diff --git a/.gitignore b/.gitignore
index 7258c6a..d1b3542 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,7 +10,9 @@ notebooks/spark/scripts/
notebooks/exercises/scripts/
TensorBoard/
.vscode/
+# ignore lightning_logs folder in whatever directory
+lightning_logs/
# Julia
Manifest.toml
-/Project.toml
\ No newline at end of file
+/Project.toml
diff --git a/drafts/FTS Seminarablauf.md b/drafts/FTS Seminarablauf.md
new file mode 100644
index 0000000..4ca0269
--- /dev/null
+++ b/drafts/FTS Seminarablauf.md
@@ -0,0 +1,78 @@
+# Forecasting auf Zeitreihen - Mit maschinellem Lernen zur Prognose und Entscheidung
+
+Inhalte und Ablauf des Kurses
+
+Tag 1 - Grundlagen und Einführung in Zeitreihenprognosen
+
+1. Start, Begrüßung, Vorstellungsrunde, Erwartungen
+ - Warum Forecasting auf Zeitreihen?
+ - Anwendungsbeispiele und Motivation
+ - Beispieldatensätze (Handel, Ökologie, Mobilität...)
+
+2. Einführung in Zeitreihenprognosen: Grundbegriffe und Konzepte
+ - Univariate und multivariate Zeitreihen
+ - Zeitreihenkomponenten (Trend, Saisonalität, Zyklen, Unregelmäßigkeit)
+ - Direkte und rekursive Prognoseverfahren
+ - Prognosehorizont
+
+3. Explorative Datenanalyse für Zeitreihen
+ - Visualisierung und Analyse von Zeitreihendaten
+ - Identifizierung von Mustern und Anomalien
+
+4. Datenvorverarbeitung und Feature Engineering
+ - Umgang mit fehlenden Werten und Ausreißern
+ - Exogene Merkmale (Kalendermerkmale, kategoriale Merkmale)
+ - Fenstermerkmale und verzögerte Variablen
+
+5. Traditionelle Zeitreihenmodelle und einfache Baseline-Modelle
+ - Naive Methoden
+ - traditionelle Modelle (Moving Average etc.)
+
+6. Probabilistische Prognosen: Quantifizierung der Unsicherheit
+ - Quantifizierung der Unsicherheit in Prognosen
+ - Generierung von Konfidenzintervallen
+ - Evaluierungsmetriken für probabilistische Prognosen
+
+Tag 2 - Fortgeschrittene Techniken, aktuelle Frameworks und Praxis
+
+1. Modellevaluierung und Interpretation
+ - Aufteilung und Kreuzvalidierung
+ - Evaluierungsmetriken für Zeitreihenprognosen (MAPE, RMSE, MASE)
+ - Interpretation und Visualisierung von Prognoseergebnissen
+ - Merkmalswichtigkeit und Erklärbarkeit
+
+2. Klassische Machine Learning-Modelle für Zeitreihenprognosen
+ - Regressionsmodelle (lineare Regression, Entscheidungsbäume, Random Forests...)
+
+3. State-of-the-Art-Modelle und Pakete
+ - Deep Learning für Prognosen
+ - Fortgeschrittene statistische Modelle
+ - Einführung in aktuelle Pakete
+ - SKForecast, GluonTS, Chronos, Prophet...
+
+4. Praktische Übung und Challenge: Anwendung von Forecasting auf Beispieldatensätze
+ - Implementierung und Vergleich
+ - Evaluierung der Modelle anhand ausgewählter Datensätze
+ - Diskussion
+
+5. Bereitstellung von Prognosemodellen in der Praxis
+ - Überlegungen zum Deployment von Modellen
+ - Überwachung und Aktualisierung von Modellen im Laufe der Zeit (concept drift..)
+ - Echtzeitprognosen
+ - Integration in Geschäftsprozesse
+
+6. Kurszusammenfassung und Fragen & Antworten
+ - Zusammenfassung der behandelten Schlüsselkonzepte und -techniken
+ - Offene Diskussion und Fragen der Teilnehmer
+
+Übungsmaterial
+
+- Lehrmaterialien, Beispieldatensätze, interaktive Übungen, ...
+
+Eingesetzte Frameworks und Pakete
+Python, Pandas, NumPy, Matplotlib, SKForecast, GluonTS, Chronos, Prophet, ...
+
+Eingesetzte Tools im Seminar
+In diesem Online-Seminar arbeitest du direkt in einer cloudbasierten Laborumgebung, die vom Trainer bereitgestellt wird. Ein Webbrowser genügt – keine Installation notwendig.
+Interaktive Jupyter Notebooks dienen als Lernmaterial und Arbeitsumgebung zugleich. Sie enthalten neben Quellcode auch Dokumentation, Referenzen und Links. Aufgrund des Programmier-Fokus sind die Unterlagen primär auf Englisch.
+Deine Online-Lernumgebung hält nach der Anmeldung ergänzende Informationen und Services für dich bereit.
diff --git a/drafts/FTS.md b/drafts/FTS.md
new file mode 100644
index 0000000..a70b07e
--- /dev/null
+++ b/drafts/FTS.md
@@ -0,0 +1,81 @@
+# Forecasting on Time Series - Machine Learning for Prediction and Decision-Making"
+
+Day 1:
+
+1. Introduction to Time Series Forecasting
+ - Importance of forecasting in decision-making
+ - Example Problems and Datasets
+ - Dataset 1: "store_sales" (daily sales transactions for 50 products in 10 stores)
+ - Introducing demand forecasting and inventory management
+ - Dataset 2: "air_quality_valencia" (hourly measures of air pollutants in Valencia, Spain)
+ - Discussing environmental monitoring and air quality prediction
+ - Dataset 3: "bicycle_sharing" (hourly usage of bike share system in Washington, D.C.)
+ - Exploring forecasting for resource allocation and transportation planning
+
+2. Fundamentals and Basic Terms
+ - univariate and multivariate time series
+ - Exploratory data analysis techniques for time series
+ - time series components (trend, seasonality, cycles, irregularity)
+ - Direct and recursive forecasting approaches
+ - forecast horizon
+
+
+
+3. Data Preprocessing and Feature Engineering
+ - Handling missing values and outliers
+ - Temporal data splitting (train/test)
+ - Exogenous features (calendar features, categorical features)
+ - Window features and lagged variables
+
+4. Traditional Time Series Models and Simple Baseline Models
+ - Naive methods (last value, average)
+ - Moving average and exponential smoothing
+
+5. Probabilistic Forecasting: Quantifying Uncertainty
+ - Quantifying uncertainty in forecasts
+ - Generating confidence intervals
+ - Evaluation metrics for probabilistic forecasts
+
+Day 2:
+
+1. Model Evaluation and Interpretation
+ - Split and cross-validation
+ - Evaluation metrics for time series forecasting (MAPE, RMSE, MASE)
+ - Interpreting and visualizing forecasting results
+ - Feature importance and explainability
+
+
+2. Classical Machine Learning Models for Time Series Forecasting
+ - Regression models (linear regression, decision trees, random forests..)
+
+3. State-of-the-art Models & Packages
+ - deep learning for forecasting
+ - advanced statistical models
+ - introducing SKForecast, GluonTS, Chronos, Prophet
+
+
+4. Hands-on Exercise & Challenge: Applying Forecasting Methods to Example Datasets
+ - Implementing and comparing traditional and machine learning models using SKForecast, Chronos, and GluonTS
+ - Evaluating models on the selected datasets:
+ - "store_sales" for demand forecasting
+ - "air_quality_valencia" for air quality prediction
+ - "bicycle_sharing" for resource allocation forecasting
+
+
+5. Deploying Forecasting Models in Production
+ - Considerations for productionizing forecasting models
+ - Concept drift
+ - Monitoring and updating models over time
+ - Real-time forecasting and integration with business processes
+
+6. Course Recap and Q&A
+ - Summary of key concepts and techniques covered
+ - Open discussion and questions from participants
+
+
+
+
+----
+
+
+
diff --git a/notebooks/data-science-learning-paths.ipynb b/notebooks/data-science-learning-paths.ipynb
index 492e4cd..232a26c 100644
--- a/notebooks/data-science-learning-paths.ipynb
+++ b/notebooks/data-science-learning-paths.ipynb
@@ -108,6 +108,27 @@
""
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Time Series Forecasting [TSF] \n",
+ "
\n",
+ " A 2-day advanced course on machine learning for prediction and decision-making.\n",
+ "
\n",
+ "
\n",
+ " Level: Advanced \n",
+ " Duration: 2 days \n",
+ " Prerequisites: DAP \n",
+ " Index notebook: 📓 Start course \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/notebooks/images/art-time-series-forecasting.png b/notebooks/images/art-time-series-forecasting.png
new file mode 100644
index 0000000..833517c
Binary files /dev/null and b/notebooks/images/art-time-series-forecasting.png differ
diff --git a/notebooks/index/mlts-machine-learning-time-series.ipynb b/notebooks/index/mlts-machine-learning-time-series.ipynb
index 222c94f..715ce2a 100644
--- a/notebooks/index/mlts-machine-learning-time-series.ipynb
+++ b/notebooks/index/mlts-machine-learning-time-series.ipynb
@@ -45,6 +45,8 @@
"\n",
"1. **[Time Series Forecasting](../timeseries/mlts-forecasting-intro.ipynb)** \n",
" About predicting a time series several steps into the future.\n",
+ "\n",
+ " 1. **[Foreccasting Metrics and Evaluation Techniques](../timeseries/mlts-forecasting-evaluation.ipynb)** \n",
" \n",
" 1. **[Classical Time Series Forecasting Models](../timeseries/mlts-classical-forecasting-models.ipynb)** \n",
" Statistical modelling applied to forecasting.\n",
diff --git a/notebooks/index/tsf-time-series-forecasting.ipynb b/notebooks/index/tsf-time-series-forecasting.ipynb
new file mode 100644
index 0000000..41f09f1
--- /dev/null
+++ b/notebooks/index/tsf-time-series-forecasting.ipynb
@@ -0,0 +1,110 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Time Series Forecasting \n",
+ "**Machine Learning for Prediction and Decision-making**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An intensive course on how to apply modern machine learning for prediction and decision-making on time series data. \n",
+ "\n",
+ "This is an advanced course that builds on practical experience in Python programming, data analysis, and machine learning. While we recapitulate some of the foundations, they are covered in much more detail in the following modules of Data Science Learning Paths:\n",
+ "\n",
+ "- [📓 Data Analysis with Python](dap2-data-analysis-python.ipynb)\n",
+ "- [📓 Machine Learning with Python](mlp2-machine-learning-python-2day.ipynb)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Table of Contents\n",
+ "\n",
+ "### Basics\n",
+ "\n",
+ "1. **[Handling Time Series with pandas](../python/python-timeseries-pandas.ipynb)** \n",
+ " Working with time series data as dataframes.\n",
+ " \n",
+ "2. **[Time Series Analysis](../timeseries/mlts-time-series-analysis.ipynb)** \n",
+ " Analysing time series data for structure. \n",
+ "\n",
+ "### Forecasting\n",
+ "\n",
+ "\n",
+ "1. **[Time Series Forecasting](../timeseries/mlts-forecasting-intro.ipynb)** \n",
+ " About predicting a time series several steps into the future.\n",
+ "\n",
+ " 1. **[Foreccasting Metrics and Evaluation Techniques](../timeseries/mlts-forecasting-evaluation.ipynb)** \n",
+ " \n",
+ " 2. **[Classical Time Series Forecasting Models](../timeseries/mlts-classical-forecasting-models.ipynb)** \n",
+ " Statistical modelling applied to forecasting.\n",
+ " \n",
+ " 3. **[Forecasting with Prophet](../timeseries/mlts-prophet.ipynb)** \n",
+ " An easy-to-use model from our colleagues at a social media company. \n",
+ " \n",
+ " 4. **[Forecasting with Shallow Learning](../timeseries/mlts-forecasting-shallow.ipynb)** \n",
+ " How to apply any supervised ML regression algorithm for forecasting.\n",
+ "\n",
+ " 5. **[Forecasting with Deep Learning](../timeseries/mlts-forecasting-deep.ipynb)** \n",
+ " Using recurrent neural networks to forecast a time series."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "1. [**Challenge: Forecasting Taxi Demand**](../timeseries/mlts-challenge.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Additional Resources\n",
+ "\n",
+ "- [**Python Test Notebook**](../test.ipynb) \n",
+ " Verify that your Python stack is working.\n",
+ " \n",
+ "- [**Jupyter Cheat Sheet**](../jupyter/cheatsheet.ipynb) \n",
+ " Some useful commands for Jupyter Notebook, mostly optional."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/timeseries/tsf-packages-chronos.ipynb b/notebooks/timeseries/tsf-packages-chronos.ipynb
new file mode 100644
index 0000000..9fec794
--- /dev/null
+++ b/notebooks/timeseries/tsf-packages-chronos.ipynb
@@ -0,0 +1,256 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Package: Chronos\n",
+ "\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/torchvision/io/image.py:14: UserWarning: Failed to load image Python extension: 'dlopen(/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/torchvision/image.so, 0x0006): Library not loaded: @rpath/libjpeg.9.dylib\n",
+ " Referenced from: <367D4265-B20F-34BD-94EB-4F3EE47C385B> /Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/torchvision/image.so\n",
+ " Reason: tried: '/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/torchvision/../../../libjpeg.9.dylib' (no such file), '/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/torchvision/../../../libjpeg.9.dylib' (no such file), '/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/lib-dynload/../../libjpeg.9.dylib' (no such file), '/Users/cls/miniforge3/envs/dslp-tsf/bin/../lib/libjpeg.9.dylib' (no such file)'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
+ " warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd # requires: pip install pandas\n",
+ "import torch\n",
+ "from chronos import BaseChronosPipeline\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "pipeline = BaseChronosPipeline.from_pretrained(\n",
+ " \"amazon/chronos-t5-small\", # use \"amazon/chronos-bolt-small\" for the corresponding Chronos-Bolt model\n",
+ " device_map=\"cpu\", \n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Month \n",
+ " #Passengers \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1949-01 \n",
+ " 112 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1949-02 \n",
+ " 118 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1949-03 \n",
+ " 132 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1949-04 \n",
+ " 129 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1949-05 \n",
+ " 121 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 139 \n",
+ " 1960-08 \n",
+ " 606 \n",
+ " \n",
+ " \n",
+ " 140 \n",
+ " 1960-09 \n",
+ " 508 \n",
+ " \n",
+ " \n",
+ " 141 \n",
+ " 1960-10 \n",
+ " 461 \n",
+ " \n",
+ " \n",
+ " 142 \n",
+ " 1960-11 \n",
+ " 390 \n",
+ " \n",
+ " \n",
+ " 143 \n",
+ " 1960-12 \n",
+ " 432 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
144 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Month #Passengers\n",
+ "0 1949-01 112\n",
+ "1 1949-02 118\n",
+ "2 1949-03 132\n",
+ "3 1949-04 129\n",
+ "4 1949-05 121\n",
+ ".. ... ...\n",
+ "139 1960-08 606\n",
+ "140 1960-09 508\n",
+ "141 1960-10 461\n",
+ "142 1960-11 390\n",
+ "143 1960-12 432\n",
+ "\n",
+ "[144 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "df = pd.read_csv(\n",
+ " \"https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv\"\n",
+ ")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# context must be either a 1D tensor, a list of 1D tensors,\n",
+ "# or a left-padded 2D tensor with batch as the first dimension\n",
+ "# quantiles is an fp32 tensor with shape [batch_size, prediction_length, num_quantile_levels]\n",
+ "# mean is an fp32 tensor with shape [batch_size, prediction_length]\n",
+ "quantiles, mean = pipeline.predict_quantiles(\n",
+ " context=torch.tensor(df[\"#Passengers\"]),\n",
+ " prediction_length=12,\n",
+ " quantile_levels=[0.1, 0.5, 0.9],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt # requires: pip install matplotlib\n",
+ "\n",
+ "forecast_index = range(len(df), len(df) + 12)\n",
+ "low, median, high = quantiles[0, :, 0], quantiles[0, :, 1], quantiles[0, :, 2]\n",
+ "\n",
+ "plt.figure(figsize=(8, 4))\n",
+ "plt.plot(df[\"#Passengers\"], color=\"royalblue\", label=\"historical data\")\n",
+ "plt.plot(forecast_index, median, color=\"tomato\", label=\"median forecast\")\n",
+ "plt.fill_between(forecast_index, low, high, color=\"tomato\", alpha=0.3, label=\"80% prediction interval\")\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/timeseries/tsf-packages-gluonts.ipynb b/notebooks/timeseries/tsf-packages-gluonts.ipynb
new file mode 100644
index 0000000..a15efbd
--- /dev/null
+++ b/notebooks/timeseries/tsf-packages-gluonts.ipynb
@@ -0,0 +1,274 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Package: GluonTS\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from gluonts.dataset.pandas import PandasDataset\n",
+ "from gluonts.dataset.split import split \n",
+ "from gluonts.torch import DeepAREstimator\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "PandasDataset"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load data from a CSV file into a PandasDataset\n",
+ "df = pd.read_csv(\n",
+ " \"https://raw.githubusercontent.com/AileenNielsen/\"\n",
+ " \"TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv\",\n",
+ " index_col=0,\n",
+ " parse_dates=True,\n",
+ ")\n",
+ "dataset = PandasDataset(df, target=\"#Passengers\")\n",
+ "dataset\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Split the data for training and testing\n",
+ "training_data, test_gen = split(dataset, offset=-36)\n",
+ "test_data = test_gen.generate_instances(prediction_length=12, windows=3)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True (mps), used: False\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "HPU available: False, using: 0 HPUs\n",
+ "/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+ "\n",
+ " | Name | Type | Params | Mode | In sizes | Out sizes \n",
+ "--------------------------------------------------------------------------------------------------------------------------\n",
+ "0 | model | DeepARModel | 23.3 K | train | [[1, 1], [1, 1], [1, 48, 2], [1, 48], [1, 48], [1, 12, 2]] | [1, 100, 12]\n",
+ "--------------------------------------------------------------------------------------------------------------------------\n",
+ "23.3 K Trainable params\n",
+ "0 Non-trainable params\n",
