3. Scikit Learn - Supervised Learning.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Supervised Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Supervised Learning is the most commonly referred/mentioned/researched/published subfield in the machine learning. Unsurprisingly, all of the predictive algorithms fall in to this category. Its premise is quite simple, when you provide a labeled dataset to the algorithm(training set), it will predict unseen dataset's (test set) output variables, whether be it a discrete label(classification) or a continuous value(regression). The learning function(regressor or classifier) will first `fit` to the training set and then try to `predict` the dataset that it has never seen, which Scikit-Learn follows these two steps literally. More on this later. First, what is the problem that we are trying to solve?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Classification"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dataset is from a telecom service provider where they have the service usage(international plan, voicemail plan, usage in daytime, usage in evenings and nights and so on) and basic demographic information(state and area code) of the user. For labels, I have a single data point whether the customer is churned out or not. \n",
      "Dataset is from a competititon that CrowdAnalytics did I believe and the original contest text is given in below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Original Dataset Explanation\n",
      "    Churn (loss of customers to competition) is a problem for telecom companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This contest is about enabling churn reduction using analytics.\n",
      "\n",
      "    The Business Pain:\n",
      "\n",
      "    Most telecom companies suffer from voluntary churn. Churn rate has strong impact on the life time value of the customer because it affects the length of service and the future revenue of the company. For example if a company has 25% churn rate then the average customer lifetime is 4 years; similarly a company with a churn rate of 50%, has an average customer lifetime of 2 years. It is estimated that 75 percent of the 17 to 20 million subscribers signing up with a new wireless carrier every year are coming from another wireless provider, which means they are churners. Telecom companies spend hundreds of dollars to acquire a new customer and when that customer leaves, the company not only loses the future revenue from that customer but also the resources spend to acquire that customer. Churn erodes profitability.\n",
      "\n",
      "    Steps that have been adopted by telecom companies so far:\n",
      "\n",
      "    Telecom companies have used two approaches to address churn - (a) Untargeted approach and (b) Targeted approach. The untargeted approach relies on superior product and mass advertising to increase brand loyalty and thus retain customers. The targeted approach relies on identifying customers who are likely to churn, and  provide suitable intervention to encourage them to stay.\n",
      "\n",
      "    Role of predictive modeling:\n",
      "\n",
      "    In the targeted approach the company tries to identify in advance customers who are likely to churn. The company then targets those customers with special programs or incentives. This approach can bring in huge loss for a company, if churn predictions are inaccurate, because then firms are wasting incentive money on customers who would have stayed anyway. There are numerous predictive modeling techniques for predicting customer churn. These vary in terms of statistical technique (e.g., neural nets versus logistic regression versus survival analysis), and variable selection method (e.g., theory versus stepwise selection).\n",
      "\n",
      "    Objective of this Contest:\n",
      "\n",
      "    The objective of this contest is to predict customer churn. We are providing you a public dataset that has customer usage pattern and if the customer has churned or not. We expect you to develop an algorithm to predict the churn score based on usage pattern. The predictors provided are as follows:\n",
      "\n",
      "    account length\n",
      "    international plan\n",
      "    voice mail plan\n",
      "    number of voice mail messages\n",
      "    total day minutes used\n",
      "    day calls made\n",
      "    total day charge\n",
      "    total evening minutes\n",
      "    total evening calls\n",
      "    total evening charge\n",
      "    total night minutes\n",
      "    total night calls\n",
      "    total night charge\n",
      "    total international minutes used\n",
      "    total international calls made\n",
      "    total international charge\n",
      "    number customer service calls made"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem Formulation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Customer Churn: losing/attrition of the customers from the company. Especially, the industries that the user acquisition is costly, it is crucially important for one company to reduce and ideally make the customer churn to 0 to sustain their recurring revenue. If you consider customer retention is always cheaper than customer acquisition and generally depends on the data of the user(usage of the service or product), it poses a great/exciting/hard problem for machine learning. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Explanation of Walkthrough"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this section, I will first walk through a classification example where I try to predict if a customer churns or not based on a variety of attributes that she has. Customer churn prevention allows companies to react preemptively to the customer and then try to provide a better experience to her. This has two advantages to the company; product gets better with the feedback that company received from the customer and as customer continues to use the product, company would not lose the customer. Customer Relation Management(CRM) tools that has the user experience and history of the user may provide a profile for the user. \n",
      "\n",
      "In order to do so, I will first do preprocessing on the attirbutes as most of the attributes vary a lot both intra-class and inter-class changes. Then, I will try to compare different classifiers. Since the dataset has an unbalancad classes(not churned users are 6 times of churned users), it provides also a nice case for different metrics usage as well and why classification accuracy may not be a good metric to optimize in this case. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Binary Classification"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This labeling schema is arguably the most common classification schema which is called binary classification as I have only two classes to predict(churn or not). Even some of the multi-class classification problems could be expressed as a binary classification schema(one vs other classes). Therefore, it is a powerful/basic classification schema that you would use approximately 83%(put some random number > 50) of your machine learning problems."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline \n",
      "\n",
      "from IPython.display import Image\n",
      "import matplotlib as mlp\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import os\n",
      "import pandas as pd\n",
      "import sklearn\n",
      "\n",
      "from sklearn import cross_validation\n",
      "from sklearn import tree\n",
      "from sklearn import svm\n",
      "from sklearn import ensemble\n",
      "from sklearn import neighbors\n",
      "from sklearn import linear_model\n",
      "from sklearn import metrics\n",
      "from sklearn import preprocessing\n",
      "\n",
      "plt.style.use('fivethirtyeight') # Good looking plots\n",
      "pd.set_option('display.max_columns', None) # Display any number of columns\n",
      "\n",
      "_DATA_DIR = 'data'\n",
      "_CHURN_DATA_PATH = os.path.join(_DATA_DIR, 'churn.csv')\n",
      "\n",
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(_CHURN_DATA_PATH)\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Account Length</th>\n",
        "      <th>Area Code</th>\n",
        "      <th>Phone</th>\n",
        "      <th>Int'l Plan</th>\n",
        "      <th>VMail Plan</th>\n",
        "      <th>VMail Message</th>\n",
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
        "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
        "      <th>Night Calls</th>\n",
        "      <th>Night Charge</th>\n",
        "      <th>Intl Mins</th>\n",
        "      <th>Intl Calls</th>\n",
        "      <th>Intl Charge</th>\n",
        "      <th>CustServ Calls</th>\n",
        "      <th>Churn?</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> KS</td>\n",
        "      <td> 128</td>\n",
        "      <td> 415</td>\n",
        "      <td> 382-4657</td>\n",
        "      <td>  no</td>\n",
        "      <td> yes</td>\n",
        "      <td> 25</td>\n",
        "      <td> 265.1</td>\n",
        "      <td> 110</td>\n",
        "      <td> 45.07</td>\n",
        "      <td> 197.4</td>\n",
        "      <td>  99</td>\n",
        "      <td> 16.78</td>\n",
        "      <td> 244.7</td>\n",
        "      <td>  91</td>\n",
        "      <td> 11.01</td>\n",
        "      <td> 10.0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2.70</td>\n",
        "      <td> 1</td>\n",
        "      <td> False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> OH</td>\n",
        "      <td> 107</td>\n",
        "      <td> 415</td>\n",
        "      <td> 371-7191</td>\n",
        "      <td>  no</td>\n",
        "      <td> yes</td>\n",
        "      <td> 26</td>\n",
        "      <td> 161.6</td>\n",
        "      <td> 123</td>\n",
        "      <td> 27.47</td>\n",
        "      <td> 195.5</td>\n",
        "      <td> 103</td>\n",
        "      <td> 16.62</td>\n",
        "      <td> 254.4</td>\n",
        "      <td> 103</td>\n",
        "      <td> 11.45</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.70</td>\n",
        "      <td> 1</td>\n",
        "      <td> False.</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> NJ</td>\n",
        "      <td> 137</td>\n",
        "      <td> 415</td>\n",
        "      <td> 358-1921</td>\n",
        "      <td>  no</td>\n",
        "      <td>  no</td>\n",
        "      <td>  0</td>\n",
        "      <td> 243.4</td>\n",
        "      <td> 114</td>\n",
        "      <td> 41.38</td>\n",
        "      <td> 121.2</td>\n",
        "      <td> 110</td>\n",
        "      <td> 10.30</td>\n",
        "      <td> 162.6</td>\n",
        "      <td> 104</td>\n",
        "      <td>  7.32</td>\n",
        "      <td> 12.2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 3.29</td>\n",
        "      <td> 0</td>\n",
        "      <td> False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> OH</td>\n",
        "      <td>  84</td>\n",
        "      <td> 408</td>\n",
        "      <td> 375-9999</td>\n",
        "      <td> yes</td>\n",
        "      <td>  no</td>\n",
        "      <td>  0</td>\n",
        "      <td> 299.4</td>\n",
        "      <td>  71</td>\n",
        "      <td> 50.90</td>\n",
        "      <td>  61.9</td>\n",
        "      <td>  88</td>\n",
        "      <td>  5.26</td>\n",
        "      <td> 196.9</td>\n",
        "      <td>  89</td>\n",
        "      <td>  8.86</td>\n",
        "      <td>  6.6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 1.78</td>\n",
        "      <td> 2</td>\n",
        "      <td> False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> OK</td>\n",
        "      <td>  75</td>\n",
        "      <td> 415</td>\n",
        "      <td> 330-6626</td>\n",
        "      <td> yes</td>\n",
        "      <td>  no</td>\n",
        "      <td>  0</td>\n",
        "      <td> 166.7</td>\n",
        "      <td> 113</td>\n",
        "      <td> 28.34</td>\n",
        "      <td> 148.3</td>\n",
        "      <td> 122</td>\n",
        "      <td> 12.61</td>\n",
        "      <td> 186.9</td>\n",
        "      <td> 121</td>\n",
        "      <td>  8.41</td>\n",
        "      <td> 10.1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2.73</td>\n",
        "      <td> 3</td>\n",
        "      <td> False.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
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       "prompt_number": 2,
       "text": [
        "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
        "0    KS             128        415  382-4657         no        yes   \n",
        "1    OH             107        415  371-7191         no        yes   \n",
        "2    NJ             137        415  358-1921         no         no   \n",
        "3    OH              84        408  375-9999        yes         no   \n",
        "4    OK              75        415  330-6626        yes         no   \n",
        "\n",
        "   VMail Message  Day Mins  Day Calls  Day Charge  Eve Mins  Eve Calls  \\\n",
        "0             25     265.1        110       45.07     197.4         99   \n",
        "1             26     161.6        123       27.47     195.5        103   \n",
        "2              0     243.4        114       41.38     121.2        110   \n",
        "3              0     299.4         71       50.90      61.9         88   \n",
        "4              0     166.7        113       28.34     148.3        122   \n",
        "\n",
        "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
        "0       16.78       244.7           91         11.01       10.0           3   \n",
        "1       16.62       254.4          103         11.45       13.7           3   \n",
        "2       10.30       162.6          104          7.32       12.2           5   \n",
        "3        5.26       196.9           89          8.86        6.6           7   \n",
        "4       12.61       186.9          121          8.41       10.1           3   \n",
        "\n",
        "   Intl Charge  CustServ Calls  Churn?  \n",
        "0         2.70               1  False.  \n",
        "1         3.70               1  False.  \n",
        "2         3.29               0  False.  \n",
        "3         1.78               2  False.  \n",
        "4         2.73               3  False.  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Discreet value integer encoder\n",
      "label_encoder = preprocessing.LabelEncoder()\n",
      "\n",
      "# Get the Labels as integers\n",
      "df['Churn'] = df['Churn?'] == 'True.'\n",
      "y = df['Churn'].as_matrix().astype(np.int)\n",
      "\n",
      "# State is string and we want discre integer values\n",
      "df['State'] = label_encoder.fit_transform(df['State'])\n",
      "\n",
      "# Drop the redundant columns from dataframe\n",
      "df.drop(['Area Code','Phone','Churn?', 'Churn'], axis=1, inplace=True)\n",
      "\n",
      "# Get the features as integers similar to what we did for labels(targets)\n",
      "df[[\"Int'l Plan\",\"VMail Plan\"]] = df[[\"Int'l Plan\",\"VMail Plan\"]] == 'yes'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('There are {} instances for churn class and {} instances for not-churn classes.'.format(y.sum(), y.shape[0] - y.sum()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 483 instances for churn class and 2850 instances for not-churn classes.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Ratio of churn class over all instances: {:.2f}'.format(float(y.sum()) / y.shape[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ratio of churn class over all instances: 0.14\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Kind of unbalanced data, do not you think? I will try to handle this unbalance in the cross validation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Account Length</th>\n",
        "      <th>Int'l Plan</th>\n",
        "      <th>VMail Plan</th>\n",
        "      <th>VMail Message</th>\n",
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
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        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
        "      <th>Night Calls</th>\n",
        "      <th>Night Charge</th>\n",
        "      <th>Intl Mins</th>\n",
        "      <th>Intl Calls</th>\n",
        "      <th>Intl Charge</th>\n",
        "      <th>CustServ Calls</th>\n",
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        "      <th>0</th>\n",
        "      <td> 16</td>\n",
        "      <td> 128</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> 25</td>\n",
        "      <td> 265.1</td>\n",
        "      <td> 110</td>\n",
        "      <td> 45.07</td>\n",
        "      <td> 197.4</td>\n",
        "      <td>  99</td>\n",
        "      <td> 16.78</td>\n",
        "      <td> 244.7</td>\n",
        "      <td>  91</td>\n",
        "      <td> 11.01</td>\n",
        "      <td> 10.0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2.70</td>\n",
        "      <td> 1</td>\n",
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        "      <th>1</th>\n",
        "      <td> 35</td>\n",
        "      <td> 107</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> 26</td>\n",
        "      <td> 161.6</td>\n",
        "      <td> 123</td>\n",
        "      <td> 27.47</td>\n",
        "      <td> 195.5</td>\n",
        "      <td> 103</td>\n",
        "      <td> 16.62</td>\n",
        "      <td> 254.4</td>\n",
        "      <td> 103</td>\n",
        "      <td> 11.45</td>\n",
        "      <td> 13.7</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.70</td>\n",
        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 31</td>\n",
        "      <td> 137</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  0</td>\n",
        "      <td> 243.4</td>\n",
        "      <td> 114</td>\n",
        "      <td> 41.38</td>\n",
        "      <td> 121.2</td>\n",
        "      <td> 110</td>\n",
        "      <td> 10.30</td>\n",
        "      <td> 162.6</td>\n",
        "      <td> 104</td>\n",
        "      <td>  7.32</td>\n",
        "      <td> 12.2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 3.29</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 35</td>\n",
        "      <td>  84</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td>  0</td>\n",
        "      <td> 299.4</td>\n",
        "      <td>  71</td>\n",
        "      <td> 50.90</td>\n",
        "      <td>  61.9</td>\n",
        "      <td>  88</td>\n",
        "      <td>  5.26</td>\n",
        "      <td> 196.9</td>\n",
        "      <td>  89</td>\n",
        "      <td>  8.86</td>\n",
        "      <td>  6.6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 1.78</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 36</td>\n",
        "      <td>  75</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td>  0</td>\n",
        "      <td> 166.7</td>\n",
        "      <td> 113</td>\n",
        "      <td> 28.34</td>\n",
        "      <td> 148.3</td>\n",
        "      <td> 122</td>\n",
        "      <td> 12.61</td>\n",
        "      <td> 186.9</td>\n",
        "      <td> 121</td>\n",
        "      <td>  8.41</td>\n",
        "      <td> 10.1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2.73</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "   State  Account Length Int'l Plan VMail Plan  VMail Message  Day Mins  \\\n",
        "0     16             128      False       True             25     265.1   \n",
        "1     35             107      False       True             26     161.6   \n",
        "2     31             137      False      False              0     243.4   \n",
        "3     35              84       True      False              0     299.4   \n",
        "4     36              75       True      False              0     166.7   \n",
        "\n",
        "   Day Calls  Day Charge  Eve Mins  Eve Calls  Eve Charge  Night Mins  \\\n",
        "0        110       45.07     197.4         99       16.78       244.7   \n",
        "1        123       27.47     195.5        103       16.62       254.4   \n",
        "2        114       41.38     121.2        110       10.30       162.6   \n",
        "3         71       50.90      61.9         88        5.26       196.9   \n",
        "4        113       28.34     148.3        122       12.61       186.9   \n",
        "\n",
        "   Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
        "0           91         11.01       10.0           3         2.70   \n",
        "1          103         11.45       13.7           3         3.70   \n",
        "2          104          7.32       12.2           5         3.29   \n",
        "3           89          8.86        6.6           7         1.78   \n",
        "4          121          8.41       10.1           3         2.73   \n",
        "\n",
        "   CustServ Calls  \n",
        "0               1  \n",
        "1               1  \n",
        "2               0  \n",
        "3               2  \n",
        "4               3  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After preprocessing, dataframe is ready to be represented as matrix that is amenable to Scikit Learn. I already separated the labels, so I would just convert the dataframe into a numpy matrix. Why I did not handle True and False some may ask. \n",
      "It turns out that booleans `False` and `True` are actually subclasses of integers in Python. If you try to do `False + True` in a Python REPL, you would get 1. That is because `True` is represented as 1 and `False` is represented as 0. Numpy uses this boolean information to convert the booleans into matrix. I just need to coerce `astype(np.float)` when I convert the pandas dataframe to numpy matrix, which I will do exactly as next step."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = df.as_matrix().astype(np.float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([[  16.  ,  128.  ,    0.  , ...,    3.  ,    2.7 ,    1.  ],\n",
        "       [  35.  ,  107.  ,    0.  , ...,    3.  ,    3.7 ,    1.  ],\n",
        "       [  31.  ,  137.  ,    0.  , ...,    5.  ,    3.29,    0.  ],\n",
        "       ..., \n",
        "       [  39.  ,   28.  ,    0.  , ...,    6.  ,    3.81,    2.  ],\n",
        "       [   6.  ,  184.  ,    1.  , ...,   10.  ,    1.35,    2.  ],\n",
        "       [  42.  ,   74.  ,    0.  , ...,    4.  ,    3.7 ,    0.  ]])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(3333, 18)"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I have 3333 instances and has 18 dimension feature vectors for each instances."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since the features have quite different value ranges and some of them are discrete and some of them take continuous values, I \n",
      "need to scale them first. Removing mean and dividing the standard deviation of features respectively. Generally, this is one of the most commonly used preprocessing step. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scaler = preprocessing.StandardScaler()\n",
      "X = scaler.fit_transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([[-0.6786493 ,  0.67648946, -0.32758048, ..., -0.60119509,\n",
        "        -0.0856905 , -0.42793202],\n",
        "       [ 0.6031696 ,  0.14906505, -0.32758048, ..., -0.60119509,\n",
        "         1.2411686 , -0.42793202],\n",
        "       [ 0.33331299,  0.9025285 , -0.32758048, ...,  0.21153386,\n",
        "         0.69715637, -1.1882185 ],\n",
        "       ..., \n",
        "       [ 0.87302621, -1.83505538, -0.32758048, ...,  0.61789834,\n",
        "         1.3871231 ,  0.33235445],\n",
        "       [-1.35329082,  2.08295458,  3.05268496, ...,  2.24335625,\n",
        "        -1.87695028,  0.33235445],\n",
        "       [ 1.07541867, -0.67974475, -0.32758048, ..., -0.19483061,\n",
        "         1.2411686 , -1.1882185 ]])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After preprocessed `X`, I could be sure that all of the features are in in more or less in the same range. However, one may think twice if the features are not nearly uniformed distribution(for example if a variable is categorical), you may do a different preprocessing as preprocessing makes the categorical variables like continuous variables."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Cross Validation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv_url = 'http://i.imgur.com/N9HZktu.png'\n",
      "Image(url=cv_url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://i.imgur.com/N9HZktu.png\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0x104366650>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def stratified_cv(X, y, clf_class, shuffle=True, n_folds=10, **kwargs):\n",
      "    stratified_k_fold = cross_validation.StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle)\n",
      "    y_pred = y.copy()\n",
      "    for ii, jj in stratified_k_fold:\n",
      "        X_train, X_test = X[ii], X[jj]\n",
      "        y_train = y[ii]\n",
      "        clf = clf_class(**kwargs)\n",
      "        clf.fit(X_train,y_train)\n",
      "        y_pred[jj] = clf.predict(X_test)\n",
      "    return y_pred"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I am using Stratified K Fold because there the classes are unbalanced. I do not want any folds to have only 1 particular class or even 1 class dominating the other one as it may create a bias in that particular fold. Stratification makes sure that the percentage of samples for each class is similar across folds(if not same). If you do not have an unbalanced problem, `KFold` would work fine."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Passive Aggressive Classifier: {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))))\n",
      "print('Gradient Boosting Classifier:  {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
      "print('Support vector machine(SVM):   {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, svm.SVC))))\n",
      "print('Random Forest Classifier:      {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
      "print('K Nearest Neighbor Classifier: {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
      "print('Logistic Regression:           {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, linear_model.LogisticRegression))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Passive Aggressive Classifier: 0.82\n",
        "Gradient Boosting Classifier:  0.95"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Support vector machine(SVM):   0.92"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Random Forest Classifier:      0.94"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "K Nearest Neighbor Classifier: 0.90"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Logistic Regression:           0.86"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Scores seem very good, but if I predicted all of the labels 0, what would I get as accuracy rate? Let's see."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Dump Classifier: {:.2f}'.format(metrics.accuracy_score(y, [0 for ii in y.tolist()])))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dump Classifier: 0.86\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is because again unbalanced dataset problem. Since one class is 6 times of other class. I could get fairly accurate prediction if I predict the common class. What I need to look at as a metric if the classifier is doing well is to see the errors over the classes. Confusion matrices give a perfect way to see the distribution, which I will exactly use as a next step."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Confusion Matrices"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you have an unbalanced dataset problem or if you care accuracy of one class over other(maybe false-positives are not so bad for you but you definitely do not want any false negatives), then you could display the class accuracies in confusion matrices.\n",
      "Very common example of class preference of one to another is in medical applications. If you are trying to predict if a patient has cancer, you could get away with some false positives(patients that do not have cancer but are told to have cancers). However, you __never ever__ want to tell a patient that has cancer that she does not have cancer(false negative). In this type of problems, not only you should be accurate but also you cannot be wrong in ne of the squares in the confusion matrix."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Human problems cannot be solved by minimizing least squares error.  \n",
      "\n",
      "Drew Conway"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Luckily, scikit learn provides a 2-D matrix for the confusion matrix under metrics submodule, which I will use to build confusion matrices for visualization(heatmap). "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> If you are missing one tool in your machine learning workflow, search for if Scikit-Learn has an implementation. Chances are, it has."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, the heatmap in matplotlib implementation does not allow to print the matrix on top of the heatmap with default options. Heatmap is good in order to get a sense of where the classifier is not doing well, but not so good if you get numbers(you need to print out the confusion matrix separately). In order to combine both heatmap and also the numbers, I will use `seaborn`(an excellent statistical visualization library for Python) where it provides even advanced formatting for the numbers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pass_agg_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))\n",
      "grad_ens_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))\n",
      "decision_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, tree.DecisionTreeClassifier))\n",
      "ridge_clf_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, linear_model.RidgeClassifier))\n",
      "svm_svc_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, svm.SVC))\n",
      "random_forest_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, ensemble.RandomForestClassifier))\n",
      "k_neighbors_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))\n",
      "logistic_reg_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, linear_model.LogisticRegression))\n",
      "dumb_conf_matrix = metrics.confusion_matrix(y, [0 for ii in y.tolist()]); # ignore the warning as they are all 0\n",
      "\n",
      "conf_matrix = {\n",
      "                1: {\n",
      "                    'matrix': pass_agg_conf_matrix,\n",
      "                    'title': 'Passive Aggressive',\n",
      "                   },\n",
      "                2: {\n",
      "                    'matrix': grad_ens_conf_matrix,\n",
      "                    'title': 'Gradient Boosting',\n",
      "                   },\n",
      "                3: {\n",
      "                    'matrix': decision_conf_matrix,\n",
      "                    'title': 'Decision Tree',\n",
      "                   },\n",
      "                4: {\n",
      "                    'matrix': ridge_clf_conf_matrix,\n",
      "                    'title': 'Ridge',\n",
      "                   },\n",
      "                5: {\n",
      "                    'matrix': svm_svc_conf_matrix,\n",
      "                    'title': 'Support Vector Machine',\n",
      "                   },\n",
      "                6: {\n",
      "                    'matrix': random_forest_conf_matrix,\n",
      "                    'title': 'Random Forest',\n",
      "                   },\n",
      "                7: {\n",
      "                    'matrix': k_neighbors_conf_matrix,\n",
      "                    'title': 'K Nearest Neighbors',\n",
      "                   },\n",
      "                8: {\n",
      "                    'matrix': logistic_reg_conf_matrix,\n",
      "                    'title': 'Logistic Regression',\n",
      "                   },\n",
      "                9: {\n",
      "                    'matrix': dumb_conf_matrix,\n",
      "                    'title': 'Dumb',\n",
      "                   },\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fix, ax = plt.subplots(figsize=(16, 12))\n",
      "plt.suptitle('Confusion Matrix of Various Classifiers')\n",
      "for ii, values in conf_matrix.items():\n",
      "    matrix = values['matrix']\n",
      "    title = values['title']\n",
      "    plt.subplot(3, 3, ii) # starts from 1\n",
      "    plt.title(title);\n",
