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  •     "    # for whatever reason \"real\" text labels work\n",
        "    return \"no\" if value == 0 else \"yes\"\n",
    
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        "\n",
        "for_plot[\"is_yummy\"] = for_plot[\"is_yummy\"].apply(translate_label)\n",
        "\n",
    
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        "sns.pairplot(for_plot, hue=\"is_yummy\", diag_kind=\"hist\");"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "\n",
        "- Points and colors don't look randomly distributed.\n",
        "- We can see that some pairs like `darkness` vs `bitterness` seem to carry information which could support building a classifier.\n",
        "- We also see that `bitterness` and `fruitiness` show correlation.\n",
        "\n",
        "Features which show no structure can also decrease performance of ML and often it makes sense to discard them.\n"
    
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      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "### 3. Prepare data: split features and labels"
    
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       ]
      },
      {
       "cell_type": "code",
    
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       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
          "# INPUT FEATURES\n",
    
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          "   alcohol_content  bitterness  darkness  fruitiness\n",
          "0         3.739295    0.422503  0.989463    0.215791\n",
          "1         4.207849    0.841668  0.928626    0.380420\n",
          "2         4.709494    0.322037  5.374682    0.145231\n",
          "3         4.684743    0.434315  4.072805    0.191321\n",
          "4         4.148710    0.570586  1.461568    0.260218\n",
    
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          "\n",
    
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          "0    0\n",
          "1    0\n",
          "2    1\n",
          "3    1\n",
          "4    0\n",
    
          "Name: is_yummy, dtype: int64\n",
          "...\n",
          "(225,)\n"
    
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         ]
        }
       ],
       "source": [
    
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        "# all columns up to the last one:\n",
        "input_features = beer_data.iloc[:, :-1]\n",
        "\n",
        "# only the last column:\n",
    
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        "labels = beer_data.iloc[:, -1]\n",
        "\n",
    
        "print('# INPUT FEATURES')\n",
    
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        "print(input_features.head(5))\n",
    
        "print('...')\n",
        "print(input_features.shape)\n",
    
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        "print()\n",
    
        "print('# LABELS')\n",
        "print(labels.head(5))\n",
        "print('...')\n",
        "print(labels.shape)"
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "### 4. Start machine learning using `scikit-learn`"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "Let's finally do some machine learning starting with the so called `LogisticRegression` classifier from `scikit-learn` package. The intention here is to experiment first. Details of this and further ML algorithms are not necessary at this point, but do not worry, they will come later during the course.\n",
    
        "\n",
        "<div class=\"alert alert-block alert-info\">\n",
        "<i class=\"fa fa-info-circle\"></i>\n",
    
        "<code>LogisticRegression</code> is a classification method, even so the name contains \"regression\"-as the other group of unsupervised learning methods. In fact, in logistic regression method the (linear) regression is used internally and the result is then transformed (using logistic function) to probability of belonging to one of the two classes.\n",
    
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       ]
      },
      {
       "cell_type": "code",
    
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       "metadata": {},
       "outputs": [
        {
         "data": {
          "text/plain": [
    
           "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
    
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           "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
           "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
           "          verbose=0, warm_start=False)"
          ]
         },
    
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         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
    
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression()\n",
        "classifier"
    
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       ]
      },
    
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "<div class=\"alert alert-block alert-warning\">\n",
        "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Built-in documentation</strong>\n",
        "\n",
        "If you want to learn more about <code>LogisticRegression</code> you can use <code>help(LogisticRegression)</code> or <code>?LogisticRegression</code> to see the related documenation. The latter version works only in Jupyter Notebooks (or in IPython shell).\n",
        "</div>"
    
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      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "<div class=\"alert alert-block alert-warning\">\n",
        "<i class=\"fa fa-warning\"></i>&nbsp;<strong>`scikit-learn` API</strong>\n",
    
        "In <code>scikit-learn</code> all classifiers have:\n",
        "<ul>\n",
        "    <li>a <strong><code>fit()</code></strong> method to learn from data, and</li>\n",
        "    <li>and a subsequent <strong><code>predict()</code></strong> method for predicting classes from input features.</li>\n",
        "</ul>\n",
        "</div>"
       ]
      },
    
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      {
    
       "cell_type": "code",
       "execution_count": 15,
    
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       "metadata": {},
    
       "outputs": [
        {
         "ename": "NotFittedError",
         "evalue": "This LogisticRegression instance is not fitted yet",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
          "\u001b[0;32m<ipython-input-15-9e1ed3d39774>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Sanity check: can't predict if not fitted (trained)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
          "\u001b[0;32m~/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \"\"\"\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m~/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coef_'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             raise NotFittedError(\"This %(name)s instance is not fitted \"\n\u001b[0;32m--> 298\u001b[0;31m                                  \"yet\" % {'name': type(self).__name__})\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mNotFittedError\u001b[0m: This LogisticRegression instance is not fitted yet"
         ]
        }
       ],
    
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       "source": [
    
        "# Sanity check: can't predict if not fitted (trained)\n",
        "classifier.predict(input_features)"
    
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       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 16,
    
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       "metadata": {},
    
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "(225,)\n"
         ]
        }
       ],
    
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       "source": [
    
        "# Fit\n",
        "classifier.fit(input_features, labels)\n",
        "\n",
        "# Predict\n",
        "predicted_labels = classifier.predict(input_features)\n",
        "print(predicted_labels.shape)"
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "Here we've just re-classified our training data. Lets check our result with a few examples:"
    
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       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 17,
    
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       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
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          "0 0\n",
          "0 1\n",
          "1 1\n",
          "1 1\n",
          "0 0\n"
    
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         ]
        }
       ],
       "source": [
    
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        "for i in range(5):\n",
        "    print(labels[i], predicted_labels[i])"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "This looks suspicious !\n",
        "\n",
        "Lets investigate this further:"
    
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       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 18,
    
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       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "225 examples\n",
    
          "187 labeled correctly\n"
    
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         ]
        }
       ],
       "source": [
        "print(len(labels), \"examples\")\n",
        "print(sum(predicted_labels == labels), \"labeled correctly\")"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "<div class=\"alert alert-block alert-info\">\n",
        "<i class=\"fa fa-info-circle\"></i>\n",
        "<code>predicted_labels == labels</code> evaluates to a vector of <code>True</code> or <code>False</code> Boolean values. When used as numbers, Python handles <code>True</code> as <code>1</code> and <code>False</code> as <code>0</code>. So, <code>sum(...)</code> simply counts the correctly predicted labels.\n",
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
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        "\n",
    
        "Why were not  all labels  predicted correctly?\n",
    
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        "\n",
        "Neither `Python` nor `scikit-learn` is broken. What we observed above is very typical for machine-learning applications.\n",
        "\n",
    
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        "\n",
    
        "- we have incomplete information: other features of beer which also contribute to the rating (like \"maltiness\") were not measured or can not be measured. \n",
    
        "- the used classifiers might have been not suitable for the given problem.\n",
    
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        "\n",
    
        "- noise in the data as incorrectly assigned labels also affect results.\n",
    
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        "\n",
    
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        "\n",
    
        "**Finding good features is crucial for the performance of ML algorithms!**\n",
    
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        "\n",
    
        "\n",
        "Another important requirement is to make sure that you have clean data: input-features might be corrupted by flawed entries, feeding such data into a ML algorithm will usually lead to reduced performance."
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "# Exercise section 1"
    
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       ]
      },
      {
    
       "cell_type": "markdown",
    
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       "metadata": {},
    
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       "source": [
    
        "<div class=\"alert alert-block alert-danger\">\n",
    
        "<strong>TODO:</strong> I propose to start separate excercise session 2 w/ SVC here (so if someone is stuck on previous, he/she can skip).\n",
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "### 1. Compare with alternative machine learning method from `scikit-learn`"
    
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       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "Now, using previously loaded and prepared beer data, train a different `scikit-learn` classifier - the so called **Support Vector Classifier** `SVC`, and evaluate its \"re-classification\" performance again.\n",
    
        "\n",
        "<div class=\"alert alert-block alert-info\">\n",
        "<i class=\"fa fa-info-circle\"></i>\n",
    
        "<code>SVC</code>  belongs to a class of algorithms named \"Support Vector Machines\" (SVMs). Again, it will be discussed in more detail in the following scripts.\n",
    
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       ]
    
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      },
      {
       "cell_type": "code",
    
       "execution_count": 19,
    
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       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
          "225 examples\n",
          "205 labeled correctly\n"
    
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         ]
        }
       ],
       "source": [
    
        "from sklearn.svm import SVC\n",
        "# ...\n",
    
        "# REMOVE or HIDE the following lines in the target script\n",
    
        "classifier = SVC()\n",
        "classifier.fit(input_features, labels)\n",
        "\n",
        "predicted_labels = classifier.predict(input_features)\n",
        "\n",
        "assert(predicted_labels.shape == labels.shape)\n",
        "print(len(labels), \"examples\")\n",
        "print(sum(predicted_labels == labels), \"labeled correctly\")"
    
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       ]
      },
    
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      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
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        "\n",
    
        "<div class=\"alert alert-block alert-info\">\n",
        "<i class=\"fa fa-info-circle\"></i>\n",
    
        "Better re-classification in our example does not indicate here that <code>SVC</code> is better than <code>LogisticRegression</code> in all cases. The performance of a classifier strongly depends on the data set.\n",
        "</div>\n",
        "\n",
        "\n"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "### 2. Experiment with (hyper)parameters of ML methods"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "Both `LogisticRegression` and `SVC` classifiers have a parameter `C` which allows to enforce a \"simplification\" (often called **regularization**) of the resulting model. Test the beers data \"re-classification\" with different values of this parameter.\n",
        "\n",
        "\n",
        "**TOBE discussed**: is \"regularization\" to technical here ? decision surfaces and details of classifers come later. Original purpose (Uwe) was to demonstrate that classifiers can be tuned to the data set."
    
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       ]
      },
    
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      {
       "cell_type": "code",
    
       "execution_count": 20,
    
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       "metadata": {},
    
       "outputs": [],
       "source": [
    
        "# Recall: ?LogisticRegression\n",
    
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "<div class=\"alert alert-block alert-danger\">\n",
        "<strong>TODO:</strong> prepare a solution.\n",
    
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        "\n",
        "**TODO**: Consider the case C=2 as this is used when describing overfitting. Or: if we find a better C here, don't forget to adapt the examples in the overfitting script.\n",
    
        "</div>"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "# Exercise section 2 (optional)"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "<div class=\"alert alert-block alert-danger\">\n",
        "<strong>TODO:</strong> finish solution - missing classification and \"re-classification\" assesment.\n",
        "</div>"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "Load and inspect the cannonical Fisher's \"Iris\" data set, which is included in `scikit-learn`: see [docs for `sklearn.datasets.load_iris`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html). What's conceptually diffferent?\n",
    
        "Apply `LogisticRegression` or `SVC` classifiers. Is it easier or more difficult than classification of the beers data?\n",
    
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       ]
      },
    
       "execution_count": 21,
    
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       "metadata": {},
    
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       "outputs": [
        {
    
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         "output_type": "stream",
         "text": [
    
          "['setosa' 'versicolor' 'virginica']\n",
          "(150, 4)\n"
    
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         ]
    
        }
       ],
       "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "data = load_iris()\n",
        "\n",
        "# labels as text\n",
        "print(data.target_names) \n",
        "\n",
        "# (rows, columns) of the feature matrix:\n",
    
       "execution_count": 22,
    
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        {
         "data": {
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           "<div>\n",
           "<style scoped>\n",
           "    .dataframe tbody tr th:only-of-type {\n",
           "        vertical-align: middle;\n",
    
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           "    }\n",
    
           "\n",
           "    .dataframe tbody tr th {\n",
           "        vertical-align: top;\n",
    
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           "    }\n",
    
           "\n",
           "    .dataframe thead th {\n",
           "        text-align: right;\n",
    
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           "    }\n",
    
           "</style>\n",
           "<table border=\"1\" class=\"dataframe\">\n",
           "  <thead>\n",
           "    <tr style=\"text-align: right;\">\n",
           "      <th></th>\n",
           "      <th>sepal length (cm)</th>\n",
           "      <th>sepal width (cm)</th>\n",
           "      <th>petal length (cm)</th>\n",
           "      <th>petal width (cm)</th>\n",
           "      <th>class</th>\n",
           "    </tr>\n",
           "  </thead>\n",
           "  <tbody>\n",
           "    <tr>\n",
           "      <th>0</th>\n",
           "      <td>5.1</td>\n",
           "      <td>3.5</td>\n",
           "      <td>1.4</td>\n",
           "      <td>0.2</td>\n",
           "      <td>0</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>1</th>\n",
           "      <td>4.9</td>\n",
           "      <td>3.0</td>\n",
           "      <td>1.4</td>\n",
           "      <td>0.2</td>\n",
           "      <td>0</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>2</th>\n",
           "      <td>4.7</td>\n",
           "      <td>3.2</td>\n",
           "      <td>1.3</td>\n",
           "      <td>0.2</td>\n",
           "      <td>0</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>3</th>\n",
           "      <td>4.6</td>\n",
           "      <td>3.1</td>\n",
           "      <td>1.5</td>\n",
           "      <td>0.2</td>\n",
           "      <td>0</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>4</th>\n",
           "      <td>5.0</td>\n",
           "      <td>3.6</td>\n",
           "      <td>1.4</td>\n",
           "      <td>0.2</td>\n",
           "      <td>0</td>\n",
           "    </tr>\n",
           "  </tbody>\n",
           "</table>\n",
           "</div>"
          ],
          "text/plain": [
           "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
           "0                5.1               3.5                1.4               0.2   \n",
           "1                4.9               3.0                1.4               0.2   \n",
           "2                4.7               3.2                1.3               0.2   \n",
           "3                4.6               3.1                1.5               0.2   \n",
           "4                5.0               3.6                1.4               0.2   \n",
           "\n",
           "   class  \n",
           "0      0  \n",
           "1      0  \n",
           "2      0  \n",
           "3      0  \n",
           "4      0  "
          ]
         },
    
         "execution_count": 22,
    
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "# transform the scikit-learn data structure into a data frame:\n",
        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "df[\"class\"] = data.target\n",
        "df.head()"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 23,
    
       "metadata": {},
       "outputs": [
        {
         "data": {
          "text/html": [
           "<div>\n",
           "<style scoped>\n",
           "    .dataframe tbody tr th:only-of-type {\n",
           "        vertical-align: middle;\n",
    
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           "    }\n",
           "\n",
    
           "    .dataframe tbody tr th {\n",
           "        vertical-align: top;\n",
    
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           "    }\n",
    
           "\n",
           "    .dataframe thead th {\n",
           "        text-align: right;\n",
    
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           "    }\n",
    
           "</style>\n",
           "<table border=\"1\" class=\"dataframe\">\n",
           "  <thead>\n",
           "    <tr style=\"text-align: right;\">\n",
           "      <th></th>\n",
           "      <th>sepal length (cm)</th>\n",
           "      <th>sepal width (cm)</th>\n",
           "      <th>petal length (cm)</th>\n",
           "      <th>petal width (cm)</th>\n",
           "      <th>class</th>\n",
           "    </tr>\n",
           "  </thead>\n",
           "  <tbody>\n",
           "    <tr>\n",
           "      <th>count</th>\n",
           "      <td>150.000000</td>\n",
           "      <td>150.000000</td>\n",
           "      <td>150.000000</td>\n",
           "      <td>150.000000</td>\n",
           "      <td>150.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>mean</th>\n",
           "      <td>5.843333</td>\n",
           "      <td>3.054000</td>\n",
           "      <td>3.758667</td>\n",
           "      <td>1.198667</td>\n",
           "      <td>1.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>std</th>\n",
           "      <td>0.828066</td>\n",
           "      <td>0.433594</td>\n",
           "      <td>1.764420</td>\n",
           "      <td>0.763161</td>\n",
           "      <td>0.819232</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>min</th>\n",
           "      <td>4.300000</td>\n",
           "      <td>2.000000</td>\n",
           "      <td>1.000000</td>\n",
           "      <td>0.100000</td>\n",
           "      <td>0.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>25%</th>\n",
           "      <td>5.100000</td>\n",
           "      <td>2.800000</td>\n",
           "      <td>1.600000</td>\n",
           "      <td>0.300000</td>\n",
           "      <td>0.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>50%</th>\n",
           "      <td>5.800000</td>\n",
           "      <td>3.000000</td>\n",
           "      <td>4.350000</td>\n",
           "      <td>1.300000</td>\n",
           "      <td>1.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>75%</th>\n",
           "      <td>6.400000</td>\n",
           "      <td>3.300000</td>\n",
           "      <td>5.100000</td>\n",
           "      <td>1.800000</td>\n",
           "      <td>2.000000</td>\n",
           "    </tr>\n",
           "    <tr>\n",
           "      <th>max</th>\n",
           "      <td>7.900000</td>\n",
           "      <td>4.400000</td>\n",
           "      <td>6.900000</td>\n",
           "      <td>2.500000</td>\n",
           "      <td>2.000000</td>\n",
           "    </tr>\n",
           "  </tbody>\n",
           "</table>\n",
           "</div>"
    
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          ],
          "text/plain": [
    
           "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
           "count         150.000000        150.000000         150.000000   \n",
           "mean            5.843333          3.054000           3.758667   \n",
           "std             0.828066          0.433594           1.764420   \n",
           "min             4.300000          2.000000           1.000000   \n",
           "25%             5.100000          2.800000           1.600000   \n",
           "50%             5.800000          3.000000           4.350000   \n",
           "75%             6.400000          3.300000           5.100000   \n",
           "max             7.900000          4.400000           6.900000   \n",
           "\n",
           "       petal width (cm)       class  \n",
           "count        150.000000  150.000000  \n",
           "mean           1.198667    1.000000  \n",
           "std            0.763161    0.819232  \n",
           "min            0.100000    0.000000  \n",
           "25%            0.300000    0.000000  \n",
           "50%            1.300000    1.000000  \n",
           "75%            1.800000    2.000000  \n",
           "max            2.500000    2.000000  "
    
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          ]
         },
    
         "execution_count": 23,
    
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         "metadata": {},
         "output_type": "execute_result"
        }
       ],
    
       "source": [
        "df.describe()"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 24,
    
