01_introduction.ipynb

   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "for_plot = beer_data.copy()\n",
    "\n",
    "def translate_label(value):\n",
    "    # seaborn has issues if labes are numbers or strings which represent numbers,\n",
    "    # for whatever reason \"real\" text labels work\n",
    "    return \"no\" if value == 0 else \"yes\"\n",
    "\n",
    "for_plot[\"is_yummy\"] = for_plot[\"is_yummy\"].apply(translate_label)\n",
    "\n",
    "sns.pairplot(for_plot, hue=\"is_yummy\", diag_kind=\"hist\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we see?\n",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Prepare data: split features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# INPUT FEATURES\n",
      "   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",
      "...\n",
      "(225, 4)\n",
      "\n",
      "# LABELS\n",
      "0    0\n",
      "1    0\n",
      "2    1\n",
      "3    1\n",
      "4    0\n",
      "Name: is_yummy, dtype: int64\n",
      "...\n",
      "(225,)\n"
     ]
    }
   ],
   "source": [
    "# all columns up to the last one:\n",
    "input_features = beer_data.iloc[:, :-1]\n",
    "\n",
    "# only the last column:\n",
    "labels = beer_data.iloc[:, -1]\n",
    "\n",
    "print('# INPUT FEATURES')\n",
    "print(input_features.head(5))\n",
    "print('...')\n",
    "print(input_features.shape)\n",
    "print()\n",
    "print('# LABELS')\n",
    "print(labels.head(5))\n",
    "print('...')\n",
    "print(labels.shape)"
   ]
  },
  {
   "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",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          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)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "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>"
   ]
  },
  {
   "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",
    "\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>"
   ]
  },
  {
   "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",
    "\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>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train first..\n",
      "(225,)\n"
     ]
    }
   ],
   "source": [
    "# Sanity check: can't predict if not fitted (trained)\n",
    "from sklearn.exceptions import NotFittedError\n",
    "try:\n",
    "    classifier.predict(input_features)\n",
    "except NotFittedError:\n",
    "    print(\"train first..\")\n",
    "\n",
    "# Fit\n",
    "classifier.fit(input_features, labels)\n",
    "\n",
    "# Predict\n",
    "predicted_labels = classifier.predict(input_features)\n",
    "print(predicted_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we've just re-classified our training data. Lets check our result with a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "0 1\n",
      "1 1\n",
      "1 1\n",
      "0 0\n"
     ]
    }
   ],
   "source": [
    "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225 examples\n",
      "187 labeled correctly\n"
     ]
    }
   ],
   "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",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What happened?\n",
    "\n",
    "Why were not  all labels  predicted correctly?\n",
    "\n",
    "Neither `Python` nor `scikit-learn` is broken. What we observed above is very typical for machine-learning applications.\n",
    "\n",
    "Reasons could be:\n",
    "\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",
    "\n",
    "- the used classifiers might have been not suitable for the given problem.\n",
    "\n",
    "- noise in the data as incorrectly assigned labels also affect results.\n",
    "\n",
    "\n",
    "**Finding good features is crucial for the performance of ML algorithms!**\n",
    "\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise section 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Compare with alternative machine learning method from `scikit-learn`"
   ]
  },
  {
   "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",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225 examples\n",
      "205 labeled correctly\n"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better?\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Better re-classification does not indicate here that <code>SVC</code> is better than <code>LogisticRegression</code>. At most it seems to fit better to our training data. We will learn later that this may be actually not a good thing.\n",
    "</div>\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise section 3 (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",
    "\n",
    "Apply `LogisticRegression` or `SVC` classifiers. Is it easier or more difficult than classification of the beers data?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n",
      "(150, 4)\n"
     ]
    }
   ],
   "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",
    "print(data.data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <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": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>"
      ],
      "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  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Al2RWYnVl2Mtj1ha3otatu5t5Ra1bc04ivrBkuFheXo7x48cDAJKSklBYWIg//OEPpo798ccfqQEhiFaKIIm6VojBvlT19ohiJItGEGGFYVjYc65A+8Ll9XJBSVK9bZZT0hqxCZV1/ybK+vWJdlcmggmOP4bjjWVWEZCzmrXFFQxiXJSpf4h3LBlwiKKIc+fOITc3FwBw4cIFmFVqDR06FEOHDrWiGARBhIHmWHsG68t5ltO1QuRYDjPyJyv7dew9+QXtEEu0OhiGBe/MgixLENxlaD9+KcSaSlQc/BBMbVWjNqF6f2MZbX3q2b47GAAl1aWadSAE0RCGt4PPcKkGHXyGCwwX/rbXrC0uz3Do2b47BnTqo+ofOIYLexmJ8GJJlBUWFuKee+5Bv379wDAMDh48iFmzZllxaoIgokjAWjHgdtKY5ldPo7to4FMoyp+s0uPOuWU6avy12PjVZpVG15mkL6skiHhGrw65hk1F1T+/wsz8SZo1HAGb0Fn9HlPVpSl54/DJ93tU9aln++4o6DoMi3avTYg1HYR5jNru3AcW4Nz//EqVxkVg7ZwzyaHpC/Ta/YzkNBR0HabJl5GcFvYyEuHFkkXjAHD8+HF8+eWX4DgOvXv3xtVXX23FaXU5c+YMBg8eTIvGiJgn3mNVb+Ehn+HStVasqHVj3s6VmjdYy2+dA5/ohyiJ4FgOHMti/s7Vmny0WVn0iPc4jWWM6tBl45aATcu86FKltQmVZAllNRW4UFMOd10Vth3/FCdKT6Jn++74xQ1jIKN+A83AYCNAa69LFKvmaKztBhBxl6qKWjfWH31bM3MxqedYVawa9SOtOaYThZBmOA4cOID8/HwAwDXXXINrrrlGk2f//v3o27dvKJchCCJEjCw2m6I5ml+/5EdmSgYm9BitdCjbjn8KQRLAyTIkyOBk2XBdhySLuFBdRlaIRNxhJDuUZQmyKMA1/JeQvB5UHPwQdcUnwKVmApAhVZXDyfFgHOmoqqtBaU25qn7KkLFw9xoAQJc2nTAjfzJyUttCBiDJ4kUb6gxVfaI1HQTQuC0uwzAGRzU43iCmW9qX+CU/KuuqVGmVdVWaWDXqR/ySHxW1btV1AbSoLER0CKk3//LLL/H2229jzJgxyM/PB8/Xn87n8+HQoUN466230LlzZxpwEEQUMWtHqAfD8aY1v3bOhge7j8DLh95UrjP9pomo8Xux/PNLFrgLBjypq0OvrPNgzf5XyQqRiCuMpCs2Vwf4S85opVR/24e0bv3xn00LmrTHDejeM1MycH+3u/Hn7/fgzqsHYvX+S3V5au/xeOfYNpwoPQlAXxdPJB56bXdKl16QqiubtMU1imne1QFnWmjLbOfsmv5hau/xsHM2U/kYMMrMh8uRjXn9H4dP8mN1glhEtwZC+laKioowefJkbNq0Cb169cLtt9+OIUOGoHfv3njzzTcxZcoUzJ4926qyEgTRAszaEeqhZ61opPmVREHpJALXWfflGzhfXapKe/P/tmBG3ylwObIB1D8gje9RoAw2AvnICpGIB4x2dBarKjTpJR+/jMw+I0zb46YnOzGr32MYde1QvHp4EwZ06oNXD29S5X350JsYdW298UrwOhAigWFYuO6armq72wweb8oW1yim3SHYMkuypOkfXj70JiRZMpXvXPUFVdpZT4ky2GhuWYjoEPJrkOuvvx6/+93vUFVVhVOnToFlWXTs2BFpabTAhyBiAbN2hHroWSsaaX79BlKpZN6uSjtafAyP3HAflg6ZrWjX68Q6skIk4hIj6Qok/XSG403b47IMi44Z7ZHMJ6OkpgxOu0M3b/u0XCwe+BRcqW3RxpFJb3gJyIIPZXveQptbJ4JNdkLyeiB5PaYkskYxbdTGm+lLBEnQPVYIskM3yhcsAkvm7WQRHWdY1iqlpaXhuuuuw7XXXkuDDYKIIQKyjIY0R3YRsPa0ZbjAO7MUbbrgKYe/sgSCpxyyLMF20QI3+DpewadJY2QgMyUdrtRsZKakg2P0jyUrRCLWYTgeKV16IbdgFi576DnkFsxCSpdeAMspb5cD8BkugOVV6QF73IY0rJ8swyKJs8PlyIbHV6ObV5AEvHRoI2wsT4ONBCW4TWY4HmxqpjqP4NeNyWCJbECOFZzPqI0305eY7YeM8gX3I17BF1K/RkQeapkIopUTkGU0lDCFIrsI6HuLN8zFj+umoHjDXPjOn0ZakhMz8yeprjMzfzJyU9sEpU1CWpDbSGZKOoryJ6vyFeVPJlcSIuZhHWnI7ncfSne8gf+8tRClO95Adr/RqPrHYbiGTdXKEdMyVTJF+avdmnoTXD8DdXjvyS8wJW+cKu/T+ZOw54eDJKVKYPTaZFHwaeKSdaSbksgaSWnTU9Jb3JeY7Yf08s3s9xjaOV2qtHZOF2Za2K8R4ccyW9xIQrZ4RLwQK7F6yVlEa7/ZXBqz+Sw/8jG4G4dAZDlwkgjxrzuRdfNIeCS/4j6VlpIOPmihIFA/lV5R64Yoi+DIpSqixEqcxiNG9aHNrRNRcfBDZN48EpwjA1x6W/Dp2coMYUMHIMaRZmiPGyBQhyVZhCjLF12qONhYDgCTMA49FKta9GIwd8wzKP3kt1pb3IkrgYvuaY1JZJt2qWp+X2L2WL18AEylJUIdiFeoNyeIBKDePrNlswXBHU9jmnUkOSDZkiDJUv00fZID8NchK2h6Xs9akWd5tE1VT5ETRKxjVB/YZCfqik/g3JZVAICO019VHuwCMkVlEOGtgiTLALTv/4LrSmZKBlgde1IicdGLQdaWrL9eQ/CZ2lk8EKONXLXR4wMvkIJtzoP7ISObXb3+ymwaEZtYMuD461//inXr1qG0tBQNJ0y2b99uxekJgogSevaIl41drGuVKyalwNM1D2v3PN/A2nYSMuzJqnOGYtNLELGGkXW01MAtR08nH6gH732zHXdePVBxn2pYHwDo1pXL09vh3y20JyVaH3oxKPm9unEJSUTxRUtmI1tcI8y23YIk4HRFsWa38GCbc+oLEgtLvtEFCxagf//+mDdvHhYsWKD8EAQR3+jZI17YsQG5BbM1+t4qWcTaIHvPtQfWwy35VecMxaaXIGINPb17zqiZcH+9R/ndddd0IOgBKlAP9KxuA/XBqK5UhGBPSrQ+9GKQz2qnXYdRMBsXdmxo0hbXCLNtd0WtWxlsBPLp2ZxTX5BYWDLDYbfbUVhYaMWpCIKIIfSm6mtPHAF75ySNVa7oKdW1KRSDbA9DsekliFgj2DoaAMo/fx/p1w9EZu/hkLwelO15Czn3FqmOC9QDI6vb+vogG9pFUx0iAhjZlwNQpcmyjNoTR1TH6tniGmG27RYM4jPY5pz6gsTCkgFH586d8c0336Bbt25WnI4giCgRrKdN5e1w9h4B3DgEEseCFSXgrzvBsBwQtGETd9EysWEH4nJkg2c5VNS6lXPaOTt6tu+OAZ36wGl3wOOrwd6TX5CdIRF3aBbWpmVBrKqA99Q38BzbreTTk1QF7D8DVrfB9QYAeJZX6kpbRzZS7Q4wqH/AHH71EGz/fqfqGKpDREOC12EInnJdmZWZNR1Afczqtd3BbbwtxL7AztlU+WgxeOsgpNZp+PDhAIDq6mo88MAD6NixI3j+0ilpDQdBxA96etpFA5+C54YBWLv/JZUWN91fh/NvLVTpgDPbtEdR/mS1brfvZNSJfizavVZJm9n3MTzQ/R6s+Hydaq2H054S7VtAEKbRW9+UO2oW3N/ug2vYVJR8/LKqfgRbjwbsP9/7Zjum5I1TreGYkjcOb/zvexj38wIUdB2mqlNT8sbhz9/vQUHXYQCA7d/vJEvQBEc3FnXWZrCONOSOmoVzW1epYpZ1mNs7zZnk0MRjUf5k+EU/nm2wdu+Z/o9jRt/JWLP/Ur4ZfSfDK/ix7LO1qnyjrxumyefx1WDZZy/Suo5WRki2uIcPH27073l5eS09daOQLR4RL8RTrFbUujFv50rVW6nn71yEZZ/9RvOmanG/6ah6abqSxme46qfuU5yo8FZBlERwLAc7x+OZHSs1xz984wNYse8lVdqSQUXIIpeqqBBPcRortMQON5hLVrcSRFlCaU053HVV2Hb8U5woPYk5/abh9b/+j6b+TOgxGhu/2ozFg+plWolkCUqxqsUoFtsXLtfMcJT86TWkXz9Q2X3c/fUeuIZObsKRqh69PsKoPV9262z4RUGxObfp9AVG8a13vqVDZpMjVZwT0gxHYEDxzDPPYNmyZaq//fKXvwzbgIMgiHqMvNKDESUB7lo3/JIIG8shPSUdXJD8wi/5kZmSgQk9RivT2wwYTdq2459CZFk4xsyBnJIGprYK/n1bIYsCbJwNrgaDhpJq/XUdybxdkybIEsqry1RlZBhW1zKRIKJNw/VNSe27IPPmkWCTneAuPrid27IKSe27IOfeIgju0nrZSooTVd4qSKg3FZUhgwEDlmEgQ8amr7fiROlJ5RrJvF23/lyenovMlAxIsohcZ/2iYEmWSIaSoMiiAC41E21unagMJCoOfqhZmyGLAlhHGrw5HS7ulZQJ1pEGWZYheMqb7EeM1lxkJKdhRv5kVR/hE4Um+wKj+NbrH/TiGwD1D3FESAOORYsW4dy5c/jrX/+KsrJLQSMIAn744YeQC0cQhDFmp9FFScCPFf/G6osOUoHdvjtmXq4adNg5Ox7sPgIvH3pTyffcoCJN2tTe42HjbFj49z9cmla/oxAI6iSASzr14DdYXsGnyudyZEOUZSze02C6/Zbp8MsiVpNlIhGDBKxIudRMZA94UCWhcg2biqq/7UNat/74z0ULUmfvu1FzwwC8/+2fNDa4AZnUg91H4J1j25RBh1fw6dafc55SPNh9hLL/BtmLJjYMb0f2wIdQ8tG6SzF413QwwW2yLQm1fYZh9f6XL/UFN0+CKPhx/u1FTVrl8iyvicee7bsDADZ+tVnVR9iDNnfV6wuM4ju4f+jZvjsq66qwZv+rl8rd7zHYWRuWkvQqbgjpWykoKMBtt90Gp9OJ22+/Xfm5++67sXHjRqvKSBCEDnqWtXoWh+5atzLYAOrfFq0+sB7uIItCSZaUgUUgX1ltpSbt5UNvoqy2Um15eHQTqnVak4BOPbAINtBR5Ka2UaXN6DsFb/7fFtU5z1WXKoONQBpZJhKxQsCKNLPfaGWwAdTXw5KPX0ZmnxHKAyAAcDcOweoD63VtcF89vAkDOvXBy4fexKhrhwKorxeu1DaYGVR/puSNw9bv/oSXD70J+eLma2QvmuDIkirWhMoSlHy0TmPs4ZEFrD74W3VfcHA9qiGassqVZRlTe49XxeNDP78Xvw7qX14+9CakoGvr9QXtnC5NfM/s9xjaOV2qtAk9CpTBhlLufa/grKeEYj6OCGmGo1u3bujWrRvy8/ORm5trVZkIgjCB0Q7HwdPofknfolAIsqsVJEGTz9i20K+Tpj4fUL/DeceM9lg6ZDb8kqBozWVZwpJBRRAkETzLQQJwtPiY6lij6XayTCRigYAVKWtP0a2HDMer0kWWa9QGN5B+eXoufjPsOXAXd2dmGRaLBj2NC9Wl8Phq8O43f1RmQISLdYHsRRObUPsCkVW/LTKyyvVJfrxzbJtKYuupqzbVvxj1BQCaTDOKbz3pFcV87GKJh94DDzwAhmGU3xmGQUpKCrp06YI5c+YgJyfHissQREITvF7DaIdjrf2msUWhOl/Lp7wbs+RkGVa72I9hVQvEy6vLdK9N9rlEzMNyaD9+KcSaSlQc/BB1xSfqN1tjOaR06YXaE0dga98FLMfjuUEz4LQ7dOtUwB43iUtCZkq6yqKaAYOP/rFLNSh3ObLBMfV12Ei6SHUlMWA4HildemkWg5vtCzhJPRthZJXLMxwyktSOVn5JMB17un0BoEm7NDtSP4Nn5+wh90NE9LFE6DZkyBDcdNNN+M1vfoOXXnoJAwYMwHXXXYfu3btj4cKFVlyCIBKawHqN4g1z8eO6KSjeMBdiXY12J1k9+82UdMzMn6Sets6fhPSgRl5vyrtDejsU5U9WpRXlT8blabma86UlOVr8+fTKeHlaLgq6Dqt349nzPDZ+tRkFXYfBGcJ1CMIqAnXyP5vmo/jNeSjd8QayBzyIlC694Bo2FRcGvKewAAAgAElEQVT+8jqy+90HZ++74b99PBbteR4Ld6/BO8f+gKeDYn1K3jjsPfmFYm0bWJMxb+dKTPtoPhbvXouCrsMUvXygHgYe1PTqLtnkJg6sIw3Z/e5D6Y438J+3FtbHYr/7NHa3aclpun1BKmdvsh8BgIzkNE2bnJ6Uhpl9rYu94Nift3Mlyr2VmNf/8SalVxTzsU1ItrgB7r33XnzwwQeqtIKCAmzZsgXDhw+3fD8OssUj4gWrYtXQ9nDiSkCWTLtUBSRMei5VQMON/+qnsmXI+O3RdzSzDIWdBkDyehSXKvmr3XDdOcmUtaIRwWUEy2H+zlWat1pkj2g91KY2H6M62W7MPJR89JIy05FWuBTz9/xas9B24g1jAMWligXLsIrLjpH96OJBRZAu2oxmpqSDb1CHg+tua3XsoVjV0hxb3LIv/wjuxiEXXapEiH/dieyb7gaAJvsRo7hcduscyLJsSewZXWPJkFlgwGikV4kQ860FS+aeqqur4fF44HTWB4DH44HX67Xi1ARBoBGNruCD7eKbqcbgWF6zx0XwruKBxrrhw3xJdSmOFh/TrK8Y13kgat9boS7jbb8wZb9rtoxGlrqk0SUiiZH1tFGdFGvqzRhyC2aBc2bBLUtKHHdp0wkjrrkNTnv9LF0bR5buA5KRZl2SJbAMC57lNccZyVWI1kdwTBrFoiRLKPeUwi+LsDEcUhkOnkN/BA79UZU3q9dQU/2InnV6vQWuX2WBGwpGsS9Iou41KObjB0sGHKNGjcJ9992HO+64A7Is49NPP8Xo0aOxadMmdO7c2YpLEERCY3a9hlnM2mjq2SC6HNlgfeoXCnyGC+Dtpux3zUK6dCLaNGo9bVAnZVFQ2eSmPrgALkc2MlMycH+3u1V2uEY2nkax/2/3WazY9xJZgCYwejF52djFmlhMuToPxf5qrG6wK/jM/Elw9r67ftBxkeb0I3rW6XoWuKFA7X7rxZKWatKkSZg7dy6qqqrg9XqxYMECFBYWokePHli6dKkVlyCIhCZgwWlGZ2sGszaaqYKIop7j1Ba2eROQ4WyrKovrrunwyKIp+12zkC6diDaNWU/r1slRswCbXWWT69v7Lop6jsOoa4dq7HCNbDz1Yn9q7/HY+t2fmjyWaN3oxeSFHRuQc+8MVSwm3V6oDDaAS+2x7aZhLe5H9KzT9SxwQ4Ha/daLZUPGzp07o02bNggsCfn2229x3XXXNXnc7t27sW7dOtTU1KBv376YP3++VUUiiFZDwIKzfeHyJnW2ZjBro8kIPtg+2YAF/UZd2lX8T78HO/Ip1a62ZXvegq3gaVP2iGYxslGkN7pEpGjMblSvTrKONLCVpapj/MUnYPtkAy43qB96EsHg2Adk/PcXr6t2ISd5YWKiF5O1J44gq1+Bqk2uBvTbY1lucT+iZ50eShuvB7X7rRdLBhyrV6/GW2+9hTZt2ihpDMNg165djR73448/YtGiRdi8eTPatGmDCRMm4LPPPkP//v2tKBZBtCokWUIVI8PPAjZGRrosgWthI2w8bc1B8JQrnRF4O+TqCtQ0WK/BZ7iAIK8JNjUTNobTtbENtt9tDgwApyhCFkUwHAOmySMIwjqakjIyDKtalCtJAsCy2mOcWeAYFs8NmgF3XRW2Hf8UJ0pPwuXIBgPAV1UGlmFUD38N12RU1LpRUaveiI1kJq0Po/VCDTGKSbG6Eue2rFLSMqb+Rt8Cl+VQBXU/wsgSxKoKQBIAlgeXlglWJ7ZsrC0iVuW0Hql1YkmU/PnPf8ann37a7M3/duzYgaFDh6Jdu3YAgOeffx5JSUlWFIkgWhWiJFi6PiIwbR28hiO5phrF//OcSq+eO2Yezr239FLamHmQ62pQuuONS2mjZoFNdqKg61CsbVDGovxJSEtOa7pAOjSqn6e3XUQEYB1pyB01C+e2rlLHukMb05IkwH/uNMr2vQ/XsKmKrCrl6jzUDRqDJXueV+rFlLxx+PP3e3Dn1QPx+/99DwU/6Y+UA9uRfcsY3fg2qq8kM2k9mG3vAlK+4HzgbcpAhM9wIZXhMTN/UlCfMRm1Qh2Wfb5OldZOZHH+vSWqGLflXqEZdDiTHCjoOgxrG6wLKcqfTFblhCksscUdO3Ys3n777WYft2jRIthsNpw8eRIlJSUYOHAgnnzySdUmgm63G263WgN+9uxZjB07lmzxiJginLFaXl2G+bvXaq0CBxVp3KfMEmyjmSrJ+M8bs3WtFWVRVN5+MRyna8GYNnGZbhlbamNr1uqRaB7UpppH8JSj5E+vaTZUcw2drIlBf+UF/GfTfAiVJUhq3wWZN48E58hAbZZLY4vrcmRj7i3T8cqRTcpMx4Kf3QPfJ68bxnei2N42JJFitTntnd5MCABNmiiJqKp1Q5BF8AwHluXwzK7VmlhceN1oVL/zK9V1Lxu3BLaMtqrrGlnWklU5YQZLZjj69OmDVatWYfDgwUhOTlbSu3bt2uhxoiji6NGj2LRpExwOB6ZOnYoPP/wQ9957r5Jn48aNWLdunRXFJIiwEs5Y9Uui5drZ4Glrf2WJoV69oWWiUT6/JOpaJurpzM1IBxrTzxMth9pU88iigNoTR1B74khQ+sMq6SGXmgFIl+K1rviEIm9xPv6Kbt1111UBAGbkT4bT7gCblA4mNdMwvhNRZpJIsdqc9i5YyhcgOI3nWGQ5L0ndz1eV6Nst25NVaUJlSf0LpiCMbHFpLRFhBksGHIFN/z755BMlzcwajrZt26JPnz7Izq5/Qzt48GAcO3ZMNeCYMGECRo4cqTou8IaDIGKJcMaqjeV09bihrI8Ixqz1rlG+JM5mYJmoPt6sdMBqK2CiHmpTzWMUg5BEFG9aoIpfNtmpm9fG6NddWZY1NrlFdxQCvD2SHzGmSaRYjUR7Z9SP6Nqc60h17SbbeILQw5L52N27d2t+mhpsAMDAgQOxf/9+uN1uiKKIffv2aWZF0tPT0aFDB9VPYM0HQcQS4YzV9JR0zMyfpLIKnJk/CekWvvE0a71rlM/QMjHoDV1jVqMtKQ/RPKhNNY9uDBbMxoUdGzTxy9jsyB01S2OT6+TsGmvpovxJAMNobHLXHt2E6tatkmoWiRSrkWjvnAyPmTc/GtSPTNbYnOeOmgUuLVNzvCQKptp4gtDDsp3G165di3/+85944YUX8Otf/xqzZ89Gampqo8ddf/31eOSRR/Dggw/C7/cjPz8fo0aNsqJIBNGq4FgeHTMvx5JBRRAkEXwzd/E2g1nrXaN8lR79ncGDZV9mpQNWWwETRHPRi0FZljUSK6GyBLK/DrbcK3DZuCUqtx+xqlxjLS0f/Aip/e8zsMm1zmKUiB8i0t7565Dyxcd4ru9UiCwHThIhHPgDuIFjNXGr51IVDmkvkThY8rSyZMkS5OTkoLS0FElJSfB4PFi4cCHWrl3b5LEFBQUoKCiwohgE0arhWL7FC8T10FtHIYgCKhkJAgvwjIQMUQDH8RcXq/phY23KYtVgvbBZ2VdzpANGWuVIIEkyKqvr4Bck2HgWGalJYFky5g3Q2u+PUj9kGZAkAHK9eQKA3DHPoGLfZtQVnwBwKX5ZlgcbtNBW4nhda+m0gQ/QjsoRpDXFqyQJpmxsg2E4HnX/+gbVX+9W0vgMFxhZBh8Ut3oYtfEcy+FCdZmyOD0zJR28xXF8yTRB3Q8R8YMlEfH3v/8dy5cvx2effYaUlBSsWbMGd911lxWnJggiDOito8h56DkUS16V5eGMvpNhZ5Ow7PMXVXacHTPaaxr7gOwr2Lo3WPZlZOsYS1IpSZJx6qwbS35/COfLa5GTlYL5v+iNK9ulx+1DipW09vsTqB9ln72LzF5DFYtbPsMF17CpcP/vDmQPfAhle96CWF3RaPwaxTuTlIqi/Mkai1GHLVn3PETLiYd4Nbu2LWC/HGzVrGdjG0yoba9+Gz8Z1UIdVjSw2i3Kn4wrMttbNuiQZAk/VhZrbKH1+iEidrEkGlhW/YWLoqhJIwgidtBbR1EFUXn4Aeqnytfsfw0P3/iAKm3Vvld0bRDNyr7iQSpVWV2nPJwAwPnyWiz5/SGseeIWZKXRA2Frvz+B+tHm1onKYAOoryclH79cn/7ROlw27ldNxq9RvF+oqcCWbz9WOf5s+fZjTLxhDFy8dTOZRHzEq9HatmBbXLGqQhlsKPm2rsJl45ZoZteCCbXt1WvjZYbFggZWuyU1ZVh74DU8O6gIbS2akXd7PcpgI3ANo36IiF0sGXD06tULq1evhtfrxb59+/D222+jd+/eVpyaIAgLCJ6OdsiyZh2FyDK6+tzkINecep25X2MLyjAsWIZFmsxAluqd6ozePsW6VMovSMrDSYDz5bXwC1JYrxsvNHZ/gj9nWoodVbW+mPvcjVkzB9YZsclOpZ4E9tZgk53g0tui3YOLAEmEDAayLIFhWMNzanYklyWIkoijxcdwtPiYqlzjf04SY6sxildJklFe5VXFJoCo1FNZFMClZqLNrROVPV8qDn6otcWV9POBZeGvLAEkEWA5sM5McJxNc51Q295gae9Zj77VrihLqKh1WyKB8kt+g/VOtFg9nrBkwDFjxgysX78eaWlpeP7559GvXz9MnTrVilMTBBEietPRM/tOQcrVeaj9/rCSj5NkXX2uV/CpzudyZIOTZBRvnKualre5OsBfciamdwY3K62w8SxyslJUDyk5WSmw8S37HPEg6WgORvcnycaqPmfvrrm4/7ZrsHzD4Zj63E3JVwLrjCSvB3yGC1xqJrIHPKiWVt01/ZKkatQs8DkdIVz4d9OSmIv1kWd5Qz08YS168dq7ay4qPXVY+oY6Nm08i0Xrv4h4vDK8HdkDH0LJR+tUMcYE2yTr5Mu57xlInoogmdVMIOcK3UGHlfBGts+QlU0CQ5VA2VgbrXdqBVjyFGCz2TBt2jRs3rwZH3zwAZ566ikkJSVZceqYw+fXujHopRFErKA3Hb16/6tIunWCygrR6ZdQlD9ZZZk4o+9k5CSlqS09e45DUkWJZupfrKowZXcbTYykFZXVdap8GalJmP+L3sjJSgEA5cEj8AY0XNeNF4zujyhB9TkH97pSGWwAsfO5m7JmDmjd3V/vgWvYVGT2G62VVn20Dpk3j1QkLZLHXPwH6uMfj+/A00FW10X5k5GZnBap25Aw6MXrIyO6KYMN4FJsni2tjk68ypIyiAAuxRhk9awqwzCafCzL6sisVkPyVIS92Jkp6Tr9xhS8+dUWjQTK7fW06BrpyU7M6veY6hqz+j2G9GSnNR+CiAghDQ+HDx/e6N+3b98eyuljEruNw/Cibaq07WtHRKk0BNE0RtPRvjqPalq+7KPf4PIxc/HsoKchShI4lkWaxOD8llUqS0//JxvADhqnOl9gZ9pY3xncrFSKZRlc2S4da564xRJpRTgkWtHE6P5cqKxVfc40hy0mP3dT1swBrbtr6OR6lyrBp5ufvfjAI1yUspiJ/0B93HPyIABg7i3TwTIsbByPzOR02ML8RjoR0YtXv1+/TibbeU1aJOLVrF24Xj4wjO6xiIBdLc/yuCKzPZ4dVARRFsExHFiG0UgFQ5FAsQyLjhntsXTIbPglATaWJ5eqOCSkAceCBQusKgdBEGHCaDqaqfHg3JZVShqf4QInA5kyC1kKaNJFjd6Xd2ZB8uvvTBvrO4M3RyrFsoxlC0qtlmjFAnr3J/hzVtX4Y/JzG+4g3mA9hoIkGsa2dPGNbX38c0jp0gvp1w9UBvHur/eo4l+SJTBg8NygGXDXVWHb8U/x9CfPweXIxtIhs2mwEUaC47W8yqsbm16f+qE4UvHamF14w/Vyevkgy/rxrCPPa2ztUkvhWV61QLyi1o2e7btjQKc+iiHC3pNfhCSBYhmWFojHOSFFWV5eXqM/BEFEn7Qkh84u5ZORkZ6j3l32gQUQPeUo3jAXP66bguINcyFKEuoG349f/f0PeGL/C/jV3/+AusH3g8u+XLMzLZOahtxRM4PSZ4JJiZ1pb6ulUrF+3UgT/Dl3HTmFuYV5Mfe59XZ1dg2bigt/+R18509DkgT4zp9W6kLFkY81se26azoqDn54Kc5T05Hd7z6U7ngD/3lrIUp3vIHsfveBddRLpAJrNxbtXouFu9dg41ebcX+3u9GzfXeSh0QBozrZrk1qVOLVaKdxsa5G3SbX1WjyybxNv+1NVcvzAmuXGp7Pd/40ZNnaGRxnkgMFXYdh41ebsXjP89j41WYUdB0GZ5LD0usQ8QUjy7Ic7UI0lzNnzmDw4MHYtWsXOnToEPHrk6SKMEu0YxUABE85Sv68HkyPQZd2Ov5qN1xDpwCypLzpAoDiDXNVb8lSH1yA5/62WTM7sqjHQ0itdqve5La9/WFc+Mvrmje8bW9/BDYTm0pFimi5RcWyS5WVcRpPLlWCuwyi+wLEmkpUHPwQdcUnwGe4cNm4JfjPpvlKXcgtmAX313uQfv1A2NpcDqmuBmxKWr2+XpJQ+fVuZPYapjoGqH/wC9iaVtS6lUW0AVyObDw7qAjZjkySh5gknLEaTZcqQDv7AIZF8RuztTE1caWq7ZZlGRWHtiPj+kEAyzaIyaGwXRyEAPV9QXAb3zBGrcIo1snGNrGJHa0DQRDNRm96XJYl1S604HlInnJkihJYQYQkSqjwlEOWRDDMpY5UTxss2ZP1LQ8ZRiXHAgDcWojaE0dQe+KIJt1KQn1wt1Iq1ZzyWH3dWEH5/H4JDFP/OVmWQduMFOU+xOLnrpeRyCh+cx6Aetvb3IJZ4JxZgCwjZ9QMiO5SVH9/FLa2HZHZezgkrwfSxTfOwWTeeDtcw3+p2JTWFZ9QafCN1lLJAA02IoAgSCiv8kIQZfBcfV3keVY3NmMlXmVJ1LfKDWq7IfhQdeiPqDr0R9XxmTferj6fyXUioUI2toQeNOAgiDhF19rzoWeB2mq1PeKYeWhz60Sc/8Pzl2wU750BqboS57asVNIuG7tYowNmfV7d9R+sT28NB2egI7aumYk1e9lYK0+k0fv8j4/pge37/omxd/ws5u9DQA+va3s7bCpqfjyO9BvvwNn/eU5Jb/fAAt0495X+G+feW6YcW7b3HYjVFcrsIVl7Rg9BkPCvs26VPfPcwjxc1S4dfIyso9Jtzwtmo83tj+D8B2ua3Xbrtb2NrROxEop1Qo+Qatrw4cMb/SEIInzoWXsygl9jjyhUnlcGG4E0seZShxVIu7BjA3ILZqt0wBnpObp2hJr1H6PngHVm1u9HELS2g0vLtOwzx5q9bKyVJ9Loff4X3/sKg3tdGRf3IaCb17W9/fhlZFw/COc/WK1KL921CTn3ztCu59i3WXVsZr/RyB09B1xqBgCy9owm5VVejT3z8g2HUV7lbeLIyKFr1bxlJcSaSlNtt15MglM/4hmtEwnEqFVQrBN6kEsVQcQpZu0RWVuyqbTaE0fA3jkJ7QuXqyRaHQGNHSEDaPIxDAsm9wpcNm6JIufi0jLBWvhWK9bsZWOtPJHG6PMH7HBj/T4E7G9Ze4q+rSjL6taTtrc/rMQ/AJz/YC3qik+ojrW36QA+06U4AJG1Z/QQRFk3TgUpdpawGsmdWJta3mXUdmf1K1DbnO95Czn3PKnKF4h3vbbbSijWCT1CehJo6ERVUVGB2tpayLIMURRx+vTpkAtHEIQxZu0RJb/XVBqf4QLDMJrFgwygu9BPb5Fh/S7NHGTIYDgu5I4sWHfNc+btZZuz1sNI390UrdHutjkYff6AHS4A+HwCKqt9zb63kYJhWDA2u74kRZIM7XMD8S94yiFWqzdYM7IkJWvP6MBzjG6c8jrtgd8votxTB1GUwHEsspxJsNnCv/u7kdwp2ILcqO0Wqys1NudWylmbC8U6EYwlrf4LL7yA/Px8DBkyBHfccQduu+02rFixwopTJwyS4GvR3yKFT/Rbmo8IHb3p8Xp7RLWsic/I0eTjs9pZPrVuteViQHc956X9mLR8J+a8tB+eWp8pe9nA2oIZL3yOh5fswIwXPseps25IOm809a7zr7NuCCbezieK3a0RaSl2je3t42N6YNeRU3h8TA+s//AYTp/34LUPjzX73kYSI5vcyq9369iNqmWCTVnsWm05SjSfjFRtnM4tzENGql2Vz+8XcepcFea+tB+Tlu/C3Jf249S5Kvj94d9Az0juxGe1M9V28xk5jcYpEDlbXILQwxJb3EGDBuHdd9/FihUrMGvWLBw6dAh79+7F888/b0UZNUTbajRctrg/LB2lm9553lZLzh8q9733WJN53h/zSgRKEj+EO1bNuFRxaZlgGFaTD4ClG0BZbblYUl6DOS/t17yVXP3LfmBYptGZi/IqL2a88Lnm2DVP3KJxoDG6zoppfeHKato3Ppbtbs3S0jgtr/Lipc3/h8G9rkRWehIynUmo9vpxvqwWW3efwD9OlyMnKwWPjOiGZRsOA2jevY0kDesSwAAsC4ZhwKQ4IXkqG5UJNmaxa7XlaKLTklgtKa/B9n3/xJC8q8CxgCgBOw//C8P7/UQVh+fLazBXpy1YPq0vciIQr3rtOaBtp/XS9Nr94DiNlC0uQehhyXxbdnY2cnJy0LlzZxw/fhwjRozAxo0brTg1QRCNwDCsVgLFsGB19r3Q61Cs7GSstlw00l3XCRIua5Pa6LF+QUJWWjIeGdENaQ4bqmr82Lr7hO6aglD13a3V7tYMfkHCoW/P4dC35wAAy6fmY8NH32HUoC4ovOta5b6nOS7toB1r2vkAenUpANfEPjLBFrsBwmE5SjQfQZTx3cly/KxTW6U9+O5kOe7MV8ehKOqvSRLFyMSrUQyaSTNq9xsSKVtcgtDDkgEHz/M4ffo0OnfujKNHj6Jv375wu91WnJogiDjBasvF5uiug0mysRg/7Gd44d2vFBvMJ+7vgSSbdgYnlOskOsFrOCRZ1r3vUoOJ9NZ6byNlOUo0H8P2IGgtEWewRozjWke8UowS0cSSNRyTJ0/GggULMGDAAHz66acYMGAAbrrpJitOTRBEnGC15WJWWrKu7trMbIIoQXm4AOrfUr7w7lcQdaTKoVwn0Qlew8KA0b3vDOof2FrzvY2U5SjRAhj9uASjHkhkOZP02wJn61iTRTFKRBNLhrUDBw7EwIEDAQDbtm3DqVOn8F//9V9WnJoIIz7RDztnazojQZjAastFnmdxVbt0rJjWF4Ikg2fNOxwJBtIIQWfEEcp1Eh2WZXBlu3SseeIW+AUJkqQvT2ublYL1zwxp1fc2UpajRPMx2x7YbByuzE3D8ml9IYoyOI6JmEtVJKAYJaKJJQOO6upqvPTSS9i/fz84jsOgQYPQuXNn2O32pg8mooads5laCA7QYvBEQW/RYnM6o8Z08C2BZRnwPAtZkMDzLFiWMbVIu7l2tTzPJswCcasJrGGRJBmllbW69z3ZzsXdrEZL6oLV8U80H7062pz2wGbjIrJAPBIYxTDFKBENLBlwzJ8/HyzLYu7cuZBlGe+//z6WLFmC5557zorTEwQRAQKWiYHdbgPT7facK6LyBixgbRvYyTpgOWvjWSxa/4Uq7cp26aoH/4DUJ/jYUOxqjcoTfO1EJHBv9hw9jTkTemHFxiPKPZpbmAdncnzNpMZaXSDMYVRHL2+birmFecpu4/Eal82BYpiINSyJuu+++w5r165Fnz59cPPNN+PXv/41jhw5YsWpCYKIEGJ1pdI5AfXuJec2r4BYXRmV8lRW1ykPDkC9BGLJ7w/hbGm1Jq2yuk51bEOpz+vzb8WaJ24JeWBgVJ7gaycigXvzs05t8d6Of+CREd2wfGo+HhnRDe9+ehwVnvi6R7FWFwhzGNXRimof3v30eNzHZXOgGCZiDUtmONq2bYuysjJkZ2cDAGpqapCVRVN2BBErmJGHRNsyMVgK4ffrW9sm29XN1vnyWl27WyNaupOwX9DXgTfn2q2VwL1Jc9hQUVW/UWmynUN6ahIKBl8NSQZKK2vBsowyyxQuaVqoskAg+nWBaBlGdVQUZaQ57Ljc5QTHAumpSUhz2CGIMkrKayCIMnjukr11eZVXlcayTNSklC2NZ4phItawbMAxatQo3HHHHeA4Drt27ULbtm2xZMkSAPWSq9aMzy/CHvTAopcWDiTBB5ZXr5WhxeBEQ8xOrUfTMlFPCrFiel9dK0s2qK/NyUoBz7FNni8grTh93qORVlyZm9bkoKO560ISCf6inWjAFvePn/8Tw/v9BMsbSKseH9MD2/f9E4V3dYVfkMIiTbNKRkL2ofGJUR1NTuIwNL8Tnv3dJSnmnAm9kJTEYuYL+5S0JY/djOpaQdU+PFOYhyQ716SMMxyEEs8Uw0SsYUlP2aVLF4waNQqpqalITk7GsGHD0Lt3b2RmZiIzM9OKS8Q0dhuH4UXbVD+RGGwAAMvb8cPSUaqfwGLwpn6IxMDs1Ho0LRP1pBBnL9ToWllmpSWrbCufuL8HIMtNni8grQg8TATSl284jHIT0opgC1gr1oW0GmQZT9zfQ7HFHdzrSrz4nvq7e/G9+vSzpdVhk6ZZJSMh+9D4xKiO+gVJWVcE1Mfcio1H4K0TVWmCIGvah2UbDpuScYaDUOKZYpiINSwZ6k6fPh1erxenTp1Cly5d4PP5kJxszpFk/PjxKC0tBc/XF+W5557D9ddfb0WxCIKA+an1aFom6kkhGAa68ojKap9KZvXmx39H0UM3Nnm+emlFy3cSDraAJZeqS9T5pfrvYewNirRK7z4HdhwPlzTNKhkJ2YfGJ0Z19FxZtW7MyUE73rMGbU6oMs6WEko8UwwTsYYlA46vv/4a06ZNA8/zePfddzFixAi88soruOGGGxo9TpZl/PDDD9i7d68y4CCIRCJUvbmZ45sztR6qZaJZ29jgfEk2Fr275mJwryuVgQTLwkDCxKi02Nd2yoKNY1Be5VXOxxvuGBzaTsIBC1ji0ncoSfX7FUy/73pwLIu1T9yCZDuP3l1zcejbc0r+nKwUVNX4YWDfJq0AACAASURBVOP1d3a3QprWWKwH1xXWkQappsqw7pB9aHyiV0c5Ttu+7DpyCgzD4JnCPCWNYfRj0+tTP+DnZKUgycaq2hwrXj5IkgCxqgKQBIDlQ5ZFUQwTsYQlT/krV67Ehg0bMGPGDLRr1w6rVq3C0qVLsXXr1kaP++GHH8AwDB599FGUlpbivvvuw0MPPWRFkQgi5glVb272+MDUenA+q6fWzdrG6uV7dlIfPHDbNVgWpJ1e9OhNePa3X2o01g212HML8yAzwIwXPledT88WNzPVrmuP2Vp2Eo4Uge/w7U/+juH9foLt++rXbPzq9UOq+woAh749p/xefL4KHXMzLbcsDmAU66wjTVtXRs1C2b73UXviCFmGtnLSU+y4/7ZrNPWe4YDfbftGSVv86E14pjBP1Q7NLcyD02FTBiKB9qXMXWfpOiRJEuA/dxrntq66FKNj5kWk7SaISGDJgMPr9eKnP/2p8nv//v3x/PPPN3mc2+1Gnz59sHjxYni9XowfPx6dOnVCfn6+Ko/b7VYdd/bsWSuKTRCW0txYNdLnti9cbuqtlNnjIzW1brRuYs0Tt6jeOOqu1yitxitbj2m000/ef4NKPiWJ0F2DsfjRPqq0Reu/wK+fvEVX/tSadxI2gxVtauA7fGREN7z43lfKv8Hfy6JH+uCe/j9FVY0f7356HJNGdkfbjHp9fTikaUaxrltXtq5Cm1snovbEkWbXPSIyWNX/u2v11249Nqq7Km3xb79E0Vh1m/Pup8cxteDnqniVZWDmi5832dY1B7GqQhlsABdj9L2luGzCMpJFEa0CSwYcPM+jsrISDFPfYfzwww+mjuvRowd69OgBAHA4HCgoKMBnn32mGnBs3LgR69ats6KYBBFWmhuroerNGzte8JSrOigZgIfj4Gck2FgO6QCsXnlgtG6i/t8axYaWYxlNvmQ7r3sswwDLNhxW0l6dPUg3X/Cz6vnyWtT5Jd0dg1vTTsItwYo2taENbmNrNtzVdZj78gEl7eG7ZWVgES5pmp6MxKiusMlO1e+hWoZKsgS31wO/5IeNtSE92QmWHg5bTEtiVc/22mjtlt7aDElStzkAMGmkui05X15j/TokST9GIfjAZ7Vr+XlbWhyKZcJiLBlwPPbYY3jooYdw4cIFPP300zhw4ICpXcaPHj0Kv9+PPn36AKhf0xG8lmPChAkYOXKkKu3s2bMYO3asFUUnCMtobqyGrM81OB6SiOJNC5Qp+JwHFuIcJ2HVvldQUlMGlyMbs/o9ho4Z7S3tQPTWTYzs3xmllV6NzWSwvt/rE0xppyVZf11H0NpPsqttBCva1ID9aFWNX/Vv8PdSVeNX/W52rYzVGNUVyetR/R6KZagkS/ixsjjs9SyRaG6s+v0iTp2r0kinMlLtptdm6KUFtyVhschmDdpzNvLrWymWiXBgSeQMHDgQ69atwy9/+UvccMMNePvtt3H77bc3eVxVVRVWrVqFuro6eDwefPjhh7j11ltVedLT09GhQwfVT7t2kR/tE0RTNDdWQ7Ut1D2+YDYu7NigmpavdJ9XOg4AKKkpw6p9r8Dd4GHLCjgWeOL+HipLymF9O+vaTD5893WqfO3apOraWeZkO1RpPM9gbmGeKm1uYR5sNpbsak1iRZsasB/ddeQUHh/TQ/m34XcwZ0Iv7DpySvk9qmtlGBauu6ar6krOqJlwf71H+T1Ubbzb64lIPUskmhur5Z46XekUx2rbjWcK85DbxqFpN9q1SW2yLQmHRTZjsyNn1ExNjDI2exNHWg/FMhEOLBs6syyLBx98EH/5y1/wySefYMKECUhLS2v0mIEDB+Lrr7/GPffcA0mS8OCDDyoSK4Jo7YS6tkLveFmWUXviiCqfZE9WOo4AJTVl8EvW7jgbsEZVrbmQZH2ZFcNoNPyAVtcvSTJWTOsLQZLBX3SfcWVAk8ay2vORXW34CNiPThv9c0iSjEkju8PrE7B8al+UVXlR7q7Dnw6cxOBeV+Ke/j+FK8uB7LTorZWRBR/K9ryFNrdOBJvshOT1wH30E7S9/WHg9oct0cb7JX9E6hlhjJF0qs4v4UJ5NZZP7QtRksCxLH74dzm6XJFtqh0KbkvCYZEt++vgPvoJ2o2ZB7AsIEmo+PKPyLrlvhafs6VQLBPhwJIBx8KFCwHUT3/+6le/Qr9+/TBv3jy8+OKLTR775JNP4sknn7SiGCERid3C9XYFJxqnObumx+MO66HaFgYfL3jKNdPyrM8LlyNb1YG4HNmwWTxVb+NZXNspS2VZG5h50NjQGtjLBqdJAa1UA8kUz7Nw6azBILvayNLQfrS8youP9p3CXX1/onxnP57zYOeRw8jJSsHyaX2VNX6Aeftkq2A4HmJ1Bc5tWaWk1UuoHrRskbiNtUWknhHGNGZ7fb68Fle1r9+IWJLrX4T8rBOjkWOatb622iKb4Xh4T32DM8d2K2mBGI00FMtEOLAkev72t79hy5YtWL9+PUaOHImioiKMGjXKilNHjMBu4Q3ZvnaEpdcI7AquR+d5jVsIJyqBXdPN8P6YV8JcmthHzxY0Iz0Hs/o9ptHjpjdYMGsFzmQbbrmho8qy9pmJWmvbuYV5yExteuAtCBL+ddat0WNf1S4dPK3PiCkC3/3cl/cr39XjY3pg+75/Ysyt/4WP9v0Tt9zQEVddtA01Y59sJZGwhk5PdkaknhHGGNlep6fYcW3ntpj3ygElfdGjN6Gssg5L3ohcHDZGpOzLzUCxTIQDSwYcsiyDZVkcOHAAU6ZMAQDU1tY2cRRBEFZjJNPqCGDpkNnwSwJsLB8Wx5EKHf30sjcOY9a4nlj0SB9wLCBKwM7D/8LIgV1gtzfe/JRXeXX12Cum9dWd4SCih953/+J7X2HpY/l4b8c/sPPIjzhw7D9YMa0veJ41ZZ9sJZGwhmYZFh0z2oe9nhHGVNcJ+Px/f9S0N3f1+4kmPs+X1WisuMMdh40RSzuDUywT4cCSAccVV1yBRx99FGfOnEFeXh6KiopwzTXXWHFqgiCaiZ5MiwGQmZIe1usKov56Db8gYcaLu1Xpd/X9CUrKayCIMniuXpoQPGthdD4hWANBRBVJkiEYaOcvVNRi55Efld8FSYZsYJ8ckqWoCSKx6zLLsGGvZ4QxfkHCh5/9gA8/U1vz33lzJ9NW3OGOw8aIpZ3BKZYJq7FkwLF8+XLs2LEDN954I2w2G3r27Il77rnHilMTBBEn8Bxj2npSlCTMf/Vgo1Ipo/PxtBg8ZgjsOK5niaxni2vjGEOdPdkYE6FiZFerF59GVtwUhwQRHiypWQ6HAyNGjECHDh0AAA888ABSUlKsOHVU8fnFiFznsumvAqhfx6H3o5RH8Okeb5Q/GvhEf9OZmpGPiB+y0pJ1rSeDbSafmZiH1//4N41UqrzK2+T55hbm0eLwGCKw4/gHe05gzoRejdriPnF/D/AXF4hbbSlKEICxXS3HMRrL7nSnHfMm5lEcEkSEIMuBRojEQnIASMlwmVoY/f6YV0znixZmF3nTAu/WB8+zuKpdepOWtX5BUm36B+hLpYzORwvGY4fAjuMB2VRAO8/zHN799Lhii1tV48ebH/8ds8b3DIulKEEAxna158pqNJbd6z/4G2ZN6ElxSBARggYcBEFYhp5lrRQ0kGAZoHfXXAzudaXS+e86ckpXKsWyDHiehSxI4HnW8GEg0jariYjePdaTsIBhwDBAVY0PyzYcVpIbylWsthQliAB6scVzDDLT1M54mWl2ayQeBEGYggYcBEGEjYDGv6EF6tKp+bj/tms01pWZQbtQ6x2rZ1tpNh/RcozuccecNMz/RW/sOXoa/Xp0UFkizy3MAwAc+vYcyVWIqJLusOu2OTIDzHjhc2o3CCIC0ACfIIiwEdD4N1yvceZcla7drcfrb/LYJb8/hMrquhblI1qO0T2uqvXhynbpuKvfT7Bi4xHNd/rIiG5YOb0vVkzrSw9yRNSorPHptjler0jtBkFEiFY/w2H1DuKS4NNdx7F97QiUlVVhwtLdIV+DIMwS61Iiv44Fqlk7Sr1jQ8lHtJym7rFoYGEsihJmr9uP1+ffGlNxSSQWooFtM8cyeKYwT5F2bt19gtoNgggTrX7AYfXCb7O7hUdqwTmRuMSDlEhP42/WjtLI4rKl+YiWY3SPk2wsTp11o7SyVvfvokTfBRF9jKyYGQb43bZvlPbzift7IMlGsUoQ4YBqFkHEKfEgJdKzqWzXJtWULapZ+1SyWQ0/RvdYlIAlvz+Edz/9Ho+PUduOzpnQCzsP/4u+CyLqZDmTdC27g+25X3j3K4g0wUEQYaHVz3AQRGslHqRERjaVAJq0ozRrn0o2q+HH6B5fqKzF+fL6n01/umQ76spywG5jMXJgF/ouiKhjs3G4MjcNy6f1hSjK4DgGHMvo23PTiIMgwgINOAgiTommlMho7YhRerBNZbBVrhFm7VPJZjX86N1jG89qLI7/8Nn/w+SR3WmgQcQoMgAGHMeQFJMgIggNOAgiTgnIXILXcIRbvtKYReqP56vIxjaBcCbbdO1Gv/imGN1+6qLvlIgJ/H4Rpxq44wUkVc9O6oNF67+IaPtJEIkKDeUJIk5pKHN5ff6tWPPELRF5wDNaO1Je5SUb2wSjwlOnazf686tz6TslYoZynThdtuEwkmxcxNtPgkhUaIaDIOKYaEiJjNaOCAbWqGRj27poKJszthul75SIDnqyTqM4FUQZriySYhJEJKABB0EQzcJ47Yi+JprnyMa2tRAsh3t51iCywyViBiO5ZmqKTTdOOY5mMwgiUlBvQBBEszCySOV5Fk/cr7ZGfeL+Hggab5CNbRwTLIf7YM8JzJnQi+xwiZjASK6ZZGc1trhzC/OQ5aT4JIhIQTMcBEE0i8YsUt/8+JI1alWNH29+/HfMGt/T1PGknY59guVwO4/8CAD1dqOSDI5lYOPJDpeIDkZyzTqfpLHFzXImwWbjolRSgkg8aMBBEESzMbJILa/yYtmGw0qakayGbGzjEz053LH/dwHjh7HIoe+TiDKNyTVtNg45WY4olo4gEhuSVBEEYQkklWr90HdMxDIUnwQRu9AMB0EQlkBSqdYPfcdELEPxSRCxCw04CIKwDJJKtX7oOyZiGYpPgohNEnLA4fOLsLdwsZhP9KPzvK2Gf9++doSSL/B/QotP9MPO2aJdDIIgCIIgCCLMJOSAw27jMLxomyrN7ODAztlw33uPNZnv/TGv4Ielo1RpjQ1UEo3m3EeCIAiCIAgifqFF4wRBEARBEARBhA0acBAEQRAEQRAEETbiUlIliiIA4OzZs03m7dChQ7iLQ8QQZ86caTJPu3btwPORCf3mxCpBBBOpWKU4JUKB2lQiXohkrBJqGFmW5WgXorkcPXoUY8eOjXYxiDhl165dERuIUqwSoRCpWKU4JUKB2tT/z96bhzlRpX3/31qSTnen0wv0wiIoIy7joyMqmw3IjiM6KOCDigr4yqYOjiI7iiD7ogMP6gi+iji8biA6jruoIyID6uNvXEYdL51RsOkF6U7SSzqp5fdHuqpTSVVSnU7SqfT9ua6+IKeqTp103/nWOan63jdhFVIZq4QWSy44fD4fvvzySxQXF4Pj4ss2lUlUVlZi6tSp2LNnD8rKyjp6OB1OrN9HKr/hMBOrmfb3y6T309HvJVWx2pGa2tG/YzNYYYxAx42zIzXVKn8bs2TS+0nH90J3ODoOS/7WHQ4HLrnkko4eRtpRVlZGK/cQ0uH30ZZYTYfxJpJMej+Z9F70SAdNtcLv2ApjBKwzzngwitVMe8+Z9H4y6b0Q8UOmcYIgCIIgCIIgkgYtOAiCIAiCIAiCSBq04CAIgiAIgiAIImlw999///0dPQii/WRlZWHgwIHIysrq6KGkBVb7fVhtvLHIpPeTSe8lXbHC79gKYwSsM85EkmnvOZPeTya9F6J9WDJLFUEQBEEQBEEQ1oAeqSIIgiAIgiAIImnQgoMgCIIgCIIgiKRBCw6CIAiCIAiCIJIGLTgIgiAIgiAIgkgatOAgCIIgCIIgCCJp0IKDIAiCIAiCIIikQQsOgiAIgiAIgiCSBi04CIIgCIIgCIJIGrTgIAiCIAiCIAgiadCCgyAIgiAIgiCIpEELDoIgCIIgCIIgkgYtOAiCIAiCIAiCSBq04CAIgiAIgiAIImnQgoMgCIIgCIIgiKRBCw6CIAiCIAiCIJIGLTgIgiAIgiAIgkgallxwCIKA48ePQxCEjh4KQUSFYpWwAhSnhFWgWCUIa5KSBceGDRuwePHiiPaXXnoJQ4YMwYQJEzBhwgQ89NBDpvqrrKzEqFGjUFlZmeihEkRCoVglrADFKWEVKFYJwprwyT7B4cOHsX//fgwfPjxi2xdffIHFixfjyiuvTPYwCIIgCIIgCILoAJJ6h6Ourg4PPfQQ5syZo7v9iy++wEsvvYTf/e53uOeee+B2u5M5HIIgCIIgCIIgUkxS73Dcd999uOuuu3DixAnd7cXFxZg1axYuuOACPPjgg1i1ahW2bNmi2cfj8cDj8Wja6FYqkY5QrBJWgOKUsAoUqwSROSRtwfHCCy+gW7duGDx4MF588UXdfR5++GH1/7feeitGjx4dsc9TTz2F7du3J2uYBJEwKFYJK0BxSlgFilWCyBwYWZblZHQ8Y8YM1NTUgOM4uN1uNDY24uqrr8bSpUsBAF6vF/v27cP06dMBBB+/+u1vf4vDhw9r+jH6hmPq1Kk4cOAAevbsmYzhE0SboVglrADFKWEVKFYJInNI2h2OJ598Uv3/iy++iKNHj6qLDQDIycnB448/jn79+uE3v/kN/vznP2PMmDER/bhcLrhcrmQNM22RJBnuhmYEBAk2nkV+bhZYlunoYRFR6KyxSliLZMcpaReRKDpCUyl+CSI5JD1LVTjLli3DyJEjMWrUKPzxj3/E/fffD5/Ph9NPPx0bN25M9XDSEkmS8WOlB6ufOILq2iaUFGZj+S0D0bvMRcJHEETaQtpFWBmKX4JIHilZcEycOBETJ04EAKxZs0Ztv+SSS7B///5UDMFSuBuaVcEDgOraJqx+4gg23zkMhXmODh4dQRCEPqRdhJWh+CWI5GHJSuOZTkCQVMFTqK5tQkCQOmhEBEEQsSHtIqwMxS9BJA9acKQhNp5FSWG2pq2kMBs2nv5cBEGkL6RdhJWh+CWI5EGfojQkPzcLy28ZqAqf8hxpfm5WB4+MIAjCGNIuwspQ/BJE8ki5aZyIDcsy6F3mwuY7h1GmDIIgLANpF2FlKH4JInnQgiNNYVmGTGoEQVgO0i7CylD8EkRyoEeqCIIgCIIgCIJIGrTgIAiCIAiCIAgiadCCgyAIgiAIgiCIpEEejg5EkmS4G5rJnEYQhOUhPSPSGYpPguhYaMHRQUiSjB8rPWpVUyX9Xu8yF4kgQRCWgvSMSGcoPgmi46FHqjoId0OzKn5AsJrp6ieOwN3Q3MEjIwiCaBukZ0Q6Q/FJEB0PLTg6iIAgqeKnUF3bhIAgddCICIIg4oP0jEhnKD4JouOhBUcHYeNZtZqpQklhNmw8/UkIgrAWpGdEOkPxSRAdD33aEowkyaj1+lBd24harw+SJOvul5+bheW3DFRFUHmmND83K5XDJQiCaDd6erZ0xgCwDAw1kCCSgd41mK63BNHxkGk8gbTFmMayDHqXubD5zmGUNYMgCEuj6NmmecPgaxZQcbIej+79HLVeH5lziZQR7RpM11uC6FjoDkcCaasxjWUZFOY5UFKYg8I8B4kfQRCWhWUZMAxw72MfYeXjR/DtT7VkziVSSrRrMF1vCaJjoQVHAiFjGkEQnRnSQKIjofgjiPSFFhwJhIxpBEF0ZkgDiY6E4o8g0pekfwo3bNiAxYsXR7RXVFRg6tSpuPzyyzF37lw0NDQkeyhJJ1nGNLNGdIIgiFRB5lwi3YgWf3QdJYiOJamm8cOHD2P//v0YPnx4xLaVK1fihhtuwPjx