      "    sns.heatmap(matrix, annot=True,  fmt='');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eIkWKkDFjRgD27NnD5cuX6dSpk3H7o0ePePjwIc7OzowbN47du3dTsWJF3N3d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\n1aZAAYAjIYMBwFxWy2FKAACGWSz3AMChkMEAYC6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06quv3jSWW43r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jJbDlMEAOAwk+UeADgVMhgAjGW2HKYIAMBhbia7LQoAOBMyGACMZbYc5haBAAAAAAC4CGYCAHCc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\nvXr10rhx4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LgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAA\nALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgI\nigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAA\nAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAAALgIigAAAAAA\nALgIigAAAAAAALgIigB55NSpU3rggQeytG3YsEEPP/ywfvjhh2u279mzp3r27CmbzWZvu3Tpkho2\nbJjXXc3W2LFjtX///mvaf/zxRzVs2FA7duzI0j558mS9/fbb2R63U6dOSkhIuOk2PXv21Ndff31N\n+6lTp9S0adNsXwMApOtnsiO+//57TZ069abbbNq0SfPnz7/l7f/p1KlTCgkJUadOneyPtm3bKjw8\nXCdPnnSo73llzZo1WrJkidHdAIBb8u+c7dChg7p06aJPP/00116jVatW+vXXX3PteEBucTe6A65i\nzZo1ioqK0uzZs1W1atXrbvPrr79q0aJFCg0Nzefe3dyOHTvUpk2b6z5XqFAhjRo1SsuXL5efn5+9\n3WKxZHvcZcuWZbvNrRwHAPJb06ZNsy1ExsTEKD4+/pa3/zdPT89rcnLKlCmaO3euxowZ8986nA9u\n9HsCAAqqf+fsmTNn1KtXLxUpUkTNmjXLldf45wd8QEFBESAfLF68WJ9++qkWLFigMmXKXHcbi8Wi\n0NBQLVu2TA0aNFCtWrWu2eb777/XokWLlJKSIk9PT/Xp00e1a9fW+fPnNX78eF28eFHnz59X2bJl\nNW7cOBUvXlytWrVSrVq19Pvvv+vll19WjRo1NGXKFJ05c0apqal6+OGH1bVrV6WmpmrKlCnas2eP\n3N3dVb58eb3++utatGiR4uLiNGLECI0cOVJBQUFZ+lShQgXVqlVLkZGRWT7lygy8hIQETZ06VYcO\nHVJqaqrq16+vPn36yGq1qmHDhvriiy/k4+OjmTNnatOmTfL29lbNmjUVGxuruXPnSpI2btyopUuX\n6sKFC6pfv76GDRtmf43x48crJiZG7u7uioiIUK1atZSamqpp06bphx9+kNVqVc2aNdWvXz95eXll\n+X6Eh4crLi5Oa9euVaFCheTh4aFXX31VAQEBufK+AzCHhIQETZo0SQcPHpTFYlGjRo308ssvy2q1\navPmzZo9e7bc3NxUrVo17dixQ2+//bZ+/PFHff3113rjjTf0zTffaNGiRbJYLLJarerdu7c8PDy0\ndu1apaeny8fHRxUqVLBvHxcXpwkTJujYsWOyWCxq06aN2rVrl20/k5KSFBcXpxIlSkiSUlJSNGvW\nLO3evVtpaWmqXr26IiIi5O3trV9//VUTJ05Uamqq/P39debMGb3yyiuy2WyaOnWqvLy8lJSUpEWL\nFmnbtm3X/d0SGxurMWPGKDk5WZL01FNP6Zlnnrlh+/z58xUfH6+BAwfq0KFDmjJliuLj42WxWNSx\nY0c99thj+vHHHzV37lyVL19ehw8fVnJysgYNGqR69erl3RsMALeoTJkyCgsL09KlS/X9998rMDBQ\nHTt2lCSNGjVKd9xxhzp27KhWrVqpZcuW2rx5s+Lj4xUWFqaff/5Z+/fvl7u7u6ZOnaqSJUtKyvgg\ncNKkSUpOTlbHjh315JNPGjlEQBKXA+S5mTNnau7cuWrXrt0NCwCZKlWqpN69e+v111/XlStXsjx3\n7NgxzZ07V9OnT9fSpUs1ZMgQDR48WElJSfryyy911113aeHChVq3bp08PT2zTGUKDAzUu+++q/vv\nv18jRozQk08+qejoaC1atEjbt2/Xl19+qb179+qnn37SihUrFB0drfLly9sLByVLltTo0aOvKQBk\nioiI0LFjx/Tee+/Z2zI/wZ82bZpq1Kih6OhoLV26VJcuXdKKFSuy7P/BBx/owIEDWrlypaKiorJM\ndbXZbLp69aqioqL07rvvauvWrdqzZ4+kjBPg+vXra+nSpXrppZc0dOhQpaamKioqSufPn9eKFSu0\nfPlypaena+bMmdd8P5o2barp06dr5syZWrx4sZ5++mn9/PPPN32PADifKVOmyM/PT++8846WLFmi\ngwcPatmyZbp06ZJGjhyp0aNHa9myZapXr57OnTtnz7fM/86aNUuDBw/WkiVL9NJLL2nXrl2qWbOm\n2rRpo4ceekjh4eFZtp80aZIqV66sVatW2XP7elP8//rrL/sU1UceeUQvvPCCKleurN69e0uSlixZ\nInd3d0VHR2v58uUqWbKkZs+erbS0NA0ePFjh4eFasWKF2rVrp99++81+3CNHjmjs2LFatmyZTp8+\nfcPfLUuXLlXTpk0VHR2tadOmaffu3bLZbDdst1gsslgsSktL04ABA9SuXTutWLFCM2bM0Jw5c7R3\n715JGbPeOnXqpKVLl+qpp566pcvHACC/3HHHHTp06NA17f+enZqSkqLly5erb9++Gj9+vNq3b6/l\ny5erdOnS+vjjj+3bFSlSRNHR0XrzzTc1e/ZsHT58OM/HAGSHmQB56OrVqzpy5IimT5+uoUOHqnbt\n2qpWrdoNt7dYLGrdurW2bdumSZMmqV+/fvbnduzYobi4OL388sv2Njc3N504cULt2rXTTz/9pOXL\nl+v48eM6dOhQlpkEd999t70/P/30ky5fvqy33nrL3nbw4EGFhITIzc1NL774okJCQtS8efMb/tH/\nb56enoqMjNTLL7+sunXrZnlu06ZNiomJ0Ycffigp46T2nyFqs9m0ZcsWPf744ypUqJAk6emnn9aq\nVavs35OHHnpIFotFnp6eqlChgi5cuKDbb79dRYsW1YMPPihJCgkJkc1mU2xsrLZu3arw8HBZrVZJ\nUrt27TRw4MBrvh9Wq1UtWrRQt27ddO+99yokJEQtW7a8pTEDcB7btm3TggULJGVc4tSmTRutXLlS\nlSpVUkBAgO644w5J0uOPP37dGU8PPfSQBg4cqHvvvVcNGjRQ586d7c//cxpo5r937typvn37SpJ8\nfHz0zjvvXLdfhQsXtk9T3bZtm0aMGKEGDRrI09NTUka+JiQk2NdlSUlJ0W233aZDhw7ZZzRIUr16\n9VSlShX7cUuVKqXSpUtLuvnvlmbNmmnkyJGKiYlR/fr1FRERIYvFcsP2zDEeO3ZMKSkp9jUYSpYs\nqebNm2vr1q265557VLZsWftlcdWrV9cnn3xyq28VAOS5zHPO7GReLlC+fHmVKFHC/rvC399ff/75\np327p59+WlJGFjZs2FA7d+7MksmAESgC5KHChQtrypQpslqt6tq1qwYNGqTo6GgVK1bspvsNGzZM\nHTp00GeffWZvS09PV/369TV27Fh725kzZ1SqVCnNmjVLMTExatWqlerXr6+0tLQsJ55eXl6SpLS0\nNEnSwoULVbhwYUkZiw8WLlxYRYoU0fLly7Vnzx7t3LlTQ4cOVbt27dS+fftbGuudd96p0NBQDR8+\nXDVr1szS7wkTJqhSpUqSpMuXL19TSXV3d1d6err9azc3t2uez/TPff+9nc1mu+ZYmeNOTU295vsh\nZUztOnz4sHbs2KHo6Gh98MEHmjJlyi2NGYBzSE9Pz5KZ6enpSk1NldVqveZazn/njiSFh4frqaee\n0vbt2/XJJ58oOjpa0dHRkrJmVua/MwuUmU6ePCk/Pz95e3vfsI8hISHq0KGDhg8frnfffVc+Pj5K\nT09XRESE/Y/9xMREJScnKy4u7pp+//M1/5mBN/vdcscdd2j16tXasWOHdu7cqQULFmjhwoVq0qTJ\nddv/ecx/S0tLs/8Oyvz9k/k94XpZAAVJTEyMAgMDr8mnzEugMnl4eNj//e9c/6d/f/iV+aEXYCQu\nB8hDbm5u9lB44YUXFBAQoNdee+2GJzyZ7UWLFtWoUaM0Z84ce3DUq1dP27dv19GjRyVJW7duVadO\nnZScnKzt27erffv2euSRR+Tn56cdO3Zc9yTMx8dHtWrV0vLlyyVlXAcbFham7777Tps2bVKvXr1U\nu3Zt9ejRQ4899ph+//13SRl/hKekpGQ73k6dOqlEiRJav369vS0kJEQrVqyQzWZTSkqKBg0apPff\nf9/+vMVi0b333qv169crJSVFqamp+vjjj68JzOuJj4/Xpk2bJGWsl1C4cGFVqFBBISEhWrNmjVJT\nU5Wenq7333//undZuHTpkp588kkVK1ZMzz//vF566SX7mAG4jpCQEPvlTMnJyVq7dq0aNmyou+66\nS8ePH7fnwtdff31NITMtLU2tWrVSUlKS2rRpo4EDByo2NlapqanXZGdmljVo0EAfffSRpIwc7tWr\nl06cOJFtPzt27CgfHx/7HQdCQkK0atUqpaSk2Auuc+fOVUBAgDw8PLR161ZJGdPvf//99+sutHrP\nPffc8HfLa6+9pg0bNuihhx7SoEGD5O3trbNnz2r48OHXbc9UqVIlubu765tvvpEknTt3Tt9++60a\nNmzIH/wACrSjR49q0aJF6tSpk/z8/LRv3z5JGeeMt3rJ6L9zLnO205kzZ7Rjxw7Vr18/dzsN5AAz\nAfLQv0+4Ro4cqc6dO2vevHn2a0RvtH3dunXVsWNHLV68WFLGdeyvvvqqhg0bZv/Ee+rUqfL09FS3\nbt00Y8YMLV68WMWLF1fz5s11/Pjx6/YpMjJSkydPVocOHZSSkqKWLVuqZcuWSk9P19atW9W+fXsV\nKVJExYoVsy/Ad//992vYsGF67bXX1KBBg5uOccSIEfYFVKSM9QLeeOMNdejQQampqVmmymbu+8QT\nT+jo0aPq1KmTvLy8VK5cOfsnRtd7jcy24sWL65tvvtG8efNUpEgRTZw4UVarVaGhoZo5c6Y6deqk\ntLQ01axZUwMGDLjmGH5+fgoNDVWvXr1UuHBhubu728cMwPlcvXr1mtsELly4UBEREZoyZYrat2+v\nlJQUNW7cWC+++KLc3d0VGRmpkSNHys3NTTVq1JDVarV/kp25EGD//v01fPhwubu7y2KxaPjw4SpU\nqJDq16+vwYMHy8PDQ9WrV7dn2cCBAzVx4kR16NBBNptNXbt2VfXq1a/p7/VmTQ0cOFB9+/ZV69at\n1a1bN3vW2Ww2VatWTX379pXVatWECRM0YcIEzZkzRxUrVlSJEiXk6empq1evZjlulSpVbvi7pXv3\n7ho7dqzWrl0rq9WqZs2aqW7durrtttuu2/7jjz/KYrHI3d1dkydP1tSpU/X2228rLS1N3bt3z7LN\nzcYJAPklc+0VKePDOw8PD/Xq1UuNGzdWYGCgXn/9dT377LMqW7bsTRcwvd6sr0zJycnq3LmzUlNT\nNXDgQFWoUCFvBgP8B5ZLly5Rloehtm/frosXL+qRRx6RJPsJaK9evQzuGQBXduXKFUVFRalHjx7y\n9PTU/v37FRERYYpr2DOLA7fddpvOnj2rjh07at26dfLx8TG6awAAwGDMBIDhqlSpotGjR2vp0qVK\nS0tTtWrV1LNnT6O7BcDFeXt7q1ChQuratavc3d3l7u6ucePGGd2tW1K2bFn16tVL7u7ustlseu21\n1ygAAAAAScwEAAAAAADAZbAwIAAAAAAALoIiAAAAAAAALiJP1wTw9fXNy8MDyEXx8fE53rdp8FPZ\nbvP9ng9zfHzkHDkMmEdOc5gMLtjIYcAcXOlcOM8XBgyudH9evwTy0Z6jGyVJyX+eN7gnyE0exUo4\ntL+bmzWXeoK8QA47F3LYOTmSw2RwwUcOOxdy2Pm42rkwlwMAAAAAAOAiuEUgAIe5yWJ0FwDAZZHB\nAGAss+UwRQAADrOabAoUADgTMhgAjGW2HKYIAMBhFou5qp8A4EzIYAAwltlymCIAAIdZTDYFCgCc\nCRkMAMYyWw5TBADgMDcLa4wCgFHIYAAwltlymCIAAIdZ3cwVfADgTMhgADCW2XLYXL0FAAAAAAA5\nxkwAAA6zUE8EAMOQwQBgLLPlMEUAAA4z2xQoAHAmZDAAGMtsOUwRAIDDzLYiKgA4EzIYAIxlthym\nCADAYWZbERUAnImjGZyamqrIyEidPn1aKSkpCg0NValSpdS/f39VrFhRktS2bVs9+OCDWrdundau\nXSur1arQ0FA1adJESUlJGjFihC5evChvb2+NGDFCfn5+uTE0ADAFs50LUwQA4DCLxVzVTwBwJo5m\n8Pr16+Xn56dRo0bpzz//VMeOHdW9e3d16NBBHTt2tG8XFxenVatWKTo6WklJSQoLC1PDhg21evVq\nVa1aVd27d9eGDRsUFRWl/v37OzosADANs50LUwQA4DCryaqfAOBMHM3gFi1aqHnz5pKk9PR0ubu7\na//+/Tp69Ki+++47VahQQf3791dMTIyCg4Pl7u4uHx8f+fv76+DBg9qzZ4+6dOkiSWrUqJEWLlzo\n8JgAwEzMdi5MEQCA4ZiKCgDGKVKkiCTpypUrevXVVxUeHq7k5GS1bt1a1atX16JFi7RgwQJVq1ZN\nPj4+9v28vLyUkJCgK1euyNvbO0sbAKDgoggAwGGOXgfFVFQAyLncuBb17NmzGjRokJ599lk9/PDD\nSkhIsP/B/8ADD2jKlCmqU6eOEhMT7fskJiaqaNGi8vb2trdntgGAK2FNAAAux9HbojAVFQByztEM\nPn/+vHr37q1BgwbpnnvukST16dNHAwYMUFBQkHbs2KEaNWooKChIc+fOVXJyspKTkxUbG6vAwEAF\nBwdr8+bNCgoK0pYtW1SnTp3cGBYAmIajOZzfs2IpAgBwmKO3RWEqKgDknKMZvHjxYiUkJGjhwoX2\nImr//v01bdo0ubu7q0SJEho6dKi8vLz03HPPKSwsTOnp6QoPD5eHh4fatm2rUaNGqUePHvLw8FBk\nZGRuDAsATMPRHM7vWbEUAQA4LDdWRGUqKgDkjKMZHBERoYiIiGva33777WvaWrdurdatW2dp8/T0\n1Pjx4x3qAwCYmaM5nN+zYs118QKAAsnNYsn2cTOZU1F79+6tJ554QlLGVNSYmBhJyjIVdffu3UpO\nTlZCQsI1U1ElMRUVgMtxNIMBAI5xNIeLFCkiLy+vLLNia9asqb59++qtt95S+fLltWDBAiUmJubK\nrFhmAgBwmNVidWh/pqICQM45msEAAMfkRg7n56xYigAADMdUVAAAALiq/F6glSIAAIflxpoAAICc\nIYMBwFiO5nB+z4qlCADAYVaT3RsVAJwJGQwAxnI0h/N7VixFAAAOY9EpRq0USAAAIABJREFUADAO\nGQwAxjJbDlMEAOAwpqICgHHIYAAwltlymCIAAIeZrfoJAM6EDAYAY5kthykCAHAY16MCgHHIYAAw\nltly2Fy9BQAAAAAAOcZMAAAOM9t1UADgTMhgADCW2XKYIgAAh5ltChQAOBMyGACMZbYcpggAwGFm\nWwwFAJwJGQwAxjJbDlMEAOAws02BAgBnQgYDgLHMlsMUAQA4zGzVTwBwJmQwABjLbDlMEQCAw9xM\ndh0UADgTMhgAjGW2HDZXbwEAAAAAQI5RBPgX/4rlNGvheH3/80f6Yut7ihgWrkIehSRJte+uoaVr\n52hbzGf64KtoPdbqwSz71m9UR++vj9L2feu1YOV0+VcsZ3/OarXqfwO66fMtq/Td7g/12tj+KlzY\nI1/Hhls3csx4hfb83zXtx46fUP0mzZSenm5ArwouN0v2D+BW3SiHI6cM0e4j31zz+OS7FfZ9g+vW\n1IoP39L2/Z/r3U8XqF6DYPtz3j5eGj15iDb+9IG+2/2hho+LUJEinkYMEdcRF3de/QYNVePmLdXi\n8Vaa/uZcpaena9jIMQpucO81j0dbP2N0lwsMMhi5zZHz4Q+/XnpNTle9s4r9+fZd22jDtve05ZdP\nNWryYHl6Fs7XseHWJCcna+TYCbq3RUs1e+RJLVq63OguFWhmy2GKAP/gXshds6LGKynpL3V++mW9\n2jdSzR5uoj4Du8vTs7BmRU3Q3p9i1Lbli4qa944ipwxRrbvulCSVLnu7Zi4Yp4/WfK52T4Tp/B8X\nNHPBWPuxw/t11TMdntLY16apW7tXFBBYUeNnDjdqqLiJbTt+0JoPP9a/L+05c+asevUboOSUFGM6\nVoBZ3dyyfQC34mY5PH7EDDW/52n749lHu+lKQqKi314lKSOH31o6Rds2/aA2D3XV5m+2a/rbY+VX\n3FeSNGxMP1W5o6J6dOivlzoNUO27a2jg69cW+2CM10aP1Z+X/9TSBfM0YfQIffjJZ4pevlJDB/bT\nN+s/sj/eW75E3l5eeqFje6O7XGCQwchNjpwPF/IopPIVy6lLm15Z8vrQb7GSpBaPNFWv/qGKHPqG\nuj3/imr9X3t3HhdVvf9x/DVsKiC44oYrEoqISZnLtV+arWbmvZa3srJMUTL3QivNlDKXTNNSozCl\n3TJtuWW3ssiktDIXFBQ1d1NB0QBxQPj9gU7NdUE9wJkzvJ+PxzyK75wz8z0Zb8985rtEtuCxcUNM\nvFo5nxmzX2bjps28Pnc2Tz8RS3zCQpb/92uzu+WyrJbDrtUbk7Vu05LghvUYP/p5du7Yw69rNvDK\njAV073UjzUKbUK16AK+8uIB9e/7g4w++YGvadq7ucCUAve+5nbRN6SyKf5+d23fz9ONTqFMviGs6\ntQXg3gd7M2faa3y/4kfSt+zgqVGTuf6mzjRuGmzmJcv/yD1xgomTp9C2TSRFRX+1f/NdEv/u159K\nPqpWn4vNZivxIXIxLpTDuTknOJKZ5XgMHt6PDb9t5v03lwHFOZu2KZ3Z015j354DzJ5e/M9WkWEA\nnMyz89z4WWxN3U5qylaWLf6Cq9u3MfNy5W/WrlvPfXf/m5BmTWl3VRS33nQDq3/5FT8/P2rWqOF4\nvPr6AiJbt+Luu3qb3WWXoQyW0mTkfrhJs4ZQVETK+jSnvD4zgvK+/nfxzsKP+H7Fj6SmbCXuqRn0\nvOsWjQZwMbknTvDRsk+JHTmMlmFhdL3uWh56oC/vfrDE7K65LKvlsIoAf/P79t0MeWgMeXknndr9\nq/qxe+desv/M4V9398Bms9HmqlY0DWlEaspWACLbhvPrmvWOc06etJO6KZ02Ua2oXiMQX78qrF+7\nyfH8H/sPkf1nDpFtW5XPxclFmTP3VdpffRVXR7V1al+56keGDo5mzOgRFP29OiAAeGAr8SFyMc6X\nw1UD/J1+joxqRZcbOzM97mVH2zWd2vL1F0lOx93dI5pVSWsAmDh2uiOz6wfXpfsd3fhp1a9lcRly\nGSLCW/LZ8i/JyzvJocOHSf5pNa1atnA6Zt2GjXybtJLYkcNN6qVrUgZLaTJyPxwS2oS9u/efc9qk\nh4cH4ZFh/Lr6r/vljb+l4unpSYuI0LK9KLkkW7duw56fT9SVfxXK27aJJGVzqu6Dz8NqOXxRRYCK\nMv856+gx1iT/5vjZZrNxd79/svqHX8n+M4dRg8cz9PEB/JL+NYs+fJlF8e+zetVaAGrVrsHhg5lO\nr5d5+Ah16gZxLOtPCvILqFs/yPFc1QB//Px9qVYjsHwuTkq0bsNG/rviW0aPGEoRzgH3zFNjufOf\ndwAKvnOxWvXTiip6Dv+08hen4wY+eh9ff57EjvRdjrbgRvU5cSKPqbOfZsXPH7Hwwzm0vrLlWe/x\n/Evj+Hzlu1SvWY35Ly0qu4uRS/L8pAls2pxGhy43cMNtvahVqxYxA/s7HfPaG4u4sVtXQpo1NamX\nrkkZXD4qeg5fzP1ws9AmnCos5JWFU/nm549IeG+WY6pA1QB/KlXy4fChDMdrnzp1imNHj1Gnbu3y\nvUi5oMOZGQQGBODt7e1oq1mjBvn5+WQeOWpiz1yX1XL4vFsE7t27l1mzZpGWloaHhweFhYWEhoYy\nYsQIGjduXJ59NM3j44cQ1jKEe3oOplZQDSbPGscnHyznw3c/Jbz1FTw2bghbUrez4suVVK5SCbvd\n7nR+QX4B3pW8KSws5KsvkhgaO5Dt6TvJOnKM2KcfpfBUId7e2qXRFdjtdp55dgpjR4+gqr9/ySeI\nE09XW+3ETSiHnXP4jHoN6vCP667h/n86zyP18/dl+Jho4mcnkjDvbW7vfTPxb7/IHdffz6GDf910\nvvbym7y78CNGPjGYeYum8e/bBpbb9ci5FRUV8cTTE6kTVJspcRPIzs5h8vQZvPDSy4wZVfyt//4D\nB1j142reSnjV5N66HmVw2VEOX9r9cNOQRlSt6sfMxKUcPphJ73t68Pq7M/nnjQ86iij2k85rK9nt\n+fj4aLFsV5KXl+dYCPKMMwWB/Hz7uU6p8KyWw+f9BPrcc88xZMgQIiIiHG0bN24kLi6O119/vVw6\nZ6YxE4bS5747GDX4aX7ftosBQ+4j+88cnh33IgBpm9KpU7c2Q0b1Z8WXK7GftJ8VYN4+3mQfyQFg\nyoTZTJk9ni9+eI+TJ+28veBDdmzbRU52brlfm5xt/usLaNQomBuv72p2V0QclMPOOXzGjd2vY/fO\nfWzakOZ0fEHBKVau+Im33yieszjj2bl07Hw1Pf51Ewvm/bWDwJnRA48/+gxf/fQhV10Tya9rNpTD\nFcn5rN+Qwq+/reOrz5YSVLv4G8GJ454geshwBj70ADWqV+erb76lUcNgIlqFm9xbqUiUw5d2Pzxm\n6CR8fLwdUwmeGzeTK6+K4PbeN7P4zY8B8Knk/OGy+Pi88r0wuSAfn0rk252LNfmnF8auXFm76riD\n8xYB7Ha7U+ABtG7dusw7ZDabzcbEaWPofkc3HhvyDEnfJAPFq05v2/K707GpKVt5cNDdABz8I4Na\nQTWcnq9VuwbpadsBOJZ1nJgHHse/qh8F+QXk5Z2kz313sG/vgXK4KinJ519+TUZmBu2vK97mJj8/\nn8LCQjp0uYGfvtNKqCXxcLEhTu5COeycw2d07tKeb5Z/f9Z5hw9m8Pv23U5tu37fQ936QfhU8uG6\nbh1ZueInx81pxqEj/Hk8W9OyXMAfhw4RGBDgKAAAtAwL41RhIQcO/EGN6tVZmfwTN3S9zsReui5l\ncNlRDl/a/XBhYeFZawn8vn03QUG1yDp6jJMn7dSqXcNRjPX09CSweiCHDzlPqRVz1aldm+N//klB\nQQFeXsUfFzMyM/Hx8SYwIMDk3rkmq+XweYsAzZs3Jy4ujg4dOuDv709OTg7Jyck0b968PPtX7h4b\n9wi39LyeEYPG8cO3qx3tu3fuc9pvGqBp88bs3rkPgI2/bSbqb6tMV65cibDw5rw6u3i+adwLY1n+\n6QrHAlVXXROJTyUf1v+6CTHfG6++TEHBqdM/FZH4zvtsTktjyqRnzOyWZXjYtMZoWVAOO+fwGa0i\nW/DG/HfPal+/dhPhra9wamsW2oSN61IBeH7WOGKHTmLFlysBaNCwHgGBVdmxbfdZryXlq2FwA47/\n+ScZGZnUqlUTgB07dwIQ3KABRUVFbNqcSv8H+prYS9elDC47yuFLux9+55NX+eo/3/HGq8UZbbPZ\nCGsZwuK3ikcBbFqfRlS7SMeaA22iWnHq1CnSUtLL47LkIoWFheLt5cVv6zfS7qrixbLXrttAq5Yt\n8XCxre5chdVy+LxFgDFjxpCUlMT69evJycnBz8+Pa6+9li5dupRj98pXZNtw+va/k5emxJOakk7N\n2n99s//pki+JHno/j40fwnuLlhIWHkL/mHuZHvcKAEsXf06/QXczYMh9rPjvSgYN7ceBfQcdC6Vk\nZR1nWOxADv2RQRXfyjz74pO8u/Aj/jyebcq1irN6des6/Vy1qj+VfCrRMLiBST2yFqtVP61COeyc\nw5mHj1A/uC5+/r6OPaf/7q2ED0lc8jJ9H+rN9yt+ovc9PagdVJPPlv4X+0k7Sxd/zqgnB5OZcYR8\newFPxY1gxZcrnaYaiDlatWxBm8gInpgwicdHDuPEiRNMen4at3e/lcDAAPbtP0BObi4hzZqZ3VWX\nZDSDCwoKiIuL48CBA+Tn59O/f3+aNGnCpEmTsNlshISEEBsbi81mY9myZSxduhRPT0/69+9P586d\nycvLY8KECRw9ehQ/Pz8mTJhAtWrVSunqzKUcvrT74RVfrqTfoLtJ37KDvbv3c/+APlQN8Gfp4s8B\neP/NZUyY8jhb03bwx/5DPPXsSJa+/5+zRg+IuapUrkzPHt15buoLPDvhKQ5nZJL49rs8M26s2V1z\nWVa7Fz5vEcDDw4OuXbvStWvFmSN9w63/B8DwsdEMHxvtaC8qKiIqpBv9+wxnzDNDef8/r5FxKJOX\npsbzyYfLATiw7yCjBo3n8acfZeCj97F+7SaGD3zS8RqvvJBAYNxIFix+iZMn7Xz8wRfMme7+c8ms\nyoaN8/0uu9rqnq7A6H8S3YCem3L47ByuWas6RUVFHMs6fta5mzduYcSgcYwcO5jhYweRnrqdR/rF\nknHoCADT415hxJhoXpwfR6VKPny9/HumPjOnfC5MSjR7+lSmzJjJwEeG4uXlxU3drmfk0EcAyDxy\nBJvNpmGo52E0g5cvX061atWYOHEix48fp2/fvoSFhRETE0NUVBRTpkwhKSmJiIgIFi9eTGJiInl5\neURHR9O+fXuWLFlCaGgoAwYM4KuvvmLBggWMGjWqdC7OZMrhS7sfTpj7Np6enox7bhTVa1Zjw9pN\nDLx3FLk5JwD48rNvqd+gLuOeHYnP6Rx+4dm55X+RUqLHRwwjbsp0Hn5kGP7+fgwe2J+bul1vdrdc\nltU+HtiysrLKbM+zwMBAIhtr/p472bCreA9u+3HN3XInPgE1OXbs2GWf/+I900o8ZtS7sed97rPP\nPiM9PZ2RI0c63YDee++9jhvQDh06EBERwbBhw5xuQBctWsTixYs5ceKE4wZ048aNbnMDapRy2P0o\nh92TkRw2msEnTpygqKgIX19fsrKyeOihh8jPz+ezzz4D4Pvvv2f16tV06NCBVatWMXZs8beBsbGx\nPPjggyxatIgHHniAVq1akZ2dzYABA3jvvfcu61rckXLY/SiH3Y/Z98Ll/YWYtSYviIhL8rR5lPi4\nkG7dujFo0CCgeFEhLy8v0tLSiIqKAqBTp078/PPPpKamEhkZiZeXF/7+/gQHB5Oens6GDRvo2LEj\nAB07dmTNmjVle8EiIi7EaAZXqVIFX19fcnJyeOKJJxg8eDBFRX99R+Tr60t2djY5OTn4/20b3b+3\n+/n5ObWJiFQkRnP4zIis+Ph4XnrpJaZNm8ZLL71ETEwM8fHxFBUVkZSUREZGBosXLyYhIYHZs2cz\nd+5c8vPzHSOy4uPj6d69OwsWLLjg+6kIICKG2WwlPy5EN6AiIpfPaAYDHDx4kEceeYTbbruNm2++\n2Wnq25ns9fPzIzf3r62Nc3NzqVq1qlP7mTYRkYrEaA6X9xdiKgKIiEvQDaiIiDkyMzMZOnQoQ4cO\npUePHgCEhYWxdm3x4sbJycm0bduW8PBw1q1bh91uJzs7m507dxISEkJkZCSrVq1yOlZERC5eeX8h\ndt6FAUVELpanwe1iztyAxsbGcvXVVwN/3YBGRUWRnJxMu3btCA8PZ968edjtdux2+1k3oOHh4boB\nFZEKx2gGL1y4kOzsbBISEkhISABg1KhRzJgxg/z8fJo2bUq3bt2w2Wz06dOH6OhoCgsLiYmJwcfH\nh969ezNx4kQGDhyIj48PcXFxpXFZIiKWYTSHofgLsdjYWO666y5uvvlm5sz5a/Hi0v5CTEUAETHM\n6IqougEVEbl8RjN49OjRjB49+qz2+fPnn9XWq1cvevXq5dRWuXJlnn/+eWOdEBGxMKM5XN5fiKkI\nICKGGd0bVTegIiKXz2r7U4uIuBujOVzeX4ipCCAihtnQDaiIiFmUwSIi5jKaw+X9hZiKACJimKeH\nbkBFRMyiDBYRMZfVclhFABExzKahqCIiplEGi4iYy2o5rC0CRURERERERCoIjQQQEcOsNgRKRMSd\nKINFRMxltRxWEUBEDLPaECgREXeiDBYRMZfVclhFABExzGK5JyLiVpTBIiLmsloOqwggIoZpj2oR\nEfMog0VEzGW1HFYRQEQMs1rwiYi4E2WwiIi5rJbDKgKIiGFWCz4REXeiDBYRMZfVclhbBIqIiIiI\niIhUEBoJICKGeVhsWxQREXeiDBYRMZfVclhFABExzGrbooiIuBNlsIiIuayWwyoCiIhhFit+ioi4\nFWWwiIi5rJbDKgKIiGFWq36KiLgTZbCIiLmslsMqAoiIYZ5WK3+KiLgRZbCIiLmslsMqAoiIYVar\nfoqIuBNlsIiIuayWw9oiUERERERERKSC0EgAETHMatuiiIi4E2WwiIi5rJbDKgKIiGEWGwElIuJW\nlMEiIuayWg6rCCAihnlYLflERNyIMlhExFxWy2EVAUTEMIvlnoiIW1EGi4iYy2o5rCKAiBhmtXlQ\nIiLuRBksImIuq+WwigAiYpjVtkUREXEnymAREXNZLYdVBBARl5CSksIrr7zCvHnz2LJlC6NHj6Zh\nw4YA9O7dmxtuuIFly5axdOlSPD096d+/P507dyYvL48JEyZw9OhR/Pz8mDBhAtWqVTP5akRErEUZ\nLCJScagIICKGeRocApWYmMjy5cupUqUKAKmpqdxzzz307dvXcUxGRgaLFy8mMTGRvLw8oqOjad++\nPUuWLCE0NJQBAwbw1VdfsWDBAkaNGmWoPyIiVqIMFhExl9EchvItxnoY7q2IVHg2W8mPC2nYsCFT\np06lqKgIgLS0NFatWsWgQYN49tlnyc3NZfPmzURGRuLl5YW/vz/BwcGkp6ezYcMGOnbsCEDHjh1Z\ns2ZNWV+uiIhLUQaLiJjLaA4nJiYyefJk7HY78Fcxdt68ecybN48bbrjBUYxNSEhg9uzZzJ07l/z8\nfEcxNj4+nu7du7NgwYIS+6sigIgYZrPZSnxcSNeuXfH09HT8HBERwfDhw3n11Vdp0KABr7/+Orm5\nufj7+zuO8fX1JTs7m5ycHPz8/JzaREQqEmWwiIi5jOZweRdjVQQQEcOMVj//V5cuXQgLC3P8+5Yt\nW/Dz8yM3N9dxTG5uLlWrVnVqP9MmIlKRKINFRMxlNIfLuxirIoCIGObhYSvxcSmGDRvG5s2bAViz\nZg0tW7YkPDycdevWYbfbyc7OZufOnYSEhBAZGcmqVasASE5Opm3btqV+fSIirkwZLCJirtLO4bIu\nxmphQBExrLS2RTnzOmPHjmX69Ol4eXlRs2ZNnnzySXx9fenTpw/R0dEUFhYSExODj48PvXv3ZuLE\niQwcOBAfHx/i4uJKpS8iIlahDBYRMVdpbxE4bNgwHnvsMcLDw52KsfPmzcNut2O3288qxoaHh190\nMdaWlZVVVKo9/pvAwEAiG19XVi8vJtiwKwkA+/FMk3sipcknoCbHjh277PN/mPhWicd0nnDfZb++\nXD7lsPtRDrsnIzmsDHZtymH3oxx2P65wL7x//37Gjx9PQkICW7duPWcxdtmyZSxbtozCwkIeeugh\nunbtSl5eHhMnTiQjI8NRjK1Ro8YF36vMiwAiYg1Ggi857u0Sj+k0vm+Jx0jpUw6LWMfl5rAy2LUp\nh0WsoSLdC2s6gIgYVsojoERE5BIog0VEzGW1HC7zIsDx9JSyfgspRwGhEQDk7Nthck+kNPk1aGbo\nfM9LXOxEyldW6nqzuyClqFrLNgDYj2WY3BMpTT6BtS77XGWw6zv040qzuyClKKjjtYCmA7gTn4Ca\nhs63Wg5rdwARERERERGRCkLTAUTEsEvd9kREREqPMlhExFxWy2EVAUTEMJvFgk9ExJ0og0VEzGW1\nHFYRQEQMs9piKCIi7kQZLCJiLqvlsIoAImKYzWrJJyLiRpTBIiLmsloOqwggIoZZLPdERNyKMlhE\nxFxWy2EVAUTEMKsthiIi4k6UwSIi5rJaDmuLQBEREREREZEKQiMBRMQwm4fqiSIiZlEGi4iYy2o5\nrCKAiBhmtXlQIiLuRBksImIuq+WwigAiYpjV9kYVEXEnymAREXNZLYdVBBARw6xW/RQRcSfKYBER\nc1kth1UEEBHDrLY3qoiIO1EGi4iYy2o5rCKAiBhmtW1RRETciTJYRMRcVsthay1jKCIiIiIiIiKX\nTSMBRMQwq1U/RUTciTJYRMRcVsthFQFExDiNKRIRMY8yWETEXBbLYRUBRMQwqy2GIiLiTpTBIiLm\nsloOqwggIoZZLPdERNyKMlhExFxWy2EVAUTEMKtVP0VE3IkyWETEXFbLYRUBRMQwm8UWQxERcSfK\nYBERc1kthy22hIGIiIiIiIiIe0lJSSEmJgaAPXv2MHDgQKKjo5k6dSpFRUUALFu2jH79+tG/f39+\n+OEHAPLy8hgzZgzR0dGMHDmSrKysEt9LRQARMczD01bioyTlGXwiIu6kNDJYREQun9EcTkxMZPLk\nydjtdgBmzZpFTEwM8fHxFBUVkZSUREZGBosXLyYhIYHZs2czd+5c8vPzWbJkCaGhocTHx9O9e3cW\nLFhQcn9L5apFpEKz2WwlPi6kvINPRMSdGM1gUCFWRMQIozncsGFDp7zdsmULUVFRAHTq1Imff/6Z\n1NRUIiMj8fLywt/fn+DgYNLT09mwYQMdO3YEoGPHjqxZs6bE/qoIICKG2WwlPy6kvINPRMSdGM1g\nFWJFRIwxmsNdu3bF09PT8fOZe2IAX19fsrOzycnJwd/f/5ztfn5+Tm0lURFARIwzmHzlHXwiIm7F\nYAarECsiYpDRKsBZL/fX8Wfugf38/MjNzXW05+bmUrVqVaf2M20lURFARAyzedhKfFzS65Vx8ImI\nuBOjGaxCrIiIMaV9LxwWFsbatWsBSE5Opm3btoSHh7Nu3TrsdjvZ2dns3LmTkJAQIiMjWbVqldOx\nJdEWgSJiWGlvi3Im+KKiokhOTqZdu3aEh4czb9487HY7drv9rOALDw+/6OATEXEnpZ3BKsSKiFya\n0srhM/k7fPhwJk+eTH5+Pk2bNqVbt27YbDb69OlDdHQ0hYWFxMTE4OPjQ+/evZk4cSIDBw7Ex8eH\nuLi4Et9HRQARcRnlFXwiInJ+KsSKiJS/+vXrk5CQAECjRo2YP3/+Wcf06tWLXr16ObVVrlyZ559/\n/pLeS0UAETGsNLafKs/gExFxJ6W1BaAKsSIil8dqW7GqCCAihl3M9lMiIlI2SiODVYgVEbl8VrsX\nVhFARIyzVu6JiLgXZbCIiLkslsMqAoiIYVarfoqIuBNlsIiIuayWwyoCiIhhVgs+ERF3ogwWETGX\n1XJYRQARMcxmscVQRETciTJYRMRcVsthD7M7ICIiIiIiIiLlQyMBRMQwm4e1qp8iIu5EGSwiYi6r\n5bCKACXYuWcv0+a/zqat6QRWrcpdPW7l/n/dAcCBQ4d4bs58NqRuoW7tWowY8CCdrmrrOPeXDRuZ\nEb+AvQcO0uqK5owb9gjB9eqadSlyDna7nb6Dh/HYo4NoH1X8Z7cxNY0XXnmVbTt2Uqd2LQY+cC+3\nduvqOOeNdxbzwSefkXX8OFGRrYkdGkOjBvXNugSXYLXgE2vZuXcfL8QvcOTwnbfdzH29ejLppVf4\n/Lvvzzq+flAQH706h14Dh/BHRsZZz/e4vgvjhsaUQ8+lJHa7nX8/0J8xo0bQ4ZqrAdh/4A+emTyF\ndRtSqFe3Do+PGErnjh0c56zbkMKUGTPZvuN3mjRuxJhRI7g66kqzLsElKIOlvExdsIh9hw4xe+zj\nAGzavoM577zHjr37qF29Og/ecTs3dmzvOP6tzz5n6YrvOJ6dTZuwKxhx3z0E16ljVvflEtjtdiZP\nf5GvVnyLj7cPD/S9m4fu72t2t1yW1XJY0wEuoKCggOHPPEf9OkG8PWcGsTEDSXjvQ5Z/t5KioiJG\nx02lWkAAiTOnclu3LoyZPJ39Bw8B8MfhDEbHTaH79V14c9Y0alavxui4KRQVFZl7UeJw0m7niWen\nsmPXbmyn9/U4cSKP4U9OIKJFGIsT5tHv7ruYMPVFUlK3ALD0P8t584MljB89nPdem0uAvz8jnpyg\nP1ebreSHyGUoKChgxKTJ1AuqzVuzpvPYoIdZsHgJXyb9wOjo/nz+Rrzj8ebMafhWqcy9d/QAYNGM\nKU7PPzPiUby9vLjrtltMvioBOHnyJLHjJrD9952OBZWKiooY9tgYqlerxnuLEujZ/VZGjXmKffsP\nAPDHwYMMGlZcMPjovTfp3KkDw2PHcjQry8xLMZ8yWMrBL5tT+c/KHxy/rydOnmTMrNmEN2vGwmef\n4d7bbmHy6wvYvGMHAJ8mfc97X/6XMf378UbcBKr6+TJm5hzdM1nCiP1pAAAT7ElEQVTEjNkvs3HT\nZl6fO5unn4glPmEhy//7tdndcl0Wy2EVAS7gUOYRIsJCiY0ZSHDdunRudxXXXNmatSmb+GVDCrv3\n7+epoYNp0jCYfnf+k8iWYXz8328AWPblV4Q1a8r9/7qDJg2DGT98CAczMvl5/UaTr0oAduzcRb8h\nI9l34A/n9l27OHb8T2Ieup8G9epyx603EdqsKb+e/nPLPXGCkYMH0LHdVTRqUJ8H7+nDrr37yDx6\n1IzLcBkWyz2xkDM5/Pigh2lQtw6dr46iXWRrftu8Gb8qVahRLdDxSHj/QyKuuII7u98MQGBAVcdz\nvlUqM/fNd3i4T2/CmjU1+apk+47f6ds/mr379ju1r/llLbt272HCk2No1qQxD/e7jzaREXz0yacA\nvPP+h7S44gpGDImhYYMGDH9kMA3q12dTapoZl+EylMFS1k6cPMn0NxJpHdrc8SF+5779HM/O4eF/\n3UH92rW57drOhDQMZl3a1uJz8k4y5N93cU1EK4Lr1KHvbbey5+BBjhw7bualyEXIPXGCj5Z9SuzI\nYbQMC6Prddfy0AN9efeDJWZ3zWVZLYdVBLiA+nWCeC52FD7e3hQVFbF+cxq/paRyzZWRpGzZSouQ\nZlSpXNlx/JXhLdmYVvyNccqWdNpGhDueq1ypEi1CmrLxdDCKudZuSOGaqCtZ+PKLTu2Nghvg7+fH\nss+/pLCwkPUpm9m5ew8trggBoO+d/+T2m28E4M/sHBZ//CkhTRtTq0aNcr8GV2Kz2Up8iFyO+nWC\neHb0iL9yODWNdZtTaRfZ2um4jWlb+X7NL4x4uN85X+e9Tz/Hw8ODvr16lke3pQS//raO9u2u5q2E\neKf2DSkptGwRhm+VKo62qDZtWL9xEwCrf/mVG6/v4nTO4sQFTtMFKiJlsJS11z5cSlTLFrQNC3O0\nBdepg1+VKnyatJLCwkI2pm9j94E/uKJJYwD63Hwjt3b+BwDZubks/eZbmjaoT81qgaZcg1y8rVu3\nYc/PJ+rKNo62tm0iSdmcqpEc52G1HNaaABfptn7RZBw9yrXXXM31nTowI34BtapXdzqmerUADmVm\nApB59OhZHwxrVKvGoYzMcuuznN+dPW87Z3tVf3+mP/MUw5+cwOz4BZwqLGTg/fc61gs4Y8lnnzN5\n5sv4eHvz8tRny6PLLs3mqXqilL3bHx5MxtEsOre7iq5/m3MK8MaHH3F9p/Y0axh81nn2/Hze+fgz\nBt93N97e+mvPFfTp/c9zth/OyKRWzZpObTWqV+fgoeKpdnv376dK5crEjpvAml/W0qhhMI8Nf5TI\niFZl3mdXpgyWspSybTvf/fIric9N5N0vvnS0V/Xz5dlHY4idOZv5HyyhsLCQB3v24Orwlk7nf/Jd\nEi8segtvLy9mPDayvLsvl+FwZgaBAQF4e3s72mrWqEF+fj6ZR45Sq2bF/vLrXKyWw9bqrYlefPoJ\nXhg3hrRt23nxtTc4abc7/WIA+Hh5k59fAEDeSTs+/3Oz6ePtjb0gv9z6LJfucOYRxj0/ndtvvoHE\nubMYN2oYb3/4EStWrnI6rlO7q3nn1Tn0vPUmRo2fxP4/DprUY5GK44WnxjD9icdJ276DmQsWOdoP\nHDrMT2vXcW/PHuc875tVPwJwW9fryqWfcvny8k7i4/M/f7f6eJNvL/67Mycnl1mvzCcyohWvzn6R\n1q3CGfjocA4eOmxGd0Xcnj0/n6kLFjHs3rvx9/V1ei4jK4u4+Ne5tXMn4p9+iscfvJ/3v/yK739d\n63Rc+9YRJDwzntuu7cwTL73MgcNnL9gqriUvLw/v/8niM5978vPtZnRJStl5vxKJiYnBbrefNeTD\nZrORkJBQ5h1zNS2aN6NF82bknbQzceYcbr/xerJzcp2OsRfkU7lSJeD0B/7TBQHH8/n5VAuoWm59\nlkv3yfL/4u/nx5MjhwLQIrQ5Bw9nMH/hW1x/7T8cx9WrE0S9OkE8MXwIv6xbz6dffs2gfhV3xVSr\nrYhqFcphZy1CmtEipBl5djuTXnqF4Q89gJenJyuSf6Jh/XqEhzY/53lf/5BMt390oJKPTzn3WC5V\npco+ZOfkOLXZ7flUqVI89c7T05P/+0dH7ru7DwCPXxHKj2t+5tMvljOg3/3l3l9XoQwuOxU9hxd+\n/CnBdYLo0u6qs577fOUq/KtU4bHTv3tXNG7E4SNHSVj6Mf93VZTjuDo1a1KnZk1GPdCXtWlpLF+V\nzEOamuXSfHwqOYqvZ+TnF/9c+W9ToeUvVsvh8xYBhgwZwuTJk5k6dSqenp7l2SeXcTjzCJu3bee6\n9u0cbU0bNiC/oIBa1auzfedup+Mzj2ZRq0bxFIGgmjXIOOK8WFzG0aM0b9Ko7Dsul+3Q4Qyan57L\ndkbL0OYkLv4QgJ9+WUuDenVp+LctAZs2asSx4xV7kRurBZ9VKIfh8JEjpG7bwf+d3j4OoElwcQ7n\n5J4gsKo/P65dR5cO15zzfHt+Pr+mbGLq6e2sxLXVqV2brVu3ObVlHMmkVq3iKQJBtWvR9H8yukmj\nhvxxemeeikoZXHYqeg5/vXoNmVnHuGnwEAAKCk5RWFjITYOHcEunTjRt0MDp+CuaNHZMGfg5ZRP1\ng2rTICjI8XyTevU4lp1dfhcgl6VO7doc//NPCgoK8PIq/riYkZmJj483gQEBJvfONVkth887HSAi\nIoJbbrmFbdu2Ub9+fadHRbFjzx7GTJ7O0WPHHG2p23ZQPTCQK1u1YOvvv5OXd9Lx3LpNaUSEXQFA\nRIsrWL851fFcXt5Jtu7Y6XheXFPDBvX5ffcep7bfd++m4en/7+MT3+a9pZ84nis4dYqt27bTtFHD\ncu2ny7HakqgWoRyG3/fsZezUGRz922rSadt3UD0wgMCq/hQVFbF52zbatgo/5/nbd+3Gbs/nyvAW\n5dVlMaB1q1akpadzIi/P0fbbug2OOf9tWkew+fQCvFC8peD233fSoF7dcu+rS1EGl5mKnsNzxj7O\nm89NYuGkZ3hj0gRuv+5aWjRtwsJJz1A/qDa7DhxwOn7X/gM0qFP8of+Njz/lw6++cTxXcOoU6bv3\n0Lh+vfK8BLkMYWGheHt58dvfdjVbu24DrVq2xMNDs8nPyWI5fME/xQceeICuXbuWV19czlWtI2jW\nMJiJM19m5569rFzzC68sepv+/+5NVEQr6tauzcRZc9i+azeLPlzK5vR0/nnzDQD0vPF6Uram88bi\nJezYvYe42a9QL6gW11wZafJVyYXcdlM3DmdmMmNuPHv27eeb71ex8L0P6Htn8SJWd//rDj767Au+\n/DaJ33fv4bkXZ1NwqpAep//cKyqrrYhqJRU9h6MiWtG0YTCTZr/Czr37+OHnX5n75js8dOe/gOL1\nAHJP5NHsPIW47bv2UC+otqYCWES7q9pSv25dxk18jm3bd5Cw6C02bt7MnaeHDt9/dx++W7mKt95b\nzO49e5n58jwOH86gx603m9xzcymDy1ZFzuE6NWtSP6i24xt9f19ffLy9qR9Um1v+0ZGMrCzmvPs+\n+w4dIumXX3n78y/oc1PxLkp33tiNT777nm9Wr2HX/gO8sPBNThWe4tZ/dDL5qqQkVSpXpmeP7jw3\n9QVSNm3m26SVJL79Ln3vvsvsrrksq+Wwlkm+AC9PT2Y+8xTT5r3Gg6OfwK9KZe7t1YN/394dgBnj\nxxL30lz6jRxDcL26THsqlrpBtQGoFxTEtCdjmfnaGyx4fwmtW1zBC+PGmnk5chGqBwby2sxpTH/5\nVfoOHkqtGjV4dMBD9Lyl+C+0m7r8H39mZzN3QSKHMzJpExHO3GnPOW0VWSG5Vq6JG/Hy9OTFcWOZ\nHp9A/9gn8a1ShXt63kafHrcCcOTYMWw2CPD3P+f5R48dI7Cq1mKxCg8PD2a/MIWnn53C3Q8+TMPg\nYGZNe556dYu/6W8V3pJZ0yYz8+V5zHplHlc0b868l2ZQu1Ytk3tuMmWwlBMbf32YqVa1KnPGxjL7\nnfd4eEIcNQMDGXRnb7qfXkPp+mvakZ2by+sfLePw0Sxah4bw4mOjHOtniWt7fMQw4qZM5+FHhuHv\n78fggf25qdv1ZnfLdVksh21ZWVllttljYGAgx9NTyurlxQQBoREA5OzbYXJPpDT5NWjGsb9Ne7lU\n2atXl3iMf/v2JR4jpS8wMJCs1PVmd0NKUbWWxfs2249phW134hNY67JzWBns2gIDAzn040qzuyGl\nKKjjtQDYj2vrb3fhE1CzQt0LaySAiBjmakOcREQqEmWwiIi5rJbDKgKIiHEWWxFVRMStKINFRMxl\nsRxWEUBEDLNa9VNExJ0og0VEzGW1HFYRQESMs1buiYi4F2WwiIi5SiGH77//fvxPL3Rcv359Hnzw\nQSZNmoTNZiMkJITY2FhsNhvLli1j6dKleHp60r9/fzp37nzJ76UigIgYZrXqp4iIO1EGi4iYy2gO\nnzx5EoB58+Y52kaPHk1MTAxRUVFMmTKFpKQkIiIiWLx4MYmJieTl5REdHU379u3x9va+pPdTEUBE\njCuFeVDlWf0UEXErFpuLKiLidgzmcHp6Onl5eQwdOpRTp04RExPDli1biIqKAqBTp06sXr0aT09P\nIiMj8fLywt/fn+DgYNLT0wkPD7+k91MRQEQMs3l4GDq/vKufIiLuxGgGi4iIMUZzuEqVKtx3333c\ncccd7N69m+HDhzs97+vrS3Z2Njk5OY4vzf7efqlUBBARw4wOgSrv6qeIiDspjekAGo0lInL5jOZw\no0aNCA4Odvx7YGAgW7ZscTx/5sO/n58fubm5jvbc3FwCAgIu+f1UBBAR4wwOgSrv6qeIiFsxmMEa\njSUiYpDBHP7000/Ztm0bsbGxHD58mNzcXDp06MDatWuJiooiOTmZdu3aER4ezrx587Db7djtdnbu\n3ElISMglv5+KACJimNWqnyIi7kSjsUREzGU0h3v27ElcXBzR0dEAjB8/nsDAQCZPnkx+fj5Nmzal\nW7du2Gw2+vTpQ3R0NIWFhcTExFxWIVZFABExzmDwlXf1U0TErRjMYI3GEhExyGAOe3l5MXHixLPa\n58+ff1Zbr1696NWrl7H3M3S2iAhgMzgEqryrnyIi7sRoBms0loiIMUZzuLypCCAixlms+iki4lY0\nGktExFylsEBreVIRQEQM0/ZUIiLmMZrBGo0lImKM1e6FVQQQEcOsNgRKRMSdGM1gjcYSETHGavfC\nKgKIiHEWGwIlIuJWlMEiIuayWA6rCCAixlks+ERE3IoyWETEXBbLYRUBRMQwo3ujiojI5VMGi4iY\ny2o5rCKAiBhnsXlQIiJuRRksImIui+WwigAiYpjNZq0VUUVE3IkyWETEXFbLYRUBRMQwq22LIiLi\nTpTBIiLmsloOqwggIsZZbAiUiIhbUQaLiJjLYjmsIoCIGHYxi6EUlUM/REQqImWwiIi5rJbDKgKI\niHEWWxFVRMStKINFRMxlsRxWEUBEjLPYYigiIm5FGSwiYi6L5bCKACJimO0i5kG50hAoERF3ogwW\nETGX1XJYRQARMc5iQ6BERNyKMlhExFwWy2EVAUTEMJuHp9ldEBGpsJTBIiLmsloOqwggIoZdzBAo\nEREpG8pgERFzWS2HVQQQEeMsNgRKRMStKINFRMxlsRxWEUBEDLuYvVFFRKRsKINFRMxltRxWEUBE\njLPYtigiIm5FGSwiYi6L5bCKACJinMXmQYmIuBVlsIiIuSyWwyoCiIhhVhsCJSLiTpTBIiLmsloO\nqwggIoZZbVsUERF3ogwWETGX1XJYRQARMc5i86BERNyKMlhExFwGc7iwsJCpU6eybds2fHx8eOqp\npwgODi6lzp1Nf2uIiGE2D1uJDxERKRvKYBERcxnN4aSkJAoKCkhISGDIkCHMmjWrTPurkQAiYpzF\n5kGJiLgVZbCIiLkM5vD69evp0KEDABEREaSlpZVGr86rzIsAAaERZf0WYgK/Bs3M7oK4EJuGorq0\nai3bmN0FKQM+gbXM7oK4CGWw6wvqeK3ZXZAy4BNQ0+wuiIswmsM5OTn4+/s7fvbw8KCwsBAPj7LJ\n9zItAhw7dqwsX15EXMQJvM3ugpyHcljE/SmDXZtyWMT9Gc1hPz8/cnJyHD+XZQEAtCaAiIiIiIiI\niGnatGlDcnIyABs3biQ0NLRM38+WlZVVVKbvICIiIiIiIiLnVFRU5NgdAGD8+PE0bty4zN5PRQAR\nERERERGRCkLTAUREREREREQqCBUBRERERERERCoIFQFEREREREREKggVAQwqLCzk+eef5+GHHyYm\nJoa9e/ea3SUpRSkpKcTExJjdDRG5AOWw+1IGi7g+ZbB7Uw67JxUBDEpKSqKgoICEhASGDBnCrFmz\nzO6SlJLExEQmT56M3W43uysicgHKYfekDBaxBmWw+1IOuy8VAQxav349HTp0ACAiIoK0tDSTeySl\npWHDhkydOpWiIm2gIeLKlMPuSRksYg3KYPelHHZfKgIYlJOTg7+/v+NnDw8PCgsLTeyRlJauXbvi\n6elpdjdEpATKYfekDBaxBmWw+1IOuy8VAQzy8/MjJyfH8XNhYSEeHvrPKiJSXpTDIiLmUQaLWI9+\nQw1q06YNycnJAGzcuJHQ0FCTeyQiUrEoh0VEzKMMFrEeL7M7YHVdunRh9erVDBgwAIDx48eb3CMp\nbTabzewuiMgFKIfdmzJYxLUpg92fctj92LKysrTSg4iIiIiIiEgFoOkAIiIiIiIiIhWEigAiIiIi\nIiIiFYSKACIiIiIiIiIVhIoAIiIiIiIiIhWEigAiIiIiIiIiFYSKACIiIiIiIiIVhIoAIiIiIiIi\nIhWEigAiIiIiIiIiFcT/A8Obg/mj91HxAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8c8810>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Seaborn is kind of neat, huh? Gradient Boosting is indeed quite good."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Accuracy vs. Precision vs. Recall vs. F1 Score\n",
      "Accuracy, precision and recall are defined as follows respectively:\n",
      "$$ accuracy = \\frac{\\mbox{true positives + true negatives}}{\\mbox{true positives + true negatives + false positives + false negatives}} $$\n",
      "\n",
      "$$ precision = \\frac{\\mbox{true positives}}{\\mbox{true positives + false positives}} $$\n",
      "\n",
      "$$ recall = \\frac{\\mbox{true positives}}{\\mbox{true positives + false negatives}} $$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Gradient Boosting Classifier not only is quite accurate for 0 class(not churning class) but also fairly accurate for churn class(class=1)). \n",
      "\n",
      "Generally, for unbalanced datasets, $f_1$ score could also give a fairly accurate estimation of how well the classifier is doing considering both classes as it is the harmonic mean of precision and recall.\n",
      "$$ f_1 = 2 \\cdot \\frac{pr \\cdot rc}{pr + rc} $$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we want to measure not just class distributions, but also a more abstract measures(like precision, recall or $f_1$ score), we could get those measures by using `classification_report` function under the metrics submodule. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Passive Aggressive Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))))\n",
      "print('Gradient Boosting Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
      "print('Support vector machine(SVM):\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, svm.SVC))))\n",
      "print('Random Forest Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
      "print('K Nearest Neighbor Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
      "print('Logistic Regression:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.LogisticRegression))))\n",
      "print('Dump Classifier:\\n {}\\n'.format(metrics.classification_report(y, [0 for ii in y.tolist()]))); # ignore the warning as they are all 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Passive Aggressive Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.88      0.93      0.90      2850\n",
        "          1       0.34      0.23      0.28       483\n",
        "\n",
        "avg / total       0.80      0.82      0.81      3333\n",
        "\n",
        "\n",
        "Gradient Boosting Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.96      0.99      0.97      2850\n",
        "          1       0.91      0.73      0.82       483\n",
        "\n",
        "avg / total       0.95      0.95      0.95      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Support vector machine(SVM):\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.92      0.99      0.95      2850\n",
        "          1       0.88      0.52      0.65       483\n",
        "\n",
        "avg / total       0.92      0.92      0.91      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Random Forest Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.95      0.99      0.97      2850\n",
        "          1       0.92      0.69      0.79       483\n",
        "\n",
        "avg / total       0.95      0.95      0.94      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "K Nearest Neighbor Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.90      0.99      0.94      2850\n",
        "          1       0.81      0.37      0.51       483\n",
        "\n",
        "avg / total       0.89      0.90      0.88      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Logistic Regression:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.88      0.97      0.92      2850\n",
        "          1       0.55      0.21      0.31       483\n",
        "\n",
        "avg / total       0.83      0.86      0.83      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Dump Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.86      1.00      0.92      2850\n",
        "          1       0.00      0.00      0.00       483\n",
        "\n",
        "avg / total       0.73      0.86      0.79      3333\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Scikit-Learn provides a lot of [metrics](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics) for your own evaluation. You could also build your own $f_{beta}$ score if you want to weight precision more than recall or vice versa by using `fbeta_score` function under [metrics](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html) in Scikit Learn. \n",
      "\n",
      "Search engines and generally information retrieval care more about precision than recall. A user could visit only so many webpages but when she visits first and second pages, she needs to see relevant/accurate results based on her query. Recall may not be very important for those cases as she is limited by time and recall may not be that important for the search results she\n",
      "sees."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gbc = ensemble.GradientBoostingClassifier()\n",
      "gbc.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
        "              max_depth=3, max_features=None, max_leaf_nodes=None,\n",
        "              min_samples_leaf=1, min_samples_split=2, n_estimators=100,\n",
        "              random_state=None, subsample=1.0, verbose=0,\n",
        "              warm_start=False)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get Feature Importance from the classifier\n",
      "feature_importance = gbc.feature_importances_\n",
      "# Normalize The Features\n",
      "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
      "sorted_idx = np.argsort(feature_importance)\n",
      "pos = np.arange(sorted_idx.shape[0]) + .5\n",
      "plt.figure(figsize=(16, 12))\n",
      "plt.barh(pos, feature_importance[sorted_idx], align='center', color='#7A68A6')\n",
      "plt.yticks(pos, np.asanyarray(df.columns.tolist())[sorted_idx])\n",
      "plt.xlabel('Relative Importance')\n",
      "plt.title('Variable Importance')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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FivokW2bPxIEDe2fZsre2yNywPaio2LxxnTowaWsT1kKh0Hp8xIgROeuss3LS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\nu3fvLfSJAAAAAJ1JSVaYvKt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TU/OZJB06dMiyZcvSsWPH1drn7bffnh133DHDhw/PjBkz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\nVZoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c4a4e50>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Polynomial Features"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = df.as_matrix().astype(np.float)\n",
      "polynomial_features = preprocessing.PolynomialFeatures()\n",
      "X = polynomial_features.fit_transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(3333, 190)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Passive Aggressive Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))))\n",
      "print('Gradient Boosting Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
      "print('Support vector machine(SVM):\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, svm.SVC))))\n",
      "print('Random Forest Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
      "print('K Nearest Neighbor Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
      "print('Logistic Regression:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.LogisticRegression))))\n",
      "print('Dump Classifier:\\n {}\\n'.format(metrics.classification_report(y, [0 for ii in y.tolist()]))); # ignore the warning as they are all 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Passive Aggressive Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.86      1.00      0.92      2850\n",
        "          1       0.60      0.01      0.01       483\n",
        "\n",
        "avg / total       0.82      0.86      0.79      3333\n",
        "\n",
        "\n",
        "Gradient Boosting Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.96      0.99      0.98      2850\n",
