intro_to_model_tuning.ipynb

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    "# Introduction: Hyperparameter Tuning using Grid and Random Search\n",
    "\n",
    "In this notebook, we will explore two methods for hyperparameter tuning a machine learning model. [In contrast](https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/) to model __parameters__ which are learned during training, model __hyperparameters__ are set by the data scientist ahead of training and control implementation aspects of the model. The weights learned during training of a linear regression model are parameters while the number of trees in a random forest is a model hyperparameter because this is set by the data scientist. Hyperparameters can be thought of as model settings. These settings need to be tuned for each problem because the best model hyperparameters for one particular dataset will __not be__ the best across all datasets. The process of [hyperparameter tuning (also called hyperparameter optimization)](https://en.wikipedia.org/wiki/Hyperparameter_optimization) means finding the combination of hyperparameter values for a machine learning model that performs the best - as measured on a validation dataset - for a problem. \n",
    "\n",
    "(__Quick Note__: a lot of data scientists use the terms _parameters_ and _hyperparameters_ interchangeably to refer to the model settings. While this is technically incorrect, it's pretty common practice and it's usually possible to tell when they are referring to parameters learned during training versus hyperparameters. I'll try to stick to using model hyperparameters or model settings and I'll  point out when I'm talking about a parameter that is learned during training. If you're still confused, [this article](https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/) may help you out!)\n",
    "\n",
    "There are several approaches to hyperparameter tuning\n",
    "\n",
    "1. Manual: select hyperparameters based on intuition/experience/guessing, train the model with the hyperparameters, and score on the validation data. Repeat process until you run out of patience or are satisfied with the results. \n",
    "2. Grid Search: set up a grid of hyperparameter values and for each combination, train a model and score on the validation data. In this approach, every single combination of hyperparameters values is tried which can be very inefficient!\n",
    "3. Random search: set up a grid of hyperparameter values and select _random_ combinations to train the model and score. The number of search iterations is set based on time/resources. \n",
    "4. Automated Hyperparameter Tuning: use methods such as gradient descent, Bayesian Optimization, or evolutionary algorithms to conduct a guided search for the best hyperparameters.\n",
    "\n",
    "(This [Wikipedia Article](https://en.wikipedia.org/wiki/Hyperparameter_optimization) provides a good high-level overview of tuning options with links for more details)\n",
    "\n",
    "In this notebook, we will implement approaches 2 and 3 for a Gradient Boosting Machine Learning Model. In a future notebook, we will implement automated hyperparameter tuning using Bayesian optimization, specifically the Hyperopt library. If you want to get an idea of how automated hyperparameter tuning is done, check out [this article](https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a). \n",
    "\n",
    "## Model: Gradient Boosting Machine \n",
    "\n",
    "The [Gradient Boosting Machine (GBM)](https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/) has recently emerged as one of the top machine learning models. The GBM is extremely effective on structured data - where the information is in rows and columns - and medium sized datasets - where there are at most a few million observations. We will focus on this model because it is currently the top performing method for most competitions on Kaggle and because the performance is highly dependent on the hyperparameter choices. The basics you need to know about the GBM are that it is an ensemble method that works by training many individual learners, almost always decision trees. However, unlike in a random forest where the trees are trained in __parallel__, in a GBM, the trees are trained __sequentially__ with each tree learning from the mistakes of the previous ones. The hundreds or thousands of weak learners are combined to make a single strong ensemble learner with the contributions of each individual learned during training using Gradient Descent (the weights of the individual trees would therefore be a model _parameter_). \n",
    "\n",
    "The GBM [has many hyperparameters to tune](http://lightgbm.readthedocs.io/en/latest/Parameters.html) that control both the overall ensemble (such as the learning rate) and the individual decision trees (such as the number of leaves in the tree or the maximum depth of the tree). It is difficult to know which combination of hyperparameters will work best based only on theory because there are complex interactions between hyperparameters. Hence the need for hyperparameter tuning: the only way to find the optimal hyperparameter values is to try many different combinations on a dataset!\n",
    "\n",
    "We will use the implementation of the Gradient Boosting Machine in the [LightGBM library](http://lightgbm.readthedocs.io/en/latest/). This is a much faster (and some say more accurate) implementation than that available in Scikit-Learn.\n",
    "\n",
    "For more details of the Gradient Boosting Machine (GBM), check out this [high-level blog post](http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/), or this [in depth technical article.](https://brage.bibsys.no/xmlui/bitstream/handle/11250/2433761/16128_FULLTEXT.pdf) \n",
    "\n",
    "### Getting Started\n",
    "\n",
    "With the necessary background out of the way, let's get started. For this notebook, we will work with a subset of the data consisting of 10000 rows. Hyperparameter tuning is extremely computationally expensive and working with the full dataset in a Kaggle Kernel would not be feasible for more than a few search iterations. However, the same ideas that we will implement here can be applied to the full dataset and while this notebook is specifically aimed at the GBM, the methods can be applied for any machine learning model. \n",
    "\n",
    "To \"test\" the tuning results, we will save some of the training data, 6000 rows, as a separate testing set. When we do hyperparameter tuning, it's crucial to __not tune the hyperparameters on the testing data__. We can only use the testing data __a single time__ when we evaluate the final model that has been tuned on the validation data. To actually test our methods from this notebook, we would need to train the best model on all of the training data, make predictions on the actual testing data, and then submit our answers to the competition. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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    "_uuid": "c02a14c73390dc52f601eabb60ba8042257036d2"
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   "source": [
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Modeling\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Splitting data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "N_FOLDS = 5\n",
    "MAX_EVALS = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we read in the data and separate into a training set of 10000 observations and a \"testing set\" of 6000 observations. After creating the testing set, we cannot do any hyperparameter tuning with it! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "a42d50c0cf134a1147c35e1aea539b12053d24c3"
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   "outputs": [],
   "source": [
    "features = pd.read_csv('../input/application_train.csv')\n",
    "\n",
    "# Sample 16000 rows (10000 for training, 6000 for testing)\n",
    "features = features.sample(n = 16000, random_state = 42)\n",
    "\n",
    "# Only numeric features\n",
    "features = features.select_dtypes('number')\n",
    "\n",
    "# Extract the labels\n",
    "labels = np.array(features['TARGET'].astype(np.int32)).reshape((-1, ))\n",
    "features = features.drop(columns = ['TARGET', 'SK_ID_CURR'])\n",
    "\n",
    "# Split into training and testing data\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 6000, random_state = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "We will also use only the numeric features to reduce the number of dimensions which will help speed up the hyperparameter search. Again, this is something we would not want to do on a real problem, but for demonstration purposes, it will allow us to see the concepts in practice (rather than waiting days/months for the search to finish)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "ecadc412aea2eba03da9349e07973f1a872ae3a0"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training features shape:  (10000, 104)\n",
      "Testing features shape:  (6000, 104)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training features shape: \", train_features.shape)\n",
    "print(\"Testing features shape: \", test_features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# Cross Validation\n",
    "\n",
    "To evaluate each combination of hyperparameter values, we need to score them on a validation set. The hyperparameters __can not be tuned on the testing data__. We can only use the testing data __once__ when we evaluate the final model. The testing data is meant to serve as an estimate of the model performance when deployed on real data, and therefore we do not want to optimize our model to the testing data because that will not give us a fair estimate of the actual performance. The correct approach is therefore to use a validation set. However, instead of splitting the valuable training data into a separate training and validation set, we use [KFold cross validation](https://www.youtube.com/watch?v=TIgfjmp-4BA). In addition to preserving training data, this should give us a better estimate of generalization performance on the test set than using a single validation set (since then we are probably overfitting to that validation set). The performance of each set of hyperparameters is determined by Receiver Operating Characteristic Area Under the Curve (ROC AUC) from the cross-validation.\n",
    "\n",
    "In this example, we will use 5-fold cross validation which means training and testing the model with each set of hyperparameter values 5 times to assess performance. Part of the reason why hyperparameter tuning is so time-consuming is because of the use of cross validation. If we have a [large enough training set, we can probably get away with just using a single separate validation set](https://www.coursera.org/lecture/deep-neural-network/train-dev-test-sets-cxG1s), but cross validation is a safer method to avoid overfitting. \n",
    "\n",
    "To implement KFold cross validation, we will use the LightGBM cross validation function, `cv`, because this allows us to use a critical technique for training a GBM, early stopping. (For other machine learning models where we do not need to use early stopping, we can use the Scikit-Learn functions `RandomizedSearchCV` or `GridSearchCV`.)\n",
    "\n",
    "## Early Stopping\n",
    "\n",
    "One of the most important hyperparameters in a Gradient Boosting Machine is the number of estimators (the number of decision trees trained sequentially). We could set this as another hyperparameter in our search, but there's a better method: [early stopping](https://en.wikipedia.org/wiki/Early_stopping). Early stopping means training until the validation error does not decrease for a specified number of iterations. In the case of the GBM, this means training more decision trees, and in this example, we will use early stopping with 100 rounds, meaning that the training will continue until validation error has not decreased for 100 rounds. Then, the number of estimators that yielded the best score on the validation data will be chosen as the number of estimators to use in the final model.\n",
    "\n",
    "The concept of early stopping is commonly applied to the GBM and to deep neural networks so it's a great technique to understand. This is one of many forms of regularization that aims to improve generalization performance on the testing set by not overfitting to the training data. If we keep adding estimators, the training error will always decrease because the capacity of the model increases. Although this might seem positive, it means that the model will start to memorize the training data and then will not perform well on new testing data. The __variance__ of the model increases as we continue adding estimators because the model starts to rely too heavily on the training data (high variance means overfitting).\n",
    "\n",
    "Early stopping is simple to implement with the LightGBM library in the cross validation function. We simply need to pass in the number of early stopping rounds.\n",
    "\n",
    "### Example of Cross Validation and Early Stopping \n",
    "\n",
    "To use the `cv` function, we first need to make a LightGBM `dataset`. "
   ]
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  {
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   "execution_count": 4,
   "metadata": {
    "_uuid": "130ac2f4bf4f58817f6c25ff88a5c19d60bbe712"
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   "source": [
    "# Create a training and testing dataset\n",
    "train_set = lgb.Dataset(data = train_features, label = train_labels)\n",
    "test_set = lgb.Dataset(data = test_features, label = test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c2a70db1edb65f88ab6fa06f0639260d12b3e550"
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   "source": [
    "We have to pass in a set of hyperparameters to the cross validation, so we will use the default hyperparameters in LightGBM. In the `cv` call, the `num_boost_round` is set to 10,000 (`num_boost_round` is the same as `n_estimators`), but this number won't actually be reached because we are using early stopping. As a reminder, the metric we are using is Receiver Operating Characteristic Area Under the Curve (ROC AUC).\n",
    "\n",
    "The code below carries out both cross validation with 5 folds and early stopping with 100 early stopping rounds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "704dce8d25515252ff5510f319eae1bb021703be"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\miniconda3\\lib\\site-packages\\lightgbm\\basic.py:657: UserWarning: silent keyword has been found in `params` and will be ignored. Please use silent argument of the Dataset constructor to pass this parameter.\n",
      "  'Please use {0} argument of the Dataset constructor to pass this parameter.'.format(key))\n"
     ]
    }
   ],
   "source": [
    "# Get default hyperparameters\n",
    "model = lgb.LGBMClassifier()\n",
    "default_params = model.get_params()\n",
    "\n",
    "# Remove the number of estimators because we set this to 10000 in the cv call\n",
    "del default_params['n_estimators']\n",
    "\n",
    "# Cross validation with early stopping\n",
    "cv_results = lgb.cv(default_params, train_set, num_boost_round = 10000, early_stopping_rounds = 100, \n",
    "                    metrics = 'auc', nfold = N_FOLDS, seed = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7af37605a7b294a07146fcba8f7eceb7517f4164"
   },
   "source": [
    "The `cv_results` is a dictionary with lists for the `metric` mean and the `metric` standard deviation. The last entry (index of -1) contains the best performing score. The length of each list in the dictionary will be the \"optimal\" number of estimators to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "e4f547977aa72be4bd3d8f364aea10e0eff987d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum validation ROC AUC was: 0.70147 with a standard deviation of 0.01872.\n",
      "The optimal number of boosting rounds (estimators) was 35.\n"
     ]
    }
   ],
   "source": [
    "print('The maximum validation ROC AUC was: {:.5f} with a standard deviation of {:.5f}.'.format(cv_results['auc-mean'][-1], cv_results['auc-stdv'][-1]))\n",
    "print('The optimal number of boosting rounds (estimators) was {}.'.format(len(cv_results['auc-mean'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e7ba4323cf99b7c5e9284c373505dfbfe46c0f65"
   },
   "source": [
    "We can use this result as a baseline model to beat. To find out how well the model does on our \"test\" data, we will retrain it on all the training data with the best number of estimators found during cross validation with early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "7d59ad26529d2f5dbd1377fd79741678cd2cf6a4"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "553e5514ef458b0db032a21a2bf89e965bf7214c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline model scores 0.72566 ROC AUC on the test set.\n"
     ]
    }
   ],
   "source": [
    "# Optimal number of esimators found in cv\n",
    "model.n_estimators = len(cv_results['auc-mean'])\n",
    "\n",
    "# Train and make predicions with model\n",
    "model.fit(train_features, train_labels)\n",
    "preds = model.predict_proba(test_features)[:, 1]\n",
    "baseline_auc = roc_auc_score(test_labels, preds)\n",
    "\n",
    "print('The baseline model scores {:.5f} ROC AUC on the test set.'.format(baseline_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the baseline score _before hyperparameter tuning_. The only difference we made from the default model was using early stopping to set the number of estimators (which by default is 100). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f493fb15e0ab416d0e848dfb0fd45ef0c0e0ef36"
   },
   "source": [
    "## Hyperparameter Tuning Implementation\n",
    "\n",
    "Now we have the basic framework in place: we will use cross validation to determine the performance of model hyperparameters and early stopping with the GBM so we do not have to tune the number of estimators. The basic strategy for both grid and random search is simple: for each hyperparameter value combination, evaluate the cross validation score and record the results along with the hyperparameters. Then, at the end of searching, choose the hyperparameters that yielded the highest cross-validation score, train the model on all the training data, and make predictions on the test data.\n",
    "\n",
    "# Four parts of Hyperparameter tuning\n",
    "\n",
    "It's helpful to think of hyperparameter tuning as having four parts (these four parts also will form the basis of Bayesian Optimization):\n",
    "\n",
    "1. Objective function: a function that takes in hyperparameters and returns a score we are trying to minimize or maximize\n",
    "2. Domain: the set of hyperparameter values over which we want to search. \n",
    "3. Algorithm: method for selecting the next set of hyperparameters to evaluate in the objective function.\n",
    "4. Results history: data structure containing each set of hyperparameters and the resulting score from the objective function.\n",
    "\n",
    "Switching from grid to random search to Bayesian optimization will only require making minor modifications to these four parts. \n",
    "\n",
    "## Objective Function\n",
    "\n",
    "The objective function takes in hyperparameters and outputs a value representing a score. Traditionally in optimization, this is a score to minimize, but here our score will be the ROC AUC which of course we want to maximize. Later, when we get to Bayesian Optimization, we will have to use a value to minimize, so we can take $1 - \\text{ROC AUC}$ as the score. What occurs in the middle of the objective function will vary according to the problem, but for this problem, we will use cross validation with the specified model hyperparameters to get the cross-validation ROC AUC. This score will then be used to select the best model hyperparameter values. \n",
    "\n",
    "In addition to returning the value to maximize, our objective function will return the hyperparameters and the iteration of the search. These results will let us go back and inspect what occurred during a search. The code below implements a simple objective function which we can use for both grid and random search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "9d904f4e3ffb10d2a9b1748fb0c236101486ede0"
   },
   "outputs": [],
   "source": [
    "def objective(hyperparameters, iteration):\n",
    "    \"\"\"Objective function for grid and random search. Returns\n",
    "       the cross validation score from a set of hyperparameters.\"\"\"\n",
    "    \n",
    "    # Number of estimators will be found using early stopping\n",
    "    if 'n_estimators' in hyperparameters.keys():\n",
    "        del hyperparameters['n_estimators']\n",
    "    \n",
    "     # Perform n_folds cross validation\n",
    "    cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, \n",
    "                        early_stopping_rounds = 100, metrics = 'auc', seed = 42)\n",
    "    \n",
    "    # results to retun\n",
    "    score = cv_results['auc-mean'][-1]\n",
    "    estimators = len(cv_results['auc-mean'])\n",
    "    hyperparameters['n_estimators'] = estimators \n",
    "    \n",
    "    return [score, hyperparameters, iteration]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "1944462565245ef7399a4905f5319b0341ada833"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validation ROC AUC was 0.70147.\n"
     ]
    }
   ],
   "source": [
    "score, params, iteration = objective(default_params, 1)\n",
    "\n",
    "print('The cross-validation ROC AUC was {:.5f}.'.format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e05fa2e22d73408eb284a28f1110649f5f1a97b8"
   },
   "source": [
    "# Domain\n",
    "\n",
    "The domain, or search space, is all the possible values for all the hyperparameters that we want to search over. For random and grid search, the domain is a hyperparameter grid and usually takes the form of a dictionary with the keys being the hyperparameters and the values lists of values for each hyperparameter.\n",
    "\n",
    "## Hyperparameters for GBM\n",
    "\n",
    "To see which settings we can tune, let's make a model and print it out. You can also refer to the [LightGBM documentation](http://lightgbm.readthedocs.io/en/latest/Parameters.html) for the description of all the hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "02a083d4b41697c47dcb21c48bc543c05cb4a4e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'class_weight': None,\n",
       " 'colsample_bytree': 1.0,\n",
       " 'learning_rate': 0.1,\n",
       " 'max_depth': -1,\n",
       " 'min_child_samples': 20,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_split_gain': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': -1,\n",
       " 'num_leaves': 31,\n",
       " 'objective': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': 0.0,\n",
       " 'reg_lambda': 0.0,\n",
       " 'silent': True,\n",
       " 'subsample': 1.0,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'subsample_freq': 1}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a default model\n",
    "model = lgb.LGBMModel()\n",
    "model.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "34854c78b2359cee125731d4f8848ca40aa08527"
   },
   "source": [
    "Some of these we do not need to tune such as `silent`, `objective`, `random_state`, and `n_jobs`, and we will use early stopping to determine perhaps the most important hyperparameter, the number of individual learners trained, `n_estimators` (also referred to as `num_boost_rounds` or the number of iterations). Some of the hyperparameters do not need to be tuned if others are: for example, `min_child_samples` and `min_child_weight` both limit the complexity of individual decision trees by adjusting the minimum leaf observation requirements and therefore we will only adjust one. However, there are still many hyperparameters to optimize, and we will choose 10 to tune. \n",
    "\n",
    "Choosing a hyperparameter grid is probably the most difficult part of hyperparameter tuning: it's nearly impossible ahead of time to say which values of hyperparameters will work well and the optimal settings will depend on the dataset. Moreover, the hyperparameters have complex interactions with each other which means that just tuning one at a time doesn't work because when we start changing other hyperparameters that will affect the one we just tuned! \n",
    "\n",
    "If we have prior experience with a model, we might know where the best values for the hyperparameters typically lie, or what a good search space is. However, if we don't have much experience, we can simply define a large search space and hope that the best values are in there somewhere. Typically, when first using a method, I define a wide search space centered around the default values. Then, if I see that some values of hyperparameters tend to work better, I can concentrate the search around those values. \n",
    "\n",
    "A complete grid for the 10 hyperparameter is defined below. Each of the values in the dicionary must be a list, so we use `list` combined with `range`, `np.linspace`, and `np.logspace` to define the range of values for each hyperparameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "b91a772cb5b4dba4bc383d396826c97ed99b3943"
   },
   "outputs": [],
   "source": [
    "# Hyperparameter grid\n",
    "param_grid = {\n",
    "    'boosting_type': ['gbdt', 'goss', 'dart'],\n",
    "    'num_leaves': list(range(20, 150)),\n",
    "    'learning_rate': list(np.logspace(np.log10(0.005), np.log10(0.5), base = 10, num = 1000)),\n",
    "    'subsample_for_bin': list(range(20000, 300000, 20000)),\n",
    "    'min_child_samples': list(range(20, 500, 5)),\n",
    "    'reg_alpha': list(np.linspace(0, 1)),\n",
    "    'reg_lambda': list(np.linspace(0, 1)),\n",
    "    'colsample_bytree': list(np.linspace(0.6, 1, 10)),\n",
    "    'subsample': list(np.linspace(0.5, 1, 100)),\n",
    "    'is_unbalance': [True, False]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1f7252347591b9f2bdcf4f59ed87f784b4c8ca32"
   },
   "source": [
    "One aspect to note is that if `boosting_type` is `goss`, then we cannot use `subsample` (which refers to training on only a fraction of the rows in the training data, a technique known as [stochastic gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting#Stochastic_gradient_boosting)). Therefore, we will need a line of logic in our algorithm that sets the `subsample` to 1.0 (which means use all the rows) if `boosting_type=goss`. As an example below, if we randomly select a set of hyperparameters, and the boosting type is \"goss\", then we set the `subsample` to 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "8f1839c3d16fb4b7a93fd732954ccd78058f8062"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boosting type:  goss\n",
      "Subsample ratio:  1.0\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random.seed(50)\n",
    "\n",
    "# Randomly sample a boosting type\n",
    "boosting_type = random.sample(param_grid['boosting_type'], 1)[0]\n",
    "\n",
    "# Set subsample depending on boosting type\n",
    "subsample = 1.0 if boosting_type == 'goss' else random.sample(param_grid['boosting_type'], 1)[0]\n",
    "\n",
    "print('Boosting type: ', boosting_type)\n",
    "print('Subsample ratio: ', subsample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5e3146caec4156fa90a3e1f81cf8eaa0bb5ae863"
   },
   "source": [
    "The `boosting_type` and `is_unbalance` domains are pretty simple because these are categorical variables. For the hyperparameters that must be integers (`num_leaves`, `min_child_samples`), we use `range(start, stop, [step])` which returns a range of numbers from start to stop spaced by step (or 1 if not specified). `range` always returns integers, which means that if we want evenly spaced values that can be fractions, we need to use `np.linspace(start, stop, [num])`.  This works the same way except the third argument is the number of values (by default 100).\n",
    "\n",
    "Finally, `np.logspace(start, stop, [num = 100], [base = 10.0])` returns values evenly spaced on a logarithmic scale. According to the [the docs](https://docs.scipy.org/doc/numpy/reference/generated/numpy.logspace.html) \"In linear space, the sequence starts at $base^{start}$ (base to the power of start) and ends with $base ^{stop}$ \" This is useful for values that differ over several orders of magnitude such as the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "a54d3b3af9f0635f26ebc310134706e3e0b25b25"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Learning rate histogram\n",
    "plt.hist(param_grid['learning_rate'], bins = 20, color = 'r', edgecolor = 'k');\n",
    "plt.xlabel('Learning Rate', size = 14); plt.ylabel('Count', size = 14); plt.title('Learning Rate Distribution', size = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Rate Domain\n",
    "\n",
    "The learning rate domain is from 0.005 to 0.5. Using a logarithmic uniform distribution allows us to create a domain where there are as many values from 0.005 to 0.05 as from 0.05 to 0.5. In a linear space, there would be far more values from 0.05 to 0.5 because this represents a larger distance in linear space but in logarithmic space each of these two intervals is the same width because they are multiples of 10 of each other. (Think about going from 1 to 10 and then from 10 to 100. On a logarithmic scale, these intervals are the same size, but on a linear scale the latter is 10 times the size of the former). In other words, a logarithmic uniform distribution lets us sample more evenly from a domain that varies over several orders of magnitude. \n",
    "\n",
    "If that's a little confusing, perhaps the graph above makes it clearer. We can also do a sanity check to make sure the spacing is correct by counting the number of values in each interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 499 values between 0.005 and 0.05\n",
      "There are 499 values between 0.05 and 0.5\n"
     ]
    }
   ],
   "source": [
    "a = 0\n",
    "b = 0\n",
    "\n",
    "# Check number of values in each category\n",
    "for x in param_grid['learning_rate']:\n",
    "    # Check values\n",
    "    if x >= 0.005 and x < 0.05:\n",
    "        a += 1\n",
    "    elif x >= 0.05 and x < 0.5:\n",
    "        b += 1\n",
    "\n",
    "print('There are {} values between 0.005 and 0.05'.format(a))\n",
    "print('There are {} values between 0.05 and 0.5'.format(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a24c021c4770b44a88f371a6348a54b9fc4668ba"
   },
   "source": [
    "As an example of a simple domain, the `num_leaves` is a uniform distribution. This means values are evenly spaced on a linear scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "7be71b814abbfd25e4514e3de127b29a1f20b070"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of leaves domain\n",
    "plt.hist(param_grid['num_leaves'], color = 'm', edgecolor = 'k')\n",
    "plt.xlabel('Learning Number of Leaves', size = 14); plt.ylabel('Count', size = 14); plt.title('Number of Leaves Distribution', size = 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b2f490bcf726d04224f092bd05cfb346a88ff25a"
   },
   "source": [
    "# Algorithm for selecting next values\n",
    "\n",
    "Although we don't generally think of them as such, both grid and random search are algorithms. In the case of grid search, we input the domain and the algorithm selects the next value for each hyperparameter in an ordered sequence. The only requirement of grid search is that it tries every combination in a grid once (and only once). For random search, we input the domain and each time the algorithm gives us a random combination of hyperparameter values to try. There are no requirements for random search other than that the next values are selected at random. \n",
    "\n",
    "We will implement these algorithms very shortly, as soon as we cover the final part of hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8b70d951ac6d66dcb5d39e0d18e82ad83e58652a"
   },
   "source": [
    "# Results History\n",
    "\n",
    "The results history is a data structure that contains the hyperparameter combinations and the resulting score on the objective function. When we get to Bayesian Optimization, the model actually _uses the past results to decide on the next hyperparmeters_ to evaluate. Random and grid search are _uninformed_ methods that do not use the past history, but we still need the history so we can find out which hyperparameters worked the best! \n",
    "\n",
    "A dataframe is a useful data structure to hold the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "cb3d66b0d87aff8da433329682988d531415b5ee"
   },
   "outputs": [],
   "source": [
    "# Dataframes for random and grid search\n",
    "random_results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                              index = list(range(MAX_EVALS)))\n",
    "\n",
    "grid_results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                              index = list(range(MAX_EVALS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b12a7681449e0f3e8ed93581fa14dd893ad3d105"
   },
   "source": [
    "# Grid Search Implementation\n",
    "\n",
    "Grid search is best described as exhuastive guess and check. We have a problem: find the hyperparameters that result in the best cross validation score, and a set of values to try in the hyperparameter grid - the domain. The grid search method for finding the answer is to try all combinations of values in the domain and hope that the best combination is  in the grid (in reality, we will never know if we found the best settings unless we have an infinite hyperparameter grid which would then require an infinite amount of time to run).\n",
    "\n",
    "Grid search suffers from one limiting problem: it is extremely computationally expensive because we have to perform cross validation with every single combination of hyperparameters in the grid! Let's see how many total hyperparameter settings there are in our simple little grid we developed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "59bfa6c52a24f259a75cfc616f1b6d1f360ecbbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2620800000000000 combinations\n"
     ]
    }
   ],
   "source": [
    "com = 1\n",
    "for x in param_grid.values():\n",
    "    com *= len(x)\n",
    "print('There are {} combinations'.format(com))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c05ac62ed102daa76ebafd4bc8030bf33ed03a1e"
   },
   "source": [
    "Until Kaggle upgrades the kernels to quantum computers, we are not going to be able to run evan a fraction of the combinations! Let's assume 100 seconds per evaluation and see how many years this would take:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "2e3faab45df397ba9470f961d238e71e396d64ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This would take 8310502283 years to finish.\n"
     ]
    }
   ],
   "source": [
    "print('This would take {:.0f} years to finish.'.format((100 * com) / (60 * 60 * 24 * 365)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c3d513c3688b8238aac5ce2abbfc6244b4443ca1"
   },
   "source": [
    "I think we're going to need a better approach! Before we discuss alternatives, let's walk through how we would actually use this grid and evaluate all the hyperparameters.\n",
    "\n",
    "The code below shows the \"algorithm\" for grid search. First, we [unpack the values](https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/) in the hyperparameter grid (which is a Python dictionary) using the line `keys, values = zip(*param_grid.items())`.  The key line is `for v in itertools.product(*values)` where we iterate through all the possible combinations of values in the hyperparameter grid one at a time.  For each combination of values, we create a dictionary `hyperparameters = dict(zip(keys, v))` and then pass these to the objective function defined earlier. The objective function returns the cross validation score from the hyperparameters which we record in the dataframe. This process is repeated for each and every combination of hyperparameter values. By using `itertools.product` (from [this Stack Overflow Question and Answer](https://codereview.stackexchange.com/questions/171173/list-all-possible-permutations-from-a-python-dictionary-of-lists)), we create a [generator](http://book.pythontips.com/en/latest/generators.html) rather than allocating a list of all possible combinations which would be far too large to hold in memory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "bc053e847d75e461d52557aea11d062cedfddb61"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def grid_search(param_grid, max_evals = MAX_EVALS):\n",
    "    \"\"\"Grid search algorithm (with limit on max evals)\"\"\"\n",
    "    \n",
    "    # Dataframe to store results\n",
    "    results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                              index = list(range(MAX_EVALS)))\n",
    "    \n",
    "    # https://codereview.stackexchange.com/questions/171173/list-all-possible-permutations-from-a-python-dictionary-of-lists\n",
    "    keys, values = zip(*param_grid.items())\n",
    "    \n",
    "    i = 0\n",
    "    \n",
    "    # Iterate through every possible combination of hyperparameters\n",
    "    for v in itertools.product(*values):\n",
    "        \n",
    "        # Create a hyperparameter dictionary\n",
    "        hyperparameters = dict(zip(keys, v))\n",
    "        \n",
    "        # Set the subsample ratio accounting for boosting type\n",
    "        hyperparameters['subsample'] = 1.0 if hyperparameters['boosting_type'] == 'goss' else hyperparameters['subsample']\n",
    "        \n",
    "        # Evalute the hyperparameters\n",
    "        eval_results = objective(hyperparameters, i)\n",
    "        \n",
    "        results.loc[i, :] = eval_results\n",
    "        \n",
    "        i += 1\n",
    "        \n",
    "        # Normally would not limit iterations\n",
    "        if i > MAX_EVALS:\n",
    "            break\n",
    "       \n",
    "    # Sort with best score on top\n",
    "    results.sort_values('score', ascending = False, inplace = True)\n",
    "    results.reset_index(inplace = True)\n",
    "    \n",
    "    return results    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a075d877eadd196d2b9b41a46b46b7178b153a10"
   },
   "source": [
    "Normally, in grid search, we do not limit the number of evaluations. The number of evaluations is set by the total combinations in the hyperparameter grid (or the number of years we are willing to wait!). So the lines \n",
    "\n",
    "```\n",
    "        if i > MAX_EVALS:\n",
    "            break\n",
    "```\n",
    "\n",
    "would not be used in actual grid search. Here we will run grid search for 5 iterations just as an example. The results returned will show us the validation score (ROC AUC), the hyperparameters, and the iteration sorted by best performing combination of hyperparameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "6672b3dd8c4047bc16a1bca70a3217bf3b761aa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation score was 0.71957\n",
      "\n",
      "The best hyperparameters were:\n",
      "{'boosting_type': 'gbdt',\n",
      " 'colsample_bytree': 0.6,\n",
      " 'is_unbalance': True,\n",
      " 'learning_rate': 0.004999999999999999,\n",
      " 'metric': 'auc',\n",
      " 'min_child_samples': 20,\n",
      " 'n_estimators': 132,\n",
      " 'num_leaves': 20,\n",
      " 'reg_alpha': 0.0,\n",
      " 'reg_lambda': 0.0,\n",
      " 'subsample': 0.5,\n",
      " 'subsample_for_bin': 20000,\n",
      " 'verbose': 1}\n"
     ]
    }
   ],
   "source": [
    "grid_results = grid_search(param_grid)\n",
    "\n",
    "print('The best validation score was {:.5f}'.format(grid_results.loc[0, 'score']))\n",
    "print('\\nThe best hyperparameters were:')\n",
    "\n",
    "import pprint\n",
    "pprint.pprint(grid_results.loc[0, 'params'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8ea3977c21fa859064f19b1dbec0cbdc465b4045"
   },
   "source": [
    "Now, since we have the best hyperparameters, we can evaluate them on our \"test\" data (remember not the real test data)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "bff132b94401f3bf83428b9b1af57c39e32486e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best model from grid search scores 0.73374 ROC AUC on the test set.\n"
     ]
    }
   ],
   "source": [
    "# Get the best parameters\n",
    "grid_search_params = grid_results.loc[0, 'params']\n",
    "\n",
    "# Create, train, test model\n",
    "model = lgb.LGBMClassifier(**grid_search_params, random_state=42)\n",
    "model.fit(train_features, train_labels)\n",
    "\n",
    "preds = model.predict_proba(test_features)[:, 1]\n",
    "\n",
    "print('The best model from grid search scores {:.5f} ROC AUC on the test set.'.format(roc_auc_score(test_labels, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's interesting that the model scores better on the test set than in cross validation. Usually the opposite happens (higher on cross validation than on test) because the model is tuned to the validation data. In this case, the better performance is probably due to small size of the test data and we get very lucky (although this probably does not translate to the actual competition data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "91dd78307ef1ff64ba1550312276ff02f9131d55"
   },
   "source": [
    "To get a sense of how grid search works, we can look at the progression of hyperparameters that were evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "a7ebf4e12d2c8359271fa185ba5d7236ded4ae08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([{'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.5, 'is_unbalance': True, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132},\n",
       "       {'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.5, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132},\n",
       "       {'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.5050505050505051, 'is_unbalance': True, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132},\n",
       "       {'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.5050505050505051, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132},\n",
       "       {'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.51010101010101, 'is_unbalance': True, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132},\n",
       "       {'boosting_type': 'gbdt', 'num_leaves': 20, 'learning_rate': 0.004999999999999999, 'subsample_for_bin': 20000, 'min_child_samples': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'colsample_bytree': 0.6, 'subsample': 0.51010101010101, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 132}],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 1000\n",
    "grid_results['params'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4dcffbc9de69cc1bd65a4c016820577881865555"
   },
   "source": [
    "Look at the `subsample` and the `is_unbalance` because these are the only hyperparameters that change. In fact, the effect of  changing these values is so small that validation scores literally did not change across runs (indicating this small of a change has no effect on the model). This is grid search trying every single value in the grid! No matter how small the increment between subsequent values of a hyperparameter, it will try them all. Clearly, we are going to need a more efficient approach if we want to find better hyperparameters in a reasonable amount of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3ada1510ec1ed54a9fee50997c2093ab16261ba0"
