwkICGDq1Kkp1klKY5f0X8R/0dilN4p6F+aH8ZMZPGYEL1+84FVCApXLV2TU4LdDLxYtX8qQPgP47nUviZwOjtz85yabf/qRBnXqpbjdiKhIbKw1h0C8eR4RldSwk83GlrFDR1KwgAcvX75k2y8/0alXV4JWrsYjf4EvLhvAo+hHJCQkYGOt2fXfxtqGf27dSjn2iAhsbN5b38aGiMgI9fNyZctRrWo1HB0cuXPnNgv8/OjZpxdrA9dgYGAAQKuWrSjg7k7WLFk5ey6Y+QsXEBERwZBBqXdRTi/Rj6KT6sHmvX1iY82tf/5N5V2fJzExkVkz5uJd2AtXt7wff8MXehT9ukzvH2dW1qnv28iI5MeClQ0Rrxsb3zQ6Jj923+7/UsVLMGv+HALWraZN81bExsUxb/FC9fZTcvZcMHv272PRnPmfV8hPlB518Tk2bt3MnEXziYuLI0/uPCxfsFixMdxRz6IBsDbTbNSxNstKZEz0B98b8jCUboHjefkqHpPMxkxtOgDnbG/H+n9XsCw5stpia2HJjYe3WfLbBkIjw5jWdEBaF+OzRTx6fexaah4DtpbWhD/6/H36LXgSHUtiQiJZrTV7GmS1NudeaMqfRYDYmOd0bziDVy9foW+gj++geniVTGpQevLoGc/jXvLzusM07+JD6x41OHv8GrNHbmDswk54FMn43CNvv8eSn5c++Pl+/3xvnfzzfeiPwwwZNZznz5+TzdaWZX7+WFlapbjN0NuhbNi0kUH9lDveI6JfH+dZNWO0sbQm4lHKw0g+VbkipfApVQnH7Pbcvn+X+euX0X3SENZPW6L+Xs8ob66TUrqOSu0c/fZ7IIXrndBbn/X/WzdrQYF8r69fzgczf6kfEZERDO374aEn4tv1rSSDHDRoEB06dPjgOi4uLuTIkYOHDx9qLH/16hVRUVGflFth69atxMbG0q5du4+uW6pUKSZNmsSLFy8++beZNDQopEqVKixZsoRnz54xd+5cMmXKRJMmTbh48SKxsbF89913Guu/fPmSIkWSkjj16NGDJk2a8Pfff1O9enUaNmyYrLvLm0YKSOpCU7x4cS5fTkrudfnyZcqUKYOenp56nXLlyhETE8OdO3fI9ToZnJeXl8Y27e3t1Qdzs2bNmDdvHi4uLtSsWZPatWtTr149MmXKxPnz50lISCDfe3cLXrx4keyH7bumTZvGhAkTNJaNHjqCscO+fBxayD83mT5vFt07dqFc6TJERIQzy28+E3+YxqRRY4mNi+P23TuMnTqRcdPf9uxISEjA3Czp4q77gD6cDj4DgEMOe34O2pLi/3qfc+48OOfOo35exMubO3fvsGZjENPHTfrisqWnWjXeZprN5+ZGPrd81K5fl5OnTql7T7Rr0/btOvnyYZjJkElTJ9OvT99UhyJ8y6ZPnUlIyE1WBi5VOpR05eqSl8ljJzBz/hzmL/FDX1+f1t+3wMbaBj295J3hrofcoO/QAXT37UrZUmVS2OK3r07NWpQpWZrwyHBWr1/LoFHDWLssIE0aQz9mz4UjzNy1Uv18ZvMh/3lbuWwcCOw8lZgXcRy8cpwpO/zxazNa3djQoOjbIWl57XJha25J3/VTufPoATmtsv/3QvwHO37bzfgFP6ifL5moTMK6b5GxaWZmBPbieexLzp8OYc3CX7FzsKJgURcSE5PG2RavUIA6LZJ69uXJZ8+187fZ99MJRRoa0lOJ4iXYun4jj6Kj2fbTjwweOZT1AWuT/cB98PAh3fv2prqPD00bNc6w+HYe2suEpbPUzxeP+uEDa3+Z2uV91H/ny52XfLldqdWzOScvnqG0V/EPvPPL7dyziwkz395sWjwzfRqlP1X7Fm3Uf+d3dcPQ0JCJM6bQv3tvrbx+Ed+ObNmykS1bto+uV6ZMGaKjozl9+jTFihUD4LfffiMxMTFZr+WUrFy5kvr163/S/zp79ixWVlafdc0jDQ0KMTMzw9U16c7CqlWr8Pb2ZuXKlRQqlJQx93//+x+Ojo4a73mzY2vVqsW///7Lrl272LdvH9WqVaNXr17MmjWLtPT+nTo9PT0SE5NaAp2cnLh69Sr79+9n37599OzZk5kzZ3Lo0CFiYmIwMDDg9OnTyVrH389m+q4RI0YwcKBmK7L+s/hU1v48y9cEUMTTm05tklrs8ru6YWJiQrvunenbrQd6r3txjB8xGi8PzfF8+gZJr00YMUbd4+LNjBK21jacfy/Pw5ukVbbWqTeqFPIoyJlzZ7+8YK9ZWVphYGBAZJTmHYDIqMhUZ7awtbVV39lWrx+Z+voAOXPmxMrSitu3b2sM03iXp2chXr16xd1793BOobdNerK0skyqh/cSP0ZGRmFr++XJN3+YOpM/D//J8lVLyZ49Y35wWVm+LtN7ydAiH0Wl2nBna2Ob/Fh4FInt6/XfvC8yKopstm+/XCKjInF3ezvbQp0atahToxYRkZFJ41b19FizYT053zs3hfxzk869u9O0QWO6der83wv7EelRF5/DwtwCC3MLcufKhXchL8p9V4kDhw5Su3rNj7/5C5V3K0rBzm970LxMSOreG/XsMbYWb+96Rj17jFv2D8+GYmiQiZyvZ6Zwt3fmyr2bbDm5J9VZJTwckv7v3aiMb2ioWroCXu5vh4e8fJn0nRAZHYXdO+eqiOgoCri4ZWhsGSWLpSn6Bvo8jtJM/Pg4KgZL69S/U/X19cmRM+k4z5PPnru3wvlp7WEKFnUhi6UpBgb6OObRvLh0zJONK+fSpvfX53r7Pfbe5zsq8sOf7/fP91HJP9+mJibkcspFLqdceHt6Uadxfbb/vJ3OHd8e8w/DH+LbowuFvbwYN3JMGpXq01QpWR6vfB7q5y9f53iKfPyIbNZvj/PI6CjyO6ftce6UwwGrLFkJDbub7g0NVcpX0siZ8PJlUuLa5N9FUeR3S3loy9vvgRSud6y/bCYvL49CvEpI4G7YPY0bREJ7JHwjPRo+VYECBahZsyZdunTB39+f+Ph4evfuTYsWLdS9yu/evUu1atVYs2aNuvc5wI0bNzh8+DC7du1Ktt0dO3bw4MEDSpcujbGxMfv27WPq1KkMHvx5vZUlR8NXQF9fn5EjRzJ69Gg8PDwwMjIiNDQUV1dXjYeT09spjbJly0b79u1Zt24d8+bNY9myZRrb/Ouvv9R/v3r1itOnT1OgQFI3/QIFCnDs2DGNzKFHjhzBwsKCnDlTniorJSYmJtSrV48FCxbw+++/c+zYMc6fP0+RIkVISEjg4cOHycrwoW48nzN26XM9f/5c3Zjwhr5+UiOISpXUKGBnm407d++Sy8lJ45HTIelHVXY7O/UyB3t7ALw9vbgeckPjwujYieOYm5mR19kl1XiuXL+WplNbGhoaUqBAAY6fOKFelpiYyPETJ/B+r2fKG96eXhrrA/x1/K9U1we4/+AB0Y+jsc2WeuxXr15FX18/2Z2ijGBoaIh7AXdOHj+pXpaYmMjJ4yfx9PpwQqgPUalU/DB1Jgd/O4T/8kU45kw+XVB6MTQ0xCN/AY6f1Ny3f508gbdnKvu2kKfG+pB0XL5ZP6eDI7Y2thrrxDyL4fzFCylu09bGBlNTU/bs34NR5syUKVla/dqNmyF06tmVBrXr0rdH7y8q68ekR138VyqVCpXq7YVyejMzMiGndQ71w9nWERszS07fetvQ+exFLJfuhlDI8fN+iCSqVLxMSL1R9/qDpB+eNuaW/yn2L2FmakZuByf1wzW3M7ZWNvx19u1nPObZM85duYh3gf/+Gf+aZTLMhEt+B86fuqlelpiYyIXTN3ErlPpUh+9TqVS8in+l3mbeAo6EvTf0Iux2BNkUmtrS8PXUk8dPvs2U/tHPt6dX8s/38b8++vlOTFSpf8xDUk+GTt27JM0uM3ZCmidr/hgzE1Ny2edUP/I65cHW0pq/zr1N9hkT+4xz1y/jnT/lqZr/q/sRD4l++oRsVp/f+Pq5zMzMyJXTSf3I6+yCrY0Nf53W/C46d+kC3oVS3odJ3wPuHD+l+T1//PRJvD8wnfenuHI96folrWcFEyI9rV+/Hnd3d6pVq0bt2rUpX768xu/C+Ph4rl69SmxsrMb7Vq1aRc6cOalevXqybRoaGrJo0SLKlClD4cKFWbp0KXPmzPnsGRKlR8NXolmzZgwZMoSlS5cyePBgBgwYQGJiIuXLl+fx48ccOXKELFmy0L59e8aOHUuxYsUoWLAgL168YOfOnepGhDcWLVqEm5sbBQoUYO7cuTx69IhOnToB0LNnT+bNm0efPn3o3bs3V69eZdy4cQwcOPCTv1wDAwNJSEigVKlSmJqasm7dOkxMTMidOzc2Nja0bt2adu3aMXv2bIoUKUJ4eDgHDhzAy8uLOnXqfHF9xcbGEnrnbRKsu/fuceXaVbJmyYJ9DnvmLl7Iw/Bwpo2bCEDl8hUYP20yG3/cQrlSZQiPiOCH+bPx9CiI3evuQj27dGP6nJmYm5tTvnRZXr58ycUrl3ny9AntW7ZJMY6yJUuTN48zIyaOYWCvfkRGRrBw2WJaNPle3e1u7cYgHB0ccHXOy4uXL9j2y0+cOH2SZfMWfXE9vKtd67aMHjcGDw8PPAsWYl3QeuLi4mhYvwEAI8eMJrudHf36JM0O0rpVKzp16czqtWuoWL4Cv+7ZzcVLlxg7eqy6jpcs9cenmg+2tjbcvn2HufPnkcvJiXJlkobqBAcHc+7CeUqWKIGZqRnB54KZMXsWdWrXzpBEiSlp07Yl48ZMpEDBAhQq5EHQuo3ExT2nfsO6AIwdNZ5sdtno0y9pCrv4+Hhuhvyj/vvhw3CuXrmGqakJTrmSLuKnT53J7l/3MGfeTEzNzIiISLqTYm5uhrGxcbqXqV3L1oyaNI6CBTzw9CjI2k1BxD2Po2Gd+gCMnDAGu2x29O/ZJ6kOmreiY48urF6/lgrlyrN73x4uXr7EuOGjgaTeSW2at2Jp4ApyOeXC0cEBv2VLyGabjaoVK6v/b9CWjRT29MbU1JRjJ/5izsL59O/ZRz0X+fWQG3Tu3Y2ypcrQrlUbde4GfX0DrK1SHv/8tdUFwOPHjwl7cJ+HEeEA3Pr3FpDUwGJrY8vtu3fYs38vZUqVxtrSigcPH7JyTdKQiQply6dLOT9GT0+P70vWZPWRn8hpnQMHy2wsP7QVWwtLKuQvpl6v7/qpVMxXnKYlki4klhzcSJm83mTPYkvsyzj2XjzKmX8vM6dlUr6aO48esO/CUcq4FiariTk3HoayYN86CudyxzX7h+e1zwh6enq0a9Qc/w2B5HZwImcOBxasWYadjS0+Zd9m2u84vDc+ZSvRun4zAJ7FxRJ67+0Unnfu3+NyyDWyWmTBwS6pATz66WPCHj7g4evj+J87SQ0stlY2ZPtAD7WMUKd5ORZP2UZedwfyeuRk1+ajvHj+ksp1kva136StWNtmoVWPpP28fc0h8ro7kt3Rmvj4V5w5do0/dp/Fd3B99TbrtarAvLGbKFA4DwWLunD2r+ucPnKVcQs7KVJGgHat2jBqwtikz3fBQqzdEJT0PVbv9ffYuNFJn+/eSd9jbVq0pGO3Lqxet4YK5Suwe+/rz/frHgmxcXEsX7WCyhUrkc3WlkfR0WzcspmH4Q+pXi1piGpSI0Nn7HPYM6jfQB49eqSO5/1E2xlFT0+PtnW/Z9nW1eS2z4ljdnv8NqzAztqGaiUrqNfzHdePaqUq0qp2Uk6u2LhYQu/fVb9+92EYV/65TlbzLNhny05sXCyLNwfwXenK2FpZc/v+XeasWUKuHI6UK1IyWRwZUs7vW7Fs9Upy53z9XbR8CXa22ahWobJ6Pd++3alWsQqtmjYHoF3zNoyaMo6C7gUo5FGIdZs1vwcgKX9HRGSk+lrxesgNzExNsc+RIyknw4VznL94gRJFi2NmakrwhXPMWDCHutVrkVWh65dPYWpkSi67t0Obcto64e5UkMfPogmLuvuBdwptZW1tTVBQUKqv58mTJ8VpKadOnZpq3ryaNWtSs+aX99aUhoavRKZMmejduzczZszgn3/+IVu2bEybNo2bN29iaWlJ0aJFGTkyKVdB5syZGTFiBLdu3cLExIQKFSqwcaPmlITTp09n+vTpnD17FldXV3755Rf1F6ajoyO7du1iyJAheHt7Y21tja+vL6NHj04WV2osLS2ZPn06AwcOJCEhAU9PT3bs2KHu3hgQEMDkyZMZNGgQd+/exdbWltKlS1O3bt00qa8LVy7RqVc39fMZC+YA0KB2XaaMmUBEZARhD95O69KwTn2excayYetmZi2Yi4WFBSWLlWBgz7dTcjat3wgTI2MC1q9ltt98TIxNyJfXlTbNU58/3sDAgEWz5jNp5jTadOmAiYkJ9WvVpXeX7up14l/FM3PBXB6Gh2NsbEw+V1dWLFhMyWJpO4VazRo1ePToEYuXLCEiMoL8+fOzxG+xep/cvx+Gvv7bvByFvQszfcpUFi5exAK/heTKlYv5c+bi9npIj76+PtevX+eXnTt4+vQpdtmyUaZ0GXr37KVuRDHMnJnde/bgv9Sfl/HxODo40rZ1G428DRmtes3vePQoGv/Fy4iMiCRf/nwsXDzvnXp4oNG7JfxhOK2av4137er1rF29nmLFi7Js5RIAtm7eBkBX3x4a/2vcxDHUb5A2x/SH1PyuBlHRj1i0fAkRkUnDG/zn+qm7B4fdv6+RN6GwlzfTJ07Bb+li5vv7kdspF/NnzMEt79uZBTq1bU/c8zgmTJ/M05inFPEqjP88P41eRBcuXWTx8qXExsXinDsPY4ePpF6tt+Xd99t+oh49YufuXezc/bbrnUMOe/b89L9vpi4O/nGIMZPHq58PGZM03VQP36707NIdo8xGnD57hrUbg3jy9Ak21jYUK1yUtcsDFOm580brMnWJi3/BjF0riXkei5dTPma3GIZRprdji+8+esDjuKfq59HPnjDpF38iY6IxMzLF1c6JOS2HUdIl6U6goUEmTt26wOaTu3n+8gV2Wayp7F6CDuUbZnTxUtW5WVvinj9n3ILpPImJoWhBL5ZNnodR5rfHbui9Ozx6HK1+fvHaZdoP66V+/sOypLHhDX1qM21wUuPqwWN/MHLO2xw9g6Yl/Vjt1dqX3m27pGeRPqqsjydPop+xecUBoqNiyONmz4jZ7dVDJyIfRKP/Tt6lF89fsnL2DiIfPiazkSGOuW3pPbYZZX3e3vEtWcmDLkPq89PawwTM/R8OuWwZOKUl7t55Mrp4ajWrv/58L339+c6XH/8Fi1L/fHsXZvrkqfgtWcT8xa8/37PmqL/HDPT1+efWLX753w4eRUdjmTUrBT0KsnrZKlzzJg0JOnb8L0Jv3yb09m186tTQiOf8yTMZVPLkOjVqRdyLOMb7z+TpsxiKFvDEf8wsjeP89v17PHrydsaICyFX6TT27XXNjAA/ABpUqcmUPqPQ1zfg2r8h/HJwN09iY7CzsqVs4RL0btmZzIbK5CTo1Lo9cXFxjJ8xhacxTynqVRj/2Qs1votu39X8PNfyqc6j6Ef4rfAnIioSd7d8+M9eqDFkddNP21iy6u1d3fa9kob1TR45joZ16pPZ0JBf9+9h8aqlvHwZj6ODA22bt9LI2/A1KpTbmzVDtqmfj2ielNts+9FNjAhQPmHv1+5bSQapLfRUKTVxiG/WrVu3cHZ25syZMxQuXFjpcL5Y/HtjUnVFolHGZn7+WsQbfB1T6GW0zHHKzFwglPFkx2WlQ1CEdYX0n6Xla3TOYr/SISjCI/OX9178Fund1s3rFrKbKh2BIjxHpD5Nqja7svye0iH8J4GmHRT73x1iAxX730qRHg1CCCGEEEIIIbRaotxfz1CSDFIIIYQQQgghhBBpRno0aJnUEn4IIYQQQgghhBAZQRoahBBCCCGEEEJoNUkGmbFk6IQQQgghhBBCCCHSjPRoEEIIIYQQQgih1aRHQ8aSHg1CCCGEEEIIIYRIM9KjQQghhBBCCCGEVpPpLTOW9GgQQgghhBBCCCFEmpGGBiGEEEIIIYQQQqQZGTohhBBCCCGEEEKrJUgyyAwlPRqEEEIIIYQQQgiRZqRHgxBCCCGEEEIIrSbTW2Ys6dEghBBCCCGEEEKINCMNDUIIIYQQQgghhEgzMnRCCCGEEEIIIYRWS1SplA5Bp0iPBiGEEEIIIYQQQqQZ6dEghBBCCCGEEEKrSTLIjCU9GoQQQgghhBBCCJFm9FQqGawixPtevHjBtGnTGDFiBEZGRkqHk2Gk3FJuXSDllnLrAim3lFsXSLl1q9zi2yINDUKk4MmTJ2TNmpXHjx+TJUsWpcPJMFJuKbcukHJLuXWBlFvKrQuk3LpVbvFtkaETQgghhBBCCCGESDPS0CCEEEIIIYQQQog0Iw0NQgghhBBCCCGESDPS0CBECoyMjBg3bpzOJdiRcku5dYGUW8qtC6TcUm5dIOXWrXKLb4skgxRCCCGEEEIIIUSakR4NQgghhBBCCCGESDPS0CCEEEIIIYQQQog0Iw0NQgghhBBCCCGESDPS0CCEEEIIIYQQQog0Iw0NQgghhBBCCCGESDPS0CCEjlKpVISGhvL8+XOlQxFCCCGEEEJoEWloEEJHqVQqXF1duX37ttKhKObly5dcvXqVV69eKR2KECINhYSEMHr0aFq2bMnDhw8B+PXXX7l48aLCkaUvXS230C1ynAvxbZCGBiF0lL6+Pm5ubkRGRiodSoaLjY3F19cXU1NTChYsSGhoKAB9+vRh+vTpCkeX/qKjo1mxYgUjRowgKioKgL///pu7d+8qHFn6evXqFfv372fp0qU8ffoUgHv37hETE6NwZCItHTp0CE9PT44fP86PP/6o3r/BwcGMGzdO4ejSj66W+/nz58ycOZPatWtTvHhxihYtqvEQ2kVXj/M3pJFFfEv0VCqVSukghFDKggULPnndvn37pmMkytixYwczZsxgyZIlFCpUSOlwMky/fv04cuQI8+bNo2bNmpw7dw4XFxd+/vlnxo8fz5kzZ5QOMd2cO3cOHx8fsmbNyq1bt7h69SouLi6MHj2a0NBQ1qxZo3SI6eLff/+lZs2ahIaG8uLFC65du4aLiwv9+vXjxYsX+Pv7Kx1imrGyskJPT++T1n3T0KRNypQpQ7NmzRg4cCAWFhYEBwfj4uLCiRMnaNy4MXfu3FE6xHShq+Vu3bo1e/fupWnTpmTPnj3Zsa9tPz4HDhz4yevOmTMnHSNRhq4e55DUyFKrVi3KlSvH4cOHuXz5Mi4uLkyfPp1Tp06xdetWpUMUQkMmpQMQQklz587VeB4eHk5sbCyWlpZA0p1fU1NT7OzstLKhoV27dsTGxuLt7U3mzJkxMTHReF0bf4QA/PTTT2zatInSpUtrXJQWLFiQkJAQBSNLfwMHDqRDhw7MmDEDCwsL9fLatWvTqlUrBSNLX/369aN48eIEBwdjY2OjXt6oUSO6dOmiYGRpb968eeq/IyMjmTx5MjVq1KBMmTIAHDt2jD179jBmzBiFIkxf58+fJygoKNlyOzs7IiIiFIgoY+hquXfu3MmuXbsoV66c0qFkiE9tCP/UxsZvja4e5wDDhw9n8uTJ6kaWN6pWrYqfn5+CkQmRMmloEDrtn3/+Uf8dFBTE4sWLWblyJfnz5wfg6tWrdOnShW7duikVYrp69weJLgkPD8fOzi7Z8mfPnmntxdkbJ0+eZOnSpcmWOzo6cv/+fQUiyhh//PEHR48eJXPmzBrL8+TJo3VDRtq3b6/+u0mTJkycOJHevXurl/Xt2xc/Pz/279/PgAEDlAgxXVlaWhIWFoazs7PG8jNnzuDo6KhQVOlPV8vt6Oio8aNL2x08eFDpEBSlq8c56HYji/g2SUODEK+NGTOGrVu3qhsZAPLnz8/cuXNp2rQprVu3VjC69PHuDxJdUrx4cf73v//Rp08f4O2dnxUrVqjv+morIyMjnjx5kmz5tWvXyJYtmwIRZYzExEQSEhKSLb9z545W/0jZs2cPP/zwQ7LlNWvWZPjw4QpElP5atGjBsGHD2LJlC3p6eiQmJnLkyBEGDx5Mu3btlA4v3ehquWfPns2wYcPw9/cnd+7cSoejiBs3bhASEkLFihUxMTFBpVJpbaO5rh7noNuNLOLbJMkghXgtLCwsxdkHEhISePDggQIRZQxdTCw0depURo4cSY8ePXj16hXz58+nevXqBAQEMGXKFKXDS1f169dn4sSJxMfHA0mNLKGhoQwbNowmTZooHF36qV69ukYPHj09PWJiYhg3bhy1a9dWLrB0ZmNjw88//5xs+c8//6wxhESbTJ06FXd3d5ycnIiJicHDw4OKFStStmxZRo8erXR46UZXy128eHGeP3+Oi4sLFhYWWFtbazy0WWRkJNWqVSNfvnzUrl2bsLAwAHx9fRk0aJDC0aUPXT3O4W0jy/3793WukUV8myQZpBCv1atXj7t377JixQp1purTp0/TtWtXHB0d+eWXXxSOMO3pcmKhkJAQpk+fTnBwMDExMRQtWpRhw4bh6empdGjp6vHjxzRt2pRTp07x9OlTHBwcuH//PmXKlGHXrl2YmZkpHWK6uHPnDjVq1EClUnH9+nWKFy/O9evXsbW15fDhwykOpdEGgYGBdO7cmVq1alGqVCkAjh8/zu7du1m+fDkdOnRQNsB0FBoayoULF4iJiaFIkSK4ubkpHVKG0LVy+/j4EBoaiq+vb4rJILW55167du14+PAhK1asoECBAurEiHv27GHgwIFafcNA145zSJqSu1evXgQGBpKQkECmTJlISEigVatWBAYGYmBgoHSIQmiQhgYhXgsPD6d9+/bs3r0bQ0NDAOLj46lZsyYBAQFkz55d4QjTni5nb9Z1R44c0Whk8fHxUTqkdPfq1Ss2btzIuXPn1OVu3bp1siSo2ub48eMsWLCAy5cvA1CgQAH69u2rbngQ4ltmamrKsWPH8Pb2VjqUDJcjRw727NmDt7e3xnf4zZs38fLykql7tdTt27c5f/68TjWyiG+T5GgQ4rVs2bKxa9curl+/rr4gd3d3J1++fApHln50NbFQSjkKIKk7vZGRUbKEgdqoXLly6izt0dHRygaTQTJlykSbNm2UDiPDlSpVivXr1ysdRoZJbfo/PT09jI2NcXV1pUGDBlrXrV5Xy+3u7k5cXJzSYSji2bNnmJqaJlseFRWFkZGRAhGlP109zt/l5OSEk5MTCQkJnD9/nkePHmFlZaV0WEIkIz0ahE4bOHAgkyZNwszM7KNzU2vjfNQ5c+Zk8+bNlC1bVuNuyPbt2xk8eLDWTvWor6//wURZOXPmpEOHDowbNw59fe1KZfPDDz+QJ08emjdvDsD333/Ptm3byJEjB7t27dKqu4KfM9ypfv366RiJskJCQggICODmzZvMmzcPOzs7fv31V3LlykXBggWVDi/NValShb///puEhAR1ct9r165hYGCAu7s7V69eRU9Pjz///BMPDw+Fo007ulruvXv3MmHCBKZMmYKnp6e6R+IbWbJkUSiy9Fe7dm2KFSvGpEmTsLCw4Ny5c+TOnZsWLVqQmJiolcMfdfU4B+jfvz+enp74+vqSkJBApUqVOHr0KKampuzcuZPKlSsrHaIQGqRHg9BpZ86cUSfF+9Dc1JK9WbsEBgYyatQoOnToQMmSJQE4ceIEq1evZvTo0YSHhzNr1iyMjIwYOXKkwtGmLX9/f/Xd7X379rFv3z5+/fVXNm/ezJAhQ9i7d6/CEaadhg0bajzX09Pj/bb1N5/tlGak0Abv52GZPHkydnZ2BAcHs3LlSq38IfLmbmZAQID6R+bjx4/p3Lkz5cuXp0uXLrRq1YoBAwawZ88ehaNNO7pa7po1awJQrVo1jeVvZl7Q1s82wIwZM6hWrRqnTp3i5cuXDB06lIsXLxIVFcWRI0eUDi9d6OpxDrB161Z1r7wdO3Zw8+ZNrly5wtq1axk1apTW7nPxDVMJIXTWixcvVJ07d1ZlypRJpaenpzI0NFTp6+ur2rRpo3r16pXS4aWbqlWrqjZt2pRs+aZNm1RVq1ZVqVQq1Zo1a1T58+fP6NDSnbGxsSo0NFSlUqlUffv2VXXt2lWlUqlUV69eVVlaWioZWrrat2+fqmjRoqrdu3erHj9+rHr8+LFq9+7dquLFi6v27t2rdHjppnTp0qrZs2erVCqVytzcXBUSEqJSqVSq48ePqxwdHZUMLd04ODioLl68mGz5hQsXVA4ODiqVSqU6ffq0ysbGJqNDS1e6Wu7ff//9gw9tFx0drZo8ebKqWbNmqlq1aqlGjRqlunfvntJhpRtdPc5VKpXKyMhIdfv2bZVKpVJ16dJF1a9fP5VKpVLdvHlTZWFhoWBkQqRMejQIocMyZ87M8uXLGTNmjE5lbz569Cj+/v7JlhcpUoRjx44BUL58eUJDQzM6tHRnZWXF7du3cXJyYvfu3UyePBlIuvunzXf++vfvj7+/P+XLl1cvq1GjBqampnTt2lWdl0Xb6GIelsePH/Pw4cNk3abDw8PV+VksLS15+fKlEuGlG10td6VKlZQOQVFZs2Zl1KhRSoeRYXT1OAfInj07ly5dwt7ent27d7NkyRIAYmNjZcYJ8VWShgYhBLly5SJXrlxKh5FhnJycWLlyJdOnT9dYvnLlSpycnICk+cm1MblS48aNadWqFW5ubkRGRlKrVi0gaeiQq6urwtGln5CQECwtLZMtz5o1K7du3crweDKKpaUlYWFhODs7ayw/c+YMjo6OCkWVvho0aECnTp2YPXs2JUqUAODkyZMMHjxYPZzmxIkTWpfoV1fLffjw4Q++XrFixQyKRBnR0dGcOHGChw8fkpiYqPGaNg6B1NXjHKBjx458//332Nvbo6enp54t6vjx47i7uyscnRDJSTJIIXTMx5JevksbE2BCUpLAZs2a4e7urr5QOXXqFJcvX2bbtm3UrVuXJUuWcP36da2rg/j4eObPn8/t27fp0KEDRYoUAWDu3LlYWFjQuXNnhSNMHxUrVsTY2Ji1a9eqp6p98OAB7dq14/nz5xw6dEjhCNPH4MGDOX78OFu2bCFfvnz8/fff6nK3a9eOcePGKR1imouJiWHAgAGsWbOGV69eAUkzjrRv3545c+Zgbm7O2bNnAShcuLBygaaxD5V77ty5mJmZaWW5U0rY+25eJW3uqbVjxw5at25NTEwMWbJk0Si3np4eUVFRCkaXPnT1OH9j69at3L59m2bNmpEzZ04AVq9ejaWlJQ0aNFA4OiE0SUODEDqmSpUqGs///vtvXr16lSx7c7Fixfjtt9+UCDFD3Lp1C39/f65duwZA/vz56datGzExMRQqVEjh6ERau3HjBo0aNeLatWvqXiu3b9/Gzc2Nn376SWt7c7x8+ZJevXoRGBhIQkICmTJlIiEhgVatWhEYGKjV3W1jYmK4efMmAC4uLpibmyscUcbQtXI/fvxY43l8fDxnzpxhzJgxTJkyJVmSSG2SL18+ateuzdSpU1Oc5lKb6dpxLsS3SBoahNBhc+bM4ffff2f16tXqYQKPHj2iY8eOVKhQgUGDBikcYcZ48uQJGzZsYNWqVZw6dUqr74ABXL9+nYMHD6bY1Xbs2LEKRZX+VCoV+/bt48qVKwAUKFAAHx8frZ1VRqVScfv2bbJly0ZERATnz5/XmdqjvPEAAECpSURBVDws71OpVOzevVtrZ9oQyR06dIiBAwdy+vRppUNJN2ZmZpw/fx4XFxelQxEZ5MCBAxw4cCDF7+9Vq1YpFJUQKZOGBiF0mKOjI3v37qVgwYIayy9cuED16tW5d++eQpFljMOHD7Ny5Uq2bduGg4MDjRs3pkmTJurhFNpo+fLl9OjRA1tbW3LkyJGsq+3ff/+tYHQiLSUmJmJsbMzFixd1rmHhjX/++YdVq1YRGBhIeHg4Pj4+7Ny5U+mw0s2pU6fYvHkzoaGhyZLh/fjjjwpFpYwrV65QvHhxYmJilA4l3TRu3JgWLVrw/fffKx1KhtLV43zChAlMnDiR4sWLq/M0vGv79u0KRSZEyiQZpBA67MmTJ4SHhydbHh4eztOnTxWIKP3dv3+fwMBAVq5cyZMnT/j+++958eIFP/30U7Is1tpo8uTJTJkyhWHDhikdSoY7dOgQs2bNUs8w4eHhwZAhQ6hQoYLCkaUPfX19ddJPXWpoePHiBVu3bmXlypX8+eefJCQkMGvWLHx9fcmSJYvS4aWbjRs30q5dO2rUqMHevXupXr06165d48GDBzRq1Ejp8NLNuXPnNJ6rVCrCwsKYPn26Vo7Rf1edOnUYMmQIly5dwtPTE0NDQ43X69evr1Bk6UdXj3MAf39/AgMDadu2rdKhCPFJpEeDEDqsXbt2/PHHH8yePZuSJUsCSdmL3/z4Wr16tcIRpq169epx+PBh6tSpQ+vWralZsyYGBgYYGhoSHBysEw0NWbJk4ezZszrX1XbdunV07NiRxo0bU65cOQD+/PNPfvrpJwIDA2nVqpXCEaaPHTt2MGPGDJYsWaL1uUdOnz7NypUr2bBhA66urrRt25bmzZuTM2dOnfh8e3l50a1bN3r16oWFhQXBwcE4OzvTrVs37O3tmTBhgtIhpgt9fX309PR4/3K2dOnSrFq1Squz8aeUCPMNPT09rRwGqKvHOYCNjQ0nTpwgb968SocixCeRhgYhdFhsbCyDBw9m1apVxMfHA0nZm319fZk5cyZmZmYKR5i2MmXKRN++fenRo4fGHV5damjw9fWlRIkSdO/eXelQMlSBAgXo2rUrAwYM0Fg+Z84cli9fru7loG2srKyIjY3l1atXZM6cGRMTE43XtSkrfaZMmejTpw/du3dXJ7cF3fl8m5mZcfHiRfLkyYONjQ2///47np6eXL58mapVqxIWFqZ0iOni33//1Xiur69PtmzZMDY2VigikZ509TgHGDZsGObm5owZM0bpUIT4JDJ0QggdZmpqyuLFi5k5cyYhISEA5M2bV+saGN74888/WblyJcWKFaNAgQK0bduWFi1aKB1WhnJ1dWXMmDH89ddfKXa17du3r0KRpa+bN29Sr169ZMvr16/PyJEjFYgoY8ybN0/pEDJMtWrVWLlyJQ8fPqRt27bUqFFDaxN9psTKyko95M3R0ZELFy7g6elJdHQ0sbGxCkeXfnLnzq10CCID6epxDvD8+XOWLVvG/v378fLySvb9rW3TcYtvnzQ0CCEwMzPDy8tL6TDSXenSpSldujTz5s1j06ZNrFq1ioEDB5KYmMi+fftwcnLCwsJC6TDT1bJlyzA3N+fQoUMcOnRI4zU9PT2tbWhwcnLiwIEDyaax3L9/v3q6S23Uvn17pUPIMHv27OH27dsEBATQo0cP4uLiaN68OYBONDhUrFiRffv24enpSbNmzejXrx+//fYb+/bt07opHhcsWEDXrl0xNjZmwYIFH1xXW89pb+jaLAS6dJy/79y5c+q8IxcuXNB4TRfOceLbI0MnhNBhz549Y/r06alepLyZo1qbXb16lZUrV7J27Vqio6P57rvv+OWXX5QOS6SxJUuW0L9/fzp16kTZsmUBOHLkCIGBgcyfP59u3bopHGH6CA0N/eDruXLlyqBIMt6+ffsICAhg+/btODk50bRpU5o2bUrRokWVDi1dREVF8fz5cxwcHEhMTGTGjBkcPXoUNzc3Ro8erZ7CWBs4Oztz6tQpbGxscHZ2TnU9PT09rf4e08VZCHTpOBfiWycNDULosJYtW3Lo0CHatm2b4kVKv379FIos4yUkJLBjxw5WrVqlEw0NL1++5J9//iFv3rxkyqQbndu2b9/O7Nmz1fkYChQowJAhQ2jQoIHCkaWfN4nyUqONyeLe9+jRI9atW8eqVas4d+6cTpRZ6AZ7e3tmzJghsxDooDt37gCQM2dOhSMRInXS0CCEDrO0tOR///ufOgu/0H6xsbH06dNHPaPItWvXcHFxoU+fPjg6OjJ8+HCFIxRpKTg4WON5fHw8Z86cYc6cOUyZMoXGjRsrFJky/v77b3WPhp49ezJx4kRsbW0VjiptPXz4MMUearowPE7X6OIsBAYGBoSFhWFnZ6exPDIyEjs7O61uSExMTGTy5MnMnj2bmJgYACwsLBg0aBCjRo364CwkQihBN25jCSFSZGVlhbW1tdJhiAw0YsQIgoOD+f3336lZs6Z6uY+PD+PHj9e6hoY3d7Pbt29PlixZNF57/Pgxa9asSfE1beHt7Z1sWfHixXFwcGDmzJk619Dw7rCJdevWMXjwYK1paDh9+jTt27fn8uXLyaZ61NapDgFUKhVbt27l4MGDKTaw/PjjjwpFlv46d+5MUFCQTs1CkNr90RcvXpA5c+YMjiZjjRo1ipUrVzJ9+nSNaZrHjx/P8+fPmTJlisIRCqFJGhqE0GGTJk1i7NixrF69GlNTU6XDERngp59+YtOmTZQuXVqjS33BggXVM49oEz8/P86dO0efPn2SvZY1a1b++OMPnjx5wqhRoxSITjn58+fn5MmTSoehKG3r0NmpUyfy5cvHypUryZ49u84kh+vfvz9Lly6lSpUqOlVu0K1ZCN4k/dTT02PFihWYm5urX0tISODw4cO4u7srFV6GWL16NStWrKB+/frqZV5eXjg6OtKzZ09paBBfHWloEEKHzZ49m5CQELJnz06ePHmSXaT8/fffCkUm0kt4eHiyLqeQlBhUGy/Qt23bxuzZs1N9vVu3bgwePFhrGxqePHmi8VylUhEWFsb48eNxc3NTKCqRHm7evMm2bduSzayi7dauXcuPP/5I7dq1lQ4lw+nSLARz584Fks5h/v7+GBgYqF/LnDkzefLkwd/fX6nwMkRUVFSKjSnu7u5ERUUpEJEQHyYNDULosIYNGyodgshgxYsX53//+5/6Dv+bi9EVK1ZQpkwZJUNLFyEhIR/8Qe3m5qaVPTnesLS0TPaDQ6VS4eTkxMaNGxWKSqSHatWqERwcrHMNDVmzZsXFxUXpMBRx8OBBpUPIMP/88w8AVapU4ccff9TJ2SW8vb3x8/NLNqWrn59fisPkhFCaNDQIocPGjRundAgig02dOpVatWpx6dIlXr16xfz587l06RJHjx7l0KFDSoeX5gwMDLh3716q0zjeu3dPqxNovf9DRF9fn2zZsuHq6qozs43oihUrVtC+fXsuXLhAoUKFkvVQe7e7tTYZP348EyZMYNWqVZiYmCgdjkhnutS48r4ZM2ZQp04d9u/fr74xcOzYMW7fvs2uXbsUjk6I5GTWCSF0XHR0NFu3biUkJIQhQ4ZgbW3N33//Tfbs2XF0dFQ6PJEOQkJCmD59OsHBwcTExFC0aFGGDRuGp6en0qGluSpVqlCqVCmmT5+e4uvDhg3jxIkTOn3xqqssLCwIDg7WmrvhO3bsoG3btsmGy4B2J4OMi4ujUaNGHDlyRCeHAJ46dYrNmzcTGhrKy5cvNV7TxkSYCQkJBAYGcuDAgRSTf/72228KRZYx7t27x6JFi7hy5QqQNE1zz549cXBwUDgyIZKT2xlC6LBz587h4+ND1qxZuXXrFl26dMHa2poff/yR0NBQ1qxZo3SIIh3kzZuX5cuXKx1GhujduzctWrQgZ86c9OjRQz2uNyEhgcWLFzN37lyCgoIUjjL9rF69GltbW+rUqQPA0KFDWbZsGR4eHmzYsIHcuXMrHKFy2rRpo1WzjfTp04c2bdowZswYsmfPrnQ4GaZ9+/acPn2aNm3a6FwyyI0bN9KuXTtq1KjB3r17qV69OteuXePBgwc0atRI6fDSRb9+/QgMDKROnToUKlRIp/Y3gIODgyR9FN8M6dEghA7z8fGhaNGizJgxQ+Pu3tGjR2nVqhW3bt1SOkSRxnbt2oWBgQE1atTQWL5nzx4SExOpVauWQpGln1GjRjFt2jQsLCzUd69v3rxJTEwMQ4YMSbW3gzbInz8/S5YsoWrVqhw7doxq1aoxb948du7cSaZMmbTyjifAH3/8wdKlSwkJCWHr1q04Ojqydu1anJ2dKV++vNLhpQsLCwvOnj1L3rx5lQ4lQ5mZmbFnzx6t3a8f4uXlRbdu3ejVq5f6O9zZ2Zlu3bphb2/PhAkTlA4xzdna2rJmzRqdTP4JSVM2r1y5ksuXLwPg4eFBx44dZapy8VXS3oGpQoiPOnnyJN26dUu23NHRkfv37ysQkUhvw4cPT7ELtUqlYvjw4QpElP6mTJnCX3/9RYcOHXBwcMDe3p6OHTty7NgxrW5kALh9+7Y6OeBPP/1E06ZN6dq1K9OmTeOPP/5QOLr0sW3bNmrUqIGJiQlnzpzhxYsXADx+/JipU6cqHF36ady4sU4OAXJyctKqnimfIyQkRN1bKXPmzOrZgwYMGMCyZcsUji59ZM6cWecSnr5x+PBh8uTJw4IFC3j06BGPHj1iwYIFODs7c/jwYaXDEyIZGTohhA4zMjJKcTzvtWvXyJYtmwIRifR2/fp1PDw8ki13d3fnxo0bCkSUMUqWLEnJkiU/ul7Pnj2ZOHEitra2GRBV+jM3NycyMpJcuXKxd+9eBg4cCICxsTFxcXEKR5c+Jk+ejL+/P+3atdOYWaNcuXJMnjxZwcjSV758+RgxYgR//vknnp6eyXIV9O3bV6HI0tfs2bMZOnQo/v7+5MmTR+lwMpSVlRVPnz4Fkm4QXLhwAU9PT6Kjo4mNjVU4uvQxaNAg5s+fj5+fn84Nm+jVqxfNmzdnyZIlGsMAe/bsSa9evTh//rzCEQqhSRoahNBh9evXZ+LEiWzevBlIShgWGhrKsGHDaNKkicLRifSQNWtWbt68meyC/MaNG5iZmSkT1Fdk3bp1DB48WGsaGr777js6d+5MkSJFuHbtmrq78cWLF7X2R9nVq1epWLFisuVZs2YlOjo64wPKICtWrMDc3JxDhw4lm0FGT09Paxsa2rRpQ2xsLHnz5sXU1DRZA0tUVJRCkaW/ihUrsm/fPjw9PWnWrBn9+vXjt99+Y9++fVSrVk3p8NLFn3/+ycGDB/n1118pWLBgsv2trcPBIOl7euvWrepGBkiaWWngwIGSU0t8laShQQgdNnv2bJo2bYqdnR1xcXFUqlSJ+/fvU7p0aUk2pKUaNGhA//792b59u3os940bNxg0aJDWTn/3ObQtbdGiRYsYPXo0t2/fZtu2bdjY2ABw+vRpWrZsqXB06SNHjhzcuHEjWUPKn3/+qTUzTKTkn3/+UToERcybN0/pEBTj5+fH8+fPgaRcNIaGhhw9epQmTZowevRohaNLH5aWllqb6PJjihYtyuXLl8mfP7/G8suXL+Pt7a1QVEKkTpJBCiE4cuSIxlSHPj4+Sock0snjx4+pWbMmp06dImfOnADcuXOHChUq8OOPP2JpaalsgArTtikPddG0adNYt24dq1at4rvvvmPXrl38+++/DBgwgDFjxtCnTx+lQxRCiM+2adMmhg4dSp8+fShdujQAf/31F4sWLWL69OkUKFBAva6Xl5dSYQqhJg0NQuiguLg4Dhw4QN26dQEYMWKEOmEaQKZMmZg4cSLGxsZKhSjSkUqlYt++fQQHB2NiYoKXl1eKXc11kTY2NERHR3PixIlkc87r6enRtm1bBSNLHyqViqlTpzJt2jT1OHUjIyMGDx7MpEmTFI4ufd25c4dffvmF0NBQXr58qfHanDlzFIoq/YWEhBAQEEBISAjz58/Hzs6OX3/9lVy5clGwYEGlw0tXiYmJ3LhxI9nnG5DzupbR1/9wDn89PT1UKhV6enopJn0WIqNJQ4MQOsjf35///e9/7NixA0j6cVWwYEFMTEwAuHLlCkOHDmXAgAFKhilEhtO2hoYdO3bQunVrYmJiyJIli0byND09Pa0ev/7y5Utu3LhBTEwMHh4emJubKx1Sujpw4AD169fHxcWFK1euUKhQIW7duoVKpaJo0aL89ttvSoeYLg4dOkStWrUoV64chw8f5vLly7i4uDB9+nROnTrF1q1blQ4x3fz111+0atWKf//9N9mwL236sVm0aFEOHDiAlZUVRYoU+WASyL