02.End-to-End-ML-Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2. End-to-End Machine Learning Project\n",
    "\n",
    "In this chapter, we will work on an end-to-end machine learning project, pretending to be a recently hired data scientist at a real estate company.\n",
    "\n",
    "Here are the main steps we will go through:\n",
    "\n",
    "1. Framing the problem & looking at the big picture\n",
    "\n",
    "We Define the objective in business terms and answer the following questions:\n",
    "\n",
    "- How would the solution be used?\n",
    "- What are the current solutions/workarounds?\n",
    "- How should we frame this problem? (`Supervised/Unsupervised`, `Batch/Online`, `Instance-based/Model-based`)\n",
    "- How should performance be measured?\n",
    "- Is the performance measure aligned with the business objective?\n",
    "- What would be the minimum performance needed to reach the business objective?\n",
    "- What are comparable problems? can we use previous methods or tools?\n",
    "- Is human expertise available?\n",
    "- How would we solve the problem manually?\n",
    "- List the assumptions we/others have made so far?\n",
    "- Verify assumptions if possible\n",
    "\n",
    "2. Get the data\n",
    "\n",
    "The second step is about data acquisition, we go through the following steps: \n",
    "    \n",
    "- We list the data we need and how much.\n",
    "- We find & Document where can we get that data.\n",
    "- We check how much space it will take.\n",
    "- We check the legal obligations & get authorization if necessary.\n",
    "- We get access authorization.\n",
    "- We create a workspace with enough storage space.\n",
    "- We get the data.\n",
    "- We convert the data into a format we can easily manipulate.\n",
    "- We ensure sensitive information is either deleted or protected.\n",
    "- We check the size & type of data.\n",
    "- We sample a test set, put it aside, and never look at it\n",
    "\n",
    "3. Explore the Data\n",
    "\n",
    "After acquiring the necessary data, we explore its properties by doing the following:\n",
    "    \n",
    "- We create a copy of the data for exploration and sample it down if necessary\n",
    "- We create a Jupyter notebook to save our data exploration sessions.\n",
    "- We study each attribute (column) and its characteristics:\n",
    "    - Name\n",
    "    - Type (Categorical, Continuous, Int/Float, Structured/Unstructured, Text ..)\n",
    "    - % of missing values\n",
    "    - Noisiness and type of noise (Stochastic, rounding error, ..)\n",
    "    - Usefulness for the task\n",
    "    - Type of distribution (Gaussian, Logarithmic, Uniform ..)\n",
    "- For supervised Learning tasks, we identify the target attribute\n",
    "- We visualize the data\n",
    "- We study the correlations between attributes\n",
    "- We study how we would solve the problem manually\n",
    "- We identify the promising transformations we may want to apply\n",
    "- We Identify Extra data that would be useful\n",
    "- We Document what we have learned in report\n",
    "\n",
    "4. Data Preparation\n",
    "\n",
    "Now's the time to prepare the data for machine learning. We go through the following list:\n",
    "\n",
    "- We create a copy of the data.\n",
    "- We write functions for all the data transformations we want to apply: we do this once for reproducibility on test and production data. Packaging functions like this will also allow us to treat preprocessing steps as hyper-parameters. \n",
    "- We clean the data by fixing or removing outliers, and filling missing values with `0`, mean, median, inference, .. or drop their rows/columns.\n",
    "- We conduct feature selection: we drop the attributes that provide no useful information for the task\n",
    "- We conduct feature engineering. Examples of feature engineering:\n",
    "    - Discretize continuous features\n",
    "    - Decompose features (Categorical, datetime, ...)\n",
    "    - Add promissing feature transformations ($log(x)$, $sqrt(x)$, $x^{2}$, ..)\n",
    "    - Aggregate features into promising new features\n",
    "- We scale the features of interest by standarizing and/or normalize them.\n",
    "    \n",
    "5. Shortlist promising models\n",
    "\n",
    "After preparing the data, we select and shortlist potential models that may solve the problem:  \n",
    "\n",
    "- If the data set is big, we sample smaller datasets for experimentation.\n",
    "- We try many models from different categories (`NB`, `Linear regression`, `RF`, `NN`, ..) using standard parameters.\n",
    "- We measure and compare their performances: for each model, we measure `N`-fold cross validation and capture the mean and standard diviation of the performance.\n",
    "- We analyze the most significant variable for each algorithm.\n",
    "- We analyze the types of errors the models make. What data would we use to avoid these errors?\n",
    "- We perform a quick round of feature selection and engineering.\n",
    "- We perform one or two more quick iterations of the previous steps.\n",
    "- We shortlist the top-`3`-to-`5` most performant algorithms that make different types of errors.\n",
    "\n",
    "\n",
    "6. Fine-tune your models & combine them into a great solution\n",
    "\n",
    "After shortlisting the most promising algorithms, it's time to fine-tune their performance, ensemble them and export a final model.\n",
    "\n",
    "- We should use the whole dataset\n",
    "- We fine-tune hyper-parameters using cross-validation\n",
    "\n",
    "We treat our data transformation choices as hyper-parameters. Unless there are very few hyper-parameters to explore, we should prefer random search to grid search. If training takes a long time, we try out Bayesian Optimization.\n",
    "\n",
    "- We try ensemble methods. Combining the best models will often produce better results than running them individually.\n",
    "- Once we are confident about the model, we measure its performance on the test set to estimate its generalization error.\n",
    "\n",
    "7. Present the solution\n",
    "\n",
    "After exporting a final model, it's time to present the work in an accessible fashion:\n",
    "- We document what we have done\n",
    "- We create a nice presentation\n",
    "- We explain why our solution achieves the business objective\n",
    "- We showcase interesting things we noticed along the way\n",
    "- We ensure the key findings are easily communicated through beautiful visualization and one-line statements\n",
    "\n",
    "8. Launch, Monitor, and maintain your system\n",
    "\n",
    "After presenting our findings, it's time to launch, monitor, and maintain the system:\n",
    "- We get our solution ready for production\n",
    "- We write monitoring code to check our system's performance while running in production and run interval-based checks to alert when it drops.\n",
    "\n",
    "During this step, we should be aware of the \"slow degradation\" phenomena: models tend to rot as data evolves. We should also monitor ourr inputs quality. Finally, we re-train the model on a regular basis on fresh data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Real Data\n",
    "\n",
    "When we are learning about ML, it's best to work with real data sets, not artificial ones. We list the following data sources:\n",
    "\n",
    "- Popular open data reposatories\n",
    "    - [UC Irvine ML repo](https://archive.ics.uci.edu/ml/index.php)\n",
    "    - [Kaggle Datasets](https://www.kaggle.com/datasets)\n",
    "    - [Amazon AWS Datasets](https://registry.opendata.aws/)\n",
    "- Meta Portals: they list open data reposatories\n",
    "    - [Data Portals](http://dataportals.org/)\n",
    "    - [OpenDataMonitor](https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex)\n",
    "    - [Quandl](https://www.quandl.com/)\n",
    "- Other pages listing many open data reposatories\n",
    "    - [Wikipedia](https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research)\n",
    "    - [Quora](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public)\n",
    "    - [The Datasets Subreddit](https://www.reddit.com/r/datasets)\n",
    "\n",
    "In this chapter, we'll use the California housing prices dataset, taken from the StatLib repository. The dataset is based on data from the 1990 California cencus.\n",
    "\n",
    "For teaching purposes, we've added a categorical feature and removed multiple ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Look at the big picture\n",
    "\n",
    "Our first task is to use the california census data to build a model of the housing prices in the state. This data includes features such as:\n",
    "- Population\n",
    "- Median Income\n",
    "- Median housing price for each block group in California\n",
    "\n",
    "A block group is the smallest geographical unit for which cencus data is published. A Block group has a population between 600 to 3,000. We will call them \"districts\" for short.\n",
    "    \n",
    "Our model should be able to predict the median housing price for any district, given the other features.\n",
    "\n",
    "### Frame the problem\n",
    "\n",
    "The first question we should ask is: \"What is the business objective?\". Building a model is probably not the end goal for us. This leads us to the more important question: \"How does the comapny expects to benefit from the model?\". \n",
    "\n",
    "Our boss answers that the model's output (A prediction for a district's median housing price) will be fed along other signals to another model to decide whether or not to invest in the district. Getting this information right is critical, as it's directly connects to revenue. In our case, our prediction will serve as input to another model that attempts to increase the company's return on investment (ROI).\n",
    "\n",
    "Since we are talking about our data flowing from model to model, it might be of benefit for us to define a \"data pipeline\". A sequence of data processing components is called a **data pipeline** Pipelines are very common is machine learning systems, since data needs to be preprocessed, manipulated, transformed to output the final predictions. The pipeline's components typically run asynchonosly. Each component pulls in a large amount of data, process it and spits out a result (prediction) into a data store. Each component is fairly self-contained.\n",
    "\n",
    "Within a company, different teams can focus on different components.\n",
    "\n",
    "The next question to ask your boss is \"what the current solution looks like?\"\n",
    "\n",
    "The problem is a supervised learning problem, and because we're predicting the median housing price for a district (which is numerical), then this is a **Regression Task**. We can also categorize this problem as a batch learning problem since cencus data is historical and does come every year.\n",
    "    \n",
    "If the data is huge, we can either split the data between multiple servers using Map Reduce or use online learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select a Performance Measure\n",
    "\n",
    "A typical performance measure for regression problems is **Root Mean Squared Error**. It has a higher weight for large errors. Its general formula:\n",
    "\n",
    "$$ RMSE(X,h) = \\sqrt{ {1 \\over m} \\sum_{i=1}^m ( h(x^{(i)}) - y^{(i)} )^2  }$$\n",
    "\n",
    "Wehre $m$ is the number of instances in the dataset you are measuring the $RMSE$ on, $x^{(i)}$ is a vector containing all of the input feature values (excluding the label) for the $i^{th}$ instance. $y^{(i)}$ is the label or the desired output of input $x^{(i)}$, and $X$ is a matrix containing all feature values excluding the labels/targets. \n",
    "\n",
    "The $i^{th}$ row of $X$ corresponds to $x^{(i)}$ and we can note:\n",
    "\n",
    "$$X=\\left(\n",
    "\\begin{array}{c}\n",
    "  {x^{(1)}}^{T}\\\\\n",
    "  {x^{(2)}}^{T}\\\\\n",
    "  {x^{(3)}}^{T}\\\\\n",
    "  \\vdots\\\\\n",
    "  {x^{(m)}}^{T}\\\\\n",
    "\\end{array}\n",
    "\\right)$$\n",
    "\n",
    "$h(.)$ is the system's prediction function, also called hypothesis. $RMSE(X,h)$ is the cost function measured on the set of examples $X$ and the hypothesis $h$. $RMSE$ is the preferred performance measure for regression tasks, but sometimes, we prefer to use other cost functions such as:\n",
    "\n",
    "- $MAE$: We use mean absolute error in the case where we have many outliers (also called the average absolute diviation):\n",
    "\n",
    "$$MAE(X,h) = {1 \\over m} \\sum_{i=1}^m |h(x^{(i)}) - y^{(i)}|$$\n",
    "\n",
    "Both $RMSE$ and $MAE$ are ways to measure the distance between two vectors. In our case, the distance between the vector of predictions and the vector of targets/labels.\n",
    "\n",
    "However, various distance measures, or norms, are possible. Computing the root of the sum of squares ($RMSE$) corresponds to the euclidian norm (also called $l_{2}$ norm). Computing the sum of absolutes ($MAE$) corresponds to the $l_{1}$ norm. More generally, the norm $l_{k}$ of a vector $\\bf{x}$ is:\n",
    "\n",
    "$$||v||_{k} = (\\sum_{i=1}^{m} |v_{i}|^{k})^{1 \\over k}$$\n",
    "\n",
    "With $l_{0}$ giving the number of non-zero elements in the vector $v$, and $l_{\\infty}$ giving the maximum absolute value in the vector $v$. The higher the norm index, the more it focuses on large values and neglects small ones. This is why $RMSE$ is more sensitive to large errors than $MAE$.\n",
    "\n",
    "When outliers are exponentially rare (like in a bell curve) $RMSE$ performs extremely well and is preferred over other cost functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the assumptions\n",
    "\n",
    "Lastly, it is good practice to list and verify the list of assumptions made by us or others, this can help us catch serious mistakes early one.\n",
    "\n",
    "To give an example, let's image we've spent 6 months working on an algorithm to predict the median housing price per district only to find out later that our predictions are being converted into categories (\"Cheap\", \"Medium\", \"Expensive\"). In this case, It would've been better to work on the classification problem instead of a regression one, that would produce better predictions for the downstream system.\n",
    "\n",
    "Fortunately, we find out that the team needs the actual median housing prices. Good to go!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Get the Data\n",
    "\n",
    "### Download the Data\n",
    "\n",
    "It is preferable to create some util functions to automate the process of downloading/extracting web-based data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"data\", \"01\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    \"\"\"Creates `HOUSING_PATH`, Downloads & Extracts the contents of `HOUSING_URL` into `HOUSING_PATH`\n",
    "    \n",
    "    # Arguments:\n",
    "        housing_url, string: the download link\n",
    "        housing_path, string: where to download & extract data\n",
    "    \"\"\"\n",
    "    os.makedirs(name=housing_path, exist_ok=True)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(url=housing_url, filename=tgz_path)\n",
    "    housing_tgz = tarfile.open(name=tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we download the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a small function to load the data using pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    \"\"\"Loads Housing data into a pandas dataframe.\n",
    "    \n",
    "    # Arguments:\n",
    "        housing_path: the path where `housing.csv` exists\n",
    "    \n",
    "    # Returns:\n",
    "        data, pd.DataFrame: the housing data as a pandas dataframe\n",
    "    \"\"\"\n",
    "    data_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take a Quick Look at the Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = load_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row represents one district and has the following (10) attributes:\n",
    "- `longitude`\n",
    "- `latitude`\n",
    "- `housing_median_age`\n",
    "- `total_rooms`\n",
    "- `total_bedrooms`\n",
    "- `population`\n",
    "- `households`\n",
    "- `median_income`\n",
    "- `median_house_value`\n",
    "- `ocean_proximity`\n",
    "\n",
    "The `info()` method is useful to take a quick look at the data. It answers the following questions:\n",
    "- How many rows exist?\n",
    "- How many `NaN`s per column?\n",
    "- What are the data types (per column)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There exist $20,640$ instances (rows) in the dataset. Which means that it is fairly small data sample by machine learning standards.\n",
    "\n",
    "$207$ districts are missing the `total_bedrooms` attribute, we will need to take care of this later.\n",
    "\n",
    "On the other hand, all attributes are numerical, except `ocean_proximity`\n",
    "\n",
    "Since we noticed repeated `ocean_proximity` values for the top 5 rows, we suspect that it is a categorical column, let's check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.describe()` shows a summary of all numerical values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should be careful, because `.describe()` ignores null values. \n",
    "\n",
    "The `std` row shows standard deviation, which measures how dispersed the values are. The `25%`, `50%`, `75%` rows show the persentiles of each columns (Example: `25%` of districts have $<=18$ years housing median age).\n",
    "\n",
    "Another way to get a feel of numerical continuous data is to draw a histogram for each numerical column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few things we might notice in these histograms. \n",
    "\n",
    "The `median_income` attribute isn't expressed in US dollars. After checking with the team, they informed us that the numbers roughly represent tens of thousands of dollars.\n",
    "\n",
    "The `housing_median_age` and the `median_house_value` were capped. This might pose a problem especially for the second attribute since it represent the output we want to predict. Our machine learning algorithms may learn that prices never go beyond that limit. In reality, we need to check with our client's team if this can be a problem or not. If they tell us that they need precise predictions even beyond 500,000 USD, then we have 2 options:\n",
    "\n",
    "- Manually collect the actual labels of districts with a `median_house_value` >= 500,000USD.\n",
    "- Remove those districts from the training/test sets, we don't want the model to learn never to go beyond 500,000USD.\n",
    "\n",
    "The attributes have very different scales, we will discuss feature scaling later. The attributes are **tail-heavy** & they often extend to the right than to the left. As a result, It will be difficult for many machine learning algorithms to find patterns within the data.\n",
    "\n",
    "We will learn how to transform the distribution from a tailed one to a bell-shaped normal/gaussian one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Test Set\n",
    "\n",
    "Creating a test set is easy, We randomly (or not if we're dealing with timeseries) select a subset of the data, typically 20% (or less if the dataset is large)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(data, test_ratio=0.2):\n",
    "    \"\"\"Splits a dataset into train/test using a `test_ratio`.\n",
    "    \n",
    "    # Arguments\n",
    "        data, pd.DataFrame: the whole dataset.\n",
    "        test_ratio, float: the percentage taken from the original dataset as a test set.\n",
    "    \n",
    "    # Returns\n",
    "        train, pd.DataFrame: the training set.\n",
    "        test, pd.DataFrame: the test set.\n",
    "    \"\"\"\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 4128)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can then use the function like this\n",
    "train_set, test_set = split_train_test(data=housing)\n",
    "len(train_set), len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one issue though: if we run the program again, it will generate a different test set. Over time, our machine learning algorithm will get to see the whole dataset. Which is a thing that we want to avoid, test sets are created to be hidden from the algorithm/your eyeballs until you're done modeling. There are 2 possible solutions to this problem:\n",
    "    \n",
    "- Save the test set in the first run.\n",
    "- Set the random `seed` before running `np.random.permutation`, in this way you're controlling the randomness of `numpy`.\n",
    "\n",
    "But both solutions will break next time we upload an **updated** dataset. To have a stable train/test sets, a common solution is to use each instance's indentifier to decide whether to include it in the test set or not. Assuming the instances have a unique and ammutable identifier.\n",
    "\n",
    "For example, we can compute a hash based on each instance's identifier then only allow (to the test set) hashes that are lower than 20% of the maximum hash value. This strategy is:\n",
    "\n",
    "- Scalable: we will always have **20%** of the data in the test set.\n",
    "- Extendable: when we have a bigger dataset, we only have to add new instances to the test set, both sets are stable.\n",
    "\n",
    "Here is a possible implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zlib import crc32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_set_check(identifier, test_ratio=.2):\n",
    "    \"\"\"Checks if an instance with `identifier` should be in the test set of overall ratio `test_ratio`\n",
    "    \n",
    "    # Arguments:\n",
    "        identifier, int: the instance/row identifier (ex. row index from `pd.DataFrame`)\n",
    "        test_ratio, float \\in [0,1]: percentage of test set taken from overall data set.\n",
    "    \n",
    "    # Returns:\n",
    "        in_test, bool: True if instance should be in the test set, False otherwise.\n",
    "    \n",
    "    # Notes:\n",
    "        - The CRC32 function converts a varibale-length string into an 8-character string that \n",
    "        is a text representation of the hexadecimal value of a 32bit binary sequence.\n",
    "    \"\"\"\n",
    "    total_size = 2**32\n",
    "    hex_repr = crc32(np.int64(identifier)) & 0xffffffff\n",
    "    in_test = hex_repr < (test_ratio * total_size)\n",
    "    return in_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, False, True, False, False, True, False, False, False, False]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[test_set_check(i) for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    \"\"\"Splits a dataframe using hash-based identification\n",
    "    \n",
    "    # Arguments:\n",
    "        data, pd.DataFrame: the dataframe comprised of an id column and data of interest.\n",
    "        test_ratio, float \\in [0,1]: percentage of test set taken from overall data set.\n",
    "        id_column, string: column name with identifiers for each row.\n",
    "    \n",
    "    # Returns:\n",
    "        train, pd.DataFrame: the train set.\n",
    "        test, pd.DataFrame: the test set.\n",
    "    \"\"\"\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the housing dataset does not have an identifier, column. We will use the row index as an identifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16512, 11), (4128, 11))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test_by_id(data=housing_with_id, test_ratio=0.2, id_column=\"index\")\n",
    "train_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use the row index as an identifier, we will need to preserve the order at all costs by making sure new data gets appended to the end of the dataframe and no rows are deleted/altered. \n",
    "\n",
    "If the previous is not feasible, we can try to build an identifier from the multiple features of each row. For example: a (Latitude/longitude) couple is guaranteed to be stable a few million years, so we can build a unique index based on it. The following procedure creates a unique identifier for each row:\n",
    "\n",
    "1. Convert latitude/longitude values from $\\Bbb{R}$ to $\\Bbb{Z}$ (by $\\times 100$)\n",
    "2. Convert latitude/longitude from $\\Bbb{Z}$ to $\\Bbb{N}$ using:\n",
    "\n",
    "$$\n",
    "f(n) =\n",
    "\\begin{cases}\n",
    "2n,  & \\text{if $n \\ge 0$} \\\\\n",
    "-2n-1, & \\text{if $n \\lt 0$}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "3. Use `Cantor Pairing` function to create unique indexes from $\\Bbb{N}^2$ tuples using:\n",
    "$$\\pi(k_{1},k_{2})={(k_{1}+k_{2})\\cdot{(k_{1}+k_{2}+1)}\\over{2}}+k_{2}$$\n",
    "\n",
    "Let's implement it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def from_Z_to_N(z):\n",
    "    \"\"\"Converts a number from \\Bbb{Z} to \\Bbb{N}\n",
    "    \n",
    "    # Arguments\n",
    "        z, integer: in \\Bbb{Z}\n",
    "    \n",
    "    # Returns\n",
    "        n, natural number: in \\Bbb{N}\n",
    "    \"\"\"\n",
    "    if z >= 0:\n",
    "        n = 2 * z\n",
    "    else:\n",
    "        n = -2 * z - 1\n",
    "    return n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cantor_pairing(n1, n2):\n",
    "    \"\"\"Creates a unique number `n` in \\Bbb{N} from a tuple (n1,n2) in \\Bbb{N}^2\n",
    "    \n",
    "    # Arguments\n",
    "        n1, natural number: first element of the tuple.\n",
    "        n2, natural number: second element of the tuple.\n",
    "    \n",
    "    # Returns\n",
    "        n, natural number: unique index from tuple (n1,n2).\n",
    "    \"\"\"\n",
    "    n = ( ( (n1 + n2) * (n1 + n2 + 1) ) / 2 ) + n2\n",
    "    return n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lat_lon_to_index(lat, lon):\n",
    "    \"\"\"Converts a tuple of (lat,lon) to a unique index in \\Bbb{N}\n",
    "    \n",
    "    # Arguments\n",
    "        lat, float: latitude.\n",
    "        lon, float: longitude.\n",
    "    \n",
    "    # Returns\n",
    "        index, positive int: unique index.\n",
    "    \n",
    "    # Notes\n",
    "        - 3 steps to do it:\n",
    "            1. Convert (lat,lon) to \\Bbb{Z} by *100.\n",
    "            2. Convert (lat,lon) from \\Bbb{Z} to \\Bbb{N} using `from_Z_to_N()`\n",
    "            3. Create the unique index using `cantor_pairing(_,_)`\n",
    "    \"\"\"\n",
    "    lat, lon = int(lat*100), int(lon*100)\n",
    "    lat, lon = from_Z_to_N(lat), from_Z_to_N(lon)\n",
    "    index = cantor_pairing(lat, lon)\n",
    "    return np.int64(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['id'] = housing.apply(lambda row: lat_lon_to_index(row['latitude'], row['longitude']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "513289261    24\n",
       "513481522    20\n",
       "513417431    18\n",
       "513353344    18\n",
       "463548767    14\n",
       "             ..\n",
       "467821317     1\n",
       "466232077     1\n",
       "459780879     1\n",
       "463061777     1\n",
       "525471590     1\n",
       "Name: id, Length: 11573, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still get duplicate indexes, and at the same time, we have duplicate (lat,lon) tuples as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude  latitude\n",
       "-124.35    40.54        1\n",
       "-118.90    34.41        1\n",
       "           35.26        1\n",
       "           35.41        1\n",
       "-118.89    34.22        1\n",
       "                       ..\n",
       "-122.41    37.75       10\n",
       "-122.42    37.75       10\n",
       "-122.44    37.78       11\n",
       "-122.42    37.80       11\n",
       "-122.41    37.80       15\n",
       "Name: total_rooms, Length: 12590, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.groupby(by=['longitude', 'latitude']).count()['total_rooms'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's time to revert back to the book:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(housing['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16322, 12), (4318, 12))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test_by_id(data=housing_with_id, test_ratio=0.2, id_column='id')\n",
    "train_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`scikit-learn` provides multiple functions to split datasets in various ways. The simplest function is `train_test_split()`\n",
    "\n",
    "Let's reproduce our implementation using sklearn's `train_test_split()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16512, 10), (4128, 10))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "train_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have only considered purely random sampling methods. This is generally fine if our dataset is large enough. If it's not, however, we're running the chance of introducing a significant sampling bias.\n",
    "\n",
    "When a survey company plans to interview 1,000 people, they don't pick them at random from a phone book. They make sure the `1,000` selected people are representative of the whole target population. this is called **stratified sampling**.\n",
    "\n",
    "The population is divided into homogenuous subgroups called strata. Each stratum represents an instances percentage of the overall population. We want a test set to maintain the percentages of each stratum to make sure it's representative of the whole population. A purely random sampling method will introduce biases that are in favour of dominant groups/strata.\n",
    "\n",
    "Suppose we've chatted with experts who told us that the `median income` attribute is a very important feature to predict `median_house_value`. We may want to ensure that the test set is representative of the overall `median income` distribution, that may be divided into categories. \n",
    "\n",
    "Most median income values are clustered around $\\$15,000$ to $\\$60,000$ but some median incomes go far beyond $6K$\n",
    "\n",
    "It is important to have, for each stratum, a sufficient number of instances in your dataset. Or else, there might be a bias while assessing the stratum importance.\n",
    "\n",
    "Let's use `pd.cut` to create the strata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = pd.cut(x=housing['median_income'], bins=[0, 1.5, 3, 4.5, 6, np.inf], labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1edca110>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the categories\n",
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to do stratified sampling based on income category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in split.split(X=housing, y=housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if this worked as expected, we can start by checking the proportions of income categories in the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['income_cat'].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a test set that is representative of `income_cat`'s distribution, it's time to remove it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop('income_cat', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We spent a considerable amount of time on test set generation because this is an important part of any machine learning project. Moreover, many of these ideas will be useful later when we talk about cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Discover & Visualize the Data to Gain Insights\n",
    "\n",
    "First, let's make sure we have put the test set aside, and you are only exploring the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16512, 10), (4128, 10))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape, strat_test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_test_set.reset_index().to_feather(fname='data/01/strat_test_set.f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a copy of the training set for us to play with it without harming the original one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.copy(); housing.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Geographical Data\n",
    "\n",
    "Since there is latitude/longitude information, it's a good idea to use pandas' matplotlib plot method to visualize the districts of interest using `.plot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like california, but other than that, we can't really see any other pattern. Setting the alpha to 0.1 makes it much easier to estimate densities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our brains are wired up to spot patterns in pictures, the tricky thing is that we have to play around with matplotlib's visualization parameters to make the patterns stand out.\n",
    "\n",
    "In the following figure, the radius of each circle represents the district's population (option `s`). The color represents the price (option `c`). We will also use a pre-defined color map called **jet** (option `cmap`) which ranges from `blue` (low levels) to `red` (high level)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1d724490>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=.4, s=housing['population']/100., \n",
    "             label='population', figsize=(10, 7), c='median_house_value', cmap=plt.get_cmap(name='jet'), colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This image tells us that the median housing price is related to location (closer to the sea -> more expensive). The relation between `population` and `price` isn't that obvious, let's check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>population</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.02692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>median_house_value</td>\n",
       "      <td>-0.02692</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    population  median_house_value\n",
       "population             1.00000            -0.02692\n",
       "median_house_value    -0.02692             1.00000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[['population', 'median_house_value']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very weak pair-wise correlation between price and population. We leave it at that & we continue with the book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking for Correlations\n",
    "\n",
    "Since the dataset is not too large, we can easily compute the standard correlation coefficient of every pair of columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at how much each attribute correlates with `house_median_value`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation coefficient is $\\in [-1,1]$. When the coefficient is close to `1` it means there exist a strong positive correlation between the two variables (For example. the median house value tends to go up with the median average income goes up). When the coefficient is close to `-1` it means there exist a strong negative correlation between the two variables. When the coefficient is close to `0` it means that there is a weak correlation between the two variables.\n",
    "\n",
    "The following figure shows multiple bi-variate plots and their corresponding correlation coefficient:\n",
    "\n",
    "<div style=\"text-align:center;\"><img style=\"width:50%\" src=\"static/imgs/corr_examples.png\" /></div>\n",
    "\n",
    "The correlation coefficients only measure linear correlations (if `X` goes up, `Y` goes up/down) and it has nothing to do with the slope. \n",
    "\n",
    "Another way to look at correlations is to use the Pandas `.scatter_matrix()` method to visualize correlations between any 2 attributes. Since we have `11` attributes and we cannot fit all plots, let's focus on the few promising ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAH1CAYAAAC3LUu8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9Z5Rk6Vnn+bs2vI/0PrO8767qrjalNuqWl1DLC4SEGUActMvMgWUPaxh2DsPCij1zRgM7wMDACAmpAXnfEmojtamqLm8zq9LbyAzv4/r9cCOjfHV1d3U3QvH7kjcir7833vd5n/d5/o/gOA5t2rRp06ZNmzZt2rR5fRDf6BNo06ZNmzZt2rRp0+anibYB3qZNmzZt2rRp06bN60jbAG/Tpk2bNm3atGnT5nWkbYC3adOmTZs2bdq0afM60jbA27Rp06ZNmzZt2rR5HWkb4G3atGnTpk2bNm3avI7Ib/QJvJ4kk0lneHj4jT6NNm2uy+zsLK/H++k4DoIgXPPd5az///LvBUFofV7/e/V+buWYl+/nevu+2bbry1dfw9XbC4KAZVmI4o19DI7jIIriFfu82fldfd22bV9z/Vffo8uv+ertr3d9L3X9L/e728XV76Zt21cc17ZtTNO84rOqqlest/4sRFFs3afLn8/17tVrdT1t/vXwStpN27Zv2jasc6N38Hpt0s3Wv9VjXH5ely9bloUkSdcs3+p13Gh70zSRZfmWl2/G5evpuo6qqgAYhoGiKABomobH47lmncu/vxm3ck9udH9eyTO/fJtb+X5+qchgX+SK/R09ejTjOE7H9Y71U2WADw8Pc+TIkTf6NNq0uS779u3jyJEjfP6FWb59OkVv1Mtwws9aRSfiVbhzKEpvxMf/8dXTzGZrbOsN80fv30k8oPL44XmencyyWmywUqpT1y36oj5++62b+Ksfz3BmqYgI+FSZkmbi2A4eRSTkkcnXdAz7pc7upxsRuJ23KKiKeFWZbEVn3dQPqBIIYFkOHlkEHOqGjWWDKkEi5EUWBQo1napuoUgiW7uDpMsaK0UNy3aQRAGPIhJQJWIBD7Io4OAwmgzy4bsGuG8syVePL/Hd0yuslTU6Qyo7+6LsG45zYGOSHb/3bSrGpfOc/eN3AZfezfGVAm//zHO3dI2N23i/AEQBHAf8qogiiTgOHNiYJOCReeZCGtsGRQLDtKkaNj5ZJOSVKWsmqixRbhj4FYl//55tvHt330seL1/V+fKxRWzH4f139uNXJb50dJGFXA2AwbifD+0bwKusd/YOXz+5xEKuzoObOtg9EL3Nd6DN9Vh/N6/m2YsZjs7l2dYbpljT+crxJUaSAV6cyXJhrQrAx+8e4J+OLSJLIv/hPdv43tlVDNvhw3v7+d++chrNtPnZ/QOcXS6zUqjz0bsHeOJsitlMjf0jcdbKGuOpMn1RHx0hlSNzBfyqyB99YAePH1okEfSwfyTGnzxxAVkU+LePbOAzP5zEsh1+8b4h/suTU9iOw4GRGD+azgMQ8srUdRPTBr8iUruscfbKIg3T/ZzwQa7ufj8QlVkouAPghF8gW3NbFa8IjebmCrD+05YEsG5QAkYArvevDh+km8frCyssldy9BSSoWi/9nNYp3+L3KqA3lzd3eZlYdVuUuE8kV3cv6urruPzcoz6ZQt29J11BiUzFPcn+iMpi0d3znt4gp1cqOMBje3p4cjxDw7B4z44uvnRiBQeIBxTifpWFfJ1NnUHOp0o0HwG/dN8QXz62RDygMput4QcyzeOvt52CIMzd6F78VBngbdr8JPDcVBbbcTi1WCQeUJlcq7ChI8iZpRIz6SqrpQa6abGYr3Gs2cEs5Opkyg2Wi3UqmoltO+SqOl86tsh8robZbKW0utFqoBqGjWHprcakzY253beoots0TP2Kjq6uW63PmmkjibSeTd2EYk3Hchw008a2wbItLq5VcRwHw3a3tG0Hx7DQTBvHcfcT8MrM5WqcWSqxrSfMQq7GXK6KabnvyEgyyOmlIgc2Jq8wvq/H5w7esC95zWleIlXdRhJsZEng6FyBsE+mYVjUdQtZBN1ycADdsKgZ7j0tNTtiHIcnzqRe0gCv6xYTq2XKDXe7qbUK8YBKtqKzXGggCuBXZZYKdcY6ggCUGgazGdc4P7tcahvgbzBnlovYjsPZ5SIz6SqW7TC5VuFi0/gG+MejCxg26JbFXz83Q9jremT/5rlZ6oZrsH3nVApZcj2c3z+bYibtbn98oUBVM3Ech8V8jZVCHcdxqGoW/3hoEcNySBUbPH54EdOyMS34m+fmaDT3+7kX5jCbL/VzM/nWOa2/c8AVxjfQMr4BsvVL368b30DL+IZLxjdcMr7hxsY3XN/4hkvGN9AyvuHlGd8vB/2y5XXjG2gZ33DtdVz+cd34BlitXDrJdeMb4MRypbX8vTOrrefx3XOrrX3lqgZVzcJxHCbTlSv6y28cX8BxBLIV7dYv7DLaMeBt2vwL4+EtnXgUkbuG43SEPGzvCZMMqtwxGOXBzR30x3z4VJnRjiB3j8Tpj/kZ7QjSG/MzFPcT8yv4FImusJefv3uIobi/6RkV6AypSKLbmXhkgc6QB6XdCrwk0m2OhIj6ZDqCKs1HgYDr+Qp5ZXyKSCygEPbKeBUBWRQIqhKJkIe+qJ+YT0GVXS/39p4QiaCKRxKQBPDIIgFVpivkoSviZawjSGfIw8bOIHsGosT8KqMdATZ1hUgEVHb1R4j6Fe4YdI3FqPfSOV7vtfiV+0Zv7424RUTcZyAKEPFKxPwKflXi/rEE+0fihLwKyaCHZMhLR1DFp0jEgyr9MR9hj0xP1IvfIxH2KfzMHTc3vmcyVf7bj6Z5YSqLVxGJ+BQ2doUYiPvpCnsZSvgZTPjpiXjpi/pa24W9Chu7gngUkd0DkZscoc3rwZ6BKKossnsgyiPb3DZ1e2+YnT2h1jo7+t1lAbhvLEEyqBLxKXzq4VGCXhlJFPjg3l5GO4KossR7dvexuTuMJIncO5pgZ18EURQY6QiwfySOIAiEvTI/f/8gflViMO7nF+4bwqtIBL0Kn3pojIBXxqtIfHRfH5LgHnt3d7B1TnG/jNJsGEIeicubHr966VfZG5Ray0nvpbXC6qX1L1uFy75GvkmbL93g+65LrzqRl44WedVc1hQxHL3kK47doI0Sr/qc8F3apj9yabkvdGl5Z28QWXTbzg/t7SfsVZBEgffv6W7tK+aXGe0IIIoC23rCqJd1Bh/ZP4wsiXRHLrs5LwPh5ZSiFwThALDRcZy/FQShAwg6jjPzio78BrBv3z6nHYLS5l8qN5pKfbV8/uAc6bKGKAh88sFR/v7QPKW668H4jYfH+NwLc5QbJicW8mzvjfDcZIaAR6Y34kUQ4PxKmYBHZt9wnN98ZAPFusGf/nCSo3M5SnWT7b1hqrrJg5s7+fDeATyKyA/OrWLaNo9u7eKzz8/yDy8ugOPQF/fTF/VRrBtYtkOuojGXq6GZNh0hD3ePxHlgYyeffX6aQt2kL+pj71CMDV1B/tsz06yWGvTH/fzqgRG+cHies8slPLLIUMLPxs4gUb+H/SNxDk5neeZCmopm0hXycOdgnPfe0UNfzI9fdRvgqXSFYt1gZ18ERbp+j+Q4DudXyngUseXpXKfcMDgym6cj5GFHX4SGYXFmqUjAI7OlO4QgCOimzR9+5xw/OLdK2KvwyQdGed+d/bft2T41scaJ+QIhr8wn7h1GvVnP+iq4+t2czVQ5Opfj6QtrPD+ZJVc1bug5A1BE2NYbpj/mZ0NniA2dQVaKDb55com4X0WRRYIemfs2JPnQ3n4EQWAiVeY7p1cA15h6eEsnpYbBkdkc3WEf23rDr8m1Pj2xxvH5AgBv2979mh2nze3hRu1mqtjg7HKRjZ0hBhP+62779MQaf/PsDLIo8r++fTOyJGBYTuv3+3J4ub/FtVKDv3hmirph8dZtXTy8pav1v2PzeSZSZfYOxdjYGSRfM4j4FCzb4eJama6wl2TwkhWcKjb40ycv4pUl3rK9ixemsgAMJfzMZd1Zmc3dIcJehcV8jfs3JPndr5xiOV9ne1+EP/u5O697jqZlc2w+TyLgYawzSKGmI4oCXzw0x2d+OIljO7xrVw8bu0KkSg0+fs8Qo8120rYd3v6fn2ExX2drTxi/KjObrRLxKezqj3B2uYRXkfj992zjwmqZqF8lEVD55/NrgPvbO7NcRDNtdvdF+OG4+/2GzgDjK2WOLxT4tQdG2NYTYTJdYUt3mHjg0jDjj75znhMLBTyyyB9/YBe9zcHyueUST5xNAfDYHX2MJAPXXPdKsc6/e/wEAH1RH//pI3ta/6tqJhfXKgzEfCQuewZ7/+D7ZKtuv/qze/v4ow+52wiCcNRxnH3Xu7+3HIIiCMLvA/uAzcDf4oYUfR64/1b30eZKhn/3269q+/UYozZtbkbIK5Mua/hVCVkUGOsIcHy+wGDcjyqJ9EV9jKfcRn0mU2U+V8OvSti2w+f+zX7+6sfTTKcrCECuqpMqNijUdEJehUTQg+1AZ8jLQq7G8YUcpg2Ta+7U3hcPL/Dd0ytUGgYN0ybok/ngnX3809ElAPYOx6joFpWGQU23OD5fIOyVyVR00mUNjyxyfKGALAnsG44xuVphV38UUQCfItMd8uD3yGzoCFFumNw9EmTvcAzdsslWdM6niuRqOodns4R9Mr/24BjgdljfOLGM4zicXS7x0KYOBuLXdtLHFwo8M5EGrm2s//ncKscXCkS8MpIIXkXi2ckMjkPzXOI8NbHGYq5Od9Nz+qaN183FecWsldyp2XLDpG5Yr5kBfvUx/8sPL7JUqONTREYSAXLVwg3X98gCiiSSCHgo1nVmMxXuHY1zerGAabshArrlkAyqbO0JtwyfqF9huVDHsGwe2JjkyEyW44tFKg2TkxTpjniv6HBvF3sGoqyVNHfQ1Xlt59zmJ4NvnVqm3DAZT5X5jYfGrmtQP7S5E9t2CHpkZEngmyfdAZ9h2ezqvxRCZNkODcMi4LnSZKpqJtPpKoNx/8v/LQpgOw6uD/TSuZmWzedfmCNd0biwWub337O99Z4/cTbFRKqMKov84r3DzOWqxAIqPREff/i+nQA0DItUsYFu2Ty0uZND01nyNYONnUG+enyJum5h2Q6m5RDxK9T1G8eQPD+V5ehcHkGAn9s/SGfIdT8PJwIkgyqW7dAf81M3LMJehblsrWWACwKEfSoJwybsU0iXNaqaGxZSrBtkKho+Vearx5f4/tlVfIrELx8YvnR7BNjdH6FuWGzpCTGVqZCt6PREvPz9oXkcB751aoXzKxUahsWFVJlfvH+ktf36TK8oCJTrBp89lyIZ8PCOnT34VQlJFK7b5gNEvAoRn0KxbtAX8zGVrjCRKrOzL8LzUxmWCw28isSvvmkEuem8ufx5q8qN5hGu5OXEgL8PuAM4BuA4zrIgCKGbb9KmTZs3mnfu7GFqrcJstsrjLy4Q9Sk8tqePoYSPg9M5JFHgQ3v7iQUU/v7QPIWazmpJw6uIfPqJcV6YzmI3E/weP7xAwCNx/4YkZ5dL3DMWZylX5+kLaVILDWbSVUI+maG4HwdIFetUdIu6YRP1K2zsDNEZ9rKrP0q5odMX9dMV8qCIAoW6gSqJLOZdo0uRRUp1k5DXpKqZlBsmewZjqLJIT9TPcMJPVTfwyLKbaNgRYHtvmL99bobjc3kWCnU0w8aybKI+FVFyExhXCnX8zY50udBgPlcjW9H4yF0D9Fw1lWjZzmXLl4L/DMvme2dSnFkuosoiNcNCEUVE0TU2NdPt1GbSVeqGiUeR+ODefpKh2zt3++CmTg5OZxmI+4j4lNu67xvx1ESak4sFvIpIVyiIJAoIzQTJ66GZDgIWmYpGWTMpNyz+8w8v0hPxYlkONd3CtGyWCjYn5vPus5dEJtcq9EZ9nFos8H9/Z5xCXcevStw5GKMz7MUjixRrBh5FpKZbFOsGwwn/q1ZOifpVPnzXwKvaR5s3nqBHptww8SnSDd+J759N8d+fnUESBT561wDjKyUsx+besXhrHcOyefzwPJmKzoGNSe4avvS/r59YZrXUIOiRefeuHg7N5G75t2jbEPGpXC96odQw0E27NVO5TrlhkCo2CHtl/vJHUzz+4gIBVeazv3wXgwl3sOhVJB67LMzqHTt7ACjWdZ4aXyVfM3jzlk7u35Dk/EqJ+8YSrJUanFkuMtYRZChxadCZKjZ44myKkFfmI5f9JoaTATZ3hTAsh7GuIFNrl2KpL12fgyQIBJtt7cauILbjkAx62Dccw3Eg4lM4v1wiV3VnaEUE7t+QRBIFNN3iT74/geU4fOLeYd62rZuKbhJUZeIBlVxFZyjuR24a2pLkKlrlqjoRn8LH7x2iJ+I6Pp6aWOOpiTSiINAX97PnBvkZc9kq2arOjt4I/88HdzGXqbKjL8JfPDOFYbmx/uuDMNtxrpj129oTJlVMIwAHNsavu/+reTkGuO44jiMIgjteE4S2a6BNm58AFEmkqlucWSpxZDbHUCJArqbjkOTgdLa1zsNbOvnY/iEMy2E6XUGVRE4tFlkp1PEqEov5OrGAyny2Sk/Ux/vv7KMz7CXuUzm7UsK2HRbyNZSSQMKv8qmHx/itfzpFVTMJeCSCHpmtPWF2D8TY2BnicwfnuLhWwavKDHtlTi2VqGgGihSgI+RlQBHZ1BViNlvj4lqVn7t7gJWixp6BKEGP2wiPJUMEPBKxgMq7dvXwxUPzfOHwPOW6gWk7xAJqy3O/mKvxyc8dZXKtzEgyyCcfHCXiV8hXdRwHDPNaC/LOwRiiAB5ZYkPnJX9Dw7AQBUgEPGimhWVD0COxZyCCR5HYN+Q2wJpl0RPxEfIo3DeWvO3PtjPk4e07ultKHK81Vc3ka8eX8CkStuPwwb19/PWzs83kxxtv1zChVNdxEPEqEpWGySp1GqZFb8THYqGGJMJioc6R2Tz3jMbpjXiRJQHTcjBsG8O08QVU9g7FOLCxg5lMlR+cWwXAsmwkSWT/SJz7Ntz++9zmJ4/H7uhjPldrxek3DAtVcgfJ68xk3IRKy3Y4vVhgqVDHdhxmMzXuaaY7lOoGmYqbuDeTrl5hgOvNgbZu2XSFvVcYvi9Fd8TLu3b1UKgZVxiEsiTy7l09nF4s8qZN7oxZVTPxq+5AQjMtTEfi6Yk16rpJXTf54fgqv3T/zfMz1ooNqrqFAMznavzBYzsp1Q2ifoXPPj9LvmZwbrnEe/f0cXA6S3/Mz1MTq2QqGrmqxtG5HMmgF0Vyc4c6Ql5M22Yw5mdHb4R8TWdnX4TJtQpLhTq7+yP0xbwUVwyGkj5EBIp1g4G4j+6wl3RFI+xX2Nob5nzKDfPrinqYSleQRAFVFlpJkRdSJb56fIly3eCj+wf5Dz+zg5VCnc09Ieq6xWy2xlhHgO+fW+XUYoG+qI+fvXuQj987DMDJhSKZioYiidxoeJ6paHz1+BKO4yogPbK1i9igO/MQ8atkyhpRv8rbtnVzdqXIcCKAKAhMrlVIBFRShWaWqgATqSpv2f7S78DLMcD/URCEvwSigiD8KvDLwF+9jO3btGnzBhHzK64hqUh4FJGoXyXscxNOLNvBweGvfjSNYdmslhr0RLws5utEfAqdIS+SKHDfhgQvTGW5uFahL+JlLltlOBkAB969qwfdtPnCoXnqhkW2qvP0hTSFmkaxbiAJbpLaQ5s6iPgUPLLY7FBUZElgJu2qTJQbBjXdpNQw6AgFGUkGiAdcr3FVtzg4neXZyQxdIZWgVyFf17BRed+dfayVGnz/3CrFmtHyTpiWjSqL6JbNMxfSFKquhN94qkS2qvPz+4c4Np8n6JGviROt6SbPT2YJeGTuHLwyDjjkVfjA3n6eOLfKWDJAV9hLV9h7jfG3sTOEbcNoR+C2a1rrps0/vDhPtqrz0ObOG3p1bieqJBD2yRiWRdCr8N0zq6TLGsYtKCHM5hp87K5+DMc1hs4uF6k0DHojPgIeBcOy0UwbVRL4hxcXWCk2uGc0wZu3dPLcxQwX1yps7Qnxnt29CILA4Zkc4HoFTdsh5lcpXaYg0eanG68isanLHTQfnsnx3GSG7oiXD+8baIUnvP/OPjc8zyOxtSfMqaUSwBU5IfGAm5h5ca3Cm7d0XnGMd+/uZXylzFhn4ArD/mZYtoPjOMiS2Dq/axHwqTKOA988scyzkxl29IcJexWGEgEkUeDBzR0s5OsEVImHN3fdYD+X6Ah7GYj5XQ9vXwRJFIg1Q1vSFY0TCwUGYn5+dGGNU4slxlfKyJKIadlIoki2pHNx1R2wbO4O0dGczavpFrsHoowQoNww+L2vnWa1rPHolg7GUxXKDYNzS2VKDdebf3a5jF9ZxbBsJlfde7qzL4JPFVkraaSKbijPgQ1JDmxMUtNNtvaG+ceji+imzdMTazywsQNFFhEQiPgUtnSH8CruoGQ6XeWir8LeoTjPTWbojXoZiPsYTQbwqRIh78sX//vQ3v5mv+hDlcWWM+Wfz61yesmdBc3X3DwYx4FCTb/5Dpvc8pk4jvP/CoLwFqCEGwf+7x3H+cHLvpI2bdq8LpiWjWk7eBWJ0Y4gH7tniA8YFo4D/TEfkiiwuTvEYq7Gjy+kObdSbsbGiQS7ggwl/G6coE+mO+IFx40nl3Dl7WRJJF3WmMvW0C2bTz28gbpucXQ+T1/UT02zSJd1BFy5qIpmspivE/IpHJ7JsbkrRDSgsq0nzP/5tTOkSgUCHlcbO1vR0c0SAzEfG7qCdIW9LGTrrJYaruyeabG5W+H4fBFwmFqroMoi5YZBPKBg2g4dIQ/DyQCabjObrTKc8DNjO+iWTdSnYFo2xbpOqW4wn63RHXETmxZyNV6czZGr6ZTXdWTDnlZs4zqPbutGEkWem8ywVKjztu091zyDd+zo5k0bk61p2NtJoaa3PHNTa5XXxQBXZIk/ev9OPvv8LIbpcGgmR6pUf+kNm7w4m+fBLW7YzFqpgYOAV5V5ZGuEct1EVQSifoWVZic8nanwsf1DbO+9pCpi2Q7fOrnETKaCR5bYOZYkGlAoVA3uGUvc9mtu85PPek5Kqtig0jCJ+N0QEZ8qs2sgikcWeXBTB47jDmzfufPSb7lYNyjWDTpDHibXKlfISyaDHg5svPWwsnxV52+fm0G3bD5x73ArMfByTMtmJlNFEl3v6tH5HPmqwVKhzu+/exsXV8ts7ApTbpj0x3wEmvHrL0XIq7C9N8JMpsIdg1G+dnyR759d5SN39RPwyIwkAoS8Mkv5BtPpCgGPzDt2dlNtmET9Cj0xH7lmSMzmLjchs6Zb7O53E9A102YxV+XccgnbcXhyIk3MryKg4lcl/B6J1VKR3qgPv8fN8wl7Zbb1hNyBtyyyrTfMSrGBJIqMdgS4a8SdbXh+Mo1ju/HypmXz+IvzaIbdDGtx27+dfREiPoWgVybqV/jmySWeuZAm7lf52bsH0S0bHxJx//XzRpJBD+/d00euqrGzL8pirsZkusL+kQQ+VboiNGedSjOmXTdtBuI+1soaogAbbjiwupKX1Ss0De620d2mzb9warrJFw8vUG4YvG17N1t7wnSFvVess5CrcW65xGK+xsHpLJbttKSWnhpfbaqQeAl6FKJ+lYVcjaOzecqaiQDsHYxyeqlEX9SHCHzwz5+nrJl0BFXyNQ2/IpMIqgQ8EpZlkwh4+OapZZ4cXyNf0/EqEr/55g18/uAcuYrGnYMxKprJTKaM7bg65eWGyVpJx6/IWLbNYr5G0CPz8XuG8Koy3zi5hGk5XFgtM5wMEFRlemNeHt3SSUmzGEn62dQV4rtnUuimw2hHiMMzWWRRYD5b5y9/NI0quYVdjszmePuOHr53NkWxZpAuN0gEPXgViZD32phOw7I5PJtjLluj1DCI+bNsbA4W1hEE4brb3g6SQQ9be8KslhrsG469Jse4HvGAh3vHkrwwlSUeUChrHkp186bawuukyg2+fnyJqm5iOw4eSUQRBd63p48nzq2wUtT4H8/PkgyonEuV6Y9e69nLVjSm01VAoCvs5dFtL+39a/PTzd0jcZ69mGYg7m8Z3wAn5gut+OX+mK9leBuWzddPLFGoGTy4qQOfKlHXLaL+V/dbPjKb41Bz5mY4keajdw9es44siewfjTORKrNvKE6m0qBYL9Ef9XF0Po8kilxcrVCo6fibXvLFXJ3+mDuDV9VMvnZiCcO0effu3pZaSqlu4FMltvVGWC01+Mw/X6Rh2JxcLPAH793JYq7OWGeQ+WyVmmHiUSWG4n52DUQJqDI7eiNuAqgiUtNtVkuu9vWzkxn+/JkpSjWDj949QDygUtVNNnQEeeyOPl6YyvKWbd187/QKM+kK/VEvpmVj2w62A30xP3O5Gp1hL0NxPwICisgVcfRDyQAeRcSwLUaTgVa4YFUzW4P1yXSFd+/q5cXZHJu6QvzZkxeZz9VIlRocnM66ybG6zcW1ClXddGd1x5Kt2RCAkWSAkWSASsPg979xlopmcnA6y+++Y+t1n+fDWzoJzebojniZWCm22sCrc4luxMtRQSlzSedcxVVBqTqO09ZoatPmNcRxHGazNcJe+QrZI4DZbIUXp/Mc2JgkHlRZyNXpDns4vlBgrdRAlQS+cmyR+8eS9Ea9dIRcPeSjc3nS5Qa27RYXkASBbE3Dq0hMrJTdgi+GxdRahcG4n/PLFvm6gdXM2q+ZNt88tcKW7hCZis7h2TxLhTqGaZNpesUVWaQz6GE4ESDsUzgxX8BYqyCJIh5FxK/KHJrNsZivt7yes5kqiiSQ8MvIssB4qkxn2Mv5VJmq5soShrwyQa/CvWMJ7h6O8+JsjuFkEEVydX5390f5yvEl6rpJtqqzoSPIr7xplCOzOb59ahnTcugMebAct+Jk3TC5sNpgR2+Ir59Y4oXJDIbt8KYNSd62o5ug59r7DvDVY0tkKxoVzWiel0L4NTK2r4coCrx9R/frdrx1Dk1nOT6XI+aTGYz7Ob1UvCXjG9wCN25RC1fTGxnWKhp/8v1xCjWTima2ppS3dIdZLNSbCZp16obF5q4QsYBKd8TLWkljc/f1PU3H5/NcXK2wdzh2jXxkG5dsRePscomRZOCGahA/yTQMi8V8nd6ol3uPKoAAACAASURBVA2dQTZ0XvsedIU9zGWr+BSpFVIBsJivc3A6i2bYxAMKb93WxVS6wv1jyZbRNxD34ZFfXu5FLKAS8spYtkMieKUndiFbYypT4b6xJLpps1bWMG2b0WSQTEVnpCNAd8TLSrFByCvzzp3d/Nenp+iP+tg3FOX0YhFFdqVP15rG8fhKma09UGqYDMV9yKJbJOpj+wepahZ6s+BXutIgGVTJVw0urlVYLWnUNIu+mI+3+rqIBVRm0lXOrRRRJJGhuJ+KZmLZDuMr5WYRInh+OstvPDzKC1M5PvXmMcY6wrx9hzuo+TefPYxmOizkGzy0KYlh2dQNi2cn08xmaizm6xyZyfG9sykEQUCVJXyqhGbaJAMqhZqBbjlMrlb47bdvYTFfZ09/lM++MMuPL6Z5564edvRF2NEsBf/5g3MEVAlVFinWDabTVVRZ4NxKkVxTLjDqU9nZf61mf023KNUNNNMmXb5xkZ2IT+GRra4D4OSiG75kOfDtU8s8uLnzhtut83JCUK5o6QRBeAy4+1a3b9OmzSvjheksh6ZzyKLAx+8dItqcQlstNfi3XzxBpqLxT0cXec/unqZ8X4N4QGWl2CBf07i4WuErx5bY2R9uJQd+/cQyDcPivrEEb97cwffPrpGr6ciSiCA6DEX9nFwsYlsW51ZKKLJI2KtQ1S1M26aq2WQrGksFmZ6Ij4Zuul4N3GIIWrNcWLaioZk2HkVAlgR0y2E46mEg7icaUDizVOJ7Z1KAw2DM14rntR23BLtfMTi9WOA3H93El44skC5r9EZ8pCsaf39wjkTQw6Nbu5jJVtnZF2U0GeDvD80xma40y5ZLpEoN/uHIPBOpMoWajuWAT5XYPRBhLBngMz+cJFfV+Jvn5gh5ZSqayVAiwFu2d1132nGddEWjJ+JjKBHgI3cNEPTIr1sy5BvF4ekM/+7xk2QqDRzHrU5565Uk4PLCfo4DhmWxmKuR88jYtoNmWnhlkWxNQ8oKvGljkpVig68cc2UrJ1crJEMeHtvTh0cWrxt3q5tuvL/jQGXCbBvgN+A7Z1JkyhqnFgv8+oNjLTm1fy1848QyS4U6Mb9yhTzd5ZxcLLCQqyEIAjPpaktmz7JtlvJ1dNMmXdL4dmYZzXQwTJuVkkapbjAY9/OBvS+t6V+sGTxxNoUqi7xlWxefuHcY07a5dzTBk+OrFGoGdwxG+b++cY6GYfH8VIazy2UM02I2U+XOwVhL6u/AhiTbesKEfQrH5wsMJwLIosCzU1menkgjCvAzu/uo6xYN0yIRVPjCoXlM29U2PziTx2wqOFmO215btsNCrs54qkR3xIthOS2v8IszOeZydSRRIF/ReHYyiyDAlq6QK2Vo22zu9mM7DnXDpi/i40tHl6lqJn/3wjy/9ehm5nM1RjsCaE2vte3AarFGqW6g2w6Fqs6hmRxeRWRHbwTbdkCApWbyP8D4Solyw8Rx4NxKmY6gim7Y+BWR751ZoVAz+NapZX7p/hHs5kzuJx8Y5QuH59nQGWA2XUOVRVRJwLEdFnI1RFG4YTx4xK/QG/OxmK+zpefWwklE8VJLOHiLA9pXHJjoOM7XBEH43Ve6fZtXT1tH/KeD9TLapu1KtkWbv+11vVmAqm6SayZ+FOsmiaCHkWQALWVR1S0M09XFNiyHlWKDTEWjYVicXirwqw/cyYObu/hPP5hgJl1lQ2eQhzZ1MJuukq5a2DaItmvYeGSxGYPtUKhqhPoieGWR8dUKXkVCEECVRGzHwbYd8nWDdNVAAFQJ9g3HuHMo2tQNr3NkNofjONQNi0LdIOZXqGgWpg3FukXVsegOe5lIlXhuKotfFWmYFn/65EWKNVe28JGtnbxpY5LBWIATiwUW8jVMyyERUBjtCJGtaMykq+SrBiCgSAIeWeD4fIEfX0hzYbWEgEAi5HGl8WyHnX0RhuI3F3p62/Yuzi6X2N4buaIoxtUcm8+Tq+jsH42/ZuEorxfLxQambWO/AuP7atxtBURRQJVFNMNCltznG/Eo9MV8OA78zXMzTKyU2dIV4OmJOn0xH9mKzrt2XRtzDzRVGryslhrXjbFt4+KRL+kX3+4E4X8JrCf9VTSzZZRdTbFutAYexcsk/yI+lTsGY1i2Q2/Mxz8dWaSimUgi6JZrNAaatRIKdbdIjnSDJMzTS0WWmgoZM5kqDzSVTabTrmOkYVislTUyFY2abrJW8lLTTNbKGgGPzJu3dnJ8vsCW7hCyJNLZDHErN6/PtB0upMqcX3E9sJu7Q3gVAY8sM5+rtZRE3BL37rKFg9UcDOumgyy5coGqJLKhM8h8rkYioLbumWU7lDWTUDOXZbXSIFcxMCybVMnV5tZNBweblUIDy7GZXC3z6SfGmc/V2NEbQRVBt13Fc1GUUCQREZjPVUmXG/hkkZ13DXJsLociibxrVw8vTGUxLJvN3SEUScRyHGIBhd/50ikKNYN7R+PkawaVhoFaEXl+KsPhmRwbOoP0Rn10hb34VAVFFtAMC8cRkSU3z0QUhFYM99WYlsNIMsBwIkDEe+VMxcXVMi/O5tnYFbxCESd7mad8/Vm8FC8nBOX9l30UcYvyvJr2t02bNrfAgY1JFMnNWL/coBjrCPArB0b40cUM79zZwx2D7jTkI1s7WStpbuOjiJxaLBAIe3hwYwdv3tJJvqZxdrlAuqSRDHo5s1Rkcq1KtqzhV0VkUeCZCxl8HgmvJqJIEn1RL3uHYpxbKXNqMY8D5OsW55ZLbOwKIeDGLuqmSTTkxauImJbDxWZ8pQNoFlxYrZCtmgzEfGzrjeCRJVcH2rYp1g02d4XJNbW6Qx4JBIHJdJW66WpF56sWM1TJlBsYloMuidQNi+cms/z54lQrtCEZVHnb9m5+4+EN/PWPpzk4nWNrT5BSwyLilanoFgensqwU6ySa9/WTD47iUdxOqCfiZTpTIRn08KOLGYIeiYc2dV7RibsVHW/uHVkp1luFfAzLbmny/qTyM7v7OLNU4qnzq+i2GwdqWM4r6ghE3HemplkUHZ0t3WEcx8FpGuWlusHRuTwzmSrLxTrFhs5I0vVm38xZKwgCH97XT6FukHgNCvX8a+E9u3qZSlcYiPlvaDz+JPPOnT2cmM+zrTeCg2vwJkMewl5XcUcSBB7d2skLU1n8qsw9I3EWm4P34aQ7q1VqGCT8Ks9PZak0TGIBlZVCg7JmgiDw3TMpLqyW6Yv6bqgdnwiqTDaTxJMhFc10Q7BqukW+qmPaDlXNrXVQrBvYjs223hDBnMS23jBjHcHWLM5ivsbXji+xrSfM/pE4pxaLDCf99Ef9TDcTNxVR4PhC0XUk9Ed4cHMH+arOPaMJOkIqJxeLfGhvH985laKum8SCCj5FolDT6WxW9fXIbtG2+8aShH1FIj6V0USA//idc3gVkTdtSPLp701g2w4bOwPUdJuGYeGRZTrDKqsljY2dQS6sVbFsh8m1Mlt7wkxn3MJBQ4kA83k3lGY8VaFYNykBPzifotiwEASLkwt55nM1arrJJ+4d4u6ROIWazlu3d/GFQ/PUdBtFFNjVH2E+W2NrT5jDMzmm1irkqzoRn8zTE2kSAZW+mI+Axw1HsRzX6BcFoaUhfjWB5mDk+EKe/SNX5tf8+GKGYt1gtdRgV3+kFYbUuEwJ6vRC/pbe0ZfjAX/PZcsmMAu892Vs36ZNm1dA0CO34swuRxAEPnzXIB++61Iiz+XJH47j8INzq9w1nECSBP7nRzfikSU00+Lj92g8cyFNb9TLXLbGi7NZFvJ1LNvB71HY3BVkNisQ9qsEPTK9US9nVsos5mvo1qWR93yuRsNwi+REfAphn4JHlrhrKEamqrOYr1HRL8UdNAw3kVISwaeIRHwyEZ/MWlmnUDM5sVigI+Qh4ldQJAnLdqgbJkdn8wRUGUkSCHplQh6FYsM1sAzLYSJVcsuhOw4+VWIg7ufukQRrZY14wMObt3aSCKg8P5mhWDfZPxLn6yeW0UybuVwNSRIJqDLb+6LUdJM/e3LSLWN/WULlYNz/kgb31fhVGaVZ3jr8KgrlpMsa51ZKjHUEWslWbwSiKPAL9w0T9MhcXCtzbqXIQq6Oab/0ttfuy40Jt3G9cDY2w8kgAY+MbtqcWChg2e6MjV+VMC2HVLHBgQ1J7h65udqJLIk3nZVo44ZhrcfL/mtkPFViYrWCDYw3PcQ+VeL+DQmePJ8m4pPpj/lbCjsHZ3KcXXY9l2/d3sX23gi9+HAch7GOABdWK+zuj1LVsngVCZ8islRwy7wvF+s39LLnqjrDCT8IbgjVwekclm3z2J4+7tuQoFQ32dId4mvHHfyqRKZs4FVkLNv1xF7On/7wIqcWizw5vsZ9owlenMtzdC7Pv3/PNt61qwdVEvHIIv0xH7bt4FVk9vRHMWwbx3EdIG4YR4VPPbyBo3M5HtnWxY/GVzm7XELTTT710Bi2DZu73KJpb76sDfz0B3cD8A+H51ktazgOLOTc36fa1NhOFRvkqjqrJZ09A1Genczwli2dHF0okKsZdIe9dIa9dARVYgEV23FQZQEBAdN0yFU1BOCZiTTPT+VwcDAti6pmUmqYKKKAabkODdOBX3tgjPMrpaayyxK5mo4kCkyslpjOVFkuNOiNeAH3GCvFBudWykiCwHS6wpaea9MY10oNnp3MYNkOXzuxzH0bLlUwXs976Yl4US/zBMgCrJeSGEre5hAUx3F+6VbXbdOmzRuPIAhN/Wl3WnJ9pO6RJT569yCP3dHH5w/OkSo2yDbl7DyyyEjcz52DMS6kyphm09oWBFJFN8ny8i7BsBwqmoUkiiSDKmG/SsSrcPdInHOpMh1hL5WM20mpkoBpWWgmjC+XmVyrEPGp1HXL1U/FjR3XTZueiI/dAxFWCnVOLRURBOgMq3SEvBRqBveNJagbNl0RLw9uSjKRKiMKYONOcXpkiXxN59RSgYZhs1ZqIDerbW7ocKcOu8Je/uO3zyGJAvPZGv/16Sm29UZ4YGOSM0tFHNxqZ+var7Gr5KvOLhc5uVhkW3eIDV1uQYiOqypdRnwKH9s/RLFuMJR45Ybzt08tk68ZnFkq8usPjr2hHsvpTJXpTIWpNddzZb/CeVDTdmcrBECRRURB4nfeuoWDs1n+/KlJ8lWdsE9hrCNAMughX9Ppjfo4NJNjpdhg/2ic+8aSFGo6AY98hXZzmzYXVt3Zt8m1Ct1NI7JhWByaznFyoYBXFdnaE0aVXaM14nc94+ve6XVKDZN8zR3sz2VrvHdPLzOZKtt6wmSrOsfn82zpDt9QB7wz5KGsmSiSSKpY56nxVWzHNeR+5U2j6KZb+fXJ8TVSxQbv2NnNhVSllUBomDYTqyVGkkFOLRaZTldQZImtzdhk23FoGBZv2ugaiRXNZHtvBMOyGe0I8D+en6XcMDmwIdm6rkJN597RBF5FYu9gjD/45rlWNdlnLqSZytRYyNcYTLjJlj5VolQ3+OrxJSRRIKBIJIMeLNuhO+oh5JOp6RYhr0SqpGE7DgdnsmimW7Ds1HKRbb1h6obFYNyPT5EwbQdBEPi9d2zlM09epD/mo1jTW9WHlws1zGb14YurVRYLroPoW6dThHwyogiJgMLeoRh7h1wv9bG5PNmqTn/Mx0ymgghYjkM86GFDZxCPIlGsGYgIOMBS8foSqj7VPb9cRWPLVYnej2zt5K7hOEGvfEXolk8RKTedTbHglYpjN+IlDXBBEP6Um4SaOI7zm7d0pDZt2tx2vnN6hcm1CveNJdg3fG3525/Z3UtFvxS7dzkvTGf55knXC9wb9bWS2voTPh5/cd6tTiaJ9HgULNsmGVSxHTBtm3LDapUfz9dNFBFSJRHDhr6oD1kWeWhzBzXd4qvHF9EMN37ctm0apuv1dEyHqmbgUSRk3cJsxhX3Rn38yoFRxldLzOfq+BQJjyzRGfbxv79zK1XNZCpdYTFXZzDhQ5HczqhhWOSqOrYDE6ky/9+TF4n4VAzb7eB29kXc81BEhpMBtvaE+eKheS6m3c5uOlNFkUWifoXRjgBV3eL+DUkObEiyXGzwxcPzRPwqH9rbjyKJfOPEMqcWC3wT2NwdbmbEd7Kr/0o97lhAbRW8eKW4yUiG+4ze4GiBHb1hnh73EPDU8TSMG5agvxVkUcCvyPTHfezqC1MzTKoN11iJ+lU6wir7RxJ84M5+xlMVptKVVqGO2UwNnAyHZnLE/Ao/t38IVb6xEX5yoUCmonH3yE9+LH6bl8ariLwwlWXfUIy3bOvi0EyO4USAI3M5NwHRdis6/sZDYwiCQKpQJ9cMCYldJjcoCm4eR6as8U5Z4LE7+lqzUImg5ybFdFxquoVXkRAFyNZ0GqaN4zgU6wZnlkoU6254yIf2DXBhtcwjW7uYyVQJehUUSeAPv3Oe8ysleiJuSKHf48ZPv3tnL8cWCvREvPRFffzlM1MoksjbdnShmzaG5Toe1mPbV8uuXOn5lRL3jib49PcmWCs3uLhaRmwakoIgNHNwbDDh26eX+MqxZQIemQc2JnjizAqiKPCJeweJ+hQ0y+at23qIB1TyNR2/IvJXP57FsFz1mPFm4nulYeJVJDf5UYBMWWe15HrK0xWNPQMxAh6JOWglXt6/sYOqlqJhOTyytYO/fX4exwFJFHlkc4LFYp23bO2ippssFxr0x3zUdJOaZlHXLd65s4fvnknRGfTw3jt6ifoVYn6VHX0R5nNVZEl05RHPrNAwbB7YmOTLx5ZYKdZ57+5etnaHKNS8jCSvzAUSBOEKKct1FLkZ5A6EPbeWjH8rHvAjt7Snn0JebRJkmzavhoZhMZEqA3BqsXiNAZ6tajx+eIGAR+Z9e/oI+2ROLRYxLJs9A268uE+VqGomF1ZdD/JgPIAiiSwVXJUL3bLJ13Q0w6I36sPG4Z07ezi3XKZQ01jINxBxR+im7bCUr1PXLe4djbN3WzfL+TpfObaIKAjsHogwna5i2jq2Y4MguI34VV718ytFvnxsgZBXYWqtTE236OpwO8qKZvJ3z88yk6nSG/Xx4mwW3XJoGDZ9MS+27Z5vybIp1XWWhDqxoMqGZIDzKyUSQQ9Ta1W+dXKFnf0RHt7SyUiHW1nOdiAZUFnM17lnLEFXyMvugSheReLQTA7DcpjLVJvTnTF3QOG4Saf5mk7Ep7Tkv24HjuOQLmuEfQrv3dPHdKZCf8z/hifMJYIeHt3WyUSqSMOwXnEikIgbq7+pO4xlOxyZzXFxrcLeoSiSJFCumgzIfnb3RxlKBBhuxn//6EKauVyNe8cSvNjUVM7XDKqaiSpfO9BZyNU4Pp/n7HIJryLRMOwbJnC2+ddDw3DbOctxmM5UmEiVqTRM9gxEyVV1gh6Z7oiv9XtaKTWo6WYr7Gk93CxX1ak2TERBYCF360Wn1lktNZhIlZFEgffv6WV7bxjLhqFEgL97foaKZpEuNXhqPE2mqlFumMxla27IjCKy1kzuS5UaHNjUwfKLrgTi3qEYY51B/B6ZE/OFpnfbzW1ZD4kpNyzGOt2iOVu7w3znzEorvOvEYp5sRaesmfRGPVxcM4n6ZLoiXg7O5BhK+BlfqWDZDqW6wcnFAmslt9DMXKbOgabHvaqbbOsNE/W79SJ+522beH4yy6ceHeMX/vpFN4GzYXBsrsBaWaOqmQQ8sjtj6ThMpMrYjkO5YfK+PX2kKxoeSeKekQTPTWbx2A5Rv4e7h2MU6wbvv6OP1VKDmE/FtB0+/b0Jzi+XuGs0xon5AhdWK8xlq/zFx/di2rCpK4hpQblhYdo6xbrBvmE3hM1NXnX70HLD4Mnm7IQqi65Sl9+kN+pjOl1hPFVmZ7OC6ImFAhs6g1cMvi5XEVJvUZ7yJQ1wx3E+e2uvWZs2bV5PvIpbPnkqXWFXf4TZTJWwTyHe9Lb+9x/PcHQuT8SnsL3X9dA+Ob4GuAazKArgQFkzyVY0BAT8Hpn9w3G+fHQJTbWwLQfDspmv6GSqOmGPzHy2RrJ5DFl0veCdQQ9Bn0y+aqCZFs9cyGBY8AffOkupbiJJrqZ31KfSP+BnJBng4mqZU0tFNMOVw1qnbjicXHCrYi4XGpjNDuB/+vwxvKqIJIqsljXSTZUAUXAvqD/uJ1fRsXGvyTQdHMG9T2XdojPk4fh8AdtxmMvVUBWB2WwVzbTZ3htBlgTqmsVyM3Hyt96yqWmwWQzF/RyfzzObrfLk+BrJoIdff3CM/rgfSXBjvU3baVVuux08fSHNifkCYZ/Cx++5shrkG8lcpsoff3ec6XSlJTf5SrABSRDoCqmslXWWiw2mMlXGUyWSAQ+KJLBcqPPkeJrN3eGWVvW6igS4neaJhTxjHa4++I8vpjm/UmLvUJw7BqLUDYuvNQv/TGUqbO+JvKJS1G1+8tjdH+XYfJ5tPeFWCXW3Ym03v/bAKKoktkqtC4JAppln4Tgwl6uydlSjUDc4sDFBd8Trhq/dYg5IXbc4Mpcj5ldRZZGOoIokCgwmA/wvb92CbtmslRscnSvg4KBIAodncxiWjWE5ZKs6pmVzZC7Pb791E18+usRDmzuYzVRJBj14JIkfT2Y4tfj/s/feQZZleX3n51z3vEnvs6qyvOnqrq42025M9xiGgaEHGFxoMUICltBCsOyuVugPVgShQNIGrGCREAsSgyTMIOgZmJmeHj/tXXlflZXe5/P2+rN/nJuv7HRXTddAd09+Iyoq8768972Xed85v/M7X6Ni0B/f06dyHwyNx3b28HcnlwlCyRN7B8glDFqO39FRADQcj3rbI5SKc11qegQhFBous+tqsVC3fT60b4C1ukNv2sLSBW0/AASZhMGZpRptN+Aj+wf4/56domZ7fHDfAA0nYO9wjqnVFqau0RaB2uXUiX7XOo9MdPE3x2z6MjG+/55hnr1UIJ8wsf2QTEw5lHzt3FonwfTYXJl//YMHKbdcxruT/Ofnp0nHVTjb1y+sKYH+GcX9llJSsz1euFTA9UNOL9Y4MlPmsycWiZs6cUNwbrmGJuDgaI6FcgvHD7lrZIRUzKDtBgzl4nzyvjHW6w6j+QT/8ZuX8QLJQrmlxpy5KkO5BL/5iQOdXbdS40r8/IWVO++C0gf8c2Af0CG4SCkfv9VrbGITm7iz2AhiefFygaeOLXa8wltuwNmlKg3HI27qrNZsvnx2lULdJpSqcGl7AYO5ODPFpvJ0RtKTinF5vcn2vjSGLig2XCbXVAZX3VYiGA1YrtqkYjrpmMFD27vpzcRpOgHPTxYAxUP8zPEFmo6viutQ4noB83aLYtNhptjE9gISpo7j3VjEaRFfGzYcVCSXCk1ihko/TJoae4cy1Gwf1w+wvZCzSzXWGy5D+TgJ00DXoeUE5BMmg7kEuYRJ3NTwghABfO3cOitVRXFZrdqMdifpTasOuIqBVgua//7KHG03YFtviqSlhsxq22OsO8kHdvfz2eOLNN2ATxwauaPUhrWaolrU2h5tN3hDesXfJ75+YY22G+DeavrOVdDgmsXWQsXmcyeX6UpaOL4qPhxP0ZRAEEjJxdUaXzqzwof2DXBupc72vlSnEJpab9KXiVOzPVarNq/PKPeBFyYLHJsr07B9WtF99tiOPh7b2cvWN/B238S7Bw9t7+Gh7VGnc7XOsxdVEmY2cYW7O19q8dnji5i60rD0pJSGo9RwCUJVUE2tNXli7wAXV+s8vufNw1VApUOeXqwC8OiOHhCqQzrWlezQ0Rq2p7IRfEk6ZiCEouCZmnLnqLoBgzmdYsNj71CWhuMzX25Rabk4fshqND64fsiZxRrrDQddU1367qRFEHWX//QlRQm5sFLnHz20hcVym4OjWf7dMxfxw0DZ+4VXPLqPLlR4+bJq3PzS4zv43R8/BMAffHOSbNxECNXVX6qo53/69DJHZyv4oRLAz5VaFOoOKUtnKBfHDyVbulPMlVoEocTxJUfnawRSUmooob6O2kmcL7eYKynN0HAujqYJQinRhBLVFhtuxx5xtWZzeLyLLd1Jlqs2E70pEGrOGsjG6ElbrNYdLEPj9GKFuu3TdAJWa3bnfZSaLq3IpjeUkl96YifFpsvhsTz/5unzXF5v8Il7R8klLQp1h3zS4stnV1mqtFmqtvH9sDMuXz0cFq4qxt8It9MK+O/AXwIfA34B+Clg/VZOFEL8r8APSikfFUL8DsrC8KiU8pejx+/osU1s4rsN13uFvzBZUD7KwD99dBtH5iokTD3iIgp0DY7NVTA0FecbMzSyCZM9QxmePrPCXLGFBGUXOJTj7HKFalsJeEJASFXcCk1wZLbKtl71/PeO54kZOmeWarQcH8vQEWGAJqHlBbi+pB0JgQxNkEkonmMoJX6oiu2YribHdMzAdv1rirYglIQhPHl4iJWaRzZhUmt7LEShGQJYKLVU50nX2TWQplB3uLzeIG5qPLqjl7ips603yYuXiyyUlb9tNm5Qarp87K5BQBA3NLpTFqWmy7mlGmt1h3u35Ll3vAtdEx1hzsXVeuSvqzx+r+d/vxW8d1cfL08VGetK3pRz+A+F7pTF7sEMuYTJUqXNesO5ZReU639MAk03JAgdYrpGqCuf9sFsnN2DWY7Pl/FDyXOXCqzWbBKWwfnlOr/w/iQxQwnBpgtNEpZONmGyezDD2aUa6ZgSjQkh2DeUiSwj0+/6oKRN3By7BjLX0AXKTZeEpTNdaLJWdzA0wQPbunny0AiuH/D9B0f42xOL1G2f/myMZy8WiBk6R+bKbL8qUbPWdjm1WGXPYPaatNxEdJ9pQrBWd7DdAD+QLFXbnQI8lzAZySeo2R57hzJMrTdYazgcHMshEMyVWhwczXFyocKJ+QrjPUnuHs1Rbft0pywe29nLV8+tMZxLcGa5GnlPC7b1pLi01iAIQ/ozMbzI8HutbncsDR03wNQFcVPZziZMjboTYGgwX2rjBSFV22OpatMdva+dfWlGuhIYQtm0HpurEkhJyjLIJkyVYhs3ubBcp9xShbLqrEtWazYNNvb7DAAAIABJREFUx+sIRlerLdqeEtz/xatzvHhZBfH88hO72D2YQReCkXycb15cJ5SS7lSMp44u0vYC6raPH0hGu5Ks1h3+3Q/fHdkF9vC//dWJzi7CY7v62N6foTdtcXy2hO2FGJpgJJckYXnomiBhKrG+lMpp6sP7VUNrvtzifETvfPbiOv/mhw6yUlW5Al86vcISbVIx/RrhbbQRC3CNhuCNcDsFeI+U8o+FEL8spfwm8E0hxDff7CQhRAy4O/r6XiAlpXxMCPEfhRD3A8GdPCalfO023tMmNvGOgu0FvHS5SMzQeM9ET2cAuN4rfLQryZfPrrJed3j+coF7xvKcX6nz/t39qkPRcNg/mOX0suKBpyyD3YNpzi9VmSm2kVLiBCGWrrFrIM37dvbxhdOreFGnJGFqSKm2buuOx6W1OumYQVfKotr2qNseLdcnaWk4HkgkXhTgsjFIyUCCDMnEDUotD12oLoJpaFi6TlfKJJSSpKkxX1ZWd4YmKDYd/uzVRe7f2sWJebUFe2gsx+R6s9NxNyLO5mK5RampwjYsQ2OlZrNnMEs6bpCKGewZzNCXiQMhfgDb+9McGMnjBiHb+9IEgUqOSEfx0WsR9WUwF2fPYJad/WlOL1YxdO0NUzO/HQzlEnzi0Jsn7d1JBKHkxcsF/FDyyPbem3bdVedN8Avv285/+MZlVr4N3rsRdfs0IBQQBBIMgYagOxWjOxXjzFKVpusjpQrW6U1bLFcdhvNxDE29rkd29DDRlyKXMElYOh/ZP8h63WG9bkeOCyke3t7bCS/ZxCaOzZX5xoV1EpbORG+K6UIDU9fJxAx+/IErlq4/9fDWKHBK8tzFAgvlVsdtYwO/9fQFLq836M/G+N0fO9TprN81kmVyrcFwPs7l9QYnFqqdtM1y08MLww4tJWUZ2L5y6tB0QS5hcXSuwvR6g2zCZLHSpmZ7zJdafN/BIXrSdYZzCV6aKvH06RWSls4j23voScfQheIvawKEpjru33NgiKVKm196YifPX1Lv4z0T3cQNnbYbkLIMaqYKdTN1jaSp4YchJhr5q6xTh/IJ4qaOqQsOjuY5OlfB9UMOjuZ4ebpEw1Ye5sWmg+OFzJfbmLpAE2AZgrSl03B84oZGKFWzBAmnF6vUbY+GIyi3bPrSMWKmxt1jXeyKEjdHupO8MrNA0wnYM5hh/0iOiyt1HtjWxUAuzvfklKbj4modL1ApphdX6jxzZpVdAxl0XSObMNCEIJ0weGLfAHrU6HnxcgHbC7hv65W/7XA2zkA2xsXVBt97YJC4qbM1EmPuG87S8gLGuhLKNebSOkO5OJoGkWkL8hatoW6nAN+IiVoWQnwMWAJuZXb4J8CngN8AHgK+Eh3/CvAeVFPkTh7bLMA38a7F0dkyx+crgHLX2Bt5mF7vFf7Q9h7+ywvTpGIGx+cr/Nx7J/jwvkE0TeD4SiX+6kyJ1ahQQQiOzFWwXZ+2JxGojnTLbWLpgl0DGVJxAz8IcT1F+QBIx0Vkb6iS1U4vlNGEYL3uRG4dAk3TENHXNqr7vdE3qDsBQSA7hbkmwPEC2rpHEIY03YByw+lszbqBxNIFtbbDi5MFBrJxbC+g5QaEoeJ8h6HigHt+oAZZASPZBLqmUay7vNoucny+rLokO3r5xQ/s4FMvzVBuujxzepWff9/2TuGp6xpPHhrh9EKV8Z4kU+uKS3puucaewSz92Tg//77t3/bf0/ED5Z/7NkkiPLdc69A4UpbBA9dx2l+bKfKVc0pH8Mp0Edf/9kSYvgRTB11omLpGKqbhBZJMwmK8J0HN9ik2XDIJkx19KT52cIiLqw0CKRnKJzo2jEKIa8KpXD+k1HTRNY0tPXF+8qGt39bvYRPvXmxQJ9puwFyxhe2GuJriXe+46ueEEOgCWlEEel8m3qHFbWC9oRafpaZHpe1yaqHGaFeC5y4V+NLZFeKmzt2jOXIJA13TIt53mUBK3ruzj4Spkl/HuhN85ewqpaZDueXSdgN6MzGqLY8wDHG8gCCmU7d9JiIx8guX1plaVwE/H9o7QDpmEDc17t2S57WZEkEoGe1KkEuajHcnkcBrM0q0/NJUkYSl0XCE6uQKJULXNMF4b5KlqkM+bhIieXW6RD5pcnKhwuU1xQ9/eapEoeEQhPDi5SKX1xo4fsDZ5VrHMnRbb5JKS7nK6JoGQuD5ktBUmhlNBIoX35VSVBFdo1B3eXm6hEDQFblROX5IXFeLl40GQdPxWak5DOUS1/Dye1IW63WbfMLkL16dY7rY4uhcmY/uH2BqvUkmYTDWneTTry8gBDy0vZtcwiQTN69JxWz7IRN9abb2pIhbaqGyWGkx2pXEC8OIShPy/KV15sptzi7VOsmiAE3vqlSeN8DtFOC/KYTIAb8K/B6QBX7ljU4QQpjA+6SUvy+E+A0gD1yOHq4C+1Fd7Dt57PrX8HPAzwGMj49f//AmNvGOwgYVQQi1hflGeHCim5cuFxnJJ8gnrsQKxwxl67dvKMt927rZOZBmpdrmuckiLSdE1wWGEHihxAtkJ87Y1MHxQq7K1cHxAu4ayTNfVrw/N5AEoUQAQhMM5eJ0JU3ips50sUndaSvfZw2VeCgEPjJSxKt/gVSxyYH0O7w6gYpPtgxBOq7TdkPqtk+lXacrYXJ0rkIQhp0ORCih5UuErwp2TWh87MAgxZbH0dmyEnnGTfpWa7xwucDFVXWddNy4wWN7Z3+atUh1v62XKAHtrVNNXp8p8dylAoO5OJ88PHqNiv4fCrmE2bGXvNn91ZeOoQlBte3ScgIycfMGXvetwgvAJ2QkH8fUNRYqintvaBpztSa1iCO7WGnzZ6/MMV1oMtKVVFzP63BuuUa15aJpytu5Zns8eAcFsZt49+A9E93YXkB32mKp3I4i5kWHqrGBqfUG1bbH1t4Uhi5AqM7yl8+ucnG1zgPbuvnk4VGeOrbIB3b385ljS6pYTVh0pUwVIhYJ+lIxE8tQHeW/m19GSklvymS+1MYNQr55YY0zS1W8QPLNC+scHMtzbrnGvVu6+NIZ5bcvgcd29vLls6vs6M9wfrlK0w1w/ZDXZ4pMrjXQBJxdrDKcTxCEkmLD4S9em6fpqiCbUErmSy32DmbIJCwMXScZU2FhCVPD0jWCyFXKtSTHZytcLqimw7FZ5b8PgiMzRabWW4RSMtYVp9h0CUPJQtlmpCtBwtQZzSd4ZaqEZWg0HZ+lqk0I1GyPJ/YM8Y2LBbpSFh/a348bhqRiBl4gKUSLmulCM9qdhHMr9ahBI1ivObx4uYjjq0L4YweHWKy0Ge9WYUfpmIGha3SlLKaLLeKmTm8mziM7e7F0jblSi9OLFTRNsK0n2XEsuTr0yNI1kpZO0wnIJUw+/fo8pabLcD7OQCbOveNdWIaiUFJuEzO1axoRG/qdN8PtFOCvSCmrqEL3A7d4zv8E/NlV31dQhTvR/xVUEX0nj10DKeUfAn8IcN99970Fx9pNbOIfHvuHc+STFpau3RD6cj1+8qGtPL5ngIFsDE0T2F7A3xxdpNC0+diBYbb3p/mJB8fx/JD/5yuXEEiSlsZwLo6uaZyLgizabsBcSQk1g+u21kIJhg6WrkJ0gohWYBlqy6/cdJguNFGGK4rfK6SMXFgUZ10DvOiyG1d3r6voVNQ96EDK0tEQVG1FGi80PYTwsHSNVEyn4VzpPoiI1jJVaPCXr8+TT1mIqLh0/JCW6/NHz07RcgN2DqT5+ffvuKEAf3GyyFJFiY5+6uGtHZeZUtPF1AWZKNr6uUvrSAmP7ey7JcHkpUjhv1K1qUcx1//QGOtO8hMPjOOH8prO8ga29qZ58tAwL10ucmG1xkK5/W0V3xuQqO6hH4Y4XkjKUoE61ZZKNdUj8dp0sYUXSCbXGvSkLD60f5CR6PVdXm/wxdMrXFipk44bpC2dXNLiFVliOJ+4JqDn8nqDpUqbe8bym17g36XoScd4cEJ5wbt+wFh3shNJ/qkXlWDx/i3d/N7XL+F4IU8eGuGHDo+yWrPZ0Z/mj56bBpSvvKlr7BrIUGg6HJkpcXy+Sjpm8K8+vpeLq3WGcnH6M3HeM6HEoNOlFpfXG0gp2dqbZK6shIkpU8cL1Li4VreJG4L+dAxdiMheU8P1JecWalTbPpNrDYzI8UMimC40KTRUguTZ5SoXVpt4gWQ4H1f0vUByfK7EUtWhFPHf9w1leGGywOHxAXpSMV6cLLB7MMNyzcXQBQ3Hp+H6FBoOCUt5j+uaQKCSa5ESKVWTJhs3oueLMd6VotnlM9qtROtV2yeXNJGFK0LP7f1Zzq002NKdpD+X4MBIDsvQSFkaTSdACMGu/jRfPb9Gyw34ufdt4/OnVrDdgPdM9HBhVXXcM3GD//ryLPOlFrsHM5FoU+1e/NNHt/HM2VXuGssxvd5iar1J3FSLoOlCExE1mdbqNk3H55OHrxA6LEPjg3sHOLdc45Htvfzpy7OA0lp9z4FBjs6V2dGfZu9gll0DyoHp337xQud87xYF6rdTgL8ohJhGCTH/Rkp5K2H3u4F7hBC/gOpO9wIHgU8DHwT+BBVr//N38NgmNvGuxshNCqPrMVds4YWKx7yBlarNueUal9bqzBZa/F8f309/Js7UeoNUTCebUKJDN5SkjCuOFb4E31Ndav2qcUUAuYTBifkqLftKt9o0BO/d2cvZ5SrFpt+ZWAxN0p2yaDgBjhsQSNWdduUVSsrG5ROGwA+kEnwKFWzQ8kJsX7JYUd7jcIVzJyTomiqsg1Di+SGGrrYxV2ouTSegqLmk4ya5hElXysLQNHJxk+lim5ihkY6ZN+36jnYnWKy0ySdN0lGg0YWVOk+fXkbXlGDw3HINL1DvL580Obylm8m1BkfnyuwZzNy0Y37/1i6evViI/GbfPsXgm/Glx7qSfLa+xKnF2i1PNG+Euu3hR/dAy/PZ0pPgwopJsakoKI/u6GWu1OKlqOula4Izi9XO50ATila1wStdqzuUWi4nFioM5eOdhMC67fG5E8uEUnXZ/r759Zv4h8Ol1TrPXiow3p0kHdN5OerM/sA9wzSdQO0KmjqlpnKvODJb7oirZ4pNBrJxlipt+rNxMnGDo3Nlvmf/ILXoZwxN0LB9NKGCymaK7Y5t6FA+zqszIem4Qdvx0YVACkGh7kb2pSE9aQtTF6qIzSV4daZMuenScH0e29HDV86vcWAky0KtzeRanWzCZLXu4kbuHXX3imPU1HqLlZqDlJKzS3VihkAiScR0Km0PIWByrU615VNpe3z13BoPTnSzrS9FfzbO9t4UX3QDBrJxSk2Xr59fI2np/OjhUV6eLqFpgsNbuzizXCOUktF8grOxGtWWx13Def7u5DLL1TZCQHfaIhalCHelLQp1l7gh+OujC8yXWkwXWnx4fz+OF6qF9noby1CR9p87ucSJBWUL+d9emmcgY+FG1rJdKQvHDxjJx3n+0roK9Kk73D2aw/VCtnQneXmmzErNoXShwHA+znA+TsxQgUgbgtnLq3WOzJQJpeRvTyyxvT9NseHyyI4evnBqGS9QC4xtvUmev1Tk7rE8Y93JjiUq0OGGJzRoR/PRRyIx55vhdqLodwohHgB+DPiXQoizwF9IKf/bG5zzzze+FkI8L6X8V0KIfy+EeA44IaV8NXrMvpPHNrGJ7xasR8EGGxZUh8byHJkt8/lTy2TjJh/aN8CBkSsTQbnlslq1CULJl86sKEV6zeHly0VqbQ9TV52WWttBCFXYbpRYGhCL6XhRh7kvbREzNVW0X1WHaUIwud6k1PRoX2Ux6IeqCHI9qZjgEtKWRvEqHrEmQNcEuq6xbyhNoelSbLhYpk7VVgmaYRRhbuqCmCaImRoJU6c3E4sU7RJxVTG+cW3HU+mdQsA/fnQrs8U2SUvnIweGWSy3Ogr46/Hw9l72DWWviTs/u1zltekSErUgSsV0lqttetJWJ7L+GxfWqNs+SxU1GV/fWVfOHLfmK/x2woMTPXzqRdUFfKvlt0At8DauJSWcX2moLeSkyVAuwQf3DrBaU4uuC6t1glCyezBDGEo+c3yRFyYL1G2PLT0pHt/Tj+OH/OGzU6Qix5SNAlzXVCFSbrlsY9OK8LsJTx1b5OhcmVw0JvphSOipKPQfvFctxKotj5MLVfwg5EP7Byi31djz3l19PH9J2as+e3GdphOwsz9DoeHyyftGmVxrsLUnxVK5zWypRXfSYqIvxZHZCpah8bWza/yPIwvomuCnH9pCOq4yAz68fwApoGH73D/RzaX1Bg3HZ6Q7wcWVBlKqFM35SpuEqVFsKDeV1SiYp9Z2O9aFhtA6H8bulMlUodkJFiu3YjRdn7uG8yRMk0urDX78/jF+7TOncf0Q1wsZ7U5QaLgIBPeO5zF0QSpm8OJkgVrbo+H4fPHsKqGUyBBOLVZJx02klJxfbXQCyP762AKVlloMvD5TYe9QlvMrNbb3pXl5qtAJbPOiNFCJZHJNde/rjs89ozleuFxA0wS96RgNR1FvSg2HatvH9UParo/jqUVToe7SsANqtnqNbSegYfvUbR/bVYsjlcoZ4/Jand5UnH/2+HZ6szFMTcMPJZ87vUIYSlZrbRUKJyWvz5SxvZCa7THixzm/3MILQk7MV9g7lOHEfIXtfWl2XuWsE7N02nbkFHaLkp7bSiSICtxXhRD/GvhtlLjyWxbg1537aPT/DVaBd/rYJjbx3YC1ms2fvzrPYqWFQDCYi1NteXz13CqX1hps70srgWWE88t1am2XQtOh0vL4rafPk0+qDsVsSdlCaZE0XaAsmlIxPYpnBgQEkYc2KC5fPNAJwmtJCJmYTt1WllPXo+1de2yDsrLxKmUkttzZn+HDBwY5MlvmyGyJhn1FICNQNJS4odHyAqXe1zSm1pu4QYgGDGYVB9L2rpznS8nF1TqP7ugjCAU/++g2YoZ+S3SRfPJaekgist7SNRVnbega79vVz/6RLP0Rb3Eol6Bu1xnIxm8ovt/JmC+1WKs7N9CRvh2IyLtLov6uYSgByVh3glrbo9b2+PPX5kiaBjOlFjFTZ7QrwVhXkmrbY6bQ5MxSDccLSMUM9g3nyMYN1usOC+U2Oweu7ACZurJc84KQhuN9i1e0iXcjglAqCp2U5JMWsxfX6UlZdF/1uc4lTX720W2d73/1w7s7FItTCxWKTZfx7gReECo/6rRFJm5yaFy5Z3SlLPoyMYZycQ6O5tk1kCUV0/mVvzxGOyoGz63UOwX/rsGsCneptLlvSxe//7XLOH5AqW6TjIR/2bjBXKlNzQ5wfIcjs0UmV+usVGyG8zFCCbqAvcM5VusOmhD0ZGIkLIMwlLQ9PxJLSi6vN/mtHzoIqGbEv3jqFFIqh6rTC1UWKm0KDZv+rAWRDWLK0lRgFhA3NQIpERIGMnFKTQ8vCHlgWzdnlmr4Ycj2nhRnvQalpsO23iQj+QQD0a5B01HzhBdCX8pgpSaULaiAybUGCUvnPRNdnQXA9v4MPekiQSjZM5QhRImst/akeW1mgbrtkY2r8dvSNWK6xsnFKjXbY7LQYLwnxbGFKjv60vzVa3OcXa6jaw2+dGaVkwtVDF3wjx4c5+GJHtwg5HsPDPG7X5uk1PL46Ye3sF63mSo02TWQZqnaVk0AAX/56jznorCwX//4/s78UbGvzLXHZoq3dF/eThBPFvgEqgO+HXgKeOBWz9/EJjZxZ1GzlahG10THW9vxA7pSKnBGCDgwrCQSLddnptikUHfxA4knA8xQ+V1X2l5HgCQleH7IcD7O7qEsQgpemlbd8SBUE1nMUG4kfiBpExBeJ/gOpFA/4795gbatN0Pd9rgUuYtIVIS0JuCZ08ucX6lHXYkbhX6Wobye3RBc3cf2lQjT0KDYdCMxqMTUIAgjSo2E5Uqbbb0pMnGTSsvl1GKT7X0pNE3wwqUCuaTJQxM9b+hMcmgsz0JUEH7inmGsmxTyHz0wyAPbum/ZE/adgprt0XbDt9z9BlV0D2RjhDLENDRihk4+aWLpypd9pCvJxZUGD2zrpj8TY7w7pYJDpCSXMBnvSREzNYJAiXIzMYOmG2AaGlt6VOKqF4Ss1x3+5ugCr06X2D2YiWK7316QUvLls6sslNu8d1cfO67ym/5ugx+EnFmqkU2YbLuJ6PZ28f13D5OOGWztTdKwfVKWCuMpNJxr6AQbcP2Qz51cotr2eGJvP1PrTaYKDe4azfHkPcNcWmtw4Lpk2lemi5HFq0vD8ZBSEIY6P3x4lIuralfnB+4e4ve+fhnXD9nSHedPX5rBDSTr1TZtLyCUsFR1VNhYTMc0dFKmRqUlVaOkqDy6a7bHqEjQn46BIKLkCUAwmE3QnbIIpaQ3EyNu6qpjbGrMlZpMrje4d6wLS9cIw5CYoVNpOYCk2fbY1Z/h9GKNLd0pdg2mObVYI2kZbO1N8fpspfOZrUYhaNt7U3xgdy8LFZsffXCcP3lhlu6UxXA+ye7BDC9cLvDgRLda/IaKytjyApASzw+YLzZZrraxDJ3Lq01evFxEaPDwRA8f3jeI44V8393DnF+5QM32ycR1Sk3ljHV+pc5wV5JKy2Mwp9ywNppOZ5aqkTC0xWyx1ZlXvnlhjVLTAwFnlur82sf24geS2aJq4KQsnePzFV6eLuEHIV84tcyewSyFhkNfJsZsqcly1abhBFHD4EYUmv5Nj1+P2+mAnwA+A/yGlPKl2zhvE5vYxHcA2/tSPLS9h5brM5CJkYyZbIni2GcTLVIxndNLNeKGzlfPr4KU9EcDiKFr9Gfj9Gcs3CDA1OgE4QghKLd8Xp+pKBupiIsNaojXNUHoq2O+L7m67NSAtuNSar45PcHSYf9wms+dWrnGSSMEjs5VMHVx07RFiRKGOn6IF73muhtiagJfKOGe6wcY2hUeuXLB0khZOiNdCZYqbQ5v6eI/PTvFpZU6OwbS3Dve1QlfGM0nGe+5cWLewEA2zj95bOIN35+miTcVyr4Tsb0vTW/GYrakXUMx+nYQSJX0ec9YnkJTRUlPrbcYzMYRQKXl8p6JbgZzceKmKhhGu5LY0ST7ob0DFOoOL08VySctvnBqmWRMZ3q9yZmlKq/PlNk/kkETglrbY6IvRXcqxkcPDL3l38Nipc2phSq7BtJM9L31YrnS8jizpCKsX58pfVcX4C9PlTqWeT/+wDiDubfm435gJNeh4v31kXnmyy0ycQPbC/jzV+eIGRrfe9dQJ6hpo2gD+NLpFZ67VMAPQ/78lXnWaw6FhstixaYnafLXRxf44L5BBAJTU04pz08WeO5igXTM5H//yG5+5YO7SFg6pxZrTEeuIp9+fZFyJDaeKjY7rzUIJTv6M0wXGuweSLNSaaNFzYCBXIylmk3c1Pj43YP89lcm6U3GiJk6UkoCKelKWPzEA+PUHZ8n7xnh+HxVjf3pGD/5x69Sa3s8sr2X7X1pFsst9o/kODlfwQ0C4qbGxTUVLjZXbhFKFXxjeyHno/h2gNOLNUUHk3B0rsx8qU3T8Tk6W6HYVFkJ1bbLyYUqUsLJ+SpJS6NqB+hCULN9tavqh5xYqNJwAoQb8NXzq9RstTv1tQvrlFsejh8weDHGazNlglDy1NHFyDZRognBcqVF2/OZKzaZ6MtgewE9KYulst2xUNzIcZACRnNxjs5VEAIycZ3PnVzG9gIemuhR44TtMdaVIJ8wabkBfekYF1ZUuNC55TrDeTU2JaMQo5tha/eb67Tg9grwCSlvsqccQQjxe1LK/+U2rreJTWziLUAI0VHXX42Hd/Tw9QtrnJivMtqVIJ+M4fkhmiZwwxBNCCSKQ/h63cbxAq6yQCWU4AYhlbbH9Qt8AbSusygJr/u6dWuLf3TgM8dXaLn+TRMS3yjq3JegR/xugSr8dU0ofmFI9LolmbhO2wvxA4kQElMXTBWa/IdvTIKUvDJVZLVmM1Nqdn6Xpi7IJq4dGhuOz2szJfrSsc5E/t2K12dKEe1H0L4D12t6IaeWqqQsg3zSom77rFQrJCyd9+7q59BYnl//2zN4oSRp6Ty2o5dj82U8X9Kbsdg3lKXp+hybq3BsrkJP2kIIsP2QpKXz8mXlhjJTbPG+XX08eWiEVOzWpr5y02Wh3GZHf5qEdW2K5tOnlik0HJ45s8LPPrrtLd8XmbjBYC7OStW+hjrz3Yir6Ws3o7LdLtZqNq/OlBjvTrJQbjNbbJKKGTx7aY3nLhUxNMFYV4JQKo7yXSM58kmTuu2zb1hRSeq2ZCATY6liU265xE2d//uZC5SbLs9PFnlsRw8XVmqkYgbnl+oUGi6FhsvnTy2pjitQbjqRHSDkkyZxQy1iD43lWSi3absBO/qSzBSbzBZb9GRirNYdbD8kaHl0JWP0pGKkYwZfPKP8+Estl+mCsipU8eoOhqERSnj24iqX1xp4oeQbF9co1G0CCScXKyQNjaYbYHt+R3Dq+iHPXlil2AxYr9tUWzaOrxIrswklQtc0wUAu3mlWLFbaHF+o4oeSZERZdHwVxHNxtcFyxWb3YJpmpB3yQomh6+iaalKUGk5H/9FyvU6q7nypyfmIC19tOR3B93rDIRVTFoHjPQlOL9ZxfUml7TPRl6LUdBnrTlJ3PHIJE00IZgsNJGpeODpfVQsaAS9dLtJyQ/wwJB0zuGsk1wnw+sj+QY7Nl/mR+8b4zy/MkIl81if60lF2gUHbC7i4WKU/E+sEyYHyOb8V3I4I880+BY/c6rU2sYlNfOcwnFcqbSmVXVR3yuTcSp3uyPkjaRlUWi412+/E52pc4eEO5WOUGjcW3zpXuNp3AnYAMQLugJGGGsCJYuw1DTvazm17EkPTcP2AtieR0qPYdNnak+KvjswTBCFxU2ckn2C8W22ZJi3jBjeUb15Y5+KqmnAGsvF3ZWf7VpGKGehCYJkaOHfmjqjZQWRfqdP2fFpuQMrSubRa56ljCyxXbUxNECYtClFRbGiCajvG1t4Uj+/po972ScdVGutHDwzy+J5+pguJcjX/AAAgAElEQVRNussmji/ZP5zjyUMjaELwlbOrzJaa7B7IcniLcm5Yrzt8eN9AR1gVhJJPvz5Pyw04t1LjR+4bu+Y15xImr8+WabsBXz67ynA+0bGo3ICUkrrjk7aMa2KrbwZD1/ix+8dwgzAKt/ruxUPbe5QzU9y8qR3m7eLrF9ZYqthcWm3wpbMrEZ9XRAWi8p+/tNroCBwtQ+OnH97aEXHfO5ZnttzmsV29HJ+vUmm5jHcn8cMQkPhBiB9KsgmLmK6xvS/N8YUKuYTFtp4UR+cW0YUgn1A5AzrgBgFDecUpD1GWgqEEzw84v6qyF16bLuFGdCk/VDaxfhBG95Kk5SpXlZlCq1PEfu38GgtVGynh8FgON1BOKYW60vKo+0twYa1JCByZKXfodlKCG0RNFQkp0wDhoQn4+F2DHBjJEzc1HtzazfF5lYSZshTFRUpYrSq++YY+xPGUa1HLC9EisY8A7h7Ls1536EpZtF1PPSlgGjoayqllvWZ35oZqy8cylEvMnsEMJxaqSCkpN5QVbBAq7+7hfILtfS6DuQT7h7M0ow72bEGN3RJIxQROECBQQtOLqxX8UBJKSVfKIuWHxE2NI3NlinWH5y4VeGRHD18/v8Z7d/Vx35Zunjmzwv3bunhussCFFRVOdPU8dj6aK94MtyXC3MQmNvGdR6Xl8tdHF5ktqC7N1p4kJxYqrNQcnrxnGD+UvDxVIqZreGHAmaUa5aZDtR2QT5r83o8fwgtCnr24TtLUWa7buF7IQDaGLxU/2o8GyI0xY6MDLYGZws37mneaNas6ErdffQuU8Ohq94xA0qFDDGZ1DAE1R9FUAo1O0I/tSwxNsFSxiemChGVi+iEf3j/Att7UTXnfr0wV+cq5VbwgZM9ghph5hXTz8lSR16ZL7B3K8sF9Azec+05Dte3xN0cX8APJk4dGblhonJov8ck/eOmO8L+vR8UOEEJZRQYRdWihou5FUxMk4wb/7AM7MAyNr55b5eJKnYbt8eq0wZHZMpausbUnxQf29JFPWuSTFgdH81TbHifmK4x2JUjFDL52fpUvnV1htthivtTG8QMWy+p5Ti9VOwV4KGVHG+H6N1JtfuCeEVw/ZLGirNMsQzkCfe38GrmEyRN7+vnyuVXOLtUY707yQ4ff3PZQCPFdX3yDEswe3vLWgpRevFzgyEyZ/SNZulOqc52OGehRAJgQ0LZ9mk6Apglans+zF9cJpOTeLXmEEBi6YK1ms95QVLxLqw0kEkNXHeZf/dAu/uyVOT56YJDnJwus1mwycZOZQoP1ukPLCTi7VOPsUg1T1/jIvn76MjGCEO4eyXN0vkLD8TE1ST0SKZ5YUrkJgVRceMMQiuonYLXm4gYh9baH4wWd3cixfIxlxV6ifVVS8XRJ+YyHEtJxA6MhkAhcL+yM+V4ImrgyH3h+5OQhIR4ziGkCXRe8PlfjlemiEpRLietLQiRN1+todFw/QNOUJiOma0yuN2i5QUQrUYsGIeDIbAnbD1mrO5jiymgiowWPlBAzdZTTNMQtDRFRCoUmMHUdhMDQdbqSFusNh95MjCMzJY7NVxjOJfjYwUGyMYMtPUlMQ0dEhb3rA1J1wGu2x8nFCmEID27rAiGYK7a4f2sXF5ZrNN2AhKVh+5K1ms1njy1RaLi8MLnOUqXdSQq+fhqz7VubLTcL8E1s4m2Gy+sNam2P6cIV/9lCw8EPJd+4sB5xmFvomsZytU3L8Vmru6qYbUk+f2qZxYpN3NRZqTs4boAXSparqqNg6aJTgN8M34ni6lvh+sCdW4FAbV1aSG6mpSs1XQazFjUn6HTGDV11nZKGhhOoSaDQ9HCrDqNdCTTEtxRdnlyoMtaVoNxy+eHDo2SvCnA5uaC6J6cWq3xgT/873u1kutDs2IhdWq3fUID/8fMz39H7Q3FDVbFhe6orvn84i+2H/MoTuzg4lkdKyckFJQartT08PyTQBALBkbkSth/wQ/eOdkSxuYTJe3f1dZ4jHTOJRV7DugZNV9lTmrp2jbDO1DWePDTC5bUGd93Ex90yNH7sgXGm1ht0pyzSMYNnJleYL7WYB3b0p5mNuL3zUeDKO/3+eCfhZESLOLlQ5Wce2kql5XLXSA7b9VmstsjGTLYPZFiuOVFx66BibWB6vcneQZua7ZOO6SxU2pGzR4pyy2Gh1CafMKm2XQxd49Jak0JDeYg7XsDp5ZqyxfMCXpst0YpcUJJxg59/73YcP+CJPQNMFVqKYtG+MpBtNBQAPF+i61fuGT8MOhTCuVKrc3yufKVp4gdXrhVEAnaBsqzVUDauqdiVRZ6Aa3Y7r97Usl0fN5ToEhYrTdbrNkIIjs9XWKm1kVLSl7Q6xbzrB1Rs1eA5vVjpmANU2x5dSZNi0yNmaKzXlIgyDCRX+xHV2n6HZ65pGhuvMhMzaTgOfhBiahqP7uxlar3BJ+8b5VMvzmIZGroQzJfbmLpGsenyR89OcWSuwivTJe4aybFctTE0jaFcjItrDUBwbrFGGDnkfPPCOoah4wchT59a7iyA/ECyXlNhacvVNv/jyDyFusOFlQa/9PgOhiOnl9/5ysXO+2je4rx2JwvwzZFlE5u4A5joTXN8vsq23hSpmM7WnlSnA/7+3X34oWSt5mDpGoPZGGeX64Ck0vLJJkyG8zHmS20MXTDRk2SpZtOwffqzMZpRV3ggG8N2A1bq7t9rwX0nEKLoAd/KpMQPoGErxbsmwNI0+rMxdE0wnE+o7UvUY8mYQcMNyL9BCuXB0RyvTpd4bGcfg7nEdY/leW26xJ6h7LuiuNrWm+JY0sQP5DUetxv4x49s5bMnlr9j90wgoe4EaEIlq/amLA6Pd1Nqufzpy7Msf/EcP3L/OO/f1c/pxSqpmM7OgQxtN+D4vPJdXqnarNbsm7pbgApA6svEaDo+C5UW55bUdvFH9g/eIHycXGtwbK5C2wv4nuuEm2Eo+eLpFZarbR7f009POsZIPsHZpRoJS6cnbfHojj6OzJXZO5h529wfL10uMl9u8fD2Hka7vrXQ+J2OgyM5jsyW2Tec5T89N8WR2TLPnF6hO2URM3SkEGzrS3FktkzC0tk/lOGzxxcjHnjIb37+HLYX8OjOXhqOTxBCoWaz3nSo2R5LVRU37ochs8UmyZgKebEMjbGuJBdXGyRMnXu35Di/UsfQNMbyCZ46toQXhiQsg/lSC1+GiPDmtpgBkIsbVFsecVOnPxvj2FyVpKVzz2iOV2cr6OL6DqyGpaljh7d08bWLBYJQMtKVYGq9iSlB168U4Btj4UYRrmuwUcNXWoqKGEpJ3fbVYhUlvo/62TSu6oI4ftgJXms4V/IdkNBoq/fY9kK+1R5Pd9LsNEg+uKefTx9ZIJBw10ieqcIiQagWOP/lZx7AC5QBwKdeVCmVAvjhw2N85tgih7d2qd9tIHH8gOVqu2Oxa5k6CctAE/Dorl7OrtQJpeTeLV08e6mA44cdSopK1YSJ/jQLpTa7BtLMl9rEDJ+4qREzde7beuNOTeoWK+vbLsCFECkpZfMmD/37273WJjaxiRvRlbKu8aMF+NEHxq/5fsNL9mp87uQSJ+YrPHN6jXvG8zw40c3PPKKus1xt89zFAoO5OO/d1YeUit/6L5863eEevpMK8UCCeV09s8FRV44nkDB1Qqlikqu2z9aeFP2ZOLmE6kbuHEijC+XjHb5Bx+LBiR4evInYFeA9Ez03FcK+U5FLmJ175mY4ON7N9G99jF//7Cn+60tzbymG/o2wQRcqNj3OLKlt+q9fKGAZGoXGJM/8yvt4Ym//NbsWuwczPHNmlZ601XHNWKvZLFdtdg9mOg4XQoiOtZ3jh5xDFeA3K5DPLavHzq/U+cj+wWuer9BwOrqAY3MVJvrSHBjJMdadJGZoxE2dfcMm+yIr0LcDqi2Pl6eUR/Hzlwr82HXjyrsJD+/o5eEdvQB86cwKAE3XV3zdUOKHIQvFNr1ptTg/v1LHMjTCEC4s11mtqQJ7cq2uNDJSYppKuNj2AlqOz/6RLN843+TusTyj3UkEgkxcWfbt6EsTt3RcH3pSMTRN8MzpFb56fjXy7w5ZrLZx/ZCe0WsFvBuuUKYm+MShUb54apnDW7t4+XIJxw9Zqto8PNHNTKlNOmZgCsmFddUR70qZlNoeEkURGcmpxst943lOLdZw/bDTkd/A1QV8OmZQail9kHfVwFioOwgRRdGHIY4fKmrLVd30mCEQrrreRmy9ctYC56rB4upxwxJ0gty6UhZlWy3Ak3GDfcNZQgkNx2XDcGlyrcG55Rpnl+s8vruPnpTapetJx1T6ccoiYeocGMkxU2zRk7KoNh28AISQtL0Qy9DQhKBue/RlLIJQYhkaj+zope0G7B7M8PUL64iICvT9B4f46rk1vu/uYbqSJp9+bYFHdvaQS1qsRbSjq3G9FuRb4XZ8wB8G/ghIA+NCiLuBn5dS/iKAlPJPbvVam9jEJu485kttTi1UmC21MHTBobEuXp8pcnm9Rcv12T2YUUKjmk1fJsYXTy0TMwReALmYQan1zgonCSRXBngUrU8D0pbOYC6p0ikrLdJxk3LLozdt4QQhD27rwQsCJvrTfOHkMrOlFruH3j5F0tsdXz6zwhdOrXzHiu8NRLIwjs1Vcf0AXVNcbEOLXI+v2wKZ6EvzP78/zeRanRcmC+wdzPJXR+bxAslMsckP3DNyw3McGstHxbJ2U7/p+7d2cXSuzL6hXOf5bE+JLh0/IJcwqNk+u67aLbhewPt2QjKm05VUn4eRrrcubnw7Y6Vqc3a5ys7+DD/z6DaeOrrI3qEs5ZbDbKlFLmEStzTOryh+9of2DWBoGqFQi7lEzOhY9p2cr2LoGpm4gaEp+zldF6zVFPe43Hb5P+7fTcLUuGc0zzcurHFhtU7C1HlkezeOH2DoGsfmyx2Xj9dnqmQSKk3yaj/ppCk6C9BM3OD4XJma7XF2qdbxCieUvDZTUimVtseDE13MVWx0ocJtgqijcn6lwVLVJpTw9OkVXF/59y+WWsqmNZTEDQ3nKo3DhsRFAwayCap2EwEM5+Is1xwEULW9jkvVas1WWQsSkjETox3gSzCExNCVcDJmate4ZxkanYI6HdcoRRnu1bZLLeqUX1qpsa03jR9K8jFl7yilchj5tadO0bB9Xp0u8sn7RrmwUufeLV08dWyBUtPhlekin7h3mJ39KXpTMY7OuZ0dUV1ANm6gCUHD8VmuKuHtUrXNzzwyQbHhMtET57e/fLGz0Hr61ApNN+DzJ5f5/Z+4l10DWfJJk+cvFXhtpkT6OleluuPe0j16Ox3w3wE+AvwtgJTyhBDivbdx/ibeZtj6f37+LZ0/81sfu0OvZBPfClJKXp0u0XR9Hpro7VihzZdanF2usXsgw1JVRejuHEjxB9+o44ch04UWx+YqBDLk9GKVQ+NdEac3zhdOFQiCkMn1pupihBAEb79gkjfFVV17CYhICBQAH94/wJHZMpmEheOHTPSl2dKT4sGJboZyCUa6EnzqhRlyCRPL0Dpiu028MdbrDk+fXqZu39nFWj6u4wQqovrq/CYvCOnPGsQMC9PQ8PyQB7d1UWw49KRjtN2A9brDSFcCXVPpff/v1ybJJUyWq+3Otvq3Su3UNEG5pVxVYoZ+A23lvq3dN2wxX1ipM7nWAODBbd08sK0bQ782hElKScsNSFr6GwY6/X3D1DV+4sEtNBz/lrt071R8/tQytbbHueU6v/j+7fyL790LwF+9PkdM17AMQcPxcP0QPwgZzMWZ6Evh+SGP7x1gOJ/A8UJ8GWI+P43uq7TVu8dyLFds9g9nKbdc5kot+jMxPn9qmTNLNRbKbeZLLVLR3/7kYpWlShtNExwYyjBbVp31oZzFTNHGDyVuEGJGRelAxmKmtBE373FmqYrjS9rFJjt6k9Qdn5iuM5hLsFh1MHRBNm6SMBUFZjAb59RiDSlUM0JEvParXXgsUycuBE03oDtlsVZXGiOBClITAJrADwN0QSR81Dri1aZ9pcBsuUHU6Q/Z1pei1PJouQHDXUniTY/1hsN4V5Lzq43OOTFD4EVtb+eqD/xa3ess7Kttn4/c1YPtBox3x/n00SVcP2RHf4rjizX8IOTcUp0n9gxQbrn0pk0ycZPJtQbD+QQvTRZ5eapEKqazfzjLpbUmuibY2pvi1ekymiZImnnyCYNQQncyxr1RqunZparSbAhBte0zmFMhdwBfOrvC2aUa/VmVTQDKpvZqtJ1bu0dvN4p+/rrB5B04a29iE+8cTBdUMhiAJgTv390PwBdOLdNyA16+XCQbddzmSyrJy/VD6rbH0bkyW3uTHc/jw1u6OLFQ5dRCBdeXBFcVsFXn7VOAbnS13/TnBOhR+9uMhKUyVOf3Z0yGcwnaXsBg1uD9u/v5+D3D10TK7xnKMFVoMpKP89BE73fo3by7kI4ZrNZtbv2v9OboS+rsHlad6KOzZSptv8NLDSQ0HZ8P7B5mar1BoeFSbfu8Ml3i4EiOZ86uslaz2Tec5fsPDvPp1+ZYrLRZqdkcGu/iyXv6WKy0uWv05h7d5abL6zNlQLlm/Gj3m1MyBnPxjvXZSFfihuIb4HMnl5lca7BnMMNH73rroT/XY7naJhM3b+i83QosQ6PbeHcX3wApSyXlJsxrF0GvTZcpNF3KbY+uhMlyVUWMH/3/2XvPIMmu+8rzd5/Ll768a1ftG42GJQxhCYJWpGgkrkgtoZEYGxrNjDSj0OysVhMx+2F3RrHjpFHMSiuFpNAaiRotRUmUSJASQXiC8EA3utHelPfp3fPv7oebnZXVaADVDo51IhrIMvleZtZ79577v+d/zlSZhquuvR+dKfLpA8PtnbMEN2zK0/BCdg5mGMnbHJqu8MC+Ib7+3BROoCQpJ5fqHJ2vkk+afOW2LSzWXHpTFs12YzHtgB1DFzhBxIN7h/k/Hj+DJgTFhtepCK80Vhe3QggMTcNDBdhkkhbZREDC1OhJmkSxRNMENSei4gTtLEzY2pciiiV37ujn1EqTOJaM5ZIs1jzCSFXWY6kakDMJnaojCH2JoanPrdwKMIQglu3PTUqqTkAsZduFavWaTxg6uq7haTGG0Ki1HUDOLDcwdFWNLzb9NSNG2MUcu2ces0scruuCubKyY5RIorblY9NTibeVVsBwzuLf/73S6p9YrPPJ64dZrnls60tyaLaCG0QEkfL4Pu/f/djxpU4lf7nm8vH9IzTcgF+6fwflpkfFCehPW1i6oBnFDGYtbt7Sw6GZCrdu6+G5s0VOtG19f+Mn9nJqsf6GDIDezFX2AQdm2jIUKYSwgF8Fjl/C8zewgQ1cIjLtrbJYyg7RBsi1U7qEgNdmKkgpqTo+mYROE+hLmwSxiub9x/dt56YtvdimTs31VYy8vDZd02bX1uLlImUKWoF8S3p3vvEoYaiEy76MpTrrkTS9iN/87km+fNtmbt3ay1LNZaXhMVdx6ElZhFHMD08XePLkCuP9yjM9axs4fvSGsJUNrEXS0nlg7xATKw3mq+vbZn07rLQi9OVGR4h6/rqUgKEJ7tzezz27BoilpOEpojNRbDKas3HDiJobMlVoMFlo8v2jS7hByEg+yZ7hDFv73zrRNGMb9Gcsig3/TZs2L8RwzuZ/uHd7W/968Sn0fNrheXlDf8bilnZ17Urx/Lkiz50tkjA1fv6u8csi4T8O+MjeQZ45XeD2C3YwBrIWtqmRNHW8MEYIRXTjSDJfcVQ6q+vzT77+Ci0v4mt3b+NT148wX3W4a0c/jx5fRgjBQjuQB1SzYt3xKDZ8Wl7IA3sHuXf3IFnb4E+eOccLEyU0IejPWNhWD7GU2JZOJGPCCGxrdTR2AvWaZLsBMJ3QccMI2zIYH0ixWPXIp0xemirjRxI/iji6UAHUPVP1QyVDkRI3UAnBmoCqFzKYTbTDhfI8dmyZCJgsNkmaBrqI0TWNXNLEavgkDI2d/WnmKy5WOzn5bLEFCAazNtNlFyklYz1JZisuui5Zqbud99EKJFYcEcXQ8kMShsANVVKxZagcCKAd6KXu/d3DGY7MVhFCcNeOgY7MRXm1a2hC4seSvrSlHFiyCQ7PVWl4IZahcXi2ymSxSd0N0NuN3JausaM/w7H5OglDY7nmdT6r+ZrLQ3dtxw1Vw+g//rNXqDshnzowtLr4CSNmy8qudKbYwm6njlqGxu89dobnzhV5/OTymmvsLUzG1uBS7tx/imq03ATMAo8Av3IJz9/ABjZwiRjK2jz04a20vGgNkfjcjaM8fXqFg9Nl8imT+YrDUM5mpeFzx/Z+dg6meez4Css1l1NLDe5sV3hfm6l2iK0mIGVptLx4zVbWldQ2r5R8AzSDtz67pQt0TRDHMUEsSFkafhhjGhpeFIFU0oWTi3V+7q5xjs5VcYOoYx94cKbCy5MlpkstdE1Q90Jmyw49KZOv3T1+VSUDy3WXbx+ax9AEP32BheH7Ff0pi/pVCuA5j8V2RWrNX15C0jL4xft2sKk3Sanpc3S+ykypRdBOak1bOi0/wvEVMV+p+0RScsvWXg7OVMglTXpS1puSVFPX+OodW2n6EZau8fDheXQhePC6obf0405ZxpvKWo4v1EhZapIOI2VRCTCaT15xpDqo5k9QISd1N9gg4G+Cvzs4x0ShxWzZ4dc+vrtzX+sCFmsuacvgxrGc+jsK0HRV2IhjybmVJnMlh1hKnjpV4Gfv2AaocWWu7FBxfJKmCgo6FzbI2jZNT2KbOromeP5cicliC1MX1N0Qoy3fCGLJ06dWCGPJ9aNZ/JC2peAqcVW+gO3HbRIdtt0/NuVtDE0F+izXVp+yoy9N06ujCTgwmuOHp4vEsWzv1KiI+pxt8Ac/dyvHF+rcOJbnkWOPAyoDZ1u/TVhxyNkG2/rTLNV9ckmDihsqP/w4Zv9YnoYfYmgaX75tM+cKTcIo5pPXD/HNl+dwAsH2wRSH5uqdzzlqO6KEkSRp6bhhqIonXfNEt/7cC2IMw0AI2NaXxDAMvDDiju19TBZbrDQ8HrpjK//vc5MYmkATGnnboOVH9KUsFioODTdASlUxV5IayeG5CsWmR80NuXdnPwsH5xEC7t05wGPHl4hiyeGZMtPFJlEseeGsWjCZutpTmKs4LFRdkqbBvtEspqFhaBozZdX46lzgh2sZ65tDLiUJswA8tN7f38AGNnB1MJBJqNbnLrw4WeLQTJVnz5ZIGBq9KaV13j+WZ+dghlLLp+GHlJoez58tsqUvxT07Bzi2UOd87oGUtLf31uK97IZiampgXmn4+JFAk5Jc0uDGTT28MqWkBG6bTJm6xs1b8hxfqCFDmCq22NKXwtI1Wu0BM5806U1ZNDwVxxzFqnHoauH0UqMT8zyxotwS3u/40dki+jXQNV943aUsDUsXfOOlaX7lo7v42HXD/Olzk52KZW/SZCCbIIhiBILpYkQsJaauYWqCmhvwzZdnSVk6v3D3eMcF5UIYukY+qfH8uSKn2zrV0Z4kN7/F3+rkYp3vH12kN23x5ds2d8h6seHxD68rx43dwxl6UxYvTpQwdUHyTc5/qbi37ewxkEkwmv9gN1JeCV6bVYu1haryqz5PwB89vkIYxUofvlzH0AQIQcMJmSq2kFJyx3gPpqEW9tu6dkZ0IXDDiGLDZ3NPkkhK+tMJJIKfuW0r33ltnq19KRpeyOHZSnvXMcAN1HjzykSZ+Yqq9/alFNFErnXgkbEicG4oSSYM3PZYFUQx3z+2zFLNodh0GetRBRddwBdv3cT1Ky1Sls5Y3ubxEyvEUhLGknzSxAkidg2l2dybYihnE3fFNmrA524a49kzBfaNZpUMp70b1XQjQCAlTBQaHF9soAvBI0eXKDaVVOYHx5apeSFuEFF34s5C2jKUhCUOY/T2eKwJMHUdTQcCZWmo6xq0e3BWmkqTLwQ8c7bIYNam6YXcvXOA//2nb1ChRULjv/zgFDU3YLnuUnbUuL1c9xjIWGiawNAEI7kUVadGwtBo+BGuH+FrMaM9Sb5822aEUHrwv3xlFilpN8vGBGFMb9ri1q29TBab/OSNo/ztoQWKDZ+05fKRvYPsGszQm7a4e2cvv//kOXYMpJmrrC6ixDr3ly/FBeU/Ab8JOMA/ADcBvyal/Pp6j7GBDWzg8uH4EYs1l009ScJIIqXS8l03kmX/WB7b1JlYaaiGGU2wczDN0bkaS3WXP3r6HAB+EK0pcQdXIwf+KqDbh/atkLVNgkiFnJxZbuBHkoSu8799fj+/+b3jvDxRxtCUFdWOwQyOH6MJQcrSeeTYItOlFg/uG2Q4n+iQtVu29bBc89g9lLmonvdKsHs4w9H5KoamMX4Rl433I+7fM6hCNryQa9k6EEaqcndopsIfPHmWT10/wta+NDU3RCAZzdtcN5YjnTDoTVk8cWKZuheST5o89OFxJgpNpkstWr6SYr0ZAT+PkZzdaTIbuiCAaLHq8vJUifH+NAc25Tm5VCeKJYW6x0rd6/hpW4aG2XZ+SJo6d+/sZ6wnST5pkk9dnd2PnpTFT944dlWO9UFG3PFyXjuwbOpNMllsYmgaB8ZyzJUcdF0jQvWSSAQVJ+QT1w3jhTG3be/lWwfnOLPc4As3jdKftsjZJtmkSdrScYOIwWyCL926ibt29tOXtvjrV2YpNjxytomli45zjxOGuGGMlGqhf16yt6M/RbGpStoxYBk6kgjb0PHDSEW4C1iuOTihRIsk8+3wnUjC0yeXman6GJrgCzeNslRziaSyWiy1lHb79bkaX/3j55ksNHnozi2dacDQVOz7ct2jJ2VRd32VEupHHNiU5myhjqkJ5ioublv2eKgdQAaq96jSJsELdbcT9NabSuBFMQ03pC9lUnNVYmYQxwxkEtTdCEMX7Qq2kvIMpHQWKmpQKTU9njq5QgwkDI3f+InryNom08UWkVRppE0vIIhUYm0YKznMct1jKGfTmzI7vuw9to4bxhia4OYtvbhhhPOIsA4AACAASURBVKFpbO5NcmBB3cu9aeXj74URAxmLyaKDbeoUGj4JXdCbUv7xlq7R8EJ60xaRFJ178dHjy50iwnlrxLfDpexdfVJK+T8LIX4KJUH5GeAJYIOAb2AD1xhSSr7x0jTlVsCWvhQ/eeMo+aTJXTsHiKKYI3NKN2fqgh+eLlBp+ThBRBDHLFRVM9Cr02XSCaPjgxujqi3vBQq+Xs2co3KEGcknyLa3Hr0w4nceO40XxCRMHV0XbO1LMZhNUHF8bhvv5cRCnaYXsVRzeWWqwn27B/nBsSV6kiYHxvLY266N9nsoa/NL9++8Jsd+t/CFmzexfyTHV/7oWbxW+PZPuAQkDfDCtpuNlCxWHPwgpumHvDJd5p6d/Xztnhv5/16c5shcjULD5199ag+3j/dzfKHGtv4UbhBTd33u3zPAixNlRnvsdTl+jA+k+do942iCN/j6PnZiieWax7H5GroQHBjLsVxzled4blVWkrVNfvaOrRQbPruGMms8xzfwziKfNJkpK1lJt6zsn9y/gziWjOYT5GyLIIqJpGQwaxFGaly8cUsP9+8ZotK2L/3lP3+VpheyWHH4wi1jPHFyhU/uH+KVqRKagCCMWax6vDRRYmt/itPLdRaqLsWmzwN7h4jiApqA/aNZzi43iRHomoZp6BiSTqjPedy0pYeJQoPbx/t4abLMUs0hY5s4bbeNWILbJdU7vlSnvdHG3x2ap9kOwfnR6RWi9i7nmZUmbhAhpeT/+tFER2ceAw8fmafaCpmvOvx3t2yi1AxIJwwKDR9d05BCkLKMdhOmYCRrM1d2kUDWtqg6EVJAzjK4b88gs2WHL948xvePLXBuucXOoTSHpqud8LS4swurmirPY6kRgFA1otlSi5W6hwQmVhr81vdPsFhz+eodWxjO2izVXfYMZ5kstoilTiahE0QRCUMjjmNmyi1MXSOIJIfn6oRtKc7jJxZZqPpoGvzLj+/h5q09NN2Qu3b188ixRcqNgPGBDCcWG0Sx6in6xP4RXp0ud0KZbFO5vmzuTTJXduhNmWvmUW+dE9qlEPDzI9JngL+QUpbeS/ZKG9jABxmxpCNlqDoqFe18OMxUscnr86p6MltRVREhVNVgKJPGCyM0Df7ihSlAbc9phrJ/0jQI3zsGKG8LJ5QEYcjLk2UyCZ2aG1HTAp45uYwXqYr2TVt6uGtnPwtVl6dPFbhhU56HPryV//bCNA0vZFt/iuvH8uweyqrP4j2SUPh+wpH5Kv41uHCcLj4fxhCiGtxMXWO27NB0A+7Z1c+ppQYzpRZjPUnMdqrfz96+hbNLDVbqdX738bN8/qYxfv7u8Us6/5t5ePenLRYqLqeW6nzvyALXb8rxi/ftuOjvDmQSSjZ2hXADJaXauD4vD8N5myCW9KVV1fQ7h+c5sCnPVKlFxQkIYslSzaMVKNnE2eUmCIkmYbbs8NTJZeYqDh/bO8RSzSWMYk4u1fidHzQpt3ymiqqHpNIKydgRv/3ICZ48tcJAxmJbXxo/iomBZ8+sELTdmY4u1PHbuujBbAKJkpaM5tPMdTU19yQNTE2jL2WS0JQExBQQ6QKnnUK5dzjN0cUmuoC9wxm+f7yAACx9VUJed6NOpXtTb4KTC01iQGvLSkCR8HJ7Ie0GMb903zaeOpPj+rE8v/X9E4RxjI5gS69N1c2hCcHu4QyH56tIKdk+kGaq1MQP1SImaRnMlluM5m0mCw5NL+TUUoN8Sklh0gmDquN3CkDdFrC6kF3knM5u7XLdZb7qEkQxf/WKRl/aIoxjskmT7QMZpkotdg9lmC61WKp5eEHEJ/YP8/CRRXpTFvmkxnxFyW1KTZ9KS/VRPHlymWfOFHCDCE0I5ssuXhgzU2yxqcdmqeaxfSDNvbsHGMgmuG28l2dOFyg3fVKWzp3b+zmwKU/S1Plfv3Os8z5K9fX5EF4KAf+OEOIESoLyy0KIQcB9m+dsYAMbuArQNcFnbhzl9FKdGzav1aYK1KDS8EJu29ZDLmGQS5pkbWXVVGr5fPvgPJV2wMH5lDUJb5kA+V5FCCCh0m6jj2OYr3qYhtrmXaq5nFpsUHYC+lImU8UWH903xC/cPU6h4TFVVLrQDf3s5aPS8rEtnYZ/7S8gIUAgSZoaizWPX/vGIWpOSH/aYiBjcVPbYrDmhizWXVYaAW4g+cHxJW7YnH9b9xHHD3n0+BLb+tPcuPniuu9P7B9hfCCNRNm+Va5xaNWhmQpPnlymP23xldu3YhlXVxr144C7dvbzajuK/g+eOsvZ5QbPnF5hKGuzUHUwdZ29I2mQMUIT2IbW0enPllo8cmyJOJas1DyGswkqTsC+kRwvTZYJ45ilmosXRKw0PMI45txynbob0vAitvSmkBIMsdqgpwJwnM7C9chMhcF0Ai+MOprr83j6dAEviPjekUUWa6oKvNwISLY36iSwczjHfM0nZRm8NFlBtm1lF7uq6VEcd8j4bMnpVGn9SHZsPi0DnK7L+W9enedbry0w3p/GCyJVJY8lO4fSnFhskDA0tg6kVGiRlCxXHbxQIiU8P1Gm6UeEccxvP3KScvs+Wap67B3NYrU/48X29yUQdC26/TBGBWsKQrlaHPLCmHMFhzCK6UuZzFY96k5Af6ZGLmmocJ2kRaGhHMEafoRp6PSnLQazardUEw0MXbCtL80LbR/wk4tVXpgoqfcop8jYBnYUEyPZM5JjfCBmrMfmibam/smTK6QtnfGBNHYnafmNi/bEOvuI1n1XSyn/NXAXcJuUMgCawBfW+/wNbGADV4adgxk+fWCUTT1rieORuVoneOT0cpPNfSl+6pZNTBRaSGDPUJa4PfRKlHn/e0F2cjUhhRq8w1iyUHUot3yabsByzaPc8nnubBFT13jy5ArPnS3yN6/ObYTvXCbcIOLlyTJ15+rKT0AtJnVWrQh1lC9xK4jxQqX1dIP2vzBiW1+qo0WVbVvA/rSFqWtkEjp/+NQ5vnt4nolCc03aYDd+74kzfP35af7jP5xguth8w8+PzFZ5+PA8mYTBZ28Y47rRLPftHuT5c0XOLjcucsQrx5nlBlJCoeFTca6O3eOPG+7eOcA/f3A3D+4bpuYGzFcdis2ApKXRcEO8ICSOQCKQUrB7JMumnhSDOZvbtvVRbvqUWz4lx2d8MM1oT5K9Izm+cPMYOwYzfPWOrSy2K+OVVsBIu28gYQqWGy5NL6TcUk4955FP6kjU+JtOGJSaHhXHx7nAPqrmhLihZKXhrRmru802ji/WqHshhaa3JjI+aa3SuvO7Q6Cq2+eP1fQizrf/eKHqwTmPP31hmumSw4/OFJirqDCrWMIjR5eZKbc4W2jyw5OqauyHcdspRb0nPwxxgggvlJS7WH0MjOYSDGdthnOJtSm6XSx0+0CabNKiJ20xlF4ltss1nziOQUiW6h5hpLYU6q2AZ88UWai6PHlyGUPXOuPBK5MlZsotjs5XObPSUG4uUcyh2UpnUTFZbKlek1gSRjEpS6fRtpH8mQ9t5oG9g3z6wCiz5SYPH55nseqQTZrMlh1MXe0mXwzuOncHL6UJ8+e7Hnf/6E/Xe4wNbGADVx97RzKcXa7TmzJJJ3SytsFfvjzL63NVig2PvrRJf9rC88OO9+r7Hcn2yJW0dCSCIIwJIjXB5JMmM6UWS3UXQxNkbIOwPUFpQm15Nv2QIIwxr3LT5Y8DXpup8NpMpePRezUhUXZwcdd1WnFCdE3p6VMJnXIzIGlqJE2DiUKT33v8DHtHsnxq/zAfv26Y584VGetJMlVoMlNu8TuP1rl31wD37R7kY9cNs1B1ODhdYcdgmn0juY5DRRzLN0ycjh/x2IklpFSphP/ornH2j+X4+vNTPHx4Hk0I/pfPXsf+sYsH/Vwubh/vpemFDOdsBq+CnOXHEUfnqxyaqXDdaI59I1kWKso55PhiHTeMCSJJqakWNxLJdLHJQs0ljiXTpWa7mRaylrrm4lgyX23xKw/s5s4ddW7cnOe/PnYKp+26tGe0h7IbkU+aNNywQ2q39KdZaqgo9F1DOSZLbkde0fRVtHy3f7Z6PQoX8rjuO67Y8AgiZXU5mEpQc5XsYThnU3WUPV5fxqTcli5mTEHZU0fongZiqYjgqmxFhRGFEsJIackjqarQKgFXUG66HQLv+qtE29R14rbHYBiunWwqTsh0ucVItNaKUxftXU2gN21zZy6FrgmytsFz7cr+tv4ExxYC4hhGshYTRSVHSVo6fhQTxRI3iBiyDXRNVaBrXqiaPiNJqm0PqQm10+G3iy/di6MwUjtPsZT8wVPn+Kt/erdyjIljXpwo0/RDfnSmwJa+NNsH0rhBjB/FF7UrHctf/SbM27se28DHgFfZIOAb2MA1w0LF4d999xh+GPM/fnIP+4ZzPHxkgZlSi7t39vPw4QVenizhhTE7BzM8sGeIm7b08Mc/PMt0qUmlFVBseuhC8A6oBd4xuKHyA3cCyQN7BsjaOg8fXgQBjh/TkGpQ9sOY2bLDriHl4/iTN47xX35wCk2oqOqfuW3Lu/xO3l+YLjT4uT95nuAaLuS6jy2EssoMYliouXxsbz/DOZvTS3ViKZkotkglDF6eKtHyIzK2wfVjeaJYNdY1/QgviDg6V2Mkb/Ox64b57uEFCg2P00sNxvvT/LMHdvGtV+fYPpBmz3CWphfy3NkiPSmTW7b2kk+alJs+XhQzU1JWlsWm1962lixWPfZfZVOSbf1pfuHujebNK8HXn59istjixYkSOpLTy3Wqjk8QRgSRJELSnzbI2AaGEHhB3JGHTBSbHY10EEsmCw0aXsRgJsE//4tXmSs73Lylh4FMgpYf0ZsxWa45lJo+QRSzZyjNVLGFoQl2D6Y5tqDsDqNYSeYkUGz4ncpxfZ12Qt2/5ber5hKodGWfL1bcDlEvdhH7infxBbPOKgFW73f1cbW1ejM6fogXSoRQ99V5NNygozPvLmd0q2oE8PpchSCCubZ7y3l08/SVusO5gosQ8JHdA9iGkrnkkwnCuEEcq88qjFUzaN0L2dSTZL7qMD6QZmKlgR+pGPu9oznmyw5JS2dTT5JjC3V0oRbyQaSsDp0g6nxWpZbXaVgtNzyeP1fk6FyVTx8YwQ/VOOKHBnfv7OfFiRK7hzNryLfO6sLGtq5yEqaU8l90fy2EyAN/tt7nb2ADG7h0PHu2QLGhqjQ/PFVgLJ/sbHs/c6bAsfkqs+UWbhBj6YLHT6hQgePtAT9oJ6V9kKC3tYt+JCGSHJ6tcP2mPAlTV9UoQ5BJmFRaAtPQ2DOc4dxKk829KZKmTk/SJIxVtPIGLg1//uLUNSXf3dCgQ1DO45XpKrZp4IWS3pTJpl7VKDWYtZguNvnE/hEKDZ+xfIpYQstXns2GoVFpBZxbaXBopkKh7vGh8V5MXSOKJZ/YP9yxiXz2bJHX2+E5wzmbfSM5vnVwlsWqS90J+crtW/hHHx5HxpBPmXx452rSYrnpE8QxQ9krD9zZwJXB9SPiWOL4ET86U6DpR7T8FmM9NoaGCloxdQxNoAvBHdv7ObPSJJKS28f7OL7QAGL8UOIEMRLJVKlJuRXgBRGH21Z8QRhTd0Im4gZeGBG1JDVX2V4amuDgXFWRVCGYqzQ7JLpbvmC3fb8vBVF7V090/que372L0+gi3W929Le6nbtHyKliUx1DwnJXk6Hf5aTVPdcIIUjoEi+CbEKj1l5kXLjU6D7/2eUGDV8dbbKgbGallMyW1UJCE1Bq+VhtYp40DeYrLkhoumqBAGp+KLUXH0Ekef5sqS2lkTx6bLEj+Vnq0sunLR3b0Kk6AZ+/eRP/7jvHqLkBh2crfGTvIMfma9y2rY/rRnNcN5oDVC/MixMlRvPJNe/j5DqlaVcSodUCdl/B8zewgQ28De7Y0c8jR5fwo5j7dg+Qs022D6SZKbW4a0c/p5canFyo4YcRp5YbDGSsTpNhpRV84LTeAkgaglDKTopc3Q05uVAnnTAYyCS4eUsvQ7kEN2zKM11qEcaSPcNZADRN8NkbRzl1kWbWDbw97hjv5w+fnnxHztU9UesCsraBlMoFSBMQxBH7hrMcX6pzaLpCGEke+vA2fuWjuwDVjHvL1h6Ozddw/IisbVJs+Iz3pfCCiCiSnF1p8L0jC0gJN27Jc8/OgY4TiqEJ0gmDlyZLBJFkqeYy2pMkiGLGepL82if2rHm9C1WHv3xpllhKPnPDKHtH1DUnpeSRY0ssVBwe2Dt0UT/4g9NlDs9WuXEdTaMbWB++9KHNPHu2yC1benhhoohAkcLbt/Xw+MkCmYTBUMbG1DWEEG1HjxRBJBnvT5OzDVpByN6RNGdXGtRd9f1Kq4ITxFiGhhar9EvL0DthP0IoHbYXRsS6TlrXCGJVOV7pIq7z1dWmyIp76duTfSmdVk3RPqX1VnVsXayScdsSHUKbNqF5BTUHvV3ilYClazhtot/dRNnoquTHsezkBNTWWeFvBbIjbVlueB09t2Wo1FoviLhn5wA1N2Cy2OKBPQOcWKop/XkUk7YNqq0AyxAsN3y8SBLEIamuJuZKV+9KsbXaX2FqGruHMzT8kDCO23NHzJHZKh/e2U/C1GnbudP0QpKmzpMnV5goNDk63xVLCpRb66tSXIoG/DusLnR04DrgL9f7/A1sYAOXji29Kf7w528DlObv2EKNTx8YwTZ15ioOX/rQJry2LZ8bRLwwUWZzT5KgHfQggtWb9vwQ9H5WokhAaBqDSUNpg4VyiNF1QRjGhJGqVP3qx3YzU2qxbyTH1v7UmmPsGMywYzBz8RNs4C1xw+Ye9g1nOLF0bZoP3wyGpv41/RjXj0gYgsWqy7cPL9DylGa12PQ5u9xgOGfzxMllDs9UGcxaOEFE04/49IER9o/lObZQ48xKAyng7w7NEUSKGE0Vm/zodIGhbILx/hQf2TtEX9pix2AaP4zZPpDmwX1DbOu/uDTktZkqB6dLjOSTFBseoAj4SkP5hwO8PFW+KAH/0ZkCQST50ZnCBgG/Srh9ex+jPUnG8kn+/U/fyO88cpJbt/awbyzP6/N18rbF/rEcUyXVUHeu0OS5syUlpRDgRTFxDIW6T942iKQkmzTw24EuDTdka3+aYsOnN21y/ViOR48t05+xVNOghCCKcMIYvU3MRVfVO7jCHubZ2qq7ispHUMjYBq2GYtq96QQNX1V5VcX98ksymlxt1hzOJaiuOO3jrmrVu6zJCS5jorENQdBeMCi9ucKZ5QZhLJDAi5Oqob7iBByarXakQ04Q8+kDw7w0UWZbX4qDs5VOM3dfxqRR9tBAyYXq6tgjOZvZsodEsm1A+fbrQjVXng/UytoGz54rslLzqLsBD782z3ePzHPzll7GB5Ks1D160pcXsnUpFfDf6nocAlNSytnLOusGNrCBS4IXRnzj5Rm8IOapU8v0JC3OFZpkEgYP7h1mstBiqaYG2hjJtr4U2YTOa7OrK/P3M/E2hGoKAjWpxdIgnTAwdEFvysTUdU4vNTANwYnFOo8eW+LEYh2g41qwgStHqeWvWpRcA4iu/+va6iTuRRBFKiY6YWrEsfJCrjk+YSSxLZ3+jEU2YfDdwwu8PFlE1zQeO7FEytTZM5LFC1XV8os3b6LhhZxYqJE0NXRdI2cbZBIGr81WMDQNJ4j5nK2mx8/eMMqD+4ZImvqFBgQdSCl5/PgSC1WX5brPr35sdXO4N2UxkE1QqHvsHLw4ed8xmOHkYp3tA++P61RKJXPTNNg3knu3X85F8XcH55mrOPSlLX7h7nE+c8MoAA/98XPMlB0WNBehbSKT0EklDPx2U51Eec9bhoahCfwwYrLUIogkr05X0DXRjlrXlCVdfxrb1EBKdgymMXTBQrnVcQ+J47hjM7hzIM1KvdJuFtcoOVdnVO5Wr9iGht4O2dHF6g+6g3su6xzRaoDOQpd0wzI13HaFO28Lqq76pQvfmXaR712IpGlQbzd1pkyDuqce65ryPwcot3y8UIUfvTZbaS+Y1A5Gj23ScANsU6M/ZVFpBuiaIGnpaKgd0H3DWaqtMkLAnuEcK40iUkLa1tGEWliN96exDI0w1rFMjcUl1fQ5U3L4k2cmWKy5HF+o8asf3Y2uiXVHz1+IS9GAPyWEGGa1GfP0ZZ1xAxvYwCUjjlU0t+OHPHWyzKZem5YfdWKxdw5mEEDDDak7PjOxsmv6IMAQ0JMyafkhQaS2WO/c3sffH13i/G7rfbvzeGHMSt1lc0+SdGJ1aHPeKdHyjwGGsipV8spqaW8ODTB1kEJNaUGXdWArUF7gQmgkbI1SwyOKVaPmYDbBndv7+L+fncRrN0yVWwG2oWGbGsWG3wnHyadMfuHucf7m1VmKDRW7/ZkbRjk6XyWbNHH8qE2k1J6RaKcAvhWEEFimRk/KIpc0CbscYkxd46E7tuJHMbZ58cTVnzgwwkf2DJKyrk0i69XG0fkaPzi21Pn6vUjCa+0Kat1VDibnA42aXkgYxcSx4MVzJZbqHqKu+ggQICRs7U9i1w2KDY/dw1keOaYi0csNny19SZbrPtv6UwxkEhyZq3DT5l6u35Rnsugwkkt2djwAluqukqAAKVPHNhSRHUhblJxLi1Lpvu/yCUG1rfFO6KJzr1TdoCPjcLpI93opYvc5ksZqOFbcdYDu6ztrG2zutSi3fK4byfD4qdJFj7uepUapSyPTvXjoz1g0Smo+C4IYITSEUFX9fNKgEAX0pS3+24szNNyQR48vs6UngaEpuUwnXlNKUpaBbRmI9nH70xZhrJxSjsypivqhmQpeGOOFEUGb+MdS5RGEUUzLC9GEwWSpxamlOhl77fiwXt+iS5GgfBn4z8CT6t3wu0KIX5dS/tV6j7GBDWzg8pC0dL5w8xh/8+osqYROoeHzoa09aruzriKPTV2j4YfU3ZCVxuog/H5H2G6mS5gGmhbTn7EY7Um2q1YRuib46L5hetMJ8kmTn/vwNhYqDoX+FFv7Ulz3DpKD6WKLV6ZL7BzMvGmoy/sZsVQaTVNf60l8tRABUaQm3+7rV6C2vzO2STZh0PIjDF1DEiOlajwrNX2W6x7TpSYp0+DB64aYKDQJopgtfUkeP7HMzrYbTiZh8MVbNvHKZJmRvM2e4WynT8ALo4tai4HStT5zpoATRNy3e2ANMX/ojm38wVNn2NKXYrRnbROmpgls7c3JtRBizaLxrRDHkoMzZTQhuHlLz5tW5a8l4i6Li/dqmNdt2/r4wbFF7tw+wLlCg6dPFdjal6InaYIETRdYps5UsYmuaYzmbTIJ1WfgBKr5rzdtMVVodMhjHEt6UhZNP6IvneD5iSJOEHN4rsqD+4bYOZSmJ2mtWZ2G8Sr5Pb3c6BDa5cal+7vrXXKPvkyCqueqe6NLXiK7Fq3deTB2Qsd11E37VtVoE7jYK+t2ONE1Ot2TmhAUWz5uENO4wkGhW5VTcbu02l2flRNKRvImlVbArsEMr8/XMDSIYknLD4lRjZcSSRRDGMU0vGjVqzyS2IZAF5CydGbLyut8ueZycqlBGEk1tkiJJgStICRrG0ROQD5lsak3yULNZTCboDdlMtaTJHPBvbve0telSFD+DXC7lHIZoJ2E+SiwQcA3sIF3ANv609y+vY/jC3WaXsjnb96EpglKDb9j+bRQddoNau/ua73a8MOYfMpgMJNk93AaJ4i5Z2c/xabP/bsH+Oi+IT6xfxghBKeX6jx8eAGA/WO5dUd5u0HEQlV5Bb8ZAXs7PH5iiXIrYKrYYu9I9rKP816FH8TUWgHaNauBK0RyrexIAk4QsyOdwDY0vCimJ2nS8EJ6Uya5pIUQYOqCvrTFUDbBQtXh9vE+hFCSgqxt0PJDak7ISN4mZ5t8dN/QG879Vn+zU0t1XpkqA5A0de7fM9j52VSp2ZGQzJYdtl9E63018NpshadPFQAlg7j+KnuQrwc3bFLn1ITgutHsO37+9eDlqRKGrnFwpkJP0qTqBByZqzJXdYmlImazxSZ1J0TTYNdwmpmyQxhJfurmMb7x8ixeGJO0zc7VHqEqqIOZBLapU2kFNL2IMIo5vljl+XMl+lIWe0cyHJypogu4YSzHU6eLaJpYu3Dpun3SOjTXwV27fcFn2s4gF9oYdvvzdyeoWl1s/K2mh24SLLt+sXsjsZtnl5sebck5x9ruQVcDYaTuZ9muWjfa0hQhodL0ccOY6XKLKFZONVEcM5q3mau4ZBI6VSdSZDyW6EIt4AWCiuNRdpR14rdfW+gs9H94aoUgVn+XastXSdFSLWIytkEUS7K2SaHhgVQ7K7du7SOMYSRv82fPT3Ve+3qJ9aUQcO08+W6jyCUkaW5gAxu4cozkbEbzNpahMVVq8fzZAt8/ukQ6YfD5m0fZ3Jv8wNjrdVM8Q1fWgnEc89JkBUuvsqk3xa98dBdb+1J8+9A82wfT3Lq1F7dr9XH+sRtEHJ2vMZxLsLk39caTAX/1yiwrdY+RvM1/f8fWy3rNwzmbcktth5raB2947MuYuGGEe5W3Vy5WkdOEavyi/TMpoe74tHQNS9doybBT0TJ1wUrdZ0tvkmzC4NXpCqmERn/a4lc/toflusdA2uLPnpui5UfcPt7HvbsHcIOIyaKyqOyuYjW9kKdPrZBKGNy3a4AwlvzWIyeZWGkwkEkwlFNSnG4oHXeDjG0wnLt24Tnd4VHWuxQkJYR4z+/wZBIGdVe5Vei64MWJEtv6U9SdQF1PMZwtNDq2fUdnq+jtxXoqYfLbX76JSitA1+C7hxdxg5i9Q1kShmoAzCR0Wn7UuQaXqw5RFOMEIbsGs0yXHCxDw1fahXZS5OpV3p3Eezl3UzcZ735+N1FeqKxKXAqN9XV9dt+H3be5ldAI20Tf0IV6X6j+jPNoXMWpxzYFW/vTNL2IG8ayfPuIop8hELbH9bMryq89krBY80gaomM/en5nJpYwnLVYqLmYmiLw5yU03ZHx6YSB1wrbSaWSwUyChhuwtT/DXLnFUtUlZ+s0vAg3Z8/u4gAAIABJREFUiDA8wa6BFD1pk94LxoL19tdeCgH/ByHE94G/aH/9FeB7l/D8DWxgA1eIkXySzb0pWn7Elt4k//l0AT+MafouZ5aa1N2wvSX9/tefSBQJFyitXyZhUGr6RHFMKDQ0AUO5BE+fXuHscoPTy3X2DGUYzFrcuaMPU9c6lbonTixzYrGOrgm+ds84OVt1rcexpNj06U2ZnYXLlSxgPnX9CLds7aU3ba678v5+gpRQbV39Bd7FKnKRpN3wJiGGhKG22jWhFqLllk/ONklaOvmkxWShyZ7hLIYulGOKFzFbdmj5Id86OEu5GRDGMQ03pOmHfHhHH397cI6FqksuafKV27dgGxqGrvHSZKnTxDuWt6m5Ia/NVAAYyCb46p1bGc6tlZnsGc4y3p/G0MQ1/dsf2JQnYWhommDnRnPxm+KLt2xiqthic2+Svzs0z63betrjiI5WhwvXx6VWiCYEmi6YKDQwdEGlFXDv7gF+8d7tHJyp8Msf2cG/+uZhaq4KazovCdGE4PX5GnNVH0ODj+wZwDkVoQswDdXcJ4RYEznfTZQvp02lO/gl02U3aGpwntt374RezqaoLladTcKuKrvX3dDZTdIF+Fdp6kkZGhMFJQ9x/IvryjVNEJ73/o5lRxN/YYLomUKTIFLSlIYXdl5z0tI7hZ50wqDshAipHg9mbaajmK29Noemy+iaYL6ipCeGLkgYGk+dKXByqfGmvR1vh0tpwvx1IcSXgHtQc+IfSSm/dVln3cAGNnBJiGPJ6eUGKUvjlq091F2f588V2dybpNLyQUrmKi3G8jYThda7/XKvGjShqm39aeXvPV9p4UUxKzWfPSNZolhSqHucWKyTtQ2ePl3g1akyuaTBP3tgF1LKTvIcKAIppdL5akLwg2NLnFysM5K3+ewNo5xYrLF/dP1b+n4YM19xGMnb2KaOpglG8h/cEJakZZCzNUrvwCUWtYMzDE1V3HRN60x0S3WXT18/0vYk1pkqNZksNlmqe3zyumE296Zo+CGfv3mMx08s88zpAsWmTyZhsLnHZqbU4v984mxHnHtupcEfPXWW3rTFV27bwkrdY6HqsLknSU/Koi+t/pVbPtv6Uh2v8G5UWwErDe+aSU+6sXv4vSn7eC/BNvWOF/vekQzHFqrsHcmyrTfFmYKDBhwYzbNQWUEI+NSBESYKTfww4tatvXz9+SmaXkTDCXh5qkIQxXzv9UWKTR8/ilmouWzvTzFRaDKUS6hAGBT5+8aLMzS8iKYXsS8I8cMYTYOxviT1tn2frq2mQF7Okrabs0ddNiiya+13pUrEbvvubl1497m7ufjVIt8AjVDitOObmxesZzWhKtspQwdDBR8NZCyW6quvciSjs9hQi6DunYdC0+98Lg13NQmz6a3mZrh+xKmlBo4f8cp0lXIrwI8kYSwZH1AOKbapU3F8qk6AF16e9v2SgniklH8N/PVlnWkDG9jAZeOZMwVemSrzylSJctNnotAiiGJ0TW2XBSEcmqkwlk/yASmAA7B/JEMrjDGEhh/G3L69n7mKQ8726UlanF1psrVf7QjYpsaR2SpH2jrEXUMZziw32w13YwxlEwznbGpOwJ8+O4eha/jtgXOp5rKlL3VRj+a3wndem2e61KIvbfHzd217Vxri3kn8xfOTTJbeOXcdiari6UKSyxg0vQg3jMjZBj0pi8/cOMrfH1nk2EKNuhsSRMr//jM3jrJUcztyINE2QdjSmyRjm8xXWhyaKaNpgo/vGyJrK7lCpRXwwkSJ2XKLmhPgZxOYuqAnZfEfvnQj/8+PJnCCmG8dnFsjU3KDiD9/cQoviNk/mmX7YIakqbOl7+Jypw1cexyerfDEiWU+vLOfpZpHfzpBzQl5dVaND2EMz0+UFBmT8Ojri/hSEseSlydLPHlqhSCKSRgaNTfA9SOaXtgO7VF64nIzJIyh5oRrotfdYNWj+4WJMpFUzcXzXTHs8krZcRe63QyvpulT9zTS3ZORMa+u3ORicL24Q5QzSYu6r8i1KcA0NIIoJmcbuGGMrsVv7N1oOykJIbB1jWZ72VBprgpE5murY1nLVylDEnDCmCiWRDLG88OOZMUPY7K2SdpSNrhJy2Cl7tGfWStBWS8uxQXlp4H/CAyxujMspZTvPf+hDWzgA4bzVnp1N8QNIpxArdzDrupuHMNc2XnLaOH3GxbqPkY7ev7YYo1/89nrKLV8Dk1XWK57mLpgNJ8mkzAY60myUHU5u9IgnzSZKrbUtqQTUG4F3DauIsOfOV0gjCVhHLF3JEvDC9k3ku3oPy8F5XaSWtUJOg07H2Qcmb96TVbrhUA1kyUtnT3DOV6aKGIKwUzJ4YenCtTcgChW1amWHxFEkpobAkpC8JXbtoCEx44vsXckx0f2DvAnP5zkiZPLZBI6r6Qs/sXHdvHM6QLDWZuRfILqKXXNlNuE/M7tfRycLlN1ArK2ScNdq/L0wrhTZXt9vsaxBSVf+ZnbNr9pz0E3lmsuhq69QVe+gcvH7z9xhmLT55XpMh/ZPcCR2QqD2QRRF1NudgmYTy3XiVFNf4+fWKbmBMRScnalTigl5ZavmgLjWJGfOKbghkQSam6IbYqO7d/Nm3O8OF3D0IT6fniewK2+vsupFq+nttLN69fjvb1e5G2NYpvpW4beYfrXqt7TfYf5XVqawazFQk01Sbb8gLofE0lJobm2MNBylZ47juUad5bu+dHoOk/aNvFbIcSSdMJguebhBzFOEJGxNRpeTD5pcvOWHjIJg960GgcMXRDHl/cJXEoF/D8Bn5NSHr+sM21gAxu4bNy3e4CkqbNnKMN/fexUZ9C78Lb/IJFvaEf+WjqZhEHSVIEZqYTB5t4Uz5wu8NJkCah3iM51oypsRQA7BtM8fnyZXNJkSxcJumFTntlyC1PXeHDfELapc2a5zh8/fY7RHpvPHBhdt4b30wdGODxbZc9w5rII/PsN//bz1/PNl2a5wkyPdUNHaXVzSZPbtvXxwkQRN4xZanikKy229qcYyia4e8cAL0wUlP5aSD59/TDHFupcN5rD0DVWGh6belPEUrJ7KMuekQzPTxTxQomhCcbySR66c1vnvF+8RaKJObK2yWLV5X/65mukEwb9aYtdQxlu3baaVun4Ed8/ukgYSXYNZUhZeiea2rtQjHoRnFis8fdHFtGE4Mu3b2Y0n7zqn+Pl4uRincWay61be8jal5f2926h5oTMlh2GsgnOrbSYr7rUvZDBbIKaqzRUQzmT+aoq5Y5mLc6VXEXaUIm6UqqqZ8UJiaTk9FKDVMIg9iJyKYty10KsJ2niBj6GjiKIUumSrx/r4flzKvglZelU3be3AnwzXOptdzXNsKru6tFaXYRWZ/1Nh5eLKIo6n9f55FuAkhORSRiEUUjKMmj5qxKU89X6mLWNot2fey6lU3djYim5eUue12ZqeGHM9r5UR1LU9CPu3TXEsYUaH9rWy+6hDAenK9y4OY+uCcK2NKUbvevswb4UAr50qeRbCHEn8DsoXvCylPJfCiF+HfgCMAV8TUoZXO3vXcpr3MAG3g9IWQb37xlkutgklgLLEHih7AxEuoCEDm601t7q/QxTE4z1JLlrZz91J2DfaI7lmstQu/mtm+9qbemHqWt8dO+qtdzX7tn+huPmUyY/e4HLyavTFRpeyOmlBsXtPoPZ9Y2gm3tT66pwflAQIehJG6ys01HhSqChQnaEEFRaPs+cKZCydHK2qSqLhsZUsclnbhjl0wdG+bff8Xh9rsZz50r8/N3bO8mHF8N4f5qP7xui1PL5pft3rHEWAbh5Sw+7hzI0vZDfffw0k4UmXhjzuZvG+MT+4TVNV6eX68yVHXRNMJBJcOeOPrLt5tD1NEmW2h7HsZSUm8F7hoBXWj5///oCUqrHX7h507v9ki4J+ZRBqqaRT5oEUYQfRoSRxmJ1tVJaa61ex76EVNsJZzhrY2gaIZKRvE0QuzS8kJF8kooTEcUuw9kE1ZZP2QlJtpt3zzeOV9vV10iqBvJs0kTXBEGXVvhqkuNuUtndnHk56K5odz/ulth0zzFWlzf5Va24JzSqbRF6yjapeuo+sQzWGG2P96eYKTnsHspQaKw2a7beZIjqfn1BpKQmErUb0vBC/EhiGRq9KbOdiplipqwkn9Mlh28dnGOh6vC9Iwt8+UObydoG+eQFVFqsz53obQl4W3oC8LIQ4hvA39L19qWUf/MWT58CHpRSukKIPxdC3Ad8VEp5rxDiN4AvCiGevJrfA765rne+gQ28z+CHMd8/ukQYx9iWjpQRXlublrZ0hCZwo2tPjK4F1kwgAr582yZ2DWVZqLo8uG+Iv3x5lhcnSgSR7MR837mjn0w7QnysJ4mUku8dWeTcSoN7dg9w69beNz3fhbhuJKeaKXM2van3V6XvnYShafSmrHeEgMe0J/pIYggVxmHkExzYnGesxyZlGCzWHM6uNNg/luPGzT00PNVc+/tPnKHQ8BjJJdkzkqXuqJ2UL31oM0IIPrp3iB2DGfrS1kUbKkE5IZyvevemLdIJg/t3D77B8WBzb4qkpRNGMeMDKUxd466d/et+n7du66XuhSQMrdM0+F6Aoaso9iCSJC/T5eHdxHLdo9gKMA2PwYxKOwxCSU/KpOlHCGD7YJoj8w0EcNOmXh45vkQkJbq2Sqb9MGLfaIb5sstt4708eWoFx4+YKjYZyCZoBRE9aZNCeyEVRBAEXTrjikPdDRACMl1Jp1dKVpM6tHN1yNkalXaFOmfrlNtVdrtdlLkUdNdvugm4JlZtCZOWjt8+sJUwaLXTha7momLfaI6DM1WkhK19GSqtCpGU9KZtlptqB8MUynO/7gVMF1uY2sUzMLo/B7PL2cULZOc1vz5b7VhSvjRV5s7t/cyWHe7fM8izZ0sYmkbW1lmousyUWuTa44auiTf0/qyTf6+rAv65rsct4JNdX0vgTQm4lHKx68sQuBGVpAkqxOer7WNeze9tEPB3COP/+rtX9PzJ//DZq/RKPphYrLo8fHge29T56Vs3ISU0/ZDrx3KUWwFnl+us1FVHdwzkTQ3HF2uCGN4v0ATYpkYUS3K2Sd2LWKh6FBo+3z40T6HhEUSSuYqDlBIhBLq21ou45UecWlLa2yOz1Usi4DdszrN/LLcuGYmUkn94fZGJYpP7dg1yw+Z3Pgjl3UIUS+ruu7PIswxBNmFy69ZegjDm0RNLRJEkn7L4vcfPYOkaXhgzmLY4sVBlvupxQq8xXWpx/aYcRrTqoqJpYt1uJb/0kZ3sHs6StHRuG3/jNdWXtvjFe7cjWevRXWh4TBWb7B7OdmwvL4YgitGFIN+ukr5XkEkY/Oz/z96bR8l1nveZz3f32rfeV6AbO0AABMFVpEha+77Ykp0omx3FjpOJMzNxksmc5GQycybLZHJO5kxm4snxOHZGlrfElmTJsqRoJymRFEWQAAhiaWy9d1dXde1112/+uIXqaoAAukHsrIeHB91VdW/dunX7u+/3fu/7+z0yRr5qs+0elDuUspWYaDXgxk0N01DYn0uRr9qYrVK1sFEPXp9dbffWnFmqoKsCT4KuarwxV6HUcHnx7Ao1O3xNvubgBBLPD/tM4qZGw3VQlPXZ1/nVZjtDHNFViq2ouTemsdhqCLyRrHU6omBXQ3WVhGWw2gxLJjpr3OlwrOyUCOysfb4W67LFHbv1OjP53q0ZD5quj6kp+FIy2RejbnuUmy4/s6uPqeXzeBJ6EgYLZQdfwmLFXpeZj2i0XUfTEY2FqocARjIRzhXCZtjJ3jhvLlYBGO+J8uZiDT+QbO2JMrVUZalqM7Vc48ntOb53Ms8zO/r4/ullMjGDhKmFxxA3ubz3PrLBZqDrBuBSyl/cyI6EEP9ISvkvrvLcfqAHWGXtOisBGSANlG/iY1263BecaCk7VJoeF1bq7B5M8v49A+zoSzCWi/Brv/cq+aqDQrjMWb9H7S8FYb2edAKEItBUwURPnPMrdY7OrBI1tHAAdnxcP1Sg+NSDw1dkHaKGyra+OH9+bA5L13h9epX9o2GALqXkyPQqTTfg8JbMFSUHwIaDn6rttTWij0wX31EB+EKpyVJl8xbabwddCSXlEqbGUqXJj84sM11s0HACEpbKStXhXL5KICVzq01Waw4N12/VZSrhTTyQbMlFiOoqpxYqfPX1OQ6OZviZ3WvlSkEgOTpbQhGCfcPJ9vUVNTQ+sn/oiuM6NlviR1MrTPTGeM/u/nXPBYHkj34yQ9P1eXOhsq6+/HKeO53nR2dXmC02+MzhET64bxApJX4QWmLfSXriJj3xW2cqdCsZSlkslpr0xk32jqS5WGwwnIlwIR8GWbYXMFOsh700MpSh82UogxHRFRq2HzZYNmxmig38QPLy+WI7I+wHYLvha1xfkomoFGpgqgoOQTvotjsSIo7rt7PKncPXjYzcdTdUCQkCSMd0pldDW3pLUyi3SjeCjoLczqbPtxsydwiJUL5FRb+L5WZbmeTsYpWjc2Uk8J2TS+367nzFQVUEvi/RlbWAG2A4ZXBmxUEXIFt6oxKIGAqmFiqkDKRNzuZrBFKytTdBzfap2B5DqQjfml9GBgE/PrtCb8JiPBcNPSMiOsdbvQQf2jfA67MlBpIW//a/nm6/9/JGbE3ZpAzhdfgMcEUALoTIAv8O+CzwEHCpkCxJGJCv3uTHLn//XwZ+GWBs7Mbc7bp0uRNM9sV57swynieZL9V55UKBR7fm+PD+Qeq2FzZ4hR4lNGyPIIDg7kmgbRofsBR4fGsPn3tsnK8cmWVquYICbOmJEdE1mq7PhZU6the0s5mFmkPT9RlKRxjJRKjZAflqgy++dJEdAwksXWVqucr3Ti7TdH3KDZcP7Bu44eOMmxoTvTHO5+vsHX7nBN8AgQy43QssQoSZw5rtYWiCC4U6S+Uwg7lnKMHeoQSKgG8cX8D2Akp1l2REoy9h0Zc0GUhZ9MRNfq5VfvK//tkbnFmq8rWj80z0xtA1Bd+XvHQuz/NTKwyno6iKYM/QtQW+XrlQpGp7vD5T4rGJHDFz/e30UoeGvM75Slg6F1vX9JGLqzy8JcuXjsxRsz0+sn+wa7azCc4sVTizVOPAaAopIWqGGfDZYgMhBMW6S77q4AXhao7sSJk2vABTFUigWPfaQd7p5Vq7ya5mr0WbioB6S6fa8SUrtVAJyfYCDLWjLrpjTG54QTuAL3VEizfyJ9VRys6FpUp7Pw27o679BvZ7t5CvuO1s7UsdE5+Ti2smBHYAcTNshIyZKo2ObPzFlVZJkAxlCyFM9sQMLTTvEVCohPreEjg+W+JioYEv4eRilSCQOJ7E1FXyVZuppSqGqlBquPQlLDxfEkjJE5M9Vxx7Jrax0PpmBuBX3PqFEBrwBeDvSykXhBAvA3+LUFHlvcCPgZv92DqklP8B+A8Ahw8fvvfW5ru8Yzk6U+LMco2zS1X+8JVpAHoTJv/gAzuZLzWpNNeMA9qGCffwFa4QBiMj2QhvLlR4/54Bnj+zQqXpcny2zPv39FNzFPo6bL6XKk1+/6Vp/EDynt19JCydqKHScH1yMaNt1W1qKuWmy4m5MqWGy2RfnG19mwts8lUbSw8VWT5xcLhdCvNOYqly+02e/NZdWCIo1j2Wqh6aEtZoHxzLUG54uH5AT9xgqdzE8deWr/cOpbB0FT9Y+65c/1JgLJlarvL6TInZYoPXZlapNF229jb52IG1jHep4fLa9Cqj2ei6spWdAwl+NLXCeC5KtFXbK6XEbTVx/dyhEc7la+wauHYg/65tOaaLNc7l64zlYhRrDuWWG+uZpWo3AN8grh/wZ0cX8APJQqnBmeUq+aqD7Unevb2X8ys1euMGL53NA+FQqSvQiqFJWRqrNS+0qe+YNbU1lwmvuaDVe5OOaJQaYaAugISlUayHsnS6Kmi0IvC4pdFsp4zX9rvZ2uxrUevIQjfuzTagK+j8GNc6VbYTrgRU7fUfvHPycUk2VAJT+ZYiuISLhWr7G8lXbPyWDvhiuY6hCoRQQNL6e5SUmi79SYvTS1W25GLYbsDXTs8zmF5vvrbRMr2bGYC/1a3/M8DDwL9qDX7/CPiBEOI54CLwb6WUjhDipj12Ez9Ply53lGLdodpwW05bAZoIB4KvHp3n+EypXYt4P6CIcOnUDSTfP7VMw/WZzkZx/IBSww01vstNHt2a5cdnC8yvNvnLj49TboQa0ADFusv+kTT/6MO7WSo32TucassJjmajvGuyB88PyMaMsK51EwH46zOrfPvEEoam8LlHx0hHjXdc8A2wWr/9ZU4+UG6EdZ6XsopeEPYMxAwVz5f0Jy2e3NbL8fkSKxUHQ1eY7IvzS09u5eRChR0dzpH/4IM7+cOXp9kzlGxP5hzPxw9CRYS67TFbbLTl9755fIGZYoMj06t8/qmtRI3wtvnYRI6HxtfKmRwv4A9+Ms1K1ebZnX0cGE23FXuuhRCCX3h4rF1HHEgYz0UpN1wOdPQ4dLk2qhDETY1SwyUV1dvlO5oi0JSwZ0QoSlurG9aXUiyU7bXsdH0tolWVtXbJMOsZBtxNT2IZKlXbx9AUanYYvHu+ZDwbobJUb7kwroWPnTHizRy7/av8fD9iKusdOqUQIGWrzOStz2qzQzax1FGgX+nYkRBrqxPIUFnL932SEQ3bC7A9ie0GlKVD3fGpOR7fObnE3Gqz3Xt0icYGy3JuaQZcSvl7wO9d9vCPCA19Ol/3r27mY1263A+8d3c/haodmjw0bPwAehMWJ+crrDbC+b3kygHpXkPSklD0QkOFqeUqvXGDuuOzfzjFKV1hSy7KZG+chXLYaLRad/jiixdQFYWd/Qk0VfBIy2gnFdWxdPUKtYqnd/YiRJgBPTi6ucBmseWY5ngBhZpDOvrONEx572W1zreLt8oWpiI6w+koUdMhKEoOjKT49Q/s5IsvXWSmUOdzj47Tn7TovywIPjia4eBo2C4UBJKa7fPgWJqhdIRjcyUmemJ8840F+pMWpYaL2bqOQs3fgDfmygymLDIxY10vwWrdIV8Jr5MzS1UObOIaE0Ksu6Y+fWhkw9veSgq1sL5+W2+C1F2uDqQogl94ZJSFUuhq+59fnmFBNEhYGihhKZAqxLqsd+fYmbZU5lrqJTFTRam5BEBEF5TCYQfb89sqG52Sgp4f0PLkIpAwlLSYKTYxNIWoqVK27fZzt5r7JSlzNZzL7nW6quAHPpamYl+lIbRzm86hRGFtdSMTNag6NkEAfYkIKzUbTQ0bdWOmynTRJ2FpfPvEIuWGx2rd4S8/GpY1R4z19xrzJqqgbJSu+kiXLjeRgZTFLzwyxg/PhG5/EV1gagoNx0PKsFlRlRJVVa4cle4hVBEOogKw/QBVBJxeqvKxg8M4XsBHDwyxrS+OpasslZsEQR7HC3jlYpGFUoOkpbNzIMlAymI4HeH3XrqI60vet6effR012rqqXNEst1Ee2Zql4YYD8Jbc5uzq7ye+dWLh+i+6xSgCkpZOEEgurtSpuR4vnl3hpXMF+lMWD49nSEV0LENlodQkkJKhdISGE2YqLzXbSimZKzUYz0bJxU3eta2HF6ZWaLo+J+fL+DKsPX/3jh5O98QYTFl89+QyZ5drWLrKX39yK4a2dqftiZvsGkiwUG7y0Pj19QDOLFVRN6HGcif4L6/MULU9js6U3lJT/24jamhMtEp2aq6PpavYXsCxmTJvLpRZKDeIWwqF+iXJPo3lVkbUMFQEoX53wtTajZGdC12dTpZuEEr+uU0fQxXUWpG5BOZLDRxfEsgAQ1kLie/dUfruRBA2tkrAdq9e9tF53jsVZ0xdoASCQEpGs3EuroarIIYWupe6fkCx7nKhEEpJ1m2fmu0TECZyHhnPcGhLlnTU4F9+/c32e2TjN7kGXAjRC/wNYEvndlLKX2r9+883uq8uXbpcH8cL+ONXZ0IJPi/AtDT8IMzACtEynxHgbsBt727m4S1pMlGTH02tAJK+hIWmCn7nhfO8a1sPD41n2tlsX0oGUxbDmQjfeGOBphtQtRts709wYr5M1FDbNb5LlSZwc5okUxGdjx+4UgnjnUZP1Lxl1tOd6AISEZ1Cff1argAme6IkIjoNx+ePX50mHTWYLdbJxS2Ozqzyn1+ZJWZqTC1V21nlrT0xzuVr9MQNfv7hMQxN4YWpFb76+hwXC3Uen8jx1961lad39ALw0HimLb+nqUp7Itds2W9fMvDoRFEEH7qG+U8nx2ZLfOuNRQA+1ppg3o20G0nv8HHcCCNpi/nVOv0JkxPzJQpVh0rTw+koQWl0ZLFX62sOi+cKa70OK5W1a7AzeyoEZGIGVbtB/LIGwNlSEy8IlWzqt0FaciN/k50yhPcLkrXv5Fo19Z1Bt9ahZ96biFD3fHxf4gQBQahrwIVCHdH+L6zpDmRYFnpJ5lECKAoj6SuNsxobXJLeTAb8y8APCfW27/cyoy5d7hi/88J5/r8fnScXN+mJGyhCYHsB9bKNKsJl8GaHC+a9jAJYmspqw8XxA6QQVFsST4vlJm/MlRlKR6g7PoGU/PD0Mo4n6Vk2+NyjY7x2cRXbD4gaKgdHM0z0xHlwLE3V9ni4VZLS5eZhe7ep70BAMqJSrLvr3i+UJNQo1l18X9L0AppumKVyfMkfvDTNQtnG0BXGc1GklFxYqXNuucqWnhj5qkOp4dKbMFmphT97vmS17rJStYm3lEyuJr/3gb0D/PB0mAX/6utzfOzA0BWlTpfz+kwof/ngWLpdstJpUW97d+/t9NOHRji7XGP7XTpBuBanl6p4geT8Sh1DbU2exFoTLkCjIyJtdPqVBx21wVfZv5RwfiXUk16urc++XtqvBOzbEPVu5B0sHZx7WRalxds9m3bHDqYLdVRV4AcSU127xlMRva1Yk4lqrDY0yg2XdFRfJ8N6dKbIl16bYzC1vsyteAsC8KiU8h9u4vVdunS5Ab5+bJ6G6zNTrGOqguF0hOU0MjIeAAAgAElEQVSKjesHzJebrOWl7n2EgLFslPMr9VAOSkocReAH0JewWhMQ+NYbi0gpqdphGYimKrxvzwDb+uIUak5b7QLgmQ4r+i43ly+9OnNb3scN4PxK84rHNU3lYrGOlOEKUdzSyNccTD2Um6s4XujYF0h8P+D5qRU0RaBrCpoq2DOYoiceZsWfnMxx5GKRQs0hGzMYzUSve1zpqEEqYhDIGjPFBmeWquvKnC5narnKt08sAeExXXLIPDiaJpASRQj2DF5bJeVOcjfqgNuej6ld35nT9gIcL8DRWqsVreuic+y8PKN9CcvQqLRKGq4mu3mtMbhzv3fL+mT5Pgi+b5SrfVe+BLelN3l0ptSebEkEUV3DcR0SEZMtOYVSw6EvGSFfLbRr+c+t1Gg4krPLtRs6rs2o/H9VCPHhG3qXLl26bJhndvSiKgoDyQj7R1I0XR/t0jKmlBiqyr1nDH0lAuhLmrw+W+bVmdW2Jq+mKjy8Ncs//uhu/tkn9rF3KAxwhBC8b08/z+7q42MHhig3Xf70tXl+cCrfDnK63Fo++8joHXtvVYQNcUEQ1maGMnKCuBGa9MRNja25KIamIGXYOGt7AY4foKsKf+WxLYznoixXbPJVmy+8eIGTCxX2DaVQFdFWzFmqNFmqNJFS8rXX5/mN709xYr7cPo6tPTF0VRA1VEYyVy4/d2J0NGka2lqEpyqCh7dkeWg8c4WaTtP1ef5Mft17dgn5ymtz/N/fneK7b17/733fYIq4qbGjL0YubqEqYQ/Ntv61mnuj49QbmkBTwuss1dEQG7XWvsONtqEOxNd2/M7TSrr7uNokqDMAjpgamipQBKQiGuWmixdIlitN9g6lCCQcHE2RssK8tS7ggeEUVdsjF1vflB+9BU2Yfxf4H4UQNuDSKjuSUt690/cuXe5B/uYz2/jld08ghOA3vjeFqggMTcFwQ21Zx/PRlHDwaHnx3JNEdIWa7XOyXqHphgGVCvTEdHb0xzk0FgYne1uGKELAnsE1h8Jy072uyUmXm0tvIhK6BN4i11VdgavtWspQQkxTBUlLYzQbw9IVZlcbSEIJusFUlMWyjecHnFqqIqUkamgMJE2Oz5d56VwBgGLd5uRCFUXAyYUyppah1HAp1hy+dGQWgMPjGZ47vUw6anBkepXdrUz1aDbKrzw9iSLEdd1TR7NRPn1omIbrs7NDCvFa/ODUMsfnwuA7FzM2JGX4TsAPJFNLoW34qcUKz+669krXxdUaNdtjZrXBwZE0s6sNeuMm5XqoSCIAXQOnVeKdtFTqrheam3WUBTkdF6RYUyS8grG0wcVVB1MV1N21quz7RJb7viRuqVTdAClhe1+cfKUQ6r0bKpoicAidTb91YpFSw+HPji0QMzVqTtjgW6h7xE2VmrO+jCyT2JhK1oYDcCnlxkaPLl3eIWz5H772trY//y8/ctXnFCWcQk/2xvnuyaXQSEa47aUvT4bB6r0Uf17eKGTpKrmYSanp4PmhzrOhKaRjBi+dK2B7Ab/wyBhJS3/LZf6kpfPpQ8MslpvXLAPocvNoukHL6OjWBODX2m1AS8YtkGiqyr/49AN8/1SeLx+ZoVAPJeMuFussVx0ajo+UYWNkJmoQs3SqtsfUcpXZYp1s1CDausmO52I0PZ/f/OFZJnpivHpxFUXAYqnJSs1hsWJf4Zyqb8IifnyTqjmXZA8VIe64Ff3dhKoIHp3IcmK+clWVmYVSk/MrNXYPJJlaqtFwfexig1zMxPZ8CjWHur1mytIZTDdc2daZLzTXGi87ysHXuVpeHovnq2GNh+3Llizd7Ss+6RxbjQ5RLI3uBOBy4hpUWydlPBdlvmzj+ZKmG2C36o2mlqpt3e+a7bJad8JseNnm0FiauuOTi5st8x/Rbs6+RCBvfg04QogMsB1oT8mllD/YzD66dOmyMZquj6YJtvfFsT2f1YZCg5ZZgAyXoe4lLp8spCyNjx0cIhvV+f7JJV5v6St7QXgzmy02OHJxlXe3lCneitFslNHs9Wt3bxWL5SZHZ0ps749vOtC6F4mZgpXanbnyLgUZ4fXR5O/90RHGsmFjpRDw2NYsL58voiuCautiCwKJ7frs6ouTNDVminUqDRdTU3h6Zx/v3t7Lfz2xyNdenyduaRyfXaVqeyRMjelinWzMZP9IctO68RthulDn5fMFJnrj6/b/5LYeeuMmmZhONvbO1Ju/Gk9M9ryl9TeEWtz/5aczOF7A1HIVU1Nw/QBDVak7oWGX4wd4HRF1Z99lqb72S62zia4juu3sl708xOrwd6F6m+0oO8fWzkns1cJATYRJnHcinbHy0mqdfC1sLC9UG+3Hi421ZvOLxSaqInB8iakpxEwNVRFYmsJg0uK50/krpGnrzsYaqzcjQ/h5wjKUEeAI8Bihqc7PbHQfXbp0ciszyPcDX3t9nv/0o/NMF+p4gaTurMlk3Q9j53LVZm61wZPbeliuOqSiJnFLZc9gkhMLFVIRnaG3kHi6m/ja6/OUGi5vLpT51We2Xbck4V5guWITSHmFgQ3An7w6dweOKEQS1udKGf5/ZqlGse5iaArlhsufHV0gqqv4ci1mUgUIRfCTi6scGE2jKQrLVYea4/NRU6PueuwfSfPjsyuUGy5vlprk4galwGXPUJK647N36OrB95HpVX54apmehMlnHhp5y4y17fm8cqFI3NTY3+Fs+b2TS+SrDhdW6mzJRjF0hagR3tz3DG2usnOp0uRPX5vH0BQ+/eAwMfNmWnzcGwghQmlWwtWD/SNJfnqxxPa+OLm4wVIldBqd7shud4bJnT93BrGdKq8bDavvZNa5895wtQD8fgm+b0QStVMFZb62FihPLa81fa87h3JN6rfmeJxbDldW5ktNzhdqbd35TsrNjR3VZmvAHwZ+LKV8VgixC/hnm9i+S5cum2Cx3EDK0PrY9X3ucbnvK2i6AfmKzZ6hJEsVm4neGD+zq4+EFWo8u0FA0lprezqXr+H6Adv74neNDfwl6+uIoXEfxN5MF+r8l5/OICV8ZP/gOgt3gK13YLWh8yYrZeg65wUBqqJQqofSYLbnY7ZMV/YMJDixUEHXBD0xk12DSVIRnZ39Cbb2xFiqNHF9yR/+ZJqxXIwd/QnGslFePh/WfyoCHhhJUW54FGoONcelant86dVZbC/gYwcG6UuEk5PXZ1Y5MrNKtemhKoLPHr6ySfXFswVeuVAEQnmzSysl/UmLfNXB1BS+8OIF/AA+fnDohox53pyvUG75X5/L196RJVmqIvjM4REuFurs6E/wnROL7B5MoqsKj27N4voB/YkIddtloSUl13ltdZaU6ALc+yRIvVlsJNjt1Nt+u3R+HxENbsWiQufxWrrSNlOKaoJ6a5aiKxKkQPoSTVGwdIGUEkMT7BtK8cLUCsOXJYo2GlhvJgBvSimbQgiEEKaU8k0hxM5NbN+lS5dN8KEHhlipOhwah28em8NxHXxubNZ/u7naMWoirPP2g4B01GCiL46qCCKGSsxUibTqXyOGSqRD6+VcvsaXXg2b435mV1/b5vubxxeYWq7xxGRuU9bfN4uPHxziYqHOUDpy10wK3g6lxlpja7F2pW7ZU7dZ4lEB1I7GTEtTeM+uXnqTFn/w8jRISbHuEjd1BDCWi7JcaRK3NB4cy/CzD41Qbrg0HJ9CzaZqu0QMFcOXxC0NVYSOsh/cN4gETi9VEAgGExFUpclA0mJqqcZAMsJ8qUHN9jk+V6ZvZxiADyRNVmsOfUmLwlucLwCz5ZYpBOvk8963p58Do2nmVht87+QyADPF+g0F4Nv74xyfK2NoCmO5O1eSdafplE18akcvPzy9zKHRDNmYiSIEEVNFdIxMnWOUqqzVe0dNlVLL2eUafZcbonMsvJmB/Wbruzs/x418Jr3DyMdSr218czPoPL5szGS2FDbPZi1BoXnjBlGZiEqxER78UMZkuhjud2tvlGNzYZPvllyENxZDMyYfweEtaY7PVnh0a4bXZ1apOQGBdDmXr/L8mTwjl0uYbvBWsJkAfEYIkQa+BHxLCFEE7tx6ZJcu9zlbe2L844/uwfEDjkwXWaw4CAnaNZQi7hYyURXXh/FMhFPLVS6VxKmqYCwbQVUUhjNRPvLAIK/PrPLTC0XO5av84NQyP//wKNv61mdenY70v+OHPzccv60W8erF4h0JwC1dvSJLfC+zayDBSs3BDwIOjl15Ps/na6ji6trINxtNgbilUWqlv1IRFccPePFsASQ0PYmuCVIRHUNTUERoqtFwPI7Nlmg4HkIIxrJRvn9qmULNZUd/gscmcvTETF6bWWU8G+Vd23owdYUXz65Qbng0W9nShuuzezDJaDbK1HKVSsNjNBPh2Z19nF6scGy2zM6BJENpi/fufuvJySNbs2RiYdPnQIdhhxCC/qRFOqozXWxgu/66EpXNMJiK8DefnrgvJoE3C0NV2JqLYeoqP71YpGr7nFqstBsUAUx1rQ7c1ARuK8IMOo143mbGY1199h3MnBgdQfON3D46/YSuFnzfqpi84froSjjuZOIWhWZYr30t1aROOicrnr+2gQzCpJAElstrJShvLq45oTqe5NhMmart88qFVVZbK00NN+B3f3yRcyt1zuXX64BvdHzcjArKp1o//k9CiO8Sejz/+Ua379Kly8ZZrthIJFFd5fRSlXdvy3JqoULdDe6JUpRC3UcAZ1dq7OhLcCZfw/cDkJK6G/CuyQwfOzDMgdE0pxcr2K7PQtlmpWrzv3+jwT/80K51mcAd/XEabh+uH/BgK9C2dIXJvjhnl6vsGbozS+6uHzC/2qQvaV7XEfHtsFRpoghxy01RNFVp27G/FfmqTXAbgwg3gJrtkYzoqEpoT//cmRUEEEgJSOKmii8l5aZLoWpTboZB92rD5Xy+RjZuApKkpdETNxjJRPnUg8N87+QyuqpwarHKwTGbJyZ7GElH+fKRWRRF8HOHR8hEDVQl1B7fM5jCa+mPn1mq8B+fP890sc6+4RSPTuSumDReQghxzUmaqal8/MDQ2z5X3eB7PbOrDeZKTXwZNmvPt+r7l0o2KzUXRUAuqjPXspq3NJWqE4ZphqZwKWtg6eB2LG5cmoDGdEHtDkbUndnvdSooavvQ12W6b3XG+lbSdLx2oB0EkLIUys2AgyMJXr5Yectt1pWudfwS0TUqLe3J3qTJXNlGSohbOosteZR2rwkQNxUqrVlaoe6iKbTHQIXw58vHxI1qF103ABdCJKWUZSFEp6/z0da/caCwwffq0qXLBriwUuOPfjLN0dky+aqNQDJfsmm2RqC7vfzkEhKou5JTS1V6YgZOECClwPUC8lWHuuPxH58/R7Xpsb0/zqmlCktlm8FUlGOzpXUBuBDiCiUKIQQfPzCElPKOBR9ffX2O8/k6ubjBX3l8yy15jzNLVb76erjY+OkHR+5oiUG1eZus6FtIwmDH8QKSEZ1C1abu+KgCDFVg6iqWrnJ4S4aXzxUJlLDp0vVaahcxnQ/uHeCZXX1YmkK56bGzP4GiiLZ5hqEpJFrmGmO5KH/j3RNXlIsoiuDDDwxwcqHK/pEUJ+bL5OIGTddnPBu9JSopXd4eM8V6OLn3fCxN4fhsiZFMFC/wwwBJQtTQuKQn5XdkvRsdKhb+ZaI/l7Kbzcs6Gd9uqcrbofNInGsotdyrdCrMnCusqZW8Ov3WwTesPyedGelSh4rT1EK5HTzPdexXSuhNmJQaDvuGUvzoXLH9XG/CYqHUJKorPDaR5UKhTm/cZKlit1+z0dKgjWTAvwh8FHil9Zk673QSmNjge3Xp0mUDFGoOlaZHtemyUrVRFEHN8e6ZwVQhHBgujXmuLxECRjMx4paK4wVs7YnztaMLFGsODdfH8wM8P2C+1EBTxTWlBy/nTmb+Vlrav6v1UObsVqigFOtOuy67UHfuaAA+u1q//otuMl4AJhJdSKSqYWkBlqaiawq2FzCWjfAr754k8Kf46cUiKUujUHMQQtCXsHh0IsdkS6lgSEpOL1VbmtI5xnJR4qaGoYWGPv2Jq69kbOtLtLPcpqawWLHZ1hfnww8MbkoXvMvtwXbDPhPXkzx3Jo+iCOZKDXIRHUUBTRGU7bVQqXN8dTsiqPoGregvlXjcCz069ws3ouZid/xc7phcdSpPBsBqzcYN4GxH2Z2phvLAqgAvCHjxfBFFUSjW37r/43pcNwCXUn609e/WG3qHLl26bIrBlEXC0klFdBwvQBJKEDbv9sJvwptQJmqyZyjB8dky+VZjmhCwezDB45M5ppZrSAnZaGiO4ktJoeawWG7i+QFBIDm5UKbh9LcMLe5ePrB3gNdmVtnZn7hlEoT7R1KUGy6KsuYKeqf42P4h/rdvnLqt76kQ1mHGLJ3t6Wgr42SgCFipOWSiJl9+dZb5cqMlN2cTNzVURWFLT4w9g2vn7Nhsmf96YhGAw1sy9CUsBpIWX3jxIvmKTU/coD9pMdEbZ1vflfJil+iJm3z8wBBJS+uWftylfPbhUb5/apmHt2R5Y67M14/NM5Cy2NEXZ7keNuMGvuRSBlzp+B4NHdxWcGYqa8FZVFvLxl7tW7/TwXdnJv52GPHENKjdBW4/15r4dJ4TS4Fm65eIAo12g/fad9tZL1+su+3vWgI9cQPHCw3JsjGDC4U66mUT8I2K526kBOXQtZ6XUv50g+911/F2dai7dLkVfPvNJVRFMJiOkI0ZLJSaTC3X7vrMiiogHTF4dlcfSUtjPBfjB6fyFGsObgC6KvjYgWGKNYeLhTqTfXHKDYdCzeVPXp3h+yfzrNTCEoNTSxUKNZth4+5WdLgdRkCmpvKe3f239D02Sk/i9tmiC2hJfhHq4NseTddj72CSB4aTfOX1+fBaWazQcH3mVpv0JUyemOxhW18URSh86IFB8jWbY7MlJnriVJouZ5erOH7AcqVJb8Ki3Oyh0FrJeGFqhR39Cd5cqPArT0+sK0Pp5CuvzXEuX2NHf4KP7B+8beeky8bJxgy25GL0xE3++/ft4OcfHiUXN/jDl2fYkqsQMVR8L+B8oY4gdB3+6XQJJKQiJjW3ZVnfEWk7HYHm5Y12d0uNdWea5u3GxRu559yO4Hsjx3Gt59etbsi3frzTZGmd4VIg26/zA/irT2zh91+e5rGJHJO5KBcLDYYzFtMdJSzR6MYSRxspQfk3rX8t4DDwGuH52A+8CDy5oXfq0qXLhvC8gOfP5EFKUlGdYt2h6QZ3dfANYSOKoSn85HwBU1f4q49v4YnJHP/ky8fRFMGFlbB8IRMzyLTqb+OmxlA6vFmOZqKAZHa1iampnM3XGL5c3qnLHSWQwW2pdRVANqbz3l29fOW1BfxAUnU8Zlcb5KsOpxcrRM3QuKZmu1xYqdOfNPnsw6Ps7E+yXAlLt3rjJv/pR+cp1lyOz5bZNZjA0tV15UKeL/nAvn5OLlRIWBqVpteyqX/rshIpZftavlCoveVrutx5XphaoeH4vDCVR1cFPziVZywX4UP7+lmtO4xkovz7758GwuDN9nwShkogYd9QnLlyGIDrylpwfRckem8rd8s952YeR+fEqbPspFPl5fIGV9HRdHlqsYoqBNOFOrVm2CBeuWz21bhZTphSymcBhBC/D/yylPJo6/d9wK9v6F26dLkF3K9OmufyNU4tVvCCgKSpk6/ad81AeC000ZJI9ANWag6/9fx5PvfoGO/Z1c90sc5jV7GQBhhKR/hr79pK1fb4k1dncbyAnQP3j7zf/UKl4d6WXgRTV9gzmGKh7ODLsIeg4QQ4vkvDbqCqCrmYwZZcFCFCOchc3OTjB4Y5Mr3KkelVAOq2x0vnilRtj8cmsvTGTYbSERQhODCaYrbY4I25EvtH03zi4DCOF3CxUGcgZV21pEgIwdM7ezk2W+o2X97FTPTEOD5XZmtPjBPzFQIZcD5fZ7nc5DsnFklEddSOr7jpeDS8sHvlxGK1nXUNOvKvG12FvNtXK7tsHClC9aVa0yMT1Tk2V2KmWKfUcPnMQ8O8Or1Kb2K9OtVGKyc3owO+61LwDSClPCaEOLiJ7bt06bIBLhRq2F6AlJKm5982zeW3gwKYhoqla61MeBBKeQH/+KO7Wak6V7iFvRVxU+MvPzZ+i4+2y43y5uLVVQduJq4XML9axw0kqpD4tBRRggBNVdA1hYihYBkqkzGTuKXx2EQOXVXIREP3VCGg1HTZ1hdjte7yxGSOg2MZepMWlqaQi5v8X989jeNJXjizwuHxDIamXLP2+xIHR9Pd4Psu5/17B3hyew8RXeVPXp3lJxeKTPTE+cn5CudWaigFgdYxyfKkQFdDm55t/XFmWgYtY9kop5Zq+BJ6YirLtetnN6M6XBLb6AbjN5fOeuyoplDfpC6vJtaaN6+2mpc2YbXVrSllqOV/ZrHC/pE0SxUbQ1MwNUEqavDEZA5LV/nh6Xx7e7nBvpDNBOAnhBC/CXyB8LP/JeDEJrbv0qXLBtjZn+DkQrUti3U3D96CMOudsHT6kiZD6Sh///07OTFfwtBV3jXZQ9TQiGavP9Q0XZ/lis1QOnLLGhq7vD36k7e+BjzU+IaZUpNczCAZMUId3pYwbzKiMZiKsKUnxvxqg3LD5Zce2MozO/v4xvEFTE3hs4dH0TWBIgRffX2OsWyMvS179ksTwbnVBmeWqqxUHT52YKjbTHkfEsoMQtMNODweKimXGyZRQ0PXFGr2mgxGRFeZ7IvjB5Jc1EJpaUHXWuUEYSCtcDW7mc5Ae89QiqOzoTOpqQqWW4XSncHf7eBmWsNvhFs12ejcb19Co2ZLbC9gKG1xJn99ZabO7S0Nqq2v3VKh3jpBaUNhpCdGoeby8f0D/D8/PI8kVDxKRTQiukoqovPASIrXpk0GUxYPjqbDGvDLkkuKvPkB+C8Cvwr83dbvPwD+/Sa279KlywYYTEcZz0WJ6Aq6qvKDU8s07hL3nc6BTBOQi+t8YN8gnzw4zBvzZR4ay7B7KMnut1DrmC7UObNcZe9Qkr7Lmvn8QPLFFy9Sarhs74/z0f1v35iky80nZRm3dP+aEjY6xQwVTRWkIho122cgFWGhHAbkh7dk+eSDw7x0rsBCqYkqwkzUC1N5XpteRVcV+hIWe4aSLFWa1GyfSsNjuWKvs4w+vVRlLBtjKBVhV7fc6b7mwbE03z+1zFg2yocf6G/JV0Y5s1jhO6fyCODQeIaGG+AHkpiptqVUPV+iqwpeEGDpKqpwQ0k6TeD5El9eGXhqKhiaiqEpRHWl3amYjqrkWxn0iAaNW1BUnrUExaZEAofG4rx8MbRX18VaA+K1AuW3o5xyq+YWnfs1FWgqIJEkW6tdcO3j7okKluvh9CliqlTd8DuImQr1enhvTcYMyk0PP5DMlZpEDYWmFzCctvj2G0tUbI+VmsO//0sPcXGlzsHRNOdX6pxdrlK317+zom7sTGzGCbMphPgN4M+klCc3ul2XLl02h5SSQ2MZFssNjs2W0VSBIdcbLNwJhlImg+kIs8U61aaPqQksQ6NUd/nKkXlGspGrlsv4geTLR2ap2h5/fnSBnzs8wsNb1ry9XD+g3AzTEoXajWmqnlqscGapysHRNEMbKHfpsnlemyle/0Vvg0CCroJCgJQqtuuHFvOK4KFWyYfnh9rfO/rG0BRBJqrTmzD5refmOLVY4fB4hkxM59hsiWNzJZqujyIE04XGugB892CCM0tVLF1hovf6ZSdd7l12DybZ3ZKj/PNj8+wcCH9OxgwsXUEVYOkaR2cLeIHkow8Mkm7JwL5rsofXZksU6g7P7Ojl+akV5ktNDo6keG2mRMMLUBXWORQvlhq4vo8Qkt5shJnVJkIIkpZBvhaqZdwqR9mmJ9sB6/HZavvxTvWPa7313d5oOlfy2sd4cmGtJO5axz2ei7NSr6AroKtr6wKO31mCJJFBeG6KNQfHC/ADqDQcak5YElpquvzbb53mjfkSL50r0JcwOLNc49TC+tI8d4MOqRt2DxBCfBw4Qst+XghxUAjxlY1u36VLl42xdziFrikMZ6LomoKlinWySHcK1w8Yz8XoT0awdJWGG+B6ktnVBm/Ml6g0PX5y/q2NcZVWo9x0oc5Kzea503nmVtdkmyxd5f17BtjeH+e9NyC553gBXz+6wMmFCt88vnDDn7HLtfnTI7O3ZL+qAEsTKAJcH9xAhAZNgWAgaaEpgmLdwQlky4nOJm7pfP6pCX72oVEurtSZKTaQQDZmUrM9vvXGItMrdUAynImwd3j9qkxfwuKvP7mVzz06TszczGJwl3sF+y0GzmQkzJrqqmA0EyEXM+mJm8wU65zL17i4UuP47CqPbM3y4Fia/WMp9g4n2T2QZDQbZSQTYXtfnN6k2V6Z9AIwOqKpQCrYnqRq++RiZqtmWFkX+Nq3aEzvdI2s3wX3jZtNZ6Bdcza2Mnx6qUoAOAGMpC0UwnvS7sFEu6Z8OG0xmLKImRpjmQiXbDeWax6TfTFipsaewSRvLJQo1h0uFOrkYiYRQyPVkYmH9ZOda7GZUeefAo8A3wOQUh4RQmzZxPZdunTZAPtHUsRNld64Rb7isFBqhlq0d7AYXAVips5ffXwLhZrNv/nmKfxAYqgKY9kok31xHE+2M0uXI4Tgsw+PEjc1pgt1DE25IujZM5Rkzw0azWiKIGFplBpuW+Kwy81n20ACji/dtP1F1LBhKaKrSARN18NQBY4fhDfCbJSHtmQ4MV+mP2lRbriMZCJXNEru6I8TM1VSEY1s3EBtSQhqqsLTO3s5NJa5acfc5d7gO28u8tp06YqStscncgynIyQtnRfOLKMKUFWF7X1xXjxXQErBaDaOqoaTwNFsjJfPr6IqAiEETS+g1HDxAtoOiQDP7OzhB6fyZBMmXhBmuIWE2VIDKSFY5w8McQOqN7bYt2Fu5LbRuU2nCdG9RmeDZcUOP5EEinWPgVRY45+M6mGtP6ApKg+OpWg4Puno2r1JCPjtv/YIr04XebCGlscAACAASURBVHhLjqf/9Xfxg7BR/PPv3sqXXp1j71CS//YPXmuft72DGytp20wA7kkpS91GlS73C3erEdPXjy5wLl8jaqhM9MbIxgxqjkd9g7P9t4Olwf7hNA3XZbrYwPVkq8HS4iP7h+hNGHiB5Nffv4Ojc2V64ibv2dVHX9LC9YNrWnInLZ2ff3iUmWKDZMvp82ahKIK/8MgYi+UmI5lu+cmtYmffza2Vtn3QNYEbSJ7e0ctCqYHt+RRqLpN9Mf6bZyZ5dLKHxXKTi4U6uwYSJKzwuvH8gELNIRc3GcvF+JWnJ5lbbfDYRI6euMnHDgzheAG7N3gz7HLv8/Wj85xarPLoRJZTi2H5xZmlKkEgUVqN3UIIxnMxAGZWmwy2ytV0VTCSieAHkt2DCU61tjNUBSkDGq6HqggKNYeq45Ov2PQmTZbKNglTZbZkg1CoNn20VpgkAdtx8Fr1Jp2OieI26KPowGZj/LQJRTsMxO+W4LuzmTRpQNm58nFYP3mI6h3Nlp0OpkJSaoRPVJteO7klZMAfvDRN0/X5wN4+orpC0w3Y2hPF8QPqth86YOoKDQcMTfD86TyvzZSYKzXXlSFp6s0z4rnEMSHEXwRUIcR24NeAFzaxfZcuXTZArWW3ZnsBqq4SNTW826BFGNUEj072EASSlZqLIlSipmA4E+UvPjrGB/cN8FvPnccLJBO9Mf7Oz2xft/21gm8/kJxarJCO6rfMOTJiqGzpid2SfXcJOZu/ecYzAohbKhFDYyhlkYnqjOeiqIpgte6QjZnEW5O0/qR1hQLLH/90ltnVBiOZCFt7YgymI+sy3RuRE+xy/+B4AW+2anGPzZY4MJLiO28u8cjWXDv4vpyDo2m+dnQeU1PoTZht1ZS5cgNVESQjOudXasytNrFb+7c0laQl0VSBKkSrYVhltebgBxLHb6XGW8RMg0xUoghBT8zkvBKqdoSrNFfWiHQ2S17ORkywOpsR45ZCoeW7PpTUmCt7V7zmci55ytxp9S1DCUtGAOImlFqygJ29UJoKfut3QWgEZ3sBilj/uvGeOKeWQgOdbb1JlqsFFCGIGBqGpiADWK46lJvhWfnh6RWGMlHqtsdEb4J/9qfHKTc8vvPmEiOZKHXHJxM1+N6pZWaKdWZXG+t6AEod6jrXYsM14MDfAfYCNvBFoMSaIkqXLl1uEh/cO8BoNsLDW7O8d08/hqqgq7du5UkTYV3crz67nQ89MMhkX5xsPHSrHEhaPL2zj/fuHlgn0yY3OTq/MJXnz48t8Icvz7BStW/yJ+hyuxhJm9d/0TXQBeSiKhFdIR3VeXwix2cOjfDpQyNkYkao4x0zGEhFiJvauqbJTqSULJSbLJSbfOHFC3z5yBx//MoMNftubyHrcqswNIV9wykMTeHBsTRn8zWihsaFlfWTRiklZ5errFRt7FaT5SNbsvQkLB4YTrF3KMlT23t5ZGuWnQMJnpjsIW7pxEyNvoTJ4S1Z4qbGMzt7+cTBYSb74nxg7wA7+xMYmkImYrBnMImuCiK6wi++ayuPbM3y9M5e/s57trFrIMm23jh/4ZGRdv3xjt5IuwdivCeG0ZowJMz1mVTzKhFb58OG1nGvUBRyUZ2kpbGjP81kb4xcTOf9+67eZ+N2BJKdd524sfbbA0NxFBE+P5Tc2EpmRA+P8lq3sr4OC/cHxzIYamhqk42vTb4DwvOqACPpSPsYhYCJnigRTdAXN9ZJ2Q6kTIZSFmOZKDsHk2SiOpmYweMTOcazMUazUQ6MpsIyI6AvafLhfQM8u7OX9+3uw2lpR9pewK7BJLsGk+xsNfbGTY2BpEVH1QoPDm/MI2AzGfA9rf+11v+fAD5OaEnfpUuXm0Sp4TJdaDBdaPDu7T3UbA/3JmTANUBVw2UytSX3pgjIxUwOjaf55IPDmJqCH0geGEnxoX2DLJVtXpjKc3y+xBOTPXzq0DBzq032DW+uVttujeqBlDfls3S5M+wbfnu11P1Jk56kRaHmMp6N8JnDo7x3zwBN1+d3X7xIueFyaCzDA8Op8AZ/lZJHIQTP7uzl//zOGRKmxvmVGn0Jk26F5Dub9+3p5317wuDytekSQNvU7NK19KOpFV48V0BTBO/b00/c0jA1hUe25tg7lMIP5BWrdP/kI7s5m6/x2ESO//e5c7xndz+qovC3n53kickce4eT/PbzF6i6PtmowScODiPEBfoSBrmExbZW6VZ/IsKvf2Anri9JWip//sYythvwxGQv0dkSpZZh1JPbejibr/LQeJbfe/Eiy1WbmKHyvt19fOONJbJxA4HkQqEJhIHnVEsPeyxrcXIpbHDf1pdgarlGIINwpag3TqnhcnA0zbfeWMQNIKYL6u6ackrCVCk2fBRg50CMkws1FAXGczFOzFdbf2MCU1NASrJRnblymPFNWYJSc218j2iChidJRTUeGkvzk/OrjGajNB2Xs/kGuirYOxDnyGwFRcB79w3x3JllBILHJjLMrDbwAslkb5xzK+FnjeoCoagIITA1FdHSaxfAgdEMjh9+f0vlBmcWq+ia4LGtPShCxdQVJnqj7B0Kg+3DW3PsGkxSaXo8ta0HicJiqcGvPrudbX1x5lYb7BpIsmMgyQtnVnh2Zy/JqM4LUyvsHUwymI5wcCzDWDbK3/7dn1Ivhuc9Ed3YpGQzAfjvElrPH+P6qyBdunS5QSrNtSzectVha0+UC/kqzg0GrqqAmKHxyESWfLlBoeGRtDRipkbTDQgkJEyD//zKDCOZCH/hkbH2ts+dybNYbjJfarJ3KMVIJnrVrOS1eHJ7DxFDJRM1GEjdejOXLrcGU9eIGir1DWhiKsCBkSRLVYelik1EV9k3mmYkE+XYTIk9Qyly8TCjbukqf+XxcWwvIH4dRRIpJS9MrZCv2jw6kaVQdYgYKp86NNIuIejS5aMHBnlzvsK2vvi6idylMgMvkCQjOn/rmUkgnNRdrS9lW3+Cbf1hED2ei3Jhpc5kb5xvvrHI+Xydcyt1Dm8JJ6eZqI4XSA6OplvlVB3lCAL2DYdBfrnpMpKJ4noBA+koT1g6dcdnz2CCmuOTihqMZKL81i8+zO+9dJEP7xvk8W09/M9Nl6ip8e++e5ov/vgiMUvj775nO//6GycxNIWff2SU3/j+OfxAsq0vHrZ+SohZOj/70DBTSzWe2tGL4wd8+8QiP//wKL/9wnnemCszkLQYSlscmy0RMVQ+cWCY71jLxE2NxyezrDYuoAjBgbE008UmUkr2DGco2+Hn+eTBAX7nRzNIIG6q7BpIUqw7jGSiPLo1x3g2TtRUyUUNvn58gaF0hPFslJmSTcRQ2TecJF91UIQgHbMYyUQIJOwcSPLS+SJNN+DQeI6pfA3PD+hJWsRLDepOQDqisbUnhi8l2ajB33vvdn7z+fPsHUzy+acmeGZXlYSlcWqxQr7qgAj7SJ7c3tv+ev7Vz+7H8YP2OHKp7G3/SJr9I2tZ7Z89NLJ2nbUafLcPxFkoN1EVwZ7B1Iau0c2MVstSyj/dxOu7dOlyA+wdSlK1PaSExyayRA2Vqu3z8vkCTddHILA0kAiEgIimUWw465YO05bKYMqiUHcpNz00VZCLmaiK4OGJKA8Mp0haOoYm2DOU4gs/voDrS1Yu0+AezUZYLDfJxQ1ixsYaS94KS1d517aeG96+y93BaDbCo1szfPdk/qqvmchZ7BpM8e7tPXzy0CjfeXOJn5wvUHc8PrRvkDfmK3ziwWGemMy1m+Eg7CG4Vh/BJeZKTV46F8pdTvbG+eTBYTJR46p1vrebudUGuhrWFHe5c/QlrCsMvyBMBuiqIBszbsgv4FMPDtN0AyKGym/+8CwQ6kZ/7pEx9g6liJsaby6UObNUJWaqvHtHL4PpCKamYOkqX/jxBaSEDz8wyK+9ZzsrVZuntvdyYSWsJT48nsFQFabyVXb2JzB1lf/lkw+03z/WakKOmzoDqQimrnA+X6M/aSGAmh3QmzDxW5OAQErmSzYf2NvPc6dXKDVcYqbG55+a4PNPTQAwv9pkz2CSVERne18CS59jNBPhl56a5MP7h4lbGtmYwWRvAstQeGQ8x0g6St31OTyW5Wy+Tk/CxNQNfu2923jpXJH/7j3bOL1U482FCoe3ZBjJRHjxXIGd/QlURfBEs4eYqZKv2G0d/qF0lE8fGkFXBdv746xUbbxAcnhrjqnlGuWmywf2DbAlG+XlC0X+4iNjvHKhyDffWODTh0ZYqtjUHZ90VGfvcJr/4xcebJ+3nS2zrbnVBg+OpRGE96VONFVB28AY9FY8NdnD1GINS1fYM3TzA/B/2rKi/zZhHTgAUso/3txhdunS5VpoqrIuWH1sIkvT9YnoCq9Nr1JzfCK6ykDK4p9/ej9N1+err8/xg1PLTBcaWLrClp4Yv/DwKL/zwgVqjo+mCLwg4FMHhzkwluaPfjKDH0ge3ZolHTX44L5BTsyX2Te8fuB4ansv+4ZSxC3thgemLvcP8v9n782j4zrPO83n3lv7XijsOwhwp0hxEVdJlmTLViw7sWM7sezYjuNOOjmZvXsyycyk+yTdZ6bPmcz0nNNr3N2TdDppp7O0E8eyZVuWZGulKFLcVwDEvhVq327d7Zs/bqEIEAAJriCl+5zDQ6BQVfer5d7v/d7v9/5eAbs6Y5ydyJFXdSxxTTMqS3bQ88X9PfzaRwYoVQ3+y7Fx0mWNLa22xeSOjigf2dx8R2OI+t343AqqbtIW89Wz6A8C56fy/ODcDLIk8YV9nU5DqAeQkNfFR2+j18ACkiThryUjPr6tlVMTWTa3hpFlO6gHO2Pakwjic8t4XQp7e+zs+NnJXL1+JlPWOLghUX/exTasf3V8gvF0mZmcyse3t644jpawj61tdoCuyDJyLcvfHPXy5KYmhIBEyEtLxE9LxM9Iqlx3/5jOVZY815GNjRwbybClNcyRgUae2dpM0OvCrch0J+wdz0szBU5N5JAk6IoH+fWnBgCYzVdoiXgpayZb28J8Zve17PDe3gSpYpWmsJf3x7PIkoQsSXxkUxObWsLEAm4yZY0/fnOE5rCXJzc2IsvX5plfPtKHbtq7Yhem81R1i/6mEPt6GzjQb8+Rz25v5dnae6SbFlPZCs1h36oL8l1dMdvgQLZ94P/ozauousVndrfTFr3987UjHuBAXwN+j7xEf34jbrUV/RZsZ5uFXJsAnADcweEeMThX4HtnZgh5Xfzup7bzb14b5NXLSbyKTGPIR7asc6CvgWxZpy3i4+JMAc20+NKBHroa/Lw/lkWSoDnspSMe4BM7Wvnr45O8OThPIuipZwUGmkOrukY4vtoOC1QNi3NTebxuhQaXzEBjiPmSRqasIQGJkAdRCwQmMpV6V9NEyLNscXe7hLwuvnqoh1LVoDnyYMmZshX79VrCtjpzAvAPNt2JQD1AvZ6V5Cxb2yKkShq6YfFo18qFeoZpMZ629dyjqfKqxz7Yn6BqmsQCHg5vSNAW9eH32A3NpvMqumERC7i5MJ1HNwUdMT/tMT8TmQqH+xNLnut6iUUssPyanynb320h7DqlrtrtLRE///untpEt62xqWWr5+f2z01yZtbsTDyWLVDSTk+NZHt/YWD83Ah4X/9vz21Z8jY2LFtdfO9R703PerchLdtVWu89CguvcVK4uEbo8W7yjAFw3Ld4fzxL0uta8W3wrAfguIcQjN7+bg4PDnXJhOo8QMJoqYVr2ZH5hukA86OWTO9pIlzT29sbZ0RFBliW2t0eI+N28sKir33CySGvUT1mziAfdHOxrQJIk5ooqPQnbSunIQOImI3FwuIZLkdBNC1W3iAbc6JbFk5uaUGQJtyIjhOCFx+wagu6GAM0RL+WqedsNllYj6HU9kN0r93THUXUTj6KwucXxH3dYiiLb2d+VmMpWmMnbUpDHNzba0o2epUXPqm5yfjpPW9RHW9TPZxdlmz+7SJfcsWjh90sHeyioxh3bv+7ujlHRTFyKxNa2pedzW9S/LHi1LMGVmhf75dkCj3REeXckzcbm8BKpmWHai/qI303fDWxk78U535sI0hj2UtVNtrbe2fl6djJf3wE5N12gZQ3B/K28mnckSdomhDh/m+NzcHBYAxem87x01m6nvrs7RjzgJhpw81hfnHRZo1Q1+MYTfXV9o2ZY/OXxCTTDYjhZ5Av7upgvVvnOqSkAfn5PBx/Z1FTfkjvQl8CjyGxvjxL1O9lth1vDpdgttVPFKk0hD8PzJf7bZwbY0R5dsu3r9yh8+UDP+g10HfC5FZ7ZcvvyBocPJ6WqwV8fn8CwBJOZCp/e1c5jvQ3L7vfD87MMzRVxyRLfeKJvTUXHsYBnxYz2reJ1KTy9Ze3yMVmWONDXwPnpPHt74uzujnNww3JP9reGUhwfzSBJ8ML+7mV+//eSoNfFVw7enWvUzzzSymi6RMzvZv8Kn91K3EoA/jjwNUmSrmJrwCVACCEcG0IHh7uItchkOxH08tQizezn93Yuu79AIGqPWXjoYp9uc1EXOIC9PfG6JtHB4VYQAjrifgqqQbpUpacxxMbm0JLtawcHh1tjcZN66wZNFurXeW69F8N6cHigkcOL6plW0mVbi+auG732B52dnTH+9Zf33tJjbiUAf+7WhuPg4HA7bGuL1C9Ga/Hb9roUPre3k7FUub7V3xS2W3FnShqPdN4d7a2Dg8cl8/ef3MBPLs/bOzN+9zLdp4ODw60R8rr47O4OpnM37rHw7LYWzk7aEpQHUYJ1OxwZaCTscxH1u+9Ig/0wsuZPUAgxei8HshYkSWoHvovdECgEdAJHgQuAJoT4+DoOz8HhriBJ0i0XrK2kwetvCsHKckMHh9umPRZY4hXv4OBw53Q1BG6q0w54XOzvW5u84WHBrcjs7flgvaa1IomHKOUvSZIP8APfBj6GHYD/UyHEL63l8Y2NjaK3t/feDfAuIIRdTet2yTwYrrYPFpYQGKbA47p9SzytZkG0Vqugu026UGUyr+JVJDa1Xst2jIyM8KB/Px0+nCz+bmqGRa6i43HJ+N12RzqXLJEsqkiALEkYlsASgpDHhWYJ3LJUtw6UpWs6cpcs1ZukWJZANy28bgXXA+Lp7fDgs9p1sz6XKvK6d0jVTYEkUbODFQgBbkXCrJ0na/G/X0AzLFyKVLcdvBsIQDdu/70yLYEpBB5FRtVNilWDRNADSPV4huuOsfh1lGtWuR6XTEE1kCVbn339e7VwDMMUVA2TgMe1ZLxCCMqaic+trHl+r+oWlhD4PcqajnEjrs6X6hbBCxw/flwIIVb8gB+qPQwhhAqo17UnflqSpNeB/yqE+Oc3enxvby/vvffevRziHfMnb4+QKmp0NwT43Ap63w8zmmHxH98aoVg12NYe4ROr+KPeiLcG5zl6NY3HJfPVQz2EfWtrGXs36f3tF2mr/fz8nnZ+7xfsZgH79u174L+fDh9OFr6bg7MFvv7HxyhlK8iKRHdzmKc2N/PapTmy03kWmrUqtX86tvOD2yUR8ruRNBMJe8u9MeylKeyjK+7HEoJTEzkM06I16udfvLB7WZMMB4eVWO26ueCl3Rjy8JVDvfd/YDUWiuplSeLxjQlevzKPEHZDoKPDKXRT8PjGxhWLLq/n5fOznJnMEfAo/PKRXryuu3OOfPv9CUbmyzQEPXz1UA/SLUThubLOnx4dRTMsNrUE+Ud/ex7JMOltjfCZR9uZr8Uziixxdb5EPOCmq8HP6Yk8fo+Czy3z/TMzuBWJPd0x/vrEJJIk8etP9pEqGVhCcGSgkWMjaTTDYldXlD97Z4xi1WBvT5zfem5LfSy/+zdnuTxbIB5w8y9f2IPrJom60xNZ/s/vXcQSgs/t6SBZ1OxjdMb4s6OjFKsGe7rj/C8/s+WGzwOw+/d/SLhmafjc/k7+6c/vAkCSpBOrPeZh76wxDWwCngY+JknSsoJQSZJ+TZKk9yRJei+ZTN73Ad4KliXqnpQLnpsO16gaJiXNbiOcvc33J1N7fzXDolS9eTvte82rQ3PrPQQHhzUzk1ep6PZ5Y7fTtifIZLGKtVAAvOj+FrVdK0tQqJqYlkAzLSqGhWZYFFWdim5S0SxKVQPDFBRUnVLVuO+vzeGDRabmQZ8t61jW+u30L8zllrAdThZEB9PZCnptxZourW0+W3iusmaiLm59fIekS/a8mKvo3OpbVajqaIY9lpFUGc2wrw/zBXVJPLMw9lzFIFW0b69oJhM1z3PdFFyu2RYKIbg8V6oXZU7nKvVjzGRVirXrw2xeXTKW+WK1fgzNuvn7M5mt1I8xMl++dox8pR5rXH+MVd8HVa//fHoyv6bHPFQZ8OsRQlSpdeWUJOm7wA7g9HX3+SbwTYB9+/Y90HobWZb45COtXJopstMpnFtG2OfmY1tbGEuX2dd7ey4ejw80osi2wf/ibaL14qe/84n1HoKDw5o53N/IC/u7+Nv3J4n4PDy/q5VNLRF2d0b5ve+eR9VMIn43yWIVEGxoDFHSTNrjfvb1xjkxksXvlmmP+Ql4XPQ1BmwrTBk2tYYYT1fY2xN/oLpbOjycPLejldMTuXqXyvViT3ecctXE45I5uCHB28MpDNPiyEAjJ8ezZMv6ssY4q/H0lmbevZqmM+5fsdHPzSio+pKM9AKf2N7CqfEcm1pCtyzN7IwHONyfIFPWODzQyFRG5dRElt98aoCNraF6PCNJcGo8x8aWEImgh6NX03TE/LRGffx/b1ylOezlZ3e18fvfvYDHJfMPPraR01N298vHNzZyeiJHulTl8EAjAa+Ls1M5fn5Px5Kx/L0nNvC9M9Ps721Yk0Xjs1tauDpfolQ1+dUn+rgyVyRVrHKov5GAZ+VjrMbvPr+Ff/LiRTyKzJ9/Y21uKA+VBnwBSZJew9aA+4UQhdptfwr8CyHE0dUet2/fPuFs8TusN987M83x0TRNIS/feGJDXf/nSFAcHlSu/24ubIX73ApfP9Jbl4vkKjoBj7JM01o1TNyyvK6BkMMHE+e6uTZU3eSP3xqhoplsb4+s2uL+ejTDwiVLzrm7BgqqjluRl8jnJEk6LoTYt9L9H6oMuCRJbuD7wC7gB8BPJUn6Wews+Bs3Cr4dHB4UvnNykvfHs0T9bl7Y3030LjRJWIne337xjh4/8s+ev0sjcfigkassbCEbXE2V6EsEOT2R483BeaJ+N18+2F3Xp56fyvPD8zPE/G6+uL/b0XY7OKwDVcOiotnykIXz92Zcni3w/TMzhHwuXtjftaas8oeVC9N5/vrEBCGvi195vI/IGurLHqp3UwihY2e+F/N76zEWB4fbZTqvohsW+YpBQdXvWQDu4HCvWNgKH0uVeenMDBG/m1DNlzhX0clXDJrCdqA9mCwihF1/MV+s0hm/s5bYDg4Ot07U7+bZbS1MZCo8tkYJ59BcEUsI8hWd2XyVvsaHKmS8r/z0cpIzEzlkSeLJTU3s6b75e+y8mw4O95ldnTHKVZPmsJd40NG6Ojx8NAQ9PLejlT87OkpJsxeSH9nUhBCCtpifpvC17/We7hiZkkYi5PnQNdpwcHiQ2NERvaUeE492x0gWq0T9bjrjzrl7I5ojPiI+Fx6XsqbsNzgBuIPDfeczj7ZT1U0e7Y59YLqZOXw4eWZLM8dGMvQlggw0hxhoDtX/VtFM3IpEZzzA1w733rMxXJktcHI8y9a2yC03sHJwcFidtqifr9YsHMuawUtnbbvAZ7a03FEvjg8iH9vajATEAm76m4Jreowz+zs43Gf+89Exjo9luDJX5JOPtN9WNbuDw3oxl1f5yeUkzREfT25s5Gd3tS+7z9nJHC9fmCXsc/Ol/d34PfdO9/3KxTnKmslUVmVbW8QpFnO4YyxL8OqlObJlnae3NNMQdGSCJ8ezDM7ZNoEdsQCPOE5tS5jOqYymyswVZPb2xNfUY8RZwjg43GdOT+SYyakMzxeXeIc6ODwMvD2cYiJT4cRohtl8ddnfJzJl/vitES7NFMiWNaZyZQzT4sRYhr95f5KpbOWujqctZm+Nt0a9TvDtcFcYS5c5PZFjLF3m2Ej6nhxDCMFPLyf5zqkpcuUHfx5ojfiQa11vmyN3Vzqp6naPgLvJ1fkSf/TmVV46O8P9cPsbmbd9y0tVk7nC8uviSjgZcAeH+0xZM6jqln1ReAhtQB0+3HTE/AwnS4S8LqJ+N4NzRfKqziMdUdyKzIkx2+EnVawiIfi/f3gZjyzTFPES8LgoayZfOtB918bzqUfaSJU04gFnJ8nh5miGxZnJLPGAhw1NoRXv0xDy4HMrqLpJR+zeaJ/H0xWOj2YA8Cgyz+249c7Od4uqYTKertAW9a0qi9zQFOKXj/SiyFK94PpusOC0EvQqvLC/+45kmalilXNTefoagxwbSZMt62TLOnt74kvqUu4Fu7vjzBc1wj4XPQ1rKzR3AnAHh/tMrqJjAZopKGkPfubDwWEx+3ob6G8K4fcoZMoaf3dqCoCiavDkpiYGmkIMJ4s8sbGRd0fSvDucwqXI7OmOs7k1QnPYi2FavHxhllLV5GNbW4iuEjyruolbkW/YHESWpTVNrpYlqOimU3fxIeeNwSTHRzIossRXDvWu+N2J+Nx8/UgvVd1a9bt5p0QDbjwuGc2waLkuo2xZgqJmEPa6bqkt/K1wcjzLxek8e3rinJ7IMZ4uE/Xbr1vVLbyu5b7990IuOZy0nVYKqsFUtsJMXkWRJQ72JW55R+t7Z6aZL2qcnshycEOCyUyFprCX2F38DIUQZMo6EZ8L16J+B01h7y0nFpwrkYPDfUA3rXpzklLV9mK1BBQq5noOy8HhtojXNLES1yZIVTcZnCuwsSVEX2M/Pzg7zYWpPMWqiSxZJIJuPr+3k864nytzRS5MFwA4MZbh6S3Ny46xoCNf8Mu/E/9wIQR/dWKCyUyF3d0xntq8/HgOHw7G0mXeG83gdSt8QV/9+utRZORbDH4XX+dvRtTv5muHeylrBs3hpV2Z//rE+sFm5QAAIABJREFUBBOZyrKGOUIIZvIqMb/njuoqTEvw2qU5hICfXEriUuzXWaoavDuS5q3BFE1hL7/4WNeaX8/NsLPsZdqi/iWL4N3dcZJFrb5r9t6IvSsQ8blvuajaW7tGeFwyu7vjPNIZxaPId3UR86Pzs5ybytMS8fHC/q47em4nAHdwuAtUNJMfnp9BkiQ+vq2lHixYluDb708yli5zqD/BwQ0JFotOSlUnA+7w8NIa9fFzj7aTKeu8enGWv3hvnKawl/aYnxOjGUJeF5IsEXQrDM+X+Zv3J/nM7g6aQl68bjv717GKvdlQzT88W9ZJlbQ7kgJUDYvJjK09H06WeGrzbT+Vw0NOQ8BLTyKA/wYLurJm8K13xymqBp98pJWNLeGbPu/x0Qw/vZykPebj83u71tTSPeR1LZNzGKbFeyMZ5otVKpq5JAB/7XKSk2NZwj4XXznUU292dasoskR71M9ktkJn3M/u7jinJ7IMNIfqAXCyUKWgGnetAPW7p6YZS5eJ+N38ypHeeuDaEvHxlYM9gN3MZoGw79bD00/vbGcoWaQrHkCRJRT57hd/T9SuI7N5Fc20bvszACcAd3C4K5ybyjGcLNk/x3zs7WkAoKKbjKXLgK11O7ghQdjrIl81kICexM0v7A4ODzIbmkKousm/f2OYgmqQLFTxuxUaQ15kSRAPeihrJo1hL2PpMt/86RCf29PJrxzp4+2hed69ahe5bbouyNnbEyddqhJwu2i9Q/2mz61woK+BwWSR/X0Nd/RcDg83+/sayKs6iaCH9lUWdbP5Kvlat8ihZHFNAfjlWXtHZyqrUlB1YnfQYE0gsIRAsLRGKFkr7iuoBqp28+DPtAQvnZ0hU9Z4dlsLF2cKTGTKPD7QyOf2dpKv6MQCbiRJojVqB/qSJFG5nKQz7icecJMuafjdyh07GS0YDpSrBqYl6ln3xWxti+B3K7hkic416qgX4/co99yK9MlNTbx5Jcm2jugdBd/gBOAODneF1qgPRbY35FsXNRsJel3s6ooynCyxt8fujLWwYyVJYApHguLwcGNagh+en8WjyAQ8Cjs7olR0k0xZY3dPA52xAImgh+mcyt+dnqJU1bk8W+Q3nx7g5HgOgDeuzC8LwG3XBZmpnMpPriR5ZkvLbY1PMyxcssThgUYODzTe8et1eLhpjfr4pVrGdTU64376GoPkKjqPdq2ta+S+njg/uZykqyFwR1ppl2Lb2E1lVba2LT0nPrKpiXeGU3TGA2vSpk9kyvWFwU8uJ+u7QG8Ppfji/mBdSraYvsYg7TEfHkXm9ESOVy7O4XXL/NLBnjU3mFmJn3mkjdMTOfqbgku004uZyam8eGYalyzxhX1dpEsaVcNka+va7EVPjWc5ejXFppbwPZOZjafLpMs6V+dL7OuJP7wSFEmSHgc2CiH+SJKkJiAkhLi6nmNycFhACEHVsNakPe2MB/jG430Ay4q8ntnSwjNbrv1e0gzA1oBP5yr0N0fu3qAdHO4TVcPEJcskC1WG5gp0xf209Tbw+X2d/NXxCRIhL7oheHpLM1G/nUn7u9OTTOdU4gEPQ3MF2mM+prIqvY3Ls11VwyJd0gA7q3g7LDgshH0uXrjHfuQOHxzcisxndnfUf7csgWbeeC7Y2BJeU6Z8LXx+bxe5ir7M2acl4uPnHu1Y5VHLaQp7CftcFKsGW1vDVHWT+aJGT2L1RjELUpqWiI+Y357LqrpFpqTdUQDeEvHx7DbfDe8znCyiGRYa8M5wiksz9uJB1S2aw14yZY1tbZFVA/hjI2lKVZP3x7Ic6k/ccYZ6Ja7O2zvdk5nKMgmKZlg16cvagvJ1C8AlSfrHwD5gM/BHgBv4U+DIeo3pfvLeSJqZvMqhDQkSIacd+YPI35ycZGS+vOairbW6KwQ8CnnVRAI6Y7e+zebgsJ4MzhX58cVZprIVuhuCfPbRdsYzFaYyFT71qA+vS+HQhgRvDs7TnQgS9buxLMF/enuEZKGKR5FpDnt5rC/BtrYIRc1YcWIPel08uamRq/NlDtymbGRoznZYyFV0ZvMqvY1r61Dn4LCAblr8xXvjzOWrPLmpqb6TCXbAeGoiy+aWCNva714iRZGlu6K9Dnhc/PLhXnRT4PcobG2LUNHNGzaJGZyzg97ZvMqBvjaqpkXM76Ep5OUH52bwKDJPbGysB5l3s8Bxa1uEoWQRRZbpjPvrAXimrPH6lSRCQKqk8fQq8/HWtgjvXk3T3xy6J8E3wOGBBMeuphloDi85xuBcke+dmSbgWbud4npmwD8L7AZOAAghpiRJ+lAIYpOFKq9fmQfAMMWSlbbD+qDqJgXVqFtS6abFyLyt3R6cK96T7SxJ4r40CHBwuJv84NwM56fypIpV3LLMTF61i54kiW8dHeelMzM80hnj0zvbefdqislMhU/vbOPkeBbDFEQDHn7zmY11reaNsmp7exrq9RS3w66uGHOFKrGAe9ViTweHG5Gv6MzVGk4NzRWXBOA/vjBHsWowlqqwpTX8QDaCcikyC3GiS5EJ38TVZEtrhDMTeTa1hOhqCJBTDWJ+N6cmcpwYzSBJttb67GQOzbT47O4O2qI3P7feH8vw7tU0m1tXl4fEgx6+cqi3/rssSVQNi46YnzMTtlzNNFefM48MNHKgr2FZhnw2r/KDczNEfG6e39l2R84uW1ojbGldvti6NJPn6nwJr0tmOqcy0Lyyx/xi1jMA14QQQpIkASBJ0ocmNRHyuvB7FCqaSSLktLhdb1Td5D+9PUqxanBwQ4JD/QncisyBvgYuzRZ4rPfuFm0ttiGczFbocyQoDg8RMb8LVTOZzqlE/G7mClUe7Y7xxmCSXEVjPF0mXzGYzlboSQSxhMYfvzVCRTMwLEFL2MvAKg1Q7jbtMT9fO9x7X47l8MGkIehhe3uEqWyFx67biXErEkPJIptaQvcl+NYMi8G5Is0RL433aOf86nyJRMhDpqzz8oW5eha6u8HPyfEsiiSxsSVMQbWllINzxTUF4MdHM5Q1Wx5yZKDxpkGwaYmaBtxie3uET+9qI1XUeLQ7dsPHrSRPOTmeJVXUSBU1xtJl+u/B9UczLTJlDa9LZq3fhPUMwP9CkqQ/BGKSJP0q8CvAv1vH8dw3/B6FrxzsIVfRaYveWBPlcO/JqzrFqn0xmc1f05rej6ItpymIw8NGb2OI5miefFWnIehhrlDl0IYEm1rCaIaFqltEfC56G4O4FVsjbloWyaKGR5EI+1x35Ol9O8zkVF6+MEsi6OHj21vXrNF0cJAkaYkV4GJyqoFpWRRUA8sS9zwIf/nCLJdmCnhcMr9ypO+mNQ2mJfjBOdsF5WNbW2iJ3Dze8LhkNNPC71ZYHMv63Ao7O6PIkkRXLICqm2iGtWI2eCUW5CEDzaE1ZaAvzxbqnUIDHoUjA40M3OZGdH9TkEszBQIehdY1vAe3Q1vUz67OGJK0dgvFdZv9hRB/IEnSs0AeWwf+j4QQP1qv8dxvgl6XE3w9IDSHfRzoa2C2oHJ4IHHPj+eSwayZn/jukU7NweFe0RLx0hz2kSxUSRWrXJjO89bgPO0xP09sbOL5nW0YpqC/OUTArTA8X+QPfzKMYVj4fC46Yn50y8KPwnsjac5N5dndHWNn540zW3fCe6NpkoUqyUKVHR1Rum7D4szB4XquJoukSzqaUbqj57EswUvnZpjMVPjI5qZljkALqLXGQYYpMCwLuPH8MZEp1zPYx0czfPKRtpuOJepzkS1phBr87O2JM56q0Brz8fSWZgR2geqj3TH2b7B3A0xL8L0z06RLdpDfukpS8chAIwc3JNa8+I0F3MiShCUE8TuwdAQYaA7z6x8J4pKle7ZIOtDXQEPQQ9jnonmNQf66RoBCiB9JknR0YRySJDUIIdLrOSaHDyf3057MWiRhK2kPrg1h72+/eNuPHflnz9/FkTg8SAw0h3n+EbtZyZXZIhOZMqWqSXvMz0BziL7GIK9dmuOH52doj/n5mR1tbG4N43HJlKoGT25qJuJzI4TgzcEUVu3/6wNw3bR45eIcumnxzJZmAp7bn656E0EG54qEfe57tnXv8OFjV1eMsK9ES8TLndQiZit6PVB+fyyzagD+sW0tnBzL0hH337CQcoHFLih9ayxAnsyqNEd8aIbg9cvzFKoGhdkiuzpjfGpne/1+l2YKaIZFyKvww3MzqLqJR5H5hce6Vn3uW9l5aov6+aWD3eimoCns5cXT06RLVT62rWVNkpfr8biuZd1HUyUUWaIzfvcW4pIkrfq5rcZ6uqD8feD3gQpgARIggA3rNSaHDzZCiLtasX27LA7AF18UHBweFjrifmIBD40hLzlVw+uSOD+VY0MiwP/78mV+dG6Wim6ytyfOcLJIf1OITS1huhr87Otd8MOX6GsKMjRXXDE4uDRT4PyU3RkvVdToawyyv6/htuQrOzqi9DeFcCvSqhZmDg63yuf3djKULNHTELijuSXqd9MR8zOVq7ClNcLpiSyXZgrs6YnT3xSqz10Rn5snNzUte7xuWrx7NY1bkdnXE69nea93QVkLu7tiDNZ07a1RPyOpMoosLdmxH0rajh8AfY0BUiUNzbDIVbQbPnepauB3K6tmoS1LcHYqh0uW2dYeqTvEjaev+ZkfH83QGqkwmipzsD9xyx1yL0zneensDACf2d2x5oXJAlXDZCxVpi3mX9LFNFfWefXSHGGfi6c3N68p076eGfB/CGwXQsyv4xgcPgRYluBvTk4ynq7w5KZGdnevrbHCvWJxEffIfImda2z04ODwoBD0uvjygW7G0xX+0zsjvDE4j2UKTk/m8bokSlUTSYJzU3nOTuY5E8nxu5/aziOdtvOJYVq8fGEWzTD5xce6VqyFaQp7cckS6ZJW/2cJcduORI4H+IcXVTe5NFOgLeajOXz3NMBhn5tHu+5cOqXIEr/wWBemJRBC8C9fHUQIyFV0ZnIqx0bSbGkN89yOlSUkJ0Yz9Y6yYZ+LrW3XdNmLXVDWwsXZAn63wnRW5bntrbREvIR97lVtERMhDwf7EpQ1g703MCx45eIsp8ZzdDUE+PzezvrtqWKVVy7OEfW7SYS8/PRysjbuaxnlxpCXiN9NQdVpCnvrLnK6afHF/d1rf3HYi4CVfl4rL56eZjRVJuxz8fUjffWs/muX5/jBuRm8LpmeRICB5ptnw9czAB8Cyut4fIcPCYWqwWjK/qqdn86vewC+GLfkZOMcHk4CHhdzhQpj6TK6YdmNSlwKXpeMz6Uw0BKiqpkMzZeYzVeZL6q8cUUnVarSHvVzYdrOaF0KFlZsCd4S8fG1I73M5lS+d2YGS4g7kqE4fHj54flZhuaKeFwy33i8r9ZgTb5nXtG3ix3MSbREfMzkVNqifs5N5RACLkwXeHbbygXEiixxaaaAIoNXaV/+xLeAWPy/JLFhBceQ/qYQP/NIK5phsaM9yt6eBsqaeUN513DS1smPp8voplUvxDw2kmEiU2EiU2Fz67WgdfGr9HuUWibfbnRzbjJPrqLTchsmFo92xeoNc7a13boD2YJhQ1kzMS1R/zzmC1XSJQ1FliivUVq6nlez3wHeqmnAqws3CiH+u/UbksMHkYjPxZbWMCOp8l3JVtwpLmBh3b2l7f7YsTk43E1OT2R55eKc3dwmEaA96qe/KUSyoHJlrkgs4Obj21t4fyzL1VSJtqgXv0fh7SE7SzeWKnNsNI1lCTa1rH4ORHxuIj43v+hzU6wa9DcFeWc4xZW5Ak9vaqbTKaZ0WAOGaQF2weCp8SxvDaUIehW+fKDnvpohaIbFKxdn0UzBR7c0r3rsz+/tJFvWSQQ9nBjLcGwkw5a28Koa6qlshdm8iiJLzORVNMtCNwTb29fWwn0xz25toS3qozXiY75Q5T++NUIs4OZzezqXyL8Wu58EPK764ti0BGXNWKZRP9zfyHujaTa1hJe4oHTG/RwbSRH2uTkykKC7IYBLkZZ1FbU7TNrH//LBbnIVnabrAv73xzK8emmOnR0xPratZcXX51LkO6r5em57K6cmcvQ3BZdISPf1NjCULBH2uehao7Z8PQPwPwReAc5ga8AdHO4JkiTxM2uo/r5XCCEoaybpksbIfInFm15nJ/P0tTg+4A4PF5dni1Q00/YMDnr4xPZWXIqM1yWTLNpuI28PpfG7FTRTcHm2yL/9yRA72qNMZCqkihrVqkFT1Ff3Ey7W9KErBRkLzgrZssa3jo4xk1c5NZ7l//r8rgeirsPhwebj21s5PZGlMxbgzKTd0KVUta/JsiQhy9yXbPilmUJ956cxZNdQZMs6u7qiS44/nVUZTBbY0W7XLmimtcy72rIEQ8kiUb8bVbfwuGQkyfbxPlqTo+iWxZ5FO74/uZxkPF3miY2NdDcEKGvmskWA36PUe198/8w0mmExl68ynVNvqpc2TItvHRtnvlDlwIYGDvdfC3S3tUfobw7iua4Go2pYuGUZhEAI6g26boTXpdAcXv55/ek7o0xkKpyZyPH4xgQ+990PcZsjPp7dtjzzPp2rcGWuQNDjqrvV3Iz1DMANIcT/tI7Hd1hnDNPi7eEUQlBvfvOgMjhX4OjVNANNIQ5suLFVoW5aDCdLNIW9zBer/N2pKXTTYixVJq8u1ZxlqzcuWnFweNBIlzSKVZ0rswXcikR/U4jpnMp4ukzVMHhjcB4hBIWKTmPYS1E10E2LY1fTzOaq9DeHKFZ1kkUN1RS0Rry8NTjP0atpGsNeXnisi0xZZypbYVNLeIl2O+BxoRrXrNhsKcGDJSNwePAIeV31YNASgmMjadqiPnTT4t+9PoxLkfjFfV1M51R002JXZ+ye2NU1R+y6BkuAS5J58bRdyFiqGiiyxESmwuH+BH91fIJ0SWNoroTXLZMqapwcz/L8jjbeGk7RFQ8ghOCH52fwuxV+8bFuMhW7CczOrigvn58DQAg7AWQJu6Pn65eTFKsGQghCPhcj82V2dER5dpVs8bb2CCOpMrGAm/bYzeUexarBfMEWNIymyhzuv/a3n1xOcmI0w4amID/36LXu36liFa9bQWBr3mN3YDm4cK3wuhXk6+SdubKO1y3fs+vF8dEMk5kKXrfM5dkibWsoDl3PAPxVSZJ+Dfg7lkpQHBvCDwlnp/K8N2Ib7Qe9riUtfu+UdEnj9StJGoIeHh9ovOMs2RtX5smU7ZbEOztjNyzo+vGFWS5MF1B1Exk4MZ6lOeIlWawiXzeOmOMF7/CQ8eMLs5wcy5Kt6MjYdRUFVWMsrZIuVdEMW0U6ISqEfAqtES8zeRWPS0EzLfIVnbDPTcDroivu5zunpimoet0SLFWs8i9fHcIwLfb2xPn8PtvWTNVNXjw9zcaWEC5J4mB/oxN8O9wyF2cKRP1uyprJ6YkcpiUwLcE7w+m604aAJZnjxczlVa7Ol9jSGmEsXeb4aJqtbZGbJmbArmv4+uN9mJagapi8NWwHyWXNrB/77aEkl2by5FUDzTTZ2REjU9ZoCnt5ezjFeLrMdLZCUTV4ayiFW5H42V3t/P0nr0W76ZJGuWqytTXMn7w9Sq6i88TGBFfni+QrBo0hDzN5KFdNhuYKqwbgPYkgv/HUteedL1ZxydKqQXIs4LG9wzNlDl33flypvb7hZGmJBvxgfwLDEkT9brrvUFL21YO9vHRuhgN9DUvkIWcnc/zV8XFCPje/+sQGov6bWzi+fiVZW0QkVtTBX8/gXIFkoYosS+TK1ZveH9Y3AP9S7f/fWXSbY0P4ISKyqFtU1H93v4pvD6UYTpYYTpboawzesd9nTyJIppylNerDexPrwIUCDMOyLzJd8QBBn8LP7+7g3FS+vj0IcHk2f0fjcnC430T8blTdZDpXwTAFE9kKmbKOZVrotQouCXtreSyjEva46E0EsQT0JAL4PQq6KbgyU2AsVSbsddEa9TGaLtGTCPL+eJYrs3nyqolbkesB+OBckbF0GUWS2dsb5+CGBEIIXruUZCJb4SMbm+hOOJpwhxsTqc01bkVib3cczbDwumX6GgP1IFhZJWFjWoK/OjFBVbe4MlekVDUoayZvDaVoifh4u5adfnzj6hrja9Z1tq46W9bZ2BJivmgX8XU3hNjSppIu6fQ1BpnNq5ydzLG5JYzfJXN8NENj2EtzyM6mK7JMuqzz3dNTuBWZgeYQJ0azgO0kki7Zu6yDc3YRpAXIksT5qTyDySL7+1Z3LqnqJt87M01rxEdTxMt3T08jIfGFfZ3kVb1ehLl4t2CxTaJmWKRK1VqzuwTvjqTZ3BJmJqfy+pV5OuJ+PrKpiY9va0GRJYpVg5fOzuBWZJ7b0XrLC+wzk/aC6sxkjsd6G+rj+snlJBemC0gSPLO5iT099mtWdZPvn51G1S2e295KvOb0kqvo/PRykqJqYFjWkgBcNy0mMxVaIr4libhM2XZpEpYgVV7bzvZ6dsLsW69jO9waVcPkB+dmqeomH9/euqbV41rY0BTii/u7EIIVXRDuhNaol8uzBXxu5Y62tBZ4eksze3rihLyum25NfnRLCyfGM3TE/IymSqRLOp/d3YHHJfP0lhb+4IeX6/c9M5G947E5ONxPPra1haPDKUZSZbJlDc20ENa14BtAlux/mWKVktsg7HXRHPHSEPQwmalQ0gw0yyIWcONWZBRZoqcxSNDjYnCuSMTvIVspU9FNRuZL9CQCdMT8dvBuWIQ8Ln56OUlz2MvJcfscemc45QTgDjfl0IYE7VE/Eb9trde16DvjUmQMU7C1LVzPjF/fq0Gq+XPIki2/OjOZY0OtOHgmpzKTU3mkI0o0cPN5sqshQFct/m0Oe5gtqHQn/HQnuhlKFtneHuW/+c8nSJc0zkzm2NgapDnsJR7wcHggQaasEfG70QyTK7NFAHTjWkld0OuiJeJlLl9lW3uEc1M5oqpBNGAXNbdEfPUAfSX+7U+HeOnMDG6XzJdqc7VAcHoiy4XpPELYAenenmtB/HCyyGS2wq6uGN89Nc1sXq3LThZsSP/ivXFm8yqzeZWgR+GtoRQhr4vexgATmQpg6+V33aJpQkmzJZ4VzcQUArn2WXXG/ET9brwumXjgWuHmULLIyLztkHZ6MsdHaosHtywxkiqTr+h1L/IFvndmmuFkiajfzS8f7q3HA95FElrXGnfc17MRjxv4DeDJ2k2vAX8ohNDXa0wOKzM4V2Rozj65T41nV2wEcLvcTkertbC3p4HOWIDjo2m+f2aapzY30xReWwc8VTfJlvVad7NrJ9JaFx7RgJunNzczlCxydtLOcL8/luHAhsSy4owdbbfWOcvBYb1RZImGoIcdHVEKqsbVZJmSuvSy7ZIlNNPWnhpVE5cM2bKMbljkKjogUDUTWZLY0OSjI+rDrdjn2qENCWJ+Dxdm8lQ0kz95e4StbRE+t6eTbzzehyUEf/zmCGXNxOeWaQx5mC9qNIU9vHJhlrJu8uSmJiJr6BTo8OFDkiR6FxUTTucquBWZxpC37jtd1gy+9e44RdXgk4+0Egt4mCuobGoJ84V9nYymymxqCdWcOxrxuWX+9uQU56ZydMb9BLw3z9yqusm3358kW9bZ2x3jT94ewxKCbFnn//jsI3TV5BiJoIfZvErE78IwbZ21S5E51G/3tAh4FKayKmen8iiSxI4OO9Cu6CZtES/ffn+SomrwaHeUJzY2MZGp8MTGRjTD4s3BFJ/Y3sJMTuX0RJaB5tCSbO9wskS+dm7H/B5iAS9uRSIacHPiaBbTEmxvjzKcLDJfrLK5NcJfvjdBXtWZylSYzVXIV3RmsnZwuiA96WkIMJmp0BD0MJOrYFqCXEXH61IwalaDbddpzmdyKtmKxsbm1d1gntveWlsQhZjNq7xxZZ62mJ9nt7cQ9ruI+Nz0NV377Bcv6nsXL94l2N4eoaqbS74rANmy/X4UVAPDEnhqY4kGPbgVGVmGWGBtscZ6SlD+DeAG/nXt96/Ubvt76zai+4iqm5SqxrLV1QKmJUiXNBqCnltq33ovxtQa8eFx2ZmBrnW2/aoaZl3DdiNdt2ZYvHZ5jjcHU3Q3BHj3aprndy53QpnNq0xlK2xti+Bz2yf/fz46Rq6i80hHdFUrowXmi1X+64kJNNPiKwd6iAY8mJbgb09OcmE6X/dGddWCi5mcuuTxee3WGwE4OKwnk+kyUb+LA31xBueKnJ3IsSjphlcBwxL1jq8WkKuYGFaVo1dTRAMeVMPC71HwuRUmMxXyFYOOmJ9/+IlNtMcC7OqKcWYyxysX55AliYlMmdcuzaGZgsc3NuJ1yZQ1k4DHxZcO9HDsaprvn7UzU490RjFNu2HPWrKQDh985goqx65m6Grws7PzWlb1705O8v/86DIuReYPvrCTXbWmaLP5KvmKHWidn87XvKsF4+kyz+1oW+J3vSBDUHWT/qYQHkWmXDWJBlaWKharBpYQpItafT4YSZWwhKBYNQhcJ7toi/mZK1Rpifhoi9rjdysSskR9HAPNIb52qBdFkXhzcJ6Xzs1gWYCwLT8B3h5K89XDvUT9blojPhRZYm9PHN2E/3JsjLOTOTpifn7309vrMcfzj7SRK+uE/S729MbrTYzOTubobghgWoK8qvGPv3MOzbB4anMT56Zy9eLoyWyFK7MF9vbE+c7JSV48Pc2+3ji/+mQ/W9sjBNwKQ8ki3z87S1PYS0Pg2rxuLmoZnS5p/Jdj41hCMNdTXTUJ2Bzx8dGIPca/fG+c6ZzKdE5le3uEZ7Zcm8tzFR1ZsjXrC4v6xS40AY+LzzzawUSmws6upa4sH9/ewsmxLP3NoSW7Iz0NQdxKCq9Lpim8tl339QzAHxNC7Fr0+yuSJJ260QMkSWoHvgtsA0JCCEOSpH8O7ANOCCH++3s33LuHqpv86TujFFSDQ/0JDq5QvPHt9ycZT5fpawzymd0dKzzL3aWi2WMqVg2ODDQu0YUlQt76l3Q9G2HopsW3jo6RKevs7o7dsCPe0avzXE0WmSuo+FzyikUmFc1mU0fpAAAgAElEQVTkL98bty+smQo/u6udim5yYizDTM72VX16S/OKC6C8qnN+Ks9cXuWd4bRdxS5J/MZTAySLKj86P0NBNehqCBDwKPUL1/VSm3TRcUFxeHgYShb58jffoaSb7OqM0RLxkq0YLJorESzt9rpwW0WzKMgGAa+Lpzc1cXW+yPB8yW6T7VaI+t0EPW4yJY1/+5NBMmWdgxsSzORUUiWNPz82ht+jcGoiy8ENcToN2N0dQ5El5ktVfG4FUwimsxWG54tcnCnwpQPd6540cFgfLs8UeO3yHAf6ElycyXN1vsTFmTx9jcG6R/Xrg/OUNAMJiTcHU/UAvDPuZ0NTkFxFZ1t7hLeG5u3Ezw0Cq854gOmcSkPQQ8CrUNYM3Iq8xN1rNq/yF8fGsQQ8t6OFloiPbEXjkc4or1ycpawZtF/XXMa0LNyKhITEM1ua6WoI0BrxLZuLF/TLybyKblhYwj7vPIpErmKwqSXEf/jpMJmKzv6+OOPpCpdnC+zvi/PjC3OMpcpEA27+wcc3895Yhr5EgI9ubaG9Jt9Y3EG0rzFId0MAVTdJBL1otRV4slBlS1uEgqqzoTHIuakcjSEvyUKV//DGVeaLVS7PFfjyge76DtWLZ2Y4P5XD7ZLpbwrW59vxdJnXLiWp6iYHNiSwhH1RWavFX3eDLWeJBdxLdsNG5kv87ckpZAk+v69z1V343sbgsuw32Lv2kU1u/NctlC7Wduyqhsl4pszBNYxxPQNwU5KkfiHEEIAkSRuAm72zaeCjwLdrj9kDBIUQT0iS9G8kSXpMCHHsno76LpBX9br37XSusuJ9prP27VOr/P1WqRomP74wh2kJPra1ZZmLR66i1zs8rTSmB8FtoKKbZGrbP9PXZZIX8+qlOV69OMeJ0Qx+t8KGptCKWjJLXMvSmZZ9ATFqkUOmrDOeLnN0OLWiaf9LZ2aYzFaoaAaqYeJ1ydR6PTCbrXKl5pOcLes0h328fGGWrx/pW6YnTATXtlXl4PAgcDVZoqgZdhHlXIGnNjdybCSFmjHrQfdKTeAW1rAW4HXJPNod472RDNmyTlfczxObmvj0rnaiATf/6tVBfnR+jqphUtZMtrSGGZwrcmW2QKWWZRycK9CXCDKTV/mlgz3s722gVDXY1h7hxFiGqWmVkppjJq86AfiHlH/+8mXSJY23BlPs6Ynz/liWeMCzpMDy0a4or12aQ5El9vVemyPcily3ysuUNDyKgs8tuNFedHPEiyJLNIY8XJ4p8KMLs4S8Lr50oLseLM/lqxi1SSdV0vjSAbuN+uh8kYszRXTT4s3hFL/yxDUvCp9bIeJz43PbO0YLzeRyZZ3XB5O2Hrw/Uc8cb24LU6gaWJagI+4jUCt61kyLizMFTCE4Mynz1uA8BdWgVNWZzqlUDYtMWedfvTrI++NZAh6F//WTWzk7mavZEPrrwfFUtsK7I+m6U9Fz21uZzlf4yqEeKppdpLi7O4YsSbxzNcXHt7Xy58fGyJQ1gh4XinxtHpzIlNFMC90SRP0u4kEPLtleuCzsECQLVT6xvZV0SVuzW9qBDQm2tEYIeBVmciovnpkm4nPTGffX5/7ZfPWWZbCvXprj5FiWzrifz+/trL/vJdUACQRSTWZ3c9YzAP+fsa0Ih7GL5nuAr9/oAUIIFVAXSQ8OAS/Xfn4ZOAg88AF4c9jH/r4GZnLqEqP6xXx0awvnpnJLtsvuhAvTBS7NFGrH9y6zTGqJeNnXG2cuX+VQ/83tlNaDSE1vN5oq3XCMo/Ml4gEPXrfMjo4opliajksV7aYCG1tCfHZ3B5PZCptbwswVVBoCHra3R0gWqnTE/YhVjrHwFYwGPPwPH93ISKpcvzBEAi6awz4MyyJQq3iPr1IIOl9ySh4cHh4eH2ikM+5nJmcXVj27tZUL0wU0fZ5UsbqkEHMBRYLuhB9FAt0E04LzU3kms2U0wyJbMfjolmb6m0IIIchVNKo1r++GgBvDEsRqBVQhrwsJUHULlyKTKWkIIWiO+PjFx+xgZr5YZb5QRZElfC6Fc1M5trZGmC9WscS1pj4OH2wWagpcikRDwM22tkjdgSdZqBL0KvQ3hXlhfw/AkuK8xfg9Cu0xH9myTscN3LSOj2YwLbvpVFkzEcLWCSfzVTSzgtelsLk1XA84dy2a2z1uhYagh4Kq0xb1kVd1JtIV+hqDbG2LMJNT2dy6VPv89nCqZu0n0d0QQJElXIrE0VoDLIBLM0U2toSpaCb9jSHaYj6mcxUGmsK8NZjC51awBDSFvRiWRdjrJlPWMC1BRTN5ZzhFsmDPl13xABdmbO//ctVgeK6IJeDYSJr/8dnN9XHN5lVKVQOfW+Gzezo42J+gI+anvynIj87P8livbRE4ma0QD7j54r4uMmWNhoCbpza3EKnVWuUqOu+NZOwmRM0h4gE3TeGFjrq2HeOh/sQNF9gLErRzU3Z2uqKZ7OyKMtAcwiVLbL2NGqyrSdtNZiJTQTOtunRlY0uIwWQJlyLR2/iAd8IUQvxYkqSNwGbsAPyiEGJt5onXiAFDtZ9zwPbr71DzGv81gO7u7tsf8F3myE1aoW5rj7Ct/fY6JI6ny8zmVXZ0ROuZ6+awvToXq0xAkiTxxMa7V1x5r9jf18D+vgZMS3BuKkfE5152Ah4eaOTo1TQ/t6sDSWJJ9lvVTf782DiaYXF1vsSnd7XTHvPzp++Mki5p7OyM8tTmJroa/LRF/Usukot5fmcbF2cKdMb9tQXVtQVBX2OI33puM2PpMgc3JKjo5qouL3u6nC6YDg8PPo/CL+zr4t+/fpVkvsqpiYxdO4FAkkCqbXtL2FlvqfaYlrAf3bRIFqsUVJ0/eXsES4DPpbC3O46qmximxVCyhEdRONCXoCcR4GuHeylrJldmC3xkcyOjqTJNYR+PdkUZSZXZ0hpeVgvys7s62NAUwqfIfOvdMZLFKgf6GmoNSOBTO9uWtbl2+ODxW89t4c3Befb1Ntj9FySJroYAg8kiP72cxOdW+OJjnViWwOtRGGgOMZQsYpiCTS2h+vdKliSKqsFEtgyigfF0maFkkR0d0SVa8M2tYWbzKu1RP0cGGnntUpJ4wE26rPHapSQAP7+nY8WuzA0BD/1NISazZXZ2RPmLY+MUVIP2mI+KZtIZD9jacUvUXTfSpSrffn+SiM/NY71xjo1kkCTq2mxTwM6OCH6Pm6SlEg24iQc8eBQZv1vmdz65hVcuJvncng5KVZPvnJrkyEAjMhKDcyXa434SIQ/fOzND2OdiS0uY8bStJ28Kegh6XRiWoDN+bW67XtaZKWmkSxqdcT9f2NfFljZ7vnvt0hzvj2UJehW+eqiXf/WlPcvkOlG/m8c3JlB1i5BH4T++NYqqm+zpiXNi1O4f8sbgPC/sv3lct60twvB8kYjPzUBTiB3tN++2uRqH+hMcG0kz0Bxaohv/+T1d5FWTkNfF3u61JTHX0wXlN4E/E0Kcrv0elyTpG0KIf32Thy4mCyxEMJHa70sQQnwT+CbAvn37VktofmDIVXT+64lJu1ihUOWTtZO9Pebn60d6scTa3TweRM5N5RhPl7EsuDRr+3q+sL+blsi1RcWmlnC9mh3sLmPfPT2Fz6XwWG8cwxQYpsXpCdvXGwGTmTJ+j4v3x7KcnrAvcD0NIVyKvGJxasDjWrVRA8Cj3XEe7Y5TUHWQWLXL51DNAsnB4WHhneEUpapB1TB55eIcIZ+buN9DyK2QKmlohoUsS4R9LqI+Ny5FJlvWaAh50Q2LvKpTNaya/7eX9pifvz05xYtnZvB7FEzLoqshwFObm4kFPMQC9jXrlYtz9DeFOLghQXPEx46OlRfHTWEvT29u5spsgeH5IkLAeyMZNrfa14TsGreHHR5uOuOB+q4IUG+j/r0zdvdJVTcZTBY5MZ61ixqRePWS3UGyajTXd59n8yrHxzIIAT+6MMu5qTy6KRhLl/nqod768+/pjrOzI4qrdq1fkJccHU5R/f/Ze+8gu87zzPN30s2x+3YOaDRyBkiCURRFKluiLGkk0UHO2Vs7rp2qLddsbe3ObNV6p8azrvXUzNgzHo3HI1u2JMtUNCnSYhBFgkhEzuicbt8cTw77x3dxAZAAAUkUk/qpYqHY6HBw+pzve7/3fYLjIckS1rVq5Wug2x79qTCZmIYfXOU5G7ZHRFNQZIlwJ2r+Cp49X6CuOzRNh5enSkyXdGRZYl1vjM/cOUZAQCoW6trsnc83ySXDxMMqvYkwH9oxyId3XD0MPLxVaKq+dHCOzYMJEmGVtul1qSSpmEbdsFFlmU/sGabtCCra/RuvNu6upXU6ntcVsr6aklHqaJ/aluhKX+GvW67wVFdlicFUhCdPrwLCvODKPakbwpyi0rYZuU374vHeGL//vo239blX4Hg+LdPtXtsVbB5IkokJC8trsWcsw6O7h+lJaK/5mpvhraSg/FYQBP/xyv8EQVCVJOm3uOqKcjs4APwO8BXgA8B/f0Ov8F2G5DvclqtluTx9dpUgEDz6VEQjCOhy6m6GV+arXY/U4UyUR/cM8a0Ty3h+wBdemGYoE8VyfNb3JRhMRbq+wn4QoNsuXzwwh257PLgpx10TNw4tmC21u4rpK4KPatvmS4fmsV2fR7b235CH/uiesR/nlqxhDW8qqm0b3fZwfB/LAy8IWNcTY6GiU27ZTORiWK5wi3B9n2w8REUXRXmz3MZ2fTw/QFNkRrIxPrS9n+culpiv6PQnwjy0pZ/9E71sG0peF551YqHG6aU6p5bqHJgq8/n71rHjFl2sTQPJLtVv/0QPA6kIXhDcdKq1hp8O3DvZi+369MRD6JaH7frYruAiX4FzjYr4ioiv1LJY1xNDlmQczyWivlYXpd6g0ZKJhSi1bUKKRDpy45IrHlZpmi75uphc/+zeES4XW+wcTpMIq0yXWoxmY9dNe2RJ6lAkJWRFJiBAQmKiN05PRxi5f12Wx/UlGobLtqEUj2ztp9yyX5M46fsBFd0mE9VwvIB83SQbC7HnzjR10yEdFaLDWEhQwGRZ4rce3EAA19Fi4mGVid4YZ1ca7BrJsHdM4tJqk50jaV6eLnNwusKWwQTv3Zzj4HSF4Uz0umL18EyFx19ZRJYkPrzjqnFCLhHirokslbbNgxv7SETUGxbHtwPddvneuQKqLPHItv7rutgvTZUot2zunezhidN5yi3BOb/WdeXJ03kurjbpTYT4/D3rsD2PsKpwYLrMkbkKmiIznI7dVibBW1mAy5IkSUEgCLqSJCnA697Njnf4E8Ae4LvA/4bghL8AnAiC4NBP+Jrf9khHNT59xwiFpnnLDeqdhpAiEwsptC2PO9dl6UuESUW1W56CB1IRJEmkm+WSIfqTEfZP9HBwpoIfCM/ibDrCJ/eOENFk+pJh/CAgGVZ5+myei/kGmViY5ZsIP2u6zePHlgDIN0w+c+coIJKxrlWH3wjTxSb3bb65m8sa1vB2QkRT2DGc5uyK8BwuNx0+sXeEoXSUJ06v0LY9HtiY5TsnV1iq6pxarOH6AZoqi9AO10eSYDQb4TfeM0G+ZlJoiHhrNwiIhxXuXt9DOioKnplSm839SQbSEQxH8GpjYZV8/fbWt3/+yCYquk1vPPym2bmu4e2Nnnio6yxWblnMVXTCqswjW/tFRLznsXvk6rMVILrpqYhGT1x0jherOpO5W8eTg9AkXNmjii2bgRuI/tqWSzYWIhsLYTl+J6DnagF37bNu2EL0/2vvmUCSBX1l62CSQzMVFElCU+RuoAzAr94/Id7BzuGg6wBzqcippTr3TvYyU2xzYLrM5oEE/ckIu0dF13swE+U3O4LQI7OV7vewXO+6QLqFik5VFx3pqWKbsKrwylyVR/cM4/sBuUSYJ0/n8YOAcytN3r9tgEf3DANiwvDU2VXSUQ3DclltiL1SkWV6EyF0SxweTi7W8f0AVZFomq4I8QmrrzE2uJamcyOcXKxzuZNrMpSJdkWtSzWDg9OV7r+v3OnSXwkGuoJCU9QBlbbN3x9d4PFjywymI6zPxXjmfIGQKvPJvcM3/fnX4q0swL8LfEWSpD9HPOO/Czz5el/QCen5wKs+fPAnc3nvXLz65X23IKTK/MI96yg2ra7o5HaweSDZ9eK+0p0WY+wwj+4Z5tnzBSDgpakSuUSY3aNpKm2bL748x5mlOg3TRXd8fm18ovs983WTqWKLREjlmQsFjs1X2TqUum4xmOiNc8c6QUO5e/LGnfP5UutHvh9rWMObjWhIYetgku1DSebKBtGQjCLBhlycluXSMByePL3Cct2gaXldEbNj+8gI8bKqSBiOz7dPrqBIMqmohuN5fHLPCL/38AZAcEQff2WJnkSIL9Rn2DuW4RfuHudyoYUXBDedRL0aqiJfZ5+2hjVci95EuEsjubjaZLokBHanl+vs61AMFUmiJ66RjKgkwirpqEY6+vqHv5pu8/1LJXpiIfaOpSm3bUKK3KVBvRrZeIj7NvSyVDW4f+Nr+cNBECBJEodnK/zgUom+ZJif2z/Gpv4k0ZDCsfkae0YzSJKY3B6br2K7PnvHMnzvfIGqbvOBbQNdquaV6W++YXEh38B2Re7ISt3k3//cHnKJMMOZCOWWzRcPzJGKanxq77BoWCkSW66heBabFl97ZZEggN2jaTIxjZouxKRfPrxA3XAY6RS6B2dEFP3F1SbfO1dgNBsloiqUmkI4vWskTS4hXFBUWeoWwd85leebJ5ZwO9xyVZGwHJ9NAwk+vvtqsfvSVImD0xU2DSR4ZGs/JxfrDKUjrOu9aic4mIogS8JHfSB1lVaaiqiENRnL8RnviTOSiTFfaXPf5PV6vfdvHeCV+Sob+xMdLUvAcs3AdsT6p6kyKzWTbbfRIHgrC/A/RNBHfg+h1XkK+K9v4fX81CEIAooti2wsdEOOctty+f7FIrGwyoMbc7eMYH8zkAiLRfBatCyXbxwXL+fHdw/dMNzoWr5W03SIhVQ29icptSxalstK3eDwbJVtHZHIQkVnuWMF2Z8Ms2VQFPG+H/DNE0v8/dElRrNRVEWiNy7+fvtQ6rqxmSxL13UiboRXO7SsYQ1vd2wdSjGajdO2fIbSEf78+WlOL9dZqZm0LYe64XQiq6/iiiBTU2S0jhj88GyV9bkYo9kIn71rjA9sG0CSJGZLbV6Zq1JsWZTbFpIkcWCqxEypzR+8f9NNBc1rWMMPi3zd5OvHl4Q15k1iz+Nhlc/tH6PQsK7TFr0eDkyVObVYE6mPvTE+0en2+n7AwekyjhewbzzDE6fz1HSbj+wcJBXRaEZfS2156XKJwx0NQ00XBWmxabFcMzi11CAb05jMxfnOyRUimgjMe/6iEH0Wmla323t0rtrVhPl+wHJduJUsVEQy5pnlhqCExMK8Z5Mo1J84tYLt+pSaFsWWfV0+SBAE4j3v/HkFv3DPOE3TpScW4uRiTYTnmQ53TfR0D85/8tQFDkyVSUU1Pr5rkOcvFshENfaOZxhMR5ElCGsyiizh+QHJsELbcjFtD91yiXZsHY1XeZ6eXRbJ05dWW8yXdV6aKpOKqPzRp3eR6TiRTeTi/Np7JpAl6bpaIhnR+OX7JmiZbteo4kZua+O9V+klH905xN8cnGO8N8Zqw8DqUOx05/YC9t5KFxQfkXz5Z2/VNfy047tnVjm30iCXDPOLd4+/psA+PFvhfMe6cDgd+Yk4B9R0m6liiw19ie4L8sNiutii0Blbnc83eWDj9QX4cs2g0rLZOpTk0GyFg9MVcskwP79/jNlSm/lKm8WKjqrIBEHAuZUGS1WDubJOT1zj/dsGuGeyh4imUDccDs9UKDRMIOChzX0osqCtfGTn4HV8sitdi9fDzZJQ17CGtyu2DaV4bP8Yz5wv4HrCjciwPeqGTctyr0vFVCTxnyyLjlNYkRjNxrhUbOF4AWeWGkz0xjEcj7PLdeqmy2QuQb4hRFfrczHSkRCXii0SIYULq83XLcBPLNR48XIJ1w94YGPutj2D1/DTiROLNY7P11BkiX1jGT6ycxDXC9g2mOSfzq5SMxwe2dqPafu0LRfX9wlxfbPqRut8uW3x0lSZeEjh8/eu6378fL7Jd8/k8QMotUyev1jAckQ8e1W3hb7JcPjwzkFWagZjPTGev1hkptRmtWHy/q39PHehwIa+JGeW61zsaJtWmyZ9SbGXFJsWjucTBKJ5tNowaVkuE71x8nWTfMNk60CCO9ZlmCm02Tue5TcenORivsm63jiyLNEwHWIdutl8Re/4gF+dJNV1h68cWcD2fD61b4SdIykWqwb7J7KEVYVwQuyDYU3h4mqLid44Z5cbvDRVYuewsFVc6VzXscUamiySbacLLaaLLWRJ4hN7RviV+yZwfJ+W6aB3bATDqszH9wyxUNHZN5al2raZKbfZ2J/gznXZbpf9mfMFHM+nqtvUDOe6+iJ1Ez3cjRp8r4eHt/Z3xav/+punO04u0ttfhClJ0gPAv0L4f6uIBkkQBMHk633dGt44XAncKbcsHN8nLCtMF1scnKmwPhfvWiypsvQjF8e3wj+8skTdcDi+UOc33rO++/Fj81WmCi3unexhtWmJjsFYhhenSnhewP0bcxSbFn/50gwrNbFZD2dEetm1mC/r/MnTF1ipm9y5LstIxzKp1LS4kG/y5Ok8q3UTVZboTYS4e30PNcPh0GyFuVKLS4WAC/km/yq2nSMzFZaqBgdnKui2R9YL8dCWfp48nScwBZftyqjrwFSZQzNCcPKRna+1nbqCmrnmyLCGdx72jWdJhFWCIMALAr58eJ6G4b7GN98LIBFWMF0fyw1QFaGN8IOgG9xzcqGGLEn8p7wQNv3c/nEmeuOkoxqxkMqv3j/BX788yzMXiizXTdb3Xk2o022X00sNhtIRhjNREZKxUMP1Ajw/6Ho/r2ENV3B8vsoXfjDDQCrC9qGkSDFWJFw/QPXF8zxX0Tm1VAfg+xcLzJUN/CBgtWnxiT3D2K5PSJX52tFFnj67yt3rs/z6e66WLlPFNs2O289q3eweGgtNk+cuFAkIyMY1Sg0L3XGxHJe6brPasFifi/On/3SJ+UqbvWMZZssiwXM4Lby0I5pCo1OQnliskQirvG9LjoWKgdJxQTk8V8F2fQZSEUKqTKFpMpgO82+eOE9Vd3hwU46NuSRBILGxP0EqonW70y9PlzkwVaY3EeLn7x7ndx4StDDL9Xj2QgFNFjaGp5fquH7AQDLMQocnfWimSiAFnF6s8+k7RkV6aCJMRbf5Ly9McW65wfPJCHdOZFnXG+vYCMe5tNomrMpkohoj2SiyJOEFQdfHe7GqdzjyYLo+G/oS3dyAv3hhmrblcWapzi/dN9GlDqVjKl89ssj6XJyhVITTS8K2+HbEkSCMHgoNi4ne2A3Fta+G6fq4vg9I3UC/W+GtpKB8AfhfgKPcOgFzDT8BPLylnyNz1a6fpeV6fP9ikarukK+b/M5Dk/ziPeOEVaX7IrxROLvcYKGq07IcXN/ncqHJv//eJXYMp7h3spe/eXmeharOE6fz7BhOIUkS3zq+xKnlBjFN4TunVsjENM6vNLsBOv3J8GvGd6eXheBCkmCm1GbrYJIZq82Dm3KENJnDsxVW6qbo1CkKrhdwudASXG4JWqaL4/r8wZdPEA+rrO+NEQSCZz+ciXIh3+RyoclgOspqw+oW4GeW67i+z9ePLbFQMfjIzsEb8vIj2q1f7DWs4e2GStvmXL6J5/mUWp2O200+t26K5V0GLNen7Dlosozj+UQ1mfV9cZbqBktVg2LTZLrY4r2b+zg8W2FdT5xy22IkGyOkyFTaNt88scw/f/8mAJ4+u8p0sY0iS/zSvevoT4ZJhlU8PyCXCBFW37j3y3Q8vnF8iabp8jO7htaoMO9QfOngAicWaqiKzObBBGM9UUKqeB5fuFQC4K6JLGFVpm25DGdinFioUzMcxnqifOP4EtPFNnesy/KdU8u0LY+nzqzy4R2DHJmrCveeADRZ7mRvXH0z6oZDMiLCbxzHw3B9TMfHcn2ePidsBQMClmuCHtK2PHoTIYbSUWIhlZohUitbloisb5qOECbKIsY9rIrDbliRCSkyR2Yr/MMrS+iOx7nNTeYrOkEAp5fqDGeiDKYiHcpYQFUXbifzZeEGU27ZNAyHmuGQjKjMFNscnxcOYaPZKC3LxfMDHM/n1FKdpuGQCMt88eUFDNvl1GKddTnhiHJvtJf5st6ZkBn8r1s2U2iY7BxO89m7RrlzPEMmFiIaUsh3QrSudWrZM5rhk/tGWK4a/OK91/t+X02zDmiaDhdXm4z3xNkykOJjnff0yFyVQzOVG9oW3wiW6/Glg/MYnSTeG3m3vxotw0FTxO+82qEK3QpvZQFeD4Lgibfw5//UYyJ3tZO0WNX5+rElZss62ZjGRC5ORFWIpW7/EQmCgOcuFCm3bd63pY+eWIjVpklPPHQdNaNluTx1Nk8QiAj4YtNisWqQimicWqrz4MZcl0PlBwGlls18pcVMScfzAtq2SzKiUm07rNQNIqpCOhYiHRPhACA4bo7vczHfpC8R4sxKAwn4wg9miGgKI9koWwdTKLJEKiKslTJRlZNLNTw/YMdQiv5kmJcul2hZLmFVpmHYzFfhnvU9RDSVz+wb4ZsnV6gbLomwQyKs8L1zq7RNsTgWmxaaIosx20LtxsLY2zwpr2ENbyf84HKJS6tNXrhYpGbYIujkFvAByw0QYc2QjamMpCOosky+1sb1fXw74OXpMhO9ce6b7OWlqRKnl+uEVZlkRBTWWwavuk9c+blty+UvX5xBU2V+730byCXDZKIahuMRCym3pILdDoQuRDggXOHLvpMRBAHltrCeu50O3zsZlutxabXFYDpCLCwTIHQJgS/oCLIsYTp+t1jWZBnXDzAcD0UWVnshRXQ2FyqCPnVptcmO4TRHO02sH1wusVwzmS62eWRrPy3LpSeusWUo1f2+WweTWG6A5/n0JaNmna4AACAASURBVCMoEkRUmalii5WqgQ8cnCox2Z/EcsUB9QPb+vn2yRUe2NBLX0dQHNEUkS/RiXQ/vVTnpemy8OjeO8R8Rcf2AoYzEVF0EzBXbrN/ooflmsEHtg3gBz4vXCpzz2QPf/SP53j2fJEdI0k+f88EL8+U2dSf5PhCjcePLRELKXz6DuHuJUmwoS/BnrEMnh+QimrUOo5fU8UWdcPB932WagZeEOB0mlq9MY3LhRY98RB/9vwUL10u8fzFIg9v7UNVZDRVYigt8koUSSKsXa0ZJEliy2CSvkSYWEjl/33qAhdXm3z2zjE+fccI08U2WwaSfPvkCvm6SVirMJyOMlNqI0s1Ng2INeNWtsVLNREeNJaNdn3Hm+bt8bn3jmc4sVQnqinXiVRfD29lAf6sJEl/DPwD0PVoC4Lglbfukt49KDRNvntmlVRE5a6JLImQ9rpd7PmyjuMFjGSi3D3Rwz2TPfywe9Zi1eh6aB+cFqfNC/mrfpmyLOH7wXV+qwBD6SieH2C6HvdO9hLSFH73oQ1871yBIPA5Pl8TxXdnQfSDgEOzFQiEvVFYlVmfizHRG+XCaoND0xWalsvGvgTLdQNVkUlGNEzHY7Vh0ZsIcWm1ycFEmPW5OJ4fUNcdDs1WOb3c4JfuHeeOdT0MpiJ8tTdOvm6wXDM4t9KkJxZmptim0LR57vwqIVUhEwvh+gFfObzAN04s43hCRf1Xv76fJ07nqbYdNg/c2LJqtrIWxLOGdx76EmEOzZRZqhnUDRv7h5xhBogQjtWWjaooBIDnBVhBwNG5KjXdZvtwmlOLdfpTYSZycf7wI1tJRFQGr+lefXD7ANmYxt8fXSRfN9nYn6BleWwfjvDEqRXO55tsHkjysd237mDdCiPZKJmYRttyb/o+N0yHUlNMwt7utodXNEADqQg/f/fYG3JIebviqTOr3cnmx3YNMl/WGc5GGe2JcT7fFI2YzoHN9nwMx+HFyyXhFx4LkYlpxDsOKNOlNudWGrxvSx+PbO1HlSXuXJdFtz2OL4hwt3sme9kzniGsiqnNXx2YJaTIJMIqpuMRBAFLNZ3VhoXpekzm4gQSXfFyKhrCdgPS0RClls3WwRQN0+Wx/f1M5OL0J8OdbnJAbzzEasPkwOUSSBKTuXjX8z6qKZ13wmX/+h5GslFOLsrsHE7zP//dKyxVDVbqYm9rmQ7ltsWd4z2MZEQB+uLlUtdCV5bgn905gibLDHXono4X0LJEh9xyfOH8NZ4hXzf50PYBvnVqhdWGiesFrNQN2rbHTKlNqWlhOD6ma/Ofn5/iXL5FWFX4/L1j/NlzUyiyzL/73B4mOhPl5ZrB85000XzD5NCMsAv8zqkVNg8kaZkuputdJwZVZal73fdO9tITDwmKy00OzpW2zd8fWcQPAvaOZ/joTnGQuWP89nIDXD+gpjuYmoem3t679FYW4Pd0/rzrmo8FwCNvwbW863BioU6paXFuuc6R2Sr9yTCP3T12U0uuHSNCbKEqMnesy3J0rsqB6TIb+xN8bNfQbS3OPfEQsZCCbnuMZKMcnxdxsZW23eWYP3exwImFOhDwvi39DKWjPHO+wEQuxs/sGup2ynePZlBkib/4/nTXQzgV1bAdD1+S0G0P1wvQbYNMVHTG/tuLs8yW2tQNhy0DSU4u1hjLRjlTM0QktqawPhdHU2XapsPXji6QjYX41QfW85UjCxydr2LYHss1k/s3Kvy7py7Qsly2DCTZOpRkKB1lqtRiqtBCtz08P2C8N0YuESIZUTmz1MCwXUCi2rYoNm0+e+couuPRGxd8esu9vlLJhN7dnac1vDtx34ZeVAW+fWLlhy6+r8DyAiotG1WCQJLQVBnXEe9VuW2h2x59qTCW62M6Hv94Ks97NuU4sVBj12iGkUyUWtvmpekyi1WjS4PZNiS6T1NFIVA7u1wnEVEZy0aZ7Ls972YQHeKzKw2CAHYMp4iFVH7tgfU3FVdfO7beNpTiIzsHf7Qb8ybhistToWnieAGh2ywa3om4ksfgegE/uFRmtWFSNx3uneyl3LYIqQqVtk0spBID5soGixUdqyPi++33bmCpZrBlMMn5fJM7xrMEARzqZEkcnq0SUkTDqarbuJ7fFfpdLrTQLQ8Dj0LDotGhfCzXTHoTIRzPJ6TJpCIquu2xsT+JIknYnocqQ9t2OT5fZdNAkpAqd526wprC1sEU2ZjGkdkKTctDAlZqbQ7OCh3EAxt6+cTeIZZrQgP1f37zDKbjs1IzOLNUx/ECjs5WWN+XQLddMlENz/c5NFNhIBVm31iGp87kSUQ0QorMt06soMgSH905yDPnC9iuzyf3jfAH799EuWVz/8YcsbDG0dkKD23O8eUji4QVmbrh0DAc4RBie9y7Psuh2RrJiErL9Li02iSsyvzF96eFk4kk8d9fmOZffXIXAPGQKrIC/IDRbJTRbJSlqsGe0TTfOrmM7frMltt89q4xLuSbTPTGyMRCrOtt0p8KU9NtDs9USERUxnuFhbGEdJ1tsOv7+J0K3nF9tgwmb2obeSP87aF5GqZLw4SvHV3iDz+auuXXvJUuKA+/VT/7pwHrc3HOrTTwA0iFle7p7GYFeDqq8eieYRRZIqIpnFkWG8+l1RbWNl+MvG6BqKbw8NZ+YiGF0WyMqCbzzPkC+yd6uoV1wxDjnHLL5rtn8uQSYX7+7nFmy23+7tACmweS7BxJYbs+jx9b4uJqk5rhsLEvgeV62I7DUs3qOi14fkDDcvn6sSWCAEzXw/fh5FIN36fLydIUif5kmN0jafrTEV6cKpOvmwSBeLkn++Jkohpt22Wu0uaLB0THYrbUpjcR4rN3jXJ0rtaN1NVk8fKu743zM7uGmCq26E+F0RQZ2/OJh1WSUZX/8OwUU8UW79/azz+7Y7S76V3BUuP2uGJrWMPbBaWWheX6zJd1RrMRdMvBvnHC9i3hBlBo2UgBJKIqQSCTiKh8fPcwmweSnY6yTViT8YOAp87kiWgKSzWTfeMZvndulVdmq2iKRCIS4ncf2kAyInis0ZCC7AhbsFfmqhyfr/GbD67v0tRuhfP5Jk+dEVHYQQC7RoWv782aEXbnoACiE/52x0Nb+jg6V2VTf+I1YSbvNty9Psty3WDncJrHjy9SMxwkw+X4QpX5si6mFQH0p8K4XkB/MkQ8ohL2Ahw/4IsH55gvt/nMnaPcO9nLxdUmd01kyddNTi7WmeyL89z5Aobtka9bVHWbWOc5S4RVDs2UCakyH9g2gKpI+IHERC7GicU6LcvlvZty6JaL7UOlbWK5AQ3DZblmUmhaXFht0jJdCk2TY/M1RrMxpootvnl8mUREhSDoRtUvVq3uc/jtkytUdRvL9QkIWK1b2J5HPqbhBYIB6XgBn9wzzNePL3Pfhl4quoPpeDRNh6lim3hYRZPhmfNFji1UUSSJZFil2LQIAuGf/uCmvs6hwuAvvj+NbnvUTZfdo2kurjbZP9HD0dkKC1WdVFTjZ/eOsFy32T6c5ORCnYruIAO5ZAg6lqWha2qOdEzjo7sGWaqa3L9B0GhyiTC7RtOcXKxjuz7JiJhQXGuVuGM4hSxLPHk6362Bji/UeGWuiiRJfO6usa57TH8ywsd2D1Fu2ewbz7BcE5Pv7Z3D962wfE1gz9mV6m09l296AS5J0ueDIPhrSZL+xY3+PgiCP3mzr+ndiI39CX7noUksx+fFyyWiIYWNr9P9uVxo8Z2TK4RUmZ+/e4x94xlenq6wsT9xw+L79FKduuFw57ps9++/d77A6aUahabFWCZCVXeJhVXO5ZsMZaIcminTGwsTH1GZKbZo2x5N06XQtDgwVaZpujxzfpWXLpeYLQvbparuoMoShaZJoWnStl38oGOZI0FIlggQXqFty0MS6yhSZ3HxXR9NBj+QmS3rlFo2qaiG5wU4ruiYncs3GM6EGe+NcXG1ydnlJssxg3hYY6wnRk8sRCKscdc6seCmIxqVto0XBPSnIjy6Z5gvHZqn2LS7FkY1w+H//s5ZmoYYE37tlUVOLNTIxq5/5d5Ikdga1vCTRr5udsI1bKptm5ru4P6YMgbPF7zSRFhlfW+YqCZzbKHK5UKLx/aP8cv3TXBysU5PXOOFSyVqukMmqlFqWqiyTDqqdWzWYtiuh+P5IhfAD8glw4z1xLhcaCHLcN2M+g1GMqLx3k19PHO+QO5HiMh+s3HFSeKnAc9dKDJbalNp26zvjXNwqkwiouG5HtOltuCAuw6/eI+wDKzrNs9dLNE0HHYNp/jj717AcDxKTYu//PW7Ge+NMZiKsGM4zT3re4iHVS4XWlxYbZGOCk3Rk6fz9MRDHJuvUm7bSMBSVSceUvECEXzjej6qLAJ2nM4hdrlukUtI1A2HYttiqWrQtlxqhsMzZ/Pkmw7nVprMd9xRJEniN94zwZmVBiFF5ufuGeNP/+kSnh+wuT/BV19ZxHJ91tdi7BlLk6+b7J/o4fhiDd3yyMbDPHFmlZW6yTPnC2wfTlHVbRwvIBVWWawaaKpMNCQJswIZ4mGF4ws1bNfnPZt6+OLLc9TaNlsGE9Q7LkfLNYNfuW8dEgHv29yHYTlUDZvJXJy/O7JIsWlyYMrBdl0kAAlGM1EKTRtFgkd3D/HydBnL9dk6mODrx5YxXQ/T8VipmR2Kq1gjlqrGazRWp5fqfO9cgaF0hJ3DKb57Rvw+bNfH8YQWZbGqdwtwEKF9DAh/8a8dXcT1AxaqOp/aN3rLZ8y7pglRb799fcCv+MS98abSP8UodIrVjf2JLvcwrCqosujINhuuEHXcxJJroaJjOC5V3WeuLOyPrtj5HJ6tcHi2wrbBFA9v7WehovP0WdEZsj2fh7cIH8xK2+LEQo0zyw3iYRVJgk39SSIlGcsRxfYsOp+7a5RkROUbx5aQZYmVapuDM2WCIGDXSIaFik6haRELKV2T/6WaieV6oqju8ORUOkW4InVP/H4A/ckQNcPBcwNCikjQNBwfz0ec7l2PbETr3IsAy/E5s9JkvmLQslxMx8f1QwxnYmzoS1A3XAzH4+O7hzifb1Bp2SxWDQzH46WpEjXD4dHdw5xYEH6yq02LIAi4lG+SjGj0pSKs1AzO5xuUWtd3vAN/zQBoDe8c1A2HqUKDk0t1LMdHt90fq6ZVOs3kAFhtiI6a7fq0LLGBmY7HfMXgQzsG2NCXYCQTI98wGU5HKDQtFio6AZCJaaL79sIMj+4eRuusCZ7n86HtA6SjKgdnKvyPl+f53F2jt+W/v3UwSRAIIfiO4avj5Jpu89yFIqmoyvs291+Xn1DVbRRZ4sRinU0DyXdlIvE7EZcLLVFINizGshGSEY2wKjNTNlBlCUkSe+DRuSqeH7BrJMVdE1nKTYtEWKWiC/rEdLnNVw4vUGrZbOhP0NBtnjyT5571vYxmo2zqT5BLhjgyV2WqKFI1Cw2DlZqJLIy1yMRCeH5AXyKE7ng4XkAirCBLHdvOkEK5bWE5PqsNYZEryxKKJLHSsPjumVUG01F2j6SZLrVRFYmG6Yqmk+/juD73bejF9UWDqNlx8jIdH1WWkSSJdFRj20CSc/kmd4yneelymYru0DBsHtjYQ1RTSEdVdEfYLaoyuG7ASt0SP69zPxRZ4tBMrXufm4ZLf0oYEOweTfHFl+dpmg6lls1izaBlepzPN+lLhqjpNoossykXpdhyhHOMIgthqQRPnyuw2hT88dX1PRyZrWC5PumIRrlts1DVWZ+LEwup3YwSzw+EK1o8xNmVBn4QsFQzkCW6riq9iRAjmSiyLL0uxeSHlURcNwS8za990wvwIAj+c+fPf/16nydJ0r8MguD/eXOu6keD5QpO12A6csMkyTcLNd3m7w4v4PkBd6zLXpe++Mz5Vb50cB4AWRYhGlc629d+3uaBBF8+skC1bVNpWzywMcdn7hxDkSWOzVexHJ/jCzXeu7mPsCYsm6aLLZIRlfsme4loCookkW+YuL5P23KYzMUptywimsLp5Trj2RgRTeGvXprjfL5BzXBY3xvjX/7DaYotsfmmIxoD6SgXCy00GXrjGi3LQ5HEKDkI6HS0xabteAESgVC1yyAhYXWEMxFNoarbeH6A519N5tNtH9eziWoy0ZCKH/gsVg3BAfOFXVq5ZVNpWfh+QCSk8CdPXSAZUbmQb4rIbdNBkSWysZBI4woCjsxWadtu1wN0tWHy2P5xPrF3hP/6wjTnVhqvTfH0fsTZ/RrW8Bag0DT4x9N5XF8Uz8mIiiLxI3fBNUVCloRVmw84vuhO6Z124KVCky2DSZ46k8dyfC4Vmnxy3ygX8k3OrTRYrhlsHkgyW26jdgKxDs2WcVyfVxZq7BhO8dJ0mUinGSEKev22CnBJktg+/Foe56GZCjOdyPL1uQTrc1ezB5Id3q8iS8RCCtW2zbl8gw19iVtan63hJ4c9Yxlc3ycbC1NtW+QbJhFN4XP7R5mvGoQUmfW5BN/vJEiuNkwWK4JScH61yZXdI/ADFqsi2C2syXzz+DJN02GxYrBpIMGB6TLpqMb9nfhyTZEot0VHOPCh2DIxHLFHSLKgRVqOTzoWQpbB94R40HJ8vAB0y+PuiSzHF+us642yUNFRJImm4fD+bX3MldtsHkhwdL7K5WIbSYJnLxS7drj5uolue/h+wErNQJYFlev0ch3L8+lNhGiaLlXDIQAMN0CRJPxAPMPRkILj+YBMvmmS6Rg51AyH3rg4SOwcTvH8xSIrDZOdQ0lsTxTtxYZFTbep6jYhVfDAHT/Atz2uuO8qMtRMVxzEJUHdkgiQJYmm6fDs+SJBEBAPia83HJeGYZNLCGHsq7fP//jsZV6eLrO+N85j+0c5uVBjsj/Bhv4EC1WDkCqTi4eJhXVUWbqpUDracX25QkG5FjXd5uxyg3W5+HWCTlWmO8UIKbeXPfBWijBvhc8Cb+sC/GtHl1htmIxmo3z2rrG37DrsTvwpvDaaVZSkdGyXJA7NVGgYDq/MVTt2euJB0VSZLQNJTi3V0G2xSVXaFsmIxs7hdDcKV5El6rrD8fkqy3WTC/kmKzWD+zfmePzYEuWWjekIznjTclmpCYFPPKJSH3aY6I1xZLZKXXfQHY+26bLatDA7pO5nLxSJhRRqhgtBgCKD7QXi39jZ5G0f0hHB7a6bHpbro8qAJHXvQ6FlEQupdOy8u92FK3D9gLbtIUsSy3UPpbPZKgi6S0DAYkVnrqLjegF2x7M4l4zgegHDmShhVeaudVlOLNT420PzuL74PaiKREiRmeiN8asPTGA4Pj+7d5jVhslELsbpTlwuwJbB9Bv/QKxhDT8hHJ+vXdVfBKD+GMU3gOkGyARoqtR5TyUk6eo31C2PM8titP7U2VX8QDgh7e+EhkjSVZeDXFLj9GKTZFSj3DnQ65bHcs3g47uGmSm1UWS6ceKFhgkSN9XF3AxD6ShnlhuENZmeV1FN9k9k6U+GSURUehNh/seBWcotm+MLNX73vRtekza8hhujaQoXklRE474NvT+2Q8toNsrhWdH9PHC5iO2JtTpftxhIhjtaoatNtMFUhLlym5ru8NCWHD3xEHpHXHt6qc5cuY3j+/TENCoti2wqxJnlBrbrU2nbpKMan7lzlFRE44/+8Sx+ECAhYXQmwQCFukXNcDBtD3yfzhCXiu6gdKiUihRQNRwkoNnZ65ZqBsmoxt+8PM+RuSrHFmr4HQFyEECpaTLZl8CwPcb7Yniej9cJvmqbLqWmyZaBBLNlnabh4Hi+uL+BsAc9t9JAt13mKz6jmSjDmSiqLLOpP8nRuRqaIvPhHQNsHEii2x49MY3vnV/Fdn0Smny1plBkarqN4waUmxZKh9styxJtW0ykHS+gJxFhuWGhSBL9qTDH5utIkkTDsLsTtsWqIe6HLOMFAU3LZa6ks6k/yXy5zYmFOvvXZ3lpqsRKzaTSttk0kKBmOFxebfGZO0eFLi2k8MpclSdO5ZElyCXC3QCiV2O4829/Nb5zaoVCw+LYQo3ffu9kt/k6kYtxqSBczT6y6/YE2G/nAvxtvVIFQUClLex5yu0fX0jXtgQXeiwbfV1PVtv1Kbct+pOR7ulNkiQcz8fxfO5eL2gjTdPhmfMFFFnisbvF4eDju4c5NFPh0EyFdb2x6/jH/ckID23po227nF1qMF/W+euX59EUiU/dMXrdIvjU2VWalkvDcPADePFyiZNLNeHr3bE+Cqsy1bZN03LxfaEwvrDSYKVqMJKNYru+CM7RFCq6EHWJKFSfYsvFdkVXW5XpjIGvvw9106c3pqLJEp4U4HugqeAEwmdckST6EyHSMY25UpsAIZxUpAC/82jJskQirOD4YDgeMhL96TCBFOB6Po4bdBYLMR6TJYmhdIQPbx9ktWFQbDmoiszfv7JAqWWJCOOhFOt6YlR1h1+5f4K/OThPTXfIJcL0xEOdbsJVDCTe2V7Ca/jpwns39/GfnpvCcHxkeEMKSh9wOlV803BRpKAb9h0LC+eilU40vdlpMd2/oZdDsxUe2zzOjuEU08UWL06VkSWxlvanItwxLpJv37upj3RM4xfuuRrgcUXAJknwqX0j3Y7h7WDXaJqRbJSIJr9GnCVJUjdbAeiu0Yok/dAj7Z9mHJyucG6lCYhC6Np7+qPgcqFNfzJCTRddWCkAJFitm/iBCIgCeHTPEI4XMJSOcGC6TEST8Xy4Y12WfE1wp79yZJEAQWv5wPYBYmGVdT0xXrxcJF8XTaPBdJjRDuVhOBMlFVFRJInxnjjn8m08P8BHFOVhVeFSh64CYHvQE9eotB360yLwRu1QqjRVoS8VJh5SmC7pNEwHWZIYzYgiFiAbC/HCpWI3mObK1nmFimJ7Pss1HbsjzNRtn3RYpmJ4hFUheGwvNUnFNB7dMwiSRG88xHhvjEe29iMBM2WdC3nx+7m82qTStoUgs9jm5/aPcWSmym88OCG8yRWQFZlMRMWqGsTDKvW2hY9orm3sj9G2XCKaQsv0UBXhUNK2hJmB7wdM9sfJxMLojsdkLtHhhnvMlFo8dTZPpW3z/KUig538jVwixOVCi2LTotSyqOs2Yz3iGaq2bZZrBpJE11ThRqh1kkkn++LXMRyueK8rsnR9/kHQqceAqn57Iuy3cwH+tk4okSSJj+wc5NxKk10jP14X0/V8/vbQPE3TZdNAgo/vHr7h5wVBwFeOLFBsWmzoT/CJPeLzXrxUYqrYQkKITT6+e5jjCzWmOy/1h3YMsGNYXOMDG3PcNZElpAgu2AuXipxaqrN3NMP9G3Ms1wziIZWVukHDcEhFNRYqOiOZKCt1g28cW+LxVxaRJNFNcFyfhiUid4stccodSIYZSEeFnVAgNljTDSg2LUKKzJnlBv3JML//8Ea+dWIZd5EOz41OF+Jqqp7r0+3gvxo1wyWqSjje1ZAPiavOJ1XD6ViYiVO0EwRM9AnO2Fylzb7xDJW2TbXtsKEvwbpclDPLTXrjwi3FDQLOdLrVpuOhyBK7hlOkohqLNYPFqk4ykuT8SpO64RAPq3z+3nU8tKWfL708x/cvFWmZHpoiUWyYvDxT7hYQV6C8i62/1vDuw/ahNBv74lxYbaEpEi3rjdEwXHkrdMcj2gnKGk5HmOxLMJyJEgBznY5dTyzElw8vkG+YjGVjLNc0vntmlTPLdbKxEMOZKJ++Y5R0VIzLTy3WefzYJUazUX5274hYGzpNkyAQm+W63h/uel/d+b4ZPrFnmMuFFut64+9qn+03Gj0JcX81Rer+Hn8crM/FODJXYetgkjvGskwV20RDCp/cN8zT54qEVZnJvgRPnc3j+wF3r+/pTJah2DSZKrTRbZfpkk4yorBYtdk8kCSXCLNjOE08rBBWVWIhhZAqM1fS+f++d5mxTBTLc9EdH6VzOGyZLn6HVmF7PrbjoUhXn6cAcSBQFWgaDh/aMcDTZwvsGklR1x0WKjrJsMb7NvVyaqlOWIVdIylmyjqyJPbRubJOEAQMdpy5HF80rF6ereH7ASeXGh2qiYQqg4XU0WPILNcM3I5dYFhTuXt9Dz2xEMPpCI9Xl9BkmX1jaZ69UMDtaCwUWRI5IukIL14u0zQdnrtY4sFNfRydrfCBbYN8/1JBmCT4Ac41G3pElakaNilfZbKvl+cuFJEliW3DCS4VWziez/bhNFsGUlTaNpO5BF86NE+lbdOfCqHbYqLQtlw+t3+MdT0xxnvixDv0mZ5EmFzi6pQr0Qn0kjsUuhvBcj3+9tACpuOxaSDB7pEMF1ebbB9O8fE9Q1xabTHWE7uOwmJ1wgODzr/pdvB2LsDf9qvVxv4kG/t/fC2p6wdd0VHtdU5Onh9Q7oj4Cg2z+/Fiy6Lcsig0bQZSQmCyPhdHkqqosvSaEeu1qZTH5kXy47GFGvdvzKFIElPFFlsHkwykIlxcbfL8hQLRkMJCReeJU3lW6gaaIjOSFaOpSs2g4TuiKybBYt0kGlbpT4VpXHPCdH3RIZcliaWayX949jIt08VyXJE0JoPp+q9Jqrryfx2aWHez9gNBR7lSoEsITpmmyKiy1L1f7jX874puE9aUTniBxeaBBLtHNXRLLK6Vtk1UE8Egf/CBTZxdrvOPp/Icmq5QNx3+20tzDKYjPLylj8m+uCgQ0lGCABRF4rkLRWZKorsQ1VTmywa261Fq2a/p4gOs1teCeNbwzkGpbdGfijBb1mlZ3hveJRF+/TL3TvbSnxJplrNlHb+TgLlUMTi93KDUsgirMl89usj//rHtAAykIpSaFpsGEiSv0VqcWa7j+QFzZZ2qbhNWZXaPZgTfVJKuE1i+0UhGtK6Y/XZQalkUGhYbfwqsAV8Pd4xnGUxFiIfU1w2Qu10sVA3GsjF028NwRbx7WFWIhlT+xQc3oykSl4ttKi3B124YDpW2Td1w2DaUJBPThH4n8AmpCoNpMbn84PZ+fnCpzP6JLAenK1wutohqCl94cYYLq00OIDGSjRD4Aa4E51fqtEyXADi92EBGdFKvf6TtOQAAIABJREFUTX0UbErRBVZkiaNzNQzH4+xKE1WW0BQZ0/U4tthAkgIcH5bqJpGQgoxw46jpoiMdBAGaIkEg9kPBrwbH87G9AN8PsNxApMbaHvGQQsP0sTwf34Zj81Vals9cWadmxBnPxkASFp1X6J5z5TaaIiMFwoZztmJg2C4RVaYnEWbLUApNFUJRxxe+5iFFNNok4OC00JiVHJtX5upENAVJgumi3v0ZixWDj+4YZjDlYro+7U4OR7Xt8sHtgzx7vsCHdwxy32QvmajGZC5OMqKxdShFNh66znyi0DSp6jaSJFHqOLCZrtfVsoHwi7/iHd+23Ou8xn/zwUn2jL02nGex1s2T5MnTK/xPj2y+5XP5di7Av/pWX8CbhYim8JGdg8yW2tzxOou1qsh8cPsAF1eb7B3LMF/WOTpfIRpS2DuW5Xy+STys8vJ0idmSiMPd0JcgHlYptywSEfW64htg50iKU4sNdgyLuNwLq016YmJ8s304xT89K3iXlwot/uADm1BlOmJIn7blElYVIh2XERAiBMcWTiphRUVVoHMwFDx0wPRFoe76PoWmCZJIyHQBw/VuevISNBJReHf54NcQuwMgrEikoiqBH7DSsCAQG7oiB/gBGI7PbLmF7UFfQjidtEyXhin46I7nM5AKk41pRDWVXCLCxdUWTcvF6HDGdUuM8h7dM4Ltejy6d4jnzhep6TYEYnEbykRQZQlVkbBdn1OLddIxlVLTZrl+9fBUv81R1RrW8HZATzzEvnVZji3UUByvywd/oyDLEvvGswymw+TrFsfmqqw2LVqmg9454O8aTlI3hFdxy3RJhBU+tnuIZ88X6IlpPHO+wNNnV3l09zC/fP8Eu0czVPQC/ckw/9e3zlBoWHxy3wg/d/f4La+nrjtcKjSZyMXJ3YZw88eBYXt8+fBCZ6NP8jO7fvz0zncybsS//WFwKd/kuYsF7p3sJRVRWULstabjstqwCKky5abJf/n+NJGQwi/sH+fAdBnPD9gxnKYvGaYvGSakymweSHBupclHdw7x5Jk881WdLYNJ/vz5ac4s1XlpqsR8WXRr66ZDWOtQj2Toj4eQJFFUp2NhFFlQIiVZRrddkWVhXLWtU2WQERHumiwaYq4v9Awf3NbHSzNV0lGVbUNJSm0LTZbZNphioWKgyBIty+0kagZMF9tYHY2YF0BMU2hZHlsGkhycqRIgLBd3DOdwPOHMEgvL5Osy8ZBCbyLEqaUyiYjKezf1cWCqjCLDvTuGePLMKl4AE70JLHcVzwswXZ8gEBROSZIotyxmi20yUa1rTuD70JPWMKvCUaUnHmKuItxoeuIatucjIZGNaR0XN490ROWvDsxi2B77xtJ4ni9oq5Josu0YTrHaMPnWiWUWqwaHZyv85nsmu9SlYtPk7w4v0JcId4L0RGDe4bkqEqIJGlJkHtgoxLPxsMrHdg+yUDHYO5bhG8eXsF1BibFdn/mKzmA6cp2pwrVLoe/f3sL4lhXgkiT1Ab8FTFx7HUEQ/Hrnzz96a67srcH6XJwL+SbPdE5y2ZuMOLcPp9g+nMKwPb748mzX+/rXHpgQNn+VNqc65v7TxTa7RzO8cKnIkdkqmZjGJ/YOs1IzcT2f8d44j2wd4OEt/d3x6Ia+BAen5+lLhvjqkQUquoPlePSnIlxYEQKnaEjF9XzqhotuGjgBZKIaUS2gZYuXrGZ4wGvH07rlEg6phFSFcstGkSQimoxue5ieKKJfr6vm+EL4dW0n/FqYrjDbtx2v23G2PZ9cIkzbchF5CzK+71LTHQ5NFUnEQpyYr6EoEr2JMPGwyh3rekhHtY6IRIhtNAUqbRfTcXn/tgG+ezbP2WUR5bxrNMNzFwocnqvwWP8Yj901xvl8k8vHl6jqNr/+wDr++OmL3XHZFdjumg3hGt45CKsKv//QRmIhlX/75Hnc29xobheOF7BUMYiFVGZLLWqGTbnlCME0oCqik/epfcNcyLdAgn/zxHke2z/OAxtzfPP4MktVg/5UmGcvFPjl+yfYPpxi21CSl6fLrNRNZEni6Hz1tgrwrx9fotK2OTpX5bffO/kTpZG4vt8tUq7Yqq7hR8f/8c3TLFYNvn1yhT99bB8HZ8psGezhOyeXAVEkfuGlWS7kW0gSNPX/n703D7Ljus48fzeXty+174UdIAhQADeRFEmJliy11ZYlWZbdtnvsGbetUY/tGLv/cMd0jCei7XF3dE+obffEdMx43B7vcluWLVmWRYoSRYmixB0kAGIvAFWoverV25fc884fN+vVK2zEygKp+iIQtaBe5s18L+8995zvfJ/LbLmFlPDGTJnHd/VTtVzGu1NcKCrVjBcnizRdn6mVFgdHu3g5+kwt1xxihlDKGkLwi49u4+/emGfPQIaq7fPSZBlNE+wcyLBQdQilZFd/mlenyqqS27EOuCG4tvp5LpLmBLXe9WZixHVBTzrOZz6wA4Cx7hSD2RiLNRsNweO7ejhkaoQSxroTnF9pEkS9US1P0TvPFxrtdTaIpDZtz8cLDAQ6QRjiS42pQpPXpkpkkwa7+pI8d3oJXdfY3puK+p5CFmotVZ0WqERaqJJdbhBy6EIZN5BUTi+zytYQAkZyCZZqSolMSkkQNXp5wZqhkBuEuIFScFmqO5iRssjkSksFzIZGzfI5sVDl1IIy+7l7OMtsucVgNkHdUXrpQ7kEf/3KBb56dAFT1/jo/kGSMQMN6M+YPHemCCj37U50Mhz+WYfW+NfenOfsUoOulMkvPb6j3QdjaLQTErnEtW3WNzID/hXgeeAZLhep/YDh3HKzzdk+MlvhhyJt7cthttziy68rl8iBXIJtvSlGupIIIdjRn6FYd3llqoQfhJxbrjNfsXCDkOWazV++eIETCzUEgoPjXXzm/dt5Y7rC37w6jR9KntjTx9beJBXLJ2HoxDSIJQxsx+MfDs9zttCISjMSxwtwI3rHtTYdrLR8RMu/JHg2tEsbLS8HCev4YxcjiCazi2krrh+gC5VlCqT6wLlBwOmlBg03bDd87hvO8S8e3U46YfCd08v8/eE5rIiX2mdoaEIjmzCZr9rMVywcP2ShajHenSRh6mzpSdGbiVFueRybq1JsuHSnYui6RtP2280+q9DEnVyE2sQmLoWmCX7xse184aUpThduPYXq/EqdYsulJ23Sk45TbXnrFjdTN/jM+3fw316e4YVzK3T3pnnx3ApJU+eFswXliOuFPLQ9x+9/8wwAW3qSTK00iRuqcfLjV+izuV74QcjEcoO+THydoceNIJsw+diBYeYrFvduubTEfaswU2oxX7G4ZzR/za6g70QoJS8Xywv4na8d59RinedOr7CzP8XEUoOYrhH4SsteIHCDUEliIkmYGoWGCqzvGsyyXHdoOj69GZNnThTwQ8nnX5mmNxOj6fgYmuB9O/spNFwGswlem64yudJirmKzfziHF4RoCHb1Z3llsoTrS6zI1wKUPOHlcLHG/jeOL1GyfKp2jWeOLVJoOHhByNeP13F9lb46OlsnGdPxQxjMJtoUzHLTRUZKJy1nLRGk6DAVKnZA026wa1CpnUkJ/3BkgZWGamT8k+9PsVx3QcCfvzTFYtT0+cr5IqFUdBY/UFUp2wuotNx2hbrphiQNrb0eF+s2ulDV9DPLDcJIaKHpBGQSJpoQ9GcTDEdUn8FcgnLL42yhwa8+sZOnji1QtTz2DWc5Nl/H1AXzlRaOH/Dy+SJbelLcNZRhYrmJqQuOzlZZabht+k1vOhaJKiS5eyiLH0pGuq6shqQJgaGrjcHr0xXOLNbpTsX4Zw96TCw3GMwllCJU9PfdmWvrEdnIpy8lpfxfNvD8dxRGuhIkTNU08FYd+XNl1SSxvS/NQD7BgY4m0JihsXsww6tTJSaW6/yHp07x4LZujs9V0QQM5pIU6g7pKIvdtD3+y7MTTK2oh0BRQ5Rs0OnFGi1PSfzVLBdfNhCSqFlSx9QlMoq7r5ULeqUM960sZeuaai6JowjiuqbTcHxWFRp1AQldI2FqWK6/ruHzXKHJl15XXe6VlkfdVjJRthcy0pXAdHxMHRKG4NEdPXzhtVn60il+7OAwg/kEk4UmMyWLL7w6jRtJRi3WbH7lh3bw+K4+vnVqed1YW87lJ95NbOJOhR+EPHVskcpVFARuBm4ANctBIPnkvcP4fsjkSpMwDImbGqYh+L1vnEHTBB++ewA3kFQtj2+dXGKy2MLUBQ9s7UJKyXcnCuhC8JKA/aN5Htjawy//0E5A0Us6+cWOH/C9iRW8IOSH9w5gGjo/fu8oZ5brUU/NWvbbcgNeny5zZqlOJXLr/YXHtrV1wDvhRW6H15I93zWQYdfA7XOnbDo+X35jjiCULFRtfvy+0dt2ro1GOm5QbXkkTZ1W1Czsh5KEbpCM6cR1wWh3itPLDTQhuHesi7mKgx+G9GcSfPXwPFIqhazxriTzVYudvRm+IdUcHgQhhZqDG0jKLQ83gN0DGTQhODpd4cR8lZihoSGx/QDNF/zFi5McW2gA0LLXnh95jetfK6rsykDyzOllzheUi+fW7mTHOuZHCidw6MKaJXogJSlT0HQl+4ayvDKzJodbiTLuTqiebyvSIb9nOEOh4aJpAtPQ1DkkOB1iAnVXVW5CCYW6SzNaaM8uN9BRya6YAYFUYwqkUkVxfEloSFL6WhiajGkMZuPomuDgWI6vH1vE9gNiOnzh1Rn8ICSuCT73Uwc5s1TnsZ29fOr/fpHlhkMuafDqVJmG43NmucHZpTovnC9FTbyKNhsi2vQ1IZRniBOE2G5Ab/rKG+gvvqbMl0a7kvSmY/Rn43SnYnz7TIHJQhNNqPtjRypn8WsUV9jIAPwfhRA/KqV8cgPHcMegKxXjlx7fTijlOuv3VbOJB7f2tBeLe0bzzFUsVhoO358o8OyJJT5+cIRP3T9GseG07ZrnKoq/dHapiRfIiMtdb+vUfvLeUf7+8ByHLpRoucols9hwmFhuUGm52J4EoRZEIdfKFFoAfhBwm9bfG8Ka1rmaeIdyCQxD58JKM5IFWl9k6c3G0IXAD0PcMGxn30tNl2MLNfwgZKQrSXfKZKGm7OcrLRdd1yjUXf79k6foSpm8b0cvjh/yzIklVhouQoChafihcuCaWGqQiml8/fgS3ekYuYTJHGtBdyx251tWb2ITnXj6+BK/+43TLDVuzwQgAdsHzfb54qF5UjEdQ9dIawIZQsPxOLGgmqj6MnF+7Yd38+ypJd6Igg3bC3l9usxeN6DYcBAo7vp8xeIXHx/CDUL+6uVpLDfgI/sGuWc0j5SSb51c5quH5yi1PE4v1vn1D+/h1GKNieUGuSiwrrSUCsNzZwqcXKhxcqFGXzbGQOQPIKXk+YkVzizV6Ys4xK9NKvrfT793fN3cvhFY1U0PuH6nv3caetOKdtidivEbP7KHv3hpmoOReoflBni6IJCKMqEJyVLdVrJ9Es4X6pwrNHF8pYJxeLZC0wk4mq1y75Y8k4UWD+/o5uvHVDDuh5K9QxnOLNXZ0pNmptSkGSVuipFEX4Bkrmq1x7fUWGvau1YmYlKH1WKzBpGaGcRNtaEQAoZyKRYqDoGUaoMRVal0AQ1XyfueWKxd8RzzEQ3H9UM+ed8oe4bzbOtN8+2TS5xfUeNPx3WInn89On8Y0ULa1xSqnq0gBEMIWpHUqATmK46qZvugx9ZScoGUFJsuAvjK4QUKdfV3X3p9gUrTIZQwWWgiEKTjRkRZEaRjOqauY+iKumLqGiXLpdLyCEPJaFeShGlg6IKBXBI5q66/2vKZKVkEoeTNuSot16fYcHl4R097My2lZKZkMVdpAZKdAxlOLdYZ7U5GzaTqWQo6+tGcazRH2MgA/NeB/1UI4QAeUQwlpbx9LekbAD8I+crheQoNhw/s7mOp5mDqGu/b2Uu55bJUs9k9kCVmaJd0vVdbHk8fX+RCscWTby7wPz2xk92DWRZrNjXLp1h3mC5ZVFou1mszWF5AteXxwtkVBnIJnrirDw2N927r5stvzOGHkqqlgsSRfJKRrgR//P0pmq7atTlewEvni/hBByfoMp+jO9G7UZkWQNJUFJEP7h2kO2Xw+89M0LpIrSFhCLJxg5brM5BN4AXKJUz5fQFSsqs/wxN39ZNNmMx+5yyzFYts3EATSkkF1EJ2dLZCPmnyndMFDE0oZ9CdvewfyXNkpozt+fihxovnVihbXpsHt4qBa5Qz28Qm7hRcKDaYLd9+9Z6WF9LywrYylK4JEoZGseFRbrq4oUQHHtnRw0PbesjEDf7guXMUaja+VEmNh7b18Nzp5ciNUDkiLlRsGrbP+ZUGlZbHeHeKY/NVXjy3wsnFOn2ZOOWWR9PxeOGc4oc+c3KJMFKrykYmO54fULFcdA0OjnXh+gH/6gvHmSw0iJs6PakYqbjBlp4UpaZLoe5suDV9Kmbwkw+MM1+12Df8rlpqL8G23hSFus2W3hQ7B7L8+H2jjPek+JtXZ3D8ED8UzJVbkfmT4MxyvU2lPDyj+qhCKXn5fJG6o9yOTy00uHe8i+W4S182sU4it2krQzcpJaWGCrp9JN1JXTlfCmUzv5oMupH9T9VZC2Lv39LFdNkilzD5wJ4+LpTUtYzlEzxrqUbMprvmUdJw1qq9zavsnUMp26Y+I/kU+WSc/lycv35luj3uascBkoYgjOvUbY/37+5j+hVVQc7ENVpRbHFxPLqaQZcoznt7jFak0Y1y5265AYGUJEyBE2XZm37Al9+YVaZEZUUD9QKJ6wfEdR1T14gZgkJduXB6Qcg9I7m2RGRMF+SSplJN0yEVyRW6XsDfHprFcgOarq/kiotNHt/VxytTJebKEaXJDSg1HE4t1PjnD21hMJdgMBfnD79zpn0dmfgdLkMopbx5/b47BNWWx1PHFMH/YweG12U5lusO0yW1WP3lSxc4OltFCPjkvSM03YBKy8P2Qj6yb2BdMySAaQgatscb02WyCYM/eO4suaTJd8+sRPbqanHSNaHs6F+ZZqbUUsYWgdIANUxl7/rI9h7+4eg85abL+ZUmhZrDsyeXmC6t7cgDCcEdwMbv5HteDwIJQRhSajh89cg8mgDbDVWmp2MCCFFW8003wNRUFtzQwfLVxDATvV/9czFmKzZ12yemC1IxDV3TSRouTTekYfu46ZCW4xOEIZrQKDVd5sqWqjgEEl1TjTld6RhOh7RRG+Ladsqb2MSdgJbj8/89f/6Wq59cDZ06R7mkgeWpRdn1fA7PVvnXXzzC/tE8pibY2ZdR/M/BLENdCV4+V8KLFA5CKYkbqk+jLxPn9FKduCF4fbpMzfboSce5d7yLgVycvnScM0sNRruSzFUsetMxvvT6LEs1m+39ae4d70bTBZ4vCUJF/fvHowvMly0qlkfSV1TCLT0psgmDnnSM4fy1O26eXW7w7KklBnMJfuzAyBUts28EQ/kEQ9cxlithlTvdKfF2J+H0Up2G43NuucG/++pxnj1TYCAbZ6VuI1FZa11TKlWaEAQdGdq65bb7dbwgpDulpGrvGVW2744f8NzplXWJnb98aZKVVsCbc1X602abrvH6TFUlrSTM19YC4jBcC8JNcfXeplV0BrJPH1ug4QQ0nIBy3SUV0zE1jW+eWm5XdI/M1Nt/rwsNgUpGdSU0yvblH2K7Qyfgv3x7gjdmqpi6xp6BVPv+jPWmKM2pYwuhUWra7SrS6hBbTtiusly8zHWe2bbXgo7potU25Vuo2fhhiJTK2Xr1mparFi+eL7JSd3hiT39bdtjUFcc8lCARkRSjxPYCCg2Huu1h6hrdqRjvGcujIdg7lOWbJ5Zx/JCEqXG+0CSUktOLtXbT52tTpYhfrmKDQMJcxaLUUgm1B7Yq5brOguDRmepl7+3F2NAODCFEN7AbaM8GUsrvbtyIbgzH5qssRNJyZ5bq67pp+zJxhvIJzi43OLlQ40JRSRA9+eZCWxze8QP+9PtTnFms8/Pv20Y6biCl5JsnlpgutUjHdAp1h3OFBpYbomuK5qBpEAYhnhAqW9RwqUVPz0ypyVLdJowmmXJT6ZtqmiCXMDm+ULs0GLwDoCSBbvz1lq/uZ81uqSaPy0xqjidxfTURutFJVyltPuD7kqmVFlMrTYIODXFTE/zsw6P83WszSOlgGjoxQ8cwNA6M5pmrqAnj5IJqfN09kGEwF8fyQiYLDXrTccZGcrw6tcbLW7kFLqqb2MTbhZcmi5Qt/63/8DYgYWgkTZ2VhkvT8RWXVEiWag5VawU/kAxk4zy2u59PPzDG984WWWm6jPeksN2QX3h0G6CaSD957wgtT3Flx7qT9Ga60DXB47v7qLY83pyr8p3TBX7y/jHyaZNvnVii5QbKnCVQgXwcjR39aZpOwCM7evnG8SWars9wPsGv/NBOejMJtvWl10mVXQ1SynYC5vCMojycLzQpNpT2+p2E2XKLL70+hybgJx8YvyUB/a1GzfZpugGG5vPtMwUatk/TCTA7wr+Ziq0SN1LiBGHbAj4R09vZbTcIODDWzdRKkyf2DHJouorrh4Th+spq1VHHDSRYHZGy3/G9wVp1OWkKnCij3bqBPEyhg1b590fnaEYKZEPZtQ1R3JCsxrerHGxQlfkrQXR8PT5fwfEljh9ycnHNrXOyw7mzsxek5a6NKWAt8F7V815F59k7k/GWt0YHnS222rHAcnWNrhOGUkkJegFvTJeJ6aphNG5o2JFAhBeEmLrADyWGDpMrTVpuiBAhYRjyM+/dgq4JzizVcCJe/YWSxd6hLHXH44Gt3bw6VeHscp0fv3eEZMyg4dhkEga7+jMEQUhXyrxE1nkVDffaMpkbKUP4GRQNZQw4DDwCvAh8aKPGdKPY0pPi9QtlDF1j9CLt0pih8bMPbeHMYo1yS5Uhi02XlbqyRj0wluf1C6pxoOH4TK40uWc0T8tVk+9ANoEbSBpOgO0pPlQQQm/aJBXTWa7ZxEyd8e4kKw1HyeaFUHd8UqFqplgNLgVAKClHIv2XC043Gjc7JCXvr9CZLdA6jr1aXtMEiCtUikIp190fXUDV9vn6sQVWWi6g0ZMyGcjGcQPJXcNZ0gkT2/WZrdgEMmSuakfaxSIyUWhcovQy1rWxJenbhW3/5ms39fqp//ixWzSSTdxKDOcSaOLaFItuFQSQTeg8saefI7NVWl6AFJA2BLlkDIRSsAhlyErD4dXJIv/ntyZw/IAzS3VGu5L8ygd3rUuMdKdj/OJj2/GCkDBUKhQf3T+EoWu8GNFONCFIxXVyCZPudIy+bDzi2CbY3pvi2EKNTMLgZx/aQso0KDVdHt7ey7a+NE/cNXhd1/jtU8scma1wYCzPh/YOcvdwlrmyxWAufs2um28n5sqKNxsA81XrjgzA7x7KEjcEg9k4Ncvn2EKNVExna1eaI3N1DB1Exwc5lzTJJT1CCY/u6GG6NI+UksFckjemK9hewD8cmScbN/D9kO50jPnqWmZ2a1eM80UlR7i9L8mJhTqaEHQnNBYaKiAbzBlMV9QGVmi37iFaDb4BrHU5ndU2SNoSwQD1q1BQVrfXEtjRl+XIbA1dU0pCpxZVA2nM0NrckZiu8ejOPAtVh08cGOJ3nzmrzhxF8oFUeujXwnN3O0rwYUdnaqcspxtITFO5Z3thSMsN8YKQsuXRn4vj+AGZuEFPKk5fJk4yplO3PKXFDizWHV4+X8QwBONdKRquTxhCTBe8eL5IxfLY3pvC0ATbetNULWUqlDIN4obGh+/up+54PLStB/MKZllb8ne+CsqvA+8FXpJSflAIsRf47Q0czw1jvCfFZ5/YoTph9cu/IbsHs/x3D29lV39G2a+Half5S4/v4AO76zx9fFFZ2BYbzFcsQim5UGqSNA1+9YM7+cobc3zhNQsZSBKGxgNbu7C8IOJiSuYrFvMVay2TG0LNCdfxzCQ3n2F+O2FEDRxXmqaUY9jadfWkDHrTMc4XW3TK6GrRg2dosOqcvcrHMwy1e6601vhxCUMw3pPi/EqTUKrXxU2duKEzW7ZJmjq6Jtg3mmcgGycIJIO5OB/aO8h0sck/Hp2nZvts6UkxmIsTBEpaUhNcohxRcTYz4Jt452Aon+RDdw3w7KnlS3idtwMCGMrF6EnH8UPoTsUoNlxsL2Awn+Bff3QvQQjfOrnEfMXm1KJyyTw2WyYdjxHTVW/NWHeKQt2hLxNrZ5lXqYJ/8tIUthdw93COj94zxCM7eujPxsklFde76fhoCA6O5jhXaDKUT/JH35tkpCvBlp4UO/szxE2tTcVYTcIUGw4XSi12D2Quq47SiWNzVaSE43M1PrR3kP0jee4eyrU1hu803DOaZ75qoQlxx3LJf/q943z/XJH7xrso1Gyars+egSwBkhNLDRKGTstfq+ZUGg65pEkYQtw02N6XwnIDDo7lOV9o4vohluuzVHfwAlUlTcWUsU1MF7x3ez8BRbpSce4by3FioUHM0JVsWBQEF5tr52s5F4/4xtHJsvQ6glhd3NxiX6hb6LrSNv+FR7fxd4fmSMZ0NBHw7TOqkpuK6fzhzz9Iw/FJx02+8NoMyzWHjx8c4ktvLALXHnN00nJ29GdpzVUJpeSR7T18e0JtjLNxnQPjXZxZrPPj947yV6/MqE2QhEe2d/N3pQZ3D2Z4/55+ao5PfzZBf9rkT16YIm7oGBr86QtTCAE/ef84T+zpx4+SnPMVCykl3zixjACWag4fvrufdNzA9kLScZPJosWOvgwrDRfL8Vmo2ZcoqByZb1zT9W5kAG5LKW2hGiDiUspTQoi7NnA81435isWZpTr7hnNvWSIUQvDA1m52D2R46tgilucxkI1zZrHG0bkqH9k3yNRKi//6/HnmKi3qlo/iPJrMlJrMl1sIufbhfObEUrt5wQsl51cu3xR18Rp5Bya9r4jwKsE30ObVgXK7NAyNqZLFxdUfGUknxgydQAbETZ2mE1B3AnRPkIhpxEyNhKGRTRhk4iYLVau3jscVAAAgAElEQVR9/l39WfaPZplcaRHTBIWmx96hLMP5BKeX6ni+ZLArwc7+ND1pk5myxWLVYrgrSSau89qU4vDrmiCfNDi+sPZwOu47ZDe0iU2gMseP7u5jstRkYqn51i+4SegarNSVJfhMqUncEErnX8JKw+Pbpwq4fkDLC/mn7xmibnvMV2wmixbb+zQGc3G29qT5wqvT2F7IfVu62h4Ly3WbIzMVzi3XcfyQmu3x3m3d9Gbi66QA//bQLM+dLiCRUVm+SjXKqPVllGTaV4/ME9M17tuZ58BYF2Eo+WLU0HVqoc6WnhTzVYv37+5raxt34oGt3RyZrXJwbE1S9k4NvkEpTX3qvrGNHsZV8fjufh7f3Q/AR//zc5xbbjBTtkgYWkSrWE+lKjQ8ZKQDXm66LNcdHE/1+qTjBkL4pOM6XlvbOiAVOV5KKZmttCi3PJxA8tQJGy+Q+KFP2lzLhnodp3Ru4dTfuU52emOF60Lz60fdUZSrUEi+e2qR44s1NGBL91q803R8Dv720zgBfPiuXhaqDoGEbxxbXGf2cy3IxFR2XkOwayDF5EqTIJSkEgYxXRBKSU82ydmlBlXL4+XJEnFDQxOQNHX+7MULtNyQb5xc5kfuGaRm+Qzl4PRSg1BKHD/g9GKdUtMBBLoIySVi1GyXD+/t56+jeWIwF+forKIaHZuvMRo5Zo93J8jEdZ4+tsBD23t54XyRo7PVSwQ0solrC603MgCfFUJ0AX8PfFMIUQbmN3A8V8RC1SJu6JeUAr9yeJ667fGPR+f5xMFRHt/V95aT5nzV4n07erhQUu5Rv/n3x7DcgNHuBKP5FDOlJsWGSyDVY9NwHQp1F6+jVGa9U1LYN4nruUrHC1moXppNNrXVDLjiyQch+FEaXKI2L7gB2YTBozt7Waq7uJE5w+r5q5bD6UWN5brN/pEcn/und1NquXzxtVm1ECOi89vsG85x35Yumk6Wg2N5/ua1GbpSJvmkyfa+NKGU6wLw3uydV7rdxCauhAvFJueXG5x9G4JvUJkzAfieSnE1nDUvgYbj89SxRQSSXDLGWHeKPYPKpr5Qd2jaPuO9KT7z+A6+c6YAqOapN6bLnC80efLNBaq2R6nhYHlKB/gP9fP8+od3U7d9/vC750FKUnGdZEynarnsG8lTajg0HJ/BbIKfenCcqWKT2bJFoW5zbL7KkdkqP/3eLYSRi0rV8nh1qgTAC2eLfPqBSwPXR3f18Whkg/1WWGk4PD9RoC8T5/FdfbfVnfOdjDemy3x3osBD23q5UGzhRXP/qib4xdDEGnf39eky1ajX4bkzy2iahu2FdCXXKhlKZUS9x14IM4UGTSfAC2SbSy4l0JGFNnRwb3MLRWdOp3WTCR5dhO1E14uTZZrRvTvbwQFfaXhtXvszp4vt31+N5nIlFNsKvZLXpioUI7WVyUKDnnSMhuOzuz/Jk8eWCSS8eL7IQCaOH4ZYXoDVobryR89PsdxwWaxZhKGMquKSuu0RSqVKM1O2+N65oqJTRWZ9ugBNaAihAv6YrlFqKS30QsPjuxMrCKGat7tTJhPLdbLx9RWudPzaGpM3UgXlU9G3vyWE+DaQB75+vccRQmwDXgZOAq6U8p/cqjGCKg1+88QSmhD8zEPjDHZkuuOGxumKRdXyOHShzHA+we7B9eIuYSg5NF1W1u89aXrTMbpTMb47sUIrsosHyblCHUPTsLzgktKuF8qb3Me++3G5e2Osat6G6oEQvpJmkqzpmgtWOWqCqZUWMUNjOJ+gWLeZqyrOvRNIzi7XMXTB4ZkKL08WKTVVo5ahCfJJkzNLtbYsYdXy+OG9A/ztoVnmqxZHZiq4fkgmblziPLer710jBrSJHwAEoeRLh2bf1rlIXuZ7LXq2m25A2hQEYUihZvMT940yW26xVLdpepK65bNUtwnCkMWaQ8zQ+PzL0xyZqUQZNcX1DqPemFcmS/zdoVkQkmNzSsng0Z29fHBvH73pOFt60nz16Dzd6RiDuQTPTxToz8RJmMrsS2lPByzXbD55cISZssW23hRfe3ORmuUx0nVp9vt68eK5YtQk3mJnf+aWHPPdiM89fZrpYotnTy6zmp9c3bxdDn4YtpMuKx3OlMVWwCqF5LsTxXWv6VyX/ShgC0O5LnlkdZRktWtTp7siOtVSkjpY0aHHczFmIoWV7qRG0YoaQm8yV1eyOnjjHWolpqHhRAfPxDSqUeBrsMYhv1lMl1rte1u1fKqRs+Zc1DgLYNkBC4GN5YXMVVrEDIHjSzU/aIJSyyVuaIx1JZWhkADT0NtU4XPLDS4Ule7582eLlFtKevLUYo2P3jPEcs3m0Z29/PUrMyxULPJJg66kSbHpMJBNYEb0nIvf1+YVNnkX420PwIUQOSllTQjR0/HrN6OvGaB0A4f9ppTy525+dJeiHGk+h1JSaXnrAvCfenCM3kyM04t1RS9IXcrz+8aJRf78xQtULY9cQmckn2IgG6NmuRRqFpomCEJwfYktgytODtoVFD3eybiVD+sVIdRuuGb79KZN4qHKggdh2BbLN3VB0/U5sVBH1wUPxw3Ge1KsND3cIKS8usP3AAL+09NneGRHTzv47k6ZlBouf3tohkrLoy8T5+vHF3E8xRmsWKqxZ8wL2dK7niv23TPzfOq9W2/3XdjEJm4Jig0nUhrYOBgCUjEN25MIJLYPoeXzwrkix+ZrKBEyQRCokvMXX5uh6QbcNZjhQrHJbFlxd01D49EdvTw/sUzD8fH8EDcIeOFcEQ1YqdkM5hO4Qch3Tq8wkk+SjnS9d/RlODxTptzyaTotfvGxbTTdYf7r85NMFpv89SvTDOQS/Mx7t5BPmfzcI1toOsEtaagcjlS1UjE9cvnbxOVwfK5CzQ4oNm360nFaFVspYwRrAXJnAN3hzr4uS53QYFWxz7toEe78KWkaaMJF08W6LHvFvlKD5PWjU6rQ6ojxqvbagesd3JZbqSrc2XhtdmT19Y5k762cGTo3D34QtpVljs6umQj5QOApdZeq5ZMyNRwkGuD6QZuyO9wV42xBoGuQjemUmh4CWGna7euqtFyIaGaagF/+oZ0sVGx29af4D0+ewo7oKx/aO0AQSvqzcUa6Uuzo9y4x2Fq5xvT/RmTA/wr4MeAQa/1zq5DAjhs45geFEM8DX5JS/v7ND3END27twfZCkqbO7ossgrMJk0/eO8pi1SZhanSlOrheQciZpTpzFdUt3nJ8arbHVNGi3HJxPKWRmTR1tvamCKSkdhV5r3db8A23P/j2JazS/FbVC4bycQoNl6mVlioNSsgklIJBCOhSUm15fGT/EJPFFisNF1/KdeoqXiiZXGnSk44p0X9dY6LSoNpycXzJYs1WpiGmrpwxhSAe09jal+bXfng3f/rCVHuM7h3kJrqJTbwV0jH9El39txupuIEQkIgpGoqGVOVnL6DleqpxW6pnvmJ5nFyoYxpK73lrb4qDY11UWi4f3DvIcD7BmaU6tqecggWCc8sNarZHPmmyZyjLYtWmUFc6wi03oDcTIwgldw/neOFcka29KXJJEycIySVMSk2XmbJFJmEyX7XIR3JlV5Isu148uK2HbX1p0jHjjtXgvl1YrNqcLzTYO5x7y81M01UJLceXKksZuRR3Km1c6WO8KmEXAlt7E5wuqIy4KSBSDmybvwVRb1ZPOsZ0qUXC0LCcyyfTbpfNRqexjneb9sedYy+vFQgoWWsnvJWn7mQCdMoYXlxEWP2fUK41e0oBS3UbN5B4QcCphQZ+qDT7nz+70q6EzHfIG5q6IJswcfxA+QFkEwxkE4RhSCBVYK7GIWg4Pt3pGB+8q5+7hrJ0pUz+j6+fWhv7NV7j2x6ASyl/LPq6/RYdcgHYAzjAV4QQ35JSHr1FxyYZ0/nIvqtLSl1OgunJNxf4ixenaDoBT9zVR0zv5oVzRRaqNhE1ULk6uYGyNe1K0JuJM1tsstmXd3MQYk1mUAM0XRDTFYfv1QuKDrK680uYyqVMWcpKkjGdT98/zs88NEbNcnnm5BI1y8fxQ6QMaXkSQ0DVUrrqXhDSn+lhJJ8gHTeoWz6GDk/s6WOmbGPqKWq2T1fS5H/70b2kLlJDyKU3M1ibeOdgstjayNgbDfD9gFCoZrNswqBh++0gCEDXBSZqMU6aOqmYzva+NFt70xydrTBfsSk1lFX3v/rhPdy3pZuRriQPbuvBD0KePbXMaxfKxA2d4VySgZxSrBrOJ8inTDxfEjd1Ht7RywNbuzGicnY+adKVMhl044BgS0+K7X3p23If+jLxt/6jdxnCUPKlN2ZxvJCJ5Qb/Q6Tr3omnjy9yZrHOwzt6ScUMmo5P3NBpeX7U83PlxXVNqwRGuxKcKSiTuvma086UG4bWdnGEtaywBI7MlHECcKLs6tuJTqObdyNd1QvW2kl7MiaLUYa5MzEGrDlNS6i21nq9ig23/X2nZGPQUc3rSsbIpmJ4fsh4T5qJpTqnFmt8YE8/D27r5tRCnQe2dqNrgvGeJOmYji/lTbnbbgQF5f6r/b+U8vXrOZ6U0kEF3wgh/hG4B2gH4EKIzwKfBdiyZcv1DveGcXimwnSpheWFHJ6u8tF7hrh3vJu4UUMgWaw47V1SKGGpZhPX9Rvzp90EoG7dYC5G3fKx/bBNEdEFJOMms9UWjqf4Yem4znA+GQXRkt2R7a0fSi6UGizVHXYPZik0XMa6EsxVbF6/UMbyfPxAUZIKddVJ/fp0iX/5gV1s70vz+VemKbdcqrYqeVteyK4B9Zi1/JCLH9UtG2xNvYlNXA8OdKh0bASEANtXhmL5pEEjMuQBJReaNA1MQ8NyfRxfYrkBn75/jE/dP8qfvjCF5QUsVG1abkDjbJFcYpLdg1ke3tFL0tS5UGzy0+8d54Gt3SRjOp84OIobhCxWbbb2pqhZHos1u62SYnTIzsYNnZ9/ZCuWF6yTHTy1WMNyAw6Mdd0yR8vluo0mxA9UIC6E0n52AEO/9D66fsiJeUVPODpb4d989C7+6pVpfuzACP9wZJ6GE2AaWru58mLoOm352kpr7W8sR2JoKiPenY7Rqtjrst+r6KBIXzEAvl3BcecVvRvzd1Ku9cEJRPv7VEzQ8mRbLrjz7nZW6jpZc533v1MxzTB09gxkqVgu23uSfObPXqXSUspI7xntYiiXZKw7yfcmVnh5sshod4rf+JG78INw3TxwPdgICsrvRl8TwIPAEdTn8gCqmfLx6zmYECIrpVz1W30M+L86/19K+YfAHwI8+OCDt/SzX2w4fOvkMrmkwUf2DaFrAtsL+M/PTHAskrCRoWSpbvE3r00jULbndTu45CFxg/Ui9Ju4fhhCNby0PLVbDoFi01UPXH1tByyEoCcV4/4tXRy6UGYoFycXWQ0XGw7PnSlwdrmBJgT9WaU/vFizEULZ2iZMg1CK6KGWVO2AvmycwXyS7X1p6rM+pxfrnJivMt6dYv9onge3dtOXibcbu1ZxLeYEm9jEnYKeVBxTvA29G1dA2BFsu36I3xEBhVHVywtC3EBi6ALD0Ng/mscLpHIPFoJ0TGsbexy6UKZu+8yWWxiaAARD+QQ//75tnFyo8bU35zkw1tUOuHszcXqvEvQauka2YzGeXGny1JtKC9n1Qx7e0fuW19h0fF6ZLNGbia0zD1rFuUKDrx5RgmGfvn/spjJwV0MYSn73m6c5Pl/j4weG+fQD47flPNcKIQQ/9cA406UWOy+ig4Iyh9k3kmNiqc7B8S4+9/VTzJYtvnp4jvft7KPUdBnpSvD69OVtwjt8X6h0cDoCgFC2Ler1KBiPGRrWdfI93m2Z6bcLuqYRrmqpt9beGwFtRoGUa1SVUELa1GhE78+V3qXO5bdpu+gChJQsN2ymSxYSeOl8kWTM4Ph8DV0TvDpVwvFCplYafPP4EicWawzcoJrZRlBQPggghPhr4LNSyjejn+8BfuMGDvl+IcTvoLLg35NSvnzLBvsWeHmyxLG5Cg07IB0zeP+efiaWGhyeLnNqsYYfhIRSMrmiSlkJQ6AJgeWFmw/iFaBH9JEb2cWrZsugze8Ko8V5talC19SuOB03CIELxRZuoNR9d/VnKNYVN7/SalFqOCTjJvMVC0PXaLl+lDkRSCnRNQ1DU4uCGT2UO/vT3DvWRSZu8OypZdxQcmKhxvb+NPmkyVcOz/Hq5Poe45qzSQLfxDsH89XWuuavtxuSyIALyCUMQimxI/tqKaHQ9NCEcrUTQnBgNM+BsTwTSw2qlovjB+QSMfLJGDXbpycdY6rYZMdAGl0Imo6ay186X+Qrh+foz8RZrjnsfOLSgO9a0JmnvZJErZSS759bQQCP7ern+YkVTi6oTO5ANnEJxbHUdNtBR6np3rYAfKXhcGiqjAS+dWp5wwNwUDr03Vfhfv/I/iF+ZP8QoBTMbC+k5TZIxTXqtsdc+corb9iRMdUvUghYfVXdXbNK967SjPxupIG83ei8h50W927HpjuQAqXcruaF1d5XCe3g+2pY8wlVm+evHJnDDaDYdNrndoOQQt0hmzRYqFrKPRe1YTu1WOHFc6V3TgDegb2rwTeAlPKYEOLe6z2IlPJJ4MlbOrJrQN32+P7ECi9NFtEEDObjpGI63zixyOnFKvXLyNDYvsTQ5A/0gxnTuCrH/WaaTa/00pgu6E2bhAhyCYNETGe62OJIw1VlxZSJDCXD+QSThQZ+CBU7wA0krqfjh6rENJCN4wQhmZhBLmngdCUotVyG8wmyCZPff+Ys+0dy7B7MsL0vzVPHFmm6Po4X8mcvTBE3NV48X7zCKC/Fzdq5b2ITtxrhHdANHqLUKJKmhuXpdKViLNcULUCtjGou2DOQ4XM/+R4G8yk0TTD7bQspoW77pBM62YRBbyZG1fL43pkVHt3Zw4fvHqRquTx9bJFXzpfoTsf49P03bjizrS/Nxw8OY7kh+0fWO0bOlFpomuDYXIU//t4UXhBGalkqwDQ0QcK8tLR9YCxPzfKUC+XI7XOh7E7H2DOUZWKpwSPb3zpzfydgoWoxudJk33COQKomylAKLhRb2G6IF3iX8IZX0UkPz8Z1rKg8mTSg1enPHuFiueDOYK6TT74ZjN8Y1t2zK8QMjrcmHendwE3u3BIXahY1R8VnE8uN9vuWMDQKdYfpUos9gxnySZNiU8kbfu9siVOLdc4VbswXYSMD8JNCiD8C/hJ1nT+H0vJ+R+ALr85wcqFGzfJw/JDnzhQ4X2jw2oUy5aukiH5APHSuiNvVob0KDaXpnYpruF6Irmt0p0wKNRs7gMWaQy6uoWkauqYW8jOLdS4Um2ztTSOFbD/5oZSUWl7E9wtwPJ+edIKx7iSLNcXB3D+SJxupMkgpOTJTZqFq8Vsf38/D23t4darMH39/kobtkU6Yl8iG2fYmB2UT7xyUWndGxcaXMFm06E6Z2F5AzNCJ68pIxQ3A8yUzpRb/458f4ok9fWzpTXFgLM/R2SqlZhMvCPnIvgH2j3ZxcqHGUs0mCCXvGesmHTcotVx6MjG296W5Z/TqvHfl0tlie1/6Ep1/gF0Dl2r9n1qstakpUkouFJt4geQbxxf5Tz95L0P5BF0pc52y1irihs4P3311YYBbAVPX+O1P7McPZVs3+U6GH4R86fU5XD/kfKGJFoVQmlD0nwCQgbw2OkJHxjVpClpRtH215Su4wvebwffNw7nC7282nOh8nyrO2jtVbq6VP0IJU8UmgYSJpTqjPUpYIRlJmWYTxg2bYW1kAP4vgF8Gfj36+bvA/7Nxw7k6Ds9UeP1CmX0jOR7e3oPjh8RMjSAM0YQqBRbrTtu5aROXx+2cjDQUxSSUUgnhS3CCANcP1zVb1J2QnpRG0tSpRZluywvJJz229KSZFYoyZHsBq/WKQILtSQoNh8KEQ9xQr59aaaFr8MtP7MT1As4VmwSB5Klji2ztTfGesTwJU1cNoSmTh7b38vp0pT2WY4sVNrGJdwq+N7G80UNoI5CqNJ2JG2TiOm4QstpGEwJ1x2dqpcn5lSaZuMHugQxuEOIFEDPgntE879vZy5NvLmC5IfmUSaFhk01k+OkHx3nxfJHRriS7BjI0HJ/XL5QY6Uq2A+pVk7V/ODyv1E9yCX7ukWvT9G/Yawu8qWtkEgaer2QMvVA1fZ5ZqvP+3X3rGjrfbgghMC/T8HgnQgjRbnI1NIGuCzRf0Q/d60xHdy7jnWY0m7izoItLKxG3Ap2BecvtyLKHKiMuAFMI/uXj2/m9ZybYP5rn8y9PX/d5NtIJ0xZC/AHwpJTy9EaN41rx0vkilhvw0vkiD23r4UffM0QQhHiB2m2busC9isTRJm4/VPCtJlwZEcElrGvUWoUfQqujGz4EapZLwtS4azDHUFeCiYUqb0a28boGMV1r88FlZGXrBhIRwlffXEAI1Z1dbHkcnikzXWpxcCzPj94zxJvzVX72oS3cO97Nv//aWqGn/zIZrk1s4k6F1O6sYETXBImYTlzXCFEb6dALVSVMCNxQ4gchpq7RdAMGs3GWajaGJhjMJUmYOg9t7+HeLV3cNZDl8EyFJ48uct+WLn71g7vaBht/9sIkTx9fAuDffnwfCVNnaqXJoQtlzhUajHUn6b6OZ/ngeBeOH6IJgRCKb71cd/gn+4dZrDlt63pT195SBncTCrom+KkHxpgutdg9mOX3nzmD44ckYwY6st2cfyUKSic6/+bO+sRvohM3G3xfy8s7Pyurhj9BKGl5Af94bIGWG1wirnCt2LAAXAjxCeBzQAzYHvG//3cp5Sc2akydWK7ZnF9pcvdQjnzKZPdAhqOzVbb2plmq2fy/z51nvmJxdqmGlJCMxQhu1vd1EzeMVUv5VcQMge3JdkOmiTKyTBqCTMIgbhpUW+66hEjDCZA1JVG2vT/FE3sHmKnYNByfrT1pfuKBEY7O1Pj26SW8QNIXM0iYKtuuCcGO/gyD2TjdqRivTJWJ6erodw3lGMolOTZXY67TwQDY1X97dII3sYnbgbH8jTUj3mqYArIJjZips1y1EJqgK2HSndAphRKhCTShzHXMSP3k/i15tvZmCICdfWl2DmR4ebJIteURMzSEBst1h4bjs1RzmFhq8J6xPIdnKjx9fImlms1ANs7zEwXqdkC56dJyfUxdqSp9/ODItY9f13hsV1/75x39aTJxg1TMoNx0MXWh5pjM5gb9etCpUrN/OMvJxQZbupO03ICm1yBp6jh+QPMyjUjxDpMdQ4C7GXlv4jIIpETTVNywWLWpWh4t78aopBtJQfm3wEPAdwCklIeFENs2cDxthKHkb19fE/z/+Ue28sG7BjizVOOpNxd47vQyC1WbQ1Ol9g6ssdTkB8yU7I7C6lypCZR0YBBieWt1RCnA1ARJU2cgm0DXVCkp7XjUI6t4L5Q0nZBcEqaLFobQyMYNUqZOwtRYrDk0nYBQqmDf8gJ+4r4xzq008YOQ/mycX3jfVv7d105QbDiqESeQnFtWWfTvn12hbq+nKG2kosQmNnG9GOm6M3SnPQkNV+LbShFEBhLXd9H1qM8mUIukoWnkUybd6RhuAJ+6f4xP3T+GQKkh/c1rM7wxU0ETYHk+ewYzNB1fmWrZHl9+Y5bpYov3jOYIQsljO/vY2pPhhXMFXpkq4QcBB8a7ScR0Mpfhf4NK5jx1bJF03ODjB4cv64jZqaLQnY7x3z+6DcsNGMzdmLrCJgChKWMWIRjvTVFoOGQTBueWL98w15lNvdngu7MhcxPvLuhIHF+iiZCx7hSnFuuXbZa+FmxkAO5LKas3Sl6/3dCica0qRxUbDn/18jQNJyBhaBwYza17YEPWC/Fv4u1H3BAM5ZI8tK2bV6ZKrHQQ+UIJBJKS5VOz6wzmEvSmY/SkY+QTJqeW6srO2g1YqNiUmy6TKypjYhoaQgieO1VQ9sZSlSi9UPLNk0vYXsCO/oySPzN0bD9ESgmaRjymgviJ5Tq7+tN8PSpjr+IOEJXYxCauGaPdd45xlHfRwxNKCPw1ZYOEqZM0dca6U9wzmmMwl8TURTsAXtUM3juU5dlTy1Qtj4bjc2A0j6kJXjpfQgileNWbifPz79vKx94zjBuEfPv0kjL6QNCwffJJk5hx+UVYNX66lJou00VFj3gr5BImuQ3kfr8boAkY605h6BqP7ewlDCWDuQQTVwjAb+XyvRkKvHsxX1OeIpYnKTdthqJ55UawkQH4MSHEPwd0IcRu4NeAFzZwPG1oEZdsqqhkZ5ZqNn/+whTllrKYTSQNfuKBUZ4+Wdjoof5Ao9OJTENltPcOZ5guW2iaIK6r5ptkVF62vRCi7PVKw6Fme2QTJn4ePn5whJcni5QaHqWWQxgovdd80mT/aI4T83VKTZe+dIxMXKfWctF0Qd32CELVad+bjvHUsQW296VJmjr3jOY4uVDnxHyNfNLkR/YP8uC2biaijDjAPeOXGm1sYhN3KjLxOycolEBcF8QMgeOHSKkkR90AkjGBJjS60zE+9p4hHt/dz0A20Q6+X50qcehCmeNzVUxDi/wamkgpmTB1MgmTfSM5ig2Xg+NdfPLeUY7OVvjia7Pct6WLjx0YZrnuIKXkM49v58B4FwLUnBJfr4qwcyDDyYUayZjOcFey/fvJlSYvny+yoz/DQ9t73ua79+7HZ9+/g2+cWOKxXX1ULTXXp2IGZofj5So1cRObuBq0qL9Mj76ubv33DmaZKa/QlbqxyuBGBuD/M/CbKIWZvwKeBn5nA8fTRt32OL/SZLw7RTZhcmK+hhCC3QMZyi2PgVycvzs0t9HDfNfgRnVSd/SnWa471CwfTQMhNL47sUIYhiR1JTWY1KAnHSMTNyjULSrWGoWkLxMjGTPIJw0mlurs6EvTm/Y4OuujxXR29qfJJEwmlpuUWy4xQ5BNGDw80svEco3JlRaVlksuaZI0dc4VmizXHfYOZdk3nGd7X5ovHprB8QJmbZ9XJsv8zo/fw397ZaZ9DeXGlQSWNrGJOw9zFWtDz/xqmxwAACAASURBVC9A8S9D1RgNoAmNTFyn6SijLFMHQ9N5cFs3Q/kE923pbmedC3WHL7w6zYvnisyUmpiGTn82zvbeFIYQlC2PQEp60zF+5r1bKDVd+rNxwlDy7KllpISK5fLZD+xkS4+SHeyJjGG+/MYsUyst7h7O8tF7httj3t6X5pd/aCeaEOvMeJ6fKFBsuCxUbe4ZzZGKbeRy/O5DNmnSl4nRlTJZaTiMdCWVEVtMpxJx/zrt52OAG722k0KyqeO9iVXzpVByUd+YT9zQuFF3l4184vdF/4zo3yeBT6As6TcUT725yFzFImZo/NJj2xjvTjLclWAkn2C61GKu0uIGOfebuAxudHKrtNzInVLxPuu2196d+n5IV9pE17TI7RJipknMDfFCSTqmsmNj3SnmKsq8oeUGJEyNe8fyxAydvkyCY3MVZssWbhAylEtwYLyLpuuz0vBwfOW6mTR1LhRb1GyfoVyc3YMZPrR3EE3AuZUGdduj1PDwgoA3L+qW1u4wVYlNbOJqaDnuW//RbYQEglA1ySlPBUkgfUbySQIpsb0AIZWE6JGZCvOVBEnDIGbo7BvJcWyuSqHucKHUwvZCYiHEDY1feGw73zq5xFzZYmd/hqGuJDFDW+dCOZJPMlexGO1SNJxOB8owlFwotgCYir52wriMjvZ4d4piQwX4icvwwjdxc/jNL7/J1EqTvz88z299Yh8vT5bYN5ylL5OgajUjd+S1Muqmjvcm3gqrog6rWG641DokRa8XGxmAfx5lPX+Mm9dTvy2oWh6//PnXaboBfWmlbLGp8/324uKSTyeq1voPfszQcP2wLT/YlTQxdMFdQzlabsBi1SYX11moOSRMHSnhxEINNwip20pa0JGS04sNUnGDlbqDJgQS6E3HScYMHt7eQzZhUGp6lJouuYRGyw3IJk2EgP2jeRKGzp+9MMVD23v47Pt38KG7+vm9b04gIMrSrSFhbKocbOKdg7ix8VlajbVMVIiinu0YyHB6oUbC0LC9QMmMugELVYtj81XuGsqybyTHjv40r0zqjP//7N13kGXZXeD577nmeZsvX3pb3vv2Xq5bLaGWQxghCRYk3C4MBAzsTswQq53YgNmNAJZhGMQIdhYQA0ISLYMkaKm71Wq1r+7qru7ylVWV3j5vrzn7x32ZlVmVVZWVmVVZ5nwiFOp385n7Km/e+7vn/M7vlwyRipgMTHmB2Hiuyn/88E7GclVePjPNhvT5ai/5qtdo5/0726jb7qLlBjVN8MDGNEdG8+xZYlrZI1ta2NOdIBowLtmmXlm+kxNFqpZDdabMibECnYkguYrN/t4E52bKBAyN2ryRNDWmplwty3LIlGu48uYbAZ+UUn5jDT8f8EYuhrMVSjWbY+MF1qcjPL6rnXdGcnz14BDHxxrdLh05t2hHWZ75UzeGdr4rqGj8T5tXVN/UoSnko267lOoOuiaoWu6CQLzuSATeVHRXIthoSV0hU7HwGzr7epO8Z2sb6xql/lIRP1/84WkqNYdXzs4wUfDSPyzHJRE0qdkOzRE/QVNHCEEq4mdDS5SHNwtGc1VminW+fmjE20/H4ZHNKY5PlIkHTVJhHw9vTvP4znb+9OlTABwazHJHXxPdTWH29iQYzlbY0hZbML25p0flgCs3j729N0CusvDOF4Ym8DU60YVMnf7mMMWajcDriJmvWKSjPnRdzHWz7E2F+c33bWYoU+Ybh0aZLtYp171qV1JKSnWbUxMlTk2UMHWNja1RvvzqEPmKl3r4ybsu3Whnf2+Sre1Rnj02yUShyoMb04uOfM+XDKsb8GultynEYKZMKuxje2eCH5yYpCsZ4pUz0/h0DYTA0KHWuA4lgxqZivcg7oPc2k72KDeBYxMlsmXba/y3DGtahrDRiv57zOs0KqX86vXciaePTfDmUI53RnNsao0yMFXiVx7ewNa2GJPFOqW6Q6HuqjywFTIEGI3atq5kwZyHoYGuadTs8xt9moauCbpTYaaLNToTQaSUvHJ2YedITXgd0FpjAWIhk1OTRUBgOw7xgMlwtoxpCHZ3JWgKe7V6v3t4nPdta+XguQxTxToVy2FPV4JIwOCRTWm+8voIEsnuzgTpmJ93bWnhR6em+KsfniEzU2c0XyUaMNjdneBPf3of3z48SsRv8tiOdnyGTmsswMGzGd63w2ugoWsCKSHqNxnMlL3qCY2DyXHVyJdy8yhU1z4q8RkayaBJPGjSmwoxlvea2JyaLNKVCGJLSdiv05UI8sHdHezuTrBpXuURU9fob47w0KY0w9kKNdvhg7vaEUIs6FBZqDVmxWzv4lq1rjxR+/q5LEfHCgC0xgJs77h8G/vVYjsurw9m8ekau9XCbgB+7r4+/v7VQd6/s4N9vUn2dCfQNME/vzni1YnXBK57/mTsMwxMzUIiuXNdmmdPTGI70BUzOJdffpqBcnObP1h4oWypiisl9jKnT9a6Ff0WvIXI85tOXdcAfLrkXVAMTcNxJK0xH4VKna++MUKubHmteDWvBamyfI6EqKkhhIvtSK9rZGMEWwiQUs6VfAwYgnjIx7p0mA/u6uC+Dc10N4V45sgYv/DXB7EbMxFRv44rJbqmMVWqM1H0fpd12yUV8XF6qsRwrsoLp2c4Mlrg5+/vZ0tbjI0tUXRN8MyxCf7xtSF0bbbspODwaIGfv7+PsN9kQ8v5aeh71jXz1DsThHw6U6UatiNpDvvpbgrxuQfXn/+eruT0ZJGa7XBirMD7trVhOS4VyxvFz1WsBaUHp0tqEaZy8/iLZ0+v6ecHDTGXAuItsvNTtlxOjBeoWg5npkv4DUHVluiijM/UeHskT3vcaxNfs12+/sYwFdulXPPWbHxwV8fcIs0dnXFKNRsJ7OqMI4TgiT2dnBgvsK09dsX9S0e9agi6JmiOXL+a6a+dzfCjU9MAhHz6kkod3uq+/fY4IHjqnXF+fF8nxyeKdCSC/NvHNvMn3z9JT1OIv3r+/PFcs5y5We6K7eA3DTThNLqhqgD8dnW5BueJsI9sreo1zlpGhsRaBuC7pZQ71/DzAXh4c5qXTs/QHPERD5qko36+8NwAbw5l6UgEGM9XKVZV3vflLDY7oAsvsJ69c5RAzXZpjvjRNG3uZBcwNPw+HaR30ov6DbqSIfJVi2TIR18qTHdTiDNTJZ4+MTn3eWGfzv0bmzk+XiRfsQiaOm2xAK4rCZg2XckQ/ekw52YqJIIm9rwUIr0R6e/pTvDl14YoVh1STT6iQZO67dKeCC5ojAEQC5r8zmOb+cGJKUpVi1zNZnf3xaNbjuNyfLzgjeY3bih0TRALGEwU6jy4MY0+L9WmKx686D0U5UblumuXKSuAnlSED+3u4F/fGePMVInpYo2tbVH2dic5PJzFcqEpZHJysoCpafzoxJRXpchvsKElQtDUyZQtJgs1HClpiwUYzVXnAlZdE9w7r0MlQGciSGdiaX+nm1qjNN3jw2w0ALpe5tcgv1Q98ttNMmSSr1jEgyZPHZng9FQJUxfcvS7FuuYw0aBJyDSo2l5w7Uh3biTw2FiBQmM2ZLJ8Pvg2hdcEChaWwVVuXRfG3/OPgdZIkImCRdCnU7aufnZwLQPwF4UQ26SU76zFh9dsh2rdpSUaoCsZ5FtvjWLZLj5DcHgkT7XuMJSpUK5ZavT7ChY7B4V9Ov3pcGMhjJe7beheU4yt7VGeOzGFpmn8xrs30JMKoQmN3lQI25W8MjDDn//gNKW6PffejpRIF2QjiBZCsj4dZUdHnKFMhXTUT2sswGM72qjaLjs7Y2zviDNRqHFyokhXMkjYbzSmajNkShb9zeG5xVZhv85MsU4kYCz4QlJKvvnmKGemStRsh+FshUPDeQxd8M9vjfHojnZ0IQg22qCahsad/U0MZSrs7PIC9MPDOXIVr1zRWL6KpjGXBD5Vtti8+r8SRbkmstdxMGI2OWv2z9FnaMSCBi8PTFOo2Zi6RrZscS5TYVdXnE/f00t3UxifofG/fe0tpot1EJLpUh1fzeaFU9N89sF1HBrK0dsUoiXuRxOCvau8DuN6jnzP2tOdIOQz8Bsavanwdf/8G9H/+v4tvHEux7aOGM+fmgK8xbsvDUzzypkMflNbcLPi1zUKjROzNS+noFx3SIZ18iWH9+9o4bmTM+QqNr1NQU5Pr21ZTuX6m3/TlanUKNUd7MsNk1/GWgbg9wOfEUIM4OWAC0BKKa95GcJy3eZvXzxHsWbzyJYWMuU6mUYqSj5rUa451G2HuuMuyAlUrkwDAqaXXzeaq7GrO4GGQNcgX7EYzlYYylSIBQyqtsu3Do9StyQBn87jO9vZ1hHlm2+OAhKBV3cbYH06wl3rmvjSy+fABVcKbNcl6PcRDhhkKxa7uhK8Z5uXdz2crXBsvMCWthgd80avnjsxxd+8dJZS1WZfTwLT0BjPV/EZOsfGvA6Z3zs6wU/d2QNAqe5wstE45/RkCV3zRsl8uldt4YvPDWDogh8/0EVLNIAQgp+7r5/RXGWuTFk6EpjrrJqO+GmPBzk3UyFgCDa0nk9zUZQb3YaWGDB63T4v6NOoWi66gKCpcWK8gOW4+DSBFN7aiol8lZMTBlvaYrxnWyt/+NRxXBd8hsBnGEgpaY74OTVZ5OWBaX76rm5iAW+9iO3KS7aQv5xcxeLMVIl16TDRG6BjpRCCzW0q7WS+WNDHts4YTWEf79vexuHhHJ2JIM8enyTsNwj6dFJhH2OFemN2JUy2mgegPxXi9WHvvB8NmhQqDgh47VyOYs3GBSZLlx7xVK3oby1GY9Z6trnfrKFMGVdCzV7eVMhaBuCPrdUHz5TqFBvl4IYyZR7YmGZ/b4ZK3WE8X8VyXMZyDqWqraaYLsOne/lRLjBbhScW9A4pXXj5kFG/yf/147vxGxp/9L1j/NPBEaSEuuUQ8BmM5apULZeQz+CtoQxff2OEt4azpMMmbekImib4yx8OYLsu+3uTxAImVcuhLxWiLR4gU7IoVi1iAZO3hnPoGpyZKnNiokg66meqWOehTem5fZ7tmCfxani2xwPEAibj+dpcGcP5ZcbCPr2xOLfIxw90ka9YDEyVKNUdtnfGcKWkbkvGc7W5tJWw32BDy/mLYU8qxKfv6UUISIR8RAMmulbBNDT8uqr/u5i+3/3Wil5/5vc/sEp7osz33m2t/J/fPnZdPsvUBSFTpz0ewHElU8U65brjnT80QSxoYOoaQZ/O+nSEqWKdw8M5jo7miQUNfKbg8e3ttCcDHB0tkClb/OiUN/r5wMY0hxs1+eu2y66uqxsF//KrgxSqNoeGfHz6nr5r8O2VlfrmmyOcnizRHg/wk3f2cPe6FOCVf7Qcl6awj+H2KMM5bxClKRzA1ItIoDkWoL/mkqvYfGR3B3/5ozNIIF+pz82IX1j5QuN8uoKmefXqlZufyfmUk7pz4Yy/l4C7zCqEaxeASynPrtVndyaC7O720hPu7G8iHjT5jfdu4rtvj/LHT51kNFfFcR2WeVNzS7gwr3v+HX1Ah01tMSqWgyYEpbo3axAJmOzvSbK5LcbAVJFTE0WiAYMjo3nuXpfCccB2Jbom+Mj+Lko1h5cHZtCFoDnq49x0mR8cn6RquWTLFpGAj//4zXfwGxrb2mPkyxY9qRDDmTJ39qdIhf1kShYBU8dveoHs8ye9hUinJoukIj6sC5YvP7TJy8HOViy2d8R55vgEFcvhoc1pAqZOXyrMlnkjSUIIPrDrfFe7czMl+o9GqNQd2uNB4kETU9fY1Hb5kez55cYGpko4rncCH5gqsjd8A5R2U5QlGM9Vr8vn+HVBX3OYTW1RfvJAD197Y4hnjk5Ssx0cx0sZ29ASoSns50Bvgorlsi4doT0eoCcVxpVw7/oUn20skB7LVfnyq+d4/tQ03ckQL5yaQtcEJ8aLWI7LxpboXBrZfJbjUq45F+Vzzy4Et9QIzQ1rPF9t/H8N15VztdY7E0F+7r5+AI6O5Hj62CRBn879G1O8enYGKaE3FWFTaxzblWxpj9IW9TNdtnhsRxtPHhql7khiAYPsvF4UiZDBTNkrg+nTzxdumB+Yq5HxG8v8340xb23W/O3zk+4u/Gvf2RHl0EiRiN9gvHBz5YCvmbrjMpytkinVyZYtWqIBTF3jyEiBiUKVmu1esuzMzUxrRNVzOZW6t0jywmtI0NTYkA5zZKyALgStMT8bWyI8d2IKR4Jp6iRDPsq5ChOFGnXLpTMZoC0eRGuMTP3Bx3fzrUMj/ODkFH/z4lkSQZN16QgRv4GhadyzPuVVBHElR8cKuC5kyvXG/kn8hsZ0uY7f0HBcl2LNpjniZ2CqRLXu8MqZGf7dB7YymKmQCvs4Nl5A1wSnJgp878gk6aifO/qT3NmXWvjdfDrv3d4297hct3n66ASDMxV+6s5uWmILF19eSAC6EOiaIOQz+Oi+rqv+PYT8OuW6gy6gaQ3yRRVluf7l7ZHr8jmakDSFTe5Zl2JXd5y/+tEAQoOgoRMMG/Snwnyukc+drzp0JAI8srmF0VyFX7h/HZbj0td8Phe6LR7g0/f24zd1SjWH/uYQ2bJFrmJhaIJTk8W5WuGzLMflSy+dY6ZU587+Ju6btzjzI3s7OTlRXFDeUFl7r5/LcGKiyP7eJO/a0sqhwSxb2qMLGh1NFWs8e2ySprCPb781wtsjOQSClogPXQhcJE1hH8+dnKZQsWiO+LCBiF9nYKpELKBTqbskQya5yvl1SvPTEBJhP6WsV+FqW1uYw2MlAPwalG/B2OJmNf9XMX/Adam/ouZYiFimRjxoqgB8qSbyNaYaTViOjRXmTqLbO2LEAiZSSvLVW+s+NebXcKQA6dXhFkJiu94iQ1g44u03NH7r0S387lcOMVOy6G4K8cc/tY+f+eJLnJ0u0xb3cp1DPp0ZvIB5LFdlqljH0AUHz2V4YGMzhqFxZqqErgkODeVojwe4Z13KS70wDGzHYjRXZSRXaeR6C5IRHxFHckdfkrv6mjgyVqAlFuCj+zoZy1Yo1hxcV3Jupoyha/Q3LrJ39HmjyEMzZYTwcj5TYX+jhNSlleoOhq7hSkm+ahELmjx9dAIhvKlK/wUtokOmwfHxAtmyteCCfDX2diU4NJQlHjSXlX+qKGvlpXP5a/4ZGqBpXrWSF05No2tefe6+VJiq5fKTd3Zz/4ZmupIh3hnNYzmSSt3hf7xybm7B9S8/vOGi940HTf6n+/rJlC2awj5GcxVKdQefri1oKz+rVLOZaeT5Ds4sbC/fGgvQeoWbdeX6qtsuzx6fREooVG1+/v7+BaVkZz19dIJnjk0QNHVOTRW97snA8XGvK7IEXj2bIVOqYTmSk+NFNCGwJIR8BkJouLgIsbCHQ62RHCyBbPF8MJat2F5VMKCqgu8byoLZictUtZn92YU1wY+PF8hXbMr1m28R5pppjwfoaw4xXayzq1GpomY79KRCfGxfJ9mKxbNHxxmYuT7TravhUl0mZ9VsF4nAcSU+3WuvbjnSq07SaEU5+xrXdQmYGuW6i8/QGM5WCfsNHtyY5nisQK7iXcA2tUU4Ey/x9nCBaEBnPF+jXHeo25Ji1QtQD57NIIQ3AtWfCtORCOI3NUDy7cOjVOo28UZTjXTUz6aWCJbj8tiOdt6zrZVCo+pCNGBSq7v4NKhLSIUXX/h0bLzATMmiULVJRa7cZe7OviYs2yXk9/JIXzubmWukkY4G2N+bXPD8V8/OkC3XkRKeOTaxrPzPT97dQ/B1g+3tsUXbWivKjerOniRvD69OEK4DNC5suvAWN0f83qLJWNBE1zQmijUODeboaw4RNHUe3dE+l8v73bfHmC7WSYZ9PL6rnT/61+OcmixxbqbM+3e0LxgBn2Xo2lyt7q5kiF96aP1cM68LJUI+7uxvYnCmvOybbeX6MXVBS9QrHdyZuPTN0WShxlSxjqlr7GiPcXysiK4JtrbHOTdTQ0pJPGDyZiGH5bg40qU7GWSiUGN7R4y64zJVrLOpLcrA1Pkbs0TYYKpkN8rMno/kTMNr/iSEIBnQGWmMlPq18104V9Ol0lxuxWaCy/lOfgG1xouSQcF0xXsQ0KDU+IeLmFCxvXNTS9ig5no35MmQj0TI4OREmXjQIBn2cS5T9WKoZbgtA3BD1/jI3vOpA5bj8tc/OsvBcxmCfp33bWnlRyen1nAPF3e5gy3k0zE1qFoOljz/XB2IBg0Cpka+4uWnBU2NYv18W3dHLjwQgj6DPd1J+tMRr5pHMsTh4Tz7+5JULId03WasUKMl5ufDe7r41YcCvD6Y5aWBGQ4NZmmO+uhqChPxG/zKwxvIVur0NIUQQvBLD69HE4Ijo3lGslUc16sNvrktSk9TiO8fncByXGb3bn6FgaaISSoaIF+x6E8vnnPdlQxhO94FvDV68Um4ZjscHs6RCvvpaw4T9OlzlVMAWqLnK5a0RC9OD9nUFiXsN6lYzpIacyzm5YEMB89mmC7W+Nl7+/BpaiGmcnN4aEuav3phdZbvSLygyZSSWNDH/r4krdEAuq5xoC9JRzzIs8cncFyvUdavPLIes9HaXUrJkdE8saCJ0Qi87lyXIlexaYn5sZa4Ak7XLn/lVIH3zUMIwScOdJGtWKTClx7Y2N+bJFuxGtdMQVcyiCYE/akIW9orSAl9zWFeOTODoQtsV2IaGq2xABXL5aEtLQxOl9nTneD7R8ap2tLr5iw0NAGaJkhFTEYavezvW5/m2eNTGLrgsR1t/L/PD+BK2NcT54UBbyFw1IDCEgquXSoGMDmfq6zNG8ldsHbLFNQsiQs0+QUztYtnvy9n/g2DT4PZQd/5rw8YML9w3FwcIsCnCyq213BvGT1rFrXUt5k/0t2fDnFi0rtxigT8TFe8gdb5TakdBF7/JYnPNBCuN8sWNHUs2+tfoglBKuInFjCI+E2my+ezxZcaj9+WAfh8b4/k+O7hMQ6ey1KoWkwV67xxLku2vPYtl2fpjRXVs6PVybCPqWJ9Xi63l5Os6wLNBZ/r4gqvJvXWthjhgE5zxE/UbzCULXNmskTAcsmU6jiOi6Z5ZQKteuMdhcBv6nzps3c1yjVaPHVkHCG8mqhDMxWqtsuR0QIf2dvJ5rYYu3uS+AyNaMAgHjTnukfFQ+bcAibbcTk0mCVg6nQnQ+zrTVCs2nxgVzsHepsYyVU4O+39YcQXKe3luN4Cmuawj5bI4iMcT+zp4PRkie5GwH+hHxyf4vBwDiHgU3f3krogB7snFeJn7+2b2/cL9TdH+OJn7mC8UGVfT/Kiny/FN94cIVuuk6tYDEwV2dx+fdpVK8pKZcurVwfcZ3g3+y2xAPt7EvzCA+tojQXwGxpGI9BuCvt4fTDLxpbIXPANXrB1R18Th4dz7G38Hf7Yrg66k0F8uuoEebsydO2KddjvWpeiMxkkGjA5Pl7g7EwZXRM8uqONtkSQuu2wpyfB2yM5SjWH+9anGS9UOTKa5wO72jgy6o2Yh/wGG1ujnJ0q0Rzx0xTxUbVd/KZGZzLMTMnC1DVyVRspBI6EkWwVhIYQXnnaRLBE1XK4a32Kp455g35Bwxt9BS7bhbs5rDPVGLLtbvJzesZLq22J+RhuBP/xkM5M2XuOJgQIbxFYXQp8OtQdSdivU5xX0WX+YsT5Abw+73KajpgM571zQV/SZCDj/beuLQzn9cZsvE/X2N4R48hYnnQ0wOB0ee5957+iJawz0fhOnQk/w408er8OtUWG9effCFxOyNQoNv4hXSHwGRoCSEV9nM1UG/+GIQamyjgudMX9nJyqIoHpYo2Q3/TSkBwX25U4UlKzXXZ2JLAcr5Tpycni3Oct9cbgtg/A3xrKIYQgFfHhSsl4vspkwSt3dT0JvNSRxf7YNrZEODrm/XIdCVtaIvyoNIMjvYWUsYBBxfIWhaSjOlXLq5M7lKswlK3wX39mH7u7vYvUkdE8f/PiWU5OFGmLBajZLhqwvjXMM0cmqDmSbY0qICGfwYbWCG+cy2LqXvpK2G9gS4kQ0JUIMJKrsrnNGwnuTIYYz9e8znOLVBR49WyGFxrtkp/Y08HnHlxPqWbP1enuSob48N5OSjWLbYsEpbGgQdhvMFWo0dW0eGe6kM+4aDHV1VpKBzuzkTeuL/le97z+VIhco0Nbu+qEqdxEcpXail6vCYj6dVpiAXZ2xtnVleDcTJl0NEBz1E/4gjUR3U2hRfOzwRudnj9CrWuC/b2qopByZV1J75g60Jsk7DPwmxpV22VgyrvO9qfD/PRdveQqFgd6k3zl4DAbWqKM5GoMTJcoVG1OTxZpjwcYzlRojvr47Ue38OfPnmZvj3dMnxgvYBoaXYkgp6dK6JqGz9Dn0g4tV5KO+qnbkqaIn7aon2LdZl93nB+dmsGWYGhevrnjeqO4/nnBeX1ewnJfOsp02UHXBD9zdy9/8dwZXCl5aHOabx4aRUroTgQ4OVnGBUI+DWFLBC5BU5trahUwNVzXZbYPUWpeQOwzDcqO9+FBn0lXQsd2Jc2xIAOZLAD2BaXjdCFwkJiGIFu10YWgUrcxTYHTqO0XMqDU+E5bOuJEs1V0IdjREePJxr6vawlzZNRbyJoIaFRsl5oN3akggzNV6o4k4jMo1+25ke6IAcXG+7YnggxMlxAIgo1yw0IIXKeRkislqYgfITQKVZu717dwZmZwLuYJ+XRsxyUaMNjVneD1s1naEgE+vr+TjW0R2mJBvnJweO57LyH7FVABOKmIj6eOjLO+JcLvf2Qn/+m7R/nGoRHqmkRK95rXAdeBSMDAlRKJRHO8FJH55Y16U2GGMxUKNQdTF0gkQZ9Ope7gNzRSIR+Furei3wXaon7OzpSpWS6TdpX/8fI5dncnyTWm5u7sb2JHR5zHdrQ1pocEpiH4LesQ2bLFvr7zF7GHNqbpS4VJhkxqtsvJiSKPb2/jO2+PeaX/5neFKtV48fQ069MRFmuGbMyb7jU0bS73e1ax5lUkqVgO8aDvogvvdKlOsWoRYECy2wAAIABJREFUDRoMTJWW9e/90KY0TWEfzRHfRaPfSzFVrPHV14eQEjKlOo9sabnq9/ix3Z1IhNcaWy3CvCZUHfFr48O7u/m9rx+96tcJQAhvIeTOzhi/9eiWudrbubKF39SuuGBaUVabEIJtHd4A0qHBDAfPZnGlZF9vkoc3e+f2XMXiyGieXMXCb2jULIdC1aZuuxweyVGzXU5NlhnOVtjeGQch6G0K0xLzekx0NYUoVr3OrZ+6u4dowKBiObxvWwu/8Q9v4jgSy5W8a2sLQ9kKH9rVwbHxEplync5kkL50mBdPTbGxJcZkscpotoquCZIhP8VqBU2DgOGV5RQC2hNhfv/jOylXHR7d0UalLhnOlPjkXT18/ltH0BxJ0GfQnQowka+xsSWC7UqOjeVpiQeZzlcZLdQwNUEs7Ge67M0QdKcC5EeKIKAvHeLoWAlNwLp0hIPnsrgSNrRFG6U9JR1xP470cqdTET+psI9SzVvzFfbpnMtUMHWN5qif8kwFTcCmdBTHFRi64J4NaY6OFbBdycf2dPNtc4zRXJVP393Lf37mJLqQ2K7gU3f3cmQ0zx19TfzFc6epWq73bxIwKRUtNAH/87vW88yxKXpTYcbyFU41cvfT8QDhTBXXlWxsjREwdap1m23tUXpSISYLNe5d30zYb3BiIs/uriS/9u6NvDgww7b2GKlogHdtuXg2fqndcG/7q/90sc72Dm/EtGq7/O77txIPmbw5mOXoWIFi1cZyJImQieW61OoOdUeSDPko1ixKdbcxneRnulRbtHzhbN61EIJ40EBIyXjRAiSbWsI8sbebQtXitbMZBjMVIn6dslXCdiTxgMFvvmcTuoAXB2bw6xrJiJ+mkI8pt0bANHAFVC0vNaW/OUxbLMBEoeZVO8Eb0R3OVvjKa0O4UvLhPZ2LLlD6qbt6OD1ZmlvkBF4+W/+8586u/I+GTLLlOjs7zzew+MFxbwrt1GSRwUz5ojztfT1JQj4vH70ndfGo1nCmQq7iTWWdmChcFIBHfQY122W6WGdr+2Ih/pX5DO2ihZVXY37BfXeZ1fffGvZO2gNTJco1m7haiKncJGy5tNxqQ6MxZSvRgXTcj+N6KV5/8PHdC2Z+ljLjpCjXmkSwLu3VkDe189cX23FpiwWI+A0ifoO+5jDpaICORABfYybU0MRcNRXbkUQDBp2JIEHTK13Y1jjeT02W+Q8/th2AF05N0dsUwnZdEkEfQZ9OwNSxHJfWmB9N82aVv/AzB5gu1UmGTD7+Z88znqvhNzR+6cF1fPngEKmwnx+/s4e/ffEshqaRDJkcPOeNSH//6ASb26JsbouiaTrJkJ98tc7Wjjj/9rEtvHbGq1j2l8+foVC16G+O0BQyKVkOIZ/B+lSE6YKFYQge2NiCZXtpPnf0pTg7XcFxJN1NQT66r5NMyeJDezr53tExhmYqfGBnG2dnKhwfL3Cgr4m+5jDfPzLBjs4Y56ZLZN8eJxny8fjOVr72xihBUyMcNLFdie16A5IPbW6lbjv0pSMYukZbPECxbrM+HWE8X+XhTWl++7EtTORrtCcCvHo2w+HhHK1xP0HTIFfO4zc1trTHeWJvNwAnJwqMZKvouuDn71+HlAPUbZd3b21hqlBnulRjR1eCO/uayFYstnXEMHUNR0rWt0SJh3w8Oq+U8SxTE1iN5Pa22NJmtm/LALxuu3zl4BDTxRptjYCyOeIjEvA6q/3qIxvJlOr80VMneGs4x4G+BHf0pjgzXeKVMzOM5ao8sqWFZMjgSy8PUrMcogEf69Nhzs2UmCjUsV2v3J9Pg/19SRCCgC7Y3BbnzHSJysAMNdtlS0ecX3zIaxZRsx3qlsMXf3iGJw8Nkytb3LUuxeb2GP/mvZv582dPYTuSnV1xmiI+vnlorBFQNjFVrLGnO0FXU4h8xRsNtxyJrgt2dyWZLNRwGgfHeL66aAD+wMY0D2xMX7R9MesXWQT50OY0X351kL5UmO7kxQG2pp0fbVhMbypEezzgdZnsuDiNxHK9G5/lto9eDemonx/b3cFM6XwFnatlu5KRbIW2uB9dX96NhKKshYHJK1dA0YU3tX92pkK1buPTBX5DZ2t7jPs2NKu0K2VNnZos8p3DYyRDPj62v3Ou1Oz6dJiNrVFsx2XzvGZsiZCPXd1xhjMVDvQ10ZkIMpgps74x8ntqwiuV++j2Nt4Y8trd65qgYjk0hX3s7kowmjuJ39R4YNP5lKn9vUn6msNM5Ks8siXN5795hOlijUe3tbKtI8ZEocbGFq+G+WzlHr/PoCXmRyDY3ZNkR1eSRMikuynE3u4EuiaYKNTmAvCmsI/pYo3pUp29PXEe297KTKnOHf1NrGuOsK7Zu46Xaxa6plGzHf6PJ3bwz2+NsaUtimkIpsoWzWEfn9jfTbHmYOoaW9tj9DSFcVzZCDYFHQlJS8zP7/3YDiYKVdanI/zzW6NYjmRvd4J3bW3l8R3tBEyNf3xtiKDPIGjq7O9L8uJAhpaonwO9Sc41RsN7m8KM52vompdepgmBqXvpMn//i3czXazRmfTimNkBvb/49AFeOZthe0eUv3tpkLrtko76F4xIb2iJ8oVPH0AIODKS9yrduJJC1eZj+73iHK4r2d/XxFCmzB19TTx/apr2eJB89dJrYO7fkOTp4zNoAn72/r4lHYu3ZQA+nq8y1ujo5jd1fv6BfkKmPrfwx8vv9YKtd21poTcV4vFd7Tx3fJLpUp1t7TEe29nGlrYYG1ui/PcXztIWD+A4kmjQpL9u8fZwnmLNIRowcCRsa+RJS7z60mP5KpYj2T4v19lv6PgNnXdvbQUh0YTgsR1eF8ZNrVH+4GO7GMxUaI35+fQXX/IWODgue7rj3LchvWDE+PGd7XzppXMETY2HNqfRNcFYroorJbu7r67t8lI9saeTJ/Z0Lvv1AVPnJ+/sueTPhfCqvcSDJj5j7QLX9ekI65d2n7Ko8XwVQxcUaw6lmq1qgSs3jf/67OmLtgV12NwRx9AEe7sS9KfDRII+KjWbr70xTFPIx0/d1UPYb7BxkbrMinI9HRnNU7ddxvNVJvK1uetmNGDyM3f3XvR8XRMLqqYBxEPedfs/fHAbbwxm2NYRJx7y8dCm8xeGX33kfC36P/3kvovedyxXoy8Vpi8V5u2RPKWajU/XOD5R5N+8ZxPHxgoc6Fs4W/s7j27mvz03wO7uONsuGKRKNGZSowGTD+5qp+64RP0GqYiPZNhHsWrTmQwRCZhzOfCzQn4vHTTsN+hIBPmlh71Bwe8cHuOedSk0Aa8PZefy1yN+k4/t75obOZ4NYGf/LZvCPlxXcnqyRDrq58REkXdtbZ1bG/bgpjSmrtGRCPK9o+NoQjBVrONK+My9fY10VYntSHRNQ9cEn32gn+FshQ/t6SBgGnQmL75uhvzG3O8g5Dfobgo1eowsNJvuZuiC1lgA25EE56XAaZrg4/u7cBqdu0N+r6P3bNrcYrqbonTES5iaQF9iZbM1u/ILIX4T+KiU8n4hxB8CB4CDUspfb/x82duupC0eoCsZZLpUZ0dnnNgiFTeaIz56U96iwtmRzrZ4gGjA8LpDNrpnPryllZFcjfF8lf50mELF4vRUiUzZZqZYpyMR5BP7uxkv1OYO1u0dcSzHZbpo8eCmiyO5nV1xdi4yujq/8cyOzjhj+RqJoMn7d7RflMbQHPHza+/euGDbYzsunja5mcSDJo/vaufsdHnBie5ms6UtRqFqkwyZxBc5OShrT+WQL+4z9/Xz1NHpucfJoM7DW1rZ3hFnR2d8QfoawE9c5oZaUdbCjo44Q5kKTWHfipsphf0G921Y3rUoHjLxmxo1y2Vvd4LvHRlnolDn3vXNPLgpvWhssK+3if+yhIXGs1WA8lULv6lTs1w6kyHeu72NQtW+KEf5vdtaiQdNtrRF5wYiAVpjfo6MCvymxrrmCGeny2hC0J4IsO+CVM7EBTGIpgn2dCd4ZzTPngsG/VpjAT681xusy5brvD2cJ+z3Zslmu1FXLYdjY0Wv2khnnORlSksuZnNrlHzFIhowLlrcPau/OcLOrgQ1y1m0eMNsmdKt7TG2XqHs8N6eJMfGC/gMbckdcoVcZh7rSggh/MAXgPXArwG/JKX8nBDiz4C/xKt8s6xtUspXLvW5Bw4ckK+++uqK9j1TqqPrYkHQbjkuM6U6zRE/jivJlOukI96KZseRJBuLDyqWs+Tk/KV4ZyRHRyJ40YGv3Nhc1+Wd0QK9qdCCOucHDhxg/vG50iBQuXmtNIBf7RuI+cfmaLbC94+MEwsa9KYibGqLqgWUypq58Lx5M5kfF1Qth3zFIh31L1pCdyWfUa47c2ksV2uqWCNo6oT9BplSHU0TC4onrIb5n7GaJvJVYkHzup2fjo8XiAfNBTd2QojXpJQHFnv+WgXgvwocAT4P/B0wKaX8ByHEx4AOvJrpy9ompfyTS31uc3Oz7Ovru6bfTVGW68yZM6jjU7kRqWNTuVGpY1O5kb322mtSSrlozux1n/8WQpjAQ1LKPxVCfB5IAKcaP84B2/FGtpe77cLP+xzwOYCenp6b9k4ZvCmZ185mSITMRRcpKje3m3kk50JTxRrvjORZlw5flG+o3HxupWNTubUs5djMVSwODWbpSgZZd4kuyopyLQghDl7qZ2uxku1TwJfmPc4Cs8k1scbjlWxbQEr5BSnlASnlgXT65s0bBvjhiSleHpjhX94eZzRXWevdUZRL+tabo7x2NsOTb4zgrlbfYUVRlGX413fGee1shm8cGqVUW0LPd0W5DtYiAN8M/LIQ4jt4I9bNwLsbP3sP8CLwwgq23bL8pvfrmi3Hoyg3Kn+jSo1P99ouK4qirJXZ85Ghi7mFdYqy1q57CoqU8ndm/1sI8UMp5f8uhPhjIcRzwCEp5cuNn1WXu+1Wdd/6ZpojfhIhc1UXcyrKavvQng5OT5bobgqt6oIiRVGUq/Xo9jbWpQu0xQJqwbByw1jTGmhSyvsb/39R+cCVbLtVaZq4Yimc29GJ8QLPHJukMxnkse1taGqEY82FfMaiZZ2ulVfOzPD6uQw7OuLcu6H5yi9QFOW24TO0uXVTgzNlvvv2GKmIjw/u6lCzycqaUUWIlZvewXMZijWbY2MF7upvIqVmB247Lw/MULddXjmT4Z71KTXqfo2pOunKzerQUJZC1aZQtRnLVRc0sFOU60nd+ik3vS1tMYSAjkRg1euTKjeHbY2Zoc1tURV8K4pySZtbo+iaIBXxLbs2tqKsBjUCrtz0dncn2NEZV4trbmOPbGnhwU1pdQwoinJZG1ujrEtH1LlCWXNqBFy5JaiTqaKOAUVRlkKdK5QbgQrAFUVRFEVRFOU6UgG4oiiKoiiKolxHKgBXFEVRFEVRlOtIBeCKoiiKoiiKch2pAFxRFEVRFEVRriMVgCuKoiiKoijKdaQCcEVRFEVRFEW5jlQAriiKoiiKoijXkQrAFUVRFEVRFOU6UgG4oiiKoiiKolxHKgBXrorluLw1lGM0V1nrXVFuIeP5Km8OZanZzlrviqIoygLq/KRcC8Za74Byc3nm2CSHh3PomuAz9/YRD5prvUvKTa5Us/mHVwaxXclQpsLjO9vXepcURVEAdX5Srh01Aq5cFctxAXClxHHlGu+NcitwpGT2UJo9vhRFUW4E6vykXCtqBFy5Ko9sbiERMmmJBmgK+9Z6d5RbQCxg8qE9HYxmK+zuTqz17iiKosyZOz/lKuzuUucnZfVc9xFwIcQOIcSPhBDPCSH+Snj+sPH4j+c9b9nbbkeuK5koVK/qDt1x5VXf0Qd9Oveub2ZDS+Rqd1G5hUkpGcyUqNvLGyHqbw5z74Zmwn41JqAoyso5jWuivcg1rm67uFcxg9vfHObe9er8pKyutUhBOSalvFdK+UDj8Z1AuPHYJ4S4Qwixb7nb1uD73BC++/YYf/viOf7h1UGkvPKJJVOq89+eO82fP3uKwZnyddhD5Vb2f//LcX7rH97k3z/5lkpNUhRlzX3rrVH+9sVzfOXg0ILtR8fy/Nkzp/jvL5yhaqlFlcraue4BuJTSmvewBrwHeKrx+CngbuCeFWy7LY3kqgBMFmrYSwiAhrMVynUHy5GcmS5d691TbnHHxvIAnJ0qU1MXNUVR1tho1qvUNZarLRjtPjlRxJWSbNlislBbq91TlNUJwIUQSSHErqt4/oeEEIeBFrw89HzjRzkgCSRWsO3Cz/qcEOJVIcSrk5OTV/W9biaPbE7T3RTikc0tmPqVf63r0xE6k0HSUT/bO+LXYQ+VW9lH93bRGgvw+K52QmqaVlGUNfburS10N4V499YWNE3Mbd/bkyQZMlnfEqE9HljDPVRud8u+UgohngE+1HiPN4BJIcSzUsrfvNJrpZRfB74uhPgTwAZijR/FgCzgrGDbhZ/1BeALAAcOHLhl58bXpSOsSy89Lzvo0/nEge5ruEfK7eTxXe08vkuV51IU5cawoSXKhpboRds7E0F+9r7+NdgjRVloJSPgcSllHvgo8FdSyv146SSXJYTwz3uYByTw7sbj9wAvAi+sYJuiKIqiKIqi3LBWEoAbQoh24BPAN6/idY8JIZ4VQjwLtAK/D1SFEM8BrpTyZSnlweVuW8H3UZQlKdVsDg1myZTqa70rylUqVC0ODWbJla0rP1lRlNvW4EyZw8M5tahcuWZWkqz5eeC7wA+llK8IIdYBJ670Iinlk8CTF2z+9UWet+xtinItff3QCGO5KiGfzmcfWLcgv1C5sX3t9WGmi3WiAYNfeGDdWu+Ooig3oIl8la8cHEJKyJYt7t/YvNa7pNyClh2ASym/DHx53uPTwMdWY6cUZdZUscaTb4xg6oIP7+0kFjDXepfm6srarkSNjVxb2XKdr70+DMCH93SSXGHzJ8vxfmO2K5FSIoS6eVIUZaFy3eGt4Rw1y6UzGVzr3VFuUStZhNkP/C9A3/z3kVJ+aOW7pSie42MF8hUvXWBgsnRDdEr84K4Ojozm6U+H0dXo9zV1cqJItpEucnKyyB3hphW93xN7Ojg+VmB9S0QF34qiLMqVkrZYgJrlEDTXol2KcjtYSQrKPwFfBL4BLK/9naJcwYaWCG8N59A1QV8qvNa7A0Ay7OPeDWpK8nrobw7z+jmvuNG65pX//psjfpo3+K/8REVRblsdiSBb2mPkKxY7O9d+0Ee5Na0kAK9KKf+fVdsTRVlESyzALz60fq13Q1kjqYifzz6ocrUVRbl+AqbOp+7uXevdUG5xKwnA/1gI8XvAv+B1tASgUZlEURRFURRFUZRFrCQA3wl8CngX51NQZOOxoiiKoiiKoiiLWEkA/hFgnZRSFUNeZbbjMlOukwr71SI/ZVVZjkumXKc57FflExVFUa6zQtXCdSEeWvuKXsraWkkAfghIABOrtC9Kw1cPDjOcrbAuHeaJPZ1rvTvKLUJKyd+/MshkocbW9iiP7VCt4xVFUa6XsVyVL786iCMlH9rdwbp0ZK13SVlDKwnAW4GjQohXWJgDrsoQroCUkrF8FYDRXHWN90a5lViOZKro/amOZNWxpSiKcj1NFmrYjc6aY/mqCsBvcysJwH9v1fZCmSOE4N1bWzgyWmB3V3ytd0e5hfgMjUc2t3BiosiB3uRa746iKMptZXNblOFsBctx2XMD9LRQ1tZKOmE+K4RoBe5obHpZSqnSUVbB9o442ztunOD71GSR7xweoyns46P7OvEb+qq+f6Fq8e23xkDAB3a2E/av5L5QuZzd3Ylr0syoULX4ymtDVG2XJ/Z00B6/uHvcocEsr5/LsKMzzoG+lTXUURRFWY7Tk0W+fYXrme24fPvwGLmKxfu2tdISC6zKZ/sMjcd2tK3Keyk3v2W3eBJCfAJ4Gfhx4BPAS0KIj6/Wjik3jndG8tRtl7FclYl87covWMb7D2crDGcqHB0rrPr7K9feuZkymbJFpe5wfLy46HOePzVFpmzx/MlppJTXeQ8VRVHgndErX88GMxVOThSZLNQ42GgEpiirbSU9Vv8dcIeU8jNSyk8DdwL/fnV2S7mR7OiMEzB1OhNBWldpJGC+nlQIn6HhMzS6kxePnCo3vt5UmOaIj4jfYEtbdNHnrG/kO/anw6oNvKIoa2J7x5WvZy1RP9GAgSYE/avQgVdRFrOSuX7tgpSTaVYW0Cs3qP7mML/88LXrRtkeD/LZB7xuhz5DHUI3o4jf4FP39F32OY9ub+OBjc0EzdVNYVIURVmqpVzPwn6Dn723D9uVBNT5SrlGVhKAf0cI8V3g7xqPfwL455XvknIrqdkOY7kq7fHgZYNrFXjfHkK+S59yZo+Vtnhg1dcZKIqizBrKlIkGTOLBS9fiNnQNdRpSrqWVLML8bSHER4H7AQF8QUr5tVXbM2UBy3H54ckppJTcvyG9pIC1ajk8+cYwlbrD47vaaYmufvrIlfzja0NM5Gt0JoJ84o7u6/75yuop1Wy+fmgEy3H54K4OmsI+ABxX8vzJKeq2y/0bm5c9YvS1g8OMNgLwn7qzZzV3XVGU28zgTJk3h3JsbouyoeV8ub+vHxrmH18dIhIw+PwTO2iO+NdwL5Xb2UqHHZ8Hnga+1/hv5Rp5eyTPG+eyHBrM8ebQ0haFDEyVGMlWyZQt3h7JX+M9XFy2bAEwU762DVMPDWZ58o1hRnOVa/o5t7NTk0XGclWmi3WOjJ4/no6NFXjtbIa3hnMcPJdZ9vvPHiMzpcsfK0fH8jz5xjBnpkrL/ixFUW5t3zk8xvHxAt9+axTXPb/o+82hHDXbZbpYZ2AVzyHj+SpPvjG8onOgcntZjSooH0dVQVk1+arFSPbiIDIZMpldt5YI+Zb0Xp3JINGAwUypzmS+xnTx4hXfVcshX7WWvb8j2QrPn5y6ZND0/h1tbGqN8v5rWHqpVLP5/tEJTk+WePro5DX7nNtdT1OIsF/HZ2jYjstT74xxbKxAplRHaxycySUem7OmizWePznFaK7C4zva2dQa5fGdl+7Q6biS7x4e5/RkiaeOjK/o+yiKcutKNmboEiETTTu/6Pvxne1EAwYbWyJsaY3y9LFxXjx1+cpMA1MlfnRyimLNvuRznjk2wTsjeb5/ZIJcZfnXVOX2sZIc8NkqKBMAQog08BTwj6uxY7ejQtXir184S912uWd9irvXpeZ+1psK88m7epFSLrkmaSxg8sSeDv6/F87y3MlJnj42wQd2tfPBXR0AZMt1vvTyOeq2y/t3tLP5EtUrLsVxJV97fZi67XJ6srjoIrx16cg17/blNzRiQZN8xSIdVdOJ10oi5OOzD6zj6WMT/JdnTqELwVSxRjLs49HtrTy+s4O2+NWlOX3j0AiZssWhoSy/9OB6+q5QcUDXBKmIj8lCTf2uFUW5pA/t7mA0V7mo0ontSLa2x/AbGv98eJR/en0EXRPo+kbuWKQ/Qb5q8fU3RnClZLJY44k9nYt+3kShxhuDWRIhE0NTVZ6UK7vuVVCEEHcBfwg4wKtSyt8QQvw28ARwFvhZKaW1km0r+E5rqlC1qdsuAGemSmRKdbqSIXY2OmJeKuCYKtYwNLHoyHjA1PEbGjPFOs0RPycnikgpEUIwUahRs7zPG86WrzoAF4ChCeqAqa/dIkpD1/jkXT3MlOq0XYMyibeTgakSR0bzbGuPLRoMCyE4O11G17wpV9f1jp+JQu2qg28As7GWwdQ0llqZ8BMHupkq1mhRAbiiKJfgMzR6U945TErJaK5KU9jHdLGGJgSWIxnLVXGlxHUkufL50EFKyUsDM+QqFnu7E+gauM7lr3PNET87OmMEDB3bUX0OlCtbiyooZ4F3SSmrQoi/FUI8ADwipbxfCPE7wIeFEM8sdxvw5RV8pzXVkQhy7/oU06U6E4Uqo2NVjo4V6EmFLrla+8R4gW+9NYomBD9+oOuiDoTRgMlP39VLT1OIiUKN7R3xuRrM65rDbG2PUqo57O+5+s6Emib4xIFuBjPluRrPayVg6nQkVA3xlfr24VFqlsuZ6RK/8vCGRZ9z7/pmDp7N4NM1hCbojAf5yTuWt2jyiT2dnJ4s0tMUWnJtcJ+hqd+1oihL9v2jE7w5lCMaMPjwnk4k0BT2sanVG3QK+XXu29A89/zBmQovnJoGQBPedW48X7vsINX9G5rRBHQnQ8RDl66uoiizrnsVFCnl2LyHNrALeKbx+Cngp4HyCrbdtAE4wF2NtJN/fWecTClHyKcTMC991z1ZrCElOFIyXawv2gK8KezjQ4tMmxm6xmM7Lp1vuxTJsG8u1065+SVDPsZy1cvmcm9ui7KvN0m2bCEE/OojG5Y9AxLxG+zqSix3dxVFUa5osuCtfypUbXymtmCdya+/Z9NFz48GDAxNYLuSZMikJRa4Yupnd1OIn2hS1ZuUpVtWAC6E0IHvSinfA3x1me+xC2gGsnjpKAA5IAkkgPwyt134OZ8DPgfQ03Pz/HG8e0sLm1ujNEV8l62JvLc7Sb5iYeraJTsQXku24zJTrpMK+9FV3ttN76P7OudqcV/Ou7e08vpghg0tkasOvks1G8txl7yYWFEUZSUe3tzCSwPT9DSFiAWuPDqdDPv41D29FGs2XcnQddhDrw9CvmKrtS23kWUF4FJKRwhRFkLEpZS5q329EKIJ+M941VP2A7PDszG8gDy7gm0X7usXgC8AHDhw4IZPzBrLVfnWW6NE/DpP7Om8ZE3l05NFarbLlrboikexV+KrB4cZzlZYlw5fcnHKleTKFq6USx5Jr1oOfkNT7cyvAb+hz+VNnpos8v0jE7TE/HxwV8eCG6yeVIie1MILU6ZU5+xMmQ0tESL+xU8t08Uaf/fyOWxXXnHh70S+SthvEL7Eey2mZjsYmqZuBhVFmdMWD8xdn2zH5ehYgUTIpCsZYixXwTQ0UuGFgW8i5Ft0kODpYxMcHytwR38T+3ouGvNblrrt8jcvniNfsTjQl+SBjelVeV/lxraSHPAq8JYQ4l9SLFDRAAAgAElEQVSBuWKaUspfu9yLhBAG8DfAb0spx4QQrwC/Avwn4D3Ai8BKtt3U3h7Jka9Y5CsWZ6cXXxh5ZqrEk2+MAFCxnCWdBGZK/z977x0kyX1feX4ysyrLu+7q7mo/bcZbjMHAO4IESdBCohUpkgoZSrq9XcXFnmLjtLFxe7erk1balVkFd+WXokhKJEiQImEIgAAIO8D46fHtu6u7y/uq9Hl/ZE3N9EwPMA4gB+wXMTEz2VVZ2VVZv9/7fX/v+55GvqYyFA/eMHJyrrEFnIXDtWC+UOfRA0lsbD66o5eht3DBeO50msOzRYbiAT52y7UR/lVcGY7MFamqBtWMQbaqXuImcCEsy+af989R10xOLpYvG6STrWropk1NNTg0W2BdV3DFhdQb03leOpvF65b4/O2DlyX0F+LUUpmnxlKEvC4+c+sAPnk1xm4Vq1iFQ7qnsjU6Q14OzRU4NFtEFARGOvz847453JLA7z28iXVvsYusmxaHZ50638GZwg0j4DXVoNy0LlwsXttcuoqbD9dDwH/Y/HO1+ASwB/iD5sT774CfCILwEjAL/Ilt25ogCNd07Dp+n58JrO0McXKxjE920RtbudHMaDpPAFfUbV1TDb7RtBvc1hfhPRu7lv3ctm2msjUCHtebkqyLIQgCD27q5ORihe1Np5arRbaqYTX9V39yJsORuSK3j7Rf9jrGU1XAceswTAvXT9F95d2O9YkQc/kGHSFPK/XycrABoxl2YZjWsp/N5upIkkBv1MdIR4C+mI8fn0rjlgQOzBTYfZH1l2qY/Oj4EgtFhTXtfkoN/YoI+ES6hmXblBo6mYp6SYV+FatYxc8PFN1kJlenL+bjJ2cynFqq4HVLxIMyp5cqeN0iqm5g2TaqYXNyqfyWBNwtiaztCnI2VWVjd/iGXWssIHP7SDvJQoM7Rtvf+gmreFfgepow/9eb/VwQhEdt2/6FFZ73Dc47p5zDq8AfXPS4P7jWY9eC2Vydg7OOpnVL77WRyRuBgXY/v3XfKILAZSUWo50h3rvJQjVMdvRfugJPVxRMy241ZKqG1bI3XClI4OBsgZ+cySII8JlbBy5Lfs/ZF174/809EYbiAaxrEPf8+FSKk4sVfG6ReNDDVK5Gvqahmxaf2H1pbL1hWsgukXRW4aHNiZuWfBum4zLSGfZekR7x7cAr41myNY27R+OXlf5s7omwMRFeFmJxOUiiwCM7e5nK1JZNTEfminzn4DwBj4tP7u5nTTzA7sEY8wUnbGql+/HAVJ7xdJVksUFP1EvPFdob3jIQJVdTifplusIeXjybwTBt7hhtx+OSyFZVyg2doXhgVb60ilW8y/HYoSQLxQZRv0zQ6wJsVMPEtCzARnaJvGdjF4WGjs8t8Z4NnVd03g9t68G0bCRR4I3pPAdmCmzsDnPvuiuTjdQ1A8vmkqLChbkf4+kqZ1MVtvVH6b0Gx6elkoJuWvS33bgihKKbPH86jUsUuXd9x0/VevjdguupgL8Vht/Gc99wPHsqRbGuM52rsa4rhOz66d1cV0J4LrdImMvXefTgPLbtJH6tT4RoC8g8tDnBUrlxSbURoKo6PbCpksKTxxZ5eHsP8eB5PZxp2Tx2yNF637++k4E2P//zJxNMZWs8sKGDVFnFtOCjO3reMkjlHDTD4sic0z4Q8rr40PYevvbaDBXFuOwC4NRShXxNozPkxS+/nbfu24snjy9xNlXFL0t86c6hd/xeWyg22DeVB0AUaAUzjSVLnFgss70v2pI+Xcm9eA7dEd8lLjxPn0i1Kk+ZisILZzJUVYMNiRB+j4u9Q23M5escS5ZY1xViJlfjz549y2y+Tn+bn6DHdcVkuSfq45ebYVBjyRL7p51IaJ8ssb4rxDf2OdrzvcNt3DESf5MzrWIVq7jZcWS+yGSmRnfEyyM7e3llPMu6RIiGblGo69Q0k0TEx+8/su2qz31OxnlwpkBDMzk4U2BDIsTBmcKy7I6LkS4r/PP+uTedL03L5vFji5iWI/H8lbuGVjxXvqbx/Ok0Mb/Mves6WmP1hRzgfZu72NxzYwqKh2aLnFysAI6m/qdZqHy34O2c+X/mGx4vxLnO47aAjFv66VXHksUGB2byNDTzrR+8AkoNnXOJusX6+Xj4TT1hHtjQtaziemSuyN+/PIVLFNjQHcK0bfJ1nedOpZeds9zQmc3XMS2b4wslZvI1TiyWydc0fnh0iYZmYtk2C8XGFV+n7BJZ1xVCEJxr87olPnfbIJ/dO8A9l6kkRHzuVlhL9Cb2Wa0qTtVX0a1lcqJ3CmGfu9Xc2xlyFju2bfPjU2mShQY/vujzvxjj6SpH5oqYV7DtEfa56I366Ap7CHhc5KoqJxbKvHg2y3A8gNct8dRxJ9L+iWOL7J/O43GJRHxuuiNe7rrGZqSL75W6brYkMufe/1WsYhXvXrQFPHSFHfncbL7BYHsAVbewLAtRFPC5RHTL4th8iTOpyjW9xqaeMIIAG7tDvNCUuTxzMkVZWTkPcKmsoJv2m86XogBhr1NgerN57vWpHDO5Oofniq0dRXCsFs9xgMoNHOs6QjKC4Cw+3kqSuIorw81bRrzB+MCWbnYOKLQH5Xd8ezpZbDCVqbEm7ue7B5NM5WoYps1nbh3g9pGr04Nt7A6Tr2mYls2OAcdfWdFN0mWVnqh3mWzjlYkcim7yxnSe37hnmNlcnbpmXuIBHfG5Ge0MMl9osL0/Sn+bn96oj7l8nd2DUQbag1i2zbb+q/NzfnhbN5aVaK3cvW7pEtcX27Z5eTxHRdG5e10Hn907gGnZaIbFXzw3jt8jcf+6Tvrb/DeN88V7N3VxcLbIYLv/p1LJD3pc/HLTYuvcboMgCHRHvMwXHNnHOZQaOt8/nMQGPrAlwauTOV6dyNEZ8lLTjLesJH9wazf9MT89UR+yJOKVJcoNna6wh/0zefrb/ET9MhXFIOJ3s6nb6YEYbA/w5XuHifll/uK5cSI+N7+4q++yrkAXo7/N37pXzlXl71vfQaGusXdoVWO5ilW823Hvug4OzLjY1BPG5xY5Ol9iYyJETTPIV1U0j4sjcwUmM3UAhG1wYqGMalh8ZEdPa6yxLJvHxxZJFhrcv6GzFd4DTijZcEeAjqCXF85kSBYaBDxO+vRKWJ8IMZuvY5iXny8FQeBTewZYKitvKj/pjvgcCacsEQucJ+obEiFKDR3dtG5Ykyg40tfP3ybjEsXVoKEbhLdz9r852FATkii8bel6i6UGk01t7IUrx1NL5ea2TpmYX+bUUgkbWCw2iPhk9k/nL0vAbdumUNcJe13LSLUkCssqyLZt883XZynU9UusAoc7ApxYKDPcEcQnu/il2wbJVzX6Lmr+FEWBD2/vWXbs//3YFnI1jYjPfYkWbP90nm/tn2uSqJEVZQzT2RqH5gqs7wqzqefSZpZ0WcHjksjVVN6YduQS5zR7AE+OLaEZFgemC0xlauwajF2zDeI7jfagh9tH2vFdIZm8Wti2zbFkCc2wuGUgtuLCZCV7v0d29lGoa7Q1F2AvnE7zty9PoRoWmxJhvrV/nkxFZTJTcyaYK9jj6os5BPurr06j6ha3DEQJyC6yVZWg7OL1qRwel8h7N3VSbhjsm8pj2rC5J8Tjx5bQTQvddBZciyXlLV1yzqGmGpd4099yAyejVaxiFT/buHWojVuHHMnlowfm6Y54KSsGZUVD0S0EwUA3zg9ir4xn+cfXZ7FtG920+IVdfaiGhWFanG02/x+eLS4j4D84usBkpkZn2MNn9gy0sjuqisEPTi/SFpC5e22cw3NFZJfI5p4IYa8bw7JwSwJlRaeumpfkLvhk6bJjXU018LoltvdHGWjz45OXF65EUbjqwt2Voj246lF+I/F2EvDffRvPfdPAsmy+czCJZlhMZKotjSrAsyfTqLrJdLZGbEAm5HXz/i1xZEmgrBgrEtNzeOZkmrFkqfXFb+gmC8UG/W3+ZV9Gw7IpNZxtqHxNW3aOhzYnuHttvEUEXaKA7BK5eAPAMC1em8xj2Ta3j7TjlhwP7vhlvoyPHV5gOldnOlfnzrVxtvdFMUyLmmq2Vs7PnExRUQxmcw3WJ0LLiNLxhRI/Op5CEgXevyWBWxLQTXvZl39zT5ipbBVRdDTk6bL6Jp/CzxZeHs+ybzJHIuLjU3v6b3jlfjxd5dmTjozEBvasoPtfCZJ4/jOtqQY/PLZIRXEsCGuqyXBHgHjAw9rOIHeOxluT25tBNUzSZQVVd6Q2mYpKd8TLXKHOvqk8E5kqazuD2Dj3n246DcM1xSBf1+gIeZnN1djSG1lWmX8z7J/O8+LZLO1BmU/vGXhTjf3FjcWrWMUq3n2QRIF8TSPqd9MV9tIWcBP2udkxEGVtVwiPW+Tls1lqzabwiUyVv39lmoZm8sCGDnqjPhZLChu6l7uknJt3shVnbj3nvPSTMxlm83Vm83XqmsGZJoGfy9V59lQa07bRdYvxbA3NsLhnXQe7Bt+6QLBvMsdzp9Ikoj4+t3dgNYX6JsdVE3BBEI6xcu1LAGzbtrfh/ONH13lt7xq4RAENLqkU98V8TGZq3NIfoy0gc8/aDgzL5pGdfcSDnjdtgEsWnG2zdFlFMy3+ef8cxbpOb9THJ/ecdxBxSyLv35JgPF1le/+lTRN+2YVuWli21WqCvGUg6mzfTRfIVlWiAblVhQ54JHYNrky8CjUNr1tiYyLE2VSFtoBMZ8iDadl88405MhWVnYMx7hqN0xn2UlGqtAflSwhotuoMZqZlY9vw+dvX0NCWVwn62/z85n2jnFwsc3qpwvarlL/8NPH8mQxH54pE/W4e3pogcoMTIS/cEbnaTvXFUoMzqSrrOoOsiQdIFhoMtvmbTZkCnSGZ92/pZqD9rSvRZUXn6/tmUXST/jYfAdlFQzc5vlDmeLJEIuxlKlujrpkMdQTZ1hvhn/fPEfO7sbDZPdhGsaHzga3dl/UVXwlTWSeWIFfVKDX0FZPlVMPk2wfmyVc1HtqSWFbVWsUqVvHugig69r22DapukaloNHQLn1vCsnF24TY70kDdtLhjJM6xpGMSsFhSeWhzgkxFYagjuOy8D2zo5OWJLDsHosvm656oj5OLZfyytIwkl1WD8XQFy7aZSoRa7mTZ6vIC0nyhzkSmxuae8LJC17MnUxyZL+FbqvDBLQk6r8I2eBU/e7iWCviHbvhVvIshigKf3N3PbDMh8EJ8aFsPi8UG3z44T76m8Y3XZxEEAUGAT+zuf1P9193rOtg/nWe0M4gsia2Ve0U1MC2b082kr56oj/WJ0CWBPumKwrH5ErO5OtmqSnfUR7mhIwgC6bLKodkif/LsGXTTZktPmGCzefNytnnPn0rz0niW9qCHz+4dYO9wG5ph0RXyUtdNMhVngPnR8SUOzhTY3BPm07f2X5I+BrBnTYyG5myzre0MIooCEd/Kr7uxO3xD/VjfCfjcEn7Z+XM1LiNXiqF4gI/u6EEzLda/CbEsKzqluk5fzNeqAj92aAFFNzmbqvCrdw3zizv7UHSDb74xx+G5IkfnbVIVjf/z/evxuN5cQpOpOLZ/SyUF24Zfv2eYY8kSM7k6LklkKlulN+pjbVcIzTD5w6dOM52t4ZVFqqpJV9jHR3f0XHUU9G3D7Tx/JkNv1Es8uPLiJlNRW9Wrk4vlVQK+ilW8i6HoFp0hL4IAyWIdSRSwbJsXTmdINeemR3b28p8/vhXTton63Ng4PTDbeiP84+szqLrFlt4I7910PkfjxGKZXFVjLFlmS+/5IlBAljBMG7ckckt/lLaAjCyJnE6VSVec7AuPS2RbX5RCXee24XayVZW5XJ3NPWG+d3gBzbCYztb4wh1rWueN+GX8skR4BennKm4+XDUBt2175u24kHczYgF5xa0iSRToCHvwuCQU3XESkQQB24aKogPLCXiprjO2UGKw3c9IR5CRC1bjH97ew9lUlc29YV4ez3JgpoAoCHzutoEVdVs/OLLIG9N5Ti2V6Y85DYx7h9vIVjVuG25nKlNreXv7PC4+fWs/ts2KOvnxdIVvH3T0wRu7w2QrKi+P58jXNOYKDR7anGDvcBvT2XqrO/xMqsL7NidWfL/8sov3b+lu/X8sWWI2X2f3YKy14k+VFU4slBntDN5Qr9N3Ag9s6KChGWzoDl9RwMyVoFDTOLFYZigeoCfqY/iiSs3FqKkGX3vNmVT2rGnjrrVOM6XHJaLoJh63hOxy/Nn/+qUkdc2k1DAIelxkKgrZqvaW/rSdQQ8zOccxRzVMnj+d4YNbE+iGxSsTWWSXo13csyaGYdmYlt2sfkMi4mEyUwWBloTEMC1eGs9iWjZ3rY1fdgHQ3+bn87cNLjs2k6vhdUutptNE2Mtgu59sVWV7382ze7KKVazi6vHejV0cmCnQ3+bnr16cIF9X8bgkDNNiMlNFEgUamslg+/lCzzmiXarrLQld5SJ3k8WS4z6SKqtYlt0qqBxfKJOuKJQVnUxFY0PCKRL95EwasBGAVEXlS3c54265ofO73z5KVTW4bbiNoMeNZliXNHN+ZHsPbQGZvpjvZ1Z+UqrrZKoKa9oDN21WxzuFa579BUG4DfhzYCMgAxJQs2375ipH/pThcUl8ak8/i6UGsiTy1y9NEpBdBNwSz5xI0RPzcjxZJl1R0U1nC+3QbIFfu2d4GQEZbA8w2B5AuaDabNk2+mWSMn2yREMzifplQMDjlpjLN9i1JkZX2EvE62LPYIyKavDF2weJhy6/1XVO+uIY//tIhL0tvXm67MTq3jES544ReHUix7Fk8YpJT1U1eOZkCtt2qhHnpAj/cmSBimJwYrHMb903clPpeOcLDfyyi2xFRTdtZNf1X/sPji6QrWocnivy5XtH3lJXXtOM1qSSv8Cu8hO7+5jJ1RmKB5xk0vkic/k63REfm3rCuESBXYMxEmEvuaqzU9Ib83J8oUKqrPDeTV2tanLDMEmEvSyWGhimzcnFMgdm8nSFvQy2BVgqK9yzroOHt/VQqGkU6hoVReehjQn+6qUpIj4XL57JMhx3FhMnFyscasZABz0u9g5fWaPRkbkiPz6VRhDgk7v76Yn6cEkij+zsu+r3eRWrWMXNh1hA5sEmoV4sNtANG8symS3WGEuWcEsiFUVb8bkRv5v3be5ioaiwZ81ynfYDGzo5NFtkw0WBZYpukq6o+NwS4gUcdFN3hP42P6Zlc0t/lFcnsqTKChsTkVYo2WJJ4f96eJi5fH1ZkQ2c4sLVyPHeaSi6yddfd2SHG7tDywppq7gU11N+++/Ap4FvAbuBXwZGb8RFvRvhuDlYK9rOtQVkbNvmqeNLJMJeQODRQ0ls2+alcZ2GZhL2upkvNBho9+N1y4grEE7VMPnaazMUahoRv5u713Zc0l19Dh/d0UNPxEumorE2EeDHJzMcmCnw5NgSH9nR4zihCAIhr5ulsroiAdcMC0GguY2mYeM0nRYaGvdv6GQqW2X3RXrx20far6pDW5ZEArKLqmoss0cMeFxUFAOfW7opyPd4usKPTqToDHlRdcfj/ZwPuHwD7PjPbUe6ROGK7Ic6Q14294SYzTe464Lo46DH1QpY2D9dwLAson6ZnQNR7vbEGe4Itpx8nj6R4uXxLEslhd6Yj8H2AGPNQB1wNNivTOaoKQZ3jsSZzFbJVjXm8nUe2pIgHvRw56hTARJFAZcoEvN76Ax7WZ8IUahreN3n35uo3/H2tm2uyof23MRm27SkWqtYxSp+fnA8WeJ7hxfY0B3C65bwuEVcosBUpo5mWmimxelUlT1D521VDdPCsGy8bonNPZFlgTbnmrdHO0OMdoZajz+xWKIvGqA76mPnQAxREJaZIuxa08a/emAthmUR9rr4nX86gmZYfHRHDx/b0cupVJlP7xkg4nMTucagm4qi891DjvHDR3b0tLIe3gnoppPQ7VzH6lj7Vriu/W/btscFQZBs2zaBvxME4ZUbdF3vKtRUg2+8PktVNXjvpkuTqYp1ja/vmyVf1yjXddwukapqkKmo1DWDjpCHo8kSO/qiqIbJL+zsXVH/VVUMKk33iEJdZ8+gycnFMuPpKu0BmdtH2ltk1S+7uHe9E71rWTZjyTJH5ouEvC5mcnUGLtDdXmxXB06TyGOHki2N+7quEGPJMlXV5Oh8iQ9u7WbHWzRGpssKr03l6Y/5LmsRJ7tEPrt3gNxF9ogf29HLTL521frgnxbGkmVU3XLIZ7OaciN9wD+8vYeJTJX+mP+KdOXZqsrJxSqWbXNqscIdox7SZYXvHEpiWjYRn4vDcwU0w+JD23uwcQj3QnGaj9/SywMbu/C6JdIVFdklohsWUlOrX1UdqcpLZzOUGwa2bXEsWca0bQzLJhaQeWBD1zISfSZVae3cjGeq7F7Txtdem2Eu16Ar7OW+9Y7X++duG8Sy7KtqPtrdlLj43NIlfRirWMUq3v342r5ZJjNVji+UeHhbglxNoy0g87EdvXz1tRlcorCsWHRuzq6pJh/YurxJ+4lji5xOVbh1aHmi7l88N84rEzkiPjf/5Re30R6Qifrdy5ooLduxVDUsi1OLFYoNHWybo/Ml7l7XQU3TifllHj+2yGuTOT6wuZvbR6/OUnAqWyPXNDI4s1R9Rwl4yOvmA1u6mcvXr8jV5ecd1zP71wVBkIHDgiD8IbAIXJlJ788ZslW1lSb5nYNJBtr8hC5oZmw0U/rCXjcVRWc6W6NQ14j5ZUoNR97RE/HRGfbiEoXLar/agx52D8b42r4ZfG6J/+PbR5Alkc09Yfpifgba/SsSVlEU+NTufobbA/zwmBM4oBomj+zsRRKFFZ8zm6szmamRrap0Bj18aHsPMb+bsmKwtklyinWN00sVtvVF8K1ANJ8/nSFZbDCRrjLcEbxso+XFntWqYXJgpoBPlm6YhvrtxqaeMPOFOp0hL2u7Qmy6QfHA5xDwuNh2FVpm1bCwmnFpjWZFfjxdpdLQeXxsEUU3EQWBiF+mouhUVWcHJltVeXUyx7pEiPVdQTb3hpFFkXvXd1KoaxydLzGRqfKlO4fY1hclFlggX9WYztUY7QjS3+XDJQmMJYvcs66zdT2D7X72TzuVomJD56mxJaazVSRR5NmTKTb1hOkMeS9rfflm8Lgk7r1MuuoqVrGKdz/aAjKTGfB7XDy8rZeHt/bicYu4JZF8XSfic7Phgmb+pbJjUqAYJn0xX4uA66bF3708xVJZcQoXFxDwubzjTFZq6FQUg96oD5+8vE/l1GKl5a7SH/UR8bqp6wabuoP8x+8fp6FbHJgpkCwq1FSDyUz1qgn4YLufUkNHNUyG4u98gWol04dVrIzrYS+fx4my/9+A3wH6gUduxEXdrDAtJ2LWJQocXyjjkgQ2JBzyGwvIjC2UiQc9vD6VbwXKgJNo9cCGTnI1lYqiY9NANSxyNQ1RECjWdToiXizb4sPb+1koNqipJhsSIZLFhrPtnwgx0uH4M0/lajx/Kk1NNVAEgVRZYaQzSPgyBBcc67rbR+Mcmi+i6hYvnM3wng1dywalC7GhO0yp4VTrXziToWFYvH9Lgo6QF0kUsG2bf/+9MdJlldHOIP/p41svOUc8JJMsNgh5XXjdIrZt8/ixJSYzVe4YjbdW0IZp8fzpDJppcd/6Dg7MFNg/XQAcWcLFOrmfRazrCr0jThuqYeIWxbesgvdGfbx3Uxdz+To+t0SxrrEuEeKHxxZQdMeLu66ZWJbNa5M5HtzYwbMnBNr8MlG/zNl0ha88N0FVdXxybx1q45tvzLJUUnC7HD/vvcPt/O8PrOWPnjpFtqZxOlVhsdRAMSxOLVYxLafBdmtfhPvWd/Ib9wwD8JcvThL1uzEsaA+4SER8N81CaxWrWMXPBk4slDk8V2Rjd4itvSH2T+cYaPMT9rpaO8HffGOWx48tAhAPytiAYdp0hT2ozUwC0zrfR6WbFsWGjm1Dtqose73P376Gb74+y6aeMNmaxrcOzOOTJT5322Br/GoPyuRrGoZlsaM/wt3r4ii6RVfEh2pY2NgU6xo11aDccIL2Sg2ds6kKa+KByxYgxlMV/uszZ5AlkU/s6WsWs5yiSe9Nskv884jrmdU+Ztv2nwIK8H8DCILwr4E/vREXdrOhVNf55huzaIbF2i4nThscTe5AW4C+mI/eqJeAx7XMl/jZkymOL5TZORDjgQ1d1FSTs+kqW3sjtAVkTi9VcEsiHQEZy7J5dTLHqcVyM75b58en0hxLlgh73fzZp3cgu52mTr8ska2qWMCHtnXzoe29K5IYy7I5segE3zR0i3VdAebyDdJljVcmcpxJVy9xlFB0kyfHlgj53LgE0E2bbMVpyPvAVqfpwrRscs1wglRZueR1Ae5f38n6RJiY343HJTUDCyoAHJsvtgj4qaXzVYOIz01DM1ksNegIefDLb0+a5M2Io/NOs2F7QGZrXwTLhu190VZDpm5aHJwpEGjqvAfb/fzZs2epaQa3NrWJHknEMC18snOfVhSDhmbyrQMLDLQHqKo6n9jVx+PHFsnXNBq6yZlUlb9+cZLTqQr5qkZvzEu6rLIm7vh+twU81DSTwXY/s/k6hZqGZtgcns2D4ERE37e+E1EUUA3HDaiqGvza3UPcMhAj4nO/qVTHsmzGFkr43BJrV+0EV7GKVQBPjC0yl68zkamQr+qAwFiyRLLYoDfqWK8quolqOLt9Z1JVSg3H5WRzbwi3JKDo0B5w8+iBeRaKDR7Y2Ml96zoZWyjxng2dy16vqhiYzbEr2ayGNzSTQk1rzb2pssJkpoph2Si6xdrOEBXF4L71nTR0R5by+dsH+H9+eBLNsPDJEv9yZIFMRWX/TIH3beri5YkcfTEf968///rPnEy3ZCcnkuXW8ZXko2+FumZwdL5ET8TXChZaxduD6yHgX+BSsv3FFY79XGC+WKeuOVv5C8VG67hlwwtn0hydL+GXXewajLK12VxxLjLctuFosshda+NMZWuYlk2+pvHILdF4aAoAACAASURBVD18/JY+qqrOV1+dYbHYoK+tTr6msaM/2ky51DEtG0U3ydd1EhEJj0virtEOlkoKE5kqz5/JUNdMPrt3cFlDCMALZzN871CSg7MF2gIyx5Ml7l0XpyvkcRpNVvhdZ/N1UmWF/piPfE2lUFcp1DVCXjflhs7huSLxoIcv3rmGVyZyPLS5a4WzgCAIy6zsfG6JDYkQE5kq2y7Qj3eEPC3f1ojPzQ+OLLBQbNAd8dEdeXMrvJ8njKer2DacTVeZztUJelwYpt1KrHxtIsdL41k8LpGgx2lsbWgmhmlzaLbAf/rhCZ44voRbEumNevlXD6zle0cWSIS9rabfeND5LHb0R3nxbIZcTcO2bV48mwEcjePBmSK/s3iYL98zTH+bny29EfweiaH2AMliw1lQhmTWJcJMZWtsvUA68+pEDlW3CMgSp5cq5GsaH7ul901/7/0zBV4ezwLwyE6RwSsICVrFKlbx7sZMrs7ZVIWeqI9bh2IsNIs2pbrGdw8miQZkBtt8FOoaHpfIpkSQ16YL2Da4BImOoIeA7CJf15kvOHP68WSZnYNRZElga1/UMR2oa0T9Mt94fYbji2XGkiX+6BPbUU2LmF9e1rvkyDsd96l0RWX3YIxcTWOwPcC/eXAdAHXVoKYYeGWJfE1vpR7aNnzv8Dz7Z4qEvW629UZaFsO3jbTx6mQOtyTw3s2JZqqwfdk4+zfD0ydSTGZqSKLAgxs7eeFMlqjfzcdv6b2EP6zi+nAtSZifAT4LDAmC8P0LfhQGcjfqwm42jHQE6W+roOgmD23uYrGk4BJFKorOk2NLTTkJzahtmwc3dSEIDpE5vlBmW0+EF85kOJOqkCzUqagGf/PyNANtfgzTCQaoawZlRWe0I8htQ+3cOtRGxOfmuwfnGe4MLgsdSUS8fHJ3P1/+2gFSZYXxVJWBNj8PblruvV2sa0R9bgSchLCwV+DAbBGPyyEyskvglYnsMq1bb9RHzO9msawgSxKbusMslhu8MZ3n2ZOpltTll24buKzX90oQBKFVQb8QXWEvX7hjDaZlY5gW0znHo3yh1LjksWPJEodmC2zoDrOtL8JzpxxieN/6jisePEzLvuHx8Bej1NAJyNIN9Und1RzM5/I1Sg2d0c4gF55+31SOw3NF2gIybpfImlCAO0bbWSwpKJrJ0WSJcsMg6JHoDHm5a20HUb9MQzfZ2hNmIlPjscNJfvfRo2SqKl6XxKbuEKZl85OzZdoCbjpDXrLVKnXd5D/+4AR/9cu7+dxtg3x93yz7pnLohk1X2MstA1HetzlxSTPxOc/vQt2pWGWrGpOZWivptFDT+B8vTOCSBH77vlH8Hhe2fX6L2FrZdfNdD910JEOX66NYxSp+3mBaFn5ZwrJtfu3uYR7e2kN7UObJ40ucTVcIety8eFqhUHOq3m/MFvjs3gGnAFbV+P4RJwwn5HGxpS9CsthgY0+I//zDk6QrKlO5GicWSrw2mWNjTxjLBsty5o+o372izenGnjCC4DymMyTz7Kk0tu1IV8/JUr1uiS29YaayTiPjgxs7eeF0hj1r2vin/XPohuXYHJYVnjy+RMwv89DmBH/5y7tahZLnT2fQTYuusOeqG/3PzX0CcHrJ4TRLJZPFknJNhH4Vl8e1VMBfwWm4jAN/fMHxCnD0RlzUzQivW+IXd53/wnU0O4+/tX+O3qgPtyRi206X8GxzewrgvvWd9MV8/MNrs8zl6/Q2PYrDXjepsspSWaU94HZW5ZJIxOfmC3euaemJvW6R7QMxtvZGLiFzAY+LtoBMqqzil114ViCgaztDTKSr/JsH19EelHn86CIHZgoMxgOcXqogiQLJgsJge6BVrQ54XHzxziFU3eTRg0myVef8tm1TUXXCPjeSKOCRbtxq+Ryx0AwnOGahpPDA+s4W+ZrIVHl5PMeJxRKJsK9VET0nBYoHZXavaVv55BfgB0cXOJuqsntNjLvXvj2Ne3/wxEkeO5RkKB7kH3517w0j+4PtAfYOtVFVDPLzRcbTNe4YOWfBZ+MSRUY6goS8rtZn+Zv3Oc6hX311mtl8nQ2JEL0xH79+zwgAW3ojLcutoNfFeLpKpqKyUGywJh7g0FyJqmJgWTaabhHzubEsG8N0POi/8fosn9rdz1yhhqqbdMe87OyP8cCGDlJl5ZIG39uG2mkLyKi6xSsTTkVnoM3PqxM5FMPkzFKFw3OOF/gTxxf5+I4+dg3G8LglfG6JoXgA07J5cmyJfF3jvRu7SES8rfvkeiwrFd3k+4cXaOgmH9zavWLE/Y3AVLbGwZkCa7uCV9RYqxkWX983Q6Guc/tIO7ddoT/6KlbxbsYtgzEmUlV6mnKTvmZg20TK+X75ZBfb+yK4JAERga6Qt+UY8tLZDDT7uSayNeJhD1OZGjv6IiyWFCqKUxVX9CxV1eC1iTxfumOQgMdFX8zXmv8vRk01WsUs3bQ5Ol+koZvLwuREUeD3H9nGbK7OSGeQ7zd3fCun0gzH/bx0NkNXxMt4qkq67CT6xvyOTEZ2SXx4ezdjyRLguFnppk2xrnPvuo5LDBzOje0X4sGNXfRGfSQiXlTdYrGsEPXJdF/G0ngV145rTcKcAW4XBKEL2NP80Unbtn8ujR9LDZ0DM3m6I75WLPpMruY0pEkCJxbLrOsKcv/6TsYztUvM/A/PFUkW6iyWGgzEfPz2/SP86ESaXFVFFARcosC2vggI4BJEOkNOwmCmrPDieBYQWCg2+PK9I8vOG/C4+A8f3sxTx5fY1B1m73A7Pz6V4ti8s41250icF85kkESRQl3ngY1dvD5VwAIOzRZZ2xl0wnoCbsJeF69O5DixWOaWgSg7B2KkyipdYQ8PbOhgvtDgH16dIRaUGYr7ydd1jswXuXtt/JpJT1U1cInLfVRll8hv3T9Ksa7jdYn8zUtT6KaNbduohkVNdTR967pCy6QTb2XFtFRS+NGJJfZN5hjpCHJqsfK2EfB/emOWkmKQr2mMJYts779xdk09UR+yS0DVLdZ1BfnWgXlemcgR9rnQTRPdMnlwY88lz/vYjl5CHheKYfHQ5kTLInAqW+OHRxcwbZvRjiB+WWK+UAcEPC6RofYA88U6qm4hCgJtQZk7RuPM5Rs0NKdZ6XtHkggI+D0utvdF2ZAI8cpEHlEo8IU7BpthUM5koOgmjx6Y5/BskXVdQTrDPp4YW2Q2X8fjkpAlEQGHSIc8Lr7ywgQel8gndve3FmnJQqPVS3BwtsAdI+18a/88pm3zyC29V2VheCFOLJQ5vVQh6HUxtlBapsG8kXjuVJpSQ2euUGdDItzaFQA4m6qgGhabus8Hf1QUvblj4MjDVgn4KlYBA21+xlNV+mK+ZXNQtqZi26AZJu/b3NVy2Xp42/lxcUd/FL8s0dBNBmI+vvn6bEvPHfC4nGwOn5sNiRAvj+fY1BNmpDPIvuk8azoCy3b2FN3kpbNZZJfIrsEYM7k6umkx0hEk6HUomOuiIsyZVJXTS2UkSeDp40scS5boa/OzvitEyOuEuIX9bmqaQcznZv90gelcHQE4NOvj8FwBw4LheJDJbA1winUXBuM8cWyR4wtlbhtubyUhn4NlO5KXNfEAv3XfarzL24XrScL8BPBHwPM4uxV/LgjCv7Vt+9tv8bwe4AfAJiBo27YhCMJ/wwnzOWjb9r9uPu6aj71TyDfT+47MFZnJ1Tk6X6I74kUQBB47tIBl2ySL9Zbv95beKHuGnMlRNUzSTQJ7YqHMfKFBR1Dm/VsS3DIYY76gMJ+rMbZY5kPbuumL+fnTZ88S87sZ7Qryzdfn8LpFPC6JqqrTE/GhGdayyRrg+EKJimJwLFmiP+bj+VNpMlWNmmZw12gcr1tqNXv4ZQmPS0QSBOJBmYjfzUd29DDQ5ifocbFvKodh2jxxbJHXJnIcmiuwrjPETK7O3uE21sQDTGar/MVzE/hliY6QB09z0Lm4Ol9TDZ47nUaWRO7f0HmJFOFsqsIPjy06PuC3DrRIGji7DYmIxJG5ouN7XlNRdIuo382do3E+uLWbkMeFKAp84Q6ngfTC51+Iqmowm6tzcrFErqrhlkQMy2bXmrfPw1QzbUwLbGyC8o2N6o0HPfzq3cOs6wpxeK5ERdF59mSKsqKjmzYjHQHemC607sN0ReG7B5NMZaus7Qzx2b2D6JZFrqpSUQy+ezBJutLg1Yk8EZ+LckPHsmxsbNoCMtv7o/S3+wl73SyVFSzL5p51cYbjQf7lSJLnzmTRDJO2gEzQ4+bu0XgrDKes6Hz/yAK9UR/xoIdDcwUm0zWePZXCtm0ms1U+sr2HH59KUVEMNiRC/OZ9ozy0pQtZkpjJ19AMx6lgLl8n0htBMyxm8zVUw8TbrIhPZWutMJ4Xz2aI+GS29Ueuyh+3oZn85GyGl8YzuESxFVb0diAR8VJq6MSDHtzS+Yl5MlPlB0cdxwbVMNnV9C1uD3rYORhjodjg9lXyvYpVADCZqdEV9jKTr1NTDE6lKvRGfbQHZWwBZJdEZ8hD2Ocm5HFRUjT+/WPH0E2bR3b1IIkiLhEQbHJVDc2wyFQVArJEXZYIeiTcLpGusAefLPInz5zl+EKZ1ybzbEqEmchUifllzGavFzjj847+KJppIQpQqmtUFZOGZvDYoSTJYoO7R+O8PJ7FsmxeGc8xlatj2TaLxQb3retgLl935jPBCaqzcBzFUhUFSRCI+t1sTDiSGJ/shA6pukVX2OvsVJoWbknkOwfnyVY1lkqNZQT86RMpxtNVXKLAr9w1dFWNnJZl83cvT3E6VeEXd/Vx69DqePRmuJ4mzN8D9ti2nQYQBKEDeAZ4UwIO5IH3AN9tPm8nELBt+25BEL4iCMIewLzWY7Ztv3Edv9MVo6LofH3fzLKod7ckIrscAicITuStZTmNaRsSYbxukelsjf/16hTHF8qs6wyhGRb7JvOY2GxIhOgKe3lqbInTS2VebDZP/nGqir9JtmuqwfcOz5MsNPC6Rbb2RegIOcmY07kq67rO2wYmC3VmsjVyFZU3co5n99Mn06i6iWpYCILAJ3f3MV9o0Bf18b3DC9jAQ1sSlBo6siTw/z1xkk3dYX793hHCXjc/OrGEKAhs6g4xla2Rr+o8tLmLoXiAzrCHH51YwrZtpnM1bBu+8vwEd4y2c9donHjI0yI9h2aLnE1VAegMe+iJ+ugIehAEAcuyOb5QxjBtbNsiVVZXJNDDHQEOzLg5Mlck6nej6iYf3d6D3+MiV1V5/NgiAgJD8QBDHQF6mrILy7JJVRTaAjLf3j9Hoa6jGRYet0Osfmnv4CX+rRciU3HO7XNLfGRHz1U3ppwLdxQEaPbj3FB4XBIPbuyiP+bnz398htl8HZtzmr4yhZrGQJuPh7Z089TYEv9yZIH5QoNT7RXWd4U4OFtEMcyWR/t8oY5hWmSqgO3YQpo2HJwpUFEM/vLzuyjUdY4li/zRU6d59OA86xMhJAGqitMkvKgr9MVEvvL8BHXdYDDmZ6QrSKqk8OTYEsPxAIW604zkcTkThk92cWiuSLGh0eb3oFsWm3vCHJjJ0xv1syERZjxdxeOSWtrEHx1f4ukTKUJeF5/a00+2qrFvMkdV1emO+DjbfPxiqcHnb1/Tes9mcjUMy76speW5htWIz03UJ5OtquRrGqcWy5QaOmu7Qjcs5Of9mxPsHowR9cvLKncXytsv1rqv+pyvYhXLsTER4rnTaW4daueZU05joVsScAlgmo7BwN++NMWzpzKIAhyYyfP6VAEbm7Kikyw2sGybwzNFArKEIDi2t7oBwaaV4US6SrGho2VtDMOgqhoYpsXjR5d49lSKsFfii3cNoegmkihQbmj87ctTWJbNroEoB2aKGJZN/LRMwOMiU1VxSwKvTGSYSFe5fSTOpp4Qh2aKDMb9/OrdQ2ztizDcHuDF8Swvj2cJeV3cv76TgFtCEkW6I14s25Gm7RyMcdfaOHXNJOxx8e++c5S5QoOP7uglXXHGsIDHGQ+PzZcY7Qy28iFswLBsxpIlIj73MpnM5bBYavD0yRS2Dd8+MP8zT8Bt2+bQXBFFN9mzpm3FgMO3E9dDwMVz5LuJHLx1prZt2wqgXDCx3I5D3Gn+fRtgXcexd4SAK7rVIt8jHUHWdQXRTAsBgbDX1dz2rtMT9bKjP8p7NnZhWjbffGOWn5zOsFBSmMk6MbiGaWEDE+kKf/jUaTpDHs6kqjQMC8MGbJuaaqI3V66T6RqCAMWGs3qdzTeIB2X6z/gZ7QghigL/uG+G7x9eIFtV0QyTmmqCbaPqJj5ZIl9T2T+dZ+dAjA2JEAvFBnP5OrbtdE7fs7aDL/39G+RrKsW6TiLiYzxdIR6QqapORb0r7GWgzc9gPIBfdvHZWweYy9d56WyWsNdNR0jG45J4eTzH40cX8bglPry9h0/v6cfjEqhrBm5R4DsH5vG6JTZ0h/HLLvZP57GxKTVlMcMdDrkq1DS8bqlFjkNeN5+/fZBMRWmSRpl903nuX9/J8YUy2apDkMYWSsSDHn77/lF8ssSTx5c4vVShPShT10w0wyJXU/n4Lb3sWdP2lo2RYwsl8jXH8mkyU2NTz8pe6ZdDM/emSYqvT/+9WHJcRS70h7Vtm795aYqnT6TIVFS8zUWhWxLRTYvFksIfP32Gum4xk6u3ElcnszX+69OnqesmAVliJt+goZloholpg0sSmu4pFqYBqYpKTTP4vcfG2D9boKGZVBo6giAwma7idYtUFAPTsgl4JJLFOrppN6s5CoWGjt8tkauqxPwugl43bQGZ//G5XZxcKvO112aoqiYiDukfag/w1y9N8uzJNKII/+FDm/jSnUOAY52lGRaH5ooslRVSFZAEgX2TOXTTJuqT+dKdQ/ztS1NOUqf3/NA3nq7wlecnMC2bL9yxZsVU1o6Qh/vXd6IYjkPL2s4g33h9ljem8gQ8LtZ1Vbh/fSdBr4vh6/SlF0VhRZnMSEeQ929JoBpWy0lpFatYxcpYKClEfDJLJYWAR2KppBD1ufnxqRTpikqmomLbJmpzQJ7NO+YHNrDQHKtsnCC9jrCXumrQG/VTrOsUG3qzmGRzOlWhO+Il7PMynqkR88u8NJFxJCSiwPtKDfI1p2n95EKZ/dN5LNumUHOSfyVRYCbnvHZNNZAEOLngSM32Teb57QdGWMg32NEf5buHFvj+4SS9UT+WbTGbr+N2iUiCzUSmigCcWSrzG/etbb0Pc/k6xbpOwS0ynasDNq9NZBnpCOJ11xntDPHk2BLFus7ppQpfvHMN3RHndzoyV+TATAFBgM/uHXjLXcOoT6bd7yZb028KN6qJTJUXTjtGDQICt4+8swuG6yHgTwiC8BTwjeb/PwU8fg3niQITzX+XgM04le1rPbYMgiD8OvDrAAMDA9dweSvD0RybLJUU1rT5OTRXYKmksn+6wBfvWMOaeIDemB/Tsgl6XGiGxT+8Ns1L49mWI4pqmCi6AYi4JUfrXKxrZCoqxbpGxOtYIFk2uCWBsFfG55FwiQJak/yfWKjg9zhuGlXVQLcsPKLE8YUyqm4yV6hhWWBbjp92V9iLJDpb6v/lqdPcOtSG7BKZaerEzqSqxOaKxPxu9GYIgdclslRutNxUPG6Je9bF0U2bRMTH+kSo1czxiV19lBo6Ya+biM/FfKHBkbkiVdUg4ndzYqHEf39O5fWpPJ0hmapqsn8mj2XBcDzAlr4IM7k6ibCXvjY/79vUhUsSOTpf5NmTaTxukV/aO0DEJ1NTDR47nEQQBPrbHEtCX7MaPdwR4Oh8EdklslhqMJWp8ePuFA9v6yHd9CXP1zQ+tqOXRw/OE/K6eW0yz2D7+Ur55TDaEeR4soTHJdEbu/SxZUXHLYqXraKbF7h26Na1l8CPL5R48tgSZ1JVhjr8fPyWPtYnQjR0k2TTNivocRGQA3hkif6oj6dPpGjoJqW6zr8cSRIPeuiKeMnXNRqawWy+jiAIzGomXpdzjxqWs7L2uSQkQcBqLiAsG1Td5EcnlqirJghwrn9+TbufpVIDEedLqugWftmFXxbJ1VQk0elb8LicRcHxhQo7+qPILpFjyTJhnwtREMnXGiiaQanheNN2hWU0wyJdUfjqKzP8yt1DNDSLZ0+m8MoSox1BKopOqqzwxNgS8aCHxVKDtV1hJFHgF3b2cmqpTKHuSKDuHo1zeqnashk7Ol9ckYAD3DbSzm3NAbrU0B1LR7dz/aeXKszl63RHfXxiV//b5p+78TLBWKtYxSqW45wtcEM3ydcUxhZKdIY8pCoqNk4BxCU6g5YoCHRHvExmnZ1byT6/41Q3TH5hfSelhs6ta9o4lizRFpBJhD08dijJQtEpVNy6Jkoi7MMtCVRV3RnnLdg3nuf4YhlREPDKEo7cW+DWwTYWyk5K9ge2dvF3r8w4xYmSimKYmBbUdYP/9vQZKorJyaUK2/sjnFqqMJOrs70/gig4Y/NsQUG3nPH36Hyl9R5kKiqPHpzHtmFrbxiPS2QuX+eB9V2kqwqLJZGOoIeQ102xrhPwuHA1LX/FplsLOHpw8+JttwtwcrFMoa6xrTfClr4o07n6JUWCqmpwNlVhoM3fsk/8acPjOj9He93vbPUbro+A28D/BO7C+dz/EqcCfbUo4lgY0vy7iDNnX+ux5Rdp23/ZvDZ279591SZlddXgyeOLWLbAQ5u7WhHyh+eKWJbNYknh4FyRhmbSG/NRUQxnSynoYVtfhKNzRc6kKowtlHhqbAkRGIoH8LpFqorJXKGOZUPEK5GqqLT5ZUTBaRARRIH1nUEqTf1qLOCmUHOaLtoCMq9OOprssM+NzyXxwpkMmmHxbx9azyM7ezkyV8QjidRN55upGhYDbX4+sr2br742Czje0VXFIFlsoJsmhbqOX5b4k2fOsnMwxtl0hXVdIZbKKpphMdoVYjbveJF/4Y4hXh7P8vuPn2R9IsQX7ljDobkihul0j+9e086auMpEpsZiqUHUL7O+K8RktoppOVX9qYyzQNAMC59boqoaDMeDJCIe7hyNt6rRC0WHNC8UGvzJM2dZ2xliY3eIdFl17Amb51A0g28fmKei6Ix0BPnMrf381j8eRDUtXjiT4eFtPTywoYuDswVGO4OsiQfYMxTjhdNZRAH8soSim9Q1s9WIeDH62/x8+d4RREG4JHFyPF3hB0cXcUsin97Tv+JA43GJNHSHeF+Pr2qxrlPXTYoNjYoicyxZZH0ihF92sXe4jelcjaG4n8/cOkB3xMdUtsZrkzlsnGbHhaJCV8jL1t4wS6UGlYZBTTObjas2dc3RqoPzZQ/7XHSEvBQbOjS3cCVRwLBsDBsEG2QR/B6JdEWhYVjols259ca6zhB7R2KMzZcZz9RwiY5eMVlUqCo6T59IcXCmwD3rOrhnXZxNPWHyNScVzjCcbvwv3bmGJ4+niPjdxEMefnImy6G5AjXVZHNPmLvXxmkPyrw8niVdVpnIVPn/2XvvKMmu+77z81Ll0Dl3T+gJwGAADBIBgoCYRVEUKZMUKUqm115JlizLks/xem3v6shrH68t7x6dlWRLNKU9S1GBIikGiSIJJoDIcTABM4NJPd3Tuaq6cnr18t0/bnV19QRgEEik+v4zPRVfvXp17/f+7vf3/U71x9g/luDBszkeaCfEKihMD8RIhnV2DcXYOSgXy4emX9x1BKQrzwcPjnNgPEXdcnnkfJ6VcgtNVXGDgJOrVSothzt2Drzm3rlnMjXOZGrcNNX3qslkeujh9Y4P3TjO6UyVvSNJ/tsP50iEZTDYrsEoz63VUYFUxOjIvLz2DrZQ5G7fJhQUPn3nDpbKTW6Z7icW1jifq3PHzgH++MELtBwf17cZSUV5+mKZib4oMwNxlootDE3B0KQLiaYqRHSVwUQYPxDsGk3wPneEjbrN22eH+NaJLJWWy4H2jrTl+iRCOoWGgwAsL2jvaAcIAXuH4+RqNomwhq6qLLWbMMf7wvzlU4u0HJ93XzfSGX+btkeqLSUxXY8jS2UqpstTC0X+4FOHePxCkVt39PGnjyzwxHyRRETndz96kFhIoy8WIh01+PaJDLqm8J6uvq1sW0YIsFGT/VhjqQhrle02wd98bp1s1SIa0njv/hG+fSrDrqE4H75p4kWTm39UmB6I8XO3TWF7/muSqP1KCPj7hRD/Fvj65g2KovxH4N++xNd5Evg14G+A9wGfB7xXcNurhlzN4o9+OMdi0WS6P4YQgo/fOoWqKuweTnBitUo0pJEI69wy00exYXNho8F3TmW4aTLNseUKT84XGIiHyFQtIoZGsenwrn3D/KO37+Rzj10kU20RDen4QpAO6RSbDomwBkgnC1VTODTTR7ZqsVRqkQrrTPdHqVkumkq7UUSh3HKIGhpHlspUWy4RXWPnYAzPD+QAEQREDI2Vssnx1Sp3zw5Sabl89JZJfu975wiCAE1R8HyB7QZEDJVERKdqupxcrZCOGkwNRDm8WKbctDmy5JOK6Dw2X6RhedKSyZGNb2XTQQipcX7PdSMoyGrE+w6MEtFV7juZQaAwmorgBoJi0yZiyI7zzXN5afjKnbsGaLketZZDptIiX7e5eUqu6DdqFgMxAy8QfPNEhkzVotiw2Tea5PrxFDMDcQoNm2RbdjAzGNtWocxULDw/YCAuJTN/+eQSDdvjHXuGOiE2l+JqMpWz2TrzGw1SUYONun1FAq5rKpoiyavrv/wK+M7BGBPpCAqgqvD8eo2KucAnbpfE/+BEioV8k888dIGp/jipiM5gIowXCFqulDTN5Rr85A3ye6kiSIakE4qmqHgiYLMOJIC67WFoLruH4uTrFgIFVZHRzSoBmgpuAOWWh8JWRSFAPqbYtHl8rkSm1kJXVSb6oty7dwjHC/j8E4tYnk+2ZvHoXJ73HRjl//jwDTw1X+D3f3CemuUxGDc4l63z8dsmaVoevpAVnvFUlPmCdDvYN5rE8wVLRZNqy2HfaJKK6fCZB+dReV/ByAAAIABJREFUFVgsmvS3rxWrXb2PhXT+wS2TTPXHrknnuIn9Y0n2jyWZy9VZLJqEDY137BkkrKv8/fF1QC4KN/19XwsIIfjB6Rx+IMjX7R4B7+Etg7F0hLG2dd7P3z7NV4+sct1Yks8+LDfNBZBrOPiBLCYUGjIiXghQNQ1NAV/AnpE4n3/yIpmqRdV0ee/1o7yr7YDUHw/hBTaJsM7z61UcX7BRt9k3kqAvZhA1NFxfSuRURcHzA+6eHUII+Xt8fL6A5wu++ZzKRt2maXtk65K4t1w5hhq6gu3JivQN42katpx7b5zqR9dlxbrcdHjqYhFNkXPKt56TzdrJsMGHbhqn3HSYHojypcMr5Os2e0elPESWWgTfez5HrmaxXm2RaedrNG0P2xPcOJXG0FQOL5Y67lIT6Sg3TskKt6EpqIqsmg/EQ/TFDFau4Mbk+QG252NoCl86vMyp9RpHlsrcsbOfib7XLnHzpYz5rzZeThDPrwP/HNitKEq373cSePwanm8A3wFuBr4H/O9ITfijwHNCiGfaj3vZt70SmI7Hs4syFbLWcgnrGi3H5+RalbChMr1Y4q7dg+wZSfBb791L3XKptlx2Dsb5N187wUrJZLWyQjykcTpTo9hwWC23iId1qi2XvliIjbrN/WdyPL1YAqBpu4R0raPh0tIRNiXyuarFjoE4mqoylgoTD2mcyTWomjauL9BUCBsKhYZNKmJww2Raxu9uNNg9lKAvanDn7ADfPJ5hbqNBxNCotlzWKxbv3DeMHwj2jyVJRw364yFZCfcC/sW7Z/nh2Q0qLQerJgMNxtJR1sstWm5A3fL5zvO5TijApt7rbK7G+Wydib4IXz+yiqEqCCGbHh86t8GJ1QqLBZP3Hxjl7tkh6i2XbLWFoihoqrSY20wD7fbH7o+H+OgtU8xlGywUmgwnwnznVJaWEzDVH8XQ1Y4LzMVCE0VRqJguyYjOz902xZlMjVt3XC4tEEJ0mjybjk/NcjuOGWtlE65CwLtxYrVCseFwx64Big0H2wuomC6jqStvs032RSk3HaIhjamXOfCYjsffHZdBEbft6CcZMTi8WKJuSVeXs5ka953KUqjbxMM6C/kmg21XjRsmUuTq8npxPJ8vHV4mW7VQFai0pPVjLKS2nVokFMD1ApJhjWzNomH70hJQkU20XiDaOxFyQbHZ9GloCoEnQIHVstludpRbv/tG46gKfOpt0xxdLvHDs3k8X5CtWvznb59hx0CUlUqLn75pnKn+KM8ullmrtDj2aIV79gyRjBocWy6jKAo/d+skH7xxglzN4u+Or5GK6lw/niSkazy/XqUvarBcsRhPR9gzkuAn9g1j2j5PLhQRQnryvpSB2PMDHji7QcPyeO/1I3z0lkn8IGC1YvHAmRyW62/rVwBpR9Zy/Mu8eH+UUBSF0VRY7na8TPvFHnp4o2N6IMZduwfZORTH8bbGqLghZXWKCrGQiqIo7bFO9r0ArFdsfnA6h+0GuF7A/rEkC/kmN0yk+MANYzx4doND033ULE9KQhSFfWNJXCHTnedyMosiEAJPCKZTYWwv4LrxBGsVC8fzSUdUapacd44uVfCFgi8gV7Pb86BAU+DWmT6WSia7huLcPTvIcrnF7FCc+05lUBQFgWx8z9flzrCiCBYLTTkXRnWWSy2atstiwcS0ZcFjJBlho25xJlNjPB3h03ft5OtHV9k3mqTWcvna0VXiIZ27ZgdR2p9vKLk1hg0mwnzi9ikqpsv+sSRPzRfxA3GZI1sg4LkVKfGLtZ1VQpp6zWF0p9Zk79UdOwde0CThjYSXUwH/aySB/l3g33XdXhdClF7syUIIF1mt7sbTV3jcZZaC13rbK8GjcwVOr8sfzAcPjjHZHyUe1jAdn1hI78TIgvSkTkcNEhFpeTeUCHEx3yAaMvjS4RWeXSwTN1SZOjgYodj02vowqV2uNJ2OxjZqQEiTXsm25xMzNGrtSqLluAzGQ+wdiTMzGOMzDy7QsF35Qx9JUGg4JMIGAzEpTfnXX3mOSsuh5QTEwxqn1mXCoesHOH5ApmIxO5Jgsdjk+EqZw4tlUhGdiKG1o8bh/rN58jUbL5Ape/2xCCfXKgTIaoEQAtGupOqqXABI4is1zkslk5Yb8Lv3nWW53CQZ1pkdSbBUapEM6zx0boN79wzy+HyBsungB5AM+zSsKO/cN3LFcJpy06Fue/THQtQsl1PrbQeK4QT/8v17meyLUW46XDeW5Gymxr7RZKd7++bpvitKARRF4f0HRttb9GlGUxFu39nPseUKZ7J1nMPLfOzWqc5221qlhaYoncpKtmrxwBnZi2x7PqOpMNePp9q2jjoXC03mNxrcNJXuNNZlKyZCgOV4rFdb7HsZ6YVeIDrVc9sLuGM8ydxGHdv1+cHpLPef3cC0PdkY7PooiqBVCnB8n4m+KONAtm5TbltpugFtPaHA8aT7jO9vvZ+qSAlKtmqRbzgEgOP7hDTZTxAPqYCg3AzwkNeyqqp4nk9IVwgCqbcrNJy2HEr+1paKJo9eKMjAJCFktTwQZKst/sM3T+P6ktj/k7t3cmAizXdPZRlNRXhutcLp9Rqm4zM9EMVohz49tVCULipewM6hOB+/dYqvHV3lbKbObkPj19812wn/cf2ASsvF9nxumbm69CRftwkbMhxrE4vFJseX5YKg0nL45Xt2s1IyOb4sVXA7BmPcsXOA/piB5foIAX/+xCKHF0uMJMP88r27f2zV6I/dOkWp6Wxr1O2hhzc75vN1Hjqb567dg3zr5DpPzpcYiBkMJgyqLRdVVUhFdTRV7uTN9Mc4sd5AINDULVJYbzkkI3E8VUor//yJRTbqNifXklRNB01VaNoet0ylOb9RY6Y/yk1TaZ5cKJEIaRyaSnNkqSwzPRSVv3pqiUDAanmQluPh+6JjzQptWZ8n2X8gBJuzlgC+cXyN+XyTlXKL3/m7kzw0VyCsq1w/lsRtc5OG7VEyHYJAcCHXQFGb7aKQRalh4fiCi/kG2TZJP79R5569QyQjOoausnckwafv2sFgIsyT8wWEkK+ZDOvcu3eIkKYynt7e+zTRF2WiL0qhYfOnj85juQErZZPf+ZmttrwnLhQIBBxbLvNfP3YjOwZjTPVFr8kONlu1+MHpHCA1/XfsHCBXs5gdTlxG9N9IeDlBPFVkw+MvvPqH89pjs4lPU6VEYtNl4dRalZrlcltXFbXUcPjiM/LH9JFDE9wy3ceFjTpRXePRCwVajo/lwkAizEKhxf/8jp0UGg7fPZWhYrq0PB+EJDchXeHQ9BAoglzNYmHDJEBW5p5cKKKoGipw3XiyEy8e0hQsTxAJafiBYKHQ5LnVKrYXdFbvm1W3TReMyT6NA5MpEHKL6emFImXToeUYuL7UYVueT1jXMDSFiXSEYtMmEPKcDCci1CwXVQHXlw4iiq6yXmnRtD1iIQ1VUYiHZOPpStmkbnlUTY/Jvigz/VFMx+fQTB///cEL5OvSb1pXFeKpCJqmUjYd/tsDc9wy3ceZTJUfnMkxPRDn3/7UdewYjBELaTieT8sNuLDRIG5oLBVMJvtiLBablJoOP7FvGEVR+KMHL+B4AaWGzUdungBFodJypTWTLSUI6ajBh2+e6JDse/cOs1GzWSo2eeZiCSHgHXuGqLYcfnB6A0WBj94yyY7BOFFDNsV6gSAe1nn77kH2jCRxPI+HzuY4sVrD0FXWKi3+8d07Aai0XALA9sEXL0+CkorIY16vyO74ZMTg3tkhPvvIAk8uFCjUbbxAdhIZmsKekRTlpkciIl1HFosmTdtrO6NsyUx0BSwBjr/d9s4XUG66eL4kyZtwfMhUbQwVNA10XSXwAoJA4CsBmiI175YXUGpK4r75wq4vWKu0yNVsapaLQCGsKyiKrJbM5RpENIVIWOeBszmSEYM9ozGeW6lRaVqY7ebMSF1lsdDgC09ZPDFfYKVkcvuOAT5y8yS6pnL37CDPr9dIRw2WimaHgBuayo6BGA+fz3N6vcadV/DQPrVW5TunMlRMh1982w5uaDcWmY7PI+cLNB1pTziYCHPHjn6iIbljdv14irLp8q0TGRJhnfddP0quZtGw5SL8TKb2YyPghqb2qt89vOXwv33tJEtFk68fWyOkKZzN1ImGNH720Di52jqJkM5wMoqh1VEUhZVKCyHktp+yWWgCxvui9MUMlosON0ym+Mqzq5SaDhv1FuczTUqmw2rZxPV9mpbHctnkc48t8viFAhFDZe9wQgbbIDi5XiVbtRBCcGa9iuX6BKK989iWvKRjBhs16bKlKFKyqLg+mqqSr7do2tKZ6shyhabtYdoKyyVpooCicHGjSdV0EcCzyyUWCya2F3DP7BCtNrHPVlv4yLHf8wMURUpukxGDR+YKnFqrEjE0bp5KcyZTYzAeYrVs8qXDK6iKwm+9dw97RpKXn3QBm634QsD5XJ2z2To3T6U5OJXm6FKZ6YEYiirlru4LNHZ2I6yraKqCH8hF0JcOL2O7AXtHE/zMTZcHy71R8Eo04G9K3LNniNFUhP6YsW2r+NLgjabt8T8enufYconpgRhzuTr3n8kxl2vQsH1MR5LgkCLw/YCmL/jWiQyHpvoYSoTJthsUYiFN+oYDC/m6rC4KGesNdBoMBT4KkhAo7SYRPwi4kKt35CpesJ00bT4fIYgYKsmwjhcITqxUKDYd9DYZVRWFeESG8KzXLJqWRzpqENJU8g0bv62L01WFVtvPNBnWydXs9pYbxMMGLVcGCmiqSjysEjEkCQ9pUoayUmoxnAwxmAjzkZsn+ZOH5ztNMLPDMXI1hyAI+PaJDDdP9/HHD13gXKaO6fpc3Ghyw3iKT9+1g6rpUGw4/Pu/P0mpaRMEgsOLJe7eM8RD5zbYqNucWqsy1R/l+EqFhuWRrVk8t1pldjjO7EgSTVUQQuq/1ystbpxMb7OPu3k6zdxGnYbt8eR8kR+czhELaUymI6xWLf7iySU+cfsU142l+MU7Z6i0XHYNxmVVJaLzr/7mFMWGlJncs2eIRFeYgdvFYDeqNteP87IwO5zoNI58+fAyX3h6mVLTpmG7knxDeyxUaLQ8apZDrQV+EJCv2fiAgt+5ZoSQ5Bsuv45ABghdabwUgBMgxd5teh4I6fAid0t8rK5q+ubE5ngBfVGdVCxEIASWG5CK6DiBwPcCTMfF0zWKpkvFdAiEdB/RFFkZ2jkYJxkxeNuuAb5/Wso+1ioWk/0Rik2no0FXVZX+WIh83ebRubx0KOqL4vkBn3v8IsslkyfmCyTCOkeWy2iKtKOaGYiRr8udnWLD4StHVjqhQ3O5BmFdYb3qcGq9xnR/jCAQ/OO376TlygbebxxfA2T1KBpSuWfvEE3HZzgZ6ownR5bKHF4ssX80ybuv+9Eka/bQw1sRFwtN6paH5fkYKjh+QGALMm27XU8EOJ5Pw/bbO7dbFrGukNapni8YToTaYTjwwJmcLFi5HhtVh6IpiXLTCTi2VKTUkva5puNRszwaNvRHJeEWAmzHo2p5IASuF8hKeyBIx0Lk2/LFgViIXJuABwIalhyjbS+g3JJjp+vTaWDXNdkU7gsLhKBueZ3x++JGg5otx+QjK1sChUDIMVQg/1WEoC9moKsKJ1crPLNYknMzgo26hen4PD5fJFuVZghnsvVtBHy5Xfg6OJnml+/ZxYWNBu87MMKXD69QbNislkz+p7t2Mjuc4NBUH989neXZxRLJiMGB8VSnV8pyfR6dK6CrCvfu3TJh6I+H+NQd05RNl4l0hFNrUqXQcromlhdAEAjOZuukozqT/bFOyviPUw54JfQI+CVQVYX9Y1dY2V2CfN0mEAEV08NyG5wcqFG3PMqm27YZEmiKkJVD0yWkq5zJVFkqNnE86RoRC+m4fsDe4TirZYv1qoXryRAfHzqVwE3ITmh5wyaJ6dxxBYR12Ryhayotxyeiqczl6vhCEn637VYyEA+xbzjBkeUK+brdqbDrmortujRdqNs+SruxEkVe+JtyHEPIZpFCQ1bKFXyatsdQIkREV1FVSEUNSg0H25eV6z+4/zx7RhJ88o5pbp5OM5KI8NWjq9LxIhA8tVBgpdxqh9UInCAgW5ONrA+vVvnD+8+3teIBXiAt7gAu5pusVy0GEyFAkVG8+QaKopCpWrhBwGAiTMTQ2KhafO/5LLGQxkduHufCRgMhRDtUJcmv3rub/3LfWU6sViRB9ALWq2FGkxHiIdnwet2YHDy6my2zNYvVSouW7ZMODN61f4SZwSgPnMkxkoxs+071V/ALPJOp8ehcnpmBOCdXq0R0lYiuMT4SZrVsYXsBLddDiICT67WOdWU3Xoot0DUWK7a9roBt5Lv7vrAu4+xvm+nnQr7BSsEkW7M7+nGAZBgSEaOd5BngtK9/TVUoNB3u2TPEQDxMxDApmy5eIJ0MKi2Hh87nede+YSb7orz3+hH+4sklXC/gz5+4yKGpfvINq6NZj4Y0zmZlz8ap9SrHV8scnOjjY7dO8uhcHkNT6YuGOo4COwZj1G2PiKFJoq/AxUKDP3viIrfN9HPn7kHePjuI6wtGU2HG0lHG0tHLGjKPtf3THzy7gR8Ibt/Zf9XU1k2Umg4Pnt2gL2bw7v0jqKrCRt2iUHfYO5r4sYdJ9NDD6xGGqsi5QZHWf5tz5hMLJepOQN32efj8Bu2oDRZyjc5j/Lb0zRcCXyhYrk/T8dijxtqWfCCU7QOi7cr/ewJqLReQY+bmPK4oCk3H79j5bdStzhxaMR2EENIyWASdMdK/pKhmu1v69VybDPu+1Hlvom67nb83Sfbm4xJhlZYTMDuS4PmMDMIznYCvHVtlsdiiP2rwC3fOUGo6GJrC4cUSZ7N16Wq2I43peGiqwkxflGpLhqyZjsdvfvEYDdvj03ft4LqxFIEQhDSV7z8vPddnh+IMJkKsVyxWy9JFrdiQDlfd9obHVyqc6koN3Wz0BBhJRToyzp+5eZzVcotDU9fmWvW3x1b56pE1QrrKr9y7kyNLFRQUPn7bZGdH9LVAj4C/BHTb0w23LybTdTE0gyfnZXRsWFdRla0KH0KgtxMyXb9deex4jProClzYaOIGgUznUthWkbwaroULOZ6MDBdtS5JC02kHoWw93xcyGOX+szlsV8oL3AB84aKpKpa7dSwd+YCgQ1wAWm7AWqlFN8/yAulnGjFUdg3Fmcs3cD1BreW2re0EFwtNDoxLD/G37x7spHGlo1IOkzQ0bMdnIKZzw3iaTMXiG8fXePR8nlrLlU1+qsru4QTv3j/MV4+sdJLEZocShHSF4WSID9wwy2//7UkcXTZ9jKUi/O3RNZaKTZIRqQH8xrEMhabNStnkY7dO8Ylbp3hyoUiu3sL1A+rtBUUqIndGFEVhNBnh/tM5dg3H2T0U50xGdojvGoxxYDzFiZUqo+kIS6Um69VWO/mzuu07KrcDfV4Oji5L+72nFgqUTDmYve/6UX7l3t189egyX3pmFTcQmLbHa+TydEWoyGvJdAUbDYfH5vI0HZ9Ke9sUtq5Py/PZk0qyayjO8+tVROCBohDSVeIhnZFUhN98zx4+99gC953Mko7oVE2H0WSIv3j8IseXy3zi9mlumEhz3ViSH57dkL7jazVu3znALTP9vPe6EIamUG65WG2P9GLdZr1sceNUmv/1A9dxYrXCWDpCuq3Xv2Eizd2zgxxeLDMzEOMTt0/znZMZbFc2Zy4Wm4yno9y2o59CwyZbtTp9A904MJ7i8XmZDXCy3WT0yTumX/D8PXOxxHLJZLkEe0YSDCbC/M3hFVxfsFKWjWE99PBWRzSsk/YDIrrGZF+YslklEdI6TfYAra0/8brJahB0rGLn83WatovjiU6BzQ8EuqKgq3RyEuIRg2Zj6/5NyF1tUISg1LA6t29U7c7fKyWrI+2by5md2y+d58M6eO052XLdjp95d7ZEd7ab0vUKuqpSNeWTs10WgQJYKMj/F02X48sVCnWbluMxnAh3qvem43P37BCqqpCrW/yf953B8QLeMTvI6fUagRB86ZnlTsHvxEqFuiXn6bLpsFQ0OZOpMZqKcGA8xXNKhXQ0RCKsU27aRA2NoYQsPsjq9NV7o7p3f7uRb+9+zw4ntjmdyR0M6Xx1PtdAtCVBpabzqhLwlZJJPKxf1cL4UvQI+DXCdDz+6qklmrbPbTv7efR8nhOrFUoNl1rL5Z7ZQU5nG3iBwHY9BApjqUi7IixwXJ+KudVZvQlPQKNrG2VbZfsaoLWN+N2rSANAVs03q/GXPSaQndaX3uf60ukhaijYruhs0XVj81gF0g9yE4YmtcEgZQu5WqtTufQENCxHan4DwelMjcFYiD99dIF6y2uHI8iKQcuV1naJkMZTFwuoispisYmhyZCbkKUylo6Qiuj8/g/O4QtZ8e+Phcg1LFaXW/iB4PELRUwnoOVId5L7z+ZAgOV6RNu2e5lqi8fni0QMla8dWeVivskziyUu5mV0+VAixIHxNIWmtJy6fUcfCwW5kj+dqXHv3iEeaidqfeCGMf71T+7n/310oe3rrnQkKIa2nQmvlUxeLq4bS5Gvy8jiTNVC0xQOTKT43OMLPLtYxvUCWo73kirXPw5s05C70jkkaPdCXArfl5OL6XgMxEJoqkoyrBExNMKGRl/U4FyuzkLBZK3Skk2etJtlVZVjKxUuFpr89ocO8LOHJnjmYpFC3WO1ZrFzKMZP7Bvi1pl+PvPQPI4XoKkKU30RTq7VmOyPsl5pcWu7og2yavXsYpkdgzHWqzYbdRvLldZaByfTnM7UsFyfi4Um3zi+TlhXGU6G2TEY51fu3bUt+AHg7j1D3DLTx+efWMJqp9S+GCb7opzJ1IgYmrRB84OO5Mhyr21Ltoce3uy4fccATy4UuHEyzen1Gopyue2rBp3CUdClDVwrbRHlXM3pVLEXCw2ajixULbZ9tzfheFIqEgQQC6vY7fcKa2pnnqxYXeS/61C6h+juufRSmFvFbRpdtRu7iwB0H5OibpY7tqryAMXW1TnHUrFBxXRpOB6JkIbjCVxFcGA8helKp7FH5/IcXSojgJoprRyD9nvomkogBPmGzVgqwmqlxVR/lHzd4sKGzAC5cTLN/rEUUUPlv/9wji8+s0IkpPKFX7mLf3jXDLqqXjOJ7cZ3TmXkLuZalX/2rtnObuDHb52karoMJUJ84rZpnlwooqvKqxpsdmSpzCPn82iqwj+8c+aawoZ6BPwaULNcnpovkq/bxEI6951Y5/BimY2alFwIITiyXMF0fFxfdOwBpWRCpWl5UiN7DXipXGmrmv7CuBoJ626Ku9KxtDZ/2F2P2eQImqp0qubd6OYAuqZgXvLhW54gCIK2/gxMu4WqyoELwOuS3vgBVDsahoALG3XG0lFU5KKg0HQwHQ/bF9w4kSYdNVAUwVK+Sd2WDXIHJlIIBNGQRtRQZQXflyEznu+39WE16paD7+ukowbFpkNIk84Xw8kw432yL2Ah3+DUWpVz2Rr5ho0QMJ6Ocn1HtiSothwMLcy/eM8ecjWbA+MpQprKVH+MVETn975/vnMuwq9ALnDbjn7Cusp3T2VZq7TwAsFv/+1zWJ7Ak8GUne/mSouv1wO8ruO60jXqs9X3EDVU+mNhdg7HMW2fPSNJ1iom/8uXj8udm/YEqCC3ff0gwHQEZzJ1VssmT82XOLFSpdC0SUV0KqZDfyzE2UyNYkNWo1IRg5nBOEJRmOyLdrxsy02Hhu3x2IUC2arF+Vwd0/YI6ypeIGjYHj95wxjvPzDKD89u8K0TGRkupSs0bem5frV+22hI51N3TJNtd/W/GG6cSjM9EG3LX+SP8WduGidTta6a4nmtaDken314AccP+LWf2P2icpgeeni9YsdQjFholERE5+hSqRMt343u5arZdWd3QUtla/xsOVvzneOLdkFFgAJlq93/AjTtrR97oYspW1dh19c6PIur/N09xbacrXvMruO4UpHuSu+drdnS5coTrFdMeb+A75/O8uR8CV1T2D+a3Don/lYfUUSXFoxzG01+8sAodVva4t4wmeaPH7xAuelQbNr8xrv3dAj6F55ewvF93FbAd09l+I1375WfyfX5g/vnqNse/+r9e7G8gHJTWh1eySUN5E79Sslkoi+Kpmw9ZtdQgv/0Dw52/r+5S9i0Pb50eAXb9fnIzRNEQhp1SxpGvFSUGja5mkVIV6lbXo+Av1r4++Pr5OsWNcuTSY75hkzTU6S43w3kxGV7bQ0XUoIhA09eftDKlXClCvm1vMOryb82q9shLiffl76XEAKzi2UpSPKrdZFsgSTaChDRQdek7eOVSKPjCwp1CxSFeEjD0FVQFPqjOplqi4NTaRYLJsslGcQSCxucz9Zw/QBVUajbssHUEwE1y6VmSaLWsH0ECpYXMBzXuW3nAE3L5c7dA8QM6X1daNjYXkCxYWJ5vmy2i4VIj+mUTbkQqLVcPvvwPKWm04lW//LhFf7pvbsZSYX58jMr28/VK5Trfu9Uph1wEMjGx64Z5XXKuV8y/Hb5qG4H1O0Wq+UWEUNFCOn8s7ktHNIUQpqsakVCurT2EnKSPLJY5umLhbY9F9Qsj+PLFX7pzw4TMqRE7KbJNP/8nbMsFE3e5gXcuXsAEQj+7tgazy6VGIyHZXQ1EA/p/Pq7ZvniM8tcN5Zioi/Ks4sl9gwn2DuSYDgRIhABY6koA3HZAPq1Y6v8/O3TV/S97Y+HXlJD0KXEeM9I8squBC8R3z6R5bunMgigP2rwq++cfcWv2UMPrwU8P+DUepVD033EQjqGJnuxfN/nxaZlQ4VN7pqK6JTbzPlS/qyIrkmsC93E/lqLb68Wuo/RexmTQPe5iYc0qm2u88xCgXK7bK+pW3H3epdsNlu3CUSdquXy1EKJ05kqmarF6WyNiungC6k5b1g2J1erBJOCPW35pqEpTPdH+I0vHEHXVGaH4vzV00sIIWhYDnXbp9Zy+fk7pvnZQ5OJGsOPAAAgAElEQVQ0bZ90bLtMZa1sslRqErSltXXTIxbWrtoXs1hsUqjL4suR5TInV6Xb3YcOjvP2PUMv6bypqiJDEA2VkH5tms8eAb8GBELW1UKayvyGjHUWQqBr0jLN8QQN58dDd15PpOpaGpDtK4wAgssHBhWk3CQa4t49gxxerLBe3q4rh3ZXeHtEa9o+8ZBCSNNQkINFttrCdn3y7fhe23RpOVI3HNZUUhEd2w/YqNmYjrRzUlWlE6se0VXO5UwaTp5dwwmyVYunF4oUGg4KQurFFKllVxRJ2BMRnWhIJxbSWSo2OZutSwvGkomqKBiaSr5m8cm3TW/THwJcyFSu8Wxv4aFzG8xtNNg9FOfpiyXWK60X7Bd4syFAypNOZ2o0nS3XlSAQhA2VkWSIasvrLA5zNYs/fGBO7n4oCtLiV2B7AZYv0D25mCs0bE6u13jHniEMTeGhc3n+/IlFcjWLpu0x3hdl11CcT981w8xAnEAI/s0HrqNhe3zhqWWOLpUJGyq/9k659el6gqVik8Vik6n+GBs1abl5NaLtB4In54u4QcDds4OXyVW60bQ9nrlYYjAR4qZrbES6tnMbUG25CCG260p76OENhu+czLBSbpGvWcwMxsg3HBJhnVLjyhNXRNlygYqFwG6rUGpXK1uzRVZfr7+UlyppvRSltlQlEFA2t5h5qb4lZyl3VfhtV7BaaREIeGqh0Nm9Pr2+RdgB/uiHF1it2JzO1NgzFG1r1QXfPJ7hiYUSCrA2lqBpS1nqmWydjZr0Lf/mcxmeX6+xXDL50I0TfPTWrdTstYqFrkr3tm8cX+e+kxnG0hH+/YcPEARyR76bjM8MxEhHDWwvIBHSObpcRgh4PFp8yQQ8W5WOauF2Bfxa0CPgL4KG7fFTB8dYyDf5+tEVfni2SKlpIwJBPGxgez726/bn9/rD1c5UgJSgmLaLafv4vn8Z+b7Sa8mFj0el5RHSFRw/IBHWt3eOewIFaQcZ1lVM26fpbIbI0NGvXT8WY2YgwVAixPHVKk9fLFK3ZICRF8gKa9l0CWlKu3ovSIQ1GpbLA2dyrJZMMjUL3w/w/QBVU2g5Pk3hSY/25cq2DnWA5bLFS0HL8Tm2XOH0epW/fnqJWsvF9oK33BUoAP8SPecmMa+ZHpYXEAQQCamyuVPIAIfhuI4TyKqW48PuIUmMN+o2datMoe5QqtvYQcB3T2XJ1SzcdnN0PKwBgtPrNX5wOsdTC0XKTZcDEynqtsdKuYkfyDCgaEhjsWji+QG7h+PM5eq8c/8wJ1crnM7WuHGyD11VWCqZvH33INMDMc5kahxup+PGDO2KvuSbeHSuHV4EjCRl5PYzF0ssFpvctWtwWwPSS8FMf5yBuLSF3D0Uf1mv0UMPrwesV6XLSL7hcGAihaIoRHQNXYUrTS6i6/buOsm1dFW8XsffV1p8t7oqZduq+l0fuLuRVWWruFa/1P6qCxu1FsWmdFSpNC3cQNrWPr9Wpdmu7FmutIdEQCKss+a3cLwAPwh46FyeuuUSCMHdewaZzzfYP5rkZ24a4xvHMtyxu58n5guslk3ydZuvHF7my0fWSEd0/p+fP8Tz6zV0VeHu2SF+6R6Z9ZKryaTkhu2xayjOZx6c4/m1Gp+6cwbLDXh0Ls/7D4xy797hK36mU2tlTq1WMTQoXqO5Qo+AvwAeOrfB4YsloiGNctPh/tMb0nqnfYE5pvuKL/AetqPpCB48l8d+iaLlADlYZCsWrUuI2WYDTBBIPV6lJQmZ0u5633yn89kmtit4esHG9XwcoXRCakAS9ZrlYruS8GoKVJo295/eIBrSsNyASEglGtKZGQxJ6yrbR1MUBpNhLC8gGd5e/bzYTl29VoR1Bcv1eHw+32m8ucp88qaHfZUPXbU8aaWpgOUEW79RAfmGh6ErWG7AQDzEnuEE57L1tm8vVMwKv3e/xY7BKKYtY+NHkhFmh+J8//kMz14scXSpIvsjHGlNlqlaHJxIslxsYjlyMdYXDWG5Hk3bp2657BpKSD/553NyR6RuEw/pKIrCo3MFfvHOmXb/gtQxXrq12o3FQpPT69IxZSQVIWKoNGyPxy8UAHjEy/PpwR0v65wWmhZ1yyMQglzdfvEnvEzs/HfffkXPX/yvH3qVjqSHNyv6YyGadotEWGexYOL7PuWWQyRk0HDdyx7fPZ6Y11bAfN0S7x8numdb7yq3X4r1mqxsB76g+5vYqG8R13zd6tgwlpo29fYXdGGjRsn08fyA89k6v/+D86xVWuwbSfD4hQLncw0OLxa5bUc/q+UW8bDG95/fYL1sklEU/uShC5zJ1lHbC7I7dg0AkIzoDMTCMvNEgy8dXsFyfYpNG9MNqFseZzO1qxLwrxxew0caB/zZoxe4e/bFK+g9An4VPLtY4ovPLNO0fWIhlYWCSdPxtumSe+T7R4OXSr670XgRcZ+MUJfxvoGyfYvOBxaK3a4k27XrKjIMYfMdPAE1O0DZ1MIrCn3REH2xEAqQrdqMpiK8/8AoTUd6RucvITXVl1AAr5gOX3h6iSNLZWx3y9VGVXlLzgQvtptytfukLEqwVrH4ypHVbV65AA3LJVtRqVkuA3EDEQjOZKrSmssNWK+a9EVDREMaumow2R9BV6WO3EcS6I26RViXLiXXj6c4slSh5Xokwjot12elLD13R1MRJvtlw8/0QIxfvHNGpo0KwaNzeXYPJ9AUOJ9rsFG38APBt0+sY7tS9//uZJiW49Mf1xhMhCg2HCb7otQsl42azc7B2BU151fDeln2OgghwzV66OGNChVBLKSiqzCaDrNWbZGI6HieJHIvJM/QeWE3kh5eGa523rvPebG5FSi0aZMIkKm5GG2nNNv1WSw0KTQsDFXhbE56mxdNl8cubGC60HR89o141EwXXVdZKsiEa4BDUzkePLdB0/H4yM0T1GwXxw/I1SxKdRvLD0iFdaq2T63l4Hg+C/kGf398nbv3DPK2XVu7lN3H/uDZ4jWdhx4BvwKEEJxYrRAP68y3w1majn9FPXMPb0z4QOtFysbdFlWCq3SRC4GmKjINNKRyw0SSd+wZ5v977CKaqlCzZIOg4wWYtkwK7Ub9Ci/ZjaPLZeaysqnlgTMbnMnUsD1/m2PIq9zn+5aC7QaEDRVNhbihEg5pBL6gZNpYbkDD9tio2YQMTQZYKdJPd89IgmzVJhlXWco3eWyuSNTQGEtFpJWgI71sb9vRT8uRA7qhKuwejjOSlH7ifgAfummc/WNbVlhzuQYnVyss5Jv4gWCjYUtC3XIpNh1cP0BvS6GGk2GWik0++/A8MwMxPnjjGGFdIx7W+PwTS7Qcn+vGknzwxsujVh0vYLVsMpaOdEKsAJZKTenYAiwVXr5FZg89vNYYSUexfMFQIsxIKkI8pNMXNTifkWTuhWbzHvl+7fFCdGtzzrNcn5Nt2UqxuX1Xo9uy8ehyBVeA6wYcW67Qzj/iu89nO4FCLcfj0fMFTMdHRWD7AX4AlbbM0/UFpuPzqT95go2Gy2cfmefIb7+PeOTy3cprvX56BLyNTWnBcDLMI3MF1isWJ1arrFdaWG7Qq3a/BXGt2j/LF1i+T83y+fqxDN89lcPQpCf5QCLEI3N5ELBaNnH8a7+SNmoWf/HEIqvlFvmGTb3lbIsZ7uGVw4d2OJXAUQR6273ItP2OxafAJxLSiRkaqYhBMqITNjQsz2ej3sK0PWxf7kRMEcVQVUzbI0C+VtP2aLk+BcendbHM3bNDJEI6Fcvlq0dWSUZ0bp7ubzcfl/ADwelsDUNVqbZcRpLhjofvVH+UVNRgIh1BU1Xm8w1KTZvjKxVOrlVl57/lUrM8bp7qu2oz0LdOrLNUNOmLGfyTu3eitC27kmG9s+0bjfQSNXt44+LePUM8dqHATVN9LBaaMnbe8rB7k/mbBvUui5myuZ2Ad+9wdN9V7BKtz+fNzmMePJMj35T3PXQ2h9ceBxu219HCd4fFWW7AIxdynF5rsG/05fmJ9wg4UGzYfPGZZVxf8P4Do1RbLvGwTjqis+j1yHcPV8eV1DKmG4DbtlwMAnI1G0NTaTnetqaWF8Pnn1jkoXMbOH5AzNCpmD3y/aOAGwhUZHS1oig0bXdb9SWsKqSjBhN9EeZyjY4/bq7WwvbkczetNNcqslGo2m4KPrJYZudgTKbJBTJR9fhqhf3jSVYrLZ5eKCGQHfS5qsVoMky2ZnP37BA10yFTsyg1ZRPZoZk++qIhbp3pZ6Nuy8Xd+TxfPrxMrmaTqVi0XI+woREP6dhuwF2zg5zN1tgxEN8W8lNtE/q6JaOg9XZA1Eo7GEoBMpWX1iDcQw+vJ3z/dI4L+Qb5uo3jBxSbLnW7V9t+q+Ba5srux2ySb4BGF2HvnrMvfc3/8LcnyTUDrs108HL0CDgyJnWz2S5ft/mJvUOENJXFYp0jy6/xwfXwhoVAeldLvPQ2yedWKugKmL6ghddbCP4IIcMzfFRl+9angvTxNVQFx5X9IC3HI1N1sFypHd/WhBTARt3G0BQatqBuuzy7WGQ4GcZQFdxAMBAPUbc8wrpGIGSIz/eez3E6U+N9143ym+/Zgy8EJ1arPHA6R75hY+gqN031MdUf44EzOU6sVomFNHYPJVCUto2mpmAEKpqikI4ZRMMaf/XUkpTGpCP8wttmOsf5gRvGOLFaYc9IcptGPF/b8nTK13oEvIc3Fn54Nse5bIM7dw9wId/AcnxWK61Ow73Tk5H28Coi15Sj/8u9qnoEHNg9lODQTB/lpsO+0SR9sRB37Oznt7545LU+tB7ewrhluo/TmSp+WxbRw48WPlDpss5SgIGYgaYpeO0sgI26I71pxZUHXQ2Zwun4QnrZlkxcHxTFYbo/wo6hBJ4vcL2Af3TXNGcyNWotBy8Q5Os22ZqFrqnowI7BGPMbDZ5bq7BzMMFvvUcmxG1utZqOz+lslZihMjMYY+dgnJnBKO/YM8SDZzcQQsYj3zLTL0OJujCcDLNzKH5Z3PN6bavZqXSNVlo99PB6gOMFPLdSBeDoUhnPk43qii8YSRhYNQdNpaP/7aGH1xo9Ag6y67Vq8ehcnvufz3LTdB9zufo1Bc300MOPCuP9UQI/eN3GyL9ZIUOZIB0xmB1J4PiCVERnIG7w7NILDwqa2ra8bH9nvt/WIgpoOgGFhsNNU2liYZ0Hz+cpNm2ajo+uSH33L7xtut1oG6ACz2drtByf9WqLv3l2mQ8cHOfmqTT5usX1Yym+dnSFk2s10lGDT94xxW07pKXWcqnF/EaDt+0a4MbJNDdMprcd5/2nc5zN1gnpKr/0jl0deUqsS6byQkFAPfTwekNIV9k3mmRuo86BiRSGLsm2ripMD8Yomg6psEG+ebkFYQ89vBZ4yxNwIQSPzRU4sVrhfLbOaqXF/Wc3cHqsp4dXCE25skb8xWC5Pl8/usb3n892SVh6+HFAVeSEvXsowUgqzEA8RDKiEzE0LEcGRwhfoOvKZa5IClf+vgUQNVTu3D1IMqJz564BdgzG+fbJDKbj4/qCgWSYX713N03H55vPrdIfM9g5FGd2OMH5XB1VgbOZOjVLymQsN2C5bJKOhRhOhhlOhqlbHqfXa8xt1Llpqo/bdvQzlAhdRqRLTYf5fIMgELh+gBsERJGP2WzGBFC1l6ts7KGH1wYfumkcIcZQFIW+iIFp20RDCvMbJrYHpaBHvnt4/eBNQcAVRfl94HbgqBDiX17Lc44tlyk2HBzf58hihbWKheMF2D3Hkx5eBmRG4pYeOGpIk/+a7REEkpxd63X15HyBbxxbpW45vWvxRwQFuUAKkNXqWEgjFZFOJwFw/USSA+MpTMfHcgPiYY1C3WEwbuD4gp2DMS4WmrKZUUiLSoGMOg5pKqYjrSJVBRIRnQ/eMEq9XQGPh6UfvOcHqIp0HtE1FU1V+bPHF8lUWuwZSXL7zn7etW+YgxMprHZFXAQCr82LDU3l03fOcN/JLKPpCAcn03zhqWUCISg2nE7CWzdajs8Xn1mmYXsYmspPHxxDVRSEECiKwodvHufc9y8A8N79oz+mb6OHHl49bC4ia7bcfrJcmZYMV88HuOw12JKYvdI49x56uBre8ARcUZRbgbgQ4l5FUf6Hoih3CCEOv9BzslWLzz+xSN1yiegqlZZHIqzxgRvG+NzjF+nJbd98iGqgqCqOLzuWIzpYnhxY+2MhCg1n2yCrs5WgCTKBMh7WqVteJygoqil84MYxzmXr2G5A1XZRBIylI0z1xzA0hWNLFeqOx/VjSbJVi6bjk29saWunrpAY/l/uO8vcRuNHcyLeIlCRchA3kH+nojqxkMZG3cYPoC9moCgKs0MxUjGDW6f7OTCR4jMPzlNpufTHDAxNJVdrslZpMdUXxRcBqqoynQ7zT39iFw+fL7JUqLNablEyPSKGykRflOFkmLlsvfM9hzUVXyjsHIiRqVr89dMrTPZH6Ysa3DLdDwrctWuQ+UKDUlNehzsGozxyvkCl5TLdHyUe1jmbrTOWjvLBG8dYKpocnEiTjhlcNy7lJUIIBuIGhYbDUDJMEAhOrFXRVYWDbQmK4we4fkBY19g5FONMts63T2a5fjzFTx0c47qxNP1R6Xl+80zfa/Pl9dDDy4TjBRQbMgDNcuUi2PECDkwkOJdtEg1pJEIaa1UZiKZy5cKIAWyO0m/VpOEefvR4wxNw4O3A/e2/7wfuAl6QgGeqLY4tlfHbISq6qtByA/qiBqmoTrln9/aqQFMkCfJ80BVQVAXPF/jt+xS2O06ENKmVvdZgmf2jcRYLTQIhG98UtuKEFZCOFe0XmxqMc+NEmpLpMJ9vEg9rvGv/CJ4fcHylgu0FNG2PsK5y5+4B7tk7zDePr5OtWRiawl27B7lj5wB/cP8c6+34ylhE5//6uZtZKpp89qF5YiENzw+4WDQpNh2SEZ3JgRiBEMyOJPmPHznIY/NF/tO3Tnc+g3IFq+Ue+d4OBRiIG1SbbifgIKyB68vJ01AhGtIwVI2iKafNAOiP6PgCBuMhDk33M5KOYDk+TcdlIW8ymgqTiBh86o5pbpnpB2SD40K+yWKxyWq5Ra5mU225LJdMyqaLrkAgQjwyV2QoEeaBMzkqLZd4SOO3P3g9g6kwhy+WiegaqyWTUsshrGksl0x2DcWwPJ/xdJS+aIg7dw3wc7dNM5wMEw/r/P1z6+wcjOMFATsG49x3MkvZdAgCQbYmUzDXKi3G01Gm+i9fuSmKwifvmKbQcBhLRTi+WuHhc3lAVsv3jyVJRw0+dOM4a5UWh6b7+PwTiwAstlMvv3ZklUo7oepvDq/w83fMXPY+PfTweoQQgt/5xikWC03u2j3YkYMJ4P/++E1862SOG8ZT/OED56FNwJWu8va2SrcGant8iRrbbek28UKV8WQINlPV9S5npe5wtx7e+Lhndz9HV6qkIgbZS1KurwVvBgLeB8y3/64CN3TfqSjKrwK/CjAzIyeTeEhnNB3B8QLUdoqhX3dIR0PcNTvIseUKVdOVfs5vUrzYttqugQgXS5JoqkgNq9aOfzVUhZYXbLN00jVlm26+P6IQC4d55/4hDk72UW25DMbDWK5HXyzEoZk+vvT0Ml87ukrd8hiMh/jIoQnuP5NjvWIRMVRsLyAZ0njH3hH2jiV4+EyGxxaqnff4Z+/aw9ePrBLSVMb7olw3luTxCwVOZ2ocnEzzidun+Kunlmk6PjdPpJgciHEh3+yQ8vcfGMPzA0JtOzjbDRhPR/iN9+zllpl+9gwn+Munlpjoi/JTB8e4e3aIc9kaf/bEEoqgo6/dN5rkk3dMs1w0uXVHH7/zd88DcjEx1R+lYXt8+KYJ9o+n2D+e2kbAf/qmqVf+Zb4BEdUV/vPHDjK/YfL1o6tkatsHLw0IGyqxkMY9+4b45O0z3DrTT75uYzoef/rIAocXSzheQCAgFdEJhKBqOXhtyY+qqrz/wAiOK3jndcN85ObJzutXTZf/n733jpLrPM88f99NlauruqpzRgYIgAkCkyiKYSxKlJwkeSQ5jOXxObK9sn28s2vLO7P2OMxZe86eHcv2eG1ZO7Y8DrKCZYnKVKCYCQIgckajc6qunG7+9o9b3egGGiQYJFBS/c7RodAV+lbV17fe+37P+zyPnV4koqur3WGAB3f2EAvlSUV1lqtBJDuA6/k0NA9NCMK6SlQPTp01yw3sBT3Jg7t6CekKc0UTTRE8sKObfM3mwESBeEjjoV29vGNPP09dWGa4M8q927rWveYf2dXDQCpCb0eYhXKTzV0x8nWd20dSHJupsFA22TOQRFWurc0OaSoDqSDaXlmj516bRr+1J8HWngQAb96S5dR8JejEE1zIrDz92oHMNm3e6DTsIJYc4PR8hURIpdZqqmztTvK/vy1Y47//xVOsjDd0xnRyreo6HVUoNILvhs6IRsjQsV2fbV0xvnMxiC7XgIdu6ubodJl37OnjE89M4MrgfPPBu4b53NF5htIR9gwm+drJJSK6yv3bu/jHA1MoCP7Xhzbx189M4XuS9942wMeengQgBFxP+RYzBHX76m/ujpCgbAU/f+fuLr56Mocv4ZahBIenq6vH7ovgO7wrfvl1K4J1u/5rLxJC6uWm1lDSYLoSXFWkQlBqHbAOrFyf9MVU5uvBAxKGoLrBsb4erNQv6YhK8eUirYG+qGC+ERxLd0xlqXWMa3dAFGBHX4Jc1eLDbx3jD79yFseD4XSImaKFT3AxlYkbLFZtIprg4T19pOJhkmGdfzzwyj2rhZTf371eIcT/AuSklJ8SQvwkMCil/NON7rtv3z558OBBAL55epHZYpM3b81ycKKIjyRmBANSvi95ZjzPwYkC47kas8UmlaaF4wc6SV1RyMRDTC43cAkWg65AzFB5z74Bjs1WmVyu47gumioYySToToZYrlmAIBvX+eLxJQC2ZMJkkhFsJ0jeu6k/QaHu8NipJXwgqsFP3DZExFDxfEmlYbNQtZjI1Zit2KgCPnj3MALBJw/NENcV3rq9B196fPrQPAB7++P0pCJM5000TfCrD2zB9iSLZZMj0yVuHUnx8I5u7vt/voPtwSO7e/hP77qJf3xuikvLNd401kk8pDFTbCKBh3f3cm6xytn5KhLJcGcUXVH4qycucH6pQU/S4F9++R6EEKRjBmF94y9y3/f5wtF5PF/yE7f2I4Tg+fE8cyWTuu2Sjhk8uKObSCsq2/d9bvrdr9F0fPaPpvjUL91DqWEzXzYZzQRBI47nM18y6U6GVn9v1XSYzDcYzcYYz9X4m6cn6O8I8xsPbUNRBKcXKni+pCOiM9wZXTeItlQxqZgum7tiCCGwbJcP/f1hFiomv/m27Tyw82qd7NdPzvPceIG37+ljd38HluuRil62e/vZjz3Nk+Mloprg1B++Y+365ODBg/ze50/wN89OogA/dfsAF5Yb1E2LYsOm2HCxveBkGtah6YKQl0+AAKGWfkZXNu7crPDQ9jSaqnN2vsxc2UII6IyFsFwf23XIxnTyDQ/PX/E6VRD4NALZM/GQIBMN4UpJPKTjOD47+uM8srufzx6eYbZsElIFuwY6aFoeJ+ZK3Lkpw0gmRm8ywiM395OvWTx1fpmpYoOnz+eYLDS4ZSjFb799Bw3bp2677OxLktgg7neh3OQLR+fwfHjn3l4++cI0R6ZKnJyvENYU/vR9tzBZaGK5Pu/Y03eV5d5L4Xo+ZxaqdMYMmo7HCxMFTMcjGdK5d1uWhu0xXWjy9PklvnZ6ibs2ZfgvP7HnqufxfcnRmRK6qqwr9K+HyXydqKGRiuo8N55HFYI7NmVesgBfi5SSU/MVdDVwh7geCnWLD//Diziez3973y2rnfaVtbnC6Ee+9IpeyxuNiT965EYfQpvXibVr82+eusTBySKP7O0loqn8/YFJfnRvPz926+VGx2cOTvNHXz1DNmbwP37+dv7Tv56iI6Lzkbfv4P0fex7b8/m7D76JA1NFlsoW79s/wK//01HGl+v86oObec/twyxWTPpTEb55epFPHpjmbbt7eMu2bp69mGcoHTSDPvviLP0dYR7e3cfJ2RJhQ2VzV4K5UhPXkwxnohyaKHApX+dde3rZ8TtfRwLbu6L8+G1DfOn4LB+8Z4zRbJz/+ewEj+zuZSgT40++cZ47xzqpmA4f/eYFwprCMx95gKWaSUTXGOyMcX6xiuP57OxL8pHPHOXcUo0/f/9tPHk+x4tTJX7x3k2Umw5fODrDz9w1ynv/8hnKTQ9dgWd/+0E+/tQl7hjtJKQr/Oanj9KTDPM/fn4/nzo0Q1RXePimHt7zsWcxbY9PfHA/ZcvlUq7GT946xNmFKi/OFPixWwb5yGeP8J2zy7xn3wCZWJi/f26S+7Z18Stv3cIfffU0d23OcnAiz6PHFgH43Ifu4KPfukhYV/i9H9vFu/7saRqOz6d/6S5+6zNHmS42+fP33cKLMxUOTxb50Fs3Mb3c5IvH53n//kFmSyZ/9+wEb9naxUM7e/g/P3+Cmwc7+Oj7b+Obp5fo6wjTEdb40N8fQlcFH3n7Nn7m4wfxJPzBu3bwU3eMYboeybDO6fkKp+crPLC9mz/+6mm+fTbHu/b284v3jvGFY/PcuyXLaDbG8dkyPYkwP/6n36bQuij55M/fzJ07gjUnhDgkpdy30dr9QSjAbwM+JKX8kBDiL4C/lVIe2Oi+awvwNm3eaFxZ5LRp80bhB60Af620C/g3Du3zZps3Mj/QBTiAEOKjwG3AUSnlh691v2w2K0dHR79nx9WmzSthYmKC9vps80akvTbbvFFpr802b2QOHTokpZQbTHv9YGjAuV7rwdHR0auulJ86n+OfD06zpTvOL9wzxldOLOD5EqTkuUt5zs5XKTYchAj8c21PogCGJvA8ifP9f/3yuiO4PCR3LXWW1tKdvR4qe0Eg/2nY3rrn0wRs603QdDwuLTfWPWZFA7iif/NbNnK6gEREJ2KoWK5H1XSJ6iqW66/OBP5DFIYAACAASURBVER0hWREY6AjwtnFGumozq8/tJXnxws8f6mA7XqEdZXuZJjbhlI8dnqRXNVCUxUSIZWp4uWI77WdtJVOztdPLvCFo3P0JMOoQvLpQzMUG+uTDH8QierK6nusCoiFVAxVUDU9VFWQjYVYqJj4UqIIVoOyorpg70CKmZJJxFDJxA1Oz5WptFItFRF8tqoIwjqGO6P8xkPbWKzZaAKeHc+zWLF402iaZETnxakSAPGQhqoI9o2kaToeNw+l2DsYOIN88/QiXzk+z1LVYrFq0hHWGUhHGO6MYTkej5/LEQtppKM65xdr3DKcYiAVZr5s8r79w7xppJPHTi8ynqsFriedMd52U8+6WHjfl6trpz8VZrbYZGdfkn2jndf9nh6aLHBqrsKtw2l2D3QgpeTxszmOzZRAwN7BFPdv7169/xdenOHX//koEvitH9nKLz+wDbi8Nk/NFXnHnz7zKj/hq4npCvfv6OG2kRSxkI7j+dyzJcPR6TKW6/HwTX10RK+WH7Vps0K7A97mjYwQ4vC1bvuBKMBfC/9yeJalisVSxWI4HWW22GShYtK0PA5OFik2bKzWoNcKPmC67cr7WkjAfJm5iNfz7ZNAbYPYUlfCmYUqG23yrMyLXhmc4sjACaPUdFY/c8tdX/w2HR/Xt8m1xtytisfHn7xEw/YCtwovcNcpN10mlxuUTQfL9RF4VM31ouzRj3zpqu3sfz0yS75mc2ymhBDih6L4BtYNPXuS1QJ65QdTdnODR0HDkbw4XVodKl4oNaja658Lgossx/aZyDf4+FOXuGtzlsOTRaaKDVzPJ1e1GMtGOLNQI6QFQ8ADqQjjyzXu2pTlqQvL7B1MUbNcvnFqkelik+OzZXRFMOk2VnXjruezULFQhcD2PFQhePJcjv5UYCf4hSNzDKajnJqrcGGphucH+QO7+pOMZWOrx71YNTk1VwHgwKU8Y9k4T11Y5rbhNMp16MCllDx5fhkp4akLy+we6KDYcDgyXeLkXDDM7Ptwy2CKdEsf/3tfPLX6Pv7Zty+uFuAr/Pu/ee5lf+8roe74PH0xB0Li+bCtJ8GXjs3jtD60Y7Ml7t3a9TLP0qZNmzbff2zYFv9hYmUwqjsZ4uahDnRVkIkZ9KbCdMYMIrqKroqr3qjrnINq8z3iWp9HzFAJa69smWuqIKQp6ErQXb/y4cHPFJJhDUUIdFXh9pE0EV1ZjTEP6QqxkMqmrihhTaxaMl5PvPeuviQAg6kImzrDaD+ki00Vl3cqBMHuxEYIIBU10FWFsK7SETUQ17ivIiCiq7xpLOgib+qKrXp+j2ai9KeiJMM6EV3Fdn2mCg1GWoOIY5mgOI7oKmNdcVRF0BkzMDSFZEQPuuvpKCOZGIYqiBoqPfEwiiLo6QjTnwqjCNg9kCQTM+iI6KRjOumYQTyk0ZUIrTvWdDS4D8BN/cF5aiQTva7iGwJbwtHWMa/8NxHWyMYNUhGdVMQgmwgRD1/uw9y3xpllz2Dyquf8t/tff1vC7kSYrkSIwXTg3nLzUIqQrqAqguHODYzy27Rp0+YHgBuqARdCRIBhKeXZ78Xvu9YQ5lLFJBUNvkhNx1v98nZcn3LTodq0+e+Pj3NitkTFcrm5L8mP3jrI9p4Yv/npIyxWLaRQGMtEefvePjzX4+nzy4RDGr4vmVxuYDoOm7oSDKUj/OvReWzXJ2wobOmKMZGrUmhCR0QhGTb4tQc3U6jU+cyhefpSBrsGs8wUm0RCKpbjs28kyfmlBgcuLKEp8MgtQ2zvTTJdbLCrJ87B6RLfOJ2jI6yRium8c28fqWiIx88skYwoHJtr4Lkr0goPJJSaDpbnU61bdKeiFCp1fKmwrTdBPKwz0hlFSo/PHp5F+j7hsEFIVwmJoFAtmia+1HjvviF29qfRVNCEYKnWpFC1ScV0wobGUsnCx+O5S2XwPWIRnUu5GkdmKgghuG2og5+9a4TPvzjPbKVO3FD5xXu3UG7aJA2NwxMFnry4zO7BNHOtbvO/u2eY3kQM07Y5u1DB8n1296cpNRxuHk7j+z4HJwsslhpczDdYKJmkoxr7RrPsGUhSbXo0PRfX9elNRlBUQczQ8aRPvmrRmQjjuy5HpiskIirbehPULZ/hzijnF6tkEgbpaIiPfuM8puuSihi8+/YhooZKR0RnvtwM3mcEUV3hJ//fZ1lo2e598VfvYfdAamV9rm6lLlZMMlED0/PJV03mS3XydZeRTJhvnlnkiTNLlJsedcshV/PQBZgS0iHoSoS5uGyyUd/8WsETEGyHrbj6xFr/rbZuC4ugk+wS2FSFFUiHYboR/Duuw6Zs8N7t6E+xpStGw3a4mGswmgohFI3nJot0hHQ8KdFUhZihMJyJ4QMP7+xG1w1OzxdRhGC4M4amKlzM1fnOuRxCwNtu6iUT06mbHl2JEEvVJjXTY3N3nFQsTN20MTQVRRFowDMTeUbSYXwpKNZthjMxmrZHJh6ipyNC3XIJ6yo108F0PTqjRhCyJCVfPDrHZw7PICU8sKObf3fPGDFDXXXI8X1JuekQ0gQLFYvuWAhb+iRCGpYnqTUdDF3BUBWWKoGEBAE1yyMTMxBC4Hp+KxhKoKnBhdyVrNwnoqvUbW/dMVwPUsqrHuf5EtMJdhfCunqVq8oz53OYjscDu3pXf7Z2bf7nzx3kb59fvO5jWKEvBMm4ys2DHWSScfaNZBjrjtHTETgPGWoQlBU1tJa9pLymg1KbNiu0JSht3si81BDmDZOgCCHeBfzfgAGMCSFuAX5fSvmj3+tj6U6GV///2hN+SFOJh3W8jggRQ6FqeQgEMxWLv/jORTqjOkXTp+FCLKSwrbeDd+4d5NxihacullioOCQjGqquElZVbh3N0JMM8ZkjCzgSeqJh0okIx+dqeEhKpo+hSUpNyYklG6HrLDUFo65kV38Hnz86B0DTcZkpWcxWXZqOz6XHL5GK6KiKQNcUXM8nFtJJxTTet3+M47NlTp/I8a49fSw3bOZLBTQFRFzFdAVdcYNbRrOcXaiyFLbYnI1x3A1CCgqmpCNuMFt1WK7a9GU6cD2ffM2hUHG4b1sXtw6nubRcpysR4qf2D6Mqgobt8tipRVRF8PDNg5xbrHJmvsotwynyNZuaXaAzanDX5ix1y0dVagghGOyMMVu2SSdC9KYjjGXj3Lm5C9fz+a9fO8uLU0VGs0lUVWEkE2cgFSGqhzg4WaQrEaLpCfI1jwu5Bg/u7F61r3twZx+eL/n4k+NMFywMzaBm+8yUbY5OF5kvmwykIlQsyUO7ulc71QPpoHNYbji4VLB9QU8yuqrV3dPyT/Z9yUg2xkLZ5OahFEOdUTxf8q0zS1SaDg/s6F7d5i/WLzu+jmbiG67JntaajGsK8VCckWxwv3LToWkvUbYg13BJhg1GuwSzJRPp+BQtqNgbF9+GSiCLcOWqNl9wOYzCb/07rAmyyXAgsXE8YobCrcNpzi1WKTddOqMG77y5j6l8neLFPEgQmmC64mJ7kqpT4T37RpkuNhnOptk/2slTF3JsagRx6mcXq/jSZ/OmTm4fyzCdb2L6Ctu64pxbqvGNU4sMphv0dQSpktl4mPHlGheW6iyGdbJxg/1dCTb3XN2h/faZJToiOg/s6OaRPQNX3b6WWCg4/XVEDVYMAlv23uwb7eRLJxYwHY+K6fLkuRwP7uzB0IJitWq5PH5uiYiu8dDO7nXa7YOTeaYLDe7anKGzMwj7WSGsXz7lKkLwzIU85abD/Tu6N7RJ1FRl9bnjreOdLTV5+sIyg6kId2/JAoHW++JSnX2jaTZ1XV5TQojVx62gKmL1tW/E3S8j+dgx0E1HJE+5+cqkUYsWmKrCaE+WX3rrlg07+Suv1XiFu1Zt2rR5Y/BaHZJ+mByGbqQG/D8D+4HHAaSUR4QQozfucK5NoW6jKQpD6QhV06VYt2k6HpP5OkarUNuUjXHTQAeZmMHBiRKKEJTqNqbjYWgK+8dS7B5I8fkjs4Q1ge2C5XocniytapR9CYWGzReOztJwPEoNm6iucWa+wmxRw5eQq5osVYIwErOlTbc9j6oVeHh6PoR1hZDmsLs/yfOXCjx9YRnPl/x/T08wkI7gS0nN9MjXnVXv63+zq5flmo3jSU7MlfF8SdUMdNBz5SaW45OM6AgBg6koE/kGlutxcq7M3sEUv3DPGLHQ5W7a8Zky47kgFKGvI8xT5/P4UrJcs7Bcf9W3u2K69HREePOWLCFNoWq6PH1hmU3ZGG8a7eSelQJjqsi3zyxRathMFxr8yK5e3ry1k/u3d/EXj4/jS8k3Ti+SjRtczNW5bThNJm6s049eWq7TsD02dcUo1m1yVYsDl/IslAOv79PzVe7bJhlMR7h5aH0M96GpApP5YJBzLBu7yldZUQQ/tW+ImumuDo1N5OucmA20ti9MFPiRm4KOorVG2vxX3znHf3jbrutei09fWObF6RILFRPpS1xfsqUzxmwx0EdLrq2vdz2Qilw3GCvhKo1805VMF5ur2umG7VNuBmtDRVKzHI5Nl5ivWMRDGk3Hw/ehYAbDylP5Bn/81TPcMZYBYK7UpGF7pKI6uWowROlLGM3GODZdpmF7zJdNdvQk+NTBaWqmy4GJAm/f3UuuZrFnIIkvJS9OFUlFDbLxEFu7Ewxn1ssTDlwqMFUIPqMt3XFG1+ipXylbehL8+ftv5ckLy5xfrHFmocpQZ3RVsnZ4qsjE8uX1sL03WA/lpsNz43kg0F2//yUkG1OFBsdb6+PApTwP7+67rmN7+vwys6Ums8UmO/qSRA2VJ84tA9A4564rwL8bPDeep/oKi28ILvCKDYdPHZrh7Xv6GPsuH2ebNm3avJG5kQW4K6Usv5Lt1BtFMqJRbjrMlkyycYNMzGC+YqIpKpqiULMcjk6XaFguAig2bDzfx0OSq5pYrs9y1eIrxxeoWS6O4yGBXNXC99dLAjRFgJTMFRvUbZ+I5oKAuZJkvmyiqQqdUR3XvxyzvoLjB5pZSdDlmi42CRsadcvFdDw6OqMsVy1URRDRNeYrNSzHw+yJc3S6xInZEkemSzRtj7CuEA1pVE2X7ngIRYHpQoOooWE6HsW6TcP2sByPFy7lMV2PO8YyLFVNxpdqPHk+x9nFGrcMdeB6GWZLTVQF7t6coWp6PHU+R9V06E1FmMo3iBoqqYhOrmaTimi4vuRLx+d56sIyH35gC93xEJ6UNGwPRcCxmRKbuuP4BLreb5xeJGqodCUMapbLVKFOel7Hcnzu39FNvm7x3HiehUoQ5b2jN0mp6TDSkiVYro/n+VxarpGJX51OOZCKcmymjK4qdMVDV91O6z1fKb6bthcM+BXqDKajDLT0rVfyzj1D11x3VdPh22dzRHWVt27v4sRchcfPLjFbbOJ6Pp4vcV2PXNVal0K6lrWJpz7B0N31sPbpJHB6vkpLnYHiehyZKeG5EikgFdXxfInSSkpVhCQV0RlfrmEoCguVJifmKvR1hHloRzffObdM1FAxVIWJfJ2J5TqJsMZHv3WeUj24wLJcn1NzZe7b3s3FXJ2lqkW6NZMRMVQ64wYHJ4KLojs3ZxhIRRhIRTg9X1m9/bXSETXY2Zfk4lIdVbm8KwEwkIpwdDoI2Fmr3Y4aKumoTrHhrCZSXotM3CBiqDRtj4HU9WudB9IRZktNUlGdeEhDUwRdiRC5qsXABhH1ryeu53NypvSa3IuqpsM/HZhkqWqTjYf48P1bSL2CkKQ2bdq0+UHgRhbgJ4QQHwBUIcRW4NeA18/f6nXE94Oiur8jTCKs81/fs5dczSQbD/HXT1zi80dmqJoeF3N1Pn9klluGUnQnQ6iK4OhMCdf3WayYeK3I2rChoPhge/66L7JUROWuTVl0TeX4fOB+YLo+nu+zVLXxfYmmwYcf2MKz43nOzVcomw4NK+iAA2RiOrePdKIogkrTZa7UZHtPgprl0p8KY2gqeweTfOnYApbvU27YqMCTF3JMFRpUTbdlyecjhEfc0IhHNOKGhu9D2XTQVYHtegghaTo+R6bL2J5kqaVrPj5T4thsGV0VLLeiuHuSISzX4227eqnZLl86NkfDdjkwXiAd00mENYqNoGsf0hUihsahySIAXzuxQG9HhPu2ZvnqiQV0TaHYsFmqBC4RuaqJripICamIwb1bs0wVGsyXTeqWx1BnlFPzZXJVi2w8xLv29rOpK0ahbpOM6Ktd95OzZRRFUKjbqwmAK2zvTdDbEcZQFSLXEdF9bKbETLFJf0eEO8Yyq0N0weesUWp1EMuWBWycUnhossjFpRoQDAl/9fg8l5brSN8npAfDpUIEn7OuKviuz5VluCDQfb98WO9lFIKiW6yJKHbXLFQfsFptdhUIawoj2Rh1y8N2fd68JUs0pFFq2lSbLsdmK9iux2yxydnFGt2JEDFD5blLBRzXX71AnCmaaKoIEmANhXLTXd19ysYNfu6uUaKGSlhXsT2fJ88HXV/H83nf/mH2DHYw3BklpCuvm3Z4c1ecD755FE0RRI3Lp8ttPQl6klevB11V+MAdI9Qs92WTNxNhnZ+/exTL8V+R1d49W7Ls7EsSD2mrUo33vWmIctN5RWmfr4Ynz+eYLpkvf8droBBcMH/p+AIg6E+FefTYHD971+jrdYht2rRp833BjSzAfxX4j4AF/BPwNeAPbuDxXBNDU+iI6CxWLHRV8BePX0D6kq+dWqRmOTieREqwfZ/ZYoPJfI1KMxBR266H413uQkqgaftsVB+Umx5PXVgmFdWRLV9qSTCQ5/rBPxQh+OyhGUK6GlitCQUhLtskNl2f+XITx4eQpnBusUpYV+nvCHNsOvD+7UoYbOqKcWahStP2ODRVZLlm07Td1Y6pJHADsTwfTVHI121qtkvTDqLJhQDPA9GSJByZKnBhqUoirNMZM4gaGr6UjGZiDKUjnJitUDEdjs2WKNQdcjULx/VJhjUsx+fCYhXHlzQdl55kmKl8nfHlBsmIzpMXctiOT8l08HyJZbqoiuDFqSI9iTD9qSin5qss1y9b93XGDBQhuJircX6xSk8izMRyA0NVODJdYrFqcvfmLJ86OMXxmQp3jKUJGyqKEKSjOt88vYjt+uweSPKFo3M0bZ/37htkuDPK0xeWyddt7trUyZePLzCZr/Pu2wa4aeCybKUnGUYRgpCuMtgZ4c++dZ5Sw+EX7x1bHYAD2JS9dseytyOM5/tMFxqcmguxXLeYKTTI1y18CZajENUVkBLHu7r4Xllvr7RbuXL/65nP9iWUTZeLS1UqpocQguapBfrTEW4dSpMM6yRCGkXPJxrSGEhFubRcp265lBpOyzFGo2Y5qEIhHdVZrgYypWzcYEtXnIl8g2w8RCZurGrzVUXQ0eqyNx2PC0s1tnTHrypkj06XmMjX2TfaeVVHutx0eOJcjo6Izr1bsxsON5YbDk+cz5GOGtyzJbPuPisOJVcyVWhwYrZMbzLEYtViIBW5pnd3WFdf1cXClYW2pipkrrEz80po2C5/8e2LOJ7HL791C6no+t+zpSuOrgrMV+mMKYGq6ZIIB7aTtuuRDP/Qu+G2adPmh5AbduaTUjYICvD/eKOO4XpRFcHtI510hHWeu1TAnilzZr6C7UkkQUcnqqvEwxoV06Vmues6hhuhAKmYQUdEY7LQXH2uuuXRcDzUVvdREATahDSFnmQgw5gtNXF9SSYewnH91ere9iSqCKQng+ko86UmkkB37fpBASskPHFumffcPsix6TKLVcFsqYnpeIQ1lUzMwPY8FKEwnI4xko1wfjGQCOQqFlFdQSJIxoOLBF1VMF2fmuWTbzTY2h1nz2AHv/XwDpZrFjv7k+iKwnLVJqyrfPHYAsW6RUdEJ6wr3Lkpw6m5Cs9ezKMIyUSrOJvMN/B8H9dTOTJVRohAZx0LB/IUXUCl6XIhV+Ut27qJGAqff3GOyUKDzpjB3sEUY10xDFXh/FKNH791gJ+5c4Qnz+eYzDeYKjSIhzQ+e2gWgIrp8Ps/uhvRktocmwm0uUenA1mOBHRN8O7bBjlwqQDATLHBU+cDfX3D9vijd18uwEezMX7+7lGEAgfGCzzV6tb+8wvT6zzkv35ykQ/cObbhGtnRm2SupcU+OlMOOsxesLA8HxTh40tBKqKx0PIkv2qdiau9zldYK095tSiAaXtYjo/bugpcqFhIBL9wT4o3b+3i/fuHODZdYu9giqcuLLNUNTk2U6JmuSTCOkOdIW4Z6sDzYe9gB8dnA13423f3ct/2bpaqJsmwvs7CUVcVfvrOYf7b189haAqPnVpkS/d6TXHDdvnWmSUgKPp+5s6Rdbc/P57nQmuHYbgzuqFm/Nkr7nOl7nwjvn5qAcvx+eqJebb3JhnP1dncFV8dwn0j85Xj87wwEazvfzk8yy+8ef3aHMrEeOTmPj55YOYVP7cAVj5CXVXRPUkipHFyrsqP3fpaj7xNmzZtvr+4kS4oj3L1938ZOAj8lZTy1e9zvs5MF+ocGM/zwkSBuu1hux7xkEahEYSqrCTt1S2XWEhrBbVcuwJfCaqxajalpoNc8zb4gI7AaRUzK7eYrk++bhEP6TRMG08KGnaQuGjaDg0nSAfUFA3b9biYq+H7fiAVEOB4Co7ro6sKdcvhD794irrtkYkH9otV08X3XAhr1GwPRUiKDYvqjEOuZiOlxHaDzreqCCQayYjWSiT08QLpOr6EyeUG//4TL9CTDPM777qJ/lSYc0tVLuXq9HaEqLakCqbjcX6xxlLVpGl7KIpgqNMgGw8zVzJXta2m41MzHWIhlbrjI6UkGQsRDwcJlWcXKmztiYOAiukSNVRiIZVc1WK+3GRLd5xUJPBbHkhFmMw3VncFUlGdUkuvGzFUHj+7yPPjRXI1i5FMlNFslLOLVRzPZyAVpSOiE9IVLCcYJH1xqkSl6TDYGXRXfV/yzMU8Ncvl3q1ZYiGN4UwUXVVwPJ+Rzui6wvf2sfRLrr2xrjjHZssIoGY6hDSVigzWnetB2fNw/Gv/qVyr+F67tl4LHiBak5wCQAR/D/GwymS+TqFhE9JUDk4W+MsnxpG+pNx0gwsJCcWGxYnZIKDonq1dxMMac6UmPjBdaFK3XLoT4Q1/d0hTGc3GmC+bV3loAxhqsHNVbjp0t25v2C5PtDTo2dbPDE0htYEExPclc6XmNbvr16IrHgrkR62OezykXZds6Y3AUGeMctPBl/KaGvY9/Sk+zcwrkjVBazfGB0MXaGrg0BI2VBJhjX85PEO56TCaiXHv1uw6V5k2bdq0+UHkRu79jQNdBPITgH8LLALbgL8GfvYGHddV/PML05xeqFJqBhKNuuXy2+/YyddOzuN50HBcpgpNBHDHWCeGqvDYqQVKLxMHKeGq4bmwFmxtL17R0RSA6fhEdElvKsZkvg6+j+N6IEQgWZEQMQSur1Gq20iCAswQUDU9xrJRoobGQsViqRrotTMxg9uG0xyeLFE2HUzHx/dBUXyW6zZSgusFEgdDU4LiN2IwkArzlq1dVEyHmuVybKbM7oEOQprCt04vsVAxmSo0+MvvXODBHT1M5uvUbZfFCtw+0snFXI18zeLcYjXYko5oRHSNX3rLJt7UGuY0XY+BjijfOpPj3GKFM/MVDM3HUBUe2NHFe/YN8w/PT+J4kjPzNcayUWzPJ25omK0B1WwixJu3ZFe7j3dsyjCajREPacRCGn/87r1M5hvc1J/k2GyZx04tMVVo0NcR5rbhNPfv6ObuzV04vsdoJghf+bm7RqlbgVTmTaNpclWLrS1XlPHl+moHMaQr3L+9m209Cf743XuoWS7be5P87uePU7F8VAG15ktvlYxlY/zcXaP864szJMI62XiIc4tV8nWLxUrwGTds/3XpZr8Wwlogt7lvWxfdiTCxsMpM0eT0qSXSMYPnL+WpmYEsK6QqjGWjxEIaZxaqlE2H6WKTfSNpnr2YJxHRmc43yNUsDkwU1kWlX8m7bx8kV7VWC+y1aKrCB+4Yptiw6WkV8S9MFDndmq94ZG8fP33HMBFDXbWrXMv4co1y06GvI8zOvsQ1JSdX8uO3DrTmDQzydZtUxPi+8bOO6Cp3burE8+WGFyUA9+/oZiQTYTy/cTLpSxELqWzvSQRhRbrClq44omXHOFNs8KaxTrLxEHsGO17+ydq0adPm+5gbWYDfKqV8y5p/PyqEeEJK+RYhxMnv5YFIKXluvEDdcrl7S2bdsBVAfzq6msi3oo/+3IuzlBoOYV0lX21SbjpICafmKxTq9ssW32tZVzz5YF1Dv+JLEK3O9IpkRQXCusAmcKTwW4/35OXntH3QpU/VdAlpKjXTXu2MVkyHsKZgui62669qzz0PpPSCwVEh8GQQwR5SBYmwTqHhcHCyiK4Kyk2HXMXi8doS8ZDKUtXE8SS+lJydr3BqrsJcqUkirNOdDGNoCrv6kzx2chHb8dEUQcMPdK07+joYSEe5mKvzmUPTdCdCLFVsyqZDKmpwYq6M6fjc3kxxfLaM40mm8jW6kyFGMnHmShapqE53IsR3zuUoNZ0g1VJTuKm/gwtLgSZ8z2AHhtbSg1cspguNVkBKMHDbnQyxqSuQJFwpO4iHtFVv5a5EmK41HdpUVEdTRCARWiM5WOdOsRLmImEgfXV31/V8nr6Yx/eDHQvT8UlFdZ4bz3NhqY7lBpKPtXMFNxJJMJSpqz4HLhVQFEEqomO6gVuOJ4OL1kA2I7GRNByP/WOdTBcalJtBR//kXJlDU0WiukrMULmYq6EqAtP22DPYQSYW4ukLywgBri9JRw32j3Wudpo3Iqyr9HVcvn3lM1EV0VonG3fXAToixqpn9itxKdFVhblSEFN/9+bM9033G4K1fX6xhufLDRNkPc/jx//8SZZqzqt6fscL5lQ6YwYRQ+OW4TTThQYRQ8XQFGaKDc4uVtjRl9gwmOh6MB2Ppy8sty4mMtedHNqmTZs230tuZAHeJYQYllJOAQghhoFs67aNZgyHugAAIABJREFUBa3fJcaX66vevZoqeOsVHbefun2Qb5xawHJ9io0gbe/4bBlNUXA8LxjAdAMbtgtL9ZcsiDbqVCYMgeWD9CVSETTt9RNOnVENVVECjXkjSKxcQVVgNBvF8wWFeuC0Yrvri38BCEVguRLT9tZpkJu2y/mlGgKxWnyvPMbzCazl/Mv396Xk9pE0R2eKnF2ooCgKjutRabrYvs9SJejECwJLxAu5Gl7rYiEdM9g3kuYnbxtkvtRkuWqRr9toCihC4aFdPezoS1Ks23zsiYurw3qZeIhkWGdbT7w1BCr50vFFIobGfKlJpOWM8eCObh7c0U0qanB4qojt+iyUmzxzwcd2fQZTEb58fB7PD3T0t42keXGqyAsTRYbSUTZ3B/7euqoQC2mvaqgtGw/xs3eNYDo+vR0bF3eNlmONBM4uVOntWF/cnZircHiySLFuY7oefR0RVAWWKha5qokQYL/ckMH3GEHQiW84Fp4PS1Vr9ULE8+S6wc6IrhI3NH7ytkFuG0nzpWPzKIrgK8cX8KQkZqjcPppmuWbzzMVlinWbubLJ5q4Yx2fLjOdqpKIGnTGDvo4wQ68grnz3QAfZeIiQprysJrsrEeLnXuaz3IjZUnPVoUVKed3+3m8Ejs6UaDrBOe3wdImbh9dLpP7gy6dedfENwTlBEISfPbK3j76OCHsHOrhtOM3p+QovTBSYLjQ5NlPi9pGNB1dfjoMTxdUZjmwidJVnf5s2bdq8EbiRBfh/AJ4SQlwkOCePAb8ihIgBn/heHkgirKEqorXtalBuOrxwqUA2HqJuu/hS0pMMc3qugqIEEdKOK1F02QqPCXy9veuoiTYqzl0ZlOWuD8KXV7lWVE03CDGpWJiut27A0/bh5HyNuKGCkNStq90wVsJWbNcj3/Dx1sheGo7k/GIVx19/bCvuGVIGRfjKQWmqoFC3aNoeVcslpKo0HJfmmoNSCB6jKIFkxfVBV4Oo6eMtqcpgOkImHsLzJQtVk6gO//T8JP/Xl0+zrSewtHO8IJuxaXtBgmPLPcTxJJbjsVixWKyYVEyH0UycUtPm8FSJ/lSEVEQnFdVbEd4uJ2bLnJgrU6jbFBs2d2/OtCQFAkUIFqsm/ekwQgrqlstAOoIm4BPPTHAxV+O24RT3bMlyMVfHdDzu2pwJkkMrFvs3dZJcI2G40jlihal8gzMLlXVBOSOdV3dvU63AI0NTVrt3qiDQyjsehqpAcMl0w7vfK3hweXuI4DNyrpBXKQQXjJ4vaToez40XODFXxnJ9epPhYE2ZLtl4CMsNrDvrVhDSY7ke+ZpFsWFjuz7HpksYemBP2J0IMZCOMldqYjo+8XDQ+VzZyTo9X2Gm2OT2kTSdMeMVFdOXlusU6jbxsHZVoiTAfLnJ8ZkyW3sSjLWGOOOGhq4KHE9ecy2sZeV805MM33DpRV9HuCU1Y0MN+K7e11bMNp1gPuWZizk0RbC9N8kdY52rybGn5itIGew+QHDxf2CisPo3t3YQ91qsSGcUIdb9XbZp06bNG4kb6YLy5Zb/9w6CAvzMmsHLP/leHkt3IsxP3zFM0/EYTEd59OgcF5ZqLFRMkmGNqKGhCkEyouP6gbQiFQ3s9kY6I3zzTO41/X5FiNWO5kYFleNDtelge9e2lKvZ15a8GAoYmmhdZATb72un86wNHqoQFNuqkHgSNAGqKtjeHadQD+Q26ahBw/Zo2uuL71Q0cKxIhBUWqzaZmEYirNGfjlBo2Hz20Ay/9uBWPnDHMI8enSNqqHznXI6JfAPPlyxWTfaPpBlMRxjujHBusU4spNJwfLoSIUpNB0NVmC0FvuXFhkPUMPmbpyeomi6GqvCh+zbxGw9t49R8hf/57AS2J/n4k5cYzURJRXT6OsJs7orzgTuGMTTBUsVioWSRimp0RHR29CR5ZrzAo0fnmC83ubhUZSLfWLXma9geZxeqAJiuxzv39l/7AybohD56bO6qzvWjR+f51YfWR6qPZmN8YP9wYAWpCEpNh796/CKW66MpwednKIJYSKVqOoETySudiPseI4BUVCMR1rFcH9Px+dyLsxTqFtmYwTv39nHX5ixzxSbFps3R6TKlhsOmrhgzhSa26zGZb7C5Oxi2rVoOdsPnUwenuXU4zXJtjp5EmNMLFfaNdOL78NCuHqqmw9dOLiAllBo279137eCjK5krNXn8bPC37XiSh3f3XnWfrxxfoNx0OLtQ5Vfu37IaxvQzd45QNV0GrxHAtJbvnMtxcanG8dkyfakw2dfBTvDVcsemDL/zrl04rs/NQ1cPCO8Z7MRQBLb/6i79PAmm7XPwUpFc1eauTVlSUZ2dfcl1634l9Oj8Uo1nLwa7k4amcPfm7Es8e8DugQ7SMYOQptzQ97JNmzZtXoobbcC6FdgOhIG9QgiklH93Iw5krdwg0fKljYc0tJaec7FiogiJ4/pYno/t+JSbDtWmHXgDt1wgXs3XUtPxVuvhlRCUK3ktBVbg2e3jehJXSgQvr4n0CYoOoQkUAUqr8TRZaGJoFr6Ul59PXPaM9gHP94mGdOqWh+n4aMIjFtbwPMml5TrThQZ//cQ4d2/JMLFc5/BUEc/3UZC40JLCBMd58FIp0GXXbWaKTTLREDUz8JA+N1/BRdKwgtfm+5J83aZuuXz95AL/28M7GOyMIAHH9XC8wM98pDNG1XT546+cxpdQadqcnAsSFLsSIVIRg+5kCF9KTMfDdAKJzVShQbFuEwupFBs248t1slGd2WKDZFjnns0ZDk4W8XxJOqrz5IVlBtMRHtzZ07og0cjX1qurdvRsHJfevSZ1saPl4GJoKpbjBoOyUhIPq3g+2G+UNvhLIIB4WKdiuvi+pNJ0qDQtDE1FCPjc4Rn++YVpRjJREmGd8VyNuuWhqzZV06ZQD9ac6XpIKVGEwFAD6ZGhKqQiOroqCGkqigj+ho/PlFmoNFGEoOkEBfyJ2fJqnLzny1VLyf1jncGF6Rqihroqo7nSq7rUsHlxqkSjJReLhQJHoMuP1TgxW+GZi8t0J0LsH8sQ26CDDpfPN4Z2dYBQxXQ4NFGktyPMzr7kRg9/3dnVd+0ufDC8rGK/iij6FXwCKVuuavHE+Ry3j6RWX9vadQ/Buev4TBnPl9y1KXPdv+PlUkjbtGnT5kZzI20Ifxd4K7AL+DLwduAp4IYU4Gt5y9YuhjujdMYMLizV+MQzEzQdD1AQClhmkGDpeZKFqk0mquMjqZkutvfKi/C1O/XfFWWvEEgEnhcMZ6pCoitBZ/2lWDmsTNwACUs1m6rlotiCiKHiuj6i1Y0117wI0/WJhTQKdRtFBFvsXQmDMwtVLMej7Hg8dnqR0wtVyg0b0/VJR3XevqePI614b13VuJCr0LA9XM9HVQKpSEdEJxpSyddtimbLsaX1O0KaWE2Y/MrJBW4f7SRXtbipv4OJfJ1N2Ri+hE3dMZ65mOeJczkczw+GTH0fXVGIGhrdCYWRTJS65bF3qANdFWiqwqVcDU9CpSk4M18laqgslJt0Rg0+eWCKmumuWhaO52oUGw6JsEZHxOCeLVnee/sQs6UmH/3m+dX3arb08uMOmqrwqw9uZf9YJ3/yjXOcnq/gS6g2ve/Oevku0JU0cFtrpGq5QTqnVBjtihLTFQ5NlWjaHsdmSqSjBqmojiIUapZL3fZoWG4Q4mIFEpWtPQl++o5hBtNRXF/SmwyzUDExNIHfCqH65AvTAIxkopQaDqWGzWOnFulJhulKhDg+W16d/YgaKjcPpdYdcypq8IE7hqmYLqNXDOI+dmqRmWITT/r8yK5etnTH14X0HJws8PjZJY7PltnaE8d0fN6+Z2Mt+H1buxhpnW+ulLl8+8wS47k6YiaQh1yPpOW7yVBnlB+7pZ+/fXbqVT+HALb2JLi0XKfUsPn75ydbXv5Xy0uOzZQoNoMU4DMLFe7cfP1FeJs2bdq8kbmRHfD3ADcDL0opPyiE6AE+fgOPZxVFEWzqCkI9IrrKmfkKS1ULSTDEuLbo8SUUG4HThvsqiu/vBZYrUZBorfa61xqSvBYql6PLLTeImI/oKr5/OXjItL1A0uL5OP767r8qBKbj43hBl1wogobtE9IULA88O/BjNh0XKaFmOdiOx0AqTG8yzFShwWypjqEKmtAqvoOOZd3yMFTR8iJvdd9bL0ZVxGrH0lAVFASzxSaO57OjN4GqKDRslxenSiyWTRRFoEqBoSlYDngySDTNJkLoqsJUocJi2aTUcNAUJYhJl5JMKkLNdhEi8MCeKTZp2h5ThUAv3BEJpBbFhoOuKhTqFp88MEkqGhTiaxnJXK1HLtRtjs+WGUiFWapYmI7P4akiF5YqSOnTstB+Q661a+F5EgevtVMk8GQQprS9J4HlejheCSmDz6BmORhakPAqRbDuNFXB9SQKAl1V2Nab4J4tl9Mr50pNLuVqmK7P5q44qaiO4/nMlZoMpMKMdcU4MuWgKYKQrlCo25yaK1NuOnREdGIhFdMJkmjnSk3u3JRhW0+CTDy04TDuSjc7amhs60lgqAqHJgu4nmTfaCcxQ1u9aNRVheg1ut+w/nxzJSs69uCi9PpdQZZrFifnKmzKxl7RkOr1UDdf/RAmBOt2vtQMdtBUSaFu89nD0/zErYNX7RIkw8Ech5SQvE4byDZt2rT5fuBGFuBNKaUvhHCFEElgCdh0A49nQz725DhLNYuyeTmmfUUmslIAuRLcl2sn32AkBFZfMiie3Suqt5WCXFVAVwSqqlBricMdHxwrSOeMGgq+L1HVYKvfcX0gCAHyJRhqYPXm+j6+56EIQSKkko7p7B/L8OzFPJNePbCRi2iUcVmuQ912OTRZIhnRqJouEo2b+pM0LI/BdIRLuRqLNRvX8xlIx7hpIMnmrjjnl2qcmC2jCMFgZ4TuRJjZQpP33zmM6/lEDZWKKfnAHSN4vuS/f/sCl5bruJ7Pz901zFA6ykA6ypePz7NYbhLWNe7elKFiOnzz9BITrSG8WEilM26wpy/FpmycOzZ18ujROU7OlCnUg273ybkKXYkQD+/uZSgd4/hciaiu8tipJQ5PFUlF9KssJufLV3fAv3pigcWKyReOBH7kBy4VOLNQxfV83OuZ9H2DoSlB5zppqOiGio/E92FnX5L/45Gd/N0zE/R1hLAdP9iJ0NSW9aKB40h29iepmS59HSFA4b37Brl5KLVafEsp+dcjs5ycrVAxHW4bTvPTdwyTiRtUTZe5ksk79vQxmIqQjhkkwzr/8PwkS5XAqeVHb+5nc3ecx88u8dlDMxTqNpeW6/zaA1uv6ZTyb3b1sKU7TnciRFhXOTlX5olzgfOJpgbJucmIzrtudglpCpuvUWC/HPdv72IkEyUbD11TwrIRXz4+T75mc3ymxC/dt/l1C7Yp1C0ePb7wmp9noWqzKRPFdH1CmsqjR+cJayrvuUKj/9DOHjpjBp6U3D780qFVbdq0afP9xI0swA8KIVIEoTuHgBpw4AYezzrOL1bJ1SzCmsC/ItXy+68ECgrwqnnZ/m6j2wFcP3AeCIv1r1Jp/c/3JaYj0X2XTDxMkyDVUAiB9CS+hIblIBwF2er+IQQLFYvZYoNUVGcyHwwXSqHg+IF9o/QEthd8GauKi+PJ1hevje377B3+/9l78yA5zvPM8/flVffR992NmwABAgQJniJlirpv2bKlkGRZXtszXnu9Hu+Gd2c2YiK89sSGPY6J9dge2zOejZl1+JK1si1LlizLoihRlETxBEmQANFAA313V1fXXVl5f/vHV13oRgMkQIIESNcT0dFAV3VWVlZW15vv97y/p4fnF6qcX2+yVnPYP5zhX7x9N7/30DRLFWU9CEKoNF2EEEyv1IlbOk03wPFDinWXx8+vtwc3fUDy3HyVmGGQjpscnejh/1ttsFh2OLlcY7niUHXUyoYQCpNo6TphJBnriXN0soe5koq0T8YMlSbqBsQtneWKQyZuct+eAWwv4Duni2jtIdaLKQ6D2e3d1bipiqWEpSOEwDQU7tL11at0vUN3XkmGxhZSTyRBhhIniNDaXO8gguWqzQ/OrHVsOgXfac8eRETtlRFdCCq2R80JSVg6t032sLM/xXMLVQ6MZOlNWQghOvdregHPzle4aTjDQCZOrRVgmYoDv3cTjs4LIuZKNuP5BFNte0ncVJ5vP4xYq7uUmu5lC3BT17bg7TZ7t2OGOk9KTRdd0zjYtqfMl2yemS8TN3Run+q5IsylcdHjAMy3z7uDo9ltlpRiw+WllTpuuyFgGRqaeLn1rq2KoogvPr2IH0b8xO0TWBexwC1d24IlfS1aqzvsaa8euEHEicUq2aTJXTv78IKI06t19gymObbj1eEIu+qqq65uZF1PCsovtv/5n4UQXweyUsrnNm4XQhyUUr6hgTwbKjZcvvr8MlLCroEM4701Su0uuHODd7pfTpv3XBeXjymPUHjCzepPm3ihVJH1QCQFewfTDGVjzBSbzKw1qDkBQQRVN8TQVDGdjuvUnJDA83lqtkI+aXS8rglTI2ZopCwDQyjf+Gi7IIokLFcdbC+k0vT5n9+xlxcnq/zuP01TcwIeP1fib59ZRAB7BtIs11q03ICZNQXSmS01uXtXHw03YKInyW/+w0kqtk/d8RnrSbBYcfjhuRLfO7POh28dxfMjZks2FdvjL5+YY1d/mr50jA8dGeWZ2Qq0sYAxQ+9YAd53aJjdAylm1ppUWz4nl+ssVlp861SBkyt1fva+naRiBp+9Z4o7d/aSiRkcGN06SHemUONdB7d6gz9wywhn1xqM5uIUGx5D2Rgnl2s4vo/gxr8A1MSF88tor4wgwI8kTT/qUHcWyi6//62zfO7eKRKW3im80SRCCGwvJGZozJVaOH6I6wXsG8rwpeOKJjOz1uCz9+wAYDSXYKI3wePnlA3k84/P8X984AC7B1KM5BLbLnxafkjCVLaije7wXTt7ycYN/uyxOZIxnX86WWD34JVh93YPpPmx28YIIsnugTRPzZY7HfGYobGzP8XfPr3AY+dKJC2d5arD5+7dcdXH1g1CvvTMIkEkmSvZfOrOyS23f+XZJSq2j64J3n3zIBO9qasKovn6iVX++qkFQIEuP33X1u0X667a3jUowuteRF/G4lN3TPH3zy1xakW9f8pNj4YbUmv5PL9Y5effvmuLv76rrrrq6q2g601BAUBKef4SP/5T4LY3eFcA1Z0VwNlig+mVOpWWr5b+b+S2Y1tX2h01NZDR5T9HL95ONmlRal6wSwgBuiaZLjRImAYpS6fqqAHIjSAeKSVNN0C20YdSKraJ+rcanCzbHhoQM3VipsZa3SUTN7hrZx8nV2p4QYQnJU/Nlqm7fif8qOH4zBUbPD5bVjsUwVrTA6k46r4fsV530HWN9abbHqJVftud/WmqrYCK7SkfuBDoukprtL0Q1ws7JI1jO/pwA4ntBazVXZpuwNOzZWbWGhwczXHfXhW9/vBLBdJxg4Spq222v2bXm6xUHW6b7LnkkFnPJYbq4qbOwVFFouhJxVhvemTjJvWWYtLf6Odh1D6vBOp7hPLKB6FEEnbOLbUq4zNTbBJFF9Jb06aB7YdEUmJogmYYdfzhZwsNDo/nKNRcvDBSKzJByKmVGhU7IGUZmIaGrgnSMWOL/7nhKh78eE+CpKkzkImTTZiUmx6nVursHkhx82iO/SMZ1hveVScxTvVdINpYm37X1FUXWtfb55omXnXKoybUBUMQhZfcxsbPkpbOobH8tttfSfFN52jC3H6+xk2da1kLW5rO2/b088JSjdWa2zk2lq4uMw1NdIvvrrrq6i2pG6IAv4wu+1dXCPG/Aj8mpbxPCPE7wDHgaSnlv7oWD5xPWtw6kecvHp+j1PAIogse51eSIdjmr34jFTdAIvDadpDN+yXlheHKUMJILoYfSMIootoKOgXQeM6i5UeUbdXtFsBazSVh6WTiOl4gSccNTq2orrcuBJm46miHEUz0JKi1AqqOTxhIkpbOSBujtlhpsVRu0XAVjs4PJZahkU2oYbtCzaFka9w8muO3P36YP/vhHPNlmz9/fE4VW2FI3DQwdZ1HpteZL9lqoE8DyzDIJw36M3GKDZe6FxLTYbXe5Oh4jpilc2Qsx6HxPF4Q8k8vrnJwNMtAJs43X1xVuEUhGM0neWD/ILdN9jDek6QvbWG7AV95dolnF6o8PVdGSsmOgRSpmM4T5ys4vhoO/eV37iWMJMO5OF4Y8aVnloikpFB3+fCR7azwhvvKOLe7d/Xx7z52iD/41jQL5RZLFeeG7IJvvGFDSQdN2UnARL1/dDQylrpPOm6Sihl8/8w6TVd5pftSJumYyWrdBSQxXcdMq0AnN5TUnIDz67YqBIHjCxXOrTV4aq5M0w24b18/B4bVfMDFw4f/eGKFuZKNoQk+eccEKzWHHf0pvvTMIusNj+PzFX7+7bv40aNjnCs2txTUV6tDY1lipoaxacDyk3dMcutEHsvQuflVIgVNXeMTx8ZZrLTYe4nu/MeOjjGz1mCq99Xt+4P7B9EFuEHEuw4Mbrs9ZunkExYt331V2wfIWhqRgIRpMJqP4/oRn75rktsme4iZGreM5fDD6DW/Bl111VVXN7Ju5AL8kmWsECKGoqcghLgNSEkp7xdC/JEQ4g4p5RPX4sGHsnFFA2lXsZtZ15eTDli6ILiOFXgUAdrWfd2wLWgaaNGFosh2A0xdwwtU99HUhWKGo3zHot2qlKgl+1TMoC8Vo+r4CAS2G+AFEXFTb1sITLwwImkZNNwQo516GEaSyd4kuqb84NmEyVLVIZLKOy6ESsnUTEHFVqsNtuvzhafmScd0BtIWp1GvQcI0SMeg3PSpOR5BpLqjkRA4QUi1BZl4SMJUloYwgpgmaAURvWnVbV6utjg21csvPZilavucLtSRQhWEjh+habB3MEM6ZvDE+RItL+DsWpO661Fr+URSWSS0doqmAFZrDv3pGDdvspjUHZ9ay2N23SbW9tJeHMRzMff5ctKEQNe0Nh2EG7ILbrTRlgIuuX8RgIwIpUbM0kjHFVM6DFWwURipi8aEpbV98IJ80kRKyMR1VqpqBWJ2vclEb5JISh49XaTScmk4ARv5Uu+/ZYTVmsOT50scGlPd8rWGo1YPUK/L3z+/xF07+sjGzY5HWhPqHMvETSxDY3a9yaHR3BYLRxBGPL9YJRM32TN4+cFKIcQ273Z/Okb/noEtP/Pb28snzMuSUC7W5cgsoDjdh8evvvO9WU03xA3aiM+Lmuy6AE28tpMvGTcYzChs5NlCk6+fWCafsnjgpoGOVShu6pd8HitVh4Wyzf6R7CWTSbvqqquu3ix6M/4F+zlUVP1vAPcA32z//JvA3cA1KcD3j2RJxk0sWwXt5GI6hYb/sp3HbFKlZtL0aPnbI+Evp+GMxXrTe0Uu95XIVQbtLT/biKKPpPoATZsathdRboUIws5+hqEkkzCo2qrI3LyVIJT4Yciu/hRNL8D2AsXnFoLepMmn75zgy8+uULZdtZSsq4TRsu2haRqFusty1eW2yTxLVQc/iig31ZBj0jLoT8eIGRq2G6LrgifOl/ne2RLDuRifODbB/XsHKdQdBjMxvvjkPN9+aY2WH2LoGqn2snnZ9rHdED+IGMrGSMVNEpZONm4QRRHfnS7yrVMFxnuStLyQdx4Y4svPLlJseJi64EePjvK9s+uM5BOUmx6PzayzXG3xjRdWGczEmC/bKqQnE+NHj45x00iWo5M9lG2fmWKDIFJdu41Ick0I1houyzWHZ+bLTK/WeWGpdtWvadX2+LW/O0Gx4RJEkpSlUXNvvB54GKlB3ZeTG6oithVEJEyDyd48o7kYX352mVorpOoE5JMWO/rS6Bp89NYxglAy3pPgTKHOH377DLWWR83xSZg6ZwsNml5ALmHiBxJDCL754irThQZhJDm71mClqkJ8dg+k+ZF9A/w/353h2YUq350u8jufuJWP3jrKdKHBjr4UQgjOFBr8Q5v04YeS26cu0DcemynxxHkV3vOJOyZec+DLo2eKHJ9TYVOfvnNyWxDNG61vvLDCn/zgPAB+FPGJi6gkbiDRtCu7aLycqrZHrRUQSslTc2WWaw49SQs/jF42UdYNQv766QW8QL3PribVtKuuuurqRtO1YVO9PtrGZxNCmMCPSCm/1f5RHtioaKrANk6VEOJfCiGeFEI8ubb2ypHxfhjx7HyF+ZK9xavZCqJXXPbXEISRRNeuJGvygrJxk3T8jWHcmrpGX9rq+Dg3F9mKXhHR8iOcYKuFJQLqTsDZtRpV21dd8wgiGeH4IZrQ2D2YpGb7NFxlS4mZOgnLwAsiao5PEEUsVVtM9SUZyydJx0wG2qEofSkLN4gIogiBYnk33YDlaovHZor81ROzNF2fwWwcN4jwwghdqK69G0S0PIWJ1DRFGxGa6jbrQlC1VXc0CCOWyjYvLFY4W2jw9FyZ2fUma3XlPX3vwREOj+XpTVqcXm3w5LkST54v03B8wkjxqzWhWOBzpRZPnCvx5z+cbXf9dc6sNnh2rsLnH5/j9GoNISBlGaRjBlLCi8s1qlfBUJZS8tRsia8+v7xlJca73PTsdVa06UsTl/eQqd42uH7IcsVhpdai1HRxAhW4s1Bu8lLb/19uepxcqTG91uDwRJ7BTEJZpaTyKAuhXpOhbILelMVa3eXkcpWlSovlaovFskqxjaSyOh2dzBNGUlmg2h3xTNzklrEcj04X+fZLBfxQMd3PFOo0N1mEpFSDj8XGdvtFseHydNsG82rk+CHPL1YpNy8fzDSzppCb14pCcjnZXqBmNy6z5OcHryGWF2gFYPsRXiAJwwjbDVittXjsbJEnzpWuCLVZano8O1/BfxNiObvqqquu4Dp3wIUQY8DU5v2QUj7S/n73JX7ls8BfbPp/BdhY88+2/79FUso/Bv4Y4NixY6/4yfXodJHj88rTq4EKlIkkrSuom+qOj9YOgrm7VDrBAAAgAElEQVSaj4XZUpOYoRPTlXf79fx4/eAtQyxUXGw3ZL3powvoAE80FaBzucd3Asly7cKB2OChrzd9nphVHd6Gp4bnvEDHEAHrTRckVOyAbEKF1hwez/O5e3dQrLvkkhY9KZNHp9c4tVqn2PBIGAEj+QR1J6BiB3z5+DJCwEMn17hzZ4ETizWEEAznEpRtj/V20RI3BOP5FLdP5VmtuqzUHGbWGuSTJq21kFBKao7qsv75D2c5U2goFF1vgv3DWSZ6k3z4yAjfP7POd6fXeGq2TMsPySVMhrIx7tnVy2Pn1ADmmcIcYRQxmI1z22QPxbpL1fH57X88xWg+wTdeWOH3Pn0bv/jgHr5/pshCucXMWvMSceeXfwu+uFzjDx4+S9MNuGk4QxBFnF5tMFdqvfoT4A3QxvCloUMQbqW2xA3BQCaOrkGh5vL8YoUnzoed1Z9aK6DWHuZdrbnMrDUp2R4py+DXP3qQz9w9yV8+PkdvyuL2qV6VjGrp5JMm3z9T5LnFKgtlGzcIyScsCjWXHf0pelIm79g/QNn2Ge9N4IURb9870MH4/cUP5/ja88sI4L69/cyu23hBxPMLFd6+T9lGXliqsVxtEYQRh8Zyne53EEZ84cl5XD9ierXOJ+/YSg55Od23p59cwuThUwWeW6hyrtjk5+7fHocwX7L5u+NLgBoWvusqYtmvRrsGUkz0JAkjyd5LWGLW6g6F+iunt16JJOqCXwgoNz2+dWqNYsPjJ+/ewX17+7fdP2bofPy2cV5YqvLUbJlvnSqw3nR5cP/QNdmfrrrqqqs3UtetAy6E+PfA94B/C/xv7a9ffYVfuwn4hTa28CDQD7yzfdu7gMde635tdBrrbkAoIyzjyrvZXqSK1OAqmzJuCC0vRLYDba6CGnZV6njBhUp7NAzBxmqyCtWRXE1DacOK7EeS6dUaZdtXXTNJ2yethjLDiDbHWqHFZtcbzK436U/HuG9PP8emepnqS2HpF7jXPSmLXMK8EHoUKUZxoaYKXV1Aw/E7HUfJBrWkl9sme9uplhLHD6k7AXFTU9vTBAJFYXH8kIqtmODTa3V+/6FpZgoN1hquOg+Eeh62p2LQB3MJ9g2n0TTVBY+k6oqGUpJJmCQto71qoIYFv/rcEglT55N3TDLeo4q1i4cuHf/S3cT5ks2ZQqPT7czGVZCRpd/4RIgNwkkQcklihi5QtqG253pzQ3djWHPjHCo1XRxfvTccP6ThqCj6gUycKJKM5uPEDJ3xniSaJqi1fNbqDk1XJbXaXkAuYbB7IEPM0ImkJBM3OTyeZ6zngn0kaK8qSNQq2IZnfyOMKookZwoNGk7AcC7RKb7nSzYvLtc6r9PVNmRNXeO2yZ4ODWfj708USV5cqjFfstX/293oIIo4U2iwVn/1Q5AvpzCifXxjnYTZzQrCq2suvJKiNt1GJaAGnF6ps1ixL3v/4Vycw+P5Dp+82wDvqquu3qy6nh3wjwE3SSmv+JNESvmvN/4thHhUSvnrQojfFUJ8F3hWSvmag3zu39ePJuCR6SITPSmCUHW2K7ZH8DqSCH1JB1Fi6eC9tlXeS8rU4bvT6/QmLWwvwNQELa/9gd9+YvpF9ApdwL6BJLYfMVd2Os/f1LZ++J0uNElbWhtPqBE3dFYbDmH7A1v4IY4XYhoa//j8Co+eKZGJG/yL+3fxE8cmeNf+Ib5/pohAMpxL8G/ev5+/O77Ad8+sU2l6tPyQ4WxceXvDiIofUW5dKGazMZ2jk718/OgYj54t0nACSk2flh+BCJnoSfLx28f5tb87QQR86s4JTi43QMALS3W+81IRiboA2TuYZv9IFkOHR8+UaDohT89V6EvF+MiRMUZzCb72/DJxXXB4ooeffttOgkjyyOk1dvQlOL5Q5bGZdf7m6UWeW6jy6x85yPtvGeEHZ9d54lxpy2symt8eE16oOfz10wtEkeSe3b3EDI3lqsvTs2U0Ia6YyHO9tBkziLzwf4HyEM+XWxiaIGHp7BpIUao5nK+oPwMSMIUKoMnEDZpegCYER8fzOH7I3zyzSNMLWG+4rFRb/P3zy5i64OaRLNOFBstVh1oroC+tsJm7BtKM5pPcu1t1jPvTMT58ZJT1hsfh8Vxnnz9z1wSpmE4mbvDg/kHKts9K1cE0lK3sh+fWmVlr0PJDfuSmAQ6MZFiutvjrpxeQEvYNp+lNbh3CvRp99NZRTq82OoOdj58v8YOz6wgBn7xjgqm+FO87NMw/vrDCStXhC0/O87P37bziId4rlS6U9zuKJMbFE5jAaM9r87xfLA2o2T5+JPGDkNVI8ldPzPPRW0eJm5f+eBrIXHgNj0zkLnmfrrrqqqsbXdezAJ8BTOBVtXKklPe1v18T9OCGYobO4Yk8zy1WWatrjOTi5JIGmoD1hveGIAZfj+IbVLBGrZ3uGISKsXxxA2kjPGXD/pmJGyRiBsVGs1N8C1THfPNuStn2XOsaRruLLRCYOvihul2iPnDtIALXRxPw7VPKc/uOm4ZIWga3jOdZrNj88SMzxAxBwtSI5xOkLINs3ODkcq2zrQ0pjrjW8faWm2rbyZhOKwiQkSSUEQ034PB4noYXMJiJU2r65BImDddvh8AoTrkQguFsHD9UvnKhCRw/ZK5kk4kb3L9vgIYbcLbQoDdlMd6TwNA19gymWa05gODEQpWmpwZCHT9kvmSzsy/FixcNYVZb25fzvTb3esNmM5iJcXpVhc+kYgaGporCG9QKvu0iVV70PZSgSUk6ZtKfjlG6yFMtNEjFDOKWTsMNySVNpgZSzK3b7eHfdiCUlJSbLpoQ1PI+pYbqlhuaxNIVFccPI9Ixg0hKvnx8kXPrTXb0JnnPweEtxWvNDTgykWf3QJqzaw1GcnF6khZNL+CFpSpnCg28IGIoG2dPO9nSD2TnfeL5Ef1pi2xc/UldqijU5p6B9MsG4VRtn4WKze6BNPfsvmAr2fA2S3mhO39gJMsLSzXOFRssV1VgzcimIdD5krLe7B5Iv2p2th9J1psuQQjBJa7ywmt85adW0BTsVF28q3N7c/hwGEmmC3V6k1ZnSHX3QJrdA5fcZFddvam149989TX9/vnf+uA12pOuXm9dzwLcBo4LIR5iUxEupfzl67dLSv3pGHfu7GV2vUnDCcgmTPzIvq5872sht/1BvlRxyFgC7zLLt5ufZ6UV8PT81qJRcuEiQYMOK9yPwCTCihs03YD79/axVHGp2C7z5RZSquErIWgnivp890yRH8ys89XnVtg7lOYbL65StT28UNlxLENgaBp37Ohlvelt4klfeFxdF5SbPo9MF3H8kEjCWE+Sw+M5/uGFVVpeyFPnVQT44+dLGO3wnX/1rr3k4ibPLlQwNY2y7THVm+Su3X3kkiZ/+XgJP4wQQmLpGmfXGvznb5/l7l19zJdtjs9XmC40yCVNPnfvTspNj88/Pk8kJXft7MMwBO/cP8hDpwrMrDWJmzrvvnmI331ounMsv31ylY8c3UpzGO9J8t6Dw6zUWjw7X+Frzy3jhxE9aYupniQrNYdi3b1hC/ArkRrElDwzV6GyacBCoC7Y3CBE1yCTMMjEVaF8vmRj6oJC3SUTMyjUHcJIognBTLGpSEJhxGA7YGe96fHUbJl80uLEYoUvHV+i1HTpS1m8tNrgf3/ffkAFQv3V4/MEkezgRr0gYiQXo+b4/O5D0/SnLExD40NHRhnvUasWk31J3nNwiPPFJqdW6pxft7l/bz+TvUm+8OQ8UiqG++bCerPCSPL5J+awvZCJ3jo/fvt457a7dvZh6QrVuJln/p6DQ/z+QzV0IfjKc0v83H270DQVc//FdoLlAzcNcHRy2zz6FenZ+TLPzFaQwPfPrG0hwAAkTZ20JWh41+bk8yKwhLKXpWM6+ZTFv/3gzSQ3IQYfmV7j+FwFXRP81D1THd9+V1111dWbWdezAP9y++u6aK3uUm157Orf3qGqOz5nVhvkEyap9jK44145veJGlwRqr/EDdOO3rU2Do1IqP7sVSDRCyk2PTEyj2hJtT2+7YJegaRLZDtYhgtOrNVqeT8vz8NvbCyUEviTUQ5arrXZnTt0mhHrsjW54KEGGEaWmR9Iy6ElZHBzL8oOZEpoQhBIqto+UKhp8udLin15cJRPXO1SY8d4kuwfSvG3PAA+dXCWMImKmjqUJLEOj5vis1hzOFhoEQYSuCdyOj1x1rktNFdU9lk8w0ZPgxEKNk8tVaCeAXsyOni9vH6h0g5C1utOhfPhhhO2F6C2fpuOjI7lhYeBXKDeCUsPBCbaupGzMKQRhhBcIckmTKJI8PVum2HDbjHHl7xcIvDAiZalJASEElqFjGgIJNJyAhKWzXLGZXmt2aDZNN2S1duG4B2FEEEkajs9itcVYe8UlbhroQigblYThrAqT2qyYodJbCzWH/kwMx4+YWWtStX2yCRNnEzGk7vgsVRym+pLETZ1S02Wp0iKbMDtJrRuyDO2Sg5bZuMlkb5Jiw8ML1KqNhsDd9DjrDY+XVursGkhddeJmxfY77+W6u30pzg8VveRayg8luoC4ZTDRk2CyL85vf/0Ub9vTz9v29OO2j00YSbyu6burrrp6i+i6FeBSyj+5Xo9dsT0+//gcQSQ5tqOH+/duXcv89/9wqsMRTsd0npurYr86uthbXu4l2rB1N6TpQnGmvK1E3PhI9yMgjNioO9YaPsWGso5EF91fk3BypbF1OxLCQHJxGdryQ0KpLAAasH84w1LF4cNHRnh2ocK+oTTFpsdK3eE/fesMCVPn4FiWd988xGg+galpfOelNYIw4p0Hhig2PPpSJn/zzBJ+GHFqpYofSsZ74+STalB0MKNCUYoNFz+UlOsuLTfgvzxSpuWFZOIGt07k+fHbx7d5dvcNbk/6+3+/d55vnSpg6oLP3DWFF0Z87bllnl90CcLoksf8zajmJd5TG699K5C4QUDdDYgiOFe0lXVFQMLUMDQNLwo7Q7h37uzlxGKNF5dqlJs+M4UmYZvG870zRWxPrbwkLZ1UTKfY8FiruwqBmY5x985e/ug7Z2k4AbYXct+efhYrNg034MNHRuhJWewdzGyh2JwpNPjNr51iulAnYek8sG+QbMLgoZMFHD/k0FiOe9pFtJTK21x3AsbyCT56dJQvPrWIEIIogg8cGr7i4/aBW0Y4sVRjV38Ko11g7x5I88BNA9Qdn+cXazy/WGXPYPqS6asvpwPDaQxNIOWlKSh/+PD0ZVfOXq025gXCMKTpBXzivzxGqeHxV0/M84Wfv4e37xsgFVNZAYOZ68tJ76qrrrq6VrqeFJS9QogvCiFeFELMbHy9EY+teNOqiLkUt7fuqpCZMJLkEyavLxjwramIq+/PdggYbW10Q6OX+cAXm75rou1bbXdIC40Wa3WX/cMp9gxm6E3FODCSJa5rOF6IF4a4QUjF9litOhwey9GXtlgs25xaqXNgJMuvvvcmdg2m0MXGgKrAdn3WasquMpZPsFx1OLvWYLXmINr8a8cPCUKJF0Q4gSJy1Fr+NurJxRcWQMeSEbQ7fqO5OKYu8MMQ/y1SfF+JNjKlNp9LynMtSMcMwpA2q1oys9bEDyWZuEHLVyhMTQAy6nRNdU3Qm7boTcVYrTp86ZkFbE+9/weyMQxNrTaYmobtqddvKBvn7t39PLh/iIneJMvVFgtlRemwvUDNDkRgampeZMO7nYoZZBNG54Jrg7oD0Gz72L0gIpcwmepPXjbZ8lLqS8f4kX0DW6wpQgiOTvZwbEdvxzO+8dyuRlU3ZCSXYCSfoHUJ3vfsevOqt3klkhJipo6uabTaIVNeGHF6tU7c0BnNJ8gnTM4UGlveQ1EkOVuo8+x8mfVL8Nm76qqrrm5UXU8Lyn8Hfg34HeAdwP/A5bM7rqmGsnHedWCI9abLHTt6t93+c/fv5De/dorBTIzelMWrzNbo6hLaeIEVTk92POQb0rjQJd8oui5Xf/elTJAbF0zqMsnxAupOQCAlthtQsn1OLNUI2ky1iu2zVG1RbQUEETgy5PRqg/NFmxOLNe7c1cvDL63R8hUSba5ks1BuMdGTZLHaoi9pslJ3Kdk+uYSBEBovLFUp2x6OH/HScp1QRtw+1cOD+wd4Zr6C7YacWKyyVHH4wC0jW57DodHMtuf10/fuIBs3cPyIM6sN3FDFtIs3ue3k1WjjgszU1YVYFCl7RisIafmKH16xAx46WegM8CUs/cJAn9SIGSBExNt29TGYi/PQyTVqLY8/ePgssyWbf/fRQ5xYrKFpgroboDVc1uoOlqHzsYPDHeTg+WKTv31mEYAPHh7h4GiOT905yVNzJXb1pXnXwSF6kxarVYeHX1rj+YUqvakYt0/1oGmCjxwZ48xanUOjOVIxgw/cMsx82eboxKvza19KScvgg4dHmCs1ufVVbPfQaI5s3CSUksNj24kuf/STx9j3b79+TVGEAHFTo+6EHM7E+Jf37+IPvn0GS9c4tVKn2HQp1j1OLFXZN5hhsi/Jp+5UrPXvnF7jK88usVJzuH2qh5+7bxe55BsTatZVV1119Vp0PQvwhJTyISGEkFLOAv9nGyf4a2/Eg98yvhVfVag5yueZizPZk+KOHT1UWwEV28fQNYKrhXt3tU0bXWpTF+20UIGUEX7UHqbU2sV2dCHkB7aXnKL9lbJ0JvtSHJ+vABGaAInAD3xqNrT8CIEaqFsstRCapNby8cIQgUTXQUgIwwhfwFrDYXa9gRso+knTC1WKZ8tn73Aa09DUcKDwaXkBTddjOJvAR7DeUDSTkAg/lJiGxvsOjSCE4MnzJdwgomx7zJe2dhCfma9uO05D2Ti/8MAeHp0uqtjzQHVULV1HyuB1o+TcSNp8qaEJSMVMwiii5YWkYxqur4KVNi7oIgkiUgMBensVIpcwqLZC0pbBWE+Sz927kz/94Swg2z7zkOVKi6fnypwp1LF0jaFsDIGyhYzlExwYyXK+2GQ4F6fuXLgSrzs+uiZ4V9u61HBU+quhaxwYzfHSqlrZqDk+QRixUG4xmI0x2XchNGbvUOYVMYJSSuZLLXIJ87KFZbHh4gZR50Jhz2B626zBlcr2QvYOpdUg6iXOM8PQGc6YLNWv3UyMKRTfXNfA80PWGi0+eWyC06sNbC9guRzS9FRKahBF1B2fmuNzZrXO3HpTrWiGEYWaQ7Hhdgvwrv5Zq0tRefPoehbgjhBCA6aFEL8ELAKD12NHZtdVZ0tK+NDhEfYOZRjIxJlZW2c0n2AgYzFfdq7Hrr2ppQuVfLgxpLkxEBa0LUCaEAhNQ5dRp6O2MWN1ucudtKVh+xGRhIWKQ6Hu4gZq+3FD4AURDU/S8HwMoZbmTR2emlOplkjVbYtbBmEYEsg29zySNL2Q4/M1BIIgirhlLEMYSfozFsW6R6npIYSyLW3Mp50vtdjZn0QIQcLUWa8rZvlSxSEV03luodpmyEscP2KpuvU8uvNlaBXHdvTgRxFxQ2O15rBScQgjDU2ooUDL0DpBMW81bb7oiiTUmj5SzfJSagZ4YdSxWugCNE1g6RqRlOqYSFhv+Oi6hpSwfyjDf3xompdWarR8dbFmxUyycYP/66sn2wE7cY5O9nDTUIaEZXDLeI6vPLvEXMmmN2XxmbsmqTk+kZQcHs8D8FePz/P5J+eotXzu3tnHr773Jnb0Jblvbz8NJ+CeXX187cQKZwsNsgmTn753R8dH/sT5Eo9OFzE0wU/ePUVPajvd4wdn1/nhuRKWofHZe6bIxrcWl6s1p0PeedeBoW2NhatVOmZQtj3CSKqwpIu0XG1Rbl3bc24jA8ELI759eo1HpotkYjpTfWkmepP0pUxKTY+jkz3cOpFnZ3+KP3j4DMfnKvSlLI5M5HhpVXF1Hn6pwFRfsuON76qrrrq6UXU9C/BfAZLALwP/DngQ+NwbvRNRJDm9UscLIkxd6/hvU5aiY9Qdn75UjELVwe02wa9YOpC1BCP5JBLBuu1hGRq2GxJoAi9SFBTLEISaRhQp3/bFlpSL1ZuO4VYd1e2EDopPF8pDqglxoXsqIB1TJBHbC0BeGPjqTSnyyWrVQeiCuKEp+ooMMHUNy9SZ7E0QRJKRbIIoguVKi0CqgBJ3E40hHTPZM6j40UJIEpZGreVTtX0EYOo6hq4SGOutrZ3DR2eKl3yexYaLlPCOmwYJI8nfPrPIUC7OYrlFJmEgUASOM4XmNbcD3JASmzji0YanW/0/FzdACLJxk/Wmix8KZJuKogmFC+zPxHhhqdpJlExYOvmEgRtEHZpIJm5weCzPBw6PdI7/xt+DastHCBX444VR52KyUHcU690LWak71ByfhKU8yxsd6aqtVkcaTtDu9KrCdoOeE0SKa36pAnzj8b0gwnbDbQV4reV3nlPlEkz5q1XDDehNWUi5PbUVoGx7HZ/766EwAjTwQpWO2ZcyOLduM5KLM5pTFJq4qVFvh3BFUnJkoodUzGSt7tJwA4JIYlzbfKKuuuqqq2uu60lBeaL9zwbK/31d9PBLBZ5bqFJqejx4YJAj4/n2AJ+riAq2R8Pxu8X3FUpvpzSGQNmVlFebnYJYA3pTauhsrtQijFS8uIzaaEKhQlj0dlc6pitc3YYP2ADmS63OtjIJA1MXOH6EqQl2D6Y4MJLjoZMrVFsqOXO8N86p5QbZhEXLC2h5IYYmyMR0Gm6gvMJCDfXF2mi4uhMQt3QeO1emN2Wxsy/FsZ09nF1rst502TOUZrnSwvYjjozn+Ol7d1CyfR6bWUeiYuqHc3HV/W55OH7AVF8SJ4h49MzWgvsTx7YywOHCigzAR46MErZTAudLNlEkWak6CCSR3B6k9FZV3NRotPEbfqBWJVp+RE/SJG7qDGRinFqu0XBChKYuThBgGTpJS+czd03hhyGff6KFoakLvcFsnErLJxM3GOuJI4H5ss25YoO/O74EwF07e6nYPnuHMqw3XD7/xDxhJHn3zUMcGsvxyTsmmC/ZfO/sOqtVhzOrDb7x4iquH3HXrl7u3d3Pu28e5pm5MrsG0sQ2VYb37O5DSkk+aTHZtz0RFeC+vf3omqA/bTGc204A2T2Q5q6dvdheeMl5lquVuojxCNsDrRerJ2m9rnkIGrCjL0kmbjCYifHD82WKDY8Xl6pUbZ+1doLpx28f5zun19g7mOLopOqKPz1bZudA6pqng3bVVVddvR56wwtwIcR/lFL+ihDiK1xiokxK+ZE3cn/Wmx6aJhjKxlX4haESFR0vwtI1ivUW9rXmbr2FlbBUyqaz6VN6s5e7Lx1j10AGQ9NYqrRwvBBNg2jT5GXK0sjGTfJJg+k1G69tMTF02AAzGBqM5xNYhoYThBwYzpJPWty5o5d3HhjiyfNlKraL40e0+iKEELS8kJrj4wURmqaRT5o03JBkTGdXf4pS08fUNeKWTszQCaOIctNlOBenL2kxlo9jaIKRfJxfesdeHjww1LETfPOFFWotn3zCRNc1xnuSzJdselMxelMxelIWpi44ubyVevLNUwV+9oF9W35WanqdhEXVcVTcb8tQ3me1WsBbhs4TNwQxQ6PqqBdXAHFT4PnqGWpiawKjBCZ7k2TiJpFUpJLx3iTnig0MXWBoKsE1ZugkLJ3elMVcqUEmZjKQtjqDmqau4foBuwfTjOWT+JGk5Yes1d3O8Q8jyW1TPQxl45xereMGIY4fdogbYz1J7t7dx0K5hR9FvLhcw/ZCUjGDUlN1pIdzcd5/ywhNN2C15jDUTnNMxwzec/Dl8YPZuMl7X+Y+mia4d0//qzvwl1C15bcpQtEWz/uGCtXXlzRimRq/8q69zBRtai2fF5dqSCkxdY16uyNfbLjsHUzzY0dHGetJYuoaQ1md9x0aZqXm4PghcVPH8UNm15v0piwGXiW+sNwO/+q9xOpEV129FfVaPeRdXbmuRwf8T9vf/8N1eOxteuCmAR4/V2KiJ0kuoZZ3dU2wfyTDHz9yhma3+L4qNS+zVCBQxfkdO3r4saPj/Ou/eY6K7auwnXZRvVFjNb0Iy5SsNnw1qInsJCRuDN31pmMYusZS1cHSBc8tVtnRl2Kt7jLZl+RsocHTcxV0ATsHUozkEiAjpgvtoa0ootz0afkhQ5kY+YRFJAXZhIGlaSRiOo+cXqPhhBRqLnFDYPshUkqOz1Uo1FxOLFX5X959E49Or/G735pmvmxj6IK7d/axsz/Jjx8b5++eXiSQkvfcPMTZQoNIwpefXe4clw/csp3TfGgsR9lWRfgtY3nOrzdYb3g0XUX+eGuU3UoacNNQmpm1C8OpEjphLxsowqDNfBeo4rpk+5RtH9sLWam59KUtPnB4lH94bpm6G+CFknRMwzI0Xlis8uh0kcGMxVR/ikzMIGHpPDtXodDwWKw6fOLYBHsG0+weUAOMDTfAdkOemavwxPky9+/t59BojlLTo+6olZQNve/QMF99fpnQkcwUVeJpLmly36bCuOkG/Oljs7S8kHt293H3JUJ2bgQ9v1DhH15YBQk7+lPbwoAOT+Q4MJTg5Or2AKlrIVMXPDZT4uhkDw+fKtDylT3o9skcn7xjkkabcvQbf/8iQSj5wC0jfOIOtYr00MkCzy9WySZMPnXnBP/1kRl+MLPOQDrGLz24l5uGtxOHXk7zJZu/eXoRieSjt46xs387s7+rrrrq6tXqDZ9UkVI+1f7+nUt9vdH7M5iJ86HDoxyZUENVYSSZLzexvaDjrezqytWxmwh1chlig3yilq8/fecUNccnZuiKhCIuUCs2JCX0pax2zLjqdpu6uoeuqTCVdMxAIDF11T0NQ0kuqYa1IglOEBGEipSRsgzeeWCQuGUy0ZPk8EQe2cbZ5RImE31JdF2wbyjN7VO9/KfP3M6HD4+iCYGhC4JIUmz4GJpg71CGSCrrzHzJ5slzJU4u1Wi6IaaukbZMpvpS3Lu7n4meJD97/y5+8u4pbh7N8eFbx/itjx/ecry+9MzCtmNo6hoP7h/inQeGMP9LhS8AACAASURBVDTB3LpNPmkSN7W3XPE92Zfkrl397WTPC9IFmMbWP0+mLtg9mKY3ZRJEEaYuCMKQlKVzft3m/QeHec/NChuYtHR2D6Y6nUs/kgQR3LOrj9/71G2M5RNomrq804Rgpebw7puHuHk0q1Iod/Yx1Z/s5AUU6i5uO+Z+90CaqnPBy69rGvuHM0z1pWi6Kmhnz0B6S2T65qK9UN/eRXaDkELdodryqNrXL3X3+cVKh3l/6hKMeiEEt09du477hoz23wAhJdOr9fYFaIRE0p+2uHUyz4/cNMCHDo/i+oqK0vJDVjYlms4UGwShohbVWgGrNbWS0fTUqsbVqthwiaRabSp2GeNdddXVNdb1sKA8z8vAjKWUhy932xuhLz2zwJefXUITggMjWY7PVf/Z+GyvRGabtdyeldp2bCLaATpSfQ/a370QSg2PP/zOGWYKDebLLUIpiekC09DRBNRaARHK2910A9X5DCPCCKK24UJGIL1Q4fzaA5cgmOhJsFxpMd6TZLInyXdeKtD0QgQhY/kELy3Xeeik8uZmEipiPJ8wySZM3ECy3mxRtX3efXCYhKXzIzcN8v5DI3zrpQJxU1FIhrNxzq410DTl816pOfzKXx3HMjRun8qzVvfY2Z/iHfsHGczGt3Q9376vnz0DGf788dktx+tn7t/1ssf7ayeWOblcI26qY/RWkhDw3puHeO8tI+STFv/3N17q+IuFgJF8nOWKzYYTwtAFSxW7TbbRqNk+kRTMrjdx/JCf/ZMniaKITNxg72CG8Z4kDTfACyJGexLs6k/z47dPEElJpeXjBhFJS6c3bWFogj97bJafumcHLS/kz344ixeE9Kdj5JMW9+7uI5cwefu+ARbKNnftvNAZrrZ8npmrUGp6HB7LsWcwzT27t3aOh3Nx7t7Vx1rD5W0X3RaEEX/xwzkWSi1KtstUX4qPHBll1yWSKF9vffbuKb4zXSSMIn7qnslL3mei59J+9deijde96kY8OVvm9GqDlq9WMmqOz1efW2Ykn+QjR0Z554EhHD/CjyLe304QfWq2xErVYa3h8qk7JxnOxfmx28b4xosrTPWmuG0qf9X7dHA0R7GhiDC3jL02ukxXXXXV1cW6HhaUD7W//0/t7xuWlM8A9hu/O1u1WHFwfBVbvW8wje2poJbordR6fA0ayFjkEyajuQQNL+Cxc+Vt9zE0dYW1US/6keqoIWCh1KLY9NA1QQyNXNJkz2CGphuQiRucWqkTMzQ0TdD0fExdWVCkVMQTDTWoqQlB0O5iCwFxQ6M/HaMnaXHbVJ7/+t2QmKG1LwYkM8VGO1lT4ev6sjFuGc+xqz/FS6t1Sksuk2NZMjGjQ3/4zY8f5ktPL/D9s+vMFBuEUpK0dMZyCSb7krywWEOifMNv3zfAh4+MAYr9bHsBlZZPveV3Ev00YO6iJMEvPjnHB4+MX/Z4ny/aan9TMUxd66ww6GJ7cuibQZq4sM9CwL27e6k1PX78tjG+dmKJU0t1QqlWAW4aTIOElWpLkS00DSEjEIKkpTy+pqYINi1fpZoKBP3pOJ+9Zwdn15qs1Rz6J3v4hQd244eSdMxQDG8hGM0nyMQNelMW+aRFoebQcHyqjiraQc2GbPZp3z7Vw/7hDGEUsVi2iZs6K9UWuhD0piwMXeODtwxTtn2VqrkJh3dxUb6hlh+yWnOoOj7VVoCUsFpzOwV4zVGrL0nr9f9zHUj4iduVpWOD1nKxXlrdzq6/loqkSvGUqBUwXQjcIOJcUXXkJ3qT/I8P7N7yOytVl0zcJBM3mWwnhB7b0cux1zCYahka77556JXv2FVXm9T1UHd1pXrDC/B26A5CiLdJKd+26aZ/I4T4HvAbb/Q+bdb7Dw3jBSGRhO+dKXK+2ETIS3d7/zlqpeqyWnM5tdK47PHw2zdseHY3OrdJS2eh0qLh+IoNLlXwhy4EN41kmV5tUKirZd9w0zYsHXRdww0idKFCfDRNMNUTo+YEOEFIueUTFZromlB0hKEMxYYHSF5crmN7AS0/RBOCTNwgiCLCKGKqL8lfP71ArRVQtX0mepP8t0fPIYAHDwzyt8cXmV23aXkBSctgojeBZWrUnICPHR3trJYU6qpTNrve5CvPLqNr6lwq1B1eWKqxULL5w4fPbEO7/at33nzZY31iscpCucnxuSq7BlP0pi3WGi6mrjr/tv8mq75pO03aux1E8DN/8lT7YkJZfTYINy0/4p9OFhBCoekk4DsBhga5uMaugTS1lsf5dZtM3KQ/HcMNJH1pk3cfHCKS8MJilVOrdW6dyPG9M0VOLtfJJ00+c9cU7z04jGVorFQdTF3j6bkyI9kE359Z5wOHRrh1Ik/N8bd0ukFxt7/wxDwvLtUo1B1ySZNffGA379g/wLdOrWHqgt/55jSaEPRnYnzmzkm0V1i6KNRdSk11sXbnzh7Ge5IcmVAd15m1Rud8+sQdEwy+ymHCK9X+4SxLFYcgirh1YnvXuFh3+McXCq/rPugC3DZf1NRgOBvn3t39fOzWscv+zt27evHCkL5UrIN/7Kqrrrq6kXU9OeApIcR9UspHAYQQ9wLXbcpFSkmtFbBrIM2vvnc/JxarPHRyFU0IskmdoWycs4VGp7h8M0sDYobADeRVX1REgCa3x8dfajtCgKVr6Bpk4iYDmRiL5RZxUycIg07kfAhM9CQ4U2iobW10SNtf2biBFBqZuCrGkpbGQDbOVG+KTNzge2eLbWuMCq0p1l36UhaTvclOeI7iLJtsdDUHMzH603HW6h5JyyBu6Egk1ZZHzfaImTqPnyu1kxcNHD9kMBsnYRpM9iZx/JDx3iQfu3WMhhtQa/m4QciZtQZBFBFJwelCg8FsnLlSi7Lt03BDrIsCQr749Hlu3XHrtmNXtX3mSjaWoZNJGIRhhJRwYCSDITSmC7WrfOWuvwxNFd2btcFxDzfNWyRiGn4gFf1FXuiaSwAJA9kE7z80wg/OFjE0DT+MuHkky9HJPMemennXzUP890dncIOIfMIkZRmcWKwSRJL1hqTpBty7p59jO3r5/Yem8YIQQ9MY60mwWG6haYJ37L+QCeYGKhE1EzdZrTnYbU9x0wsxdI2Vmsvn7t3ZoSVNF+rsHcywVLapO8ErJjMuVxxGcnFGcnHu2zPAoU12h+mCOp/cQDK3br+qAjxqc8azCQMhXv5iwDI03nfo8tSV+XIL53VMBdZRqa9NT1nQsgmTt+3p51+/fz+GJig3PTQhthxT2Wbzf/TI2Cte7LwRaroBuia6OMSuuurqZXU9C/CfBf6bECKH+mytAj9zvXZmY4J+NB/nE8cmuHkkw1hPgpm1Jm4Y4PjNt0TxDapYbl0G5rs5Avzlfn+zNoYow02/aLTTCcMowtR19g1l6GkPSVZtH02DMFRDXe+5ebAdGy87hZjRLroMXdBwI4SmCtC4oVGyQ0IJP3X3Dvww4qXVOi8sVgmlCjfZOZDiXLFB2faJm3qnY7xQdhAIXF+FrxwZzzNfttGEYLneoukF/M4/TVNteeiaxp07esgmTSZ6k9yT6qPuBNyzq4ff+vppWp6yDYzkE9heyCfumODkcp1nZiss1xwe2DfAA/sGMTQNre03H87GmC/ZrNYvBKZ89u6tS+kAD58qcHy+QjpmsH84zflik0LdZbHSou4EHX/9m00X120bXHjT0Ki2/E4B63vRhYIbdSG3UZ9HAFIy0RtnsZLk7FqD/SMZ+tIWzy9UycZNLEPwtRMrrDc8DoxkKNseaw3JesNjR38Kqz3cKQQ4Qci5YpNbJ/KM5ROdYewNNdyAv/jhLLYX8u6bhzB1wVzJJh3XmepPMppPcO/ufuKmztv39XO20OSOHT08fq5EseHxhSfn+fRdk6Ril/9Te2QiR7HhEjM09g1dIHV8/0yR43MVzq030BF870yR8Z7kJXngL6cvtVdxbhrO8IFbRq7qd7ft63ie4azJYvX1GRQNURfLmYRBJm6yu00e+eNHZtAEPDNXIZcw+dCRER7cr+whXz+xwqmVOpO9ST5+++XtXG+EzhWbfPn4EoYu+OQdE/SnY9d1f7rqqqsbV9cziOcp4IgQIgsIKeXrayx8Bc2WlP18qeLghRExQ6cvFaM/bbFWdztYtNdbG5aN8Dq4C3ShKCPeVSRNC2gXNBFN/8J2RvIJWp5PGEEmZvChw8MsVhwWSjbW/8/ee0fJdZ5nnr/vpsqpc26kRgZIEAwgxSCSoiUrWZZljSxb41mPRz7rGfnYs+Pj8e7xeHY9s7bs8do+jpIlW7Y0CqNsKosixQiCJAgQOXQ3OsfqyuHmb/+41QU0AjMBUqzfOX0OUF1d99atW1Xvfb/nfR5NkC3bGGqQSNgZD7FctsnEjIYHs8TQFKKGRt3xA+u9hv5aEYKIoRAzNGzP4+3bunno9BIhTcF0fbyGQ4np+GiNgJ2wrhLSFXRF4MvATaM9bmA0kjN39CUpmw6GFniT9yZDFEwX0/XZ0Zvio3duaBZQ3zoy27BUVBnLVtnWlyIdhesH03z14Ay6KuhLBcvgEUPlrVs6uX1TB5FGrPdC0WTfH/6oefy+dXSaLX1rZShTuRq261OWDu/aNcRsweTgRB75E+bKkwir/Npd6/mFm4Z5x188St02AwmKAiDQpMSXQSpt3fFxvEATnInquF6wunLX5i7iYZXeVATHC177/WMrSBl4N/elI2iKYCJbJRFS6YgZLJZMOuIhyqZD1NDY0ZeiNxVu2tlJKanagbtKtmxRtYI3xHTjM2JDZwwB3LWlkxuGMs2u8t7hNnYPpHFcn6nGfSuWS65qN8+f1ceO6mqzW5sI67xvTyCvWO2eqkpQ6KuKIKKpdCXD+BLminV6UuEgUEoVazTml0NKyXQucApZ3adVKpa7Zj9eDIoiGO6IM1u8dPbj1UJRggCk//Ke7Uys1PjRiUWWSiZVy6VYd5BIJldqWK6HlOc/u6fzQVCVogiqlktYV5s+/Rfjej6257/quvqZfA1fSmw3CMxqFeAtWrS4EtesABdCdAP/L9AnpfxpIcR24FYp5aevxf7cOdLB0xN5RrrPp9XtHUrxwxMLCHFp9+61QnJtim8a2/VeQvENwf5WL1oa8CTM5OvNgcmy5fLH3ztNdyLMVL6O6/uENAXbk1Qth798cIxYSKUtquP4YLkS0/WoWsESf80ONPl1xyeiq0hcSqbDJx4e569+dJalsoXjSTRNIRM1sFwfkFieT65qoymBN/d3qvMNj/ckt2xo577tXTw2uoLteHQlQ5ycL7OjJ8mPziwFA5u+ZN+G9jXdyzs2dpKMjlGs2bxrZw/dyTBbeuI8Nprl7FKZbMXm8HSBbxya4y0j7Yx0JajbPj+9q4fN3Qk64msDPW7qv9TSzdAUTi+WGemKY3s+h6cKnFksYzpeU57zk1CK257Pnz8wxucOzDDUHmW2YALBDMGO3hgzBYtkRGdzd5xHzy5je40BPcdHVYLj9NxMkb3DGfZtaMdyfE7MFzm7WGYqV0VXFbLHLcKGgkA0rEUDWcPjo1k6EyFGuhLEwxq3bTz/Otx/ZJ6xpQpbehK8fUcP2/uSTWvAw1MFlsoW0ZDKQ6eWyVbsZlBO2XT4wlNTPHBikZLpkIzo/NvbN6zRJP/wxCLH50oMt0d5/w1ru7VPncvx+GiWjrjBh24eYkdfkodOLxPSFNrjBt3JMNt7k5ycL/H94wvEDO0Fu+tCCO7a0smJuVJTVw7wwIlFjs4WGWyL8oGX0DWeWqlyYrb8ou//cvB9eOj0Mk+MPcqu/hSnFsp4vmRDZyxwMQprjHTF+dSj5/B9yc7+FPNFk229CRRFcGB8hSfGVuhIhPjQTYOXXKTUbY/PPzVF2XS4d2s3uwZePYeT6wbTLJaC1YyR7qvvYtOiRYs3DtdSgvIZ4B+B/6vx/zPAl4BrUoCPdCcYuWD5V0pJoe6yqSvOibkSjv8Toj+5gBcjN3m5yMYGVqPp67ZHrm6jCAhrKlFDoTukkq3alE0Pz1cxXUlPMtCJr3auDFWhLnxkw4/XUCSuhLCuslKxsFwXSeAnvq4tQjKqB39je0hpEwupFOsOH7ppkHhYQ1EEd4x0cl3jS/cdO3qoWA4xI4iif+DEImeWy4AgHTXYO5xZ87xWajb3betGAP2ZCO+7vh9VEXzmiQmihkbU8LAdD0URHJkp0ZcKHBkmslU2dyeoOWuvcD7x2Fnu3Nm15jbb9Zu2Z2PLFXRVQWno6RX8oBB91V+xq0tYBc+TeMIjV7HoiOpEdIWa4yMkbOlJ8cVf20nZclgq1Tk5X2KlYuNJSSaqM180sVyfbb0JynWHVFjjZ/b0cWq+hOcHKZOqIqhaHmlNo2x5dCXCVG2XxVKdiumiqwqJsMYH9g6scZOZyAZONeeywVDv27Z14/o+X3t2FlUV9KXDwcColJxeKDUL8KVGt3ypbDUcawR3jXSu6TBPNFxwpnK1ps+95fqEdbXh8hHYW5ZNF1VR2NIdx5eSLT1J7trciev5jC5VkDLoYC+XLWIhDd+X2J5/Wd3x9YPpSwYqV/djOlfD9Xy0F+ikr3JoukD9NexGpMIqtuNR9yRCepxaKKEpCr4vqdkuGzsTbOiMYTpew6kGQprCB28coGYHK2Wrzy1btqiYLtGQiqYozc+UlapFqe7g+ZLRpfKrWoAnw/pLuqBp0aLFm5drWYB3SCn/lxDidwGklK4Q4iX2X187/nn/JN85Os94tvqaDh1dS17rLupqR10BEjGdt450MJU3mVipBoNtNYmuqhiqxPMlqoCFkhn8nevjSXC8oCBb3de8GbwWZctGU4Jumd8I5CmaHr+0r5evHZ4LJAQhjY54iPUdcT67f4rTi2W6kmG6EyH+4bFz1GwXx/WZL1ls6Ai8l88uVQjrKomQjudLPvHwGO+9vo/h9hhzhTrfOjLHRLbKXCEYRvv6oVn2DGYo1Gxmi3WSYY22WAjX9/nlfcMkozqlusueoaCQT4bXDuT9/vu2XnLcbt3YzoHxFTZ2xtnRl+ILB6ao2B6OG9hj/iScjeYF73Tb8zg4XaSRtYQLfOvoPA+fWaJkBhcz7VEN2/MRCM4uVfCPzJGtOGQbrjmPj61w3/YunppYYbli0xEziIc1LNdnqWLTFjFYqljcMJjG88HyAg/wrT0JPvXYORzX5317+hlsi3L7SAfHZ4tcN5imZrt84alpKqZLJqpzcr5MdzLEzeszfPrRCVRFMJAJutnDbVFGuuPcsqGNXMXm9pFOwsbagvi2jR0cmsqztTeJAL58cIbZfJ2b17exb0M7f/3QKKbj88xEjlvWtzOdr1M2He7c3Em+avOlZ6bJV22SYZ31nTEGMhFs1+dLz0yTLVu8dUtn81x7Pm7b2MHByRybuxMvuvgGuG9bF50xnZnXKJK+eMGJ4fggENQdl5AavCfzVYsJIbh9pIN1HVEcVzLQFuX3vnmMiWyVO0Y6ecfOHh4fW2EgE2GpbPG9/QvEwxq/cPMgUUOjLxWhPxPhgROLOJ7PbKHeck5p0aIF8MptJCf+6F0v+r7XsgCvCiHaWW2WCrGPYBDzmuO4HkdmCvgNB4bVLi4ExaShBm4cF7vAvZYd5TcqCqBrgu19CX797k2kogYf/+4pjs2VWC6bbOiMEdICb+blskk8pKEIF98H25NoikBVBcL1cbwLBvMIBvMUJXBlSRgKQ21ROhMhOqIGuqKQjur85r0jfPvoPCfmSkGKpq5waDpPvtEFK9ZshKKwUKzx6Nkl+jJRdvenuXtrJz86uUTVdBhbrtCXjjCxUsXzA23xXNHE9XyWyxbzxToRPXDK8X3J27Z3c+O6DHeMdL7g8fnT743yiX+z1upuc3eCjQ0PaCklbTGDmKFhKYG2ffUo/KSdbxdKr+qOj+34zefnhFS64iEsz0dTBCtlG19KLM9DEbBSMXlsdJlkxKBUd1jXHkFVFTZ1xRldLKOqwdDkYFuUUt3hLRvbSYQ1tMZqCcDkSo3Btig3DGXYM5jG9QItd6keyE9m8rXmyoTnBW4dnu9zcDLHe6/rA+Bdu3p59+6+Kz7Hnf2ppstJ1XKZzQf67LHlCrdtbKcvHUFKGFuusrUn2fS0Dtx66tRtj7CusmsgxZ2bg/NrqWySbSQ9ji1V2NWfesGientfku19ycv+zm/YQV5OPx0N6XQnXrsCfBVB4JqjKdCZiNIWM9jRn2JypcamrjhzeZP3Xt+HriocmSkwX6jjSzizWOYjtw7z4ZuDEKEfHF/Al5JS3WG5bDHcHqyCbe5ONI/95Eq1VYC3aNHiqnMtC/D/CPwLsLHh/90JfOAa7g8QRNF/7dAsjufjEbhlXChx9lnbvbuQn6Ri6NXCJ9B0P3g6y4N/8jCqCLS77TGdWEjH82GwLcwPjhfJVhxWj6LjBUWA48vzMXkXIBs/q37hhbrHUsnk//nWSXJVm3hYY0t3gr99eBxDFfSkQuRrNicXSpxZUnA9iecHg32e77BcsTizVMXQBCOdceaLNb57bBHb9fnh6UXu2NTFrRvamC3UyVctepNhTlRt4iGVXf0pcjWbk/MlIrraTGaMGip7h58/COS3f3rkktuWyxZfOTjDctlEVRR0TSEeUrFdD0MTuLZsHoOfZC58my2U7cA+UxfEDBUpAqtGRUDFdBsyJnA9Sb5mM1dcJqwF93WlYF1HlEzUoGp7PHJ2icVSkFr69h09DLZFsV2fHY2C1HI9vnBgisdGs3QlAnvAVFTnusEujkwXSEUN0hGNuUKdpZLJ2FKFsaUqm3sSbOqK869uGmzOkTwfsZDGDcMZxpcr3LK+HSEEt23s4PhckesH0/Slw2zqipOv2dwwnCYTNTi1UMZyvTXJjJ3xENv7kkyt1JjM1fibH4/xzl09bOpKPM/WL0+uGji3eL7k/Tf005taW5h+6akJDs5cGlH/ahO892Gp4rBUcYjogkzUwPN9JrJVMlGdv3pwlJGuWGOuBKIhlXu3dVN3PD775CQCwZ2bO8hWLFJRY02Rvbk7zpnFQFu+o6+VctmixeuFq9mBvtZcSxeUZ4UQdwFbCBoep6WUr4231UugVHeYK5is74hTd3xmc3VM93WjjHlDoSqgSrAvqBQ9CY4buMy8bVsXuqqQjGgoQqCr4EuBoQp832t2RFf//GK/cU0RCCGx3cCusGy5mHbgIuF7Phs745xaKHFdf4pN3QlUReHgZB7XC7rIhqYgpY+qKJhOYH3nepK67XFyvoTn+yBgqWiRr9qML1foT0foSQSBOO/eHVi63bq+jTPLFTRFCbSpDS3u6YUKe4fb8BvLJ4oimv9e5c9/MMZffWRtkT6VCwr4hZJJVNdojxvcvbWL+aLJoakCSIf6Bd3hNws+0BkziId1BtIRwrpC2XShEejk+YGzxerFmeVKIkaQmtmXCnPdYJpT8yVMJ9Be1x2PU4vlQAPecM+AQMs9k69TqgcJlNcPppsOKdcNBFrqrxycIWpoaKrA9iRzhTqJsEYmapCt2PSnI7iuj9awPLzw8S/krs2d3LX5/ErJzevbuHn9+fPhPdet7aZfTl8shODtO3o4l63y9WdnGtrmyssqwKdyteaKwLls9ZIC/B8fn3zJj/lqYDsS03bZOZDG930WSia6Ak9P5EhHQ9wwlOaGoQx3bu7kmckclhPMjdQsl1+8ZRhgzfGPGhofvHHwmjyXFi1atIBr64KiAu8E1jX246eEEEgp/79rtU8A6ajO9r4k07kaH7hhgOOzBUpXanm3eF48f20XcxVXwvRKjc/tn8RZ7UI3qklDAU9R0RSB68k1ftcXa5/txh8pQCyk0psKc3qhjOv5SEPlKwenURTBXMHkl/YNs649ypPjK42AFg3L8bB9iYJEVQW+K/H9YLjNR8NQg3Ce6wZSJKM6Z5YqPHRqiYoVWMAlwzrSl3zmiQmG26Lct6ObHX1JDFVhplDnxnUZjs0W+dsfj2G5HrdtbKdkrk3C/A9vW3/J8dncneD0QgVDC4Yve5JhDE3hU4+eo2o5DZeXNydTBQsKFqcXyigisIN0XB/Xk5RMD005H9yjq9Ae1cibHo+czbJ/LIcv/cDCT1PpT0XYM5jmn/dPUKw5vHN3L47n86lHz3F4Kk/d8TA0ha29lxay23oS5GsWpiuJhxQ6EyGKZhA/35MM86lHx/nRySW29iT4qZ3dHBjPBVrxPa9NWIzvSw5O5ji9WKY/HWH3wKUpli+Gka44p+ZLOL5ke++lEpU/+Jnt/Pwnn3qlu/uS8YBHR7McnCoQ0hWQgbtSVyLM7Zs6ODpX5MB4jq8fmmGkO4FEcmK+xMmFEsmnp4kYKtcPZl6z49+iRYsWL5VrKUG5HzCBo7yO5spWu0mrfPKRMVaqNtZV8gH/SWA1mGf1kAkgHlJxfYnpBIOEHmB5Hr5/XvsrgJCuMtKdYLFoYXseFcvFvKDbe7Hu2VAF7fEQv/OOLXzhqWk6EyEKNScYVpQC6UvSUZ1z2Spv3dzJs1MFbNdHVQS6KpjO1ZAE1nTLDR2tqip0JsLsHW7jp3b0cN/2br5zdJ5vHprFcoPOmuNJHNfHdD1URbBYNrl5fTv7NqzVc3/xqSmyleBxHzubZddFhdGffOcsn/63a60IE2GdD98ytOa2Yt3hC09NkwjrqIoLDVu9N1stvvr6r4YRqYpCKKwSMzQWyyaZmIHnS4bbgwCX6wZSHDiX41y2Ss1xQUrChsa69hjv3zuA7QYBPQBnFsrB0GbJRFEEnYkQ1w2mm8E9F6IqAkNV6EmGSEU0bm/o/aOGiuP5PDuVx5eSseUKT53TkTJwHHkxyZgvh6rtMp2rs7UnSUciRN/L1DTHQhofunnoir+/aUMn1w+mODx99cd1PD9wjFEUQc0OLrbqjkfNdomHNFYqFp7vs64jRkcsxFBblONzJQpVh7a48Zoe/xYtWrR4qVzLAnxASrn7Gm7/EpbLFl8/NIMiBB/YO0A6anDXls5ApBGoUAAAIABJREFUd3nZXm6Ly3Gxj7kEKlYwLAdB8aQAHbEQrpSU6i6O5xPWFRxfcnS2SFtUJ1uxkaxNfbywABeALwPN73/71gk2diWIhzTaojqmKzEdF+lLxpcrlOouuwdT9CTDnFoo0ZUIMdgWRRGCqu2xZzDFs1N5ZgsmqYhOOqoz3B6l7rj8xQNn6U6G2NGf4NRCibmCiS8lsVAQXz6ZqxE1NEzH5chMge8dW+DJ8RWihsoNQxmqtouuKLz/hn4KtbUqq/9856