   },
   "source": [
    "#### Application\n",
    "\n",
    "If you want to run this on the entire dataset feel free to take these functions and put them in a script. However, I would advise against using grid search unless you have a very small hyperparameter grid because this is such as exhaustive method! \n",
    "Later, we will look at results from 1000 iterations of grid and random search run on the same small subset of data as we used above. I have not tried to run any form of grid search on the full data (and probably will not try this method)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "88f78cd29ffc2a94806acd37876c5c7ad1323f39"
   },
   "source": [
    "# Random Search\n",
    "\n",
    "Random search is surprisingly efficient compared to grid search. Although grid search will find the optimal value of hyperparameters (assuming they are in your grid) eventually, random search will usually find a \"close-enough\" value in far fewer iterations. [This great paper explains why this is so](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf): grid search spends too much time evaluating unpromising regions of the hyperparameter search space because it has to evaluate every single combination in the grid. Random search in contrast, does a better job of exploring the search space and therefore can usually find a good combination of hyperparameters in far fewer iterations. \n",
    "\n",
    "As [this article](https://medium.com/rants-on-machine-learning/smarter-parameter-sweeps-or-why-grid-search-is-plain-stupid-c17d97a0e881) lays out, random search should probably be the first hyperparameter optimization method tried because of its effectiveness. Even though it's an _uninformed_ method (meaning it does not rely on past evaluation results), random search can still usually find better values than the default and is simple to run.\n",
    "\n",
    "Random search can also be thought of as an algorithm: randomly select the next set of hyperparameters from the grid! We can build a dictionary of hyperparameters by selecting one random value for each hyperparameter as follows (again accounting for subsampling):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "5679964bab15104f395235d95fa0259baa01d488"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'goss',\n",
       " 'num_leaves': 88,\n",
       " 'learning_rate': 0.027778881111994384,\n",
       " 'subsample_for_bin': 220000,\n",
       " 'min_child_samples': 175,\n",
       " 'reg_alpha': 0.8979591836734693,\n",
       " 'reg_lambda': 0.6122448979591836,\n",
       " 'colsample_bytree': 0.8222222222222222,\n",
       " 'subsample': 1.0,\n",
       " 'is_unbalance': False}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.seed(50)\n",
    "\n",
    "# Randomly sample from dictionary\n",
    "random_params = {k: random.sample(v, 1)[0] for k, v in param_grid.items()}\n",
    "# Deal with subsample ratio\n",
    "random_params['subsample'] = 1.0 if random_params['boosting_type'] == 'goss' else random_params['subsample']\n",
    "\n",
    "random_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the `random_search` function. This takes the same general structure as `grid_search` except for the method used to select the next hyperparameter values. Moreover, random search is always run with a limit on the number of search iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "92287f56a80bd35a9cd8e31a91e417fcc344a97f"
   },
   "outputs": [],
   "source": [
    "def random_search(param_grid, max_evals = MAX_EVALS):\n",
    "    \"\"\"Random search for hyperparameter optimization\"\"\"\n",
    "    \n",
    "    # Dataframe for results\n",
    "    results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                                  index = list(range(MAX_EVALS)))\n",
    "    \n",
    "    # Keep searching until reach max evaluations\n",
    "    for i in range(MAX_EVALS):\n",
    "        \n",
    "        # Choose random hyperparameters\n",
    "        hyperparameters = {k: random.sample(v, 1)[0] for k, v in param_grid.items()}\n",
    "        hyperparameters['subsample'] = 1.0 if hyperparameters['boosting_type'] == 'goss' else hyperparameters['subsample']\n",
    "\n",
    "        # Evaluate randomly selected hyperparameters\n",
    "        eval_results = objective(hyperparameters, i)\n",
    "        \n",
    "        results.loc[i, :] = eval_results\n",
    "    \n",
    "    # Sort with best score on top\n",
    "    results.sort_values('score', ascending = False, inplace = True)\n",
    "    results.reset_index(inplace = True)\n",
    "    return results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "04a714aac0572eb2a7b718cfc518c3f199d3b368"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation score was 0.72420\n",
      "The best hyperparameters were:\n",
      "{'boosting_type': 'dart',\n",
      " 'colsample_bytree': 0.9555555555555555,\n",
      " 'is_unbalance': True,\n",
      " 'learning_rate': 0.2797162853084688,\n",
      " 'metric': 'auc',\n",
      " 'min_child_samples': 365,\n",
      " 'n_estimators': 16,\n",
      " 'num_leaves': 50,\n",
      " 'reg_alpha': 0.6326530612244897,\n",
      " 'reg_lambda': 0.5510204081632653,\n",
      " 'subsample': 0.8434343434343434,\n",
      " 'subsample_for_bin': 220000,\n",
      " 'verbose': 1}\n"
     ]
    }
   ],
   "source": [
    "random_results = random_search(param_grid)\n",
    "\n",
    "print('The best validation score was {:.5f}'.format(random_results.loc[0, 'score']))\n",
    "print('The best hyperparameters were:')\n",
    "\n",
    "import pprint\n",
    "pprint.pprint(random_results.loc[0, 'params'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3c879a16aa5c217b664da41f86f980e30412cdd6"
   },
   "source": [
    "We can also evaluate the best random search model on the \"test\" data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "41b7a4dc042776f7f28c0c8d371f927290e18cd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best model from random search scores 0.72466 ROC AUC on the test set.\n"
     ]
    }
   ],
   "source": [
    "# Get the best parameters\n",
    "random_search_params = random_results.loc[0, 'params']\n",
    "\n",
    "# Create, train, test model\n",
    "model = lgb.LGBMClassifier(**random_search_params, random_state = 42)\n",
    "model.fit(train_features, train_labels)\n",
    "\n",
    "preds = model.predict_proba(test_features)[:, 1]\n",
    "\n",
    "print('The best model from random search scores {:.5f} ROC AUC on the test set.'.format(roc_auc_score(test_labels, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c8b40b3b6a25ed849fa4ef014e71830a76e7c721"
   },
   "source": [
    "Finally, we can view the random search sequence of hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "b3a337e380aea5c07c59663d170f4fced1ee29a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        {'boosting_type': 'dart', 'num_leaves': 50, 'learning_rate': 0.2797162853084688, 'subsample_for_bin': 220000, 'min_child_samples': 365, 'reg_alpha': 0.6326530612244897, 'reg_lambda': 0.5510204081632653, 'colsample_bytree': 0.9555555555555555, 'subsample': 0.8434343434343434, 'is_unbalance': True, 'metric': 'auc', 'verbose': 1, 'n_estimators': 16}\n",
       "1     {'boosting_type': 'gbdt', 'num_leaves': 104, 'learning_rate': 0.08633904519421778, 'subsample_for_bin': 200000, 'min_child_samples': 290, 'reg_alpha': 0.8163265306122448, 'reg_lambda': 0.1020408163265306, 'colsample_bytree': 0.6444444444444444, 'subsample': 0.9444444444444444, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 41}\n",
       "2    {'boosting_type': 'dart', 'num_leaves': 130, 'learning_rate': 0.005116582891651224, 'subsample_for_bin': 80000, 'min_child_samples': 235, 'reg_alpha': 0.3469387755102041, 'reg_lambda': 0.5510204081632653, 'colsample_bytree': 0.6444444444444444, 'subsample': 0.6262626262626263, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 551}\n",
       "3                                {'boosting_type': 'goss', 'num_leaves': 137, 'learning_rate': 0.009062087736871187, 'subsample_for_bin': 100000, 'min_child_samples': 415, 'reg_alpha': 0.26530612244897955, 'reg_lambda': 0.6938775510204082, 'colsample_bytree': 1.0, 'subsample': 1.0, 'is_unbalance': False, 'metric': 'auc', 'verbose': 1, 'n_estimators': 110}\n",
       "4       {'boosting_type': 'gbdt', 'num_leaves': 41, 'learning_rate': 0.40260198354127363, 'subsample_for_bin': 60000, 'min_child_samples': 240, 'reg_alpha': 0.12244897959183673, 'reg_lambda': 0.44897959183673464, 'colsample_bytree': 0.8222222222222222, 'subsample': 0.6414141414141414, 'is_unbalance': True, 'metric': 'auc', 'verbose': 1, 'n_estimators': 6}\n",
       "Name: params, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_results['params']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c69590cfed06017904f88dd0752f6746b7a0a517"
   },
   "source": [
    "This time we see hyperparameter values that are all over the place, almost as if they had been selected at random! Random search will do a much better job than grid search of exploring the search domain (for the same number of iterations). If we have a limited time to evaluate hyperparameters, random search is a better option than grid search for exactly this reason.\n",
    "\n",
    "### Stacking Random and Grid Search\n",
    "\n",
    "One option for a smarter implementation of hyperparameter tuning is to combine random search and grid search: \n",
    "\n",
    "1. Use random search with  a large hyperparameter grid \n",
    "2. Use the results of random search to build a focused hyperparameter grid around the best performing hyperparameter values.\n",
    "3. Run grid search on the reduced hyperparameter grid. \n",
    "4. Repeat grid search on more focused grids until maximum computational/time budget is exceeded.\n",
    "\n",
    "In a later notebook (upcoming), we will look at methods that use the past evaluation results to pick the next hyperparameter values to try in the objective function. These methods (including [Bayesian optimization](https://sigopt.com/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf)) are essentially doing what we would do in the strategy outlined above: adjust the next values tried in the search from the previous results. The overall objective of these _informed methods_ is to limit evaluations of the objective function by reasoning about the next values to try based on past evaluation results. These algorithms are therefore able to save time by evaluating more promising values of hyperparameters. This is a really cool topic and [Bayesian optimization](http://gpss.cc/gpmc17/slides/LancasterMasterclass_1.pdf) is fascinating so stay tuned for this upcoming notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e9c87697fc9414741b4816c73dc8676b82689b65"
   },
   "source": [
    "## Next Steps\n",
    "\n",
    "We can now take these random and grid search functions and use them on the complete dataset or any dataset of our choosing. These search methods are very expensive, so expect the hyperparameter tuning to take a while.\n",
    "\n",
    "I have made available a script that implements random search on the features from the `Fork of Fork of Lightgbm with simple features`. (https://www.kaggle.com/poohtls/fork-of-fork-lightgbm-with-simple-features). Anyone is free to use this script as they please on any dataset. I am currently running this script on the set of features for 500 iterations and will make the results public when they are available. \n",
    "\n",
    "For now, we will turn to implementing random and grid search on the reduced dataset for 1000 iterations just to compare the results (I took the code below and already ran it because even with the small dataset, it takes a very long time. The results are available as part of the data in this kernel). \n",
    "\n",
    "## Writing to File to Monitor Progress\n",
    "\n",
    "When we run these searches for a long time, it's natural to want to track the performance while the search is going on. We can print information to the command prompt, but this will grow cluttered after 1000 iterations and the results will be gone if we close the command prompt. A better solution (although not perfect) is to write a line to a csv (comma separated value) file on each iteration. Then, we can look at the file to track progress while the searching is running, and eventually, have the entire results saved when the search is complete.\n",
    "\n",
    "### Extremely Important Note about Checking Files\n",
    "\n",
    "When you want to check the csv file, __do not open it in Excel while the search is ongoing__. This will cause a permission error in Python and the search will be terminated. Instead, you can view the end of the file by typing `tail out_file.csv` from Bash where `out_file.csv` is the name of the file being written to. There are also some text editors, such as notepad or Sublime Text, where you can open the results safely while the search is occurring. However, __do not use Excel to open a file that is being written to in Python__. This is a mistake I've made several times so you do not have to! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9541ebef4cdc1032822a25053fc600c59e769b47"
   },
   "source": [
    "Below is the code we need to run before the search. This creates the csv file, opens a connection, writes the header (column names), and then closes the connection. This will overwrite any information currently in the `out_file`, so change to a new file name every time you want to start a new search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "c5c7d3bd7e7bb7383eace282fb0c3025112076c2"
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "# Create file and open connection\n",
    "out_file = 'random_search_trials.csv'\n",
    "of_connection = open(out_file, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "# Write column names\n",
    "headers = ['score', 'hyperparameters', 'iteration']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bfd483777c1ee9eafcc62c43edb64d9424d25a7b"
   },
   "source": [
    "Now we must slightly modify `random_search` and `grid_search` to write to this file every time. We do this by opening a connection, this time using the `\"a\"` option for append (the first time we used the `\"w\"` option for write) and writing a line with the desired information (which in this case is the cross validation score, the hyperparameters, and the number of the iteration). Then we close the connection until the function is called again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "2ef7f51ecb7f0e884b867d1b45daeb5bd74ac3ae"
   },
   "outputs": [],
   "source": [
    "def random_search(param_grid, out_file, max_evals = MAX_EVALS):\n",
    "    \"\"\"Random search for hyperparameter optimization. \n",
    "       Writes result of search to csv file every search iteration.\"\"\"\n",
    "    \n",
    "    \n",
    "    # Dataframe for results\n",
    "    results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                                  index = list(range(MAX_EVALS)))\n",
    "    for i in range(MAX_EVALS):\n",
    "        \n",
    "        # Choose random hyperparameters\n",
    "        random_params = {k: random.sample(v, 1)[0] for k, v in param_grid.items()}\n",
    "        random_params['subsample'] = 1.0 if random_params['boosting_type'] == 'goss' else random_params['subsample']\n",
    "\n",
    "        # Evaluate randomly selected hyperparameters\n",
    "        eval_results = objective(random_params, i)\n",
    "        results.loc[i, :] = eval_results\n",
    "\n",
    "        # open connection (append option) and write results\n",
    "        of_connection = open(out_file, 'a')\n",
    "        writer = csv.writer(of_connection)\n",
    "        writer.writerow(eval_results)\n",
    "        \n",
    "        # make sure to close connection\n",
    "        of_connection.close()\n",
    "        \n",
    "    # Sort with best score on top\n",
    "    results.sort_values('score', ascending = False, inplace = True)\n",
    "    results.reset_index(inplace = True)\n",
    "\n",
    "    return results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "b3f63599e98af370afc24976679e10c19e3f97b3"
   },
   "outputs": [],
   "source": [
    "def grid_search(param_grid, out_file, max_evals = MAX_EVALS):\n",
    "    \"\"\"Grid search algorithm (with limit on max evals)\n",
    "       Writes result of search to csv file every search iteration.\"\"\"\n",
    "    \n",
    "    # Dataframe to store results\n",
    "    results = pd.DataFrame(columns = ['score', 'params', 'iteration'],\n",
    "                              index = list(range(MAX_EVALS)))\n",
    "    \n",
    "    # https://codereview.stackexchange.com/questions/171173/list-all-possible-permutations-from-a-python-dictionary-of-lists\n",
    "    keys, values = zip(*param_grid.items())\n",
    "    \n",
    "    i = 0\n",
    "    \n",
    "    # Iterate through every possible combination of hyperparameters\n",
    "    for v in itertools.product(*values):\n",
    "        # Select the hyperparameters\n",
    "        parameters = dict(zip(keys, v))\n",
    "        \n",
    "        # Set the subsample ratio accounting for boosting type\n",
    "        parameters['subsample'] = 1.0 if parameters['boosting_type'] == 'goss' else parameters['subsample']\n",
    "        \n",
    "        # Evalute the hyperparameters\n",
    "        eval_results = objective(parameters, i)\n",
    "        \n",
    "        results.loc[i, :] = eval_results\n",
    "        \n",
    "        i += 1\n",
    "        \n",
    "        # open connection (append option) and write results\n",
    "        of_connection = open(out_file, 'a')\n",
    "        writer = csv.writer(of_connection)\n",
    "        writer.writerow(eval_results)\n",
    "        \n",
    "        # make sure to close connection\n",
    "        of_connection.close()\n",
    "        \n",
    "        # Normally would not limit iterations\n",
    "        if i > MAX_EVALS:\n",
    "            break\n",
    "       \n",
    "    # Sort with best score on top\n",
    "    results.sort_values('score', ascending = False, inplace = True)\n",
    "    results.reset_index(inplace = True)\n",
    "    \n",
    "    return results    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eb1e6314d25e7cfe3922f09d50d73d83705f04d7"
   },
   "source": [
    "To run these functions for 1000 iterations (or however many you choose) uncomment the cell below. Otherwise, I have run these functions on the reduced dataset and attached the results to this kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "f6df200465e7263ce209d7a6fc34f55efb6abde4"
   },
   "outputs": [],
   "source": [
    "MAX_EVALS = 10\n",
    "\n",
    "# Create file and open connection\n",
    "out_file = 'grid_search_trials_1.csv'\n",
    "of_connection = open(out_file, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "# Write column names\n",
    "headers = ['score', 'hyperparameters', 'iteration']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()\n",
    "\n",
    "grid_results = grid_search(param_grid, out_file)\n",
    "\n",
    "\n",
    "# Create file and open connection\n",
    "out_file = 'random_search_trials_1.csv'\n",
    "of_connection = open(out_file, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "# Write column names\n",
    "headers = ['score', 'hyperparameters', 'iteration']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()\n",
    "\n",
    "random_results = random_search(param_grid, out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "667fc1640f701179e688d3a914d4e82d67d79eab"
   },
   "source": [
    "# Results on Limited Data\n",
    "\n",
    "We can examine 1000 search iterations of the above functions on the reduced dataset. Later, we can try the hyperparameters that worked the best for the small versions of the data on a complete dataset to see if the best hyperparameters translate when increasing the size of the data 30 times! The 1000 search iterations were not run in a kernel, although they might be able to finish (no guarantees) in the 12 hour time limit. \n",
    "\n",
    "First we can find out which method returned the best results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_results = pd.read_csv('random_search_trials_1.csv')\n",
    "grid_results = pd.read_csv('grid_search_trials_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we save the results to a csv, for some reason the dictionaries are saved as strings. Therefore we need to convert them back to dictionaries after reading in the results using the `ast.literal_eval` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "# Convert strings to dictionaries\n",
    "grid_results['hyperparameters'] = grid_results['hyperparameters'].map(ast.literal_eval)\n",
    "random_results['hyperparameters'] = random_results['hyperparameters'].map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a function to parse the results from the hyperparameter searches. This returns a dataframe where each column is a hyperparameter and each row has one search result (so taking the dictionary of hyperparameters and mapping it into a row in a dataframe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(results, name):\n",
    "    \"\"\"Evaluate model on test data using hyperparameters in results\n",
    "       Return dataframe of hyperparameters\"\"\"\n",
    "        \n",
    "    # Sort with best values on top\n",
    "    results = results.sort_values('score', ascending = False).reset_index(drop = True)\n",
    "    \n",
    "    # Print out cross validation high score\n",
    "    print('The highest cross validation score from {} was {:.5f} found on iteration {}.'.format(name, results.loc[0, 'score'], results.loc[0, 'iteration']))\n",
    "    \n",
    "    # Use best hyperparameters to create a model\n",
    "    hyperparameters = results.loc[0, 'hyperparameters']\n",
    "    model = lgb.LGBMClassifier(**hyperparameters)\n",
    "    \n",
    "    # Train and make predictions\n",
    "    model.fit(train_features, train_labels)\n",
    "    preds = model.predict_proba(test_features)[:, 1]\n",
    "    \n",
    "    print('ROC AUC from {} on test data = {:.5f}.'.format(name, roc_auc_score(test_labels, preds)))\n",
    "    \n",
    "    # Create dataframe of hyperparameters\n",
    "    hyp_df = pd.DataFrame(columns = list(results.loc[0, 'hyperparameters'].keys()))\n",
    "\n",
    "    # Iterate through each set of hyperparameters that were evaluated\n",
    "    for i, hyp in enumerate(results['hyperparameters']):\n",
    "        hyp_df = hyp_df.append(pd.DataFrame(hyp, index = [0]), \n",
    "                               ignore_index = True)\n",
    "        \n",
    "    # Put the iteration and score in the hyperparameter dataframe\n",
    "    hyp_df['iteration'] = results['iteration']\n",
    "    hyp_df['score'] = results['score']\n",
    "    \n",
    "    return hyp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest cross validation score from grid search was 0.71957 found on iteration 0.\n",
      "ROC AUC from grid search on test data = 0.73470.\n"
     ]
    }
   ],
   "source": [
    "grid_hyp = evaluate(grid_results, name = 'grid search')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest cross validation score from random search was 0.72737 found on iteration 7.\n",
      "ROC AUC from random search on test data = 0.74224.\n"
     ]
    }
   ],
   "source": [
    "random_hyp = evaluate(random_results, name = 'random search')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations\n",
    "\n",
    "Visualizations are both enjoyable to make, and can give us an intuitive look into a technique. Here we will make a few simple plots using matplotlib, seaborn, and Altair! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we can plot the validation scores versus the iteration. Here we will use the [Altair](https://altair-viz.github.io/) visualization library to make some plots! First, we need to put our data into a long format dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('notebook')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boosting_type</th>\n",
       "      <th>num_leaves</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>subsample_for_bin</th>\n",
       "      <th>min_child_samples</th>\n",
       "      <th>reg_alpha</th>\n",
       "      <th>reg_lambda</th>\n",
       "      <th>colsample_bytree</th>\n",
       "      <th>subsample</th>\n",
       "      <th>is_unbalance</th>\n",
       "      <th>metric</th>\n",
       "      <th>verbose</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>iteration</th>\n",
       "      <th>score</th>\n",
       "      <th>search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gbdt</td>\n",
       "      <td>56</td>\n",
       "      <td>0.005742</td>\n",
       "      <td>100000</td>\n",
       "      <td>455</td>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>True</td>\n",
       "      <td>auc</td>\n",
       "      <td>1</td>\n",
       "      <td>640</td>\n",
       "      <td>7</td>\n",
       "      <td>0.727372</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gbdt</td>\n",
       "      <td>125</td>\n",
       "      <td>0.046552</td>\n",
       "      <td>260000</td>\n",
       "      <td>435</td>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>0.964646</td>\n",
       "      <td>True</td>\n",
       "      <td>auc</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0.725473</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dart</td>\n",
       "      <td>80</td>\n",
       "      <td>0.124844</td>\n",
       "      <td>100000</td>\n",
       "      <td>355</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.767677</td>\n",
       "      <td>False</td>\n",
       "      <td>auc</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dart</td>\n",
       "      <td>22</td>\n",
       "      <td>0.021262</td>\n",
       "      <td>120000</td>\n",
       "      <td>240</td>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.955556</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>True</td>\n",
       "      <td>auc</td>\n",
       "      <td>1</td>\n",
       "      <td>399</td>\n",
       "      <td>5</td>\n",
       "      <td>0.721275</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>goss</td>\n",
       "      <td>96</td>\n",
       "      <td>0.007747</td>\n",
       "      <td>280000</td>\n",
       "      <td>255</td>\n",
       "      <td>0.979592</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>auc</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720909</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  boosting_type num_leaves  learning_rate subsample_for_bin min_child_samples  \\\n",
       "0          gbdt         56       0.005742            100000               455   \n",
       "1          gbdt        125       0.046552            260000               435   \n",
       "2          dart         80       0.124844            100000               355   \n",
       "3          dart         22       0.021262            120000               240   \n",
       "4          goss         96       0.007747            280000               255   \n",
       "\n",
       "   reg_alpha  reg_lambda  colsample_bytree  subsample is_unbalance metric  \\\n",
       "0   0.959184    0.448980          0.600000   0.611111         True    auc   \n",
       "1   0.693878    0.285714          0.688889   0.964646         True    auc   \n",
       "2   0.530612    0.591837          1.000000   0.767677        False    auc   \n",
       "3   0.693878    0.836735          0.955556   0.818182         True    auc   \n",
       "4   0.979592    0.673469          0.688889   1.000000        False    auc   \n",
       "\n",
       "  verbose n_estimators  iteration     score  search  \n",
       "0       1          640          7  0.727372  random  \n",
       "1       1           70          1  0.725473  random  \n",
       "2       1           37          3  0.721928  random  \n",
       "3       1          399          5  0.721275  random  \n",
       "4       1          124          0  0.720909  random  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine results into one dataframe\n",
    "random_hyp['search'] = 'random'\n",
    "grid_hyp['search'] = 'grid'\n",
    "\n",
    "score_df = random_hyp.append(grid_hyp)\n",
    "score_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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\"verbose\": 1, \"n_estimators\": 132, \"iteration\": 9, \"score\": 0.7112919265323201, \"search\": \"random\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 98, \"learning_rate\": 0.1344000510768803, \"subsample_for_bin\": 200000, \"min_child_samples\": 35, \"reg_alpha\": 0.18367346938775508, \"reg_lambda\": 0.6326530612244897, \"colsample_bytree\": 0.8666666666666667, \"subsample\": 0.7777777777777778, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 11, \"iteration\": 8, \"score\": 0.6822475866256285, \"search\": \"random\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 0, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 1, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5050505050505051, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 2, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5050505050505051, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 3, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.51010101010101, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 4, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.51010101010101, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 5, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5151515151515151, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 6, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5151515151515151, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 7, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5202020202020202, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 8, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5202020202020202, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 9, \"score\": 0.7195722262099563, \"search\": \"grid\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 20, \"learning_rate\": 0.004999999999999999, \"subsample_for_bin\": 20000, \"min_child_samples\": 20, \"reg_alpha\": 0.0, \"reg_lambda\": 0.0, \"colsample_bytree\": 0.6, \"subsample\": 0.5252525252525253, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 10, \"score\": 0.7195722262099563, \"search\": \"grid\"}]}, \"mark\": {\"type\": \"circle\", \"size\": 150}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"search\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"iteration\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"score\", \"scale\": {\"domain\": [0.65, 0.8]}}}, \"height\": 400, \"title\": \"Score vs Iteration\", \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.4.3.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#63c9b580-1eae-4712-aa4b-ac7c49ddd9cd\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#63c9b580-1eae-4712-aa4b-ac7c49ddd9cd"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#63c9b580-1eae-4712-aa4b-ac7c49ddd9cd"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_random = random_hyp['score'].max()\n",
    "max_grid = grid_hyp['score'].max()\n",
    "\n",
    "c = alt.Chart(score_df, width = 400, height = 400).mark_circle(size = 150).encode(alt.Y('score', scale = alt.Scale(domain = [0.65, 0.8])),\n",
    "x = 'iteration', color = 'search')\n",
    "\n",
    "c.title = 'Score vs Iteration'\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average validation score of grid search =   0.71957.\n",
      "Average validation score of random search = 0.71618.\n"
     ]
    }
   ],
   "source": [
    "print('Average validation score of grid search =   {:.5f}.'.format(np.mean(grid_hyp['score'])))\n",
    "print('Average validation score of random search = {:.5f}.'.format(np.mean(random_hyp['score'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Search Values\n",
    "\n",
    "We can show the distribution of search values for random search (grid search is very uninteresting). Even though we expect these to be _random_, it's always a good idea to check our code both quantitatively and visually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"af237783-0425-4f87-ad7b-7c62241e4f77\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
       "    font-size: 14px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#af237783-0425-4f87-ad7b-7c62241e4f77"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
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\"iteration\": 0, \"score\": 0.7209086885269749, \"search\": \"random\"}, {\"boosting_type\": \"goss\", \"num_leaves\": 42, \"learning_rate\": 0.03340371957847807, \"subsample_for_bin\": 40000, \"min_child_samples\": 250, \"reg_alpha\": 0.061224489795918366, \"reg_lambda\": 1.0, \"colsample_bytree\": 0.6, \"subsample\": 1.0, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 29, \"iteration\": 4, \"score\": 0.7189576580336821, \"search\": \"random\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 85, \"learning_rate\": 0.02949448212754248, \"subsample_for_bin\": 260000, \"min_child_samples\": 175, \"reg_alpha\": 0.42857142857142855, \"reg_lambda\": 0.04081632653061224, \"colsample_bytree\": 0.9555555555555555, \"subsample\": 0.5303030303030303, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 52, \"iteration\": 2, \"score\": 0.7187784008578119, \"search\": \"random\"}, {\"boosting_type\": \"goss\", \"num_leaves\": 60, \"learning_rate\": 0.038005888089776646, \"subsample_for_bin\": 80000, \"min_child_samples\": 90, \"reg_alpha\": 0.8571428571428571, \"reg_lambda\": 0.1020408163265306, \"colsample_bytree\": 0.6, \"subsample\": 1.0, \"is_unbalance\": true, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 21, \"iteration\": 6, \"score\": 0.7135839811927724, \"search\": \"random\"}, {\"boosting_type\": \"dart\", \"num_leaves\": 59, \"learning_rate\": 0.014302977675878709, \"subsample_for_bin\": 280000, \"min_child_samples\": 35, \"reg_alpha\": 0.673469387755102, \"reg_lambda\": 0.7755102040816326, \"colsample_bytree\": 0.7777777777777778, \"subsample\": 0.9191919191919192, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 132, \"iteration\": 9, \"score\": 0.7112919265323201, \"search\": \"random\"}, {\"boosting_type\": \"gbdt\", \"num_leaves\": 98, \"learning_rate\": 0.1344000510768803, \"subsample_for_bin\": 200000, \"min_child_samples\": 35, \"reg_alpha\": 0.18367346938775508, \"reg_lambda\": 0.6326530612244897, \"colsample_bytree\": 0.8666666666666667, \"subsample\": 0.7777777777777778, \"is_unbalance\": false, \"metric\": \"auc\", \"verbose\": 1, \"n_estimators\": 11, \"iteration\": 8, \"score\": 0.6822475866256285, \"search\": \"random\"}]}, \"mark\": {\"type\": \"text\", \"align\": \"center\", \"baseline\": \"bottom\", \"size\": 20}, \"encoding\": {\"text\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}, \"x\": {\"type\": \"nominal\", \"field\": \"boosting_type\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}}, \"title\": \"Boosting Type for Random Search\", \"width\": 400}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.4.3.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#af237783-0425-4f87-ad7b-7c62241e4f77\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#af237783-0425-4f87-ad7b-7c62241e4f77"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kvf8ogGSvt+mD8xVJT0u61joqwXO1y65Asu73g23VJUiutvF32Q4k6/5HwUxylL8GyT9a86oTkBzVL1UczBLKHhuBZBV9vrUIIDnY33slfSLp7Z4Z5uAg0Q8EktEdHJ5/mj3aGatu+n2SAcl9CsV+HUju9S8tsdqBq9/gByT39ks1B7QhaUV9tOaL8Sy3VtPXowsBknsla0Ny9R+qgeTefqnyAJZRdtjKTLLKnv+uKCA5yt++HfKjAkQ/GEhGd3Ba/mnn6x+s+YL8NumA5LSminIWkBzlVNr5aiet8jYQIDmqX6o6ePW71oBkVf08uBggOVgqO3DXrWSjAkU9GEhGdW5/3rvufVp943NNcn8D1XoEkLzB2bTj/Zc9T+Ha9T5Sa4tcVxeQrNvmvqdopGWT7pN46lZiRHUst44QK+ChQPIG09rPd+7eO73tPSSg89NSBpLTdIty1q5nt7K7dYuLQDJKe0/LE0j26rbr2a2rvmUMSE77dxbprL5vAVn9vU8st0ZqYd9cgeRWPfs+VK/+0ZVA0vffH9EqUICZZAUm7igBSNbtr3d1pUIy3cdn9fbNerqfeHZ+2jl79uzmzJkzp7zFI16dCgDJOn1NVQHJuv31rq5ESNry4DOSzkn6VtLLks53vtLJIGrfbmFfEGrAfELS652n13+nFZD0bpu64wHJuv0FknX7611diZBs12jA7IOk3dRqX+ti35jdhmrv950BSe+2qTsekKzbXyBZt7/e1ZUMybTk2rfc2oXkdyA9Ojp6cbPZvNQV6vDw8AbtPju+qvcuHntrSrxCFLj79pv16P0Ho7N56p1PR5/DCXEUePOxe0Yn++7Hx/r8y6ujz+OEGArY+4S9X2z7OTg4uO5yXWnX7tpLq6kGl5nkj5+98OD3Tm0uxLCRLMcqsNlsfvFXR//8obHnMZMcq1is46fMJP/xmZ//4pR04yftWKWT7RYFNtKDf3X2p/+57+USZ5J2jfE1Sc9JsuXTPki6XJMEknX/mwGSdfs7tTogOVW5es+LBklzor27Nd3Ian9Py6pfN5t2Hmhsm7S7FUjW2/RWGZCs29+p1QHJqcrVe15ESLq6sW3jDpB0lbm4YECyOEuKSAhIFmFDUUkAyS33SQLJovrUPRkg6S5pFQGBZBU2uhYBJIGka0NFCQYkoziVN08gmVfvCKMBSSAZoU/dcwSS7pJWERBIVmGjaxFAEki6NlSUYEAyilN58wSSefWOMBqQBJIR+tQ9RyDpLmkVAYFkFTa6FgEkgaRrQ0UJBiSjOJU3TyCZV+8IowFJIBmhT91zBJLuklYREEhWYaNrEUASSLo2VJRgQDKKU3nzBJJ59Y4wGpAEkhH61D1HIOkuaRUBgWQVNroWASSBpGtDRQkGJKM4lTdPIJlX7wijAUkgGaFP3XMEku6SVhEQSFZho2sRQBJIujZUlGBAMopTefMEknn1jjAakASSEfrUPUcg6S5pFQGBZBU2uhYBJIGka0NFCQYkoziVN08gmVfvCKMBSSAZoU/dcwSS7pJWERBIVmGjaxFAEki6NlSUYEAyilN58wSSefWOMBqQBJIR+tQ9RyDpLmkVAYFkFTa6FgEkgaRrQ0UJBiSjOJU3TyCZV+8IowFJIBmhT91zBJLuklYREEhWYaNrEUASSLo2VJRgQDKKU3nzBJJ59Y4wGpAEkhH61D1HIOkuaRUBgWQVNroWMTck75X0iKT/IOk/SrpL0n2SLrlWcYJgZ4HkCdSLeyqQjOvdnJkDyTnVjRl7TkjeJOmNRpb/I+lnzX+/LelpSddKkAxIluBC/hyAZH7NI4wIJCO4lDfHOSF5i6S3mv89L+lKM6N8VtKTkr7JW2r/aECyBBfy5wAk82seYUQgGcGlvDnOCck0k3y8KelPJT3QwJKZZF6fGa2jAJCkJfoUAJL0RVeBOSFpY9k1yU8kfSTpTyS9LOklSZdLsYKZZClO5M0DSObVO8poQDKKU/nynAOStsz6YTNr7KvEgHma5dZ8JjPSjQoASbqCmSQ9MEQBIMnu1iF9Ut0xQLI6S10KYibpImNVQeaAZFsg27BzsXXLhy2//kjSq6WoyHJrKU7kzQNI5tU7ymhAMopT+fKcE5Jp2dWAmO6LNEgaOE+y3Jquc5pKfbeTdDcM7bzlBEjma7aSRgKSJblRTi5AshwvSslkTkh2YZVqPsl9kgbe1yQ911zTNNjaj10DTT/dY3ZqDSRLacW8eQDJvHpHGQ1IRnEqX55zQtKq6G7i8d60Y7PK2zqQvEPShebpPpbDzif8AMl8zVbSSECyJDfKyQVIluNFKZnMDck56zQAPyHp9c7Te9rXPW02+4ykc9t20wLJOS0qNzaQLNebJTMDkkuqX+bYc0IyzSLteqT3Rp2hS6oGSbs387zdm3l0dPTiZrOx+zSv+zk8PLzBnc+Or+q9i8dlukZWJ1bg7ttv1qP3H4yO89Q7n44+hxPiKPDmY/eMTvbdj4/1+ZdXR5/HCTEUsPcJe7/Y9nNwcHCq/dp1v+wpMV2T/OvOcuhJlbHrkPbQ9G2bf9rXKS0HO/b9bc+KZSZ5Ujtins9MMqZvc2fNTHJuhePFzzGTtEfRtX9Ocl2ye73R4j4s6VetGeNXzYPV7XF4X0h6aNcTfoBkvKb1yBhIeqhYXwwgWZ+nJ60oGiRPWu8N5wNJd0lDBASSIWzKniSQzC558QPOCclUvN0X+UrzywszXJ88kchA8kTyhT0ZSIa1btbEgeSs8oYMPjck24BMAhUFSiAZsm9PnDSQPLGEVQYAklXaeqKi5oRk2t1qm2bSzf77Nt2cqJgpJwPJKarFPwdIxvdwjgqA5Byqxo4JJHnAeewOnpg9kJwoXOWnAcnKDZ5Q3pyQtHRYbp1gCqfMrwCQnF/jiCMAyYiuzZvz3JAc9bDxeUvtj85y6xKqLz8mkFzegxIzAJIlurJsTnNDctnqBowOJAeIVOEhQLJCUx1KApIOIlYWAkhyTbKylh5WDpAcptPajgKSa3N8f71zQ9KekGPPSn1S0q2t//5mf2p5jmAmmUfn0kYBkqU5UkY+QLIMH0rKYk5IpuuR9lVWduvHnZI+2fJFyYtpAiQXk37RgYHkovIXOziQLNaaxRKbE5J2n+RbzezxclOhfY2V7Xjd9nDy7EIAyeySFzEgkCzChuKSAJLFWbJ4QnNCsruzNRX7tqSnt30rR25FgGRuxcsYD0iW4UNpWQDJ0hxZPp85IWnVpafupG8COck3gMyiFpCcRdbigwLJ4i1aJEEguYjsRQ86NySLLt6SA5LFWzRLgkByFlnDBwWS4S10L2BuSLZnkvb9jr8r6fyu73d0r3BPQCCZW/EyxgOSZfhQWhZAsjRHls9nTkh2r0kaJH/QlMw1yeW9X3UGQHLV9m8tHkjSF10F5oRke3fr7zcD/7q0eyWZSa7zHwWQXKfv+6oGkvsUWt/rc0KS3a3r66cwFQPJMFZlTRRIZpU7xGBzQtIEYHdriDZYX5JAcn2eD6kYSA5RaV3HzA3Jrpo2u3xG0jlJRTyajuXWdTV8qhZIrtP3fVUDyX0Kre/1uSBpz2y9IOmu1mPobNOOPZauqHslgeT6mt4qBpLr9H1f1UByn0Lre30OSPZdi3xB0iuSvpD0ELeArK/RSqsYSJbmSBn5AMkyfCgpizkgma5Dvi/pV5LekGS3fxT1OLpkAjPJktoxXy5AMp/WkUYCkpHcypPrnJB8VdKl5oHmv1PS81rb0gLJPI1W2ihAsjRHysgHSJbhQ0lZ5IKk1WzQLO4HSBZnSZaEgGQWmcMNAiTDWTZ7wnNCMj3UvFsEG3dmt5UB9ikAJPcptM7XgeQ6fd9VNZA8e3Zz5syZU12RfvzshQe/d2pjO3T5qVABIFmhqQ4lAUkHESsLMQckQ0nEcmsou9ySBZJuUlYVCEhWZadLMUCSmaRLI0ULAiSjOZYnXyCZR+dIo8wByXSf5H+V9NPmgeaXSxWFmWSpzsybF5CcV9+o0YFkVOfmy3sOSKZv/3hL0pMzQPLe5sk9pkrfvZfd58U+LOnDbRICyfmaq+TIQLJkd5bLDUgup32pI88FSYPSHLtbDYCvSXquefbr6UbYNgQNorc1YLTjn5D0uqRrfSYAyVJbc968gOS8+kaNDiSjOjdf3nNA0rJ9vnkMXV/mnreAtIGYxjJwXmkeZLD3gepAcr7mKjkykCzZneVyA5LLaV/qyHNB0uptf+nyHNckt80Su5B8WdJ5e17s0dHRi5vN5qWuGYeHhzf489nxVb138bhU38jrhArcffvNevT+g9FRnnrn09HncEIcBd587J7Ryb778bE+//Lq6PM4IYYC9j5h7xfbfg4ODq67hfCG+wkHlNmeVdqDzj2evNNddm2nwUxygClrP4SZ5No7oL9+ZpL0RVeBOWeSNlbfsutJQWkQfESS/X/fd1JyTZI+36sAkNwr0SoPAJKrtH1n0XNCsv1tIGljzT7A7XOo/T2V6VjbvWrfNpKWVb9uNu2kjUPsbt2n6gpfB5IrNH1AyUBygEgrOyQaJN3tYeOOu6QhAgLJEDZlTxJIZpe8+AHnhORcy62uogJJVznDBAOSYazKmiiQzCp3iMHmhmR6+o596bL9FPfFy0AyRJ+6Jwkk3SWtIiCQrMJG1yLmhqRrsnMEA5JzqFp+TCBZvkdLZAgkl1C97DGBJA84L7tDZ8oOSM4kbPCwQDK4gTOkDySB5AxtVX5IIFm+R0tkCCSXUL3sMeeEZLoF5JLTAwRmUZLl1llkLT4okCzeokUSBJKLyF70oHNCMm3a+etd38KxtDpAcmkHlhkfSC6je+mjAsnSHcqf35yQ7H5lVarO8wHnJ1YMSJ5YwpABgGRI22ZPGkjOLnG4AYAk1yTDNa1HwkDSQ8X6YgDJ+jw9aUVzQvKkuWU5n5lkFpmLGwRIFmdJEQkBySJsKCqJuSHZXnK1Bwr8bvraqlJUAJKlOJE3DyCZV+8oowHJKE7ly3NOSHaftmOQ/EFT2tOSruUrc/tIQLIEF/LnACTzax5hRCAZwaW8Oc4JyfaXLv9+U9avJdmXHj+55Wuu8lYvCUhml7yIAYFkETYUlwSQLM6SxROaE5LdmWQqtqjntwLJxXtwkQSA5CKyFz8okCzeouwJzglJK6Z7G0hRt39YgkAye88VMSCQLMKG4pIAksVZsnhCc0Ny8QL3JQAk9ylU5+tAsk5fT1oVkDypgvWdPzckmUnW1zNVVAQkq7DRvQgg6S5p+IBzQpJrkuHbo94CgGS93p6kMiB5EvXqPHdOSKZZ5KuS7CHn9nOvpOclnWZ3a50NFaUqIBnFqbx5Asm8ekcYbU5IWv0GxIsdSP6opG8F4ZpkhDb1zxFI+mtaQ0QgWYOLvjXMAcltDzZPmRe1wxVI+jZUlGhAMopTefMEknn1jjAakOQB5xH61D1HIOkuaRUBgWQVNroWMQck2wnaNchPOhkzk3S1kGBTFACSU1Sr/xwgWb/HYyucE5J8n+RYNzg+mwJAMpvUoQYCkqHsypLs3JB8q3lW6+Us1UwYhGuSE0Sr4BQgWYGJM5QAJGcQNXjIOSFp0nR3txYnF5AszpIsCQHJLDKHGwRIhrNs9oTnhiTXJGe3kAGmKAAkp6hW/zlAsn6Px1Y4JyS5JjnWDY7PpgCQzCZ1qIGAZCi7siQ7NyS5JpnFRgYZqwCQHKvYOo4HkuvweUyVc0Jy7muS2653dp8Zu/P7K7kmOaZd6jkWSNbjpWclQNJTzTpizQnJuZZb2xC8r/XIu+SIjfuapOeGPB8WSNbRyGOrAJJjFVvH8UByHT6PqTIiJH9P0jVJD3aeC5vqvkPSBUl3NX/oA+l3GgHJMe1Sz7FAsh4vPSsBkp5q1hFrTkjOrdC25VbbUZseom6zzmcknds2qwSSc9tUZnwgWaYvS2cFJJd2oLzx54TkXMutScUh92AaJF+WdF7S5aOjoxc3m81LXRsODw9vcOaz46t67+JxeY9cFYEAABJySURBVI6RkYsCd99+sx69/2B0rKfe+XT0OZwQR4E3H7tndLLvfnysz7+8Ovo8ToihgL1P2PvFtp+Dg4NT7deu+2VPiUtB0r6r0n4+lGSQfETS+80S7Q0pM5OM0ajeWTKT9Fa0jnjMJOvw0bOKOWeSfXm2l0JPWkd7JtmeMX4l6Q1Jj0v6QtJDNovcNhiQPKkNMc8HkjF9mztrIDm3wvHi54akbaqx5c4nh+w8zSEnkMyhcnljAMnyPCkhIyBZggtl5TAnJOdebnVREki6yBguCJAMZ1mWhIFkFplDDbIEJB9urhcWIRSQLMKG7EkAyeyShxgQSIawKWuSc0IyayFTBwOSU5WLfR6QjO3fXNkDybmUjRt3DkhuW2ZNKn0kyXagflOCbECyBBfy5wAk82seYUQgGcGlvDkCybNnN2fOnLnh1pYfP3vhwe+d2tiTe/ipUAEgWaGpDiUBSQcRKwsxByT7JLLbNV5pXtj5mLjc+jKTzK14GeMByTJ8KC0LIFmaI8vnMzckbVn1g6bMojbsJOmB5PJNuEQGQHIJ1csfE0iW71HuDOeCZPsh4zu/qip3wd3xgOTSDiwzPpBcRvfSRwWSpTuUP785IMnGnfw+MuJIBYDkSMFWcjiQXInRI8oEkmzcGdEu9RwKJOvx0rMSIOmpZh2x5oBkKGVYbg1ll1uyQNJNyqoCAcmq7HQpBkgyk3RppGhBgGQ0x/LkCyTz6BxpFCAJJCP1q1uuQNJNyqoCAcmq7HQpBkgCSZdGihYESEZzLE++QDKPzpFGAZJAMlK/uuUKJN2krCoQkKzKTpdigCSQdGmkaEGAZDTH8uQLJPPoHGkUIAkkI/WrW65A0k3KqgIByarsdCkGSAJJl0aKFgRIRnMsT75AMo/OkUYBkkAyUr+65Qok3aSsKhCQrMpOl2KAJJB0aaRoQYBkNMfy5Ask8+gcaRQgCSQj9atbrkDSTcqqAgHJqux0KQZIAkmXRooWBEhGcyxPvkAyj86RRgGSQDJSv7rlCiTdpKwqEJCsyk6XYoAkkHRppGhBgGQ0x/LkCyTz6BxpFCAJJCP1q1uuQNJNyqoCAcmq7HQpBkgCSZdGihYESEZzLE++QDKPzpFGAZJAMlK/uuUKJN2krCoQkKzKTpdigCSQdGmkaEGAZDTH8uQLJPPoHGkUIAkkI/WrW65A0k3KqgIByarsdCkGSAJJl0aKFgRIRnMsT75AMo/OkUaJDMnnJV2UdKkj+C2SPpT0QPP3h5vfe305CyQj9atbrkDSTcqqAgHJqux0KSYiJG+S9IakxyXd1wPJeyXd1oDRgPmEpNclXetTDEi69FG4IEAynGVZEgaSWWQONUhESP5eA7wHt8wkT0u60sDTgPqMpHOSvgGSoXpz1mSB5Kzyhg0OJMNaN1viESGZxNi23NqF5MuSzku6fHR09OJms3mpq+bh4eENAn92fFXvXTyeTXgCL6vA3bffrEfvPxidxFPvfDr6HE6Io8Cbj90zOtl3Pz7W519eHX0eJ8RQwN4n7P1i28/BwcGp9mvX/bJwiUMhyUxyYaNKHJ6ZZImuLJ8TM8nlPSgtgxpnklyTLK3LCswHSBZoSgEpAckCTCgshVogadce07Lq1+xuLazLCkwHSBZoSgEpAckCTCgshciQdJGS3a0uMoYLAiTDWZYlYSCZReZQgwBJ7pMM1bBeyQJJLyXrigMk6/LToxogCSQ9+ihcDCAZzrIsCQPJLDKHGgRIAslQDeuVLJD0UrKuOECyLj89qgGSQNKjj8LFAJLhLMuSMJDMInOoQYAkkAzVsF7JAkkvJeuKAyTr8tOjGiAJJD36KFwMIBnOsiwJA8ksMocaBEgCyVAN65UskPRSsq44QLIuPz2qAZJA0qOPwsUAkuEsy5IwkMwic6hBgCSQDNWwXskCSS8l64oDJOvy06MaIAkkPfooXAwgGc6yLAkDySwyhxoESALJUA3rlSyQ9FKyrjhAsi4/PaoBkkDSo4/CxQCS4SzLkjCQzCJzqEGAJJAM1bBeyQJJLyXrigMk6/LToxogCSQ9+ihcDCAZzrIsCQPJLDKHGgRIAslQDeuVLJD0UrKuOECyLj89qgGSQNKjj8LFAJLhLMuSMJDMInOoQYAkkAzVsF7JAkkvJeuKAyTr8tOjGiAJJD36KFwMIBnOsiwJA8ksMocaBEgCyVAN65UskPRSsq44QLIuPz2qAZJA0qOPwsUAkuEsy5IwkMwic6hBgCSQDNWwXskCSS8l64oDJOvy06MaIAkkPfooXAwgGc6yLAkDySwyhxoESALJUA3rlSyQ9FKyrjhAsi4/PaoBkkDSo4/CxQCS4SzLkjCQzCJzqEGAJJAM1bBeyQJJLyXrigMk6/LToxogCSQ9+ihcDCAZzrIsCQPJLDKHGgRIAslQDeuVLJD0UrKuOECyLj89qgGSQNKjj8LFAJLhLMuSMJDMInOoQYAkkAzVsF7JAkkvJeuKAyTr8tOjmoiQPC3pg6b4jyTZ79+0xLhJ0huSHm/+9rakpyVd6xPsLJD06KNwMYBkOMuyJAwks8gcapBokDQAPiPpXANGA+QVSZdaqt8i6TVJz3Xg2WsMkAzVr27JAkk3KasKBCSrstOlmGiQNAA+Ien1ZmZ4r6TbJH3YUuMOSRck3dX87b4ORK8TDki69FG4IEAynGVZEgaSWWQONUgNkPyRpFdbqhs409+6M88bzAGSofrVLVkg6SZlVYGAZFV2uhRTAyS7M8m2MAbJlyWdl3T56Ojoxc1m81JXucPDwxvE/Oz4qt67eOwiMkHKU+Du22/Wo/cfjE7sqXc+HX0OJ8RR4M3H7hmd7LsfH+vzL6+OPo8TYihg7xP2frHt5+Dg4FT7tet+WaDEIdck7Tql/dgSrB3/iKT32bizgFsFD8lMsmBzFkyNmeSC4hc6dLSZpMnYt7v129aM8avW7tYvJD1ks8ht+rPcWmhnzpwWkJxZ4KDhgWRQ42ZMOyIkXeUAkq5yhgkGJMNYlTVRIJlV7hCDAUnukwzRqN5JAklvReuIByTr8NGzCiAJJD37KUwsIBnGqqyJAsmscocYDEgCyRCN6p0kkPRWtI54QLIOHz2rAJJA0rOfwsQCkmGsypookMwqd4jBgCSQDNGo3kkCSW9F64gHJOvw0bMKIAkkPfspTCwgGcaqrIkCyaxyhxgMSALJEI3qnSSQ9Fa0jnhAsg4fPasAkkDSs5/CxAKSYazKmiiQzCp3iMGAJJAM0ajeSQJJb0XriAck6/DRswogCSQ9+ylMLCAZxqqsiQLJrHKHGAxIAskQjeqdJJD0VrSOeECyDh89qwCSQNKzn8LEApJhrMqaKJDMKneIwYAkkAzRqN5JAklvReuIByTr8NGzCiAJJD37KUwsIBnGqqyJAsmscocYDEgCyRCN6p0kkPRWtI54QLIOHz2rAJJA0rOfwsQCkmGsypookMwqd4jBgCSQDNGo3kkCSW9F64gHJOvw0bMKIAkkPfspTCwgGcaqrIkCyaxyhxgMSALJEI3qnSSQ9Fa0jnhAsg4fPasAkkDSs5/CxAKSYazKmiiQzCp3iMGAJJAM0ajeSQJJb0XriAck6/DRswogCSQ9+ylMLCAZxqqsiQLJrHKHGAxIAskQjeqdJJD0VrSOeECyDh89qwCSQNKzn8LEApJhrMqaKJDMKneIwYAkkAzRqN5JAklvReuIByTr8NGzCiAJJD37KUwsIBnGqqyJAsmscocYDEgCyRCN6p0kkPRWtI54QLIOHz2rAJJA0rOfwsQCkmGsypookMwqd4jBgCSQDNGo3kkCSW9F64gHJOvw0bMKIAkkPfspTCwgGcaqrIkCyaxyhxgsIiRPS/qgUfcjSfb7Ny21b5H0oaQHmr893Pzea8hZIBmiUb2TBJLeitYRD0jW4aNnFdEgeZOkZySda8BogLwi6VJLlHsl3daA0YD5hKTXJV3rEw5IerZTnFhAMo5XOTMFkjnVjjFWNEh2odcGYlK8Dc4uVG9wBUjGaFTvLIGkt6J1xAOSdfjoWUUNkPyRpFdbonQh+bKk85IuHx0dvbjZbF5qC/j973///3377bd/x1NUYqEACqAACtSvwJ133vm/fvKTn/xWu9JTC5ftPpNcuJ5iht82oy4mQRLJrgA9kV3y4gekJ/ZbtDQk3a9J7i95HUfQ/OvweUyV9MQYtdZxLD2x3+elIWkZ9u1u/VZSWlb9eszu1v0lr+MImn8dPo+pkp4Yo9Y6jqUn9vtcAiT3Z8kRoxWg+UdLVv0J9ET1Fo8ukJ7YLxmQ3K9RyCNsU9Ozzz5rs3F+UOA3CtATNEJXAXpif08Ayf0acQQKoAAKoMBKFQCSdRnf3S28977Susqnmi0K0Be0BgpMVABIThSuwNOel/RKT15vS3p62xOKCqyDlHwVoC989STayhQAknUZvvexfXWVSzUDFaAvBgq1ssPszgL7ub35gL3zudgr0+a7coHkWp2nbhRAgTUrYB+cXpP0F5L+tPX/z3W+YGLNGv2mdiBZVwswY6jLz5NW0/0GnXa8vm/cOel4nB9HgQTJ9yX9U0n/XtK/kQQkOx4CyThNPSTTvjdF3gyHKFf3Md0NXHyYqtvvodWl69X3SbJnZn+562sIhwat7TggWZuj1IMCNyqQZg1pltD9Hc1QAAW2KAAk62uN7m5GZpL1eTylIvsauk9aJ9rsof29rVNick5sBdi4M8A/IDlApECHtC/GPyTpXUn/SNI7gWogVRRAgfkVYOPOQI2B5EChghyWGv/fSvqXDSTt/8+xYy2Ig6SJAnkUYOPOQJ2B5EChAh1mSyh235NdhP9A0gudL7EOVAqpOirAcqujmJWEYuPOACOB5ACROAQFgivAxp3gBs6UPh+cBggLJAeIxCEoEFwBbgEJbuAM6fPBaaCoQHKgUIUfxk3jhRu0UHr0xULCBxgWSA40CUgOFCrIYcwYghhFmihQgAIstw4wAUgOECnQIXw6DGRWxlR3zSi5XzKjEQwVTwEgGc+zfRnz6XCfQut7fdsKg91Hy/M619cPVDxCASA5QiwORYGgCmxbYUjfAMFDrYMae4K0WV0YKB6QHCgUh6FAcAX6Vhh4qHVwU0+QPqsLA8UDkgOF4jAUQAEUqEgBVhcGmgkkBwrFYSiAAihQmQKsLgwwFEgOEIlDUAAFUAAF1qkAkFyn71SNAiiAAigwQAEgOUAkDkEBFEABFFinAkBynb5TNQqgAAqgwAAFgOQAkTgEBVAABVBgnQoAyXX6TtUogAIogAIDFACSA0TiEBRAARRAgXUqACTX6TtV51Eg3YeW4yHidnP4E5Jel/QDSX8p6SFJl51L7T6pxTk84VCgLAWAZFl+kE1dCuSE5POSfkfS05KuzSjjaUmPSLL//2bGcQiNAkUoACSLsIEkKlUgQfIFSa80NbZnlQaaD5q/f9QCT/fh0w9L+lCSzeLekPR4c47FfbU5rx3nTUl/3swk7dALkn7ZyqEvno1/pYm7DbTtJ7R80eRusdKMNQH0SUlvNfFuk/SApJSrDdGuu/33StuAsiIrACQju0fupSvQhmSCWZqF3SnpE0kGzc8a+Fk9f9YAzv7bYGVLp+k4+1v7v9tLqu2ZZHu5tQ1Jy8GOs7wMVH8o6WcN5L5uQGyg3DUbbc8kLbbB+31Jv2pq+J+SzjV/T0C8tQH1v24MsxwsTvt8i8MPChSnAJAszhISqkiB7nJr+3f7Bo728qi9ZtA7I+msJAPKpdbs0eDziwY2dzUapRmh/boPkileG3J2DbOdw5Al2+5yq51jPyk3G+d/NJC0/A3MaWZsv9tPmlUnq5lNVtT0tZUCJGtzlHpKUsAbkgacBMQEmgTKpSBpNdrYtlxry6rtGeI2SKaZLNc0S+pWculVAEjSGCgwnwJzLLemJda0PGpLnbZUOQWSJ11uNci1r5+mGWH6mylr0Owut6YlY6vBrpemWe58ThAZBSYqACQnCsdpKDBAAe+NO91Z5Nut64dpM4zN6Po27vQtt37b2gg0ZOOOjZ9qso07acOOAdpmtmlTUoKkXd/s27iTjrd4LLUOaCQOWU4BILmc9oyMAksrYGBNG3fsfsoh1yT7cu6e174GmZaIl66V8VFgkgJAcpJsnIQCVSjQvdXEZof/onkoQbrNpF1od9Z3R2sjUfvWFiBZRXtQhCkAJOkDFEABFEABFNiiAJCkNVAABVAABVAASNIDKIACKIACKDBOAWaS4/TiaBRAARRAgRUpACRXZDalogAKoAAKjFMASI7Ti6NRAAVQAAVWpACQXJHZlIoCKIACKDBOgf8PRyJ8i/YALpMAAAAASUVORK5CYII="