///jsDI8tY//777yevmzt37nSMRIhPIzkahNBB69evZ+jQoRrLgoKC1D+u1q1bx6JFi6ShQUssWLCArl27YmxszIIFCz64rrYmjNNVgwYNolOnTkydOhVTU1Olw8lQmTNnTnGGFW01YsQIBg8ezIQJE7CwsGDbtm3Y2dnRunVratasqXR46Wb48OFMnjyZgQMHYmFhoV5etWpV/Pz8FIws/XXv3p3ixYvzv//9D3t7e62dhaFBgwbcu3cPKysrGjZsqHQ4ipHGA/GtkR4NQugge3t7jh07pk6Wli1bNk6ePKl+fu3aNUqUKMHjx4+VC1KkGWdnZ06dOoWNjQ3Ozs6prqenp8fNmzczMLKvT48ePZg0aZLWzDphZmbG+fPntaaHRmoaN278yetqa1Z6CwsLzp49S968ebGysuLPP/+kYMGCBAcH06BBA27duqV0iOnC3Nyc8+fP4+zsrNEj6datW7i7u6uTJWojMzMzgoODcXV1VTqUdKevr0+JEiXw9fWlZcuWGo1KumL16tXY2tpSp04dAIYOHcqyZcvw8PBgw4YN0hAhvjrSo0EIHRQdHa2RkyE8PFzj9cTERI3Xxbft3Wz0upqZHlLPVQDQrl07AJYsWaJEaOmmRo0anDp1SusbGrJmzap0CIozMzNT52Wwt7cnJCREnZ8gIiJCydDSlaWlJWFhYckaUc+cOYOjo6NCUWWMUqVKcePGDZ1oaDh06BABAQEMHjyYgQMH0rRpU3x9falQoYLSoWWYqVOnqr+jjh07hp+fH/PmzWPnzp0MGDBAaxtRxbdLGhqE0EE5c+bkwoULyaZIeuPcuXPqGQmEbrh58ybdu3dn7969SoeSLj6Wq+BNQ4O2qVOnDkOGDOHSpUt4enomm3NeW6Y0DQgIUDoExZUuXZo///yTAgUKULt2bQYNGsT58+f58ccf1RnqtVGLFi0YNmwYW7ZsQU9Pj8TERI4cOcLgwYO18nN97tw59d99+vRh0KBB3L9/P8XPtzbNPFChQgUqVKjAwoUL2bx5M4GBgVSqVAlXV1d8fX1p3749OXLkUDrMdHX79m11o9JPP/1E06ZN6dq1K+XKlaNy5crKBidECmTohBA6qF+/fuzfv5/Tp08nm1kiLi6O4sWL4+Pjw/z58xWKUGS04OBgihYtqjXJw96XL18+ateurXO5Cj6UpVybksW9659//uHVq1e4ublpLL9+/TqGhobqIWLa5ubNm8TExODl5cWzZ88YNGgQR48exc3NjTlz5mhtt+qXL1/Sq1cvAgMDSUhIIFOmTCQkJNCqVSsCAwMxMDBQOsQ0pa+vr55dICW6NPPAjRs3CAgIYO3atdy/f5+aNWvyyy+/KB1WurGzs2PPnj0UKVKEIkWKMHDgQNq2bUtISAje3t7ExMQoHaIQGqShQQgd9ODBAwoXLkzmzJnp3bs3+fLlA+Dq1av4+fnx6tUrzpw5Q/bs2RWOVGQUbW9o0JVcBQIqVapEp06daN++vcbydevWsWLFCn7//XdlAhNpTqVScfv2bbJly0ZERATnz58nJiaGIkWKJGto0hYy84CmZ8+esX79ekaMGEF0dLTWfocBtG7dmitXrlCkSBE2bNhAaGgoNjY2/PLLL4wcOZILFy4oHaIQGmTohBA6KHv27Bw9epQePXowfPhw9Z0RPT09vvvuOxYvXiyNDEKr6EquApE0Nr9cuXLJlpcuXZrevXsrEFHGUKlUnD59mlu3bqGnp4ezs/NHpwL81qlUKlxdXbl48SJubm44OTkpHVK604XGg09x+PBhVq1axbZt29DX1+f777/H19dX6bDS1aJFixg9ejS3b99m27Zt2NjYAHD69GlatmypcHRCJCcNDULoKGdnZ3bv3k1UVBQ3btwAwNXVFWtra4UjEyLt6Uqugjc+No3pG9o4namenh5Pnz5Ntvzx48dae7fz4MGD+Pr68u+//2o0HDs7O7Nq1SoqVqyocITpQ19fHzc3NyIjI7W2B0NKTp8+zeDBg/n555/JkiWLxmuPHz+mYcOGzJs3D29vb4UiTB/37t0jMDCQwMBAbty4QdmyZVmwYAHff/89ZmZmSoeX7iwtLVOcsnXChAkKRCPEx8nQCSGE0AEfu7MZGxvL9evXtfaHmK7lKvjQNKZvaOt0pvXq1cPExIQNGzaox+cnJCTQvHlznj17xq+//qpwhGnrxo0beHt7U6pUKfr164e7uzsqlYpLly6xYMECTp06xblz57S2N8+OHTuYMWMGS5YsoVChQkqHkyFatWpFgQIFGDNmTIqvT506lUuXLrFu3boMjiz91KpVi/3792Nra0u7du3o1KlTqgmttdkff/zB0qVLuXnzJlu2bMHR0ZG1a9fi7OxM+fLllQ5PCA3S0CCEEDrgU+94jBs3Lp0jESJ9Xbp0iYoVK2Jpaame+u6PP/7gyZMn/Pbbb1r3Y7R3795cvnyZAwcOJHtNpVLh4+ODh4cHCxcuVCC69GdlZUVsbCyvXr0ic+bMmJiYaLweFRWlUGTpJ2/evGzfvj3VWSXOnz9PgwYNtKohsX79+vj6+lK3bl2tS/D5qbZt20bbtm1p3bo1a9eu5dKlS7i4uODn58euXbvYtWuX0iEKoUEaGoQQQiRz5MgRihcvjpGRkdKhiAzg6enJrl27tGaM+7179/Dz8yM4OBgTExO8vLzo3bu3Vg4NK1SoENOmTaNevXopvr5jxw5GjBihtYniAgMDP9hb6/2koNrA2NiYy5cvp9pz6Z9//sHDw4O4uLgMjkykpyJFijBgwADatWuHhYUFwcHBuLi4cObMGWrVqsX9+/eVDlEIDZKjQQghRDK1atXi7NmzWtXd+tChQ8yaNYvLly8D4OHhwZAhQ9R3vXXZrVu3iI+PVzqMNOPg4MDUqVOVDiNDhIaG4unpmerrhQoV+qyZCr41LVu25NWrVzoxRv+NbNmycfXq1VQbGq5cuYKtrW0GRyXS29WrV1PMt5I1a1aio6MzPiAhPiL1QatCCCF0lrZ1dlu3bh0+Pj6YmprSt29f+vbti4mJCdWqVSMoKEjp8MQXOnfuHImJieq/P/TQNjExMZiamqb6uqmpKbGxsRkYUcYIDw+nVq1amJubkyVLFkqXLq1ObKztfHx8mDJlSoqvqVQqpkyZgo+PTwZHJdJbjhw5UjzG//zzT626KSC0hwydEEIIkcy73TK1QYECBejatSsDBgzQWD5nzhyWL1+u7uWgq771/a2vr8/9+/exs7NDX18fPT29FBvLtDHxp76+Pr/99luqw0IiIiL47rvvtK7cnTp14tdff6Vv374YGxuzdOlS7O3tOXjwoNKhpbuQkBCKFStG/vz5GTRokDop4pUrV5g9ezbXrl3j1KlTuLq6KhypSEvTpk1j3bp1rFq1iu+++45du3bx77//0r9/f8aOHUufPn2UDlEIDdLQIIQQIplv/Yfn+4yMjLh48WKyC+8bN25QqFAhnj9/rlBkX4dvfX//+++/5MqVCz09vY8OE8idO3cGRZUxPtawolKptLKBxcnJiRUrVlCjRg0Arl+/ToECBXj27JlO5JY5deoUHTp04NKlS+ocFSqVCg8PDwICAihRooTCEYq0plKpmDp1KtOmTVP3UjIyMmLIkCGMGDEiWSJUIZQmORqEEEJoPScnJw4cOJCsoWH//v1akwBRl73beKBtDQkf888//ygdgiLu3buHt7e3+rmbmxtGRkaEhYWRJ08e5QLLIMWLF+fChQucPXuW69evo1KpyJcvH4ULF1Y6NJFO9PT0GDVqFEOGDOHGjRvExMTg4eHB0qVLcXZ2lmSQ4qsjDQ1CCCGS+VAW92/RoEGD6Nu3L2fPnqVs2bJA0swagYGBzJ8/X+HoRFq7fv06Bw8e5OHDh+rcDW+MHTtWoajSx+c2rPTs2ZOJEydqRbLA96c5NDAw0Lr8Mh9TuHDhjzYuZMmSReuS++qSFy9eMH78ePbt26fuwdCwYUMCAgJo1KgRBgYGyYYFCvE1kKETQgghkvnWu9KnZPv27cyePVudj6FAgQIMGTKEBg0aKBxZ+rl58+Yn7cOgoCAaNGigFZn7ly9fTo8ePbC1tSVHjhwajWZ6enr8/fffCkanPG350amvr0/WrFk19m90dDRZsmRBX/9trvOoqCglwvuqaOP5XJcMGzaMpUuX4uPjw9GjRwkPD6djx4789ddfjBw5kmbNmiVrdBPiayANDUIIIYSW0tfXp1KlSvj6+tK0aVOMjY2VDind5c6dm549ezJs2DClQ/kqacuPztWrV3/Seu3bt0/nSL5+2rLPdZWLiwvz5s2jfv36XLhwAS8vLzp06MDKlSu1rveh0C7S0CCEEDqkSJEiKV6Y6OnpYWxsjKurKx06dKBKlSoKRCfS2tmzZwkICGDDhg28fPmS5s2b4+vrS8mSJZUOLd1oyx379KKrPzo3bNhA/fr1taLXzufS1X2uLTJnzsw///yDo6MjACYmJpw4cQJPT0+FIxPiw/Q/vooQQghtUbNmTW7evImZmRlVqlShSpUqmJubExISQokSJQgLC8PHx4eff/5Z6VC/mLW1NREREQBYWVlhbW2d6kNbFS5cmPnz53Pv3j1WrVpFWFgY5cuXp1ChQsyZM4fw8HClQ0xzzZo1Y+/evUqHIb4y3bp148GDB0qHIcT/27vzqKjr/Y/jrwFkEQTxGuDCNuLVEMwFzSWXq+gBTHJJvTdQBnH9pWK5VyaWuKTXCPEqKcwA7ldJRTTNDRc0Cwo0VBybSCHTXNhc2Ob3h6e5TajXe+/MfOI7r8c5neN8Zv54erRz5D2f7+fzH6utrYW1tbXutZWVFRwcHAQWET0f7mggIjIjEydOhIeHBxYuXKi3vmTJEhQVFWHDhg1YtGgRMjMz8fXXXwuqNIyUlBT89a9/hY2Nzb/dZm0u26sfPXqEf/zjH1iwYAGqqqpgbW2N0aNHY8WKFWjRooXovP9afHy87teVlZVYvXo1hgwZAn9/fzRq1EjvszNmzDB13h+KuX67ba6/b4C7fBo6CwsLBAcH665tzcjIwIABA+rtzklPTxeRR/RUHDQQEZkRJycn5OTk1LvmUa1Wo2vXrigtLcWlS5fQrVs3lJeXC6okQ/v666+RnJyMbdu2wd7eHhEREYiKisL169exePFilJWV4dy5c6Iz/2ve3t7P9TmZTIbvv//eyDV/bOb6A7e5/r4B8/69S0FkZORzfU6pVBq5hOg/w+stiYjMiK2tLbKzs+sNGrKzs3UHBdbV1Unu0MCysrInrstkMtjY2OhtS5WS1atXQ6lU4vLlywgJCUFqaipCQkJ0p/J7e3tDpVLBy8tLbOj/SKPRiE5oMMLDw+Ho6Cg6g4ygtrYW58+fh6enJ5ydnXXrBw4c0D3fTw0PBwjUUHHQQERkRqZPn44pU6YgJycH3bp1AwB89dVX2LhxI9555x0AwMGDB//tvewNTdOmTZ95Onfr1q2hUCiwaNEivavxGrp169Zh/PjxUCgUT300wsXFBUlJSSYuM76qqipoNBq0adMGVlbm8c+dkydPIjExEVevXsXOnTvRqlUrpKWlwdvbG6+88gqAx38nSBpmzpwJf39/REVFoba2Fv369UN2djYaN26Mffv2oX///gCg+7MnIjIlPjpBRGRmNm/ejISEBFy+fBkA0K5dO0yfPh1vvPEGAODBgwe6WyikIjU1Fe+++y4UCoXuxoVz584hJSUF7733Hm7duoVVq1Zhzpw5uoELNUz379/H9OnTdedyFBYWQi6XY/r06WjVqhXmz58vuNA4du3ahbFjxyIsLAxpaWkoKCiAXC5HQkIC9u/fj/3794tOFEqKjw+0bt0au3fvRkBAAHbv3o0333wTx44dQ1paGo4ePYrTp0+LTiQiM8ZBAxERSd7AgQMxefJkjB49Wm99x44dSExMxJEjR5CWlobY2FhcunRJUKVh5OfnP/dnO3bsaMQSMaKjo3H69GnExcUhKCgI+fn5kMvl2LNnD2JiYvDNN9+ITjSKzp0746233sK4ceP0fqj+5ptvEBwcjBs3bohOFMrPzw8HDhyAu7u76BSDsbW1hVqtRuvWrTFp0iQ0btwYcXFx0Gg0eOmll576yBgRkSmYx15CIiLSU1VVhZs3b6Kurk5v3cPDQ1CRcWVnZ2P9+vX11jt37owzZ84AeLy9+McffzR1msF16tQJMpkMT/se4df3ZDIZamtrTVxnfLt378b27dvRo0cPvcdlOnTogKtXrwosM67Lly+jb9++9dadnJxw79490wf9wVy4cEF0gsG5urqioKAALVq0wOeff657LOb+/fuwtLQUXEdE5o6DBiIiM3LlyhWMHz8e2dnZeutS/sETANzd3ZGUlITly5frrSclJem+4bx9+7beAWoNlbkfjHjr1i24uLjUW6+srHzmOR0NnZubG9Rqdb2DPU+dOiWpxwUAwNnZ+bn/LO/cuWPkGnEiIyMxevRotGjRAjKZDIGBgQCAL7/8Eu3btxdcR0TmjoMGIiIzolAoYGVlhX379un+cWoOVq1ahVGjRuHAgQO6QzC//vprXLp0CTt37gTw+FDMMWPGiMw0CE9PT9EJQgUEBCAzMxPTp08HAN3f8Y0bN6Jnz54i04xq4sSJiI6ORnJyMmQyGUpKSnDmzBnMnj0bCxcuFJ1nUHFxcaIT/hBiYmLg5+eHa9euYdSoUbCxsQEAWFpaSvYsEiJqOHhGAxGRGbG3t0dOTo5Zftul0WiQmJiIwsJCAI8PwZw8eXKDv9rxWVJSUtC8eXMMGTIEADB37lx8+umn8PX1xdatWyU1lLhw4QL8/Pxw+vRpBAUFITw8HEqlElOmTEFBQQGys7ORlZWFrl27ik41Cq1Wi6VLl2LZsmW4f/8+AMDGxgazZ8/Ghx9+KLiOjO3hw4eSOsCXiBo+DhqIiMxIt27d8PHHH/O6MzPRrl07rFu3DgMGDMCZM2cwcOBAxMXFYd++fbCyskJ6erroRIOxsLBAt27dMGHCBLz88stYs2YN8vLyUFFRgS5dumDevHnw9/cXnWl0VVVVUKvVqKiogK+vLxwcHEQnmczDhw9RVVWlt+bo6Cioxvhqa2uxdOlSrF+/Hj///LPuhpWFCxfCy8sLUVFRohOJyIxJ57JwIiL6t1asWIG5c+fi+PHjuH37NsrKyvT+k7KTJ08iPDwcvXr1QnFxMQAgLS0Np06dElxmPNeuXYOPjw+Ax4ckvv7665g0aRKWLVuGkydPCq4zrKysLHTo0AGzZs1Cr169UFVVhVWrVqGgoACbNm0yiyEDAFhbW8PX1xfdu3c3iyFDZWUlpk2bBhcXF9jb28PZ2VnvPymLjY2FSqXCRx99BGtra926n58fNm7cKLCMiIhnNBARmZVfDwsbOHCg3rrUD4PctWsXxo4di7CwMOTm5uLRo0cAgNLSUixduhT79+8XXGgcDg4OuH37Njw8PHDo0CG8/fbbAB5fi/fgwQPBdYbVp08f9OnTB2vWrMGOHTugUqnQv39/+Pj4ICoqChEREXBzcxOdaVAjRox47s9KaffKb82dOxfHjh3DunXrMHbsWKxduxbFxcVITEysd/ir1KSmpuLTTz/FwIEDMWXKFN36Sy+91OCv6SWiho+DBiIiM3Ls2DHRCUIsWbIE69evx7hx47Bt2zbdeu/evbFkyRKBZcY1aNAgTJgwAZ07d0ZhYSFCQkIAAN99951kz6awt7dHZGQkIiMjoVaroVQqsXbtWixcuBBBQUHYu3ev6ESDcXJyEp0gXEZGBlJTU9G/f39ERkaiT58+8PHxgaenJzZv3oywsDDRiUZTXFys27H0W3V1daiurhZQRET0Lxw0EBGZkX79+olOEOLy5cvo27dvvXUnJyfcu3fP9EEmsnbtWrz33nu4du0adu3ahT/96U8AgJycHPztb38TXGd8Pj4+eOedd+Dp6YkFCxYgMzNTdJJBKZVK0QnC3blzR3d9p6Ojo+46y1deeQVTp04VmWZ0vr6+OHnyZL1DXXfu3InOnTsLqiIieoyDBiIiicvPz4efnx8sLCyQn5//zM927NjRRFWm5ebmBrVaXe9b/FOnTul+SJGipk2bIiEhod764sWLBdSY1okTJ5CcnIxdu3bBwsICo0ePlvTheBqNBjU1NWjbtq3e+pUrV9CoUSPJ7mCRy+XQaDTw8PBA+/btsWPHDnTv3h0ZGRlo2rSp6Dyjev/99xEREYHi4mLU1dUhPT0dly9fRmpqKvbt2yc6j4jMHAcNREQS16lTJ9y4cQMuLi7o1KkTZDIZnnThkJTPaJg4cSKio6ORnJwMmUyGkpISnDlzBrNmzcL7778vOs9oTpw48cz3n7TLoyErKSmBSqWCSqWCWq1Gr169EB8fj9GjR8Pe3l50nlEpFAqMHz++3qDhyy+/xMaNG3H8+HExYUYWGRmJvLw89OvXD/Pnz8fQoUORkJCA6upqrF69WnSeUb322mvIyMjABx98AHt7e7z//vvo0qULMjIyMGjQINF5RGTmeL0lEZHEFRUVwcPDAzKZDEVFRc/87O+34EqFVqvF0qVLsWzZMty/fx8AYGNjgzlz5mDBggWws7MTXGgcFhb1L5eSyWS6X0tpsBQcHIzDhw+jefPmGDduHMaPH4927dqJzjIZR0dH5Obm1ntmX61WIyAgQNKPCP1WUVERcnJy4OPjI9kdWkREDQF3NBARSdxvhweurq6wtbUVWCOGTCbDu+++izlz5kCtVqOiogK+vr5ITEyEt7c3bty4ITrRKO7evav3urq6Gt988w0WLlyI2NhYQVXG0ahRI+zcuROvvvoqLC0tReeYnEwmQ3l5eb310tJSSQ2U/h1PT0/JDkyJiBoS7mggIjIjjo6OGD58OMLDwzFw4MAnfuMtJY8ePUJMTAy++OIL3Q6GYcOGQalU4r333oOlpSXefPNNzJs3T3SqSWVlZeHtt99GTk6O6BQykKFDh8LOzg5bt27VDVpqa2sxZswYVFZW4sCBA4ILjeODDz545vtSezTK2dlZb1fSs/x6MCYRkQgcNBARmZHPPvsMW7ZsQWZmJpycnDBmzBiEh4cjICBAdJpRzJs3D4mJiQgMDER2djZu3bqFyMhInD17Fu+88w5GjRpllt9+X7p0CQEBAaioqBCdQgZSUFCAvn37omnTpujTpw8A4OTJkygrK8PRo0fh5+cnuNA4fn+7QnV1NTQaDaysrNCmTRvk5uYKKjOOlJSU5/5sRESEEUuIiJ6NgwYiIjNUXl6OnTt3YuvWrTh69CjkcjnCw8Ml9+2fXC5HXFwcQkNDceHCBXTs2BEKhQJJSUnP/a1gQ/b7W0a0Wi1++uknLF++HDU1NTh16pSgMjKGkpISJCQkIC8vD3Z2dujYsSOmTZuGZs2aiU4zqbKyMigUCgwfPhxjx44VnUNEZJY4aCAiMnMFBQUICwtDfn6+5J7ltra2hkajQatWrQAAdnZ2OHfuHPz9/QWXmYaFhcUTbxnp0aMHkpOT0b59e0FlRMZ1/vx5DB06FD/88IPoFJN4+PAhqqqq9NYcHR0F1RAR8TBIIiKz9PDhQ+zduxdbtmzB559/DldXV8yZM0d0lsHV1tbC2tpa99rKygoODg4Ci0xLo9HovbawsMALL7xglgeCSlF+fj78/PxgYWFRb/fK75nbDQylpaUoLS0VnWFUlZWVmDdvHnbs2IHbt2/Xe19qg2Mialg4aCAiMiMHDx7Eli1bsHv3blhZWeH111/HoUOH0LdvX9FpRqHVaqFQKGBjYwPg8YBlypQpsLe31/tcenq6iDyj4+n70tapUyfcuHEDLi4u6NSp0xN3rwCPb6SQ6g+d8fHxeq9/fTwoLS0NwcHBgqpMY+7cuTh27BjWrVuHsWPHYu3atSguLkZiYiKWL18uOo+IzBwfnSAiMiONGzfGq6++irCwMISEhKBRo0aik4wqMjLyuT6nVCqNXCJOVlYWVq1ahYsXLwIAfH19MWfOHN2BgdRwFRUVwcPDAzKZDEVFRc/8rFSHTt7e3nqvf921M2DAACxYsABNmjQRVGZ8Hh4eSE1NRf/+/eHo6Ijc3Fz4+PggLS0NW7duxf79+0UnEpEZ46CBiMiMlJeXS/of3qRv06ZNiIyMxIgRI9C7d28AwOnTp/HZZ59BpVLhjTfeEFxIRP8tBwcHFBQUwMPDA61bt0Z6ejq6d+8OjUYDf39/3ipDRELx0QkiIokrKyvTHQqm1WpRVlb21M/y8DBpiY2NxUcffYS33npLtzZjxgysXr0aH374IQcNEnPlyhUcO3YMN2/eRF1dnd57UrtRhh7fqqPRaODh4YH27dtjx44d6N69OzIyMtC0aVPReURk5rijgYhI4iwtLfHTTz/BxcVFdwvB72m1Wkk/x22ubGxs8N1338HHx0dvXa1Ww8/PDw8fPhRURoa2YcMGTJ06Fc2bN4ebm5ve/+cymQy5ubkC64ynsrISy5cvx5EjR544YPn+++8FlRnfxx9/DEtLS8yYMQOHDx/G0KFDodVqUV1djdWrVyM6Olp0IhGZMe5oICKSuKNHj6JZs2YAgGPHjgmuIVNyd3fHkSNH6g0aDh8+DHd3d0FVZAxLlixBbGws5s2bJzrFpCZMmICsrCyMHTsWLVq0eOIgVap+u1MpMDAQFy9e1J3TYG63jBDRHw8HDUREEtevXz8AQE1NDbKysjB+/Hi0bt1acBWZwqxZszBjxgx8++236NWrF4DHZzSoVCp88sknguvIkO7evYtRo0aJzjC5AwcOIDMzU3cGiTnz8vKCl5eX6AwiIgCAhegAIiIyDSsrK6xcuRI1NTWiU8hEpk6dim3btuH8+fOYOXMmZs6ciQsXLmD79u2YPHmy6DwyoFGjRuHQoUOiM0zO2dlZt2PLXJw5cwb79u3TW0tNTYW3tzdcXFwwadIkPHr0SFAdEdFjPKOBiMiMvPbaaxgxYgQiIiJEpxDR/yg+Pl7368rKSqxevRpDhgyBv79/vatrZ8yYYeo8k9i0aRP27NmDlJQUNG7cWHSOSQQHB6N///66x2TOnz+PLl26QKFQ4MUXX8TKlSsxefJkxMTEiA0lIrPGQQMRkRlZv349Fi9ejLCwMHTt2hX29vZ674eGhgoqI6L/lLe393N9TiaTSfZQxM6dO+Pq1avQarXw8vKqN2CR4iGYLVq0QEZGBgICAgAA7777LrKysnDq1CkAwD//+U8sWrQIBQUFIjOJyMzxjAYiIjPyf//3fwCA1atX13uPt05IQ7NmzVBYWIjmzZvD2dn5mYfj3blzx4RlZGgajUZ0gnDDhg0TnWByd+/ehaurq+51VlYWgoODda+7deuGa9euiUgjItLhoIGIyIz8/uo3kp6PP/4YTZo00f3anE7hJ6CqqgoajQZt2rSBlZX0/5m3aNEi0Qkm5+rqCo1GA3d3d1RVVSE3NxeLFy/WvV9eXl5vZwcRkanx0QkiIjNRV1cHlUqF9PR0/PDDD5DJZJDL5Rg5ciTGjh3LH0iJGrD79+9j+vTpSElJAQAUFhZCLpdj+vTpaNWqFebPny+40Liqqqpw8+bNesNUDw8PQUXGM3XqVOTl5WHFihXYvXs3UlJSUFJSAmtrawDA5s2bERcXh6+++kpwKRGZM946QURkBrRaLUJDQzFhwgQUFxfD398fHTp0wA8//ACFQoHhw4eLTiQjCAwMhEqlQllZmegUMrIFCxYgLy8Px48fh62trW49MDAQ27dvF1hmXIWFhejTpw/s7Ozg6ekJb29veHt7w8vL67nPsGhoPvzwQ1hZWaFfv37YsGEDNmzYoBsyAEBycjIGDx4ssJCIiDsaiIjMglKpRHR0NPbs2YO//OUveu8dPXoUw4YNQ0JCAsaNGyeokIwhOjoaO3bsQGlpKYYMGYLw8HCEhIRwW7UEeXp6Yvv27ejRoweaNGmCvLw8yOVyqNVqdOnSRbLDpt69e8PKygrz589HixYt6u3MeumllwSVGV9paSkcHBxgaWmpt37nzh04ODjohg/Xr19Hy5YtYWHB7xeJyHQ4aCAiMgODBw/GgAEDnrp9eunSpcjKysLBgwdNXEbGVldXh8OHD2PLli347LPPYGlpiddffx1hYWHo16+f6DwykMaNG+PChQuQy+V6g4a8vDz07dsXpaWlohONwt7eHjk5OWjfvr3olD8sR0dHfPvtt5DL5aJTiMiMcLRJRGQG8vPzERQU9NT3g4ODkZeXZ8IiMhULCwsMHjwYKpUKP//8MxITE3Hu3DkMGDBAdBoZUEBAADIzM3Wvf/1mf+PGjejZs6eoLKPz9fXFL7/8IjrjD43fKRKRCNI/jpiIiHDnzh2969B+z9XVFXfv3jVhEZnajRs3sG3bNmzatAn5+fno3r276CQygAsXLsDPzw/Lli1DUFAQCgoKUF1djU8++QQFBQXIzs5GVlaW6EyjWbFiBebOnYulS5fC39+/3mNBjo6OgsqIiMwbdzQQEZmB2traZ151Z2lpiZqaGhMWkSmUlZVBqVRi0KBBcHd3x7p16xAaGoorV67g7NmzovPIADp27IiXX34ZBQUFOH36NGpqatCxY0ccOnQILi4uOHPmDLp27So602gCAwNx9uxZDBw4EC4uLnB2doazszOaNm0KZ2dn0XlERGaLOxqIiMyAVquFQqGAjY3NE99/9OiRiYvIFFxdXeHs7IwxY8Zg2bJlCAgIEJ1EBpaVlQWlUolZs2ahrq4OI0eOxKpVq9C3b1/RaSZx7Nixp753/vx5E5YQEdFv8TBIIiIzEBkZ+VyfUyqVRi4hU/riiy8wcOBAnjZvBiorK7Fjxw6oVCqcPHkSPj4+iIqKQkREBNzc3ETnmUx5eTm2bt2KjRs3IicnB7W1taKThONhkEQkAgcNREREElZTU4Pjx4/j6tWreOONN9CkSROUlJTA0dERDg4OovPICNRqNZRKJdLS0nDjxg0EBQVh7969orOM6sSJE0hKSsKuXbvQsmVLjBgxAiNHjkS3bt1Epwn321tIiIhMhYMGIiIiiSoqKkJQUBB+/PFHPHr0CIWFhZDL5YiOjsajR4+wfv160YlkJJWVldi8eTMWLFiAe/fuSfKb/Rs3bkClUiEpKQllZWUYPXo01q9fj7y8PPj6+orOMzqlUokxY8agcePGz/zctWvX0LJlS1haWpqojIiIh0ESERFJVnR0NAICAnD37l3Y2dnp1ocPH44jR44ILCNjOXHiBBQKBdzc3DBnzhyMGDECp0+fFp1lcEOHDkW7du2Qn5+PuLg4lJSUYM2aNaKzTGr+/Plwc3NDVFQUsrOzn/o5d3d3DhmIyOR4GCQREZFEnTx5EtnZ2bC2ttZb9/LyQnFxsaAqMrSSkhKoVCqoVCqo1Wr06tUL8fHxGD16NOzt7UXnGcWBAwcwY8YMTJ06FW3bthWdI0RxcTEyMjKgUqnQv39/yOVyREZGmt25HET0x8QdDURERBJVV1f3xC3z169fR5MmTQQUkaEFBwfD09MTa9aswfDhw3Hx4kWcOnUKkZGRkh0yAMCpU6dQXl6Orl274uWXX0ZCQgJ++eUX0VkmZWVlheHDh2PPnj24du0aJk6ciM2bN8PDwwOhoaHYs2cP6urqRGcSkZnioIGIiEiiBg8ejLi4ON1rmUyGiooKLFq0CCEhIeLCyGAaNWqEnTt34vr161ixYgXatWsnOskkevTogQ0bNuCnn37C5MmTsW3bNrRs2RJ1dXX44osvUF5eLjrRpFxdXfHKK6+gZ8+esLCwwPnz5xEREYE2bdrg+PHjovOIyAzxMEgiIiKJunbtGoKCgqDVanHlyhUEBATgypUraN68OU6cOAEXFxfRiUQGc/nyZSQlJSEtLQ337t3DoEGDJH/bxs8//4y0tDQolUp8//33GDZsGKKiohAYGIjKykp88MEH2LZtG4qKikSnEpGZ4aCBiIhIwmpqarB9+3bk5eWhoqICXbp0QVhYmN7hkERSUltbi4yMDCQnJ0t60DB06FAcPHgQf/7znzFhwgSMGzcOzZo10/vMzZs34ebmxkcoiMjkOGggIiKSoOrqarRv3x779u3Diy++KDqHiAwsKioKEyZMQM+ePZ/6Ga1Wix9//BGenp4mLCMi4qCBiIhIslq1aoXDhw9z0EBEREQmxcMgiYiIJOrNN9/EihUrUFNTIzqFiAxsxowZiI+Pr7eekJCAmTNnmj6IiOg3uKOBiIhIooYPH44jR47AwcEB/v7+9a47TE9PF1RGRP+rVq1aYe/evejataveem5uLkJDQ3H9+nVBZUREgJXoACIiIjKOpk2bYuTIkaIziMgIbt++DScnp3rrjo6O+OWXXwQUERH9CwcNREREElNXV4eVK1eisLAQVVVVGDBgAGJiYnjTBJGE+Pj44PPPP8e0adP01g8cOAC5XC6oiojoMQ4aiIiIJCY2NhYxMTEIDAyEnZ0d4uPjcevWLSQnJ4tOIyIDefvttzFt2jTcunULAwYMAAAcOXIEf//73xEXFyc2jojMHs9oICIikpi2bdti9uzZmDx5MgDg8OHDGDJkCB48eAALC54DTSQV69atQ2xsLEpKSgAAXl5eiImJwbhx4wSXEZG546CBiIhIYmxsbKBWq+Hu7q5bs7W1hVqtRuvWrQWWEZEx3Lp1C3Z2dnBwcBCdQkQEgI9OEBERSU5NTQ1sbW311ho1aoTq6mpBRURkTC+88ILoBCIiPRw0EBERSYxWq4VCoYCNjY1u7eHDh5gyZYreFZe83pKoYenSpQuOHDkCZ2dndO7cGTKZ7Kmfzc3NNWEZEZE+DhqIiIgkJiIiot5aeHi4gBIiMqTXXntNN0AcNmyY2BgiomfgGQ1EREREREREZDA8epqIiIiIiIiIDIaPThARERERNQDOzs7PPJfht+7cuWPkGiKip+OggYiIiIioAYiLixOdQET0XHhGAxEREREREREZDHc0EBERERE1QLW1tdi9ezcuXrwIAOjQoQNCQ0NhaWkpuIyIzB13NBARERERNTBqtRohISEoLi5Gu3btAACXL1+Gu7s7MjMz0aZNG8GFRGTOOGggIiIiImpgQkJCoNVqsXnzZjRr1gwAcPv2bYSHh8PCwgKZmZmCC4nInHHQQERERETUwNjb2+Ps2bPw9/fXW8/Ly0Pv3r1RUVEhqIyICLAQHUBERERERP8ZGxsblJeX11uvqKiAtbW1gCIion/hoIGIiIiIqIF59dVXMWnSJHz55ZfQarXQarU4e/YspkyZgtDQUNF5RGTm+OgEEREREVEDc+/ePSgUCmRkZMDK6vFFcjU1NQgNDYVKpYKTk5PgQiIyZxw0EBERERE1EHV1dVi5ciX27t2LqqoqeHh4ICIiAjKZDC+++CJ8fHxEJxIRwUp0ABERERERPZ/Y2FjExMQgMDAQdnZ22L9/P5ycnJCcnCw6jYhIhzsaiIiIiIgaiLZt22L27NmYPHkyAODw4cMYMmQIHjx4AAsLHr9GRH8MHDQQERERETUQNjY2UKvVcHd3163Z2tpCrVajdevWAsuIiP6FY08iIiIiogaipqYGtra2emuNGjVCdXW1oCIiovp4RgMRERERUQOh1WqhUChgY2OjW3v48CGmTJkCe3t73Vp6erqIPCIiABw0EBERERE1GBEREfXWwsPDBZQQET0dz2ggIiIiIiIiIoPhGQ1EREREREREZDAcNBARERERERGRwXDQQEREREREREQGw0EDERERERERERkMBw1EREREREREZDAcNBARERERERGRwXDQQEREREREREQG8//p+LUfipsJgAAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"df.hist(column=['Age','Annual_Premium','Policy_Sales_Channel','Vintage'],figsize=(20,10),layout=(4,1),grid=False,edgecolor='black')\nplt.suptitle('Histograms')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:45:09.618291Z","iopub.execute_input":"2024-07-10T14:45:09.618698Z","iopub.status.idle":"2024-07-10T14:45:12.155191Z","shell.execute_reply.started":"2024-07-10T14:45:09.618663Z","shell.execute_reply":"2024-07-10T14:45:12.153777Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Ux8AAAAAACg7lbLoGTYsGGZP39+6TVzpmedAwAAAAAAdbdKPHqrriorK1NZWVnfbQAAAAAAAKu51fKOEgAAAAAAgC+CoAQAAAAAAChbq8SjtxYtWpRp06aV3k+fPj2TJk1K27Zt061bt3rsDAAAAAAAaMhWiaDksccey6677lp6P3To0CTJoEGDMmrUqHrqCgAAWF1Nnjy5vltgNdK+fXt/SQ8AoIytEkHJLrvskqIo6rsNAABgNbd00ZtJRUUOPfTQ+m6F1UjzFmtkynOThSUAAGVqlQhKAAAAvgg11YuSoki7vU5K03Zd67sdVgNL5s7M3NsuyJw5cwQlAABlSlACAAA0OE3bdU1lVY/6bgMAAFgNNKrvBgAAAAAAAOqLoAQAAAAAAChbHr0FAABA2Zs8eXJ9t8BqpH379ua0AYAGRFACAABA2Vq66M2koiKHHnpofbfCaqR5izUy5bnJwhIAaCAEJQAAAJStmupFSVGk3V4npWm7rvXdDquBJXNnZu5tF2TOnDmCEgBoIAQlAAAAlL2m7bqmsqpHfbcBAEA9MJk7AAAAAABQtgQlAAAAAABA2RKUAAAAAAAAZUtQAgAAAAAAlC1BCQAAAAAAULaa1HcDAAAAAKubyZMn13cLrEbat2+fbt261XcbAHwMQQkAAADAClq66M2koiKHHnpofbfCaqR5izUy5bnJwhKAVZSgBAAAAGAF1VQvSooi7fY6KU3bda3vdlgNLJk7M3NvuyBz5swRlACsogQlAAAAAHXUtF3XVFb1qO82AIAvgMncAQAAAACAsiUoAQAAAAAAypagBAAAAAAAKFvmKAEAAACAlWzy5Mn13QKrkfbt26dbt2713QaUDUEJAAAAAKwkSxe9mVRU5NBDD63vVliNNG+xRqY8N1lYAl8SQQkAAAAArCQ11YuSoki7vU5K03Zd67sdVgNL5s7M3NsuyJw5cwQl8CURlAAAAADASta0XddUVvWo7zYAWA6TuQMAAAAAAGVLUAIAAAAAAJQtQQkAAAAAAFC2VqmgZMSIEVl//fXTvHnz9O3bN4888kh9twQAAAAAADRgq8xk7jfccEOGDh2aSy+9NH379s1FF12UAQMGZMqUKenYsWN9twcAAAAA8KWZPHlyfbfAaqR9+/bp1q1bfbex2lplgpILL7wwRx11VAYPHpwkufTSS3P77bfn6quvzimnnFKrtrq6OtXV1aX38+fPT5IsWLDgy2t4NbFo0aIkSfWsaal595167obVwZK5M5P4mWHF+ZmhrvzMUFd+ZqgLPy/UlZ8Z6srPDHXlZ4a6qn7l/YDk0EMPredOWJ1UNm+RiY89mq5du9Z3K6uMD/KCoig+tbaiWJGqlezdd9/NGmuskb/85S/Zd999S+ODBg3KvHnzcsstt9SqP+OMM3LmmWd+yV0CAAAAAACrk5kzZ2bdddf9xJpV4o6SOXPmZOnSpenUqVOt8U6dOuW5555bpn7YsGEZOnRo6X1NTU3eeOONtGvXLhUVFSu939XJggUL0rVr18ycOTOtWrWq73agwXGOwcrj/IKVyzkGK5dzDFYu5xisXM4xGoKiKLJw4cJ06dLlU2tXiaCkriorK1NZWVlrbO21166fZlYTrVq1clGDlcg5BiuP8wtWLucYrFzOMVi5nGOwcjnHWN21bt16heoareQ+Vkj79u3TuHHjzJ49u9b47NmzU1VVVU9dAQAAAAAADd0qEZQ0a9YsvXv3zvjx40tjNTU1GT9+fPr161ePnQEAAAAAAA3ZKvPoraFDh2bQoEHp06dPtttuu1x00UVZvHhxBg8eXN+trdYqKytz+umnL/OoMuCL4RyDlcf5BSuXcwxWLucYrFzOMVi5nGOUm4qiKIr6buIDf/jDH/Lb3/42s2bNytZbb53f//736du3b323BQAAAAAANFCrVFACAAAAAADwZVol5igBAAAAAACoD4ISAAAAAACgbAlKAAAAAACAsiUoAQAAAAAAypagpAEYPnx4vvKVr2SttdZKx44ds++++2bKlCm1at55550cd9xxadeuXVq2bJnvfve7mT17dj11DKuXSy65JD179kyrVq3SqlWr9OvXL3fccUdpufMLvjjnnHNOKioqcuKJJ5bGnGPw+ZxxxhmpqKio9dp0001Ly51j8Pm8/PLLOfTQQ9OuXbu0aNEiW221VR577LHS8qIoctppp6Vz585p0aJF+vfvn6lTp9Zjx7D6WH/99Zf5DquoqMhxxx2XxHcYfF5Lly7Nqaeemu7du6dFixbZcMMN8+tf/zpFUZRqfI9RLgQlDcC9996b4447Lg899FDGjRuXJUuWZPfdd8/ixYtLNT/5yU/y17/+NTfeeGPuvffevPLKK/nOd75Tj13D6mPdddfNOeeck4kTJ+axxx7L17/+9eyzzz555plnkji/4Ivy6KOP5rLLLkvPnj1rjTvH4PPbYost8uqrr5Ze999/f2mZcww+uzfffDM77LBDmjZtmjvuuCPPPvtsLrjggrRp06ZUc9555+X3v/99Lr300jz88MNZc801M2DAgLzzzjv12DmsHh599NFa31/jxo1Lkuy///5JfIfB53XuuefmkksuyR/+8IdMnjw55557bs4777xcfPHFpRrfY5SLiuLDESENwuuvv56OHTvm3nvvzc4775z58+enQ4cOue6667LffvslSZ577rlsttlmefDBB7P99tvXc8ew+mnbtm1++9vfZr/99nN+wRdg0aJF2XbbbfPHP/4xZ511VrbeeutcdNFFvsPgC3DGGWfk5ptvzqRJk5ZZ5hyDz+eUU07JP//5z/zf//3fcpcXRZEuXbrkpJNOyk9/+tMk7593nTp1yqhRo3LQQQd9me3Cau/EE0/MbbfdlqlTp2bBggW+w+Bz2muvvdKpU6dcddVVpbHvfve7adGiRf70pz/5HqOsuKOkAZo/f36S9/9HbpJMnDgxS5YsSf/+/Us1m266abp165YHH3ywXnqE1dXSpUszZsyYLF68OP369XN+wRfkuOOOy5577lnrXEp8h8EXZerUqenSpUs22GCDHHLIIZkxY0YS5xh8Xrfeemv69OmT/fffPx07dsw222yTK664orR8+vTpmTVrVq1zrHXr1unbt69zDOro3XffzZ/+9KccccQRqaio8B0GX4CvfvWrGT9+fJ5//vkkyb/+9a/cf//9GThwYBLfY5SXJvXdAF+smpqanHjiidlhhx2y5ZZbJklmzZqVZs2aZe21165V26lTp8yaNaseuoTVz1NPPZV+/frlnXfeScuWLTN27NhsvvnmmTRpkvMLPqcxY8bk8ccfz6OPPrrMMt9h8Pn17ds3o0aNyiabbJJXX301Z555Znbaaac8/fTTzjH4nF544YVccsklGTp0aH7+85/n0UcfzY9//OM0a9YsgwYNKp1HnTp1qrWecwzq7uabb868efNy+OGHJ/F7InwRTjnllCxYsCCbbrppGjdunKVLl+bss8/OIYcckiS+xygrgpIG5rjjjsvTTz9d67nTwOe3ySabZNKkSZk/f37+8pe/ZNCgQbn33nvruy1Y7c2cOTMnnHBCxo0bl+bNm9d3O9AgffA3ApOkZ8+e6du3b9Zbb738+c9/TosWLeqxM1j91dTUpE+fPvnNb36TJNlmm23y9NNP59JLL82gQYPquTtoWK666qoMHDgwXbp0qe9WoMH485//nNGjR+e6667LFltskUmTJuXEE09Mly5dfI9Rdjx6qwEZMmRIbrvtttxzzz1Zd911S+NVVVV59913M2/evFr1s2fPTlVV1ZfcJayemjVrlh49eqR3794ZPnx4evXqld/97nfOL/icJk6cmNdeey3bbrttmjRpkiZNmuTee+/N73//+zRp0iSdOnVyjsEXbO21187GG2+cadOm+R6Dz6lz587ZfPPNa41tttlmpcfbfXAezZ49u1aNcwzq5qWXXso//vGPHHnkkaUx32Hw+Z188sk55ZRTctBBB2WrrbbK97///fzkJz/J8OHDk/geo7wIShqAoigyZMiQjB07NnfffXe6d+9ea3nv3r3TtGnTjB8/vjQ2ZcqUzJgxI/369fuy24UGoaamJtXV1c4v+Jy+8Y1v5KmnnsqkSZNKrz59+uSQQw4p/dk5Bl+sRYsW5d///nc6d+7seww+px122CFTpkypNfb8889nvfXWS5J07949VVVVtc6xBQsW5OGHH3aOQR2MHDkyHTt2zJ577lka8x0Gn99bb72VRo1q/+/hxo0bp6amJonvMcqLR281AMcdd1yuu+663HLLLVlrrbVKzwhs3bp1WrRokdatW+cHP/hBhg4dmrZt26ZVq1Y5/vjj069fv2y//fb13D2s+oYNG5aBAwemW7duWbhwYa677rpMmDAhf//7351f8DmttdZapTm1PrDmmmumXbt2pXHnGHw+P/3pT7P33ntnvfXWyyuvvJLTTz89jRs3zsEHH+x7DD6nn/zkJ/nqV7+a3/zmNznggAPyyCOP5PLLL8/ll1+eJKmoqMiJJ56Ys846KxtttFG6d++eU089NV26dMm+++5bv83DaqKmpiYjR47MoEGD0qTJ///fWL7D4PPbe++9c/bZZ6dbt27ZYost8sQTT+TCCy/MEUcckcT3GOVFUNIAXHLJJUmSXXbZpdb4yJEjS5Oc/fd//3caNWqU7373u6murs6AAQPyxz/+8UvuFFZPr732Wg477LC8+uqrad26dXr27Jm///3v2W233ZI4v2Blc47B5/Of//wnBx98cObOnZsOHTpkxx13zEMPPZQOHTokcY7B5/GVr3wlY8eOzbBhw/KrX/0q3bt3z0UXXVSaBDdJ/uu//iuLFy/O0UcfnXnz5mXHHXfMnXfeaW4uWEH/+Mc/MmPGjNL/uP0w32Hw+Vx88cU59dRT86Mf/SivvfZaunTpkmOOOSannXZaqcb3GOWioiiKor6bAAAAAAAAqA/mKAEAAAAAAMqWoAQAAAAAAChbghIAAAAAAKBsCUoAAIBVxqhRo1JRUZEXX3yxvltZKc4444xUVFTUdxsAAMCHCEoAAKCB+OMf/5iKior07du3vlv5Un0Qrnzwat68eTbeeOMMGTIks2fPru/2AACAVZygBAAAGojRo0dn/fXXzyOPPJJp06bVdztful/96lf5n//5n/zhD3/IV7/61VxyySXp169f3nrrrfpureSXv/xl3n777fpuAwAA+BBBCQAANADTp0/PAw88kAsvvDAdOnTI6NGj67ulL93AgQNz6KGH5sgjj8yoUaNy4oknZvr06bnllls+dp3Fixd/iR0mTZo0SfPmzb/UfQIAAJ9MUAIAAA3A6NGj06ZNm+y5557Zb7/9lglKXnzxxVRUVOT888/P5Zdfng033DCVlZX5yle+kkcffbRW7eGHH56WLVvm5Zdfzr777puWLVumQ4cO+elPf5qlS5eW6iZMmJCKiopMmDBhufsaNWpUaezJJ5/M4Ycfng022CDNmzdPVVVVjjjiiMydO/cL/yw+8PWvfz3J+yHSh4/r3//+d775zW9mrbXWyiGHHJIkqampyUUXXZQtttgizZs3T6dOnXLMMcfkzTffrLXN9ddfP3vttVcmTJiQPn36pEWLFtlqq61Kn8FNN92UrbbaKs2bN0/v3r3zxBNP1Fr/o3OULO+z+kBFRUXOOOOMZdZ9/vnnc+ihh6Z169bp0KFDTj311BRFkZkzZ2afffZJq1atUlVVlQsuuODzfoQAAFAWBCUAANAAjB49Ot/5znfSrFmzHHzwwZk6deoyAUiSXHfddfntb3+bY445JmeddVZefPHFfOc738mSJUtq1S1dujQDBgxIu3btcv755+drX/taLrjgglx++eWfqb9x48blhRdeyODBg3PxxRfnoIMOypgxY/LNb34zRVF8pm1+mn//+99Jknbt2pXG3nvvvQwYMCAdO3bM+eefn+9+97tJkmOOOSYnn3xydthhh/zud7/L4MGDM3r06AwYMGCZz2batGn53ve+l7333jvDhw/Pm2++mb333jujR4/OT37ykxx66KE588wz8+9//zsHHHBAampqvtDjOvDAA1NTU5Nzzjknffv2zVlnnZWLLroou+22W9ZZZ52ce+656dGjR37605/mvvvu+0L3DQAADVGT+m4AAAD4fCZOnJjnnnsuF198cZJkxx13zLrrrpvRo0fnK1/5Sq3aGTNmZOrUqWnTpk2SZJNNNsk+++yTv//979lrr71Kde+8804OPPDAnHrqqUmSY489Nttuu22uuuqq/PCHP6xzjz/60Y9y0kkn1Rrbfvvtc/DBB+f+++/PTjvtVOdtftT8+fMzZ86cvPPOO/nnP/+ZX/3qV2nRokWt46qurs7++++f4cOHl8buv//+XHnllRk9enS+973vlcZ33XXX7LHHHrnxxhtrjU+ZMiUPPPBA+vXrlyTZfPPNM2DAgBx11FF57rnn0q1btyRJmzZtcswxx+S+++7LLrvs8rmP7wPbbbddLrvssiTJ0UcfnfXXXz8nnXRShg8fnp/97GdJkoMPPjhdunTJ1VdfnZ133vkL2zcAADRE7igBAIDV3OjRo9OpU6fsuuuuSd5/ZNOBBx6YMWPG1HpUVvL+3QgfhCRJSgHFCy+8sMx2jz322Frvd9ppp+XWrYgWLVqU/vzOO+9kzpw52X777ZMkjz/++Gfa5kf1798/HTp0SNeuXXPQQQelZcuWGTt2bNZZZ51adR8Nem688ca0bt06u+22W+bMmVN69e7dOy1btsw999xTq37zzTcvhSRJ0rdv3yTvP+rrg5Dkw+Of9TP7OEceeWTpz40bN06fPn1SFEV+8IMflMbXXnvtbLLJJl/4vgEAoCFqcEHJfffdl7333jtdunRJRUVFbr755jqt/8Fzfz/6WnPNNVdOwwAA8DksXbo0Y8aMya677prp06dn2rRpmTZtWvr27ZvZs2dn/Pjxteo//D/yk5RCk4/OxdG8efN06NBhmdqP1q2oN954IyeccEI6deqUFi1apEOHDunevXuS9+8E+SKMGDEi48aNyz333JNnn302L7zwQgYMGFCrpkmTJll33XVrjU2dOjXz589Px44d06FDh1qvRYsW5bXXXqtV/9HPsHXr1kmSrl27Lnf8s35mH2d5+2/evHnat2+/zPgXvW8AAGiIGtyjtxYvXpxevXrliCOOyHe+8506r//Tn/50mb85941vfGOZRxYAAMCq4O67786rr76aMWPGZMyYMcssHz16dHbffffS+8aNGy93Ox+dJ+Tj6j7sw5OSf9hH72JJkgMOOCAPPPBATj755Gy99dZp2bJlampqsscee3xhc3hst9126dOnzyfWVFZWplGj2n9frKamJh07dszo0aOXu85HA6OP+2xW9LP9sLp8hp+0n8+ybwAA4H0NLigZOHBgBg4c+LHLq6ur84tf/CLXX3995s2bly233DLnnntu6ZnBLVu2TMuWLUv1//rXv/Lss8/m0ksvXdmtAwBAnY0ePTodO3bMiBEjlll20003ZezYsSvtd9kP7kaZN29erfGXXnqp1vs333wz48ePz5lnnpnTTjutND516tSV0lddbbjhhvnHP/6RHXbYodYjwr4MK/oZAgAAK0+De/TWpxkyZEgefPDBjBkzJk8++WT233//7LHHHh/7H2lXXnllNt544y9kckkAAPgivf3227npppuy1157Zb/99lvmNWTIkCxcuDC33nrrStn/euutl8aNG+e+++6rNf7HP/6x1vsP7nb46N0NF1100Urpq64OOOCALF26NL/+9a+XWfbee+8tE2J8kVq1apX27dt/6mcIAACsPA3ujpJPMmPGjIwcOTIzZsxIly5dkrz/qK0777wzI0eOzG9+85ta9e+8805Gjx6dU045pT7aBQCAT3Trrbdm4cKF+da3vrXc5dtvv306dOiQ0aNHlyYW/yK1bt06+++/fy6++OJUVFRkww03zG233bbMnB6tWrXKzjvvnPPOOy9LlizJOuusk7vuuivTp0//wnv6LL72ta/lmGOOyfDhwzNp0qTsvvvuadq0aaZOnZobb7wxv/vd77LffvuttP0feeSROeecc3LkkUemT58+ue+++/L888+vtP0BAAC1lVVQ8tRTT2Xp0qXZeOONa41XV1enXbt2y9SPHTs2CxcuzKBBg76sFgEAYIWNHj06zZs3z2677bbc5Y0aNcqee+6Z0aNHZ+7cuSulh4svvjhLlizJpZdemsrKyhxwwAH57W9/my233LJW3XXXXZfjjz8+I0aMSFEU2X333XPHHXeU/gJTfbv00kvTu3fvXHbZZfn5z3+eJk2aZP3118+hhx6aHXbYYaXu+7TTTsvrr7+ev/zlL/nzn/+cgQMH5o477kjHjh1X6n4BAID3VRQNeHa/ioqKjB07Nvvuu2+S5IYbbsghhxySZ555ZpnJDlu2bJmqqqpaY9/4xjfSqlWrjB079stqGQAAAAAA+BKV1R0l22yzTZYuXZrXXnvtU+ccmT59eu65556V9jxnAAAAAACg/jW4oGTRokWZNm1a6f306dMzadKktG3bNhtvvHEOOeSQHHbYYbnggguyzTbb5PXXX8/48ePTs2fP7LnnnqX1rr766nTu3DkDBw6sj8MAAICyt2jRoixatOgTazp06LDM3eIAAAB10eAevTVhwoTsuuuuy4wPGjQoo0aNypIlS3LWWWfl2muvzcsvv5z27dtn++23z5lnnpmtttoqSVJTU5P11lsvhx12WM4+++wv+xAAAIAkZ5xxRs4888xPrJk+fXrWX3/9L6chAACgQWpwQQkAANAwvPDCC3nhhRc+sWbHHXdM8+bNv6SOAACAhkhQAgAAAAAAlK1G9d0AAAAAAABAfWkQk7nX1NTklVdeyVprrZWKior6bgcAAAAAAKhHRVFk4cKF6dKlSxo1+uR7RhpEUPLKK6+ka9eu9d0GAAAAAACwCpk5c2bWXXfdT6xpEEHJWmutleT9A27VqlU9dwMAAAAAANSnBQsWpGvXrqX84JM0iKDkg8dttWrVSlACAAAAAAAkyQpN12EydwAAAAAAoGwJSgAAAAAAgLIlKAEAAAAAAMpWg5ijhE82Y8aMzJkzp77bYDXSvn37dOvWrb7bAAAAAABY6QQlDdyMGTOyyaab5Z2336rvVliNNG+xRqY8N1lYAgAAAAA0eIKSBm7OnDl55+230m6vk9K0Xdf6bofVwJK5MzP3tgsyZ84cQQkAAAAA0OAJSspE03ZdU1nVo77bAAAAAACAVYrJ3AEAAAAAgLIlKAEAAAAAAMqWoAQAAAAAAChbghIAAAAAAKBsCUoAAAAAAICyJSgBAAAAAADKlqAEAAAAAAAoW4ISAAAAAACgbNU5KLnvvvuy9957p0uXLqmoqMjNN9/8qetMmDAh2267bSorK9OjR4+MGjVqmZoRI0Zk/fXXT/PmzdO3b9888sgjdW0NAAAAAACgTuoclCxevDi9evXKiBEjVqh++vTp2XPPPbPrrrtm0qRJOfHEE3PkkUfm73//e6nmhhtuyNChQ3P66afn8ccfT69evTJgwIC89tprdW0PAAAAAABghTWp6woDBw7MwIEDV7j+0ksvTffu3XPBBRckSTbbbLPcf//9+e///u8MGDAgSXLhhRfmqKOOyuDBg0vr3H777bn66qtzyimnLLPN6urqVFdXl94vWLCgrocBAAAAAACw8ucoefDBB9O/f/9aYwMGDMiDDz6YJHn33XczceLEWjWNGjVK//79SzUfNXz48LRu3br06tq168o7AAAAAAAAoMFa6UHJrFmz0qlTp1pjnTp1yoIFC/L2229nzpw5Wbp06XJrZs2atdxtDhs2LPPnzy+9Zs6cudL6BwAAAAAAGq46P3prVVBZWZnKysr6bgMAAAAAAFjNrfSgpKqqKrNnz641Nnv27LRq1SotWrRI48aN07hx4+XWVFVVrez2AAAAAACAMrbSH73Vr1+/jB8/vtbYuHHj0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scipy\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_auc_score\nimport warnings","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:45:12.157045Z","iopub.execute_input":"2024-07-10T14:45:12.157592Z","iopub.status.idle":"2024-07-10T14:45:12.251319Z","shell.execute_reply.started":"2024-07-10T14:45:12.157542Z","shell.execute_reply":"2024-07-10T14:45:12.249727Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"X = df.drop(['id','Response'], axis = 1)\ny = df['Response']\n\n# splitting the data into train and test\nX_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.1,random_state = 0)\n\n#using min max scaler we scale the values and then transform the values\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:45:12.253242Z","iopub.execute_input":"2024-07-10T14:45:12.253767Z","iopub.status.idle":"2024-07-10T14:45:20.393024Z","shell.execute_reply.started":"2024-07-10T14:45:12.253721Z","shell.execute_reply":"2024-07-10T14:45:20.391770Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report\n\nmodel = LogisticRegression(max_iter=1000)\nmodel.fit(X_train, y_train)\ny_pred = model.predict(X_test)\n\nprint(classification_report(y_test, y_pred))","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:45:20.394728Z","iopub.execute_input":"2024-07-10T14:45:20.395236Z","iopub.status.idle":"2024-07-10T14:45:39.657587Z","shell.execute_reply.started":"2024-07-10T14:45:20.395191Z","shell.execute_reply":"2024-07-10T14:45:39.656199Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0 0.88 1.00 0.93 1009313\n 1 0.30 0.00 0.00 141167\n\n accuracy 0.88 1150480\n macro avg 0.59 0.50 0.47 1150480\nweighted avg 0.81 0.88 0.82 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from xgboost import XGBClassifier\n\nxgb_model = XGBClassifier(n_estimators=100, use_label_encoder=False, eval_metric='mlogloss', n_jobs=-1)\nxgb_model.fit(X_train, y_train)\nxgb_pred = xgb_model.predict(X_test)\n\n\n\nprint(\"XGBoost Classification Report:\")\nprint(classification_report(y_test, xgb_pred))","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:45:39.659070Z","iopub.execute_input":"2024-07-10T14:45:39.659437Z","iopub.status.idle":"2024-07-10T14:46:56.542660Z","shell.execute_reply.started":"2024-07-10T14:45:39.659408Z","shell.execute_reply":"2024-07-10T14:46:56.541408Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"XGBoost Classification Report:\n precision recall f1-score support\n\n 0 0.89 0.99 0.94 1009313\n 1 0.57 0.09 0.16 141167\n\n accuracy 0.88 1150480\n macro avg 0.73 0.54 0.55 1150480\nweighted avg 0.85 0.88 0.84 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report\nimport lightgbm as lgb","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:46:56.544045Z","iopub.execute_input":"2024-07-10T14:46:56.544416Z","iopub.status.idle":"2024-07-10T14:46:57.981907Z","shell.execute_reply.started":"2024-07-10T14:46:56.544387Z","shell.execute_reply":"2024-07-10T14:46:57.979973Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"# LightGBM\nprint(\"Training LightGBM...\")\nlgb_model = lgb.LGBMClassifier(n_estimators=100, n_jobs=-1)\nlgb_model.fit(X_train, y_train)\nlgb_pred = lgb_model.predict(X_test)\nprint(\"LightGBM Classification Report:\")\nprint(classification_report(y_test, lgb_pred))","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:46:57.983626Z","iopub.execute_input":"2024-07-10T14:46:57.984264Z","iopub.status.idle":"2024-07-10T14:48:13.969708Z","shell.execute_reply.started":"2024-07-10T14:46:57.984225Z","shell.execute_reply":"2024-07-10T14:48:13.968155Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Training LightGBM...