       "metadata": {},
       "outputs": [
        {
         "data": {
    
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rvV8/FhdVanYMzz8wUsUzBjrZtscvnoSKax7DcPyYiWUAEDvRyFu+/FB3ZJe2NG/XNWVXBswnwWL64Dzwuz3VtusAAAAYqVw92fHII49oy5YtKiws1Nq1a8MuRr5//3719PQoPz9f5513niTps5/9rNatW6ennnpKVVVVuvTSSyWdfTXW1772NZ08eVKf+9znNH78+KgfD9zNNE119nTp/po12t3aOGBbzb6N/Z+6ykrLjPrDiGma6opRX3xtvfPhe7qk8EL9Q+VN/dtaTrXp1T0bdPnkS2Ny3L76Z+WXaXZB+YA++m8H4Lx4xsBotO17PdUF+TNU4RdPfJ/29U10ROOYiWUAEH3RiOHec3U+4Ffn+sZ3tLSkSktLqgaMk0PF9GB5YHBOIi8AAAAM5NrJjmPHjmnt2rWSpHHjxuk73/lO0LIPPvigJOnGG2/UwYMHdf/99/cvJrZgwQLddNNNeuqpp/Q3f/M3mjNnjsaOHas//OEPOnbsmObOnas77rgj+gcE1zMMI+DDks/u1kbdX7NG9y35alL1xb8tI8XQpOxCZaVlqrOnSwc6DsnsM7W+qTYmx+0z+B36PAAC0RfPGBjNtj2GZ9DPAz99G81jJpYBQPREI4Z7DGPARIcknew+pRfqf6sXd6zTpOxCzcibppvnft5STB+cBwbnJPICAADAQK6d7Ni0aZNOnz4tSaqrq1NdXV3Qsr7JjmBWrVqliy66SD/72c+0fft2maapKVOmaMWKFfriF7+otLQ0R/uO5OM1TdUd2Rn0Yclnd2ujtjXX64L8sqg9nMSyL4PbMvtM7T9+MCptAUhc8YyB8Wo7keI+AMCeaMRwr+lV3ZFdQev0jZP3Hz+oykmX6IL8siGTGQAAABge1052LFu2LOii4sG88cYbQbddffXVuvrqq4fbLYxQHsNQdVOtpbLrm2oHfB3dzX1JpOMGED/xjAXxapv4BwDuFY0Y7jE8luusbqod8DoqAAAAOIOPkgAOaT/d4Wi54YhlXxLpuAHETzxjQbzaJv4BgHtFI4aTFwAAAOKLyQ7AITkZ2Y6WG45Y9iWRjhtA/MQzFsSrbeIfALhXNGI4eQEAACC+mOwAHOA1TS0qqrRUdnFRpUzTTIq+JNJxA4ifeMaCeLVN/AMA94pGDPeaXst1LiqqlJe8AAAA4DgmOwAHeAxDFQXlKs0tDlmuNLdYswvKo7pIbSz7kkjHDSB+4hkL4tU28Q8A3CsaMdxjeCzXWVFQzuLkAAAAUcAIC3CIaZpaXbUy6ANOaW6xVletjMmne2PZF/+2jBRDU3MmambedE3NmSgjxYjpcQOIn3jGwHjFIeIfALhXqBieaqSqaup83bnwH2zFcK9palWYXLiqaiXf6gAAAIiS1Hh3AEgWhmEoKy1T9y35qrY112t9U63aT3coJyNbi4sqNbugXKZpRuXTvV7THPDpsD4pbF+8g/oyuA6r/I/7TG+30lNH9W/z/WyapvpC9NlreuUxPH7bBv7sVsl6XEgug+99J2KB0zEw3L1kNQ4NjHnDvz8jaRcAkBhCxfBwOcI3tg2UP311bm2uV7VfLlxUVKmKc2Nwj2GEzb+B8rFTOXs4EqEPAAAAwTDZATjI9wetWfllml1Q3v/vvk+EOf0HL98f0eqO7Az4MGWa5pC++D5JtuPobr3R+HbAfSLt586WhoD9kKTdrXv1akNNwPZ2tzYG3ObWhydfv+uO7Ar5kAvEk5X4YTcWOB0DI7mXQsWhSOu0Ily7AIDEtbOlQVuat+vasiUam3l+0BzRfrpDnhRD56WPCTqZkXKuzgvyZwzKP17tbz+oF3esk6QheSdUPpbkeM62KxrjBgAAAKcx2QFEweA/lEVj4G+apjp7unR/zRrtbm0csK1m38b+16dkpWUO2KfL4j5W+2ylH3dU3aKxmTnafKhOJ7tPWd626lxf3DQx4D13Ph4IcT7ceFxILnbiRyTxy4kYaOdeSpEsHU9mWqaj92e0zyMAIHr8Y/ihE0f0wNJV8qQYuvO17wWM6RUF5VpVdau6es4ELeMf9z2GR6Zpqtvbo1/Vv6KXd76uXrO3v/zmQ3V68FNfV5qRFjIvra5aqYy0dH2n+sfa2lwfss1o5RryHQAAcAtGIoBLGYYR8IHDZ3dro+6vWTPggSOSfZzox3drHtOEMXlaWlJla9sDNWtcNyHgMYyAD6w+bj0uJJdoxAKn2bmXrBzPOwfed/z+dMN5BAAE5h/Dl01fqPwxefrum48FjenTxxUp1UgNm0f8475hGPp29cN6sf53AyY6JGnZ9IUal5ljqb5UI1UlY6daajMayHcAAMAtGI0ALuQ1TW1trg/6wOGzu7VR25rrZZpmRPs42Y+tzfVaWlIlI8Wwvc0tizh6Ta+t8+GW40JyiUYscJqde2lbc73O9J5RQ9u+oOWMFEOXFF7g6P3phvMIAAjMP4YbKYaWlCzQB83bg8Z0K2V8fHE/VJ6wW1+wsfLgNqORa8h3AADATZjsAFzIYxiqbqq1VHZ9U60Mw4hoHyf7Ud1Uq7zR4zQpu9D2Nrd8C8JjeGydD7ccF5JLNGKB0+zcS+ubapWemh4wfvhMPr9QeVnjHL0/3XAeAQCB+cdwX47Y0PRu0PJWyvhbfy6P/PHITkfqCzVW9m8zGrmGfAcAANyEkQjgUu2nO2yXi2Qfp/vhv4aInW1uEY1zDDjNDdepE7HFJzM1M6I6Y10OABA7vthsJUdEmke6vT2O1hcq10Uz15DvAACAWzDZAbhUTka27XKR7ON0Pzp7uiLa5hbROMeA09xwnToRW3y6ersiqjPW5QAAseOLzVZyRKR5ZJQnzdH6QuW6aOYa8h0AAHALJjsAF/KaphYVVVoqu7iosn/NDrv7ONmPRUWVajnVpgMdh2xvc8vaFl7Ta+t8uOW4kFyiEQucZudeWlxUqTO9ZwLGD58Pjx9SS2ebo/enG84jACAw/xjuyxELiy4LWt5KGX+Lz+WRC/PLHKkv1FjZv81ordlBvgMAAG7BZAfgQh7DUEVBuUpzi0OWK80t1uyC8v41O/z3MVIMTc2ZqJl50zU1Z2L/gof++zjZj4qCcr3aUCOzz7S9zS1rW3gMj63z4ZbjQnKJJH7Emp17aXZBudJT01UybmrQcmafqc2H6hy9P91wHgEAgfnHcLPP1GsNb+qigllBY7qVMj6+uO9rY0lJ1YCxdiT1BRsrD24zWmt2kO8AAIBbMBIBXMo0Ta2uWhn0waM0t1irq1YO+HSVb58vz/28fnzNt/X9q76ue668Td+/6uv68TXf1pfnfl6rq1YO+URzqE84W+nHHVW36PDJFq1r2GBr26oAfUl0XtPUqjDnw43HheQSSfyww04MCVWH1XvJyvFcPmmO4/dntM+j05z4vQBAsvCP4ev2bNDhky26Y8EtQWP6nrYm9Zq9YfOIL+77YuzNcz/fP9a+ftbVGjNqtCRp3Z4Nau08Zqm+XrNXDW37wrbphEC5wm35DgAAjFwpfX19ffHuxEi0fPlybdy4UfPnz9fatWvj3R24lGmaMgxD25rrtb6pVu2nO5STka3FRZWaXVDev92f1zTlMQxtba5Xtd8+i4oqVVFQLq9pqqn9Q/1m1xtDtgWqz2o/drfu1bqGmoDbdrXu1at+2/z74sZvP1g5x248rkRGTLUvkvhhtc5g177dOu3cS1aPx+n7Mxrn0WlO/16Q/IipGCn8Y/jm5u26puxKjcvMCRov2093yEgxlJ0+JmjcD5dn2rra9eudr+viglmqKChXj7dXaZ7UIeX984ikqOeacLnCd1yJnO8SVbxj6rW3vdj/3y/94LqYtw8AQCylxrsDACLne6CYlV+m2QXl/f/u/1DkzzRNdfV06f6aNdrd2jhgW82+jee+aXGrxowarc2H6nSy+9SAbaurViorLXNIvVb6MT13msrGTw+4rTS3WDP9tvk+UebGCQHfOW4+eUTl40tV4Xc+ery92tPaqIIx+QHPIxBLduNHOKZpqjNMfAkWQ4LVZ+desno8vrhyQf6MAXVGGnecPo9Oc/r3AgDJJFgMD5YjfAtwm6YZMO5bGWuvrlqpv6n4Cx3rOq5ntr6otz78g66YPFdLS6r0D5U3DajPv4+B+ulUrrGTKxI13wEAAEhMdgBJYfAf54I9bBiGEfAhxmd3a6O+W/Oo7lvyVS0tqdIL9b8dsO3+mjW6b8lXI+pH6G2ekPW4if85TjVSdWnhbOVkZqu9q0PvHdqmXrNXpbnFIc8jEEtW40c4VuJLuBgSrD4795LV43E67jh1Hp3m9O8FAJLR4BgeLkcEivG+f7Mac1/dW9M/1n6h/rd6ccc6Tcou1Iy8abp57ucDthGtXDOcXJEo+Q4AAEBizQ5gxPCaprY21wd9iPHZ3dqorc31WlpSNWAhRd+2bc31vI83iMHnuNfs1bsHN+t3e6r17sHN6jV7JXEekXzsxBcr1z73kjOc/r0AAIKzO9ZeMm3BkEXL9x8/qNcaamIak8kVAAAgmTDZAYwQHsNQdVOtpbLVTbXKGz1Ok7ILh2xb31TLJ7iCsHOOOY9IJk5f+9xLzuA8AkDsODXWlmIbk8kVAAAgmTBSAUaQ9tMdtsplpWVGXMdIZfccA8nC6Wufe8kZnEcAiB0nxtp26nEKuQIAACQLJjuAEcS3qKLVcp09XRHXMVLZPcdAsnD62udecgbnEQBix4mxtp16nEKuAAAAyYLJDmCE8JqmFhVVWiq7qKhSLafadKDj0JBti4sqeVdvEHbOMecRycTpa597yRmcRwCIHafG2lJsYzK5AgAAJBMmO4ARwmMYqigoV2lucchypbnFqigo16sNNTL7zCHbZheU867eIOycY84jkonT1z73kjM4jwAQO06MtX3bYxmTyRUAACCZMFIBRhDTNLW6amXQh5nS3GLdUXWLDp9s0bqGDUO2ra5ayae5wrByjjmPSEZOX/v+9aUaqaqcfImumr5IlZMvUaqRyr1kETEJAGLHSsxdVbVS7ac79NreNwNuj0dMJlcAAIBkkRrvDgCIHcMwlJWWqfuWfFXbmuu1vqlW7ac7lJORrcVFlZpdUC7TNHWo47DmFF4YcFuoT3N5TVMev+3+P0e6LZK2Ymlwu32SpXPMp+KQbKzGF6vXvn99XtMrj+Hp3+b7OVB9TseGRIk1kXL69wIAOGtofjibm6zG3EevuU/vH/qj3tq3Sempo4Zsj1W+8bVDrgAAAMmAyQ5ghJqRN02zC8r7fz7Te6b/v6fnTlPZ+On9P/s+xRXsAcf38FN3ZKeq/R6OFhVVquLcw9Hu1r16taGmf9vSkirNyJ0Wdr/BbVppKxYPYlb6MSu/bMA5DncegWQRKr7Y0Xfu/+uO7Ap4n/X5lXU6NiRKrHGCr5/EJAAYvrD5oc+UISNoLjx+5oRe2vmaLi6Ypcsnz9FlEy+WYRg603tG7+x/T2/u36SM1PSo55vBx3Gmt1tXTJmrOR+7kFwBAABci8kOYAQxTVOdPV26v2aNGtr2aVJ2obLSMtXZ06UDHYdUMm6qVletVFZa5oD9Qj3c+Ne5u7VxwLaafRv7X401NjNHmw/V6WT3KY0ZNVqfvfBanew5pe/WPBZ0P19ffO1baWvwPtFgpx/+eEhEMrMTX6zcC95z9T0Q4j5bda6+FMnR2JAoscZpgz8h7Ka+A0AisJIfVlWtVPeZE7pj3f3KycgekgvvWHCLrpq+SKtffUCF5+VrVdVKefu8uv2396rjzIkh9UUj3wQ7jo0Ht8hIMfTxKXO1Ys5fKyM1nVwBAABchZELMIIYhtH/UGP2mdp//KB2tOzR/uMHZfaZ2t3aqPtr1th6qPGvM5DdrY36bs1jmjAmT0tLqiRJy6YvVP6YvIATHf77De6Llbbs9j8SidIPIJE4HV88hhFwosNnd2ujHqhZI49hOH5Pco8DAAKxkh8eqFmj3KyxunLaFQFz4Xff/Ghc7Cufk5GtK6ddEbC+aOSbUMdh9pl6c99G3bfhR+Q5AADgOoxegBHCa5ra2lwf9OHMZ3dro7Y111tagNBOnVub67W0pEqpRqqWlCzQB83bbfUlGv2PRKL0A0gkTt8XXtNrqb6Gtn0609vtcNvc4wCAoSIZ9xopQx+3B2+3Ut7JfEOeAwAAyYzJDmCE8BiGqptqLZVd31Rr6ZNcduqsbqpV3uhxuvRjs5WXNU4bmt611Zdo9D8SidIPIJE4fV94DI+l+iafX6j01FEOt809DgAYKpJx76TsQkvbw5V3Mt+Q5wAAQDJj5AKMIO2nOxwtF0mdORnZEfclGv2PRKL0A0gkTt8XVsplpmbGrW075QAAycFufhi8fluw7VbLO4U8BwAAkpWjC5R3d3fr/fff16ZNm3Tw4EG1tbWpo6ND6enpysvLU1FRkS655BLNnTtXGRkZTjYNwALfRINT5SKpc/Ckh536o9H/SCRKP4BE4vR9YaVcV29X3Nq2Uw4AkBzs5ofOni5L262Wdwp5DgAAJCtHJjveeecdPfPMM6qurlZ3d7ckqa+vb0i5lJQUSVJGRoauvPJK3Xjjjbrwwgud6AKAMLymqUVFlarZtzFs2cVFlTJNM+zX1u3UuaioUi2n2vTen7appbNNC4sus9WXvnN1ONn/SETjPAJu5/R94TW9lur78Pghnentdrht7nEAwFCRjHsPdByytD1ceSfzDXkOAAAks2FNdmzatEnf//73tW3bNvX19Sk1NVWzZs1SaWmppkyZojFjxigzM1MnTpxQe3u7Dh8+rC1btmjfvn369a9/rZdfflmXX365br31Vs2dOzeiPni9Xj3zzDN64YUXtHfvXnm9Xk2ePFmf/vSntWLFCqWnp1uqp7GxUT/60Y/03nvvqb29XVOmTNENN9ygL3zhCwzukBQ8hqGKgnKV5haHXJCwNLdYswvKHa+zoqBcz2x9Ub1mr15reFN/PfsztvvidP8jEY3zCLid0/eFx/AMqM9IMTT5/EJlpmaqq7dLHx4/JLPPVMm4qUpPHeVw29zjAIChIhn3mn1DF/cevN1KeSfzTbjj8OXc0txp5DkAAOA6EU12HD9+XN/+9rf18ssvKz09XZ/+9Kd1zTXX6LLLLlNWVlbY/dva2vTWW2/phRdeUG1trd555x1df/31Wr16tcaMGWO5H16vV7feeqvWr1+vrKwsXXTRRUpNTdUHH3ygRx55RNXV1frpT3+qzMzA7z712bFjh77whS/o5MmTmjNnjmbPnq13331X9957rz744AM9+OCDlvuE5OM1TXn8Jrz8fw61LRGZpqnVVSt1f82agA83FQXl+rcrviKv6ZXH8PT/++Cf7dRZmlusO6pu0eGTLVrXsEGStG7PBn2i+OO6o+oWfbfmsaD7ra5aOeDTZFbaGrxPNER+Hq1dH267rpCYYn0dOX1/ek1Tq6pWqvbA+7qk8ALlZY3r39bS2abNh+pUOWmOvKapFMl226HOT6LEGqcRWwCMVEPjX+ixbrAxnJX8sKpqpVo7j/WPewdvv2PBR+NiK+WHk2+Cxf1AxzFm1Ggtm75QS0oWDMi55AoAAOAmKX2B3jcVxoIFC3Tq1Cl98Ytf1I033qicnJyIO9Dc3Kyf/vSn+p//+R/l5OSourra8r4///nP9c1vflNlZWV64oknNGHCBElnJ1NuvfVWbd68WTfffLNuu+22oHX09fXpuuuu086dO/W9731P1113XX8dN954o3bu3KlHHnlEV111VcTHGMjy5cu1ceNGzZ8/X2vXrnW0bjjD91Cxtble1U21aj/doZyMbC0qqlRFQblM09Su1r16taEm4LZE/QOYr2/bmuu13u+4lpVUqTR3WshjDvawE6zOxUWVmn3ufOxu3at1fufKv71Q+w0+j1baisW5H855DNZHK9dcol5X8UZM/Ug8ryNfjLAbQ5yoz2pssHp+EiXWOIHYAruIqUgW4eJfW1e7fr3zdV1cMEsVBeXq8fYqzZMaMl5KCpgffGXC1eFr85KCWZrtl8uczDdW4r7/cWxu3q5ry5ZobOb55IooiHdMvfa2F/v/+6UfXBfz9gEAiKWIJju+8Y1v6J/+6Z+Um5vrWEeam5v14x//WPfee6/lfT772c9qy5Yt+ulPf6rKysoB23bs2KHrrrtOEydO1BtvvBG0jjfffFNf+tKXAg483n//fX3uc5/TvHnz9LOf/czeAYUR7wEPQjNNU509XWG/rdDVc0arX31AJ7tPDdi2umqlstIyE/qBINAnvTp7uvRAmE+qZaVlBv1j5eA6Q32K2uo2q/2P1wNYoPPYFebaCXR9WLnm3HBdxQsx9ax4Xke+GFL74Xu6pPBC5Y32+ybGqTZtPvRHVU6+NGQMceJYQsWGSOpMlFgTKWILIkFMRTKwNJ5fcIu6es/o3upH9K1P/qvSjLSQY2H/eBnu23LhvuE7OJ84lW/sxn3fNyTJFdET75jKZAcAYCSJaKRyzz33ODrRIUkFBQW2JjokaezYsZo2bZoqKiqGbCsqKpIkHTlyJGQdNTU1kqQlS5YM2TZnzhzl5ubqvffe08mTJ231De5mGEbQwb4k7W5t1HdrHtOEMXlaWlI1ZNv9NWsS/kFg8B8bPYYR9OFOOntcD9SsCflHysHb/M9BpNsiaSuWAp3HcNdOoOvDyjXnhusK8RXP68gXQ5547xn9/ct36fbf3qtvvP4D3f7be/X3L9+lJ957JmwM8RfpsYSKDZHUmSixJlLEFgAjlaXx/Jtnx/NfvvRzGpeZE3Ys7B8vA40BB/7sCbndTv6yw27c9xgGuQIAACQNV49WHn/8cb3yyisB1wnZtm2bpLOTKKHs2bNHkjRjxoyA24uLi2WaphoaGobZW7iF1zS1tbk+5MKD0tlB/9bmei0tqZKRYgzZtq25vv8r4onOa3ptHbPXJccVa3auHf/rI9L9AH/xvI4GxxCzz9T+4we1o2WP9h8/2L/gqtUYEo1jGYn32Ug8ZgCQ7H6lkUMAACAASURBVI/ny8eX6oPm7a6Pl5HEfXIFAABIJhEtUG7F6dOnFe4NWeEWDo9UX1+fHn74YUnSsmXLQpb1ffNj/PjxAbf7/r2lpSVsu88//7xeeOEFS32sr6+3VA6x5zEMVTfVWipb3VSrf6i8SZOyC7X/+MEB29Y31Wp2QXk0uug4j+GxdcwVLjmuWLNz7fhfH5Hul+yIqfbE8zpyOoZE41hG4n02Eo8ZwRFTMZLYiX91R3aqoqBcG5retVQ+keNlpHGfXGEfMRUAgMTk6GTHSy+9pKeeekq7du2S1+sNWTYlJUXbt293svl+P/zhD7Vp0ybl5eVpxYoVIct2dXVJkjIyMgJu9/17Z2dn2HYPHjyojRs32uwtElH76Q5b5bLShk7cWa0jUdg9ZgQW6Xnk/A9FTLUvnteR021H41hG4n02Eo8ZgRFTMdJYjWtnvD22yid6vIzkOJLl2GOJmAoAQGJybLLj//7v/3THHXeE/TaHTwTrolvy8MMP6yc/+YlGjRqlhx56SOPGjQtZ3uM5+y7VlJSUkOWsfF134sSJmj9/vqV+1tfX68SJE5bKIvZyMrJtlevs6Yq4jkRh95gRWKTnkfM/FDHVvnheR063HY1jGYn32Ug8ZgRGTMVIYzWupXvSbJVP9HgZyXEky7HHEjEVAIDE5Nhkx5NPPqm+vj4tXbpUN954o/Ly8pSaGrW3ZA3R29ure+65R88++6zS09P1ox/9SPPmzQu7n+9VWqdPnw643ffvo0ePDlvX9ddfr+uvv95Sf5cvX84nQRKU1zS1qKhSNfvC/34WFVWq5VSbDnQcGrJtcVGlTNN0xUJ+XtNr65i9pml5keGRxM614399RLpfsiOm2hPP68jpGBKNYxmJ99lIPGYER0zFSGIn/l2QX6Yeb68WFl3m+ngZSdzvk8gVESCmAgCQmBwbpTQ2Nio3N1cPPfSQLr30Uk2dOlUTJ04M+T+nnDp1Sl/5ylf07LPPKjs7W08++aQWLVpkad/8/HxJwdfkOHr0qKTga3og+XgMQxUF5SrNLQ5ZrjS3WBUF5Xq1oaZ/8V3/bbMLyl3zIOAxPLaOmYmOwOxcO/7XR6T7Af7ieR05HUOicSwj8T4biccMAJL98Xz90d26qGCW6+NlJHGfXAEAAJKJYyOV0aNHa/z48f2vhYqV48ePa/ny5aqpqVFhYaGefvppS9/o8CktLZUk7dmzZ8i2vr4+7d27Vx6PRyUlJY71GYnPNE2trloZdNBfmlusO6pu0eGTLVrXsGHIttVVKy29+iyReE1Tq8Ic86qqlfK67Lhizcq1E+j6iHQ/wJ//dWSkGJqaM1Ez86Zras5EGSlGVK8j/xgSrG07MSQa98RIvM9G4jEDgBQ6/hkphqqmztfXqv5erZ3H9J/vP6f20x1hx8JuiJeRxH1yBQAASBaOvWfq4x//uF577TW1t7crJyfHqWpD6u7u1s0336y6ujpNnz5dTz75pAoKCmzVUVVVpf/4j//Q66+/ri984QsDtr3//vtqa2vT/PnzNWbMGCe7jgRnGIay0jJ135KvaltzvdY31ar9dIdyMrK1uKhSswvKZZqmDnUc1pzCC/u3XVl8hWZNmCGv6R3wqSev6ZXHiO1E4EdtD3xdjO/nQK+R8R3z1uZ6Vfsd86KiSlUUlCf966uCnSs7rF47gz8VF+l+gD//6+hMb7fSU0f1b/P9PJzraHAs8//ZY6FtO/dUNO6JkXifjcRjBgApcPw709utK6bO05zCC/vz1Ghl6Qef+ro8hkemaVqKl/EaE1sZq0YS98kVAAAgWTg22fHP//zPqq6u1u23366HHnooJpMDjzzyiLZs2aLCwkKtXbs27GLk+/fvV09Pj/Lz83XeeedJkubPn6/S0lK99dZbeu6553TDDTdIktra2vStb31LknTTTTdF90CQkHyD+Vn5ZZpdUN7/775PNBmGoem501Q2fnr/Nt8nluuO7Ir7ZIHvgaTuyM6Afekxe/T01l9rX/uBAf9umqYuyJ+hCr9j9h1Xsk50hDtXkfxBVQp97Ti5HxDIzpaGgNdzJHyxK1xsSwnTdkrIVoaKxj0xEu+zkXjMACANjX++MV2wD/YcP3NCTe0fatb40gHx8kzvGb2z/z29uX+TMlLTIx4jRsruWDWSuE+uAAAAycCxyY7Jkyfr8ccf19/+7d+qqqpKF154ocaOHauUlMB/2khJSdFDDz0UcXvHjh3T2rVrJUnjxo3Td77znaBlH3zwQUnSjTfeqIMHD+r+++/vX0zMMAx95zvf0Re/+EXddddd+uUvf6n8/Hxt3LhRx48f1w033KBPfvKTEfcT7hfo01KBtnlNU509XXqgZo12tzYO2Kdm38b+V7hkpWVGfdLAPNeX+0P05Y4Ft+iq6Yu0+tUHdLL7VP+/rz7XR3/JOskhWTtXvnNi9yEv1LUTjf2AaFzPdmJbV4LdS7GuM9GNxGMGAOls/LM6Pv7YeQVa+eu7VFFQrhVz/lrd3h7d/rt71XHmxJDykeY1O4aT2yOJ++QKAADgZo6NXPbv369/+Zd/UV9fn7q6urRp0yatW7dOv/vd74L+bzg2bdqk06dPS5Lq6ur00ksvBf1fOBUVFfrFL36hq666Svv27dNbb72lj33sY/rWt76lu+++e1j9xMjhMYyAfwz02d3aqAdq1sRk4sAwjIAPRP59+e6bj2nCmDwtLaka8O/316wZUQ81Vs7VSDsncK9oXM92Yts7H77HvQQASEh2xsdXTrtCb+7bqPs2/Eg5mdm6ctoVAcvHIq8xVgUAALDOsW92PPjggzp69KgyMzN15ZVXauLEiUpLS3Oq+iGWLVumnTt32trnjTfeCLpt+vTpeuSRR4bbLYxQXtOruiO7gj6E+OxubdTW5npdkF8WtUkPr2mq7shOy31ZWlKlF3esk9ln9v/7tnN9TPaHJjvnaqScE7hXNK5nu7HtksILZaQY/fFkOG0DAOCUSMfHwcbL/uWjmdcYqwIAANjj2GTHxo0bNWrUKD3//PMqLi52qlrAFTyGR9VNtZbKVjfVRvzefGt9MWz15R8qb9Kk7ELtP36w/9/XN9UOeFdvsrJzrkbKOYF7ReN6thvbAsWTSNsGAMApwxkfh8tv0cxrjFUBAADscexjH2fOnFFJSQkTHRix2k93OFpuOOz2ZfAaHbHoY6JIpN8bMFzRuJ6HG0+G0zYAAE6JNJ+Fy2/RzmuMVQEAAKxzbLKjtLRUR48eVV9fn1NVAq6Sk5HtaLnhsNuXzp6uiPZPBon0ewOGKxrX83DjyXDaBgDAKZHms3D5Ldp5jbEqAACAdY5Ndtx4441qaWnRU0895VSVgGt4Ta8WFVVaKruoqFJeM/D77J3pi2mrLy2n2nSg49CAf19cVCkzin1MFHbO1Ug5J3CvaFzPdmNboHgSadsAADhlOOPjcPktmnmNsSoAAIA9jq3ZsXDhQn3uc5/T97//fb311luqqqrShAkTlJWVFXSfRYsWOdU8EFcew6OKgnKV5haHXECwNLc