diff --git a/.gitignore b/.gitignore
index 959f691..3ac7b25 100644
--- a/.gitignore
+++ b/.gitignore
@@ -64,7 +64,6 @@ wheels/
 MANIFEST
 .idea
 
-
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
@@ -145,4 +144,4 @@ venv.bak/
 
 # Exceptions
 !docs/requirements.txt
-!data/rainbow_beach.parquet
\ No newline at end of file
+!sam/datasets/data/*.csv
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 52ba4ba..cd68299 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,20 @@ Version X.Y.Z stands for:
 
 -------------
 
+## Version 3.1.0
+
+### New features
+
+- New class `sam.models.LassoTimeseriesRegressor` to create a Lasso regression model for time series data incl. quantile predictions.
+- New class `sam.preprocessing.ClipTransformer` to clip input values to the range from the train set, making models more robust again
+- New abstract base class `sam.validation.BaseValidator` for all validators.
+- Renamed `sam.validation.RemoveFlatlines` to `sam.validation.FlatlineValidator`. `sam.validation.RemoveFlatlines` is still available, but removed in future versions.
+- Renamed `sam.validation.RemoveExtremeValues` to `sam.validation.MADValidator`. `sam.validation.RemoveExtremeValues` is still available, but removed in future versions.
+- New class `sam.validation.OutsideRangeValidator` for checking / removing data outside of a range. 
+- New function `datetime_train_test_split` to split pandas dataframes and series based on a datetime.
+- New `sam.datasets` module containing functions for loading read-to-use datasets: `sam.datasets.load_rainbow_beach` and `sam.datasets.load_sewage_data`.
+st outliers.
+
 ## Version 3.0.4
 
 ### Changes
@@ -19,6 +33,7 @@ Version X.Y.Z stands for:
 - Updated package dependencies for scikit-learn
 - Changed the DeepExplainer to the model agnostic KernelExplainer, so we can remove all the v1 dependencies on tensorflow
 - Fixed pytest MPL bug by temporarily setting it to a previous version 
+
 ## Version 3.0.3
 
 ### New features
diff --git a/README.md b/README.md
index de3ec3d..cbb53c2 100755
--- a/README.md
+++ b/README.md
@@ -32,11 +32,11 @@ Keep in mind that the sam package is updated frequently, and after a while, your
 Below you can find a simple example on how to use one of our timeseries models. For more examples, check our [example notebooks](https://github.com/RoyalHaskoningDHV/sam/tree/main/examples)
 
 ```python
-import pandas as pd
+from sam.datasets import load_rainbow_beach
 from sam.models import MLPTimeseriesRegressor
 from sam.feature_engineering import SimpleFeatureEngineer
 
-data = pd.read_parquet("../data/rainbow_beach.parquet") # Requires `pyarrow` package
+data = load_rainbow_beach()
 X, y = data, data["water_temperature"]
 
 # Easily create rolling and time features to be used by the model
diff --git a/data/rainbow_beach.parquet b/data/rainbow_beach.parquet
deleted file mode 100644
index 81c0e06..0000000
Binary files a/data/rainbow_beach.parquet and /dev/null differ
diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst
new file mode 100644
index 0000000..a82cfa7
--- /dev/null
+++ b/docs/source/datasets.rst
@@ -0,0 +1,18 @@
+.. _datasets:
+
+=============
+Data Sets
+=============
+
+This is the documentation for available datasets.
+
+Rainbow Beach
+-------------
+.. autofunction:: sam.datasets.load_rainbow_beach
+
+
+Sewage data
+-----------
+.. autofunction:: sam.datasets.load_sewage_data
+
+
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c574c7d..ca13c8f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -16,6 +16,7 @@ Welcome to SAM's documentation!
    data
    examples
    data_sources
+   datasets
    preprocessing
    exploration
    feature_engineering
diff --git a/docs/source/preprocessing.rst b/docs/source/preprocessing.rst
index 14eefc7..ef463fe 100644
--- a/docs/source/preprocessing.rst
+++ b/docs/source/preprocessing.rst
@@ -6,6 +6,13 @@ Preprocessing
 
 This is the documentation for preprocessing functions.
 
+Clipping data
+-------------
+.. autoclass:: sam.preprocessing.ClipTransformer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 Normalize timestamps
 --------------------
 .. warning::
diff --git a/docs/source/validation.rst b/docs/source/validation.rst
index 4ad3e1d..a319f35 100644
--- a/docs/source/validation.rst
+++ b/docs/source/validation.rst
@@ -6,9 +6,23 @@ Data Validation
 
 This is the documentation for the validation functions.
 
+Base Validation class
+---------------------
+.. autoclass:: sam.validation.BaseValidator
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Detect Outside Range
+--------------------
+.. autoclass:: sam.validation.OutsideRangeValidator
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 Detect Extreme Values
 ---------------------------
-.. autoclass:: sam.validation.RemoveExtremeValues
+.. autoclass:: sam.validation.MADValidator
     :members:
     :undoc-members:
     :show-inheritance:
@@ -23,7 +37,7 @@ Testset image:
 
 Detect Flatlines
 ---------------------------
-.. autoclass:: sam.validation.RemoveFlatlines
+.. autoclass:: sam.validation.FlatlineValidator
     :members:
     :undoc-members:
     :show-inheritance:
diff --git a/examples/feature_engineering.ipynb b/examples/feature_engineering.ipynb
index c5f3712..2d49110 100644
--- a/examples/feature_engineering.ipynb
+++ b/examples/feature_engineering.ipynb
@@ -130,10 +130,9 @@
    ],
    "source": [
     "import pandas as pd\n",
+    "from sam.datasets import load_rainbow_beach\n",
     "\n",
-    "data = pd.read_parquet('../data/rainbow_beach.parquet')\n",
-    "\n",
-    "data.head()"
+    "data = load_rainbow_beach()"
    ]
   },
   {
diff --git a/examples/lasso.ipynb b/examples/lasso.ipynb
new file mode 100644
index 0000000..f1c980c
--- /dev/null
+++ b/examples/lasso.ipynb
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lasso regression for time series example\n",
+    "\n",
+    "This notebooks provides an example on how to create a linear model (Lasso) with SAM.\n",
+    "\n",
+    "The timeseries model utilizes the feature engineering capabilities of SAM. To learn more about feature engineering, see the notebook `feature_engineering.ipynb` and the [Feature Engineering](https://sam.nist.gov/docs/feature-engineering) section of the SAM documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-18 15:12:22.079152: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2022-08-18 15:12:22.079547: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sam.models import LassoTimeseriesRegressor\n",
+    "from sam.feature_engineering import SimpleFeatureEngineer\n",
+    "from sam.datasets import load_rainbow_beach\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>batttery_life</th>\n",
+       "      <th>transducer_depth</th>\n",
+       "      <th>turbidity</th>\n",
+       "      <th>water_temperature</th>\n",
+       "      <th>wave_height</th>\n",
+       "      <th>wave_period</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TIME</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-06-15 00:00:00</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>1.495</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>0.136</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-15 01:00:00</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>1.420</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-15 02:00:00</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>1.478</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>0.114</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-15 03:00:00</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>1.518</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-15 04:00:00</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>1.507</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     batttery_life  transducer_depth  turbidity  \\\n",
+       "TIME                                                              \n",
+       "2014-06-15 00:00:00           11.6             1.495       0.85   \n",
+       "2014-06-15 01:00:00           11.6             1.420       0.87   \n",
+       "2014-06-15 02:00:00           11.6             1.478       0.79   \n",
+       "2014-06-15 03:00:00           11.6             1.518       0.76   \n",
+       "2014-06-15 04:00:00           11.6             1.507       0.77   \n",
+       "\n",
+       "                     water_temperature  wave_height  wave_period  \n",
+       "TIME                                                              \n",
+       "2014-06-15 00:00:00               16.6        0.136          3.0  \n",
+       "2014-06-15 01:00:00               16.3        0.117          4.0  \n",
+       "2014-06-15 02:00:00               16.1        0.114          7.0  \n",
+       "2014-06-15 03:00:00               15.9        0.111          3.0  \n",
+       "2014-06-15 04:00:00               15.7        0.107          3.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = load_rainbow_beach()\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use the model, we need a feature engineering transformer. `sam.feature_engineering` contains a number of transformers that can be used to create features from the data, suitable for time series problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_features = SimpleFeatureEngineer(\n",
+    "    rolling_features=[\n",
+    "        (\"wave_height\", \"mean\", 48),\n",
+    "        (\"wave_height\", \"mean\", 24),\n",
+    "        (\"wave_height\", \"mean\", 12),\n",
+    "        (\"wave_height\", \"mean\", 6),\n",
+    "        (\"wave_height\", \"mean\", 3),\n",
+    "    ],\n",
+    "    time_features=[\n",
+    "        (\"hour_of_day\", \"onehot\"),\n",
+    "        (\"day_of_week\", \"onehot\"),\n",
+    "    ],\n",
+    "    keep_original=False,\n",
+    ")\n",
+    "\n",
+    "X = data\n",
+    "y = data[\"water_temperature\"]\n",
+    "\n",
+    "\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# for now LassoTimeseriesRegressor does not support predicting for missing data, so we include\n",
+    "# a SimpleImputer after feature engineering.\n",
+    "feature_pipeline = Pipeline(\n",
+    "    steps=[\n",
+    "        (\"features\", simple_features),\n",
+    "        (\"imputer\", SimpleImputer()),\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "    ]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following example creates a model for nowcasting (predicting the current value of a certain variable)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoTimeseriesRegressor(alpha=0.0001, average_type=&#x27;median&#x27;,\n",
+       "                         feature_engineer=Pipeline(steps=[(&#x27;features&#x27;,\n",
+       "                                                           SimpleFeatureEngineer(rolling_features=[(&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    48),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    24),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    12),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    6),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    3)],\n",
+       "                                                                                 time_features=[(&#x27;hour_of_day&#x27;,\n",
+       "                                                                                                 &#x27;onehot&#x27;),\n",
+       "                                                                                                (&#x27;day_of_week&#x27;,\n",
+       "                                                                                                 &#x27;onehot&#x27;)])),\n",
+       "                                                          (&#x27;imputer&#x27;,\n",
+       "                                                           SimpleImputer()),\n",
+       "                                                          (&#x27;scaler&#x27;,\n",
+       "                                                           StandardScaler())]),\n",
+       "                         quantiles=(0.1, 0.9))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoTimeseriesRegressor</label><div class=\"sk-toggleable__content\"><pre>LassoTimeseriesRegressor(alpha=0.0001, average_type=&#x27;median&#x27;,\n",
+       "                         feature_engineer=Pipeline(steps=[(&#x27;features&#x27;,\n",
+       "                                                           SimpleFeatureEngineer(rolling_features=[(&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    48),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    24),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    12),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    6),\n",
+       "                                                                                                   (&#x27;wave_height&#x27;,\n",
+       "                                                                                                    &#x27;mean&#x27;,\n",
+       "                                                                                                    3)],\n",
+       "                                                                                 time_features=[(&#x27;hour_of_day&#x27;,\n",
+       "                                                                                                 &#x27;onehot&#x27;),\n",
+       "                                                                                                (&#x27;day_of_week&#x27;,\n",
+       "                                                                                                 &#x27;onehot&#x27;)])),\n",
+       "                                                          (&#x27;imputer&#x27;,\n",
+       "                                                           SimpleImputer()),\n",
+       "                                                          (&#x27;scaler&#x27;,\n",
+       "                                                           StandardScaler())]),\n",
+       "                         quantiles=(0.1, 0.9))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_engineer: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;features&#x27;,\n",
+       "                 SimpleFeatureEngineer(rolling_features=[(&#x27;wave_height&#x27;, &#x27;mean&#x27;,\n",
+       "                                                          48),\n",
+       "                                                         (&#x27;wave_height&#x27;, &#x27;mean&#x27;,\n",
+       "                                                          24),\n",
+       "                                                         (&#x27;wave_height&#x27;, &#x27;mean&#x27;,\n",
+       "                                                          12),\n",
+       "                                                         (&#x27;wave_height&#x27;, &#x27;mean&#x27;,\n",
+       "                                                          6),\n",
+       "                                                         (&#x27;wave_height&#x27;, &#x27;mean&#x27;,\n",
+       "                                                          3)],\n",
+       "                                       time_features=[(&#x27;hour_of_day&#x27;, &#x27;onehot&#x27;),\n",
+       "                                                      (&#x27;day_of_week&#x27;,\n",
+       "                                                       &#x27;onehot&#x27;)])),\n",
+       "                (&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, StandardScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleFeatureEngineer</label><div class=\"sk-toggleable__content\"><pre>SimpleFeatureEngineer(rolling_features=[(&#x27;wave_height&#x27;, &#x27;mean&#x27;, 48),\n",
+       "                                        (&#x27;wave_height&#x27;, &#x27;mean&#x27;, 24),\n",
+       "                                        (&#x27;wave_height&#x27;, &#x27;mean&#x27;, 12),\n",
+       "                                        (&#x27;wave_height&#x27;, &#x27;mean&#x27;, 6),\n",
+       "                                        (&#x27;wave_height&#x27;, &#x27;mean&#x27;, 3)],\n",
+       "                      time_features=[(&#x27;hour_of_day&#x27;, &#x27;onehot&#x27;),\n",
+       "                                     (&#x27;day_of_week&#x27;, &#x27;onehot&#x27;)])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LassoTimeseriesRegressor(alpha=0.0001, average_type='median',\n",
+       "                         feature_engineer=Pipeline(steps=[('features',\n",
+       "                                                           SimpleFeatureEngineer(rolling_features=[('wave_height',\n",
+       "                                                                                                    'mean',\n",
+       "                                                                                                    48),\n",
+       "                                                                                                   ('wave_height',\n",
+       "                                                                                                    'mean',\n",
+       "                                                                                                    24),\n",
+       "                                                                                                   ('wave_height',\n",
+       "                                                                                                    'mean',\n",
+       "                                                                                                    12),\n",
+       "                                                                                                   ('wave_height',\n",
+       "                                                                                                    'mean',\n",
+       "                                                                                                    6),\n",
+       "                                                                                                   ('wave_height',\n",
+       "                                                                                                    'mean',\n",
+       "                                                                                                    3)],\n",
+       "                                                                                 time_features=[('hour_of_day',\n",
+       "                                                                                                 'onehot'),\n",
+       "                                                                                                ('day_of_week',\n",
+       "                                                                                                 'onehot')])),\n",
+       "                                                          ('imputer',\n",
+       "                                                           SimpleImputer()),\n",
+       "                                                          ('scaler',\n",
+       "                                                           StandardScaler())]),\n",
+       "                         quantiles=(0.1, 0.9))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LassoTimeseriesRegressor(\n",
+    "    predict_ahead=(0,),\n",
+    "    quantiles=(0.1, 0.9),\n",
+    "    alpha=1e-4,\n",
+    "    average_type=\"median\",\n",
+    "    feature_engineer=feature_pipeline,\n",
+    ")\n",