\n[LightGBM] [Info] Number of positive: 1273892, number of negative: 9080426\n[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.323682 seconds.\nYou can set `force_row_wise=true` to remove the overhead.\nAnd if memory is not enough, you can set `force_col_wise=true`.\n[LightGBM] [Info] Total Bins 732\n[LightGBM] [Info] Number of data points in the train set: 10354318, number of used features: 10\n[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.123030 -> initscore=-1.964044\n[LightGBM] [Info] Start training from score -1.964044\nLightGBM Classification Report:\n precision recall f1-score support\n\n 0 0.88 0.99 0.94 1009313\n 1 0.58 0.07 0.12 141167\n\n accuracy 0.88 1150480\n macro avg 0.73 0.53 0.53 1150480\nweighted avg 0.85 0.88 0.84 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\n\nnb_model = GaussianNB()\nnb_model.fit(X_train, y_train)\nnb_pred = nb_model.predict(X_test)\nprint(\"Naive Bayes Classification Report:\")\nprint(classification_report(y_test, nb_pred))\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:48:13.971496Z","iopub.execute_input":"2024-07-10T14:48:13.972004Z","iopub.status.idle":"2024-07-10T14:48:20.167018Z","shell.execute_reply.started":"2024-07-10T14:48:13.971959Z","shell.execute_reply":"2024-07-10T14:48:20.165314Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Naive Bayes Classification Report:\n precision recall f1-score support\n\n 0 1.00 0.59 0.74 1009313\n 1 0.25 0.98 0.40 141167\n\n accuracy 0.64 1150480\n macro avg 0.62 0.79 0.57 1150480\nweighted avg 0.90 0.64 0.70 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.linear_model import SGDClassifier\n\nsgd_model = SGDClassifier(loss='log', max_iter=1000, tol=1e-3)\nsgd_model.fit(X_train, y_train)\nsgd_pred = sgd_model.predict(X_test)\nprint(\"SGD Logistic Regression Classification Report:\")\nprint(classification_report(y_test, sgd_pred))\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:48:20.168602Z","iopub.execute_input":"2024-07-10T14:48:20.169110Z","iopub.status.idle":"2024-07-10T14:48:52.463127Z","shell.execute_reply.started":"2024-07-10T14:48:20.169065Z","shell.execute_reply":"2024-07-10T14:48:52.461754Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_stochastic_gradient.py:163: FutureWarning: The loss 'log' was deprecated in v1.1 and will be removed in version 1.3. Use `loss='log_loss'` which is equivalent.\n warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"SGD Logistic Regression Classification Report:\n precision recall f1-score support\n\n 0 0.88 1.00 0.93 1009313\n 1 0.33 0.00 0.00 141167\n\n accuracy 0.88 1150480\n macro avg 0.61 0.50 0.47 1150480\nweighted avg 0.81 0.88 0.82 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.linear_model import RidgeClassifier\n\nridge_model = RidgeClassifier()\nridge_model.fit(X_train, y_train)\nridge_pred = ridge_model.predict(X_test)\nprint(\"Ridge Classifier Classification Report:\")\nprint(classification_report(y_test, ridge_pred))\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:48:52.465094Z","iopub.execute_input":"2024-07-10T14:48:52.465502Z","iopub.status.idle":"2024-07-10T14:49:00.998850Z","shell.execute_reply.started":"2024-07-10T14:48:52.465468Z","shell.execute_reply":"2024-07-10T14:49:00.997552Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Ridge Classifier Classification Report:\n precision recall f1-score support\n\n 0 0.88 1.00 0.93 1009313\n 1 0.40 0.00 0.00 141167\n\n accuracy 0.88 1150480\n macro avg 0.64 0.50 0.47 1150480\nweighted avg 0.82 0.88 0.82 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.neural_network import MLPClassifier\n\nnn_model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=300, solver='adam', random_state=1)\nnn_model.fit(X_train, y_train)\nnn_pred = nn_model.predict(X_test)\nprint(\"Neural Network Classification Report:\")\nprint(classification_report(y_test, nn_pred))\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:49:01.000749Z","iopub.execute_input":"2024-07-10T14:49:01.001275Z","iopub.status.idle":"2024-07-10T14:49:57.747753Z","shell.execute_reply.started":"2024-07-10T14:49:01.001229Z","shell.execute_reply":"2024-07-10T14:49:57.746592Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:693: UserWarning: Training interrupted by user.\n warnings.warn(\"Training interrupted by user.\")\n","output_type":"stream"},{"name":"stdout","text":"Neural Network Classification Report:\n precision recall f1-score support\n\n 0 0.88 1.00 0.93 1009313\n 1 0.52 0.02 0.03 141167\n\n accuracy 0.88 1150480\n macro avg 0.70 0.51 0.48 1150480\nweighted avg 0.83 0.88 0.82 1150480\n\n","output_type":"stream"}]},{"cell_type":"code","source":"df_testing=pd.read_csv('/kaggle/input/playground-series-s4e7/test.csv')","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:49:57.749338Z","iopub.execute_input":"2024-07-10T14:49:57.749824Z","iopub.status.idle":"2024-07-10T14:50:11.805937Z","shell.execute_reply.started":"2024-07-10T14:49:57.749766Z","shell.execute_reply":"2024-07-10T14:50:11.804724Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"df_testing.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:50:11.807561Z","iopub.execute_input":"2024-07-10T14:50:11.808067Z","iopub.status.idle":"2024-07-10T14:50:11.829733Z","shell.execute_reply.started":"2024-07-10T14:50:11.808018Z","shell.execute_reply":"2024-07-10T14:50:11.828363Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":" id Gender Age Driving_License Region_Code Previously_Insured \\\n0 11504798 Female 20 1 47.0 0 \n1 11504799 Male 47 1 28.0 0 \n2 11504800 Male 47 1 43.0 0 \n3 11504801 Female 22 1 47.0 1 \n4 11504802 Male 51 1 19.0 0 \n\n Vehicle_Age Vehicle_Damage Annual_Premium Policy_Sales_Channel Vintage \n0 < 1 Year No 2630.0 160.0 228 \n1 1-2 Year Yes 37483.0 124.0 123 \n2 1-2 Year Yes 2630.0 26.0 271 \n3 < 1 Year No 24502.0 152.0 115 \n4 1-2 Year No 34115.0 124.0 148 ","text/html":"
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idGenderAgeDriving_LicenseRegion_CodePreviously_InsuredVehicle_AgeVehicle_DamageAnnual_PremiumPolicy_Sales_ChannelVintage
011504798Female20147.00< 1 YearNo2630.0160.0228
111504799Male47128.001-2 YearYes37483.0124.0123
211504800Male47143.001-2 YearYes2630.026.0271
311504801Female22147.01< 1 YearNo24502.0152.0115
411504802Male51119.001-2 YearNo34115.0124.0148
\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_testing['Gender'] = df_testing['Gender'].replace(['Female', 'Male'], [0, 1])\ndf_testing['Vehicle_Age'] = df_testing['Vehicle_Age'].replace(['1-2 Year', '< 1 Year', '> 2 Years'], [0, 1, 2])\ndf_testing['Vehicle_Damage'] = df_testing['Vehicle_Damage'].replace(['No', 'Yes'], [0, 1])","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:50:11.831376Z","iopub.execute_input":"2024-07-10T14:50:11.831737Z","iopub.status.idle":"2024-07-10T14:50:30.637409Z","shell.execute_reply.started":"2024-07-10T14:50:11.831709Z","shell.execute_reply":"2024-07-10T14:50:30.635998Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_655/155575545.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df_testing['Gender'] = df_testing['Gender'].replace(['Female', 'Male'], [0, 1])\n/tmp/ipykernel_655/155575545.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df_testing['Vehicle_Age'] = df_testing['Vehicle_Age'].replace(['1-2 Year', '< 1 Year', '> 2 Years'], [0, 1, 2])\n/tmp/ipykernel_655/155575545.py:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n df_testing['Vehicle_Damage'] = df_testing['Vehicle_Damage'].replace(['No', 'Yes'], [0, 1])\n","output_type":"stream"}]},{"cell_type":"code","source":"df_new= df_testing.drop('id', axis = 1)\ndf_new = scaler.fit_transform(df_new)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:50:30.639144Z","iopub.execute_input":"2024-07-10T14:50:30.639650Z","iopub.status.idle":"2024-07-10T14:50:32.439056Z","shell.execute_reply.started":"2024-07-10T14:50:30.639604Z","shell.execute_reply":"2024-07-10T14:50:32.437737Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"test_predic= lgb_model.predict(df_new)\nprint(test_predic)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:50:32.445069Z","iopub.execute_input":"2024-07-10T14:50:32.445518Z","iopub.status.idle":"2024-07-10T14:51:25.927224Z","shell.execute_reply.started":"2024-07-10T14:50:32.445482Z","shell.execute_reply":"2024-07-10T14:51:25.925862Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"[0 0 0 ... 0 1 0]\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({'id': df_testing.id, 'Response': test_predic})\nprint(submission.shape)\nsubmission.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:51:25.929555Z","iopub.execute_input":"2024-07-10T14:51:25.930007Z","iopub.status.idle":"2024-07-10T14:51:25.995350Z","shell.execute_reply.started":"2024-07-10T14:51:25.929971Z","shell.execute_reply":"2024-07-10T14:51:25.993669Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"(7669866, 2)\n","output_type":"stream"},{"execution_count":30,"output_type":"execute_result","data":{"text/plain":" id Response\n0 11504798 0\n1 11504799 0\n2 11504800 0\n3 11504801 0\n4 11504802 0","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
idResponse
0115047980
1115047990
2115048000
3115048010
4115048020
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"},"metadata":{}}]},{"cell_type":"code","source":"submission['Response'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:51:25.997256Z","iopub.execute_input":"2024-07-10T14:51:25.997689Z","iopub.status.idle":"2024-07-10T14:51:26.083772Z","shell.execute_reply.started":"2024-07-10T14:51:25.997656Z","shell.execute_reply":"2024-07-10T14:51:26.082574Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Response\n0 7560129\n1 109737\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"submission.to_csv(\"submission.csv\",index=False)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:51:26.085513Z","iopub.execute_input":"2024-07-10T14:51:26.086987Z","iopub.status.idle":"2024-07-10T14:51:37.451486Z","shell.execute_reply.started":"2024-07-10T14:51:26.086916Z","shell.execute_reply":"2024-07-10T14:51:37.449814Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"import pickle\nwith open('model.pkl','wb') as file:\n pickle.dump(model,file)","metadata":{"execution":{"iopub.status.busy":"2024-07-10T14:54:58.650573Z","iopub.execute_input":"2024-07-10T14:54:58.651146Z","iopub.status.idle":"2024-07-10T14:54:58.659380Z","shell.execute_reply.started":"2024-07-10T14:54:58.651104Z","shell.execute_reply":"2024-07-10T14:54:58.657609Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nfrom sklearn.metrics import classification_report, accuracy_score\n\n# Assuming these are your stored predictions and true labels\npredictions = {\n 'Logistic Regression': y_pred,\n 'LightGBM': lgb_pred,\n 'XGBoost': xgb_pred,\n 'MLPClassifier': nn_pred,\n 'Ridge Classifier': ridge_pred,\n 'SGD Logistic Regression': sgd_pred,\n 'Naive Bayes': nb_pred\n}\n\ny_true = y_test # Assuming y_test contains the true labels\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T15:12:46.603755Z","iopub.execute_input":"2024-07-10T15:12:46.604279Z","iopub.status.idle":"2024-07-10T15:12:46.613154Z","shell.execute_reply.started":"2024-07-10T15:12:46.604245Z","shell.execute_reply":"2024-07-10T15:12:46.611561Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"def get_classification_report_df(y_true, y_pred, model_name):\n report = classification_report(y_true, y_pred, output_dict=True)\n df = pd.DataFrame(report).transpose().reset_index()\n df = df.rename(columns={'index': 'class'})\n df['model'] = model_name\n # Separate accuracy\n accuracy = accuracy_score(y_true, y_pred)\n df_accuracy = pd.DataFrame({\n 'class': ['accuracy'],\n 'precision': [accuracy],\n 'recall': [accuracy],\n 'f1-score': [accuracy],\n 'support': [len(y_true)],\n 'model': [model_name]\n })\n return df[df['class'].isin(['0', '1'])], df_accuracy","metadata":{"execution":{"iopub.status.busy":"2024-07-10T15:13:00.741890Z","iopub.execute_input":"2024-07-10T15:13:00.742351Z","iopub.status.idle":"2024-07-10T15:13:00.755209Z","shell.execute_reply.started":"2024-07-10T15:13:00.742316Z","shell.execute_reply":"2024-07-10T15:13:00.753304Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"dfs = []\naccuracy_dfs = []\nfor model_name, y_pred in predictions.items():\n df, df_accuracy = get_classification_report_df(y_true, y_pred, model_name)\n dfs.append(df)\n accuracy_dfs.append(df_accuracy)\n\ncombined_df = pd.concat(dfs)\naccuracy_df = pd.concat(accuracy_dfs)\nfinal_df = pd.concat([combined_df, accuracy_df])","metadata":{"execution":{"iopub.status.busy":"2024-07-10T15:13:07.575230Z","iopub.execute_input":"2024-07-10T15:13:07.575735Z","iopub.status.idle":"2024-07-10T15:13:23.819119Z","shell.execute_reply.started":"2024-07-10T15:13:07.575690Z","shell.execute_reply":"2024-07-10T15:13:23.817691Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"final_df=combined_df","metadata":{"execution":{"iopub.status.busy":"2024-07-10T15:17:01.314990Z","iopub.execute_input":"2024-07-10T15:17:01.315622Z","iopub.status.idle":"2024-07-10T15:17:01.327462Z","shell.execute_reply.started":"2024-07-10T15:17:01.315581Z","shell.execute_reply":"2024-07-10T15:17:01.325691Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"accuracy_data = accuracy_df.melt(id_vars=[\"model\", \"class\"], value_vars=[\"precision\"])\naccuracy_data.rename(columns={\"value\": \"accuracy\"}, inplace=True)\n\n# Plotting the class-specific metrics and accuracy\nmetrics = ['precision', 'recall', 'f1-score']\nfig, axs = plt.subplots(len(metrics) + 1, 1, figsize=(12, 25))\n\n# Plot precision, recall, f1-score\nfor i, metric in enumerate(metrics):\n sns.barplot(x='class', y=metric, hue='model', data=final_df, ax=axs[i])\n axs[i].set_title(f'{metric.capitalize()} Comparison')\n axs[i].legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Plot accuracy separately\nsns.barplot(x='model', y='accuracy', data=accuracy_data, ax=axs[len(metrics)])\naxs[len(metrics)].set_title('Accuracy Comparison')\naxs[len(metrics)].set_xlabel('Model')\naxs[len(metrics)].set_ylabel('Accuracy')\naxs[len(metrics)].legend().set_visible(False)\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-10T15:18:31.761023Z","iopub.execute_input":"2024-07-10T15:18:31.762063Z","iopub.status.idle":"2024-07-10T15:18:33.579876Z","shell.execute_reply.started":"2024-07-10T15:18:31.762023Z","shell.execute_reply":"2024-07-10T15:18:33.578535Z"},"trusted":true},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\ No newline at end of file diff --git a/Health Insurance Cross Sell Prediction/Model/model.pkl b/Health Insurance Cross Sell Prediction/Model/model.pkl new file mode 100644 index 000000000..291211b46 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/Model/model.pkl differ diff --git a/Health Insurance Cross Sell Prediction/Model/scaler.pkl b/Health Insurance Cross Sell Prediction/Model/scaler.pkl new file mode 100644 index 000000000..2e3212c54 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/Model/scaler.pkl differ diff --git a/Health Insurance Cross Sell Prediction/README.md b/Health Insurance Cross Sell Prediction/README.md new file mode 100644 index 000000000..6329b390e --- /dev/null +++ b/Health Insurance Cross Sell Prediction/README.md @@ -0,0 +1,92 @@ +# Health Insurance Cross Sell Prediction +Dive into the intricacies of predicting which customers are likely to purchase additional health insurance based on their profiles. This project utilizes various machine learning models to analyze customer data and provide insights that can help insurance companies tailor their marketing strategies and improve customer engagement. +webapp + +## 📝 Abstract +Health Insurance Cross Sell Prediction involves using machine learning algorithms to predict whether a customer will purchase additional health insurance. This analysis provides valuable insights into customer behavior and helps insurance companies make informed decisions to optimize their marketing and sales strategies. + +## 🔍 Methodology +**Importing Libraries** + +- Libraries such as NumPy, Pandas, Scikit-Learn, LightGBM, XGBoost, and others are imported for data manipulation, visualization, and machine learning model building. + +**Loading the Dataset** + +- The dataset contains customer information with various features such as Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage, and Response. + +**Data Preprocessing** + +- prepare data for analysis: handle missing values, encode categorical data, scale features, perform feature engineering, split into train-test sets, and normalize data. Ensure data is in a suitable format for machine learning algorithms. + +**Training the Models** + +- Each model is trained on the training dataset and evaluated using metrics such as accuracy, precision, recall, and F1 score. The models used include: +Logistic Regression +XGBoost +Naive Bayes +LightGBM +Neural Network +Ridge Classifier +SGD Classifier + +**Model Performance Analysis** + +- Training and validation loss and accuracy are plotted to visualize the models' performance. + +**Model Prediction** + +- The model is given a test dataset to check the accuracy and precision of the predictions. + +**Deploy** + +- Using the Streamlit library, the model is deployed for real-time prediction of customer cross-sell potential. + +## Project Directory Structure +```bash +Health Insurance Cross Sell Prediction +|- Dataset + |- dataset_view.csv + |- dataset_review.csv + |- README.md +|- Model + |- Health_Insurance_Cross_Sell_Prediction.ipynb + |- README.md + |- model.pkl + |- scaler.pkl +|- Web App + |- app.py + |- README.md +|- Images + |- correlation.png + |- dis_age.png + |- distribution_response.png + |- f1_cmp.png + |- gender_response.png + |- gender.png + |- histogram.png + |- model_cmp.png + |- precision.png + |- recall_cmp.png + |- README.md + |- webapp_run.mp4 +|- requirements.txt +|- README.md +``` +### How to Use +**Requirements** +- Ensure you have the necessary libraries and dependencies installed. You can find the list of required packages in the `requirements.txt` file. + +**Download Data** +- Download the train.csv and test.csv datasets from Kaggle or any other source mentioned in the dataset section of the project.[Kaggle](https://www.kaggle.com/competitions/playground-series-s4e7/data) + +**Run the Jupyter Notebook** +- Open the provided Jupyter Notebook file and run each cell sequentially. Make sure to update any file paths or configurations as needed for your environment. + +**Training and Evaluation** +- Train the models using the provided data and evaluate their performance using metrics such as accuracy, precision, recall, and F1 score. + +**Interpret Results** +- Analyze the model's performance using the visualizations and metrics provided in the notebook. + +## Connect with Me +Tanuj Saxena [LinkedIn](https://www.linkedin.com/in/tanuj-saxena-970271252/) diff --git a/Health Insurance Cross Sell Prediction/Webapp/README.md b/Health Insurance Cross Sell Prediction/Webapp/README.md new file mode 100644 index 000000000..334207e15 --- /dev/null +++ b/Health Insurance Cross Sell Prediction/Webapp/README.md @@ -0,0 +1,39 @@ + +https://github.com/user-attachments/assets/d0554627-f3f2-4f1f-a375-e3ba23c46db3 + +# Health Insurance Cross Sell Prediction Web App +## Goal 🎯 +The goal of this prediction web application is to accurately predict whether a customer will buy a vehicle insurance policy. By analyzing customer data, the app helps in organizing and prioritizing potential buyers, detecting trends, and ensuring targeted marketing efforts. It streamlines the process of understanding customer behavior and provides valuable insights for stakeholders. 