4qut1nO2LYasvrzW8qcnnF+rD44dk9pkqzS0eMe/7tXOuRso5gXtF43q2G9te3bMh6OLkdtsGAMAp4XKkkWJo8vmFKs2dpoqCcv186//1j4srCsr1zNYXA+a3aOc1xqoAAAD2ODbZMW/ePElSX1+f3n77bb399tshy6ekpGj79u1ONQ/Endc0tapqpR6oWRPwYaSioFz/dsVX5DW98hgev/0++tlrmvIYxoA6fT/b3ba6aqXuD9KX0txirapaKdM0taRkgZaULFBLZ5v+eHinLp98aYA+Dqw/1DkI1sdYcvpcrT53row4HAtghxmF6zlcbPPFE69p6vLJl2p9U63ltkPFw4F9sFbO118rcchOnQAAdwkU41OUMiRHjhk1WsumL9SSkgXKyxrXX37p9Crljh6ryklz1Np5TOsaNgxpY3BeC5dXwuWnQPnKa5pKkSzn9j4pIcbiAAAA8eLYZEdhYaFTVQGu5DEMZaVl6r4lX9XW5npVN9Wq/XSHcjKytbSkSjNyp8kwjCHbFhVVqqKgXKZpanfrXr3aUDPsbb72AvVlcVGlZheU63TvGf1s6wva136gv77FxZfLNE3tCtFWoD+Q+v697sjOoMcWi4kCK/0Ida62NddrfYBzxUQH3MIwDGUGiUO++8Br83oOFdv86/TYaNtXvu7IrpD1WS0nWY9DduoEALhLuBifnpren6e2NG/XtWVLNDbzfG1trtczW18cUH5pSZW8pqnz0kfrXy9fEXSM6FuxMlibPd5e/Xrn6yoZN2VAngmVryT157Qtzdt1TdmVIcf1vvwa6jmDsSwAABgJHJvseOONN5yqCnAt3x/ILsifMeB1VV7TVGdPV8BPZNXs26jS3GLdUXWLxmbmaPOhOp3sPjXsbRUF5br9ir9T+fjSAX3pNXv16p4N+p9tL+pUd6etfqyuWqmstMwBD0umhWMLtJ/TrPQj1Ln6tyu+oln5Mwa8AsD/YRNwA9M01dXTpXc+fE+XFF6of6i8qX9by6k2vbpngy6ffKmt+9FqnZlpmZbLdfZ0BfymiO9eXXUuZlgtlyJZikO+Plopx4QHALiLb7wdMm8suEVneg1Nyi7Uhfll6uzp0p2vfS/sGHZWflnAMWKfZClXXVP2SX399Qc16fxCpRlpQctXFJRrVdWtOt1zZkCuWt/4jpaWVGlpSdWA/GqaZn+ejvdYHAAAIBF47r777rtj0VBbW5syMjKUkpISi+YS3gsvvKCDBw9q4sSJuv766+PdHTjMSDEG/Zyib61/KOi7dtu62lV/dI8+M3OpOnu6tKNlz7C3HT7Zoj8e2aml06v0yq7fa3/7QU0bN0XffOMHWtdQox5vj+1+bD+6W0tKFgzYJ8XCsQXaz2lW+hHuXC0pqRpSJ9yBmHqW7z54taFGr+xer9oPN+vNfZv08q439PM//p/e+9M22/ejlTrzx+Rpem6R5bY3NNXq1YaagO21dbXr+vJPKT11lO4Je0+frc9OHKoO03Ys4hWQ6IipcCMjJSV83mjZo6tnfEK7WxtVcF6+5dxhDBoT+saIlto8ultLpy/U7PwyjR+dG7L8FVPmqaKgfEi/ur092tGypz+/NrUf0KUfm62UlJSEGYsjuHjH1GfW7ez/789fNTPm7QMAEEuOfrTD6/Xq8ccf15/92Z+pu7t7wLa7775bH//4x/XYY4+pt7fXyWaBhOY1vdraXB9yUUFJ2t3aqK3N9VpaUjVksmS42+ZNvEgXFV6gD5q3a2fL3mH1Y1tzff+n2bymafnY/Pdzmp1+WD02wG0G3wdmn6n9xw9qR8se7T9+sH9hVTvXupU6jRRDlxReYLntrc31uqTwwiH3oE9GaobSU0fZuqe9pleNxz60VDZU275y25rr5SUWAIBr2B1vl48v1QfN24c1hrXb5vjRuSHbNFIMLSlZELKML7++1lDTn6sSYSwOAACQKByb7Oju7taXv/xlPfzww9q7d68aGwcOuI4cOaJjx47pkUce0cqVK9XX1xekJiC5eAyPqptqLZWtbqpV3uhxmpQ9dA2c4W7LyxqrDU3vDrsf65tq+78C7zEMy8fmv5/T7PTD6rEBbhON+9FKnZPPL1Re1jhH7kFJ+qsLPm07bnoMjy4tnD3stn3WN9XyGisAcBE7eaPuyE6leVItj4uD5cxIctXBjuagZXz51E6/PIahPx7ZGb6wGOcCAICRwbHRzn/913/p7bffVl5enh566CGVlJQM2P7000/riSeeUGFhoTZs2KCf//znTjUNJLz20x22ymWlZTq+zel+RFpntETj2AC3icb9GK5sZmpmRG0HugclKTdrXET15WRmD7vtweUAAO5hNXafOfcqVydypv06gr8iNdJ82h3g1bSh+wAAAJC8HJvseOmll5Samqr//M//1FVXXaXU1IFrn3s8HlVVVenRRx9VSkqKfvWrXznVNJDwcjLC/xHOv1xnT5fj25zuR6R1Rks0jg1wm2jcj+HKdvV2RdR2oHtQklo72yKqr70r/B9xwrU9uBwAwD2sxu50T5qt8qHK2a8j+NsNIs2no84dj/U+AAAAJC/HJjv279+v4uJilZaWhiw3c+ZMTZ06VXv27AlZDkgWXtOrRUWVlsouKqpUy6k2Heg45Pi2ls5jWlh02bD7sbiocsCaHVaPzX8/p9nph9VjA9wmGvejlTo/PH5ILZ1tjtyDkvSLut/Yjpte06v3Dm0bdts+i4sqWbMDAFzETt64IL9MPd5ey+PiYDkzklw1MbsgaBlfPrXTL69p6sL8MsvlGecCAIBk59hkR0ZGhuXBU1pamlJSgn+FF0gmHsOjioJyleYWhyxXmlusioJyvdpQ07+Yr5PbXmuo0UUFs4bdj9kF5QPW7LB6bP77Oc1OP6weG+A20bgfrdRp9pnafKjO1j24+dAfh9yDPqd7T+tMb7et+jyGR8VjJw+7bV+52QXlrNkBAC5id7xdf3S35XFxsJxpt82jp1pDtmn2mXqt4U1b/UqUsTgAAECicGy0U1RUpL1796qhoSFkuf3792vPnj2aNm2aU00DCc9rmlpdtTLog0hpbrHuqLpFh0+2aF3DhqhsW7dngw6fbNEdC26JuB+rq1YOmdQ0LRxboP2cZqUfdo8NcJto3I9W6rx80hxLcW511Up5TVOVky8NWc7bZ8prmloVpr5V5+qzetxe09TlYdr2lQMAuIuVvHHH/8/evYdHUd7943/P7Cab7C5LEgIJCYEkEDBAEgmCYNXEglj0q7Y8LdL6UEuvPp7b/qxWRfy2Vr8o9rK2Wn1qPbT2QVqVRy1treAxgHKUUzhEjuEUCAkkIWw22WRn5vfHspvd7GlmM7s57Pt1Xbkudu7j7DKf+569Z3audM8FX972V5xzNEfMH2nMVDtWdUld+O2GVyO2eajpKFyyS9NY3l/m4kRERET9gaAoSugfDtVgxYoVeOKJJ1BYWIjf//73AQ8oB9wLHffeey8OHjyIhx56CD/4wQ/0aHpAWrhwIbZs2YLp06dj+fLlfd2dmJJk2e8K2Z6vE4Vnv3fX16Dq6Ca0dLQiLcWGyvwZKMkuhizLOHjuCD48vF7XtAPnjuCji2lj0kZh/uT/gxSjKep+BLsizLM9XJ3xuJJMTT+07hsNDIkUUyOJxfGotk61+TzxsLq+Bmt98lXkz0BpdrE3XW0+LX2MFIsTdYwi8sWYSv2JlnOJSONGU3sL/rX/E1yaPdFvHOnNmBmpzS7JhX/t/wTjMkb7jTPh2gSgaSzvL3NxCq6vY+qN96/y/vufv7k57u0TERHFk26LHS6XCwsXLsSOHTsgiiJKS0sxfvx4mM1mOBwOHD58GDt37oQkSSgpKcGKFSuQnJysR9MDUl9PeOLBM6kONfFP1Em3JEswiAaf190nbD1P3nzfo+jTgrcXbX3BKLIMQRTh6nLCmGTybve89qTHi577RgNDIsRUtTz/p50uJ0zG7uPR87o3/+fVHj/q84WOh0B3bAnM534dLLbo1TZRImNMpf6gN+cSocYN39eusydxfutqmAtLkDJuqt8cNpqxMtK40rNONeOV1nkr57n9U1/HVC52EBFRIjHqVpHRiD/+8Y948sknsWrVKuzcuRM7d+6EIAjwrKcIgoC5c+fiV7/6VUIvdCQCWZbh6GrHU+tfxMFztX5p649t8d5ObU5KTbgJuO9JkPu1GPTfAPzem+jTgrcXbX09KbIM2dmG+jeXwnn6MJKH50E0mSE7HehsPAHTyLHIXrAEoskStwUPvfaNaKDxjb2Hm45hlG0kzEmpcHS142TraYzNGNOr2Kv2+FGfL3Q89IstDSeQcfV8GIZmQjp/Fk3r3oZpRF7Q2KJH20RE1Ld6ey4RGOPdC+SyqxMtn/8vzm/+JyC7AAD2nR8CggjLxK8hc+7tEJNSohwjw48rPetUM15pnbdynktERESJTrfFDgCw2WxYtmwZ7rvvPqxbtw7Hjx9HS0sLUlJSkJ+fjyuvvBJjxozRs0nqp0RRDHpy4nHwXC2eWv8ils5+MM49I70Jouj+MvLUQQBAZ8Mxv3TnqYOof3Mpchct64vuESWUnrH3+Pk6v/SBFHt7xpamT//HL52xhYho8IrFuYQgiqhf8Zh3XPGjyGjbux6u5nqOK0REREQDmK6LHR5ZWVn4zne+E4uqaQCQZBl7G/aHPDnxOHiuFrvrazBpxARedTRAKbKE9mN7gp80+nCeOghHbTVSx0yO689ZESWSwRR7GVuIiBJXLMYzjitEREREiSGqGdz//u//6t0PyLKMt99+W/d6Kf4Mooi1Rzepylt1dFO//bKNIhNEA+zVVary2qureNJIFEODKfYythARJa5YjGccV4iIiIgSQ1SzuMceeww333wzqqqqdOnEBx98gOuvvx6PP/64LvVR32vpaNU1H/Vfkr1FXb625hj3hIgGU+xlbCEiSlyxGM84rhARERENflEtdvztb3+Dy+XCXXfdhZtuugnLly9HU1OTpjqOHz+O3/3ud7jmmmvws5/9DCkpKVi5cmU03aF+KC3Fpms+6r8M1jR1+SzpMe4JEQ2m2MvYQkSUuGIxnnFcISIiIhr8onpmR0lJCVatWoXXX38dL7/8Mp588kk89dRTuOSSS3DppZdi3LhxyMvLw5AhQ5CSkgK73Y6WlhacOXMGO3fuxI4dO1BXVwdFUZCRkYEHHngAt912G4zGmDxChOJMkmVU5M/A+mNbIuatzJ8BWZb79c+pUGiKLMFaWgn7nnUR81pLK6HIMn8WgChGBlPsZWwhIkpcsRjPOK4QERERJYaoVxeMRiN+9KMfYcGCBXjjjTfw9ttvY9++fdi3bx8EQQhZTlEUAMCoUaPw/e9/H/Pnz0dKSkq03aB+yCCKKM0uRtGwgrAPFiwaVoCS7OI49oz0JogGmAvKYMopCvvAR1NOEcwFpXHsGVHiGUyxNyC2CCKSh+dBNJkhOx3obDwBKDJjCxHRIBSL8SzinPXiOGPKHc9xhYiIiGgA6/WtFFarFXfeeSfuuOMO7N69G5s2bcLWrVtRV1eHpqYmXLhwAcnJyRg2bBgKCgpQVlaGK6+8Epdeeqke/ad+SpZlLL7qHjy1/sWgJylFwwqw+Kp7+vWVxX1BkSUIoiHk6/5Sp3/9MrIXLEH9m0uDnjyacoqQvWBJXK+Qk2QZBp+2er4mGqxiHXtjHU/823LHlraajUgZV45kW6Y3rbP1LDoObYeleGbMY4vafY7ne0NENNhpGc8EKCrjdOCcVUy1wlZ+HWzlc2D0GWfiEcM5XyUiIiLSn26/GyUIAkpLS1FaWorbb79dr2ppgBJFEeakVCyd/SB219eg6ugmtHS0Ii3Fhsr8GSjJLuZChw/PCVX70T2w766CZG+BwZoGa0klzIVlUZ1web4AbD+2B/ZqnzpLK2EuKNPtC0JBFCGaLMhdtAyO2mp3W23NMFjSL7ZVGreFDs//qb0N+7HW5/9cRf4MlPL/HCWAWMXeeMUTX4IoAiYLbOVzUF1fg7X7/ul/TJfPgRTD2KJ2n/vivSEiGuxEUURqhPFMkiWIoghH7S5V8bfnnLX98A4MnXETjNZ0OI7sQtNnK+ISwzlfJSIiIoodPiSDYsYzSZ84YoLfLeayLPulJzpFliA7HUHvjLDvWee9M0I0mVUveCiyDNnZpqJOi24LHgCQOmaS363/ysXPOl4LHY6u9qBXAK4/tsV7BaA5KZX/92hQ0zv2xjue+Pa3vY+OafX7bFYZv/V9b4iIBjtFlgFnG87v24AJ46aiZMYib1rn+bM4v20NrBOvgKurEw3vPQu53e5NDxd/feesqWMmQ3a2oe7PD8cthnO+SkRERBRbnEFRzPW8HZsTd3+CaAj5E1AA4Dx1EPVvLtV0Z4cgiirr1PtKbEOP1/H7rEVRDPlTBwBw8Fwtnlr/Iv//UcLQK/b2VTzpy2NazT631WzUEL8Zd4iItPDE4XOrX8bJF+/CyVd+hlP/8yhOvvIznHzxLpxb/TLq31wKo20YbOXXBZSPFH8F0dAn4xvnq0RERESxxVkUUR9SZAmOI7vCPtwbcJ9sOWqrociSujprtdQpa+pzfyTJMqrra8I+xBJwn0Durq/xXuFOROH1VTzpy2Na1T4LIszjyjXGb8YdIiI1AuKwIqOz4Rg6TtSgs+EYoLjjqfPUQTiO7IJtyrWAEHhaGy7+9sX4xvkqERERUexxsYOoDwmiAfbdVary2qurVN3dIYgG2Ku11Dnww4BBFLH26CZVeauObuLVckQq9VU86ctjWs0+Jw/Pg9GWqTF+M+4QEamhaezZvRbGocORPDwveHqI+NsX4xvnq0RERESxxxkUUR+T7C3q8rU192md/V1LR6uu+YjIra/iSV8e05H2WTSZVeXz1jeIYi0RUTxoja+euBwqXY829MD5KhEREVFscbGDqI8ZrGnq8lnS+7TO/i4txaZrPiJy66t40pfHdKR9lp0OVfm89Q2iWEtEFA9a46snLodK16MNPXC+SkRERBRbXOwg6kOKLMFaUqkqr7W0UvUzO6ylWuoc+L8HLMkyKvJnqMpbmT+Dv4FMpFJfxZO+PKbV7HNn4wm4Ws9qjN+MO0REamgae0oq4DrfiM7GE8HTQ8TfvhjfOF8lIiIiij0udhD1IUE0wFxYBlNOUdh8ppwimAtKVT+zw1ygpc6BHwYMoojS7GIUDSsIm69oWAFKsov5G8hEKvVVPOnLY1rVPisyHIe2a4zfjDtERGpoGnsKy9C64yPvQ8sD0kPE374Y3zhfJSIiIoo93WdQkiThq6++wsaNG7F27dqwf7Hw7rvvYsKECfjyyy9Vl3G5XCgpKcGECROC/l199dUx6SsR4L6yLHvBEvfJliAiecQYpOQVI3nEGEAQYcopQvaCJaru6uiuU+6uUzTCcslM2KZ+A5ZLZgKi0afOwXPFprEnGwAAIABJREFUmCzLWHzVPSFPIIuGFWDxVffwKjkijfziSRCxiid9eUyr2WdL8Uz/+B0i32CLtURE8aB27HG1nkPr9o/CzJ9Dx19FlpH9vV/AMulqQDRGVYdWnK8SERERxZZRz8pef/11vPDCC2hra4uYVxAE7Nu3T8/msWPHDjzxxBOayx06dAidnZ0YPXo0ysrKAtLT0tT9niv1b4os+d0Z0fO13uXU1ge4H6qYu2gZXF1OGJNM3u2e15HaDExXIJosyF20DJIsweCT5nntLiMGrSPe75Ue77EoijAnpWLp7Aexu74GVUc3oaWjFWkpNlTmz0BJdjFkWeZVchRXoY6/eNArdgmi6I0njtpq2KurILU1w2BJh7W0EuaCUiiy7BdP1O53uD5Ge0yr3e9w+bTss9b3hogo0amJ0+riqwQhdQhG//gliD7zZ7nLCTHJ5Bd/e7bhGZcMJjOyvvlTSDfd6zdOdc/B9Y3hnK8SERERxZZuix2rV6/GsmXLvK/T0tJgNpv1qj6iNWvWYPHixXA4gj+cLpyamhoAwLx583DXXXfp3TXqY56TlPZje9wnSvYWGKxpF0+UykKexERbTms/bNNvQFJWIQwA9p07grU+Jz0V+TNQml0MGQKCfUWpps69DQdC1Am0fvEuOo7uhsGahiHlc5CSOyGu75Xe77HnxHDiiAkoyS72bvdcHccTR4oXSZZhEMWQx58nPRb0Pq4AePOnjpkEc0GpX1u+6Wr3W20fBSgAgOJhhX7HtKvL6W73Yrq7L+4vstqP7oF9t0+dJZUwF5Z501W3rXKf1eYjIkp02scnd4xPyR3vF1/lzg6/ejtOfhW0Pneb3WND+5GdsM24CUnWdOxtOICd9ftw44TZSE8dGnLcUgAIOr8PnK8SERERxY5uix1/+ctfAAA33ngjFi9ejIyMDL2qDqu+vh7PPvssVq1ahdTUVGRmZuLs2bOa6vDcYTJp0qRYdJH6kCLLkJ1tqH9zKZynDvql2fes896eLposPe5yiK6c1n4Y07MwbMTdaO9qx1PrX8TBc7V+5dYf2+K9nT01KdXvy1E96rRdfiMu7PoYzjNHkF6xAHKHHfVvPRmX90rv99hXzy+RedJI8STJclTHtB5ieVwBCHrlrYf6/U4BnA4VfTRD9uQ7fRjJw/O82zobT8A0cqw3H4DuvGrrVPn+hNtnte8NEVGi0zo+KbIUcgxw2Zsx6ke/gWBMilxfcipO//VxOOsPI+eHv0aH0YjHPv41Tl9owLJrH4ZBELHk41+HHbfMSakxmUtyvkpERESkP91mVAcOHMDQoUPx5JNPxm2hAwB+97vfYdWqVZg8eTLeeustFBYWaq7Dc2fH5MmT9e4e9TFBFIOeBHk4Tx1E/ZtLA76Uirac1n5kzr0DBkNS0C8HPQ6eq8VT618MOCHSpU5jEjK/cQds5dchKS0r6EJHpH3uL+8xUX9hEMWojmk99OVxpX6/DbDv2xC2j201GyGIhu59UWR0NhxDx4kadDYcAxTZZ18M/nlD1OnJ29VUz7hDRBRnWsencGOAbcq1MA7JUFefwQhTzjjYyq9DcloWln3+Bxw8V4s5467GCGsmnr74OhjPuMVFCCIiIqKBQ9eZW25uLpKSkvSsMqLCwkI8/fTTWLlyJSZMmKC5vKIoqKmpwfDhw/HJJ5/g29/+NqZMmYIZM2bgZz/7GY4cORKDXlM8KLIER+2ukCdBHs5TB+Gorfb+5Ei05TT3QzTCNHoSqutrQp5keRw8V4vd9TWQIvUxijpNYybBVj4HjiM74/Ze6f0eE/UXkixFdUzroS+PK637nTpuKiCEmIIIIszjyuE4omJfTh+G3OXUtN+mrHxADH1jK+MOEZG+tI9PUugxQBC1zVuP7IJtyrWwls9Bdf0+HDxXC1EQMXvsldh18XU4nnGLDwwnIiIiGhh0+xmr4uJiHDhwAE6nEyaTKXIBndx+++29Kn/ixAnY7XbY7Xb84he/QHl5OS6//HLU1NTg/fffR1VVFV555RVMnTo1Yl3vvvsu3nvvPVXteu4modgRRAPs1VWq8tqrq7y/BRxtOa39sIyfBoPBiLVHN6lqq+roJu/v+upepy0TTZ+tUFVGj/dK7/eYBqeBGFMNoiGqY1oPfXlcad7vGYuQPDzPfZdGD8nD82BUGZOSh+dBTDJp3m/L+MvQ9lXo/jLu0GA0EGMqDQ5axifF5XLn3x08v5YxAgDsu9dixM0/AQCs3fdPAEDe0JHINGfgb9WrVNWh93hNgwNjKhERUf+k22LHD37wA9x77734/e9/jwceeECvamPO87yOrKws/PGPf0RxsXsi63K58Jvf/AZ/+tOfcN999+Gjjz6KuIhTV1eHLVu2xLzPpJ5kb1GXr61Zl3Ja+mGwDAUAtHS0qqqjZz6964z3e6X3e0yDz0CNqdEe03roy+NK6357nrXRk2e7mn3Rkhfo3m+DJU1VPqLBZKDGVBoc1MZpISk5bP5o4z7QPf6kGlP9XkcSi/GaBj7GVCIiov4pqsWOtWvXBmxLSkpCeXk5XnvtNezbtw+zZs3CiBEjkJycHLKeioqKaJrX1XXXXYeqqiqIooisrCzvdqPRiJ///OfYvHkz9u7di48//hg33HBD2Lpyc3Mxffp0Ve3W1NTgwoULveo7RWawhv9Cy5vPkq5LOS39kNrOAwDSUmyq6uiZT+864/1e6f0e0+AzUGNqtMe0HvryuNK637LTETTds13NvmjJC3Tvt9QW/ksyxh0ajAZqTKXBQW2cVro6w+aPNu4D3eNPu6vd73UksRivaeBjTCUiIuqfolrsuOOOOyAIQtA0RVGwceNGbNy4MWwdgiB476roS4IgYOTIkUHTRFFERUUF9u7diz179kRc7Jg3bx7mzZunqt2FCxfySpAYU2QJ1tJK2Pesi5jXWloJRZYhiGLU5bT2o+3AVkiSCxX5M7D+WOT/C5X5MyDJMgxh+hhVnZILSlsLrCUVcXuv9H6PaXAaiDFVkqWojmk99OVxpXW/O8+fRWfjiaDpnY0n4Go9C2tJ5H3pbDwBucupbb9dXWg78GXkfIw7NMgMxJhKg4OW8UkwGt35Q4wB3WOEynlrSQVc5xshCwIq8i/H+mNbcOL8aZx1NOHqi68jqcyfAVmW+aBy8sOYSkRE1D9FNWPLycnByJEjg/6FS/P9y87O1ntfYiIzMxMA0NHR0cc9Ia0E0QBzQRlMOUVh85lyimAuKPV+qRVtOc39kF1wHt+L0uxiFA0rCFtH0bAClGQXe78U1bNO57G9aN3+IcyFl8btvdL7PSbqLwyiIapjWg99eVxp3e/2Q9sAJcTDXhUZjkPbYS702RdBRPKIMUjJK0byiDHeh5ubRo6FmGTStN/OM0cB2RUxH+MOEZE+tI9PBv8xwJcio3XHRzAXXoohU671GxOC1ldYhrYDW2Hf8RFKsyeiaFgBZEXGx4c/R9nF1+F4xi0udBARERENDFHd2fHpp5/q3Y8+s2LFCmzduhXz58/HFVdcEZB+8uRJABgwizOJQJElCKIh5Gv/vDKyFyxB/ZtL4Tx1MCDdlFOE7AVLAq7gjbZc6D4Hr+/sB39E7u3PYfFV9+Cp9S/i4LnagLKl2cX4+dfuhCRLMPjspyRLGPn9/4eG934Lx/5NmuosGlaAxVfdA8nVhbMf/BGysw1DymYh+5ZHUP/Wk0H3ObWgDFnfeSjo+6/uvQosN/I/f4UzK59Ge+2uMOW63+NwnzVRfyHJsrrjT8e7OjyiiV09+xGuX+Hir/r9lmCdeAXs1Z+F7KOleKY3trTVbIR5XDmMtkxvHlfrWTgObffmA6A6DiVlZMOUU6RLbCciSnRq5+VaxidAARAY18VUK2zl18FWPgcAMPz6OwG4x4TW7R+idfsayO32gPqGTrsergvn0NV+AQ9feReWff4HfHhoHa4puAIPXXkXnv78D2HHrWjv6tAyvhIRERGRPnR7QLkWdrsdx48fx8SJE/uieT8nTpzABx98gKSkpIDFDqfTiTVr1gAArrzyyr7oHvnwfPnUfmwP7NVVkOwtMFjTYC2thLmgLOiXU4IoQjRZkLtoGRy11e5ybc0wWNIvlivVtVwo4errajyO1KwCLJ39IHbX16Dq6Ca0dLQiLcWGOWOvQtGwQoiiiOr6Gqz1SavIn4HS7GIMn3c/us7UonXL+6rqrMyfgZLsYkiyhNaNq5CUngWDJR0uezNShg4P2sch5XOQkjsegijCUbvL7/13p02I+F511O3Hhe0fBnxu2QseRUfdAXdaj3JyZweaPl2OzjNHI37WRP2FQRSRmpQa4fiLzRceWmKX58ubvQ37g8YW3y931MRf936nRIw7BtEAxWSO0Ef3l2WiyQxb+Rw4juxC02crutstqYStfI7fl2qihjoj52N8ISIKR+u8XN345I6/jiO74Dx9GGmX3+jN3354B4bOuAlGa7p7TPj0Db8xIaPye7BN/QbOb/4nUsdOgbmgFK62FjT+60VAkWEtqYS5sAyi5PKOU2sOrcX/mTALS2c/GDDP9oxb0Sx0aBlfiYiIiEhfgqIoih4VFRcXY+rUqXjjjTci5p03bx4aGhrw+eef69G0H8/vYa5YsQKXXXaZX9qpU6fQ3t6O9PR0ZGRkAAAOHTqEm266CbIs47nnnsN1110HAOjq6sKvfvUrrFy5EldffTVeeeWVmPRz+vTpWL58ua51D0aKLEN2tkW8Gkw0WUJ+SRV45ZnauzKiKxdNfcHu3mjv6oh4pXRqUopfufB1dj/3I1Q/gl2lJzsdId//1IIyZH37QQiiAYIxqbucqwuKLKF+5dPoOFodUK77czP7tye50LrzEzRX/RVyhz1EmdCfNSWe/hpTQx1/8RDuGJdlGY6u9oixxZyUCgFQGX/NkJ0O2PdtQOq4qUge2n0nRuf5s2g/tA3WiVf45bOMK4dx6HBvPtf5RrQd2u6XT027gADZ2aa6zsj5GF8osfXXmEr9Q2/n5cHGJ0AJiPnG9CxkfuMOpI6eCIgG1W1Kjla0fvlvtG5b4zeP9M0jQwmYc/u+jnZBQsv4ygWPxNHXMfXG+1d5//3P39wc9/aJiIjiSbc7OxRFgZp1kwsXLqChoQGtra16Na3aQw89hC1btuDee+/Fj3/8YwDAuHHj8PDDD+PJJ5/ET37yE5SUlCAnJwe7du1CfX09CgsLsWzZsrj3lfwJohjy5AYAnKcOov7NpchdFPqz6nlLvfq7MqIrF019hh5pBtEQ8mQJAA6eq8VT61/E0tkPaqiz+7kZocoEphnCvv/ttbtwesVjyF20DOe3/httNRtgKb4CQ6ddj1N//r+aP7dTy38BZ91+TWWI+qNQx188hDvGRVHUFFsixd+2mo2wlc/pzieISB6e511c6Gw8ASgy7NWfIXfRMtj3bcC51S/jXIh8ppGFSMkp0hT3PXlD1am2bU8+IiIKrrfz8lDjU886Xc1nUP+3x5F25XxkVNyius3WLz9AyxfvhM3T84e2eo7X0S5EaB1fiYiIiEhfUS12HD58GLfddhskSfLbvmvXLsycOTNkOUVRYLfbIUkSxo0bF03TMfH9738fRUVFePXVV1FdXY39+/cjJycHd955J26//XZYLJa+7mJCU2QJ7cf2hDy58XCeOghHbTVSx0weFFfkSrKEvQ0HQp4seRw8V4vd9TWYOGJ8wImaHjS9/0d2wTJ+Gs59/BeM+Ob/B8eRnZo+N0BxtxVioSNYmcHwWRPFkyTL2NuwX3VsKR5WCOfpw6EzCiLM48rdPzviOd4VGZ0NxwKyeo5dy7hynBPE4PlEI0xZ+f71hdAdCybBWV/b+7bB+EJEFE4s5uWKLKH9aIg6BRG2KbPUzymP7IJtymy0bHgXUORe9UsrrePrpBETeHcHERERkc6iml2NHTsWs2fPRnNzs/dPEAS4XC6/bT3/Wlpa4HK5YDKZ8MADD+i9LwCA5cuXY//+/QE/YeWb5rmrw9fMmTPx2muvYevWrdi9ezfWrFmD++67jwsd/YAgGmCvrlKV115dNWi+nDKIBqw9uilyRgBVRzfFZKED0Pj+714L49DhsIyfBqMtE/bda9WVu/i5JepnTRRPBlHUFFuMSSYkD88LmSd5eN7F471KVZ326ioYhw4PWadl/DQIhiRN9QmiAZbxgeO+1rb962R8ISLqKRZzNUE0hIz53WOMyjnlxblouDgfqxivdXzlQgcRERGR/qL+Gauf//znuP766wG479i47bbbMH78eDz66KMhy4iiCLPZjNGjR8NqtUbbNCUgyd6iLl9bc4x7El8tHep+7k1tvmhpff8NlqFRlYu2DBFpozW2uJ+LEZwnTeuxG6rOaOOHwZLW67Z75iMiokCxmKuFqlPvMUZrv7TqL3N3IiIiokQV9WKHxWLB9OnTva+nTZuGCRMm+G0j0ovBGvlLLAAwWNJj3JP4Skux6ZovWlrff6ntfFTloi1DRNpojS2y0xEyjydN67Ebqs5o44fUFvmLsEht98xHRESBYjFXC1Wn3mOM1n5p1V/m7kRERESJSrd7Z5cvXx72rg6iaCmyBGtppaq81tJKKHLg7/MORJIsoSJ/hqq8lfkzIMlS5IxR0PT+l1TAdb4RbQe2wtV6FtaSCnXlLn5uifpZE8WTJMuaYoury+l+eHcInY0nLh7vlarqtJZWwnW+MWSdbQe2QpG6NNWnyBLaDnzZ67b962R8ISLqKRZzNUWWQsb87jFG5Zzy4lw0XJyPVYzXOr7KHGeIiIiIdBfVYsepU6d0+SNSQxANMBeUwZRTFDafKacI5oLSQfM76wbRgNLsYhQNKwibr2hYAUqyi2P6zA7V739hGVp3fATILrRu/xDmwks1fW6J+lkTxZNBFDXFFmOSCaaRY0NnVGQ4Dm2HuVD9sdt2aHvQB8cCAGQXnGeOaqpPEA0wZYffH1Vtg/GFiCicWMzVBNEQOuYrsrY5pWcuGiLOxzLGax1f+cwOIiIiIv1FNcOaNWtWr/9mz56t977QIKbIMrIXLAl5kmPKKUL2giWD7kpcSZaw+Kp7Qp40FQ0rwOKr7onZXR0eqt7/Wx5BV/MZtG5bAwBo3b4GXc1nkH3LI5o+t0T9rIniSZZlVbFFlmX/Y1IQkTxiDFLyipE8YgwgiDDlFMFSPBOKLEXM5z52JVgnXuHOZ0xBxqzbMPxbP0PGrNsAYwpMOUVISs/2ry8I3/rUxw2ftsPmY3whIgolbMwVjRg6cx6yb31MUyxVZAnZ3/sFLJOuBkSj3xjSuuMjuFrPqYvzkgsddQfD54lhjNcyvhIRERGR/gRFURSthS655BJV+YYMGYIhQ4bA6XTi3Llz3u1paWkwGo34/PPPtTY9aCxcuBBbtmzB9OnTsXz58r7uzoCgyDIEUYSjthr26ipIbc0wWNJhLa2EuaDUmx5d3RIEnzsjfF9Hm6YXSZZgEA3YXV+DqqOb0NLRirQUGyrzZ6AkuxiSLEOEormPWvuu5v3vqDuAC9s/9KYNKZ+DlNzxmj+3WH7WNDgNhpiqdzyJVJ8syxBFMWRs8aS7y7qPObnLCTHJ1F3HxdeedK35Qscn//TQscA31qmLG4wvRJENhphK0VE7FvWMpYoApM34JpKH5UIwhC6vyBIAwS/OBuSRJL86useQ8GOCq60FEEQYzbY+jfFaxldKDH0dU2+8f5X33//8zc1xb5+IiCieonpA+fbt2wO2dXV14Z577sGuXbtw++2349vf/jZGjhzpTW9pacF7772H559/HgUFBXjttdei7zUlJM+JSeqYSTAXlHq3e67OiubExXPS1H50D+y7qyDZW2CwpmHIlDlIGTUBgigGpNmm3QBTdkHQctaSSpgLy3Rd9BAhAAAmjhiPkuxi73bp4tXMBlGEo3aX+4TO04/SSpgLyvwXIHqmubrQtO4NdJ45GlAm3HuZkjve7/2Xuzp80oqQmte9GOpbl5bPLRafNVF/FSoORRtPPMdd+7E9IeOCIIreL1omjpjgF1s8V5sG+yKm4+RXQev0aT1CPsUvX6h97s7njn+h447g3aY2bjC+EBEFUjt2ePSMpd6FiCO70H5kJ4ZefhOMQ9ID4/zF+iRHKxRZgdE6NORY4LrQhPOb/4nUsZe6xxBZAcTAMUGR3Hc4Gy1p3n3pyxgfzfhKRERERPqIarHDbDYHbHvhhRewbds2PPPMM7jhhhsC0tPS0rBo0SLk5+fjrrvuwnPPPYfFixdH0zwluJ5f+vXmbg7Z6UD9m0vhPNV9u7uYakV6xQLIHXbUv/WkX5oxPQuZN9wdtBwA2Pes894iL5rMvV7wUGQZsrPN3VZ9LSzjL4PBkga5swPpV88HklMD+ujXj1segdGaAcfh7ZDb7f5p8xfDNvUbqPvzg5CP2nv03dLjijuffpw+jOTheRBNZshOBzobT8A0cqy3nC//k2Ltn5tenzVRfxUqDgHRxRO/YzVsfd3HuKHHcdXzSxj1dZpV74vmfBHiTjSxhvGFiMgtmrHDw3O3sCdedzWfRu4Pfw1BFFH354fD1mdINeP0Xx9He+2u4Hl85qpJ6SPdP0Hl6MTJ1x6A0ZrePSacrYOleCYy594OMSkl7IU08RRpfCUiIiIi/ek24/r73/+OkSNHBl3o8HXNNddg9OjR+OCDD/RqmigqgmgIelJnK78OSWlZQRcRMufeAdGYFLSch/PUQdS/uVSXOzsEUexuS3ah7atNaN22GkbbsJB99OvHW08iKT0LtvLrAtPefiogrbvvPU9kffqhyOhsOIaOEzXobDgGKHLIckQUXqg45KE1nvgdq2HrU3+sqqmzrWajpn2x79ugKl9XUz3jDhFRjPV27PCN/9559NtPRa7PYIQpZ1zoPD5zVU8Zo20YbFOu9R8TZBfa9q5H/V8f55hARERElOB0mw02NjYiLS1NVV6z2YwLFy7o1TSRZooswXFkV+BJmCDCVj4HjiM7A9NEI1JHTwxergfnqYNw1FZf/F3iXvSxVmMfg/XjyC7YplwLCKKqtO6+y+H7Eawtn3JEFF7IONSD2ngSi2NVVZ2CCPO4ck37YhlXHhCTguUzZeUDYuibUBl3iIh6p7djh99YptMcNVSeSGU4JhARERGRbosdOTk5OHjwIM6cORM23+HDh3HgwAGMHj1ar6aJNBNEA+y7qwK2Jw/Pg9GWCfvutQFplvHTIBiSgpYLxl5d1au7OwTRAHu1tj4G7cfutTAOHY7k4Xmq09x99/y+ffB+BG3LpxwRhRcqDgWjJp7E4lhVU2d3TFLfdqiYFNBHYxIs4y+LnI9xh4goKr0dO3zHMj3nqKHyRCrDMYGIiIgosek2E7z++uvR1dWFu+++GydPngya56uvvsLdd98NRVHwrW99S6+miaIi2VsCtokmc8g0g2VoyLSg9bc196J3CNlWuD6G64ennJq0nn2P5z4TJRK9j61YHKuR6tQzJgXLZ7CEv2uUcYeIqHd6O3Z4ysdiPOiZJ1IZjglEREREiS2qB5QHs2jRInz44YfYu3cvvvGNb6CsrAxjx46F2WyGw+FATU0N9uzZA0VRcPnll+PWW2/Vq2miqBisgV+gyU5HyDSp7XzItKD1W9J70TuEbCtcH8P1w1NOTVrPvsdzn4kSid7HViyO1Uh16hmTguWT2sJ/aca4Q0TUO70dOzzlYzEe9MwTqQzHBCIiIqLEptudHVarFa+//jrmzp0Ll8uFbdu24e2338Zf/vIXvP3229i9ezdEUcR//ud/4g9/+AOSkpL0appIM0WWYC2pDNje2XgCrtazsJZUBKS1HdgKReoKWi4Ya2llr5/ZYS3V1seg/SipgOt8IzobT6hOc/e9+5kdwfoRtC2fckQUXqg4FIyaeBKLY1VNnd0xSX3boWJSQB9dXWg78GXkfIw7RERR6e3Y4TuW6TlHDZUnUhmOCURERESJTbc7OwBg2LBh+O1vf4uf//zn+Pzzz3H06FHY7XbYbDYUFBTgmmuuQUZGhp5NEkVFEA0wF5bBlFPkfaBi8vA8iCYz2g5swdDLru9O85BdaD++z79cCKacIpgLSnvfxwKNfQzWj8IyNFX9FVBkVWk9+x7Qj3Bt9XKfiRJJQBwKQe2xFYtjVVWdigzHoe2wlc9R3fb5bWsCYlKwfB11BwHZ5Rf/ZKfD/SWXIjPuEBH1Um/Hjp5jWeuOj5BR8d1ezVED8/ytO+YHK3NxjDDljueYQERERJTgdF3s8MjJycH8+fNjUTWRbhRZQvaCJWir2QjzuHIYbZndaa4uZN+yBPVvLfU7UTv7wR8x6vbnkL1gCerfXBr0JM6UU4TsBUugyJL3gcK+/9bWR1lzH/36ccsj6Go+g9ZtawLT5i8OSOvuu+z3cEdPPyLvs8yHQhJp4IlDWuJJ+Pq0H6s96w58HblOS/FMTftinXgF7NWfRcyXlJGNzLl3BMQ/V+tZOA5tv9gu4w4RUW/0Zp7nvutQ6J6vFk1VX5/kci9qB+Hbpq18Doy2YbAUz4Sr9Zx37iqmWmErv+5ius8cOcp5NxERERENfDFZ7CAaCATRANFkhq18DhxHdqHpsxWQ7C0wWNMwZMocpIyagNxFy+CorYa9ugpSWzMMlnR0NZ1GcmZu0DRraSXMBaVQZAmtOz6G46tNMFjTYC2phLmwTPPJlyCKUfWxux8yOs+dgnnc1IA0ubMDrRv/DtPIwoAyPU9k3f2wRGyLXzgSaeOJQ5Hiidq4oeVY9dTbfnQP7LurvLGlZ7zyxCE1fYxFvmDxz1pSCVv5nIv5GHeIiHojmnme53X7sT1wnjqMtBk3euN1++Z/YejlN4atT3K0Qmq3I+fWX4bM47rQhKYvliO18NLumJ86BCPm3Y/2wzswdMZNMFrTA8eI0kqYC8o4NyUiIiJKQFEtdsycOROCIOCtt95CXl6ed5sWgiBgw4YN0TRPpAtFliE7HUGvOrPvWYfUgjJkfeeatqLcAAAgAElEQVQhpI6Z5HdLvCJLkJ0OdDXVIzWv2D/N1YWOuoNISsuCufBSNK/9K+Sjdtj3rPNeoSaazKq/uIy+j+5b+wVRREpuEVLzLglMMyZh2OzbgpYJxrM9XFtEpJ0nHgSLNb7p6uuLfKx64lio2OIbrwABstMB+74NsIwrx4ibf+LN6zrfiPPb1sA68QrvT0xFzmdRtc+R4l93Hy2MP0REvaRlnueOz22of3MpuppPI/eHv3bH67ee8sbrC9Wfuu+6mHKt33ggOx2QOtpgMNu823rOp12tZ9FU9Ve0blsDucOO85v/4RfzU8dMQuqYyZCdbaj788McI4iIiIjIK6rFjubmZgiCAJfL5bdNC0EQommaSDeCKIa8vR4A2mt34fQbv0TuomU9yhm6y4lGWMZfBoMlDVJbi/tBurILppwi5C5aBlv5dWj54h0AgPPUQdS/uTSgvtj0UfT5tyFoWqjtkfsUXTkiCi/w2OrdT3CEO1b94lgQPeOVJ++5EM/OsFd/htxFy2DftwHnVr8cMZ+afY4U/6KJqUREFJ6aeZ5vfE772n8gKS0rYNFBbrej5Yt30LLhPQy7dhGGTrseZ/7+O7Tt+wIA/MeIs3VIzsyFKXc8hl9/J1p3fOSdP3sEi/kcI4iIiIiop6gWO/7nf/4HgPvZHD23EQ0EiixdvO0+9IMTAfeJkqO2GqljJnuvhvYrJ7vQ9tWm4OWO7IJtyrVo2fCe9yGK3fVNivhFZrR9JCIKR5EltB/VElsmwVlf6ymMzoZjIfNaxpXjnCBGzBcpXjH+ERH1T37xWRAv/nTVzrDx2jJhOhxHdqJt73rvtp5jRGfDMXQ2HIPlkpmwXTobLV+8G/Dgct9xiWMEEREREQUT1WLH9OnTVW0j6q8E0QB7dZWqvPbqKu+t9ZrK7V6LETf/BMnD8/xO6Hzri0UfiYjCEUQD7LurVOX1xBbL+MuCLuz2zBss5oWqM2IfGf+IiPod3/icPDwPRlsmmj5bETK/mjy+Qs2fvekXY3770T3q6uMYQURERJRQdLvEZdGiRVi+fDlOnDihV5VEMSXZW9Tla/P/iTat5dy/eR+6vrB1RNlHIqJwtMYWgyVNdd6eMS9Uvoj1Mf4REfVLnvjsiffh4rWaPH51RxhLPOlKV6em+oiIiIgoMUR1Z0cwGzduxKZNm/Dkk0+isLAQX//611FRUYGpU6fy+RzULxmskb+8AwCDJb1X5WSnI2x9YeuIso9EROFojS1SW+QvqULFvFD5ItbH+EdE1C954rMn3oeL12ry+NUdYSzxpAtJyZrqIyIiIqLEoNudHS+99BK++93vYtSoUTh8+DBeeeUVLFy4EDNnzsSDDz6If//737Db7Xo1R9QriizBWlqpKq+1tBKKLGsvV1IB1/lG94N5A+qTYtZHIqJwFFmCtaRSVV5PvGo78KWqvMFiXvA6w8crxj8iov7JNz53Np6Aq/UsrCUVIfOryeMr1PzZm35xXErNn6yuPo4RRERERAlFt8WOyspK/OIXv8BHH32E1atXY8mSJbjyyivR0dGBf/zjH7j//vsxY8YM3HbbbXj99ddx7Fjo3/MmijVBNMBcUAZTTlHYfKacIpgLSr0PNdRUrrAMrTs+8nu4Ynd94R9O3ps+EhGFI4gGmAu1xBYDTNkFqvK2Hdoe8EDZ4HWGj1eMf0RE/ZNffFZktG7/EObCS0PHazV5Lgo1f/ZLvzgucYwgIiIiomBiMvPLz8/HwoUL8corr2DLli149dVXsXDhQowaNQpbtmzB008/jblz58aiaSLVFFlG9oIlIU+UTDlFyF6wJOBqMFXl5i9GV/MZtG5bE6S+yHd19LaPREThKLKkMrZIGuKQBOvEK3SLV4x/RET9k298bt2+Bl3NZ5A9f3HIeO08dQiK5FIV012t5/zmzz3Tu++25hhBRERERIF0e2ZHKM3NzWhqaoLD4YAsy1AUJdZN0iChyJLfHRA9X/eWIIoQTRbkLloGR2017NVVkNqaYbCkw1paCXNB6cUTpJ7/Z5WI5eTODrRu/DtMIwt71KdtH9T2kVesESUOtbExXD5BNEA0mSPElu78auOQnvGK8Y+IyF+s58Za+uAbn1u3rcbQy2+MGK8jx3QJQuoQjJh3f8SYzzGCiIiIiILRfbGjrq4OW7duxZYtW7B161acPHkSAKAoCgRBQFFRES6//HLMmDFD76ZpkPCcmLQf2+M+cbG3wGBNu3jiUhaTE5eU3PEwF5R6X8tdHd5/d9QdwIXtHwbtR+qYSX7lPFePCcYkDJt9m892990c0ZyQevY1ZFs8iSNKCGpjo+eLqPaje2Df7ZOvpBLmwjJvuiceBcaWwHilNg7pHa8Y/4iI+mZuHKkPcqcT1slXwjy23C8+p46OHK9Dx/RQ41LwmM8xgoiIiIh60m2x46GHHsLWrVtx+vRpAPDewVFQUIDLL7/c+5eRkaFXkzQIKbIM2dmG+jeXwnnqoF+afc867y3posnS6xMYv7ZOH0by8DyIJjNkpwOdjSdgGjkW2bc8AqM1A47D2yG324P2w5fvsz38t/f+qrvAOnkCR5Qo1MdGdwxTk8/3Lg9f4eKV2jikd7xi/COiRBXPubHWPjgObAYEEZaJX0PmdT+C3OVE3Z8eRGp+CTLn3g4xKSVonyLFdK0xn2MEEQ0GR5b+R9DthUveiXNPiIgGNt1mgqtWrfIudJSXl+PZZ5/F+vXr8cEHH+Cxxx7D3LlzudBBEQmiGPRkzsN56iDq31yqy0mMX1uKjM6GY+g4UYPOhmOAIrvbeutJJKVnwVZ+Xcz6QUQUiZrY2FazEYJoUBlD4/uzJ0REFJ14zo2j6oMio23vetS/+f+QZBsG26Wz3K//+jjnyUREREQUd7rNQEtKSiCKIhRFwfbt2/Hoo4/ikUcewauvvorq6mrIfDgcRaDIEhy1u0KezHk4Tx2Eo7a6Vw8c1NTWkV2wTbkWEMTAtF72g4goElXxShBhHlcOxxEtMVTSuadERKSneM6NdemDz5yZ82QiIiIi6gu6/YzVypUrYbfbsXnzZmzcuBGbNm3C+vXrsX79egiCALPZjMsuu8z7c1YTJ06EIAh6NU+DgCAaYK+uUpXXXl3l99u8MW1r91qMuPknSB6e577rQ8d+EBFFoiZeJQ/Pg9GWiabPVqiqk7GLiKj/i+fcWJc+9Jgzc6whIiIionjT9QHlVqsVs2bNwqxZswAAZ8+e9S58fPnll1i7di3WrVsHALDZbNi8ebOezdMgINlb1OVra457W6LJHJN+EBFFEileeeJTPGMoERHFXn+I69HOmTnWEBEREVG86brY0VNmZiZuvPFGzJ07Fzt37sQ//vEPvPvuu3C5XGhtbY1l0zRAGaxp6vJZ0uPelux0xKQfRESRRIpXnvgUzxhKRESx1x/ierRzZo41RERERBRvMVvs+Oqrr7Bx40Zs2LABX375JTo6OqAoCgDgkksuQUVFRayapgFKkSVYSyth37MuYl5raSUUWY76wYea2iqpgOt8IzobT+jeDyKiSNTEq87GE3C1noW1REsMlfigciKifiyec2Nd+tBjzsx5MhERERHFm26LHXV1ddiwYQM2btyIzZs3o6mpCQCgKApSU1NxzTXXoLKyEhUVFcjKytKr2QDvvvsuFi9ejBUrVuCyyy5TXe7MmTN48cUX8cUXX6CxsREjR47ETTfdhP/6r/9CcnJyzPpL3QTRAHNBGUw5RWEfgmjKKer17/8GtCWISB6eB9Fkhux0uE/SFNndVmEZmqr+Cij+D1jUox9ERJGojVeOQ9thK58TlxhKRESxF8+5caQ+DJlyLZx1B7xjTtA++MyZOdYQERERUV/QbbFj9uzZAOC9e2P06NGoqKhAZWUlpk2bFpcFgx07duCJJ57QXK6+vh633HIL6uvrMXHiREyaNAnbt2/H888/j02bNuFPf/oTkpKSYtDjxNDz6mHf18HSshcsQf2bS4Oe1JlyipC9YIkuV4kpsozsBUvQVrMR5nHlMNoyvWmu1rNwHNoOyyUz0dV8Bq3b1viVTS0oQ9Z3Hgq5b+H2OXyfoitHRPrQ+xjUUl/4WKkiXhXP1BBDw8fhaPpIRET688T/aObGkWK2mpju2Tb8+jsBuMec1u0fonX7Gsjt9u4+zF/snTP79glQOG4QERERUdzotthhMBgwbdo07wJHfn6+XlWrsmbNGixevBgOR+BzFSJ57LHHUF9fj5/+9Ke4++67AQAOhwP33HMPNmzYgOXLl+OHP/yh3l0e9DwnXe3H9sBeXQXJ3gKDNQ226TfAlFUAQTSg/ege2Hd3pw2ZMgcpoyYgd9EyOGqr3eXammGwpMNaWglzQalut8MLogjRZIatfA4cR3ah6bMV3n5YSyphK58DRZbQdb4B5nFTvf0YUj4HKbnjIYgiHLW7/PbN3ccyKK4uNK17A51njvpvD9H3UO9VpHJEpA+9j0Et9anNqyZeCaIBoskcIYZ6FmX17yMREenLHf8tmubGnjjfc55tLamEubBMVTogBI/7JZXIqPwebFO/gfOb/4nUsVNgLiiF60ITWretxoh597v75Opyz5WP7PJvg+MGEREREcWQbosdmzdvhsVi0as61err6/Hss89i1apVSE1NRWZmJs6ePau6/JEjR1BVVYXRo0fjzjvv9G43m81YunQpZs+ejTfeeIOLHRopsgzZ2RZwFZoxPQuZw++G7HQEvULNvmed966J1DGT/G5/d18dBt1OjNx9DN0Pz1Vppqx8jLj5Jz7lpKD75ldu/mLYpn4DdX9+EPJRu199osnS44Q0+HvVsx89yxGRPvQ+BrXUB0BlXrOqeCWazN4rZgNjqAQA3oUO/fvIGEVEFAue2Kpmbuyep0YeL5SuTjT8/Vnv3Rme9NSCMmTf8gjkzsh1ZHx9IQD3Xf3GIRkYNvs2uFrPQmq3A7KM+uX/l+MGEREREcWVbrPLvljoAIDf/e53WLVqFSZPnoy33noLhYWFmsp//vnnUBQF11xzDcQek+2cnBxMnDgRdXV1OHTokJ7dHvQEUQx6gpQ59w6IxqSQt+IDQHvtLpx+45cBt7jrfTIUqo8ezlMHUf/m0iD9MEQu9/ZTSErPgq38uiD1+e+H+n7wZJAoFvQ+BrXUpyUO2fdt0BSvgsWu2PaRMYqIKJbUzI1VzVPfXAqjbZjfPNXDlDMOgsGoYYxw96nx3y/h5Cs/Q+uOj2BItaL+7Sc5bhARERFR3A34GWZhYSGefvpprFy5EhMmTNBc3rOIUVRUFLJ+ADhw4ED0nUwwiizBUbsr8ARHNCJ19EQ4jgRJ68F56iActdXeK5Hj1seQ/ZC1lzuyC7Yp1wKC6L892vp8yhGRPvQ+BlXXd/ow5C6nprYt48r94knoPoaPm1r3We7qgPP0YVV5GaOIiPqOIkvq59lB5qkQxIs/l7hT01zdUbsLF3Z8hM7GE7BNuVZjeY4bRERERKQf3X7Gqq/cfvvtvSrf0NAAABgxYkTQ9OHDhwOAqp/Gevfdd/Hee++parempkZlDwceQTTAXl0VsN0yfhoEQxLsuwPTgrFXV/ndqq+nUH2M1A9N5XavxYibf4Lk4XnobDjW+/pi+H4Q9UfxiKl6H4Nq60sengcxyaSp7WDxJJZ99K0vUrtq2yaivsN56uAniAb18+wg89Tk4Xkw2jLR9NkKdXVcjPvtR/f0qjzRQMSYSkRE1D8N+MWO3mpvbwcApKSkBE33bFfz4PO6ujps2bJFv84NYJK9JWCbwTI0ZFrQOtqade1TQP1R9kNrOdFk1rU+okQRr5iq9zGopj5PXOhtPAmVL2J9OrerpW0i6hucpyaG3sT3aMcmpauzV+WJBiLGVCIiov4p4Rc7DAb378wKghA2n6ziFuvc3FxMnz5dVbs1NTW4cOGCqrwDkcGaFrBNajsfMi1oHZZ0XfsUUH+U/dBaTnY6gm7vbT+IBrt4xVS9j0E19XniQm/jSah8EevTuV0tbRNR3+A8NTH0Jr5HOzYJScm9Kk80EDGmEhER9U8Jv9iRmpoKAOjo6Aia7tluNke+qnXevHmYN2+eqnYXLlw4aK8EUWQJ1tJK2Pes89vedmArFKkL1pLAtGCspZVQZCngYYyx7GPofsgQRFFbuZIKuM43orPxhD71+ZQjSgTxiKl6H4Nq6+tsPAG5y6mp7WDxJHgfw8dNrfssd3VEbLe7bcYoov6K89TBT5El9fPsIPPUzsYTcLWehbWkQtNcPTV/Ms5veDfK8hw3aGBiTCUiIuqfEn5m6XlWR6hncjQ2Nvrlo8gE0QBzQRlMOT0e+i670H58H8yFQdJ6MOUUwVxQGpOFjrB9DNkPUXu5wjK07vgIUGT/7dHW51OOiPSh9zGour6RYyEmmTS13XZou188Cd3H8HFT6z6LSSkwjRyrKi9jFBFR3xFEg/p5dpB5KhQZrds/hLnwUk1zde+YElV5jhtEREREpJ+o7uxYsULdQ+ciufXWW3WppzeKitwT8UOHDgVNP3z4MABg/PjxcevTYKDIMrIXLEH9m0vhPHXQu/3sB3/EqNufC5rmYcopQvaCJTG7qyNSHwP74X/Fmapy8xejq/kMWret0ae+IOWISB96H4Na6gOgOh5aJ14Be/VnusTN2PSRMYqIqK8psqQqZrtaz/nNUz2cpw5BkVya4r7vmNK6fQ2GlM1C9vzFqH/7KY4bRERERBRXUS12PPHEExGfcaFGf1jsuOqqqwAAn332GR544AGIPhPuU6dOoaamBrm5uRg3blxfdXFAEkQRosmC3EXL4Kithr26ClJbMwyWdHQ2HocpKz9omrW0EuaC0pgvdETqY3c/Ak/C1JSTOzvQuvHvMI0s1KU+ngwSxY7ex6DW+tTmFU1m3eJmrPpIRER9SxAN6saL1CEYMe/+kOla4n7PMaV122oMvfxGjhtEREREFHdRLXZMmzZN737ExalTp9De3o709HRkZGQAAPLy8nDVVVdh/fr1eO6553DfffcBABwOBx599FFIkoRFixb1ZbcHLM8JTOqYSTAXlHq3+57cBKZJF8vGdqFDTR990zWXMyZh2Ozb9KuPJ4NEMaX3MailPrV5PXFRr7gZiz4SEVHfUzteqBlP1Mb9UOMExw0iosQy/627VOV7+5Y/xLgnRJSoolrsWL58ud79iIuHHnoIW7Zswb333osf//jH3u2//OUv8d3vfhcvvfQSPv30UxQUFGD79u1obGzE1Vdfje9+97t92OuBr+cXcP5foPVMi88iR0/h+hhNOb3rI6L40PsY1FKf2rx6x81Y9JGIiPpepPFCzXiiNe5HboPjBhERERHFDmebcN/dsXLlSsybNw9NTU2oqqrC0KFDcf/99+OFF16A0RjVmhAREREREREREREREcVBzL7FdzqdkC/epuwhSRKcTicaGhrw6aef4p577tG93XB3nYRLGzlyJJ566ind+0NERERERERERERERLGl62LHv//9b/z3f/83amtrAxY6gonFYgcRERERERERERERESUW3RY7Nm/ejPvvvx+KokTMm5aWhq997Wt6NU1ERERERERERERERAlMt2d2LF++HIqi4IorrsCKFSvwzjvvQBAE3HzzzVi9ejVef/113HDDDQDcPxm1bNkyvZomIiIiIiIiIiIiIqIEptudHTt37kRycjKeeeYZZGRkAAAKCgpQXV2N/Px85OfnY8aMGRg2bBiWL1+Ov/3tb/j+97+vV/NERERERERERERERJSgdLuzo6WlBXl5ed6FDgAYP348jh07hra2Nu+2e++9F0lJSXj//ff1apqIiIiIiIiIiIiIiBKYbosdJpMJJpPJb9vo0aOhKAoOHz7s3Waz2ZCfn4/a2lq9miYiIiIiIiIiIiIiogSm289Y5eTk4OTJk+jq6kJSUhIAIC8vDwCwf/9+lJaWevMqioL29na9miYiIiIiIiIiIqIQjiz9j6DbC5e8E+eeEBHFjm53dsyYMQMXLlzAs88+C0VRAACTJk2Coij4xz/+4d12/Phx1NbWYuTIkXo1TURERERERERERERECUy3xY7bbrsNJpMJr7/+Or7+9a+js7MTEydOxMSJE/Hll1/iRz/6EZ5++mksXLgQkiRh6tSpejVNREREREREREREREQJTLfFjlGjRuGFF15AZmYm7HY7kpOTAQCLFy9GUlISvvjiC7z++us4c+YMbDYbfvrTn+rVNBERERERERERERERJTDdntkBAFdeeSU++eQT7N2717tt2rRpeOedd7B8+XLU1dUhPz8fP/jBD5Cdna1n00RERERERERERERElKB0XewAgOTkZEyZMsVvW1FRER5//HG/bU1NTcjIyNC7eSIiIiIiIiIiIiIiSjC6/YzVrFmzcN9996nK+73vfQ/f/OY39WqaiIiIiIiIiIiIiIgSmG6LHXV1dWhoaIiYz+Vyob6+Hi0tLXo1TURERERERERERERECSyqn7E6fPgwHn74YSiK4rf9q6++wre//e2Q5RRFQWNjIxobGzF69OhomiYiIiIiIiIiIiIiIvIT1WLH2LFjkZubi9WrV3u3CYKAtrY27NmzJ2J5QRBwxx13RNM0ERERERERERERERGRn6gfUP7oo4+ioqICgPuOjUceeQT5+flhFzEEQYDFYkFRURHy8/OjbZqIiIiIiIiIiIiIiMgr6sWOzMxMfOtb3/K+fuGFF3DJJZf4bSMiIiIiIiIiIiIiIoq1qBc7evr000/1qoqIiIiIiIiIiIiIiEg13RY7fO3fvx/r1q1DbW0t7HY7nn/+ebS1tWH16tW48cYbkZycHItmiYiIiIiIiIiIiIgoAem62HH+/HksWbIEn3zyCQD3szwEQQAAnDhxAkuWLMHzzz+Pl19+GRMmTNCzaSIiIiIiIiIiIiIiSlCiXhV1dnbihz/8IT7++GOkpqbi61//OkaMGOFNVxQFNpsNZ86cwcKFC3Hq1Cm9miYiIiIiIiIiIiIiogSm22LHG2+8gb1792LatGn4+OOP8eKLL2LUqFHe9OLiYnz66aeYNm0aLly4gFdeeUWvpomIiIiIiIiIiIiIKIHpttjxr3/9C0ajEc888wwyMjKC5rFarXjmmWdgNBqxfv16vZomIiIiIiIiIiIiIqIEpttiR21tLcaNG4esrKyw+bKyslBYWIgzZ87o1TQRERERERERERERESUw3RY7BEFAe3u7qryyLCM5OVmvpomIiIiIiIiIiIiIKIEZ9aqooKAAX331FU6ePOn3rI6ejh8/jkOHDmHy5Ml6NU0DmCTLMIhiyNdERESDDcc+Iv3weCIiIqJ4mv/WXRHzvH3LH+LQEyIKRrfFjhtvvBF79+7FQw89hBdffBFpaWkBeVpaWvDAAw8AAObOnatX0zQASbICgyhg96Gz+GTrCTRfcCJ9iAmzpuXh0vEjvOlERESDBcc+Iv3weCIiIiIiop50W+z43ve+h/fffx/btm3D9ddfj5kzZ+LkyZMAgD//+c84cuQIPvzwQ5w/fx5FRUW49dZb9WqaBhhJVuDo6MKvXtmE/ceb/dKqtp/EhNHp+OV/zYA5JYknqURENChw7CPSD48nIiIiIiIKRrd7vJOTk/Hqq6+isrISTU1NeP/999HQ0ABFUfDrX/8aK1euxPnz5zFt2jS89tprMJlMejVNA4xBFIKenHrsP96MX72yiSenREQ0aHDsI9IPjyciIiIiIgpGtzs7AGDo0KF46aWXsGfPHnzyySc4fPgw7HY7UlNTMWbMGFRWVmL69Ol6NkkDjCTL2H3obMiTU4/9x5ux80AjSsZl8kSViIgGNI59RPrh8URERERERKHoutjhMXnyZD6AnIIyiCI+2XpCVd5PvzyOS8cPj3GP/n/27j0uqjL/A/iHmeE2ggqIpCY3cUZTEQgEzVtopbnmhmllmVpa67pZvy6alZmmtZXZRbsppptpq7WWm5mZtzJBlBUFxUAIRFERFZSLXGbO/P7AGRnmdgYHhpn5vF8vXy+Z85znfM/t+Z7nPGfmEBERtSzmPiLb4flERERERESmtMhgBwCcOHECp06dQkVFBfz8/BAREYHQ0NCWWhw5kLKKWpuWIyIiauuY+4hsh+cTEREREREZY9PBDkEQ8PXXX2PlypW4cOGCwfSIiAjMnj0bd911ly0XSw7Gz1fc+1rEliMiImrrmPuIbIfnExERERERGWOzF5RrNBo899xzWLx4MUpKSuDl5QWlUomYmBgoFAp4eHjg5MmTmD17NpYuXWqrxZKDUQsCRsR1F1U2MTYYakHTwhERERG1LOY+Itvh+URERERERKbY7Jsd3377LbZv3w4fHx+8/PLLGDt2LNzd3XXT6+rqsGXLFvzzn//E6tWrERcXh2HDhtlk2SkpKfjss8+Qk5OD+vp69OnTBzNmzMDQoUNFzX/u3DkMHz7c5PSYmBh8/fXXNonV1UklEkQpOkMZ7Gf2xZLKYD/+xjIRETkF5j4i2+H5REREREREpthssOPf//433Nzc8PHHHyM+Pt5guoeHByZMmICgoCA8+eST+PLLL20y2LF582bMmzcPHh4eSEhIgCAISEtLw4wZM7Bo0SI8+OCDFuvIzs4GACiVSigUCoPpYWFhNx0n3aAWNFgwIwELVx0w2klVBvthwYwEqAUNpBI3O0RIRERkW8x9RLbD84mI6OaMfX6L7v8/vDeu1eZ1ZH8uGW/08/BX/tPKkRhnKj6g7cRIRNQabDbYUVBQgLCwMKMDHY0NHToUISEhOHbs2E0vs6SkBAsWLICvry82bNigG6jIzMzEtGnTsGTJEgwfPhxBQUFm6zlx4gQAYPr06bjvvvtuOi4yTypxg9zLHUufGYojuaXYnV6Esopa+Pl6IjE2GFGKwOs/OaABcKODqhYESCU2++U1IiIii5rmnubmIrG5jzdmiSyz5nzi9SMRERERkeuw2WCHp6cnJAwGQq8AACAASURBVCI7Et7e3igrM/21c7HWr1+Puro6PPXUU3rfyIiMjMSMGTPw/vvvY+PGjZg9e7bZerTf7OjTp89Nx0TiaG/m9IsI0PuJAbWggXC9c3oktxS7Dp3WdV5HxHVHlKIzbwYREVGL0+aarLyLNstF5nJf4+lEZJmp86mmToXfjxTj14xieHlIef1IRERERORCbDbYMWjQIGzbtg3/+9//cPvtt5ssV1hYiNzcXIwcOfKml7lv3z4AMFrXyJEj8f777+O3336zONhx4sQJyOVy/lyVHRh70q6qpt7ozxLsPXxG97MEci93dliJiKhFqAUNqlswFzXNfcxnRM0nlUigFjSorVPh0/9k4rcjxRAavZSc149ERERERK7DZt/pnjt3LoKCgjBr1izs3bvXaJnc3FzMnDkTvr6+ePHFF29qeRqNBnl5eZBIJAgPDzeYHhoaColEgry8PGg0GiM1NCgvL8fZs2cRFhaGNWvW4L777kP//v0xePBgzJ8/HyUlJTcVJ1lHKnEz+fvLAJBTVIaFqw6wo0pERC2GuYjIsUglbnjt81TsPXxGb6BDi+csEREREZFrsNk3Oz755BPcdttt2L17N2bOnImuXbuib9++6NChA65du4b8/HzduzECAwPxf//3fwZ1uLm54ZtvvhG1vCtXrqCurg7+/v7w8PAwmC6TyeDn54dLly6hqqoKPj4+RuvRxnT8+HHk5uYiLi4Ot9xyC7KysrBp0ybs2bMHX375pdEBFbIttSAgK++iyZtLWjlFZTiSW4p+EZ3YaSUiIptiLiJyLDxniYiIiIhIy2aDHf/+97/h5tbQcdBoNCguLkZxcbHRshcuXMCFCxcMPtfOL8a1a9cANLz/wxQvLy8AMDvYoX1fh0KhwCeffILu3bsDAKqrqzF//nxs3boVL7zwAjZv3mwxps2bN+O7774TFb92kIVukEok2HXotKiyu9OL9H6fmYicD9tUsgfmInJWztqm8pwlIntw1jaViIjI0dlssOOtt96yVVWiiH0ZOgCzP2M1depU3H333WjXrh38/f11n8vlcixevBiHDh3C8ePHceTIEURFRZldTnFxMQ4ePCg6LjJUVlFr03JE5LjYppK9MBeRM3LmNpXnLBG1NmduU4mIiByZzQY77r//fltVJYpcLgcA1Naa7rTU1NTolTVGKpXqvs3RlLe3NxISErBlyxYcP37c4mBHt27dMGDAAEuhA2h4uqOiokJUWVfi5+tp03JE5LjYppK9MBeRM3LmNpXnLBG1NmduU4mIiByZzQY7WpuPjw/kcjnKysqgUqkgk+mvikqlQllZGTw9PdG+fftmL6dTp04AbvxsljlJSUlISkoSVe/kyZP5JEgTakHAiLju2Hv4jMWyibHBUAsa/uYykRNjm0r2wFxEzspZ21Ses0RkD/ZuU8c+v0X3/x/eG2fTuonaqokbZ1oss+nBT60u25LaShxErkT8b0G1MW5uboiIiIBarUZhYaHB9IKCAgiCAIVCYbaeFStWYPbs2cjJyTE6/cyZho7TLbfcctMxk3lSiQRRis5QBvsBACQSN4R2aY/bwvwR2qU9JNc7pspgP0QpAtlRJSIim2uai0xhLiJqG7Tn7KiEEL3rxaZ4zhIREREROT+H/WYHAAwZMgSZmZnYuXMnIiIi9Kbt3LkTADBs2DCzdeTk5GDHjh0IDw+HUqnUm3bp0iXs378f7u7uiI+Pt23wBKDhaTxpo/evqAUBS2YOwu9HziJSEYjAjjdeQF9afg3p2SUYHNW1WU/lGVuW1Ip3v7RWnUREZF9qQYMFMxKwcNUB5BSVGUxXBvthwYwEvVxkz3wgdtnWxOhM+c2Z1sUVWNpfpv6eNaHh52ZLy69he2ohfkopQEV1PQDDc5bHBBERERGRc3LowY6kpCQkJydj1apVGDx4MPr27QsAyMrKQnJyMry8vDBp0iRd+aKiItTX16Nz587w9fUFADz44IPYsWMH1qxZgyFDhuD2228HAFRVVeHll19GZWUlHn74YQQGBrb+CjoxbWczK+8idh06jbKKWvj5emL0oFAoQ/wxYkAwMnIv4Msfs3XTEuO6Y/Sg0OsdUvEDHdoObGbeRexutKzEuO6IVnRuVgfXVPwj4rojStGZP5FAROTApBI3yL3csfSZoTiSW4rd6UU3ckdsMKIUgQY3TW2ZY8QSm4usyVn2XB9bY652LJb3l/lj89KVGnz/ax5ienXG5NG9ce+gMGz5LQ8xyqDr56ygWxaPCSIiIiIi5+TQgx233nor5s6di0WLFuGhhx7SffsiLS0NKpUKb7/9NgICAnTlp06diuLiYrz11lu639ccPHgwpk2bhjVr1uDRRx9FTEwM/Pz8kJ6ejrKyMsTGxmLu3Ll2WT9npRY0qK6pN3hi1lfujkdH9UJldR3eWJ1m8DTt3sNnGp7Mm54AubdM1M0WtSCg+poKC5MNn85tTn3m4terc0YC5F7u7DATETkobfvdLyIAUYobDzyoBY1uekvkGLHE5yIZqmtUonIWoLHb+tgac7VjMbe/ck5dxsdzElFTqzZ7bM5/Ih5j7gjDcx/8iq6dfLBgRgKmjOmDsqvXsPPgKQy/vTuu1fKYICIiIiJyZm27pyrCI488gs8++wz9+/fH4cOHcezYMcTExGDNmjUYN07ci7peeuklfPDBB4iJiUF2djb27duHwMBAvPjii1i7di28vb0tV0KiSSVuRjuaoweFISigndGBDq2cojIsTD4g+iaLVCIx2jFubn3m4terc9UBdpSJiJxA0/zQuG1viRwjPi7Luej3I2cbYhSZs+y5PrbGXO1YzO2vvz/QH+4yqcVj843VabgloB1GDwzT27/bU0/h3KVqyKTizwUiIiIiInJMDv3NDq0777wTd955p8Vyu3fvNjlt9OjRGD16tC3DIiPUgoCsvIsGHU2JxA2jBoYiI+eCyU6oVk5RGY7klqJfRIDZGy5qQUCmkWU1tz5z8ZuusxM7zURETqglcow1y7aUiyQSN8TeFoSMXPF5tVeoH06eKRdV1pbrY2vM1Y7F3P6SySToE95J9PVhRu4FjBoYgm/3nNT9fU9CCODmZuU1Jo8JIiIiIiJH1DZ7qeS0pBIJdh06bfB5cJAvAjt6Y3e64TRjdqcXWbzJIpVIsNvIsppbn7ZOY/GbrpMdZSIiZ9QSOcaaZVvKRbq8akWMXh4yBAf5iirbVgc6AOZqR2Nuf8XfFgR3mUT09eGe9NMI9JPrjmPt39ZfY/KYICIiIiJyRG23p0pOq6yi1uAzuZfM5DSxdbRGuZaqk4iIHI8984GlOpubV7Xz3cyy2wLmasdiaj908PE0O91UPcaOfx4TRERERETOj4Md1Or8fD0NPquuUZmcJraO1ijXUnUSEZHjsWc+sFRnc/Oqdr6bWXZbwFztWEzthyuVtWanm6rH2PHPY4KIiIiIyPk5xTs7yHGoBQEj4rpj7+Ezep8XlVSgtPwaEmMNpxmTGBsMtSBYfGdHopFlNbc+c/GbrlPDn0IgInJCLZFjrFm2pVyky6tWxFhTp0JRSYWosrZcH1tjrnYs5vZXWnYJ6lWC6OvDO2O7o7SsWncca/+Gm5uV15g8JoiIyLb+XDLe6Ofhr/ynlSNpWyZunGmxzKYHP22FSIjIWbTNXio5LalEgihFZyiD/QA0vEA1tEt79ArxQ9qxc4hW3phmijLYD1GKQFHv7IhW2K4+c/HfFuaP0C7tIbneMb5RJzvKRETOqCVyjDXLjrKwbEHQID27xKoYvTxk6HlrR1Fl2+pAByBu+wDM1W1F0/0lk0lwR2QX3DsoFPG3BeH4nxcRreyMUQkhetdaTSmD/RCt6IztqacgCBrd3z8fOIXtqYVWXmPymCAiIiIickT8ZoeLavpEpvZvU583tz7jZTVYMCMBvx85i9jbghDY0Vs3rV6lxmvTE7Ao+QByisoM5lUG+2HB9ASr4lowPQELbVSfpfhLy68hPbsEg6O68qlAIrJKc9tfV9BWt01zc4wt1kebixauMr3shlwkWCy3YEYC1IIGgMbmOdNexGwf7XozV9ufdn+du1iFsK4d4C5rdH6oBQDArAlRABqutbanFuKnlAJUVNcDaNif85+Ix/lLVdiWUqC3f0cNCsWRnFKo1OLPBR4TRERERESOiYMdLkbbgcvKu4hdh06jrKIWfr6eGBHXHVGKzqivF7D252wUnL2q97mpjp+l+ozNJ5W4Qe4lw+hBocjIvYAvf8zWzTd6UCiUIf5Y+sxQHMktxe70It20xNhgRCkCrbrJIpVIIPeW2aw+S/EnxnXH6EGh1+tkR5mILGtOO+oq2vq2sTbHaP+fmXcRuxutT2Jcd0QrOluZ39wg93K3sGyNLmdZjtENgJvNc6a9WLN9yP60x6ki2A8ZuRdw+I8LuH94BPzbeyEz3/B8mTy6N+4dFIYtv+UhRhmEKEUgLl2pwbaUAsx9LE7396adxxHTqzNGDmj46TUeE0REREREzo2DHS5ELWhQXVNv9Im2vYfP6J6KG3NHGJ774FdUVNfrPl8wIwFyL3e9DqCY+kzPpzI5X7QiEC9PG4B+PQIQpQjUTatXCcg9dRldOvlA7i2zasADAPpF6NenFgS96WJZit/UehMRNdXcdtQVOMK20eaDc6WV6BPubzxnBfpA7uUOQIPqayqj35rQrc/0BCvzW8N6G+Y3jW66WhBQfU2F348WI7Z3EJ6bdLuuXGlZNX5KKcDg/t10y7V1zrQnMduH2gbtcbow+QDOXqzE+88Og1Tihhc+/M3s+T9lTB9A07A/Azp44fGxfXGx/BrWbTuBbSkFqLxWj+9/zddrLwAeE0REREREzoqDHS5EKnEz+dV9AMgpKsMbq9Ow9JmhGD0wDJt25eo+X7jqAJY+M9Tq+pozX0ZuKV79NAVLnxmKvf87gxOFl1BeUYuDJ0qgUglQBvsZ1ClG0xs0zb1h09z1JiJqiu2JaY6wbRrHKJNJMKB3EDr6eprIWW4mfx4KuL4+yc1bH8P85qY3TbtcicQNwUG+kHvJUF3T8DJyQdBg16HTRnK1bXJmW2Bu+1Db0Pg4nThSgaCAdkYHOrQan/8bfs5BatY59Arxw6wJUdieWqi7hjVWXrs8/eXzmCAiIiIicgaO23Mlq6gFAUdyL5jsNGrlFJUhI/cCRg0M0XsBZE5RGY7kluqefLOmvubOl5F7AX3C/bH9wCmkZJ2DSiU0qVOwuN621tz1JiJqiu2JaY6wbZrGqFIJSMk6h20phXo5K//sFajVAjKsWh/b5De1oL9cQdCg8NxVZBdcRuG5qxCubzd75lWixsepROKGUQNDkZEj/lrxrgHBKCqpwPYDp5CRewH3JIQYfYm5K7alRERERESuhoMdLkIqkWDXodOiyu5JP41APzmCg3z1Pt+dXqR78s2a+po7n6k4btTZ+odvc9ebiKgptiemOcK2ERtj/G1BkEol2G3V+tgmv0kl9lkukTUaH6fBQb4I7OiN3enNu1Y0d+0IuF5bSkRERETkatirdSFlFbVWlZN7yYx+3tz6bBWHNXW0hOauNxFRU2xPTHOEbSNm2R18PEWXtaacWI6wHYmaXvM191rR3LWjNfUSEREREZFj4mCHC/Hz9bSqXHWNyuz81tZnqzisqaMlNHe9iYiaYntimiNsGzHLvlJZK7qsNeXEcoTtSNT0mq+514rmrh2tqZeIiIiIiBwTBztchFoQMCKuu6iyd8Z2R2lZNYpKKvQ+T4wN1nv3htj6mjufqThu1Gmfd3Y0Z72JiJpie2KaI2wbsTGmZZdArRaQaNX62O6dHfZYLpE1Gh+nRSUVKC2/hsTY5l0rmrt2BFyvLSUiIiIicjUc7HARUokEUYrOUAb7mS2nDPZDtKIztqee0r24VPt5lCJQ790bYutr7nzG4tCv0z7v7GjOehMRNcX2xDRH2DZiY+zRtQOkUgmirVof272zwx7LJbJG4+NUEDTYnlqIaKX114rmrh215V2tLSUiIiIicjXs1boQtaDBghkJJjuPymA/zH8iHmVXa1CvVuOOyC6QySRQBvthwYwEgyfhxNRnaT6JxA2hXdrjtjB/hHZpD4nETRfH+UtVuHTlGu4dFKofy/QEuz592tz1JiJqiu2JaY6wbRrH6OPjgfnT4vD+s0Mxf1ocfHw89GJUCwIWTLewPi2Q3xov11TOtXdeJWp8nP58oBAXy69h/hPxUAb7QSaT4I7ILrrrQQ8PKYbH3IqFTybgamUt9mWexQuTYvDm3weh+lo99h4+Y/w4d9G2lIiIiIjIlRh/ex85BbUgNHlKU4N2Xu5Y+sxQHMktxe70IpRV1MLP1xN3DQhBv4hOUKsFSKUSPD62b0Md1/8WBA000ABw06tPbqK+xNhgRCkCr3cqTc9XW6eCp8eNw1D7tyBo4OPtjmcfjrmxPtdjabpejf9uiWlNSSVuItZb0Hty0Fx9ROS6xLUnGqd9Etlc29vcbSO2PbdFucYxanMUAKC7H77qfcv1nKWN0Q1yb5mI3GE6pubFKNEt11TOvZkcZU3+tEd9ZF9ij2ljx6la0BieW4DB35++eKfubw93YOW8EXrTG9enbS94nBEREREROScOdjghbYctM+8idh86feNmSlx3RCs6Qy0I6BcRgChFoN48AJCZb3weDYCcU2X4KaVQN230oFAoQ/wBAL1C/fTqq6nTvhhSgzMlFfh2d55uvkdGKRHoJwfghhOFl7Gr0fJGxHVHlHZ5RfrL08WiAb7ZlYujJy/qxSGVuCEr76LR+tSCxiD+cUPDEdatg8VtZazzq+0sm9qO2X9ewo60IqNxOOtNSyJqHtPtiUZvujPRtoXm2uyG9W4YLDeXY7SD6WJyn1QisaKcdTGayp+GA/7mcqZ2+9h2XbTLN5Vzm8Zn231on/rIvizvT/1jN7RLe/x1WA8ADcfpydPluH9YBCAxfW5dulKD73/NQ0yvzkb/VgsCfj9SjA4+ntePcx5nRERERETOjoMdTkYtCKi+psLC5APIKSrTm7b38Bndz1XIvWVWzTP/iXj4t/fC//4oQUV1PXzl7nh0VC9UVtfhjdVpOHmmHMFBvpB7yVBdo0JRSQV63toRC6Yn4NbOvjhzoQJ5Z64gvk8QAv3kVi+vafzjhvXAL2mn8GdxuV4cYuvrEiBHSJco0dvK9Lc8Gj8VqEF1jQoLV5mpb0YC5F7u7EgTkYGm7YyzthMNbWW9iLZSpmujzeUYbT4T256LLieqPRdfX+MYLa2PfdZFfG4Svw/F1Wnr+si+zO3P//1RguUv3AkPmVR37Ebc2gHPPhTdcJwmH8DVqlp8PCcR12pVWPix+WvFMXeE4bkPfkXXTj4Gfy+YnoDBUd3w2ucp2LwnD69NT2iok8cZEREREZHT4ve1nYxUIjF640Mrp6gMC5MP6N1UEzPPG6vTcEtAO4weGAYAGD0oDEEB7XQDDIKgQeG5q8guuIzCc1chCJoby5JK8OKjsQCAFx+NbdbymsbvIZNi5vj+BnGIre/vD/SHe6OOtthtZY5U4ma0A61X36oD7EATkUsT01aeu1illyvM5pjrP4FjqT2vrVeLKvf70bMN5US15xL8frRYVB6xZn3OXawUF6PIHPb7EQsxWpmbbJ3vmD+di7n9OXpQGAI6eOsdu3Mmx0IqvXE8i71Ga3xtZ+xv7fE/97E49Az2g0wq9rzmcUZERERE5Kg42OFE1IKAjNwLJjtxWjlFZTiSWwq1IFg1T0buBYwaGAKZTIJRA0ORkSN+WUH+cnTs4AWZVCJ6Pu3yJE06ndo6+/bohNFWxKGtz8NDij7hnayK39KLW9WCgCNWbXu+IJOIXI+YtlImkyCsawer8llNnQonz5SbLOfhIYWfr6fFOiUSN8TeFmTVsmN7BxnkqebE2LhsWLeOkMmMX6K1ZIxicpOt8x3zp3Mxtz8lEjeD60cPDyk6+8l1n8lkEquu0RpfKxr7+0huKdp5u1t93crjjIiIiIjIMXGww4lIJRLsPnRaVNnd6UW6J03FzrMn/TQC/eQY0DsIgR29sTvdimVJJXhtahykUono+bTLCw7yNVqnu0yCTlbEoa3v3oEhcJeJj0O7rcyRSiTYZdW251ODROR6xLSV8bcFNbTRVrSpXh4yo7lCa8zAkIb8Y6HO4CDfhvxmxbJN5SlrY2xc1l0mwYDeQa0eo5jcZOt8x/zpXMztT92x2+j6S3duXv9Md/4381qx6d/aazirr1t5nBERkYMJf+U/Jv8ZK0NE5Kw42OFkyipqrS5n7TwdfT2bNV8HH69mzSf3Mny1zM3EH9Deu1nztXY5IiJnZKkN7ODTvBxjLFdo+Yts97V12HLZ1pRrXFaba1s7RmvqbKvlyL5M7Sdjx27Tc/Nmz39TfzenTiIiIiIicjx8QbmT8TNxc8RcOWvnKb/eCbR2viuVNejsL7d6vuoalclpzYnj0tVrzZqvtcsRETkjS23glcrm5RhjuULrssh2X1uHLZdtTbnGZctN3HBt6RitqbOtliP7MrWfjB27Tc/Nmz3/Tf3dnDqJiIis8eeS8UY/b61vUUzcOFNUuU0Pfmpymr3XwV5sse2IqO3gNzuciFoQkBjXXVTZxNhg3Ts7xM5zZ2x3lJZV4+CJEpSWX0NirBXLUgtYtPYQ1GpB9Hza5RWVVBits14l4KIVcWjr25Z6CvUq8XFot5U5akHACKu2PX8Lmohcj5i2Mi27pKGNtqJNralTGc0VWj+mnmrIPxbqLCqpaMhvVizbVJ6yNsbGZetVAg6eKGn1GMW+s8OW+Y7507mY25+6Y7fR9Zfu3Lz+me78b+a1YtO/tddwVl+38jgjIiIiInJIHOxwIlKJBNGKzlAG+5ktpwz2Q5QiUPfODrHzRCs6Y3vqKahUAranFiJaKX5ZldfqMH5oDwgajej5tMsTmnQ4tXUWnL2CA8fOWV1fXZ0ax/+8aFX8Yt7ZEWXVtudvQROR6xHTVqpUAgrOXrEqn3l5yNDz1o4AGl5wfkdkF9w7KBR3RHaBTCZBXZ0aZRW1FusUBA3Ss0usWnb6iRKDPGUpRktlC4rLoVIZH2RvyRjFvrPDlvmO+dO5mNufgqAxuH6sq1PjQlm17jOVSrDqGq3xtaKxv6MUgai6Vm/1dSuPMyIiIiIix8TBDgfX9BsHakHAgukJJjtzymA/LJieoDefmHnmPxGP85eqsC2lAADwU0oBzl+qwvwn4k3ON6hfF/xz1h1QCwI6+Hjhr8Mj4C6TNixvhnXLMxa/ItgPfxkcjnqVGq9ZGf8n3x5FnUpt9bYyRy1oLK7XghkJfFqQiFyamLayS6d2VuUzbdn3nhmKTUvG4KUpAzBzfH+8NGUANi0Zg/eeGQpPd6moOgf37yoqTzW05wIG9+9mVYxiynbp5CMuRpH1DY6yEKOVuak5+c7Y9crN1Edtl7n9+VNKAS6WX9M7dt9Zlw61+sbxLPYarfG1nbG/tcf/W2sP4mRRGVRqsec1jzMiIiIiIkfFd3Y4KLWggVTihqy8i9h16DTKKmrh5+uJcUPDEdatA5Y+MxRHckuxO71INy0xNhhRikCoBUHvmwpSiQRyb5mFeTQoLq1C3G1BummXr9ZCGSI3Ot99Q3ogrFt7SCUSZORewO5GMY4eFApliL/Vy9NOU6kFbN6Th6MnL95UfUXnKhDWrb1V28ocqcQNci93i3HwaUEicmXWtJWWc9ONNlruLYMi2M8g5yTGdUe0orOurNg6WypGUcu2dTkb5yZr9qGp65URcd0Rpeh8YzsyfzoNS/uzU0dvqNWC3vTvf83HX4f10H3239/yMW5ohNlj4tKVGmxLKcDcx+KM/q0WBOw/WowH7+qlO4Z4nBEREREROTcOdjggtaBBdU09Fq46gJyiMr1pew+fwaB+XfDio7ejX0QAohSBjeZreIrS2M177Wem53GDMqQjbgu7XW+aSqVGQUkF+oT7G8xXfU2FhcnGY4xWBOKVaQOsXl7uqct456v/oeRytcj6NGbrs7ze1n35SdtBNhcHEZGrE9NWavPIuYuVBjmmXtWQD7p08oHcu+FSxlzO0T7lLfeWiW73xbbn1uQR8cu2dTnb5yZx+9D89Yr2SXq5lzvzp5MxtT9Ly6qxbtsJ/C+nBFPuvQ19e+hPFwTBYB5Tx3dABy88PrYv1Oomfzc6/odGdzd6DPE4IyIiIiJyThzscEBSiZvRGwdaKVnn8NLH+7H0maFN5rN8475pGWM3aRr/PffT35FTVAaZTIIBvYPQ0dcTowaGIqxrB6M3nbQyckvxyqcpZmNsurxXPv4dxwutrc+t0f+tW7ebYVgfO89ERE2ZayulEokujzTOMeUVtTh4ogQqlQBlsJ+u3TeXc3KKyrAw+YBejhDb7ottz63JI81ftq3L3XxuMr8PzV+v5BSVYeEqS/uF+dORaffnx98cwR+nylBUUqF7f8xrK1N157YyxA9Jd/aEpNH+//XwaZw8XY4xd4ShSycfbN5zEjmnynD4ZCliegbq2oP0nAvoGtAOvUL8MGtClKhjiMcZEREREZFz4mCHg1ELArLyLpq8caCVU1SGI7ml6BfRqUU6cE3jUKkEpGSdg0wmwfRx/ZCRc8GKGAPM3hTSLsvUQIdhfS2zzkRE1DrUgoBMIzmmqfyzV6BWC8jMtyYvms85ZBtt5XqF7Et7HGw/cMrodO25nZJ1DuHdOqJfRCcAGmTlXcTS9Yfh4SHFtL/0QUbOBazZmq2br2l7UHjuKgrPXcUd/bvxHCciIiIicmHsCTgYqUSCXYdOiyq7O72oxW4cmIoj/rYguMsk2J1uTYzmD8O2ss5ERNQ6pBIJdoto9+NvC4JUKq4sIC7nkG0wdxPQvOOg8TxjBoY0nOM2vK4kIiIiIiLnxd6AAyqrqLVpOVvG0cHH06pl26scERG1bWLa85bKOWQb3C8ENO840P7fv713Too4SgAAIABJREFUs+sgIiIiIiLXw8EOB+Tn62nTcraM40plrVXLtlc5IiJq28S05y2Vc8g2uF8IaN5xoP3/5avXml0HERERERG5Hg52OBi1IGBEXHdRZRNjg6G+/hLI1oojLbsE9SoBibHWxCg0a1mm62uZdSYiotahFgQkimj307JLoFaLKwuIyzlkG8zdBDTvOGg8z4+ppxrOcRteVxIRERERkfPiYIeDkUokiFJ0hjLYz2w5ZbAfohSBLfrODmNxqFQCjv95EdFKa2K0/M6OtrDORETUOqQSCaJFtPs9unaAVCqurNicQ7bB3E1A846DxvPU1alxoazapteVRERERETkvJyiN5CSkoLHHnsM8fHxiImJweTJk/Hbb79ZVUdBQQGee+45DBs2DP3798fYsWOxbt06CG3w6TC1oMGCGQkmO33KYD8smJHQ4k9Jmorjk2+Pok6lxoLpFmKcniD66bu2ss5ERNQ61IIgOo9YU5ZaD3M3Ac07DhrP8866dKjVPMeJiIiIiMgymb0DuFmbN2/GvHnz4OHhgYSEBAiCgLS0NMyYMQOLFi3Cgw8+aLGOP/74A4888ggqKysRExODfv36IS0tDYsXL8bRo0exdOnSVlgT8aQSN8i93LH0maE4kluK3elFKKuohZ+vJxJjgxGlCIRa0LT4U5Lm4ig6V4Gwbu0txCiIfvqurawzERG1DqlEArm3THQesaYstQ7mbgKadxw0neeXg6dw14AQnuNERERERGSWQw92lJSUYMGCBfD19cWGDRugUCgAAJmZmZg2bRqWLFmC4cOHIygoyGQdGo0Gc+bMQWVlJd555x2MGzcOAHD58mVMnToVP/zwA+666y7cc889rbJOYmk7hP0iAhClCNR9rn0qrrVuHJiLw/Q04fq81nVI28o6ExFR69DmCTF5xJqy1HqYuwlo3nFgah6e40REREREZIpD9wrWr1+Puro6TJ06VTfQAQCRkZGYMWMGamtrsXHjRrN17N+/Hzk5ORgwYIBuoAMA/P398frrrwMA1q1b1yLx20LTjp29bhqYi8Nw2s0ddm1lnYmIqHVYk0dsnXPINpi7CWjecWDpnOY5TkREREREWg7dO9i3bx8AYOTIkQbTtJ9ZeneHuTpiYmIQEBCA//3vf6isrLzZcImIiIiIiIiIiIiIqAU47M9YaTQa5OXlQSKRIDw83GB6aGgoJBIJ8vLyoNFo4OZm/MmxvLw8AND7ZkhjYWFhuHTpEvLz89G/f3/brQAREREREREREVErC3/lP6I+IyJyNA472HHlyhXU1dXB398fHh4eBtNlMhn8/Pxw6dIlVFVVwcfHx2g9Fy5cAAAEBgYana79/OLFixZj2rx5M7777jtR8Z84cUJUOSIiV8U2lYjIdtimEhHZDttUIiKitslhBzuuXbsGAPD29jZZxsvLCwDMDnZo69GWNVVHdXW1xZiKi4tx8OBBi+WIiMgytqlERLbDNpWIyHbYplo29vktuv//8N44g88af06tb+LGmRbLbHrwU6vLEhHZm8MOdkiseBmhRqMxOU0qlQKAyZ+50hIEweJyunXrhgEDBoiK6cSJE6ioqBBVlojIFbFNJSKyHbapRES2wzaViIiobXLYwQ65XA4AqK2tNVmmpqZGr6wx2m+GaMuaqqNdu3YWY0pKSkJSUpLFcgAwefJkPglCRGQG21QiItthm0pEZDtsU4mIiNom8V+PaGN8fHwgl8tRVlYGlUplMF2lUqGsrAyenp5o3769yXo6d+4MwPQ7OUpLSwGYfqcHERERERERERERERHZl8MOdri5uSEiIgJqtRqFhYUG0wsKCiAIAhQKhdl6evbsCQDIy8szmKbRaPDnn39CKpWiR48eNombiIiIiIiIiIiIiIhsy2EHOwBgyJAhAICdO3caTNN+NmzYMFF17Nq1y2Da4cOHcfnyZdx+++0mX3BORERERERERERERET25dCDHUlJSfD09MSqVatw7Ngx3edZWVlITk6Gl5cXJk2apPu8qKgI+fn5ei8HGzBgAHr27In9+/dj06ZNus8vX76MhQsXAgCmTZvWCmtDRERERERERERERETN4bAvKAeAW2+9FXPnzsWiRYvw0EMPIT4+HgCQlpYGlUqFt99+GwEBAbryU6dORXFxMd566y3dy8QkEgnefPNNTJkyBfPnz8e3336Lzp074+DBg7hy5QomTpyIxMREu6wfERERERERERERERFZ5qbRaDT2DuJm7dmzB8nJycjOzoaHhweUSiVmzpyJgQMH6pVLTEw0GOzQysvLw0cffYS0tDTU1dUhJCQEDz30ECZMmACpVGrzmIcOHYqSkhL4+vqid+/eNq+fiKipXr164ZVXXrF3GC2CbSoRtTa2qUREtsM2VZx169bZKKq2ZfLkyfYOwSRn3ebU9tjyPHDmNpXIEof+ZofWnXfeiTvvvNNiud27d5ucFhERgY8++siWYZlVXV0NAKioqMDBgwdbbblERM6IbSoRke2wTSUish1btqlKpdIWIZEVuM2JiByLUwx2OKJbb70VZ86cgVwuR0hIiL3DoTbkxIkTqKio4NOUZFJzj5FevXq1YFT2Zcs2ledg28T90na56r5hm+pcXPU4bgnclrblKtvT1dtUV9nPjoj7pm3ifjHPmdtUIkuc4mesiJzJ5MmTcfDgQQwYMIBfmSWjeIy0LG7fton7pe3iviFnwOPYdrgtbYvb0zVwP7dd3DdtE/cLEZkisXcAREREREREREREREREN4ODHURERERERERERERE5NA42EFERERERERERERERA6Ngx1EREREREREREREROTQONhBREREREREREREREQOjYMdRERERERERERERETk0DjYQUREREREREREREREDo2DHURERERERERERERE5NA42EFERERERERERERERA5N+vrrr79u7yCISF/v3r0xYMAA9O7d296hUBvFY6Rlcfu2TdwvbRf3DTkDHse2w21pW9yeroH7ue3ivmmbuF+IyBg3jUajsXcQREREREREREREREREzcWfsSIiIiIiIiIiIiIiIofGwQ4iIiIiIiIiIiIiInJoHOwgIiIiIiIiIiIiIiKHxsEOIiIiIiIiIiIiIiJyaBzsICIiIiIiIiIiIiIih8bBDqI2pLi4GC+//DKGDh2Kvn37YsiQIZg/fz5KS0vtHRq1AVu2bMHEiRMRFRWFyMhIjBs3Dv/617+gVqvtHZpTUKvV+OqrrzB+/HhER0cjMjISY8aMwccff4za2lp7h+ey1Go1vvzyS9x3333o168fBgwYgMcffxx79+61d2jUSHl5OYYMGQKlUmnvUIhEY7vfctgm3Dz2C1xHSkoKHnvsMcTHxyMmJgaTJ0/Gb7/9Zu+wqJHNmzdDqVQiPT3d3qG4POZuIrLETaPRaOwdBBEBWVlZmDZtGioqKqBQKBAcHIxjx47h/PnzCA4OxrfffosOHTrYO0yyk3feeQerV6+Gh4cH4uLiIJVKkZ6ejurqaowcORIrVqyAm5ubvcN0WGq1Gn//+9+xd+9eyOVy9O/fHzKZDEePHsXVq1fRv39//Otf/4K3t7e9Q3U5c+bMwZYtW+Dj44Pbb78d9fX1OHToEOrr6zF79mzMmjXL3iESgP/7v//Dtm3bAAA5OTl2jobIMrb7LYttws1hv8B1bN68GfPmzYOHhwcSEhIgCALS0tJQX1+PRYsW4cEHH7R3iC4vIyMDjz/+OKqrq7F+/XrExsbaOySXxdxNRGLI7B0AEQF1dXV44YUXUFFRgVdffRWTJ08GANTW1uLFF1/Ezz//jOXLl+PVV1+1c6RkD3/88Qe++OIL+Pv7Y8OGDQgLCwMAlJSU4OGHH8bOnTuxY8cO3HPPPXaO1HF988032Lt3L5RKJVatWoWgoCAAwOXLl/H3v/8dGRkZ+OSTT/D888/bOVLXsm3bNmzZsgVhYWH46quv0KlTJwDAyZMn8fDDD2PFihUYM2YMQkND7Ruoi9u6davupiaRo2C733LYJtwc9gtcR0lJCRYsWABfX19s2LABCoUCAJCZmYlp06ZhyZIlGD58uK59otb3888/Y968eaiurrZ3KATmbiIShz9jRdQGbNu2DYWFhRg7dqyuQwMAnp6emDdvHjp16oSCggI7Rkj2lJqaCo1Gg/vuu0830AEAQUFBmDRpEgDg0KFD9grPKXz33XcAgJdfflmvQ+nv74/XX38dAPDjjz/aIzSX9t///hcA8MILL+gGOgCgZ8+eGDt2LARBwP79++0VHqHhRs2iRYsQHR0NqVRq73CIRGO73zLYJtw89gtcx/r161FXV4epU6fqBjoAIDIyEjNmzEBtbS02btxoxwhd1/nz5zFnzhzMnj0bgiDoXYeS/TB3E5EYHOwgagN27NgBAJg2bZrBtC5dumD//v1YvXp1a4dFbYT256lKSkoMppWVlQEAOnbs2KoxORs/Pz+Eh4cjMjLSYJr2WwMXLlxo5ajoo48+wg8//IChQ4caTKuqqgIA3kyzs1deeQV1dXV4++237R0KkVXY7rcMtgk3j/0C17Fv3z4AwMiRIw2maT/juzvs44MPPsCWLVvQt29fbNy4EeHh4fYOicDcTUTi8GesiNqA7OxsuLu7o1evXjh37hx++OEHFBUVoWPHjrj77ruNJnNyHUOGDME///lPbN++HStXrsQDDzwAmUyGHTt24Msvv0SHDh0wfvx4e4fp0D777DOT07KysgAAt9xyS2uFQ9d5eHjoPemotWfPHmzfvh1yudzoDQJqHRs2bMC+ffswf/58hISE2DscIquw3bc9tgm2wX6Ba9BoNMjLy4NEIjF6Iz00NBQSiQR5eXnQaDR8N18rCw8Px9tvv4377rsPEgmfEW4rmLuJSAwOdhDZWV1dHc6dO4dbbrkF27dvxyuvvIJr167ppq9atQpPPPEE5syZY8coyZ569OiBN954A0uWLMF7772H9957TzctOjoab731Frp06WLHCJ2XRqPBhx9+CAC4++677RyNa6upqcGcOXOQl5eH/Px8dO3aFe+88w5/VsBOTp06hXfffRcDBw7EI488Yu9wiGyG7X7zsE2wDfYLXMeVK1dQV1cHf39/eHh4GEyXyWTw8/PDpUuXUFVVBR8fHztE6bqefPJJe4dAVmDuJqLGOERNZGeVlZUAGi54586di5EjR2L79u04dOgQ3n//fXTs2BGrV6/m77W6uJiYGAwcOBByuRwJCQkYNGgQ2rVrh6ysLGzYsAEajcbeITqlZcuW4dChQ+jUqROmT59u73Bc2tmzZ/Hzzz8jPz9f91lOTo4dI3JdarUac+fOhUQiwZtvvsmnTcmpsN23HtsE22G/wHVoB7G8vb1NlvHy8gJw46c7icg45m4iaozf7CCys7q6OgANF7yDBw/G0qVLddPuvfdeyOVyPPXUU/j4448xceJEdiBd0JEjR/D444+jW7du2Lp1K7p16wag4R0e//jHP/Dll1/Cx8cHzzzzjJ0jdS4ffvghVq5cCQ8PD3zwwQfw9/e3d0gu7ZZbbsGBAwcgkUiQkpKCJUuW4I033kB1dTWfvmtlycnJyMjIwOLFi9G1a1d7h0NkM2z3m4dtgu2wX+A6rPlpJD7URGQaczcRNcVvdhDZmfaJHQB4+OGHDaYPHz4cQUFBKCkpQWFhYStGRm3Fm2++iaqqKixZskQ30AEAQUFBWLZsGWQyGdauXav3MwfUfCqVCq+99ho++eQTeHp6YsWKFYiLi7N3WC5PLpfDz88PHTp0wOjRo7FixQq4ubnh888/R21trb3Dcxl//PEHli9fjuHDh2PChAn2DofIJtjuNx/bBNtiv8B1yOVyADB7DVNTU6NXlohuYO4mIlP4zQ4iO/P19YW7uzvq6+tx6623Gi3TtWtXlJSUoKysDGFhYa0cIdlTTU0NMjMz0b59e6MvpOzevTvCwsJw8uRJnDp1Cr169bJDlM6jqqoKzzzzDPbt24f27dvjk08+4UVzGxUVFYXg4GCcOnUKp0+fRkREhL1Dcgnvv/8+6uvrUV9fjxdeeEFvmiAIAKD7/OWXX+bTddTmsd2/OWwTbIv9Atfh4+MDuVyOsrIyqFQqyGT6t2ZUKhXKysrg6emJ9u3b2ylKoraJuZuIzOFgB5GdSaVS9OjRA3/88QdKSkqM3qy+ePEiACAgIKC1wyM7q6iogEajMftVd6lUCgCor69vrbCc0pUrVzBt2jQcP34cXbp0wcqVK6FQKOwdlsvSaDR49913ce7cObz77rsGNwEA6F7oqVKpWjs8l1VdXQ0A2L9/v8kyP/zwAwDg2Wef5Y1NatPY7t88tgm2xX6B63Bzc0NERAQyMzNRWFho8NBGQUEBBEFgm0TUBHM3EVnCwQ6iNmDo0KH4448/sH37dgwbNkxv2p9//oni4mJ07twZ3bt3t1OEZC8BAQHo2LEjysvLkZmZafDtjpKSEuTn58Pd3R3h4eF2itLx1dXV4cknn8Tx48cRERGB1atX45ZbbrF3WC7Nzc0Nu3btQmFhIf76178atI2nT59GQUEB5HI5n2xtRevWrTM57bbbboNareaL48khsN23DbYJtsd+gesYMmQIMjMzsXPnToPBjp07dwKAwTFA5MqYu4lIDL6zg6gNeOihhyCXy/H999/rnn4DGp5aePXVVyEIAh555BGrXmRHzkEikeCBBx4AALzyyisoKSnRTbt8+TJeeOEF1NfXY/z48WjXrp29wnR4H330EY4cOYIuXbpg3bp1vGhuIyZOnAgAWLx4Mc6fP6/7vKSkBM899xxUKhUmTZoET09Pe4VIRA6K7T61VewXuI6kpCR4enpi1apVOHbsmO7zrKwsJCcnw8vLC5MmTbJjhERtC3M3EYnhptFoNPYOgoiAbdu24cUXX4RKpUKfPn3QuXNnHDlyBGVlZUhISEBycjLc3d3tHSbZQW1tLaZPn46DBw/C09MTcXFxcHNzw9GjR3H16lVERUVhzZo1fHlhM5WVlWH48OGoqalBnz59zH5DZunSpa0YGdXX12PWrFn49ddfIZfLERMTA7VajaNHj6K6uhrDhg3DihUrdD9nRfbFp7jJUbDdbx1sE5qP/QLXsX79eixatAju7u6Ij48HAKSlpUGlUuHtt9/GuHHj7BwhAcDkyZNx8OBBrF+/HrGxsfYOxyUxdxORWBzsIGpDTpw4gU8//RSHDh1CVVUVunfvjnHjxmHatGns0Li4+vp6bNiwAVu2bMGff/4JQRAQGhqKv/zlL5g6dSpv9t6EHTt24OmnnxZVljdsWp9arcaGDRuwefNm5OfnQyKRQKFQICkpCRMnTuSTrW0Ib2ySo2C73zrYJtwc9gtcx549e5CcnIzs7Gx4eHhAqVRi5syZGDhwoL1Do+s42GF/zN1EJBYHO4iIiIiIiIiIiIiIyKHxcUgiIiIiIiIiIiIiInJoHOwgIiIiIiIiIiIiIiKHxsEOIiIiIiIiIiIiIiJyaBzsICIiIiIiIiIiIiIih8bBDiIiIiIiIiIiIiIicmgc7CAiIiIiIiIiIiIiIofGwQ4iIiIiIiIiIiIiInJoHOwgIiIiIiIiIiIiIiKHxsEOIiIiIiIiIiIiIiJyaBzsICIiIiIiIiIiIiIih8bBDiIiIiIiIiIiIiIicmgc7CAyYvPmzVAqlUhKSmqR8m1Nfn6+3t9nzpyBUqmEUqlEVVWVzZe3du1a9OnTx2C59qJWqzFmzBg88cQT9g6FyCXYq81MTEyEUqnEnj17RM8ze/ZsKJVKLF++3GBaZWUlSkpK9D5bvnw5lEolZs+efdPxNqVSqTB+/HhMmjTJ5nU3V0ZGBpRKJf773//aOxSiNstW1zuTJ0+GUqnEV1991SLl2xK2r2xficSyVxtrC2lpaVAqlYiPjxc9T1VVla6vfubMGYPpxrZHc66BxcrKykKfPn3wzTff2Lzu5nrttdcwePBglJWV2TsUIrIDDnYQubDCwkJMnToVH374Yast89SpU3j//fcxYcIE9OjRo9WWa45UKsULL7yA33//HRs3brR3OETkALZu3YpRo0YhOzu71Za5atUqHDt2DHPnzm21ZVoSHR2Ne+65B4sXL8bFixftHQ5Rm1JaWopnn30Wc+bMsXcoDoXtawO2r0TmsY3VV1lZiddffx2TJ09utWXW19fjpZdeQnh4OMaPH99qy7Vk9uzZqKqqwuLFi+0dChHZAQc7iFzYDz/8gNTU1FZd5pIlSwAAs2bNatXlWnLnnXciJiYGy5YtQ3l5ub3DIaIWsHbtWmzbts2qp+dMWbZsGUpLS20QlThnz57FZ599hrvuugv9+/dvteWK8eyzz6KyshJLly61dyhEbcq+ffvw008/QaPR2DsUh8L29Qa2r0SmOUMbGxkZiW3btmHTpk03Xdfx48fx9ddfQ61W2yAycf71r38hLy8Pzz77LCSStnN7sVOnTnjsscewdetWpKWl2TscImplbac1IiKnd/DgQfz6669ISkpCYGCgvcMx8Le//Q3l5eVYuXKlvUMhohYQHByMHj16QC6X2zsUqy1fvhw1NTV48skn7R2KgfDwcNx99934/vvvkZuba+9wiIiswvaViOzF29sbPXr0QEhIiL1DsVpFRQU+//xz9OzZEyNGjLB3OAamTp0KLy8vvPvuu/YOhYhaGQc7iKjVfPbZZwCAiRMn2jkS44YMGYKgoCB8/fXXuHLlir3DISIC0PDU8X//+1/06tULkZGR9g7HqAceeAAajQaff/65vUMhIhKN7SsRUfOsX78eV69exQMPPGDvUIzy8/PDyJEjkZWVhf3799s7HCJqRRzsIJs5ffo0Xn31VSQmJqJv376IjY3FhAkTkJycjGvXrpmc57XXXtPNEx8fj6eeesrkTysplUr069cPgiDgiy++wD333IN+/fphxIgReOONNwxeZKhVW1uL9evXY8qUKRg4cKBefF988QXq6upsth2aEgQBmzdvxiOPPILY2FhERkbi3nvvxQcffICKigqD8tqXL65evRpnzpzBnDlzMHjwYPTt2xcjR47Eu+++a3Q+ACgoKMC8efOQmJioW84XX3wBtVqte4mZllKpxIoVKwAAP//8M5RKpdHf91SpVFizZg3Gjh2LyMhIJCQkYNasWTh27JhV2+HUqVNISUlBjx490Lt3b6NlLl26hA8++ABjxoxBVFQUYmJi8Mgjj2Dbtm165bQvUB83bhzq6urw2Wef6Y6FYcOGYfHixaiurgYApKamYsqUKYiJiUF0dDSmTp2Ko0ePGl2+RCLBvffei+rqar4MktocV21jn3vuOSiVSnz00UcG0zZs2AClUom+ffuipqZGb5pGo8HAgQOhVCpx9uxZAOZfzvjLL7/g0UcfRWxsLOLi4vD000+joKDAoJz25erFxcUAGr4RplQqsXnzZoOyZ86cwbx58zB48GD069cPd911F5YuXYqqqiqrtsGmTZugUqnwl7/8xWSZ9PR0PPvssxg+fDj69u2LIUOGYM6cOfjzzz/1ymlzzNq1a1FYWIjnn38eAwcORP/+/XH//fdj69atABp+g/nzzz/HPffcg759+2Lw4MFYuHAhKisrjS5/0KBB8Pf3x88//4zLly9btX5ErUl7Dr/22msoKSnB888/j4SEBERHRyMpKQmbNm2CIAhG562rq8PatWsxfvx4REdHIyoqCvfffz9Wr16N2tpavbKJiYmYN28egIafFlEqlUhMTNQrc/r0aSxZsgRjx45FTEyM7lybNWsWDhw40DIb4LpLly7h7bffxj333IPIyEjExcVhypQp2L59u9Hy2vbz8uXL2LFjByZNmqS7tnrooYfw448/mlzW9u3b8eijj2LAgAGIiYnB448/jvT0dN2+eOmllwCwfWX7Ss7AFdrYyspK9O3bF0qlEqdPnzaYPmHCBCiVSrz44osG0/bs2QOlUokpU6YAMP+C8srKSixfvhyjRo1CZGQkRowYgeXLl6O+vt6g7OTJk/HYY48BAMrLyw36/o3t3LkTjz76KGJiYhATE4OHHnoIP/30k1XbQBAEbNq0CW5ubibbz/r6eqxfvx4PPvggBgwYgMjISIwdOxaff/65Qd9Am2OuXr2Kbdu2YeLEiYiKikJ8fDyefvppnDp1CkDDPtUeU/369cN9992HLVu2mIxzzJgxAIB///vfVq0fETk2mb0DIOeQn5+Phx9+GFeuXEGHDh2gUChQXV2NrKwsZGZm4pdffsFXX30Fd3d33Tz79u3D7NmzUV1dDW9vb/Ts2ROXL1/G3r17sXfvXjz99NP4xz/+YXR58+fPx7fffgt/f3/07NkTeXl5+Oqrr7Bjxw6sXbtW78XXFRUVmDJlCo4fPw6pVIrg4GB06dIFxcXFyMzMRGZmJlJSUpCcnGzz7VJXV4enn34ae/fuBQB069YNHTp0wMmTJ/Hpp59i69atWLNmDbp3724wr7ZMdXU1QkJC0K5dOxQWFiI5ORmpqanYtGkTZLIbp/DBgwfxt7/9DVVVVfD19UXPnj1x5swZvP322zh06JBB/TExMTh37hzOnTuHjh07Ijw8HAqFwqDc3/72Nxw+fBidO3dGWFgYCgoKsHPnTvz6669Yt24doqOjRW2LHTt2QKPRYPDgwUanZ2dn46mnnsKFCxfg7u6Onj174sqVK0hPT0d6ejpOnjyJZ555Rm+e+vp6PPHEEzh48CBCQkLQrVs3FBYWYt26dSgsLMSoUaPw6quvwsfHByEhISgoKEBqaioyMjLw/fffIywszCCOO+64A2vWrMH27dtb9eVuROa4chs7bNgw/Pjjj0hJScHs2bP1pmk7qvX19cjIyMDAgQN107KysnD58mX06tULXbt2NbuMd955B6tXrwYA3HrrrfD19cWuXbuQmppqMG9AQABiYmJw7Ngx1NXVISIiAu3bt0dAQIBeufz8fCQlJaGiogIem/2EAAAgAElEQVQRERHw9PREUVERVq1ahZSUFGzcuFFvf5mzY8cOAA3fPjNm5cqVWLZsGTQaDQICAqBQKFBUVIQtW7bgl19+wddff41evXrpzXP8+HF8+OGHUKlU6NGjB86fP4/s7Gw8//zzUKlU2LJlC1JSUtCtWzeEhIQgLy8PGzZsQEFBAdauXWsQg0QiwcCBA/Hjjz9i9+7dbfYpPyKt0tJSTJw4EefPn0ePHj0gCAKOHz+O+fPn4/fff8eyZcv0rrPKy8sxY8YMZGZmQiKRoHv37vDy8kJOTg6ys7Px448/YvXq1fDz8wMA9O3bF+7u7igsLIRcLkevXr30fsLz999/x6xZs1BTUwNfX18EBwejtrYWp0+fxs6dO7Fr1y4sXbrU7E345jp+/DhmzJiBS5cuwcPDA2FhYaiursaBAwdw4MABJCUl4c0334Sbm5vBvJ9++im+/PJLyOVyhIaG4uzZs8jIyEBGRgZKS0sxdepUvfKLFy/GunXrADS0r+3bt0daWhpSU1MxbNgwvbJsX9m+kvNw5jbWx8cHsbGxSE1NRWpqql5fvqKiAsePHwcAo33w3377DUDD+yLNuXjxIqZNm4bc3Fxd37i8vBwrVqwwOlCjUChQXl6O3NxcyGQyk99UW7lyJQ4fPgxfX1+EhISguLhY14afP38e06ZNE7UNjh07huLiYvTu3RudOnUymH7lyhXdfQSg4Sf5pFIp8vPzsWzZMqSmpmLVqlUGbfWyZcvw9ddfo1OnTggJCUF+fj527NiBjIwMvP/++5g5cyZqa2sRFhaG0tJS5OTk6F5QP27cOIM44uPj4e7ujr1796KmpgZeXl6i1o+IHJyGyAaefvppjUKh0CxevFhTV1en+/z48eOa+Ph4jUKh0Hz33Xe6z0+fPq2JiYnRKBQKzQcffKCpra3VTdu5c6du2i+//KK3HIVCofu3dOlSTX19vUaj0WguXbqkmTx5skahUGiSkpI0giDo5nnrrbc0CoVCM3r0aM2ZM2d0n6tUKs3atWt19R09elQ37T//+Y9Gofj/9u48rorq/x/4iz0QEZFAURESh01FSxBFREMlUXKpFEtz4auYBq5J2mJmqT0+boAfK/2UJZ/UT5iolJqWZioEYqipLFqXJUC5giAIss7vD37ndIc7F4nFC/F+/uPDmXtnzszlvu85c855H0GcMmVKo65f0+vXr1/Pz339+nW+vbCwUHz99df5e2pqavi+iIgIXqZp06aJmZmZfN/JkydFR0dHURAE8bvvvuPbS0tLRS8vL1EQBHHNmjVieXm5KIqiWFlZKW7dulVy31Sxc4WEhEi2Z2dn89cPGDBAPHz4ML+nt2/fFidOnCgKgiAGBQU16v6IoijOmzdPrdxMRUWF6OfnJwqCIAYHB4sFBQV833fffSc6OzuLgiCISUlJauUbNGiQeO7cOf76mJgYvs/JyUnctGkT/5tUKpXimDFjREEQxA0bNsiWs6SkRBQEQXR1deX3kRBt68gxtqCgQHRychJdXFzEkpISvr2mpkb08PDgxw8PD5e8b8eOHaIgCOLWrVv5ttGjR4uCIIinT5/m237++WdREASxf//+4okTJ/j2rKwsHusEQRAjIiIkx5c7lihKY/ikSZNEhULB9506dUo2hjckPz+fxzrV+87Ex8eLgiCILi4u4r59+/jvyYMHD8Rly5aJgiCI48aNky3fjBkzxLt374qiWBeHg4KCeOz08PAQ4+Li+PtOnDjB35eSkiJb1qioKFEQBHHlypWNujZCtIHFH0EQRA8PDzExMZHvi4uL4/Fx7969kvcFBweLgiCI06dPl9TNcnNzxZdfflkUBEF87bXXZM9VP9ZVVFSII0aM4PUR1RitVCrFOXPm8LiqaubMmaIgCGJUVFSjrlXu9ffv3xd9fHxEQRDEt956SxJXk5KSeLn27NkjORaLeSyusjJXVFTwWPPMM89IfqOOHz8uCoIgDhw4UPJ7k5OTI77wwgv8eGFhYbLnovj6F4qvpL3oKDF2z549oiAI4pIlSyTbT506JalPZ2VlSfb7+vqKgiCIGRkZoiiK4i+//MLvlaqlS5eKgiCIkydPFnNycvj2o0ePiq6urvz42dnZfJ+mY4miNIZv2rRJLCsr4/dq+fLloiAI4uDBgyUxvCGffvqpKAiCuHbtWtn9YWFhPEampaXx7ampqfy5xc6dO2XLt2vXLrG6uloURVG8deuWOGjQIB4/586dy58VVFZWiosXL+b3SZMpU6aIgiBI4i4h5J+N0liRFsEWzJs6daqkd97FxQUhISHw8/ODkZER3/7555+jtLQUkydPxpIlS2BoaMj3+fr6YsWKFQDA0yzVN3bsWKxYsYKPBrGwsEBERAS6dOmCa9euSXIyJiYmQkdHB6tXr0bPnj35dj09PcyePRu2trYAgFu3bjX3NkjcuXMHBw4cgIGBASIjI+Hi4sL3de3aFZs3b4aNjQ2uX7+O06dPq71fX18fERERvHzsuj09PQFAkoopOjoaSqUSrq6uWL9+PR+xYGBggGXLlsHPz6/J1xEcHIxJkybx0X3W1tZ47bXXAADJycmNOkZNTQ0f1dGvXz+1/d9//z0UCgV69uyJ7du3w8LCgu/z9/dHYGAgAMhOUV2wYIFktsjkyZNhbW0NABgwYADCwsL436SlpSWmTJkCAEhJSZEtq6mpKWxsbPhIcULago4cYy0sLODm5obq6mrJCLmUlBQUFRXh6aefBqA+eu7cuXMAoJbSoL7du3cDqIslqrGyd+/eiIyMhJ6eXpPKraenhx07dsDOzo5vGzNmDB89zGLio7Dreuqpp2RHWe/atQtA3SKMM2bMgK5uXdXOxMQEGzduhLm5OTIyMnD58mW18m3evJmPmDY0NOSj+Wpra7F06VLJTBk/Pz/+WaampsqW1cHBQVJmQtq6DRs2wN3dnf9/2LBhWLNmDYC675YoigDqZoqdOXMGXbt2xc6dOyV1sx49eiAiIgImJib48ccfNX4/VF27dg1lZWWwtrbGqlWrJDHa0tISixcvBlCXnlRTupem+vrrr5GXlwcPDw+sX78epqamfN8zzzyDDz74AEDd9culSvH29sayZct4mQ0NDfmo2pKSEvz+++/8tTt37gQArFixAmPGjOHbbWxssHPnTpiYmDTpGii+EtI+/JNjLJuZlpCQwK8D+GvWsVz9VKFQIDs7G0899VSDC5Ln5OTg2LFj0NPTQ3h4uGSWcUBAABYsWNCkMgOAp6cnwsLCYGxsDKAuPrHP5MGDB7h582ajjpOYmAhAvm1/584dHD58GDo6OtixY4cke4SjoyPefvttAPJt++HDh2P+/Pm8/t23b19elzc0NMS2bdv4swIDAwM+mzAtLU3yOahiZWRlJoT881FnB2kRrELy3nvvITExEdXV1XzfK6+8goiICIwfP55vYw/3WQ7F+iZMmAAdHR2kpKRAqVSq7ZdLL2Rubo6xY8cCAE8bBdTlDb169aps+qTKykqYmZkBgMac9031888/o6qqCi4uLpKUL4yRkRFv+LHprKocHR35Q3tVLPWSal5fdj9feukl3hBTNWPGjKZdBCBpnDKswVVaWir5rDW5d+8eX0NDLmUX+7wmTJggO7X09ddfx/fff493331Xbd/IkSPVtrEKoZeXl9o+Ns22oZzO7O+Z5YwmRNs6eoxl3/O4uDi+ja07EhgYCFNTU1y5coXn/y0uLsbVq1dhaWnZ4IKzDx484A/Fnn/+ebX9dnZ2GDJkSJPK7OTkhF69eqltZ/GzqKioUcf5888/AcjHzrKyMt5we+GFF9T2GxkZYf/+/bhw4QIGDRok2de3b1+1FF2q/5f7PNmDO03xkzXcb9++3ajfBkK0qWfPnvD19VXbHhAQAGNjY+Tn5+PGjRsAgB9//BFA3YM61QEZTLdu3fjDa7k6XX1PP/00Ll26hJMnT8p2qLKHULW1tWp56puLXYu/v7/sA/6RI0eiS5cuKCgo4KlYVNVPPQUA3bt352Vm9dPc3FykpaVBX19fNj5ZWVnx35S/i+IrxVfS9v3TY6y9vT1sbW1RWFgo6YCJj4+HiYkJZs6cCUDa2cEG4jwqhdX58+cBAG5ubpKOH6Y5qezk2vbdunWDubk5gMbHT9ZOlovFZ8+ehSiKcHNzk+0M8fX1xZEjR3D48GG1fXJtezaYqn///ujSpYtkH2vb19TUaGxrUNuekI6H1uwgLYIt8nX58mXMmjULnTt3hqenJ0aOHIlnn31WksextLQUeXl5AIBt27bh448/lj2mnp4eqquroVAoJPk3gbofOjnsx5QtYMUYGhpCqVTi0qVLUCgU+PPPP/H7778jNTWV/yhqGgnQVGxkW2ZmpsbOBvaQUW4RXCsrK9n3sM4A1VEobIFETYuQqc4q+bvkyqE6Eq+iokKSb1UOW0zRwMBAtjODLewmVxkC6kZ2y1V8Ach2CLGR73LvYWVt6PNmoxxpEUjSVnT0GDtq1CiEh4dLFlZnI+eGDRuGQYMG4fz587hy5Qrc3d1x4cIF1NTUYOTIkbIP85jc3FxUVVXBwMBAtjEJ1MXVhISEv11mTTGcxc/GLtp+7949AEDnzp3V9uXl5fHyy61BBNSNWJbTUOwEwHNiy+3X9Fmy2CmKIoqKimRzOBPSVmiKc4aGhujTpw9SU1ORmZkJV1dXXqdLSkrSWKdjD87l6nSaPPHEE7h+/Tpu3LiBrKwsZGVlIT09XXKMlp7Zwa4lKioKR48elX0Nm9GhUCjUHuTLxQ6g7uF/eXk5Ly87T69evdCpUyfZ97i4uDS4sKwmFF8pvpK2ryPEWB8fH0RFRSEuLg7Ozs5QKpW4desWRowYwWe0qM4maGxnR0ZGBoC/OnDrs7GxQefOnVFSUvK3y6wpfnbq1AlFRUWN7vxh7WS5+JmVlQVAc9vewMBAba0jpqlte+DR8ZPa9oR0HNTZQVqEm5sbYmJi8PHHH+P06dMoKSnBqVOncOrUKbz33nvw9/fH2rVr0blzZ8mIJTaaoyH1f8T19fU1NprYdtX3FBcXY9OmTYiNjZVMxzc3N4eXlxdSU1N55aklsZFtRUVFj5xOrzpLg1GdbitH9cecjcDQlA5A0/1qjEeVozHu378PABoXBHtU+RvCRubIaeghZ2OO2ZQKJCGtoaPHWBcXF1hZWeHmzZtQKpUwNzfHpUuXYG9vDysrKwwdOhTnz59HYmIi3N3dG53Cil2HsbGxxnjBZqb8XS0RO4GG4yeLnQ2VX5NHxdumxE/VeHz//n16GEfatIa+2yzWsfoZ+zc/Px/5+fkNHrexdYeLFy9i48aNktkTOjo66NOnDwICAjR2RDQXuxbVdFOayF3Loxb+ZvVT1fikSVPrpxRfKb6Stq8jxNhRo0bxzo6goCA+EGfo0KGwsrKCvb09FAoF8vLyYGFhgcTERJibm/MUV5qw+9FQ/DQzM2tSW7Wl4qdqHbq+tta2Z+Wgtj0hHQd1dpAW07dvX2zevBmVlZW4fPky4uPjcfbsWVy/fh2xsbEoLy/Hv//9b8kPWHx8vMYR+5pUV1fzkVb1sYd8bMSUKIpYuHAhfv31V1hYWGDmzJkYOHAgHBwc0KNHDwB1KVBao7ODXecrr7wim36ppc9VVVUl22kCNJyy6XFgawmUlZWhtrZWLdUWa2S2dCqxpmIVIdU1EAjRto4eY318fBAdHY34+HjY2NigrKwMHh4eAOoalUDd6LnFixfj3LlzMDQ0xPDhwxs8JpsKX15eDlEUZRtRDx8+bHbZm4PFIbn4zj7rhsr/OLEHhwDFT9L2NVTnYN83FuvYd23VqlUICgpq9rnT09Mxb948VFZWYsiQIZg0aRIcHR3Rt29fmJqaQqFQtFpnh7GxMUpKSnDw4EEMGDCgVc7BzgM0XAdtK/VTiq+EtLyOEGM9PDxgYmKCS5cuobKyknd2qNZPFQoFEhIS0K1bNzx8+BBjxox55HpwrKOIpYGW0xbqp2VlZbIdCG2tbc/iZ0t19BBC2j5as4M0W21tLbKzs/kUTUNDQ3h4eGDJkiU4dOgQPvzwQwDADz/8gAcPHsDMzIw/fGPpl+qrqalBXFwcMjMzUVNTo7Zf02g0li+TTflMTk7Gr7/+Cn19fRw4cACLFy+Gt7c3fwgH1OW+bQ1s0URN1wjUXcdvv/2G4uLiZp2LrQnCFjGur7ELjbUWloe4pqZG0lhj2L3StIDxjRs3EBgYiHXr1rVaGVWx0Sis3IRoE8XYOixPfHx8PM9/7OnpCQBwdXVFp06dcPnyZVy5cgVKpRIeHh6PHDVsY2MDQ0NDVFVVaYw/jRn93JrYZ8nSraiytbWFrq4uqqqqeMqA+iIjIxEUFITvv/++VcsJ/BU7dXR0KH6SNk/Td7uiooKnEGH1K7ZeQkN1uhs3biAlJUXjwBNVUVFRqKysxLBhw7B3715MmzYNbm5uPNVGa9VNgcZdS0JCAn7//fdGp4OSw34ncnJyNN4TTfXWx4XiKyGtpyPEWENDQ3h6eqK8vBxXrlzBxYsXYWJiwlN4qQ7GYWuNPGrWMfDXGp0pKSmy++/du4eCgoKWuIQmY/FTbo0PVn5Ndevq6moEBgYiNDQUd+/ebb1C/n+sjDQjjpCOgzo7SLMplUqMHTsWs2fPxp07d9T2q46sZTkx2UOrAwcOyB4zNjYWc+fOxeTJk2VHNMTExKhtKywsxA8//AAAfMFDtgiVqakpr0SpunDhAs9t39KL/Xl7e0NXVxeJiYmyFbfq6mosWrQIL774Ivbs2dOsc7HF32JiYmRzVR48eFD2fWyUWkuvV1Jfjx49+AgPub8Rb29vAMCxY8dkG9bHjx9HcnKy7HtbAzsP64QhRJsoxtYZPnw4DAwM8MsvvyApKQnAXyPn9PX1MWTIEDx8+BA7duwA0LjGpLGxMby8vAAA0dHRavvv3LkjWRRd1eOKnywnvFxaB1NTU55PX26Rx8rKSsTExOD8+fOPHEXYEtjfZ/fu3TWmLSSkrUhJSZEsKsscPXoUFRUV6Nu3L//+jRo1CgBw8uRJ2ZzfJSUlmDNnDiZPnozjx4/z7fVnsjIsdjo6Osp+N1XrbXId0s3BruXrr7+WjV8XL17Eq6++igkTJiA3N7fJ57Gzs4ODgwNqampk1+UoLi7mvyn1UXxVR/GVtDcdJcaysh89ehSZmZkYMmQIX0di6NCh0NHR4Z0d+vr6vN3bkNGjR0NPTw/Xrl2T7fCQi0mA5vvRGliHhlzbxMvLCzo6Orh8+TLv2FIVHx+P5ORkJCQkyK5h1NKobU9Ix0OdHaTZrK2t4eHhgdraWqxcuVLyg1daWootW7YAAAYPHswXsPq///s/GBkZITY2Ftu2bZMshHXu3Dm8//77AICXXnpJdtGr//73v5JKilKpREhICEpKSjBq1CgMHDgQwF8/aEVFRdi3bx9/fW1tLU6dOoXly5fzbc0ZvSbH1tYWAQEBqKmpwcKFCyX5QouLi/HGG28gIyMDJiYmGhdia6zp06fD0tISV69exbp16/j9rK2txWeffaaxQsRGPTenMdsYOjo6/DO5cuWK2v6AgAD07NkTWVlZCAsLk0yHPXHiBO8MmjNnTquWE6hr8Obl5UFfX5+XmRBtohhbp1OnTnB3d0dubi4SEhLg4OAgGaHFZnmwkXOs8fkoixYtgq6uLqKionDgwAH+cO3OnTsIDQ3VuFAjy//b2vGT3eu0tDTZlAULFy4EAOzevVvyQPHBgwd4++23kZOTAzs7u0bfj+Zg8X3w4MGtfi5CWsLSpUslC9X+/PPP2LhxIwAgNDSUbx86dCjc3d1x//59BAcHIzMzk++7c+cOFi1ahOLiYjz55JMICAjg+1icyM/Pl8RAFjuPHTsmOVZxcTE2bNiAb7/9lm9r7GKxjfXyyy+ja9euSEpKwpo1ayR1rt9++43HbV9f32Y/GFq0aBEAYPPmzfjpp5/49rt37yIkJETjzGaKr+oovpL2qCPEWDbAiA0UYrM5gLosAf369UN2djYyMjLw9NNPN2otuG7duuHll18GUHefVGfJnDlzBuHh4bLvY/ejtLS02ZkjHsXNzQ0AcPnyZbV9dnZ2eO6551BbW4uQkBDJZ5Camoq1a9cCAGbNmvVYOospfhLS8dCaHaRFfPjhh3jxxReRmJgIX19f2NrawsDAAFlZWSgrK4O5uTnWr1/PX+/g4ICPPvoIq1atwieffIKoqCjY29vj3r17fCTG8OHDsXLlStnz2dvb46233kJkZCS6deuG9PR0VFVVwdHRUXKeAQMGwNfXFz/++CPWrVuHTz/9FJaWlsjNzUVhYSGMjY3h5uaGK1euPHIxtKZ49913kZubi4sXL2Lq1Kmws7ODiYkJFAoFysvLYWBggIiICFhbWzfrPGZmZti8eTOCg4Oxf/9+fPvtt7Czs0Nubi4KCgrQv39/XLt2Ta0y4ejoCAC4du0a/Pz84OjoiIiIiGaVRZMRI0YgMTERycnJmDZtmmTfE088gR07diAoKAjHjh3D6dOn0bdvXxQUFPBpxqGhoXwUd2tii8kPHjy4WQu7E9KSKMbW8fHxQVxcHKqqqiSNSUDauBQEAT179mzUMQcOHIi33noLH3zwAdauXYuPP/6YX7Ouri68vLxw4cIFtfc5OjoiPT0dmzZtwqFDhzBz5kxMnTq1eRcoo1evXrCzs0NGRgauXr2qFgd9fHywZMkShIeHY9WqVdiyZQssLS2hUCj438b27dv5KMPWlJycDKAu3hPS1j355JMoLCyEv78/BEHAw4cP+QjUefPm4bnnnpO8fsuWLQgKCsLVq1fh5+cHBwcH6Orq4o8//kBVVRVMTU2xe/duyaj7fv36QUdHB0qlEn5+fujevTv279+PuXPnIjY2Fvn5+fD394e9vT10dHSQkZGByspKODk54fbt2ygqKkJ+fj6efPLJFrvubt26ITIyEosWLcKhQ4fw3XffwcHBAaWlpfyBlKOjI38g2RwTJkxAQkIC/ve//yE4OBi9e/eGmZkZT18lCALS09Nl66cUX6UovpL2pqPE2O7du8PJyYnPYpGrn7KY15hZx8yyZcuQlpaGxMRETJw4EYIgoKysDFlZWRg8eDAUCoVaCqk+ffrgiSeewMOHD+Hv7w9ra2t88cUXjepg+btGjBiBHTt28NhU37p165CdnY1r167hueeeg4ODA6qqqpCZmYna2lp4e3tjwYIFLV6u+goLC5GRkQFjY2MMGTKk1c9HCGkbaGYHaRG9e/fGN998g8DAQNjY2PDRC9bW1pgzZw6+/fZb9OvXT/Ke8ePH4/Dhw3jxxRdhbm6OtLQ03Lt3DwMGDMCaNWuwa9cujYtIffTRRwgJCYGenh5u3boFW1tbLF26FPv27YOVlZXkteHh4Vi9ejWcnZ1RUlKCmzdvonPnzpg+fToOHz6MkJAQAMDZs2d5CpiWYmpqij179uD999/HM888g4KCAqSnp8PMzAwBAQE4ePBgo6ayNsawYcPwzTffYPz48TAwMEBqaiq6du2Kd955B2vWrAGgvqDh8OHDsXz5cnTv3h05OTm4fv16i98Dxt/fHzo6OoiLi5M9h4uLC44cOYLZs2fDysoK6enpePDgAby8vLB7924sXry4VcpVH3uoOXHixMdyPkIag2JsHdXRs/UfSjk7O/MFx0ePHv23jjtz5kx8+eWX8Pb25o1xd3d3fPXVV3BxcZF9z5tvvglfX18YGhpCoVBIRi62NDaKUa7TBagbPf3ll19i9OjRqKysRFpaGrp06YLp06fjyJEjcHZ2brWyMRUVFUhKSoKRkRHGjRvX6ucjpLmsrKwQHR2NMWPGICcnB4WFhRg2bBg++eQThIWFqb3e2toa0dHReOONN+Dq6oqcnBz88ccfsLKy0vhds7e3xwcffABbW1solUpkZ2fj7t276N27N44cOYIpU6agR48eyMjIQF5eHpycnLB69WpER0fzFHtnzpxp8Wt3d3dHbGwsXn31VfTo0QO3bt3C7du3IQgCQkNDsX///hZ7OPb+++9j06ZNcHNzQ0FBATIyMuDp6Yn9+/fzUbb10zJRfJWi+Erao44UY1n9tHPnzmr1RtXOj78zC6xTp0747LPP8Oabb8LBwQEZGRkoLy/HrFmz8J///Ee2k9XU1BTbt29Hv379UFxcjLy8PI1rDjXXoEGD0KtXL+Tk5MimqurSpQv27duHsLAwODs7Izs7G7m5uXB2dsa7776LTz/99LEsGM5i+7PPPstnvhBC/vl0xNZOhkpIC2IzEWJjYyEIgpZL036cPXsWCxYsQJ8+fXDy5EmtlWPx4sX44Ycf8Pnnn/MKZlvy8OFDjBgxAvr6+jhz5gyMjY21XSRCHiuKsW3T3bt38eyzz8LCwgKnT59+rDmZGys2NhYrV67EjBkz8N5772m7OIRodOjQIaxevRqurq44dOiQtovToS1duhTHjx/H0qVL8dprr2mlDBRfCWlZFGM7ji+//BIbNmzAwoULsWzZMm0XR9a8efNw4cIFREdHU4pqQjqQtlebI4T8bdu3b0dAQAC+/vpr2f3nzp0DgMcy+qwhwcHBANBmK74nT55ESUkJZs2aRR0dhJA2w9LSEi+88ALy8vI0LpiubYcOHYKenh6CgoK0XRRCSBsxfvx4BAYG8vSJqioqKpCYmAgAGmfQPQ4UXwkhpGmmTZsGCwsLHD58uNWyQzRHXl4e4uPj4enpSR0dhHQw1NlByD+Ak5MT0tPTERERwfOFAnWLBMfExGD//v0AgMDAQG0VEUBdbnwfHx+cPHmy1RedbIq9e/eiS5cumDlzpraLQgghEvPnz4eRkRG++OILbRdFTVpaGuLj4/H888+jd+/e2i4OIaSNsLW1RXJyMjZv3ozS0lK+/d69e1i1ahUKCgrQp08fDBs2TIulpPhKCCFNYWxsjKCgINy+fRsnTpzQdnHU7N27F8jP53YAAAKhSURBVLW1tVi0aJG2i0IIecyos4OQf4Bx48bBy8sLSqUSkyZNwrhx4zB16lR4eXnhzTffRE1NDUJDQ7XemATqFm3X19dHZGSktosiceLECfz2229YvXo1z/tPCCFthY2NDUJDQ3Hu3DlcvHhR28WR2Lp1K7p27YpVq1ZpuyiEkDZk+fLlMDMzw7FjxzBixAhMmjQJEydOhI+PD06cOAFLS0ts3br1seRtbwjFV0IIaZrZs2fDyckJ4eHhqK6u1nZxuNu3b+Orr77C1KlT1RaNJ4T881FnByH/ALq6uti1axf+9a9/wcPDA+Xl5bh58yaMjIzg5+eHL7744rEt8P0ovXr1wooVK3DkyBHcunVL28UBANTU1GD79u3w9vbGlClTtF0cQgiRNXfuXAwcOBBbtmzRdlG4pKQk/PTTT3jnnXdgYWGh7eIQQtoQR0dHHDt2DPPnz4etrS1ycnKQk5OD3r17Y/78+YiJiUH//v21XUwAFF8JIaQpDAwMsHHjRvz55584ePCgtovDRUZGwszMDKtXr9Z2UQghWkALlBNCCCGEEEIIIYQQQgghpF2jmR2EEEIIIYQQQgghhBBCCGnXqLODEEIIIYQQQgghhBBCCCHtGnV2EEIIIYQQQgghhBBCCCGkXaPODkIIIYQQQgghhBBCCCGEtGvU2UEIIYQQQgghhBBCCCGEkHaNOjsIIYQQQgghhBBCCCGEENKuUWcHIYQQQgghhBBCCCGEEELaNersIIQQQgghhBBCCCGEEEJIu0adHYQQQgghhBBCCCGEEEIIadeos4MQQgghhBBCCCGEEEIIIe0adXYQQgghhBBCCCGEEEIIIaRdo84OQgghhBBCCCGEEEIIIYS0a9TZQQghhBBCCCGEEEIIIYSQdu3/AQxTKopqC+rpAAAAAElFTkSuQmCC\n",
    