U+4ACm4/HVZ2co1BxuXtfGx793kpMLZXzXx/El50cx31xcmI6pqQqluoMEapaLIgSFms3WniTzxTq6qtAWM+hMhJhcCRJPfRlIl7Z2J9g9kOLsUoXHRrPYjkdPMsy+je0cni4Q0hQyMYPBTJR1jWIe4NBUnkfPZulLh+lNRzi7VCEVCSLquxIh9g5nCOsqA+kIJ+fK9KZCLBYtFkomsZDKt4/O8YG9g5ct6l8Ko0sVvndsnrZYiJ/b2088pLG9L8lEtsqewRff/X6g4Uu+dzjD7SOXPxcvxPclcePqR6yvrmpYro/p+iiAJ0DKIJynbAayLE3xKJkuAsGPTi0RUhX2bWgjYmhs7UmQjFzLr7wWLVq0OM+1/DT6rhDip6SUP7iG+7CG0aVKM/VuPFvlhiGDG4ba+NU7JAfGc5RNh+PzVw6huDAy++Wy+kVzIapyftjw1UARwc9L7Z6Kxo9/0W3P95QboYZICYmwRt1xkVIQ0hR+874tTW0twHeOzvNf/+UYddtrdLgEngwsBrsSYUKaQsl0iRkqyxWLZDiwCrRcD9sLrAw//+/20X5B+tx/+vJhDozncH2fk3NlfnHfEI+PrgBw+0gHN607r7c9OlPkgZOLAE0Xk797eAxfBt7Mv3HPZn52zyC//y/HqVoubTGD//Ke7XzxqWkAJrI15gqBK8piyaQnGea5mQL3NiKz79naTWcixB9+91Rzm3/zdIk/u3QOk8WSyVIp6Jz/4MQC80UTVQiEqqCowUqCoQpUJQgq+kkpxg1N0J+KBD7ZjSeVCmvcuD7DM+cKVCwHIQT96TC9qQgHp3IIBK6Enb0JXF+yvjNG1NBIRIIk1euHgpj5x0aztMcMOuIhfuvtm+lNRfj2kXnCuoKhChZKJsNtUf7re3dccf+OzZXw/CBd8o6RTta1x5jK1eiMh7hjcyc3Ns6njkSI917fx3PTBdrjBrmqzWAmymLJYqlsMpCJvqLjdHK+hOPJ5nky2BZds3L3YpBScmyuiJRwdLb4ogrwsulSqLtX1YEnrAlu2dDOsdkihZoTXMArgdd7PKSzUrXZM5ThzGKZbT1JOuIhjswU6EmGEQJ+674tZGLGC2+oRYsWLa4i17IAfxL4uhBCARxWneWkvLw31muM6XgcnS1ybLbIroEUEU3lY59/lqIZDGKNL1deMIzn1UgKv7j4hle3+F7dxuW280KsDrddfNvzbuuCO/kShATLkziex2ceH2euWOfQVJ6q5TGTr1GoOWiqgpAyCDtp/GE6qlOs2WTLFlkR2AkuV6zAIhKasfNfenqKx8dWiBoa1w+mcTxJzfGwHI8D57JIJI4r2dKTYFNnnC8/M8U/PD5BzND4rftGaI8b2A2ZyV8/NIrleOiaws7+FIoSODEoIhjUu3Fdhu5EmG29CcaWq1w3mEZXFRZLdWbzdSKGyrt29ZGtWihC8L+emaY7GV5zfD6271LP5kNTeR45u0yuapGKGDiuj64qRA0NXZHMFKzAAcaVXF5l/8YliPCurVlFKZsuj5/NYrvB+aBIyUrVpmIFnU5PBl3Z43MlVEWwXDZxPYkUMJOrMdwe5dhcCUNV0FXBroEUj57J8vholtHlCqbjsbk7wfWDaXwp+drBWZbKJj+1vYdNXefTDB8+s8yZhTK263Hn5i5UBQ6Mr+B6Pr6U/MvhOT75yBj3bu1iV3+aJ0azGJrCsdkiNw5nAj/zeOiSc+BinpnIceBc4NF93/buy96nPWbw1ekCHXGD9MuUVAghuG4wzYm50iVBPVciGdGomvZVveCzXMljZ7P4jTwAAUR1lZCmoKmCG4YztEUNFCGYztcwncAl5sC5HDv7UqQigQTte8cWaI8ZvG9P/ytegWjRokWLV8q1LMD/FLgVOCrlq1G6vjJm8jWqlsvO/lQQ97xQYqmhCc7bLrGwRsV0CalgvUY1jypeegz9akKi4/mvaYS9rgRL/m7DHtBvJFNKgv1WFS7x6dZUgefJZsfcl5Jk1MCu2Ahgrmjx5PgKuapNqe5QqDukozqdiRAz+ToRAW4janogHWU8W2GhZOHLQKuqOD6O5xPRFLb1JrluMM2Dp5ZxvMCqrD8dxnF9tvckmCuamI5kvmCysz/FL+4bIqSpPHByiVLdoWy6HJzM8xv3bgbg8wemsF0fIQT/et+6pm40X7PZ0ZdiR1+Kke44iiJ4x87eNcdqZ3+K37pv7fH7xqFZzmWrTOdqa27/yyfz/NmGtfd9brqA70NbLMTugRRHZor8zPX93LO1iz/5/ikWyzaO95NpRSgAuxHeBME5pAiB7frBvxVB3NCa5197zGCgLcLkSg1dUajaLp4MikshQCI4l63RlQiTier82l0b6UtH+Oz+SSZXqtRtj/5MhHft7m04zdSbr9Gx2eKaAvzwVIG2mIGhKbxrdy8Pn1ludrzbYgZnlrKU6i5HZkrcs607GKJt6K72rmu7ZD7gShyeDuYUjs0WuWtz52WLxZLpcF2jaM5WbBLhl1eE372li7u3dL3wHRsIIVhqaOavBqoItuk1OgaqgHhYa6R7Bhevv3HvCKoiGF2qcP9zc0AwyPwf7jm/tHR8rojpeMwWgtWpwbZXtgLRokWLFq+Ua1mAnwWOvR6Kb4D+dJSOuMHphTLPTuUxHY+pXA3f96nbLmXrhW3fLrbJe6m8nALal1yVpE7HByn9y1lyNxMvL0TCmuIbAh14pXH1sjqYuVAMYrcNNRhqc1y/4fIRyCykhOMzBQpVm7AeDHL6EpbKQay9pkDU0OluFFgDbREePLkICI5MF5gvmdSsQM6SiRn4UjK1UuXx0SyHpgrMFuo4nk/U0FgqWTx2dpnxbJW5fL0Z2DGdr/HFp7MkIxqFqs10vsZQW5SQpvCJh8cYbIvy0zt7EOLK7gqJsMbh6fwltm6/ftOlCz67BlI8PrrC+o4oy2WTIzMFNnUnWNcR496tXewfz/3EFd6rSM6/D7SGzKBsuviAJyUdUYO641E2XaQMhmdnc3Usxw864SGNzriB5UrKpouC5PrhND88scTZJR9DO8e/u2MDM4UajidJRTRMx+PEfImb17Vh2h6jSxV8KXn7jrXd55HuOF97doaeZJg/+u5Jvnt0AUMXvGVjJ0+dW+HsUoV4SOWtWzpRgKfOrXB6ocSOvhSbuuJMZKv88MQiHQmD9+zuu2JYzmr3dnN3/Iqd2q09ScaWq6QiOr2p5++ov9rsGUrz2GjuqmzLk6xZWvQkFOsuR2eLTGSrvG1bNx//3ilmcjXu3tpFR9ygZLps7Tn/vjoxVwo+C4om8bDGvzw3x9u2dbOlJ3C38XzJ/c/NsVQ2edu2buJhjW89N09YV3nfnj6ixov7mpRS8r1jC0zlaty5uZNtl3GRadGiRYtVrmUBPg/8WAjxXaAZq3atbAgjhspHbl3Hpx87R6Fq8/RMjoFMhOWyRb52vuOzqn28WAOpK4L/7S3r+MrBaXI1l1eTK23zteD5tqEogrAicN3AH9tQFeIhjZLpXtKBN1SBrirYro+isMZFRhPQkw4zmInhS0lIs9nSk6QnFeZcNhiYFAhst4bnSzwkCIHjS1IRnWI90IFqqsLGzhj3be/hlvVt3Lapg9/92hG6EmHqTuCgIoM/pTMZ4p6t3Y3tqfzg+CJl0yVmaLxtWzeaqpCJGjxwcpFUxCCkq9wx0sGN69r4nwcmqdkex2aL9KcjDGaivGtXL89M5qnZHqcXyty2sZ109Mo607LpNjt2F/JHj2T59MhaEfje4Tb2DrdRrDn8w+Pn2D2QpisZIhXRuWNzJ5kfj1Ex3aty4fVaojVWfHRV4EkIawpV+/yVXGc8xLuu6+M7R+YxXY90xOB91/fx4Kklzi6V8WVwDiYjOoqqsL49yk3r2/nYPZv4ix+dRUoI6Qp3jXRyeLpItmIxulTh4TPLDLfFGG6L0ZcOM1cwATgxX6Jqec2udzy0tqs8mImyoy+F4/l8/9gCru+Do1C1XEqmS0hT2NqTZGd/ivFsDdPx2dKT5LZN7XTEQzw+mqViuVQsl4XSlXXgt2xo55YX6Jav64jx7+/e9EoO/8vm528c4sC5fHMV5rXm4pVBVYDleKTCOhMrwWpFxXI5MlPkP963+RK996HpPBFDozcVQRFguz6Hp/PNAnypbHIuWwXguZkCmahBse5QrDtMZGtXTAy9mGLd4dRCMCN0aKrQKsBbtLgGvNIgn6vJtSzAzzV+jMbPCyKEuAX4MwLh6zNSyt8SQvw28DPAJPBvXkmYj+P5jC6WOTCRoycZIqJrzBdMLqgJmsXpxV89ji/55KPnXu6mn5crbfO13NblsD3Jhek4ru/juDbOZf7I9mQzTEdeUCcqBA9RqNnsW9/GfNGiPWYwuVLloZOLOL6PoamkInpgV+hLVAXGl6uX7Jvt+pyaL7NSsZnIVvjTH5zG9SW2J6laHnXbo2q5uJ7EdGp889BsUMhGDe7bFlgLji9XqJoug+1RXF9y43CG7x9fQAA/e0M/ANt6kyyXl+lLRTi1UEJCczBUCBjIREm+gARgS0+CyZUqz00X1tz+2++8zARmg0RYY6gtylSuxkS2ykf/+RluGMoQNdSmdd4bjQsv8FavyVb93C8svgXQkwpx/+FZypaL6/kUag5/++MxBtoi6GoQnlR3PLyqj+/D6cUKJ+ZL/N3DoxiKQipqsL0vydu3d6OrCtmKTbnu8u0jcyiKQnvMIGq0EwupzWLy6GyBhaLJnqE0h6bz/ODEAhs744wtV4iHNEYXy5iux+6BFM9M5unPRHjnrm5mC3Vcz2eoLUpvKsyPTi5xcqHEjt5k00Vla0+Sg5N5JrJVZvI17tnaRV86wsHJPJu7E81o+dc7Q23Rq1Z8w6Urg56Euu2zXA7ciKq2h5TB5/dHPn2AhaKJqgQylJvWt5GrOuSqFpMrNVRFcONwptkhN22Xv35wlGen8uzsS/GOnT0kIzrH50qENIXBthdv55gM6wy2RZnJ19h2Ge/4Fi1atLiQa5mE+X8/3++FEH8ppfzYRTdPAvdIKU0hxP8UQtwB3C2lvF0I8TvA+4Avv9x9mi+YLJYsOmIG6YjBL906zH+7/zgqb/xRt5AaBLrMFs3mUOcLfYWGNYHVGHy7HKte31fq0AsBhiKwGt+gAhjpjuF4gbwkFQnxW/dt4fhcib95aBTT80FKfBlIA6KGSkiFuZJFruI0H1tTgudjuoEEp2Q6Dd/lIOFuuC1CrqqSq9mU60HipeMFnt1lM9D5p6M6XYkQpuNRqDskwzqbu+IMZKJs6Q6+PLNli3XtMW4YynD9QJovH5zG0ARPTeQp1h0MTeE37hl5UcEe23qTJMMaT4ytrLn96wcX+d13X959QlEEP7d3gLlCnT/41gmKdYeDkzl6UmHyFYuy/cbqgKtAd9JgrnTli4eQKhjpjnPDYLrhOCQwVDVwOvE9LC8oun/xliEOTxeYXKkRNVQkUKq7TZcMS/hEdIW6HQxXD7VHmc3XqNoeuapDPKySjESp2T4/u6ef4fYonzswRUhTGWqLcufmLr5xaBaAbx+doz8dZXy5Sm86QtRQ2TOc4ePv343RsOS7a3M3IBFCcGi6QM322Nqd4Ob1bU1Xni09Cbb2JJgvmMzk6xyZKXJ2sYIEDk7m2beh/Q0xHDjR6BZfDS6W9cUNhVVVihCBK5IiAu/8XNVmpWoFSZ4SRpUKJdPlrVu6mC/UCevBa7WpO9HUzx+ZLXF2qUIirBM21GbX+tffuvF5JWWX3VdFXJKq2qJFixZX4vVsivqWi2+QUi5c8F8X2A38uPH/B4AP8woK8LCuYLkeS2WLeFjjmXM5LFe+flKCXgGWBzN5MxiM5MpF9YWYVwgfWi20X8gRxfVBiPO3GppCxfKYK5hI4EtPT3J6scRCwWxqcgGSmqBm++SqFpbjoykCVYFVE5rgcc/bPtqOjyICa8NS3aFQszFUQb5q40rwXR9FQNXy8HyLw9MFpnJ1BJKQrtJnqORqFueyKjv7U4SNoOAbbo/xwxOLfO3ZGbb0JLhjpIPZQh1VEZxZKLO1J4GiCB44uchXDs6wpTvBb75thM8dmOQfH5sgrCv8p7dv4Z6GDWFnIsz6jhgHzp3Xz/7ybVcOPVlFEUHU+vG5Io4nUYTEvYodyFcLj0C7/3xYnuT0QhnHl+zoS3J2qUzF8tBVgZTBsdjZl2KgLcY3Ds+xWDKJhTQ64gY1222eg2FNoT1hsFw2+erBGUzHw/UDF4+YodMRN7Bcn4OTOSzX46Z1bfi+5PB0gZ39SfpTYbqTYZbKJhs7Yvzo1DLzhToSyc6+FN3JMJ/PTnH31i6G22ONgisouobaokQMldl8nW8+N8f9z82RjhoMZKL0pSO0xw0kkoFMlP5MmOemi2zojL0hiu/lksl//tqRq7a9iz97JQLT8fAlhPXgPSUaDkh1x0NTFAQ+rpRULZdQRnB4usD2ngTH5ktULJfJbI1DU3n2DGXY3B2nI26QqzprhmRfavENgSPOifkSe4YyL9pVpkWLFm9eXs8F+BURQuwGOoAC55vTReBSke1LYDxb5cZ1GXrzYVRFYXS5SiysXlXP29cSD0iFVMK+pG57vJwGqgD6kgbZqr3GDSaqBh0g0wvi3BWFIPjEl6zviJKK6MTDGqcWys1jWbV9Dk8VUBWB3Sj2DTX4Uh3MRMlVLVRVEA/r/Nz2flbKFvcfC3y6XQ/ee10vD51aIhpSiRgqH3//bj72xUN4vqRiucRDOhKHsKbSnTAoWy5RQ2e+YBJtFNmf/MheTM/ns/snUYRgPFvl1+7ciCB4Pvc/N0euarN/bIWf3zvA+67vbwQABamdAPc/N0e+avPk+AoTK31849BsM/3ym4fneOvmLhRFYGgKf/j+XXzx6enmcfunJ6b43Xdf2Xca4ORCma3dcZ4cz+J6wRBqMqxRNF/dWYOrwYu5bnB8cF2f4bYomaiBL218Kdm3Ps2u/jRv39XLyflSkGqpqygC1rfHWKnYdCVCGKrCP/3Kzbi+5Pe+cYyi6VK3PT544wBbe5Lct6ObUt3hs/snOT5XZHSpQkgLEix3D6QI6yqGrvILNw/i+pJvHp5DUwUVyyWsq1Rsl5l8DUNTeXoi30zdXKUjHuKjd2zgk4+O88iZZYp1h7ihoqsKQ21R/uB9O4EgEEgIwR0jnehXGMh8vfGJR0avSSqw3vg8SUU0ZMOiNB3R+Pd3b+Kdu3rZP7bCoekCSyWTsaUys4Ug+EgRCrsHUkQMlU99ZC9/8+MxEIL94yvsGcqQjhr85S/cgOV6RF7ksOXlkFKyf3wFKeHJ8ZVWAd6iRYsX5A1XgAsh2oC/Aj4I7AX6G79KEhTkF9//o8BHAYaGgm6j70t+cGKBlarNPVu7ms4Uw+1RDk6q9KQiSCSW4yOQnJovN3Wqb3Re6YCoBBbK9iWFVM1jTXXlX2AAPp6tEdIUYiGF8gVFowRsz0eVAlUF4QEISnWHGYIhNsv1MR2PH55c4tYNmWZQkSICzbChqRRrLu3xMN88PEehZmM6Ph3xEI7nIxBoqsDQVTKqiqIIYmGNxZLFSFcCT0q+e3SeA+MrOJ7ProE0u/pTnJwr8vmnpilULXI1h1REBwF96QipiEHJdNjQEadYc5BSkq1Y7OxP0REziBkani+JGiqaIvjywWnevqOHdNS4pLP2fB3w6VyV//NrRynUHXqSoWAgtXGI34jFN7z4C9mxbI2/f+QcuhrEjgMcmi5wbqVGIqrTl4rgepKa7WGoCpbroyqCUt1luD3Cn/7wDJ1xg3hIY75o0hbTMTSVrb0JwnrwunSlQjwz6WO5gQe9qghiIY32mMFn90+wviPO7SMdbOyM0ZsMM5OroasKu/rT9KQi5Gv2GptC2/X53vEFqpbL+o4oM7la0z7PdH0sx2O4PXpJsX254vvJ8RXOLpa5cV3b8w7zlU2H7x5bQBWCd+7qJfIKUyrrtsd3j83j+jLQQ1802/Du3X186rHJV7SNl4PT+DxZKgfe/44PhbrL+HKF0aUqVdvjW8/N4fo+0peULZeORIjrh9IoQlC3Pf7L/ccpmy49iTB1R+F/fP80UUPh5vXtTTvJF+KRM8scmw1WojZ2xXjHjp4gt0AINnTGGVuqsLEz/sIP1KJFizc9r+cC/JI1QCGEBnwO+G0p5YIQ4mng14E/Bt5GEO6zBinlJ4FPAtx4440SYK5Y52Qj0fKZiTzvuS4owHtTET5654bmxoPgB8mh6TxzefOyFnxvNlaHKF8KkiBCWlFASoHSKMMC7aaGocI7dw1RsxxOLpRZrti4viSsK3h+YP9YqbssFG3esbWTA5M54mGDmuVy47oMSyULXVN4ZjIPBN3hsK7wnuv6mM3X6EmFiRoaN29oY1dfiq8cnGGpZGJoCs9OFTg1Xw6kLK5P2XT58ell9o9lmS/WqdleYG2XCPHomSwfunmIX75tHa7vE9JUHj27zPqOOL3JMO/c3ct0Phjg29KToCNmULUDyc1zM4Gn88X8/SMT/P77dl32uH390CzjDb1t1fIIawp123/DxtALgtf8xZ4/FTt4zjFdoWL7+BJyVZuJbJWq5dKeCFGxXXQ1SAPd0BFr+tQvly2WyxZbexJs6o5jqAq/dOsQ7bFAj62pgTvKQtGkarksFE02dyfoz0SwXZ9sxSZbybFnKM2eoQzbepO4ro/r+6SiRnC+eH5TVwwwsVJlbKkCBF7uq4E7sZCKoSps6k68KP9p2/XZ35gVeHw0+7wF+LHZErP5OgCnFgL5wyvh9GKZyZXzPui3bVw7n7C1N0V/ymC2ePWGgMMqmI3VNs8P0lKTIQEoTObqPDaaZTZfa4ZuCWBzd5wb17XzO+/YylLZ4m8eGmUiWyMWUunLRBq+/zlSEQPbk9wwlHlB3XbJdDg4mWdsuULN8vClZHtvjQ2Ngvs9u3ux3LXnRIsWLVpciddzAf4Xl7nt54GbgI83Oom/CzwihHgMmAL+/MU8cHssRDKiUzYd1nesXT6+uBs1V6hRrrut4rvBy9XDS8CyfXRN0HAxRAK5qoOuCh45s4yuqSwUa9RsjxVo6J0FuipQFMnocgXb9SnWPaq2hUSyMGkFsphMhKrlYje8wws1h28dmWPf+jbiIZ3vHJ3nKwdnuHd7FwqBLlRVBLqmsFS2KFkO3YkwqYjO+HKZuYKJ4/pkojrxsE5YV9k1kAJAVQRnl6ocnMyTier4UjKZr/G5J6cCXbgQJMI6t2xoZ/94DscL5BSX4+dvHL7iMbtpuI1vHJ7D9XxuXJfhoVMOVfvqphC+mlzo8f1iMV0fs/Hv1VWorz47S1wX+EJgOz7xsM5Id5wfnVrCdnw2dsZZLltEtECfbzoeXYkQFcvlf3/rRg5O5inVXd6yqQNDUzk2W2SlamO7QbCTEAIBDLZFiTSKqbCugh7c99vHFtjZl2J7X5JvH5mnbDroqkLZclmpWMwV6vhScmSmyOauGINtUU4ulEEIpnO1ZhE+tVLjsdEsg20R7hg5f3Gmq4KBTISZfJ0NnWs/ny5msC3CwclAytKfefGOHVeiPx3B0BR8XzJ0mXM2pCmkY6GrWoCbF7lQSSQVS6Iqkv1jWQ5N5dFVwWKpjuMH0p7lss1gJozl+vzdw6M8cHIRVRF0JkLYnk/MUMlEDVxfMpuvc3S22BzMvBIxQ6M7GWalYhHSAgvWrgtSTYUQreK7RYsWL5prVoALIe7n0kZeEXgG+ISU8jMX/42U8gvAFy66eT/w8Zey7Yih8su3DmM3Aliej88/NYXzcnLbW1xC2FDoT0fwfclcMbAQc9zAZnCu2HApaCQYOp4MvjDjOr9x7xYOTKzwxOgKxbrTSDiULJas5jBise6QCGkQFRiqoFB3MHyFlarNz+0d4DNPnMPzg7jwd+zooWK6JCIaB8ZzrO+Isrk7wc3r2ujPhPm9bx6nLaazsTPG//jgdU1N+4Vpg4+cWaZqeSyXLW5Z38Z8sc5UroYEfvb6PvauayOsq2zvS+E3tMoA+erawuXLByf4/YHdlz1et27q4Cu/dhuO59GdivD3D4/y8e+dbl0MAhVHYqhBKuJdIx30p6OoAjQ16FS+ZWM7h6fzlOoOtufj+5LjcyW+cnAGM9AzcGK+xIaOGBPLFeq2hyIE2YpNXzrCvo3t3LKu7ZKu6MNnlrFdn4cry4R0hTOL5SDJ1XRY1x7D9nx6U2GemcwTC2nNYdJi3UERgifHV5oF+BNjWRZLJoslk139qaaPvBCCn7thgJoTrL48HwOZKL96xwaEgJD2you/zkSIX71jPVJy2WJSCEHdurbyJ1VR0YXE8wJ5Wtl00VXRkKkE1qXpsIbnw8n5Eg+fzga6cU9yXX8Ky/F5+/V9/NK+YT756DhSSh4+s8zugdTzDl+qiuBDNw1Sd/pQRCBte6No91u0aPH641p2wMeBTs4X1P8KWAQ2A38PfOS13LimKmuS6PaPrTCdq3HrxvbmF+RiyaRuBw4bPymDmNcS3/fpSIRABgW400jKtF2JEJK67WNowUBm8F0qcX347tF5npnMUW10t5EgGr+TBANaMUPD8SQhTTR9gU3HY2y5yv3PzRPSVQo1h954iOWKRaEe2Bp2JULMlyzqjs+nHx8HKTFUhcWSSUcixGy+zpHZIt2JELYng4G6kMrDZ5YD3e3uXjZ2xUlHDI7UikGXLBUmrKvsH1vh6Eww5YBBAAAgAElEQVQBn8C54/aRDuLhtW+59++5cgd8IlvlwLkVHE+yUDRZKtZa5+BF2J7ksbNZYqECUgbnUM2q8YMTNnXHp2Z7qEoQ4jRXqFO3XMZXapgNqULJdBhdqlCyXPrSEdJRHUNT2NgRu6T4ljIY7h1dLHPH5k66E2FCukKiIXkSAnb0JZkvmnQnw9he4OBzcr7EbL5GxQ404KsMtUUZz1ZZLlvsH1/h7dt7mttUFPGCxTfAo2eXg4THkEbFdNd8fr1cXqiQv9auUDXbQxIE8lRtn1X/mdXAHglM5Wr80/5JPuR4RHSFXNVjQ0ecWFgnFlJpjxl88/BsIzUXrh/M8OnHxjkyU6IjbvCbbxshGVkbT/HcdKEp89ncfXmf71zV5sFTS6QiOvdu7WrZEbZo0eKKXMsCfI+U8s4L/n+/EOIRKeWdQojjV3NHijWHJ8fPay4/dHMwGLd/bIV4SOMDN/Tz4OlFJlbM53uYFldAJehM9mdi7B3KsGsgzX//9gkKtaA7KZCYjgx8xX3ZHLTURKCzPThdoGb7zeJTJejEqUIiBOweSJOO6qzviPHUuRwdMYHje5RND9eXPD2RY0dfkvaYQU8izOGZAus7YqgKbOyMoyiCZ6fyTDW0r9v6krxlUzvJiMGXnp4Ogjlmi+iqQtRQefLcCkjwhGBXf4reVIS7t3SiiODCbjVF78mGLZnfcJ3Z2pugo+EJvcqz01l2DaUue9weObvMYtHk6Ykger5sOoQ0Qd2RVy0Z9fWKrsC6tghlyyVXc6g6HsNtEWyvjml7ZCuBLCSkCdLREGFdIWZoTOZrJMManuczV6xzLltFIulNhtnRl+IX9w2hXEFKMFc00ZVg2C4V0cnEDH7lLevxfImmChxPEg9pFGsOuiaomC5ffmaaYt1lvmixZyhNvnY+J+y2TR0slk1CqsKp+TJbe5KXSOKej6WyyTMTeSzXY3y5yrbeJI+ezfLhW17Y2vLlkq1YrJStF77ja8B5o8fzxbamBLrwTExHSoFpO5iuT9XxEFWLbz43x1s2tlN3fIbbo3zo5iFCmsrYcoUHTy2RrVhEDY1TCyUmVqpMZAOv9+8dW+SDNw02t+37kodOLyElFOtLVyzAn57IMZ2rMQ1s6oq/pNezRYsWby6uZQHeKYQYklJOAQghhgisBQFeU4GhlJJHz2ZZqVrcOdJJMqKTierka84aHWWuanNyvkQirL4qy7tvVjzA82BsucpnnpjA9yU1J+hcXdJNu+AGT0LVcnG8tYVm8HgN28JGh2lipcbh6WJzaDOqqxhq4Kiiq4JDkwVs12OoPUYspFIyXdpjIcqWy2LRxLQ9clUbVQikL1GVYHVkU1ecR88uM52voQqFzd1xTMdnoWiyoy9JT0MDOtwRIz4VmPD0pIKwlraYERR7viQZ0UmEL3273b2l95Lbjs8W+ePvn8K0fdZ1RLA9iaYI0hGDedXEdF2kfPMW3xC4YEzlaygy0Aibrs/JuTJu47goAhzfRxVB0ej7kkw8xGAmylOzOQo1GynB8nySIY2eZARfSr57dIGb17c1u8jFusOPTy8RD2ns7E8yla/huJJ9G9v44lNTnJwvEdZVrh9Mc8dIB598eIzpfJ0P3TzIjr4UQ+0xHjixgC99pJT0p9fqtEe6Ekxka4R1lUxU58FTi5RNl7du7iIVff501WQ4OKf8umx+br0aOvDnw7btaxYAdeEQr9cIAHMbu1KzPToTYRzPw23sn+X4GKqg2Hivr+uIN2VkXYkQ7fEQYV0lHdUZzEQ5NlckW7VRVcGGrvOrCF89OM1TE3niIZV4SKc/feUVhv50hBNzwTnRHj/fQX9iLMtC0eQtmzqaw7ktWrR4c3MtC/D/A3hMCDFG8Fm6Hvh1IUQM+KfXcsNzRZODDccMXV3h3bv7+PAtw1Qsl7ZY8KG5VDIp1h36UmHOLJZbWr9XAQmULzAPf74Csj2qYrmSuuNf8X4qMNwWoWK5lM0ggEdVArlQJqrjuJJ17VGWKzYVO4ikn8xV2daTZENHjIFMhOlc4Ixwct4iGdZxfR9NVUhFdf71vnVM52s8PpalXHcJGyrZioWhCHpTgVf5asrhQCbKr9y+HqApHfjwLUO857o+BBALaZcNWvnmc1N87N6ta277xCNjjC5VkBKihsJtG9qxfZ++VJiBTJiHTi1TMp2m5vXNysVSZOeCE8VQgtUITwarKxDEji9VTMqWy//P3nsGSXbdV56/+3z6zPK+qruBRqMbQBMeoAEpSiRFDJ1EihQlkVqtNNKMIlYRq52I3ZmI3dnY2YkYfZiRREVoYnZXO0ZaWVIkJdGJDiBAEmiYBtr78r4qfebz7+6Hm5VdbdEwjQakOhEdXZWV+fLlezff+99zz/+chqfGg66DSJm8e28flVbAXDmmHcZ87hElDXphtsyFdeVEU2kFjBaV/eHsRpvnZyucW2ti6qKrRf7uqTUA/vzQPP/mEwXeMVHkyEKVsVKasZ4UHzoweMk+3zVaYLyUxjY1lqouL8/XAEhbZT6w/9LnXg7H1Pnco5O0/ZisY9DwLl6/bhZ+689fvqnbvxZMAeM9Dk0/oelHSCmVhG3bOX9gssSplQZapU2USHoyFuM9GQbzDvuH8/zUnQPd5/Zmbf63j+5nte7hGDrPz5Y5slglZ+vkHRNDU4RL24/4qxcWkFI5Nv3OJ++hlL72Md5+PrdWUTaaPs9eUOFbUm7wyfvHbsIR2sEOdvB2w62Mov+6EOJ2YB+qAD8lpdzSeNyQm8lrRTFlkrJ03CBmuKDYCMvQ6DEuXlhzjsl6w+Pkch1dg+bb1Hf57YpWKEnpGm2Sa0otdF3JMSb7TJZrnroZJ5KUqZF3TBIJ7TAmTiRJomQbSSLJ2DoZ22C27OKHSYdh1qi6Ppausdn0SZs6hbSJH9kq3MXQCMKEIFYFgGPp7LvMHu5yza4uBEcXazS8kMf29nN8qcZcuX3Jc963b4DLMVxwcIMY29S5e6zIyeU6dS9CB04sN9A1ga5phMk/7gpcomwxLx8bhgChKW9mFcSqzr1jqr6PdhBvS1JVzXRtP2a23KbtxxwQeaRUsfJD+RQvU1O68IEsC1UXxxDcNpjl9EoDx9QxO2FRcZLQ8iMsQ+u6l5TSFr1Zm/OdQn294V/inAF0me7eTGecRUn3uvRKsI2Lq3Pbi+8X5yrMl9s8tKunm3NwIwjjhCdOrxMnCe+7Y+AKKc777xjg+bnaDW/vjUIowdR1vHBLtiauOO9uGDPQCdyqtHyWa+p2EieS3ozNk2fWqXsRu/synOv4dW85GxXX1PUiSZSs7QvfOU0s4fG7hhnI2azWfaZ6M/R28gW+d2KN6BrH6PKVi6xtkOtMkG70vO5gBzv4h49bbUN4PzDV2Y97hBBIKf/bzX7TjG3wy49O0QqiKzS5W2h4IVGilLaOqSNvrirmLQ2x7f/rlXwCpc0NO82ROpB1NExNuQVYhoYXJjT8mFgqdnqx6tH0VUNcKaV3i2pdCHIpg0LG7CQGQhJLTEOwpz/LZE+KJ8+VsXQdxzAYyNkkiXK1uWe8wG+89zaCMOEPvn8WL4wpZSziJGGslGaqN0suZWAZActVjz0DWSqtgLSjU24G7OnPdpm1gbzD//TBO7hjMMeRRZWaePdYkeGi84pa25nNFi92VlpkAufWm1c8Z7UWcPfopY+NFFN88MAgtqHzkXuGeXa6TCIlXz+2Ql/Wpjdnc8dQjhdmyrT/gVqiGMD2Ke/2SVjGEsQJ3dCZomPQChOiWDXp3jaQQdN01uo+AknVDUnbBvuHs9TdmKneNF4YM1JwWG0E7OnPsFBxCcIEQxckUjJXbjPZm2H/SJ6hgtMJkjLY3ZftOuIcGC7Q8EIMXe3PF19Y4NE9vRRSJr/86BSgiq+P3TPCf/7RNBLBD85u8KlrMKCFtMl/984p/Ch5XUx23Qt58vQ6oIJ1tnpabgQnl+scW1QFdjFtXRLPDvCbP7mXp86u8+OZKzLPbjrWmj6OqRPESs6TsQ0l8ZKSgZyatP76Y3tImTq//scvEEQBKzWPqd4Mc+UWx5ZqjBZTPHVmnZFiipnNFrcPZnFMndVGwPvu6Gd2s4WU8NJ8FSHUysa//PCdWIbW1X2fXK5z9DrH6HI4ps4vPTJJw4voz139frODtw6m/pev3epd2ME/EtxKG8I/BvYAL3ExTl4CN70AB3Xzvjw1LkkkT5/bwA1j7p8sMpB3mCu36clYneYp983YtZsOjVfnZLBV+LxSP78EpLj4XIFiGINE0gxjUqakL2vTCjziJKHqRhcdDDTIpUwsXUeIkDCWrDd8glglSg7lbXJpi5Fiil99926OLFQot1aJYh9dpCm3AsJYYhkaEz1Zml7Ecs3FNjQq7RBTE9imjh/GxElC04sopiz8KCFjGezuz7BYdTtLzSaLVZff+/YZMrbOYN5hojfNUtVjxfYopU3uHlWewYemy7w0XyHvmLz79j7GShf1oaW01WU0R4oOa02fuhti6dAJd2Tf8JVNWoN5h3IrpJQ2Gcg79Ods1hs+QwWHpheyUvfRhUDXNYjiK17/DwGXrzdt6bp1TeBYBi0v7kgQJHUvZqjoUGmFtPwIN0hwTA1Tv6gRFqhmvc2OZWDeMdA0jbxjsFb3WG/42KbeCdiRFLc5YGwvhrezm6WM1QlnqbK7L9NdAbl7rHiJ+0VP1mIg59D0IzZbPl8/usy79vRdVeOdsQ0yr7NGS5k6+ZRJ3Q1fdcFXTJnMbraIJVfIZbbwTw6O3JICvO3HmHoCCBWSJmG0lMbQNZpeRN2LqLrq+0FHomIZyvFkK/00ilXy6cxGi72DWf7s0Bw1N2QgZ5MyDWxdpxVEGJ0ekJ60cj86tlTlL5+fpz/rUEybnFyug5Dcvi0JNU4kT51VNpWP7e2/hBl3TP2meoS7QcwPzq5jGxrvub0ffcd9ZQc7eMvjVjLgDwD7pZRvGQrv7Fqzqw1PmTq/+b49LFZHSGLJXx9epNb2War6XQeKy4vYLfeOtyo01DK89xrFw1uvsnQYzFo0g4iKe+m2tM4/21AyAEMXeGFCEClWW0oVcLLe8GgHEZqAsYLNbYM5dvVn0DWNXX1pvn18lSfObHR145OWzlhPmv/18f0s1VzOrrVIOg2XC1WXIErIOgbDhRQ//9AYXzuyAkDDDUmbGmGsGHfL0LAMnbSl88EDg+zpz9IKYnozFidX6gzmHJ4+t8FTZ9Y5slAliiV3jRZ4cFeJz79zCmWNKBnKO6zUPJ46u85z02VKGQs3jPmVd+3qHotSxuLzj052lsYdDowWqLQD/vK5WdabAZahUXcTuCy88IP7hzg4XqSUVoXDv/2Zu5krt7i9P8en/68fYRsayzWXvqxF3pEs1t7+7jwanSL5On8fKzr85vtv40fnNjm6WKPcDBDb5CUHhvMs1lwavmrIy6XUysjJ5TqaJmj6MWGiGvM0IRjI2egFh9MrDeJOAM/dowWGcvYrNkBu4e+Pr9L0I6Y3Wvzqu3bR8CMG85cWvVsM6JnVBt87tcrpFZXC+/jdVzbgvhEwdY1ffHiCSjvoNgnfKGpuxGDeQUpoXEN2t6s/SyllUHHfPFmeQDkfSVR/R5RIBgs2P7FvgJ9/cIL/+6kLhFHCN4+tEMWSUsZC1wX3jBV5z95+3n17P7ah8YMz6+iaoOlHhFLyvZNrBHHCwbEC90/0KL/wRPLxe0cZyafYN5Lnj388wzPny8yWWxTTJoN5h6hDDJxda1JzQwopk1MrdQ53GrGzjnFFiujNxItzFU4s1QEYyDnsH7l2euoOdrCDtwZuZQF+DBgClm/hPlyCUtpE14TSDGYtFisu59aa3D6YpZi26Ms6rNQDEqks88Q2JwrtbeAJl6DcIm5kNy//ONt/1wS4MUSJ6EpOthBJ0HSBZWokUhXfW7Z57SBmpe4x1ZtGCPCCGE0TpCzlTDNXVoX0ywtVXC/qaHRV8dXyIkYLKb768iKnVxqsNXxMQ0dPFAtqdiQCewYy/PDcBl9+cYF82mSylGK14QMxEkneUQmoFzaaFNImZ1ebjPekGcw7aELwwmwFDdFNRkxbBnU34MJ6i/fvG2Sj6XNmpcFdowVKGYtUh9lKmfpVZQM5x+w6LzimznAhhWXoxImarA3kr2QoNU10dbtSSr58eIG1us/Aow69aYvpjTaaAE3TSFsXLdnezkhQ5/lakEDWNml4ETU3xA2UjCmMEsIEGm5Ef8ZR1pZRzOnVBsW0SduPqLkR+bTy6taFIEYloPZlHYYLNotVjziR9GUtMrbB8DanknYQ8cNzm2RsnUd393J4vspa3efR3b0U0ia9WYumH1HKWGQcg8xVnG5Arbjt6s9gndMJXqfE5EawNdZeLfIpg/WGT5TIq7r2gDoPUfzmr7zEUqJLQbUdYhkaGoIfndvg+GKV9WZAytQptwOSRNIKYkpp1b9R8yJemK3wrtt6uX0wy6mVBoWUxWRPmhOLdRJfMph32Duc4+RqHRO4b6LEZK9anerN2mQdA9vQSFsGecek6qqVlCBSqy2gVko0oSRMva93GeNVYms8aULc9LG1gx3s4I3BrSzA+4ATQohDQNdYVkr5sVu1QwN5h889ohIyezMWf/jEeeJEslL3+OR9o5xfa7BQadPyQ5IELFPJGjQE7SCiHb71q6BEdpbyUQ4bKcugJ20xU27hbtv/2/rT1NohlbZKnuzLqtjmcjtEdj6v02GSW0FEFCVYpk6UwFDeJu+YmLrg7FoTP0q6ARlhlFBphwzlHVp+RCIFbhAzU27Tl3VYqKgoekPXuGs4R9hpiiqlbdpBxIsnKixVXcZ70nzsnmE2mgEpSxXB77mtj4yj86/++hjldkAriHnv7f185sEJvn9mHYFyxmj7ES0/4mtHlunP2RwYKVBImXz7xCpSKu323sEcQZzw+N1DfOv4KppQ6ZczGy0VX111+bX37OZzj07y03cNYeraDTdYRVGCqauCs9wM6Mte+3XPTpf5xlHF5rf8iMneDAtVJeGZ7E0rK70wYr6qvkKWIfDfBrrwrebJ7fKmy9dlxooObhhRbUfKOtIP+dH5TZaqLjnHIIolpq4RJ6phcrYjF5sttxBCMFduM15KIZH0pC3uHS+ypz9L24/Z3Z/hUw+MU0yZXQ3vYEEV8NutAp+dLnc10aau8fTZDUD503/s4AgfPTjCSs276kTqcuQdk889OknTU6E/b0U0vIi+nE0i5TUZ8JmNFkHy5jAOGqrZWqCKzJYfIhOpXFCihDOrDartkHzKRKDkW9V2xL7hHPeMFXlwsocfnt9go+Fj6IKfuGOAX3zExNA0SmmTeyeKuEHMrv4MtqHziw9Pdq53F8/nJ+8b4517emm4IY5l0Jsx+U9PXsCPEtLWxSbY4UKKzz06SRQnVzTa3mzcOaxyDkxdo7RTgO9gB28L3MoC/H+/he99TWxdvM6vNVisuGQd5c/7zIVNnp8tU3VDVSkI8COJrgtMXSARaELecn9mpacWnQbSq4e1JFJ9hCCWGHHCYs3raB4vslqLVZeRYpqwkyhp6DpBogy5VYEnMQW0QzqFrY6lCfwooeaqImZ6s0XTjy7ZgURK4kSiCw1N0xBSMb6mplFMm9RcEz9K8IOImqdz/0SJEysNkk4K4Xrdo+6G1FoB/TkHTdM4vlRjMO+wZzCLQDW+qTAVVZwFsWRXX4alqst63afcVnrxjG2QMnU2Wz5HFqpoQhBLpVPP2AZDjsG+oTwvzFa7rGWlHbDZDCikFKu9neG+UaQdE9lSlok92evfLPs7DGsQKYeNWEqCSIUMbTR8Gl7Ict3vOoKYmoZ/TSHHWweX9xVcPk41oB1GeEHSXV0KY0nUWZVwTI2NZoAXRKRtg0LKIpESXROUUhZhnKAhiCWkLYNS2mLvUI7ZTRfPibl9KEdPxmK55vLCXIVaOyRlG7xjrMB8pU0YSx7d3Uuxc551TdCfvehSkrZ0nji9hmPqPLyr57oR5tuRd0zyr3K8vJkopEzSltLCX6uQ86MI+SY58EjUeQfVXBp1JvOptEZf3qYVxCCU/lrXBOtNHzeIOb5YRxcCQwhqbkAhZXXP5UDuYnE82ZthveHz5Ol1pvoyVwTsnFyus1BxmepNs1TzmOrLUMrYDOQdXpqvdNnvLdxK9vnNLvp3sIMdvD7cShvCJ2/Ve78S4kTy9aMr9GYtdE0QxJK/fG6ehYpL3Lnv6EIgBfhRTJwIShnFEJu6UB7DSULTvzk3qa3Gxe1b14W6WfVlTNK2Ts2NaLoR1yNDg0gSxKpAzjkGewfSXFhvE0nwQhW7/s49vcxstNhsBUiZXKJxb/oJuqb2o7+Qoh3G5GyBH0tq7YCNho+uCSxT0JdzaLgRiZQMZC0SJD+1b4C5isue/ixZx+CXHp7ENgV/dWiePzk0TxhLTq02uHe8yFy53bEVTLBNTdkBBhGGLqi0Q7ww5pvHVviVd+3idz/zDp6fLWPpGqdXmxxdrLG7L0OUk0yvt2h4Ebv7M3z2oQkcU+fvjiyzWPUYKTo8vKuX4YLDcofVTFtqv2puyHhPivsnS6zUvNfFYO4byuAFEYXr+AlvwYsSHpgocmZNuTPU3ZA4UQmAc5tt2mF8iQQojBW7Hr6Fa/AtyYwGGLrgvskC51ZbNP0Yr9M1KQQ0vbjjhW6QsXQGC6oB7rc/cAcnV+p84btnERgM5Wz+z48fIOXorFR9pnrT/NUL8yxWlInlnUN5Htvbx0RPRvUttALGO82y3zi6wpNn1lmstOnL2hxfrFFImTimjm1oPLK7l/6cGgc9GYtfemSSWjtkrtziuRnVL9KTsa6ZjPh2w3hPml94eIIkgaGrrOj4UcyhmSq9WZvl+s1NxLy8WbwdJBiawNBg31CeTz8wzsvzNcotj5lNlySRbDR9/CgmiBJ+dH6Dmhty92iRj79jhN392au+z9+fWGGt7nNyucFET7rbLNnwQr51fAUp4ZvHVhgrpTi53GC8lGa06DC7aaFpgtnNVleusoMd7GAHN4o3vQAXQjwtpXy3EKLBVWTGUspb3j2iCeVG4EcJQZSw3vBwo0h13nefJRVjmsiutZ5aJjUJo4TWG5ySsr3o1gVXFNZbGuBKKySRkiCM0XSBiOQ1GfkE0DpseJQk1L2oW2AnQLUdkLUNEglBFGMItRycxEoDr3ea+nUhCJMEr2MFZxkabpiQSIGhq+apaitA1zRSpo5t6mQdk8m+LNmURRDFTK83+crhefaNFChkLHRNxdIP5BTbdG69wfm1JhKJF8TYhk7TDVmounhhhK6ZvDBTZrnm8sjuXvIpk76szXdOrTG72abS9rF0naYfYmiCsVKaO4bymLqgkDIJooT+nM1UJzp6aluEdCFtdpvyHFO/5G83gvPrTWY3WxwcK9KbtdlsKqcGL4pxjOt/BfOOSdo2EUL5HJdSFjnHYLkW4UdRd0K4dc7CWGLpEF5zi7cectv/hq5xYKTIQtmn7l3ca0NTDXe6pmxANV2j5obcMZzn8HyFbx1fIYhiQBBK+NrRJVKWgaEJ/uOT5xgvpck5Nm4Y4ccxUQJ/emgOJHzk4DBhknDofJlyK8DSlZTMMpQriqErNntLA73d2aaQMimkTC5sNJneaNKTscnaKu300HQZieTByRJHFuu0/IiHdvXcVPeLajvg8FyV0VLqDZsEbGeIL4ehdSQ/t6h3PpEqFbbmhuiaxkRPmrNrDRpeiG0oZ6uqq67LjqZRawccXazSl1N+7IWUSRgn/Pj8BqdXGiQoaRcSUrbOesPjqy8tMVZK86H9gzimyovoyajvf8pSjeW9WZtiWhE0mY77zemVBktVl/smSjfcxAsQRAmHpsvYphp/S1XvVW9jBzvYwdsPb3oBLqV8d+f/tyxlJITgMw+O8/cnVjm/1mBm08UxdTKWjhvG2LqgP2eTdUzOrzW7DuES2GyFr2jX91ogUe4jhq6WwK9VVYcSyu2oE0YiyNo6fhwTRur1mhCEiSRMOq4oBiA0pJRstsJLNhsm8MNzG+Qck8F8ioyt896+DGdXm91jsdkKGSnYzFdcgjACIUhbGrapM9mbwjI01moedS9E1wQHJ/r4qTsHef8dgzSDCC+I+U8/OM/Z1Qaz5TZPntlgIO9w53Aey9D4nZ+9m9W6z1Nn19VydBhjGToCyfHlBn4YkzYNUobG0cU6x5frPHF6nQ/fNUw7iJTspOHxgzMeAzllJ3jPaIGPHBztykh+8eEJNlsBu24Ci+WFMX/38jKJlKzWfT770AQvLdSUnr4VcXqlzv1TPdd8/XhPmv6czR2DWSSCX350kq8fW+ZPn5kjjGJ0LUGTym5vS1MtUWE0bwUpuI6KR19veoSRxDJUcWRoSrY0UcpwZqXB/ZNF/vblizafhZTBncN5nM5k7fxak0SqNMv/+qNZyi0fQ9fozVgg4YsvLpK1DVbqqqHypblqp59D0PRivvDds6zWlVuMRLKrL8vzMxVA8nMPjDOYd9A1wXgpjddhULfi6K+G+XKbnoyNbWj052yOL9V45sImAOVWwNnVi57vj+3tvynHFuA7J9eYL7d5eaHKcMF51XKoV4uGF6r3SdvQuLnZCLpQ4zjujO0t8iFlGuia4PnZMllTZ3qjTZIkZGyDO4aK2EadIEroyzlIKZneaOEdXUbXBL/yrl0cnqvyd0eWObJQw9AEB0byfGD/EA/v7uH3v3OWo4s1hIBdfWqVbKPpM1ZMMV9xGczbmLrGA1M99OeUVK0va1P3Qr5xbBkpodIO+Nn7bjzt8sW5Cs/NlPGjmKYf0ZuxKbeCncTMHezgHzhuWb66EGKPEMLu/Pw+IcRvCSGKt2p/tqPcCnh+tkLO1rusbU+H7QCBFBo5x2Kt7uFepcq5WXWPH0MrSF4xglxKCBK6evQtq2gpBKapX/SIFaDreofFl0SXWWkIoNr2ubDRouWHNP2Y40t1DowW+L+/EvgAACAASURBVMkDg8RSMZS9WVux8xIEkiQBL1QNXH6Y0PCjzt+UTiaIE86sNYjihNOrDdwgJkwkfhhTaYfU3ajDNFp8//Q65VZAzjFo+xFSCFKWTj5lYegwV2mzUHWxTQ3bUC4X6Y6/e9Y2yNoGhqYRxZJYquaqoUKKLz4/zzePLZMkkmLaYk9/9hLv5u3YWs5+af7GvY83mj5PnF5jueZ2daJb+7VlnafcEq4vQzmz0uCl+QpzlTaH5yr8/YlV7hzMk7F1glgiO24q21llIcRbovgGQChZTJKArmuEkZr8eZFyeAmThNlyi8NzlUvkBnUvYmazzbm1Fj++sMlyrU3DC1mqerhBTBAn2IbGrv4MpqGRdFaijI6LkRvGLNZcejvNdIWUiSagFUSs1f3uOWl4EYfnKlTbAXeNFih0bOauV3yDksUUUib9Obsz5i5yGaW0heh87rNrDU6t1G/4cEkpeXGuwhOn1/j+6TVOLNVJEslzM2UOTZeJL/M5zXTGlGVomPrNv5ybusZSzWWxcmWo1BuNqDOutW0fK0zUalzNDVire4SJaib3ooSNps+5tQY1NySfsujLqr6AMEpoBTE5W/l3f+vYChtN/6KvvKmzbzhHzjG7k3JDU2m6hZTJnv4sCFire1xYb3X3JW0ZHFussVBpY2oXj3+lHfKDM+uKWb8BZDpjR9cuXrsy9s1bNdnBDnbw1sCtbML8EvCAEOI24I+AvwH+FHj8Fu4ToPR+q3UPQxN85J5hUpbO90+vMb3R5uxaA1MXrNZdNpoXl8wvd3V4s2FAN+Bl67JvGerGtFVXR7EkFAmakKRNxR62w5iWH3eLn5ShCrqUaSBEQt1LSKRkqeaRd2LV+W9qjBRSGJogZaqgG9tUjiiRhN6sRd2L0IRgo+mr7VnKFs3UNb5xdIV7x4osVF0ytkEziDkwUqDSCpBAIW2QMg28KOaLLyxy32SRAyN5lqsuE6UUu/qyfP7RCb74wgKHLlQQQk2a/s3P3MVG0+e+8RJ1L2K46FB3Q/7D35/G0NUN9TMPTvBffjjNqZUGpq4xWkx346ivha3iByDvGNfUkm7H148us9kMOLZY47MPT7DRCNjVka4YnTpfFYTXF2t/4XtnWam5nFlt4pgaf3Zojn/1+D5MQ8fQO44nlw26t5ILSiJRwSgo5nl7s6UfJt2wpeZlxYoXSebLbve5hgbtQMkPhKZs4+6fKPHbH7yD0ysNvn50GZBkHYMnTq3T9ENOLNZ4554+DowUGCs5fOPoCs/OlLudnx89OMzvfvssG02fE8sNpvoyN6zl/cjBYWY22owUHTRNcNtAlk/dP4aUMNGbZk9/lq8fXabmhnzj6Ar9Wbs7Gbgezq01efL0OufXm9iGxlgpzUbT7+YTmLrg3omLxvEf2D/InoEsAzn7pkpdttAOIg7PVmgGN3+MCdTKjqkJtpun+p3xEkYSTYOHdvXw/VPrVFoBmw2flGUQJwmjxRRhlNAuJuwdyNKXtfivP5pheqOFZWh8/tEpJnrSTPVluj0dv/7e3RwYyTNaSrOr/+JYODRd7qyYqMncVF+GvzuyRLUdcnypzj9/7x5+4aEJzq03eerMOi/MVqh7IR+5Z+QVP+fdYwVyjoFlaKQtnbWGz+5XKXPbwQ528PbDrSzAEyllJIT4GeD3pJR/IIQ4fAv3p4stdsw0VMJa1Q2ZKKVJWTpCKEssU9dBhCDVjSLnGMqO780xB7gEhgDbFASR3DJo6Vr+bSd1EwlemCAEZGyBoWtosfp96/6m6xpCKiYmiAASpfcW2sXCKZYs11z8OKHmhTT8qKMJF0RRQtUNyVqminZvBYhOFH3bj5R1nKahaQIvivHCmJShYZsaOccgiJWHbtrSWW96NP2Ql+aqHBjJoesa7SBhqj/DVH+uY1eown1iqRqzuu4kfsTL81VGiinuGStydq1JLCUjRaer2TQ0cYWLwdXgmBrllk87UE2Bp1cabLZ87psoXbPocTrWZLahU0pZl/gC67pqLxOCbnritZAy1ZjThCDp2K/NbrYppkxSpk4US1WcdE6OQE0G3yo9mJeXadt/9xOIvRBDu+iGcklTSOdBKZUMwQ0j1hoq/nuo4NCTsfjW8RX2DuZI2zrzm21avkrFnNtM8CLVFH3HkFK73TdZ4njH1SKMExxTx49iojjBdIyundzVkCSSw/NVhIB3jBWxDb273S1sZ82HCg6agIVKm7FiCtO4MXZ6azwZmvp+Ko2xfsXft2Do2pvaAGobNzfRcTu2rmWXG8xIqUK9mr6yQh0dSnFssd7pIRBomsDQ1HfGj5LuitOFzRbrDY/lmttd0bxvUn2Ha27AF59fwDF1JnszVySIbn1mIcDuXDPUYyG2oSmr1IzFHXqOZ85vEiWSVOc1QZRweK5Cxja4a/Tqk/3tfSXFG2jO3sG1sRMlv4O3C25lAR4KIT4L/DLw0c5jb4muk8fvHubCegs3jHjizDoA900USZk6/VkLN0jYP5InY+ks1z1u688QxAnn15pXRGi/GdCFYge3ihcdVYDFUlJ0TOIkvEQjLDrWglGSUHRMspbBYtVFoGQojqmx2QpIWzq9WZOsbXLveImzaw3lQBJJ5stt3DCm2g6xdQ3DUBKQOJH4YcJP7Svxcw9M8MJsmWenNzm2WCeIJVU34F98aB8tPyKKVfNq1tbxo4ShvGDvYJb9owW+fXyFQspioezSFBHPXKiQtXQaUjGnf/LMLCOlNI/s7qHphRwYLfLNY8t85sEJAL5zYpXpjRaaELx3b59ikA2dH53b5Dd/4jZ+fH6D0WKK22+geJnqzSCEIO+YHJre7DK6LT/mA/uvHtf90YMjXNhoMlZKXyFtSVkGuhth6hrXbpFV+JeP7+P/e2aWPR0bRdvUafox79zTy4O7Srw0V2G9HrDR9Kh6ofIBTtus1j2CbZKijKURhwnem0COqwnblRr0q1lixol6fFdfmrWGpyZmQYKhqcTFh3eV+MrhZcptNYa9MGZXf5pP3z/ON4+vcGypzhOn18jYBuc3WmQs1SSbsw1KGYv1xkWnjvGeNLqAnG1wdq3Ji3NV9vRlqGVt/ulju6/q+rGFI4s1ftC5Fli6ds1CagvLNZfNVqAKur7MDVsPjvek+dT9Y7QD5RjUk7EZzDv0ZpQ3942svtxM5FMm/+MH9vKvv3q8E3B1c2Eb6nsnkdTaIaahYRs6vVmL4aLDZG+aDx4YYldfhvlym6Vqm+PLdcZLGdabfreRd7nmYuqC+bKLLgTldsDRxRr5lMmHDgzxR09P88z5TZZqHu/a08v0RpPPPTrV3Y8HJksUUyZp2+gGHH3iHaNc2FBBXls2lHnH5DMPjbPZDLox9c9Ob3bZ87xjMtF7fXnTDnawg38cuJUF+K8A/wz4t1LKaSHELuBPbuH+dOGYOvtH8sxuXtT7Ke/lEEvXMVIaXhgz2ZchZRsMFh28IGJ6vcUriVCuZiH4enEtt0PFHMqrNoUamqb+6RqaSDB1DU2ofU+kYno0TTCUT3HncJ6za03myy0GcinaoWpulJ2KPkokdod1qrkqXt6xDJpBxN6hPO0w5tx6i2bDp+aGvDxf5e6xAo5l4FgG/Tm7Wyg9uqeXlbpPuRkgpUQi8cOIWEIidQRKi5mxDYI44d7xEhstn/NrLdbqHg9MldjTn0MIWOpIXEoZi4G8jexMPI4u1Dg4VmQg73ByuU47iDg4VsTQNabXm/z4wiZ7+rOM96SZ3WwzWnLoy9psNn0Wqx51V4X83Dl8sXifL7dZrLrcNVogaxvMbLbwwrir0b382G+x2tdjXQF6MjbjPRnmKy67+jNsNgP8KGa4kOH2gSxnVpr05QS5lMnJFdV8lnd0mr5B0FYSqYwJB8eLnFyq4bk3nxs3dTWGLv8qXOubIVHa4rRlYukCQ4uJEhUEY5k6I8UUDS9UvQ8Cklhyz3iRJ8+sU2sHxFL5vG95zPthQsOL2Gz5LFU9vDBmd3+WR3f30pdzuimaS1UX29C4czjHesPvSj62EMYJRxaqZGwDIRWb3fQj9g3luGu0gB/FvDxfoyejJqnTGy32D+cppFXQi64J+rL2FWzqK+Fq+vNi2uTkcoOMbTB4i/2enzm/Qc29+cU3XOxvGMynCGOVs1BImeQcCy+IOdoJSTo4XuTRPX3MbrZIXlhgteHT9ELcIKIVRIRRwqmVOn4Uq0CfQCXjzmy0OLfW7K5YaUKtKlyupxcdNr3hewx1GnZTls54T5oTS3XGe9LdAKeBnHOJk8z2bZnG1a7Gqmfk7KpKXe67AanSrUaSSI4s1tAE3D1auGEf/B3sYAcXcSt9wE8Av7Xt92ng392q/bkaJnsz/My9o/hRwl89Pw+oovShyRIgcMOYKUsHBKajYZk6fqw4cF2DOL5SCvBaCcjLPXGvh+3v0QqiS+QJhqaiiu8Zy5G2LRYrbVZqIZYh6M86jBQdZjddhIB7Rgs8tLuX5zppgGEsqXvNbtyyrglyts5QPoVpapRbQef9JefXGqzUFKM5UnQYKaRo+RGbDZ+vvrTI7Gabn39wHNPQ2DuQ5dx6C9vQaPoRPz6/SSuIaQURGdtASlVUxXGCFyd4UULN8xjI2QwVHBarLqdXG6QtjS985xy/86l7SFmq2dTQBJah8an7x6i5Ic9e2OTUSoOX5qs8fvcQ3zymUib9MOHASIH/5+lpzq016clY9GaVdKQ/Z/PTB4b4Lz+aIYoTFqvKI7zuqnPdDiK+cniRKFFF3f2TpYvbjRLeuafvkvNTSBmsNwSpztL19XB2tcH0RgtDE2w0A8ZKaSxd+VP/+aE5lmseuZTBZE+KM6t1TF0V9j95xwBfOryomnATwf7hPDMbLSpvQgFuaAIhIA6uzu+bGkz0pGgFCdW2T9Y2WKi4DBcd9g/n6c1ZfPG5eZZrHn9xaIHbBnLcO1lirux2Anpi/vblJT5+7wgvzVdJmTrrjYB7x4vU3JCNhstM2SWIEgxd48RSnTuHc3hhzKcfGGeh0ubIQo2MrRp7Ewk/Pr+JoQn+6WO7u3KDZy+UeW5Gaf9HiynqbsRq3eP52Qp3Duc5v97kyEKNREqiRGLpGhc2mvziw5P05+zumNs39PqdVf/25SU2mgGH5yv8s8f2XLNh+GbjuydX+YvnFrhGSOYbjiSRhHGCrqvJqh/FlDImj+7u4dnpCqdWGrw8X+XMaoPfeO8eJnszHBgpUD67TtOPKbdU87iUahLfn7V43x0DPDTVw4mVOtV2yN8dWeJT940x3pMmZxv0ZFVT9nacW2vw7ROrgCI1Huw4F33z6AqLVZfnZ8r82nt2X1We89BUD4WUSXYbe345vnJ4kYYXcXypxq+9Z/cbfBTfeBxdrPH9U2uAWmE4MHL9FaEd7GAHV+KWFeBCiGmuUo9KKd9SV58tbZ5hKLY4ZUFvxkbXNTaaPk0/ImvrzGy2ux7aUiqNuCAmvkq981qK8Nf0GnnpJEAXio3JOgZeKIllSBAnBFFM0NELhLEk1WFtH97dx8HxIi/Nq5TI7ZW8pjxNlD2iqSLty82gm1q40fApt0NMXVBrh1TbPgXHpNyJDdcE3XTL52fLPDdT4baBHBM96gaVtnQcS6PlRwgEtXabrGOSN/WON3nIYN6hkDLJ2Mq72QuT7iTFMXQG8w5BHPP02XUKKYtH9vTyYqeZTdfofCZ1Qz2/3iSXMrsOMZqgkw6qbjBDRYehgoMfxdimRhhLam7AczNlBnJ2t5DWO9rTLehXqbDjJCFKJJGUvFIdtVVolTJWJ91TZ7I3zWK1zVLNo+4F2Ibo9B4o7X4+ZREmEtvQCOOEMJF84+gytdbNtY3bghde/FwCdSy3G+wkEpIE+rJWt0jW4oi6G3Jyuc4jqR7COCFKto5VQiljcWDEoOHHaEAUJ6zVA4ppS03QOkml/TmHowtKq7/13kIoHe4Ls1VyjkHaMpjdbNEOYkaKKTKWzrk1NbGU2/ytt7tvGLqgkDape2E3bVbrjhXRkYFFzG/G1NohhbTJWCnN2MV+ydeFrfe6fDx5YcyxxRpDBecS9h5U8Xp8qY6hC+4cfmPiFVTq75vX5BtJ8MOYth+SSPX5m17EQqXN+fUGLV9ZRp5ba/Jnz87y7HRZXTMEzGy2kYnKOLANjVhKTF1juTNhH8zZnFpuUEyr9N2hgtNdjWsHcbdXBLjkOy0EvDxfxdnmKLW1onU1aNrVj3/Tjzi1XFcyNXFxLL0Sgijh6GKN3oz1qjMJQKUcL1ddxnvSzJXbTPakX3WKpr7twmVot8xMbQc7eFvjVkpQHtj2swP8HHBtQ+RbjH/+3t38fhhR92KEJrh/qsShC+UOM+nTcCOCKO7cJCBlCGpvIEt0SXPaVR7bgq1DIjsNe5c9RwIDOYuGG3K8FSCEktt4UUKcJMxVXIJYEidJd4l9/0ie//7du9jdl+aPfzyHlCpoZ6I3S7nls1T1qbshmiYopA1afogUgvVWiIa6WbUCxW6P96T5lXdPYRs6u/oyfP/0OgtllzOrqqmxL2vz2x+8g8fvHiaRSm/w/EyZ75xcJW0bFFMmn3vnJAM5B0tXnXv7h/NM9ip9dNuPuavjHf3OPb0U0yZPnl7ny4eXAFit+3z83lHOrTXZ1ZuhlLH4xL2jPHF6jfWGz3dPrvKp+8eY2Wgz1ZdmpOAwV3HZO5gjaxt86v4x1ho+T51dY6Hs8txMmYYXYZs6n7h3lPWG3/Wu/vg7RmgHMfuvcuNdqflEsaTlR9S9kIH8tVM19/Rn+ejBYYJI0pe1WKy66JrguyfXWKi0CCPJRjOg5Uf0ZCyytsFP3jnA9EaLd9/ex/GFCiuNkMXaRcmAqakiWBO8oqXla0EC3cmoalgW6FKyZfgSS5iruIyXVMFjGYIwMjm33mSzFTCz0UYKgYaklLaIkoSVmsc7xgp8/tEh1YNQaTO90eK2gQxZyyRMYpp+zC89Ms5ETxrTWAEpuWu0wFDR4dB0hVPLdZ6bKTNScHDDmImeNA9OlcjaBi/MVsilTE4s17l/Ul2GHt7VS95RE7zxUordfRk2mgG7+jOM96QZLjj0pC16MhYpU+ML3ztHxjb46suLfH6bfviNwMcOjnB2rclUb+YS9vvbJ1Y5t9bseFxPXeID/vJClSdOK926rok3pFnzvskebhvIcHqpedPCni7vFQhiyXLNp+AYuImk5kZ87egKXph0oupdYil5+uxG11VICDXOYwkjhRT5lEF/zmGh0uYHZ9d5+twmD+0qYXakJ989tUbLjziz2mDvYI7nZyr8xmO7u8d6d+d76EcJbT/ie2cU+/uhA4Ps7s8wWlKZB68G3zi6zELFxTI0Pn3/OPPVNnv6Xlnj/4Mz612v8s89MnlD7jpbaAcRf/3CAlEiWa55DBccnjM1fv09uzFehY3lgZE8uibesHG1gx38Y8Qtm7pKKTe3/VuUUv4e8P5btT+vhLRlMJRPoQtYb6hghpW6x7k1xcJIKTF0DUNTLhcZW++27wsuWs9dDg1lH/hqcD2SRBOiy9xdXqDrmkacJLTDiCiRJHJLH67Y7CRRRWHWMdnTnyOIE758eIG6G/Ib772Nh3b3kLZNNE3jzqEcpi4IYhUfHscJpbRNb0f72FYWKkpLLpQFYt0NGSukmerLslBp8/S5DaptlYDYDmLiOOHF2TJn1xrcMZhnrKTcI9wgxgsTRgoODTdksxkw2ZsmTpQjyHhPmn3Dee4aK1Dq+GobusY9Y0VyjknDC1V4EaoJ6r6JUvd5u/oyTPVmaHgRJ5bqtPyY+yaLJBLyKYs7h/KcWW2wVlfx8+8YL1JKKy9xfRvzM5h3uHeihKVr/PDcOseX6kz2Kobp6ELtUv9moSQaN6qb1Dvpg4auddxt1LYc0+hYESadYxTTl1NBIXU3pO3H9OedK1j2jG0wUrDZP5Lj9fpZWDdwBYkTSXjZSpDS9qobuKHr9GTMTrKs6ilIYokQaqWiHcRKWqMLzq01Waq2Wa56HWZTZ6w3RdY2GS6kKKUtRkspJnrS3DvRw+cf3cUvPDTJWDGFpgmiOKHmKhZ7qJCi6cfMldsM5p2uHzMoTf+xxRp3DOU6+nJACPYN57ryBEPXODheZLwnTW/WZrSYUhPDbQiihJfmqyxWXV4Pcp1x2/MKvvHXwhspWDGFQGqXbvON3P4VzjlS6fFbQdzxe48IOxGwAtXIW3dDwjjukg6J7IRTdfpZerM294wVuy5JoK6VAzkbL0yo3MDq0G0DOQ6MKL1zIiWLlTbfObFKtR1est3XgnzaeFulXwqhWP2d4nsHO3jtuJUSlPu2/aqhGPG37Lf5B2fWKbcDTiw3ODCS54+enubwXIXNlk/G0nlgsoe9g1nOrjeRiaQdJthGRBzHZG3BRE+WM2vNS5wpQLGFQXx1h4irwdSVV3c7vJgQtwW9U+wGkexqxnVNFUmxFNgmrDWDDispuWesxOmVBilTFeaGEFimxntu7+d9d/Tz9SPLfPfkGpah8T//9B30Z20aXoQfJfz1S4u0fGXhVmkF3DWa58BIkbrr88UXFklU8g4Hx4r0ZCy+dmSZSjvgX3zpCB+5e4i/PaJS4xaQ9GQtosSgmLb48flNnrlQVpaFCbw8X2Z2s03G1llt+HzpxSVA8uyFTdK2gRDw8w9O8Kn7x5gvt9l3GeNs6oKp3gyGJvjw3UNXPaZ3jxX4bz+eodwK+M8/nGaiJ03GNlite7T8iNnNNpah8Wvv2YVt6Hz04AhnVxt84t4RNpuBYlw7hddzM2X+8InzRLHk6GKta0VW90LedZvSgt87XuDZ6TK9WYv+7PWXfherLl85vAiohL1S2qKQMvnpu4a4YzDL73/3LLom2Wj4IESHFc7y8kKVzVaIpSkHlGaQdFlpL1Syj6VqgGkI4tfhGx50GPTLx6+GOvZCKE/vy2FoAkNT/u2ljEXBcRjIKfeWsOOMglSBPMW0RU/aYqXq8r2Ta3hhwr6hLHsGcli6xnrdZ6yU5r7JIsMFh799eQlNCFKW1nWc+NX37OYHp9c4NFNGE4KRYoqD4wVe7oQrTfVluG0gy10jBTabPl96cQEpYbPl8/59gzx5Zp1jizU0Ifjld05eYRUnhOCT948xs9Hi9oGLl7HvnVrj5HIdXRP88junXnehdjk+sH+QkaLDYP7KFMyDY8VOo7W4IbefG0HDCzi23LjCbvX1ilKud/3TNEUONPyou6pmaAJbU+FiEjWmU6aOoUMUx2xNCYRQP33snhEm+zJ86r5RvnR4kVLK5BP3jvKXLyzgBopAOTCa52fvUytZk5etNGzHfRMlXp6v8kznWvXUuQ1afshHDo6+qs/84buHObWsGjhfqRl7Ox7b208po4KGXg37DYpI+uT9Yyx1JCjz5TYTvelXxX7vYAc7eGNwKyUo/37bzxEwA3z61uzK9RFECRfWlatF2tKpu+HFpU7ADROkhL6cTbkdMF9xWa97xHGC3rljNIOIlKkRxfFrdkBRBbbAD+UVxTfqbbrFy9bfNNTNKgglni+RHeZV0wSFlElTRRJiGYJ2kFBuBixWVMF7Zq3B3GYL09D46ktLrDfUZMONIoJIdB0DjA7D7QYxKdMgZeq0AuWGUnVD9g5lO0x2hCTm7FqTsMNwSlRSXdrUsQ2NuXKbtKUzs9kiiBMq7VC5qpg6lqHhhhF1N2C23Ga06NAOYo4uVDkwWuCBq0S6m4bG7v4scaIcDxYrLo6hEUvl3Z5PmZxeqZN1DNodhi3p6ICTRLlqBFHMZsun1g4ZyOvMbbaZr7TZN5RjqjfD2bUmDT9iT+d9pISWH3F6uc7+kTyWodJGq+2AQ9Nlmn6M3vFGf6XCJdnGnG+x6ImUjJVSLFXbmIZGFCdIIYiimGo7QEiJbegkMiCMJIahoYuLnyuME+puCFK+YhPojeLyz5Gg+gmuNtZF57PMdZp9kyTBD9TEThOXa7dV+I5t6sqS0lPsZzuIGc47eJ1KcKo3TcuPeeLMGlVX9QfEseT/fXqayd40/Tmb2wezHFuq05uxGCml8MNEafoNXfk7GxqnV+u0g4t73SFaWWt4rDU8BnJOdyKz3vBZrrmMlVL8zUtL5FMmHz04conrhWrQTFhrqOTGN7oAd0y9K5m5HJomXjFk6tUi6bDKbzR0jWtmKMTJpc8TnRWkQspSbjl+iESNtbytE8QafpQQywQpBfmUST5t8cCU6i14dFcvx5aqfP/0On0ZJcmb3mix18sy0ZNmsjdDkkgOTStbwqG8zZ1DBZpBRKUVsG8ox3hPmrStd69fFzbanX6gS2+ptXbA906tsW84f4UOPGsbPDDVQxQnvDCr0ljfMV58xaLaMjTun3ztzQUjxVQ3eOhWO+rsYAf/mHErXVB+4la996vF906tUW0HuB3ruRdmKzimzp7+DHGSEIQJz06XcUyN5dpF/+Uty8GGl9DyleWZqQv87X8XFyPjXwmx5LpsZZSomPPtNodBAuF2NwoJhpDEccJTZ9a7Ewkt6jTKxZJvn1hhvtxmvtym5qlC+i8OzTFaSmMZOgXHxDI07hzK44YxfpiwWvdYbwQ4lkY+ZRInCZVWSCuoc2G9ialr1GKJY2icXm2wpz/DQsVFE7DeVFHz59ZVIeuGMefXmmy2QgbyNhM9adK2wWN7+ziyUOeleZ9yK6DhhTimzpdfWuTEcoPPPzrZlZZs4cN3DXF8qc4Pz23wlcOLLFZdejIWtqEzkLfZyrCZ7Mnw0FQPB8eVbGW94XPPWIEwTvjdb5/B1DX+5uUlHr97mH/3jZNEieT0SoN/cvcI3zmp3BE+enCEh3f3slRt8x+fvEDTj1hr+Hz2oQnuGSvwhe+e5bmZCi/MlEmkkunU2uF1gzfGe9I8fvcwTT9krJhierPNbQNZ/vrFRU4sq4hzL4yxdEHDU6mnkYSHdvfgzFe5sN6i5ceE2wt5CXF8E6qoy3CtieZ2mQAS1pohG62wcbXZzQAAIABJREFUW9ilO7qWOJYkMsGPEs6uNii3fNwwwtA0Egn3T5VIWwZhrCaOf3pojpWax+2DWQZzNt8/vc70RhMQyiHFC9nTnyHjGFRbIctVD4FgT3+Ws2sNnjm/iRBCpWxOlkhZOveMFVShXXUJIzXx6clYeGHMXz4/TxAlHFmocGa1qazqwpjPPjzZ/azv3zfAmdUGlq7xreMrjJZSl8TWv90wUkyRsjQa1/I+fY24XoDZ9pFq6hq5lEHdU7IkN4zRhSBCEsaKJbdNHcsQ1NoSXVeF+cO71CTlidPr/OH3zzG72SJl6Xz4riEsQ2N2s8VfPOeTsQ0+dGCY52bK/MH3zrJQcRkppjqvV9KTjabPY3v7sQyN08t1zqw18cOYLx9e5HOPTF6y7//hO2c4tdzAMjR+7zPvuGpx/dTZDf700BwNN+ShXb38Dz952xU2iDvYwQ7+4eFWSlAKwL8GHus89CTwf0gpa7dqny7HWsNTvsthjKFrDBdT5GyDM6tNpISp3iyxhJmNFg0vvCojrW85QHQYvZxj4LfCLstn6II4lleElrxWbC++t3D5pg1NPdENk6sW/mGs7AvDDpsrpdLhVtsBg3mHMNYRSEpZi71Zm81mwKmVhoqCbvrkHMVW170YU4IXJWQtlexn6CpII20Z9GYtFiou/3975x0e13Ud+N+ZN33QK0EQJMHeRUqUSKr3Ysuy5aas5SYncZTmxF5717vezSbxOnGLkziJi+y15GRtR1bWkmW5SG7qXSJFipIoikXsBNGB6eXsH/cCHIAAK8oAvL/vw8eZy1fOvfPefeede0quoFREAsbabcelO5nBJ0I04JEK+YmF/LT1pamMBqgIB+hPZUll89TEQuQK5gWgrS9NJl+gM55hUWM5mVyBPZ3GUr1pbzft/Wlr0S7Ql85RHvbjEwj6PaqigSGFN8rDfnYeiVMe9gh4pshQzlrD81ZTTOcKZIrMc9l8Ac8nbFhQz3+8eABVJRbyWDu3hkLBZOqAowVqMnlIZUeO1N3VHkdt4ZXiiouNNo1ZNl+gUFBCng8JB0lkTIGRgOdDVVkxs5LyUIB9XakhMpYqAy+h5qXU9CNdyBH0fGRyBXw+42owkCUincvT1puiqSpisuwkMxzqSbG/O0E44GP5zAoSmRyJdI6g30dnMk1XPEtlOMA5s6po60sTT+dI5/Ic6EnYfOIwsCbRVBkedNvIFTJ4Ph/N1REy+QK72+M0VIQGVyRSWR3sQ2qYs3s44BEJeHRb//ZsrsC2zj4qIqOnpDsd2vvTtPWmaagwbjzz6soGsxmNJQPzwWTh9/uojYXI5VOkciaDk+cTCgXwPMUnQiZvYlLEZ1bogp7wysFeGspDPL+7k/50jryaeSCXV1pqIkTsS1HSrn5kcgX6bZB0oBdePtBDbSyE3xOe2dnB2jnVtNbFaKoMc6g3TXu/CXJu60vR3pehqTLEgZ4U/akc6WyedC4/6Lc+nEy+QDydI5HJk8nlUIU9HXFePtDLuXOqmDFCkPbOIyZrz+lkQTkVjvSlOdKXZlFj2YS6qRQKyva2fsrD/kFrvcMx3ZhMU8x3gJc56nbyAeBO4J2TJlERvaksdz+7l1xBWdhQxrrWGhorw9TFQtRXhCgU4JKFdTy09TA/f/kAAS/Kgvpy9nYl+O1rh8jkIez3MacuSjhgyjcHPR+72hNWwcxRyCvZnBLwC4W8UjjJB1vIg/Qo6ZxHW/JXjDtKJOAjGvJI55VM5qjyFw0IfZkiK2m+gNo0edadm65EBr/noz+VI5PP87PNB6mIBFhQX8bVSxu464nddMQzHOk3JdMHfChbqsIc7stQFQnQUB5i/YJaeuNZth3upT+Vw+cz1QlnV8d4cU+XUYo9b3B59HBvingmzxtH+lk6o8JWtkuRzOVp70+zoLGMTN7khhbrpnOoN8WR3jT7u5PEQiYryQ7r+vLsrg7a+tJk8wV+/5J57GyPs7DxaPaBRCbH3c/tpTuR4WB3iiZbvv4da5pprAhz+2Xz2X64n3esmUlNLAQons/HEqsoN1dF+KPL57PtUC9vX238Qn0+4SMXt/KbV9t4Zlfn4Lk6+o8N/tp+uI8HNh8E4IaVM0bMI33T6pls3tvDA5sP0BFP40mEqkiQBQ1lfPzqxeRUeXl/D73JDE/s6KAvlSUa9PD7fIQDPtp60/SkbI54VQIeg7mdJ1pdLwt5RPxCezyHYiqMCnk8T/DySlAVLSgt1VF2HjFxFHu7kvxs8wEO9mYI+n2Uh/3s6ojTHc/Qk8wNuoqFAh51sQARv8fuRILXDvVSUxbkvWtncefju3nlYC/P7OqkKhLgdy9upSoaJBL0WNBw9Hpoqoxww8oZvLS3m72dCe7duJ+3nTOTt6+eyb6uJO88t5kfPLOH8rCf37lgqAV0y74euhNZepNZrlrawNYD5nw+Ed63bvYpF+kZiYHrNZ3Ns687SUt1lOaqXt57fssZH3s4v3rl8OCLJJhMI+ORSWc0qiMBGitCZPMF9nWb1cacfQHy5ZVg0E9nPGviXzApY6NBP//+7B72dppUsUFPaK4MUxUL0Vpfxn+6YDZBzxROunFVE4CZU3IF8gXlSH+GdL6P8lCKbCFPZTjIX/5kK81V5npMZHMc7E6xuqWKr/5qO7Vl5iWosSJMdTRAedhPXZlRyGeM8NI1rzZGbSxINOBx7pwauhMZvvTgNtr60jy8LcpfvG35ENeWVw708uBWU2fgbefMHHKtjiX96Rx3P7eHbF7Z11XBtctHjqEZD57e1cEzOzsRgfetmz2ksJHDMV2YTAV8vqq+q+j7X4nIpkmTZhi5vA5aOhG4cMHRYio3FQXbLJ1ZwaHeFABXL21kd3uc7W19dMWzREMeLdUx3nt+C+fNqeZrv91BMlsg5I+w9UAv8bSxdsRCfvqSOQYy7EYCxodx+LLsQABSfXmYjniG5LAnn2As7sVp4AbaQ37B5zOKSm0syL7u1GDmAk/A5/Phlzw5NSnLQgE/sVCBvGbJ5RlUbD0fhIM+CmlFrT9oTSxIWdhPPJMjXzBW9aAniAjlYT/hYACfZJlRGWZhQxnvXtPCvRv3maqinhD0fDTaDBYtNVG6ExnqysPMro2SyyuvHeqjUDA+5n5PaKoMs7M9TsoKVh42VvG0tYb5fcLOI3Ha+9Kksmapur4sNGhx3NedJOj38PuEuvIQTdbCksrmebMjQUXEuDXkC0oqlyeZydNcFRl8Ibh8cQOXL24YHN8BH9w9HQlEjNvIpYvquXRR/ZDfp6kywq3r5/CZ+14ebBuwnBWTKvpdU6NoNw3lYS6Y5/HygR5Cfo/uYIYlMyq4emkjsbC5rS9aUMeu9jjhgJ+X9/cwrz7GmtnVvGdtC//21G6+9dhOm8HBz3mzq3l4ezuZbJ5ktnBM7u7ia3As9a3ykEdztcnF3Z3sORqAifHJNi+uHp5dqYiG/PhzJqA0njVWyiovQDJr8oP7bB72dK4w6E4SDnhURYOEAx6xkJ/OeIZEJsfM6ghbD/aiqkSDfpbNrByxCiXAkhkVJDJ5DnSbez2VzeP5/LTWxYgEPN553izm1cWOsRKmcnmCNgVnXVmIgz0mG0q+UGDb4T6AM1bCs3lTrKbYAp/KjU/Bpa6EWZnyiRINesyqCvPa4fiJdxxG0DM51wuYOeRkjA8+u2pXHg4QCmTw+yBn01UiEAqY1SoERCHoF+bURokE/WTzSiZfIOj3bHXZCPXlYaqiATbv62Z2bZQL59ciIuxu72fTnm4iQY/ykJ9kroBPBL8niHikcnm641maq6AvlSNXMDL5fcLhvjThgEcykyeTy9OTzLHKVtkdvjryZkfcrOiISXMIUBExOckHXMbSuQK5YZbz4t92+DHHklze1Csw55zY1/K0nfdUj34G81KwvyvJnNroiEWPHI6pxGQq4EkRuVhVHwcQkYuAM8vVNYbUxILcsKKJtr4U584ePeBlZXOlWSIXkxv1jbZeWqojVIQDzK2LoWosFhfOr+M9a2fRXB3mp9a66fl8nDu7kqDfY8eROL3JrLEoN8TojGfYfrjfuBqocRtRhIqwccfoS2VJZbEKO+TyQsATasqCVIT9gy4MFeEQZWGPbN4UKplVHWVHW5zaaICI30c8kzUp+5or2dedNBkAaqJ88rol3PnEbg71JikLeXQnckSCHufPraY8bII3wwEfsVCAtXNr+B/3bqYnaSyYVWEftWVhgn6P2rIgB7uTg0vxv3vxPFrrY1y3YgYzq8I88UYHFRE/f3T5QpLZHI9vbyfgCTMqI5w3p5odR+K8friPrcks0aCPpU0VbJhXS315mF++epiGsiDvWjOLmrIQM6siZPMFfrxpP9sP9bKzPcGMihBXLW0cohi9ZcUMXj7Qy7y62BBfy/s27udgjwmUu3HVTA50J3lqRzuHetPs7UpQKOiomRFeP9w3+Lu+7ZwmFjSMnnVi5cxythzoIxbwceXSY61Ky2dWkLY55Vc2jx5EVxE2cu7rTKBiVhGWzxxqLb/B9jUW9PNmZ5x9XUni6RxvWdmEiMmz7vNBXSzEypmVBD040p/mUG+a6kiQfV1x0jm1mSeggJDJDS3F4mGKPQ2stET9ZoWmWIEfGOXix7gPU1Z88YwK5lRH2Ly/F0EJ+mG+Tfm2uqWKF9/s4umdHezuiFMXC9JaF2NWTYR4Ok/I7yMW8vO2VU1896k3OdiTYvnMStbMruK5nZ28cqiXRQ1lBAMeH9wwh13tcQoF5ckdnaxrraGlOsKh3jRrbDrB43HOrCpyebMqlM0XuG/jfuPSlDcZOJY2lXP9iqYh+6xpqSJfMAVgltiUhiG/x44j/Ty3q5ONb3bxwQ1zzyj9nLlemzjQneKtq5o43Jc+5joYK957fgub9nXxwu5ublzVxPa2Xl5vi5/06p1fTLB6yIMj/VlUlaAtcjYQTxLwIG0XZ8J+k6EJTDBmZzzNkzs6qAj7KQ/5WTojQk0sxN6uBNGgn3gmSypXIBb0MaeujJmVEeorQqxpqSbgE7a19bOgoYzmqjCHe9OkMjm+/OA2FFM8bGFjOd9+fCf7u5JUR4Isn1lJa22MroRxa3tpXxevHOhjQWMZc2uj7G6PoyiLZ5QTDnjkVOlMZHj/htn86MX9VERMpqb182pZO/foc+TVg72D1XJvOqeJyxbXk84WOG9ONQHPx20XzWXjni4uXlB/TIxI8XU4Up2BsaIqGuStK5s42JPi3DMI+jwdNsyvJej3URkJDN6Xqsrdz+2lN5llZlWYW86fPaEyORxjzWQq4H8IfNf6ggN0AR+aRHmOYfGMchbPKGdPR4J4OjdYLexAdxLFuBp4PuGC1qNZCNI5ZeWsanwiNFaEONiTojuZZU9ngsU2ev7hbW34fUJFOMB1K2ZyuCcFCNl8gTWzq1nYEOOxNzrwfKYS5MGeFPlCgYpIgNbaGIlMHs/noyxkfBWbKsN0JXLUxoJURvysbqnm4gUNdCXTFPKK5/nY25lg2cxKqiJ+drbHKQv7aaoO44kp/+73fCy3FsDaWIj182t5aV8P+UIVFWFTKtyzFd3edd4sDvWkyOYLg5NjPJNnoFhmNGSUqlk1UXqSGXqTRnlvrY9ZF4McIb/HhQvquaC11ljoAz6aq8tZ0HDUd9vvmWj/B6oidCWyBD0fc2zKrGuXNQ5WqlvQWM6KIkX1hTe76Ipn8PmgpTZKRWToZV5bFuKyYdbpgT6AcXkJ+31cuqieLft7KM/kyeYKFFTxIXT0p+lN5ZhbGx3M5Z3IHLVExdPFn3Mc7EkyuyY2WKhjQWM5Ij5CAd9gfvJifD4ZMaPLSCxoKDtmCbpQUHZ1xKmJBgf72p3IgBjf6YdfP8LaOdW8f/0cfuf8Fv72Z6+yqz1OdczPn1yxkJ+/fJiCqr2uMmzc082+zgSJbI54Ok9fMkc6X7AVIY1ins2bypZ1ZSEuX9LAvRv3k8jk6E3mCHrG/98n0J/ODxYBCgc8FjaWs7ChjI54hpDfRyFvMulsmFfLJ65dzIHuJD6Ep3d24Nmo5euWz+CqZY185/Hd1JeHaaoM01ARYXVLNatb4NJFdSQzBTr6M/SkskSCfhorw3xww1wee6OdF9/sQhWaq6NcvPDY62A0iu/1J3e0k8zk2NeVJBr0aK6ODrkGBvB7PtbPqx38Hg54XLSgjr6UyWefs6sslZxZdpQFDeWDL31Lmk6w8Qk42JMkX9BjKmuC8c//5LWL+e6Tu6mLhXi4K4XfZ+IZfAK1sSC5fIHysJ9DPSkyBXN91MSCNFdFCFhXj2wuTzJnMogMVLPNq3K4N40ngufLA0p5OGDiAPJ5jvRmTIahTJ6KsJ8lTZVcvrieD2yYi6ry+Z+/xqa93SxrCnHJonp6ElmCfh9z62LMrYvRWBHisiUNpLJ59nUlmFsb4zfbjEuNqtKdzJLM5gYtzvUVIT566bwh49CfzhENmpz1LTXRQR/sda21dMUz7OlIEIv5yefMi1HI71EdDbBhfu2QcUwUuf8ls4UhRh5VpaE8zHvXzh4x7/vwZ854Mqs6Opj2cSIZuE+KUYWkHbf4aD6YDscUYjIV8FeBLwLzgSqgB3gHsHkSZTqGzfu6+fWrbYjALee3kMjkuX+Tqaw4kqXz2uUz2LS3izm1MepiIX665QAdB9L8bMtBPJ95UF63bAa72hM2FVucvEI44KOpKsx5c6p4ZlcXW20O6epogB1H+snblHjRoJ9Y0GPJjHJe2tdDwO8jns6zsrmSPZ0JuhI5Xj5g4lj7Ujk64xnqy0OUhfy096c50J0gmyvQlsiyurySXpvW7XBfGn88Qyzk54Mb5g7mu97VboJ9dnXE6UzkeG9zJXs7E4N5kq9d3sjymZX8zc0r+G8/2kImVyAS8BEL+lnQUMbs6ijrW5Ps60qSzRf4yUvGZzka9LPtUB91ZeYBM6c2xq3rZlNbFuJnWw6yqz1OedjPbRe18pGLWvnploM0lId49WAfL+3tMX7582rwRI6xAl2/YgaN5SH60jlmVkVYPvPkUrHduKqJX75yiB1tce55YR+XLaqnN5mlO5HhvDnV+D0fPYks339mD7mCsq61ZtA1aWVzJUn7EjLwMlAoKD94dg99qRxzaqO889xZAKxqruJAd4rZtVHKwmN/Cz66/Qgb93QT9Pv40IVzKQv5uWJJA2F/B/dt2sfGPd08tPUQ/+tty/n5ywd56JXDtPWlKQv58WQH71s/h75Uljm1Mf76/lc42Gsyx0TzftLZBH6/ya4zEDiZtSkxj/RnyOQL9CRz/OdrFvF3v3yd3mSOVE6RXJ5Y0PifV0UCNFdHWDKjkgsX1FAeDvLItjaiQc9UkvV8bN7fw7ce3UlfOsdzu4wvaCKTJ5dP8s1Hd5DM5bnxnCbe7IizuqWaqkiAuA2uO2dWFXlVFGVlcyUiyqLGCnw+YV1rDT4xbl+tZxDAtmJmJd94eAft/WlmVIa5cknjMQrD8bhkYT3hgEddWaikUsHt6TD3Nowcf5DLF7jtzud57VAvBTUpIrN5s5oR9psA4P6McWPKFaVEXVBfRl15CBHYdrCXdhuIHgr4ONyXRvrTNJSFOGdWxWCazlTGpGw93Js280rQI5M3q42hgMe1yxu4colZQRIR3r9+Dg3lIXYcidOXzJJXZVVLJZ39GX7y0gFiIY/bLmrlRy/uZ0dbP7s64syqiiACsVCAlc0VrJpVxc1rZvF6Wx8XzK055iWkeH5vrY0NzsurW6p4emcnPcksyUyeX287TC6vLGgo49IRXvLOmVVFOlcYcf56ckcHz+4yVZbPdHXkTLnnhb109GdoqgzzOxdMrsXZ5xPevrqZ7W19Jz2nOxylzGQq4D8GuoEXgf2TKMdx6bORaaoMBvUNuCL0po7NYFETC3LlksbB78tmVnK41/j5DmxfUxZiZXOlydqRK+B5PhY0lFMdDbCgoZxHX28nFjKBO/FMjoDPZ9ILisndHfB8rGqu4M2OOIgQDfpZ1FiOiFgfUyES8OhP5cwSr+ejPBxgTk2UF/d0UxUNkskX8Hw+KsIBZlSG2d+VJOCZDAMDCkFrXYzWuhiPbT9CbSxEbSyEzyf0pXKDmRAGxueyxY184tosz+3qYHdHgtryIAsbylhjLTvZfIGvP7zD5sPOEvD5SGdN9UYRIV9QEpk8tUBfyhS5TmTy5AtKS02U2y+bT386x7cf2zn4fzfOH1nhqSsLcc1pBAw1VoRZNrOSI30mMPJIv/HnbK0rG0wdN1BF1PyeR4txez45xsqVVx20ivYVXSuRoMf6ebWIQK5QIDgGBWkP9aQQMX0YOFcmVyCVzVMW8lMRDrB+Xi0PbDYvj8lMnng6R2d/2lr2AZRkNk99WYjz59awrytBXk05+GjQz6zqCLlCAZOnKEehYJQsY6E8WpmwMuLn3DnVzKqO0t6fJptXPIFoyE95OMCli+v55LWLB8f0pb3d+ERorSujoz9tg36FTludMJUtUFsWMrnvVckVTPDq/PqywaqUAOuKLM1+jJI7nHDAG7H9VFGgLOwnnSvg9/k4v7XmlBSlWMg/JI6gVCi+pnuTx85vuYLSETfXTKEA6jPW0UjQR1UkSNDvoz9tXkTFB9GABwhz6mI0Vhif651tCUL+PLGQn4An9FtrZjQU4LLFjXQnjAy1sSAb93bRGTerXz6BvnQOv0+oLw9xzbKmIRbilpoov3vJPO56YhddiSyhgHDZwnp++Lx5oUhmTNaTvlSWTN7cG7lCYTD3d65gViyuWNLAFUtG/m2Gz+/F11wmn2dObYw9nQmyeaUyYjLuNIzwguX3fFw4yvw1MP/lCkoimzvj1ZHTRVUH55Li62IyaamJntBVzOGYKkymAj5LVa8fiwOJyN9jKmm+qKp/NhbHHOC8OdVk8wUCnvDMrg56UznEB+fPrWHVcfxzB1gxs2Iw2HJg+4sW1JEvKPu6Epw7p4bqaIA32vpZNauKGZVh3rqqicbKMOlsjs54lkPdKSrCHmtbawkHfFw0v5bORI6FjeU88voRLmit5YrF9Tz+Rju9ySyxkJ959TH+z+O7Cfp9LGos55JF9VRFA1y9tIEHtx5mRlWYqkiQXKHAzMoIO9vjdMUz3Lzm2Gpu58+tIVdQYkE/8+uNX3tP0jzEipdOr1rSQFnIozOeZXZNdIj/csDz8dZVTexo6+e65Y3s706xZnbVoA92Q0VocGK9bvkMNu3tZl592aDbBpjCFdevmMHeziTnzq46vR/0BKxqriRhFYh1rTXUlYXoSWZY12oetE2VES5fXE9nPDPk4TsSAc/HjauaeKOtn3Najsp79bJGNu7pZk5tdExyQr/R1j+YAebtq5u5dFE9kYDJIlNXlHe4MhrgwxfN5ZHXj3Du7GpaaqL8pwtmE0/n2XGkn/qKENctnzG4rD6rOsqt62bzysEe3rKiic5EhpbqKH2pLC/t66atN42iNFSEmVERYnd7goUNZSxqLOfejQdY1FhGyO/jcG+KZU3l1JSFqAgHuWHljCH9XtFcSX86x6yaKKC82ZFk+cxyzmmp5vXDfWZ1pytJfVmA1w/3U1cWmnRrXGUkwG0XtfLItiNsmF8zJtlMSoGlTRX0prLk8saqO5w9nXEqwgG64hkaq0MsnVlBfypHVSRg7xHhF1sPcqA7RXnYI5VTrlnWyMpm497WWBHGJ8L2w30sm1lBwOfjsTeOkMkpFy+s44YVTRzoTnKoJ8X5c2tY3lxBedhPWdCsmmzZ340g3LCyaUT3DIDrVzSxeV83CxpM6rxrljWyaW83c2ujRIIeb13VxNb9PaxrrSES9PBs4O5I/T0V1s+rRRDWzauxK4HeaWUouXhhPQHPR00sOKapKk8VEeFtq2by2qFelp/Es87hcJwaopOU1FVE7gD+SVW3nOFxzgVuV9WPisjXge+o6nMjbbt27Vp9/vnnT+s8yUyebz66A1WjLN5aVGxjvPjttjY27TGlsm9aPXOIte9k+JffvkEmVyAa9PiDy+aPh4iOMWTt2rWczvX5wptdPPr6EQCuWNJwxorEyfCb1w7z0l7j6nTzmuYh+Yjv27ifXe0mM8aHLpw7qqLkmDoMXJtPvtHOP/56O2CME//l+iXHbLvjSP+gm97q2VVcUYKWfsf0Yfi8OffTP51EaRxnO7s//9Yh30XkBVVdO9K2E24BF5EtmBVcP3CbiOwE0lh3QVVddYqH3AD8yn7+FbAeGFEBPxMiQY/rls/gzY74cbOijCXrWmvI59VYtE/DX/UtK5t47WCv85eb5qyaVUlfKouIsGKcsl8MZ11rLfmCKVg0p3bokvBli4x/c1Nl2Cnf04wLWmt4y8omjvSluHX9yEaIeXUx1s+rJZ7ODVagdDgcDsdQJsMF5cYxPl4VsMN+7gGWF/+niHwU+CjA7Nlntmy9tKmCpeOY9mk40aCfq5c1nnjDURjw4XZMbwKeb8L9iWMhP9eMcm1Wx4Jcv2LiinY4Jg6/ZwJ7j4fIsfEQDofD4RjKhCvgqvrmGB+yGxjQiivs9+Lz3QHcASAiR0RkrM8/1agD2idbiBKgFMfhXBF5cbKFGGNKcZzHirOpb8OvzanQ91KXsdTlg9KXsQ6YLSJ7KG05x5NS/43Gi5Lst3zhmKZR/ZUnMwhzrHgK+APgh8DVwF2jbaiqZ57+YIojIs+P5o90NuHGYWKYzuN8NvdtKvS91GUsdfmg9GW08s0tdTnHk7O179Oh32ee/2ySUdUXgZSIPAYUVPXZyZbJ4XA4HA6Hw+EYjelgAWesUw86HA6Hw+FwOBzjxZS3gDtOmTsmW4ASwY3DxDCdx/ls7ttU6Hupy1jq8kHpy3jHsH/PRs7Wvk/5fk9aHnCHw+FwOBwOh+NsxFnAHQ6Hw+FwOByOCcQp4A6Hw+FwOBwOxwQyLYIwHQ5HaSEiK4AVwA5VHfPKtI6JRUTOw1QZrsbUWnhaVZ8//l4Ox/jgrkd3ynkAAAATx0lEQVTHdMD5gE9jRMQD3sGwiQq4T1VzkynbROMm7PFHRH6hqteLyJ8DVwE/BS4C9qvqpydXujPnbLiGRnpxEpG/B0LArzDVhiswNRfyqvqxyZJ1OKX8+0yVubiUxxAG5fsSEAM2Ay8ChyjB63E8mCrX0XhQ6tfm6eAU8GmMiPwbZpL6NUMfnOeo6vsnU7aJZKooEFMdEfmNql4pIo8AV6hqwbY/rqoXT7J4Z8R0voZO9OIkIo+q6qUj7Ddi+2RQ6r/PVJiLp8AYDsh3GfAxhslXStfjeDEVrqPxoNSvzdPFuaBMb+aq6geGtW20RYvOJs4bYWK+V0QenRRppi/LRORfgfmYyTJp28OTJ9KYMZ2voaD992aOvjh9Q0Qet+3Pi8g3MA+/XszD7yqM9bFUKPXfZyrMxaU+huep6qUi8hXgPZjr8cfAP4jI1ymt63G8mArX0XhQ6tfmaeEU8OnNj0XkAeBhzIOzErgU+MlkCjUJTAUFYjqwzv77P4EcgIiU2e9Tnel8DR33xUlVPyEia4ANwCLM8u8dqrpxMoQdhVL/fabCXFzqY1gsXz1wHbAK44JSatfjeHH/sOuoArMicP9kCjUBlPq1eVo4F5RpjojUARdgJvxu4HlVPTK5Uk08RQrEwDg8fZZM2I4xougaqsJcQ08B/qkeZCoic4q+HlTVjH1x+oSq/vVkyXWqlPo9PhXm4ikwhiUt30QgIhcDKzH97wGeA+ap6jOTKtg4Y3/79Rydf+tU9bOTK9WZ4Szg0xgbsHEZZsKqBrqAmIhM+4CNEfDZPz/g2T+H46QQER/wkv0bbAZ+AVwzKUKNHXuLv9i+JoFLJkec06Zk7/EpNBeX7BhaSl2+cUVE/g5oAPJALfARVT0iIncDV06qcOOIdbFRzJw7wDIRuWYq+/07C/g0xgZsbOHYwIVpHbAxHBvAEeTYwJUpHcDhmDhEJIHJNjCkGVilqrWTINKYUdQ3wTzkYIr1rdTv8akwF0+BMSxp+SYCEXlEVS+zn1cBXwU+BXxBVaezAv4JjLvRXar6sG37uareMKmCnSHOAj69OVsDNoYzLQM4HBPKq8DNqtpT3Cgiv5wkecaS6dC3Ur/Hp8JcXOpjWOryTQR+EQmqakZVN4vIzcD/BZZPtmDjiap+RUSCwO+JyO3A9ydbprHAKeDTm6kQ+DMRTMsADseEciNHgxOLmdIWGMt06Fup3+NTYS4u9TEsdfkmgo9jfKDbAFS1S0RuwmSFmdaoagb4mojcAXyAoe6AUxLngjLNmQqBPxOBC95xOKY3pX6PT4W5eAqMYUnL53CcCs4CPo2ZQoE/E8FZHbzjcJwFlOw9PoXm4pIdQ0upy+dwnDTOAj6NmQqBPxOBC95xOKY3pX6PT4W5eAqMYUnL53CcKs4CPr2ZCoE/E4EL3nE4pjelfo9Phbm41Mew1OVzOE4Jp4BPb6ZC4M9E4IJ3pgAi8jDwSVV9XkR+BrxPVbvH6Ni3AwlV/dexOJ6j5Cj1e3wqzMWlPoYlKZ+IzAUeUNUV43T8J1X1wvE49plS3HcRWQt80K1GnDzOBWWaMxUCfyYCF7xT+hQr4JMti2PqUer3+FSYi6fAGJacfOOtgJcyZ3PfxwLfZAvgGD+KAn+uxPjKXQVcJiJn48qHC94ZB0Rkroi8JiLfFpGXReR7InK1iDwhIttF5AIRiYnId0TkORHZKCJvt/tGROTfRWSzreQWKTrubquwICL3icgLIrJVRD5atE2/iHxORF4SkadFpPE4cv6liHzSfn5YRL4gIs+KyOsicolt90TkyyKyxcr0p7b9Kiv3FtuPUJGMfyMiT4nI8yJyrog8KCI7rMV94Nyfsn3fLCJ/NaY/gKOYkr3Hp9BcXLJjaClV+TwR+Zadox6yc9tqOy9tFpF7RaQaBueftfZznYjstp+X2zlpk91noW3vt/9ebvf9Dzvnfk9ExP7fW2zb4yLyVbvaMiJ2LvyulXO3iLxTRL5o57dfiEjAbneeiDxi594HRaSpqP0lEXkK+OOi414+cF477z9p580nRWSxbf+wiPzInme7iHzxeIMqIl+3c+vW4rlztP6O9qwpVZwCPr25C5iPSVr/N8D3gFbbftYgJnjnI8AB4ElgP3CbiHx1UgWbPiwA/hFTqWwJ8D7gYuCTwH8HPgP8RlXPB64AviQiMeAPMW4hq4DPAeeNcvyPqOp5wFrgYyIyUJ0xhrGAnQM8Cvz+KcjsV9ULgD8H/pdt+yjm/lhjZfqeiIQx98stqroS8+D/w6Lj7FXVDcBjdrt3A+uBvwYQkWuBhRjL52rgPBGZsqWTS5UpcI/fRYnPxaU+hiUu30LgX1R1OcYy/y7gX4H/aueSLRydZ0bjduAfVXU1Zq7bN8I2azBz1jJgHnCRnaO+CdygqhcD9Sch73zgrcDbMYV8fmvntyTwVquE/xPwbjv3fgczRwPcCXzMznuj8RpwqaquAf4Cc80PsBq4BVgJ3CIiLcc5zmdUdS3m2XKZiKw6QX9He9aUJKX29u0YW6ZC4M9E4IJ3xpddqroFQES2Ar9WVRWRLcBcYBZwk1gLNBAGZmN8YL8KYKu6bR7l+B8TU/ENoAXzsOsAMsCApecF4JpTkPlHRfvNtZ+vBr4xkBZOVTtF5Bzbv9ftNt/FWH3+wX6/3/67BShT1T6gT0RSIlIFXGv/BpbJy6z87tobW0r9Hp8Kc3Gpj2Epy7dLVTfZzy9gFNwqVX3Etn0XuOcEx3gK+IyIzAJ+pKrbR9jmWVXdByAimzBzVz+wU1V32W1+gDEmHI+fq2rWztEe8AvbPjBnLwZWAL+0RnYPOCgilcP69W+MXLCrEviuteIrECj6v18PVN0VkVeAOcDeUeR8r5hVTz/QhHnx8B2nv9cy8rPm1eMPx+TgFPDpzVQI/JkISjJ4ZxqRLvpcKPpewMwxeeBdqrqteCc7sR83CEVELscoxhtUNSHGTzxs/zurR4NY8pzafDYgY/F+MoI8cpLHKe73wHe/3f9vVfWbpyCb49Qp9Xv8/mFzcQXGJeX+4+00wZT6GJayfMX3fh5TrXI0chz1PhiYy1DV74vIMxjL9IMi8nuq+psTnGdgjjkteVW1ICLF82jxvLV1uJXbGhVOJnDwsxir+s1i/MQfPkEfjkFEWjGrqOfbip93YcbreP0VRnjWlCrOBWUao6pfBj4MvAL0AS9jlvOP63c13VDVT2CWrBowbg71wB2q+ueTKtjZw4PAnxb5K66x7Y8Ct9q2FZhlxuFUAl1W+V6Cce8YLx4CbhfrlysiNZil1LkissBu8wHgkVH2H4kHgY+ISJk9ZrOINIyhzA6OucfX2n/vwLh6TDqq+iXg80AcMxe/gnGnKAXrLTA4ht/BrNCss/++WSrzZNFvXM/RefxAqcg3jB6gS2x8CUPnjd0cdbd798AOIjIPY9n9KubFbKT5cCReA+ZZRReMe8eZsg2oF5ENVraAiCy3Wal6RORiu92to+xfiXERAqODnA4VmPulR0x8z4Cl/Xj9He1ZU5I4C/g0RqZO9bWJoFSDd84GPotx2dhsJ8bdwI3A14E7revJJuDZEfb9BUYp3ox5KDw9jnJ+G1hk5cwC31LVfxaR24B7rGL+HPCNkz2gqj4kIkuBp+wzoR94P9A25tKfxYiID3jJ/g02Y66fU3FNGhdE5O8wLwV5oBZjCDkiJvj4ykkVziIi/8d+zGCVW6BXRO5Q1RO5NIw71l1HGWoBXSYi14zgmlIKfAj4hohEgZ3Abbb9y8APReQDQLGF+xbg/XbuOYSNIzkRqpoUkT8CfiEi7Yw8j54SqpoRkXcDX7VuJ37MHL7V9uM7IpLAKLwj8UWMC8onGNrHU5HhJRHZaM+5E3jCth+vv6M9a0oSl4ZwGiNToPraRCCugprDMa2xysDwlzMBVqlq7Qi7TCgi8oiqXmY/r8LEPnwK+IKqlooCXizjFhuUh4j8VlWvmFzpwCpzq4C7VPVh2/ZzVR3JB/msQkTKVLXfKp3/AmxX1b+fbLnGi+nSX2cBn95MhcCfiaCUg3ccDseZ8ypw80Bw1wAi8stJkmc4fhEJqmrGBhzfjMk+sXyyBSuiWB/470WfT8fHeMxR1a+ISBD4PTFpPr8/2TKVEL8vIh/CGJo2Ylx1pjPTor/OAj6NEZFPYVxQHmZo4M+j1ifxrEBEvgJEOTZ4J12i/oOO00REPgO8Z1jzPar6uZG2d0wPxOQo7lDVzLB2fym424nIBcBuVW0ravOA96jqv0+eZEcRkeXAa6qaL2oLAteraikFi2LdwT4ALFbVT0+2PKWIdZ37s2HNT6jqH4+0/WRig09Dw5o/MJBda7riFPBpjg2WWInJTdqD8WGdp6rPTKpgE4wNxliPiU7vBupU9bOTK5XD4XA4HI6zEaeAT2OOE/jzm1LxO5wIRgvewaRZKsXgHYfD4XA4HNMY5wM+vVk7LPDnHuuWcrZxLy54x+FwOBwOR4ngFPDpzVQI/Bl3XPCOw+FwOByOUsIV4pnefJyiilyq2gXcxLGBGdMe+xLyNUwO5lqG5gt2OBxnKSJSZfMKH2+buSLyvpM41lwReXnspHM4HNMV5wPucDgcjrMWW1HvAVVdcZxtLgc+qarHLepxMscq2rYkMrQ4HI7JwVnAHQ6Hw3E283lgvohsEpEv2b+XRWSLiNxStM0ldpuPW0v3YyLyov278GROJCIfFpF7ROQnwENiOOZ8x2m/XEQeEZEfisjrIvJ5EblVRJ612823273H7vuSq3fgcJQmzgfc4XA4HGcznwZWqOpqEXkXcDtwDlAHPGcV2E9TZAG35cWvUdWUiCwEfgCsPcnzbcBU6Oy051s9wvkuHKUd27YU6MSU6P62ql4gIn8G/Cnw58BfANep6n4RqcLhcJQczgLucDgcDofhYuAHqppX1cPAI8D5I2wXAL4lIluAezBpTU+WX6pq5wnOdzw5nlPVg6qaBnYAD9n2LcBc+/kJ4C4R+X3AOwXZHA7HBOEU8CmKiDwsImvt55+NpZVDRO4SkXeP1fEmErvE+8+TLYfD4ZiSnGzZ9Y8DhzHW6LWYktgnS/wkznc8OdJFnwtF3wvYVW1VvR34H0ALsElEak9BPofDMQE4BXwaoKpvUdXuyZbD4XA4piB9QLn9/Chwi4h4IlIPXAo8O2wbgErgoKoWMCXRT9fKPNr5Rms/KURkvqo+o6p/AbRjFHGHw1FCOAV8ArGBO6+JyLdtgMz3RORqEXlCRLaLyAUiEhOR74jIcyKyUUTebveNiMi/i8hmEbkbiBQdd7eI1NnP94nICyKyVUQ+WrRNv4h8zgblPC0ijScQ91IReVJEdg5Yw08QGPRA0bn+WUQ+bD9/XkResXJ/2bbVi8j/s318TkQuGmW8fLZvVUVtb4hIo4i8TUSesWP0q5H6M9ySLyL9RZ8/Zc+9WUT+6gRj4XA4pimq2gE8ISZ94AZgMyZN6W+A/6Kqh2xbzs6fHwe+BnxIRJ4GFjHUqn0q3DvK+UZrP1m+ZOfolzHKvEu76nCUGC4N4QQiJkXVG8AaYCvwHGZi/F1Mfu7bgFeAV1T1/1rF81m7/R9gAoU+Iqaq5YvAelV9XkR2Y6petotIjQ3uidjjX6aqHSKiwE2q+hMR+SLQq6r/exQ57wJiwC3AEuB+VV1QFKB0PTYwCFgHLGZogNI/A88D9wNPAUtUVUWkSlW7ReT7wNdU9XERmQ08qKpLR5HlH4FNqnqniKwDPqeqV4tINdBtj/t7wFJV/c9W8V+rqn9i+/GAqv6HPVa/qpaJyLXAu+2YipXzi6rqsgU4HA6Hw+EYd1wWlIlnl6puARCRrcCvrRI5EEAzC7hJRD5ptw8DszFLkF8FsFUtN49y/I+JqXgJZtlxIdABZIABK/ULwDUnkPM+u7z6SpF1eTAwCDgsIgOBQb2jHKMXSAHfFpGfFp3/amCZyKCbY4WIlKtq3wjHuBsT0X8n8Dv2O5hxultEmjD+l7tO0J9irrV/G+33Msw4OQXc4XA4HA7HuOMU8InnRAE0eeBdqrqteCerrB53uUJMsYirgQ2qmhCRhzEKPEBWjy535Dnxb18spwz7dzg5hrozhQFUNSciFwBXYZTnPwGutNtuUNXkCWQAY0FfYP0g3wEMWO3/CfiKqt5v+/2Xx5NLzAAOBEoJ8Leq+s2TOL/D4XCcEiJyHfCFYc27VPXmkbZ3OBxnH84HvPR4EPhTqzAiImts+6PArbZtBbBqhH0rgS6rfC8B1o+xbKMFBr2JsWiHRKQSo3AjImVApar+DJObdrU9zkMYZRy73WpGwb403At8BXjV+muC6et++/lDo+y+GzjPfn47JnUYmDH+iJUPEWkWkYYTd9/hcDhOjKo+qKqrh/055dvhcAziLOClx2eBfwA2WyV8N3Aj8HXgTut6somRI+J/Adxut9kGPD3Gst2LCVJ6CWONHwwMEpEfYoKGtnPUtaMc+LGIhDFW54/b9o8B/2Ll9GMU+9uPc967Mf7mHy5q+0vgHhHZj+ln6wj7fcue/1ng19hAKVV9SESWAk/Z95x+4P1A28kMgsPhcDgcDseZ4IIwHQ6Hw+FwOByOCcS5oDgcDofD4XA4HBOIc0E5ixGRzwDvGdZ8j6p+bhJkuQ34s2HNT6jqH0+0LA6Hw+FwOBzjiXNBcTgcDofD4XA4JhDnguJwOBwOh8PhcEwgTgF3OBwOh8PhcDgmEKeAOxwOh8PhcDgcE4hTwB0Oh8PhcDgcjgnEKeAOh8PhcDgcDscE8v8BF+JaytdM5egAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(frame=housing[attributes], figsize=(12, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most interesting attribute to predict `median_house_value` is `median_income`\n",