        "          1       0.92      0.77      0.84       483\n",
        "\n",
        "avg / total       0.96      0.96      0.96      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Support vector machine(SVM):\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.86      1.00      0.92      2850\n",
        "          1       0.00      0.00      0.00       483\n",
        "\n",
        "avg / total       0.73      0.86      0.79      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Random Forest Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.95      0.99      0.97      2850\n",
        "          1       0.92      0.71      0.80       483\n",
        "\n",
        "avg / total       0.95      0.95      0.95      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "K Nearest Neighbor Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.89      0.98      0.93      2850\n",
        "          1       0.68      0.31      0.43       483\n",
        "\n",
        "avg / total       0.86      0.88      0.86      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Logistic Regression:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.92      0.97      0.95      2850\n",
        "          1       0.75      0.54      0.62       483\n",
        "\n",
        "avg / total       0.90      0.91      0.90      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Dump Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.86      1.00      0.92      2850\n",
        "          1       0.00      0.00      0.00       483\n",
        "\n",
        "avg / total       0.73      0.86      0.79      3333\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Scaled and Polynomial Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you think, when some of the features come together, they could form a much more powerful feature, or just by getting the square of the feature would be powerful feature, then Scikit-Learn has something that quite fits to your needs. Let's aasume I have `[x, y]` feature vector and I am interested in  `[1, x, y, x^2, xy, y^2]`, in the preprocessing step, I could use `PolynomialFeatures` of Scikit-Learn to build that feature matrix. If I just want to only get the interaction features(not `x^2`, then it is enough to pass `interaction_only=True` and ` include_bias=False`. If you want to get higher order Polynomial features(say nth degree), pass `degree=n` optional parameter to Polynomial Features. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = df.as_matrix().astype(np.float)\n",
      "scaler = preprocessing.StandardScaler()\n",
      "X = scaler.fit_transform(X)\n",
      "polynomial_features = preprocessing.PolynomialFeatures()\n",
      "X = polynomial_features.fit_transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Passive Aggressive Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))))\n",
      "print('Gradient Boosting Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
      "print('Support vector machine(SVM):\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, svm.SVC))))\n",
      "print('Random Forest Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
      "print('K Nearest Neighbor Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
      "print('Logistic Regression:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.LogisticRegression))))\n",
      "print('Dump Classifier:\\n {}\\n'.format(metrics.classification_report(y, [0 for ii in y.tolist()]))); # ignore the warning as they are all 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Passive Aggressive Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.92      0.93      0.92      2850\n",
        "          1       0.56      0.52      0.54       483\n",
        "\n",
        "avg / total       0.87      0.87      0.87      3333\n",
        "\n",
        "\n",
        "Gradient Boosting Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.96      0.99      0.97      2850\n",
        "          1       0.92      0.75      0.82       483\n",
        "\n",
        "avg / total       0.95      0.95      0.95      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Support vector machine(SVM):\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.91      0.99      0.95      2850\n",
        "          1       0.92      0.43      0.58       483\n",
        "\n",
        "avg / total       0.91      0.91      0.90      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Random Forest Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.94      0.99      0.97      2850\n",
        "          1       0.93      0.65      0.77       483\n",
        "\n",
        "avg / total       0.94      0.94      0.94      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "K Nearest Neighbor Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.89      0.99      0.94      2850\n",
        "          1       0.79      0.31      0.45       483\n",
        "\n",
        "avg / total       0.88      0.89      0.87      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Logistic Regression:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.93      0.97      0.95      2850\n",
        "          1       0.76      0.55      0.64       483\n",
        "\n",
        "avg / total       0.90      0.91      0.90      3333\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Dump Classifier:\n",
        "              precision    recall  f1-score   support\n",
        "\n",
        "          0       0.86      1.00      0.92      2850\n",
        "          1       0.00      0.00      0.00       483\n",
        "\n",
        "avg / total       0.73      0.86      0.79      3333\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Regresion refers learning how to relate the input variables to output variables. This is true for classification as well with only one difference that the regression would deal with continuous output variables where classification produces discrete output variables. One can use a regressor to output discrete variables as well using basic thresholding although it is not common. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets.california_housing import fetch_california_housing\n",
      "\n",
      "california_housing = sklearn.datasets.california_housing.fetch_california_housing()\n",
      "california_housing_data = california_housing['data']\n",
      "california_housing_labels = california_housing['target']# 'target' variables\n",
      "california_housing_feature_names = california_housing['feature_names']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "california_housing_feature_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "['MedInc',\n",
        " 'HouseAge',\n",
        " 'AveRooms',\n",
        " 'AveBedrms',\n",
        " 'Population',\n",
        " 'AveOccup',\n",
        " 'Latitude',\n",
        " 'Longitude']"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Cross-Validation\n",
      "- Split the data 80/20 rule, test_size parameter determines the ratio of test data in the whole dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import cross_validation\n",
      "X_train, X_test, y_train, y_test = cross_validation.train_test_split(california_housing_data,\n",
      "                                                    california_housing_labels,\n",
      "                                                    test_size=0.2,\n",
      "                                                    random_state=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(X_train.shape)\n",
      "print(X_test.shape)\n",
      "print('Training/Test Ratio: {}'.format(X_train.shape[0] / X_test.shape[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(16512, 8)\n",
        "(4128, 8)\n",
        "Training/Test Ratio: 4\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So far so good!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Regression model parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters = {\n",
      "              'n_estimators': 500, \n",
      "              'max_depth': 4, \n",
      "              'min_samples_split': 1,\n",
      "              'learning_rate': 0.01, \n",
      "              'loss': 'ls'\n",
      "             }"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### We will use an ensemble model to predict housing price"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import ensemble\n",
      "from sklearn import metrics\n",
      "classifier = ensemble.GradientBoostingRegressor(**parameters)\n",
      "\n",
      "classifier.fit(X_train, y_train)\n",
      "predictions = classifier.predict(X_test)\n",
      "mse = metrics.mean_squared_error(y_test, predictions)\n",
      "print('Mean Square Error: {:.3f}'.format(mse))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean Square Error: 0.295\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### This may take some time, but it will worth it I promise\n",
      "parameters = {\n",
      "              'n_estimators': 3000, \n",
      "              'max_depth': 6, \n",
      "              'learning_rate': 0.04, \n",
      "              'loss': 'huber'\n",
      "             }\n",
      "classifier = ensemble.GradientBoostingRegressor(**parameters)\n",
      "\n",
      "classifier.fit(X_train, y_train)\n",
      "predictions = classifier.predict(X_test)\n",
      "mse = metrics.mean_squared_error(y_test, predictions)\n",
      "print('Mean Squared Error: {:.3f}'.format(mse))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean Squared Error: 0.194\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Yep, this is much better. But instead of setting the parameters like this and then look at the mean squared error, maybe I should use another way to optimize the parameters based on mean squared eror. I will see how to do that in the next section; GridSearch to optimize the parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(16, 12))\n",
      "\n",
      "plt.scatter(range(predictions.shape[0]), predictions, label='predictions', c='#348ABD', alpha=0.4)\n",
      "plt.scatter(range(y_test.shape[0]), y_test, label='actual values', c='#A60628', alpha=0.4)\n",
      "plt.ylim([y_test.min(), predictions.max()])\n",
      "plt.xlim([0, predictions.shape[0]])\n",
      "plt.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v0NefwMra961eo+q/m/luFQUoAIgnorXfqz9m8zVsde073f94LIxwJAQlHEFy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Y5kMfi+Gz3s5fFeTq0ywnA8gpJchRgOoeTSygKwRLdBoKu0bTOw+g4RStAf3fOw6xjpfa/5/51V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\nfsInkA8yb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ALo/+EqLu5mTeQQH61JndAyvNFlWLrvLDvnJfeHUtK8EaUb7XaE0yCNIPCe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GAW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\nl0mBYcyjU374KFH6wsb9HGOArw+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kLhx1QVVq4vDoHAJIh9v779bBGlgpwmDFMYzbWaPX\nI0EZBGikCwmBIIrUnkEubey5XpkVBVOFV1ichZQQ8OZDBbmWBOGGxe4Z21EGrCo18eaijkd9Ai7O\nrlFIABDiEDgWE7mkY/uM8X06RcRVZ+tiDPC+3MBgKkHRlkXED/zG2fC1sPvVKja//AFn1RsMrSxi\n7udPMJ2PIvNpTcbcBtCb9/NYndOgLZrlwhSE0RH09/EQsvrvGBVZod2K1CcQ2q+ghtvbKXRKcPmv\n6+12bDA+M8L23w97blQ+YcBp/jNaSh5dogEQDbb31C6Vc9TkJmpSC31iHJlM2gT6ozvA5u89lRcj\nSQYQub80Q377XP356vuiRhd1VKr56LsokpjRgDd63bt6I+Py7BqZ7V2k4zFkeA6V9R3kZgoAxIBV\nDiJcSiBO/uExAEAYGwKXCjr24Pbu+jjC5dfPIYwNITE2fHuME23ARskRQIqse0uWA1wzCvvmjHER\n5O81GzfZB77xGHKlFuKbmSXNc2g2gZm8iGyCw9cH1wTYkhcQKwSlbQuXrE1yhFazLMloLM6Ce/0a\no9mEIRHsnTAHADvAojNrg1yy00d2TnHtHMTSjb0QZozZpoSi0IqYjSeomNe5NRjHyfoGsk/m28W7\nba1jwwjKedMEPl8YbbM8qB1ZxNdRR5KE8eE27bWb6BgS3ex4dbLN8YF8qD1xu8xj5m/DN6qo7e9D\nRVtI7++DbzwGeNVTCquz7FgDsGCWAQp+/P334AFIHO/CdGY9M/RSPz7F5TcvwLdp0nVA25TjOaRJ\n7tr1B1Bd30L9t88BAH1Lj3C5/lrD27irMVXXd+kwoRy5txx8Bs5cLVBnZho3N8j96udQioSrUUXr\nNVZ4H2TSqEpNjB3/CO71a7T4uEsGLLgMvDtA+iVRUqOfLuJ1ahxvLurgWDayINYa+CUXZ/B9iWSA\nBY7VqI6imcJ2Npbly2u8+eMazio3AICT5xuIPxzDaEaIPJi6DaA3fwnimOvKSp0dBYCx0gWKq29Q\nNFWzdBHjHJ4MZzqoyAYLuOjWlZ5PfqpPQJONgW+PDARR1N2vHjuA6FFmz92u52+wzUaLTsdF6whU\npSbef7cOaWMX8vU1mGIZlZFhZD+ax09/8bEDqny3OyTuimbIT/zem8FoJoGXJ6zGr7x7VvPQd3eZ\nxKRLPIl8DJN9Sfz95ilyAGIMA55n0WwpiLFMGwXfDDhJW+XgMmkM/uYvcfnNCwBA7ulHlPvcf/+r\n50htNa4dHhNmjmQCcoVmPK4Tca+wyKWKoWMBmJ6axKv8GGIMPIsC0dg3HeMixgATchnzA2kEm8VV\nbZyC0vahy/7xT3JZk2v9aR4ph3fxp21zupe3HfajXKxP9YFvPLGAChrf3Xx/05y6A8Bi5z6Iv55w\n2x90tp5gNbT29lEq1UOw8UQt+jjN4VER9VITQ+kYBgwYjsb98/FIBqNZHo+HrG3juq8jPppwYcPQ\n94/aNRtjgA8VUjC7v9J54cA7we2mY/UzIDdYwmKRJP9tBYzsTGeZz5oVs+z0Ty9ws7kLKAqSY0Oo\nt1vkdXFOkFnfl00ldBrl9t6o7R+guL6L4uqmlkhW9VpQe+1FkS2Xyqhv7GAozeOkLKG++xbDPFxB\nfa2YJ0ZfVe9E6OaZdYsdDMwzt0s7GETshsI6MzMnAhO9+qyONStVvixi8/wI53wcgFMGLJhYKVj6\nUxwOv91Az68HUIsJhgMTvP3SLmYal1d1BTubpwBg4jhW27L4PeKguAVZ/sFYtyqmRrm7kYAw4r5m\ndsXyy8kcWpWKbQ7Rab4/2opsVOLPJ6+erd6cgKvLMKCC3awee4Pohbmev8F2mx/r9LrBkietShmV\n9R2ISQ6XW28AKEgN5lFZ3wH/dB6i6JV1vy978LaqJX56jqGmSLzLJCZ9cp3BRI+AJz0sgCSElceo\nrO9gKpdE38eLmJsexY/vr7XfVgHnaKscQmEC8YF+h5lZd/DTII6w0mohvfCItAwzDPjejMbl3C36\nXrfv2tzetXUsCHtvMDlKqMWiLgq4PVf1RsbeRQ2rxyU0WwgBTOu1f1KUTrMzmKUTHZk7bVv4CqSb\nHRF5jpw/kU7/m9ZBARXAYtDEdqeBlV9yVX2HTPtbRFeZjADDxnWUknQ1NOQWXp1XcXAlIcaWHbG+\nuEwauacfI7MwY3kWff8Yu2abCpDmY5CaLfAxNvKOR/L9Z/HhawL8NvjsYySH+wPtCS/QdLlSpcQg\ncB+9pfcxFjos0NDuEVar/sulMhqv9jD68RyOfthG4+gDZn7983bHExG3M1PgFQwPki6RD+UKfnem\n0yhP9JKY6PjvvgAYaHYj3t+H3NMnkBICdo8uNFBrI2aCU3LFCtDrpge1pAoD5ObnUdl5S5KLC1Mg\nwX7LMk4yh5PRCU2Xa8D0EbX2eyeKrPrErIPnhwXMDTg/Q+ReDb0idTYUxpmZGAO8qgHDQ4IBxdPs\n7CU5DiOZJPIiqayHon0xiZmCBQDOKva2weiqRuTjVSUZb44utPYqG8fxXD/kZauytF8raDCWzmUx\n+bNl1A0jA1Pj/SGVq7fxv49Ag25r5gbCmOTc8BV0xd2qlJHk/IJGLwmWWY5uXfWz1SMmIbW7RtRn\n0o2Czv4RDlQljNhR1I0gekDw96YJuByN1mSfp44LEsjRntckF8NQmkfxpoV0Iga52QTHMMileQsY\njj5aAMCH8/42xb6nhcIY6nLLlyYuzL28HJj7qYc6EQXN7V0M/LFNqbo0i9n//X/UEuksy9reeciV\nutB83dLapqk7ILO8oP2MtGyutlt8GZPDS7P/jb4Cl83gwX/+WyRGh3H2xR9gTbjeRjcKAcizdyzI\nHIfjUgO8TydSVPtKxXFR5dV51QWYVk/8KQq9vXFzxI3v4VRtNINZ2unIaO/VJ3AOHU1OcpddSFEl\ntnVdpDJldQv4zh+EzP63UYy+Afoo5eB4j2nkoSoRhHs+xvpgfQFO39u6f9SuWT7GQoxz+OmDLMVe\nCpMgV3BcknAkkd9tlSRk4EwpSi0MAU2//n4N5c3XEMaGMPibv2zrVO9zoBYgq+3xpqh8DH+dRWyA\nEafJ/3nVP2WQG+hF5lc/BQCMf/IYfp1O1fUtXGzuQi6WUL8qYp/PIr00i3JhCttVEgtqdNwKwGUz\nyD1bQf7zZ0gOD6AqNZHe33PBTCAJqmGWxHNsKoHfvbnU7u2k1402Su+mm0NmYRalzV2UNl6jtLGH\nzKKOfwcAp883cIgUYgnBEZi+c6HvAHXSwbeWEKBVpH5GSc2QA8BYT9I1Q955BsyuLMwULAoePVvG\nq7YzMNMvwtp+6W1Qzffyy7Sp7eaAneOYzjgGB7dRW306BRX0zpKT+41mkm1uYKbtVDgp6qAKvNOK\nKL3j4Zfwam7v3vJsOXn+ztrD/VkWjC2XVvaP4O8Y/PsaUdQH07zWKqiC6Dlfq3sV6ei6IOj2HpdJ\nY/qzj3D67xtQfrqIm4trxHMCBp8t2yIvr4AAACAASURBVBIY779bR71tEN8vzWL0809tWB5VqYli\nrQHViYxG3L+raU8zwPmX3+DqXxkcS4TtY+TTpYgSFzROLu15UcA3apgT0SFDQDihTa6Ttd3WqxjH\nh5j47DEIx7cq5ndu4iNcr277XLeMD//4rxp+wM3ZhaaH1Nl/e8tmELH6CoTHWmq2tN9wT1p0drad\nHGE1iWvqWACQXZ6npE/tfExH5GOYyCWxelwCAAw6Jm7IOXtzWdWSoZ+AsTBJuO8fSWtj1cV4La8C\nh+aM8ymE6ZKp3sj4/f6VVtX1P/Od2nYFUiJpazmm8xHp/QLnwMoOWDy9PBdqf6gAuo31LbQaUnt0\nRyTXN1AXJsaG0fNkgcom+9N+e38b6zuPj+ZdO0WsSa4wnVYqHoEqM/00XZjObeneBQ0F56eX2PnD\nqoaa/+aPa+ibmUB+oC+0r5hZmEJ5+40Gnlg7PMblNy/ac+/eOATBuxSNEhz0VRW5VMarr15odN3X\nX73AcuGBxbbYxZTshT7yYxTr/pkTgfq3O2BiMQJe22yCn0iisr4Doc28oV/bCCY9h+TwAAAGfKOG\ntCtmgmKq4icXZyDEyXu463f3eLays6/9u7z9Bo1qDUwiQd192LlE3/Hdpb5HP0WqoFUpQ5Bqtg9h\nnIcEiEF0pm7TDYU3JYyXuFe0rY7KmCEbXg1lUL0dVePh8OY4jkKc3rsH6a5VEVvY2v+Ag+samsmU\nppTJrKSuDEhFK2X6//5Og7mlbCKXDDFzaRcvxGI3BdEphzS9BJ/J9LqWW9uvKsb3cmL/UNtQ6Ry3\nMDNeeqJJ7aDpTcbxeMirsuxP6UnjKHrtA7fv6uyIi7ZZtWDCILO8AKEwgdLmLqqv37avYd7n6miB\nKpX1HbSezgO8OlKgr3/6rIqRRFTtz/TflWFZXL18hYsHDwGOR2V9B4ejI5G04gc5g8bgxul91P3D\nQ8FPFmchLs3f8ghG0CqlEczTuYtJW9/2XvK6rlypofbuRPvv2rsTyJUa1G4AOLRsqueHvrVWp5lT\nqV1juQGcvHyFNB/Ho2fLsHNvR1HZdHKE9edWOxbIrHkKMxab5G6bnWfYaXWjXKpgOgn8Zm4Ar86r\nDu3QenJ09bikJUe3zyptJgnzvXVatpjWnmzVaZN9Sby5qGt/4xyshVtz471UKtTBFOdxH/N6dNaJ\nafh7reU4CSkhapXW8GfZnpCiBSwWQ/sDDGIpATGYKcaYmI7QH+/vC+93MAbab9AxK/glwNTvv3dO\nvqE1wSWXCEaYa4HMslefjGQNBSX/7+dUpOp9u4/6xq7LO5I9c3RSxklFQpaPgedYnFVvgKMiptgE\npvMiVXeMVX8DCsrb+57PS/sOfl2KwcTdd6zLTS0ZAAAnZQkzchP2neU0/u1nu8xdtVyjgTPDTzmW\nRZ/I41gCWIboKRW/xR1MmnHFTLAWJcp/+h69MilKpNtFCWe97h/PfihJkMYe4HJrH0NpHtOfLaM1\nmtcwucpSsyujLW7PY/wWTj6sm9wBhoBqWDbRU5EQKxRQLkyZHO2JHoEg2MIdvCGKlv0gnNRGIKEw\nBtXfUe2kuhAsi96tuVinjcc3qtj5w0t8/bs1KACGVhaxi1n0CZz2e+n9PYLZ0C8iuzyP3dSgx7OZ\n3zXKmUuzeH2P7rTEG1vy3ZV9tCBn9n25CWZ8BIl0AvTVY/pn6nTv0QI2ep83J0fRbZ4vzLm0JxSD\nJbncrwtAc9gAxaZH1NEC1YgPWUYKunX2/a5rA1AdG4LERU3LSSv+wY15/zCob+yid6YA8Lfdwmyf\nB7QGIuGAPP11GJcSScC/TSrl6blH4FIC6rIMqUkcHGvLJllDJTBXtwaa+XIH70oN9D+eBjtTwKtk\nCmNSy2R/o0y8mpNC7k5sJ7aZTjea9+TEkzlMzs3a7mekhwX0Fmqq+2o/M+s0QDH5L04Sfs31exmp\nUGmkU11l/fvtKlDjgYOjCwC01WIAaKF+TEKV5HA/AMZVfwQBLA7S4WLEEFCaLQ31vhPxov0GaL+x\nP16L+v3Heszjfc3tHRwbupSckw9+9jeY7ytINRTXtrTEqfUdVYo5AHj4dAnvv3+Jq4aMkU8eo5lM\nYfesiobcouqmUZ/fCJqeXZrFzdW1NjJAOj3CBfK3wfTDptImZrfU0izYlP4+VrBHwLwmNDpZ66pl\nAD6XQfn8GsLcJFCuIpFLI/HwIXbYBCpt/JbRTELDrgDMesG7Y1yB1CTPm+A5lDf30PdkHtlsnHTV\n8Y8RrGOHnB2p2UL54UMyNjPxENcAYnNjmObZ9ngCDzZF1kPtita7MxFxF6uzvTHhg6XvEaigblja\nmZzjQww+nTe1Gok8h8dDadeMfBhu4GgXPbhBNT6L3hJpbfsJU+WlcTjCU3sEE0tb6vYODle3sP/P\nf0Jvbw5X2T6cPN/A+MMxAARcRAO4aktxbQvCSsbWOaJWV62KZzSTCDBzqQrtfgj2PcIj7Dtz3jop\n++6CnCk4rUh4dXiNWI3VqsfG91JaLQz+1S/aIwPkWVUgl+48kz3RRA/YqJ81Y2LRyVE045RYJcy5\nZAxORjcpkhTUZRmsJEPkY9poQY9h5u9+sAGYg63s/lvUvlrDSVkiIwOj+bZu6j6P++118tBIENuk\naC3CDMsiPVdAz8oyALYrziGXESFOjKL+43sAgDgxgn2Jwe6ZhPTwONL7+xhI8cguzZqSAX4dR06i\ndrawDKC0FJyu72J8ptDxO7iLe1LIjR86VIs8pb6278ltDHu0Ehvp3ABgrj9lSpr439f4PkoEs+1e\n55bc6y6xOwSpBpYBXp23NFvgXy0GgBY+/MO/4MM//R4AMPhXv0Df//A0MEaGen0VsDiK2X0u1S4c\nvNhCeuERmsUy2ASvdaT5Vd+dKthetN/hhWn79BltvI9v1LRkAOCngxmt40JuOAMOAnR0yxO9gucY\nkpFiLrU0i4d/+2uUG02gHQTHGGidO0Bw8Eh1velBBcN1KXY+aqPf28jsZrTTTmCPQLA1IXpvU/PT\nTt+do/7pChrxBCZ6BfSm41j7cAO1dKBikLiLO8r+vsTg/fA4Kus7GMsJEMeGwCYS4A2FPnrR71OX\nZWx/IAUYY9xiBhkkPr1aGFK7M4eODkyje50C6Hrpff17tJz/GHfOMkBaHO1AbQT8Qf3wfSKP8Itk\nVb5ziGmZd7YDA+VuUOdEovBINYk8tzr3os7jpJZm0ZIYTHc4w+tv+OmoPaIzzGRd5FJZU/iKAvDn\n5xAyWVQZDhM9AvpEHjP9Ig6PiENjBLma6BVMM7tGqhknxeM/c2mUTo2xt+MT3DF3B+fqbheCvboo\nNVsoFwqo8QLSMO8la8XbWFVxGqFxk3BOYfAqAZcRUZuaxJs/El7cyZ85tR47ye0g9LcqZcgNN5ou\ndzE7mQpqU5NY+yABuNDOdmxuFoPjo6Y2YVW65ZTTXVd3YDLLi1guTGBGbhpABaMAa40GDKz79JlA\nUF0klyravHB58zUuv36OltxC7unH6EbXklyqQrosQnz0EABQPrsm1TNeQLkwheboCGYsIGJhky3G\nzpb+VByNZgs8y2LcYR+F+zZm3Xe/kkL0YjxnYpzDXy8MYKJHwMRoD87Oyp5/W5fdRpb8u6C815y2\nCyJYt1U4XWUH8Xv/3Toq6zvoT3FgJqdQLkwB8K8WA0D9+IwkAxQFYBhc/OF7iI8mQNc956yL5FI5\n8N5T17+1tw9AX39rQK/aPhKMkEAruzyP/pVFOI9ROtF+d0vnGenwguhkZz0Zjm6ZRcmlgiyXKqhv\n7CAvcvhQllB5uYv5qRG0sinNH53IJQMU/5yErLeqg+RShWoULFiHUpTAws73thY5jGCPwYQUoU7K\nElgGuG7cYCCeQI0nMUBvXgBwY/qLJMe1Ywf9/cx6wQOcsg1+eQ5g5KZkSgaqyTo/PC2jf8hl0khD\nwUzLrP9Iwst8xpn/n7o3+ZEjPe8GfxEZGVvuS1XWxmJWsXayulpUi1K31CNL9ifD39iDmYOBGcAn\n33zwSYAg3z7D/4DPAwjwYWYuM5gBxmN9Nj7bsqxWt7pbTTXJYhVrYbFYXayFtWblHhkZMYc3IjIi\nMtZcyPYDCOpiZka86/M+77P8flPj2DnvlF8cHl2AfrjhqX/etLxxh0Cww1z1TLENc1BYD34Vux8/\nRgkxg9KwmOYxoaFQ9n4B6Gya2vozND7fxgnsgGsUIosLKCEGHkCFFQJ40vq/nLgpzf45yoO1jaFg\nUI8U4iwmvvsNLBZHAdCdMWvdt9RxJ27lMSZ1sijMSKBOimdpJI620vHcOjlkdOnPEOw2fLrr2cIY\n5mpocK7eL3TukTs9vVfmWMPT2S3WflnLacK0qdfSGLcInTtbyZNkAZf3yXsqyZSReuze3uFwztvf\nN3fxyqCuDO+Qsnqmn5jmyymdsViuQHf86OtTH/9MVkSz0uy7f3q7ws0rBSaRsNQiBs+k8NM93nvQ\n7lThVxYgcTwYi5E/TPpMIh1dRDJZzh4+hVCcghdgE0V36oUBEoVILM97Ropu6k3UtAyS8H2gQHMa\n13fb6virs4KWPtrPuGhzyQmY+2AVqYebAIDk6iLExUk4A8+GnRsnhoupYO1C/xG2ILoxuJPDeZ9R\nlNVBagfDTHARI13fWa+561fdQB5M1lqYTIvwFyH7PBeLk6BPDg3WqIu9PbQ1gDLvaLG9KRToKIOb\n9S2c/+JTiNPjFmBdf4yMIOIfbEjcX8TVZc0y/vbnE4fDpnHRwi8eYmFkFLO3RuCnk92iq/5ZTOH2\nC5MQEVsoovxkS8tqWHYdQz2KrDQ1YLvHm4iO5KBwQUvOrGvOXXeQy+lFTUaUiaCAJuh//zUYjjdh\nyNBgaLrvbJrwQang+yYYsHAY8X63E9hj0Gw/iRNQKRYBra3ynTsoRTiwIA47tsk6gnUWjg5A61mQ\nWAFyy13PdhM9ii8uTpIyQMBYe364U3a8sjkeIHcZsQs/5M2JO5MJoeZWEbT89y1kCPgf5kHS3Xq5\nVEhtBacVCTysdesj9+8OgOuYAtts4HJjx3iO02UzGFoxEPRy0vsFsXeESlX1b5vZwMmMpDH2F/8L\nhIWixkOqH8JaKuH9u5AtG5My0h/1UgHAS/FQRjqau0OGKCdFlhG8Nt4q9nX56PgGT0/pEIjJVjEu\nBJQ7OFe39Lb23Rwh9f2vLNkz8xp3KhBuLYW9DA6qnMBNVzRkGc8v6wBNjIaryzoasgwdHySI13tw\nqf1W8BzdGQD06hUmRiYtyQA6pRr2dMbj366DPjk00eV0nJMiyyApcDiruDmAehH/S0X/F+tBOG30\nM2gKB9d1PKkBeHHp8Kxeo+6kv9a0ULgyqhiGOwBc1zHrkkbKJGKILxZx9QlJaRWmxrSsIvd27F5U\ncXxURqXcCD1W9kvqyP0VAygJcNYP4aL3trmcuI1icRodB5ZXxkjwuXHTfYPFaXEzfoPqxjBODi/9\n2Q2GSS/NWWr3/XB5zGtzULR0vUvws8LJwTYxkgUboS177E6Kw+RYBiIbcY0W68KP5TH6hx/i8tdf\n4GZ9C4nlOTCJGKTrMvJ/8D0wMbHnLK8b7RKUvLcAgjPjt+Yo8OkkmJa/E0O3dXU5uK5jrNAOMJbd\nmCX+ayDsfiFo79WdfUREAfHFIhKri57fb746IWwnFAV+LIfX//AvoDkOc7Mz2M1NGu8Naq846Q7z\n5ZSLRnB1XEIimwYLK4ZMv8G0wWYn2c9WQD47g/rwS7A0A0kD7bUCC/cv/mCPwbP9dMpKAOAogocS\n399DfH8f1zEWhbUlTJjwVHRWnU5kfcuXqcH1nmTCBHLP3CFZsA1ZNvDKXl7WcfnlBsoXRxhP8Eit\nLRmZ54DzORjPJDGvdALdUxM5jGAFfmw/3tK9P3UmkxdXNby4bODFZQMzWR7TKcF3/7+lkoFBpDcG\nOyjsExO7t4A2YKlbd9+QXoZseCPXeVHSNvRxFZc1CU1Zwc551Xiu++XE2+AYRopwuSEFStUKY+C4\nrYfgKLNeDpmYRTnNzc5A2OtEaHtJi9NxC3Twy74ujw58qm/iQi1Xa7j67BEAgOZYlB5toVi8hYmZ\nbA/R437bpKCi4RJYnUa9XSZ5JoLROIvXmlE0agPXG6RTIphopTTN3ig9naSrPtKUzqhjc8gZohdK\njzbfUjpa8EtFEF01OAwNChInYKvWHxVWt5D+Xnz0uYlr+vs4nbhtiSwsjZC0UX5lAaemmtWtGjAm\nuRnuFFL3V6HISqDImj5Wce1sCN+/bh2eAHyM4eB63w09W2+f7qDT60udLrL9OJrc2tkLRpG38RtU\n1/RvFzmCYc54ZUOo2L2o4OkpGce7hRjmcsQhJZcrtvPB2U56m9gA3WJzsLUoJFfmcfqL3wEge2y3\nTmMSFIJhb9AY/e9/iPjdebz++S/BJDVnnQrNGdDrfKloG7pHRU1SBuaMZhIxJFeXAJNeqQQORFkl\nyOU17H4xP1NtKyhv7CE2f8djLClEknGAosDERVT3X4EtjAKgIOy9wIfLM6ays/7OVv1yyraaKP3i\nN67tebO2g5t0n62AiqtPfwf86jdATAQ/fwep25M222cQ2U/e946gwZWO7iB/v5MXMUa38PrzQ7Ax\nvYTWG09lEO11F2tWQKkhEdyWahVHD5+CSvDIiayReS5xgnG+O+mWruzMnD/bj5eYg4oUTeNmfZu8\nk+sAeNZaMv5+8wwL+RjuFuJYGf9asQy4iXmR9lPbbxezgaJCkSij9qTgWW/eSecGiBfXHG12MnL9\noyPd9Uz20oiTcgOfHJQgRmkoKjCXE+G8SOzUEmZvv7VWsP/ygF5lMI6ffttvP7B2c5N4MD8Njolo\nl89gz7MbPv54Bd5iT5uzgnMNVpzWZvPoCDePNgFVhTA1ZpQrDCd67CUKdj5+hOefkHr/O++vYv6D\nNQDuaM66eBmj372dthi6foe4t2E7mCj3YGvTu/WJOZ2RZyjsXZFDoRBnMdYFYjpo6R6jcNEQZyo4\nKzjWcESQ6v5fCiByuYqb9W0L1/TrT77E4ftx1KhoFxOKeG8JvMaFHMxop5F58C4Sy/MAhocu3ZHu\niKE3XaPTb3oVFa+rTYtTD8YaNp/BpOzDjRbSvUSkO926Fwl3IVIMh3+KZyCy0a72Dlp4ppsViYyj\ngspVCY92L/CiSUEFcN1oGW2v7uw5nA/OMpHgkRUY8IxuiwyrT+562M3B9u2lefBRAmLcvceCrFUa\nYnEauQ+/5ZpREp4xYMsoxSk92kJ6asKnDWGEQv7+ChZGRnFwXUeFFd6yk8ZLSDZHQ5YR98jc1AMm\nVITgOHSEAs8wYAZwQTdfTuusgLn3V8HuvQAwWDyFQdkA9rP1Zn0biiSh/GQH+eU7uNnaA3d5juL/\n+AMLTsTgyiIH4Rhxpuvs0Od2S+/j591ep+eaAbPbKhDnIjitSChQAM/QqEpt1FuEmlGV21i/KlvO\n927d0m1fhz+DOnc/FipAwcAVAgDxzjS4d1cBoAtwfee8httZFjHOeRwiP/3pT/9LyNa4Cs/zqNWC\nXCJIh1ptBVGNP2f3ooqHRzd4eV0HRZGL8GSSRzEjoJAIgijuJRRoLgpFaiEdUTFWyGIkFgVXuoYe\nJRZuTVjeIZerOP3//gU3v9tA4/AY7Xod8aU50BwLuVzFxS8/M77bPD2HOHMLitRCNJ1CfGkW/K1x\ncKM5AyjO3JZohEY0QmojHx51UvluGi385qsSaIqCrKigKBUCEzHq4rNRGCmi9vHKilGPf++8cxAH\ndSYpoF5v4rJGwD7m8+IA5shLgrWf5qKgKDIfAIx5bbVVvLzubIpaS8ZhXcWrumIZoyDtyIpRTCZ5\nTKcFZAQGlzXifAk/BiRtrry5C5plkVieRfJdN+CfQQgFrpCHOHML8aU7iKaTuPz1QzDJGKTzK8jl\nKnI/eB+xWZKyG4txAfeyk9j3t/eYVK5KePT3vzL+vjp8jbHlImi57bjPrGnSnTmx6goKWZHFdErA\nnayIQoL3bYf7s7So7y8/Q2VrDxQFcIU8AITqp/6OzjzMQi0U0GqrIX7f/TyyNyjUJAUJjkExwyOX\nFnFea6H1mjg/sXAHhbnb2h4i0t8c28V5jBSphcqWFbk6vnTHI9XdvNfR9cz4xAgoCgPRPdEIBYoC\npI0t1D95iNzpMTIio81tb7pMkSRUt1+gcXjcec/YCFpjBby8qqNJEUOHoigthS8CmWFw2kSI/lCg\nOdbhbHHuX6WtQpLkAehppznO9bAHrO1zm8tWW8XBdR1XdaJjC3EW87k4ohHadAaTiPDO9iFO01nI\nDIOsyEAuV6FITTSoCFptFfGJEYgzt3A5NoHHVKzrfAzTLn0s9H4DKl5eW4HGihnBsteIKPho/wof\nb77Cr7ZfY+NKQlZk+gRNJqLvZbczMCuymEzyUKHi5VWDtHd7B+WPPsPOF8+QFaIox1OoSm28MxZH\ntNHE5a+/cD0fzOOg2xyvbppgGXog/XEWNz3cycRALovTdBbMnSJqOUJfPJeLgeLYkHvM+t6a1Aad\nzyE1N4340h2TvejXps7v9T3ipBPTK3OIcKzvmmvTQLnSDLDXKGRSIkYz8b5saLf1ZH5WsP3i9Mwz\nUrM/NY3daNJ1P+rfbxyfQVVUJO/OQ2nJcLPdw4s+PyoKCdY4/8eK4xBn7PPt/Huib8j+C2JnmG0x\n4dY4apJirI+g57IiSahsPYfUVtBWVTARGmpbQfPoNVRFgTg1hvQ37iL3333HcD7Z7xyXtRYmk7yD\nrupFOuPAiWyINWG17/3XnH38+p3/Tjvsz7XfHaI0jYzIoKxEMBbnkK7doBBjIc/P4qHSibzr57vb\nuHbPcVCbWbXc8SIci0xUxfk/f2w4bpWWjNTcNCIci6t6C6cVCaNxFkmO3BFnczxYxrldbyFDwBmY\nbTi1u513lp9s4sxExZW/v9JVt24WuVrrgDdRFBpfHUO6uACgQq7WYalDp4Dy5g7hB6cANp2wAM70\nUnenApDbJMoZ3d1D4/NtHGiIseK9Rcfx0v/b/u+DTm+iqLeZdeAlzumq5shvhAIqUhujMTImvaTR\n6t+dy8UxkdAASkKOgbnesSm10Hyyg9j8rCegWP/SiYbI5YqlVAGAFnXsdx6dWS38uZ6HIfpcmTyq\nzTrIOLhTMblTgpnB3yaxL9E9eNp1EDWesGdYOLF7HRtnnWquzes1ZTSouGcCiOBX5jU0b9ozxT3o\nMweneygUWdUAHWMjdKB6RC9hEiLEO9OQzi5Q3X0JYWoMiYkRXH36W9AnFUyuLKA1d8eUWTRMXUqe\nfTfO4eqy1vezu+djE2fpnIURJtwa9u97Tm1hJMugwQpoOyS42Guld851KrltnFUlVIpFVIqz2p7g\nsVUjbDX6d3spw3Pab/N5AWY6XKdo7GVNwukX67j+fB08AHplAY8SHCYS/rWdwcW7ZENPIxWkOp5/\n8gSzGR55MYrzjW2kRwvI5FOE9aktBzofeivh8UpZdo+2B8k2EtkIpiZyODy6gCDVDQTy3veYd0TV\nv03O52F3hDOOOXiV4/SCBzKYCK5/WUV4DKHE6hKoqXHsHpYMXC2v/ejFctSvM8Brfv3PgV5xNnRb\nrPv9+XwwG1BnUjJnVo4lWLRrdYJHlU4iff/uUDPrOuJeyw6E3XPB1pzz3PRbDkF16RM75fVsTsCI\nWAcmVjD3/gqm0zyUBqBungEIljmsqp1MbkDFi6u6BQzauSzaWd+OFgpakCWqOX6IQ0Hfk5Mp3vJs\nt+wA4C04BJw65M0r2b/I5YpB9wcApY8fY7V4y/PyxcQECJMF1F+dgo4yqL86wen/+89Q6k1wU2Ng\nM0nj0p9YnkV54zkAChRN4/U//QrJtWXQHOeZImtfbDNZEUKUxicHJQDAd6bTGKFknGjGjY4Yeyef\nR/+R5H5ToL8udVR2cVIUnc3RkGULwFK/79JTnKz8uEElOKDYoMWcHqW2FVe2D+c14q50nfa3H9dz\nPJPEnfdXLQebXsoRjJHEKwWu87kOVDMSI7XX/ge3FYjSPFftqxp26509GNQQHgR3r13cjHJzbZ53\nyuiwqBY1tpjYKIT7CUynBSRu9R5978ggdQ/lmZ4YTjpgWexoDulvr0G4PYnzf/4YY3EeagF4/XIf\nseUZG53eMHUphaTAocl6RZx6m3+preDgug74GvTe7XMDomxvbSNloukdf++eMWa6/jp7+BRAp05a\np5IDQPbq+jaEiXHsnANZgUGtJRsplGaA2uDtct5v35/J2pzD6CrdU6tV1De2jaqHxsY2xHt3uvrt\nPxd+Z3fwkg0mQuNWSgDPRMDnRUyMJzWwrSDnQ1Axt1f0YJAaDIihMwJ5b3usX8wSt9+7XXj81lzv\neCD9SJD1FHZ8KdCxOOqeesm9DYPCwul3fvsFCHR6/93bTmPSvedrkoLd3CSEHxGWtF1WwORMFlPF\n2zZA287+GRbeh9s4iD3PUy9lZ8Nhieouh0JXIHApoViYzvzsrY2jEr54cYlaS4bUVnBYamI0zmI0\nxoUCK4/v7+H89BA0BbTOr8BNjpl0NdmTOuB6p73upaNfixudzis5LFCahty2RBFOKxLm5Ta8lhuT\niGP0P/8erh8+xdWnXyJ9/y6quweAqiKaz1pQZgEV5Y09j6e5SbdndS4nYmWUtCwrsoQL2hYFeVVq\nYno0Y/H66OMVbBzfFnrwm+F4dxaN0WCgCrH3cQwPKDZo8fbCqqo7TWEYpRuE6xmgMf/BGsaXZwBY\nQQX7ZSTRP9dB9qoAUjwT4OC2cvmOz9zG6W86l48XDRpSWwmFIWFva+/cvcEkWNTG36ESiF7Up/5O\n5xUe06gfg8hg8RaG/w4LWJasoLpzAOH2LehjNq4BEI1OpRDPBMsmGRw7g/s7gu5n+1glV5dCsOaE\nE73OeiTGIsUzwMkhptm7pnZ1mCJwXe/whGtUcjoQoV0qUtv234PD1TDj+DiNaYqPYirJo62ouKy1\nkI+xWByJWXBK/OfCXS/7t69z4JC6hQAAIABJREFU9plrpEdiLG596x7EhUnLHg8CDul/nlrby6/M\nYyc2anxq1td+l6sge7UXBPJ+pJ965rfJN/7mxIlxRXd+vG0wShUNWQ59jtufIWl0rKxvSa5qoNWb\nL5dB3uG15+06mEnEPQEa32x2bxibv9fzjvyOsABYs8X6c5o56WOCj9L9TNpy8db77dTnmtTGlhYU\n2rusQ4zSkBUVrysScmLUE6zcvGcEqY74/j4BX0wmEE2nXEDJ7e39GjkE3JSAE5hUTZJtm6e3hUvH\n4ojdWzCYBWL3FjT+ZC+hkFhdRnQkD1VuIyLyqD7/qvOxBWVW7XjTFQWjf/ihlj0Q5ICwTxaFrMgb\nfxHE2EW0/4UAqSTfWUKFFTCTETGTEaFUK+AZ1fiteRzdFqX3wTssI3Q43rvwMjiF2J93mOoBUGyQ\n0klddxqDZunGsW8Sxztm+PAMcbZ0Id8H5nqmEc840eK8HcPJzuV7vroA+g8+BEvTZK40wE8np1wQ\ncafQDC9kzAUbgCKZU7/D0NuhEmbPdjuYaqbLV28Slm/+6/cOJmZOD6Ywcv8u4plUgHcEB83rR8Jy\nyNtTd+dt0d7BG/TmDI7ufkucgLGCgLTcAjk7WY1KbhOFOEtKBjRQNZ6JYDTGIc2TWkp/A95Z/C4y\nrmOaSOCbP7yPic+foqWoKNxfRt7EDR9kLtzOHIwkA7TcdvYt5iGvuoFTBrUDvM/TLuCzJ88g3E/0\n6Ega5l51vrj4X1q92zSoS6/+nOOm0tdz3qw4Ma78HhKrJGPjzV9OrW3TzzZZUXDdaGE0xoUcVxX7\nEoXjsSlU17dRiLOY+2AVEsdDkmRbf8j7Hh3fYO+yjtE4i+/eTmMuF+taHwmeRdMUAHSPvnf/Nijt\n4qCDTs6OsSBUmrr0Glzr/E5qtxEfm0KlONtjL6w6IHz2CMkYDnvPqbcUzGQFfKXZkt4lBzYKa42J\nASqgthUtQN37HnoLGQLuSsDuYe/ePL2lU4tsBOPv3cOhVk87rtWVBWkrPzaCzIM1lB4/Q3z5Dto3\nFY3uyXzRH2aNE3B9ewb0H8TwqtTERTKOD/ICRJZG+cmW4wbqZVESGV7mwODowuzSS9bB16PcQa93\nfPMe8sE5Z2otGb/av7akN9mR7/24nvsRP4PLjBwcv7dAPKoRWqPoUVC5unakLFKqFQs1afnJNooL\nRbxo0sZ75nIibrPEQItn/BWxHcvibiGO0RgBIeofiIuCwHTq0gchvRyIXvV3va3vN+EQGsw7iFG0\naMGqYRIxlwuD94XLjFtxVpVw+ouH4KMJTE3k3pIjVRfrWA3LoPeOvBKd/+KqhoPrBmotWcOFIQb9\n3OoihOItjEGFxFnT+AcTkeztIlOT2ogsLmK2SID53iyeii7Ws8+tDrf8ZBNXnz0GAGQevGO6xPk9\nU/VkBWEjNKbTggV3wqBe5nik1hZ9eLm992pvEXuv8zDIXHu1qZ9Lr9W26cYDsTOwvC2d4CxyuYKL\nR5u4froDhqJQPzzB1WePTRkbwcpjhpFVaj7bxCiDBMvgvakksqIb8F13O4xnaFg9JQAHoxkcvOjG\nBapJbexd1IxypdcVCU9Pq5hICF3rg6IGh8HiLYMM/jkHBILaD70G18y/YyM04vv7aE+Mox6aWcMZ\nhymsBOmzyEawmI/hi3IDs1kBFamNFBfFu8sJTKcEHJUbPng0mr5lU1AN29rMnuPO1uEnb+lW5H0h\nq0ltHB1f4Oq8Bppm0bqpYO9Q7gN8J+zGsW5+80LXwQS7N5BbjVM3DWAYMRaYGMe0ltUwkRAgl2ud\nDUQRypHoSA782IjvonQ7NPuth3rzYq3J1vk/h4fUb5X+043d1uVwU4WDKC0ulXTsGwNYLrXOAI0R\nCzXZcKO81jFkm3XI5arpPabPZ7JgmyTtuL7/Ek/+j59b6pPNFy2eiaAQZ41SnUKcxVxOxDTXycoh\nzjNivKqBnGcU5nIimrKC3Ysanp5W8Kn5IuOQHhxkHfTjbBtu2uYwI0Bvs/zIXU4npnHWIuOuTExA\nR6mRODIG5JPgjlcn0LxB1g73P//Dcq66RV6Jzt+7qOHRSRlTKQ6HJQIfn+ajpmi8lmZue2rw9ei3\nvtyxD9hmHYsiLJfeo3LdYuQ5OXWCzEX4Myf8PpHLFbz++b+hfngCAGidX2p2gB/YmdO6XtTauwlJ\nA0WeupXHmKRobbJSLy+m85j4g5yWfRkLeQ6qmtNlHmPFKQR1uvjrz37XeC+/d3ZSdPBA3kTZZz92\nCGEI+eq6gUZFQpKLIGFLWw5SHvOmskrbKilddnMGOLejI3VWQITq1JADYXR1eDo8czCyt7U5jPXT\nq2NdL91o95y1pb9/JMZifirlGOTxEm8cJq+z0a5fg7VzZSKFuNZvQNUybMn6m8sxAcHKO2VzB9d1\nPKkBeHHpa0uyOdHpYQC+JhgCVlFQefgl2v/+JZi6irsTWRy/ukArxqLWfg/ifXMdoV28FFjQjeO8\n+Xtd6INUaE4oy6A6HJTtWh2ZB2uILM77POlNpOJaZRgXD30T60BRZv7PXi5W4WUQ49hd32MoagpI\nLM9q7ANvBmzQaBXl3jc/gEang2a4TiWSFVN+8gwnjgecaYzZBORyBWcPN42LVnV9G4cT4zanWRxz\nH7yDlCXaG0ddy8qhIjQaB68QzWcAUCg92gzkPKtJCg6uGxAYGo9OyJq0XGRMkbY3g/PhlbE1mAj/\n4C+MVnyHtx81J6KXmeiZJfK9BUz84D3N498ZwyKr+jpe3UDzBi/+iPrDdbzYdTMs77PvJ13n60wB\nl7UWZEUFQwePqvmvx17P7c6eZaHiW1qZB6Dily+ujG95XRS6Aay6HRHBz5ze+iFX66i/OjX+rr86\nhVyt+zoESEBh08BvIDpxCpHFeVQSGZyUmyRyd1EzRU5lo33x/T1sr28DeREj9+8CUC3ZAt767+tS\nkjgYcbug6DL84E1/509NamOrBsTnZyFWKrh59hyZ4gQyD94JHD0O5+gOZ+OFOdvc22F9xmSKxe5F\n3fEZIhvBbE5EWZKNrOdOaZ+fDN5eD79+3M4B93EPNsb6vpUQH5uygD4HCa51O0uWA5bkBZOwbDNz\nOTHwurLbBQSfIKxQkDgBW7XOunO2JTsse2PfWkb63WXHp32NHAJkYd2sb+Dof/97sE0Zc9OTOPrX\nj5FbWcBEkkNjYxvyfNEjza3/i1R/qe3WzRH8We5Gl+um0lCAb9a3jfosmmNRevQMY8Uph9/QXZkK\n9nEcLojXcKKFEQp4cVlHRiGXu92LGmYyolEDBADtre0QhkVY6dDHDMJwNhS15uj56u/+byTXlpF5\nsDawdgc/DN08vu4AjWyzblzKgTeXZdKPgURT5H9WIRgiQnEaALozaCig8vwlqP1D0AwDYWoMgwQo\nC9Of4UV5v570onZ8h2Pt4v22y4DsZSbV9W3U789j57KDpbBzXsPYKAsCRkUuTm71gpHFeYxOjQOV\nJrZqZNyDzW34qIU5o8f8nOFesuyXjkWcTkwHep+OwfHiso6ZrICmrARAdw4mbue2PkZuBrl1z1Jo\nbOwgPV80skO8xQ3AykmCReK8LjJeZxUTExFfvkNolwHEl+9otan+fTAzsRTiLKTrGnbrdTw6qRIU\nbdbZDtJBXwFAbiu4eLQJutkErY2dnz7/+mZJ/ceUQTkcdOrb5I+amJoZHehlrSO9OC8GcbZ1nqGX\nL8mKiutG0wGPwMp0FR4XzWvPDxt81j1Dwnvc/cfYvG8rxVm0J8YxP5UKsU4G4yxx1wHh2GYmErwr\nHl7nbwrlhmT8VpDqODyqG7p50GeunWWv/OvH+NbbcwgEWaxkQ9+sb+Hkd09ROr1ES4whW7rGnWQU\n6UIcnGttD5FhXaQiFNCQ7QAhzv20bw7/SD35nfcCcN9UidUlREdyaNfqoDnW4zfWtDw71Y9becQw\n0rsHabSLbATTGR4XX26gvrGNBBdB9v5dYC5vRBC5VhP8548xooFvGCBMmgymj4M3nCmaJgaZSi6Z\ng71YD+qi1/0cfa8PTrovN/0efkwihpH7Kyh9/BhfleqoFmfQkmhMlOs2nBJvwzuajEOuNQCKQiTp\n5HjsdhKZDx69fsz5IhMGvXi4XPZv+6JtF6eLt/JgCWCdQCnDSH+GlVOZCcfQAKzginQs3sUfLXGc\nCYjKzuQRw/cLzrzETn0Ix2vtbkwPD/eFiP3ScfZwA4eIedIYmvePGGXwx8sjmE7pmRNBxqc3qa0/\nw6UjdWpnj7MuDsEgF06nsR6jZfAMYymXGITT2e+sYhIx5L73LUTTKQBA8t5CoMCAxAmoFIuAtjdr\nM0UclFUIDHm2E6uKPjaHRyTCxTMU9q4aEDgGKVVGnvO2+9xEkOpQqhWADXKx+Po5Pv3WzJtgYOlH\nzO2vswKmJnIWZpUge6KzNjqZYE6OGufMlCC2UrCzTW/H3gVp62zOetEHYIALJ1gGSY7BNyed8Aj0\nQEpvKf5uFNC9ZHKEWT81ScbTU4JVwUZoi4PUEeDU9I6w9kOdFTTAd/8MBP1zL3Ds4NKfDohYvmrF\nVPEqN4nv7xm2TK11H7i3FNqJ67eXnFj23GTIlp5zTVnNqB8jHdMNAxkqzs/KiI1koVbquLyo4O6f\nfAjUmgCoQErP+yLlf6CaB1cHK/r0qxvfS56TR9U5Uu9vCOgLwLwR3CJ4Buihw8bWIxo1dCPDe3mj\nwl063y6V4BwPlC+O8DrGIkITUBGluojj326hsb4NimPQPD5GauE2QaumgPLmDqrb+wAGkzEwSMNZ\nV9Q3mpIQpsY0Z8/gxTkqGFbsIFWDNFS6lWnh6MAx28MPhMx+qCRWlzE/NY6zg2uoUUE76OqeOCWW\nd6gAN5pH6tY4AAqqYqc5c3cS2ZlAui8yTujF7/iM49fv4j4s4RkaYyxwWZMgMSwKcRY8Y3Wm2J1I\nQcCr+i3RcCoziWdSmFeszlhAtfBHP6Gi2Ni9MBxDEwmuZ30SnNeayH883JjhX97sBtaiCDQ+3zbe\n0xmjWLd+cgTFC9/m+P4eXv/mEGwkYthN7k59/37oGDuAEmBt9R510yPCAFDlBEBWjGwOHVBtJsuD\n6D3VMjal5hoe/fIRAICen0UZQPKkMwZe+s/c1/j+HuL7+7iOsVDXlgPu46+b/vRbM0NmRxkaPlLQ\nz4kUjg5A6/oUK0DOKbLZnZkyFjhbL6j9qqIuK8Z/O4leugoAE0lecwgMQtxpBytXJZw9fGoEDdz1\nd3c/g60fFQelOrbPif00GmcxGnPpVw+2tfdl1o9pR8HT04qB29B/IM5LB5jHjzbusPN5AY+Oy0YZ\nyJElqKTistbE09NKF65EnmexKIKUSIGs2cbGNviZKQDEoQl0KCV7DRoDDix7qwuuI+CpAWVZxt/8\nzd/g+PgYrVYLf/7nf44PP/zQ9fuN6xLkcs1YXE6eu7N0zgK2Y/aWKIoCcXEW1a09xL+5CmZpDtkf\nfNs3VU//LLW2iItHzyArChK3xm0XqaBRXDK4WYHBr/avHQDTwhwavRsvwY1Tp41t/T2/Mg/ERrt+\nWbkq4fCo5BmR8ZY3Ubfnp7Bpg98bIB5MpdYwFn9daiOWSEBtNgFRRGJ5FuWN58Zzvn4GcGc+xTvT\nqG6/QFBnWHAZZn16L4aKsxfYfrk5PLoA/XDDxDFtnjt3EDK3vtKxBNp8C8HdLdZ31Pd154TaNT/e\nTiJ3QDKdp3jn3IpeHFmcxGDmp7OfVHVwJQ5vTlTU918hzajAVy/BTxYw+cEPTPu320gAVOxptZ2z\nOWcdNZiLcXeZidMZoI+/DkT1/KRs4R3OCl+Pi4nThVJkBwfYar90jNxfgWJjXdFBea26f9iXt26g\n0hOHvee4xxcXMGab/yBtNo+1hV8aZC1SU+MIz7FtBTF9cdkI1n3tt/5r33o2d/pAPp3PkfnbOa9D\njDL4k+URtBUVLy4beHHZsNgLIssA79wFz5EsHx0vY/TBki1LwquvMYzRMnGk2DICw9dGfx3Eb50P\nk4FlGPhI4T4nOnnLdN5vmRgKOmLPTKkUi5A4IUC0M5j9SvZ53XSpswYORDaCmSyPv988A0AuzQfX\nDa101asVwdae89k0hX2JwuFRCY3zGgpx1siCDdNPf8yjNl5cNgyH3uuKhPcmUzCXLesXdm5uFleb\nuwGcE2ZxvyN5M+2IeHZWwc+3OmMOYKAZbB2xjl+Ci6DcJPM2k+XB0BQW8jFbUCligN9un1e7HCkU\nRWE6LQB5UoqljxnPRDB38crIHpx7fxWYyQRy4rr1W2StLHsT01nXnnqO3D/+4z8inU7jr//6r3Fz\nc4M/+7M/83QIHP5f/4RyuWEyuq2eu3yMQeW63n0BNQyDTcSzKbD/859AGRnByEiaGCBsMKV3OjGN\nQ8QgZPJQD48wgo5nMyzfMs8wAfnTibh7VIMbAkCvddjWjS2XKxYF0tjYxuL3Ry2OmPbWNs4ebqBx\nXkP83kJP3J29RcbDpOX6K2wy7lZaOyEZ66Tuqiri2RQm/4ffAxOLAVBR3tgL3VcvcfZy0qhcXQMA\n4pkkwjEfkPnMPHgXiWU3nujeZfhRwTCGirv3W6lWIEj1EJzV9veqaJyc4eqzR0ZJjbmvvdWOdt7h\ndPlzE/cU1o5RoCORRyjgdZXUIYbn6/baX9b99E1QGGW9yhGGXZsY/p362mWSSYx89z0AgFC8Dd35\n8+ysgv+6dYa0IiEjRLFHAZeNlsHxW5bkIRgN1jZ3r33rGWBfd3beYZ5hAqXKOonTmrbzWpvFD8Ha\nfqFkaHqATt/uS0cCMIzDF1dV/DdT5sSbBYkzA5HGHcdIktqOv+xNj1r5pV/zDKS20ifqdgfEVF9f\nLy4bmE7zRopz7/XyzmezU/2sjpjtB64ost00vOY0c/O7nXWEbrf1TjX2HxmIMLz46fdeHA5v48yw\nZqZYwVfd2+MH3BjcQURhOiUYTt1g94b+1l5DbhMHISsgfm8Bp+vbSPEMRu7f7QocDSKDVYwyWBsj\npWczJoplM8r97nU9gHPCSZzuSB3mAQBdTDtZgcHBVcfBqZcjhZNgDhnz+EUo4JODa8MBQNpAdc25\n+Te6MyXNR3G3EDf0IpOIY+T+Xcu5AgDC3gss6I6CvRdQlmdC9ssuVqdLOu6+Pj1XxO///u/jhz/8\nIQASvY8EVLS60W333DVnZ1GKcA4RuY5hMAYV1zsvcfPkMdgI4TDvjl56cIKyAupzSxCmb7uCU+jp\nGF4S/rLQq0f1TdRhE2/UWIEoSuJw2AIbocnFeX0bwsR4KMOzNwkXmQ6myJwzJOypu/zYKPRLw+Br\n77oxG3Y+fmSpEZ7/YA3h6RDfBAd7vxI85c7pe27Oifr+Vyg92kSqKiFSLKJSnMXURA4jWPHhqe68\nj+CSbOPm0SaEyQK4yTFb+4Jm8LgboG7z05XC+vIlSgkO7cUZpO6vgqwFK23mcYU4AdoqEGcjxoUg\nuNHuvb/s+2nrvIp4XnTJwHpTLAfB22/7KtS2tUyjJrVxcNXA5MlXqG9sowRg9P1V7HMjiGiokXuX\ndQ0Txnr09Z4i6+7Qct8X1nVnp6QTWTpgqqyTdK9pb15r73PLfqEcNI6A0x4S2QienZXx8y0SanaO\n/vjpneFybBMj1s1G8GqbHxNSBLtlpqtcKJ5JdpWe9HpWz2REzajX01/DR8i9zmb72tD/1oG13CWI\nPvbWEeFqo/u9KDmnEX+9Mg3c0eEHr98H+8ygc9mp8Sd/z+c6+zBse3RwQMDMRU9hPi/AixteZBnc\nLcQD788wa89pHEid/SUA4gyJTY4jVYgjMZLx7F9YMeu4turULxPKfQDnRDCxMg8IL0gAz860Yy