+ "23.3 K Total params\n",
+ "0.093 Total estimated model params size (MB)\n",
+ "11 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 0: | | 50/? [00:00<00:00, 72.58it/s, v_num=1, train_loss=5.870]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Epoch 0, global step 50: 'train_loss' reached 5.87072 (best 5.87072), saving model to '/Users/cls/Documents/Work/Training/point8/data-science-learning-paths/notebooks/timeseries/lightning_logs/version_1/checkpoints/epoch=0-step=50.ckpt' as top 1\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1: | | 50/? [00:00<00:00, 82.65it/s, v_num=1, train_loss=4.800]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1, global step 100: 'train_loss' reached 4.80319 (best 4.80319), saving model to '/Users/cls/Documents/Work/Training/point8/data-science-learning-paths/notebooks/timeseries/lightning_logs/version_1/checkpoints/epoch=1-step=100.ckpt' as top 1\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 2: | | 50/? [00:00<00:00, 77.49it/s, v_num=1, train_loss=4.500]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Epoch 2, global step 150: 'train_loss' reached 4.50422 (best 4.50422), saving model to '/Users/cls/Documents/Work/Training/point8/data-science-learning-paths/notebooks/timeseries/lightning_logs/version_1/checkpoints/epoch=2-step=150.ckpt' as top 1\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 3: | | 50/? [00:00<00:00, 82.00it/s, v_num=1, train_loss=4.350]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Epoch 3, global step 200: 'train_loss' reached 4.34816 (best 4.34816), saving model to '/Users/cls/Documents/Work/Training/point8/data-science-learning-paths/notebooks/timeseries/lightning_logs/version_1/checkpoints/epoch=3-step=200.ckpt' as top 1\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4: | | 50/? [00:00<00:00, 78.37it/s, v_num=1, train_loss=4.190]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4, global step 250: 'train_loss' reached 4.18728 (best 4.18728), saving model to '/Users/cls/Documents/Work/Training/point8/data-science-learning-paths/notebooks/timeseries/lightning_logs/version_1/checkpoints/epoch=4-step=250.ckpt' as top 1\n",
+ "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4: | | 50/? [00:00<00:00, 77.83it/s, v_num=1, train_loss=4.190]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/cls/miniforge3/envs/dslp-tsf/lib/python3.12/site-packages/gluonts/time_feature/lag.py:104: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+ " offset = to_offset(freq_str)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model and make predictions\n",
+ "model = DeepAREstimator(\n",
+ " prediction_length=12,\n",
+ " freq=\"M\",\n",
+ " trainer_kwargs={\"max_epochs\": 5, \"accelerator\": \"cpu\"},\n",
+ ").train(training_data)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "forecasts = list(model.predict(test_data.input))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAGdCAYAAADXIOPgAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAqwdJREFUeJzsnXmYY2WZvu+TnKxVqX2v3qoXemVpaLYG6WYTkUUHRlRcYMQRdURRGGaE3ygzo4A7MyCjIrLIAC7YoyiIjbLvzdpN73t17VtSlcqec35/fDknSVVq7aSSVH/3ddXVXcnJyZd0dZ0n7/u8z6fouq4jkUgkEolEUmBY8r0AiUQikUgkkkxIkSKRSCQSiaQgkSJFIpFIJBJJQSJFikQikUgkkoJEihSJRCKRSCQFiRQpEolEIpFIChIpUiQSiUQikRQkUqRIJBKJRCIpSNR8L2A6aJpGe3s7Ho8HRVHyvRyJRCKRSCSTQNd1hoaGaGpqwmKZuE5SlCKlvb2duXPn5nsZEolEIpFIpkFraytz5syZ8LiiFCkejwcQL7KsrCzPq5FIJBKJRDIZBgcHmTt3rnkdn4iiFClGi6esrEyKFIlEIpFIiozJWjWkcVYikUgkEklBIkWKRCKRSCSSgkSKFIlEIpFIJAWJFCkSiUQikUgKEilSJBKJRCKRFCRSpEgkEolEIilIpEiRSCQSiURSkEiRIpFIJBKJpCApyjC3w0XXdaLRKJqm5XspEsmsxGq1YrPZ8r0MiURS5BxRIiUSidDd3U0gECAej+d7ORLJrMbhcFBTUyNToSUSybQ5YkRKIBCgtbUVq9VKZWUlLpcLq9Uqd1GWSLKMUan0+Xy0tbUBSKEikUimxREjUnp7e7HZbMyfPx+r1Zrv5UgksxqXy4XH4+HQoUP09vZKkSKRSKbFEWGcjcViDA8PU1VVJQWKRDJDKIpCeXk54XCYaDSa7+VIJJIi5IgRKSB65BKJZOYwzLPSAyaR5IaYFkPX9XwvI2ccESLFQPpPJJKZRf6fk0hyS3egm5gWy/cycsYRJVIkEolEIpktBKIBBsIDRLXZ206VIkUikUgkkiKkc7gTgLg+e9upUqRIJBKJRFJk9If6CcaDALLdI5FIJBKJpDCIaTG6A91p389WpEiRSPLI/v37URRFGkwlEsmk6Qp0pbV4ZLtHckRiXDyn+rV+/fp8L10ikUhmJYFoAG/Ym3bbbDbOHjGJs5KpU19fn/H2/v5+otEoTqeT8vLyUfdXVVXlemkSiURyxKHrOh3DHaNun83tHilSJGPS2dmZ8fb169fz7LPP8tGPfpT77rtvZhclkUgkRygD4QFC8dCo22ezSJHtHolEIpFIioBANJDxdulJkUgmyZVXXomiKNx8881EIhFuu+02jjvuODweD4qi4PV6AViwYAGKovDMM8+MeS7D47J///6M9/f29nLTTTdxzDHH4PF4cLvdrFy5khtvvJG+vr4prftb3/oWiqKwZs2acY/75S9/iaIo1NXVmdstAHR0dPDTn/6Uiy++mKVLl1JSUkJZWRmrV6/mm9/8pvm6p8L69etRFGXcatVE76Pf7+e2227jxBNPpLy8HKfTyZIlS7jmmmtobW3N+Bhd1/nlL3/J2WefTU1NDTabjbq6Oo4++mg++9nP8sQTT0z5tUgkksMnokUy3h7X42i6NsOrmRlku0eSE0KhEOvWreOVV17BZrPhdruzev6XX36Ziy66yBQjdrsdq9XK1q1b2bp1Kw8++CAbN25k6dKlkzrf5Zdfzr/927/xxhtvsGvXLpYsWZLxuIcffhiAyy67DFVN/ve55pprePTRR83vy8vLGRoa4u233+btt9/mf//3f3nmmWeYM2fOdF/ylNmxYwcf+MAHTJGnqioOh4Pdu3dz55138uCDD/LHP/6R0047Le1xV155JQ888EDaa/H5fPT09LBlyxbefvttzj///Bl7HRKJRBCNj22QjWkx7Fb7DK5mZpCVFElO+PGPf8z27dt55JFH8Pv9eL1e9u/fT0lJyWGfu7W1lQsuuIC+vj4++9nPsn37doLBIMPDw2zZsoUPfOADtLa2cumll056Y7uFCxdy8sknA0khMpK+vj42btwICFGTypIlS/jWt77Fe++9RzAYxOv1EgwG+dvf/sYJJ5zAnj17uPrqqw/jVU+NwcFBzj//fPbv38+HP/xh3nzzTYLBIH6/n3379vGpT30Kr9fLpZdemlblef7553nggQewWq3cfvvtDA4O4vV6CYVCtLe3c//993PGGWfM2OuQSCSCJ/78BOuPWc/zTz2f8f7Z6kuRlZQEuq4TCGTu9xUbbrc777kbfr+fJ598kve///3mbfPnz8/KuW+66SYGBgb48pe/zH/913+l3bdy5Up+//vfc9JJJ/HOO++wYcMG/v7v/35S57388st59dVXefjhh/nGN74x6v7f/OY3xGIxFixYwKmnnpp236233jrqeLvdzplnnsmTTz7JsmXLePzxx9m3bx8tLS1TeLXT4/vf/z779u3jQx/6EL/73e/Sfh4WLFjAAw88QF9fH48//jg///nPuf766wF45ZVXADj33HP5yle+Yj5GURQaGxv59Kc/zac//emcr18ikaTz6O8epbOtk+/+v+9y2lmnYbGk1xhi+uwUKbKSkiAQCFBaWjorvgpBbB1zzDFpAiVbBINBfvWrXwHwta99LeMxdrvdFCZPPfXUpM/90Y9+FKvVyvbt23n77bdH3W9UWD7+8Y9PSQRWV1ezdu1aQLSpZgLDx/LVr351zLV+/OMfB9LfI4/HA0BPTw+aNjt73BJJMdLdIxJm9+3ex9N/fnrU/bKSIpFMgZGVhmyxadMmIhFhHjPaM5kIBsWeFgcPHpz0uevr6znrrLPYuHEjDz30EMcdd5x536FDh3j+eVFmHdnqMXjttdf4yU9+wksvvcShQ4cYHh4edUx7e/uk1zNdWltbTVPsRz7ykVGfuAyM9zH1PTr77LOx2Wy88cYbnHXWWXz2s5/lrLPOoqmpKefrlkgkY9PdnYzBv+e/7uGs889K+wAS12bnhI8UKQncbjd+vz/fy8gK2TapTofa2tqcnLejIxlk1NXVNeHxU60qXX755WzcuJFHHnmE73znO+YvgUceeQRd1zn66KNZtWrVqMd9//vf54YbbkDXdQCsViuVlZXY7cLI5vP5CIVCGYVLtkl9j3p6eiY8PvU9WrJkCXfddRdf/vKXefbZZ3n22WcB0SI677zz+Md//EdOOOGE7C9aIpGMS29Pr/n3d994l00vbeLE0040b5OVlFmOoihZMXVKBFarNSfnNVoQlZWV9Pf3Z/38l1xyCV/4whdobW3lxRdf5PTTTweSrZ5MVZT33nuPf/mXf0HXdb70pS/xhS98gaVLl6a9B5/61Kd48MEHTRGTS1LbND6fj7Kysik9/rOf/SwXXXQRv/rVr3j66ad54YUX2L9/Pz/96U/56U9/yr//+79n9OxIJJLc0dcrJhnXnLqGTS9v4hd3/OKIECnSkyLJC8b4big0Oj0RxMU1E0ZU/8DAwJiJuIdDWVkZF1xwAZAUJjt37uTNN99EURTTx5HKo48+iqZpnHfeedxxxx2sWLFilEibTNVnJBO9R5D5fUrdzmDr1q1Tfl7jHF/+8pfZsGED3d3dvPrqq1xyySUA3Hzzzbz11lvTOq9EIpk6sVgM74AXgK9+86tYLBaef+p5dry3wzwmqs/O/XukSJHkhYqKCgDa2toy3v/6669nvH3NmjXmxft3v/tdTtZmVEuMaR5DrKxduzbjhNKhQ4cAWL16dcbzDQ8Pm1MzU2Gi92j37t0ZQ+JaWlpMoZKN90hRFE466SR+/etfM3/+fHRdN/05Eokk9ximWUVRWLV6FededC4Av7jjF+Yxs9WTIkWKJC8cffTRAPzf//3fqPt0Xec73/lOxsd5PB4uvfRSQKTEjlehiMViDA0NTXltF1xwAeXl5fT09PDUU0+N2+oBzE0WN2/enPH+b3/729Nah/Ee/f73v894/2233TbmY6+88koA7rrrLrZt2zbmcbqupwkdw0ybCavVanpswuHwmMdJJJLs0t4pDPcVVRVYrVau+vJVAPx5w59pOyg+xMh2j0SSRS677DIA/vSnP/Gd73zHNJTu37+fj3/842zatGnMx952221UVVXR0dHB2rVr2bBhg3nR1HWdHTt28P3vf59ly5bx6quvTnltDofDbG3ceOON7NixA1VV+chHPpLx+HPPPdd8LbfeeqtpRO3p6eGf//mfufXWW6murp7yOv7+7/8eRVHYvHkzX/nKV0wx0d3dzZe//GV++ctfjmmS/td//VcWLlzI8PAw69at4/77708zhu/fv5+f/OQnHH/88fz2t781b7/xxhu59NJL2bBhQ9rWAt3d3Xzta19j165dKIpivmaJRJJ7OrqFGb6yuhKAFceu4JR1pxCPx7n/rvsB0NBmZzS+XoT4fD4d0H0+36SODwaD+tatW/VgMJjjlR0ZrFu3Tgf0K664YtR9V1xxhQ7o3/zmNyc8zyWXXKIDOqBbLBa9oqJCB3Sn06k/+eST5n379u0b9djXXntNb2pqMo9RVVWvrq7W7Xa7eRugP/XUU9N6jRs3bkw7z/nnnz/u8R/60IfMYxVF0SsrK3VFUXRA/8xnPjPm+7Jv3z7zcZn46le/mraOiooKXVEU3Wq16vfee68+f/58HdCffvrpUY/dtWuXvnz58rT3uKqqSne5XGnn/PnPf24+5itf+UrafR6PR/d4PGm3/fu///uk30f5f08iOXx+9sDPdEBfs3aNvqV3i76ld4t+96N3i9+XLqf+3Pbn9C29W/RwLJzvpU7IVK/fspIiyRsPP/ww3/72t1m6dCmqqmKz2bj00kt59dVXJwyCO/HEE9m+fTvf+c53WLt2LR6PB6/Xi9vtZs2aNfzLv/wLr7/+Omefffa01nbWWWfR2Nhofj9Wq8fgN7/5DbfccgvLli3DZrOh6zqnnXYa999/P/fcc8+01gDwgx/8gLvuuotjjz0Wp9OJoiicd955/O1vfzNbOmOxePFi3nrrLe666y7OPPNMqqqqGBwcRFVVjjnmGK655hqeeeYZ/uEf/sF8zFe/+lXuuOMOPvzhD5v7HoXDYebOnctll13GM888Iyd7JJIZxshIqaqpMm875YxTWHHsCkLBEA/9/CFgdrZ8FF2fgZnILDM4OGhuejaZ8cpQKGTGkTudzhlYoUQiAfl/TyLJBl++4cvc8b07+Og/fJR/+96/mbc/+fsnue6q6yivLOeZrc/QUtlCuaM8jyudmKlev6dcSWlra+OTn/wk1dXVuN1ujjvuON544w3zfl3Xufnmm2lqasLlcrF+/Xree++9tHOEw2GuueYaampqKCkp4eKLLzYnJCQSiUQikSTp6RWhjIYnxeCcC89BVVV8Az76evqI67NvwmdKImVgYIDTTjsNm83GE088wdatW/nBD35gjkoCfPe73+WHP/whd955J6+//joNDQ2ce+65adMN1157LRs2bOCRRx7hhRdewO/3c+GFF056x1qJRCKRSI4ENF0zg9xS2z0gJu7Kq0TlxNvnnZXtniklzn7nO99h7ty53HvvveZtCxYsMP+u6zq33347N910kzkdcf/991NfX89DDz3E1Vdfjc/n45577uGXv/wl55xzDgAPPvggc+fO5amnnuK8887LwsuSSCQSiaT4iWpRBnoHgBSRogv/SUyPUV5ZTl93H96B2SlSplRJ+cMf/sCaNWv4yEc+Ql1dHatXr+buu+8279+3bx+dnZ1ppkeHw8G6det46aWXAHjjjTeIRqNpxzQ1NbFq1SrzmJGEw2EGBwfTviQSiUQime1E4hGzkhJ3xXm983Ve6XiFTV2beLv7bdRSUWsY6BuYlYFuUxIpe/fu5X/+539YsmQJTz75JJ///Of58pe/zAMPPABgxpSnxnIb3xv3dXZ2YrfbqaysHPOYkdx6662Ul5ebX3Pnzp3KsiUSiUQiKUrC8TADfaKSYi+3jxIipRWlAPgGfLMyGn9KIkXTNI4//nhuueUWVq9ezdVXX80//uM/8j//8z9px6VuHw2iDTTytpGMd8zXv/51fD6f+WVsQy+RSCQSyWwmFAnhGxB7dDnKHKPuN0TKQN+AbPc0NjayYsWKtNuWL1/OwYMHAWhoaAAYVRHp7u42qysNDQ1EIhEGBgbGPGYkDoeDsrKytC+JRCKRSGY7Xb1d5od4m8c26n5DpHgHvLLdc9ppp7Fjx46023bu3GluutbS0kJDQwMbN240749EIjz77LOsXbsWgBNOOAGbzZZ2TEdHB1u2bDGPkUgkEolEktxBvbyyHMU6uttQUlECgLffi4Y264TKlKZ7vvrVr7J27VpuueUWLrvsMl577TV+9rOf8bOf/QwQbZ5rr72WW265hSVLlrBkyRJuueUW3G63mdhZXl7OVVddxXXXXUd1dTVVVVVcf/31HH300ea0T64owtw6iaSokf/nJJLpo+s6Xd0JkVKVOaTNrKT0ewGI6TGsWGdkfTPBlETKiSeeyIYNG/j617/Of/zHf9DS0sLtt9/OJz7xCfOYG264gWAwyBe/+EUGBgY4+eST+ctf/oLH4zGP+dGPfoSqqlx22WUEg0HOPvts7rvvPqzW3LyxFosoGMkcFolkZjH+zxn/ByUSyeSJalH6+/qByYuUuBZnFmmUqYkUgAsvvJALL7xwzPsVReHmm2/m5ptvHvMYp9PJHXfcwR133DHVp58WNpsNm82G3++ntLR0Rp5TIpFAMBjEarVis43upUskUyI8BA7PxMfNIlInezyVmV/7SJES1WbXhM8R8fFGURQ8Hg8+n49gMJjv5UgkRwTxeByfz4fb7Z5wuk8iGRdNg8H2fK9ixonEI/T3ikpKaVUpw9HhUceMavfMsgmfKVdSipWamhqCwSAHDx6krKwMj8eD1WqVvzwlkiyj6zrhcJj+/n40TaOuri7fS5IUO/EIRANHXDUlqkXNSkr0qCh3b76bY2uPZf2c9ea1yzDODvuHiUaixJxSpBQlVquVuXPn0tvby9DQEF6vN99LkkhmNSUlJTQ0NGC32/O9FEmxEw+LP/3dR5RISa2kRKojALzT8w42i43Tm08HwO1xo1gUdE3H2++lzjO7PhQcMSIFhFCpr6+nrq6OaDSKpmn5XpJEMitRVRVVPaJ+vUhySTzhswgPQjQENmd+1zNDpIqUmCtZIdnUtQm71c5JDSdhsVgoKS/BP+BnoH+A2FxZSSl6FEWRn+4kEomkWIiFk38f7oaKeflbywwS0SJmuyesivdgRdUKtvZv5aX2l3BYHRxbeyylFaX4B/x4+2ffJoNHpEiRSCQSSRERjyT/HhwATxNYZ/flKxqPoqMz0DeAxWkhqohq0rq56/DYPbza+SpPtz6N3WJPM8/G9NklUo6I6R6JRCKRFDGpIkXXINCbv7XMEOF4mHg8zkDfAPZaUfl3qS4cVgenNJ7CcbXHAfCXA3/BfYwbYFZWUqRIkUgkEklhkypSAIZ7xFjyLCaiRfAN+NB1HXudEClldrFvnaIorJuzjhVVK9DR0d6nodgVBvoH0NFnlVCRIkUikUgkhYsWh5EXXS0m2j6zmEg86UcpnSvaOeWOZOqsoiicM/8c7BY7qGCvtuPrF7slx/XZk64uRYpEIpFICpeRVRSD4e6ZXccME41HzckeV7MLgHJ7ejS+RbHgsYuRbFu1jYF+IWpkJUUikUgkkpkgdbIn7fYQhHwzu5YZJHWyx1HnAKDCUTHqOFOkVNnMfX6kSJFIJBKJZCaIj7MXTaBv5tYxw0S1ZCXFUiku1WWOslHHZaqkzKb9e6RIkUgkEknhEh+jkgIi2G0WEtfixPW4ECkKkNgXN9WTYpAqUtJ2Qp4lSJEikUgkksJlLE+KcZ+uz9xaZgijEjLQN4CtygYW4T8ptZWOOtZjS7Z7BgcGAWZVVooUKRKJRCIpXGLjiBT08UVMkWKIlP7efjMjpdxejkUZfclO9aT4B/3EYjHpSZFIJBKJZEaYSITEZl/LxxAZ/b39yYyUDH4USG/3APgGfLLdI5FIJBJJzonHYKLMj3ErLcVJarsntZKSCaMFZLFZsHqsePu90jgrkUgkEknOmUwrZzxjbZFitnv6+rHViQpJJtMsgNVipcRWAiTNszLMTSKRSCSSXDMZATJWjkoRE41H0TQNX78vWUkZQ6RAunm2r7dvVkXjz+5tJCUSiURSvIyXkWIwG0WKFmXQO0g8Hh/V7rFb7DhVJ27VjVN14lSdlNnL6Ax0Yq+209cnsmNiWgzVUvyX+OJ/BRKJRCKZnUxGgBhjyIqS+/XMEDEtRl9vHxaXBdUjLtPljnIWlC2gobRh1PEVzgogUUlJESmzAdnukUgkEklhMqnxYn1WVVNiWgwNjYHepGnWpbqwW0UFJROVjkpAeFKMaPzZ4kuRIkUikUgkhckEIqW3fwBN02aVeTZtsqcu3Y8ylkipdlUD6amzs2XCR4oUiUQikRQm44iU3z3+VxpXn8e13/z+rKqkjBfk5rRmFik1rhpAtHtm207IUqRIJBKJpPCIR0HXkt+n7NPT1dPH5274NrFYjFff2jKrRIohLkZWUhxWh9jHJwO1rloA1HKVQd9g2nmKHSlSJBKJRFJ4pAoPHejaApqOrutc/S/fpm/AC0Bvv3dWReNHExNN/T39aePHLtU15mOqXFVYdAuKRWEoPgRIkSKRSCSSGSCu6YSis8MEOSVShUc0CBE/eA/w4KN/4vdPPmPe1dvvnVXR+Ea7p6e3B1ttIsjNXj6mHwXAYXXgsggRE7YIcSdFikQikUhyTigap8M3ey7CkyZVpMSCABza+Q7X/L/vAvDVf/wEAINDfiLB4VmzG7IhUvr6+rBXJyspY/lRQIgUYw+fuCNOOBKeNTshS5EikUgkBUwoGscfijEUmh3TGpMmrZISQNd1PvudR/AN+Tnx2JXcduOXsVjEJazf65s1vhRDpHijXhRVwaJbKLWVjtvusVltVLgrAFCrVPoG+ojrcbRUT0+RIkWKRCKRFDChmLjQdB5p1ZTUjQNjIX7+2Cs8+doOHHaV+//zi9jtNqoqxM7As6nlY7RphpVhAEosJSiKMq5IAah0JrNSenp70s5VzEiRIpFIJAWM4UcJRTX6h2ePQXRCUiop+w+08rU7/wDAt//xgyyvjEIkSE1VBTB7zLNRLYqOjqZpRF2iolJmL8NqsaJaxw+IN0VKYv8ekCJFIpFIJDkmHE2W7LsGQ2ja7PBejIuup4mOa267D38wzOnHtHDtR84ANOjdkS5SZkElxZjsGfINYasWptmqkqoJqygA1U4R6GavttPb1yvONwsC3aRIkUgkkgIlGteIp4iSWFyn1z87vBfjEo8i5o7FH8+/swuAH13zYazWxGUr5KXGI8ykQqTMjkoKJILcEhkpla7KcU2zBmmBbn0DaecrZqRIkUgkkgIl0+hx91CYaLz4DZHjkhJzH/T78PlFlWRxc03aYdWlotog2j3FL96M9kxvT29aRsp448cGde46AKwlVnq9vWnnK2akSJFIJJICJRQdLUZ0XbR9ZjXxZAWgq6ODJVUWLlhqp7zEkXZYspIyINpDWnGLN6Py0d3dncxImWD82KDMUYYSFZG0fQHpSZFIJBJJjhkrxM0biM7ugLeUceLOznb+72Mu/vgxJ8rep9PyUGrKxMW7b8AnbijyaoohUtr72lFLhVF2oiA3A7vFji0qhM1gdPZE40uRIpFIJAVKOJZZiOg6s3vSJ8U0293Ryopaq/hm//Ow56+mUKkpLwGgNxGRX+xZKaZIGWwHQAkp2Ky2SRlnbVYbTl2ImaAeJBqPSpEikUgkktyg63rGdo9BJFbcrY1xSREp4Z79AJgvd/8LsHsj6HpSpPR7xX3FLlISba6+sGjX2MI2bBYbVot1zMc4rU4sWHBYHbgtbgDC1jDh+OxInZUiRSKRSAqQcEwbN+l9VptnU0SKMiSqCvsCblj6QXHjgZdg15PUlIuLsilSirjdo+u6KSqGdLFJoEt3jVtFsWBhjmcONosNRVEod5QDEHPEiMQjsyJ1VooUiUQiKUDC41RRACKzVaSMyEhxRvoBGKQU5p4Eyy4Udxx8hWXht4DZUUlJbc0ErWKvolJr6bh+lIaSBhxWBzar8KLUlCamn0ogkngPi73lI0WKRCKRFCChMfwoBpoGsdkoVNI2FoxQYfEDELGLRFXmrIHlFwFQMfAu/3KaHf9wgFAoXNQiJTXTxEibHW/8uMxeZqbMqhZhsq0vrwfA4rEQSoTbSZEikUgkkqwzmemdaHwWps+mbSwYpN4hhIeltDZ5e/MJsORcAC5dLqoIfQM+0KJFO4ZsiJS4FkfxiFHi6tLqjO0eu8VOU0mT+b3NIt6DebXzALHJ4IB3dgS6SZEikUgkBch4plmDWdnySclIITzM3FLxGp2VjenHVS0CoKVKXMaSLZ/izJAxxMRQYAhLhXhNjbWNozJSFBSaS5vTzLSGSGksb0TXdCw2C23dbYCspEgkEokky2iaPqnpnVk54ZMiUnTfQZwqBKM6VbUN6ce5KgCocSmU2KDPGEMuUvOsISZ2HNiBRbWgazr1tfWj2j117jrcNnfabUa7x6W60IbEz0THQEfaeYsVKVIkEomkwAhPUnzMygmflHbPUNt2AHb2adRXl6cfpzrFFzC/wpJSSSnO/Bhj/Hj3od0AaEMabpsbRVHMY0ptpeYePakYlRS71Y7uFy3A7qFucV7Z7pFIJBJJNplsmuysrKSkXFQDXXsB2DeoYLepo49NVFNa0kRKcbd7DvYeBMASsozyo5TZyzI+NlWkWEOiDeQL+cRYs6ykSCQSiSSbTDTZYzA7KynJi2rcJzJSOoL2zMcmplsWVCjJ1Nl4kVZSjH17BkUFxB63j/KjOKyOUY8DsFqsWLBgtyaj8f0xP5F4RIoUiUQikWSXyZhmYbYaZxMiIx7FFhYZKX3xkszHJiopC4q8kqLpmhnk5ouIfYhcimuUH8XIQ8mEzWJDtai4ENWXIEGiWrToU2elSJFIJJICY7LtHk2DuDaLxpB1PdnuiQYp1cVGeQFLGaCMPt5ZAYxo92gx0Ipr88XUakdADwBQ5ixLEykWLGZbJxOGgPFYPQBErVGiWrToU2elSJFIJJICIhbXiE0h/2RW+VJSx4/9nbgtiYu3uxrmnASlmSd80iopUHShbkarR9M0ojbx9ypPVZpIGavVY2BM+BgBbzFnbFakzmZwIkkkEokkX4QyiA5/KEYgFiMYiYuvaJxyl42FNaVE4houxt6ArqhInUTp3w/Afq9GdXU12F1QtwzKm6F3F4QHU0SKQl//QObzFAHGZE84HkZ3C4HaUNOQ5kkZr9UDSfNsnaeO93gPXBCMinj9mBbDbh3D11PgSJEikUgkBcTIVk84FuftQ95Rx9mtohA+aysp3gMAbO/VaFiaMnbr8EDz8TDUCYmCU7XbQmioP+U8xVU5MCopHd0dqJXisjy/cX5ah8tuGV9kGCKlvrIeLaxhsVvoDfWygAVFPYYs2z0SiURSQIwUKcPhzBdc47hZNeGTKlJ8hwDY1hunsaF+9LGeBmg8jnii2lCa2DkYKLpKitGO2XVglxnk1lTelHbMRO0eQ6RUV1cT7Revvy/Yl3b+YkSKFIlEIikgRk72DEcym0CN42aVSEkVF0OdQKKS0tCQ+XjVYZpn650xAkHR3kgTO0WAUenY17EPAH1Yp8SePtE0UbvG8KTU1NQQ7ZMiRSKRSCQ5IBybXCVFTxw7u9o9ifFjLYbu7wISIqWxKfPxqhNLSRUALZUW+vp95uOLCUOktPWJ/XasYeuojJSJRIpRSamtqTUrKT1DPQBFPYYsRYpEIpEUCJGYNmoT37EqKSBMtrMqK8XwkoR8EPICsNcHlTUZ2j0AFgXFXQ2MCHQrUpHSO9wLgENzpKXNWhWrWSkZC6vFilWxUlZaRtwnfmZ6/eJ8spIikUgkksNmZNKspuuExhMp0fjsykoxKin9+1CAgaCOxeFBsbnGfkxJLQALylPGkIuo3aPpGnFd/BsPRYSvpkQtSfOgTGSaNVAVVUTjh8W011B4iFg8JkWKRCKRSA6fka2bQDjOePIjnDDPzpqWj+FJ6RfejO29cRqqKzLmuJl4RJWlpdJCX29v4jzFc1E2qii6rhNUhKem3FWe1t6Z7PiwzWoDRVRiAIZjw0S1qJzukUgkEsnhM9IEOxyJ8dKeXv74bjs7u4aIjegFGbslz4qWjxYHIxnVKzbZ29ar0VBbNf7jPMKvsqDCQm+P8GCgxxnVNytQUjNS4g4hOusr6tPHjycrUhK+FLfFDUBIDxGJR4o6dVbmpEgkEkmBEI2l1006BoM8v0tUB95rH8ShWjiq3sOKpjLmVbln1xhy6saAg8JAur1Xo7G5ZowHJCgTIqXCqeDv60zerkXBMv7YbiFgVDl8fh8Wj6gbNNc1px0z0fixgeFbKVVL8eMnaokSjov03WINdJOVFIlEIikQoiM+/R/oE/u42FULpQ6VcExjc5uPX73eyv88s4edncLDMCvaPfExxo/rJhApznL88cTn7aGO5O1F0vIJxkSLp+1QmxnkNrd2btox4+3Zk+m4KncVuqaDAv0hEXJXrL4UKVIkEomkQBhZEWkbEBewuZUuvrh+ER8/aR7HzqnAabPgD8fYdGAAHX2WVFKiyT+HRdtm3IwUA9XJkC7aG8auyWnnK3AMkbL74G4sNgu6rlPjShdmU233VFZXEvMJUWJkpRSrL0WKFIlEIikQUjcWDMfi9AyJUn1ViQNFUZhX5eYDqxo4d4W4cHuD0dmTlWJcRH2toEWJarB3QKOhoXH8x6lOwpZSANzxwZTzFX7lIK7FCcVDABzsEj4cAuC2uc1jVEWdcPzYPDZxXPPcZqID4v0cCIs9jWQlRSKRSCTTJhrX0FMsKYFInL5h4dOoLkn/JN21/XUAugeGCEVnSVaKOX68F4B9Xojr0NhQJ24fq5pgVdESqbPllmDK+Qq/cmBUUQA6fKJVpUbUtMrJRBsLpmJUUhYuWUjMK0TJQOgIEik333wziqKkfaWW4nRd5+abb6apqQmXy8X69et577330s4RDoe55pprqKmpoaSkhIsvvphDhw5l59VIJBJJkZJpsqffECmlyYtWJBxi489vE39HZTAQnR1ZKYaoGBAbC77XLS6qDXUirI3Ssds+Fo/ISql1RpOBcEVwUQ7EAubfBwJCTDhxpuWiTDYjBZKBbouPWmy2e8xAtyJNnZ1yJWXlypV0dHSYX5s3bzbv++53v8sPf/hD7rzzTl5//XUaGho499xzGRpKbvx07bXXsmHDBh555BFeeOEF/H4/F154IfH42IFFEolEMtuJxtNFxsBwFF9QXLirUiopz274Jd72fcSD4vfqIW8w8fgir6YYIiUxfry1JzGOW1MtqijuKhij7eGsFNMwjW4NPTIsbiwCD4ZZSdHBH/MDYjIntZIy2ckeA1VRqaupQwmJGebugW7gCKmkAKiqSkNDg/lVWysUrK7r3H777dx0001ccsklrFq1ivvvv59AIMBDDz0EgM/n45577uEHP/gB55xzDqtXr+bBBx9k8+bNPPXUU9l9ZRKJRFJEjBQZrf3iU7bLZsVtFxfngH+QJx/6CQAxr2gPdPiEpyFc7L4UQ1QMtQPCNFtRVoLT6RAiRVEgEYE/Ek9DCwDzyxUCPlGRMCsqBYwhUkLxEBGrqJpVlFRMK8jNwGa1YbFYKFWFT8c77EXX9SNHpOzatYumpiZaWlr42Mc+xt69if7hvn10dnby/ve/3zzW4XCwbt06XnrpJQDeeOMNotFo2jFNTU2sWrXKPCYT4XCYwcHBtC+JRCKZTaSKFE3XaU9USFKrKBsf/hmBIR82u4OYV4zpdiZESlFXUnQ92Z4xx4/jNNYmRIlxoXbXkCl+1l27AACPQ6E/sZNwobd7QrGQGYcfiobQnOLfr66iDkVJvsYpixRjDLlUhOANx4aJxCNHhkg5+eSTeeCBB3jyySe5++676ezsZO3atfT19dHZKX6w6uvTN4Kqr6837+vs7MRut1NZWTnmMZm49dZbKS8vN7/mzp075rESiURSjKQGuQWjcXpH+FF8fd387dH7APjAJ79IbEBUUnr8QqQU9YSPYZoND4kvRmSkGBdq1Q7OslEPV5xldCa6PP4u8cG50Ns9qabZru4urBViv5159fPSjpuKJwWSEz6NtWIqKmKJENWixPQYul58vqUpiZTzzz+fSy+9lKOPPppzzjmHP/3pTwDcf//95jGpChBEG2jkbSOZ6Jivf/3r+Hw+86u1tXUqy5ZIJJKCJzXIbTgSo99vjB+Li9TjD9xJNByiZeVqTv3ApUQTlZQ+fwRNL/KsFNM0K/wo/rgdfwQa6jJM9iQ2FExDddIZEJezcH9iEEPXRNR+gZJqmm090IqtSlRAaj3J16cqKlaLdUrnNSopLc2iBaY7dUIxIWSLsZpyWCPIJSUlHH300ezatcuc8hlZEenu7