    "\n",
    "Let's zoom in the correlation scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='median_income', y='median_house_value', figsize=(12,8), alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This correlation is indeed very strong, we can clearly see the upward trend\n",
    "\n",
    "The price cap that we noticed earlier is clearly visible at $500,000$USD, but the plot reveals other less obvious lines at:\n",
    "    - USD450K\n",
    "    - USD350K\n",
    "    - USD280K\n",
    "    - ...\n",
    "\n",
    "We may want to remove the corresponding districts so that the model to not learn these quirks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experimenting with Attribute Combinations\n",
    "\n",
    "We may want to transform tail heavy distributions using the logarithm function ($log(.)$).\n",
    "\n",
    "One last thing we would want to do before feeding the data into an ML algorithm is to try to combine features. Examples:\n",
    "- The number of rooms per household, not the total number of rooms in a district.\n",
    "- The total number of bedrooms isn't helpful either, we want to compare it with the number of rooms.\n",
    "- The number of poeple per household is also an interesting feature to look at.\n",
    "\n",
    "Let's create them all:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the correlation matrix again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687160\n",
       "rooms_per_household         0.146285\n",
       "total_rooms                 0.135097\n",
       "housing_median_age          0.114110\n",
       "households                  0.064506\n",
       "total_bedrooms              0.047689\n",
       "population_per_household   -0.021985\n",
       "population                 -0.026920\n",
       "longitude                  -0.047432\n",
       "latitude                   -0.142724\n",
       "bedrooms_per_room          -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that `bedrooms_per_room` is much more correlated with `median_house_value`. meaning that the more expensive the house, the less the `bedrooms per room` ratio. `rooms_per_household` have a moderate positive correlation with `median_house_value`, the more expensive a house is, the more rooms it will have.\n",
    "\n",
    "We don't have to go through every correlation/engineered feature, the goal is to start at the right place.\n",
    "\n",
    "Data science is an **iterative** process, once we have a model up and running, we can analyze its outputs and go back to this step to explore the data again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Prepare the Data for Machine Learning Algorithms\n",
    "\n",
    "Instead of doing this manually, we should write functions for this purpose. We mention the following reasons:\n",
    "\n",
    "- It will allow us to reproduce the transformations on any fresh dataset.\n",
    "- We will gradually build a library of transformations that we can use in future projects.\n",
    "- We can use these functions in our live systems to transform the data using a pipeline before feeding it into the algorithm.\n",
    "- This will make it possible for us to try various transformations or a combination of them to check what works best.\n",
    "\n",
    "Let's start with a fresh dataset again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16512, 9), (16512,))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()\n",
    "housing.shape, housing_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "\n",
    "Most machine learning algorithms cannot deal with missing features, so let's create a few functions to deal with them.\n",
    "\n",
    "We saw earlier that `total_bedrooms` have missing values, we have 3 options:\n",
    "1. Get rid of the corresponding districts\n",
    "    - `housing.dropna(subset='total_bedrooms')`\n",
    "2. Get rid of the whole attribute (feature)\n",
    "    - `housing.drop('total_bedrooms', axis=1)`\n",
    "3. Set the missing values to some value (zero, mean, median, regressor preds,...)\n",
    "    - `median = housing['total_bedrooms'].median()`\n",
    "    - `housing['total_bedrooms'].fillna(median, inplace=True)`\n",
    "    \n",
    "We can also use `scikit-learn`'s `SimpleImputer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the imputer can only work on numerical attributes, we need to create a copy of the dataFrame without the `OCEAN_PROXIMITY` text attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can just fit the imputer to the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The imputer has calculated the median of all attributes and stored them in `.statistics_`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the \"trained or fitted\" imputer to transform the numerical attributes by replacing missing values with their corresponding medians:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 8)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a numpy array containing the transformed features. If we want to put it back into a Pandas DataFrame, it's simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "17606       710.0       339.0         2.7042  \n",
       "18632       306.0       113.0         6.4214  \n",
       "14650       936.0       462.0         2.8621  \n",
       "3230       1460.0       353.0         1.8839  \n",
       "3555       4459.0      1463.0         3.0347  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(data=X, index=housing_num.index, columns=housing_num.columns)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn's API is remarkably [well designed](https://arxiv.org/abs/1309.0238). These are its main design principles:\n",
    "- Consistency: all objects share a consistent and simple interface\n",
    "    - `Estimators`: Any object that can estimate some parameters based on a dataset is called an estimator.\n",
    "    - `Transformers`: Some estimators can also transform a dataset (like imputer), these are called transformers.\n",
    "    - `Predictors`: Finally, some estimators, when given a dataset, are able to export predictions, these are called predictors.\n",
    "- Inspection: All the estimators parameters/hyperparameters are accessible directly through public variables.\n",
    "- Non-proliferation of classes: Datasets are represented as Numpy arrays or Scipy sparse matrices, no home-made data types.\n",
    "- Composition: existing building blocks are re-used as much as possible.\n",
    "- Sensible defaults: Scikit-learn provide reasonable default values for most hyper-parameters, making it easy to create good baseline estimators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Text & Categorical Attributes\n",
    "\n",
    "So far, we have only dealt with numerical attributes, now, let's check text/categorical attributes. \n",
    "\n",
    "We have only 1 categorical attribute, which is `ocean_proximity`. let's look at its values for the 10 first instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19480</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8879</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13685</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4937</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4861</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a limited number of values, each of which represents a category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most ML algorithms prefer to work with numbers, so let's convert the text into **ordinal categorical** numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_encoder = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat.values)\n",
    "housing_cat_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the list of categories using the `categories_` attribute of the `OrdinalEncoder`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One issue with this representation is that the encoder will assume that two nearby categories are more similar than distant ones, but this is not the case for us (ex. categories 0 and 4 are clearly more similar than 0 and 1). To fix this issue, we create one binary attribute per category:\n",
    "- One attribute is equal to `1` if the category is equal to `<1H OCEAN` and `0` otherwise.\n",
    "- One attribute is equal to `1` if the category is equal to `INLAND` and `0` otherwise.\n",
    "- ...\n",
    "\n",
    "This is called 1-hot encoding because, for any row, only one binary attribute will be equal to 1 (hot), while the others are 0s (cold).\n",
    "\n",
    "The new attributes are sometimes called dummy attributes, let's create them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot = one_hot_encoder.fit_transform(housing_cat.values)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is a sparse scipy matrix instead of a numpy array. If we use numpy, we have to store all of the zeros in memory, comprising of most of the array. Instead, we store the information as a Scipy sparse matrix which only stores the locations of the non-zeros (which is more efficient).\n",
    "\n",
    "We can mostly use it as a normal `2D` array, but if we want to convert it into a dense numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a categorical feature has a large number of possible values (ex. country, postcode, city, ...) then a 1-hot encoder will result in a large number of input features. This may slow down training and degrade performance.\n",
    "\n",
    "We may want to replace the categorical inputs with numerical features that relate to the categorical values. Examples:\n",
    "- Replace country with its GDP/Area/Population\n",
    "- Replace `ocean_proximity` with `distance_to_ocean`.\n",
    "\n",
    "Alternatively, we could replace the categorical features with low-dimensional learnable vectors called embeddings. Each category's representation would be learned during training. This is part of what's called \"representation learning\" or \"deep learning\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Transformers\n",
    "\n",
    "Although `scikit-learn` provide many useful transformers, we will need to write our own for custom tasks such as data cleanup or feature engineering. We'll want our transformer to easily work with other scikit-learn functionalities (such as Pipelines).\n",
    "\n",
    "All we need to do is create a class with 3 methods: `fit`, `transform`, `fit_transform`. We can get `fit_transform` for free by adding `TransformerMixin` as a base class.\n",
    "\n",
    "If we add `BaseEstimator` as another base class & avoid the use of *args* and *kwargs*, we get two extra methods (`.get_params()` & `.set_params()`). \n",
    "\n",
    "For example, here is a small transformer that adds the combined attributes discussed earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin, BaseEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Engineers new features from existing ones: `rooms_per_household`, `population_per_household`, `bedrooms_per_room`\n",
    "    \n",
    "    # Arguments:\n",
    "        add_bedrooms_per_room, bool: defaults to True. Indicates if we want to add the feature `bedrooms_per_room`.\n",
    "    \"\"\"\n",
    "    def __init__(self, add_bedrooms_per_room=True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self  # We don't have any internal parameters. Only interested in transforming data.\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `add_bedrooms_per_room` hyper-parameter will easily help us find out whether adding the attributes helps the ML algorithm or not.\n",
    "\n",
    "We can add hyper-parameters to control any pre-processing step that we're not sure about. The more we automate these data preprocessing steps, the more combinations we get to try out.\n",
    "\n",
    "We will more likely find a great combination of steps that maximizes performance (and will save us a ton of time)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "With few exceptions, ML algorithms don't perform well on data features that have different scales. However, scaling the target value is generally not required.\n",
    "\n",
    "There are two ways to get all features to have the same scale:\n",
    "- `Min-Max` scaling: for each value, we subtract by the `min` and divide by `max - min`.\n",
    "- `Standarization`: For each value, we subtract the `mean` and divide by `std`\n",
    "\n",
    "Standardization produce values with a mean of `0` and a standard deviation of `1`. Unlike Min-Max scaling, standardization doesn't bound values to a specific range. This can cause problems for some ML algorithms (Example: NNs usually expect inputs range from 0 to 1). However, standarization is much less effected by outliers.\n",
    "\n",
    "As with all transformers, It's important to fit to the training data only, not the full dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Pipelines\n",
    "\n",
    "There are many transformation steps that need to be executed in the right order. Scikit-learn provides the `Pipeline` class to help us chain transformations in a sequence.\n",
    "\n",
    "Here is a small pipeline for the numerical attribtues:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have handeled categorical/continuous columns separately. It would be better if we had a single transformer that is able to transform all columns.\n",
    "\n",
    "`ColumnTransformer`s to the rescue: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attribs = housing_num.columns.tolist()\n",
    "cat_attribs = [\"ocean_proximity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_pipeline = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attribs),\n",
    "    (\"cat\", OneHotEncoder(), cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**That's it!**, we have a pre-processing pipeline that takes the data and transforms it into data that we can feed to an ML algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Select & Train a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to select & train an ML model.\n",
    "\n",
    "### Training & Evaluating on the Training Set\n",
    "\n",
    "Thanks to all the previous steps, things are going to be much simpler from now on.\n",
    "\n",
    "Let's first train a Linear Regression model, like we did in the previous chapter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the model on a few instances from the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data_prepared = full_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:  [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictions: \", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:  [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Labels: \", some_labels.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works, although the predictions are not exactly accurate. \n",
    "\n",
    "Let's measure the performance of our model using the RMSE metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = lin_reg.predict(housing_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most districts median housing values range between 120K to 265K, so an average error of 68K is not good.\n",
    "\n",
    "This is an example of a model overfitting the data. When this happens, it can mean two things:\n",
    "- The features do not provide enough information to make better predictions.\n",
    "- The model is not powerful enough, meaning its hypothesis space is narrow.\n",
    "\n",
    "The main ways to tackle underfitting:\n",
    "- To feed the model better features.\n",
    "- To select a more powerful model.\n",
    "- To loosen the model's restrictions.\n",
    "\n",
    "This model is not regularized, which rules out the last option. We could try to input more features, but let's start by testing a more powerful model.\n",
    "\n",
    "Let's try out `DecisionTreeRegressor`, this is a powerful model, capable of finding non-linear relationships within the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_reg = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is trained, let's evaluate its performance on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_mse = mean_squared_error(y_true=housing_labels, y_pred=housing_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is either the model is absolutely perfect, or it badly overfit the data. \n",
    "\n",
    "As we know, we shouldn't touch the test set until we are ready for deployment. The solution is to partition the training data itself and extract a validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better Evaluation using Cross-Validation\n",
    "\n",
    "One way to evaluate our model is to use `train_test_split()` again on the training set, extract a validation set and evaluate our iterative models on it.\n",
    "\n",
    "A great alternative is to use K-fold cross-validation. We randomly split the training data into 10 folds, we iteratively train the model on 9 folds and evaluate on 1, doing this 10 times.\n",
    "\n",
    "We will endup with `10` metric scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=tree_reg, X=housing_prepared, \n",
    "                         y=housing_labels, scoring='neg_mean_squared_error', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`scikit-learn`'s cross validation features expect a utility function (the greater the better) rather than a cost function (the lower the better). That's why we used `ned_mean_squared_error` and we negated it at RMSE evaluation\n",
    "\n",
    "Let's look at the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scores(scores):\n",
    "    \"\"\"Displays the scores, their mean, and the standard deviation.\n",
    "    \n",
    "    # Arguments:\n",
    "        scores, np.array: list of scores given by the cross validation procedure.\n",
    "    \"\"\"\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard Deviation:\", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70295.85879837 67208.2294927  71916.75200996 68928.55067415\n",
      " 71046.20989839 73194.27731825 69429.16053652 71235.46411182\n",
      " 75889.22508973 70339.47885913]\n",
      "Mean: 70948.3206789036\n",
      "Standard Deviation: 2272.8560419542605\n"
     ]
    }
   ],
   "source": [
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision tree seems to perform worse than the linear regression model!\n",
    "\n",
    "We should notice that cross validation allows us to not only get an estimate of the performance of your model (mean), but how precise it is (std). We would not have this estimation if we used only one validation set. However, cross-validation comes at the cost of training the model several times, which is not always possible.\n",
    "\n",
    "Let's compute the same scores for the linear regression model just to be sure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=lin_reg, X=housing_prepared, \n",
    "                         y=housing_labels, scoring='neg_mean_squared_error', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
      " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
      " 71552.91566558 67665.10082067]\n",
      "Mean: 69052.46136345083\n",
      "Standard Deviation: 2731.6740017983493\n"
     ]
    }
   ],
   "source": [
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's right! the decision tree model is overfitting so badly that it performs worse than the linear regression model.\n",
    "\n",
    "Let's try one last model now, the random forest regressor. Random forests work by training many decision trees on random feature subsets then average out their predictions.\n",
    "\n",
    "Building a model on top of many other models is called **Ensemble Learning**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_reg = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//miniconda3/envs/research/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_mse = mean_squared_error(y_true=housing_labels, y_pred=forest_reg.predict(X=housing_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22029.216324549794"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=forest_reg, X=housing_prepared, \n",
    "                         y=housing_labels, scoring='neg_mean_squared_error', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [52402.35340164 49150.98777662 51917.13835708 55214.67790364\n",
      " 51984.68506744 57732.63314668 51068.95231905 50490.24426029\n",
      " 55393.77710271 52942.68273899]\n",
      "Mean: 52829.813207413725\n",
      "Standard Deviation: 2452.545263218794\n"
     ]
    }
   ],
   "source": [
    "display_scores(scores=forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is much better, random forests seem very promissing. We should notice, however, that the RMSE on the training set is still much lower then the validation RMSE, meaning the model overfitted, but not as badly as the decision tree model. Possible solutions to overfitting are:\n",
    "- Getting more training data\n",
    "- Simplifying the model\n",
    "- Regularizing the model\n",
    "\n",
    "We should save any model after training so that we can come back to it at any time you want. We make sure to save both the hyper-parameters and the parameters (weights) of the model. We can easily save scikit-learn models using Python's `joblib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/01/forest_reg.m']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(value=forest_reg, filename='models/01/forest_reg.m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# & later\n",
    "forest_reg = joblib.load(filename='models/01/forest_reg.m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Fine-Tune Your Model\n",
    "\n",
    "Let's assume we have a short list of promising models, now we want to find a way to fine-tune them, let's do that:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search\n",
    "\n",
    "One option would be to try out many hyper-parameters manually, but this would be considered tedious work and we might not even have the time to do it. Instead, we should just use scikit-learn's `GridSearchCV`.\n",
    "\n",
    "We just tell it which hyper-parameters and values to test and it will test out all combinations of hyper-parameters and use cross-validation for evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_reg = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=forest_reg, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5, return_train_score=True, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we can't guess an initial quality search grids, we can start with powers of 10 then zoom in once we have the best estimate.\n",
    "\n",
    "The model will first explore $3 \\times 4$ combinations of hyper-parameters, then jump to the 2nd hyper-parameter space and try $1 \\times 2 \\times 3$. For each combination, it will train 5 times using the cross validation strategy, all in all: It will train **90** different model variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the best estimator directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features=8, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When `GridSearchCV` finds the best estimator, it will retrain it on the whole training set. This can be controlled by the parameter `refit=True` (by default)\n",
    "\n",
    "Re-training the best model on the whole training data is generally a good practice. & ofcoures, the evaluation scores are also available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63751.20022020855 {'max_features': 2, 'n_estimators': 3}\n",
      "55370.922775715415 {'max_features': 2, 'n_estimators': 10}\n",
      "52904.227253068806 {'max_features': 2, 'n_estimators': 30}\n",
      "60731.88068265941 {'max_features': 4, 'n_estimators': 3}\n",
      "53108.94765264947 {'max_features': 4, 'n_estimators': 10}\n",
      "50435.77072997419 {'max_features': 4, 'n_estimators': 30}\n",
      "58821.23720793129 {'max_features': 6, 'n_estimators': 3}\n",
      "52418.38775416207 {'max_features': 6, 'n_estimators': 10}\n",
      "49815.96410841989 {'max_features': 6, 'n_estimators': 30}\n",
      "59453.91629016195 {'max_features': 8, 'n_estimators': 3}\n",
      "51895.55638084359 {'max_features': 8, 'n_estimators': 10}\n",
      "49807.061426784 {'max_features': 8, 'n_estimators': 30}\n",
      "62083.74010960627 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54300.69562195466 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60568.5849318819 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52238.21182418438 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "58376.9906904098 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51595.970549461395 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "for mean_score, params in zip(cvres['mean_test_score'], cvres['params']):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the best hyper-parameter combination is: `50110.7370892457 {'max_features': 6, 'n_estimators': 30}` with an average RMSE of $50110$. The model performs slightly better than a random forest with default hyper-parameters.\n",
    "\n",
    "We should not forget that we can treat some of the pre-processing steps as hyper-parameters to test if adding a feature is better for performance. We can do the same to functions around handling outliers, feature selection, scaling method, and more..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomized Search\n",
    "\n",
    "The grid search is fine when you're exploring a few hyper-parameter combinations, but when the search space is big though, it is better to use `RandomizedSearchCV` instead. It works almost in the same way of a grid search, but it try out a limited randomly selected number of hyper-paraemeters for each iteration. This approach has two main benefits:\n",
    "\n",
    "- If we let this approach run for 1,000 iterations, it will explore 1,000 values for each hyper-parameters, instead of combining each unique value.\n",
    "- By setting the number of iterations, we can control computing resources much more effectively than doing Grid search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Methods\n",
    "\n",
    "Another way to fine-tune your model is to combine the models that work best. Usually, the ensemble model will perform better than any part of the model, especially if its models are producing different errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the best models & their errors\n",
    "\n",
    "We will often gain good insights about the problem by inspecting good models. For example, the random forest model can give us estimates over feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  6,  4,  1,  1,  1,  1, 32,  6, 11,  6,  0, 18,  0,  0,  0])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "(feature_importances*100).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_encoder = full_pipeline.named_transformers_['cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_one_hot_attributes = cat_encoder.categories_[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = num_attribs + extra_attribs + cat_one_hot_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.06614195992562232: 'longitude',\n",
       " 0.06344029958610363: 'latitude',\n",
       " 0.04469855412347374: 'housing_median_age',\n",
       " 0.015494148837422152: 'total_rooms',\n",
       " 0.014381096417454978: 'total_bedrooms',\n",
       " 0.015314670216386326: 'population',\n",
       " 0.014510586727973786: 'households',\n",
       " 0.3256163085267954: 'median_income',\n",
       " 0.06813486800603749: 'rooms_per_hhold',\n",
       " 0.11376029387676202: 'pop_per_hhold',\n",
       " 0.06515241883166968: 'bedrooms_per_room',\n",
       " 0.002568336193539316: '<1H OCEAN',\n",
       " 0.18594650472353433: 'INLAND',\n",
       " 0.00010550288726181512: 'ISLAND',\n",
       " 0.0017313638948778165: 'NEAR BAY',\n",
       " 0.003003087225085202: 'NEAR OCEAN'}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorted(zip(feature_importances, attributes), reverse=True)\n",
    "dict(zip(feature_importances, attributes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this information, we might want to start dropping some of the attributes to simplify the model (ex. only one `ocean_proximity` value is important). \n",
    "\n",
    "We should also take a look at how our systems makes mistakes, try to know why, and fix that from a data engineering/model perspective."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate your system on the test set\n",
    "\n",
    "After tweaking the system for a while, we finally have a model that can be evaluated on the test set. There is nothing special about this process, we reproduce the same steps you used with training data to benchmark the model.\n",
    "\n",
    "However, we should call `transform()`, and not `fit_transform()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(labels='median_house_value', axis=1)\n",
    "y_test = strat_test_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prepared = full_pipeline.transform(X=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions = final_model.predict(X=X_test_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_mse = mean_squared_error(y_true=y_test, y_pred=final_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48739.038026174676"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases, such a point estimate of the generalization error won't be enough for us to launch it in production. We might want to create a confidence interval of 95% around the metric.\n",
    "\n",
    "For this, we use the individual predictions for each test set element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "confidence = .95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "squared_errors = (y_test - final_predictions) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([46681.93025447, 50712.77002048])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1, loc=squared_errors.mean(), scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do a lot of hyper-parameter fine-tuning, we will endup with a slightly worse performance on the test set because we will sometimes overfit to the changing validation set. This didn't happen now, but when it happens, resist the temptation to go back and do more fine-tuning to have better results for the test set.\n",
    "\n",
    "If you want to improve your model, just start over again.\n",
    "\n",
    "Now that we are in the pre-launch phase, we should **Document Everything**:\n",
    "- Highlight what we have learned.\n",
    "- What worked & what did not.\n",
    "- What assumptions we made.\n",
    "- What our system's limitations are.\n",
    "\n",
    "We should create a clear presentation with nice visualizations and simple-to-remember statements.\n",
    "\n",
    "In our case with the California dataset, our system didn't actually beat the experts system (with 20% error). But management still decided to launch the service to free some time for its experts to work on other tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Launch, Monitor, & Maintain your system\n",
    "\n",
    "Perfect, we got approval to launch. Now you need to get your solution ready for production (document code, write tests, ...). \n",
    "\n",
    "Then we can deploy your model to a production environment. One way to do this is to save the whole pre-processing pipeline, the predictor (using joblib) then load it into the production environment and use `.predict()` to output new predictions. We want to load the model upon server startup, rather than everytime it is used:\n",
    "\n",
    "<div style=\"text-align:center;\"><img style=\"width:50%;\" src=\"static/imgs/micro-services.png\"></div>\n",
    "\n",
    "We can hot-load the model within a web application, or alternatively, wrap the model around its own API end-point and design the web component separately. This will allow us to upgrade the model to new versions without interrupting the web server. The Second option also allows scalability around the micro-services architecture, another advantage is that we don't have to write the web app using Python.\n",
    "\n",
    "However, deploying is not the end of the story. We also need to write monitoring code to track the performance of the model within the live environment and send alerts when the performance drops. This can be caused by either bad system architecture or model rotting since trends do change over time.\n",
    "\n",
    "Sometimes, model performance can be inferred from downstream tasks. Like having a recommender system proposing products and then tracking sold recommended products to non-recommended products sold.\n",
    "\n",
    "Sometimes, It's not possible to determine the model's performance without human intervension. For example, a model that is supposed to detect errors in a production line for products. In that case, we would need to send a random sample of products that passed through and the model wasn't so sure about to be checked by a human expert.\n",
    "\n",
    "All of this can be a lot of work, and most of the times It'll be more work than training and building the whole model. If the data keeps evolving, we would need to re-train the model regularly.\n",
    "\n",
    "What we must do is automate the process as much as possible by doing the following:\n",
    "- Collecting fresh data regularly and labeling it.\n",
    "- Writing a script to train the model and fine-tune the hyper-parameters regularly.\n",
    "- Writing a script that evaluates the new model's vs previous model's performance on the new test set and deploy if the performance increased.\n",
    "\n",
    "We should also make sure to evaluate the model's input data quality by trigger an alert if:\n",
    "- More and more inputs are missing a feature.\n",
    "- The data's mean/std drifts too far from the training set ones.\n",
    "- A categorical feature starts containing new categories.\n",
    "        \n",
    "Finally, we must have a way to archive model history (save all exported models) and automate a way to roll back to previous ones. Also, keeping backups of every dataset we produce so we can roll back to any one and test combinations of dataset and model versions. \n",
    "\n",
    "We also would want to save different test sets that target different data properties (ex. a test set containing only recent data or a test set containing one category from a feature). This will help us analyze the strengths and weaknesses of the model.\n",
    "\n",
    "Much of the work is in the data preparation step. Building monitoring tools, setting up pre-processing pipelines, and having human evaluators.\n",
    "\n",
    "It's preferrable to be familiar with the overall process and know 2 or 3 ML algorithms very well than to invest our time in advanced algorithms without practical knowledge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Try a Support Vector Machine Regressor `sklearn.svm.SVR` with various hyper-parameters**\n",
    "- `kernel=\"linear\"` with various values for the `C` hyperparameter\n",
    "- `kernel=\"rbf\"` with various values for the `C` & `gamma` hyperparameters\n",
    "\n",
    "How does the best SVR predictor perform?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\n",
    "        'kernel': ['linear'], \n",
    "        'C': [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]\n",
    "    },\n",
    "    {\n",
    "        'kernel': ['rbf'], \n",
    "        'C': [1.0, 3.0, 10., 30., 100., 300., 1000.0],\n",
    "        'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=svm_reg, param_grid=param_grid, cv=5, scoring=\"neg_mean_squared_error\", verbose=2, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed: 10.1min\n",
      "[Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed: 16.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                           epsilon=0.1, gamma='auto_deprecated', kernel='rbf',\n",
       "                           max_iter=-1, shrinking=True, tol=0.001,\n",
       "                           verbose=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
       "                                10000.0, 30000.0],\n",
       "                          'kernel': ['linear']},\n",
       "                         {'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0],\n",
       "                          'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70363.90313964167"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_mse = grid_search.best_score_\n",
    "best_rmse = np.sqrt(-best_mse)\n",
    "best_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 30000.0, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the optimal value of `C` is at the maximum, naturally, we would want to launch a new grid search with even higher `C` values and removing smaller ones to minimize runtime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Try replacing `GridSearchCV` with `RandomizedSearchCV`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, expon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_distribs = {\n",
    "    'kernel': ['linear', 'rbf'],\n",
    "    'C': reciprocal(20, 200000),\n",
    "    'gamma': expon(scale=1)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(estimator=svm_reg, param_distributions=params_distribs, \n",
    "                                   scoring='neg_mean_squared_error', n_jobs=-1, cv=5, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                                 epsilon=0.1, gamma='auto_deprecated',\n",
       "                                 kernel='rbf', max_iter=-1, shrinking=True,\n",
       "                                 tol=0.001, verbose=False),\n",
       "                   iid='warn', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a274e60d0>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a1d94fbd0>,\n",
       "                                        'kernel': ['linear', 'rbf']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X=housing_prepared, y=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60051.456669637686"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_mse = random_search.best_score_\n",
    "best_rmse = np.sqrt(-best_mse)\n",
    "best_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 63786.48609569545, 'gamma': 0.8914099487077451, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomized search tends to find better hyper-parameters than grid search in the same amount of time. \n",
    "\n",
    "The reciprocal distribution is useful when we have no idea what is the best hyper-parameter range.\n",
    "\n",
    "The exponential distribution is useful when you roughly know the range of optimal hyper-parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Try adding a transformer to the pipeline to select only the most important attributes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 6])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [-15, 30, 5, 89, -34, 15, 88]\n",
    "indices_of_top_k(arr, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y=None):\n",
    "        self.feature_indices_ = indices_of_top_k(arr=self.feature_importances, k=self.k)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This implementation necessitate that we have feature importances before running the pipeline, probably from the base model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a pipeline that runs the previous pipeline and adds feature selection of the top k features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "preparation_and_feature_selection_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281, -0.61493744, -0.31205452],\n",
       "       [-1.17602483,  1.33645936,  0.21768338],\n",
       "       [ 1.18684903, -0.5320456 , -0.46531516]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(X=housing)\n",
    "housing_prepared_top_k_features[:3, :3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Try creating a single pipeline that does full data preparation + prediction**\n",
    "\n",
    "Using randomized search we found the optimal hyper-parameter setting, now we can just load a new model instance with the fine-tuned hyper-parameters, but the step of optimizing its **parameters** still needs to be done using `.fit()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
    "    ('prediction', SVR(**random_search.best_params_))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('preparation',\n",
       "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
       "                                   sparse_threshold=0.3,\n",
       "                                   transformer_weights=None,\n",
       "                                   transformers=[('num',\n",
       "                                                  Pipeline(memory=None,\n",
       "                                                           steps=[('imputer',\n",
       "                                                                   SimpleImputer(add_indicator=False,\n",
       "                                                                                 copy=True,\n",
       "                                                                                 fill_value=None,\n",
       "                                                                                 missing_values=nan,\n",
       "                                                                                 strategy='median',\n",
       "                                                                                 verbose=0)),\n",
       "                                                                  ('attribs_adder',\n",
       "                                                                   CombinedAttributesAdder(add_...\n",
       "       1.43810964e-02, 1.53146702e-02, 1.45105867e-02, 3.25616309e-01,\n",
       "       6.81348680e-02, 1.13760294e-01, 6.51524188e-02, 2.56833619e-03,\n",
       "       1.85946505e-01, 1.05502887e-04, 1.73136389e-03, 3.00308723e-03]),\n",
       "                                    k=5)),\n",
       "                ('prediction',\n",
       "                 SVR(C=63786.48609569545, cache_size=200, coef0=0.0, degree=3,\n",
       "                     epsilon=0.1, gamma=0.8914099487077451, kernel='rbf',\n",
       "                     max_iter=-1, shrinking=True, tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(X=housing, y=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [183198.80606101 333025.53597133 171102.79065145  53697.40747689]\n",
      "Targets:\t [286600.0, 340600.0, 196900.0, 46300.0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictions:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"Targets:\\t\", some_labels.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Automatically Explore some preparation options using `GridSearchCV`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\n",
    "        'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "        'feature_selection__k': list(range(1, len(feature_importances) + 1))\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_prep = GridSearchCV(estimator=prepare_select_and_predict_pipeline, param_grid=param_grid, \n",
    "                                cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed: 11.0min\n",
      "[Parallel(n_jobs=-1)]: Done 240 out of 240 | elapsed: 28.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('preparation',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('num',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('imputer',\n",
       "                                                                                          SimpleImputer(add_indicator=False,\n",
       "                                                                                                        copy=True,\n",
       "                                                                                                        fill_value=None,\n",
       "                                                                                                        missing_values=nan,\n",
       "                                                                                                        strategy='media...\n",
       "                                            kernel='rbf', max_iter=-1,\n",
       "                                            shrinking=True, tol=0.001,\n",
       "                                            verbose=False))],\n",
       "                                verbose=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid=[{'feature_selection__k': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10, 11, 12, 13, 14, 15, 16],\n",
       "                          'preparation__num__imputer__strategy': ['mean',\n",
       "                                                                  'median',\n",
       "                                                                  'most_frequent']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.fit(X=housing, y=housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_selection__k': 8, 'preparation__num__imputer__strategy': 'mean'}"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3314361096.081166"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}