9F\nmsuF0YmDcwbeTnM4LreM55izACMUwZVYG0vgvSk7rkT3uULOBsri1OSZQbM92ctbO+Kp+QSBDH61\nWsVf/dVf4S/+4i88X9lotUxckkTMnrsbVsCcq3eaGAZyuYLGxo5LWjDgB9Kgix2cQl/YOi1UIc6i\njXeA1WU4LwI/jngVEmfniuz1AjfYOmzn38fB6JewZmdBjsRYpHgGo1MpxDNiqOi9E9+yHRjNfBgN\nLzLdPe7u0dtB1d45AcaROaxcXRvOAAB4/skTjC/PIJ7xAz17M+mLg6vzD6pUwylfuVrT2qatz5ND\njD5YItGiXLCovBlkVEfMjuazyDxYs/XVL62xVyPHlML66SHodgNXn2zg6pPfQZEVZB6822UUmEG3\nxCiDb99Kavy03o6WjgPRn87OLm5gaW+jtjzIO/3WLtusI7b/AnWtGoLd3cP8eyPYI1T1GO3CG9Cl\nN73glc7pvd47624uF7fwDgdNlXWXsCn1b9Lx6K/jdMeOLt3RHz998qY4tp1sBK96T/92GTqhq1yI\n7qn80N1pQdrzJiPkTtgMbLOuZYD6lVYQPUfOhk0ozSZabQVnD59CKE6hQ4nY//luRwS3t8Oe0QVY\n04jfTKaBk2Opm8LTbX6Hod97B/dzk3AUm6jr56Bgao97H+3rcSbLW8pqHh3f4OkpbTjlvz9DLtvO\n62K4wL5OQS8z1tlriUb1so15uta19nrLhOytX/p9j9wnkj05ZZ2YB4oJDi+atNF+nokYmU5rYwlM\nZ3gsjQRnlAvjkLE7Rd6fTqPcbBsYc22VrJ3plF5GQkFkaQhRCp8clMDQFN6fTiMrOmVNUAHsmzjm\ngCGtLav4Wg2np6f4yU9+gj/90z/Fj370I8/vbmpIlHPvr6FQLICiKHwTFLY0QIpv5mNYHk+i0iTe\nlATPdkUvGlEFVa3jivZROitCSBOFf1Nv4viojLj2neOmgrtxDnmetbxrMR/DLdO74lwUVLsJ9vIY\n7HQafJSBsvcSiftL4NNJqKqKckNybZeqqmiUbnD5aBO17X0clxqozBTRmJvHYj6GlYmUTyQmnOR/\n8C007y8CALhUMvSzvX6v5uNgvveuZdHl5iZx8cU6qqZ/y7+36vheVVWB/X1UHz1DVlXxeyvzSH1j\nBUmBM76vqio2jkrY0g//fAx3MqIxt7pksiL4tDMKtqqqXXM6HWacR5K9feYjTn0zzz/dboDnrVsr\nmeSRG3FH+/Z7Zq/tdFvTQdbXiEd7Afe9mBS4wN9zWouJqTyatnUyPpoEn9bmLMDcmfUI4jxSkyMo\n/KfvInlrItSYNq5LKO3tI6HrpL19JO4vdtriI4moilY6hvP1TXAcWRPt5y+Q+O4auLiIuOlQWp6g\nwDER8CyDxXwMCz7zr6oqzn/7xNizscUZxOOc5Tfm/dW1nwQVzMGBY98s4+fwrGFIg1FwHSE3eUbg\nQVGU4zvd1q6qqqhUq9iIcUiKLHIii6mMiOJCHskSMVLfmUhieiLtPq4h9UIjqqAS59CUySWCYxiw\nYhTHZamz3hsyilEahaQI6abc1e7uZ6oDH3u/vewmvepgJ90TVMfd1JsQzmtYnqCM6M/a7bQxb356\np989a+5Ds3QDIPgZ7NW2IO26qTdNOoF8L5MVu3Sqk7jNcT6fMIAlzeeA3zh6nR9h14U+lt8oiFi5\nlUbpy000v9hBlaK6bA37ewEYek6hgObWDq4uK7hpyuCmCojLCpZzMeveCrCPnfpQhoptl/VpWb+q\nguNSE+MpAYCCh69usDwaB88yrufgoCSfj1v0fmptCblv3uuy39i7C+5rcQj6vcEo2GmrRnp3Toji\nlsggl4/3Zxv7zGWp1sDVv2+DfUIyB69WF8H+0XeQCKBHzXtDVVWctbQ0A1XBzquGdU5vxwY2p377\nx3Ev28Yhn0+gOFbFL3YuMBplAIrMcTFKI8ZFLXvWTQcMpy853B5PBr5P2MWqAwGAxzvzOaxpZ8hX\nV3V8oX1+71YKd0bilruHWxvNurzckGzv6NaziqLg9KYGgMKD+VHcvU3O+DgXxelNzTLuZy1gLUvW\nh6qq+N3BBR6f1xETosiJUShRBlyctzzfVV+HuPv1ckaVSiXXzzwdAhcXF/jLv/xL/OQnP8F7773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UrVqeLil5+RX0RoXPzbp2DzWVAMg+bpBVJzt8GJPAAK0QgFqlaHIrVAc1HbcylEI7TWRueI\nBM2xYAUOFEX6BJDFUEjoRiCFrBjFZJJHMSOY/r0j0Qhl+f2dRBTswVed71EAPzUOqCpojiFRVEly\naC+RVlvBy+u65d/eHU+imBFc26AbUk9PK9h4XQGvtrU2AEmORjnKQ6EotOkIsHAHhbnblrmLxTi0\nmi2jH7WWjMt6C9f1NigKyIrObaW5KCgKaJycgUnGEOE5sPkMEsuzEItTLuPev8jlCo7/9RM05TZU\nAPLrC4gz075ewv/44rymzXsGAJqn5xBnbqFBRbDx6hLy85dgGBqlWgtpkUFsekJbj5151eeyeUq4\nwlNrSxBuTYBETLz3P3n/pzirSji4buD84ATR6SlMjySxUohjIR+HXK45ttF/zrz3oNN+KWYEm24i\nirvVVhCN+PendyGG3sUvP0Nlaw8UBXCFPAA4/HvOVRcokoTK1p7lyfGlO6C5KHYvqnh4dIOX1/Wu\nvRlpNXD4j782fuM3xm7rJvw+opAVGUhtBa+rEspNXW+wyKRiGM3ETXPnNBZ5OOm0ylUJZ//2KSI0\n0cXN0wtM3Z3FrXzSQxc694u/NY7aHtGJUlvBwXUDzJ0i5EgUl7UWxhIsXt10yhtUkDKBaMRuWHX2\noCK1UN3dx3mTPO+i1gJzp4hMyvmyZT8rrGdNcInFONRq4QHOgu0VN7HqHq++6O+JtmXIz18CAHJi\nFBGa1tax8/rS96Ugkd+eNoGcyODxSQVyJIo4S+Pq8DXSAoPsN1YM/URzLOJcBELpGjkxilsP7kG4\nNYlWW8XL67rx29brC2QFBtm1ZeO3fn3OT49BKE5BnZkGxsa1frIoP9nyXMNe+jSIeM2xsb4pYiM1\nX19ALE6ZxtWqhyRZwROZ1T4Bruuyj75TTbqJQfnJMzz7+UfY/HQD57UmKokEts6qUKOssX+sz+vW\n2dFIxNEWk8tVNH/zBdICg5wYBV8qQZyZBkChuvsSFMMYv/FaO51+b+L4Xz9BaXMXTITqmpcge0Bf\n2ydsHEJxErPfXMbk0m3sXtQsujefFkFRVOg5NusnjmNwc3CCzHfuI750B/GlOx7PcD9fzOeb3Qmt\nnzH6frD3vdVWjfOQl+po/IY4wGiKQrZWxsTKDGiO87GnO230s3G9xb2PXmffoIXmoojwHJRGE43j\nU0QiDEZvjWBm7hbGBRqR/QNIbQVtVUWEplzXJs1FwfNkjoGweoCMZaQtgxPdzwk3/Wd3pFjXR6/z\nZCoFY4DLGnEMuJ1lfnqw6/N3FiFMFlA+fo2L19dQLi5QPb1Cq91GfHwEQrRDCS9MjiE2P+OzZ8xC\nQYgyEKIRi/4260eg2x4UojRUFTgskaDs+9NpLI7ELfogytCIWfZer2K9J+u6qHp9g2xUBcWySHJR\ny3hHIu7OiLccvh2WB0+F1DaDKpD/N2cSJJZnEJkpuoJWeLXTL5vAO4Ji9+LRKK8tWzIczv/5I4Ci\nLNkO3V59skFZm3fdyjPsLLrnN0IBd7IE+OKbd+eRfvkSDE0D33gH9MS4UQ7g3g8RGaWJL46qEGPE\nk+YdRbKCZV0+3UL58RZUW43nYIUAFq2flnFebSHBRTCXE12zHsI+++1FfaztGNQ+0iMd7d09gKLA\npZNkPXatwf5q66W2gtOKBJoCzmotKBUJI6JCMlxSIvpz1bhzWbPNOhZFgi0AOJdS2KOOE4nh0Ed1\n0uMI2JrOqQ1Q1rS5x88g1xtGza1dF7iVWA0l5ZyCUaerp6r2IjWJXIr1GlNz28ztk8uVAGm2OkiS\nU1S4t6wbJiZaxtRewsAzjGNafE2yZ0mZnpmIgV9ZwKmpLGKrBoxJTjgJ3QBzbLPuAcI6+LPUDQcm\nKOCVXT+611zq7yERxvj+PtgIHQjAM76/Z0Rl4/cWoGbnIUgE5NQdL8g5xVhvx95FDfWZWSyszPYA\nSEpjqVjAtGkugqWKDwvEU8O5UNqgq1VUNonjULwzjcyDdwF0c9LfPHkG4X7CkgHo9my5XEV5cwfV\nbcKekViZxcWTHSP6ePVoEy9jaXwlM5hIcgZYYLeE2afmCCGRXsBf5XLFwEECgNLHj7Fqy9gJlvXp\nnBHjpHsHOcd+EWbndTdFarm7opv2EqklJFYXHftuj0KykQhmMzyYCK3p8+CXxX4zztz2lsTxHmef\nl93Wq01HwNCpzx4huapnqWxjrHgbTCaJ49kZS1aLFeTU+pz8e6uQNQaw4G0IM5ZeJRbOWVflJ1s9\nzFP3s+zlNs5t88Y16v4cULMZ7P9v/wApOwKp3cbWx49RLRQwOzWCIkuwzZhErKesjPD6sTu7oCYp\nVmyr8yrieRFss6H1o18cuY4Ujg5Aa+VZ31pdhDizHNgmGIpDIJgSHUwarvkgUBUFo3/4Pbw6vMDp\ndc0CdmdMKgXINzfY+F//T9RnZhB99x7G37vn8W7ndvaX2m09/PQFLldrxuWLomm8/qdfaSlwXFca\nsHnzF9YWkL1zBwC0Ohf/MdTpsCitT2vFb4Btkk2uKJEA6KoEVK708CmU8xri9xZQKc4G6juTEPFs\n/zWe/2odEZpGQZV6TE33l5rUxm4dqBZngI1tlKU2rm8XIXFCn4u/n8PMzXjvxagPv4/cjCdG+/0O\nZpGdL6LYbkL99AvjWU6GrKTVaZvHMggmh7leXFhZwDHNYkauI6rQnm20jxNx+AVBUe3MFwsV31pZ\ngHhvqet3bobc8AAfVZxVJcM50r6qGqmWulA0raW6EWO6ex56u0wQcNDgRjSTEMGmE3j9T78CAIz+\n4Ycehk0wsaMF9yLGnGnOLDuLjJ+4rTWz81KxGdFss44iC+OyflSuG5RlXswu4r0l8FFy4XB3tjoA\nK0HFiSlF2O4c7u0s9TN+rYBVPBPR2uXWDu8+JFaXXB3FxoVqJgu2eRe6AefVB7ZJUnGrAEBRSG9u\noHq4h5RMI1IsolKc7cILMr/T+axRUZeJo0sRdQMyrHHWa+nXoHFG9HUhIVsYh/L//ByJaATC1Biq\n2y+QWJ6DxAkajgtxkOssENNp3sNpSp5tpmIWJgvgJsdQ2dqH2iSZMzQFnNdaUGQVM1kBX103kOaj\nuFuIu9oUfuef+7kQXgc25LbhDACA04qEebkN6wwExfYJOufh57h/ppuONOQ2ds47fdbPN7bZcLxY\nO/XdfL7r2ChmOtVgbdMyuh4+NZwI3efaMAKGXnZbvw4KGxg6BcjVGhqy3M1EIykQWfdMK92uIqn2\n/vZNeHwK53XoCPhLyz1hX/RTcuaHmWD/XEzGkUnHcVaVSOCPj4ClaRz/dh30iRt2Q1jnj9qlH80l\nj1ahbZgB3cGT2vozXG7sGIFg9+BvcCHrYMtwmDY2dpGenwHYYDpnSBkC/kp0cNGrbvCK0tarLrA7\nPeJI0TSun+7ipt5CFARQ43Bi3PXdwwV2Mik8E92Xn1g3v4rdj5+ghDjqrIBFEQGiGioqei0LgFJD\nxnG5iYPrTjqnnydPbwMboUk90vo2hIlxTE3kfC5LKp6dVfDR/hXoqoSE9t0UP9xklVdjt5AeLQAA\nbkYzrm1zAqhyUhq9AwS5Ge/d4EVBjPre1qfdeBKNPs7lREwkeGSyIqrHFzjxWEPu7fXH5Mib6sUF\nKor5ved4+a+bSHARvPr+NzD/wbsuBl7nvfWWDLVaQ06M+lDndBsfjY0dQk0XUFF696e3qII5Yiy1\n26gWZ/B4v4o/4kRMr3VqROOLRY2320scDkkfx4w/y4BV5HIN0tUNkmvLAADp6kZjogh/iXFCCxYX\n813fC2sMd0eFvSmJiLiPg963OajG/Le3ti2X88jivOFABdzZZfSMrqmJnKdzqSsisb6Ndq1OjE2K\n/B0dyYEfGwFA9YyLEdT4PSo3jBr/1MePjewLL53Xi4FqtJcNjqmQY1QkExFERAGVzx4Ba8sEkPDk\nEKMPllycAc5CxrFuylqpE7aTPs/6QV7owoh5XdQnpqDMzGA0I4ATiSNKp/oDgLnZGVSePMNppYXY\nvQWMizF8v9Bdx6+LnYq5fniCaD4LmueQfXcZhd9t46wqQVhZgJyMI8tHkRuP4puTdpwNXfxxZ/Sz\nOeFKSRvusk3H4oiZ6v5j9xZAx5xpQINQqpnPgMGyBnX0UyYrotwKlmrstO5I/y69f2h7d3ffrec7\nZjJQlmfAM5GADjSvjC7rd3zrtP2CG7bx99JL/QJ72ll/9Gxfqa0gPjblEDBzshtUbByV8MWLS9Ra\nMm6aMrICcaLN5d4UUv3XSYI5hXSmidjn62i1FSTfWUIb5H4HDYDQK6jqdf7poKhmVrVxMYZpto3d\nizpeXDbA0LSnvW7XB4uCisbGNvyDv4MSU+mGhyNqiLew4F5yPUrkL+5IuWbwCqdoE5MQEVsoorK5\nCwDgJgtoMG+zhtw5Ncea7fCh5jVyNyL01GseJH1yW9sAI/fveqYMjcY4pDUkXIGhsXtRd0zf9ReK\nGGA8oxnh3gZYTWrj4KqBa5rF5MoCyhvbEKMRDYRn8EaSyEYwmxNRkWRcnkvICFENtC5IxopTqtQi\napJiiqqEU9DuHN0YqOPJn+GD8lSMSYFDM+HOU+9/CfHb/zRmb41grNBGvVTCb/7rcxRiLCI0jeef\nrGN8eRbxTLpLKZrLXZob26hvbIOPsTh+ZwmZ+wS1WkgltXd3HAjexkdH3A059zKE3qMKlJalkMDh\neQ0lmkVakfDq5AIzywsYM1L6RNBRtocLRZDoVnhaJndK2OAil6vgNp9hPhEBxXFg915AXp13aIu/\n08I+Z52osFMarNPcdKhX/SKAcrliOAMAYmSkp8Z9emvP6DKD3YaIfFEwuJTbtToyD9YMMNCwEtT4\nte/z04qEFM90pWy/eVFR3z+EIrVQ33yO2J1bECYLRi0lG4mAZ8h+DV7iMCwZVjlAcKmzAuLv3gN1\ncgiAAr8yb6H6e5GfBL6TBk/TLqxBzkKomO8Q1iAAqXfIGZlYnkdDbuOlROPZWQWPTsoYjbOYqbc0\nh4BVvM+T4YC6imwE4+/dw+EE2b/jvsEMN3E6AxZ9udTDCdHTfDqB8lk58G+c0qsdzzfW6WJtB0W0\nl8ZGuuclQKuCZHT5rQfzJdFtb/XM2mQqiwsnnfE2sn0pChzLIP3qK7Qnxg1QWjfQ5JrUxpZm32yd\nVXFWlTAa53DdkLX+d7NX6P12ttXCZVk42T/xjAi1B4dm/06xMPteL4O4Bea6ji0NzLHgQn0ZzvnT\nzaq2d1HrUAojiL1utcUKIo3NIZwBzutAtIzj0piAxRHn+XtrGAL6Yjn+7Tqq69soxFm08Q7gShUT\nDCkXgAPKPkm5re7sg+ZYjP/oA6jHJTR0b4/HQTBYT6811dm/xsxcVyKasglEg8ZHbiuI3VvoeMM0\n8Vrg9j5NZ3i8uGyE6ol14VEYuX/XiMj5XUbbKjCTFfACJGp/p5hBvjjq+N1uCRuNJVgH6Zf7uNzb\nABOhMSKuALlly7eCpUpt4iydM1Ip52ZnekiVG6w4rc+jct1S8uFlPLkpRowkMXxDllyyFCZiUDYF\nFV6qo76xrflkKChffIlPHz7GSZOgey9+dw1zubiz8SEwyN2bB/mx3akTjv6x36iCyEYwWsjgs0sZ\nkydfgXn+HAobRa19H+L9u8a7e5+HwTFXDC7SqaK6s4ebR8+g87xjcgx6DT5gH3d/p8VEgkdWYMAz\nEcMZFAZ/QN8/M1ke0ykBHYdSd9t1fBo924RnvM+I7jWyhTEPBHT7OCfvLQBQcbO+Y9CqkRpV0h8x\nERtiSUtHdHwRnBwCsBqddp087Ki4PqYG/RoFiLPTFpwNJiGGctYN9qy3yzBoR73F3p/x9+5hmiU6\nxUz1p8v/z9yb/saRp2eCTxwZmZGRJ5lMJu9DvEVKKtXRreqz3O1uwz2Gdxe7+2W/7F82GGBngV1g\ndsZG2wY8bnfb7nKVqrpK1RQpUTxEURRF8T7yzsjIiP3wi/uOZFI9L1BAiZkZ5+94j+d9nkaMR8fk\nPDcl733c/H7ZTBpj/+f/AmF2Wq8Qs+k0UgCmxDZ2zuuYKwjgGLor1EV0BEzYIIj4Bn08OU7Ydku7\nOea3C+cLukzc3dyc485rf7P7nkFM6nWxg4PDc/Aga4MXMsrrXfBiA53hISRceT68zD1I9FJX4GBH\nfHmvS9pnN5OvND1vitITuBSApaUppOYfgGs10Dw6w/Xqhn5c3e9SWwUoCjirGa0dJ1URTaljSgg4\nn6tb8qcbiWW38dGd/+Eu4xk2QevlkydY1rPFjU2nMZ1OoaRKYsrtuxZJ9+A9yHu83qStUbs+bW7E\nc0axLWzx136NWhuf1VdxVx6wP8f/4RICAIVJTgF9dAAUkuAY2lcqJtjx9nbszL9VJBntShOLf/0Z\nZuPxENnbXunDO6/P63zm+9equPaJjeExXcaHTacAdZJ5ZcT874kGS9MRHaFwC5CZ0INMCEPf+F4p\njfF8AgsDKQBhAsLuqrFSpY7m820kOVYlb+tO7ouwjTcAdWHY6R/BjxanfBYop/k5nbOFJA4OzwEg\nROuFZjb4HhS9lxnoRYuLuyPbS+c5lc/gzqMVC+lOKp+BW6Chnff09RWG40BVYZDmYyivrqM8OQmZ\nYnH85DliEyOqzrlhGpw8VTtF5flLVJ7veoyh9ymXSZHxX6vj5Vd7YLgYBlMcms+3IM1Omp79+w8o\nnObnGPhtUlYjRGS7emWR6Lx/n5BdHZIgJUofvNPp6S7YqLcl/HrjFHMFQYVoOgmn9kQK70qjehJ7\n5tN7BK4I9GCP0Mx9bY0NFNTWAXvgEn2PChuw2+f5yNI0hu9PgRUIDFOq1NDY23flFehNMjEgAawA\nitrXmV6c1bXt2XQS1ctyQI+y3brZ6/9HIZZ1M+feIKprqB1SPd1PSDG1RHI6zuCrN8RBddvHw79f\nKoQ/0sv9xLkeOK/d/F170BuF3Iu8e6lWsyQJKZoJ4HzxPl60nvmgseeNqPUi19OuMVhuUUF9/QWa\nKg+QO4eUF/+WtV2M7Pdjluv3Gg/hkkN+fqLfuCXEgN3JV1qNTQtIL07jzX/6r3rSW3y5jxbN4GJr\nj6h9HB4jMTYEgDlUuioAACAASURBVNIJepMcg/mCgD9WmxjPJ3BZl8DSFKb7eB3x5Fc5N1+n2zv0\nD6g1c28T8eJc8R6z9s/CIvbcLfN6F4f/vo8kxyJ7f9H3t1qbG4RBzPx4AEPpuCXhFKaSbn6u2ng0\n++faesmLDYznePhxQtjN2a4Zdt0h7371XRm7Fw0UUxx+MJGztZN07yu+B6/Xb8A42WKDjqWROnAs\nDalmPED7QqGxlbs+WAVgBQGJ0A/t5gGC6/U5kAzuz8L+Wx2uomWsWh18784g5M8e6n0pwVkm6z11\nl/TwX4CchB7zDn3j8MmAm1ZjDfI2AMBVA9Omfje3DYgW4kgszerPNLOy4MgS0kIKrAeU3L4Y2vv0\njc8ItN3MDjoAJ4rB24wNvnp5rbNsh7HuK3l+1ZUwjo2MC1Umqy/JYfbT+xhanEJL6oDPZgB4bR4q\ni+r6cxQzHNrlKmLpLBojRZyIgAwJiZgxnhxomBwPcXVfv6ab9mv1phJK+s+4IrkGK1Nzr4mVDCdQ\nKXRzz+5a18GblOMnYDMppD65DwCIzU5h+yR6y4yfgxjl3TAUsHvhxgPAOFFdamLpGgAzb8g1eV1r\nd2PE+ZwTpQHkP7nvcZyoe1SYgI6MPYK+YNB6voXOH17izKSAQzEMmvtvER8pwRl03zSJ5e3Y+5HL\nATJ235zh+PQK175tQua5ResOWfJGzN7zoR2795NI8IB291tVH7gWGfvDU31oSpKeDADc9nEjuAp6\nv+EDfe9kTFiSai0h6e+DGEGE29qRe71nqSgGk2Zu4LTehiTwOD+6wmCKw/THC6g833X5jfNajeRp\n1GpuUHHkpp/7m1Spofl8S9ezr61vYX5p2vJevNZmrtUEv/sKc2pvt3u7WPeFuGA/0W/cBrXEhUef\nCLPTOt8OHecsiSJFkUHFGJz//g+gWdZE0EthaTiLFBSMZBN4q0rXTfcbY75bXrPU3i5OHnsR7HVj\nfpB+98RcFP/dMu/rVRx++wwVtWDiR0JuT/L/zXEDcwXgrly38FyFqaSbn6vdP0+vLCD3+jXKay/A\nsTSuF+9YUFLaM/KLf53FX3+rix3sntd1X+WkKuLZcc0XdeW2fnrZLScEZDw7rmLnvA6OoR2LXBRH\niU0nUZmYwO7jNTAMg+mJgkUOjZmf9byKPxWpj93sjNpT+aTOKG5M9qDeb3dLsCySD+9Cmp0E0I2T\n0duqKC82HIQe1OgQNIbbjmIkbaJk1rzNe+KZydsAL7kv6waks4YLRcz/pKgSNQqYtVUUvNiS7Yvh\n4KF7FU0zOzuoMzMdtBEZDkq2Juos28GVlm4reV5z28+x0ZxgGY/P23i8f4WcLOJeKY1PFoZxJLPY\nvhCBi0vMJwHODVKnPhuOZoBcFom+PNI//gSvC8PIf7OOi3ob6eUFjJT69edkd3y9iRK7sd5UQtl0\nCgMP74bOWHdnVieQ/eEDYHIywvHcx2DwJmUNftxIepoSA6Dd5X15WXj+gd1z8oyLNoSVF6orGnyQ\nQnplHrGBfgBAoqRpgUdd63vdwmPwiLiRqWr3Xm9LQK0O4fNVDKY4FDMJnQSJSfJovD0mhHI94JYw\nm79j7/UsFJw9eYaNf/4WoCgUh/I4PrpyUZ2wrtEz528t7V/hkGduUPEGLlVpv4GHSx5w45vLrUUx\nP0c3yRHliCPTtSRMvpTbPh622u2fAHczLx/EiWi0J9t3zmvYPa+DooCTWgtFIR5w7QrkWtWSPOfF\nBnHu9T04mDRT7Mg4rrSAqojsZ9/HdSyO2PwIsrG4h7/pnjwdTvvJ5DktKOi96efhkjAGfxRAku32\nCrFewHOgRMIUA53j4XbbeoLVjbwIod32RTadsiRwzeTAFEWjvvcWmeV50HHOQtBLUeS+7w6mMMGR\n55fKexQ3fSzJ0ZhPEvJQAETOVSC8ab0grvNbV1zh/sWonG1k3vfxLL7ZriGttktoPDZB5p3kN3z+\nsPfv5p/HBgpoPt8GxzKQyhW8+U//FZn7ixZuH3cJawqK0gvZ87BmXT9zqT8JqSBhk/+HzVMAxNEC\n4Hgh4Rwccqy/V3LIffw9jHAKdr/8BnOFJDiGwfXqC5QmR30Wij81qY+CzuYWsqoDnFqew9BHy7bN\n0Tvb5tbz35EV7F+19O9qxwo/wXuvxWq/Tq/2BUY9XCfinPAjTvEPnMLKfVHqYibBzBq+WQdKgzxY\ntZIb5NzYF8ODw3PQT56HcjTcLZhcxdjgqS5Yto3AIFz12HtuA17kiIzuBNfFNg7SRYwoQOP5Fp5R\nFIb+/AMcpAf0doz9qwbGOzI4lvEm+FHhwnSSR+vODKbGRzBNkV7YKcsGamYwdyNKTN6QeKwXcP7o\nGeuo5uYECv0DoRE23UrbuQU/dpKeagTElNmCHcRgCSNtTo9kree387yYUV3eckNez8BMTjqP4+Hx\nLhM97giN7lEk7u/VTN65e9FAXm4jIRMC27xgOHYaqZxmUWTHbl4hdz4LqVLF3tdrOhKsIZ1h5Bc/\nRHGqaIGMmucWLzbw8ss1iz9B1mch0jVSNIXXX63jSAWhuenak2vU5qGitrA9Az85CjadugWZNX9z\nWxPsvlS4NkSz3UbCw0DBuTnZWoBNARjOcBA7MnjHtStoShJoUUJncwvXqy8syfPxHB/xPs23rKAV\ni+vJBS9/0yt52se7k8X96cy/Qm/2xbSKs52b5bDSgiTLlsSHN4Fh2AJdMHIguADoX7X1e3du+7EO\nT4dTbckOCzeTA/PDg2AzXig6656hmOZQWMRMZW0T3OoGxjsy0gszaAlWFKc2F97XWkML3iTV3kYh\nwbLoJASkVEUQmgL4u7PwkjzWEiEH12Q9GAi5fnWbbKJoGtWNl5A6HbQ7HVyr6AUxbk3yrb4r49kx\nDY6h8SEoFDkzEtRs3ntjkiMk6RVR0ueVO0m68ziG3+iUQNTs1hICGpu8ZidVUWe1t1qwM60dSwFw\nSXOAKGJItt9UsNSZfcF6X5uvll3Ss6lHBzq5j2b+zr9xb68u6zoBoMGTwETW09Q3bApIL06boC7w\n+Sw4sDS/g3r7Lsprm+AYGtn7i0jlM0hfX+LL/SsAwKPxXER0gPtiHdzvRt5xkNxX2GvQnrfU6l1y\nyW8Tix4Ymlm2w0FfSV/3NmpbpErmVz0OP7cNkypVXH69ql4eDWb3FTr1BigqhoLcwtH/9TeQJyeR\nerCM6uQ0GhyPzMoCql89sRH8LDieUyqfwaxcx/YZOfxsv9+7dW7SZ0+eq5UhOrA37XYtXGLBvbIc\nIcCiyCYmR3AKgqqMXpsUWfs29EqRtlkiHsc1QyrKWnjpREyFC5BvzvGiVWTSlvMbrQKGaZ/vnNdD\nyQ0BzqDr9Mlzwv+iBg83S/TcjIHdHPibr8VslPpfbnEKlc3XkM0kSArQ/8OPwU+Ogozf7pNE0R17\npzWlDs5qEgpCDGe1NspNCct9mUDCMpoCpI5sadcJukb79XHTkzg6fKp/7q5rbzyDd5UmTqoiIVS9\nakAWgbBksFEsuqNrzKluiLluSrRqNauf5rYOCTFaD7AVAIdlEf/Hg35kE4SomlTyO2hNT+Hbdw3w\n7UsMPF7FgBC3Jc8zqNxfDIlY1d79BgZTHKqTk6jqDPJRizME4emE9RKS1W6C3u4+d6oK+PkYfkU2\n7T0lYyzul0hCjHD6kO/crEAXhGb157oJXi/Dvzt7i0pQFdp8XXbuFfP7scwhyik1G7TnmQtEHMOg\ntfMKwtyUSnSpoDE9hbUTEcBFyLXGm8/JbV3x/GxlHpSqykN4ooLfu96/Lw4hkc+hKcnYjAuonddd\nrttIhIx2ZAxMT2FPGHNcn/3etDXGC83kNl8SpQKy9xdQXt9ERZTQ6uvDUaWDQUVECUa1kxcboCng\n2YWE2QJ5x5tnNaQKSZdxHLQ3Wtdmb76m7pKyt9oy0FGA6T5eX6xnfB11uxkDEPGE5VhXNIcffH8F\n3OvXAFQWV/X74fr/unGiuid8gT44zDCp6E4rAL2CBRiQe6c0HoGreAUI+mJBAVK5jFf/8b8gfW8B\nxUcfgJ8cM33mBoMJCkBMPYvCIPiHGYzneKTHCqiLMiqtDubUSVFpdVAX5YCkgBfxjVWxIczzCxs4\n+DtQwRPN/vvR4X4MYIks/pSC+Mw0mpKEFGQY/Ak3Q7F03xZD7qe8voXLL5+AHxlEfKRkqh4LruPe\ne24rOmmk1kqQ5Ghcr++ivLqhEuwMYjTTh9f1BlIJFsL2S/BJDjEhjuvtXeQG+jAwMYzc8Djq3607\nCH7cnlO0oFAbQzL2tt9g958eQ2S5wN60921u49BdQSIcWY+dRflyqIjLRJ/eWtJ9AOK3SVm5OwZT\nHEqQvatHXZ4/XDAdtIYbVUjte7MF3vK8AQX7V80uJVp7b71AkdTbkj6Pp/tIksI89j6oHuNidQOV\nOIe5TxYx/mAeRLc7Gvma9t3wAWP0NVHTlsf6FkazDITlOYwPFxy/M8NpL6kYuJlp7G7v6kSRQJh+\nV+v1ifEEhKumi669vWUmidrUBNb+6VsARBVlpwE0KrVbGlfe66PWvnOq9cY+XNL3DTMx1/xPBtW2\nudvUQnfy7rhDbq0WZ2kUUxxO1DVmIMUhm4iRZ7eygNNcAQfXDfz7qYjG2zLGYzKuL+rIJmLgGMaU\nPKcjjDfj3ZegQIwb88bv+fglT91aFjXCsmnVfyo3WiD+TtDcsErgsYIdFelMjAepCrg9gzD7ZEch\nQZFcqwKclpi7bZJc9+PfZL10248TLBNBNt16XUQmT5MW9njWqo9+9WwHzUoVxUcf6G1IkdYGxSBd\nbUqSmgwg7+XgsBGo2uHlX3j7Xe5tPq8u69i/Iueeld0CencbPNyH/M06jt5dIlGrotk3gHfLcxj+\n7CPLni3XqtDUGziGAff6NX60fMeHPD5sLOg2n8jfqNEh0Jk+NF/sAQCqk5MQ44RkUCPPpClgcWYa\n9OCc730698YNcnzL9VNqIcb7/XeblL01L8Y8ee5b2OTDQNPtA3AesyrE0jjWFKQH8wBIpu3ob3+j\nfjc4ExJ9USAkRftXDV1HNCrhS/b+vGc20P68gHAVbIYCGtfXaDzdAKXB0Z+SF9/YexMYIFA0hXer\nWyg32ri4bKD8xVMsqn2uGgwGaq+LeUApin9QbH6+DY5X+/UNREd4WJ7XeZyOgt1x9yIuCreIei90\n4Saay+/7iUbq62+fYf3zp8DnT3Hn0QpmP70PgDJJSnr3OQf183WTUNAquBIASZbRODhCrNAHpHl4\nLZbecxs6aWRebmEoFddZm62s8idY+Oz7mKRZ1J9vo3EYBz9aAsPIwLMtpDkFxdgHwOSYR1+y22bv\n9m7922LOnjzH6y+eor2xjcRwEccYCNWb9v4snIIE12qGDrA0FmV+eQ5v2hSa61vgh4ewew708Swy\nnTbI8yUs7QDJ5IeB5rttUmKcR3VyElCDpOrkJMoMh+2zsmf1yN+6RXYFbfzaWCFKAubv/WQqDy3R\n6YYaCDJ7sm7g4RLknqCVemEKqqZ7Iv9PAo6ZfgElWsLJ40OMDZK9m3v9BniwCIAO3WpiX8MJmiCs\nhQ0cNJkxBUMf3cVuaRAAMDha8IXTDosSlOExNOfnkJgaM4giW03L98WOrCZxnTKl2vWxkNH3YAmN\n0iA4mlbljJ1648z8LF4PjEH+KUGbbNAcFhyIx16b9953PDyuKxbJw/1Iw+kjGW1zZtmwJOqiwY7u\nV00LTk67twK4+Wn2dagvSRKKz47JWk8CbHKvdVHGZh1g4zxOa1UoAM5icQh37kCqnpjg7gY5p5W3\nx4vnw7y3pMCGXof8K3zmlsXU3i5q61vYoihIH87hsK8Epj+PobiBSgqaG429fUuyx8ppEUVVIJqZ\n94vU3i5Se3s4SbBoriyg8HAJ/kTS7w+9G82cQW5l7QWyX66henGNTqWC7PiIST7duyinHS9Iera8\nvol3q1t69bns0Ybk9XsnAkSGyEgARH18AUC9bZU5NlsQl4sfisTc5rN7XsfqUQXFFIeiEA89xvT+\n/RiLxtYuoCjgMlnU1rcgf7IAcFl97eDFBrI10UQiS1oO3Im/o8eCbnEVLaRwMbMAfnwCgNGSLFXq\nFvLM63dvcD01hgbHY74g2AqhakJDkgB9jyHFlJ2DazQ4saeoMS+7Ve93klNQKnIe2RnvoNJJ1rOJ\n0YF+lIpWmUBSpajqgTbglVExzqmxu5qNoQj0x+v710+fYetfCdw5tTyHbUxHJnwp/fWfB2QDg6uc\n5kW23pZQFTtYa8noP70CtUvQEqnFO5Bqdd8AQa8Urm6g3JQQHxlEk+VwWhMxzrDI3p9HeZ1ABPnR\nkipzZVjrutxV9sk9qKA9+7e9FiJ7b872WR0/mepDiSabSDcELE4LkTxQoddSre6aobcTqjWlDra/\n3dK/8fLLNQwtThEyvQAIW7gKeDdZd7WCW2tDGB0Du/caGSjI3l9APc7rMnCAdbF0ux7NqdA2mpcA\n8p89RG52AlBgaIYDSC/OI58WIM3fQWXhDuovX+Pyy++QHhtCPMnraIDuew39k1ZSpYby2gs0xA6E\nhTuov3gJLptFZuVhhHOQ89wuWzhlcm6lgO+GOx4dj4PqdACpDT7OQmk08Oa8DmFrA8m37zCQ4lBP\n8NjdIxwRWuKqW2i+JvcIOPk7ovGIhMnmexMfag4DAGyfmflsjLEidjp4VxoFVAktu4PQbZ8hPzmG\n2EA/WIFUWdPojUzhzUm2KBSFOIYYMrY4WoZcq+mVvATL4Lop2RAe4V+a1xreW5Jf81xXoExPAYUR\n/f68r4kCy9BoPdsGPzJsIYo0Q8JPayKqk5PYPBExK9c8xxypfjXB8AJK+YSeDLXff250CB0FyBey\nOjJjKp9EnKVCJLXd77/caJng5e4kZ+bva2uWvpfa2le8zPycG9NT2Okf0a/Xr3c6aI10c8w1BRu7\nua1DM/0pXWbW7Z4VhXB+aK2n8aU5DI888JFg82dQvxlHQnCFTydEpCik2w28/I//H+JLcxAefYDt\nwbGQgVRVJ24FzJwWqfegcGFOKB7gGgq2zurA755gbqCI6bEBj/PerAXK3xRwrQbmkyTBpR3fjP4M\nTkQY+7EWexTTcVBP3wCKgr7pYd13CVOU8zYyh5S+HBr7ZxBZ4oNb25CicCFYESD9iogLNRngLnPc\nO7O3pWktpt6FQSdSCABiDI1MgkW50davO8EyjgIkMzmJrElZpFcE8l57WTItqHsw+bs2pqQWoCEV\nAGBAoDE7mgUtpDA+nMXZWVW/XwvB7fQU+N1XEDsyqpOT+r4UJUnXLWL4lhICzgUTamVXs9CQBhW6\nfvL3vwMdj7sey3xe74yK9aGn4wyBrLcl1MQOvjko22TwyPcPDs8h/tsqKq020vEYampFrRsLnmzB\nvVEa6+bv965QFFhAbOD89BK5TgcsTaNTriIYQm9AXY7zBVy+2IPY6aA2OYXPT9u4OzyOyckxJO+M\n6z3l3gPKWT3xdlLdMqxu7Q7RF//6+gu9z1GxHKe3AZs+0Z6+gFSuoFOu4uy3n0O4MwF+YlTt8TJI\njMzzID47BQDoqNUghqbRkjqEWV8170nfSxUIU7tFPKFXcGscj/z//Jfo++QuCrOj2D+88jmG+/Xo\njoxq5bUXyM1O6ouT0pEtEmGEifcB+PERdOpNh8Z6t20UYdYXjqExKMRwDID/6D4mfvkDFGateshO\ns0qV9Wr8hjHPeRWBpMkYvxsoKSKOj85R+Yff4A6toLp7gPZwEZmFCbz69b+AW5yFFOPw6jFJXKXy\nuchj0Lhm6Nfcl+S6CmKDs/n+zqS5IpJangOm+gA4x4q2xrsrCURtT/EOHrrlDLA7gXYVDalSCz1X\ndFjj43UIYgNCu4GrqTEoKpeGG8JDjPNgb1jF6yXJr/n9iR0ZL79cQ+IXfWhwfKATxTE0BlMcrtV/\nm/cqbY8k/oThlPXxLFH1McHa7dUvrZ1PT6WryWMASLDe6Em/oNbdCHrxtC3jok2byDC9uAis4zGx\nNAsIRQAG2a92fvsc5VoNHK1uQG6JaHc6ePnlGnjP52y09nUbPLv11Xv3trvPJ/N9jGd5DKbi6Oc5\nTPcnfQl3/daa3nIkOC3JMRjP8diTRMQTMWBzT/coamub4PMF2y/c52JT6hgyyzCCScXlfURPLIYL\nnkkrBnBcNRRk9q8aKA12XN9Xr4l0zderjUMOCj5emkNyecF07TdJRFBgaVpF06oFh4CiXDijwA4M\nIPZgGaJLG1IULgQ7AqRTr2NKoBDjeVhljp3WK5U2e5upFxmi876Sus89dH8O+asKuFJObW9KQbSh\n9qqT0yh+smBK9nkr+nitc1KL0n/rbP22G5HgLtHkHCmVZ0iMJyyy5RqPWl2UUWmK0JAA2pjP1Yi/\nvdM/gh8tklhh80R0OV8Y6w4xfCsJgZsumOYBSKlBLjtScj2W+bt+GRX7QlNpdfBgKIWvD8oQOGD1\nqILVowo6MnB3MG1UlADICulT68gyGJrGeI73XSxvV+aQLLLm7FqzbwDpiRJiDAM6zoEVwp0/wTIY\nuLeA1sQEDs7q4DIpcAxNnttUH/KfPEB6cVa/J21AxbMZn+oJgSUNpxO6xIa9t8iaYQ1GMtjvgwUs\nk3g+CTT/sKWfw8wSHeyMuEtuBWVeYwP9OPn734IdK0E8PsPh//N3SIwOofSrz1D81Z8BoB3zoLWz\ni8G7d/Dd42cAgHs/egA+mwYu/ALvm5hbMsS64E71JSwV3AuOBy2k1AXL2UPtN+41R0ZLB5gZnoP6\nHb011qNBho3j+xsZW4vA6gayCRaZlQU1GWCFcdmPaX52XtKI3lJuNzXvYDRq72tsoB+J332OWFrA\n1j9/DTorgJU7aL09hnJnGAAgyjIOr5sABRxcN7GQN8Olw+sxu1f0bkoE6DQ/Z5JrNZDa24O2taf2\n9sC17gKcFXppDxDnk6pGO2dudwsfzPeaYM1PicYuHxdOOo/AGheKAq4eb4GlKMjDg/p1koShE+ER\n1nn23gtvu4fY26zXRGHm03ukTQDOPlhaSKHBGU5ZvS3h93tX4BjCQVBMxbB91vasftl5Owg56hvM\nrCy4jv9ogQ9pe9r63RMkEixSM9PYpabRkGRPLgL7eGw+38L8T4r47troaT+sNDDTn3LMUalSRevt\nERoHR5BkGYlc3vO6tLWBazVCjX+vgNR7nehu/ZFrFQBUj1CEXnbzIsTA1RmUFIvGm0O0G3XwM1M4\nYTkkAJP/aUWmANa5qPFpmDktAHi+j/BrcvjgmU0LyKwsACbJZ2+Vp5ua93OXKjVcP30BSp0XzY1t\n5GYn1XXde+/QzJfY8ekLpBbvoFOugo5zZI1zcDYY1xiE3jGjfZIcg6GPlnGgrr+kDcmdXNOZOHHn\n2Urt7eL62TaK8Q6UoxNgpBQQo3QTXFoLJ9rcTsZY/NXigEqG7iTD87ov8/m1QNqP3NBI9gUrkJnH\nfWdzC0em1m7CJWP82731264IYVIREqyy5RpKI3VWN7X+AIVvHuPin/6d/P+f/wD0//aX5L7kmmNN\nDG/R99g/WcOsf9BsJXA4+83nCHJw+ckxNCUJmyeiDgsN0oqOszR4lsbqUU3/2855XWeS1o6RUkmK\npvMJ9D1YQmGsENgbdHsyh1bYU4PjMf39u6B2XoFWWdLZtBBwfmu2dHbhDi6Lg65asW7BDUURXW2x\nVMTpYRmdBHlv22d1tCTZdXPyux+nVq2xsKdX5l3vw77Jn9gYooEwzrh7NtJOruNMbFBghSToeBw0\ny+Dsd49J1khRiD73h/eQKBUddypKMq5HRjHzv5Ie2tMYD4AslgeH5wAIAWF3/cROmJVbMsS+4BpS\natDv97DSwLdndVQrTUcPtf+7pDA9VkDms4cW1v5wAUCUOeMM1J33Oh+QFAs6n7OyxczPWp6dLo3o\n0FN274cN9wyDzFurO/ziT8ZvLMkjUW8hx8dQLteRXZiGsv8WHAVM/tWP8cXaW4AipGevWjTGxY7u\njHg5oeGvOYxah3VM3wweb9XMNq8VdmK1mU9XwMyPENTRH7ZwhPDa9Ldpt1e1pBBjaFOVyzA3hAep\nQIet4t2+5K/Zl+AYGncerWBH3ftvyrdiHnMMRTgWigKL1N4utta3IOUTSI2Mozo57VH9Mng77OSo\nyRsmRLS2J81q61tIjgyhQbvxrngZhWIqBrYiY64gqAWBBobTvAusnQKTESApCkBR6B/I4Vz9xLhf\nxZk0dfAuuF+HV0Dqxg0TRQ/enDAznHb/+ey31vj7rkGIiOBEhtYzHc9lkejPQ5oaA8UyyLExDD26\nD36MIATMyJTBFIdSKo7dc6v6iz2YTLB+6NFwic5oVXwKhYdLmBsoYv+qYVNicFp3azx5pgQlqlVj\nDb4prTdbKpdR3dgFAItUqpcF73GUT6AK1x7+IB6bnfMa3h1WdN9rpl/oMnnuLPyM5+I4PblCbX0L\ngykO8ZQAqi+P/s8e6eoF9mdqPufNZLLd2fvDm9/5vdeOcGOVUvfQqp4MAIiyQ6fe0HmsvFq/7YVN\nu4qQxr8iirLrtdAX55B+/5W+90q//wr0z74HlAZvpXDiZ7eSEAhXIQ9mSWXTKbDpJBJLcy4BBhzf\nTUHGzMYqXn65BgCYebSC5DxZPN0Wmr4kh/F8AqtHFQBET10LSs3fr05OY35pWs/yhIMJ30YFxAl7\nOh4Zxy7GwGcKOpu/4ei6n9/qPFLovHiJlZ8MefZV2Sd3f38KO+d17F52sHoto9hpoSjEwVDQ2eWB\nYLgXm06iMT2lv687j1bAppMhIYYmNYOzNlKlUaT29ojTr44RA+rjbl59i+a/mbVDzQu4NsYrG6RN\nIZbPgGat92mfB5mVBdck1eDhPmiN+AdLQP+i73U7zfmORts1XH69qkPwzRU/u5nl3jTiupS6CEWD\n7ClENWJ5gfAGmLK44SzMnHEnKdP6Wg1d7xEw87PIjQ4hwTJwZ8f2Pp9bgJUbtbYKadKIdkku+7jy\nGkO3Y+Eq0z9FsAAAIABJREFUZ/r4/fc/GjC8kSL6/+JTCLN3IMYTGJk5AAA94QcAu29OcXDdwNcX\nEgYikgPZr9OLMFS7fjdFBb8NMtiJ95YUsxOrTbYauHi+rR/fP8D2fuZe5E5evCndmQKpVofcajla\nboLMt8rloeARnVjx9hnF7f2yIy5kd91dk5UE7qs3ZUtbFMvQSO3toTM8BHC8R/WLspGj9k4DXEe0\nSOR+R7M8hpME1aUx1JvJq9zGIy2kwTFt1+NbTcElk0B5cQkJjkaCofDRaBq0kNHvww5N3qwDHy/N\nhZQtvFlA6q0HHwWpYySbvYMYb9/V/zwRYekKoEgymJSAws9+AFZIojRVwtlZVX/ODEV+fdEQ8bbc\nAktTGMkmcHcwDfvc5VoNSLUGhLkJVX7OqxXUWt0NrybiZTSmxwZQGuweUeZtRmtv83dPMJjiMCBw\n6nMf1QliebGBzNkVODXosrfW2veOqb6ELu8N+Ldyeq0j9jESFJxqn2u+18HhOUq0hFQ+6zhvUOLE\nfK56W8KvN04xVxCwlI2DKSSJj65QUDoyWMGOljHGqVnlwp8I0rBeKzmE43joZUutv3W7l8m1KnK1\nK7RicQBmP5xCmmPBqzAzlnFHVL8Pu6UzBVff/FjVzd/bOa9jWyiCf5h2BLx2s7M6cruvIK3M6sGA\n20KzMJBCRzYCWe9+d/I3B8z96QuVLCoZGkrTLZzMHsiX117gNJYGON7C5u8v4+dmFMZzPEqDzv5F\nt8k9WTIq6FpVJJeIYX4gaVlEg6wuytjpHwH/C9LLu8PxGLoMT1hoJgrrDA/hengIs6NZXXP6pq0b\nDAXsXjR0mUTrwmaMcZqL4ey3jwEAxV/+CIlSAdp75ifHTBrdScza0AekL3NTrzJr1aMoi479Hb37\nZh3U8QEq3z1DenwYibEhkPdJNkCCCDAy39b33S1x3W2SARnm5nDFBvqh8YdoPZNnb8uotwkj/+hw\nP2YincU9wDL3/gLkHgvzS5BmJwG4z2f/MdRri/IOKBQ+WoHUP6D/1pzAYaFgenTAcqz6+ga2fvcd\naAoYGR3H29JYADmQt7k7zoYDx1DAuypJNALW5+bHauwng+SUDTJIZrfPrMRqpSLneganBUMS7cGq\nM6E8H8rpdneWVBb7py8giyLaZxeIj5Q8Eufuz8wPjql9x/zMw1Xx3jdTuNUxj7YHhpOj1O774JAk\nqbSWqAGB08mivKHFBgKlMzsdUQPc3cxtT8MJFsz0NApq9Tj3+rVeRKmofBDkHG6+GUK8T1UxZGIC\nyrd/xN7aNrihAXCzr7D4ow98r58kiCdN53MigW4KsTfrwTMULJVyfzOf20vC+SYJJcPCcKBo12KB\nJt/zhqB3FPK+/nX3AgOpOIopDvtXTUzlrRrn9fUXOPnqW1Q3dsGPllD4+SMIs3dcEuU2grPzt+B3\nDR6p7vgGgGiBTfjvmlt7AcKTkE2w4BiGkDifGe0+V5yA8fksOD6uJuisAaVd0UfzZRnX4RhmzHaf\nCNX4bk4KSSgP77qMw3CJE83/0GynQeNjlyKG2fTEhKZyASDz2UMUPFQIemte99WdfxllrDoKeMtz\nsLcMmJFARgybDFQR6mxu4fzzPwCrW0B/HxK//CmG7s6TaykVUPzlj3Dyj78HYI4h3r/dYurBazKE\nf7Fe8nX+mz3lAuE1PnMuNDTuDqb1CqlXv7vH4VTCw9/qhIf+sNKbstNaz00zNOLtVmBrhN3cA+Uo\n0jmGadJhH41m0JfkwNJ0ZEhv1Os3m5koTFieAz0/AqszPg9Kreym8hmYn7UXasT8NzNqxGlkjJf+\np18i9/0PAUCdyFRo3dYgFEMU48UG4u0WzlY3UKZpZMbGIR++RXl3H/zECOpr29jpJ+0KU30J1z4u\n7Zm8a8n6M+kN4dtNzNST6kLswgoERXSs9immV+bw8ukOMq/3wNC0Ta82+FzeAVYKM3Cywwf1w/qP\nobAWHGBFfQcUFR6Gx7Ua2F8jG6OsAMLeK+SKgwCELnrb3M3uwJn7sb2VYJzX7X6/Kqx0e0+FlUJn\nSWco4KRmJB8AomcfJpkYFpKoPWe3hLLUaKoVu+AgxG390I7HZtKI5bIeEFDns9COEd1xDXJGw+7x\nvUgaBPfkmqudTpk841oZCjZiYe/7rrcfWuDJWhLayzQECk0BeyINLQ13s3XSCO7zfUlU2iTglypV\nNJ9vm5LM9qS6832HrcoqI0M4+uIJMHsHVxSL3cdrGFu+g1Q+p//WnfjUa3xF94nczpFgVXWFtqQH\nQOZKuRdSx3zu9NI0Ks9f6ud2L0b4B4L25A8hPguTmHM+h9Jf/xwAZZHVZn/4AJictDwDnmWwMpRG\nHx8DzzKWY2rVc+qLp+DWt5COMWi8eYfr715AmJ1xXL95PePFBl5+uYa5QhIcw3TJNxDGwqwD/t/R\nWns1X5AgX1IAiEoSc/gOjCKj+uIl0mNDKP7lT12RxubkzGwhidV3ZQe3BgBU1jZ85BzdLSg41T4/\nv67iUoX1cwztUxTzjk+0Y+2eO30QI0GnQIzzqKutgObr9yKHDrNP3Ky1z/2+/PdZvzlJCP80xZK+\npB+Czj1Z6lSHc9nfbK3NZhUhrtXA8fo2qhu7SMcY8FeXKL5+ieIHc9CStMVf/RkyH94DoMUQN/UX\nu7P3ziFwm4FD9xXh8BnJKISHdusZ2aKJ4T5RE5GanER1cjrCxPNCcDgXXbfJPZhJWv423Z9EX5Jk\nXKNsFO5kIEkoId9hMFGYijBRg4xZue5LKOKGDLHDlt2fL23hDPAnS7SOtV4QUOpM4V+ugU6wYM/P\nQBUHIadSOK80wU5P4DQu4Ppfv8PAf+hHg+Oxf9lEUeBcIXAz/QLupuK4vKi/p+qenzkX30EXYpfk\n8gISMfLeOxTQ+OIfkRE40BTQeKbp1eYCzxYmwAq3CUUdQ9GeQZjsePgg2stMBKAtSocnH1dFcAyD\nn0znMVLqdySVwpg7dNlw4DRGYgC6xOpXb8pdVlSdsNJsgrWwpKc4BqLKQ6K/o1B95m4cKOTvYYJd\niiZKFeY+RX5yFGLci23eZ69S4AEBdT6LmyN5vK8jLOHVza/D6xgKqpdlAMCRzOhzLx1nUL8mf58e\nLWCmP6VfqxZImomFg6ve4dqi9OfB8WAo4PiigmyXyBqnkeA+kUujclq50XGCfKAkx2AoE8cORUoH\nOZ5FzMT7o433KP3C3flEbvs2SXL/euMUAFwq5U6fx37u6uYe5FYLEhsDYJ/P5B7DJC/s7UeaN+IX\nKHk9B/L/m5a/C/0DjqD8sNLA6ruKziewf11DUeAs1fNyUwLPqIz4N7ZeQZnDScn6kam6t/am9O8e\nHJ6Tvvm+LAYmigAoNcjzTywPp+N4dkw7uDW4VsNDzjHtczxyTH//mHw+l2OxU0iCldqQW6JD9tvb\nrHuOdq5xTsZRpYUGrMpEO+c1XVba/kzHcwlsqGpYw5l4xLXqdkiD3S2Ys8POCRbEd2Rfe+z/9tzf\nHIUhw3eyHI+mwbF2n4mC3Nen//+fym45ISDrGzMtGEGnv7mzUwJejM9mu30CI/M5ggkPe202hvuR\nEgYAZI8OUPxkwVdGx+1Y9sy3g4RErSDbJzdN0w75QBIcKR6OrJdz7L5whHuHMsTzC+Q7LWRVOLad\nVDAKoYj92Wh/C9I3vnmV6+Zj1twqI8kyjvNZtCURFFhUszlwHI8YiLJGVpZ157chybg7mHIly8nw\ncbQ4s+SJ/33ePCvsbq7vcH4OuVHChK+hPpIcg1EVpsWLDUzneZRbEo6vm0gnGJxUW0jlw5Bbmcwz\nwArjGEYZQ9GfQYmWHBra5ncQPoiOwjlgU2UYL6L7THYwdPn+UAZ9PIPf710TiVV0l0B2g5UKMRqg\nKNCtBhhZRjIh4HtjGZOcHKVel19gouCw0gLXquPtdQtcJoVPJ3JIcrSnA2tPhKTmJ9WqpHHM/asG\nNutesnFWi5pQvFlCvndtAPbr2D0n/C1aYrmbY2j38vabNZIcpYD2zDTo+TkwFPDq8VO0NrZAUxTa\nD5cw/BffAwB0FBmnJ1fI0xSuaE4nFo5CZheWE8Kc6NKO4dZuEQ1Cr6B5dQ2pUgebFnqSZHY3CrPj\nRUg/+xAvvniKy0Yb3NIcjmQGOK/qiRcv9Flvzblvj2d5vTXLPYDxR8IocgftO1PY/pZURjVOI83C\nJC/MyR9ebODg8NzSZtjbQMl4BsPpBJ4d1zBXECDJMn69cYbFooDzehtFgUdqdhrJahU4eAN+tIT8\nJ/fc4c8mn7vB8Zh5tALu1StQNI3U/KTpefRGzjnMeuT/Hf9nOtMvoERLOFH75pWOAmsM4p/YdxtH\nXnKO4cp6wdLi+WI/UoWcBUJuHofu5k7k19ncRmp1A+MdmezZ80sAnFwf9ucuJwXk7i+gvboOpQVk\nP70XcQ3pbe+7l38ZNCdvF7kazti0gMzyHNpX17rSTOH7DwBVoSA6SbPZ/Pbk6HP0Fp+KjO0vCMGf\n2OmAWZgFPT+H2UISmpQZQwEjWQ6ADG2SerFThmd87rZvJ4qzoxEe+hNV2a03G7XBcG+uThHIXLDj\n4DVAvEhItIDRO2jWgiNDglBDK2jOUlCPbRSiFmIyTv7+tzj575+DjjGgWAbp5QXPvtmwqhPu5rew\nuTuI0d9zL0i3SKsMx9BI9WXR/OgDtLk44mPjoLd3ISvAxCfLaMR57J7UdAhZuAWSaF3vXzXQUFmC\n3ZII7ysrTBZPJ+rDfP56+z6++mdSDc7cW8BmnUJJ7Ohj1os0Kcy7i17V6u3mmNrbxcnjA3AMY1sL\nyTPo41n8fu8qMIhWlChqCLeRbPWHLnOtBpqtlu6UufdyhjcNVipv74KLsxgaSuLJ3/0bQFF4+Oky\nMsN3wboGpO7rZl3sEL6O9S2MU6RlaXh5CHUPNmH3SmUSdIzTx1NiaQ5rxk9DQCPfRxKcPAO3vdkJ\nwSdzajyXcOHlcbfgBGV4a1xf4+WXa+AkERQFvH3yHCMTI+BZGmd/fI60OgeOnzxH49EiLuk4hL1d\nNP7lOyRiDB5+sgQ6P+F+nR7OZWdz2zc5aHdm7w9lMJyOQ67VXFjfo0Lo1e/v7qFSaerf7+2YMAo7\nqXwGE9+7hzeZPggghKO75w1d6tDpOyR9+TF6mbxIcizuDqZCJ6Xt504szmFNKIL/BWkx3OF4lZxS\nRQAFFrMMM7cy1tsPkdT7r933Ar/nYP87XJ8PhWynBZoCvr4gPEAdGTrySaueD8cUle9KK6oFMMLP\nF3DJApW1TVSevwQd45BemY/AtXBTUyDXquDFho//5re/Ukjls1Ae3nV5tv5zzdKWYSLorMMp50jQ\nbcH3EiZAE8sViFcV5B89JP++qqjJPu+2m4t6C8+OqxZC7xIt6dKqHMOg+Xwbkg7790K2GescJ8m4\nkBmcNhTwV00sImJBpaf2PlEH/ha9+OXkLypMDeHsrGJRCilGJmn2Q9d0155+awmB6mVZz9Kf1drA\nk+cYnRjB9hnwk6k+tCQFa8cV9UFU8IOJHIbTCdcNl2s1IzA+d2Pdwhf9e9Tdv3/zjVrrU9NgS8Ly\nHGSRwozvhA03QOwkJOZ3YFyzYVpwJHZkkjVd3wI/PITtM6OHJlqGLjgx0zw6I9lTRYEsKmA5Dn0/\n/BBJGwzMDKUHrKoTNzcF1ctrHBxe61BQM5HR+3HSoR+fOA0bEDsyxj66i+TyFAAKh0N9OJgi1zE6\n3I8+nkFH9qqgGPdmVJySutY1AKSW57CNaU/G3V5nXqOx/5rOv7wEIZYFAJvucTBpUrR3RzbWpiQh\ndUsbpt0xSe3tgRMIjNCtPzjBsqEgfpWmaJmbwWoI70M73pAIO1KVI2amp7CWGcRLRy9nuGdtH0NZ\nRYIitnG58wojhSIEsYGj//tvkHq1i+Kjhw7Iode6KdequlMoK0BlbQvyxwuuzmFTkvRrsT9H83gT\n4wng1YXLXfit3+Hfi0GMF03m1C0YbkkyTk9MxJ16AphUO3iWxng+gZl+DWFjOMTJdFLvcdV6dMMn\nKK33Yt6343QbiYtTtA6OAQBjw0VSblCA4WwCtSbZWwpJAgs/ODxH69kOufZqFa3/8ncY+mgZHXwE\nhOgLlmvu7WGauSs10J4BVdRko1Sp4vTJMwgCB0KApX1fIGMJN3XyjMIOQKrmIx8tW5RHNHPzHXKv\n9ywEZm5Iqt7tlVGDBsp17pkDT3vlzq1dzWxJjsF8EthS14XBFIfm8y1TIBbmWqy93eZrLKgqA3Yy\nxM7mFrJfPMVpTcTI6DjaM3fAMWQtd0M+aRZUQZUqVdS29iwtTbGB/hu1vtqfl31/J8VBknzRik3Z\nmghmMmprrGbuY8y/tZP8bqY/idzrPRtB57xDzjH4eiIGaCo6MdgUPajcOqvpQSUAyPWGlRiZAqRa\nHYCCQ5GCJMv62vuDiZzlHoR2Aztfr+tgCoMrJNsTZIj5+sMfz+lfBiUUbwe52k1ygtLXZA1bos09\nrdBh5kwKY7chQ3y7uAmKQiLOIM3HUG0Z7OVNScLb6ybeqAvtSVXEs+Ma+vhYj04cDdrYPawkqEfd\nzaI61W73QoGZn8M1BCRAAp4gyGXQAPEjIamvv1ATMmTCFT77OML1R7EuEjOKAqlaB53gHd/zV50w\nzhl9gSOL++mTZ2ie1cEtzuC71CAU2ImMbjt40owkpk5z/UYVv9JSZdrscHXg7mDHZ4G0VpzSS9Mo\nr22jo/aT1dSEz/szb/ZfPzO3EADGfYYlTfJ7d+YEjIaK2TwRMSvXMMm5sbTfxMj8H04n1KoihyvB\nX1qum01QaDfw9qyOgWIewJ8GWqeZXU2F2X6J1IOcq056OKMM6OjjA3D5FCQArbcnSBULqG68hKYB\nbF8X/dbNBMvonAoAcf4TLAPW9vzTcQZfvSEV1qBEC6uSWUWFRrobWduaMRnavgG4yZwuRFoDebEB\nmgJOvnuJ1jOyL2jEnYCRAO4owKuLJsazSSQ5J9HqzMo8+nhWry5HM+u80BJJUqWK/oE8Dt+ekHsd\n7MPUeA60kMbbn3yAV4/XAQBT318Bn80ARwTqn+E5tJ6/QUGIoS8Zc1V7cSezI8GLXm1jaVQ2tm0k\nkfMWJMVNuYTMz2D/qoGtszoSVRFZlige9I4nwijsaPbyyzUMLU45OIQABbvnJBmg+Q652hXOv/4O\nMZ4PJEcLd+9h/LqoSWnvueeafJ6fQ8lBMmY93niOBwpJSB0ZrOu49vI5yL7h1tutXSNFOat/Ghni\ngEC4UfLnh6gtTuk941HacAB78vK2zdjfX13W8eqiiVcXTcwWkpjkFL3CPSBwXbbGGufpxh+TKnVX\ngs6oAWHwnDfGdn9/NgRqhqB2WlIH2xfkd2bFr6Wrdyh/88ogRh4tgctlcPabzyF2ZLwrjSI5OY37\nJcJ7QHxEAxkxlIljRz1TOs6AUXkngpMaUWKvXpCsB8vX3w6ywEpA6WwZs89xK1L6Q1BqAcloI9OS\nqb1qt+3Gbs3bS+UzmJ4o4NWv/wVJRcHgZ5+ilhAsjLAAeXQ5WQTfppFgCx4MtVFgZQaBFGCuXPS+\ncnf7/Sn+G7uWye4N5NKYOCNZQ5ZuPgk0/7ClH+969QVaD+eh9Q6bg6PBFEdaBlRYuTaone+U1uXt\nzBM07PNMRJLpsKtOKKZze1dq/Exb3DmGRkFg8fTrdeR+mkcsk3KV/HkfVhdl7F85kR1uMm1+C6R2\nb2n18+rmK5TLdXQUBWe1NtIJBnO5RMCC5eXwdNODbP9NOImsm20EQUkisglRo0PYObjW5+G7b9ZB\nH7lB+bs1t/mfgRLYphTu3tMJoqjx7pt1NJ5tIdtqo+/hXVQnpwOv63Yl5RSIHVVVQnWoOZpGJwDV\nEiQdR5ATZKywlILhB/Moq58O3BkBFY+r57X2mFqvxTgum05h5tN7yFoYxa0St5p2vWbBe0SvHBgZ\nl1+vorK2iWp/BsydKaRXFtT5bZI5fboJqdEKVDnQguF336yjtr6FoVwcl7sHQB/hy6+tE+JOL+is\nl0PclxYiwbyJ+e2LFPIDOaR/TvgBWApIsDGwHIvZTx9gaJGMbQ3NNzrcj3dqG0k6ziI7MaJXQ53m\n9m4UNKan9KB57pNFYOuVniC4Xt3Aaa4fm+q2pgU6XhYFQl8XO9isE9RWZ2cXx1URYx8vQ4zz2D68\n0FvlNLReL/ckN1K/4TSv+w6pvV1g9xX2vlpHfLiI7MSImqzo1t6HrG245HNglS0toDN7B7smVIW5\n994vCArjA7mTIYqg43FwDIOhdAI5VxnMYNJoZ/Iy6RiPiVKhxxwV5Po0n1O7b6v0K/HjEjoZW9Q9\nyLmn36xdJWziiZxXq8y7X6d1bC9LHfTPz6Cky1Xbx7mB2qEpQFZ5UjTFrw/zDBpPCOpRI0bOP3qA\ni8+/1Y+jFXbc2zAMrpDdx2tgaBp3Hq0gwdIqao+YW1IjyhztXWI0KNnTe+SqYW7zmbTUlFWUUGZ5\nDsz8rGVeb57V8GHBIGdPxlj81eIAxrM8uFYTUqUWmJz3K/x0O7ZvLWqRKnUkGw3MfEqkFChKQrHI\nqdk9klWuihJaz7fAvnyJgb4kOnQDMysLrs5QWFiZ1tfZVF9GGMmxJEdjPglLj/TtZGiiVaP9Ngdz\nRT8M5DLcAKHUPjxDhlGuVXDSkR3OsNn4yTHEBvpREhIQ4075RjtcMoj1M7jnnw4l0+G853mic65m\n3+eTALe6oZ87+oJEoY/nMChwoDNxiIn3X00lphCODROs3z+oC79AttoSWtOT4F7sYTTLQFieQ2mw\nH95j18vhCeKScDMZz46rll5kgnoIGzA579O8iOqkSbuv9Gu1S1H5BfZiPIFrpgkOJpkeFY3Si7Ym\nr/kfbi30576QKjW0YjImORn00QHQn8R1s41j1VHwhpLftmOuYE+k8K40SpihUxxmPl1x6PraUS1h\nrsmyHigUJn/2fVCjw6h/7y7erm5j66xua7+Cy7WYyZUopFcWXWSJyGfdOyHRoZHac9D2l9r2Dt7+\n578BFAX0zBhQbVqg7PqZLCoHCk6fPAM1OuQio0dhklNAHx1AyifAcSzq5+eQM1mILOeJjtARDi3v\ne42aAPHbF9m0gOw9U8Lsnvk50bo8nmYz/QKGP/sI8icLaG8vqIkRyseBsr6butjBTv8I+F8Qhuh9\nAML6Ns7rJOlcEFhUrxqAupdtn9UxPNWH7P0Fi8NoHVPRIPTVyWn0zU4CtRaS8yMArD3sqeU5YKov\nNPGh2VL5DO48WrG0DBhErva1lfgOE5yMw3/fx3ZNRHpuGo2tXVxmsxj7+PshnFK3QM+9R/p2EExO\n6bmocOO6KFvGhMZFoJEv9wpur5kiy0ivzJuSeosu89dbXtMteUkIERue+837aIm0Sr8qSCzNQYwn\nwEKOyByveMoEBt3HzZIGJl+IArh8BuJlGfb1xc7f9d/WjzGR4jyLe2bUjqwAnRfbiE2MoJMQMN2f\nRJZT0NB+o7YeEAQt9OT2YCqGa/V47uOaxuKPPsDY8h0AZB3QuEC87H0USHvbrnBzc5vPsYF+nH/+\nB4I8BNC+LqOvLwum2UIjxlvQcG6tZEem8Z5cXvBFQ/ntncz8LHKjQ2Rf1jlD/O2WIxgK8aTBjpkw\nSS3M9CdRbFbw7uINYsNZFVJGYHpJU8bJvImFWTTNfZ2AUbnwlhxTUFnbBOfCxBlk0aC55kApzMsO\nMoNAzB1y6dxYwy/iJHNMNpA2UqVRpPb2CGzr/iLi2Qwqah9buODJ2GT92E2j9fxbpf7C3LMYT2Db\n1J+7f9XAeEc2EAR6j5X/82HTSSSWZvW+ssGHS/iXqgKlWsejccIy/j5NqtTQfL6lw5dr61uYX5ru\nKqmlbYDy7h4AILO8gE2hCH6cEG2VA4gZvRweMe7OD+IXtL44reIfNk8BEBgqAE/Ug/l33puGC8x4\nvgBpZVb/fjiHzXCuJFnGVbONiRh03eDbtpttisacraUTwNiwnuwbEGhkEzEUR7OesEy3Tb+PZz17\nVLVzhq3m6MefnAY/PIRrAMz8CGY4xnPj01UEAiui7msg/fABroUBvf1KG5va/dmvxVxxC3oXveth\nDFq/jfdKMTTEk1P9k8bBEeKptP47d5UDBac1EcdVEfsH1xiVWVdn9LopkRYJisLIwgyKyTioeNwV\nHaHdv1GN89Jn72UFJ2qwop6bywGffID04lzI31lNS14zFKBMTQFqK0VrehrXTBzO2riCTr2h/7/9\nmqLqfYuJJEazKb1twiLH+/o1Wn/kcLH1GkBUaC6N2U/vY2hxihwrn4G/sgiFBMug3O6gLsloMHEI\nD5bB//h7SC5PB5zTneBu57zu2iN9+3YztE43JMbu64WBqFQUxRmk3iNVyfSisY/Zr1NbI5ttCe2T\nU1wdAJ27U3p7o3n+uREidt/m4WXWtdN+35qiGLsyD35yFPtXDUK2+uoigEfIaVKlip0vVnFYJlnJ\n6y9WTTKBwdXlbpMfFl9CIeSAhZ//UCV1NKDlTUmC2JHBszR2LxqIJ2Kh7kszjmHw4XAGfDar7y1u\nqA4zkunOoxXMzw9D8/Xd78maQO21eon38dyTgjdvL+ilkWuUJQl25IfcbBhtiBSF+s4erv/ff0BT\nYlCfnML1zB08nOozoXQMvg4tPjytiTj+3RMkYukAlLvb3umyjoa8q1tLCOgv++kLV8mSytomKutb\naD/fATsyCIyWQNE0YWFUB1k3A8Crr9PLpEoVl1+vAgC4OGdj4gy2SU5BMdUBneSRynvrPxuLQ/iX\nHexMUuhLxl0gl36yV+Huy+xkd4aHcD08hNnRLFL5rNrH1kvID9Tjhen59zJvSSiNXMnueDU4HpmV\nBUJ6RAFcLq3LSHqPN5U3QiiCf5jGSDaO0waNe+rXKq0O6mo14P0apfcQAiB9jKbAxT0gc+enSK8s\nIP1wHpcXhFRw9ryO7TPyi7CQXi8G27BWFzvYv2yqdwa0y1UwjASgz+dXfpuGO6N+XZSRjOj8m4Pi\nZIzjYJqVAAAgAElEQVRFmmNxbzSDGO75Ek5FNbf539ncwpHpHFE3RfucrWzsQpib0iujAw/vulSX\nvK3elvD7vSsPIkKS0CUyetDvISyiwOpQ+weNqb1dyNu7AIDM3DSaYxnP1gG39STIeXd+Hj4Z2psW\nAO82HA2Sem1CO9V2DyDMTKC28xqggPTKvEd1j6gcnD55phPUmpMi5mcuxnlUJyeB9S1AUXC1uISp\nTxYc8pde78pLnz2qeQVNUYsHTguzP1rJ3ABgPGe02I3nE3g1dQf8CJFFLXM8ZkyfzxaS4FoNHK1u\nmojanHwFYa9XG1v5viRa1Ra0Ndy8F8Q5FpW1LQsxXLR92oms8DMxzkNJCUh+8R2qYgfUn32KzEgp\ncN/wSjbqXAWmHum7g6kQ+1AvLHqyKiqUF1DQPDoBK/CkDckHUfkhKBQ52jVIDXqfDAXwX34J8bdf\nAACqv/ox6v/7r/T7654QMaq5r53afdsVxZj5WV2GFQD2L5sQO+F5R5qShJ2LOioqoWhd6mBWklSZ\nwDBV56BnG7JyrcCk8ED+YC4snDdI0qeUioPzkdRxQ+30D+RhTtTZx4cdybTD8RjRx7a5jdYfcekm\nFWxuwb0p+77By2b1428DWdO92Yipp6csxNRcfx78yCAaB0dgBR7Vg3coDwyBYxkkDvaRWJrCWJ6H\nWGu7Hl0naFctKtLiJkiNW0QIEKIzqdG0SZYsGC+XAlKLd1B9sQuaT0BuNHH2m8+Rvb8IfnLUZwAQ\nR7MpdRx9Ut59ne5Zp9r2LsqrG4CigB8bQmJsyJSU8M9mV9Ze6NAQfmQQyl/+VIcieVm0l+3mTMIx\nee3f6RV0x5wpFpbnQFuqY91ZmCSHtec/nHnd82GlaTmXJnmp/bswvwRpdhJSra4nA0AB5WdbYNJJ\ncP19FriN+TwNjserJiJtTrdhZidD618P7uvyg/BTSOQyYNvknqIENGw66chEs+kkWFCRK6UdBbjT\nx6P1fAuN51vID6bQoRue7N9+m4Z9fHgx6kfNgmv7doJlkfSEj4cx9+SMXYZPSwZY70/ovqdfAdKL\ns77VJbOZ5y9DAVWx45A31KyzuYXTJ8+xdVbX21iC1qJuKupcq4HcxnMc/nETkqKAu7rC06Gh0Pwx\nfuf0+ns0B+WmFXAZZ0+e64ik7P1FvQ1Hg8LSDI3O2yPER0pQZBmphWnQ8Tjyjx5i8IN5sAtzpufg\nVDmgRoewf3BtU+RwWlVFSwAETUELKbAh7s2szw445RSjjd/wrP1Wi5IYtf5Oc/gbe/tq0o9wB+z0\nE4j+VF8C41keSY4BS9OWBOpMf1JvwdNIBXtnZGxl+DhOVb+CrGFG20RqfkpFgdyWWQMiuVbF+dEV\n+KU5JCmgXalhgtOILaObhvwBx+N+KY2PRjORifLer/klAa1BVWNvHwf/+b/pPmRR9SHtiErtGWye\n1ZAqJNX2mPDBUJJjMIMa/vDbLwBZAc9SuP7nL1D/8YdIjmt+nUGICDg5U9wtCl+Qe/LSvHa6KYrl\nRq0kxh2FIIzNSTa/PUKM85Du3AGeE19WunMHYpwEnzevOnsfI8iXcBYWgMUBARWFQbXa9LmvMKid\n4MS3XKuiWgNei7TlWfrvmVSAb5mMnPxu7L3Rn1FiaRbbgoH81faJm7CP9NrsvuRO/wh+tDhlSowD\nxb/8KS6/fgqKZYBMCmcsuQNZIf9pRVWzaePl9MkzANCT85p10/JlNu33XH/S8zu32jIgVeoOyRJL\nH6MCsJk0+n7wEOLZFeIjJQCU3ofhbqQfyCy5N/TRsiWYcfZ1wkE0qOkAVzZekqTE5itI12XUWk0o\nHdnkdHlX/MvrWzo0pHFwhMuvn3pm+oNetrdZe9q8grhe99JxrYYVdri3B651F+BSFkm66BAi783y\nppAkO/dAU3K2J/xkqs/Gum9e4EgyQKpUUd/Zw/m/fQ1hYkTfpN3GQtTN6XaMJN+0OUM4FZwJDIDI\nbJVoCbSQ8kgaMS7M5Nr4klG9JJ1nxiYko3l0pp/Xu3+SjZRY0IK0g8NziAf76CulUEolcPrkOajR\n4UhVbKlSVaFdxDR5rLmCYLt3N514exac0a9t9V1Z5+/QpPC6y1j7k6TpkLKWOxw/Sk+/9xwLu8EY\n87dxfY21loyG6dP9wzMcVQg0M/vFU71K6U9i5H58ICSDc60Bavc1BpIsjqsi5J3XiLdbgcorYc75\np9c+VrD75kyX/RxMccDqhi5Ppu2DoCiMxBOItVqg4wn0//Bj8CopVUmXKvMyotU9KrO+iRgjcQLP\n73Rzf25OpVO73hl4OKGWxILafDRN8ekxsk76zx9rK0Zz/y3iIyWIHRkvv1wD/4s+khhW1RTMPdna\nMyPXajjsvYXeksCr3GjBgK66o0B6RwJnPb89IOInR1SUJvnGoOCP0tTMLTEXe/kS2S/WcFwVkVJ9\nvfebDOi2b9nPJyM+h1Spory+HehDmgsz/R/fBQr3u7gPCsMZHqPpOMrNNmQADYn4BldCzYIgHXh4\n1xXG7XwOUfiCknrSjmJotNTkZZjnmWCd7QTjSWAqT3yMoHU5wbKIL82hVhwEAMQLWSTYbqTZnM+A\nHGPDQiJqHCNau0FHAeYKaeT7kri8qAfcVzTUjn1uzZy/xfa/vMBprY1zVaZyQIiHauWsix1ViULB\ns+MKSBEvmmSsZvZ3UF57Af5h2uEn9LpdoddmT4ybY9DG3ms01DVMWJ7D0HA/0gkOLVNhmJg2XkYB\nG6oyCjI0CFna93AOuQeLrr/9k7CgWcmdAGFmArIowXyDrOAebEqVGk6fPNer7LX1LRwMD9kGojU7\nVhclC9Hg68UZPJucRrbTQrYqYiCbQubePI7+9r8jMVwCw8VxDfQYkuL9ssM6VWEr/73pW7XCDrVs\nsVmSTmPUjN5f5bVZdtuvpeg6vGbHwcsJcTu3NibL61to7h+idXKOWDbj2KTdnu1MfxJDaZIB7Et6\nScLdJjO7EqpCpjkWJ4UkMisLgFB0QPvN/eX09KTpOE496tlP7+Pk739rUXtI/flPAZCkDC82jOoO\ngGiJK5NUXH8SHEM7epzNcmNeGXlzRW/GVNEr+vb7B2XBBQyn43h2TN9ACs+wsPPa7f40RvGg35rv\nTZtj+XwSx3VZ17uOMiY7m9to2PShp8/e4NVjkvDML9/BcVVENsFaWrjCrUXREpyswIMfGYS0fwia\nohAfKeKaYrF2VImgvOK9JvmtF7ftoNTFjkU95LjWRl7gUC9X0OaM5wpFwVs5hom/+CmSmbRl7XSr\nRjjNqozgvnaGSda4B1Bee5Lb2Hdq1893pQZjNu082hq4BSDz2UMkpkZxcHjtgVxwOqyNt8eIFfoA\n1m98Bo3fXpGyGWtT6qyOoThtSSTaUSDO892cpMsrqHJHaQaZGyJqCwMpDnmBA04PMcrd7eo6u7Ob\nVJB7s987CjOv9sB9MAdw0RtuEqUiZv/6Mxz8w7/iuCpi+Jc/wpmQw1WIhDh5DiTwzawsoPBwCVKl\nHpovqERL+ncVWQaTSenKCOa1031dTWEGcG0nCPM+khyDe0MZ7KpzcrrfTHQatr3RaywY3CsASdiW\nLK2p3u0GXmtiho+jxdmDxZuaMbfkWhUnj1/iuNoGTQGN51uQi4MQEzHbM3AqPO2c17D6roxXFw2U\nshy2T+pIxdkI3B5ELhHQikpW4xga4zneJT7yWzNvW/XIauHiK+O9p1eWsDI5jlkTot17T6bAptOY\nTqdQUu/JGxnqtaZ6IUvJWD998vxPkxDwq0bZe0foWMzRf9srJlMz0SBNAa+/XsfoyBAaCQHM5CTy\np4do7rxGLJsBzbJoHByRTT/g3jLLc2hfXetwr/wn9wKcQufLvp0BfPO+VTvsUOt1M0vSRevjCet8\nRCer0aS09ATG0QHGubtEaip0YoSMydhAP2RRhFStQ9Mmt38vqmpC1Cqut7k/Q79Mt7nSrjGlcwyN\n5vMt9H+Uw7+ekWruo/EcuFbdU1bGTY96YDBHkgHqczr5x98j8+EKZguCLk82mOLQwT0V5h+VEI9C\nKp9Bc2UBF398bulx9oL8m9cMQMHR3/5GPxa/+wo/WpwCLaRwWGlYWke8xoVXsK5lxd+vOTdFbSOM\nehw2LeBlXca3r6x612HGpDHeDH3owvIEzh6/AigKPMegubOP/MIkcHaCAYHD2MfLSM6PWOCj4Tbx\n4O+xaQF9nz0C881ToEOhNj+HbypKoPLKzez9MG0DJLmWWp5D7dk2BLGBs5fneHFcRXplDvLsNGiV\nO0G4Owt2YCAUhN/L7C1WzjHhr17hHUCF25N4saG2RjAABZTXSeuWF8QYCErOkDVTliRDCQQAKArX\nX36D629W0ax0QiizKGi12+DnpwAo4BgixbWjJhJmC0lovbhh2x5uWmyI1hpoP18vSbqcQZW3+kaw\ncS2T1B8FSOWqzthdmZtC/pMHIY5nRa75EyGSe7CvM2G0473g8l5teuZzGD7kFa6e7SAxMoj8Jys2\nH9JamOnLmrmBohqN4q9+Bm5hBspZDSc5OymzcU7zWCEInA0j8P3dE8wNFFWeomDjxQbkuibnSunI\n4MLPfmAj2CPn9lpX3doJwvmfTp+tLnaAeAKN6Um8/HIdgF0e0mwKqpfXOH3yTC+MGckPE68KgOrk\nJMpMDImQ68BwOqGS8jJ6QuZ2jIxVDgBMyV5ZAQrJGE7Ufxu+kDvnkqZqlozR+O5NGcPZBMSOgpOq\niI9Gsj7+NeBVVLKu3YtIjxVQEsl6Ym+3cb5rBTvnVTw7JvPw7qCAmX4vRv1eJQ6ixlck7gu32rus\nQ67I0OBrtCJLFZzW2yiLHVCUglmPX926yoCf02TuHcneX0Dpr38OzVnVvmcfAGxawMDDJVybWwY8\nJbKI2YkG03EGHE2jAVWyZ3kCV2IbibESqhvEuTJImILvTUtqhJV2sDpVYQk9olb+b9pG4Hx33fc+\nvi+G0P+fujd9jiO9zj1/uVTWDhT2HQRALAQIkt1UN6WWWtZthyVfb+GIuXMj5uP8Gf44f4pjYm44\nJmLmLvb4xh1btiyrJbV6YYs7sRAA0SCJfas9K6tqPryZWZlVmVlZBXQ75kQ4rCaqst7MfN/znvec\n5zyPk38gbBDq3tRjo0Nk7q9SLRQDEj3hVBMsux5Oh26foaPSPpiwNzS9WqVQqXJvVFQasuUqJeM6\nWh0a8mSYv2eRZjWv9/bjbxA4Jh+kyecr1DM9bSH/ls+weqacY4upKqqmMj+Qamkd6cQSmsx0JuaS\nROwWPt3punb6xG7RQAW9yto18IxYa05TFDRVZrJW5ODzdQxZYfL2LNN/8DNADhkwt65Nq9UrWikz\nMpQxYd7uKkb2yRr5jR0ifRlml25QvXWLY5Po8Nu1qx/qvK3hkxJpU6uYOTJD/UQ//Q2HEeHPsk/W\nkf7wY+QbE2iy7NgHWys7YeyqfspJ0CtHNY+APVgGFAQZqqbI5kEwS+7FK2q6jr5/FAAxFi1Tktlr\nbMniuX1mnekbU1j6QxOZGNmHj0nfvcVIKuIrt2mpIzRaFG8xsTLHdCaOmk4yYQau22d5/nHzxIdc\n87u1ML2m3cOl62b/tXWo9eaM6W5teOxxy7N889f/RXA8TY6SX98mvdyOaLjWglwb/rM/xD8p4H+A\n72isd5Yo6DXPVkUvPqP5gSTpO0scZQYofv+c80gUtYlss7kwk7l3i0I0hh544PRPVGSfrJF79AI5\nr5OamaE4O8d0X4x2JMjN3Fe750VGR/o9E3EqDe6V1M4WqZ0dLtMakZ4E5aMzgQq4u0xsdMhn/N+G\nX5VsRNKzgxy75yUUCfZjQ0z/7CeAWx6yYdYedEHpuMBISmMo6UaBOnlVjqRIAMkuLdd1z4dv6zjW\nOleH7i9zYcLY++4ts3h7wuRAETGUV1zbH1ebrgqVap07oyk0WTZ5Uvx9nldRaWx51vOMGJaUu6Ab\n/Pr1OYfm3DwvVcw9K9LyDLoryvklEbzZ/a+qAOU1xquiEdV0ktjKIrl/+A2ll6+ITfklAr+TlgHv\nxd09a6TgCLgzM+WCYAQ9fJto8OvnyLJM7cYka47Mfs9AAunDu1w8fknfR/dJLc3Qe/9O4DUb95ZE\nj8bQ6eZhdjpJvUkGuyObCJMtc787a2JaknRhJ2bLu34sOCJas8OdmGP8gVwGnVe0uk/0eI8PvJAG\nnVnQemnvMESPcN3RG9hz55bo03IMza376+4fjKkKC99bZOMrEVLf/OgOmZs30P/4xxz+v7/CqNUY\n+unHxEYHMbKFFmJIQSTU2Xp3HlDykTgkI7Y8TzDkX1jwcwmXMPNjNN88KbB7XiKuykz3xZgfCN4M\nW829eTjhfDG1k/lyXSz24c3rucZGB1FTcfK/+DVJSSK1skB07y18b7XlHYc9eBb0Ku++fIry9WMu\nX76iPDFMz3/4YwbvN2DDznVRr9bIPt9mdOGmh/LKd83r0c6Cgna3T5q/s2TODY3DJykOHc8urirc\nvTnikH1s5csZHOyWz7+z+3ER9E6Omgf49uaukilk7y3b/DzxiREkVfGFGFu/LVBaZltKreDBTC2h\n7X7D/R+tkFvbIV+tcd6bYT9bZSSlsDiY8JHblFCWFrkgactSrhVgdCSOikxCk3h5lOW/r4lqdLM8\n6rdpnauQ1B1J/U58TI2zzx8JEmVDIme2CFlVQ2/OGPehKkyg7LXHDf7Rj+i5J9BlctSvHc9tpf1j\nD+TaXWKjQ57jaPZHNvdIQFzROtYXHGUGWCsIjprDfNkFofZLEgACHp3MuP7dOrwCjrbMOofRBF9t\ni7Xth0b0Kxw0I7uMrS2KoyNs10GVZd/YU00nRYuhyWNiofRq+RzxmSmbq8T5TOcHEvTVypz8epdE\nSsO4yHH+u8f03r9NeuUm6TtLnr8VZFc7GIlYe+ukwKP9LMMpjbFUVLS1mW1/XuYkQ+2/NcPZ03V6\nYxl67txCj4pEisWrokiQzZVbSHa9/ID/Hqg4OLpa3237dRQO6TL6lz9tgbE3X6+ZkyumqswNJMjq\nBtunRVbH0iiShCLJdhtGd9Z9AqhkVO1kAMBhTqdkVFsSAt0luzs5n1296Bk0xquhESVis5NUJJnI\n8gLllmRJw76FHeu76OcQEyiWzUO5BJqfdmXj8+k7tzCKZbJP1lBf7/FhMkFi9VaI/pQguxoUvLtJ\n6kYXuCfhEsqS0FEOyh4X9CrbZ4WQzKLu5+qWpOviMG9Wfg7//p/t4K4bZteW525umg35kvYWfMgO\nRxDnf2B0j69Z3eB6Dyh+87f13TmJphaaWh0SmgJWf3l/gmxFvBNrjkWlOt/7+C7xxRmbVHD4z/6Q\nwuIC77JlHiczFE8KzA94BFIh30mQJSIq35/qIaaqISH/1wHrDlbxqNaxCcUSmhQaBu9VXaqubXS5\noXQnjbU0mOSrbBgyzNZApHkuXTx8wvnnT1DicSI9SSiVqZWD+yCbA45mq+Vz1Da3qb3aQVNlym8O\nufjyEZm2MljfRpLkOve1MEG7MMsnJdIp0HqpW+i4fIW+23OMxKCnqqOaRGvNfDnvVhe5nO4LNaqr\ncM8YWUHQG1+aJfviFcU3B/R/8lFAwO6/D1mtW9VCURwC61IAxNh/H21lppbomZkmNjXB7qmOMjEN\nT9c5yOlMfbgaSFTqnKNxvUgtnwOt1yWPCiIgzcT8A66rWes6dMoO5t+dBPSats653ntLLW2aXr95\n8fAJb/6P/4pRrXLR20cppxMfH2PjGLtq6E8WaiUT1pCjWgBZcx0jX6BWLrsO/moyQd+De9fC1REm\nYC9UDLZOiw3ukZAcSXq1Jrg+tDjVOqQ0xVYfWhhM2Jwc7XyeZV5xpUB1NVCaXvFicKGtjl5ttJid\nFAwSikxJat9uMnh/hcWhYXbPi+S0OPMnbzj/oiG11px42jwpsLefo3RWYqIvjmQSJ9aNKtnnWyQX\nbnZxCOx+P7d8hKUKZK3Tuf7Gewjyd6mdLS7WXxNLJTBmpvgiOQzbpy5m/ZJh8LtvLj3ecfi9o5Wj\nqyGb7Efg6JRB9W5f9TY3jL3uKCwmqK6tkfmXrzgt6KTeW2Xsg1USmuLDMyOF2hO95BK9eAT8rfU5\nxlTVliMFIU0aC+R3CW8Fvcre2xPiiHfZrIzj9MXfvizi1VAzMVWhL5PiIKcjBySDrzUhUK+HPyA3\nsn0NohLv/h3PX2o5CB+MTwf+brPiQen5BpmFGdAacEarJ1cPSbJ1XfJ+3Zp7EgrG6QuSFLV4YC+7\nM0s6HMgs6v0+Y5leW5IujDkzu5IsU73MoZrVo24Wjt9zr3YMSQ+ysE48nOxjq7pBq3xkUAY4DArA\nj1UbnPCjxmf81R5SxDJpskdZN4N3XaK8sU3fyjwW/LKg13hRi0PSTcrlRUzUaXbfyX8AotppMUyH\nh/xfD/zQ7mvV/Mfcjq3cWZ1r3jwiQwPXsKF0cmiVWBnvJWVuDkEJRH9ZpUZrRm5tByNXIHFjgtz6\nNrFYzLftKqHJzJ+8sQOD+Y/ukFgabPlcTFUYlgy+2dmDep3M1AiRpkpOEE+NtRa7IU1sfgbNVfer\nQMLD9CZ7E1010HETz9bIfvWU3N//A69tubJbFC8uuHz8EkUW38s/Xcf4o/dAidHerpJIqXOU0zko\nK8RvLdOfiJBenvf5fvt9KDY65D4I3r0VADH2NtfckEDLpDn++afo1RrK6KQN9ZUlkOdG7LG1qhk0\nEiUWBPo8qVG/t4yytEC1jisonb9StczP/NehJTuYD3g2flXC+Mx0YBLdyObJre3Y/11+c4DW22v/\nd0wN4ulpJBMs1Ig3WbN5b49fUtN1KsenRCdG6b23TDCXlPd+GRsdZNhEroFoGVCTMY5/HsyzY/VH\nt3CPBKhGtSDuTHMmr602Hm+f1yrHC3VfNEH3VmdHl3g3Okn+6TqDSZXI8jyfnxrUyboOxt4mMzc1\nxOhIlVo+ZycDoNV/OavqqdVFTje26K3VSE+Nmcmeq9jV9vPmtXpvrKeFnNhpCU1hKQFPH73gOF+h\nL65y8JunDP35aMsh0W9f84oLvJKvWrnA7sNnJJMaVoxgPVe/PcNuw5QgOj/LXqKvlSC1bezo9i3p\nlTmO//HXqHv79NdqxGolZn64Yj8bca/dnHHCyCX6mXc8m9AU7o31kDbHMx8H+fQEI5lwIXs7T3bX\nBYGliYpxc8y4W9FiK4vEZiexOTK6NK8xWi3lVy1C2Cj5h89RlO8oIZAt6R2R3Fj9U7vnRXGIPSn4\n9pI6A93mxXH08Dl7JD2YghVb91S8rKAXdl3Eb0Hmvo/rUQMQZvV4WdtGUC97c5a0LVyq6Zqdm5tv\n4fjnn3LdyJFaPoz0lNv8DxNBc8ErCAkLPw+Sj2zI8lhjcSc0OsmOCxKcIPZs65rfXvKqdeMOg2Jo\nvqeRt7vIFls1KzBgsaP6jf26EUre1fx2QZxb8q6hFw8wcGeeq24eXuPs1H9JUvv3HzbzXa/VSN2a\nI/dyi547Swz/xR/S9+Ce5+8b2QLxrW0WB0296+1tSnMTntVfLZdj/M5Ncus7aFXDI8ngj465Ln9u\ntS44q+7jn3zwrawd/55sy4S/qWy/obi2TUOu7BFHmUF2z4tU63WKusFQOkZ/IkIiqpEzPH/Ow6w5\n0Vk/pJNcq1g2OF+YQ4/GPQOMcPtQeH/nv4967zuaIpPa2aE6PsaJFCGnV8l/c8nCYIKRt5YqidsH\n2zwsn+2hmT3EF49eMjozaf/2vdE0030xbg11QmoVzl+FWYfdQKrD8LrUazUhzfziFT3xCPLqIqdm\n0SGhyb7EaM3JBD+yZue9qT1pIpleBj75yJUA8iIT86/4C+Raz/fuAthtbP4m3m9/XKVo1EJyj7Si\npJoRd055RCObd/u8rW2MO4ILwaug4GVhUF1+c8CO5cwkWEGC10WJerECQE63SP+arTlmVTHK4cnv\nrKTb+J0ZSpuvXWP6Ls3pIxIRlb9YHnL1zPubxHAqgiJJjKQ0IorM3mWJ3lqt5ZMt+9rWNrnlWbuV\nCdxxWDPR4dY3x6wfF4jldHpV2eQq8De7DVMC4/KSw//9/0KfukHq/TtNBKnBUspauejyLfnN1+Rf\n71GTxDow3h1h5Euoaa9qfqd98zKpvl4zUV8joUkhvhNcfLWeY+HpS3L/9BWbL7aI20lySy68Mz4x\nqFN6vm5zz+WfrrO0Mtd0/jQVJn7xkFgkzcTcLPGtBmqmdY43rl8f9DqXuMf4Nlvkl9tnwHWcRyWb\n6DUSaZ27lv2byA5aVtBron+q6dBimbMn0uloZ7Rw/W/ZJy85+fQL0Ys4OUr69jz6mdDNbH5h3VT7\nOzvQeweozU6hk8XV7Pyt3i5/q6OUxPVv9sd5ddo5hL2Wz1E6l7EWWdBvOaFM1oYcGx0UJDmPBVog\ntTRjBrzhgyWv595Z37Vl3kGnP1Gg0gZ22BhvUIuA11wblY02hFxivMHZcfH7uYe/J/t4LSR7tvu7\n4CYiaxdkXi2pFXxos9QjLD4Ci5wwiPjK21dIIdZTZwoOQUGcE3Z6Jy0zHqk19OKBkS/XmHlvgfx6\nI0AS66J74hi/ORVT1S5bJZwWLM2kppP03r1lcrC8T2J2kvTKAl7rojE3JJtJ3r+FSCI6PkJksJ/e\n91ZQYlHi05Me42tdF9eJ3nKq1ICoutce3AItvA6004LWVEGvsTkwQeYT8d+byYxHT3arVao1vjET\n6333lok+fUpk7QWJyTFyGzswO4vfITW8vrh/AFUzDKrjY8RMcq3gfUhYc7XOOmA6OXG6laszsnn7\nfpykhtb/H0pqTI+m+PSoYvf87r09QX743OFvnD5YwFObuVFAkKiODmtt+Iy8UCZ+EF/v79fKQhHG\nv5feP4niNee8kEqeSQZ7bd9naukGkdVVwE9hp4l4zJFMgCCyZqePqaMmnZwsfvrvQWOXiY02yLOs\n+78013HP6qKd/Leu3Z9OdMg94vY7QfxOVgK4df6I6zj9UlCCy4nqsvxqM9mZ3xxwQtkVCQbkGiyy\n/X0AACAASURBVAPmodMiG3ZbdyRnzci+iewp+t4+SiJBammmK/6A7q3xfi1ovzXGsGOQk2l67t4S\nfr8ONx6skovE0fCaI37v2Mvc5NRrBVGJrm5u2W1M1nO1yE2PnFKeJqpHUiQOnm5SKFep1evsP3zG\nUAtBqr+U8lICNEeBol6rIc/N8O6pWLPj7835IIiC9gnnni/b8b9/W0MwQtZpbilrcW9aucTZ2itB\nCm8nyS258AYheiKAe6EZJQG4FD6Eqkbju81Em5sDEzxYmCaqKg5iW+/rqx+/BzMzHmMRc0KcQRr3\neT3oczEHItp3lBBIx7QrV7ybewrH09HWwG7WzW46dH+F2vhAE/ymyIFJTES9TvGbd0QG+xn+008c\nlahGlsw7O9qOsC88xDIoQLUqMp2TUjidf52aLgU8+xpvvnxK6dePKNfqDK7c5C9+cM/sffbOkjZv\nTFbfWDUdb9KobzbHvZhQTf3sEisRk76zhFEskX2yRvb5K+RIxKPlI8hxtMrIGNkCycUZ8utBGbqr\nWbv+vObN8yezfYgF3n7zKa7vdEXI1TDx+ydfPuL8b/6WnqjC8MQIhz7s2V7fdY69QUTmX4HtbKNt\nnd/K0sI1HdrEWARxU8NBv/vyKfL+HpqitFlP3a09ryDOgp2OpDT6v9lh/ek69aEEF7tvoH8IgINs\nmYWFOUaXBd+Hd1/+1Q7xqZ0tUc1se+/BpqYTZG/cYOuzJyiy7CPN1Bh79sUG+fVt8hu79lpvXccN\njgm/FiKoUzKqxFYWKb1Yx7jMob874lj/lN57y55EVt1bcJW2WaVmJKU5+ie92deDDjNe1UW7MhGN\niX5V8+CSWl2EWXdVVU23St72fnCPdTPQj0lVLh+9tPu7zx+9JDU4HLrKGg4VUheol6+fU8/mqFzm\niPX0Ubw5S2l2PnDv96/WKW1QUo3f9kZpiYTtvsf3Ww8xyyi9vWinp55j9DKvg1Bxx40owOzrbTYv\nlEn/j98L6f/qFHf2qOm6/b6H//Tf+exvfknjblWD/P1SO4Wd5mSCH1mzPypGrMvC05eUnq/TiB+s\nZxxWQ96yOtVC0f7fYg6755tF4AndIMyce0Ird0IwZ4Nbn90vrhSoLqUN+qm1fbC6tk6vmZROrS4y\n9sEqAtXmXxS6CsmZheyTFZnqm32YGDVJX7vlD+jGrkPdSpwL+t9boTo+ZqvdjI60xnZe/iHV18NC\nzYOrycdyM3P0L8xAvkxiacI11oPxaYGEBmrjA8ybpJfHn33Fxfpr1EyKWDaLPDjEndEUAz7Jxeb3\nulaAD1cWKT3fACC6OM9Z3yixmHhHfmgv/30i6Zqfi++2iLzaRo5Gia0sspFsJOrCFtusPePdl08p\nPlunP6FS5R7cabQy+FmYOdB8L9kXWyQXZ8mv79gxVLPM7dHDZ0CjEFuoGHx6VENTqiaxbaMQ6pV8\nTQ4MfUfrILxda0JAkjrrQWw+cM72x9g+bRD0bBy3Sl1Y1uyQ0uD6Xc/Nrl43kwHuA1xcLzKdibdU\ndYMZe+27vmLWRmx6AvLup7UclDlrOP956r7PPnd2yavPntJX1SmuvaLy8iX9QwkSP/iexz01ru1k\nPw/qG3Oac3FJsixYfu8tI0ejdr+0k8/Bq+WjP662CZYa0FZn8iG9cpPkwpyrfyjo2Xs5C78MvVH2\nr9J0UpFsvv5SAspfbtmVlPaEXK1mEaBIG6LqfFkyGDk4YuHeICOe7Nnu7zaP/fYNJyHc1TWsvTaP\njCkT5mfhILCNsejVKimrN9jSHDfhe2Hna/Nnw8Nw3bDT3mrZripXqzWUdBrN0NFVjeTqInIy5aEX\n331/pHNOxfUiqZ0dE9p8FYKbOi+Pcvx9PUPmw+/TF4+wmenxqViLd59f38HpI6TJMU/IpG8LkQTZ\nF+vsfL1uy8pOfPg+9X/5jUiSSRInn36B/PmjQFLS8MgV7/adRkUj6eq/A7M64yJqExrdTvZ16xpW\ngsR9mBEHWC+yt/TKLKmdHazdK7Wzg1a+DZpTKcBL8jZJeueMlzuHXHy9zkBEJaJpFPf2Maa9k4t+\n8975SiXZ+6BlZHNs/uYxl5Ua+S+eo1erlOZkotmXrK7MtlHd8I4TXHwlBCUiOiNktPYCL9isc81M\nZ+JCjuvRun3toPYUqLP/337eZrzCvFAm9ft+atBus+5L7UnT99F9AOIz03SeCPNWDQrj25z8So12\np3aovHBJTgsV06xU8DZbYu/tCaVfPLTl3hrPuF1rjVeb6Zodc1w8WiMyNOg5X7w4AzozkzvhP/1X\nAKIjgh9l9C9/Zr635mfhrc/uF1eGQ4M17l9Awtca1c79Paa126jpq0jv+u9VTmSfpMjk3hwQGey3\nn731jL5t8vGrEb0Jkr3d86JAMQNzl8dENrfRFJmqJzmm93z3PhP5tw/rsQSTvSnXfuXkZYDGPhqf\nmaT22df0fPIDLtZfw9tDMj/6kHivP0GqlyVWbwlONUCPxshtn9pSiv5oL+942Dk/+3c22Pybv2Uk\nGSE9PS5Uou6nW0g1278riRmtxtnWFpfVGsd5A+U3T7gzM42aTnsmyfse3CUMCsrn1kgvL5BeFj7a\nvV6t9zwJ5vxQJNF241SYyLzesZMsAnHg1Sbqfba7znbyTuxaEwKXxXKHBE7uxQJ1V0IAgkhrWh2S\n04H6TZAGMqBqkwTln66zDnzwyX1+siqyz1q5GMDY27l5v2DZlZTozeuib0iSkGRZBHvpdr3l7ucZ\nlJyIawqFxwIxAZB9vEb/7Vtt7knquG/M20yGWwlqpVY2YacpEpQNf1iL01yOpF7n5MkGyuwNwryl\nICfkBf8LX6VpZ60Q132TSdsK+IQj6vx5F/UqyVs3Kbx8BZJE/3srgezZ3dh1MarG1HZOr31A6RyL\nszdYlmAwqXagRR9E5Ba2ci/Rn4hyeyTF3luxUY5qEKsZpPp7KX3wPnIkytBoH1aF6vreS1PyLhlO\noivILAb1OnAma5yVYbEabl22N8mRcGnobKeX5zj+8iln50VQNc4eveBdJE7yosJ4XWG4JyZ6mU0p\nMv+5Fy457RVcOzfyhhTpckswbx1gbeigicbZOIbM6x3Ka684++1D4hMjRCdGPcfavJZya68ZUOv0\nWn2oLiivO4h0KqEUdINsucp0X4yKIhNZmMPY+wZVlum55wfT9jbroGi3202MUNzZdfRjCrmng5xO\nIqZSrFTJlg20OpyVDLbPSkyMtmtz6C6RfjXf0xozzA8kyLze4fLJS/Gs7y0z+pd/RCv6xAu2nsPb\nvNiwW1EmvbEIC4PR8EFfHerXtv7AL0niRXZrxSmFimEGvtHQ6jl6VKzBdm/beUiwZPqsfznI6fTG\nGi0bfkmEhCa1HOQarWOdIgq6MyObJ7e+Q7VYovzukMLWLvV6Db8DwcnRGeu/fmQTgVr67Km+cC1J\nrWgwNyrLCQlvwNktVE24llhFgum+GFq5gFGWQyOzrJYRy0TSKdEFv0unvep1SobhUlXoJBbIPnnJ\n0cNnrB8XBIx/fIztz56Z/ABSS/LUiVTy2ovczzm4fTilGOiFSsixSkTjMaKDA8R6xHwZuivagRpk\ndLiene9h0yRYV6ljSSm6/t70jPziYd30fXG9SMksUIHgEOkZ7GM6E3etTVFsa3+nJaPGScGw18lB\nTmfBqJqxvleSPBUSBRVMTuxtEmo6zVw6xahetRUmLIvrRXNPEc/NiTiwrk86EVBYC4pfOl0L4U35\nq7/6q//tui72T6/O2TjIIknQn4gQNikQUWT7/yQJTs3FsDCYIB1VSEcjzPTFmOlLMJJuELW0u250\nZJDUrZskPrxL+gf3Sd+YwGK1rFRrHB6eUvytYJGUJYienzG4NEM0EaemV8itbYkqiSIjyRKpxVlK\nkkKlWiNiMiIZ2Tw1XUeOtrtfif5EhImeGDN9cUbSUQp6jYdvxSQylAhQJ5m/gHyR0rsDKifn1I2K\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DVALQc+dWy8Gyls9hlFVX5dq6xyBfcl18LCC4dywJR+jct3vtv1Dn1WkRCZgbSCABf7o0\nSG9Ma9vX6sebZHEUVNfWOPz6BbWLEqnFeQ95XHHt3fOS62CVjMjoD58Rn5kkFm0geopaXKhy7O/Z\nB7nE0oT9/MO+l+t5JzJ9D95zkJIlkCNa4H6Q6U2RdFT7S0a1hZDVTfYrnpE3kV2CUVk851Sfm+Cz\nk/trvEOheJLa2UFT5BAqTnXX3hVrQk5OZ+I24a//Qd+KgXRSo5OuZFDQbzsVfxKaTI+hosoSqQ64\nzZqfqbWnFrW4UIF5tsFwtEr5zQXRidG2Y2q+vgvhkdIwLnPkXm4hR1Tye+9QxsZ8EbxGNt9CVmup\n7jT7n8nxAYZY4fKp2Bfik6OulrXmueBE1XT6jPySGM7qdfbJS5fkuIUYsP7ePnZp+AQQLSevfvuE\nxcEEmqJ0sFbbx8zi2bygVhZr7uLRi878QNuCUPtn6IyPFAk2i3B/ZZHLJw2FvWZePMtXFiWo1mod\nvNNrTgj80eIgZ6eFlpdpERDFoyrljW1KlSrVmlkBH3RKKrm1K9sF9u0OTGEOmAlNYdJDstAoC7im\nFQhoqszcjUG2fvEZICr1wYzEzeafbbI3bC1F790ljr5+TurBPfpW5oTm7/Zph9XL68kAFyoGv9o5\nt6sBYQiE/GwgoSGQgHHkyUHeraxynmyuZHdWkbTZ5WtVTkbGGBifJD0qMuiCPbcNjE6TUdMpTm7c\nIPfVMzJxlZ67S+S0uB3giXda59Tste5PJ3BW3sIzNeP6Tnp5jm/++j9jyQzm17dJLy/Yzib8wf27\nyPZ7WYgkhIS5ce10NLaEJpOOKny2myVT07k7mvZIgPknvILmkB86IwwTcNBcbP1cJ4zD4hotFXat\nwZCrV2tsf/aE7APhB7Jlg7eXZTIx9yHM3yd++6zO4Na6lpOpNkzgrZabmWP4wS0XY7b97rUUPXdu\nUf7l19Rmp9Euc5zLKrnZWfRo3K6ONFjF3UHv6Mwkk+MDvFtdpLaxhbr+mvTsBHJUawok2lUUwqy5\n8FUJPRonNzMDZsUsNzNjyzw5/Ut/XGP2B3c4HehFVSTm+ht7wlICSl80uGbCBkbO9VAtFHn7f/4d\n0eFBUvMzV0rWez1/CyJtzZGN4+ZklrB2+7YVqOm1GvGlWYpr20QG++l7cK+DoNxp4VEJIA53C2Zi\nI64XmTp5x/kXb7GC86tJ13VuCU1haTDJ73MiSB5OaV22Frb6p4JuMJzSOMzpFCo1hk0yxGbYv/d6\ngBmtzuiwhpxMIUiUG88t/j9+zfmzV1TrdaoXl8THR5kcHwyMK/RqlY2TAoqiIO+fMjna55orYx+s\nMq0J6LmFdrFaMTpRwLoec/uAdvtBlbsuRI5AtoWRxfRSAWoQUYdPDja+74QoT1YKjA5HkWc/EIon\nvgnChjXWv+B2OviXr+lFY2Qow9zUIGo6xdD924FxkzMGcrf/9bSJPdyKPytDYi1Uw9e0aH6mlk/a\ne3tit5tFU0mk/j4GPvmI2KgbUepMpPTcWSS6tdWUSGkgPKrLN+1YMDo8Qs/iDeLf/6DlmtY6u3y6\n3iCrnRxtUd1p8T8DolUpcXO6RZbbeo7O9ggIG9/77XHu+eNEi1w8fmkXws5++5CaUTMLYXRBNNnO\n/GKedvu3qfxmGJTf7FPc2wdEMqVThIl1/SCUYSjUbMVgy0zMapMjcF+cc2rjA76t27U6DKejFIw6\nWqXskB7/jjgEeuJRylorSZWcTJE0g7DiZYHMg3tIFxcgiV5z69DlJf/k58DDHZjCHDBbez/cUoNL\nLnmsRSurubVN/C9/ijQpOAUERDOMgxTmF7A5tUaN8QHm2yZGuv+tZmtmlW2W0fjJbD/j6Th9/QnK\nubLv/TYHdFaWsrHZLWGMj3PeVDmwoESJEBU1ZyBbz+Y4/+e/JbK8QPy9VY9Kg7cV9BrZcpXqwjzx\nwWH2smXivUnef/eK8y/eIRznIo/So/x29xyAj6YzfDzTRyMp0L6/0+uwmFyYs5nSw5HVCfim+zqd\nv2uvYLuRAAvrgL2rfwDJxRl700kvz5kwybCHFPGsSoZgTP+odETh6TrnwHHhvsntEK41PbX5BgAA\nIABJREFUIbAq5oPOCMME3Pp8xOe2TsTn5gacnwvDOCzMv8LutpJRZ7ovxu6ZCP7nBxr+wN8n+ulW\nh7Fw6JOEJjN/8sY+/M9/dAdm+9pyMyQ0melMjM2Tgp149JfIlBi8v8Ls4CD/unVGHeiLRyhletg+\nK7j66We0mockUoNnw1i9wXlcwo+sMsius6prWW5mzkfmyelfarw9qbDl8EX3x9OALNBlTX6nZBjI\nukG93j6IkRSZ3MtNyoenVPUKtVKZgcG+ru/Hz5x9w6nVRZjtpx1yrnXfNg8ZOR0khYn3Vhn+0z8g\nNjpMp+/Sul4tnyOuFwMS7+61bKkT5J6vk/v8ETQpSYSVrusmoew1tpXxXlLUmeiNudZB50inZtSH\nyo9uZHh2IN6P4Idw+y+/9VDc+cZ1X4WlBdsHxY0yrx+ukVBlCnoV5fkrVv/neotcZfPB6u1Xz0hH\nNQZGenn5t7+kNphg5P4K40uL9ueD9oj2aIk6ejRGbGWB0nORYPOD8naeYG2zH7RwTLSiGtIxjXLO\nnwjWasM4evjMrrw3+6d2HEBO2HHmxXOkrdeNPuhQXC3iOnq1ilGtcZCvkNSLFP/xX1lXNXo+EXt5\npygqK8kcTnWrofhzNeRfwzdZqIvDwYR4tnWJerWGmvSSWBUojbJRY7M6Q+/YGHomTnqqmQCuNRaU\nkTyvaa0zyULE7O2jjQ55qu40+x81nXIgVkR7W0GvknAUPVtRNd35Uj/uhPjMZEvrQvbJGunlBfRo\nLOA8594fnDFsUYsz/9EdtK1twDmXxTj23goCwsnxATsuC96/3S2MfZpGvV5HlWWUnjBS5hAePdHe\nEprCbH+Mv3txBMBEb5Qv9i5ZHEyiKbLrObWgeV6/pq9aopTNoj15zFDEgIHlwN/7TjgEnGQQmdlJ\ntDfvGErPk1qapff+HfsQYL2EaKXMnl7sEsrcbGEgr+7eD8u8tDAbsi11NwmHg7CwM2tMdntRNGmN\neknguaWBaAqourVGINYso2FZQlPpiUc5CtyUPAK6AfdmlwbH3+UOpBWbfkmWKa5t0xNVKCIgT0sr\nc6EdvyLB6/MSsqSxWD5k4IunZNfWYGqM6MQo7754xss5GWRxOPvt7jkrw0n6E04GYP9Mqd+hUjjo\ne76BYPPBPR1V7PcRdDjVqzVKhkHKt8+oXQKs/XNvcaiPX2IUSwIJIEF65SbJBZGUyT7f8riCd2+x\n9awUCYrnl1R+/4LjvGBt7fvdYzILN1DT6SsdyMKgMyA8QqOgV3n07tKG6mZ1w7WR+QeNre0BzSYn\nUw2EkiIz+4M75Hp6OT8t8uFUL++Pp02SP9GWVTO8+wf97qW9hUefGNkC8a1tV8K0tjwLiEAurhft\nyrDz+n6EYs2fcz6rhelh9GjCTiJMZzQ2T/K2b944LpDJH/pIhJr7wcQo0oP3mtZfoqm/sZnQ69up\nKDo3ckWC2b5YEyJG+AshLXjKPRMFlS1XEQRNqkCXOXrhi3OzPDnUgVO+h8Sw5g3LtQ4H2RcbnLzc\nRltZ4HJ3n8rJBdOLM13vJ16HjpgqZEEtIajUzg5a+TbZtb1g5FyTuRAV9TrnE1PU+gfoDgljzfEX\n9OZ1lJkZcjNzIaD+BUrPN4iYc85COwYRMXrb9QSPkiSe1+2RNLN9Yg12N2e9kjOd69Ub+UKLj86Y\nJMMA5UiU2MQw+b19JElCHh3idRmmWtohHTHJVA+VsTGilTInJlJTXHuN0SYOESOb4+jhM0BUP8Mm\no+3EYhNZZLPvdu5VDbLVsCg2P/OSxQxP9ts4BF1QOhatR0OehIP+c84JO65tbPH292uMJCMU9/Y5\n+/yxD1dL6zh2dIl3o5MUn61Tk6CazaL3DwFw+eQlGZMvIOhafsWL/dB7f/vEouUrMOOl9pXkJept\nkA2WWYSMmiJTVOImP1lru69XLEiA320mq41PjIRUHRNFn82TPBtvBfJkYTDB/J0l4jOTlIyqieTp\nfq8L4k6Iz0yRWprhzFR2a25f8LlbzxjE9V6XBjHuWK05FueZwbsvn9p8Qe9WFxn/5IO2Z0FXC6NR\nZfuyzo2lJYaSUdRoJ8mk6+BwEteZ7o1zr9ecM3GNvQu/tnXHfJ/tRz6d5fDv/xltfBSw1so02oA/\nSvM7IhVEQMcme5GXfuiAkSRsCJkiQW1tHeX5cy5fviI6MUzhP/yxL/OoFyGfVi6axCnXH7h5sZlv\nnheJIwLerRPRb9+f8K44qekEsZUFW+9Y9Ae5dShjKwuQHMZUNHRAnNxkIs2HzFa1gyWPDHDCQ19Y\nXK+190/1dMb+AZLXxtca0DUvEOvvzcRZYQ541vu4NBf88Nwk0qhoQZjOtG8XEL+vMN0X49F+lt6a\nTm19E8kMfOx2Fql7veJ2pJbBgaB/csZ5HSfT+VFeJzczw+aRzrSR9QlSxLXDJcDCmSTLDQblukgC\nJBduehwKltCjMc4fPnNVX9J3brmeVbUOqZjCca5IAlBjUU4KBiVbc9bbwrAmd4vO8LOSYbj6drdO\ni5QMI6Aq705+NP7m03vcBFWe0EXCp0He5uYZmJ+bdSmhCNhvQ4u5E+s88SKhKTJ6tYZerRFTRcXf\nyepf5S6YVabmd94gFHPOWe9WiqWExGzfINtnBd5clFg/LjCc0hhORhsawIGkPq0wcHdScomD8Wk7\nuJnOxJmbGgRkc14vCZJc3H183ZmjmnQiyJJUWfZNMHtDXxv3UzIMNvcuiOtFAYU/ypIaSvkEQ+J7\n9f4M2uYeFxu7xFcWiM5OEV0NWw0MHg9gQ0aHkprNAq8pMka+HWuyt3kjKjrhXRHWmOOSGNv+HsMP\nbgWgVNwWRknC8WkXQajdhpbQril4hHAFED8L0dboYZ4V52Qr0iKmNmKKfCRO30//gINffAFA/4f3\nqMb8AtVGTDI5PmBX/UbM1gi9Wm1Kgotizbo5DxoHY//7tlBej/azth/xI4u0/JYF4320n6Vag9sj\naYIPk9208oV/n07i59TqIgdP1+mNqQzdv+0xJ6/rwBIwDnONasUi+V9+hkLjnYUzdwwUUxUol9p/\nraWi7Ed8nbffo0DDRl0+w2//u57qr3uMzmsOzo5yfJxr+YZznTnJagUSx4/81W3OAzs02rbe6pLJ\nV3Eaym92a81kmxZPgoo3x0dQDOJ8r81zuZbP2Ug0EIXC2oNboGVCobIUCR5n60wsL1B8vcMbvcbY\nJ/evuM83W5jkdZ3q2gZDnz22VdQ+WlowiwHeCF/7XJVMCG6i84YfzBgV6mWDZNTbp3wHCYFWx6ia\njlEoCYjBauUi0s42uRebyED0+ITC1098mEeFWf1ppe09Sl+ss48fy2S4qkHwRHEHOOcbO5T+4Zdi\n7MvzfJ0aoWjUfJh9azw7yLEZG6T3/odMm9AhI+vOopeerzPwQYZfHogM0EfTGReU3iI3cR4y/dQO\nmoNdbzInAjatsP38/1Y97I330dobFXajk7g1lKJagzf7J9S0CMmIRHr5JsU1AUEa+/A2t9LDrpaB\n/kRYHYP2v9+OVdUvGHC2DwiVhTE29y44lSK82s/6BCmBP4Ukyxj5Qlun17xOUkszPgzKzjUjqgZ7\na28o/eKhHaTZxINRd8U6eXCAFpMprm0RnxxB+d6PzJ5K73Va3AkvAdoOnQHh+EdABLlWjy2I/t2Y\nqgQmg/z+5rfe3L2MsmtOuJNpEvGtbX68POvK9HdO1tnOPAj1zEPy5m8aG1dNl5nRasj7ezaB1NHD\n5+aG7i9a6rTmZ/Xuy6fI+3toikJsZYHd5DAAc/1xtk6LZGIRlizpwBCkPtazbU5KHj18zh5JUm/f\nkX+6zjrYUFdwt3YF9fGFNae8F1Xv/nqv9gr3exTvofTwGaVfPESvVinMzqJPTjBUKXFrZhi/BKE6\nNETle/eJ9WSoA/rCnL3eOjM3D5BurmvV7FtMLs7acl1+B8d25kRUgDvxdTXCVIEAFCS6wT7TLzgP\nknp1Ju72xyb5l8gAdbza0P5trHvklVdyG49YKsU8NJLczDH6H0d4c1FiW43yJ5l4G7/klDO7T+n5\nup0EXzvUWajlGU9HKRlV1gp1YrcXKDxZ4yCnM/XhakAltyFTB3CY08mYSgp+1kzguHlSYLYv4Shy\ndPIsu+V38VbLgUbCbNiW3BWo0lo+FyhJ7IIdL8wxbpTsloG+B3d9nqH/OIpaHLQ49z95n+KzDUcx\nLHzL2v7BKbvnop1nYTDBSKD6Sbh41PnOrfeYiUVC+oxwyRT/fdd7jNY1/VEg3kUkPzlTP2tu27oY\nS/HsIGfP9723J4zKRqBUc/t79iOhlJrINhvjvU6Oj5iquDgRRsy4TFiQBHaDkLIOVOZvkjSRjoml\niSuNyWn1un9Lg9OEH1lrJNL395j+4Qp6VKzpIIRvMzfR6dQ0//SuTPxU58+XhzzH9a0nBDpxjJqs\nMJDSkAFVDpIYaSwoSZEp7b4xWYaD9UCD4V0QpmprSUGVnq8zkFA5zuu8/vwps38y3NLTYf3+y6Mc\n/31N9IAMpzSKJnSo+VipV2sUKlUWB83DernK8cPnNnO0kynbyvBFK2Xq5TK0yJ80nJYz8QKNQEkr\nl9q8m/bZ6evop+2+j9KC3ns7mHAm2zDL15e3ef35M44lhcX/+OeMv7eEmk7zMXVWhsV4RDIgXOB2\nXQexMO0DcjKFHtV5tZ+1v9ccpDSb/dxNopfqZY7jn39K771lBj/50PFJd5CfO7tEmhxjdGYCUTFt\nZVB2JtIsuPPG21Os8N+Sh7ISWc57jOtFEts7MJhB6hGEMxOuNhD3OoU6+//t5/Zow1Sy22f5wyXE\nvHtsFU4LOnq1Froa0mApd44lXAZZr1WRZZmIIlOvg1rWUdUSaI130M1m670uE74Bl7K0yAVJYmCr\nh4wOi4qQ3fMNcF5kLp3qeH1YMEQruXD55CXx+2mKWpxEROX7/Sq3hyIMDPV1IB3ob9FKmYun6yBJ\nxDWF3PN1Mgszvq1dV21v8+6vtyxce4WRzVN6vs5gUuXJfpnBZ0/pfbXJTkRj8BMnD4fbnG19AGM2\nAVFYa4JcI/yUVcmwpaEkyW4pstZn576/u/nsZVfZe9r7kMZBCRoEoQXd4PGvfk/m3/2YM1nzaUP7\n/5u1HpL8g2618Z1Uijnze7N9Xu1Crb9jyZnlZifZ3LuweR8evbvk2YFMXBXkheXEMJkPM/TFIyRW\nb7S9drXeSCyCvz9yIgvBj8RR9NBDEIu++Fx7VEtjH2jwgbRXy5kcHzCRLoKwzYnUmv/hXdKefAAO\nhYLhW8R+uIKRL6EmvVon2o/DQleNLd3BWLqJq3+97bqtc/zwOeu/EBDz1OoiG8wxvrTIqM8BuNt4\nVAIUGVQZG+F3HfweM1qNvn4FKZk0Y0epQ0SsSXDnSOR48VCEjbe1ctHVtlV9ucHGxBjrpwYjKY3Z\nozcUn63zNhNj7MNVnzkSZG7YujcJpd94vVDF3b0DNZ1i/od36XWh+MI8o8b4LT6WItdTRHHGcpfF\ncoctDc52dTk0wtdKDMoSfH5qsECwfScIAT/H2EIOcX+ZiFIh92IrMCPZvOiLbw58+/esTGCxYnB6\nfMHuW6guTzgqp17VrnYTp8a7bImzizxxRSIdVRhIqDR3yFq/bxGAQSP7bP2el5yRlSiwoa+O6v/o\nzKQg7/rsKUm9SNQooQz0Un6z34X8SRj7LtjJr9pH2Qn8zbu9AWBneIr4zwYBWNPijEYTJlxQMoM1\n61nUQm1mRjbPjFZn3Azs27VqBN1f+/aBMEFK63XTd24RGRrg8O//GdWU7rp49JLy/SVE4sMdsAzt\n7XD8ezFfb350h4Uf3gPk0O/PkoayDj7OzLFLiseEePZGRYW3tQ3EXd3t3MLMmXYJMesdw/i8mDdO\nJm2jVuO8VLGhiNaG0nwQnj95w/kXzTq6YVh3BeriTNE4/cdf0xOPMPXRXY5//mnTtbxbeMI8Iy/Y\nd1Aw00zKJidTxFYWOTCDuuTqotlLKWQ92x3smp+VE3Iq+ANEb6ZFYFRMamTvLdtksNa4O6n4Agzd\nX0ZP93Bar9NTKaGuvyanqWQXZ4m+d8f1XUVqBJHd+sfmQM3qr0cT2INw7RWWSfTHNW4M1Ch/uUHq\nvRXyNXfvrtd3uj9kCwTc9lmBF4dC9nUsFeW3u+csDibprZZd0lBWS5F1/e58f+t87i4Be5W9x19r\nvfmglF6Zw+pT/vat09517M9eneDQaf4+1vmuqvVuAm5JqJiYJNZWpdcqplTrNSq1OmeyRl8yuIXQ\nOZZEROUvloeY7o3brX1ev20hC73ROo0eeucB3OtZtke1uPdfiw/Ezw978U3lzi44en1o77kHOZ3e\nh899+ACcCgUNuUDVxx/4j0OQblotspZP3jwpuPvXfbiQrEOwJa8GDUk8aIWIN3+/nWSx853f7I9z\nUTbYPMlzVjB4my3zoxsZ5gdSLYWH1nXuP4bskxcuxNzYB6sdkPmKa4RP5IQ1ya42G9UaW2clYmYi\n7Oz4guPfvzD/HfK/ecwdTzRfuzOBUzkt7fpe536pW//shZxoxIrBiNLr4mOxrDXpt1At+7Y0OO0q\nPrmZm2goVTbXw3ekMtBq7RxjUxBikkMY+QJqMoYeTbTNJFo9fJb59YOUn68jPxcv4LB0h9k//JCE\npnQBd6+zWYK8opL//TP0SITJn37MoRJDw3tja848N5jB3ZBqPRpn+qzA0eEZsgRjvdEWh2bki8S3\ntrk1nOT8s3VUSaI6MogyMcrgH/3Ik2nZN1DS2k02s9XBsek1O7TrCyK+vZ62hrWHkwUToIXtT/XW\n4G39+wv0ao2eO7cYvL9CMPKg3YFOBCnkC+xeFKnGoiGCLHPjjWq+vfTOgCVZKfL4V79nJKmhyDKv\nfvuEseVZUn0Z2r0/5xzMzcyxtDLnQdjUkOKp31vm4tELQCTKgubU9QeyYcx7LjmfVyKiktZUPpjs\naeIWaUp+mMkAaARUTtZdRYKtk0bix0lCuvf2hNL+OdryArVYhOPPn9D//m3kaLRDtI7fJh9+XXr5\nGagj35onFhGBQa6Fxb39vPaX5lomPTXI8Nklh5/toZkEWt1xYQjFG8kkPtuvKWwfFxm+O4/+f/89\naU0lPjFiElDO2/dp9Z/+7pvLK/ZemoFaXLXRHt1cx8kpMpSMcjI5QlXTGFHlUAlC/3fhL+FkIeAS\nEZlcWbQyDSQi3pfx+V2L4bmZaLOzZHS3SY1u9x7//aT5oJR9sUVycZb8+g4JTeXuj9/jX0yi2nBt\naOElULtv4bs+duwwv3VVlEezv3EmwY0q3BlNoclyCLm5bsYiMduXYCytOfhcxHeae+gvAKVLuHFz\nwmDtOE9qMNGCLnWOy5lMyD55ydHDZ+hZndjpEaV+N1S4mXenRS7wFw+JRdK+kGY/s0g3NVWx5X0t\n2VHLvJFVbpb33pLBSCrCQU6QC0+3aStR04mQUrfuIstvds/Yz+qkoiqHOZ1nB3nG0yIppKaTHa6p\nOqX9Iw5/+zUHuSpIErWNLY6G+hlPz5EIFa/UOS2U2do7otaUyJEmx7sm/xP7QwM9l1xdJKfFSQDD\nQwnOgHg0Yv/eQgtvU+c8Ldb3ruKXwhBZeiUbnC1rVqLLjSoPiheC98OwyQ2vpN9CpiegpcE9Bj+f\n3D7udfu1t9kilmy8n32rCYFwjtH90AUkxoMJc8DnQdRh4OMPic9MYmXsm1EIs7Eae8/XoS5gjOVn\nG9S+v4xRVjuGFxX0Ktmvn3D0y89J9vVQ6c3wYuuAH/y4zsxEnyNgFwd8Mf44G8dF7o2mHS0LzoBb\n3O/Wu1OSO1vUX6yT1WtId28xOjcroJY4ey4lIoqMKstkyxUOTwvoqkGtIuEtuOe/4fkHAK2tDiD6\n/y6LZQp6oyLWeRDRXQWju+95wzahmaBEcROgpaNUKksYCzdNyHv4/lRLJ7tWLnNaqLDftKlaf7dh\n1L94yOLQMHNTzbqzrRbcl7ZG6tEL5q0kw9JKwPXcjn2+aZ5Fe3vIehDbSJJEPC50qMuVoP7slm92\nEHSJw9lRZqDRO3jip+IhAmVlaYHRmUmADiCJnZpbX9dK6ABcPHrhyYVQrePTk2wSO5aD+5Ut4iqA\nid4Y04d79oE4trIA5kFbVzUUpXsysbCbvP8mJJ7NeDrGeFokP95mi/xy+wyAdDrlIsJxVtPar+lW\naS6RNBa+MKaqDjhdt1Y3kR06igTvcmWGk1HyY5PU5uaI98WJ2m1ZDZ3rX+2cu6RZu20dsAK1k0+/\nsJURiju7djUofPW74Y9HqXO+8ZrC2galoo5y6yZ6NGr283eyLvznhxMBV6zU6EuoFCt1qjVx0M2W\nqxQVP2mo4N+Auh3AtINSN9Z6J0iYVjhu++s3rCN4ch3Sywt2a9soNWZLFRtKXNBr+CPPwq/P9mNq\nt96+i8R847eu1mbjHfBW6/ADc+6FRx90MpYaW98cu/ra5we8v+svYyms27ZC4S8WefeFUFIY+7CV\nONCaC5oq05+MUOjrpWbo9GVSDN1f8amSCtOrtUaLF/6+zW8/MLI5arpONZcnv/kagMjsBPRNB96X\ni+Vdi6PMzDCxv0dvLCJimqlBV/uiWDeN9VnQay1St2Nnl8RU1WO+N965JiuosrdP7JQPIvvkJZdP\n18k+fkmstw8lnaLw+BXVfIECOokmycVm5IHVX751UmD3rESvXiGtiUP6SUFnd++CoqZ3mYB2FyFr\nutRAfw73wXvLduU6ubqInEwFcpiF2/OCpDCTXZ4F3Nf3altx8qYtJUB79MK+fhCqvLvf80puNGS0\nmy3Sk260NEgwsDpvf0dcx+2nvZFora2zrQiWxhy31GIiEf+4/TtTGWjnGJ3WftJ1cggVmdzzkRSH\nOR1FlhlMCkmwIOiEt9UpXlyQXdsmpcocHFzA4QUD769wmNOZQeL44XMun7zkolQhNzNrSxj9ZLYP\nK7BrHqslXXZ2fEHsd08o6jXGe6Lkn67z5mc/4YM/mzUdWoKCXhMauS/WiS/Ncvz2BF3VWqC4Xs/B\n79+9HJtfq8P2WYGvjgvksqUWGb2wz7C7TKHf94KqSJ3ANiVmtLogQBtKIOfzfPPX/4Wee8v0Pbhn\nczeEvcfym32yu285yOnEJkcAB3cDrRvu7nmR0RHnu/Ov2HodrBt9aaLXqPR8w4OQ00Pi0rTNgQl+\nvDxrb5wWsY0zYMlH4txfGiP7q8+53Nhh4O489b27EJp8xmsOegeoBb0mJD0D+7R9DhFtIYndmPu3\nlhJwkS/blYuRlMYo9Y4DPL+ASgWX/uxwSuPo8Iy4g0C09Hyd6Z98zBuzBaNHUxj/kz9AP8u6rtXO\nOtvkW9n5m3vHFwYTjKejrmx0tlzl+1M9xFTVMZ+78wXN+ubeqiqdIUSan4Hd2qXFSb23irS/h52U\nNeepSERcFxGcRHxmEvnzR/TcW0aOalw8WjNhvcmWZEu7hJpVcT+/McPraA+7J3len8nMPt234bBh\n10W7+eFEwBlV+LOlQeYHGlwaAP0e0lBBv7F1UqBoNDg42kGpO1/r7eC4V7u+t6qQF5HvEpummoXf\n74SBlVtJei0wpvmuCYC/m3bDRsCbZFQWv5fq62k5LF6POfraJYnB1TneViZd76OzPSA4Ud6ipjWY\nNNuE6jxKjwpJZOBWepiPvWIbCYzLHNKLV0TrdWb/lz+j/+4yJaPG4d/+3POA1nvvli3ZaFWQ/c2b\nULK4s0e9VuXo558S6U3Ts7qEvrnL0k+mG1LdIZIfuZk5hh/csuNf5yHPyVHSjF61zhupnS2BHlMU\n3/me0BTmBhJkdZF8H05pNhdQp2YnDyTI3J6nur3H+fO3RCfH6M2kKD1ft+MyP+RBtqTb99g/2Ev5\n5k2qr3cYSWsUZ+fse+s+Ad2I1+epu+S/1SYemWbSOmVpvm07htuCpTCvwx95JWya0Si750WmqzU0\nRQmFKm++B2eMGi5B5N4/mudqTzyKbhY3si82yD7fIvt8245nsk/WuHj8EkmWSS3NIEdULh412mz/\nP+be60mS7Drz/LkID61S68rMSlW6urq7gEY3SICkETNDG9vl2Ozjmu3L/g98nP9k+TJm+7BD2gwN\nNAoQBNiNbrSqRsmszKxKVVlVqVVoDxf74OEe7h7uIbKyAR4zmKErw92vX7/i3HO+833uUtD2CJZW\nxQ1F+QMFBC6f3dpt3R9C5XSapU/u0v/gGUdllcrMLJ/u11lM1MkvXLH00z3OXpA1BvZukWJVI7Uw\nQ+LxC6IRgZH3rnGsxNl+c8jmvz5AFOCsWiddtGqeghij3WZLl+Ub/11UNXRDwZ5vYjKFrLhkzJJD\nLP7REMOpCK92zh0Sr8s2f6nDTN4i2Ug1FpCLLEgXJX0Jvm6CTVeE0+9MueFvqm5w9GSN/NIspVUr\nYm3L4KkutIMiSQiSyMnyOjQIfGzuhtaxLIZIOQpImRQIAggCUjqNW/hNTifJ3FoCV1219/uFOaPv\noovudQZtiUu3WePM/y298PYzU6NQqWIODnB4UET69QOWBgeQk8kLRHgbbXItfNl7t+iWtLH3Q0Qv\n5nVm/c96UYHIlWl4ahF+FqenUaOWRJVDygSkAomygiWHmllvAEt/1q6JVSQRWspYBKZycUZ++gHG\n/aVGljOJVig7976IIxxcE+/tD/vAGSTXtXZoSbD6LSbLKLUqWs1CTxmlYqMsxFsu0W4t6EYKqrda\nz1az1z3bRj+4yZTSSo50+fubEJCxuPjBtKzqrB+V2aoJLJ8bmBi8Oa954LDB1grdd5t7fLj7wI+A\ne7cDe+d3exdFgbKqW+U2gXXVSY7LNQ/zdtD926FlXhyVWUsOEb+XdlSFvIFby2w1C3/g07bO46kR\n2HhTcIL0YSzs4Xtv8ns4uL9rwKb357llQ813DnYEBzO0QsmqaxcEYtUS6//P32AuXGXrz37AtR+/\n56y9c/0JZw20yeTCrR06wRswmBrLcnhY5Lhcs5SPGmUnQcSUcjpJ+tosr/76b8AfTiLDAAAgAElE\nQVQ0SU+MoG685sX4FbbPKhgBB7TmAX8CTitdHt69vrg9xuVUksTsJLpuNpIGFuR/ZDje0q+2Ba2p\ntgSomxxbEnA4Srxk3l5y4tTmZqOUrN3+0ipv6C4BuVBJoglyJsPoB7fI7h8jplMtHGph8zGaau4B\n8YhM6u513v/pLaKyyKf79fbP7dm8Y8891pRaxUdat4ya60czDCdw8vGVXNs1qp0UZjvE7ju8DoIo\nYpQrQLNdFSVO5taSRdDeAVXuNYsLwi0x7KAralbAwSq3DXnvhvkTIlbCzXqmff60+yAy2MfBd08x\nC0UqKxsUHj9HTMZIXJnEG7yzObTC1/WwJGqYfc8IgXbRz/DocfeOVrcQcotgQpgYa8Bt4qQ211l9\nssrCYIL+m/MdJIO8Azt/e4nKszXm/vQ+9akJjqfnmemLsbt74vy+UNNJRLpzDm3psoMijC/OM/by\nJZIoWEQkDbbnllqyMowMp5kYi3wvAZcgR28qG+P1udr54t+TVTU9pCbN6i9D03Bq4RrZ+IWfTjDV\ngG1un1bYXnntgv0lyN5Z4rzhJMYnRlyTvXXTsByeYEirnEkz+PH7SJU6r0+rxHB/H4GBe9dZGBxi\n+7RCsfF8W0/Un7233+tNoRrqYHXatPyLRvXZKot/PNT1hm/B2yVelmu8LutgQkw0qJ4XeP2/fkE0\nkegJ7WFDdc8eLaOdFykuv+TkiwcYmkH+/t13OmzZ8lGda0fD2+d3Zq3MrNfOp2bIjo8BXi309o5p\nMLytNeu9hEVsk7KkaXSLNXqQ6z5nP2URX7rIaLqptyurOueVGvhQDf6aeDf83z/W28l1xWSZxQQe\nWK2VbXjuSIXpY6NkS6qjSw/49MSD2x5MUtspUt7e/OPtzmjGeXelViHYcbg8tnsInsNqNO4gXuDy\nFA3CLeggl2jLmRDUB2HKNu6stjdz4e3/2f7WkoFO899W6ni32vdmkGv1sOQEuYItGKnofveKEm8g\n9wyCCSBbLQhtM5WLef7NzbGwdlj2BunbsLCHve9FaoLb+V7hARvpEiDCrXYZakdNa98niiQymlV4\n/tXvAJOELLH19TP6Iybqi1eASWV2hhf9477rLxrQbx7awiXpgq9Lzs+SuXMNsA4uu8Uqv9s84VRU\nuDY/y97aunNAc3N7yek0s+kUI+8QKDJ1HXP6Cru/WyFyUmHy5vUG+q09V9K7raneBMZ+THap/XQO\nynSHimstXXKXCceuz1N9tgqm0CgTMgMDdO7rVd1A0w2qWp3haKRlLezvtwKNU6qX0+vyEqzefgDQ\nat7+UnWD7dMKCSXOnRGrVHEs3Z6w021+KcxmKe+7mbNvutSyzH/9gjnfHBxYvI42P+1c0027tULR\nIYYEOGsQLSr5DPv/+CkAQz/7cQhPhdesYECnfdtkt1Dl1blK9ZtlMlGJfNKk+nqf2MhwTyUOYWui\n0h/eVumv/uqv/lvXT+hgsViMctl/YLTq3b0kSdaC++DNOVunFQQB+hIR198F+hIRxjMxpvNxhtNu\nQi6cexQeP+fo119RXFlHECA6PBDwu2Y7NEnmRUEjrlaofGFlZ/sTEfSjM1JLV9t2dl032Dq1nBM1\nlyd2ZZxbP7zByMIs0/k42Zh175Qiou4fEZUFUreXqA4MWVH7wHewLCKJpBSL/CYyNMB7Hy5y44c3\nGF+Yca5zPx+sQ89QKsJ4Ntamn6yFylBVxGiEXhdW+xtM5eKMZmJEJAlBgKJuoqpax/cKMjEaQRCg\ntmeJSGfvLBGfHOt4j6DrpNFRT58ATOfjbJ1WePDmnO2STioqsLO+C1iZ+LfJPJMDGQrPX/L455+i\nvdwipYjsKinGMzFSY0MkpieIjg1h1DVs5IjVRtg6rfBot8jOWZVnByWSjQl+XK4znokRkUSnrdW3\nh8QlgakPbzJ9fdbXVwL5bIKhfIrpfJzE5jpHv/6a4so6pmnyRvFuGiPpCI92mzX97ufZ94sOD5CY\nmSS1dLWlTw21xvmjZUxdR5CtTXb87iKTA5nAsZNMRlvmclWQ2D4po1Uq1I9PSV2dRK+q5If7kUSR\n2t4hiZnJwIhp05pz/+CkiLC9Q/3JioPGEESR5PwMYjTqjL+RdIR8PEJEEjzjOSKJCILVFwBzAwnk\nSonVt2dslXSu9ie4GiDN1snKqs6DN00lh+Nynel8AkUWPM8aSkXYq4EmRZy5oBVKHP36K+daf58E\n/V0ZGuDki+8Cr4m8fEnkwUOyb98wlFKsQMHMVOA37s6a/b9TVKmrGn0Jhb6EQn9CZuesRqZBKvT6\nrMLu/gn7J0U29s7JiAaaFHHGHljzwQRSUYmTikZ/QmFpKElicx3tywek3r5mJhVhMK1w8sV3qLrB\nxnHZeu+5GcqI7G6+Za9Y42jyCq/iuYD9oNn29WKdN+dVdjd3EQQY+/AG8clRtEIJ9eSMky8eONd1\nNx5ta91zIpJI9dlqhz0maH+7qLXO4bpuBq5xkY5wTZO6bqKbJhoCuqFjmjCVj/P+eIbhdCywvUFj\nfzwTYzgdbRkfzTVIaukD/37lbXfw3t2XUDz97/9vd3sjEkiVMuVSBU2KMHf0Gv2r77r0BbDWElmm\npunU948YTilM3r+JOTzCgzfnzpjeOauRi0dYGkqG7HUCYjSCodYx1DpiNNL2m/n3sb73riGNjTrr\nykxfjC1Xqd7OWYXNkypFVWemL8adkbTn29n9rERl1AZabTofJ5qItRDFBu2h5vBw4PduP75av5+U\niDnvb/krOmtHZRS1TMLUMeQIU7l4wHzqp6zq1HWDiNTMmvVqhqpSXFn3/Jvl03Uz971+UtgcaH4/\nAePwmMLWG9JTowjZLPGoTOzwEDkSQdUNXqy+Jj497lkvbb8k2Oftzux9OR6xJGZ3zqyx8tFUjsXB\n1oSWGFUQIzLq4SmqbnA6dYWXkQw5Q6UUTZC7cZWl+zdIX5kIaMvF1jZ7nBVe77NZNsj/yQ/R773H\nfrqvi7Hlfy7O+Egozf3eBK72J2ig132+qEBEEtgu6wF7RdieaXYYh9Y8rwoSdd10/caeC1+y8eA5\nW6dlNocmyM9PMX53kfjkGNHhwUC/zOonk531N7w4KrE1NMFTMUU2EWEq7V/7LEWLjeMKiiQw3Rdr\noAe7LVnr/SzgXy+SNxfZiucad7P+12kvikiCx0ebGc4y0pd2+jP4LECPbbX2TWWon/L6NpFGCWvk\n5ITF9+aYGco2+lC05kMIgXaQlcsVlr985vx3SdUZmZ+k/Hyd+NQY8clR9EqNxMyUZ63xv7f/rGTP\nY38fxK4v8DCSIy3pVJ+sUNMMcuNDJK+MIkgygix7+qms6uiyjCIL1PaOPP1oqHWKq+sIkoggWmM1\ntXiVSBvC398bh4DbuoP7tSd5uUhE2M5C7LyxNuthnzSbn3E16Fq73U2N12aQY34gwRpWFOxqNkr/\nQNYVFWofnbQJH+xnhT1/580RFU1n14x0YLa+jHrB1m8w15/kRirKyXH5ghmxd5ERabKAp/IZQAhk\nNXePrb3sAIk/vo8RjTtZ3HZyNlZ0PEX+/l2HACoo8yMIcOjKiPb2jl50jFYoeaBZQdn7YAbS1j4K\nI7qpbO5YTlODsGzoP/2kkV225mO3JHznV2aRB4cQTgqcATMrT9vUkrWigPykQeXxMaIPHoFpEhsf\naXHg3KgIR8ucpqSeO5tQfrLMi3/9jlHT5PrNq6gFmbKa7Toz18mCJJ3azdmerAF1AzANy8ux1rgV\nl+yoXVN+cUhdu7XXXRNvK7NEt7dI6jWEvWNKoyNkbi9RnLaoS91rUhy40ZDrUmpVazwLAmKpzN5/\n/1uq799E3T3gJJuzou0CDNY0VlLDvPezUTaOq5yKCvO6EZoFDyOpdbMH2/KrF/sW3vWuN73oXqxd\nbbV3Dl8MKePNcN6ZzPJRznpGPJvtYj8Kst44E4LbLaIVimilcmipSKuUYJAPYKFwlIfLTOkG6aU5\nauutSh3tv1Mjk+gpt0k53wUslZA7I+kAlRB/W547dcPpa7Mk52fafLPWvSENrjIBk41j65Dnl9Kz\npSbd7XDIcGsGkgBT+Vib9a5V1SiI8KqTeXwvEY4//xbz06+RMylL+ePWIm8KNVKb6+x884SyqjN1\n/xa78XkPsdfZw2WHOBZow2TfmYvg4kozrX5Se76gJqRenxhj7VsLTTh5Zx5l61XoVVWtE2KmVxP5\nZDrP9SHrHa2yhGAJUvubVzWNlX2Vuy9fsPXVE6vdf/o+qfwVLgup4X6mMDHK9s4Zx65yyN5QPMFo\nJXfNezBPhNks+elK6aEblEzwb+y54HBCNXzJFazSCLlxjzCpV2lxgYKQZP+wzKmoYBZVHr055+Ox\njGdsuH3PYNnZdnPkomeBVr6geReHQ7dkneGIj2D+iYu2VU4mfElda89qLYPt3sRkiqRLJjt5cwEx\nEQcTTL0dqXZ7dLybmN3dB2o0BhvHHE/P0/d//AWlJ6soIzn6793wlTj4ZKnHppiennT9XUBOJ1By\n6Z6QDH+QgEB3dlEm+nbW/Ejl+j0L1tM4XLTXpfReC8GOnP33jZMyL06rbOwUAlQFwtvWaXMYfrON\n8fUT1o7KzNxeopicDd1YLhdC521nJh6lprizx72SB12Eydjg+eZ+Q1IvybxRDoSruh261OY6pSer\n9CdkqrOz0CB4jMkmiiR6ZD9a5WzC21iua2wcV5AlOCypjGViAYtj+OHcv7FMK35suxBQa8eFIfT2\nWJAzafIf3QNw9Fl7gYs2nXzIJVNM5WMMpzQKT14gxaOkFmdcC044D4Lbjqfnmf1LkdffPEWVFa7O\nNu/hPrzG1YpHy9x/iNAKRXYfr4AoMqBXKf+Pv8ecm6Vc/yGJezdC36n9e/r7Okj73M+6nCC5ME3h\n8QpiVPGQiVl/b3VcYyMDgQu3XY/6fVhcraBUDZpOpIlSqzgw/7hW4/zlS1LpOMVvnqEYBpGhfkpP\nVlm8PuuMveE324iu+rrE4DUHaiiIIsXll2Ca1uaZSnB+VmYgGaE0PcOrukg+LqHHYpyIvShWNEmj\nLB6CRnmGYSBlUhg1FTEavUT5yaauNT0T0bbeqzeItn/fETvui/6gz+43T+jf2ECRJJQ7S3BribD9\ntV0AotfgxLRiMjKkNCSyxODAjSAgiKLFn9E1jNPe2yzOF3V9y/nmvZktc9ostwkqXQgPBnhJxLTz\nAq/++m/I3LnG8P3bjC0uOPdsF/SxftMspQiT0rPNz1sz159ALqk8PC2zcVxFFsU2Y6qpamSXovgJ\nr7qGIQuglyvs/fyXxMZHSE5PcAYIE6PsvDlDf/4CQ4eYJMHKC/YmhhhuEHsBqIbBwaPnqCubALy9\nscDYTz/wrandzpeLJRpa/KRHzxkY7GMxIbQpp7Mg9Vd/cp/hO1ZNbiqfoZBLWaz+ksjVj27xQok7\n13cX0O/VRA9nQDtTozHEKCwmjlhdfsFEJkZfQia9tYV2d/ES/EK/CaTyWSYM2RPQtyV2uznoBQWv\n++Kyh5i2NfgVVPLTnl+rmwRl2G8MTXNK2C5qVSXOidh7YK5p7edI72cB996QcEn3XVzWtR1HRhD/\nROe2tu5fQb6V9bvihc+QCUVi1Ee0mMonMLsKPgaTaPs5X8bSUYha5VQyTT//eG6JxdtLAfLc3gAR\nNMbjTJ8voVFGPTl3SobUk3O0QrltycAfJCDQ2bHoHNHyfnxL612NxrqQVGrUCt274dSTgMnu//yF\n84uL6VLiPHf7tOrIhT3cLaAbcGM43aFdYeaqt374HFkSkUTRyWr3ot7gvh9cFoHQZZEHtQsAGaz8\n5js+/+dvARi+d501FpxF2/1NPEiKp6sMJGVGUjHU3R2G7i81UB2WhjkPl8nGZEfOxh3BC+qjhCJ5\nmN/74gojqWiHDJLX3BuLoy8/3RdwiEw50WXbOi/GHYJovqhm78RcPk34lVXOXmxj1uuU9/YxdQMx\nYsHa29WQuuf+TF+Mda4SHxojhiUTNN5Dva3bFFnkStTk1d/+G5gmg6P9HlZfuxM6j/+LbnxW5rK0\ntomUiJNanCZ9a9F3bavjasHdWxfui2e9wi2hiMwdvbZ0mqMSs+8tkVgccMgdxfNzRk4LxCbHiGhF\nCljrS188wlBfAiEaZSpn/ZslJfTMxVTdZMVv4eJQIgiyRPyj24iRKLFonH4sKcXt0yqzfXGKqt62\nNjJo34jJrgO6aXF3DPzpx8jJxDsEkhtjJBoje2eBF58/Zq+okry5gKEKzHXcY8LtYmR49r7TvdyR\nHcDI6jVKj1foT3mJtYI4K6z7tM/otMt6uJ1IN49G9s4S2vREa+BGrWPUauiFEkf/+jmpxZmeSEWd\ntzUM0rcWG+RM7zpPLjb37eCXpuvUdb1BRHsRNE+4dvT8QCKEy8Pg1ZnaNZlqJ8KrTu/rnt+V9VdE\nchlEWaays0tkoM/zW3f4rBaJEr8xT+XpGookkl6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MJGYLmuXidulY2aCN7mKM9906yALT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