+    "\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rrrvwrne9C5RSvPzyy7j00ktP8dF3B2vaM8nhcDicjYkrJnt7e7mY5HA4HM66ZHBwEPv378dFF12EYDCIzZs3e4/Jsoy/+Zu/wTXXXIORkRGcf/753mPf/va38ZGPfASf/vSnYZom3vOe93AxWQMuJjkcDofTMlxMcrodSilMi8Inn5IpaRwO5zTxne98p+Zjd9xxB+64446K7bt378Y999yzloe1YeBXUA6Hw+G0jCsmR0dHsbCwcJqPhsNpnqxq4aXjKcwua3j+aPJ0Hw6Hw+F0NTwzyeFwOJyWMQwDhBCcd955+OUvfwlKKfKmahzOukbRbByeyCIcFAEAlu1AEnlsncPhcNqBXz05HA6H0zKGYcDn8+Gcc86Boig8O8npGjTdRtAvoicggQDIqfbpPiTOBsBxKGIpo/GOHM4Gg4tJDofD4bSMKyb37NkDABgbGzvNR8ThNEdWtRCQBYQCIijlYpLTGZJZE88fTcG0nMY7n2FQSk/3IWxY1sNny8Ukh8PhcFrGFZNnn302AC4mOd1DTrUhS6wk2y8LiKV5NomzemJJAytJA8mMeboPZV0RCAQQi8XWhejZaFBKEYvFEAgETutx8J5JDofD4bSMKyZ37twJSZK4mOR0DVnFhiyxWLrfJyCR5ot/zuqZXdYQDohYiOsY7vef7sNZN4yOjmJmZgbLy8un+1A2JIFAAKOjo6f1GLiY5HA4HE7LuGJSkiTs2rWLi0lOV0ApRU6zMBj1AQB8koBklotJzupQdRsZxcJAVMZCTMfFZ5/uI1o/yLKM3bt3n+7D4KwhvMyVw+FwOC3jikkA2LVrF6ampk7zEXE4jTFM1s8mCMT7/zYtbOdw2iGZNUFAIIkEumHDcXhJJ+fMgYtJDofD4bRMsZiMRqPIZrOn+Yg4nMbopgOUT7ChFAY3TeGsgoUVDX4fYeORCA9OcM4suJjkcDgcTssUi8menh4uJjldgWY4AC1XkwS6wRf/nPZwHIqFmIFwoNA5Zlo8M8k5c+BiksPhcGrwr//6r/jGN75xug9jXcLFJKcbySl2RWISoDyTxGkbRbdhOw5EMX9mUcIz3ZwzCm7Aw+FwOFWwbRsf/ehHAQDvf//7T+/BrEO4mOR0I4msAb9cGkcXCIGiW6fpiDjdjmnRkgAFpTw4wTmz4JlJDofDqcIzzzzj/Tc3U6ikWEyGw2FomgbL4gtyzvomnbXg95UufWSJIJmxsJTQT9NRcboZ03JQLCdFgQcnOGcWXExyOBxOFe69917vv588GIdl80hzMeWZSQDI5XKn85A4p5D5FQ0nZrOw7e4JtNg2RVa1IUulha6yJGB2WcPLx9On6cg43YxlUVAUfgeSSJBTHR6E5JwxcDHJ4XA4VThw4ID33+PTy1A0+zQezfpD13X4fD5QSpHVWcfE/FLiNB8V51QxtaDiucNpLMS0030oTaMaNgDKHDeLCPgEhHwiNMMGpVwAcFpDNxyIReeULAlIZAw8/Sq/HnLODLiY5HA4nCrEYjHvv2fnFpBVuJgsJpVKoa+vD4bpIKUwMfnIs7PQDf45bWRmFlXEUgZWUgYiIRFppXvK+Zhja6X9DiEEkbAEEMJdODkto+oWRKFwXkkiwVLcwFLS6KrMPYfTLlxMcgCw8h8z7z7GI7McDivZHBkZAQDYahyxtHGaj2h9EY/HMTAwAN10EAqzMldFySLHM7gbmslFFa+MpUEphV8WkO0iMWk7tIqULIJSWHzxz2kR1XAgiYUzyycLGO7zQSCAxUtdOWcAXExyAABzKyoeei6Gx16K4/g073taD/AevdOLoigY3rwDAKArMSxzcw4PwzCQzWYxMDAAzXAQCIYBAJqmIKd2j7jgtIZtUyTSJpJZEyCsnK+bMva2TeE0CJby6y6nVTTDwbHDL2B5ad7bFvSLIARekJ7D2chwMckBAGRyFjTdRiZnYW65e3pgynEcilhqY2SQXhlLcwFzGlEUBYN5MZlKsJ5JvtBkJBKsF2hgYACqZiMYCgEAbENFLGWezkPjrCFu3/BAxIf+Hh9kiUDRu6fP0LSc+plJXubKaYNMJoc/+fA78L+//LmS7ZQSWPx84pwBcDHJAQBkFBuRkIT+qIyManVt31M8beLZw8mujwaaloOpRRVzK1xMng4opUxMDm+FJMmIrSyBgi80XeLxOAAmJjOKhUjezdUyVMR5OfCGJatZACj8PgGSSEAIgUNpvhdx/WPaDgShjpyk4GWunJZ57pknoGkKpibGSrZTXjbNOUPgYpIDAMioVsEunQLpXHeWqh2fziKVs7r2+F1SWQuUEswta9xe/DSg6zocx0EgEMLg0CbEVpZAQLs+SNEpXDHZ39+PtGIhEokAAExDgaLyDO5GZX5Zg08qXzYQ6F0yoN0waIlRSjmU/8Y5LWLZDp59gjl/z0yPlz5IWACDw9nocDHJgW1TaHqhgVwSCeLp7itV0w0bKykDPQERK8nuzo4sxTUEZAGWQ7teGHcjiqIAAPyBAAaHNiO2sgDwzKRHcWYyq9jojTIxqSg5gACGyT+njUY8bWB6SUUkJJVsFwBkFKsrgl6G6UCos+oRCYHWpVU5nNPDH/zBH+KBu78PAFhemoemKt5jhAJmlwRaOJzVwMUkJx9VLsze8knd5dDnYlgUBAQ9QQnzK93b96kbNiYWVGZVD5qfjcY5lbhiMhwOo69/EKlkAhQUBs9aACiIyZ5IHwzTRiTCylxVJQcmuvnntNGYWlAR9IkVZaI+meC5I0lML6mn6ciax7TqZyYlkUDX+bnLaY6xsTH8r698GQNDm/GW234TADA7M+E9LooEWpeUgHM4q4GLSQ40wy4xJZAlAdkutPdnC1gKn0yQVa2uLbWbWlThOGxhIxICRes+Yd/teGIyFEa0tx/pVAICCAy+MABQEJO+YBQAgSz7IElyXkxSnsHdgCi6DbmixBWIhmX4JRFaF4gwo0HPpCjwxT+nee655x4AwF9+5hu4/bc+CACYmSqUuvLziXOmIDXehbPR0U0HxZc7WSLM+r3LsGyWmXQzrKruIBLqvnjJUlxHT1AEwL6LbK77hH23k8ux8TihcAjR3j6k0wmIIoHKFwYAmJsrIQQWDUIgLCMVCoVZmSsIDF7ateHQ9dJZesUIAroia2+aFAKpn5nkZa6cZrnnnnuwa/fZ2DayCwGBVUPNTJ/0HpdE0rVmhhxOK3TfSpvTcVJZC3JRI4kgENi0++YjmZYDCpYRoRTQ9O68iOc0B3J+0datWeJuJ5NhYjIcCiHa249sJg1Cbahdek51mng8jv7+fiwmTIQCLPARDIWhKlkIBHxBvgHRDLummBSF7sja66YDsV7PJC9L5DQJpRQPPfQQrr3+RvgkAZFoL/r6BzFx8pi3D89Mcs4UuJjkYCWpI+AvOxUohap31+JZM2wv6kwIK8vqNhyHwjBtiEViMqfaiKUMPgz+FDK3mAQABEMhRKP9AABNTXfV72EticfjGBgYQDpnIuBj1w4mJnOQROLNI+RsDGybwnZozRJRUSTQuyD4aDcoc5UlAYpud20gknPqSKfTUBQFm7bshE9m18BLLrsWzz79K2/uqih2j9Mxh7MauJg8w7FtimTGgl+utHufWlAxNp09LcfVDqpWKMPySQJS2e4TXyy7Cq9UVxIJDNPGwZOZrvx7uhHbpjg2yXoCAwGWmQSAXDbJF5l54vE4+vr7gaKy8lCoB0peTHLRvbFgVSr1ew3Nde7g6zgUtoP6cyYBgAIp7qDNaYDbNy4He731074bbsb87JQ3b1ISCbIKCwZzOBsZLibPcNwMQvkNloJiMa4jo3bPolA3CmLSLwtIdWHfp+tIWwwhwErSQJZnJk8JmmEjk2FBlEAgiGhvHwDg6Ksv4Iff/zqefvrp03h064N4PI6+vgEABQFRyEwKULvAjIXTPMxQqbZYFIT1n4EpHx7/7FOP4D9+8E3EVpZKtvskAUsJ/VQeGqcLccVkqKfXWz/tu/4mAMCTj7O5k4QQ9EckPH0o4WUrOZyNCBeTZzhMoFRe5ERCkMpZXWWTrui2Z/vuk7t0vEmVBZntAJblINOFf083YlgUmsZMZQLBoJeZ/Nzf/TG+9PlP4Df+0+1dMVNvLYnH4+jt62eRjjyhUA9yuQwrc9VtLMb1M/5z2iiwwev1MpOAadnresFsl52LH//wu/DZ//7H+P6/faVkeyggYinOxSSnPq6YjET6vG3bRndiZHQXXnruCW9bwCfCtimc7llKcTgtw8XkGU5Wtaq62wX9Inxydznb6WYhMykIBLZDu9BEqDIDEA1JGO7zIddFWeJuxjAd6Dpz5gsEw56YdLfNzU4hnV3/M/XqMb2orir7Eo/H0dvbXyIeItE+ZNJJiCJzc33mcIKXu24QTIvWS0yCEALqVAq29YTtUE8OO44Dy2KVK8yBuIAkEj7ahtMQT0zme+pddu4+BzPT46U7E7KufxsczmrhYvIMRzOq272HAiKGe/2wHdoV8xoppdD0gnENg1SUNq13dMOuKHMN+kUE/SJyqrWuI/8bBU23YbiZyUAQvb2FxcLmLSOglOLE2MlaT+8KVlIGkpn2ysBt20YymUQk2ldypkZ7+5BOJQGwHjrbqiwt5HQnpuWAkgbfpcD6jdcrtk09PewKSQAwDK1iX55R5zTCFZPhSG/J9tEduzEzNV52r6b83s3Z0HAxeYaj6bXt3hkEVoejtDNLasczFpZNAVIwA2F0X2ZS0ap/HyzTCh4xPwXkVAuGroAQAp/Pj56ixcK+/TcDAI6NHT9dh9cWDz32Es65+HV41+23Q1VVpLMWMm2ajKRSKVBKEY70eWXlABDt7YeiZGGaBoZ6fRAEfr5uFFTdhtTAuIZgfQcPio+tWEyaRqk5CiGAwxf+p4zJeQWzS91X6eGKyWhRmSsAjG4/C4qSRTy2XNhICXh8osAXv/hF/PznPz/dh8HpIFxMnuGoulOWzSuHdnRBaJgOXj6eRrzD7masDKvsOEn3lSvVEpMukwsK0rnuMxbqJnKaDdNQEQyGQQiBKIroiUQBANfuZwYLJ050V2bya9/8EcYOPoIf3HUXDjzyNDKK2XYPbsF4oq/kXHXLgd3sJCFurx2n29EMpyRwUA1K13clSHGZq2UWzn3DKC33dgOSPJN0alhK6DgymYVhOus6s11OPB5HMBRGIBgo2T66YzcAYGa6+B5BeZlrEf/4j/+Ib33rW6f7MDgdhIvJMxxW5lrvNCAdXRAuJ3SkcxYWO+yWZ1UziKCdFcKnglTO9GZWVeOVExneO7nG5FQbpqHBHwh629xZkxddfCVC4QhOnhg7XYfXFgsLC95/P/38YVDKRHM75XyumAyGeksERm/e9TadTgAACLpjkD2nMYpmQ5YaLxfWs5hkY5fY8dXLTDJIRWySszYkMhbSOQsPPruMqYXuyVC6fePlwd/R7XkxOVXUN0kA2qFLoePQri/D1jQNpsmD4hsJLibPYCilMEwbdbUkOleqNrWo4IVjKQz3+bCcNDoa+TUtWqElCdiMxm7BtBwoml1TTIYDIiilvARrDaGUQtFtGLqKQLBITPb2wx8IYmjTVmwd2Y2TJ06cxqNsneWlRQi+XhAiYOz4GAhhifxExvTGAzWLKyYDoWhJIKqQmWRiUsy7unK6n5xqNWiHYKzn7Itm2J7ZnG3XzkwCAPh19pSgGzZ008ZQrw+gQDy9tvMYVb1zjsPxeBw90b6K38XWkR0QBAEnTxwpvBftnAHPzJKK49O5xjuuY1RVhVE1iMPpVriYPINhIrG8z7AU2sG+w5MzCiJBCT0hCabpINfiIrYeplXZ4C4KBEoXjTbJqTZoHfv9UECEXxJ4xHwNMS1m4a5pCgJFmcmBwWHs2Hk2BEHAyPZdGB/vLjGZjC9DDm1GtH8LZmfGwX73wCtjaSy3WCXgiknRH8G/3DWOv/jXIzh4MlNR5iqJBBrPTHY9lNJ8ZrIJMbmOM5O6XijVtayCmDTNKotagWcmTwU5zQYBG+UVDUmIt2kK1gy2TfH0q0kks50ZsRWPx9FT1jcOALLsw7aRnfj217+Iz/3dn3jbOxWcWIobmFhQujo7qaoqz0xuMLiYPINhIrH+BUkSSMfMclS9NOumdVDomZZdIcMkkUDrosxIVrXqSEkGIWRdL9i6HTYKh0JTWc+ky8f/7O/xN3//rwCAke1nYW52qqtuhunUCsRAPyID27E4N4FwQIRDKRbiOowWg0WumHzyiImXjqcRSxn47n2zXilwJp0EwII53fT741THsFhxaL2gI8AKQ9az4ZlqFPwBStxc9Uo3V1Rpwed0npxaEHaSJEA3HOhVZi2vFtNyEE8bmFvREEt1psUmHo8j3NNbNWP/t//jX3HOeRfh4MvPets6cT45DsVyUoemOUi1aaB2urFtG6Zp8szkBoOLyTMYNzNZD1EgUDsg+kzLge1QCEVRPL2DJaiaXmkQIYqko4J1rYmlTPgaRP8JAR9+vIYomgMKQNPUkp7JHbv24JzzLgTAemIsy8LU1NRpOsrWUTMrkAID8EdGMDU5jqBfQF9YRjggtuzW7IrJIzMEF58dwWsvH8DcioaevKuhW+Yqddnvj1Mdw3QqxhVVQxRJR6tNOk3xHOLS0SDVFrV8lMOpYHpRQ8gvlmxT1+AcevJgAs8eSSISFDG73Dkx2RPpK1nTuFx82TW48prXYGaajQgh6EwJeE61YTuALBGsdNh34lShqqwvtpuCsZzGcDF5BmPZjW+YokA6Em02TAfFgW1JEDpqJKNWcRuUxM5lVU8FqawJv68JkwuuJteMTM6EJBDomlpS5lrM9rxb39jYWFcsOA3DgqklIAYGkLSGoWQTeP7VOfhkAX5ZgNlipjuRSCASiUDVCTYP+DE6HIRpUeRMPwRBKBWTRvf8/jjVMUynUcwRAOCTSNsOwaeC4jJX2yqcl6ZZuSin4JnJtSajWIilDYQCpWKy1R7uRli2g5PjU3jw59/BzInnkM6Zq66YoJTmeyb7a+4zun03NFVBbGURFLQjQeBExgBAEfAJSHWoXPdU44pJnpncWHAxeQZjWqUCrxosE7b6u6pRlgWVOrzw0I3KkRqSSJBRLTz6Urwr+gtUw27grAsIhPfyrCWJrAm/LEBRsggGQ1X32bHzLABMTL50PNXxxU+nOTm1AFAHon8Avsh2AMCTTz8P22aVAlaLbs3xeBx9ff1QNBuRkISRTcwafz6moyfS6/VMstmolJdldzmG6YA2cf2UJQHZ9Swmq2QmQ6GeqotaAtKgAYSzWhbjGoSy2dCiwNo9OolmOPj2nZ/HP/79n+DjH34ndE2Buspe7nQ6DcMwEO0dqLmPNyJkahwUq19HUUpxYpb5TsiSgLTCRHFyDftM1wKemdyYcDF5BtNM+RLr0evMexXfnWWJdHQRXm1epiAQhHwiEmmj5ezLqcZxKEzTaeisCwI+/HgNSWYs+GUB6VTSM5QpZ/OWrfD5Azh69BjmY8a6z34fPT4DAIj0DiIwdDFARBw4cD/+y2dewuS80rLYcyPyFEBPUMTIMBOTM0saor39XmYSACi4o2u3o+hWwxmTgHtNZ7MC11sAwbYpTNvxShJdMRkMhWFWcXMlpPvHL6x3cqpT0dbBqok6W3mj6Q5mp5lhmq5rOPjSk9BXKSZP5N28t4zsrLnP6A4WdJyZOglCV2/AE0+byCoWAn7R+63NrWiYWuyecSoAz0xuVLiYPIPJKlZDhz6BAHYH6jMM04ZTpCZlkZW52k2U2jaDZjiQqix4ImEJggBY69gYAigYVzQyuRAI4PBB8E1BaWvnlmE6MEwWlEinEjXFJCEEW0d24/DR40hljFUtTGIpAydn19bm/eTkLACgf3ATRLkHgcELsTL5JEyL4r6nV1oOtMTjcQTDUQBAJCQh6Bcx2CtjdllDtLfPmzMJAH6Z4LnDyXVtzMKpT1ZxmnJyda9dE/MKTs6tr9EF5dl31801GApXHQ1CaSNrOs5qYWPJysWk0HHTLkWzMD8zjje95Z3wB4J44amHV92XOTbG5gxvG9ldc58tW7dDlCRMT4+DENJyb3o5yazhfV7st0Yxs6RC6bK50zwzuTHhYvIMJqs2HkRNCEEntEtOcyALhfcSRVZe9+LxFOLp1V1UvHmZNeag0Q7OeForTJs2ZXIB8Mxks8yv6C1FbVmGkUJVc7AsE9Hevpr7bhvdhccf+xW+/bX/saqyrMWYhoX42hopjJ1kmcnRka0AgLMvuAF6cgwhIY0XjqWwEGt9NIgv0AsA6AlJAIBdW0I4OplFNNqPVLIgJnvDMnKqvSYOjZxTQ06zGpbfF6CYXFCQXWcLXHZ9ZRimg+/dy8yzgsFQ9TmT4O0Ea412inwOFpaSSMSXcPY5e3H5ldfj+aceQlZbXSmtKyZ37NhVcx9JkrB12w7MTJ2EIADmKhdSquaUtvIQgrRiQdHXb2l5NXhmcmPCxeQZTFZtnJkkpDOzwxTNglT2XoQA04vqqktOTIsCpP68THOVUcG1hh1f42Ps1PdxJqAbDmKp5m9YrLyKeD1/tTKTAHDb238TvX3D+NkP/hUnJ6bbOj5KKWZXdKSz1poa+Tz//LMgYgAfec9V+KPbd+ND73s7AOCyzSdBKRuC3QrLy8uQ8mIykheT117Yh1jaBBXDyGbTJftzM5PuJqNYJSOdmtlfX2cuvpZFvfvDzx9fwrGpFAC3zLXKNYLw1ORa41aBFLMWs2kPHWXCb3T7blx3w82YmRrDsWMnkFGstu+lY2NjGBzejEgkUne/c8+7CK+8+DQArDoor+llYpJSODaFqttdYQTnwjOTGxMuJs9QHIdCLb84VUEggN2BC1VGqRx6bdksO6oaq4usWXaDoWA0vw+AgyfSrH9zncHKAJsrJVt/R78+MW0HiRay3hnFhEAKoy3qickb3/g2fO6fvgEAePjAg20dn6LZ0A0blu2s6Tl56MVH0Lv1MvT3hnH1BX24+qor0D8wjBOHfgUALWUCDMNALBaDHGTGEz1B5sR4xfm98MsCUpoP2Uy64nndtNjhFLBsB6bR+D7hIgkCTIuuu0y061yuaDZ++qsFgLJznhnw6BXnJ6WkY0PmOdUxzMrMpCAQmLbTsYCpbjo4euwYANbDuG//zQCAR375AF4ZSyOVa0/QjI2NYevI7oZBlmv334SlxTlMTxxddauNapRWX4X8InyyCFq0vukGeGZyY8LF5BmKe7NvOIiarL5EVDdsZHLM2KSYoaiM4V4fVG21mUkHtJ4QIxSW7YBSNvB3PRqmsL+hicwkAKeLbhynglwuB12vLFWzbYqsajUt1NJZ13wnLybr2L4DwDnnXYTBoc149Jf3tnVjnI9p+d9f500nXE6cOIF0bAo7ztvvbRMEAddefyNefvaXoNRuaRbk0tISew0fE5NuZjLgE3H+rh4ohh9KLlPxPL4u7040wwFpYZXQF5GwdTAArYMzhDuBG6w7MpmFYVFcuIuN/XllgomJ4rmTALvO8nN27bBtCqts7rQLIehYj/X8soaFmUkAwOj2Xdixaw+2btuBpx9/CItxHYbZfmZyy7ZdDYMs+/bfBAB4/qmHVy2QWfC/8GMMByX0R2QQgnUZIK8GpZRnJjcoXExuEGIpo6Xof7OlpYSsXrwk82V85cJVkgTI0up7JEyL1jVyFwiBYVAYpoOsYne8jKYTKJoNodGcFuS/jy5Z5cRiMQwPD+Ouu364Zu/xk5/8BD09PRgYGEAqlSp5zLRZ9r1Z1+Bk1oRPFpBOJwHUz0wCLNBy7fU34bGHf4ZzzjkXTgtGVSdOTuGyvaM4/sqjoKBrFuC4/4EHAAAXXf7aku379t+MTDoBPXEcuuk07Vy5sLAAABAD/ZBEUjIXNRwQATEIXddgmgVxTQhfmHcruuGA0uaykgD7TUgigdnCOXUq0HQHAgEOnsjAJxFceV4PAECQmKgs75skTYX2OO1i2U7t8C8lHWtLOTmbw+LcSfQPDCHcEwUhBPv234SXnvsVUlkDRj7ocXI2h3i6uYCgqqqYn5/HlpFdDffdvGUUu88+D88/88tVZQ89X4gqK3bawc9rLaGUQpZl3HHHHQB4ZnKjwcXkBuHoZKalURu6WediXoQ7K45dzNoTYQsxrSIr6SKJwqqzMuy4av81gsCGpxsmhaY769L9rJmSYyBf5rr+tHBV7r33XqysrOB/f/3f1uw9jh49CgBQFAXT06W9i5bNxtE087uwbAeqbjMx6WUmexs+7/f/8M/x2je8A1NTk5ifn2/6uP/vj36CXDaFA/f/B2RBQCK7NlHag4dPAkTABXvPK9l+7fU3ghACZfFZtvBvUu25YhK+fkRCUkmAyC8LoCKbzZnLFrKTtAO2+BsJSinGZnItz/c8HbTfz07WVemdolmQRIKDJzM4b2cPwmyaDQSR/YdRVtlAQdaVGN5oWDatO+Pa6EBm0nEocpqFF555BJdcvs/bfu75l0BVsqB6DEp+7bGSMpDKNNduk81mAQDhcP1+yeL3m54cg7WK88mwWHCjWiUZBe0Kt+zDhw/Dtm3vPskzkxsLLiY3AJRSpBUbuRbEZGv22wSprIXDE9nWDw4saxr0i1Ufk0QC1WDHMruktmSY4qIZNuqNQZMEAs10YFgOLIcivQ4Ha2uG3aSY7EwP66ng3nvvBQA8/thDa3bjSCQL/XnxeLzkMdumCPoFLMa1ku2U0oqFfHFAoxkDHpfNW0bxhre8B0DB4a8ZHnzgPgDAU48dQE9QwNh085HxVjg5PgvR34+dW0Il2/sHhnDe3kuhLD4Dw3KaNofwxKTU7/VLuvh9AhzCMj3FYhI8M1lCOmfh8HgG6dz6uQ7Nr2h45IWVil7HrGo1nn1bBULouhKTqu7AtNhcvr27erzRIL29TBBUmzXJWTssm8Kpk/HuxLlj2hQzU2NYmJ/Bvutv8raP7mDjPOJLU15FiKo5iGeau/66LRWyz9/U/qM7dmNpYQaKqjXeuQam5dRc4xB0RnyvNffcc0/Jvy1rbY3nluIaZrpsBmc3I53uA+CsHsum0A0bqYyJTf3NXeAyTcyYLMAWBqk2sieUshK+YKS6mBRFAtNkA6LnV3T0RiQM9vpaeg9Fqy/ERJHAMBzoBpuXllHWX0SM2X43XrURALQLIuaO4+Dee+9FJNqPTDqBxx5/CralY2pqCm9729swPDzckfdZjtUWk6ZNEQlJmI/puMSmnnnB1IKKRMbEZecWMo/F2e10KgGfzw9/INjUMezcyRYnY2NjeN3rXtdwf9M08divHkYk2s/MGSaPYXjbORibyeGaC1o79xsxMzsPKdCPbcOBisf27b8JR772T8ikUy2XudpiLyLB0tsHE5P5zGRR3yR1KBeTRcwuqUgrFhIZAwPRzn7ftk2h6LbXy9rsc54/moJpUWQVC/6i6y+7T7SuJh1K8tmS6tf9U42iF8bT9PbISK+we0AgyM5XwywXEvycBYBf/vKXLQXJyjn//POxf//+iu31MpMUFPMrKgQCDDe5nqmGaTl44emHADAjHJeR7ex6vTA3DkVj5f+KZjVdKeCKyWCg8ppajdHtZ8FxHMxOTwJXbW36+IthZazVPzCBEM+ZttZ4tPVAuZgE2L3Q5+vsNRBg17SXT2QQCUkY3dzcfZyzOriY3ADYNoVts1KNc5p8TmuLBOawllNtmJbT0uLCtCgoSNVG+8LLsxLaeMasW/pSC1VvICYFAt10oOgWgj4RmVz1Hs7TheMwo5jBqNxw307N/VxrJiYmsLi4iPf9/l/i/3ztM/jWt/4PvvmN/w3HcfDRj34UX/rSl1b9HobpYG4x6f17cXGl5HHLYjdXW6NI5UwMRH0wTAdHJrNwHIpL9kS985KJSbZ6TKcTiPb2N31+jIxuhyTJTS+6XnnlFeRyGfyXj/0V/veXPo2XX3gKb9t9PhKZzgc5YiuLkIIDCAUqF/VXX/d6fONrX8Dc+IuwnX1Vnl3JwsICor190EwRmwZKbx8BnwgihwGgxNGVEMLdXPMomo3xeRVb+v2YX9Fx9khPR18/njbwwrEULj47Ct20sWtruPEx6TYoBXwSQSJjlATzcqoNXxtikmB9OUyqhu31lQV8AuImy0wGg+zzr8xMcjdX27Zxyy23VDU3a5bBwUGsrKxUbGcO7NWf0xOQcHxagSQKqxKTlkXx3JMPY+fuc7BtZIe3ffOWEUiSjPmZCag6W9PYDgt6G6bT0KHVE5PBJjOT23cBAKYnTwJo7jpb+bc4NYMbQb+IIxNZ2DbFhWdF23r9tcZxHDz22GMV2w3DWBMxuZTQkFUsmCZdV2u9jQwvc90AWA6FJBEkM2bTi7aMYsPXbGaSUFgWhWa0bl5jWk7jGjdKkNNsqJrVllV3uctZOaLAXAlzig2fTGA5FMY6aljXDHajaOaC1y1mJm5fxLnnX4xz916Ob33zTjiOgx07duDuu+/uiLiYXFCgKDn0DwwBABaXYyWP2zaFQAh8koBXxtLIKBZWkszBz8oLeBdFtzwDpHQqgWhvX9PHEQ76sHnrDhw/3pyYPH78OADg2utuBADEVpY80xLd6Gw/byq5jGDPUNVza9fucwEA8eXppjOTi4uLGBjcBN10EAqU/uYCPgGCXJmZJOAj+1xeOZGGKBKEgyKSGbPjvU6KbiOeNvHkwQSOTSlV9ykvp86pNkDZonQ5Uf5YKxUsBVgf1/r41h2HwjRpkZgUPffWQCifmSw3A2kwbepMIBaLQdd1vPcDn8D/+dHz+NaPnsfk5CSmpqaa+t8HP/hBaFr10k7Lru1eHgqIGIjIqx4vk87kcPDFJ3BdfhyIiyiKGBndhbmZcWhFGWvmqt34+uu6kYaazUzuOAsAMDU13vzBl1FvDnUoIKInKK2rNU058/PzUBQFolga1Fyr9pfJBRXRkATbcdbMKZ1TCheTGwDbZpEXy6bIKs2ZjRiGDanZiDMlXiltq4YM9coziklkTIgiE5WtWmgrjTKTIoFh2shqNmRJYD0G68jRld3Amv+b7S5w4JmbY2JyZNtWXHP9TbBtG0NDQ/izP/szjI+Pr6p0yiWWNGHqCgaHNkMQRSyvlIpJ06YQCNAXkaHpDg6NZzAf0xH0CSAgJWXb2VxhDmo6lWw4FqQYQgi2juzCsbxIbIT7t5919rno7RtALLbkPdZK33MjHMeBkomhJzpU9fGBwWEIchCplammTZ0WFhbQ2z8M3bDh95X1TMoCBKmKAQ9o15hGrSW2TbGU0NHfI3vivtPzGFNZCwMRGduGAjBMuyI4oZsOXjyWQi4fSMnkWLmtJBKWsUub3vXXtBzYFPWrSupgWuvD6My0HICwYCgABP0CbJv9/eEQy0waepnoIbzM1S1p375rD87Zswt9Q9swtGkE27dvb+p/0Wi0ZtBQNxyIdYKnokhW/dt49FePwDC0khJXl9EduzE7MwGAQNFs7/fYTCtPVmHnij/QXGayr38Q4Z4IZqdPNn3s5eRUq67bu9AB1/21xL3nXXPNNSXb18LRVdNtLCcNhAIiKOBd6zhrCxeTGwDLZlMWiYCmDGw0w2lG33mQ/JxG3XSg6q39MJuJvMsywfhcDgC7KKotZGdsm3rljLVwhfbCigZZEkBp5xdxq4EJ9Oa+EIEAXdAyifGpWQDA8KbNuDZvfvCmN70Jb37zmwFU759oFVW3oak5hMI9iET6sLxS2jPpFM0x6+2RsJTQsRjXEQ6KCPkFTC1o3mLHDTQAbmayeTEJsMXJibETTWVcjx8fw8DQFoR7whgc2oTYSkFMtuLI3Ih4PA7q2Ij2Ve9PJYQgGB1BOj7d9CzZhYUFRPuGoRkOgr5qmcl8mWs2XfIYH7TgmmQUfucE6Hj2Lp0zS5yzs2XO1ablIJ42MbmoQjdsPPpSHNOLGgJ+wXPuXsnfQ3SziaqSGoiErJsRTIZFAUq8eap+n+BlJsM9eTFZ0TPZXedsLGV03H3WFZMDA5vZBkpbWpgLglBzXJJm2BDrBClEgUBfZUbpgQfug88XwOVXVfZsju7YjfETR/Gp//ab+IOPfgi2aSHkFzC73NgkJ5NlmUlfkwY8hBCMbj8LczMTLR1/MYmsiYCv/nJ9PZdlu2Ly6quvLtm+FplJt12EEAKRkLaq3Titw8XkBsBdCIb9UlMXQ91w0EpzIqUEhulAFgVkWnQgZAuo+he5vrAM6rAB6LTopt8MplW7XKaYrYMBjAwHIIkEhAD6OhqqncqaTTm5AgAIaXrhfzqZm1uAIAjo6x/ERRdfgTe+5d342Mc+hrPOOgt9fX1eqedq0Iy8mAyFEe3tRzxWEJOuY6t7mhNCQEBgWUxghoMSYikDCzH2e8mpNuT8d5BMxNDb15qYvPCiS6AoWTz19IsN9x0bG8PW/IyygcHNiK8sAgB8ktDRvkl3Mdg/UNvsKNw3iky8+TLXWCyGnkg/KEXJjEmA/dsVk0pJZhKg60NXnFbKRyu519ViVjMuhFKKTM4q6fnKli3+TYvCJwkYn1WwENe9AKErQHsCIo5OZkEpbSnIVY4kriMxmf+M3SBl0Cd6bq49YXa+6lplz2S3nLOqbuOZw0kkOzxeyL1+DG9iYlIUWluYC4JQM7jWaBSW1IHM5C8fuh8XX34dAlWM1G58w204/4JLsbQ4i7u+9y0cfvUZhAIiYmkTqWz98vNcjt0zmhWTABOv8zMTLVdduSQzVs3xakDeS2H9LGkqOHHiBCRJwqWXXlqyfS0yk4pueVlvnyQg10S1Hmf1cDG5AbBt1mQc9AtYTup49nCy7kxIzbBbjjjrpoOAT0CqRTGpaHbd8gyX3h6ZGXgQtCRYm52XCRR6EiVBaKoc+FSRzlXeKOaWNfzgwHzFzVgg3bEwn5ubR2//EERRhM8n4Y4//yfs23cdACASiSCXy63q9S07b5qg5hB0xWSiICYdNmKypFdwMCpjoMjkqLdHwuHxLCv7Nh1IkgDbtpGIL2NwaEtLx+NmX3/RRMb1xIkT2DbKHAWLM5M+WejouIjpmTkAwNDQppr7RPq3Q0nNQTcavy+lFOl0Gv4Ay+aUj/vx+0QQwQdRlEp6JoHuyvKsFcUmTwDyxmOl16GjE7m2XLPd17eKsvEBWayoVDEtB6JAQCkwNp1DT0DEloGA95xwUEIqayKjWDCaDNRVQ5aEjmbZV4Ob/XWDlAF/ITPZk89M5pTSEQKU0q44Z587ksSh8QxiSQOJJkdbNIsnJvPO2wGfgFiy+XOTzUSufrNq5MAuCKyaqN1s68TEBE6eOIar833p5Vx25XX42r/diy9//RcQRBEvPvNLdq+gwIHnVjC7VDson82fK82OBgFY3+Ti/BRyauvfkW4W7k+1IARw1vH5OjY2ht27d2PTptJ70VpkJvWiQIUoEuRaGoPHaRcuJjcApsUEFSEEw70+zC1ryNSZpZhRbEhC8189IWyhEgww04hWLvCNIpDlBH0ClhLNu8dphgPUmVdVDVkiFRH700mqLJsAAH/xlSP4j0cWqorebhim7Rq1AAVB50Z7w+GwN/i5XVyXYEXJIRjsQW9vP1LJBJIZEznVglPFwU0QSIkTccAnIqvamClaOKSScdi2jcE6AqwamzZvw66z9uKB+++tu18ul8PCwjy2FovJ2BIopZAlgZmhdIgT46zUePOW2sI4Orgd1LEwMz3d8PUURYFt25DzYrK87MovCyCEIBCKlLq5cjMTAKxKo/iMlAQBOa1yrqMrwpIZsyVDJlV3QIrewe8TkM6WZyYdOKDo75EQS5kIBytdfgWBYCmhQ9Gsun1t9ZBEAqUD57Lj0LazOS6GyYKnbs9kwCfCtiwIguBlJnNKqXggWP/nrDtOa2ZJw+YBPxZWOi8mg6EwolHmEOqXhZZmNDfMTDb0bKAw2/zu3RnH+8rMd8oZHhrA3ouu8kaIDPXK6A1JWIzXXoNks25msnkX0u3bd8O2LYydaN2EJ5HMQVVyMOrMQl3vI8PGxsawZ88eDAwMlGxfi8ykajheCbUskRZnqnPahYvJDYBuOl5kWZIEiCJBMmtA0Ww8cyhZkelr1aGPUsAwKcR8T43Swo9T1e2WRokE/SJiaaPpBURWNdGCLgbAoublvUStksiYHRmIa5gODKtUcLMSTfb3l7vLEbK+eyNclpcWMThYLMiI5zbX09PTATHpgIBCVViZa29fP9LpBA6eTCOZdQMejT+nUEDEi8dS8Mns83ezhK2KSQC46rob8ezTT9T9244ePQoA2LJtp/c+uqZCUbKQ8kZRqyl1LGZ8konJkW21Z5v1DTPL/JMnGxsipdNMIEo+tgAPlBnwuOLSH+gpdXMlZNWCYCOQU0v7xCSp0j1SMxwvO/3yWBrTS81fY+aW1ZJroZQ3NCte0CuaDUkgkCQBO7cEq5rrRIISphZUZJXWAoHFSCKBatirdm1+4pUEHnxmGZre/u9CyQsXzWCD32WJwLIsiKKEcJhll7K50s+ZdEE7gVt9tGXAj56giETG6Kg78MLCAvoHNpVkeXTDrghmHpnIVP2ea2UmHYeNAmvm3LLa/HsefPBBDG8ewVlnn9tw3/2vuRlHD7+EVCrB+sj9IlZStdcg6SxzSW4lM+nOtjx6rDXjuccffxyjW/tx+y1n4+Z9O/HU4w/hTTecjbFjh0r2W+8jw06ePImzzjqrQkyuRWZSMxzPQ0MSmcFSp0dTzS1r+PpPp3DjRx/nY6/ycDG5ATBNp2SREvQJWIobeOZQAhPzCmaWS2+UimZDatHu3XbLpyhpqURUUeuXs5QjCASOQ5HTmouAJjOVWb1GyBKBolptXQRWkgaOTGRwbDKDE7OrK9UEKkuOj0xm8YkvHfH+XS7c3Wzber+ArSwvYnBoc9EW6i10OiMmmUuwqrhlrn3IppNYTrAgiu2gqYx1JCRhy4AfvWFW/hqPsf7FgZJjb47r9t8M0zTw8MMP19znwIEDAIALL2ZGBAOD7H1cEUsIWnZMrsX07AKI6MfIloGa+wwMM1F79FhlDyultKRcPpVKAQBkf15M+ssNeJi49AfCJW6uhIC7uYI59cpFI4xY9q70OmfZDlI5E6puI5ExMTmvNvVb1w02v7IvXCjjFgQ2K7H4O1R1p67xCcAymlnFxonZXN0+rXqw916dwZBtUyQyJgyLIqfZeHks3VZFiaJZrIdTdxDwi8yQzTIhSTJ6wqyfTlUrsz7OOk9o6GahsJEQAhC05DfQiLn5BfQNDHsBh/IKE4B9R4txo2pbTa3MZLM+BxSk7Vmlk5OTGN15Tt2RYS7n7b0EADA1zq6B7rl7fDqHWKryvMjlXAOe5jOTozuYmDxytDWvgKeeegq2beM33vNhmKaBH/7715FOJXBy7HDJfut9ZJiiKIhEIqckM6kXmTuxc5Z03OhsbCaH//HNMcwsaeuqZep0wsXkBkC3aKmY9IuIpw2kcha2DfoxtaCWRBPLFzXNQCkbsyBJBIl0cxcAJgrtNuaUESzEmit1TWfrN6ZXg7kWtrfQiacNvHIijcWEgbRirbqEQtVLy3TvfXIZcyuFkqtafUfreXHu9h0ODZdlJs3OZSYNy4FpGTAMHcFQGJFoH5RcBllVh6I5rMy1ydcqLoeNx5YBtJeZvPLq6+APBOs61d5zzz04+5y9GBkZLXmfuOvo2kGn4fn5eYj+AQz11V709A9sBhF9ePXw8YqF3/icghePp7x/u5lJn98tcy3vmWS/QznQU+HmandDo+8ao6hWSRBPFgnUssCBZVMkMxYSaQOCwH7/qWxjAZXKsdLucldrAnY+udd/tcEYJZfNA36MDAUQ8FeWwTYNpavKlLGyVAoQiqxiYTlhtCWW3FYLzbC97LllWZAkCT0hNitQUcvKXAngYH2fsxV+AZTkDe86g5uZLIWUzDO0HTYyrNosP0II6z0tu64YJi0px64FAfs9aLrdlEt9MfMLpUK4HqP5rOHMdKEE1ScRHJnM4NWT2Yrjd0eD+HzNzZkEgKHhLfD7gy2PxBobG0Mk2ovf+8ifgxCCZ558GABzHC9nPY8Ms20boiiiv58Z20UiEQBr1DNplGa9CaEd/V04DsVKsrA+5W6xDC4mNwCG6ZSUNwkCi8REghIkSYBuOp7du2U7sCyn7iiNajj5eWMhv4jZFa2uwY+LGzmtNjC9HgMRGUcmsg1vILbdrlgFANrUgOJyklkTkaCMSFBiswpXaZhSXKbrOBSHJzK44rxefOjtrPyw1sDd9WwOsbQch2WZGBouzUxmciYW41pnxKThwHCHR4fC3lxIP1SoGivFaucziuWdVdsRk4FAAJdccQPuriImbdvG97//ffzqV7/CFde83sumu+/jvi9F58Tk4sICpEA/+nrkmvv4/RJ8PdswM3XSi7BSSqFoNg5NZLEU173fuismvcxklZ5J9nhpmatAyLoOfpwqFN3xHIMBVjao6TZeOZH2PmPTcmCYNsZmFYT9IojQ3GKl1vWYAoinTS8ooOlOU9maTrGaQepa3k3WJwqYjzEfgHZcuF0BrRmOFwCxLBOSXJSZ1KqIyXV+zuqGXXaNo03dl5tlcWEBfRVisjQz6TgUqmFXde4V8je2CjHZ5MKegsK0KGIpA8enm79fUEqxuLCAgSrHXo2tIzsgCEKJmOzrkbF1MIBk1ixx2HYcCi2fxW4lM0kIwcj23ZgYb23W5NjYGEa3n4VQKIRNm7dBybHPoVxMrueRYZRSOI4DURQhyzIikYjXh9vpzKRt00IVnXcApYmDnGrh+FT76w/ddHB8ulCV1kyw70xgze4qhJDthJCHCCGHCCGvEkL+KL99gBByPyHkeP7/t+bBz6nAMCtLl7YOBhAKsBtnJCjh8Hgm36vQzhWHuaoJhMDvE6DpDo5ONv4xqroN0saCXswP0J6p46gGFCLXrYpVBqlrUlSLVNZCJCQiFBAhi8yowrKdtl4LKC3TnV5SkVVtXHNBH87dzhbs1QXv+l6cHz7CynQ3bRnxtkmCgKlFFSdnlY6ISUW3YRisdyUYCqN/YBAAoOXiUHVW5trOeRFbWUIwGEYoP8y8Va649vU4eeJERQT6Jz/5Cd7znvfAMAxcef0tXgBk02b2GU1NngDAhJfaIRfMeGwJ/p7BukYXPlmAHN6G+dkJLCd1zK+oOJwXkaxuing9fG6Zqzv+o9zNVRQJZJFA9vcgk04WHujgQkc3HcSbrIxol9X059XCtpkBjFgSMSfQTYrjU1nohps9ZBb/6YyFcFBCQKp0ZK2GotmQqzSPEwCLMR1LcfYazWYmOwFB5eiTVnCv7/68y7FmOC2XlFHKMluSyPpTg/nSbNtmPZO9kRB7r7IyV4L13YMGAFmltMJIIJU9uO1i2zaSyQR6+0rLEilK1xC2w/r7c1plwKOWmGQL++YuCJbtYCVlIp5u3vgvmUzCMIyqQrgaPp8fm7eOYmaq0hzHJwmYKxq3ZpgOTIudK630TALA6PadmJ2ZaMlAb2xsDFtHd0EUCEZ3nOVtr8hMruOAnds3K4rsfnHBBRfg/PPPB9B5MWla1Waol16HdNNBfBUjuH748Dy+/vMZ79+dHsnTraxliNIC8F8ppRcA2AfgY4SQCwB8EsCDlNJzADyY/zdnFZiWU7ecIxQQkcpaiKXc3obWFxMsM8n+ezAqY2pRbbjgYiVJ7S1cwgERi3G9br9QWrHa7hPwSQSxVGsXAdNyoBu2tzgP+kXEUyZiKbOlyGkxxWW6r55kr7F3V4+3UK9e5rqe85LAfffdD0IIrigaFu2TCZaTrEzNFZOtziwtRtVtmDoTk6FQGNtGdwEAFuYmoBluZrJ1YitLbWUlXa7ax0aElJe63n333YhGozh+YgoXXnKtJ3Qj0V6ct/dSPP04cxOUJYKZJQ2HJ0pHa7RDJrWCSHSo7j5+WYAUHsHC3CQOT6RxaDyLiXkF00sqwgEJkkiwGGeLKc+AJy8my+dMutv8oQHEY8veb5cAcDplKjSbw6Hx1QUiGvHyiXRT83pbIadZnuN2MZv7ffDJAsz8qBsQYLBXxmAvyyYH/EJT16laoxZkUUAia0I3beimA8uuLIVdKyhoS2605aRzFiRRgE9mY0aCPgFZxWI9d01e+I18bzUhLDPpnrOWycpcIz2sVLFaZnI9u2MC7Dv3FVXluGYjncCdwxnwlwomAaQkO+w4rLe0WnbGPdfLTXh0w26qzFUk7L1iKVYd0axQ9ubrDtaer1vO6PbdmJmuzBpGQmx2tysADcuBmRdAstx8ZhIABoe3IBFbavrvMAwDExMT2LR1JySRYGT7Lu+xdDpVsq9A1q/Lu50fgOmKyUcffRSf+cxnAHS+zNW0KUiZVwIFK5N3fxuO09r4uXKeOpiEXxbw3ltYILjdUU4bjTUTk5TSeUrp8/n/zgA4DGAEwK8D+GZ+t28CePtaHcOZgmnThqYKAR/LDLVVO07hZSaBQoN6pkGUOK1YkJroWagGK8+1sZQwcGI2i3SVUq/5Fa2i1K5Zgn6xpO69Gcr7dXwyQUYxsZzUkWwz0qUahUXgy2NpbBsKYLDX52WVq9142BD49XnjAIAH7r8P5+69vCSqHQ5K2L4pCN0qiMnDE+k6r1KfdNaC5WUme7yo7ez0OGyH9Ui0Y1IUjy21tAgp56K952LLyC789Gd3e9sopbjnnnvwhje8Ab39myoCIPv234SXX3oa2UwaoYCERNrEcmJ1EVvTNGGoyYbReTczaegaVpYWkFNt2DZFKmsh6BcQDUk4MasglTWRTLIFDJFZNidYQ0zKwUHmUJsvdSUdyEymcyaeejWBsfyxrNXCSdVtzC5rWFhp7drQiFojX2RJAEHxTD02IkbwrO0FaIbdUJQpenVTNVkiSGVMCAJBts3qiXaRBAHKKgxh0jkLPolAEFi/dTQsIasyI55mK0FMywEh7FzRDRvB4jJXSUbQLwGCDE0vy0yS9W9yllXtkpJl1723E7iLfL+/tEReLBv54lAKQSTI5Crft1ZmUtHspgIaQb+IqQUNisYqr5oVyt58zBZM1Ea3766amZRE1iPqtrMomgND1yHLvpYrXwaHNiGdiiOdbe7aMjk5CcdxsHnrLogiwej2osxkurJncr2er+ViUpIkBAIsiNPpzKRlV7a3SIKAQ+MZjM2w0lTbodBMu+2qiaWEjqFeHy46i1UvJTO8zBU4RT2ThJBdAC4H8BSAzZTS+fxDCwBat03klGBZDhpd1yIhCfMrGjI5q+W8lmvtX9KXSYCFFQ2HxjPI1XDYS+dMb+RCOxBK8PzRJF46VrgQuDgOxWJMRzggtfXaPlmAojdepBWj6KURVddBb35FR06ttExvhGmx0jZBYL1TRyazuOxc1ksgSwIz6KgiJklbxcOnhkQigReefwZXXls5LFoSCSzLQTgchuM4WI5n2xIFhukgqxaLyTB6e/sRjfZ5fS+64TQV/S4ntrK4qswkIQTXXn8TfvXIL6Hlsx2HDx/G9PQ0br31ViiaXVFFsG//zbAtC88+/QgkkWCoV161YcD8POvBHB6u/7f4ZQFyzzYAQC4xjU39PoQDIgSB/S2iSBD2i3jhaArLK2wBY5MAxPx4iXICPhFSkAURih1qV6v9cvl5oCEfc4jsVDlfMbph4+RsDpJAsJwyOlrqGksZ8NUoN3ZAYVmUlWZXeZwADUcZqTUyk0G/iKFeH0i+XPlUXjl8MqkaBGwGSllAw63a2LkliJ6ghKxqYjGuN+3yaVrUC96ouuM5ENuWCVGSIIoEgiBD1yoz0et5mo3reF7sFyBLQsdK5FWNCR5/WV+gnBesbmbHdij8MpvbXH4tr5WZVHS7pHe4FqGAiKxisR44QpBWmjuXPDG5qQUxuWM3Usk40qlkxWM+ieDJV+J4ZSyNl8dSINSE3EK/pMtg3rl7YnK+wZ4Mt1Via77qxnWEBSrLXAnBuh1l437/QtECUpZZkKLTmUk2Sqb03OrtkdhvQ3czkxS60b4pz3LSQF9EwlAfy9onMmvbdtEtrLmYJIT0APi/AD5OKS1JRVAWSqn6CyCEfIgQ8iwh5FkA9Wu1zmCYWxoaupYJAoEoEByezLTs5OouBosjcSG/iMOTWRyfzuKpV5NVo2KZXOtjO4qJhET4JQHDfb6KzF9WZTeZ1ZRsEZCaBjflUEpxYqbSKp9SeIK01UgXWxCx4z94MgPLprjsnKj3eNAvVo3GEkLXbUnLyZMn4TgO9l54aY09CIL5fsRkMt2W2QyLvhOoaqHMFWCzvGamTgIgbbk+UkqxOD+LTZu3tfzcYm547Rugqjncfd8vARRKXm+55RbE0gb8ZaLi4kuvRijcg6ceY2NDBIGsqjwQAA4fnwIAbNmype5+PplACrJMbDy2BEIIwkEJg9HCYqknJCGrWjhycgn+QBCWLXq9Z+UEfAKkAGuDL4jJ1c/sy6oWgj4B4aAESmu7HK+GY9M5HJ/JYTDqA3Wo1yvaCVaSRoX7rYsAAt1gZa60yjgbQgjSdUweXHFdTUwKAkFPiAXc0jnrlAah/LLQdtRe0Wzopl0SsHArYtI5q+n+sOK2jhIDHtuGJLEFbTC6BbNTx0qeRwjgrGM1mdNsOE7pfd/tC9X0wly9VkqCi8nks2eSXJqZlPI+AU8fYmLG/R4orcy+18tMNtu3O9znw3CvD0G/gHiTbSmumNy8uf61r5g9514EADj48jMVj/X1yOgNy96INccx4fc37+Tq4gYpT+bn/zZibm4OADC8id2Prrz6Brzu5rfh8iuvryIm1+/IsPLMJFAwL+p8z2Tl3y8IBEG/4I3csh0Kw2g/M5lIG+iPyAj6RfgkgpUkF5PAGotJQogMJiS/TSn9YX7zIiFka/7xrQCWqj2XUvpVSulVlNKrAKys5XF2M+za0dyFeSDqQ29IRm+4tWyekLf4LiYUEDEU9WHLQAA5zar4YVo263GQ65h/NCLgFxEOSl7PTLGLXHtGQqW4tvnNEE+bWEkaiJR9dj5RYBcwQloWRsV/z5HJLHwSwbk7CsYvwYBYXezS9ZuZTKVZP1tvtLqBDSEUwbz4S6aybc1UTOdMgFCoKstWB4Ps9UZ3uKVKzGGw1Qx8PLYMRcli+46zWz6mYq685gZIkowf/fjnAJiY3Lt3L3bs2IFE2qzoNZRkGVfvez2eeOxBNoJHIKAUNYdmN8OhY9MAgJ3bt9bdzy8LEPJlq9lM7T7NwagPpp5DT08UatGivNrrEZ8rJll2lGD1/TxZ1fYyewJpbdZts+QUG70hmQWoSOcEq21Tdvw1qjTE/NiKWp9R0CdgKVG7NK64L7AWFBRpxYRUxaRnrZAkAYbptBUwqiXkTZO1dDQTnKCUYnwuB7/sisni0SAsMwkAey68ASvTLyCVLp6NStb1YJCsWllhJAgENgWePJjwyoDHpnOIp1vP/ih5QyJJLP2d+2QB1GFZXseh+e+BAKAVWWjX4bU8M6nqTl1TsGLc6gi/T0QsZTYllubn5yHJPvT39zX1HgBw2ZVsrNOTjz1Y9XFRJBiI+NAblmEaesv9kgAwkBeTk9NzTe0fj8cBAJFeVunR2zeAz/3Tt7Bz9zlVM6jr1Zivmphcq8ykZtiolleRhMLYHMN0YNrtmVGquo2MYnvB1lBALHH7PZNZSzdXAuBOAIcppV8oeugnAN6X/+/3AfjxWh3DmYBTO7lbFZ8sNDV7qZhaNunuopgAFb0a1azCV0vx4s4oGti8GpqdE5nMGlWjqdGwhOE+HwiYq9fUgtL0exdH0ZJZE/1RX8l7hPysFLccivU7oDiWYAuyYDBU9XFKifdYKp1p6zyJpQwEZBGpBLvZupnJ0e27sTA/DUotzC6pCMqtzchzDRiKjQ7aIRTqwaVX7MMjD9+PufklPPLII7j11ls9E4lq2fp919+EhblpTOYHZ1NgVWWWx08yt7mdO0br7ueTBQgyE/65XO0eVkEgUJUMwj0R6EWL8nL8PgGQ82IyVlTmuloxqRRK+vw+AYls56PBim55vz9ZEBqWljaLZbMh7bXEniQQaKZTUyC5c4NrfYam5TQMJ8oCK4FcTdtBu7RTejkf0xCo8jsZGQ4g6Bfy973aHJvK4oFnVrAYMxANy7BtNmbCnZtpWyakvJh83Y1vAHVM/PyeA97zOxEAWUviSQO+KhVGpulgMa5795aMYuVdcVtD1dnvy83euggCyc+tpTDtQoWMXxawXJShGZ/LYXKelQ4Xi0nmKO+07CgsiQSmTZu6X8zPL6Cvf7ikrLIRfn8AV1y1H08+dqDhvoZhwNeikysADOZ7OBcWFppad8Tj8Xx/Yem9NNrbj3QqUUVYtzcOa605lZlJ1kdcvULDqyCzKERC2poRPr3IstP9Ufa7CAXEtv0yNhprGabcD+B3ANxECHkx/7+3APgsgDcSQo4DeEP+35w2YdfptV0gENLYP7Q8iq8b7Tu5VoOWvYeiWxDbGglSQJaaHw+SU52q8ywFgeSzrxQzixrmVpp3gTQsx/tc0zkL0bKsZzAg1liIVQ6CXi8kUywzGchnC6vhPqZrSlsLzaxi44FffBef/x9/BgAIhdkA5JHtu+E4DrT0Aob7/BVZ5Ea4BgzFFuztct0NN2Py5BGMbNsMXddxyy23QNFs0Bq/iWv3MxdYNzIuEDTdF1aNqSkW/T5rV/3MpE8WQMQACCHIZes7yOayGZaZLOo9KycSkqDaIYiShHhsGUA+y9OBnkm3ykGWSE1Dm3YpLxWVJFKzF7xVDKv+kHZRJNB1J38tr16mZTu1TXxYVUj9D1jKf2araTtoj/b6W5eTBkI1+uEp6gu9pThzQ/ZJrvCBJ6gKmUnLE0pvveVGENGPX/2ySEyuY3dMAFhJm1V/g5v7/QgHRU9Mqm2MUwEARWGLfFGqPqOWENafZjsUoBQhv4ilhIFMzoJtUxyeyIJWKb1kQeA2P1dKm/rdLywsom+g9c6offtvwtTEGBbmZ+ruZxp6Wz2TA3ljt2R8GdNLasP94/E4+voHQEjp9xzt7YNlmV5ljgch6zLIXC4mbZuuWWYyp1pVq+Hcah93zrrfJyDbxvV9aoF9b30RdvzhoMQzk3nacy9pAkrpo6itJm5eq/c906CUAmRtryACIaB1AoI+SUAiY2J0U9DbpmhWR4/LJwqIpQxsHQrkX7+6uGsFWRKavtEqZc55Fa8lClhJ6eiPNH+TMQwHQv4nkslZ2DRQGu0M+kUs5aqVt63PmwYAJDwxGay+Ay2ISWqpbRl0aIaDh+77D2zZOoqP/fHfoifC+kzdnpRkYgU7drZeqjozdRKiKGLrtu0tP7ect7/z/ZAkGfGUivPO2oQ3vvGNWIgZNVPK20Z2YOfuc/DkYw/iPb/zETiUrEpMzs1OQgwMoDdSW9QDTPwRQuAL9CDbSEzmMgiFI9DrlLn2R2WkcjYGBoYRz5e5Aqh7/WiEaTmwivqjJVHomGtl4T1Yv2LBRbVzgpWVs9f+LkWBlcgXSgYroaDIqlbVAIlh0oYlmZJIvHmLpxJJFJDIFK7bzeA4FKZJIYWrH6sAAqtKbxTA+tdfPJZGX49cco66GS1PTJqmJyZ7o2GE+3dhYbYwG7YTAZC1glKKTM7EUG/lvUYUCUABIz++QzecthbNmZzbM1lD0OevT6bJyvIlSYCt2HjkxRguOzcKx6Gev0BxZtK0aEOzwFqIAkEya3gBgprHnskgHI7W3aca5+1lff4TJ49iy9baFR26obc8YxJg2c9IpBdqZgVjMzns3haqu6aIx+Po7e2vuCJEe1nlRzqVLJmHTCldl+dssZhUNBsvHkth7w52Xq1FZjJUy+GfMCFr2uzcTOWYaVQqZ6EnKGJ+RcOOLdUrqlzG51jlWV+PKyZF3jOZ51SHKTkdhgUG1z4zWa+o1O8TkCjry8jkrKpDtNslEpIwuaB6vZmNxF0zyBJBVmvuRqvoVl3xGvSLsJ3WShOLLdLTOQvRUOmNO+QXq5a5AuvzpgEUeiYDgRpiklCvbMe2VGRaXOhQSpHOZPDic4/j9W/4Nbzxzf/Je6z4JtsOM9Pj2LJ1e1v9MOX0RKJ4z+98BO/7wB248c3vhSAISGXNuj3E+/bfhBeefRyaprLI/yrE5NLCJALRkYb7jW4K4IJdPaBCCOlUCgsxvWZGJpdNI9wTgVqnzHUg6gOlQLRv2DPgEQhgr6KZRzedkriUKLDFciczR8UjJAAWHOrYzD67cWZSa/D3yIJQs48wo5gNqzT8soCBaOvjDFaLTyINR0iVY9r1WzcIQU0nxrllHYZVGexw7xtuZta2rZKyu8jAdqRj0yXPWU0AZC1xe/RrfZesB5cZ7xim3Va5dlZhFTblZa7lx2HajjcybCAqAwSYmFNACODLC9GSzKTlAG2uV0IBEeNzasPfZU5R4K91/6mD65ZabURIMaahw9fmPWJgaBMS8SXYduPRavF4HJHevooAUOE+V+nouh6jzMViUtVtzCypmFth68VOZiZtm0I3nJqmjISyUmnLchAOioinDIzPKXjkhRW8PJbG068mGwrDmXxGuT+fmYwEpaar2zY6XEx2OY5D19zuPRQQsaW/dmTZJwlIK1aJkEqt0sm1HFFk2dHFOIuYFvc3tQuL1jsNF6WU0oYOdH6fgC39/qpuYrVQ8pkCx6HIKLXKXKutaCoNkdYL6QwrvSnv83ARCIHsY9kyx1RaLsEyLYqDLz4Jw9CxL18a6uLeZJOJGJ57+lctf0YzU+Ml9uudIBQQEUsZMEynZNRBNfbtvxm6ruHF5x4Hoauzek8sTSLUW79fEmAZmNtv3gaIIRwdX8Z/++IhPPBMdb+zXDYDf6AHK0nDcwgtZyDfS9LTO+SJSaxyNIhuOCXrT3cRbXbQbdM1sXERRcJKojowHsS06pf2iQLrdbNsWnMx6JMJUjWy+OkG5xXAyrzc0qxTiSwJLZcLNxp1JQi1s/bpnFn1sygXk8VlrgDQN7QdSnoeVn5xSwhgr9NrbL3zBGAZPM1g5xOlgKJaLV8Lcwq7z4pireI1JiTLv6vekIS5FR0hv+SZ7Kha4bw1GmTp68ECthQHT9SfT6zkcjXvP/UYHNqMQDCUdwSvjWkY8LXh5goAA4Ob8tdFimyDUSfxeBw9PX0VQexotLqYpHR9ZtNdMSkIzJBLlgSMzbLzq5OZSTfAVCvIQgFYFstMCgIBEYBjUzn4JRFTCyoGojJePVn/3FpJGSAE3lotEmKzb2Opzs4l7ka4mOxynBbcXNtFEEiFA2X545SiRBhklM7354SD7Edfzwq/FQhhMrxRU7+7EGkU1ReEUofWRmiG4w2adigqStiCfjYbqdpCYJ2uc5DN5sVkjTJXUSAQZfaYoSuwHNrSZ2bZFIdefhKiJOGyK68vecwVkz+66+v42Ad+HT/7j2+3dOwL89PYNrKzpec0gp0zBKmsiZRi1v1NXH7Vfvj9ATz56IOg+dmD7ZDL5aBklhHpb65c9+zREPyBMBaXkwAKAZtiKKVIpRKYjbN+rFv3DVd9LVdMBnsGEXcNeFbpMpjOWhVXOALS0nnTCNNyqq5xdaO90QrFMIfB2tcONq+WlaHWMkfzyQJSNcaDpHL1z6vTiSyxYfOtfIZMBNX+vARS2+k4q1V3EHcdZf2emDRLxOTQ5p2gjo2FBdYvt54NeMwqs/SKkURmNmLZLINpO26wpHmUvAB0TYoqoCwAYlql86dlScBQfpSHkO/1my/yEdC09ub/uvSGJMRStc2oAEBR28tMEkIwMroL09P1M5OGqXsGMq0yOLQJsdgSArKI5aRR9xoWj8cR6umtOJ9rZSYFQju2LujktdUtcxZFERnFQsAnYMtgEIIoQtM6JyabcYa38kZcAiHoC8twKEV/VMa2oQDCQQmZnFXzb7dsB6mMhd6w5F2nh/p8sG2KfR94rK7j9pnA+rwDcWpSPoLjVPRMNgMhFKksuwFZtgPD7Hx/TtAvIJ4xkFXtkv6m1SAQNpaj3s2JWUg3fi9XnDY70sHtYXLL18ozkyG/WEPsdu6m0UlsmyKXy0GUpJqloqJIIPrYjV7XFIC25vxrWg7mZiawbWRnRSltT08UhBDMTk8AAB45cHfTr+s4DlLJOHr7Bpt+TrNIIsHsksb6wOr8JgKBIC6/6no88diDqxJLJ0+yyHrvUHNikhCC/v4+OBYLBIhVflfx2BI0VUHCGMQNl/aX9EcXM5DvGZaDA4jHluA4zqrNTJYSOoIVwSzaUhVAIwzTqSjlJwBeOZHG3DJbDC8ndEzON+/W7KJqTbhXUlb2Xkt0ypIAXbcrzgnLdqBo9qr7x9cK95rYyky3RuXdgkBqZqWLjZqKqcxMmiX9gFtGWEXC9OTJwnGvw2sskP986nzdosjmJ1tWwe7LaOEaSyktjAapUeYqiYKX/Sw/Z0MBEaSoDPf4VMY7bxWtfrtII4T8WJjq7R8MRVFq9+w3YHTHWU1lJmW59Z5JIC8mVxYR9AtYThh4+LkVHJ7IVA22JBIJBMPVylz7AFSKSWD1cyYt24Fu2Hj8lXjHXEqLy1wziuWNeJIkGYrWOQFWq/S9AM2LSQeCUAh8lEBIzfWIaVGkFMsrcQWAm68awkd+Yyf+23vPxnCDXt6NDheTXUQyY+LJVxMlCzM2GeT0LyRCPhGL+ciMbtQvU2oXQggIJViI6SX9TathMCpjekGp68JqWE7TyV+C5maguWMiJJEg44rJstLBgfwso+VEefSONLTGPx0YlgNd02r3SwKQRQLNZKYvWt6NrtWF5vzMBEa3V5ajiqKISKQXyUQMAHDsyMtNv66Sy8JxHO9G3Ul6giLmYzqaKe+69vqbMDl+HCtLM1DbHK8zNsaMRAY372j6OZuH++CY7PuoVk45ne8jooGt2NRfeyHVExIhiwSCrx+2bSOVjDflBl0L26aIpQ0E/eWGP5ViezULKUWzIZWJaAfAxHyhR0vVbcTSrUfSdaOxmKSUlbrXbTOvstBx/32qeyFbgZDm5/kCjUshhbyTaDn1gphuZs7v9UzaJULJLW8/eXLMO+b1m5ms7+Yt5ctcTdsByTt/tzIexLQobIvdk2qJSVFk5x7LTNboUcufzKZNvYBpWqnuttka9c2xNFWtOZqqEaM7dmNuZtITQNXQda39zOTgZii5LExDRVa14TjA8alsRd+daZrIZDLoifRVfL61vAE6Ueb6zKtJHHhuBXNLupccWC3FYjKrFII9kiR3NDPJRn3U/gAoXDfXygBIYR9ac2SIaTlIZ82SdgFRZLPB33Xz1nV9DT4VcDHZBeimg2cPJ3FkMovluF5yIXUo1rxnshmCfhGxJCs/May1y5yFAgIOjWc6JqAJIRiI+nDoZKZmJsgwnabrSiloU4sQ9+ZBCPEcTaPh0hv3js2sL2NqsdJGfB1qSTagXFe9fhXLprjzp1NeZgcAAn4RWc2BPxBELpcF0NpCxzAdzM+O1xzf4d5oAWBxYRZzM5NNva4b5S1+fqeQJYH13jVxzu67gRldv/DMQy2J7GJcMTm8pfmS3a2bB+AXdWwZ9Fc1enGj9XLPSEUGvRhCCPqjMhypDwAQW1kCIQRWm142OY057pUvqByUlkcbpoMXj6XaexOwIfDli9y+sIxoSPJEvW62OWZBt6tme4uRJTZTtl45LK0yZkPV7HV5LSiGOmhpQLhpVi85diGkemay3nt4mcl8VswyzZJ+wG1bt4KIAYyfPOFtW48BO4CZT9U7m0SRwDBZmSsFq+Bp5bw1LQeWxe5JolTdtVnMz+2zHafqkHigEOAg1EEiw0RDo77xZpAEgulFFc8dSVbcs23bhmHoCNUZTVWP7dt3wzQN/NWf/h4y6erXE9Mw2nJzBZgBDwDEY8sYHQ6gLyJDEISKIFEiwe5HkWhfxWsEAkH4/QGk0mWZSULbbUcFwAJa8YyJsF/C5gGflxxYLcU9k+w6m3fllmXklOZHqTVC1R1IdaJxIiHQDBuW7dQM2gkgNQ2eTIsirViek6sLATpaJdOtcDHZBaiajakFFYsxHT6fUDJOwXGoN8/pdOKWn2RVi/UZrdH7hIMStgz4G9qDt4JPFqCbtKZboqJbdRd5xRA0Z5ySzBrezTadF5blPZNbBgOQROINyi2wPstcDdOBrileZvLYVBYPPRfDt+4undvlkwRE+4YQjy1DEoWW3B7nFxagKtmaRjnlYvDkiSNNve5aikkAkGXS1MD4XbvPxZato3juyYe8IcutZHUAYG5uDkQKYGBgoOnnRCJRmHoWW2uKyXEIggg5tBm9PfWNXAaiPpikDwAQW1kEQfuZSUWrfi2RhNKbvmVTz+q9HWIps6KU1icLCPgEL9ihGw5ybZiZ6E0Mae8Jisgodt3MpE8USgIzAOtT70S5/1rCMpPN/8a1Oo6MAOtNr9ZPXO93olcrcy3qB4yGJUihTVhcYPNZhVWaRjVDJmdhtol5g+XohlM3OOH14BqFPtF4pvkMUHOZyXwprY2a5597z/TJBItxA7phw7Trf7fNEAqImFpUMbuk4dWTpeOMFIWVoQdD7ZW5Xr3v9bjwkivx0P0/xSMP/aLqPqZptDVnEiiMr4rFCmOTCEHFvOV4PA4A6IlWvx9Fe/trGPC0f9KydRuF3ycg6GcjLzqRnXfFpEMFUNCi8UsyFLWDmUnDgVg99gGAtZu4hoa1sog+mdScG6noFnKKXWFk5lDaEaO2boeLyS5ANx34JAFbBv0IyEJJoy8rc+3cXa+dxVIB1v+narY3P7FbIKT2cO1Mzvbq/JuhGbORpbiBUL50z128R8rKXEWRYGQ4UDHgmNLV90asBazMVUEgX2K0kmI3ilCg9Arf1yNhx46zMDN9ko0OaGHW5PFjLOu2fXutzGQfAGBkdBcAJmaaIbXGYrK/x9dQhAHsJnft9Tfh+WceQSarI6daeGWstYzb8nIMoi+KnkCdO2sZ4XAEuqaiJ1A9qDIzfRIDwyMggoTeOplJgNmm62Bz3uKxJVbm2ua91jDtqi3hkshGTrilWLZNYRhOW9lcTbehGbbnPln6PgI0vZCZ1AynJTMTSlnZVKMFtCwJsG1aVyREwxKml0pHI8TTBgLr1HzHRRRam9mpNRBLAiFVF2/1gpiN3FwjIRlSYKDIgXh1plHNoOh23faKWqgNPh8ArAdXZcZVAZ/Q0mB13XTg5AVAzZ7JfCmtYdXJTOYjI34ZSGRMZFV7VeY7Ln6fgJGhAIZ6K7NnrpgMhdorcx3Zvgtf+z/3YmBwE5587MGq+xiGDr+vPTfXwaHNAFA4z8BaP9JlZa6umIxE+qq+TjTah0w6WbJNIKtbCuqm4xlfCQI7/zsxa9cVk7pVeh+QZR9yaucyk7pZvwJEltzfQR1XfllEqkZSYTGmgwIVYpK5J3d27nE3sr7vQhwALGrlBlJCfhHLyaLMJG3OHKYecysa/vAfD+J798/i9z/3Cl4eqz+8vBau0M2ussn+dMAyZLXmuFmQm8gqAUwM1MpMugug2SUVi3Hdm9WXyVkIB8Sq2Ysdm4OYXixccKcWVPzJPx/CydlcU8dzKkllTBi65pkfzK+wG325+CCEYPvO3ZiZGocsCUhmmw9gjJ1gZWg1M5P5SO6ecy8EUHrTBgplUIahwzQLUdF0vmSodw16Jltl3w03Q8ll8cyzTyGtWEhkWhutsLgcgyBHEWxFTPZEAAAB0UQ6Z1Z8HzNT4+gfZj2YjURxT1CEJfQByGcmSftzJhXdqTpPVpYETMwpeOk4s3K3HArVsJswYaik3iLXndkHsMWWlRetzdJoJmAxOzYHEKjoDS0gCOwol5OFBXQsbdac+ble8Em1r63V0I365m1sNEjldk2vHcQ0Gri5RsMSxMAAUgnXgXjteyYdh7Yk8ly0Bp8PwKodUzkLokAgS6yMstlAi6bbXplrLTdXSWIZe9N0avdMFp3zjk3zrq6d+0xFkcA0Ssf3ZPKjqdrtmQRYOea+/Tfhqccfqto7ycpcV5mZLBaTVX4frpis1cPPMpPJ0o2UriozqRlOhSAoz/YvJXTP46FZ3M8wnbNLSpwlWYauV95r2kXT62e9A77as3pdZIl5WFQzUZzLr2f6y8RkccbzTGZ934U4ANym9XyduSTk+yHYyes47ZeQuTzyQgyJjImfPcYucPOx9qJFwYCIpbiBdLYTTfanFjZcu/qFJlvkQNYIiuplrrph49EX45hf0fD80RSGen3eTTin2QgHqy8i01MH8Mw3XovZefbdPHM4CQD42WPNZdxOJYmsBdMo9Ey6A361KouY0R27kU4loCopaLqNZI2xB+WcPDkGQRCwdaS6uYybWdyybRTRaB/iRTftVDKON7/2HLz2yq147ZVb8ZortuCnP/o3APD6Y9YqM9kKV1/7OoiiiOeefAhLcR2KVtuuvBorKzGIvoiX+a5FPGNgKalDNxyEe1gm0SeosB1mSOM4lF0b0gZmpk8iMsDcYev1TAIsE61aPgQCIa9nst31glZjBFDQL2K4zwc1HxFmmUnaUm+eSzJr1MyuiALLjjoOha47LUehDctp2my7GcEZ8LG5pQC7puhm9YzqeiLgF7GSMppeNDYscyWAXSUzWc8ptDwzadtWST9gJCRB9PcjnVoGAC8A0omF7vyKhoUq91TbociptlfO3iyNFs0AEPYLWFjRvbI/Stl9phlSOQug9ctcAcAvCVCNQqC7HHc0CKUUkZCEyQW1bk9bOxChtFc2nWGB8NWISYDN/E2nEjhy6MWKxwxDh6/Nnsm+/iEIgoB4UcWMT64Uk7EYM5Eb6K/eqhCJ9lWWua7Sgbia8Uz5fWc5riOZba001R0NkshY6AkW7h0+nw+GobU8tqYWuulUmKgVIwiuu0gdc6/886v1TS7EmJgs75lko3i4mFzfdyEOAGYOUTpHjHgNv5TSVeUlHYfi8VcS2DLoh5sAaHe+nSSyrNxKSu+6zKRPFqpG3AzTge3U7gupgFaPaMdSBpYSBp4+lEDQL5aIbVW3q7hVMl589PsAgFdeOQigcCFbineu16ATUEqRzpowdNXrmZxZYguoahdmt0x1dpplJ2eqmAxVY2pyHJu3bq85esSN5A4ObsbA0KaS3pQnHzuAdDqJ33rfx/CRP/prbNk6ivvv/iGAQs9kNcODU01PJIqLL70Gzz/1EGIpA5bdWoN/IhGH4IvUzUxSymzSzxntQUoxvcykBPY9pLIWvnjXBL764yncde8xZDNpBKOjCPqFhjMNWWCEoN8b0M1u3+1ketQ6JaKyRGCaDhyH/S266bTUm+eSTFs1s3vunFDTptAtJ5+pbP49Ot0/HvAJiKVY1iin1TdiWS9IIvueahlblNPMotB2Kh1NlTojWNy+Vfc6Xl7m6vcJ8IcGYeoqFCWbX3iSjhhrTC2oODKRrThe02TnrKq3thDVjPqfD8C8BaJhyVu8+ySCl46lajpVFpNRLIDmHThrzZkECyr5JKF2ECS/nTpOflwIagZN24XS0uxZOuPOOV6dmLzmuteDEIInHy0tdXUcZk7UbmZSFEX09Q+VZCbd30excJuYmAAhBFu3jVR9nWhvpZhc7YUmlSsNxgiE9QkWY9i07cwkiFByLR/etBWxpbmOzLSklMIwG7cTENCG5esUFFmt8m9028v6ImXtSALxgppnMlxMdgHFdsoubqR1tY5zJ2YVxNMmfuN1W3DnX14GUWg+glmNwaiMgFy9ZHM9I+UNBcrLG1jEqZXPmKJaL/bUooqhXhm9YbmiN1LVnZpiMhRmN8VkikVc3e97Obm+xKRmOLAcCk1TEQiGoBm2d4zl5gJAoUx1dnoc0ZCE2WWtYRaAUoqpyZNVx4K4uJnFgaFNGBzajHhs2XvsycceRF//IP7gT/473vdf/hg3vvE2vPjcE9BUBelUAsFguO2Ic6e5dv9NOHHsZUzPzEMSWxsinUolG2YmM4qFLQMBL8vYk89MipSJyV+9FPey4FP52XtSeBt6w437PsMB9pp9/cOIxwplg+1cqtQamUmgMMPQtClsx4FDKZQ2enyUBj2NhFAYpgPLchCQBaSbzKID7Drdyf5mOV9eqBs2W/R3zWWWNGW0RSllBjNN3D/KK0DqfY+GSUuCIOVlrgAQ7RsGUChBJGWOwe1g2Q5WUgZSOauipFW32JiCWr361XAcdi428/mEAqInnvt6ZKQVqymHzmIxWS8zKQik7pggQShkJgFgMOprGIhqHVJSvpvNsp7JeuOpmqGvfxB7L7y8om/SMNjn52tzziSQnzUZWyrbWjr2Z2xsDMObRxCqYSQU7e1Huqxnslkn+VpklNKkhSQS5NTS898wHaRzrV1jC26upeueke27MT872bZreTGWzUwJG1V3REJS4/51UUCiygio5YQBAlTcAyWx9mzKMwkuJtc5lu3AqNIj4f4AmQFP+6/v9t7t3RWBJBKEA1LTEeRqEELQF5G7buYOO15a0SPQTmN1+QXdMB2sJA2EAmJV0ahodoVJjUs4zCzOXTHpLjxSWbMjEb1OoeosS6KpzM11Yr6QaawWnNiWN8iZnhpnvS92Y2MTw8rPmKzRLwkUicnBYQwWZcYcx8GTjx3ANdfd6C1y9u2/GYah4/lnH0M6lViTGZPtct1+NiLk+9/6Fzxy/w8xOTnd1PMopcikkxB80arnFKUso6ObFNs3B0EI0yNuZvLwiweQmbof3/3edxENmLj83Cimp5mYTBiDiPbUL3EFCoZLPb2DnpgE2ivHbzSj0Z05aBgUfllAto1rl2ZU78t0odQtYyII+ATEW+hzy2lWY7OUFiFgfZ6xlIFAjdEN6w1JJIinGwsZzXCaWhQCpCJopxp2ze/RsByvX+v5Zx6DbVkV/YBbtm4BwMTk4YkMJuaUVZfgZRQ2ukUWiTcew8WyHPgkgmQL8/y0fKa7nftr0Ccgnq7/XmwsiAPquGKy8e+9Fu4xOu26bzXzHigtz8xkO5OZBIB9+2/Cq688h5/96Nu4+6f/jrt/+u+452f/DgDw+dsXkwODwyXXRYD9HcXrruPHx7Bl266a175obz80VYGuF8qnhVUMiTNMNr6luKdRloSKQLBlORVmQY0oHg1SzOj23chmklhajrV51EXHZTPvEEopnn/msZoBvIBPxFBv/axy0CdgJVn6OzEtB/G0gUi4UoyKIhuTsx5NEU8l7V8pOKcEVXNQ3pRAQT2jCdumqxJuEwsqomHJS92HAiJyVVL8ZwqqXirsNMNu6QJNUOk0qGg2KGqbcKi6jZC/+iKop6cHQEFMuhGwRMaEqtmQe9ZHPMi9EWoa65k8Ps1u6pfsiXhGPMUEAkFs2rwNM1Pj+S0Emm7XnUG2sLiCTDpRV0zu2LUHoiRh1+5zMTC0yeuZPHbkFSTiy7guP8cRAC678jr4A0E8+diDSKeS66Jf0uXcvZdgy9ZR3P2jO3H3j4BXnnkr7rvnZw2fl8vlYFsmRDlSNXCxlDC8/t9wUERWZb/1TZu3QRRF/PSur3j77t3yRxgZ/i84sDAJgCBLh5uqOHBL2XoHtuKV5x/JGx3l3TFb0D6WzSoF6pWYU8pKEU2biYWc2tq1i2V6bERD9Q6MQskvWH0yE5PVZl9WI6u05gTdLKmsycaZ1LhurDeCReW59dBaqAShRUE726YwTQopXLtn0icLyKRT+NgHbgMADOSdNV1279iKRwEsLS7gl0/3wScLeNcbqpcZNstyQgchrCQ7W5bRMW2KSFjC9IKKc7aH6wY0XDTDhtCmZAj4RMRT9StatHzQpGDA07gSoRbE7ZlcQ1tcWSIlv/msKyZXmZkEgNfe+Bb8f//r8/j03/xhxWNDw1vaft1NW0Zw+NUXYds2xHxTqyCw3/TmASZST5wYw1XX31rzNdx7VSadhD9/LKyVoL1jWknqoA4puaZJYuXMRcOkUHXWw9+sL4YrJkWh9Brrzoo+duw4zj97a3sHnsdtzXri0QfwJx99N/75f/1fXHv9jW29lt8nYCVVeo1XdRua4SBcJUDregJYNu269q5O0h13ojOYas3OsiB4ls2W49RsgG+GyXkVO7cEPaETCoirykx2N5WN1DnNgdyCaQAhgFkmJlnWrvYCoF7PZDTCxGQ6xQxiXDGp6g4W450ZKtwJ0jkTsijky1yDODbFZhZu7vfXLOMa2b4bM/msFwEa9g4dO15/LAgAXHTJVXjw8QlsG92JwaHNUJQsFCWLpx4/AAC45rrCDcbvD+CKq/azXspUYl2JSUEQ8O0fPoq7fv4sbr71djz6yEPQ9cbft+sCKPoiCAUK520mZzG3S/f8JMwZWsj3BA4Nb8HPDhzGXT9/Ft+460mcfe7FWJl8AluHAjCys5BCwxBEHwYbRHUBeDfcXeddC01V8PKLT6OdvKRpNZ6h6/Z+GqaDgE9EVm0tQmxazWV6NN0GCFtcUFoQl43IqRakDi8woiEZYzM5aEZl+8N6xTUZafTdNB+8oyVlrqbloJ7TERuvRTAzfRKUUvzFf/9n/Mbtv1uyz7lnMYOpk5OzyCoW0jmrZXOcYjTdxvHpHPp6ZMiS4AVuXKx8tlQ3HcwuaU3NqtMNp+35lz5ZgKo7dedxajr7/bhiUhTbzzeUl7muBZJIkFGLM5NZAPBM4FbD+Rdehp8dOIS7fv5syf9+dO9LuPmWt7f9ulde/RqkUwkcPfySt63YWCuZTGJlZQVbR+pU4OR7+0v7JmnbZoxTiyrCgdJriSSyXsDiSisznwFspazTFZOyXHoubc8Hhd37+mpgay6Kx3/1AABgYvxY26/ltk8U/04UzYFWZ50GkHx29MylO+5EZzDzMb1imLYkEa900LYLw4FbxbQczC6r2LW1cOENB8WOzBbqRiSRIFU281Cv07NVDUEgMMuSI4mMAblG1JlSCkWza5qlBAJsnpU7B7G4pMfN/q0HMjkbskSgqQr8/iCOT+dw7o5wPtNdfYE/umO3l5kUCJDT6mcujh877j2vHm6J08Ag64GKx5bx5KMP4ty9l3jW7C77brgZUxNjOHL4pXVV5goA4Z4otu84C6+56W1QVQWPPfZYw+e4YlLwRUt6JjXTxlLSQF9Uhu1Q9AREiCIpCUT1Dwxh+46zcP755+J1N96CV19+FlGfCjM7B39kBH//4fPw27c0ztS4mf3te66BKEl46rEDoCjNJDWDaTkgDaxQ3eCNbjrMkMdyWnLWMyzacPadKBC20MvPYKOobipVjazWecHn9wnQDacjM/tOFa5pTqNFaDZnNTSXcSnOwpgWrZvQdDOTM1MseHXhxVdWBBD2nrMNgICp6TnkNBtZxYKyCsv/2RUVFBSSyMZzlJdgm/mse1+PjJeOp/HisXTD10znrJr3kmY5PpXNj+mo8vqKCVEgsCzXzXU1Za7s/69lmasslVYjZNyeyeDqM5MAy0Bu33FWyf+2btu+qmqwa6+/scLcx50FSinFifz4q62jjds5SseDkLZbnhJps0IoubMmnz2cRDJf2m9aNgTSmnupKyZ9cunrbxvdBUIIxsZWLybdMlc3aFyoeGoPAaW9zKmsCc1warYjoQP91d0OF5PrGMt2sJwwKk5gWSRIZU28dDzFDGPavK7NLmuwHWDnlsKF90zOTPokUmGuoTZhw16M28NVTDxtwl/DLVI3WaS5llmKGyF2BxQXL8aOTWWbPq61JpWzQC22QLHgR1a1sWeUiUlKUXURuX37WYjHlpDLZeCTBXz9zjvx9re/Hd/85jervsfRY+ym4/ZbNsIVjn//N3fg5Zee9voQi3G3aaqyLpxcq3Hl1TdAkmTccccd+Pd///e6+y4vrwAAfIGINzKCUrbQHtkUwO6tIYSDEvqjLMNYkEel7LvhZjiOgy995oPQUyeweesu7NzCntsIdx+bhHDJZdfiycceBGmxn0fTNHzk9z+Iz/71hzA1eQJf/MLfYmryRMV+BASG4cA0aX4OI2nJQIwtAOofWW9Y9rIGACASdv1thGU7MM36PZ/t0hOUapbGr1cEoKHZTDrX7BgmAqsoOMHaPmp/zobJsoDT+UXmSJVryLbhEKRAH+bmFqAZDnKa3dJ8zHIyOdvraXUdO4uzj6ZJIRACnyxguM+HhbjeMDuZzpnwNTnzuBo+mWBiXsXThxJYrmLGE0uxuaVemau8+jLXVc2raIAssVJM93PLdsjNdS3pHxjC+RdchieKzH2YcGOBZVdMbquXmcyLyVQy7m2jbc6ZrN9OQDG5oLJ1ikNBKQEhtKV2AldMSmX93X5/AMObtuEn//fb+O53v9vycRdjWRTzcxOYzt8j3KBRu1CgRDAnMyYM06nbVsAzk5x1i6oxJ8DyH7kksf6TiXkFhuXUnJHWiJW826Zbpw+c2WLSLwtsxlYRzQyILkYQSL4UhGGYDlJZq2YvoFvaWat8wjTYd5TLJr3jGd3EspWTi2pLJUTPHUnir75yBB/9f17Bg8+uNP28RpgWM4myLLY4MWwmJkaGA14gpNo5VXB0nYAkUPzz5z+FH//4x/iLv/iLqn/X9PQ0+geGm+6HOf+Cy3DZFdchm0nhvL2X4Na3vatin+07z8Ytb30Xzj3/Ytzw2lua+4NPMZFIBO/67Q9jfn4ef/3Xf11336VltrgIhnu9baZFEQqKuHpvH7YNB7B5wI/+fI+0O/qinAsuuhKvef2boWs5DGzajV/79bc3fbwBnwBCmPnMZVfsw9ixV2FZektrymeffRbf+fY38dhDP8VnPvVxfPvrX8QD+TEuxYgigWo4MCwnb3RDkW1BALCMVoPMpEggEAJ3bRwNSzg6mUM6V19QasbqWhDqEQqITQn79QQFgdaglD2dKx+DVZtM0edvNAgKFDKT4xjetLWq2BAEgkDPIFaKZgAutjlzGWDussUlzoQAyYzlCVTTduB2UAgCAXUoUlkL8bRR4fzq0srnU43esIzhPh+CPhELZW0SlFLEUyYCPhG23XjOZCM8A5417Jl0r1/u0PhsjmUm/f7OZCbXin37b8KrLz9bklmklK0H3Ezd9h07az7fDZQuLy9429p1zK7XTuBWr5iW44mlgCy2ZELmfv8+ufJ69bZ3vBfJRAyf+4d/bv3AizBMB7OT7HPbtHkbZqZXl5kUBeLdRyilSGRYZjJQp8z1TKe77kZnGLWirZJI0N8jQdGdVS1Y3HLW4lEV4aKyxG5zZF0tkiTAyLukuqVpmuGgt8GQ9mKKM5OG6eBXL8ZBSO05la5bWjBQfYHAzEsANZfvmdQdRMMSenskLCUMaEbtsSLlfPz/fRUZxQIhwHJSx81XDTX9d9WDnYMEmsocXDWLLUCG+/3eokjRbAz2lj7PbcCfmR6HoWvIZpK47OrX4cVnfomDBw/i4osvLtl/cXEBg8Olphn16OsfxFe++fO6+xBC8N8/+7+afs3TgSQSvP/Df419l+/BH/3RH2F8fBy7d1ePWi+vMGe8YjGpmw4GemXvnN4zGvay7TW0JCRJwj988dttHa8gEIQDIhTVxvadZ8NxHCzNzcCh25p+DXdB1TcwjBeeZeW91RYIkkCg6jYMy0FQFhDwiVhJGdi5tbnMhGHaoA1KaQGU9IrKkgCfRHBsKoer9vbVfA6bMXlmXUPr4bYRbBsOVH3ccShymo2hXnb9ePJgArPLGv7TjZXmHEGfgIW4jh1b2PdsNCj71fOjQY5Nn/SuO9UIR4aQSsXQl//30ipGMCmajWCR8KOU4PBEBsP9fpy/sweWRSH6C8csCASHxjOIp0xEQiJuunq45PVsm/XqVjMBaZWAT0Ss7G/TDAemTSGKBJZpQRTFVa0BiNczuapDbfw+YMI9EpaQyWYhy75VleeeCq7dfzO+/tV/xLNP/RI3venXAbDvP5OzMDY2hsHhzYhEIjWfPzS8Bf5AsCQDRwipGG3WDKbl1PzlDER8ADWg6oXeSb+v+uiMmq+f7/sp75kEgA997JM4fuwwJk4ehWXXd9WuR1a1YGisUmvvhZfj0Ufuzc+Sbe888EkC0vnEQla1YVoOtDoj3Dg8M7muqTcwORyUAMJGWbR7wXcNAYoHCYcCIiybdmRYc3dSiJ7bNs1Hj5v/fOX8RYhSioxiQdVtDEZrm5a4Rh61ylyNfGZSU1g/jWbYCPpEbOrzI54ym+5vdRyK5YSB//yWUbz7DSM4NJ7tyHwnwDWFoNBUFhVWTAl+WUBfj+T9XdXK20a27wIATE2M4YlHHwAhBH/0X/8OAHD33XeX7EspxfLyIgYHN5W/zIZHkgQYpoM3vOFNAIB777235r6xWF5M9vR523TDQX9P4RwMBUQvU56fiNNx3F5ZN/s8Nzve0kJnbGwMoijibe/4z9626SqlS6JIoOusnFQQ2OiOlZTRtHGKqrdmsOXSE5SwnDDqznXTjc7OmOx2/A1mdLomOu797PFXErj/6eWq+4YCIpYTBizbwcR8Dq+cSNctQSvOTNYdLdQ3DEstlA7GkmZbC3THodAq5l6ya3BOtfLGUaWB4GhIQiJjYlO/D0oVoxzVYAZanQjy+mSCrGqV9HnlVNsTFZZlrsp8BygcJ13Dnkn2PkBGyQctFQX+Dji5rjUXXXIVeiLRklJXvyQgkTUwNjaGrSO762agCSEY3b67JMBGSHturoZZ//wWBTaD23IoCGGOpUpeYDWDbrDfvCBUX+MMDgwgm056jqztkFVs6CorcT5v7yWwLQuL8zNtv55PLojJTM6Ek++hrDe7+UyHi8l1jGnZqLvSowS2Rdsuc82qNkSBlaW5uAPHW+k72lhQb7YkMwBp7dk+WYCi2UhkTCSzRsPnuyKrVsTLymcmTS0FSilU3YHfJ2C438fKoZqMECYyJmyHYrjPj0v3RGGYDo5OdqbnUtXZ8HRXTOZ0CcP9PhBCvDLXaudTOBzB4NBmfOVfPo07v/IPuODiK7D3woux86zz8Yu77ynZVzMcJGLLGBxqPjO5kaAAdp21B7t27cL9999fc7+VWByC6EOwaEFFQdFTY/QFWSM16VY4jOadd+dmx5t2QAWYmNyydQdueB0rPRZFEbPTExX7+WQBiYwBK2/jLksCHIfiyYOJpoScqrVWxu4iigS242BmWcPdTyzi5eOpin3SitWWUN2oVBs1UEy5eEplTS8rUI7bY5bKWjg+raA3LNct+zUsB4KjIR5bqusG3TcwDEtLeAIoo5htzRqu5hIc8ouQZZLP8rBtxY/7ZAGb+/1e8LL8s2LBic5kupljJSkJRrLrOPvNWJa5qhJXABDydeFrWeYKsPVLMs0W/oqiILiO+yVdJEnCVde+Fs88+Utvm98nIJkx2bWvzoxJl9EdZ1X0Bjpo/bNu1DcuiQRa3tWV9UwSgDTuf3Yx8mJSrJF1jPb2I5NJeuPuJueVukG6amQ1C6rKAu7nXXApe53x4y29RjFy3uTStimWEgYEsE+oW0YxnQ74J3MKUDS7rSyQqjsNB147qyhHVTQb4aBU8nw3S6m0OK9tw0AKixrTbtxPVQ2fJGByXsVS3GgYyXLLXGu5hBl5MWkbGWbrni9rHerzIZkxMbGgNHVMbn/sUK8Pl54TBQD87qdfxL8/MNfU8+uRVW3IooBMhi2oc2YAm/pZH269nkkA+Ju//xJ+/w//Er//h3+JP/urfwQAXHHNjXj8sUeRzRbEbk61kIwvVbixnikQsEqFCy64ABMTEzX3W1xchC/UD7+v+HwiNXs9SNH/7SThoARFtdE/MIRQqAdLc5OeI2AzjI2NYfPILlx8yRX4u899Db/9u3d4Zk3FSCJzCS1erw5EfMgoVlO939lVjO6gYCZYIiGYi+kV4jWzSrOUjYa7KK2FYZZeb5N5k6Na503AJ+DQeAaaYTfsIzRMB0qaZSrciohqDA5tBqgFx2DnWVa1WxqDUHi/ygLncFDCQMQHRWNZnkbL5XKTE0UriL1OQICSvt+sakPKBz9WUyLovb6XmVzb7Lw/P/vVtilUVT2t5jvf/Pk0Hn0p3nhHAGedfT6WFmYLozMkgngig/n5+bpjQVxGd+zG7PSE93yBENht5AA0vX6JuCQK0HQHbKJU4bucmFebyk6qBjvHamUmI9E+WKaBdCYHSimmFtWWfnOsCsCBqmQhCAIuu/I6SJKM5575VdOvUY577uY0C0sJAyR/XeJlrrXhYvIUMLuseov5VlD1+k6A7lShdqtesqqFnmDpj6NeJulMQCICsmohM9lO1iYaljC1qGI5aZRkfauhNDDgcTOTjpnF04diUA0bAR9z/3Moc+Qtn19WjWVXTPb5sHWICb1U1sI/f391rmcAE4qySLyZV2ktgE39rKzSO59qlONee/1N+N0P/Vf87of+K87bewkA4OrrboJpmnj44Ye9/eYXV2BZ5hkrJgEW5Ni8eTMWFhZq7rOwsAhfcKDM8InWPA/XKnHmlrkSQjC6YzcW5iYQTzcnJiml+VKvXRBFAW96y3/yzo1q2cm+Hhnlv1NKifc7rvc+6Vxtc6xGhHwisoqF3h6Z9dSULYAyitU1cyBPBYLAerpqOZYWB1wpZVlHAEjWKI2NhCTEUkbD0ViUsnaQXIKJyXo9k5s3s8oHS08wsaVYTMS1SD2/A8N08n31tY/bJ5GK30sm19lMdyQk4vh0zivjzSk2fPnAim1bEFeZmSz0TK5tZlKSBOimjVjagK4pTRu0dRrHoXj4+RieP1pZpVCNgcFNcBwHyQQzwyOEYH52AgCwtQnH8u3bd8M0DSwvzbMNBG3NIFV1q+46U8zPm7TtQgBkMOrDydkcphYbB7PVfAmyINaYpZ0fybWyvALLplA1u+4s1HJ0k1UBKLkMwuEIwuEILrvyOjz52IGmX6M6LCtpmDaM/G+Ei8na8DvdKYBSNGUlXw5zEq39FYmEwLTat3jIqbZX1upypotJWSJeRNiyG8+gq4YgEAxGZUgiqdtvefBEGj97jDkH1iqfcHsmAeBrPzgE02LCwM38rSTMpkpd3dEGQ32s/PSf/+RCAGhqCH0jlHypoOtM54g9nkNwJCQhGpYwNtP8TMwrr74OgUAI99xTKHUdn5hlx3uGikkKCt2wsWXLFiwtLdUsHVteWoQcGvAyNZZNIctCTVFDQDqa7XCJhCQv6zG6fTfGxw7hySefaKp8KRaLIZVKYcu2Xd42z6ypSt+kLAnYMhAo20YQS1VmC4vRDQd2FbfsZukJSdg6yN6XlJUMMpt/NvuSU0T+nlUNRbe8lg027oHtVyszSQjBYNSHgUh90WPZFJQCmfg0AGC0TmZy61Zm9uNocQxEZSiqXVPM1sMwa/fLUrB5m/UWX0G/iOVE6fmbXKWTazkBH5spvZRgjrXFwQ9W5rq6zKTgiclT0DdMgbllDZp2+jKTqawF027eTXogfy+Lxwp9wQtzEwCaE5Mj21n2cmbqJJ5/5jH86sBPkEk1J2SLUfX61ylRAEzTgWkXAiCCQNAblrGw0njtkVGN/OvUEpNszMlKLM7EpGG3NMeSzdwFspkMwj3MtGjf/ptw4vghLC22X3kliYI3j7XQjsQlUy34J3OKSLYjJhuUuUoigWWtzoAnXJaZ7Mn3ncRTrR/vRkCSCotC03LgtNlPJktCXeMdAPjs/zmBhRizZ685GsQsXKxNg5V9Bvwizh4JQRYJjk5lm1rsLCcKYhIA3nL9Zvz6azc3ldVsBLPAF5BOs8yk4Itg6yATk4JAcNFZEbxyItN0H0S0J4iLLr8edxeJyclpFn0dOEN7JmVRQE5zsGXLFliWhXi8eilVbGURUqCQmTQtx/tNV4WgrVLuRgz2yvkSQRvn7r0ES4tz+MRH3465hcYjaV5++WUAwOiOs71tozt2QxAEHD96sKn3D/oFzK/oOPDcSs1ZgarhdKxdlBCWOXJxo+VnmiN2M9QqjcsptreoLb6m1RqTAbA+w0bBADfjmY6x0ULhnmjNfUe3seuL6CTRF5GRU622AsGqbte8d5N8v1m9U88nC8iVCdmM0lkxCQA9QRGTC2zElKIXPn/bsiCutswVaz8axEUUCGIpA7qaPW09k8tJdi9vdjapGxiNFY2iSa2wgMeOOn29Lm6A7ZWXnsFHf+/X8D/+6oO46/vfaumYAXb/rjdLm/XXuu7UhbM26Gf96idmcnjuSLLm7MlcPjNJamTVXTG5vBKDabFWHrWFZIZusut4Lp+ZBICr970eAPDS8080/TrlBH0C4mkDAZ/YsB2Jw8XkKaN8fmEzqHp9cwhJZDMNVzMapLzMdfOADyPDARx4buWMdCL0SQJy+bImRbMhruFisK+ncLOutSAyizKT1GEX5YBPQMAv4qKzI3h5LI1UpnF0cCVlwO8TSr7vnqDUsBSwEbZNYZo0n5lMQJIDECU/zhoJe/tcsieKjGJhckFt6jUJIbji2htx8sQJb0TE7GxeTA4O13vqhkUWCXKKhS1btgBA1VJXy7KQSsYg+guZScNko2RqQQhA1iAzOZTPeMdSJv7zBz6Oz/y/34RtW/jFPfc1fO59990HWZZx4aX7vG2hUA8uvPhKPPXEQ029v19m5eqa5uDlsXTVa5mq2R0bWxD0CVhOFeb2GWZtu/0znVqZyZxeqMQpFnDtBGKLcd0qEytTdZ1cAWDnjhEAgGAlEQ1LyKg20jmrZUOQVM6Cr1aJM2XumKRBJMMnC5hZVPN/g+NdZztJKCBiJWUio1igKPgvWObqDXiIe087BeuIoF9EPGUilVg5bSZtS/mAbfNikh1nbGXJ2zY7PY6+/iFs3TzQ8PmbNm+DLPvwxKMPeNuSyUQrhwwgHwxuMJJDyAdAhKKrmjvj8/BEBjOLGiar+DfYNvXKxGsa8ERdMRmHaVHYDpBpIcit6Taba5zNIBxhgaKdu88BAExPtt/G4/cJUHUHIb/Y0CiRw8XkKUPTW6sDZ70ltG7ESBRYZrJ9N1erwgGPEII3X7cJkwsqjnTI7bObcPt6TMtZVT9VM/SEGkd+TdOAmO81oDa7WQXy5ipX7+1DPG3i8ETjEtKVpI7hfIlr8fu3MuC96vEVZRnSqQQkfxTbNwVLIngXncWihYcnmj+frr/hZgDAPffcA910vEHig4NnaGZSEpDT7Lpicnaelb8SX38hM2lTROucZ2sleNwM+ErSgCAIeM3r34xwTxS/+MU9DZ7JvvNLL78Wfb2lc9b27b8Zhw++gFSyscEFIQRbBvwY6mOux9UMHVI5s2M9jaws0fD6ATs1dmfjQT3XxnIUrTgzWRCQP/nVIj77rbG23zGeD7YllqewvU6/JAAMDfaDCDJgJhANy0hnTVgWbbrf13vPfPCuGpSysSGNGlSiIQkzS1rexbuBs3ubEEJAHdZ7X1yhYNudM+Bx1rhnEmAB1qBfQDy2dNoCjq4vQUaxmwrEu8dZLCZnpscxun1XU6X3oihi2+hOvPrys962XK61NZtpsbFKjYMUrP2nPLYe8rPAdjQsIZGpXEuohg3q1B8N0uv2TK7EWFuGSJoW5EDeNVsiyGXTXmYyEAhieNNWzFTpsW8WQgh2bw2xntEG3hYcLiZPGbrJUvcT8zmsJPWG+1u207CXqZCZbH1JaNlszER5ZhIArrmgDwBwYqY5p9CNBsk7uqayZsfLiorJKhYuPjuCv/vgeTX3MU0DoVAPACASYOdDIF+3f/Yoy/7Nr+gNAxUrSaOiP7InKMK0aEtBjnKMIpOiVDIBSD04Z3u4ZJ/eHgk+ibTk5nneuedgy8gu3H33PdB0G8n4MmTZh0i0t+1j7WZkid1gh4ZZaVRVMTmb7w+R+zwXUUqBYJ3SHEEgHRs3UIx7rq2kDDz47Ar+6qvHcfW+1+OBe3+CO+64o6L0bXl5Ge9973vxjne8Ay+99BKu3HdTxW9v3w03g1KKpx5vLjvpQoDqYjLTuWCRO6rCnU1mWI2v32ciBARGle/Ctpkjo7uoTeYXpm6G++DJTFvlpgAwtaDCsXUkY/MY3V4/M0kIgS80iKWxhzB/7D6kcxYCPgEnZ5vv+TZMB6ru1Lx3iALBzJLasJ/WHT+T0+z8YnZtQj+9YQnHp7IlJYzMzXWVmUly6nomBYEgKOnQdc3rRTzVLCfYus52qCc+6hEK9SAU6kG8qMx1ZupkXYOockZ3nAXbtiGKIqK9A8ikM42fVIRm1Hdy/f/Ze+84uc7yevy8t01v27u0arYk9yoXbNywCSX0kEAgCSEkoSSUXwr5EnpJCCQBEggdApiQUEw34G5ZEu62XNSl7X13+sxt7++Pd9477c7MvTOzK8naw2c/WDOzd+7O3PI+zznPORyyRDC1kK+Sbgd8EkI+CV5FQCKlVX3XzAWWfRa1ikkuc11cWoJumJAk4mr8JlFYp6XTxZlJoBCdMn4Upmni8//+YUxNjjneZiXWZyYbY/2TWSNoGsWe/Ut46Jm4IzMSVW8cSyGKLGeyGSUmlx7YZXP5vSL8HhGLDjMMn2uglF0EM3UG058+lnQc2mv/HhTprIENfT5sGqw946GqeesCGfaxCzW/oPNGQCavNwxpX1hR0R0tLyb5d++GnZxbypXlS6katVRMC4tLgBTClqHyv4cQgkhQRjztfDFICMFFlz0fd911F+LJLA4feAIjG7dUNU50g+LE9HO/6cGKPgp/sAtAjWJyqvCYUu7mWq9gYimT7V/sxUIyRIEZP52YzmBiLocXvewNiES78NnPfhb37X4QibSGffuXsRhXceutt+I73/kODh8+jMsuuwyXPe/FVTLBs3dcAI/HiwPPPO56f3I2mWjxTHubRaJArNnkdNaARNZvr5WQRGKNEZRipbAQ5ed3PKVBFgkW4sV70KHxNJZdMoQAMDabhWSyRbaTQuOaG18BQrPY8/PPwDDZ/XVuOe+YbW5kXhcNygj6pIILcX1QMNlkKqs1rUBqBE/B0K2rZMZf1zWIYpuiQZqxGG0CiydZvTJf4tjvxoRncZExk/l8DrMzkw2l2KUYLjRH+vqHEYnEEE+mXBXvqubMFSISkBELyjVHJgSBwKC0yjgnk9NhFvJKxBpurh6vD4riwfLSErJ5A4okQFPNmq7PlUhm2O+kU0kES+ahh0ZGMTF2DE898RC+8eV/xT996J2OtmcHnpHsVdaZyVpYv9utETojMsJ+GUPdXsyvqLaLm1I4uXERQrChz9eUGyE3mbFjJgGgIyyfsSY8RCCYWsiCwJ71PTKRxse+cRjfv2u66fdQNQrNoHWDtgE2u+IPMGZyyxC72XPTJP67mVzjLLTFhGaxRZRSrCQ167u36wLaHZ+mSfHEkSSmFoqzj6pWNIBaWVmGqIQsJ9dShAOSxdo4xZVX34hsNoOf/+LneOqJfdh11fVVr/nM947hH/7rQMty3dMBAiHIGR54vV7bYnJ6mh2PkidWViTVK5gK+dNthyAQdIQVLKyo1uJ60/Yr8MVv/hwAcNtPfo4njyQxPpvF7FIOv/zlL7Ft2zY8+eSTuPveB9DTN1J1XeOyromxY672RRSqZVP5VZhBC/oky/0vnTOazq98LkMqkYyVYnY5X/ZdrKQ0hIMSXn5tH7yKAEKAL942hrd/ej8+8c3DrhbMYzNZ9MXYtj2exrERH/7wR/AXb38vlhcmoKUmreuWU4dJZkRSe/9EkThuYngkAQsraltZdDsQUu48rusaJLk9bq5rYcADFF1RT5bj9/yyao13uDHh4TLXqckToJRiyIH5Dgd3dB0c3ghfIIBsJu2IFeVwI59uaHZFi0UXRzytg6A+M0kIQSgSxfLykqVOIMT+OlGJvGZa42CpVKKMmRwe3oSlxTmMFxzAW2mOZPMGPLJQd+zsTMd6MblGICDWSQIUoxpqIZs3HMmkaoWRN0I6a4BSEz7F/mLfEZHPWGYy5JUws5BHrXvgxDxbMC62UGzzAq5WMc+hqnlrDuC8TV7881u3Y3PB3EYSCbwKc/07MZ3FYh35dDKjI1ToKmbzJh45sGItTlKZ6sLxicOJKhfFeFpHPKVjbCZnPZZIFzvmqeQKRCWEcKC6415ZTP5q3zwOjddn6K+8+hpIkoxPfOT90DUVu666wXoukzPw6MG4lenlZsbidEXQJ2F6MY++vj5MTU1VLainplgxKXpLi0laX05XMFFYDXRGFCzGVatxtZTU0NXdg23bz8evf/VLzC9n0RmWcGJyGXfffTduueUWAPUXOEPDmzAx7q6Y9MhClUSyUTOvGXgUAcksUwlksjrk9YVHFRRZQDxZLofLayYmZnMIlcz2Ti3k0ROV8LJrevCFv9mJgU4PMjkDAmGS11omPpWglGJ8NoeeKDsfPJ7qRpcddhVmtjMz+wrHDsvac4KVpFbbfMclfB4RU/M5LCVXd+SiEm2RufJokFVQPtjBYiZPQjGZzupYiKs4ewNr/DouJjt7rP3mTTI3zCR/7dDIJvh9AeSy6TLlUCMk03pD8x3nIMhVFIDxlAahsI6tZcADAJFwDPH4MlN0iAQmqKNrNFNkUeiahnwua62VgOJn89C+ewEAsY4ut3+QhWzeXJe4NsD6p3MS4JGEhvNjybQOabUSxQE88/QTOPqj38FfvHoLrt81grHj5QYHnWHljGUmPYrAZglqrAV5IyBUxyWzEYrFZGNmklvZa6qKge7yPL2gT4JuUCzGVTx8IG4rDeF220GfiJe//OX40Ic+hLmlPLRCjpsdMxlP6xgruLPxUOvphSz8HgGpjI49Ty5hfDaLY9MZRAqfQya1AkEJ2UphwgG5bEH/zV9M4INfOVj3b/f7gzjvwl04cewgvF4/zr+o6O758wfm8KnvFJ3a3HRjT1d4FAGprI6+vn7ceuuteOWrXmM996EPfQj/9LH3w+cPQZB88MgCTJNCFEjdxYKwerUkuiIyFuJFZpJLFK+8+kbsf/y3ePnzh3D9Zb34nedtRDabxc033wygYEFfY/05NDKKifFjrpgpWRKqWPFs3lydRS5l5046Z0JqU0HxXIIiMyMp3qhSNRP3P8ZmpbgZkmFQPPyrT+Mnn74SV13Qjasv7MHTP/8LEADXXcwWhE5HDBbjGjJ5A90F9Zvi8db/hQKGhkfRN7gRC0/8Jz7/L+8EARzHFTRyYXcDRRZAKStQ1zKz1GhHMQkuc12bazOXi54MN9ejk+xeef5WdqA5LSY7unpLikl2P2tkElWKkQ1brN/x+QPIZtPu5g0zOpQ2HVeKVC5Lp5QiWcJM1ooGAYBQJIpUYgWJjAZRIBAIQcbB+cYUWQTpDDMe4m6uQDE6Ze/uOwGgpaibXN5Yl7g2QGs6hnU0BUUWGtqdt/Mkt8P42GFQU8MV17wYe+79KcZOHMHIxi3W8x0RBYmMDlWrbSTwXIbfK9a8eU8WmEmtBeMazgZW5nxWgjGTwcL7VTPFQb+ITM5ALCRjblnF1HwOI33lM4ucGfJ7BPziF7/AxNQcLrvxLZicZ3LVhM0so2FQjM9kMdzrw/4jCezcFMb4XA5hv4S8ZiKZ1vHYwTgIIZAkAblcFrqWh+wJ23bwwgEJyQyz2Hcz4/uuv/s47r7jZ9i+83woSpFV4DbkW4cDODSerpLXPJfxrr//CD724Q/iJz++DfOLcXR3RvCtb30L287aiRe+5p342dPsGqMbtKH7HCFYNev+aEhGPKVbM77cEfP3Xv8WQPRAAJM0pXI6tm/utYrJVEavuRgfGtmEfC6LhfkZdPf0O9oPWSJYSRvM3KFQWCfT2qrE/siSgLklNl8XaaHZ9FyGRxZwfCqDjrCCpYSGTF5HT7R4bk8vZBE/cQeGN52HF77wRXhw37148rHf4n2v2WhdN/OaiUBjxSqWCuqagIddq0uvIY3wt+//LP7ub96Ng0/thSwRx/Fe2bzZRraHzR9HAtKaZpbqugaP11nhXQtc5rpWEWNLC3OQJBmhcNT2+QMnUjg0nsaLr25/sXlkMgMC4PwtvJh0dj/qHxxGKplAPL6MyfHjCIUiliGNEwwOb8SHP/llXHHVjdj/+IPIZ09gOaFhQ5+z309l2iefDnhFzCzmce5mNh6kaiYMSq37Sy2ZKwD4fH4sLy8jmTbQExMtw74NDS7xmawBAiCdTLB9KGEmN20+G/5AEEuFJkM65c6cqBQ51azpzrwOhvW73UmAp1BMlhoOVCKZ0eFdxSIunWISwxe9/A3Yc+9Pq060zjDrSi4lNPR1Or8BP1cQqhOnwIvJVjIaecBvvfehlELXNfgLcwCqWi1jZVmRbFt+j4D5ZbWqmOTPq5l55PN5HD92FNGQzEyeAMs0pOx9DRNeRcT9jy8hpxqQxBRU1YQUkC3GJacakAqFQiLO8q2CoajtMR0JSDBMNkvm5ua1ZdsObNm2o+rx8dkcrjgnhhdf1YN/+K8DrqQ9pzN8sghP7zl49R++A489dC9+/LNf47qrL8KhQ4fwrr/7BM659Cb87OnD8CismGwUskwapt01j0hQLrDmrIjkbFSsowt//tb3WK9LpDXEwopl0JDM6jUjO4aGNwJgkjCnxSQhjCNJZw1EgjzHcHVm0II+EUcm0zDWyHTkdETIL2FyPoetIzom5rLwyeXH6J4HH4WRW8JLX/U+/OEfvhGKx4NHH9qNczbKeOQQ+1x5dmQjcEZaEQv/75CZBIDLL78SwcErsPj0NwFTRSLtjJnIqgaC3vYurZrxRWgFuq63bMDDu4ZrNTO5uDCLjs5uq4itxF0PL2DvUyt40VU9bS/Mj0ym0d/lRWdEhkCcM5NDQ0yKOTl+DBPjzMnV7b7ddMsrAAA+fwD5XBqzy3mYJm14zOiGiWzeaKiOcgpJEpBPakhlDYT8ElMLUQLDrG/AA7BZZk2dBsDm2BVZcNS84bEgy2m2fi2dmZRkGZdefi3uufNnAForJvOaCe96MVkX65/OSUAt5ysOZpNutC0DzQ48j6i/ny3IUqlE2fMdBbOWpTN0btIOOdXAF390AhNzBZMNF3KSSvBCtB4zyZnIQCEaRM1XF5MBn2hti12Aq1lG3s2PL44DABbmZ0CNHGIh1jCYWy7fLr/3hwISusIKhrt9mFvOVw2fexXRKiwT8RUAQLBGVzgcZDesRFovO+6b6VpncgYW4iqGe70W8+ZUgna6I+ATkUjruPKKK+H1+fHVr3wZH//EJwEA2y+8xgpX98gCdMOsGwsCrJ4BDwCLmeOFVa1rCZsFzVkzMqmMUVMVwM0pJsbdh1GXMvCJNju5csiSgL4OL/o7WmN1nssQBAJZEvDowTgm5nJV18C9998BALjpphcAKEYHJOIr1nfm1FmVL0hlgf2/05lJvp/hjmGAUizOjSOZ1hter0yTtt3Y6WRA17WWZa7FYmatZibn6kpc55ZV6EbtdVcrODaVwaZBPzOT8UuO1018rm9i7Bgmxo5hsNAsawY+fxDZTBqaZjoqZlcjboYQYo2z5FQ2SsDdXOsxkx6PF2o+h8FuH4vnkQRLyVQPyYzOpPOFQrHUzRVAmc9CumKN6wb5dWayIdY/nZMFCmRrXNRyDgf9W0GmoDEfGBgAUN214cxkI6OgMwn7jyRx72PFwHQ3swmVcDIzaRWTnJnU7JlJLmOVJYJMzrRmHCvfa3G2mLM0MzVmFWIzi+XfMVv8s5uMKDKXv6BPQsTGWIdjeYk56UWjHbbPc1OeeEory7V0MhdRifFZJs8d7vVZxdKZInMVBILhHh98Pi+uvPpGPHDv7fjyl76AkY1b0Tc4ahVuiixA02lD04DVZDzCFdEHlYZOZftAi02NdE6vaWDS2z8ERfHg8MGnXO2LVxExV2DgTZMik6tdsLYDa80knW6IBiWoeRNdEbnqszp64GH4YxvR18cancVichmeQn6q02KSM5MiYceeG5krAHT2bAAATE4ch27Shq7Zmr5Ks7hrDFZMthgNgrVlJufnptHZXa+YZNeXdpu1qZqJlZSO/oKC65zNITzwxDIefnal4e8ODm0EAJw4dhDTU2OunFwr4fcHkMmmQQjBkYk0njpan4lbDTWPVxYwu8Q+52zOgAAC0+TFZO17kcfrRT5fNPYTBALDoGX3jMp1DcDUJrLEMiYBWM73HFdecyM8Hi8IIdZrmkFONeGR12cm62G9mDxJoLT2RY0PFa8mspk0IEiIRqMQRbHqROuOKvAqAg6MOQ9rfq7j4HgaAgHe+8YtuPr8jpZkrqmsAUWqbxGvqWzxy2Wu/N+lCPpEpLJ8FpGlBlY2I3ixOTNZdMGcGD8KRSYQCCvwSi/UpkmrFkQhv1S32z5ZcNjsHdho+zxnqRgzWdy/pSZz4wBgpNdXZCbPAAOeSrz/41/Arbftwa0/egCf+cov0BVWkNeKzKRJG8tcAcZOrsZcU+nMoCSSuhmBfo+ImcKsoWHWLsYkScKFl1yJfQ/c5Wpf/B4R88sqKKVWttpazqCtoxyEEIQCkq36JhWfRyg2YP07HC4Wk/z1qkMDnkRKQ8ArwtDZtdONzBUoRi9MjB0FAWkYb6Tq1FEI/KkOwzDa5+a6BpJvSikmx49jcGiD7fN5lRV8QGvjKXbgBU9HoQH/ppeMoDum4M6HFhv+rtfnR3dPPx7cdy8Mw3Dl5FoJnz8AQ9fhk01MzDFzvFzewNxy3pYUSGeMth+pXo9gjTWwcQUCwzAhCELd663H40M+ly17LOSX8MThBAyDwjAo9hQyiTl0w4SqMbMrTo5UFpO9fUP42V3P4oUveW2LMldjXebaAOufzklC2C/hsQNx28B1N9k/zSKbTUOUmKQgEAhZA8wckiTgorMieOiZFduO0JmIQ+NpbB4KYMdoCGG/1FK2YSqjN8yY5Myk31cw4LGZmQz4JFBa0mUkpKp7zpnJyfFjiHUw2/SJsaMghMDnEZFTzbLFmWFS1wHZE+PHQAQZ/f2Dts+HS4rJ0v1rRkb99LEUYiEZHWGZzVdI5IyRuZbC4/FidNNZGN18Nvp6OizTA4BnSxJHUnmBkFXx4IkEi8f3QJcH8bQOvca1xO9lxV42bzRMRNp11Q04fvQgpqfGHe+LKBLoholUlmWyPhcW/M9V5NKLCEe7rX+XM5PseM67kLlGgpLFenhcMpPnbx+EIAdx+PBhKBKpGgmohKabqziFvHbQda0l90ugxIBnDT6PpcU5ZLPpmszeQklsVruZSX4Pi4XZaJAiCxju9TlWdQ2NbMITj+6z/rtZ+PwsMkzXsujt8AIUePZECg88voQHn16pahgup9S2SzdlSUBeM5BXDUuCappGXYkrUM1MAuyekMoaSGTYbPXkXNZSJQGsQcDr02yGkR7+wmdQimAojGAoVDXK5Qb1ZK7snnb6n/OtYr2YPEnwekQEfSJmbW5OiZQGuY1ucHbIZTMQZWaHFwiFbSUAl26PIpU18MyJ1Kruy+kAVTNxbCqDbcPsYhX0i1B16lhuVYmlhFa22LYDZyIVjweyrEC1Yyb97CLN2UdQikyu/GbJi96J8WPYctZ5iMY6MX6CzZyxYtIo+ztME6DU3WJ77PhRSIF+REKK7fMhvwRRYDfe0oWg26xOVTPxxOEELjorYnU6fR7xjDHgaYR8WTEJR3l3RFidYjLok6ymBI+0sXMOBhgTSSmba2p06PEMwH2773C9Twkus16/95+S0HUDWnYJ0Y7SYjIKgBWT/Lh26qSdSOsIB2Rr3twtM3nu5jDk4CAOHjoEf8Gtsh6LzxaWp3+jQtfaIHNdQwOe8QYZjXMlJnOtNIHtwNU1nJkEChm7LucmAWC4BWaSF1K8sPLKAo5OptEZUaAZtEq9k1glEzICglTWsCSohmnUNd8BWGM0n89VnVsCYY2AZ8dS6O/0YmI+a83W57VihBT/m32+cmaSIxgMI5NONX0s5lUTHlnAYw/vwa3f/E/r8SOHnsa/ferDSDp0en4uw9GRRAjZQAi5sfDfPkJIqNHvrKMxPIqI5YoAZ4B1U1c7jiOfS0NSmOtnIBCylQDsGGUnph17eqbh+HQGukGxtVBMclYx3YRkhlKKY1MZjPb7676OM5OyrEBWFFtmks9ccumOLApVF7Z0zgClFGMnjqJ/aBRbtu3E/iceAgD4PALyqllW4JlNVBZjY8cgBwZsMyYBViz0xDyYWcyXGSC4lbk+dSyJvGbi4rMj1mM+r3jGzEw2Am8K8EVCIwMeoMBMrsK+CAKxslgHu9gifiVZ+6brUQgOjacaFnobR7chEu3AgWeecLU/Xpk173J5w1U8zTrWDjOzCwA10FUSPG9nwOOYmUxpCAckqCpjPRQXBjwAMNTjRbBzC04cfgIEJnKqWXduUtNNmM+BToXejpxJsnbRIHzMohazV8oot5+ZLBSToZJiMiwjmzcdeQJcd8NLsHnrDlx7w4vR0dnT8PW14PPxYpI1/4N+EV0RDxRZAEG5P4FpUqSyqzM3TgjLRVVVJkGlplk3YxIAPF5GbFSykwGviBMzWeTzjBmURILdTywhndVZMVl4XYYXk377NVUgGAKlFNms+7Et06TQDAqvIuCr//VJfOZf3of4yhJSGR3f/sZ/4v/++98wfuJZ19t9rqFhxUIIeTOA/wPwX4WHhgD8aBX36YyBJBIrUL4Ua1FMqrkMJKXATAbtJQBWiPS6zT0OFmZHeTEZLDgQ3vrrScw3kD5VYnaJBblvGqxfTHImUlYUKIoHedtismCis5TD5/73GI5NZ7CcqmYmjfwyspk0hoZHcfmV1+PIoacxPzdtsXqlc4xuv29KKaYnjkEODiJcJ+qkv8uL6cV82UJwOelO5nrgRAqSSLB9Y7EDuc5MFlF6LfF7BUedZ0HAqmVNctMmzkzG6+Trhv0yQj4JsQaMPSEEwyObLDbCKXxeEXNLKuJpfU0D4NfhHCfGpwAAPT3FoDyv1wePx4t4oshM8lijRkikdUQCUpGZVNwxk4QQbDv3eVBzSTyz/xEQUt80LJMrxiWdzjB0DWLLxST7HOgaMJMTY0chiiL6+4dtn59ayMNTKKraPTO5lFDh94rwlmT6dhbc8J1IXa+85iZ8+wf345/+7ZstzXH7C67vvLAihFjSTEJQ1mRmc+O1o+laQdAr4tB42iLoTcOoa74DMGYSqC4mvYqAlaRmzSvGggrymomjkxlkcrqVFZzNpFnTXbZXRgUKLq+V41xOwJtHhObw6EMPgFKK3+65C/G0hr333wkAePrRe1xv97kGJxXLWwFcBSABAJTSQwCab5+soxyUIF1yc1I1E7q++tbiaj4DpcBMBoNhW2aSX2fWKHP4lMah8TR6OzyIFBwqOSP4wJPL+J87plxt6+gku9hvHqzW95dC10uYSdmemeyOeSAKwJdvG8Pep1bwuf89jgPHk2Xd4FTWgKhOAwCGN2yy7LL37b4TXVEFi3EN6Vy5zNWNDnBxYRb5fBZycKCu4Ut/pwezS3lrMRbwio7DnTniaR3hCuMOv0dANrf6C5bTAXnNhCiwJlV3zBkLIxBgtfpFXMo9WCgmV+oUkwCT50oOpLlDI5tcx4Pw5t2xqfSqN+vW0Rwmp9h1aqC/PEM0HIkxmavk3M1V102kcwbCQRl5NQ9RFJuSbo6efQVABOzdfQdAaV0X70zOPO1jQQBAN/SWZa5rOTM5MX4M/QMjkOTqAljVTOx7ahnnbQkh4BNXhZkslbgC7orJdsFXIXMthVcWyvwJ8trqzY17PSIMk1oRY4ZpQmw0M+kpMJMVJjyEEHRHlDLFUywo4/hMBs8eT1vrjUwmbf39duCO+M04uvIG7dTRhy212AP33YGZiYNYWpwBADz+4N2ut/tcg5M7ap5Sah2FhBAJ6xMnbQQt0/AzJ87V/3g1NQPFWyJztTnJBGvm4cz+uimlODSetuYlgSIjCBRzGZ3iyGQGikSsBXYtqIXiUZYVlsNkMzMZC8n4i1dshGECL7m6F3nNxBNHkmUMVSqrgxSKyQ0bNmHLth3o7unHR/7x7fjmBy7Cs3d+GLOLxY5gaTSIE5w4fpjtZ2DAcle1Q3+XF7pBMbXA3qszorieX0ll9Ko4Fb93nZnkyOQMJl3WTXRF7Lu0lVhNM5pIgM1N9nawwnYl1Z6F3ODwRsxOT1jniFP0xDyIBZW6x+k6Th6mp9nibHCwr+zxcDiKRHzFYtobFZOUUjx+mLEQ4YAENZ9zzUpyxGIx+Du34ytf+CQ+9Devr+tKnM7pq5oPvVYwdL1lAx6sYTTIxNixmvOSv316BamsgRsu7UawReM8O9gXkzxazb1bebPgxVTGrphUBCzEVctMsVmvB6foiijojrL7j2kYEBrNTHrtmUmAFaelDKogEAS9EsJ+qejmnknZmu9wBAKFYrIJR9e8xtYWx559AB6vD9de/yL84iffxVvfcB0A4JWvfi32P74PqdSZ7S3i5GpxDyHkvQB8hJCbAPwlgJ+s7m6dOQh4JUzM5bChMD+Xya3NxIWuZqF4CvN/wZBtoCtXJpzhtSRml/JIZHRsGylerEoz9Bo5/FVifC6L4V4fxAYdbF1jNyJrZtImZxIAdp0Tw4XbIvAoAu54cAHxlIasalqym3TWgJmZhiCKGB4eASEE//jR/8Rjj+zBfffeg0PP3IPHDywgHJBw3pYIiwZxQUc/vO9eECLA23F2XfvsvkIO1/Fp1n3sjipWYekUqYyBkL/8xuT1rM9MciwlVHSEZRCgKgy+FgRxdQx4AOCczWFkVROyJCDoE+vKXN1gaHgTKKWYmjiBjZu2ufrd9fDpUxfzc7MAgA3D5a7QnJkURQJCGi+Gj05l8K/fZTLovg4PHsnnXc9Lcvi9IjrOexu2rnwTTzz6ABZXal/vUxkDAQdzyqc6WM5kazJXK95nlaVNlFKMjx3BjnNebfv8w8+uoCuiYOdoECGftCpurhv6ImWPRYMyRAGOTXjagUoDnlJIkgBV0zC3nEd/lxc51Vh1xph//47cXLnMNedsPVCpgMo2YCaDISZzbcbRlTfm56ePYNPms/G2d38Qgxu3oyem4Jwd23DeuTuw7awdkIQze6HspJj8WwB/CuBJAG8B8HMAX17NnTqT4PMImF9Rkc6yqIh4SluTmQtDy8LLmcmaMlfGWZzpzCR3gitlEmMhGX/3hi3Yt38Ze/YvwzSp46DyxbiGjf2+hq/jrIuiKJAVjy0zycEXyLGwjHhKRzZnIFYwBEhldKjJKfT0DkFWWLfw0l3X4tJd12J06wX4f+/6A9x7z70YGXgJztoQYsyki0Nw7wN3YnD0XIhKuGxupBL9VjGZgUCAaEjGgTF33bxkVseGSPln51+fmbSwnNSYRT0h8CoOi0nSnOmSE1x9fgeuPr8DAFtgtauY5K6HE+PHXBeT6zh1sbg4ByJ60NkRLns8HImx+CFC4JGFhgY8YzOsYfX+N23D1uEAfqTmoLiMBeHwe0V4Y9vw/Etehscf2YOJyUnoRhekCsd1nnsXqWFCdrqAUlrImTw93FwT8WWkkomazOTYbA6bBv0ghCDoF9vKFmq6iURKr2ImBYEgFlKwsHIyZK7299SgT8LB8TT6u7zMfGeVEwM4HLm5WgY82bqvq4WGMtcWmEk+M7k4cwIXXHQJhkc24ff/+D246vwOa4116aWXNrHXzy3UPZoIISKAZyilX6KUvppS+qrCf5/Z1UUbQQgBIQRzS+yis5zQHC8CW4GhZeHxFZlJVc3jsUf2Vl34BWGdmeSOrZW5kOdsCmGkz4ecauINH3oM7/y3p7D/aP2LFaUUS3HVmqmwQzaTxpFDT1vRIJKsQJEVqDYSkErEQjISaR0rqRIr9KyBTHwC/YMbq15/9dXXgAgKnnz0HhDCbo6GQSE4rCZXlhfxzP5HsfHsqwEAvjqsTzggwasIyBUym0J+ibGmLg6wVEZHqMLkx+cRkcubZ3zTA2CSq1iokL/pcC5QIFiTwYVIUKrr5uoG3LXxnjt+ijtu/xEWF+bast11OMNyUsMXfnjCsulvB6anxjF1Yj8Uf0eVYUc4EkUysQKAzdVqNQx4TJPi8UNxjM/l4JEFbC6YnOXzeYv9cAvOgnT2bmD7OXnM1tFV1ehzwiVY11mx1bKbK5+ZpO0tJp996jHc+avbEF9ZAgCMj7HZaTsn11zewNxSHsO9rFgJ+iWMzWZxx0MLbdmXhRUVFLAknaXo7/JgbLa54qgZ+OvIXAF2HCdSGpIZHZmsYc0frzZcGfA4ZCYrkc2mLTdbO1gGPM3MTGomqKlhYX4CQ8OFhgWh8HvWFS6lqPtpUEoNAAcIISNrtD9nJEI+EWOzGVBKsZLSViX7pxSmacLUs1Ynh3dt/vyNv4Nf/vR7Za8VCDnjF+nccMFOvsTZNgCYX1Gx/0h9GUUirUMzaN15to/849vxuldcjXic3Sw5M6nVYSY5YmEZ8bSOeGE27f7Hl3BkIoXk8hj6h6o7t16fH52D52LmxGMQCIGmU6i6iQbXfgsP7r0blFIMbr0SskjqmqcQQtBRCHb2yAJCfhEUKDOgqgfTpEhnDZuZSQEUqGvZ3woSaa1tjNpqQtNNy72ysuCuByKQNWkYRYJyQwMex9uKdqC7px8/+eG38Q/v+RN8+hN/15btrsMZnjqaxP2PL+HZNmYQf+KDf43Z4w8jGKtebkSinVhZXoRpmlCk2szkk0cS+OS3j+KeRxYx0O211CKqmmtJ5goAsS62X9MTx21lts+V/FJDZ/cOUWzRgMeKBml5lyxkMim85Y2/g/e++4/xuX/9AAA2LwnYZ0xOzOdAAYz0smKFF31f++l4W9QsnHm0Mzs7aySIiblc22c0a8Hrqy1z5SACsLCsIpU1qpj11YJpmo5nJnmEj1tks5m6zGQkEgMhBLMzk663nVdNaOlZUNPE0AgbrwDFuolbBZx8GjEATxFC7iCE/Jj/rPaOnUnwekQkMjqWEho0w2w4S9cqkskMAGp1srieHKjuagkCWTUJ3OkCy33UZgatv6vY7Q76xIYFjV3AcSWeevJhAMDxY4cAFJhJRYFaY2ayFB0hGYmUhpWUBk038ccffgxLS0vQcimMbLDP4ArHepDPrIAQ5nap69QyX2qEPfffiXAkhnDP9roSVw5uTOBRRKvgcTrDks4ZoEDVzCRnjFer4PvLT+7HW/9l/6psu51YTrK/P+SXauZ92kF0QaeomonjJbmzlFKsJJ197tEQKyabEbY8dTSJnGpgKaFC100QQvCtH9yP7/xwN264+WXY98Cd0PX14Oi1Aj9nHzkQx/u/dKAtrpWJ+Aq8oX5c/9pPVT03MLQBqprHwtw0FFmoOTM5PscWo3nNxFDJWIKazzdtwOMvXNd8kV5IkoypieNQtepjmM2hnf4wTHa/E1ssNlZD5vrIg7uRz+fQ1d2HvfffAUopJsaPghCCgcENVa8fLzCDnJl80ZW9eOXzmbmTW68DO8zxYtKGmTxrA1tfHRx3n23YDERRhMfjRaZOlmLIK+HQRBrxtLZm8UimaVqNhVoourk2WUxm0vDXYSZ9/gC277wQv91zt+tt51QDWpoVoUMjo9B0Cr9XXJVYldMZTq4W7wPwYgAfAvCpkp91tBEEBE8fS65JZ3MlztgzfwUzCaBKjkCIe7fS5xrSWQMeWbB16esIy3jNDf345Nu2w6MIVZmhlVgoLLrqyVxjsS4AwPEjBwAAiuKBongdM5MmZUUrnxvS0iy6ZONG+2IyGIpByyUgECCbN6AZ1BEzSSnFvgfuxGVXPB+qjrrmOxy8iPYWZK4AHHdu+QI2WMG6cSkbzwJtJ04nVp43KkIBqW7eZyUEwXmwuKabkOXicZ7KGkjnjYbHPcBYfE2nmJhzt2A4PJ7Gx795GN/+5STe8emn8P27meNnJBLDpi3bcf1NL0UqmcDT+x92td11NI9EIbPu7kcWcWQyg6eOuZePVSKXy0CJbkV3V7jqOS4vGx8/BkUmNYvJ6YVigVA6466qrRnwAEBeIxgY2oDpyWNlubwc2ZzhuAl3KsM02GfbKjNZlLm27xq6d/ed8Pr8+OO3vAfzc9M4cugZTI4fR2/foK2MeWw2C68iWEogjyLggm3MLIePFrWC+eU8RIFYs3Ol2DwYgCSStrL3jeDzB+oyk16PCIEAIZ/k2OOhVThyc62RM+kUjQx4AGDXVdfjqSceQiK+4mrbec2ElmJu+EPDm6AbtGrkaR0OiklK6T12P2uxc2cSokEJ2Zzh2M6/FHPLeatwcIJ4gl3cAkEWcss7kUD18PY6M8ns3mvlJxJC8NLn9aG/ywuvUp+ZNE1qyWI4Q2eHaKwTAHD0yLMAAFmWGTPpIAYhFmLHzyPPruDJgjX+ZZvYsbFhdLPt74TCEZhaCrquI5MzoBWYn0Y4fPApLC7MYtdVNyCXN+FzMEPAZa6SSKyi0CkzyYvOSgnnYLcXYb+EZ463vqCtxMxS693rtQKPLIgEZfhcOEq6WQDrBkU0yBwR01kdmbyBwW4vUrnG3+GF2yIgAB56ZsXx+wHAzx5g85B3PbIIAJicL19wXLrr+RAEAXvvv8PVdtfRPHgxyXstE7PNLQJLkc1mQYkHHTYLcz4PNzF2tC4zOb2YsxiXkb6iUZeqNs9M8vGGdM7A0PAopiePIW2TNZnMGGvG9qwmDIP9bY0cOBuB30PaNTP5X5/9KH79i+/j4kuvxjXXvRAA8LEPvAN7d99pOy8JAOOzOQz3+soKp54YuwfNtoGZnF9R0RVVbAszRRYwOuDHoTViJoHGxSTA7p/18qDbDcM0GudMtmzAk2pcTF59A0zTxIN778Z/f/UzOPDME462vf/huxA/eht8/gA6Oruh6SaCzwHH5naj4eqPEJIkhCQKPzlCiEEIce+vu466kCUBkaDcVLfo3Z95Gu/9wrOOXx9PsEV3MMCKyYsvex4u3XUtABuZ6zoziVTWcBSzwFwGa89h/Pv/HMO3b5+EKBBHM23Tk2PweH0IhaOOZyY58/fj++bwj19izGaXd7GmDAgAolHmtrm8vIJs3oBuUDg5DA8ffAoAcO75lyKbNxwZR/H9y+bNEmbS2exKsvC6yplJQgjO3hjE08dTbe2CAyiTdJ7q4KHUkaDoiCW2QJybbBkmRcgvIa8xhjISkLB5MADDwTUiGpKxdTiAh56NO9612aU8Hnp2pWw2uVIiHo5EsWXbTjz79OOOt7uO1lDZABqfa91oJJNJg4hexGxGAHr7BiFJMibHj0ORBKh6bWbyqvM68Pdv2IJzNxcVN2o+D0+TzCRvzGRzBrZs24kTRw9gbnGl6nXxlPacmKMyDHadbcQmNUJxZrL1a7KmqfjaFz+F+MoSXvbqP0J3Tz9e9Lu/j3QqiXAkihtveXnV71BKMTabxUhvuft3wCch6BMx14ZG4fyyaitx5Rjt92NsJrtmChe/L1DTgOdkwTQNCA0k060Y8FBKmcy1QTG545yLEQyF8cP//Qb+418/gNu+/01H29/zm29Az8zhRb/7B4VRIFqljlqHM2YyRCkNU0rDAHwAXgngP1d9z9bhGPxa7ZThSRSKyVCIFZORSAyf/dIP4fNVd7XWmUnYmr7YwVtH5mqaFA8fYItow6R1mb9Sx7GLLrkKiuKB7JCZLF1oZ/MmJJHg+LEj6O4ZqOlmGIvFAAALS0vI5E1WTDqoJhcXWCZcV09fIdfSOTOZzuoIFmYfHTOTWc5MVi9ytm8MYimhtdX2fX45j7seXrT+vVoLAt2g+OS3j+BAi3Ko5aQGryLAq4iuHKFFgTiW1+smRW+nF1ecF8PV53fgmgs7EQvJhaZT442cvzWMEzNZaw65EX65dw4CIXjnazdZqg07B9Gh4VFMjB9z9keso2Uk07rl9xwOSNZsWivI57IQJI+tZFAURQwObcTE2FF4ajCTyYyOZEbHQJcHOzeFyq6xLRnwFGYmMzkDl195HQxDx+77ysVZlFKksjqUOgZkpwtMPjPZIjPJrW1pG7rR3BToL//6/Xje828BALzvI/+B//nxPvzPj/fhZa96Y9XvLCU0ZHIGhnur73s9MY8V+dUK5ldUdMdqF5Mb+33IayZmFtdG4cKYybWT1TqBaZiNcya9XObq/jqSz2VBKYXPH6z7OkmScNmu5+OhfezcnSi4ADfC8vwYQoNX4T3v/ScA7FbpWYPEhdMNrq58lOFHAG5end1ZRys44ZBFSabYxSYULD/57CQS68wkkMnpjoKoPYXYCzvMlnRBb768u+520ski8b/r6hsAAIrscVRMRoIy3vy7IzhnE+vK93V6MHbiGIZrmO8AQGcnYyaXFpeQzRksGsSB9HFxYQ5enx9+fxC5vAGfAwMeXuxmcoU5VJE4LiZrzUwCRQMEp2YwjaAbFB/5+iE8c7x4Y14tt9jphRweP5Roee6MM+g+j+iKIREE4jjAmlCW89ff6bVijUSRMe212KJSRHn2qY1MsBKZnIF7H13ClefFMNDtxb+9cyeGe7zI5qvfZ2hkE6YmTqyb8KwR4mkNl2yP4E0vGcYLr+jBSkpvKQyeUgo1nwURvTXNyYZGWMNAkQXkbQxwphcYq9HfWV08tGLAI4rsmE/nDJx34eXw+QJ44L7flL0mp5owTKzZHNpqgheTjdikRuD+C2YbZK7NmALxWI5KZhIAejqUsntyM9B0E8mMjs5wvWKSzfOvlcLFicx1reEoZ7IFAx7OxDaSuQLF9RRQdAGuB1XNI7k8DX90yHqMkGKu9zqKcCJzfUXJz6sIIZ8A0PqAxDraBj4DcNzh3ORKvJyZ5PD7qyUS7WAmf/R/38BfveWVLW3jZCKdNeB3LHO1v3EeK9xMPvrnZ+EPXzhk+xrr/UqYyV1XXg8AkBXFkcwVAK69sBOXbmcmA70dHkxPHsPg8Maar+/uZjOaS0tLyKsGlpPOnN4WF+bQ2dkDQghyqulK5qoZtBAiLSGedj4zKQrEVsIZKriXJtpgw55M6/j1b+exGNfwsmv6rOLfKZvmFnwGkMe5NItsjkmN7WSC9SAQpm7I5Y3GBQGB7bERDcmOTHiChfOIZ7fWw+xSHnnNxMVnRazHfB4RWRvzk6GRUei6hrkmrN/X4R7JjI5YWMF1F3dhY2E28REX8uVKqGoelFIIktea+67E0Mgoxk8chSSatszkfGEevaejmoHM55tnJgFmwpPNG5BlBZdcfg0e2ncX1JKRhkzOAHlOeLkCBjfgaZGZFKyZydY/F24K5GaOk7PlQz3VxWR/pxcLcbVhLnQ98Lnhes7ZA91eyCJxvDZrFX5/8NSTuRpGw+9NkiSIktSUAQ8vnhvJXIHiekoQBMxMj0PT6q+ppifHQKmJQLR0zUbXLKPzdIKT8volJT83A0gC+N3V3Kl1uAOX1jjtfj1ziC24to32lz1uz0ySlnOinn3qMTzy4O7WNnISkcoaDplJseaC+thUBrJIMNhdfWOrRDqVxA03vwwf+qcvYcPoVrZtj9dRNAjHthHWKAj5gPjKInp7B2u+trebuccuLi3B7xXRHbE3FKjE0uIcOrp6ADAXWCcGPHzwnxe7g91ex+ZR2byJQA1Lbu5emnBYmNbDJ/77ML59+ySGur145XV9OGukkN/VxoB2ADAMiu/dMYUnCkZJre57Jm/Aowh1O+V24MVkMscMdRpBtmEHIkFnzGTAy74nOwOTSvCol2iwWBx7PQJydszkMGPexx1Kl9bRPDTdRDZvIlJYRG8fDeGskQC+/vPxpuV8uSy7d3k8vppd/3POuwTZbBrx2Wdsi0nuZtxp00xR1Tw8TTKTADPh4c2kXVffgLnpcex/+oD1fLuvDScTRWby1JG5NrNPE3M5dEZkW7OZGy7pwmC3F/9661Fb2bwT1DKEK4UkEgz1OL/HtYpTkZk0TbMqJcAOHo8X+Zz7z8kNM9nTN4iP/stX8Wdvey9M08T05Fjd1/PRiY6eot8EpcTW2f9Mh5NP5MuU0j8u/LyZUvpRAFtXe8fW4RycDTs+3fhEzKkGjp6YAghBV3dP2XM+W2ay9VmxfD4HTVMdyTRPNegGRV4zHc1M1osGOT6dxUifD1KDDFFKKdLpJIZHNuMFv1Nkc2VFgaHrjjO7Bru9GOrxIuZl32dnV0/N1/b1MGZyeXkZ4YDsOOd0cWEWnV09oJQi75CZJITgC39zLt76yo0AgM1DfozPZZGzYZsqkc0bNecyLWYy3ZrMlVKKibksokEJ7/i9URBCLAOOdjOTz55I4cf3zeLex5YAMOlgK8jkDHgVwbVtOSEFToWSuk6ZAEBBbZlJViQ2Pm64kVXawWfJmdpIsPj3eBXRduHOA8vX5yZXH7zpwRfRkkjwRy8ahqZTHJlsbiGby7FiMhj013zNZVdcB0IIJg/tsW1cLMVV+L2ibd6tmm8+GgRgJjz8mOXsxi9/8Uvr+URKs22ynI4wuQFPq8ykFQ3S8i5Z8nU3cSWJtF7WiCpFNCTjVdf1I6+ZVe7QjrfPi8kGmb5Bv7RmzQafP4Bs9tQyjTNNw2Ex6WuOmcyyURQnzCQA3HDzy3DRpVcBaCx1PXCQZX3vumg7AM6y298Dz3Q4ufp91uFj6zhJ4DfWuaU89AbswJNHksinFxEKd0CSyy+0Pp/fOjE5BNK6zJV3m1Ils4CnCziD4sTN1asILLja5vOamMtZwcn1kM2mYZomgqFQ2eOKzBZCTgtyQSB4z+s2ozfMbiwdXb01XxvrYDOTiZVlR9vmWFyYQ0dnD/KqCQo4MuAB2M1VKnT2tgwGQClwfKpxI4QVrPbv4VVEKJLz+ctaSGUMGCbw4qt7MdDFmAzLgKPNC4Inj5SfD60yk9nC3KoThrgUQsGAhwLo7/AgWYc1JIBtV5a9Z+PrBD+PUg5krry4jpQsCH0e+/idru4+eDxex6YK62ge/Bwrlfdx+XqzUu1clp3/oWDtBWEk2oEd516E8YMPQNNpVZNzKaHVnLdU1RxkpTWZK28mDQ5vxMDQJvzqV7dbzyfSxnPCyRVobj7RDlxB0o6ZyWZMgbJ5o24EBs8hbbaYTHKZawNnT0USoDlQbbQDfn8A2Sxr6IydOIJkonnpebtgmqYjRtnj9bYkc61kJk9MZ2oSIYNDPLe2/v3i8ScPQJACuPZypnwxTECWBUfRaWcaal4