4/Hwww/jkUcewYIFC5I5nKSTDCM4VUglCCLdIHMukY4YXYMB0HWUIDqYpN3hqKurw0MPPYQ5c+ZEbAsEAvj4448xbtw4AMDEiRPxxhtv6Pbj8Xhw/PhxzU9lZWWyht1uEm1Mowqp1sFqsUp0ThIRp2TOJVJBPLGqF390HSWIjifmHY53330Xb7/9Nv7973+DZVn06dMHl19+OYYMGRL1uPvuuw933XUXTpw4EbGttrYWTqcTPB88fXFxMaqqqnT7eeqpp7B9+3Yz7yUjIROcdejssUpYg0TEKekSkQoSpakUrwTR8RguOH744QcsWbIELpcLw4cPxxVXXAFJknDs2DE8/fTT2L59O1avXo0zzzwz4tgXXngB3bp1w+DBg/Hiiy9GbJflyGcnGUb/W7Bp06bhmmuu0bRVVlZi6tSpMd9cJqCY4ELFkkxw6Ulnj1XCGiQiTkmXiFSQKE2leCWIjsdwwfHoo49i8+bNOO200yK23Xjjjfjpp5+wdetWbNmyJWL7a6+9hpqaGkyYMAFutxuNjY1Yu3Ytli5dCgAoKipCfX09RFEEx3GoqalBSUmJ7jhcLhdcLle87y+tEAQJtV4fBFEGzwVv+/IxBE8xwYU/e0omzPQjk2KVyFwSEaehulSY58B1Y89C965OyHLQ3xH6GJUkyXA3NJOng2gzidLUtlxH47lOEwQRG8MFx6ZNm6Ie2KtXL93FBgA8+eST6v9ffPFFHD16VF1sAIDNZsMll1yC1157DVdddRVeeuklDBs2rK1jtxSCIOE/lR6s23VUFbwl0wfg9DJXVDGjiuQEQaQbii49+IdhOOn2Ye2TR3XNuJT0gkgHzF5H471OEwQRm5ifIJ/Ph1dffRV79uzR/MTDsmXLcODAAQDAihUr8Pzzz+OKK67AJ598gj/84Q9x9WkVar0+VcSA4POj63YdRa3XF/NYMmESBJFusCwDSYa62AAizbhk1iXSBTPX0fZcpwmCiE5M0/icOXPg8XjQs2dPtY1hGNPPUE6cOBETJ04EAKxZs0Zt79GjB55++um2jteyCKKsa1oTKBc4QRAWJZYZl8y6hJWg6zRBJI+YC46qqiq8/vrrqRhLRsNzjK5pjae7FQRBWJRYZlwy6xJWgq7TBJE8Yqr+WWedhZqamlSMJaMpzHNgyfQBmgqoS6YPQGGeo4NHRhAEER+xKotT5XHCStB1miCSR8w7HJdffjl++9vf4qyzzlLrZgDA7t27kzowKxEIiKitb4YoSuA4FoXOLNhsnGYfnmdxepkL628fAkGSwbPRs19QZhctkizB46tHQArAxtrgcjjBMmzMbQRBJA89M25eth3uhmZIkgxJkpHjsGHd7cG6TQygTt5qvT7StxTS2TVU75oqSXJERqrepXlYd/sQzfWcDONtx2xMdYbYI4LEXHBs2rQJs2fPRq9evVIxHssRCIj4scobkdWid2me7qKjuDAnZp+U2UWLJEs45q7AxoOPoqbxFIpzirBw6Fyclt8dAAy3kWgRRPJRzLhAq3bteeNrXDX0V9j23Geqhs2b0g+vHPwe0688DwFBIn1LIZ1dQ42uqRzHYOXOv6ttK2YOgijKFJvtJFq8hcaU2f2IzCDmXzQ3NxczZ87EuHHjND9EkNr6Zv2sFvXxZ2GhzC5aPL56VZAAoKbxFDYefBQeX33UbQRBpBZFu0b1760uNoCghm177jOM6t8blb80kL6lmM6uoUbX1OpTjZq26lONFJsJwGxMdYbYI1qJueAYMWIE9uzZg+rqatTV1ak/RBBR1M/CIorxZ7WgzC5aAlJAFSSFmsZTCEhC1G0EQaQWRbvycmy6GpaXY4PDzpO+pZjOrqFG11SHXfuQB8VmYjAbU50h9ohWYi44du3ahQceeADDhg3DoEGDMGjQIAwePDgVY7MEHMeqBjOFksJscFz8t1+VzC7hfXbWzC421obinCJNW3FOEWwsH3UbQRCpRdEub2NAV8O8jQH4/ALpW4rp7BpqdE31+bUTW4rNxGA2pjpD7BGtxPwUff755/j666/xzTff4KuvvsIXX3yBr7/+OhVjswSFziz9rBbOYBYWxZRWXduIUx4f3PXB/9d6fZAMcntTZhctLocTC4fOVYVJec7T5XBG3UYQRGrJz83CmtvK0bssD6tmX4oVtw7E2b0KVQ/HgY9/RFmXXNK3FNPZNTQ/NwsrZw3GilsHYt1t5Vhx60CsnDUYJUU5mjgsKcrB8hkUm+3FbEx1htgjWmFkWY767M+RI0ewZs0a/OUvf8F3332H6dOnY/v27ejXr1+qxhjB8ePHMWrUKBw4cEBTkLAjkCQZ1XWNCAQksAwgyYDNxqKkIGgODzeq3XldP+x+9WvUen1RzWiUpUpLayYLATaWN8iwErmto0mnWCUIIxIVp3pJNJZOH4BCVxZkOWgwVyZvpG+pxaoaGk48sSoIEn6s9GBtWFyeVuKEu8GvZo4scGahqrYRlb80wGHn4fMLKOuSi+5dnRSfbcRsTFkp9oj2EfO+1YYNG7Bu3ToAQN++fbFjxw6sXLkSzz//fNIHZwXcDc1Y9sihiEJBm+8cBgARBrStz36GWyecj7W7jmL1E0ew+c5hujm+QzO/EADLsCjIdrV5G0EQqUMvicbaXUex7vYhKAnL0Ef6llo6s4bWen3qYgNojcv1tw/RZI6s9fqwYsdh3es5xWvbMBtTmR57RCsxFxyBQADnnXee+vq8886D3+9P6qCsRCyDt5FxMnw/giAIq5OMJBoE0V4EUdaNSyHssWZK2EIQySPmfavs7Gx88MEH6uvDhw8jJyd2LYnOQjSDt9E2b2NAsx9BEEQmkIwkGgTRXniO0Y1LPuwxKUrYQhDJI+anaNmyZVi6dCmGDx+OESNGYNmyZVi2bFkqxpYSQk3d0YzcRsdJkoxlMwbomsz0zN93XtcP+979rlOZ0SRZQl2TBzUNv6CuyQNJpm+LCMJqxNJKSZKRbWejJtEglPoIOQAAIABJREFUkg/pbSSFeQ4sDYvLpdMHRDwmRQlbrAXFurWI+UjVb37zG7z//vv417/+BY7jcMYZZ8But6dibEkn3ore4ccNPK8Ua+aWg2WZCANk7zIXNt85DAFBAs+x4Fhg4c2XdBqjJFUSJQjrE0srQ7ePGdALa+aWQ5ZlcByLQmcWbDauo99Cp4D01hi7jcXcSReoZnC7LfL3wbKM5prdWa7TVoRi3XoY/lX++Mc/ork5WF2T53n8+te/xtlnn60uNpqbm/HQQw+lZpRJIt6K3uHHHfmqCssePQQbz6Iwz6ERJ8X8XVKYgyKXA/nO4P/D98tUqJIoQVifWFoZun3Pm99i5tp3sPxPHwUfLaXFRsogvdWn1uvD/Tv/jpWPH8GSRw5h5eNHcP/Ov6PW64vYN/Sa3Vmu01aEYt16GN7huOiiizB58mQMHToUI0aMQK9evSBJEo4dO4YPPvgA77//Pu6+++5UjjXhxGsQI2OZeaiSKEFYn1iaR5qYHpDe6mPWNE5YB4p162G44Bg2bBguvvhi7NmzB+vXr8cPP/wAjuNw+umnY9y4cXj22WfhdFq7OItiEAtPgRfLIBbvcZ0RpZJoqDBQJVGCsBaxNI80MT0gvdVHMY2Hx2e4aZywDhTr1iNm4b/2sHXrVrz55ptgGAaTJ0/GjBkzNNu3b9+Offv2weUK5mD+7//+b0ydOjVmv4kqUtUeD0dosT+AgSOLRUCQYedZgGEgiLGf/+wMxf06+3OWVPiPsAKx4jSaVipmckGUwDAMZFmCIAJZdhYMGMgtxVAzUd/Sjc6gt/EW/qtxN0IQZLVAL88z6JLnCBb+E2XwXPBRKt7kIrkzXL/Tmc4Q65lG0paCR48exd///nf85S9/gSAIuOKKK3DZZZehT58+6j5ffvklHnzwwQ6rWh6vQUySZNQ3BjTVdBdP649Pv67Cr/t0wdZnP4u5gIl3sWM1WIbFafndsWb0IqokShAWxUgrJUnGfyo9Gi1cMn0A/vnDSfTulo9tz8XWQiJxkN7qI8syGpoETZyumDkIx6rrNdXHl0wfgNPLXDEXHZ3l+p3OUKxbj6T9ZQYMGIDdu3eD53n88ssvEEUxon7Hl19+iZ07d+Kqq67CqlWrVJN6KB6PB8ePH9f8VFZWJmyc8RjEar2+iGq665/6GMMvPk1dbCjtRib0eA3rVkSpJFqcW4SCbFfGCkKyY5UgEkG8caqnlXpauG7XUQw4r5u62FDaM1Xf0o1M0ttEaWptfXNEnFafaoyoPr5u11FdI3k4nen6nc5kUqx3BpL6sJvNZsO2bdvwxBNP4PLLL0dpaam6raGhAeeeey4WLVqEHj16YPHixXjkkUdw1113afp46qmnsH379mQOs80YGdBkWb9dzzhJJsvMIx1jlSDCSWScJkILCcKIRMWqKEZebx12Pm4jOV2/CaLtmFpwiKKI+vp6hNo9CgoKTJ1g3rx5mDlzJubMmYPnn38eU6ZMAQDk5uZi586d6n633HILli5dGrHgmDZtGq655hpNW2VlpSmvR7IwMqAxjH67nnGSTJaZRzrGKkGEk8g4TYQWEoQRiYpVjou83vr8QtxGcrp+E0Tbifnp2LNnD/r164dBgwZh8ODB6r+x+P777/H1118DALKzszF27Fh8++236vaKigrs3btXfS3LMng+cv3jcrnQs2dPzU9ZWZmpN5csCvMcEdV0F0/rj/c/PYY7r+tnqkopVTTNPNIxVgkinETGqZ4WLpk+AEe/OoF5U8xpIUEYkahYLXRmRcRpSVFORPXxJTrVx/Wg6zdBtJ2YdzieeOIJPPfcczj33HPb1PHx48exbds2PPPMMwCAAwcOYNKkSep2h8OBTZs2YeDAgejZsyf27NmDMWPGtHH47SdapglBkFqyr2gzWPA8i14lTqy7fQhEUQbHMbDzLC67qCd4jsWm3w9Fc0BSjzHqr2exs90VTWVZgtjghiwKYDgeXG4+mJbnGCVZgsdXj4AUAM/yYBkWftEPG2sjcxVBEO2G51mcXubC+tuHQGjRwiw7i0HndwcgY93tQyBJMliWgY1n4W5oVidlmZjhJ5oet5VQ/baxNjizclDf3Ki+Jg03j83GoWdX5ZotgeNYuLLt4DhGcx0vdAZjs6a2MWrmqkyrSJ7IuE0E4bHvcgRLMIS3Ufxbi5gLjvz8/DYvNgDgsssuwz/+8Q9cffXV4DgOY8eOxfjx4zFz5kzMmzcP559/PlatWoW5c+ciEAjgoosuikibm2xipXrUy75yelkwhe9P1fVYt+soCvMcuHn8uZrMVHde1w+7X/0atV6fqf7MpuELR5Yl+Kt/QtUL6yG4a8DnF6P02sWwl/SCDESkjLtt4M34f5+/jLomN6WPIwgiIfA8i8I8B/5T6cEH/3sMQ/v1xPqnPtZk8Dv42XFcfG4ZXjn4PaZfeR4CgpRxGX6i6XFbJ296KT/nl8/G3q9exScVn1MK0Dbi8wk4frI+4vpbXOjAkoc/VNvW3FYekYHS6DqtJFGwOomM20SgF/sLhs6FnbVhzd+2UQpcC2P4l6qrq0NdXR0uvPBC7Nq1CydPnlTb6urqTHU+b948vPbaa3jllVfw+9//HgCwc+dOnH/++QCAcePG4a9//SvefPNNrFu3Dna7PQFvyTzRMk0YZV+p9fo02yaN7BuRmWrrs59h0si+pvuLF7HBrYoEAAjuGlS9sB5igxseX736gQWCFTgfObIbE84Zi5rGU9h48FF4fPVxn5sgCEJB0bfRA05XFxtAawa/0QNOx7bnPsOo/r1R+UtDRmb4iabHbUVPv7ccegzDzxisviYNN4+nya97/W1uljRtgYCU8Ot0upPIuE0EerG/6eCjqKyv0bRR/FsPwzscgwYNainiFDSKr1+/Xt3GMIzqz7Ay0TJNiAbZVwRJBmSo2/JybLr75eXYzPcXJ7IoqCKhILhrIIsCAoykqcAJBD+kTnuO+v+AJMR9boIgCAUlWxXHQlfnlPZQXQzfx+oZfqLpcVsJSIGo+q28Jg03h16WquraJoiSNuZYRj8223OdTncSGbeJwCj2Hbw9oo3i31oYLji++eYbAIAkSWBZ7Y0Qs3c40p1omSYYSFEzWCjbvI0B3f28jYE29RcPDMeDzy/WiAWfXwyG42FjORTnFGk+uMU5Raj3N6r/t7FJzYpMEEQnQclWJUrQ1Tml3dsYgI3PzAxW0fS4rdhYW1T9Vl6ThptDL0tVSWE2uLC5jSTrx297rtPpTiLjNhEYxb5P8Gv2o/i3HjEVPtTorXDDDTckZTCpJlqmCaPsK4V5DhTmObBi5iCsuHUgCl1ZWDXrUgw8r1Td787r+mHfu9+Z7i9euNx8lF67GHx+MQCoz15yuflwOZxYOHQuinOKAED1cLz8zVvqM5GKESsagiTgZMMpVNbX4GTDKQj0jQJBEGEomtjUHMCq2Zdixa0DcXavQtXD8c7R/2DxtAHId9rQszQvIzP8GOkxGBYBdw2E+lrIsrm7OHr6Pb98Nt7/92H07XIGFg+9HcuH3wlRFlRNlmQJdU0e1DT8gromDyST5+oMuLLt6jV73W3lWHHrQKyYOQhZWawmDm02Vvc6XeDMQq3Xh+raRtR6fZAy6I5HtHlEojETo3qxv2DoXJQ5izVtC4fORa49m+YnFoKRQ4trhDBt2jR88cUX8Pl8cDhaJ8WSJOHcc89Vs091BMePH8eoUaNw4MAB9OzZs119mcpSJcng2dZsFXpm88XTBoCBjKZmEWVdcyDLMN1fezCbpUqWZZxqrIMECT7Bj9LcLijLKwYX5RsCQRLwU10Fthx6TGNc7FXQHTx9s2CKRMYqQSSL9sapniYunT4ABXlZAGT8u8KDZ9/6l5pI47SSPHib/BmR4SeUcD0WmxtR9cwDcZlxW/VbgI3l4czKQYO/Eaea6rD5Q60mn5bfDRXeKo3RNlNNtfHEqiTJ+M8JD9Y82Rqfy2YMRK/SyDiUJFlznS5wZuF4TX3GJTlQkGUJ/lMnINRWgrU5IAV84AvLYC/qllDTuJ4Z3ChGw2Nfm6Uq2JZrz8Yx9wman1gIwwVHfX096urqsHTpUqxbt05t53kexcXFEY9ZpZKOnsTVen24Z+sHEbddb51wPtbuOoqSwmxsvnNY2mSwqG04heXvbom4Rbl65HwU5hYZHney4RRW6By3cuR8dI1yHNFKR8dqR+EXA7Bztri3E6mlvXFqpInrbx+CxS1ZgELb00kfk4VQX4uKXUsiHlXpPn0deGdhXH0aafL9I+fjfp32NaMXoSDbFf+bSEPiiVWj+DQTh+051gokI071qGvyYNk7GxIWozQ/sR6Gy0Cn0wmn04ndu3encjyWwMhsHm4UTxcCkqhrwhIkMepxgqx/nChHP44g7JwN//3cXMPtz095NIWjIZKNkSYKkn6yjHTSx2SRDDOuoSYbaDyZaoNESxCTzGOtQKpM40Zm8HhjlOYn1sNwwXHOOeeAYYxvF2ZClqp4MTKbhxvF0wUjAznPclGP4xn94zgm+nEEQXQujDSRZzPTIG6GZJhxDTXZQOPJVBskWoKYZB5rBVJlGjcyg8cbozQ/sR6Gf+nDhw9DlmVs3boVPXr0wJQpU8BxHF588UVUVFSkcoztJppPI3Qbz7GALGuqhOt5LBSzeegznXffcDGefOWrpBsg46kI6sp2YUH5LGw6tAM1jadwSfcLcPOFkxGQRdQ2nEKeIw/1/qaICp4F2S7ML5+teUbyniFzwDIM6po8YZVveeRKAAR/VC8JVQgliMxA0U7FQLt0xgCsffKo5jl3m43FA7MvRcXJeo2Hw+oGcSM0+szbUXrtYlS9sB5cbgEKhl4LW1F3dT+GYdtcTVxfk2fDzvG4d/idEGUJPqEZbp8XZc5iU4lBOgP5uVlYNmNghIfDTBzqXe+tEsN68wUAmjY2J0+N01CvkZFpXJQEeJo8CEgibCwHV7ZL1wuqF9sLh86N8HDoxWhADKDO54UoieBYDgWOPNjCHsHV+yzML5+dcY8QZhKGHg6FSZMmYd++fZq2iRMn4sUXX0zqwKLRlmc4o1UTBxCxLbRKuFGFUUmSUXGyHpW/NMBh5+HzCyjtkoOcLBtYlkmaAbI9FUEVkZABuP0N2Pzhn0I+pLOw96vXdCvYCpKAuiYPRFmCDBm7P9ur7hde+Xb+JTfB9sYuyA11USueZ6qZUY/O6uEAQI9UWYi2xqmiq3ve+BpXDf0VXjn4Pa4dfRa8DX5VE0uKcrD71X/iyFdVQRP5jAHomu9AXk5mGMTD0dXnKcsgC80AGFTv26TRbb64J457KlVtvKT7BZh83njNBCpcKyVZwsmG4GMoDBgwDCBKEtZ9sF09Zs6Am/D6v97Df59/FXploM7Go6nNzQKq6hpRfapRE5+lBTnIyor9DXu0Ly3TFd14vP5eQAhEzCFsxT0hNXpjfpEpSgKO1f2sfnlZnFOEBeWzcFpBD82iw8gg3sNV1rKgbjWDh8dnQAzomsFPy+8WsehonZ+I4BgOBdkuMoynMTGVqKmpCT/88IP6+ttvv0UgEEjqoBJJtGriettCq4QbVRh1NzRjxY7DWPn4ESx55BBWPn4E9+/4O9iWzFPJEqL2VATlWB6FuUVgAHWxASgVbHcYVrDlWR5dc4uQxdnxwHt/xCcVn4ccp618u+WTp2EbOilmxXOqEEqYwR8w/yxuW/Yl2o+inaP691YriG/c/YlGE1fu/DtG9e8NIKita588CklG2k/U4kVXn59bA5bPUhcbavsL6+Fp8mi0cfgZg9VJFqCvlR5fPVa+9xDuen0l/vD6/TjmPqEuNpRj/nT0aQw/YzA2kc6quBv9WLnz7xHx6W70xz4YUK/tJYU5Sb3GJxK9eBRqK3XnEFKjF7yzELb8YvDOQsMvMD1NHnWxAbRUAT+0A54mj3Y/g+t+fXMjCrJdKM4tQkG2S3cxXOfzRnwOthx6DHU+b8S+yvyk1FmMrrlFtNhIc2L+df7whz9gypQpOPvssyFJEr7//nts3rw5FWNLCLEMX7HM33oVRjvKRJYIc5eRgTxWBVuzlW/l7DzNuIwqnpOZkYiF3cbhqvkvm9r3lS0TkjwaIhRFA/NybJp/QwnVUuV1phht9TDSZ7Csbnu4FjvtOTG1MlyHjY5R2klngxhWGhczp55GOHrxyNoc7ZpDmE1A0x6DuFECBDFGkhsi/Yl5h2Ps2LF44403cPPNN2PGjBl4/fXXMXTo0FSMLSEohq9QFMOX0bZQ87dehdFofSYTxdwVSlvNXYqBPBQzFWwVw1es45gmr2ZcRseRmZEgrIuigd7GgObfUEK1VHmdKUZbPYz0GZKk2x6uxfX+xphaGa6nRsco7aSzQZRK46GUFGaD49L/TkW86MWjFPC1aw5hNH8IT0DTnus+Z3AOLkaSGyL9MfRwvPzyy5gwYQKefPJJ3QNnzJiR1IFFI1UejhUzB8GZbYcgSsiys2j2SxDFoLm8OSBixY7DKS0E1B4Ph4IoCaj01qCq4Rc4eDt8gh/FuV3wzOcv6Xs4xAC8TR5IDODxN2q8H/cMmYMcWzZESYQMGZwkI7B/K3k4QiAPhz5mPRx0hyM1JMLDcdXQX2Hbc59pCv+5nHY0+QKorm1CWZdcdO/qtMTjKG1FMedKfh8CpypQd/AFiA11QQ+HvwmsIxeB2srW9skLwbq6okZoQmV9DRy8HTIAV1Ye1rc8InVJ9wswrd9ksAwLBgx4loMgiRAkARXeauz752vIz8qL8H0oHo4p51+VkTobK1YDARG19c0QRQkcx6LQGSzmd6ymHut2tSY1WDJ9AE4rdprycFgR3YJ+XYO/L0YIAAwDyDJk3gZ7frGpOYTe/EGviHBsD4dxApmAGMAxzwlsCS1uOWQ2euaVoTHgo+QzFsbwk/bjjz8CAP71r3+lbDDJgGUZ9C5zYfOdwyIMX5Ikw8azmDvpAjjsPGQZKO2SjXtuvBh2noW7wY8F2z7ABWd2xRXlZ2D9Ux9rLqZb/jAM/kDqTGQMw8Je0gvdp69rU5aq8D4Csoj/++kzraavIXMxu/+NmCEGNEYuQQzguLtCk93q3uF/AMey4FkO9f4mrHrvIY2xq8eUJeAkSR0XA+C0/O5YM3pRVKMYQRDWQdHV26+9EJIkY9Y1F4BhgHW3D4EgSqioqcej+z5HrdeHxdP64/PvatAlPzt2xxZE94ugyQvBubpC9J5C9f4HNe1ioBknX98B1lkI/6jrtVo8dC7WjlkMURLhbvZi1Xt/VLV30nlX4MEQs+49Q+agKLsAufbsFn0NgAEDlmEx65KpnVJnAwERP1Z5dRcWLqcNa+aWQ5ZlMAwDlgve+chohAB+eWOn1jQeaEbl3o2tbZMWQnZ1MTWP0J0/DJ0bcSzLsBHXfWdWDn4OSZJg9OUjx3LI4rLwfy6+Xl3UZLFZONlYizV/29Ypv7jMFAwXHPPmzQMAnH/++Rg3bhy6dOmSskElGsXwFY5i/tarIApAvfNxzfC+WPn4YY25fO2uo1h3+xCUFOZE9JtMGIZtV/VPj68em8LMXJs+fBRrRi9CcVh1Tm+YQeyTis/xY91xrB5xN0RJUr+JU/rZcuixYJVPp7YfJcUuQRCZg56uVtc24t4/faTR1PVPfYwVtw7GyscPZ0x15lB0zeJ7N6LbTat127uMmYHmiu+QM2UxNoWZYzcdDGoxy7Ca5B7DzxisLjaUfTd/+CesGb0IPMuTvrZQW9+sLjYAqMlf1t0+BEsePhRxrV9/+xAUp/ganiqMTOPKAkRpq9oXjFU2v2vMPnXnDy0xGx6D4df9urAkCYqRPPxYj68ea1sWFgrFOUX4PxdfH/NYIr2JuTT8+uuvcfXVV+PGG2/E7t27UVVVlYpxpYRo5u/QbRyrby63ouGsLWaugEElT0EWIZCxiyCIMIzMuYqGZqJp3NAsLum3sy11B+TsPEMtNmsOJ1O4FmNzuH67XlKYTKEtpnGYjKP2mMHNHmu0n4O3x3VeIn2IueB44IEH8MEHH+Cee+7ByZMncf311+O6665LxdiSjllDuSghYwxnbTFz2RgDgxjDgSdjF0EQYRiZcxUNzUTTuKFZnNVvl1pS1TJNXkMtNmsOJ1O4FmNzuH67XlKYTKEtpnGYjKP2mMHNHmu0n0/wR7RR/FuLmOrv9/tx+PBhvPXWW/jb3/4GAOjbt6+pzrdu3YorrrgC48eP1zWff/3115g0aRLGjRuHZcuWQRBSu1pVKogqQhRaQTR02/73v8Piaf01+y2ZPgCFzvSvNBqOy+HEwqFz1Q90tGqfeS0VykP3XVA+C3nZLhQ48jA/bNv88lkocOSl7s0QBJFWFOTasWT6AI1WLp7WH+8c/Y9lqjO3FS43P1jIr2Uip1ZqziuIbJ+0EJ5/vAcAkD97N0JfFS0O1+n3/30YdxvsS7RS6MyKiL8l0wcgPycyLpdMH5Bxj/eFoheXfGEZSicvjIhJLq/AVJ9tmT/Ee6zefguGzkWZs5ji3+LErDR+wQUXID8/HzNmzMCIESNwxhlnmOr46NGjeOihh/D0009DEARcccUVePzxx9GnTx91nyuvvBKrV6/GhRdeiKVLl+K//uu/cMMNN8Ts20xGldDKoFk2FqIECGKkwTtaBdHQbBc8x4JlGfgDEjiOQX6OHY1+oU2VR5VMJnqGb0kSIHrrgrc2WR6sMx9yU33EvkrF8IAkwsZyyHPkod7fhIAUAM/yYGUZzWIANpaDK9ulyRyh/m5kCR5fvWrmyrE54G6uhyiJ4FgOLtYG1u8Dw/GQHNnwNDdAkCTwLAc7x7f0b0Nu2HEFjjzwnC2k/+B+zqycmJkpjMcYfx/pAGWp0oeyVKUXbc38F6qZedl2eJv8kCQZoiRDkmSNFnItCToK8xzgLXyHQ6PfvB2yGADEoF4zuS7IDR4AcvhBAMNC5nk0QIIAOVghHAAry8hmedTLIkRZBMsE7w4zgFoUTdFAgAHHsBBlCYAc1F97Ntw+L4SWKss2lgPAtEkrw3XWCroaK1abmwW4G/1qlqr8HDuysnjddpuNs1wFcUB/LgEgok2SREj1dYAkAiwH1lkAWZaCsdrSxuS6wHK2iDhgdPpjGBbNQjM8zQ2t84WsXGTxkV8k6MWWIAqaOUN+lhM8x0fsJ0oi6nxezdyCYznNvMUKsUpoiXk/at26dTh48CCeeeYZfPTRRxgyZAjKy8tj3uUYMGAAdu/eDZ7nUVVVBVEUkZPTas76+eef4fP5cOGFFwIAJk6ciG3btplacMQiNBVuYZ4DN48/F1uf/Uw3ha2RoVwQpIhsF4un9cdrh/4Nb6Mf1409R7MtVlrcaCltZVlCoOonVO0LZo7I7tsfRUOvRVVLdVr1G7OuPTRZo5S7Cnu/ek1NaXvbwJvx/z5/GXVNbiwon4XTCnpELDpCzVwBMYBj7hOatIrzy2fB+dVRoLkRjRcN15wvtH+9LBHh6fAu6X5BRNrGWNklEtEHQRCJJzzN+MDzSnHd2HPw7FvfRKTFXTJ9AJ596xsc+aoqZanDk0WofnO5BSgacSNq/ro9JNPPAni++hDOX/VD3cevoaD/Fah59ZGgnp81AM2jrtPo6JwBN+H/q/gK5b0uwZaPtO2v/+s9TD5vPHoVdIczKwc/1VWE6fNs9HSVRej23eWz8G319zin5ExTWmmUutTKuioIkmH62/D2FTMHQRRkrH4yMmV+Oseo0VwCvA1VzzwQkZGqKjQj1eSFAGdD1XNr1LaSKctQyTPY9KE2DkpFFtXPrNKcg+nSDT97qnTj0R7isdCLrQVD5oJjOTXZjHJsrj1Hk+1yxYi7UO9vjDhHr4LuZBC3ODFVZfz48Vi/fj3efvtt3HDDDXj++efxu9/9zlTnNpsN27Ztw/jx4zF48GCUlpaq26qrq1Fc3PosYXFxsa4h3ePx4Pjx45qfysrKqOd1NzSrF8RJI/uqiw0gaBRb/cQRuBuao/ZR6/VFZLtY/9THuGZ4X4zq3ztiW6w+dTOZvLAeYoMbordOXWwAgOs3I9TFRui+4VmjgpmhdmD4GYPV148c2Y0J54wNZo84tAOeJk/U91nn86of7NA+mX4jwF08OuJ8of1vPPgoPC3PIyt4fPWaTBTDzxgc0b/ecYnuoyOIJ1YJItW0J05DtRWAqoWj+vdWFxtAa3agUf17q6/N6G66EqrfBZdeoy42ACXTzybk/2Ykal59BK7fjFAXGwDA9BsZoaN/Ovo0RvS5VF1shLYrelfX5EFdk0dHnx+Du7k+ov3BQztwcc8LTGtluM6mo662NVb1rtvrdh2Fu9Ef0V59qlFdbChtVohRo7mEUFsZkZFKWWyo++3dCMFdrWlz1/+iLjaA1jhwe6ojzuFubjCMx1D0YmvTh4+ipuGXiGMFSdC0BSRB9xx1MeYyRPoT8w7H4cOHcfDgQRw8eBB+vx+jR4/G2rVrTZ9g3rx5mDlzJubMmYPnn38eU6ZMAQDoPcnFMJHfKjz11FPYvn276fMB2uxTeTk2w0xU0RBE2TDbSjx9GmUykUUBkETNNtbh1N3XKGuU056j+7qm8RSEGFmjRKNsU7IEmeWins9Mhol4sqtYNUNLPLFKEKmmPXEantlP0UIjTczLsWleWzVLVah+G+kzWFbNQhW63SgbFcuwhvoa1GARcktb+D5Gui3LkvkshO3IOJQq2hqrRtdtvSxVDjsf19ygozGaS7A27ZMaRhmpwveT7A7dOJDs2v0Ed43xfCFsntGWTFPBBwxbYcAYzEkoA6bVibng2LhxI8aMGYMtW7bgrLPOMt3x999/D7/fj3PPPRfZ2dkYO3Ysvv32W3V7aWkpTp48qb6uqalBSUlJRD/Tpk3DNddco2mrrKzE1KlTDc+tZJiqrm2CtzGg/l/BTLYUnmN0jxMlxNWnkjEiVAD4/GIwHA8ZjGab5KvX3VfJGhWen7re36j7ujinCHxg1ZXoAAAgAElEQVSMrFEcq98nx7BgpUDU80XLMKEco2RXCe8jWnaJRPTREcQTqwSRatoTp6HaCrRqoZEmehsDmtdWzVIVqt9G+gxJUrNQhW5XslGF65ckS4b6GtRgTm2L0GcD3WYY1rRWhutstH07irbGqtF1W8lSFdru8wtxzQ06GqO5hBTwafZTMlLF2o/1+3TjgPVr9+Pzi43nC2HzDKPY0ss0JYf5nmTIBnMSyoBpdWJ+svbv34/bbrutTYsNIGjsWr58Ofx+P/x+Pw4cOICLL75Y3d6jRw9kZWXh008/BQC89NJLGDZsWEQ/LpcLPXv21PyUlZVFPXdohql9736HO6/rp5uJKhqFeQ7dbCv73/8OBz7+MWJbrD4NM5nk5gezmUxqzRzh+cd7KJ2yDKVTlqLbjatQOmUpSq+/Vzdr1Pzy2Xj/34fV17cNvBkvf/NWS0ap2XCCQ8Bdg0CDG3VNbtQ0/IK6JjcCDW4E3DVwsTbML58dkW1K/uw9iJ++E3G+0P7NZJh4/9+HI/q/Z8gcSLKIuiYPJDny2yQzfaRjhop4YjUd8YuBdm0n0pv2xGl4Zj9FCw98/CPmTdHqrNKuvE7XLFWyLEGor0XAXQOhvhayjiaF6nfdR/tRMvEelE5Ziu63bEDP2VtRdsMKSP4mlEy6B55/vIfi8bepeq6XjWrOgJvw3g8fYf6lke2K3hVku1CQ7dLR59nI5rIi2u8un4VPj38e0R6sBM20aH+r5rYn41CqaGus6l23jbJUlRTlYPkM/SyV6YzRXIIv6Y2es7ei55xt6Dl7K/iS3pEZqSYvBF9QomnLd3bFgiGRcZDvKok4R35Wrm485meZyDQ1ZC6Kc7tEHMuzvKbNxvK65yD/hvWJmaWqPWzbtg1vvPEGOI7D2LFj8fvf/x4zZ87EvHnzcP755+Obb77B8uXL0dDQgF//+tdYt24d7HZ7zH7bmqWKYYDKk41gmOC3GmVdctG9qzOmMUwQJNR6fRAkGTzLIMvOwufXZmZJRJYqWZbgd9eAEQIAwwAsB7m5CVV7N0QYzCVZgqfJA0ESwbMcsgMC3LUVkOwO8BwPW14R/GIAvCwjJyCg+tnVYHILELh8OrZ88nSrCeuSm2B7YxfkhjqU3LwGXo5pyQjBIqfJB7ahDlLAB66kN5o4Vj0fy/Hwi9GzRIRnwQrNmiLJEp76bK9qco9maNTvw1oZKqyapSoRGaYoS5V1aG+WKk+jH4IoQpKgZqtyODhwDKubHTBdiJbMgwnTF1W/ZRlSgxunPnhOYw5XUoyKQjNY3gbW5gDDchC8p+D54n3gvHIwXbuDZXmwDANGlpEDFo0t2asQ8nBJQbYLfMudBkESUNei+aIs4i/fvI0vq77BihF3gWVYNcNVZJYqATzLoSngw5qWys3hmhuus1bQ1VixGn7dLsxzgGUZVNc1IhCQwDKAJAM2G4uuruw2X8fTgfC5BBzZEGt+1iaambQAyO8KptEbnFfIMmS7Awg0Q6itBGtzBO+CFJaBLyyFN+z6qpelSpQluH31kGQRoiSBY1mwDId8h1ONVwW92DKbfUqSJdQ1eSC2ZGAL/TwQ1iWpC45k0ZaLY63Xh3u2fhBx23TzncPSJge3UF+Lil1L1FufpZMX4pe3n4y4Fdp9+jrwzkLD45T9uoyZAQBqHzlTFuOBr1+KuEV577lXo/G59WrfAHT7Cz9vvNQ1ebDsnQ0R41gzelHGfntBC4729UELjtTQ3ji1gs7qYaSh0TRPOabLmBm6Ot1lzAxU7d1o+DoePY1XOzNRc+OJVavGp1kC7hqcePreiFjsduMqHHu4VYdLpyzFL2/sjPsaf7LhFFa8uyUinlaOnI+uuUVRjiQIEx4OqxNucgTSzxgWbgIzMiXKohD1OGU/tuWWuLLNyLQoZ+dF9G3mvPFiBZMiQRBtxwo6q0fUZB4xjjHSaTbkkSS91/HoabzaSZobxKrxaZqwxDNAy7U87PFAIyO52ZgUDBLXkKGbMIPhguOqq66KeuArr7yS8MEkg3CTI5B+xrBwE5iRKZHh+KjHKftJLWkNlW1GpkWmyRvRt5nzxosVTIoEQbQdK+isHtGSecQ6xkinpZC0snqv49HTeLWTNDeIVePTNCynn8gg/FFlAyO52ZjkDRLXkKGbMIPhI1VHjx6NeuCAAQOSMiAztPV549BCVelY3EeWJTSfqoTbUw3J7gAny3DlFEI89XPwOUuWRXNBMUSW0VSD1X3+eNJCMNm5gCxDEgW4vSch5TjB5xaguvEUGAQvQkXZ+QiIAXCShDxw4G1ZkCURUoNb1zuieE2MKqWbQa8Y0LLL5iHb5oAgCVEr3VqxIi7QeR6p8gdE2G3Bi44k+MHydnqkykK0N06toLN6mPFwhHo3IEkAJECSUPfpm3D+ql+Yh2MBTh18AU3ffay+FgU/WIcTjQ4HBAAsy4FlGLAMp6kWzkd5Vl2SJZxsCN6ZYMBAhgwby6NrblEU/1sAPMtH9XDoHZPu+hpPrFo1Po3Q9XDU1kBwV7d6M/JLgJw8CCe+b23r2hOyKKpzDdbvQ76rBFlFZaau5YIk4IS3BjUNv8DB2+ET/CjO7YIyZ1c0+Js08aP4MEJjm2XYiDgDYCr2rBSjhD6Gy9rQBUVdXR2ampogyzJEUcRPP/2UksElApZl0LvMhc13DktbY5gMoIqTsPHLF1qrcpbPQtZn7wSzp1w+HVvef0j3gmEv6YXu09dBFgOAKOHkO7vQ9N3HLdVtr8emL19AQXY+brhgAh45slvtI7Ri+Pzy2XAefgP1R15Gdt/+6Db1foDlIo3tJs2VRrAMi9Pyu2PN6EUISALsnA21PjfWvBP7YphpFXEzDbuNUxcHNPnvfFhBZ/VgNBoa+UWKonun/vZspEF88iKweYXodtMDwYUIy0FiANdFY1BYPhFsthOyJKL+qw/R2O8ybHr/j6p+qZXGe/fXraisZ8DVq75clFMQUysXDJ2LtWMWwy8GdI3hnUFfrRqfeuhei6+/FxAF1Z+hxCd8DZq2kuvvQyUjYJNmrjEbp8kSOBN/ayVJwf/99Bn1+KWXzcPP3ipsCos5jtFWFV887A6IsqjZb9ll8+CXApo2mgNkLjH/Ulu3bkV5eTlGjx6Nyy+/HGPHjsX69etTMbaEwbZkqigpzFEzVqQTulU5D+0A028kbEMnqdmllG2h1WAZhm0xe7E48f/uR9N3Hwfb+43EppaL04RzxqqLDaWP0IrhWw49Blw8GgDQ9N3HOLHn/uBjA85C9cIbrVJ6W2AZFgXZLhTnFkGWZVVo9N5btN9PulXEJYjOTrrrrBGKhtryizWaB7TqXnj18GDV5g1gZBm2/GLYCkvBcByqdi9H1XNrUbFrCQI1x1D17BpwF4/Gpo92avRLrTRusqKyUcXx8H11ryUHH4UsyyjOLVK/ZY51TCbqq1XjMxy9a3GwqviGiPgMrz7u9lSr8wJAmWs8Bo/JKt4eX33ENbuqviaibdPByKriNQ2/ROxXqXMszQEyl5gLjpdffhnvvfcexo0bh7feegvr16/HmWeemYqxdRqMjH1ydp6h4TvC9CdpzY+hxxlV6g6tGC5xraHQFoN6ewzlZg2NZHwkCKIjiGUQD9U/o+QfIsu1qdK4ngHXrFk3Hq0kfbUWetfi9lYVFyRzpm+9WHHwdlNVxfX2MzqW5gCZScwFR1FREUpKStCnTx988803mDBhAn788cdUjK3ToBj7QlFM3YrhO3xbhOmP5dUiPQA0xymVa8P7CK0Yzoqt2SyiGdRDaa+h3Oh9G1Uvj7UfQRBEIgk3iIcSrn/hGqkcw0mirn4plcbD2/UMuIpZN9a+8Wgl6au10LsWK2bwUKJVFQ+lOKcIPGvO9K0XKz7Br9tneFVxvf2MjqU5QGYSc8HB8zx++ukn9OnTB5988gkEQYDHY+72W2fETNXacHSrcpbPgvzZuwgc3Icll87C4qG34/4Rd2Hx0NuxdNg81WylnA+Q0O2G+5Hdt3+wPaS67cvfvIXbBt5sWDF8fvls4NN3AGgroIcSrVJ6vJitdGuFirgEQWQWkiRAlkSU3XAfmBwXiq/6fVjV5kWQZVnV+XCN9PzjPZRMXADx03ew4NKZEVXBA6KAxcPu0Gj74mF3wM7ZI6qCG1UcD6+lEY9Wkr6mD6ar3l9/L0qnLEW3G1ehdMpS8F176lcVLyzTVhV3lWBBeCX68lnIc+SZmre4HE4sCIuVUmcxlg6bFzFHCa8qXpzbJeLYMmdxRBvNATKXmIX/3nvvPezatQuPPvoorr76ajQ2NmL48OFYvXp1qsYYQbpm/onXWB2epYr1+5Bf2B0cy0JiWVQE6rH5w1az4D1DZqNnXhl4jo883+RFYLOdABgwznzUt2SOYMCgquGkJktVsxhQs50UMjZA8EfNPtXeLFV6mK10a8WKuED6xmos4skwFW4apyxV1sGqcZosJElAoOonVO3b2Kqt1y0Dw3KQm5vA2LPxy4HdrdmoWnQeaK3ODDAAzwOCAJnnUS9LaJYFnPBWY98/X0N+Vh4m/9d4bNFo+xy88OVf8UnF5xHGWKXieKzqy/FopZX0NVNj1ez8wdA0DkRUEAfHQzh5XNPGFZTA6/NCkETwLIc8Rx7Ekz+bmrdIsoRKbw0q62vULFU9XGVoDDRh84d/ak1oMGQ2euSVwtvcoInX1ixVrXEGIKPnAEQrMe9HjRgxAiNGjAAQ9HP8+OOPOPvss5M+MCtiZKyOVcVTbHCj+plVmmcwm1uqf3oZSV1sAMHnFjd/+BhWjrwbBTIbeb69GzTnK8i2GVabndbvWmw+9Fhr5dmwW7LhtBrUE4diIk/UfgRBEO1F9Napiw2gRVufXYMuY2YAgKbCeLjOG2kk2+TB6ndaqzTfUz5bXWwAirb/CdP6XYtPKj5XjbFKVXC+JQ1uLOLRStLXjsfs/MHINK5XQbzL5TNR9dxaTVv36etQ6GyNI6G+1vS8xeOrV1MsKyweeruatQpoSWjw4WO4f+R8FOvEq16c0RygcxBzwdHQ0ICHH34YH374ITiOw8iRI9GnTx/Y7fZYh3Y64jVWRztOYKFvFpQkyJJk6nxGhqtQ0ziZrwiCIFqQ9DVZqRoej86H63CsZB7Ka9LmzoHZ+UN7TOPtTQjTFtO4aNKITnQeYt6PWr58OaqqqrBkyRIsWLAA33//fYc+TpXOxGusjnYcz7L6ZkGWNX0+I8NVqGmczFcEQRAtsPraKvnqTRnI9QjX4VjJPJTXpM2dA7PX8/aYxtubEKYtpnHOpBGd6DzEVLJ//vOfePPNN9XXgwYNwvjx45M6KKuimAbDn4U0MlaHVrAtnbwoosI3GBZ5DIv55bMjCj7lZ+WB43jN+bLPGoCscTNQy0iwNXnUZxwVw1Vo0Ryl8J9VzFdUZZQgiEQSzZPG5RWgdNJCVO3bCC63AAVDr4WtqBtEby28n7+P4ivvQM1ft0fodcBdY+hvC9fh9/99GPPLZ2HLoR0h2j4Le796DUCkMTaVGkh6m3oUM3i4D0M3gcuNK8EIAYBhAFmGbHfozj3krBzk3nCvpqq4UUIYM/MWvblEqbMY84fM1niR5pfPRoEjL+7fBcVfZhJzwdG1a1ecOnUKRUXBFWxjYyMKCxP7HH+mEKtqbSjhxq/wCt9icyMqnlwELrcA3a78Pe4fegdElgEnycgDB57jNeeTZBkVQgNW/22rbiXO0ArfPMuBZVj8YfCtljBfUZVRgiASSSyDLsvysJX2QrdpayE11KFqb4h5fPJCcK6uGp1X9Dqa6TZch20sj1ybAytHzocoieBYDvlZTsy6ZCpmSFM02pxKDSS97UCEgLZa+LWLI3aRZQloakBlaEKDSQvBl5ymiUkmJw/HPZXYGFJVfOHQuTgNQGjJw7bMW/Ri2OVwQpRE3B8SxwWOPNg4W1y/Aoq/zCXmX69r166YNGkSNmzYgM2bN+Paa68Fx3FYvXo1PVqlQ7SqtaGEG79CK3wDQNUzD0Bw16Dg0mtQ89wD8D58Bxr/53Z4H74D1X++T63wrZyvkeexqSVLBBBZiTO0wndhdj7yHXmGlWfTDaoyShBEIjEy6Cq6CgAsy4NhGHWxoe63dyMgiarOA616bdSX2meIDhdku2Dj7eiaW4TSvGJ0zS2CjbdrtivanEoNJL3tGMzEJGCQ0GDfRkj1bs3cw9vcaPrvaHbeAkTGMMuwsHE2FOcWoSyvGMW5RXEvNgCKv0wm5h2Ovn37om/fvuprepwqMcQyainbzFS4BTK7EmcmvzeCIFJPewy6sSqMG/XVHlKpgaS3HYPpODJIaIAMqc5t1XETsYm54Ljjjjvg8/nw448/om/fvvD7/XA4HLEOI2KgGLXC09gpdziUbYpB0Wg/BcXMFZ76NhMMh5n83giCSD2x9Lct+5ntqz2kUgNJbzsG03HE6u8Hg+rcVvs7WnXcRGxiPkvzj3/8A6NHj8bs2bNRXV2Nyy67DP/7v/9rqvPt27dj/PjxGD9+PDZu3Ki7fcSIEZgwYQImTJiAPXv2tP0dWJRolbtDt9V9tB/FV94Rs8J3JlfizOT31lnxi4F2bSeI9hBNf9u6n9m+2kMqNZD0tmMwHZMtCQ00+01aCC6vQLOfVf+OVh03EZuYS8YNGzZg165duOeee1BWVoaNGzdizZo12LdvX9TjPvroI3z44YfYv38/GIbBrbfeirfffhtjxoxR9/nyyy/x4IMPol+/fu1/J0kmWtaEeCpwMwwLW3FPdLtpdfBWKMuDsdkheH4J9tG1B/JmrEVAEuFjOXSbsSFqJXAjM5cyRlES4GnyICCJsLEcXNkucCa+MUiHbBGx3hthPeycLSGVyAkiHDN6bMYoq/TDOgvQ7aYHAEkEWB5cXoFmv/DkHQ0s4IEMm68+QqeUPkWGgRcSBFkEH6VquIKeBjqzcpKizaS3HYNRTMqyBMFzSp0ncHkFwYQGIXMHJSaF+lrNsXp/RwaI2E9vvmJ07ddrB2CqzUwMUfxlLjFnnD6fD2eeeab6+rLLLsNDDz0Us+Pi4mIsXrxYLRD4q1/9ChUVFZp9vvzyS+zcuRPHjh1D//79sWjRImRlZbX1PSSdaFkTGCBqthMjZFlCoOa45rjiK+/Aqff+DNZZiOZR12FTSLpEM1kajCpxipKAY3U/a/pbUD4LpxX0iLroSKdsEVRllCCIWMTKPhWKYpSN1s+pvz2Lgv5XoObVR6L2xzAs2Nx8/OyuwMb39fVS6dP9xQeo73cZtnwUmg53NnoVdI+56FA0MNnaTHrbMYTHpCQJCFT9pJrElbsZttJesOV3VfeLFvehf0eznw+j+OrhKsPPnkpN+7LL5sEvBbAppG3B0Lmwsza1Knlb45PiLzOJ+ZfneR5utxsME0yk9sMPP5jquG/fvrjwwgsBAP/5z3/w2muv4bLLLlO3NzQ04Nxzz8WiRYuwf/9+eDwePPLIIxH9eDweHD9+XPNTWVlpagyJIlrWBLOZJcLRO67mr9tRcOk1YPqNVBcH4eeLa/xNnoj+Nh3aAU+TJ+73TUSSDrFKELHI5DiNV4+N+nH9ZoS62IjVXyy9VPrExaPVxYay35ZDj6Euhh635VyZQibHqhmMMlKJ3jrtfmYzXJnczyi+6po8Ee2V9TXqYkNp23TwUVTW12R8fBJtI+Ydjrlz5+LGG2/EyZMncffdd+PQoUNYtWqV6RN89913mD17NhYtWoTTTz9dbc/NzcXOnTvV17fccguWLl2Ku+66S3P8U089he3bt5s+XzKIljVBFsW4MpQYZaRgHU7IPJfQLA0BSdTtT5DEGMdRtoi2kA6xShCxyOQ4TVTGKKUfs1kCgdh6qfQpcazufqIcXY/bcq5MIZNj1RQmM1IlMusaYBxfghw5l3Dwdt19Hbw9oi3T4pNoGzEXHCNGjECfPn1w6NAhSJKE2267TfOIVTQ+/fRTzJs3D0uXLo1Ip1tRUYGPPvoIkydPBgDIsgyejxzOtGnTcM0112jaKisrMXXqVFNjSATRsiYwHBNXhhKjjBSSrx4MxyY0S4ON5XT741kuxnGULaItpEOsdiSvbJnQ0UMgTJDJcZqojFFKP2azBAKx9VLpkxUl3f04Jroet+VcmUImx6opTGakSmTWNcA4vngmci7hE/y6+/oEv6bPTIxPom2YetiTZVnccMMNKC4uxhtvvAGv1xvzmBMnTuD222/H5s2bdWt3OBwObNq0CceOHYMsy9izZ4/GUK7gcrnQs2dPzU9ZWZmZYSeMaFkT4s1QondcydV3oe6j/ZA/excLymclLEuDK9sV0d+C8llwxXhGkrJFtI10iNVU8cOaSYY/RHqTyXEaTY9lWYJQX4uAuyZompUlzbGh22VRRNlNq8DkuFB81e9N6XssvVTGhk/fwfxLtXo8v3x2m55Z7yzanMmxqockCQi4TyJQW4mA+ySYXBdKJ4dlpJocmZEqkVnXAOP4Ksh2RbSXOYuxIKxtwdC5KHMWZ3x8Em2DkWVZjrbDfffdByD4TcO0adMwdOhQNDQ0YNu2bVE7Xr16Nfbt24devXqpbddddx3effddzJs3D+effz7efPNN/M///A8CgQAuuugirFy5UjWZR+P48eMYNWoUDhw4gJ49e5p5n+1CliU0n6qE21MNye4A6/ch31WCrKIyMC1mwLZmqZIkAYFfTkBwV4O1OSAFfODzS8Dm5IGRZTA5efA2NyYsS4OSpUqQRPBxZamibBHxkOpYTRSxMkhFW1j0WbbPVB9mslRdNf/lWEMFQHdX2otV41QPPT0Goif30DPTFo+/DXUfv4aikVPB2rMBWY6p77H0MjxLlSiL4ExkqYrnXJlKJsVqKLoG8ckLAUcuhJPHW+cJhWWwF3WLiEGz8xCz+xnFl147AFNtnSE+CWNiKtyXX36JvXv3YseOHbjmmmswf/58TJoU+1vM5cuXY/ny5RHt119/vfr/cePGYdy4cW0ccuoRG9yofmaV5jZkc34xuk9fB95ZGDXbiWGf3jpUPbcm4tZmt5tWg2/JPpHILA0cy6Mwt6jNx1G2CIIgrISeHgv1tbpmWUXDdZN4vPoIuoyZgapn16j7xSKWXipj4wG0Nx8jaXNmoWsQ37sRXS6fiarn1qr78SFzj1DMzkPM7mcUX0btZtuIzkvM5aYsy2BZFocOHcKgQYMAAE1NTUkfWDqRKCOiBpNmMIIgCKJ9xNLwaEk82q31BGEGgzkBa3NEtFE8ElYk5oKjV69emDlzJo4fP44BAwZg/vz5OOecc1IxtrRBMVqFEo8RUQOr32e4GYwgYiGFmfPMbuus+APmswG1ZV8ifYml4UbbFdN4u7SeIMxgMCeQAr6INopHworEjNp169bh7bffxsUXXwybzYZLLrkEV199dSrGljQkSYa7oRkBQYKNZ5GfmwWWZQz3V4xWmud/r78XABBw15j2bWj6zCtA6aSFEQV9ws1giSIenwlhDVjebuipUPwURCt2G0fekATSVj3tCLjcfJRefy+E2krNs/CKv0NP4xUPR+m1iwGGjar1pK/WJJ1il8srQOmUZVpfZ0EJwLBqZqloSQskSQjW5wipPs7SF5hEGhEzGnNycjBhQutFN9SDYUUkScaPlR6sfuIIqmubUFKYjeW3DETvMpeh0DAMC3tJL3Sfvi54QeHtEOtrUfHMkjZVFw/vE9m5KLtuOcAwgCxD5m1JuUi1pfouQRCEWeLR0w5DCOCXN3ZqNFAhXOMBBmBZFF8xG2JzIyqeXGSonaSv1iTdYpdhWEASI2LUVtytde5hsJiNVpGcFh1EutDp1NDd0KwKDABU1zZh9RNH4G5ojnqcYrSy5RcDstTuarZigxtVf16B44/dieN/mofjj92Jqj+vaHNFXNPnSkD1XYIgiFDi1dNUY0YDQzXelt8Vtrxgko2qZx6IehzpqzVJt9g1iiOp0avGpZKkJuJYkxXJCaIj6XQLjoAgqQKjUF3bhIAgGRwRSSJM5EkxoqfBuQiC6DwkQk9TQbwaaOY40ldrkm6x2644oiQ0hAXodAsOG8+ipDBb01ZSmA0bb/5XkQgTeVKM6GlwLoLoCMjc3TEkQk9TQbwaaOY40ldrkm6x2644oiQ0hAXodNGYn5uF5bcMjHhuMz/XfFZ0XRO5ieriie4DaC3oF5BE2MIK+qlGRllG6eRFqNq7oV3nIoh0hYzgHUMi9DQVxKu3sczmhn1PXgi0FBSMXlQtABtro6JoHUC6xW575gRtSUITbc5AEMmk00UZyzLoXebC5juHxZ2ZIsJEHkdWkkT0IUoCjtX9jE2HdqCm8RSKc4qwoHwWTivoAZZhNUbG7L790W3q/QDLURYVgiASQiL0NBW0S2+jmM0j+vb74D9VgZOv74DYUKdrHpdkCcfcFdh48FFVtxcOnYvT8rvToiOFpGXs8jZ0uXymurgFbzN1GMvysJX2QrebVkfNUhVtzkCLDiLZdMoIY1kGhXmO2DtGIZ7q4onuw9PkUYUDAGoaT2HToR1YPXI+8mRGY0Br+u5j/P/s3Xd8FHX+P/DXlN30bAqbhFDEwk+FE5Uq0tsdghzSFI6qIh0xhlAkIC10Dk8R0a8eICodRARsBD2IHGA58bCAJygYEkLapm2yuzO/P8IOmZ3ZZJOdzZa8n4+HD8nszOxs9pPPzKe8359r1y+7vGIuIYS4Qov6tD7Upb51FsjrWI/aGxTX3lsim0uvtq/JXCw1NoDKenvNideQ1ncurcxcz3yp7NpKCmUJCgDnq4qrYVkerKFRtftU98wQHRbj3gcgpAYNssERKCyCTao47HJK82AVbBAFUCAjIYS4oTaBvK7uaxEsqvW2hQJ8G7T6SD5Q3TMDIZ5GDQ4/pmM5GENjZBWIMTQGPMuBYRhpsSA7CmRsGBrP2IwQhwDCqipsFug5HQRrBVheX49XRoh/sQfyulKPurqvjtWp1ts6mtLSoNWmrBQGxLUAACAASURBVNVVdc8MhHgaTRj1Y5EhkUjpMgnG0MqhUPt8zMiQSCkAzZ65ggLFG44QgxGP75rq9D89p8OvacOosUFIDWpTj7q6b2RwOOZ0myqrt+d0m4rI4HAPfxriy+rjnl3dMwMhnkZdKn6MY3k0i2qC5b2TYRVs4B0yTrgblE4IIQ1ZbYLNXd2XZVg0MyQire9cWAQrdCxPWaqIJolkalLTMwMhnkSlzM9xLO802EuLwHZCCGnIalOPurovy7AUIE4U6uOeXd0zAyGeRF0qhBBCCCGEEI+hBgchDZRgrfD2JRBCCCGkAfDolKqNGzfi6NGjAIAePXpgzpw5std//PFHpKamori4GO3bt8eSJUvA8zTLi5D6wPJ6/Jo2TLH9jgX7vHA1hBBCCAlUHhvh+PLLL3Hy5EkcOHAA77//Ps6fP49PP/1Utk9KSgoWLlyIjz/+GKIoYvfu3Z66HM2JogBrcT4shTmwFudDFAVvXxIhhBAXUR1OfA2VSRLIPDacYDQaMW/ePOj1lak377zzTmRmZkqv//HHHzCbzXjggQcAAEOHDsXLL7+Mv/3tb566JM2IooCK679LK9Da09fp45pTFihCCPFxVIcTX0NlkgQ6jzU4WrZsKf378uXLOHLkCHbu3Cltu379OozGW4uTGY1GZGdnK85jMplgMplk27Kysjxwxa6zlRRKlQJQuRpo9p5VSJywkrJCNWC+WFYJcUTllOpwf9GQyiqVSRLoPB4wcfHiRUyePBlz585FixYtpO2iKCr2ZRhGsW3btm3YuHGjJy+x1kSbVbYaKFBZOYg2q5euiPgCT5VVrVcEr7BZpDiNusRrVD3eU+yroR9aP1j19eyCIkxclu70+DcX9kZ8VITT1w+tH1zjOQCgwmKDXufaKrzlFhuCXNi3Nuf0BF+sU+sb1eH+oSGVVSqTJNB5tMHx9ddf49lnn8ULL7yAgQMHyl6Lj4/HjRs3pJ9zcnIQFxenOMf48eMxZMgQ2basrCyMHj3aMxftAobjwRuMssqBNxjBcBTw3pB5qqw6C+62q+3Dv57T4fFdU52+vvuJ19w63pVz1MTda4yPitDkGvU6DoOSD9a4H1DZiHFlX2eNqPrii3VqfaM63D80pLJKZZIEOo+V5GvXrmH69OnYsGEDOnfurHi9SZMmCAoKwtdff4127drh/fffR/fu3RX7RUZGIjLStxZI4sIMiB8xTzHXkgszePvSiBf5YlklxBGVU6rD/UVDKqtUJkmg81iD46233kJ5eTlWrVolbRs5ciTS09Px7LPP4r777sO6deuQmpqKkpIStGrVCuPGjfPU5WiKYVjo45ojccJKiDYrGI4HF2agwC5CCPEDVIcTX0NlkgQ6jzU4UlNTkZqaqtg+atQo6d/33HMP9u7d66lL8CiGYSmQixBC/BTV4cTXUJkkgYwmBxLiA2oTGN54xmaEGIyybQ1tsb4Km0URC+Ht2AhCCCGEqKMGByE+oLrAcMfGRIjB6FZAdSBwN6icEEIIIfWHJgcSQgghhBBCPIYaHIQQQgghhHjR6dOn8eyzz3r7MjzGL6dU2Ww2AIG74ijxrISEBPB8/RR9V8tq06ZN6+NyiI+4evWqS/vVV1mlOpW4wxfrVELU1GdZJXKMqLbkt4/76quvAnLhH1I/jh07Vm8P+FRWiTvqq6xSOSXuoDqV+Iv6LKs1KS8vx4IFC/D777/DbDYjKSkJBw4cwMsvv4wdO3bgyJEjsFgs4DgOr7zyCgAgKSkJgiCgrKwMqampuPfee5GUlASTyYTS0lJMmzYNffv29fInU+eXzbw//elPePfdd2E0GsFxnLcvx+vsK6++++67SEhI8PbleF1Nv4/6/B25UlYD7fsLpM/j7c9SX+/pzTrV279jV/jDNQLeu05v1qn+8t24KpA+jy9+Fl+5DgDYsWMHYmJisG7dOuTm5mLWrFmIiYmBIAjIycnB1q1bwXEc5s+fj5MnTyIyMhLR0dFYtWoVLl++jPz8fPz+++/Iz8/H//3f/6GwsBDnz5/39sdyyi8bHMHBwWjfvr23L8PnJCQk+EzL3Rf4wu+jNmXVF65XS4H0eQLps6jxhTrVH37H/nCNgP9cZ104K6uB9pkD6fME0mfR0q+//oqHH34YABAbG4uZM2fi3XffBcuyCA4ORlJSEsLCwvDLL7+gQ4cO6N69O3777TdMnz4dLMviqaeeQsuWLfH4449j9uzZqKiowIgRI7z8qZzzywYHIYQQQggh/uquu+7CuXPn0L9/f+Tl5eHpp59G79698dNPP+HDDz/EBx98AIvFgnHjxkEURfz73/9GTEwM3nrrLVy6dAlJSUlYtWoVioqKsHnzZhQXF6N///545JFHvP3RVFGDgxBCCCGEkHo0cuRILFiwAKNHj4bFYsHmzZuxe/du3HbbbYiJicHIkSMBAJGRkbh+/Tp69eqF5ORk7N69G1arFc888wxuv/12bNq0CUePHgXDMJg4caKXP5Vz1OAghBBCCCGkHun1eqxdu1a2rWvXrgCArVu3qh6zZcsWxbaXX35Z82vzBG7x4sWLvX0RxH1BQUHo1KkTgoKCvH0pPsHffh/+dr01CaTPE0ifxVf5w+/YH64R8J/r1FKgfeZA+jyB9FmIe/wyLS4hhBBCCCHEP9BK44QQQgghhBCPoQYHIYQQQgghxGOowUEIIYQQQgjxGGpwEEIIIYQQQjyGGhyEEEIIIYQQj6EGByGEEEIIIX5i165d+PDDD719GbVCDQ5CCCGEEEL8xLfffouKigpvX0at0ErjhBBCCCGEuEAQRBSWlMNiFaDjWRjCgsCyjNvnzcrKwuzZs1FaWgqWZZGamgqWZbFy5UqYzWZER0djyZIluHLlCtLT0/Hvf/8bRqMR9957LxYsWIDMzEzwPI+kpCR0794dp06dklYyNxgMWL9+PWJiYrBhwwacOnUKhYWFiI6OxiuvvAKj0ej29deEGhyEEEIIIYTUQBBE/JZlwvJ/nsb1/DLERYcg9alOuC0h0u1Gx969e9GzZ09MnDgRp0+fxtmzZ3Ho0CFs3rwZiYmJOHHiBBYuXIitW7eid+/e6NixI7p164ZZs2bhoYcewpNPPokrV65g1KhReP/997Fp0yYsXrwYbdq0wdtvv40ffvgBzZo1w6+//oqdO3eCZVnMmTMHhw4dwlNPPaXRb8g5anAQQgghhBBSg8KScqmxAQDX88uw/J+nsW5Wd0RHBLt17s6dO2PmzJn48ccf0aNHD/To0QObNm3C1KlTpX2Ki4sVx/373//G8uXLAQDNmjXD/fffj++++w59+vTBjBkz0LdvX/Tp0wddunQBAMydOxd79uzBpUuX8J///AfNmzd367pdRQ0OQgghhBBCamCxClJjw+56fhksVsHtc7dr1w6HDx/G559/jiNHjmDPnj1o2rQpDh48CACw2Wy4ceOG4jhRFBU/22w2TJgwAb169cLx48exdu1anDt3Dt26dUNycjImTJiAv/zlL2BZVnG8p1DQOCGEEEIIITXQ8SziokNk2+KiQ6Dj3X+cXrNmDQ4ePIghQ4Zg0aJF+Omnn1BYWIivvvoKALBv3z7Mnj0bAMBxHGw2GwDgoYcewt69ewEAV65cwTfffIMHHngAI0aMQElJCSZMmIAJEybghx9+wNmzZ9GxY0eMGjUKd911FzIyMqTzeBoj1lfThhBCCCGEED/lyRiOa9euITk5GSUlJeA4DhMnTkTjxo2RlpaG8vJyhIeHY/Xq1WjevDkOHz6Mv//970hJScGDDz6IRYsWITMzEwAwa9Ys9O3bF6dOncLKlSvB8zyCgoKwZMkSGAwGzJgxA2azGTqdDnfddRcEQcC6deu0+PVUixochBBCCCGEuMBTWaoCHTU4CCGEEEIIIR7jlzEcVqsVV69ehdVq9falEFItKqvEH1A5Jf6Cyioh/skvGxxZWVno06cPsrKyvH0phFSLyirxB1ROib+gskqIf/LLBgchhBBCCCHEP1CDgxBCCCGEEOIx1OAghBBCCCGEeAw1OAghhBBCCAkgO3bswI4dO2p93P79+zFv3jzNr4fX/IyEEEIIIYQQrxk1apS3L0GGGhyEEEIIIYS4QBQF2EoKIdqsYDgeXJgBDOP+hKEZM2bg0UcfRf/+/QEAQ4cOxeLFi7FhwwYUFBQgODgYCxcuRKtWrTBv3jwUFBTgt99+Q0pKCs6ePYuMjAxwHIc+ffpgxowZeOWVVwAAM2fOxKFDh/Daa6+BYRjcd999WLZsGaxWK1JTU/Hzzz+DYRg8/fTTeOyxx2TX9J///Eda6Tw6OhpLly7FbbfdhrFjx8JgMODixYt46aWXcO+999b4+ajBQfyCIAowmYthESzQsTpEBoeD1eAPvCGj3ykhhBBf48v3JlEUUHH9d2TvWQVrYQ54gxHxI+ZBH9fc7UbH4MGDcejQIfTv3x+XL19GeXk5VqxYgUWLFqFVq1b45ZdfMH36dHz88ccAgKioKGzevBl//PEH1q9fj8OHD6O8vBwLFixAeXm5dN7s7GysXLkS+/fvR0JCAlJSUvDFF1/gm2++QXR0ND788EPk5eVhxIgRuOeee6TjKioq8Pzzz+Oll15CmzZtcPToUTz//PPYt28fAODuu+/Gxo0bXf581OAgPk8QBVwpzMSaE68hpzQPxtAYzOk2Fc0MiT5TCfkb+p0SQgjxNb5+b7KVFEqNDQCwFuYge88qJE5YCT482q1z9+jRA8uWLUNxcTE+/PBDPPLII9i8eTPmz58v7VNaWor8/HwAQJs2bQAA8fHxCAoKwsiRI9GrVy8899xzCAoKko759ttv0bZtWyQkJAAA1q5dCwDYtGkTVqxYAQCIiYlBnz59cObMGYSHhwMALl++jMjISOl9HnnkESxatAhFRUWy93eV9789QmpgMhdLlQ8A5JTmYc2J12AyF3v5yvwX/U4JIYT4Gl+/N4k2q9TYsLMW5kC0ub/yvV6vR8+ePZGeno6PPvoIQ4YMgV6vx8GDB6X/9uzZg6ioKABAcHAwAIDneezZswezZs1CQUEBRo4ciUuXLknn5Xn52EJeXh7y8vIgiqL8s4kibDab9LMgCIprrLqP/f1dRQ0O4vMsgkWqfOxySvNgEdz/A2+o6HdKCCHE1/j6vYnhePAGo2wbbzCC4bSZMDR48GBs2bIFBoMBTZo0QYsWLXDw4EEAQEZGBkaPHq045ocffsCYMWPQoUMHzJ07F3feeaeswXHffffhu+++Q05OZUNpxYoVOHbsGB566CHs3bsXQGUj5NixY+jYsaN03B133IGCggKcO3cOAHDkyBEkJiZKDZ7aoilVxOfpWB3aJ7ZBz9s7I1wfiuKKUnx+6RR0LBXfutKxOhhDY2QVuzE0RvV36svzaesi0D4PIYT4GlfrWcf9eJZ3+d7kDVyYAfEj5iliOLgwgybnb9euHYqKijBy5EgAldOfFi9ejDfffBM6nQ4bNmwAwzCyY1q1aoUHHngAjz76KEJCQnDvvfeie/fuOH/+PIDKKVcLFizA008/DUEQ8MADD2Do0KEoKyvD4sWLMWjQINhsNkyZMgWtW7fGzz//DKByxGXDhg1YtmwZysrKYDAYsGHDhjp/NkZ0HFPxA1evXkWfPn1w7NgxNG3a1NuXQzzMYrPgSuE1rM94XZrTmdxlMpoZGkPH6bx9edXy1bLq6jxZX59PW1uB9nm04qvllBBHVFZ9nzv3lwU9nkWFYMFaH66jPZWlKtD5RpOREAdVez0ARmpsAJXDq+szXseS3sloFBbj3Qv1UyzDopkhEWl958IiWKFjedUeKGfzadP6zkVUSKTivL4+elDbz0MIIaR2nNWzy/vOAQNGuj+IEBX7pX3xMlb0m1fjvcmbGIZ1O0C8IaIGB/E5jr0eLz2yWHVOp020OTkDcQXLsDU+ZNdmPq0/jB74+vxgQgjxd87qWbO1HMs//4d0f0jtOUt1vwqbBUbqTAw4vvEUQEgVjr0jgijAGCqvfIyhMeAYzhuX16DYYz2qcjaf1teziwC1+zyEEEJqz1k9e63ouuz+cK3oOtXHDQg1OIjPcewdOfTzZ3i+yySpYrLHcNAUGM+LDA7HnG5TZb/7Od2mIjI4XLGvP4we1ObzEEIIqT21enZ21ynY98MR2X77fjiC2V2nUH3cQFAzkvgcxwxKxy99iXBdKBb3ToYg2sAxHKJCIsFTL4jHuRrrAdQu85W31ObzEEIIqT21epZhGBSUFcr2KygrRExIFNXHDQR9q8TnqPWOdL+9E2JDoxAfbkSjsBhZY0MQBRSUmZBTkouCMhMEUblYDak7e6yHMSwGUSGRTm8G/jd64HcJ+gghxM9U1rNh+hDV+0NEUJhL9xfi/3yn65GQm2rTC+0PgcoNhT+MHlB5IYQQz3JWzzaJTPDp+wPxLPqmSb1ydTTC1V51fwhUDkTOvkdXvzdvofJCCCF158o93Fk9W1xe6tP3B1+0YMECfP/99y7vf+zYMfzjH//Q9JxaoREOUm880bvsD4HKgcafRwmovBBCSN24WvdTPaudtLS0Wu3fp08f9OnTR9NzasW3nw5IQPFE7zKlOa1//jxKQOWFEELqxtW6P9DrWU/Fjc6YMQMfffSR9PPQoUPRtm1bnD59GqdPn8bw4cMxdOhQzJ07F0VFRZg6dSoGDhyIKVOm4LHHHsPVq1exf/9+zJs3DwDQu3dvvPTSSxg+fDgGDhyI//73vwCAsWPH4vTp0xBFEWvXrsVf/vIXDBgwANu2bQMAnDlzBqNGjcKQIUPQu3dvHD16VJPPRw0OUm880evhf4HK/s+fe6+ovBBCSN24WvcHcj1rH+VZ8NlqTP8wFQs+W40rhZmaNDoGDx6MI0cqUwdfvnwZ5eXlaN26tfT65cuXsW3bNqxevRqvvvoqbr/9dhw+fBjTp0/Hzz//rHrOqKgo7N27FyNHjsTrr78ue+2jjz7CN998g0OHDmHPnj3Yv38/cnJy8M4772D58uU4cOAA0tLSsGnTJrc/G0BTqkg9EEQBJnMxBFHEvG7Tse+HI7iYewkA0D6xDRgAOSW50LG6aoPDTeZiWASLbD9/CFT2d46/ez2nd5r+1tn3VN/X6Ox9qbwQQkjdOEt9rud0KCgzyepfd+pZV+tzb9xvnI3ypPWd6/baYD169MCyZctQXFyMDz/8EIMGDUJGRob0+u23346IiAgAQEZGBtatWwcAuO+++3D33XernrNbt24AgJYtW+KTTz6RvXb27Fk88sgj0Ov10Ov1OHjwIABg7dq1OH78OD766CN89913KCkpcetz2VGDg3iU2pzPaZ3G4b1zB2EIisDw1gPxYvr6aueD1jRv1B6oTLSn9rtP6TYVC3o8i7QvXpZ9H+FBoV6J7ahtTAmVF0IIqT37yEXVunZBj2eRby7EWpX6ty71rKv1ubdiCT05wq/X69GzZ0+kp6fjo48+wuuvvy5rcAQHB0v/5jgOolhzavegoCAAAMMwitd4Xt4EuHr1KmJiYjB27Fh06tQJnTp1QufOnTF79uy6fiQZr3frpaenY+jQoejfvz+WL1/u7csJWN5aq8JkLsau7w9h/IMjsLhXEsY/OAKHf07Hc50n4qm2T2B9xus1zgf155gBf6f2u1974jWE6IKR1ncuXn00DWl956KZIRHF5aUuf09q5bGuZZTKByGEeB7LsGgSmYAlvZPx8sClWNI7GSG6YKmxAbhf/7pan3ur3vd0fMrgwYOxZcsWGAwGNGnSxOl+Dz/8MA4dOgQA+Pnnn3Hx4kXVRkV1OnTogE8//RQWiwVlZWWYOHEifvnlF1y+fBmzZs1Cjx49kJGRAZvN5tZnsvPqCMeVK1fw4osvYs+ePYiNjcX48ePxxRdfoEePHt68rIDjzaxCgmjDI/+vFzaf2S6995SOY8ExLGyizaWeAn+OGfB3zn73VsEGY1iMS/s6fk/ORk30rE4xauJKGaXyQQghnieIAv4wZcnq7tSeszStf12tz53vZ6nT+7pKbZRHy/iUdu3aoaioCCNHjqx2v2nTpmH+/PkYNGgQmjdvjkaNGslGQFzRr18//Pe//8XQoUMhCALGjRuHNm3aYMSIERg4cCDCw8PxwAMPwGw2o7S0FKGhoe58NO82OD799FMMGDAACQkJAIANGzZIwz92JpMJJpNJti0rK6verjEQaDXnsC7zKgFGamzY33vzme1Y0jvZ6XxQx54CV/fztkAsq7X53bu6b9VRr3B9KIorSrH7+0Poe2e3OpVRfykfviIQyykJTFRWfYvas8S1ousu17+uPEPoWB3aJ7ZBz9s7S/eHzy+dUpyPAaP6vgxq18tfW/URB/jZZ59J/96+fbv0706dOkn/PnbsGCZMmIB27dohMzMTY8aMQXR0NIYOHYqhQ4cCqJxBVPVY+/FVz5mUlISkpCTZ+8+bN0/KdAUAixcv1uRzefWO/Ntvv0Gn0+Hpp59GTk4OevXqheeee062z7Zt27Bx40YvXWFg0KIHuK7zKlf0nav63iJEl3sKPN2joJVALKu1+d2HB4UiuctkaZqcMTQGyV0mIzxI3ivibNQrVBci28/VMuov5cNXBGI5JYGJyqpvUXuW2PfDEczuOgXrTm6utv519RkiPCgUw1sPrPE+wjIMpnQcq7iP1EcCEF+IA7zjjjvw4osvQhAEsCyLpUuXgmW9HiVRLUZ0JeqkGhUVFdi1axc++eQTXLp0CRzH4Y477kD//v0xZMgQ6PV6p8empqbi22+/xfbt2xEaGopp06bh0UcflVpngPMejtGjR+PYsWNo2rSpO5ffIBSUmbDgs9WKngB773F1vQ5VX1uc/nen53D2XrO7TMbnl04peismtR/t8N7V9xS4up83+XJZdSebh6u/+4IyE7787Su0a9oGoiiCYRh8ffUcHr6tvayM3CjJkxIF2BlDY/BCj5lIOrpEts3VUTh/KB++wpfLKSFVUVmtH67eH5w9S6zoNw+iKFZb/9Z8bOV7ixCR+tkal5413vjqXafPFsT3uDXCcebMGSxbtgzt2rXDxIkT0axZMwiCgCtXruDEiRMYNmwYXnjhBXTu3Fn1+EaNGqFz586IiamcC96nTx+cO3dO1uCIjIxEZCQVHndU1wNcXa8DAOm16Z3G12le5deZ32NY6wH4e8Ybqr0VrvYU+EKPQk18tay6G8Pj+u9exN1xd2Lp8Q3S+zzfZRIA0WEvUbUscQwnDZHXdpTCH8qHr/DVckqIIyqrnleb+4OzUewwfQj4Gqawqo2ORIUYkFdWIBsdcTUmJDI4HE/cN4hGtv2IWw2O48ePY8eOHQgPl3/Bd911F3r16oWioiK8+uqrThscvXr1wty5c2EymRAWFoYTJ07UuCQ7qb3q5hwWlJmcxncAkF4rriitU7xFu8T7pMaG/fzrM17XJGc1cY0n84ZXZRFsiu/67xlvYHHvZNl+zmIugng9rY9BCCH1qDb3h+LyUuw9f1gWf7f3/GGXRhXU6v1hrQZIjQ37e7saE0JrKvkftxocc+fOrfb1iIgIWeCJo/vvvx8TJ07E3/72N1gsFnTp0gXDhg1z55KIE856gKuP7xDxp/h7MOjuvuBYFgt7zsLb/9mHrzLPSZmFGIaRLdrnOJoSGRRBGYS8rL6yODnLOiaI8pR6kcHhSOk2VZa3PaXbVITrQ1FoLoJNtIERKnve6OZBCCGeU5v7g0Ww4KvMc/gq85xs+5PCEzW+T2RwOBb0eBZZxTkI5vUwWyvQ1NDYpZiQlBpHLtyKDCD1RJOg8ZycHBw4cAAFBQWy7XPmzKnx2OHDh2P48OFaXAapg+oy/LAMiz/f1R0r/7VR+sOf3XUynmr7BDiWQ5nFjBc+XaUYhq3a68DcPB9lEPKe+srixFeZElX1fTiGU+yrZ3V4ut0o6cYToQvFlcJriqH65lGJNQ7VE0IIqRtPZCJ0pkKw4K2vd1R5npiC9oltZA0YQ1AEdA73Bz2rU5zLm+n+Sd1ociefOnUqEhIS0KxZMy1OR+qousAvq2BFQZkJVtEGHcuDAYMKwQKe4fBiryQsqTLv3j4PMq+0QDFFZt3J17GkdzIYMNKaCfbXqg7D2kdTBFGgDEJe5iyGJzwoFAVlphoDBauWHZ7hEBUSCZZhFWUtKiRSdX6vIThC9j4MIy87ALDhkRcVi0Cuz3gdS/skg2P4OgW7E0JIQ+ZKMHhtsvw5G5125X5uMhdjt0M69D3//RBPtX0Cfe/sJjUumhsSVZOKrOw3HxW2Cuk+xLFcvUwVJtrRpMFhsVgodZ2XVdfaF0QBvxdkyh4Ep3Uah/fOHURBWSGSu0zGyn7zUW6rkM2DdDZFxiYKEF0chqV5lt6n9h2EB4UqFnBS6x2yClZF2UnuMhnh+lBFI7WZIRHNoxKxpHcybKINHMPBEByBzKJs2fvM7joFUSEGWflhwKiWpwqbFcs/X089WIQQUguujgDU9h7tODqtNvqgfj3KdOhJD09EUUWJYtTD8f7wp/h7cONmJ1Rtg8uJ79Dkrt26dWtcuHBBi1OROnIW+GUyF6OgzCTrPY4KMaDcasHUjmMx/sER2Hv+MMptFXCcB8nenCJTlTE0BizDSIvuOL6mNrRqjx8xhsVIveOkfjl+B8XlpU7LS1WOZcc+8mARrNUeay9JxRXK91l3cjOGtRogex8Romp5ulZ0vcZrJIQQIlfdM4EjV+/RJnMxjv3vJOLCYhEVbEBcWCyO/e+kS3WyIIqKRYBN5SWKoHG1+8Nf7+mnuA/Zg8urounavk2Tb6Zt27Z47LHHYDQawfO3Tnns2DEtTk9cUF3gV9WRipaxt2PkfX9VLJYjigJmHF4o6wXhnCysU1Jhxu7/fqCYPkNTpfyHq4GCViejXI6ruVbuIypGQ5z1QiVGxMtS4OpYXlGeZnedjDe/3lnjNRJCCJHzTLIQEQ/f1l4W16mW+lz9SGU69GBeAz2t3gAAIABJREFUr3qNjSPiZPcHjuFcCi6nZxDfpkmDY+PGjVi3bh2aN2+uxelIHagFc7VPbAMGAANgXrfp2PfDEQy+58+KXobNZ7ZjUa8k6Wf7PEiW4XD0wnHZnMujF46j5+2d8VXmOTQOj8Pim9Nnqs7tJ77P1eA/Z4HgosMNxhgaA4tgU/RCCaKgejzPcooh/JjQKNl0LB3HwxAUgdk3p3DZF3aiHixCCKmeJ5KFuJr6XA0DBu0T28gW6mPBql5jqC4YS3onS/EaLKPcr6CsEIagCJqu7Uc0uXMbDAYMGDCg5h2JxzgGfrVPbIPhrQdKwVf2uI1gPli1R6GkolT2s0WwIjY0SrGwzpSOY7Hz+w/QMvZ2PJDYGovTaX69P3I1UNBZILiO5RWL9KnF/AiiiOe7TJIt/Ph8l0ngWU4R2McyLBqF3RoitwpWDG89UBk/cnPRSEIIIepqEwzuKldTn6vhWU5lEeBJmN99Jlb+6xVp24IezyLfbFIEps/rPgOrqoysJHeZjMjgcMpi6Ec0+aZ69uyJ1atX489//jP0er20vXXr1lqcnrjAMfCLAWSZHnJK87Dp9NtY3Pt51R6FGyq9IFXPWW6rwB+mLOz8/gNczL2E2V0mK0ZKKEOE/3AWKAhAkbnKMRDcPpLleGxeaYGibLEMg33nj8hGyfadP4Kn2j5RY4as4vJS1fgRKmOEEFI9TyRsqS71eU31uVVldGR9xhtY2idZdo0iRKR9Js+AufbEa1jed47iPkSNDf+iybd16NAhAMDHH38sbWMYhmI46lnVxf1ySnJVeyIAqPZY7z1/GAAUvSD2cwqiALPVjIKyQgCgBf0CgONikNVlNak68mDn+NCvNhrCMqzqQlHjHhguG31TGx2rrwULCSEkEDlb8LeunI14C6KABZ+trrY+V4vhqBwdEREbeusanT27WAUrjGGxmn0WUv80aXCkp6ejpKQEYWFhKC8vR3FxMWJjqWB4k7P5mwCDvecPy3qcf7r+C55s+wTGPThcEYtRNY93ZFAEVvSbhwqbhRb0C0DOspq4OqLAszyaRiZUxvUINnAshyBOvRxmFmXX+D71tWAhIYSQmvEsrxjx1nN6zP90paI+X9FvHkRRlEY99JzeSX0uT6trz4DpuJ9johLifzSZbH/kyBEMGTIEAJCZmYlHH30U6enpWpya1JF9/qY9bZy91wEAvso8h3UZr2Px8Q04+NMnaBadiMXp6/Hs4UV4MX09/jBlQRAFqcd7wWerMf3DVKR+tgYF5kLEhkYhJjRK9fyUIcJ/uTuiYBWsuGrKqixLRxZhcfp63CjNx4u9kmTlZHbXKdj3w5Ea38dZGaYyRggh3sGzPBqFxSA+3IhGYTEot5Ur7htRIQbklRVIzw4LPluNfHMhFvR4tsb6nGc5PN9lkmw/e9wf8W+adBVu3rwZb7/9NgDg9ttvx/79+zFt2jT07t1bi9M3eBabBQXmItgEG/ScDgxTOR9Sx+oQERQKsbQIos0KhuPBhRnAMKxi/ibPVmZ6MFvNUsaqi7mXVLNW2XubAVTb490kMkGWSYKyVPk3HatTZBH5/NIpBHF63CjJk33PanNnC8pMitGzvecP46m2T8jKiZ7TS1Pz7NRGLmjRSEII0Zba6uMMAFtJoeI5wpXj9Zxecd8I04cq1tewx2HUVJ9bBVud4/6Ib9OkwSEIAhISEqSfGzduDEEQtDh1g2exWXCl8BrWZ7yOqBAD/tZmMDadfvtW9oYukxB0bCfKLpwBbzAifsQ86OOaS40Oe/yF49x8+0rjzmMxLNK/la9ZIYiCSytVE/8RpgvG8NYDsL5KFpHZXaegsLxIkR2keVSiotEhAoqVZKd0HAsRkGUzc5ZxRC37lNZzkAkhpKFyFqcXb2NxfcdSWAtzFM8RNR2f0nUqRrV5TFafO1t/yZU4DBFineP+iG/T5JuKiYnBzp07YbVaYbPZsHfvXjRq1EiLUzd4BeYiKUBr8D1/lhobwM1eg4w3wDxYOZJkLcxB9p5VsJXIe4/V5uZvOv02nuv8NGJDo1VX62TASHPoHV/TsXytVjEl/qGwvFhqbAC3Vn2tGsRnzxRVUGZSPYfaGi+iKCh6ukzlRRj/4Ags7pUkrXZfXF6qek5CCCHuc3bfLjRdh7UwB4Dz5whnx689+ZriHuFsFXBX4jDsMRyOx1ZNyUvPG/5JkxGOpUuX4vnnn8eyZcsAVKbDXb9+vRanbrDsw5Y24dYfWbg+VD3zlLEpQiakgSkrguXEPog2+Vx4i2BBVIhBNkR58KdPADBOVxNnGbbaPN65pfmUQaieqQ2Fu9q7YxWsKCgzVTstyiqo51gP5vWKbTbVvOvqWUiKK0oV2xgA6zJel21/UnjCpc9CCCGk9pzF6Qn6YNk2a2EORJsV1uJ82TQrZ8eH68NkC7SevvotkrtMko2WT+80waX7FevkmcRsLVe8Lz1v+BdNGhwtWrTA/v37UVhYCI7jEB5OQZ3uqDpsOb/7DCljQ3FFqXrGn5JcrDr5auXUlP4TAIcHRD2nV0zFmtZpHPScDqIoqq4mPqn9aDAA4m0sFv1pBAR9MNgKMww2Fgwog1B9qy5lbU2VuFWw4veCTEUqQ8dpUTyrnmPdbK2Qnc+ed91R1cUAq+5bYJaPhjg7J5UdQgjxHN5JHc2Jonw/gxEQbMjcvlA2zUoXGa16fHhQKP5x6k3Z84UhOAJPtxuFYF4Ps7UCQbwOnAuB3yzDqT6T9L2zm2w/umf4H7emVL3wwgvIycmRfjYYDLLGxvXr1zF//nx33qLBEEQBBWUm5JTkIq+0ALu+P4Sc0jwc+vkzKWPDwZ8+wbRO42TZG6Z1Gidl/IkKMaBAtCJPrEBBmQmCKEjndpyKten02xBEAZHB4XjivkHY9u0eLD6+Adu+3YMn7huEyOBw2EoKcX3HUpS8twxlWxeg5L1luL5jKWwlhZRBqJ65M4WtoMykuoCe47SoqOAIzO46WZFRKi4s1mHbZNW4ijABmN1xvHzfTk8hPqyRbFtKt6lICDdS2SGEkHrEMqzqM4Q+Kq6ykYHKxkb88Lm48elWxTSrMAFIcbjvz+46Be/8Z7/i+SKvtBDWmyMQVsGKfeePSs8k1VF7Jnn8vkF0zwgAbjUPx44di8mTJ6NZs2bo1asXmjdvDkEQcOXKFfzrX//C5cuXpWlWxDm13uspHceisLwIxy99CQCY330GeJaHntNhed8UWAUBgIiXTr2Fi7mX0DL2doy876+yYUh7D7hVsDoN4KouE5DNZpUqHDv7UGt1ox9Ee+6krLWK6lOlHKdFsSwHPRck65XSs3rwHCffxgWpjqqIgg08w8v25cGgEatXlBNdZCxlnyKEkHpUYavAe+cOykYP3jt3EM91fhqJE1ZK06dEUUTZxbOyY62FORAggmPk94MwfagiwDunNA+h+hC8VGXUY0rHsRAdRlLUOHsmAUD3DD/nVoPj3nvvxb59+3D06FF89NFHuHTpEhiGQYsWLdC/f3/0798fLEsFoiZqvdebz2zH+AdHYF3G6zh+6Uv8N/snpPWdC8PNkQfRZkMxz8EQFIHZXSYjMTIe14tzERViQE5pntQDvqR3MgD1RfrsAVzOMgExHA/eYJQ1Ouy9IFZTHvKPvS2rlMoNRiROWAk+PFr7X1ID584UNp5RnyrlOC3KZC7Gii9eVuw3s9OTN3uq9LAKVrz73X5Maj9aUWZKWGDHzx+j5+2dpX13/Pwxnv7TYJS8d6vjodxgROKTqxEuChBtNjAcLelECCFaE0VBlu6WB4OCskJZ/JwxNAa8CNl921qcr3rvL4KAHefel9XxOSW5qveX7OIbimca+/NITZw9k1DGQv/m9gQ4hmEwYMAADBgwQIvraZCc9V5HBkUAuDV8GBEUiorrvyN7zypYC3MQ3mkwhreVpzGd0nEsdn7/AS7mXkJOaR5ulOZj+3f7MK3TOFkMx5SOY2ucT8eFGRA/Yp70frzBCOOjM3B9/3rYSgpgHDgNQkkByjMvArg1+kG0V10Af00iwCL54UlY/+WtcpL88CREOJQAtXIYFWKAjtdh2+k9srIjqASNiwyjmhZXEOT7cmFRsBXlInvvmhrTMBJCCKk9URRkzwu8wYi4kamY3WEc1p299SyQ3G40wmzykQe1e3/8iHkogjL1edLDEzG76xRp3Q37NKs3v94hO2dOaR5E1DzCQQIXRdz4AGe9141Co/Hqo2nS8KFQUihVAAAgtLhXkca06siIMTQGpvIiXMy9hPfOHcTT7UYhPjwWf5iycfTCcTzTtvqsQAzDQmdsisZjlwOCFaLNioJTB6UGRs7hTYjt9ySy964BUGX0ozi/2oWDSO25swgeJ4oI//YLvNh1OgSOBWsTgK8/A/fQINl+auVwWKsB+LtKGVvSO1mRwUQQRdW0uIu7z0T88Dlgg8MhmIvBhEZKjQ3g1vzgxAkrAcClxacIIaShchy5UKsrbQ7PC9bCHFzfuRzxw+diaddpsLEcOMEG29efgek4UFGf6+Oay6ZZcWEGiKUFijp+w5dvYlmfFNm9iWEYJ4u76urnF0R8EjU4fICz3uuY0CjZA6VjTIUYor5oX7g+VDbaAQAXcy9h1YlXsbhXErZ9uwcpXSYhIjii2usSRQGWnKvyEY6B02C5cQXlmRdhLcwBF2oAAMXoB/VYa6+ui+BxYQYY7uuO7HflCztxYQbZfmrlsHFEnGoZEwUbMrfJM5iI4erlUWRY5H665da+w+eAC4uSlWVrYQ7ECjOuvbeERj0IIcQJtZELtbpStFkUMZhcWBQ4iCh5d9mtY4elQBBsyH47VXE+x+nRgpN4QItgQXy4scp+Qp1H5EngogaHD3C199oxpoIpK1IdGTGGxmBJryT889s9uJh7SfHa0q7TYPv6M+ChvwLh0fLeEl4PiMLNqVGMooek6qgGbzCCi2yEZtM2oSL3KvKOvyONfth7rCmew/sYhlXtrXJ8kFcrh4LNqh7/AShGKCKeXKG6r5ibKd937xrE9n8G2btWSPvxBiMq8jJVRz2oDBFCSCW1kQu1EWKAQUjLDoi8v1f1o8v71iK2/zMu1b2ci/GA7ozIk8Cl2befnZ2NH374AefPn5f+I66z914bw2IQFRKp+odpn1dpn7okfpuOlJspc4HKP/zk9mNh3rMOlqNv4fFWj8hfazca5j3rULjpWRSf/gCizSr1lmRunY/r+9fDknMFmVvn48rGKbCZbqhmqWKDw6VeED4yBmBZZO9aITU27PtRPIfvYBgWfHg0dAYj+PBop6MGjuUwnGGR8vAz8rS2Dz+DULNZdpy1MAdhYBXlMaXLJOB8hmJfXXSCLA1j3LAUFJzYo9iPyhAhhNwiOsseWWGW7t2ZW+dDZFlEdx2B3E+34No7i5D76RZwuiBwYVGKY1md+sJ/jqKCI5Ds+MzRZRKiVGZLuPJMQxoWTUY4NmzYgC1btqBRo0bSNoZhcOzYMS1O7xPcWeXZrrp5l46vsaEREEqLFPs69lQjOAzLez0PqyhAuPEHKj7aCkvmRVgAhABY/kjla6zVAmvGAZTcbBRUzTZl7y2J7fckcj7cKFVmttJC1UwVfFQcEieslK7JWTaryl4WohVX5u06U9fyy1itCP32C8WcX+HujrK4DNN3x8FYLQg6thMLH3wMYkgEmLIiiMd2IvzBvij5Ll06J28wQjCXIOGJBQDLAoIAwVoOW0mB7L15gxEMr1fMLaYpVoSQhsrZ/dZxhBjlZbi+f61Lo8sAFPU5eD0KykyyewbP6dDckIglvZNhE2zgWA5RwRHgOYrNIDXT5Inw0KFDSE9PlzU4Aok7qzzbVTfvEoDytWFzkHdiN8ounlXM0bQPcwqCFZbs31G0bw2Mg2bi2nvyNU/KLpxBdJdhyNwyV4qxsOZcqcwwdTPeIrbvBKlCYoPDZZVYwZcHYBw4DTmHN8mumY+MlT30Octo4RgjQOrO1Xm7atwqvzyPyFZdcb3qnN+RCwBLBbKrxGXEDU0BRAFlF84AF87ITtGozzjpBmm/brAcsnal3dr2xAJlGRq1ELbi/Dp9ZkIICUSq99vhc3Dj6Buy/YTyUtWREH1MY3l9PGwOoNMj59Art+rzJ1Lxh6UIa1XuGTynQ6OwmPr8yCRAaNLgiI6ODtjGBuB8lee0vnNdDuJVm3eZ98VONPrLRECwwVqUKwXSVs6rXIPYfk+i7OJZp3MqbUUFyN5XOR9TMBer9nrYinIBVAaLCdZyxD32nCzbVNVRDMFcrJjzWZZzBY3HLgMEG8Dy4CKiFA97rsYIkLpzOm/3ydVSzI2z37vz8jsH4bbqj4XVivyTexDb78lb84BZDln75D1n1/evReOxy9TXbeF0UqYzsDwYnR6Z/5wj/yy70pD45Gr56B2AzB3zKa6DEEJucsweCZYHeB5sWJRslEK0WlTrY2tRvqw+t1nLcWOfPK6jsPgG1v53j1vPPIQ4cqvBYY/TaNWqFZYvX45BgwaB52+dsnXr1u5dnY9wZ5VnO8d5l0GJLRHVYQCubb+VGcI4cBryPn9PygDFVsnooDqnUrh1TrXRCPv5ghJbIqbn3xSvWW5ckR1XcuErRHcbges3HybtGSxufPyW6khLVVVHXoj21ObturqehbPyW2ExI/PtF2sYPahccbbqAo+JE1aq9pwBUC2DgIhr26tktHKWpcpaAZ3hVqYTy83Gt+P7UFwHIaShUsseGT98LqJ7jsb13Q6jxsPmSJ2SVUdCqsZbNh6zVFHPCvpgt595CHHkVoNj5syZsp/T02/N0w6kGA53Vnm2c5x3GfXwEOnBDFDPACWYi6XjVWMi2FvnLM+8iLzP30Ns/2egj20CsDwKzh5G1MNDoIttAktBtmwEJefwJiSMWgTLjSso/t+3lb0lVR4M7deUvW9tjSMtxPPU5u1GdRvhdD2Lqt+Ps/Ir5l6r8Vi197WVFSlGwkzfHQcAFJw9Ius9Kzh7BI3+/KRL84gdyzfFBhFCiJzqaPfe1Wg0eJas7s07/i4MnQfLR6eDQhWxcoLFrKhn2Qqz2888hDhyq/TYGxhZWVlISEiQvXbx4kW1Q/ySO6s82znOu+RCDdVngLoZwwHAaUwEFxEl68GwlRSAD4sGZ6ic3hbZupusd8NxBMVWnI/cT7cgftgccBFRsBXlO72mqj9TD3P9U5u3q4tJdGkEQK38pnSdgor3X63xWLX35eNuQ0y3Ech2GAmDTo+oDgMUIxy2ijLF++hjEhVxHYryTbFBhBAi42y0m+X1uF4lrs44cBoYXofMmwvzAkB4m96IH5Yiq7t5Q5yinjVExtE6GkRzbjU4CgoqW8qTJk3C9u3bIYoiGIaBxWLB9OnT8cknn2hykd6mRU5pxzgHgKk2AxQbGgHjgMkQbU87XzeB5aGLby6by8lFRIFleViL86XGBuB8BMUeL5I4YaXTHuUaR1qIx6lmKANcGgFQK79hgohralmhHEcZVN5XtNlwzSGGI3tfZQyH2ghHZNt+yvfRB9cY80OxQYQQIudstPu6Q52cc3gTEkamyo41//Y9onuPrhKXyYEJiwTH6RT1bDOA1tEgmnLryTE5ORkZGZU59jt16iRt5zgO/fr1c3aYX6rrKs9VVY1zEEVB1qsQ0rIDGvWbIO0riiJEmw0QbBDBQBQFMAyrmhpVZ1AG7DvL1W0fQbGPdti3izYr+MhYZY+yCyMtpH44xsk4lqHqvh/H8uvsWDY0QjUNbdX3teRnqcdwCAJi+oyFNT+rciPHI6bPWABQHc1wpeFAsUGEEHILF2ZA/KiFsOZngdUFQ7CYnY52MyynyEjFBodBLCuGiMrGC8fpVOtZBqAAcaIptxocb731FgBg/vz5WLlypSYX1FDIem9FEUJJIa69uxjWwhxEdPorIlt3dZiyMgd8XDNYb/zhUppQZ6MVvMGI2P7PSFOr7NsZjlftUXZlpIV4hzsjAM6+a0Uwolr5clK2wHFAeQlyP/o/WZAib2xGoxSEEKIVq0VWzzYes0S9Tub1shkQbLjB5WcIQrSmydyYoUOH4uzZW1lsGIZBcHAwWrRogfBwmvPnjL1XwVqcj+y9q6XKwnB/b+Qef1ceAHZiNxr9+Wll+lwnQdxO579HxoAvL5UCxxx7xdV6OljqYfZZ7owAOB5rrbLmBeA89S7D62F8dIa0SKR9jRcwrDKIfe8aNB67XHUUjhBCyC2uLO6qFjRuKcpD3NDZsJUWSqMeXKgBYBjoImOlY53W8ZQIhtQDTRocK1euxE8//YS77roLHMfhwoULMBqNKCsrQ1paGvr27VvjOVavXo38/HysWrVKi0vyK4rpT06Cb0UIyP3o/xTB32pB3NX1ftO8eKLG5dS7w+eg6PsvHDKivIO4x55zMtXKVp8fgxBC/I6ri7uq1dMsr4NYUS4b9TAOmqmoe51NtaZEMKQ+aPKUmZiYiC1btuCDDz7AgQMHsGPHDrRt2xYHDx7Eq6++WuPxp06dwoEDB7S4FL8iCFZYCm8Aolg5/HkTI0I1ZS4DFrH9nkTB2SOIengIACCkZQcATOWaBSWFsBbnV/67OB8AwIdHQ2cwgg+Pliote8+243bi20RRkH2/oiiobqvrsWAYWTkEnKTe3bsGYfc+hOy9a3DtnUXI3rumcsTs5nzhqniDEWCpfBFCSHWcLe5qKymU7WefLl0VqwuWVgq3H5tz6BXFe6gdS4lgSH3RpJRduXJFFjTepk0bXL58WZEqV01BQQE2bNiAKVOm4KefftLicvyCIFhhyf4d2fvWgAuLkk1REcpLVXshhDITcj/dUpnuLjgUIS07IKbb47i2PRVcWBRieo2RTXOhuZmBw1nvF3gdsncsq75HTO3YUQsBq0W+beQCGAfNlG5cvMEIXXSCallUS2sLXqe+8B/L1fevixBC/Iqrow9saIRiQT+wnProskMHFKUaJ96kSYOD53mcPHkSXbt2BQCcPHkSOp0OeXl5sFqrH6pbtGgRkpKScO3aNdXXTSYTTCaTbFtWVpYWl+0RrszBBABbUYFUYVgLc5B3/B3E9n8Guqh4acTDMQDMarohjXY0HrsMjf4yUVqpPLbfk1JjA6C5md7gybLqrPcrtv8zNX7nasda87Ok4Xfp2J1piBs2GwlPLKgclRAEadRCGYyoTKNoKylUTYtrHDBZk98B0Ya/1amk4WpYZVU9VT5YTpY5EADyTuyW1bO2ojz1YxlWkXWQplQTb9GkwfHiiy/i2WefBcMwEAQBQUFBePnll/Hmm29i5MiRTo/bs2cPGjdujM6dO2P//v2q+2zbtg0bN27U4jI9ztU5mAAAQd6bUZ55Edm7VqDxmKUoOH1IsTiPYxpbQKzyb4ANDqe5mV7mybLqNM2xLlixzfE7V53zqwtWPR8XFIpr7y2tdtTDOHAaoBKszoUZENNjJPWe+Th/qlNJw9agyirLKkaI44YkQygplJLK8AYjEv72IsounkXZxVuJeoISWyJuWIq0FodUTwPI3Dpf8TxCnZDEGzRpcLRp0wbHjh3DhQsXwHEc7rzzTnAch3vuuafa444cOYKcnBwMHjwYhYWFKC0txYoVK/DCCy9I+4wfPx5DhgyRHZeVlYXRo0drcemactYLrTrKwKqnFuXCoxF5fy+Yzp+U0tlV5P6hSGMLsGC4W73PgrnYpUXgiOd4sqw6XZTRYpbtxxuMYHi9vEeMUfacCRaz6vkq8q4pRj0aDZ7l0qgFJSTwD/5Up5KGrSGVVYZhFCPEorUC+Rn7ZNvUZkDYSgogWCsU9XRs7zE064H4DE2eRnNycrBr1y5p5XG71NRUJ0dU2rJli/Tv/fv348yZM7LGBgBERkYiMtI/Fp+pTQYINtygGMWIG5qCgi8PoPhcOgAgqsMAgNeD5XSyNLaV8+JZ2XzMgi8PKFKVUu9y/fJkWXU29xa8Th5LMWohbFVSHzobpeBCIhE/fK6s5yx++BzcOPqG7H2thTnQhUXh2sF/uFSuaKE+3+dPdSpp2BpSWVUbIU4Yu1SRsTJhzBJlrNygmWBDwpFdtZ4eloKCUwdl70GzHog3adLgSEpKQkREBFq1agWGYbQ4pV9y1gutNsoglhXDdP6kbL584XfpCPt/7VF8Ll12nLN58YoeZV5PvcsBytnoAQDZNgDIOfa6PGVt+rswdB4s25afsQ/GgVNlx4qiKDVs7ZzFa1C5IoQQ7TAMC52xqWyhPrCsImOl5cZVmL75VP5McPoQjAOnovHYZZWpcFkOjC4I5t++l70HzXog3qRJycvNzcU777zj1jmGDh2KoUOHanE5XlObDBCiKCL8zgeRtStNNueSCQ5VHFfdvHjqUW44nH3XVbdZivJU13DhI2OVoxShEbKGgyBYFdlP4ofNARceBZalmxRxXYXFBr2Ok/5PCKmeKAqw5FyV3+uHz5EW+rUrOLEHjR6ZJF8bSaU+F0WBMlIRn6LJU0RiYiJKS0sRGhqqxen8Vq3msAuC6lobjccsQ+Oxy8FFREnH0bx44jJn5Wrs8hrLEMvy0MU3l/WwcRHqjQ1Xs7GRhkmv4zAo+SAOrR/s7UshxC+oxoDuXYPY/s8ge9eKKvsVgIuIVR3tpoxUxJdp0uCIi4vDY489ho4dOyI4+FbWnJpiOAKR6yMOomq8h60kH9cP/F2W3YpGMYjr1MsVILpUhliWB2toVP071CYbGyGEkBo5iwFVW/NIbTTDWZ1Mzw7EV2jS4GjSpAmaNGmixakaDGfxHrbifMomQeqsNnFEdVWrbGyEEEJq5LTu1gfXOEpBdTLxB5o8hcyYMQNmsxm//fYbWrZsiYqKCtlIRyByd0qJWrxH1bU2uLAoiDYrLIU5NBQawLSemlS7OKK6vXdtsrERQgipWXV1d031MtXJxB9o0uD47rvvMH36dPA8j507d2Lw4MF47bXX0LZtWy1O73O0mFIii/ewVKAi96q01kZQYktOfn6DAAAgAElEQVTE9BqDa9sX0pSVAOaJqUmuxhG58971MYpCCCENiSgKAMshtv8zYHXBlWsssRxEUaA6mQQETZ5eV69eja1btyIqKgoJCQlYs2YN0tLStDi1T3I2fGkrKazVeeyxGXyUEXxErJSSNKrbCGk9DXfOT3ybVuXIkb1c6QxG8OHRqjcrd97b3hNXuQAlKPsJIYS4yVZUgOxdacjetQLX3lmE7F0rkL0rDbaighqPpTqZ+ANNmr9msxl33XWX9HOPHj2wYcMGLU7tk7QevnTslYYg0PBoA+DNYXB33ptWFCeEEI0J6nUyBKqTSWDQpMHB8zwKCwulRf9+/fVXLU7rU6rOdwcYhLTsgMj7e0kL75i+O+7W8GXVTFTW4nwaHm0AtBgGV4vDAFBjbIa7702Z0wghREMsr/pcARfXQKI6mfg6TZ5gp0yZgjFjxuDGjRt4/vnnkZGRgaVLl2pxap/gON89otNfEdNtBLL3rZUtkMaGRmjyfrUJ/CX+y93v2VkcBngdsncsqzY2g8oYIYT4DiYsUuW5IgVMWKS3L40QTWjS4OjduzfuvPNOZGRkQBAETJs2TTbFyt85zncPbXaPVCkAN+e/71uDxAkrwWrQw0DDow2Du9+zsziM2P7P1JgekcoY8QRaWbx2KmwW6DldjdtI4BNLTCrPFWsrF2KtYW0kQvyBWw2O8+fPy36+//77AQDl5eU4f/48Wrdu7c7pfYbjfHc2ONzjc+9peLRhcOd7dhaHweqCFdvUyiaVMaI1WmG8dvScDo/vmirbtvuJ17x0NcSr3IjhIMQfuNXgmDlzptPXGIbBsWPH3Dm9z3Cc7y6YiynGgnidszgMwWKW7UdlkxBCfByrXp+7GsNBiK9zqySnp6drdR0+zXG+u+m744gfNgfZ+9bQ/HfiNc7iMMDrpBsXlU1CCPF9XESU8rli2BxwEVHevjRCNEFNZxeozXdnQyNu/czrAVGA1ZRLc+FJvXEWhwFAdZu1OJ/iNQghxAexLA9dfPPKmA3BCrA8uIgoMAxLdTcJCNTgcJHafHc2PNojq0UT4ipncRhVt1EZJYQQ38eyPNgqAeJUd5NAQiXWTZ5aLZoQrVAZJYQQ/0N1NwkkmmapchQoWaqq483VoglxBZVRQgjxP1R3k0BCWarcpMVq0YR4EpVRQgjxP1R3k0BCWarcRCs2E19HZZQQQvwP1d0kkGjSTM7Ly8MHH3yAkpISiKIIQRDw22+/Yf369Vqc3qfRis3E11EZJaT+CNYKsLy+xm2E1ITqbhJINGlwPPfccwgODsYvv/yChx9+GF9++SXatWunxan9Aq3YTHwdlVFC6gfL6/Fr2jDZtjsW7PPS1RB/R3U3CRSaNJMzMzPxxhtvoHv37hgzZgx27NiB33//XYtTE0IIIYQQQvyYJg2ORo0q80a3aNECFy5cQHx8PKxWyqJACCGEEEJIQ6fJlKrY2Fi8+eabeOCBB/DKK68gPDwcxcXFWpyaEEIIIYQQ4sc0GeFYunQp9Ho92rdvjz/96U94+eWXkZKSosWpCSGEkAatwmZxaRshhPgqTUY4Pv74Y4wbNw4AkJKSgpSUFLzxxhtanJoQQghp0PScDo/vmirbtvuJ17x0NYQQUntuNTh27NgBs9mMrVu3ory8XNpusViwfft2TJo0ye0LJIQQQgghhPgvtxocPM/jwoULMJvNuHDhgrSd4zgsXLjQ7YsjhBBCCCGE+De3GhwjRozAiBEj8Nlnn6Fv375aXRMhhBBCCCEkQGgSNP7QQw9hyZIlGD9+PAoKCrBo0SKUlJRocWpCCCHEKwRrRbU/exMFkhNC/IkmQeNpaWmIi4tDbm4ugoKCUFxcjEWLFmH9+vVanJ4QQgipd46rhvvSiuEUSE4I8SeajHD8+OOPSEpKAs/zCAkJwbp16/Djjz9qcWpCCCGEEEKIH9OkwcGy8tPYbDbFNkIIIYQQQkjDo8mUqg4dOmDt2rUwm804ceIE3n33XXTq1EmLUxNCCCGEEEL8mCbDELNnz0ZoaCgiIiKwYcMG3H333ZgzZ45Lx27cuBEDBw7EwIEDsWbNGi0uhxBCSD2qsNiq3e7sdX+jFjTuSiC5LwefE0JIfdBkhEOn02H69OkYM2YMOI5DeHi4S8d9+eWXOHnyJA4cOACGYTBx4kR8+umn6NevnxaXRQghpB7odRwGJR/EofWDXdrurxyDyAHXAsl9OficEELqgyYNjv/973+YM2cOfvrpJ4iiiLZt22LNmjVITEys9jij0Yh58+ZBr9cDAO68805kZmbK9jGZTDCZTLJtWVlZWlw2IZqiskr8AZVT4i+orBISODRpcLzwwgsYMWIEhg0bBlEUsWvXLixYsABbtmyp9riWLVtK/758+TKOHDmCnTt3yvbZtm0bNm7cqMVl1pogiCgsKYfFKkDHszCEBYFlmTrvRwKbN8tqoPLE31ZD/3ulckr8hZZlle7nhHiXJg2OsrIyjBw5Uvp57Nix2L17t8vHX7x4EZMnT8bcuXPRokUL2Wvjx4/HkCFDZNuysrIwevRot665JoIg4rcsE5b/8zSu55chLjoEqU91wm0JkbLKx9X9SODzVlkNVJ7426K/VyqnxH9oVVbpfk6I92kSNN6sWTN888030s8XLlxA06ZNXTr266+/xoQJE5CcnKyoWAAgMjISTZs2lf2XkJCgxWVXq7CkXKp0AOB6fhmW//M0CkvK67QfCXzeKquByhN/W/T3SuVUaxQA7jlalVW6nxPifZqMcGRnZ2Ps2LG4++67wfM8fvjhBxiNRgwaNAgAcOjQIdXjrl27hunTp2PDhg3o3LmzFpeiGYtVkCodu+v5ZbBYhTrtRwipHU/8bdHfK9EaBYT7PrqfE+J9mjQ4XE2B6+itt95CeXk5Vq1aJW0bOXIkRo0apcVluUXHs4iLDpFVPnHRIdDxbJ32AwCrVUB+kRlWmwieYxAdEQxeZT+A5pGShsexzPOc639brnL298pzLPKLzPT3RkgAcvXv3lmdQ/UDIe7TpMHRsWPHOh2XmpqK1NRULS5Bc4awIKQ+1Ukxl9MQFiTbLyJEj/kTOmLl1jPSfvMndEREiF62n9Uq4HKWSbFfi4RIRaOD5pGShkatzC+Z1BkvTOiIFTX8bdWGs7/XCqsNCzZl0N8bIQHI1b97tTrnhQkdUWq24MU3TlH9QIgbNGlwBArHHtYmjcKwcnpX2GwCOI5FdPitXg2LxYb84nLYbAJiIoOwfMrDEAQRNgH47MxlxPZqieiIYOnc+UVmqbIDKodpV249g1XTu8IYHSq7DmfzSNfN6i47JyG+Rm1kDkCN21gGijL/4hun8MKTHfDixM7gWEh/W8beLSGIqFNvY1FZBf71zRXFOdu0NNLfGyEBqqisAqZiM1ZO6wqbIIBjWVy9bkJBkbnGOsdis0qNDft+y/95Gn9/rnud6yFCGiJqcNzk2MPaqXU8Rv75HtURCVEU8Vt2EVZuPYPoiGCMG3gv/rHzW2m/Z594EIIgys5vtYmqc0OtDvsBNI+U+CdnoxQWq6AYrdPxrKzH8IUnOyI6IlhW7qMjgiEIDJZsubXfnHHtkVtoRtqWM3XqbRQEEe3uTfj/7N15mBTVuT/wby3d0zM90zMD9MyAg7jERKMYUAFlMyxuQUQEBINsRjbhknCRHUUQGBSRK0FUMBFMvIqBqCExioImLAbU+FMxKF43GMdZYPalp7ur6vfHTBVdXVXT1fv2fp7HR7q6ltNd55yeU1Xve7Dq2fdU7dVuU3eF1N5IpHWd+zQyc51RPYZb8MDKWQIuSzccCziybVi69ZDq99xhV38ven3Okin6fdOZOhfWhdgPEZKO0n7A4XtF1vcK67A+PVR3JK78URdkWDhU1TaD41jlvXtH9VQGG0DbHyqbd32EdfcNQFVNM/KyM9Do8gAA1s0eAAkSWIZBQ7MH+9//DrxO5xRMXAghiULvzlz52SZ88mVVwDsK6547htljrsSqZ48q+5tw449x8CP13YiWVo8y2JC3Nbob4XsXUr5DKYoSNu/StteHpquTVhi1t3BiqyguK71l5jpx567ZqmUvj38qosewcpaoHyPRud1e1Da5lXafZ7fC5Rbx0r7Pce+onsjJsqCh2YOX9n2OGbdfiWVT+yrLsjMt2PWWer1db32OCTf+WNM3rTPZDxFC2qT1gMP3iuz8u65S/YGfk2VRXg/v0x2/GHAhHtredtXjkbkDlfd815NV1rSgpqEVu/ef1Nwl+fWE3tjx1/+gpsGFpVP7IteufR7dbFwIIYlE785cQX4mBvUuVt1RWDKlDzIs6j/mK2ta0K1LtjLQLsjPxIXdHMjNzlBtu3pmf3PZZjyCchfStw3l5Vh1t/cNFjWK1wontorisgiJPrfbi1OVjZp2n5ttxchBFysXG+Q7m2CBZ1/7VFlWMmeA7npdO2ep+oduXbLpKQRCgpQWAw6jK4t1Ta1454NTWHnvdbDwDFbe2w8v7TuJL07VoKHZg36XF2JYnx7o0TUHZVWNym3Vuka38l5udoZqO6Dtj6ysDIvmLkllTQueeOkj3DuqJ9btONZ2hWX0lUCTW1Wuhha37tWYOeN60dUTkhD02pSFZ5V2IdfbTJsFq549pGoD63e+j/VzB6quLO5//ztkZfJYP2egksUNYLB+5/uqbX8406h7948BVFlkPF5RN2aqZM5Agyw0DB779eAO7z4YxVaZeZab4rIIib7aJrdhu9978CvVb+reg19hxugrVcsYhtG9A1oyZ6Cqf5Ak0FMIhAQp5QccHV1ZBKC5+vrrCb3x/N9O4MQ3ZzR3J+aN740/vH4Cxz77AeNv+Inyx5DvdjUNLswb3xtuj9fw7kdOlgU/OT8fIwddjKVPHtKUy+MVcfSzChz9rEK17YzRdPWExJ9Rm+rW2a5pM8um6j//XN/oVl1ZXDn9WtTUt6r2qbftS/tOarLIzBvfGxIk3P/EP5Vla2bp3wkRRAnzxvfWXMFkWSbgH/56d3DMPstNcVmERJ8g6LczhoH+HQ5Iqn5Ir8+R+40Cn/5BFCVTWSwJIeek/IDD9y6G/Bz4/ztZgexMC0QJmiuoT7z0EdbdNxAMA2UwIL+3eVfb3QlAf7vVM/vjVHkD9h78CsP69ACgfxWkodmDMUMv0VxxeeGNE5gzrhfFcBDT4hEXYHS1vmTOQM3VxXU79GMz1vmtV1ndjKf2fBJw25oGF/JzbVg7ewAkSQLDMDj22Q/o5LCptmUYRrcNcSyje6XzvrG9AubZ12uXZp/lTvc27fYIsFo45f/EGAV5h44zmEdDkqB752Lt7AEB+xy53/DFsgx6FDkC3hUlhJyTFr928l2MWY8cwKpn38NPL+qCZ175BFU1zbpXQ7yCCI9HMLw70TnXpvteQ3PbVdvbBl+MPQe+xP73v8OSKX1QkJ8JAMqdkD0HvkRBp0yMHHQxnn3tUyzdehjPvvYpRg66GKIoKXOA+G5HV0+IP/lOw/1P/BO/WvMW7n/in/iuvF6TIS3SjK7WG11d7ObMVtXlbk7t8882K6+7bdcu6m1XTr8WNXUuLH/qMGaU7Mfypw7jpxd1Ae/3N+y7H57G0ql9VdsundoXeXYrJt58mardTbz5MjS7PAG/R712afZZ7nRv01YLh5ELXqPBhgly4Lfvf8ScDAur2+4FUT9LpH8br6xpwXl+/ZXcb/iT74oW5GchP8dGgw1CAkj5Oxwer6i5G1Gy4xjuHdUTDc0e3ashgiAp//Z/z5mfCQb6V0875djwmwlXQYKEqbf+FA3NHhz8qBTr5wyEIEqoqmlR3rPbLJoro5t3fYT1cwbS1RNiSrziAoyu1htdXeQ5tn0+GwkcxyjLfddzub262zY2u1V3Jy0cg1Xb/6Vpz2tnD1CV8a1jpzBiwAWq4+ZnZ8Bi4TRti2WA//6ffwb8HvXapdlnualNExJ9ogT85+szmjug1/bspv9b7zfgaLubwWqy6o0ecgms1pT/c4mQqEr5OxyCwfwXOVkW7DnwJeaN7626mrFkSh+88u6XeOXdLzV3J5ZM6YOX9n2B5laPZrt543ujqdUDCRKeeOkj5erpkGvOR+fcTLAsg2VPHcbyp45g6dbDqG1s1S2X3P35Xj3JtWegrqkVlTXNqGlwRf0KNkkO8YoLMLpaz7KMbrvgWAYF+Vno2sWOgvZJLv3Xy7FbsWxaX80+M2286u6k/Bn9P7MkSZpt7ZkZquNa2q+u+1+ZbPUYf481DS5Vu/PfNi/b/J0LuiJKSHTl2jPQ80dO1R3Qnj9ygmOh83veFxzHaPorCRLmbDiAWY8cwJwNB/DKP76mWCtCIiDlh+w8p383oqHZgy9O1eAPr5/A7DFX4jxnDryCiD+/8yXefv80gLaUtyXtdyd4lkGGlcVdN10KlgH++PcTuhkvcrOs2DBvMLyC+iqm/1XhmvpWU1dGKZ0mMRKvuACjq/V1Ta268RFzxvVSb89As96f3j6J+8b+TLNPUZTaMle1t0H5M+rdRQn17oHR9yiK6kB0vXZHdy4ISRxG7fFMXQsOflSquXNxbc+umv5qyojLVftMp1grQqIpZVuRKEqoaXCB5/Wf6dz//ncA2oJQO+dmwpmXCa8g4pP/O6OsN+Sa89ElNxNdO9vhzM+Cw952ddLCs7htsDr+4rbBFyPDwqJTbiY6ObRXMf2vCu9//ztNufSujBo9NlPX1Br9L5EktHjGBehdrc+1Z+jGR/iXhzdoP3r75HkWzvwspQ3m59h023N+ji3kuwd63+PyaX2V7DVAx+2O7lwkJ9HrNrUsXbgFj6llySjDwmLwVd1Vd0uvv6o7srOsqn5owo2XwmJh0zbWipBoSsk7HP53BUZffxFK7hsIQWq7SpqXnYE543phxmj1FUmzVypbPSKe/5v6DsfzfzuBRZOvMSyT3v5zMq0Bj0fpNImRRLy6buFZzB5zJWxWHi63V/fKoDuE9iPjeRYXFDlUdz3yc2zgw7gCqfc9iqKkSUtN7S61sLwVX68do1p20fI9cSpN/KXCLOVGTwTYrDw++E+5Krbj3Q9P44Zre2j6EpYNPCcPISR4KTng8L8r8Mo/vsbhT35QBYHmtz/PHUpaUQvPoqbBhXU7jinLzNx21cv1Hyi4N93TaZKOmZk/Ilbqmlqxctt7mrqqlyI2lPYjk+96+Ao3PbD/91jT4KJ2R0iS6Shl91vHTuGFN79Q1i3Iz8SwPudr+hIg8O8yISR4KfnrafauQKhpRWP5KEu6p9MkycNsu4t0nY5GemBqd4QkH6M+yChdbl42tWdCYiUl73CYvSsQalrRWD7KkoiPzRCix2y7i3SdjkZ6YGp3hCQfoz7I7RHx0r7PVY9xvrTvc8wZ10t52oEQEl0pOeCQr076P8fpf3UynPiIWD7KkkiPzRBixGy7AyJbp6MV50TtzpxQZg+Px8zjotcNltdO4BbsOslGb+byVJ3N3KgPkiTg6GcVmrisGaMpJouQWEnJAYfZq5MUH0FI5MTrrgC14/gKZcAgzzy+d+OoKJRIn3+QuF6AuJl1kk0qBIOb1VHKbuojCImvlG1tZlJV0nPahERWPFLEUjsmhMiMUnZTH0FIfKXkHQ6z6DltQpIftWNCSEeojyAk/tJ6wAHQc9qEpAJqx7Ejx120egRkJHDAbTzjMbrOfRqZuc64HNuMVI3h6Aj1EYTEV9oPOAghhJjnG38R6ziMYMQzHiMz15nQcRPpFNdBCEkMKRvDQQghhBBCCIk/GnAQQgghhBBCoiYpH6kSBAEAUF5eHueSkGRUVFQEno9N1ae6SsIRq7oaTD0tLi4O61ilpaWqfZSWloa1PyPhlpO0MXN+qE8lySKWdZWoMZIkSfEuRLA++OADTJw4Md7FIElq//79MftjhOoqCUes6irVUxIO6lNJsohlXSVqSTngcLlcOH78OJxOJzgucbOkxEp5eTkmTpyIF154AUVFRfEuTtwF+j5ieYXDTF1NtfOXSp8n3p8lVnU1nn1qvL9jM5KhjED8yhnPPjVZzo1ZqfR5EvGz0B2O+EnKb91ms+Gaa66JdzESTlFREY3cfSTC9xFMXU2E8kZSKn2eVPosehKhT02G7zgZyggkTzlDYVRXU+0zp9LnSaXPQkJHQeOEEEIIIYSQqKEBByGEEEIIISRqaMBBCCGEEEIIiRruoYceeijehSDhy8jIQL9+/ZCRkRHvoiSEZPs+kq28gaTS50mlz5KokuE7ToYyAslTzkhKtc+cSp8nlT4LCU9SZqkihBBCCCGEJAd6pIoQQgghhBASNTTgIIQQQgghhEQNDTgIIYQQQgghUUMDDkIIIYQQQkjU0ICDEEIIIYQQEjU04CCEEEIIIYREDQ04CCGEEEIIIVFDAw5CCCGEEEJI1NCAgxBCCCGEEBI1NOAghBBCCCGERA0NOAghhBBCCCFRQwMOQgghhBBCSNTQgIMQQgghhBASNTTgIIQQQgghhEQNDTgIIYQQQgghUUMDDkIIIYQQQkjUJOWAw+v1orS0FF6vN95FIaRDVFdJMqB6SpIF1VVCklNSDjjKy8sxbNgwlJeXx7sohHSI6ipJBlRPSbKgukpIckrKAQchhBBCCCEkOdCAgxBCCCGEEBI1NOAghBBCCCGERA0NOAghhBBCCCFRQwMOQgghhBBCSNTw0dz5li1b8Pe//x0AcP3112PRokWa9/fs2QOHwwEAuPPOOzFx4sRoFokQQgghhBASQ1EbcBw5cgSHDh3CK6+8AoZhcO+99+Ktt97CDTfcoKxz/PhxPP744+jdu3e0ikHaiZKIelcjPKIHFtYChy0bLBP4Bleo20V6HyT1eUUvalvq4ZUE8AyHvEwHeNZcF0V1jKQLo7ouLxclAaIkQYJEbUGH2yPAauGU/xNCYiNqAw6n04klS5bAarUCAC6++GKUlZWp1jl+/Di2b9+O06dPo0+fPli8eDEyMjKiVaS0JUoiTteV4dGDT6GquRrOrE5YNGg2uud26/CHKNTtIr0Pkvq8ohenasuw8fAzSj1ZMGAmzs/rFnDQQXWMpAujun6eowjf15dj16d7ccuPh+DpY3+gtmDAauEwcsFr2LtxVLyLQkhaiVoPdMkll6BXr14AgG+//Ravv/46rr/+euX9pqYmXHbZZVi8eDFeeeUV1NfXY+vWrZr91NfXo7S0VPUfTfgTnHpXo/IDBQBVzdV49OBTqHc1RmW7SO8jWVBdDV1tS70y2ADa6snGw8+gtqU+4LbpVMcigepp8jKq67Ut9Xj04FP4+YXXKYMN3/eTtS1QXSUkdUQ1hgMAvvzyS8ycOROLFy/GBRdcoCy32+3Yvn278vqee+7BsmXLMH/+fNX2O3fuxJYtW6JdzJTmET3KD5CsqrkaHtEble0ivY9kQXU1dF5J0K0ngiQE3Dad6lgkUD1NXkZ1XWhvP9nWrJRqC1RXCUkdUR1wfPjhh5g3bx6WLVuGESNGqN4rKyvDkSNHMHbsWACAJEngeW1xpkyZgtGjR6uWlZeXU3B5ECysBc6sTqofImdWJ1gCPKoS6naR3keyoLoaOp7hdOsJxwR+xjqd6lgkUD1NXkZ1nWtvP43u5pRqC1RXCUkdUeuFfvjhB8yZMwebNm3Cddddp3nfZrNhw4YN6NevH4qLi/HCCy+oAsplDodDyWKVrsINiHXYsrH8+nkob6yCjbfC5XWjKNsJhy074HYLB83GBp/nhRcOmh1wO/99LBo0W/PMcTD7SBapWlfDrX9mts/LdGDBgJmaGI5cWw5qW+o73Dad6lgkpGo9TUbBtC2v6IUoibh/4Cw8duhpVV3Py3Rg0aDZ2PXpXszqO0kTw5GsbYHqKiGpI2oDjt/97ndobW3F+vXrlWUTJkzAgQMHMG/ePPTs2ROrV6/G7Nmz4fF4cNVVV2HatGnRKk7SilRArFv04HcfvqgaOJhhZS341dV3KQMVK2sJqvwsw6J7bjesHb4YHtELC8tT1pQkEm79M7s9z/I4P68bVg1dAEESwDEccm05KGuoCLgt1TGSjIJpW75JFfIyc/Grq+9C15wC2PgM5NpylDYw45qJECURq4YuoCxVhJCEwkiSJMW7EMEqLS3FsGHDsH//fhQXF8e7OFFV21KP5W8/orlFvnb4YuRlmrvyE+o+InHsdJfsdTXcOhDO9lT/YifZ62kyCqZ+n2mqxsoDGzXrrhq6AF3snWJW5kQQibpKWaoIiT267JHg4hm4TcG4JNw6EM72VP9IKgumfoeTVIEQQhIBDTgSnBwk6CvUwO1g9xGJY5PkFm4dCGd7qn8klQVTv+WkCv7rmkmqQAghiYB+uRNcOAGxvgGJDw75DXZ+tBsflH0CZ1Yn3D9wFkRJQG1LveEzvv7B5hKAQnsXeEQPalvqYbdmos7VENLM0MEKZxZqos9MwGqw9c//POXacnQTFmRZbKhqqoYgCuBYDnm2HFg4i+bYoSQ7ICQZ+NbvXFsObHwGOIaDV/SiurkGHMtDlER4RS94lseSwXOx/p9bVEkVrJwVXtGLxtZmpR1nZ2SpXocSwxFuoghCCPFHf7EluFADYvUCEu8fOAv3Xj0BHtGrGnx0FATsH2x+X7/J+N9PXkNtSx0WDJiJ3Z/9TdmP2ZmhgxXOLNREn9mA1WDqn955un/gTFi5DFUdWjZ4HkobyrHxkPp8ds/tqhl0hJrsgJBk4BY9ePurg7jlx0Pw+OFtSj2f3/9eeEQBW/71nKruPzxsIdyCB4Ik4C+fv4WG1kaMvXyEpm/07ZeDTTISqUQlhBDii3qPJMAyLPIyHXDaOyEv02Gq09ebkfaxQ09DlCSsfud/8EHZJ8pyo5lo612NSkpced2tR5/HqEtvVGaC/vmF1ynvmZ0ZOljhzEJN9AUzO7fZ+qd3nh479AwqGqtUyyqaqpTBhrxs4+FnUOtq0JTRv/5tSOJZkwnxJddvvdnB61ublMGGvH4LFCoAACAASURBVGzDwacgSiIefvd/MP/vq/DON0fw8wuv0+0bffvlYGcaD6ZvIIQQs2jAkaICzUjrv1wvUNFoH9nWLM2/ffcfaRQwGXnRCMg2Ok823qpaZuOt+udTVJ9PChonqUyu33qzgxu2Eb82ZjSzuH+/HIskI4QQ0hEacKQoo4BEziD4UC9Q0Wgfje5mzb999x9pFDAZedEIyDY6Ty6vW7XM5XXrn09WfT4paJykMrl+y7OD+zJsI35tTG9bvX45FklGCCGkIzTgSFFysK/8w+E/I63/cr1AXL193NdvMl77fJ/yrPC737ynvLdgwMyozI8gz0LtW45oHStdGNWPcAKy9c7T/QNnojDbqVpWmO3EgoE659OWE/UyEpIo5Pr97jfvYVbfSap67siwY+610wL23+9+855u3+jbLwfbZqjdEUKigSb+S2HnMo2og331Mgk1uVt0M5L47oNnObAMC7fggYXllSxV8szQ0cwc5RE8qHU1dJjVKBElcl01qh/G6wXOWKN3njiW0xxHEAVT59NsGUP73JSBR5bI9TSVyXVRlESIkggJEhgwYBkWHMu1Z6kSYGF5VfaptnUYiJIEnuXgFQVlZvFz6wWXZMS3TYSyj1ihif8ISU50jzSFycG+vkRJxPf15aoMJB1lNdHbh69YzHIrSiLKGiooa0qEBTq3QHAZazo6T/7HYTkWThN1x0wZg0EZeEgiMVu/9eqtfr+dH3SboTZBCIkF6k3SjF4GknCzmkQbZU2Jn2C++2Q4T8lQRkL8RbPfpjZBCIkFGnCkmUCZp+TXiZSRhLKmxE8w330ynKdkKCMh/qLZb1ObIITEAg040kygzFPy60TKSEJZU+InmO8+Gc5TMpSREH/R7LepTRBCYoEGHGnGYcvGQr8MJPcPNM5qIkoialvqUdV0FjUtdahzNaCq6Wxb0Hl78Ln8WpREzTa+y4Phuw+GYTRlpqwpsaFXXxZ2kNXswSHzsemWlfifWx7CpltW4sEh85GdkWWqPkSi3pj5PJSBhyQD3/YgSF4sHfxffv32LDis2bik84XKa4Zhgmo3oiSCYRjcP3AWtQlCSFTRJYw0ZGUt+NXVd8HGW+HyumHlMjCzz92Y1p59Ss5IohdMeF+/yfjfT15DbkYOxl4+QpnlVv6ROs9RpAlKDzYAUe+4y6+fhzXDFykZWxIpa0qq09QXVj87mCAKaHI3q+rEksFzUVpXjg2HOq4PsQpcZRkW3XO7Ye3wxQmZgYcQwChIfAb+q980iBDh8rohiF785Yu3cO/VE9DqcePZD19EbUud6Xbje4y8zFz86uq70DWnADY+A7m2HGoThJCIogFHmql3NWLtPzarntl1ZnXC2uGLNVmD9IIJtx59HlN6jwMA5Q9L+b1HDz6FVUMX6AYgrh2+2HTmFL3jrv3HZt0ykujqqL74n89aV4OmTlQ1ncXvPnwxYH0wClwNpt6YFenMV4REmn6Q+DZM6T0Ojx1+BkBbO5zSexweO/QMpvQehy/PfgMAptuN7zGqmqux/uCTStumwQYhJNJowJFmIhEELAcq6r0nSELYAYgUxJg4gjkXgqg99zbeamp7OueEnGM2SDzbmhVy8Di1OUJILJm6jOF2u/HNN9/g1KlT8Hg80S4TiaJIBAE3upvR6G7WfY9juLADECmIMXEEcy44VnvuXV63qe3pnBNyjtkgcbkfDiV4nNocISSWOhxwfP7555g7dy769u2LSZMmYfz48ejXrx8WLFiAkydPBtz5li1bMGLECIwYMQKPPvqo5v0TJ05gzJgxuOmmm7B8+XJ4vel7ZcU/YNYoIDtcekGzy66fB0HyoryxCmeaquFtv8Klt+59/Sbjtc/34d1v3sOCATM1gYZ5mY6wg3L1jrv8+nmQIOl+H7EINk52oX5HwQSN59lysGTwXCwZNAcPDZmPJYPmoGtOIRYO1NYH/0Dy7IysmAVzU30h8eJb9840VaO6uQZnmqpR21KHM03VHbaH/x4wQ5XcY1bfSUo/bJT0w+jY/se4pPOFWDJoDlb8/NeQIFGbIIREHCNJkqT3xpNPPokPP/wQY8eOxfXXXw+73Q4AaGxsxKFDh/Diiy+iT58+mDt3ru6Ojxw5gs2bN+P5558HwzC49957cffdd+OGG25Q1rn11luxZs0a9OrVC8uWLcMVV1yBX/7ylwELXVpaimHDhmH//v0oLi4O5XMnFPOzyEYmgFaURNS7GuERvbByPKpbavHYoWdUxz4/rxt4llety7McWIaFuz24PDsjC42tzZrgW99tQg3KVZfRghpXHTboBBQDSOhZchOhroYTkO0VvfihoQpVTWeVoHGnvTO65jjB+10JFSURp2rLVAHiCwfORnFukaqeZGdk6SYWOM9RpFufEuW7SGWJUE9TnV7dm9V3Ev5f2Wfo3+MaPH54m0F78IABA57l4BUFSJDAgAHLsGAZ1rAfDnRs+RhN7pb234Cnk6JNRKKujlzwGvZuHBXhkhFCOmLYm1x66aX4/e9/j1/84hfKYAMAsrOzcfPNN2Pnzp249NJLDXfsdDqxZMkSWK1WWCwWXHzxxSgrK1Pe//777+FyudCrVy8AwB133IE33nhDs5/6+nqUlpaq/isvLw/pwyaqWM/+LQfNOu2d4BG8ymDD99i1LfWadfMzc5Fry4HT3gl5mQ7wLK+8l5fpUH6cfLfxXR5qGSVJUgYb/t9HIs2Sm6h1NZzvqLalHuv/uQXrDz6Jh97ZhPUHn8T6f25R6of/ceTBhnycDYeeQmNrs6o+NLY265bHf71o/LGTSPUlXhK1nqY6vbr39LE/YMhF/ZXBhrxc3R46o4u9E/Iyc9HF3kl53Skrr8N+ONCx5WNIkqQMNnzfS4Q2QXWVkNRh+LDmsGHDAm48fPhww/cuueQS5d/ffvstXn/9dbz00kvKssrKSjidTuW10+lERUWFZj87d+7Eli1bApYlmcVz9m+vQZC3IAkRP1aoOg5ulBIm8DFR62o4waHB1A+zx4lnsCoFyiZuPU11RnWPZdio18lk6UP9UV0lJHUEjA47evQotm3bhrq6OtXy3bt3mzrAl19+iZkzZ2Lx4sW44IILlOV6T3IxDKNZNmXKFIwePVq1rLy8HBMnTjR1/GQgB+/5px6NxezffHuQt/+xOYaL+LFCZfT9yN9HR+/FUqLW1UDfX0eCqR9mjxNOecIVz2MnikStp6nOqO6Jkhj1Opksfag/qquEpI6APcqKFSswadIknH/++UHv/MMPP8S8efOwbNkyjBgxQvVeYWEhzpw5o7yuqqpCQUGBZh8OhwMOR2rnzJeDpPViOADgmm5XYnLvsWgVWnGmqVq5jR6MczERHvAs3x6L4YaVs+D+gTM1MRyxmqfAt1wW1qL7/LHe9+MbGNnRe7GUqHXVYcvG8uvnobyxSonDKMp26n5HHsGDWlcDBFEAx3JwZNixZPBcTQyHXv0IdJ6CXS8a4nnsRJGo9TTV+da9vMxcjPnpL1CU7QTPcbh/4CxVDIXvrOGhx7+d61OTpQ/1R3WVkNRhGDQumzBhgupRKLN++OEHjB49Gps2bcJ1112nu86tt96KVatW4eqrr8aKFStwwQUX4N577w2471QMcPQPtJYDAUVJQF1ro+rHyDeo2+y+jWYMr22pw7Lr58HGW+AVBXAMF9KAJhTBBPB2FIgeiSD1aEmEuipKIk7VlamC7hcOmo3z/b5nj+DB6bofVDOFLxg4ExlsBtb9c3OH2/oey8y5iOc5S+T6Ei+JUE/TgSiJaGht0gRpz+9/L1oFDzpl5qG6pRb/+8mrQc0a7rt/oz4VQFL2of4oaJyQ5BSwRxk6dCheeOEFnDp1CmVlZcp/gfzud79Da2sr1q9fj1GjRmHUqFF48cUXMX36dHz66acAgMceewwlJSW45ZZb0NLSgsmTJ4f/iZKUf6C1HAgo6gT0+QZ1m2E0Y/ioS29EVXM11v1jMziGR2G2E13snWIy2DAql1GwYkeB6JEIUk9l9a5GTdD9Bp3vWW+m8I2HnkFFU1XAbWVmz0U8zxnVFxIvLMPqBmlvOvIsmtzNWPPuE2hyN+PLs9+EFLzdUZ9KfSghJJ4C/mVZXV2Nxx9/HJmZmcoyhmHw73//u8PtVqxYgRUrVmiW33XXXcq/L730UtOxIOkqEkHdZmYMj0eAIAXwxobZ71lvpvCq5mrYeGvAbQkh5nTUH4ebLIT6VEJIogo44HjjjTdw6NAhdOnSJRblIX4iEdQdKCg9XgGCFMAbG2a/Z3mmcP/1XF63aj06R4SErqP+ONxkIdSnEkISVcD7pp07d0anTp1iURaiIy/ToZnRO9ig7o5mDI9rkLVOuRIlWDGVmP2e82w52ro2cCYKs510jgiJEL32GOys4cHsm9orISQRBAwaf+ihh/D5559jyJAhsFrPPVoxbdq0qBfOSLoFOHpFL2pb6iFIAiysBZBEeEUBFpZDji0Hje4WTfYpC2vxmYHWAytngUfwwisK4FkOHMuiVfCAZzjk2nLQ1L4P/0xRZrJIBWKUIUtdxsQPVgxFotRV3zrUUWIAt9eNutZGJUtVbkY2WJbVbAu0TQrolQTwPvvT257neE0dkiQR9S318LTXY0emAxxdhY2bRKmnqcorZ3+TBLAMB45hIUgiOIaBIEkQ25fnZmSj2eNS9YeiJCptjWM4WFgOAKPq3337UlESIEoSJEia34FQ+/BEQkHjhCSngL/wra2tuPDCC/Htt9/GoDhED8/y6GLvBEH04nTt99jQPitt292OGdj92ev4oOwTTfYpObWu73v//PYYBl/QF1uPPq9JwSuv55vVxGwWKSOBMmQFuz8SPFES8X19ecDzKEoifmisVK23/Pp5cIseTYYrjuGw/p9bVHWom6MAZfWV6ixXA2bCbs3C6nc2qba3MBzW+Wy/cMAMdM87jwYdJOV4BQ9O1ZVho0+/PavvJPy/ss8woEcfVXvxb5de0YtTtWWqdf57wAx8UfkVLi34kbL8mm5XYuzlIzT7Os+Ra6rtE0JItAXscUpKSnDHHXegpKQEixcvxrBhw1BSUhKLshE/9S31ymADkDNWbcPPL7xOee2bfWrj4Wc07438yXBlsHFuH+r15KwmwWSRMixzgAxZwe6PBM/sedRbr7yxSjfDVVXTWU0damht1ma5OvwMvKJXs32F3/YbDm9DfRCZ1whJFm3Z39T99tPH/oAhF/XXtBf/dlnbUq9Z5/HD23B18ZWq5T+/8DrdfdW21IfdhxNCSCQEHHBs2rQJmzdvBgC4XC5s27YNW7dujXrBiJbHIIuQf1YT3+xT/u+xDGtqHx7RG5GMJ4maISudmD2PeuvZeKvpzFVGWa4YMKa294rmM68Rkiy8Bu3CqC/2bZdGWQolSVItlzNc+a8nGGxPfS4hJNYCDjj279+P3//+9wCAoqIi/PGPf8Trr78e9YIRLUt7FiFfellNfLNP+b8nSqKpfVhYXsl44r9uKFlTjI5HGVSiz+x51FvP5XXrbquXuYozqJ8SJM0yve151nzmNUKSBW/QLoz6Yt92KWcp9F+HYRjVcjnDlf96nMH21OcSQmIt4IDD4/HAYrEory0WCxiG6WCL1CQH7lU1nUVtSz1ESYx5GRyZDiwcMMMvY9UMVVYT3+xT/hlP7us3GXu/eBv39ZusyXqllxklEhlPEjVDVjpx2LKx0O8cLNT53vXOVVG2U3dbp72zpg7lZGTpZlTjWV6zfaHf9gsHzIAjiMxrhCQivd+Jtuxv6n57Vt9JeOfrI5r24t8f6mUp/O8BM/Bh6Seq5XKGK/995WU6KGsVISQhBMxStXTpUrjdbowdOxYMw+DVV18Fz/NYs2ZNrMqoEeuMKnqBz/EKvBNEL+pb6pVsU+eyVHnBs1x7BigPLCyvygDFsxwkSVIylTAM057pijfMFCVJIlqry1FXXwnRagPrdiHXUYCMTkVggvjc57JUacuY7BlTAkmE7D+iJKK8oQrljVWw8Va4vG4UZTtRlOPUfPe+50o+PwA0y+Q/rHwzV3EMi9amOjQwIgRRBMeyyJFYWLJy0OBXv+QsVXI9pixV8ZUI9TTZdfQ7IYqCKksVA0DytEL69BCk4kvAdO4Kq8UGhy1H0yZ9M8yxulmqvB3243ptOpn7XMpSRUhyCvgL/8ADD2Dz5s0oKSkBz/O47rrrMHfu3FiULWEYBd2uHb44qPkwIoFjeeTb1bfI8zItBmvDdPn01hOa6lD54mp466qUZa25TnSbWgI+O99kiQGWYWP+PZFz6l2NWPuPzZrJwPTqr9G50luvi1899DbWoHLHUlV9aWmvL3n+9YVhNfWYkGQW6HdCbi/exhqU+bUTPteJTlNLdAcCcpZCPf7tUq/tUv9LCEkEhgMOt9sNq9WKrKwsLFmypMN1Ul0kgqeTkSR4VT+KAOCtq4IkpPbnTjWxqr9UX0g6M9vOqJ0QQtKR4X3V2bNnY9++fRBFbayCJEn4+9//jlmzZkW1cIkiEsHTyYjhePC5TtUyPtcJhkvtz51qYlV/qb6QdGa2nVE7IYSkI8MebvPmzXjsscewfv169OvXDz169IAgCCgtLcXRo0cxcOBAPPHEE7Esa9zIwbT+z+YmQuCdKHohNNQCohdgeXA5eWB1/pCUJBFCUx0kwQuG48HZcwPGYXD2XBSOW4KKP62Ht64KfK4TheOWgLPnBlfGCMxWTkIXTP0NpZ7IOHsuCu56UBPzY7a+6B0bQMjlISSW9NrZwkGzYRcleOqqlPrr269y9jzkDRoHS6e2iVYlSTRdv6lfJYQkE8MBh91ux8qVK1FeXo79+/fj66+/BsMwuOKKK/Cb3/wGhYWFsSxnXLEMi+653bB2+OKECrwTRS88FadQsefRcwOCMYtgKTxfNeiQJBHuylOagYO14PwOf9wYhoW14Hx0m1oS8h98iRRwn67M1t9Q64myPYAKTsSjx/+kPtcAAuW10z32XQ8AXk/I5SEklhgAhQKLB68Yd27A7QHO7HsKLV++r6q/1oLz0W3aIxAazqJi96NB12/qVwkhySZgz1RUVISJEyfigQcewIoVKzBx4sS0GmzI5MA7p70T8jIdCdGpCw21ymADaHsOuGLPo213PHzXa6pT/mhT1vvTeghNdQGPwTAs+Ox8WHKd4LPzg/5DLxKzlZPwmam/4dQTILxzrXdsb015WOUhJJbkJBtN//swWnYsR9P/PozKXWvg+NkQAOr6yzAsIInKYMP//UCoXyWEJJv4/9VMQifqBx8igYIU0zXgPhmFW0/COdd6x2YtNgquJUnDqP2wPo8u+tbfcNob9auEkGRDA45kxuoHHyKBghTTNeA+GYVbT8I513rHFj0uCq4lScOo/Yg+dx1862847Y36VUJIsol679TY2IgJEybg6aef1kzSs2XLFuzZswcOR1uO8DvvvBMTJ06MdpHipqOAXLPBuqr1LBkoGL8CdY1nzj0znN0FXE6eZt2iKevQKLjhZQBOFGFH20SA3sYasFk5EJsbgi6XmTIncsB9spEnffSIAixBTpZn5lwZJQlAZjZqmqpVx2UZVrM/hy0bCwfOxoZDPkGzA2cj25oJT90ZVWIDxm97NitHc2w+vygiSQsIiTS99qTbfiasgNhUh653r24bQHcphiQKcNedQRPHwj51DUSvB97WJrDNjaaTLFC/SghJNgH/WnG5XNi3bx+qq6vhOyn5tGnTAu78448/xooVK/Dtt9/qvn/8+HE8/vjj6N27t/kSJ6mOAnIBmArW9d9Hdr/b0HzVEGzwCdJdOGAmuvutm3FBT7RcNwIbjmw/t17/Gcj8x26IzQ3oNOhOdeC5iXKZLXOiBtwnG0H04nTt99hweJvPuZ6B7nnnBRx0mA0G10sSgMxslNaV+R13JookXpkUUhkIdO6GIq+kDpqVeAhVpajY/YgqsQEy7aj440rV9hZnsSZBAYCwkhYQEmlG7YnrXASwHDrfPB2sxQaJZQGPG1V7f+tT9xei5rNDaPxpH+z+6h+45cdD8PSxPwSdZIH6VUJIsgnYOy1YsAA7duzA559/jpMnTyr/mfHyyy9j5cqVKCgo0H3/+PHj2L59O0aOHInVq1ejtbVVs059fT1KS0tV/5WXl5s6fiLpKCDXbLCu/3rc1cOx4fAzqsDBDYefQX1LvWpdfsDtymBDWe/INvADbofjZ0O0gecmyhVMgHEiBtxHQzTran1LvfJHPyCf622ob6kPuG0w58o/SUCDq0HnuM+grr5Ssz+pqQ6Vu9aqgmaF6u+VwYay7p5HwXg9mu3F5gZNgoJwkxYQrVTpU+PFqD1JTQ2o2LUWFbvW4Yc/PgipuV4nqccGcFcPx8YPX8DPL7xOGWwAwQd+p0O/SnWVkNQR8A7HyZMn8eabb4Jlg+/M1q5da/heU1MTLrvsMixevBjnnXcelixZgq1bt2L+/Pmq9Xbu3IktW7YEfexEEyhA0EzwoP8+BJbTDRz0igIk8dw+jdYTWA6sLTvkclFAr1o066pHFAzPdSDhBafqH1e02jT7gyiYDvwGw2iWpXPdiaVU6VPjxag9+dd/o75V7o+zrVkU+B0A1VVCUkfAAUfnzp3h9XphtVojemC73Y7t27crr++55x4sW7ZMM+CYMmUKRo8erVpWXl6edLEecoCg7w+Qb4BgR+8Z7YMTBTizOql+tJxZncCzHBiGUdY1Wo8TBYiuxpDLZabM6SSaddXCcobnOpBAdS+U47Jul2q9tmQFnOY4cuC3/7Hh83hmMOUh4UuVPjVejNqTf/036lvl/rjR3azbtijw+xyqq4SkDsPbFs899xyee+45OJ1OTJo0Cdu2bVOWPffcc2EfuKysDLt371ZeS5IEntd2tA6HA8XFxar/ioqKwj5+rMkBhXJWEt8A2I7e62gfwodvY+GAGUq2Evm5fkemQ7Wu9/CrWNh/unq9/tPhPfwq6j9+B4VjFgVdLrNlTifRrKuOTIfhuQ4knHOlf9yZyHUUaPbH2HNROGahajmfW4DCsYvV645ZBIm3UN2Jk1TpU+PFqD0x9hxV/W/rWxf61f2FED58Gwuunoh3v3kPs/pOUrUtCvxWo7pKSOpgJMnvUmO7pUuXdrhhSUmJ6YMMHToUzz//vCpLVXV1NW655Rbs3r0bxcXFWL58OXr06IGZM2cG3F9paSmGDRuG/fv3azJfxVuoWZ1CylLVHtTb4GqAVxRg5SwQJBFe0QsLwyHHlgO4mtoeVbFkoFHywisK4FkO2QwPeFqVDEFKlireCkiisn8mMxtiY11bhiGOB8NZIHnd2u04HmBY5T0K7m0TyboqZ6mSz2G4WaoktE0g5hE9sLAWOGzZYADNeoIooKGlHl5JAM9wyMl0gGWYtgkmfTJPsSwPr7cVUlM9IAoAy4GxO8AwLMTGWmUZm50HxmB7Eh+J3KfGSzD9tdJPsmxbPVd2IgK+/WD7hH8Sy6GJkSCxLERJggRJaYNGsRiiJGraayrGbQQSibo6csFr2LtxVIRLRgjpiOEvvDygePvttzF8+HDVe6+++mrIB5w+fTrmzZuHnj17YvXq1Zg9ezY8Hg+uuuoqU5mvElmgbEByAKyejt4LtF6+vRO8gkcnm9AMFOd2g4WztK3XwX7Z7Hz98o9ZhOqDL0NsqkWnIXej6q9bQs5YRcLDsTzy7Z0Cr6jDv96IkojTdWWatJqFAqvOPnXXA4DXgwafc5s1bgkE3oKKFx9WnW++y3kQqr5XZzwzyEgFne2pvpBEEUxfLopeeCpOKfU+85I+6DRoHCr2bFC2LbhjIWoO/QktX76v7CsviPpu1F6753ZLy0EHIST5GPZUBw4cwL59+7B+/Xq89dZb2LdvH/bt24fXX38dmzZtCuogBw4cUK5EbN++HT179gQA3HTTTfjrX/+KN998EyUlJRGPE4m1YLIBRVqDQRajBhNZjGS65d/zKBw/G4K8/qOVwYbyXggZq0hiqHc1Kn+8AOcy5Phnn/LWlOueW29NuTbLVGOtTlYe/YxUettTfSGJIpg+TWhQ1/u2zH8bVNtW/nkDHD8bEnBfRozaq9mMVoQQEm+GdzhOnDiBf/3rXzh79iyef/75cxvwPH71q1/FpHDJJpxsQOHySAZZjKTAWYxkRuVn258ppoxVqcMjekxlnzLKMsVazGWpMspIpbc91ReSKILqy0X1ukbZqVif2Ixg67tRe6WMVoSQZGE44JgzZw7mzJmDF154gTJCmBRONqBwWRiDLEZM4CxGMqPyi+1X0ShjVeqwsBZT2aeMskyJHnNZqowyUultT/WFJIqg+nJWva5RdirR525EsPXdqL1SRitCSLIw7K3kOA273a4bs3H77bdHr1RJSs5eopmB1iD7jih6zwXOWqyAILYH0XJg7DmQmhpUgbZceyyGnpz2bEL+M0JnMzw8dVWaoG69AG/d8vvEcDhvnauJ4ZA/WzCfm0SOqg51EHztH+Sak5WDRYNma54Jz4UV2eOXgbXY2gYbXYpReNcD8NaUn1uWXwTY7Og+5yklKFbkebCZOSgcuwgVu31iOMaey0jlH8Phv4zqC4kHveBw377Q1qMn8q4bBcaSAUnwwlNTDrT3oXKAeNHEhwDBC9Hjguj1oHDMIlUskxzDAYSWlc1hy9Ztr5TRihCSLAyzVM2aNQsAUFVVha+//hrXXnsteJ7H0aNHcdlll2Hnzp0xLaivRM6oYjbblG+gIWfPUwVk5/S7DY7LB6qCDgvHLARfcL7hoEOSRLTWnUGT5IXAsuABZHm8qHxpjbIP561zUf3OHyG0Dx7kf6uCv6t/UP9x2aUYHG/VzWAVSpatdBPNuuofrCoPEC2F56sGHUYBsLyzGA2tzfCIXlhYHjkZWfBWlarXu+sBwNPqN4hYDHA8KnatVdVPznkehLqzmsGJJb9Qlc1M/kOL6kviSOQ+NZo6Cg4HAKGlEUJdFaoP/gl5fX6BGxlfqAAAIABJREFUqr9tPdef3vZrMByPylc2nls24j7Uvv86Og2ZCLG5HgxvAZuRBSYjExBFABIYzhJSfT+XpaqtvVKWKspSRUgyMbzD8fTTTwMAZsyYgU2bNuH889s64LKyMjzwwAOxKV0SMpttyjfQsPMN01QB2bk/G4ry9j/mADn4dgO6TnoYXHtOd83+mupQ+ccHlW0Kxy5C5VvPqfZR9dct6HzDNFTsflT174o/rUe3qW1ZyeTMQTI+14luU0tgMThusJ+bRI5/sKocpN110hqwuV3OrWcQANttagnyfM6Zt7FGs563phxn39iu3nb3I+h883Td+mlUf/TqBtUXEm8dtQ0+Ox+SpxUVeza09dHtgw15vaq/PKFpB1V/29rWr+5aq/SvQMftwCyWYZFnYt4dQghJRAEfAP3hhx+UwQYAdOvWDeXl5VEtVFrwCTTUBBmyrH7wrWgcAO4f5BgocNH/3xT8nYRE/cBW+AWSmg2A1VsvEkHjVH9IogrYNtrrtGF/qtMO5HXDCRInhJBUE/B+rNPpxObNm3H69GmcPn0ajz32GLp37x6LsqW29kBD4FyQoUIU1a9xLijXiBzkqOzCf5/QBoD7/pvheM0+fN8jCYjVP1/wi+Ewe1711pODxv237ShoPNBxCEkUAdtGe5027E912oG8bjhB4oQQkmoCDjjWr1+PL774AqNGjcLtt9+O77//HuvWrYtF2VIal5OHwvHLUTh+GThHZxTccb/yg1b38YG2mI3218oEaiwHT90ZiDqpEDl7LgrvegCF45eh692rwWQ5UDh2kWofzlvnovbIK5p/ywGMcqCk6rgUzJuwuJw8FI5ZpKknXE6eej2D88pkZsNTdwaemnJ46s6AyczWrMfnF2nqUeHYxeBzC/yOuxCMPUdVBwvHL0PhXQ9Q/SEJq6M+T5JEMLwVhROWg8lyoMCvT3be9mtwmQ7VsoIxC9vWvXM56j9+BxndLkHh+GUo+uVKAG0xI4QQko4Mg8YTWSoEOPoHK2Ze0gddbpja9iZvhSh4wHg9bXMYSBJEUcTZv21tC/I2Gxh81wPgMrKUwNxAWark/VAwb+REu66GmqWKycyGt/K0dlZwuwPeyu/UGak4Ht4zpaplXG5nVRY1xu4Ax1loxvkklQp9aqj0+jwAcFeeQvU/XkJev5Go2vtbcPY85A0aB2unbgDLwtNQDZa3gLXYwLAcvA3VqD7wh7Y+euxisDn5EBuq1QkXqD2EjYLGCUlOhvd4f/3rX+OJJ57AyJEjdd/fu3dv1AqVDvyDFVu+fB8/VH57Lnj7jys1wbdKkLfZwOAXHzYV8O2Lgr+TC8vyqnpgxP+8eurO6AacF01YgYpd5+5g8rlOdL55umaZXr3SCzr3DcAlJBHp9XlyXe58wzRU7f1tWwKFuipU7FqHwvHLVIkUgHP9c2vZlwCAit2PoOukNcpgA6D2QAhJb4YDjunTpwMAZaSKkkDBigFn/A4xMJgQAMYB52HMCk51kKQKuS7rBYsbJlLwCxI3amPUHggh6cjwvu4VV1wBANiwYQM+/fRTdO3aFX379lX+I+HpKFjR6D3fIO9QA4MJAWAccB7GrOBUB0mqkOuyXrC4YSIFvyBxozZG7YEQko4CPki6dOlSVFdXY/r06Rg9ejSeeeYZfP3117EoW9KQJBHexhp46qrgbawxFRioG6w4djEkSQIYVvOeKsg7iMBgCthNP3r1URS96gBxu0M34FyeFVxZNm4J+PwiU/WK6iBJBKH0x/7bg2FROHYR6j9+B84R92kTKfjX8zFt6yqvxy1pS+pA7YEQQgAEGTT++uuvY8OGDSgvL8eJEyeiWa4OJVKAY0cz1XYUGChJomZWby4rF2fffLYt6NAv4FuSJMDrDiowmAK+4y/WddUoeYBmtvAxi8A5iyE11asCzhmG1Q2gNVuvqA4mp0TqU8MRan+st70cJG7pUgwIAhiGAWO16bYJNisHYnODpt5Te4g8ChrXEr1usLzV8DUhiSDgvd0///nPOHLkCI4dO4Zu3bph9OjRGDhwYCzKlhQCzVTb4XY6szIrgeEU8E1CoFcfdWcLb088YNEJOA9nVnCqgySeQu2P9baXg8TlfvnsW8+h29QSZcDgvz9WZ//UHkgssLwVX68do7y+aPmeOJaGEH0BBxxr165FVlYWZs6ciZtvvhldugTOiJNOQg2UNdpOb/ZvQswKZrZw/8QDhCS7cBMXdNQvU59MCCGhC3hv9+jRo9i4cSMqKiowY8YM3H777XjkkUdiUbakEGqgrJnAcAouJMEKZrZw/8QDhCS7cBMXdNQvU59MCCGhCzjg4Hkeffv2xY033oihQ4fC5XLh3XffNX2AxsZG3HrrrSgtLdW8d+LECYwZMwY33XQTli9fDq83+a4ehRooq7ed3uzfhARDr17pzhauk3iAkGQXbuIC3X55xH2o//gd6pMJISQMAS/XLFy4EO+99x4KCwtx44034sknn8TFF19saucff/wxVqxYgW+//dZw32vWrEGvXr2wbNkyvPzyy/jlL38Z1AeIFb2ZmsXGOkD0gs1yoNvUkqACAxmGhcVZjK6T1ihBu4zFioI7FihBiL7Ho5nBU1Mw589oRmT/ZXyX89B10sPKLOBsdh4YhlHVNTlA3NtYE3KAOCGJhmFYWAvO1+2PRdELoaH2XH9rd6iSJrDZuZBaGsHa7G1thWUBUQRYFl1uuhfgeXjrqwFI1DYIISRIAQccPXv2xPz589GtW7egd/7yyy9j5cqVWLRokea977//Hi6XC7169QIA3HHHHdi8eXNCDjj8M5/k9LsNjssHomLPBlXWH0vh+brZo4z26akq1c2mAkCTacV561xUv/PHtgxWHawXTEYWEl/BZNQxzD7l9Wi2B8uhYtdaTd30DRA3OjZ4i5LMgOoTSUZ6gdqi6IWn4hQq9vhmaluI6oN/QsuX7yPzkj7oNGicuk8ftwQWZzE8VaWo/sdLyOvzC1T9bSu1DUIICUHAnnLy5MkhDTaAtoDza665Rve9yspKOJ3nnpV1Op2oqKjQrFdfX4/S0lLVf+Xl5SGVJ1T+mU9yfzZU+WECzmX9ERpqQ96nnE1FaKrTfa/qr1uQ1390wPXk90jsBVtXgzl/Rtmn9Lb31lUGrJtGx/bWlFN9SnGJ0KfGmtBQqww2ALldbIDjZ0MAAI6fDdH26X9a37bdn9bD8bMhymBD9T61jahKx7pKSKqKWwSc3vQfDMNolu3cuRNbtmyJRZEMaTKXsGzYWX8CZVMxm8EqnIwsJLKCravBZNQJJvsUa7FplvnXTcNsPDrbUn1KLYnQp8ac2HFWQDkLlf/78nZG71PbiK60rKuEpKi4DTgKCwtx5swZ5XVVVRUKCgo0602ZMgWjR49WLSsvL8fEiROjXkaZnLlE+cERRfVrBJ/1R7NPqLOp6L2nl8Gqo32Q2Aq2rgaqA4HWlbNPaeqJx6XaVq9uGh1bb1uqT6klEfrUmGMN6nt7nypnodLr032zVFFfG1tpWVcJSVGGj1R99tlnHf4XrvPOOw8ZGRn48MMPAQCvvvoqBg8erFnP4XCguLhY9V9RUVHYxw+Gf+aSuo8PoHDMwrCy/nSUTcVsBqtwM7KQyAq2rgZz/gyzT+lsz+cWBKybRsfm84uoPqW4ROhTY43LyUPhGP9MbQtR//E7ANCWhcq/Tx+3pG27cUtQ//E7cI64j9pGjKVjXU1UbsET1HJC/DGS3rNNAIYOHWq8EcNg//79pg8ydOhQPP/88yguLsb06dMxb9489OzZE59//jlWrFiBpqYm/PSnP0VJSQmsVmvA/ZWWlmLYsGHYv38/iouLTZcjHB1lqZKz/pgNGDfaZ0fZpyhLVXIKVFejkaVKkkRVNh6juml2f1SfUl88+tRYM5ulyr/uK+1EktqyVlGWqriKRF0dueA17N04KsIli69YzDR+567ZmmUvj38qKsciqcfwL+QDBw5E7CC++9q+fbvy70svvRS7d++O2HGiSS/zCZcb3qzrevs0814o65HEFMz5M1rXfxnDsGBN1E2z+yMkFbAsr20X/q916j71sYQQEr6Al+Srq6vxl7/8BU1NTZAkCaIo4rvvvsPGjRtjUT5CCCGEEEJIEgs44PjNb34Dm82G//u//0P//v1x5MgRXH311bEoGyGEEEIIISTJBXwAtaysDNu2bcPgwYNx991348UXX8SpU6diUTZCCCGEEBJBotfd4WtCoiHgHY4uXdqecb3gggtw8uRJ3HbbbfB6Kfc4IYQQQkiyYXlrTILMCfEVcMDRuXNnPPvss+jVqxd++9vfIjs7G42NjbEoGyGEEEIIISTJBXykavXq1bBarbjmmmtwxRVXYPPmzVi4cGEsykYIIYQQQghJcgEHHG+++SYmT54MAFi4cCFeffVVfP3111EvGCGEEEIIIST5GT5S9eKLL8LlcmHHjh1obW1Vlns8HvzhD3/AjBkzYlJAQgghhBBCSPIyHHDwPI+TJ0/C5XLh5MmTynKO4/DAAw/EpHCEEEIIIYSQ5GY44Bg3bhzGjRuHt99+G8OHD49lmQghhBBCCCEpImAMx7XXXotVq1ZhypQpqK2txYMPPoimpqZYlI0QQgghhBCS5AIOONauXQuHw4GzZ88iIyMDjY2NePDBB2NRNkIIIYQQQkiSCzjgOHHiBObPnw+e55GZmYnHHnsMJ06ciEXZEpooSqhpcKGyphk1DS6IohTvIhGSFqjtJQc6T4SQULkFT1DLSeILOPEfy6rHJIIgaJalG1GU8F15Pdb8/igqa1pQkJ+JFff0Q48iB1iWiXfxCElZ1PaSA50nQkg4rJwFd+6arVn+8vin4lAaEgkBRw59+vTBhg0b4HK5cPDgQfzXf/0X+vXrF4uyJay6plblhxQAKmtasOb3R1HX1BpgS0JIOKjtJQc6T4QQQnwFHHDcf//9yMrKQk5ODjZt2oSf/OQnWLRoUSzKlrA8XlH5IZVV1rTA4xXjVCJC0gO1veRA54kQQoivgI9UWSwWzJkzB3fffTc4jkN2dnYsypXQLDyLgvxM1Q9qQX4mLHx6P2pGSLRR20sOdJ4IIYT4Ctj7f/XVVxgzZgz69++Pvn374u6770ZZWZmpne/duxe/+MUvcMMNN+CFF17QvL9lyxYMGTIEo0aNwqhRo3TXSUS59gysuKcfCvIzAUB5PjnXnhHnkhGS2qjtJQc6TyTRuT2C7r/9l+m9F22i193h63D3R0g8BLzDsWzZMowbNw5jxoyBJEnYtWsXli9fjueee67D7SoqKrBp0yb8+c9/htVqxYQJE9CvXz/86Ec/UtY5fvw4Hn/8cfTu3Tv8TxJDLMugR5EDj/16MDxeETzHgmOBM3UtsPAscu0ZFBhJSJBEUUJdUys8XtGwHfm3PWpvicn/PDHty+qaWul8kYRgtXAYueA1AMDejaMM39d7L9pY3oqv145RXl+0fE9C7Y+QUAQccLS0tGDChAnK60mTJuHll18OuOMjR47g2muvRV5eHgDgpptuwhtvvIG5c+cq6xw/fhzbt2/H6dOn0adPHyxevBgZGclxBYxlGeTn2CgbCyEREEw7ktseSWwsyyDXnkH9IyGEkMCPVHXv3h3//ve/ldcnT55EcXFxwB1XVlbC6XQqrwsKClBRUaG8bmpqwmWXXYbFixfjlVdeQX19PbZu3arZT319PUpLS1X/lZeXBzx+rFA2FiJL9LqayKgdxU4s6ymdVxIO6lMJSR0B73BUVFRg0qRJ+MlPfgKe5/Gf//wHTqcTI0eOBNAWp6FHkrSTPDHMuStadrsd27dvV17fc889WLZsGebPn6/aZufOndiyZYu5TxMHlI2FyBK9riYyakexE8t6SueVhIP6VEJSR8ABR6gpcAsLC/HBBx8orysrK1FQUKC8Lisrw5EjRzB27FgAbQMUntcWZ8qUKRg9erRqWXl5OSZOnBhSuSKNsrEQWaLX1URG7Sh2YllP6byScIRTV90eAVYLp/k3iT634IGVs2iXe92w8lbT65vddzDbk/gJOODo27dvSDvu378/fvvb36K6uhqZmZnYt28fHn74YeV9m82GDRs2oF+/figuLsYLL7yAG264QbMfh8MBh8MRUhmiSQlw9YhYP3cgys80g2EAl9uLos523WwsHo+AmsZWCIIIjmORn50Bi0EnaCaAliSWRK2ryUDOauT/rH+2zYKqmmZ4BQk81xa7wZv8Y9WoDektF0UJNQ2ukI5jVqK06UjXU//PZc/gUdvkhiBI4DgGJfcNwKmKBry07yRqGlxYcU8/5GRaUdPgivt3QRJbOHU1UFA4iZ6OZgkPd/ZwvX3T7OPJIeCAI1SFhYWYP38+Jk+eDI/Hg7Fjx+LKK6/E9OnTMW/ePPTs2ROrV6/G7Nmz4fF4cNVVV2HatGnRKk5E+Qa45ufYMHnEZXjipY+UP5SWTe0LUZRUP6Aej4DvKhpQsuOYst7SqX3RozBHM+igQHSSbvSyT2XbLLpt5oIiR8DBgFEb6l6Qg9OVDZrlHMdg1fZ/BX0cs1K1Tft/rtHXX4TBV3VXnbN543vjjfe+xcw7roTH60Wmjdc9B8n+XRBCCDEW1fvaI0eOxF//+le8+eabmD59OgBg+/bt6NmzJ4C2zFXy+yUlJbBatbfaEpFvIOSYoZcogw2g7fnkdTuOoabBpdqmprFV+RGW1yvZcQw1jdrgSQq0JOlIzj5VkJ+F/Bwbao3ajF/b0mPUhmoaXLrLK6ubQzqOWanapv0/1/C+F2jO2eZdH2FYnx5Yv/MY6ho9KK1oSMnvghBCiLGo3eFIZb6BkDlZFt2gSK+oDpoXBP3gSUHQBtdToCUhgFeQTLUtPUZtyGifNiuvXdfEccxK1Tbt/7k4FrqfU+4nc7IsyjL/dZL9uyCEEGKMIvdCIAdCAkBDs0f5t6wgPxO836MBHMfqrsdx2kcIfPfvuy