gELk/CLF+uaVFqrQOWHq5r6W9R7UqFUyV5tl28l18ouzkORlBYM5vZecSeHiFSu4ub5\nJmprLx2cDU54dHdjJbOHXgh1XTadRN/iQ5S0loH0/Ay21Rj6Qs15+7h3khjqtCIeVDwx/p4C0g/a\n8Y9hjbxLZnYag8VRkPERIJWryEkyIUZjGUtBiHxeqVZILOlCkLRTHGm9t3qhSfrw0JO53s/oZMoS\n+4a/t6l8EsOfvg/l6QOfZ3b9kmelljyb/5qPNp8IBxa3tIZRWUHf5AT2cmNoinVkDw7Baao419+v\n4ZJJQlIU7FyS1oVP+hIBRSgF589Wba1QXvH25FgBmU8Xjc9lHpN2vyjvAzCLBkabhdYucnBYxyAt\neRRdwswv98H+d7ukOt86Qe6nVNMehxxAKu3HS5s2gmqvlpNuIGzCmB5L6DHi0nEZsgItRgQax2e4\n+nYZSrOBytqOqwXN6bf87O2X3z2tnSxlp71gre+JTaeQgoppxfwdJyOrm+V2A4PFcQjpZFvMzk7o\nK8lsmllA/4O2t/ap8XlaReovfoWjdC+WkznUL5z6nm4Zj4YkOTYW8rkMH0eT82InJ2Y//Kg4e/YC\n1OiQw1G4AxQrw/TF59/aFAMYLbAHwsrNUKCTKdQ50SBF0609MiUFL/Mj4H9BqmkvOR4jkSpMNN7/\n2fuYeEIqq1ZSQes9t14HXkGpHblgVfMAzKRSFBNvyhCvbpBZIAd08eomEpzML2CwOibSH1/Vqt2U\nr9PtLJjt7sGidXXZj6/D3wSOwURvAn+zdka+k+Kwf92wMIlToLkYpFwGqacLiDG0RvTpfaC2+pd0\nnEG5ScYuzGblGfAmvdebf3DsVa0I469NctTN86pBjur3bM5ruJMuxBdH3cRdJKVQwT2c9Ohhd1dq\nnUgdL9N/X6+yj2bjEGXVRz2mc4tnM5Y2tUk0rsuQWBZ9C7Mdw3ApAPxtHVwzAUEguite72gqLyD3\nas8IRssLc+CNw8zdt/C8rcDNNAVbXyzZEshEvtXq82kHaa97HHTCM1FRoJYruHZxiISxoOpSVAtm\n/Q+Cxafn5zBfHMO0dmi9CSiYdGr+79u+x+p7S/h9xf8grP99eW0L1c0dZKsiGEv1tBvzTVX9fCA0\npn8RgKj9vUmAevTdcxuy0l/1I+xeG4ZA0SOeyZtqAs7vdXeNavFkYBHE5zsRfyfs2cXcn4isHffq\nFT55fB8Ah2uLUkdTIlxdAscbRHGDA72QPQlDVey8Psem1u4QTPRNo7D4yCJBTdrUcqPDIZMmfkNA\ngd9/hav1PZwWBKiLj9psHTQ5IPQqPxA1Xu+cQw4ApGZ7iQQThdhNGURiOkeCbrrajLyxRZLCL/dQ\nPzhCLEvaee0taKbfisX8ESFvJSEQbqFHr1R0ypgbzvkFM7L6s9y2y+xsohMAu9OMynRsY+i9VbD5\n3SYSv/gJAAJL7uVZDz4COyS0P8Xhx/dyHrrt4ap9OnPr/kEJowprbF5+AYpe8dChrXrFY7g45jGH\n6MCFp8+7nYvu9Re1z3StoiYq4FsQU0ZdB17IhcRgn09SKeidudmAAQp0vJNDitdh3HRMen98sMPs\nVuIvOgN0O9Y4Pvfh6+gLaMuhMJ7lDTIc5xyNgk5x+sVyU3aRkAabV8CrV79MWSby+96f9UpahvHX\n1s/oXCi5RAyPBggjeXd8PnnGUEGcRlIKAOM5HoMD/pUHL988WBz1PZQMp+N4cUJjppAEx9Co30pg\nGlVwNN2l1hnL01L6exrF/nUdxxoxrDKcRytRUKcJHIPJvICyKGHvso5HN8foXX2F65UE1IU5MLPT\nrr7hQZoklg2UCQVcfP4t6G+WtP7gbnJP+FmYd969Knrl6sZYr4Ap3+om1WyVsFRwVG6gLCm4/f0a\nMnEGGaja3j8KMc4jPnsfTKOK5h82wI8MoOfpE1uiJ7i6ZN8DWvnIlqz/tBQA8abAptNIab8VHTUS\nxYLeN2k/2Xl9jv3rOuoaRDzKvmKXMTZ/E4BG8qtJQB4foP/pg67x1pQboi/qyO/vDw4vjPbBYIK9\nsHttUGHKr9Bjmv98vxtJx7u3dg+hlLFWVMthP/NkDnWV7DWyhaLFKxEVnejbVKlwveuQd20WAtYw\nkOIgFsdw9XwbA1p87X0usc4Lum1yxLdl0ThjrO//bgrVAuetNqNUKwYqiZ8cw83zDTBJAcniqEcL\nGnn3Me4HTgh4wTG8iA/erkV5cf4LPph4pH1HcZdwST0wqEuKpiNP7leHGVshoacVES9Oqjbddv8s\ntY5yIIvp7NkLg1HYqh4AIBD+4m1uZtOXDt6BoP6lkWxnOvft9zgBreda+4GoH3LBnVTyrtz5VW9/\nNDMQiUQrmpH53UomizxfdxJ/0TTE/S04ualCadRwK4qgGAYsbQ1wgyGWAsfi0UDKp+e9M3QKSQZE\nGS+3/0nPz+IslzcDaAvCyPpZqVzxJt2KyGivy+hEYRpufxMPc52UQSga3oKrkXrihxfr6N1/hZ6T\nY7AMfQetM8Qakmy8PyBYAiqI/0V/pp2DU7z6v7ZwTtNgKBFYWkNudMj4pK7ScFoQkJ+fhs6Xo0uK\n6eij7nFPtLKg/bibVXTnz1LgOQZKrQ5E4jBR8bIBvMwPg93dBSfLiOf7IbExcLAoNyT78d6//TNM\n/epnFtkw+2/4VZesgWynPjK1t4PTrw7AMUyI71M21AgAnFv03ztPzCioXJHiDZHltCc+zp+tGlXV\n1OMZ7FCTPgUSp0WLGzmG8SFHu6t2V2IMBQPh2dq8VX/8/IN/YUrAi5OKTQ2jncOQt6RjJ/Y2igGt\n36f//mSPDQaKA5jePPFMJHv5SC+Fp1axWmdxlXm/g1DRkGScHh85pHatZl8zUxdvwO/sAqA8/cTb\nR3N5WXvtpt0rWrjvx0ttJsFqGSMVYAQeA3/2J1BlBWwm1RYRJ/OXf/mX/0uHd2pYIpFAreYHJacQ\nYyg0Vjdx8btvUNnYAUUB8YHwTL9WizEUKAq4rJl6jQPp8BIVNVHGs0Oz2n9Zu8VIJgGqVoMiiqDj\nMde1/H4zPlCAMDGG1IP74MeGoR+ypHLVci0YDoNjgcuaZFwjj1so4q3nb1qNjsdAUUDzhPQnZxce\nWH4v+PMMTSH9ZBYXmV6snxE4biYeM5771XUdzw5vcFxu4qIu4kaDGQNAXuBwv1dATAtgZRr4YvvC\nNXYxo7JJIT5QQOzeCE77BlHLm2oGxR4SjB6U6lg/M/vJKYpUSWWWhaooQLMB8fwa/OggCj/9kfGc\nMYZGjKF931/MhQAg3+lPcRjJJFDs4R3zxPme/AOYXiFmXKMs3mJt7xRvLgj8tlfw/67XvQ4xEmhJ\nAh1n8fKihmeHN3h1XQdFARm5GWo+AMCtrODVdR0SE4PExIwxjjEM6DinZQkp4x5EWYGsqig1JGO8\nvO5vsj8FpqfHY15HMbJJ3soKYgzl8X3KZ+24n89q5PnCB+pSuYqL331j/Ll5cg5hYiyAxCvI7PPA\nnEtEVmvrn55BYhmUdl5Diccx9q/+GNnFx9pnKNs7CXfdoHfsHoMofjGZjAf4a7vVRBm/P6sbv++3\n3hRRRGVjx/Z3qQf3ERfiLe/Lee9TBQEjWd74TOtnaz2fwln06wT55hhDaf61bKzxgTQHigLE1Q3g\n2TLoz79EmmMRz6ZRPjxF7N4IOD78fhZkgsDh5OtlnP3j1zhZeYkUR0PMEf6SYg+PW1mxrFGC8Aje\noyncygq2jq6h7OwDAKqijBzPIv9oGkycQ/36BvUvn2EgxSETZyGeXyM5M4Hbi2tQNAVKVRCzHI5T\nD+63uR67Y7oPrN1KWD+rYvO8hlwipkmPRX8HHM9BhYqrN2dIinWkqiWw1zegKCp0zFMTZawcl/ES\nCdAjQ0iODEASJfQlOSQfz2KZMoO9Mh3DUD6LuGCuF91iDIVbRcGmhYk7E4/ZfEhYH+kVU5yme8GL\ndWRWniMTJ4ffMD5WKldx/fk3uKzfYv+6gcO9I9Bjw7iQVNtaCdpbvY20ayz9zWfY/8MGVKjIjw0Y\n15DKVZz+7itcaH6kenSKw0we57cUuEYdaVrx8dH+caM+juFiNHJAsu75YZ+xJ8OjXm+6fGCMoQ3f\nWLuVcFm/xXVdBsfSyKR5NCUFt6cXGEhxGHv62Ban6uv9Zn0bTUk2fAPgvc8QH79txBIMTSH1YBKb\nNyL+dv2MjCsFNCXVJybzM9Xhe1TIPT0B8UM712z/zBH0G2HfJyMkQA/3g380jfS9EZgJRzM2SKUS\nSKiqT7xqN31PPOZS4IsjmHxvDiMP7qFVIjN8XOUXH+v3GwfHx8EyNJon5EzgnPPWNcOLdZz97hvk\neBYMTfv4Cf9Y6O7N+rzk+fx8gZeFHVfv2KvVWYRCf4rDeJY3xoWOc6a/UYGeD54g/5MfITE2jHh/\n3vPeGUaFn71VDoGokPdgCwcBjJIZrD1fx2WgrIf/bzrhMfZs+yxOHPJOP5noAUBB3tjEcaAuq/2Z\n/bNWXs/qZujN10TUJcWuIV8q4eC4AnA8ZBWIMzTGcgm8vm6gP8Xh0UCyjQwdhVRPFqMK65np84K/\n7F7VSBU/2Y/Zf/0rTP23qm/Fw2l6Rjyo9SOQYwGmdrA/nMnscTr67oUPi39wppgX6+D3X+H0q1NS\nPXg4ja2kmTA5+u456OOwVZYo2VQVp9UmTjVeARLo+jsG/XmjkmiZc1AIgeBo/Rv+z/d2WgC8zT2X\nDFmtmyYg5JD7+R8jtvAIqXcfIHyV0buCGS1j/sPCLoOqIGH4MYI/0y7HRjsW9Tr+vtmvalDkVNDH\nB6BzCVQ4FvWDY5SFFI5FOFqsOnt/usqAwS2ikUCNDuddrMxuThzrHm2uOcQTPpWpFKYADNISDnsS\nYPV9RoUhkQoA9T27Hnn30EftV171KjoFIKeIeHN84aiiRzEa0x8tYODeIM7+9rdEvhVU5JhHVoGJ\nXh47l8DV0AT+9Y8eYzyfjKjc4F1daq/q5s36r1QruE5ykVV4nPrvB6U6rspKRwSbYdo19LVwVhVx\nXrtFLx9D7+s9bD7fBAoC+hYftclx0bqy2EkVkaL8UUdWnXurutVPJnp9CfZcqj97e5A1eVW/OeLV\nwibGE9g/ujI+c1oRkUvEPJ7Af992ck+9/GIZJSQDWjpar/Xunjm8zet92uU0yWdqz9fx+tsVnFRu\nkXw8g6H3H7eFSnZ+rp39PlxMERY1FK6azot1xG+baLj+xfu53r7CXOcoqU4I8r3JY5Ou84hzXJz+\n2Kk6FuUZ/oWQCrZrwRDAoJfrfHGzAtD4dtP4d3/H0XqiOp3Q2bNVHCDpkqDhmg0jGRD8m/bf9+r9\nCiIbtH6+V4jboMlTF29Q+mobjfMaUo9nUClOgo+x+OkYIaZwkwoC6QQXctL7OytngDLRm8Dupekq\nNmrA4ETel2HW+v5qtxIqooyvX99Egqm5NovldUj1htYD6L+YvKQhCYt/1hNOqN/r0XfPobzcBVZX\nIY/0AeOjuFlZB7+YRp3jTZ3YAukPDzsfwm0IFFIcg1PtTynL57ycWCoew9VlrcU1rWafg85ER3f7\nvb371P3uMaqGeCcmKwqgKLguK0gIXi0w7SQyglVEvHrzhC4nSYJaZnQouH5f6flZxPryAHSVBbOq\noHMB+BN5tfKv3QwUup1UcvpmLePvq/ZAGT31qbn7uH6xhcuaiOQ7j20tVt17Xq2vOcGifzQLOhk3\nmJwBL04cQlbakCSkoDgCjVlMD49jC0T1xS5Bq+JYYXEyMk6IzNJxTL5PGK1NgiN/crH2TWkbsqz3\naC4flzFy/Brs9jYULoaavAhh8VGb90dDyKQRF/zUaIIPNNY1tzCYxnhPAuPZBETQEDjaHr8Uki1a\niGg8Gkh7KGMQc/vIWYjxBETPljgP1n8ug6OIKjxe+u91jgekqDJo0UxvVcPSGpIxGrGH06CEGKqf\na3s65c9xEZYP627bX8h9eMUaus69bnqhpFeIhyDYI/5hejQLOpny5BmSyhWUGiJWc0PIWlrY8pIM\nWTW5XwCi424fm/AHLj1RpKc93L6QVOUPDklVenQ432VS8fatdivZ5DQBFQeHlxD/8RlKjVuk4zFU\nn2/iYHioa3Dyu+DRcSdT1kCNDvnMDf85L3A0pi7eYPvLFTQoCpPFPnB1MkfuMhaLat1JHrWXoDF+\nmwKkmzJe/4e/Quadh0gM5CFe3QDwbq/Qf1O/R7+WzbDP0NWEQOO6pDGRey/MtxmYt365HjIT/wKc\nSTsWbSKbz61UK7j+dhfwqBwF9dI5s9TRSfIIwR6R7tIhjqotIdDaKN+MuJ5sAbx1m43WDagAVChN\nUqFghATKK+uQ2BgkWYH47AX44ijYtP1g5ycNGZT5L3Iq6JMDUJyMs9dvcLV/AFVRIRTHMJ7jDQbz\ngbaID02FhkAtWq0vG7CT1bidGI21oxv8XqtAhQmsnXPQmujo3OxzKLrT67b8oHuc2bQAefo+5JPv\ncV67xcDiQ4wO97YdEHk9g9c6itKb5+6rjWL2ObJ7VcXnaweQFRVnagx9ybg2TwSUV9ZxZiG0NBmt\n71oZJoqZWso6USLpX+4WsZmdjX3KwcYucAzAmfshm0lj5N//Ba6FHpu8YTfMUBnQGK77Fh8h1ZM1\n/KDVTE4cU2N641TEbOUc3NIa9Pd49mwVg6PDGJ7oAdBr2wOcErHi/itcrLxEeXWXwEnbqry2qgaq\nWD+r4O82TKUOAJH6Yh/0pYBqDdtf74HhYhhIcWisbkKaLrZ9wAtS4mi9FrykuK4tnxeMfxsfzuL8\nvNLyGe1jYfdjpo9UCWN9BP/vxXOSr4ktyEyJ/nuxt4D9Uh2VRBLTeXKACq8l7vbFqZ4M7n84b0tO\n2P0dZXtWWqSMg+VAikM8xuLKl+OicwRWN3qk/WMN/0LJsAa9tiNbnfNzzkM2DtBb4l5+sYyzqoja\n6DhKU/fRpymjJFjWlbwiHBx+CAD3vu28F517yu/5j7577oPSNO1tnDmsY85QQEWUjXh056KGuqQg\nq3223JQhxBgwtDc0HwDUwtuSW42SSCAE4S8PSqhzYsTCWw38zi5mtEIX16ij8POPNfSvN2ll54n6\nKEgx1ZA514sbnVlwobomyripNz1/S+fXgaqCYmhCSr0wBzoed6H17oKDpKsJgYP/89colxsdQ0ve\nnlleHBdEDhjNnE6ob/EhlOG8ewOwBIQ6TATaIfVux4U8t9Q0K//WylE4RtxozqQ1lBwtNkn7NfRK\naCKecGXES8svIK9vw30wcgdhmZ40Tn/9OQCg/5c/xulNA68vyjiv3iKdYIDrGiYdLQtsOoniuzPI\nfL8GKh43WPxFjwDbajFVxdX360hNT6CyuYvG4Sn6fvHHGB0rYFAkVREZT9qE0rZCxNAYzyUCIKPm\n+6yJEjY6JEbhGNqW6AgX9LiVDrrn9LpVtfEeZz0gzvyqFzkKaHC8pocbPiCKatagkBfr2P5yBTMF\nARzDeFzbLYOW//OPI/4iCSbXz8pY/t0fIG9sodyUkHsyB/HxA4Nl/KWmNQ4ApS+WMV8cA5tO3yHh\nTnTT5dx0FRT8wzPM9PVjcqwP0eaZ98Zsf9cU+J1dfDI34aquOOF+ow7f6L9mwgcEpsqAfd/1PZjM\nz6KZTaN8WkE1TQ54+9d1jGtw8IuaiIuaRNoahlu3NVyt76GvIARWXoPN6/BMJLP056+JMvavzISy\nP2Q5yGhM5ZPg+sma8SfJimLeY99abcj8fhgpLoqKep/efoxNp8hvHZrtCFHWqZ4AtlZJZwWi0mFC\n1fW9nCQedps0GD6JiZ4EpvJEvSSclrjfnkfaNYbmJgDoyU83t5DuG6egYjidQO12EY1VghLlRwZc\n/AdSmSRc2HSyTZ9lrllrMqe7bV1moeS325fIaYzzS0c3eHFCeyBnwsXkRkuc5teTe7s47R+AaFGB\nsRaaEizVxjPZEzWKSPn6Qn+UplvF4+7PHP5KZLrVOR6pRzMoLK0BIMmOIUNNRsHVN0sor2yAjnNg\nP34XKBYd93m3JJReZj3HiLKCSrEYgpDWzzQ0HACoMJIBXvLEnRNARyk82KH6XE/GVpHvbvLIvK/U\neQ1DcdpCzEzG+kab0/zoIGguZnxPlGXj/4Oer9ME2HKQ8VsAACAASURBVJ1EYv6Brjap44k7n9TR\nB6YTx+FerM5rpQHPDcCqXVte3UZ5dSd630fkZzUP19mFWe0AalaOuvteSGZZrxhm5mexlRww/tXq\nWIJg0bZFm0tbFq0GXdUy4qjWsPT5EgpJFr08h5JFk9V5IDk4vMD4RUnTGgcqlze4HRvG+ZEG3bl/\nHytlFYOibIOqlVc2UN16hUQ6hdRsMQScUEV97wDK7S0arw8Ry2XQ9/OPwPUXtJ5a2oR7+kJpg7Om\nhl61FrRbn5s4EMLPwLM0xi2BVytjWn7EXNPmXCKVhrQl0dGOEoOzUtQtp9eJ+R3qdRZ9AxHRip7B\nZXfLieDVVzv7ozmAacX+b7+vmijj9OQK9dVNcDSFxq2C6osN5GfvQWaSaEqyrSf4pCJiWpIRPuXx\n9rghnP3L+9d1DA7IEYKcaIzjCZb1aIGyJ6rCVR/bYcT3lgB1/56KrS+WsPvVCg5uGhhYfAh6lkC5\nm5MTeP3tC+Pv6z5tDU4/OKBV6yVVbUtdwCuR1JQUm2rMcJpDXVIM7hsyltErr2w6hb7FRz6+pZ2g\n/IfkOvEysxpWMhAfnScn/aqkqb0dW19+en7WaD0RZRlHg6NAcRKyCuxeNjCeJZwNYdZgcIKV9pB4\n9DOSdBEWH0HSNNqdHBfOP9/dISXcHGuFMjititi9InvnaDaOg1LTkLX1krqO+t45hsFMQcDjYs6C\nJlUhb2x5HObgEyN47dvuRI3XWPihNL3trls4yG+QeWt/L5MWxEulOInHDyfRn4qBTqYNf3v29TLe\n/K9/BZaiwI8O4vr7NaTyfZZ77gRZ14n/Mc8xDUnCxmk4AmKnecdrgufBvxtFE32/4EUy/7fOYaBn\nnGNh+z0VEK/LLdALugWNq/e/BRdEzLEW7o+jurkLVVHR/8uP8ebgAifXNaTnZyA2VIzHb0Ndx/ve\ngu0tlmb8JjW6uGHanWn0gWnHcQRla+z9pH6/CQDVzV2fw5z3bzrHLPyzujNwg//m59APWt0OWKRy\nxVYxLHyzAv7HGR8ouTfqwLpoKdoJo9lAsTiG3rEMPtu7RlaI4UaWsXzUwECKw3AmgcGg05lFaxwK\n0Bi/B0XoAwC8oTlMB9yLKisor+4gOX0/EE6oQ9x1WZDy2haYVAqZx7MGfNQ6b705ItZ8YNjmZ4xq\nJ8gGqT+31RHZAy/vA4TAMZgtJPHZZQU7l3X0pzgcluuYyjvJHR1zf3gcxeIYrHMprDSe01nqUDtv\ncqmoTu/ug3I9ENi5MAMB52EkCD5cXlnD1TfLAICep0883q/XbxLUx5vjC4Cmcf/DeXA7u45rd2Ju\nX8HMTkNRgYIQw0X1Fr1CDIkYA46mMVoQwKfjSFqI5pKPZ0AnU7YxCkIBhasOdF4t8epfjgrVD9rg\ng5NWQfffGnkVXlc+jNl/r3JVMhJHBSGGk2erGL03gvHhPHYxhuQvClDOa1ijOUzLik97k90PvinP\nYPPL5+DjLNJ9vR2pCTAUQFEm9wxgJggkRcHBdQOj2TjeHU67IMvhzM+3tBOU+8/nqLBxr89zzTqk\nJhVhnZv3QzE0mm+OER8ZdD1De5B2d5XU4MTRrLS0BrU3h/NnL8AxNCRZwc3yOpIaid0Pb2bMZuW4\nAFQc//XfG59q75Ai4cUJQRhwDO1TYQ2fNJDKVRQ5FcMTBMHjbNnZvWwY/fyXGmli9FZE09h0En2L\nD1HSYrnk4xkMDdlbS70Pc6Ok/SQgRvC2YB/PplOY+ugJspaYKNz7uOtKuzfnkTfiRcXO63O8/nYV\njYqITJwBDo4hjQ/artg+sq4b1XayJlJQMa1UI/oE8xpOn+pbWOmS6ZK3AJEVxUSvz1g4zEAvmMkY\n93zxIwAk3+mkLZRNp9Dz9B2DfFeMJ1DaeANVkvFlWYW6doZfTud9vm+51zbj3DtJCETRE/fOKLbX\nM6I7U16sYzzHY3Ks0MJJdH5QCLdYW+vQ+x3mvO7Zb8KFcYh3x7rqDfeuN0RbFe68KqGYjmO3Sf7c\nHY1RysgOV2JxVIsTwCpxBpViEWKcBwt3oDM6nEcfHhoZ677Fh1CG8jhDzDgIR1dYCAjoFYBJJdHz\no0XkP/0QiUGSeGgVBDiTKlYYtm5inEelWASeu587ulEY7UmApWnMFJJaAFPHcJq3PZvn3J/o7QgC\nrmd1xXirADFs8q4bm6JprQ56dYMQy2v9eh82pHIZp3/3j6gfHAMAbs8vtTXpRUqom4Kd1+eoLK8D\nG3tICSwGf7wA/t/8KbyCLa++2p7+PC4uqt6Xh7evGCyOYnQ4j6OFOSRebKJXYFF8+hj0/QGNgJTB\n0PuPcTBM9OhNWCR5/qAKeDjf1C0eAtK/PNPXj/3rOiouVu1OA8duHizd1lpXvnPjGAajGQbvDWfA\nZ3nsXjZQjfHoKcRwdWnt8aZtcGryLDrU/RaruWHwP++BStOoHh5BtKiohDnM6n576egGO5d1jGbj\nEGUV/Vr/MqMlCIQYiycaR8p4Non2+SDcvqWdoDx4PkftRbd/3qlOVPj0g5ZPZUtmKwqYTApKUzTa\nN8x3EU7Byb0+7FXSg0MyR3RenLOqiNcnZZTOa4izhHdAVlVc1G4hBLDa+1lrpJjfGg4T99mJujoz\nFfulOjbPyW/2pzhj7lotzBxT1Vb7mQqlWiH7KMcbnEEj2YQNURM95iJEoPPFMUxLsi/poCKKoBga\nNBeDqhCN+q1zjaOJIon+4WKrGCEcv4aZtCG/U7kq+bSHmPPAqapyNxw2bq4OndMKnDk/a6KM/es6\nGqKM5IP7uFnfBh9jkFmYDeUXW5lUruDs2QvyswzdYaxvT/j5ozH8vx/md6OhP73XN9esI7W3Bz2y\nSe3tgb6cwKWnLw6O57zmoScB4MIcep4ugC+O+vr88IlWc6xEUYIY5/H8qmz86+5VA1N5AfvXDQMh\nKHB0V+KKriYERv+7X+Lq0p9U0Gq8WId0doabLsHWdGeqZ4Y2AWQ+XUTBlyFYwfmzVdysEDmm7MKc\ny7GapEt2Fu127003p6OPcph7GzIq4czObu7kBdBhUgwFyNOTYLaINnny8QzGhwvQc+9hxtRYtMtk\nEfb9/EPc3pDf0uFHexc1UiEqNXHWM4T5X40iKcQcVT+PQCdvh+jrrR2603MqLLQDV7d9RwUyj2e0\nZEC4XtJGSBh2RSPxAmB77jDVa6dRFOXJVux8X/oBvtMKj5WJFiAHVmp2KgK5lLd1f73YD3p6cCnG\nE9g6r1uqltYEip3Mz/nbUrWO+psT48/1NyeQqvWAhICK82ereP3FMhrfLSE+3I/zfB96n61hvHjP\n59ncfbW0i9go3PNP5ZMWGSsaeyKNLa1v0kl21k4F3G1mAE/GuVs8BDQmx/owOOCuAITZXFtv8N05\nWDpNZ8R3yrYS6yyR4UwcTfzRPPJ9RCJXf1YhxuLP5/ownuUhcIwGAV+DqChIz06h5+GkMXftByEW\n/cVJ9D99QNonIqhsDKfjeHFiJihrtxJEDaEw3mMq1MiRW3V+SIu6FjTun3LFpU7UXJxFpASICrCZ\nNAo/+7EPPDaYGCt4fZj7rN6Xr/cgV5I5JB5N4ft//APScRbD7z1CoieD90czyMi3kMrVSPMiSIbZ\nD5EaNUFM9u9ZgtCjgPzjKei9vGHu01mxP62IeH8k29Z+pkuI6uYkGtOJUrNVEUyxiEpx0vDJfgoT\n4Y0Cm077tH+RtkipVML5P3yFWC6Dkf/hzzR02KWBZgJIciIIzRTeRxIyXyc3zvRHCyBrgfhCXc6a\noYCjStNIxuxcEHnAXoEzOKn0d9o9JGFwAodwC0yj+mIL/PsLGPngIYY+eWpL1LeH2FGxf13HpqVt\nqy/ZPjJLt8Nyo8W+GLT/WCvYgq88cTj0Z5APMtV0gFZcMNHlgvVRtBIAAmQt6upKfr+l+0UuGYNY\nbd2C4bfX62t57bSCjTPCx7J72XTda9S4qKsJgUQuC/bWH4asT+rU3g5Se3soZxK+sDW7ecM2nBVp\nJ0TtZmUdOU+GYALV2dTgogMpDnD1XFdtTK39Bou2fYJGXaz6Acppfoc5MxhWIcZ5TcJK24goMiml\nargkDNAd1lVVtS9Gp2SgFe4tq4A4OYn8cAExmkbfveE2NiQiZSbVGyivbECVZaQf3jdgOvrCFWIs\npvMshtJxMDSNegvyPHNM7PMjuIexnR6dzvp66GTKF4Zt3rM+D8mfveDYwdVru1mlJWu3EqqijOXt\nEwN5A1CQNzaR1ZALKU1Xt120h42JlgLir14hPzmM4Ym89nz0HUH92jlAkYDBKbOIZL/HZ91kfmbA\nQoxNCkjN3SebC4gEHZv0l+ySylXcrJhBYfPNCbhsFkCQzJcKqVyLdBDz6/3Tx0tnqm+XgCzs74Ub\n53bN7Q+8AoFBWvIYu84Zx9u9Z29deb8qQRTzJ2Tzb4daw1ntFpXLEmq//d8x8N4j3P/p+2BmZzwP\nQqme9lobrAlKIcbiR2MZjcWeBkvTbcJZw1k7QfkPyXUS6n6ePDAS01Es3IHN3pev9yAzFLCWHUL9\nYwG8wGGVjWNaUqCsv8Tx6ha5rzZgtmHvkWs2IiSIzb3hZHgMB0iC33+F0rfr6Fvd8SgiBZtV5YcE\n9N1o1TDNTH5rB6LjA/Q/fWCQRIdt3/M3/71SKldx83wTtb03ECZInNM4vkBvs46J3gT+Zs1U/9i/\nbvigmcj1G74yrW7z4sYZmptAqodIQO9c1LB0XEZ/isNQKm6QjUqKgp3LOuqSgofXR+B3dyHdVCDf\nVBAfGYz8bv0sqCBhvG9NunUsx6NvrACKojQ5X3Oco+4zNVHGRo3A5avPN3FSETH2weOO/E/rdR90\nSPf4t/lZn5i4NZrA6156eZbsB2kB2YU5lJbXQdE0UrNFJAYLAb44Wqu4JwGg1gbHJv0SHaYdlhs4\nOq+hUm6EqOR77/WAgu/elHCqFQprt0S9wiBubNPeIocAmdSDtITTrw7AJTmoiuoDWwuuPk/lvZjq\nBYzneOjpgCD5Nh2qo9tJRTSySfq/6+Q4elYzl4j5bnytFqu+8I++e050mVMcZDwBtD5h/8OcmfHV\nJaAqxUlMTU7YHNj5338ewYF1zrpaboi2xbh/1TCqNV5WeLOPgTf7YBkafTEJyM+F+BX75iOVa6hu\n7oGOxwEVRu++895lFeBtwWKYID1q60g7XBPe3wkOAsh9cVADYNjm9f3mIZnPXtVrJ8mKyeoPwCbr\nOHG2j/qLTbwEkPnpInLT91Ba2jAzsccHGOf80DhO8wssKHAsA+mmjKu1bci1BnqeLiA9P+ujTNFJ\nAoxU7Q9KDew2aeO6RS5chcC50TdWNzH7k36XqoK1JxswA5YEyxr3x6aTyH/8AWI9WVA0heTUPdg1\nvN3JT46hkeEY0A/uo7a+jV6B0/oovTZ8vypFK3P6CrffJVJWXhY10ULmOl8cs8nShR3n1tZ+5Ty1\nt0P2LA3mbvez0aq8nQb9prl15f0Ctnau7U3I5v2soqzgRpTRWCfVksuaiMyzVfSPDpN7cxyE2mnT\n8xo3a/+yneH8LmAC7SR/wuy10eell0+LZzMoh5AdfPvcK/Ye5J2LGmq3CgaHenFRvQUAzApA49mm\ncf0fDvVoNTtx51GliXsxopgBANkEG/o+rXNXVoOhwq3mmF1CNCjJRFjdyT7TyZ5s/nsoSLIKo2Kq\n38d4NmEQGvrzGNivn44zKDdl47ei+khrDA+YqiOTvTwYGtg8Jy2hWbmJ7S9XMN0noPxiCyxFIVbo\nfUtz0JtvYPWwhNWNNwBIW6s+zu0k2a2FRmF2BHeZsA5KGPj+W5fG16pqoicbxOsSyiubKK9ug45x\nSPsmILzNf692EwACpipBkI81kOzaOzfHiAnkFZoVKNzjEkarzmWtaSQDAOBNqYmfT/XiqHzruNdo\n9pb1ngjTsk1+wgVbQ2D1Wc8EeU2uybECMp8u2toA/DJidY43smcAkJk3kxF6D1brPmbzuYIXK4Ui\np4B6tQ2kGcQFDqWlDfDF8cB+Qp2MzmDDfr4JfngIL/Mj+PG9QVz/l9+B1dAV0RxYd1lXZRVGTwtg\nZ1blRdLPIyQ57T6tz+00uxyRdSGSg5q3tQoWg83rsDRrQMjunhnaLwiw39fAwiyGZ2ccn3FfK8qm\nYX/uWZxoag0A8B4o9HM0EiyLrNzE1dIazrUArufrZcwP9xm/aWYlw423V2BhzbpW1rYN2afS0jqo\n0SGjDxGIWoX2CoK9mdSPvnsO+tjv4Nf6d8ZzPAYHWshlURRqGy9x/fI1ADLuzOwMmNlpCPW6YwMj\nh3YvybXswhywtIZsohfpT59aYNru3/Ql8OnLuD7r9Vz6WvVqb+nlWYznnP2ptFGdAYg/CE7gROF5\nCDnOjutH6a+z+hPdf3Ea3LIbLSfdRBV49aa+TTPIGb8ghJjxkQE0WDJWCdZ9EHL2vkepBLcat+7x\nEfnfg19V0++egvfaMPPSeSgnQSUzO41BS+IsvOygfj+tDvvhCREnehNoDaE335/ezz6aThDFmwRw\n3OU91jeY5/yJXZ1qKlZfd1oRMZRtn5Mi/Jr328PJ/TVjiu/BxpYoopwy1kESvq3nYavqMJtOIvN4\nBrfXJWP/7nn6hCS8ATwaSAUmQZ3XLzflUEUdL26cVE/GkCSVVRgIJQBYGMqgl2cgK1pyQqxDlGW8\nKjVwqxH7ZUIgKMNaa5SQ9X2rqFyVcPBsDdTaLkBRuJgax+DT6Opf1vlfb4Ofo9U1gW4jsaIlH633\nYlU1AcjczL58iZP/4z8Dqgp+dBAloI19O2jdugkA7WvRSkpoRXt4WxCywumHMU9a7qzzeqKXx0wh\njZmCOT7t7H1vXQC6FWzNGXS2qj7bjUZh8RFymnSM38RyQnXGczwKY2QkrT1YlWIR90fGURZlH+32\nsKaiurWL+vNNkj0dHdTaJKwW7TBHCzyplrdlnWX+rXBywDykTPQIRoWGTScxnOZRL5VQSgQ9l3kv\nurSPVY4IMMnqvODEOuFPe7q+KhrHZ7j6ZkmD/FAoLa9DqjdQ3dwzfsfJLdF9xnr3u3cf4jYw6JtI\nCTYvR8416zi2XP/s2SoOkAS0dpWN8ypS2nwfycaxUyPJgHScwUVNgqQheqLCYa0bPy/WcXBYNwKL\n9PwDxPrykGt1433o1hlXgT0o16v2NPG3OHm2iqnhAkkOFkjFtdXBz3ujT4G1zZMKEixjC1hmPngA\n8eU+dGf/8otllEDGLfuHTa3Pz0zw+fXMp+dnQY2SzL83kVI3zH0YsZo1Kz/Rm9B6ykk1QCeAA4Cy\nKAUmcIJgla3HufX9NyQpYkKJslWcr5P2uRje/JEwnZP/eScyCZnbBQBSXeo2dN5thJzxXjqNy/FB\nnG4dkH5VjfV7CqbcLvE59t73aEGa/7j9MNw67oOUHWEUrKAUBoIblLRtnxTNPXesqBxSjAhHiLh7\nVcPuZQO7lw0LYsiPb4m8PyeyRb+H7rZW+AfzTtRTTZRRe76OxipBKehqKrrph8o6TaP38QxJEDJ0\n5PuMlryz+l7BkGqsphOgJ4s+yS4z+W2XsQ6eN14KP6S3PmxRxf7bUrUGNskbcwUIK6dqN5IMaOUn\nvVucBM6P84Rc79EAeWYxzoN5MA3x5S6SD+6jWS5DYmPo63gOmu8vXGVawdU3S7he3YT4bA3yyTm4\nQi/qX32Hk1d7UD9cjFyg6H47mzeiQY/BhbTgmzBohYSNTvzsVjXRjRfrqGqcZQBQPzhGrNAbcC2v\nmD4scstEMrr5Twjx8/513UjKTOXJGJVPz8DWb9F3bxikeOq9D/j5YSGdxMJQBmltPpu8YJ2947ee\nELBX7EhffE2UfR/GWX0m1V/3gdScXGGq38EVecDswXr36QPQSR2y096AS+UKLp5vgZ+dQH1jF/U3\nJ+j99MOWDscMhtcwkOJIy4A2sVI9AtS2NtHOWdcpisJUXsAgTTJfKa0fzl6h0TahSxmpwVGk9vbI\nmNpQG3YZpMb+GyNRUtXQENYDoHMj1zfJ9p6D/PbN803cLK2BHxlAfGQQFE1IsvRkizdpz11WoVrf\ndzs97875rjvAMN8dGcxj4o/mUVvZAEPTBoeB0EHriVUWpna7CEEj/0wM9qHn6YJtjNWDN8h+sdIV\nrgKrKSpQSMZwriU7gtqM3BZMaGWdJ4MLsxj6n/8bNCUZcZbG9SuCDtCRPzqoW29dCtMHRqD75JA7\nrdQCDwbRe5nJBlle27LB4dLzs5gVgP1rgp6yZuWPj68wyioAl0VDkmwM+DuXdY2Qsp3txh1ohjPz\nHYgy8UEVLcFo/UxL+T8uC9XRj2hv5/D//ejV3/Drxy9QGDjcB61LceFhyPasTkxFeWUD7NIacoqC\nsY+fuNAq+nuXmiTQ0uV1O5FB+5dgNVHGweEFeJBEpRNhBKjoRLve+Y6dSdvWiS3v+WW7LgVcfP4t\n6G+WjLZNVzsRBQ+uInItPS4DgKWjG7w4oU3Yrq9Pcid2Om1j9Da/BJKJknh5UcXB4QUa//DMIF7T\n1VSsMebCUIYkO6YK4JqPYE2etLaocYP98+mHkyivbhufD052kc+Qggb5/yjzRif/q0sKHg2kLHD1\nMNVhSkviet9XkP/vrPrs1eIUfCC2HSbVGSQmxqA3S/aPZiNX5O0W/X2Xnq3gzf/2/yDe3wPpxQbY\nvjwaG9vgYiwYSbKx4oeP/7wRTZ0VtOyIhvLKum1vnFqc9x3z4XRC6/O3k3W3kiAMUgKxqproc2c8\nx4OlYONlSs/Pask/vVpPmyiSjU2Hn47Somq2dYuygsz8AxQWHwKgcP5s1eCpSz2ewRYmMZxOIPPV\nl7j97T+DasrI/OqPgT/9NNzQO579LviLfoCEAKA705cXVYOQSh90L8fgxZDaniyO/R5awfz1HixW\ny9RIzXYWkc72WQefSGDoR+8gxTEazCR8D+KgljyxPk87m6gzGLh5volYXz4SuRCRvjEP46pWYbAF\nL39YxdktCz4WR6U4CXl4CNMOR+t0BPU3J4gVesHFOQykOJS0v7f27lilgIIrQsHvXypXcPXNEkBR\nSM1NorK2g1jh/6fuzZrbyLI8z587AMcObuBOUSRFEiIlSpGKJVORkZUV1VlVVlVj3WZlM2b9MA9t\n/TTfop77A7T1U9u0tc3jWC82VtOdVlkZU5VRoYxNCm2kREokRVEUxZ3EDgLwebhwh+9wgKCi8rxk\nhki6X7/33HPPPcv/30/PTxYah2+LuXN8ZzdEZJZL1RqRxTlT1kL0cHdKLWLWd+slcfDOIvWxAf3Z\nmXQcDYQopgS58vES2+OiJ7iJYdB+60lMCZCJwWojGDCcUCgtr1LVwT/tKP67//03DlgF2IB3/Iqx\nzFAJBPj5n97iyq1r1MJ108HQOsDm/P1Oe+w8EmelHgVqAgNkfQMQAJEaiGji5jzsbpveHwSbPfSK\nKNtFdSwxBpXD3X1yZyVLhUEzUHZ8774eKNMqZ5TVDSZrdaI35qglGxUKjeDOXjqGeucGkcwsQwlF\n73EbSiieNEV+AhbFzddtBeKMa6AEZBKbm9QaAUZvAD6nrFsT0NTczuH+/vazv50EF1XqZTHHclih\nmi9w+vC5HlDS2rP8tYa0fpf35VJCkQOU1zZgcdbwHWZk6eLMtKm8119wpfl+p+yjNqb3C+Cniqxy\nw+Hrv3mN4ycv9QqjsyerjUonp8CykMsuwfWjX1JAJvf8JYn5a3qLlomO69EzqmfZllhFGt6S1ive\nPrhod9sYzeKsu9oe1UKM5oCsh4+peNHB2qVdv8H6+7nnmzrO1mVITJGZ7BXVHhrdsqAaNq7hZQOo\ndvP5/ugmzZdJ8Rsi2XaxgFQn6517vglArVAm1pdEDcqElACRgV4dsK7d1je7XPTMMc9fNZvX7UNu\n5SXH9+5Tr9bp++QDyzOdxu1hFyQayYhNn+O06o5M9pYI4vfdvUMiM0XPnSXTJV/DqIhWivR89chU\nmWlsUbVWsVpFrPVKkzb+i/vMDw4x2Rs1AT9rCc7q/j57v/6ScDhITaqz9+svSX14i7l03FbBWy0L\nFg0vMMRu0gzDew8I+KOPcjIMdoRUr+iXvQddQ1v2io75QbludxNpaJ/pkR7K//Qdm6ubjN3JkLx+\njeTSoo/nNA9K+2Jd4BCV0DdyrVBsgLf5+y4n6hsT3YYEajZH8e/+kUpQIXFznuL0DJWwO8CVqtZJ\n3phrHHwKs58ucX5tGID+WLslu2ZAoMm+CNcHExgvPfm1dUF5qapEr4zSd/cnDHz+KZGRNHJI+ZGQ\noVWyj1d40UDuj9+cZ/yXn4mIp4FNQZOLILo7BZQ0ykWAybEeDgwgVVOKyshEjwv/cHvvneyN6o6z\nNyWMJvas4sUORhck9SUzBeWFHZ7GHjt5usbR1gGJD26Sm5rhxcA4v1iYJhIMUDfYqdGPburAjMb3\nW+2hEfjRW9wcCIGh8ObBCqVS1cR8oDkzUmOetXK7QCyqV84ogQC1Zy/J/HKUrRPB7DKcDBNWgpw9\nWWV4aoKfX+3l6Ttha28Mx1sGX72Cm50FMVXqZUHDI4dF5m/OoL/t7CUToCndCQReNLgYTMZQ+lLs\n/fp3AAz9+S8IxtsFEPRbbdSpI+nARjMwTvTPRPnmCyXKeKXucLY7v//wy2/1/uShv/xjkg1QXiGt\nAuTdbfWqZvOUlgVA8LtchdLaK0YUS9WDJOn7SK3XHZ7ifQlqFbT1CiB4t+HEiM9PkX2yilqqcn50\nSm7lBVFTK6Okt3Dt/e1vHbGKrAGNobYqrN6XtNZdK56UkQKt2w631zi1vahYetjVusANMF6QvHyS\n9vRG5cWhoOULB2TS8RBDrvR02nxcRtuk8fkXkfbpJn8cphjLqOt1kdF+tk5ifgpiMUKpJPViWa/c\nqYSjegI1IInWjnb8v4udOc5tRlbqvezj5yQX5kzP9IM/YdTX5EI7FTGamHXHehYYxxCQ4N7WCfPp\nOFHcKzPdqlitomO8NWTrpBlU084HEJULSsAJsYfcdAAAIABJREFUn8Ksg3acnfbAEC8i7zEgYFYo\nb/qozriqjSWith70ZMQH6JDdqehGVjhaKVI4ylHNFgiMDFLNFTn+5jFRV87wyxMv8LaLOLlGug1J\nliFXoK+3n3e5CscPVzjvTfO1ar68mbIQp1lq2TyRK6MkMlO8G7tq4TY3bwSvjJC2+bXyt4e7WWp1\ndO5bUQ69rpcUFbd36f/jn+kXDGfHUmzk+PyUDVW0M7E749Vsnv37y7oByT9ZZWtslJHh/hY9082q\ngvYu7PaAkrbvmiBVzqAmF5FgMsHgnRuuwE7mUvWMLatYCUdY2znWn9dZYMS5zLAb+9G6x0KjQxTl\nIBVDG4wcTxBUgsyi+nBGzPbQb2ZRz4A18BfWDtD74l/ee0ykge3RpGpqzofuoGhUiJkpS+WMCOwM\nJRT2BmPI+TzH90SpeuzaJLOf3GYs2Q7wn3Xum06ntvf0IOazdeqVCqmleXruLGHHTxCc2PVKxXSB\nvFgZaHvSdvZXgmreP5BpNVugcpKl7+4dAConWUBukSV3Zu+JVooGOlH7ha59jAezHdZEwwOiTRwQ\njdJMczyL27scf/PIAZzWbf9eVquXge9agr7r18mvvgIgdXOe4tY2e7/+EmgEbByrIbx8He+gbWeX\nF1Hdl1/bJJiIkl9/ReqnH5BdeenQyigRjMc8MtNmR3YnW2TtQNgaf9UOl3WxbIov2reDArmpGTKL\nM3rwvZtjcd8j7gms3sVZzh4/F1gFtxZILmVILszR1x8je95s2bCLma2lmi8SjEeZos7IkGLzD4x7\ntFqHVDhIpaaiBCSXNfzn0DbpLp3RTUp6G2WzCtgLhNFb2q1WCibj9Ny63sho/4SBxWkYGiIYN9Li\nNVsFNL8WYLwnovu13qJSqlap1Gq+kzC2agCH+Utkpji+J6qkjPR77Ym9KjS7vO75F36e6ceX04KB\nxsrMRF+KTO7Ao4q1KTqobqNSTKv4lOMJA/BzULQSNHDqhv78F5z+wz2QJIb+/BdERgSNt8AMyF0Q\nZ+di8t4CAl70UVoW9yI8qdbnW3vQ6/lWJeaadLd0LaYEmOyN8hqoygFS4QBB+ceMojcj/07gbX7E\njfrG2Ot78JsvGQTiIZm1wwKpWIgi1subJQsxlkCtqRw+fsF2qK9Fz5t3RshIGQmi57rJfatSqdYg\nHqPv7k/Es0wtHPbLiX4ISpBcvEZ8bsbDcWiN4KxFsc3OuJBaI5sUsOiJ8yVDtlUVjH50s0OwKbtc\nTpuE09rhWKq+f3+ZN3c+JvJnvwREVnGg6jdD/mOJKDNX+3t5u3fMQVWmVqtRqJwTwY550n7w039W\nwxjlTtych2kvcB2zMxNMJbnyb/66oetxh8qZBAmgdnOO1//pv6Ah+uZXN0guzF2AUsjudPbcznD2\nZI3cs3Uiw/2cfv+E0++fOJYoajobTCX1C7Oo/Gj+TkyRdTyEVijMnZWjt5H9lUDpTXLwmy/RAo2+\nnGwV1Jo58+yFa+HE3qPpxyqQ+vwOaZcsiOXTTMGLVq1rRkBQKx7Q5YMeXo4NM65fk5UkQ3JhvvEb\nKqcPV0jdFhgOleMzqtlCB+90D9r6HR80ddY4F2qtzsHmLsWZGIHrC/THQiQXjO0e7aGkzw4k2ggC\n/nO4WF5Gdtip6sb9vHNKYD18e0YwMkjPnRSTvVGSjUBdMJkg0psku591fbd2hiJBKBmncnKm01OH\nx0fEJcUjoB9rQdn844B3ti/tgRB3G7yz3XZeMxhkaX2b3KOXtj0RUwJM90f4f1b2AVGRs3VSMvi1\n7t8nbH/FA8/Laz400FG79NxZol6tN6oHnZ+p+a12sFuzn2xs9+12+5eV+vPuZK9Oa+lUmem/ilWA\n6s4PDrF1UtQx3mJKAFx0YOiv/oSJP/mEs9NSIxjwz6eq6kfCEAAx6RGKirisbRyVCMqybx5wL9FK\nNYw96J1yE1+8N1ESdIif3uJUqlB8vmGiZPlxxBm8zX085kNOktwNXjNrtCCc8oBM6tZ1vU/a+lzN\nIHQSmHAL3sSUAJN9ER7uioPTXMooovFvRyZEqXNCYfZT77UwHYIqZJfXic9dcxmvO+e7tV3G7owv\nUpu7Ru3dDxwUzhm+s8jEWL/p8ugEDmitKtgeG71AK0E3xRu0zbh2GiaEtVSdoPgG48EeCQYvsfe2\nGyLKMNfzAQ5Hpwi+fElSCTHwwQIL14bbRG92k9aBBKUsKPO0PHvi1Svko2mUeIxrd5d482AFMFI1\nCUwGtxI1tz0fn5vRLz6d7WOzODmdI//qTwkNpqlXKpx+/8SzRFEXhwuz9oPs4+coD1eY1ICAMl7t\nW53htXiX2Eq2AGo7JZJO55L2Hj+I9lvHJQKlvB4sAjh78ozE2JCBAliz6a2CFxm9HcyokTFFZvbw\njV7dM3t3ibnMbab7YpSqVU9cCeu3ulGa/XjirBNGjBuQLq3vu9PxGaV8XqU6dVVUXZSrnMzNUAlH\nLU6he8Wck06/n/Jl/9JOQEOcV61wabzONK8eb+fzThMtgVUJR3UshqIS5XkBRmxtNe54HlpbTTAR\no3xwRP/PP9Ira0Lpfts8X4yy+TKlE/BkZ5sTy6SbdJMGADyoN/apasa/ahu800mM663qSPxeAHna\nuRSLuVHcSkz2RHWcDr/tOUbb74bnZRTb3nwkWoKdq2MlV+q9pqj0vtqk/sMywYDcALu97gEK3ul5\n6yV2nAENVNBJv9yrWJ1EZubKICPDdn11tmcyPZPjVBwCe+8fC8fy/vf2omScntsZ9jUE5DuLVMIx\ntnaOdMW+CA+4eSIlZj+9RSAzDvihtvHO6AYyc/ROjBIJBnyUkzkZsiYdohsoktszRLls5wwH7uJ3\n09kPuXRalCi5H95mJom6pRxOiw4as+5KX4rK8RkgtdUr6fb+64MJanURbDJSRurGcUpQTp5CQ0/a\nwygoVavIlaptXdwcHSMoWmRxjmgoaXbGHz8jMj3Bi4FxUn/RT68EJSXayLg0s3ydlbF1+ncXMVCd\nAeBYS9XddMF/Zqfzb+9UNB0LSPBm5Aq9Q8MkUmHO4gkiwSa6bmu56NjNJc1yPs/e336BHA4zcjvD\n/P/x12QboIJ2VN0YR4UKFMoNDA8RJXfa84KP129wsXOJjAySWpoXAQHcSxTdsGA0p6zpAArg2NLy\nmmM5oFk6rRzzyoRKJmevPTHb7+LmFrv//TcO73CWmgrTPVE2G/89nAw39OO3FrT5VsGLFfZ7B3je\niDUY93k1WyC6vsG8lmlZ36C6NMuOI76P2yXT+K0TFM5yyLFoA/ejE9+gm/rprhP+3nnZJfP28ZnG\npQI/uYU8NkoEXIL2Ts9xD3p3bq/cz9TOxVw+751o8scK4vU7nWL8WBNYXlgMAtDZ2Z5U84VmP7eq\nUt7dp35+7jo32lq1Q9ms4094ZIS153eu217z3Aos2snmiICxDg77ZJXjr3/g4O/+ifD4CMnFa2h+\ndnfE3Jrljw2riWtVDgeR0wMO1OSiOujGcOJCyRCtZdHX9zba9Pb+9rfIkbBeHQuYgs/uZ6PK+usD\nHW1/OKGgPHxGaDDdIiDY7nnrx1eytl56BVTar/LoTgKuedZpLcDvU95rCvHd2KSIvAH1sQHGDOAp\n0UqxLR5wu7ReQLdIt7vD5mCYPMfQOkLs/T1CqTeOBchL4bzaoPYKX4Bz2Etabzon3vizyb62nu3U\nJ22KkKuiBzb9q8/07NTFeyVlne+4ns+ZKkScy8ncDzCzc6dSnJnm8V4FOHJ1HIwgeAL929wuM/XT\nD9H+xUp3V1SiRCtFlHLR9Ewn3Qom4wzeWeTU2DJg4h+/KDJtZ9Hadp0jt1J1gClUxhql7kZdaG2A\nL/rtF5OaCmMphe+2S+xT5xf9gTbaoi4+dmOljhSQKWULhK6IrMDp4+cMzE4QCQYpVOq2tdo+LfLt\ntsDwuDvZy2dTfYDkcuh2N6LvfpmSfJUoOh2qwsaL3rzk4gzddQDdxU8mtPMLq7CxwpZqfYcq+/ef\nIk2MmjJATpnAsWSYnl/epvh0jbASpLR1SHA86TJO5+BFpVZn66Roy6YB1KtVCxioRKla0xGcjb8f\nUwItS8g3KxJruQDkKi2pNq3zdDm0dhd5Z3dYJrQzS0134ie5Beu9xVmnJ1yAnJ2/p/0ztV1pb379\nnFfGQK/b7/gVrwSWFxaDE6CzjucRjxIdH6a4vUs1X6T/Zx+IbPjCNWpnuYa9FMHR9df7Fn70VvMt\nfNPCk2eU1jYIxKIkMlMklzIOf3cx3XZfi4BvZhgn6l4NHDYQjXD2w7KomhgcILe6SfTqGMVXO1w8\nIWU+tzMxUB6u6GN0u9cYca3ON155UJN7tbk4+7Dt4tkYdVOSZWpnOQEqqkJ2ZZ16tW5hAMi4ZtsL\nlZo4HxqiAfh1Vy7Lz7tM5hNvEba0m/bQn7y3gIC+yS2Og1FR2+MBd5JWC2j/uZfD1u6l5mIo8EKp\n1w8LPNzNMtETZvtUoGT3RkKsHRQYkauCBrGDiOtFs6TGXuTTcIXovLnX0P1C7fPdKo1ggJGWqRP1\nNEdnrUCSUHfgtJdbHGBNJ6pUrTYcFyF2xFQHai0b+rfExEg/ic/vcPb4mQAOur1Aoi/FXL3A2++e\n6O0MNW7B0oKnbiWXFliausKcA6hgezrp5mC+D8NodaBjPiPr7uLv2616681Eov2Nl05rB/D6YYGd\nswofX+lhIKqQLdco+EJVv6gt0cSc2d39r/+D43sPQJKITY2x899+Q6km2QBegzLc2zolKEtIwLPN\nPRbicCyH31NwxbnnVsvw931yu0WJohDtUI1W3piohbIr68Tnp32jdl++dOvCqur0R1vbp0zUgzrC\nttWR3MkW+YeNY0iOkvmTUQZCKge/+aeW77UGL1JL1219ulpAu3heJdw3QmBtXW/NEhmPI9tzWwVO\n3AAy/3nQ2rX/Tv8l81Zb07RPxc0tPRAU/OwDmJrCb4DEK1jfib+gBXr8r0/rM1WTTnyWy2pJMAK6\nzfSb9b69i5f7nm8Pi6EpwWSCob/8Y46/eQRA3ye3Gs8XayrWPebKj+6+VsI33d45pPTFfYYTgrVF\na51sx6e+iPg5E5v2qckL70ptKknUcnnOflgm9cEiPR9c17GhOk1IaWPUAqFbJ2Uma3XHAIVNVAim\nkvR89hGFfMWDmtyZYc2rEm12IMaILNphEn2xFt/jXhEmybLONgTeFWKaWFk8UkvXiYykL1i1Za6i\n7h771o8v/n2/7leY/egzZqJb4Bbt8YBfhjRL16Az3IFOxBp9PiqcU62rBGXxD4nNdfZ+30k7xcWi\nZ0688eXlNUKjo6YeKbcSQrd3X04Zp3d0VuOGtnLaV7MFHweYcKKEXtgdWk0KlTovrNRaYScu0QTp\nO4tEpgVYSyQoU80WmFLqyLvbnMclglKtMZZJ8KBsFGNL4nzc2in73ObOuIbtOZh26Yxj29yDe3k9\nps3DpPZ81WRzRDm5kfLFus/87Cdx+eqPBqGYR5FrFN+fKbGINqcqtbM8qCrBeJTTB8uk/uKPADPA\nK4iqhuBbEQwY331NcXmV3Rc9HF25SnRsFLA6/J1khFodZpKDfWk6eek7izixC1idhGilSPi8bKYW\nUiG5MOcrqHBR8W/nOr+wau/Yv/9UrxLKKVHbxUpzJAuVqp6BBESf8vSAXk3SapzWwN2cod1EAysM\nSPDyqIjUO8qtX41zKssEMuMmu2AEE66WW39nuwCZfzgibHSpWiVhqlyx25rhHREEkAIypa03jbJi\nQQcYHxjU97r/C731ctHavjnptBxPkNj8rs31cT9TtaCS2xi6Le7nlYHtJBwmZ6B8Ff9fJXd8Agj6\n2vZACu2tGK3WzQ3QWXtesgV1bjWbc+RH9xI9GNf4bzeatm6J21r4o9sVgL77vQPNCojDgl5NqeEI\nJBauoVYq5F+8EkxbSsiGDdUKqM8tAbaXL7PXwHQaTijcXMpQWn4BuNtV456SVcFS0o4/7B2EUU3J\nFdXX+axVNMdN50IiM8Xpg2Xq5TJyWHGtENPmTl9LRIvuZG+0gbYvk1zKIE0I3Wu2gPm55NoBcrsn\n77/F1E28gTENAKIIfekGKOt7Cwi4G1zDgdRtHnAfi2svXZvi+7cloMSN4Thz6agHlY75+Z1dgsxS\nU0XkeeOoyHR/lHK1Tk+tTGJzEyXuBjbiLhfPNNoRNzVKOntPLqbxaeB5zu/uRlbMPP824KyTokt0\n1lhS1t47/a6xdRM7ZTxF33aFxOa6jvyaXJzhZOsN+y/eADA4O84I9Q51S2UnW6Zar7N+JLhRf361\n1/Hv9ANFElHg40fPSOoOph+x77X2otLtiD+j7cbKYGR4MGaOtYCRF9+8934yHmYxQi/XGfz9I0s1\nij970Ol6O8+LSqlaQxoZIpXuQwoGqeaLpr8cSij0DkSIBIUdOy7WeLa5R3F5lZm+KFFFhgePqP/w\nhEpQMTn87WeE2gsgiOev6NlvvrjP/OAQM1cGTd9odRK0C2RJkhgY7UWuV5ECssHZeh+H/fsoVxfv\nkCZG2do+9egFd5aABKVqlX7ffMfmi4zxEgQqG0cl/WcqUAxFqenBSGEXytW6CUx4dsApaNp0iG0A\nmZubKOUboCTb+lZ3uXwKPKMYM5n7+Qq5qSme71WYq+f1y6/V1mzvHCLfX9bPruKbd4TS/Rbgwosl\nAPz5C070zLmO18dq67SgknUMxt9vt5KldcLBqRTbzF0fWZxjKD5EbyQECF/o1dePePXNU0BUA859\nervjykY/6+YF6KyJE8CnURz50ctFqmVRQVDNOtOfWjO9fi63Xr/nLs5l8f7pdusiuO1wSTXOXVXH\n1GkXCNd4fqlEFueJ3byujzOhBNhr/GZcCRCbXyAyLd7pjn3SXNfW1JLtifl8NraUpVzX2mlcIKqS\n6pVzHdy1509FUiFaKbpcXN1aHFTd/wUaLWD+qkKtNmrjqMRkb6QL7DU/boupdgYpqM7AmAYxAogC\nnJ+cdiVh9h4rBPxQvHSzrM/v4hpL18757UaOdwfiWDspnfO/3RxxKd9yfn6nlyCjsYuFgvwvC4NM\n9miUiXlO4hdH7+5UnBA3s4Zyxc57ct25x1s7Zfb5H0uaEZ2LSpTU0nVKy2uAiKJZM8DaQeXvABPj\nm1Kce9q1/3YLfJkvllX9Upp/skoe6IkEOV5ZpxIIQSPokg9FKVXrJHztH7NohjMWCnJ7RDhmZpBC\ni0gNnveVlxQiQRgZsVG6GecBjMEN616IdRCVboq7U9GO0bYHJaz98t3LdpgvucnFGbLLL23VKP73\nSKv1tpcSO8+L9u8GyqFIQPCin1cgVzL071b0v/tsqo+FOByu9xJTgkgBmeDmK86npwEnh99eiWJH\nVhZjLp6ecnr/qd5X7ifAWanVdQcWRLBvZLimO99WJ2F395j4+rp+QYnsH1ANSNQUhRROzAPdEmf0\n9ffRcpPo62GiHrTZHlU1o6cbbVTxvIqaL/CoXGRibKBDB8iM1K49e6Y/Sq5SM4G6gnDYt05Kup5o\nDrv3RacJkFmt1QleqLXQKj8GBV4ziPNi+1R3pv0G7DXw1cajiGem0faXW/+1MVjphbCtiZahchu/\ntYJOt3W0ouqyP8srqASdVAyYsRIq4SiFSq3FuWmulrBWqTUrqYTuzUbrvGwEAwBe3nvM6MI0ib5e\nf59tkPYSN24I9jEHcFhz9hogPj8Nqxs6P3r4ZJ/dfxBUhUpvUgd31vaB0V7kpmbILM4w2esFjO0n\nCNo6qK+UGzqgOLc9dQJcWajUICz2QzAZ7wgMt3nBbrRofXGfSCjJxNgAY8mwyd9SykW2dg7YKAud\n8cY+EevqTS3pLE6g7U4gpsaWsvGVDaLrTdYAd5tnxqrR6XwlCFQr9Hx7r0XSw97i4KTvffVy236B\nJtN9Mab7RNKy08x+d9o0O5XmGSQFZOStN8yPiDZOIzCmJiYAUSC38lIA1v/hBATASTEuSzoxsJVC\niXe5ZunaXq5CqVqjP2YvSXF7vrFnvb1LkHOEuprNQzDYuBy1307RjaoFq4FP9sXY/k//Xf+pW09u\nsPE+f+++GAjQ9s4hIxM9Nr7TdGaR6twU0Ly8OlWhtAKAEqAvaybaFefxXfDQUqEQDBP56DYAeVOZ\nXOf7p9aiZD2YjJNcmGnyyY83+eRbIUwHMnO2vTAiVy9Q8i8O7kBmjhELOrQWSDG+ywsTwRqUCGTm\n9J9qGQ92twHvgJEmbvvJmiXPPd+kXq4gh8MdV6O4r7dzMMxpXrT/H5CgOG2kHEqRDMHbvTNXTIyB\nwX6Uj5d04Kuh6XGkEeHsGh1+J9yM4uYrTh82M0nJpQwvDgs8fHvG8cEpPTunzPbHGIy3prgKJuOk\nlq5Do+9VK4lvJQMxhYGYghyQefvld+xPz1Ap1zj76jFLU5MEk93KLmvSHaC4zksWnc+Q5Z1Tvt8Q\n51ozcC1aWh7+4wPKT9coAW9vzjP2+UcXPKPtlzstu9f6W1qj9r8wgKfWKxKzXQCH/PG41SXkeIKi\nUnH8qdXWTIwNMMiisE8qDHz2MdGpCbIrL8g9Wyf33YoND0QTY0azODPNiwEBYGe9NGrvdMKx8Zpn\na2lxax/FHjhzCiqBe8VAaz0V58aLwzxrO2b996czKvWy6GXRssiTvVFGhrVETZaXPp5yeaLaKhjW\nDGtvrV4zsjoZ0eI1dhJJltn79e9I3V5ADod1sMhKOMpYMtJIuHSyl620e+6tpE7fZe2H98I60DKs\nXpWBxnc6l637E2uQWpvvTEwErAM7b4lurLOZr5K4OU9uauZSL5lW0HbtdAsmY0QW5zj6YVm3nTVE\nAGs+HUMJBNqzeQ06Xykgk328eoGkR1MK51W+38lTPyiQjgfpj7rjyXkl3f5QxXoGFd+8I2Wr/mqK\nEUAUIDo+TDDeXmWg43Mv/ASb/PPpwWhXIsEgM/2CB1YCbiQlEaX0yctaz1+079l8KFqN4si/+hXG\ny5HfZ14sqtp8jtaPVM5m9R4ikBx6cmP6Ye+X0sbdKYu3rBrQSoP3BmOkBnqYPBAEPoLvdME2/87r\n4eaMNnt1ju/dJzo+THh8pMXatr64N42a6LdMbG42wAWvcz5TNl2uxEHVvrQLcmTkk08MpMjl7I29\nTuvUO+Hdg9iedK9sy5HPfmrCNCejH93UDzGvgFFT/O0ntV4nuZS5FOA6p2Bkf9Rd34xAWNcaQFjV\nbIHw9Ahyvo47Joa9XNApWGLFzXgDBO5/qwdCTh8+Q5oYZf2wImyrrBC/do2dV5v0REIM3rnRspw3\nfWeRqf40W6dFcpG4TZe9Lk9VVMoDA1SCouXqXa7CXLXmgrnRuVz8YtkN3TfbnkKlynOXAJpSLlF+\nuqb/LP9klfon10FpP8PpNQardIovEsjMc0pcp8j7QwePglZz4WBrBq4TGhTlo5GRtI6cnmz8jhUP\nZC4dQykX2W3oZaVW5+W9x0T/rJ+i4xxKTCkq8u42pGMoAZnTh8+JTk220ON2WmNaBc5aVwwYn+Xl\nb3ae9VMpbm5Tr1T08uihv/xj0VetvUPp4drdpR/hrBZitTdnj58RvZN0LN02/a6KoV/eS1S2Too8\nLzRbZv3ZI2/aPacEgnFNOrOjZp0avp1hLDPfGIO9nVR75062ZClbtyeDrPplxGwBc4C68OQZPH7G\nFQnO3+yijI1wkK/qWA3OZfXtin1MOgZZ2FppFBBVI/Eh4p8kyefPUXtTLap/nMVatZnITJFdfgm0\n34JrBF6WJIHHEYvHURZmefTNE4bjCvM/v40zIGS37jTOY7pY8tRN7BWdrr9prP7C2XcMJuOiXeO7\nh4QCMn2f3O5KALutk/TJkyf8+3//7/kP/+E/OP5cVX/sHoymdLK4MSXA7dEUSSVIfHOd2MomJ08V\n1NsLtkyP0/ON1HYXle5mLbpVmSGMbn19k3qlwvnBEeHxEQP1l0Trw978PG2TKC4Ajm69Wtr8b+8c\n6pmMcCjI3q+/JPXBoqCWebLm4sj4b03Q1kFqRCuL27sOfZudiMGoTfeL8uvGYZNEZXShUZrdl8IO\nntbOO/y3sAg++Vvs31+mXK3Sczvj6xIbCdr3QqIvhtp2H6HKUaHM03c5WzmxDaSmY6Pd+jBpvcfs\n+8nW4nBLZMWTC3OilKsL0VsviQSDjlmRw/1jark8UkOHwuvr7H2/ixIIEPzsA2JTV1vMZzNQZgSs\n0vaQcU01Z8fL2eirC+drZ+QKA5mrDE32mejxnEX0G26UZQLRONN9EWYHrLrsdHkS4y1Vq7wefAaN\n3tf4zfn3zu/rx+ZcZsmiE0BRJBgw9RIPJxQiwcsBCjNLuw6dOCdK1WqXHGqzdNbz3C3MgVZzYU0S\nmC9X0akJ2/OMWWxjq4A/UanmiwSrVR00TPt3P9/ixz/x59u4VwwYAf8u5m+6BxO0Merl0TQDxcb2\nm7lPb3fxrL7YJUcJykxFYLPUpBMU31h19a+Muq/W6wz9+S8aLQMQWZzncdMc+bRHrWn3uptAEGLX\nqeeMtAhilaqtqg3d9UujteWkqAffMjF4/f895l3unGg4iPLqLeND/QwnQuyclanU68yloy7+SlMX\nVdVrr7mNSQT+N46KREMyEz1Cj4xnSj4UhXhI7GklyuzdJZT1DcCvzbPT+faEQhcAglcpVutohQAB\nCR4khun94z5CfRFeROKMu7Iyde9OY7TjlxFocFqzdNpcnWg6gwzVX8ZEVdPuyHqQJ3r3MyZ7oySv\npLsyVt8z+p//83/mf/7P/0k06n4gZ0sVH/1r3QoQtKpE6MTAir8Zkavs3dtugPg59bK49ZL77UX/\nwxTN6CaTEYKpJKHeHgY+v0tkpAnuVc3mOP7mISDK7LzolGwIyrczDojvzr1aWtnriFxlr5HJAAkj\nlQxA7NqkpQ++vYBFqVqlUquhSCqJhWs6iIfG52vvk25HDEbNBL4kddSHaBd7yXzUUoJvFa3sLBoP\nM9CTwFpU7ew8J5jFTtOTXMoQGhwARDaKki39AAAgAElEQVSr9aVP0G6uHeSZDqsMxEKOF4ApRWVk\nSLFRLFrFi8/+MrKKgcwcvROjRIIBXd+Lm6+73pvsFhQx2zuZta8esvH7x+TOSly/s0B0aoLad+sw\nIKLuGjK5fzsp9Ma+fzLupc2N30n0pZheeUjpmx84KJxz/c4iMzduk+hz60NtitGpqakNEKGemIOj\n4BSoSZBAZfSjm2w30LRHxwYuEPl3P3e8cC/ef486jTHKTO++5skX3wNmgKJgMsHsp7foMfWdvq9A\nid89aD4nkuEA2bKY/+5lcNoFfuz2etqR/p38JbeLdM/t69TXN/WxmLLYmPVSCchcu7vEi4ZdNc9h\n47sePaNeKXOw8469WJ+hPaPuA4TsMsTZl/PTPuYeQG4VTLC3C7j9TXfOavGd7ZxLJnsjgdKbgq+/\nZ7LBxHKSviqoRXH2r7Q1NFLMNQPXkgAn3HBnVHISP8DOTgkE4z42sgFIskwiM+WSKfYvzgk8b9vh\nHaAVzE4zyQQjjfOgur/H5kkeggrFSg156irnNRWQ6b19nXzUH2r+h0gMKc4YHG5jApV8pUYwAC8P\nC+zlKswONPvq9XkIBfnplRSRYJBYJk11qX2mHSOd72Rvmsl/NQHIbT1DfIcIUtfqkFACVOp1VCCU\nSlCL+Em4CTtp1Vunll83sGUnO96qJQUwXNJbV8M7rdmNq9Y2Ma8zyA6YrFVMFZWoYAnySWfdSnxb\nnytXrvDv/t2/42/+5m/afkn3nSH/gIHtO/4SkaAXyJi3M9AtROnOkVr90XYY6bnEPDkptBk1Xbsc\nq6qq9xEF41qmTvzu2ZNnnD1cEX3oEyMNWiS7WDfJ+mGB/mszjDR4c42tAm69WjElSKKvB7UBeKjW\n6wz92c/Z+/WX+vutffDt8D/bwNhSCa78m79ucNXGfSGi/phiYg4IyBx99T3SNw+Rw2HH8eprokQJ\nRCIuGQF3w2V1asX8tKKKs7wb+EnuHa9++4RIo2ysibBq33s06C3t4o5D4E/aaX1ysEeI4JhWVihK\nb1eQJkZbBjJai3uwU1uD3PGJXso6GAtx+MMKSixJvFDkLKSS7jMHoPzaSbf945adB/S9HF3f4OZw\nUoDCHe8yqdxuaw780mc62bbuRP6bfNyAAwif897wS6FprHrSnn/RC281WyCx9Yp5jSXGBFDUmqbs\nxxbrOZEt15oObVdbEv0DP14u5kD7wYbk0nWSdzIcHxVc25ysVJHjDqCCxu+qx+NkayF6Pv2Ik3gv\nawcFel9t6pR1g3cWSS4t6H8nnutPfzrzbdxtlDc9VyfBBOd2gUo4qmMR2P+mXelGhYkzX7wSCHD2\n+BnboaS5hDwzz4jLXncKXDtjQMmmColW47YCO3slEMzflaFaLJF9/Jzs8kvkkOK5D1rrlDO+Sjfw\ntWJKgOzjZxw8eEpIrSMfHyCNjKDcuEHvneu83s0J3VSddcZq454f5EmkY23qlcRIIszOWZn5mERQ\nrvPisMh0X9z2jf2GFuh2aUq1sWptuqtA9Je36bl1o83+czs94+czfUz3xXyyBQg7efjlt+SerRMZ\n7kcKBPWK5aauuN8V27PjVruc4d3YpGOlhjVoUM/nPFgYjOJ8BtmCbMclKrV6Cz+oM/G9hp9//jk7\nOzuev5OMKDblM/avQfvgYk5Gs1Cpsb1zSBRc+uAuJl7GpbUSdQtR2m/Wwnxpd76kYvodDYm2cF4l\nV6kxFA+3AHZpAhElRiYo774hGZAMrQKNPvunqxx//QBlqI/K3jHFN+/o//xuy8O+eF7l6OAUinlm\nJgb1cXj1arnNE9Qp7QqH2k4no1LNF8z4By5i3IS5KSMYmyhv9uvk/+giCeaA0vYuudV1UksZYjNX\nL9yC0urv/FHF2SVaKVJaecFEKsJUb4So4QJjpUY8e7Lqu/qk3VJS+8XPHZnbLWq/e1JktfHvwwkF\nUFl/c0pJqTAzcNF2Kv+X+HAwgKpWmSicIock9u4vE5m/wsS//kvoWgWTc3be+jtKMEBYCen/7Udi\nikwyHODelkBevjvZ6xINr7P++oCtkyJHUoisxbZd9HwoVGq8/e4Jlaei9eDtDScQvovZ/+GdLWQt\nY9/AQLmoSJLkEeB+HwwIFxfjpU8EA/5wcQO8xMu/8Kp4ivSmCJ5b94TZ0Teuc8tskgrFchU1JDJ1\n0UqRja8fcVgQl8HTrx6xNHWF4uZ2B0Hx7lBxxhS5JT2X9r529MWtXcAZ+rET6WaFiWTyPY1SrNZ4\ncixs5kwDO8Zpr3vpnLXqzJvFwDkTbwd2dkogWMcksDG8KICt89Bap+x64BUo9m5PNLS7loucPlwB\nFSJ9KepBmXL2DGXzNepgimJ82PU72xWvMU32RTj8YZni8irJcID+OzdgNt2SrahdX0ljxgKo1Grc\n/+IB8XBvmyw1dnrGSDDEjeGIL7YAkXxcJbfykmA8ytG9H4hMjBBK95t0pXUbnp0dye19xj2yf39Z\nADha2GGMoPLGKueefIXA1BS5qRnm0jGSEYVyrjOLUlMFNtvFaRbt0tVTVZIkfpYZ0cshkhGF8ukZ\neQOHLEBff4xIrzfCs6qqHHz3mLxhctMfLQHw+qsH8I/fApBeylCanaOvP0YqetG+7qakP/+Y8p0M\nAOGeFFKDBq4Uqnf0PR3LoDtIjXWO4plpai83dICh+vomiTsZcmuv9N9RFmZ5mxgmkVBYeyMUaiwc\n5G25zo1EWJ/D0skpp+sCrKh0fs6LByv0/8UolaVFzuamuHG1l/7hNJIk6b8bPC9T2XpD5fUb0r/8\nKcnJMa78/DbRXvs3qKrKh0g838+S+/Y5g2vrKNEQx7cyKH/xM3oazA4Df/wRqcVpAod5ts5DJCTI\npONMjvXoa