     },
     "metadata": {
      "jupyter-vega": "#af237783-0425-4f87-ad7b-7c62241e4f77"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar chart\n",
    "bars = alt.Chart(random_hyp, width = 400).mark_bar().encode(x = 'boosting_type', y = 'count()')\n",
    "\n",
    "bars.title = 'Boosting Type for Random Search'\n",
    "\n",
    "# Add text for labels\n",
    "text = bars.mark_text(align = 'center', baseline = 'bottom', size = 20).encode(text = 'count()')\n",
    "\n",
    "# Display\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boosting type should be evenly distributed for random search. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, for the numeric hyperparameters, we will plot both the sampling distribution (the hyperparameter grid) and the results from random search in a kernel density estimate (KDE) plot. (The grid search results are completely uninteresting). As random search is just drawing random values, we would expect the random search distribution to align with the sampling grid (although it won't be perfectly aligned because of the limited number of searches). \n",
    "\n",
    "As an example, below we plot the distribution of learning rates from both the sampling distribution and the random search results. The vertical dashed line indicates the optimal value found from random search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_hyp['score'] = random_hyp['score'].astype(float)\n",
    "best_random_hyp = random_hyp.loc[0, :].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 8))\n",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "# Density plots of the learning rate distributions \n",
    "sns.kdeplot(param_grid['learning_rate'], label = 'Sampling Distribution', linewidth = 4)\n",
    "sns.kdeplot(random_hyp['learning_rate'], label = 'Random Search', linewidth = 4)\n",
    "plt.vlines([best_random_hyp['learning_rate']],\n",
    "           ymin = 0.0, ymax = 50.0, linestyles = '--', linewidth = 4, colors = ['orange'])\n",
    "plt.legend()\n",
    "plt.xlabel('Learning Rate'); plt.ylabel('Density'); plt.title('Learning Rate Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code repeats this plot for all the of the numeric hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterate through each hyperparameter\n",
    "for i, hyper in enumerate(random_hyp.columns):\n",
    "    if hyper not in ['boosting_type', 'iteration', 'subsample', 'score', 'learning_rate', 'is_unbalance', 'metric', 'verbose', 'iteration', 'n_estimators', 'search']:\n",
    "        plt.figure(figsize = (14, 6))\n",
    "        \n",
    "        # Plot the random search distribution and the sampling distribution\n",
    "        if hyper != 'loss':\n",
    "            sns.kdeplot(param_grid[hyper], label = 'Sampling Distribution', linewidth = 4)\n",
    "        sns.kdeplot(random_hyp[hyper], label = 'Random Search', linewidth = 4)\n",
    "        plt.vlines([best_random_hyp[hyper]],\n",
    "                     ymin = 0.0, ymax = 10.0, linestyles = '--', linewidth = 4, colors = ['orange'])\n",
    "        plt.legend(loc = 1)\n",
    "        plt.title('{} Distribution'.format(hyper))\n",
    "        plt.xlabel('{}'.format(hyper)); plt.ylabel('Density');\n",
    "        plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence of Search Values\n",
    "\n",
    "Finally, we can plot the sequence of search values against the iteration for random search. Clearly there will not be any order, but this can let us visualize what happens in a random search!\n",
    "\n",
    "The star indicates the best value of the hyperparameter that was found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RYQ3MJlzUrwW2S5lnEqGCcTYtjIfcjkOs31TCjYHCk5LJz5OEC7vWonlqyusQ+XhzTP/zovC5ifwcW2Lewn5wW4XftSCmz5U4DgrH2aJKlqfPlvMp7ItFYXUtc+I8Q9Vj5crNQppO0htsCk9FLSwKXzxE+F0py2oiPHVdXKcXjrE2Up4GJDy9v9m2rPK3KHyHU8UT1on5XxLn/Wslv3NRfF3rasLbA05846sozghPyjuh//BC+BxCI9gGNn+KcX5M/1hReOHC46wS+SicB15XFL6sVP6q+K2eSvxehU8foQF1swvKWvM6RD7+HOd5eYl9/O8kbioU7eeFtxBeX+F3LYnpr0qJm83gzcohz7f1mZifxHFwQkrctxi8ZplZFDeZwbdydyuKKxyrc0uEp70t8eoYt2wY61KoVyp6UpfB66BlpNcRhYcbFhSFL6k0nMHz9z5gl5TvKNy422x7VbHehSfQZ9RhfxiqfPpTyjz7JNbho2X2oy+V2P5dxftXjP+fGH9NBdu56msCYPv4/4N12G6qPzefR/WnPlv0J3GcnJQSV9j/lxSFL4zhi0sss1BuLioR/hSb30SfweDbzl9NWeapMe4Hw1jXwjG4MBE2mdAwlyXlAYCYplBvH13h96ReN41WeUqd7heje3u6tzfGPxpzKij0h3qluw9UkP7gOP1xifjFcXpIBctaGqdfs9D/9PShZjCzaTHtOWZ2WeyXczFh54TwJlCa+9x9bTLA3XOEk0cIr6EX+2ecluoTdp27/7Y40N1vJjwlsWOZ/ABgoW/t/YDV7n5HiWS3x+lryy2rAg+6+6Mp4YXf8tVm1poIv4JQQH7EzFoS4e8krNst7v5MjXm531MGODazSYRCLUd48jFN2vY4Ik6vTZvB3VcSXreeRahkh/IGwsXNHWn5JDxxugHYgdCtRsEP4/S4ZGIzm0N4Mq3QBWTBGwlv6f3B3den5DtP6KoD0n//1O04FHfPuPvJhCcQTidst2Uxeg/gbOAeM9uuHnm14BALY/NcYmGcgcWEJ3ig/HFSaj8olF3/W2beNM+UOA4ej9MXVbk82TLVXOYMox6r5Hhf6u5rUsJr3b9/UxwQzxWeSlneG+L0BnfvS1nW1VV+dzlt7n53uQRmtr+ZfdbMLk6UOWfG6LJ1c8qyRqKuLnk+5eGs+kfx30MS4asI3YBtQ6iLkz4Upz8sCi+7rzJ0vq8vEV6Wuy8hbOejCBd3fyE8Nd9MeAvtT2Z2cr3yamGMmY/GfumvSBxXO8YkpX7zG2L9VWw+oR/3f7j7n0rMW0racbOGcAHYRLgglC3b71LCCtcd97n7umREvGZ5Ov5b7VgVm+2PDPOcJ47NcGj8t9rzsD+UqCPqcR52MIPn78+nxF85jGUXFK5fr7YwXvGQ41gNo3wqt59Abdevvyvev6JCXXRwSlyxqq8J3H014S2afczsfDN7WQXfU4rqT9WfIqXUvc4rY4m79ycD3L2DsL9CbWV0rfYjNBo94O5PlUhTrhyo+LppFMvTqu8Xl6B7e7q3N6bVZUDUCWDXOH2iwvQ7x+nTJeKfKkpXzg8JJ43HEw6gnJn9jdBdwo/dvXhwtNcR+hgtV5CnDrxL6LovTXeZ+EJcS0ochD4zS3kG2I7w2uPjZdLNjdPtbehB4qsaJLZEnjbj7hvMrIPwlMeLCK3ruPsaM7uGUMG8m9AFBAwWOt8bRl5KbbtZhFeLATZY+hiLBcntMTdOv2Nm3xniu7dn6P297H7u7m5mywhPL+4MPBbD7zezh4FXmNkB7n5vnOX9hDLnZ+6eScn3u2v8/auuvJJi5XdB/GBmexLepDsVeAWhz+aPDyevZrYjoRJ6TZn0pY7bVUXbK6nasqugVFnQFaeljneRpLlxWlWZM8x6rJLjvd77dzXLK5SbpR5aqPVhhjQlt0W8cPkp4aZOKaW2cSlz47SedXWt51M/Iry5dxyxwS/eHD2W8FRg8c26uXH6cBV1alLN9Yy7ZwndZ9wS8zmVcDH3NcLF34Vmdou7PzecvJrZfxCeyJxaZp5qj6ta6xgof9zMQvWMlL/uGOqaZdjluLt3xWOsefPkFZkNtALd7r5iuPmJ6nEeVigvl6VFunuHmW0Ath3Gd3yO0AXREfHTY2b3EG5C/rB4ewyzfEr77boT5WMt16+l6uJlcbqjmU2J5Xcpc+O02uuX4wj18+nA6Wa2mjAmyi2EN2Z6hlhWgepP1Z8ipaTtQyN1nV+uvp5dIr7Wunwoc+N0fpX3amq9bhqN8rSq+8Vl6N6e7u2NaWqc2tRQO2RB2ZKkmuXF1tiFZvZ14G2Ep8YPIrT6f9rMvuruZwGY2VaEAdHmAJcTBhh9Euhy97yZnUR4lbFU/tKe6qkmPnUVysRZBWmAF97gW0d4zbacxyrJVBm15PdiQuPUx4CfmNlehFb2ZwhPS9Sqt0R4YXv0M/RT9sm3Awrz/YEwgGw5a4eIh8r3c9h8m/2IMJjrhwiDYsLg0xY/KkpbyPffCePXlPNISlip7VgTd38S+FR8g+1UwtN5hQqs1rxeQai87iT0G/wQYRDcbNyfHqf09q5k/SotuwpqOdZFilVd5tShHqvkeKj3/j1Wj5dy2+I8wgXWI8D/Izx1t87dB8ysmfBGcLVGs64uVlzG3UR4O/stZraju7cBRxIuHH6fuFFVUMj7VYQuOKpVt3omXpDcYGZ/JbxZ2Eq4uXt5TFJ1Xs1sf+A7hK4sPkXoq/15oDdebF4FvI/aj6tq6xgYu8eNjBEl3jYoGE/l+Fg9PmrJV2ULdl9pZgcSBgU/kvAWz+sJ10dfNLP3xF406lE+ld1WQ+xHJWerQ5qargnc/U4zewmhkeVwwnb7t/j5opkdPIxeOdKo/ixD9adMRDWWi6UM1dvWSNxjrFUhr88SGm/KuSfxd63XTSNenlZzv3gIurene3tjmhqngsIJ4EvLphr0fEy7O6Ef4mK7x2nFT9G5+98JB8XXY2v7uwndA55pZlfH1/TeQLihd5+7n5SymH+p9PvqaLcycYWW3+VDLKNQaPe4+8Jh56i81Pya2baEJwhzhH5qX+Du98aT4Neb2SuAjxAKm0vrXPEXrGWw24KTS3T7kaawHa9y9+/XIR+FFvjd0yItPO4xN/5bvK//mDC2yrFmdjqhS6/9gCdTujYo5Pv+Ufj9q/F7QgWWfKKj6rzGm/FHEvatt7v7hqIkwzlunyE8ufpSwlgwIqOpljJnLNZj9VSo70rVjeXqzHp6d5we6+4PF8XVuo1Hoq5eThiwdXfSzxVSz6fcvc/Mfkq4uHg/cD6lu9CAkPeXEPp4f7IO+R42d19hZo8R6sbieqbavL6LcF5yobt/KyW+1t+82vNjkS3JGqAHmG5mO9Xw9tRIKZSlc9MiE9c8wxKvge6IH8xsG+AMwltVVzDYjcxIlU/DUaounhunbbEbyXJqvn6Jb0ddHz+Y2a6E3jiOJNwkfV8Fi1H9qfpTpB4KXfKV6i7uxaOVkToolMvPVlku13TdNJrlaYX3i8vRvT3d2xvTNOZUUOjL+oNWQZ/ZxJNw4IMl4o+P09tLxJfl7ll3/2n8HiO8fgih32JIeUI9tugfU8v3DdNMM3tzSn6OJHTp184QryXGi7mHgF3MrNyrkfWwr5mlnSS+P07vc/fulPiL4/Q0wu87QOm+QAsVfE2Nv7ELg9sIAzoW919bTqFv4XeXTVW5Owmt9gfHi6Zi7yBc3LZT1G2jh7FmbiN0PfBWBo+J4icrILx1MQAcMYw+dKtiQ7xHHRUqluRrsrXkdVtCWbsxpfKCyi5ASymUXQuHsQyRWtVS5ozFeqyeCv1SH22bjlNYMJzjvRolt/MQeRiAF7qk2MQI1dVDnU8VLvDSzqcK9clxZjaD0K1GF+HNvGL1rh+HNFQ9Y2G8mrnx32Q9U+/jah6wbxXLSrqP8MDMS8zsoBqXITIhxcaL38d/FzYwK8XuIJy/H2JmaV3Mlypvh8XdO4HPE54w39HCOIUwcuXTcLzFNh13oqBwPVhqXMWkul2/xG6Izo7/7l3hbKo/A9WfIsNTaIjY7B5ZvDZbMKq5GZ6/Enp4OMDMqmlUq/W6CRpQnpa5X1yO7u0Furc3RqlxKrgB+D/gZcAVsTX0BWa2Y9GNmMsJBc7hZvbhorTvI7wqmKGC8YjM7ENmttlJj5ntArwq/lvod7PQTc6h8WSpkLaJ0KfmnkN93wj5ZuICpDA43v/Efy9y90peS/xSnF5tZocUR5pZq5m9z4Y3cCyEwvs7ycIndq3wxfjvhSXm+ynhCcmPEiqva929vUTawtNrw8nrVwndC1xiZps1UJlZk5m9PXapUfBL4AFC4fqt+ARj8XyvKN5nS4ldSlxPGHj1u8njIlb2hd/4ghK/ceFpkYWEiz0nZRDm+Przdwl9El9vZnuk5HtHMzu1wsbjSmxrZkvN7Fgzm5byfW8ACq9HXzPMvLYDHcAMM3t/UdoPAh8YxnpcAawE3mxm58SyILn83c2s0otckWrVUuaM1XqsXm4HHiYMoH1e7EIAADM7gNDn9WgobOf/SAaa2ZuAT5eZb6j6q9519YWEJ88+bGaHFy3r/xHOg1YzON7jC9z9bsLF06sIdWYLoW5Oe8Dkm8BGYJGFAc83Of81s0lmdmhxHobp7HhcbLYtzGxrwrnkzJivW4aZ18Lv/aGi85tZwA+o/WGZAcJT/BC6Nd7k4tfMppjZW2tZtsgEcQ6hDPuimb2rONLMDozXRaMmnr/fSOiB4TsWxukp5Gceg9c8NTOzT8dr1WJvIZTFnYRzXxih8mmYpgPfTp43x2v9/yBcr1w01AJquSYws91iub51cVrCDU2ofLwN1Z+qP0Xq4a+EMaBekazHLDRMXUCJt3DHonjcnU2o/24ws32K05jZDDM7wcx2SATXet004uVplfeLy+VT9/YC3dsbq9xdn3Ds7UkYONQJre03EgZsv4fwJswFRemPieFOeDLmJzGtExoVPpjyHcti/NxE2C9j2LOEPkt/TGgx7Y3h1xQt4+YYniGcjF0T5+0mnKQ6sLhonoVp4Yn4JTF+QUrc3Bi3rCh8QQz/C6FC6yA8HXA9sCHG3Q40F823KMYtSvmuzxJOsp3Qp2ehu4OlhMZAB46o8fctbIMbCYMAtsff91eJbX3VEMs4L6Zz4JAy6U6JaTqBXxAKmSuAWZX8HonlfCD+zg78M+4fvwDujtvbgY8VzbNr3HZO6P92CWHcqt8zuH/fXcV2m0N4fdgT2+zGxO/xK2BKiXmnJfYFB24v8z3NhAEenXBc3RP37VsIN3qzMW5qpfv1EOs1I5GvHsJAxD+N+9tDibg/AlvVIa//lVjmnwn9DD8Q/z+X9GNsblp4yrocQLj4dMKTT9fG/eQBQv+zp6Uct0uGOE6q3qb6TOxPYf9NCa+6zGEE6rFK0lCi/iF0ieDAwkrCE/FLSKk7CU/ZFsq+f8TtcRvhyawLCmXHMH6LssdxTPPviTLn/piHu4rKnLTf8/wYt4pQJl4BXFGUpq51NaHOzMfPnYTzqUI53A0cVmbezyfW04E3lkn7priPOuHJyN/Efe/PcX0dOK9onmUUnbdVsV4XJPL1NKHu/Anh2CjsHxngmOHmlfCm+nMM1tXXEh686iRccF9fyz4e01giXZZQX14V12MVoY/1IY+LemxTfSbGp1T5E+MWUsM1S6n9aqj9rVxeqlifExk893uEUN7+ijCeogOvSqRdRInroBifekyWWe9S4Tsn1n05g+epGcK15zPDOQ4J1yF5wnnvL2KZ8JcY5sB/JNKOSPlUy36U2P5XEsrYZfH3upVQRztwbhX7XVXXBIQbiYWy/y/xu39OuLHphJuWB1TxO6j+HGZeR2r/jGlUf+ozqh/Kl4tzKXFvAfhCjMvF/fD6eFy0EXoL2qzeYuj6rOT+SgXXMhWsa8ljkMHryRzhOujnhHtpDxLe7nVgXiJ9TddNiflHrDylyvvFQ2wz3dvTvb0x+9GbU5GHPj/3JRSyzwGHEV5ZnEFoJf5hUfrrCDvONYQLgH8n7HDXAge6+48r/OrzCYVne1zeewhPLP+Z8Erg+4vSH0No9X2KMOjsAsLJ7XxCQTra+oBDCZXDfMI2W0d4YuEId+8vPeum3P1z59N/AAAgAElEQVTrwP6Ebd1K6MvzUMLTbbcQui24s+QCKrMOODAu72DgzYSK89MMDupXym1x+qi7l+uy8TuEpxKXEwYt/Gj8pD0lV5K7/wR4JXAJoWI9jDDo6yzCK7wnESqU5DzPEn6H0wgXyPsQ+tKeR9jHzo7zVZqHVcBrCU+BrCU81fdGQkH9H8A7PHRDmDZvb1H+0l77LaTtd/d3Efbv3xL6gv83wv6QIzwhd4SHgXDrYQNhvc4kHGs7EtbtrYQn8W4hdIdxmBc99VJLXt39f4BjCQ25e8fv6SAMvHnpcFbE3e8l7CffJFxIvZWwXzcRypZfDWf5IuXUWOaMtXqsrtz9AUJ9/nNCefJOQv/WpwDfiMnWjHAefkYoB+4k9C3+NsLb8se7+xllZv0C4byki/A7Feqv5LLrWle7+4Vx/psJ5z/vIdws+iGwn7v/vszsVzI4COyzhAvqUt9zG/BywoC+6wmD0L+dMC7Kg4R9uNTb07X4KuEc7geE8v4Awrni/oSbbRcAL4/nk8PKq7uvj8v9X8LF6lGEuub7hLourduJiniwkHBc30Y4tt9F6P7lAeD0WpctMhG4++WE4+8nhO5e3kUYKHsN4Txz1MfpcfflMQ+XE8r+dxK6tDmHUMb6ML/iE4Ty1wjXJ4V67mfA69z9kkReRqx8GoYnCWXyUkJd+QbCtc2Hh6gjN1HDNcGTwKdi2jmEcv0thJtfFwCvjOf1lX6/6s9h5lX1pwi4+38TyvXHCffKXkcoE+ZT+ducY4a7n0IoG68llLVHE9apidDwdAyJunkY100FI1meVnu/uCTd29O9vbHMYouaSFXMbAGh5fl2d1/Q2NyMDjP7X+DDwCnuPmR3DyIiIklm9gHCE283u/vbGp0fERGRLYWZLSJ0T/tld1/U2NyIiIiICGjMKZGKWBiX6gOE1usfDpFcRES2UGa2rZntlRJ+AIP9easeEREREREREZEtWiMGIBUZN8zsPODFwOGE/kjPcvfOxuZKRETGsBcDD5nZY4TxAjPAHoSugw242t1/3sD8iYiIiIiIiIg0nBqnZNwxs9cDJ1SYfI27f2YYX3cssCvwPHARg0+9i4iIpFkOfJvQh/dBwDaEQc7vILwxtbiQ0MzmAZ+rdMFx7AIREamzUb6+GDPMbDaD4yFW4jPuPqLjJoqIyJbDzE4gjL1UiT+5+xUjmR8RGX0ac0rGHTNbSBggtRLPuPvckcuNiIhIbRLjN1bE3W3kciMisuXaUq8vzGwu8HQVs+zu7stGJDMiIrLFMbPFwPEVJv+hHtYTmXjUOCUiIiIiIiIiIiIiIiKjRt36AbNnz/a5c+c2OhsiImPGfffdt8bdt290PqQyqsdERDalemx8UT0mIrIp1WPji+oxEZFNVVqPqXEKmDt3LkuXLm10NkRExgwze6bReZDKqR4TEdmU6rHxRfWYiMimVI+NL6rHREQ2VWk9NmmkMyIiIiIiIiIiIiIiIiJSoMYpERERERERERERERERGTVqnBIREREREREREREREZFRo8YpERERERERERERERERGTVqnBIREREREREREREREZFRo8YpERERERERERERERERGTVqnBIREREREREREREREZFRo8YpERERERERERERERERGTVqnBIREREREREREREREZFRo8YpERERERERERERERERGTVqnBIREREREREREREREZFRo8YpERERERERERERERERGTVqnALy3ugciIiIiIiIbJkcXZCJiIiIiGxp1DgFDOTytHdmGMjlG50VERERERGRLcpAboDuge5GZ0NEREREREaRGqei7r4sz6/vZUPPQKOzIiIiDWRmk8zsdDN7zMwyZvacmX3TzLaqcP4zzOznZvaUmbmZLaviu78e5+mqeQVERERSxPol7bNZnWNmLzWzX5rZejPrNrM7zezQEsvd1swuMrPlsd58xMw+bmZWTf5W96xmTe8a3PUWlYiIiIjIlmBKozMwlrg7a7v7yGRzbD+9hUmTqrqeEhGRieFbwCnA9cA3gZfF//c1sze5+1Cv2Z4DrAPuB2ZU+qVm9irgdKALUAUkIiIj4U7gsqKwTZ7OM7M9gbuALPB1YANwIvBbMzvS3W9LpG0GbgX2BS4CHgWOBC4BdgAWVZO5rv4usvksc1rnMMn0HKWIiIiIyESmxqkU3X1Z+rN55mzTQsuUyY3OjoiIjBIzeznwSeA6d39XIvxp4ELgWOCqIRazp7s/Fed7GJhewfdOBi4Hfg1sA8yvaQVERETKe8rdfzxEmnMJD1e82t0fBDCzHwGPABeb2TwffL3pBGB/4BR3vyiGXW5m1wKfN7MfuPsz1WQwk83Q1t3GnNY5TJmky1URERERkYlKj6OVMJDLs7IjQ1dfttFZERGR0fM+wltLFxSFXw70AB8cagGFhqkqnQL8K6FhTEREZMSYWbOZpT44EbuwPRpYUmiYAnD3LuAKYC9CY1TB+wn14+VFi7oAaALeW0se+3P9tHW3MZBXl+siIiIiIhOVGqfKyLuzqjPDuu7+RmdFRERGx/5AHrg3GejuGeBBNr0hVxdmthvwVeDL1T5dLiIiUqV3ExqTNprZqjhW1LaJ+L2BFuAvKfPeHaf7QxijEdgPeCDWk0n3EurTmuvNbD5LW1cbmWzxokVEREREZCJQ41QFOnr6aduQIZ/X4LwiIhPcTsAad+9LiVsOzI7ja9TTd4GngfPrvFwREZGkewljQL0bOB74A/AJ4M7Em1Q7xenylPkLYTvH6XbAtLS0sR5dm0i7GTM7ycyWmtnStWvXpqbJeY72nna6+rvKrJaIiIiIiIxH6sS7Qj39WZZ35Nlhm6k0T1GbnojIBNUKpDVMAWQSaerySq2ZvQ84Ani9u1fVj6yZnQScBLDrrrvWIzsiIjKBuftrioJ+ZGb/B/w3cGqctsa4tLowWQ8yRNpC+tYScbj7ZcBlAHvvu3fJpwDdnTW9a8jms8yYOqNUMhERERERGWfUylKFgVyeFR29dGscKhGRiaqH0J1RmqmJNMNmZjMJY3J8393vqnZ+d7/M3ee7+/ztt9++HlkSEZEtz/8QHrg4Kv5fqOPS6sLierBc2kL6utSZAB19HazLrKvX4kREREREpMHUOFWlvDvtnRnWaxwqEZGJaAWh6760G207E7r8q1cF8CVgK+ByM/uXwofQRZLF/19cp+8SERHZjLsPEOu+GLQiTtO64yuEFbrxWw/0pqWN9egs0rsHrFlnXydretfUc5EiIiIiItIgapyq0fqefto7NQ6ViMgE81dC3XhAMtDMpgKvApbW8bt2IzRO3QP8I/E5gNAN0j+AX9fx+0RERDYR67ddgPYY9BChm74DU5K/Nk6XArh7Hrgf2DfloY4DCPVpPetNALr6u9RAJSIiIiIyAahxahi6+7Is7+ilP5tvdFZERKQ+rgEcOK0o/ERCg9FPCgFmtqeZzRvGd30NeE/K5++EcTreA5w+jOWLiIgAYGazSkR9lTAO8U0A7t4V/15gZvsk5p8OnEB4cOLexPxXE+rHk4qWexqQBX5Wj/wX6+rvYlXPKtz1oKCIiIiIyHg1pdEZGO8K41DN2aaF1mZtThGR8czdHzKzi4FPmNl1wC3Ay4BTgNuBqxLJf094+8mSyzCz42I4wPZAs5mdGf9/xt2vjN/1l7Q8mNkngN3c/Rf1WSsRERHONLPXAn8EngWmA28F3kh4g/eiRNozgMOA35nZt4BOwkMaOwNH+aYtQpcDHwbON7O5wKNxuf8GnO3uT4/UCvUM9NDu7cxpncMk0zOXIiIiIiLjjVpT6iDvTtuGDDO3amZGa3OjsyMiIsNzGrCM8BT4UcAawk27s2IXRkP5KHBIUdhX4/R24Mr6ZFNERKRiS4B/BY4njAWVI7wF9QXgfHfPFBK6+z/N7HXAecDngGZC931HuPttyYW6e7+ZvQk4G3hfXPaTwCeBi0d4nchkM7R1tzGndQ5TJunSVkRERERkPNEZfB2t6+6nL5tn++ktTJpkQ88gIiJjjrvngG/GT7l0c0uELxjm9w9rfhERkWLufgNwQxXpHwXeUWHaDuAT8TPq+nP9rOxaycxpM9mqaatGZEFERERERGqgxqk66+7LMpDLs8M2U2marO4lRERERGQceeJWuOvb0PEMzNgNDjoV9npzo3MlUlbOc6zuWU1PUw8zp85k8qTJjc6SiIiIiIgMQa0nI6A/G8ah6u3PNTorIiIiIiKVeeJW+PVnYGM7TN0uTH/9mRAuMg50D3SzomsFPQM9jc6KiIiIiIgMQY1TIySXd1Zu6GVDz0CjsyIiIiIiMrS7vg2TmqG5FczCdFJzCBcZJ3KeY1XPKtb0riFf0VCRIiIiIiLSCGqcGmFru/tY1ZnB3RudFRERERGR0jqegaZpm4Y1TYOOZxuTH5Fh6OrvYkXXCvpyfY3OioiIiIiIpFDj1Cjo6suyvKOXgZye3BMRERGRMWrGbjDQu2nYQC/M2LUx+REZpmw+S1t3G139XY3OioiIiIiIFFHj1CjROFQiIiIiMqYddCrk+6G/B9zDNN8fwkXGKXdnTe8a1mXWNTorIiIiIiKSoMapUZTLO22dGY1DJSIiIiJjz15vhiO/AVvvAJmOMD3yGyFcZJzr7Oukvbtd3a2LiIiIiIwRUxqdATM7A9gPeDWwO/CMu8+tYTkfAk4H5gGdwE3AGe6+un65HT53Z213H325HLO3amHSJGt0lkREREREgr3erMYombB6s72097Sz/bTtmTxpcqOzIyIiIiKyRRsLb06dAxwKPAmsr2UBZnY68ENgA3AqcClwLLDEzLaqUz7rqisTxqHKDKibPxERERERkdGQyWZo62kjm882OisiE1Y+r/G2RUREZGgNf3MK2NPdnwIws4eB6dXMbGazgbOBvwKHuXsuhv8VuJHQWHVOXXNcJwO5PCs3ZJjZ2sy2rU2Nzs64teSxVVx6x1M8t76HF2/XyskH78GCeXManS0RERERERmDBnIDrOxeyZzWObRMbml0dkQmlLVr17L33nvz0EMPMXPmzEZnR0RERMawhr85VWiYGoZ3Aq3ARYWGqbjcm4CngA8Oc/kjqtDN33Preujq09N71Vry2CrOuvERVm3MMGNaE6s2ZjjrxkdY8tiqRmdNRERERBohp/FdZWi5fI627ja6B7obnRWRCeXGG29kxYoV3HjjjY3OioiIiIxxDW+cqoP94/QvKXF3A/PMrKq3sRphIJdnVWeG59f30NOvRqpKXXrHUzRNNlqbp2AWpk2TjUvvGG6bp4iIiIiMOwO9sOG5RudCxgl3Z3XPajoyHY3OisiEsXjx4k2mIiIiIqVMhMapneJ0eUrccsASaV5gZieZ2VIzW7pu7ZqRzF9V+rN52jZkWN7RS2+/xqMaynPre5jWtOlgxtOaJvP8+p4G5UhEREREGqK3AzpXgMY6kSp19HXQ3t1O3rXviAxHZ2cnd999NwB33303nZ2dDc6RiIiIjGVjYcyp4WqN076UuExRmhe4+2XAZQCvfNV+PjJZq13fQI6VG3ppbZ7C9lu3MHmSNTpLY9KLt2tl1cYMrc2Du3LvQI5dttvsJxcRERGRiSifg+410Lex0TmRcaw328uKrhXMaZ1D8+TmRmdHZMxbv349zz777CZhl197OT453F7JTcrx+W98nhPfdeImaXbddVe22267UcuniIiIjF0ToXGq8IpMC9BbFDe1KM2409OfZfn6PHO2aWFq0RtCAicfvAdn3fgIPf1ZpjVNpncgx0DOOfngPRqdNREREREZab0d0LMWfMw9aybjUDafZWX3SmZPm81WTVs1OjsiY9p5553H17/+daZOnUpzczO5fI6+XB/Z3jBMQbY3y6XfuJTF31rM5EmT6e/vJ5PJ8NnPfpavfe1rDc69iIiIjAUToVu/FXG6c0rczoAn0oxL2XyelRsybOjV4M7FFsybw1eOfjlztp7Kht4B5mw9la8c/XIWzJvT6KyJiIiIyEjJ9kHHc+GNKTVMSR1pHCqRypx77rmcc845mBmdnZ10d3W/0DBVkO3N0t3VTWdnJ2bGOeecw7nnntugHIuIiMhYMxHenPorcBJwIPDPorjXAI+7e9eo56rO3J21XX1MmWRs1TIRfrb6WTBvjhqjRERERLYE+Xx4UyqzodE5kQmuo6+DgfwAs6fNxkxdrIsUmzRpEmeccQaHHXYY73jHO1i1dhX5gc3HbbMmY8fZO3LDDTew//77NyCnIiIiMlaNqzenzGxXM5tnZk2J4BsI3fl9wswmJ9K+HdgT+MkoZ3NErenqI5vTQL0iIiIisoXp64KOZ9QwJaOme6Cbtu42BvLqwUKklAMOOIDHH3+c1pnp4z5vNXMrHnvsMTVMiYiIyGYa/gqOmR0H7Bb/3R5oNrMz4//PuPuVieQ/Ag4BdgeWAbj7ajP7IvAN4DYzu5rQnd+ngceAC0Z8JUZRLu+s2tjHi7adqif4RERERGTiy+ege3VonBIZZX25Ptq62pg1bRatTek330W2dM3NzfR39KfGDWwYoKWlZZRzJCIiIuPBWHhz6qPAV+NnDjAj8f9HK1mAu38T+DAwE7gQ+DjwM+CQidClX7HMQI71PXp6T0REZEJ44lZY/Da44JVh+sStjc6RyNjR3x3ellLDlDRQznOs6lmlcahESrj11luZ2jIVgCktU7ApxuSW0LFNS3MLt96qcxsZHjPby8y+YmZ3m9lqM9toZg+a2RfMbKuU9C81s1+a2Xoz6zazO83s0BLL3tbMLjKz5WaWMbNHzOzjpifCRURGXMMbp9x9gbtbic+CEmmXpSxnsbvv4+5T3X2Ou3/E3VeN1nqMto6efrr6skMnFBERkbHriVvh15+Bje0wdbsw/fVn1EAlAtCzDjpXhnGmRMaAjr4O2rvbybv2SZGkK6+8ks7OTlpbW/n4SR+nc30nHz/x40ybNo3Ozk6uvPLKoRciUt5HgNOBJ4GvAP8FPA6cDdxlZtMKCc1sT+Auwtj0X49ppwO/NbM3JRdqZs3ArcDHgGuAT8blXgJ8aWRXSUREGt44JbVb1ZmhM6M3qERERMatu74Nk5qhuRXMwnRScwgX2VLl86FRqmddo3MispnebC8rulbQn0vvwkxkS9Pf38/NN9/MNttsw3XXXceFF17I9OnTueiii7juuuvYZpttuPnmmxkY0L0LGZZfALu4+wfc/SJ3/567vxf4b2BvNu156VxCr0yHu/u57n4J8AZgBXBx0RtRJwD7A59y90+5++XufgxwHfB5M9sNEREZMWqcGufWbOyjo0cXRiIiIuNSxzPQNG3TsKZp0PFsY/Ij0mjZftjwXOjOT2SMyuaztHW30ZvtbXRWRBoul8tx/PHH88QTT3D44YdvEnfEEUfwxBNP8KEPfYhsVj2/SO3cfam7b0iJuiZOXwEQu/g7Glji7g8m5u8CrgD2IjRGFbwf6AEuL1ruBUAT8N66rICIiKSa0ugMyPCt6+4nl3dmTdcgoyIiIuPKjN1CV37NrYNhA70wY9fG5UmkUfq6oKsd3BudE5Eh5T3PLU/dwk1P3URbdxs7T9+ZhS9fyBt2eUOjsyYyqqZNm8Yll1xSMn6HHXYoGy8yTLvEaXuc7g20AH9JSXt3nO4P3Gtmk4D9gPvdPVOU9l4gz6YNWSIiUmd6c2qC2NA7QNuGDLm8LuZFRETGjYNOhXw/9PeEG/L9PeH/g05tdM5ERlfPOtjYpoYpGTeWti/lu3/7Lu3d7Uxvms7q3tWcc8853Pn8nY3OmojIFsHMJgNnAVngqhi8U5wuT5mlELZznG4HTEtL6+59wNpEWhERGQFqnJpAevqzrOjopS+ba3RWREREpBJ7vRmO/AZsvQNkOsL0yG+EcJEtgcaXknHqun9cR9OkJlqmtJD1LC2TW2ia3MTiRxY3OmsiIluKC4DXAme5++MxrNAdQV9K+kxRmnJpC+lbS8RhZieZ2VIzW7p69erKcy0iIi9Qt34TzEAuz4qODLOmN7PN1KZGZ0dEZNyJ3TucCpwMzAVWAz8jXPQMOQiKmZ1B6B7i1cDuwDPuPjcl3VTgOOBtwD7ADsBK4B7gK+7+aB1WR8aDvd6sxijZMuUGoHNFmIqMM+094Y2pgoH8AE3WxPKutIf1RUSknszsq8AngMvc/dxEVE+cpo17MbUoTbm0hfQ9JeJw98uAywDmz5+vV79FRGqgN6cmIHdnzcY+2jvVzZ+ISA2+BZwP/B34JPBz4BTgpthwNZRzgEOBJ4H1ZdLNJVzMzAS+T7i4uho4HHjQzN5YY/5FRMa+gQxseE4NUzJu7dC6A/25/k3CerI9zGmd06AciYhsGcxsEXAm8APgY0XRK+I0rTu+QljhKYL1QG9aWjNrAWaR3j2giIjUid6cmsC6+7L0DeTZeuoUWlsm0zJlcqOzJCIyppnZywkNUte5+7sS4U8DFwLHMtifeSl7uvtTcb6Hgekl0q0G9nX3B4vy8BPgAeB/gPm1rIeIyJg20BvemNL4UjKOHfOSY/je374HWWie3Ex/rp+B/ABH73k0q3tWM2vaLCZV9EyLiIhUysy+BHwJ+BFwgvtmJxMPEbrpOzBl9tfG6VIAd8+b2f3AvmbWEseZKjiA8ED/0nrmX0RENqWz5Qkum8+zvqef5et7eW5dD2u7+sgMaEwqEZES3gcYof/ypMsJXTp8cKgFFBqmKki3trhhKob/HXgYeEUlyxERGVf6e9QwJRPC/B3m87F9PsZ2U7eja6CL7aZux8f2+Rjzd5hP90A3y7uW05vtbXQ2RUQmDDM7C1gEXAl82N3zxWncvQu4CVhgZvsk5p0OnAD8A7g3McvVhHGlTipa1GlAltC9u4iIjBC9ObUFGcjl2dCbZ0PvAFMmTWKrlsnMaG1m8iRrdNZERMaK/YE8m16w4O4ZM3swxo+o2HXgi4D2kf4uEZFR1d8NG9vUMCUTxvwd5jN/h/SXnHP5HO3d7WzdvDUzp87ETNdcIiK1MrP/BL4MPAvcBry/qFxtd/db499nAIcBvzOzbwGdwImE7vuOKnrb6nLgw8D5ZjYXeBR4K/BvwNnu/vRIrZOIiKhxaouVzYeGqq6+LNtt1cw2U5sanSURkbFgJ2BNUZcOBcuBg8ys2d37U+Lr5eOExqmvlktkZicRn/DbddddRzA7IiLD5A49a6G3o9E5ERl1G/s3ksllmD1tNi2TWxqdHRGR8arwkOCuwA9T4m8HbgVw93+a2euA84DPAc3A/cAR7n5bciZ37zezNwFnE3rRmEUYO/iTwMUjsB4iIpKgxqktXC7vrNnYx8ZMlu2nt9A8RT09isgWrZXQR3maTCLNiDROmdlBwDeB/wPOKZfW3S8DLgOYP3++XkMQkbEp2w9dbWEqsoUayA3Q1t3Gts3bsm3LtnqLSkSkSu6+EFhYRfpHgXdUmLYD+ET8iIjIKFJLhADQN5BjRUcvGzMDjc6KiEgj9QClHmuemkhTd2b2auBmYAXwVnfPDDGLiMjYltkAG55Tw5QI4O509HXQ3tNONp9tdHZERERERBpOb07JC/LurN7YR+9AjtlbtTCpgrGoljy2ikvveIrn1vfw4u1aOfngPVgwb84o5FaS9DuI1M0K4F/NrCWla7+dCV3+1f0uq5ntR+iGYgPwRndfXu/vEBEZNfkcdK0KY0yJyCYy2Qwru1Yyu3U206ZMa3R2REREREQaRm9OyWa6MlmWd/TSl82VTbfksVWcdeMjrNqYYca0JlZtzHDWjY+w5LFVo5RTAf0OInX2V0LdeEAy0MymAq8Cltb7C81sX0LD1EZCw9Qz9f4OEZFRM9ALHc+qYUqkjJznaO9upyOjcdhEREREZMulxilJNZDLs6Ijw4be0t38XXrHUzRNNlqbp2AWpk2TjUvveGoUcyr6HUTq6hrAgdOKwk8kjDX1k0KAme1pZvOG82WxYeo2oJvQMPX0cJYnItJQveuhc0V4c0pEhtTR10F7dzt5zzc6KyIiIiIio07d+klJ7s7arj4yAzm2n755N3/Pre9hxrSmTcKmNU3m+fUjMhyLlKDfQaR+3P0hM7sY+ISZXQfcArwMOAW4Hbgqkfz3wG7AJoWjmR0XwwG2B5rN7Mz4/zPufmVMtxvhjantgAuBg8zsoKIsXe/uev1ARMa2fB662vW2lEgNerO9rOxeyZxpc2ia3DT0DCIiIiIiE4Qap2RI3X1Z+rN55mzTQsuUyS+Ev3i7VlZtzNDaPLgb9Q7k2GW71kZkc4ul30Gk7k4DlgEnAUcBa4CLgLPcK3q0+aPAIUVhX43T24Er49+7A7Pi34tKLGt3wltVIiJjU7YfNq6EXOm37UWkvIHcACu7VzJz6kymN09vdHZEREREREaFuvWTiiS7+XN3AE4+eA8Gck5Pfxb3MB3IOScfvEeDc7tl0e8gUl/unnP3b7r7S929xd13dvdPuXtXUbq57m4p8y9wdyvxWZBIt6RMusJn2civsYhIjfq6YMNzapgSqYO851nTu4Y1vWteuN4SEREREZnI1DglFSt08/fcul429Axw8F7b85WjX86craeyoXeAOVtP5StHv5wF8+Y0OqtblAXz5uh3EBERkdHVvRY2toFuoo9bZtZqZk+bmZvZd1LiX2pmvzSz9WbWbWZ3mtmhJZa1rZldZGbLzSxjZo+Y2cfNbLOHOKS8rv4uVnavZCCvRl8RERERmdjUrZ9ULZvPs7a7j/U9/ey/+0w1gowBC+bN0e8gIiIiIy+fh6426NfYlhPAV4DZaRFmtidwF5AFvg5sAE4EfmtmR7r7bYm0zYQxFPcldIP7KHAkcAmwA6W7rpUS+nP9rOxayexps2ltUlfdIiIiIjIx6c0pqVnenVUb++juyzY6KyIiIiIy0rL9sOFZNUxNAGa2H2GMxS+VSHIuMAM43N3PdfdLgDcAK4CLi96IOgHYH/hU7Ab3cnc/Br4hbtEAACAASURBVLgO+LyZ7TZiKzKB5T3Pqp5VdGQ6Gp0VEREREZERocYpGRZXA5WIiIjIxPfC+FI65xvvzGwycDnwG0IDUnH8VsDRwBJ3f7AQHsdevALYi9AYVfB+oCcuM+kCoAl4bz3zv6Xp6OugrbuNbF7HnoiIiIhMLGqckmErNFB1qYFKREREZOLR+FITzenAPOATJeL3BlqAv6TE3R2n+wOY2SRgP+ABd88Upb0XyLNpQ5bUIJPNsLJrJT0DemtRRERERCYONU5JXbg7qzozrOvub3RWRERERKQe8jnoXAG96xudE6kTM9sd+DLwFXdfViLZTnG6PCWuELZznG4HTEtL6+59wNpE2uK8nGRmS81s6dq1aytbgS1YznOs6lnFusw6XA3FIiIiIjIBqHFK6qqjp5/2zgz5vC6YRERERMat/h7o0PhSE9B3gaeB88ukaY3TvpS4TFGacmkL6VvTItz9Mnef7+7zZ82aVSY7ktTZ10l7Tzu5fK7RWRERERERGZYpjc6ATDzdfVkGcnnmbD2V5ilq/xQREREZN9yhZy30djQ6J1JnZvZB4C3Awe4+UCZpoUWyJSVualGacmkL6dXCWWeZbIYV3SuY0zqHlsmlNr2IiIiIyNimlgMZEf3ZPMs7eunMlLvuFREREZExYyADG55Tw9QEZGYthLelbgHazOxfzOxfgN1ikm1j2AxgRQxL646vEFboxm890JuWNn7nLNK7B5RhyuVztHW30T3Q3eisiIiIiIjURI1TMmLcnTUb+1ilbv5ERERExq58HrpWw4bnIavxQyeoacD2wFHAPxKfJTH+g/H/E4CHCN30HZiynNfG6VIAd88D9wP7xsaopAMI15tL67USsil3Z3XPajoyalAWERERkfFH3frJiOvqy9KXzTNnmxZapkxudHZEREREpKBvI3SvAY1fM26Y2auBmcCd7p4ZKn3UDbwnJXx74BLgN8D3gf9z9y4zuwk4xsz2cfe/xe+dTmi8+gdwb2IZVwOvA04CLkqEnwZkgZ9Vum5Sm46+DrKeZdbUWZhZo7MjIiIiIlIRNU7JqBjI5VnRkWHmVs1sO60J4IW3qSZN0gWUiIiIyKjKDUD3aujXcEBjlZl9BjjE3d+eCLsKeG/89ykze727tw+1rDjG1C9SvmNu/PNJd0/GnwEcBvzOzL4FdAInErrvO8rdk90iXA58GDg/Lu9R4K3AvwFnu/vTQ66sDFtXfxe92V5mtMxgetN0NVKJiIiIyJinxikZNe7O2q4+1nf3k4/Xs5PMmDW9ma2nNjU4dyIiIiJbAHfoXR8+rm6Xx7hjgXsK/5jZoTHsakLXe2cCnwU+Xe8vdvd/mtnrgPOAzwHNhO77jnD324rS9pvZm4CzgfcRxpl6Evgk/5+9+46Pq7wS//85Mxp1yQ0X3GU6pmN6N6ZYTgiQJQFCCC2EzZeEkv1tskmWFBLIJksSUgihQwihhLIJFtiYZtNsUww2YGxiuVfJkqwy9d7z++MZ2WMhyRpZ0lU572Re47ltjniBR3PPc86BP3Z1bKZtnu9RHa2mLl7nklS5xUGHZIwxxhhjTJssOWV6nJ9xI8RXZUt9nFjSZ4/iXFvhZ4wxxhjTXZIxaNxsc6X6jonAgxmvzwU2AJeoqorIHsA57EZySlVXAq3+Aq6qHwNf6OB1aoFr0w8TsJSfoipaRX2inqEFQ8kLtxwHZowxxhhjTPBCQQdgDEB9LMm62igN8RRqq3iNMcYYY7qOKjRsgbq1lpjqW4qAzL6LU4E5GS31PsK12TOmVXEvzoaGDVRFq0j5qaDDMcYYY4wxZidZJ6dEJCwil4rIwyLygogcnt4+JL3dviCZTkmkfDZvi7F6axNbGxMkUn7QIRkz4Kgq0YQXdBjGGGO6SjIGtashVhd0JCZ764BDAERkAnAg8GrG/iFAPIC4TB/TkGhgXcM6amI1+GrfsYwxxhhjTO+QVVs/ESkEZgPHA41AIe5LEbghub8A7sP1PzemUzxfqW1KUNuUIBIOUZgbpigvh/xIOOjQjOl3VJV4yifh+cQSHk0Jb6fWm8YYY/qwpq02W6pv+yfwTREJA8fgElEzM/YfBKwMIC7TB6kqdfE6GhINDM4fTEluSdAhGWOMMcaYAS7bmVM/BqYA5wFvAJuad6iqJyJPAWdhySnTRZKeT13Upy6aJDcnRGlBhJK8HJtNZUwnpDyXhEqk3COe8kl6tnq2X1DP3Xy2vxuNMQBeEho2uaop05f9FFc59U1cYup6Vd0EICIFuO9k9wYXnumLPPWojlbTlGxiWMEwckI2htoYY4wxxgQj299ELwDuUtX/E5Fhrez/FPjy7odlzGclUj5V9XFqG5OMHJRHXo5VUhnTmsxqqOZEVNLz8XxbOd9veSmoqYS8QZA/CMJ2o8mYASu2DRq3WLVUP6CqNcDpIlIKRFU12eKQU4A1PR+Z6Q+iqSjrG9YzJH+IVVEZY4wxxphAZHv3ajTwfjv7mwD7zdZ0q5Tvs6E2xqhB+dbqzwx4vu8SUfGUZ9VQA53vu/ZdsVrIK4WCIZakMmYg8X1o3AzxhqAjMV1MVbe1si1K+9/LjNklX32rojLGGGOMMYHJ9rfPamBMO/snA+s7H44xHeOrsrEuxsjSfApyLUFlBpZY0qMxnqIp4VkiynyWKsTqIL7NJakKh0EoFHRUxpjulIy6Nn5eKuhITBcTkRLgBuBMYCRwqaq+KSJ74Nr9Pa6qS4OM0fR9VkVljDHGGGOCkG1y6kXgchH535Y7RKQMuAL4S1cEZsyu+Kps3BajOC+H0oIca/Nn+rV4yqMx7pJSlpAyHdKcpEo2QvFIiBQEHZExpjs0bXUP0++IyHDgNWASrn36JKAAQFWrRORrwGDgxsCCNP2GVVEZY4wxxpielu1vnD8B3gYWAn8DFDhbRM4ArsEN6r21SyM0ph2qSn0sSX0sSW5OiJL8CEW5YXLCViVg+j5LSJku4aWgbh0UDHZVVCJBR2SM6QpeEuo3QioedCSm+/wMGAUcA6wGNrfY/3/A6T0dlOnfmquohuYPpTi3OOhwjDHGGGNMP5bVHXxV/RT3BSgF/BQQ4D+A7+KG8Z6uqlkN5RWRkIjcICJLRSQmImtE5DYRKerg+SNF5M70eQkRWS0it4vI4GziMH1fIuVT3RBn9dYm1tVGqYu2nBltTO+XSPnUNCZYs7WJdTVRapsSlpgyXSNaC7WrIRkLOhJjzO6KbXP/PVtiqr/7HHCHqr6LWxTY0gpgXM+GZAYCX32qolVsatxEyrd2ocYYY4wxpntkXauvqu8Ah4rIQcABuATVclV9r5Mx/Ab4NvA0cFv6mt8GDheRaara5l1ZERkBzAdGA38GlgAHAf8OnCwiJ6hqUyfjMn1YPOkRT3r4vjKkKDfocIxpVzzlEU14NMRTJFKWiDLdyEvCtnVQMMQ9rIrKmL7F96FxM8Qbgo7E9Iw9cO382uID+T0UixmAoqkoGxo2MCR/iFVRGWOMMcaYLtfpRtKqugSXDOo0EZkMfAt4SlW/mLG9EvgdcCHwSDuX+D4wAbhYVf+Wcf4b6fNuxLXDMANUTVMCERhcaAkq03vEUx6xpE8s6RFLenh+a4uhTVBEJARcB3wDmAhsAR4HblLVxg6c/1/AEcCRQBmwSlUntnP8McDPcW2bFHgD+J6qLtqtH6Qtqm4+TaLBzaLKyeuWtzHGdLFkFBo2uVadZqDYCOzVzv7Dce3+jOk2nnpURatoTDYytGAokVAk6JCMMcYYY0w/0anBPCJysoj8TETuFpH909uK09uzaad3Ea7y6rcttt8NNAGX7OL804Ao8GiL7Y8BMeDyLGIx/dTWxgR1TdbizwQnnvKoa0qyaVuMVdWNrKuJUt0QpzGessRU7/Qb4NfAR7gFFE/gKnr/mU5c7cotwFTgX0BNeweKyLHAq7gk1k3Aj4B9gHkicnBnf4AOSSWgbq1LVBljei9VaKx2s+MsMTXQVABXisieLXekFzZcips71ec1JBvwfC/oMEw7mmdR1cXrUN3599d5a+dx5awrOfvJs7ly1pXMWzsvoCiNMcYYY0xfklVySkTCIvIY8DKuaukKXEs9cHOongG+mcUlj8K1o1iQuVFVY8Ci9P725AExbfHbcboVYBSYJCJ7ZBGP6aeqG+NsqIsSS9qXXtP9Wk1GNVoyqi9oUdF7vqrerao34ipxT8NV9O7KXqo6TFXPANbv4tjfAQngZFX9jar+BjgZV0F1W6d/kI5qrqKqXeOSVcaY3sVLuiRytN08t+m/foL7jvUecCvus+FrIvI3YC7uM+Z/gguv62xo3MBVL1zFY588Rk3M/n3vrVSVmlgN6xvXE0u5GZbz1s7jlvm3sCW6hdLcUrZEt3DL/FssQWWMMcYYY3Yp28qp7wJfxN2ka543BWxPKD0NlGdxvdFAlaq2Ns15HbCHiLTXj+1DYIiIHJa5Mf16SPrl+NZOFJGrReRtEXl7a3VVFiGbviqa8FhfG2XzthjxlCWpTNfwfSWWtGRUP7K7Fb2o6oqOvJGI7I1bhPGEqq7LOH8drlprmoiM6mDcuycVh7o1dgPcmN4ktg1qV7v/Ps2ApKobgWNxM3avwH0+fRX4EjAbOElV+035a1W0ioc/fpgrZl3Br97+FR9Wf/iZCh3TOyS9JBsbN1IVreL+JfcTCUcoyClARCjIKSASjvDAhw8EHaYxvZaqkvSTRFNRGhIN1MZqqYpWsbFxI2vr17Kmfk3QIRpjjDE9ItuZU5cCD6nq7SIyrJX9H5NdcqoQaOsbdyzjmLaWc/8WOBd4XESux83AmpzengQi6fM/Q1XvAu4COPiwI+xbzwDSEE/REE+RHwlTkp9DcV4OIrLrE02/l0j5xFMevg++Kp4qqqAo6f9vf+0rpDzfkk/9T5sVvSLSkYrebN8L4M1W9r2FuxF5JDCzC9+zbc2twxKNUDQCcmxWnzGB8H1o3AzxhqAjMb2Aqq4BviAipcB+uATVp/0pKQUwsXQi5+19HnNWzaE+Wc/ctXOZu3YuE0snUl5WzqnjTqUgpyDoME0LDYkGVtevZlDeoJ2254fzWdewro2zjBkYVJWUnyLpJ7c/ml/vqo1pWMI9FKUxxhgTrGyTUxNpv81QLTsqljqiCRjRxr78jGNaparzRORCXFuk5pt3HnAPrqrqPGBbFvGYASSW9IglPaoaEoQEQiKEQkJJfg6l+TbodyBIej7RpEcs4RFL+qR8P+iQTPB2VdF7vIjkqmpX9MBrbovb2t2b5m1juuB9spOMuSqqwqFQkM1HujFmtyWjUL8RbPaOaUFVtwELg46ju0RCEa446Aq+csBXmLd2HhWVFSyvXc7KbSu54/07eODDB5g6firlZeWMKxkXdLgmw4jCEdTEaijIKSAcChOSEDEvxpjinv8VxpigJD2XfEp4iZ2SUVb9aYwxxrQv2+RUPTC0nf17A1uyuN564EARyWvlRuAY3A3Cdm8AquoTIvIUcDBQAnyiqptFZAGuR/unWcRjBiBVxVPwUPAgnm7RNrQol6K8bP8TMb1dNOHRmEgRTXgkPUtGmc/Y3YrebN+LNt4v1uKYzxCRq4GrAcaPHd3WYZ2TWUVVMBRy2wzDGNMVmue/WWtNM8DlhfOYNmEa0yZMY1nNMioqK5i3dh5NqSaeXfEsz654lkP2OIQZk2ZwzKhjCIesuiBo5+9zPne+fyekIDecS8pP4fkel02+LOjQjOlyKT+1PQGVmYiyJJQxxhjTOdneeX8NuEREftlyh4gMwbUgej6L6y0EzgSOBrZPTBWRfOAw3KDfXVJVD1iUcf4o4HDgVVVts/LKmLYkPZ9N22LkhELk54YoiIQpiITJCWc7ps0ESVVJeD7xlE886dOUsBlQZpd2q6K3E+8FkNeZ98psTzvlsIO751/sZAyS6yEcgfxSyBsEIft70Jgu5SVdtZTNlhrwRMTHdRHOhqpqv1xNte+Qfdl3yL5ccdAVzFk1h+cqn2Nj00Y+qPqAD6o+YFj+MM6aeBZnTTyLofntrZ803WnKyClcc+g1PLX8KTY1bWJk4Ui+uM8XOXTEoUGHZsxuU1WiqSgxL0Y0FSXpJYMOyRhjjOlXsv0i83Ncguol4IH0tkNFZB/ge0AR8IssrvcY8H3gejKSU8DXcavF/9q8QUT2AiKqurS9C4pICNfmL5yO15hOS/k+DTGfhlgKgLxImKLcMEV5OUQsUdXrqCrxlE804RFLuVZ9torNZGm3K3qzfK/m67bUvK13DGzwkq6SKloLJaMgYnM/jOkS0RpXMWWfVcZ5iOyTU/1eaW4p5+9zPufufS7vbn6XihUVvL3pbapj1Tyy9BEe++Qxjht9HOVl5Rw07CCbJRuAKSOnMGXklJ22bY1upSHRwJD8ITYvzPQpST9JNBUlmnRJKfs+aYwxxnSfrJJTqvq2iJwP3Avcn978v7jBvJuB81T1oyyut1hE/ghcm27NVwEcAHwbeBV4JOPwF4EJ6fcCQESKcUPrnwYqgUHARbgB8j9Q1Zez+fmM2ZV40iOe9KhpSjKkMMLgwtygQxrw4imPWCI9Oyrp4duXB7N7uqSiN4v3AjgONysx07G4G5TvdOH77T7fg23r3SyqQlulbkynJWPQuBlSXZXrNv2Bql4WdAy9WUhC25MgGxs38vzK55m9ajb1iXpeW/car617jfEl4ykvK+e0cadRGLGWtEFLeAk2NW6iMFLIkPwhREI219f0Pqq6vTLKqqOMMcaYnpV1CwhVrRCRicAZuESSAMuBWZ1soXc9sBI3N2MGUAX8HrhJVXc1ECYBfABcDOyJa3+0EDhbVWd1IhZjOkRV2dqYIJr0GF6cZ+3+elAitSMRFUt61qbPdLUur+hti6p+KiJvAxeIyH+r6vr0dUcDFwAvqerGzv0Y3ah5Nk6yCfIHQW4x2Cp1YzomGYNYLcQbgo7EmD5tVNEoLpt8GRfvfzGvrXuNisoKPqn5hNX1q7nzgzt58KMHOW3caZSXlTOhdELQ4Q54Tckmoqkog3IHMShvkFW3mcCl/NT2ZFQsFcPf5a0nY4wxxnSHDienRKQAd7PsE1WdDzybfuyW9Lyo29KP9o6b2Mq2BHDh7sZgTGdFEx7raqMU5+VQmJtDfiRkX7a6kOcriZRPIuUTT3lELRll2iEi44Cf4CqfRuAWKrwkIsOB/wH+pKoL27vG7lb0puP4ano7wHAgV0R+mH69SlX/knH4dcDLwDwR+X1627eAEPCdDv/wQUjG3CO0xc2iyi91s6mMMZ+VaHQt/JKxoCMxfVB60cLngUnpTSuAZ1W1d7R+DVBuOJep46cydfxUPq39lIrKCl5d+yrRVJSKygoqKis4aNhBlE8q57g9jyMn1C/Hc/UJqkptvJaGZAND84daZZvpEZ7v4alHyk/hqUfCSxDzYlYdZYwxxvQS2fx2Hgfuxt1Im9894RjT93i+UhdNUhdNEhKhIDdMYW6YotwcQiFLVHVUIuWT8PztyahEyifl2wo20zEiUga8BeSnn/ds3qeqW0RkCnAVO1rptWd3KnoBrgROabHt5vTzq8D25JSqviEipwI/Sz8UeAO4QFXf78B7Bc/33U33aI2bRZVfatVUxjRLRqGp2pJSptNE5L+BH+K+t2X+xfp7Efm5qv4kmMh6n70H7823D/82V0y+gjmr51BRWcGGxg0sqV7CkuolDMkbwlkTz+LsiWczrGBY0OEOWCk/xeamzRTkFDC0YKi1+jOd5vkeKU1tf075qc8ko2xelDHGGNO7dTg5paq+iKwBSrsxHmP6NF+VxniKxniKKklQEAlTnJ9DUW7YKqoA31eiSY+Up3iqpHyflOeqo2xWlNlNPwd84CAgipuDmKkCt+p8l3anoje9/dSOvE/G8W8Cp2dzTq+VjLqHVVOZgc73oGGzq5gyppNE5FpcRfBC4DfAR7gE1YHADcBNIlKtqn8ILsrepzi3mHP3Ppdz9jqHRZsXUVFZwcKNC6mJ1/DoJ4/y+LLHOXbPYykvK+eQPQ6x39EDEk1FWd+wntLcUgblDSIk1ibd7Fo0FaUp2URTqgnP94IOxxhjjDG7Kdu+Bg8CXxWR21U13h0BGdNfqCpNiRRNiRQhEYrycgiHBF8VXxVVSPmK5ymKUpSXQ0l+Dnk54aBD7zJJr7kln5sTFU/aFwjTbaYBv1fVNSLS2nLoVcDYHo5p4MqspsotgsJhkJMbdFTG9IxkDOo3uASVMbvnW8AC4ERVTWVsf19E/g68nj7GklOtCEmII0YewREjj2Bz02aeX/k8s1fOpi5Rxxvr3+CN9W8wtngs5WXlTB0/laJIUdAhDziqSl28joZEA6V5pZTklliSaoBRVWJejHgqTtJPkvATOyWdFP3M8cYYY4zpP7JNTr0BnA8sEpE7gOVAU8uDVHVuF8RmTL/hq1Ifa7+v9bZokm3RJLk5IUoLIhT3graAnq/Ekh4K5ISEcEjICUm7K0xjSY/6mEvK2Xwo04NKgQ3t7M8l+8880xUSjZBsgvxBUDAUQnbTyfRj0VrXxs9unpmuMR64o0ViCgBVTYrIX4Ff9HxYfc+IwhFceuClXLTfRby+/nUqKiv4eOvHrG1Yy12L7+Khjx7ilHGnMKNsBmWDyoIOd8Dx1KMmVkNdvI6S3BKrpOrnkl6SqBclmowS82KWcDLGGGMGsGxv1L2Q8efbgZa/RUh6W/8p/TCmhyVSPlX1cbZKwlVSRXb85+Srbp/HlPR8wiEhNxwiNyeEICiKr25FmZeuzvJVCYtLKDVXbnm+e4REyMsJkRcJkRMKkfL97bOf4kn3Hq0Jb09UhQiFQBBCAtGkRyJlc6JMINYAk9vZfyzwaQ/FYlpSdTft49vcPKq8Ejefypj+wvehcTPEG4KOxPQvq4GSdvaXpI8xHRQJRzh13KmcOu5UVtSuoGJlBa+seYWYF2PWylnMWjmLA4cdSHlZOcePPt7mIfUwX/3tlVSD8wdTktvev/6mr0h4CWJejFjKVUh5apXFxhhjjHGyTU5dwWcTUsaYbuCrUhdNQrTtiivPd8kqdqPJZlMi+3Oak1sJLBFleo2ngGtE5F52VFApgIh8EbgA+FFAsZlmvg+xbe4RCrskVV4J5OQFHZkxnZeKQ/1G8NqvkDamE/4A/KeI3KuqO1UHi8gY4BqscqrTJg2exLWHXcvlky/nxdUvUlFZwbqGdXxU/REfVX/E4LzBnDnhTM6aeBYjCkcEHe6A4qlHdbSa+kQ9JbklFOYUEg7Z+te+orkyKp6KE0vFLBk1gInI0cChqnp3xrYvAD8DhgIPqur3s7jefwFHAEcCZcCqtuYAi8gDwNfauNQFqvr3FsfnAT8AvgqMBtYC9wP/o6r2S54xxnSTrJJTqvpAN8VhjDHG7I6fA58D5gNzcYmp74nILcDRwCLgtuDCM5/he66aKloL4ciORFXYVqmbPsL3IVbrZqtZSyLTPeqATcBSEXkYWIr7fDsQ+AqwDNgmIpdmnqSqD/V0oH1ZUaSIc/Y6h89P+jwfVH3AzBUzmb9xPrXxWh5f9jh/X/Z3jhp1FDMmzeDQ4Ydau7kelPASVEer2SpbyQ/nU5JbQkFOQbstxk3PS/pJVxXlxYmmojvNjDID3o8AH7gbQETGA38DGoEtwHdFZLmq3t/B690CbAXeBQZ38JyvtrJtQSvbHgO+ANwHvAkcB9wM7A1c1sH3MsYYk6WsklMich/wZ1Wd38b+o4FrVPWKrgjOGGP6mgUrtvLowjVs2BZlz9ICLjxqHEdPGhp0WP2eqm4TkeYvEBfj2syeAdQCdwA/UNVYgCGa9nhJaNrqHjl5OxJVtkra9EaWlDI954GMP/97K/uPbHEMuOSVJac6QUQ4dPihHDr8UKqiVTy/8nlmrZxFbbyW+RvnM3/jfMYUj2H6xOmcPv50inOLgw55wFBVoqko0VSUcChMcaSY4kgxEVvQ0uNUlZSmSHgJoqkosVSMlP+ZsXjGNDsUVwXc7ELc97TDVHWdiDwHXI2rUOqIvVR1BYCILAF2+Rexqj68q2NEpByXmPq1qn4nvfkeEakFbhSRu1T1jQ7GaIwxJgvZtvW7DJiDW5nemjJc2awlp4wxA86CFVu5/aXl5ISE0vwcqhvj3P7Scq5jH0tQ9QBV3QZcB1wnIsNxX3y2qE1Z7ltScfdoqoZIIeSXQm5R0FGZgc73IdnoZkolmywpZXrKaUEHMFDtUbAHlxxwCV/e78u8teEtZq6YyYfVH7KuYR33LLmHhz5+iFPHnkp5WTl7Dd4r6HAHFM/3qIvXURevIyxhIuEIeeE8QhJCRNj+v/Sfc0I55IZzgw47UL76pPwUmjGhIUdyWm2VqKr46rtzNEXST5L0kiT9JCk/ZYkok61hwMaM12cBc1V1Xfr1P3CLCzukOTGVDXGlliVAg6q2NZfg4vTzb1ts/y1wI3AJYMkpY4zpBtkmp3alCLBerMaYAenRhWvICQkFEfdFryASJpr0eHThGktOdTMRuQl4SlWXAKjqlhb7JwNfVNWfBhGf6QRVSDS6RzgH8gdDXimErJ2S6UGphKuSitdbQsr0OFV9NegYBrpIKMJJY07ipDEnsWrbKmZWzuSVNa8QTUWZvWo2s1fNZv+h+1NeVs6Jo0+0Sp4e5qmHl/KIpdovjg+HwhTkFFCYU0hhpLCHotvBVx/P90ipS+40J3wSfgI/fa9cVXck19LPIQm12kZSEMKhMGEJbz8mJCEEcf9MMt/LS7Y586k5uaeq+Pj4vm/zoUxXqwVGwvaZTsfiWvM1U6Cgm2OowyWnEiIyF/hhK92gjgLWqeqazI2qukZE1qf3G2OM6Qa7TE6le8JOzNi0v4ic3MqhQ3HtJj7tmtCMMaZv2bAtSmn+zn+t5kdCbNwWDSiiAeXHuM+fJW3sPwjX89ySU32Rl4LGKldNVTgMCjraYt6YTmqu3ks0BR2JMaaXmFA6bJO4wwAAIABJREFUgW8e+k0uO/AyXl7zMjMrZ7Kmfg1Lty5l6dal3Lv4Xs6YcAbTy6YzonBE0OGaDJ7v0ZBooCHRsKMtYG4xkdDOyURVdckd9XaqwPLU2149pCh54TxyQ7nkhHJQ3DmqSsJLkPATJLyEq1RKJ306WsSvqq66qYfWQjQn94zpRouAq0RkDnAekA/Mythfhput2B02Ar8B3sHNuDoUuB6YJyLlqjon49jRwEdtXGcdMLa1HSJyNa4tIePHj++isI0xZmDpSOXU5bgbepp+/CD9aElwgw4v77LojDGmD9mztIDqxvj2yimAWNJnVGl3LwYzHZAPWB+Svk7VJalSMSgaYVVUpns0bbV5UqbXEJEiXLuhfXDtkaTFIaqqV/Z4YANYYaSQGZNmUF5WzpKqJcysnMlbG96iLlHH35f/nSeXP8lRo46ivKycw0cc3mrliwlOZlvA5mqj7fusasiYrnYzMBtYgPv8ekFV387Y/znaHhuyW1T1ey02PSMij+ASZn/Cfa42KwTibVwqlt7f2nvcBdwFMGXKFPvF0RhjOqEjyalngJW4D5L7cH/xvtniGAUagIUty2CNMWaguPCocdz+0nKiSY/8SIhY0iflKxceNS7o0PolESkFMktohqWrfVsaCnwFsM+n/iLeAF4CikdBzsCeI2G6kJeEhk2QbL89lDE9RUSOx83jaK83sAKWnAqAiHDw8IM5ePjBVEermb1qNs+vfJ6tsa0s2LiABRsXsGfRnkyfOJ1pE6ZRklsSdMimBb/N8TPGmK6gqm+IyBG4WVN1wKPN+0RkGC5x9XQPxrNcRB4HLhORfVV1WXpXE5DXxmn56f3GGGO6wS6TU6r6PvA+gIhMwM30WNzdgRljTF9z9KShXMc+PLpwDRu3RRlVWsCFR42zeVPd5wbgpvSfFTewtuUQ22YC/GdPBGV6SCoBtatdcipS6B45+VZNZTonVueq8qxayvQuv8d1pvgCME9VawOOx7RhWMEwLtr/Ii7Y9wLmb5hPRWUFH1R9wIbGDdz34X08/PHDnDz2ZMrLytlnyD67vqAxxvQT6QTQsla2V+O+z/W0lennPdgR13pgTBvHj8G19jPGGNMNOlI5lWmOJaaMMaZtR08aasmonvNK+llwSaqngQ9aHNNc2fuWqr7Rc6GZHpNKuEc0fc82nAPhXMgrhbziYGMzvZ/vuWopmy1leqcDgZtU9Z9BB2I6JieUwwljTuCEMSewpn4NFZUVvLT6JZpSTcxZPYc5q+ew75B9KS8r58QxJ5IXbmuhvjHG9B8iUgacDowE/qqqK0UkFxgFbFTVRA+G07xCIHPW1ULgKyIyLrMblIiMw82j+kcPxmeMMQNKtsmpeSKyFLgXeEhVt3RDTMYYY8wuqeqrwKuwvbL3TlXtlp7lpg/xUu6RaIJ4ERQNdwkrY1qK1UFTNfjW1sn0WhuAZNBBmM4ZVzKObxzyDS498FJeWfMKFZUVrNy2kmU1y1hWs4x7F9/LGRPOYHrZdEYVjQo6XGOM6RYi8j/AjUAYt3DwTVz1Uj7wEfBD2u5+0dn3LAI8VY212H44cAHwsar+K2PX33Bt4K8HvpOx/fr081+7Mj5jjDE7ZHu35nvAZcCvgFtE5J/APcAsVeuDYowxJhiqennQMZheKNEIySYoGAK5RZBjK9QNbqZU42ZXcWdM73YPcLGI/F5VvaCDMZ1TkFPA9LLpnD3xbD7a+hEVKyp4Y/0b1CfreerTp3j606c5YuQRzCibwREjjyAs4aBDNsaYLiEi3wD+P+B3wLO4GVMAqOo2EfkH8Hk6mJwSka8CE9IvhwO5IvLD9OtVqvqX9J/3AZ4TkWeA5UAjcChwBeABV2deV1VnisizwI0iMgiXQDsON9PxYVV9Lasf3BhjTIdllZxS1V8CvxSRE3B/SV8AnAesE5H7gftVdWWXR2mMMcZ0gIiEgf2BIcBnhg+p6tweD8oESxWatrpHKORmU+WVQm5h0JGZnuR7EK+HRINLThnTN9yKayf0poj8CbfS/DNJqo58tonIfrgWuEekrxkBVgMVwK9UdUMrx/8PcAqQC7wL/EhVX2rl2oOAnwHnA8OAfwF/wFU02wLGNBFh8rDJTB42mZpYDbNXzeb5lc9TFa3inU3v8M6mdxhZOJLpZdOZNn4ag/IGBR2yMcbsrm8CT6vq9SIyrJX9HwDXZnG9K3GfS5luTj+/CjQnpzYCc4DTcBVRBbhq5MeAW1V1aSvXvgBXxXUJ8FXcnKmbgF9kEZ8xxpgsdarPjaq+DrwuIt8CLsJ9QPw38AMReQm3yu9pVbU2FMYYY3qEiHwXV+Fb2s5hthx5IPN9iDe4R06eq6iyuVR9nyrEaiG2DUQAcc/b74kreMmM18b0GQW4ZM+RuO9XLQmuRVJHPtvGAnvi5jOuBVLAwbjV4xeKyGGquhlARPYC3kgf80ugDvg6MEtEpqvqnO0BuJkhLwCHA78HPgamA3fgZov8OKufeIAYkj+EL+/3Zf5tn39jwcYFVFRWsGjLIjY1beKBDx/grx//lRPHnMiMshnsO2RfRCTokI0xpjP2Bf7Uzv4twB4dvZiqntrB4zbiEkwdlm4B+MP0wxhjTA/ZrSEMqtoI3CMiM3Er6y4BpqUfW0Tkf4FfWxsKY4wx3UlErsKtMH8V1y7i58BvcLM6rgRW4G6UGeOk4lC/EWIFUDwCwpGgIzKdEW9wc6M8Ww9l+qU/Al8CngHmATWdvZCqvgi82HK7iMwFHse1bv9levOtwGDgSFVdlD7uIeBD4I8isn9GRdRVwFHAt1X19+ltd4vIk8D3ReR+VV3V2bj7u3AozHGjj+O40cextn4tz1U+x4urX6Qx1cjLa17m5TUvs9egvZgxaQYnjTmJ/Jz8oEM2xphsxICidvZPAGp7KBZjjDG9UKeTUyISAj6Hu+k3PX2t14C7gDiuNPcXuA+bbMp0jTHGmGxdA7ylqqelW0b8HJipqi+JyO3AIqxqyrQmGYXa1VA41FVSmb7B96Bhs5srZkz/9QXgPlX9eje+R3PiaAhsHyJ/DvBKc2IKQFUbROQe4Ke4ZNSC9K6LgSbg7hbX/S2uzd+X2ZH0Mu0YWzKWrx/ydb564Fd5de2rzFwxk8ptlfyr7l/87r3fcd+S+5g2fhrTy6Yzunh00OEaY0xHLMCNArmt5Q4RycdVN73e00EZY4zpPbJOTonIvrghgpfiWjVsxbVwuLtF39YnROQOXNs/S04ZY4zpTgewowVD82ruHABV3SAidwHXAfcFEJvp7VShsdrNJCoaARFbmd6rJZqgYZNLUBnTvwmwsEsv6G4GFgP5wIG47hfgZk8BHALk4YbBt/RW+vkoYEF6seIRwLvpdkiZFgB++liThfycfM6aeBZnTjiTpVuXUlFZwWvrX6Mh2cAz/3qGZ/71DIePOJwZZTOYMmoKYbG1N8aYXutXuJawf2HH97BRInIW8BNcy9mLgwrOGGNM8LJKTqXbPpyA+6L0KvAd4ElVTbRxyjzcanZjjDGmO3lAcwlF8/PQjP0rgX16MiDTB6USULcW8kuhcBiE7IZfr6LqWvhFrfuLGTBeAY7FdaboKlfhFhY2Wwlcoqrz0q+bS3LWtXJu87Yx6echuLlYnzlWVeMiUp1x7GeIyNW4mVeMHmeVQC2JCAcMO4ADhh3AlfEreWHVCzxf+Tybo5t5b/N7vLf5PYYXDGd62XTOmHAGg/MGBx2yMcbsRFXniMi/A7ezIwn1l/RzAvi6qra2GMIYY8wAkW3l1P7Ar4G7VHV5B46fA5yWdVTGGGNMdlYDZbD9htga4CTg0fT+o3CVvsbsWmybaxdXMgoiBUFHY8AlDhs2umdjBo7rgZdE5EbgD+0sCMzGM8BSXPXU4bgWfsMz9hemn+OtnBtrcUx7xzYfX9jGPlT1LtKJt0MOP0TbOs7A4LzBXLDvBZy/z/m8s/EdZlbO5N3N77IluoWHPnqIRz5+hBPGnEB5WTkHDD0AEQk6ZGOMAdzf9SLyD+AC3D1FAZYDj6tqawshjDHGDCDZJqfGqGqHJ06r6hZchZUxxhjTneYCM4D/Sr9+ArheRAqAEHAJ1tLPZMP3YNt6m0UVNN9z7Rabql3llDEDy8u4QfK/An4hIhtwlcKZVFX36ugFVXUtsDb98hkReRJYKCIFqnorbn4UuNZ+LTX3PG1q8dzasc3HN7Wxz3RCWMIcvefRHL3n0axvWM9zlc8xZ/UcGpINvLr2VV5d+yplpWXMmDSDU8aeQn6Otak1xgRPVTeyc9WuMcYYA2SZnMpMTInIfsCk9MsVqvpJVwZmjDHGZOF24P30zbUo8CNgX+Br6f2zge8FFZzpo5pnUaXibhZVKBR0RP2XKvipHY9UApJN7p+9MQPXanbMUewWqvqBiLwHfBO4FVif3tVaO77mbc0r3WuAaGvHikgeMAxbqNhtRheP5sqDr+QrB3yF19a9xszKmXxa+ymV2yr5w6I/cP+S+zl9/OlML5vO2JKxQYdrjBmAROQp4AGgQlVTAYdjjDGmF8q2cgoRmYpb8bB/i+1LgW+r6otdFJsxxhjTIekFEp9kvG4EzhGRQYCnqg2BBWf6vniDS5KU7Ak5uUFH0//EG6CpCjy7Z2FMJlU9tYfeqoAdcxoX49r0HdfKccemn98GUFVfRN4FDheRPFXNzCYfjatcfrt7QjbN8nPymTZhGtMmTGNZzTJmrpjJvHXzaEw18o8V/+AfK/7BYcMPo7ysnKNHHU3Y5ikaY3rOdOALQLWIPAI8pKrvBhyTMcaYXiSrJcDpxNTzwHjgbuAG4EbgHmAc8Fz6GGOMMaZHiEixiNwnIhe03KeqdZaYMl3CS0LdGpdIMV0jFYe6dVC/0RJTxnQzERnVxvbTgIOAtwDSn5n/BE4VkUMzjisGrsLNCVmQcYm/4eZKXd3i0tcDKeDxLvoRTAfsO2RfbjjyBu4/634un3w5IwtHArBoyyJuWXALV71wFY998hg1sZqAIzXGDBAjgWtwiwi/hWsju0RE/kNE9gw2NGOMMb1BtpVTtwCbgGNbDi4UkZtxX2p+Tusr7Ywxxpgup6oNInIh8HpXXE9EQsB1wDeAicAW3M21m9IVWV12vriJ5RcB1+LaEObh2jg9BvxWVbd1xc9kuoiqS6SkBkPhMLCB89nzUpCo31GNZozpKX9K3wh8CViFmwd1JHAhUA98J+PY/wJOB2aLyG+AbcDXce37ZqjuNADubuBy4NciMhH4GCgHzgN+pqqV3fgzmTYMyhvE+fuczxf2/gLvbXqPmZUzeWfTO1RFq3j444f529K/cfzo4ykvK2fysMmIfZ4ZY7pB+rvM3cDd6c+IrwFfAX4J3Coic4AHVfXRwII0xhgTqGyTU4cAt7RMTIEbrisif2bHMHpjjDGmp3yESwR1hd8A3waeBm4DDki/PlxEpqmq34Xn/wz4Pu5m4U+AJHBq+s/lInJci5uApjeI1rp5SMWjrM1fR/g+JBogXg/JaNDRGNOniMgJuO9XxwBDgJZZBFXVjnyn+xvupuBXgeG4WVargD8Dv1LV1RkX/DT9vr/AzWvMBd4FzlbVOS3ePCEi03CfZxfh5kz9C7dC/o/Z/bSmq4UlzJRRU5gyagobGzfy/Mrnmb1qNvWJeuatm8e8dfOYUDqB8rJyTh17KoWRwqBDNsb0U6q6Evcd5ycicjxwKXAxcAZgySljjBmgsk1O1eFW1rVlG1Db+XCMMcaYTvklcIeI/EVVl3X2IiIyGXdD7SlV/WLG9krgd7gV5o90xfkikoNre/QucEZG0upOEUnhVhUeCizq7M9julEq4dr8FQyB3GJLUrVGFaI17mE5VmOyJiInA3Nw38Hm4yqSXgKKcTOdFuM+Q3ZJVR8nixZ7qvoxbk5IR46txVUAX9vR65ueN6poFJdNvoyL97+Y19a9xszKmSyrWcaqbav40/t/4oEPH2DquKmUl5UzvnR80OEaY/opESnCdYzYFygKOBxjjDEByzY59QRwkYj8UVV3Gg4gIhHcarknuio4Y4wxpoP2B9YAi0XkWdxMjKYWx6iq3ryL61yEW5X+2xbb78atIL+EdpJTWZ4fwQ2h39hKNdb69PMu2wiaAKlC01b3CIUhtwhCOelHGMJ5EM72V61+IpWAhk3Wus+Y3fMDYAMwBVfptBnXxeIlETkT+DvwzQDjM31QbjiXqeOnMnX8VJbXLKeisoK5a+cSTUWZWTmTmZUzOXiPg5lRNoNj9jyGnNAA/RwzxnSZdCvzM3DVUufiZhVWAX8AHgwwNGOMMQFr9zdNEWm5ZOpO4Hhgbrr/+FLcF6UDgRuAcPoYY4wxpif9OOPP57VxjAK7Sk4dBfjsPOwdVY2JyKL0/i45X1WjIjIXOFtEvgs8iRsefyruZuPDqrp8F+9negvfg1grI8JyciFSBLmFkJM/MOZUxeqgscqqpYzZfUcDv1bVLSIyNL0tBKCqs0XkL7jPtalBBWj6tn2G7MN1Q67jioOu4MXVL1JRWcGGxg0srlrM4qrFDM0fytkTz+bMCWcyrGBY0OEaY/oYETmIHe379sR915mJS0jNbLno3RhjzMCzq2VQK3E38zI131Vp2RO2efsSXJLKGGOM6SllXXSd0UCVqrZW7rEOOF5EclU10UXnfwX35ewX6Qe4z92fAzd19ocwvUgq4R7RGpeYihS4R04B5OT1r2SVl4LGzZBoWbRojOmkPNxnB0Dz50pJxv5FuIpcY3ZLSW4J5+59LufsdQ6LNi+iorKChRsXsjW2lUeWPsKjnzzKcXseR3lZOQfvcTDSnz67jDHd6YP089vArcDfVHVrgPEYY4zpZXaVnPopn01OGWOMMb2Kqq7K5ngRyQe+BMxS1U0ZuwrZcQOwpVjGMW0lp7I9Pw6swN18fB73mftF4Ifp43/ezs9wNXA1wPixo9s6zPQmqi5x05y8EYFwrquoCkdcsioU2XG8hCAUCibWbPgeJJugcQv4LTtUGmN2wwZgLICqNopILXAQ8HR6/1jcKnRjukRIQhwx8giOGHkEmxo3MWvVLGavnE1doo7X17/O6+tfZ1zJOMrLypk6biqFkcKgQzbG9G6/BB5MzzE0xhhjPqPd5JSq/riH4jDGGGN60iDgflzv88zkVBMwoo1z8jOOaUuHzxeRQuAN4F1VvTDjuEdF5FHgpyLyd1X9pLWLqepdwF0AUw472BaS9EWqbiZTe3OZQmGXtArnuedIgdsWpFQC4ttcQspLWvs+Y7rPQuCEjNezgRtEZBWuvd+1wPwgAjP938iikVx64KVctN9FvLb+NSoqK1i6dSlr6tfw5w/+zIMfPshp406jvKyciYMmBh2uMaYXUtXvBR2DMcaY3q1bp5uKyEjcUPczVPWl7nwvY4wxJkut9aRZDxwoInmttOYbg2vZ11bVVLbn/xuwD/BfrVznCeDLwIlAq8kpM0D4XrrSKiMnGs6B3BIoGNwziartSbQYJBogGdv1OcaYrnAvcJmIFKhqFPg+cBLwQHr/RuA/A4rNDBCRcITTxp3GaeNO41+1/+K5yud4ee3LxLwYz618judWPsfkYZMpLyvnuNHHEcmsADbGmDQRKQYGk56dmElVV/d8RMYYY3qDbk1OpVlDamOMMX3FQuBM3BD6ec0b020ADwPmduH5Y9LPrWUXclo8G7ODl3IzrGK1kD8Y8ge5hFVX8D1INLqKKD/pnr2EVUcZEwBVfQF4IeP1ChHZFzgd8IDXVLUuqPjMwLPX4L249vBruWzyZby05iUqKitY17COD6s/5MPqDxmcN5izJpzF2WVns0fBHkGHa4zpBUTkQlzL8gPaOczm1htjzABlN72MMcaYHR7DrUy/nozkEvB13KyovzZvEJG9gIiqLu3M+cBH6eevAY+3iONr6eeFnfopzMCg6pJU0RrX8i+3yLX98z2XUPISboZVXgnk5LZ/LS8J0VrXrs8SUcb0WqraCPwj6DjMwFacW8w5e53D5yd9nve3vM/Mypks2LCA2ngtjy17jCeWP8Exo45hxqQZHLLHIYjYelVjBiIRORd4BFgG/Bm4Jv06BzgXWAw8G1iAhvq5c9l6730k164lMnYsQ6+8gpKTTw46LGPMAGLJKWOMMSZNVReLyB+Ba0XkKaACt8rv28CruC9TzV4EJpBRIZzl+c8CC4ByEZkLPJm+1vm4tk1PqOq73fKDmv6nvdlV0RqXnGo5uN73QL0dySxLShnTq4hIGMhT1aaMbYOBK4GhwKOqujio+IwREQ4bcRiHjTiMLU1bmLVyFrNWzaI2XsubG97kzQ1vMqZ4DOVl5UwdN5Xi3OKgQzbG9Kz/AD4GjgSKccmp+1T1JRE5CHgdWBRgfANa/dy5bPrpzUhuLqFBg0ht2cKmn94MN/23JaiMMT3GklPGGGPMzq4HVgJXAzOAKuD3wE2q6nfV+arqicg03Myp84FfAgosB74L/LprfhxjgFTCPYwxfcmfgWOBgwBEJAK8BhyY3n+jiBynqnZjzwRueOFwLjnwEr68/5d5Y/0bVFRW8FH1R6xrWMfdi+/moY8e4tSxp1JeVs6kwZOCDtcY0zMOAX6mqjERaV4lFQZQ1SUichfuu9D/BRXgQLb13vtcYqqgAAApKMBPb7fklDGmp1hyyhhjjMmgqh5wW/rR3nETd+f89LH1uDaA3886UGOMMf3dicBTGa//DZeY+n/Ae8CjwPeAC3s+NGNaFwlFOGXsKZwy9hQq6yp5rvI5Xl7zMjEvxqxVrrLqgKEHUF5WzgmjTyASjgQdsjGm+4SB6vSfo+nnQRn7PwH+vUcjMtsl164lNGjQTtskP5/k2rUBRWSMGYhCQQcgIiERuUFElopITETWiMhtIlLUwfOLReT7IrJYROpFpEpE3hCRy8SaWxtjjGmb9TAzxhjTm+0JVGa8ngF8qKp/UtW3gLuA4wKJzJgOKBtUxjcP+yYPnP0AVx98NeNKxgHw8daPue2d27h81uU89NFDbG7aHHCkxphushbXBh1VjQKbgSkZ+/cDGgOIq8up56GpVNBhZCUydiwai+20TWMxImPHBhSRMWYg6g2VU7/BzeJ4GrfKvHk2x+EiMq29FkoiEgKeA44HHsS1TSoELgLuT1/ru90avTHGmL7KFjAYY4zpzYR0+6O0U9m5kmoDMKInA+pN3t70Nk8tf4pNTZsYWTiS8/c5nykjp+z6RNPjiiJFfH6vz/O5SZ9jcdViKioreHPDm9Ql6nhi2RM8uexJjhp1FOWTyjls+GGEJPA1tMaYrvEGMA24Kf36H8B1ItKEWyz//4B/BhRb10qlSK5dixQUEi4t2d4qrzcbeuUVbPrpzfi4iimNxdBEgqFXXhF0aMaYASTQ5JSITAa+BTylql/M2F4J/A7XouKRNk4HOAbX7uK3qnpDxvl3AEuBb2DJKWOMMS2o6iZ6QfWwMcYY045K4CzgThE5AVdJ9XLG/tFAXRCBBe3tTW9z5/t3EglFKI4UUxOr4c737+SaQ6+xBFUvJiIcMvwQDhl+CNXRamatdG3+tsa2Mn/jfOZvnM/ootFML5vOtPHTKM4tDjpkY8zuuQM4T0QK0pVTPwCOBn6c3v8h8B8BxdblVBVtasRvaiSUm0to0CBCRUX01qZOJSefDDf9N1vvvY/k2rVExo5l6JVX2LwpY0yP6u7kVB1wOe4DpzUX4VYE/rbF9ruBXwCX0H5yqjT9vD5zo6omRKQKyMs2YGOMMX2PiNy0i0MU1+d8NfCKqlr/GGOMMb3d/cCvRWQJMAbXDmlWxv5jcAvyBpynlj9FJBQhL8d93cvLyYOU227Jqb5hWMEwLj7gYr6035eYv2E+MytnsrhqMesb13Pvknv5y8d/4ZQxp1A+qZy9B+8ddLjGmE5Q1YXAwozXW4DDROQQwAM+bq9bUl/mJxL4W7YgNTWES0oIlZQg4fCuT+xhJSefbMkoY0ygsk5OiUgY+ApwJjAS+E9VfU9EhgCfB15U1XUAqhrDtdtry1GADyzI3KiqMRFZlN7fngVALfCfIrISmA8UAJcBRwLXZPXDGWOM6at+zI4ZUi2XprXcnhSR/1XVH/REYMYYY0xnqOpvRaQEOBd4D/i+qjYBiMgw4FjgfwMMMTCbmjZRHNm5qiY3nMumpk0BRWQ6KyeUwwljTuCEMSewettqKioreGnNS0RTUV5Y/QIvrH6B/YbsR3lZOSeOOZHccO5nrmEtHo3pW1T1g6Bj6CmaSpGqqUFqawkVFxMuLUVyP/v3mDHGDFRZJadEpBCYjZvx1Iib7zQkvXsbrtrpPuCHHbzkaKBKVeOt7FsHHC8iuaqaaO1kVa0RkXOAe4DHM3bVA19U1Wc6GIcxxpi+7SDcYog4cDvwSXr7/sB1uM+7bwHjgBuB74nIalX9cwCxGmOMMR2iqjcDN7eyvZoW86ZEJAIcB7yvqv263d/IwpHUxGq2V04BJLwEIwtHBhiV2V3jS8dzzaHXcOmBl/LK2leYuWImq+tX80nNJ3xS8wn3LLmHMyecydkTz2ZU0SjAWjwa0xeIyMnsWOB+m6ouFZFi4AjgA1WtDTTAHqCqePX1ePX1hAoLCZeW9om5VMYY092ynbfxY2AKcB4wiYzV6arq4Qb0npXF9QpxNxJbE8s4pj0NwBLcqsHzgauAT4FHROSMtk4SkatF5G0ReXtrdVUWIRtjjOmFvo773DhFVZ9Q1Q/Sj8eBU4AkcKGqPpl+vRg3l9AYY4zpL4biZlIdGXQg3e38fc4n6SeJp+KoKvFUnKSf5Px9zg86NNMFCiOFlJeV84epf+DWE2/lpDEnEZYw9Yl6nlz+JFe/cDU/efMnvL3xbZ5c9uT2Fo8i/z979x0fV30l/P9z7p2mbsu9qdiYZhPAGIcSCL3IqQSewJIQAmm7YcM+TzbZPMmG1Cftt5uEELKEJATIhtAWQhKL3ksAGzDFNqZYxZIty7Ykq2ttK+rrAAAgAElEQVTa+f1xZ6QZWZI1sqRROe/XS6/RfG/RGbA9unPuOUcI+oL4HT/3vH1Ptl+GMdOeiLgicgfee9PXgSvwblIHiAJ/Bv4pS+FlTbyzk0hDA+G6OqLNzWh4wPvxjTFmWsg0OXURcKOq3ofXjq+/d4CyDM7XyeBzoUIp+wxIRI4CngMeVtWvqOq9qvo74H1AA/CbRBvC/ajqjaq6WlVXF8+anUHIxhhjJqCLgTsTN0qkUdUoXnXtJf2eHzauERpjjDFjb2JOXR9lq+et5gtHf4GZoZm0R9qZGZpplTJTkIiwcvZKvnr8V7np3Ju49PBLmRWahaJs2LWB7zz/HTY3baYz2kks3vcroLV4NGbC+DfgY3idK44g/Qb3buBeoCI7oWWfRiLEWloI19cTqa8n2txMvGew+/eNMWZqynTm1ELg1SG2dwIFGZxvB3CkiAQHaO23CK/l31C3EPxvvCTWXamLqtopIuuAq/CSZe9mEJMxxpjJpyjxNdzte+ibRWWMMcaYSWb1vNWWjJpGikPFXHz4xVx06EW80PAClVWVvLr7VeIap6m7iebuZvL9+RQGCxHEWjwaMzFcBtyqqtcmZiX2t4VpnJxKFQ+HIRwm1tKC+P24BQU4BQWIk2lNgTHGTC6ZJqf24iWNBrMCL+E0XOvx+s6uAZ5OLopICDgGeOoAxydjGag6ytfvcVBd4SjvNraTF/SRH/SRG3RxZFrcdGiMMVPFq8A/icgfVLUmdYOIlOG1i9iYsnwYsHPcojPGGGOMMQfNdVxOWngSJy08ie1t27l5082sb1iPorRF2miLtOFzfKyat4qeWA9Bd7BGLaNrw64N3PP2Pezq3MW83HlcsPwCS54a490s/p9DbG+hb469SdBIhGhTE9LSgpOfj1tQgAQC2Q7LGGPGRKbJqUeBT4vIf/TfICLleP1j/5DB+e7A6zv7L6Qkp/Bmh+QCf0w5/zLAr6pvpuy3GS+5dTnwk5R9ZwAfBpoZRtXU9uYuPvuHl9LW8gIueUFfImGV+D7gJa/ygm7i0deb0MpL7JNcD/kcxBJcxhgzXr4GPAhsEZE/A28l1g/Dez9wSLT1E5EgcCnwtyzEaYwxxhhjRsGSgiV884Rv8tyO57h1863s6thFVKNE41H+8u5feKz2Mc4qPYuK8goW5C0Yszg27NrADa/egN/xk+/Pp7m7mRtevcFaTRoDbXjzEAdzCLB7nGKZdDQeJ9baSqy1FSc316umys3NdljGGDOqMk1OfQfYgFfx9Ce8lkjnicjZwBeAHuCHwz2Zqr4uItcDV4nIPUAlXh/aLwFPArel7P4oUEp6H/Wf45UJ/ygxf+pZvDe+zwILgC8mZotkrCMcoyMcg7aR9Xt1HSEv4JIf8pJaef2SWvmBwZNcyTW/a+W7xhgzHKr6pIicBfwUb/5Uqg3Av6rqU4l9e0SkFIiMc5jGGGOMMWaUJSupVJUtTVuorKrk2fpnaY+08+d3/syf3/kzq+auYm35Wo6bfxzuwGOpR+yet+/B7/gJ+rwqraAvCFFv3ZJTZpp7BviEiPyk/wYRmYl3g/sD4x7VJBTv7CTe2Yn4A7gzinDy8uyGeGPMlJBRckpV3xGRM4GbgO8mlv818fgG8ElV3Z5hDP8CVAOfA9bizQG5DrhGVeMHiKdGRNYA1wBn4n0g2YXXuunLqnrPcAJYPjefP/zTSXT0RGnvidLRE6WjJ+Z9H46mrMd6v+//PBpPH10Siyut3VFau0eUGwMg4HO8RFUgvSproEqtZCIsP+Cz9oTGmGlJVZ8B1ojIXKAc72aGbaraOMC+NmnWGGOMMWYKERGOnHUkR846kitXXslDNQ/xQPUD7Onaw8uNL/Ny48vMzZnLeeXncU7pORQFhxpXOny7OneR789PWwu4AXZ17hqV8xszif0/vATVY8DNibWjRWQ5XueLPOBH2QltctJImOju3UhzM25hIU5hoSWpjDGTWqaVU6jqS3hvJivxqpwEeFtVXxlJAKoaw+tBO1QfWlS1bJD1d4FPjeRnJ4kIRTl+inL8IzpeVQlH43SEY7R3ewmtZJKrPSWB1dETTdsnNdHV2RND+503HI3TFA3T1DHy15banjC1kmvABFdqZZe1JzTGTFKJZNR+CSljjDHGGDM9zAzN5OOHfZwLl1/I+l3rqayq5JXGV2jsauTWzbdy25u38b6F72Pt0rUcNvOwg7rmnZc7j+bu5t7KKYBwLMy83Hmj8VKMmbRUdYOIXAD8Dvh9Yvk/8D5HbAQ+qqqbsxXfZKbRqDeXat++viSVY92XjDGTT8bJqSRVfQOvWmraExGCfpeg36U4b2RDCuOqdIVj6VVa4QEquXoGSHyFo3R0R+mO7l9oNlrtCfvP38ofZAZXaqIrOacr4LM3SGPM+BCRXLzBu7NIbwMLQLK1nzHGGGOMmfpcx+WEBSdwwoIT2NG+g8qqSh6pfYSOSAdP1D3BE3VPsLRoKRXlFbx/8fsJ+UIZ/4wLll/ADa/eAFGvYiocCxOJR7hg+QVj8IqMmVxUtVJEyoCzSbnBHXhQVTuzGNqUoLEY0eZmpLUVp6AAt6AA8Y34o15jjBl39i/WBOGI9CaA5o7wHNFYonqrN6HVl8AarD1hbxIsUc0ViY1Ne8K8wP4JrPx+SSyvqmv/9oQ5ARfXseotY4brxW1N3L5+Oztbu1hQmMPFxy9hzdKh5tBOfomk1E+BTzPwe5vgzUkc3SEDxhhjzMSxG6+tbUO2AzFmIlqYv5DPHPUZPnHEJ3iq7inWVa1j275tbNu3jV9u/CW/3/R7ziw5k4ryChblLxr2eVfPW80Xjv4C97x9D7s6dzEvdx4XLL/A5k0Zk5Boqf63xJcZAxqLEWtpIdbSgpObh1uQj+TkWCckY8yEN2RySkS2jeCcqqrLRhiPOQg+16Eoxxlxe0LwWgm2p1VnDa89YbKyqzMcpd/4LcLROOFonObOyIjjyg24+yWwhmpP2Levtx7yW3tCMz28uK2Jax97G58jFIZ87O3o4drH3uZqlk/1BNW1wJVAJV5P873ZDccYY4zJjIhcNpLjVPXWxGMcqBnVoIyZgkK+EOeUncPZpWeztXkrlVWVPF3/NB2RDv7y7l/4y7t/4Zg5x1BRXsGa+WtwnQPf27R63mpLRhljJoR4Zwfxzg7EdXFyc3Hy8pBQyD4TM8ZMSAeqnKqF/UYhmSks4HMo9gVG3J5QVemKJBNX/edrxfolvFLWU1oYdkf2b0/YGY7RGY6xu31k7QkdISV5lVl7wuT3ftfaE5qJ7/b12/E5Qo7fu4jO8bt0RWLcvn77VE9OfQT4k6pemu1AjDHGmBG6Ge/aK/XTo9RrMRlgDeDWMYxpWnPEu8EtrnFU7bJ4qhERDi8+nMOLD+fKlVfycM3D3F91P41djWzcvZGNuzcyO2c255Wdxzml5zAzNDPbIRszoYnIYyM4TFX1zFEPxnjVVG1txNraEBEkFMLJycHJyUECI/vMzxhjRtuQySlVPW2c4jBThIiQG/CRGxh5x8hYXNOqtwZqT9jRP9GVnNc1SHvCuDJq7QkHTF6lVWq5A7YvzA34rD2hGXM7W7soDKX//Qv5HRpau7IU0bjJAZ7IdhDGGGPMQTi933M/8GO8OYo3AJvxElRHAp8H9gD/Np4BTlYigiMOrrj4HB9+x4/P8eGI0/uVpKo44vRuT4rGo8Q01puo0kSOUBI5Q0WJxCJE4hHC8TCR2Mi7RpjxVxQs4sJDL+Sjyz/KS7teYt22dbzS+Ap7uvbw31v+m9vfvJ2TFp1ERXkFRxYfaRUIxgxsKXaD+4SkqmhXF/Eu73MB8QdwcnNw8vNxLFFljMkimzllJhzXEYpy/GPQnrAvoTVUe8Lk92PVnrA3sTVIosvaE5qDsaAwh70dPb2VUwDdkTjzC3OyGNW42AAsz3YQxhhjzEip6pOpz0XkO0AIOEpV21I23Sci1wPPA6cAj45flBOXiBB0g+T4cvA5PnyOD1dcXHFH5fdnn+PDd6DL55TLl2g8Sne0m+5YN3GNIwgi0rtuJiZXXNbMX8Oa+WvY2bGT+6vu55GaR2iLtPFU3VM8VfcUZYVlVJRXcNqS08jxTfnfsY0ZNlUty3YMZng0Eia2L0xs3z6cYBCnoMBr/+dYxyBjzPgaUXJKRILAaXh3RQBsA55UVfst20wIB9ueMK5KVzg2YAKrPWXGVkc42tvCMG0mV0+Mrkhsv/Mm2xPSNgrtCQM+8kP7J7CSM7hSq7ZS9wn47JeNqezi45dw7WNv0xWJEfI7dEfiROPKxccvyXZoY+1rwF9F5C5VXZ/tYIwxxphR8GngF/0SUwCoaquI/B64Cvj2eAeWbSKC3/H3VkAF3SAhXyit0inbfI6P/EA++eTvty0Wj9EV7aI75l0+J6uv4honpjFiGiMaj1orwSxbkLeAK1ZewaVHXMoz9c+wbts63m55m+rWan716q+4edPNnFFyBhXlFSwpmPK/axsz5kQkD/gycKuqVmc5nGkj3tNDvKcHaWryKqkKCqyayhgzbjJOTiUG9f4UmEl63/MWEfmyqt48euEZkx2OSG+CZ6Rice1tRdjenVK9FU5PYg3UnjC5NhbtCf2uDD5by9oTTnprlhZzNcu5ff12Glq7mF+Yw8XHL5nq86YAPgfUAX8Xkb/j3TTRP0OsqnrluEdmjDHGjMwcwB1iuwvMHadYxpQjDvmB/N7qIvASNqnPk632km35JjPXcQdNXCXF4jE6o510RDqs0irLgm6QM0vO5MySM3mr+S0qqyp5qu4pOqOd/G3b3/jbtr/xntnvoaK8ghMWnIDrDPXX1hgzhHzgW8AzQHV2Q5l+NB4n1tpKrLUVJxTqq6ay7j3GmDGU0SfvIvJxvEG9tcB/kN73/AvA70SkS1XvGOU4jZl0XEcozPFTmOOHopGd40DtCb1qrli/7UO3J4zElObOyEG3J8wL9FVp9U9gpSa5ehNfie3WnnDsrVlaPB2SUf1dnvL9yYmv/hSw5JQxxpjJ4k3gsyJyo6o2p24QkWLgs8CWrEQ2ynyOj9k5s7MdxoTiOi4FgQIKAgW9M66SjzGNEYvHiMS9GVfJWVdxjWc77Cnv0JmHcujMQ7li5RU8UvMI91fdT0NnA6/teY3X9rzGrNAszi07l3PLzqU4NO1+HzdmNNgHBRNAvLubeHc3sncvTl4ebmEhYtVUxpgxkGlZyNfxLpJOUNXWlPX7RORXwAvANwBLThkzCg62PaGq0hWJDZjAGo32hLvbR/a60toTBhNVWhm0J8wP+vC7E6dti8k+VbU/EMYYY6aabwP3AFtF5CZgK96NFkfgtfwrBi7MWnRm3Ih4VWTJj2x9+AasqeuJ9dAR6aAj0kEsvv/v8Gb0FAYKuWD5BXzkkI/wSuMrVFZVsr5hPXu793Lbm7dxx9Y7OHHhiVSUV7By1kq7Mc8YMylpPE6srY1YWxtObi5uYSFOjs3aM8aMnkyTU4cB3+yXmAJAVfcl+p5/ezQCM8YcPBEhN+AjN+BjTkFwROfo355woARWe9rzKO0plVtj1Z4w4HPIC7iDtycMDlzZZe0JjTHGGDMZqOp9InIhcC3w1X6b64CPq+qfxz8yM1EF3SBBN0hxqJjuaDc9sR7vK9pDTC1ZNRYccThu3nEcN+84dnXs4oHqB3iw5kHawm08U/8Mz9Q/Q0lBCRXlFZy+5HRy/bnZDtkYY0Yk3tlJvLMT8QdwCwtw8vMRx+4RNcYcnEyTUw0MXWIbB3aNPBxjzEQzmu0J05Ja4Vgi2dWviqtfa8LkWv/2hOFonHA0PibtCQecwRVK7mvtCY0xxhgzPlT1XhG5DzgOWIp3LfYu8JKq9XAzgwv5QoR8od7nyTaA4XiYnmgPndFOVHWIM5hMzcubx6dWfIpLDr+EZ+qfobKqkq3NW6ltq+WG127gls23cPqS06kor6C0sDTb4Roz6YjI/wVW4b0nlgM1qlo2xP7vBf4f8F68yuPngK+p6sYB9l0I/Ag4H2/21Sbgx6p61yi/jElPI2Gie/cizc04eXk4ublITo59NmKMGZFMk1M3A5eLyK9UNa2hl4gUAlcAvx+l2IwxU8TYticcfAZXamXXWLUn7E1q9Zuv1duuMDXpFXBTZnF56wGf3Wk0Eon2Rgp8TlVjiecHoqp6wJlTIuIAVwOfB8qA3cCdwDWq2jHax4uID/gnvLlZhwFRvA8ef62qvx7G6zLGGDOFJZJQ6xNfxoyI67i4jkuIEAQgrnE6I510RjsJx8JE4yPvaGDSBdwAZ5ScwRklZ/BOyztUVlXyZN2TdEW7qKyqpLKqkpWzVlKxtIITF5yIz8n0Yxljpq0fAE3Ay8CMoXYUkROAJ4B64JrE8lXA0yJykqq+nrJvMfAMMBf4KV518j8Ad4rIFapqn3MOILXln4ggObk4ebk4OTmIO0DvWWOMGUCmvwU9DXwAeD0xY+pNvA8HjwT+EdiD9w/9qakHqepToxCrMWaaGs/2hB09UdoHSHIN1p6wrTtK20G0J/S70i+hlUhyBfo9H2CfZJJrmrYnvBzv/ecfgVji+YEocMDkFPAz4EvAvcB/4s32+BJwrIicNYw71Yd9vIgEgL8ApwN/BG7Ae29eDtgttcYYY4wZE4445AfyyQ/kA16yKpmkisajRDVKJOZVWlmF1cgdMuMQvnTsl7hixRU8WvsolVWV7OjYwRt73+CNvW8wMziTc8vO5byy85iVMyvb4Roz0S1T1W0AIvIGXoXTYH4BhIFTVbU+ccydwBa8a7RzUvb9Gl4l1odU9a+JfX8H/B34DxG5q/8N+iadqqKdHcQ7vXsxnZwcL0mVk4MTGNlNysaY6SHT5NTDKd//GO+DPuhr9Vfabx9J7GMpc2NMVo1me8K0yqyU9oSplVx9ia+h2xNGYkpzZ+Sg2hPm+N0BZ2ylticcqJJrMrcnVFVnqOcjJSIrgH8G7lHVj6WsV+Fd4FwM3DaKx38TOAs4W1UfH43XYIwxZnISkW0jOExVddkwzn0o8Am8D+OWASG8Kt27gJ/3r+wVkcPwrvfeDwTw7lL/lqo+NsC5i4DvAxcAsxLn/SVwg1pWY9JwxElrA5gqEovQHeumI9JBd7R7nCObGvID+Xz4kA/zwWUfZOPujdxfdT8v7nyR5p5mbt96O3e+dScnLDiBivIK3jP7PZPud3NjxkMyMXUgInIIcDxwUzIxlTi+XkTuAj4tIvNVtSGx6R+Ad5OJqcS+MRG5DrgVqMDrhGGGKd7VRbyrCwDx+bxEVSgHJydkVVXGmDSZJqc+PSZRGGPMJDB27QljKRVdKWsDVHYN1J6wKxKjKzJ67QnzQ9P6l8VL8G6s+Hm/9d/g9SD/BEMkpzI5XkTy8Nr/3aeqj4v3KUS+qrYd7IswxhgzKdXSd/PfaLsC+CJete4fgQhe1e73gf8lIieoaheAiCzDm8sRBX4C7AM+CzwoIuer6iPJkyYqgB8GjgWuw7sj/XzgV8A84Ntj9HrMOPK7fvyun4JAAZF4hM5IJ+2RdiKxkd9cNV054rBq7ipWzV1FY2cjD1Y/yEM1D9HS08JzO57juR3PsTh/MRXlFZxRcgZ5/rxsh2zMeIoBNUDXQZ7n+MTj3wfY9jzee+JxwDoRWQAswntvHGjf5PksOTVCGo0Sa2uDNu8y1wkGvYqqnByc0MA3RRhjpo+MklOqestYBWKMMVPdaLcn7J/Aau//PByloztKezg25u0Jp5DjgTjwYuqiqnaLyEb6LnRG4/hTgALgJRG5Fu8iKV9E9uAls65RVfufYowx04SqnjaGp78b+KGq7ktZu0FE3ga+gdf29peJ9R/izfI4Ljk0XkRuxRsOf72IHJ5SEfUZvPe2L6nqdYm134jI/wBfF5Hfq2rNGL4uM878jp+iYBFFwSLCsTDtkXY6wh3EdP8bqMzQ5ubO5ZNHfpKLD7uY53Y+x7pt69jStIW69jpufP1Gbt18K6ctOY2K8grKi8qzHa4xY05V9+C11ztYCxOP9QNsS64tGsG+aUTkc8DnAJYsXDjQLmYA8Z4e6Okh1tKCuC4SDOGEgtYC0JiEtqeeoul3NxGpq8O/eDHFV15BwamnHvjAScombxpjzCSS1p5whJLtCYeawfX9UYx5vIhICfB5vHlNs+hrOZukqnrmAU6zENijqj0DbKsHThKRgKqGR+H4wxLr/4LXD/2rwF7gUuD/4l0EfWqwQFMvhkoW28WQMcaYwanqhkE23YGXnFoJvVW9HwKeSCamEse3i8hvge/iJaOSN2H8A9CJd1NFqp/jtfn7OF71lZmCAm6AYreYGcEZtEfaae1pJRq3+2oy5Xf9vH/x+3n/4vdTta+KyqpKHt/+ON2xbh6ofoAHqh/giOIjWLt0LSctOAm/O/LrAGPGm4iU4l2zHMw1WqZyE48DXZN199snk33TqOqNwI0Axx11lLWxHQGNxdJmVVmyykx3bU89xa7vfg8JBHCKioju3s2u734PrvnmlE1QjSg5JSJnM/Qby/cONjBjjDFjYzjtCSdbckpEzgfuxZuL0QY0jfBUuQx8YQLpFyeDJacyOb4g8bwYWKmqbyae3ykijwOXiciPVXXzQCdLvRhafYxdDBljjBmRxYnHXYnH9wBBBm+FBInklIg4wCrgZVXtP4joRbxK4gNVHJspwBGHwkAhhYFCOiOdtIZbbTbVCJUXlfPFY77I5Ssu57Hax6isqqSuvY4tTVvY0rSFGcEZnF16NueVncfc3LnZDteYIYnIh/BmG/qBVqB5nH50Z+JxoHYloX77ZLLvqOrYsIF9d91NtKEB3/z5FF10IXmrV4/Fj5o0Bk1W5YSQUMiSVWbKa/rdTV5iKicHAMnJIZ5Yt+QUICKH4334dyj7J6WSFLDklDHGmPH0Q2AP8JEh7g4fjk5gsCv94VycZHJ8spf68ymJqaRbgdPwBtEPmJwyxhgztYjIY3jXUueqajTx/EBGfMe5iLjANXizpZLzFDNpbzQTyBloX1XtEZG9DNIKyUxduf5ccv25hGNhWsOtdEQ66OsCaYYrz5/HB5d9kA8s/QCv7XmNddvW8ULDC7T0tHDXW3fxP2/9D8fPP561S9dy9JyjccTJdsjGDOTHwHbgo6r6+jj+3B2Jx4Heg5Jr9SPYd9R0bNjA3l9ej/j9SEEBsaYm9v7yerjqi9M+QZVq0GRVMOD9twt4j8ZMFZG6OpyiorQ1CYWI1NVlKaKxl2nl1K+BJXgtiJ5m/O56MMYYY4ZyOPDvB5mYAu/i5EgRCQ7Qmm8RXsu+waqmMj0++dtFwwDn2Zl4nJlB7MYYYya3pXjVRpLyfCw/1f85cALwdVXdmlgbrVZIyf0HbIUE/drTlpQMM2QzWQTcALNzZjMjOIO2cBvt4XabSzUCIsLRc47m6DlHs6drDw9UP8CD1Q/S0tPCCw0v8ELDCyzMW0hFeQVnlpxJfiA/2yEbk6oM+LdxTkwBrE88ngj8tt+2E/DeW18CUNWdIlKfWO8vuXaw15j72XfX3V5yJZS4fzHxuO+uuy05NYT+ySoAcRwvSRUMIoEgTsBLWhkzGfkXLya6ezeSqJwC0O5u/IsXD3HU5Jbp7TVrgP9PVa9T1Y2qWjPQ11gEaowxxgxhN4O32svEerz3xjWpiyISAo7hwBcmmRyfnNcx0G8ZybXGYUVtjDFm0lPVMlVdqqqRlOflB/oayc8Ske8BVwE3quoPUzaNViuk5P6DVhur6o2qulpVV8+ZM2d4gZtJx+f4mBmayeKCxczOmU3IFzrwQWZAs3Nm84kjPsFN597EV1d/lRWzVgCwo2MHv33jt3zqwU9x3SvX8W7Lu1mO1JheVQz+HjFmVPUdvOuui0Skdzhv4vuLgMdUNfUGwT8By0Tkgyn7usA/Ay1A5YF+ZjwcRsORYccYbWiAYL//NMGgt24yovE48e5uYvv2Ed3dSLi+nnBNDZGdO4k2NRFr70DDo/FRgTFjr/jKK9BwmHhXF6rqPYbDFF95RbZDGzOZVk7txWubZIwxxkwkfwA+BvziIM9zB/B1+iqEkz6Ld/f3H5MLIrIM8PdryTfs41W1SkSeBU4SkVWq+nLivG5i/yjw0EG+HmOMMSaNiHwb+Hfg98AX+m3OpL1RM16L2v32FZEg3nziJw8yXDNFiAj5gXzyA/lE4hE6wh20R9qJxqPZDm3S8Tt+Tll8CqcsPoWa1hoqqyp5fPvjdEW7eKjmIR6qeYjDZh7G2qVrOXnhyQRcqyAwWfNz4Msi8itV7Tjg3gcgIp8EShNP5wABEfn3xPMaVf1Dyu5XA48DT4vIdYm1f8a7kfDL/U79I7yk1W0i8lO897lL8OYmfkZV2w4UW6SmlqqPfhT/woUESkvxl5USKC0jUFaKf+FCxHXT9vfNn0+sqam3YgqAnh588+cf6EeZYdB4HO3uhu6++YdWYWUmg4JTT4VrvknT724iUleHf/Fiiq+8YsrOm4LMk1O3Ax8Brh+DWIwxxpiRuhk4XUTuA67Fu0tvv94xqlo71ElU9XURuR64SkTuwbtL7gjgS3gfsN2WsvujeBdHMsLjwbtAehp4RER+gXcTyMfxKq++e6B4jTHGmEyIyLeAb+HNNvyM7j8M6HW8Nn0nDnB4WnsjVY2LyMvAsQO0s12D9wHgqLdCMpOf3/EzIzSDGaEZdEe7aY+00xnpJK7xbIc26ZQWlvKPR/8jnzryUzy+/XHWVa1je9t2tjZvZetLW/nt67/l7NKzOb/sfOblzct2uGaaUdUbRaQQ2CQitwDVDHyNduswT3kl3kzeVMmZ90/i3bCYPOdzInIa8P3ElwLPARep6qv9fv5eETkZL0n1RSAfb+7vxap6xzBjg3icSF2dNxvm2Wf71n0+AiUlBEpKepNWeWeeQcsfb/PaWQWD0NODRiIUXXThsH+cycygCatg0EtaBYI4oSDiy/SjcmNGV8Gpp07pZFR/kntRKKwAACAASURBVMlw0sQdcHfj3c39CwZ/Y5lUH6Yddcwqve/hp7IdhjHGTBjL5ha8pKqTptm1iMTxLjiEIeZzqKo72LaUc7l4lU+fw+uTvgevIuoaVW1P2a8aKFVVGcnxKfu/B++C6VS8FkhbgGtV9eYDxZq0+pijdMMj9w53d2OMmfJkzvJJ9T42GBE5Ea/93nK8SiTpt4uq6rJhnusa4Dt4H95drjpwJkBE7gIuAFYlP8ATkXxgE17i6rBkUktEvgj8EviSql6Xco7/AT4EHKqqVQeKbfXq1bphg+WxpjNVpTPaSVu4je5o94EPMANSVd7Y+wbrtq3j+Z3P9875EoTV81ZTsbSCVXNX4UimEx7MeHLFpaSoZNK/j4nIPOBeBp7nlKTDuUab6FYdeaQ++r3vEa6uJlJTS7immnBNrZcMGYQEAuC6oIo7cyb555xN4dln4xYXI9L/7d6MF3HdRLIqYBVWxhwEERnW+1im6eAI3kXJV/AuNgYz6d9YjDHGTCrfZZSGxqtqDPjPxNdQ+5UdzPEp+7/G0O+pxhhjpiERuQyv9V4EeAsY8Q2AiSTSdxLneAT4h34ffO1S1YcT3/9f4EzgIRH5GdCK1252EbC2X7XVb4BPAz8VkTK8GywqgI8C3x9OYsoY8Nr+5fnzyPPnEY1HaQ+3W9u/ERARjpp9FEfNPoq9XXt5qOYhHqh+gKbuJtbvWs/6XetZkLeA88vO56zSsygIFGQ7ZDO13YDXGu9neN0imrMbztgR1yV31SpyV63qXdN4nOiuXYRraonU1hCuqiZcW0u4thai0bQ5SNGdO2m55VZabrkVJz+fQGkpgbIyr9KqpJRAWSluYWE2Xtq0o7EY2tUFXV29a+ktAQM4ieSVMebgZZqc+jHwf4BXgGeYwm8sxhhjJg9V/Xa2YzDGGGNG2TeArcBZqrrjQDsfwPGJxxLglgG2Pwk8DN4g+ZT2Rl8DAsDLwHmq+kjqQaoaFpGz8CqAL8Gr7noXr22ttYI3I+JzfL1t/zojnbSGW62aagRm5cziksMv4aJDL+KFnS9QWVXJa3teY2fHTm7adBP/veW/OWXxKawtX8vymcuzHa6Zms7E6wjxr9kOJBvEcfAvWIB/wQI44b296xqLEdmxg3BNDZGavqRVpL4e4nHi7e10b9pE96ZNaedzZ8705lmVesmqQGkZgZISnLzc8X5p086ALQFFvISV359SaRXYb76YMWZomSanLgPuVVVrgmqMMWZCSLQa+gvwR1X9XbbjMcYYk0XhTujYne0oRksp8JVRSEyhqpcDl2ew/xbgw8PctwWv9eBVI4nNmKHk+nPJ9ecSjoVpDbfSEekgk9EExkv2nbzoZE5edDK1rbXcX3U/j25/lK5oF4/WPsqjtY+yfMZyKsorOGXxKQTdYLZDNlNHD/BOtoOYaMR1CSxZQmDJEnjf+3rX4+Ewkbo6wtU1XqVVdQ3hmhqiDQ0AxJqb6WpupmvjxrTz+ebOJVBWir+kL2nlX7IYJ2h/l8eSqqI9PdDTk7YuPp/35fd786uSz10XXBdxrK2qMakyTU7lAg+NRSDGGGPMSKhqu4gcD/wx27EYY4wZI7EIdO6B9t1e8qljN3Q0QseexONub1ukI9uRjqY6wD5ZMgYIuAFm58xmRnAG7eF22sJtvfOUzPCVFJbw+aM/z2UrLuOJ7U9QWVVJdWs1b7e8zbWvXMtNb9zEWaVnUVFewfy8+dkO10x+64Cz8dr7mQNwAgGCS5cSXLo0bT3e1UW4drs3zyolaRXbuxeAaGMj0cZGeHF9yskc/Avm4y8tS7QITCStFi30EiZmzGg0ikajaVVWqUSkL0nluuA46Qktq74y00ym/yI9D6wYi0CMMcaYg7AROCLbQRhjjMmQxqGrpS/BlEwypSWfdkNXU7YjzYYbgEtF5GeJeYbGTHvJln9FwSI6o520h9vpinYd+ECTJseXw/nl53Ne2XlsbtpMZVUlz9U/R1ukjXvfuZc/v/NnVs1bxdrytayatwpX7INSMyL/B3hARH4BXAtsUyt9zJiTk0PosEMJHXZo2nqsrY1ITS3hmmrCNX1Jq3hrK8TjROp3EKnfQedzz/Ud5PPhX7zIawnYm7QqxTdvniVExomqejPHhthHXDe9VWCyfWD6vFBjpoRMk1NfxhuM+6Sq3jMWARljjDEj8C3gXhFZp6qPZzsYY4wxQLgjPcnUnpKASl2PRzM7byAf8ubs/5U/t+/775wyNq9pDInIqf2WNgAfA14UkeuBKmC/JJWqPjUO4ZkJ6um6p7l5083Ut9ezKH8Rl6+4nFMWT74//5kSEfL8eeT584jGo141VaSNWNzyuJkQEVbMWsGKWStoXtnMQzUP8UD1A+zp2sNLu17ipV0vMTd3LueXnc/ZpWdTFCzKdshmctkDKHAc8EVgoA/XVVWtlGcE3IIC3JUrCK3sqyFQVWLNzQMmrbSrC6JRItU1RKprSK01l2AQf8mSfkmrMtzZsywhkgUai3n/v7r6br7onXEVDCLBIE4iaWXMZJfpG8DPgTbgLhGpZ+ALJFXVM0cjOGOMMWaYPgHUAo+IyKvAW0Bnv31UVa8c98iMMWaqiUXS2+mlVjilJqAi/f8ZPgA3kEguzYa8uQMnn3JnQ2DKDv5+Ava7kTb5idBvB9mmgN3qPE09Xfc0P3jhB/hdP4WBQnZ37eYHL/yAr/P1aZGgSkpWU80IzaAz0klruJXu6MDtlMzgZoZm8vHDPs6Fyy/kxYYXqayqZOPujTR2NnLL5lu47c3beN+i97G2fC2HzjzUPrA2w3Er+793mTEkIviKi/EVF5Nz7DG966pKtLGRSI2XqEomrSLbt6PhMNrTQ/jtdwi/nT4izMnLw19asn/SaoYlqsfbQDOuxOfDCYWQnBycYBDx+7MYoTEjk2lyaineG0tt4nnJ6IZjjDHGjMjlKd8fk/jqTwFLThljzGA0Dl3NKQmmPent9pLJp+6WDE8skFucSDIlkk75c9Kf582G0AyY3h92fjrbAZjJ5eZNN+N3/eT4cgB6H2/edPO0Sk6lyvXnkuvPpSfWw76efXRmmiQ3uI7LiQtP5MSFJ1LXVsf91ffzaM2jdEQ7eHz74zy+/XGWFS2joryCUxefSsgXynbIZoJS1cuzHYPxiAj+efPwz5tH7po1vesaixFtaCBcXZ2etKqrg3iceEcHPZu30LN5S9r53Bkz0pNWpSX4S0tx8/Mziqtjwwb23XU30YYGfPPnU3TRheStXj0qr3k60GiUWHs7tLcDieqq5Nwqv7/3C2sJaCawjJJTqlo2RnEYY4wxI6aqTrZjMMaYCUsVIh0ps5x29yWdUuc7de6BTFtiBQu8aqb8ASqdkl+5s8C1OzkPRFVvyXYMZnKpb6+nMFCYthZyQ9S312cpookj6AaZmzuXSCxCc0+zJalGaHHBYj571Gf55BGf5Mm6J6msqmTbvm28u+9drtt4HTdtuomzSs6ioryChfkLsx2uMSZD4rr4Fy3Cv2gReSef3Luu4QiR+jrCNbVe4qq2hnBNLdGdO0GVWEsLsZYWul99Le187uzZvdVVyUor/5IlOKH9k9gdGzaw95fXewmUggJiTU3s/eX1cNUXLUE1QqqKhsMQDu+3rTdZ5fN588V8fsTv89Yc+zjFZI/1dTXGGGOMMWayioXTK5za+yWfktsiXQc+V6reFnvJdnqJ6qb+SSh/zti8LmPMAS3KX8Turt29FVMA3bFuFuUvymJUE4vf9TM3dy7hWJjm7ma6ohn+W2gACPlCnFt2LueUnsObTW9SWVXJMzueoSPSwX3v3sd9797HsXOPZW35WlbPX40r1m3UgIgMq9uSqtYeeC8zniTgJ1BeTqC8HHh/73q8u5vI9u29c6ySFVexPXsAiO3ZQ9eePXRteCnlZIJv/nwvaVWSbA1YSssdd3qJkWTiKvG47667LTk1BjQSQSORAbeJ6/YmrvD5EH/AS1wlE1nGjCFLThljjDHGGDPRxGNei73+lU79E1Dd+zI7rziQU9yXdMpLba83u289WDjdW+xlnYh8B/iYqq4cZPvrwB2q+v3xjcxMFJevuJwfvPADwKuY6o51E4lFuHzF5dkNbAIKuAHm5c0jEouwL7yPjkgHqjYKJ1MiwhGzjuCIWUfwmZ7P8FDNQzxQ9QCNXY280vgKrzS+wpycOZxXdh5nl57NzNDMbIdssqua4c2csk+/JwknFCK4fDnB5cvT1mPt7Yl5VrVelVV1NZGaWmItLaBKdOdOojt30vn359NP6Pd7s5ICASQQAL+fyM6d4/iKDHjtHTU2cPcEcZxE0sqP+FwvYZXaMtCYg5RxckpElgH/G3gvMBPoX/unqrpsFGIzxhhjhk1EZuLNlBrq/enMcQ/MGGNSqUK4PX2GU9p8p8bE93tAM22xV7h/S738OekJqNxicOz+tEnio8DDQ2x/CLgQsOTUNHXK4lP4Ol/n5k03U99ez6L8RVy+4vJpO29qOPyun9k5s5kZnElruJW2cBtxjWc7rEmpKFjERYdexAXLL+ClhpdYV7WOlxtfZnfXbv6w5Q/86c0/cfKik6kor+CI4iNs3sn09F32T075gGXAh4HXgfvHOygz+tz8fNwVKwitWJG2HmtpSVRX1fbOtIrU1BDv6PB2iESI96/mEaHuqn/urbAKlJYRKCvFnTPH/h3JAo3HB28VKOJVWrkuuG5fpZXr8yqvEmvGDCWjPyEichTwDBAEtgJLgU3ALGA+8C5QN8oxGmOMMUMSkVLgWWAhsA8oBJroS1LtATqyFqAxZnqI9qRUOu3ul4Da01ftFO3O7LxuMJFkSlY6zU75PpGAyp1tLfamnnLgzSG2bwU+M06xmAnqlMWnWDJqBFzHZWZoJkXBIlp7vCRVLNMbAgwArrisWbCGNQvWsKN9B/dX388jNY/QHmnnybonebLuScoLy6lYWsFpi08j5Nt/9oyZmlT124NtE5GlwN+BDeMWkBl37owZ5BxzDDnHHNO7pqrE9uyl9ZGHab3nXjQeh1jMS4Cogirhd98l/O67aeeSnJzeOVbJpJW/rBR3xgxLWmWJqsIQ7QIhmcDyI4FElVyyUs7mXJmETNOX3wXCwBpgL9AIXK2qj4nIZ4Ef4N39YIwxxoyn7wMzgDPx7sBrBD4OPA98A7iY1GbZxhiTiXgMOpu85FJnarXT7vRWez0jaLGXO6uvpV7+3PT2eslqp2CBtdibvmYMsW0m1grJmIPiiMOM0AwKg4W09rTSGm61SqqDsDB/IVeuvJJLD7+UZ+qfYV3VOt5peYeq1iqu33g9v3/j95xZciYV5RUsLlic7XBNFqnqNhH5NfAdYF224zHjR0TwzZlN8SWXEFy+nH133U20oQF33jzyzzoTX0FB70yrSE0N4bo6iEbRri563nyTnjfT79txCgvTklb+Uu/RLSgYVjwdGzb0xuCbP5+iiy60mVejyEtghdFIuK9iDhJtAQNIwN8770r8fptxNQ1lmpx6H3Cjqm4VkVmJNQFQ1d+IyCnAj4APjWKMxhhjzIGcCfxGVR9PfX9S1U7gGyKyAvgxcGnWIjTGTDzJFnu9yabGlPZ6ydlOjV5iaqQt9tLmOg3UYs8uwMygNuHd+Pfj/hvEu0X4QwxdWWWMGSZLUo2ukC/EWaVncVbpWbzV/Bbrtq3j6fqn6Yx28tdtf+Wv2/7K0XOOZm35WtbMX4Nr74XTVT1wZLaDMNmTt3r1gImgvBNP7P1eo1Ei9fVeW8CUpFVk506Ix4m3ttL9+ut0v/562jncWbP6Ja3KCJSW4IT6qjc7Nmxg7y+v95IiBQXEmprY+8vr4aovWoJqjGmy4qpz/23i83mVVT6/1x7Q50vMvUq0CrQE1pSSaXKqAK91H3gVVAB5KdufBX54sEEZY4wxGZoFvJH4PllTntrf6mHgW+MakTEmu6LdfbOcBqp0Siagoj2ZndcXTFQ3pVQ4DZSA8gXH5nWZ6eR3wK9F5GbgK6q6G0BE5gA/AU4ArspeeMZMPckkVUGggH3hfbSF27y7vs2IHTrzUA497lCuWHkFj9Y+SmVVJbs6d/Hq7ld5dferzArN4ryy8zin7ByKQ8XZDteMr48AzdkOwkxs4vMl2viVwql96/GeHiLb6wjXeDOtIjXVhKuriTbuBiC2dy9de/fS9fLLaefzzZ+fOF8JHX9/3rtZLRj02s8lElf77rrbklNZpNGoN6xugDlXkGgV6LogDuI64LiIzwXH6avCSia0zISX6f+lXXizpVDVNhHpAA5N2W6tJYwxxmTDbiB5NdsGdANlKdsDpCerzERQ/Sy8cgu07oDChXDsp6Ds5GxHZSa6eLSvxV5qtVP/BFRPa2bnFddrsZc22ynlK5mACuRbiz0zLhKdKd4PXAZ8UkR24g2WX4jXveIOVf2vbMZozFTlOi7FoWIKA4W09LTQEemwJNVBKgoWccHyC/jwIR/m5V0vU1lVyUu7XmJv917++OYfuX3r7Zy08CQqyitYMWuFzZCZAkTkmkE2FQNnACvxbrYwJmNOMEjwkGUED1mWth7v6CRcW9svaVVDrNnLg0YbGog2NND5wgt9BzU2gt/vzUIKBIi1thLevh3/woVWpTMBqSpEo973g4+7Ssy7SlRb+XyQrLpyfYgj3jVd8r0m5T2nN/nlOPZeNA4yTU5tBI5Pef4kcLWIvIg3cP4q4NVMTigiDnA18Hm8DxJ3A3cC16jqkMPrReTbDH0nfFRV/ZnEY4wxZlLaBBwNoKqaeF/6JxH5C9770+ew1kcTS/Wz8NSPwfFDsAg69nrP+TdLUE1Xql5CKXWGU//5Tu2N0NUEmbZaChX1tdLLm5NIQKW018ubDTnWYs9MPKr6icR72aXAIXhJqb8Af1TVu7ManDHTgM/xMTtnNkXBIvb17LMk1ShwxeX4+cdz/Pzjaeho4P6q+3m49mHawm08Xf80T9c/TWlhKRXlFZy2+DRy/bnZDtmM3LeH2NYA/DsDtK415mA4ebmEjjic0BGHp63H9u0jXJNIWlXXEKmtoXvLmxBLtO5OtJnTxFykus99Hnw+AiUlve0Bk/OsfHPnem3nzITmzbtKtA8coWSiShwnvVorueY4vX8WvF8PFOJxNBbz/mypph0viWQZfv+QiS9V7zzEYmj6Bm9dNfkD0xNsiYSagndsLO7F5E1l6kvKua7385OvIXEOjUTQWMyrXotE0HAEohEvnv6//yR/bjzuvWbV3raLEhx+F5FMk1O3AV8UkRxV7QK+iZegejyxvQv4eobn/BnwJeBe4D+BIxLPjxWRs1SH/PThHuCdAdbfA3wF+GuGsRhjjJmc7gO+nPL+9F3gQaAqsV2BC7IVnBnAK7d4iSl/oue3P+Q1ZHzlFktOTUWRrsQsp93p85w6U5JOHXsglmmLvZCXXMqfk558ypvdV+mUO9ta7JlJTVXvxLt5b0gi4gdOBF5V1X1jHpgx04jf8TM7ZzYzgzNpj7TTGm4lFs9wFqHZz/y8+Xx65ae59IhLeab+GSqrKtnavJWa1hr+69X/4uZNN3PGkjOoKK+gpLAk2+GazJUPsKZAk6q2j3cwZnpzi4rIec9R5LznqN619vXr2fuL6wAvGaBdXWg4jDgOGg5DNEp42zbC27alnUtCIQKlJQRKy/AnHgNlpbjFxVZpM8Ukq7TG4raU1OQQiHcDZiL5NN43wvQmpw7y52oshsZiZJK6zSg5pap3AHekPH8lMWT+AiAK3K+q2wY7vr/Esf8M3KOqH0tZrwJ+AVyMlxAbLJ7XgNcGOO+vE9/+brixGGOMmbxU9VfAr1KePyYiJwL/AMSAe1X1uWzFZwbQusOrmErlC0HrzuzEY0YmHoXOvekJptQEVG+LvbbMzuu4XlIpmXDar9IpUf3kz7MWe8b0Kca7afBs4LEsx2LMlOQ6LkXBIoqCRXREOmjubiYaj2Y7rEkv4AY4o+QMzig5g3da3mHdtnU8VfcUXdEu1lWtY13VOo6afRRry9fy3gXvxefYHJHJQFVrsh2DMUPJP/545Oovse+uu4k2NOA75BCKLrqQ3FWriDY29lZYhatrCFdXE96+3UtUdHfTs/Utera+lXY+Jz+fQFmpl7QqKyVQ4lVcuYWFWXqFZiJTVa+6Kpb9m12yWRV+0O/oqroduHaEh1+CV1f2837rvwF+BHyCIZJTAxGRXLykVj3wwAjjMsYYM8mp6gZgQ7bjMIMoXOi18ktWTgFEu6FwQfZiMn1UoXtfeoIpmXRqT0k+dTZBpveRhWZ4VU25swef75Qz01rsGTMylq01Zpzk+fPI9eXSGm5lX88+4pm2nDUDOmTGIVy96mquWHkFj9Y+SmVVJTs7dvL6ntd5fc/rFIeKObf0XM4tO5dZObOyHa45gMQNg1cBy4FZ7P8+paq6bL8DjRkneatXk7d69X7r/vnz8c+fDye8t3dNYzEiO3Z4iaqaGiI1tYSrq4ns2AHxOPH2drrf2ET3G5vSzuXOnEmgNNEWMJG8CpSW4ORa21JjDjo5JSI+4MN4d+r9VVUbMjj8eCAOvJi6qKrdItJ/vtVw/S+gEPiFqmY/9WiMMWZcicghwDzgDWtrNIEd+ylvxlQEr2Iq2g3xiLduxlakK32G034JqMRXLJzZef25fS318uYm2urN7pd8mg1uYGxelzHGGDPORISiYBF5/jyaupvojHRmO6QpoyBQwEcO+QgfWvYhNjZuZF3VOjY0bKCpu4k/bf0Td7x1BycuOJGK8gqOmn2UtdKagETkMuD3eL/xvwXUZjciYw6OuC6BJUsILFkCp5zSux4Ph4nU1RGpSVRY1dQSrqkh2uB9RB5rbqaruZmujRvTzuebO9ebZVXSl7TyL1mMk8G8HmMmu4ySUyLyE+B0VT0+8VyAR4BT8O5++IGInKCq7w7zlAuBPao60ICBeuAkEQmoaiafjlyJdwvvTRkcY4wxZpITkQ/gVfKWJZbOBh4TkbnAc8DXbHj8BFJ2MvBv3oyp1p1exdSxn7J5UwcjFulrsdc/+dS5p+/7cIYt/h03MbtpTt8cp+RXaru9QP7YvC5jjDFmgvM5PubmzqUz0sne7r02j2oUOeKwat4qVs1bRWNnIw9UP8BD1Q+xL7yPZ3c8y7M7nmVJwRIqyis4Y8kZ5PqtEmEC+QawFThLVXdkOxhjxooTCBBcupTg0qVp6/GuLsK1tV57wJq+pFVs714Aoo2NRBsb4cX1KSdz8C+Yj7+0jEBKpZV/0ULEZy1NzdST6Z/q8/CSUUkfBE4FfgJsBK4DvgZ8dpjnywUGm3zdnbLPsJJTInIY8D7gUVWtOsC+nwM+B7Bw8ZJhBWuMMWZiEpHTgHvx3otuAb6d3KaqjSLyLl7LV0tOTSRlJ1syajhUobslMc+pcYCKp8Ssp5G02MuZuf8sp/5Jp5yZIJmMNDXGGGOmp1x/LiFfiNaeVlrDrdbqb5TNzZ3LZUdexiWHXcKzO56lsqqSLU1b2N62nV+/9mtu2XQLpy85nYryCsqKyrIdroFS4CuWmDLTlZOTQ+iwwwgddljaeqytLdEWsIZwTQ3hKq9NYLytDeJxIvU7iNTvoPO5lLHZPh+BxYu9WValZV7SqqQU3/x5iGPXambyyjQ5tQR4O+X5B4EqVf0agIisAC7N4HydwNxBtoVS9hmuKxOPvz3Qjqp6I3AjwFHHrMre1C9jjDGj4RrgVeC9wExSklMJfwcuG+eYjDmwcGdKgml3vwRUSgVUPJLZef15XoIpd3a/aqf+Lfb8Y/O6jDHGmGnKEYcZoRkUBgstSTVG/K6f05acxmlLTmNbyzYqqyp5ou4JumPd3F99P/dX38+Rs45kbflaTlx4In7Hft/JkjrA+pMZ049bUEDOypXkrFzZu6aqxJqb05NW1d6jdnVBNOq1DKyupoMne4+TYJBASYk3zyql0sqdPcvanZpJIdPkVABIrU8/nfRKqm1AJpPMdwBHikhwgNZ+i/Ba/g23asqH98FjE97d88YYY6aP1cC3VDU+yC9gdcD88Q3JTGuxSKKV3u79K5w6Uqqewh2Zndfx9Wur16/NXm+LvbyxeV3GGGOMGZbUJFVbuI3WnlZiNhZ71C2dsZSrjr2Ky1dczmPbH6OyqpL69no2793M5r2bmRGcwTml53Be2XnMyZ2T7XCnmxuAS0XkZzYT3pihiQi+4mJ8xcVw7LG966pKtLExMc+qhnCt9xiprUUjEbSnh56336bn7bfTzufk5XkJq0SFVW/SakbReL80Y4aUaXJqO3ACcGOiSmop3t3qSXOBTAYZrAfOAdYATycXRSQEHAM8lcG5PgjMA64dZIbVqHpxWxO3r9/OztYuFhTmcPHxS1iztHisf+yEi8EYYyYIl8HbxALMZvgtYh3gauDzePOrdgN3Ateo6gEzCQdzvIjcCVwEbFLVlUPta7Kkt8Vev9Z6aW32dkNXU+bnzikeOumUPxdCRdZizxhjjJlEHHEoChZRGCikLdJGW7iNSCzDimhzQPmBfD607EN8cOkHeXX3q1RWVfJCwwu09LRw51t3cvdbd7NmwRrWlq/l6DlHW0XB+HgJ+BjwoohcD1SRfsM7AKqayWd/xkwrIoJ/3jz88+aRu2ZN77rGYkQbGryEVXV1X9Kqrg7iceIdHfRs3kzP5s1p53NnzMBfWuK1BuyttCrFybObG012ZJqcuh34ZmK4/AqgFahM2X4s8G4G57sD+DrwL6Qkp/BmVuUCf0wuiMgywK+qbw5yrmRLv99l8PNH5MVtTVz72Nv4HKEw5GNvRw/XPvY2V7N83JJDEyEGY4yZQLYApwC/GmT7B/Da/g3Hz4Av4VXh/idwROL5sSJyluoB+7KM6HgR+QDexVvXMOM0oy3ckV7h1D/h1NtiL5rZeQN5/Vrqzfa+T01A5c6yFnvGGGPMFCYiFAYKKQwU0h3tpj3STkekA1WbMjCaRIRj71cQAAAAIABJREFU5h7DMXOPYU/XHh6ofoAHqx+kpaeF53c+z/M7n2dR/iIqyis4Y8kZ5Afysx3yVPZoyve/Zf/hqJJYc8ctImOmCHFd/IsW4V+0iLyTT+pd10iESF0d4ZpawjXVve0Bow0NoEqspYVYSwvdr76Wdj53zhwC/ZJW/iVL+P/Zu/P4uusq4eOf89vumput2dM0ZZNFVLQwam3VF4gszjgPyog+jDCi6OO4zYwj4ijPDDyyOKIOKDOijEVcRtEqzKs4CCgUEcQqzFS0LNI03dukaW6Wu/++zx+/m61N0iZN7r1Jzvv1yus2399yTwLJvfmd3znHCocPfWql5tRMk1M3EMyd+nOgH3i3MeYggIhUA39GcFHuqBhjNhfvnviQiKwnSHSNXMR7BPjOuN0fIhimeNjtLSLSCpwHPGmM2TzDr2nG/uPX23EsIeIGr58R1yaVK/Afv95essRQJcSglFIV5A7gFhF5ELi3uGZEJArcCLyGo5g5VawK/jCw3hjztnHrW4FbgEuY+No0J8eLSJwgsfYVgtdSNZcKucPb6U1W8ZSbyZhLwHKLFU0jiadlh8x0GmmxF52fr0sppSbaD6wE9pQ7EKXU9MJOmLATpiZUw0A2qKbSuVRzb1lkGZeecinveMk7eGLXE2zYuoFnep9h5+BOvrb5a3zz99/k9e2v58KVF3JczXHlDncx+qtyB6DUUiOui7dyJd7KlcDrR9f9dJpcd3cxabUtSFx1baPQ0wNAYf9+Uvv3k9r0m3EnE5zm5tHqKm9FZ5C0amtDXL2xUs2NGSWniu3yrmCsSmm8AYJ5UzO8ssPHgC7gSuBCoAe4laD90dG+O7uc4E6Lr8/wuWdldzJFIjzxWxd2LfYkS3ezeyXEoJRSlcIY868ishr4GkG1kgG+C9QTvD58wxjz7WlOMeKdBDdBfOmQ9a8RJLkuZZrk1DEc/1mC1+RPo8mpo2d8SB0cSzqNznc6tMVe3wxPLBCtm7yt3vjPwzWgLWGUUvNIgr5T5wAnErymHfpLxxhjriv+wwe2lTZCpdSxcCyH2nAt1aFqkpkk/dl+raSaB67lsqZ9DWva19DV38V9W+/j59t/TrqQ5qfbfspPt/2Uk+tO5sKVF7K6dTWuVrPPCWPMneWOQSkVsMJhQiedROikkyasFwYHg6RV17axpNXWLvxkEowhv3s3+d27GX78ibGDilVbhyatnOZmxNZCSDUzM62cmlLxj6H+WRxXILiQePMR9uucZtv1wPUzfe7ZaklE6B3KjFYtAaRzPs2JSKlCqIgYlFKqkhhjLhWRHxIkgE4muID3K+CbxpgfHuVpzgR84MlDzp0WkaeL2+f0eBE5C/gQ8E5jTFL73xPMdcoNTZFwKrbbGy7+e8Yt9uKTzHMaqXoqJqAiddpiTylVdiJyIvBjxl7TJmOA60oWlFJqXlhiUROuIe7FOZg5yGB2JqO81Ux0VnfywVd8kMtPu5yfbf8Z9229j+0D29lyYAtbDmzh65u/zrmd53Je53k0RhvLHa5SSs0rOx7HPvVUwqeeOmG9cPBgsSVg11iLwK5tmOFhKBTIdXeT6+5maNyUHvE83I6OYnvAIGnlrujAaWzUOX9qStMmp0Rk7WxOutiHGV5y5nL+5WfPk8oVCLsW6ZxP3jdccubyJRWDUkpVGmPMjwhmPc1WK9BTrBQ+1E7gtSLiGWOyc3G8iDgEVVU/NcZ8/xjiXjgK2Ykt9gbHt9nbN7YtN8NKYNubWOEUXXZItVMxAeXqTRxKqQXjVuB44CrgZ0BvecNRSs03x3JYFllGlVfFgdQBMoXJ3lKquRB1o7zluLdw4coL+V3P79iwdQOP736c/mw/dz93Nz987oesal7FhSsv5BWNr8ASq9whK6VUydg1NURqaoi8/OWja8YYCj09o3Ossl1dZLu3kevejslkMNks2RdeIPvCCxPOJZHI6Byr0aRV5wrsmhpNWqkjVk49zOEDC6ezJIYZnnVcHR/lRP7j19vZk0zRnIhwyZnLSzrrqRJiUEqpchGRI86Pmowx5ptH2CUKTHUVID1un6mSUzM9/u8JWjX9ryPEdRgRuZKgJS4d7a0zPXzuGT9onzd+htNk7fbSMy2yFojWT0w6jc54GpeMCiW0xZ5SarF5HfAlY8znyx2IUqq0QnaIlngLg9lB+jJ9FPxCuUNatESE0xtO5/SG0+lN9XJ/1/3cv+1+DqQP8OSeJ3lyz5O0xFq4YOUFnN1xNlVeVblDVkqpshARnIYGnIYGoqtWja6bQoH83r1jSatixVVu507I5zGpFJktW8hs2TLhfFYiMSFp5a4IHu0q/T27lBwpOaXDC6dw1nF1ZU8EVUIMSilVJusIboaYSTbCAEdKTg0DU/XvCI/b55iPF5ETgGuA/2eMefEIcR3GGHM7cDvAqlecPn/DCYyB7ODEGU6D+yZWPw3th+EemOmFk1DVxJZ6scZi4mlc8ilaB9acdSFWampdj8FTd0JyFyRa4YzLoHN1uaNSS1sW2FruIJRS5RP34sTcGAO5AZKZJPmZtjNWM1Ifqeddp7yLv3jJX/Cr3b9iw9YNbO7ZzO6h3dzxuzu46/d3sbZ9LRcedyEn1JxQ7nCVUqoiiG3jtrbitrYSe81rRtdNPk9u5y6yXVuD1oDd28h1dZHbvQd8Hz+ZJL15M+nNmyecz66vPyRp1Ym3ogMrHD70qdUiMO3VHh1eqJRSqkK9cZ7Ouws4VURCk7TmayNo2TdV1dRMj78ZOAD8qJioGuEAXnFtyBize9ZfzZHkMxOTTocln4rVTvn0kc81nh0al2Q6dK5TMQEV1RZ7qoJ0PQYbbwLLhVA1DPUGn3OVJqhUOd0PrAa+Wu5AlFLlIyIkvAQJL8FwbphkNkl6pu/N1Iw4lsPqttWsbltNd7Kb+7bex8+2/4xUPsWD3Q/yYPeDvKT2JVyw8gJe1/Y6PNsrd8hKKVVxxHGK86c6Jqz7mQy57TuKc6yCmVa5bdvI79sHQKG3l1RvL6nf/nbCcU5z88SkVWcnXls74um86IVMb0VWSim14BhjHpmnU/8aOBc4C8Yme4pIGHgFcKSZijM5fgXBjKpnpjjX88AG4C0z+gogqGBK9R3SWu+QaqfB/ZCZYYs9scZa7E0536kxqIjSFntqIXnqziAx5RbvxnPDkCuua3JKlc/fAhtF5O+AW49wc4RSagmIulGibpRsIUsym2QoN4Qx81dAr6Aj0cEHXv4B3n3qu3l4x8Pct/U+tiW38Wzfszzb9yxf/93XOXfFuZzXeR7NseZyh6uUUhXPCoUInXA8oROOn7DuDw2T7e4uJq22kesO2gQW+voAyO/ZQ37PHoZ/9atxJ7Nw29pGk1buihV4nZ24LS2IvainDi0aM05OiYgFXEYwH+O44vKLwHrgm8YYf+7CU6r8LBGs4kXWvK//eyu1yH0P+BTwMcYll4D3EcyK+vbIgogcD7jGmC2zOR74OFAzSQy3Ecyn+lvg6KqmBvbAfX83lnQa7gUz0xZ71WPt9eKNk1Q9NUC0VlvsqcUpuSv4GRjPCUNy/goXlToKjwEx4HPAjSKyCzj0l7sxxhx/2JFKqUXNsz2WRZZRG6qlP9vPQHZAk1TzLOpGuWDlBZzfeT7P9D7Dhq0beHzX4wxkB/jh8z9k/fPreVXTq7hw5YW8sumVWGKVO2SllFpQrFiU8CknEz7l5Anrhf7+oC3gSNJqWzDXyh8cBN8nt307ue3bGfrFL0aPEdfFXb58YtJqxQqcxkbE0t/PlWRGV5hEJALcB6wlmN0x8hf7BcCFwLtF5AJjjNaYqznlWBa2LdgiWBZk8z7Z/MwSRZYIIdci5Ng4tmB88I3BNwbbEixLcKwgEWVbI8818c5/3zfkfJ9cwSDFc4pMLBAwBgq+Ie8bCsUPYwy+GXs+M/pv9I8IpSqIMWaziHwF+JCIrCd4zTsF+AjwCPCdcbs/RFD9JLM53hjz4GQxiMjngUFjzA+OOvDUAXjx4cm3OaGx5NL4aqcJyadlwYV4pZaqRGvQys8d93OQT0OipXwxKQXdBH9zKaXUpGzLpi5cR8JL0J/pZzA3qH9fzjMR4aXLXspLl72UvnQf93fdz/3b7qcn1cOmvZvYtHcTTdEmLlh5AeesOIeElyh3yEoptaDZ1dVEXnY6kZedPrpmjKFw4ECxLeC2saRVdzcmncbkcmRffJHsixPHe0s4XGw12IlbbA/odXZi19Yi2v2lLGZ6+/OngdcDnwduMMb0AYhIDXA18PfAPwCfmcsg1eIiIlgCwuGJnZEqJUvAsS3CxWSSbR3+C6LgG9K5AvlC8c23jJx/7FO7mGxyLMGxjz0zbllCyLIJzWHhgBmXpBpJYI0krwzFJJYfbMv7QVIuVzD6R4dS8+djQBdwJcGNFz3ArcA1R1kdfKzHz1yoCk6/eCzRNJKMijeCF9cWe0odyRmXBTOmcgSJ2nwa/FywrlSZGGPeUO4YlFILg2M51EfqSYQSHEwfZCg3VO6QloTacC2XnHwJF590Mb/a8yt+svUnPL3/afYO7+Ubz3yDb/3hW6xpW8OFx13IiTUn6oVPpZSaxtCmTfTf/QPye/bgNDdTffHbia1aNem+IoJTX49TX0/0Va8aXTe+T37fPrJd28hu6yK3rTtIXnV3Qz6PSafJPPscmWefm3A+Kx4vzrLqxF3RgdvRQWjlSuyE3mAw32QmF7hF5AVgkzHmkim2/wewyhhzwmTbK9Xpr3ilueeBI40RUbM1UrEUdR3CXpBsUsfGGEOumJQTGVe2UVQobs/mfdK5AuncDNt7qSXv+Maq3xhjJn8XoCrOqlecbjY9+KNyh6HUwtb1WDBjKrk7qJg64zKdN7WAScOJ+jq2gKxatcps2rSp3GEotWhkChn60n2k89rUptS2D2znv7b+Fw91P8RQfixJeELNCVy48kLWtK8hZIemPYctNh3VHfo6toC86vTTzS/vvbfcYSi1YA1t2kTvl7+CuC6EQpDJYHI56j/011MmqGbCFArkdu06LGmV27kTphnhYtfWFtsCdhaTVyvwOjqwotFjjqmSzSRROBnL8/Da24/qdWym9R/tBFVTU3kE+PMZnlMtAo5l4diCYwuebeHaxc8ta9KqJ3VsRATPmfr76kBQ3VV8z5vOFegbzpLKapJKKaWUmlTnak1GKaWUWhRCdojmWDOD2UH60n0UZjqLVM3a8qrlvO9l7+MvT/1LHtnxCBte3MDW5FZeOPgC//LUv3DH7+7gnI5zOH/l+bTGW8sdrlJKVYT+u3+AuC4SLrZZLz723/2DOUlOiW3jLV+Ot3w5rHnd6LqfzZLbsSNoC9i9jezWLnp//3sig4MAFPr6SPX1kXrq6Qnncxobg6RVx4qxiqvl7Vih6W8+WAjGJwqlqorCgQP0fvkrMEeJwkPNNDl1EDhxmu0nFPdRi8jIvCe32BrPtoKZTM64Ry1Pr2xh16alOkI6VyBX8BERBMgVfAYz+RnP71JKKaWUUnNLRLYCPnCyMSYnIi8e6RjAGGOOn+fQlFILVNyLE3Ei9GX6GMwOljucJSXshHlz55s5d8W5bDmwhQ1bN/DYzscYzA3y4z/+mB//8ce8svGVXLDyAlY1r8IW7TCjlFq68nv2IFVVExdDIfJ79szr81qeR+i44wgdd9zo2qWXXMJvn3uWt73ylXzhfe8L2gJu7SLb3U2htzeId98+8vv2wZO/HncyC7elpTjLqnNstlVbK+LM4XyYeTbficJDzfQ78wDwQRH5qTHm/vEbRORc4P8Ad89VcGr+ja94ciwLWwTLGreuiadFJezahN2Jb3proh6ZfIF01geBkUK3/lROk1ZKKbVUjLa02wWJVm1pp1R5bANM8QOge9y/lVJqVmzLZllkGREnwoHUAa2iKjER4ZT6Uzil/hTee/p7eWDbA/xk60/Yn9rPb/f9lt/u+y2NkUbOW3ke5644l+pQdblDVkqpknOamykcODCaCAEgk8Fpbi5pHMmBAZ58+mmyxvC9zZu5+TWvof7cc0e3FwYGgnaAXdvIdm8L2gR2deEPDIDvk9u5k9zOnQw/9suxkzoOXnv7YUkrp7kJsaySfn1Ho9SJwpkmpz4NvBm4T0SeAp4prp8GnEEw9P2auQtPzYQlMlbVNK693qFt9Ub2swRNPCkAQo592CywqrBLMp2jbyhLwdfrIkoptWh1PQYbbwLLhVA1DPUGn3OVJqiUKiFjzBum+/xYicjVwCuBVwErgW3GmM5p9v8T4LPAnxAkyX4JfNIY8/Qk+7YCNwLnA3GCvxNvMsbojYtKVYiYGyNkh+hJ9egsqjKpCdVw8UkXc9GJF/HrPb/mvq338dS+p9iX2sc3f/9NvrPlO7yu9XX86XF/Wu5QlVKqpKovfnvQOg4mzJyqvvjt8/acff39bN+1a8La/d/7Hq7vkwVc3+erN9/Mm9/xjgn7LF++nNqXvnT0c2MMhb6+oMKqq4vctm1kizOtTCoF+TzZri6yXV0M8cjocRIK4XV04K4ozrIqJq/sZfVlvV5f6kThjJJTxphtIrIKuAH4U4I/bgAGgO8CnzLGdM9tiGq8IKkkWFbQZi/k2IRci5BjaaJJzblE2CXmOQxl86SzBVK5giaqlFJqsXnqziAx5RbffLphyBXXNTml1GJyPXAA+C1QM92OIvJq4GFgJ2M3H34IeFREXmuM2Txu3zrgF0Aj8AVgB/Au4Psi8h5jzDfm+OtQSs2SYzk0x5oZyg1xIH2Agq9VVOVgi82rW17Nq1teza7BXdy39T4e7H6QodwQD+94mId3PFzuEJVSqqRiq1bBh/6a/rt/QH7PHpzmZqovfvu8tJEb8fl//Vduvv12QqEQIdfFFAqYbJahQvDaOJTPc9O3v83n7r4bsW0yuRyZTIa/u/JKPvvJT46eR0Rw6upw6uqInnHG6Loxhvy+fcVkVZC4ym7rJtfdjcnlMJkMmeefJ/P88xPisuJx3I6OIFk1bqaVXVOaytpSJwpn3PCwmHz63xJkQhoAAfYZY/SK9RxyLItYyCYWcoJqqGJCSqlSsy0hEXZJhF2A0RaAqVyQrNIffaWUWuCSu4KKqfGcMCR3lycepdQoEXkX8NcEc3/rJ9nFGGOO9m+6440xLxbP+zuCCqep3AJkgbXGmJ3FY74P/AG4GTh33L6fJKjE+jNjzH8W970DeBz4vIjcbYzRYTdKVZCYGyPiROjP9JPMJvVvujJqjbfy3tPfy6WnXMqjOx9lw4sb+GP/H8sdllJKlVxs1ap5TUYd6rpPfILqRIIbvvxlkoOTv1UdLBQglQIgEg5z7cc/zsc/8IGjOr+I4DY14TY1ET3rrNF1UyiQ2707SFp1bSO7rZi02rkTCgX8wUEyv/89md//fsL5rOrq0UTVWKXVCqxYbJbfgcmVOlE462lcxWTUvjmMZUmyJGjB59oWjhXMfQq51mFzgZSqFCMtAKtx8X1DMp2jP5XTiiqllFqoEq1BKz93XNl+Pg2JlvLFpJRCRD4N/BOwl6ClXt+xnG8kMXUUz3sCcCbw7yOJqeLxO0XkbuCvRKTZGDPSeP5dwB9HElPFfQsicivwTeAC4PvHErtSau5ZYlEbriXuxulN92qrvzILO2HetOJNnNNxDi8cfIFzJ9wDoJRSaq5ZlsUnPvhB3rh6NW+/8kr6enrJGv+w/TwR6pYt4we3386ql7/8mJ9XbBuvvR2vvZ3Y6rFOJSaXI7dzZ7HCqlhttbWL/N69YAx+fz/p//4f0v/9PxPOZzc0jM6xGklaucuXY41vyzdDpUwUzig5JSJ/DfwvY8w5U2z/KfBDY8xX5yK4xca1g/Z7I634JpsHpdRCYllCTdQjEXbpT+U4mMrpXXdKKbXQnHFZMGMqR1AxlU+DnwvWlVLl9EGC1nrnGWNyJXzeM4uPj0+y7QngPQRzqzaISAvQBnx7in1HzqfJKaUqlGu7NMeaSWaTHEwfxJ/kwpwqHRHh5LqTyx2GUkotGWe+/OVsfvBBXvna17JjaOiw7Y3RKL998EESVVXzGoe4Ll5nJ15n54R1P50m191dTFh1B5VWXdso9PQAUNi/n9T+/aQ2/WbcyQSnpWWswqrYHtBta0Ncd16/jpmaaeXU5cCmabY/R/DHiianCKqiIp4dfLg2rm2VOySl5oVlCbUxj1jIYd9Ammxe/6BRSqkFo3M1cFUwYyq5O6iYOuMynTelVPklgO+XODEF0Fp83DnJtpG1tlnsq5SqYAkvQcyJkcwmtdWfWvBEZKr/gYeMMfFD9n0JcBPwesAjmM34f40xP5vfKBcXsW3EtsFxgkdrFtdApfw38ItIELsIiIVYEvx75OuZLkZjwPcx2Swmm8XPZDG5bGkCV8fE8zx6spP/t+rJ5Qh5XokjGmOFw4ROOonQSSdNWC8MDgZJq67iPKvuoE2g398PxpDftYv8rl0MPz7ufjPbxm1vn9AW0FuxAqe5Ofi5LYOZJqdOBKYbaPsMQVuHJUdEcG3Bs4PKqLAXPCq1lHiORVtNhANDWfpTpb6OopRSatY6V2sySqnK8xSwvAzPGy0+ZibZlj5kn5nsO4GIXAlcCdDR0THzKJVSc862bGrDtVR5VRzMHGQwq+Pi1IL2KHD7IWsTLlSIyPEErXPzwOeAfuB9wP0icr4x5sFSBFrpRCRIOllW8DjuY/Tz2SSjFqtIZPSfxpjRZJXJ5yGfx/g+FAojO4w9GD9Ibvl6w3epPfSLX+CGQqRzOSK2Td73sS2LdKGA63k89ItfcMHZZ5c7zAnseBz71FMJn3rqhPXCwYNBldUhSSszPAyFArlt28ht28bQxrFjxPNwOzqK7QGLc606V2A3NAQ///NopskpF5iuYWH4CNsXDREh4o5VRXmO/hJWCoKfjfp4iKqwy0A6x0A6j6933SmlKlXXY8WKoV3B7CWtGFJKVY5PAz8UkfXGmN+W8HmHi4+hSbaFD9lnJvtOYIy5neJFw1WrVumbRaUqiGM5LIssI+7GOZA+QLagd/6rBelFY8y3jrDPDUAN8CpjzNMAIvJNgpvvvyIiJ5tFWkYoIxVOjoO4blD5NJJssu2xCiGReb84vZiJCBIKQWiyt0qTM7kcfjqNn0phUilNVpXAt3/0IwYGB4mEw/zVO97BtR//OJ/5539m3fe/z8DgIN/+8Y8rLjk1FbumhkhNDZFx87GMMRR6eorzrEZaBHaR696OyWQw2SzZF14g+8ILE84lkUjQanDFitG5Vm7nCuyamjn7vTDT5NRzwJuAL0yx/Vzgj8cUUQVzbYuIZxMtJqT0l7NSU/Mci/p4iLqYx2Amz0A6TzpXKHdYSik1puuxYNaS5UKoGoZ6g8+5ShNUSqmyM8Y8IiJXAE+IyONAF3DomyljjLlijp96V/FxsnZ8I2s7Z7GvUmoePbrjUdY9s46dgztpi7dx+WmXs6Z9zTGdM+yEaY230p/p52DmoLb6UwuOiHiAZ4w5rAxQRGLAnwEPjySmAIwxgyLydeBagrmJT85ZPJYFdvF6om0HbeNsCywr2GZZYNlBKzl74nXH0Z8+Y0arbSY8jq/AGTnCmGKSSYJzWtZYQkqvaVYscV1s18WuqsL4Pv7gIIXkgLYInCfZbJb/+vnPqYrH+c6Xv8yb1q4F4Iv/+I+c/8Y38r8//GF+8rOfkcvlcCtsXtPREhGchgachgaiZ545um4KBfJ79x6etNqxM6jyS6XI/OEPZP7whwnnsxKJQ1oDduKu6MCexVyumSanvgvcICLXAdcZY7LFL9AluLPv3OLjouBYFmHPIuzahB2tjlJqNkSEqrBLVdglm/cZzOQZyuTJFfTOD6VUmT11Z5CYcos397vhoNHHU3dqckopVXYi8ifAOoK/2dYUPw5lgLlOTv26+Pga4OuHbHt18Tl/A2CM2S0iO4vrhxpZm25msVJqDjy641Gu/9X1uLZLwkuwP7Wf6391PZ/iU8ecoAKoDlUTskPsH95PwegNh2rBeDtwKWCLyH7ge8CnjTH9xe0vI6j8fXySY58oPh5dcsq2xyoJpkg6Hes8F00lLU1iWdiJBHYigZ9KBdVU6TR+ZrKOymo2Cr7PpRddxD985CM0NTRM2Hbu61/P7x56iM/ecgv5QmHBJqemIraN29qK29pK7LWvHV03+Ty5nbvIbusaTVzltnWR27UbjMFPJklv3kx68+YJ57Pr6/E6Owkdd9xRxzDT5NQXgfOBfwD+j4hsIfjj5BSgjqCf680zPGfFGJ+Mirg2rq3JKKXmkudY1DkedTGPXMFnOFsglS2QyhX0LjylVOkldwUVU+M5YUjuLk88Sik10b8QpMzfCjxqjDlYiic1xrwgIpuAi0XkM8aYXQAi0gpcDPzMGLNn3CHfBT4uIn9qjPnP4r428GHgIHBfKeJWailb98w6XNsl4gRzTkYe1z2zbk6SUxBUUbXEW9g3vE/b/KmF4EngbuAFIAFcAHwIeL2IvLZYSdVa3HeyCt+Rtckqg4HDZyc6tbVzFLpSk7MiEaziPCvj+5hisspPpYJ5VmpWIuEwt1x33ZTbmxoapt2+GInjFNv4dUCxkgzAz2TIbd8+NtOqOL8qv28fAIXeXlK9vaR+85ujfq4ZJaeMMTkRORf4G+BdwBnFTc8BNwL/YozJTXV8pXJtYXldVJNRSpWQa1tURyyqIy6+bxjOFRjK5BnOaqJKKVUiidaglZ87blxmPg2JlvLFpJRSY14G/ONIwudYichfAiuKnzYAnoiMdL3YZoy5a9zuHwV+DjwqIrcW1z4MWMDfHXLqGwmSVt8RkS8QXNB7J8Hd5u81xgzMRfxKqantHNxJwktMWAvbYXYOzm1XTcdyaIm1cCB9gIGs/mirymWM+ZNDlr4pIv8DfJbgNe6zQLS4bbISlHTxMTrJtpHn0NmJqmzEspBYDCsWA4pzqjKZoKoqncbkFtzlebUAWKEQoRNOIHSAUTn0AAAgAElEQVTCCRPW/aFhst3bRpNWue3d8OyWozrnTCunKCafPlf8WBQsEU1MKVVGliXEQw7xkEPBNwxlgxlVGZ1RpZSaT2dcFsyYyhFUTOXT4OeCdaWUKr99wFyWJ1wBvP6QtZHbQB8BRpNTxphfisgbgP9X/DDAL4GLjTH/Pf4ExpheEVlNkKT6ayAO/B64xBjzvTmMXyk1hbZ4G/tT+0crpgDShTRt8SmLPmZNRKiP1BN1o/Smesn7ere+WjD+Gfi/wIUEyanh4npokn1H7l4bnmSbUhVnZE4V8TgA/vAwhYEB/GH9X1jNPysWJXzKKYRPOSX43PPgu989qmNnnJxSSqn5ZFtCIuySCLsMZfIcGMrqfCql1PzoXA1cFcyYSu4OKqbOuEznTSmlKsW/A5eKyJeNMcd89dcY84YZ7v84cPZR7rsT+MtZhKWUmgOXn3Y51//qeiComEoX0uQKOS4/7fJ5e86IE6E13kpfuk+rqNSCUOwGtQtYVlzaVXycLIs7sja35YdKlYgVjWJFo5h8HlMogDFgDCaToTA4hMlpe1ZVGTQ5pZSqWLGQQ9SzSabyHExlKfhaKa+UmmOdqzUZpZSqVL8A3gI8ISK3AVuBw8rKjTEbSx2YUqqyrGlfw6f4FOueWcfOwZ20xdu4/LTL52ze1FQssaiP1BNzY/Sme8kVtI2UqlwiEgbagSeKS5sJWvq9ZpLdX1183FSC0JSaN+I4iDPu8n8kgl1Tg5/NYoaHg7Eavh8krgoFKCazTEE7GanS0OSUUqqiiQjVUZeqsEMynaM/ldMklVJKKaWWggfH/fvrBK31xpPiml2yiJRSFWtN+5p5T0ZNJeyEaY21cjBzkGQ2qTOEVVmJSL0xpneSTdcRXAf9TwBjzKCI/CdwkYi8fKRtrYjEgfcCzwNPlihspUrK8jzwvCm3+8PD5A/0aYWVmneanFJKLQiWJdREPaojLoOZPKlsgVSuoIkqpZRSSi1Wf1XuAJRS6miJCLXhWmJujJ5UD9mCXtBUZfNpEXk18HOgm2AW4gXAG4FfAbeO2/dqgha2PxWRLwJJ4H0Ebf0uNJppVUuUFY3iRaMUBgYo9PdjcloZq+aHJqeUUguKiFAVdqkKuwCkcwUGM3kG03l8fd+olFJKqUXCGHNnuWNQSqmZ8myPllgLyWySg5mDWkWlyuFh4FTgMqCeoCXu88A/AF8wxqRHdjTGvCAiq4EbgU8CHvBb4DxjzIMoVQIDGzdy4I5/J7djB257O3VXvIeqtWvLHRYAdlUVdlUV/tAQhYEB/FSq3CGpRUaTU0qpBS3s2oRdm/qYx2Amz0A6TzqnvXGVUkoppZRSqhxEhOpQNVEnSk+qh0whU+6Q1BJijLkHuGcG+/8BeOv8RaTU1AY2bmTvtdchnodVXU1+/372XnsdXPOZiklQAVixGFYshslmKQwN4Q8OYvL5coelFgFNTimlFoXxFVXZvM9AOseAVlMppZRSSimlVFm4tktLvIX+TL9WUSml1CQO3PHvQWIqEgFAIhH84nolJadGiOfheB7U1uKn00G7v0IBYwwmm8WkUvq7Xs2IJqeUUouO51jUx0NUR1wODGcZTOvdHEoppZRSSilVDiNVVL3pXtL59JEPUEqpJSK3YwdWdfWENQmHye3YUaaIjp4VDkM4PGHN+D4mlcIfHsYfGtJElToiq9wBKKXUfHFsi8aqMK01ETxHf90ppZRSSimlVDm4tktzrJnacC0iUu5wlFKqIrjt7Zj0xKS9Sadx29vLFNGxEcvCisVwGhpwOzpwli3DCoXKHZaqYHq1Vim16IVdm7aaCFVht9yhKKWUUkoppdSSVR2qpiXWgmNpIx+llKq74j2YbBa/2A7PT6Uw2Sx1V7yn3KEdM7Es7Koq3NZW3NZWrFis3CGpCqTJKaXUkiAiNFSFaEyEsfROPTUNEbFE5G9EZIuIpEVku4jcLCJH9U7qaI8XkVoR+aiI/LS4T0pEnhWR20Vk+fx8dUpVqK7H4EdXwp1vCR67Hit3REoppZSaJ57t0RJrIeyEj7yzUkotYlVr19J0zWdwGhrw+/txGhpouuYzFTlv6lhYoRBuYyNeezt2dTViaUpCBfRWFaXUkhIPOXi2xb6BNNm8X+5wVGX6IvAR4EfAzcApxc/PEJFzjDFH+h/naI//k+L2h4AvAz3AS4H3A38hIq81xvx+Tr8ypSZj2eBGih9RsBzwC2AKwSMGjAkeARAQGVsb/2j84NHPg5+DQq54jml0PQYbbwLLhVA1DPUGn3MVdK6ez69cKaWUUmViWzZN0SYOpA8wkB0odzhKKVU2VWvXLrpk1FTEdXHq6jC1tfiDg/jJJH42W+6wVBlpckopteR4jkVbTYSewSwD6Vy5w1EVREROAz4MrDfGvG3c+lbgFuAS4DtzdPwW4CXGmD8eco4NwAPAtcDb5+DLUgudCDghQIrJHz9IHJmRxNC4/URALBA7SDqNJJIOfRQLbA+cMNiTvB20Heb0baJfCBJWhSxkhyE3BH4xT/vUnUFiyi3ePe2GIVdc1+SUUkoptWiJCPWReiJOhN5ULwVzhBtalFJKLQoigl1VhV1VhZ9OU0gmMcPDmPF/36olQZNTSqklaaTNX8Sz6RnI4OsLoAq8ExDgS4esfw24EbiUaZJTMzneGNM12QmMMQ+KyAGCKiq1VIwkoOxQMbkkQXLJCRcTU1MYSVCNJKYqlVVMljkhCFUFa7k05NMwsAdCiYn7O2FI7i59nEoppZQquagbJWSH6En1kMqnyh2OUkqpErLCYaxwGJPPUxgYwB8YwBT0ZoWlQpNTSqklTdv8qUOcCfjAk+MXjTFpEXm6uH0+j0dEqoEq4HcziFstNCJjrfScSJC0mU1yqdKTUtNxw8FH3XEwsBe86Ni27HCwXrdyrF3gaOvAQvC5XxiryPJzxcfCxGoypZRSSi0ItmXTFGsimU3Sl+7Tu+eVUmqJEcfBqa3F1NTgDw0FLf8ymXKHpeaZJqeUUouO7/tYMxiuqG3+1DitQI8xZrJ3QDuB14qIZ4yZqinysR4P8GnABe6cLlARuRK4EqCjvXW6XVWlsOygasiLBZVBCzWpNNde+1H4ycchS5Csy6XAz8LqjxVbE85QYdy8q0Ku+O9s8G+90KWUUkpVtISXIGSH2D+8n7yfL3c4SimlSkxEsONx7HgcP5sNklSDg3rTwiJ19FdvlVJqAejt7WX58uUcOHBgRseNtPlrTISx9ILxUhYFpro1Jz1un3k5XkTeDvwdcD/wjWmeB2PM7caYVcaYVQ31ddPtqsrJCUG0Dqrbgyqg2LIgAaO/Z8ac9CY4//NQ1QTpg8Hj+Z8P1mfDdoLvcTgBsXqoaoaajqASq2Y5xBuCKi39b6CUUkpVpJAdojXeStSd7m23Ukqpxc7yPJxly3CXL8euqUHsWdy8qCqaVk4ppRaVe++9l127dnHvvfdy+eWXz/h4bfO35A0DjVNsC4/bZ86PF5ELgG8DvwH+wuhtQQuTCLjRoDrKi82u8mcpOulNs09GHa2RuV5OCMLVQRVVLhW0AzTFFoEj7QKNX/wYWdPXA6WUUqqULLFojDZqmz+llFKIbY+1/BsYoNDfj8lrde1iUPbklIhYwEeB9wOdwH7g+8A1xpihozxHHfAp4M+BdmCAYFbHNcaYR+chbKVUhVq3bt3o42ySU6Bt/pa4XcCpIhKapDVfG0HLvula8s3qeBE5D1gPPAOca4xJzv5LUCVnO+AWk1FaFbVwiEycc3Uk/riE1UibwEK2mNzyxyW29OKZUkopNVcSXoKwHWbf8D5t86eUUkuciGAnElhVVfiDg0GSKqfX7RaysiengC8CHwF+BNwMnFL8/AwROccYM+2tqiKyAngYiAN3AM8B1cDLCC4EKqWWiGQyyRNPPAHAE088QTKZJJFIzOpcI23+Ip5Nz0AGXy82LhW/Bs4FzgJGb24QkTDwCmDjXB8vIm8meA3cApxjjOk7ti9BzSuRIAFlh8B2g9lRjlfuqFQpWBZBR2wnqL6ajDGQz0A+XXxMBTOwlFJKKTVrnu3RGm+lJ9XDcG66JgZKKaWWAhHBrqrCrqrCHx7GHxrCHx7GaMeLBaesySkROQ34MLDeGPO2cetbgVuAS4DvHOE03yL4Ol5mjNk9X7EqNZ2Ht+zjqxtfZHvfMMtro7x/7XG84eSpOnstTqX+HvT19dHd3T1h7avfvQdfHCBLQWw+ef2XeP873zphn46ODmpra4/6ebTN35LzPYJK3I8xLrkEvI9gVtS3RxZE5HjANcZsmc3xxXOcC/yY4MaKs40xMxuWpkrHsoNWcOFqbdWnpiYCbjj4GFHIBe0Dc8OQHdLKKqWUUmoWRtr89Wf6OZg5qG3+lFJKAWBFo1jRKMYYbfm3AJW7cuqdgABfOmT9a8CNwKVMk5wSkbXA64CPGGN2i4hLcKFQb6VRJfPwln1cc+8zuLZQE3HZN5Dmmnuf4VpYMgmqcnwPbrzxRj73uc8RDofxPI+8b8jkChQywY9/Pj3M7V+6iTu/8nkcS8hms6TTaT7xiU9w0003zei5tM3f0mGM2SwiXwE+JCLrgfsYq+h9hImvSQ8BKwhex2Z8vIisAu4pHv8N4Hw5pB2cMeZbc/01qhmw3bFWfV6s3NGohcp2g49wIkhMZYeCRFUuFSSulFJKKXXUqkPVhOwQPakebfOnlFJqlLb8W5jKnZw6E/CBJ8cvGmPSIvJ0cft0Lig+dovIfwLnA7aIPA9cqxf1VCl8deOLuLYQ9YIfp6jnMJzN89WNLy6Z5FQ5vgc33HADNTU1XHfddSSTk4/nKWSGGS5O/YlEIlx//fVcddVVs3o+bfO3pHwM6AKuBC4EeoBbCeYYHk353NEe/1JgpLzii1OcS1/HSskNF9v0FT/scr9NUouOCITiwQcEM6qyQ5BJQi5d3tiUUkqpBSLshGmLt9Gf6ac/269VVEoppUaNtPyz4nH8oSEKBw9qkqqClfuqSyvBcPhDh8YD7AReKyLeNMPnX1J8/BrwPHAZEAL+FrhLRFxjzDcmO1BEriS4cEhHR8cxfAlqqdveN0xNxJ2wFnFtdvQtnQK+cnwPLMvi6quv5uyzz+atb30re3sOYPKH/6oQx6W5YRn33HMPZ555pHz3kWmbv8XPGFMgmIF48xH26zzG49cB62YTo5pDjgehKvCqNBmlSs+yg4qqcCKYUZVOBlVVWlGllFJKTUtEqAnXEHNj9KZ7Sef1Jg+llFJjRAQ7HseOxykMDlI42I/JTZViUOVS7qswUWCyxBRAetw+U/2fU1V8HADeOJLEEpEfAS8C14vInZPd6W6MuR24HWDVqlV6m42ateW1UfYNpEerhgBSuQLttdEyRlVa5fwenHXWWTz77LO0HncyQ72Hj52LVi9jy5YtJBKJOXtObfOn1AJnO0EyKlQVJKeUqgROCOINwb99H/JpKGSgkAc/FySv/EJ5YyyVrsfgqTshuQsSrXDGZdC5utxRKaWUqkCu7dIca2YgO8DB9EEKZom8ViqllDpqY0mqkUoqTVJVinInp4aBqXp+hcftM5VU8fG746urjDF9InIv8G6C6qo/HGugSk3l/WuP45p7n2E4myfi2qRyBXIFw/vXHlfu0Eqm3N8Dz/PIDR6YdFtusI9QKDTnz6lt/pRaIGwH3Ggw88dywfY0IaUqn2WBFyW4R2ucXDqorMoOwiTVwotC12Ow8abg5zVUDUO9wedcpQkqpZRSU6ryqog6Ue7beh/f3fJd9g7vpSnaxEUnXsSqplUljWXT3k2sf359WWNQSil1ODsew47HKAwO4fcfxM8u0r+pFhCrzM+/C1gmIpNdOW4jaPk33f8lO4qPeybZNlJCUXsM8Sl1RG84uZFr/+w0GqvC9KdyNFaFufbPTlsy86ag/N+DBx54gHAxAWV7YcR2sb0gvx0OeTzwwAPz9tzxkENrTQTXLvevU6XUKNuFaB3ULIfaTog3QqQ2mPOjiSm1kLnh4v/bHVC3EqqaIVIDbiRIaC0GT90ZJKbcMAjBo+UG60oppdQ0frnrl9z29G30Z/qJu3H60n3823//G5v2bipZDJv2buLf/vvf6Ev3lS0GpZRS07PjMdy2NtzGRixPrxGUU7krp34NnAucBTw6sigiYeAVwMYjHP8k8AGgfZJtI2v7jj1Mpab3hpMbl1QyajLl/B7cddddJJNJotEoV1xxBddffz1XX301d9xxB8lkkrvuuou3vOUt8/b8Y23+Mgxm8vP2PEqpaVgWeHEIJYKL2UotdpYdJFxD8bG1Qj5oB5hLQT61MKurkruCiqnxnDAkD2/dq5RSSo237pl1uLZLxIlQ8AuICORh/fPrS1a5tP759biWS8gJbp4MOaGSx6CUUuroWLEYViyGPzREob8fPzPV9CE1X8p9i+X3AAN87JD19xH0Mfn2yIKIHC8iJx+y348J5k1dKiLxcfu2AH8OPG+MeWE+AldKVYZsNsuGDRtIJBKsX7+eW265hXg8zq233sr69etJJBJs2LCBXG5+Z0NZltCYCFMfm/sWgkqpKbgRiNUHFVJ1xwUVUpqYUkuZ7QTJqnjDWHVVoiWotvKiIFLuCI8s0Rok2MbLp4OvQymllJrGzsGdhO3gvaBt2UGSyA6xd3hvyWLYO7wXz554F75neyWNQSml1MxYsRhuaytuUxPWPIwGUVMra3LKGLMZ+ApwkYisF5H3isjNwBeAR4DvjNv9IQ6ZHWWM6QM+TtAC8AkR+VsR+STwBOABHyrBl6GUKqNCocBll13Gc889x5vf/OYJ28477zyee+453v3ud5PPl6aiqTrq0pQIB3fpKaXmngiEE8GF9+q2oF2fo28elZqUZYMXC5JTiVaoXQlVTUECq1Jfp864DPxcMF/LEDz6uWBdKaWUmkZbvI10YewGB0ss8iZPc7S5ZDE0RZvIFiZWLmcLWZqiTSWLQSmlZmpg40a2XXY5L5x9Dtsuu5yBjUdqZrY4WdFokKRqbsYK642vpVDuyikIqqY+DpxGkKi6BLgVeIsxxj/SwcaY24G3AYPAdcA/AM8CbzTG/HS+glZKVYZIJMJtt91GU9Pkb/abmpq47bbbiEQiJYspFnJoqQ5jWxV64U+phciygwvsIzOkdHaUUjNnWRCqCmZV1R0XJKwiNUHFVaXoXA1rrwqqIjPJ4HHtVcG6UkopNY3LT7ucXCFHKp/CGEMqn6LgF3jf6e8jZJfmZqaLTryInJ8jk89gjCGTz5Dzc1x04kUleX6llJqpgY0b2XvtdeT378eqria/fz97r71uySaoAKxIBLelJUhSlfB64lJU9r9EjTEF4Obix3T7dU6zbT2wfm4jU0qp2Qu7Ni3VEfYm0+QKR8yzK6Wm4kaCSimvgis9lFqIRIJWf14UYssgnYT0wcqYU9W5WpNRSimlZmxN+xo+xadY98w6dg7upC3exuWnXc6a9jUYY+hJ9TCUG5rXGFY1reIDL/8A659fz97hvTRFm7joxIt03pRSqmIduOPfEc8bTcJIJIJfXK9au7a8wZWZFYlgRSL46TSFgwfxU6lyh7TolD05pZRSi5XnWLTXRugbznFwuAIu9im1UFg2hKuDCg/bLXc0Si0N4UTwkR2C4QOQ12HASimlFp417WtY077msHURoSHagJfxOJg5iDFm3mJY1bRKk1FKqQUjt2MHVnX1hDUJh8nt2FGmiCqPFQ5jNTfjZzJBkmp4uNwhLRqanFJKqXkkItTFPKKezf6BjFZRKTUdkWCGVKRWq6SUKhcvFnxkhyF1IJj5pJRSSi0S1aFqQnaI/cP7KZhCucNRSqmyc9vbye/fj4xrX2fSadz29jJGVZmsUAirqamYpOrHH57fatyloBJmTiml1KIXdm3aayNUR7QKRKnDiEAoDjUrgrlSmphSqvy8KFS3B3OpXB0GrJRSavEIO2Fa4i0lm0OllFKVrO6K92CyWfxUMK/PT6Uw2Sx1