jarKw0NDUQiEQYGBsY8JhMOh4OysrK0L4lEIplNpIqM4VAsbfy4u20/L/7p1wB8+B//GU95pelJGRgOE4rFi1ykJCopPvEBtDssLrSN9YlKipoiUhweUJ3pj1cd9IbFMdpQd/L2Ar4oB6JJkbK/fX8yyM2ZDHKbToy9IVKWLlqKHhMFgN6AmPApxkC3wxIp4XCYbdu20djYSEtLCw0NDWzcuNG8PxKJ8Oyzz7J27VoATjjhBGw2W9oxHR0dbNmyxTxGIpFIjkRSg9z84eT4cVWJgz/+4na0eIyVJ6/jjDPO4JSjGrAm8i8GQ3GGw7HiNs6afhQhvDqHxaXJHD8eebEeWU1RnQxqQrhYw95kBaVAs1JiWoyIltyr6FCPqP4oIQWX3WXePh2RYrR75rXMIzYo3te97aIFVowiZUobDF5//fVcdNFFzJs3j+7ubr71rW8xODjIFVdcgaIoXHvttdxyyy0sWbKEJUuWcMstt+B2u7n88ssBKC8v56qrruK6666jurqaqqoqrr/+erN9JJFIJEciI4Pc2r0hYpqORYGhtl1s+tsfAfjXb/w7i2rF1IZHVdCiYSw2Bx3eELWlTuKajtUyfnu9IDHEhL8LgIM+IbgaaqsBZbRIcVXBYLvY7RhxSNDiAby4tGGIBsFRWrC+lFQ/CkC3T1R/bFEbjpRNEac6fgzJSkqpqxQCQBXsbd0LxxRnu2dKIuXQoUN8/OMfp7e3l9raWk455RReeeUV5s+fD8ANN9xAMBjki1/8IgMDA5x88sn85S9/wePxmOf40Y9+hKqqXHbZZQSDQc4++2zuu+8+rNap9d0kEolktpDaqonE43QNCg9BZYmdx+75FgAf/PBHOPaY48zjyisrGfJ1Yq+Zz6GBAMfMqSAa16bsYSgIjHaPX1ysd/eJ7xvqasBqE+PHqVgsIjclsccPQMxZCbRSaglCLCRESoGOIae2etDBG/YC4FJc6Wmzk9xYMBUj0M1utWOLi8e39YhdpWe9SHnkkUfGvV9RFG6++WZuvvnmMY9xOp3ccccd3HHHHVN5aolEIpm1pAa5BSJxs9VT4bDwt9eeA+Cfrrsx7TGVVVUMDAiR0jkoTLbhmIbTVoQixbh4JrwT2zqFSGusqxk7Dt9dkyZSlFJhsq2yRYRIST1vgZFaSQnHwwT1IA4ceOyew8pIMVAVEa1fYi1hmGF6h4o3dVbu3SORSCR5Js00G07u2eNITIBU19Yxd0FL2mPKK5Lm2e6hIs9KMSopAbGL8ZYOcRFvqKseW6TYnOBIDlE4KpvRdB2nVYdhsfNvIbZ7dF1PEymReISoKtZZWVqZNnI8nXYPiEA31aJSXlIOwFBirLsYKylSpEgkEkmeSRcpSdOsJSAuts1z5496THlFlRno1pvYLbl4RUoUYmGIiGj4Vp+GqqpUVZSPLVJAtHwSVNbU0jaYqEglUmsL0TgbiofQSP47+fw+KBF/r6+qx2IRl2VVUbEo07tEG22ihioxdRtSjtARZIlEIpEcPqlBbqntnuiA8BI0zZk36jEVlVVEE5WU/uEocU0rzkC3eAyxeY1o3cQVlYGQMM1aLBZQx6kmqMlJmJqaWvZ7E68/kVpbiDkpaX4UYP/B/WZGSmNlo3n7dFs9kBQp85oSPzcu8A/7iWrRogt0kyJFIpFI8owR5Kaj0z8cxh8Wn3gD7buAzJWUispKM3XWG4wQjBZpVoqZkSLGcAMI4ZEcPx7HPKo6MGLyU0WKnjDgFmK7Z+Rkz4GDB0yRUu1K7k003VYPJMeQ59TOEd9XqOzbs4+4Hh/1/IWOFCkSiUSSZwxxEYjE6fWLi3apQ8Xbvh+A5nkZ2j2VVcR83aBrROM6XYPh4sxKGbGxoC8mKggN5r4941ysFcUMdqupq2efV1QJooNilLkQ2z2pSbOapnGw7SAWm7gUp4qUbFRSal0iT0YtU9mxYwcAQ5GhaZ83H0iRIpFIJHnGCHILRpJ+lOoSO/2dorowJ2MlpQq0GHrAC8ChgSCaBnGtuMr5ppBItGi6g2I6Sezbo4xfSQGzHeQuKaF9WFRVYkO9iVaPXlAtn6gWTQtU80f9dA+Iqo8SUnDb3OZ9hyVSEu9ZuaMcw/6y68Au8zmLCSlSJBKJJI+kBrkFo8nJnqoSO70JkZLROFspTKNxn6gatPtEGb/ofCkjgtzaEtfQxrEyUkaSEpHvjYm/K2GfMOKmnr8AGOlHGYoO4R32AmCP29NaPFPdWDAVVRHtHqfqRI2Kvx/sEvsiheIhopN4T8Lx8LSfP5tIkSKRSCR5JNVHEopq9A+Li4NbiRAJh7FYLDQ0zxn1uIqESIn0C3Ntl69Id0M2PCmJzJMDA6Ly0VBbPX6rxyDFWOtXRBqvPeaHaOIiW0C+lJF+kKHwEP7ERJPb4k4Lb5tOkJtBaqCb2yqqM93e5J5GQ9GJWz6+sG/az59NpEiRSCSSPJIa5BaMxOnzG+PHIjOkvrEJm230Bau8ohKAcI/YlK8nsWtyOF447Y1JYQa5iXHrHb1CVIiMlElcqFMqKTFHhdgagDgMJyZ8CqiSMhwdTvveH/Wb48FlzjKzkmJVrIedHGyz2LBb7XicIvHdG/aakz2GMBqLmBYrmLaQFCkSiUSSR1IrKYFIjIGAECnxRIUkU6sHwFNWjqIoZqBbr9/ISik2T0oEdB2CYsPE94wgt9px0mZTSREplRWVHDKyUgYTWSkF4knxhX2E4iHz+2A0SCQWIWoTIqrKU2V6SQ6nimKgWlRUi0p1ecKM64KeTlGtGo4Oo+ljV9wGI4NQID9GUqRIJBJJHjFEiqbr9AxF0HSwWRWCvQeAsUWK1WqlrLzCzEoZGI4SjRdhVko8KkLcEm2ftw4OAtBYXzN+RoqBxWK2hWqqK2kdTLx+IzK/ANo9uq7THehOu20oMsRg3yBqhfCM1FXWmaPD2RApxjkqXaLiplaq7N+9HwANbZQ/JpVCafWAFCkSiUSSV4wgt1AsTm/Cj1JVYsfXlaikZBg/NqioTKbOBqNxBoYjxSVSNE3sZJyoemiqC++wEBX1tVWTa/eAKWZqqqvoGEqUAIbFfjWF0O4ZCA8Q0SJptw1Fh+hr7zMzUmpKasz7silSKhwVAKjlKvt27zPvH6udE41H08ak840UKRKJRJJHjCC3UDROv98YP3bQ1zH2ZI9BeWUVeiSIHdHSaB0IJKaFCqRWPxEjgtwiVpEPX+Ypxe1yTc44C8mslOoa2v0JkRYUnp58bzKo6Ro9gZ5Rtw9FhujrTIqUamcyI8U2WXE2DkZVpsopDNa2Chv7diVFylh5Kb5I4VRRQIoUiUQiyStGuycU0dLGj7vbxcjoeCLFmPBx6MLr0OYNousQKZbkWS09I2UoLsRG42QzUgwMkVJTnaykBL2J58ivSOkN9o7afTgWjxGKhXjjuTew2BNBbu4UkZKFSophvDXEj1qpsnfXXvP+iBbJOGZcSK0ekCJFIpFI8ooR5BaKJTNSKlxWejpFC2T8SkrCb5CY1ugotjFkMyNFiJSuoLgkLZo/Z3IZKQZGu6eqgg5/QqSEvML8mcd2T0yL0RfsG3X7UHSIN//6Ju+98x4ADhy41WSQm1EFORyMrJQqlxCyaqnK/gP709cxopoSioXSzL2FgBQpEolEkifSgtwiMTMjxRkdJB6PY3c4qK1vGPPxFRXiAmQJeQHoGixWkSJMpfsTGSmLW+ZOvtUDZiWlurKC9qHEaw/7IR7OayWlJ9CTtuOxQXtPO//77f/FVplIhnWVpyXMZmu6B6DSUYkVUVXpHeolHEpWT0aOIhdaFQWkSJFIJEVCW1sb9913H5/4xCc4++yzaW1tzfeSDpvU8eO+4SihqPjeMiTSVxub54qdgMfASJ1VEjsI9yQ8LUXX7kmYXLd1i/UvaZk7+VYPgFUFiyoqKYl2jx4ZSqTO6omdlmeWSDzCQHgg4313/cdd+Hp9VB8lWjGl9tK0tNlsihSH6qDELrw+arnKgb0HzGMCsQDxlBHtQvOjABx+TUkikUhyxHPPPcejjz7Kxo0b2bZtW9p9Dz/8MDfccEOeVpYdUjNN2r0iH6TcZSPQO35GioER6Bb3dkAz9CeyUoqnkpIwziZMru8cEp/sl7TMm9z4cSqqi+rKcrPdo0SDEBkGZ5kQQ9aZvdx1BbrQM4SNPP/U8zy74VkURWHpBUtpo40aV41pllUVFWWyba5xsCgWLFiwW+yU2koZjAyaY8hHrTgKELtu+6N+yh3lBKKBtH2FCgUpUiQSSUGya9cu1q1bZ36vKApr1qwB4PXXX+fAgQNjPbRoMCop4Vhy9+OqEjsDuyYIcnOqlDpV0zgb7hVVpcFQDH84itNWBEVyXRciQotDSHyCf2WPECuLF8ydXJBbKqoDl8tJWHEQjuk4VEXsB1TWKNpKNlcWlqxPSkAEY0ERiDYC/5Cfm792MwBnXX4WPpsPojCndA4OixBl2ZjsMbBarKiolNpLYXj0GDJgipRCbPWAbPdIJJICZdOmTQAsWrSI3/72t/T29vLaa6/x2c9+FoCDBw/mc3lZISlSNAYybSyYISOlrszBgpoSXDar2e4J9BzCZhUXz/aBEOFiqKSEB4VfZLgPdA0dhX19UVRVZf6cxmmIlMSET0o1JRnolp12T19otAk2EyOD2wx+9B8/oqu9i5rmGs66+iz8UT9WxUqzpxnVmr0gNwOj5VNmLxPnrrSZgW4G/ogfXdcziqpCQIoUiURSkLz3nph8OPvss7n00kupqhIX5Hnz5gGzRKQkgtyC0Tj9ASMjxU5Xm3htc1IqKYoC86rc1JeJi7FDtVCRmO4Z8g1Q4RYX9UPeALqe7ncpSBJ7E+ETVaCwxYWmw8J5zaiqOq1KCkB9TTUdhnnWCHTLkkjpDfYSm+BcgWggY1Daay+8xq/u/RUAV9x8Bd0xIWQaShooUUvM47IqUhITPuX2cvF9hZqWlQIQ02MZx6QLBSlSJBJJQWKIlJUrV6bdPqtESiLILRyN05+opNSXOWhvTc9IsakKi+tKKXcnL2Cq1UJlQrj5vANUusR97d4imPBJafEwlIj1jwpRsnjBXKaUkWKQqKQsmNtMu5GVEshu6mxcj9Mb7B33mK5AV8bb77ztTgDWf2Q9y09eTuuQEGdzPXPTJnuyMX488lxGNL6twsaBPaPbpD3B0WFzhYIUKRKJpCCZSKR4vV4GBwuzRD1ZjGqHPxzHm6ik1JdY6ekWuSHN8+ajKLC4thSnbfSuuPW1Iko9HovhSVznugaFAbegRUpwAHMHu4RI6RgW7Sox2WOffEaKgWoHxUrL/OZkuyeQmK7J4hhyf6g/YwgaiNyRTJHymqax7V1h/D77k2ej6zqHhkRLb07pnKyPHxtYlUSgmyMR6FahMjQ4RMCfvsZMBt9CQYoUiURScIRCIfbs2QOMFimlpaVm66fYqylGkFvXYAhNB9WiYE34KEpKPZRXVOK2W1GtmX9VV5R5cDhEBaHUEkucKzHhU8jtnmDKaK5fVB7294v1L1k4b+qtHgPVScvcJjrMaPzsixQdne7hzJ6TTPH3AIf2HyIYCGJ32KmbW0d/qJ9ALIBVsdJQ0pAzkWJG4ycC3Yxclq6OzNWeQkSKFIlEUnBs374dTdOoqqqivr5+1P2zoeWTGuTWkRg/rnDbGew2kmbnoSgKbvvY5X+7ajHNsyWIcxhTQgVbSYmFxa7HBgnfyNZO0aYS48fTFSkOWualtHsiQ0KgZDl1djA6yHB0OO02X9hHMB7MePyOrTsAmLt4LlbVyiG/qKI0lTahWlTslhSRksXpHkOk1LnrALC6rSh2he6OzCKrEJEiRSKRFBxGq2fFihUZRz4NkVLMY8hGqyeu63QPGbsf2+jvSt9Y0GUf3eYxsFuT5llHVESc9wciaLpeuBM+hmHW/F5MzLzVKoTLtMaPDVQnLXObTeOsHh6CWCgZGpdFuobTqxFjVVEAdm3dBUDTkiaApB+ldC6AWUlRUHI23WP83VZuo6tTVlIkEolk2ozlRzGYP19cwIu7kpLYsycaZyCQHD/uahMXMEOkuMcRKQ6bxQx0swYHsCgQ13T6hyOFW0lJbfWkfL+nL4rNpjKvueEwRIqDec0NdCaKHHpoEGIRUU3J8s7QwXjQzBbxhryEtcw+FYCdW3cC0Li4Md2P4pkDJEVKNgUKJD0pDquDElsidbZSpatdihSJRCKZNlu3bgXGFimzpd0DQqT0D4tP+o3lLtpaRXWoed58bKqCbQw/CohKitHuCQ97KXWIT8udviBxTUfTCswQaeynYxBNtn5aB3UWzpszvfFjA9WJw2FHd1YAYIklUmchJ3v4dAW6iGvxCadjjEpK85JmeoO9hOIhbBYb9SWilWlE4mdbpBjVE9WqUmorFX+vUGlra8vq8+QSKVIkEknBMVElZXaJFM2spDRXOJMiZe583Lbxx1FVq4VKI9Bt0IfHmTBGFqp5Njii1TMoLpZR3UJ/UGfxAlFZOJxKCihU1DQmtxxI7GuUi92Qo1qUA4MHiGiRMY8JDAc4uC+Re3PUHFr9olLWVNqEVbGiKMkWTzb9KCBEioJol5qBbhU2Oto7svo8uUSKFIlEUlAEg8ExJ3sMZoUnJRHkNhSK4guKC+jcKhdtB5MiZTw/ikF1tRgvHfYN4HEKUdM9JEyoBeVL0TQIetNvS4iUgYi4OC9pmQco0zfOKgqoThbMa6bTTJ1NmERztBvyWGZZgz079qDrOp5KD+XV5Warx/SjWOwkdIQZvpZNjJaPGehWqdLdKY2zEolEMi22b9+OrutUV1dTV1eX8RjDk9LW1kYsVphJmRNhBLm1+8RFzq5acOhhBn1eQEz3jOdHMaipESJlyOelLBHo1lOIEz4hL+jx9NsGxSRTW2LYZ0nLYYwfG6gOWubNTQl0S0TZ50ikTMSubaLVM+eoOWi6Zk72GH6U1OpJtispkGz5VCRaYLYKG72dvehZ9ujkCilSJBJJQZHa6hlrM7f6+npsNhuaptHe3j6Ty8saRrunfUCIlCq3HW+nqCxUVtdQUlqKK0OA20hqa0Sgm887QGUikbbPX4DtnpGGWTAzUvb2CQGxuGXu1Hc/HonqpGX+3GRWiiFSctDumQyb390MCD9KT7CHSDyC3WI3x4INPwpk35MCKVkpTtEWVMtVfL0+fMHC3FBwJFKkSCSSgmIiPwqAxWJh7lxRLi9GX0o0rpntnuT4sZ2ejuRkj0O1YLFMnLpaWysqKb6BfmpKxQXPiNgvmEpKPArhodG3+0Xb4b1OIdSyV0lpSqbOmoFu+REp29/bDohKitHqaS5txqKIy69LTe7OnAuRYrR7DJFiq7ShxTUOdhTH/xspUiQSSUExGZECyZZPMfpSAmHR9gjH4vSZux/b6E6MH8+ZOx+3Y3L+hIbaWgC8KSLFm/C4FIxIGe6BTNHrib119g/Ek+PHiT14po3qZMHcJtoTWSla0AuaDpHRcfW5JhKLsG+H2NBvzpI5afv1GBiGVshtJaXWJX5ObNU2UIpH3EuRIpFICorJipRinvAZjoj2RihlY8E6T3Kyp2nuPNyTaPUA1NeJi4/P209dmRApg8Eomq4nUm3z7D3QtORuxCNvT7RiDvo0Fs6bg9Vqnb5p1kB10txQR3dAVKHC/gEx9hwNiOecQXYc3IF/wI+iKNS11NHmF+08w4+iKIo5GmxVrFgtk/s3nwqGSGkqbUJVVCx2C/YGO+0d7Rl1Y6EhRYpEIikYAoEA+/aJT56zWaQETJGSHD9uqnDSlrL78WQmewBqDePs4CDVLisKonDgC0TR9QLwpQT6RhtmQbRhEjshtw7qYmNBAOthelIsFqx2F3GbB4BYwCei+NHT4/hngLfeeQuAunl1+HQfUS2Kw+owqxoemweLRVyGc1FFgeTEkEt1mT4Yd4ubvq4+/NGZfT+mgxQpEomkYNi2bRu6rlNTU0Ntoo0xFsU6hqxpOqGoEA4DgQjDidZPc4XbFClz5s/PuOtxJioTsfgA8aCfEiPQbVCMIee15aPryRHgkfjaIC4EWqtPSxk/PkyRAmB1oHqEodgaCyQD5GZQpHhDXvZtH93qmVM6xzSEp7Z6jIpHtjGqM6pVpaGkAQBXiwtvt5fBSOHvIi5FikQiKRgm2+qB4o3GD0TjZkJ724DwSbjtVsrdqtnuWbSwZdLnU1WVsvIKAEJ+r5mV0jVUACIl5DWFyCgGxAV8MGIhGEvZs2eMia4pYbVRWiX2yXEShkgiyyQ8cyKlO9BN265Ee+eozH4Uj91j/j1nlZQU8dNc2gyAa4GLge4BBsNSpEgkEsmkmSgOP5XUSkrefRdTIBBO5nW0+4SQqCqxExzsJxQMoCgKi1smL1IAqqrE5Ibf56XMEClGJSWf7R7/GHHx0RAkNug7lMgzWbJwXnaqKABWGzXNC4hrOhYFMDb/iwZAy9B6yjLReJT+UD+Hdoppnrqj6szJnvllQlwrijIzIiUlIG6eR/yfcc534u31MhQdKnhfihQpEomkYJhKJcUYQfb7/fh8xZH5ADAcSV4kjZZMpdtOf4f41F3X0ERlmXtK5zRSZ33eASrcwnjaO5TnMeSwH6LDme8L9pt+lD19YhIpK+PHBhYbC+bPS0mdNYy7M+NLOTB4gHgsTvtekeETa46ho1PrqqXSKdpzJbYS048CM1NJaShtQNWFeXZIHyKuxQvelyJFikQiKRimIlLcbjc1iSCzYvGl6LpummbjmmYKifSMlMlP9hiYImWgPxnoNpwIdMuXSBnLiwIQSIqU/V4Nu93G3Kb6wx8/NrCqtCyYl8xKCaRMF0XGEE5ZosvfRW+wl+7WbmKRGHannXZFiJWllUvN41L9KJCbtFkQFRsjK8VpdVKlJjak9IifD/8Yoi2eyeycB6RIkUgkBcHw8PCkJ3sMis2XEopq5hRsMGWyp7rETm+X2PStec5c1HF2Ps6EkTrrHRigqkRUI4zR5qzs3xMLTy2xNRY2RcgodMQePsZkj09j4bzm7IwfG1jttMyfR0ciKyU6mCKYcuhL8Uf87B/aD2C2euYcO8ccPV5SucQ8dpRIyVElBZItH7vVTmNZo7itUSUcCGc0z/YH+9nWty1n65kKUqRIJJKCYNs28Uuxrq7OrJBMRLGNIRv5KAChSMwUEvXlTno6EyKluXnK5zX27/F5+6k2At0CQlToejKCf9roOoSmYLL0j1NFCflAj5mbDbYOGpM9HP74sYHFRm11Jd1BYcL19abs+psjX0osHmPXwC7TH2WYZqtPF/82jSWNlDvKzeNT/SiQW5FiTPg4rA6aK5LmWW9P+oSPpmns9e7lxc0vcsWpV3DhhRfm3e8lRYpEIikIptLqMSi2MWQjaRagZzhsVjmaKpx0Jyopc+fMmfJ5DeOsd6CfWo+40PuCUfMCk5WWT8g7uePiMeE5GYtgv9jsb0i83nc6NTHZk63xYwCrDUVRCCvC2xMc7E+pBOXAl6LDbu9uwsaoM8lKCovEH0dVHmXeV2ovTQtuUxV1zH2qsoHhS7Fb7eYYsnOOk97uXmJajEA0wHB0mM29m82pJG+vl/b29pyuazJIkSKRSAqCwxEpxVhJOdQvxmLLnCoeh42erk4A5s2deiXF8KSkipSYpjMUEs+XFZESHppcBSLQC/o4zxfsF7sfazF8EQvberWkaTZbF0RFAYuK7hQtlXhwMBHolmCilk98ajsmH/Ifwhv2pt+26xC2ahtBd2Jfoopkq8djm7kqCiTbPQ6rA4/Ngx7QUVSF9gHhlTngO8CW3i0EY2KtbbtFFWjVqlU5XddkkCJFIpEUBNMRKcXkSYnENGLxZOm8zSsuCJUldpw2C12d4oLRMm9uxsePR6pxtsSu4k6k1RpZKYFoNtobOkwmV8PYdTgTxkaDXvHv9VqiC7OkJYvjxwYWGzYz0G04GegG41dSYpHMOzanENfihGNhAtEAvYFec7zYIDQcoretl/KTRHtnTukcSu2l5v1ljpnzo0CykmK1WHGpLlSf+L4nJEazfRFfWlunkERKbiLuJBKJZIrM9kpKIJL+6dzIMakqseOwWsxKypw5h1NJGcCuWvA4VQKROJ2DIZbUeRgKRQHX+CeZDCEfuCrHvj88NHZ4G4ipHoAB0Z57cmeiyrBwbvZFitVGaVUzsB23Ek6vpESDoiqUaa8cf+eY3phNnZuIaRNXWYyLfNVpog2X2uqBDH6UHE32GKS2ltw2N+6ImyGGGLRkFp2FJFJkJUUikeQdv99v+kpWrFgx6ccZIqW9vZ1IZJyLYwGQmo8S0zS6jfFjt52Q30c0sf7GxsYpnzu1kmKzKnic4qLXPSguzNGYTigb1ZTQIIxnpAyM40UBUaHQNbOS8re9Uew2lTmN9dkzzRpYbVQ1i1C8cltcBMiZjOFLiYXHfQ2TESggWj32ejv2OXYUlLSpHrfNPSoCP1eR+JnOX2IvocqSGEMuCY86NhaN0blPCOapfGDIFVKkSCSSvGNM9tTX15sX3MlQW1uLw+FA13Xa2tpytbyskJo06w/HGEhM9lSW2BnsF9Mw1TW12O1TH8M1RYpvAJvFYqbO9g4lL0KDoSmMEI+FHhfVkkxo2thjxwbBfhjqgniYGCrvdGksmteU3fFjA4uNxgVHoek6qgUYHpF+m8mXMtRBNiJYD+08RPnJotUzr2weLjVZxRo5egwz50kBKFFLTPOsVqalmX0Bug92E4/Fcbqd5oeAfCJFikQiyTvTafUAWCyWomj5xFM2FQQxeWNkpNSU2unrFp9cm5qapnV+Q6REwmGCwQDliUC33uHkBcgw0R42YwmRkDfzbscGYb9oBXlFxawtVo6mkxw/zlaQm4HVRsuChXQPC9Ex3LU//f6RlZRocEIvymRp29VmipSRrZ68iJSUSkqprZS62joivREURaE7kD4ubrR65h01L++TPSBFikQiKQCMSspUWj0GxTCGPDzCj9LuDRBL7CtTX+akJzF+PF2RUlJSYlZgfAP9VLnTA90AgpE4cS0LmRdjiZSJLvDG/QmRsrlPrHGxsftxtiLxDaw2yiqr6A6IC21P+7706aSReSlDHWQDXdfp8nfhbHZiwcKi8kVp92cUKbn2pCgp485WlbqGOoL7hB+oc7gz7VhTpCzJfxUFpEiRSCQFgFEFWbhw4ZQfWwyVlNR8FICDifHjCpedEoeV7kSQ23RMsyCiz1PHkI3U2YFAssWj6+DPRjVFi46OljemdsYj0CcWkTDNPntArGXJwgXZHT82sNjAYmEwKgSAt69ndIvHWHNkeOJW1SQZ6BrAsVL4a+Z55uFMqRC5VBeqNd1/oqDkvJJitVhRSL6/TU1NhPYLj06bN71NaoiUuUumPmWWC6RIkUgkeae1VexbM2caQWZFIVJSKinhWNw0tFaW2HCo1uRkzzTSZg2MQDffwAA1idRZXyCaNlqaFV8KjL6gBwcY18uhxcVjAr0QDaBbVB58RQizo5cvzv5kD0CiOhGyCD9IYKgfIiOElNHyGcxOFQWgdVer2epZVr0s7b5MVZRcm2YzPU+5qxylT4iW7mB6u6d9txiFn3eUrKRIJBIJAIcOiZwJY2fjqVDoWSliU8FkJcUfTsbhV5XYcahWMyNlOpH4BpkC3SJxbdRzZ4WRImUyUz0kqyg+azWdAwEqy0o4efWqHIoUBRJR9FpwcHQlJTIsqikjxcsIdF3nBz/4AU/971MTPu3WXVtx1DsgBgvL0yuDI/NRIPd+FIM086y9BFc4Id70gBniFglF6D4oRIts90gkEgkQj8fNyZzDqaQUqiclEImnTe36QzHTNFvlFkFuRiVlup4USBcppQ4Vp038eu9OmfCJxfVReS3TIhZKjvRGApC4yI3JcGIX4oQfZVOnWNsHT1+NqqrZHz82sKjYykSgmxoLjG5JRQPgm3gqbMOGDVx//fU88p1H6DnUM+Zxuq7TXicEZ3W4GnuKz0ZRlLS9ewxmTKSkVFLcqpvK8krCHeJno2u4C4COvR3ouk5pZSnl1aPXmg+kSJFIJHmlu7ubWCyG1WqdVkZIarsn35uhZaJzMJT2/VAoalZSRNps0pOSjUqKb6Afu9WCxyEufh2D6QIiK74USFZTxtunxyCYHuL20CYhWi468xRxey4qKQBWO6XVQvi5lTBEhzG3oTaYQGANDQ3x5S9/2fx+ywtbxjz23c53sTZZiYfinDHvjLT7yu3lGVs7jlwJtBGkBro5VAeVdZWmebYrIESK4UdpXtxcEJM9IEWKRFKQDA4O8pnPfIbHHnss30vJOYYfpbGxUeRlTBGjRRQIBOjvn8QFcwbxBaKjTLP9wxFzh+LqEgdWPU5/r/h0no1KytCgF1sidRaSgW4Gg9kUKbo+8VSPkUIb9EJ4EB0Lv3qjD9Vq4QPrTxbH5EykqFQ3LQCg0hEXInaKmwv+27/9W1oGz+YXNmc8LhQL8UL7CwAMPz08KmOk2pU5/8eZ7dHrMUht9wDUN9YT3J9ZpDQd1cQLbS/w+92/n5G1jYcUKRJJAfLII49w77338nd/93ezXqgcjh8FwOl0Ul9fDxSWL0XT9FFVjGA0RrsvhA6UOlQq3Crevh50Xcdms1FTUzPt5zNEyqC3H7uqmCKlZyhdpAQjcWLxLGw4GB0WXpSJUliHE3v5JFo9ndFSAlE449hFlFfVkpPxYwOLjYYFwrxaXwJ9Xv+E/pNU3nzzTe644w4Avve97wGw/bXtRMOjDcgvd7xMVIkSag/RFGxKq0QoikKlI/N2Ak7rzIgU64gtABqaGgjuTY4h67pO2542Kt5XgfdMLxsPbuRHb/yIQDQwI+sbCylSJJIC5K233gKEX+MjH/kIzzzzTH4XlEMOZ7LHoBB9Kb3+MNFYevtpKByj0yfaPw1lTpw2le6EH6WhsRGLZfq/kpPtngFsVqsZjd/nHx19nrVgt8FJpPwGEn6URKvnucTo8YVrV4C9NDfjxwZWG84q0UKzWxVa2zom3gE5QTwe5+qrr0bTND72sY9x3XXXUVlXSSQUYecbO9OO7Q50827PuwB0/LKDZSekT/WU28tHjR6DyC/JdUaKwUjvy5zmOQQPBtE1nUAswJa+LUQvjDLnqjlE1Sjl9nK+tuZrM9aOGgspUiSSAuSdd94BxIU7HA5z8cUXs2nTpjyvKjccbiUFCm8MORLT0gyrBkOhmOlRaSh34rBZ6E5M9hzO+DGkG2etikK5S1wU+4ZH72mUNZEyXsIsiB2FjfaK1/CjiMrKRevWgMWSu1YPCAFkL2EgnBi37eqYOM8lwV133cWmTZsoLy/nRz/6EYqisPI0kYi8+flky0fXdZ5ufRodncHXBxneNszSE5emnWvMVs8MVVEgPdANYO6cuegRnUiH+Pn468G/Yqu3EfPHOKX6FK5ZfQ0XL7p4VAVmppEiRSIpMDRN4913xaey3//+95x55pkMDQ3xgQ98wExmnU1ks5JSKCKl0xfKuA+fPzSikqJmZ7IH0kUKQKXbCHTLIFLC0ZkxGRtVlPAQBPrQgecORFk2v47FixNJrLkUKRYxOTQYE1UEX3+PGDueIHm3rb2Dm266CYBbb72VhoYG/umv/4TrPBflJ5fz3rvvmcdu699Gx3AHVt1K+0PtVDVUUdOcbNuN2+qZIT8KjM5jMT4U+LcJEWnBQs9jPXR/r5tT5p8yY1NHEyFFikRSYOzdu5fh4WGcTifHHHMMv//971mzZg19fX28//3vL6iWRjYwKimHI1KMrJRCeG+GwzF8wdGeBR2d/uGwWdmoL3emTfYcrkgxwtwGEiKlOpE6m2ktmkZafkq2aG3r5H/u/w3DgYQXx8hPSex6fHDYjjcEF61dCU6PuC+X7QSrDSwKESPQbbAf0IWfZhyu/dd/Y2hoiJNPPpmrr76a7kA3zx16jg5nB3O/MJfq66q5++27+fP+P/N82/MAeA54iA3EOGrNUWl+lLFaPTBzkz0w2pPiKfPgcDnoerSLk0tPZsWBFXQ92kXTnMP7Ocw2UqRIJAXG22+/DcCqVatQVRWPx8MTTzzB8uXLOXToEOeeey7Dw+P/ki0mjErKbGn3dPgyj7QOh+N0+kQLyONUKXWoot3Tdfjjx5DiSfF6icfj1CQC3UJRjWAGQZK1lk8K/++7d/HFG2/lH756M3o8Pmq/nid3ivfmotNWghFsltNKiqgGKK4KAKLDXnH7OC2fJ595id/+3x+xWq389Kc/xWKxUGor5Yfrf8hxtcehdWvoms6wNsz2/u0EY0EqHZV0PSYmZJauSW/1VDmrxnyuGa2kjJjuURSFqroqtKBG1WAV3TtEiFvTolkkUm699VYUReHaa681b9N1nZtvvpmmpiZcLhfr1683dzg1CIfDXHPNNdTU1FBSUsLFF19sfpqSSI50DD/Ksccea95WU1PDX/7yFxoaGti1axdPPTVx8mUxEI/HaW9PeDJmgXG2fzhCMJJ5csaf6kcpExenXFRSdF1n0OelzGXDbhW/4nv8oVHHD4WixDWdSEyIGH+iAhSJTX/yZ9c+IRJ/88eN/PTeB5OelYRpduPuMFVlbk5duQBspeK+nHpSVEChtFJMf1kMf8w4Y8gPbfgzAJ///OfN/4Num5tz55/L+rnrWd6+nG1f3AZPwMkNJ7OkYglnN53N3nf2Aozyo4wlUhSUGa2kKIoySqjUNdYB4O320r4rkXq85PDEcraZtkh5/fXX+dnPfsYxxxyTdvt3v/tdfvjDH3LnnXfy+uuv09DQwLnnnsvQUFK5XnvttWzYsIFHHnmEF154Ab/fz4UXXkg8nv3yo0RSbBgi5bjjjku7fc6cOZx55pkA7NixY6aXlRMON8jNwGj3dHV1EQxOkH46RYZC0Ywtk5EEIrExqyggfCCmH6VciJTUfXsOt5Jis9nMUez21oPYU7JSjOdNJRTV2No+yI7OIXZ3+9nXM8zBvgDB6PR/D7d1JveBufbbd/H2rjaIBsEvqgzPH4jzwVOWo5aUg0Uhp+PHBlY7lc3C/7KoQqN7YGjcSsp7O/cAcPbZZ2e8/+jTj0YLaez8/U5OqDqBCxZewNCOIeKxOJX1ldTOqTWPHa/VY7fYsSgz28wY6UtpaGwAhEhJDXIrJKb1Dvn9fj7xiU9w9913U1mZNATpus7tt9/OTTfdxCWXXMKqVau4//77CQQCPPTQQwD4fD7uuecefvCDH3DOOeewevVqHnzwQTZv3jxrPh1KJIeD0e5JraQYLF0qPqXt3Llz1H3FyOEGuRlUV1dTWio+mWe75aPp0NofyDjKazAcjrGvd3hUmGkq/nB6JcVmVbAqStYqKQDLlonR1727d2CzJkVK19BokZJtNE2jvUsYZU9evYpwJMpHv/kAwU7xs7rXp9A1rItWj93wo+Rw/NjAasPRtAqAE5usbNuXGEPO4J3VNI1tu/YBsHLlyoyna17SnBxF3iRe2443xIeGkX6UsaZ6YGZbPQYjJ3yamsXP3MEdBxkaGEJRFBpbpv9hIRdMS6T80z/9ExdccAHnnHNO2u379u2js7OT97///eZtDoeDdevW8dJLLwHwxhtvEI1G045pampi1apV5jEjCYfDDA4Opn1JJLOR/v5+88I9skoJcNRRRwGzR6RkY/wYRCl7wYIFgPg9lG10Hdq9IboGM7dNJhIocV1nYDhixuE3JEyzgWE//iHx++xwKykAy5cvB2Df7p0iGj+RlTIy0C0X9PQNEIvFUBSF3//0W8ypq2Bnaw8vPvtXAP64PYRqtXDeSUtnxo9iYFGhch7+qEKJXaGzdS+gZTTP7m9tJxAM4XA4WLRoUcbTKYrC0e87Gkimz+54XYiUQvWjGIyspBjtnq0vbQWgZk4NDnd+c1FGMmWR8sgjj/Dmm29y6623jrqvs1OULY2So0F9fb15X2dnJ3a7Pa0CM/KYkdx6662Ul5ebX4f7C00iKVSM0eOWlhbKy0dv8DXbREo2xo8NDJGyf//+wz7XWHQPhmntD5jju75glAN9gYzjxqmIKooQCmVOFbddxalazCC30tJSPB7PYa8vTaSktHtmQqQYrZ762mrq3fDINz+F1Wphgd0LwMa9cdYdt4jyUldysmcmRIrVBqqL9oh4Tm0gUWnL0PJ5b4do9Sw7avG4lb1Vp4vKzJYXtxAJRdi3WQjjVD/KeK0emNmMFIORIqW+UVyrhwbEe1ForR6YokhpbW3lK1/5Cg8++CBO59hv8MiNiXRdn3CzovGO+frXv47P5zO/jF9sEslsY7xWD8CSJUsA4b3w+Xwztaycka1KCghhB7kVKQDeQJT9fQH6hyMJwTLxY/yhKJ0Jv4rpR7FZ6MnSZI9BUqTswGZVUiopo7NSsk1bhxApzQ21EOjltGNauOPq9SyushCN6zyzPyZSZhUr2ErEg2aimmC1g9VO0CUuyJVaIqY/Q/LsezuF+XXlsqWj7ktl+cnLsapWug508cofXyEWjVFRV0Hd3DrzmCrX2FUUEJv8zTRjVVIMil6kvPHGG3R3d3PCCSegqiqqqvLss8/y3//936iqalZQRlZEuru7zfsaGhqIRCIMDAyMecxIHA4HZWVlaV8SyWwk02RPKmVlZTQ0CLPbrl27ZmxduSIXlZRctHtG4g/FaBsITkqgQCJp1jdiskdVs+pHgaRIObBvL0o8TlmikjIQiBAfrx+VBdo6xSaJzfW1EBYtrKtPF7/TXz4Uxx8xRo9Lwfg8mmvTLIh2j+rAXStE7KLSRMsuw4SPUUlZseyocU/pKnWxZLX4wPCHn/wBEK2eND+Kc2w/iqqoeQlLG+lJqWuYZSLl7LPPZvPmzbz99tvm15o1a/jEJz7B22+/zcKFC2loaGDjxo3mYyKRCM8++yxr164F4IQTTsBms6Ud09HRwZYtW8xjJJIjlbEme1KZTS2fbFZSZqLdM13STLPlIlhMZKRkZ7LHoLm5GY/HQzwe5+D+vVSWiAvhUCiGLzTxhNLhYLR7mqpLzdssA6IyscXn5rKzjmNRcw04UtpaM9XusTpoWiQE3JIqhd6envErKcvHr6RAsuXj7fYC6X6Ualf1+K2ePPhRYHQlpba+Nu37ohcpHo+HVatWpX2VlJRQXV3NqlWrzMyUW265hQ0bNrBlyxauvPJK3G43l19+OQDl5eVcddVVXHfddfz1r3/lrbfe4pOf/CRHH330KCOuRHIkEY1GzUyhsSopkBQps2EMOZuVlJlq90yVaFzDG4gyEBAiIZmRkty3J1uVFEVR0iZ8qkuECAhG4+NOJ2UDQ6Q0VySEh65Bv6hqffGqT/Grf/+0uN0wzc7E+DGIQDcFSirr2ecVN3Xs2wZ6DCLJkfF4PM623YnJngnaPSBGkVM5ao34fzmndA5LKpaM+9h8bdo3spJis9uoqhVtKatqpX5B5m5GPsn6kPYNN9zAtddeyxe/+EXWrFlDW1sbf/nLX9JMYT/60Y/48Ic/zGWXXcZpp52G2+3mscceO6wRRImk2Nm+fTuRSISysjKzKpCJ2VJJSQ1yy2Ylpbu7u6ASeQeCEXMqqNxlw2W3YlFEkFu2MlJSMX0pu3ZS7lJRLaIFcWjgMMaQo8Pg7xn3kPauRLvHECmD7RALCd+JJ0WEmePHttyPHxvPA2C1s3dYVLFCPfvFbZGkeXbfwTZCoTBOp4OFLfMnPG3T4iaqGsQFvry2nOaWZpZVLWNO2ZxkO2sM8mGahdE7IQPUNwhh0tjSSHVJNVXOKurcddS4a0Ydmw8OW6Q888wz3H777eb3iqJw880309HRQSgU4tlnn2XVqlVpj3E6ndxxxx309fURCAR47LHH5MSO5Ign1Y8yntF8tmSldHV1mUFuhs/mcKisrDQnovKdPGug6ToHewOjkmZLHCoK2c1IMTBEyt7dO3Goalqg27R9KboG3e/BUNeYh7S1JRJLaxNTaX3C30FlCxj7xlhUsAuhkNM9e1KxWIVZ1+pgUBUX3pJIInQuZcJnayIfZdmiBZP6wKwoitnyWXnSSo6pPYYKZ8WklpTPdo8yQkEZ5tlVK1exvGY5R1UdxcKKhcwpPfzqZjaQe/dIJAXCRKZZg9RKyozsZJsjDD9KU1NT1qqoM2menQztviChmDYqabbUIYRDtvbtSSUpUnZgU5MTPkOh6OH7Unq2wVCGqIjQYNI4a4iUfuHvoHph8jh7qh9lBlo9BlYbqHasVWL7hLmOYSG8Unwphml25dKFGU8BsLpuNatqVrGsahmLKhZx9fVXc/7Hzuemb9w06WmdmY7DT3tuRRlVTVmweAEAK4/LHF6Xb8Z29kgkkhllovFjg4ULF2KxWPD7/XR2dh5WnHw+yaYfxWDBggW88847BeFLicTjtPYHAEZVUkodKrqum+2eXFRS9u/ehapgTvh4g1F8gShVhxvW1bNdpNuVJX7uoiGCB99kYEi81uaacoiFwZeIiqhKCUVzpkxmzuSF2pIwz85fQuDAC3jsQKAvzRPz3nZRmVzZVAaDbeAZ7c9wqA4cJNddu7iWY+8c///rqHNYHRNGcuQSh9VBREuOpH/ua59j+dHLOfuCzNsA5BtZSZFICgBd1yc12QNgt9tNk2gxt3yMSko2RUohmWdb+4PENQhF43gN06xRSXGqeAf6iUbExSKbQnPhwoXY7XZCoSDe7nbqEsKo0xdiMFs7H/fuEBdyTYOuLWYVxe20U17qhIH9olLhqgR3Sl6II1+VFBVsLpYuaGBTu9ibyN+5F7QYdL0HB18xTesrm0sgmrttBPLV6jGwjzArl1eUc+FHLsTlduVpReMjRYpEUgB0dnbS09ODxWIZc8+QVGaDedaopGTTj1Yo7Z7hSDIXxfizwm3DaROmWbfdak721NbWYrdn74KtqqoZ+ndo/24aE8KowxdiKBjNXl5K7y5oewMiftp6RLBgc025qBIYrZ6qEa2TVJEyk5UUqx0cHjxuJ1sHEu2vjoRnZriHeDjA9gPCb7Oy5fD9UeORr1aPwUiRUuhIkSKRFABGq2fp0qW4XBN/opkNY8i5qKQUSlbK/t5hc/+6MU2zOZjsMTBaPof27aK+zImiiKyWwXBsUjs6T5rE/jdtvQmRUpto5xim2eqUVo/Vnp6LMpOJqxYb2N2gqPTqFQDYhjvMu/e29xGKxHA5bLQ0jp8Ue7i41PxWLOwWKVIkEskUmWyrx2A2VFKyGeRmUAjtnoFAxMxEAcY2zeZgssfAECkH9+zGZrVQWyoEQYc3lF2RkiC1kkLIB4FeQBGTPQaOFD+KRU1O/MwERrCaoxTNI0RhlWVIeGeA9/YJwbh8fj0WS24vi/mupOT7+aeKFCkSSQEw2ckeg9kwhpwr4yxAX18fQ0OjN5DLNTo6+3rTM1pGVVLsiU3/cjDZY2CaZ/fsxGqBpgrx6b3DF8yeLyWFZCWlPNnqKWsCW0rVwJ5Mop3RVg+ISgqAs4y5c+dx0KdhURBZLsB7+4VIWZHjMDNVUUelvs40Nqtt1BhyISNFikRSAEx2ssfAqKTs2bOHWCz7F51ck+0gN4OysjKqqkS5Ph/VlC5fmEAkbn4fjMTNykXqZA/MTCVl764d2CxKmi/FH4oRy/I+Pm09Yq+eppryzK0eGDHZM8P71hjP5yhjxYJ6XjmU+DcaFNU8o5KSaz9Kvls9BsXU8pEiRSLJM8Fg0KyITLbd09zcjMvlIhaL5d1/MR2yHeSWSr7Ms4OhKPv70veD2dcnqiqVbhsOmxUFcDtEmyMXGSkGS5eKze583gFCQwM0JvYL6vSFiOs6g1lu+ZiVlOqyFNPsCJGS2u6Z6R2ADbOovZTlKSIl0idC/2ZKpBRKq6VQ1jEZpEiRSPLMli1b0DSNurq6SV+wLRaLOcFRjC2fXAS5GeTDPNvnD7OlzUcspUCh6zov7+kDYHmjuEC7HVYsiYyMnhxWUlwuF/Pmi2j37oN7qS61Y7daiMQ1+vwRvNkWKQlPypLKOEQDQhSUp7TxVHfSFwIz3+5RFOGDsTkpKytjXyIeH98hYtEY2w+KBNpci5R8jx8b2Ga6knUYSJEikeQZo9Uz2SqKQTFP+OTCj2Iw0+bZdm+Q7Z1DaCPCf7d3DtHrD+NQLZy4QLSgjFYPkJN9e1JZuky0fDoP7sGiKKZxt8MXZDCYuUUY0zQC4am1DzVNI+Af5JyFVhbHtosbKxekG2NLR+wDM5MZKQaGL8XuIV5STySuY9dCHDy4n0g0jttpZ0FDZc6eXlVUSm2lEx84A8hKikQimTRTNc0aFPOETy4mewxmst2zr9fP3pRxYwNN13lxTy8AaxZU4bSJC7YhUqLRKH29IgAtF5UUgOWJ3ZDbD+wGSPOlDIdH+1LCsTjvHvLhn4xIiQxDxzvw3v+hvXQnPdeXsPFTJZT4don7a0bsAjxys7p8XCRNX4qHJfMbebtTvP7eg0LkL59fJyZ74lHY8TgE+rP69DWuGqwzOdE0DsWUlSJj8SWSPLN161YAjj766AmOTKeYRUouKykz0e7R0dnZOUSPP5Lx/u2dQ/T5I6KKMj/56dwQKX09Xei6js1mo6YmN7vNrlhhZKWki5R2bxAd8AWjVJcIsTAcifFeu49IbIy9oHRNJMz27hJfg23mXcZFZL9PZ8FRR0PlfGhanXys6kw3zaLkqZKSWKlTmGdffTfOSc1WdJ8QzMcvqoMDL4mviF8Ez63/l6w8td1ip8qZ2/yVqSBFikQimTR79wqj4eLFi6f0uGIWKbmspMxEu2dvz/CYAkXTdV7cLaooJ7VU4UhUUYRpVvzK7Ur4URobG3OWy7FyxQoAWveK6oZhnu3xh4nGNVOkDAQibO8cJD7WwM++5+A3V4q9blIpbYCaxbzWDhd8+wnmzWnmjUv/fvTj3dXp38+0adbAuDA7ylixoIE7H49xzUl26unjm+sc/MsJ+2GXEHS4a8QIdZaoL6nP6349I7FZbFiwoJHdKa9cIEWKRJJHIpGIWVVYuHDs3VczYWSlHDp0iOHhYUpKSrK+vlyRy0rK/IRh1Ov14vV6qaioyOr5D3kDdPjG3ttlW8cg/cMRnDYLJ6RUUVx2K1bDNNuVO9OswYqESOnpbCcU8ONxlVDqUPGHY3QNhqh02+kaDLG72z+qXZVGZUtyM76qRaKVU73YrI689d5L9AZ0TjV2Px6Juzb9+3x9ijfaPVaVFYvnmxM+C0oj3LzeAcTEPkPzT4c1n4VF67LytG7VTZm9bOIDZxi71U4onrs9irKF9KRIJHnk4MGDaJqG2+2mrq5uSo+tqqqiulp8St29e3culpczchGJb1BaWkptrbgwZrua0u0Psb83MOb9mqbz0m5RcThpQRUONelBSDXNGhkpuTLNAlRWVlJTK36mOg/uRVGUlJZPCH84xq6JBApAxVy4/New7gY49qPQfHxa+yYtbXYkFhu4KtJvy3clBSivriesetjVJ4TKWx1xuuecD6d+Sby+LE6/1LtzGxA3XYql5SNFikSSR4xWz8KFC6dVDi7Glk88HqetTXgactHugdyYZ33BCLs6/eMes7VjkP5ABJfNyvHz0ydFSjJM9uSykgKw6ChRbes6IALWGlMmfKZE8/FJT8cI0tJmR+KuZlS4ab4mS1LFkcPDigUNnPVAgJPu9vO+X0apOepEULJ7SSy3l+O2ubN6zmxRLIFuUqRIJHkkVaRMh2IcQ+7q6iIej+ckyM0g2+bZ4UiMrR2D41YdNE3npT0pXhQ1fZKj1Jm8yPd153b82OCoo8SET0+rIVKMePzslfnHraSU1o6+LV8ZHVY7pmJyeFi5oIFDgzqvt2s52bNHQSnYKgoUzxiyFCmSgmN4eJg333wz38uYEbIlUoqpkpLLIDeDbJpnw9E4W9t9YxtLE7zXMchAICqqKPPSqygKUGpPvtbe7pmppByVGEPubE2vpPiCUQKR7GynYFRSmmpG+C4UK2SaaMlXu0dRUsyznrR9elYuyL5YrnZWF3Rommz3SCTT5IYbbuCEE07ggQceyPdScs6RKFJyaZo1yGa7ZzAUJTzWaG4CbyDCMztEaunJLVXY1fRfrU6bBWvKJ/WuDrFvUa4rKcsSIqVjvxApDpuVqhJxcerwZqeaYlZSRrZ73NWIXfxGkM9P8IZAslhZuWSBefPKluxWPFRFpcaVm9HybCFFikQyTV588UUAvvOd76DrE9r6ippstnuK5b3K5fixwUxG44eicX7zxiECkThWfw9LK0f/Wh2ZNLt/n/h3nzdvXk7XZmw02NV2gFhUjEw3GebZqfpSMhAMRxgYEucZJVJKMlykLTbI0cj1pEipbCxfmgycy3YcfkNJQ8EEt42FalGxKmOvsVBGpqVIkRQUuq6za5fIddi6dSsbN27M84pyh67r7NkjPuFOV6QY2Sper5e+vr4Jji4MZqKSktruyaV4i2kaG95qo384gjbcz4H7r+eph3486rhUP8ov7/4x0WiUtWvXsmjRolHHZpO5c5opKfWgxeN0H9oPTM+X8s7mLbQn2jqpGFUUl8NGRWnqDr/K6HwUyF+rxyClilNZ18jRCxtxOWysWZY9wVxuL6fcMcY4doExnnm2UCL8pUiRFBTt7e0EAskRz9tvvz1/i8kx/f39DA6KLe6NT/5Txe12m5/Gi6XlMxOVFCMrZWhoiP7+7MabG+i6zp+3dHKwP4DdqtD5q28Q9/fx3O//l77OtrRjjcke70A/v/7lLwC46aabcv5p1a5aaVksqm2dBxO+lIrkhM9kBNx7773HCaefy7prfkx8hDGnvVf8/DbXlqe/FldV+t49BvluMaQm3do9PHfnl9jxv/9KXaUnK6e3WWw0ljRm5VwzwXgtnzJHYWS7SJEiKSiMC21VVRWKovDEE0+wffv2PK8qNxitnqamJlwu1wRHj02x+VJmopLicrnMyaFctXxe2N3Le+2DKAqcXq8R7RHPE4tG+dP9/512rNHu+d9f/IRAYJjVq1dz/vnn52RdqagWhYUJkdJ3SPy81XmcWBWFUFTDG0jfDXk4HOPBVw4QT9kt8ZFHHiEej7P7UC9/enlr2vHm+PHIyZ5MrR4oqEoKdg8VHjdz67O3qeCc0jkF3+ZJZSyR4rQ6C2b6R4oUSUFhtHpOOeUULr74YgD++7//e7yHFC2H60cxKIQx5F27dk16ImsmKimQ240G3znk5aU9or123ooGLH3i39JTKVocr/5lA+37hGh02iyoFgv+oUEeuvenANx4440z0vNXrRZaFgvvRV/bfgCsFoW6MnEBMvJSonGNl/b08rPn9nL/ywd49I1D5jkeffRR8+93bXgx7fxjmmZLMoweQwFUUlIuvBYFHNlradQ4awo2E2UsxhIiHnt2KkvZQIoUSUFhVAOOOuoorr32WgDuv//+nJXs80m2RUq+Kim6rrNu3TrWrFnDww8/PO6x8Xic9nYx2ZLLSgrkzjz75sEB/rxFjBCfurCaY+dW0NUqhNDKk9dz7Pvej65p/OGeH6IALdViu4Jf//IXDPl8HLV0GZdccklW1zQeCxMipftQUqw1VYjKXZs3yLuHvPzsub08v6uXSFxjSV0pLbVizdu2bWPbtm2oqoqiKDz52g52H+oxz5MxI8VZAdYxdlzJdyXFYk0PpcvSxdhldVHnnlpidCFgs2QekS6kGH8pUiQFRapIWbduHcceeyyBQICf//zneV5Z9pktIqWnp4eOjg50XefTn/40jz/+eMbj4vE4V111FbFYjLKyspwFuRlke6NBXdd5eU8fG7d2AXD8vAret0S0Nbpaxb9l/dwWLr7qaygWC++++BSxju1UlzoIBYM8cLcw1H79X/81Z5sKZmLJEvHzcWj/Hkh4UIy8lDcPenliSyf+cIwyp8pFxzRy5+WrOXGByDcxqijvP/cczl9/KgA/+f3L5rkzps16xvFkFEILIXUNY/kuppA8a8FCs6e5YKZhpkKmSorD4sCpOvOwmsxIkSIpKIx2z5IlS1AUxaym3HHHHUSj0XEeWXxkW6Ts3r0bTZv5XU0PHDhg/j0Wi3HppZfy/PPPpx0Ti8X41Kc+xf3334/VauVnP/tZzoLcDLLZ7tF1nWd39vDcLlFFWLuomnOWJ3e27TqYECnzFtE4fzGnnicqJff91y3ous7vHnmA/t4emufO5xOfuPyw1zMVFi5ajMViwT80RDwwAEBTedID5VAtrF9ayz++byErmsqxpFxsDZFy6d99mC9ccRkAv3j8dYJhMc6cVklRXdC4GjxjZI4olrErLDNJqnnWmVJJUVQhsBpXQ1XLpE/XVNpUMP6NqWK1WFGV9H+TQjHMGkiRIikYYrGYOZJrXHg/9rGPUVdXx6FDh/jd736Xz+VlnWyJlPnz56OqKqFQyPR7zCRGpWLNmjVccMEFhEIhLrzwQt566y1A7PT8sY99jIcffhhVVfnVr37FRz/60ZyvK1vtnrim8+R7Xby6T7Qcz1xay/uW1JoCRdd1s91TP7cFBfin6/8Vu8PBG6++xDMbn+De/xG+qi9+5WvYbDObQup2OWmaIybAhjoPAlBZYufsZXWctqiaq89YxMkt1ajW9MvB3r17efvtt7FarVx84QWcf9ZpzJ/TyMDgML96RbTszErKgiUw50RwjTN6WygX8lRfjK1E+GfqVsD8tVC7dPzXMILGksaiGTcei5GpuIXU6gEpUiQFxIEDB4hGozidTtOv4HQ6+cIXvgDMrnHkaDTKwYPignG4IkVVVTNvIx8tH6OSsmTJEn7zm99wxhlnMDg4yHnnnce7777LpZdeyqOPPordbud3v/sdl1566YysKxtZKdG4xm1PbOedQ14APrCygZNa0vM/fH3dhIPDWCxW6prmsbi+lKOPWszHPv1ZAP71mn+kq6ON2roGPvnpT0//BU0Tm9XCgkXClzLQvt+8fc2CKk5fUovLnrmiZVRR1q1bR01NDVarlc9/6u8BuOu3f0WrW2WOIDctPW7ikDa1QBJOU8WSAtSvhNK6KYfM1bvrqcoU+19kpFaB7BZ7QbV6QIoUSQFhtHoWL16c1rP/whe+gN1u55VXXuGVV17J1/KyysGDB9E0LW1U9nAwKk/GeziTGCJl/vz5uFwu/vCHP3D88cfT09PD6tWr+eMf/4jT6eQPf/gDF1100Yyta968eSiKQiAQoKenZ+IHZOCHG3fy9I4eLApcfGwTx86tGHWM4UepbpzDsuYq6j3il/xVX/oaJaUegoFhAK64+ktUekqm92IOA9WqsGChCP3rbN2XMak+E2arJ0VUfuZjH8Jut/H62+/x5Gs7iMbiADTWjTHNk0qhVFKyIJbqXHUFH3s/WVID3QqtigJSpEgKiFTTbCr19fVcfrno48+WakpqqycbhrslS8Qn5XxUUox2ihGgVl5ezp///GeWLl2Kpmm43W7+9Kc/cd55583ouhwOh7mB33RbPlefsZBlDR4uOX4Oyxsz/wI3Wj0LFi6mviz5KbSyqporr74GgIrKKv7+k/+AyzbzGRo2S7KScmDvbspdE7ebDh06xKuvvoqiKPzd3/2deXtdTRUfufAcAP7fd39s3ma3T6KFle/JHoPDFEs1zhpq3ZMQZUVCalZKoflRQIoUSQExlkgBuOYa8cv+//7v/4hEIjO6rlyQLT+KQT4nfIxKSmpqbm1tLX/961+5/vrreeaZZzjrrLNmfF2QfH+nW2GqcNv5748fx6LasfM0uhOVlOWJzfxSufLzX+Yfr7me7951L+VlpaN8HzOB1aqwYJGopOzfs4ty98SCwvB/rV27lsbG9GmdL35aGGjf3CxCFpsbJjl6m++MFAPVjujzTJ1qZzX1JdndjDDfGCLFbrHjUqcfKpkrpEiRFAypkz0jWb16NZWVlYTDYTZv3jzTS8s6uRIp+W73pNLc3Mz3vvc9TjzxxBlfk8HKlSsBDutnxjJBpasvkT9iVCtScTidXHPD/+OU09flpYoCInV2fosQKW2tByiZxDIMkZLJP3TqmmM4dkXyg0RzwySrCoVSSYFpr6WhJLdj8/nA8KQUUoBbKlKkSAqG8SopiqJw0kknAfDaa6/N6LpyQbZFiiHs9u7dO6Oj2l6v19x/KNc7+k6Ho48+Gjg8kTIebruV3kSSayaRkkq+RIrNaqGuoRF3SSnxeJyBzlZs1rGFV19PjzlCnil0TlEUvnjFR8zvJ1dJUQqnkgKFtZY8Y1EsqIpakH4UkCJFUiCEQiHzE3kmkQKYIuXVV1+dsXXlimyLlKamJtxuN/F4PCcx8GNheD1qa2spKZl5U+hEHHPMMQC8++67WT+3zaqwuNpO+yExpdUygUhx5EmkWC0KFovC/IViAmz/nl3j+lI2/vmPaJrGmjVrRlXHDC7/u/Mp84gW2KREirsKCinsrJCqOgVAqb20YCP9pUiRFAR79+5F13XKysqorc1cPj755JMBWUnJhMViMaspM9nyGavVUyisWrUKEEbQgYGBKT/e6/Xy6CP/S393e9rtFgVWNpbT1XoAXdfxlJVRVTN+2yNflRQwJnzEz8f+vbupdI9dSfjLn34PZG71GJSWuPnXf7oSq9XKuWecPP6TuyqhosCqbIUyaVQgFPKkkhQpkoIgtdUz1rSLUUnZvn07Pp9vxtaWbQYGBvB6vUC62fRwyYd5ttBFSkVFhdmGmkrLJxQK8f3vf5+FCxfyr1/5Avd+62tp9y+uL6XUqbJ/rxCE8xcuGXdKy2pRsKv5+3WrWiymefbA3t1UjGGeDQz5eOWF54DMrZ5Uvn7NZwjueYlTTjhm7IOc5VBRgD8b1pkN1Ct0CjkxV4oUSUFgXFgzmWYNamtraWlpQdd1Nm3aNFNLyzpGFaWxsRG3O3sl1nyMIY8cPy5EpuJLicfj3HvvvSxZsoR//ud/NqsvezZvor9LVFOaKpzUlYpR4/17dgOYAmAsxgpMmylsIyopDtWK0zb61/+7L/6VWCzGqlWrxmy7pp13vPRcRxlUthRWm8dAtnuKBilSJAWB0aKY6BfjbPClZLvVY5CPCZ9M48eFhuFLmUikvPzyyxxzzDF85jOf4dChQ8ydO5d7772XNaesBeDNZx+n3KXSUpP03uzbIwShIQDGIp+tHgDVakkbQwaocKW3fGpK7ex/82lg/FbPpLB7CleggGz3FBFSpEgKgvEme1KZDRM+uRYpst2TjlFJGc88q+s6H/3oR9m6dStVVVV8//vfZ+fOnVx55ZVc+GFxwX7rmcdZ2uBBScnYSFZSClukOFQL8xOpswP9ffgGBqhwi43lKlwqx82pYFG1izdefgHg8JKB7aVQtXDKMfMzisUCFtnyKQYK+KdIciQxmXYPJM2zr7766rT3Y8k3xiaK2RYpxnvX2tpKIBDI6rnHopjaPVu2bBlzl+g9e/bQ2tqK3W5n165dXHfddTidoqVz3gUfQrFY2LftXboPtZqP0XWdAwlPykSTPU57fn/VOm1W3O4S6hubAdi/dxflLjsrm8pY1VxBqVPlvXfeZNg/RGVVFatXr57eE6nOwhcoBrLlUxQUwU+SZLYzNDREZ2cnMLFIWb16NVarlc7Ozrzs+JsNclVJqa6uprKyEkgKoVwyPDxMX18fUNgiZenSpdhsNoaGhszKz0hefPFFAE488USqqtI3jaupq+O4k04D4C9/+j/z9v7eHoYGB1EUhXkLxv63tFjAoea3kmJUclJbPjarJW3K55UXngFg3foz0/bOmjQWG1QtAkt+X+ukkVkpRYEUKZK8Y3go6urqqKioGPdYt9ttegyK1ZeSK5GiKMqMtnyMC355efmE/275xGazsXz5cmBsX8oLL4g2x2mnnTbqviq3nYsSLZ8/P/Y78/Z9CW9H09x5OJxj7xxb6lCnt/AsYrUo2NR08+xIXn7uGQDWn3n21J9AsYgKSqHsdDwZZCWlKJAiRZJ3JtvqMShmX0o0GuXgQRH+lW2RAjM74VMMrR6DicyzRiXl9NNPH3Wf1Wrh7PMvwmq1sn3LuxzYJ6pUxoV+PNOsTVVoriiM/VCcqnWUedZg2D/E5rdeB2D92VMVKQpULgB7YYaBjYmspBQFUqRI8s5kJ3sMUn0pxUZrayvxeByn00lDQ/b3AZnJCZ9imOwxGM8829fXx7Zt2wCxoV4mKquqOfn09QA8+dgGAPbvTkz2jDF+rCiwoLokL5sKZsJps45ZSXnj1ZeIxWLMmbeABQtapnbi8jkiD6XYGE+k2MaujElmlsL43yM5opnsZI+BUUnZtGkTsVgsZ+vKBUarp6WlZXp9/wnIR7unGCop42WlvPTSSwAsX76c6urqMc9x3oUfBuDJRMtnokrK3Eo3zjxP9aTisiUrKQf37yUej5v3vfy8GD0+5X3rJz6RRRUGWbsHPE1QUrhppeMyVrvHVlKcomuWIkWKJO9Mtd2zbNkyPB4PgUCArVu35nJpWSdXfhQD2e7JjNHu2blzJ6FQKO2+8fwoqZz9gYtQbTZ2bd/K3l07zJZJpkpKXZmD8jFSXfOFw2ahoWkOdoeDaCRCe+tB875Xnn8WgFPed2bmB6sOqF8FjcdBw9FQtxxqFoOnfgZWniOsNuGlGUlZ48yvRTImUqRI8oqu61OupFitVtasWQMUny9lpkRKT0+PGb2fK4qp3dPU1ERlZSXxeNxs7RiM50dJpayigrVnnAXAH3/3a9paxetvWZT+c1vmUqkvK7x2gUO1oKpW5i1IbDSYGJ/u6epkz85tYqfxte/L/GBFSVzUCzScbbqMDHVzlIHDk5+1SDIiRYokr/T19ZkX08WLx48WT6VYfSm5Fikej4fGRvFJMNe+lGJq9yiKktE8GwqFeP11YRidqJIC8P5Ey+dXD9xNPB7HXVJKbX3SW+S0WZhbWZgGUkVRcNpSkmcT7Spj9Hj5qmOpqKwa6+Gzk5HTSJ7s+8Qkh4cUKZK8YlRR5s6di8s1+SmIYp3wybVIgZlp+YRCITo6OoDiECmQ2Tz7xhtvEIlEqK+vZ9GiRROe48z3fxCb3c7Q4CAACxYuNjcWVK0K86rdWCyFW21IM8/uSRcpp54xRqtnNpNaSXGWg71k7GMleUGKFElemepkj4FRSdmyZQt+vz/r68oVMyFSZmLCp7VVJK+63W5qaorDOJnJPJvqRxlvF2MDT1k5p68/x/zeiMNXrQotNSV5D22bCGeKeXb/3l3ous6rCT+KMb10RJFqnvVIL0ohIkWKJK9M1Y9i0NTURHNzM5qm8eabb+ZiaVlnYGDA3FW3pWWKY55TYCYmfFJbPZO5uBcCmdo9hkiZyI+Syvsv/Dvz7wsWLcZqEQKlkCZ5xkKIFCGsDuzZzd5dO+ju6sDhcLJ6zcl5Xl0eMMaQXZVgK4w8G0k6UqRI8spUJ3tSKRRfygsvvMCmTZvGPSYej3P77bcDUF9fT0lJ7srKM9HuKabJHoOVK1cC0NHRQW9vL5qmmePHk/GjGKw/9wM4HMIY27JoSdEIFACnamFBYqPB7q4O/vrnPwJw/EmnjpuaO2tRHYAiqygFjBQpkrwy3XYPFIYvpb29nfXr13PiiSfyoQ99iPfee2/UMXv37mXdunX8x3/8BwD/8A//kNM1pbZ7crUJYzGZZg08Ho/ZZtu8eTPbt2+nv78fl8s1pQ31Sko9fO4r/8yxx5/I5ZdehMteHAIFQLVaqK6upKqmFoDf/u99AJw8mXyU2YjVDu5qGZFfwEiRIskbuq4XvUh58803zVCsP/zhDxxzzDF85jOfobW1FV3Xueeeezj22GN58cUX8Xg83Hfffdxyyy05XdOiRYtQFIXBwUG6u7unfZ7HH3+cFStW8Nhjj426r5jGj1NJNc8ao8ennHIKNtvUMk0+9+XrePGll2iqKw4/TipO1WpWUzrbxSadp5y+Lp9Lyh+KAmVN+V6FZBykSJHkjdbWVgKBADabbVoXuzVr1qAoCgcPHjR3UZ5pDH/DmWeeySWXXIKmadx7770sWbKEU089lc9+9rP4/X7OOOMM3n33Xa644oqcezgcDof5fk635fPuu+9y2WWXsW3bNr74xS+OCkArxkoKpPtSJhviNhJFgZaaEkoKYOPA6ZDqSwGoqKxi2cpj8riiPFMsuzYfoUiRIskbRmvkqKOOmvInWRDl+xUrVgD5q6Zs2bIFgHPPPZdHH32Ul19+mTPOOINwOMyrr76K3W7ne9/7Hn/7299mtOpg+FKmM+HT3d3NxRdfzPDwMACHDh3irrvuSjumGD0pkD7hM9kQt1QsluIWKCCyXFKj/E8+bV1OtmiQSLKB/MmU5A0j0t4QGtPBaPkYgVwzjVFJMS5+p5xyCs888wx/+tOf+OIXv8jrr7/O9ddfj9U6s5/WpjvhE4lEuPTSSzlw4ACLFy/me9/7HgC33HILPp8PgFgsRltbG1C87Z633nqLPXv2oCgKp5xyyqQeOxsECqSPIcMR7EeRFAVSpEjyRjZEyvHHHw/A22+/nY0lTYloNMr27duB5MUPRLLnBz/4QX784x+b7YWZZjoiRdd1vvCFL/DCCy9QVlbGY489xrXXXsuyZcvo6+vj+9//PgBtbW3E43HsdntOdnLOJYsXL8bpdBKNRgHR/ikvn3gzOUOguO3FLVBAxOO3LE5WUk5NESmKIjYilEgKBSlSJHnDaPcYo6HT4bjjjgPEJ+OZZufOnUSjUTweD/PmzZvx5x+P6bR7/uu//otf/OIXWCwWHnnkEZYtW4aqqqbR94c//CFdXV1mq2fu3LlF1yZQVTVNFE/Gj2LkoMwGgQJCRC89agl/99FP8omrPk/z3GTLrtxlw64W17+pZHYjfxoleUHX9axUUo499lhAfLrv6enJytomi9HqWbVqVcEFmqWOIWuaNuHxf/7zn7nuuusA+P73v8/5559v3vfhD3+Yk046iUAgwLe+9a2inewxSK1uTcaPUupQZ41AMXDaVP79+3fyLzfflnZ7rUeO4koKCylSJHnh0KFDDA0NoarqtILcDDwej7kx4Uy3fAzT7KpVq2b0eSfD/PnzsdvthMNhU1SMRTgc5pOf/CSapvGZz3yGa6+9Nu1+RVG47TZxMfvpT3/K008/bT5HMZLampvqZM9sIVO2S7nLVjShdJIjhymJlP/5n//hmGOOoaysjLKyMk499VSeeOIJ835d17n55ptpamrC5XKxfv36UeFW4XCYa665hpqaGkpKSrj44os5dOhQdl6NpGgwqihLlizBbrdPcPT4GEFcM93yGWmaLSSsVqvZRpto24DNmzfT19dHVVUVd911V8aq0Jlnnsn73/9+otEo9913H1C8IsXwMc2fP7/g2nQzRSYxUlcmqyiSwmNKImXOnDncdtttbNq0iU2bNnHWWWelpWx+97vf5Yc//CF33nknr7/+Og0NDZx77rkMDQ2Z57j22mvZsGEDjzzyCC+88AJ+v58LL7zQDMQ60hkaGmLfvn35XkbOyUarx8AQKfmqpBSiSIHk5NNE2wYYk1EnnngiDsfYF6pbb7017ftibfesW7eOO++8k4cffjjfS8kbzhG+E49TlVUUSWGiHyaVlZX6z3/+c13TNL2hoUG/7bbbzPtCoZBeXl6u/+QnP9F1Xde9Xq9us9n0Rx55xDymra1Nt1gs+p///OdJP6fP59MB3efzHe7yC45zzjlHt1gs+m9/+9t8LyWnXHXVVTqgf+Mb3zjscz3++OM6oC9btiwLK5scQ0NDOqADek9Pz4w971T4xS9+oQP6unXrxj3uH/7hH3RAv+mmmyY850c/+lHzdT/zzDNZWqkkH2xt9+nvtnr1d1u9+nA4mu/lSI4Qpnr9nrYnJR6P88gjjzA8PMypp57Kvn376Ozs5P3vf795jMPhYN26deYmXm+88QbRaDTtmKamJlatWmUek4lwOMzg4GDa12wkEonw7LPPomkan/70p/MysTJT5KKSsmPHDjOALNcY629oaKCmpjCj0Y1KyqZNm8atVBqbI5544okTnvM///M/sVqtKIpieoEkxYkxalzqnH3GYMnsYcoiZfPmzZSWluJwOPj85z/Phg0bWLFihRlLXl9fn3Z8fX29eV9nZyd2u53Kysoxj8nErbfeSnl5ufk1d+7cqS67KNi6dauZ3xAIBPjQhz6Ut7j3XKJnabLHoKGhgfr6enRdN30iuSZ1sqdQWbZsGR6Ph+HhYfP9Hsnw8LDZrp2MSFmyZAl//OMfefjhh2lubs7qeiUzi9HekRM9kkJmyiJl6dKlvP3227zyyit84Qtf4Iorrkj7BTjSdKfr+oTjmRMd8/Wvfx2fz2d+tba2TnXZRYHhqTjhhBNYunQpra2tXHLJJYTD4fwuLMu0t7fj8/mwWq3T2lgwEzPtSylk06yB1WplzZo1wNi+lLfeegtN02hqaqKpaXIbrX3gAx/gox/9aNbWKckPTpsFt8NKaZEn6EpmN1MWKXa7ncWLF7NmzRpuvfVWjj32WP7rv/7LTJ4c+cm/u7vbrK40NDQQiUQYGBgY85hMOBwOc6LI+JqNGO2d973vffzhD3+goqKCl19+mc997nPoup7n1WUPQ9QuXrx4XKPmVJjpCZ9CN80anHzyycDYexsZpllDzEiOHJw2K3WyiiIpcA47J0XXdcLhMC0tLTQ0NLBx40bzPsNjsXbtWkBUCGw2W9oxHR0dbNmyxTzmSMaoAqxevZqjjjqKX//611itVh544AF+8IMf5HdxWcQQKYeTNDsSI3l2pisphdzugYknfKbiR5HMLpw2Kx7n1Df2lEhmkimJlBtvvJHnn3+e/fv3s3nzZm666SaeeeYZPvGJT6AoCtdeey233HILGzZsYMuWLVx55ZW43W4uv/xyAMrLy7nqqqu47rrr+Otf/8pbb73FJz/5SY4++mjOOeecnLzAYkHTtDSRAmJn3dtvvx2AG264gccffzxPq8suhgciG34UA+M9e/fdd4nFYlk7bya6u7vp7u5GUZSsvoZcYIiULVu2ZDQVp44fSyQSScExldGhz3zmM/r8+fN1u92u19bW6meffbb+l7/8xbxf0zT9m9/8pt7Q0KA7HA79jDPO0Ddv3px2jmAwqH/pS1/Sq6qqdJfLpV944YX6wYMHp7KMWTmCvGfPHh3QHQ6HHolEzNs1TdOvvvpqHdCPP/74PK4we5x22mk6oD/00ENZO2c8HtdLS0t1QN+yZUvWzpuJv/71rzqgL1q0KKfPky2am5t1QH/22WfTbh8YGDDHiXt7e/O0OolEciSR0xHke+65h/379xMOh+nu7uapp57i3HPPNe9XFIWbb76Zjo4OQqEQzz777KhyuNPp5I477qCvr49AIMBjjz02a6d1poLhpVi1ahU2W7IEqygK//Zv/waIVsZMjdjmCl3Xs7Kx4EgsFou5j0+uWz7FYJpNxaimjPSlvPHGGwC0tLRQXV094+uSSCSSiZB79xQIhkgxvBWpNDc3M2fOHDRNMz0ExUpnZyderxeLxZK1yR6DmdoRuVhMswaGeXakL0W2eiQSSaEjRUqBMNKPMpJTTjkFgFdeeWWmlpQTDNPsokWLcDqdWT33TI0hF4tp1mCsSooUKRKJpNCRIqVAGK+SArNHpOSi1WOQOoas52hkW9M08zUUSyVlzZo1KIrCwYMH0yIC5PixRCIpdKRIKQC6u7tpb29HURSOOeaYjMekipRcXYBngmwmzY5k5cqVqKpKf39/znbWPnDgAH6/38wLKgY8Ho/5fhvVlK6uLlpbW1EUhRNOOCGfy5NIJJIxkSKlADDaE4sXL8bj8WQ85vjjj0dVVTo7Ozl48OAMri675CIjxcDhcJgX48PxpYRCIb7yla+wbt06du7cmXaf4UdZvnx5msG50BnpSzG8TUZ0vkQikRQiUqQUABP5UQBcLpfZCirWlk/qZE+u8kUON9Stra2N9evX89///d8899xzvO997+Odd94x7y+2yR6Dkb4U6UeRSCTFgBQpBcBEfhSDYveldHd309/fj8ViYenSpTl5jsOJx3/ppZdYs2YNr776KpWVlaxcuZLu7m7Wr1/Pyy+/DBSfadYgNR4/dUpM+lEkEkkhI0VKAWBcUMerpEDxixSj1bNw4UJcLldOnmO6Y8h3330369evp7Ozk6OPPppNmzbxwgsvsHbtWrxeL+eeey5PPfVU0Y0fG6xatQqXy8Xg4CA7duyQlRSJRFIUSJGSZ4aHh03fw2QrKW+++WZR7oyc61YPJN/DAwcOjNrIMhPxeJwvfOELfO5znyMajfL3f//3vPTSSyxcuJCKigr+8pe/cO655zI8PMwFF1zAtm3bgOKrpKiqahpkH330Ubq7u1FVdcKfOYlEIsknUqTkmXfffRdd12loaDB3kh6LhQsXUlNTQyQSmbHdfrNJLid7DCoqKmhpaQEm50v53e9+x09+8hMUReHb3/42v/71ryktLTXvLykp4bHHHuOSSy4hEokQj8cpLy8vypRkw5fy05/+FBDVoGxn1UgkEkk2kSIlz0zGNGugKAqnnnoqUJwtn1xmpKQyFfOsMe1y9dVXc+ONN6IoyqhjHA4Hv/rVr7jyyisB4e/IdFyhY4gUYzxb+lEkEkmhI0VKnpmsadagmH0pM1FJgamZZw0j7EQiUVVV7rnnHh5//HHuvffew19kHjDMswbSjyKRSAodNd8LONKZrGnWoFhFSk9PD729vSiKwrJly3L6XFMxz05lpNhisXD++ecf1tryyfz586mtraWnpweQIkUikRQ+spKSR2KxmHmRnGwl5cQTT0RRFA4cOEBHR0cOV5ddjFZPS0sLbrc7p89lGES3bt1KIBAY87i+vj7zPSw2I+x0UBTFrKY4nc6ct90kEonkcJEiJY9s376dcDhMaWkpixYtmtRjPB6PeUEduattoaHrOt3d3bz88sv86le/AnLf6gFoamqisbERTdPG9aUYArGlpeWISV01fCnHHXdcUSXmSiSSIxMpUvKIcQE99thjsVgm/09RqC2f7u5uHn74Ya666ipWr15NeXk59fX1rF27lp/85CcAY+5NlG2Masobb7wx5jHFmh57OFx11VWcc8453HjjjfleikQikUyI9KTkkan6UQxOOeUU7r777ryLFF3Xefrpp3niiSd46qmnMlYtFEVhzpw5LF68mBUrVnDNNdfMyNrWrFnDH//4RzNZNRNHokhpampi48aN+V6GRCKRTAopUvKIcVGfaqCWUUl5/fXXicViqGp+/hkfeOABcyzX4Nhjj+Xcc8/lfe97H0uWLKGlpSUvWRyykiKRSCTFjxQpeULX9WlXUpYtW0ZZWRmDg4Ns2bIlb6mhTz/9NABnnHEGn//85znrrLOor6/Py1pGYoiUbdu24ff70wLaADRNK9qIe4lEIjlSkJ6UPHHw4EEGBgZQVXXKUxYWi8Wc0shny8eoUlx//fV8/OMfLxiBAtDY2EhTU9OY5tkDBw7g9/ux2+0sWbJk5hcokUgkkgmRIiVPvPjii4Awkjocjik/Pt/m2UAgYIazHX/88XlZw0QYiaqZWj5Gq2f58uVyykUikUgKFClS8sSzzz4LwPr166f1eEOkvPzyy9la0pR499130TSN+vp6mpqa8rKGiTBaPpnMs9KPIpFIJIWPFCl54plnngFg3bp103q80e7ZuXMnfX192VrWpDGqEyeccELB7mMzmUqKFCkSiURSuEiRkgc6OjrYuXMniqLwvve9b1rnqK6uNr0U+Qh1My78hdrqgWQlZfv27QwNDaXdJ0WKRCKRFD5SpOQBo9Vz7LHHUllZOe3znHbaaQC88MILWVnXVHjzzTeBpBAoROrr65kzZw66rqeZZ8PhMDt27ACkSJFIJJJCRoqUPHC4fhSDM844A4DnnnvucJc0JUKhkLkXTyGLFEi2fFJ9Kdu3bycej1NRUUFzc3O+liaRSCSSCZAiJQ8crh/FwGgVvfbaawSDwcNd1qR59913icVi1NTUMGfOnBl73umQyTyb2uopVD+NRCKRSKRImXG6urrYvn07iqKYlZDpsmjRIhobG4lGo7z22mtZWuHEpLZ6Cv0in8k8K/0oEolEUhxIkTLDGK2eY445hqqqqsM6V6rxdiZbPqmTPYWOscYdO3YwODgISJEikUgkxYIUKTNMtlo9BkY15vnnn8/K+SZDMUz2GNTW1jJv3jwguaGjFCkSiURSHEiRMsNkyzRrYFRSXnrpJWKxWFbOOR7hcNjc86YYKimQ7ksZGBjg0KFDAKxatSqfy5JIJBLJBEiRMoN0d3ebUfLTzUcZyapVq6ioqGB4eNisFOSSLVu2EI1GqaqqYv78+Tl/vmyQ6ksxBNa8efMoLy/P57IkEolEMgFSpMwghm/k6KOPpqamJivntFgsnH766WnnzyWprZ5CN80apFZSZKtHIpFIigcpUmYQw4+SrVaPwUz6UoohxG0kxlp37dplBt9JkSKRSCSFjxQpM4jhR8mWadbAaB09//zzaJqW1XOPpJgmewxqamrM1tSGDRsAKVIkEomkGJAiZYbo7e01/RCHm48ykuOPPx63201/fz/btm077PP9+Mc/5j//8z9HCZ5IJMK7775rPmcxYfhSQqEQIEWKRCKRFANSpMwQhl9k5cqV1NbWZvXcdrudU045Je15psvzzz/Pl770Jb7xjW/wjW98I+2+rVu3EolEqKioYOHChYf1PDNNauVHVVWWLl2ax9VIJBKJZDJIkTJD5MqPYpANX4qmaVx77bXm99/+9rd56KGHzO+L0TRrYFRSAJYtW4bdbs/jaiQSiUQyGaRImSFy5UcxSE2e1XV9Wue47777ePPNNykrK+Nzn/scAFdddRWvv/46UFwhbiNJXbNs9UgkEklxIEXKDNDX12d6OXIlUk455RRUVaWtrY39+/dP+fFDQ0PceOONAHzjG9/grrvu4oILLiAUCvGhD32Itra2opzsMaiurqalpQWQIkUikUiKBSlSpkl3dzf33nsvfr9/wmMNn8iKFSuoq6vLyXrcbrfZ0piOL+WWW26hq6uLxYsXc80112C1WnnooYdYsWIFHR0dfPjDH+add94BilOkAFx++eXY7XYuuuiifC9FIpFIJJNAipRpcvPNN/OZz3yGD37wgwSDwXGP3bhxI5C7KorBdH0pe/fu5Yc//CEAP/jBD0y/RllZGY899hjV1dVs2rSJUCiEx+Nh0aJF2V34DPGf//mfhEIhGYcvkUgkRYIUKdPEaN88//zzfOQjHyEajWY87mc/+xk/+clPADjvvPNyuqbp7oj8z//8z0QiEc4555xRVYaFCxfy29/+FlVVAeHtsFiK88dGUZSiM/xKJBLJkUxxXm0KgN27dwPiwvenP/2JK664YlSuyJ133snVV1+Nrutcc801XHzxxTld0/9v7/6DoqzzOIC/l9+QyyoQSyuGnnpnGWXSDQpNUiLBnWeNM1bScVrm3ZlyOeY5NjFT6SCnM2mW10XdHHd5Htp52Y+LSDtXTUUDXOygCSGtTFnFH7BEsIh87o9mn3MFhV2e59ldfb9m9o99nu/ufj4fn2U/fr/Ps5ueng6DwYCGhgbY7fYBPWbXrl14++23ERQUhHXr1vX5IZ6RkYHi4mKEh4dj1qxZaodNRETUJzYpXnA4HDh16hQAoLS0FCEhISgtLUV+fr5yZc2LL76I/Px8AMDSpUuxfv16zf8XP2zYMOWk0IEs+Vy8eFG55Pg3v/nNVZdBHn/8cbS1tWHhwoWqxEpERNSfEF8HEIhcsyg33ngjHn74YYgIcnNz8eqrr2LYsGGIjIxEQUEBAODZZ5/FypUrdVtmuOeee/DZZ59hz549/c56bNq0CYcPH8bQoUOxYsWKfp87NDRUrTCJiIj6xSbFC64mZezYsQCARx55BC0tLViwYAEKCwuVcStXrlSaFb3ce++92LBhA7Zv397v2LfeegsAsGTJEtV+lZmIiEgtXO7xQkNDAwBgzJgxyrbf/va3WLVqlXJ/zZo1ujcoADB16lSEhITgyJEjOHr06BXHOZ1OWK1WAND8XBkiIiJvsEnxgqtJcc2kuCxfvhybN2/G+++/j9///ve+CA0mkwnp6ekAgA8//PCK4/bt24fvv/8eZrOZX25GRER+iU2KFy5f7nExGAx4+OGHMX36dF+EpcjJyQFw9Sblo48+AgBkZWUF7CXFRER0beOnkxf6Wu7xJ64mZefOnejs7OxzjKtJ0fq7W4iIiLzFJsVDDocDp0+fBtB7JsVfJCcnY/jw4ejo6FB+2PBSdrtd+Yr7adOm6R0eERHRgLBJ8ZBrqSc+Ph7R0dE+jqZvBoPhqks+rit/Jk6cqNlvCREREQ0WmxQP+ftSj4urSSkrK+u1j0s9REQUCNikeOhKV/b4m8zMTISEhKChoQFffvmlsr2np0eZSWGTQkRE/oxNioeudGWPv4mOjsbdd98NwH3Jx2az4cyZMxgyZAgmT57sq/CIiIj6xSbFQ4Gy3AP0veTjWuq57777EBYW5pO4iIiIBoJNiocCZbkHAH72s58BAKxWKzo6OgDwfBQiIgocbFI80NraiubmZgCBMZMyfvx4JCYmorOzE7t374bD4cD+/fsBsEkhIiL/xybFA4Fw+fGlLr0UuaysDFarFd3d3Rg9ejRGjx7t4+iIiIiujk2KBwJpqcfFteTz4YcfcqmHiIgCSoivAwgkgXJlz6WmTp2K0NBQNDY24syZMwDYpBARUWDgTIoHAunKHhej0ahcitzS0oKQkBDce++9Po6KiIiofx41KUVFRfjpT38Ko9GI+Ph4PPjgg6ivr3cbIyJ4/vnnYbFYEBkZiYyMDNTV1bmNcTqdyM/PR1xcHG644QbMmDED33777eCzGaSuri6UlJTglVde6XN/IC73AP9f8gGA9PR0GI1GH0ZDREQ0MB41Kbt378bChQtx4MAB7NixA93d3cjKykJ7e7syZs2aNVi7di02bNiAyspKJCQkYNq0aWhra1PGLF68GNu2bcPmzZuxd+9efPfdd5g+fTouXryoXmZe2LFjBx5//HEUFBSgtbW11/5AXO4B/v99KQCXeoiIKIDIIJw+fVoAyO7du0VEpKenRxISEuQPf/iDMqazs1NMJpO89tprIiLS0tIioaGhsnnzZmXMiRMnJCgoSMrLywf0uq2trQJAWltbBxN+LxcvXpRbb71VALjl4IobgAAQh8Oh6utqraenR8aNGyfBwcFSW1vr63CIiOg65enn96DOSXHNNsTExAAAjh07BrvdjqysLGVMeHg4pkyZonw/R3V1NS5cuOA2xmKx4LbbblPG+EpQUBCWLVsGAHjppZfQ2dmp7HMt9ZjN5oBbLjEYDNi+fTsOHjyI8ePH+zocIiKiAfG6SRERLFmyBHfffTduu+02AIDdbgfwwwf5pcxms7LPbrcjLCwMw4YNu+KYyzmdTjgcDrebVmbPno3ExETY7XZs3LhR2R6oSz0uI0aMQEpKiq/DICIiGjCvm5RFixbhs88+Q2lpaa99BoPB7b6I9Np2uauNKSoqgslkUm4jRozwNux+hYWFYcmSJQB+OL/GdZ5MIF7ZQ0REFMi8alLy8/Px3nvvwWq1IjExUdmekJAAAL1mRE6fPq3MriQkJKCrqwvnz5+/4pjLPfPMM2htbVVux48f9ybsAZs/fz6GDRuGxsZGbNu2DUDgXtlDREQUqDxqUkQEixYtwttvv42dO3di1KhRbvtHjRqFhIQE7NixQ9nW1dWF3bt3Iy0tDQCQkpKC0NBQtzFNTU2ora1VxlwuPDwc0dHRbjctDRkyBAsXLgQArF69GiIS8Ms9REREgcajJmXhwoX4+9//jn/84x8wGo2w2+2w2+3KL+waDAYsXrwYq1atwrZt21BbW4u5c+ciKioKubm5AACTyYR58+bh6aefxn/+8x/YbDb88pe/RHJyMjIzM9XP0Ev5+fmIiIhAVVUVrFYrl3uIiIh05tHX4v/pT38CAGRkZLhtLykpwdy5cwEAy5YtQ0dHB5588kmcP38eqamp2L59u9sVMevWrUNISAgeeughdHR0YOrUqfjrX/+K4ODgwWWjovj4eMybNw9//OMf8eyzzypfKc8mhYiISB8GERFfB+Eph8MBk8mE1tZWTZd+jh07hrFjxyonzyYkJKCpqUmz1yMiIrqWefr5zd/uuYpRo0bhoYceUu5zFoWIiEg/bFL64fpyN4AnzRIREemJTUo/JkyYgOzsbABAcnKyj6MhIiK6fnh04uz1auPGjdiyZQsee+wxX4dCRER03eCJs0RERKQLnjhLRERE1wQ2KUREROSX2KQQERGRX2KTQkRERH6JTQoRERH5JTYpRERE5JfYpBAREZFfYpNCREREfolNChEREfklNilERETkl9ikEBERkV9ik0JERER+iU0KERER+aUQXwfgDdcPNzscDh9HQkRERAPl+tx2fY73JyCblLa2NgDAiBEjfBwJEREReaqtrQ0mk6nfcQYZaDvjR3p6enDy5EkYjUYYDAZVn9vhcGDEiBE4fvw4oqOjVX3uQMEasAYurANrALAGAGsAqFMDEUFbWxssFguCgvo/4yQgZ1KCgoKQmJio6WtER0dftweiC2vAGriwDqwBwBoArAEw+BoMZAbFhSfOEhERkV9ik0JERER+iU3KZcLDw/Hcc88hPDzc16H4DGvAGriwDqwBwBoArAHgmxoE5ImzREREdO3jTAoRERH5JTYpRERE5JfYpBAREZFfYpNCREREfumabFL27NmDX/ziF7BYLDAYDHjnnXfc9p86dQpz586FxWJBVFQUsrOz0dDQ4DYmIyMDBoPB7fbII4/0+XpOpxMTJkyAwWBATU2NRll5Rq8ajBw5steY5cuXa53egOh5HHzwwQdITU1FZGQk4uLiMHPmTC1TGzA9arBr165e+123yspKPdLsl17HwpEjR/DAAw8gLi4O0dHRSE9Ph9Vq1Tq9AdGrBocOHcK0adMwdOhQxMbG4te//jW+++47rdMbEDVqAAAVFRW47777cMMNN2Do0KHIyMhAR0eHsv/8+fPIy8uDyWSCyWRCXl4eWlpaNM5uYPSqQWFhIdLS0hAVFYWhQ4d6He812aS0t7fjjjvuwIYNG3rtExE8+OCDOHr0KN59913YbDYkJSUhMzMT7e3tbmPnz5+PpqYm5VZcXNzn6y1btgwWi0WTXLylZw1WrFjhNqagoECzvDyhVw3+9a9/IS8vD4899hgOHz6Mffv2ITc3V9PcBkqPGqSlpbnta2pqwhNPPIGRI0firrvu0jzHgdDrWPj5z3+O7u5u7Ny5E9XV1ZgwYQKmT58Ou92uaX4DoUcNTp48iczMTIwZMwYHDx5EeXk56urqMHfuXK3TGxA1alBRUYHs7GxkZWXh008/RWVlJRYtWuT2Fe+5ubmoqalBeXk5ysvLUVNTg7y8PF1y7I9eNejq6sKsWbOwYMGCwQUs1zgAsm3bNuV+fX29AJDa2lplW3d3t8TExMgbb7yhbJsyZYo89dRT/T5/WVmZjBs3Turq6gSA2Gw2FaNXh5Y1SEpKknXr1qkcsfq0qsGFCxdk+PDh8uc//1mLsFWl9XvBpaurS+Lj42XFihVqhK06rerQ3NwsAGTPnj3KNofDIQDk448/VjWHwdKqBsXFxRIfHy8XL15UttlsNgEgDQ0NquYwWN7WIDU1VQoKCq74vJ9//rkAkAMHDijbKioqBIB88cUX6iYxSFrV4FIlJSViMpm8jvGanEm5GqfTCQCIiIhQtgUHByMsLAx79+51G7tp0ybExcVh/PjxWLp0qfLryy6nTp3C/PnzsXHjRkRFRWkfvErUrAEArF69GrGxsZgwYQIKCwvR1dWlbQIqUKsGhw4dwokTJxAUFIQ777wTN910E3JyclBXV6dPIoOg9nHg8t577+HMmTN+87/n/qhVh9jYWNxyyy1488030d7eju7ubhQXF8NsNiMlJUWfZLykVg2cTifCwsLc/kcdGRkJAL2ex98MpAanT5/GwYMHER8fj7S0NJjNZkyZMsUtt4qKCphMJqSmpirbJk2aBJPJhP379+uUjXfUqoGqvG5vAgQu6xS7urokKSlJZs2aJefOnROn0ylFRUUCQLKyspRxr7/+uuzYsUP++9//SmlpqYwcOVIyMzOV/T09PZKdnS0rV64UEZFjx44FzEyKWjUQEVm7dq3s2rVLDh8+LG+88YbExcXJvHnz9EptwLSqQWlpqQCQm2++WbZu3SpVVVUye/ZsiY2NlbNnz+qZYr+0PA4ulZOTIzk5OVqmMiha1uHbb7+VlJQUMRgMEhwcLBaL5br6m1BbWyshISGyZs0acTqdcu7cOZk5c6YAkFWrVumZYr+8qYFrRiQmJkb+8pe/yKFDh2Tx4sUSFhYmR44cERGRwsJCGTt2bK/XGzt27HVTg0sNdiblumtSRESqqqrkjjvuEAASHBws999/f79/WKuqqgSAVFdXi4jI+vXrJS0tTbq7u0UksJoUEXVq0JetW7cKADlz5oxa4atCqxps2rRJAEhxcbEyprOzU+Li4uS1117TJBdv6XEcHD9+XIKCgmTr1q1qh68arerQ09MjM2bMkJycHNm7d69UV1fLggULZPjw4XLy5EktU/KYlsfCpk2bxGw2S3BwsISFhcnSpUvFbDbL6tWrtUrHK97UYN++fQJAnnnmGbfHJScny/Lly0Xkhyblxz/+ca/XGzNmjBQVFWmTjJe0qsGluNzjhZSUFNTU1KClpQVNTU0oLy/H2bNnMWrUqCs+ZuLEiQgNDVXOct65cycOHDiA8PBwhISEYMyYMQCAu+66C3PmzNElj8FQowZ9mTRpEgCgsbFR9ZjVpkYNbrrpJgDArbfeqowJDw/Hj370I3zzzTfaJqACtY+DkpISxMbGYsaMGVqGrTq1/ib8+9//xubNm5Geno6JEyfi1VdfRWRkJP72t7/plYrX1DoWcnNzYbfbceLECZw9exbPP/88mpubr/o8/qK/GvT1fgeAW265RXm/JyQk4NSpU72eu7m5GWazWeMMBk+NGqjpumxSXEwmE2688UY0NDSgqqoKDzzwwBXH1tXV4cKFC8o/0Msvv4zDhw+jpqYGNTU1KCsrAwBs2bIFhYWFusSvhsHUoC82mw0ArjrG3wymBikpKQgPD0d9fb0y5sKFC/jqq6+QlJSkeexqUeM4EBGUlJTgV7/6FUJDQ7UOWRODqcP3338PAG7nY7ju9/T0aBe0ytT6m2A2mzFkyBBs2bIFERERmDZtmpZhq+pKNRg5ciQsFovb+x344dJz1/t98uTJaG1txaeffqrsP3jwIFpbW5GWlqZfEoM0mBqoyus5GD/W1tYmNptNOat87dq1YrPZ5OuvvxYRkbfeekusVqt8+eWX8s4770hSUpLMnDlTeXxjY6O88MILUllZKceOHZMPPvhAxo0bJ3feeaeyvHM5f1vu0aMG+/fvV5736NGjsmXLFrFYLDJjxgyf5Hw5vY6Dp556SoYPHy4fffSRfPHFFzJv3jyJj4+Xc+fO6Z7z5fR8L3z88ccCQD7//HNdcxwIPerQ3NwssbGxMnPmTKmpqZH6+npZunSphIaGSk1NjU/yvpRex8Irr7wi1dXVUl9fLxs2bJDIyEhZv3697vn2ZbA1EBFZt26dREdHyz//+U9paGiQgoICiYiIkMbGRmVMdna23H777VJRUSEVFRWSnJws06dP1zXXK9GrBl9//bXYbDZ54YUXZMiQIcprtrW1eRTvNdmkWK1WAdDrNmfOHBH54XySxMRECQ0NlZtvvlkKCgrE6XQqj//mm2/knnvukZiYGAkLC5PRo0fL7373u6ueCOlvTYoeNaiurpbU1FQxmUwSEREhP/nJT+S5556T9vZ2vdPtk17HQVdXlzz99NMSHx8vRqNRMjMz3S7h8yU93wuzZ8+WtLQ0vVLziF51qKyslKysLImJiRGj0SiTJk2SsrIyPVO9Ir1qkJeXp4y5/fbb5c0339QzzasabA1cioqKJDExUaKiomTy5MnyySefuO0/e/asPProo2I0GsVoNMqjjz4q58+f1yHD/ulVgzlz5vT5Olar1aN4DSIi6s3LEBEREanjuj4nhYiIiPwXmxQiIiLyS2xSiIiIyC+xSSEiIiK/xCaFiIiI/BKbFCIiIvJLbFKIiIjIL7FJISIiIr/EJoWIiIj8EpsUIiIi8ktsUoiIiMgvsUkhIiIiv/Q/8ySHBK4AeTkAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot predictions\n",
+ "plt.plot(df[\"1954\":], color=\"black\")\n",
+ "for forecast in forecasts:\n",
+ " forecast.plot()\n",
+ "plt.legend([\"True values\"], loc=\"upper left\", fontsize=\"xx-large\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/timeseries/tsf-packages-skforecast.ipynb b/notebooks/timeseries/tsf-packages-skforecast.ipynb
new file mode 100644
index 0000000..9c0de1f
--- /dev/null
+++ b/notebooks/timeseries/tsf-packages-skforecast.ipynb
@@ -0,0 +1,411 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Package: SKForecast\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Libraries\n",
+ "# ==============================================================================\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.linear_model import Ridge\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "\n",
+ "from skforecast.datasets import fetch_dataset\n",
+ "from skforecast.preprocessing import RollingFeatures\n",
+ "from skforecast.direct import ForecasterDirect"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "h2o\n",
+ "---\n",
+ "Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health\n",
+ "system had between 1991 and 2008.\n",
+ "Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd\n",
+ "Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman\n",
+ "/fpp3package, http://OTexts.com/fpp3.\n",
+ "Shape of the dataset: (204, 2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download data\n",
+ "# ==============================================================================\n",
+ "data = fetch_dataset(\n",
+ " name=\"h2o\", raw=True, kwargs_read_csv={\"names\": [\"y\", \"datetime\"], \"header\": 0}\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Data preprocessing\n",
+ "# ==============================================================================\n",
+ "data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')\n",
+ "data = data.set_index('datetime')\n",
+ "data = data.asfreq('MS')\n",
+ "data = data['y']\n",
+ "data = data.sort_index()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Split train-test\n",
+ "# ==============================================================================\n",
+ "steps = 36\n",
+ "data_train = data[:-steps]\n",
+ "data_test = data[-steps:]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Plot\n",
+ "# ==============================================================================\n",
+ "fig, ax = plt.subplots(figsize=(6, 3))\n",
+ "data_train.plot(ax=ax, label='train')\n",
+ "data_test.plot(ax=ax, label='test')\n",
+ "ax.legend();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "
ForecasterDirect \n",
+ "
\n",
+ " General Information \n",
+ " \n",
+ " Regressor: Ridge \n",
+ " Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n",
+ " Window features: ['roll_mean_10'] \n",
+ " Window size: 15 \n",
+ " Maximum steps to predict: 36 \n",
+ " Exogenous included: False \n",
+ " Weight function included: False \n",
+ " Differentiation order: None \n",
+ " Creation date: 2025-01-13 12:24:49 \n",
+ " Last fit date: 2025-01-13 12:24:49 \n",
+ " Skforecast version: 0.14.0 \n",
+ " Python version: 3.12.8 \n",
+ " Forecaster id: None \n",
+ " \n",
+ " \n",
+ "
\n",
+ " Exogenous Variables \n",
+ " \n",
+ " \n",
+ "
\n",
+ " Data Transformations \n",
+ " \n",
+ " Transformer for y: None \n",
+ " Transformer for exog: None \n",
+ " \n",
+ " \n",
+ "
\n",
+ " Training Information \n",
+ " \n",
+ " Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
+ " Training index type: DatetimeIndex \n",
+ " Training index frequency: MS \n",
+ " \n",
+ " \n",
+ "
\n",
+ " Regressor Parameters \n",
+ " \n",
+ " {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.0001}\n",
+ " \n",
+ " \n",
+ "
\n",
+ " Fit Kwargs \n",
+ " \n",
+ " \n",
+ "
\n",
+ " 🛈 API Reference \n",
+ " \n",
+ " 🗎 User Guide \n",
+ "
\n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ "================ \n",
+ "ForecasterDirect \n",
+ "================ \n",
+ "Regressor: Ridge \n",
+ "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n",
+ "Window features: ['roll_mean_10'] \n",
+ "Window size: 15 \n",
+ "Maximum steps to predict: 36 \n",
+ "Exogenous included: False \n",
+ "Exogenous names: None \n",
+ "Transformer for y: None \n",
+ "Transformer for exog: None \n",
+ "Weight function included: False \n",
+ "Differentiation order: None \n",
+ "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
+ "Training index type: DatetimeIndex \n",
+ "Training index frequency: MS \n",
+ "Regressor parameters: \n",
+ " {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None,\n",
+ " 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.0001} \n",
+ "fit_kwargs: {} \n",
+ "Creation date: 2025-01-13 12:24:49 \n",
+ "Last fit date: 2025-01-13 12:24:49 \n",
+ "Skforecast version: 0.14.0 \n",
+ "Python version: 3.12.8 \n",
+ "Forecaster id: None "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create and fit forecaster\n",
+ "# ==============================================================================\n",
+ "forecaster = ForecasterDirect(\n",
+ " regressor = Ridge(),\n",
+ " steps = 36,\n",
+ " lags = 15,\n",
+ " window_features = RollingFeatures(stats=['mean'], window_sizes=10),\n",
+ " transformer_y = None,\n",
+ " n_jobs = 'auto'\n",
+ " )\n",
+ "\n",
+ "forecaster.fit(y=data_train)\n",
+ "forecaster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2005-07-01 0.952188\n",
+ "2005-11-01 1.180630\n",
+ "Name: pred, dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Predict\n",
+ "# ==============================================================================\n",
+ "# Predict only a subset of steps\n",
+ "predictions = forecaster.predict(steps=[1, 5])\n",
+ "display(predictions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2005-07-01 0.952188\n",
+ "2005-08-01 1.004327\n",
+ "2005-09-01 1.115190\n",
+ "Freq: MS, Name: pred, dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Predict all steps defined in the initialization.\n",
+ "predictions = forecaster.predict()\n",