          "text/plain": [
           "<Figure size 806.85x720 with 20 Axes>"
          ]
         },
    
         "metadata": {
          "image/png": {
           "height": 706,
           "width": 797
          }
         },
    
         "output_type": "display_data"
        }
       ],
       "source": [
        "import seaborn as sns\n",
        "sns.set(style=\"ticks\")\n",
        "\n",
        "for_plot = df.copy()\n",
        "\n",
        "def transform_label(class_):\n",
        "    return data.target_names[class_]\n",
        "\n",
        "# seaborn does not work here if we use numeric values in the class\n",
        "# column, or strings which represent numbers. To fix this we\n",
        "# create textual class labels\n",
        "for_plot[\"class\"] = for_plot[\"class\"].apply(transform_label)\n",
        "sns.pairplot(for_plot, hue=\"class\", diag_kind=\"hist\") ;"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "<div class=\"alert alert-block alert-danger\">\n",
        "<strong>TODO:</strong> hide tech stuff below.\n",
        "</div>"
    
       "execution_count": 25,
    
        "scrolled": true
    
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       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": [
    
          "/Users/uweschmitt/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
    
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          "  if __name__ == '__main__':\n"
         ]
        },
        {
         "data": {
          "text/html": [
           "<style>\n",
           "    \n",
           "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
           "    \n",
           "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
           "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
           "    \n",
           "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
           "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
           "    \n",
           "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
           "\n",
           "    \n",
           "    body {\n",
           "        font-family: 'Lora', Consolas, sans-serif;\n",
           "       \n",
           "        -webkit-print-color-adjust: exact important !;\n",
    
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           "        \n",
           "      \n",
    
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           "       \n",
           "    }\n",
    
           "    \n",
           "    .alert-block {\n",
           "        width: 95%;\n",
           "        margin: auto;\n",
           "    }\n",
           "    \n",
    
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           "    .rendered_html code\n",
           "    {\n",
           "        color: black;\n",
           "        background: #eaf0ff;\n",
    
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           "        background: #f5f5f5; \n",
    
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           "        padding: 1pt;\n",
           "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
           "    }\n",
           "    \n",
    
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           "    p {\n",
           "      line-height: 140%;\n",
           "    }\n",
           "    \n",
           "    strong code {\n",
           "        background: red;\n",
           "    }\n",
           "    \n",
           "    .rendered_html strong code\n",
           "    {\n",
           "        background: #f5f5f5;\n",
           "    }\n",
           "    \n",
    
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           "    .CodeMirror pre {\n",
           "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
           "    }\n",
           "    \n",
           "    .cm-s-ipython span.cm-keyword {\n",
           "        font-weight: normal;\n",
           "     }\n",
           "     \n",
           "     strong {\n",
    
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           "         background: #f5f5f5;\n",
           "         margin-top: 4pt;\n",
           "         margin-bottom: 4pt;\n",
           "         padding: 2pt;\n",
           "         border: 0.5px solid #a0a0a0;\n",
           "         font-weight: bold;\n",
           "         color: darkred;\n",
    
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           "     }\n",
           "     \n",
           "    \n",
           "    div #notebook {\n",
           "        # font-size: 10pt; \n",
           "        line-height: 145%;\n",
           "        }\n",
           "        \n",
           "    li {\n",
    
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           "        line-height: 145%;\n",
    
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           "    }\n",
           "\n",
           "    div.output_area pre {\n",
           "        background: #fff9d8 !important;\n",
           "        padding: 5pt;\n",
           "       \n",
           "       -webkit-print-color-adjust: exact; \n",
           "        \n",
           "    }\n",
           " \n",
           "    \n",
           " \n",
           "    h1, h2, h3, h4 {\n",
           "        font-family: Kameron, arial;\n",
           "    }\n",
           "    \n",
           "    div#maintoolbar {display: none !important;}\n",
           "    </style>"
          ],
          "text/plain": [
           "<IPython.core.display.HTML object>"
          ]
         },
    
         "execution_count": 25,
    
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         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "#REMOVEBEGIN\n",
        "# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
        "\n",
        "from IPython import utils\n",
        "from IPython.core.display import HTML\n",
        "import os\n",
        "def css_styling():\n",
        "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
        "    base = utils.path.get_ipython_dir()\n",
        "    styles = \"\"\"<style>\n",
        "    \n",
        "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "    \n",
        "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
        "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
        "    \n",
        "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
        "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
        "    \n",
        "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
        "\n",
        "    \n",
        "    body {\n",
        "        font-family: 'Lora', Consolas, sans-serif;\n",
        "       \n",
        "        -webkit-print-color-adjust: exact important !;\n",
    
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        "        \n",
        "      \n",
    
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        "       \n",
        "    }\n",
    
        "    \n",
        "    .alert-block {\n",
        "        width: 95%;\n",
        "        margin: auto;\n",
        "    }\n",
        "    \n",
    
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        "    .rendered_html code\n",
        "    {\n",
        "        color: black;\n",
        "        background: #eaf0ff;\n",
    
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        "        background: #f5f5f5; \n",
    
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        "        padding: 1pt;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "    }\n",
        "    \n",
    
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        "    p {\n",
        "      line-height: 140%;\n",
        "    }\n",
        "    \n",
        "    strong code {\n",
        "        background: red;\n",
        "    }\n",
        "    \n",
        "    .rendered_html strong code\n",
        "    {\n",
        "        background: #f5f5f5;\n",
        "    }\n",
        "    \n",
    
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        "    .CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
        "    }\n",
        "    \n",
        "    .cm-s-ipython span.cm-keyword {\n",
        "        font-weight: normal;\n",
        "     }\n",
        "     \n",
        "     strong {\n",
    
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        "         background: #f5f5f5;\n",
        "         margin-top: 4pt;\n",
        "         margin-bottom: 4pt;\n",
        "         padding: 2pt;\n",
        "         border: 0.5px solid #a0a0a0;\n",
        "         font-weight: bold;\n",
        "         color: darkred;\n",
    
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        "     }\n",
        "     \n",
        "    \n",
        "    div #notebook {\n",
        "        # font-size: 10pt; \n",
        "        line-height: 145%;\n",
        "        }\n",
        "        \n",
        "    li {\n",
    
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        "        line-height: 145%;\n",
    
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        "    }\n",
        "\n",
        "    div.output_area pre {\n",
        "        background: #fff9d8 !important;\n",
        "        padding: 5pt;\n",
        "       \n",
        "       -webkit-print-color-adjust: exact; \n",
        "        \n",
        "    }\n",
        " \n",
        "    \n",
        " \n",
        "    h1, h2, h3, h4 {\n",
        "        font-family: Kameron, arial;\n",
        "    }\n",
        "    \n",
        "    div#maintoolbar {display: none !important;}\n",
        "    </style>\"\"\"\n",
        "    return HTML(styles)\n",
        "css_styling()\n",
        "#REMOVEEND"
       ]
    
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      }
     ],
     "metadata": {
      "kernelspec": {
       "display_name": "Python 3",
       "language": "python",
       "name": "python3"
      },
      "language_info": {
       "codemirror_mode": {
        "name": "ipython",
        "version": 3
       },
       "file_extension": ".py",
       "mimetype": "text/x-python",
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
    
       "version": "3.6.6"
    
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      }
     },
     "nbformat": 4,
     "nbformat_minor": 2
    }