4FWpJ0wnOMqbalx6gNGe3T5faGdByzUrVN+38uQYTu55T7yYZmD1xub0p+F4TESiLNHO4WPPEuAkkSaXmHwzfIUZ4lvNWjDV40CvLMtWdg1YzrUH62CXnZGVgypQ/W73xf9fy3/8Rk+dkZWDq1r+Z59PxsbXC57/5tVr7DQHSS3hIlEDka5cnPsem2mVy7NWAguVEQen6OTbt8Wj9wPKNkUzJqw+EwKk+yt+lcewbW3jcAHo8Iq4UFAwZrZvXH91WNSpD4vPG9sffgV1gypS88Xi865Wam5HdBSKwk0szhFMRNzEq7AYde8OavJ/TG8387oWRQ6VHUlhXDKMgTaHuU4Kk9n6CypgX9Li/Emln9IUmAIErgeZ27FhYOPQpzUDJnoJK9paMsVb77l49NiK9EC0SOdHlYlkF2lgUPTb8OLAOIEmDP4nGqsjFgILleELqcjYrjGMwec6UymOd4BkX5WVg/ZyC8ogSejXyWKqPyJHuQtChKaGz24KV9n2PkoIuxeZc6eYYj2wpJkjBq8I/wzJ8/UfrY7gU5KfddEEIIMZZ297D1gjefeOkjjBl6iSp4saMgT//3jn5WgRVPH8F35Q2479EDWPHUEd2AU4uFQ0F+Frp2saMgP8twsJGqAaYkshKtnkS6PHVNrVi+9TDue/QAZj1yAPc9egBNzV7TgeT+Qegsy6CmwYVV2/+FVc8exdKth7Hq2aNYtf1fqGtyw5mfha6d7XDmZ0U8Ja5ReZJdTYMLJTuOYVifHspgAziXPOP/Ttdh6ZOH0djiwRenapQ60dDiTrnvghBCiLG0u8NhFLzpG8woBy92FNgYaB/hBJymaoApiaxEqyeRLo/e/lhGv+2ZbW/hBKITLfn7NEqe4R8sLi+nvowQQtJL2t3hMArebGj2KP+28GyHQZ5m9hFOwGmqBpiSyEq0ehLp8ujtT5T0g5LNtrdwAtGJlvx9GiXP8A0W911OfRkhhKSXtLjD4TvDN8+xWDXjOqzc9p4Sf/Gr266AKAHPLBkGi5VFq1uAlWexfFo/rH3u3PPoy6e1TejHcwwemnEdKtqDuiUJKOycCY9XwjNLh8GWwaG1VURVTTPysjPQ6PIoAeqQJLR6RPAcg1y7FXVNbk3wq16AqXzsmgYXPe9MAMQ3EFkvODzXnoEV0/phzXPqgGy92cIFQURdsxuCIILjWORmWWGxcKp95mRa8fj8wWhtFSGIIjiWRWYGi2VT+2KdTwzHMoNAcgCa2cPzc2xYOf1aVFY3KzEcBZ2ydAPEzQbAJ1rgfrT4f85smwUWnsXK6deisdmNh2f1hyC0TXjKsQxYFgAYlMwZAEgMfrdiOE6VN6Cos51mGieEkDST8gMOvRm+l03ti42/GQxBkFDb0IoVTx/RBJDn5VgxecRPVcGlPM/iraPf4cfn5yE326YK6vYNPF8ypQ9eP/wNGprdmHDjpapjy+v99MJ8zYy8vsGvvgGmoijh2dc+xdHPKuIeGEwSR7wCkY2Cw8/rYgfHM35thkF5dRMe8pnFu2TuANQ3elR1f+X0a+H1Sn4D/H7gefUM4Mum9kVWJq86hsXC4myDCyueOteOH5p+LdweUTUwWTq1L3oU5kAQJE1CBv/vzGwAfKIF7keL/+fsd3khJtx4Kf7z9RlcflEX/OvTHzCod3es36meYXzvwa8wctDF2HvwK0y48VI0t7Td6aCZxgkhJL2k/H1tvRm+1+04BrdHhCBKyh8k8ntyAPmwPj10g0t/fnV3FBc4DLerrGnB+p3vY/TP2/bhf2x5Pb0ZeX2DX+UAUwvPYvlTh3H0swplPQogJ7J4BCIbBYfXNrk1beah7f9Chd8s3qIATd2vrG5WBhvysrXPaWcAX7fjGL6vbNS0S69XUq1XUd2saaMlO46hptFcYLvZAPhEC9yPFv/PKfdtfS/vinU7jmF43wuUwQagnmFc/n/JjmO49ILOKD/blBbfGSGEkHNS/g5HxzN86weQdjQbriRJEKTA23Gs8SzkOVkWwxl5/YNXEy0wmBCjOmnU1vxn8ZZ02o/Nypva1miZ/zjLaH+CQdC4f3sy2+7SpX36f065b5PPpZkZxitrWiCIouG5SbXvjJBIEb1usLw15O3dgkczV8dFy/egpa4KP2yZpdl/uMcLpXxWzhJ4RZLUUn7AIc/w7fsDd26Gb0b3Pd/gb//3GIYBywTeThDPzUKut57DnqH7nn/wqhw4678eBV2SeDGqk0ZtzX8Wb0an/cizTwfa1miZf5Ipo/1xnH7b9W9PZttdurRP/88p923yuZRnGNfr63z/z7Gs4blJte+MkEgJd6I/vcn5gHMT9MV7IkGaPDA9pHwPL8/w/f/bu/OAKsr1D+Dfcw7LQZBc2NxQMxAXNFdkSUVBBCnB5VppQmrmrdSwFFDSUvSiYih4Nb2Z1wxvWlwibLkqiCGbSiVkmphCLigIiaCs5zy/P/gxcdhkOTAceD7/6Dkz884zM8/7Du+ZmXeqRlCp/obvuqatfHEUImIzEHM+q87l4lJv4lbOw3qXM+muBz+vcYiMq7uMqvlOncusO64aD69WPRhcfT5+Ky8TU3052U1fp1ZOr/UeD9MeXVS+k8pQaz6THl2w7lXVMte9agOTGsvWVZ6/93hoaUlUvjPt0QVr66n3jalPja13naV+1tzOqrbt3KVs+HuPx6lzmfDzGqeyH1bMG4WY81nCv/7e43ElMw9mPfU7xT5jjDH2lw57haP6iCrG3eX1vuG7+tu/tWQSyGQSvLNgDLSkEnTV0xGmyXVlKK9QYtLovpDJpDCsNk1LJoFUKsE788dAJpNAW0uKvzlZQiaTopu+jvBQb9UoVVXlP6Wv88S3G3fUNxQzzdVQTvYzNvj/evHX6FMAVL/T08FTeroqdbJqlKqaZZaVKVTmM9TTgUwmqVWfJRJJrboEoM761Zj6VN82Aqg1ulJnqJ8194cEgI62FBOse0NbSwr35wZBJq08Lkpl5UhVEgmw1HOE8K9cR4qeT8mF/djR9xljjLG/dMgOR1NGjql6+3dDZYx4xghu9gMRdOh8rRFvqr8tvDkj1hjrPPkQSKv9AcVYe1BXTlZUKHEzt0hl9KnAv9vhUXEFao7G1t+0a531rmaZcrkW5PLadaSuZY0b+V1j61PN+Rqq352hfkqlEjylr1vnPuhn0hU3cwqRfi0XQ582qnW8zU0MoFOjresM+4wxxlilDnlLlTpGjqlehudkC6GzUVVe1Yg36l4vY5rqz8KSWqNPVVRQ3aOxFWleneD6Xf8++LOwBIGfpGD8sF51Hu8Hj8rEDJsxxpjIOmSHQx0jx1Qvo74RWCpHulLvehnTVBV1jAAllTSu7mgCrt/174MKJamMWlVzuiYeb8YYY+rTqh2O6OhouLm5wdnZGeHh4bWmX758GbNnz4aLiwvWrVuHioqKOkppuqoRVapr6igo1cuoGoGlZnmVI12pd72MaSotmaRW/iupcXVHE3D9rn8faEklKqNW1ZyuicebMcaY+rTamfLevXsICQnBkSNHEBUVhaNHj+LatWsq86xevRrvvfce/ve//4GIcOzYMbWsWx0jx1QvIzIuo9YILFUj3qh7vYxpqu5d5bVGn9LSktQ7Spym4fpd/z7o3lWOgEU2wqhVNY93N/22G9OfMcZY+9NqD40nJiZiwoQJ6NatGwDAxcUF33//Pd566y0AwO3bt1FSUoJnn30WADBr1iyEhobi5ZdfbvG61TGyU80ydHWk9Y50pc71MqaptLSkGGBmWGtkKCNDemLd0QRcvxveB/3NDNGtqy6kEqgc7276OrUeGGeMMda5tNpZICcnB8bGxsJnExMTpKWl1Tvd2NgY9+7dq1XOw4cP8fDhQ5Xv7t69+8T1q2Nkp1pl6DdjGdZpNDdXOxItLWmdI0PVNaqUJuoI9buleVrfPugI+4a1L+2tTS0rV0BHW4bScgV0//9Hk6rvNFH1N5BX/Vta3nkGwWBtq9U6HESSQEeaAAAZHklEQVS1HxKUSCSNnl7l0KFD2L17t3qDY6wVcK4yTcB5yjRFe8tVHW0Znn8nCtE7ZuL5d6IAANE7ZoocVfPV94bvht5KzlhztVqHw9TUFBcuXBA+5+TkwMTERGX6/fv3hc+5ubkq06t4eXnB09NT5bu7d+9i/vz5rRA1Y83Huco0Aecp0xScq4x1HK3W4bCzs0NYWBjy8/Ohp6eHEydOYNOmTcL0Pn36QFdXF6mpqRgzZgy++uorTJw4sVY5hoaGMDQ0bK0wGVMbzlWmCThPmabgXGWs42jVKxw+Pj5YuHAhysvLMWfOHIwYMQKvvfYaVqxYAWtrawQHByMgIACPHj3C0KFDsXDhwkaVrVAoAHS+++OZepiZmUFLq20eYuVcZS3RVrnKecpaoj22qX379lX5fOvWrTq/rzm9ruWbumxL1beepqgekzrKa+8aewzaMleZKgnV9TBFO3fhwgW+pMqaLSYmps0aYM5V1hJtlaucp6wluE1lmqItc5Wp0sgOR0lJCX755RcYGxtDJtPM0SHUqeqe1vDwcJiZmYkdjuietD/a8heOxuRqRzt+HWl7xN6WtspVMdtUsfdxY2hCjIB4cYrZpmrKsWmsjrQ97XFb+AqHeDRyr8vlcowdO1bsMNodMzMz7rlX0x72R1NytT3Eq04daXs60rbUpT20qZqwjzUhRkBz4myO+nK1o21zR9qejrQtrPla7U3jjDHGGGOMMcYdDsYYY4wxxlir4Q4HY4wxxhhjrNXI3n///ffFDoK1nK6uLmxsbKCrqyt2KO2Cpu0PTYv3STrS9nSkbWmvNGEfa0KMgObEqU4dbZs70vZ0pG1hLaORo1QxxhhjjDHGNAPfUsUYY4wxxhhrNdzhYIwxxhhjjLUa7nBouNjYWMyaNQvTp09HYGCg2OG0C1FRUZgxYwZmzJiBrVu3ih3OE+3evVuId9u2bWKH0yK7du2Cm5sbZsyYgYMHD4odjlps3boVfn5+YofRIWlS7rf3POis54Lo6Gi4ubnB2dkZ4eHhYofTYkVFRXB3d8etW7fEDqVFNKlus7bBHQ4NdvPmTWzYsAF79uxBdHQ0fv31V5w5c0bssERVXFyMzZs34/Dhw4iKisKFCxeQmJgodlj1SkxMxNmzZxEZGYmvvvoKly5dwsmTJ8UOq1nOnTuH5ORkfP3114iIiMDhw4dx/fp1scNqkaSkJERGRoodRoekSbnf3vOgs54L7t27h5CQEBw5cgRRUVE4evQorl27JnZYzXbx4kW89NJLyMzMFDuUFtGkus3aDnc4NNjJkyfh5uYGMzMzaGtrIyQkBCNHjhQ7LFEpFAoolUoUFxejoqICFRUV7Xp0DGNjY/j5+UFHRwfa2toYNGgQ7ty5I3ZYzTJ+/Hh8+umn0NLSQl5eHhQKBbp06SJ2WM324MEDhISEYNmyZWKH0iFpSu5rQh501nNBYmIiJkyYgG7duqFLly5wcXHB999/L3ZYzXbs2DFs2LABJiYmYofSIppSt1nb0hI7ANZ8WVlZ0NbWxuLFi5GbmwtHR0e8/fbbYoclKgMDA6xcuRKurq6Qy+UYP348Ro8eLXZY9bKwsBD+n5mZiW+//Raff/65iBG1jLa2NkJDQ/HJJ59g+vTpMDU1FTukZlu/fj18fHyQnZ0tdigdkqbkvibkQWc9F+Tk5MDY2Fj4bGJigrS0NBEjapnNmzeLHYJaaErdZm2Lr3BoMIVCgaSkJGzfvh3Hjh1Denp6u77s3xauXLmCiIgInD59GmfPnoVUKsWBAwfEDuuJMjIysGjRIvj6+mLAgAFih9MiK1asQFJSErKzs3Hs2DGxw2mWL774Ar169YKtra3YoXR47Tn3NSUPOuu5oK5R/SUSiQiRsLq057rN2h53ODSYkZERbG1t0aNHD8jlckydOlWjf91Rh7Nnz8LW1hY9e/aEjo4OZs2ahXPnzokdVoNSU1Ph7e2Nd955B56enmKH02y///47Ll++DADQ09PDtGnT8Ntvv4kcVfN8++23SEhIwMyZMxEaGorY2Fhs2bJF7LA6nPae+5qSB531XGBqaor79+8Ln3NycjT+dqSOor3Xbdb2uMOhwRwdHXH27Fk8fPgQCoUC8fHxGDZsmNhhicrKygqJiYl4/PgxiAixsbGwtrYWO6x6ZWdn480330RwcDBmzJghdjgtcuvWLQQEBKCsrAxlZWWIiYnBmDFjxA6rWQ4ePIjjx48jKioKK1aswJQpU7B27Vqxw+pQNCH3NSUPOuu5wM7ODklJScjPz0dxcTFOnDiBiRMnih1Wp6cJdZu1PX6GQ4ONHDkSS5Yswcsvv4zy8nLY29tj9uzZYoclKgcHB/z666+YNWsWtLW1YW1tjaVLl4odVr0OHDiA0tJSBAUFCd+9+OKLeOmll0SMqnkmTZqEixcvwsPDAzKZDNOmTeOTDatXR8p9sXXWc4GpqSl8fHywcOFClJeXY86cORgxYoTYYXV6XLdZXSRU102QjDHGGGOMMaYGfEsVY4wxxhhjrNVwh4MxxhhjjDHWarjDwRhjjDHGGGs13OFgjDHGGGOMtRrucDDGGGOMMcZaDXc42oGUlBS4u7s3+nt1SEtLw/r165u1nuDgYMTHx7c4hkePHmHJkiUoKSlpcVmsbbUkN3ft2oWvvvqq1vf5+fkYPHgwAKCwsBALFy4Upg0ePBj5+fmNKv/MmTMICQlpVmw1vfvuu/j999/VUhZruS+++ALh4eFPnG/KlClIT09v9PfqsGjRIiFHm7Key5cvw9/fXy0xBAUFISUlRS1lMfVoac4+SXp6OlasWFHntNdffx3//e9/AQC7d+/GqVOnAAB+fn44cOBAo8ovKirC4sWL1XKePnXqFHbv3t3icphm4g5HJ3Xt2jXcu3evycv9/PPPuHbtGp577rkWx6Cvrw93d3fs2rWrxWUxzbFy5Up4eHg0OE9BQUGzTr5FRUUIDg7GsmXLmhueipUrV8Lf3x88enj7kJqa2m5/oEhISGjyMkqlEuvWrcPbb7+tlhjefPNNBAYGttt91Bm1ds5aW1sjNDT0ifOlpKSgoqKiyeUHBwdj7ty5kMvlzQlPhZOTE1JTU3H58uUWl8U0D7/4rwGPHj2Cv78/srKyIJVKMWzYMGzcuBFSqRSxsbHYu3cvysvLIZfL4evri1GjRiEsLAwZGRm4f/8+8vLyYGVlhc2bN8PAwACnT5/Gvn37UFZWhvz8fHh4eDT6RFNWVobg4GCcP38eCoUCQ4cORUBAAAwMDDBlyhR4enoiKSkJ2dnZcHV1xZo1awAA+/fvx5dffgl9fX2MHTsWMTExCA8PR2hoKAoLC+Hv7w8PDw88fvwYPj4+uH79OkpLSxEYGIixY8fWiiMsLAwLFiwQPn/55Zc4ePAgpFIpunfvjq1bt+KPP/7Ahx9+CBMTE2RkZEBPTw/Lly/H4cOHcePGDUybNk14W6+rqyuCg4OxePFiGBkZqeGodW5i56yHhwfWrFkDOzs7fPPNN/Dz88P58+chl8sREBCAIUOGID09HRYWFli8eDFOnDiBkJAQ6OnpYfjw4UI5/v7+KCkpwcyZM4Vf6MLCwnDx4kU8ePAAixcvxvz582ut/8iRI3BwcICenh4A4OLFiwgMDERxcTG0tbWxZs0a2NrawtraGt7e3oiLi0NRURFWr16N77//HlevXoWJiQk++ugjdOnSBf369UPXrl0RExMDJycnNR+tzislJQXbtm2Dqakpbt68CblcjqCgIAwaNKjeti4pKQmxsbFISEiAXC6Hi4sL1q9fj7y8POTm5qJPnz7YuXMnevbs2agYGqoPt2/fRm5uLm7fvo0ePXogJCQEpqamSEtLw/vvv4/y8nKYm5vjzp078PPzE67YeXl5Yf/+/QCAo0ePYsOGDcjPz8fMmTPh4+NTK4bvvvsOffv2hampKQDgxo0bWL9+PfLz8yGVSvH3v/8dbm5umDJlCtzd3REXF4cHDx5g+fLl+PHHH3Hp0iVoaWlh7969MDU1RdeuXTFq1CgcPXoUXl5eajpaDBAnZw8dOoT09HQEBwejvLwcNjY2WLt2LebMmYPU1FT84x//wOrVq7Fp0yYcP34c9+7dg5+fH3JyctC7d2/k5eUBAMLDw/HLL79g27ZtkMlkAICffvoJL774Iu7fvw8LCwvs2LEDXbp0UVl/dnY24uLiEBAQAKDy/BIYGIgff/wRMpkMTk5O8PHxgb+/P3R1dZGeno779+/D1dUVPXr0wOnTp5Gbm4vAwEDY2toCAObMmYPdu3fjn//8Z2sdKtZeEatXZGQkLVq0iIiIKioqaN26dZSZmUk3btwgd3d3ys/PJyKiq1evkr29PT169IhCQ0Np4sSJlJubSwqFglatWkVBQUGkVCppwYIFdOPGDSIiunv3Lg0ZMoTy8vIoOTmZZsyYUWv91b8PCwsTyiEi2rFjB23YsIGIiBwdHSkoKEgo19ramv744w/64YcfyMXFhQoKCkipVJK/vz85OjoSEVFERAQtXbpUWM+QIUPo559/JiKigwcP0sKFC2vFU1BQQCNHjqTS0lIiIrp8+TLZ2NjQnTt3hOXee+89obxLly4REdHixYtp3rx5VFpaSnl5eTRs2DC6e/euUO7y5cvpyy+/bM4hYjWInbNVeUpE5OvrS/b29hQfH08KhYLs7e0pJyeHfH196eOPP6bc3FwaM2YMZWRkEBHRRx99RJaWlkREdPPmTXr22WeFci0tLenAgQNERHTp0iUaPnw4lZWV1Vq/p6cnJScnExFRWVkZ2dvb0+nTp4mIKD09ndzd3UmhUJClpSUdOnSIiIj27dtHo0aNort375JCoSBPT0/6+uuvhTI/++wzWrNmTfMOCKtTcnIyWVlZ0fnz54mI6MiRI+Tp6UlEDbd1VblDRPTvf/+b9u3bR0RESqWSlixZIuSIo6MjpaWl1Vpv1fdPqg9Tp06lwsJCIiJ6/fXXadeuXVReXk4TJ06kuLg4IiJKSkqiwYMHC/lmaWlJeXl5wno2btxIREQ5OTk0fPhwoZ2sbvny5RQRESF89vDwoM8++4yIiO7cuSPE4ejoSFu2bCEiom+++YasrKzo8uXLRET0xhtv0N69e4UyTp8+TfPnz2/UcWCNJ0bO3rp1i2xtbUmpVFJycjLZ29vTqlWriIho69attH//fpW2+I033qCQkBAiIsrMzKRnn31WyK8FCxbQd999J8Q0Z84cevz4MVVUVJCnpydFRkbW2ubDhw+Tr6+v8HnLli3k4+NDFRUVVFpaSvPnz6fk5GTy9fWluXPnUllZGeXk5JClpSV9+umnwja/+uqrQhmFhYU0YsQIKi4ubvpBYBqNr3A0YMyYMQgJCcErr7wCOzs7eHl5oX///ggPD0dOTg68vb2FeSUSCf744w8AwPTp04Vf6+fMmYMtW7bA19cXH330EeLi4nD8+HH8/vvvICIUFxc3Kpa4uDgUFhYiMTERAFBeXq7yq8jUqVMBAKampujZsycKCgpw5swZTJ8+HYaGhgCA+fPnIzk5uc7y+/Xrh5EjRwIArKysEBERUWuerKwsGBsbQ0dHBwCQlJQEBwcH9OrVCwCE/ZGSkoK+ffti6NChAABzc3N07doVOjo66NGjB/T19VFQUCD8qmdubo4bN240aj+whomds87Ozli1ahV8fX1x4cIFeHt7IyEhAfr6+jA3N4exsbEwb2pqKiwtLfHMM88AAObNm4cPP/yw3rKrnhkZMmQIysrKUFRUhO7du6vMc+PGDfTv3x8AcPXqVUilUkyePBkAMHz4cERHRwvzuri4AKjMP0tLSyEf+/bti4KCAmG+fv36ISoqqt64WPNYWVkJV1Fnz56NjRs34s8//3xiW1fFy8sLFy5cwMGDB5GZmYmMjAyhDXuShISEBuvD+PHjYWBgAAAYOnQoCgoKcPXqVQDApEmTAAATJkyAhYVFveuoyldjY2MYGRkhLy9PaCurXL9+XXhW6cGDB7hy5Qrmzp0LAOjVq5dwzz0ATJs2DUBlPhoZGcHKygpAZf7WzFduT1tHW+dsnz59YGZmhvT0dMTHx2Pp0qXYv38/iAgxMTH417/+hezsbGH+xMRE+Pr6AgD69+8PGxubest2cnISrgRbWFjU+Yzc9evXYW5urlK+v78/ZDIZZDIZPvvsMwBAZGQkHB0doa2tDWNjY3Tp0kW47drc3BwPHjwQyjAwMICBgQFu376NQYMG1Rsf63i4w9GAfv364eTJk0hJSUFycjJeffVVBAQEQKlUwtbWFjt37hTmzc7OhomJCU6ePClcsgQq79GVSqV4/PgxPD094eTkhLFjx2L27Nk4depUo+8NVyqVWLt2rXCye/ToEUpLS4Xpurq6wv8lEgmICFpaWirlV4+rJm1t7VrL1ySVSqFQKFTKk0gkwueSkhLcvn0bAIROSRUtrfpTTaFQ1JqfNY/YOTt48GCUl5cjJiYG/fv3h6OjI3x8fKClpSX8wVSlZp41lCPVp1flXF1xSCQSIUdr5idQ2Ql5+umnAajmfPX/11S1P5h61WyPiAgymeyJbV2V7du3Iy0tDbNnz4aNjQ0qKiqa1J42VB+q369elacymaxW+Q21qdXzub42VSKRQKlUqsxfPWevX7+O3r17A1BtUzlfxSFGzjo7O+OHH35AQkIC9u3bh+PHj+Pbb7+FXC6Hubm5SoejKW1qY/JTKpUK+Vm1TPX8zM7OFupKU8/5DdUd1jFxq9SAI0eOwN/fHw4ODli9ejUcHByQkZGBCRMmICEhQRi95syZM3jhhReEBiYmJgaFhYVQKpU4duwYHB0dkZWVhaKiIrz99tuYMmUKzp07h7KyMpXK3BAHBweEh4cLy7z33nsN/hoMVP4Sd+LECRQWFgKofN6iikwma/IDZP369UN+fr6wnTY2NkhKSkJOTg4A4PPPP8f27dubVCYA3Lp1CwMHDmzycqy29pCzTk5OCA4Ohr29PQYNGoSioiJER0cLVxSqjB07FteuXcOVK1cAQHhWA6g8WSkUiiY/rD1gwADcvHkTAPD0009DIpEID/NeunQJXl5eja5zVW7evCl0Upj6XLlyRTj2R48exejRo2FoaNhgW1e93Tp79iy8vLzg4eGBnj17IjExUeUHkYY8qT7UZdCgQdDR0cEPP/wAoHKkv6tXrwp/gDWnTR04cKCQrwYGBhg2bJjwPEh2djZeeuklof1uLM7X1iNGzjo7OyM6OhoKhQImJiawt7fH9u3ba7WnAPDcc8/h6NGjAIA7d+6ojFjWnPys3p4CgK2tLSIjI6FUKlFWVoYVK1bg/PnzTSqzsLAQpaWlQkeadR58haMBHh4eOHfuHNzc3KCnp4fevXtj4cKFeOqpp7Bx40asWrVKuJKwd+9e4YErIyMjvPbaa/jzzz8xbtw4LFu2DDo6Opg8eTJcXV1haGgIc3NzPPPMM8jKymrUr/tvvPEGtm7dCk9PTygUCgwZMgR+fn4NLmNra4u//e1vmDdvHuRyOSwsLIRLqKNGjcLOnTvx5ptvqgw/2hBDQ0OMGTMGycnJmDRpEgYPHozVq1djyZIlACpvHdiyZQsyMzMbVR5Q+TD8Tz/9hM2bNzd6GVa/9pCzzs7OOHDgAOzs7AAAdnZ2+O2332rdTtKjRw8EBwfj3Xffhba2NsaNGydMMzY2xtChQ+Hq6or//Oc/jd7+6dOnIz4+HhMmTICOjg7CwsKwZcsWbNu2Ddra2ggLC2vy1bT4+HiVgRKYehgZGWHnzp3Cg9nbtm0D0HBbN3HiRGzatAlA5YhM27Ztw549eyCTyTB69GjhlqgnsbCwaLA+1EVLSwthYWHYsGEDPvzwQwwYMABGRkbCL7zOzs54+eWXsWfPnkbvAxcXF5w8eRKzZ88GAOzYsQMffPABDh8+DIlEgs2bN6vchtgY8fHxmD59epOWYY0jRs5W3XJa9dC1g4MD9uzZU2eHY8OGDfD394erqyvMzMyE2+4AwNHREVu3bkV5eXmjt9fJyQkff/yxcEXirbfewubNmzFz5kwoFAq4ublh2rRpiI2NbXSZZ8+exeTJk/muhs6oDZ8X6RRCQ0Ppgw8+EDsMIiJKS0sTHowlIvrkk09o5cqVLSozNTWVXnvttZaGJoiIiBAeMmbiaE8521KFhYXk6upKjx8/Vkt5WVlZNHfuXOFhUKYe9Q060N4FBQVRbm4uEVU+1D1u3DgqKChodnkVFRX0wgsvqAyi0RIPHz6kGTNmUElJiVrKY3/R1JxtqYCAAPrmm2/UVt4rr7wiDHjAOhe+paoDGzhwIC5cuAB3d3c8//zzSEpKavELpkaPHo2BAwcKtxW0RFFREY4fP47ly5e3uCzGgMrbUlatWtWkX5kbsnPnTgQGBtZ6FoR1Tn369IG3tzc8PDywbNkyBAYGCoNyNIdMJsOmTZueeHtsY+3evRtr165VeaaPsZZYvXo1jh07ppZ3iZw8eRJjx45VufLCOg8JEb/RijHGGGOMMdY6+AoHY4wxxhhjrNVwh4MxxhhjjDHWarjDwRhjjDHGGGs13OFgjDHGGGOMtRrucDDGGGOMMcZaDXc4GGOMMcYYY63m/wBKMUVJYOPNCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 806.85x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "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>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mikolajr/Workspace/SSDM/machinelearning-introduction-workshop/.venv/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
      "  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",
       "        \n",
       "      \n",
       "       \n",
       "    }\n",
       "    \n",
       "    .alert-block {\n",
       "        width: 95%;\n",
       "        margin: auto;\n",
       "    }\n",
       "    \n",
       "    .rendered_html code\n",
       "    {\n",
       "        color: black;\n",
       "        background: #eaf0ff;\n",
       "        background: #f5f5f5; \n",
       "        padding: 1pt;\n",
       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    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",
       "    .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",
       "         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",
       "     }\n",
       "     \n",
       "    \n",
       "    div #notebook {\n",
       "        # font-size: 10pt; \n",
       "        line-height: 145%;\n",
       "        }\n",
       "        \n",
       "    li {\n",
       "        line-height: 145%;\n",
       "    }\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": 24,
     "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",
    "        \n",
    "      \n",
    "       \n",
    "    }\n",
    "    \n",
    "    .alert-block {\n",
    "        width: 95%;\n",
    "        margin: auto;\n",
    "    }\n",
    "    \n",
    "    .rendered_html code\n",
    "    {\n",
    "        color: black;\n",
    "        background: #eaf0ff;\n",
    "        background: #f5f5f5; \n",
    "        padding: 1pt;\n",
    "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
    "    }\n",
    "    \n",
    "    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",
    "    .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",
    "         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",
    "     }\n",
    "     \n",
    "    \n",
    "    div #notebook {\n",
    "        # font-size: 10pt; \n",
    "        line-height: 145%;\n",
    "        }\n",
    "        \n",
    "    li {\n",
    "        line-height: 145%;\n",
    "    }\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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