V7yn3KFVLCsUwm1qxG1rw4rFyh3OgqbJKaWUKhERoT4eorUmgmvrr1+lcDyI1UNtJ1Q1g60F3UpVnJEkVXVbMANOKaWUWgQcy6E51kyVV1XuUJRSqqyq1q6l6ZrP4DQ04Pf34zQ00HTNZ5b8vKmjYXkebmMjniapZk2vAimlVImFXZu2mgg9gxkGM/lyh6NUaVkWhBLBPClH71ZVajK+72NZFXYTgxsJElS5VDCTKqfDgJVSSi1sIkJ9pJ6QHeJA+gC+0RbsSqmlqWrtWk1GHQMpJqlMNkshmcQfHJzX2YaLiSanlFKqDCxLaEyECadz9A5m9UVLlV7XY/DUnZDcFbTtOuMy6Fw9f8/nRiBcHcyy0bZ9Sk2pt7eXl73sZWzevJm6urpyh3O48UmqoR7IZ8odkVJKKXVM4l6ckB2iJ9VDpqCva0oppWZHPA9n2TJMbe1YkiqvN6VPp8JuyVRKqaUlEXZpq4kQ8exyh6KWkq7HYONNMNQLoergceNNwfpcC1VBzfLgYnYorokppY7g3nvvZdeuXdx7773lDmV6biT42Y43gqWvYUoppRY213ZpjjVTHaoudyhKKaUWOLFtnNpa3PZ2nIZGrJB2jZmKJqeUUqrMPMeipTpCc3UYz9Ffy6oEnroTLBfcMAjBo+UG63PFsoI5UlVN2r5PqRlYt27dhMeKF05AzQqI1GjyWSml1IImItSGa2mMNmKJ/l2mlFLq2IgIdjyG29qK29qKHY8j+jfTBPpqq5RSFSLqObTXRllWFcK29MVKzaPkLnDCE9ecMCR3z8353QhUdwSVUkqpo5ZMJnniiScAeOKJJ0gmk2WO6ChZFsSWQfXyINmtlFJKLWBRN0pzrBlbK4OVUkrNESsUwmlowF2+HKe2FnF02hLozCmllKo4ibBL3HPoG86STOd1HpWae4nWoJXf+IvI+TQkWo793NG64EMpNa2+vj66u7snrD30H7cRsgpkAU/yfP2Gv+fsSz44YZ+Ojg5qa2tLGOkMOB5Ut0O6P5hHpa9fS4aIWMBHgfcDncB+4PvANcaYoTKGppRSs+LZHi2xFvYP79c5VEoppeaM2DZ2TQ1WdTX+0DD+QBI/nS53WGWjySmllKpAliXUx0PEww77khlyBb/cIanF5IzLghlTOYKKqXwa/FywPluWBfFm8KJzFqZSi9mNN97I5z73OcLhMJ7ngZ+HfJqBdPD7fiBd4J++9HX+6cvfAsshm82STqf5xCc+wU033VTm6I8gXA1uFAb3QS5V7mhUaXwR+AjwI+Bm4JTi52eIyDnGGH0jo5RacBzLoSnWxL7hfaTzS/fCoVJKqbk30vLPjsfws1n8ZBJ/cHDJ3aCuySmllKpgIcemvTZCz2CWgXSu3OGoxaJzNXBVMGMquTuomDrjsuL6LLgRiDeBrW8rlDpaN9xwAzU1NVx33XVTtu9Lpn1gGIBIJML111/PVVddVcIoj4HtQnUbpPpg+IBWUS1iInIa8GFgvTHmbePWtwK3AJcA3ylTeEopdUwssWiKNtGT6mEop4WgSiml5p7leVjLlmFqa/EHBykMDGByS+MaoF5FUkqpCiciNFSFiHg2vYMZCr5e4FNzoHP17JNR40VqIVZ/7OdRaomxLIurr76as88+m7e+9a0c7NlLOn/47/eQI9Q1NHPPPfdw5plnliHSYxSpBTcGg3shr22RFql3AgJ86ZD1rwE3ApeiySml1AImIjREG7DTNsnMApkHqZRSasER28aursaursYfGqIwMICfWtydKDQ5pZRSC0Q85BBxbXoGMwxl8uUORy112sZPqTlx1lln8eyzz/KK45vZ2nP4Hx6tNWGe3rKFRCJRhujmiONBzfKggirVp1VUi8+ZgA88OX7RGJMWkaeL25VSasGrC9cRtsP0pnopmEK5w1FKKbWIWbEYViyGyWaDJNXgIMZffJ2yrXIHoJRS6ujZltCUCNOYCGNbUu5w1FLlhqG6QxNTSs0Rz/PYPTD5Ra49AwVCoVCJI5on0Tqo0d8di1Ar0GOMmaw0biewTES8EseklFLzIupGaYm3EHbC5Q5FKaXUEiCeh1Nfj7t8OU5dHeK65Q5pTmlySimlFqB4yKGtJkLU0wJYVWKRWki06XwppebQAw88gBcKLnJFPRvPFqJe8DbdDYV54IEHyhne3LJdSLRCVXNQUaUWgygwVc/G9Lh9JhCRK0Vkk4hs2r9//7wFp5RSc82xHJpjzVSHqssdilJKqSVCLAu7uhqvvR23qQkrujhu+NPklFJKLVCObdFcHWZZVQhLtIpKzTPLgkRLMF9K/39Tak7dddddJJNJotEoV7z/g/QeTPKeKz9IJBIhmUxy1113lTvEuReKB1VUNcshUhP8jlEL1TAwVXlfeNw+ExhjbjfGrDLGrGpoaJi34JRSar7UhmtpjDZiib6GKaWUKh0rGsVtasJra8OurkYW8N9SCzdypZRSACTCLs3VYU1QqfnjRaFmBXixckei1KKTzWbZsGEDiUSC9evXc8sttxCPx7n11ltZv349iUSCDRs2kMvlyh3q/HBCEFsGtSsh3hh8rhaaXQSt+yb7j9dG0PIvW+KYlFKqJKJulJZYC669uNosKaWUqnzieTh1dUHLv/p6xF14nSk0OaWUUotA2LVprtY5VHNBRCwR+RsR2SIiaRHZLvL/27v7IMvq+s7j7++9/dzz0DO08yzgxo0SkywmSKm7STCxFDG1bCRGXImgKxMjD4GskZQSID7gJhseSgKphRhFFraQFFhgjNmYEnV9gnGLLaIxg8iTojADAjMMMwwz3/3jnIZLc3vs293c06fP+1V16sw9T/fbP+70Z7i/c36/uCAiZtUz0+v5EXFMRHwtIh6PiIcj4rqIeNHC/lRzFFF8abxiA7TaVVcjLUn79u3jxBNPZOvWrbz+9a9/1r6jjz6arVu38va3v52nnnqqogr7JAJGVhRPUglR2xYAABdkSURBVK3cBMPLfUqzPm6l+P/KIzs3RsQIcDiwpYqiJKlfBtuDrBtb5zxUkqRKRKtFe8UKhjZtZHDdOlpj9bmx2M4pSVoi7KBaMBcBFwLfAU4DrgNOB26KmNWYHbM+PyLeBHwWGAX+CPjvwK8CX42IDQvy08zV8PLiaanRiUrLkJa60dFRLrvsMtauXdt1/9q1a7nssssYHR3tc2UVGhyB5Wth1aHFUKLejb7YXQskcMa07SdTzDV1dd8rkqQ+a7farB1by7KhZVWXIklqsNboKINr1zC0aVMx5F97cd9o7GzmkrSEDA8UHVQPPLqHp/bvr7qc2omIl1F0KF2fmcd1bL8L+BhwPHDNQpwfEYPAJcB9wK9k5s5y+98D3wLOAzYv4I83OwPDMP6C4sthSapSqw2jq4pl7xOw+zF4cidkVl2ZOmTm7RFxKXBqRFwPfA44jOLGjC9xgNyUpKUkIpgcnWSwNcgjex4hzStJUkVicJCB1avJVavYv3Mn+x97jP1PLr6Rtn1ySpKWmOGBNusnRhhs+yt+Dt4KBHDxtO1XUEzmfsICnv9rwAbgr6c6pgAy8zbgZuAtZQdWf7TaxZMKEy+0Y0rS4jM42vE01SQM1G889SXuDOC9wMuASyluxrgE+M3M9G4ZSY2ycngl68bX0XZYbElSxSKC9vLlDG7cyOD69bTGx4lFNHy631xK0hI02G6xfuUIQwP+mu/RK4D9wC2dGzNzN3BbuX+hzp/689e7XOcbwArgZ2db+JxFwNjq4gvf4eXP+9tJ0ry02sVwoxMHF3NTjaxwbqpFIDP3ZeYFmfmSzBzOzI2Z+YedN19IUpMMt4fZML6B0YEGDctbY/Odd1iS6qA1MsLgmjUMbtpEe2JiUQz557eWkrREDbRbbFg5yrIRR3DtwQZge2bu6bLvh8BkRBzodv1ezt/Qsb3bsQAbZ3qjiNgcEVsiYsu2hx4+QEkHMDRefME7ttovdyXVz+AILFsDq14Ey15QPF0lSdIi0W61WTu+llUjqxbVXerqar7zDktSbcTAAAOrVjG4aRMDq1cTA9V9b+g3lpK0hLVawZrlIywf3sf2nXvYu8+RdX6KMaBbxxLA7o5jZhqot5fzx8rX3Y7vPLarzLwcuBzgiMN/obcB7Vvt4gvdIW8ElLQEtFowsrJY9j1VzEslSdIisXJ4JaMDo2x7Yht79+2tuhxNM995hyWprqLVor1yJa0VKyqbl8ref0lqgNGhNptWjTIxNuRdewe2CxieYd9IxzELcf7Uutvxs3mvuRkaK56WsmNK0lLUHiiG/ZMkaREZag+xfnw9y4aWVV2Knmu+8w5LUq09a16qdetojfXv+yI7pySpISKC1eNDbJwYZWSw+nFlF6n7KYbe69ZhtJFiyL4D3UbSy/n3d2zvdix0H/JvbiJgfBJWbCienJIkSZLUN61oMTk6yerR1d4wuLjMd95hSVoyWqOjDK5dw1Cf5qWyc0qSGmZooMWGiVEmlw/7P0XPdStFNh7ZuTEiRoDDgS0LeP6t5fpVXa7zSuAxYOtsC59RBIysgIlDfJpAkiRJqtiKoRWsHVtL2xvGFov5zjssSUtODA4W81K98IUMTE7SGnp+fg3aOSVJDbViZJANEyMMto2CDtcCCZwxbfvJFPM/XT21ISJ+JiJeOtfzgS8BPwLeFRFPj+8REf8OOAq4LjPnNyj9VKfUsjXFUFeSJEmSKjcyMML68fUMt2caEVx9NNt5g58lIjZHxJaI2LJt27bnrThJqtKzhvxbv37Bh/zzmypJarDhgTYbJ0bZtnMPj+95qupyKpeZt0fEpcCpEXE98DngMOB0is6kzolw/wk4hGJ88p7Pz8y9EfEHFB1aX4mIK4AVwJnANuDcOf8gEUWH1PDyOV9CkiRJ0vNnoDXAuvF1PLz7YXY8uaPqcppsF7Bmhn0zzgWcmZcDlwMcccQR+fyUJkmLR2tkhNbICPnUU+zbsYP9O3aQ+/bN65p2TklSw7VawdoVI2zfuYfHnpjfgzpLxBnA3cBm4I3AduAS4JzM3L+Q52fmdRHxBHA28BcUd+z9E3BWZs5tvqmBIVi2rlhLkiRJWrQigoNGD2K4PcxDux8i0z6OCtwP/FxEDHcZ2m828w5LUqPEwAADq1aRExPs37GDfY8+Sj41txve7ZySJAEwuWyYAB5teAdVZu4DLiiXAx136HzO7zj+s8Bne6uyiwgYmYCx1cWfJUmSJNXCsqFlDLWHeHDXg3ZQ9d+twOso5g3+ytTGjnmDv1xRXZK0qEUE7RUraC1fzv7HH2ffI4+Qe3v7TtGJRiRJTzto2TCrxnzipnaiBRMHw/hBdkxJkiRJNTTUHmL9+HpGB0erLqVpepk3WJI0TUTQXraMwY0bGZichIHZPw/lk1OSpGdZNW7nVO20B4tFkiRJUm21W20mRyerLqNRepx3WJI0g4igvXw57eWzn//czilJkiRJkiRJTTXfeYclSXNg55QkSZIkSZKkRup13mBJ0sJwzilJkiRJkiRJkiT1jZ1TkiRJkiRJkiRJ6hs7pyRJkiRJkiRJktQ3dk5JkiRJkiRJkiSpb+yckiRJkiRJkiRJUt/YOSVJkiRJkiRJkqS+qbxzKiJaEXFmRHw3InZHxH0RcUFEjM/y/Jxh2fl81y5JkiRJkiRJkqTeDFRdAHARcDpwA3ABcFj5+uUR8drM3D+La3wFuHzatr0LWqUkSZIkSZIkSZLmrdLOqYh4GXAacH1mHtex/S7gY8DxwDWzuNT3M/N/Pj9VSpIkSZIkSZIkaaFUPazfW4EALp62/QpgF3DCbC8UEUMRsWwBa5MkSZIkSZIkSdICq7pz6hXAfuCWzo2ZuRu4rdw/G79N0Zm1IyIejIhLImLlglYqSZIkSZIkSZKkeat6zqkNwPbM3NNl3w+BV0fEUGY+eYBr3AJcB3wPWAEcA5wK/FpEvDozdy500ZIkSZIkSZIkSZqbyMzq3jziTmAwMw/usu9TwO8CqzLzkR6v+37gI8DZmfmRGY7ZDGwuX74E+Nde3qPDJLB9jucuJbZDwXawDabUvR0OycwXVF2EZicitgH3zPH0un9WF4rtULAdbIMpdW8Hc6xGzLEFYTvYBlNsh0Ld28EcqxFzbN5sg4LtULAdCnVvh1nlWNWdU7cDazJzbZd9nwbeDAz/lCenul13ENgJfCszX70gxc78Xlsy84jn8z3qwHYo2A62wRTbQXXhZ7VgOxRsB9tgiu2guvCzWrAdbIMptkPBdlBd+Fm1DabYDgXbodCUdqh6zqn7gcmIGO6ybyPFkH89dUwBZObeqWvPsz5JkiRJkiRJkiQtoKo7p24taziyc2NEjACHA1vmctHy/E3AA/MtUJIkSZIkSZIkSQun6s6pa4EEzpi2/WRgDLh6akNE/ExEvLTzoIg4aIbrfggYAG5auFJndHkf3qMObIeC7WAbTLEdVBd+Vgu2Q8F2sA2m2A6qCz+rBdvBNphiOxRsB9WFn1XbYIrtULAdCo1oh0rnnAKIiEuAU4EbgM8BhwGnA18Ffj0z95fH3U0xkVZ0nHsR8Ergi8C9wDLgGOA1wDeB12TmE337YSRJkiRJkiRJknRAi6Fzqk3x5NRm4FBgO8UTVedk5s6O4+7muZ1TxwLvAX4eOAjYB9wBfBq4MDN39+enkCRJkiRJkiRJ0mxU3jklSZIkSZIkSZKk5qh6zqlaiohWRJwZEd+NiN0RcV9EXBAR41XX1g8R8bMR8cGI+EZEbIuIHRFxW0R8oCltMJOIGIuIuyIiI+Ivq66nnyJidUT8RUR8r/x7sS0ivhgRv1J1bf0SEcsi4v0RcXv592J7RHwtIk6KiPjpV5D6wxwzx2Zijplj5pgWu6ZnGJhjB2KONTfHzDDVhTlmjs2kyRkG5lhTc2yg6gJq6iKKebFuAC7gmXmyXh4Rr52aJ2sJeydwCnAjcDWwl2Kerw8DvxMRr2zwXF8fBCarLqLfIuIQ4GaKed8+DmwFVgK/CGysrrL+iYgW8PfAq4ErgUuAMeCtwCcofk+cVVmB0rOZY+bYTMwxc8wc02LX9AwDc+xAzLEG5pgZppoxx8yxmTQyw8Aca3KOOaxfjyLiZcDtwA2ZeVzH9tOAjwFvy8xrqqqvHyLiCOCOzHx02vYPAx8ATsvMJvbw/xJwC/A+in9gXJqZp1ZbVX9ExFco5ow7MjN/VHE5lYiIVwFfAy7OzDM7tg8B3wVWZ+ZEVfVJU8wxc2wm5pg5hjmmRc4MK5hj3Zljzc0xM0x1YY4VzLHnanKGgTnW5BxzWL/evRUI4OJp268AdgEn9L2iPsvMLdMDpHRtuf75ftazGEREm+Iz8Hng+orL6auI+FXgPwB/npk/iojBiBiruq4KrCjX93duzMwnge3A432vSOrOHDPHnsMcM8cwx1QPjc8wMMe6Mccan2NmmOrCHMMcm67JGQbmWKmxOWbnVO9eAeyn6M1+WmbuBm4r9zfVpnL9QKVVVONM4KVAY+5q6HBMub43Im4CngAej4itEdGIf1iVbgEeAd4XEW+OiIMj4iUR8VHgl4HzKq1OeoY5NjNzzBwzx8wxLW5m2IGZY+ZYU3PMDFNdmGMH1tQca3KGgTkGDc4xO6d6twHYnpl7uuz7ITBZPnLXKGUv/znAU8CSfwS5U0S8CPhT4IOZeXfF5VThJeX6CmA1cCLwX4Angasi4h1VFdZPmfkT4D8CDwOfBu6hePT2FOC4zLyiwvKkTuZYF+aYOYY5Zo6pDsywGZhj5hgNzjEzTDVijs2gqTlmhgHmWKNzbKDqAmpoDOgWIgC7O455sj/lLBoXA68E3p+Z/1p1MX32V8BdwIVVF1KR5eV6B/Ca8pFTIuIG4PvA+RFxZUMm9dwJ/DPFpJ5fowjVU4BrIuLYzPzHKouTSuZYd+aYOWaOmWNa/MywmZlj5ljTc8wMUx2YYzNrao41PcPAHJvSyBzzyane7QKGZ9g30nFMY0TEhygePb08Mz9adT39VD5e+jrg3Zm5t+p6KvJEuf5fUwECT/f63wis45m7IJasiPgFivD4x8z8o8y8ITM/TjFu7o+BK8o7gaSqmWPTmGPmWLk2x8wxLX5mWBfmmDlWrhubY2aYasQc66KpOWaGPc0ca3CO2TnVu/spHrPtFiYbKR7PbcwdDhFxHnA28Ang3dVW01/lZ+BC4HPAjyPixRHxYuCQ8pCV5baJyorsjx+U6x932fejcr2qT7VU6UyKf0xe17kxM3cBf0fxuTi0/2VJz2GOdTDHzDHMsSnmmOrADJvGHDPHMMfADFN9mGPTNDXHzLBnMccanGN2TvXuVop2O7JzY0SMAIcDW6ooqgoRcS5wLvAp4F2ZmRWX1G+jwAuANwJ3dCw3l/tPKF+/q4ri+mhqIs9NXfZNbXuwT7VUaWO57nYnw8C0tVQlc6xkjpljJXOsYI6pDsywDuaYOVYyx8ww1Yc51qHhOWaGPcMca3CO2TnVu2uBBM6Ytv1kinFhr+57RRWIiHOA84CrgHc0YNzPbh4H3txleU+5//Pl6xsrqa5/PkMxLuwJEbFsamNErAf+E3BHZn6vquL66Dvl+qTOjeVdLscCPwHu7HNNUjfmGOZYyRwrmGMFc0x1YIaVzDHAHJtijplhqg9zrGSOmWEdzLEG51g0q1N6YUTEJRRjod5A8fjlYcDpwFeBX1/qv1Aj4hTgL4F7gT8Bpv+8DyzVSdpmIyIOpZjM8NLMPLXaavojIjYD/wP4NvA3wBDw+8B64Dcz839XWF5fRMQhwP+leNT4aorfB6sp/pF5KHBKZl5WWYFSB3PMHDsQc8wcwxzTItb0DANz7Kcxx5qXY2aY6sQcM8cOpIkZBuZYk3NsST4O1gdnAHcDmykev9wOXAKc04QQAV5Rrg8Gruyy/0tAI0OkqTLz8ojYDrwP+BDFPyy+DvznzPxqpcX1SWbeExFHAucAvwEcTzGp423Af83M66usT5rGHCuYYwLMMTDHVCtNzzAwxzRN03PMDFPNmGPmmKYxx5qbYz45JUmSJEmSJEmSpL5xzilJkiRJkiRJkiT1jZ1TkiRJkiRJkiRJ6hs7pyRJkiRJkiRJktQ3dk5JkiRJkiRJkiSpb+yckiRJkiRJkiRJUt/YOSVJkiRJkiRJkqS+sXNKkiRJkiRJkiRJfWPnlFRjEXFURGREnFR1LZIk9cIMkyTVmTkmSaozc0yLgZ1TarSOX8TvLV9PRMR5EXFUxaU9LSIOL2s6tOpaJEmLhxkmSaozc0ySVGfmmDR/A1UXIC0yE8C55Z9vrrCOTodT1HQzcPe0fV8GRoG9/S1JkrQImWGSpDozxyRJdWaOST3yySmpjyJi+UJeLzP3Z+buzNy3kNeVJGk6M0ySVGfmmCSpzswxLUV2Tkml8rHbu8qX55aP5mZE3D3tuLdExP+JiB0RsSsivhkRv93lehkRn4yI3yiP3wncVO7bEBEXRMRtEfGTiNgdEd+JiLMiot1xjfOAT5Qvv9hR0yenau42PmxEjEfERyPizojYExE/johPRcQh03/mqfMj4h0R8e3y+Hsi4n1zb01JUj+ZYWaYJNWZOWaOSVKdmWPmmObGYf2kZ/wLcCZwEXADcH25fefUARHxYeADwOeBPwH2A78FXBcRp2bmpdOueQRwHHAFcGXH9l8E3lS+z53AIPAG4L8B/wb4vfK464H1wGbg/LJGynO6iogB4B+Afw/8LXAB8G+B3wdeFxFHZOYPpp32bmAt8HHgEeAE4M8i4geZec1M7yVJWjTMMDNMkurMHDPHJKnOzDFzTHORmS4ujV2Ao4AE3lu+PrR8fV6XY3+p3Hd+l32fAR4Dlndsy3J5bZfjR4Hosv0qYB+wvmPbSeV1jjpA/Sd1bDu53Pbn0459Y7n9qi7n3w9MdGwfA7YBX6/6v5GLi4uLS/fFDDPDXFxcXOq8mGPmmIuLi0udF3PMHHOZ/+KwftLsvY3il+6VETHZuQA3AsuBV0075/9l5hemXygzn8jMBIiIoYhYXV7nHyiG2zxiHnX+FsXdFx+d9p5/B9wGHBsR0//ufyIzH+k4dhfwDYq7IyRJ9WeGSZLqzByTJNWZOSZ14bB+0uwdBgTw3QMcs3ba663dDiofk/1j4O3Ai8vrdlo1xxoBXgTcn5k/6bLv28DhwCTwYMf273c59iHgoHnUIUlaPMwwSVKdmWOSpDozx6Qu7JySZi8o7nJ4A8Vjst18e9rrXTMcdyFwGnAt8BGKX+p7KR7z/TOY11ON00NpNmb6eSRJS4MZJkmqM3NMklRn5pjUhZ1T0rPlAfbdARwN3JuZ/3KA42bjd4EvZ+bxnRsj4sU91tTNncDRETHR+Vht6ecoxrHd3uM1JUmLnxkmSaozc0ySVGfmmNQj55ySnm1nuV7dZd9V5fr8iGhP3xkRa3p4n31MuxshIsaBM3usqZvPUPzd/uNp138D8HLgxszc30OtkqR6MMMkSXVmjkmS6swck3rkk1NSh8x8KCK+BxwfEXcCDwCPZ+ZNmXlrRJwL/ClwW0RcB9wPrAd+GTgGGJrlW/0t8HsRcS3wBYpxZd9JMSbrdLdSTEb4gYhYBTwO3JWZ35zh2p8ETgTOiohDgS9TjEH7nvLnef8sa5Qk1YgZJkmqM3NMklRn5pjUOzunpOd6G3ARcD4wBtwD3ASQmR+MiG8BpwNnAOMUY7v+M/AHPbzHHwI7gN8BjgXuAy6nCI0vdB6YmfdGxDuBs4C/AgaBK4GuQZKZeyPi9cDZwFuANwGPANcBZ2fmfT3UKUmqFzNMklRn5pgkqc7MMakHkdnr0JOSJEmSJEmSJEnS3DjnlCRJkiRJkiRJkvrGzilJkiRJkiRJkiT1jZ1TkiRJkiRJkiRJ6hs7pyRJkiRJkiRJktQ3dk5JkiRJkiRJkiSpb+yckiRJkiRJkiRJUt/YOSVJkiRJkiRJkqS+sXNKkiRJkiRJkiRJfWPnlCRJkiRJkiRJkvrGzilJkiRJkiRJkiT1zf8HtSx7Og8DH4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize = (24, 6))\n",
    "i = 0\n",
    "\n",
    "# Plot of four hyperparameters\n",
    "for i, hyper in enumerate(['colsample_bytree', 'learning_rate', 'min_child_samples', 'num_leaves']):\n",
    "        random_hyp[hyper] = random_hyp[hyper].astype(float)\n",
    "        # Scatterplot\n",
    "        sns.regplot('iteration', hyper, data = random_hyp, ax = axs[i])\n",
    "        axs[i].scatter(best_random_hyp['iteration'], best_random_hyp[hyper], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = 'Iteration', ylabel = '{}'.format(hyper), title = '{} over Search'.format(hyper));\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Msal4j8qZ/zNyfhimzj/PkF/Zfl1OmXJkTNtAfgVPcoNmRLa7Qv4kjtzzMfDJOP/eTPpNMT2v9ftf4rzDUmkvjWmPkbnBE+dPIzSsKJJqOVhNeTHI9s2m/4IwO/0Z+AjbXpAMKdZB4khajn41k74wFc9hZZa9KM6/rMr3WhTzt5Jf2ZW0Sl9U6/7UpMndIdyszj3vl8m/nOHdCPtizrw59Jc/f5dK7yC0HB720wnlzqeMkXKk3L4djXNYzjqS88zinHk1X2dltmXQltVVxDc/9TmUe3K5j3BzMd0TQ+52jfR5lfCkeh/hxujeOfPfGde3loE3Rb8V0z+Ts8zP47xsY5ICoaX+7JxlJhEqj5yBFS3J9nbnxVfF9jURWrAnTw6npyeBz7Bt48UhxTpIHK+P+a+rcHxs08tHzHNGnH97le+1MOYvkTonpeYnZfl5wz2+NWlKT6hMXsIOVCYTnppKnvra5tqBMN6us235dnpMvzRnmW+TuU9AqGBZRziP71smlqTsOyaVlpwLy17bDLJ9RqiMLMA25cdq4CvArpllhhTrIHGUvc+ciqfcfZXkvvajecdPTv75qXVu0ziH/vJo8XC/RyM5acypseNnZdLfGF+vz5vp7muAvwK7Eg54CCeQacAT7v5QzjK/IpzUh83MXmBm/25mF8S+Lhdb6Fd7CuFH3zZjPg3i6jLpV8bXI8vM/2VO2uPxtdzYCYeY2X/G/ml/FOM+J87ev5pgMzH9pMz8xfH1NUmCuxcJ2zqJ0Oov7V8JJ8Rr3L2QSh/sWLifULAcbGHw2Kxyx1hF7v5nQhd9Cwkn7zsJF1OTgX8CbjWz7CCCQ47VQt/r/2pmX7HQ13FyTL0kZin32dzm7u3ZRAtjZ7yE0KrymkrbmmOb4yp+dsmAzbnHlsgOZFyWXeUMo0z7dU7a3+JrkXChU27+nmXW+dN4vslKypp0eZiULze6e2fOMleUeQ9gRMvyJKYb887H9JeHr8z0u52MNzJgLCUzewVwIOGYuCc1Kzm+bnH3nuybxH2wLMafNx5JbnkxGHdvcfd/JjzVdBbh5t2aOPslhIuxX5vZ1JGINfZd/iYLY01cnPqtkoy9Va48XJvZX2lviK8/LLedZSxz94056RV/a4lUYVl8/b6ZHV3mN+xI2uZaw9030H8eT59blxFavF5uZq80s0GvncdxOVLOcM+3w1XzdVbGkK5/Ksg7fv5CqOiYSmgxXq2ROq8eQbgBd5e7P5Mz/2eEboXmAi9OpSdl74npzGa2O6FBUNK9U+K1xKcH3H2b32Pu3kd48h3gsJw4HigTX0XuXnD3DxGeLjqd8NtyeZy9L+HG4702cNyvIccax/14jYWxxb6bKntPjVkq3ScoNwbKUMveFe7+aE66yl4ZLSqTgx2lTH4loYHok3nXDu7+AKEB3lQGniuvJFTevDt9jJhZA+HJ2z4GltuvIDy186C7P0W+O+NrXvlR6dqmLA/OITxFfCqhe9/0+fM/gIfMbJ+RinUY95nL/V5Jyo8ryhw/5SRdBGaNyfJDAxiPHeV+qM2Pr9+2nMHkMuYQHtubF/9fUSHvCkIL4CEzsy8AZxIqKcqZUeNqy8W8PL7mDoxI6DM6K7nxlL5JhJlNJzwq/JYKcdQSd7K/ny4z/6lMvsTlhCeMTgS+mEpPbs79OJN/fnz9uZkNFtOuhNYgaTVfDCQ8VLHfGScsDAZ7JGGsr0OBs83sZne/dzixmtk7CD/aKw1UXO6zKbd9e8fXp2s8mUP+cQVlji2RHdC4K7vKGWaZlneu6Iiva929VGF+ufNILeXhvMy8cstsY4TL8orlobtvNrOthLHF9iC0eIZQybMZOMrM9nT31TF9sPLwU5YZqD3HnJy0IZeHAO7+GKFbIQDM7KWEi5sTCTfETiN02zHkWM3sAMITZZUuYmotD6G/THxikFiyVB7KaPkKYSyWNxIaBfWY2TLCBfXl7v7XEX6/Ws6tHyaMC/PeOG01s7sJ3YRdnr3pPc7LkXLmx9ehnm+Ha6jXWYlhne8ztrh7a5l5ywljtVSzTxMjdV4drOx1M1tO6F53HqHFPe7+gJn9BXiJmb3K3e+Li5xAuE90baah5Pz4+i4z80FiGo2y9xnCGJoXAJjZfoQnlj9BaCByPuE7O+RYzWwPQgVTpUrGct/h9Zn9laayV8YLlcnBjlImD1bGQihnX5rKi7uvN7NfEe6pHkMY/4j4/26EhoDpfZjEvaBO5cd6whO43wcws3nAvxGevN2L0H3sm4YT6wjcZx7sfmat5cdad+/NSR+T5Ycqp8YId+8qMytpDXAH8Owgq9lUw1sOWmNQcWGzdxG+yK2EmzC/JRz8hTj/buDwIbxPuS+/VZofWz9V60uEE8bDhLEslgGb3b1oZo2ELgdqUe02Dojd3ZeZ2cPAgWa2IP7/EsKFw9/c/e7M8smxcDODPz2Qtw3ljrGaxZPcHWZ2FKHmfR6hUEoqp2qO1cyeT2jJ0EToB/xqQgHeES+qzie0VC+3vwfbvsEKljy1HFciO5zxVnaVXenwy7RK54qhnkeGUh6WWyY3fRTK8lo+n+dicvceM/sf4N8JN8W+FhtBHB/zZVvMJ8fXfYQuDirJuxAcsfIQnnvC+CQzm0Ho8uMY+iunhhrrTwkVUzfGdT0OtLp7yczeQLgQH0p56JnXaqk8lFHh7h3Am8xsAeH3+ZGE886rgbPM7MPufmk166qmFTU1nFvd/ZFY+XwU4UbdPxKeKHkTcK6ZHe3uy+J7T5RyJGu459vhGtJ1VspInu8r7a9a9mlipM6rQyp7o8sJN6NPInzG0P8k1eWZvMmx8AhhfMlKHs5JG+my90ngk/F7/wlC2ZtUTg011ssIFVNLgf8iPDGwxd17zWx/Qlk81GtRUNkrY5zK5OfsKGXycMqPHxOOkZPor5warPx4hvBZVHJvTtpIlx+rgC+YWTthHKrXm1lzvLcx1FiHdZ+5wn2V57IMMj9rXJUfqpwa+5Kbele5+w+qXCZ5YuYFFfLsXWFeNZIuZT7j7j/KmV9rd36JcjEn6dmngYYiif242A1D2lDiXknoImEf4Pc585NHRFfnzLuccNPpJMLJ632p9KxngQOAC939N0OIc8S5e4eZ3UvoyzzdwmEosb6FUDF1fXz0Nmuox1RSKO9rZg1DeHpKRGo3VsuuckarTBuOWsrD5O/5ZZbZp0z6SG930kIu9/1ilzszCT+W12Vm/5hQOXUSYWDeowldKtyR0w1Qcnz92t0/W2OMo+k3hMqpbHkINcQan5o6kLCP3pXTOnM4x+MzhN8sLwayjWBE6ibeUEpuKjUBHyQ8IXGRmV3r7ltj1qQbm+k5q3l+FW/1AsIN57x0yFxrxN+Nv4xT0uXZlwnj6FxEuLkF478cKafe59vhXGeNtNlmNqPM01Mjea1aq8HKXqP/90F2P/2E0IPHcWZ2OqGr5VcQunj6XSZvciw84O6LhhnzSPoNoXIqr+ytOlYL3Q2/iTAOzdtS55zEcL7DKwjXxi8G7h/GekS2C5XJo2IslskVy4/MvGz5kTREPzp+DkXCPb12whNuaUncz4zB8gPC03WzCZVgQ411pO8zJ5L7mS+umGuc05hTY18y5s27KuYa6AHC+Dr7m9nLsjPN7GiG3y3SLvF1mxbx8WmaoXbrcFyZ9OPj611DXG9a2dhT71OLJKbs2FGJpMLpzpx5PyHcpDvOwhgVJxBqxPPGBxnKsTAsVkWffPSfaNOP7Q4l1krH1G6EQWhr5u5rCT94pgHvHso6RKRmY7XsKme0yrTheFd8eijrhPiaLg+T8uXtZtacs8y/lnmPkd7uJKa3x64NspLycFlsmfmc2J3QY8BL4+dfrks/6D++3lFlq8xhG4HysJZYk89lTZluQ8r9VqpG0of+omGsQ2RUxTFmLiSM3zCVgd1bJjdH8i7S35CTlrXN98fMdqX/d+bS7PxMbOsJrbEBDkrNGu/lSDnb/XybMZzrrNHwnmyCmf09oeeLbvJbfI+2pYTrxyPNLK8Rz7GEhiHr6B9rAnhuDNDbCd2sv5nKDSXvINx8fGOZMn7EDaPsHUqsMwn3x9pyKqZgaPcJEip7ZdxSmTxixmKZfD+hO8L9zCxv/KSXEfZrDzBgzCd37yY8MZX0dnEc4fi4zrcdA/mPhC7cXxV7TNouqihDkvKjG0jGgBxqrCN9nzmRlB/vLXP8TAiqnBr7bgQeJPyw+mbsMmYAM3uJmf1b8n+84bM4/ntheplYo/21EYjrsfj6gfiIYrL++cD3hrHeV5vZqekEM/sI4fH6jYRu34Yrif3fM+/zOuCMIazvUkLrgKPTn0Nc5/GELgYKwMXZBeO4GrcTCsevEwZPvMvdl5d5n5XAh8zsTBs44Hryfoea2UhWwHzYwsCR2wymaGZTzez/EQqrEqELouHEmnwu/2xmc1P5diJ0sVBpHKrBfC6+XmRmAwaatOANeTGKyJCN1bKrnNEq04Zjb+Bz6R/VZnYMocKvm9hndrQUeIhQlnzVzCanlvkH+ru5yRrR7Xb3uwgXObsCF1gYFDdZ50vpv3j8ZplVJDfDPkooOzsIA59n3+cBQmu9A4GfpMuM1PvtE38/jJSDzOw3ZvbWvAsDM3sn/YOlXzPMWP9KaLjyEjM7IpXPzOws4IjsOmrwDcJ+PcXMPpK9aIvfy32HsX6RmlgYqPxFOekvJbQm7mPgTeeki5VPxd+ISf5XAp+v4i0/ZmaHpJZrAC4kDAh+u7s/EtOnmdnpsYFU1tvia/qpzvFejuSq0/k2bcjXWaPkvyyMdZTEMB34NqFrpCvcfct2iuM57r6CMJB6A/C9zPfi+cBX478XxHGEs5JGIIuo0FAyNvj7HmE8kZ/llRVmtoeZfWIEb6DNNLNlZnac5TS+iWXkufHfdNk7lFjXAVuAWWZ2Qibveynf0KcalwFrCF1HnZ/+fRTXv4+ZHZS/qMj2ozJ5VI25Mjl2JZe877fT+9dCjxfJvvpRmfItKT9OonyXfskTb18AGoGbzOzgnLhnmdkpeds0DPeY2QfK3Yug/5r0Z+7eM8xYR/o+c+Im4P+AvwMuS3/P4vr3MLNK4ySOD+6uqY4T4cefD5Jnb0K/lU54bHIJYTye3xAGp3PgnswyMwlPizihUuenhJuFWwktuv4Q5716iHG/MK7LCY8ZXgv8L+ExyDsJ3S44sDCz3JJB0i8iFHjLCBVR98f0IvDWWvYfofsCB5Zn0v8lWY7QUv9qQtc2TujWYNDPJOe93kloTeAx5ivjfnagF3hvhWVPSMXjwL9VyHswoSbeCT+gbycMuncn4TFgB/6nmn1e5XadloprNfCLuG2/AtbH9BLwseHGSrigeiimbyWchK8HNsTlfxjnnZd5n/Py0nPi+VxqW5Lj65eEH1cOzErlXRzTFpVZ15D3qSZNE2Gq5jzJGCy7ysXOCJdpcd58csqg1Pzcc1fq/HMx4ULl0Xi+WkooHx04NWd9LyW08nJCy8ZkX/cC3xrJ7R5k/+5PKC+cMMDv1fFcm5SRF1dYdi9CmZKcq39cIe8s+luLd8RY/4fQuuzxmL62mn1e5Xa9LBXXVsLF+FXALXF/J/P+B5g8ArF+h/7fELfH/fh4/P+rcd6SzDIL89JztuWtMQ4nfBevIXzPku/r21N5F8W0xbUcx5o0VTvR/9vvr4Qb7FcSzq3FmP6VTP7dUueYlYTfir+P+c/PO9fF5ZLv6IUx7230j2+a/M7dJ5V/Vuo7eH/8nvwPoeFFcm1yTCr/RChHlsd58zPpNZ/DajwGBjvP1HydVW5bhhhf8jmsIFyfdAE/j59xcj30CDC7mu2qYntzP9dBYtw9xpBcd11LuIHZHtN+Dkwps2xz6th14M4K79NI+M55/EzuJXw3fgH8JX4eDjRVu72DbNesVFydwO/isfcz+n8vOqFM3mkEYv2P1Dp/T/jeJN/55D7B8sz7zM9Lz9mWVxGua5PzzfWE37kPEr6Xp6XyLqRCeT6cfapJU6UJlclL8tLjvIrfdcZpmUwoA5L1bY2f+/X0X1feB+xcYfnkPT3GbBXyXkj/PcQHgOsI11IPxX3jwAGp/Aup4tqmwvttob8MuC8eBz8l3A9MYv4zMHcEYh3SfeZy6Zk8+9F4ndg6AAAgAElEQVR//2QzoXy/llCu9RAan1R7nA5rn47WVPcAdvSpmgMx5msm9KX8O8JNvh7CDf4/EFokHJSzzCxCC9ln4pdnOeGGxk7030jZfxixvzB+QVcRTvKPEU7IUxm8EqpsOmFww7uAtjjdRpkbkZX2X6UvJfC6+B4t8T3uAU6q5TPJWefLCIXCWkLhuI5w4jukis82KTg7qHDij/lnA+cQHjdtJbQWXEEoZM8C9qtmn1e5TTsD7yDcJPtj/Kx7CBc6jxBaWbxsBGOdGY/Zv8a8K4EfAPMoX9jnppeJ57WEwnZt3I41MZbTSF2wocopTZoqTtWeJxmbZVe5H4YjVqbFefMZ3gXMIsKNjF8Sflh3Ei4+tmmokVp2P8IP4Y1xG/5MeArJRnK7q9jHcwn90j8ZP8OtcV3HVbHsbUmswFGD5J0S99PtcZuL8fy+jPA08qsz+XP3eZXbNAV4DaEl3Z3A03F/dcVj9KfAsSMY6yRCy7s/EX4bbCLcXDycMhcV5dLLxPNCQhn+VPyMNhMu8M8H9kjlW8QI30TVpCk9ESpLv0+42N9I+P23nHAD4E1lltkvnrc2x+/gg8TGXRXOdcl5xYCPEypjC4SbxT8G9srkn0J46vQawo2XVsLv38cIv01fkvMe47ocoUKFDjWew2o8BiqeZ2Kemq6zKm3LEOJ77nMgVHicR/g90h0/64uAXardrsG2t9znWkWcMwiN8R6Jx19yjfthMo0mcpa9lP7vyMlVvNc7CN/R5JpqA6G8+h5wdK2fb4X3MUIPKp8h/D74G6FMTPb9rYTW+pNGItaY/z2Em5ht8XvzW0KXh88dB7V8TzN59yD0BPBY/Iy2EirKLgBemMq3EFVOaarDhMrk3PQ4r+J3nXFcJsd9cgahUqUjxvYn4EygeZBlP5P6PD9fxXu9llCxspL+a5CH4356B9CQyruQ4VVOvTRu163x828llANJ4/mPAFNHItaYv+b7zOXSc/LNBP6L/mvDdsJ34VLg5TUcp8Pap6M1WQxOdiBm9gLCDaMOQguvvjqHhJktIdz0ea27L6lvNCIiMtaMxbJLRERkrDGzxYSxg/7N3RfXNxoREZEdl8pkkcFpzKkJzMwW5IwlMI/QB+hk4Ce6uSciImOJyi4RERERERERkYlvpAaqlLHpl0CPmf2F0B3MXsArgGmEfk4/U2FZERGRelDZJSIiIiIiIiIywalyamL7CvA24CBgF0Lfmk8Qxty5wN1bk4zxUdNqfcndHxvBOEVERBIqu0RERMYhMzsTOKDK7De6+42jGU+Wme1GGPOnWp9y942jFY+IiIjIjk5jTgkAZlbLgaBxoUREpO5UdomIiIwdqXGEq/Hf7n7e6EWzLTObDzxdwyL7uPvyUQlGRERERFQ5JSIiIiIiIiIiIiIiItuPuvUDdtttN58/f369wxARqcr999+/0d3n1DsOGT9UzonIeKOyTmqhck5ExhuVc1ILlXMiMt5UW86pcgqYP38+y5Ytq3cYIiJVMbMV9Y5BxheVcyIy3qisk1qonBOR8UblnNRC5ZyIjDfVlnOTRjsQERERERERERERERERkYQqp0RERERERERERERERGS7UeWUiIiIiIiIiIiIbHdmdpaZXWdmT5mZm9nyMvnmx/mVpn+tMv9fyrzHoWZ2u5m1mVmrmf3SzF5WJu+eZna5mW0wsy4zW2Zm7y6Td6qZfc7MnjazbjN70szOMbOGIewyEZEJo+5jTpnZWcArgFcC+wAr3H3+ENZzEnA6cADQCtwCnOXuG0YuWhERERERERERERkh5wObgQeAWRXybQBOLDPv20Az8KuceT8DbsikbclmMrPDgCXAKuDcmPxRYKmZvdrd/5zKuwvwO2B34BvASuAE4Foze7+7/yiz+muAY4EfAn8ADgc+D7wQWFRmm0REJry6V05RfSFUlpmdTigM7gQ+AewFfBI43Mxe5e4dIxSriIiIiIiIiIiIjIz93P0pgPhE0/S8TPHe3k+y6WZ2ODAT+Km7b8xZ9P/cfZvlclwI9ABHuvuquO5rgUeBrwNvSOU9k9DA/hh3vyXm/QGh4ulrZnadu7fH9DcTKqa+4e5nxOUvM7MtwCfN7BJ3v7uK+EREJpyx0K3ffu6+q7u/Hlhd68JmthvwBeCPwFHufom7nwscD/w9obJKRERERERERERExpCkYmoYTomvl5XLYGZNZjatwvwXAocA1yUVUzG2VcB1wOvMbI/UIicATyYVUzFvCbgI2AV4cyYvwAWZt03+f2+5uEREJrq6V06NQCH0dmAacFEsCJL13gI8hU7yIiIiIiIiIiIiE4qZTQf+BXgGuK1MtjOATqDDzJ6NYz9NzeQ5JL7+IWf5ewAjDEeCmT0PmBfT8/Km15f8vcrdn01njP+vzuQVEdmh1L1yagQMVoAcEAsrERERERERERERmRjeQ+gG8Ifu3peZ1wfcAZxNaNh+CvAI8Fng52Y2OZV3z/i6im0lafOGkDfJn5c3yT8vb4aZfdDMlpnZsg0bNpRZXERkfJsIlVODFQqWyiMiIiIiIjJumNmLzexKM3vUzLaaWaeZPWZm34itt/Py32hmLWbWYWZLzeyfyqx7ppldZGarzKxgZg+b2YfNzHLyTjKz0+N7F2Lr86+b2U5l1v1mM7s7xrDZzK4zs30qbGNVMYuIiKScQqiE+lF2hrs/4+5HuftF7n6zu//A3Y8GLgVeBxyXyp50+ded8x6FTJ5a8iZ/5+VN8ud2NxiHLVng7gvmzJlTZnERkfFtIlRO1VooAGqBICIiIiIi48JewPOAnwFnAacRui76IHC/me2eZDSz/YC7gcOBrwD/QWhR/isze116pWbWGNdzKnAN8DHgceC7wH/lxPFN4BuEVucfI4zB8XHgFjMbcF1pZu8Efg40xxi+ChwJ/N7M9szkrTpmERGRhJn9PXAYcJu7P1PDov8vvr4lldYZX7Pd/QE0ZfLUkjf5Oy9vkr+zzDwRkQlvSr0DGAHpQqErMy+vUABCCwTgEoAFCxb4qEUnIiI7NDM7C3gFoY/yfYAV7j5/COs5CTgdOABoBW4BznJ3tbAQEZnA3P03wG+y6WZ2F3AtsIhQqQPwRWAW8Ep3fyjmuxx4GPiOmR3g7sm1zymELtI/7u4XxbRLzex64Gwz+5G7r4jrOJBQIXWDu/9zKoangQsJrc+vimkNhAHhnwWOcPf2mP6/wP3AeYSKtUQtMYuIiCROjq+X1bjcs0AJ2C2Vtjq+5nWxl6StGkLeJH9u130xvVyXfyIiE95EeHJqsELBU3lERES2t/OBfwKeBFqGsgIzOx34MbAV+ATwfcKNwCXlulMSEZEJb0V8nQ0Qy4NjgCVJJQ9ArBy6DNifgYOun0BoxHdpZr0XAA2EcTwSxxO6S78gk/fSuI73ptJeQ+hW/bKkYirG8RCwBHhPrMAaSswiIiJJQ4gTgQ3ATTUuvi8wGViXSvtjfD08J/9hhHuL9wO4+xpChdJhZfICLMuse56ZPT+dMf6/ZyaviMgOZSJUTlUqQA4FHk9fFI2EJY+t5/hL7uEfv3wHx19yD0seWz+SqxcRkYllP3ff1d1fzxAaS5jZbsAXCOXdUbHv8XMJNwr/nlBZJVK7J26DxW+FC14aXp+4rd4RiUgFZtZkZruZ2V5m9gZCQwWAX8TXgwi9SfwhZ/F74ushcV2TCE/1PujuhUze+wjjd6QrhQ6JafelM8ZlH8rJS4U4ZhAqnWqKuRJHD1aJiOxgjgXmAFe4ezEvg5ntmpM2iXBtBaEnCgDc/W+ESqJ3p7ufjX+/G7jD3demVnU1sJ+ZvS2VdzLhKeMt9JfNSV4I3fKmJf9fWWYbRUQmvHHVrZ+Z7U0YP+rJVOFzE6EriY+a2VXuXop53wbsB3x2JGNY8th6zr35YRomG7OaG1jfVuDcmx/mc8DCA3YfdHkREdmxuPtTw1zF2wll30VJGRfXe4uZPUVorX7+MN9DdjRP3Ab/+ymY1AhNs6FtXfifr8H+r693dCKS7xRCd3mJ5cB73X1p/D+5mZbXPVCSlvQ2MZswHtQ2ed2928w2MbBnij2Bje6eN87vKuDVZtbo7j01xPFwjTGX1dvXS0uhhdlNswfLKiIiY4yZnQi8IP47B2g0s3Pi/yvc/Yqcxarp0u9SM5tBGNfwWUI3fv9M6G79JuCnmfyfAH4LLDWzpLz9GKFh/xmZvF8iVFpdZWbfIJRZxxMaVJzi7m1JRne/1cx+DnzSzGYSGmQcHrfhJ+7+uwrbICIyodW9cqrGQuhyQjcR+xAuxnD3DWb2WeBrwO1mdjXhAuYM4DG27XpiWL5/11M0TDamNYZdN61xCp09vXz/rqdUOSUyTH19fUyaNBEe6BQZUYO1QD/ezKaP9FPCMsHd/a1QMdU4LfzfOA16Yroqp0TGqhsJ1zfTgZcTusObk5ofv9DkVSAVMnkq5U3yT0v9P22QvEmenhGMI5t3ADP7IHHsqnnPn8fW7q0U+4rMaZ6DmZUJVURExqCTCff60j4fX+8EBlROmdlewBuAu9390QrrvZXQ9d8HgV0IZc3DwEeAi929L53Z3e82s4WEJ6u+QOjK727g3e7+p0zeTWb2D4RKqo8QyuZHgOPc/ZqcWN4NnENoWHgioTLr3Li8iMgOq+6VU9RYCOVx96/H1n2nE56iaiUMDnzmSN+se7alk1nNDQPSmhsms7KlcyTfRmSHs2nTJg466CD+/Oc/s8suu9Q7HJGxZLBW5RbzPJGekb5pt/fee49mfDIebVkRnphKa2iGLc/UJx4RGZS7rwRWxn9vNLPrgT+aWbO7f5Ew9hOEbvKymuJrZ+Y1L2+SP32B0wmUa4lXy7qHk3cAd78EuATg4Fcc7ACdxU7W9q1lzrQ5TJk0Fi51RURkMO6+sMb8KwljRg2W7wfAD2pc9x+Ao6rMu4pQ0VRN3gKhcuqcwfKKiOxI6v6IgrsvdHcrMy0sk3d5znoWu/vB7t7k7ru7+/vdfcQHg3r+7Gl0FUsD0rqKJfaandugT0SqdPPNN7N69WpuvvnmeociMtYMqVV5HJtqgbsvmDNnTna27OhmvQCKXQPTil0wSxWZIuOFu/8f8CDw7zEpGdcwrxu8JC1p6NACdOXlNbOpwK4MbBSxGtgtzstb98bYpV+tcdSStyrdpW7WdKyhp9QzeGYREREREambuldOjTcfOnJfiiWns6cX9/BaLDkfOnLfeocmMq4tXrx4wKuIPGfIrcpFynr1J6CvB3o6wT289vWEdBEZT5oJXRUB/JnQkOHwnHyHxddlALEroweAl+dUOL2KcJ24LJX2x5j2qnRGM2sCXpaTlwpxtNL/tG/VMdei1FdibcdaOosqHkVERERExipVTtVo4QG787ljDmT3nZvY2lVk952b+NwxB2q8KZFhaG1t5Z577gHgnnvuobW1tc4RiYwpg7Uq91Qekers/3p409dg57lQ2BJe3/Q1jTclMgaZ2R5l0l8LvIQw/iCxO/NbgIVmdnAq33TgFOCvwH2pVVxNePL2g5lVnwb0ErpJT1xDKG9Oy+T9QFzHlam0O4E1wCnxvZM4DgYWAte5e3GIMVetz/tY37me1h79rhQRERERGYvUEfcQLDxgd1VGiQxRS0sLzzwzcEyT7199E302BeihZJM58/wL+NDxxw7Is/feezN7dmZ8FJEdwx8JNw4PB/6WmXco8PhIj68oO4j9X6/KKJHx4Xtm9jzgDmAF4anZVwLHAW3AGam8ZxHGyvi1mX2T8JTSBwiNGd7i7p7Keynwb8A3zGw+8CjwZuAdwBfc/ekko7v/2cy+A3zUzG4AfgH8HfBxQmXUVam8RTP7BKFCa6mZXQrMIIwPvAH4r8z21RJzzTZ3baZYKrJr867DWY2IiIiIiIwwVU6JyHb1pS99ia985Ss0NTXR2NhIb5/TXSxR6g7drvQWOrnkgi/z4+98jSmTjJ6eHgqFAv/5n//Jl7/85TpHX3/dvaXBM8m4ZWZ7E1qgP5m0KgduAi4k3BC8yt1LMe/bgP2Az9YlWBER2V6uBt5HGHR9DuEJphXA94GvuvtzrX7c/W9m9g/Al4AzgUZC931vdPfb0yt19x4zex3wBeB4wjhTTwIfA76TE8dpwHJCg4m3ABuBi4BzYzeB6XVfZ2ZdhIHfv0bouu83wKfjAPLpvFXHPFRtPW309vUyZ9ocJpk6DxERERERGQtUOSUi29UXv/hFZs2axec///my3feVujvp7A5/Nzc3c/755/PpT396O0Y5Nm3tKrK5Q4N7jzdmdiLwgvjvHKDRzM6J/69w9ytS2S8HXgPsQ7gBiLtvMLPPEm7u3W5mVxNak58BPAZcMOobISIidePu1zKwi73B8j8KHDtoxpB3C/DROA2WtwR8PU7VrPvnwM+rzFt1zEPV1dvF2o617D5td6ZM0mWwiIiIiEi96Ve5iGxXkyZN4qyzzuKoo47i2GOPZd3GzXjvthUuNqWBPebsxk033cQhhxxSh0jHBnenq1iitauXzp7eeocjQ3MyocIp7fPx9U7gCgbh7l83s02ELpEuJHR5dC1wprr0ExERqU5PqYc1HWuYO20ujZMb6x2OiIiIiMgOTZVTIlIXr3rVq3j88cfZc98D6Ni0Zpv502buxmOPPcaMGTPqEF39FYol2gqhQqrUN6yhFqTO3H3hSOR198XA4mEHJCIisgMr9ZVY27GW3Zp3Y1rDtHqHIyIiIiKyw1LllIjUTWNjI8X2zbnziu0tTJ06dTtHVF/uztauIm2FXoqlvsEXEBEREZGa9Xkf6zvXM7tpNjOnzqx3OCIiIiIiOySNBisidXPbbbfRFCugJjc2YZMbmNzYBEDT1EZuu+22eoa3XXX1lFjZ0sXmjh5VTImIiIhsBy2FFjZ2bcRdT6mLiIiIiGxvqpwSkbq54ooraG1tZdq0afz7hz5A65bNfPiDp9Dc3ExraytXXDHoUDzjXqnPWd9WYM3WLlVKiYiIiGwHy9Yt4+zfnc3Jvz6Zj9/xcW5+8mZ6+zS2p4hUb+nKpZz8q5N54/Vv5ORfnczSlUvrHZKIiMi4o8opEamLnp4ebr31VmbMmMENN9zAhRdeyPTp07nooou44YYbmDFjBrfeeivFYrHeoY6a9u5eVrZ00l7QzRARERGR7WHZumVc/KeLaSm0ML1hOi2FFi568CJuefIWukvd9Q5PRMaBpSuXcv6957OhawMzGmewoWsD5997viqoREREaqTKKaC35PT1qSsHke2pVCrxvve9jyeeeIKjjz56wLw3vvGNPPHEE5x00kn09k68ipu+Pmft1gLrWwuUdO4REREZqLen3hHIBHbDX2+gYVIDU6dMxcyYOmUqDZMauO6J61jbsZbOYme9QxSRMW7xw4tpmNxA85RmzIzmKc00TG5g8cOL6x2aiIjIuKLKKaDkzqotXXT3luodisgOo7m5me9+97vMnTs3d/7cuXP57ne/S3Nz83aObHS5O+vbuunsmXiVbiIiIsPiDq1roLut3pHIBLaucx2NkxsHpDVObmRd57rwO61zPW09OgZFpLxV7atomtw0IK1pchOr2lfVKSIREZHxSZVTUbHUx+otBVoLE7cLMRGpvw3tqpgSERHZRqkXtj4LPR31jkQmuLnT5tJTGvh0Xk+ph7nT+htMberaxJbClu0dmoiME/Omz6NQKgxIK5QKzJs+r04RiYiIjE+qnEpxdza2dbO+raBu/kRkxG1q79b4UiIiIlnFQqiYUnd+sh2880XvpNhXpLu3G3enu7ebYl+Rd77onQPybenewobODbjrulBEBlp04CKKpSJdvV24O129XRRLRRYduKjeoYmIiIwrqpzK0V7oZdWWLgpFdfMnIiOjpaOHrV16MlNERGSA7jZoXQV9+t0t28eCuQs49eBTmd00m/ZiO7ObZnPqwaeyYO6CbfJ2FDtY27GWko5PEUk5Yq8jOPvQs5nTPIfWnlbmNM/h7EPP5oi9jqh3aCIiIuPKlHoHMFaFbv66mNHcwC7TGpk0yeodkoiMU1s6e2jpVGtwERGRATo3h0lkO1swd0FuZVSe7lI3qztWM6d5Dk1TmgZfQER2CEfsdYQqo0RERIZJT04NorWryMqWLjq61RWXiNRuS2cPmztUMSUiIvKcvhJsXaWKKRk3Sn0l1naspaXQom7+RERERERGiJ6cqkJvXx/rWgtMa5zCrtMbaZisOj0Rqayju5etXUV1DyoiIpLW0wnt69SNn4xLW7u3UugtsPu03Zk8aXK9wxERERERGddUOVWDzp5eulpKzJ7WwMzmBszU1Z+I9Ost9dHe3UtboZdiqa/e4YiIiIwd7tC5Cbq21DsSkWHpLnWzpmMNc3eaS8OkhnqHIyIiIiIybqlyqkbuzuaOHtq7e9lt+lSaGtRiTmRH1tfntPf00tHdS1ePWoGLiIhso1gIT0uVivWORGRE9Pb1srZ9LXN3mkvj5MZ6hyMiIiIiMi6pcmqIenr7WL2li52bGpg9rYEp6upPZIdSKJbY2lWks6eksQdERETyuIdxpbpa6h2JyIgreRiHas60OTRPaa53OCIiIiIi444qp4aprVCkvbuXnZumMHtaI5Mnqas/kYmsu7dES0eRzp7eeociIiIydvV2h6elenvqHYnIqOnzPtZ3rmfXpl2Z3ji93uGIiIiIiIwrqpwaAe5Oa1eRzu4Su89QV38iE1GpL3Tp2VZQl0QiIiIVFVqhY0N4ckpkgnN3NnZtpOQlZk6dWe9wRERERETGDVVOjaDevj7WbC2wy06NzGzW4LgiE0GofO6lpbOHPt1kExERqaxjI3RtqXcUIttdS6GFzmInuzbvqnGoRERERESqoMqpEebubGrvplAssctOjTRoLCqRcadQLFEolugqlugu9qlSSkREZDClIrSvh2JXvSMRqZvuUjer21czY+oMZk+djdm2Xb4vXbmUxQ8vZlX7KuZNn8eiAxdxxF5HjHps9XpfEREREZFyVHMySjq6e1nZ0sXmjh5cN7ZFxrzOnl7WtxVYsamD1VvCd7erp6SKKRERkUpKvaFSasszqpgSiVq7W1nbsZZi38DuoJeuXMr5957Phq4NzGicwYauDZx/7/ksXbl0VOOp1/uKiIiIiFSiyqlR5O5s6exhxaZO1rUWaC0U6S311TssEUlp7+5lZUsna7cWaC/0UupTZZSIiEhVurbAlhVhjCk15hAZoLvUzdr2tXT19lfaLn54MQ2TG2ie0oyZ0TylmYbJDSx+ePGoxlKv9xURqZaZnWVm15nZU2bmZra8Qt7FMU/e9K6c/FPN7HNm9rSZdZvZk2Z2jpnljsdhZieZ2YNm1mVm68zsMjObUybvoWZ2u5m1mVmrmf3SzF5WJu+eZna5mW2I615mZu+ucheJiExI6tZvO+hzp6O7l47uXgAaJk9iasMkpjVOYafGybndPYjI6HF3Wgu9tHYVKarCWEREpDalIrSvg2Kh3pGIjGklL7GuYx2zps5iVtMsVrWvYkbjjAF5miY3sap91ajGUa/3FRGpwfnAZuABYFaVy5yYk3ZfTto1wLHAD4E/AIcDnwdeCCxKZzSz04FvAHcCnwD2Aj4JHG5mr3L3jlTew4AlwCrg3Jj8UWCpmb3a3f+cyrsL8Dtg97j+lcAJwLVm9n53/1GV2ywiMqGocqoOiqU+iqU+2gu9TJk0iZnTGpjRNEWVVCKjrLfUR1uhl9ZCUU9IiYiI1ModulrCpCelRKq2pXsL3aVu9txpTzYWNtI8pfm5eYVSgXnT543q+8+bPo8NXRu2+/uKiNRgP3d/CsDM/gJMH2wBd//JYHnM7M2EiqlvuPsZMfkyM9sCfNLMLnH3u2Pe3YAvAH8EjnL3Ukz/I3AzobLq/NTqLwR6gCPdfVXMey3wKPB14A2pvGcC+wDHuPstMe8PCJVlXzOz69y9fbDtERGZaNStX5319vWxqb2bFZs6Wd9WoLOnV2NUiYywrp4S61oLPLO5k5bOHlVMiYiI1Kq7PXTh17lZFVMiQ9DV28Xb9nsbPb09dPV24e509XZRLBVZdOCiUX3vRQcuolgqbvf3FRGpVlIxVQsLZphZpXubJ8TXCzLpyf/vTaW9HZgGXJRUTMXYbgGeSuc1sxcChwDXJRVTMe8q4DrgdWa2RyaOJ5OKqZi3BFwE7AK8udK2iohMVKqcGiP63Gkv9LJ2a4Hlmzp5dnMnq7d0sb6tQEe3KqxEalXqc7Z2Fnl2cydrtnY9162miIiI1KC7HbY8C21roaSytB7MbP84VsY9cZyKNjN7yMw+Y2Y7ZfKeV2Ecjk/lrHuSmZ1uZo+ZWcHMnjWzr2fXm8r/ZjO728w6zGxzHB9knzJ5X2xmN5pZS8y/1Mz+qUzemWZ2kZmtinE8bGYftgnWtcTLd385pxx0CrOmzqK1p5U5zXM4+9CzOWKvI0b1fY/Y6wjOPvRs5jTP2a7vKyIyyrbGqcvMbjOzQ3PyHAKscvdn04nx/9VxfjovhKeZsu4BDjCz6VXmNeCVAGb2PGBeTM/Lm16fiMgORd36jUHuTrHkFEtAEdoLvUwyY6epU5g1rYGGyapTFCmnUCzRWijS0V1Spa6IiMhQuEN3W+i+r1SsdzQC7wc+QuhS6EqgCLyW0PXQv5jZYe7elVnmdGBjJu3+nHV/E/g48DNCF0R/F/9/uZm9zt2fG5zTzN4J/BT4E/AfwEzgNOD3ZrbA3Ven8u4H3A30Al8h3Dz8APArM3uTu9+eytsI3Aa8nNCC/FHgTcB3gbnAeYPvovFjwdwFLJi7gKYpTcxums3UyVO3y/sesdcRqowSkYliLaH8uh/oAA4mlEdLzezN6TIG2BN4pMx6VhHGlErnTdLz8lrM80QVeSFUSFWz3nTe55jZB4EPAuy99945i4qIjH+qnBon+txpKxRp7+5l+tQpzJ7WwBRVUokAoUK3rbuX1q4iPb19g8BUDHIAACAASURBVC8gIiIi23KH7tZYKaWnpMaQnwJfdPetqbSLzeyvwGeAk4FvZ5a50d2XV1qpmR0IfAy4wd3/OZX+NGEcjeOAq2JaA6Hi6FngiGRcDDP7X8LNwfOIN9CiLxIGtH+luz8U814OPAx8x8wO8P5WRKcQWox/3N0vimmXmtn1wNlm9iN3X1FpW8ajQm+BNe1raJrSxMypMweMByUiIuW5+5mZpBvN7CrgIeB7wItS86YB3WVWVYjz03kpk7+QyTNaeZ/j7pcAlwAsWLBALW9FZEJS7cY447GS6pnNnaza0sXmjh4KxdLgC4oMYslj6zn+knv4xy/fwfGX3MOSx9bXO6RB9fT2j9m2sa1bFVMiIiJD4R4qpFqWQ/sGVUyNMe6+LFMxlbgmvr4kb7k4DkelxojHE1qBZ8fhuBToZOA4HK8htPy+LD1ge6x4WgK8J1ZgEbsEPAZYklRMxbztwGXA/gzsvuiE+H6XZuK4AGgA3lNhG8a9Qm+BdR3rWN2+mvaedj35LyIyBO7+V+Ba4IVmtn9qVidQ7hHVpjg/nZcy+ZsyeUYrr4jIDkWVU+NYd7HEls4eVm/p4plNnWzu6NHNeRmSJY+t59ybH2Z9W4FZzQ2sbytw7s0Pj9kKqo7uXtZs7WJlSydbu4r06SJeRESkdn190LkZWp6Gjk3QpwZP40zSFdG6nHn/R+hKrxDHiHpTTp5DgD7gvnSiuxcIrc9rGYdjBqHSCeAgwg24cnmfW18cxP4VwIPxfdPui/HtEONw9JR62Ni1kZXtK+ks6h6liMgQLI+vu6XSVpPTZV40j4Fd7a1Opefl9VSewfKSWncteUVEdiiqnJogevv62NLZw8qWTlZv6aKtUKSvTzfspTrfv+spGiYb0xqnYBZeGyYb37/rqXqH9pzeUh8tHT08s6mTda0Funp0A01ERGRI0pVSnZvD/zKumNlk4FzCmE5XpWZtIXQB9DHgWOAs4AXArWa2KLOaPYGN7p7XzdAqYLc4HlSSN0nPywtDG1tjNtCclzfGtYkyNxXN7INmtszMlm3auCkvy7hU6iuxvnM9m7o26SkqEZHaJN35pRtt/BGYZ2bPT2eM/+8JLMvkBTg8Z92HAo+nnh6ulPcwQkXW/QDuvoZQzh1WJi+ZOEREdhiqnJqACsUSG9q6eWZzJ5vauympkkoG8WxLJ80NkwekNTdMZmVL/Vttdvb0sq61wLMtXbR09tCrG2giIiJD19MJW1aESind+B7PLiDc0DrX3R9PEt39Anf/kLv/2N1vdvevEp5kWgd808ymp9Yx2DgcSZ706/YchyPJv804HBDG4nD3Be6+YNfddi2z+PjV1tPGmo41FHqzD5SJiOy4zGwnM2vKSX858G7gUXd/MjXr6vh6WmaR5P8rU2k3AV3AR2MjkGTdbwP2S+d1978RKpTebWZ7pvLuGeO4w93XZuLYL64ryTuZ0JhkC/CLStstIjJRVeqDXMa5Pne2dhVpLfSyc9MUZjQ10DhF9ZGyrefPnsb6tgLTGvtPCV3FEnvNzr0XMOr6+py2Qi+thSLFkiqjREREhq2vDzo3QqG13pHIMJnZ54GPApe4+xcHy+/um8zsYuA84NXAr+OsTmD3Mottr7E1KuVN8te/tVSd9JR6WNuxlqmTpzJz6kymNdTnt7mIyGgzsxMJT/oCzAEazeyc+P8Kd78i/v0i4H/N7Ebgr0AHcDDwfqAEfDC9Xne/1cx+DnzSzGYSupw9HDgZ+Im7/y6Vd4OZfRb4GnC7mV1NeHr3DOAxth2j8RPAb4GlZnZRTPsY4UGAMzJ5v0SotLrKzL5BeJLqeELXtae4e1t1e0pEZGJR5dQOwN1p7SrS2lVkasNkdm6awvTGKUyaZPUOTcaIDx25L+fe/DCdPb00N0ymq1iiWHI+dOS+2zWOnt4+2gpF2gq9GkdKRERkpPT2QNsaKBXrHYkMk5mdB5wD/Ag4tYZFl8fX7Dgcf29mU3O69ptH6PKvJ5U3SX80Jy8MbWyNFkIr9W3ymtlUYFfgzpz17FC6S92s71xPw+QGZjbOZHrj9MEXEhEZX04GXpNJ+3x8vRNIKqf+P3t3Hif5WdZ7/3PV1tX77DM9+5KExBAmCZMAyQGDoA+CRzS8OAYEAQXEAxKQcwQU48KurEY4PISIUUEWT0CQ4+MJRCAmSBKzkoUks2emZ+uleqv1V9fzx11VXd1dM9M90z3Vy/f9etWruu7fXb++a3pmqut33dd1HQG+C7wQ+HVCadhe4KvAh9398QbnfiXhvfM1wGsJ70E3EAJGE7j7x82sD3gn8JfAEPA14D11Jf2qc+8ys2uAD1RuDtwFvNLdH5w0t8/Mrq58z7cCHcCjwHXu/tWT/qmIiCxyCk4tMfliRL4Y0W8F2lsSdLUmaEnET/9EWdSuuXANf0boPfX0wBgbl7fx2y/YzjUXnmwz7exxd0YLEcO5ovpIiYiIzLb8CIwcVQm/RcDM/hj4Y+BvCbusZ/JDPVkfjl8ArgTuqPs+aeBS4IeT5kLYbf7dSed+LuHi3ROVxw8TyvSdrA8HVHpruHvZzO4DLmsQJLuSsPtcfTgqilGRE9kTZAoZBalEZFFx92umOe8IIcA0k3PnCMGp951ubmX+3wB/M825PwJeNM25h5jh2kVEFjsFp5aosnslQ6VIMh6jLRWnvSVBSyKGmTKqlqJrLlxzToJRVaWozHCuxHCupD5SIiIis61chmw/ZAebvRKZBWZ2A6Es398Bb3D3Kb88mVkCaHf3zKTxTcDvAH2EHd1VXwX+gNB344668TcR+jzV9+H4AWFn+hvN7JPV3eNmthO4BviiuxcB3H3EzL4NXGtmO6u7xyv9rt5IKMN0d925/wG4mlCK6ca68XcAJcKOdalTDVINFYZY1rJM5f5EREREZEFScEooRmUy2TKZbJGYGelknNZknNZUXD2qZNblihFD2SKjhYiZbfgVERGRaSmMwegxiErNXonMAjN7K/CnwAFC1tKrJ20mO+rutxFKBO2t9OF4jFAy7xmEgFAH8Cp3z1af5O4Pm9lnCI3fbyU0Y78IeDshGPXlurlFM7ueENC6w8xuAroIZY+OEzK66r2XsJP8/5rZJwmZVW8ilO972aSsr5uANwCfMLOtlbW/FPhV4APuvnemf2ZLRSEq1Mr9dSQ7aEu2kYwlm70sEREREZFpUXBKJii7M1YoMVYowSikEjE6W5K0t8RJxBWokjNTjMqM5kuM5EsUSsqSEhERmRNRCcb6IK+e2ovMFZX7zcAtDY7/ALiN0LvpfwPPAX6FEJA6QQho/bm7393gue8g9KN6M/CyyvwbgRsmZ2e5+9fNLEsoi/QxQum+7wHvrpQqqp/7VF1vjfcAKeA+4CXu/t1Jcwtm9mJCv45XEfpM7SY0lf/Mqf5gJChGRQaiAQZyA6QTaTpTnbQl2lQRQ0RERETmNQWn5JQKpTJ9pTx9lUBVeypBe0tCGVVyWsWozFg+YqRQIl9ULykREal44ja469MwuB+WbYGrrocLfr7Zq1rYymXIDkBuUL2lFiF3fz3w+mnMyxOypGZy7gj4eOU2nfn/DPzzNOc+Brx8mnMHgbdVbnIWcqUcuVKOeCxOR7KDlngL6USamOnzm8h84O4UyoVmL0NERGReUHBKpq1QKlMoFRgYK9CSjNPRkqCjJUE8ph15EpSiMqP5iOF8URlSIiIy1RO3wb/8D4ilIL0cho+Gx3xMAaozlcvAWD+UtRFERMZF5YhMfrz9WEu8hc5UJ+3JdmVUiTSBuzNcHCaTz5COp5u9HBERkXlBwSk5I/liRL4Y0TeSpyUZpz0VJ52M05KI6cPOEpQrRgyOFUM5SBERkZO569MhMJVqC49TbVCojCs4NTOF0VDCr6Td1yJyevkoTz6bZyA3QHuqnfZkOy3xlmYvS2TRK0ZFhovDjBZGibyykSTe3DWJiIjMFwpOyVmrBqoAzIyWRIx0Mk5bJWAli1cpKtM/WmAkr6CUiIhMw+D+kDFVL9kKgweas56FqDAaMqVK+WavREQWoMgjhvJDDOWHiMfitCXaaEu2kY6ntclQZJa4O2OlMYYLw+RKuWYvR0REZN5ScEpmlbuTK0aVTBqIx4y2VIK2VJzWZJyYSgAuCqWoTCZbZDhXoqzeFiIiMl3LtoRSftXMKYBiFpZtbt6aFopSAUaPQVEXuURkdkTliOHCMMOFYWIWI51I05popTXRSiKmSwUiM1GMihTKBQpRgZHiCJHK7YqIiJxW03/jNLMYcD3w28BW4DjwNeAGdx+dxvM7gLcDr6o8Pw88AXweuMVdV86bKSo7w7kiw7kiZkZrMk5rKpQBTMTVlHchKEZlcpXMOAdyhYjRQoT+aYmIyIxddX3oMVUgZEwVs1AuhHFpzD1kSuUGw9ciInOg7GXGimOMFccASMaTtawqlf8TObnhwjCDucHxkn0iIiIybU0PTgGfJASXvgF8HLio8vgyM3uxu5dP9sRKYOtfgKuAW4AbgTZCoOqLlXO9e05XL9Pm7owVSowVSvQByXiMlmSMlng83Ktf1bxSLjsDYwWGciUFokREZHZc8PPAx0KPqcEDIWPqquvVb+pk8sOhr1Sk8rkicm4VoyKZKEMmnyFucVLxFMl4klQsFb6OJfXZTZa0XCnHQG6AfKQyuyIiImeqqcEpM7sY+F3gVnd/Rd34XuAvgeuAL5/iFM8B/gvwKXd/Z93zPws8TsjGUnBqnipGZYpRmRHCBZeYGa2p0KuqoyWhDztNlMkWGRwrEJUVlBIRkVl2wc8rGHU6xSyMnlBfKRGZFyKPyJayZEvZ2piZkYqFgFUyliQVT6lvlSxaZS9TLBcpRAVypRy5Uk6ZUiIiIrOg2ZlTrwIM+NSk8ZuAjwCv4dTBqa7K/eH6QXcvmNkJQPUHFpCyO6P5EqP5En0jBTrSCTrTCVoS8WYvbcnIFiJOjOQpRidNWBQREZG5UiqETKnCaStbi4g0lbuTj/ITskbisTgdyQ46Uh0kY8kmrk7k7BXLRUYKI+ofJSIiMoeaHZy6AigDd9cPunvOzB6oHD+Vu4FB4PfNbB/wY6AVeD3wbOAts7xeOUfK7gxliwxliyTjMdpScdpSCdJJlf6bbVHZGakEBau9pURk9qi3ooicVjmC7ADkMuorJSILVlSOyORDKcCWeAttyTbak+0kYs2+7CAyfcVykf5s/4RMQREREZkbzf4tcT1wwt0b1Sw5BFxlZil3LzR6srsPmNkvA18gXOirGgZe4e7fnPUVyzlXjMpksmUy2SJmRmsyTjoZwx0id8ruJGMxUolwS8ZjzV7ygpAtRAzliowVIvWUEplb6q0oIo25Q24wBKbKyloWkcWjmlU1kBsgHovXelUlYgniFicRSxCzGDGLYZg2IMq8MFwYZiA3QPnkv56LiIjILGp2cKqNsAO8kVzdnIbBqYoR4CfAt4C7gBXAW4Evm9nL3f22Rk8yszcDbwZYv3HTzFcuTeHujBVKjJ3ib0QyHqv1rmpLNfuv+PwznCuSyRYplPQLt8hcU29FETmpwmjoKxUVm70SEZE5FZUjsuXsKTNRqgGr6i0VT5GKpYjH4sRMmw9lbuVKOTL5jLKlREREzrFmX7kfA9ac5Fi6bk5DZnYJISD1Tnf/XN34PxACVjeZ2Q73qZ0q3f3zhJJIXHLp5UobWUSKUZlitlwrCdjVmqSzJUEstrR3443kSwyMFtRPSuTcUm9FEZmolA9BqaIugImIVEUeEUXRhB5WVWY2McuqkmmViCVIWIJ4LE7c4uP3FlcmlpxWMSoyWhxlpDhCqVxq9nJERESWpGYHpw4DP2NmLQ1K+20glPw7VdbUOwlBrK/XD7r7mJl9B3gboT/H7tlbsiwkxahM30iegdECibgRj4VbazJOe2pxBqzcnUJUJl8qUyyVKUZOoVSmpHJBIs2g3ooiEhRzoXxf4bSt5kREpI67h+AV0++PWw1WxSxWKyVY/TpmsVpQS1lZS0OpXCLyiFK5FIJSpVGKylwWERFpumYHp+4BfgG4ErijOmhmaeBS4Ienef6Gyn28wbHEpHtZwsruFErjCXIjuRInrEB7Kk5HOkFrcmHurnN3csUyhVKZfBTVAlHqISUybzStt2J9+drNmzef6fpF5GxFRRg9DoWTFgMQEZFZVs3EOp2YxWiJt4RbooV0PL0gPxdKUB+EKkQF8lGeYlQkmlpMR0REROaBZgduvgr8AfAO6oJTwJsIvaa+VB0wsx1A0t0fr5v3KCG49Xrgz+vmLgNeDgygrCk5CXdnJF9iJF8iHjPaUglSiVjIrjLDcYolp1guk4gZrak4LYlGcdCgGIUgUchSCsEhx0nEYrSl4qSTJ3/uqdZYiMqUIq+dM2bgQK4QMVaIKCsQJTKfNa23Yn352l3PvswpFSCRmvkrEJEzlx2AsX7Qe7WIyLxU9jLZUqUfVj6UEGyJt5CKp3B3HMfdKXu59nW1pGB9Nla1nKCyseZGISpQLBdJxpIkY0kASl4KmVDlIvlSnlyUIyorCCUiIrKQNDU45e4Pm9lngLeZ2a3A/wEuAt4O/ICJfTi+B2wh9O6o+hTwG8BHKv2n7iRctHsT0AO81d1VPFhOKyo7w7nTpPWPQiIWI5WIEYtB3IyyQyEK5fNOFSQaHBt/bvhQE8YTcSMZixGPG+WyU3YolSvBqMhVik9k4Wtab8UJohIMHoBYHFLt4ZZsA+0MFpkbxWzoK1U6WWxaRETmI3cnV8qRK+VOP/kkqv2xakGrSi+sRCwxoU+WnF5UjhjIDzBSGGn2UkRERGQONDtzCkLW1D5C6aGXASeAG4Eb3P2UV+bdfb+ZXQncALwIuA7IAg8A73L3W+dw3bIElcplSoUzCxg1fK7KXIssdvOrt2I5gtxQuJlVAlUd4V6BKpGzV8rDWJ9K+ImILGFlL1P2MqXyyffJVrOvqoGqZCxZC2BVHy9lxajIaHGU4cKwSvKJiIgsYk0PTlV2e3+8cjvVvK0nGd8NvG72VyYiInLW5m9vRXfIj4SbWcikiqcgnqzcUiHTSkROr1SAbH/49yRLloVGNS8GzgdWMrHiA4C7+/vP+cJEZN5xd4pRkeJJdiua2YRsq2QsWcvIMmzC12ZWG1uI/bLq+0KVyiXyUZ5CdKq9WyIiIrJYND04JSIisogtjN6K7lAYBUYnjptBLFGXVWXj4xaDeAskWiCRhrh+pZAlJipBKRv+7SgoNdW+O+H+W2DoMHSth8teB1uvbvaq5oyZnQ98E7iQqUGpKgcUnBKR03L3Wk+lmYpbnFgsRozQ+6oasKoGsqqBrWrPrHr1wa4JQa9ZDIK5O9lSlrHSGNlSVn2iREREljBdSRIREZkjC763ojtEp6o/Wle6LBaHZGsIVCVbQ9BKZCErl6E4GsphejncyqXwOCqEe2ls353ww49CLAkt3TDaFx7z7sUcoLoR2AG8G7gd6GvuckRkqYo8Iorm/j0qZtV+yqGhcrW/VrXXVjXry90pUx7v5xXlas8RERGRpU3BKRERkbm1NHorlqPxMoEQsqsS6cqtkmEVX9r9E2SBKJchNxhu5TPrM7nk3X9LCEwl0+FxMh36bN5/y2IOTv0X4FPu/rFmL0RE5FwoT/o1thoUU0k+ERERmS4Fp0RERObQku2t6A7FbLhVmVX6WbVAIlUpB9gCsdjJzyNyrpTykB+G/JCCUmdr6HDImKqXSMNQb3PWc24UgL3NXoSIiIiIiMhCoeCUiIiInBvuUCqEW75uPJ4cz7BKtoXAlci5EJWgMByCUiXt9J41XetDKb9q5hRAKQddPc1b09z7V+Bq4P9t9kJEREREREQWAm1VFhERkeaKiiE4MHoCBg/AwD4YOQ65DBRGQ9BAvQlktpTL4e9W5unwd220T4Gp2XbZ66BchGIOnHBfLobxxev3gOeZ2bvMTBH2c+zo2FG+s+c7PNb3GNlS9vRPEBERERGRplPmlIiIiMwvUQmizMQxM4inxvtXJdLhsVlz1igLT2EslOwrjCrYOde2Xg28O/SYGuoNGVOXvW4x95sCuBNoB/4c+IiZHQaiSXPc3Xec85UtAZl8hs899DkADGN9x3q2dW9je/d2dnTvYFv3Npanlzd5lSIi0oiZvRe4HHg2sA3Y36jkuZmlgdcCvwTsBNYCvcCPgT9z98cmzd/KyUvuPuLuz2zwPZ4DfBB4DmGLzV3Ae9z9gQZz1wMfAX4R6AAeAT7q7l9vMLcF+MPK+tcDTwNfrMwvnmSNIiKLnoJTIiIiMv+5h55Apbp6gLWAVaUkYKodYvHmrVHmH/cQkMpllB11rm29erEHoyY7QLiINevM7ALgNcAvADuANLAb+DrwKXcfnTT/GcBHgZ8FUsB9wB+7++0Nzt0NfAC4FlhZOe9fAZ9znxjFNbMYcD3w28BW4DjwNeCGyWuozH8p8D7CBcQ88D3g9919yoXCmay5kY5kB2ta13AsewzHOTRyiEMjh/j3Q/9em7MivWJKwGpd+zpipmIiIiJN9iGgn/B//7JTzNsKfB74d+Bm4DCwHfgd4Foze4m7/1uD530DuHXS2ODkSWb2XOD7wCHghsrw24A7zOwqd3+4bu6KyjrWAJ8gBJteDXzNzH7T3b846fRfBV4O/DXwI+B5wPuB84DXn+I1i4gsagpOiYiIyMI0OWBlBi2dkO4OwSpZmsplKIyEDKnimLKk5Jxw92vm8PS/CbwV+BbwJaAIvJAQVPpvZvZcd88CmNkOwi7vEiGLKwO8CfhXM/tFd/9u9aSV8oO3AZcBNwKPEXZ/f5awG/1PJq3jk8DbCRf5Pg5cVHl8mZm92N3Ldee+FvhH4EHgfwLdwDuAO81sl7sfrps77TWfzPqO9dz8/9zMcGGYPZk97M3sZU9mD3syezg4fJCyl+nP9dOf6+c/j/5n7XmtidYpAavNXZtJxpKn+5YiIjJ7drj7HgAz+wkhC6mR48Blk7OYzOxLwP3AXwC7GjzvIXf/+2ms4y+BAvACdz9UOffXCO+PHydsEql6DyHL65fd/duVuTcTAk8fM7Ovu/tIZfylhMDUJ9z9XZXnf8HMBoHfM7PPu/td01ifiMiio+CUiIiILA7ukBsKt3gyZFQl0xBvCRlWMe2OX9QKo6F3mcr2yeLzj8CH3b2+3unnzOxJQomg3yJkOwF8mLDr/NnVi3dm9reEUkOfMbML6zKi3ghcAbzd3W+sjN1kZv8b+AMz+6K776+c42Lgd4Fb3f0V1UWY2V7CxbzrgC9XxpKEYNdB4Pl1F+f+BfhPQtDrzXWvZSZrPqXOVCc7V+9k5+qdtbF8lOfA0IFasKoavMpHebKlLI/2PcqjfY/W5icsweauzbWgVfXWlmybzhJERGSGqoGpaczrA/oajD9aCWpNKdNXVSkJGHP3sZMcP4/wnvjX1cBU5dyHzOzrwBvMbJ27H6kcejWwuxqYqsyNzOxG4G+BlxKyi6tzAT416dt+itCz8jWETRoiIkuOglMiIiKy+ETFcMsPj4/F4iFIFU+F4FXtXjvkF6yoNF62rzy5vY/I4uDu957k0FcJwalnAphZO/DLwPfrd5W7+4iZfQH4M8KFt7srh14NjAE3TTrvpwhl/n6NkMkE8CrAmHph7SZCv43XUAlOEUrzrSeU+xupW8cDZvZ94NfM7K3uXjyDNc9YS7yF85efz/nLz6+NRR7RO9I7IWC1Z3APmUKGkpdqY9/je7Xn9LT3TAlYrUivwNT7UKRp7j16L7c+eStHx46ytm0t155/LbvWNkqckcWsUna2Bzh6kinvIpTpMzOr9nr6oLvX1Qvnisr9jxo8/z8IWczPBr5jZj3ABkI2c6O51fN9re7rQ+5+sH6iux+s9Ki8AhGRJUrBKREREVkayhGUs1DMThw3qwtWpSZ+rYuO80u5DKVcuBUb/CxFzpFKxlAZuLASZJnOrm939x2zuIyNlfvqxbhnAS2c/MIaVAI9lQt5lwP3uXtu0ty7Ca+t/mLZFZWxCUEid8+Z2QMN5nKKdfwccAEhM2raa25w/IzFLc7Gzo1s7NzICza+AAB3pz/XXwtM7R7czd7MXo6MhU3yvaO99I72ctfh8c3ty1qWTSkL2NPRQ9zU/1Bkrt179F4+9+DnSMaSdCQ7GMgN8LkHP8dbdr5FAaql53cIwan3TxovA7cD3wT2A6uB/wb8EfC8So+q6u6m9ZX7Q0xVHdtwBnOr8x9tMLc6f2OjA2b2ZiqZxps3bz7J00VEFjYFp0RERGRpc4dSIdwmiycqJQGT41lW1QCWzC330DOqMAZRHqJCCE6JzA/7Aa/cAA7UfT3nzCxO2AVeYjxjaSYXy5YDrY3munvezPqYemHtxKRd5vXnvsrMUu5emME6HpnhmueUmbGydSUrW1dyxbrxWNtocXRCD6s9g3s4MHyAyCMG84Pcf+x+7j92f21+Op5ma/dWtnVvqwWstnZtJRVPnYuXIbJk3PrkrSRjSVoqfUZbEi1QCuMKTi0dZnYVoR/UQ8CH6o+5+wHgRZOecrOZfZ7Q2/A6xrOfqrVbG73P5SbNmcnc6teN5lbnN6wb6+6fBz4PsGvXLtWsFpFFScEpERERkZOJSuE2mRmk2iHZFm5x/Up11tzHM6Kq9+odJfOUu19zqsfnwKeA5wJ/4O4/rYzN1oW16vyZXFirzinM4joaXeCrqd9RvmHT3MWv2pPtPHPVM3nmqvFWJsWoyMHhgxPLAmb2kC1lyUU5Hu9/nMf7H6/Nj1mMTZ2b2Na1jR3LdtSyrTpTnXO2bpHF7ujYUTqSHRPGUvEUR8dOVtlNFhszezbwHeAw8NIGmcAn80FCcOpljAenqr2oWhrMT0+aM5O51a8bza3Ob9gHS0RkKdCVFBEREZGZcof8SLhBCE4lWiHRArFE6G9l8bqygHbqAFY5CjcvN77F4pXsrdTCD4SVo0oAKh8yosqlyk1ZUSLTYWbvfD/oOwAAIABJREFUB94GfN7dP1x3aLYurFXnT76wtuYUc6d77rO9wFdTv6N85+U7z2kkOxlPsn3ZdrYv214bK3uZo2NH2TO4h92Z3bVsq/5cP2Uvs39oP/uH9vP9p79fe87q1tWhJGBdwGp162r1sRKZhrVtaxnIDdQypwAKUYG1bWubuCo5V8zscuA2IAO80N0bZeCezEEgAlbVjR2u3Dfa7VAdO3QGc6vzT7aLYgONs4dFRJaEBX51Q0RERGQeiEoQDUN++ORzzELgKp4MwS2vD0jN4LpqLA6JNCTTEG9ZOAGrchmy/ZDLKCNKFi0zawGuAapRiz3AD2awm/t05/8T4H2EZu5vmXR4JhfLBoBso7mV17AS+MGkc/+MmbU0KO23gVDyr1A3tzr+2GnWMdMLfPNWzGL0tPfQ097D1Ruuro0P5AYmlgXM7OHwyGEc53j2OMezx/nxkR/X5ncmO0Ogatl2tneH28aOjcRj6mMlUu/a86/lcw9+DkohY6oQFSiWi1x7/rXNXprMMTO7jBCYGiYEpvbP8BTbgTjjPRsB7qncPw/4wqT5zyWU7v1PAHfvNbNDlfHJqmP3Tjr3r5vZJnc/WPc6NhHK235rhusXEVk0FsCVDBEREZFFwB2iYridjXIEhdFwq4rFKplasXCrBsHiyfGMq1hs6rmiYujrFJXC8Vhi4u1sd++7hwyp4hjkBpUdJYuamf0G8AlCP6fqPx4HBs3sXe7+N2d5/j8G/hj4W+CN7lOivA8TyuM9r8HTJ1wsc/eymd0HXNYg4HQlEGPqhbVfqBy7o25NaeBS4IeT5lJZx3cbrGMIeGKma16olqeXszy9nMvXXl4by5ay7MvsmxCw2je0j1K5xHBxmIdOPMRDJx6qzU/FUmzp2sL2Zdsn9LFKJ9KNvqXIkrBr7S7esvMt3PrkrRwdO8ratrVce/616je1yFUCU98FRgmBqb2nmLvS3fsmjcWAD1Qefrs67u5Pmdm9wCvN7I/c/XBl/nrglcDt7n6k7lT/APwPM/uv7v7tytw48LvAIPB/Js39deAdwLvqxt9Ruf8SIiJLlIJTIiIiIgtduQycJvAzIYBloZReo35aE55Tn6WVCmMNs558/Fi5BFGhEogrKEtKlgQz+zXgb4ADwMeARwkBqp8hZDjdbGZZd//qGZ7/BuBPgL8D3uDuU/7Bu/uImX0buNbMdrr7g5XndgBvBJ4E7q57yj8AVxP6Nt1YN/4OoAR8rW7sq8AfVI7dUTf+JkJPqPoLaz8AeoE3mtkn3X2kso6dhKyyL7p78QzXvCi0Jlq5aOVFXLTyotpYqVzi6eGnp/SxGi2OUigXeHLwSZ4cfLI2P0aM9R3rJwSstndvp7uluxkvSaQpdq3dpWDUImBmrwW2VB6uBlJm9r7K4/3u/neVeVsIGVPLgb8ErjKzqyad7hvuXt3BdZOZdQF3EUr5rQJeATwb+CfgHyc993rg34A7zKz6vvi7hA0b75o09yOEoNWXzewThCzfVwFXEDaQ1MopuPt3zOyfgd8zs27gR4RNGb8F/L27//s0/phERBYlm7rhbum55NLL/Z9u++HpJ4qIzAM71nT+p7vrU5hM265LL/F7v/uNZi9DRGR6WpdjHasW1HudmT0IJIHnuvvQpGPdwI+Bgrs/6wzO/VbgrwiBrz9iaiT6qLvfVpl7HiGYUwQ+SchSehNwCfAyd//XuvOmCBfsdhIu8j0GvBT4VeAD7v5Hk9ZxI6HX1TcIO8IvAt4O3An8XH3AzMxeSQhoPQjcBHQB7yREsp9d3xtkJms+mZ2X7/R/+rd/Ot20BcfdOTZ2jD2ZiX2sTmRPnPQ5K9MrQznAurKAa9vWqo+VyDzSnmxnTfuaBfU+N5fM7PvAz57k8A/c/ZrKvGsIwaNT2ebu+yrzfwt4LXAhsIKQqfsIIQP5c402epjZ8wiZVc8hvGfdBbzX3e9rMHcDIUj1i0AHYWPKRxttRKlkGr8PeA3QQwhmfRH4SHXDxqns2rXL7713QScSi8gSY2bTep9T5pSIiIiIiMjZeQbwR5MDUwDunjGzLxIyn87EFZX7zcAtDY7/gLCTvFqW6GrCxbL3ACngPuAl7j6hxJ67F8zsxYSLcK8i9JnaTdgl/pkG3+cdwD5CptXLgBOEjKsbJl/gc/evm1mWcCHuY4QLgt8D3j25af1M1rzUmBlr29eytn0tz1s/Xvkwk89M6WN1aPgQZcr05froy/Vxz9F7avPbE+1T+lht6txEIqbLASLSfNXg0zTmfZ/xsrnTmX8zcPMM1/Ij4EXTnHuIEPyaztwc4T3xfaebKyKylOi3URERERERkbNzhFNfMCszsfH6tLn764HXz2D+Y8DLpzl3kJAN9bZpzI2Aj1du0zn3PwP/PM25016zQHdLN5euuZRL11xaG8uVcuwf2j+xj1VmH4VygdHSKD/p+wk/6ftJbX4ilmBL55YJAautXVtpS7Y14yWJiIiIyBKk4JSIiIiIiMjZ+Rvg9Wb22WqPpapKv4vfJJTvEZkT6USaZ6x4Bs9Y8YzaWFSOODRyaGIfq8E9DBeHKZVL7M7sZndmd22+YfS097Ctexs7loU+Vju6d7A8vbwZL0lEREREFjkFpxaQu/f085V7DtI7lKWnq5XrrtjEldtXNHtZMsv0cxYRERGZ38zsBZOGfgj8EvCwmX0WeJzQq+JngN8hlMC745wuUpa8eCzO5q7NbO7azDWbrgFCH6sT2RNTygIeHTuK4xwePczh0cPcefjO2nmWtSyrZVdtX7adHd07WNe+jpjFmvTKRERERGQxUHBqgbh7Tz+fvv1JEjGjK52gbzTPp29/kus5X4GLRUQ/ZxEREZEF4fuE4FO9alm/j9Ydq45tIfSFis/5ykROwcxY3baa1W2rubLnytr4SGFkSsDq4PBBIo8YzA9y37H7uO/YfbX5rYlWtnZtZUf3jlo/qy2dW0jGk814WSIiIiKyACk4tUB85Z6DJGJGazJ8nm1NxskWI75yz0EFLRYR/ZxFREREFoQ3NHsBIrOpI9XBJasv4ZLVl9TGClGBA8MH2D24uxa42pvZSy7KkS1leaz/MR7rf6w2P25xNnVumpBlta1rGx2pjma8JBERERGZ5xScWiB6h7J0pSf+uNLJGEeGsk1akcwF/ZxFRERE5j93v+Vsnm9mMWAjcMTdC7OzKpHZlYqnOG/ZeZy37LzaWNnL9I721vpXVbOsBvODRB6xb2gf+4b2cfvB22vPWdu2dkpZwBXpFZhZo28rIiIiIkuEglMLRE9XK32j+VpGDUCuWGZdV2sTVyWzTT9nERERkSVhNbAX+Hng9tPMFZk3YhZjQ8cGNnRs4Pkbnl8b78/1TwlY9Y72AnB07ChHx47yo94f1eZ3pbomBKy2d29nfcd64qbKlyIiIiJLhYJTC8R1V2zi07c/SbYYkU7GyBXLlMrOdVdsavbSZBbp5ywiIiKyZChtRBaNFekVrEivYNfaXbWxseLYlD5WB4YOUPISQ4UhHjj+AA8cf6A2vyXewtaurRMCVlu6ttASb2nGSxIRERGROabg1AJx5fYVXM/5fOWegxwZyrKuq5XrrtikPkSLjH7OIiIiIiKyGLQl27h41cVcvOri2lixXOTp4afZPbi7FrDam9nLWGmMfJTnpwM/5acDP63Nj1mMjR0bp2RZdaY6m/GSRERERGQWKTi1gFy5fYWCFEuAfs6L3917+vnKPQfpHcrSowCkiIiIiCwRyViSbd3b2Na9rTZW9jJHx45OKAm4J7OH/lw/ZS9zYPgAB4YP8P2nv197zurW1VMCVqtbV6uPlYiIiMgCouCUiMg5dPeefj59+5MkYkZXOkHfaJ5P3/4k13O+AlQiIiIisuTELEZPew897T1cveHq2vhgfnBKH6vDI4dxnOPZ4xzPHufHR35cm9+Z7GRb97YJAauNHRuJx9THSkRERGQ+UnBKROQc+so9B0nEjNZk+JDcmoyTLUZ85Z6DCk7NM2YWBy4ElgOxycfd/YfnfFHnyr474f5bYOgwdK2Hy14HW68+/fNEREREZsmylmVcvuZyLl9zeW0sW8qyL7NvQobV/qH9FMtFhovDPHTiIR468VBtfjKWZEvXFnZ072Bb9zZ2LNvB1q6tpBPpZrwkEREREamj4BRwoG+M9//zo6zrTtPTnaanu5V13WnWdraQiE+5HikicsZ6h7J0pSf+15tOxjgylG3SiqQRM3s38B6g6xTTFuc23H13wg8/CrEktHTDaF94zLsVoBIREZGmak20ctHKi7ho5UW1sagc8fRI6GO1N7O3FrQaKY5QLBd5avApnhp8qjbfMNZ3rJ9SFnBZy7JmvCQRERGRJUvBKSBXivi3nx6fMh4zWNXRQk93uha4WtfdyvrK4xXtKWKqaS0iM9DT1UrfaL6WOQWQK5ZZ19XaxFVJPTN7I/Bh4AfA/wU+CHwSKAK/BewBPtu0Bc61+28JgalkZUdxMh1e+f23KDglIiIi8048FmdL1xa2dG2pjbmH0n+TywIezx7HcQ6NHOLQyCHuOHRH7Tkr0ivGA1aVoNW6tnXqYyUiIiIyRxScAla2p3jJxes4MpSlN5Pj+HCeskPZ4dhwnmPDeR58OjPlecm4sa5rPGg1nnmVZl1Xms50Qr/IisgE112xiU/f/iTZYkQ6GSNXLFMqO9ddsanZS5NxbwH+w91faGYrCcGp77j77Wb2aeABFmvWFIRSfi3dE8cSaRjqbc56RERERGbIzFjTtoY1bWt4bs9za+NDhaGQXVUXsHp65GnKXqY/109/rp97j95bm9+WaBvvY1W5beraRDKWbMbLEpk3zGwT8KfALwBrgJdUPi+tBj4K/C93v6eZaxQRkflPwSlgZUcLv/+SZ9Qel6IyR4fzHMnk6M3kOJIJQasjQzmOZHIMjBUBKEbOwYEsBweywMCU87an4rUSgROzr0LwKp1cvNc2RaSxK7ev4HrO5yv3HOTIUJZ1Xa1cd8Um9ZuaXy4C3lf52iv3CQB37zWzzwPXA3/dhLXNva71oZRfsq4XQykHXT3NW5OIyDxmZh3At4AvufvN03xaP/BCwoYHETlHulJd7Fy9k52rd9bG8lGe/UP7x/tYDe5h79BeClGBsdIYj/Q9wiN9j9TmJyzB5q7NEwJW27q30ZZsa8ZLEjnnzGwb8B9AunJf+6Dg7sfNbBfwRkDBKREROSUFpxpIxGNsWNbKhmWNy2xlixFHMrnx4NVQlt7BHL2V4NVYIQJgtBDx1PERnjo+0vA8y9uSE3pc1Qew1nSmiceUdSWyGF25fYWCUfNbBIxWvq7e1//A9gHnn8sFnVOXvS70mCoSMqZKOSgXw7iIiEzh7iNmdgXwpRk8p0goHysiTdYSb+GC5RdwwfILamORRxweOTylLOBQYYiSl2qP6/W090wpC7gird/5ZVH6IFAGnglkgWOTjv8f4L+e60WJiMjCo+DUGWhNxtm2qp1tq9qnHHN3hrIljgzVZV0N1QWyMjlK5bARf2CsyMBYkUd7h6ecJ2awpjMEq9ZPyrrq6W5leVtSJQNFRObGAWAbgLvnzewg8HzgK5XjVxB2vC9OW68G3h16TA31hoypy16nflMiIqf2ACHzVkQWgbjF2dS5iU2dm/jZjT8LhM/6fbk+9mT2sDezl92Du9mT2cPRsaMA9I720jvay52H76ydZ1nLsikBq572HmIWa8rrEpklLwZudPeDlTLok+0HNp7jNYmIyAKk4NQsMzO625J0tyV5xrrOKcfL7vSNFDicydYCVkeHchweDIGrEyN5nNDv6shQKCX4wMGp36clEWNdV6VEYC2A1VoLYHW06EcrInKGfgi8DHhv5fHXgXeYWSsQA17DYi3pV7X1agWjRKR5omKzV3Am/hj4hpl9x93/rdmLEZHZZ2asal3FqtZVXLnuytr4SGGEvUMT+1gdHD5I5BGD+UHuO3Yf9x27rzY/HU9P7GO1bDtbOreQjKuPlSwYXcCpGtKm0PVGERGZBr1ZnGMxM1Z3trC6s4WdDfaRFEpljg2HoFU10+pIJpQM7B3MMpQrAZAvldnfP8b+/rGG36cznWBdV5qeZWl6uiYGrtZ1pUkltFNLROQkPg08aGat7p4lXHC8AKjWtfu/wHuatTgRkUUnOwBHHobeB+HIQ3Ds0Wav6Ey8hpB5+10zexB4Apj8i7q7+2+d85WJyJzqSHVwyapLuGTVJbWxYlRk//D+0L8qs5fdmd3sG9pHtpQlF+V4rP8xHut/rDa/mqk1uY9VR6qjGS9J5HQOAhef4vhzgafO0VpERGQBO6PglJktB34LeA6wnLCTvJ67+4vOcm1LUioRY+PyNjYub9xMdaxQqgWtJtwP5ejNZMkVywAM50oM50Z48tjUflcGrOxIVYJVrZXg1XjZwFUdLep3JSJLlrv/FPhp3eNR4JfNrBuI3L1xI0ERETm9cgT9e0Iw6siD0PsQZA40e1Wz4fV1X19auU3mhM9QIrLIJeNJzlt2HuctO682VvYyR0aPsDuze0KW1WB+kMgj9g3tY9/QPm4/eHvtOWvb1tYCVdWg1arWVSrxL812K/AWM7uZ8QwqBzCzVwCvJGzwExEROaUZB6fMbAtwJ7AeyBDSefsZD1KdYLyBvMyytlSCHas72LF66g4qd2cwW5wauMpkOZzJcWw4T1R2HDgxUuDESIGHDw1NOU8iZqzpaqGnK03PstaQgVXX96q7Vf2uRGTpcfdMs9cgIrLgFEbgyE9CRlTvg3D0YSg0+KgQS8Dqi6DnWbD5efCnv3Hu13oW3F1lCUTklGIWY33HetZ3rOf5G55fG+/P9U/oY7U3s5fDo4cBODp2lKNjR/lR749q8ztTnRP7WHVvZ0PnBuIWP+evSZasDwK/BPyYUBLdgfeY2YeAKwl9GD/evOWJiMhCcSaZUx8AlgEvAh4GjgG/BvwH8IfAdcDPztYCZfrMjOVtKZa3pbiop2vK8ajsHB/JTwha9daVDewbKQBQKjuHB0MfLA4MTjlPazJeKw9YH7Sq3relVC1SRBYOM9t8Js9z90Wx1V9EZNa4Q+bp8YyoIw9C324qm6knal0B6y6BnktDQGr1RZBoqRxbfk6XLSLSTCvSK1iRXsGutbtqY2PFMfYO7WVfZl8t02r/8H5K5RLDhWEePP4gDx5/sDY/FU+xtWvrhIDVlq4tpBPpZrwkWeTcfcjMnge8H3g1oUDPzwODwGeBP3T3XBOXKCIiC8SZRBFeBNzk7v9mZisrY+buY8AfmtnFwEeBX5+tRcrsiMeMdV0hqHTppqnHC6UyR4aqGVfZKWUDhyv9rrLFiL0nRtl7onGCXHdrMgSq6soFhlsra7paSMa1sVRE5pV9NLxyelranioi89u+O+H+W2DoMHSth8teB1uvnr3zl3Jw7LHxXlFHHgr9oyazGKw8D9Y9K9x6dkLXBliEmfgWygtcBmyvDO0B7nf3M3mfEZElqi3ZxsUrL+bileNtfUrlEgeHD9bKAVb7WY2WRilEBZ4YeIInBp6ozY8RY0PnholZVsu205WaupFVZKbcfQi4HrjezFYTAlTH9X4nIiIzcSbBqZXATypfFyv3rXXHb0O1ZRekVCLG5hVtbF7RuN/VSK4UglZDjXteFUqh31UmWySTLfLTI8NTzhEzWNXRMp5t1TUeuFrXnWZlR4rYIrxQISLz2p8xNTj1y4R+IbcBjxI+bP0MYYPGA8C3z+UCRURmbN+d8MOPQiwJLd0w2hce8+4zD1CNHK0Eoir9oo4/HnpITZbqqGRF7QzBqLUXh7FFzsxeQtgxvmXSoX1m9t/d/V+bsCwRWSQSsQTburexrXsbLyK0+HZ3jo4drQWrqoGrvlwfZcocHD7IweGD/ODpH9TOs6p11ZSygGva1qh0v5wxdz/e7DWIiMjCdCbBqePAisrXw0AO2Fp3PMXEYJUsEh3pBOenOzl/beeUY+5O/2ihLvNq/HYkk+PYcI6yQ9nh2HCeY8N5Hnp6avuWZNxYW18usCvNuu7WWvZVZzqhX5pFZFa5+5/UPzazVwPbgGe7+wOTjl0OfA94AhGR+ez+W0JgKlkp6ZRMh21l998yveBUVIQTT4xnRPU+GIJTjSzbUglEXQLrdsKKbSFbagkxs6uBbxF67/4l45v5LgZeD3zLzF7o7nc1Z4UishiZGeva17GufR1Xrb+qNp7JZ8b7WFXKAh4aOYTjnMie4ET2BHcfubs2vz3ZPiVgtbFzI4mYSvaLyqCLiMjcOZPfNB4BdgK4u5vZ3cB/N7NvATHgzcDjs7dEWQjMjJUdLazsaOHi9d1TjpeiMsdH8hOyrer7Xg2MhSS8YuQ8PZDl6YFsw+/Tnqr0u6plXrWyflm61gMrnVSVLRE5a+8G/mpyYArA3e8zs88A7wX+4ZyvTERkuoYOh4ypeok0DPU2np8dCBlR1RJ9xx6BUn7qvEQa1lwc+kSte1YISKlHFMANwBHgOe4+4Q/ZzP6C0DT+BuAlTVibiCwx3S3dXLbmMi5bc1ltLFfKsW9o34SygPuH9lMoFxgtjvLwiYd5+MTDtfnJWJItXVsmBKy2dm+lNaG9yEvQPlQGXURE5sCZBKf+CXiXmbW6e5ZQDulfgb2V4w5cO0vrk0UiEY/R091KT3fjX2RzxWhC1tXE+yyjhVAyZrQQsfv4KLuPN+53tbyt0u+qkm21ri4La01nCwn1uxKR07sAOHaK40eB88/RWkREzkzX+lDKr5o5BaFHVFcPeBn690DvQ6E835GHYXB/4/N09owHoXouDb2j4slz8xoWlucAH5scmAJw914zuwl415mc2MzeC1wOPJuQ2bvf3beeZO7fAK87yale6e7/OGl+C/CHwGuB9cDTwBeBj7p7cfIJzOw3gHcCFwJDhDK3721U0snMngN8kPBn48BdwHsabf4ws/XAR4BfBDoIGyI/6u5fP8lrEZEZSifSXLjiQi5ccWFtLCpHPD3y9IQMqz2ZPYwURyiWizw1+BRPDT5Vm28Y6zvWs717O9u6t7Gjewfbl21nWcuyZrwkOXcalUEXERE5azMOTrn7Zwm11KuPbzez5wGvBiLgGypXITOVTsbZurKdrSvbpxxzd4ZzJY4M1ZcLzNYCWEeHchSj8HvSwFiRgbEij/U27ne1prMu62pS36sV7SmVDBQRgF7gWjP7zOSGvmYWA15B2B0vIjJ/Xfa60GOqSCjvlx+C0lgIVt10DRQabPSJJWD1haE0XzUzqmPNuV75QpUilDw/maHKnDPxIaAfuA+Y7hXg1zYYu7vB2FeBlwN/DfwIeB7wfuA8QjnCGjN7J/AJ4AfA9cBG4PeA55nZle4+Wjf3ucD3gUOEjDGAtwF3mNlV7v5w3dwVwL8Dayrnf5rw2fJrZvab7v7Fab5mEZmheCzOlq4tbOnawjWbrgHC5+/j2ePszexlT2YPuwd3szezl2PZYzjOoZFDHBo5xB2H7qidZ0V6xZSA1dq2tcSWWInXxWpyGfS5MJONGJX5c7IBYr5s2hARWSpmpYCwu98L3Hsmz61c6Lse+G1C76rjwNeAG+o/4JzmHCuAPwB+hfAhaZhQ5/0Gd7/jVM+V+c/M6GpN0tWa5IIG/a7K7vSNFGpZVtUgVjV4dWIkX+t3dWQox5GhHA8cnPp9UokY67rGe131LKv/upWOFtXbFlkibiJ8aPhXM/sE8FPCh4eLCBfhng+8r3nLExE5BXfIPA25AVi2FQ7fB6W6csnH66pvt60c7xPV8yxYfREkWs75kheJx4DrKhsbSvUHzCwB/FplzpnY4e57Kuf6CeHC2im5+9+fbo6ZvZQQmPqEu1ezur5gZoPA75nZ56ubDs1sFfAB4B7gRe4eVcbvIfTaup4QRKv6S6AAvMDdD1Xmfo3wZ/Bx4Bfq5r6HcCHyl93925W5NxOCZR8zs6+7+8jpXo+IzA4zY03bGta0reE5Pc+pjQ8Xhsf7WA3uZk9mD0+PPE3Zy/Tn+unP9XPv0fHLQm2JNrZ1b5tQFnBT1yaSMWXfSkPT3ogxxxsgmr5pQ0RkKZkPV9s/Cbwd+Abhg8pFlceXmdmL3b18qieb2RbCf/AdwM2EJvXdwLOADXO3bJkvYmas7mxhdWcLl2yc2u+qGJU5NpRvGLjqzeTIZMPml0KpzIH+MQ70jzX8Ph0tiVq2VU999lVXK+u606QS2hUmskh8BFgL/C7wogbHP+PuH57uybQJQ0TmVCkHxx4b7xV15KHQP2oyi8GKHdCzM9zWPQu6NoCyxmfL/wI+D3zPzP4ceLQyfjHwPwm7pN98JieuBqZmwkI5gE5g5BSfp15duf/UpPFPES6uvYawqxvC+08bcGM1MFVZ27fNbE9l7ocq3/s84Argr6uBqcrcQ2b2deANZrbO3atZyK8GdlcDU5W5kZndCPwt8FLC+6aINFFnqpOdq3eyc/XO2lg+yrN/aP+EPlb7hvaRj/KMlcZ4pO8RHul7pDY/YQk2d22eELTa1r2N9uTUCioy/5nZlcCvAtsrQ3uAb7r7j8/gdDPZiDEnGyDm0aYNEZEl44yCU2a2mXCR7XxgJTD5U627e6MLepPPczHh4t+t7v6KuvG9hP+4rwO+fJrT/D3hdTyrUY13kWQ8xoblrWxY3rjfVbYQ0ZvJhqBVtXTgYPXrLLli+Dw/ki/x1LERnjrWeOPmyvbUxJKB1SysZa2s7mghHtPFH5GFoFLK7x1m9lnCxbhthPe53cC33P2nMzylNmGIyOwZOVoJRD0c+kUdfxzK0dR5qY66XlE7Ye3FYUzmhLt/wczOB/4H8F8aTPkLd7/5HC4pQwhOFczsh8D7GlwsvAI45O4Tagq4+0EzO1w5Xj8XwsW8yf4DeJWZdVQu8J1u7m8SyjZ9x8x6CO9lXzrJ3Or3VnBKZB5qibdwwfILuGD5BbWxyCN6R3rH+1hVglaZQoaSl2qBrO/xvdpz1rXrUnOtAAAgAElEQVStY/uy7ROyrFakV6js/jxlZnHChozXM/V64O+b2d8Cb6zfzHA6092IMccbIObLpg0RkSVjxsEpM/tFwgW2al31/rP4/q8ivJFN/o//JsLO9ddwiuCUmb2A8OHv7ZVGw0kg6e6NU19EGmhNxdm+uoPtq6desHF3MtnihGyr+uyro0M5SuXQkqZvtEDfaIFHDg9NOU88ZqzpbJnU66qV9ZXSgctak/rFW2SecfcngD8/m3NoE4aInJWoCCeeDEGoIw+FoNTI0cZzl20Jwahqr6gV20O2lJwz7v7uym7slzN1Y8MT52gZRwibIv4TGAV2Au8glA16qbt/t27uesYzvCY7RMjUrZ9bHW801ypznpjGXBjfXDGTuSKyAMQtzsbOjWzs3MjzNz4fCJ+r+3P9EwJWezN76R0Nv9oeGTvCkbEj3HV4vH15d6p7SsCqp6OHuMWb8rpkgvcBbwC+Sfi8VJ8t/PvAbwD7gD+dg+89lxsgmr5po8FxEZFF7Uwypz4MnAB+pdJr6mxcAZSZ1JzX3XNm9gAT/+Nv5KWV+wNm9m1Cc8O4mT0J/Nl0aq2LnIqZsawtxbK2FBf1dE05HpWdEyP58cBVJsfhTJajlQBW30gBr8yrlhFsJJ2s63fV3Tre66oSzGpXvyuRpjCzZ1BXpuIMsqa0CUNEpi87EDKiqiX6jj0CpfzUeYkWWPPM8UDUukugdfm5X+8SZma3Ax909+9VHv8G8MNKEOovmrUud3/PpKFvmtmXgQcIpQfPrzvWBjT4CwZArnK8fi4nmZ+bNGeu5k5gZm+mUipxwybFr0TmMzNjZetKVrauZNe6XbXx0eIoezN7J5QFPDh8kJKXyBQy3H/sfu4/dn9tfjqeZmv3VrZ1b2NH9w62d29nS9cWUvFUM17WUvabwG3ufu2k8R8Bv2pmt1XmzEVwai43QMyHTRs19e9zmzdvPsmyREQWtjO54n0hoSzE2QamIPwHfcLdG30YOQRcZWYpdy+c5PnPqNzfBDwJvA5oIaTb/p2ZJRs0NxSZNfGYsbYrzdquNDs3TT1eKJU5MlSXdZXJ0lt5fCSTYygX+mXnimX29Y2xr6/x9eaudGI8aFWXfdXTHb53Mq6d0SKzycx+DriR8J5XP/44IVD0vYZPnEqbMESkMS9D/x7ofShkRvU+BJkDjed29tSV6LsUVp4HcTWUb7JrgC/UPf4i8FrCTvF5xd2frPS1eL2ZXVCXxTVG+OzUSLpynLq5VOZnG8ytn1M/t9F5z3TuBO7+eUJZKXZevtMbzRGR+a092c4zVz2TZ656Zm2sGBU5OHyQPZk97M7srgWvsqUsuSjH4/2P83j/47X5MYuxqXMT27q2sWPZjlqWVYdK2c6lNZy6wsQ3gY/N0feeyw0Q82HTRk39+9yuXbv0Picii9KZBKeOE5r4zYbT/cdfnXOy79dZuR8GXlgNYpnZNwiNGD9kZrc06udRvwNh/cYGUQWRWZBKxNi8oo3NKxpu+GQkX5oYuJpUNjBfCn91h3IlhnLD/PTo8JRzGLCqo2VK4KqafbVS/a5EZqQSmPr/CO9PNxF2zxnwM4RMqH8xs5e4++3TOJ02Ycip7bsT7r8Fhg5D13q47HWw9epmr0rmQmEEjvxkvDzf0YehMDp1XiwBq58B6y4dz4zqWHPu1yun00so3Vc133/Z+v/Zu/P4uOtq8f+vM/tkmbRpm6ULUKCyt4Ativ6ueAG94m65XBDRL1cQ6wYIiKiAgAjCBRFQbgUXREU2y6IoChQUFy6tSClYoNBSStukaZs0SZNJZjm/P96fSSaTSZqkyUwmOc/HYx6T+Xze88kJ2k7zOe9zzuve83TcLm6AzQzcMm8WfXd4b846/mqetZq1JnttvuuSde3hrDXGTAJBf9C185uyL8dzPABpTdOwq6GnLWAmYbUjvoO0ptnQuoENrRt48s0ne65TE63p0xZwbtVcZkRnWDv90fEKUDfI+Xp6P2tG21hugBgPmzaMMWZSGUly6ufAibhZGXuqA7fjIp+h/AWd+QD4VfaNPVVtFpGHcH1uDwDW5L4xewfCYYcfaTsQTFFUhAPsX1PB/jX55101dySyZl119rQGzMy7Squ7E9DU3kVTexerN+3sd52AV91V3yd5Fe35OhYJ2D/QjenrKqAReHv2wFoAEfkWri/4t4Gjh3CtcbEJY6/ZM3NPm/Hg9b/Cn68BXxDCVbBru3vNVy1BVepUYeebWbOinoftr+I+tXNEq3uTUPULYMaBEIj0X2fGm8eBi0VkIdDsHTtLRI4f5D2qqmeMfWh5Zdr5ZQ8tWwF8QkTmZM/XEJE5uM0VD+WsPQv32ZebnHob8LI3VyOzFm/tj3LWvh33B+EfAF7L2k3e8VyZY6PRscMYU8J84mNmxUxmVszknbN6/43UHG/uN8dqc/tmFGVr51a2dm7l6S1P96yvDFX2JKr2q9qPuVVzmV0xG7/P5lgN09XALSJyn6quyj4hIkcAnwc+N0bfeyw3QIyHTRvGGDOp7DY5JSK5jU1vB/5dRB4EbgTWA6nc96nqAH1J+tgMHCwi4Ty7ymfhdpsPVqX1pvfckOdcZmi8NeA3JUlEqC4PUV0e4uCZ+eddNbV1sWVnpzfrKqt9YGucHbvcH51kWtnU0smmltzNPE5ZyN9TZdXbLtC1EKyrihAN2j/UzaQzH7gqNzEFoKpvisgPga8N8VrjYhPGwsMPs00Y49E/f+YSU0Hv/wrBCCS845acKi3JOGxd01sV1fC8mx+VS3xQvZ9LQtUvcG36YrPBNomUoi/jbjwdj9s9rsC7vMdAFBiz5JSIlAMpVY3nHD8COAlYo6qvZZ36FfAJ4Fzg/Kzj53rP2QPkH8RtTvyiiNypqinv2h8C9gMuySxU1VdFZCVwkohcoqqbvbUzvTiWq2r272+/Ai4QkQ+p6m+8tX7gS0AL8Lvh/9cwxkwGUyNTmRqZypG1R/Yc60x2sn7n+j6zrF5vfZ1kOklbdxurmlaxqqk3nxLyhfrMsZpbNZd9YvsQsY0ig3kLbqPcShH5I/AS7jPuYOA9wCrgABG5NOs9qqrfGoXvPZYbIIq+acMYYyaboVROvU7/bZ6Z36A/OMj7hnJHewXwXuAo4Kmei4tEgMOBP+/m/c8AS+g7lDAjc2zrEOIwpuT4fdKTQMonnkjRmNUisKdlYEucLa2d7OpyOeWO7hTrmnaxrilPayFgSjTYZ8ZVXVbVVW1lmIDNuzITz05cpdJAWnE3y4bCNmGYgbVudhVT2QIRaN2Sf70ZP9obvSTUalcd1fQSpPvt1YJQRdasqAVQe4g7Zkqeqm7HtVoFQETSwGmqeudofy8R+SSwt/dyBhASkYu91xtU9efe1/NwrWcfwLWC3QUswA2lT+FV02b9DA+LyG+B80SkCjfI/mhcAu0XqvqXrLVNInIJbobIYyLyK9zn2Pm4m5Lfywn7HOAJ4CkRudk79iXAR99EGMB3cEmrO0Xku7jd4x/HzWU8U1UH+0w2xpg+ooEoB087mIOnHdxzLJlO8mbbm33nWLWsY1dyF93pbl5pfoVXmnu70PnwMatyVk/CKlNtVZX777bJ67Ksr0/wHtmO9B7ZFNjj5NQYb4AYL5s2jDFm0hhKcuoK8vYgGRV3A1/H/UX/VNbxz+DaHPX8xS8i+wFBVX0pa90DuOqt00TkysyuBBGpBz4KrFXV3B0MxkwKkaCfvaeVs/e08rzn2+KJPomr3LlXiZT7Y9/SmaClM8FLDf3vC/jEzbuaOSVCXSzab+ZVdXkIn+0GN6XnXuDjIvIDVU1mnxCRIO6G2b1DvJZtwjADi810rfyCWZsMknGI1RcvJtNfKgHb1ma16FvlklP5TNnba8/ntemr3tdVS5nJ4HLg+aEu9j5PjgZWqWr/vsx9nQEck3Msc4PvT7i26+A2MjwG/Dvu5loUt5HhbuDqnN+jMk4CLgZOAz6JSwxdiksY9aGq14vIdlzV2E24zRr3ABdl7Q7PrP2biLwbuNJ7KPA34KTcFlCqul1E3ul9zy8AFbh5j6eo6t0D/lcxxpghCvgC7FO1D/tU7cOxHAu4NvqNHY29bQFb1rG+dT3bOreRJs3Gto1sbNvIn9/s/ef69Oh05sbmujlW3jyr2rLaydgmf+7ulwzPMDZiwBhtgBhHmzaMMWbSENXidvrx/lL+InA/bsfCQcDZwF+BYzNzNETkdWBvVZWc958F/BB4EfgJEML1tq0HPqiqf9xdDIcdfqQ++Oju7g8aM3mkVdmxq7unXWBjztyrprYu0kP4qyPoF+pivRVXdVURZmYlsCojwbH/YSag/Woq/6GqC4sdx0SRp31tBXAHbg7UDfRtU/Fl3MaOT6lqv1Z6ea59GK6txf2qemLW8S/hbux9UlV/4R3rtwlDRKYCG3A3AA/M2YSxFtisqm/ZXRwLDz9MVz52/+6WmULLnjkViLjEVDoB77KZU0XV2ewqojLt+ba+CMk8o+MCYag5tLcqqu4wiFoh46iITkUqpk/ozzoRqcVV175HVZcXO55St+DIBfrgEw8WOwxjzASws2tnz/yqTFvATW2bSNNvxCsA5YFy5lb1TVjNqZxDwDfwXvDyYDk15TUT+nNuuETkSfpvxMj4k6q+O2f90bjND2+jdwPE11T12TzXnoVLUp1A7waIa/JtgPA2EWY2bdTjklk/Bb6jqok860/H/Y54AO53tt/iNm3020A4nJhzLVy4UFeutBGMxpjSISJD+pwbSuXUWDsX1zrwLOADwDbgZuDSfAPec6nqrSKyDbgQt4MwjdvdcKqq/nWsgjZmIvOJML0izPSKMIfO6t+6IJlK09jW1dsuMKviaktLnJZO92+2RErZ2NzJxuZOemeF9yoP+6mPRXPaBnrPsQhhm3dlCuN1Bm5fe9cAx19gCO1rVXW1iPwA1+5hGX03YfwJyG7/9Dhut6Bkvb9ZRC7AbcJ4WkSyN2GEcJs7TKna553AV92MqdYtrmLqiP9nialC0jTsWAdbnvcqo1ZDy4b8ayvroG6B16ZvPkyfB37bZGH2yKTbam+MMeNdVbiKI2qO4IiaI3qOxZNx3mh7g9daXutJWq1vXU93qptdyV28sP0FXtj+Qs/6gC/A3pV79ySs5lbNZW5sLmXBsmL8SCUhN/k0hPV/B44b4tpNuOrgoayN45JTF+9urbf+duD2Ia4dcszGGDNZjDg5JSL/BXwM2Nc7tA63M/ye4VzH68t6vfcYbN0+g5xbBiwbzvc1xoxcwO9j1pQos6ZE857v7E65RNXOzpy2ge65M+HmcuzqSvFqUzuvNrXnvU51eSir8qr3eWZVlBmVYfw+u6djRsVYtq8F24RhBrPPOy0ZVUjd7dDwQm97vsYX3LFcvgDMONBr0eclpCpqCh+vMcYYY4ouEojwlqlv4S1TexsWpDTF5vbNvNbyWk+l1Ws7X6Otu41kOslrO1/jtZ2vwRu916kvr2ffqn05qPqgIvwUo0tE9sb9fjMPmEb/DReqqpaIMcYYM6hhJ6dEpAw3+O9Y3IdPi/e8CPgvEfks8GFV3TWagRpjSkc05Gfu9HLmTu8/70pVaY0ns5JWvQmshlaXwEp6PQN37Opmx65u/rWltd91fAK1MS9plXmu6p17NbUsOBl7f5sRUNXLxvj6tgnDmGJQhZ1vZs2Keh62v0reXHS02psT5bXnqznItVo0xhhjjMnDL37mVM5hTuUc3j3n3YD7XXdb57Y+LQFf2/kaWztch7ctu7awZdcW/rq5tPeXiciHcTN4g7hWdv3bpBhjjDFDMJLKqatwZag34XquNgCISB1wEa5V0bdxO8WNMaYPEaEqGqQqGuSAusp+51NpZXt7F1ta41ltA3uft7V3oUBa6anI+mee7xMJ+KjtaRfoWgdmV2FVhMdDV1NjjDGjJhmHrWt6q6Iannfzo3KJD6bt782KOtw9x2aDbWgwxhhjzB4QEWaUzWBG2QyOqj+q53h7d3ufhNXrra/zAi8McqVx7xpgI/AxVV1d7GCMMcaUrpHcnT0ZuFdV+ySfvCTVud6gwZOx5JQxZgT8PqEmFqEmFmHB7P7nu5Nptrb1TVr1ft1JazwJQDyZZsP2DjZs78j7fWKRgEtY9VReRXvmXtXGIoQCvrH8MU0JEJF5DNymAlW9o+BBGWN6tTf2VkQ1rIKmlyCd6r8uVOHNiTrMez7UHTPGGGOMKYCKUAWHzTiMw2YcBkB5sJwaSrpd8D7AVy0xZYwxZk+NJDkVA54Y5Pxy4P0jC8cYYwYXCviYPbWM2VPzD5Pd1ZV0865a4lnVV651YMPOOPGkG/HTGk/SGm/nlcb+s0YEmFYR8qqsolltA93z9AqbdzWRiUg98DN6h9Xm+x9bAUtOGVMoqQRsW5vVom+VS07lM2VvrypqgWvTVz3XVUsZY4wxxpjRsB4IFzsIY4wxpW8kyanncTvJBzIPsN0TxpiiKA8H2G9GBfvN6L8rXlVp7kjQ2JrbLrCTzTvjbG3rIpVWFNjW3s229m5Wb+o/7yrgE2piYepjEeqnRPu0C6yvilAVtXlXJe5W4N+B7wFPYT3UjSm8zmZoWN3bnm/ri5Ds6r8uEIaaQ715UV51VHRq4eM1xhhjjJk8vgecLyK32Lx5Y4wxe2IkyamLgftF5ElV/U32CRH5CHAm8NHRCM4YY0aTiFBdHqK6PMRB9bF+51Nppam9q0/SKrtt4PZd3QAk08rmljibW+LwRku/60SD/p5kVU/FVax3/lU05B/zn9XskWOBG1X1gmIHYsykoGnYsa63PV/DamjZkH9tZZ2rhqqb7yqjpu0P/mBh4zXGGGOMmcRU9VYRiQEvisjPgNeBfr2VrQ26McaY3RlJcuoTuBLeB0TkZWANrr3RwcABuKqp00TktKz3qKqesafBGmPMWPL7hLqYSyQdPqf/+a5EisbWLjZ7bQK37Iz3VmG1xmnz5l11JlKs27aLddvybyKrigazZl1FmDklk7yKUhMLE/Rb+6kiawdeLXYQxkxY3e3Q8IKriGp43iWjuvu3WMUXgBkHQN3hvZVRFSU9n8GYbE3AXKCh2IEYY4wxwyEitcBiYC/gkgGWWRt0Y4wxuzWS5NTpWV8f6D2yzfce2RSw5JQxpqSFg372mlbGXtPyz7tqjyfZsrMza9ZV1nNrnG5v3tXOzgQ7OxO83NDW7xo+gekV4T5tArPnXk2rCI3pz2gA+C1wPLC02IEYU/JUYeebXhJqlauO2v4q7p+GOaJTvdZ8XlVUzUEQiBQ8ZGNGSkTmAJcD7wVqgPep6nIRmQFcA/yvqq4AUNU0MECJoDHGGDOuLQUWATdgbdCNMcbsgWEnp1TVtvQbY0weFZEA8yKVzKut7HcurUrzru6eRFXfmVdxtrbFSSukFba2dbG1rYtVb+7sd52g32ZZFcD5wOMicgNwM7BeVfPcSTfG9JOMw9Y1LhmVmRfVmed+hfhcS766w1ybvvr5EJsNNq/PlCgRmQs8DUS85/rMOVVtEpGFuPbnK4oToTHGGDNqjsPaoBtjjBkFI6mcMsYYM0w+EaZVhJlWEebQWVX9zidTaZrau9jSEs9JYLm5V80dCQASKcuRjDVVbfF6p98AnA1uXln/ZWqfoca0N3qJKK8yqullSCf7rwtVeIkorzKq7lB3zJiJ49tAGjgU6AS25pz/HfChQgdljDHGjIEurA26McaYUWA31owxZhwI+H3UV0Wpr4rmPd+ZSNHgVVmddk2Bg5tkRORC4GqgEXgGa1NhjJNKwLa1LgmVqYxqb8y/dsreLhlVv8BVRlXPddVSxkxcxwM3q+pGEZmW5/wGYHaBYzLGFMHKxpUsW7uMxo5GastqWTxvMQtrFxY7LGNG08PAe7A26MYYY/bQbpNTIvKTEVxXVdVmTJk99sy6Hdy1YiNbWjupj0U5ZdEcjtq3uthhGVNw0aCfudPLmTu9vNihTAZfAp7EzQpJFDkWY4qnsxkaVve259v6IiS7+q8LhKHmUNear26+S0pFpxY+XmOKKwZsGeR8CNsYaMyEt7JxJUtXLSXoC1IRrKA53szSVUtZsmCJJajMRHIe8IiI3ATcCKyzNujGGGNGYii/IJ0+gusqYMkps0eeWbeDG5evJeATYpEA23d1cePytZzDPEtQGWPGUjVwjyWmzKSiadixzmvP5z1aNuRfW1nnJaEWuMqoafuDP1jYeI0ZfzYChwxy/u1YCyRjJrxla5cR9AUJB8IA7jnpjltyykwg23D3/d4KfAGsDboxxpiR2e0HhapaDxZTFHet2EjAJ0SDfsBVjnQmUty1YqMlp4wxY2kVsFexgzBmTHW3Q8MLvYmohtXuWC5fAGYc6CWivJlRFbWFj9eY8W8ZsEREfkxvBZUCiMiJwEnAN4sUmzGmQBo7GqkI9p2pGPKHaOwYoA2uMaXpDrzPOGOMMWZP2C4GM25tae0kFun7f9FI0EdDa2eRIjLGTBLfAO4RkftVdWWxgzFmj6nCzje9JNQqVx21/VXy3lOIVmfNipoPNQdBIFLwkI0pQd8GPgj8H/Bn3B+wi0TkKuAo4Dng+uKFZ4wphNqyWprjzT2VUwDdqW5qy2xjh5k4VPX0YsdgjDFmYrDklBm36mNRtu/q6qmcAogn0tTFokWMyhgzCXwS2AQ8LSJ/B9YBqZw1NlvRjF/JOGxd45JRmXlRnc3914nPteSr82ZF1c+H2Gzo35bFGLMbqtoqIkcD3wJOBQQ3LL4FuAX4hqrGixiiMaYAFs9bzNJVSyHpKqa6U90k0gkWz1tc7NCMMcYYY8adESWnRCQAfBR4GzAVyG39ZzftzB47ZdEcbly+ls5EikjQRzyRJplWTlk0p9ihGWMmttOzvn6n98hlsxXN+NHe6CWivMqoppchney/LlThqqLq5rvKqNpD3DFjzKhQ1VbgHOAcEZmBS1A12ZB4YyaPhbULWbJgCcvWLqOxo5HasloWz1ts86bMhCUiFcAU+t8XRFXfKHxExhhjSsmwk1MiUg08ARyK+4VLvWeyvrabdmaPHbVvNecwj7tWbKShtZO6WJRTFs2xeVPGmDFlsxbNuJZKwLa1LgmVqYxqH2COxZS9s6qiFkD1XFctZYwZc6raVOwYjDHFsbB2oSWjzIQnIqcAFwMHDbLMP8g5Y4wxZkSVU1cCBwJnAk8CrwH/AbwBXALM814bs8eO2rfaklHGGGMmr85maFjttedbDVtfgGRX/3WBMNQc0jsrqu4wiE4tfLzGTBIistdI3me7yI0xxpQ6EfkocCfwCvBDYIn3OtNlaTXw26IFaIwxpmSMJDn1AeAOVf2piEzzjqVU9WXgNBF5Erga+NwoxWiMMcYYM/FpGnas89rzeY+WDfnXVtZB3QKvTd8CmD4P/MHCxmvM5PY6rlvEcNkucmOMMaXuAmAN8FagApec+omqLheRQ4G/As8VMT5jjDElYiTJqTpghfd1ZqBBJOv8A8BXsOSUMcaYEiUiU3HtaQebrXhcwQMzE0t3OzS80JuIaljtjuXyBWDGgV57Pq9NX0Vt4eM1xmS7gpElp4wxxphSNx+4UlXjIlLmHfMDqOoLInIr8DXgwWIFaIwxpjSMJDm1Ayj3vm4DEsCcrPMJ3I08Y4wxpuSIyN643X4zgZ1ADPfZl0lSbQN2FS1AU5pUYeebXhJqlauO2v4qee9tR6tdRVSmRV/NQRCI9F9njCkaVb2s2DEYY8x4k06n8flsvuUk4Ae2e193es9VWedfxjasG2OMGYKRJKdeAQ4GUNW0iPwTOF1Ebsd9QH0KWDdqERpjjDGFdSUwBTgO1y99K3Ay8DTwDeAU4JiiRWdKQzIOW9e4ZFRmXlTnjjwLBabt31sRVb8AYrNBpOAhG2OMMcaMVPOOZk54xwk88vdHmDJ1SrHDMWPrTWBvAFXtFJGtwELgPu/8AdhmPmOMMUMwkuTUH4ELROSLqtoFfBe4C7erXIEocNbohWiMMcYU1HHAbar6RNZsRVHVDuAbInIIcA3wiaJFaMaf9q29FVENq6DpZUgn+68LlUOtVxVVvwBqD4FQReHjNcaMCRE5CvgYsK93aB3wgKr+X/GiMsaYsffY7x6jsaGRx373GP/5if8sdjhmbP0NOB641Hv9EHCOiHTgOk18AfhNkWIzxhhTQkaSnLoKuM5LTKGq94hIEjgNSAH3qerdoxijMcYYU0jTgBe8rxPeczTr/KPANwsakRlfUgnYttYloTLzotoa8q+dsreriMpURVXPBbF2N8ZMNCLiB24FTgdySx8vFJE7gDNVNTXC638NOBI3fH4usEFV9xlk/duAb+NmJyruRuJFqtpvQL2IzAS+A5yAG2z/InCNqt6bZ20YV0X8SVz72zeBn3rrE3nWfwr4MnAg0Iq7Wfk1VW3ak5iNMePPfXfe1/NsyakJ7xbgYyISVdVO3OfCUcBl3vkXgQuKFJsxxpgSMuzklKoq0JVzbBmwLN96EQkCRwOrVHXnSII0xhhjCqgJqPa+bgPiwD5Z50P0TVaZia6z2bXla3jeVUZtfQGSXf3XBcJQc0jvrKi6wyBqYziNmSQuBv4beAC4FviXd/wQ4EJc6/PXgctHeP2rcJ0qnsW1nh2QiLwdeBLYRO+u9i8CT4nIO1R1ddbaauAvQA2uI8abwKnAPSLyaVX9ac7l7wY+AvwE+Dvu97xvAfvjEnPZcXzZu+afgHOA2cB5wNEicpSq7spaO+SYjTHjT1trG8+tdHnkf674J22tbVTGKosclRkrqroCWJH1ugk4XETm4zatr1HVdLHiM8YYUzpGUjk1XNXAE8B7gOUF+H7GGGPMnngRWABuQ4aIPAN8XkQewrWpOAt4qYjxmbGkadixrjcR1fA8tGzIv7ayDuoWuCRU3QKYPg/8wcLGa4wZLz4NPKqqi3OO/x23u/xRb81Ik1P7qeo6ABF5AVfhNJCbgG7gXaq6yXvPPcAa4IWLhCMAACAASURBVHrgvVlrL8JVYn1YVX/jrf2xF/d1InKvqrZ7x9+PS0x9V1XP997/IxFpAc4TkVtV9W/e2um4GY4rgOMyFWMisgKv/RMu4TaSmE0JWdm4kmVrl9HY0UhtWS2L5y1mYe3CYodl9sDOlp1s2ripz7G7H76btN/lItL+NNfdch0nf+DkPmtmzZlF1ZSqgsVpCk9Vny92DMYYY0pLIZJT0L+1hTHGGDNePQicn9Wm4grgD8B677wCuTcfTanqbofGF2FLpkXfancsly8AMw5wSah6r01fRW3h4zXGjFc1uIqpgTwAXDfSi2cSU7sjIvsDi4CfZJI83vs3ici9wH+LSJ2qZnqRngq8lklMeWtTInIzcAfwfuCerLUA38v5tt/DVUSdhmvFB/BRoAy4ObuVoar+RkTWeWuvGmHMpkSsbFzJ0lVLCfqCVAQraI43s3TVUpYsWGIJqhL2vzf8Lz+88YeEw2GCoSApTdGd6ibV6f6oJzuT/PLGX3Lv9+/FL34S3Qm6urr47Dmf5aLLLypy9GY0eHN5a1R1TdaxubjPgmrgDlX9wxh978sYvMV6UlWDQ1j7FVXt87ksIj7c5onP4jpnNOE+Ay/NrvbNWv9+XOX0AlyHqceBC1V1fZ61B+DmFh+D68TxLPBNVbVN/MaYSa1QySljjDGmJKjqLbg+6pnXy0XkaNxNuRRwf2ZnuCkxqtD6plcRtco9b38Vl2/MEa32KqK8WVE1B0EgUvCQjTEl4xWgbpDz9d6asbbIe/57nnNP46q33go8LCL1wCzglwOszVzvnqyvN6nqxuyFqrpRRDZnfe+hxPFxEanwqrKGHHOe82YcW7Z2GUFfkHAgDOCek+64JadK14XfvJBYVYyb/+dm2tvybOgBUp0pOukEIBKN8JVLv8KSc5cUMkwztm4E3oKbM4WIVABP4WYRApwsIseq6p/H4HsvA17Nc3w+8BXcbMNcXwa25Rz7R551NwBnA/fjqnYP8l4fISLHZ7cqFJHFwH3AKu/7VgHnAn8VkYWqujlr7X64zRtJ3EaWncBngD+IyAmq+tjufmhjjJmoLDlljDHG7IaqrgRWFjsOM0zJOGxd47Xo8yqjOpv7rxMfVO/nVUR586KqZoNY4bcxZsiuBm4RkftUdVX2CRE5Avg88LkCxJG5Mbgpz7nMsVkjWJtZ/688azPrZw8jDvHWvDKCOAAQkbNwrXaZNaffaTMONHY0UhHs24Ey5A/R2NFYpIjMaPD5fHz+vM/zjmPewVkfP4vtzdtJJ/qPF5KgML16Orf96jYWHLmgCJGaMXQ08Ius1yfj/i5/P/Ac8Chu3uKoJ6e81oH92geKyA+9L3+c520PqOrrg11XRA4BvgQsU9UTs46vx7WePQW40zsWBG4GNgL/ltX+9ve4pNdleJ9Pnqtx8yLfqqrPeWvvwLWT/4GIHKiqeXbLGWPMxGfJKWOMMcZMDO1NXkXUKvfc9DKkk/3XhcpdAipTFVV7CIQGG99izOhIp9P4fL5ih2HGxluAdcBKEfkjbjahAgfjZu+uAg4QkUuz3qOq+q1RjqPMe+7Kcy6es2Y4azNf51ubWZ+7dqziAEBVbwVuBVhw5AK7qTcO1ZbV0hxv7qmcAuhOdVNbNr7b4kYCESL+CPFUnHgy3nPcJz78Pj+JVKKI0Y0fh7/1cB5f+ThHLzqaXY39Op5RVl3G4ysepzJWWYTozBirBd7Ien0CsFJVHwEQkdtxLf4KQkTKcMmjTcAjA6yJAR2qmueXAwA+jts4kdu69jbgO7h2tHd6x47BJeMuzSSmAFT1ORF5Elc59gVVTYhIOfBh4MlMYspb2y4iP8K1kF8EPDOMH9kYYyYMS04ZY4yZ1HJuFA7VWNxQNMORSriWfD2zop6Hti3511bt5ZJQmVlR1fu6ailjCmj7jmbmH/NBVv/5YaqnTil2OGb0XZb19QneI9uR3iObAqP9WdLhPYfznIvkrBnO2szX+dZm1ueuzVy7c5TjMCVi8bzFLF21FJKuYqo71U0inWDxvPE3ulNEiIViVIQqCPqCPcdT6RTxVJygL0jIHwKgrbuN5ngzae1fLTTZhEIhulu6855LtCQIhUMFjsgUSAKIZr0+Brg963ULMK2A8fwXEANuyp5zmOV5oBJIicgzwLdU9fc5axYBaXKSRKoaF5HnGF7r2mNxm1ZexLUbDA+yNnM9S04ZYyYlS04ZY4yZ7C4bwXvG4oaiGUxni5eEWu2qohpfdG37cgXCUHOomxdVv8A9R6cWPl5jcjz0yONsbtjKQ488zukfP3H3bzClZm6xA/BkZlzk63OXObZpBGsz6wfqnzcrz9rM8dzZILNwn6Ob86wdShymRCysXciSBUtYtnYZjR2N1JbVsnje4nE3byrsDzM9Op2gP9jvnN/np9xX3udYZaiSSCDC9s7tfSqrJqOnnniKcChMoiuBP+wnnUrj8/tIdaUIhUL85Ym/cNz7jit2mGb0vQKcKCI/AD4EVAOPZ52fA+woYDxn4D5XfpJzvAVXYfs3oBk4ADcX6mER+bSq3p61diawTVXzVfFuAt4hIiFV7Wbo7WhfHMbafrLb1+611175lhhjTMmz5JQxxpjJbrzcUDQZmoYd63srorasgpYN+ddW1EL94S4JVTcfpr8F8txcMqbYbr9rWc+zJacmHlUd4C+pglvhPR8N/Cjn3NtxN+/+AaCqW0Rkk3c8V+ZY9rzFFcAnRGSOqm7MHBSRObibbw/lrD3LiyM3OfU24OWsVkhDjtmUnoW1C8ddMioaiOL3+RGEoD9ILBQb9jWCviB15XV0JDpo7mqetK3+7r/7ftrb2olEI5z8qZP5yiVf4dorruWen99De1s79999vyWnJqYf4CqlmnFtV9fRNzn1LmB1IQIRkQOA/w94XFXXZ59T1dwWfYjIT4AXgBu8OZGZz6Ldta7NrOmmAK1rvfh72tcuXLjQ2tcaYyYkS04ZY4yZ1Pb0hqI3EPdoYJWq7hydqCaZ7l2uEqphFWx5HhpXQ1db/3W+AMw40JsV5bXoqxjfcyuMAWhta+Ppf7gxA0+v/CetbW3EKm0Ghxl9qvqqiKwEThKRS1R1M4CIzAROAparakPWW34FXCAiH1LV33hr/bih8C3A73LWfgK36/z8rOPnes+/zDr2IG6A/BdF5M5MmyUR+RCwH3DJHsRszIiE/CGmRacR9g/UnXL4yoJlRANRWrtbaelqQXXy3D/u7u7miT88QUVlBd+//fscc9wxAFx+7eUc+95j+eJ/f5Hlf1hOIpEgGLSNQxOJqt4hImngY8BO4CpVTQCIyDSgCrilQOGc4T3nbm7IS1W3i8hSXPeMdwB/9E51ADUDvG1P2tFa61pjjBlEIZJTTbhd6fYLhTHGmImoGngCN/B+eZFjGf9UoXVTb0VUw/NudlS+uQ3RqV4iaoF7rjkIApH+64wZR5pbdvLGm5v7HHv8wTsJS5puIORL86ObruW4j5zaZ81es2cydUpVASM1o01E9sZVC83DzdqQnCWqqiMqIRCRTwJ7ey9nACERudh7vUFVf561/Bzc59JTInKzd+xLgI++SSVwQ95PAu4Uke/iWgx9HDf/4kxV7dkpoKoPi8hvgfNEpAo3P+No3I3BX6jqX7LWNonIJcB1wGMi8itc26LzgZfoP3B+ODEbM6BIzr8TBMEnPkL+ELFQDJHcP5Z7TkSoClcRDURp6myaNFVU6VSaE089kbO/ejYzamb0OXfM8cew/B/Luemam0glU5acmoBU9RfAL/Ic3w68tRAxiEgA+BSuheD9w3jr697z9Kxjm4GDRSScp7XfLFzLv+6stZnja/KshZG30DXGmEll2MkpEfnUbpYobujtG8CzqpoExkubC2OMMWYsjP6djoki2QVNa1xFVMMqNzOqY3uehQLT9vcqoha459hsGIObSMaMpe/c+EOu/f5tRMIhQqEQpFOQ7KKtyyVg2+IpLv/fe7j8tgfB56e7u5t4VzcXfvEzXPPNC4scvRkpEfkwcC8QBFpxrY5G0xm4gfPZMrMP/wT0JKdU9W8i8m7gSu+huHkbJ6nqquwLeDvI34lLUn0BqAD+BZyiqnfnieMk4GLgNOCTuBtql3rv70NVrxeR7cCXcVVUrcA9wEVZbZSGHbMx+YgI06PTKQ+W737xGAn5Q9SX17MjvoP27vbdv6HERaIRvnX9wCNYZ9TMGPS8mRhEpIzezRMbVLWQVUAfAmqBGweYFTWQed5zY9axFcB7gaOApzIHRSQCHA78OWctuA0aj+Vc++24z7tXvNercS39js4TR74WusYYM6mMpHLqdtwvCxmZu0a5xxTYLiLfUNXbRhaeMcYYY0pKe1Nve76G511iKp3svy5U7qqhMpVRtYdAqKLw8Rozyq6+5AKmVFXyretvobUt/83J1q40dHUCEI1GuOob5/HVsz9byDDN6LsG2Ah8TFVHfc6Gqr57mOv/DgypSktVN+ESTUNZG8clpy7e3Vpv/e243x+HsnbIMRuTzS9+ZpTN6Fc1VQw+8TE9Op2wP8yO+I5J1ebPTC4icjCuOvZ4wO8dTonIY8CFqvpCAcLItPT7cZ74AkB5btt1b07i54DtuE0QGXcDX8e1qn0q6/hncDOhslvX/gnYApwpIjdkNlyIyALg3cBPM20OVbVdRH4DLBaRBZkNFyJSAZwJrAWeGf6PbowxE8NIklPvwe2MqwaWAi97xw8EPgtsA67G9RL/ArBURHao6q/3PFxjjDHGjBuphGvJl2nP1/A8tG3Jv7ZqL5eEysyKqt4XxFfYeI0pAJ/Px9fO/RzHvesdfOSTS2jZsZ14sv/NyXBAqJ42nQd/vpRFR8wvQqRmlO0DfHUsElPGmIEF/UFqymoI+sZX27jKUCVhf5itHVtJ5tukY0wJE5HDcQmaCuBRXMWtAAfjqo/eKSLHqOpzYxjDTOB9wDMDfPZWAOtF5AFc671m4ABcQqgC+LiqdmYWq+pqEfkBblbiMtzMxYOAs3E/651ZaxMicg4uofWUiNwGxHCVwk3AN3Ni+Rpu88UfReQGXGXVZ3Bt/T6glsU2xkxiI0lOvRM3yO+wnHLdh0TkFlzv8UNV9UoR+SGwCjgPsOSUMcYYU8o6W7wk1GpXHdX4IiTj/dcFwlBzKNQd5s2LOszNjzJmEjnqyAW8/PQfOfyod7B+W/8/JzOnhHnu738gVllZhOjMGFhP/mHnxpgxUhYsY3p0Or5xutkl5A8xs2ImWzu2Es/37yVjStd1QBpYpKrPZp8QkSNxc3j/B7e5faycjqvY+tEA5ztx9yHfBnwUl5DahmvDd62q5qtWOhc3j+os4APe+puBS1X7DshV1XtFpBNXSXwdrnXf47iNKpty1r6a1UL3IiAEPAu8T1Vz2wIaY8ykMpLk1BnATfn6yHrlqrfjhtde6b2+Axtia4wxxpQWTcOO9b0VUVtWQcsAIyQraqH+cJeEqpsP098C/vG1g9mYYggFQ2xpTeU919CWIhwKFTgiM4a+B5wvIreo6q5iB2PMRDclPIUpkSnFDmO3fOKjtqyW7fHtk2IOlZk03g7ckJuYAlDVZ70KpHPGMgBVvQq4apDzXbgqqeFcMwVc7z2Gsv63wG+HuHYN8JHhxGOMMZPBSJJTNfT2kx3omrVZrzeP8PsYY4wxplC6d7lKqMy8qMbV0NXWf50vADMO6J0VVTffJaeMMf08+qe/EAqHiXcnKAv5SKaUgF/o6E4TDIV59E9/5YPvPbbYYZpRoKq3ikgMeFFEfobbed0vM6mqdxQ6NmNKgV/8BP1BQv4QaU3Tkegg3bdQAXDVUlPDUwmW0CYYEWF6dDpBX5DmeHOxwzFmNMSBhkHOb8ZVLhljjDGDGknS6BXgDBFZqqqt2SdEpApXWfVy1uG5wNaRh2iMMcaYUaUKrZt6K6Iannezo/LcBCI6tW8iquYgGAcDx40pBT+/5wFa29opi0Y447STuOrr5/O1b1/Pj395L61t7fz8ngctOTVBiEgtsBjYC7hkgGUKWHLKGCASiBDxRwj5Q4T8IQK+vrcmNKJ0JjtJpBN93hP2l273zKpwFSF/iG0d20hp/qraPbWycSXL1i6jsaOR2rJaFs9bzMLahWPyvcyk9jvgw8APBjj/YeD3hQvHGGNMqRpJcuoK4B7gZRH5KS5ZBW6w4Om4yqqTAUTEB5wC/HWPIzVF9cy6Hdy1YiNbWjupj0U5ZdEcjtq3uthhGWOMGRKFLc+5iqhMm76O7XnWCUzbvzcZVT8fYrNBpOARG1Pquru7efjRJ4lVVnDPj27iP479NwBuvvpSPnD8uzn5zHN4+NEnSCQSBIOlUwFgBrQUWATcADyFG7xujMkSCUQoD5ZTFijD7xusGYurNioLlhUossKJBqLUVdTR1NFEd6p7VK+9snElS1ctJegLUhGsoDnezNJVS1myYIklqMxoOw94RETuBa4FXvKOHwRcCFQDpxYpNmOMMSVk2MkpVf21iJwKfBc3yC/bFuA0Vb3Pe+0HTgCa9ihKU1TPrNvBjcvXEvAJsUiA7bu6uHH5Ws5hniWojDHGfcbNZfDWFsXV9BL8+oz+x0PlLhHV8zgUQhWFj8+YCSiVSvP/TlnMNy/4ErU10/uce99x7+KV/3uUy6+7mWQyZcmpieE44EZVvaDYgRgz3pQHy4mFYyVd9TSagr4gdeV1bOvcRkei3yjvEVu2dhlBX5BwwP13DgfCkHTHLTll9oSIpHHVv30OA0fiqoZzjwM0YiM+jDHG7MaIPihU9W4RuQ94K+6GnADrgZXeAMHMugR9W/yZEnTXio0EfEI06Ha3RYN+OhMp7lqx0ZJTxpgJR0Q+tZsliuuh/gbwrKomgQ1jHtieUO93ySl7Q91hUOdVRVXvC+IrbmzGTFDRaIRbrr18wPO1NdMHPW9KThfwarGDMGa8EBHKg+VUhasI+iwBn8snPmrKamiON7Oza+eoXLOxo5GKYN9NRiF/iMaOxlG5vpnU7qB/csoYY4zZYyPexeAloZ7xHmYC29LaSSzS9/8qkaCPhlabb2mMmZBup+8vX5ndf7nHFNguIt9Q1dsKFNvITJkDZ9zr5kcZY4wZCw8D78G19zNm0vKLn8pQJZWhyt227jMwNTLVzaHq3Ibqnt37ry2rpTne3FM5BdCd6qa2rHZPwzSTnKqeXuwYjDHGTEwj3i4tIu8SkStF5DYROdA7VuEdnzJ6IZpiq49FiSfSfY7FE2nqYtEiRWSMMWPqPcCzwOu49rUf9R5f846txLWvuBBoB5aKyInFCHTIQpWWmDLGmLF1HjBHRG4Skf1EbFifmVwCvgDV0WpmVc5iSmSKJaaGoTxYzozoDPb0r43F8xaTSCfoSnahqnQlu0ikEyyel9t1zRhjjDFmfBh2ckpE/CJyN/AE8HXg08BM73QSeAD4/KhFaIrulEVzSKaVzkQKxT0n08opi+YUOzRjjBkL7wTCwGGq+j+q+pD3uBZYAJQBh6rq9cB8XMLqvIEuJiI+EfmyiLwkInER2Sgi14tI+XADE5EyEVkvIioi3x/RT2eMMWYsbMO1PP8C8AqQFJFUziNZ3BCNGX1hf5gZZTOYXTmbWCiGz9oFj0hZsIyaspo9+u+3sHYhSxYsYWpkKu2JdqZGprJkwRKbN2VGnYjsNZRHseM0xhgz/o2krd9XgRNxN+IeAdZkTqhqXETuB94PXDWUi4mIDzgH+CywD26w/D3Apaq6aziBiUgZ8KJ3nR+o6heH836T31H7VnMO87hrxUYaWjupi0U5ZdEcmzdljJmozgBuUtV+E6pVtV1Ebge+BFzpvb4DOH+Q690AnA3cD1wPHOS9PkJEjlfV9CDvzXUFMH0Y640xxhSGzeMwk0o0ECUWjhENWDeN0RINRKktr2Xrrq2kekd5D8vC2oWWjDKF8DpD+8yzEkpjjDGDGkly6lPAHap6o4hMy3N+DS45NVR2064EHLVvtSWjjDGTRQ2D/yIVALKb929mgM9TETkEl8hapqonZh1fD9wEnALcOZSgRORI4FxcO8Hrh/IeY4wxhWHzOMxkICKUB8upClUR9AeLHc6EFPaHqauoY2vHVhKpRLHDMWYgV9A/ORUA9gM+AqwGfl/ooIwxxpSekSSn9mHwm2ItwJAGW9hNO2OMMePQK8AZIrJUVVuzT4hIFa6y6uWsw3OBrQNc6+OAAN/LOX4b8B3gNIbwOScifu89jwDLsM85Y4wxxhSIX/xUhCqoDFUS8I3kFoIZjqAvSH15PU0dTXQmO4sdjjH9qOplA50TkX2Bv+Pm9BpjjDGDGsm/LNuAwUpo9se15hsKu2lnjDFmvLkC1172ZRH5KS5ZBXAAcDqusupk6GlNewrw1wGutQhIA89kH/Ta4D7nnR+KLwMH4trqGmOMGcdEpAKYQp75vqr6RuEjMmZkAr4AsXCMimCFzZIqMJ/4qC2vZUd8B61drbt/gzHjhKquE5EfApcDDxc7HmOMMePbSJJTfwFOE5Frc0+IyFTg07gk0VDYTTtjjDHjiqr+WkROBb4LXJRzegtwmqre5732Aycw8KaMmcA2Ve3Kc24T8A4RCalq90DxiMhc3C93V6jq6yKyz1B+DhE5CzgLYK/ZM4fyFmOMMXtARE4BLsa1KR+Izd8w417YHyYWjlEeLC92KJNedaSakC/E9vh2VG2snSkZm4CDix2EMcaY8W8k25++DcwDlgMf9I4tEJHPAs8C5biqp6HY3U276SISGuwCuTfthvh9jTHGmAGp6t3AXsDbcVW+pwJHA3up6q+y1iVU9WVV3THApcqAfJ9xAPGsNYP5X2A9Llk2ZKp6q6ouVNWFM6bZzEBjjBlLIvJRXMeHAPBDXHeIXwH3Agnc70lXFC1AY4YgGohSV15HfUW9JabGkYpQBbVltfjFctumZHwUaC52EMYYY8a/YVdOqepKEVkM/Bj4qXf4OtwvYFuBj6nqv4Z4uaHetBtwRzkjvGmXvaN85uw5w3mrMcaYSUBVU7jK3md2t3YQHbg2gPlEstbkJSKnAe8F3qWqNhXbGGPGrwuANcBbgQpgCfATVV0uIofi2r8+V8T4jMlLRCgPllMVqiLoDxY7HDOASCBCXUUdTR1NdKcGuz1izNgTkUsHOFUNHAscCvTrtmSMMcbkGlZySkSiwEm4QfD74G6YHYhLTK0F/qCqA95ky6NoN+1U9VbgVoDDDj/S6uONMcb0ISLvwn3G1ALXq+pL3hyRI4HnVbVlCJfZDBwsIuE8VcKzcNXDee8wiEgYt/Hid0CDiOyf9T6AKu/YtiHGYowxZuzMB6702pNnKmL9AKr6gojcCnwNeLBYARqTzS9+KkIVxEIx/D6ryCkFQV+QunKXoOpMdhY7HDO5XTbIuQZci9trChOKMcaYUjbcyqku4EfA2ar6f8BvvMdI2U07Y4wx44qI+HGtmf4Tt/lCca2ZXgKSwAO4iuGrhnC5FbgE11HAU1nfIwIcDvx5kPdGgRnAB7xHrtO8x1e8eIwxxhSPH9jufZ25a1yVdf5l4HMFjciYPAK+ALFwjMpgJSJS7HDMMPnER215LU0dTexK7Cp2OGbympvnmAI7VLW90MEYY4wpXcOaOaWqaeANIDZK33+FF8NR2QezbtqtHOS92Tft1mY9nvTOn+a9PnOUYjXGGDM5fBU4ETgPN9S+586NqsaB+4H3D/Fad+N+UTs35/hncG1rf5k5ICL7iciBWWt24aqVcx+f984/4r1+aIixGGOMGTtvAnsDqGonrt35wqzzB+D+XjemKML+MDPKZjC7cjaxUMwSUyVuenQ6ZcHdjS01Zmyo6obcB27z+X+IyJkiUlfsGI0xxpSGYc+cAn4GfFJEbsxT7TRcdwNfx920eyrreN6bdkBQVV/yDmVu2uWaAdyCu2n3Y+D5PYzRGGPM5PIp4A5VvVFEpuU5v4YhJqdUdbWI/AD4oogsw1X7HgScDfwJV6GV8TjuxqZ4700A9+VeU0T28b58TVX7nTfGGFMUfwOOBzJzOB4CzhGRDtxmvC+wZx0njBmRsmAZsVCMSCCy+8WmZIgIM6IzaNRG4sn47t9gzCgSkWuBf1fVRd5rAR4D/g33u8x2EXm7qr5WxDCNMcaUgJEkp/4GLAaeE5FbcNVJ/eZCqepgrYoya+ymnTHGmPFmH+D6Qc63AFOHcb1zgdeBs3DVvtuAm4FLvYpkY4wxpe8W4GMiEvUqp76B6w5xmXf+ReCCIsVmJhkRoTxYTlWoiqA/WOxwzBgREWrLamnssASVKbj34ZJRGR8C3gVcCzyH+13nItzGc2OMMWZAI0lOPZr19Y24dkXZMvM5hjpV1W7aGWOMGU/agOpBzu8PNA31YqqawiW7Bkt4oar7DPF6r5PVatAYY0zxqeoKXMvyzOsm4HARmQ+kgDX2u40Za37xUxmqpDJUid831F/HTSmzBJUpkjm4jeoZHwLWq+pFACJyCPCJYgRmjDGmtIwkOfXfoxmA3bQzhRQO+plWHqKjO0VLR/eA6/w+IeD34RfB54OuRJpEavTvJ4gIAZ8Q8Iv3vQSfCKpKWul5TquSSuuYxGCM6ecvwGleu4o+RGQq8Glc61hjjDFmUKpqLcbNmAv4AsTCMSqDlTZLahKyBJUpghBu40XGv9O3kmodUF/QiIwxxpSkYSenVPVnYxGIMWMpFPAxpSxERdj9Xz4S9FMW8tPU1kUyrYQDPiJBP+GAj1DAR9Dv63eNXV1JdnYm6E6mCfiFgM9H0C+EvPf4RUipkk5DdzJNRyJJPJFGVSkLBaiMBIgG3Q7GTLmh3ze8Xx47upO0dCSIJ1K7X2yMGalv4xJUy4HbvWMLRGQerj1FOfCd4oRmjDFmPPJmFNao6pqsY3OB83DVuHeo6h8KFEtuZ4uMXapakbP2AOAa4BjczcZngW+q6vI8160CrsS1eJ8GvAZ8H1iqqpqz1gecA3wW1y63+s0IlQAAIABJREFUCbgH1x1jV55rvx+4GFgAdOFaul+oquuH9lNPTmF/mKpwFWXBsmKHYoosk6Da1rmNXYl+f8SMGW0bgbcDt3pVUvvSO3MRoAZoL0ZgxhhjSstIKqeMGfd8IpSF/ERDfqJBP4E8yaZI0M/sqVGAIe0wLA8HKA8P/kcmczYa8lNFkHRaSavm/f4jURYKUBYK0JVM0R5PsqsrRTJt1VTGjCZVXSkii4EfAz/1Dl+Hq8rdCnxMVf9VrPiMMcaMSzcCb8HNmUJEKoCngJne+ZNF5NihzOUdJU8Bt+YcS2S/EJH9cPOEk7g5ITtx80H+ICInqOpjWWtDuPbuR+BasK8BTsDN2qqld7ZWxg24OcL34zpkZOYKHyEix2e3OPQ+c+8DVgFfAapwrd//KiILVXXzyP4TTFxlwTJioRiRQKTYoZhxRESYUTaDYDxIS1dLscMxE9tdwCUiUgMcArTiZshnHIHbwGCMMcYMypJTZsIQEcpDfsrDAcpC/iElnMa67YXPJ/jGoMtkOOAnXOFnWgXEEyniiRSdiVRPpZYxZmREJAqcBLyM2+n9XuBAXGJqLfAHVe0oWoDGTGYiECoHX9B9nfkMz3zupboh0Qlpqy42RXE08Ius1yfjElPvxw2HfxS4EChUcmqdqv5iN2uuBqYAb1XV5wBE5A7gReAHInJgVkXUmcAi4GxVvdk7dpuI/Br4uoj8VFU3eNc4BPgSsExVT8x8MxFZD9wEnALc6R0L4pJdG4F/U9V27/jvgX/gkl5njfw/w8QhIpQHy6kKVRH0B4sdjhnHpkSmEPKH2Na5jbSNujNj42rc3KmP4jY2fEpVW6CnyvbDuE0KxhhjzKAsOWVKXsDnozISIBYNDrtN3kQQCfqJBP1Mwc2oiifSdCZSdHQn6U7aLyPGDFMX8CPczbf/A37jPYwxheIPQCCCywmrSz6FyiBUCb4hVCKnEqBpEJ97pFOQ6nLH00n3WlNuDbjvkzluzMjVAm9kvT4BWKmqjwCIyO24Fn8F41U7hTIJn5xz5bibh09mElMAqtouIj8CrsAlo57xTp0KdAC35Vzqe7g2fyfjqq8APo77A/y9nLW34drinoaXnMK1E5yJa/fXE6eqPiciT+Iqzr6gqgkmKb/4qQxVUhmqxO/zFzscUyLKgmXUSA1bO7ZagsqMOlXtAs7wHrnacPOmbEOfMcaY3bLklClZ0ZCfykiQ8iFWSU0GIuJaGYb8VJeH6OxOsbMzQUd3stihGVMSVDUtIm8AsWLHYsyk4g9AtNqrjNrDm6+5FQU+PwRCu39fKgnJOHS1QbfN6zDDlgCiWa+PoXduIUALbk5TofwnLgnkF5Em4G7gYlXd6Z2fD4SBv+d579Pe8yLgGW9+1JHAs6oaz1n7DJD21pL1vv+fvXuPk7QsD7z/u+pc1dWnOfQMMwOi5jUq2XgIIuorwUQTxUSz8XWjWTeSCJisKGDceAwaNJoDeCL4RogbD9EsksUET0nQBGFFosSQZYkETyiCMgeYY5+77/3jqWZqmpqZ7pnufrqrft/Ppz419Tx3PXU93dN9ddX13Nc9y8HCFgAppfGIuK3DWI4Qx8+QtUu84zDn2bXKxTIDlQGa5abvdXRMaqUaIw0LVFpZrbate446UJIkLE5pjSkXCzRbaz9VSkuzjlM3mytUTUzPsGd0iv0TFqmkBfgI8F8i4n2tqwIlLZcIaKyD2tDBNn15KZag2IRqMytOHdiZzbaSFuYu4EURcQXwi8A64Itt+08EHlihWL4KXAN8i+xii7OA84Gfjoint2Yoza2FdW+H589t29q6HyYrvD1sbEppIiJ2tY2ldeydh8mh9wJPj4hKSmlyEXH0THGqVqoxUBmgUW7kHYq6gAUqSZK0mlmc0prQrGZt+2plW1kci2qpyMhAkeGZWXa3ilSuTSUd1s1kLYpui4gPkK019bC2FCu4qL3UHSKg2g/VASiUspZ7C2nTl4dKH5QbML4nu1mk0tFdQTZT6kGgAXyHQ4tTZwC3r0QgKaWnztv00Yj438DvAxe07ucqH50KSHOzoxrz7g93wcZ425i58UcaOzdmcpFxHCIizqO1HtXWE7d2GrKmNMoNBquDVIvVvENRl6mVamxsbGT76HbfA6orRMTh/iMfSCk15439ceAPyWY0V4CvA29NKf1Dh+MOAu8gey+4Hvg28CfAn6Z5PzytWcUXAK8kW6t4B/BJsja1D5uCHxFnAW8BnkCW874I/E5K6bsLO2tJ6k4Wp7RqRQT9tRKD9TLl4ir98GqNKRcLbOyvsq6vwp6xKfaOTTHrGxRpvuvb/v0+YP4PSWshHKyWS0dSqkCxkhWiiuWFrxm1WkRAfSi7TR6AqbGsRWAUDxbXopCtXzW221aAPS6l9NGImAX+I1k7o3fOrZMUEeuBQeADOYb4x8BbgeeTFafmLrroVAmpte5H590frmpS49CLOEaBkSOMXeix5489RErpSuBKgCc8+Qlr8g/aiKBZbjJQHaBcKB/9CdIxqpfqbKxvZMfYDgtU6hY30coBbQ65migiHk124eE02bqIe4Bzgb+LiOellL7QNrZC9j7wScDlwDfI1o/8ANm6km+b91rvAV4DfAq4DHhc6/GTIuLZrfaGc8f+ZeCvgH8F/hvZ3wQXAl+OiFNTSvcd25dAktY+i1NadYqFYKBWZqBepliwv/pyKBaCdX0Vhupl9k9Os298mokpF4KXWn497wCkNa1Ugcb6bPZRt6j0Hfl8ynWYnszWq0ozMDsDs1PZNvWMlNJfAH/RYfsu4KdWPqJDYpiKiPuADa1Ncx+EdZpyNLdtrq3eg8BYp7ERUSW7uvxLbZvvAx4fEdUOrf22krX8m2wbO7f9G0eJo2tEBAOVAQYqAxSPd509aYEa5Qbr03p2ju3MOxRpKXynlXeP5F3AEPBTKaXbACLio2StYq+IiMe2zYg6h2wdxNeklC5vbbsqIv4n8KaI+POU0vdaxzgFeDVwbUrpRXMvFhHfBd4PvAT4RGtbmazYdQ/wzFZrXSLi88A/kxW9zjv2L4MkrW0Wp7RqlIsFBhtl+qslF/1dIYW5QmCtzOT0LPsnpjkwMc3UjP3I1btSSh/JOwYpV4ViNtOpUM5mD01PwMwktF9pHZHNiirXs7EPbS9mazb1olIFSusP3TY1nrUFnNx/6NdPXS0iGsAjWg+/l1LqOPNnJUVEDdgG3NLadDtZW6GndRh+euv+VsgWt4+Ir5NdDT6/4HQaUJgb2/I14Oda+26aF8MTgRvnjaUVxxc41OnAXrL1vLpGs9JkqDpEqeBbca28ZqXJLLM8MLZSS+BJy6c126kyV/CZt68PeAFww1xhCiCltD8i/gy4hKwY9dXWrl8lm6l71bxDvZeszd+vkM2+AngpWTeN984bexXwB8DLaBWnyNoJbiFr9/dQnCml2yLiBuBXIuJVc7OtJanXrKHeKupWfdUSJwzWOXFdg4Fa2cJUTiqlAuv6Kpy4rsGWoboz1ySp1xQK0L8J1j0SBrdl/26OwNCJsP7RMHxytm/do7Lb0InQtwFqgwdvvVqYOpxyLfs6Dp8M9eGsqKeuFRGPj4jPAbuB/9O67Y6Iz0XET6xQDOsPs+vtZBcmfhqyD+da/z4zIp7Q9vwm2dXj3+TgB3YAf0m29tP8q7svJGuX9Mm2bVeTtb+9cN7Yc1vH+Hjbti8BPwTOab32XBxPAM4ErumWD+xqpRpbmlvYUN9gYUq5GqgMMFwbzjsM6Xj9f2TFpH0RsT0iLm+tGTXnJ8laxn6lw3PnLtR4Cjy0ftSTgX9JKY3PG/tVYHZubNvzZjk0T9J67m0dxnKEOAaAx3Q6QUnqBf5VrFwUIhiolxmolSi5ntSqUysXqZWLbGhWGZucYffYJGOTtv2TpK4S0SqWBJQbWaHpSO2liv7ZeMwKRehbn61fNbE3a/c3Mwmz063vQ+HhtzSb7Z+dyR7PrXc1O521DJydcTbWKhIRTyQrtDTJ1qz4N7Krqh9PNovoGRHx0+1Xby+Tt0TE6cA/At9vxXMW8Czgn8haC815I/CzwN9HxHvIZimdS9ZO7/nzFn+/iqzt7bsj4mSyFnxnka2x9Y72Bd1TSrdHxBXA+RFxLfA5Dq7F8SUOXk0+127wArKC1k0RcRXZB3UXkS0u/9Yl+JrkqhhFhmvDNCsW71faTT+4iQ/f8WHu3X8vW5tbOfuUs3nmtmfmHdaqMFgdZDbNsmdiT96hSMfiq8A1wLfIcsZZwPnAT0fE01sXYGxpje3UGnZu21z72GGg3mlsSmkiInZxaGvbLWQtaue3rp079tMjotJqYbvQOO6YvzMizqN1UchJJ53U4emStPb5KYNWXH+tzHCjbFFqjahXitQrdcYmZ9h1YILJaVv+SdKaVChms5tKVSjVjlyI0vIoFLMZVEshJZgag6nRrG3gzPTSHFfH6lJaV1anlL7eviMingz8A/DHwHOWOY4byApiLydbC2qGbBbUm4F3t18RnlL6VkQ8g6wF0RuACvB14Lnti8S3xk5GxLOBd5C1M1oPfJtszY0rOsRxIXA32Ydqzwd2khXGLm5fJL517GsiYgx4C9nXcQL4IvD6lNKaXm+qv9LPcG2YQvi+Z6Xd9IObeOc/vZNyscxAZYAdYzt45z+9kzfxJgtULcO1YWbTLPsm9+UdirQoKaWnztv00Yj438DvAxe07hutfZ0KSHO5sDHvvtPYufGNtseNo4ydGzO5yDgOkVK6ErgS4NRTT/WKJEldyeKUVky1XGRDs0K15Idha1G9UmRbpcG+8SkePDDF9KxFKklaM8p1aG5y9lM3iYBKI7v1bchmY03uz4pVU/M70mgFnA68Z35hCiCl9PXWTKILljuIlNLfAH+ziPHfAF64wLG7ya5MP38BY2eAy1q3hRz7M8BnFjJ2LagWq6yvr6dSrOQdSs/68B0fplwsUy/VAR66//AdH7Y41WZ9PesEaoFKXeCPyWbbPp+sODW33mO1w9ha63503n2nsXPj29ePHAVGjjB2oceeP1aSeo6fUGjZRQTDjTJDDd+cdYP+WplmtcSesSl2j04xa0shSVrd6sNZSzl1t1IFSuuAdVnLv8kDrVlVB2z/tzLGgR8dYf99wNgKxaKcFKPIUG2I/kp/3qH0vHv338tAZeCQbbVijXv3r+nJeMtifX09EcHeib15hyIds1ab2PuADa1N97Xut3YYPrdt7hfCg2Q5+mFjI6JKNlv4S22b7wMeHxHVDq39tpK1/JvsEMc3jhKHJPUc+wtoWTWrJbYO1S1MdZmIYKhRYetwnXrFmXCStCqVazC4zcJULyoUoTYA/Zth3aNgYEvW0rHgn/7L6HPAC46w/wXA51coFuWgWWmypbnFwtQqsbW5lfGZQ2eRjs+Ms7XZ6XNqrautY7A6mHcY0jGLiBqwDbi/tel2slZ6T+sw/PTW/a0ArXazXwee1CpGtTuN7LPTW9u2fa217bQOMTyxw1iOEMde4K7DnZckdTvfoWpZ9FVLbB2uMzJQo1Lyv1m3KhcLnDBYZ2N/lWIh8g5HkhSRFaX6N2eFqXLt6M9Rd5tr/9fcCEMnZ8VK1xtbDq8F1kfENRHxlIjob91Oi4i/AtYBF+Uco5ZBtVjlhOYJbKhvoOjP1qpx9ilnMzUzxdj0GCklxqbHmJqZ4uxTzs47tFVruDbMuvq6vMOQjigiDnfV1dvJukN9GiCltL/17zMj4gltz28C55Ctx/jVtuf/JdnaT+fNO+6FwDTwybZtVwOpta/dua1jfLxt25eAHwLntF57Lo4nAGcC16SUpg5zTpLU9WzrpyXVVy0x1Ci7rlSP6a+V6auU2Ds+xZ6xKWZmbR8kSSumVIFSPVtXqtxwdowOr1DI2jzWhmB8D4w9AK4heUwiYpbsg6lDNgNPBn65w3bIrub2/VeXsIXf6vbMbc/kTbyJD9/xYe7dfy9bm1s5+5SzXW/qKAYqA5SixI6xHSRbwmp1ektEnA78I/B9oAmcBTwL+Cfg8raxbwR+Fvj7iHgP2Sylc8na6T0/Hfqf/Crg14F3R8TJZC34zgL+I/COlNJ35wamlG5vrSV5fkRcSzZ7+nHAa8iKUZ9oGzsVEReQFbRuioirgAGyC1Z2kK2TJUk9yzdHWhKlQoFNg1WLUj2sUMha/Q3UyuwZy4pUrkclScugVMmKUOV6VpSyGKXFioD6EFQHYOxBGN/tulSL91EeXpxSj2hWmgxXh50ptco9c9szLUYdg0a5webCZrYf2M5Mmsk7HGm+G4DHAy8nWwtqhmwW1JuBd6eUHurnmVL6VkQ8A/gD4A1Ahax933NTSl9oP2hKaTIing28A3hp69jfBl4NXNEhjguBu8lmWj0f2ElWGLu41Saw/djXRMQY8BbgUrJ2g18EXp9Scr0pST3N4pSOW7lYYPNgjXLRD8eUFamG+yoM1A8WqbzqTpKOQ0RWjKr0ZfdF/3zTEikUsjZ/tUEY3QUT+/KOaM1IKZ2ddwxaeZVihfX19VSL85ckkbpLtVhlc3Mz20e3MzVjxzGtHimlvwH+ZhHjvwG8cIFjdwPnt25HGzsDXNa6LeTYnwE+s5CxktRL/HRDxyVbc6hGycKU5ikWgnV9FfqqRbbvnWBqxrZBkrRo5Ro0N0GxnHck6mbFEvRvyopU43tgcr8zqaQ2hSgwVBtioDKQdyjSiikXymxuZAWqiZmJvMORJEldyOKUjlmtXGSkv2phSkdULRXZOlRn5/4J9k9M5x2OpNXu7i/Dv3wE9t4HA1vgSS+Hk5+Rd1T5aKzLbtJKKdeyWxqBqTGYPJAVqmZt63Q0EXHSQsallL6/3LFoadnCT72sWCiyuW8zO8Z2MDo1mnc4kiSpy1ic0qJFBOsaFQYbXsWthSkUgpGBGs3JaXaPTjE+5Ydckjq4+8tw4x9CoQzVQTiwK3vM63unQBUB1X6oDWVrS0l5iIBKI7uxEaYnYHoc0uzBGVWFEhSK2ePpcZgahenJXMPO2d0sbA0qKxxrRKVYYV1tHbVSLe9QpFxFBCONEXaN7WLfpO1fJUnS0rE4pUWplYtsaFaplJwtpcVrVEo0KiXGp2bYMzbFAWdSSWr3Lx/JClPl1geB5RpMtbZ3e3GqUMgKUrXB7AN/aTUpVbPb4VSb2f3MNEwdyGZcTY31WmvAS3h4caoEPJpsrYvbgc+vdFBaPFv4SZ2tr6+nVCjx4PiDeYciSZK6hMUpLYizpbSUauUitXKRyelZdo9NcmBihtRbH2BJ6mTvfdmMqXalGuz9YT7xrIRiGerD2WypiLyjkY5PsQTFwazIOjsLE3tgfC/MTOUd2bJLKb3tcPsi4lHAV4BbVywgHZNmpclQdYhSwbfJUieD1UFKhRI7x3b6/k2SJB03/+rWUZWLBTYN1JwtpSVXKRUY6a8x3Zhlz9gU+8anmfVNjtS7BrZkrfzKbS2Upsdh4IT8YlouhSI01kPNK/PVpQqFrPBaH85mUo3u6tm2fyml70TEB4HfAz6bdzx6OFv4SQvXV+6jGEV2jO5gJtmuXZIkHTurDTqiSqnAlqG6hSktq1KxwPpmlZPWNRhuVAhnD0i96Ukvh9kpmBrPmmNNjWePn/TyvCNbOoUC9K2H4ZMtTKl3VPpg6CTo35TNFuxN9wKPzzsIHaoQBYZrw5zQd4KFKWkRaqUam/s2O8tQkiQdF/+S0GHVykU2D9QoFCwUaGUUCsFwX4X+WokHDkyy3zWppN5y8jOA12drTO39YTZj6kkv7471piKyNaXqw1mBSupF1f7sNr4Xxh7I1qjqHb8EuFDLKtJX7mO4NuyH69IxKhfLnNB3AttHtzMxM5F3OJIkaQ3yL3F11KyV2NisOoNFuSgVC4wM1OifnGHHvgmmZ2fzDknSSjn5Gd1RjJpTKGbr79QGs39LymYNVvthYi+M7e6KNaki4uLD7FoH/AzwE8AfrVxEOpxyscy62jrqpXreoUhrXrFQZFPfJnaM7mBseizvcCRJ0hpjcUqHKESwvlmhv9azLVe0itQrRbYO19mxb4LRyZ66ulrSWlcsQX1d9gG8F3pIDxdxsHA7eSArUk2Pw9pde/JtR9j3I+AtwB+uTCjqpBAFBquDDFQGvABPWkKFKDDSGGHH2A5Gp0bzDkeSJK0hFqf0kFq5yMb+KuWi7Ya0ehQLwebBGvvGp9g/Mc341Cxp7X5wJanbFYrQWAfVAYtS0kJV+rLb7ExWqIo1+bfoIztsS8ADKaX9Kx2MDipEgWalyUBlwBZ+0jKJCDbWN7KTnRyYOpB3OJIkaY3wr3MREazrqzBYd7aUVq/+Wpn+WpmZ2cSByWn2jE4xNWO7P0mrSG0AGhtcU0o6VoVi9nO0BqWUvjd/W0SUgBdGxDDwmZTSj1Y+st5VLBQZqAzQX+mnsDYLntKaEhFsqG8AsEAlSZIWxOJUj2tWSwz3VZwtpTWjWAgGamUGamUOTEyze2yKiamZvMOS1MsKBWhuymZ+SOpJEfFHwLNSSk9pPQ7gC8AzgQB2RcTpKaVv5xhmT6gWqwxUB2iUGrbvk1aYBSpJkrQYFqd6UCGCgXqZgVqJkkUprWF91RJ91RJ7xqZ48MAks7b7k7TSqk3o25jN+JDUy55LVoya84vAGcAfAbcBlwNvAM5d+dB6Q1+5j/5KP7VSLe9QpJ5mgUqSJC2UxakeEhH010oMNyoUC15FqO4xWC/TVymyc/8ko5PTeYcjqRcUillRqtrMOxJJq8OJwDfbHv8i8N2U0hsAIuIU4D/nEVgvKBfKbGxszDsMSS1zBapEYnRqNO9wJEnSKmVxqkc0KiXW9VWolJwppe5UKhbYPFhj/8Q0u/ZPMDPrLCpJy6Rcg/4TnC0lqV0FaO8z/CwOnUn1HeCEFY1IknIUEWysb2QHOyxQSZKkjqxUdLlCBBv7q2werFmYUk9oVktsG27QrFl7l7QMagMwsNXClKT57gFOh4dmST0K+FLb/hFgfw5xSVJu5gpUjXIj71AkSdIq5Ke3XaxRKbGhWXFdKfWcYiEY6a/RX51h5/4JpmZm8w5JUjfo2wD1obyjkLQ6/Q/gdyNiBDgF2At8rm3/k4Bv5xGYJOXJGVSSJOlwrFp0oWLh4GwpC1PqZfVKkW3DdQbr5bxDkbSWRcDACRamJB3Ju4APA08DEvBrKaXdABExCLwA+GJu0UlSjpxBJUmSOnHmVJdxtpR0qIhgfbNKX7XEzv0TTE47i0rSIhSK2fpS5VrekUhaxVJKE8ArWrf59pGtN+WUAUk9yxlUkiRpPisYXcLZUtKR1cpFtg7VWd9XpViIvMORtBZU+2HwRAtTko5LSmk2pbQnpTSVdyyrVUQUIuKiiLgzIsYj4p6IuCwi+vKOTdLSmStQ1Uv1vEORJEmrgFWMLtBXLbF1qE5/zdZl0pFEBIONMieta7C+WaVU8FegpA7KdRg6Efo3QdFJ5pK0At4DvBv4N+DVwDXAa4BPR4R/sEldJCIYaYxYoJIkSbb1W8uKhaxdWbPqt1FajIhgsF5moFbiwdEpdo9O5h2SpNWgVIHGBqi4HoIkrZSIOIWsIHVtSulFbdu/C7wfeAnwiZzCk7QM5gpU20e3MzY9lnc4kiQpJ16FtkY1qyW2DTcsTEnHISJY11dhy1Cdsu0wpd5VLEFzBIZOsjAlSSvvpUAA7523/SqydbpetuIRSVp2zqCSJEl+GrvGVEoFtgzVGRmouW6OtERq5SLbhm2NKfWcQhH6NsDQI6A2kHc0ktSrngLMAl9t35hSGgdua+2X1IUsUEmS1NssTq0RlVKBDf1Vtg03qJWLeYcjdZ2IYGN/lQ39VSIs/GrpHM8i7xHxmIi4JCJuiYgdEbEvIm6LiDe7SPxxqg/B8MnZvT/zkpSnLcDOlNJEh333AhsiojJ/R0ScFxG3RsStO3bsWPYgJS0PC1SSJPUui1OrWETQrJXYMlRn23CDAWd1SMtuoFZmy1DNNn9aSsezyPtvABcB3wYuAf4b8O/AO4CbI8J38YtVLMHg1mzGlEUpSVoNGkCnwhTAeNuYQ6SUrkwpnZpSOnXjxo3LFpyk5WeBSpKk3uSCRatQuVhgoFamWSvZuk/KQbWUtfnbuX+SfeNTeYejNWwJFnn/K+BdKaU9bdv+NCK+CbwZeAXwJ0seeLeqDUJjPRQsPkvSKjIKjBxmX61tjKQuNleg2jG2g9Epf+QlSeoFfjqzivRVS5wwWOfEdQ0GG2ULU1KO5tr8bRqoUfKDbB2741rkPaV067zC1JyrW/c/cdwR9oJiCQa2QHOjhSlJWn3uI2vdV+2wbytZy7/JFY5JUg4igo31jTQrzbxD0Sq1mLbnEfG2iEiHub2uw7EX1Y49Is6KiJsj4kBEPBAR10TEIw8z9scj4q8j4sHW+Jsi4meW5qsiSWtX7jOnWi2NLgBeCZwM7AA+CVycUjpwlOc+huyDvZ8DHk12Zd23yVomvfdoz18NIoL+WomhepmSbcSkVaevWqJeLvLg6CR7xpxFpUU77CLvEXE8i7xva93ffxyx9YbaADQ2WJSSpNXra2Tv504DbprbGBE14InAjTnFJSkHEcGG+gaCYN/kvrzD0erzG8CrgOuAjwNTwLPI2p7/p4g4PaU0Nu85FwE752375w7Hfg9Z+/VPAZcBj2s9flJEPDulNDs3MCJ+mazLxb+StV4fBC4EvhwRp6aU7msb+2jgZmAa+CNgD3Au8HcR8byU0hcW/VWQpC6Re3GKRfzy7+BYktKqMVAvM9yoOENKWuUKhWB9s0qzVmLn/kkmpmbyDklrx9EWeX96RFQWc0V4RBSBi8ne3By2JWBEnAecB3DSti2LCrorFIrQHIFKxwsdJUmrx9XAm8g+1Lupbfu5ZGtNfTyPoCTla319PYUosGeiUxMB9bBjaXv+1ymlu4900MW0Y4+IMnA5cA/wzJTS/tb2z5MVvd5G631Yy7uAIeCnUko+eQrRAAAgAElEQVS3tcZ+FLgDuCIiHptSSgv9AkhSN8n1MuJ5v/x/OaV0VUrptcBryYpMLznKIf4K2JZS+s8ppctTSn+aUvoV4PeBnyRLSqvShv4qG5pVC1PSGlItFdk6VGd9X5UIf3a1IMe0yPtRvBc4nWyG8b8fbtAhC8WvX7fIl1jjqv0w9AgLU5K0BqSUbgeuAH45Iq6NiHMi4jLg3cCXOPLajJK62HBtmOHacN5haBU51rbnETEQEUe6QH8x7dh/muwixD+bK0y1YrsNuAH4lVYBi1ZLwBcAN8wVplpj9wN/BjyGY++mIUlrXt49bnpuLY6IYNNAjYFaOe9QJB2jwUaZbcN16pVi3qFo9RsFOq2hAcewyHtEvB04H7gypfSu44yt+xSKMHAC9G+yjZ8krS0XAq8DTiErVL2E7Kr0XzhKJw1JXW6wOpi1+fPiQB3Zkdqe/2+yVnrjrTWintdhzGHbsQPz27HP/fsrHY5zCzBAVnSC7ML56hHGth9PknpO3p/cLOaX/2KsyrU4IoLNAzX6qquhm6Kk41EuFjhhsM6G/ioF3yjp8JZskfeIeBvwFuDPgd9csgi7RaUBQyc5W0qS1qCU0kxK6bKU0o+nlKoppa0ppde2X5EuqXc1K0021jdSiLw/wtJqdIS257uBK8k6Nr0QeCPwCOCzEXH2vMMcrR37hoiotI2d295pLGTv9RY79hARcV5E3BoRt+7YsaPTEEla8/LO7Iv55b8gC12LY6VlM6aqzrSQusxALZtFZdFZh/E1slx7WvvGtkXeb13IQSLircBbgY8C59iTfJ76MAxsyWZOSZIkqes0yg02922m6N97eriObc9TSu9NKb0ypfSRlNJ1KaU/JpvJdD/wnohoth1jMe3Y5+47jT+esYc4pE37xo2HCU2S1ra8i1O5rcXRfgXCA7t2LvIlFm9jf5VGxQ+vpW5UKhbYNFBjZKBGyVZiOtTVQCJrV9TuYYu8R8SjI+Kx8w8QEReTLar7MeDXbW/UJiJr4de3Pu9IJEmStMwqxQon9J1Apbioa5jVxRbb9jyltAv4U2AIeHrbrsW0Y5+77zT+eMZKUs/Ju1oyCowcZt+yrsWRUrqSbHov/+GJT17WK9A39ldpOqtC6nrNaol6uciuAxPsH5/OOxytAiml2yPiCuD8iLgW+BzwOOA1PHyR9y+StZl4qE9kRLwK+D3g+8AXgF+d12///pTS9ct6EqtRBFQHoLHO2VKSJEk9pFQosblvMztGdzA2PZZ3OMrRcbQ9v7t1v6Ft233A4yOi2qG70/x27Pe1bf9Gh7FwsGXfffO2H2msJPWcvCsmi/nlf0SrdS2O9c0q/bVy3mFI6uCGO7fzwRu/wz0PjnLicINXnvEoznzs4erlC1MsBCP9NfqrM+zYN8H0rJNcxIVkb4DOA54P7CRb5P3iBcyCmlt78STgIx32fwnoreJUpQF9G6FobpUkSepFhSiwqW8Tu8Z2sW9yX97hKAfH2fb8/2ndt69T/zXg58jasd/U9jpz7dhvnDcW4GlkFxC2Ox3YC9zVenw7Wceop3WI4/TW/YJavUtSN8q7/1RXr8Wxvq/KYN0Pz6TV6IY7t3PxdXewfd84Q/Uy2/eNc/F1d3DDnduX5Pj1SpFtw3UG/B3Q8xa6yHtK6eSUUszbdnZKKY5wO3NFTyZPEdDcmK0tZWFKkiSp562vr2e4Npx3GFphC2l7HhGliBjssP1E4LeAXcDNbbsW3I6d7ALBHwLntK9bFRFPAM4ErkkpTQG03vN9GjiztX9ubBM4B/gm8NWFnLckdaO8Z05dDbyJ7Jf/TW3bO67FAZRTSne2H2C1rsWxrq/CYMMPz6TV6oM3fodyMR5aC65RKTE6Oc0Hb/zOcc+emlMoBBuaWVvPnfsnmJxeFb+epLWnXIfmJijm/WeLJEmSVpPB6iDlQpkdYztYJdcpaxktou15E/huRPw1Weu9B4EfJysINYGXppQe6gu5mHbsKaWpiLiA7DPNmyLiKmAAuAjYQXbxfLs3Aj8L/H1EvIdsZtW5ZB2jnr9aLrCXpDzk+ilPN67FUS4W2NCsUq+4Boa0mt3z4ChD82Y11ctFfvDg0q9FWisX2TpUZ8/YFA+OTvmmSVqoCOjbALWHXfQoSZIkAdAoN9hc2Mz2A9uZSTN5h6PltdC252PA/wSeCvwSWUFqJ9lnh3+UUuo0W2nB7dhTStdExBjZ8iKXkrXu+yLw+pTSvfPGfisingH8AfAGoAJ8HXhuSml+W0BJ6imr4RLkrlmLo79WZn1fhUIhjj5YUq5OHG6wfd/4QzOnAMamZtg23FiW14sIhhoVGpVsFtX4lG+apCMq11qzpZyFLEmSpCOrFqtsbm5m++h2pmam8g5HyySldDZw9gLGTZDNklrMsWeAy1q3hYz/DPCZBY79BvDCxcQjSb0g7zWnumYtjvXNKhv7qxampDXilWc8iqmZxOjkNCll91MziVee8ahlfd1KqcCWoTrrm1UK4e8L6WEioG89DG6zMCVJkqQFKxfKnNB3ArVSLe9QJEnSAuRenFrrIoKRgRqDdT9Ak9aSMx87wiUvOIWR/hp7xqYY6a9xyQtOWbL1po5msF5m63D9kJlbUs8rVWHwRKi7sLUkSZIWrxAFNjU20aw08w5FkiQdhZ+KHodiIRjpr7m+lLRGnfnYkRUrRnVSLhbYPFhj3/gUDxyYZGbWtajUoyKyglRjXd6RSJIkaY2LCDbUN1CKErsnducdjiRJOgyLU8eoVi4y0l+lVHTymaTj018r06iU2LV/gv0T03mHI62sUjVbW6pUyTsSSZIkdZGh2hClQold47tIyQsBJUlabSxOHYOhRoXhRplwvRhJS6RYyFqE9k1Ms2v/JNOzs3mHJC2vudlS9eHs35IkSdISa1aalAoldozuYCbN5B2OJElqY3FqEcrFAhv7q9TKtvGTtDz6qiXq5SK7Dkyyb3wq73Ck5VGqtGZLVfOORJIkSV2uVqqxuW8z94/ez/SsnSokSVot7Em3QIP1MtuG6xamJC27QiHY2F/lhME6ZVuHqtuU6zCwzcKUJEmSVky5WOaEvhOoFv0bVJKk1cJPPY+iXCywZajO+mbVNn6SVlS9UmTbcJ3BejnvUKSlUe2HgS1Q8M8PSZIkraxiocimvk0WqCRJWiX8dOgInC0lKW8RwfpmlS1DdSolf2VrDWusg/5Nri8lSZKk3BSiYIFKkqRVwk86O3C2lKTVplYusnWoznCjknco0uJEZEWpxrq8I5EkSZIsUEmStEpYnJpnqFFxtpSkVSkiGO6zOKU1pFCEga1ZOz9JkiRplbBAJUlS/ixOtczNllrXV3G2lCRJx6vSB0MnQbmWdySSJEnSw1igkiQpXxangGIhnC0lSdJSiIDmCAyckM2ckiRJklYpC1SSJOXH4hRQKoSzpSRJOl7lWjZbqjaQdySSJEnSgligkiQpHxanJEnS8YmAxjoY3AbFct7RSJIkSYtigUqSpJVXyjsASZK0hpUq0NwEJd/Ia5W763q4+X2w+3sw9Ah4+gXwmOfkHZUkSVol5gpU9x+4n4mZibzDkSSp6zlzSpIkHZv6EAyeaGFKq99d18PnXwf77ofacHb/+ddl2yVJklqcQSVJ0sqxOCVJkhanUISBLdC3IWvpJ612N78PChWoNLL/s5VG9vjm9+UdmSRJWmUsUEmStDIsTkmSpIWr9mct0SqNvCORFm7396BcP3RbuQ67v59PPNIyioi7IyId5rahw/gtEfHRiNgREWMRcWtEvPgwx65GxCUR8d2ImIiIb0fEWyKi44KDEfFrEfEvrePeHxF/FhEbDzP2qRHxhYjYFxF7I+JvI+KJx/fVkKRjY4FKkqTl55pTkiTp6AoF6NuYFaektWboEVkrv/ai6tQYDJ2UX0zS8roT+P0O2/e1P4iIdcD/AkaAdwM/AH4V+GRE/EZK6c/nPf9q4IXAfwe+AjwNeDvwY8DZ8459UeuYXwIuALYBrwWeFhGnpZQOtI09HbgBuBe4uLX5fOCmiHh6Sun2RZy7JC0J16CSJGl5WZySJElHVmlA3wgU/bNBa9TTL8jWmJokmzE1NQazk9l2qTvdn1L6iwWMewPwSOAFKaVPA0TEh8gKT5dGxDUppf2t7WeRFabenVL67dbz/ywidgOvjYgrU0o3t8ZuAN4BfA342ZTSTGv714DryIpV72yL4/1kP6FnpJTubY39JPAN4DLg547x6yBJx8UClSRJy8e2fpIkqbMIaG7M1peyMKW17DHPgeddCv2bYHx3dv+8S7PtUpeKiFJEDBxl2K8C354rTAG0CkmXA+uAs+aNBXjvvGPMPX5Z27ZfAhrA5XOFqdaxPw18p31sRPwY8BTgmrnCVGvsvcA1wLMjYvNRzkOSlk0hCow0RmzxJ0nSEvOTJkmS9HDlGjQ3QbHjMiLS2vOY51iMUi95KjAKlCNiD/A3wBtTSvfNDYiIE4CtwMc7PP+W1v1TgE+2/fvelNI97QNTSvdExH2t/bSNhWwGVqdjvzQimq1ZWUcb+xvATwGf7XSikrQSioUiI40R7h+9n8mZybzDkSSpK1ickiRJB0VAYx3Uh/OORJJ0bO4A/oxs3akScCZwDvCzrbWe5gpUW1r39z7sCAe3bW3btgX4t8O85r1ka0q1jz3SsaM15q5jiAOAiDgPOA/gpJNcP07S8isWimxqbLJAJUnSErE4JUmSMqVqNluqVMk7EknqaRExBFy4iKe8P6X0AEBK6fnz9v2PiLiRbIbU7wHntrY3WvedFlEZnzdm7t+HW3BlvMPYhR57sXEAkFK6ErgS4NRTT02HiUuSlpQFKkmSlo7FKUmSel1ENlOqPpz9W5KUtyHgrYsY/xfAA4fbmVL6RET8PtBeuBpt3XdaRKU2b8zcvw+34Eqtw9i5Y48d5diLjUOSclUsFNnct5nto9sZnx4/+hMkSV1hdnaWQqGQdxhdxa+mJEm9rFiGga1ZKz8LU5K0KqSU7k4pxSJu31rAYe8GNrQ9nmvv97CWeW3b2lvt3XeYsXPj54890rFT25jFxiFJuStEgZHGCLVS7eiDJUlr3q5duzjxxBN54IHDXg+mY2BxSpKkXlUfgqGToOybaknqAT8G3D/3IKX0Q7Kiz+kdxs5tu7Vt29eArRFxYvvA1uMtHcYCPK3DsZ8K/HtKaf8Cxp5OVsj65w77JClXhSiwqbGJRvlhnUclSV3muuuu47777uO6667LO5SuYnFKkqReUyjCwBbo2+BsKUnqIhGx7jDbXwVsAz49b9dfAo+OiF9sG1sEXg3sBj43byw8fC2succfb9v2N2Tt/M5vHW/u2L8IPLp9bGvW163AiyNiS9vYLcCLgX9IKf2o03lJUt4igpHGCM1KM+9QtIpFRCEiLoqIOyNiPCLuiYjLIqIv79gkLcyHP/zhQ+61NFxzSpKkXlJtQt/GrEAlSeo2vxYRrwD+lqyNXwk4E/gl4Ns8fB2rPyArAH0iIt5NNpPqpcBTgHNSSvvmBqaUPhsRnwFeGxGDwFfIZju9AviLlNL/ahu7IyJ+F7gU+EJE/CVZi77fBu4E3jsvjguAfwRuiojLW9teTXYx5W8f81dDklbIhvoGClFg78TevEPR6vQe4DXAp4DLgMe1Hj8pIp6dUprNMzhJR7Z3715uueUWAG655Rb27t3LwMBAzlF1B4tTkiT1gkIhK0pV+/OORJK0fL4G/AzwK8BGIIDvAn8I/EFKaXf74JTSroh4BlmR6lVAE/g34CUppas7HP/FwFuAlwH/hayYdXHr+YdIKV0WEbuAi4D3A3uBTwJvaGvpNzf25og4E3hH65aAm4EXp5T+dfFfBklaeetq6yhQYPfE7qMPVs+IiFPILri4NqX0orbt3yXLjy8BPrGUr7nvxht54EP/nakf/IDytm2se8Vv0H/GGUv5EqvutT3n7n/dlXrtBx98kO9///uHbPvclVdSnk1MAqWZGa544xs567zzDhlz0kknMTw8vKSxQPd/nyOltOQHXWtOPfXUdOuttx59oCStAhHxzymlU/OOQ2vHqU9+Yrr11n92tpSkNcNcp8Xw/Zyk1WTv5F4eGHvgsPv7yn2M9I2Y53pERLwDeDNwRkrpprbtNWAX8KWU0llHOsZP/eQT0lc+97kjDXnIga/+EzvffzlRLhPVKmligjQ1xYbXvJq+0556HGeyel/bc+7+113J1/7DP/wD/vRPP0i1WqVSKZOmp0mTkxyYOTjBsa9YICoVolRicnKKiYkJfvM3X8nrX/+GJYsD1vb3uXritgXlOYtT+GZG0triB3ZaLPOcpLXGXKfFMM9JWm32T+5n59jOjvssTvWWiPg74NlAI6U0MW/fl4HHpJQ2HukYP1Grp2tOPnn5gpSkJfb4f79zQXnOtn5SBzfcuZ0P3vgd7nlwlBOHG7zyjEdx5mNH8g5LkrRc7roebn4f7P4eDD0Cnn4BPOY5eUclSZKkNahZaVKIAjvGduBF4T1vC7BzfmGq5V7g6RFRSSlNtu+IiPOA8wBOqdaWP0pJyoHFKWmeG+7czsXX3UG5GAzVy2zfN87F193BJWCBSpK60V3Xw+dfB4UK1IZh3/3ZYy61QCVJkqRj0ig3GIkRdozuYDbNHv0J6lYNoFNhCmC8bcwhxamU0pXAlZC19Xv0Atv63fu61zGzaxdRO1jQSuPjFNevZ+ully4y9MXJ67U95+5/3bxee//+ffz86adz34HRh+3b0tfg7265hWZz+db1XtPf5xO3LWiYxSlpng/e+B3KxaBRyX48GpUSo5PTfPDG71ickqRudPP7ssJUpZE9rjSyt4Y3v8/ilCRJko5ZvVRnU98mth/YzkyayTsc5WMUONyHSbW2MYcVlTKVbVsX9GIb/utvcf8lb4eZGaJWI42PQ0ps+K+/teBjHKu8Xttz7v7Xzeu1G+Pj/J+xMcanpx6274Hxcfoe+Ugq1eqyvDb0xve5sKRHk7rAPQ+OUi8XD9lWLxf5wYNH/FtBkrRW7f4elOuHbivXYff384lHkiRJXaNarLKpbxPFQvHog9WN7gM2RESnT7C3krX8m+yw75j0n3EGmy7+XUobNzK7Zw+ljRvZdPHv0n/GGUv1EqvutT3n7n/dvF77+uuvp9KaPVQvFilHUCtmv8sr1SrXX3/9sr029Mb32ZlT0jwnDjfYvm/8oZlTAGNTM2wbbuQYlSRp2Qw9ImvlV2n7PT81BkMn5ReTJEmSukalWGFz32buP3B/3qFo5X0N+DngNOCmuY0RUQOeCNy41C/Yf8YZK1IsWE2v7Tl3/+vm8dof+9jH2Lt3L41Gg1e84hW8853v5I1vfCMf+tCH2Lt3Lx/72Mf4hV/4hWWNodu/z86ckuZ55RmPYmomMTo5TUrZ/dRM4pVnPCrv0CRJy+HpF8DsJEyOQkrZ/exktl2SJElaAuVCmc19mykXynmHopV1NZCAC+dtP5dsramPr3hEko5qcnKSz372swwMDHDttdfy/ve/n2azyeWXX861117LwMAAn/3sZ5maenjLPy2cxSlpnjMfO8IlLziFkf4ae8amGOmvcckLTnG9KUnqVo95DjzvUujfBOO7s/vnXep6U5IkSVpSpUKJodpQ3mFoBaWUbgeuAH45Iq6NiHMi4jLg3cCXgE/kGqCkjmZmZnj5y1/OXXfdxc///M8fsu+5z30ud911F7/2a7/G9PR0ThF2B9v6SR2c+dgRi1GS1Ese8xyLUZIkSZKWw4XA3cB5wPOBncDlwMUppdkc45J0GPV6nQ984AOH3b9p06Yj7tfCWJySJEmSJEmSpGWQUpoBLmvdJEkttvWTJEmSJEmSJEnSirE4JUnSMoqIQkRcFBF3RsR4RNwTEZdFRN9KPF+SJEmSJElabSxOSZK0vN5DttjtvwGvBq4BXgN8OiIWkoeP9/mSJEmSJEnSquKaU5IkLZOIOIWsoHRtSulFbdu/C7wfeAnwieV6viRJkiRJkrQaecW1JEnL56VAAO+dt/0qYBR42TI/X5IkSZIkSVp1LE5JkrR8ngLMAl9t35hSGgdua+1fzudLkiRJkiRJq47FKUmSls8WYGdKaaLDvnuBDRFRWcbnS5IkSZIkSauOxSlJkpZPA+hUWAIYbxuz5M+PiPMi4taIuHXHjh1HDVSSJEmSJElaKRanJElaPqNA9TD7am1jlvz5KaUrU0qnppRO3bhx41EDlSRJkiRJklaKxSlJkpbPfWSt9zoVmLaSteybXMbnS5IkSZIkSauOxSlJkpbP18hy7WntGyOiBjwRuHWZny9JkiRJkiStOhanJElaPlcDCbhw3vZzydaK+vjchoh4dEQ89lifL0mSJEmSJK0VkVLKO4bcRcQO4HvH8NQNwM4lDme167Vz7rXzBc95LXhESslFhNaIiLgcOB/4FPA54HHAa4AvAz+TUpptjbub7Hsbx/L8o8Rgnls4z7n79dr5wto8Z3OdFsw8tyiec2/otXNei+drntOCmecWxXPuDZ7z6regPGdx6jhExK0ppVPzjmMl9do599r5gucsLbWIKJLNfDoPOJnsj4mrgYtTSvvbxt1N5+LUgp6/TLH33M+G59z9eu18oTfPWVqIXvzZ8Jx7Q6+dc6+dr7RQvfiz4Tn3Bs+5e5TyDkCSpG6WUpoBLmvdjjTu5ON5viRJkiRJkrRWuOaUJEmSJEmSJEmSVozFqeNzZd4B5KDXzrnXzhc8Z0kH9eLPhufc/XrtfKE3z1laiF782fCce0OvnXOvna+0UL34s+E59wbPuUu45pQkSZIkSZIkSZJWjDOnJEmSJEmSJEmStGIsTkmSJEmSJEmSJGnFWJxahIgoRMRFEXFnRIxHxD0RcVlE9OUd23KIiMdExCURcUtE7IiIfRFxW0S8uVvPuZOIaETEdyMiRcSf5B3PcomIdRFxaUR8q/X/e0dE/GNEPDPv2JZDRDQj4k0RcXvr//bOiLg5Is6OiMg7Pikv5rrey3XmOfOc1EvMc+a5vONZLuY585wE5jnznHmuW/RKnivlHcAa8x7gNcCngMuAx7UePykinp1Sms0zuGXwG8CrgOuAjwNTwLOAdwD/KSJOTymN5RjfSrkE2JB3EMspIh4B3AA0gQ8BdwGDwE8CW/OLbHlERAH4PPB04CPA5UADeCnw52Q/26/PLUApX+a63st15rkuY56Tjsg8Z57rOuY585zUxjxnnus65rnuzXORUso7hjUhIk4Bbgc+lVJ6Udv2VwPvB/5zSukTecW3HCLiVOCbKaU987a/A3gz8OqUUtdW5AEi4snAV4HfIUvqV6SUzs83qqUXETcBJwOnpZR+mHM4yy4ingbcDLw3pXRR2/YKcCewLqU0lFd8Ul7MdYds74lcZ57rTuY5qTPz3CHbzXNdxDz30HbznHqaee6Q7ea5LmKee2h71+U52/ot3EuBAN47b/tVwCjwshWPaJmllG6d/8u95erW/U+sZDwrLSKKZN/fvwWuzTmcZRMRZwD/L/BHKaUfRkQ5Ihp5x7XMBlr397VvTClNAjuBAysekbQ6mOsO6vpcZ57rauY5qTPz3EHmuS5hnjvIPCeZ59qY57qEee6gbsxzFqcW7inALFk1+iEppXHgttb+XrGtdX9/rlEsv4uAxwJdd8XBPGe17r8fEZ8GxoADEXFXRHTdHy4tXwV2A78TES+OiJMi4scj4l3ATwFvyzU6KT/muoN6IdeZ58xzUq8xzx1knuse5jnznDTHPHeQea57mOe6OM9ZnFq4LcDOlNJEh333AhtaU+u6WqsqfzEwDXTVVOB2EfFI4PeAS1JKd+ccznL78db9VcA64OXAK4BJ4GMR8et5BbZcUkoPAi8AHgA+CXyPbFrsq4AXpZSuyjE8KU/mOnoj15nnzHM5hiflyTyHea4LmefMc9Ic8xzmuS5knuviPFfKO4A1pAF0+uUOMN42ZnJlwsnNe4HTgTellP4972CW0f8PfBd4d96BrID+1v0+4FmtKaJExKeA7wDvjIiPdOGimfuB/0O2aObNZAnuVcAnIuKFKaXr8wxOyom5LtMLuc48Z54zz6kXmecy5rnuYp4zz0lzzHMZ81x3Mc91cZ5z5tTCjQLVw+yrtY3pWhHxdrKpolemlN6VdzzLpTUl9OeA30wpTeUdzwoYa93/5dwveHioSn8dsJmDVyl0hYj4D2S/2K9PKf23lNKnUkofIuth+yPgqtaVNlKvMdf1QK4zz2XMc+Y59STznHmuG5nnzHPSHPOcea4bmee6OM9ZnFq4+8imv3b6Jb+VbNps1155EBFvA94C/Dnwm/lGs3xa3993A58DfhQRPxYRPwY8ojVksLVtKLcgl94PWvc/6rDvh6374RWKZaVcRPaH2TXtG1NKo8Bnyb7fJ698WFLuzHVdnuvMcw9jnpN6i3nOPGee6w7mOakz85x5zjzXHXomz1mcWrivkX29TmvfGBE14InArXkEtRIi4q3AW4GPAueklFLOIS2nOrAReD7wzbbbDa39L2s9PieP4JbJ3EKZ2zrsm9u2fYViWSlbW/edrjIozbuXeom5rvtznXnuUOY5qbeY58xz5rnuYJ6TOjPPmefMc92hZ/KcxamFuxpIwIXztp9L1q/14yse0QqIiIuBtwEfA369C/t3zncAeHGH239t7f/b1uPrcoluefw1Wd/Wl0VEc25jRJwA/BLwzZTSt/IKbpn8W+v+7PaNrStLXgg8CHx7hWOSVgNzXffnOvNci3nOPKeeZJ4zz5nnuoN5TurMPGeeM891h57Jc9G9heSlFxGXk/Ut/RTZ9MnHAa8Bvgz8TLf98ouIVwF/Anwf+F1g/vnd3y2Lrx1NRJxMttDgFSml8/ONZulFxHnAB4E7gP8OVIDfAk4AfiGl9Pc5hrfkIuIRwNfJpv1+nOxneB3ZH2wnA69KKX0gtwClHJnrejPXmefMc1KvMM+Z50KlLrsAAAacSURBVMxza595Tjo885x5zjy39vVSnuuK6V8r6ELgbuA8sumTO4HLgYu77Zd7y1Na9ycBH+mw/0tA1/+C7wUppSsjYifwO8DbyZL5V4BfTSl9OdfglkFK6XsRcRpwMfCzwEvIFli8DfjtlNK1ecYn5cxcdyhzXRcwz5nnpDbmuUOZ57qAec48J7Uxzx3KPNcFzHPdm+ecOSVJkiRJkiRJkqQV45pTkiRJkiRJkiRJWjEWpyRJkiRJkiRJkrRiLE5JkiRJkiRJkiRpxVickiRJkiRJkiRJ0oqxOCVJkiRJkiRJkqQVY3FKkiRJkiRJkiRJK8bilCRJkiRJkiRJklaMxSlplYiIMyMiRcTZecciSdJSM89JkrqZeU6S1O3MdVpqFqfUNdp+Qb6u9XgoIt4WEWfmHNpDIuKJrZhOzjsWSfq/7d1f6JblHcfx9wcrKBHMxcJOctEO1kHU8mR0EhSldSC2sQRrtaC1jf2pERn9wTY2rUF1FMHGSCcMwrBQBgVCIYMtZOBg5tjm7B+yrSQpcw3Rbwf3LTzePIq/n/WY1/N+wZff777u67nu61F+9+fgeu7r0ZnFnJMktcyckyS1zqyTjnXW6Z6A9BmaD6zuf3/1NM5j1BV0c3oVeGNwbhtwLnBoslOSJJ2hzDlJUsvMOUlS68w6TTWfnJJmKcm8T3O8qjpSVR9X1eFPc1xJkmbDnJMktcyckyS1zqzT552LU2pS/zjsnv5wdf/IbCV5Y9DvliR/SPJhkoNJXkvyjTHjVZJ1Sa7t+x8AtvTnLkryRJIdSd5P8nGS15OsSjJnZIxHgWf7w1dG5rTu6JzH7duaZG6StUl2J/l/kn8n+W2Si4fv+ejrk3w7yc6+/5tJ7p/9v6Yk6fPGnDPnJKll5pw5J0mtM+vMOrmtn9q1C7gXeAp4AdjUtx842iHJz4GHgJeAR4AjwHJgY5IfVNXTgzEXA18Hfg2sH2m/HLi5v85u4GxgKfAYcAlwd99vE7AQ+A6wpp8j/WvGSnIW8DJwNfA88ATwZeB7wPVJFlfVO4OXfRe4EPgNsB+4FXg8yTtV9bvjXUuSdEYx58w5SWqZOWfOSVLrzDqzTlVlWU0UcA1QwH398aL++NExfb/an1sz5tyLwAfAvJG26uu6Mf3PBTKmfQNwGFg40nZHP841J5j/HSNtd/Vtvxz0valv3zDm9XuB+SPt5wHvAn883f9HlmVZ1uzLnDPnLMuyWi5zzpyzLMtqvcw6s846ttzWT9NqJd3NcH2SC0YL2AzMA742eM1fqmrrcKCq+l/1d9Ik5yRZ0I/zMt3WmYtPYZ7L6T4VsXZwzd8DO4BlSYZ/x89W1f6RvgeBP9F9akGSNB3MOUlSy8w5SVLrzDo1z239NK2+AgT42wn6XDg4/vu4Tv3jqw8A3wIu7ccddf4s5wjwJWBvVb0/5txO4ArgAuC/I+3/GtN3H/CFU5iHJOnMYs5JklpmzkmSWmfWqXkuTmlahe7TB0vpHl8dZ+fg+OBx+j0J/BB4DvgF3c32EN3jt4/DKT2hOAyLk3G89yNJmh7mnCSpZeacJKl1Zp2a5+KUWlYnOPcPYAnwVlXtOkG/k3EbsK2qVow2Jrl0hnMaZzewJMn80cdde5fR7S/73gzHlCS1wZyTJLXMnJMktc6s01TzO6fUsgP9zwVjzm3of65JMmd4MskXZ3Cdwww+JZBkLnDvDOc0zot0f6cPDMZfClwJbK6qIzOYqySpHeacJKll5pwkqXVmnaaaT06pWVW1L8k/gRVJdgP/AT6qqi1VtT3JauCnwI4kG4G9wELgKuBG4JyTvNTzwN1JngO20u33eifdXqlD2+m+JPChJOcDHwF7quq144y9DrgdWJVkEbCNbm/Y7/fv58GTnKMkqTHmnCSpZeacJKl1Zp2mnYtTat1K4ClgDXAe8CawBaCqfpbkz8CPgHuAuXR7rv4V+PEMrvET4EPgm8Ay4G3gV3Q3862jHavqrSR3AquAZ4CzgfXA2Bt8VR1KcgPwMHALcDOwH9gIPFxVb89gnpKk9phzkqSWmXOSpNaZdZpaqZrpNpKSJEmSJEmSJEnS7PidU5IkSZIkSZIkSZoYF6ckSZIkSZIkSZI0MS5OSZIkSZIkSZIkaWJcnJIkSZIkSZIkSdLEuDglSZIkSZIkSZKkiXFxSpIkSZIkSZIkSRPj4pQkSZIkSZIkSZImxsUpSZIkSZIkSZIkTYyLU5IkSZIkSZIkSZoYF6ckSZIkSZIkSZI0MZ8AXBFT4O30PSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize = (24, 6))\n",
    "i = 0\n",
    "\n",
    "# Scatterplot of next four hyperparameters\n",
    "for i, hyper in enumerate(['reg_alpha', 'reg_lambda', 'subsample_for_bin', 'subsample']):\n",
    "        random_hyp[hyper] = random_hyp[hyper].astype(float)\n",
    "        sns.regplot('iteration', hyper, data = random_hyp, ax = axs[i])\n",
    "        axs[i].scatter(best_random_hyp['iteration'], best_random_hyp[hyper], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = 'Iteration', ylabel = '{}'.format(hyper), title = '{} over Search'.format(hyper));\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score versus Hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final plot, we can show the score versus the value of each hyperparameter. We need to keep in mind that the hyperparameters are not changed one at a time, so if there are relationships between the values and the score, they do not mean that particular hyperparameter is influencing the score. However, we might be able to identify values of hyperparameters that seem more promising. Mostly these plots are for my own interest, to see if there are any trends! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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295tqXHcy3yZ6CuRUosqKtxal7yauDLklnjCzpcAZRE8DXE79mvQXbvxd6e5fr3KdwtMfB02S58BJllXjtfH8X9z9m2WWT7eboEplLqRX82TLVAplPyXu0qGYujcSaTKKS4pLMzRb8Womaol1hf+vqrDOwRXSm/G4RWaNYsXcx4r4xlPh5lMncBpwEfAlM7vG3QfjrIXubhaX2czTqtjVQcD9FdKh5PdB3FXPDfGEme1LdI7WEo2LcmyctRmvk/X4LVSI0z9z93+deZFq9iTR39vBwK/LLJ/u39B07G1mSyu0nqrn70uRBUGxVrG2qFyKtZFGxdqWoDGnpCybom2rmSXZdQOkOEgU+vlcO9U+4ibAhYBU2i9r4YL6+vjlzVNtr0RhbKHXTpqrdlcQ1bqfYmYdwD8S1ZZPOpZDQfyF90yiIPsUM6tXs83pHO/dwBjwTDM7onShmb2cmXedtCye7/bUvJm9hOk3Wz2lQvob4/kt09xusYplL9qPiMwRxaWKFJfqY7bi1Uy8tkL/8/8Yz4tjXeHz+Coz6yqzzpsq7KMZj1tk2hQrKmqKWOHuaXe/mGichw4mjt9auLn4rDKrvqxMWqndfh+Y2XLgb+OXt05Rth1ET20DHFa0qBmvk7XEh0oKn7VXV/m0fL0Vyrjb31CscFO31r+h6XpDaYKZPYeo1UMG+M0clUOk6SnWVqRYq1hbqtGxtiXoxEgl/2ZmF5rZs0sXmNkSoma5y4Bh4v5lY/9J1G/nP5vZe0qDlpn9hZkdUpR0YTz/uJk9vyhfErgAeApR08f/q7H8lxEFwdPN7JNxYCg9jmPM7HW1bDTu9/XnRBfGC4gGzrvF3TeV2f5HzKxcH6QvIwoQQ8BALfufxI+Ae4AT4/dtt35Y43P/T4XXcfPfdfHLi4vXiZ9mOL8O5Xownr8j7gqvsP1VwFdnsN3jzOydxQlm9h6igWp7gCtnsO2CQtnfXbKflwIfqcP2RaQ2iktlKC7VzWzFq5k4EPhc8WfWzP6e6Id0Bri0KO+twL1En4P/iD+vhXVeSNTneznNeNwiM6FYUUYjYoVFA5o/o0z684ieOg6ZeNPyl/H8o2a2R1H+FwBnT7U/4H1mdlTReingYmAP4Ofu/oc4fZGZfcjMVpTZxivjefF4Rs14nawlPpTl7ncTdWn1XOAKM9uvNI+ZHRz/xpoNlxGNwfTy4u8C8X7fCPw9kAYumaX9l/q0ma0uKsNi4MtE3Td9x93r9f1IZD5QrC1DsVaxtlQTxNrW4O6aNO02ETX/9Hh6lOiP6bvATcBgnJ4GTi6z7klEAceJBii8mugm1QNx2qtK8p8fp+eIBv+7EngkTtsBPK8k/6p42aYpjuFwolp3B7YTBYnvET1VsTlO/940zk3h6YfC9E8V8g0QBYLfAz+Ij+v2OM2Bd9ewT4/+XCfNc2DROe4H1gNXxe/Zxjj9jpJ19iRqkutElTo/iN+rQaKnw26Plx03zc/R04s+L48B1wD/S9T/781EXTg4cELJeuunSP9SfB43xOf1rqLP0Em1nL9Knyeip3AK7/Hd8bm8LX59bjXviSZNmuo3obg02XYVl2o7X7uVnTrHq2o+F8Bn4uWfKUlfF6dfQvTD54/xe3Vr0Xv1zjLbex7QFy9/uOhc54Ev1vO4NWlq1gnFism2O6exgqjC3InGHbkufh/Wx+fLgS+U5F9B9ES3E91I+2F8DcoB55S7hsXrFY7n4qL34qr4mubxNg8uyr9XnJ4n+g1xdXx+7yl6P/++KP98iA+b4mWrStL3itd1os/+r+Nz8TPgT3H6thn8Pa6Nt7GuwvKTibqY8vi9+C5RrC+8P2+u9limWb7C+/AY8N/xe/o/8Xu8I172B2Dvao6riuMt+75q0tRqE4q1k21XsdYVa0vSZzXWzoep4QXQ1JwTsJyo67JvxBePbfHFYyi++F0IrJ5k/acT1SJvjP+Y++LtnAM8pUz+k4kC2UCcfyPRk0ory+Sd9AJTkndv4FPAb+Oyp+ML3c1EfblWPIZJttlVdNEcBZZUyPdm4FtEQbafqKuih+OLUE031aiyIiQu2weAX8X7zBIF1tuJnoI4rMw6exE9wfJ4fO43Af9B9OTDw/G+nzmDz9LTge/H5RgneiLiM0RPg6ynhgBTnE40oOItRE/jDBMFx7LndbLzN9nnCXhpvI/+eB93AKfW8p5o0qSpPhOKS5NtU3GpDmWnjvGqms8FU/8gWgscTdQdxED8fv2aMg9hFK27muiHYk98DPcD7yV68rtux61JU7NOKFZMts05jRVENyAvjc97T3wMm4AfA6+osM7q+HrUF1+P7iG+sTfJNaxww8yA98flTgPd8XEcUJK/jag16dVEN4WGiFrwPAh8HfiLCp+Llo0PTFKhE5+PtUQ3ZnuI/l62ET0EeEEt73mZba9lksqaOM8R8Wer8Le6nehG7VG1Hss0yrfzfQDa4/P+MNHf8maiByKXVXtcUx1vpfdVk6ZWm1CsnWybirWuWFtm2azF2vkwWXySREQmMLODiJ5IGSV6WixscJEws/XAXwF/7e7rG1saERGZS80Yl0REROaKma0jGovpn9x9XWNLIyIiMv8o1s49jTklssCZ2Zoyff2uBL4NJIErdANQRETmiuKSiIiIiIiIyPzX1ugCiEjD3QBkzez3QC9wAPB8YBFR36r/0sCyiYjIwqO4JCIiIiIiIjLPqXJKRL4AvBI4DFhGNB7IQ0SDKV7k7kOFjHHz1mp93t0frGM5RURkYVBcEhERaSJm9kng0Cqz/8jdfzSb5SllZiuA82tY5aPu3jNb5REREZHqaMwpEamamdVywdC4UCIiMqsUl0RERGZf0di/1fisu39m9kqzOzNbBTxawyoHu/umWSmMiIiIVE2VUyIiIiIiIiIiIiIiIjJn1K1fjVasWOGrVq1qdDFERObUXXfd1ePu+zS6HDIzimEishAphrU+xS8RWYgUv+YHxTARWYiqjWGqnKrRqlWr2LBhQ6OLISIyp8zssUaXQWZOMUxEFiLFsNan+CUiC5Hi1/ygGCYiC1G1MSwx2wWZjJklzOxDZvagmaXN7Akzu8DM9qhi3c+YmU8y5aZY/91FeVfU76hERERERERERERERESkkka3nLoQeD9wHXAB8Oz49ZFm9lJ3DydZ91rg4TLphwEfA35caUUz2x84FxgBFk+v6CIiIiIiIiIiIiIiIlKrhlVOmdlzgfcB17r7a4rSHwUuBk4Brqy0vrv/Dvhdme1eGv/365Ps/ivARuD3wJtrLryIiIiIiIiIiIiIiIhMSyO79XsjYMBFJemXAWNMo9LIzBYRVWptBm6okOfVwN8DpwNBrfsQERERERERERERERGR6Wtk5dRRQAjcWZzo7mng3nh5rV4PLAW+6e67VTyZ2VLgy8Cl7n5n6XIRERERERERERERERGZXY2snNof6HH3TJllm4EVZtZe4zbfDjjwjQrLzyM65jNq3K6IiIiIiIiIiIiIiIjUQSMrpxYB5SqmANJFeapiZs8CXgT8wt0fLbP8OKKu/D7s7oO1FNTMTjOzDWa2obu7u5ZVRUREREREREREREREpEgjK6fGgI4KyzqL8lTr7fH88tIFcQusy4Cfu/tVNWwTAHf/mruvcfc1++yzT62ri4iIiIiIiIiIiIiISKytgfveAjzHzDrKdO23kqjLv2w1GzKzNuBUoA+4rkyW9wCHAh8xs6cXpS+J5web2VJ331jTEYiIiIiIiIiIiIiIiEhNGlk59VvgZcDRwK2FRDPrBI4AbqlhW68E9gO+WGEMq4OIWon9b4X17wRGgcU17FNERERERERERERERERq1MjKqauBM4EPUlQ5BbyDaKyp7xYSzGw1kHL3Bytsq9Cl39crLP8m8Ksy6e8BTgDeBvRXW3ARERERERERERERERGZnoZVTrn7/Wb2FeC9ZnYt8FPg2cD7gZuBK4uy30TU+slKt2Nm+wMnAne6+/0V9nUfcF+ZdU+K//tjd++ZweGIiIiIiIiIiIiIiIhIFRrZcgqiVlObgNOAvwN6gC8BZ7l7WOU21gJJ4PJZKJ+IyIKXzgWNLoKIiIiIiIiIiIg0uTCdrjpvYhbLMSV3D9z9And/lrt3uPtKd/+wu4+U5Fvl7ru1moqXnePu5u6XTWP/a+N11WpKRKSMgbEsO4bKDeUnIiIiIiIiUh0zO8PMvm9mG83MzWzTJHnXxXnKTa8tk7/DzD5nZo+aWcbMHjGzT5lZqsL2TzWze8xs3My2m9nlZrZPhbzHmNnPzWzYzIbM7AYzO2LaJ0JEZJ7yMCTf3U3Q21v1Oo1uOSUiIk0oDJ3ukQyjmTxtiYY+xyAiIiIiIiKt7xygD7gb2KvKdd5SJu3OMmlXA/8AfAO4HTgWOBt4OlGPSzuZ2YeA/yQaUuQDwAHAh4Fjzexodx8tyvuXwHpgM3BWnPxe4FYzO67S8CIiIgtNmMmQ7+7GczkS7e1Vr6fKKRERmSCbD9k+lCYXVNu7qoiIiIiIiMikVrv7RgAz+z2weKoV3P2KqfKY2f8jqpj6T3f/SJx8uZkNAB82s6+5+21x3hXAvwG/BV7i7kGc/lvgeqLKqnOKNn8xkAWOd/fNcd5rgD8CFwAvm/KoRUTmuWBggGBgAHeveV09Di8iIjuNZfNsGRhXxZSIiIiIiIjUTaFiqhYWWWpmk92//Md4flFJeuH1m4vSXgUsAr5UqJiKy/ZjYGNxXjN7OnAU8P1CxVScdzPwfeClZvaUGg9JRGTe8Hye3LZt5Pv7p1UxBaqcEhGR2MBYlm2DacJpBhQRERERERGROhqMp3Ezu9HMjimT5yhgs7s/UZwYv94SLy/OC1HXf6XuAA41s8VV5jXgBVUdhYjIPBOOjZHbsoVwfHxG21G3fiIiC1zx+FIiIiIiIiIiDbYNuBC4CxgFDgc+SDTW0/9z958X5d0f+EOF7WwmGlOqOG8hvVxei/M8VEVegJWTH4aIyPzi7gT9/QSDg3XZniqnREQWMI0vJSIiIiIiIs3E3T9ZkvQjM7sSuBf4KvCMomWLgEyFTaXj5cV5qZA/XZKnlrwTmNlpwGkABx54YIWiiYi0Fs9myXd3E2azddumuvUTEVmghtM5jS8lIiIiIiIiTc/d/wxcAzzdzJ5ZtGgM6KiwWme8vDgvFfJ3luSpJW9pWb/m7mvcfc0+++xToWgiIq0jGB4mt3VrXSumQJVTIiILThA624fSdA9nNL6UiIiIiIiItIpN8XxFUdoWKnevt5KJ3fJtKUovl9eL8kyVF8p3+SciMm94GJLbsYN8Tw8e1v/hdlVOiYgsIOPZgM394xpfSkRERERERFpNoTu/7UVpvwVWmtnTijPGr/cHNpTkBTi2zLaPAf7k7iNV5P1Looqsu6ovuohIawkzGXJbthCOjs7aPlQ5JSKyQPSNZtk6OE5+Fp50EBEREREREZkpM9vDzDrLpB8JvA74o7s/UrToqnj+wZJVCq+/W5T238A48F4zSxZt+5XA6uK87v4wUcXW68xs/6K8+8fl+IW7b6vx8EREWkIwMEB+61Y8l5vV/bTN6tZFRKTh8kHIjuEM6VzQ6KKIiIiIiIjIAmRmbwEOil/uA7Sb2afi14+5+3fi/z8D+F8z+xHwZ2AUOBx4GxAApxVv191/Ymb/A3zYzPYEbidq7fR24Ap3/1VR3m4z+1fgfODnZnYVURd9HwEeBC4qKfYHgF8Ct5rZl+K09xE97P+RaZ8MEZEm5fk8+Z4ewvHxOdmfKqdEROax0UyenpEMQaixpURERERERKRh3g78VUna2fH8ZqBQObUN+Dnw18CbgC5gK3A1cK67P1hm268DPgW8GXgL0VhQZwGfL83o7heYWS/wIeBiYAi4BvhkUZd+hby3mdkJwL/FkwO3Aa9z9/uqPXARkVYQjo1FY0sFc/dwuyqnRETmIXenZyTLcHp2m9+KiIiIiIiITMXdT6gy3zaiCqZatp0mqpz61FR54/zrgHVV5r0deEkt5RERaSXuTtDXRzA0NOf7VuWUiMg8kwtCtg+lyeY1tpSIiIiIiIiIiIjszrNZ8t3dhNlsQ/avyikRkXlkPBuwYzitbvxERERERERERESkrGB4mKC3F/fG3UPnCZkxAAAgAElEQVRU5ZSIyDwxMJalb7QxTzqIiIiIiIiIiIhIc/MwJN/TQzg62uiiqHJKRKTVhaHTM5JhJJNvdFFERERERERERESkCYWZDPnubjzXHGPUq3JKRKSFZfPR+FK5QONLiYiIiIiIiIiIyO6CgQGCgYGGduNXSpVTIiItaiido28kS9hEQUVERERERERERESag+fzUTd+4+ONLspuVDklItJisvmQnpEM6VzQ6KKIiIiIiIiIiIhIEwpHR8n39uJBc95DVOWUiEiLcHcGx3P0j+WaqgmutBBX948iIiIiIiIiIvOZuxP09REMDTW6KJNS5ZSISAtI5wK6hzMaW0pmJshB/2PQtRd0LAWzRpdIRERERERERETqxLNZ8t3dhNlso4syJVVOiYg0MXendzTL0Hiu0UWR+SLIwUg3jPVCx57QuSck9XVARERERERERKSVBcPDBL29LdPjUqLRBRARkfLSuYAn+8dVMdWkzCxhZh8yswfNLG1mT5jZBWa2RxXrfsbMfJIpV5L/I2a23sy2mlkmnv/SzF497QMIQxjvh/5NMLwN8plpb0pERFpLy8cwERERERHZyYOA3PYd5Ht6WqZiCtRySkSk6bg7faNZBlUp1ewuBN4PXAdcADw7fn2kmb3UfdIBnq4FHi6TfhjwMeDHJelHA5uAnwI9wDLgdcC1ZnaWu589g+OAzEg0tS+CrmWQ6pzR5kREpOnNnxgmIiIiIrKAhek0+e5uPJ9vdFFqpsopEZEmks2H7BhOk81rbKlmZmbPBd4HXOvurylKfxS4GDgFuLLS+u7+O+B3ZbZ7afzfr5fkf0OZvBcBdwEfN7Nz3D2YxqFMlB2LpvY9YNEyaOuY8SZFRKS5zNsYJiIiIiKywOT7+wkGBhpdjGlTt34iIk1iKJ1j88C4KqZawxsBAy4qSb8MGAPeXOsGzWwR0Q3BzcANU+V393ycdw8gVev+JpUdhYEnou7+cuN13bSIiDTc/I5hIiIiIiLznOfz5LZubemKKVDLKRGRhnN3ukcyjKRbr/ntAnYUEAJ3Fie6e9rM7o2X1+r1wFLg4kpPkJvZMiAJrCDqEulE4Jfunp7G/qZW6O4vmYKOJdGU1D1EEZEWtzBimIiIiIjIPBSOjpLv7cWD1u98QJVTIiINlA9Ctg9nyORaP6AsMPsDPe6eKbNsM3CcmbW7e7aGbb4dcOAbk+R5CFge/z8P/BB4dw37mJ4gB2N90dTWDqk9oGOxuv0TEWlNCyuGiYiIiIjMA+5O0NdHMDTU6KLUjSqnREQaJJ0L2D6UJgi90UWR2i0Cyt3UA0gX5anqxp6ZPQt4EXCTuz86SdaTgU5gJdFT511ET6p3T7Lt04DTAA48YP9qijO5fDaaxvujVlTti6OKqmQ7mM18+yIiMttaIoZNiF8HHlhNUURERERE5qUwmyXo7ibM1vL8WPNT5ZSISAOMZPJ0D2dwV8VUixoD9q2wrLMoT7XeHs8vnyyTu99S9PKbZnYV8Csze46791dY52vA1wDWHPG8+n7gglxUSTXeH1VMtXVAWyekuiC1SJVVIiLNqSVi2IT4tWaNvjCJiIiIyIIUDA0R9PXNy3uIiUbu3MwSZvYhM3vQzNJm9oSZXWBme1Sx7mfMzCeZciX5P2Jm681sq5ll4vkvzezVs3eEIiK76x/NsmMoPS+DygKyBVhhZuX6tVtJ1F1StU+ctwGnAn3AdTWW41vAU4ieRm8sd8ilYXwAhrZC30YY2hK9zs+vJ3tERFqcYpiIiIiISJPzICC3fUc0vtQ8vYfY6JZTFwLvJ/ohcwHw7Pj1kWb2UncPJ1n3WuDhMumHAR8DflySfjSwCfgp0AMsI+pO4lozO8vdz57BcYiITMnd6R7OMJLJN7ooMnO/BV5GFFtuLSSaWSdwBHBLhfXKeSWwH/DFCuN/TKYrni+rcb3Z5w7ZsWgCSLZBx1Lo3BMSycaWTURkYVMMExERERFpYmE6Tb67G8/P73uIDaucMrPnAu8DrnX31xSlPwpcDJwCXFlpfXf/HfC7Mtu9NP7v10vyv6FM3ouAu4CPm9k57h5M41BERKaUD0K2D2fI5HSZmSeuBs4EPkjRjT3gHUTjdHy3kGBmq4GUuz9YYVuF7pC+Xm5h3JrY3H2kJD0JvCd+eUetBzDngjyM9UVdALYvho4lUReAiYY24pYywjAkofdFZD5TDJOddM0XERERaS75/n6CgYFGF2NONLLl1BsBAy4qSb8M+DzwZiapnCrHzBYRVWptBm6YKr+7581sM/A8IAXorrGI1F0mH7B9MEM+nKwxqLQSd7/fzL4CvNfMriVqlVto/XszE+PXTcBBRDFvAjPbHzgRuNPd76+wu2cAN5vZD4A/EXWdtJIojj4L+Ja731ph3ebjDpnhaAJIpqJKqsJ4VW0dGquqgXp7eznssMO4//77WbZMjRlE5iPFMCnQNV9ERESkeXg+T767mzCdbnRR5kwjK6eOAkLgzuJEd0+b2b3x8lq9HlgKXFypFZSZLQOSwAqibv1OBH7p7gvnXReROTOaydM9nCGcp33DLnAfJOou9jTg74i6jP0ScNYU3dIWW0sUkyYbRP5J4ArgRcCrgSXAIHAPcDY1PsjRdIJcNBUqq8wg1RVPe0Bbe2PLt8Bcf/31bNmyheuvv561a9c2ujgiMnsUw0TXfBEREZEmEY6ORmNLBQur7UwjK6f2Jxpst1zf5JuB48ysvdrBeGNvBxz4xiR5HgKWx//PAz8E3l3DPkREqjI4lqN3tNbhF6RVxA9BXBBPk+VbNcmyc4Bzpli/h11dH81/E8aq6o3GqmorVFYtil7LrFm3bt3OuW5UisxfimECuuaLiIiINJq7E/T2EgwPN7ooDdHIOzyLgEp3bdNFeaqqnDKzZxE9kXeTuz86SdaTgU6i7iReRzQQ71Kge5Jtn0b0VCEHHnhgNcURkQUsDJ2e0Qwj6fk9aKHInAjyEJR0A1hoWZXsUMuqOhoaGuKOO6KhX+644w6GhoZYunRpg0slIiKzQdd8ERERkcYKs1nyO7rxXC1tc+aXRlZOjQH7VljWWZSnWoXBeCfrVgJ3v6Xo5TfN7CrgV2b2HHfvr7DO14CvAaxZs0Z9c4lIRaOZPL0jWY0vJTJbCt0Apoei12bxWFWdUUVVW2dUgSWT6u/v5/HHH5+QdtP3/ouOREAWaLc8l5/7MV5yysTG5QceeCB77733HJZURERmqtw1/7IfXoYno5+2QSLgzPPP5B2veceEPLrmi4iIiMyOYGiIoK8PX+DDgDSycmoL8Bwz6yjTtd9Koi7/qm011QacSjTA7nU1luNbwClELaq+XuO6IiIAZPMh/WNZRjNqLSUyp9whNx5NBYlE1BVgWwe07xHNZYLPf/7zfOELX6Czs5P29nYI85BPM5yOKtaH0wGfvehyPvvlKyDRRjabJZ1O8/GPf5zzzjuvwaUXEZFalF7zgzAgE2TIj0ffW/PjeS49/1LWXbiOZCKpa76IiIjILPEgIN/TSzg22uiiNIVGVk79FngZcDRwayHRzDqBI4BbKqxXziuB/YAvVhjDajJd8XxZjeuJiJALokqpRnfhF4YhiUSioWUQaRphCNnRaBrri8apal8cjVmV6opaWy1w5557LnvttRdnn302Q0NDZfMMpUMKjdi7uro455xz+MQnPjGHpRQRkXqo5pqfH8+TJ/o+q2u+iIiISP2F6TT57m48rwfbCxp5J/NqwIEPlqS/g2isqe8WEsxstZkdOsm2Cl36lW35ZGZ7mNniMulJdg3Qe0eV5RYRwd3pH83yZP94wyum+vt6edERhzLQ39fQcog0rSAP4wMwtAX6NsLgZhjvjyqxFqhEIsEZZ5zB+vXrecpTnkJnW/kKu44246lPfSo333wzZ5xxhirBRaQhAg94YugJesZ7GM+PL/juT2pVes1PpMpfyy2la76IiIjIbMj395PbulUVUyUa1nLK3e83s68A7zWza4GfAs8G3g/cDFxZlP0m4CBgtzsnZrY/cCJwp7vfX2F3zwBuNrMfAH8i6v5vJfBG4FnAt9z91grriohMkMkHdA9nyOab48b2Tf/3U7Zv28rP/++nvPaUNze6OCLNrbgbwPF+6NwrmhboDbijjz6aP/3pTxyx+ik82jO+2/L99+rk3gcfZOnSpQ0onYjILoEHjGRHGMmOkLQkXakuFrUtoqutC1OL2KoUrvkrn7mSke0juy3fY9kePKhrvoiIiEjdeC5HvqeHMJ1udFGaUiO79YOo1dQm4DTg74Ae4EvAWe5e7V3ftUASuHySPE8CVwAvAl4NLAEGgXuAs5lYESYiUtHgWI6+sWxTPbH7g+9FDU1/+L3vqnJKpBZhGHX7N94fjUuVbIdkKponUtH/F8ANz/b2drYOB2WXbRsO6OjQmF0i0lyKK6oSlqCzrZNFbYvoaOugzdpUWTWJ9vZ2sgPlh3bODeZ0zRcRERGpk2BklKC3B1/AvbZMpaGVU+4eABfE02T5Vk2y7BzgnCnW72FX930iIjULQ6dnJMNIprma3w4PD3HfXb8F4N4NdzI8PMSSJXraVaQm7pBLR1MxM0i07aqwSqYg2RFVZM2jG5833ngj7R2dpDNZFrUnyQchbUljLBuS6ujkxhtv5KSTTmp0MUVEygo9ZCw3xlhubGda0pK0J9vZu3Nv2pPtDSxd87nxxhvp7Ogkm8nS1tFGEAQkkgmCTEBHe4eu+SIiIiIz5O4Evb0Ew8ONLkrTW5h92IiI1CCbD9k8MN7wiqnBgX7++Pv7J0znf/GrhBY9ZxBaG+d/8au75Rkc6G9ouUValjsEOciORWNWjXTD4JPQ+wj0PwajvbtXaLWg73znOwwNDbFo0SLefvq76R0Y4m2nvZuuri6Ghob4zne+0+giiojUJPCA8fw4W0a20DPeQxCWbx26EBVf89912rsY6h/iXe94l675IiIiInUQZrPkNm9RxVSVrJm6pmoFa9as8Q0bNjS6GCIyR9K5gO1DaYKw8dfK8z73r3ztyxfR3tFBe3s7Qehk8yFBpuhJ4Y5FtLclSCaMbDZLNpPhtPd+kE+cdfa099uWSHDQij3ucvc19TgOaZw1RzzPN/z8ukYXY/5JtkHH0mhKNrrH5Npks1n23ntv2trauOaaa3j5y1++c9kNN9zAG97wBoIgoL+/n1Qq1cCSikyfmSmGtbgjX3CkX/eL6ccvM6Mz2UlXqouuti5SiYV5PdM1X6S1KH7ND7qPKLJwBENDBH19TTUUSCMk2ttpP+CAqmJYa91BERGZQ+PZqGIqbJKg8rFPfZYle+7JVy74AiMVnsAIMmOMZ6L/d3Z18ZF/+TTvfN+H57CUIgtQkI/GrvrDj+HeK2B4C+y1Cl74QXjm3za6dJMKgoC3vvWtfPrTn2a//fabsOzEE0/koYce4rOf/Sz5fF43KkWkZbk74/lxxvPjQNTtXyqZoiPZQUeyg862ThI2/zsV0TVfZKJbn7yVdQ+sY/PIZlYuXsna567lxQe8uNHFEhGRFuNBQL6nh3BsbOrMMoFaTtVITzyILAyjmTw7hjNN+bTDfXdv4PRTT6Gnrx/P7z6gtSVTrFi+jK9952oOO/IFM96fWk7NH2o5NYs2/RpuOQ8SKWjrhHwaPA8vOxee9fJozKp5NE6VSCvRk+etb6Ytp6ZiZjsrqjqSHaSSqQXbukpkobj1yVs55zfnkEqm6Ex2kg7S5IIcZx5zZtNUUCl+zQ+6jygyv4Xj4+R7evB8c41R30hqOSUiMgPD6Rzdw5lGF6Oiw5+/hhtvv5vjjl7DWO/W3ZZ37bWCG2/fwJIlSxtQOpEF6p5vRRVTqc7odaozGo/qtotgv+dEacm2qJIq2Q7J1K7/J5KNK7eIiODupPNp0vld4wgmLEEqkdpZUZVKRC2tkrpmi8wL6x5YRyqZoqutC2DnfN0D65qmckpERJqXuxMMDBAMDDS6KC1tysopMzsD+LG7/34OyiMi0lCD4zl6R5q3YqqgPdVObriv7LL8SD/t7R1zXKLmpBgmc2ZoC3TsOTGtrROGiiqQg3w0UdLUP5GArr2jSUQkphjWWKGHZIIMmWDi98JkIkl7op32ZPvOllaqsBJpPZtHNrO0feLDfJ3JTjaPbG5QiRpH8UZEpDaey5Hv7ibMNP/9w2ZXTcfa/w7cZ2Z3mNk/m9ni2S6UiEgj9I9mW6JiCuBXN/+CjvZ2AJLtnViyjWQqqpBqb0/x65t/0cjiNRPFMJkbS/ePuvIrlk/D0qdOvW4YwmgvDD4JZbrqFJEFSzGsCQVhwHh+nMHMIDvGdvDE8BNsHdnKYGaQTJAh9LDRRRSRKqxcvJJ0MPG7WzpIs3LxygaVqKEUb0REqhSMjJLbskUVU3VS7aivG4E1wKXAVjO73MyOnb1iiYjMnXwQsm0wTf9Y69wUvu7732NkZJjOri7edOpa7vvzE7zp1LV0dnUxMjzMdd//XqOL2EwUw2T2HflWCHNRV35ONA9zUXq1cmkYfCKqqAp1c1NEAMWwlpAJMvSn+9k6spXHhx7niaEn2Da6jYH0AOl8uinHMBVZ6NY+dy25IMd4fhx3Zzw/Ti7Isfa5axtdtEZRvBERmYSHIfmeHvLdO3D9Xq+baiunPg0cBHwG2AG8DfiVmf3ezD5kZitmqXwiIrNqKJ3jyf5xxrKtM3BhNptl/Y03sHjJEr76zSv59Dn/wR6LF/Ppc8/nv775XRYvXsIvb7yBXC7X6KI2C8UwmX2rXgjHfwL2WA6ZoWh+/Cei9Fq4w3g/DGyC8YHotYgsZIphLSjwgHQ+zUBmgG2j23hiOKqs6kv3MZobJRtkVWEl0mAvPuDFnHnMmezTtQ9D2SH26dqHM485cyGPN6V4IyJSQZjNktuylWB4uNFFmXemHHOqwN03A2cDZ5vZS4B/Bl4FXACca2bXA5e7+89mpaQiMq+sf3AHl96ykSf6x3ja3os4/fhDOOHQfeds/+lcQN9olnQumLN91ksYBJz8hjfx/o+ewYp9J56zv/qbv+WmO+7l4vPPJcjnSaVSDSplc1EMkzmx6oW1V0ZVEoYw2gNjvdHYVW2dkIrnGttEZEFRDGt9oYek82nSJd2/tiXaSCVSpJIpUonUztdtiap/povIDLz4gBcv5Mqo3SjeiIjsLhgcJOjv14NFs2Ra33rd/SbgJjPbC3gL8HbgtcBrzOwJd19VvyKKyHyz/sEdnHX9A6SSxl5dKXYMpznr+gf4HMx6BVUmH9A/mmupllKlOru6+NwXLqy4fMW++066fKFTDJOW4g658Wgaj9OSbVElVftiaN8DzBpaRBGZO4ph80s+zJMP84znxyekJyxBe7KdPTv2pKutq0GlE5GFTPFGRBY6DwLyPT2EY2ONLsq8Vm23fmW5+4C7f8ndjwCOAr4G7FmXkonIvHXpLRtJJY1F7W2YRfNU0rj0lo2zts/xbMDWwXE2t1gXfjJ7FMOkZQV5yIzA8DbofxRGuiE7CmHrtQQVkelRDJvfCi2tto9uZ8fYDnKhumoWkcZQvBGRhSgcHye3ZYsqpuZA3foLcPe7gLvM7EP12qaIzE9P9I+xV9fE7ua6Ukme7K//RT+bD+keyZBpwe77Gm3bYJrbHunljo29jS7KrFMMk5YVhpAejCaAZCruBrAjmpIdkJjRs0gijeUeVbx6AGE+/n9YZvLd03DomP/3zxTD5rex3Bjj+XHaE+2kkinak+20J9ppT7aTMF3fRWTuKN6IyHzn7gQDAwQDA40uyoJR986s3T09dS4RWcietvcidgynWdS+6xI0ngs4YO9Fdd3P4HiOvlENOF2t0J0/bRvm9o293PZILxu7RxtdpDmnGCYtL8hFU6ZooNZkKq6oao/Gq7IkWGJXd4Cl10gzSKSi7gNF6s09rmTKR5/V4nmYjyuhwnrusI7bam6KYfOXu5MJMmSCzIT0tkQb7cl2Uom40ir+v4i0rtBDQg/xJo5fijciMh95Lke+u5swk5k6s9TNlHcd3F2PY4lIXZ1+/CGcdf0DjGXzdKWSjOcCcoFz+vGH1GX7Qeh0D2fUfV8VMrmAux8f4LZHerl9Yy99o9kJy/fsSnHsIcu5tEHlmynFMBF2VVjVyiyqzEqkINEWVXIVKrcShQquBBDn09hXC08YFLVkKmnRFBa3YorzFVo+SVUUw2QqhTGriiUTSbrauuhq66It0UbCEiQtqVZWInMgCANCD8l7Pvo/Ie4+ocIp9Cht5/+L0kKv58MZ1VO8EZGFLhgZJejtwev6kJxUQ4/EisicO+HQffkc0dhTT/aPccDeizj9+EM44dB9Z7ztdC5gx1CGvAJKRX2jWX4Tt47a8Fg/mfzEc3XgskUct3o5xx6ynOfsv5SOtmTLVk6JyAy4R+NbBVVW9BdXZrV1QqozmmOA7+p2jXhuiag1lyq1mlNYqFTKQz4N+Uw0FXejJyJNJwgDRrIjjGRHJqSbGQkS0dwSJCyBYSQTSYxdaaXLEkSvk4lkg45IZO65O4FHFU2FeelUSM+H+Z1pIiLSWjwMCXp7CUZGps4ss2LGlVNmtifwduB6d3945kUSkYXghEP3rUtlVLGBsSz9Yzl141fC3dnUO8btj0QVUn/cOjShk4iEwfNW7smxq5dz3Orlde9esZkphonUUXFlVm4cxqtcL5GMKqmSqXhqjyq1dCN0esqO0RSUjNVUUlHoPvH/iqMtQTFMauHuBATT7unSzEhakrZEG22JNjqTnXS2ddKW0POu0rwKLZQKrZJKK5x2zsPdK6JkF8UbEZmPwmyW/I5uPJedOrPMmnp8k1wB/AfwKKAgJSJzKgyd4UyeofEcuUA/IgryQcj9mwe5La6Q2jo4sVvwRe1Jjlq1jGNXL+eYg5exZ9eCHZ9AMUyk0cIAwvGoQqtYsg3auqCtPa68ao+7FFwAPc94cUVRsHsF04TXxRVPqlRaYBTDZM64O3nf1Y3gCNETxslEkjZr29m6qs2iyqtUMrWzMkukkkKFUHElUtl54f+lr+Pa1kLlUyE97/mdXeVJXSjeiMi8EgwOEvT3K040gSm/KZrZxVNk2ZOov5Z/NrO/BtzdP1CPwomIlBOGznguYCwbMJrJEyqYADCSznPnpj5uf6SX3zzax0hmYldc+y7p2Nk66vAD9qK9bf7f4FUME2lhQR6CYSgdj3bTr+Geb8PQZli6Ep7/Vjj4RUVjYSXj8bHadnUbWGgZZIl4DK14jKww3L1VUWlrolI7WxZNkq/c8sny7fw/u/9fFizFMJlNG7Zv4No/X8v2se3st2g/Tn7GyazZb03N2wnCIGqRNYlCpVUqmSKVSE0YC6vQ3WDCoi4HpXXtHFupZJyl4u7vAg+iz4wHOyulpPEUb0RkofAgIN/TQzg21uiiSKyax5jeS/SrebJvig68ouj/ClIiUje5ICSTD8nkgmie11NwBVsHx7ntkV5uf6SX+54cJAgnnpdn7reYF65ewbGrl7N6nz0W4o9+xTCR+WTTr+GW86IKpo49YbQHbv48+Cdg1QshyFW/rZ2VViJNSzFMZsWG7Ru45L5LSCVSLE4tpj/dzyX3XcI7D3/ntCqoplKowMoEpU8b7C5pSRKJuOKqaEysQkVW0pI7K7IKY2MVV3RNprh1zW7L8J1/bcVjcJWTD/M78zTjd+tCi6Kdx+pMeF3c4qig9HiKtxF4QBjGLZzYVenk7oQU/V9d4bUyxRsRmffC8XHy3d14MPlDNTK3qqmcegxYBnwauK7M8lXAL4F3ATfUrWQisqDlgpDRTJ6RTJ5sXj90CkJ3/rRteGd3fY/2jE5YnkoaLzhob45bvZy/PGQ5KxZ3NKikTUMxTGQ+uedbUcVUqjN6neqEXJy+6oW1bUsVU9L8FMNkVlz752tJJVJ0tEXfEzvaOiAfpc9G5VQtAg8IgoAcNTxsECtUVu3s6q2oAma6D7YVKssSJMh7niCceEOr0PKruLKs0O1cYf22RBtt1raz4sfMojHAisY7Kq48KrQkS1hiZ0VQSIgV/sXrF/6FHoKzs8WSyDQo3ojIvOXuBP39BIODjS6KlFFN5dRzgLOJ+pf9a+C97v5EYaGZFbbR7e6P1b+IIrJQBKEzkskzmsmTzulJhoJ0LuCux/q5/ZFebt/YS//YxB/re3Wl+MtDlnPs6uWsWbU3Xalkg0ralBTDROaToS1Ri6libZ0wtLUx5WlWm34dVdgNbYGl+8ORb6298k6agWKYzIrtY9tZnFo8Ia092c72se113U+9ug6s1my03ClUllXi7lG3hhXqhKZTySbSAIo3IjIveS5HvrubMDN1621pjCkrp9x9HPiomV0JXAb8wcw+C1zo7rp7LCLTkgvCaMo7mSAgFzhZdde3U99oltvj1lF3P95PpqT12EHLF3HsIdH4Uc9+6lKSiebrUqQZKIaJzDNL94fR3l0tpwDyaVj61MaVqdns1vVhb/SaT6iCqsUohsls2W/RfvSn+3e2nALIBln2W7Rf3fYx110Hisj0Kd6IyHwUjIwQ9PbioXpjambVtJwCwN3vNrOjgA8DnwHeYmbvBHbMUtlEZJ7IBSHpXEA6F5LJRxVRqoSayN15tGeU2zdGFVJ/3Do8YXnC4LAD9uTY1Ss47pDlrNy7q0ElbU2KYSLzxJFvjSpackQtpvJpCHNRer20equjenZ9KE1BMUzq7eRnnMwl910C+ajFVDbIkgtznPyMk+u2j2buOlBkrsx168GZUrwRkfnAw5Cgt5dgZKTRRZEqVF05BeDuIXC+mf0Q+CpwK/BTKjZiF5GFJgidfBiSzYeM5wLS2ZC8nlIoKx+E/O7JwZ3jR20bSk9Yvkd7kqMPXsZxq5dz9MHLWNKZalBJ5wfFMJF5YNULgU/ElUdboxZT9aw8mg+tjtT14bykGCb1tGa/Nbzz8HfO6k3zueo6UKRZtWrrQcUbEWllYSZDvrsbz6lb3VZRUxt3IA0AACAASURBVOVUgbs/CpxoZm8GLgDUn5TIHFn/4A4uvWUjT/SP8bS9F3H68YdwwqH7zsm+3Z1sEBKEvnPKh04+iCqk8oETqkXUpIbTOe58tI/bHunlzk19jGYm9pKw39IOjlu9gmMPWcbhT9uLVDLRoJLOX/WKYWaWAD4AnE40SHA3cA1wlruPTrHuZ4gGHK4k7+6pOK8BbwJOAtYA+wM9wL3Av7v7b6ZTfpGWteqFs1dRNB9aHanrw3lNMUzqZc1+a2b1BvlcdB0o0sxavfXgbN33M7MzgOcDLwAOBh5z91WT5D8G+HfgGKIKstuAT7r7vWXy7g98HngFsBh4ADjP3b9fJm8H8C/AW4hi05PAN+P8u93VNrNTgQ8BhwJDwI+BM9y9u9pjF5HZFQwOEvT3q6emFjOtyqkCd7/CzK4BuoBJf8SIyMytf3AHZ13/AKmksVdXih3Dac66/gE+B3WroHJ3cnFlUy5w8kFIPozGg8oFagE1HZsHxneOH/W7JwcIS+LkoU9ZwnGrl3Ps6uUcsmIPovs4MtvqEMMuBN4PXEf0g+3Z8esjzeyl8VOHlVwLPFwm/TDgY0Q/dgo6gO8Q3cj7HvAo8FTgncDtZnaqu18xjfKLSKn50OpoLro+lIZTDJNmNxddB4o0s/nSenAW7vudA/QBdwN7TZbRzP4SWA9sBs6Kk/8/e/ceJ9lZFXr/9+y6dnV39W0mk7klM90JEIgkk0wSZuKLeOTw0SCe4ygKKoaLkHyUm/L60YMaPeD1KEhAjgkXPYBgwPeMvqAcXxSEID25TG6EQDDpnkkmM5OZ7up7V9dl773eP55d1VXV1d1VfauqrvXlsz9VtfdTu54ZMr269tprPW8HvmmMOSoij5eM7Qf+HbgE+CA22fRzwBeMMW8Wkb+uOP3ngf8C/BVwAjgCvB+4AnhjxTx+NTjnN7A3duzDtj48Yoy5cbWbOpRSm0tcF3d8HH9hodFTUWuwruQUgIjkgNwGzEUptYq77x0lEjIkovafbiIaJp1zufve0ZqTU4W2e5VVT17wXBNQ6+eL8OT5WYZHxhkeSXE6lS47Hg07XHdZb7FCaqArtsyZ1GZbawwzxrwEeAdwXER+qmT/KeDDwOuAz63wud8Gvl3lvHcHTz9ZstsFXiEi36gY+3Hs3YAfMMZ8bpULiUqpWmyHqqPNbn2omobGMNXMtqJ1oFLNbDtVD27wdb8hERkFMMZ8B1vhtJwPB5/7chE5G7znC8D3sDdWvKpk7G9iK7F+QkS+FIz9JDbx9GfGmL8Tkblg/y3YxNQHReQ9wfs/YYyZAn7NGPMxERkOxu4Afh94EPgREfGC/Q8CX8Qmq/5wPX8hSqm189Np3PFxxPNWH6ya0qrJqaDk9ksi8p0tmI9SagVnJtP0dpSvO9QRCfHcZBoRm2zyfJtgKk0+eSJ4wWOzlLc+MDrBPQ+e4fzMAruTHbzuhv3cONjf6Gmt2ULe4+FnJhkeSXHfaIrJdHkngL5EhCODtjrqusv76IiEGjTT9rKJMez12NYWH6rY/3FsK4lfYIULe9UYYxLYC4JngX8u7BcRF3uXXhkRuWCM+QZwDHuH4PP1fJ5SqortUnW0ma0P1ZbRGKZa3Wa3DlSqmbVS9eBWXvcrJKZWY4y5ArgB+KtCYip4/1ljzN8BbzLGXCoihfjxc8BIITEVjPWMMR8BPg3cgm1fWxgLS+Pgh7AVUb+AbR8I8F+BBPCRQmIqOPeXjDGjwVhNTim1xUQEb3ISb3q60VNR61RL5dQfAL8f3BXwCeCewt0GSqmtUah22tPTwcXZTDGxIUA657KjK8ap8dapJH9gdII7v/YUYceQjIdJzWe582tP8S6ubKkEVWouy4nRCYZHxnn42SlybvlNvwcGEsV2fVftTuJsYbu+7Zb8W4fNimE3AD7wQOlOEckYYx4NjtfrZ4Ak8OHSLz6r2Ie9m3BqDZ+nlKqkVUequWgMU0qpFtVi1YPNeN2vEItOVDl2H/Bm7LpV/2SM2Q3sBT67zNjC+b5Q8vysiJwpHSgiZ4wx5yiPg6vN4/XGmK4m+PtSqm1ILoc7Noaf00Zu20Gtbf1GsQvY3gD8uTHm88AnRaTaD2elVAXft1VLvgi+b9u+eSJIyXNfBBEQCfb5UnwsOHZoL3d+7Sk8X4hHHDJ5ux7Uzx7e38A/Xf3uefAMYccUk2wdkRALeY97HjzT1AkUEWF0fJ7hYP2o7z8/W3bcMXDN/t5ihdTe3o6GzHO7JP820GbEsD3AuIhkqxw7Cxw1xkSDFhi1egs25/xXtQwO2lHcCHxGRDIrjHsb8DaAy/btqWM6SrUprTpSzaVtY1hp/Nq3f18dU1FKqebQYtWDzXbdr/DF5WyVY4V9e9cwtjD+u8t87lnszRO1zsMEY/5jmfMppTaQNzeHl0ohvnaE3i5qTU79LrYdw5uxCwO+GVtC+z1sT/HPiMh4vR9ujHGw/VlvAw4AY9g7Ge5YbUFBY8zvBfNajisikWCsAX4e+HFssN0DjGMX5v0DEbm/3rmr9iEi+EHCSEoeF5NN9rjn25Z5XpBQkmCfv4Ft9G4c7OddXMk9D57h+ZkFLm3RipjzMwsk4+U/fuIRh+dnmm/xwrzn89iZKYZHUpwYTXFhpvw6TmcsxI0H+jk6tIMbD/bRHY8sc6at06rJv020GTEsAVS7qAeQKRlT04U9Y8wLgR8Evioip2oYfyV2gfmzwHtWGisiHwM+BnD42h9ojr6eSimlatW2Maw0fh26/pDGL6WU2lybct1vHRLBY7V4lakYU8/YwvOV4mDl2HrODVTcIHjZZct8lFKqVuL7uOPj+POt0zVK1abW5BRBj9f3A+83xvwI8EvY3qsfAP7IGPNF4BMi8pU6Pv/PgXcCfx+c56rg9SFjzCtXWRj3OPB0lf0vBX4d+FLJvhj2C9CjwD3AKWA3cDtwwhjziyLyN7VMWIRi667lOnQZwBiDY+yjql/pukgiS/+uS/9eJUgW2f2Lx/wgMbQkscTia4J9hXFSrGIKqp02OLm0EW4c7G/5BMPuZAep+WzZukuZvM+lycZUGlWaWchz/6kJToykeOD0BOlceXeaS5Nxjl4xwNGhAV66t4dwyGnQTKtrpeTfVtmEGJbGrpFRTbxkTK3eEjx+YrWBxpiDwFexd6j/mIiM1fE5SinV/E5/K2iteA6Se9q+taLGsLU5eeFkq7TTUkqpprBJ1/3WqhCHYlWOVcaqesYWnlcbWxhfObZw7sov1MvGzLIbBA8fbq6LSkq1GD+bxR0bQ/L51QerllNzcqqUiHwV+Koxphd4A/YLyU8DP2WMOSMiB1Y7hzHmJcA7gOMi8lMl+08BH8YuqLvsQrwi8m3g21XOe3fw9JMlu13gFSLyjYqxHweeAD5gjPncKskwAPK+z3OTtX9XKyapMGUJlsLzQiLFlO7DPhEWky7CYhLGcew5Q8YUzx9yjH2fqTh/xWeUHislxSRNeVJHgmSOYwwhxxAy5XOT4vvtM8eY4ro6hQqiQvKnML4y8VR4FBY/U21vr7thP3d+7SkW8l5Ze8LX3dC49oRnJxcYHhlneCTF42en8Sv+M7xqdzdHhwY4OrSDAwOJpk48N3vyr9E2IoYB54AXG2NiVdoi7cW2S6r1jvMw8IvABPZmjZXGHgD+DegCfkREHq/lMzaEXixWSm2F09+Ce/8EnAjEemA+ZV/zG/ozh/aNYfP5eU5Nn6I72k13tJtYaLlritbJCye567G7iDgRuiJdTGYmueuxu7j9mts1QaWUKtIk9vI2KN6sx7ngcW+VY4V9Z9cwtjC+2tjC+Mqxhf2VN8jvxV7mOodSalN4U1N4U1N6rXgbW1NyqkBEpoCPAB8xxlyPvavidTW+/fXYfMmHKvZ/HPhj4BdYITlVjTEmEXz+WeCfS+bpYsuTK+d/wRjzDeAY9u7B5+v5vFrYShxYTONsgFqXGFaqSTVDe0LPF753fsa26xtJ8cxEedI5Fna4/vI+jg4N8LLBAfo7o1s2t/VqxuRfM1pnDHsQeBV2vYxvFnYaY+LAtcC9dUzlNcAu4M5l1v8onPty7EW9HuCVIvJIHZ9hrfUXOr1YrJTaKo98yv6siQQ3I0fikA/268+bonaLYWdmz/DOf3tn8XU0FCUZTZKMJosJq+5od3Hf/zn1f3B9l7ATto+hMCLC8aeO64VnpRSgSexarTPerMeDweMRllbmvgx7ke2hYI7njTFng/2VCvtOVpz7540x+0XkTGGnMWY/dhmQL1aMfVswj8rk1E3A90VkrtY/lFKqNuK6to3fQvt2AGoX60pOlRKRh4CHjDG/WuNbbgB84IGK82SMMY8Gx+v1M0AS+LCI1JrC2Yftpz61hs9TSq1RI9oTLuQ8Tj4zyfDIOPeNTjC9UF4S3N8Z5cjgAEeG+rn+sj5iJZVHraQZkn+tZg0x7PPAe4F3U3JhD3grtuf4Zws7jDFDQEREnlzmXIV2SJ9c5njhot7XgT7gPwfzrd/Y9+CumyHeY7dYssrzXohX7H/4r/VisVJqa8ycs0nwUuE4zJxvzHxaQDvEMEN5xXrOyzG+MM74Qn3Ln5yfP88vf/WXy5JaxeeRbpKx5JJ9Iac1fx9USq3s+FPHiTgRYmFbiRkLx8BFk9grWEO8Wc9nPW2MOQm81hjzOyJyDsAYswd4LfA1ESm9wfxvgf/bGPMaEflSMDaE7dg0BXy5YuzPY+Ng6dqH7w4eP1uy7//Fdnd6e9BxyQvO/RpgCPidDfkDK6WK/HQad3wc8bQ6ox1sWHKqQEQyq48C7N0I48vcYXcWOGqMidbaUiLwFuzdE39Vy2BjzC3YOwY/U8e8lVItZGw2y4lRWx318LOT5L3yypHBHZ0cGbLrR73w0u5iW8pWtx3WJmuEWmOBiDxujPko9kvKceyXncK6id+gvPL3q8DlwJL/uIIvVz8KPLBceyNjTDf2bvMD2LsWXxgsPl/qX0TkQi1zx83AXAbmahu+OBHHbk4ITLDNX4QTHy1PZhWTWj0Q64ZQpL7PUUq1t+QeW51ZSIaD/bmV3N24ObWI7RzDXtD3Aj71Y59iNjdb3GZyM8zkZspez+Zmmc3Pcnb2LK64S87j43Nm9kyVT1heZ7jTJqwqElfLPdbSdlAp1XgX0hfoinSV7YuGolxI1/k7chtaz/UzY8wbsHEFYCcQNcb8dvD6GRH5TMnwd2FjyDeNMR8J9r0DcChPKoHtwPRa4HPGmA9iryu+Hnvj+y+JyGzJ/P/JGPOPwK8ZY3qAE9jKqLcAfyMi/14ydswY8zvAnwH/aoz5W2w7v/cAT7K0G5RSao1EBG9yEm96utFTUVto1eSUiDib9NkJYLnWD5mSMbX2O38h8IPAV0XkVA3jrwQ+gw1YlUGtcuzbsGW87N2nbbGUamYiwsjYfHH9qP+4UF5hH3IM1+7rCRJSO7i0J77MmdR2sIkxDOyddaex8eHVwDj2wtsdtaxhGHgjEGLlReQHgIPB83csM+aHgdW/SfcdgFf/FmSmIDMDmWnITtvHzEz583zF+ori282vuNj30Cr3g0Q7baKqMoEVSy5WaZU+jwdJLWfD759RSrWCQ7fatqF5bMWUmwE/b/e3GY1hi4wx9MR66KmsqlvGyQsn+ctH/5KwEybkhMi6WXJ+jv+0/z+xo2PHYkIrX5Lcys4wm59lwS1vHzPvzjPvzvN8uvYO8IW2g92RkpaDsSTJSLKY5Kqs0OqMdDb1mqZKbTe7EruYzEwWK6fAVmXuSuxq4KwaY5PjTaW3AD9Use/9weM3sNfpABCRYWPMK4DfDzYBhoHXishjpScQkZQx5mZskupXsOsbfhd4nYh8vso8Xgv8NnZJkTdgrw3eEby/jIh8wBiTAn4VW0U1A3wB+E1t6afUxpBcDndsDD9XT42K2g5MoxYUM8Y8DlwiIksivzHmC9hAEatjMd7/Afw68HoRuWeVsQexQS8B/HA9i/G+9NB18g9fqacNe7kHRie458EznJ9ZYLe22lJqQ+Rcn8eemyquH3Vxtjzv3RUL87LBfo4MDnDDwX66YnrRux5hx+HyHZ0PiYj2t2hxh6/9ATn5ryuuV7/IywfJqxk4/e/wyGcAY6unvJy9WLzjBbYyKjO9ODa/QT2ho13V2w/GS9oPVrYljHXbyi6lVGs7/S3bNnTmvK2YOnTr2luIdvRiunZqDGtxh64/JH//tRrjV+DkhZMcf+o4F9IX2JXYxbErj9XUqivv5ZnNl1RnBUmrstdBhVZh31xuDlnnGsMhEypbP6s7EqyhFVtMYBXX1Ypp20Gl1qt0zaloKErOy5H380215tTB3oMav7aBw4cPy8mTJ1cfqFSb8mZn8SYmEL/W+6NUs3OiUaL79tUUwxp5hfYc8GJjTKxKa7+92JZ/tSamwsAvAhPAit9ajDEHsGXBXcCP1JOYWq8HRie482tPEXYMyXiY1HyWO7/2FO/iSk1QtRBNMDaH6YU895+aYHhknJOnJ0nnynvR7u6Jc/MVtjrq6j1JwqGtvBlMqW0gFIHOHXbrH4T+ocWLxX2XLX+x2MstJqsKCavS15mZYN9U+XO34leB3JzdZs7WMWljE1TFCqyeiueF14WKrUJiq8sm3ZRSzeHAzbqenVq3w7sOr+kCcyQUoT/UT3+89t/vPfGYz8+XtxwsSWLNZGfK2g4Wxrgl1cieeExlp5jK1rcUcmXbwUJSq6xiq6SKKxlLattBpbA/I26/5vY1JbGVUkqtn/g+7vg4/vx8o6eiGmhDk1PGmF8B3iMigzUMfxB4FXbNp+JCvMaYOHAtUE950muAXcCdy6xhVTj35djEVA/wShF5pI7PWLd7HjxD2DF0ROzdbR2REAt5j3sePKPJjRahCcbGOjORZngkxfBIiifOTeOX3KBqgKt2Jzk6NMDRKwa4vD+hrVFUXeqMYe2n1ovFoSh07rRbPdwMZGZtoqqY0JpamszKFJ4HiS+v9D4WsfuyMzD9XO2fbRyIdpe3HoxVa0PYU95+MNqpSS2lVFPQGNZYIROyFU3RZM3vEREyXqbq+lmla2hVJro2qu1gaSXWio+xpLYdVNvWWpPY7UzjjVJqI/jZLO7YGJLPN3oqqsE2unKql8WFDVfzeeC92H7n3yzZ/1Zsu73PFnYYY4aAiIg8ucy53hI8fnK5DwsSU18H+oD/LCIP1TjPDXN+ZoFkvPyvPB5xeH5mg1ogqU2nCcat5fnCE+emOREkpM5Mlv9biYcdrj/Qx9HBAW4aHKC/M9qgmaptop4YpjZaOA5dceiqM6mVX6hSoTWzzFpaJVvp2lni2zHZaZg+U/tnm1BQqbVc68HSiq2S/ZFO0At8SqmNpTGsxRhj6Ah30BHu4JLEJTW/r6ztYKEiK1+e0KpMblW2Hcx5OVJeilQmVfPnOsYpX0MrunTtrEKCrnS/th1UatvReKOUWhdvagpvaopGLTWkmkvD2vqJyOPGmI8CbzfGHAe+DFwFvBO7HtTnSoZ/FRv8llzJMcbsAX4UeGC5Fn3GmG5sxdQB7EK/LzTGvLBi2L+IyOqLya/D7mQHqflsMbEBkMn7XJrs2MyPVRtIE4ybL51zOXl6kuGRFPeNppjJuGXHBzqjHBka4MjgANdd1kssol94lWprkQ67dV9a+3tEbFIrGySvMtNVEljLtB/0S1qIihccr68FEyZUvd1grAc6qlVtBccjCU1qKaVUm9uotoNlLQiXqdrK+4t3M/viM52bZjo3Xdd8C20Hl0tqVUtyxcPxuj5DKaWUUs1PXNe28VvQa6hq0arJKWOMt9qYdXg3cBp4G/BqYBybPLpDRGpdBe2NQAj4xApjBoCDwfN3LDPmh4FNTU697ob93Pm1p1jIe8QjDpm8j+sLr7th/2Z+rNpAmmDcHGOzWYZHUpwYGeeRM1PkvfK7J4Z2dnJ0aIAjQwO8YFc3jl6cVTXa5BimWpUxEE3YrXt37e8TgXx6sbVgZfvBsv0V1VtSkdRamLRbPZxw9baDlcms0vW04j22Kk1/birVcjSGqY2y1raDWS+7JHlVWpFVbd+GtB10oqtWaFUmuBKRBI622lVqTTTeKKU2m59O446PI57+uFHlaqmcEuAc8FgNY68AXlDrh4uIB3wg2FYad2CFY38I/OEq7z9NlaqrrXbjYD/v4kruefAMz88scGmyg9fdsF/bwbUQTTBuDBHhqYtzxfWjnr44V3Y87Biu2dfD0St2cGRogEuTevekWrNNi2GqDRlj15mKdkKy3qTWvE1ULUwv03KwUMFV0p4wO2NbDhb4LqRTdquHE1naWjC2zPPSNbX0znWlGk1jmGoYYwzxcJx4OF5f20E/z1xubkkSa6Wk1lxuDp/FeJfzc6QydbYdxKEr2lW1ImulKq2w07BmMko1E403SqlNISJ4ExN4MzONnopqUrX8JvYUcEFEXrPaQGPMbwHvW/estrEbB/s1GdXCNMG4djnX59EzU0GFVIqxuWzZ8e54mJsO9nN0aAc3HOijM6ZfFNWG0BimGs8YiHbZLbm39veJD9m58vaDS9bXqtwXtCAsWVsEPw/pcbvVIxRbJoG1XPVWkNgKx+r7HKXUcjSGqZYTcSL0xfvoi/fV/B5ffNL5dFmVVrVkVuX+sraD+MX3n+VszZ+dCCfqajmYjCaJhWIYrUhW24vGG6XUhpNcDndsDD+Xa/RUVBOr5ervQ8CqAUqpdqEJxtpNpXPcf2qC4ZEUJ09PspAvL9/d29tRbNf3A3t7CDn6JU9tOI1hqnUZJ0gIJaGnjveJD9nZZdoPzi4+z1Y8z85SltTysjA/Zrd6hGM2URUL5l58XtF6sGxMD4Si9X2OUtufxjDVFhxjq566ol3sYU9N7yltO7jaOlqlz9Nuuuw8aTdN2k1zIV17h/+oE6Ur2rVsm8Fqj52RTm07qJqZxhul1IbyZmfxUilEZPXBqq3Vkpz6JnCzMeYyEXl2lbGPAZ9e/7SUUq3q2Yl0cf2oJ87N4JfEIQO8ZE+ymJC6rD+hdx2qzaYxTLUf4ywmgaij7azvLSasskFia0mlVpUKrlx5a1bcLMxdsFs9Ih3lFVjV2g92VFRpxZIQitT3OUq1Do1hSi1jrW0HXd9dNZm1pGIrP4sv5W0HJzITTGQmav7cyraDq7Uc1LaDaotpvFFKbQjxfdzxcfz5+UZPRbWIVX/TEZG7gbtrOZmI/CPwj+udlFKqdXi+8J1z0ww/neLEaIrnJssXQY5HHA5f3s/RoQFuGuynL6F3xqutozFMqTo4IejotVs9fDdIak0t336wWoIrX/GFJb9gt7qTWp2LFWaVFVuV7QfjycWEl17wU01OY5hSGy/shNfVdnC19bNK9+X8xTZGa2072BHuqKvlYHe0m3gorjcAqrpovFFKbQQ/k8EdH0fy+dUHKxXQb+VKqbqlcy4Pnp5keCTF/aMpZjJu2fGBrqitjhoc4LrL+oiGtYWFUkptW04YOvrsVg8vX9FasCKBla32fNomsErl5+02e76+z492BomqkjaDZRVbpcmsnsVKLSdU3+copZRqaaVtB+uRcTM1tRycyc0wl5tjNjfLvFt+48aCu8CCu8DF9MWaPzfiRJZdR6s70k0yllyS5NK2g0oppdbDm5rCnZxs9DRUC9LklFKqJhdmMpwYsdVRj56ZIu+V9429YmdXsV3fC3Z16d16SimlVhaKQKLfbvXwciUVWbOL7QdXq9pyM+Xnyc3bbfZcfZ8f617aWrDqWlola2xFuzSppZRSbabQdnBnYmfN7ym0HVy15WA+eJ6dWdJ2MO/n19R2sDPauZjMqkhiVXvsjnYTcbS1rlJKtTNxXdyxMfxMZvXBSlVRc3LKGHMJdjnupyVYzcwYcxB4I9APnAD+VnSlM6W2BV+Epy7MMTwyzomRCZ4eK19TJOwYrt3fy81XDPCywQF2JeMNmqlSq9MYptQ2EopC50671cPNLq3CWq7lYLbkuZctP0921m4ztbdlAmOTWtWqsaolswpjYl12DTHV1jSGKdU+1tN2cDYfJKsqq7XyM8X9hTW0ZrIzS9oOFo7Xo2rbwUiQxIpVb0WobQebl8YbpVQ9/Pl53FQK8bxGT0W1sFWTU8YYB9t79k2AAb5vjLkFG5juBRLB0F8GbjXG3CIi+l+lUi0o5/o8/OwkJ0ZSDI+mSM3lyo4n42FuGhzg6NAAhy/vozOmxZfNyBhDyBiMgZBjcILnha+Agk0+er7g++CJsF2/X2gMU0oVhWPQtdNu9XAzJYmrqRVaDlZUa3mlMVTsvuwMTNfx2cYJKrVqbD/YEayxFe0CvfDX8jSGKaVqUdp2cHfn7prfV2g7WExY5cqTWNWqtjai7WDYCVetwiq0HSwms0qSW12RLm07uIk03iil6iEieBMTeDMzjZ6K2gZqubL8BuAtwJeBZ7HB6i4gAnwY+BTQAbwTuBV4a3BcKdUCJtM57hud4MRIipPPTJDJ+2XH9/V1FNv1Xb2nh5CjF7u2WsgxhEMOkZAh4jiEQjbhVEhAGQOOMcHGmu5E9H3BCxJWXvDcD563eNpKY5hSan3CceiKQ9eu2t8jEiS1KpNZNbQf9EvWcRR/8XhdSa3Q0paDsYrEVnFfSYIr0qlJreaiMUwptWnW0nbQ872yloLF5FV+tur6WoXH0raDru/W3XbQYOiKdC1ZS6v4GAvW06pIdkVC2nawRhpvlFI1kVzOtvHL5VYfrFQNaklO3QZ8RUR+HMAY813gTuDzIvLeknFvNsZcDfwcGqSUaloiwrMTaYZHUgyPpPjuuZmy5INj4CV7eooJqcv6E8ueqx0ZY4iGHaIhh1jEwTEGEZvAWan4qFC5ZIzBQLGaCWwVky/2/xvHmGK1U8gxW5YMdByDgyGy/ZZE0RimlNp6xkCkw27dtd/Fjgjk00sTWoXE1UJlQqsk2VV6A7N4sDBpt3o4oaAaq7Qyq5DAWqFiK9KhSa3NzDJFhwAAIABJREFUoTFMKdVUQk6I3lgvvbFe6K7tPSLCfH6+LIFVVqGVX7qm1kxuhlxJBbIg9v35WZhf4cMqdIQ7itVYle0Fi/tiybKqrY5wRzu2HdR4o5RalTc7i5dKbdvOO6oxaklOXQG8v+T1P2Ovsf5TlbH/ALxnA+allNpAni985+x0MSF1dmqh7Hg84nDjgX6ODg1w08EBehJ6h1lpEqrwGA4ZIiFtJ9FiNIYppVqHMRDttBv1JrXmlyawlms5WHw+W57U8j1YmLBbPZxISeJqucqsnqX7w3FNaq1MY5hSquUZY9bcdnAuP2cTWfmKqqygamsuP1dWoTWfX6bt4EJ9bQdXSmgteR1L0hnpJGRa+i4/jTdKqWWJ5+GOp/DTddwdoFSNaklOdQNzJa8Lt2CerzL2eWq+f0YptZnmsi4nT08wPJLi/lMTzGbcsuM7uqIcGRrg5qEdXLu/l2i4fZMuYcchEQsRj4QIO8ZumoTaLjSGKaW2P2PsOlPRLkjurf194kNuvmJNrdLk1szSdoSFaq3Sums/D+mU3eoRilZJYAXtB5dNciVtUqs9aAxTSrWtQtvBHR07an5PWdvBQtIqSG4t13ZwNjeLV3Kjhuu7TGYnmczWXn1c2XawauvBKo9N1HZQ441Sqio/k8EdG0Ncd/XBSq1BLcmpceCSktd54CGg2qpnu4CpDZiXUmoNnp/OMDyS4sRoisfOTOH65aW2V17SFSSkBrjikq52bFcA2DWcYuEQsbBDR9QmpdS2pTGs2Zz+FjzyKZg5B8k9cOhWOHBzo2elVHsyDsS67dZTx/vEt1VXq62nlS1JeGVm7HtKk1peDtLjdqtHOFal/WBP9eqtnn31nbu5aAxrYycvnOT4U8e5kL7ArsQujl15jMO7Djd6Wko1tbK2gzUSEdJuuqylYGWLwcq2g7O5WbJedvEca2w7GA/FV6zQ2kIab5RSS7iTk3hT+s9dba5aklNPANcUXojIDHDDMmOvBk5twLyUUjXwRfj+87OcGLXt+kbHyn8TjoQMhy7r48jgAEeHBtjZHWvQTBsrGnaIhUPEIw7xSEhb87UXjWHN5PS34N4/sS3AYj0wn7Kv+Q1NUCnVSoyzmPxhf+3v870gqVWtxWBplVbF69xc+XncLLgXYb72Nk0tSmNYmzp54SR3PXYXESdCV6SLycwkdz12F7dfc7smqJTaYMYYOiOddEY6ubTz0prfl/NySxJYlY+VSa7KtoMZL0NmIcPYwthG/7HqpfFGKVUkros7NoafyTR6KqoN1JKc+hxw3WqDjDH9wE8CH13vpJRSy8vmPR5+dorhkRT3jaZIzefKjifjYY4MDXBkcIAbDvTTEW2/qqBIyKE7Hi5WRzlOe1aIKUBjWHN55FM2MRUJWnJF4va+zEc+pckppdqBE4KOXrvVw3eDpNYUZGZL2g/OLm1FWJrcyrV8X3yNYW3q+FPHiTgRYmF7Y1ksHAPX7tfklFLNIRqKMtAxwEDHQM3v8XxvyTpZy1VpzeRm+A7f2cQ/QRmNN0opAPz5edxUCvG81QcrtQFWTU6JyKeBT9dwrmnsys3p9U5KKVVuYj7HfaMpToykOPnMJFnXLzt+WX+CI4P9HBka4CV7egi1aTImEnLoTUToioXbtmWhKqcxrMnMnLMVU6XCcZip1s5eKaUCThg6+uxWDy9v1+P671dtzrw2mcaw9nUhfYGuSFfZvmgoyoX0hQbNSCm1EUJOiJ5YDz2Vvw8v4yAHN3lGlsYbpZSI4KVSeLOzjZ6KajO1VE7VREQ8bKBSSq2TiHA6lebESIrhkXG+d362dIUGHANX7+0ptuvb359o2FwbzRhDZzREVzxMIrphP9JUm9EYtkWSe2wrv0LlFICbgeTuxs1JKbV9hSL1V2m1II1h28+uxC4mM5PFyimwLcR2JXY1cFZKqXan8Uap7cnP5XAvjiH53OqDldpg676Sa4wZAH4X+ISIfHv9U1KqPbmez7fPTgcJqRTnp8t7uyaiIW44YKujbjrYT09HpEEzbQ6RkEOyI0J3LNzQtn1ff/Iid987ypnJNPv7Etz28kFe8aJLVn+jagoaw7bYoVvtGlN5bMWUmwE/b/crpZSqi8aw7evYlce467G7wLUVUzkvR97Pc+zKY42emlKqDWm8UWr78mZm8CYmEJHVByu1CTaizKAX+BXg3wANUkrVYS7j8sDpCYZHUtx/KsV8tryn6yXdMY4M2eqoa/b1Eg07DZpp84hHQvR0ROiMNb5K6utPXuSOLz5BJGTo7YhwcTbDHV98gveBJqhah8awrXTgZuA37BpTM+dtxdShW3W9KaWUWhuNYdvU4V2Huf2a2zn+1HEupC+wK7GLY1ce0/WmlFKNovFGqW1GPA93PIWfbvk1WlWLW/XqrjHmi6sMSQAG+G/GmDcBIiL/ZSMmp9R2dG5qgRPB+lGPPTeN55ffnfDCXd0cHRrgyNAAQzs7237tJGMM8YhDIhqmMxoiHGqeBN3d944SCZliO8FENEw653L3vaOanGoSGsOa0IGbNRmllFI10BjW3g7vOqzJKKXUltB4o1R78TMZ3LExxHUbPRWlaqqc+nFAsIFoOQIcLnmulAr4Inz/+VmGg3Z9p8bL70qIhAzXX97HkcEBXjY4wM7u2DJnah+OMXTGwiSiIToioYa27VvJmck0vRXtFTsiIZ6b1PVhm4jGMKWUUq1KY5hSSqmtoPFGqTbhTk7iTU01ehpKFdWSnLofuBr4HeBOqWhCaYy5AvgP4KdF5PjGT1Gp1pPJezz87CTDI7ZCajKdLzve2xHhZYO2OurwgT46IqEGzbS5NMs6UrXa35fg4mymWDkFsJD32NeXaOCsVAWNYUoppVqVxjCllFJbQeONUtucuC7u2Bh+JrP6YKW2UC3JqaPY3rJ/ALzBGHO7iDxYclzvmFAKmJjPcWIkxYnRFA89M0nW9cuOX96fKK4fddXuJKEWSL5sFccY+ruiJOOR1Qc3kdtePsgdX3yCdM6lIxJiIe+R94TbXj7Y6KmpRRrDlFJKtSqNYUoppbaCxhultjF/fh53fBzx/dUHK7XFVk1OBXdM/IUx5h+AvwBOGGPuAt4rIjObPUGlmpWIcGp83lZHjab43vnZsuOOgZfu6+HI0A6ODg6wt6+jQTNtbolomB1d0aZaS6pWr3jRJbwPu/bUc5Np9vUluO3lg7reVBPRGKaUaimnvwWPfApmzkFyDxy6Vddoa2Maw5RSqrWdvHCS408d50L6ArsSuzh25bGmXEtO441S25OI4KVSeLOzqw9WqkFqqZwCQESeA/6rMeangTuBY8aYXwMeXPmdSm0fec/n8eemi+tHPT9TXg7bGQ1xw4F+jgwNcNPBfpIdrVUJtJVikRC9HRE6YzX/GGpKr3jRJZqMagEaw5RSTe/0t+DePwEnArEemE/Z1/yGJqjanMYwpZRqPScvnOSux+4i4kToinQxmZnkrsfu4vZrbm/KBBVovFFqO/FzOdyLY0g+1+ipKLWiuq8Ki8j/Y4z5CvCnwGeBR9ESX7WNzWbyPHBqguGRFA+cmmA+55Ud35WMcXRoB0cG+7lmfy+RFqwA2kpdsTDJjghxXWdLNYDGMKVU03rkUzYxFYnb15E45IP9mpxSaAxTSqlWcvyp40ScCLFwDMA+unZ/syanCjTeKNXavJkZvIkJKpaPU6opralkISjrvc0Y8xngo8AMoKlYtW2cm1ooVkd9+7kp/Iqf5y+6tJujQwMcGRpgcEcnxuj6USsxxtAVC9ObiGjyTjXcRsUwY4wDvAu4DTgAjAFfAO4QkflV3vt7wO+uMMQVkWLppTHmRuAXgOuBa4BO4E0i8r/qnbdSqknNnLMVU6XCcZg535j5qKakMUwppVrDhfQFuiJdZfuioSgX0hcaNKP66HU/pVqPeB7ueAo/veKvcko1lXX10xKRf8d+wVCqpfkifO/8DCeChNTpVLrseDTscN1lvcUKqYGuWINm2lrikRCd0TCdsVBLrimltrcNiGF/DrwT+HvgA8BVwetDxphXishKq40eB56usv+lwK8DX6rYfwt2keIngcewixYrpbaT5B7byq9QOQXgZiC5u3FzUk1LY5hSSjW3XYldTGYmi5VTADkvx67ErgbOqn563U+p1uAvLOCOjyOu2+ipKFWX1l7sRal1WMh7PPzMJMMjKe4bTTGZzpcd70tEODJoq6Ouu7yPDm1DVxNjDMl4mJ6OCOGQw9efvMjd945yZjLN/r4Et718UNdoUi3PGPMS4B3AcRH5qZL9p4APA68DPrfc+0Xk28C3q5z37uDpJysO/SXwpyIyH/SA1wt7Sm03h261a0zlsRVTbgb8vN2v1AbSGKaUUpvv2JXHuOuxu8C1FVM5L0fez3PsymONnppSahsREbypKbypqUZPRak1qTk5ZYy5BOgBnpagaaUx5iDwRqAfOAH8rWhDS9XEUnNZToxOMDwyzkPPTJL3yv9zPbijkyOD/RwZGuCq3UkcbddXl65YmL7OaLF139efvMgdX3yCSMjQ2xHh4myGO774BO8DTVCpLbUJMez1gAE+VLH/48AfY9sXLXthb5k5JrAXBM8C/1x6TERao/+HUmrtDtwM/IZdY2rmvK2YOnSrrjelNIYppVQLOrzrMLdfczvHnzrOhfQFdiV2cezKY0293pRe91OqtUg+jzs2hp/NNnoqSq3ZqsmpoB/53cCbsF9ivm+MuQUbmO4FEsHQXwZuNcbcIiLeJs1XqbqICKPj88X1o77//GzZ8ZBjeOm+Hrt+1OAAe3o7GjTT1hYNO+zoihGvqC67+95RIiFDImp/1CSiYdI5l7vvHdXklNoSmxjDbgB84IHSnSKSMcY8Ghyv188ASeDDGkeValMHbtZklCrSGKaUUq3t8K7DTZ2MKtDrfkq1Hm9uHi81jvgrdWJWqvnVUjn1BuAtwJeBZ7HB6i4ggm378CmgA9uj/FbgrcHxVelCvGoz5D2fx85MFRNSF2fL7yDojIW46eAAR4cGuPFAP11x7W65Vo4x9CWiJDvCmCpVZmcm0/R2RMr2dURCPDeZXjJWqU2yWTFsDzAuItVuUToLHDXGREWknkWD3wII8Fd1vEcppdT2pTFMKaXUVti0635KqY0lvo83MYE3O7v6YKVaQC1X5W8DviIiPw5gjPkucCfweRF5b8m4NxtjrgZ+jtqDlC7EqzbEzEKeB05PMPx0igdOT5DOld/Ec2kyztErbELqpXt7CAdt59TaGGPoioXp74wScpZvfbi/L8HF2UyxcgrsWl/7+hLLvkepDbZZMSwBLFc7nykZU9OFPWPMC4EfBL4qIqdqeU+tjDFvA94GcNm+PRt5aqWUUpurrWNYafzat3/fRp1WKaXUUpt53U8ptUH8XA734hiSr+f+IaWaWy3JqSuA95e8/mdsme8/VRn7D8B7avlgXYhXrdfZyQWGR1MMPz3O42en8Su6Hl+1u7vYru/gjs6qlT2qfomoTUpFw6sn+G57+SB3fPEJ0jmXjkiIhbxH3hNue/ngFsxUKWCTYhiQBpbrTRkvGVOrtwSPn6jjPTURkY8BHwM4fO0PaH94pZRqHW0dw0rj16HrD2n8UkqpzbNZ8UYptUG86Wm8yUl0yTe13dSSnOoG5kpeTwaP56uMfT4YXwtdiFfVxfOF752fYXgkxYmRFM9MlH9njoUdrr+8jyODA7xssJ+BrliDZro9GWPo74zSU9GmbyWveNElvA+79tRzk2n29SW47eWDut6U2kqbFcPOAS