tCCFXEELeDaCbEPKukp8PAFi3MjqFoGomwn4JJgVmGziULa6oMPJLVawkUEPmKrRuwMMvEKWzgKcLeDfakcxVFmBSxmaWIpHWkMzoZSHaNd8vxT6jQKA8uFtWmHRRzTtndwNeEUsLLKOvHjMpSRJEOYBkwnkxqWkqEvFldHb2WIYoviYczjYPMSbisANGI6vWjx8JBSTrBt8seLxGqVS0NBqgnXjySBJyCQucSOlIpnUYRnPNG85MSi4lOIIAmJRCFIDhPl/N9zdNClEktsw1+15Iw/koNzLXlZQOf4WZkM8j2Hb5BUHAyMYtVlzNOlYHqmbiwUJOaGkx6feKkETStDIgV2g4BgINsuKuvB7TJ56AoSYsWSvHYkK1lXjrug7DMOBpoZgM+cuvLRdeejUefmiv9e9MXrdlK1TNxC/3zDV9Tp8MGDovJluLPyBtjAZpxhQok6tvCtcT80ASSfPFpAOZKwDIMoGqr8337/MHkMtmYBgG3vonL8WXP/+JNXnfejCMxjmTQPMy13SaESA+X9EDZGIui3/4rwN4/LA9gdHR2Y1gKNzwfjF24jh8kQFr7tYwKLzyevljh3pnpgIgCEACECr5SQB41erv2jqcQtVMbOhnB/vUQv0LYzytw8gtobu7mqny+4NVEgk2M9m6zBUoFkqnE7hRiBPpIJ/1qZS6Ts2zAtBJMZlKsYtfJTPJu+qai7lJn0cEVVmB2FmHmQQA2RdGKrXieNvLi/OF7fZYhihOmclShAMyuiIKTjiQaOcbxI+EA3LLBjyLhQVqKcOxGsxkMq3jxEwWt1zRg63DAZy7OYRM3sBf/et+/HzPnOvtUUqtuVW3hIIgsM825JcQDcpQZKGqIQKwJkmtG6koEsgyaZg3qcgEkkgcSYbjKQ3hYPl5x5lJu47zxZc9D48/steav1tH+/GbB+dx272zkEWC7lixOCOEIBKUsNIkM8nvO4FAbZkrAOy66gZQaiIz9wgeP1S+UKzFTKoqu/+0InONhWSspHTruOvs7EY6lQSljCHN5AzbEYbv/noK37p9Eo8cPPkMkVO0a2aStDMahJsCuShwMw2YSVEkGOjyYKIFmSshjVVLsiRAqzM60E5wdi6Xy2BleRFHDzvPH18tsJxJJ8ykD7kmmMlEnK1xIpGo9Rh38l1O2De3CCG4+LJr8OCeu+uub1OJBYSixUa8YVJ4niMKhHaj5qdCKb2HUvpBALsopR8s+fk0pfTQGu7jOupANygME9hQcNSbanBhTKQ1mOoyOjptmEnbnMk2MJOFblM6dfrJXPmit95NiYOzZpUyPN75bIWZVDgz6dDRlWOxwEx2dNaPI/H6IsimnX8/i4uF7Xb1WoYobrINSxEJSo6iIrL5+nOZYX/rzORinH2+HZEiw+FfhZlJHvK+YzSI979pGy7dHgUAqDrFU004DKoauw54FZHlRrqAABZpEw3JEASCgW6vZS7BQSnFUlLDcF/tY9griw3nqwkhCPhEZzLXVPXME5c527km77rqeqhqHo8+/EDDba+jOezZv4KN/T7859+cW5UHGQnIlmmSW+T5zGQDZnL7ORchFI7CXHoYjx0qFmiabiKR1svOWw6u5mg2GgQAokEJhkmt61QwGAClFMlUmkkYCWylbzzu53RKDGn3zGRbokEMPjPpQuaaM6xGYC0MdHsbrplqIZnWEfRJDc3qmMx1bZhJf4GdSyXj0DT1lJghZ26uTg14mikmVwAA4UjMemx6gZ3z9dYVV1x1PWamJ3D82MGar8mlFxGKdFn/NgwKTxNN8zMBTj6VDCHkk4SQnxNC7uQ/q75n63AEbpYRDsjoCMtW1lYtxFMa9OySrezRTuZK2jEzeRozk3zRateNopTiS//5CUyOHy97TeVCd3IhB68i1JznKQX/jPzBipnJwkJIc2litLQwh3A4CqWBxCsYjmJ5+in84H++7mi7iyXy2WSGLSDdzupxlM4j1UNONWrOTAJM5trq3OFSQoMowHKqBFgUhigQ23zDZsFdL/sKMQalJjOHJ9KuTa84axrwSa7nOQQR0E3TcsHs7/RWNUQyOQM9MQWbB2sv9mWZ2DKalQh4JUfRIPGUVva5ALDMVeykrhdcfCU8Hi/27r6j4bbX4R4zi3kcm8rgynNjtvLBSLD58y+VYsVkOFS/mJQkCZdd8XxkZh/E/iMJaxZt2UZRwKHmW2cmeT7qSpL9ff5CQPr8Qpxd7ysO+0zOwJ0PLViNxHZeO1YbxZnJVpnJwrW6LdEg7gpc3aBQdWqNKNTCYLcP8yuqo1ijSiQzRkOJK8DuH06crtsBzkwuLy4AAGanJ0668aGTnEmgUEzm3ctck4kVKIoHHm/Rk4Kvg+vdZy6/imVOfvR9b8f7/uZPse+B8rJG1w1o2SXEOoprZd2k8K4zk7Zw8ql8G8CzAEYBfBDAcQAPruI+rcMFeDGpyAR9nR7c/8Qy/vP7x2u+fnEpDtNQrUiHUvj9AeTzubLgb0EgbWAmT9+ZSf752tnVL8zP4Cuf/2f86etvBlBkTSqdSafmcxjo8jpa5HP2NhgsZybD4SgAYHl5wdX+l8Z31MMlV7wA1NTxxf/4uKPtzkxPAAC6e/rx4DNxeGQBmwbqS9RqIeC4mGzMTCYyekuy7KWEilioPBqFEOK44HWKmaU8JJGgs8CkhAPFRXBONXHHQwuuWFY+z+nEKKoSokAQ8EqIFvL9OiMy+ro8WC5hmfKaic6IUvcY9siCoyI44BORzjmJBtERCVSYhBUaFnbxIF6vDxdechX23r/e61wNPPTsCgDgsh0x2+cjQRkrTTKTiSSbewo1KCYB4IKLrkA6MY9sahFjs1mYJsWiNetcXUzy0YFAIFj1nFNwFpb/fXzRPjsfh6rRqnrptntn8NWfjlv/Xq2M2tVAsXBrkwFPW6NBnO0Tvx76GzQ4o4VmlRNlTCWSGb1uxiSHIq+tAQ8ALC2xMRQn8RerDdMwHTUBFMXjyhOCIxFfRjgSK7s3TVnMZO3zrn9gGDe/6NVIJFaw+55f4Uv/UT5fOj07D1CjjHgxDNq0Auu5DidnZiel9CsAtIL09U8AXL/K+7UOGxweT1cxj7zjpciCJZV74MnlmgvqpYI8sbOzeobO0tuXzBwJBG1gJk9fN1fesbRz6ssUBr+XCxfuypnJB55YwqHxNMbnso4krkCx4A5UMJM8+oCzoE6xuDjXcF4SAP7wjW9B7KzXYmV53lHRPzF2FB6vD6FoD/buX8ZlO6O2lvxO4KRQo5QiVycaBGDMpKbTprrMHItxDR2R6gVpLeOXZjG7mEdvh8cqWiMVi5Jv/HwC3/jFuN2v2oIzk04ibCpBCMry2AghOGdTCIZBLXdow6QNLfA9sgDDQTEZ9IkNmUlVM5HJG1XMJDd5ytaIktl11fU4cfwQpiZONNyPdbjDE4cTGOrxoitqn2MaKRhgNRMlFU8wRUwk1LjgG97AnBW19CR+cPcM/uwTT1gz13Yy16KKos/1fnFwufVysryYnJxdQV4zUNpjSWd13PHQArqjimXcdjoVk0ah+Gtd5lqYmWwDM+l2ZpJfDxsxk9ZoShPX9kRaR8jvoEiSBBgm1sSEyWIml4pNZ7drhnbDpCaIg8aELCvQdffNqERiGaFwpPh+JsXMImcm6zcJPviJ/8L3fvJb/P4b/gJP738E8XjRhHBsfBoA0NtbvG6YoDVzcM90OPlU+Lc7TQh5ESHkQgAdq7hP67ABpRQf+MpB/H+fe6bscbVEhnnTZd147U0DAKrn9vg2lpdqu3vyC1EmU4wHYcxkm2Sua8RMfu2n4/hlEyYmdqgnc02VzIBOjh8vKyYppfj6zyfw9Z+NI57SMdLXOBYEKJ2ZLC8m+wdHIAiC6+gDFt9Rf14SAPq7vPBFhgDA0UJ8YvwYhoZHcXAsjZxq4spz7dkKJ3BSTGo6hUlRV+bKu8SJjI655Tze8en9rkPUlxIqOkLVxeRqMJNc4grAMpo5Z1MIl+2IAgCeOJR0fO7xTnwo4H4BKIkCRnrLWeWAT8LO0RCWLUMV0tCt12kxGfA2Lib57F3lzKTPkrnaNwx2FaRLex9YZyfbiZxq4OBYGudtDtd8TSQow6TNsTypVKGYDDcuJjt7WYC4kJ/B44cSyKkmHno2DkKAbptCd9GBo3UjFGWu7LjkDND03DLSWaNsTvmOhxaQU0389e+N4gt/ey5kibQ9Vmg1wZnJVmWuvMKm7ZyZdFjgWl4HDRqcvDmZbaIBmcrojmWuANZE6losJuetxxrFX6w2TIdurpIkNVdMxlfK5iWXEprlnutkNh9g9w3TNPHgnrutxyanWDHZ318sJgkIpPWMSVs4KSY/QgiJAHg3gPcA+DKAd67qXq2jCvM1Ij94saMU4gB45pGdRC6nmsinFwHYG7LwOZDSuUnGTLaw4yiRua7BzOSRyTTueGgB37p9si3bq1dMlv49D+6929LS5zUTyYyOTM7AiRnWMR9xkDEJFJ3JKplJWVbQ1z/cMGSXQ9d1/N5LL8fE2FFHzKQgEGwodPzHHRSsE2NHMTQ8as0Q9Xa0luGmGdRqjNiBS4frSUz4QnJ6IYexmSyWEhoOjqdqvt4O6az9HIzf075i0jQp5pby6O0sfmZeRcSH3nwW/vq1o3jHa0bxtldtRCZv4PCEsxB4vlhtlHlmh94OD0YHqyXK/d1eABSUUlDQhjOxHkVwJIn3+6SGMle+CAhWdP69lszV/rvYMLoVfQPD+OcPvxtXXdCNf/zbP2u8Q+toiGeOp6AbFOduCdV8DW/mNJM1mUqz4zwWrV9MrqQ0+MN9EEUJXjprPX5oPIXOsGKbgbq4yF7n5DpYC4oswO8VrWKSL9pXEinMLavW+6qaidv3zuPczSFs6PdbEvl2xwqtJixJaavM5KpEgzjbJ64iaWSc52uSmTRNimTWWTHJVU1rIXW1ZK6LxWLS6ZphtcDcXBuXGpKsQNeaKSaXEQ4Xi0nu5BoOSI4iqABm7BUOR8vm7aenZwAAQ4P91mME1Na1eR0NiklCiAhgK6U0TindTym9jlJ6MaX0x042Tgj5KiFkjhCyv+SxCwghewkhjxFCHiKEXNbi33BG4OA4u9lWnpOaxi7U/IIVKmFnKpFI66AGK+z8gepFAb9BlhWTbWEmCzLXNXBz/cl9bOHQ10JxU4q8yizf7bL1Sv+e+fkZeErcXCsZseFee5nriZlMmWnJ44/uw5ZtO227woPDGzEx4ezGsP+Jh3Di2CH8zktfi1f//psd/c7WrZsBNL75mKaJqYkTGBoZLQaYN1HEcDhxS7UcY+sUNKMDfhAARycz1n7NLjlnJimlNe3kO8Iy5lfcOenWwlJCg2bQqmN006DfKpbP2xKGKKAq/qAWMhYz2djkySk8soBoSEYmZ0AWhYah7LIkOJLEB30isnmzruyL/z2VRi/886klOSaE4P996LP4oze/GxdcfCXu/NVtZUqLdTQH7nhZz4CJs8jNOLqm0xkAAsKh2k03Sik0g6K/O4C+/mEgP209Z5hAT4e9/HZpYQ4ej7eqQecW0ZKZUJ+PfQ6mmkUipVkM2MPPxhFP63jxVcXC1e8Rrbzi0wGG4a5wq4XizGQ7ZK4FttRhgcuvD/VyJtnzzTGTOdUEpc5m1IuNhrWQubJmTKnM1a2aqd1wmjPZPDO5jHBJLMixqQwIAXZsDDpmJiVJwqVXPB97d99pNT/m59hacmRowHodBYHsNnvrDEHdT4VSagD4/Ra2/3UAt1Q89s8APkgpvQDAPxb+vY46+M2D8/jmz5nhSbRCgmcxk7I9Mzk2k8X37pgCpRTxlAZqsAUxj5oohd+SubaPmdQ1zboRrDYzqeumFVLrRG7nBKpGay6iMyV/TzqVLJG5GpgpKWKiQanMYKUUlLLXA0wG/Pgjey2pXiWGRjY57jLu230HRFHEO//24xgc3ujod4b7OyB6ojh+7Ejd183PTUFV8xga3oREhoV1tzJH4KiYVBvHj/g8Iga6vTgymbFcJWddyFz5AsFuzmakz4/lpNZ09EEp4oVg98pohVL4vSKiIbkqmL0W+OIpFmy+qLfDQJcX83EVsVBjl1hJJI5Mpng3f2659neTqcEsWAY8dRZ/l1x+Df78Hf+AP/6zd0PXNTzy4O6G+7SO+kjnDAikvmMznx9zuoAr2346DSJ56s78prMGuqMKvB4R/UMboaemcNmOKHpi7F7WG7NvIC4uMBOy0uKmGZYoFpKqZiZh5tAZUaxmI29ebR0uFt1+r9jWeevVBpeUSq0Wk3xOro3MpCi5lLk2mpn0NMdMOi1WAUApyCLXkpnkMtf+wZGTHg9imqajxoQsK9CaYibLZa5HpzIY7PaiI6I4ZiYB4IqrbsDC/AwOH3waAPObIKIHnR1FaT8FtWTL6yiHkxXgbkLI5wghzyOEXMR/nGycUnovgKXKhwHwbycCYMr57p55yOYNfP1nE9biqtLFsNTNFahmJn+5dw4/vm8Wt++bx4e+egjUZCerLFffeC1mMts+ZrLU6nl+dgr33vULPPv0401vrx6OTmWg6RSxApvSDuQ1o2ZILZ8B9Xh9SKeTZdEgM4t5CITNhw3XkLiaJhvm5vr+h397P3Rdw66r7P2thkZGkYgvY/e9v2ooHdq7+07sPO+SssH0Rujt8EAODuKJxx/Ggw8/UfN1vKDlzGTI7z6OohROTCq4zNXXoGjdNOjH0akiMznjgpmsN2fDc1y5hKYVcMlbI3MIn8f5IjSTM0EABFtgiO3QFVWwsd+Pc+rMynGwBXXja8XFZ0cgCmy2rBYsmZqnspisz0yW4rwLL4fPF8BPfvAtjB0/3PD166iNdJYx9nXdfAuNHrsM0EbIZjMQRI91LbBDRjUw3OOFAGBgaBTzsyfw9ldvxHAPOzdrSe0XF2bRWZKrvJTUsBh3v2iNhWQrgsSu8QowGW7QJ5Y1INs9b73a4O6rbWMm2zEzqbuT3vKZ6oYzk0rj5pQdePHpxNnTYibXcGZyqcBMbt66A1OTJ8oc+tcaLGfSCTMpu2YmVTWPbDZtFZOUUhybymB0wI+gT4Sq1x+fKcVlV14HAPj+d7+Mn/z4Nowf3Q/F31HMSzUpJEFYN+CpASefygUAdgL4EIBPFX7+pYX3/GsAnySEjBe28/d2LyKE/FlBBvsQgC6715wJePYEk2hdeW4M528NI5Mzyoo7foHy2DCTlBYD0H90D9N/E8pOVrvMLW76kihxtBIIaYmZzJWE0D768AP4m3e8Dm9+/c2rkn3EP6uLzopUfU7NIq+aNS8e3J22u6cf6WTC6lh9745p/Pi+WXTHPHjrqzbitTcO2P6+aVJ4ZBFawanusYcfgKJ4MTB6ge0FcOu2cwAA737ra/HIg/fX3OdcNoNnn34Ml15+jfM/FGwxpkQ2Y+L4M/jrt/wu8nn7CzufqRwa2YRk2tncSD34vez360nB+M2+0YV804AfibSOo1PMkXhmMe94ZqfenA03UOIzsK2AL3QazSD6PaJj445M3oBHEeCR22tbHg7IuHxnDAEHLrGyKAAOegodYQWX74zh7kcWsZSwlw7XKrhlicCrCNasbj0oige7rr4e99z5M7zjz17RltmtMxXpnN7wGOANzWbclLPZLATJB8lm5rEIgmhIgSASDI1sRiqZwOzMpDV73FOjmFxaKDpa67oJQgCPR7Ccip2iM6JgOanBMGjJSEi5hHo5qVWph3xtnLdeC7jNdKwFPjPpJBqEUopnj6dqnqNuZyYzLhp2gLPmVCm4LLbe2AUHPy80ffWvP7KsQJQkLBVMp7Zs3QlD1zFbiPI6GXCaMynJsuuZyWRiBQCsYnIpoSGR1rFpwG9dr5xkGgNAT+8Atu+8ED/6v2/go//wx1gYfxjhjhHr+ZxqIupAoXOmouGZUJiTrPxpJRrkLwC8k1I6DGbk85Ua7/tFSukllNJLALgL13sO4amjScgSwZ++dAQ7R0OgKO+i8aKDd788igBZIkhkdMwu5bFY6KSmsgYu3BbGS65iRrx2IfZ9A8MghJS5eQpCazMP3Mm1FJqmlg2ItwsHTqQx2O1Fb4cHFO5vEHbIa2ZdZtLj9SESjSGdToIQghsv7bJYLN0wcd6WMDb02+cvGiYgiswhDADGTxxF/9BGnLOlE0tJrerGeumua/GdH+6GLCt44L5f19znxYU5UErRP7jB1d/a2+FB93l/ga7z/hJGPo477tlj+7qJsWOQZQU9vQOOs7bqwdnMpDNZEf/sj0ywYjKnmo6D1OtJo0J+CZ1hGWNtKSadzvOIjsPOlxMqQn4JinLybnSiSJwQkwCAl13DHPI+fetR28ZJxiq4yz8jQgj6u7yYWqi+rtjh/R/9PP7sbe/FzPQEjh151tnOraMKmZxRlzUEYDUynDIBpcjlspBk+7nyhRUVum5CFFgDRiTA+RdfDQDY98Cd6C8Uk7Xm5BdLsnYXExrO2hDAYLfXtRy3K6rApKxg9HrZNT1byUwmtSr5esDrvCl0KsDgktI1lLne/cgiPvL1Q3jo2bj9PrmcmczkDSgSaWiWIksEAmmemXQicy01Z1ptEEIQDsesCLgt23YAACZOoqMrNU1HBVgzzCQnPvjMJI/OG+rxIujjsnvnrOxnvvgDfON7d2PzLV/Ci99yK77+ze9az+VVE7Gw/Vz2OhwUk4SQXkLIVwghvyj8ewch5E0tvOcbAfyg8N//C2DdgKcOnjqaxLbhABRZsJUDqhVuo4QQFt6e1rG/wEpy5mjrcACGrkGUJNtAYo/Hi57egTKNvUBISx39UpkrAFxy+bUAmPSo3RibzRY6UuxzaofpQd1iMpVEMBhGMBi25kH/6EXD+Mc/2YYtQ3686Mr67oGGSSFLAghhn+/42FEMjYxiy3AAXRGlqrgihGDTlu244OIrsPf+O+w2CaAkS9SlFb4iC+iMBRDacBMAAb/+9e22r5ucOIaBoQ0QRdGSubYCq5iss+CyOsENmEkudWOfbfkcUyM0mrMZ6vVhYs5ZEVMPjotJr+DYBfL4NMsyFYSTV0yyhZuz9x/o9uIvXrEBx6ez+IVNjE82V3sxONjlweS8s+/B6/PjRb/7WgDAnt21z5l11AeXudYDP9+akbnmcxnbYtI0mSnWSkpH0MdYAUIIRjaehZ7eAezdfQeuPLcDf/mKDbYmZ7qmYWV5EZ1dPUhndUSCEjb2BdDb4XEtO+wsZFguxFUIggCfL1Alc11OatVxNqebm6vFTLYm53Mjc330IFP51GoAW9EgTt1cc0ZDVpLvo9cjVo0PNdw+b3Y5kDxyp/21YCYBVlhxNnjLtp0AgPGT6OjqfGZShqa5M7mLx1cAwHJzXSk4SUeDsmtmEgBC4Qg6+s4CCWzClbsuRmdHcUxIM82q3ON1FOHkavF1ALcD4Fq9g2BS1WYxBeDawn9fD+BQC9t6TiOe0jA+l8POTUx+ym/mpUPFlQY8AJubTKZ1PPRMHL0dHlx0Fpt32jocgKrmodjMS3Iwk5diF4sxk83/Dflc+aKPL+x49le7YJrMYCgWlq2i281FpBZUzaxpwJNKJRAIhhAIhMqcXT2KgA/86Vl4weX18x1Nk+LAk3vxyQ++Fd/++ucwOXEcmzdvASEEHWHZmqWsxK6rrseRw8/gI+97G+IrlSPJrBMPNGeF39vhgaiEEezZjkfu+19848v/WvWaiTGWMQnw4ObWLrBOZibzDgx4AGYHzgvOTQOMPXCaNdkomywalKxZzFbgdCHidGYyldExv6JiqNdXlne31nBrmX7x2VFcuj2C2+6dqfpcM/nai8GBbi+Wk5pj5UFv3xBGN5+F//3Ol/Dd//48AODgs0/iW1/7DABganIMX//Sp9dlsHWQzhkNnSsFgUCRSFPFpJrPQvZUF4M51UQsLCOeLspHicCaFpdfdT1+u+duiIKJK8/rsGU/lpeZqKmjswfpvIGtwwGIImu4Oux7WOgqFJOLcbbg9fkDZcwkvwdVylz9Hja75VZWe7LgVlJaC8TKmWx8XvHmUC2HZ7dznJm80XBeksOnCE3IXAszk46YybXLmQRQZkYzNLIJHq/vpDq6OndzlUEptVhoJ+DMZKTwN3ODvEiwuA5MumAmpxZyeKzgoD7UU349IkBDdcaZDCfFZBel9HsATACglOoAHH3bhJBbAewBcBYhZKLAaL4ZwKcIIY8D+BiA9SCwGnj6GJvH2DnKikk7xo3bTZc6TIX8EibmsnjqWBK7zoniwm0R9HV6sGkgADWfh2zj5MoxNDxaxkwSQhzZ/dcCZyZf9do/xQ03vwwXXXoVgCJ71i6kswZMCkQCkvU5tWNOpd7MZDqVRCAQQiAYasqp1jAp/u/W/8I9v/4BPvupf4Sm5rHj7K0A2Hdda/Fx/Qtehi3bduKnP/oOfv2LH1Q9zwt1uyzRRtg5GsI5m0K4+gWvh26Y+Py/fxgL8zPW85RSVkyOjELTTeRUs2WZqywRiAKp+33xm32jGRVCiMVObhr0QyDOTXg4M1qriAn6pKYC2SuRzRuQJdJgPsz5zOTxaSbpHerxntRiUhAYk+hGFn/dxV1QdWrJkziydRaD/V3sJu9U6goAr/mDtyCfy+Kzn/pHpJIJfPMr/4bPffoDmJocwx23/whf+MxHMD+77gVXC5lsY5krwEx4mpHzaWoWiqd6HCCTM9ARliEIBNECK8DU1BTnXXAZ0qkkZqbHa26XRyTI3ihAgWiI3fu8HhEeufY11g4WM1mICPL7y5nJZEaHYVa7NDtRXpxKMAoz/E4lpbVgFZMNutGGQS31SC01UTPRIE4kqAA7FrjBm1PkHM69A4C8hjmTABAORwGwUSZJktDbN3hSr20sZ9KZmysAV+zk4YNPgRCCvoFhAEA8rUORCHweAb0dHngVAXc+tOC4Ufg3n3sG3ygkJwz1FI0T+e83UmecyXBSTKYJIZ0oTMMQQnYBsBe2V4BS+vuU0n5KqUwpHaKUfoVSen8hq/J8SunllNKHW9j/5zSeOpaE3yNitMCw2M2WqTqTYZZ2ZUN+CYsJDZQCV54TwyXbo/iXt+9gzqFa3nZekmNoZBOWlxYsc5l2MZM33PwyfPRfvoqOgqveUpuZyZWSjpTF4K62zDWdRCAYRiAYtpxd3cA0mfyks7PTemz72dsAAIosAjXmDPoHhvHf/3cvBoc2Yu8Dd1Y9v7QwC0IIojH3vlUvu7YPf/eGLfidF78GA1d9AgCw74G7ittenEM2m8bQ8CaLTWrVQZQQwuaKGhjwiIIz9osXk9GgjO6YxzUzWWvRHPCJ0HTalMFI2fs47Jr7POz9Gi14j0+zhs3wSS4mAXaNctOB5wvvygiUTK62rHKQF5MOpa4A8PLX/BE+/ulvwDAM/HbPXfht4Zjet/tOq7u92OYG13MFlFKk63wfpVBk0tT5oWs5KEo5E6DpJkxQDHT5Ctd1dp0RRDaG19XN5m7rNSb5dxsMRRH0S2V/Q2dEdpUv6FEEhPwSFmowkytJ+8if4n37zGImASaVrbeQn1vK40NfPWj9u9Y9wO0+qRp17LrpVQTHs+kcvPh04uZqyVzXIGcSKDKTXi8rhsLhKBIFo5qTAcdurjI7bwwXc5N7d9+Bs3dcgGiMraHiKQ2RoAxCCPxeEa++vh9PHkniySON12d6BSteeq1QNYqgT4K0njFZE04+mXcB+DGAzYSQ3QC+CeDtq7pX6wAAPH0sie0bg9YcVMCmSFI1syr3htPzt+zqxmBPeSyFpqoNikkmX+TspNAyM8kWfB4v2ydZVhCJdrRd5srlDdGgjAB3B20Di1R/ZrIgcw2GkEmnXMkzAGbQM3b8KF73utchEmHa/PPPPRsAW5SROm4mhBDsuuoGPLzvvipn3MWFOURjXZCk5ou8Df1+KJFNCEW6sbdk1my8NBakYGwTbkMchd8r1v2+FuMqvEr9aAIOXkyGAhL6OjyuZiZFgdTMkeJy3lbZSaddc34za7TQGZvNojPMmignu5hkjLrz6wVfePOFOEe9z6inw8MYZxcZogBwznmXwB8I4qv/9S/W4mrv7jusgmM1TMGeC8hrJgyTOnL09chiUzJXXctB8ZTfq+aWVYT9MmJhGX0diqU4EQrLFi7jr/e98e/5rM392DoUKHuuK6ogp7k04YnI5TLXkhgtnkFp5+YKtEcpsxbgM5Oi2Pp1vZGyafeTyzg6mcGV58YgCrU/I0N3NzOZV42a4ymV8DXBTGbzrLnpJHOwnTJXSikefnalphwYKJrReHgxGYkhUZgtPBlgbq6NvwtJYueN06zJRHwF+x9/EJdfWfQDjaf0srnGay9kRaYTF/a5knXCrnOiZc/lVGPdfKcBnLi5PgI243glgLcA2EkprR1Ct462wDRpYQ6q2K21Yybtip0XX9WLr/7D+Xj9LUNV29XURjLXTQCAN73uJlx32TCmDu9ukZlkJ7GnZB6ms6sHi4vtNeCJ88ImKLXXgKeBzDUYDCEYZDOplc5+jbC4OINcLouzzz4bN954IzweD4aG2HemyEJDY8zLr7oe2WwaTzy6r3y7C3OuzXcq0R1V4POI6N98BX79ix/geRf14XkX9eGtf/ISAIVYkAIzGaohczVN5xlP3TGlprnNs8dT2PPkMnadE7N9vhLc1TESkNDbyYpJJzKXbN5EoE6WHg9Ub72YNB3Jo7ikt9E8z0pSQ0dEASGwwtNPFoI+yZXRRMAnQpaItRDnyOTMmkyYJBKEApJjl17r92QZl+56viWNuv6ml+LBffdgpTB3vBqmYGuNRFprSyRSKfjsuSOZq0yakrkaWs5a/AKMJQj6JVx7USc8soALtkWsokwQGDPJZfz1GpO8UbB5Qy+GKvJ+Q37JsfswR2dEwcJKMWsyUxINUjT/KL8e8sUtL0JPdejc7KZFAx4AIESoK3N97GAcmwb9+MtXbkRX1FPT58AtM5nXaM0mcCW8iuDagCdXaHY5aW4qbZS53vnQIv71u8dw/xPVXgkc3IzG62OKtnAkhkRiuebrVxumYTpiJrnM1Ymj6577f4OX3LgTpmmW5XKvFJhJDq9HhN8jWvmw9TBZGJv4wJ9uw1tfubHsOVU30RFeN9+ph4afDiHEC+AvAVwNdum9jxDyBUpp67aG66iJbN4ApcUFLMA6aATlxaSmmZYmn0MQCJQaDIXagJncsm0H3vGeD2NleRHf+cZ/YO7EI/BuOb/pv6PITBZv5B2dvavKTHoVAQJpvRNMKUW+jgEPk7kyZpL9O4FgqHG4O8f0+HEAwJYtW3DjjTfi9a9/vdV5ZdKY+jeqiy+7GpIkY+/uO3FJSabk4uJsy8WkIBD0dXpwsPtV6NkZxs27Oq0Ik87uXgwNj2LPndMgBOiN2R9PCwkV1KwdJl6KczaFcOuvp7AYV63ZJI67HllAyC/h92+yz+usxFkbghjo8mC414fZJRU51UQ8pVcxBpVI5/S6DoC8SZHKtHZcOWYmOaPRqJhMaRjs9sGkp0Ax6Rehu+g+EUJYGLwLZhIAQr7mzJD+/O3vxcbRrdi4aRuSiTju/PWPMXHiCID2m4KtNeaW8/i7/3gGr7yuHyO9Poz0+coWVs2ikctxKRRZcM1MUkph6jlr8QswxU3IX3y/0kU7Iex3YrEuEEKwVKcJwBmZ7u7OquecFhul6I4peOJwApSyrMnZmUnrOd5kqjQk29DngyIRPHM8hUt3RF2/51rDrdlNPdSTuSbSGo5OZvDy5zO5sr/OqIPbOc68ZjgvJj2iZajjFFnVdCRxBYrRIO1wc33o2RUA9ePauMzVZxWT0bLs8LWG05lJSWbnjRNm8lc//z4UWcGfve3vcd6Fl1uPx1M6zhoJlr22IyzXzDMuBR+bGOr22jYJgv7Wr6XPZTgptb8JIAngs4V//wGA/wbw6tXaqXUUWbVSBz1BIPB5xbLuXV4zLU2+E2hafWaSEII/eONbAQB33/FTJJfHobQhZ7KSmdz/+INNb9MOKykdcmHwmuvlW3Vz1Q0KSu0XHaZpFg14AqyYTCUT6O1zvv3pKSYZ3bJlC0ZHR7F161brOVkiIITCNGnNuIdAIITzL9qFvbvvwNve9QHr8aXFeWwc3eZ8R2pg20gQx6cHoZz9x3jjn51bNhupaibuengRF22LIBauvshquglRIDAdtv7P2xLGrb+ewpNHknj+ReULv8W4hr4ujyPnPADo6/Tgn9/G8rV4ITu9mGtYTGZzJvx1GEO+SGxVPp3LG+iONi6wnQZqryR17ByVQAAoDmRXqwmPLDqJlStD1KaYzOSMuuxt0N9cMTm6+Wz8xV+9DwBwx+0/AgCcOH4YwOlfTN527wxUneLnD8whntax65wo3vaq0Za3a3cvqgWPIri+7qqaCWrkrcUvwK4fPTH7+5QgEFACSJKEaKyrITMpKx6EQ4Gq5ySpsfqjEj0xD1SdYiWlw+cLlqlRsjkDhKBKySJLAraNBPH0cfdz9ScDxWiQ1otJJnO1by48fSwFCuD8LawBG/CKSOfsz+nVnJn0NclMNjKD45BElmXZas6kaVIcGmfHW73ClBeTpTLXdCoJXdOsucS1hGmaEIgTmaszZtI0TezdfSeueN5NeN0b32Y9rhsUqYxeFd8RC8tVM/l2mFrIoTMsV60z8qoJkHXznUZwcjacQyl9E6X0rsLPmwHsXO0dO9NRlBaVnxjMqKQkGkQ1HV/UACCfrx8NUoqh4VEkFsfRimqqKHMtMpNM5jrXViv+REpDJCBbHaWAT6p5Y3IKbiRhV0xms2lQShEMhS020q0Jz/TEcciyjOHh4arnCCHwecSqofBK7LrqBhw++BTu/PWPoWsaKKVYWpizjI7soOmmo4LoNTf045WFrjFnfjkeO5RAMqPjpsvsTX7iaQ1bhwMAnOWUDvV4EQvJePxQtbfXclJDR4NCsBY2DfjhVQT85P7ZhvtRz/QFKKoEki02KZzKXJ0Yd6iaiUzeQDjAzEWcyK5WE8wMzN3vVDKTpskUAfW+i5BfQrJFhpjP3PHFSz2G61TH+GwW9z22hK6IYkn+hTYdC7XuRXbwNMFMJlMZALSsmFR1s6Z8XiDE0mx0dvXULyYTywiForbqErkJFp8XuPPL+So3V3Ze25+DO0aDmJjLIZF2F8p+MmCYfGayDcUk1yTb4PBEGrJEsKG/aDBYm5nkM5POpIZuZia5m6ub9UhONeFzyEwCrKHQyszk2EwWf/LRx5ErrEnqjfDwmcmiAQ8rLpNJR76ZbYdbZlJv4OZ66MB+LC/Nl8lbASCZ1kGBKjWGG2ZyoLs6nmg5peGy7dGmlAxnEpx8Oo8UHFwBAISQywE8tHq7tA6gKJkJVnSDoyEZUwvFQeGsy4tao5nJUgyNsGKSS0yaQaUBDwDEOrqRy2bKzAtaxUrF4HXIL2Jm0dmsXC3wRZFdhzO+wl0CIxYz6TYeZHZ6DEPDIzWNcvweqWExefW1LwAAvPddf4Sf3vYdzM9OQVXz6O7tt329pptYSWnQTNpQBuxVRJy1gUlG+DwQx9hMFoQw9tIOpgn0dXoRLDigNgIhBJfvjOKRA4mywpVSiqWEast+OkEoIOFV1/fjicNJK2qnFjL5+sVkUebaWpOCsW5O3Fwbz0xy45qgX3Jshb+aUGT3C/RYSMZyQitkjFH8Yg8rDuo53ob8Ysuzqx0VUvDT1c2VUoqv/Wwcfq+Iv3vDFuu4aVdfgTflnDCTiiy4dnNdSbDz0ucrseIHah7P7O8qSO4bzN8nVpYRDEdtXaAFgUAWhbpmJpXoKUj655ZVhCNRpFMJi52s59LMr5NHJzOO3+tkoa3MJIglm63EkckMRvv91ncT8NVWExWjQRovWU2TQtWdz0z6PAIM050MNeuCmQTYedGKzHX/0SR0g+Kygkw6U6dRXnRz9Zf9O3kSHF1N04RhGJBcRIPoev3r+uOP7AUAXHLZ88oetxz9K5pQHWEF8ZRedy1lmhRTC3kMdJUXk5RSiALQXWOUZx1FODkbLgbwACHkOCHkOFhu5KWEkCcJIetGPKsEqxtccQO/+KwIjk5lLIfKXN5wLOcAAFVTodiEQ9thaHgTNDUDNdu83j6fy4IQUjan6Q+wG6tbw5p6iKc0REs6Us+7oBPHp7NWAG0zsIpJm5vS1MRxAMDg0EZrZjKVcvdeicQyurtrx3eE/PaZbemsbhWCo5vPxv/9/GH09Q9hz32/wW/33A0AuOiSq223mcubGOzx4aJtEUcyQf6Z8gJPNyhOzGQwOZ9Fb4fHtvubzRsIB2WE/BJCAckxU3HdxV0wTIov/3gMjxe+t3TWgKZTdLTgpHb5TnYznV6sPeb96IE4FlbUusWkIgvwyO5lfKWglLJ5QAeSGScyV34DDfpFxyHdqwl+PLgxgYmFZOQ1E9m8iaePJ3Hrr1kmWr3PKOSXkMroLZnNVM4Vn64y14UVFQfH0njp83rR1+nBv/7VToz0etvmHupmZtIjC67lfMkkuw8EAkUpKkGd2IVCziTA2OV6MVPx+DIikVhNxt4jCzBcHENdUQUEwOxSHudfdAUMw8AjD+0GwOd87e/FnWF+HW3dYXy1UZSUtsGAR7BXpugGxfHpDDYNFtnoevFQbuY4OQPo3IDH2ThBKTgL7RSy1JwxFcexqQw6wzLe8ZpRxEJyXbWKVUz6uMw1CgAnZW6SK9N8/mqZeSWKbq71WcT4yiIIIejsLp8pKvXNKEVHWAZFtWN4KZYSGvKaWcVMGiaLaTvZip/TAU7OtlsAjII5ul5b+O9bALwYwEtWb9fObNSSFnFHy7372YUhpzqTzHFoah6KC2YSAHKJCcfbr0Q+n4PiKR9o9hcuLJk2FpOJjF4mi7r2wk50RxXcvrd5u3/eYa8smCilGDtxFAAwODyKQMHN1S0zmUoso7Ojo+bzHVEFqg0rnMmbSJUUNEPDo7ji6hvx4L57cN89t6Oruw9btu2w3WZOYyHgXVEFoth40c/nDDkz+f27pvH/vnAATx9LYdBGEgIwCc5IIZImGpIc30QHu704Z1MIjx5M4Nu3s2NuuUZ2mxvw6JJa7p+UUvznD44jFpJxy676xkVBn4hkC4yYqlGYtDbrUgpHxSRnJn0S/A6Yo9UGIQRej+hqgc6/2zseWiiL+6i3GAz6JZjU3QKwEn5/0DJ98Xi8bc++XStwWWt/oavO8hQl19l5tWAFtDtoWjZjwJOwikl/yaMESo33I4X/AUBHJ5O51lKgxOMriESjNd/b4xEaqj9KIUsCOiIy5pfzuODiK+Dx+rDnfhadVM80ikvv4qeDzNWl2U09EEJAbWYmx2ez0HSKLSVxLX6vCM2wdwB3w5aqdRRFduiKsvWQ0wgpgEVFeF008RWpNWby2FSx8K4nBwZKi8lyZvJkxIPwNZ7Pb69gKoVcmOdsNDMZj68gFIpUybB5o6ZyZrIjzLOMaxepUwUn18EKZtI06Un3IThd4CQa5ASABIAIgE7+Qyk9UXhuHauAWtKirqiCjf0+PH2MFS7ZvOHYVQxgOZOy05nJERYTMvfE13Ds6AHH71GKfC5XZr5zeDyNvU+zblU7mclc3ixjZiSRYOtwAHPL7rLoSlHrprSU1HDo8GHIsoKe3gGLmfzEB9+JQwf2O9q2YVCkkivo7Kx2GeQI+qQafq4URCgvBHdddQMy6RTuueOn2HXV9XU6aYRtlxBHMlqfR4AsEcRTGhbjKn65dw4UrGAcqlFMmiZFsODEGPDKrqJl3vO6zbjp0i7ML6swTWrdADqalLkCzOE06BNrsgI5lbFiz7+4E8MV8QGVCPiklmSuvPhx0gBSZAGSSOouHHiRH/CJp4xBgCK5Y3su2BbB2RuC+J/fTOHuRxYhCgRveskwLtwWqfk73AypGRMeDkIIOguzxRs2bUMmk8K//fM/uM6LPdmwy3v1eYSWCu1S5DVmpiU5MHrzM+C9+wAA50JJREFUKKyYdDNekEoVislCk9E0KSSR1GwmCALKZiY1TcWnPv53tvK4ZGIFsVjthp1bZhJgUte5ZRUejxcXX3o19j1wJwBmwFOLTVdkAV5FOM2YyTa4uRJ7N9fJgnPmSMn11sqHtrneWTOTDrKTazWBa2G0MLN5dMq5BDnXDDPZ5MxkOqtjZimP0YHSYrL2ccSjyiplrvGTwEzyUSa/K2ayfjGZiC9bf1MpeKOmemaSNQvqmfBwJ9eB7vK1sWHSqrSEddij4adECPkwgCcAfAbApwo//7LK+3XGI5U1IEvE9oLYGWEacEqpawMe1QUzOTC4AUObL0V67lHcefttjt+jFPl8tqyY/MBXDuLJ4+ykbhczaRjMsKNygd4RlrEYbz53rZbM1TAoZiaPY2BoA3SDwO8P4rIrrocgCPjud77maNuqbiKVWEFHHWYyUAihr9x/AoKARyy76V56xbU457xLMLJxC170u39Qc5sU1MqL8/vEhtlXhBBEgzLiKQ33PbYEXafoKkR31GImBaEoUYwEJdCCK60TSCIzZNAMik/fehSf/DZjgFthJtl+yDXNL2rJY+wQ9ItlrLBbFItJZwsRv0esyzCtpDSIAvsu3bg6ryYUSXDVQPB7Rfz17zEVxImZLIZ6vLju4q66zAKPjWg1poVnFT7/hhcDAL7735/H5Pixlra51kjY5L02Yi/coF7WbiU8sgBK4YrtSxaKyVCQLThVzbSaUXYgKMZDXnjxlegbGMb/3folHD38TPW2E8t1r7E+xd3MJMBMeOZXWJNr11U3YPzEEUyMH2torBUJyqcHM6m7M7upB0Kq719AUTpd2iy3y9G29smamXTBTDosAmJhGbGQjGMOi0lKKbJqMzOTzRWTfM62vJisfW6LoogX/e7v49Jd1wIAIpyZPAlZk1mLmXRQTHJmssliciWlw+cRqtbMXVGWwTwxlwOlFB/9+iE89MxK2WsmF3II+kSEA+VrANOk8K4Xk47g5FN6DYDNlNLnU0qvK/xc3/C31tES0lmjLGOyFJGAhHhaZx1g1JktsYGq5iHXyZkshSRJePXbvgYQEaraHMOXy2WrLiREYt3IbKa+IYrj91DtF+idEQWGSV2Hm3NwqXEp45NTDQT9Emanj6OrdwNWUhoIIfiHf/o2tu88D9OTx53tc15DKpVALFZ9UeQQBIKOsFLFMJig6OvyQDeoNTPn9wfx5W//Ct/7yW9x4SVX1nhPA7IkWAvDoLexWyzACsKVlI4DYykM9Xhx9flscTbYU11MUkpBKbFMoXweEQNdXlcMUl8nOz5L511bLiYL54wdVmrIY+wQ8ksWE9QMeGHo9JztjimYmMvaPveje2bw4/tm4fOIEEn1TfRkQZbtF5D1EPRL6CvEuPDvv9HrgdaYSQDoKDi6Xnn1jfiXz90KwL0r88kGv76FS4tJj9hGZtJ5Zh8/Bt2Y8KTShWKyEN+h6iZCdTLdmHSSHV9n77wAH/qnLwIAlioMlFQ1j1w2g76e2uoPj8c9M8kbU6ZJLUfJvbvvrGvAAxSuQacTM+nA7KYRiCCA2nSW7O7ZvLC0cxp3M8eZc1lMAqxQc1pMHhhLgzocVeCQJQGq1lxT+/HDCcgFpRXAI1Tqn9vv+8h/4JrrXgiAmQQCJ2dmMpPh5lrOmclGMtdEYtmaAy1FPKXZ5ur6PCJG+/14+ngS6ayBZ46n8OSR8mt8LSfXdWbSOZx8SvsBRFd5P9ZRgXRWtxikSkSCMlIZ3Sp23Gj33TCTACAQgIiK5crqFrlc1rKo1gudOcEqJtvDTPIFul0xCQCL8ca20Hawm9dLZQ0M93owPXEcIxs3gQiFAtMn4qytWx0Xk8vLKwBQt2sOMAamctaCgKAn5sFFZ0egN5jDoJTNoOTyBjJ5AxduC1sS2KBfcjTHEQmynKZD42lsGwni5l3d+MNbBi2JkmlSUEqRzupYSmoI+kSIJe6JG/r8yLu4kfbbFBNOJHb1EA5ISNRYyCVcMJO9HR7Mr+StY9kt8pq7c3bLUADHpjK2Rf9vn14BALzgcsauyacIM2knHVxJag0bF3wmyEkxGfK1p5jkJjzhSKxpV+aTjWRahyKRsgaFr8BetCN+KeeSmQTgam4ynWbNkiIzScsku5UgBGUy/s5O1hCoNFCan18EAAwP1p6D9ioiDJefUdgvwTAZgza8YTMGBjdg3+47685MAgVm8jQoJg3DhCAIbTEdEQTBMksqRTZvQhSY/JODN0PsZhfNwhznasxMAqyYnF7IN2TzMzkDn/zWEfTEFOzaWbsRXAmPLFgFtBtQSvHIgTh2jIas89ut6kAURYRCkZozk8+eSOF/75zCbx6ct7w42gW+xnMic7XcXBsykytW3Ekp4im95j18x2gQRyYymFtma8H5FXaM6QZTTU3M5WyVVsyA59S4r57qcPIpfRzAo4SQ2wkhP+Y/q71jZzrSOaOmFXs4IIECltTGTYdMU1UoSvWQcTKj217sBIGACArUZovJbMYaBJ8o6NJ5Mbn3gTvxh6+6Brlsa3bptebQuIPeooPAWjuspDTIIrGYybmZSbz+pefinC3dyGYzuOKSnfDIIuJpHRv6fdi0eTPmpsehaxre9Ze/h//9zpdqbpvbdDcqJuU6Mz2yZH+jLkUmZ2B6MYd4WsPOTSH0dhS/e48iwnQQ2x0NyphayCGnmjhrQwAhv4Sbd/VYi42lpIbZZRXpvAHdoJZpT/F93GUP8sxEAHjVdf34w1sGnf9yrW3WkZhxZjJcI9euFIPdXhimO7OGUriVYG0dDkDVKcZny9lJ06SYXcrjll3deMXz+wFSvjA7mVDkaplrKqc3zDblZhycoawHzky2Gg/SVWAmw5FY067MJxuJtFYlz/J5RJgUTbMhpcirpuPjtZliMpth1/9wqMSMxYWZFJcqVxaT03MLAIDOztrXWEkUQGpMptcClxMnMjoIIdh19Q14aN+9UFW97txyJCidFjmTpmm0JRaEgdgzkwWvh9KCdaTXh66Igt1PVBc0emFmUnIgvXU7MwkAmwZ8oABOzNRfi8yv5JHXTLz2xgHLuMcJOiJs5MYtphbymFtWceFZYesxn8d9oygciSG+smT73M92z+K2e2fx9Z9N4PM/ONE2RQPgzoCHR6Q1cnOtLXPVaqqLdoyGYJgUDxbkrQuFtfN7P/8svnjbGNI5Axv7/FW/Z5oUnlOkSXuqw4ko/hsA/gnAkwDaYw+3joZIZ3V0Re0XVbz7whe0TlkOSik0Ta1iJpeSKjrCChbjGghIWUdPIIAgKsjnm5S5ZjNWFAjX/vNi8sG992B6cgxzs1MY2bilqe0DtefQODO51CQzuZLUEA3J1g3v3rt/iZWlefz5X7wV3V0d+OM3/gGOzok4PqUiFlKwecsWGIaOhx+8Hw/c92sQQvDqP3iz7badFpMeWaiemSSsyFck0rBIM0yKzoiCdM5Ab8UiXZGJo25SZ6S4UN02XH1ToBToiSroiCi2BZkiEYA4v/ERQtDf6cH0Yh4vfV4vBKH1IikSYO6WqmZWLTLiKQ0CKZq61APvXk7O5zDYU9+sxw5uFzq8wDo0nrZmZgDGmuc1E/2dheYApbZZeicDdsykR24c2n3elhC6IkrN7NJS+DwCRIE0LWHneOkr/xADQxsRDIUt44rTjZmsdLIGihmdGZfRUXbIa86ZSX5cu4lBSGf4zCQ7vtnoRu33EwQCUtIE8/kD8AeCWFooz5ucn2GO0CMjIzW3JYkEtWrJTM4+d5Zf45JpHegCtu+8AD/4n69Cz8zC59lQ870iAQmpLGu4nSrnqh0M06hyymwWgmBvwJNVqw1sBIHgeRd04Ef3zGAxrlr3b8CdKVCtht3MUg5dYcVW5bKxYMJzbCqD7RtDNbe9XGhMx1xGVXVHFdawzxvwumj+H59ma6bSfQp4WaOI+WU421ZP3yBmpsdtn0ukdWwa8OPmXd34/A9O4KmjSVyyPep4H+vBDTMpOciZNE0TycSKrcw1kdJtZa4ALInw44fiABgRk8kZmFrIYbrg5Lqhr/p+blAKRTl1z9VTCU6KyQyl9DOrvifrKEM6a2BDfy2ZK/va5ngx6fCCwjs+lTOTFATbRoLI5HQ8diiBnpLnOTPZisy1o7MHk/M5fO+OKYgCYBaKyblZlifXqpa/VjEZ8InwyAIWWigmSyWue++/A30DG/C5z34GYmF2YzmTwrQiIOSXsGXzZgDA97/7FQDAxFhtI49kknUJGzKTklC12KGUFfmyJIDS+hc63aAY7vKiIyzDUzGn55EFB7wkcPPlPeiOekBBbbuxBBRnbQwiVlJ4l0ISBaDBfla/ZzdSWaMthSRQXAAm0jr2H00indVx9fkdiARlrKR0hAOSo/fq7/SCoOhG6BZ5lxKszggzhzgymQbQbT3Orcz7uzwwDMoYllMkC0uSiA1jXmqbYo++Ti/+7Z07Hb0HIQTdMaVphpijq7sPt7z41QBgMZOnXTGZrl5E+bzs+MrmjJbnjfOqWVMlUwl+XLspJrkyhbMXFLRuMcmOpPJjvbOrF4sVM5N85GDLltqNSlki7IJaAS592zzoL5PsA9VRQ0PDzPVcS0/VNWUJF76jRFprKTeXQ9VMGCYz4VtOamXNplZgGu1jJgkhMG2iQbJ5ewObS7ZH8MN7ZnBgLIUrzy3eG4vRIM3NTPLjMZM3ELYpJiNBGZ0RGfc+toQ9+1fwN6/bXNWgAZqPquophN7Pr6gNHcNLwY+xaLDcXAtgf4vTtd/QyCh23/Mr2+dWUhq2bwzh8h1RfO2n43j8cKLtxaSbnEm9DjOZSiZAKa1iJlXNRCZvIFJDXeTziAgHJIzPsfumplMcHGPznBTsmjLUa28oKLZhdvhMgJNi8j5CyMcB/BiAdeemlD6yanu1DqSyRtXM5OT4cRw7egBdA2zB5ZaZ1AomOnLlzCRlN29CqqMoSGFmUlWbK8j4zOSv9s1DNyg+8Kdn4f/917MgRLBc41rNPyrOTJZ/DoQQdITlupbQ9bCc1KwLv6apeOi39+KFL32tVUgCQCggo6/TC1kSsLlQTN57188BAJMTx2EY1V1eSilSDplJ1sGu+FYIhUAIZImAFJxSaxVChkERDkgY6K6+gTllxzyKgCvOrT0fQgHIdYoZUSSQRALDoFULs1q48rz6n4tbcBng7ieW8L93TgNgN6CXP7+/5uC+HTyKgO6Y0nQxyWWHTj97QghGen1VMtfpBXYu93d5YZgUinxqFJIAu/lWL88bCbLdY6TXZ3Xu2wE+M3m6yVyTaR3DFSx5KTPZKvKaWaZOqAdPEwY82UKwudfnA6WsTKx3fhBS3ZboLORNlmJ68hhCoRC6u7tRC2xUoBomZVLbbN6wJNUcpTJXoJjHrCbHEJ87ApaeVg2+0I2n9LYUk1+6bQzHpzOYLmSzfusDF7a8TaAgc3VgdOMEpWZJpcjVmC/lTuEryXJ2yjBNx2yp3cxkMqujO+qpuxbYNODHg88w5urYdAbnbQlXvWYpoYEAVaMcjdBdaMLOLbsvJkWh3ASw1PW2o3oXbTE8vAlLi3NIp5PWdQ5gaxE2ayhBkgTs3BTCXQ8vYmwmiw+++SzH+1kLGRfFZGXOZCqZQCKxgoHBorKAO9JWFpNOHNm7o0qZkmX/0WLTsL/LY2+KR3FKqwhOJTi5YlwIYBeAj2E9GmRNYJo86qL84H7P238f73nb7+M/PvlXAIrFpJMwaQCWVFUpyZlkF3pm3iBL1fMjgkBaM+DJZuD1+zE+m8WGPh829PlYIeQpdlFbzT/izKRdl64n5sHYbLYpI4rlgswVAA4++yRy2Qyuvfa6std0RmTsKEhQ+gcGEI6wIijW0Q1d1zA7M1m1XcMEsmm2YG1UTNoVX4QyaRYhBD5PA0dWgppuZLIkwOcRXbEItd5EajCv13A/Vxmczf/RPTPoLFjBTxbYvXhKc2S+wzHY7bXmf92CG/C4cRoc7vVicj5f9vlNL+bgVQREgxILVj6FTAIk0Wa1DwJJFJo2LrLDSK8Pc8sqcm2a8ZFkGR6vb9XdXCfnczg03h7zMUopEhm9Sl7O7x3ZNsSDrPbMZC6bAYgAWVZgGBRej1iXZWcGPOUHWGdXDxYrZK5TE8ewefOWutvyKgIEm/gKSinL6rTxEbAyTgsL067uPiiKF0tPfx0f+/9+B9NT9nJCfo3h81qtIJXR8dunV6xCsp0wDNNRBIcTEGI/M5nNm7ZNcL9XhCIRrCTLiz5D1x1HldiNEhgGxWCPFxRM1cHveemsbkXDlDK7teTzy0kN4YDkusAoMpPuvq9EWkfIL5Udw4E6ESq1wBsek+PHyx7P5JjsmjdTX3VdPwgBjkxmmo5UK0U2k4IgCGXxcLVQmTP5rrf+Hl5xywVlsleuYqsuJhs7sndXqKp4MelVBEvmXAlC7Ndg66hGwztESRzIdevRIGuDnFrNtOmahrETRwAAjz20GxJymHEtc61mJjWdwu8VIIos07Ly8iEQAiLIUJudmcxl4PX4MDGXw1CPD4LADG1kpdidazX/qF5232U7ophZzLtevOXyBnKqaclLFuZmAABnbdtc9jpZEqxOtSgQfPYbd+Hr370T7//YfwIAJsaOVm3bMCiy6RUAQDQarbsfdjctCgpORPo9UoMijUCu02Xu6fC0ZeBebCAR9XoE6MbJG7nujioQBQLNoHjpNX0Y6fVhaj6HT996FMems47Mdzh6OzyYX1abalBwV1s3xeRQjw+GSXFoPGUtIKbmcxjo8oIQAsOkrra32hBFgqrDgVJ0R+WGlvZuwDv8XLrUDgQCIaSTq8tM/u1/PIMPfuVgW7aVU01oOq2emSwsONtxbrOZSXcy15wLZlLNZyFK7FjWjcbHMiGkSjbf0dWLpRJm0jQppiePY+vW+rP4hBBbV2tKa1/TWBNOsJjJ2SUVntAgTC0NaprYe/8dtr830udDOCDh7kcX6+6TE+x9arnqvG9Xo4Yxk+2bmbQrSnKqPTNJCEE0JFty0mb2qXJmUjcoZElAf6cXpmlaTsfLKQ15nWI5xYr7S86O4qwRxqBVvj/HckJDLOxeNh70i/AqguUm6hRJm0YRP7fdOAMPjTApduV6hBftvNEx3OvD617ADO/ace3IZtLw+QOORjAsN9cCM/nEo/sAAM/sL4ogi8VktOx37bJ2K8FHdLjB28RcDtGghL9/wxb83o0DNX6LQGrTqM1zHQ1XIISQXkLIVwghvyj8ewch5E2rv2tnLriraintPjMzAUPX8YIXvpLNPiafKsmsc7aQ5FLV0i6RqpmIFCSAisSmUUoXyYLAZa7NMpNZUMGDTN7AUCGXMOATIcolXcAWmclMrvbncPnOKLyKgJ8/MOeq08bzG/lsBJ/HGRzor/k7hAAdnT04e+cF2LT5bADAhE0AumFSpFMrCIfDloNZLbAFTfX8GZe1+hwUafVcPruiCvItLELYsUIbOon6PGKZKYth0LZ06J0iEpTxH+85B//+zp24/uJODHR7MDabwyMHmKxpQ79z6VFXVEFeM5HKuL/ZqpoJSSSuup08guWjXz+Mf/7WYVBKcXw6axkGmCagnEKOc5JAqkdkCcHoQMCaZ2oH+N8/Nmufw9kMAsHQqjKTbuSfTsCjUSrNo3yWzLX198urznMm+aK01mLcDrlcFpLM7g2GSetmNQIFZrLisY7ObiSTcUtBk81pmJ0eqzsvyREOSFXqDErZ7C+I/aI65C86s/7PbyZhevqs5/buti8mFVnATZd24fFDCUw1qWzg2P34MoZ6vHh9idN1OyTNgDsWsBGIIMBuVjqbN6vGUjhiIdm6/3KwOU6HCizNhCwW75GZnI7eDgWKLGCwy4uhbi80w4Sum7hwWwT89jnQ7cX7/mQbfB6hdjGZVJuaQSaEoDuqYH7ZLTNZ7dQ82O1FOCDhJ/fPOl7TDA5vBABMVDCTdhnLlqqhDdeOTDYNvwMnV6DUzZV99oNDGwGUn098JKoyGoQ3WQPeeswkKyK7ogq2FZoGHWEFm4cCZWZPlVhnJp3Bydn5dQC3A+Cl+0EAf71K+7MOFLu6pcUR7yi96GV/AI/Xh8zsg9ZzTgPQrZlJuXji5DXT6nwRwljD0i4tYyabc3PVNQ26riGvs+3zuR6/V7QcXYF2zEwaTK5k00HyekS88IoePPRsHF+6bczxNvnMhlVMLsyCEIK+vt6av1PqMdjV0w+Px1uTmUwnVxpKXAHGTFYrBov2E35fI/lofefAkL96TtYNTJNlQDbqPLKit7ifec1EKquvqfQ16JfQGVFACMFAV7Gh8u/v3IlbdtXOoqsEn+tpxtgpr1a7yTbCQFdRln54IoOFFZVZmRdkWcYpJnMVRbuwBYqOsIzOsOJKnlUPnREZPo+Aibn2FZPBYHhVDXi46UO7wGWsla6j/iakcHYwTQpVp44No7weEX6v6NhBO5MzkE5nIBXiqnSj8XsVWp5lj/G80KXFeQDA9NQ4DF13VExGAlKV0zClFF5ZwGXbo0hk9KpFeyQgWzJXSgE5wIq6kY3b8OC+e2pm5V1zIZunfOpY88fYzGIOhybSuPq8Dlx3cRfe8jI2U9aOxT/Ao0HKJaIf+/oh3PuYe0aVgMC0lbkaNdctMRtmUjcMR7EgQLX7cE4z0Vdwvb7o7CgGur0wDGYM19uhIBqUyxoGsZBcJbPVdRP//YsJjM3mmp537Y55rDg3p+Ay11J4PSJed/Mgjk5l8OjBoopC082aaplAIIRYRzcmxiuYST5rWFIgc2OkdjKTTmC5uRYMeLhh5J6yYtJe5pqxroO1rx3dMbb9SFDCq65jpIATtdT6zKQzOLlDdFFKv4dCLAilVAfQPq3SOqrAu9el0iLeUdq05WycveN8qHEmeZVdsBycmeQy1+WkBkki6I4VF6uVsskiM+m+mMzn2SIvo7P3G+TMpFcEKSsmW5e51sv3euV1/bh0ewTPHHe+kOMXWT5LsLQwh0i0Ez5v7a5kacdcEAQMDm+0dXTVTYojh57B6Ohow/2wXZhTAlIonP1esWYOJcB6wnZW6Bx8/qLZ+QjDYQ6TzyNCL3kPSikiIblh9mA95FXTmndxC15MDnR56nYl7cDlMm4XBkBhoeOy8JMkAX2d7ByVRIKjU8x0ZmOBTTXMU8u+XBRJmUGmaVKIAgEhBLFQNQvULAgh6IooTRts2SEQDK2qAc+TJaYP7fgcsnwkoqIA443IVotJt7moAMv3dZrt+7WfjiOfyyASZuyFblDLibYW7BpXPb2s1z07zeJAxk6w666TYjLgk6pyUU3K/AJ6OryIBOQqGWwoIFms8GJCQ6D/CvSOXoE/eONfIpNOYWryhO17dYRl+D1i0wZeAHD/48sgAK48jy2o29U44DAMZnZDKcU3fzGBz//wOJ4+nsIP755xvS27aBCz4EBbKx87alPMuZG5VjbsCIpu3oJALJVMV6GxGAvLZeeincz28EQGt+9jjYp6a416YMyku/GIRLpa5goAF2xlzjuzS+w40nUT7/j0U7jvcfssSYDNTVauR+yMa4rMZOvHUyaTtnLGG4EbLOm6BtM0rcbQ8SPFkQCuGuFxc9b78GKyjqqB37cjARk7RkN47Y0DeNNLascG8e9pvZh0Bid3iDQhpBMFrQIhZBeA+Kru1RmOosy1nJn0eH3o6u5DV3cf9BwrwDQXi2nOTGY1AYtxFZph4nkXdJZ1pXxesaxbIxACQVSgNmHAky1YvicyAjrCstVhC/gkELHIDLVeTNa+MXFEQ7IrGVA6y14b9LF9XlyYQ7Sjh0V11EAlMzo0vKmqEwgA87MzePaZJ/GCF7yg4X6wRTjKb0AE1kxaI1aagDUcaj5PCLwesbViso4dPocsCRBKymLTZAtgVW/ufXWDYjmlYSGhNiUdHCjkRe4crZ0pVgt8kL8ZmW5ecy4ZLMXH/vxsvOGFQ9ANikcPxCGQItNvUAqP3K6Q8dZROaJLabEACPgazfi6g93CsxUEAqFVYyYffHoFt+8tzvW1pfNfY15cEBj7/ujBeFOzvRxuo2wAoCOiYNEhM/nsiRQifhPRCFscmpTCp9RnoNihVHGt5TNhhbECft11Ukx6Fbv4paJDdtAnVjEYIb+EmcU8fnjPDOaX83jxC6/HbT/+meU8yRfC1ftOmIFXC3O+B8dTGB3wWwxZOxf/AGcmRSysqPjVvnns3b8CwD6HryFsDHjymsmyRGvcN6IhGTnVLPt73MSVlGYJ82O/9PzwKgK8imA1EStvj6XM5MxiDm/95JO446EF6/lNTUaw9MTYeARvQjSCppvIqaZtMen3stgz3khL5wwkMzqOTNR2tx4e2WQzM6lDkUiZ5LidMtdsJg2/zxkzSQiBLCvQNA3JxAp0XUMwFEY2m7a+R05qKBXxdpm8AUUidRvnXREFYb9kRYC8+OpebB6qvW+aThH0iW2LJ3uuw8kd4l1gsSCbCSG7AXwTwDtWda/OcNjKXMePYWh4FIQQdHb1IJNcqPXrNaFaMlcPTEox0OW1yWYUymWuhZzJZpjJXMHyfT5BLI06wNgwKpQUk20w4Kk1f8Hh9zCbd6cLq3SOXfB5vtriwixiHd11CzMAZYXf0MgoJidOVMl8Hv7t3QCAW265peF+EEIgSyLK1jKURYMAhc+yxu8aBoUs28t/S+GRhTLW0A0MB4YZAJsXMks+e8OkiIVkeD1CUx31lZSKHaNBbB0ONDUrFPJLeOsrN+Ilz6stW64Fv5eZKTRTTKpac5JURRasAnjPfjYvxbdDcGrNdVQ2QEoX5ooiwGxjSEgs1Hz0jx0CwfCqzUz+8J5pDHR58UcvGgLQHiYpx+fmbZppL76qBydmsnjsUPNMq6WScclMOvlOKKXMNdPIwevlC/TGztACAVDh5trXPwxRkizmZWriGHw+H/r7a8+4c9Q6H6US9Udl0+vynVF0hBV8/65ppLIGegoSOi63rXSWLcVgj7elmclURi9vAHvay0yaBmMBD4wx07oXXdmDSEBqqlgVBKFKksyPWV8dmStQPndrmNURW7VQqv7QC+7ApfdAQgh6YgrCweJ4T+ntj8tsKaW48+FFxNM69uxfRiwk44t/dx4u2R5xtB+V4AqweYcmPNxR1q6YZCqPIoPKv5u5OjOZQ8OjmJudstZlAFNgRYLl+dB8LZWzcTJ2CzcyV4A5uuq6Zp0/Q8ObQClFvrDPmqpCkuSq+dlMrr46DWDn+WfetRPPO99Z7FhObT2j90yCkzvEUwCuBXAlgLcA2Ang2dXcqTMddguEibGjGBpmssjOrl5k0kmYursbUrGr48XZG0PYOlx9kvs9UtmFVSjkTGrNFJNZdgHIahLOGinKEvxeESZhC2NRFNsyM9mImfR5RVDq3GUwlTUgS8RaaCwuzKGzu7dhYSaQosRvaGQT8rks/vrPX4Uvfu5j1mse+e1d6Ontw/nnn+9oXzxytSMev/Z7FAGyJNiyPYZJ4XXAWHkUoWm5qFMn0UiAzSty0wqTUvi8Ii7cFkGyCakrBWPm7FgDp7ji3FhT8y/cTKGZmUm1Yp7HDXoLC1bDBHZuKmdU6zn2rjUIYR1i/rVQAFLhMFQczNe6QSwkI57S2mJjDxQMeJIJPPv047jm4n7Mzky0ZbuZnIHx2Rwu2R5FZ+GYa8fiv8hMVn//V57XgYBXtEymmkEzzGRnREEyozeU8WZyBib3fhSTRx+GryCFI6B11R/sReUyaoCZd/T3D1uM5NTkcWzatNnRscZkbBVSTAoIhQZN0F99jTlvSxhvf81G69+8UOjsYs2pyszLUgx2e5HI6Hj/lw40lZOazBgI+qpzB9vFTBqmAVEQcWAsBb9HxO/dOIAN/b6mmCq7mUket1LPgAdAmeLA0J0Xk6WxI5pOq/K6AWDHprD1PoJQHmUUC8nQDYqVlI7dJbLR0QE//N76sTX1UJo16QS8mKycmeToCMtYTvBikn3GPC7ODpy9n5o4bj2WzlbnqLbVgCeTgt9FMSnLMjRNs84fbhzE8ypVNV/FSgLOiknAmb8DR14zEW3CufdMhZM7xB5KqU4pfYpSup9SqgHYs9o79lzB+GwWv3hgztV8jJ3MdWF+Bj19bMi/s5N1Pzd05rFj1JlTFsC6OgAbdGaOYNUnSsgvlTFIRWbSfSc1l2M3SiJ5cfaG4n4G/RJQYCb7+ofXRObqdykFSmcN6yZEKcXi4hy6uhqbtBChuNDhbmS/3XM3fvR/34RpmjAMA489eA9uuOEmxxc1WSbWXCRne3hRSwhBR1hG3qaLqOn2WV6V8Mj15y7rwTCdmXMIAsH20ZBVzHMH0lhILhTgzt9f100okgivRyzMFa89K9cVVbDgMjMMcJfZV4nS2c5Lt0et/z7VmEmAMeZawdSExSywv1mRBFRVAi0gGpJh0tq5cG4RCDA312997TNQ1Twe/u39bdnu0ck0KIBtw4HijFsbFv92zt8ckkjQFW1tprQZZpJHJywl6i+a4ykduYUn0T9yNt745ncCAChIwxklAlTlIQPFmTDdoJhxEAvCwd+v9BpES9Qfta4xwz0+izXizGQ4EoMoSZb7tx2GCgqDI5MZ7HtqxdE+liKV1csKAH8bF/8An08UcHAsja3DARbn5RGbOl6JQKrO93q50EDxOje9ULy+cumtEywlVKtJqBsm/DYOn35vka0UhHJDJ15kfvDLBxFP6zi/MJ846sLx2w7d1qy9s/sGl8PWiq2KlSgA+Ge6GFdrNoZ51mTp3GTGRtXVTtm0W2ZSlGQYerGY5AVwtqSYLI2243BaTLoDscac1tEYNe8QhJA+QsjFAHyEkAsJIRcVfp4PoDnR+BmIJw4n8Ln/O+5KEpevkLnmclmkkglLQtNR+P/XXR/Ae9+41fF2LZmrotS8YYcCEjrCimWMUspMup29yRVmJr1eHwa7i7LWy3dEISnsEBoaGUUysWLr+OYUjmSuvHvrkA3I5HTrQpJOJaGpefT19jX4rQIzWfjv4cKFEACWFudw+OBTeOapR5FMLOOmF9zsaD8AwCMVmUO7/LOOsFzFuFJKkcwYjiIvGDPpeHfK4FTmCgAhv2jJHykoJInZtyuyO2Y0q5rojPA4m5PDyHXHPJhdUl0zYnnNvZsrRykrvqVk1oMSnHJZWJ0Rpdg4oBRclaTINrkOLcBOEtcKgqEQTNPETCF4vtLooVkcGk+DANg8FGirYUqjeKhS9qIZ5JphJgsL+cV4/feNpzUYagLnXvg8nHfB5exBQgsRVbXBarzq847PqGeyGmYmjzual2TbI5BloWyUgF1n2X/XusYIAsE5BYUAD6UXBAEdHd1YqiNz3Tjgt6IY3M775gu5oqXMJL/3tU/maoIQZhLE1Us+r+j43lkKQRBg0vJ7kyVzrXHP7okp6AzLePJIUZ7NpbcN992kWEpo1v1B0ymC/vrHrlDR39q+MYRd50QRC8l46ys34k0vGUZXRMF5haKyWXg9IsJ+ybHMtVbsDweXuZomta4Dhoma88qDBWXbeImPQzZvVJnW8GtJ+4pJ59dQxkyqWFxk589wYZ85M6nVYCadqNOaQfsL1Ocu6pXdNwP4IwBDAD6F4hIgAeC9q7tbzx1s7GdF09xKHjvhzOwjV9EN5sP8vJh0IqWphKapeOC+3wAAlDrFJABsGvDh4QNxBHwSYyZFhbFqug5Jdk77c21+NBwqWwj3dHiwbWMX9jwD9PZvhGnehXQqiVC4uVmETM6BzNVl7loqa8BfMi8JAP39DopJgXe4CXr6BiFJMiRZRi6bwd7774Cq5UEIwQtuusnRfgBszqzITBblVxzRoALdLHeqzeQMdEUV9HZUX3gr4SvZvltQAsgOiyNJFMqiZzhbFfCyOd16kZvzK3nEQgpAKXKqga4IW+ScrEiMkV4f8pqJuWXVclp1gmbcXEvx5y/fAKkkQw0AQE89ZjIaknF4gi0AKC0WwrIkVAXOtwI7SVwrCATYgpEbufCOeKs4NJHGYI8Xfq+IbL6dxWTtWCSA5agdrmPK0QhFZtL5ooov5BcbMJOLy2lQI4eOjhKbf0ogNZBsCzUUHUMjo0glE/jxD/4bqpp3XEwCgFcumJBZLGWxaedRhJpTvi++qhfDvb6yRWdHZ0/de3PIL+Fz7z4HH/364bqyRDukstUFhiQJkCXSNpmrbugwqQABRQd2X8FzwC2YzLUGM1ljZpIQgvO2hrHnyWXoBou24tLbRkhmWNwUZyZZbmmDLGcilBWToYCEt72q3Gn93965s+F7O0F3THHsAl7LXIujI6zAMCmSGb1svnFuOY8em/t+JBJDOBwtYyazNmsnQSDwyELLxxOltJAz6XZmUsfSwhw8Hi+6etiaK5tl6xtVVW2LyXTOsJjfdsA0KQihjjPc11GHmaSUfoNSeh2AP6KUXk8pva7w87uU0h/w1xFC3rgme3qaYqTggOaGmcypBiSx6EzFu5ydnayItIb860hpKvGLn/wPfv7jWyEIAjo7OupKLH1eyTowBAIQgS0O8i6lrpyZDAariewtmzeAiF509bMb/vKSvftdI6iaibxm1uzecbhlA9JZo8zJFQAGBp0wk8UupyRJ2LJtJ669/kXYdva52LP7DuzbfSe2nn0Benu7HO0HUAjVNrhkkFYtpmIhGV5FKJNS5zUT3THFkZRWkWsvlhqBALYzKbUQDcmFBSqxOv9+nwS9gaurYQILK3nMxzXoBrVkXopcPe+0FhjpZef12Ky7jEO1BWYSAK4+vwO7zolVPOo8HmitEPCKVveRUkAkXFLGmOh2ObpG28xMRqLss11ZZpl67XB2NU2Kw+OZIsvTRsOU0vkwO8RCsqP5xVrIa9UjF43AF/JLDZjJ2Xk2j9bVxbIXVY0F2Tc6lu3cXAFg89YdAIDPffL/AwCce+65jvdZlknZMUlLGjSKJNQk00f6fHjJ1eUmXp1d9YtJgBVMvR0ezLgsJjlbVTnn1qwM1Q6mYUA32V88WIhQ8nuYCZHb85ZU0n4oNsvrqYnO2xJGTjVxaJwVEYahQ5Qa32c4G84bGoSQhtdbYQ3rhaEeHw6MpXBkonGTquidUWO2NFy89pUWfrN1mM+RjVtw5NDT1r+zeRM+m/u3zyO0LJvO53MwdN2VuoO5uapYXlpErKPLkshmS5hJO5lrNmfY/h3Ngo3vND8feyai4WlEKf1+g5f8VZv25TmJkF9CNCg5HroGWDe49ObNb0xc3hqNdYEQUtcxrhLzs9MAgG9+/wF0d9cvZGSJTaUAbOaBiOzkVfPubnycmQyHqi8mN938Cmy45b8R62Uy3Vq5XI3AO7WNtO1+l0G86ZxuFUmW5GJosOHvVRZ6n/niD/D37/9X7LrqBjzx2D489eTDuOSK6xt230sRCcigBTaHojp6QRQJNg36Ec8UF2+6SR3r/ZvNUaIs88GVFCQWVJAvzNJxNiXgFaHVMdGhlEIUmNTqorPCuObCTouRkkQBchuLE6cY6vGCEGBsxl0xWRmoXQ+mSRvmcDIWvLE0cK3h94oFoxTKvr+SQ8TnEaDr7ZnvigRlELSvmDz/oivK/t2OzMnphRwyeQNbC9JkryKAkPbNTNZTZXSEW2Nui5nHzq9Xiiwg5JcaMpPzC6xg7ykUkznVcJT5WhWVVMAll1+Dz3ztTtz+m/vw5JNP4oorrrD5bXtUmpyVSrNFkUUnaA6P2c6uHiw5aPT2dXqQSOuumgopLn30VZumNCNDtYNpsmJSFGAxXG7HRDgIqTbgccJ279jI1gyHxlkRYRqmo5lJfsyVGqs1GoUQBFLmE7GaeM0N/YgGZXzlJ2MNX5tVDRBSe165I8RnkzWr8BMIMFenQXHxZc/DU08+jHQqUZDHVstcgeaZ6FJwRV1HZ7fj35EkCbquI5dl+ZT+gkTWMuDR7JnJTI2/o1kYBq3K7l1HfbTj0zq1VjGnIPq7vK6YyaxqlklAOAPJGUlJkhCNdWLJhcw1kVhGIBhC/+Cmhl1mWSraeQuEQBAKxWSTzGQoVC1z6Ix5IXk7IIdYgVYZpusUqUwhD9Lf2M0VcMdM8lgQ/jlvHBlo+HuCUO40GI5E4fX5seuqG2DoOkzTxLXXOZe4AiyehIAWFuZFeWgphrp9ICDW7CEBcbwAlCQBpAl2L6+ZCPslV4VxKCBZizZexAZ89Q2ADIN1Ca8+vxPDvT50hJUyWZ/fI7atOHEKRRbQ3+l1zUy6MeCJp3SspOoXAdz6/lTroEqigKBXhKrRMpkrwBam7Sr+JZEgHJSacta1Q2dXD7ZtP8/6dzuYyYOFBTFnJnlwettkrnUWUUUznCaLSa185MIpOsJyQ2ZycYExk5wNzuvFWeh6IMQmexfM1GvLth244flX4ZxzznF1Tnjlcqk/Y9OLfzPPPnSCjq5eLC3ONfQB4CMIbqSuqaz9/c7nbR8zaRgmNAPo7fBa12ivy2YsB7GJBnHiEBzwSeiKKNb11TCdzUwuFa4D/DiioAX1Sp19JEV39NVGJCjj4rMjmF1qfL3KFVQHtY5jXuiPzWSRy7PCs7fDUzce5PIrr4eh63ho333IqSykyY4hbk8xWVi3djqP35JkBbqmIp/PwePxOWImNZ3NEbdzvlE3aMMM73WUox3F5NprzE4zDHR7LQeveIPFIQDkVaOCmZxluUKxIqPYaC6jEon4CsKRmEXf14Nc4m4nCLCYybxrZpIVk9FwNTMZDcogBNAQgc8XqArTdQrnzKRzB0XdoMipJgKFbS7Mz0KSZAz1N5am1uqan/v/s/fecXKkV7n/81au6tw9OUkzylrtaqVN2pzs3XUOOGBMMBgnwgWMAZMucC/XcA2XeDHgCwb8wyQbsI1tbOP12usN2pzzKo+k0eTp3JXe3x9vVXWq7qrumZFGUn/9kW1pqrtruqvfes85z3nO5VdB06KIxhK4+qqrAp+nFlHgEI8weahNqe+NT5F5bB2LeMEHQfh+QoEjoF3khMoVG30hNn+1KDLnnb83e1BoL7M1bda70MqWPaLyMM5yZRIAJoaUjiqTpkVDj1KhlEK3bEgBvWqmtbY30bUkpgkwTBuU1hsExTUB+hoG/1MjGl4+vja9jQBw4LrbvP+/FjMnXzlRQFTl63prI10amjRSrtht5YJe9SLXXbBdcIKXTsfZZBJSYGVycckJJhOObJvCW3ODqB3B5JIvmRjuU7qSfEs+feO1sUsmLnlBUBCZzAAsy/Kk0q0Y6iKY9GSuaqPMdfWyRBfbtmCYBKP91eu1k/tnLQQEtCGodiXXQfenTUOqt77aVrieyYWsAVEgiGkCdMOGJvPBMldSVWKdDRJRERXDDpzjWNattgFNTBMwOaLhqVeyTK4q8RhMy20D1UsvvwpaJIqD99/tBYt+94+1kLkuNijqwiCIAkzTYMGkokBVWTDpVSYrFUhifWXSTcqt5X3Qsnv9kp3Sq0yeBUb6ZCznTXzl/jP4yd9/NnC2VFmvl8Itzs8imcrUmd8MDI5gpoMZaNmVJcTjKVhWcP8L62viYdlsoe1W5loosN8zEW/umeR5gkREwHLewOj4Zpw40W1l0u0hab+QyI60LMwGzpUWujLXubkzSPcNgA9RgeM4/xqfKEp413s/hDe948eRTig+R7SnPyWhVLHrXAYbGelXPLkObSOPaSRoSHgrDMtGqsM5jbXn5BpcsJt963OwrPYLe19CWpMBy50ylFawkG1txd5I2E0UwHpZmCV8++c2Tbqm8p61RHOCfJvSOtOoqCZ0bfjkxyWTMZxZrHSk/mjH6970Lly27xrE40nkc6uXuZ6YLWPzsNYwGJwPbQbWjlLFajn8HYD3/ezW0fWFo3lsGlKDZz82kAlRmVxxRkLFnWCSdhJM+hgOVQwbw32dr60AW5dqv8c2aF1QGtWE0BNt3BEMr7z0bNvjBtMyCAFOzoVX/HiVSbWhMrmGMlfLsmBYwEjNe9mtAzHHcU3J1Yphg+eC2ysmhlScnq+grFusZzJkZTIdZ14B+ZKJkX4luEJN0FQ9XU9cJ99svn0LQykgUQQAl2+L49XpAmaXKlBlDgNOZbKV874oStixay8OH3qxrcGPKvMor7Iy6RY7Mh0Ek6IgwTAMVMolyLLimfd4o0GM5srkegSTpkWhKL1gshPavluEEI4Q8q6A57h/Dc/ngmTcMev4p/86BQA4Nd/+5lFu6JlcXJhDOlP/hRwdn8TJE0dCj+vIriwhnkjCtMLNHlQkDqZls8pklzLXXL4IwkmIR/3dLtNxNgONzQfrrjKZ826u7TchhBBHuhG8gSuU62/Y83OzGBwIJ9XgfTLmLh/+b7+KH/ixj3VlYZ2MiTBsu6XMFWDBL88xIx6BCzYecPEb2u0ys1huY94R/jVcRIGDwLP+JM/gIuA5XClnK/qSMij1rwivJ6m4CEoRKEV16UQyWKpYzriB9hshw7I7MkA6m0RV1gtLqyaZAFrPl+uWS5zxDM8fWX0VEQAmt+zEpz/7nxganViTymS+aHobSBdNWRuZa1lvv+HUFB6KxHVs9AKwpNrLx/PenL1OSCckFCtWW6lcdrkaTJoWhSRxoRNghANq8xGsfxtIRsMFo42IIlf3VSPOf1w0hQ8dbuy74jqIooSHHvh22+NkiUnlg5LLteSLJlSZ88z5XNZS5moYJgDek0gD3Q+y9+uZNMxwJmSbhlRQANOzZdi2HU7mmjO8fnrLpt7IlnZwpP6z7hbToqF6kxPOfO+g+wZzam7/O1++PQ4K4MlXslBlHgMpNpKp3dzdSDSGUqnofZatgsnVViYXFpoVdUEIolhTmVQhKyoIISgVmRGToeuQ5PqEkXvdr2VS1aY9mWuntP1GU0ptAL8YcMxPrekZXYAcuCSF267IeH9fCchIVfT6PpiF+TNN2Z3xiUkU8rlAKY2LK3MNO8pBlVmmliPEc3PttDKZy+dBBLkpi+qScmagjY1P4dT0MVhdDDss+FiltyKs413RCVC9nsmFWQwND4c6HxL41pKuFqmoIoAATWYmtXAcwUBaQrZgeuceBoFnPUi1BhSnFyqYWahAEtpvertxJo2oPCioV2GVRQ5cw+vXEiQ50RQeyZi4ZjKvsKQ7dBLVO5jZZ9muvX2Qyy31RthsNBSJObpSAFxNOZ19lmsnaBkbUBDXBLx4LB98cAdEIjEU16BnMl8ymypuaxVMBvVMAsC+7Qk88PQSVvKG018U7nvy7OEcbMqqH52SCejVLJYt5HLLIByPSDQGPYQjdy0EpM4ltKLbSETEwBaOVjTOaaWgdWu5KnMQuNZrVC2qFsHlV1yLg/ffHXjs5mEVxzqQyueKpm/iNBERkC2Ya9I7bhgmQLi6z6PbQfaEI6ANcyYrhh1qPrA7Vu3BZ5ZghZS5FssWoirvJRaZuiPgHNdo9O1STkfZCH5/3MTSSpuADwhOFAHApiEN7qWryBwGneC5neGjpkZQKha89cfvNRR59aNBFuZnkUimOxonJwgiTMNApVyGLCvgOA6qGqnKXPUKJLG+Mllah8okAelYjXGxE+bd+hYh5GOEkHFCSNr9s+5ndgHB8wRvu3kYn/3vl0MUSODms7Yy+bMffgeeffpRb7aky9jEFACEruhls0uIJ1Ksly7El0SVmUlGfc9kZ5XJQqEAjldaVg3TMRGLOVaZNAwds2dOdfT8ADPgkYRwFTJN4UJJgdw+THcDuLQwi6HBcJVJrkXPZBXa0uq7HaojuWg0M2lkICWhUDa9zVwYCCHoS8peRRZgAWZU4zExpLZ0WqVduohGNQE8RzyZK8cRJKNCy56kMP0LAynZcwnsBsuiHUsBPWv2kI9zzy9UAE6AWIB02z0wzPf5XOAGza4br/fvIteUvFgNhLAkSrcmM7W8Ol3Ax//sBRTLFiLR2KrdXE2LDRSPNQT8yaiA+WV9Ve8BpdSRwrW/Tl745i/i+c+/Dh/95U/gA7/zNP7fl4KdJAHg8MkiRJ5gy2gEjxz8Lt737ttCz91MJ9zxIP6b2lNzZVh6DlokDkIIDNMO7T4NMKk/Baty2TZFvmxhpD/8vNdGGit9BKTOmZsQgogavtf3wA234/CrL+K2AxN40+2XYOa0f0vKxJCKxayBXEBg4ZIvmU1jQQAWeJkWxYnZzu7RfhimBdIQTGrO/adjmStplrmGHY/Ul5Twmqv68I2H5pAv6qEqk+7weteQJUxAQMBkzavB/Z0iqhA4hsetTAb5Z5RDJIoEnqDfCSBVmfdMeX7rr1/GvU/4FxpUjQWT7XsmmQHPatanxflZZDLhJa5AdTRIpVKCLKt15wuwyqTY4OZabFNh7RYK2rXL/cVKmF3IuwH8JIB7ATzm/Hl0PU/qQoXjCFIxMXDzyVy82BfjkYfuBQC854d/ou6YsXHWlzEdoteQUorsyhJi8SQIIU2SKz9UhY1bqOuZ1DurTBYLJRC+fWWyWLYwOLwZQPjAuJZci5urH2rIyqQ7qyquCbAsCyvL8xgeDp4xCTS7udaylDPQn5K7yngJPIdYhDkKtgsm+5Mybrw8jZ2bYx09/6YhrS4TSUFx074MRvsVX3MeStm/dvO7RFUeAl/vUpdOSN5crWYIxIAZY6rMZNndUtYtrBSMjm6eqQ4rk1WZa/ublGnakEU+dKZ1o2ZQZWd+KTPgqZ4jxxFEVQFGwGzRTkhERGQLqw8m/+YrJzA9V8bxmRKi0fiq3VwLDYkpl52boyhWLBw93ZkbcC2GSUMlWp554n5Qq4wjL9wH06J44JmlUImXlYKJeFQAzxM8/OB38OLzT+KxR+4LdW5uMuv0gv8948RsCbae9cx3DJMiEaKK5OIa8CxkDcxnWVA+mOquXxJwpf7138vGZTaq8aGv2Te+9b344ff/LG65/U2Ymz2N55993Pe4zc4c6mMz4aSuxbLlK2vfMsqqeIdOrt6IyjBNgOPrgvtuK5MgpGlN1Y3wjtY/cMcoUjERC8vlUD2TbnKlYthIRMMlVDlu9TLXlaKBLWMa0nEx8LsVizCVUZBCrazbocZTuEZOqsyjP1mt2j3xsn8iTNUiKBbzbXsm+5MSbArMraIPfWH+TEfmOwCgqCoqlTIqlQpkRfHOt1RbmWzomXT9ErpJ0remV5nslDBzJid9/kydjZO7EEnFxBCVSQuyxMEwdFimiQ/99K9i+876AczDoxPgOC5UAFYs5GBZFhQ1geE+OdQoB1USYFiOe6jbM9lhZbJUKoLwSktThckRdgOcLTIJ8MkuTHjyLWQ/foSVlj364jIGUhL6UxJWlhdh2zZGR8PJXFsZ8FgWhWnbuGxr55Ixl3RcRKlieRU9PxSZx3Cf2jbgbPXcxJFx2TaFwLHFVFN437Ehlg1IIt/x6wBARBGaMtOpmASzVSBH3NmnrWnseeoUy6aIaUJHm6WYU2FdDHCtdHENYlKx9qZFZd1GOiZ6wXZQL2g3UuOzgSRy4JzNZKNCLRbh19TRNREVsBywOQuD66xpWjYikdiqeyYLLUY5XDLJkj3PHu6+8uluotpl5C3LQsWZ9xvj5vBT79jsvG7w77WSN7wqyvQJdp85eF+wdBNgVaVNQyq+8dCcr0HV9GwZ1Mgjlaqa73TSS8t5qgbWpz7SryDWQTDaiCQQUGf8kkujcUtMFUJLhBOJFH7iZ/87fv6XfwdA69FXm4c1iDzBp/7tGI6cCg4oW1Wi+5IS4pqAQyfD91+2wvSpTIoCB5EnHQeTHMehUapfCVmZBNgactPlaRRKOiy7/QLPKvUWVJn5BiRjHVwPqyxCUcpGqaRj1TnKrWCqHyZLdufw+hFGwg4Ag45LtCJxkEQOP/PuybZzUTUnOHMTXX7X0/gAS3JMz3af7FpYmG1S1AXhBo6sMql451ssVYPJxspkdW7p2t0HKag31aBHOII7vAjRCCG/Rgj5tPP3bYSQN67/qV2YpOLtg8lCyYRuUqgy72XFI5HmKpMkyRgcHsPTTz6MMwGurisrywAANZLAaL8a6jwlkS1MuYLVdWWyVCqCE5SWTqt7pmKYHNHwnecoJEnuytE1X7JCygGZSc9S1r/6ZFoUMwsV5IomnjucwzWXpEAIwezsGQDA6EjIYBLwbXMrli2M9CmrkmIkIkxmtR5rnCRySDqW5ZZNvb5aSWQBZaN0xzRtT/rUKbLEQWvIIrY1kaHBwSSTetYfU9Ytz5EuKCCzLBYcFjtwheU4gnTA97mWmYUyCFA3IsIPw6SIOhtjvk2lm/1OwXPUzhWEEGgyD92iTQmQTHxtHXgTURG5ghnaWdcPSqk3S7BQI3NdjbFTq9FFiaiI8QEFzx/prs+TUopHXlgB0D4j72b0VTWCuTPT2LtFhSpzePyllcDXyBZMr+fMDYYOBpjKuBBC8PZbhnBmsYJHXlhu+vnLrxwCrcx6Tq4gwS7j9c/P1myR53DV7iR2bmoeP9UJssRjICV7bqnua9QSUQV0Kn6IRONIpfu9YLyRqCbgl39kK/JFEw8/vxz4fMWy5bvuEkKwZUzD4TUIJi2T9Uw23rc1ha97f8JA0GzAoxu0owTYzfsyALWRLbZ/8ysGM/tSZR6UAlE1XGWSEICs4jterliIR0REVQFRTQilbnGd7P/1nhn8/J8877tulSrhzBLdyqR7j75qVxLbx6MtZbSqFmVqtXwBBP7fu5F+FshNdymbppRicWGuIydXgPVzFot5z4CHnW+9zLWxZ9INJtfSMIfQ7l3uL1bCfKP/BoAO4Drn7ycB/Pa6ndEFTiomYjlntNyg3Pskm721b3tVYhWN+VeztmzbjUcOfhcf/ci7275m1rFgj8aToR2vMgkRr7mqH/EoD47rLpgsl4vgBLllxogQgjuv6cfcsomB4U0ts7ftyBfDy1z3bosjWzTx/NHmDdy3H53Hxz/1Ar735CJsCly9OwkAmDl9EgAwNBRO5ko44o3nqKVkWN6i3y2Kc5PsphoYBleiY9m0rg9vuE9BrlRf9TEt2nVgrMo84pH6G72m8OBaGlzQwGq6wDfXT7MFE8sFE7PLFSwESMstm7IeyA73FKmYGLpX7/RCBZmEFOxea9ueNLydrX3FsBGLiKGUBucKTeGZK3TD7xH2OxuWRFQARXUOXzfM1ZhW5EsWotE4LNPsuFe8lnyDmVctW8cjHZmv1HL0dAl/85UT6EtI2DoaaXlcocAqnzt374Vt25g7M43t49FQDqLZAnOhpZRi+vgRaFoU08cPY3bmZKhzvHxbAoD/6It7/+knUVw+jsGhUQCsEtBJZYEQlixKRkX0p+TQI0XaMTmieSZeBLRpne101qYLcytvfW/bPhFFzDHQCcLtCfR9nQEVMwvlVbtam5YFnm/uNxwfVHFoujMZLfEZDdKJzBUABtIyOGKjHLDMlspO75zCgxIaaF7jwhHSnDkIoPZ3Kus2+hxZt6bw4HkS6J4cjwo4PlPCVx84g9klHc81OFEbJrsPh7nHDqbZ/qzW0CcRFVoa/KjOuI1sLg9F5nz3E5rCoy8hdR1MlkoFVMolpNL+Tq4f/t9P46//o7l3W9UiKBbysEzTq0yqaoPMtcHNtazbzlzttdkXuU7zG/m+uhEJ825toZR+EoABAJTSInqzJbsmFWPDj/3klpRSfOuReWwfj2DzsOaZP/hVJgHgN/7Xn+O97/spHHr1BZw+daLla9YGk2G/cIQwmeP4oNr1nEm9UoYoqW03xJucnpFEZqyrnsl8yQotc92/IwFV5nD/U4tNP3t1ugDTovivh+egybx3XiecTcCWLVtCvUbrqiEJ3cPRClnkwPOkq6HcYUjFJCZttuslI5uGNNh2faBnWN3PN9QUHrun6q9pnifoT8pN3wvX2CBo8yEKHDhSv2khjomNJDRvaBqxQZFJSIionUldXUfiMJxeqARWJQFH8udkWTmOoFWiu1Sx0Z9c3TW13qgKD9OkTS7HUTX8qIUwuN+tIFOLVuiGjc9/+7T390LJhBZl1+hq+ibdObgxnzUqGRWRL3ZXTT29wDZ5H3vvlFdF8H19Z07mjl2XAWAVxpF+Gafm2cgfs8Vr2zZFtmAgHhGxuDCLUqmAqw7cDAA4EXKd5nmCqMo3BUmWZaGSn8Huq9+Kn/mF3walrDe/k2CN4wjKuo1kB0ZjQdRK/SlIU89kt3Ly8YmpwHtbIiIEJkJsmzoOn/7rbiLCKqerdQm2LAui0Hy97p6M4sRsuaPvGCEEtKkyaXe88Rc4iorR/jHuuq1IzJ0pbKWK1BsDB5IrmHW9wIZtI+74UGgKj5v3ZZhBVJsnTUZFzDkGXIrE4cFnlup+7voHhKlMpp1ZsrUvl4iKWMmbvufgzm6856Hpttf02IDStcy1VGTJKkVtTnQtZQ3kSxbueazZIEhVI945y7LsnW+xmIdt2zBNo2nOpNsWFjhPNCS6aa9JcupiI8zqqBNCVDg5e0LIFgCdD63qAaBq2uE3j+jMYgVnFiu4YS8zy/VkrlH/YDIWT+CNb3svAOCh++8GpRTHz5SatPK57LJzfKrjG2JME2tkrp1lqXS9BElqb4owlGGDm5X4GKY7mJvpwmQ/4W4aksjhih0JPPHyStPrHHcqBHPLOqZGNS9bN33iKDQtgsGwbq4+skTDZHKV1S5QsshBEriWo0FWiyrzoCCOzLV+xtrUmIaFmt5Ay6JrasUNAMN9MkoN5gXLeRM7N0UDA2iBJ6C0/hgCgsmRCC7fnkBQ/su1At8xEemouuX2QAddt5RSnJ4vY7ivdTBp26znh5BqMM+12ZSYJkU6vrpq93rDRgyhqTIpSzwUiQ/dgxaEa94SZLffiu8+sYAHn13Cm28chMATZnQScYPJ7vsa21Um3Wpq1ud6M0277UZudpF9F4Pm6Lk9nzt27wUAnDhxGCN9CgyT4v3/6yn82l+86P+4sgXLZu+rm1A7cP3t7Dk6SPrFtOYg6czsPEBtjE9ewiRsFRvJmh7hMPCEMNOeLudK+iHwXNVVmtCmYlW3PVlj45OYPXMK5XLrzzOmCYEGUq4Eu1UwGVvld8DFsixIYvNr7Hb6fF/wUfa0guM42H6jQTp8L3mOoqy37i8EqsFkxbCxZSwS+jUIAdM1hkQ3mQleNblKoCnV6zCiClBkvm2S6DVX9eGuA/34hR/ciqt2JfHYi/V7kpJnKhN8jx0bUPDO24bxwbdOeP+WiAqwbOr1bNfiViZtq9TWBGhsQMGp+UpXjq6umkORm/d/bp+4X6DsnhuAJjdXw2BrnuTTM9mtasAPw6Cr6r++WAnzCfwmgK8DGCeEfA7A3QB+aT1P6kIm7WRS530s093m+W3j7AvlViaj0damLZsnt2NoeAwHH/i2F1g1VlayTs/kQH+m4+wNz8Ez4FlY6kziYuplSLLW9hhR4DCQkkHUYVTKJczPzYR+flcK0kmvzeYRDfmSVbeI6oaNU/PVQHlqtHrOp6aPYHJqS+j3jfgEk+WKjb5ke9OVMEgiB1Eg4IOHWXaFpvDeyAa5QeK0bSyCqCpgbrkCy6JMlrbGQ31Zdamxukgx3Bfs0sh6Kus3GxRszlhfws3ctr8pigLBQFp2MtXhbqDpuNhSaVDLSt5EWbcxnGn+XVbyBmaXK1jOGywwBSA51zRPWvdMgtCW/cgbBVe+7KfMziTENg6+nZGMhhsE3orjZ0qIqjzeedswIgqPQsny2gtWY8KTL5ngiL/9viv19gsivvfUIn7lz19s2Y97ZqmCVEwM3DC7Ccmx8UloWhQnTxzBqFPJpACmfSSo7JzY+ljOHsdn/uKTAID9V10PUZQ6akeI+VTcpqdZBbi/nyXoihUToyG+47UQjt2bOhknEobBlKOOoKRpzZdEDnzIWZO1uGO82hnMxSNioMy16r7p/5m7/a1hx4y0wrZtiGLz+zo5rEGRuI7muRJCAkeDFPJZ/MHvfhz/53c+3jJxw3MUNuXwO3/3Kp5rYR7lvj8xje+oh5YQAtDw+yKLUsQ1AcWyMzqDNu9BNIWH0SaY3D4RxQ/eNYY9UzGMD6ooVqy6e4i7LoZxcyWE4C03DdUlltqNH3Erk8Qq1wWgjaTjEiyben3fnVCpsMSJ68jq8g/fOIm//CKTt/rtieqCSc/NNcqCSZ3tmcXGnknDXtN+Sd20EY9s7PvqRiSMm+s3AbwdwPsA/COAKyml96zzeV2wjA2wL8jxM8038UMni5BFzrvZuxsBrUVlEmALyTXX3YaHH/wu5paKGEzLTW5i83OnwXEchof6Oz5fjiMgHA9eTuLRJ5/v6LGmXmrSt/sx0iejQpi2fqaNXLeRchfzhcbcxvKaTdTJuTJsCq9PbUtND9Lpk0exdcvW0M/PE9b/U3eepoW+xOrlWBxHvM35euDKSf0yx6LA4dpLU9g0pGEpb4BlY9f2RNyRIW5G1zRtSGLIWWGEQBLrs8EErLrA8wSKzLWU9DHYXCmB5xCPBNu7u7jOrEEmPG4PTaPMlVKKssF6PgyLIqLy4Ai8+Z2tRs3YNuvpamtctAEQBZYA8UvGZBISSiGGfIfBlZllu3R0PTlXxmi/4swT5FEoW9AibENaLHRnkgMwN9eIKvj+/m411e+czyxWYFNg+ox/NWt2sYKBVHCCyktIxuJe795ogyzWDY4efGYJX7mPGY65m9CnD34JDz/4HVy27xqMjk9iZGxTSzMZP+Ka0BTcnDrNgskBR+1BaXVma1g4AvSl5DrH0bUgGRNh2RSEUN8ESKSLkTZTW3cBAF549omWx4TpmSwGDGePO++FX6W7E2zLguQTTPI8wWBa9lypw+ArczXreyYfeuAe/MvnPo3P/8Oncfc3v+T7PBwsgHB4/mgeX/qef8LZ7XeNaf7ft9bn2FnfFoHT3pA3cGapAoA0BTOazOT9YfBTq4Vxam6HW7H3q1IrCtvfXLldxk2XZ1o+h/vdyhU7X6MrZba/cquLAEsifO3BWe/vfu0kmk9lMpFMIZdb8dayxspk7Vz2tcCmtK7S3CMcYdxc76aULlBKv0op/QqldJ4QEs4fvEcTEVVAJiHi+EwJtk1x9HTRu5kfPlnA5uHqWAc3I+7KrVplRA/ccDuKhRz0pRcwMaQ2KTZOHD+C/sFRJGPhnFxrcQe3qgNX4NiLDzQ5s7XDMsuQlfaVSQAY6VOQM1gVYHFhLvTzuwtuJxKHMcfy+mSNhOy4s2G7/ao+SALB1jF2zqZpYubUMWzfHj6Y5Hif/gtKENXWprcnqgnr1jMJsOx2ueIvG5ElHuODqpdZXuuNHCEEfQnJu8noZmeVN1Xm6saL2CCei2gsYBNIUR1c3pcUUdJtmCEkmO4mOKhv0t1QN87SyxeZMdNgWgGlFJPDGpOWk+rog8bkBMCysYlIZ5umcwELJjlff4uoKqxZ870i8VAkrqvKJKUUJ2fLXpAVUQQUSiYUx02wnTwxiFzRbDlnt92GzzV1aqwc2jbFIy8s4/R8xRtQ3o5aR/Cx8UlMnziCiCrUXYcreROPvLCMP/vXo/inb51y+iXZOZXyC+gfGManP/ufEAQBYxNTHVcmG4ObmRkWDIwMDzOzC67ztUQSOW+24loS1QSAAJQSX2OSiNK5NHvLtl3o6x/Cg/e33jYlIgLKut124H27uYBAVebqfnaHpgv4w388HJBEa8a2/YNJgN0fwhgFuXAc17R+NVYm3espEo3hofv93YIJoSDOfKFWvfruDOlGc7cgCNCRzBUgGBtQcesVfRgbUKEpXNM9OaLwMEJa/7ojTGoTkm5g3Mm4nFoSbZQaNsfWDYlrnxRwXfK7MTXzq0y6Rlw/cMcIbrw87avmqZe5sseOjU+CUopjR14GgJY9k2vJWlY6LxZafgKEEIUQkgbQRwhJEULSzp/NAEbP2hleINRKPSYGVbx4LI9f+8sX8Wt/+RIefn4ZR04Vcex0qa4q5ko+FC2GmcVKywHQV159EziexwtP3YtEREDjEjZ94jCGRye7kgS5i6Q2eBUqxUW8/OIzoR5nGgaoXd2QtWO0XwFEZhG/sDAbcHQVr4ekg4UkERUQVXn823dm8BnHTezUXBkCT/D2m4fxxz+3x1uIp6enYRo6tm/fFvr5Od+vFF2zClJME1Y9YLn987OBz62qgfGIAFXmMdonr0sg05+SvH4Rw7Q72mQqEtdQmaw638a05vEmtRDAmyuVjsvIFkycWqgEbhxd2XqQo2uh7PbO1f8+ZcPGaL+C0X4ZQ2kFowMqNg1XvzNcC3OIim57Ge2NjCQQiKL/xrxVkNUtnW50XbIFE4Wy5QWTmlOZ9ILJUvfjFgql1nNw28lc3Y2luwGzbYpD0wU89tIK/vifjyBbNEO5Q3sJyVgcYxNTODV9DKZp4pKpmLcmLWR1/Mu3TnmPeeZQDv/hVCgL2XmkM1V7//HxSZw8cTS0DDyuCcgXzbpE6NycM25pdBimxSSCnTpUT41GQplZdYoicWwcD6jvKhvRhLbyRT+YeuhWPPLgd2BZ/lWeahWo9fXrBpOtginvOZzvwAtH83jspRUs+rTUtMI0bdi2BVn2X1sS0WA5bj0EtOazNy0Kq8Hg7cSJw0hnBnDba9+Mhx/8Dkyz+fmpbWH3ZAKj/UpLFUjJCyY72+cw9UcnlUzmF5BJSNixKYrJ4WaTGU3lYYWUQ1cl+tXfu85MqAuSNUqNfNGsC9wMyr43AmnvgRENcU22wq8yecJJ2u/bnmCjoSp2U4FE1aryZDcQHRufBAAcOfQSADT5cFTWuDJJQdb0+S4W2r1jHwLwGICdzv+6f74E4P+u/6ldGHz+85/Hvl1DmJ2pyjcnhlQs5QxP6vrUq1n8z8+8DE3lcePlae+4Qj4HQRCRLRHsnoxCU/ybuiPROHbtuRLf/OY3IUs8ZLE+ezp9/AiGRjd3leVyKzva4BUAgIMtMoeNuJkpRQ3OHu/bkYAWTQOEYGH+TOhzc4PJTn4vJofkUChb+PZjCzAtitklHf1JCTxP6hqvjx5hcq6wTq6Aa5hS/btu2NAUfs0Gyw9l1l7aVUvMycy3qn5yHJtnNjLQeZU71OvXvP+GSX1dMFuhynxdFp6Q6nzDqCa0zBRbFpur6W5oIyqPeFTAjk2RwEqXG9AtBshc3QHRzRI1iqgmYCAt45ItMWgKj01D1e8Mz3G+waRhUSRjq+/DXW9EgYPEc76SQVniIYl8qApwGJJRsavKpBuwucFkVGU9k4qjqlhNZTJfsqC1CJpVmQ2C9zPBcCvdJx1r/vueXsRv/NXL+PK9VYlfOoQ0tJDLghACVY1gbGISpmlgduYkfuL7NuNX3scUFw8/t4zTCxXcup9J3v7080e8kSUrS/Wz4kYnJlEqFfC2u/bhsYfvw3veeh3OzJxqfmGHWESATavJFABYWJgD4RUMZBKw7e7mpMY0YV2s+93ZqMyB2K8yyYWuONVy4Ibbkc0u44Xn/KWu8Yaqoh8lr62D/d6f+9s/xW/9yke8n4sCB03mvUqwe38MOwcXcD4naretTK4Ugg3HXLiG0SBuQq82WTl9/AjGJiZx4Prbkcut4EWf98i2bQxlVEyNaG2CSfbc8Q5d051u+9DH05q5xzFNwKRPhVwSuNDPmPSRuc44hYNuRyhpCg+BJzhyuoiPf+oFfOSTz+A/HYlpxWTBJEfb+2i6+4x8V5VJJ5isqUweP1OCJDCptKYwN+9GqaufzNXtOT58iJmFSU2VSRuyj2FUN1DKkkjdmm1dzLR8xyilf0wpnQTwMUrpFKV00vmzl1LaCyZDEolEUCwWsFhTcRvrZ1+SkT4FE4MKHn5uGbpJ8dPvnMT4YHWTns9noUViSMVEbBmNsOqKz8bLtimuuvY2PPbYY5idncVYv+xlk7Iry8iuLGFkbLIrKQDvVJ8EJQ1eSePIkXD9MiUnm6+GCCZjmoDbrx4ALyVw+lR4A55yl9m7vduqhkbLOQOzS/6SsYWFeQAI7eQKuFnO6m2kULbWNIOejkvr6jSmyoKXnW/FtvHoulXF3CoOpez23kmiIBEV6/qFKZhJB8Aqgq1u7qZFodTcjKIqj6t3J7FjUww8T9pWNEWBSfSWsu2z/4WyBY7Um2e410nE6RX1q2CRlm6uFOoG75cE2KZLErkmN1eXTFz0Nr2rxbXD7xRX5j7qJEiYzNXyEmHuWnbkVLFj85WybresJBFCEI82V1MppXWVSUop7n2CjTM6crqETUMq3nj9APbvSAS+fqGQgxaJguM4jDoZfteN1TWm+tYjc5BFDm+/Zdg751RMxEfevgmLC7N1weS+A6/HW975o5g5dRz/89d/GkcOvYiHHn6o7txraayWAcDy4iwEJQVN4WHTqrx8oxDThJamUYrEd6UM2X3JfgDAkUP+7rnhgklH5up87++95z/x9a/8C5aXqiMWWO8lu3bcNpBOgsmKbgPUhthicx6PsHaBsN9ZQkhda0zFWUvlOpnrYYyNT2HXHvYeHfZ5jyzLBMfzSMXZnG6/72GpYkEUCCIh50tWzzF8z6Rtsz58IaDVRBKZSuZ0iLmfqswk+u7nZJg2vvXIHPZMxbq+zxJCsG97Avc/vYRc0UQyJuCRF5YBAGWTfe+JHVCZVFdTmXRkrjWeGdNOK4Hr/QBUEwAufgY8yVQGkWgMR15l14Xo4+a6VpXEYtlCJiGu2yzvC5kwBjx/SgjZQwh5FyHkh90/Z+PkLgTcYffLS9VewEu3xLBvexwffc8UxgZUVAwbHAE2D9dXewr5HCKRGPqTEjiOIB4VfTe2NgWuvYHZtn/zm9/ESL/qSXFOTrN+hJHxSU8D3wlczaLJyynMzISrHBYKbjAZroJ19e4UeDmFk6dOBx/s4I6R6FTf/kN3jeGDb2EuZotZHWcWKxj0sdgvFZnxRjze2k23EY6rN4YzLBt9yY09vqEWWeKgyFzgzXK9kEQOsQhzSCWks37YgbQMAtRZtrtS3ERUcDY2zTf2sm7VubcRQpCOS5BFDnum4oFVR3c8SDsKJctxy62+rxXdRjwitq2wCC1uahSA1uGm6VxACIGi8C13a8m44G0wV0siKnQ1Z/K5wzkMpCRv4xZReRQrlmceVikX8ep0Ab/+6ZfwlfvDKycAdm212+jEIyJWGmSuxbKFimFjICWhWLHw9KtZvHgs71VD9u9I4PtfOxpq1FAhn/PcwMedYHLacRXVFB6yyEE3Ka7anUQqLiLjGIV9/2tHcO2eJBYX55DuqybT4ql+/MNnP42hoWHMnDoGoOpSOrtUwfxK/e/iZwqTXZ6HpKVBCBtDtFaqjbUi4pyzXwJEEv0rTrrRLNmrZXB4DLwgtOw39ZxYi2bL53Klim7P5PRxNkrrkYPfqT5PjeGRayLmN4asFSXdAqX+cyaBGtOokFJX0tAzaTjfdbcaXS4VMTd7GmMTkxgcGoUgiL7vkW1Z4HkBqZgIm/q/fqliQZF4iB1eT2xNDne/My0KVeYDWzwkkYMgsJEhYQLvWlXFYy+uYDlv4vXXDQQ8qj0feusE9u9I4D13jGLPVNyrdhZ0HgAB7PaKC1lihnz5bgx4Kv4y1zGnWOIGk4Vy/eeo+fRMEkIwNj7VpjLZfo3thELZwuRIs2y5RzBhDHh+A8CfOn9uBfBJAG9e5/O6YHCDyaUaY5moJuDnf2ALhjKyJ60aG1CbgqJCIQc1EkPC2eQkIkLLyuTO3Zehv78fX//615GIskV3brmC515gTcuX7t7elSyodqahoKQxH1KGms2xQEzTwpkkDGdk8B08P1DNvCrOpnrm9DQeeuAe78/S4rzv4ySR8/rSjp8poazbvs6IxSLrN0okgisALsQnbx1fR1nqWqNIHFSJX1eTnyAGUhKKZQuUdiY3kUUO44NqNZNKqzJtgecwlJZ9526VDRubhv2v09F+BZLgH4S6pOJiiJ5Js7lfUrfRlwzIPBOWLKrFtChEnttwm/BWDKXllp9jRBFhdThbthWJqIh8yerIcMS0KF44mseeqapjtttLaIMlgcqlknfdtBpL0IqgGWiJiNBUTXUTE6+5qh8cAf7080ch8AQ/cAezKqg91yDy+aw3p7hvYBiyrHjBHyHECx5vctorXIOySyZjyK4swTJNZJyeScuiEHkCVeZx5513eq8xc/IIVvKGr/FJzGdcRT47BzXKJLW2Tb1e5Y1CTGPrn1+8IIucb9gxu6S3HaEgCAJGRjdhusWMTve9O71Qxs//yfP4hf/7Ag6drB/FVaow12dF4lAs5r2WkAfvq7ae1I5icYOYTqTfbmWytczV7fMNF0xyhKtzc22sTJ6cPgqA9cXxPM/cgn3eI8u2wPOcl/DxS96VyhYUmetYosg+53BrhhtMBiGLHPZtS2BqVK2TeLciGRO9oP/EbAmEVOd6dosi8/joe6Zw14EBDPcxH4BDJwt45UQRnKCiUg7uBY9qfJcGPPUy12zBwErBxLizvriJ0FK5XWWyGoiOTUx6ZmJ+lcm1MOCxLOboHnhP7uFLmE/gHQBuBzBDKf1RAHsBhN9dX+T09/eDEIKlRX9jGffm7edMV8hloWlRb/FSZd5/TAAFZFHAHXfcgW9+85uglOLaS1O45Yo+6CtHwHEcrrh8R1fnXyt3FNU0VmoqrO3I5thCFY2Gy/JENQFqNBP6+YHqaBA3K/XzP/Fu/MyHvs/784nf+JmWj03HWfD40jF2w/aTuRbyrN8oEgmfqSKOYYph2jg9X0Z/Uu7ake1cIAock12eQ5lHX4JVZCSR6zjjOJCWoZu2Iy2idUHx6IDimfu4lCoWElGxpZyISXLaO8GmY2Kgm2uhZDWZMBmWHWiKJXCkaZ+jGzaS0c6GvJ9LNg9rLQNfReKa0i8LWR3zK+37efyoVk3Cb54PTRdQ1m3s2VJVH7g9jiWdQpJklMslTy7diU2+bTM5YLvNZ201lVIKw7S9xMTUqIYrdyVR1m3cfmUfXnNVH37nIzuxo4MZeq66BWD9ayNjm3DyxFHv50MZBQMpyZvLd8Nladx+ZR+SMdELVlyZa1m3kIqz6+6tb30LACCZSuP0yaOoGDY2DasgDa6YsYbKpGmaKCzPIJYYdN4jbLikiCLxkAR/UyD3XBuli5JIAivszE33qO/PVJlDf1LCl793Bss5Ayt5A//5QP2eoVixvKqY+xkqqoanHn/QO4b1NDqVSaPznslSxQYNkLkC/vML/WiUuepeZZK9j4dffQEAMLGJ+RK4jsON2JYNjuM99+yFFR2f/PtX8cXvVttiShUmdwwzSqr+HMMfa5jtv88uHEcwOqCiPymHksYnY4L3OS0s60jHxDVVB7nzjX/7M6/gqVezIIKCUjF4bnhUbZ4TGwZX5uqa5Zxw/EEmBh2TM2f0RmOgrdS4/9dKZN2+SQCQauZMWhaFYa3NzOtSxUJ/Ul6XXuyLgTDvWplSagMwCSFxALMAxtf3tC4cRFFEMpWu65msZfOwCp6D7wahUMhBi8a9nhtF5n2lN7ZNIQgEd911F+bm5vDEE094PVgP3n8PrrzqGmwaSXZ1/rXBZDzZj2JuIVTzfc6pTHYSiCVT4Z8fqKlMOgvJ8vIibrjlLvzlZ7+GS/dehbnZ1pLZqMpD5Ik3gHnQpzJZKuYRi8XAdTLYkbCbU0W3MT6k4ppLkuEfu0HYsyV+TgNg1zH20i3RjnsX3ICNUjTdFPqTTLpaKxWv6Db6E+0Ds6jKw2xjujHSryBbND23Oj+KZQuRJiMWEngTJByB3fB9qOg2kh3O5duoKBLXZMtv2+jIXdHFdWHupG/ylWm2odq9ubr+TgyqIAA+942TkBUVpVLR25h3srFyr7N2WfOhjIJlx3Hx898+jR/97acw58zxS8dEvOmGQeyejOItNw2CEFLXUx+GQiGHSLQaKPf1D9WZnP3YG8fxq+/b5n3Prr00hR99I7u9L8yze1bGkbmWddurZL7lLW/BM8+/jNe/7vWYOXkMNgUycakp2ZmICBB44pkcPffMo7CMIka3XgWAzXQTN9jmrV0Si+PYzNpaIzxKqROQtL9mR8cnMX3isO/9jRCC//auSUgCh5v3Z7BtPOLNpnUpVSyv59oNuHbtvhxLS1UFTiLCZK6mRbvsmWxvwOOOswkvc623o640BJMPPXAP4vEktu7YAwDeLNTG98iyTPC8gHTM7fOdx9Ov5vCv95zGS8493JW5dqqq4ULKXEsVC4ZFMT4QPDvbxTWK8jNOrMVtlVhY0TG/YqAvubbmasOOb4Pb/qSqkVDBZEwT2lbcW6Hr7Np1K5MnnFFs457M1alMNhjwcBwHVWV7xlqJrCvRB1A3u9y9ntZC5loyLPSnN76p3UYlzCfwCCEkCeD/gbm5Pg7gwbaP6FFHf/9gy8pkJiHh9396N667NNX0s3wui0gk5m1GNJmHpvJNWUGbUggcwR133AEA+PrXvw4AmJubw6OPPoo3vP6urrMttY/L9A3AtgzksiuBj8vl2UIVjYSfBdY/MATb0pHPZUMdX9ZZr6nbS1SplDA8MoG9+w5gZGwzsitLLR9LCGGDh3MGCODb11jIZxGPd1aEd29MujPW4nypHtXSzQiZtUSWeFy9O4WhTPibtoum8AAFLCfBUovAc9g+Ea3rUTMsO9AxT1N46G0qkzfsTUMSCL5+sPVYG7/KJAH1rt1W+CXZTdv2NnXnO7LEzHnqs/e0qcIVBm9uYweyvpW8AVnk6q6BzcMa3v3aETzxchaCqKBSrgaTnWysPLfpNhudSUduf/R0Efc8xoxUHn5uCQJPkI6LmBzR8Cs/sq3j2XkuhVzOk7kCLDCsHb+UjInIJPw3UG4wme4bgG6wGYgJ5zwIIdizaxu2b9+KudmTILaOqMY3bZwFgcPl2+J46Lkl2DbFPXf/F0A47Lz0OgCARSnELtxc1xNF4tr2ozauB6ZFwfMcgqSS445Ur9Ywp5bJEQ1/9HOX4MfeOI7hPgUzC5W6oKpcsTzzHbevcOcll6NYyMM02DU/3KfApsCZhXJXPZNsVqMNSWohc9U6DCYJgU39K5OUUjz0wD246tpbIDg9mmPjUygW803zppnMlUciKoDngGedPueYJuBbj7BgeiGrIxYR2prH+Z9jK5OzegolC3umYsh04IHAcQSZpOgF9q24YkcSBMCvf/olnJ4vr3kwOZCSPEOpP/iZ3RgeSKJYChdMdqLGcKmUS8w535GknjhTQlwTvISfW91tN2uy1gm2rjJZ0zNZLSisPpiklHjrW4/OCbMjiQN4J4DvAPg6gDil9On1PKkLjb7+wZaVSQDo9zF/+cfPfgqnTh7DgRtu9wISnie4encSdz86X6czdq2qBwYGsH//fvzVX/0Vjh8/junpaVBKcdddd3V97rU9k66r6fzcDOKJZNvH5R0DnlhImSsAjIyw/tLj06dwye7gIK5csaHUNMNXymVPGhFPpJDLLrd9fComYnZJx87NUd/qQbGY68h8B6g6w5kWPedB2flMtzdTgWcbwYruPytzpF/Bc0dysCwmgaVoPQTcJaq1H0Ad0wRcvzeN+55cxI++ccJXnlQoWz4bVBIo8WObsfp/oyAtHULPNwghiKpMRixL7vtGQKlb8Qm/MUx2UZnMFUzfuXSXbYnjn/7rFARRQblc8jbm7eTOjZRDGIS5vbpHTpfQlxCRK5p47kge28YibV1ODUPHX/7pJ5DPtU/szZ45hb1XHPD+nukbwOL8bKj3dnGhKnNdzBnYtyPRFHhu3boVlFJ85v/+Om7Z/6eIqGwsFV9z47jusjQefXEF/+0XP4Fn7/8HqJndePMtTNZI0HoM0blCFDhcvq31/SeiCCgUywDY72iYFHFNwHKeuYy2UlO4G+ITxw8hle7zPcaVBQ9nZJR1G8s5E6m4iD/7wlF8557/wlAmAmAXpo8fRirdh5HRTQCAbHYZ6Uy/VzU7MVvuajRIqcyOlSX/TbUgcNAUvsk0qhUcaRgN4nx/ZJHDoVdewNzsaRy4/jbv52M1jsO1LsK2ZYHjmPT4/W+ewNOvZnHj3gy+ev8ZzK/oKFUsLKwYuOYSuWN5KCHEc80O+k50o9gZTMmYXay0TVDs3BzFR96+GX/8LyxJsNbBpCBwGEzLiKg8BlIytEj4ymS3PZOSrHjv54kzZYwNVoND14DHL5jUtAiWFquBKMAq1i61PZPuNb7ankk2EoQi2oVJZQ9GmN3uXwO4EcyAZwuAJwgh91JK/3hdz+wCoq9/EIcOvRr6+DMz0/jj3/s1RGMJvPn1t9f9LKIKkATO2wwDTBbmbjw+9KEP4Td/8zfx5S9/GQBw44034oorruj63N0s39YxDSMys44/duIUprbubPu4QoEtVPFY+P6ezZvGAAAvvnwEl+zeFXh8WbegOouIZVkwDN3LZsUTSeRyK7Asq25jU0veMdU4cElzVRgASoV858EkABvsprQWTeE9OicdF3H8TAkpH7dTSeQwOazi6EwJmbgUyjFWDjEOYHyAOSiXylbT6BZKKQols2nGJAUC+3t4vrlnkiA4AD6fiEUELCzpoKBMniwQiDyBYXY2g9ANCjsxHFkpmL5zW93eLF5UUS6V6qTRJad3LYgwWfOYJqA/KeHo6WLdRnXbePsk3MsvPoO//5s/QTyerNtcUcreQ0LYJjkSjWHfFdd7P0/3DaBSKTOX11j7te3MzEmoWhS6JSOmcpgYUps229dffz3GxjfhS//6WXzj3W/E1r13YGFZR62m4PJtccSEZTz6jf8DXorhbd//gzU96qTjStLZoF2AG1G5uqSCadlIJyRYNoVu2i2TB1Nb2T3thWefwGWXX9P29d1xUs8dyWHf9jgefHYJC8/8JYT+DIAfxOyZUxgcGkM8we5dOSeYHOlXwBFWCXKvv1LFdhwvg6/ZYol9d1rJXAGWtAkyHPMgBLQmG1Zbmfzu/d8CAFxzXTWYnNrmvkeP4/L9LAli26wHnnPu4zddnsFNlzMDp4PPLuG5IzmccmTUQ2m5q+QET4j3vWmFje4qYPGo6Ot10cjOGql9Xwu1wGr4ie/b7N3rVFVDNiDZDrBEarHMTM06CdJrE/sAG3F0876M93dR4CAKxKmE16NqkbpAFGCKCkXVUC4V63om3STfaudMlnUbyVh7Z/Ue7QkMJiml9xBC7gVwFZib64cBXAKgF0yGpK9/AEsLc6Ez7QfvZ+5s//Yf38Jrb76y6eeqwjvSGieYrOk7+eAHP4gPfvCDa3buksDhp9+1GZdOxXHfQRYgHj3eeki1ixtMxmLhK5OX7mYmQa+8Ei7wLuu213OmN1hRuzfZfG4FiWTa9/EDKQkn58q4yqcKSilFoZDF0Kah0OcPOFlOEBBC18yuukdnpOIiXjqRRz/nL0ca6Vdx+BSrnFOKwL5FucU4gFpqrc4bg8mybsOmqOuZdA2CpACZKyGo65k0TOZct9FMS1ZDKiri5GwZJZ2iYtgY7mMOurmC2dHvKYlsaPtKSAkewHogEz5Dzt2easLLdT2TALC4onszKdsRRuYKsL75I6eKdb/r1oBg0m0F+OSffA6XX3Ete72KhYppY/t4BM8cymHAR/GSyTB1ycL8mcBgcvr4EQyPbka+bGHfpojvvWvz5s149pmnkUwmcOjQIVx+gMfMfH0VXxI5vG7XSTwB4G//4WvYtuOS6g8JNmQw2Y6IItSpBQyTIqJykEQJh08VWwZtwyPjmNi8FQ/edzfe/YMfbvsarmHKX/z7MQxlZFBqwy6dhkgcd8zsEhLJlKcQcls6RIHDUEbBidmyN3/PrXAOZYI33KUKCxJbGfAAwMSQgpePB1e1ANYDVzsaxN38SwKHg/d/G1u27cbA4Ij386HhMWya3IaHHvg23vPDPwGAVSUBgOebt6uZhISlnOHNih3MyF1dT8QJJtsfQ7saZh9V+VCDR2KagNF+BSfnyuhf48okwGTULrKsolIOnumdcYwK55f1juZlVyolL5jUDRsVw0YyVv/5aQrfUuZaG4gC7PMZHduMQ68831CZXBuZa7FsYdtEbyTIaggzGuRuAPcDeDeAlwBcRSltX5bqUUd//yB0vexZG7ejWLZwz7e/ib7+EVx39eW+x2gyV2cIYvv0h60VhDBXQU3hsXWSVQ6nTwYvQsUi26wn4uErk9u3jIPwMo4d87dPb6TWkKDRijoeZ8HkSpu+yQ+9dRN+6wPbffuRKO2yMul8DDbtfP5lj7VBlXnYNlr2I0Y1HiCEWYFzJDCgk1qMA6gl4gWTzTdHd6xEbc+kadGmuZN+CA3mT6WKhYF12GicSwYzMiilkCUOsYiAeERALCJCN7rrm+ykRyxXMH1H9xBCkIyJACfX9UwCwPyKHuq5Kw0GYa0YG1Axt6TXOQJvG2u/sSkU2L2kth8yX7KwdSyC4T6lpZ+IKx1s7EnzY/oECyb3bU9gpK918JxIxDE4OIhXX30VEUWAnyL8oQfuRl//ELZu313/A3r+BZONCQ6bsgBzclhjLQ4+47tcDlx/G5549H6Uy+1n/KVrDLZmFiqwygswjQqyK8sAgOzKMuKJlJc0ra0yjQ0omJ5l0mzXeCXsd8INJlupeQBg62gEi1kDi9ng7wFBvZvrYlYHRwCelPHU4wdx4Ibbmx5z7fW344lHH/DeI8t2g8nmc8okWNXv2cM5CDxBJiF1dT1xHGmbMGQyyO7URqLAeTLaILY7Ac1ay1wbkWXF2zO1wzXMOd7GXM6PSrnsjfbw7n8NMt+IIvi6oGs+wSRQlYnX9kxWQibsgrAp9Rz+e3RHmE/gaQA6gD0ALgOwhxDSmaXcRU5ff7XXMIiKbuKZx+/DtTfe1lJjr8p8/Sw1so5SIVKV2U2M9oFwIl4+dLzJhauRUoktPolYeAMeQeChJUZx5tTRUMe7mVeAZcKAqp10wr3Jtgkmo5qALaP+mzbLpih12TNpOlKnjdYLdLGgyDx4QlrKcgSeQ9xxqYuowSZJcotxALW4VceizxxLdzBz7ffZMGmo/huOqzNDRMWg677RONuoMo/RfgWTwxq2jmlIRAQkIgIMu/WmvBWTIxqeO5yrk6W2glKKbLG5kuySiomwiVzXMwkACyvhNuZeZdJHbl1Lf1ICBUtE3LI/g59996Qns21FMe8Gk9X1yQZFTBO85Iff9eoGkwsB83xN08Sp6WMYHt2MqVEtUNa7detWHDp0yHfNo5TikYPfxTXX3er7XTvf1kmpRpo+s1BGqcIq6IrMY2pE88ag+HHg+ttRqZTxhlt34nU378BjD3/P9ziOI3j7LUO4bCtLFhh5pgbKriyx63ZlCfF4TTBZc58bG1Awu6TDsKhnYha2b7LkrFUc1yaYdBIdh6aL+Nw3pvH3X59ueSzh6udMnlmqoC8p4enHH4Bh6DhQI3F1OXBD9T264/opvPFWJn31CyZdOejTr2YxnJHBcehqpAbHAe1Kk4ZJocnByb+Wzx+i8gkAN+5N4/JtcV9DwLVEVhRvfEc7xgYUEAIcn+kwmKxUZa6uaVmjAd3+HXE8fSiLMw2uxZoWhaI27xvdvslaN1e3n3M17vPuOunX7tAjPGFkrj8HAISQGID3AfgbAEMA1vdqv4AYGWNW66dPHcfmqe1tjz0zcxKFfBZ3vea6lsdEFKEumKRo3fS/NrDnFgQOkWgCZ+aW8O/fnfGGaPtRKhVBOBGq0pk7VrJvHEtzx0MdW9YtxDR2GVbKjTLXJID2wWQ7bJvNaOs8mCTQTYoxH+lcj7ODInGQJNLWKbU/JeHJl1ewd1vw58txbJBxseRnosPQ2lQm88XmyqRlUURC3AB5njQEBbRl8HM+s2dLHBxXrVK5gVinJjw37cvggWeW8NiLK7jWxyG7lopue+YpfiRjImxIKJdZz2RU5VEoWaE35tU5uO0/5/6asURjAwqu3JUMfO58nslcayuTBKwFghACTeF9e07dMR8LbQzhAGB25iRM08D4pi2h3v8tW7bg29/+tmdAVku5VEQ2u4zNU36zjul5WJkkAChsm7m47hqLeN//dELyxs34cfWBW/D+D/8CstkVfPVL/4Cvf/XzuOLqG32Pffstw5hf1vGzf/QcZJsF/7peQalUQC67jHgi6RtM1pokudLEsNdssejMB/SpDLlMDKngOYJDJwt45hBL3PzgXWO+x3JcvQHPmUUdAykZB++7G4qqYe/+A02PufKam/H+j/yiV4UFWCB5+51vazrW/V1LFRubhlSIvP980CA4giajs1oM017VuhtGRgsA2yei+Nh7w6u5ukWW1VCVSUnkMJyR24698qNSKXkqMfeeGG0YjXXnNQP4+sE5fOPgHH749ez6eeKlFVxyw4/ijW9trnq/4/t/HJOT2yHW9Ew+/lIWiYjgVeC7oVi2kIpJF1TryLkg8NtBCPkpMAOeKwAcBfAZAP7ptB6+bHIG8rp23u1whxHv2N466FRkDlbDyrdefcONy3ImnUKRKzVlkxopl4rgBLXjhX1odDNOvfogdN1saU/uvYZue1n/Jpmrd5Nd7uj1XXTdQLlcRCLR6WgQAKC9eUXnEFHgoMpCWzfMdFxCf1LGlrFwN+6xARVPvLzSMpiMtBjCTCnFNx6agyQQjPZXN2i6aUNrmjvZTI0wAOWKBY4j3mtdSDTeyFWZR39SahvA+7F7cxSZhIiDzy0FBpNuBanVJjEdF2FSCWWnZ1KVefAcCR9MOjLXIGlcf00VolVg24jbMhHR2PXLRqtUXX4jqoBCsbnnNBZPQhBELM63DyZPHGetBpsnp9oe57J161Z89rOfRblUapILupLcaNQ/cXPeVSad97Ss20jFRezZUv293OpGrmhCErimz14QRXzgJ38ZALAwN4OD993dNmHSl5QwlJaxcLpaST598jhs20Y8kUI0GgchpC6YTMeqicxUTIQkkNAyV7c9RVFai88kkcPYgIJjMyUs54y2Bi2No0FmFys4sCeFr/7zt3HF1TfWOXa6CIKAD/zEx0Odb23gfMWuJGJdunHyAbOkdZMGjpBqB+GCBsecXWQlnMwVYHN3D50sdvT8zIDHlbk2K3MA5m2wdSyCozPV5/7Wo/M4NdeH9779EjQyPDKON77tvd7fi2ULT768gtuu7OsqgaAbNuZWKuA5DrsmY8EP6NGWMCGIAuAPAOyklL6GUvpblNJvr/N5XVD09Q9AUTVMnwjuBTx98igAdnNuhShwdTcfQsi6VSYb5/lGojEQu9R2llt2ZRlHX7wPHN95QLVp8xZQW8ff/sOX63otaqGU4rf++mXMLule1t+VbCiyCt2wfTO2neBugLqRuaoy35tXdI6Ja0JbuVNfQsKBS1OhDRXScantbqCV1flLxwt4/KUVvP3WYdaD52BR6o2yaAdHCAghMC2KXMnElTsT593mu1smBlUUAuT0jXAcwVi/GsptMucY9bStTBLHgEdnxkdJZ7h4GNzqqhoQTKbjoledC1v9KBRyUFQNgijCtikKZQvxiODdB2JqQyuEA8dxSGf6A2Wu0ydY4nNqKnwwCQBf/tK/4slHv4cnHz8I02Tvr2sWVFtF9aDrlwhdL9zKb7ZgNrluKhIHWeRRrliB4zMO3HA75mZP41tf/zdvyLsfv/RDW9AnV2dTugnneCIFnucRiyWwUpM0rZVIKzK7ZsM6HLvBpOojM6ylLylhbklHvmTBpsygxQ/ChjgCAPJFE4WyBdE8jenjh30lrp1SG6xvHdUQ6/K+W3OavhiWXbd+dwrPBbzAWUaWmcw1TB/n+KCKuWXd1yynFfUyV7dnsjnQT0aFulFOFd3GUs5omDvsz9OvZmFYFAf2tE8atqJYtpCOS4iqfJ06pEd3BC7jlNLfp5Q+RCntfNhMDwBsQR0e3RyqMnl6+ggkScLYmL9sBECTdIlgnU0MahYcLRIDNYttZ7n9/id+EbnFaciRTMtjWnGtYzr0mf/z4/jyf37H95hi2cIrJ+qlRG6WrWLzmFmseL1EQbMmW5HLsg1Q56NBCCIq35tXdI6JR/m2wSTPk47mgGoKD1ny36AD7Dsp8MTLwrocO802ZzfubXYUDpXpJkzGrhs2MgkJA+nW8rMLDddlVTfsjmadRTW+6XPwwx287jdnEmBVHSI4cyYNG7LIIR0XfU0j/CjrNniOtK2QA85g8wT7XcNWJvP5LCIRFpwt5nTYlGKgZkMUcWZ3+tHXP4S52dNtn//Iqy9CVtS296Fa9uzZAwD46Z/4cfz6z70TH/6R1+NbX/93AP5mQYDTq3QeurkCrDe3rFtNAQYhBENpCds3RSAIJMCM53bwPI9f/8UP4J/+v79oeVx/SsaJYy9Bi7Aq9AknKe0mTOOJVH1lssZIRJF4pDpIgJScQfaK0j6YzMQlzNSok84s+le5CKka8JxZYscvn34KAHDVgZtDnVMQ33frMD701gmYNkUi2l31kAWTrQMYAqxqbjTXPOHpnOKa47RLYri4UulWCQM/KuVSkwGP3/uXiIp1hYmKzqrcYdb7BccIbWygu3uiYVFsGdVw3WXp3kiQNaD3Dp4lRkYnvWxvO06dOopNmyfbuqmJAhtV4C5+dB37TghrD/GIxmKw9AJW8kbLxXdm5jQIr+CHfuYvO369G66/Hr/0P/4aAHDihH9jv7vQ7NocxeuvY4YSXs+kpCIdF0HBIxqLI5vtrjKZc3qSOpW5CgLBUFrpLU7nmKGM4uvSuxoUic139cOtVjTKXM8s6ZBFri5gMUwbssiHmlXIOaNmDNNelczqfERTeMgij/kVHWXdarsxryWiCl6fajuCZK4DKRkcL8M0dJQq7HPsZGNeaxAWhDvGI3RlMp9DNBoHpRS2DdywN4Odm6vBmiLzLQshw6MTODl9rO3zP/TgPbhs33WhJcaXXnopnnzySfzH176F3/6jLwBgHgHuuQL1ZkGAMx+Z57o2NTmXbBrSsHNz1DcRsXsqhm3jUUwOa1hpsykeGBzB3//bfdiydRceuPebLY87MzONo4dfxk23vh5AtV3G9QVoDCbVGsMnpcNqesnpmZTV9h6L6QaDqDOLLSqTNT2Ts84xZolVWUfGNoU6pyDedvMQbnTmTmpdGrHwfGs3V9tm+6tGA5lOIAjXM3m2cOXF7r6pHW71fa6TYLKmMlkomSDE33E1ERVQ1m2vJcBVc4RRlqwUTIgCWYWTK0VEFS6omc3nkt6O9ywxPDaJU9PHYFntNzmnp49gampL22MkgYMicTg5X4FlURA4bmTrAEG9o0IkEoOpF6Cb1PviN3Jmbhlq/1684ZbOJ8gQQnDllVcDABYXF32PcSsKb7x+EIPO8Ouy4+Y6OpRAKi6iotuIx1Nd90y60qxOK5OqzGP3VE9/f65Jx6VVyZL8kKX6kTyNRHzmZs0uVjCYluo2zKWKVVdFaof7sHZGMRcqhBAMZWQIAsGOiSgWQ0r1YiqPYsVqGfi7BMlcp0Y0r3esXCpBllgwmSuaMEIEtiXdCnRydXEdejvpmdQiUZxZqmAwLXsyaxdZ5FqOBxmbmMLpU8dhGv7v5/SJIzhx7BD2XX1rRxu1vXv34vobbsLlV1yPeDyJBacv0zMLitSvi5bdbBB0vsBxBHu3JXw3om6APNKvBF6Dk1M7cMOtr8MzTz3s3XMacedO3/H67wMAnDzhBpNuZTJZlzStXWvcazZsz6TXLtKmZxIA0on6tXWmhYcCM55h78HcMjumUlxAPJ707ZdcHaRrV0/OCfYKJdMLbCilmF/RcXqxjHRcWlUr0foaJHaOWzV0XfDb4a5NCyFHIrHnLdcZ8EQU3vc9cFs9so7SzR3BFCb5kc0bSETEjpNRlFIYpg2KZofZHt3TCybPEiNjm2EYOmbPnGp5DKUUp08ewZYtrfslAZZFu2V/BlvHNORLJihd557Jml1JJBqDXskDQMu+yXw+i3gs3vUIg4EBlmVcWfavKrqVydqssJthGxmMIxWVUHH6Jrvumcx31zPZ48JFkfgm46taIo7bZy2zS5Wm4fEVw/ZkjUFwjjSA0tXZn5+vDGVkbBmNYGKIbX7C9Pi41TR3JEsrCmULPNfaIIfnCTaPsg17Pl+AJHJIORLCMJtzVpkM95ldd2kKdx3oD5TEeueez0JSopga1XDp1uY1Kqrx4Hn4yrLHxydhmSZmZvyVH27wsv/qWyEKnV1z7GolSGX6sej0ZVbHmNQHk6YVbjzO+UpMYzNTywF9v9defzssy8IjD33X9+cH778bA4MjnvOpV5l0Zim3S5oqIqtMlnU7cJwXAJQr4Xoma6W0cU1oacjHkWplcmHFQFTlsbI4h7QzomZtoV1Xqdw5kMWK5SWr55YNjPUr2DkRZbNbVwEhbJbhRsGtGoYx4YlHBIgC6Vzm6hjw5EtmS4mw28rg9vRWvMpk8GutFMyWLQrtKJYtnF6oQOS5noPrGrKu7yQh5DOEkFlCyLMN//7ThJAXCSHPEUI+uZ7nsFEYGWUzcqaPtzbhWZg/g0q5hC1b21cmASZ1He1XUTHtszAapLoIRqNxVEp5UGq37JvUy3lofmYLIZFlBZygYqVFIOgu9rVzgdwMW186iqjGwwbF0MgYDr36QqgNaCPHj70CABgfH+/4sT0uTJjMtfXPtYbKpG1TzC3pGEg3Z+DDyge9ZA5BaLOgC4mBlIxdm6OQnd6vUiW4Iuj2KwdJXUsVC2rA7Litm1iv69xCzpO5AuEy52XdCr253T0ZazlewY9CIQctGkMmLresjk0Oa74mMO7w71b3ouefeRyZvkGMTEy1Ha/jh6uQyfQNeuNHWrm5WhbtWpZ4PkAIwfig6hmQtGLPZVciEo3h4H13+/78+Wcex74rr4emRSEIIk6dZBJlV+YajSdaegPIEo9RJxB6+Hn/Y1wsi8KouJXJoJ7JajJs23gEZxZaVCY5zuuZXMzqSMdFLCzMeiNq1grTopBEHmLIZEwjgrt/cswMl3IGkjEBe7bEsXsq7iWzuqWxXehc4wWTIWZNEkKQSUiY77YyWbJ8zXcAZsADwNtLdiJzzRbMrnpkbRsY7pMxNqiclxL7jcp6707+FsBdtf9ACLkVwFsA7KWUXgLg99f5HDYEI2NuMNm6b3LacWnb1sbJtZZkTEBEESCLfHUxXGMaXc4i0RgopaBmuWVl0tQLLW3gwyIqceRzy74/86tMlkpsUcwko1BkHgTA1dfeiplTJ3D86Ksdv/4TD38HO3buxsjISMeP7XFhovqM5Kkloghez6RlUTz47BIMi2KwoTIJEr63x73XUQSPmLhQcW/4o30qipVgYwY3C54PMOFxg8l2jAywnmnbqtQFk2H6h8qOA+x6UMjnoGmxts6+w32Kryvi6PhmANX7TSMnp49gYtMWEKDjYJK1RVBk+gaqMldHvukayLgYlt0kz73QUBWW2GyHIIq48pqb8dAD325KfFYqZcyeOcU+D0I8aaumRb15e9FoHIV8ru6xkvO5ySKHvdvi2DYWwT//1ylPwunHk69kQS0WFMoBMtdUTAQBS+iO9CuYW674SnqZzLUaIKTjEhbmZ5HJrG1l0jDtlgFLKAhTjGgyh9F+BZJAsH/H2jlnt+vJPBdUZa7hxoP0JaTQlUlKKSqVMhQ5OJisrUyapu3dX0P1TOaNrnwRbMdJfc9UT3W2lqzr7oRSei