2KcJ1VVCf6Ln5o2ZXpaVCkcfPeY8sNnhGWV88NDElfH6f1ggfT0iPlZwFmxTMJgSCDC\nwJV0c36CdU4CotcrGI0gSdLl6kELUVWV8qno/dP0VU0n4MMFNv7P/5tQMkb9LEvuwRPCUYWeuWnb\nePU1adBufjg9YJ9nn1IK1TmMBDk9LRFpUFHtn9dZMOiYdfwfIrGxvQ+RIMOJMEM9UdO8anuvdHTC\n2coLAOq3Z0n/0U9NYzwrlnm7kyXR2AdW3W41j0fvDjj74TE8E/11x0sZNj+8wetTYdcy6TiLhnmx\n6woo8RBvAhEGPr5B/vFzTs9r1GdneFqQUAslKrLE4mSfruediNOaazIwEOfk8w95ce8hqCrXfrpA\nbnmdg2qQ+M0b1AZjJOamiPalPJ9je2c6QfCzD2z7q5WOaH/37ovfc7byguj4CMGDPV9/e1Ysoyoh\nfnZNtJ+okkw4ETGtp6qqvP7qAVt/9y2oKrWJSc7GrjratnbEODdS9Zzc8ir7hXMABpdXif/qA9KD\nfb6+vdWcCTv6ioHeRmZh/RXJO9eJuNhPP2umphMc3L4ODvbQZi9UlWypeXa3s+/b0aF2pF6vs/Tk\nudBjYOnubYaiA0hy3fE9lzUO0zs63AN+RLNx9cajZNXsX7j5JQCDg2ZbvrxzqgNKGm2W2xwZv0tV\nVW4uLbAx0EcCmA4FeVFDt+Wn1TpyrUJ9fdPkbyTvZBz11VE8fBs/Ujo5ZXDvLamromQ/vPeWZOi2\nL9+yfHLGh0NhnhfFtxt9CjcfL9yTMp2Pjn5Iq/eenlE+y9n8tHbmzbjO+rMtOhm6fo3va3HC56Lt\noSYH6O2LOp43fn1aca7mWp6r6XTSdAeQJAkGe3x9W7tjsskFdcoqTt9i21tRlWyjijWsBJA3XnHt\nZ0vEkwnYes2Hv5x01DNNrH5XK71ynF8gEahTzu2x1xcjFJBJ775hOPYJEQ+gSydfab5WJh5RXO1r\nLp9nORIEVaVcVIlHQ0Tj4bbO2bNimaH+BENaS4Mkt3WHK4XqZOMKhXAANSQjByQUJUgsphCKRXRd\ncfLNtPfUB+LsLmbYapwts3dvMzw1jCzbAwNWfSydnxONhwlEmv+WCFQ5NtjD6sYmtXyRZDJKIhGh\nnN1jdPYD/d7ktI+dxEk/FkZT5MrCD2n3rD49PXX9WdfD7AcHOf3/iwiIhDwzZTo8s+eShWfTXrJS\nzebY/fIH/TeyX/5AdWAQgMNvHtMTlHmXq1D69inz01co58rsdxhxcRehGNmDnGmM8sxVUy+W/Xve\nj1jnKPfgObVC0QR4d/D6gAPD71TuPaR252Mq4SjlklCoQqFCtVzl+KhAuUGBVM0WyGZFwKBSq1Eq\nVSFfpnguk0jGyNWC1BtrXc0WyBUqHH+3TGRqktzqBicrGyR/+iG5c4mcy9wMKTIBpcbZ2ksCskyp\nXKX07VOOlq5R6evF3jsTYrInSkyRTXpmk6kp4g1dIRnn4CBnnislgjQUI/Kzj2Bk0OVZKqNh2RQ5\nbeqYKIfPneRMJYX+9UDoUj2fM/Sb+9nQbmWGHhmH8XEiixmOv3lI4uZ1cqsbnG7skPrlp47jHVJk\nEukYff3iez3n2VMkAjMzlF41qeKOzmWTjlllSJFJTKQp3L1FaXmVXK5s2V8SXBlj/+9/r7eE7N1/\njnzlqilzUKhUyWXNqNRe721Kowzt4Qr7v/ma8NgQpYFBKt895binn1pEXNS+z5ZIoJqizFZdqeQr\n5LIlcsNXiPalCcjw/F2eaCHPiaywsnPO7liSSuy87Zk1jtUryzRxZ4HeaUEv9jZb5He/XwYVapLK\n6/0CS+UKuf2z9rNVDvvLj1QH0pTrEuHMHIQVtv/pIdX0kF626FZC62c9q9kcb+89pFSqIkuQ/WGF\naG+aQkGx2Tb/Yp7j8NwU+dI51XNxDuRKkD0rogZ87HnTnMXY3di1favR5hq/0ynz64Tj4FbBkv5o\nST87xXplHf++VfbPXS4PI6GazcHycyYTiqh2+vo7lh88dml7eo9YDR3uAT9SDgbZ+R//CMDYX/wR\nx0c5MLUDGP0SAJVwIszxUcFUEv+9of+7abMC3nNk+K7eZIz+ShNUMDA3o2cG4zfnKagBn/p6OVLN\nFkxMOOfUfLzfXPm4oAMVyxwcZHUb5OjjHeT08xFo7Yc03qf5jbXnq5w+fI4UkCltvWm0U4p59ztv\ng4NJ9t18DMPaVcIREhtHXE0IZhUlIHFyXKSSdzpvJOSZqyYOeyffoJNztbMsqEo1W6A+OmKgec7w\nLl+H/HGXWoXaZ0PTvsW4t6KVIs8kqA2PUz5YQ1JB7e2lXJdRG7o5EpT5MK2BfDrrzJAiEewVazU3\nmvJtT4zzW80WGVCCJHuFHikBiePjAsGqu15Z1zSxuc6Lv9tGCQRc7edQXxL57i2OflimVK4RmJvh\n6FyG81Ib56zmMzWz3O3d4SSC01cprb9l5+EqvTcXKIRDlGoQnpky6G/TN4ufFxlNhSnnSuznKhQq\nVR7HB4n+0c8BeKxE6X976lK5Yr3HZhjoSZh9vsK5yR5KAZlaoYJca85fpVDl4CBHOp1ga+cE8Kd/\nVrtzeNgEim13jymK+8/aDgi0HwX3BkuoZnMNahPxL8ZeDK9n6tyXwGRvlEs59HWxlNWMTTIyMQpI\nbXOXNqVOroHmqj3joj1ljlRnFnRzJSAz2SuAKGb6o+QqNZSAbALb0YA64vNXya9umoCIopUimWi4\nUXJo+IbMVY7v3ad+XmXwV5+iDKUbNIRe3yHRE1EYS0Ucka6t5T4amNiF19qGf+A8NrdSK3cEYn/O\n84vDvInnefbTW41+TO8+dTdHzrvMNEFqaZ7T75/4XBtRVpeKhi8YYBNUcfODQ2ydFMk1UI+9S5vE\nu2N3bjiWGIKZGtGt7aNTJgJtHkMBmVQkyNmbdyg9PaR6YuRkGXfsfO/+xKISZebgNbtf/0C2XGN8\ncZ7z2WttIrqbHRh//W+yDng1Ho8z/bMlCo+fE5Bl4jfnCaaSFHaPO2h96bTMXHJh56hzcH+Zs8fP\nGtSbZlYXv+upBGQdNT8dC3EeDVlsW3tinePs85dcuTXHwfI6AKlb15HjfiuCmrStbvvYb2+1fe1X\n2O8dcDhDxfxJknm9zO1OKvv3n1IZGWLtqKb/TjtteN662J1eaSUQQArIHK+s6/vfqquX29tvH9Nl\nPLeazfNm+5DjK1dBkgisvaa0e4Acjrg46I3zZCdLLltq6T8150iUyu7ff0p0aoJgUtNj83c1Wwvs\nwJyRoEp8fspwaXu/IMqdYBGYdUSitLxG79wUKHawVDPNMzqAcMy3Hjf9xmilSM9XjxiMK6j1OoFU\ngnq5oge2tNbLi+2V5toFXZkZnMWNw94ol0vLponBPkqQXLxGfG6azYrM2sYRAUmUxl8fTNA5o0N3\n2NC0PnpZgtDiHJE/+yUqENh5C7vbgDPIp/N4msHYajDkCiroJWI/LLS1H4xrGq0UBf11XNwY3e2n\nTPrODXrnrhK03N3864MX6LufYI2goT0kTvruJ1RCCmehMPOGtt7me2KcLz9n/fePKcgy3F1i7tPb\n+pNOA8In8bgn43SPTWLFwTCDyqduziNaBqxAnqLCRAsq+dO/ywHCtkpbbxgbG+M//sf/2MFrnA5P\nsfH37z9l9aBA4uY8xekZ1g8bjoiHsW8i5gb0DWeXToyrkyKq5I5P2d45RbtaP3pb5aksC2fTkbvU\n+dnGPv61rx6a+GtHkgqnD1f17/OT2fCiOmt+t5X5YIHklTQjFSOdkKQrtA7UsfKS6MQI6V/dJT53\njWAyTl/Daa8/Uzi4NsNxqcLLe0/0bxj/3/8l2cdryGGF1E1/lHXtIl3X8zmq5aAnpoKTw91tIKPG\nq1B1WiZ/ouFflBqZlne5Cj33l1vyPPt2dhv99dV8M6PUc2eJerVO9vFzy9q4G15VVS/IoAAgM3Nl\nkJHh9iLxXs62oEa83WIdWzGMeNsGta4yenue6NMXJPuiDN29zbuxgRaOkHt/Yj2f4+TbV8z2x9g5\nK8OrTRbuLrRh4O0OzJRipSYSjBhypeo4zzElyJWPl9geHwOEQ5+Khinw/sR5D8ZYf33A6heikmQ4\nocDDFRsuS2uqSOEM8XCFnkiQ1NJ1Yjev0rRtrfBXWuu4IssM3rpOcfIq0Blbgfc+7qy3ulKrs3VS\n1PsZt3cOGZGrrekcJZXjco2jwjnb73LsVWSG4t1ru+tGxl6sa4bjbx4hBQNEx4cbgcAfU7pFOWiX\nUrUmguNBhWg4yM53D4l9eotwI3AjTYya1lUH+WrsDSOzk9PlrVoW498vVDguCzwATgrM6FVqbtVr\n5j1Ye77K7sPnhkvbTBuVbt2S7mARQOugVid6bE1ovMtV6IkIwOpgKkn6X/ycYDymn1/ZxyumLH3r\nJIGX+GfZMoIJg1cw8HL4341iWgcVssvrBKavsnZQ0rGmtnagtjDOjeGky/u99+dF6V1jSoBMDFYb\nPtxgXIFXm5yOjwm6x49uMqncAKASjlKo1NpCoW8NKuj2fZ3sB8nsp8QVn+C9dpaF9vXByb9uL1gT\n2HnLvpFOODNu82lzx2e8+uYpgUYrwMt7jxldmCbRl/KJTWT8ZisQsHn89vlv0pVq61GoVPV2E+gu\nvfBF5UcbgdUAHz9cYS/cw7GsMN4T4cZw0lW5Wyt9JwbcSREFSN/+g6cEdo6oZbNUBgYpT0wSuynA\n+vxys5pLT6f1YADA2wcr1AM14gnxLf4zG84GwPp3Tr/jpPjVbE4H6kBVKb5+y+mDZ8TnZqlmC5SW\nVzktVdnKVZD3HnFynEVp0FC+vPeY0X/7LxlbyJje01qMSNeqyYBanZrZwzecfNvMRiSXMjYaJK+D\n/cdngOhUvMEQjTQ91bMstbMcB3//JcmFpqPW98kHtkCRu+EV2BRai4Xf/eN2SHXX0F3UCfSXoQ2m\nUkz/2/9Vnz97NNifbseUINWyoCkajIfpiQhAvcneJjtHq2i4owMz3W/QQQH6+XivAhy5HKIiUt4f\nFWvRH1MameP3qct2KspCpXGZbYjmNDv9rbcedZ86zj43jWDqsHNpvvHZnV8YW2eereNKLV3XEYy1\nrNVeOoZ654YOKuv093v/+DU7364QHh9CfvOW5Pikjl7cTrbHTYe6l7GXCMSjSAGJxMIM52cF03ta\njeNiYqeausy2BDmeIH5znuLTVSQJwuNDSOGwDs66tX3KRD3oK6PkdHkLJuNEFuc4+H9/x9HTlyij\ngzx7sMbIcD8xJciLwzzvvhfVa4Nxa/Vaw55lcyIYIKaH7PI68blrXZsD7cH+9lB7lRpeuuom3dDj\nohIlcXO+mTm+dd1C15zlxVeP9EpJDbSxWbnRSqzniBi3rFGyKfaEnP7NYS8cG7v+K+VGWbTitbda\nrV97NjIgQXl5FXlZXP72SktM/8nHjhfKy28bkkRVssbc0rg4z030GBiEGv7VTjtZYC9pzldxc8uU\ndTZ/n3E/+E3qNM5WJcXbmWlTojKYjLVglfDj3/lf63aCNUpZVDRoOzexucnuu2meF8TzjdVSSlXs\nq0qwGUwuVOqCtaZfPDtbrlG4MH2f3R79IYD2avKjhySUgEw6HuTRrjAyQwmFrZMS030iQuY8md6H\nQCcG3EkRR+Qqpw+fEQ4FCW6+olA8R+npIfjyJT0L0xQDrWgkHMYjQWF9k1ilQEGJUanVyOZqlNQq\ng1JIRBu77EC2e2g6STWfJ5/L8/q0SiQSJBIMolYqyEGZeqiZVXKqBPFT/hNMxk0GdLo/wkSwxpQS\nYGy6v5Fl3dD//vTRM6rFkqk9wsn5tb/HOL5OHffOL6UxJcDE2ABvb87rLQOiKsLtcDVyQ1c4PzjS\nqVWafyPGExocYO9vf0twYoTqWY7X/+m/kLq9QN8ntxtVEuY+ezfDW83mdRBK8LN/Oj2E2+/j077X\nW587pZrxXtdO6aUAoldHya9uoETCJBeuNX5Wd+nbtvLbOos21lK12ggGCHFC0nVqzUqnU6ZvNvP5\nXkYWVKOibOpJIDOnO8taf3JqqdOLXLep45z1wdth8N4L3bm02mnk5g4LbO8c6jZF0FyKbyqFRE9u\ncx0lolMTcO8+oYU5SkEFnqzSPzbKralhA6UfPinGupeptUo1m9cDnbmVlyBJjP3rvyJ18zr2jLTb\nONqjEDWz9jwzZW2jU1cutS0hpshImTm2wj1IwO3rs7D9ine5is6yY9zfWtD8bVkEqcyBHCdnXUK6\nMsbhOTB3jRMpiHp/meLdBejpYW1rj7dfPQJVVCv4qV7rTLzsy+Ve6gKZOXonRvWWrWo2TzAZo+d2\nxrTWFw0mWRMao3rm2E6Bq1eGNORdrsJctYa/Wbefd8M7Wxx++Z0J46gZ2Klz/M1DvWKw5/Z15sYm\nHehO7etQLRbJPl5t/N2Cy7q0Wr/22qYSfSmms3tsL6+CCslwgPLTNeo/XQCl1/RmP3a9vdYHZz0N\nJhMMNuium+M0Vu540VqaxTqeTDpuOWOa82XFn3C3P+3voUKlzouBcaJ/1g/ACyVK5V3OhGbfflDD\njNkR0TE7ulFdYm4dB8RYLZUu6vYOg2GZnR+eExkfYvyv/oREX4pCpW5q+0jdnIfp/guOqbXElACZ\ndJzvs91nCbio/GgBAePG748pXP/ZIrm+CLVIJyWL9uhod0UiqQSJBiSSvVF6VRkNp7GtxZQEBVzu\n2Tr9V8ZQj/Z5G0oydGeRaDjIuyer9ESCDN65cckZZ2cJJuOkbs5zfnKqHyR9n9yiuPmKF/eekD0q\nkH17wLuBIa5PD9J7GqKwskZ0Ypjxv/oT5Hicgqlk2X5QjSUFB6oXdVvxvMqDL+7z8tUmY6kIs5/e\najh5zd+XZLlxoFlpadwcbruOXMzp8Hv5sDujswNxxj7/iPon11uCChoPuGAqSai3h4HP75qyC/p4\n4jHkcBhJlvUqD/PcXE6ksrMMSnf6+JzkYiWB3ewNNnPlxq9dITzYS3b5JdnldSKLc6zFh2zj3MmW\nms5BDCZ7Iy4OjBirXKkCR47vL1RqFJ484+zxM1tr1mKxCchl5aG29r51wyF30pORqQnxbcwQHRtl\nsjdK+kra5V3tBinMlztzNZFf8QoiWp/ph9u41eXZ7+XVPK7ZgTgjcpW9dKyRsZJAguzKGvm3u2Sz\nJcs6SoRjUfrUgH4RmeyNGjiq292fTpmR7mTsTfZMVcktvyR1062c2r5e/r/D7EQnF6dZ//aZKWu7\n0Khu6Vy811fLWg0OCeaK/cAA07euEdnNOVDugnae3LCACnpJOBggFo9y0ACYS8dChIMBStUqb07L\nemf2Qf6cqkNLnNbG0fnl2fuycnlYEGZdmD18Q3TdAFrn0kdv0mMJkgsziECvSis7NTsQY0QWQbVE\nXwy3vnetMsQI2ijH/X2v9bzb3jkk+PBZs9Jze5fjbx41AjtxTu8/5s3/9d/QgHlPJRgolpBXBDaK\nRndqTWQdff2A0/tPkQMB8XfguC5u62ecH+f1jVOo1Ahk5hiZmqCaLzYC1BLTfTFOhhPs5SoEZNmA\nNVXncP8YgAFPthfzvvPX+uClp6LaTZoQmBqdY4mBtZpncqzHBCponc/im3eE0v0ueDzGv1nRy/9P\nbW147qJVmwUkeHFY0CsgnBINrc4qI2bJfuGcs68eoaAweHXM0Q63E6xp0pIvi32ZuWb7nXo+x+nD\n5/QN9pL81U8BmFiaBWSUcp7E5ibHtRoH+XNS95cZnx5hYmTA0yf3Fn8J0MWxHhKNZFFnwZHLaVu7\nhICAvcSoVb9LdmWV0wernD1dJ3FzntGPbrZxsXcu9W/XEXFSxERfDLVRip1YuEbtLEc4FmX29nUC\nmXH971othqa4Wjl+dHyY8Gia6FA/0uJN8sl+ckB0bJQhGyjGZUjrHiRjtnDrv/ya/VyZnYpMcnKM\n4K0bPP/qAbdHe7j6pz8DoH5ljH/YEIZZc7qsB9XDt2c8fSfrJalOBiEgwdHBKfLyKqm4ovfYT05N\nmtY0kZkiu/zS4ducHG57efyU4nwoadKdTebujIoSrV7vP7c/zhMM0ahnANGJEdd+Wy/Dqz0na2gZ\n6HaAyvvS3mnlQGt5nyXyRq5cVN2vnuYAACAASURBVJXy7gG51U2Si3MEUwnOHj8jeiepH8AApWpV\nB/iJbr1i9dkmpGMM31lkLDMP+OVLlnlxmBd4FV/cJx0Xpl5rzTqRFaZ3ThmPhlDKJdNeOHuyamIr\nubyAkkqpWmMsmWAsGaZUTTWcPDfnrJ0eWzM4ldKbpHJ8hnD+RbuRux646V+rZ7aqUNLELejUSZCs\nOdZEXwrVkLVKLsyQXX5JMin0y7iOTpgLxkDMRftste+8aOVAMBknkZni+J7AmPCyaU7SznfY2hiX\n1zk+yUGjzPRdrsJcAwTOmPGqhCMEXS+HRvFaX3Em16vi8mjs34329DAhhz2cZIlUNOybRSPRl2Lh\nsw/Y+L0oDZ7+2VIjY1ZDSSUILc43ucw/WHC0j35A6Nzk/YI/NsWoC9FKkZf3HjOfjqEEAjqveNGx\nj97oq67pAV03kEc3G6F6BFZjisz44gzvBvsph8Id4ZP4kWo2T+75pv7fxe1dlJFBso9XUXR7/1zv\ne9ZEkmWK669NfxdK+8ymaoHJRjVnclELqJjnzT1YI+z19c8+YMAShPru77/j0e+En3LrFx/w0b/4\nyBGnxmnftbJl3npa5+m7HC8OS45YYjFFJhODrZOiwBXwCagMeNP+1uskFpoXX3f/RdXbjACGk2HS\nee8LpNWPmOyLsHFUsv2e9vx2zqpKvU7u6JTSs5fU8gXefnCTsc8/ujBOhWaHoluvOP3hOT2oBKam\nyU3NMJeOEQk2sJZUyXDZbd53eiJBds4khpNhemtlNv7rb6gP9zKot9u1Vw3hd04kyVq91c4F//Iq\nqLoaEFBV+0C9M03if/Orr5qlH7vbelmVH9FA2qKIyJZmyC8CrtH4GgoVgdrfvCgKI9ZJX6hW0i0c\nbQVUibAsMzHcr5fxTowNkOjrLkiRXVopk4SGTg9NZF2Auqpylj9nMJkglQwzEA0RRKJSqzmW6hgl\nIMH6UZH5dNz0O9qm0IzR+qGYjGQ4oIOAaGItk5VDiotDb3a4ncq3RoYsTqXl0OrGJuuGU93eBbbp\nuMSuTbZAf/YyvJKJqqyVvnf3kn3xygHvKPPllTd7iiQhBYMUNl6j1mrEpyYIj4/ojB/aOCNBmcjG\nS+prLzn5/jGRsSFIC0qooYkxl2ChfS013dNCDQf5Kn2RAI/3itTqKv3JEP/j2QFXkwpLSRnF14XG\nTfwdZmY9MeMeJMMBsuWaPg/WNa9mc2312BqdOUmW2fv170jdXkAOh3XHzq283E+7idszvfdCq+xw\nu/bCYayW8yrbYESwy/vaBxetuLGDoprbpS5P6vU68ZvzVJ5tAiJrCyIrGp0aZ+ukxOMCsOGG22EW\np/Xtj4r2O42SDlRmZ6Z5MSASDprt6i6Ym8zcp7cZXZgGtOymTEyR+PRqL08j1xnIXGW2P0b6irUS\nrR0Qus7k/WP0+BHNV93U/79TIMOfjXAq736O8nCF4Vqd1NJ1epW+RitD631pPe8mxgYY4Dpkc6ZK\nTw0rQbtU5lZEQiU+P0Xh5Wvbc63rEOpNEbkyQq5RSZBccgaOtv6dFpjUviO7sk58ftrka1XCUdZ2\njohWioTPy6ZgTdNeL2AEaDvcPxLBgMad79HvfmD61jUGLEmtQqXeheCmUVSe7ee4t/IGACUl1nNE\nrhIJBhutRmI9Jxvrmc4s4r2OzbNBVc2Awab5VGHgs49Fy5fHXaQSjpKbmoInqyBJVONRdn79O086\nQbsfIROUzXTKYpxVQPU1p9rYDx+uUHj2UlA5BxUqT1apf3LdJSnmD3fK6OMcN2z0fDpGz+42Q59c\nb9ylrKDqTVsSTMZJLV0n8MV9AiGJvc0jTpL9JAoVlDYqKqzjaTUndmnvgn+ZAdWuBgTKp2cXyDQJ\naiHt/7cW4YTmnj6j9E8iapi4OU9uakZ/RifgRTEl0FaJof+ojkRkZNCCkG5G/L8M1Far+FemhoEK\nR+hbmGbo66eU4yFyU9OUege4/ektlPVNwAxqZRTrQTXU6G11lqYx2uiJcFS+Yemxj9nmulXprRdo\njhxPeB5a7ytr0VraddyF3ttBBJtZTmPJsxtAkJWqrLtjdL+0uxlVbZydBPfs+6pJAXdZlQjaWLUW\nnNLrtxRebTPw8zuUdg8pvnlH/+d3mTDtf5n11wdkHz2jXAelWiN1eMDZWZrditQCUMz5ELX25ydu\nZDiORnl1VGQgJfbs8wJ8vDhPaXkN8KLLcRKvw8w+v064BwEJ7m2dMJ+OowRkx4P0oj221Xqd81qN\nsCnwYdfxiwfx3PvYu90ioztElWJjrDQYepqgUj23r1Nv2Gn7OrrvcbeqE3+YAt0W2cOeeUsnJaia\nLg/+ZJH62KTOKjGSPeLkb38LSEQW53ju0O7TziWjcF7ld5sn9NTKOiUdSETXN/jFwrQBoEzLOvpn\nJWntlzQpSZsiMTuQYCypcab7W+NopUg9nwPFX2Vj6wv/5QSrjLpQVKLM3l1CWd8ARMa53pJJxq+o\nlAtF5JBYr/NaDalWQwkK9h8wf1PTJ5NQggFyX9+n8OCJTknYOjHhcN4NiMuzdinWSqCDyTg9tzLs\nP1gm8clt+hZn6Llzi1Ai4bAe5nUobm5x+ug5fXfvkMhM0XNnyWVc5r8TgcmXVGriLFACMsmFOdN+\nLlRqej+3FA4SOdqH9FXbc/36JMYWuMjiHBj2ql9x09NCpcbhg2Xkrx6J77kxTyQZZu/3b1ACAZKL\nTV9SCQQoLa9RnZvyGLv5bPgQyUI72Nl+yE2JNrzweZnDL37PQAMEsaXPj6W9taFXO9miXgU83e8F\nRGkULRnaz8lxjt3GEW6kF++2KIEAkaCo7AEvEHpBjX0tnebBi3fkj4oklACHhSoDsfbYw/yLsM2l\nUL0xDj9thu9PfnRQwc6iwcIJPXuyyvG9+wyl+9mL95F/skpmceZCjot/h7CTso1OQKp+DGkaqMTm\nOolXrxgaTDE+OYE8N83o1WEqhUGqSyJjooFaOfU4Ww3K2oFwXjMxgRIq0G+bGzSmBLkxnKRg6rGP\n28DImiA0zqW35rXJMDc2aR9fi0OrGQ110yPvC2X3eHs7C255As24ljx33gfXCjPA67DxpOV58oyj\nxmW153aGgEvpvHU8rZg/LnZB8+N0N/d75fCQg7//CjkSJjotHB3hEMn6/i9UqmydFMWBFpKIL80T\n3triqHBO/IObNkAxLzHqXm5qhtmFKd6cllGkEPVsmWy5ylwsqAfo5Ouz9E5PGDAtrHQ5WrWQYAIR\n75Ap7R5w/M1Dnf3CmCl3m19v3ANnabfHtnmurLCXrVC9+zFruycMqxVmP71lytpbW9xaP/MZar3O\n0J//orF/3CqUmjrvJ6PSib3QnGgQAXEzKJLQv+SdDMdHBd+OpPa31myRMwDm+woKdFppYGfXADew\nRHeu6Xo+x8nffo8W3Dn6YZnAh3FqEfdAmdU+GNc3IEGuUmMoHoSamZIOJJFp7BDEtLVf4nVutc7O\nGb8jsblOYnOTk7iC6goyZxU/F5xuYrk0n2nS6Uya6lLzUtqKScbakpe6OW+zGcFkjEI0ysZvvgZJ\nYvxnS6xla5ArMnM1zcFvvsTrvNXwMlK3F4B2Lgf2dTNWehrF2O5RHRugx3M9JP0SHMjMM9LAcvKb\nmBBSp+iAXm/EUzAixhcrNcYH+whWz0GnFrfvs4HBPm794gNTy8DAYJ/tglVaXiXzyyFTJV7Trnrv\nBad5qedzZB+vko6FOMifE1jfIBmRUAZSAOSeb1Ivl6kGBaOQN32fX9pBP/uhaXNiyVhjj4qfDHsm\n48TferW3iirbJhvQxlGJyd6ICXDQ/aySiIwM8f9T96bPcZx5mtiTWVlZlVlZN+6LBRA3AbIbojRS\nt3rU2pnp3p2JibF3PV8c4QiH/ylH+IPDYTvCGz4mdmc86+1d7ahnRi2pRTXVJEDcBEAQIHFfdSey\nKv3hzczK+6jKAtm/T5RQlfnWe/ze3/k893/+GCmf9OJ+pKWHyN0n7O2BjdCBAt8AjeGBPFZLQO/H\ni6i/UJIiFmBj7+SR9/3d0s3lZBz0RCFAm2FLullBFWpAIJZOGemQfGWanFCunUU98JSywdnTc0wN\n9ICKxTCWiXfFcDHTd7Qf1enGRedf/GwmfeapvLKJMkUhdVvF5dc/IPVoDuKf/BFQMEY83crPVcWm\nZh8qK+uoPdnEkePFaOyxl4qlQHNtBVRZR6EwiiHFWNaPz3JpfbsCjo1g9NEU9kS40Mb4cSi7z9sb\nRNorZwxDPLAUdGJB3OWB2pNNaFnWr5/jWun17ORst5cJbl0KrRJfr2Cg2oKTQPajCnEmG01HJa7P\n6BejHCb+u/8aN4keB0AxN7G2QG1XL8EBmMozGEzGIAgxNOoNJGMR/PY1cWynenhM6satfpdcZGs4\nrdxCvDeC6tAIhosX4F+9ws2zNTADvWCHBzSjw2t+9evckAkHsL5lwGxc8GwEg48XcDBEQJy8e2zJ\nvUKNDGL74BpVlgMnVnENKPgvFOydpxnLpa4GBnk2YmlbMoMKtsS45/1lVILpCzvaJbb+AGD1bRQU\n4pkUmNt2gs6t8xkENfv9EtliD5DgTFX7b6MOobTSarVkW08dqu/NlZsyTsp19CViFifDySlX17cm\nSdqZs1DSdWDgedslwUAWpWLZkmVWf8cALeHk2wOwCXMw0M8d0g07qImbw2MAQGq4H/YAfsY7x5lX\nXIZULKImNQxUcoCMRqWq/dsspcsb7Lw6A/f4ESgK+P64gok//xRRmsbOl99ayuBbeB4tm4wb7g+E\nkxFE3No9wsM1Mb/Til4/bEPvlmdkpJIRULEYWCaBnj/5FADAJOIOzhiF+Z8tYXh+HCxDId+bg9Oa\nj2U4DPRzaJZLrb5ynzaceV7iTAT9AsG3GskwGM7FkSi3aCvlZgO398ex9TsSOGoFQLopVp0zuTij\n3SUNPHT1w9qxh8azPMaz5Hd527Z6evEwqn6IbiqwMrHrx3Pk7vMVrDI69yQJKWArch/c8JANsLHf\nM+B+fzvrZr8Ofivgo29lf29BBSnKHtDNzyYwo1w70ZmoFxQgG3qhopEIsksPIMZ4zYkDnDa2c3bP\nLsrT2NjUeHeDgUfdpfhHpw5afsSxERTXNsFQrXL6RL7XpCgp02Vqz39KX1wo5XCs9qzgjqiMmiSB\nNjAaGP9uAFQRWAzAveRSvbRyM0WUVzax/WQN0bIEjBMAF/M+8q9A/fVDvROhgKZ4C4qJgGajXXtN\nsMvGqFTZehVHyvqKjSaOSyLivp4TtrQuBU6sGkp8/e1h73On6R4d4v7gaA/KttU31vHZ6bRWqwVv\ncMAfDqYwP5bF0WlRc0wA+zlVLzIVGKjy9VMIP57H4ckZxmbuQZ64h1e/30A0Esfozz4Ak+Qhil4l\nd9YMdMW1daqd4BoFOiGgqoCtmduanC5opzJJ1RDQr7PTmpv3vP+MShB9YaRd0tgF7lD88Vy/OzGv\nw855BVWp6YqcbefMq0bb6dMXLQpAXsAgBTweSenYGbyccrK+QSjp2hNZC4jrHdegFZDnXz1xoK4j\nVQytNs93LU1s/j+/wu7f/RoAMP6XP8f0v/klnFD93YUAmKqYJQkF6JoAEeuZjTbsKRllGdW6BJoi\nOBS1aAzRxq3ChW52DGUDsj5AWXjmrc5BdxDGzWNS/x1WINCurVR9R3XvAE3xFlVlr6X//Oe4PT3D\n9bN1nJZFlAoFDShOpeY1Omkc8lqSx97BqtpQ3rbz25ikgMmfPET6h1XIxRJwfAYkedQPjxAbHkB8\nbhrLiT5wvyDBa6cAiCretIPW+TKvv5PO0drHOnTGnVrI1IC4ueXUXsJq1bQP5KuISYzps+YKQDvn\n3s22CGq/Bj8bfnyy7lKxqtIFS9qOfqgVnbYr1bNs5ufriPbmwSR43eSYyp2zKYiXN2BSSYz+9/8a\nsaEBMAke3ji73qB6FodEUc6A36iOX6YF5zEG/fz2eQkvjsl3HvQnMJl3o81wP5jmUpzM4WtQNG2K\nXDuV0zvNL7Q2j5tna4RpQeFS9RI3MDK7aJ0BUAVAqVCAGOM8NzsnVgk4CcOCBlBZ3kBieMjlImtX\nun2h24uh5FluIvP4AW6ebaC0uYvk/CRSC1NILnqB39yF6JQqa8R6ULm4OxX/5dlKz5ckYevM2MPe\nKvEl4uwc+V1ve6fX2xG2vyDNbTaFwjAG+lgt45Xm47hiqvArNE2jst6is6wfnuB2tA+HzSi4x4/A\n/uQxthMZxfjxM7/qOpMAYlPJxjlL0MuWjNN/aaPxPeYySTvn0W1d1d5+VcazuQAZFW8h53nuTsDX\n7Cp36r9/jgsFZC18A+Vd6EgZtaNThxYYQas42T+41nRQQ4ahX9W/hFE9ZgUiI3tixuDMNkUKkwgG\nGmpmSDFS1wm6d70f4H83h8d4/e//CxhZhkRHsPt3v8bAxz9CangQ9nvJeX9JxRJOvvkBl1dVgGFR\nXtnEwdAgsgMCxEbDNfAmZFO4/8kiXn6zjKZMytirR8e4frGF4RgN+egEGB5QgDHt0e/dMql2wN3O\n5869bUV9J9l76mdl7ImUBu48lvXbK+4sXneBav8zqSSynywBAKK9PTj74jdaEgArm+CGBjWcFADa\n8zixioM3VVtWCPW32znMGYUyUNXT/u08ku2O9vbg5O//AXQ6BVAUopkM8p9/gmYuB+xeBHqeG+2g\nUZzta693uCUs1DU6eHMOgABTmplMzAF8pzZed+m84sS6lms4zeQNLSFq0Mg8V25BoM4SS973lbu+\ndF+fu8IZuMPUpc+NQAHSTZEcNB2oimFCZEC8KqLnTz8Fk+BR3dvH2RdfA3DpF1dEz4spNpo4ffoC\nXGEERrTqluEp1YNk2klpZWVlHbVVUubcYlpYh6giji7NwzlyrT/whNaIX5h177EWJfzm1RVOlIz4\nVe1W2eRuWV+39dAdfqUUp7gwqaHWpx5OY7UiY/v1mYVC0I2DVg3maFUdPTlkP3rkw4iwByMDnKN1\nKqAKAF8OJM9GMJbhsAmApoAczyBCUbhW/m7eR8H7fVsKw5wB6Eakz16M7Tnnv/4aTUkCPz6KRqWG\ny++WwRXuha5kzBQ89vgR3mMGZDRFKiTgJy+DnBjblZV13CyvQ2o0IQyPaaCl2dkCcHYCgKyh85oG\n7+kNWmVid+aivfnW/6OA86+egP7umaZToRgRzoaAEXyS9M5uIBVnUM/lUGzQGP7RDBiaBuQG6KkJ\nXCX0IGV+HR77bFznbV7Wkv2xNKcZjAQLIY74/JRBV/t3aNzBFBsbG0j85jmqFzdolktIjw2jgYce\nVIlB5S6YAlp7cygZQ45j0FzfQv3pSxx+84MW2A3XQLFiwFhxQ/wFDMx6eiJvbRlQ97t3wJqCkE1j\npMm46iD/jrKxqs4b98h4JvWtEHogssjMNK6RQByw4I6Eh2tDxm9tobn7QDcgo/7qEI23J4DcRDST\nwq2Q1P7mJ1iqP7vlrR0C7HpTAzs8gNueXlSlBr46vUVuYATC3h6pzLFluzAyOMQZGvt/85+APAeW\nSYCiI8h//gniA72umUe1b18UG4b9YAbudmcucG9bAaCtfXF5HdfP1yHJMo5yvVgWhtAEUBIlzPQK\nAQOqZvF5Fyi0ykFEj6FSuV0Cv/QAqg3rpYviTAST54catsHkJ4vgZ3p8vpkCk+BBx1itEoeNyCSJ\naWLMmsj7D0IDcKUddPZfhI6Dc/1v9kGrPf6YB/JzjmMM2sarSjh0tkYRG03sX1UtbTBmOmV9ECiI\nBMEGANxs+pa+zOZ4FG8pm8+8W7mzgICX8lM3M0XTaNyUwAwPALDyw2siQ+Fjh2aIq/926hfXf1lf\nUo6rKiaS9s6J/6iObOD97hdY9CZYhWmhgnOJIu/78imme/swYUPnAxgP/GlZxPGXTxGPJjEylHc0\nkmtSQwsGAMBJSURNargGBOxKKXMcoyt91GdpkzqUZxkHERb/4XvS79gnkIoBspYR1CTJGEWnoCHr\nQslSRLNp5D79ALk//hjxAft5sIp/MDJ9hQNgFwVXwH9MJcoToz2of/QAO98u46ws4f4ni5iZGUJN\naoCtVyEVS5Y+Sn8ZnpbCoCI0avuHoAYI6u11G/Qm7uJlKOv2rEwBTWgZX7/PD1bu1aJUGms2Ecuk\nIN+UNPwIL/oc85gnIfuecz+gf/YXUess33zxBHWpiWQsinhpGzlJxOX6HtLJGPKPZ5CYIi0lR//+\nC+3b+ssx3J7e9sQJqAq9BATJagjM2hjMMxqd5aVCO9W79AG4wgioq5oDYJN3Rl8qlnH6dFXTxWo2\nzqmM2a/DYVuyn+ahGv3anCf6MPNZH8Yy+h5pIm6GgNu6EorEZVQaQHP3FTiGQn5i0LnEuCMJ0ovd\nTuUZmafKrYSS2MC9aBPpJy/QlyJnUOUjV8uowxDj3NrhhvABslP2DpAZSV8zcD0D1n70ftBAjR8d\nQAJnp8o5TS3OYCvRr/3VDETmnJ30d2/pGVLM1HXm56nVht0taXXL6JdR39vH8J99gsP//A2k6xLu\n//W/RGq437b6NJLkHatApGIZxbWXiEwW0Hi6gdPdQwx+/AFeyVH0RmiUChNoDA1iaiTtQAEL6Bkc\npGKpVUWmOLzEbu1kP8g6J9Q+seSnbaX12RKun6+hfnWDi5VtHF5VMfVf/StsDo7j5UUVP59wq2zy\n36rqdBdoNvZzYv8LMwXEB3o0UNh+gSUtA8r5V8/wDA9sKsGAfoFFbXUT0lTB8Fx1PHZ2vFSuILa2\njmkFfZ/d2YW0OOVbnzJJ3gEskcxLVbK27IQj9v5LJ8Fhsl82tL3anbsqHDGvpRPLmZ3EmXYCogSY\ndoAmAVshazy/wbL3RF/GM0kUT4u+xgzcXTXWe9PcHJmZQmZkEEy9jrMvfgO98qtJkkITN2PprdKo\n5Qzirnzi89M4/vIpAFKCvFEBBsSGY0+In/6Oi0odL45LSCv/Ry0pjkUiuG00cVxqaJ/ev6pioN/p\nfUS0UilF3CJpcYbBRI7DzgXJekzkOKWM0Z9UbiXsXFRRlZp40C84AmYwSQEVUcLeaauc6aQkIhMn\ngQdyiYkQ1Ci6wILNpAiyLkWBzaZQ2T9EaW0H3HA/bufOlICAf/FbimztT27oKjeAqonreTKfQEVs\nYq9vFIl/SaLE2ywH8bKKi9+vahSIkz/RZ/n8lTCbDdyT3UNUwUJkWAXfIKwLw79RZmfw8T9egBiL\ngzGMR3/hB0ca11MqxaJRXH7xGwJkGI9ZstZ+EarD5nU1i+pMJijgrHILyAAfjYCiGWRev0JvDw82\nQqO4uovE1KSvZ3q9S5V2IuZ2l0XLoGoBVQEymvW6AbDKzhCI9vY4XnDZj36M5Ny09l6AwkQyiYEu\n0jcS6dzhqEmS9iwDqnMFGOjnwNjovHbKumtSA8flW2RpCdd7B7iVZaSHepEcH/M91vAl+PypezNC\nQbtbBtM0uds4xsBl7pcdyJ/zIGtsLwAsuCEDtOTDADM6kFbwUofzJUMrXVYzuXZ3off59B+o8aMD\nSJDpuWYT9Hy3DO6nKV90v9Y70t/4zWDP5oCZXrpb0upj7zYppEf6IfwP/xoAMPznn8NShalUn57/\n43eubYui1MRGlYI8P4c4Q6EyOoZ0nDGUlpPWpqDtjsZz4rRObvuBTSVdnNB2RcZlvYE3T9ZAQUYE\nMiqrm8j0DyOaEhBn7KrWyPfCCWZTSC7OQKrWUFzeQHH1JegoqwWhB6BntmnpjbEMB6jOfIQGKArF\ntS2UN/cAmPeJlULx/MtvcfNsPVD7ql6cwBIBYOusqsMpqWIoyQW6053Ey3/prgNvrRoM6qCGU6Fk\nrUzSs5ypFaj2Z0/AJNzZROx+tz4ALYcQ7JRlZ5w1e7mLasA7DAi4laYalUoC/Y9mFcdfRvHePfzu\nbQ1sRMTU0BgGRgZBSvdSUDOLwTYmBX5hFvEoKSnz14/sdrmT8e+cV7B5Vka/wGJcR4+VWphG7LYB\n6A6w2zv14EV+Pg+QuX00mEJSUTh+SpTU9dg5r+Dy7BoTXNSRA1z9nRWxoRjVtCEAMamVYZI1LBUm\nIA8PYjQVQe2fvgNAATJwWywDMqUg27NtRiH9Gumt/mRz5UY6zuDlN8vgfpFDVVdSqYpqZEUo4PDo\nHE1lLY9LItJPV3VjDt7nWr+VIBUI7RxNAZVxf/gGfiRopLJl8JXx5pbCkwqAXYLL0NOThF3Z9e5F\nTXuCX2T+Zr0OAIjwrTlun17JW9ozTo1ASgBQYzlw89OoKkGk+NQ9RI/eKs4zcVpqkgQhm3LUQXcT\n2bW/LPT/7+KfvsHJr74CAPT98mcdGJF2urB94EwmmUDv0jyu9S0DNgwCQdfUbHjomRRUxH9/vaP2\nv81tXemEgOyDCVz9279DYuoexNML1I/PMfDLz95Zj3VYDls1yiGnIOKr+D2JqQlXZ5GIX+eB9DC/\nHRhBeWUTPQmmDdyQ4MEPw3rK5N72X73WrpD7o6kFqpylJjUMCYKzsoRCMoZdolpNQGRhsduoDCnv\nNkvotXf1axeBrBn+5r9p1acpwbEKhEkmwD2Yws3ubwEA3Pw0dkoyflE6xMETUp0RtLTcjdLPfp2c\nKwBK9VtfiP1Mkkd8fgo3y+sKFZtdewMRMcahMjwEgBQLZu4NocJzyGXjmBjJOOr2MMu/pWIF5c09\nHWCjfo1liGLD8h0mKaB36YG2N5JzEyiuvoQ6v1YdR2kVXNfPNnxUA/mT8DGm3KQd/6XT/nbyDLvW\nm+AOarDKWudxG+0Q9ZlWBjP7MQbZo0H0DyiyD4kN6YTbIuPs+2UcfUXoMoMkw7qti++0QsCuR8VW\nqSh8p7uXZfz9Xhmy4mhXbiW8oGnSt96saAZF0MgJz0YwMpQPpY9OP37VSU6PFrA4P6GVoSYhY7q3\nD/tXVZRYTjFInTaM+ntGgKuqQymuWdpD4J7M80jsvsTtkx8QoWlieCk90kYxGnP9mTj4KINHA0mM\nZeOY7RW08nuAVBysXEiobbRJzgAAIABJREFUNZroLYvIM+R3RiJx0DE2hPJS/w6IRqGo/PdxSUQi\n6g/tdSwbx1G9iprtp4Nl4/XGJn78EGJfHw6u67iiWWQuK3jQn7T9rrOEAbpFFIwYi2Nj90JzkLbO\ngAf3RMvZ3L+sQWw0XfhsrWCaBD1YJFUIIwPo++WnEC9JqVQweqVugoxZnZWpHg5bZ1XE5qfB3htB\ngo8iP5RH75ssrp+1kI83TkSiixwvR3f9FGZPrxgj5791MloGkHhV0gCbxKsiQQfuTfuqLuguWBgB\naFosjGLKQPHV6fraU7wBpH1g9Hgfr367gghNBzTw9eO2X1eejaB3fgrl2UmIFIXexw+R46JKy1W7\nv+vuQfb0e/N+joNcriDdqLeFiO/XedA+p2DAlAD09mVR0vUwC1kessv+dDfgnKoU7iYD0xIjVtCk\nqWLN3OJGJwQkdImGxMI0xoZ6oNacWIHI7pbdpr3AZ6do46q4O936SoezL74CZMqlCoRC+uEDDEaS\nOLyu45Bm8SBJgX++13Zpubshb16nJo6OL8HWK3hRlNErsPjpvYzlTnB3QhWqzUQfuKUkxjIckgYa\nNatcFKaQ++u/QHllEyLD4sPPHiE9N+rYcttKDnVb3LGu9OsOyCiu7gR5tMs+8Na3bnd3eDgdVgnm\nv7Qog90xzNz1X7gVQN2o8qTA1mu4WN3SPuM+xrB0D3m3OnfFtS0UV1+iuLrjiBdVOzpF6ZsftO92\nCyCwHbmzG8OpRwUxexRTMcZht1bVipeLdQmHN3XM95FJMxoUQSMn3eGHV51kMw0RQGFitBcD/Q3s\nXlawe1HD0dElxjIcJkZ7YHc4mWQSE0khQClucANAKlbQWH+JoVQcxyUR5ZVNzMxPWJSL2ZgrN4A/\nGk0hzjDauHiW0ioOdi6q6BNYNOIxSAKPi2+egKFp9P3yZ0gtTOH6GTFq7hKVWM/vzkRo3P9kEdvK\npdpSqNZWA4am8Vb5Xr/AondpXmtVaScbD8gQazK+XTsFaBZ9Aov9qxrGs3yA9XOnxmrHkdOD8wgL\n05CXhi2facikGsQeYMg6Jq4wAjN6cO6PP9Ge502v5P57nc5D0Hmwc1Y+G89pfcZq4I5nI0B+DtTI\nkMZtr35+KBlvUfxYxN0g9MMi4G6cePecNmsklaj2zerf71Vd0H3niOg7Nw2uoqerPdTqObQX82Vv\nkkoJr367gmiERo5nwNka+P4wKJzO+8RoD1J/9rGWoUstzNiM138Jvfv+935Oe7qBMmRebtbJd3sx\nD6ZDcEROrKJZLgGsUx92y+mx47tub396nZPuZ2BUMd4fFLidXfxsbtzAeW8GVhx8vIADBSi3VUVD\nxu4GRHY30g3cBGjPSj+aweV3zwHAA8vAfmyk0kHHyuFSBcKzEUyN9SFyXsEYgEkuDEpPe6YDs546\ne7qKzS+fggLwyeI0ygMTyj1E3p2Ms57Opv4+q7KcUlLug/YOs+DG7mFaobu1B742rlsyFkGx3nAc\ni19x0k/egUQjjpcfHeddDeQfJM7p7nbrOe9c/PsvRM+stTAHXDHM7k7/eUn7AQgZzTqppHJONgVr\ndfF3d5LvlhXWHfsxt4BrKz+8AJ3Pt9Wq0k155xgCbn1UDRmG0vS+BOuSnXQTczkwMe7DiKCbxz/J\nAanGLQBzFpws+v5VTXO+NgGkPl9Cj4aMapa7iPIbuazHMq3Lx01IMEA/NqKkchyj8TxzYhXnx9eY\n/fjHiEYiEK+KyP3xx+CUkvm7yHTp16dUmMCMVrmR0Pq9zJkiPfJzgZUx8NEs8NEs4kzER2msk7SU\n7VjsFtM9RKEE289kHzfLJVw/W4Od4mnHUGbrVQh7e1DROIS9PcTqH9mezcm81TgHnJHulWEb0IPV\neXCjV9JL8MuhM4c2QpF+c2NQryV6bvtwxO2cexsn7kaTbKzSUPjEzUBpdm0A74txoMrx0BgOQMbd\nHMojafspu8ue1/ZxhAKKYhMJmkZTJqXXOa4JcrZUlPd2KZX0QqNn6QEyU/dQ0+gUnVHi3YwSr4y3\nP+PG60w4BUBamRf/gFPOdGcHb87B7b8Ce/AGVwkW8qM5w9w6Z938G69+nAsrVdm7FgpxhgHjiOKt\ngCXPkEBt97A6OpFwcRPMz47wnPbvdsfn716woVjriN7TvtzafP4LrIyb5daaF5c3ER80oqJTVFjJ\nLGMwwg/jjvpv/bpJxRI+GhDApdNdrPLw396VXJwBpSDJq23F/t9F9JZUrjjaV3bPsmuvDbvn3N97\n7cWMR+YHw8wsdwVq174otKsCh7PvngFwbo3U654IRQDVvXRPGAkS7R6ngNTcJE5/WO+oVaUb0oWb\n0D5b4byh7BWc3jBQS9MZmrKhC/IejxkpuS8RCxHNm3LpX7EqAU6saplYALhZXkdmqnDHxreyRrG4\nBtTIRiI6w8lIf2Q20oz9inqhkONjeNAvtNoLElEwsHcI70acL1CniDkR6yXO6NazEwXJs4xhjvzt\n5dY+5sQq0mURvQlztpf83uD4BsagEAHoIf/f/mz6C2IwCTvgGd6wv7q3F8zGqXMWVb+/VR3x29c3\ntpl2qVgGCznkkkBn9otOS/XsOJ5JEOZ9cybcRbvEzVUZTiXniqifM7QPyDCUX6cWZ7EnUth6Q9hL\nJrkmIk9faFnB4CV9rb32RgS2zkREqAvcO32NxO4rABTi81PYSvRZxsnWSfVN2KX4RJwcNncKRalc\n0YFReo3JmSe7/80+mGfruPr2ByRHB4GEHV1hGA6Pu2PgTFXmVzovN223YuP9CF60I9Z2Mr/SmQ60\nrpW/7xnn2r9DYL137cZPjQxi64w4aWpwamAkDTZCo19gNQduLMPZ3C3udJV+aNLsgoh+gAP1+Cvc\n/itcb+zhPBPH4IcLwOKc9p3g7U0tm1SPpQAAhZPXePUdwdSa/GQR7FAcUp0Gobms6N4DJchC5k7f\nVmwVq32gZ4KqHx61nb29K854P8IkE0gtzvrGMLMXsjaRmSkM+GKF6lyC6cdW5v3y2x+QuD8Ktq9H\na410nPdKCftK2+5wOu7RtuutN3yPWQbiuYwHcO27kVBvF1n23+9jzkLYcW/7oQvyEjuk5Ew8Ggr/\npX6sfvpXeLbFdQ8QqpT2Kh68xM1gMa3R0BgKhVGQDe+ctdKvhbVfUS/Gzzbw0GdZeCfiZaC1007h\npdQ7iRoGN3rNZYCRQgHpowNDIKclzka5c7DOmAGJpVMonpXgd+6clKHZMA/KUuD2bP/ipJegGS6T\neR7ZZh2/e1MGnyB/M2fa9XPa/2gGQxZu9HbE2EtsZr8osN4MFDxLYywTx/Z5heCr2AUo2uB47q6E\n2cPnRyhtTsYycewrPepjGQ58fxZPdi8BEPyTf3x7jcGTEoZScYegm9tvaO21CAUclepIsBFcnl3j\n4Ne/w2K/gMFkHDfL6+CWkoaMV2VlXblDjE55Z/tfxk21joooOc6zVdetaWf2dmsH5a09NEURt2cX\niA0PuAKUOelN8u8NRCMRMDTtQVfYqeMr2zoGPBtxpCoLEuzRr6+KoWNfXu0m7veHoUWGAvILk3AH\nqnqfxT5Dbg8yHe57w6N09RNIINSQ+taGpOYk24s+OFX7fAnpR7PAs3Wk4wzp93bt/Xf+fW72RZAg\noh19a+HkNU6ebeDsye9B9/diR+5H+evnWFTso+BsMPZVXdvnFQIG/d0L9CQY5HgW9LPneLO2AToe\nA5tJQry8AZQEXGRmqm2QQ73ekptNRFICmnVRY0DqyG6lWtTbd4X/on95z9K8AcMsWAKj+7TI9uLf\nvlbXjorQgCyjvL2PiCA4YpXxLI2Js9f4zX/+HWgAc0vz2L9KBmzbDT5m8z3eGXBtdzCFQg0IFGui\nlsEECDiZyk+vGU4BHSffdEF/EKL0ln6+5Av9td1Iq9sBtr0MxnPgWQYVUXJRqK218O5X1K2boSyc\n74IT8K4UFuA/22D/3U72cqkwgb6PZkmZqS8AmBGSBbU1ZK2KLHhPqp0yNEbsx6WST5YC677vpGTL\nyQBqbGxpJVxsJonS2RWaZxUINuCa1jndwEAIPL3654qNppX9Yjznif67fV7B/lUNHENjLBtXWD86\nr2TpnljPLAl8OGce/IIvOn/OmukaS2cVvUd0UoQCdi+qyALIzI3jeOMV0nEGvUsPbObMXu+Y95qK\nfaOSq56UROR5Eggey3AaaOwMD9SebGq/3RiAtFLA+ZsXMsa3b4ooFWs+daOM08otDr9+jtruAdiV\nF+gbH0ZsZADRTLrjjIbcbLZBVxhM3BweC1VZwN+hPlul6n12VESjiTZAYYlYgUBborbIcPuvcP1k\nHb2rO0ibWiz+EMTpPkptbWDw92uIcpwGMm0n7eowr1aqsA1qqVjCyX/4NaoHRwCA27ML7Qybxy9k\nU5gpnVmCU5m/+oWPNroWqJ8fey1M4cQqYju7GEmx2ARAH58ims/iuARMSQ3E28iO261TjmMMYNBn\nZQk9QhyltR2kHs2Bommc/OqfFcaqGK6frSMzMmj/gqCigA32/MlPwST4QLa3uqdI5e06xGYTMZV6\nG86Vw90VWsMwA4LZ3sFbe8IF6wtiX/m9W6RiBdHtXfQnCMZAZGsH8fFRy+faE3csE/Uez+Z4FG9J\nojy4dE7B7CShVwiYwckwnnuHDpuR7nAhSeFUjjpn0ToQ/5eW2lta0L7npPSDLTpRRuSSaPULqcq1\nBQB419IqYe/GPgiT+kYv6nre6Ogj36UjZUfbKWT9z19NamDrTHQxZI2KTJatpYjeYnyGOcDkzVIA\nuO37ID3LXkJwGMg7WsbFLCnXXNnUssdsvQqwnZb6dXZJugVD9Pu/IZMMzliah56C7G4BAr3FfGbf\nfr8CWlftEhTAyc/nzHtRnSd9ie3OeQXDR6/BvHyJYoxFduE++j56ACFrBb5z0jt6acjASDqO1ZMy\nrmgWc0vziGwRFOz0ozkkR3swoLSHsPUqjjzWpbr32vZcOM2LOkZB+ZuTbtTfXWKjCfHeCC5XXoKL\nMbipSeD232jZfCbhDpDldg+q/z8YXSEQppFppiprJyChrzYESNAzeHbJX+CeA3C5vkfGGmcs+BGq\n3pN73i+8D1ehZLx+8hyb/+vfArKM3vvDBHPINsDaKlfOjJASbufecL/SHYNaKldRPTzW/rt6eAyp\nXAWTFJTxDxpwiJyCU25OkL4KN0IBJ+U6+hJerE3G8xOE2cbCvJThwEZoVG4lJGbuo7pJ9FliYZrg\npNTtOZmMY6ANbXFuogeDBtyZieJM+4w9Fr31cNYCNOiug3R7igKSc+OQ/ugDHBfruP7yWwJInWBD\nbB8IavPcRbuRHxvfuA/MLR/t6Hn92vm9W9gIrYGpA06tOd0QYsfGM0kUT4ttPaGbLSmh7pBYvW4B\nJ2uWZ7B1dqt9xr/D5geV1d+G0dMdfrg4A358rgtlqkEMb+/IV7BFbykjsdGAMDCiZTgrtxL+ee9K\nC4LoAbYAo9IMkwLNLN1y3LsrMhqVqvbvTp/VvlFLvpt5tQd6uYX0HSSjQicERKgLn4asjNU31/jd\nLumpnuGhUWh2cmbcWQqIBFd23sad3b6OM8p6UjIkyFAhk3oTLNIcA6F8CvHZvoHPlsxpi77HDrDG\nbnxul6R+rVgX9ovOlP37BxCoioap0sMDkHH69AWokUFbJ9y/URPU+CFO9QAtYe38Dc7ZKCDLYPde\nI/6ThzbjsBeVhmssE8P+FWF1eNCfVMpfq6D7pzH942nDWdICN6w1i6h3VN3PRafGXuvuqkkS9g+u\ngZWXqIoNJGbvAwevbcfk9SzAeA92ygzgt0Tf/R7rLEDGsxGMZeN4dkSMub422/46vw+Neo/59EdA\noQC9AxNOBjx8vITY5ARePlnV/n768hDpATvaTwf8p2YFk3lz/7hzVZG5LaFbBjWT4A0ZSmHuPpgE\nZ9X/2rzYBad410C8WoULkPtUYCMQG02tOsyK72N///hvWbQCLG5NjGPnm2WI0RiEf/MXoAv3WqwX\nrF1AkHdlJrCzSXN8i01BBYMeyXBKUHQDcrOJvl/+TGkZUN8jYBJoE3/ETS94O7p6wDipWMLr//P/\nw/XgEJhZstrHJRHpOKMBswYXoyPdOeit8dlOuiKIT6AFMS0V4i28C/08Tp4fgtvZBamcmMHx0Fib\nCcNgOl1tkcWzNZ+tOW5y162P3ZVwvTEKVnCy0EoirKisfjaMme6wtrqNzNS4R9av3cv03Rje+guO\njdAQ9vbQGBqEGOOUS5SshxlgC7Ai7HeDjrGb4l9hBVtTdd+ofUgEWXsUYiwYhoX67varI8h3D96c\no/blU12k2Q3p2758fywbx/OjIjJNEVku6mjIVsQGNpSxCns7pKyxh0fv0gMD2JiXIgzCUtCu+DPu\nKEzmeeQ4cg5yPOkLTz+awd5/+RZvnm0iM9yDszcn6BsfRX5hCsXVl9rY1GcmF2dwmslj/6qKKsth\n6twNtIiIH9ok41rxDuwX9hJk/78vF5d5zCqWikqNtH9wjZEmE2olmfc8EZT3wWQceV7J2rvcX+bn\nJWMRfLlDAm79AouPRlNKpQZZ5/FsQvueU2VD2JUc6hjf1psOv9n4fiYpQICMkSaD89kCyiubEHJp\njP/iIySm7gcICDrTMQa9H9sr0fe6x1pVa8HPBIXZXgGNJtwxO2ylxRLjFWBu7S9SaSns7SkthiQo\nY6f3Evle7XeFkwEPq6rPuLdrkoTqb5aRmL2PyjpxnpMPrbScbvhPmVd7qNngbZhFnwxyC6KHIUwy\ngfynHyKaSeG20YQwPw0xFsfWm0vtM+ayfrPuD2rj8lEGg8koXl3VsXtRA0PT3i2iyvv9B59aAceK\nKGE7PwzuFznEQejBzTTb5DeNaOwqZlT3b/ev8DBNg6VpzWm0O692/y9pakG1Cwq1Hxy1109BgndU\nhEZlew+l9R3U3pyDrYnILsy0qnzaapFq2YCAUqXhmwXB+9nuuiKYT2BXIa6KmeXl5TfLmO7hwUYi\nOH26ShiETKDBqnjr5yB3S1h37btpV+5mG2i4FQLplAWcTMimMNWs+IowqaKPtlE0jZuVTQMqK9DN\nDLM357O9Yxmuwd3JoqfjDMYGBFAJHr99bfcJt4wSpUXRpXp4Jcbdqz7wx+PemYEkQ2w0sX1exm69\nxXLhfPiNe8TpQvGDKK4vHQWCRJqtCnK2V4C8voWdJ8uI0CQbzc/YZWaImBkxWhdPwjfVmX5t2HoV\nUrHiiiMSnrLzpnijRoZwWm8iOjuJy2gMHBvB5OefgE8JKK7uWJ5YEZuk59sD6b79caoUbU4ZRzvd\n42//2/Xs16JN7Rl3K1bg0dOnqzguiRoKcvj63Xue7MA1nfde63k1ScL3Bzea03JcErFxWsVYOriR\n6tRT3i4q/WQ+gQdCDJcXFd/3Uv+bfdDnJ+gfziE7P4H00kMEB80LT9or0feqmvBrzNmdORoP+pMB\nA5sKnsP3KyivbKJfYDG5OI3t/Ij2fnOAStuv4zmwdcKE4D+Y3XkGPFgVg7f9o+5tIUnj/scLePnt\nCrjHjzD6aAp9Hy95/i5VOLGqYDCpNJj2v8+cDGoF0btlUFOWoPH4ddXzO+q43TGciCTjrMGOGs/F\nsXtR04L7d1V5qYGhyoSC2rx2BK9IBHChMROoMnz0Gre/3UOTpnVOoz2wuN3/06/z+1L5pu6p4toW\nSpu7iGVTkDkWN2vbYP7sM0w/nG27yrIiNvD2+xXUFFvsdOE+ehvNDqoNWuI3oeJnP9nRV7P1BwBr\nTxDsJbuXFUM1abjOdufJ2/aqvIievKnW0T5AbNCAhlk3O0uoWoOi7AfaVtaZAqSbolZ+FR0fBrJj\nPkdidAaCKH8vzmcn9PbwI0X+F711wa3htCyiVChg46KBKbqBqR4OwagauwVY0c3qA3eF5YZ8DdjP\nrXlOK+MFfLNfRq/Aoi8RcwXEM89fZGbKMiYnRHEnMffStWPESMUKkq9eYSpPDFluZxfS4pRFMfJs\nBDM9Cayek8yGmQ3DnyK0lrgd+dpTQZSdDDEWR3x+CrVVAsimlijq18CJ4g0AREbpR5RlVOsSaJ5c\n2nY6Q9S4mP2LF+ibPSq11SmpiA2Unv4etRdbCvKxHlzMff879eyXMwnQE4Uughy5OQlG4FFqZAj7\nB9dtUCIFES/DJuhFG2ZfppeD2m5Wg0KKi6HOqsF0d8dNc6JoskeLq7tITE12yfD2V2UURom+Wbx1\nGBlbZWXdoFv8njm79x28OdeM+uOSiPTWDn42NwE6ITj8ft07TEa1dj89XwdF00jMjPtsYeqGeO1d\n69+nfvIIg3PjAFRaSNrwPDO960SOVDuyERpjSa7DPRB2NU5rHwOyIWi8e1EjrCYubXKBRk4Z7ShA\nNgD1miWcRIxqT8tAjPP8PXbMBGOZOE5PLhG7rYN9+xo1mqxfp07jXYi/OSR7KtqbR2lrF+LROaIA\nsoUhjIz3ObTA+ZNmuWRIzFy+2MH4T+Ygbgdp5boLsaGv1v1m/TxWWY5QSO7sAgB6l+bRHMpbAl2q\nvK8txmp7RNWX3dLSg8JZBYMxtZoHbVSk+w1oWHXv/KDzPdGF2bUbqHqx+cuiM8kEknMTeP2//A0A\nIFYYQWV7HzOfjWKjQr7jVhpr127QmfInAGsEiMxaqiPG4jh4cw4OaKGDh7J5/XLKE2VEjQxi++Ba\n25xbZxV8Np4LRNXYXQ7Vd82jTDL9oIDi2ibKm68AODmoxjkVYxzkoyJOSiIycedye7v5GyiMGC4U\nd0Rxo+gp5dReuvb7+WWtLBsgjv6AbekqhfmhNATIqNwumZxtP46xUQnN8NCVuLX6xOmEmhF3Kun1\n+Y5EH2Y+69PmxbwGdhRvADFE73+yiJffLAMA7n+yqAFW2RmM7RlXzoEwZ1RqvXFEfuf5989w/W//\nFokojYH7I7imgGhvPiAKsrlnP4zz7Vwx5T9ISkHIpjHSZLpMQ+ZHgmcOeDaCiTyPokjK2vsEFg/6\nEzbjt3Kxq/8txuKuIIXOWBJBWqECljgqFXpSuRJapVjwsXRSot/Z2KxtWuHziccZBkxbdyLJRkvV\nGorLGyit7yByKyO5OBtaBtyvvvMKrjj9XchmbN7qTO/ayqjJqMxPW+4ls7jPQ1jtnVb2ErOMZ/21\nyfm/X/R2lOzxnU4TMep6tJJNpcKEwtLCaa0PXtJ/uA9ueR10hEajdgNqoB+AW1vW+9PiFmQOm7kc\ncv/qT1D54TluGzLSjx91DIIZZyIE7Fi12xJR5B7MAA9IMrIT3RxmtYx3hZ1pHmd6IC1Oad9NAr4D\nXe9eZDQ2NpH++jmOSyKEhWkMPl5wvZc82a4QLmOA0zvv5VgkYvZ3The9M6vxE8RATExNIPWjOdxc\n3GDn+TaAbUyODOGzn3wAeyeCSKcOrfWAzJDypzcX4MQq0mXRwk1dWVlH7cunAKBRlqkAU50rM78Z\newp0QkCVFS1PeN+ianctZE1nsK0c3uzCfVz/sKmto/Me0c2pDEzkOK10NZhRai2d90IUJ2KllJto\ni/OaiBjjUCoUACXaXCoUIMY4WyVAUcTo4JceQDIxYngZLmYltH9VxZjCLqAGJDb2LnAiFwlIVBsV\nNeZ3bFSAgX4OjE12HQAGkjHsEpw3A1ifc6bKPrDZnnFlHwhzRqVuBQTUzOLtxg4uqxIuKzLw8hBD\nXBwnf/8PGk8yVxhRxmydR6ee/c7FWTe1U27sFzTz3Yh7tYO+fSDORGwMZetcATKun20AIFUs0FWx\nAH5KJoNVc/lZE33mWboponFTwtkXX4VOdxdsf7RTou8ubjqs/TYt9/eNDOXxVqny6hdY9C7Nd5TZ\nk4oVlDf3QMdioCjjPdYugKN5j981ppA7vatzELjzarP2xAyktnsBhwy6v3vCfb5l3FTrqIiS7m9+\n1qj9RIy6HmKjSRxShYFn9wIaS4tZzGdLTYCwkQj5eDqFCGTQLOvA3hScljZ4ACHo5wO0ICX6kP/p\nH6Ny28CmT6whN2GSAiZ/8hBpFQtjaT5AQsjrd4Z5Rvw8izLdN+bqVL+Brnchrblk61VcP9toVUQc\nHWCMJW1dQUTPdgWEnYANLl3yFO3Lpp0NAOumZZICuAezWPsf/w9AlhEb7sfW7zbR/2jWMbJcERto\nShK0SHJbGQ7jphZjcWwpaOtVlkOkUEBaR5EFyKitbqI/GcON2EBzawf5eyP47WuCftpp+0CQAEf7\n5WG6+Q/YYvGHIRQiM9O4RgJxAFUAlysvfRl5+jnlowz+cq7XJjIebP6YZAKJ6QKKyxugYyzSj+Zs\n59idUq69CHqpMAFuiPD1+ivP7twxrrIcUouzuFlex3FJRHJxGt8UZcgQNZCoMMvB7FpoSnUak1wT\ng8kYhKzekKEd9ImTdFrlortUEpwNKrW1nIumgIPLGnpmxlHZ2MVtPIbaTRHJXBaggPOvnoD+7pkW\nHLCrdjH37KuOaCfnO5xqonZAM+9a/GSzlQCaw96wVK2sbKJRqWqgpbXVTcx81kfKjeGvZLI71Vyt\n0teTv/8HMMOE6q3dtZWKJQ1crDOHMuzqMm8d5tym1R4Y4WQ+gaHPH6P50ayBfq47EjQD7rzHvebd\ny+4ICz/IfxBYL+50tWFkoc1AauOfPzYFrwg+gH2w0DoGp6Dp9nkZb98UUSrWTDroXVdemsUjASLL\naJSrkKUG7MA1g9HSkvkL1m8ePhicGTjxH4/rmO5JgIV9O1JQykAjmGJwhhbAvQorvHu23We9+2Ck\nu9hUvSp+ZsuHcB+fnR7U2K66JHbvdKoOAEIOCNSurhXUT9liqGRGBh2+5bxpuekConOkpKTG2HOP\n2j1jcmIc3O4upJtSmxmO1qYWFQoYVUqFCfR9NEtK/RS0X1AUso0aqPVtNGUZt+MjwCQp57nb3pd2\nDpHN/HfcYmF8fjj0R52LvmRcWJgGjg4A+AcPA+yj9t7zZ6yOmTw/BLe7iwjPQZgpILk4g2Dz0t6F\npkeuVr/XftTV2QjA1JHbAAAgAElEQVSxU0I9M/OIj49g/+AaZaX9ohNxNzKtLTTC3g5ermwi0sND\nNrAl3KVY163/08eIZtIAYJst4dkI+vqzOJybwtnaFvr/6BHS8+OIHR0DoEDRNEprL5F6RLLpbtUu\n+p59rjCGbI5H8ZYYlGGLuj5eLQDdyMaGLX4xMzrTc4SXfKBfnYkAJZNK4NtLgpQkMwleC1a0JzKK\ny6tY/+oZTkoiUg9nMfh4QedkdgtkNoiTZ6/D9GOztmnBgPg9MpQP5Eyw9Rqg2A6dnrswy307o0H0\nZnUwV9A4STcRtI3ijzPd60zbAak1yzOgE0ltP2+fl/Hs7Y3WTvTTexlM5lt7yc89rq6PoMzxXdmV\nanXl5XfPMMACxWkC+up9XvX4Fy1MHoqm0bgpIaYFGu3Ym1ri3uJG1nDnvIJnR0X0eeI7EXm3FNjt\nYnS1z9Ciyvvah68/i5xYxViGw8RoDwD6vRmrXTDyw/lpA9OJX5DfoWQc2RyPeqmuffdGCSjaV8x0\nIoRha4AmfqyQdceZCXW2D/7vX6FYrCE5PwEzgmKcsTcA3DatkE1h9Gcf2PT4GsX8jO38MH56bwBX\n//EfHTIc/o0GvXGg8iAL2VbZth7vgKEoMEP92FvfAzd2zyfQhLsEvyCDsQQ4coeG1F/XHYDC4GI2\nQAcfL2glPp2AhznuX9386dGDjXQrFIqrOzpKL/cx+zkz7nJXUVf796h94maQqPacAW8jVG33cGZL\nuKvsM9E3NckGRXpmGv2ukX+lh/qzJRzO3ANL00gO5dH7Zl/L8nPD/aBjbgFTs6h7nhi87TonXrop\nCOVXGKCZ70689Zx5rlIL09C3DKg82q1sp3fJpPrM86+eoLT2EtxwP6p7+wowZQufQO5R97n/888k\necTnpxQ0d9qxislJpGIJG795huWjEgCg8vQFGkODBso1dSzNcimkTEl4NHlO81QRJQPi99uFaQx9\n/tiH7u3GXdiqZvQX3OtmcN47S/2mWPOVrXQqOw4ziOQHVNJvq6YeSO26dovvD25QZW8x1cNjKBnD\nznlFazU8KYl4cVzWcJ3+MBw2ChGeRy8vY6RXAD+eC2g7tNZUKldw9sVXuu/K2L+qYqOiZ2/iLS1u\nTEOCDBk0G9WeqqelBOCJ79RN0e/Nhgx8MpZBsd7QfpO6T7uL0fWHKVqwS6m02QSQ+nwJPUvBS/Db\nl+C6kV+YRcbUTustRE+muBhOSyKInyyjUVHZSJzuwXZ1t4zi8oa25+RHs8h/MOP46a5onuLaDhLT\n4yhv7gFoGTuTQEBnhPZAo3X5Js85ZDicjAYz0iO0oMFknkddauLw6BxH9aqJ61XBO1AydHSMRX9Z\nxLXyts4zH8EpJrxoE80UFMLeDppbhGZNmJowcId2It7Kr1sGil3A530rQfIj3Rhzu+WFwcvcjIjd\nJEI5mecNwDFqAKu93+X+W1pZaqJsw+udDyItfROhgJNyHX0Jo17yNgZseqjzrRLC6t4+grUAKDpi\nZw/FYq0D58RZNzlTflkZLZyzse/H+fRyRPxSN1nnCi5loP56g7nCMBrfPAW3MI0Yz2nzXN17rY2J\n+fRHQKEAVQ/6cV63zyvYSvSBW0piLMMhOdpj835nqUkNXFQkZZRAqd5Autm0fK6xsaUh5QszBaSX\nFgFQbd0LYWfEVFpYsK3zZEb8Lq9sovnRLMC6tx11zxEgmcN4JoniqVvVlbtd0L2KDSLB1oYy2Qgk\nqMQkEy5nIlwQOvv1GjFlsikDkJrYaKBUGDeAOue4cExsdX3e1skZuqueavM81Fa3iBPEBt23ZE3N\nwHPx+Wkst7aFti/061zaz+Psy29R2txFcn4SqYUpJBfnte80AuI7ee91P3vJq8SdxkWF4HnleNbh\nGd2Vbp/pMMWctLlZXkdmqnBHgRLv4J/jXAY+B0ZR7STVV7W3k9oPJpPnrxEwdQDXz9aQmhpGNGU/\n7u6EImUgOTeF5FwLQZIMXrZcst6b1rvH1+4ZQpaHbJO98of0OIPjoTEDcuzF71fRXNlEDdasAJMU\nkP3okfb9yZ88RGRmWBtbGE6c34PhRZtoBWtpIrO2ije/Jw7FkFQD+xM3GhhZaw1hkrzSItKOQ9+t\n6gG7gE9rnHzImRE/Slf/GTPdih8HLviZCVs6Wasmdl6fabzM/gCC/I/Li31DvaTt2BLuQsw4EAIb\ngagALAYFpjQbz6pOUPsLpXIFTMK7KknVEUnFeOnMOem0//APIVBnX3anBrlYx6i+9TnmuXKfO/ug\nWmuOZOxf1bBVJIZpv0wAb6VyxWAEXP1+DUK+1/c66fdsleVIr7bYBMEu8Sd0QgD3YBqZH17g1VUN\n8flpsEnB8AypWFbAC29w9WIbp7/5HvekBhgubghwmQPa3Qoi66mKW1kVGfH5afALs+DZiBXxW2Bd\nS+DfF/EOSLyP59D+3rGj+vWiPbT26Qe9Q+0y2eQdaqCvJknYODGCOscZxoWBRNYYhKz3gXWfT+YT\neCDEcHlR6fL6tN7tnLFsV/SBURk3kSjKLy8QjVCmNjGi+6RiCbfFCpqSBH58FI1KDZffLYMr3AOf\nTPjAd7Ifg1tgyU8riRPoIe8BoB5+S4w/sFvj352+827ae3k2grEMBzUccNeJG78B/a7rR6X1z4x5\n11kw2cosNury6a4EBFqb3HiInJyKTmlRKmIDQ8k4hpIx6DOOXpl1tUzejPR4+nQVB0hoXLInx5co\nPl9HROnTtGYFwkLq7O6BtAuG9AkNUDuv0J8gpVjUzitI5YolEq6OT8sslmpgM0mIlzdQnSxrmSwP\nK3ge70rh6N+5sJ8ru9+YebVn6PUJt23BXybPTLdSmyABo/iAW+atW2cmmGgKSVFYNyubPtdKxtnT\nVWzqGDje7sEFICiI+C/pdGJLeBfCRxn80WgKcYYJdd30GeF32ZqjSjDD530DxTKLuezOGDCe6uHR\n/2gG1883tEx3ME54r4yUvZFaERvYqEBrtTguiRj9cAFMIm4wAu6BwlTohr278GwEvUvz2Iil0cuQ\nQEj1VkbFFFigaApvn23ipnoLAGB+WEeKoUDH7IJV3cpyG5+bnJ9AcZUAfZ6WRRx/+RTxaFLDC7BH\n/HaXzh2BuzDWu3cOg60N+a1qYMvLRnCvPnB28NzuUPN6OWWyVeeTSQoQIGOqWbb8RnsGElgYhCbz\nKthtE5ffPTPYTeo+T3Ex1G2YpMIT6xlLP5pxqUBrZ18Sx3j7vIznby9wcFPFZUXCRJ7HT+9lrPtC\nBqAUFzEJHpQWgOvEDrLf634qWbxAD90B1MNE9fcPduv9HYSUoPOzH6yfmRjtQerzpbZb1O5GwteP\nKkbH6Q+rkIsl3N6UUP/VLXqX5pXWv870vD2zWBxRh8+H+utG/ptf4vLCjOivoP+7OoDtlzG7Hwhr\nRkYrvfp+RaP/aXxI2AKcJr8pA30Ci3OlBNI+K9BppiycjHlQw4PmOdJ7qnChc8P9eHNLYUNhVtDP\nqVQs4fTpCyQSLCiawsmv/hmpR3OgYzHbdoDi8gbKW3sG8Lzt84qGWWBH4ah+112p+J8rTqwqSkYt\nXW4PS8JdvClpWu+hDc6F29jdI4N37ERRgHRT1BDx+ftjyH70I5iDfvr5lIpl3Cy3xt/c2kGjXAEG\nSCCtk8x08KhpmEi6/sXOEM7xJHAZlgSdC1VHNHf2AHSrXz94q9P7Aj5qFvP8mgPGKhZEolpHcXkD\nxdWXoKOsT/0te4LUORmpquiZQ/iZYYiA0QgYd6YXtZP2HGurLh1Lc+jtywKAbcaHSSbAThRw86tv\nAACx4X6c3wIc1YRds1+3stzm55Y29tCsi5AYRguqAK15bw/x2895cM7cdWobGOwCCkjOqThPznZP\nuOJ3bVq/lYrQqB8eKQB0gNhooiZJEAKM2d3Bc7tDrWxTUGyidn6jmYFEjytkZBACLr75HQ7+t38H\nOhIBNzKAa+DO+sztztjAX/2Zw35vf19WxAZ2zit4eVEFBWJf5/mogq1gDMykFqZxe32DyvYeqodH\niGZSOqyUdxtMVkvdpawawFxzAVBXpRNbpHVPirF44BYpp/PA1muhMAZ57wenz9DoWSLA04DaHh6G\nXvK28e8OyNRejofGcHrL4PpXv0ZJ5iCcVXD99XMsFkbBJJMd4/kEYRYL9STFM2kwt/qL34ge6ewA\ntift9QxSKLAy6KMDoIcHG6FRXNs1YB70Ls2jOZTXnj0ylEf/p49w+nRN+3vYyrnzHsOWokg6sAQ4\ntlb8+c9x+d1z8pkfL+CJbSQ8gv2rKjbPKoiXROS5KCgA1dtbUJQVDVT/e+RGE8XVHUTG72HrjBhY\ndhSOKtCZl1JxmyvzbxzLcA7lR+FTz9iLDV1JR5UR/t8bVlmYHjgTsgxuZADlzV0k56YMASC7Mjo2\nQhtKbHP8u+jjJ+N7Nw7n+1iKS4zd5NKMTQA33Pf4LWt7X8BH25VmuaRxwgP+z3VFbAQCqYvopsTc\niqQHHdUbAbV8NuCvCbpn7XUpzzJ40C+4BBYoJH68gMxf/wXKK5uoMSwSUxNI9woB0ZuNY/fvILQy\n0XrHWG42EH8wiatVgquTWJhGA62KwvbFnQLP6T4KB3+AQnJxBtTIICob2yiuvkRxdeeOz5r32hjs\nhmYTkZSAZr2Oc4lCqVDAxomIqWbZcFeHFcCyG686x4wPkE+/v9FNrp8u4+Tv/gG1128RzZC2zWhP\nOJhORgl2H9rttfYrB60Sq9fA1u3+0qJBPfqb/whhblJJQHWHltYPO455v8UZCjuXpK2zX2AxwNBd\nY1DR35Px+Skg0ef6eTMuWk2StJbFsMWPnnL+TEJJFhI7capZCcEet9reRnwib5+p26L6sAk2hjd1\nAHIDXDSC45KIKakBoUM8n9ZeJf99p7SDZjH3Ito7gOEJJ1bRLJcANg1zhQJgBJgz9CvZYB4kYQJA\nzM+DK9zT/t7dDSMHjIb7L5+29sLShoyHUyRcX57a2N7B4VUNfZ88xpONt5BRxcOf/QgjgcpkrRSO\nahVCZ8aPFdylqAOx8cKSsOtP7MSRNL9n/6qKsUbTF61a+5FLJ+NSdixFdBcrcKb5O2Ye3p3zCnIj\nKcTnp9G7uol0nEFqcRaxKIXrZ+0iyRv7fFtzQ/p8xVgcjO158Qba7G6woLtZjPb2CYV4JmUK4L4b\ned+Rl83zaw4Yd8In7ARSVwH57Twb0S51PXXZm2IVk3nB1nE3GwEf9CR89P/75UO3ipsu9Qos8GwE\n+Y+XUB27hziAwaE8evK8bXtPuJkcnU6gADabUtrfgOr4OJazw+A+7EUuGkH91QFq/+kfSTUhHqII\nuGActCfdpwgjhuXBm2vUfrOKfoFFb4J9786aQWSASSWR+vnH2LkQNbC+gzfnGKAlCFnVznPLzNsF\nC+gAyYDW3aAHxO00sGs3LrZexeXGHqRyFcL0OEqbu4gkBSQXZ0K2lZ3vw8BnjIKGAQIA/P1RZD/6\nMbzmhmcjmMjzKIkS6qubYF6+RG+OR4OuApZSaUKDyqT0uFYyapIEWpRCD7J7s+MYaTRfvJkylWTz\nmExGQk8CWAEeNzHzWR82FLVhxqAwrnGrzU1qNnFVu0VfIqYDx3u3WfKK2MDBm3NwIL5iGPpPr1OF\nvR1sKvSVvUsPkFyc8V2pexdSYzlw89OorpJ9lFiYBp0QQsDzsdONzrbKndba2DmAwaWloPlki55E\n2NuBsLeHqwQLWXN07ME9nJWe2VixB/HqhrTGtIbTsugYDW+JOg8yalIDp09fKFE/M8Wi8TtmCorW\nReAeCVeNxVJhArmpAiLVOv7z6S3yg5MAgF/TLCYqInJ83PR7WnMsZFOYalZMFQrB94H3pWU0Ztvv\n2Qo/c1llOaQWZ31nwLjCKKK9eTAJ/8jrTsbl7coLHP7v/07L8gcpRTQDZzqNu3IrYfeiCiYCvL6u\nYSjVh5nP+gw83u0F1uzWYgZcYQT7V1XS37l7YWvceQFt/qFnpzsrR25H/KEwd78iI1xkcWexzq8l\nYAy0ZVDZgdSdlOoaOJm6n4eSMbw4pjHdkwAbobF1VsVQkrOUIqvj1RsBY0NpnJ2VXEbRzWopVRc7\nrZW9M2d3d0nFMrjCKLjCCFTnxd8YrZmy0uV1686UKYhXRfT86aeQYiyWT1pVbBCrGDrcB7JxMBGS\nBW1UaoErQTqRMAIhGr2w8t/HJRHpOOMrMB2eeOsEy299OItIby+qJZKkUKnJTnp4yEsPdLraKYBF\nkiAq2n+OZ1ERmw7Bl4hpjwLF5TWc6vAiwujrbY3LuO+lYhlyswlhdgKl9R2kFmfQ95f/AtmPHoX0\nTiJerYh+7SVSOTiO1f/p/8JN9Rax4X7IP2wqlYNOoNSqkN8/QEt4890JmKE02AjtmPk37gsZ1Ylx\n5Zza3/lW8Xcf+WXHAQjYc1OSHEuy7VhKwhUKYxkOA/1WvC+3Njc+yiDJMng8kjK0L3aKbeBHT9l/\nhsfV01XUdFhTpcJEoHe7iYHBgAJuVjYRSfIdYpiFI/rAYGx+Guy9EST4KAaVyhRVH3UmVhwJJ+lq\nQMC+RL3FOKCnk/F7sZuN98nFGQzQEk6+PQCrtCOoi+vWYxMesEdYQhQxNTKI7YNrA3WNNVKmzgMJ\nHoiFUVyeVbSoPwDb6KnlIni+rjibvGEOhpJx5DhGB4BjROUV4zxyiTjo83Ncwon/XL1YRlCTGqAT\n5LLxyhj5M378OUBqOaidM+2nzLD9zKURM2CGl7F9UUE1yuFBfwI9M/M+AO6cHFUzArA/w7hZLqG0\nsaf9d/XgKGAponnOecMFy7MRjOfi+Lu1U/BRGiclEbcNoCfBYqNCY6Cf0zjW/ZaRm/EI7NZCjHEa\n8jMQPLP2fmWngzq4xs8HL0dub4x+UJiDBFnac3juquVHFTs8GuMea+dOYZKCAaQutTiDJ5XW99Rs\nKJ0QApZ5Go0At7u20+y0HxovL6wfLwyW9oN29pmy01cnqB5dIZsRyJ0pkyxkMxYHYKyQu65LGn7Q\ncJZDugsAje5zGB4QWZXlNBBK4C4zgf6rGO3oOdUybhX3iTiPfnS1rOEWqc8h4NOt9puGspx2DEzb\nXz/XgnX6vt5wxLjvmWQC6YezuH6+juzHP9bRcHpX97yrdrjIeAHV8XFEAdQYFjWt1Nnf9wm4rhPE\nmfGzejaH5RMrtodbdaeZmrezoL9+L8uYnBjHdp6ARKsVKH566YMGs+3vSUGzq4IIW6+CrbMAr0dr\n6ZwxyFtPWT8jFcuorW5qgfHyyiZm5ic6brNotX8otNPJGOhyGZdrO2iKIsSjU8SGBzQ2HjdHORxp\nrbksq+8y+kSqTd8+G0pnEmpA4KZaR8XghDrTXrRzwTsZ73FGiXQrvUz+5N2AjLkLBTohoOqBIqvO\ng9hoksvqxQ6yswUcr+8hzTHA+D18f3CNKitiPBfXUbEYXgXppoiTv/8H0LGYlnE1X56TeUb7grqW\n2RyPeqmGy2oD3+xfAQA+GcsofKtG2RMppS+oFcm1U9x65ejP+HHvxywur+P8qycorb0EN9yPvj//\nuS6678xM0Z4YS9n1czh5fojS8jpQvkXPwjTQv+AxdiJOe72691qh6iqicVNCbHjAUvpPlAiHF8dk\nTA/6E4gzwFWzCWHuvgYMGLwUUR23/fkdS3OY7kkgQgOVWyvnuH+xAq0RWp/25F2DxvgTI97KWIbD\nxGgPnI1B/w6xncOX4xjEqnUEBRXz4zy2A/gY1OHpfol1O9LOnUI5tmzps6G9S/OYMjEb+EXQP/t+\nGUdf/R5Atypg3AO9na5VJ0E7S6bsh1Wc75/heuMVEnIT1/uHSE/fQ+/SAzDJBBjAYIANDGRxOjGh\nlQRfDY9isCOMAyfxCpab95YxMFyLNqHaXHaiNyxLhQnMzE+Y+mk7FXenNNgaWs+Rmk0+UXCf/I7Z\nae8lYxGD7QJYqwayuYgBVPI4kLPrR6xzFjzwE9yWdr8Pgz2PTgiI/mhBCzCppc5+JSgbDZMUQIsS\ngAsN06NqC5RmBKes7R8q4JRuFbT+xmPYyxQF/tUr/PTeAJjeXtfkhRvekr9gdrDqDac2N/tq6rDu\nAz93oN1nKPQmWKTj5E4YyxjBJdsdi552urS6iatvVxAf6sctRSGSSuDssoQjkezbpkhhsmsgq8Y1\n/wAU+lhVj7mDm94lBlWo1tMXm2coFWumDW79sWFn5dTNf/7Vd7h6sY34cD9Se6+QXJy7Y772zqWt\niJAskx7MsXuI3xzj+bdrANbAzk3i/z3ux1RPAg/6BUzmW33XFE2jcVMCoyD4Xj9bBzUyqIF6AHaG\nG1nLFBfDaUnEp4Us5vuIsiTBAKPz4s8QtFeOnQRrpGIZNyubxPGVZVQPjnD53XOl9CvhWxn7u6ys\nQC9bCtALJ1ax++0ybhtNRGgaxeVN3A4Otu24EBomcsGpvy3ak3M4PxQ4htb+zST5VvbhkyVd9iG4\ncnE6v3wyoYGITeQ4lMSGDb+yl8i4qNTx+sky6i+I0a0CrdmVmoliE2OZOPavSHme/bucL9L3JVig\nnhXVCdwEkPp8CT1LD2C3RkEwMMxAQpVbCf+8d4XclYjBGN3lzLpfuYsA7fvKZGBt2bJmQzdQKIxi\naJxU9QRB0C973LXhZCHed+pIIjRNE+eFYVFmOXCDAnr+9KdIKs4CAEvGZtdUEsyP55DpCoWp3zlU\n7hxdYLg0U0Dk/rjOuLfnsXc2LDtpv7mLtisKQjYNeemBodqjJknA5ZWCSu4vGVSTJBTrDUz3KHRr\n9QZqkjWATSUSSOiqKYI6uy2x0ztuffz+39GeLe18HwZ9Hs9GMPh4AQfK+Ri0AeFzl+BBEJ6lMXl+\niJffLAMAJj9ZBD/TY/iM+XdUD48R7clprT6hjIdqMS81KjXS2qG0KrtJZwFSp/1hPb92bW5O1dTv\nMjmqt8Fa+HJhjYck+97cK+AULK5fn6EkRyCcVtAjsLj96QeIR2Mo+cItaF9Hmtd846wMoYf3veZ3\ndbd25S1mWiT/k+c+4W69/1xhGFe/pnAxeg8iw6L69TIWC2OYzAvvGcK3lwQpq19Dv8CiVCigxHKY\n4YHa030AAE0Br75bQebnWQAJHWUSURJSuYKzL76yPNtZWpea3KMeVlrBDGjf0A4/0ydDKlfQFO2r\nLIK9z/tyMF88N8vr4JaSDlFr/2K71xP+nmn+jTvnJCOcC4CkKsvttPS4lz95CwkOvXl7jt1vl5Fk\nI0jGohrQmoqSDRBaGn0lhrESJkhFSafluOE5mYZeN5C9lJkquF6OXlkSNfilAgkNCjGUxAb6EmS/\nh18efjdBFj9o0EZ518CSfoVyyYZ2yzDoZhZCBluvYoaHI/hVN2mhzN/Nzk8gWxZJ5leWkYrS4FPm\nLLl+nlVMHdPY2XdnQKt3jj4wLI0NoKRD7Hba6/b7p7P2Gz9OZDg6Qa+rZewub+Pl//y3AID7nyxi\n6iePYA4K2OkrlTJa34ITZxgbeli2Q2cXcNI7775VLawAbDu6w3rmgwVBKuB2djHdQ0Cs2Z1dSItT\njs+QlcpIVbz3nvt41L2sJp244X7QMT1A510mGYidtn9VNehX9fyaf4dWTf3OhIxXbSN2Cl6Eef+q\n9nBEyODyPgHu46IR1MfHcRZP+WzF67xF0d1Wez+ka2GH3cuKIWunnzwnYAk/E+4EslaTmjgSATCk\nbL1V3uV8Cb4fBqCdUBqgREVs2JYPqgdoADLEGNlgbL2KI4Xm7bRMHOIsFzVteKIkyBoY0fftQP9U\nVEr9pcZ8+iOgUIBXpPuu+1/0mZNGtYb4QB61o3Nww/3IfvQQTDIBMTBIR6tM3k90kI3QGMsQNNAq\ny2Hy40WUltdxXLpFYmE6gFFh30uZfkSy/MLcBGpXRUgMg95HzqX/lVsJOxdVVKWmUiXirwzx7Mlz\n7P8TAXkxgym5X3h2501GRSQ9uG5zpwFfyUAPH8VZ+RZ8NIKhFAk6vTguYf+K7OtxqYTdi5r2XZXH\nub1z3K5xFF5mjGcjGMtwUMMBambY+fPeWRJ9YEgFElocENB4G3h4OvFjAFKGwKM1kBUOGKA3GnRL\n3g9gSb+/2y4b2p5RSfqSZ3CuO8v2z+lGsKE1ryxkfDg/DX5h1tA66M/IMjqCYoxzuBftxIp9Mhll\nNcyGFoVwMNBDP7/9bgAvrRLU2byb9pswjH5lTmNxNMtFTfcBwMtvljE4Nw4hm7G818w6JBXLtgEq\nu3XuNFDmtBZhSdjOZ3vPC0b1GQ72C+Xq2Bp+hwzkP/2wDTBS47j1/oJKhdioVG1Yl9z3enh2MdGv\np09fYPOsooHxuVULAkBiuoDy5i4A6o4rImUUl9c0XI7EwjQGHy/oghcET0Aqlrvmkx0OjCLT149o\nNo6beAKTntWlRDrTkTIaG5tIK79bWJjGzOcfwj9TQOs53b5TuhIQGM/FDca6Xem5OdtXurzBwZtr\nwAKmF/EABiFCJwRLeZcYi6MiSmDrVRgVwfuOLO5HaerLTBVhBaQfzQHP1pCOMxj98AGO+SggVm0y\naPZKy+4CtKMCTOR7wSQFT8RaP7RTYQUN9GNhkgKimRSG/tu/ApvPaeNp733O62G9QOeQHO3BgEhK\nEPmZHkiLk5gyRUT9idVRVYEa96+qOLoiEcfmUB56mCP1N+6cV7B7UcVETEa6UcfWGe1LiUnFErb+\n6Qe8Un6vFUzJP6gjICs4EsEMgP4H9xHf2EMhEwc1O4mvT2/x7OgCfQKLvkQM+5e1Nvl0w1Oq4WZ5\nKEyM9iD1+RKuv3+GaETWXdatudQH/4JkSQACnpWOs3dWHl7de22jY+2ZX9oztP2hQft7VtB1NOKG\nSMWK8m9nkFB/vzt8buST4XvYXyLzZNYV3RTjvFKorW6RUnslux60Wktr93pDMBaC9N7q11KP2aAa\nw52BHpqlu4CX2p3zfB3C3H3S+sfFkb4/rhnVgKyBZbXDOV69vkaTifgqxffvRHaSlTbO6SQXBKOm\nxXThHqCyAvJX2qYAACAASURBVIV2qyonPEc+7Oyq8XmqPgrLQQsj+BQO8HSQhKC9vxAf6HVhXnLb\n6+FUZJnvrfLKJrihQU9MBVBAcv4+ElMThqRqt0UqlnH6dFXD5SivbOJgaFDz87rlk+lt/vEch5LI\nohGPKXqZx3iW1z7XjblQbRUNI+HoAPf5j1CUglWi/v/t3WtsXOW5L/D/mhmPPTff7Ti2kzhOHMeJ\nnaTZEBIKu0WlFO12n24dpCO1tBVFqDRUaSWQWiiiNNDSopYKWiGKWqqKj63askVPxTnd3dqczWXv\nAClOQnzLxSQhiRM7F8/F9ng863xYM+O5rDXrMmvNrDXz/32CxBmvy6x3ve/zPu/zlqOIsqkt3e1b\n2nH1SgyAmBMQyCV1yKUMAumLsXlsFO6pk1jMinKlaS0Mkr+WSQgF8d9n5zMFNDoCq/uuVz5dqzjj\njWbuzNzCmbOo//s7cLlcaFnuB9p2QCmokP1nZs8MFP88s9NVszpCoohkUxPi9b6sL7r+35eZuU6l\n/EzNIut+yL94sqN/nlDIxEJEwmplfcWdKKRzbPV5UHfqJJbemcIipO1csFF9Z4HFxApmonH46qXP\nm4kuyxRTUi/qKBW+XMGFrl4g9UwX+y5Ls97nMPnmEUQFAUM3DaFzeAD/eWkZ6SHrpUgczQ1S1svm\nNr+m6G72cSlV29cWJMgPJphNQH2dGw2Z65oOXk4gezvSSF8/Bv2AFyg6S6IU/MotDroE616CcrtC\nKO/8YiVzZ9Ly9rBvDqX2sBcUOzJa66nkd4rSs1pGxeIrmJiNqexaY3dS530xkVCpcaNVbtsViydM\n/U5aP+OeO6ECiGhs9CHplmYqPSE/Fvo3ZmbQN+0bgSfkV/y0/Hai49w03vnzeObfyqXiFx6Ptbs2\n5V/TEwsu9O3Zjg8PfZA5Til4oUwtQGWFYstczbtmZtdfkb5Ds4ePY/7oOLxuV0HhYmupDdZzd7AC\npEmMwsGt8np75cFn4e8uNl4wfg+19LO1Z6Vmb12rumOWCISPn0JgYJOO47WWtWMytUr+6gFT5YlE\nPYGlrKwWnZe9XEWUTf20Rl89lrxxrK67K7x4J+aiODUXw+jFMDqDXmyoS+Lk20expd0vfalTUa7e\n7jYpBV5zYZDVm76YSOC/z85n1uRGATQ1eExP17KrhemzmD82iav/9Xc0rGlF7OIcIodGkUyIaNmz\nC3q/jdJLbTCzH2/TjkEg1Zk2a22gGV/s/I5Q+66tCnvV6v996WJvgNzA2k47Vqy+RBpX4mj+8EPM\npP4mOD0N79J2wBss2oi5AkE0tzdi5i//DwDQ9umP6yimJGLx4mVcPTSaSqVTi1yvSoQjiL53DH4x\ngWW3F9ePn0Ln8AAAaUZ8OCTgg7CU9paO7m7wSsGfYIv6cgGlRvV8eFHTzG1hMMFv4iBT/qVY19GG\nnB1FUtdyAj7cuG2LSrVz5eBXdnFQa4mWbOujv90xr7Bk9n0SXC5c+j//icadQ3DV15fUkcmtXg3M\nvXEIK28fhlBfb/Ie6OWhdl21ZWvlBheDXb2m7lHtTOmt9OKILScQia+gf40baxsS2NwWQCyexIm2\nHvjukN5RJ7w+9MSTRTq+WdW4L17EkXePZJZeKqfiF35Gud+BG27aiXXD0tpwPUUFzaU2aCs28C/X\nNdM6aFk9l4szc5hM7Qu/JugFRsdMGaBp2aJU60zxdFzAhXfHM8VXN9+8IzXhVzxbS3nwqVR7oxit\nqe76txRWmxHObl87Al6su3EY/sEe29ZK84QC6Ni9DdezlwykMpcTS1b/9lLHGHJ9KWj6rsq9B+ub\nGhGejRg6ksDygvoPGWTRNIF8RzQdiU/v/3opEsfaptVGPJ1S0dnbhGBLINOIAVoLg2i76Vqrx1eq\nxkApafTZBYc8AR+uvP0+Gnq74PJ4ED46gdBQ8bRiJTPd63EO0jUK9vSiM/M35qwNNONaZ3eEXALw\nH1cSGEillZcSUfMuLSA4PY30t3F1YF2u5NtcxaKV2S+RQT9ytnJJFyhTa8S8SwuoX1hE3ZA0GPdE\nYtKyG9XzlV7m88cmMT86Bl/PGtT3dGFN0IvrqZ9Q/i6LuHr8FGbe+wAQRdT3rMGM2IEBCJm18gKA\nf967Hes2r0+lmE1kzkM0mGImzTqqR14Vt68ydWZMRHJJejMWrkss5B/eqqHaeWWqv6eDiNnrBZNx\nAZtDLpOWCBlpd6wqLKlOb5suuARcGJ3EwsaNiHtWDO+B7ve6MdgewHthPZk0SvS202rXVT1bK7vz\n7nW7EJyexkoquKgUQND7LjG71o3xz9N27JkiWQJw6orUOexuTWAqksgp5qy3eNXKxBQio2NYHptC\nQ3cnFts6dP17KyleU69aoGKV+cXetNfAsHbgX2ywqXWAvXoubgEIXw0jfWVmIvFMH6J0xZ95rTPF\nsfgKzp2fw2JqkmYmuoyWI+OIX5vHwofnUSxbS0mxQEFp2zTqT/fWNiNsfBvCyuymJG2xO9K3rmAJ\nrdo1tke9t9y+lNxSavmgWeF9EgR9ywXShXlPHz2BE4eOIVTvxkef+BgGbt4FMwOhFvYUlTuiKyLQ\n3+qTip3V+bBt3wi8p6QiFx27tyPY0oSCL4mOwiCrLw9pNjc4PZ1Ke9KarlX5GgN9XhFdnV4D684l\nYjIJX/86zB+Tai74ersyM7Z6ZRqnVAejcMuM0tYGmn2tF7w+uAVARNjwZ+QSZAfWlSG9/LtDDegO\n1SM79Sk/9XUiBty4bQuQNYsMiBoaMQFrm3yoT83oaj3fzAtVAIJDmxAZO4m69lZsvnkH3IM9AJSj\n44lwFOGJE/Bv3YTY+Eksnb+Elo/fCAC5a+VPfwjvjkEklqA7xaxYxWnjzOrsiViYPodkPJ6pXNz5\nT59EQ1e77I4idqh2rmZ5Uz8uJ31ocLlytvUxb4mQmR1t7Z+V/V4Sk0l0fubW1JKB4uunte8eM47l\nlSSW2loRLyiSq/+8tnU3IZh6lv1el4EaGlKHLDw2lVOMSm87Le1akv/u1hOwktrhgd4mhfei0XeJ\n2cvWjFVeN7NAqd6ARLrt9ghA965BnH9/At6mJqy79R9UU/HLw4x7ZG7gz1gar9mFwYoPNvUMsLPP\nZXpJwN7BDYgfn8Kyx4vGETMHkGrPvM76F4KAQHwBkaPnMP/mu/D2dsPb04XrClkNSoNP5dobpW3T\naG26t9b3lvVB71xKg3hBYQmt0vFVfixmjtILV7sgAsdPY01LG9wuF06+fQxrh/o1ZG9pV9apo+wX\nlb/Og38e6khtFdaJxMhA6qdyi5jIVVqPxVcQL1plOOvlsbFVms2V6Ygo3aDK1hgofADk9zaVf7Fk\nN3Zuvw8b7vtfWLp8Fa76ejTtHLIoKih3LNpefGZea+n75cMHM1JDtG99E8JLUuOev9WVnoij3I4M\nuddR7VzNinDqjzTnzyJnZ90o8YQCaN41hMgb7wMwEE0WAU9jCC37dqPttn1o6OooeoxpXpcLwdYm\nJG/YCQDo2LE1NWAvXlFYO/m0Ly2dZ6t3zMh0yFPXDUCq8JlLdkcRu6YFSrKWhl1PojPoQWdOLNIZ\ne9YrK6xcr15UUPp3avVU0p+7mEjgbMc4UPIe6IAgrBZVOzEXzdmqUb0wkVQZem50HNf+6+8IrVuL\n+p4uzYUX07u+JObDWJmPoL6nS9d6ZLmirekJg3ylvUvKXTsnl55jz26L+lt9iMRX0FDnwdqQN9Mu\nGB88C2jtakXjZ/ah5R9vQmPPGlQmFV+O0XuUWwA0Xi9dF4/ie9mKat7Ft4czyorB5ooI7IrM4PLx\n01gW3Wgb6MO1DRvQrv5PS6a1/oXf60ZvdxsuDG9BcuoUPJMfIjC0ERevRXBh4gzq3A1oaQ6iS3aZ\nmvzgs/jvLu+SGDN3Isj+jldqDKM1MJt/fHau91aOrIv880+cvwRfUxPiLmOTu2rK0CvL/UIqvaiU\n1+9kf0n0DIiyXh4VSu02QtsDUOw6GO2sKstvnAbbA1nrEeXXVqf3iHcLwPqWBmztCKI8HQsBPo/0\ne7pCDdjdnTuLbqyxKhZdVftOSh3qq4eOAABa9uzQsRY4t2Oi9vJXTqvUux+0gPYbRpBIpYxq/d7k\nZ/Q0Dm/RHAxIB10wOoamejcaR7aifV070rORcsdrrDEu7FRq6zxbuVd7FhEQV/IraMvsKGJj2d/T\ndCZYc0Mdtq8JmhpEqazczou5mQoBNIQj6NnWjzOqe6BrDzbG4iu48O6xTJrtheEt6L7thqKDh0Q4\nghNvHcH8chKLkTgWTp1DT3sLXPUNiv9m9d+uLl+LjJ0ERBF17a05qbjqx65nZktEfEVqLyubxSUd\ni3VproVFslpaA3kFQvUNntNt99wb72QylIQrV4GeLhOPu1RGrmn2+17EQv9GnGiTstU2z30E36ns\njJdBJMJRzYN27YM2vdvDmUfroCX7XHzxBQTPTGNRAOoEAfHJaZzr24DukM/yQK72+hepZ+C2G5AY\n3oBrPgGJujok+jYA4ycBAJG+vryC0rn/vjBLQm/tDW3X19jg3oz+RmVm1+08iDeXWVkX8hOqiXAU\niahUpB8QMtlbl1OTnFoKqeplcf9S/gsp16hUPvUm66aEzC0WZjb162B2ZzW3cVrf3YTZVEEMuWNp\n9XkwNRtDbDmBU1cWMHoxjJUksH1NCMXSZIHSrrWW74fxxko+Qqz2OxPhCC795T+wcO4iAGB59krq\n96mvx88PNEhLBIofo5494gHlRkwQjETES2kglf+t3j/XT2vn2bqZbXus8TOfv86DnV0h3NDbiFa/\nFJyrfqXMMOZujTYsszWa3M8C6h2+ZDSSKYwKSMU+k3u2Fl2HvZhYkYpZCgICWzdhfvwkOleS6DTh\n+6n92LW0RdL2phe6enOKjFXmGdLfEZd//v2pZRby9Rey26LSC4QK8PX1wnVoNFUg04tStvI0R/Zz\n5MqpGaN1cJP9vo+vSAWs0wO+dDFrr9stZbEsLOLq2Ekdg3ZtgzZ928Ppoz7Y1PpOXj2XZDSCSz4v\nZqOJko/PKG3XJvUM9HRB2LMLlw9/gKjXh5Yv/A8sdPciYvD66rsvWq6v0cF9af2NahiY5xc079i9\nzWb9olIzR+QnVDNtnQB4WxqlJYmigL5P7cXG3m4A1hRStTQgoP0LKSIRjSG5tKSpmJb5ZG6KSXtA\n62XNwEBPB1V57U+6cdJSECO74BEAnJiLYWOLX6aBK/fapvJKRBew8NFM5v8XPppBIrqgGhBQCjSo\nR5q1vESsTH+T+2ytMzvKRd/0/XkxdilQk89Jz0Hxa5jfSe1v81c4GFDOe17afsF6tkbT2+Fr8Lix\nJujF1WvSILOlOahaQ8MVCCIwvEXarSfV2V6zZ7ti2n62zLvsyDiCQ5uwMh+Bq96roZaJ/oBKpr3s\n64evey2uA6m6JeX/zhnriBdm9hkZAJdGUNjBqRIKC+R6R8eQPn/dgxsBcLld8NV75JPIXVLAAR7p\n3al90K590KZlezj9tE0C6Fo2422CqFAN3lyF7bLR2fT0NoTIy+7Qc8zG0/S1XF8nL5MzssTWvDFM\ndkHzZHcbzM33rmx/UK6f3+VKrL4/RCB+LYz222+BJ+C3/Bht8A1dXWeYjMexPHsls87QnNQbdYqz\nvBWJpK12DBLRGDyBwheSvuugp4Oqd2ZjtfpldiPc6vdifUsDzqTWqnobgyrFYfQO7OQfYi3XxezG\nSu13egL+TIE9QCq25wlkr4nT1yCVJW3dVHYqCqN2LFasHdXDTttXKtFyP61aXlFqynD6eAdLXkal\npFz7BRvhCQXQ09sG4ai0d3v38D+qtn1+rxtrbxjGudTShbbuNgRbtF6z3EFuJvWxaC2T0gIqgP7q\n+vax+vxrr2BtHvtkKYm4ElvCBzORTL/hzLUFrM9aSqWp2BwKl0KEOloRPX8Bkb5+bM4UswaCg30I\nHz8JL1Bk0G5s8JB9Xa3aHs67tJj6DzPul3I1+NKtXsOF6TO4PioVvc5+jxidTfeEQugPBdFl+B1u\n5bLA8g88zXmezV5iq+93Z9qBVJtudla4ffqm+XKLW0rBAOv7hpb2UrSkwWVH0z2NIdQ1NxUpRGb2\nAys9pMlE5VKjlCxMny3yRdV+HfR0ULXObIiiVCAnXXXaC6ma/WpqK9Bz8Sx2jo3iUiSOxh1bsXbD\nsIHgjfzaGuWH2LyUee2K/05PKIC2W25EXXMTAGldvZYta5QDDc6KNNspba34sRgdiFQ6iFBe2u9n\nse+pWQN7fSnDADKpwdHJaV2fUy56OnF6O3yJcAzxaxF0fPwGAED8WgSJcEx15rq0d658kEvp2I0G\nVKwu+qmHfQbWetkhS2m1IOnkbBSdQS86A/VY8Pqw1L8RHx6SgllKxeYKFS6FiF88h849WxEc3Jkp\nZu0J+eGq8xYZtJcyeBAQGhmE0CsF1cxN9bVqUKNUDb4Uq8cquF1YPPMR6nu7ILjcmD82mfUeKaWP\nU/p+8+b3r/Lv0SDcg1sAWN1nKP15NnuJrURLn0m+HTCT+X1Tc7a8Dbb4cSGvuCWQRCIcMdgm5x+X\nMotHFuppcNI2gimpglrSDKpyWrH50SERm7OKzVSyMwFo/aJWamAoYvbdo5h5+wiuvn04s9d8dmqr\nNLsxibWhBrT5vcDFc1jvlXZ60PN75AZo6temEinzxX6n0S1rylTEznYD2sqkcBndQqrU2czaY9LA\n3uDLO50anE6NNjtAVfrAVE8nzkCHT7Zopfoxqbepep/b8gZmy6v0c1MPKmjv5OlT2SwlpYKkgx1+\nnMY6+O6Qat1rKfi2KncphNftRoPHA8CVc67F7llp7Y+YKrIsZR0MRmaxvtmXec+Xwk4BdzX5x7pw\n/hJcvgZET3wIAPBvWo+WPbtQbe/P3PMWceKtI7iOABZS2wdb22ewW9ahtj6T8woTm7flbSy+klPc\n8uL5C3D/8f/C63EjNNSPwEC/StuRRCS1DXKwpbFgzN32D4OKR1OGEaV6Glwloun5azV9p07j1qGN\nJqdHVZ6eDqqWmY1EOIpodqN+7iLq2lsV1h5mbxen73oqDdDUN9uw4xpxo41ydkdcLFJgyigrB7Sr\n96Fp52BOaqDy821tCpfZM3d2Tg+3SqnXsNwd2PzjTacGW8ec/dK1Xw/tP2vdzLV520yVGlAxN3W6\nFKV2xIvvbJN/vdtvu7Gko7Wj7IKkDR43Tl9Z1LAcpPD9r/17b83gKfs9EZw+hcljk0C7Hx27t1u6\nfMnOxGQSoZEtuH74A7jcbvh6uxCdPI3Q0ICOe2DHvl5x8ZUkZiJxpPcHsXufwQ59JqsKE5t5blZs\nebvg9cEXX5AKAXf4kZgP4+xv/4jGnUNo2bNT4R2bxNRbo5nsgi17huD98Gzm566PjqNxoAd1jfLH\nZYtvYeXT1ABAQIPHA48NHkxzH0IBm9v86HJJyyKCLcWzLwpT2+R/Vkwmc9bGZx+jmcfvi0uFCdMd\ngeKfbec1QYW0XydrzsvorLj6S7jweLs+fzvSnTOl47Z+sKjcydY2ELFqVs5JKpNWbLxNKcxSS6cG\n6/scPey6rMeae2fuc2s0oOKstl8b+QGq3PVe2j2I8mzray3lgqQiBv1SLYH0zKpc+6z0HdDar1Gi\n3P5oH5RmOvcpZixfctLylJxjFYHm3dtTBaqFrGLiImJxqa9a/Nl3zvOef48Cw1sM74BQfpV535en\nMLF5tQ7MLIqff+5rgl7U13lwNbV1L6D8jo1cnc8EAwDg7OgUer0i6v1alleVOSCg3HiVP63F3g2p\nmQ+hmJMyIhZtOHNT2waSsYIZ4/R1C7/xPjyNIay753/KpLCUfvx+rwub5z7KfLk37xuBf7C96GdX\nLn3OaKRa23WyT1qgtpewfY5XjvKOBcUHIvLF6eyybrm8jLfX5g3s9T1nWlODq5/dUkjl6A+oSG3O\nGJJL0rvr+uiYjdoco+y2nKsc5NphIHx0At7RMaxfSaJxZCvaB7dB+3snoNqv0XJche2Gtq0z0x38\nc+elyY01QS+8bpdJy5fsUPdBK7Xg7CCm4wKmzl8BUDxj0d59jHzZ5y0iGRdwLlVwu9eS3RvMZmw3\nJ7m2a/VZUDv/ci0BK33bQK1F8bPHCWK79ro9K9iB+WNTAABfb1cq6KDNQnwFoRuGED9xFoDURill\nBwBlzxCwU+Nlp2ORY07HTU/DqW3GWED7DSNItHUAKL6vbSnHnwjH4Dt1GlvapciW99RpJEYGMoEH\n+zT8pUaqK3cuetNzrX4JVz5IpzwQUTp3+6xbVmK3tErzBvalHIN92g/nq/xzCwAilj66iIVzFwFI\nHSfIbjBnt+dBifJyLrnrXd/UiPBspILHa6bcdjiz1FQQUO+tw9LESSQG+jQ/w+Yt7cptN7TvBLHa\nwY8t78bi8UkAgoHlSyIWr11PFQLN3RLaOe2ZcnA2Xt+AqdNXMn9n93R6fdLnLWLN0TG4Dh8HAHRg\nG9A2pONznNB+FV+Kuub8GY3nb9dMu1Xai+LnjhM8t+wC+vqgfP+yzn1kCL6+9fBvWo/o5GkAguI7\nNtjSiE37RlYLEu4dRufenUhs3yr93lRWk5IKXG07NV52OhbnEIRyXbfsGgTqKtExLUek2rrzsiYK\nW5lZ4Eoo5wxe+SrzW4/tbnWxw3MrwN0YQCKVUululDsGuz4PhYoPYguvt5R6XcUEIDEfzixRlCtA\np/Teiafa6MqSOvj+3duRGOgDoHf5Uuq7e2oa4fCirb+7+qy+C+Jx7bt92SMIqZ/UX5zI9Guvj07A\n17de4/vQGe1XsbartPO3q+wtAkXZovhy44RAW4fmGgOeUBAte3YhNJTeFUXpHevCwM07sXZoI4DV\nHU1yf4+tAgJUTjkNpyCifnM/FhMJBJFE/prD8m/dpDzIMdbg26FjqpeWQaWV56U9CqunOJP68Srd\ne3sOFgvPXXt6Y+kqW5mfSF2ln1sRV90NmN8qzTaJbjd68zo+1fU8VPp6l48nFEBoqB9nf/tHQBSL\nFKCTf+9Y1a8x2kcxsnwp/d0NpYL3zv7uytN3n5zY1yuN2rbJ9s8c0Mv+5+QJ+bGQt0Wgtu1QjdDa\n5rsQbGk29BsYEKh66YazFx++9wGOvXEEeOMINu0bwcDNO5EbFCjn1k1qgxyjDX55O0qlRar1VPm3\nQwdQ35ZoxfahdUKkO5dQsfRGUwYyAiC4XEhE89NNq5H9OxJkrni9D5ENG7CQKtrmGuhHvN7n2A5O\n+YPzdiYgMNCPxp1SsEetcFe8Xuq/rN57q/o1ZgxK7fBetws9BbCln3fatdPXX5QrYpw9G+3K/Jmd\n+lPF2q5KF9I2WyyezNkiUGk7VLnzhg0zWpz6viRdBCwmVjD13mqF25NvH8XaoY0ykaTyrNtJF4FK\nN27yRaCc0OAb7xQ4c9u60u+Jc2fqCtMb83fBsJPMS+jIOBLzYazMRzD7b2+gaedQ6uVajcrRkVAL\nODAgUQmRvn74uqVK8nJVvJ2VZqxe5DQ3s6y6SSmzO1XuXbEAu1X9mvL0UdLf3eSpaQB2/+4apacA\ntlNp7S/KFzGWm422X3+qWNvltELa2qj3/3LPu31jF2Y113wp39JUm408arGqrt2UqzMr4nI0jpmI\nVPl3TdCLriJrW+zNCYELMpPyLhjmK7UmQ11HGy7973+Hp6cLgJB5uaKj0ZLjrSTrOxJqAQdnzGxU\nm9VZKen/5WfUnZZmrDSILRz4treHyntoZad+75wZYNdKOv/Q7kFcvVKdWV5OGwQap95flLsWQu9a\n2dlo7TXn5VjV3y8WgKue/rLeZS7p89Ze80VPFnHpbNRSlvfEa01B9cl9I6mCE9nK15mN1/sQ6esD\nUimekb4+R6d4GlGraaHOmqmTV3wXDLOVWJk/4M9sbUWlUeu01k6n1m60poU7vzMqN/DdviFewSMq\nF+ffu9IIaGhuhGfZpf6jVLXyZ6ON96fsG7x2Th/R2mXW5Q5y2mb8Vd3RXTuQrz6ZrdydWbUUz+pX\nzpoNdmLXmTq90XJ9u2CUxnhn2Dkv19LV0rlSPvtvU0XWqdUAe7Vg271K7loEWxoxkIzJfL+N9afs\nHby2ax9RTvW8d6rjLGTVyjpOPedpvPqkOVaP1R/ya0jxrAXV05joY7fZHn3Rcmd1Xpz0ci2Vteeq\ndt+d9b0gJ5Ib+IYavFiKVFuWgP4AbW0G2KtFLb2n1Mhfi2Jr8+3VnzJDNZ6TPuUOctpmJGLuids3\nFcZc5p6ntZ3ZwmPdPDLIlzfZgv5oudM6L7X0crXyXNXuu9O+F+Q8hQMD7WtSldhtAsVo36ZWA+zV\nopbeU2rkroV532+pvz+Iy4ePAwA6dm9j8Np2yhvktFHLad6J2zsVxjzmn6d1nVmlY/VX2T2hWsLO\nizq7DTTMkH/fC4vh8ntBxmh9Xswc+NpvAqVW+nBktmp831hnpns9zkEKAiS721DtpUmdqXxBThsF\nBABGd+2AnVmqPUz1toL9BhrmYzFcMktlnhcOvqk61ML7xjyZum2p+l21U7eNQSMlVVmuNN25T6vW\nzr2TztNJx0q1SMqO6fr8p9H1+U/XYEdCRCyeQCyeAEza/lNuoJF+EVcLuWK46WwBIj1q4XnRiv0F\n0ovPD6mTgkYX//WvuPivf0X46DjM6u9UgyoNBdXKOk4nnaeTjpVqU61mx3CWm6hW2TM7iv0FMkJE\nfCUJAPC6q3K+0zS1uCsHs6GKq9KAAFA7nXsnnaeTjpWoNli15as9BxrmqsVOFVmjcs+LXQff7C+Q\ndp6QHwv9G3Hy7aMAgE37RuAJ+St8VHbGXTkoVxUHBIiIqHLsOtAwEztVZJZKPi8cfJOzxeJJnGjr\nge+OVgDACa8PPfEk/F5mCiirrbpttTBJUYra+SYQEVEBa2e5a2GgUVudKrJSLTwvRNZZSBXJIypU\nC5MUxrEXQ0RU0zjLTUREzsXlW6QNg65KGBAgIqp5nOUmIiKnYmCbqBTsARIRERERkYMxsE1kFJ8c\nIiIiNa+TewAACTRJREFUIiIiIt1ExOIrAJybncKAABEREREREZEuIk7MRXPqV2xuc17BQu7HQRYT\nkQhHkAhHAIiVPhgiIiIiIptY7SeLIvvJThOLr2SCAQAwNRvLZAs4CTMEyEIiwkfHc/b8DI1shdOi\nZkRERERE5srtJ3tu2QX09YH9ZCo3ZgiQZRLhaKaRA4Dro+NIhKMVPCIiIiIiospjP9n50ltepjl1\ny0tmCBARERERERHpUh1bXjJDgCzjCQXQtHNr5v+bdm6FJxSo4BEREZG9se4MEdUG9pOrhbTlpbTt\npfOCAQAzBMhSAkIjW+HrWwcAqUbOmQ8KERFZjXVniKiW5PaT2zd2YXY2UuFjolrEDAGymABPKAhP\nKAh26oiISAnX0xJR7VntJwsC+8lUGcwQICIiIiIiIiogZoLT1ZrtzIAAERERVVx6PW32kgGupyUi\nosqpjaVsDAgQERGRDbDuDBER2YfcUjZf37rUUujqwYAAERER2YRQdR0tIiIiO2NRQSIiIiIiIqIs\ntbI1JDMEiIiIiIiIiHLUxlI2BgSIiIgMqf7Kw0RERLWt+peyMSBARESkW21UHiYiIqLqxhoCRERE\nOslVHk5nCxARERE5BQMCRERERERERDWIAQEiIiKdaqXyMBEREVU31hAgIiLSrTYqDxMREVF1Y0CA\niIjIkOqvPExERETVjUsGiIiIiIiIiGoQAwJERERERERENYgBASIiIiIiIqIaxBoCRERERERkgIhE\nOAqAxVWJnIoBASIiIiIi0klE+Og4ro+OA5C2Xw2NbAWDAkTOwiUDRERERESkSyIczQQDAOD66Hgm\nW4CInEO4du2aWOmDICIiIiIi51iej+DCK/+W82dr/+V21DVyO1YiJ2FAgIiIiIiIiKgGcckAERER\nERERUQ1iQICIiIiIiIioBjEgQERERERERFSDGBAgIiIiIiIiqkEMCBARERERERHVII8ZH5JMJvH0\n00/jxIkT8Hq9ePTRR9Hb22vGR1OFfPnLX0YwKG0b093djXvuuQdPPPEEBEHApk2b8O1vfxuCIOCV\nV17Bn/70J7jdbtx777245ZZbKnzkpObYsWN4/vnn8cILL+Ds2bOa7+vi4iIef/xxXL16FYFAAI8/\n/jiam5srfTokI/seT0xM4KGHHsK6desAAHfddRduv/123mMHSyQSePLJJ3HhwgUsLy/j3nvvRV9f\nH5/lKiJ3jzs7O/Hggw9i/fr1APgsV4OVlRU89dRTOHPmDADg4Ycfhtfr5bNcReTucSKR4LNcha5c\nuYKvfOUreP755+FyuRz1HJsSEHj99deRSCTw0ksv4dixY3j22Wfx05/+1IyPpgpYWloCALzwwguZ\nP3vooYewf/9+7N69Gz/+8Y/x+uuvY3h4GL/73e/w8ssvY3FxEV/72tdw0003oa6urlKHTipefvll\nvPbaa/D5fACAZ599VvN9/cMf/oCBgQHcd999+Otf/4rf/OY3ePDBByt8RpQv/x6PjY3hC1/4Au6+\n++7Mz8zOzvIeO9hrr72G5uZmHDx4EPPz87j77rsxODjIZ7mKyN3j++67D1/84hf5LFeRN954A4Ig\n4Fe/+hUOHz6c6XfxWa4ecvf41ltv5bNcZRKJBH70ox85tn9typKB0dFR7N27FwAwPDyM8fFxMz6W\nKmRqagqLi4s4cOAAHnjgARw9ehQTExPYvXs3AODmm2/GO++8g7GxMezYsQMejwfBYBC9vb2Ympqq\n8NFTMevWrcPTTz8NURQBQNd9PXLkCPbt2wcA2LdvHw4dOlSx8yBl+fd4fHwcb775Ju6//3784Ac/\nQCwWw/Hjx3mPHexTn/oU7r//fgBShp7H48H4+Dif5SqidI/5LFeXT3ziE3jkkUcAAOfPn0coFOKz\nXGWU7jGf5ery85//HHfddRfa29sBOK9/bUpAIBqNZtLLAcDlciGZTJrx0VQBPp8PX/rSl/CLX/wC\nDz/8ML73ve/l/L3f70ckEim47+k/J/u67bbb4Ha7M/+fHjQC6vc1Go0iEAjk/BnZT/49Hh4exre+\n9S28+OKL6Onpwa9//WvEYjHeYwfz+Xzw+/2IRqN45JFH8PWvf53PcpXJv8f79+/H9u3b+SxXIbfb\njYMHD+JnP/sZ7rzzTj7LVSj/HvNZri5//vOf0dzcnJkcF0XRcc+xKQGBQCCAaDSa+f9kMgmXi/UK\nnWr9+vW48847M//d1NSEubm5zN+nv9CBQACxWCzz57FYDI2NjWU/XjJOEITMfxe7r6FQKOfP039G\n9vfJT34Sg4ODmf+emJjgPa4CMzMzeOCBB/DZz34Wn/nMZ/gsV6Hse3zHHXfwWa5ijz/+OH7/+9/j\nhz/8YWbZJsBnuZqk7/FTTz2Fm266ic9yFXn11Vdx6NAh7N+/H5OTkzh48CCuXr2a+XsnPMemjNp3\n7tyJt956CwBw9OhRDAwMmPGxVCGvvvoqnnvuOQDA5cuXEYvFsHfvXhw+fBgA8NZbb+FjH/sYtm3b\nhvfffx/xeByRSATT09PYtGlTJQ+ddBocHNR8X3fs2IE333wz52fJ/r75zW/i+PHjAIBDhw5haGiI\n99jh5ubmcODAARw4cACf+9znAPBZrjZy95jPcvX5y1/+gt/+9rcAgPr6erhcLgwNDfFZriL591gQ\nBHznO9/hs1xFXnzxRfzyl7/ECy+8gC1btuD73/8+9u3b56jnWLh27Zqo/mPFiaKY2WUAAB577DFs\n2LCh5IOjysiubgwABw4cQFNTE5566iksLy9j48aNePTRRzPVMl955RUkk0l89atfxW233Vbhoyc1\n58+fx2OPPYaXXnoJZ86c0XxfFxcXcfDgQczOzsLr9eLJJ59Ea2trpU+HZGTf48nJSfzkJz+Bx+NB\nW1sbvvvd78Lv9/MeO9gzzzyDv/3tbznv2QcffBDPPPMMn+UqIXePv/GNb+C5557js1xFFhcX8cQT\nT2Bubg6JRAL33HMPNmzYwPdyFZG7x2vWrOF7uUrt378fDz/8MARBcNRzbEpAgIiIiIiIiIichQv9\niYiIiIiIiGoQAwJERERERERENYgBASIiIiIiIqIaxIAAERERERERUQ1iQICIiIiIiIioBjEgQERE\nRERERFSDGBAgIiIiIiIiqkEMCBARERERERHVoP8PvBFFq7n0L0cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106346090>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_score = [classifier.loss_(y_test, y_pred) for y_pred in classifier.staged_decision_function(X_test)]\n",
      "\n",
      "plt.figure(figsize=(16, 12))\n",
      "plt.title('Deviance');\n",
      "plt.plot(np.arange(parameters['n_estimators']) + 1, classifier.train_score_, c='#348ABD',\n",
      "         label='Training Set Deviance');\n",
      "plt.plot(np.arange(parameters['n_estimators']) + 1, test_score, c='#A60628',\n",
      "         label='Test Set Deviance');\n",
      "plt.annotate('Overfit Point', xy=(600, test_score[600]), xycoords='data',\n",
      "            xytext=(420, 0.06), textcoords='data',\n",
      "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc\"),\n",
      "            )\n",
      "plt.legend(loc='upper right');\n",
      "plt.xlabel('Boosting Iterations');\n",
      "plt.ylabel('Deviance');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6rRwAAABQJUWFCVs5AAAAoFoKCxO2cgAAAECVFBUmbOUAAACAaikqTNRrSYeJ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\nmOgOEkwAAAAAAFDoSi6YqPYmgokQwQQ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FLzAoVLRNFDLaJwoVbROFKhvBhDML\n9djpGKapqn1nqmrfmal94S2d6l2yQt2JoKJn8XJ1vv6OOv/1dsa1jopy+aY0qWxKszyTxsvbNFHe\nxgnxZdMEuevrZJjZ/+O5VBiGock1Pk2u8WXsT81Z0R7Qexu6tbLNr9WdfRnnOAxDzdVe7VYXv76p\nyqvJNT41VnpyElhkn6F4OJEeSNBrAgAAAEBxo8dEDkX9AfW+v1I9i5cr8PE69a1tUd/q9epbvU5W\nMDTkNYbLKe+kCfI0TpC3aYK8kyZkhBeucTVyVlbI4fXs4FdTXGzbVkdfRGs6+/TRlj6t7wrq4y0B\nfbSlT8FoZqKXnLtiUpVXTVUeNVf7NKXGp+Zqr+rKXDK30cNlR6fXhhH/lrVtMfcEtor/WUGhom2i\nkNE+UahomyhU9JgocM7yMtV8crZqPjk7Y79tWQq3dSjYslnB9RsV2rBJwfUbFWzZpOD6TQpu2KTO\n19+J/+U5DEeZT+6GcXI31MlVVyOHzytnZbnc42rlrKmSs7xMjjKfTJ9XjsRX+npy2/S4S3JoiWEY\nGlfm1rgyt/afVJ3ab9m2NvaEtK4rqHXd8SEhG7pDaukJ6t0N3Xo3c0SOXKah8RUeNVZ61Fzt1S7V\nXu0+rly71vnk3ME9W+I9JeJBRDKcAAAAAIBiRzCRB4ZpyjO+Xp7x9areb68hz7HCEYU2bo4HFS2b\nFGyJBxeRLV2Kdvcq3L5F4bYOdb+zRHYsNobKGINCC9Pnia97vTLLfIl1j0yvR6bHLdObOF7mk7Oi\nXI5ynxxlaV+JY+nnF0r4YRqGJlV5NanKq4MGHAtGYlrfHdLarj6t7uxTS3dQm3rD2tgT0vruoN5a\n35U612EYmlAR72UxbUKlal2mmqq8mlLjy9lTQpI9JOLDNzLXGc4BAAAAoFgRTBQo0+2Sb3KTfJOb\ntnqebVmK9vgVC/Qp2tOrcNsWRXt6FevxKxYMKtYXlNUXX8b6grICaet9wbRzQooF+hRu35I6ljXJ\n8MPjlul2y/TGl4bbJYfHLcPtlulxyfR4ZLpd8XOS53pc8SCkzBcPStxOGQ6nDKdDhtMZ33YnznW7\nZLpdMlyuzHWXM2O/4XLKcDgGhQdel0PTxpVp2riyQS+hNxRN9bBY3urXx1sCaukJ6a31XRmBhSR5\nHKbqy93apdqbmA8jPvFmc5VPZW7HWN7ItE408YkwTVOyLCbBBAAAAFC8CCaKnGGaclVXylVdKTWO\nl/bITrm2bcsKhhQL9ocWVjgcXw+GZAWDivr7FOuNhyLxr2B86Q8kzgmlyogFgrJCIVmhsKxQRNEe\nf3w9HJYdiW67QtlmGBnBheF2yUwFGvFgxHC7ZDqd8SDDGQ83Gj1uNXnjAYrhcCgqQzGnU+3+sHoi\nljrDlnqjlnqitvpsUx84TK10OGQZpmzTlM/j0rgKr+oqPKqp8KiyzKOaCq/GlXvlcjtkmA4ZpiE5\nHDJMU4bDjE+G6jDjxxymZJqZx1xOGTJkmIYMh0My4suB56bWk+UNEc4AAAAAwI5GMIEhGYleDg6f\nV6rN7b1sy0qEFJF4+JEMLEIRxUJhWcGgYolwxI7FZEeiqaUViV9jh6PxayOR+HokkliPyIpEEwFI\nRFY4Gl8OWLfDEVnhiKI9vfF6JOoja+QTuZQnvrZHSNKmxFe+GA6HZBoyzMTS4UgFG0YyEHEmg47E\nfmcyOOk/NxWmJIOQ9GtT58aXcpjxUMQ040GKYcQDE8OQDPWva8C2odR1RuKYzERZ6dfKGFzWcNcm\n6+ZMfBlm4v3ov06GkbjGSNRXqfWMbSW3+6/r306rwyjLMtLOi78EY+hrE1/OunL1dAYkZdZhYFmZ\n5WaWkzqmgddtu07Dvg9SajloX2p1iPsCAACgJBFMIO8M0+wPQQqMbVnxACSaCD6CiQAlFJIdjcm2\nLNVUebWlrSd+biwmO2b1ByjRqKxIVLKS+y31BcPa4g+pKxBSb19Y3f6QuvwhdQdCCoZjMmxLhm3L\ntOJLw7ZkWJbKHIbKXaYqHIZ8DsnnMOU1JY8peUxDLtnxIMWy4nWx4veTFZNt2Yl1K1VPWbbsxDHF\nEuck6qnka0i+HismKxKVbYUHH0tb354gBxiTtODCSAs0Bu0fOP9K+mZa2DEo+EjfHnQs/dBWzpMx\n9KER3mtwnUZWP0Mjq/uIXn8q3DOHeHlbeR1D1Stt2+EwFUv/vNjKezd0/bavLlv/9x14aOt1Sc7t\nY9t2fNVO3x5mYuLh3uvtqsf2sbcygfa2GKZjiH+TLBpJ3UZc/22fN6L3Iu0Up9NUNDrMz7MRlDWy\n+43w9W1n3VHaHE5TseHa5vYaybf4EJ9Xqc+mIT6/+j+3hvicS10/RPlDfd6kt/0B3wcZm1s5L/2b\nI+P7ctBpw51nD3ueMk4b/r7DXbN997KHPW3AmzFsecO+/q2eN7L3zPS6ddSbz2qsCCaArTBMU4Yn\nPrRD5YPnnpCkcQ2VsrL06KZQ1FJHIKz2voha/WFt6glpY29IG3tCWtkbUqs/LGuYX0CcpqFan0v1\nZa7UE0nqylwaV+ZSnc+dWLpU7s7tEI5kIBIPP6x4gBOL9Qc3iUAn8YiReDBi27JtS0qt25nr6j9v\n4HWpc+zMa/v/SBj+2uQ9U+FKJNp/LFHH9LJlK1Wf/vtpQD3Uf9+064YsSwNfQ3bL8nld6guE+svS\nwPcrrSzLSpyyjTppYB3T3pf099weWFbmdrw2yXopc3/6H3ip91z9+5R+btp+e5j9qcY59A/vQb9Q\njOoH9vBl2MPcd3AZw12zlXsNqt9wG4NusJV7pa3GrP7v2WFvNNT2wMOZO0wjPj/OSMoa9DK29h4O\nca+t/TK17WuHKCvVqymtZ096kDPg83VEvwxuq52N8jN7NJ/1/Z+hVo57Ko2g7JHefgT1HNFrSfvD\naXC72777jejljfT9HdF59CrbGZim0f/ZOSYjKGOonxP24GMZP7sHHctcGfJn6MDzRxrCZyYcQ64O\nvG6r/5kwTFC81WB7uP+AGFTeCF/HCMswBifZQ5ex1XsN7q2aLHv492no+5oet7KBYAIoIB6nqcYq\nrxqrhu49ErUstfrD2twbVnsgrPZARO2BsNoCEbX74/uWt/ll2f6t3mNChVsTKjyaWOFRfblb4yvc\nmlDuUUOFWzVelxzm6H/BSc1j4eLjJd943jkKFW0ThYz2iUJF20ThGntPHv5yAIqI0zTVWOlVY+Xw\nw15ilq2uYETtgYg6+uLBRUdivSMRZmzsCWlN5/BPXqlwO1TjdWlcebL3hUv1iV4Y9Yl9Yw0wAAAA\nAEAimABKjsM0VFfmVl3Z1rtV9YSi2pwYHrLZH9bm3pA2+8PqDkbVFYxqS19E67qHDy9MQ4mhI27V\nl7s1qdKjxiqvKtwOVXtdqvU5VVfmVplrLI9IBQAAAFDqCCaAnVSlx6lKj1PTxg3/LJFw1FJ7X7yX\nRZu/f/hIW3IYiT+sVe0BLW8bfuhIpcehOp9btT6X6spcqvW5VON1qtrrUnXassbrksdp5uKlAgAA\nAChgBBMAhuV2bnvoiGXb6uyLaH13UJt7w+oJRdUVive4aPOH1ZoINFZ39m3zfl6nqRqvS1VeZ0Z4\nUTMwxPA5Ve1xyU2QAQAAABQ9ggkAY2IaIxs6kux90dkXVVcwoq5gVJ2JZf92fP3DjoCiI5h1jUZt\nSgAAGHVJREFU2uc0Ve1zqdrjVI0vEV54nKpO65VR6XGo0u1Urc8lL8NKAAAAgIJDMAFgh+jvfbHt\nc23bViASGxBexEOLzr6oukLxZXcwoq5QVKvaA4pt9dluceUuhyo9TlV4HCp3O1ThdqoqEWrUePuX\n8TkyXKrI8aNVAQAAABBMAChAhmGo3O1UudupScM8OjWdbdvyh2OpHhfpvTB6wlH1JCbzbO+LqDcU\n1bquiILRbT/WyGEY8WEj3szgIr6M99CoTVt3ORhaAgAAAGwvggkARc8wDFV4nKrwONVcve0gQ5Ki\nliV/OJYKMbYkhph0BqPq7IuoMxhJLKPa0BPUhx3bDjKSTySp8cXnxUiGGNVel6o8TlV5nap0x3ts\nVLqd8rlMemQAAABgp0cwAWCn5DRNVXtNVXtdknzbPD8YTQwtSYQV8TAjknq0anLYSWdfRBt6ghrB\nFBkyDanC7VSF2xEPVhLDSyo9/dvJp6dUeZwqdztU5ooPQ/G5HDIJNQAAAFACCCYAYAS8Toe8FQ5N\nqPBs89yYZasnlAwq4nNidCfmyegNR9UTiqk3HFVvOKbeUHzZ6g8oMpI0IyEZalR5nKr0OlWZFmJU\nJkKNiZt7FQ1GUmFG+pJhJwAAACgUBBMAkGUO04jPQ+FzSbUjvy4UteKBRSimnnBUvaF4iNETiqo7\nFJU/EpM/HJU/HFNvOKaeYHx/ywh7aKRzmYZ8Lod8LlNep0NlLlNelyM1QWhZ4lj8nHiYUeZyqMKd\nmDg0EX4QcAAAAGCsCCYAoEB4nKY8TrfGlW3fdbZtqy9iqTsUVU8omgg1YnJ4XdrU4VcgHFMgEpN/\nwLIvElMwaqk9ENbaSGy7ww1JcpqGyt3xQKMsMRSlLLGd3ktj8LozdZ3bSbgBAACwMyOYAIAiZxiG\nytzxYGBiZf9Qk4aGSrW29oyoDNu2FYnZ8kfiPTT6IjH1RS0FwjH1RWMZ4Ua8x0Z8CIo/HD/mj8TU\nHogoFNv2JKEDOU0j1SOjzGXK53akbTtSvTe8Lod8zsFLn8shb9qSXhwAAADFhWACACDDMOR2GnI7\nTdX6XKMuJxKzFIj0hxXJICMVakRiCiSGo/iT56WFHxt746HIKDpvpDhNIyOo2NayzOWQ12XK5xy8\nTA51cTsMnqACAACQIwQTAICscTlMVTuSTzsZHcu2FYxYqeEm8aWlvmh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       "text": [
        "<matplotlib.figure.Figure at 0x10f4b1390>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The point that red line(test) is equal to blue line(training) is our sweet spot. We want to minimize the training error but also do not overfit. Test error stays same after 600-700 boosting iterations even if training error decreases up to 3000. Training error decreases because we are overfitting after 700 boosting iterations. That is also obvious that test error slightly increasee after 700. In general, both classification and also regression, in order to prevent overfitting, one not only looks at the success rate of the classifier/regressor in the training data but also in the test data as well."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get Feature Importance from the classifier\n",
      "feature_importance = classifier.feature_importances_\n",
      "# Normalize The Features\n",
      "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
      "sorted_idx = np.argsort(feature_importance)\n",
      "pos = np.arange(sorted_idx.shape[0]) + .5\n",
      "plt.figure(figsize=(16, 12))\n",
      "plt.barh(pos, feature_importance[sorted_idx], align='center', color='#7A68A6')\n",
      "plt.yticks(pos, np.asanyarray(california_housing_feature_names)[sorted_idx])\n",
      "plt.xlabel('Relative Importance')\n",
      "plt.title('Variable Importance')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9+vXMqKP2qPYYAJsMoSNJS9OSNC1sq/YYAAAAwHpyjw4AAACgGEIHAAAAUAyh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\nDh0DBw7M1Vdf3fJ4/vz5aWhoyIABA94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       "text": [
        "<matplotlib.figure.Figure at 0x10f848990>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In here I am plotting the relative importances of the features as RF could estimate which feature could play more important role than other features. This part generally is done in explaratory data analysis part but it is nice to have at least some idea on how classifier treats the features as well. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Classifiers both in terms of their architecture but also capabilities differ. If you do not know beforehand which feature is important to predict the outcome of the particular class, you may want to choose a classifier which has both feature selection capability(which could score the features based on how useful it is or minimizing least squared error like RandomForest does in this case). Some of the classifiers support class weighting(if one class is more important than the other one) through `class_weight` parameter (e.g.: Suppor Vector Classifier, SVC). When you want to choose a classifier, apart from its merits, these out of the box capabilities could also influence your thinking and make your decision about which classifier is best."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms',\n",
      "            ('AveOccup', 'HouseAge')]\n",
      "fig, ax = ensemble.partial_dependence.plot_partial_dependence(classifier, X_train, features, \n",
      "                                                              feature_names=california_housing_feature_names, \n",
      "                                                              figsize=(16, 12));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/bugra/anaconda/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n",
        "  if rotation in ('horizontal', None):\n",
        "/Users/bugra/anaconda/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n",
        "  elif rotation == 'vertical':\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Cm91mcEIRcWbqaBFxRZUOCT799FO++OILAgICqF27Np9//jkff/xxdWQTqTa/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\nERE5S0UCEREREREREQHg/wMi/XCGFeVlEAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fb8f610>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this part, one could also look at the partial dependence plots to see which feature influence more and also how tdoe they disribute across different feature categories."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I made an intentional mistake in this notebook. \n",
      "\n",
      "- Can you think of what might go wrong in the feature set? \n",
      "- Are features scaled? If not, can scaling help? Why?\n",
      "- What type of scaling would be useful to preprocess the targer variable(price of house)? Why?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### TODO\n",
      "- Change the `target` variable to `np.log(target)` for prediction and do a similar feature importance and deviance plot for that target variable.\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}