82xsSqtEXai22XVOsd52HgF4EJbCWxUkopBRrDlFJKbY3NijdKqXUSz8MdH8dP6/IYanuqJTk1TvmddXngIWCmythdwFSNn60L8apVLeQ8Tj4zyfDIOPeNTjC9kC873peIcGTItuu77rI+4pFQg2a6vUVCDju7Y2v6+33Fiy7RZJRqpM2KYQ8CrwJuBL5Z2GmMiQPXYhcOrtVrgs++c5n1P5RSSrUnjWENEHJCRJwIIRNCEHzxERHyfh5/xa7zSinVsjYr3iil1sFfWMAdH0dct9FTUWrT1JKcegK4pvBCRGZYPnF0NVBrn3FdiFdVNTab5cSorY56+NlJ8l55yerBHZ0cDRJSL7y0G0fb9W2qzliYHV2xFdeWUqqJbVYM+zzwXuDdlFzYwy4OnAA+W9hhjBkCIiLy5DLnKrRDqmxHq5RSqr1pDFsjYwyOcQiZUPHRGFN8LSIU/ufgEAlFiDh2W6kVeN7Pk/fy5LwcWS9Lzs/h+UvviTTGUPif3YF9VbK/8Dll+43BF7+YEHPFLT5XSqlNtFnxRim1BiKCNzWFN6V5YLX91ZKc+hxw3WqDjDH9wE8CH63xs1tiId7g3MXFePfu27+Rp1bYH7ojY/MMj4wzPJLiPy7MlR0POYZr9/VwZGiAI0MD7O7paNBM28ta2vgp1YQ2JYaJyOPGmI8CbzfGHAe+DFwFvBP4BuVtab8KXA4sudpljNkD/CjwgIg8vsL8LgfeELx8SfD4GmNMYYX4z4jIM7XMXSmlVMvQGFYiGoouSTg5TsXrkkTUZigksBKRxKacfzme7+GJh+u7eOItqeDyxbepNhHCTrg4z0LlV3FjMdG15O/SODjGKZ6vMLaYLAvOX/yf2M3HL86nMLYwT02qKdUyNuu6n1KqTpLP446N4We3RUG6UqtaNTklIp8GPl3DuaaB3dS+eG5LLMQL5YvxvvTQdfob9gbIuT6PPTfF8NMpToymuDhb/kO3KxbmpoP9HB0a4IaD/XTFasmjqo0SCTlckowRC2ubRNXaNjGGgb3j/DT25oVXY9thfAS4Q6Tmvj9vBEKsHrsOUr5IMcCxYAP4d0CTU0optY1oDFsUMiH2dO2pcVrbT8gJESJENBTdms8z9vPWK+/lWfAWyLgZsm4WTzvvK9WUNjneKKVq5M3N46XGEV/bCKv2sWFX/IM1nqbreIsuxNtmphfy3H9qguGRcR48NclCvvzLye6eODdfMcDRoR1cvSdJOOQ0aKbtracjQn9ndNPuOFWqGa0hhhXe84FgW2ncgRWO/SHwhzV81tepcte6UkoppTFMNatIKEIkFCEZTQLg+i45L0fOy9kWiX4e13d1LS+lWsRa4o1SanXi+3ipFN7c3OqDldpm6kpOGWMuAa7HJo8S2LslzgIPicjFOj9bF+JtA89NphkeSTE8kuI7Z6fxS+rODHDV7m6ODu3g6BUDXN6f0IRIA0VCDju7Y8QjWi2ltqcNjmFKKaXUltEYpraDsBMm7ISXtEUUkWK7woyXIZ1Pk3Ezy5xFKbWZNN4otbX8XA734hiSr6k+Q6ltp6bklDHmJdi76l6JzSmUZhAEEGPMV4H3iMh3avzsbb8QbzvyfOG752YYHhnnxOgEz06UV3vHww7XX97H0aEBbhocoL9za1pTqJUlomF2dscIOZocVNvPJsUwpTaHMbDcGh2hsD0m/vJjlFLbisYw1Q6MMYSNvTQRDUVJRpO4vkvGzZDzg0orL69tAZXaRBpvlNp63vQ03uSkrtGo2tqqySljzDXY5JEA/wu4D3vXRAa7NtRe4Ajw08CwMeb/EpHHVjtvqy7Eq5ZK51xOnp5keCTFfaMpZjJu2fH+zihHhwY4OjTAof29xLQyp6n0JqKaJFTb1mbFMKVWZQwYx25OCEyo5NEpeR2MMcFzx7GJJy8PXs4mokJRCMfsOUv5HvhuyVbttV7IU6pVaQxT7SzshOmKdpXtK20LmPWy5LycJqyU2gAab5TaWuJ5uOPj+Gldvk2pWiqn/hi4APyQiJxbZswnjDG/g23F90fALTV+fkstxKsWjc1mGR5JcWJknEfOTJH3yrP8gzs7iwmpF+zqxtF2fU3HMYad3TE6Yxu29JxSzWgzY5hqZaVxyZTcHFrcb8qPFR6LCaWQ3VdIOJUmoQpJpvXMLRy120qcIMFFbPkxvg/uAuQzkE8HCS+9M0+pFqExTKkS1doC5v08eS9fXMMq7+XJ+bmyu9BDTgjHOOS9fCOmrVQr0Hij1BbxFxZwx8cR1119sFJtoJar0kexyaLlAhQAInLOGPMXwH+v9cN1Id7WISI8dXGOEyMpvjWS4umL5Yv0hR3DNft7OTo0wJGhAS5Nxhs0U1WLWCTEJd0xIqF1XDxVqjVsWgxTLcAYCEVs5VEoap87keCxTap4HQeinXZjIKjKyoGbtY+lFVrFJJ2Ap1+WlGoCGsOUWkXEiRBxIkv25708Pj4RJ4Jj7HeerJdlNjfLfH5eWygpVU7jjVKbTETwJifxpqcbPRWlmkotyangKkVNBE0AbRs51+fRM1NBhVSKsbls2fHueJibDvZzdGgHNxzo0wqcFtGbiNKXiGC0mk21B41h242pqF6qrFYyoZIklMalJYyxLQLDK1RbgU1iFRJYpS0DxQdkMcmlF/eU2kwaw5Rao0hoacIqFooR64jRH+9nwV2wW35BWwMqpfFGqU0l+Tzu2Bh+Nrv6YKXaTC1XbR4A3mGM+YKIPL/cIGPMpcA7gPs3anJq602n89x3yiajHjw9yUK+/Bf1vb0dtl3fFQNcvaeHkKO/k7SKsOOwsztGR7RNqgWUsjSGNSuncq2lKkmmJftDS9ddUpvDGIjE7bYcLw/pCcjObt28lGovGsOU2gSOceiMdNIZ6YQO8HwPX3w88XB9l4yXKa5tpVSb0Hij1Cbx5ubwUinEr3X1GqXaSy3Jqd8Cvg48aYz5AosLI2axixwUFkZ8bfD65zdlpmrTPDuRLq4f9cS5GfyS+2UM8OI9SW4eGuDo0A7293doxU0L6oqFGeiKaTJRtSONYVvNcSra6IUrqpyc9mmpt92FItC9Czr6IFfe7hc3Yzf9EqbUemgMU2oLhJwQIUJEsNVWXXQB4ItPzsuR9bLkvBwZN6NVVmq70nij1AYT38dLpfDm5lYfrFQbWzU5JSL3G2N+GPgw8EvBVlruW7jafRJ4l4joHRRNzvOF75yb5sRIiuGRFM9NLpQdj0ccDl/ez9GhAW4a7KcvscqC7KppRcMO/Z1RElFtbaXak8awTWRMeRIqHINQTFvptaNwFML91Y+5OcjPQ3bOtglUStVMY5hSjeUYh3g4Tjy8WEWc9/IseAvFyqq8n9f1q1TL03ij1Mbys1ncsTEkn2/0VJRqejVdQRKR+4AbjTFDwA3AHiABpIFzwIMiMrJps1TrNp91OfnMJMMjKe4fTTGTKV/ofKArytFB267v0P4+omGnQTNVGyHsOPR3RenSdcCU0hi2EcJRCHfYx0JCSqufVC3CUbt19IHngniAsclN3wN3wSat8gv2tVKqjMYwpZpLJBQpW89KRMj7eZus8oOElZfXCivVcjTeKLUxvOlpvMlJvXFBqRrVdeU6CEQajFrEhZlMsTrq0TNTuH75D8YrdnYV14+68pIubde3TXTFwuzoiuFoCz+lymgMq4ExtgoqFAsSUTEIx22rPqXWKxSm7FfPUGRxTSsRyEzDwqQmqZSqQmOYUs3JGEM0FCUaKu824vouOS9H2k2TzqfxRdvcqtag8UaptRHPwx0bw19YWH2wUqpIyyq2ERHhqYtzDD9tE1JPj5X3NQ07hmv393LzFQO8bHCAXckVFjlXLccYw0BXlGQ8svpgpZRynCD5FLOVUIVHvVFBNYIx0NEL8R7Iztgkleeu/j6llFKqCYWdMGEnTCKSQOLCgrvAXH6OBXdB76ZXqkbGmOX+scyLSFfF2BcCfwL8EBAFHgZ+V0S+VuW8PcDvA8eAAWwy7i+Au6TiH6gxxgHeBdwGHADGgC8Ad4jI/Jr/cGpb8dNp3PFxxNOb7JSqV03JKWNMDHgN0Af8q4icCvb/LPDLQD9wAvjvInJ2k+aqqsi5Pg8/O8mJUZuQSs3lyo4n42FuPNjPzVfs4PDlfXRqm7dtKRJyuCQZIxbWNltKVdIYVsIJQ3KPTURpWz7VjIyxCapYUpNUSqExTKntwBhDIpIgEUng+i5zuTlmc7Pa+k81lSaON98EPlaxr2whn6AV4TDgAv8DmAbeCvx/xpgfE5F/LRkbBf4FOAR8BPge8GPA/wR2Ab9X8Vl/DrwT+HvgA8BVwetDxphXimhZZDsTEbzJSbzp6UZPRamWtWqmwhiTxAaDq7GLIGaNMa8BdgKfBS4AceyCia8yxhwSkcnNm7KaSue4b3SC4ZEUJ5+ZIJMvj4X7+jo4EqwfdfWeHkLa3m1b64yF2alt/JSqSmNYBScE0USjZ6HU6gpJqniPXY8qNw+5OU1UqbaiMUyp7SfshOmN95KMJZnNzTKTndEklWq4Jo83oyLyN6uM+SOgF7heRB4FMMZ8GngC+Kgx5kUlFVG/hF1T650i8pFg38eNMf8beK8x5q9F5JngHC8B3gEcF5GfKnyYMeYU8GHgdcDnNuRPqVqO5PO2jV822+ipKNXSaimjeTfwIuA3gGeBPwY+CGSBHxWRrwAYY24FPgm8B/jtTZltmxIRnplIF9eP+u65GUrrjB0DL9mTDBJSO7isXy88tgNjDH2JCL2J6OqDlWpfGsOUanWRDrt17gAvD/k05DP2UdenUtubxjCltinHOPTEeuiOdmuSSjWDpo43QbVTVETmqhzrBH4C+HohMQUgInPGmE8A78Mmox4IDv0ckAY+XnGqD2Hb/P0stvoK4PXYZN2HKsZ+HPt39AtocqoteXNzeKkU4mvhnFLrVUty6rXAZ0TkzwCMMXngfwMfKAQoABH5lDHmJ4AfR78UrZvnC4+fnWZ4ZJzhkRTnpjJlxzsiIQ4f6OPmoQFuOjhAT0LXGWonkZDDzu4Y8Yi25VJqFRrDlNpOQhEIBRVVAG4O3AVbXZVPg35BVNuLxjCltrlCkioZTTKTm9EklWqUZo43P41NAoWMMWPA54HfFpFCH7WXAjFsy8FK9wWPNwAPBOtHXQc8LCKZirEPAH4wlpL3+SwmtgAQkYyCFeLQAAAgAElEQVQx5tGKsaoNiO/jpVJ4c0vypEqpNaolOXU5dmHAgpPBY7Uf/N8CXrXeSbWruazLydO2Xd/9pyaYzZS3rtnZFePI0AA3XzHANft6iYadBs1UNVJXLMwObeOnVK00him1nYWjdismq7LllVW66LxqbRrDlGoTxpjyJFVuBk+rg9XWadZ48wDwd8DTQBK4BXg78EPGmKNBJdWeYGy1dbAK+/YGj31AR7WxIpI1xqRKxhKce1xEqvVtOwscNcZERSRXedAY8zbgbQCXXXbZin9I1Rr8bBZ3bAzJ51cfrJSqWS3JKbB3ChSkg8epKuOmAS3hqcPz0xmGR1KcGBnn0eem8fzyiygv2NVl2/UNDXDFJV0YowmJdhWLhOhPROmIarWUUnXSGKZUuwjH7NYRvM5n7FpV2VltAahalcYwpdpIaZJqNj/LdHZak1RqqzRdvBGRmyp2fdoY823gD4B3BY+FdS2qJZAK1VGJisflFgnKlIwpjF9pbGHMkuSUiHwM+BjA4cOH9W6pFudNTeFNTSF645tSG66W5NQFFu9EABukPgA8U2XsZcD4Bsxr2/JF+I8LswwH60eNjs2XHY+EDIcu6ysmpHZ2xxo0U9UsIiGHvs4oXbFac8lKqRIaw5RqZ5G43Tp3QG7etv/zXbt2lZfTyirV7DSGKdWmjDEko0m6I92apFJboZXizZ8Cvwu8GpucKiTSql08iweP6YrH5S60xUvGFMZfssLY0nOqbUhcF3d8HH9hodFTUWrbquVqd1kfVRFZAH59mbFHgO9twLy2lWze45EzU0GFVIrUfPlNFcl4mCNDAxwZHODwgT4SUU1CKAg7Dr2dEbpjYa2YU2rtNIYppaxop91KFdoAulmbsPJdkODGYScEJqRJLNVIGsOUanOFJFUymiTv5cn5ObJelpyXI+fl8EXXWlQbomXijYjkjTHngB3BrnPB494qwwv7Cm38JoGFamONMTFgAPhGye5zwIuNMbEqrf32Ylv+LamaUtuDn07jjo8jnt4YoNRmqiUL8mfAC1YbZIzZhf1B/vH1Tmo7mJjPcf+orY566JlJMm75L42X9Sc4MtjPzVfs4KrdSUK6fpAq0ZuI0peIaFJKqfXTGKaUWl6hDeBK3CzMXbSPSm0tjWFKqaJIKEIkFKEzsnijRc7Lkc6nmc3N4oleQFVr1jLxxhgTB/YB9wW7Hse23jtSZfjLgseTACLiG2MeBg5VSTjdCDgsrrcF8CB2fa0bgW9WzOFa4N51/4FU0xERvMlJvOnpRk9FqbawanJKRO4H7q9h3AXg+o2YVCsSEU6n0pwI2vV97/wMpffYOgau3ttTbNe3vz+x7LlU+4qEHHZ2x4hHdF0ppTaCxjCl1LqFY9C7HzLTkE6Br3epq62hMUwptZpoKEo0FKUn1sNcfo6Z3Ax5L9/oaakW04zxxhgzICKpKofej72W+aVgTnPGmC8Bx4wx14jIY8H7u4BfAp4CHih5/98CNwNvAz5Ssv/dgAt8oWTf54H3Bse+WbL/rdi1pj675j+gakqSy+GOjeHntCBOqa2i/ePWwfV8Hj87XVw/6vx0pux4IhrihgP9HBka4KaD/fR06BrFannd8QgDnVEcraJTSimlmk+8B2JJ2wYwO2vXsNJ2f0oppZqAMYbuaDfd0W5yXo65/Bzz+Xldp0q1st82xrwM+DfgWaALuAX4YWwirTSx9N+AHwG+Yoz5c2AGm0DaC7xapOwXto8DbwI+aIw5gG1ReAvwk8Dvi8ipwkARedwY81Hg7caY48CXgauAd2Lb/31ug//MqoG8uTm8VArRG9GU2lI1JaeMMbuB1wLzwD0iMm+MCQG3Aj8UnOcB4JMiMrdZk20Gvi987cmLnBhJcf+pCeaybtnxS7pjHBmy1VHX7OslGnYaNFPVKkKOYUdXjM6Y5oqV2gwaw5RSG8aYxbWrRCC/AG5mcd0qTVapDaYxTClVr2goSn+on75YH/P5eaZz01pNpVbVhPHm68CLg88fADxsFdRvAR8UkeLd4SLytDHmZuCPgd8EosDDwI/K/8/encdLdtd1/n99aq976669L+kk4LCYmYxgUFmCgCCrDj/5qYAIkSXMKEtAdIRxorgEFwII8psfQRBkUcBJMMwIKCqCQAaiI8QAExajIR1Id6c7vd2tqr7zx6nbXX1zl7p9b92quvf1fDzuo7pOfeucTwW6P7fO+3y/J6VPtO80pTQbEY8HfgN4Vmvf3wBeCrx1kTquAm4nm2n1VOAwWTB2dUre9G0zSM0m9cOHaZ461etSpC0p0gpfoiPiYrJ1VieAAG4lW8v1A8CTFwz/GvCIJabebgrlPf8m7Xnem87Z9sBdIzz8/pM88v7bud+OYe8TpI5VS3l21MoU8oaY6m8R8fcppct6Xcdq2cPOddlll6Wbb7555YGSVq/ZbAVVUzBzr8v/9YvqOFHbYQ8bcPYvaW1OzZ3i3pl7mW24VNUguXj84g3pX/ab7rKH9a/mzAz1Q4dIcwb40nrKlUqU9u/vqId1ckb81UCF7CqBpwF5sjVafwj4T8B2YHdr3HcBv3KedQ+MYj74/osnuerx/4YPXPkD/LfnPJTnPvwi7r+zZjCljk0MldgzVjWYkrrLHiZpY+RyUBqC4W0wcTHUdkDeWdFaE3uYpHUxXBxmb20vO4d2Us6Xe12O+o/9RltO49gx6nfdZTAl9Vgn35gfB7wtpfQOgIhoAB8FXp9SelvbuN+OiH9L1shetu6V9ol941U+/LOPpFrK97oUDahcBDtHywyVPGElbQB7mKSNF5Hdo6oyBtPHYeoeaNRXfp90LnuYpHU1VBxiqDjEVH2Ke2fuZbo+vfKbtBXYb7RlpHo9W8ZvaqrXpUiis5lTe8mm9M67pfX4mUXG/h3ZDQc3rVqlYDCl81Yq5Ng7XjWYkjaOPUxSb1VGYfzCbEZVztnSWhV7mKSuqBaq7B7ezZ7aHqqFaq/LUe/Zb7QlNE+fZu7gQYMpqY90cob8CNkU3nnzf55YZOwkMLPWoqTNaLRaZNtwyaUfpY1lD5PUexFQnYDKOEwfg6lj0Gz0uir1P3uYpK4q58vsGt7FTGOGe2fu5fTc6V6XpN6w32hTSynRuOceGseP97oUSQt0cvnm/waeHxETkZ1V/wWyRvRjEVGZHxQRY8CLOPdqC2nLy+eCXaMVttfKBlPSxrOHSeof8yHVxEVQ2wlFr1bXsuxhkjZEOV9m59BO9tb2Mlwc7nU52nj2G21aaXaW+sGDBlNSn+pk5tTrgE8DdwHTwAjw/wN3Av8UEf+jtZ+nA3uA3+xOqdLgKeZz7BqtUCq4jI/UI/YwSf0nIlvurzKazaCaPQkzJ2HOJUZ0DnuYpA1VypfYMbSD8eY4x2eOc3LuJCmlXpel7rPfaFNqnDhB48gR/x2T+tiK4VRK6XMR8TTgFWQN6uPANUAT+G7O3gSxAbxh/gaK0lZXKuTYPVqhkDeYknrFHiap7+XyUBnLfuaDqrlpaMxmP36Z3rLsYZJ6pZgrsq26jbHyGPfO3GtItcnZb7TZpGaT+uHDNE+d6nUpklbQycwpUkofAz62yEvPiYhXk90M8f+klI6uZ3HSoBouF9hRK5PLuYyf1Gvd6mERkQNeDrwYuAg4BHwQuDqltOxvwRHxq8CvLDOknlIqLnjPA4HfBn4QKAH/APxKSumvV1O3pD7WHlTNm5uCqaMw631AtiJ7mKReKuQKZ0Kq0/XTTNenmanP0EjeN3Gz8byfNovmzAz1Q4dIc3O9LkVSBzoKp5aTUroDuGMdapEGXj4XTA6XGKkUVx4sqefW2MPeSHYV4Q3AtcCDW88fEhGPTyk1l3nv9cDXF9l+Kdka7x9p3xgR9wc+C9SB3wHuJVvv/eMR8eSU0ifO8zNI6nfFavZTn4Hpe2HmhLOpBNjDJG2cQq7AaGmU0dIoAKfnTnN89jjT9ekeV6aN4Hk/DYrGsWM0jh1zpqc0QNYcTknKDJcLbK+VyTtbStr0IuIS4KXA9SmlZ7Rt/2fgzcAzgfcv9f6U0peALy2y37e1/rhwqYzXAePA96aU/rE19o/Ibkb81oh4UPI3cGlzK5ShthOGd2QB1cwJ71Gl82IPk7RWQ8UhhopDTNenOTl3kqn6FI2ms6kk9Uaq17Nl/Kb83VgaNCveDCciZiLiQxHxpIjwrLu0iPGhErtGKwZTUp/pYg97FhDAmxZsfztwGnjOancYEUNkJwTvpG1JjYgYBn4U+OT8ST2AlNJJ4A+ABwAPW+3xJA2oCKiMwtg+mLwYajuymVXadOxhkvpdpVBhe3U7F4xcwL6RfWyvbme0PEqlUCEX3nt5UHjeT4OseeoUcwcPGkxJA6qT3xaKwDOA/wn8S0T8akRcuF4FREQuIl4REV+NiOmIuCMirm19kVnpvb8aEWmZn/ssMBoRD4yID0fE0Yg4FRGfjojHrdfn0dYSEewYKTM5XOp1KZIW160e9jCyGwR/vn1jSmka+EfO70TbTwCjwB+mdM5C/pcCZeBzi7znprZ6JG018/eoGtsHExdCdRxyngzcROxhkgZGMVekVqoxWZlk9/BuDoweYG9tL5PVSYaLw4ZV/a2r5/2kbkgpUT9yhLm77yY1nLkpDapOfzv4XbIr20aBq4FvRMTHI+LHI2KtN9d5I/AG4Mtky0t8iGy984+0btS7nOuBn17k53dbry+13vnDydY7/wWgRrbe+ePX+Dm0xeRzwe7RiveXkvpfN3rYXuBwSmlmkdfuBLZHxGpT6xcACXjnIsea3+9ix4LsBsWStrJ8EYa3w8TFMLLL2VSbhz1M0sAq5UuMlkbZMbSDC0YuYMfQDqqFKk7O6UvdPO8nras0O0v94EEax4/3uhRJa9TpPae+mFJ6f0RcBfwk2ZePJwCPB+5prRn+zpTSras5uOuda1CVCjl2jVYo5r36SxoA3ehhQ8BiJ/UAptvGzHays4h4IPAo4K9SSv+8yLFY4njTC8Ystu8rgSsBDhw40Ek5kgZZBJRHsp/GXHZvqtmTUO/onyP1ny3bw+xf0uYSEQwXhxkuDtNoNjhVP8Wp2VPMNJb650gbrCvn/aT11jhxgsaRI3j6VtocVnVmPaU0lVJ6V0rpcuBBwLVAHXgF8KWI+GxEPH8Vu3S9cw2cWqXAvvGqwZQ0YNa5h50mW6ZoMZW2MZ16QevxD5Y4Fkscb8VjpZSuSyldllK6bMeOHasoSdLAyxdhaBLGD2Q/1YksvNLA2Yo9zP4lbV75XJ7R0ih7anvYV9vHWHmMQq7Ta6fVTV047yeti9RoMPedu6kfPmwwJW0i5312PaV0W0rpF4H9ZGvTfpQs3Hn7KnbjeucaGBHBtlqZnSMVlyGQBtw69LCDZMseLXaybR/ZckmdXnFeAJ4L3APcsMSx5ve72LFg8eWSJOmsQgmGt8HERYZUA84eJmkzKeaLTFQm2D+yn93Du6mVat6fqk+s03k/ac2a09PMHTxI8/SpXpciaZ2tueOnlBoppRtSSk8DLgR+eRVvd71zDYT5+0uNVV1qWdpM1tDDvkDWQ7+vfWNEVIDvAW5eRRk/AuwC3rNEP7yFbDmkhy/y2g+0HldzPElbWS5/NqQamsyeayDZwyRtNpVChe3V7VwwcgE7h3YyVBzywtA+sMbzftKa1I8eZe6uu0j1eq9LkdQF63o5SkrpYErpdat4S6frnXekbb3zv17v9c4j4uaIuPmew4c7LUebRKmQY+94lWrJkzfSZrbKHvYBsgshrlqw/UVkveR98xsi4v4R8aBl9jW/HNLC+yTO13US+AjwmIj49237rQEvBL7GghnIkrSiXD4LpyYvhpHdUKz2uiKtgT1M0mYSEQwVh9g5tJP9tf1MVicp55dajVQb6TzO+0nnJdXrzN11F41jx3pdiqQu6mRR39cCX+rS8U8DO5d4ra/WOweuA7j0IQ91YdMtZLhcYEetTC7n1VrSgOpKD0sp3RIRbwVeEhHXA38OPBh4GfC3wPvbhv8V2RWG9/mHJCL2Ak8CPp9SumWZQ74a+CHgLyLijcBxspOI+4CnJhfdlrQW5Vr2U5+F6Xth5jj4z0o/sIdJ2vLm7081WhpltjHLidkTnJo7RTM1e13aZtLN837SqjVPnaJ+5Aip0Vh5sKSBtmI4lVJ6bRePfxD47ogoL7IMhOudq6fGh0pMDq92VUlJ/aTLPewq4HbgSuCpwGHgLcDVKXX8bfkKIM/iF1WckVL6ekQ8Evgt4JeAEvAPwJNSSp84n+Il6T4KJajtgKFtMHsiC6rqHf0qri6wh0nSuUr5Etuq2xgvj3Ns5hgnZk/0uqRNocv9RupYSonGkSM0Tvh3W9oqOpk51U1fAH6YbL3zT89vbFvv/FOr2Nf8eue/53rnWot8LthWK1Mr9/qvh6R+llJqANe2fpYbd9Eyr10DXNPh8b4C/IdVlChJ5yeXg8pY9jM3nYVUsyedTbWJ2MMkDbJ8Ls+26jZGS6PcM30PU/WpXpckaY2as7M0Dh2iOeuFUdJWsuI9pyKi0AqL2rftjIjXRcTnIuKrEfGpiPjPEdHx/aFaXO9cfWWkUmT/xJDBlLRJdLmHSdLmV6zAyC6YuAiGt0G+2OuKtgx7mCQtr5gvsmt4F7uGd1HKu+rJ+bLfqNcax49TP3jQYEragjo5A/9HwCTZeuJExHeRzXLaBTTIloC4P/Ao4NkR8eiU0r2dHNz1ztUvSoUc22tlKsV8r0uRtL661sMkaUvJ5aE6kf3Mns5mU82ddjZVd9nDJKkD1UKVaq3KidkTHJs5RqPpfWpWyX6jnkiNBvXDR2iePtXrUiT1yIozp8iWvPt02/PfA8aBnwEqKaU9wDDwcuC7gV9bZQ1XAa8CLgHeCjyTbL3zp3VjvXPgkcBNZOudvx44Rbbe+cdXWbc2gYhg23CZfeNVgylpc+p2D5Okrac0BKN7YPxCGJrMgit1gz1MklZhpDTCvto+xspjRNznumYtzX6jDdecnmbu4EGDKWmL62Tm1B7gIEBE5IHHA7+RUnr3/ICU0izwltayez9G1rA64nrn6pVyMc/OkTLFfCcZraQB1dUeJklbWr6QhVNDkzBzEmaOZ7OqtF7sYZK0SrnIMVGZYKQ0wrGZY5ycPdnrkgaB/UYbqn70KI1jx3pdhqQ+0MlZ+ZNkV0wAlIEi8KUlxn4J2LEOdUldVasU2DtWMZiSNj97mCRthHINRvfCxIVQHYecv2OtA3uYJJ2nQq7A9up29tT2UClUVn7D1ma/0YZI9Tpzd91lMCXpjE6+NX4WeAZASuk08A3g8iXGXk7ragupX20bLrNzpOI0f2lrsIdJ0kbKF2F4O0xcDLWdUPSE4BrYwyRpjcr5MruHd7NzaCfFfLHX5fQr+426rnnqVLaM3/R0r0uR1Ec6Cad+C/iBiHhbRIwDvwC8NCJeGxH3i4ihiHhARLwReBbwJ90sWDpfuQh2j1UYG/IXUmkLsYdJUi9EQGUUxvbD+AXZn70waLXsYZK0ToaKQ+wd3stkdZJ8eK/EBew36pqUEvXDh5m7+25So9HrciT1mRXvOZVS+lxEvAj4b8DzgK8CJ4Bfbv3MC7IbKP56F+qU1qSYz7FrtEKp4BIz0lZiD5OkPlAoZ7OohrZn96WaOQ712V5X1ffsYZK0viKC0dIoI8URTtdPc2L2BNN1Z3HYb9QtzdlZ6ncfIs35e5+kxXV0pj6l9IfAQ4H3AzuBSbKmFMAUWXN6EfDYlNJUd0qVzs9QqcC+8arBlLRF2cMkqU/kctn9qMYPwNg+KA33uqK+Zw+TpPUXEQwXh9k9vJt9I/sYL4+Tz23t2VT2G623xvHj1A8eNJiStKwVZ07NSyl9GXg+QESMAiPAVErpni7VJq3ZWLXItlq512VI6jF7mCT1mWI1+6nPwNRRmDnZ64r6lj1MkrqnmCsyXhlnvDLOVH2Kk7MnOV0/TUqp16VtOPuN1kNqNKgfPkLz9KlelyJpAHQcTrVLKR0Hjq9zLdK6iQi210qMVLy/lKRz2cMkqY8UyjCyG6qzrZDqRK8r6mv2MEnqnmqhSrVQpdFscKp+ipOzJ5ltbM1ZH/YbnY/m1BT1w4dJ9XqvS5E0INa8zllEbIuIN0fEpetRkLRWhVyOPWMVgylJK7KHSVKfKJRgZBdMXAjlEYjodUV9zx4mSd2Rz+UZLY2yt7aXPbU9jJRGyMXWvU2A/UYrSSlRP3qUuW9/22BK0qqsR3cdB34OuP867Etak3Ixz97xCpXi1l4vWlLH7GGS1E/yxSykGj8AlVFDquXZwySpy8r5Mtuq27hg5AK2V7dTKVR6XVIv2G+0pDQ3R/3b36Zx7FivS5E0gFZc1i8iblxhyBDZDRJfHRE/A6SU0n9Yj+Kk1ahVCuyolQlPYkhqsYdJ0oDKF6G2E6oTZ5f722L3/7CHSVL/iAhqpRq1Uo255hynZk9xYu4EjWaj16Wtmf1G56tx8hSNI4dJzWavS5E0oDq559TTgETWiJaSgMva/ixtqMnhEuNDpV6XIan/2MMkaZCdCakmYeqerRZS2cMkqQ8Vc0XGK+OMV8aZqk9xcvYkp+unSYPbn+w3WpWUEo0jR2ic8F6hktamk2X9/hdwGnglkE8p5dp/gAeQNbD/t7XN9dS0YXIR7B6rGExJWoo9TJI2g3whC6nGL4TK2FZZ7s8eJkl9rlqosmNoB/tr+5msTlLKD+S5CfuNOtacnWXuzoMGU5LWRSfh1COAVwOvBW6OiIcteN0rJtQTxXyOveNVhkqdTACUtEXZwyRpM8kXoLYjC6mqE5Db1Deot4dJ0oDI5/KMlkbZW9vL3tpeRkoj5GJgepT9Rh1pHD9O/eBB0txsr0uRtEms2ClT5veBS4A7gM9FxO9HxGjXq5OWMFIpsn+iSqkwML/sSeoBe5gkbVL5Agxvg4mLs7CqMJBXqi/LHiZJg6mUL7Gtuo0LRi5gx9AOKoVKr0talv1GK0mNBnPf+Q71I0cGeflKSX2o4zP7KaVvpZSeDjwT+H+Ar0bEM7tWmbSIXAQ7RyvsGCkTW2M5F0nrwB4mSZtURLbM3/gBGL8AKqObbsk/e5gkDaaIYLg4zO7h3ewf2c9EZaKvl/2z32gxzakp5g4epHn6dK9LkbQJrXraSUrpT4EHAx8B3gd8EKf4agOUCjn2TVSplV3GT9L5sYdJ0iZWKGf3pZq8X/ZY7O8r1VfLHiZJg6uQKzBWHjuz7N9YeYx8rj9v3WS/EUBKifrRo8x9+9uker3X5UjapM5rTbSU0vGU0ouBHwQKwHHABUfVNbVKgX3jVYp5l/GTtDb2MEna5CKyGVRj+2FoW6+rWVf2MEkafKV8iYnKBBeMXMDu4d3USjXy0V9Blf1ma0tzc9TvuovGsWO9LkXSJremKSgppb8D/v061SLdR0QwOVxirFrsdSmSNhl7mCRtAZtsib959jBJ2hwqhQqVQoVUSUzVpzg1d4rT9dN9c18f+83W0zh5isaRw6Rms9elSNoCXB9NfauYz7FztEy50F9XEEmSJEmSJK2XiGCoOMRQcYhmanJ6zvv7aGOlZpPGkSM0Tp7sdSmStpB1XSMtIi6PiKvXc5/amkYqRfZPVA2mJG0Ye5gkaVDZwyRp88hFjlqp1usyFmW/2Zyas7PMHbzLYErShlvvG/g8GviVdd6ntpBcBDtHK+wYKRObdBkWSX3LHiZJGlT2MEnSRrDfbDKNe++lfvAgac5biknaeC7rp75RLubZOVKmmF/vzFSSJEmSJEkSQGo0qB8+TPO0S0hK6p0Vw6mIeOcq9udNEnVeRqtFtg2XnC0laV3ZwyRJg8oeJknaCPabrac5NUX98GFSvd7rUiRtcZ3MnLoCSECnqUE672q05UQE22slRirFXpciaXO6AnuYJGkwXYE9TJLUfVdgv9kSUko0jh2jcexYr0uRJKCzcOow8HngeR2M/XngP6+pIm0Z+Vywa7RCpZjvdSmSNi97mCRpUNnDJEkbwX6zBaS5OeqHDtGcmel1KZJ0Rifh1N8D/zaldGSlgRFxau0laSvIRbB7rEK5YDAlqavsYZKkQWUPkyRtBPvNJtc4eYrGkcOkZrPXpUjSOXIdjPkH4EBEbOtgbND5NGBtURHZjCmDKUkbwB4mSRpU9jBJ0kaw32xSqdmkfugQ9UN3G0xJ6kudhFNvAR4LTK80MKX0GymlTvapLWx7rUS1ZDAlaUPYwyRJg8oeJknaCPabTag5M8PcwbtonDzZ61IkaUkrLuuXUvo28O0NqEVbwLbhMiOVYq/LkLRF2MMkSYPKHiZJ2gj2m82nce+9NI4eJaXU61IkaVle7aANMzlcYmzIYErS5hARuYh4RUR8NSKmI+KOiLg2IoZXsY/JiHh9RHy9tY9DEfE3EXH5gnEREf8xIv53RExFxLGI+FhE/MD6fzJJ0mZnD5MkabA0O1iWLzUazH3nO9TvucdgStJAWHHmVLuIuAx4GLAPGAJOA3cCN6eUvrD+5WmzGB8qMT5U6nUZkrawLvSwNwIvA24ArgUe3Hr+kIh4fEpp2W8PEXEh8EmgBrwDuA0YAy5t1dju/wP+Y2v8L7bqvxL424h4Ykrpk+dRvyRpQNjDJEkbwfN+/enIkSNceuml3HLLLUxOTi46pjk1Rf3QIVKjscHVSdL56yiciojHAW8FHsDiNz5MEXEb8NKU0ic6PXhE5ICXAy8GLgIOAR8Erk4pnepwH5PAa4CnA/uBE8A/tfbx6bZx0TrOi4EHATPATcCvppRu6rRmrd5YtcjksMGUpN7oRg+LiEuAlwLXp5Se0bb9n4E3A88E3r/Cbt5L1ocvTSndtcyxvofspN7HgKek1iVwEfE24KvAdRHxoJVOJEqSBo89TJK0Ebp13k/r48Ybb+TgwYPceOONXHHFFee8llKicfQojXvv7U1xkrQGKy7rFxGPJfsyUQV+Bfhh4BLg/q3HHzPkpscAACAASURBVAZ+leyKij9vje/UG4E3AF8m+4L0IbIr9j7SCq5Wqu1C4O+B5wF/CvwscA1wO4tfsfffgGNkV+y9Dvg3ZFfsPWYVNWsVJoZKbKuVe12GpC2qiz3sWWRf2t60YPvbya4ufM4KdT0aeBTwOymluyKiGBFDSwyfr+ndqW1thpTSMeDPyHrZIzusW5I0IOxhkqSN0OXzfgNvzUvhNpuken1NNbzrXe8653FempujftddBlOSBlYnM6deC9wCPHqJ2UxfAT4REW8EPk3WsP5mpZ16xd7mt2247D2mJPVaV3oY2VIXTeDz7RtTStMR8Y+t15fzlNbjv0bER4AnA/mI+Brwayml97aNnU/4Ty+yn/ltP9CqX5K0edjDJEkboVv9ZrNY01K4qV5n9o47iGKJXKVMVKrZY6GzO60cP36cm27KFny66aabOH78OKOjozROnqRx5Aipg3tRSVK/WnF2EvBQ4O0rLbOXUjoJXAd8b4fH9oq9TWz7iMGUpL7QrR62FzicUppZ5LU7ge0Rsdx6pg9sPb4dmCSbAfwCYBZ4T0T8TNvYW1uPj2vfQWu52h9sPb1gqQNFxJURcXNE3Hzo0KFlSpIk9Zkt3cPsX5K0YbrVbwbeggvrfyyl9PaU0iuBV5Kda3xmp/tKc7M0TpygfuhuZu+4g9lv3Un98GEaJ0+duU/U0aNH+eIXv3jOz28+4IEU57KZV4W5OX7zAQ/g7//mb/iHT32KL956K1/6ylc46swpSQOqk5h+FhjtcH+jrfGd8Iq9TWrHSJmRisGUpL7QrR42RHbvwsVMt41Zan8jrccTwGNTSrMAEXED8E3gmoh4d+sqvI+SLX/7sxFxELi+te9XAv+27ViLSildR/YlkssuuywtNU6S1He2dA+zf0nShulWv9kMlruw/rfILqxfdtWn5tQU0/90K+QCIpc9EkTb84ggSmXe+s538M73vY9isUihVKJ+6jRNEqP5HDVyNBP8yT338MdPfCJUq0zPzTE9M8PPXXEFV//8z0MERBDzx4mAXI7smhBJ6j+dhFOfAl4RER9NKd2y1KCI+HfAK4C/7fDYK12x94iIKM1/2VlE+xV7XyO7Yq9M9kXnPRFRTCn9YWtM+xV7N7bV3NFV5+pMRLBjpEyt3NnUZEnaAN3qYaeBnUu8Vmkbs5Sp1uMft/e5lNLRiLgReC5Zn/tKSqkeEU8G3g38dusH4EvAL5EtLXG8w7olSYPDHiZJ2gjd6jebwVovrGfujm9x8Bd+oaODPQN4xsX3W32Vn/kst3/ms8uPiQVhVS53Nsya/3PrMdpey8aTBWmLvT8XBAueR/v+aO0vt+hrxCL7awvxznke8/W1aotctu8lXjv7/OyYaNvvmefRPiYLC4kc5HNn/7zwtYXvz+U4Gzq2/bdsDwrbn+dy93nt7P7Ohpbz+z1z/NyC/x1ikePlc221LlGLoaX6RCcpwi8CnwP+ISL+BriJLDyaIQuD9gEPBx4D3Ev2JaMTA3HFXmufVwJXAuzbb4a1mIhg50iZYYMpSf2lWz3sIPDdEVFe5CKLfWQXXyx3ReG3Wo/fXuS1+XsoTsxvSCn9K/DYiDgAXAQcSSndGhE/2xry1Q7rliQNDnuYJGkjdKvfbAbndWF9+3nES8qVRd7aAyllP80mC6cjOz15C1smtFwYVGZBYPvzsyHgOe+HxUPMOBtI3jeoXCRIXDQEjXPff6YGlggxFwaJi7y2SAgaC0LDM2HhYsfOLRFaLhaCngkKlwoOFwSTi4SW9w1JFwTKiwXOi41dIkA9s/8NsmKSkFK6LSIeRjZd9UeAxy8ybIbsxoCvTil9o8NjD8wVe+1LSlz6kIf6b/YCxXyOHSNlKsV8r0uRpHN0sYd9Afhh4PtoWxI2IirA95BdfbiczwP/Edi/yGvz2+5e+ELrBN+/tm16CtmVfB/vsG5J0oCwh0mSNkIX+81mcF4X1refR/zeSy9NF19/fRYKNZttIVECUrat2YQEKc3/OXHi5Eme/JSncqhRJ4AckCOIgN2FAn/213/NcD5Pc3qGND1NatRJbQEUKWXP54/ZTNn+E9A6zpnxjWarlnTmtTPvb71GatXaen9q2y+pbV/N1jHn99ds3OezkRLpzH5b+5t/3nr/mf2lVi1t+z3zWc7U26qN9npan7P9s7UfPy14fs7/Ns2zr7d/lrTwsy3+/oX7OnO89mP2i2VCSzC43LIWhpYLw7RlZ14G5DrPCDqa5pJS+ibwExExBFxKduXAEFl4dBC4ZaUbJy7CK/Y2gWopz86RCvmcU0El9acu9bAPAK8BruLc+xW+qLXv981viIj7A8WUUnuf+TDwe8BzIuI3WjcXJiL2AE8HvpZS+vpyBUTEjwJPBd6dUvqXVdYvSRoA9jBJ0kboUr/ZDNZ6YT0Akc9DPk+nZ84igpEDB/jKzDQziwQZB+fmGP2e76FcLp/Z1pydJU1NtcKqqVZYo360ZIgIi4SY7UFdWiE4TCu/v9EktYV4i4aYzbP7Ped5o3mfEPA+tbQHh7S9Py083mIh5kohYDN7vlSI2WwPHluh6Jnwt+2/06LvX/D8nFA1nfv+tjFpydcW+W+7cMxi7+kXGxharmoNtpTSabLpvevBK/YG3PhQicnhUq/LkKSOrGcPSyndEhFvBV4SEdcDfw48GHgZ2Rrs7TfF/SvgQjj7XaQ1y/dVwNuAmyLinUAJ+E+tx5e0Hy8i3tF6/z+SzRx+FPBTZL305evxmSRJ/cseJknaCOt83m8zWOuF9asWxRKFHdv5+F/8BeWREWaOH6cSQQPIA9MpUR4Z4S//8i952tOeduZ9uVIJSiXyY9nzs2HVdDazqp9OfG9x5yzpBh2Hltr8zpmxt9iMw6XCrWYiNRvzO1k8xGyfEXif2YjLhJitoO7cIHGZ2pqJyOfghS/s6DP38gZBXrE3oPK5YMdImaGS95eStKVdBdxOtpb4U4HDwFuAq1v3O1xWSum6iDhMtsb7r5NdKPE54Nkppc8sGP554MVk98gtAV8HrgbemFKaQpKk1bGHSZK0srVeWL8q+VqN/LZtRC7He97zHo4fP87Q0BAveMELuOaaa3j1q1/NO97xDo4fP8573vOec8Kphc6GVVla1ZzJlv8zrJL6V0RAa6YlDG5wmSt1Ppkl0grrXEbEDHAj8A7g42mlN6xCRLyF7Mq6Gzj3ir3PAI+b/2IUEbcDF6aUYsH7ryS7Yu9WoP2KvT3A01JKf9E2dqkr9v4BeEJK6d5Oar70IQ9NH/6Lde09A6VczLNzpEwxn+t1KZI2UET8fUrpsl7XsVrd7GGD6LLLLks333xzr8uQpA1lDxt89i9JW9FG9S/7zdIi4t8BXwRuSCk9o237S4E3Az+dUnrvcvv43ksvTZ/9sz9b/ji5HPlt28nXhgGYnZ1lYmKCQqHABz/4QZ74xCeeGfuxj32Mn/zJn6TRaHD06FGKxeJ5fbbmzEw2s6oVWhlWSVovuVKJ0v79HfWwThKGItlVbv8T+JeI+NWIuHCtRbZcBbwKuAR4K/BMsiv2ntbpFXut2k6SXbH3X4D/Azy2PZhq+TzZVQ2/TtZAHkp2xd4PdhpMbXVj1SJ7xyoGU5IGSTd7mCRJ3WQPkyRtBPvNElJKt5Cdr/yxiLg+Il4YEdcCb+C+S+Gel1y5THHv3jPBFECj0eB5z3set9122znBFMCTnvQkbrvtNp773OdSr9fXdNz8+DjFXbsoHjhAce9eChMT5KpVIud5P0kbo5OZU03gd4Fx4CeBUbJlG/4K+APgwymluS7X2Te24swpl/GTNMBXndvD2njluaStyB42+OxfkraiDZw5Zb9ZRkTkyS6uvxK4iGwp3A+QLYV7cqX3LzdzKj8+Tn58PFvKq4+klEgLlwF0Qp2kDq33zCmAL6aUXky2XN7zydYTfwLwJ8DBiLg2Ii4574rVt6qlPPsnhgymJA0ye5gkaVDZwyRJG8F+s4SUUiOldG1K6YEppXJKaV9K6ZWdBFNLiUKB4u7dFCYm+i6Yguy+N7lKJZtZtXs3xQsvpLhnz9mZVX1Ys6TBtKp5mimlqZTSu1JKlwMPAq4F6sArgC9FxGcj4vldqFMbLCLYNlxmz1iVfM6mI2nw2cMkSYPKHiZJ2gj2m+7LDQ1R3LuXXLXa61I6tlRYlR8fN6yStCbnvYhoSum2lNIvAvvJ1qb9KPAw4O3rVJt6pJjPsWeswtjQ+d1UUZL6nT1MkjSo7GGSpI1gv1lfEUFhcpLirl1EPt/rctZkPqwqTEycDat2787CqkrFsEpSx9a8VltKqQHcANwQEXuB5625KvVMrVJg+3CZnLOlJG0B9jBJ0qCyh0mSNoL9Zu2iWKKwcwe5UqnXpXRFRBDV6pnZYCmlc+9XNTPjPaskLWpdbySUUjoIvG4996mNkYtgW63ESMXZUpK2JnuYJGlQ2cMkSRvBfnMe8nmKe/cQufNevGrgGFZJ6lQn4dRrgS91uxD1TiGXY9dYmXJhsKcVS9Ii7GGSpEFlD5MkbQT7TRdFPr+lgqnFLBtWTU2RZmcNq6QtasVwKqX02o0oRL1RLubZPVoh7zJ+kjYhe5gkaVDZwyRJG8F+o412Tlg1MUFqNrOwambGsEraYtZ1WT8NllqlwI5a2RsVSpIkSZIkSdpwkcsRQ0PkhoYWDauaMzO9LlFSlxhObUERweRwibGq95eSJEmSJEmS1B+WDKta96wyrJI2j3ULpyLigcCXgZRSMvTqU4Vcjp2jZSpF7y8lSfPsYZKkQWUPkyRtBPuNeuWcsArODaumpmjOzva4Qknnaz2byRxwB+CioH2qWsqzo1amkN/aN2KUpEXYwyRJg8oeJknaCPYb9YX7hFWNxrnLABpWSQNj3cKplNI3gYvWa39aXxNDJSaGS70uQ5L6kj1MkjSo7GGSpI1gv1G/inyeGB4mNzwMtIVV88sAGlZJfeu8wqmIKABDwOmUUn19S9J6ykWwa7RCteQyfpIE9jBJ0uCyh0mSNoL9RoPMsEoaHB2v7xYRz4yI/xER3wFmgKPATER8p7X9WV2rUuclnwt2jxlMSZI9TJI0qOxhkqSNYL/RZhX5PLnhYQrbtlHct4/SgQMUduwkPzpKFF1lSuqlFWdORcQQcCPwOOA08I/AJ4FpoALsAx4DPDkiXgj8SErpdJfqVYfmg6lywWBK0tZlD5MkDSp7mCRpI9hvtNVEPk++Ngy1szOrmlPTpJnp7HHOmVXSRulkWb9fAx4NvAx4e0ppZuGAiCgDVwLXAq8FfmE9i9TqFPM5do1WKBU6nhgnSZuVPUySNKjsYZKkjWC/0ZZ2n7CqXqc5PUOansoeDaukrukknPoJ4M0ppd9fakCrcb0lIi4CnolNqmeGywV21MrkctHrUiSpH9jDJEmDyh4mSdoI9hupTRQK5GuFJcKqadLcXI8rlDaPTsKpHcBXOtzfl4Ht51+O1mJyuMT4kGulSlIbe5gkaVDZwyRJG8F+Iy1j8bBqmjQ9bVglrVEn677dDjypw/09pTVeGyifC/aMVQ2mJOm+bsceJkkaTLdjD5Mkdd/t2G+kjmVhVY3C9u2U9u+ndMEFFHbsIF+rEcVir8uTBkon4dR1wDMi4oMR8YiIOOdvWUQUI+KREfEh4Omt8dogpUKOveNVqqV8r0uRpH5kD5MkDSp7mCRpI9hvpDU4E1bt2JGFVfv3G1ZJHepkWb83ARcBLwGeATQj4jAwA5TJpvPOh1xvbY3XBqhVsvtLRXh/KUlagj1MkjSo7GGSpI1gv5HWURSL5ItFqNUASHNz5y4DWK/3uEKpf6wYTqWUEvDyiHgb8GzgMmAvMAQcB74IfAH4QErpn7pYq1oigm21EqMV03dJWo49TJI0qOxhkqSNYL+RuutMWDUyApwNq5pTU6SZGcMqbWmdzJwCIKX0ZeCXu1iLOlDI5dg5WqZSdBk/SeqUPUzawm77S/js78Gxf4HxC+ERL4cHPKHXVUkds4dJOh+f/tanedet7+LOk3eyr7aPKy65gsv3X97rstTH7DfSxpgPq/LzYdXsLM2ZGcMqbUkdh1PqvXIxz66RMoV8J7cKW51PfvVu3vapb3LH0dNcMDHEix99Px7zoJ3rfhxJkqQNc9tfwkdfBbkSVCbgxHey57zegEqStGl9+luf5pr/dQ3FfJHR0iiHpg5xzf+6htfwGgMqSeozUSqRL5UMq7QlrX/Koa4YLhfYO1bpWjB19Y23cveJacarRe4+Mc3VN97KJ79697ofS5IkacN89veyYKo0BBHZY66UbZckaZN6163vopgvUi1UiQiqhSrFfJF33fquXpcmSVpBtIKq4s6dlC64gNK+fRS2bSM3PEzkXUlLm4szpwbAWLXItlq5a/t/26e+STEfDJWy/zsMlQqcnq3ztk9909lTkiRpcB37l2zGVLtiFY79a2/qkSRpA9x58k5GS6PnbKvkK9x58s4eVSRJOl9nZlaNZv+up9nZ7J5V09Ok6WlSo9HjCqXzZzjV57bVyoxVi109xh1HTzO+4BjVYp5vHT3d1eNKkiR11fiF2VJ+paGz2+amYPxA72qSJKnL9tX2cWjqENVC9cy26cY0+2r7eliVJGk9LAyrmrOzpOlpmlPTpBnDKg0Wl/XrU7kIdo9Vuh5MAVwwMcTU3Ln/cE3NNdg/MbTEOyRJkgbAI14OzVmYPQ0pZY/N2Wy7JEmb1BWXXMFcY46p+hQpJabqU8w15rjikit6XZokaZ3lWkFVcddOSgcOUNy3j8LkJLkhlwFU/zOc6kOFXI4945Uzy+x124sffT/mGonTs3VSyh7nGokXP/p+G3J8SZKkrnjAE+DJr4eRXTB9LHt88uuz7ZIkbVKX77+c13z/a9hR3cHx2ePsqO7gNd//Gi7ff3mvS5MkdVmuVCI/NrZIWDVE5IwC1F9c1q/PlIt5do2UKeQ37h+LxzxoJ79Gdu+pbx09zf6JIV786Pt5vylJkjT4HvAEwyhJ0pZz+f7LDaMkSeRKJWgFVgDNmZlsGcD5e1Y1mz2uUFuZ4VQfGakU2V4rEREbfuzHPGinYZQkrUJE5ICXAy8GLgIOAR8Erk4pnepwH5PAa4CnA/uBE8A/tfbx6QVjnwy8CrgEGAG+BXwE+N2U0nfW4SNJkrYIe5gkSdLWlCuXoVw2rFJf6Hk45RcjiAi21UqMVrp/fylJ0rp5I/Ay4AbgWuDBrecPiYjHp5SW/Y0uIi4EPgnUgHcAtwFjwKXAvgVjXwRcB/w98NvAKeBhwFXAj0XEv+u0Z0qShD1MkiRJLBFWTU2dCa0Mq9RNPQ+n2OJfjEqFHDtHKpQKrvkpSYMiIi4BXgpcn1J6Rtv2fwbeDDwTeP8Ku3kvWR++NKV01wpjXwXcBTwqpTTd2nZdRHwH+C/AE4APr/qDSJK2HHuYJEmSlnImrAJSSqTZ2Sysmp4mzcwYVmld9TSc2upfjHq5jJ8kaU2eBQTwpgXb3w78FvAclulfEfFo4FHAy1JKd0VEESimlE4v8ZZR4J623jXvYOvRK84lSZ2yh0mSJGlFEUEsDKsWLgOYUq/L1ADr9XSd5b4YnSb7YrSkti9GvzP/xSgihpZ5yyhwtNdfjCKC7SNldoyUDaYkaTA9DGgCn2/f2Oov/9h6fTlPaT3+a0R8BJgCTkXEbRGxWO/7OPDdEXFtRDw4Ii6IiB8D/ivwt8Bfr+GzSJK2FnuYJEmSVi0iyFUq5MfHKe7eTfHCCynu2UNhYoJctep5bq1ar8OpLffFKJ8L9oxVvL+UJA22vcDhlNLMIq/dCWyPiNIy739g6/HtwCTwPOAFwCzwnoj4mQXjXw58qPX4ZeBfgf8OfBR4Qkqpcb4fRJK05djDJEmStGZLhVX58XHDKnWk1/ecWumL0SMiopRSml3i/e1fjL5G9sWoDLyS7ItRMaX0h23jXw4MtR5f2bb9D4EXd/uLUamQY/dohUK+15mgJGmNhoDFehfAdNuYpfrXSOvxBPDY+T4XETcA3wSuiYh3t913cY7sZN4NwEfIZhc/EXg+0ABetFShEXElcCXAgQMHVvxgkqRNbyB6mP1LkiRpsEQEUamQq1SA1jKA7UsAzsy4DKDO0etwauC+GO3bf0FHH2yhWrngMn6StHmcBnYu8VqlbcxSplqPf9x+AUZK6WhE3Ag8l+wCjK9ERA74GFnPfmQ6+5vcn0bEEeA/R8QHUkqfWOxAKaXrgOsALrvsMn8LlCQNRA+zf0mSJA22iCCqVXLVKmBYpfvq9RSe02QznRazpi9GwI3Ablqzq9q+GD0C+ImU0h+llP40pfQi4HeBF0bE4xc7SErpupTSZSmlyya3b+/wo501OVxi52jFYEqSNo+DZMseLdbD9pHNCl7qwgqAb7Uev73Ia3e1Hidaj48CLgf+e7rvb20faj3+4MolS5IE2MMkSZLUAxFBrlqlMDFBcc+ebBnA3buzZQArnjvfinodTm3qL0a5CHaNVhgfWm7JdknSAPoCWQ/9vvaNEVEBvge4eYX3z99rcf8ir81vu7v1uK/1mF9kbGHBoyRJK7GHSZIkqeeWDavKrkC2FfQ6nNq0X4yK+Rx7xisMl/2uJUmb0AeABFy1YPuLyJajfd/8hoi4f0Q8aMG4D5MtSfuciKi1jd0DPB34Wkrp663NX249/lREFBfs54rW4xfO83NIkrYee5gkSZL6zjlh1d69FA8coLhrl2HVJtbr5OQDwGvIvhh9um37ol+MgGJK6att4z4M/B7ZF6PfSCmdbI1d6YvRG1NKc237uaL1uC5fjCrFPLtGK+Rz/oWRpM0opXRLRLwVeElEXA/8OfBg4GXA3wLvbxv+V8CFQLS9/2hEvAp4G3BTRLwTKAH/qfX4kraxX4yI/w48A7g5It7L2Xsm/ghwE/Bn3fqskqTNxR4mSZKkQRC5HDE0RG5oCCYmSM1mds+qmRnS1BTNmZlel6g16mk4tRm/GI1UimyvlUxyJWnzuwq4HbgSeCpwGHgLcHVKqbnSm1NK10XEYeAXgV8HmsDngGenlD6zYPizW8f7KeDXyGYd/wvwOuA3U0qN9fhAkqQtwx4mSZKkgbJkWDU9fSa00mDp9cwp2CRfjCKCyeESY9WFq1VIkjajVs+4tvWz3LiLlnnteuD6Do41C/xO60eSpDWxh0mSJGnQnRNWwblh1dQUzdnZHleolfQ8nNoMX4zyuWDnSIVqabHbWUmSJEmSJEmSpG65T1jVaJy7DKBhVd/peTg16EqFHLtGKxTzuV6XIkmSJEmSJEnSlhf5PDE8TG54GGgLq+aXATSs6jnDqTUYLhfYUSuTy3l/KUmSJEmSJEmS+tFyYVVzapo0Z1i10QynzkNEsK1WYrTi/aUkSZIkSZIkSRoki4VVzalp0oxh1UYxnFqlXAT7xquUCi7jJ0mSJEmSJEnSoIt8nnxtGGoLwqrpKZrTM4ZVXWA4tUqFXBhMSZIkSZIkSZK0Sd0nrKrXs5DKsGrdGE5JkiRJkiRJkiQtIQoF8rXCgrBq+sx9q9LcXI8rHDyGU5IkSZIkSZIkSR3Kwqoa1GqAYdX5MJySJEmSJEmSJEk6T0uGVVNTNGdmDKsWYTglSZIkSZIkSZK0Tu4TVs3NZSHV1FQWWtXrPa6w9wynJEmSJEmSJEmSuiSKRfLF4rlhVfsygFswrDKckiRJkiRJkiRJ2iBnwqqREWBrhlWGU5IkSZIkSZIkST1yn7BqdpbmzAzNqSnSzMymDKtyvS5AkiRJkiRJkuZFxO0RkZb42b7I+L0R8UcRcSgipiLi5oj48SX2XY6IX4uIf46ImYj4RkT8ckQUlxj/3Ij43639fici/iAidqz3Z5akdlEqkR8ZobhzJ6ULLqC0bx+F7dvJDQ8T+Xyvy1sXzpySJEmSJEmS1G++CvzmIttPtD+JiEng74CdwBuAbwHPBj4YEc9PKf3hgvd/APgPwDuBzwEPB34d+C7gigX7fkVrn38LvBzYD7wSeHhEfF9K6dQaPp8kdSxKJfKtwApaM6vmlwCcniY1Gj2ucPUMpyRJkiRJkiT1m++klN7bwbhfAi4GfjSl9BGAiHgHWfD0+oj4UErpZGv7U8iCqTeklH6+9f4/iIhjwCsj4rqU0mdbY7cDvwF8AfihlFKjtf0LwI1kYdU16/RZJWlVzoRVo6NAW1g1NU2aGYywymX9JEmSJEmSJPWdiChExOgKw54NfGM+mAJoBUlvASaBpywYC/CmBfuYf/6ctm1PB4aAt8wHU619fwT45oKxktRT0Qqqirt2UjpwgOK+fRS2bSM31L/LABpOSZIkSZIkSeo33w+cBu6NiGMR8e6I2Ns+ICL2APuAmxZ5//y2h7VtexhwZ0rpjvaBrecHFxkL2Qysxfb9oIiodfphJGkj5RYLqyYn+yqsclk/SZIkSZIkSf3kVuAPyO47VQAeA7wQ+KHWvZ4OtsbNh1V3LrKP+W372rbtBb68xDHvJLunVPvY5fYdrTG3LfkpJKlP5EolKJXIj2XPm7OzpKkpmtMzpOkpUrO54TUZTkmSJEmSJElaVxExDly1ire8OaV0D0BK6akLXvuTiPgU8D7gtcCLWtuHWo8zi+xvesGY+T8vNnZ+/MKxq9k3ABFxJXAlwIEDB5Y4lCT11n3CqpkZ0vQ0zelp0vT0hoRVhlOSJEmSJEmS1ts48CurGP9e4J6lXkwpvT8ifhNoD65Otx7Li7ylsmDM/J8XGzs/fuHY+X1PdbDv+TqvA64DuOyyy9ISx5KkvpIrl6FcJj+WpVUbEVYZTkmSJEmSJElaVyml28mWvltPtwOPbHs+v7zfvvsOPbOtfVm+g0uMnR+/cOz89q8vMja1jZGkTWXRsGpq6kxotR5hVW7Ne5AkSZIkSZKk7vsu4DvzT1JKd5EFSj+wyNj5bTe3bfsCsC8iLmgf2Hq+d5GxAA9fZN/fD/yflNLJVVUvSQMqVy6T5ccuyQAAIABJREFUHx+nuGsXxQMHKO7dS2Figly1SuTOL2YynJIkSZIkSZLUFyJicontPwfsBz6y4KU/Bu4fET/SNjYPvBQ4Bvz5grFw33thzT9/X9u2PyNbzu8lrf3N7/tHgPsvGCtJW0ZEnA2rdu/Owqo9eyhMTBCVyso7aHFZP0mSJEmSJEn94rkR8QLgY2TL+BWAxwBPB77Bfe9j9VvAjwPvj4g3kM2kehbwMOCFKaUT8wNTSv8zIv4H8MqIGAM+RzYz6gXAe1NKf9c29lBE/Ffg9cAnIuKPyZbz+3ngq8Cb1vlzS9JAiogslFpFMAWGU5IkSZIkSZL6xxeAxwE/Cewgu2/VPwO/DfxWSulY++CU0pGIeCRZSPVzQA34MvDMlNIHFtn/jwO/DDwH+GmyMOvq1vvPkVK6NiKOAK8A3gwcBz4I/JJL+knS2hhOSZIkSZIkSeoLKaXPAD+6yvfcSRY0dTJ2miyc+uUOx78LeNdq6pEkrcx7TkmSJEmSJEmSJGnDGE5JkiRJkiRJkiRpwxhOSZIkSZIkSZIkacMYTkmSJEmSJEmSJGnDGE5JkiRJkiRJkiRpw/Q0nIqIXES8IiK+GhHTEXFHRFwbEcOr2MdkRLw+Ir7e2sehiPibiLi8bcxFEZFW+Pmp7nxKSdJmZA+TJA0qe5gkSZKkXiv0+PhvBF4G3ABcCzy49fwhEfH4lFJzuTdHxIXAJ4Ea8A7gNmAMuBTY1zb0EPDTS+zm94Eq8PHz/hSS9H/bu+94S4o67+OfLwM4DkMUUAYkmAgmsqDoI4juAy4PBkRX0qwgioKKCiKmwZwAwyIuEkZQFJYkujySlAVUgogEhVGQAck5DTPMAL/9o+pA09Pn3u57T7z3+3696nXurVNdXadOd/+6T3dX22TkGGZmZsPKMczMzMzMzPqqbyenJL0c2A84LSLeWci/Cfge8B7gxFGq+QnpM7wqIu5oVygi5uWy5TZsSTqIOiUi7m38IczMbFJyDDMzs2HlGGZmZmZmZoOgn8P6/Rsg4Dul/B8BjwG7jjSxpDcAWwHfjIg7JC0laVrDNuyVX49uOJ2ZmU1ujmFmZjasHMPMzMzMzKzv+nlyajPgKeCyYmZELAD+nN8fyfb59RZJvwTmA/Mk/U3SiAdUAJKmAzsDtwDnNmy7mZlNbo5hZmY2rBzDzMzMzMys7/p5cmoGcG9EPF7x3m3AypKWHmH6dfPrj4CVgD2APYGFwAmS/n2U+b+bNEb6saONqW5mZlbiGGZmZsPKMczMzMzMzPqub8+cAqYBVQdEAAsKZRa2KbNsfn0E2DoiFgJIOh34B/BVST8e4YBnL9IVg8eN1lBJewN7A6y55pqjFTczs4nPMczMzIbVUMQwxy8zMzMzs4mtnyenHgNWbfPe1EKZdubn15+1DogAIuIBSWcCu5Ou6ruuPKGkDYAtgLMj4pbRGhoRRwFH5WnvkXTzaNPY01YG/JDjZtxnzbnPxqZJv63VzYYMoYkUw7z+1ON+qsf9VI/7qZ5O9ZNj2LMNRQzzMVjPeHs08fk7Hl6OXxPAFVdccW+HYpjX5c5xX3aO+7KzJlJ/1oph/Tw5dTuwgaTnVAwpsTppqIl2V+sB3Jpf76x47478umKbaffMr40fwBsRqzSdZjKT9MeI2LTf7Rgm7rPm3Gdj434blwkTw7wc1ON+qsf9VI/7qR73U9cMXQzzMVj3eD2b+Pwdm/VXp2KY1+XOcV92jvuysyZjf/bzmVOX5/lvXsyUNBXYEPjjKNO3HuC7RsV7rby7y29IWgrYDbgH+EWD9pqZmbU4hpmZ2bByDDMzMzMzs77r58mpk4AAPlbKfz9pjPOftjIkvVjSeqVyZ5DGOd9V0vRC2dWAtwF/j4gbKua7I7AKcEJELBr3pzAzs8nIMczMzIaVY5iZmZmZmfVd34b1i4hrJB0B7CvpNOAsYH3gI8D/ACcWip9PGqdQhekfkPRJ4D+BSyQdCywN7JNf920z6zEPh2RjclS/GzCE3GfNuc/Gxv02RhMshnk5qMf9VI/7qR73Uz3upy6YYDHMxs/r2cTn79hsYvC63Dnuy85xX3bWpOtPRUT/Zi5NIV2xtzewNumBXycBn4+IRwvl5gJrRYQq6ngHcCDwSuAp4A/AIRHxu4qyawA3A5dExOs6/XnMzGzycAwzM7Nh5RhmZmZmZmb91teTU2ZmZmZmZmZmZmZmZja59POZU2ZmZmZmZmZmZmZmZjbJ+OSUVZK0hKT9JV0vaYGkf0o6VNIyNaZ9maQvSrpE0j2SHpH0Z0mfqZpe0ixJ0SZ9sjufsDvG0295+nb98Gib8utKOkPSA5LmSbpI0jad/VTdNc5lbaRlJyQtalB+aJY1SZ+W9F+S/pHbPneM9ewu6UpJ8yXdJeloSau0KfsaSefl9flhSb+WtOG4PoiNWwe2OY2ml7S9pN/n7c39eTlcp7OfqrMcz+px/KrHMasexymzZpquM02Wd0kzJB2f49h8SX+U9K42ZZ+T495Nkh6XdKOkz0paqk15r6M1jGF/onaMlLS8pO9Lui3Hpb9I2kdS1VCcXdvvG9a4bjbIxrPfOaxG2Pdd7JhiGLeV3eL9iM5p0peSZo+wzO5UUX4g+qfJd9oLS/ZrxjbwDic9FPl04FCeeUjyRpK2jYinRpj2fcCHgTOBnwKLgK2BLwM7S9oiIuZXTLc/abz7oivG9Sl6bzz91nIRiz8Ab1G5kKQXA78HngC+CTwEvB84W9J2EXHemD9Fb42nz04DbqjIfxVwAPDLNtMN+7L2VeB+4E/ACmOpQNL+wGGkB59/FFgD+DiwpaTNI2JeoewWwAXAbcDnc/a+wEWSXhsR14zxc9j4jXebU3t6pWeLnAJcRVq/lic9r+R3kjaNiNs7+sk6x/GsHsevehyz6nGcMmum9jrTZHmXtBJwMbAqaX26FXgvcLKk90XEcaXqTwJ2BI4lPUNsS+BLwEuAmaV2eB2tr/b+RJMYKWlp4FxgI+D7wHXAdsAPgOcDs0rt6Mp+35DHdbNB1on982E06jHFMG4ru8z7EZ0zluOY3SryLqvI63v/jOE77b6IcHJ6VgJeTnqo8aml/P2AAN47yvSbAstX5H85T79vKX9Wzl+735+9n/2WywYwu+b8TgaeBDYs5E0nPWx6DvmZcoOcOtFnber9zzz9Wyfosvaiwt/XAnMbTr8yMI8ULKcU8nfI/XNwqfxlwMPA6oW81XPeOf3uj8maOrCtrj09sBRpR+dmYHohf8O8HTqq3/3RpT6aFPHM8at3/dSm3gkXsxynnJyapSbrTJPlnfSDXQA7FPKm5DruK8X07XPZQ0t1HJrzX1vI8zra7PutvT/RJEYCH8rT71eq91RgIbBWIa9r+31N2uzk5FQvNVlnJ1Ki5jHFMG4ru9xv3o/oT1/OBqJmvQPRP02+054tv72eodPgJ57ZSX59KX9qXjnOGmO9r8z1/rCUPyvnrw0sByzZ7z7oV7/l6WcDS4+0QQCWARYA51e897lcz+b97pN+LGvANNJVM7cWN+ITaVkrfaax/Oi3V+6H3SreuxH4a+H/l+Syx1SUPYa04/aCfvfDZEzjXX+aTA9sm8t+rqKe8/M6t1S/+6TTfTRCvRMqnjl+9W95mgwxy3HKyalZGmmdabq8523LDRVld8v17FzI+0nOe2Gp7Atz/g8KeV5HO/NdP2t/ommMJF35PA+YWir7+lz2wEJeV/b7hjmuOzkNcurGfucwJGocUwzjtrLHfej9iB70ZX5/dm6bSMdrS4xQdiD6p8l32qvkZ05Zlc1IC++zbkGMiAXAn/P7Y7FGfr2rzftXkzbeC5TGa91ujPPpl071207AY8Ajku5WGhd3+VKZVwHPId0GWnZJoT2DrhvL2s6koHBcRDzZpsywL2vj1erXdsvPepKm1ywrYJPONs9qGu/602T60ZaD5YCX1W14Dzme1eP4VY9jVu84TplVq728S1qNdNXsJW3KFutr/X1bRPyzWDD/f3tF2ZHa4XW0nvL+RO0YKWkJYGPgyhyHii4jxavyd9aN/b5hjutmg6xbxzHDYLRjimHcVg4K70d0x0M5zZd0rqTXVJTpe/+M4TvtCZ+csiozgHsj4vGK924DVs5jttYmaQpp3MsngBNLbz9IGk92P9LYm58G1gL+W9LMZk3vq07022WkK6V3AvYAfsMz44ROL5SbUai3al6QNjiDruPLGrAn6Wz/sRXvTZRlbbxGW35UKDNRlrWJaLzrT5Pph3U5cDyrx/GrHses3nGcMqvWZHlvum7MaFO2Vb5cdqS6vY6Oos3+RJO+WhF4blXZHKfuY/HvrBv7ff5+zbqjG/udw6DOMcUwbisHhfcjOutO0jPK9gHeTnpe1aak5XXbUtlB6J+B7Mslez1DGwrTgKoNMaRbZ1tlFjao8zvAFqRxMecU34iI75QLSzqWdPvk4ZJOiYhHG8yrX8bdbxFRPrt+vKSrga+QHoD3lUI9tJnfglKZQdbRZU3SusBWpNu7byq/P4GWtfFqsvxMlGVtIhrv+tNk+mFdDhzP6nH8qscxq3ccp8yqdXPdGG0bVy7brXZMFlX7E53q11b5Jt9vq0zT/T5/v2bd0Y3jmIFX85hiGLeVg8L7ER0UEQeVss6QdCLpLrsjgZcW3huE/hnIvvSdU1blMdItslWmFsrUIulLpCsdjoqIr9WZJiLuA34IrAC8tu68+qyj/VbwLVLge2tpXrSZ33jm1Wud7rM98+vRdScY0mVtvJosPxNlWZuIxrv+NJl+WJcDx7N6HL/qcczqHccps2rdXDdG28aVy3arHRPeCPsTnerXVvkm32/duv39mvVGt/bPh1H5mGIYt5WDwvsRXRYRfwdOBl4iqTis4yD0z0D2pU9OWZXbSbeqVi2sq5Nuca17VfAs4LPAccAHG7Zjbn5dueF0/dKxfiuKiEWtukvzatVbNS9of7voIOnksrYksDtwP3B6w3bMza/DsqyN12jLTxTKTJRlbSIa7/rTZPphXQ4cz+px/KrHMat3HKfMqjVZ3puuG7e3KdsqXy47Ut1eR9sYZX+iSV89AMyvKpvj1PNY/Dvrxn6fv1+z7ujK/vkwqjimGMZt5aDwfkRvzM2v5ePgfvfPQPalT05ZlctJy8bmxUxJU4ENgT/WqUTSF4AvAMcDe0VENGxH6/bHdg+cHzQd6beyPP0aPLsfriHdhrllxSRb5Ncxza/HOtlnOwDPB05oMz7wSIZtWRuvy/Nr1fLzGmBOYaiokcpuQQqKV3S2eVbTeNefJtOPthw8DPytbsN7yPGsHsevehyzesdxyqxa7eU9Iu4g/cCwRZuysHisX13SC4sF8/8zKsq2a4fX0TZq7E/UjpER8RTwJ2Cjih9RNyfFq/J31o39vmGO62aDrCv758Oo4phiGLeVg8L7Eb1RdbzW9/4Zw3faGxHh5PSsBLwSeAo4tZS/H2mh3rWQ92JgvYo6Pp/LHg8sMcK8lgSWr8h/IenBhPcCz+13n/Si34Dntan3W3n6A0v5/wU8Cby6kDcduJkUANXvPunFslZ4/1d5mldO9GWt1P5rgbkjvL8msB6wVCFvFdKtupcCUwr5O+Q+/GypjstJO1YzCnkzct55/e6DyZo6sM1pMv1SpKtsbgamF/JfnbdDR/e7P7rRRzl/wsczx6/eLU+F9ydNzHKccnJqlmqsM7WX98J2eIdC3hTSA+cfAJYt5L81lz20VMehOX+rQp7X0ebfa939idoxEvhwrnO/Uh2nAouAdQp5Xdvva9JmJyeneqnJOjtREg2OKYZxW9nDfvR+RA/6ElgGmFqRvxHp5OlfS/kD0T9NvtOeLbO9nqHTcCTg+3lhPQ3YK68si4ALKOxMk25VjNK0rQ3/zaQha3YtpTcXyq6QF/7jgAOB9wPfBh4EngDe1e++6GG/HQ78AfgqaYiHTwK/yfVdQulHKOAlpOGA7gIOAj4EXJn77V/63Re96LPCezPy5750hPlMmGUN2I00HMhn8/f/QOH/3UplL8j9u3Yp/xM5/7fA3sAhwKPAdRR2rHLZ15KC643Ax3K6MZd/dTc/q9Ooy8K41p+60+ey7yLtqF+ZtzcH5eXvTmD1fvdFN/qISRTPxtlPkyZ+jXedy+9N+JiF45STU6PUcJ2pvbyThiuaCzyS16G98zoVwJ4V7fhlfu9o0nPxjs7/n1BR1uto/e+3yf5E7RgJLE26wnkRKR7tRYpPAXypoh1d2e9r0mYnJ6f6qck6OxESDY4phnFb2eW+835Ej/uSdCfdHcCRwMeBDwA/ABaQTixtNYj90/Q77cny24+ZOg1+Ip01/QQwJy/gtwGHVawAc1n8R6rZeaFuly4olH1OXhmvySv8orxynwJs3u9+6HG/7QicnadZAMwD/gwcTMXZ+DzN+sAvSD9WPQZcDGzb737oVZ8V3js4L1vvH2E+E2ZZ45kf8kZcv0pl166oZyZwVV7e7gaOBVZtM88tgfNzcHskL6sb97svJnsa7/pTd/pC+X8lHRg8ltejU4AX97sfutVHTKJ4Ns5+mjTxa7zrXH5vwscsHKecnBqlJutMLl97eSc9Q+AE0h2XC0jDG727TdmpwJfzNuxx4B/A5yjc2Vgq73W03vc7e4Tvt2q7WDtGki5m+A/S1fuPA38F9qXibiW6uN/XpM1OTk71UtN1dtgTDY8phnFb2cW+u6BujMnlvR8xzr4EXpD75XrSHUqLgFuAH9NmBI1B6Z8m32kvknKjzMzMzMzMzMzMzMzMzLpuiX43wMzMzMzMzMzMzMzMzCYPn5wyMzMzMzMzMzMzMzOznvHJKTMzMzMzMzMzMzMzM+sZn5wyMzMzMzMzMzMzMzOznvHJKTMzMzMzMzMzMzMzM+sZn5wyMzMzMzMzMzMzMzOznvHJKTMzMzMzMzMzMzMzM+sZn5wymwAkhaTZ45h+Vq5j7Y41yszMJhxJa+d4MavP7ehI3HL8MzOrR9JsSdHvdvRSr2KEpJUlHS/p9jy/C7o5vzZtmJnn/caa5S+QNLe7rTIzm3gcT82ebcl+N8DMzMzMzMzMbJI6FHg38BXgH8Bd/W2OmZmZWW/45JSZmZmZmZmZWX+8GTg7Ir7Y74Y08BZA/W6EmZmZDTcP62dWImmKpGn9boeZmVmR45OZmdmE9ALg/k5XKmnZTtfZEhELI+LxbtVvZmZmk4NPTtmkVhhbe1tJn5N0I7AA2Dm/v6mk0yXdK+lxSXMkfUbSYncdSnqnpKskLZB0i6Qv5HpD0syG7Voiz+dCSXdKWpjrPFLS82rWEXks220lXSLpsVzXdyUt02ay50j6qqRb8+e9StL2FXV/SNI5km7LbbtD0k88fqyZWWcManwaob2140IhPm0j6Q85Pt0q6VP5/RUlHSPp7vzeryTNaDPrZSR9L8e3+ZIulfSminkuIenTkm7K/XCNpF3afJb1JP1A0l8kPZLbcIWk94+ji8zMekrS1PyMhzl5O/Zg3vZ9K7/f9hmCIz0bQtIqSs9Huk/SPEnnS9qootzuki7L850n6R+SfipplUKZzXM8+Ftu4yOSfifp7RX1zc5tel7++95c/gxJL8hl9pZ0Xd7OXy9px1IdT39mSf8m6epCbJxVFUPb9O3ykr4h6YYcg++R9DNJL6ozfaGeWUrPHRGwR27bs2KzpL0k/SnHuIdyrN2qoq5WbH2TpIslPQr8skl7siVzu27On+1qSe+pmN9iz5xq5Umakfvjgfzdny3pZWNoi5lZ3zmeDn48teHmYf3Mkm8DSwE/Ah4G5iidlDkduIE0Dvj9wJbAF4ENgXe1Jpb0buBnwI3AIcATwB7ADmNsz9LAAcCpwC+AecBmwJ7AVpI2iYiFNerZGNgpf67jga2BjwCvkPTmiHiqVP7HwCJSfywNfAw4Q9LLImJuodwngUuA75H65RXAXsA2kl4ZEfc1/8hmZlZh0OJTO03jwka5DUeR4tPOwNclLcjtmwvMAl5CilvHA9tWzPd44EngG8CywAeAX0vaLiLOK5Q7DPgocCFwOLAqcATp2R5lbwTeAPwKuAlYhtSnR0laOSK+VqdDzMz67AjgfaTt5OHAFOClwDbjrPfXpO38LNIdP/sCF0raMiKuBZC0K+m44iLg88B8YE1gO9L2955c19uB9YCTgZuB55FiwGmSdomIE9vM/9ZcbytGnC7pNGBv4BjSxRwfAU7JxzE3lerYgXSccwRwJ/D/gC8AawH/PtKHl7Q88Pv8eY4F/gKsBnwIuFTSphFx80h1FJxGiuUnkPrqqJz/+zyvbwAHApcBB5Pi3N7AbyXtGBFnlerbFHgnaZ/hxzXbUPYNUtw7EghSf/xM0tSImF1j+mVIsfaS3OZ1SPH3F5JeERFPjrFdZmb94ng6+PHUhllEODlN2gTMJO10zwGmFfKnkjasFwJLlqbZP0/zxvz/ksBtpAfXrlgoN530o1cAMxu2S8BzK/L3zPXtXMoPYHZFXgBvK+V/N+e/p5A3K+f9ClAhf7Oc/7VSHctUtO1NueyB/f5enZycnIY9DXB8WjtPN6uUXzsu5LyngNcU8pYG7sj53yuVPyxPs24hrxW3LgWWLuSvATwKXFfIWzfXez4wpZC/cc4PYO1RPssSwAXAQ8BS/V4+nJycnEZLpB+8zhrh/crteX5vVsW2cXbOO610vLBJ3pb+upB3GumCiiVHaWPV9nZajn1/LeW35n9EKb8VI24Blivkv4rScUzhMz8JbFzIF+mijwC2GKUfvkv6cfDVpXaslT/z7DF8V1XHcq3YdXEpzs0AHiRdxDGlVEcA245xeZmZp78ZWL6Qv3zOu5/C8WmOiXNLdVxAddw/IOf/S7/XCycnJ6emyfF0eOKp03AmD+tnlhwZEY8V/n8z8HzgOGAFSSu3EtC6Qu0t+XUT0kHC7Ih4oFVBRDwK/HAsjYlkPjz9jJEV8rx/k4u8pmZVcyLijFLe1/PrYrf3At+NSNEgt+Ny4BHSVSHF9s3LbVsi34a7MnAV6Ue7um0zM7PRDVR8amcMceEPEXFpYfqFpCvDRbr7quii/PpSFnd4FO4kjohbgZ8C60laP2fvmOs9LApXbEfEn4Bz232W/HmmKg2nuxJwDrAc6apEM7NB9xDwckmv6HC93ywdL1xB2pZuK2l6Yd7TgLdKUruKStvbaXl7O410zLO+pOUqJvtO6f9WjDg+Ih4u1H016cetqthxbo4BrbIBfDP/W3WM1GqjgF1IF4jcVorB80h3C72l3fQNtWLXN0tx7nbSD4trke5CLroqnn3X8FgcGREPFeb3EGmfYUXSncWjeYrF43jrGLbquzAzG3SOp8MdT23A+eSUWfK30v+tH7SOJd0mW0zX5/een1/Xya9zKuqtyqtF0s6SLiVdSfBAnndr+KEVa1ZzXTkjIu4gXW1XNYZr1fBG95NuCS62bRtJF5CCxoM80zfLN2ibmZmNbuDiU5UxxIWqeNM6gXZTm/yqZy4uFueAv+bXF5Verx+h7NMkTZf0bUm3kGLwvaTP8pVcxHHOzIbBx0jbq2sk3SjpaEk7ShrvbwDttrtTSCdMAL5KutvmDOAeSacqPTtp2eJEklaVdJSku0jxo7W9/WAuskLFvMrxo13saL031thRZZVc31tYPAbfwzMXkHRCK4b/peK9a/Nrua3lfYaxGGvftNweEQtKea2hfWs9O9nMbMA4ng53PLUB52dOmSWPlf5vXZFwAPDnNtPcXirbMZLeAZxEuor8o8A/SWO9TiGNC1s3CEab/HZtbjcG+NPlJW1Gunr8BuAgUuCan+f18wZtMzOz0Q1UfKoyxrjQ9pkT0f55FFWfpyrOlcu1/q9TFuBE4F9Jz/64kHSRxhPA9qShEx3nzGzgRcQvlB7Avj3wf0jP7dsTuEjStrQ/ToDmvxM8a1saEX+XtAFpeNc35fn/CDhE0hsi4sZ81fQ5pIsuvgdcTrpC/EnScyreS8X2doQYMd7YUUerrvNIz2bqprHE8PI+w1jUjZXtjPRMqZ7sl5iZdZLjaXXbWtWM+olGrqsX8dQGnE9OmVX7e36dV2NohNZVBetWvFeVV8dupJNRWxeHc5LUdCihDcoZklYjXcleddV6He8lnSTbLgoPQ5S0DL6a3Mys2/odn6r0My5sAFxdymvdXdaKczcW8suxb/3iP5JWIJ2YOiEiPlh6b9txt9bMrIci4n7gJ8BP8o9XXwcOJA0Z9+tcbKWKSUe62nl90nA75bwnSVd3t+b9OGm42bMAJG0P/DfwceDDpGdYvBr4YkR8oViZpL1qfLzxWOwYqZA30jHSPaS7g5frwPB5o2nFrpcX/m6p09ax2gA4s5RXjqtmZpOK42lbwxBPbcD5yk+zamcDdwMHSVoswEh6buE22j+SHuI+U9KKhTLTeeYW2qaeJF2B8PQ6mgPgZxvWs66kt5XyPpVfy8+iatI2WPyqiYPxNsXMrNv6HZ+q9DMu7C9p6dY/ktYgnSybExGtYSbOJMXUj0uaUii7MenKx6LKz5Iv7Oj2wZ2ZWUe0nllbzMvPgbgy/7tSRDwC3AlsU3yOhaQXAeXjh6IDS+Vb29Lz8zMNyc+MKGs9k6IVu9ptb1/BCM+p6JA353a35inSj4wwwjFSRDxFeq7h5pJ2qiojadUOtbEVuw6QtFSh/tVIV8LfzDPfZyftI2n5wvyWJ+0zPAj8TxfmZ2Y2sBxPRzUM8dQGnO+cMqsQEfMk7U7amM6RdCxpuKIVSA9CfwdpI39BRDwh6ZOkDetlko4hDf8zkzS+9jo0v9X1FOCdwG8kHQ8sRQpq0xrWcw3pyo4fka623xrYiXRgcVLDulpOJw1rdJako4CFpPFgX0Ua19bMzLpkAOJTlX7GhSVJQ2r8DFiW9APac4GPtApExPWSjgD2JcXVU4FV8/9XUXigfEQ8IukcYFdJ80nDYqwFfIB0J5qfl2Fmw2BZ4A5JZ5J+QLubtM2WFpANAAADlElEQVTfh/TciF/mcv8BfBn4/5LOAGaQtqPXApu1qXst4Oxc92qkbel80nCzLedIeog0NOo/STFqJinmnJDLXEd6ntKBkqaRnoX4MtL29lpgY7rnKlI8OIJ0EceOpB8ET4iIP4wy7WeA1wEnSzqZdNX7QlK/bA9cQfqs4xIRcyR9i/Qj34WSTiJ9r3sD04FdRhiSaTzuBS7N+xcinQhbE9irOKKHmdkk4Xg6soGPpzb4fHLKrI2IODs/R+MgYFfSA/seIA2rcBiFYYQi4kRJT5DubDoEuAs4Jpc5jRRgmsz75/nK9/2Bb/NM0DuIZx4oW8efSLf6foUUGB8mBc2D85UKjUXE7yS9E/gc8CXSZzuPNPbthWOp08zM6utnfGrTnn7Ghd1J8e0g0sHa1cDMiDi3VO6jpCsa9wa+Rbpg48PASymcnMp2JQ3VsQOwRy77GWARcFxXPoWZWWc9BnyH9HyKbUknM+4g3Y3ztYhoPZvwG6ThvncD3kh6iPmewCa0/zHt/5JizSGkiwEuAQ6IiOIQq0cCO5N+GFuJdPxyJbBfRPwW0rMuJL2VdKyzB7AM6Ue0PUjDE3Xzx7QzST/efZo0zO3dpPj1pdEmjIiHJL0O+ATpM+5IuvDjVuBi4OhONTIiPiXpBuBDpLi0ELgUeG9EXNSp+ZR8Cng96UfS55Ni4C4RcWKX5mdmNsgcT0c2FPHUBpvS3Yhm1g2SPkEKEFtGRHks2W7PO4AfR8TMXs7XzMwGXz/jk5mZWT/kB9rfBBwSEbP62hgzM7Mh5XhqneTnw5h1gKSli8+xyHnTSVdl38czY8KamZn1jOOTmZmZmZmZmQ0iD+tn1hkvIo0t+3PS1QOrkW6hXQfYJyIW5h8HV6lR1/0RsbB7TTUzs0nE8cnMzKyHBiWuSlqaNAzUaO7p0vOrzMzMzEbkk1NmnXEPaXzYXUgPWX8CuAY4KCJOzmVeSPphcDRbAxd0oY1mZjb5OD6ZmZn11qDE1dcCv61Rbh1gbhfbYWZmZlbJz5wy6xFJU4GtahS9IiIe6HZ7zMzMwPHJzMyskwYlrkpaEdikRtGLI2JBt9phZmZm1o5PTpmZmZmZmZmZmZmZmVnPLNHvBpiZmZmZmZmZmZmZmdnk4ZNTZmZmZmZmZmZmZmZm1jM+OWVmZmZmZmZmZmZmZmY945NTZmZmZmZmZmZmZmZm1jM+OWVmZmZmZmZmZmZmZmY9878SaJPhgwc9MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize = (24, 6))\n",
    "i = 0\n",
    "\n",
    "# Plot of four hyperparameters\n",
    "for i, hyper in enumerate(['colsample_bytree', 'learning_rate', 'min_child_samples', 'num_leaves']):\n",
    "        random_hyp[hyper] = random_hyp[hyper].astype(float)\n",
    "        # Scatterplot\n",
    "        sns.regplot(hyper, 'score', data = random_hyp, ax = axs[i])\n",
    "        axs[i].scatter(best_random_hyp[hyper], best_random_hyp['score'], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = '{}'.format(hyper), ylabel = score, title = 'Score vs {}'.format(hyper));\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize = (24, 6))\n",
    "i = 0\n",
    "\n",
    "# Scatterplot of next four hyperparameters\n",
    "for i, hyper in enumerate(['reg_alpha', 'reg_lambda', 'subsample_for_bin', 'subsample']):\n",
    "        random_hyp[hyper] = random_hyp[hyper].astype(float)\n",
    "        sns.regplot(hyper, 'score', data = random_hyp, ax = axs[i])\n",
    "        axs[i].scatter(best_random_hyp[hyper], best_random_hyp['score'], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = '{}'.format(hyper), ylabel = score, title = 'Score vs {}'.format(hyper));\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to avoid placing too much emphasis on any of these relationships because we were not changing one hyperparameter at a time (although we could carry out experiments where we only change one hyperparameter and observes the effects on the score) and so the trends are not due solely to the single hyperparameter we show. If we could plot this in higher dimensions, it might be interesting to see if there are more promising regions of the search space but here we are limited to one dimension (a single hyperparameter versus the score).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "# Testing Results on Full Data\n",
    "\n",
    "We can take the best hyperparameters found from the 1000 iterations of random search on the reduced training data and try these on an entire training dataset. Here, we will use the features from the `Fork of Fork of Lightgbm with simple features` (https://www.kaggle.com/poohtls/fork-of-fork-lightgbm-with-simple-features) kernel (I did not develop these features and want to give credit to the numerous people who have worked on these features. Please check out their kernels!). \n",
    "\n",
    "The code below uses the best random search hyperparameters to build a model, train on the full features from `Fork of Fork of Lightgbm with simple features`.(https://www.kaggle.com/poohtls/fork-of-fork-lightgbm-with-simple-features), and test on the testing features. The test data is the actual competition data, so we can then submit these and see how well the score translates to a full dataset! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "93d0f7ca4d0e5a7dab72c32b9d9bd724f7479d63",
    "collapsed": true
   },
   "source": [
    "## Model Tuning Next Steps\n",
    "\n",
    "From here, we might want to take the functions we wrote and apply them to a complete dataset. The results are likely to be different because we were only using a random subset of the training data. However, this will take much longer (300000+ observations instead of 10000). I'm currently running the random search on the full dataset from the Kernel referenced above, and will see how the results turn out. (Sampling some of the observations is not inherently negative, and it can help us get reasonable answers in a much shorter time frame. However, if we are using such a small portion of the data that is not representative of the entire dataset, then we should not expect the tuning to translate to the full dataset.)\n",
    "\n",
    "In an upcoming notebook, we will turn to automated hyperparameter tuning, in particular, Bayesian Optimization. We will implement automated optimization of machine learning hyperparameters step-by-step using the Hyperopt open-source Python library. I'll provide the link here as soon as this notebook is finished, but if you want to get an idea of Bayesian optimization, you can check out [this introductory article](https://towardsdatascience.com/an-introductory-example-of-bayesian-optimization-in-python-with-hyperopt-aae40fff4ff0), or [this article on automated hyperparameter tuning](https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a). These topics are pretty neat and it's incredible that they are available in an easy-to-use format for anyone to take advantage of. I'll see you in the next notebook! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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