+ "display(predictions.head(3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAEmCAYAAAAdjog2AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAnR5JREFUeJztnXd4HNXV/7+zfVVXvdiy5G65YmyKTTHVxoDpgR8kgEMLoQV44U1ICC0ESEINBEihJoQQOiS8YAMGDDYGNzC2cbdl2epl1bbO3N8fM3d2drVlqmTJ9/M8fkCr1d6dLXPPfM/3nMMRQggYDAaDwWAc1NgG+wkwGAwGg8EYfFhAwGAwGAwGgwUEDAaDwWAwWEDAYDAYDAYDLCBgMBgMBoMBFhAwGAwGg8EACwgYDAaDwWCABQQMBoPBYDAAOAb7CahBEATs378fubm54DhusJ8Og8FgMBhDBkIIuru7UVlZCZsttQ4wJAKC/fv3o6qqarCfBoPBYDAYQ5a9e/di5MiRKX8/JAKC3NxcAOLB5OXlDfKzYTAYDAZj6NDV1YWqqip5L03FkAgIaJogLy+PBQQMBoPBYOggU8qdmQoZDAaDwWCwgIDBYDAYDAYLCBgMBoPBYGCIeAjUQAhBNBoFz/OD/VQYA4DdbofD4WBlqAwGg2ESwyIgCIfDaGhoQF9f32A/FcYAkpWVhYqKCrhcrsF+KgwGgzHkGfIBgSAI2LVrF+x2OyorK+FyudhV4zCHEIJwOIyWlhbs2rUL48ePT9tsg8FgMBiZGfIBQTgchiAIqKqqQlZW1mA/HcYA4fV64XQ6sWfPHoTDYXg8nsF+SgwGg2E6ncFO3LXyLswum40fTf6RpWsNm8sqdoV48MHecwaDMZyJClHc+tmt+KjuIzy05iG0BlotXY+dURkMBoPBOAB5fN3j+LLhSwBicPDq1lctXY8FBAwGg8FgHGB8se8LPPvdswCAhaMXAgD+veXfiPARy9ZkAcEwoaamBo8++uhgPw0Gg8GIo76jDz94egWWbGwc7KcypFiyZwkA4Nzx5+K3R/0WJd4StAZa5dutgAUEg8hxxx2HG2+80ZTH+vrrr3HVVVeZ8lgMBoNhFv9cVYevd3fghZW7B/upDCm2tm8FAMytnAun3YnzJ54PAPjn5n9atiYLCA5gaLMlNZSUlLAqCwaDccDx5c42AEBDZ3CQn8nQgRd4bO/cDgCYUDABAHDehPPg4Bz4tvVb7O/Zb8m6wy4gIISgLxwdlH+EENXPc/Hixfj000/x2GOPgeM4cByH559/HhzH4YMPPsDs2bPhdruxfPly7NixA2eeeSbKysqQk5ODww47DB9++GHc4yWmDDiOw9/+9jecffbZyMrKwvjx4/HOO++Y9TIzGAxGRvrCUXxb7wcA7OsMaDpHHszs7d6LIB+Ex+5BVW4VAKDYW4zSrFIAQFugzZJ1h3wfgkQCER6T7/hgUNbedM8CZLnUvaSPPfYYtm7diqlTp+Kee+4BAGzcuBEA8L//+7948MEHMWbMGPh8PtTX1+PUU0/FvffeC4/HgxdeeAGLFi3Cli1bMGrUqJRr3H333fj973+PP/zhD3j88cfxwx/+EHv27EFhYaHxg2UwGIwMrNnTgaggBgGhqIC23jCKc9yD/KwOfLZ2iOmCcb5xsNvs8u357nzs790Pf9hvybrDTiEYKuTn58PlciErKwvl5eUoLy+H3S6+8ffccw9OPvlkjB07FkVFRZgxYwZ+8pOfYNq0aRg/fjzuvfdejBkzJuMV/+LFi3HhhRdi3LhxuO+++9Db24uvvvpqIA6PwWAwsGpne9zP+zsDg/RMhhY0IJhQOCHu9jx3HgDAH7ImIBh2CoHXacemexYM2tpmMHv27Life3t7cffdd+M///kP9u/fj2g0ikAggLq6urSPM336dPn/s7OzkZubi+bmZlOeI4PBYGSC+gco+zsDmD7SNzCLb3wL+Oge4LxngMqZA7OmScgBQUF8QJDvygfAAgLVcBynWrY/UMnOzo77+dZbb8UHH3yABx98EOPGjYPX68V5552HcDic9nGcTmfczxzHQRAE058vg8EYWvj7ImjvC2N0cXbmO+skEObxTX0nAGDaiHxs2OdHfccAKgTfvAy07wB2LBuYgKBtB+DOBXJKDT9UyoDALQUELGUw/HC5XKrGNS9fvhyLFy/G2WefjWnTpqG8vBy7d++2/gkyGIxhyaXPfYWTH/4UDX7rNui1dR2I8AQV+R7MGVsEANg/kJUGbTvE/5LM51jD9LUDT80FnjvV8EP1hHuwr2cfAGC8b3zc72hA0NVVD6x4HAh1G15PCQsIBpGamhqsWrUKu3fvRmtra8qr93HjxuGNN97A+vXr8c033+Ciiy5iV/oMBkM3O1p6EBUItjSau6EooemCI8cUYYTPC2AAPQR8FOjYJf6/MAABQdsOIBoE/PWGH4qWG5ZmlcLn8cX9Tk4Z7FkOLLkd+O51w+sp0RwQfPbZZ1i0aBEqKyvBcRzeeuuttPd/4403cPLJJ6OkpAR5eXmYM2cOPvhgcKoADjRuueUW2O12TJ48GSUlJSk9AY888ggKCgowd+5cLFq0CAsWLMChhx46wM+WwWAMBwSBoCck9jdp9Ft3xf75dnEQz5FjClFJAwILFYk4/HWAIPVwEdT1cjFET6Npa6VKFwAxhaCzr0W8IWhu6kBzsr23txczZszAj3/8Y5x77rkZ7//ZZ5/h5JNPxn333Qefz4fnnnsOixYtwqpVqzBz5tAyepjNhAkTsHLlyrjbFi9e3O9+NTU1+Pjjj+Nuu/baa+N+TkwhJKv37ezs1PU8GQzG8KEnHAU9PTRYFBC09YSwfm8nAODYCSVo7xX9TvsGykNA0wUAYEHv/9ZAK3b5d2FW2SzYOBvQPbABgZ+mQUw+Ns0BwcKFC7Fw4ULV90/sr3/ffffh7bffxrvvvnvQBwQMBoMx0HQHY5uWVQrBp1tbQAhQW5GHinwvspziVtPWG0YwwsNjUkVWSpQBgYkKwbaObXj2u2fx/u73ERWiOKz8MNwz9x6MpAEBCCAIgIHR7Ns6tgEAxheM7/c72UNAH3+wAwKjCIKA7u7utM1xQqEQQqGQ/HNXV9dAPDUGg8EY9nQHY5tIQ5c1AcGyLaKkfcKkEgBAnteBbJcdvWEe+zsDGFOSY8m6Mm3bY/9vkocgKkRx6fuXojss+i4cnANfN36Nc945B096xkMuFhcigE1/86WmviYAkDsUKpE9BHYaEKSvNNPKgJsKH3roIfT29uL8889PeZ/7778f+fn58r+qqv4vDIPBYDC0E68QmC/hR3kBn24R+52cMEksweM4LuYjGIhKg3bzFYK67jp0h7vhdXjxr9P+hXfOfgczSmYgEA3gnz07TVmPECK3JS709L9ollMGNhsEQAw+TGRAA4KXX34Zd911F1555RWUlqau1bztttvg9/vlf3v37h3AZ8lgMBjDlziFwILNeW1dJ7qCUfiynDikqkC+nQYE+zr7TF+zH3EKgTkBwc5OcdMfkz8GU4qnoCq3Cj+q/REAoINXBFYG1uuL9iHIi+9Jkaeo3+/zQuJrJ3Acejlu6KYMXnnlFVx++eV49dVXcdJJJ6W9r9vthtvN+l0zGAyG2XQFYhtWdyiK7mAEuR5nmr/Qxsffi+rAvAklsNs4+fYRBTQgsFghiIbiy/9MCgh2dIqqw1jfWPk22kq4U3mlbiBF0R4QWz17HV5kOftPr3XvWwOvICBgs8FvtyHX5JTBgAQEL7/8Mi677DK8/PLLOO200wZiSQaDwWAkQakQAEBTV9DUgGDZ9/HpAoqVvQi6w914bO1jaOhtQCHsmJ7jxQ+6e8VfmuQhSBYQ+Nw+AEAXp+gLYyAAaQumThcAAOq+RB4NCGw2jBzsgKCnpwfbt8fkmF27dmH9+vUoLCzEqFGjcNttt2Hfvn148cUXAYjBwCWXXILHHnsMRx55JBobRTem1+tFfn6+SYfBYDAYDDV0BeM3rAZ/EONKc0157PbeMLY0iaa7Y8eXxP2u0ucBYH5A0Bfpw3UfXYe1zWvl294qLsLhgRCqo1HzFAK/GBCM842Tb5P7AtjsIAA4wJCMTwOCZOkCEALsXo58h4AmB+C32cUGTCai2UOwevVqzJw5Uy4ZvPnmmzFz5kzccccdAICGhoa4Bjt//vOfEY1Gce2116KiokL+97Of/cykQ2AwGAyGWrqTBARmsX5vBwBgbEk2CrJdcb8b4RMl8H0mBgRhPoybPrkJa5vXIteZi18c/gsU20UlotkhlTaaEBBEhSh2+3cDED0EFKoQhG0cghxneD3ZUOhNohB88SjQvAn5UhMJv91mepWBZoXguOOOS9r0hvL888/H/fzJJ59oXYLBYDAYFpGYMjCzF8HaPZ0AgJmjCvr9rjzPY/p6r2x5BSv2r4DX4cWTJz2JQ0oPwQdrnkIrAujw5AHBFlMCgvrueoSFMLzgUPn6T4CL3wbsDmQ5suDgbIgSAX6bDV6eN7Ree1D0EPRTCHZ+Ik5uBJBfMgXo3g6/zfyAgM0yGESOO+443HjjjaY93uLFi3HWWWeZ9ngMBmP4QVMGeR7xetDMAUfrJIVg5ihfv995nOJ2E+HNm8Oyvnk9AOCq6VfhkNJDAAC+qLhJduZXiHcywUNA0wWjQ0HYdn8OtInNgziOQ75NDHTk3gAG1ktactjXDrx2GUAE4JAfIb9semw9k6sMWEDAYDAYBxFUIZhQJvoGzEoZ8ALBN3vF3vqHJlEIHNKGKRBxnoIZbOnYAgCYXDRZvKFtB3x9nQCAzmxpUzVBIZANhWHpsZo3y7/L58TAqpN2DzRDIfAqFILdy4G+NqCgBjjtQeR5Yr0IhnQfAkaMxYsX49NPP8Vjjz0GjuPAcRx2796NTZs24dRTT0VOTg7Kyspw8cUXo7W1Vf671157DdOmTYPX60VRURFOOukk9Pb24q677sILL7yAt99+W348lq5hMBhA/AZMPQTjpYDALAl/W3M3ekJRZLnscrChxGGPlSBGTJjW2hvpRV2X6FebWDAR6GkG/nEOfBGxy22HBQHBmIi0Abd8L//OJ720fhMCgqSmwoZvxf/WHAM4vbFuhTamEGSGECDcOzj/0ngrEnnssccwZ84cXHnllWhoaEBDQwOcTifmzZuHQw45BKtXr8b777+PpqYmuatjQ0MDLrzwQlx22WXYvHkzPvnkE5xzzjkghOCWW27B+eefj1NOOUV+vLlz51r1KjMYjCHCiu2tmH73Ery6WmzwRhWCiWVi+2CzFIJ1dZ0AgBkjfXH9BygOxW28CQrBto5tICAo9ZaiyJUP/PMCoGM3ClxibwC/IOXXTQgIdvrFpkTjwtIG3LxJ/l2+FNx02qmJ0UCVgZQyiFMIGqWAoGIGAEWpowUeggGfZWA5kT7gvsrBWfuX+wFXtqq75ufnw+VyISsrC+Xl5QCAO+64A4ceeijuu+8++X7PPvssqqqqsHXrVvT09CAajeKcc85BdXU1AGDatGnyfb1eL0KhkPx4DAbDOB9tbsIdb2/Ew+fPwBFjkpSDHeAs3dyEnlAUH3/fjB/MrpIVAnoV7w9E0BeOIstlbDtYVyf6Bw6t9iX9vUMx8CfCGw8Ivm8Xr9InFk4EGtYD+9cCrhz4jrwW+PZP6IhK3giDHgJe4LHLvwsAMJYqBM0xhSA/EgYcSoXAQGMiKWUQ5yGgCkG56B2Q2xczD8HwZs2aNVi2bBlycnLkf5MmTQIA7NixAzNmzMCJJ56IadOm4Qc/+AH++te/oqOjY5CfNYMxvPlgYyP2dQawfFtr5jsfgOxoERv0NEmDjLoC4iZS6fMi2yVe1ZqRNlgrKQQzq/r7BwDzFQIaEEwqnAT0iAOBUDIRvqKJAIBO2k7YYJ59X88+hPgQPDYnKqOS2tC+A4iIr5lPaiccMxXqUyQifARdYXGQn5wy6GkGehoBcEDZFADKeQb2odu6eMBwZolX6oO1tgEEQcCiRYvwu9/9rt/vKioqYLfbsXTpUqxYsQJLlizB448/jl/96ldYtWoVRo8ebWhtBoORnI4+8aQbNckIN9DsbOkBADR1hcALBL1h8Qo21+NAhc+L7c09aPAHDU0g9Aci2N4srnNIkgoDALDZONg40VQYNaHSYEu7aCicWDgRaGsQb8wuQYFHDEg6o9LMBIMpg91duwEA1S4f5KHNRBArDUpqkR/qAbJ96HR6AXTqXo+qA3bOLrdEltWBonGAW3x/8mhKhKUMVMBxqmX7wcblcoHnY/LSoYceitdffx01NTVwOJK/NRzH4aijjsJRRx2FO+64A9XV1XjzzTdx880393s8BoNhnI5e8aRrxiY20ATCvNwIqLk7GNeDINfjREW+Rw4IjLBxv1hdUFXoRXFO6jk0DpsNYV4wHFxFhSi2dYqlf5MKJwF1G8RfZBXHugeaFBC0BkRlqEwqL5Rp3gxkFSFfShH4XR5D6ynbFts4SW1o/Eb8b8V0+X7KlAHhQ+jv1tAPSxkMIjU1NVi1ahV2796N1tZWXHvttWhvb8eFF16Ir776Cjt37sSSJUtw2WWXged5rFq1Cvfddx9Wr16Nuro6vPHGG2hpaUFtba38eN9++y22bNmC1tZWRCLmykkMxsFIe58UEAxBhWBXa6/sdY7wBHXt4ibpdtjgcthQmituYjSdoJfWHvE1ovMKUkErDaIGPQR7uvYgxIfgdXhRlVslluUBQHYxCtyiQtDDBxEBDHsI5N4AXMJFWvNmoLsR+VKg6KcpEZ3thNX4B4BYQBDlOPSZNKeBwgKCQeSWW26B3W7H5MmTUVJSgnA4jC+++AI8z2PBggWYOnUqfvaznyE/Px82mw15eXn47LPPcOqpp2LChAm4/fbb8dBDD2HhwoUAgCuvvBITJ07E7NmzUVJSgi+++GKQj5DBGPp0yimDoacQ7JDSBZRtTeLPeV5xmFGW5CEIRY0dm18KmnxeV9r7UR+B0bJD2VBYMFG8mu5tEX+RXYxcV658he232wwrBHJvAEHa8D0+8b/Nm4GeJvikY/FzUpCjVyFIW2EQCwg8dg9cNvH98xOWMhg2TJgwAStXrux3+xtvvJH0/rW1tXj//fdTPl5JSQmWLFli2vNjMA52BIGgkyoEJjjjB5qdkqGQsl0KEHKlLoVOuzndA6nPoiA7/dRE2pzIqKkwzj8AAL2S4TO7BHabHXmuPHSGOtFhs6PYYEAgKwQ0iKk6HNi2BGjZDNR9KSsEnTAYECROOgx2Ae1iuaNSIeA4DvnOHLSEOuAnUZhZU8cUAgaDwUhBVzACuncNxZRBKoWAjjt2ShJ+xKBC0EEVgiyVCoHBACSuwgCIBQRZxeLzkGr1O01QCORmQbTCoOpw8b8du4EvHpX7EHRBEEMCvabCgKREuAuA9l3A3q/EX+RWAtnFcffNd0klo2ApAwaDwbCEKC/g6r+vwSNLtwIQx/kqfzfUoAFBcY4r7ue8BIXAaLBD0yoFWRkUAikgMKoQNPSKVQXVeWI/FvRRhUCU2+VKA5vNsIdAzu1HpM9C4RggOzba2XfoYgBAlAN6OU73enLgse1D4I+HAC+dK/5CkS6gyJUGxNzPJAsIGAwGQ2JTQxfe39iIpz/dAUKIfOULABELFQJCCNbVdSAQNu+KTxCInDKYM1a8wtzTJv6cmDIIG04ZqFQI5BSFsdcyLt9OSFzKAIgZ7zrsduMKAV0rLFUtuPOBcqkh3Pj58Jzye7jtYmWFEc+CHHg0bIz/xcSF/e6bTwMCTtDUITcTLCBgMBgMiX0dYoleKCrAH4igozdWqcNb6CFYvq0VZz+5Ajf/e71pj9nQFUQgwsNp5zC7WrxipjFNnpQycJiUMqAKgc9rvUIQ4kPojnQDAIq9xUDQH2s+JKUMaKWBqBDoDwiiQhSdoU4AQFFQSr948oAF9wMn3wOc9xxgd8RKHQ0MHJIDDz4KlE0T++ncugOYtbjffbOdYml9wIAikQxmKmQwGAwJWrMPAI1dQbnkELC2yuA7qY7//Y2N2Nveh6pCY03OgFhDolGFWf3KAalC4DItZSC+TgXZmRQCWnao/7WkG6eTsyHXng10iW2F4coFnGIZpU+qBOi024Cw/oCgM9QJAgIbZ4MvKL5H8OQDJROB0kny/fLd+Wjuaxa7BxpVCHgemHSa2E8nRU8dl0M8zjDHiQGI3ZytnCkEDAaDIVHfoQgI/EF5owPM6b+fCto6mBDgH6v2mPKYO6TOgWNLclCeH99UJ9FUaDxloNZDYDwAoY2CiiNhcDs/7ucfABSmQpvd0BU0DT58bh/sQbGtMGgXQQV0PTFloH09gQix8kZeEAOCNDjt4vsZ4jhTuxWygIDBYDAk9isUgqauINqVKQMLPQTKWQL//novghHjMjCdYTC2NAelefHdA6lCIOf0DaQMeIGgS+qAmNlDICkEBtQWOSCI8uLUQbkHQczoR1MGHQarDORSQLcPINJ74kkTEOhMUfhDfvDS4xfmjIh5FFIQpxCYOM+ABQQMBoMhEZcy8IfktsWA8VK5dCg7BXb0RfDfbxsMP+auVjEgGFOcjaJsd9xIYqoQmJEy6ApEZF9bvkoPgRG1pa23EQBQxPNA67Z+JYeA8Q1aXovm9SUTHzh70pk11PWvt8xxb7c4mro0GoVz0mliC/40uB1igBfhwBQCBoPBsIJED4GyysBKhYDOEjhliji6/O9fGk8b0JLJ0jwP7DYOJYoZA3KVgcN4XwD6GuW6HXLVQipoysDIa9nauRuAJK23bVdUGCgCAslD0GFwRLCc13dIuXxPXtLNOi4A0bHe7s4dAICaSDRjugAAXDZRiWEKAUMzNTU1ePTRR+WfOY7DW2+9ZegxzXgMBuNAojcUld3ygHjVPhBlh1FeQGtPCADwk3ljAADr93aCGCwnozI+7TlQpvARyFUG0gYdNpAyoP4BX4YuhYCiqsGIqbC7HoBCIehLEhDIjYnsAAigM0Uh9wZwSKpAEv8AkDCSWIeHYFez2KK4hheAUXMy3t9pF19rswMCVmVwENLQ0ICCguQzyxO566678NZbb2H9+vW6H4PBGAoo/QOAmNdXmu2sakzU0hOCQEQ5fXRxzFUe4QlcDv2z7LqDonRN0wNluUkUAhNSBnKFQQb/AAA5bWFEIWjrbQIAFPM80OcXVQIgqYegx2ZDBIBTiAK2zM8vEbl7ICf9rSc/6f3iTYXaUwa7/WKlxGjOq6pigCoEZpsKWUAwRAiHw3C5tH+gk1FeXn5APAaDcSBRLwUELrs4ojdxAqBVKQOaLijNdcPliIm2UUGAS6eIKwhEHnWc55UUgrz+CoHLhJSB3INARUAgByAGPAStoQ4AUkAAxFr8KjwEua5ccOBAQOC326R5BtrPn7JCAHEIVKqAIE9SDvT2PdjVI6oeNc5cVfd32cVjibAqg+HBcccdh+uuuw7XXXcdfD4fioqKcPvtt8syYU1NDe69914sXrwY+fn5uPLKKwEAK1aswLHHHguv14uqqirccMMN6O2NDTBpbm7GokWL4PV6MXr0aLz00kv91k6U++vr6/H//t//Q2FhIbKzszF79mysWrUKzz//PO6++25888034DgOHMfh+eefT/oYGzZswAknnACv14uioiJcddVV6OmJ9VFfvHgxzjrrLDz44IOoqKhAUVERrr322rgRzU8++STGjx8Pj8eDsrIynHfeeWa81AyGKmhToqkjxJN7W284PmVgkULQJAUE5fkeWcIX19O/afaGo/2aEJXl9VcIzEkZ0EmHKlIGNlplYEAhkJoSFdH3IySVAyrKDu02O/KlTbrDQG8AebARJKUmRcqAKgRdOgKCqBBFXVBMe9R4SjLcW3oaUmdEsQ+BsU6MSoadQkAIQSAayHxHC/A6vOAyuEOVvPDCC7j88suxatUqrF69GldddRWqq6vlzf8Pf/gDfv3rX+P2228HIG66CxYswG9+8xs888wzaGlpkYOK5557DoC48e7duxcff/wxXC4XbrjhBjQ3N6d8Dj09PZg3bx5GjBiBd955B+Xl5Vi7di0EQcAFF1yA7777Du+//z4+/PBDAEB+fv8Iua+vD6eccgqOPPJIfP3112hubsYVV1yB6667Tg4gAGDZsmWoqKjAsmXLsH37dlxwwQU45JBDcOWVV2L16tW44YYb8Pe//x1z585Fe3s7li9frvq1ZDCMQlMGUyrz8d3+LoSjApT7llUKQWNXLCCgfQEAYykKmi5w2W1wS6qDUiHIMTVloK4HAWBS2aEg+i2KbW4AodgvsuM3U5+7AJ0hv6EBR7HeANLrk6TkEADyXVKnQh1r7e/Zjwjh4RYEVORWqPqbmIcALGWQjkA0gCP+ecSgrL3qolXISlKSkoqqqio88sgj4DgOEydOxIYNG/DII4/IAcEJJ5yAW265Rb7/JZdcgosuugg33ngjAGD8+PH44x//iHnz5uGpp55CXV0d/u///g9ffvkljjhCfA2eeeYZ1NbWpnwO//znP9HS0oKvv/4ahYXi2M1x48bJv8/JyYHD4UibInjppZcQCATw4osvIjtbzIE+8cQTWLRoEX73u9+hrKwMAFBQUIAnnngCdrsdkyZNwmmnnYaPPvoIV155Jerq6pCdnY3TTz8dubm5qK6uxsyZM1W/lozB4cNNTVi1qw2/WFgbV9Y2FKEVBiMLvCjLc2Nve/yFhVWNiWgPgrI8DziOg8PGISoQQ+t1KdIF9CKFBgRZLrscCJiRMlA7xwCIKRJ6j60v0oeANGa4eOSR4hhiiiJlACibE+lrFkT62tEu9Two5KVNPoVCEN9KWFtAsLtrNwBgVDQKe8JUw1TEVxmwlMGw4Mgjj4xTFObMmYNt27aBl3Jjs2fPjrv/mjVr8PzzzyMnJ0f+t2DBAgiCgF27dmHz5s1wOBxxfzdp0iT4fL6Uz2H9+vWYOXOmHAzoYfPmzZgxY4YcDADAUUcdBUEQsGXLFvm2KVOmwG63yz9XVFTI6sXJJ5+M6upqjBkzBhdffDFeeukl9PX16X5OjIHhvvc246/Ld2HVzrYBXTcU5Q278BOhKYNKnxfleZ5+v7eqdTFVCCqkKgAznPhdgXhDISA2KLJxQFVB7KJF3qANpAw0KQSyqVCn61/aoL2CgKyxJ8T/MmEzjZUe6ksZdC//AyJSs6AiOukwhUJAc/ohmw1Eo+t/FzUUhiP9jiEVdD0xIGApg5R4HV6sumjVoK1tJsoNFgAEQcBPfvIT3HDDDf3uO2rUKHnz1ZK28HqNP2dCSMo1lbc7nc5+vxOkE0Nubi7Wrl2LTz75BEuWLMEdd9yBu+66C19//XXagIYxeBBCsN8vbqJ72vswd4DWDUZ4nPbH5fA47fjP9Udr+ryng6YMRhR44+R1t8OGUFSwLmWgUAgAwGmzIQjBkIzfnVByCAAjfF68/tO5KFFUG1ClwEhJZWdAg0IgBzv61mvtFDfPIp4HRs+L/cKdBzjiuzHSZkF68voA0OYXe0HkwAZ3iA42Sm4qpDl9AIgIEU32RRoQ1ESi/VSOVMgBAViVQVo4jtMk2w8mX375Zb+fx48fH3cVreTQQw/Fxo0b4yR9JbW1tYhGo1i9ejUOP/xwAMCWLVvQ2dmZ8jlMnz4df/vb39De3p5UJXC5XLJikYrJkyfjhRdeQG9vrxzEfPHFF7DZbJgwYULav1XicDhw0kkn4aSTTsKdd94Jn8+Hjz/+GOecc47qx2AMHF3BKIIRMaCr7xg4NeeTLc1yW95QVIDHmfz7ooUIL8hX6iMTFIKSXDfqOwLWpQyohyAvXiEw4iGgKQOlQgAAM0fFlwqbkjLopVUGmRUCu8HGRG0dYolhEWxA8QTA5hSH+yS5svZI/f4jHHQFBO3SlMNCXogZF1OkDOgGDQBhIawpIKApg5qIBoWApQyGH3v37sXNN9+MLVu24OWXX8bjjz+On/3sZynv//Of/xwrV67Etddei/Xr12Pbtm145513cP311wMAJk6ciFNOOQVXXnklVq1ahTVr1uCKK65IqwJceOGFKC8vx1lnnYUvvvgCO3fuxOuvv46VK1cCEKsddu3ahfXr16O1tRWhUKjfY/zwhz+Ex+PBpZdeiu+++w7Lli3D9ddfj4svvlj2D2TiP//5D/74xz9i/fr12LNnD1588UUIgoCJEyeq+nvGwNPSHSvLS8y3W8l/NzTK/2+W87/RH4RARBNecY47bhgQvaK2QiEghMgKQUW++D2lV+1GBg5RUyEtOUyFOSkD9X0InAaDHdqlsNjmEev1C8VGTsmurONkdR0egrawVM0QCQKddeKNKVIGTlssGArpTBmM0aEQRFinwuHDJZdcgkAggMMPPxzXXnstrr/+elx11VUp7z99+nR8+umn2LZtG4455hjMnDkTv/71r1FREXOmPvfcc6iqqsK8efNwzjnn4KqrrkJpaWnKx3S5XFiyZAlKS0tx6qmnYtq0aXjggQdkleLcc8/FKaecguOPPx4lJSV4+eWX+z1GVlYWPvjgA7S3t+Owww7DeeedhxNPPBFPPPGE6tfC5/PhjTfewAknnIDa2lo8/fTTePnllzFlyhTVj8EYWJq7YsHhQCkEwQiPjzY3yT8bqWdXQg2FlT4PbDYOpQqFoFQKCMwsO9zZ0oMN9X74AxGEpM2YDiAyo1a/K0BTBumv2p0O4ymD2KRD9aZCvemQ1p79AIBip7QxF0lqaXb/cj05r6+zNK+NF1WoQl4QhygBKRUCjuPg5sRzZljDFbs/5JcrGaojkbjSyXTEHxvrVDgscDqdePTRR/HUU0/1+93u3buT/s1hhx2GJUuWJP0dIDYM+s9//hN328UXXxz3c6IZq7q6Gq+99lrSx3O73Ul/l/gY06ZNw8cff5zyeSnLDynKdspHH300Pvnkk5R/zzjwaFIoBMqxwVbyyZZm9IVjV3sRk4x++ztjhkIA/VIGgLHSPCU7W3qw6PHPEYwKePj8GQBEQx5NfZhRmtcldylMf4p3KgyM6bxAqQhGeASkyYxaWhfrfS3b+kQTcpFXSm+WTAC2/BfI6X/RE68Q6EgZ8EEAdtGvQEnhIQAAF+dAiPAIadigabqgNBpFDiHaPQSs7JDBYBwIKBWC5u4QghHelHx+OpTpAsA8haBJOhaaKogLCHLE/+cFomvTVBKM8Ljun+vQKwU19/53s7RuLK1nxkTAmKkw/SZNpx0SIh6fw67t2PySEmG3cch1Z95OYsemM2Ug5fWLsqVU5OzLgb524Iir+93XaPOeFiECMSBQPNe0AYH42dcSEOzpEo2LNZEoYHcDbpWdCm0HSMrgs88+w6JFi1BZWal6wM2nn36KWbNmwePxYMyYMXj66af1PFcGg3EA0dwd7yfZ12mtSpCYLgDMCwjoxubziifaUkVXP6Ur36hKcN97m7GpoQv5Xic4DmiRXsPyvCTOfxPKDvMyjSNWTCfUc2zKLoVqAiWHwVkGbVFRxi/OrRJv8FUBZ/wRKJ3U7740r6/LQxANY4dkuByt6KaaKmUAAG6bGBBFNKxFOyGW8rxoKFQZbMaXHQ6iqbC3txczZsxQnR/etWsXTj31VBxzzDFYt24dfvnLX+KGG27A66+/rvnJDic++eSTOMmcwRhqJPb6tzpt8MX2VvSFeYzweeWrUbN6A9CAIF/aQD1Ou1xXrwwIjBgLtzR248WV4hXho//vECyaXin/TmliNMVDIFcZqEsZAPpMjFoqDIBYAKJX/WgTxM2vyDcm433jUwbarqJJ0I/tLvGYxoUVf5vCVAgALikgCBH1a3VKioeP54Esdf4BcS3x2HiOQzTa3+itF80pg4ULF2LhwoWq7//0009j1KhR8uZXW1uL1atX48EHH8S5556rdXkGg3GAQBUCGwcIBNjbbq2xcGuTWAs+u6YAn21tAWBeXp9uoPkKV/5ZM0fg060tOKTKJ98W4fWXOW5pEl3rs6sLcPzEUlQVZOE/3+6HQOLbCpvSmIhWGWQyFSpnJ+ioNNBSYQAopx1qX4tEI2iVnm5xUebqIyMpg6bOHeix2eAgBDXOPCDSJpY4Ovo3rJLXkxQJLSkDGhDkC4LqkkMgocwxGjQt9295lcHKlSsxf/78uNsWLFiA1atXxw22YTAYQwsqd08sF6+arFYI6tpFubi6KFtxpWmOQiC78hUS+52LpuDj/zkurgOfkav2/YrWyAAwrjQH/+/wUQCA2dWxHiBOg058AOgOqFMIbDZO3qS1rPd/Gxpw59vfyUGOWoUgVnao/di6/LvEzR1AUVHm/iY0ZRDS0YdgW5vo7ajmAWf5NPFGT15aSZ+mDMJEfcqgM9gJACjgBdWGQiA+IIhEg2nuqQ3LTYWNjY39atHLysoQjUbR2toaVzJHCYVCcfXuXV1dGdcxu40p48BnKL3nEV6QpeDhAk0ZzKr2YXNDF/ZaXHq4p018/OrCLDht+jeWZCSmDJQoZzQY2aQbEioZAODeM6fimuPGYqSinbDThGZBskKgYgKh086BF4imiYe/e/977G6Lvd9quhQCxsoOtzZ/AwCo5AV4XNkZ7m1MIdjeuQMAMA5OoHw6sPOTtP4BQBGAaPAQ6FUIHDYHbAAEAGHevIBgQM5QiWYTeiJPZUK5//77kZ+fL/+rqqpK+di0HS7re3/wQd/zxJbIBxq3v7UBs36zVG5AMxzoCUXl8r9Dpe53VisEckBQlCUrBFZ5CJRwHBe7sjWw3r5OqQGRIiCw2bi4YAAwPgAIUA43UhMQaFdb2nrijWxq5hgAxkoqN7V+BwCoFdRdxxppTLS9W/R6jLNlARViaSiy0s97oSmDMFEffNCAoECjhwCIVTWETawysFwhKC8vR2NjfKlQc3MzHA4HioqSvwC33XYbbr75Zvnnrq6ulEGB3W6Hz+eTh+RkZWWZ1tuccWBCCEFfXx+am5vh8/lStno+UFixow1dwSg27vfHmceGMs2SOpDtsmNCmVgqtc9ChSAU5eW5CWLKwHhpnpJkKQMldhuHCE8MKRIN0vMf4Uv/GTDazS8Y4eWr/UwpA3E9bVftEV5Ad0jc9K49fiyWfd+ChdPUje01Euxs7hTbFtfac1TdP67fv9aUQW8DAGC8Mx+YdDow+zJg4mnp16PthLWkDKipUKNCAABO2BAEj5CJCoHlAcGcOXPw7rvvxt22ZMkSzJ49O+WVndvthtvtTvq7ZNDRvDQoYBwc+Hy+tGOZDxQC0pV0T8i8qWSDDa3bL8vzoKpQvMJt7QmjLxxFlsv800p9RwCEiKN7i3NcsTy7CQEBIUSW2JMpBIA5A4eoh6AiP/1AMXnT1LkWbVvMcUCOiveCBiBqUwY0eAKAm06agFsX9C/5S4WRssPNPfUAgFqXusmscf3+NQQEvMBjZ0icqjjOUwQ4PcDpj2T8O9lUqDIgIITAH/IDAPI1egjE9Rzo5iOI8INYZdDT04Pt27fLP9M+94WFhRg1ahRuu+027Nu3Dy+++CIA4Oqrr8YTTzyBm2++GVdeeSVWrlyJZ555JmkLXL1wHIeKigqUlpYyo+JBgtPpPOCVAUqvFAgMREBACMFXu9oxoSwXBdlaRqxoo1nqUliS60a+14lcjwPdwSj2dQQwvkxdcxUt1EnpglGFogIoKwQmpAx6QlF5g0oVENgNXrUHwrzc4lfpIUiGfGw65wvQdEGO2wGbLbNaqjVl0KkwLDo0+mL0VlD0RfqwKyy2+J2cPULV3+j1EOzr2YcQ4eEWBIz0qpvFIq5HFQJ1x9Yd6QYvBQ+iQtC//XI6DoiUwerVq3H88cfLP1Np/9JLL8Xzzz+PhoYG1NXVyb8fPXo03nvvPdx0003405/+hMrKSvzxj3+0pOTQbrcPmU2CcfBAc+29AxAQfLWrHRf85UucMqUcT188y7J1aIUB7fk/siALmxu6UG9RQLCnjVYYiGqEw4RafQpVB1x2G9yO5Buc0R78NN2R7bLHjSROhsugP0LtHAOK1pRBZ5+23gNK7DoVgi0dW0AgtvgtzlGnCur1EGzr3AZAHDZk9/pU/53Tri1l4A+K6oBXIPAQojllIAcEwiC2Lj7uuOPSuruT9ayfN28e1q5dq3UpBsMSOvvCuPofa3DWISPksi+rCEdjMnNP0PqAgBrvNjVkrswxAq0wKJOa9lQVeC2tNNjTTg2Fors8VmVgXCHw98X8A6n8R0bK5QCgQTIUVvq8GT1ORv0R3RoqDADFPAOVioQ/QLsTaleg9DZd2tQmDheqDUdUm+/0DjfaLo1YHh8Op21VnIhbSlGEoO517Ah1AADyabCi0VTopGWObPzxwYsZJ8CDnZU72vDlzna8IHWNs5K+cOxE1BPSPoJVK1QubvAHIFgwrpfSLCsEYkBAZfAGiyoplBUGgGLTNOEYYxUGqa+PYrX6+r5/sn8gQ7oAMK5+qO1SSNE6btmIQuDQ+TpulvoCTA6FNQcEEY19CLZL5sVxkYi2gMAhpShUlkPHKgwEsemRhrUARd8DE6cdsoBgCPHA/32PQ+5Zit2tvYP9VIY09Aqqvdc8M04qehWT+XpC1vtbqPwd4Um/WQNmQgcbleaKKQOvS5QvgxFrgh45ZVAoKQTypmk8QI51KUy9wWmV1RPZr7LCAIipH3r7EHSr7FJI0RqAdMgBgXaFQO+0w03tVCFQHxBQD0GI40A05NnlgCCsLSBw0fVUKgSyoVCQSg41Vse5OKnMkQUEBycrd7SiJxTFur0dg/1UhjS0ZKq9N2x5c6M+hW+gdyAUAoUDfF+ndWWAdPQxVQhcJncOBMRhSZsbuiAIBHs7aMmhpBDo6K6XCn+GkkMgphDoPT61FQaAwuRn2EOgtl5f27H5FQONtKKn7DAYDWJn504AQK0GhYA2CiIch6iGTbOpVxygNSIa1aYQUBOj2pRBUDyPF/DaSw4BxfAmHZMcU8ECgiEEnTtOJTuGPnoUV9HdFhv9+hQKgdVrAbGrXcDaRkEtCQqBSzLjael2l4mLn1mF0/64HP/d0IBwVIDDxqFC6uNgqqkwTVMiitEpfTSVkqnCADAvZaDeQ6AxZRAwnjLQMstgW8c28IRHIS+gjOczNgii0A0aAMIqS/PCfBjdEbEdcxHP61IIwtCWMsgXBM3+AXE9ljI4qGEBgTl0KzbN9h7zDDnJ6FV6CIIDkDIIxNazahxxXzgqBzf9FQJzFJcoL2BXay8EAvzyjQ0AxBkAdLPUm4tORrouhRSjEwjpe1GpojGV0cZEsZSBOoVAawBCzz/pXi+z1gKAze2if6A2FAIHAF6VfQiUA4BUGu/ag2Jpo4MQ5AkkY7viuPWkwUchjQGBj9decggoGyExheCgJBAWTxD+AAsIjKDsB9DWa21A0KdIEwxIykARdOyzSCGgj5vlsstjiM1WCPyBCGg2hwYftMIAMD5GV4maMj09A4AohJC4KoNMUFk9rFchkPsEqNuwtaYMYgqBdg+Bntfx8PLD8YtpP8W53T0AZ1d91W7jbHCIIQRCKgcAtQXbAACFPC/+pcen+nm67WJAoFUh8Ak8kFOqeh2KXFapsS1zOizvVMgwD2rY6uizdhMbTASBYOP+LkyqyLVsGJBSum+3OiCIKE2FA5AyiPMQWBMQbNgnmqEmV+TJJXT0vQqZFBAk+4xT/wBgctmhKoVA/3r+QERW99S0rqbDjfQeW2ywkbYqg4HwEOh5HWvya1BTeQzQFwCySzWZ71ywIQoeEZWmwvaAqBAU0efn0aMQqIOaCn06PQRMITiIIYQM+5RBlBdw/cvrsOiJz/GXz3Zatk53UBkQWFtpoDQVDkRAoDy2/RYFBN/Wiyey6SN98m1UITDLVNjeK37Gq4uycEiVuM7E8ljDI71u9WSoCQhkU6GO9WhgVpzjgseZuXGa0fHH3UFtCoFWtcWYh0DnsfWJV+7aBwBJgapKDwFNGRTyPODMBuzqj9FNAwKV8QrtQ1AgCGKgoxGmEBzERHgiG5o6h2HKQBAI/ve1b/HfDeJQkXoLB+Uoc/lWpwziyw6jIIRYOnwrTiHoCFiy3jf1nQCAGVUx6dbslAFVboqyXfjrJbPx0eZmnDmzUv69w8SqBjWjgulVtBYzHIWmC9RUGAD62/tSqI9EfadCjSkDI30I9AZyRgICot54F0sZCJr7AriklEEEAAQBsKW/3qadCvN5vSkDt7SeeQEBUwiGCAGF9OwfhimDRz7cijfW7ZN/DketKweMUwgsNhUqFQJeIKZJ6snghfiqid4wb7rfJMIL2LRf7IIYpxAY3MQSoSmDwmwXinLcOP+wKrgdsavrWMrAzLLD1NdHDrnsUPt6tAdBhcpJl0aDne5g5uNRoqVklBeIom+Djj4Eess3+8Qrd7UVBhSt7X1jKQNtFQYA4HKKAZ+azoiEEEOTDgHAJTVCCmWYndAb6cXS3UtVPSYLCIYIyoYvw1EheP87cUT25AoxZ6e2BEoPA2oqTGjU021h+2Jla2Qqf5tderilsRuhqIA8jwM1ipy+rBCYljIQ35eCFMY1h8FafSXqUgZUIVAfEBBC8Pqaejz2odgbf1RhVoa/EHEZbJNMFQ/1KQP1wU6Xwuypq8pAx+sIQLdC4NY4AEhOGQjaAwK3QwwI1AxTCkQDcpAi9iHQrxBkGqZU312PX6/4tarHZCmDIUJAIT37AxHwApHzmsOB1h4xxze7pgCbGrp0T3pTg3LjtL7KIP7E0BuKoiRX/WhvLdArN4/ThpqiLHxT78e+zgCmjtB2YkuH0j+gTEXItewmpwwKc1IFBOYpBGr6EOgxw93+1nd4aZU46G18aQ4uO3q0qr/TM/44GOHxyNKt2NvRJwe8assOtZgK6cVItssuB4Fa0P2+yQGBXoVAY0CgI2XgdooBX8iWOSCg6oCTEHgJ0Vd2KKco0r9v1KugBhYQDBGUKQNCRFlQT9nPgUiEF/qNhjWz450SQSDoCQ+cqVDpIQCsNRbKDWk8Towo8IoBgckKwbeSf2D6yPiTpUtjc5tMdNCAIMVnXDbeGVwvGOHlNI66ToXqN7Ll21oBAD89bixuOmmC6g3UoSP4+HBzE/6sMOL6spyqGxNpSRl00goDnece3f0j5JSBVg+BuMWFNHsIeE0VBgDgdKhPGdBN2sfz4Nx5gFNdOkmJS+XsBNoRUQ0sIBgiBBKk586+4RMQtEl5fLuNQ6l09WxVyqA3HIXy+2O1hyAwkAFBIGaOGyEFVmaXHn6TpMIAsMBUKG08BdnpFQK9w41e/qoOUYFgwWRx3r2NA3JcqU+HMVOh+vVogHbOzBGarqa1lgECQKPUCfGQKh8uPrIaM0f5VJftakkZGKkwENcSn5NAxODcplbl1GsqtIkKQURle18jHgJaZRBB5oCAGgr1+gcAwGmnZY7pPydU9VADCwiGCMGEjWU4+QhouqAo2yUbx8xsgaskcUNuk+YZWOX8V3YqBKwdgRxTCByxgMBEhSAQ5rG1SWzrqqwwAMxPGWRUCOgGrSNl0N4bxi/f3ABCgJHS65TndabdnGQznMorW0KI5jHEFKeOpkst0nfo0FEFOHfWSJ3rZT42v4EKAyAWfABipYHL6oCAKgQk8/mSEGIsZSCXHapPGfh0+gcAwC2ZGMMgomyc4hxG11IDMxUOEforBMOn0oCezEpy3XKu1iqFgJ6k3Y5YI52+sHllO4n0JXQnTAwQzKRLMaRnRIGYzzRTIVi/txO8QFCS60Z5XrzE6Ta7D0EmhUDjBq1kc0OXrBJ9sFE0s2YyyNGNTG0A0hfmZTVBbflf4lpaZPXWbqlMM4XnIh2aPARyUyJjKQNAY9pAb5WBPCI483e8K9yFqNTkp1BPlQHtC2DLPF1RThkYUAhcUooiwnFAmvVYymAYkixlMFxokcb0Fue44TR5Y0mEBgSleW40d4UQigpo7w0j223NV4EGAG6HDaGoYGmVQZdi7K2ZKYOuYAR//HAb/rFqDwDg0FG+foqK+QqB+PkuTBEQ2A2UHW5u6JL/f+kmcbJdpk1bq9GPqjUOGwePU9t1V8wfof7Y2iQvTEmOdsOqU4PRjyqT+XoVAkVtvqYSTr1VBjb1A4CofyAXNrgA7QqBcphSNIB070SsS6G+HgRALCAQqxoiAJJ/V7SkDJhCMEQIRuJPtMNJIaApg+IcN9wmbyyJyF3c3E4USZuNlZUG1ENAhwD1WuohiNWf04CgvTccV7Kqh7ve2Yi/fb4LwYiAmaN8+OWptf3uE+tUaNz1H4rycmonU8pAz3CjzQ3d8v/T9z6TQmDX2CpZ6efQmo5y6FDJ5O9Qrn6FQM16clMiHSWHQLxCoNqPEe4DolJgq3KwEYWmDMIk83dA9g8QaVvUqhDYYq99ptkJ8T0I9AUETqciIEgzvElLyoApBEOEfgrBcPIQSHJnSa5SIbCmMRHdaHI8DthswH5/0NJKA6oQlOZ6sLc9MGBVBtnuWBOfYIRX1TI3GYQQfLqlBQDw4A9m4NxDRyTd4JR9CIx6Mqg6YLdxKZvraDHCJaJUCCiZAgJ6Fa12E1P6ObSiZ7KinDLI1qMQ6Kky0BcQ2GwcbJxoKlRdRUHVAZsTcOemv28CLpv4PEMq+v3HehBIr7vGgMBhc4AjBITjEI6k77TaGewEoH+OAZDQ98CklAFTCIYI/UyFwyhlEFMIXLHyNatMhbRpi9uBQunk2WZhpQH1EJRJCoGlKQPFVandxskeIyOv5d72ANp6w3DZbVg0oyLlRq90tBv1fyibEqVcT2fZYYQXsL25B4A4TpmSqauf1n7/sW6B1g8AIoTIKYNiHT0unHqqDHR6CAAd8wwCipJDjYEmDQjUeAjkgCAqfUc1BgQcx8lpgnA0faqOrlWgc9IhoGhMxCFtQMBSBsOQRIVgOI1Aph4C0VRobj17It1yFzeHnDKwauIhIUTuVFiaK5rwLE0ZyENtHOA4Tg6ujLRLXlsnXl1MrsyLax2ciNuhMzechFjb4tSbqd6e+DtaehDmBeS6HTjn0JgbP9PGTaVutbMMtM4TiF9Lo18hEJVf86IUnot0aFMIjHkIAB3NiXT6BwDALQ0nUpMykHsQhHrFG3Ir09w7OS7pkDKlDOhaxVFeV1MiQGFiTJMyEIgg+xXUwAKCIQINCHIlCXI4jUBWeghcDnN74ifSrUgZWO0hCEUFWWKm3Qmt7UMQSxkA5rQTXicFBDNH+dLeL04hMKjuZGpbDOjv90/TBZMqcjF3bGyDyVhloNNUmKsrZaDtO0CrdHLdDl2pIU1lhwFjHgJAR3MinRUGAOC0qQ8I4noQ2JxATpnm9dyQ/B8ZAoLWgNi0qthA2aGTBjtpUgbd4W7wKo6dwgKCIQI1p9EBKcMxZVCS64bLbnEfAkkhyHE75ba4VqUMlOWMpQMQECTWvbtNaBa0bm8nALG+PR12G6fo5qd9vXBUwBfbWxGM8HGDjVKhd7gRNRTWVuRh5iif/BqpLTtUbyqMD8604NDoIWjr0Z8uADSmDAx2KgQUx6dW3bHZgcKxQEG15rWo0S+sYiJg3Ojj/BEZpxUmXU/6b4hPHRBE+Ihs9Cvmef0eAps07TCNQkCPKduRreoxmalwiECd4uX5Xmxt6hk2KQNl2+LiHDdCUV6+3Qq6FVdusZSBNaZCmh7wOG3yJj0wpkLxa23UjxGM8PJkw0wKAV0vIPCa1wuEeVz54mp8vr0V1x0/Tt58U/UgAJTDjbQGBOLx1FaIKZC5Y4uwbEsLRhakHzwUu6pV6yGgwZn1CkGrFNAW6+hBIK6nzo8hCEQ+7xQYSRloDRynnC3+00EsZZB5rbiAoKhK13oucAAIwtHU5xSaLrATgnzOodmrIK8lpQxCctlhf6ih0OfxqXpMFhAMEWjKoFJWCIZHykDZttjndaKjTzzhRniirbWpSuiGnOuJmQqt8hDQ9yzL5UCu1OdgYMoO41MGeoOrDfv8iEqNiGgZYzqcdg6BiDbPQl84iiteWI0VO8ST5LItzZhVLaoRqUoOAX39/oH4gAAAfnfedGyo9+OYcemv0rRetSsrPrTi1HgFrUy56SHm20m/XncoCvqU9JglKTE/hnUjzilOm7qJgIDCQyAIQL62bo8UNw0I0igEdJ0inoctu1SzUZIS7yFIHxAUeNIrfBQWEAwRaMqgXAoI/IGIJRvmQKNsW2yzcXLZISB2oXPb9JXLpSI+ILDWQ0A3/yyXXW58ZFXrYkEgsj+in4dAp0JA/QPJGhElw+WwA4hqCkDuemcjVuxoQ5bLjr4wj00NXfL7kj5loH22QHN3EK09Ydg4YGKZWL5WmuvBibWZB8to3cSUFR9aUa6l5jtOUwZ6uhQCyhLODFPzpO+Jx2nTXcYqrmdtabES2iwoU79/IOYhEFMGRhQCIMSnUQgCNCDQ7x8AYv6ICMeBRENI9imROyK6fKoek3kIhgj0apO2jBWItSVsA4WybTEQk7kBa04YXQoPgdVVBtRDkO1yIEeS8a1KGfQohjZRIxsNCEI6FYK1ezoBADMz+AcoejwLX+8WT1gP/WAGxpRkgxDgy53SlVralIF2v8L3kn+gpjgbXpe2DU3rekZMhQ57fFCciRY5ZaBPIXCpTBnsahXd96MK06dXMjGQCgG9io5kUAjCfBjdEfHzUcTzBhQC+p1LHRDEDIX6Sw6B2LEBQCRFmaPWlAELCIYI1EOQ73UiSzqZdQaGftqgtTte7jTTrZ6MHulEneN2yKbCvjBvuJtfMmhA4HXZkeOOBQQkw7hSPdB0gdsRu3oz6iFYt1eqMKjyqbq/1tw3ELu6HV+WgyPHFEl/L74+6TwETh1lh83SZ01N+qPfehpr542YCpVBsZoUhVkpg0wBOB1sNb5MW3OgRNSme7qCEQTCvKHvi5xnR/rHoP4BBwHyBKI7IHCBpl8yBwRFvP6SQyC+VXIokjwgoMflc/tUPSYLCIYIVCHwuOxyOdZwqDRoSTiZGXWrZ0LZhyBXMb/Aiiv3PqlLYbY7FhAIpH8bajOgEnWuYgMyMl/A3xdBU5f43kwdoc70pDVFEeEFWbEpzHZjzpj4OvO0HgId/f6pIS5TRUEy7LpNhXqqDBQDgFQcX5uisZce1LZK3iY1dBpfmqNrHXk9lcHVXe9sRO0d7+Mvn+3UvZbcvCdDQKD0D3AA4Bulbz2OBgSpL9biFAIDAQFNGQCpGyHJKQMWEAwvqIfA67TLJ7Th0L5Y2baYYmW3QqWHgOO42HRFC9bqDcVMhVkuu+wd6g6Z/77JJjaFq92Ih2BPuygPl+S6VQ9+ogGI2hQFDWg5TtykjxgTX2dekKYxkZ6gUa6h1+GQ11x2mOT9UL2WwjOgJmXQOkApg22SQjDBLIUgw7F1GQjgKHQkccaAIEAbBUnfzbwR+tbj6HcgdUAgr2UwIOA4Dk7psFKlDOQWySwgGF7Qq0qv0y6f0IZDpUFrkqsbq0Yg8wKRZXx6xW5l8CErBC47OI5DjotWGpifnkgmUbsNNCba0yb2YteSL5arGlS+ltS74ZNaLZfmejBOcfWZ1lSotZYdxjYYLe12CSFJFRu1cBynqTRvIFIGhBATFQJ1fQ/krogGAgK1CkFco6CsIsClzycRSxlkVgiKeAHILde1jrweR8+VyVMUcotklVUGLCAYItCUgdelDAiGgUKQYCoEqFvd/JSB0uGfk2C8syI9EfMQiGtZWWnQlUSiNqIQ1LWLAUG1loBAY9tpuSOhYuM/UlIJ3A4bvGmc7HpMhUZSBloUglBUkF8DPcONAPUDjvrCUflzZrTKIN37tq8zgL4wD4eNQ02xuiY3KddTWSFidNQyALgctMpAbUCgv8IAANyc+JlNqxAEFQqBTiVCXo8GIEqF4NtXgX9eAHTWsZTBcEWZMvANJw9Bd/+rG5dFMj6V6l0Om9yXX3biW5EyUCgEACytNIgpBIqUgc72vgBQRxWCIu0Kgdr3jXYkLIoLCIrk29KVOjoH2EOgxcRI0wU2Tqww0YPagIf28XA7bLLqpRU1KQOqDowuzo4z/upB7bHFRi3r74rosospg0xnyjijn05DIQC4pDLpsKBGIeCBPO3zEpQ4aVWDslXyl08CW98H+ce56JBKKfPd6nxArA/BEIAQEjMVOu1yH/GhXGXQF47CYbOlUAisuWqX/QOKE6eVw5TopMMsd4JCYEFAkMzEZiTYoR4CTSkDjQFIspkFJ04qwwmTSnFUxkZBGvvhI5Zi02cqVB+AKNMFevuEqE2JKE25ekdO07UEIl6125M8Z7P8A0DM/5FOISCEwB8wNmoZiHkIQhleGlkhiPK6DYUA4OJoQJA8BAlEA+iN9EprCUBuhe61xPVsAAEiys6Ikm8g0LYNoVxR7bA0ZfDkk09i9OjR8Hg8mDVrFpYvX572/i+99BJmzJiBrKwsVFRU4Mc//jHa2tr0LH1Qojyhe5y22JWmhX0IBIHgb8t3Ys0e9bO01fLdPj+m37UEU+58P65tMUU2p5mtECgqDChGm/ekg0q5tEzUym6FyTrjGTm2ve2iBFmtQSHQWtVAAwKlV8DrsuPZxYfh8qNHp/1bh6L/vtqyNH9CJ0ctODUM5DFiKKTQPHum17LNYNtiIL6qIVUwt61JVAjGGfQPAOrSIX1hXvYYGAkInLKpMD1xRj8DCoGbE9/zkJD8O07XcQsCcrJLAIf+9w1QVjUoAoKQGLx1OKQGZbAhy6Hue6w5IHjllVdw44034le/+hXWrVuHY445BgsXLkRdXV3S+3/++ee45JJLcPnll2Pjxo149dVX8fXXX+OKK67QuvRBS0AxJMfjtGvO1erh8+2tuPe/m3HhX77ER5ubTH3sL3e2ISoQ+QtfnOOOm56mti5aK/JgoySyutWmQkAsPwRiExfNhOb8lSdPeVCUxs9JKMpjv18MCEYVqs8Xx6YrqnvfknkI1OJUDJ5R2+DGL12565GgtZQd0vRNrtvARqZSITBqKFSuBaQOCLZKKQMzFAI1cyFo8Oayp/eSZMLtEHtOhFMpBIQArdvRGmgBIJkKTQgIUqUMlOZFzmC6AABcoN9xpUIgtufumHkRAMBHONXqkeaA4OGHH8bll1+OK664ArW1tXj00UdRVVWFp556Kun9v/zyS9TU1OCGG27A6NGjcfTRR+MnP/kJVq9erXXpgxaaLnDaOTjtNlOm2GWC5gzDvICf/mOtqUHB/k4x33X+7JF47seH4fWfzomTVs2+al+ysREPL90qb0DKE7WVr2VvOFZ2CIjdEQHzlR1/IIJPt4gntGPHx8qY9L6O9R0BECIqG1quPM3wEKglrlZftfNfv0lNyyyDLgODjShOlSbGxMZe+tZK3x2UEILtclMi4wqBmnQP9Q/keZ26UyEA4JIUAp7jwAtJqns2vgk8MQutXfUAjJsKnZKHIJNCUGSCoRCIeRZkE2M0BEjBQUdZLQCgMBoGVKpomgKCcDiMNWvWYP78+XG3z58/HytWrEj6N3PnzkV9fT3ee+89EELQ1NSE1157DaeddlrKdUKhELq6uuL+Hcwo/QOAtc54ym6pTWmO24EwL+Bn/1ofp1QYoUG6+pxckYfjJ5aiuij+KtSIGS4RQghue2MD/vjRNjz5yXYACQqBya9lhBfw95W7sb8zgD7FLAMAyJEUArNTBv+3oQFhXsDEslzUVsSu4PSaM6naMKowS9PJWG/KoEDHKF3lJqYmIAhGYs5/Q6ZCFZ+TbgODjShq+/3TORx6KwwAUf2g8Xiy78F+fxC9tMKgyFiFARCrMkh3bJ0m+AcAwOWMPd+kV+3Nm9HHcejjJLXScJWBNF0xhYdAOdjIqKEQiHkWIlQhkNIFANCRI/pwCqJhoHOvqsfTFBC0traC53mUlZXF3V5WVobGxsakfzN37ly89NJLuOCCC+ByuVBeXg6fz4fHH3885Tr3338/8vPz5X9VVfrfoOEAbavrdVrvjKfsbhMDgl8snASXw4aeUBTtJvU92O8XFYKKFC1kzdykW3pC8klzR4t4TEpToctArX4y/rmqDr9+eyNuefWbmIfAAlPhx983ya1k31i3DwBw9qEj4jZwvQpBnY4eBEBMbdFqKkzXbyAVyuY9ajZpKkHbbZycwtGCtpSB/i6FFLV9CGjwRoee6cWZJginhsKa4mz5M2WE2CyD1MfmlysMDAYEjtg5JmlvgFAX2qTUmlcQkGV3A9npDa3pcNuklAFJfvEUV95ohkKQaGIM+qVf5KAjKp7vfLwANG9S9Xi63t3EqwZCSMoriU2bNuGGG27AHXfcgTVr1uD999/Hrl27cPXVV6d8/Ntuuw1+v1/+t3evuuhmuBJU9CAAFLlhCwMC2phmXGkOPDQAManff0OnqBBU5icPCOjVmBkBz9bGnn635SbxEJgVXH38fTMAYOXONtR3iK8h3YCowmN0rZ0tPbjs+dU444nP8erqvfhqVzs4DjhjRvwVh95gR09TIj3rdRgICJROeDVeE3rFma9TgtbSCMnI6GMKfS3TyeqEEHxbL24AUyrVlZWlXC+NIrFVrjAwni4A4g2hqeg00FUybi2HGzZJLg9HkowkDvrRIp1PizkHcOiluscRA4BLaiecKmUQ1wDJhIDASQMQGuyEJDXdnYf9PfsBAGVRHmj6TtXjaUpyFRcXw26391MDmpub+6kGlPvvvx9HHXUUbr31VgDA9OnTkZ2djWOOOQb33nsvKir6l1243W643fpzYsONQDjWpRCwrpMfJRwV5M1sdHE23E47EIyasmmGo4JcKlXhS35VY6ZCQE9mUyrzsK25B+GoEJcyMNLvP5FghMeqXaIkSEgsl0w9BG5Z2TEWWO2RrgqDEQG3vvYtAODI0UWoTFBc9JpP5aZEGioMAGhuA00VJz0BAW07HeGJKue/0StOLa2SZVOhzqZEgLpufo1dQbT2hGC3cZhckad7LSB9b4DN0pTISeXG1qDYVTQmivWMMObCh80ONyEIcBxC0b7+vw/60Sp9T4rLZgALf29oObcUEIRJpoDAnJSBvJ6sEEgBgScPu7t2AwBqIhGgyQKFwOVyYdasWVi6dGnc7UuXLsXcuXOT/k1fXx9stvhl7FJEZsXUt+FIKg+BVQpBfUcfBCIGIKW5bsVGZny9pq4gCBGPIZWZzMxNeluzeDI7fmIpfn1aLXLcDswdG5MEzXwtv9rVnnRwUVaiQmBwuBE1kik5e2b/qw3a8VG7h0DqQaAxX6ylqqEvHJVfKz1VBoBCxlehEBgpOQRiVQ1qKhqMDDaiqDExUnVgfGmO5nHOiaRLGWxuEDcZo0FHbK3Mfgy5KZFBhQA2B5xUIUjW7z/YhVaqEHj1pwooLhstO0we9LfFNUAyw1RIqxqkgIB6CNy52O3fDQCoiUSB5o2qHk9zCHvzzTfj4osvxuzZszFnzhz85S9/QV1dnZwCuO2227Bv3z68+OKLAIBFixbhyiuvxFNPPYUFCxagoaEBN954Iw4//HBUVhqPkA4GAik8BFYFBNQ/UF0kmsrcJqYMGqh/IN+TUro101S4VaqfHl+WgzMPGYEfHlGdvKLBhLU+2yo6/U+qLcWyLS3y5pElmQnNUgioJ+K06RVw2jjs7QjgtOn9lTY9nxNCiK62xQDgdKhXCKh/wGW36crpA+ImHYSg6nNipEshoGxdrCVlYLzKIN2xbZACgukjjaULxPWSpwxCUR7bpYqj2kpzAgI1cyH8ihSPIWwOuOWUQQqFQAqcizxF/X+vEXnccgoPQVufeI4o4nnDTYkAwCWXOUqKhJQyCLpy0NArTomsiUSAwD5Vj6f5E3vBBRegra0N99xzDxoaGjB16lS89957qK6uBgA0NDTE9SRYvHgxuru78cQTT+B//ud/4PP5cMIJJ+B3v/ud1qUPSAJhHk47J0f0VhAMx3sIjAytUcPuVvGLQx3FtM2vGQoBrTCoSGOCMivgIYRga2N8h7XEznFmlh1+tk38sp81cwQiPMGnUoBA29dShcDo+GM67naEz4tfnlqb8n56XseW7hCCEQE2Dv1SEJnQEsh19IqbZmGGFsXpcGhoJ2w4INDSmMigGgGkv2KnfLtPDAimjfTpXie2XvIAZFtTD6ICQb7XiUqDxkWKmvfNTIXAJS0TjibxEIT8aHOYpxC4pYAgkiQgEIiAFjrHwOUDHMbT4k6buF5iymCP2wUSJMh15aIwvwrdgd2qHk9XCHvNNdfgmmuuSfq7559/vt9t119/Pa6//no9Sx3QNPqDOO7BZThqbDH+dulsQ/Wy6einEFhsKtwjKQR0iInbaV7KgPYgSGUoBJTthI2llBq7gugORWG3cRhTklz+NkuN2N8ZwNamHtg44OhxxQhHBTkgoO+bWQoBHXebqX5fj4eA+hMqfV7NjnItwRX1D+hNFwDK0jz1m7R+hSDW3lcQSL/AkhcIPtzchDlji2J9CIyUHWa4iiaEYEN9JwBghqkKQfxruUlKF9RW5Jp2flNTQWHGpEMAAGeDS1IIQskCgqAfrfniZ9CclIGkEKD/d7y5rxlhIQIHISjLMTblkOK2Uw8BVQjEC6DdUtA1Om80OBsHNO5W9XhsuJEBNjX4EYwI+Oj7ZvnkbwUD7SHY1UYVAlEyNmsjAxQKQQpDIWCeqZCmC2qKsmSVI9VaRl/L5ZI6MKPKB1+WC/OnlGNsSTaOGV8sbx40sDKqEKjtTqdH/dgllWbqqTdXc1VLae8Vj6Ew28BVtAYPQafBgCCuqiGJSvDfDQ34yd/X4NJnv5I3MyOmwkx59vqOADr6InDaOUwsN949MFXKIOYfMB50UBwqZhnEqgwMmgo5DvQRIokeAkGQPASSqdAUhYCaCvu/b3u69gAARkSjcOaZU0rvSlQkQqJqtJsT16/JrwHKp6t+PDbcyADdio5zDy3ZinkTSixRCeRJhy7xg2t27Xwi/RQChzlmOCCmEFSoUQgMbtKJ6YJ0axlVP77YLkqBtFNgjtuBpTfNi7uS9MipF5MUggzNaPS8jjta9Pes19Ifo11OGeiXTWXjnZoqA4NlbE5FZ8RkG9mWRnHjXFfXKd9m5OrWkUElo4bCSeV5KYNdLcgpg4T3btP+mEJgFmqaLlFFx2gfAgBwS8v0UwjCPQCIyaZC6iFIHRBUR6KmVBgAsZRBKCFlsJuIAXdNXg3gVh98MIXAAF2KgGDDPj8+2Ghuz39KqsZEVigEEV5AfYcYScc8BOalDKhCUJlOIdAx6z4ZW1VMaDMruKJB1GSF8aqfX8EkhaBNpUKg59hoQDA2RYolHVoCELkHgYEcsZp6dorRKgOHolIq2Xo00FViJGUQUz+Sv5bf7usEAEwzIV0AKPssxNYjhMQUApMMhYBy2mG6lIE5nQoBgD5CmE94j4J+CADapYCgyGuGqZAOU0odEIyKREwLCFxOUcWVuzDSlAEvXdTl1wDl01Q/HgsIDEBblFIJ7OGlWyCoHLSihUQPgbIPgdmlm/UdAfACgcdpQ6k0ktjtNOfKFlBWGaRWCMwKeNQMZDFrrX1Ss6URaYx4bhMUAkEgcpWB6oBAk0IgnkjGluhXCNQEcm0GBhtRnBpGEptlKgSSKwT0/VdWTOQYShmk9xDIFQYjzA0IwjxBIMyjNxTFvs4AuoJROO0cxpeapxA4M1RshKOCPAfEsIcAAP2WhKMJ5bpBP/w2G6KSqmtGlYHbLq4WAYGQoBLUdYlm+5pI1NAAJSUujw+AwjAZ6gIBsDssfj5G540Wg4+b1JUdsoDAADRlcOYhI+Cwcdja1CNPiTMTmjLw0CoDuY+DOoe1FmjJYU1Rdiz3bZJCEIzwcrmZOlOh/vUEITaQJV2HNTOmHQYjvCzjjyxIfVweE8yZ/kBE3pAyNfTRaioMRXm55HCsgZSBmvWMdCmkqBmSQzEaENgU/f6TXbXvlwKCu86YglyPA7UVeXG+A62kaxQExMx+U00KCOh6oQiPUx77DHPu/wgvrNgNQAwOzWhZLK+VwTBJ3yuOA3INqCwUF+gFVIJCEIr1IPC5fXDaTQg+FOOME1sl00ZBZioEbm8BACASDYkbQrALbXYbeoQQbJwNo/JGiS+kR93nhHkIDECn1o3weZDlsqMrGDUsBycjVR8CQNzInCaWPNKhRsoudbE+BPqObV9nAL2hqPw8s1z2tJPgYlea+oOduvY+9EolodQLkQyt/feTobw6TLfhUIUgaKCfAzUU5nudGU/SWhWCurY+8AJBjtshq0NakCs2opnfNyNdCimaJhAaDAgAcSML8wIiCRsZLxA0dYmbzVHjivHZrcdb2iioJxSVjYtau0mmgr53+zoDcuvqvy7fBcDcdAGQOZCjPQjyPE5DQRXFRWhA0F8hMNNQCMRSBgAQ4kPwSNMWo0IU9T3iRMWaSNSUHgQA4JTSHCEIoici1IVdTvEzXpldKZsO1cICAgPQlEGuxyl1hYtaktcPpqgyAMSTvQFfVj92tcYbCgHjUvdFf/0S9R0B3HTSeADpmxIByly0/o2TDvyZVV2QNmAyI2WwT/JcjCjwpj0uZflmuvkf6VBrKAS0Vxko/QN6npsuhcCAi9ypsjcAIcSUMjaHnUOYB/iEAKS1J4QIT2C3cSjNdZvSk8SRpoKCzgLJ8zhMuYIGYt85+v1XYlaHQkqmaYem9SCQcHH0e5AsIDDPPwAADrsLNkIgcFycQtDQ04CoEIVbEFDG80CWOevlZYkm5k6bDehpBkLdckBQk1+j/fmb8qwOUmjKINfjsLRZUKJCYLdxsNs48AIxfb3vpGYnypOAkT4EwQgvX3E8vHQrgMwNb4wqBBFewL++EvN1PzqyOu19zUhPqPEPALGAjhBxPT3ucLUlh4B2U6ER/wCgLf3SboKHQK2psC/My/K0kU1Grp9PCEDo+1+e5zGtQZkzzXeArqe1cVQ66GtJA4IjRhdidk0BPtrcjAVTzKmZl9fKUHboN7HCAADcNGWQOP442IVmE5sSAQDsTnl2gnK9Pd2iobAqGhXz9G5zPBnVeeL5bbfTCdLTDC7Yhd054rZek1ej+fGYh8AAsYDAaanzX/YQOGMbiBm570SivCDnJpXT04z0IaDmMUBs6gKk71IIGD+2pZua0NwdQnGOG/Mnpz+ZmTFKmioEmU7QboWyo3e9WIVB5o2Uvo68QFT14N8hmTBTNXHKhNrgShAIOsxIGci56PTr0Q3GaefkoFrXeorXUwn1D2T6XGshnfpBKxoyBaBaoJ8V2oeiuigLty6YhPdvPBZVGltYZyKTP4IqBEY6PSpxgn7H+ysE21zi52903mhT1oLNHmuEpEhRxJUcOrMBm/FSUQAYlTcKNgJ0221o69gGhLuxW1IIRudrPyYWEBigKxhrQGJmC9xEAlLuXpmXtKIXwY6WXgQjArJddoxJljLQ4SGgG5jSpZ2uwgAwftX+95Xil+/Cw6sy59lNCKzohjAijaGQrkWVeL0+AhpgFanIEyWmljIRSxnoVAhUfgf8gYgcHBYYSBmoVQiUhkIjfUJSddjbb8kVe2oPgRXr0e9cd0i8yBlZYG4QoMSeQSEwrSmRhJumDBIVgpAf37vEzbO2KHULcE0oZycoUgaxgCBimjoAiI2JRnDi67Sr+VsAwDbpmMbkj9H8eCwgMIAyZeAycBWdicQ+BIC5EwEpG/bFZqsra+iNVBm0STnvSRW5uGC22CDjsJrCtH9jRG3Z3tyNlTvbYOOACw8flfH+Zig79SpTBvGDovStpyVloPROZDo+QkgsZaCjwgBQH1zRSpaSXLch97pDZdmh0R4EsfWS5/XldtwWSPjJex5YlzKgpKuWMYozgxnUT3sQmKQQuEAvMOIVgmCgXc63TyyYaMpa4nRF6fEVjZBoyWF1JAp4zPVkjHaKj7erYwc6bTY0OsSUwaTCSZofiwUEBogzFVqwQVPkToWKgMAKRYL6BxJLmWIeAu3BTou0gRVlu/HAudPw9a9OwtHj0+fr1Ex6S0ZvKIqbXvkGAHBibZmqE6YZSgtNGag5iRo1aGoxFSq764X49Ou1dIfQE4rCxul3rqvtQ7CtifaH0Bd4UJwqyw6NlhxSYp0Rk6cMRqRptqUVl7xp9j+2mIfAxBRFgvfB7DSBkkyDojoNdpVMxEkVgoQywG2BJggch0K7F6VZpaasBZsDhdJ3rakv1qiOlhxWR6KmKgQAUOMRz6e7+hqwWVIHqnKrkOPS/v1iAYFOCCHoCdEhJg5LJHyKbCp0xd4uK9b7Tp6eFh/BGpl22KbYwDiOQ4mKcjY9swyivIAbXl6HDfv8KMx24fbT1EmARssOo7yAxi6a0818EvUY7FaoRSHgOE61QXO7lC4YVZh67kMm6FpRgaRt0EU7SBptdqOmBS4A+PvMMak5UswX2O+34IpdNjAmUQj86hQpLSQqNVYqBJmmHZo22EiCpgzk9r4S34c6AAATs8rNazlvs2NiWDznbW7bDEAMRBp6GwAA1VFzUwYAMDpnBABgV8SP793ihYIedQBgAYFuesO8nAfNiUsZWF92CJhvKuQFgo1S3/KplQkKgQGZm3oISlRsYBQ9x/bHj7fjo++b4XbY8LdLZ6Na5XAeo6mXpu4QeIHAaedU1e4bVQhogKXGVAgAbpXHZ7TCAIhXJNIFqttUdJBUtV6G9r6CQBCOCuYpBCly32rmc2heS+7pEH9svEDQ6LcgRaFIETrtHMpyzVMf+q+VPpDzm+whcHHidy4iRICPfgN8fC8A4HteDEwn5aSvRNKEzYnasPj8v2/fDHQ1oL67HgIRkG1zoYgXzA8IfKJXYLcN2CyZJCcXTdb1WKzsUCe0KZHdJjqXrTIVCgKRgwxlysDsqoadLT0IRHhkuewYk7ApmFFloEbipugpO1z2fTMA4I5Fk3HoqALNa+l9HWm6oDzf0292QTLkboUGFYIilQGWy2EDQioCAmmT1usfkNeSCPNCXACrZBtVCAymDDJdaV7x4mos39Yib9TGAwLpc6lYLxCOdd8084rdmeLYEnsemLde7L0b4fOq+izrJRZYpU8ZmKUQ0IAgFOkFVj8o3nj4VfheGgBU6xtnyjoAAJsDtSFJIWhaB/LwJGw65qcAgDHOPLEA0m3e5EgAqCkU1dD9DjuI1KiZKQQDTLeiwkCUZvXL6ukIKNzoniQBgVnrfbc/1n8gsTtYbJaB9rVaFR4CteiZQNjSLa4zTWMrV6NKy36VhkKKkfRLXziKPslPolYhUBvwGBlqRHEqBgClWq87GMF+6Qp3gkkpg2TmtL5wFMu2NCPCE7kds3EPQX9Fgg7rys7QfVMrqToVWtHzAIgP5qysMAAyd5j0mzjYCIgFBOFwn3wb37IF2+ziaztR5+aZFJsD4yMR2AnQzgfQYrdjVct6AMBsp2SmNlkhKCwYizyeB+E47HfqNxQCLCDQTZeiwgCwpi8AENtwsl12ZLmSpAxM8hBsqJfSBUk2VCNVBtQEV6zhaiZd29ZkEELQ1qs+t65EDqx0vo6xpkTqTqIxD4EOtUV6LV0OG3Lc6jafmNck/Xo7TUgZ2GxcRkMoTReU5rqRb/CEn65Wf0tjNwgRN5WFU8tRlO3CUeOMNZ+JmeFiG5mywsDM0eepNk25K6aJagQQnzKoKrTOPwDEyg6TKTu8QOTPuXkeAvG8GYjGAoI9DV8hwHHwCgKqi0yqMAAAmwMeQjBa+khudjnxZbgFAHCETQoETA4IuNwyjI7EJu+WcE7djZZYykAnskLgFj+0VjUmorndMSU5cSccuZOZwfUCYR4fbGzEBxsbASS/wjaUMpAVAvUpA63pF38gIqcXtKQmgPj3TU874XpF22I1GFEIWhV+DLXP06VCbekLR+XAxkhAQNeL8HzK9257kzn+ASC9qXBzg5iWmDYiH0/9aJbhtZTrKTdpuSmRyRu0M2PPA3Nz/MqUgdUKgTOFORMAPtvagu5QFPleJ0aZVOlQZfMAArAj2g0CgAPwfeNaAMD4cAR2b/oyaE1IDYcmRQRst9uwJDsLjSQMp82JmYK03ZpcdghXNmp4gm+kH2udPt0PxQICnXQnKARulVdiWtnZmlzKNUMhqGvrww/+vAJNXeJG43LYcPjo/l8OvY2JtIzqVaJVIaAbZZ7HodkhTydHAuIVi9OuLSCgG+lIlRuCEYVAS8khRU2gStWBwmyXoVbCgBSohvmU7x2tMBhnwKtASeX6B4DNUsdNM/vwJyuX22dBySGgCHZSlDiaaSgEYhcYgLUVBkD6aYd//1Js4PODWSNTelC0MtGWDVeUoNPGY6/DgVHRKL7v2AY4IBoAdZTnpUSamDgpwuM/HhveyxHP2zNKZiCrQ5oTYbJCAACjbV4A4us5ya1fCWMBgU6UbYsBa/oCALGTdSqjn971AmEeV/9jDZq6QqjI9+D82VU4b9bIpPXHemcZaBnVqySxfC2TwamlW3taInEtQN/kyH0dogxppULw2Ifb8PyKXbKRUE9wle5zYoZ/gJJJkdhqUoUBEPMsJNtYaEBQa2ZAkEYhSDfOWw+prqL3WdAECYgpEsBAeAiSpwz2tvdh2RbRHPzDDDNItOC0O1EbDuMbjxvfuF0YFY1iU6QTcLgxUbABNm3f+bTYxC21NhQCcp2ISkreERVHAI3/Ee9jRUDg8gEQyyhrs/WPVmYeAp3QlEEe9RBYVHZIT9aJ/eWNpCgIIfjVWxuwqaELRdkuvP7Tubjp5Akpm5HoTRnQvL6aUb1K1JavUbTU5qddS+Nr+f53jfLgJvWmQu0KwZvr6tHRF8F2aTNVaygE1FVsmFFySMkUgGyXFAKjTYmA/j3xae8DQSD4vlFcx9SAIIlCQBUPsxv5pOrmp9XEqnU9AKiyXCFIng7551d1IAQ4ZnwxRqcZWa4ZuxPTQuI5YoPbjV6OwxqP+B2aBZODHykgmNjbEXfzkRVHAiExSIXb5JQBgBpPifz/k3JrdD8OCwh0kpgysMJUSAhJafYykjL4aHMz3li7DzYOePyimSqG8sSuaglRXwqoR+IG4k9OatIGrTp6HVAcdhvoxZGW1/JPy7bj6n+sQVQgOKm2VHV3P60VG7xAZFn6sqNGY8GUMlw6t0b181STyjI6wyDZeskCEGWFgdGmREB8K+EVO1ox+c738a+v6lDfEUBPKAqX3aZ7UFPa9aTAwx+IyO2+k6XazFirn4fAgiZIQCxl4HbYVDUPMwJNGSj7OYSiPF75ei+AzBNKNWNzYLpUCvitx4UVXg+iHIdRkQhGu0zenCUPQb5AMEIy+mUJAqYUTASC1gUE1blVmBMI4LjePoyQGhXpgaUMdEIVgpwEhcDMgKC9Nwx/IAKOQ7+I2Ygi8a10Ejt75kjMHZs530RTBoSIJ3qXQ12eXb5y11ByCMSCHUDd69mqYQJg0vUcNgQjgur37tv6Tvzhgy0AgEvnVOPXp09WbfLTqhA0dgUR4UVvw69Oq+1XEpoJNYFqrAeBCSmDNN8DMysMAGWeXcCK7W0IRgQ8tHSr/J0cX5ajOQWUjsSr9i93tkEgonpn9gatPDZKbygqd/Ez3VQofa5GFJhbLZGMmPcjFhD848s6tPeGUZHvwYmTTGojTLE5ZIVgi8uFJdli8D6vLwAu12f6WpTacBj7nA7MDobgDPcCIVFNsiJlYM8tx18axWoGeH26H2fYKgSCQPDdPr/ulrSZoFPBqIfAaPlaMqiUW5nv7WewMRKA0A1Ufd479jHRkjZo06kQ2Gyc4gopsyLR2q3duKhEjRNfCc1PHzWuCHefOVVTPbhHo0JQp0hJaA0GgMyfE14g2NVqfsog2fduSyNNF5hzQlQqBLS7XUt3CH/8aBsAc9MFQP9yuS+2twIAjjZYzpgMV5KUwV7Jr5LrccjnHbOYXuVDcY4bp02rMPVxk5GYemnvDeOxD7cCAH524nhT+ysAAGx2jIjyKOR5RDlODgiO6wuY7/i3xd6X03t64RUEnN/VDQQ6LA0IkB1LGRhRIIZtQPDON/tx+uOf4+GlWy15/P5VBlLzCxMVgp1Uyk3iyDYSEMTaCatsbmNXBgTq12szkNvXcnx0gJIeU6G4ltTaVGUwt7ddlG315Dm1KgR7paY6enPUmZSk/Z0BhKICXHabKWaydOutr+sEAEwbaU6nNvmKXRDkUeQAsFUqbTQ7IEisavhcCgjUqGx614rEXUWLDvxDqnymrzfC58XXvzoR/zPfxJr8FNANXyDihdsjS7eiKxjF5Io8/ECaiGoqNgc4ANOD4nlC4Djk8gJmBkOAx9yugUqF4MS+AL7aU495gSDQ3QjQaYtmByEAkKNQVQwEHMM2IFi1qw1A7IRqNspJh4A1psKd0pXbmCQbj9bSPCVysyCVG3Xc2F4Nx9eqo20xxanBI2HEQwBor9iol67U9GygWhUCelWotyZb6TVp7gritjc2YJM0swKIDTUaXZytS4FIt14ia+tEo5WW1tLpUG6aXYFIv9+bWXIIxHsIGvwB7GzphY0D5owpMnUdoP/Ez/qOPjnHft3xJrbaVWB1qoCiHLW8qaELL60SA51fnz7ZlM9gP6RNelooNu3w6EAATsD8fL4tRalkZ13s/80sc6TklMX+30DAMWwDAtqYxIphQ0ASU6G8qZjXhyCdQmCk7LBVxxV1bMCR+uNr7dbWd1+JlomHdB29CgE9+ao1FdZrGHeciNbAqs4khSAcFfDmun14+as6PPXpDvn3ZvoHgNQNs/yBiOwhmDnKZ85atOyQF+TOoQUKb4LpAYFCxv9iu3jBMW2kzxQ/RL+1FMcGAI9/tB0RnuDoccU4woIAZCBRdkV8aVUdBALMn1yGOWMtOi45IAjJNx3bJ36HrVQI4ugUgx64clIHDUZgKYPUCAKRy4GsDgjyLKwykMvBkigERqoM5A1Uw0atZ56B3JRIR7Mbta8nIUSheOg3FapZi0Kv2qsMKARqUwY0INCtECiOjQaCdW298u/NLDkEUn8u1+/tBCAeh16vRyLKenaqEFx57BhwHDC+NMf0jVrZKjnmH7BmE5PVD4Fgd2svXltbDwC4ef4ES9YbSByKun/6uZg/pdy6BRUKgVcg8NjdOCYgVrtYHhA4pIuGDikgsMI/AAB5I4DcSqBwjCEFYlhWGezt6JOHwGi5otVCYsogVt5lTkAQjgryZpDYlAjQ7yEIhHn0ahyQA+ibZ9BmILefqSc+pSsYlV9z3aZCDa9lKMrLnR0HQiGgfgXDKYOoIE+R2yspHIC5JYcA5AqUxPdtnZwu8JmyDhB/xU49BMeOL8GRY4p0p4/SYVeM7aUBwVEW+AcApalQwBtr68ELBPMmlJiWbhlMlAoBvXCrrbBoowTkK/IcQvBc0Auc8Tzynz8X6G4wP59vTwhCSyYADd/EFAILSg4BAA4XcN3XAMcZarQ0LBUC2pQEsEYhIISkbl1s0np17X3gBYJslx1lef1Pbno9C/QqUcuAHEBfykCuMtCjEKh8Penx5LoduludaqkyoMNsvE67pu6LFC0KQV84Kh+fHjUCiE+90JK19t4weqUqmZ1mBwQplJ21kqHw0GrzNjTlFbtfMTL30FEFpjcKAmJBanNXEM3dIXAcMNOiDVppvNssnc/mTShJ9ydDBpuNk3t/8FK7cDP6UqReMHaem+Itw5SiKcAIab5FQY25a3EJW2qJOJpY9hBYpRAAgDsHcBlL/Q3PgKDB2oAgGBHk0iOrTIV0ZnziUCOK3pSBngE5gPaWu8EIL5dm6vEQqDUVGvUPAMqJgJmPLeb611evrUUhoOpAnsehW/5Wfk46+2Kmqr0dfejsC8vpFrMa+CT7HggCkRWCmVXmbaB00+wL8whKczbyTC7Hi19PfL/pBcfIAi+8LgvywYg33tFyTTPmPxwoKNMG40pzNXUy1YxSxqe59kWPAZe8DdQcY+5aHBe/XolUtdG1X/yvlQGBCQzLlMH3jTEXtZ4JfZmg6QKOA7KkKz6zGxN99L3Y03tWiisqvSZGvfl2rfMMqH/AZbfJPgstqGm5C+g/nvi1pLJDFccWMxTquwKVX0cVCoHRkkMgfoPuUAYE7QH0hsTnUJHvQbYGtSgdyapfdrT0oDsYhcdpwyQTpWG6abZLnzWOiyl2VkBTBttNVlWS4VRsmDR1mMxcPFRx2DlImUtr0wVA8oAguxgYc5x16wlR0Z+QS/s6SOcxK0oOTWRYBgRblCkDjRP61ECvfHPcDnnwjpkBQYQXsHRTEwBg4dTkZhs1Q2uSobfvv9Z5BrEKA5euK2m1x2dkjgHFpaHKIGYo1NeZTkvZoVFDIRD/ufQrSvP2tvfJAYKZG1vi90AQCFbtagcATB/pM7dzoLRp0uBT+X20ApqioMdmaUCQMHXT67SjIs/c7oSDidJHYHY1SD+Urv7sAUi70AAkpxzISmhpzRSCgSUQ5rFL4aK2RiGgFQYxedLMxkQrd7TBH4igOMeF2TXJe6Sn6xmfDj0VBuJ62kYgr9kjSsR6h5S4M5QddvSGketxoEXn8SjREswZVgg0NCYy2oMAiFcIqIeAPjb9nRlTDuX1FIHcNS+twXsbGuXfmW2Isyds0FamCwDAnrBJWxkQJNbjjynJtjTYGWiU3QgnV1odECg+F9nWmEDj16MBQSngTfjMW2UqNAld4fqTTz6J0aNHw+PxYNasWVi+fHna+4dCIfzqV79CdXU13G43xo4di2effVbXE87E1qZuKOfvWKIQyBUGsXjKzNbF//edeBKdP6U8ZaMOvYpErAeBxpSBRo/ER9+LCscJOvuSp1MIvtnbidm//RD/+9q3JikEWgIC2pTIeoWApgxGGgkIpGPr6A3HjZvd2x7Ajmap5NBEKZp+Lvd1BuKCgXyvE6dPN7ctbuJVdJ7X2oDAmeDeNjOQSoTjuLjjG07+AWCgFYIkKYOBWC+3HPAOc4XglVdewY033ognn3wSRx11FP785z9j4cKF2LRpE0aNGpX0b84//3w0NTXhmWeewbhx49Dc3IxoNGr4ySeDpgvGFGdjZ2uvJabCxAoDIH5TIYTo7vrFCwRLNoon0lOnpj6BajHCKZEnEGocOBTrQ5D5yrYrGMGqnaJMfGJtWYZ7Jydds6C31+8HLxC8sW4faqQpg1oDHCXaTIWiQqA3r69FITAzZdDUHYy7fW97n6y+mJoykL4HNE0wqTwXb117FNwOm+md8BJ73ud7rRU8HYkKgcWbtMNmQ0SaUmmlGjEY0ICgMt8DX5b+764q4gKCgVQIyoacQqD5G/Twww/j8ssvxxVXXAEAePTRR/HBBx/gqaeewv3339/v/u+//z4+/fRT7Ny5E4WFYrRUU1Nj7FmnYbNkKJxR5cPO1l6EeQGCQEyV2xJ7EACIc8mGeUGW2LXy1a52tPWG4cty4ogxydMFgP5GSHq6FALaFILlW1sRFQjGlGTrThm40qRglm9rkf9/tzT8x0jdudoKkWCEl18/qxUCQojhHgRA7H2jvRM4TpxaWdfeJwdAZm42tFMhNfrNqi7QXQ6aCUfCd9rqlIFyvXyvU1c5rRacdg7U9jHsAgLp/GV5ugAYRA9BWf/GRwe4QqApZRAOh7FmzRrMnz8/7vb58+djxYoVSf/mnXfewezZs/H73/8eI0aMwIQJE3DLLbcgEAgkvT8gphi6urri/qllu9QedYZigIpZzYIoyRQC5URAIz6CDyR14OTasrQGLKN9CDRXGch9CDKv99FmMV1wkk51AEjdmGh/ZwDbmnvAceLmRjFUdmhXN9yIpgty3Q7k65Sn6evIC0RuS5uMxq4gAhEeNs7YqNvE1AsN0AIRPm2fC724Ej6zqapkzCDx+2F1ykCpSIwtyba897/y+IZrysDydAEwCAqBFIDklAF2R3xQcIAHBJoUgtbWVvA8j7Ky+BN9WVkZGhsbk/7Nzp078fnnn8Pj8eDNN99Ea2srrrnmGrS3t6f0Edx///24++67tTw1mf2dYqChlPNCEcHUq5SuNCkDwFhAsHKH2CM9U+7daNmh1ivqWB+C9OvxAsGyLWLJpF7/gLheclMhVQcOqfKhMt+L/25oAGCOQpDpfaMd/ozMjFd+DoNRATkpgr7v9olB8PjSXN1qE4B+9d1luR70hqKyYjC2NHmfC704HQMXECRK+JabChUKwUBcsdPjs3FAdZH5jZYGE/q5NHsiZVJoQGB3DYxk75AC+FypQsxbAAT94v8f4GWHukyFiSeQdDlzQRDAcRxeeuklHH744Tj11FPx8MMP4/nnn0+pEtx2223w+/3yv71796p+bs3Sia5SMT/e7EqDDfWdAMSTK8Vm4zQPyUnE3xfBFqkh0WGjU6cLAH2NiZSlZ5qrDFT2IVhX14GOvgjyvU7MNrAZpDIVfrZNbBl7zPgSXH+iOPXN7bChxJBCEO9WT4XRCgNxrdhXLl0vgg37xBPI1BHGeq0nXrEXZDvjuh6avbG5FesV57gMpTsykWjyy7PYQ6A0+Q1ETwDavKeqMMuytMtg8dPjxuKsQypxvIGLBtXQgCC7JF5WtIp5/wsceglQPVf8WWksHE4eguLiYtjt9n5qQHNzcz/VgFJRUYERI0YgPz92YqutrQUhBPX19Rg/fny/v3G73XC7tZ/ge0NRuUdAWZ4HbocNfWHeVGNhoz+IT7eKV6mnz6iM+53LLpqA9FY2rN4jGrHGFGdn3LD1VBm09YrBkt3GaZa81fYhoCmP4yaW9DN9aSEW8MSc8bxA8LkUEMybUIxJ5Xl4dvFs2DjO0AlT7WtJJwPq9Q8AYuDostsQ5gUE06z3nRQQTBth7ASSqBDke13wOOxYLZWFmu2UV6536KgCS2X1RIVAbxpH9Xo2ZcrA+oCAvpbjhpl/AADOPGQEzjxkxMAsRiX8gUgXAMC088R/FKWx8ABPGWg6Y7tcLsyaNQtLly6Nu33p0qWYO3du0r856qijsH//fvT09Mi3bd26FTabDSNHjtTxlFPTLNWkZ7vsyHE7NDfTUcPra+shEODw0YX9DHN6nf+Ur3aLAcFhKXoPxK2l6HWeLhetpLU7NltAq8lSTR+CKC/grfVii85TpxkrMXMm2aS/re+EPxBBrseBGSN9AIATJpXhuInGrjLUvG8RXsB/vhWPzeiY1kzdCgkh+LZeCghGGlQIEgKCgixnXBmj2RubMu9tZboAGPiUgdJUaFarZzXrDacOhYNC2VTA7gaqjx6c9YdrQAAAN998M/72t7/h2WefxebNm3HTTTehrq4OV199NQBR7r/kkkvk+1900UUoKirCj3/8Y2zatAmfffYZbr31Vlx22WXwevVfaSWjqUssrSqTOnrRTSxoUi8CQgj+vVpMX5w/u6rf7412K/xaKtXKlC5QrgWob07U2qu/Zl9NlcHn21vR0h1CQZYTxxvcpJO1wP1ki6jMHDW22JD6kIiaio2PNjejtSeM4hy3IW8EkPlz2dQVQmtPCDYOmFxhLCBw9wsIXHFdFs3ebJSfS6sDgv4pg4ExFTpsnKWpkMT1rOx3cFBQOgn4+W7glPsGZ31lt8LhlDIAgAsuuABtbW2455570NDQgKlTp+K9995DdXU1AKChoQF1dXXy/XNycrB06VJcf/31mD17NoqKinD++efj3nvvNe8oJPoFBBr772di1a527GnrQ47bgVOn9W8prHUAkJJghJfzxoerUQgSqhrUDFkxMggo9lqmVlteX7sPAHDGjErDw0oSp0d2BSP4+5fiCNGTJuuvXkiGXOKYRiF45WvxM33erJGG2+96MryW9HMwvjTX8PAcWkFByc9yyj0UrDCr0ffdaecM+x8y0V8hsNZDQN+3muJsU1swp2J2dQH2tPVirkUjlg8qXINoylQqBK4DW+3R9Q265pprcM011yT93fPPP9/vtkmTJvVLM1hBLCAQNzyzUwZUHVg0owJZrv4vnRGFYP3eTkR4grI8N6oKMysnDhsn15SHeB5A5qsjI4OAMo0I7gpG5IZK584yngpKLDt8+pMdaO8NY0xJNs48pDLdn2om0/u2vzMg+0YuOKy/MqSVWHOi5OuZZSgEkqUMXJg6Ih+V+R5MGZFvqIIhGTVFWbBxounTaiPcQJcdHlZTiEvmVOO4iQMzhvieM6fg9tNrTX+PGAMMDQhcuYDN+kDSCMNqlgEtpUpMGZjRvjgcFbBko1hff16KDS+2aWoPQOR0QU2hKiMWx4nmtFBUUB2AKEcfa0XuVJjitXzv2waEogLGleZgmhkbmaKKYn9nAM98vgsA8ItTJpl+debMUGXw2hrRN3JEEt+IHjwZuj6aZSgEknsIctwOLP/5CbCiNX51UTZW3nai5QY/oH+/f6vX9DjtuOfMqZauoYTjOBYMDAdolcEBXnIIDLuAQFQISuWAwDyFYNWuNvSEoijJdaec6W5EIdBiKFSupycgKNKhEGR6Ld9YJ6YLzj10pCnOcqWp8KElWxGKCji8phAnm5wuABTpiSQpA0II3lhbD8AcdUC5XiaFwKihEOjf79+XJW6aqWZkmEHZAE3l69epcACCEAZDM1mSCfkA9w8AwywgaJYVAillYKKH4KPNYrOdEyeVpnTo660yEASCdXWdADQGBBp6EUR4QXau6zlhJ5oK93UGUJ7ngd3Gob03jNVSQLNohjkDbKgK0NoTwsffi6/9badOsqSMLV0gt6OlF7vb+uCy2zB/SvJR1FpJpxA0dQXR0m2OoRDorxBY3jd+AOE4Dg4bh6hAYOPE6iIG44Cj5ihg8pnAxFMH+5lk5MBOaGiEDnAxO2VACMHSTWK6IN2wnkQjnFp2tvagJxSF12nHhDL1phMtisSLK/dgV2svCrNdusr0lIbJL7a34qgHPsY9724EAHyypRkCEQfZGGnaE7+eeGxr6zoRFQgmledipsnjcynUeJfsdaRtmI8YU4gctznxc7qKDdqpclxpjmFDIdC/MdFASPkDCTUW5nmdlrcSZjB04fQC578IzPh/g/1MMjJsAgJCSMxUKHUQzOTmVsuWpm7s6wzA7bDh6HGpHb96A4Jv9tKccb6mcjq1AUFbTwiPfrgVAHDrgom6NgVllcGqneKm9fLXe9HZF5bVEyOzCxJJ9AmcZrCvQTrSKTsffW/+sckKQUIfAn8ggvv/bzMAYP5kc9QI6jUBxBkMA+GOH0ho6aHVPQgYjIOBYXN26ApE5ZxsqVxloL8MUMmHkjpw9LjitFdtegcOfSO1Qp6uMWesNmXw4JKt6A5GMaUyL2n/BDUohxvRsbzhqIB/r94rO/BPrDWvDWnile2p0wcgIEh43zr7wlgjdfQz2ntASSqF4N7/bEJTVwhjirNx3QnjTFuPHp8ve/htmlQhGG7KB4MxGAybgICmC/K9TvkKTMvI3nR8SP0DGa4S9Y4k/kbK7U+v8mn6OzUKQXN3UK6hv3PRFN1mMmVwRQMCAHhk6Tb0hKIoznHJ3QPNQDkkZ1J5rqWtYlPNoPh0awt4gWBiWa5cu28GscZEMYVg2ffNeHVNPTgO+P15000t2ZMDAu/w8Q9QqKJm9RwDBuNgYPgEBFK6oFxhmItd1epPGbT3hrF+byeAzFfAekyF4aiAzfvFyXYztCoEKgKC/9vQCIGI0wEPV9EBMRXKKoO69thQqoD02h4/MbXZUg9KheB0C9UBIHWqJxYImjuAxZNgdt3V2osbX1kPAPjx3NGYrcFYqgb6WtIKg+GEU/rMsZQBg2GcYRQQiBUGpYrZ7nLtvAGFYHODuFnXFGVldOfrSVFsaexGmBfgy3JqboeqJmXw7jdi//1FM4w186EegmBEkMsXF06N5bkzqSdacTliwYXRuQgZ10piKozwAj7dYk1AQD+XwQgPf18Elz//NfyBCGaO8uF/T5lo6lpALHAsGEYVBhRZIWABAYNhmGEUEMRXGADmpAy+bxTHEU8szzyUQk8fgvWyf8Cn2SWdab19nQGs3tMBjjNuyktskJLnceCnx40FIF7xHjPe3Paqo4tzkO2y49gJJRhj8bS3ZMrOt/V+dAWj8GU5cUiKvhN68Sg+l//z6nrsbO1FZb4Hf7l4tiXd/eSUwTBUCGJVBixlwGAYZdh8i5oT2hYD5jQm2koDgjL1AYGW9b6V0hFa0wVAZs/Cf6XpfIfXFKI831izmMQhOaOKsjB9pA9/uuhQ+LKcyDapJI9SmO3C6ttPHpDx5fR94wUCXiCw2zh8LfVVOLym0PQmPlQh2NXai+XbWsFxwF8vnY0SHTMm1OCUUwbDTyGgVQbMVMhgGGfYBASJbYsBc6YdbmkSA4IJahQCHaZC2ixoug5DXibPwrvfNAAwni4AkgQEUnrjNAvz+2bU4ashfnKkALvNLreSNuK7SAV9LVdIPQcOHVWAKZXWDQKKpQyG36ap7EPAYDCMMWxSBo20bXGuIiAw2IdAEAi2SQHBJAtSBr2hKLY1i4+vSyFIs97u1l5s2OeH3cbF5fr1wnFc3MZpput+sFG29w1FBQgCkRUCLZ0j1UIVAl4Qx1ab2eMgGV7pe1CYPfwUAuYhYDDMY9gEBGlTBjoVgn2dAfSGebjsNlQXZR5qk64nfjK+qe+EQICKfI88f0ELSlPhs5/vws2vrJeDn2WSIe7IMYUo0jHMKBlKlWAg5sEPFMqKhnBUwJambnQFo8hy2TGl0vz+454EteUkk02LiVxx9BicNq1CV4fKA50x0rCpCSpSegwGIz3DImXQ3BVEc3fqlIFeU+EWyT8wpkTd/HOtnQpXbBcl4yN0ytIuRcDzp4+3ozfMY8HUciyYUi63wD0qTWdFrbgddnQjCmB4BQS0m1+YFxDmBVkdmFVdoKlzpFrcCuNgdVEWxpVaa5o8aXIZTrJgKNSBwO/OnY7/mT/BtJbZDMbBzJBXCPZ1BnD+n1ciKhCMLs5OUWWgL2VA/QNqKgwA7Z0Kv9jRCgCYq3PTpuvVd4hKBiDNFRAIVkk58DljinQ9djKGq0IAxKdfvtplXboAiH8dT5xUxnrwG8DlsLFggMEwiSGtEHT2hXH+0yuxrzOAkQVevHjZ4XGOcI/BPgRbdQYEahSC7mBENhTqvYqnUveWpi75tmXft2BTQxf8gQhy3A5MG2GeWY16MmwcUOnzmva4BwIuhw0Iie+dlf4BAHGlhSdNHn4yPoPBGJoM6YBgycYm7OsMYITPi1evnoOK/PhNyqiHYIuGkkMg/dS8RFbtbAcvENQUZWGEzs2VBiDbmnrk2xq7gnjui90ARIe8mZI3TcFU5HuH3ZAcGlztaOlBU1cITjuHmaN8lqxFx/TmehyWBR0MBoOhlSEdEFBJf/6Usn7BAKCvyuDzba14ZfVeXD1vDHa29AJQb1iSAxAVpkKj6QIgtoklKiBvrKsHYG66AIgd33BLFwCx4Opjabrh9JE+S5oEAcCMKh8uOmIUjhhdOOwCKwaDMXQZ0gGBLOmn2LD1mAr/tGw7Vu5sw/vfNSDCE2S77Kqv4LWkDKih8KixBgKCBLf6xLJcbGnqBhGr2TBnLAsI1EJLD99evw8AcLKFJjyn3Yb7zp5m2eMzGAyGHob05QkNCManDAi0ty7e3SaqAhGeyI+tdmiP2k6FLd0hWd0wsmknBgQ/PqpG/v88jwO1FeaWzFF3/Kii4RcQuKTgMcIT5Hkc+OERowb5GTEYDMbAMmQDAn8gIncnHF+WvGxLVghUTjsMRng0+MV+Bovn1sBu4zRdKapVCFZI6YLJFXmGmsUkBgTzJpZgTIlYl33EmCLTW+5OG5EHjtNfJnkgo3wtF8+tQS5rdMNgMA4yhmzKgHYQrMz3pOxS5nZqUwjq2vsAiFfXdy6ajF8snKQpj6y2dfGXO8V0wVyDkr6yoY7XaUd5ngfnHjoSf/hgiyntihO5Zf5EXHXs2GHZN94tvZZepx2Ljxo9yM+GwWAwBp4hGxBsyZAuAGIpg6hAEOWFjI773a1iuqCmOBscx2k2ldE595k6FX65U+oRYDQgUFzVjpae80/njcVZM0forlxIB8dxwzIYAIBst/heX3TEqGHZ4pfBYDAyMWQDAlpqNyFFugCIH9kbimYOCPa0iQqBmjbFyVBTdtjUFcSu1l5wHDDbYMmZUiEYLaUKbDbOkmBguHPt8eMwqjALN5w4frCfCoPBYAwKQzYgoD0C0pUEKq+gQ1EB2Rla+lNDYY1O05yaToU0XTClMs/w1bby+GhPd4Y+ZtcUGg7QGAwGYygzZE2FdEpguoDAbuPkcjI1vQgMKwTSBs0LRJ5klwhtKXzEaOMlgXEBQQkLCBgMBoOhnyEZELT1hNDaEwaAjINhYpUGmY2FRhUCZY/6VGkDqhAcaULToLiUQbG1A3IYDAaDMbwZkgHBVsk/UFXoRbY7fdZDbS+CUJTH/s4AAOMKAZA8IGjuDmJni+gfONwEeTrRVMhgMBgMhl6GZEAgpwtKM7cUjg04Sp8yqO8IQCBin/niHH0uc4eNAx1cF+L7r7dKqi6oLc9DfpZxtz71IJTneYat+5/BYDAYA8OQNBVu3CdO90tXckhRqxDskdIF1UXZusfRchwHl92GUFRAKCKgqSuIkhy33Olw1S4xXXDEGHPMa2NKcvDbs6dibAlLFzAYDAbDGENKIegKRsALBB9JA2iOVLGxulROPNzdKhoKa4qNteWl6322rQVH3PcRrv7HGggCQVNXEO9+0wDA3KFDPzyi2hQ/AoPBYDAOboaUQvDhxibUVgOtPSHkeRyYq2IwkFtlykCpEBjB7bCjG1G8uVYckrNkUxOe+nQHvt7dDn8ggmkj8nH8pFJDazAYDAaDYTZDKiB477sG7O4Wy/lOnlzer5d/MtSmDHZLJYd6KwwS11tb1yHf9ocPtgAQ1YOHz5/BRt4yGAwG44BD18705JNPYvTo0fB4PJg1axaWL1+u6u+++OILOBwOHHLIIXqWxapd7fJ42lOnlav6G7fKCYRmKQQ0SBEIYOOAhVNjz/N/F0xU5XtgMBgMBmOg0RwQvPLKK7jxxhvxq1/9CuvWrcMxxxyDhQsXoq6uLu3f+f1+XHLJJTjxxBN1P1lCgI6+CHLcDhw9PnO6AIj1IQim8RCEojzqO8SSwxqjAYHi6n/qiHw8fP4hOKm2DOceOhKXsaE5DAaDwThA0RwQPPzww7j88stxxRVXoLa2Fo8++iiqqqrw1FNPpf27n/zkJ7joooswZ84c3U+WcmJtadycgnTIEw/TjEDe3NCNqEBQmO1CWV6G/sYZUKYx5owtgtdlx98unY2Hzp8hVxswGAwGg3GgoSkgCIfDWLNmDebPnx93+/z587FixYqUf/fcc89hx44duPPOO1WtEwqF0NXVFfcPEOv8AWDh1ArVz1mNh+CbvZ0AgBkj83WXHCauB0CV6ZHBYDAYjAMBTabC1tZW8DyPsrKyuNvLysrQ2NiY9G+2bduGX/ziF1i+fDkcDnXL3X///bj77rv73X7rggmo6+FwYq16l77culhNQFDlU/24qaAKgcPG4bCaAsOPx2AwGAzGQKDLVJh4FU0ISXplzfM8LrroItx9992YMGGC6se/7bbb4Pf75X979+4FAPzwyBrcf840TS59NabCb+o7AZgbEMwc5UOWa0gVcTAYDAbjIEbTjlVcXAy73d5PDWhubu6nGgBAd3c3Vq9ejXXr1uG6664DAAiCAEIIHA4HlixZghNOOKHf37ndbrjdxnL58mM50zcm6gpGsKNFrDCYMdJneD2v1PfAzOZDDAaDwWBYjaaAwOVyYdasWVi6dCnOPvts+falS5fizDPP7Hf/vLw8bNiwIe62J598Eh9//DFee+01jB5tvevekyRl0NwVxCtf78WPjqzGpgbRn1BV6EVhtr4ZBkouPrIaUYHgh0dWG34sBoPBYDAGCs2a9s0334yLL74Ys2fPxpw5c/CXv/wFdXV1uPrqqwGIcv++ffvw4osvwmazYerUqXF/X1paCo/H0+92q5AVAkXK4Df/3Yx3v9mPjfu7MG1kPgBz1AEAmDuuGHPHMTMhg8FgMIYWmgOCCy64AG1tbbjnnnvQ0NCAqVOn4r333kN1tXhF3NDQkLEnwUCSaCrsDkawZKOY8nh/YyM2NvgBAIeY4B9gMBgMBmOowhFCyGA/iUx0dXUhPz8ffr8feXl5mv72H1/uwe1vfYdTppTj6Ytn4dXVe3Hra9/2u9+rV8/BYTXmTCFkMBgMBuNAQe0eOuyb6idWGbwltT6+7KjRyPWIAomNA6ZUags0GAwGg8EYTgz/gMAZa13c6A9ixY42AMCPj6rBz04cDwCYUpnPSgQZDAaDcVAz7HdBpULwzjf7QAhweE0hqgqz8OOjRiPb7cDMUb7BfZIMBoPBYAwyB01A0BWM4uWvxAZHZ80cAQCw2zhcePioQXtuDAaDwWAcKBwEAYGYMtje3AMAKM5x4/QZ6mchMBgMBoNxMHAQeAjiD/H+c6Yhz+McpGfDYDAYDMaByfAPCBTTB885dAROnty/xTKDwWAwGAc7wz5lMMLnhcdpQ1G2G3cumjLYT4fBYDAYjAOSYR8Q+LJc+OSW45HltrNUAYPBYDAYKRj2AQEAlOd7BvspMBgMBoNxQDPsPQQMBoPBYDAywwICBoPBYDAYLCBgMBgMBoPBAgIGg8FgMBhgAQGDwWAwGAywgIDBYDAYDAZYQMBgMBgMBgNDpA8BIQQA0NXVNcjPhMFgMBiMoQXdO+lemoohERB0d3cDAKqqqgb5mTAYDAaDMTTp7u5Gfn5+yt9zJFPIcAAgCAL279+P3NxccByn6W8PO+wwfP311xY9sxhdXV2oqqrC3r17kZeXZ/l6ADs2M2DHZh4DdVwAOzYzYcdmDgfysRFC0N3djcrKSthsqZ0CQ0IhsNlsGDlypK6/tdvtA3aiB4C8vLwBW48dm3mwYzPOQB8XwI7NDNixmcuBemzplAHKsDcVXnvttYP9FCyDHdvQZLge23A9LoAd21CFHZs2hkTKYCjQ1dWF/Px8+P3+AY9IrYYd29CEHdvQhB3b0GQ4HNuwVwgGCrfbjTvvvBNut3uwn4rpsGMbmrBjG5qwYxuaDIdjYwoBg8FgMBgMphAwGAwGg8FgAQGDwWAwGAywgIDBYDAYDAZYQMBgMBgMBgMsIIjjs88+w6JFi1BZWQmO4/DWW2/F/b6pqQmLFy9GZWUlsrKycMopp2Dbtm1x99mxYwfOPvtslJSUIC8vD+effz6ampri7nPGGWdg1KhR8Hg8qKiowMUXX4z9+/cPi2OrqakBx3Fx/37xi18M+WP75JNP+h0X/WdlJ7SBet/Wrl2Lk08+GT6fD0VFRbjqqqvQ09Nj2XHdf//9OOyww5Cbm4vS0lKcddZZ2LJlS9x9CCG46667UFlZCa/Xi+OOOw4bN26Mu08oFML111+P4uJiZGdn44wzzkB9fX3cfX77299i7ty5yMrKgs/ns+yYKAN5bAN9LhnIYxvoc8lAHdtgnUvUwAICBb29vZgxYwaeeOKJfr8jhOCss87Czp078fbbb2PdunWorq7GSSedhN7eXvnv58+fD47j8PHHH+OLL75AOBzGokWLIAiC/FjHH388/v3vf2PLli14/fXXsWPHDpx33nnD4tgA4J577kFDQ4P87/bbbx/yxzZ37ty4Y2poaMAVV1yBmpoazJ49e0gf2/79+3HSSSdh3LhxWLVqFd5//31s3LgRixcvtuy4Pv30U1x77bX48ssvsXTpUkSjUcyfP19+3gDw+9//Hg8//DCeeOIJfP311ygvL8fJJ58szzYBgBtvvBFvvvkm/vWvf+Hzzz9HT08PTj/9dPA8L98nHA7jBz/4AX76059adjyDdWwDfS4ZyGMDBvZcMlDHNljnElUQRlIAkDfffFP+ecuWLQQA+e677+TbotEoKSwsJH/9618JIYR88MEHxGazEb/fL9+nvb2dACBLly5Nudbbb79NOI4j4XDY/ANJgpXHVl1dTR555BHLjyEVA/W+hcNhUlpaSu655x5rDiQJVh3bn//8Z1JaWkp4npfvs27dOgKAbNu2zeKjEmlubiYAyKeffkoIIUQQBFJeXk4eeOAB+T7BYJDk5+eTp59+mhBCSGdnJ3E6neRf//qXfJ99+/YRm81G3n///X5rPPfccyQ/P9/aA0nCQBwbZaDPJVYe22CfSwbqfRuMc0kqmEKgklAoBADweDzybXa7HS6XC59//rl8H47j4hpTeDwe2Gw2+T6JtLe346WXXsLcuXPhdDotPILUmH1sv/vd71BUVIRDDjkEv/3tbxEOhwfgKJJj1fv2zjvvoLW11dKr6EyYdWyhUAgulytu6InX6wWAlMdvNn6/HwBQWFgIANi1axcaGxsxf/58+T5utxvz5s3DihUrAABr1qxBJBKJu09lZSWmTp0q3+dAYKCObTDOJVYf22CeSwbqfTsQziUUFhCoZNKkSaiursZtt92Gjo4OhMNhPPDAA2hsbERDQwMA4Mgjj0R2djZ+/vOfo6+vD729vbj11lshCIJ8H8rPf/5zZGdno6ioCHV1dXj77bcH47AAmHtsP/vZz/Cvf/0Ly5Ytw3XXXYdHH30U11xzzWAdmunvG+WZZ57BggULBnUkt1nHdsIJJ6CxsRF/+MMfEA6H0dHRgV/+8pcAkPL4zYQQgptvvhlHH300pk6dCgBobGwEAJSVlcXdt6ysTP5dY2MjXC4XCgoKUt5nsBmIYxusc4nVxzaY55KB/EweCOcSCgsIVOJ0OvH6669j69atKCwsRFZWFj755BMsXLgQdrsdAFBSUoJXX30V7777LnJycuS+1oceeqh8H8qtt96KdevWYcmSJbDb7bjkkktABqlppJnHdtNNN2HevHmYPn06rrjiCjz99NN45pln0NbWNuSPjVJfX48PPvgAl19++UAfThxmHduUKVPwwgsv4KGHHkJWVhbKy8sxZswYlJWVJT1+s7nuuuvw7bff4uWXX+73u8Rx54SQjCPQ1dxnoBiIYxusc4nVxzaY55KB+kweKOcSypAYf3ygMGvWLKxfvx5+vx/hcBglJSU44ogj4owg8+fPx44dO9Da2gqHwwGfz4fy8nKMHj067rGKi4tRXFyMCRMmoLa2FlVVVfjyyy8xZ86cgT4sAOYem5IjjzwSALB9+3YUFRVZfhzJMPvYnnvuORQVFeGMM84YyMNIilnHdtFFF+Giiy5CU1MTsrOzwXEcHn744bTvrRlcf/31eOedd/DZZ5/FjTgvLy8HIF5xVVRUyLc3NzfLV2jl5eWyoqG8ImtubsbcuXMtfd5qGKhjG4xzyWC8bwN1LhnIYzuQziUAmKkwFUgwcCVj69atxGazkQ8++CDlfT766CPCcRz5/vvvU96nrq6OACDLli3T+Wy1MZDH9u677xIAZM+ePXqfriasPjZBEMjo0aPJ//zP/5jxdDUxkO/bM888Q7KyskhHR4fOZ5seQRDItddeSyorK8nWrVuT/r68vJz87ne/k28LhUJJDVyvvPKKfJ/9+/cPuqlwMI6NYvW5ZDCPzepzyUAf22CeS1LBAgIF3d3dZN26dbLD+uGHHybr1q2TP4D//ve/ybJly8iOHTvIW2+9Raqrq8k555wT9xjPPvssWblyJdm+fTv5+9//TgoLC8nNN98s/37VqlXk8ccfJ+vWrSO7d+8mH3/8MTn66KPJ2LFjSTAYHNLHtmLFCvlxd+7cSV555RVSWVlJzjjjDMuOa6COjfLhhx8SAGTTpk2WHtNAH9vjjz9O1qxZQ7Zs2UKeeOIJ4vV6yWOPPWbZcf30pz8l+fn55JNPPiENDQ3yv76+Pvk+DzzwAMnPzydvvPEG2bBhA7nwwgtJRUUF6erqku9z9dVXk5EjR5IPP/yQrF27lpxwwglkxowZJBqNyvfZs2cPWbduHbn77rtJTk6O/Hp2d3cP6WMbjHPJQB3bYJxLBvIzScjAn0vUwAICBcuWLSMA+v279NJLCSGEPPbYY2TkyJHE6XSSUaNGkdtvv52EQqG4x/j5z39OysrKiNPpJOPHjycPPfQQEQRB/v23335Ljj/+eFJYWEjcbjepqakhV199Namvrx/yx7ZmzRpyxBFHkPz8fOLxeMjEiRPJnXfeSXp7e4f8sVEuvPBCMnfuXEuPR8lAHdvFF19MCgsLicvlItOnTycvvviipceV7JgAkOeee06+jyAI5M477yTl5eXE7XaTY489lmzYsCHucQKBALnuuutIYWEh8Xq95PTTTyd1dXVx97n00kuTrmXVVfRAHdtgnEsG6tgG41wykJ9JQgb+XKIGNv6YwWAwGAwGqzJgMBgMBoPBAgIGg8FgMBhgAQGDwWAwGAywgIDBYDAYDAZYQMBgMBgMBgMsIGAwGAwGgwEWEDAYDAaDwQALCBiMg4LjjjsON95440G3NoPBUA8LCBgMRhyffPIJOI5DZ2enKX/3xhtv4De/+Y15T5DBYFgCm3bIYDAspbCwcLCfAoPBUAFTCBiMYUZvby8uueQS5OTkoKKiAg899FDc7//xj39g9uzZyM3NRXl5OS666CI0NzcDAHbv3o3jjz8eAFBQUACO47B48WIA4kz33//+9xgzZgy8Xi9mzJiB1157LePfJaYMampqcO+998rPsbq6Gm+//TZaWlpw5plnIicnB9OmTcPq1avjnveKFStw7LHHwuv1oqqqCjfccAN6e3vNfvkYjIMWFhAwGMOMW2+9FcuWLcObb76JJUuW4JNPPsGaNWvk34fDYfzmN7/BN998g7feegu7du2SN++qqiq8/vrrAIAtW7agoaEBjz32GADg9ttvx3PPPYennnoKGzduxE033YQf/ehH+PTTT9P+XTIeeeQRHHXUUVi3bh1OO+00XHzxxbjkkkvwox/9CGvXrsW4ceNwySWXgI5a2bBhAxYsWIBzzjkH3377LV555RV8/vnnuO6666x4CRmMg5PBna3EYDDMpLu7m7hcLvKvf/1Lvq2trY14vV7ys5/9LOnffPXVVwSAPA6YTljs6OiQ79PT00M8Hg9ZsWJF3N9efvnl5MILL0z5d4QQMm/evLi1q6uryY9+9CP554aGBgKA/PrXv5ZvW7lyJQFAGhoaCCHiNMarrroq7nGXL19ObDYbCQQC6V8UBoOhCuYhYDCGETt27EA4HMacOXPk2woLCzFx4kT553Xr1uGuu+7C+vXr0d7eDkEQAAB1dXWYPHly0sfdtGkTgsEgTj755Ljbw+EwZs6cqfl5Tp8+Xf7/srIyAMC0adP63dbc3Izy8nKsWbMG27dvx0svvSTfhxACQRCwa9cu1NbWan4ODAYjHhYQMBjDCJJhmnlvby/mz5+P+fPn4x//+AdKSkpQV1eHBQsWIBwOp/w7GjT897//xYgRI+J+53a7NT9Pp9Mp/z/HcSlvo+sKgoCf/OQnuOGGG/o91qhRozSvz2Aw+sMCAgZjGDFu3Dg4nU58+eWX8kbZ0dGBrVu3Yt68efj+++/R2tqKBx54AFVVVQDQz7zncrkAADzPy7dNnjwZbrcbdXV1mDdvXtK1k/2dWRx66KHYuHEjxo0bZ/pjMxgMEWYqZDCGETk5Obj88stx66234qOPPsJ3332HxYsXw2YTv+qjRo2Cy+XC448/jp07d+Kdd97p1yOguroaHMfhP//5D1paWtDT04Pc3FzccsstuOmmm/DCCy9gx44dWLduHf70pz/hhRdeSPl3ZvHzn/8cK1euxLXXXov169dj27ZteOedd3D99debtgaDcbDDAgIGY5jxhz/8AcceeyzOOOMMnHTSSTj66KMxa9YsAEBJSQmef/55vPrqq5g8eTIeeOABPPjgg3F/P2LECNx99934xS9+gbKyMtnJ/5vf/AZ33HEH7r//ftTW1mLBggV49913MXr06LR/ZwbTp0/Hp59+im3btuGYY47BzJkz8etf/xoVFRWmrcFgHOxwJFPSkcFgMBgMxrCHKQQMBoPBYDBYQMBgMBgMBoMFBAwGg8FgMMACAgaDwWAwGGABAYPBYDAYDLCAgMFgMBgMBlhAwGAwGAwGAywgYDAYDAaDARYQMBgMBoPBAAsIGAwGg8FggAUEDAaDwWAwwAICBoPBYDAYAP4/35/kXHnEM7AAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot predictions\n",
+ "# ==============================================================================\n",
+ "fig, ax = plt.subplots(figsize=(6, 3))\n",
+ "data_train.plot(ax=ax, label='train')\n",
+ "data_test.plot(ax=ax, label='test')\n",
+ "predictions.plot(ax=ax, label='predictions')\n",
+ "ax.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}