📈🚗 + +## Model(s) Used for the Web App 🧮 +The model used in this web app is a pre-trained LightGBM classifier, which has been fine-tuned for predicting customer responses. The model analyzes various features such as Age, Gender, Vehicle_Age, and others to predict the likelihood of purchasing insurance with high accuracy. + +## Video Demonstration 🎥 + + +https://github.com/user-attachments/assets/d39b0cd7-9b31-49c9-939e-060409edc74b + + + +## How to Run the Web App +#### Requirements +Ensure you have the necessary libraries and dependencies installed. You can find the list of required packages in the requirements.txt file. + +### Installation +**Clone the repository:** +```bash +gh repo clone tanuj437/Health-Insurance-Cross-Sell-Prediction +cd Health-Insurance-Cross-Sell-Prediction/WebApp +``` +**Install the Dependencies:** +```bash +pip install -r requirements.txt +``` +**Run the Streamlit app:** +```bash +streamlit run app.py +``` +### Signature ✒️ +Tanuj Saxena + +[![LinkedIn](https://img.shields.io/badge/LinkedIn-%230077B5.svg?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/tanuj-saxena-970271252/) diff --git a/Health Insurance Cross Sell Prediction/Webapp/app.py b/Health Insurance Cross Sell Prediction/Webapp/app.py new file mode 100644 index 000000000..c65d8b83f --- /dev/null +++ b/Health Insurance Cross Sell Prediction/Webapp/app.py @@ -0,0 +1,56 @@ +import joblib +import streamlit as st +import pandas as pd +import numpy as np +from sklearn.preprocessing import StandardScaler + +# Load the fitted scaler +scaler = joblib.load('Model/scaler.pkl') + +# Load your trained model +model = joblib.load('Model/model.pkl') + +# Function to preprocess and predict the response +def predict_response(gender, age, driving_license, region_code, previously_insured, + vehicle_age, vehicle_damage, annual_premium, policy_sales_channel, vintage): + # Preprocess input data + gender = 1 if gender == 'Male' else 0 + vehicle_age = {'< 1 Year': 1, '1-2 Year': 0, '> 2 Years': 2}[vehicle_age] + vehicle_damage = 1 if vehicle_damage == 'Yes' else 0 + + # Create a numpy array with the input data + data = np.array([[gender, age, driving_license, region_code, previously_insured, + vehicle_age, vehicle_damage, annual_premium, policy_sales_channel, vintage]]) + + # Transform the data using the loaded scaler + data_scaled = scaler.transform(data) + + # Make predictions + prediction = model.predict(data_scaled)[0] + + return prediction + +def main(): + st.title('Insurance Response Prediction App') + st.sidebar.title('Input Parameters') + + # Input fields + gender = st.sidebar.radio('Gender', ['Male', 'Female']) + age = st.sidebar.slider('Age', 20, 85, 40) + driving_license = st.sidebar.selectbox('Driving License', [0, 1]) + region_code = st.sidebar.number_input('Region Code', min_value=0.0, max_value=52.0, value=25.0) + previously_insured = st.sidebar.selectbox('Previously Insured', [0, 1]) + vehicle_age = st.sidebar.selectbox('Vehicle Age', ['< 1 Year', '1-2 Year', '> 2 Years']) + vehicle_damage = st.sidebar.selectbox('Vehicle Damage', ['No', 'Yes']) + annual_premium = st.sidebar.number_input('Annual Premium', min_value=2630.0, max_value=540165.0, value=2630.0) + policy_sales_channel = st.sidebar.number_input('Policy Sales Channel', min_value=1, max_value=163, value=1) + vintage = st.sidebar.slider('Vintage', 10, 299, 150) + + # Predict function + if st.button("Predict"): + prediction = predict_response(gender, age, driving_license, region_code, previously_insured, vehicle_age, vehicle_damage, annual_premium, policy_sales_channel, vintage) + response = 'True' if prediction == 1 else 'False' + st.success(f"The predicted response is: {response}") + +if __name__ == '__main__': + main() diff --git a/Health Insurance Cross Sell Prediction/images/README.md b/Health Insurance Cross Sell Prediction/images/README.md new file mode 100644 index 000000000..d0fbb1dcd --- /dev/null +++ b/Health Insurance Cross Sell Prediction/images/README.md @@ -0,0 +1,47 @@ +# Image Folder Overview +This folder contains various visualizations that represent different aspects of the dataset and model performance. Below is a detailed description of each visualization. + +## Visualizations +- **Correlation Matrix:** +This matrix shows the correlation between different features in the dataset, providing insight into how variables are related to each other. +correlation + + +- **Distribution of Response:** +This chart displays the distribution of the target variable Response. It shows the frequency of positive and negative responses in the dataset. +distribution_response + +- **Distribution Based on Gender:** +Description: This chart shows the distribution of the Gender variable, illustrating the proportion of male and female customers. +gender_response + +- **Gender Distribution:** +Description: This visualization represents the count of responses from different genders, helping to understand the gender demographics of the dataset. +gender + +- **Distribution of Age:** +Description: This chart displays the distribution of the Age variable, showing the age range and frequency of customers in the dataset. +dis_age + +- **Histograms of Selected Columns:** +Description: These histograms show the distribution of values for selected columns such as Annual_Premium, Policy_Sales_Channel, and Vintage. They provide insight into the data distribution for these features. +histogram + +- **F1 Score Comparison:** +Description: This bar chart compares the F1 scores of different models used in the analysis. The F1 score is a measure of a model's accuracy, balancing precision and recall. +f1_cmp + +- **Recall Comparison:** +Description: This bar chart compares the recall scores of different models used in the analysis. Recall measures the ability of a model to identify all relevant instances in the dataset. +recall_cmp + +- **Precision Comparison:** +Description: This bar chart compares the precision scores of different models used in the analysis. Precision measures the accuracy of the positive predictions made by the model. +precision_cmp + +- **Accuracy Comparison:** +Description: This bar chart compares the accuracy scores of different models used in the analysis. Accuracy measures the overall correctness of the model's predictions. +model_cmp + +### Usage +These visualizations provide a comprehensive view of the dataset's characteristics and the performance of various models used for sentiment analysis. They can be used to gain insights into customer demographics, feature distributions, and model effectiveness, aiding in identifying areas for improvement in the analysis. diff --git a/Health Insurance Cross Sell Prediction/images/correlation.png b/Health Insurance Cross Sell Prediction/images/correlation.png new file mode 100644 index 000000000..db182c6c4 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/correlation.png differ diff --git a/Health Insurance Cross Sell Prediction/images/dis_age.png b/Health Insurance Cross Sell Prediction/images/dis_age.png new file mode 100644 index 000000000..f0f589e77 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/dis_age.png differ diff --git a/Health Insurance Cross Sell Prediction/images/distribution_response.png b/Health Insurance Cross Sell Prediction/images/distribution_response.png new file mode 100644 index 000000000..380d7f4d5 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/distribution_response.png differ diff --git a/Health Insurance Cross Sell Prediction/images/f1_cmp.png b/Health Insurance Cross Sell Prediction/images/f1_cmp.png new file mode 100644 index 000000000..af937e012 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/f1_cmp.png differ diff --git a/Health Insurance Cross Sell Prediction/images/gender.png b/Health Insurance Cross Sell Prediction/images/gender.png new file mode 100644 index 000000000..ed349b0d2 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/gender.png differ diff --git a/Health Insurance Cross Sell Prediction/images/gender_response.png b/Health Insurance Cross Sell Prediction/images/gender_response.png new file mode 100644 index 000000000..85579ea8a Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/gender_response.png differ diff --git a/Health Insurance Cross Sell Prediction/images/histogram.png b/Health Insurance Cross Sell Prediction/images/histogram.png new file mode 100644 index 000000000..92bcdec50 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/histogram.png differ diff --git a/Health Insurance Cross Sell Prediction/images/model_cmp.png b/Health Insurance Cross Sell Prediction/images/model_cmp.png new file mode 100644 index 000000000..73732c792 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/model_cmp.png differ diff --git a/Health Insurance Cross Sell Prediction/images/precision_cmp.png b/Health Insurance Cross Sell Prediction/images/precision_cmp.png new file mode 100644 index 000000000..50d200793 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/precision_cmp.png differ diff --git a/Health Insurance Cross Sell Prediction/images/recall_cmp.png b/Health Insurance Cross Sell Prediction/images/recall_cmp.png new file mode 100644 index 000000000..a522d24ba Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/recall_cmp.png differ diff --git a/Health Insurance Cross Sell Prediction/images/running_test.mp4 b/Health Insurance Cross Sell Prediction/images/running_test.mp4 new file mode 100644 index 000000000..2eab71f3c Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/running_test.mp4 differ diff --git a/Health Insurance Cross Sell Prediction/images/webapp.png b/Health Insurance Cross Sell Prediction/images/webapp.png new file mode 100644 index 000000000..8e0dec651 Binary files /dev/null and b/Health Insurance Cross Sell Prediction/images/webapp.png differ diff --git a/Health Insurance Cross Sell Prediction/requirement.txt b/Health Insurance Cross Sell Prediction/requirement.txt new file mode 100644 index 000000000..eca966494 --- /dev/null +++ b/Health Insurance Cross Sell Prediction/requirement.txt @@ -0,0 +1,11 @@ +pandas==1.3.3 +numpy==1.21.2 +scikit-learn==0.24.2 +joblib==1.0.1 +xgboost==1.4.2 +lightgbm==3.2.1 +tensorflow==2.6.0 +streamlit==0.86.0 +matplotlib==3.4.3 +seaborn==0.11.2 +