+Axsa3jwD4XUppxTmm/cCrC4RM/zAkSW47HsTNFO/Yvi3Ucwo8h5v3ZXDzvvS6jQsghKA2eeP2vdhmASuF5s1apVIBtXTEYqvrG1S0OIr5Fd+f5YomZLFeouAaB2iaBk3mEY+I2HrJDQCYTKgTSsUCnn3qIG5/zR1dnn2PCxFR5NpuDGsrk/c9tYg//zdWQRiomTlqWRQC1zx/rhWEuMLBi7MyWUsyLoLS4HWuGky2rwgVy1ZgMNOXZusYtcqQJQ7DfTLScRH/cd+Zls6+LuWKDTWkzLVT8vksItF4W8fiVhLSvv4hyIrasjI5ffwIhscmIYlc5zNNCQBKkMkMeLMsvTEmQv1zmTatM4u5EJGEcAZD115/G2ZOT+PokZfr/v3UyWOglHrV5KrpTtI7JhKNwTSNOmfOj713C/bvSCCisl61113bj2zRxEyLCuLCio4//KfDIGxbBiXAgEcQOMSjAhJRAUNpGZbt31NXK3NdyhpIx9mM07WWuRqmvTq3VY6gVLbQl2RKiBsuz3Q03zbw+TeYAU+1MhkumOxPhq9MejO/Q8hcY5oAAlZldPslAWApQOZq28zxtRuZq00Rup2gR3jOxTu6HcCNhJCHCCHfJYRcdQ7O4azDcRxGx9uPBzlx/DA4nsfk5ObQz8vzBPI6bVg8amyzIzGWzbHNAlbyJv7zwVl8/FMv4IvfnQEAnJlj0tROXVAbUSNJlIstgsmCWSdxBYCSI9dQVRU8T3DD3jTGJzZjYvNW/Nkf/hbuvHErfvOXPxzqtZ947AGYho7X3tELJntUEXmOVV5awHomTVBKcWKWXY/9SQmbaiRSFcNGIiqEltdwhJnvaAq34UwczjaSQEBI8I4s6mTBgyuTdmBlUtMiAADbLEMWOYgChx95/RimZ8s4+GzrfuzFrI6TcyUM9621yQhg2zaKhTxULdo2mHSrU43VLkIIxsYnfRUbxWIeC/NnkBmcwI5N0Y6TlOyypkj3DaJYzKNYzHvOs41wOAv3rnOMJHCh5gtec/3tAIAff+8d+NqX/8n7d3e/4FaTa8eBuLguuW6fP8Bk0x99z5S3ZrgVoFYjF9xK0DU7mbw1SOYKAMMZBQMpGYPO6AhfEx5CQG0bumEjWzQRlQ0Ui/k1l7kaJkVU6347y3MEZcNGJi5Clvg176UjGyx2cSWoYQx4AGbCky2YbSvbLl4w6bxG0THg8YPnCWIRAct5o87QMagyWShbsGx0KXOl591s2/OBc/GWCgDSAA4A+AUA/0J8dlaEkA8SQh4lhDwKoO8sn+O6MDY+1XY8yIljhzE0PA5R3FgD7wmq90O3MhkRdNz31CI+942TmJ4t45EXlgEAs3Psf5OJ1UkIIrEkKqUV337HbNFsstAvl8sQRQmc07TDcQSqwuO/fex38Oa3/xAmp3bgm1/7Qih318OvvgAAOHD11av6HXpcWLTbuAMsy2pTlmU9NV/GpiEVf/izl9RJcSq67Zm4hIEQQDc7e8yFiiRyTo22Pe4IlXA9k+1vgX39bNNrlua8DeZljuFNO9nX3Y/Ow7aB265Y+1tXqVQApRRapL3MlRDmbuknP7zksivw1BMHmxxdTzrS15GxyWZ5dgg4R8mScSpPiwtzdTMxa6GgXfe4nS9IYr0beiuGR8bxc7/0CUQiMfzHv3/O+3e3ejzuVCbf/+FfwDu+/8fxgZ/8Ze8Y9711XXP9CPpOuC0rhFYgSXLb0WQuP/l9m/H+N417jup+Jjwcx9xc3R5j0WYJ4nSmP/D5O8GmFFqnVfQaBIFgaiSC/i6u+TBwAXMszzZu1TBsZXLLGEuquUWDduhOMClJCiilKJQtaG0kyMmogOWc4ZnvJCICFrOtR88BwTOC20Gpk+Tpsaaci3d0GsC/UcbDAGz4BIuU0k9TSq+klF4JYP5sn+R6MDbBZk22+pJMnziCsfGps3xWwXA1sb7ryHfTZSpyRRNbxzTcvC/jSVxm55mqOZNOruo14/EkLD2Hsm7jyKki/v7r095r5Apmk4V+pVxqkuZoMod9V9+Mj/3qJ/ETP/ffYds2Hjn4ncDXnj5+BPFEGv19zYPme1y8sP6x1je4yWGWzT98sojT8xWM+DgA6qaN/g5cjgkh4DnSNNftYkTgCTjAtw+wFlXmwBFm89+OMD2Tw6MTIISDkT/pSZNFgYMqc96cSj8OPruMS7fGfM2XVkvB6UGMRGOBvfKqxMHwCSYPXH87Cvkcnn3msbp/P+EELyNjk131e7Iee+IFkwvzZ1DI55qcXN1jpQ57Ms83RIEDKAkVSLz7Bz+MO9/wTjz95EMoOIHh9PEjiMUSXiXymutuw8d+9ZO48ZaqFYX73hZrKpONxBxTqlaVSbdlhVrlwLEgLqm4iERURDIqQBY532CSEAKb2lV3TmsZQDXZsHaQVQUIW0YjuGp3ct3cPTmywWSuSng3VwDYMxXDrfsz+Mr9s4EjQgyd/VyUmKs+pWi7zo70KTg2U/JkrsN9CiqG3TRmqxY3+ZHopmcSdN3awi5mzkUw+UUAtwIAIWQ7AAkXSLAYxOj4JCrlEubnmrM7lFKcPHEE45s2XjBZWzh25UoTfTb+6OcuwS//8DYMZ2TkSxaKZQvziyzz2JdJruo1E8kULD2HZ17N4tc//RK+fnAODzzDZGXZognOXKjrESmXS1CU+s17ROG9vqbde65ANBYP1T954vhhjIxP9hacHnUIAtfWRGFyRAPPAc8dyWF+WcdIg8SxXLEQiwgdBYbusOdGWffFCCEEqswH9ioSQhBVhcCeyTAyV1GUEE0OwSicqqvwxTQBWR+nVJflnIHR/tWNE2iF646qaTEIAcGYqvi/X5fuuwEcx+NBx0E0l13BY4/ch0cfuhcAsGVqS1dOh+4jXBnjU48dxMryom8wSSm54CuTHEcgiVzgvEmXAzfcDss08djD92Hm9DReeuEpjE1Mtf0s3Huye134EVFYb1qrYDLrbM5tqxJovtMIIQTJmIivH5zDn/xLVXn1vScX8OQreZimjf/1t0xSbZfZPXytZa6E0FWZXckit773e7KhzFyrlcmQMlcAuGYPS2i0GgPj4u7LJElG2XHfbudqvWNTFItZA9NOa8iwI5tu55jtVia7kbkSsARtj7VlvUeD/COABwHsIIRME0LeD+AzAKaccSH/BOBH6Eaq/68j4xOtHV1z2RUU8lmMT2w626cVTI2ja6TmxhXTBMgSh35nAPvccgULTjDZv8pgMp1KAbaBf/zGYSgSB4EnODlbwuMvrWBufhlf/NO34fP/+P+843W9ArWxMqkIMEx24oIg4OoDt+Dg/c2OeY1MHz/iyYp69HAR+XYdk8xtdWJQxfeeXAQFmmaTZUsmpka1jjbpnBNAraUZxPlMmGASYP2rrTbOLmFkrgCQHtgEI3/S28AATF6V8zEgA5iUuWLY65YAyOXYGqtFY4GbIk0RYJr171exbMHmIth96RVeMPm7v/Wz+MkfezP+7Z8/g/7BUQwNpPyeLhBCmHx1YGgUPM/jU3/8P/DCc080BZO2TUEI7dgt9nwkonChrlkAuOzyq6FpUTzwvf/CB37wTjz79KOY3LKz/fM7763rmusHzxNoCt+yj3ilYCKq8tArJSgBMyb9uGIn80hwx4+Ypo1/ufs0zizqMMxqL5zpBJOpdOfyb9um0GtMWmqhdGMblAncxoomOzXgAarjYIL6GQ2jWpl0+yDbzdvduYm5PD/1Crt+3T7zdq/z2IsrEAVSN++0llzR9GZX+nGx+w+sB+vt5voeSukwpVSklI5RSv+aUqpTSn+QUrqHUrqfUvrt9TyHjYQrYfVzdF1ZYfLQ8ZGhs3pOYSA166Dbn1Hb7N+fZF/+3/m7V/GdR44DWH1lcmSY9VScmpnHzfsy2LkpimcO5fAH/3gYxdknYOglnDldHbqtV8pNwaQs17tvHrjhdszNnvZ6Iv3Q9Qpmz5zE5GTwrM8eFxccxwxDWm1oAGDreMQbD9Ioc2WOrJ0ZjnCEBS4beaN0NmGVtjCzJoW64K8R3bBhWjTUaIrt27bCKJzCJVPVgCgeEVsGq9XRResjTV5cmAPA+jmDEhOazMG0q++XYdrIl01kEiLuuONOvPzCk5ibPY0H778bt772Tfizz3wZv/epLyHSbSBMCAgIEokU/v5fv4fJLTsAoKln0rRo4IzPCwVF5n2lxn6IooQrr7kJ//mVf8Hc7Gm8/yO/iI9+/HfaPsYNJttVJgFWTW8pc82biEdElEslqF0Ekz9wxyjedfswAJZMeeDZJaeyxEaDHLgkid/7qV0ol9g5xuLJusfrhl0XdPqRK5o4s1Stihkmk89aFoXAcxu6yk04ArqBokmlQwMeAF7gFiRzrXg9kzIqjmFPu8rkaL+CiMLjqVedYDLDzq1VMHnoZAEPPruE11830HL9LlUs6Kb/+01B0Ysl156N++27ABkcHgMvCL6VyewKy9iNj691L8Hqqa3HuLPCapv93cpkvmTBNooAqhXMbhl1gskbLxHxlpsGMTageH0d2+MvAkCdmY5fMNloy37gutsAtB8VcmqaWbFv2dILJns0MzWq+o7Ecbn+0jS2jUdw0+VpX5ljkIlPI4rM47Jtq3NGvpDQFM5TG7RjakTDoelCy76bUoX9e5DMFQB27twGW89hNF393GNtgtUVR/66XpVJd37j8GBw4pG5pVavOd2wMZCScf3eDN74htcBAP76zz+JYiGPO9/wTlxx1Q3IDIx6PXadwpHqy01u2Ykbb2Gv4Rqjudg2Xbf+tI1GRBVCVyYB4Jrrb/OGyb/9XT+KaKz9vdQvwetHVONbG/AUDCSiAkqlYscyVxcv2MjqePi5ZQykJIgCD1CKS7fEMdynoJDPQRBESFJ9C8BSzmgrawSYE3Y8IqBYtrCSN1iwQZhz8lBmfYxz1gqOhDL1PWt0asADMOVNTBNC90xKkoySHixz5TiCLWMaSo4k1nUHXmwxHuTJl7MgAN5wXRupNAEIqK8KjRDSq0yuAxfHar5BEAQBI6ObfOd7zS8sAABGhzaecS3HwVsJeZ6HpkXrblzRGqcu23SDydXNmXQNB67dJSIeEb2NeTIq4OVn7wMALC5WR5jqun8wWZsMHBgaxZatu/Dvn/87fPELf+f7uq7b7vaQsz57XFyM9qkAaW0Cs3U8gt94/3Z88K2bmnpwCNBVX06vKllFU3jYYUYtXJKEYVE8/pL/eKFOgslxR1FyssaJOx5hVR6/zYorf0104TQYhsX5MyCEoG8g2BFTFAhM08aSk+U3TIqYs17fcN3ViCcz+OIX/g68IGD/lTc6jyJtZWntILXW3wCuue5WAMCLzz9Vd5xlX/hOri5RNVw13cVNem7fdVmo3kJP5trGzRVoX5nM5k0kIgIq5WJoA55GMglWiZ9b0vHisTwu3RJHKiGCgmLbBHMDLeRziMbidUneYtmCqvAIKlITwtQec8sVZBISrrssje0TEZQqNiaGujvns8VGi10EUQTP86ENeFzScRELATJXt2dSrK1MBqyztc7RUZVHIiJ4a1YjMwsV9CWlllVJ3bARUXgkYyKKZaspkUPQk7muBxfHar6BGBufxLRjv17LwoLjgprJnOUzCqbRwzIWT3iVVKDeoOeOKzQIguhp8rvFtQ6fm2VmRWMD7GYxHl/AzKkTAIClpWowWamUoTUGkyKBZbOZf6408S3v+BEszs/i9377Y8hlmzeaLzz7ODiOw67d7ftUelycKDKPdFysm4kVFkrQm2+1SphjY/BGYOtYBJm4iO88vuAb+LtZ8DDB5NS2XQCA55953Pu3mCbAsuFb+XRdXhvHF60ViwtzSCQziCjBrsCyyCGiCRBFDoWSCcOyvTERosjj/e//ccSTadx2xztQtGRvney2akhA6u4Vl+27Btt27MFP/txv1B1n0+5f43yD9TuH37yOjm/GLa95I972jveFOl6SZEiSHKIyKSDf0s3VQDwqOkZ2nctcASDjVCYff2kFZd3G7skoNg/HANvEUNpRL/mMicmXTGwb09q+RbZNAUIwNRrBNZeksH9nAn1JCSN9KrZNRJCKbWy3a+Zqe67Poh5ZVr0KeFgyCQmLQZVJw61MSt46264yCaBuJIsssntsK5nr6YVy20p0oWxhKCNjMCVjIWs0VTgp7d2H14PeW3oWKZYtDI9uxvSJw00Z7eVlFpyl0xtvHAVpkGhk+gaxuDBbd8zHf2gLfvmHt0IvFxCJxlbdCzMyugmEEK8aMD6oYGJQgVR4AgCw85L9KOSXvayTXilD0+pvgqLAIRUXsXsy5jVjv+u9H8QffOqfYVkWHnnou02v++D9d2P77v3oS3dnQNHjwicdl7yZWJ1AgJ6L3CphsyaD4TiCN904iBeP5fGFe043/bxamQy+BY6NT2J0bDMOPlBt73fnm/lJXd3K5HrKXFOZAUgh3CsjqoBb9mdw+fa4425L6qoEf/D7v4v5uTl84yv/gD1bYphbqUCWuJZDxoMgDWMVRVHC//eFe3Hg+tvrjrNtCukicctm72VnkcTv/uFn8bZ3vS/8a0RjwT2TLRyOK7qNUsVGIiKwYFLtrsqXcgxavvckU1nt2hzF1k19AKg3e7BQqB8TY5o2BIFgdEAFT0hLxUdZt5GMCtAUHpuGqyZmqszj8u2JDd97SzaYAQ/AxoN0WpnMdFKZFGXvPhmkdHBbpQBWBEjFJcw1zPG1LIpc0cTMQqXJ3K4W06JIx2UM9SnYvyPRVPHuVSbXh14weRbJl0xkhjahkM9heWmh7mcFx6Evldp4QUyjh2W6b8Dr23HZsyWOS6bYDc1vQHWnyLKCgcERT3aqSDw+8ZFdOPbSA9i0eRumtl2KQm4F5YrFXN4qZWha/U2Q4whuuCyNHRNRxDTBmzu357IrEYnG8ND99d5Py0sLeOHZJ7D/6lt7m/4eLUlGRZhdpJlt2nnPZI96VJkPXeO5/co+XHdpCl9/cNYLHl3cimKYyiTA5jI+9tD3vI2SG0zmiiaOnCridz/7Kp5wJLXZggmBJ6EC1W5YWDiDVHogtPxZFDik4yIzgjHtpseJTm/5UFoBzxHsmIh0vdkK+zjLphAvksqkJHLQlPbGXaslEo1780dbEdV4VAy77jwWVnS8/xNMghyPCCiXil25uQLsOkpEBOgmxeSwinhEhKYxeWuxWAAAZ+ZotQd0pWhicliDKHCIR0Vv1mAj+bKJsf6NLWVtB89hQxnwAGyP1bHMNSGhWLaa1tNaqj2TEsohDHgAYKBm9jIhBLs2R3FqvozHXqyqx7758Bw+8slnUNZtb3yIPxQRhUdME1h7FGn8Ka2bnd5jbbg4VvMNAiHAyCgbD3L82Kt1P8tllxGLxyEIG28EQOP3LpNpDiZd8vnsqvslXcYmpur6S3W9gicevR/XXH8rovEkctklLBd0/PSH342TJw41VSYBtrnheYK92xPMIMimEEQRV15zM77ypX/A627e4f151xuvAqUUV1x9a+D8th4XL91UGgCA0O56JntUkUQOiswFOj8CbFPymqv6oJsUj76wXPczdzMUxs0VYE7QpVIBTz/xEIBq1fHwySL+52dexrOHc3jw2eoc3HhEWLdqycL8LBKpvsANWi2EEIz1y21HKERUAfu2J9pm/cO9FgLHL9n2xdULnIlXN9brQSQSojKpVRMgp+bL+MTfvYIHndnNt+zPYP+OBAsmu5S5us8NADftY+06qhNMlrxgsn5/YNOqxDEVFXwVH5bFNv/D6zS39WzAcRvLgAdwZK5dVCYBtJW66jp7TlGSa0aDhJe5AsBrr+7H2ICCf/7WKe/fXjyW9/5/q2DSXXdUhb2eJDLfjLr1qNdusi5svMjlAsWmlLlW7bgMAPDEow9g774D3s9zuSWkUxtP4gqwjUi9zHUAy0vzsCwLPF+/GVtenEcytTZ9n2Pjk7j3nq95fz85fRSVShmXXHoljp84Bdu2cezQs3jswW/huhtuxUc+8pGWz5WKidBUHrbNPof3f/gXkOkbaNr0ZPoGsGXXXjYXqkcPH9wbFaU0dMBAKXVuYr3rarWkYiKWskYoA5dt4xH0JyU88MwSbry8ui5VeybD7SquuPoGCIKIg/d/G1dec5NXmfzCPadBCEF/UsS8s8HKFcx1k7hSSrE4P4tUegCxDkeP9KdkTM+W2/YqTgx1H0i4cM79ot1Xg4JeVAm7VFzE8dkSVudx3ppINBbYM5l2AoEXj+Xxj988ieW8icMnixB4gve9YRwCT5yeye4rgDxPYJsU1zoD7lW/ymStcolSyE6gkUlKeHW6iHLFqpNi50smxgaU8zr5wG3Auk03MldXyryUNzHaYvCA7lQmZVlGWbcg8ARCwFrdmNQTeIJ92xP42gOz3p7t6Omi9/OhjH9iQTcooqoAwYkWOY7NVzVMCkl01htKNrws+nykF0yeBTgOKFYsDPcpiCgj2L7zMhy872687wMf9Y7JZ5eRyWzUYBJ1hZh03yBs28by0gIyffUrysLCLPZOHMBaMDYxhaXFeSebGfdGqoyOb8biCltYDj31DQDA337mL7FtW3sHVr6mCX77zkvxi7/2+77HzS1XLqqNTo/OEHgOUVWAblDIUrjrxLYBUeR6N7E1IB2TMLNQQRj9AyEEuydjePLlqlzq+SM5fPY/2YzasDJXTYti7/4DOHj/3fipj/4mUjER+3ck8MTLK3jnbcM4s1jBky9nsZQzcGq+jMH0+owqyOey0PUKUun+OhftMCSiIsaH1HXvF2pMPvpBAfDcxttgrxdSh/NlOyUajePUyWNtj9kzFcdwRsZf/Fv1uLJuY3JYhcATmKYJw9BXFUz+9x/bjvkV3TN5cmWu5ZITTBZyiNaODauplA+mFezfyb5TtcGkadF1S86cLTZmZVLp2IDHNTpabjPGxajpmSxXch0pKGpJRAVYNmVzmymwsGLghstSSMZEzzm4kbJuYbAh0IwobJyM5D2E9pK668DFs5qfQ0b62GKdjotIJ0Tsv+ZWPPPUw8g7PQ6maaOQW96Q5juAI1uqiSbdALLRhMfNmjcGmN0yNsEkwff813/gmScfxvGjrwAAxiemvKHHX/7i5zE1NYWtW7cGPl+YTZRbqewtNj3aEYsI0E0bVsj5cZZNIfW0NWtCLCJ0tDFLx0VkC6Zn1nX/00zat2080tF4igPX345XX34Oc7OnwXEEH33PFP76V/biTTcMYjAtY6Vg4pc/9QJml/R1NN85AwAYHhnueLSGJHLYtXltWhDawXEkUAROcHH1D4sCafIeWEvCGPDwPMF77hgFzxO8/80TuNmRom4eZtXoSpklaBWfdpGwTI5ouGpX0vt7bWWSUop8ripzNS0KSeLrrmNNae6Jtmn4pM9GJYz0+2wjK53LXJNRpzLZJpjU63om7dBjhn74dWP4/teOeH9POMqLbN7A0Rl2bd5weQbf/9rRlknZimkjFatfeyMqj+W84SlHAGfcXY81pfeWngU0hceeLTH0J2UMpCRcuv9mWJaF+753DwDAsCgK+Y0dTNZVJp2xHe7GxqVYyKFSKSPdIpi0LIrTC+XQRgRTW9h4jt/+7z+ND/zQXfi7v/ojxGIJxBMpRGNskPv09DTuuuuuUBUfQhA4o86yWRa5V0Hq0Y5EVIBu2Dg5V/aMndrBhrT3rqm1QO4w052KiaCoZtMXszomRzT8xvu3d/Q8riPpQw/c4/2bJLrmNawSmS9ZuO7SFF537dok1BpZcBJ4U5tG1+X514LaucStILi4NnSrDZxtm+LoTLFlQBKJxpH3GXXVyP4dCXz645fhlv0Z7NocBQBsGmaVyFLJCSZX0TPZiKZFnecuQNcrME3DCyZ1w0ZMqw80/CTYlJ7/M0nDJFjONrKier2sYVFkHqrMtQ0mjZo5kyyYDPfZ3XFNP954fXWuaiLKgsLlvImjp1kFdXPAPFHS4FYNsF7himFDcsYj8VxP5roenN/f0POIrWNR9CUlDKYV3PmaG6FFonj84e9gIavDNCly2eUN6eQKNM+ZdAcpN5rwuH/PZPwHLc86w4bbOYHVsmlyG/72n+/B//2rL2JoZBzZlSWMTUyBEIJovPpe3XXXXaGej+cQ6Jti2/S87s3ocXaIqAIMy4am8iiGuJ4tm140c/XWG7HDjbnX5+NsgBZWDPS1kEm1Y+v23ejrH8LB++9u+tmgYwjBc8D73jCOyZG125DX4qpBJjdyMBliCgLFxVWZFHgCQroPJ3TTBkcIDNP/OdKZfuRyK57bcDvcdWjf9jiuuSSJ/TtYYjaXXQYAxBPJrs+zkVoDHren03VzNUy7qYIvCVxTsy0h5LxPxG1E99B0uq9pqkAYUjGxrcxV1yvgeR48z6OsWx0n/1zcKuhKwcDR00X0JyVPPt2a5sSDLPGYGFJx2dY4VooW+J5CaF3ovavngH07M7jjta/FEw/fA9OyUdRNZFeWNm5lskHyWa1MtggmfSqT+aKJ/qSMTUMqKiGcGF127t6LK6+5Cdfd+FoAVelrLJYEAIiiiFtvvTX07xE00cG0esFkj2BUiUfFoEhEBVCwalc7yatt47zPrm8UmCNu+I152unzWcrqoJRiYUVHOi4FPKoZQgiuue5WPPzAPbCs+gSC2yO5YyIa2iG2GxadNXZkZGjdXmO1EAT3TJKLzIxK4IPfk3YYJkUsIrR0hK22nsyFfs6IKuCn3znpfRdWVpYBAPH42iW1VbUqc83nWVuPa8Cj+wSTokDAE9TNm6Q4/9dOQrDhqmFpZ164bXc2siYZEwNkrhWIElsPy7rdtUTZrUyu5E0cO13C5uHgXl5CAKnB7yKTEHH5tgSSUQEEFML5rZjesJzf39DzFI4juOuuO3HixHH81R/+Ev7iD34Zpmlu3GAS9aolTYtC06JYrJG5PvTAPfjm174AwD+YLJQt7NgUhaYIXQ3vPXDdbQCA0bFJWBZFKs1ueDfccAOi0Wio5wizebFtCvE8z4L2WH8UiYPAEwz3yRjJKJAlDoVya7mr1ZO5rhluRStsD5JbmVzMGSiWLVQMG5lE58EkwEaEZLPLeOG5J+r+XZF4vOaqvnWTt7oszM9CEET0962NY/Z6wIcInJgBz8XzfWDukt3/vrphI6ryLROxVbXQGd+fhyG7wnqJ17IyqXmVyTyKXmWSBZOUEkTU+mCSEAJNEbz+ZgAgoKxieT5DgK42PutIJjMAy7KwsrzY0eOS0faVSUPXIbnBZKX7yqSm8BB4gpmFCmYWK15vbzsoJU2JB1Fgc15liUekxum1x9rSe1fPEW9+85sxOTmJe+/5Cu6/50sYHBzE1Vdffa5Pyxc/44B034DXvwMAf/L7v45///zfAqje2GrhCJCMCZAlrivJx5UHbsKey67ENdfdCsumiEcjeN3rXocPfvCDoZ+DC9EEb9kUSq8y2SMAWeIQ0wQkIhKu2JXE5HAEutH62rLs87/vZ6PAZG88rJAJ9ZgmQOAJlrIGFlbYJqiVG2AQVx+4BYQQX6nr+94wjn2OZHC9WJg/g2S6f0NLphsN23y5yGau8jwBz9VX3Fwquo2lfOvNOcDWj1RMQiulbDeVyUa8YHINK5Oy4wxbKhY8g6BoNA7TohAEtidoRFM4LKzoOLNYQaFkQuC58/5aWU/zpW6JxvsANCvMgkg5lclWeyndqFSDSd2G2mUwSQhBIiLg6VdZRTuoMmnbFIRQiG2c+DMJ8by/ljYq57ff8nnM8PAwDh8+DEopbHtj31iJz1qQ6RvwFiFKKaZPHPF+5jqtuhimDVXhIfAcVLk7VzNNi+KvPvdNAECxbEGROXzta18LeFQ9HBecMbdsCqnLxa/HxQMhBMMZ2RvPoAWMaej14q4tssjBsmiovjtCCFIxEV99YBZffcCR4nchcwWARDKN3Zfux8H77saPf+SXunqO1bAwP4tUZmBDJyY4gsB2AuDi6pkEWALKtCgkpyJrmjZ0k6JQNkPIgpkb/OFT/j/PZFgwuTaVybULJjmOg6pGmMzVca+PRGPIFUxsGlJ9q0QRhVVgr9yVxJMvZ9Gf7O67upFgJoYb53q3bQpbTAJg18zW7btDPzYVE2FaFPmS5etabeg6RIl9ZhXdhhzSzdWPRFTE4VPMGCqoMmlaFKrc3jwxk5Awu6i3/HmP7tm4d6SLBELIhg4kAf+KXiYz4N245udm6uYVcQ02fRXd9myeBZ6DJPEwO+ibbMQw7SZ5TBhCBZO9nskeIdmzJe45xykS17YaQ8n6z5q7mJBFDlaYiMWh8dhuZa4Ac3V9/tnHseJsvs8mCwuzSKb622bfzzVBvem2TcHzF5+joixydZXJXMnCct4ABbs3+VUtXQhhPZMDSRkrThWz9p6ccnwMFjusMtWSyy6D4zhPhrpWqFqkrjIZicZg2nbTPECXiCpgICVjakTDcEaGKp//92NCsBqV85pj2RRDQ6zvunHEWxBu28Dhk0Xfn+t6BZLIKpMl3ep6ziQA77OfGFSQiLZXk7jBZDviEbHj+bw9wnH+f0t7rDv+MtdBLDmSmpMnjrZ9fMWwkaiRsyRjAkp698FkxbAxnOl8KDjPkWD5FWHD5Xv06ARF5tteWQRMvtVjbZDlzoLJxllnrrlDN1x7/e2wbRuPHvxu18/RLQvzZ9DXP7ihAzE+4NwuVsm3IvF116xtU/SnJAwkZcQ0wXdklmVRnJwvgdosGN01GUVZZ/Ntj58poew4SYuihEQy3bFksZbsyjJi8WRTMni1aFoEd3/ji/gfv/oTAJjMlQItN/WJqIhdm6MghGD7RBTpLiXpGwmOhNh7nEUsi2J0ZBhA59XsS7fEMZCS8OkvHfMdi6VXKhAlCbphwzBp06iOTnDdi3/wrrHAY03LhhbwWqmYiB2bwnls9OiMnsy1RyBs4G79v2X6BpDNLkPXK5g+fhgA8Au/9vsYHdvU9HibAjGtekPYNh7F955cgCrzHUudbJuC4wiSsc5vMJzP7+GHcBEZQ/RYGySBQOCJd33W4lYQ1tPl82JDFkhb99xGPvqeKSznDFQMG6+cKDR9Rp2wY/decByHQ688j9vvfGvXz9MplmVheXEeAwP+o5c2ChzXvpXBtnH+G6p0gStzdbEpxaYhDQMpCa+cKODUXLlp413WLaRjEgSBGYuIAofRAQVHThYx0qdgMWt4j8n0DWBhYRUy1+zSmkpcXVQtgukTRyDLCn70Qx+DFk2CM2hL+SO7t7P7e19SQt8FInPdSH2TpkWRiMcgK2rHCQhN4fEjrx/H733uEA6fKuLSLfG6nxtOz+RLx/MAgnsd2/FjbxrH0dMl7J4MrpabFoUa4h67mrW/R2t6wWSPYHy+e7UN/ydOHAEvCHjL9/0wBKH+kqKUwqa0TqqSionYNKTizEIlULrQSLFsYTAld5XZ5kPIXCnIhja36LExIYR41YXGDaFhUkSUnovcWtJpZXIoI2PIUTPs3RYPOLo9oihhcHgM08ePBB+8hqwsL8K2bQwObvBgMqAyaVo2IoHz4i48mqXZLECUJR7JmOgNZq9FNyk2DSvYMVGtpkyOaJieLWHXZAzPHsqiVLGgyjzSmcFVViaX1tTJ1cWdNblpajve94GPIlc0kY6f/9XGjtlAMYxlU6TiElLpga6umf6UM04m71OZdNxcn3olC5En2L25e9n02ICKsYFwwahhUUR6EtZzRm930yMQjiNNAg3XsfXlF5/BC88+juGRibpAcmaxDMO0cWZRx2i/gmhDj2NU4+uytGHRTbtriRrx+T2ajiG9nske3ZGIiqj4SNXKuuX1mfRYGxSJh7WawX2rZGx8EtPT/sHkwvwsSsUC5udm8OTjB70/q+2xdDd9I8PDq3qedSfAgMeyg3ubLkQaK5NA1YQoogq+1VzTokhExLpqSiom4vLtCaRiIjaPaMiXmNQ1nenHzKkTOHXyeFfnl11ZXlMnVxd3PMj4OJsRXdYt9K2iZ/l8hHQ2GnfdMS2KRExApm8Ac7OdV7OTThEgW2h2ITZ0JnN96pUsdm6Odj0apBMqug1Z5NCX7Lz9qcfacPGlB3t0jN+IJNc97hf/23sBADfd+nrvZ+5NcXqujG3jEVyxM9n0nJosdFRZcLFs2pX5DhA8GsS2KQjIWVn8elx4JKMCjs34BJOG3Qsm1xh+jfu6OmVsYgp3f+OLTf9u2zZ+9Ptvw7U3vAZPPvYgjh19xfvZgetvwx/9xRe6fs35udPstUc3djDJc6Ttxtm06KpMOc5XMgkmV3UriYRUg8lEREBUE1CuWA3KBgqlwYCGEIJNQ8zZsj8pg9IsKKUYGd2Eb37tC/ihd9yI/7r/SMe9j9mVJUxs3rqq39EPSWJGO6Pjk1jJG+B5ctGth2SDRZM2pVAlAVsmx/HQw4+CUtpRH7YqcxB5guUWlUktmsTphQpu3re+83CLZQsCT7BSMHDFzkSvEHAO6b3zPQLxW2LSjnscAHz/D30Ev/Jbf+z93bIoFInHpiG1ZbOzJHItF6980fRt7AZYz2O3MlSeb98zaVoUEaW9tXSPHq3QFKGl/XuQMUCPzhAFck57kMYmJpFdWWqqNr70wlOYPXMK3/jaF3Ds6Ct47/t+Cn/y6X/DtTe8BodeeWFVr+kanW3fvvYb/rWECzA6Y5XJi2/rEVEF7N+RRK7IKom0ZtYmxxHsmIhgMWeg5JjqAKztotE8qhZN4RHTBBgmxfs+8HN413s/iEI+1/EgesCVua59ZTKXXQbAvjO6aePaPWnEIxdZMFnz3xsDAkEguP22m3Hm9HG8euhQZ48mBPGogKzPfFTDqIBw7PNdbzlzoWxhfkWHJHIYTPu7A/c4O1x8K3qPjiE+m4NUTTD59nf9KJKpagbKdGRMB/akmuStLsyAwX/DUdRtFGpuqHXnQgBJ7G5R5sC1DSZ1w/adm9SjRxhaGeyQXh/umnOuZxSOj08BAE6eqJe6Hrz/2wCAconZ5r/9XT+Gq6+9BZdefjXmZk97/94N08cPQ1E1bBof6fo5zgbBRmcEonBxJlcyCRGEgzcGpPY6HsoouHp3EoQA88s6M/MiCKziqgoPw7ShqBouu/waAJ2Pe7AsC7ncyrr0TC445zI2PgV6kSp/SPti/VmHgM3oveuuuwAAD3zv7o6fIxERsVLwd3OFE0zGI+u7n6Jgsu+pUW3Dj9i70Ln4vtU9OsbPzVUUqz0P45u21P3Mspg0p12FTxKJb6LOtCh4/x85J9O9EyAfsH8xTIpoL5js0SWKxIGv2SjWcjGOQlhPFEcm2Imj61oyNsH6v2qDyS/962fxlX//HEZGNznHTGF0fDMAYNw9fvpo16954sQRDI1sDuVYeC4J45q9kedkricCz2E4I6Pg9DnyNb2QHEcwOqDihr0ZCAJBoWQhHhEC3SdVueo/4HoZdGKqYts2/vrP/zcArEtl0h09MTYxCQJ6UX72HCEgZH3XKkpp21mldcc65k/btm3Dps2TePTgPTi9UOlo/nciKrQw4KmAOh10sXUOJgko9m5PYGoksq6v0yOY3g6nRyCtlv7XvendeN8Hfr4paLRs2laaAziba1rfw5gvmZhZLGN8UAXPcU0LI6UUoN1XeTiOwG6THzTsXmWyR/cw6Y+IcuMMVUq7rqb38EcWOUwOq1j2MYA4G4yOT0KSZDz3zOMAgOWlBfzub/0cFhZm8QPv+ym89nVvx9ve+SN1xwNYlQPs9PHDGBuf3PCJiUCjM0IvytEgLiP9CkqGBepUhxrheYLxAQWzyzqmRrTA59MUriaYZF4GnQSTx46+gs/85e8DAHZdcnnox4XlF3/t/2BsfBLpzCB4jlycrtYELVsg/Jhb1pGrqfrZdnCgWCxbmJ4rA2jvDcHOh0LkCQghuPbaAzhx9EUkYwJyJX9FmB+JqIgVX5mrDkrcyuT6ylwpmCKoV5U89/R2zj0CaZUY/Y1P/Lnvv4cxWOA4AlniYVkUgpOpLFUs7JmKYaRfRbFsoVAy68x2TKfi2e2cII6077Lqme/0WC3puIhjp0qe5JVd39zFuYFaZ8aHNBw+1b1sdDXIsoLLr7gWDz3AZK0PP/gdUErxqc98Gbv37Mc7vv/9dcePObLY6ROHu3o9y7JwavoYbrr1das78bNAkAEPpcRb8y9GIgoPSll1ttW9bDCjYHChgoF0sDulKvOwaX0wudjBvMligc0D/IM/+2dPJruW3PmGd+DON7yDjU0KSDJfqJB2aqsWlAwLMWeLvpDVATC/iESLAM2yKWSJw5mlCiybYiTTpoeQVtUyiXgctlnB7skYHn5uOfT5JSICskWzabayrldguZXJs5CcF3uB5Iagt8PpEQghwfMZa7FCuvVpCo/ZZR3FcjUbNtynIKYJyCSbKzyGSVc1+N1PruvCMnm0V5nssSpSManO/j/sIOUenRNVeaY2cDL2gdn4NebA9bfjyKGXMHN6GgfvvxvJVAY7d1/ue2w8kUQ8keq6Mjk3exqGoW948x3A7Q9r/VlcrFJHF0XiQe327RrpuIird6dCVaHZ87D3U4t0Poi+VCwAqM6DXC9M6+IcCQM4hckOuyY5AhRKpiflv/qSFCqG3bJCSSnrUeQIC7BarYfFsoV4RPSqeZqmoVgsIBUTARJeKpuIiqAUyBZM3P3oPP7vF45gYUWHoeuwqICoyq9rb7ttU4h898WFHmtLL5jsEUynzeMEEENIUZNRAeMDCnJF03uca+08kJJh2rRuQTRMu6WhT6jTIgSt2jiLZQuZhNQzSumxKiIqD5DaYNKGdhE6V54NCCFIOLJi07Rx6OTZrVIeuP42AMAPv/NmfONrX8DVB25pO45hbGISX/3SP+IPfvfjHb/Wc8+/CAC49JId3Z3sWYTjWiftmKnMRSp1dJBEDqrCt73XEEJC95uJAufd1wghyGQ6G0RfPEvBpGVdnC6+ADp3iKcUm4cjACGYXa5AEnn0JyVsGY1gyUda6jwEmbiI6/dmEFWZw68fuZJZ57LPgskiBJ6gPynXuQm3w533/U/fOoW/+coJHHx2GY+9uAxdr8C0hXU33zGt3kzwjUTvk+gRCOnYioyE6onZtTmGK3YlkUlIyBdNgBIvExuPiBjtV3B6sYK5ZR3zKzoqho3BELKflmdF2Ln5UazYGOnvWUv3WB3uaBk3u2uYbNxMj/UhHRdRMWwYjgReN8IbSKyWyS078cGf+hXc8po34k1vfS9+6P0/2/b4H//wL2FoZBz3fecbHb2ObVM8/cQD4HkeN99w9SrO+OzAk9au2W6rwsVOIiKsWeJSFOqrUJm+gY7cXN3KpHY2KpMX6VrItlDhAkrbpuB5gkumYrhhbxoCz2EwLbNEQUKC3SLWoxQQRR6pmIhkTERZbz7Qsli/cn+yaqCoaRps24au6xhKyyj6PM6P8QEVHAHue2oR+3ewGY+n55hk2rCFde+XtGwKubeWbBh6mr4egXSqIqAIZ93vyhM2Dat49IVlxLR657o9UzGMD6qQRQ4Hn12CAcqkGF3S/vdY3XP36AGwa5rdyG1oCnNZ1JTeMrteJGMiDk0XYFkEUVVAqWKdNXUBIQQ/9qGPhT7+uptei0cfvhf/+s+f6WhI+ErexLOPfxcHDhxAMpns8mzPHm2KsyhVLKTiUusDLhLiUaHZqKtLRIFDreQm3TeI6WPh5waWiiwAWHeZq21ftJVJjiC0m2utHFgSOezaHPWqfKJA6pQvtVBQuAX/VFzE9Gyp6RjdtBHV+Lp9ViTCPvdisYhENAoa0ihopF/Bb31gBx54ZhFvvH4Q//v/exWnZnPsdSx+3VuGrF5lckPR+yR6BBLUA9N0fAuXulZEVQG6SRFp2HTLEo+BlIxEVMT28ShG+tRVbRTZ5q3173Gx9nP0WFsG0xKKjlSIopc9XU8iCg8QtgHTZB56B9b254JYoh+VcgnFQi7wWMuy8Nwzj+ORh76D5599EnfeeedZOMPVw3H+/Vq2TVHWbWyf6Nn4J6PimgVWokBAUG0JyfQNeLMdw+DJXNX1/lwIJPEivcd2oO5q7C3dMhZBf4opsiSRQysbQUoBwcnkRFUBfj09hmkj1tAqpGnMMbhQKLCEPvEfb+XH5IiG9945hkRUxGBawcw8S0zoJr/uMlfLplB7hokbhl7KvEcoOvO2IBA6sH6PqDxkkSCitb7RbBpWMWavTobaSuZqmDZkid/wlvs9zg8yCRk2LTh/I4j2KpPrhizxACWwbIp4VMTKORoVEob5FR1px23zzJkzmIrG2x7/1S/9Az7xGz/j/f0Nb3jDup7fWkGI/xSEbMHExJCKRLSnAOlLSkiukRKGEAJFYioIUWA9k8tLC9D1CiQpuC3ElbkqavAYktXRWZL5QoLU/HcQpmVDVfyr96LAtRxvRmlVFRDVBBCwwLT2PTdMimikfp/lBpPFYhE8z5Q1FcPuOLk+lJHxwKNZ9jq2yPwD1hHT6iVqNxK9T6JHIO1kS43ohg1J5CB14NYnChySMantppvjyKqDvVaqMt2wkeptcHqsEXFNgMCx64rnyLrfVC9mJIGA4wDTpkjHRURVgfVfbzBM04YgENx09RYAwOxs8OiG793zdfQPjuKLX/oqDh48iP3796/3aa4JrSonumljfFA9y2ezMREFbk2VMImIiIojmx0Z2wwAODV9LNRji6UCZFmBIKxf0suymKz7YnVLZ3uPTmSu/nsdSWDfLr/KP6XUc2iVRQ7bJyJYyul1x9i0ue2iVuYKAIrE1TmSh2UoLcMonAYACJEhjPStrweFRYPnmfc4e/SCyR6BcM5lYpo2yrqFmcVKy2NXCia2jKkdu5cNpqV1b85vJXMt6zYSsYvzJtdj7eF5guE+BWeWKhhISz3r8nWEEAJN5mGabMbaFTuTyJfDD94+W5R1G31xCaMjwwCAuYBgcm6pgEceuhe3v/YuvOXNr8c116z9/L/1wp2plyuY3r1CN2zIEtfrS18nMgkJZcd8anxiEgBwcvpoqMeWioWW/ZL5oonZpQpml1rf88OwUjQwOby6NpXzmYAOmzosu/UINEIIFJn3D/YIm6XtsmlIQ1QVkK1RaxCgqc+wtjIJAKrEeeNIOmEgLcEonAIAfPKjt+L6y1IdP0cnEFzcrtAbjd4n0SMQQtjojJPzZSzmDHCkdWYMAEb7Os8+bxuLYiC1vsYMXAuZq02BmNbb5PRYO3Zsijp9JN27D/cIR0TlYVo2BJ6NU1Dk7jLr60nZsJGOSxgaGgIAzM+1DyafefJhlEsFvOvtbzwbp7emuInEss4q87phYzFrYM9UrJdYWSfiUcFrRRmbmAIATB8/HOqxrYLJim5Dt2xce2m689EWDZgWMNJ/8ValCYiv9LsR07RhWWirwnKN3Xxfp+Y1JJHDlbuTIBzBolOhpEBTNa+2ZxIAFJmHFbJnspbJYQ0xbha8IGJyctOqr5kgLvZ5tRuNXjDZIxDiGFz0pyTEIwIiCu87w8hybN+VLuQ7PE/WfaPRWmpCoSm9r0KPtUOVedy4N42hTC+YXG8iKg8CAtHJUkcUHuY5NuKxLIqT81U3RQq24c9kMuB5HouLc20f/9hD90AQBNx++23rfKZrD6vCEFBCkUmIODVfxmVb4xd1MLHeRBTeM8lLJNOIxuI4cfxIqMcWiwVoPuY7xYqFzUMa+pISeC68KYsfhFy8MyYB1irk7m4WVvS697I2Mb+QNTAyILedp606Sgw/GgO4qCrgxr1pUMrWJIrgyqQscbA7M8kAwILQTakVjI1tBs+vv/yUgiCyirnjPdaWi/fb3SM0hDCtfV9SxvWXppFwGrQb0Tf4TD220NYvtpRSgJAmJ9kePVaLIvM9Gc5ZIKIK4AXiZanbDew+WximDYCgUDKxnDNAHekax3EYGBjE8mJrt01KKZ54+Du4/vrrEY+3N+nZiERVHiDMXXdqNIKxAQUTQ71Acj1RZB6yyJIohBCMjU91UJnM+1YmDdP2TIIiqtC1UzKlFKDtq20XOm6Qpxs2SrqFkuP2bdsUJ2bLKFcsUErBccC+7YmWMlfASZb5VCYJiK+/hSzxGM7ImF/REdcEr6/Se76GnklRaO0YG8T08SMYc2TW64lu2NAUrjcaZAPR+yR6BMJxBLbNZkcqMhuKW/GZkWWYNqIbuMHerzJZ1m0ko80LbI8ePc4PFImHInIQnGAyop77ESGWTZGMCphd1pGMixgdUKA4NvaDQ0NYajO6YWbmNA6/8izuuuuus3W6a0pEFXDFziQ2D2sYSEm4+pJUb309C8Q1NmILAMYmJjF9Ilxl0pW52jZ1kiAMQuCpjCKqvxopDO6oi4td4kwIkC+Z6E/KKDn7p4WsgYGUhELZgm5QRFUhMAGpyhwMu3l9o6AtQ8CRfhUV3cb2iWjTzxorkwJPOhgEV/P6lJ61YLJUsbxxKT02Br1gskcobFq1mI5qgu9iY5i0aYbRRoIjpMnRtVyx0Z/sLUo9epyvyCIHVeG8TVhUFbAKRd6aYFoUyZiIXZui2L8jgf07kl51YnhoCIsLZ3z7zgHg4P33AMB5M1fSj+E+BVvGIiBk9S7cPcIhy5zX6zY6PonTp47DNIJH5RSLBWhaFCsFE3PLVfdPCkBzpKlxTagLNDvB8JkhfTGSjEkolCyM9CuglPWkigLBtvEoLJuirFtIJ4J9I2Ka2CJobN0qlIqJ2DUZ9e3hb+yZFHkOIJ0voIsLsyiVChgdOwuVSdNGJr6+Hhs9OqO3yvcIxJW5Ss6mQJV4fwMeoKt+ybMFIQBt6II3bbpm87569Ohx9pFErk4RIYmcn8/WWcW0KGKagL3bE03B1L59l+Po4RewuLji+9iHHrgbmb4B7N2792ycao8LBFkkngvn+PgkLNPEzMx04OPcyqRlUQgCe46KbkPkOc99NaIKsLos9humDa03HglX7Ehg20QE4wMKAIrFrI7LtsaQjosAYW1C6XjwXiQeESAKnG9feCvPG0nksGdL3DfYbKpMCqRpnxSGxQXWB943MNTxYzuFELLu7v89OqMXTPYIhDj/cWVkUY3HQFrGUq4+60kIa97eqHAEXsatWLachnSKeKSXNe3R43xFU3hcOlXtLZRELrQN/3phUdpyjuCdd94J27Jwv1OBrHucZeHxh+/FTbfcDq6TAb89LnoUqerC6Tq6ngwhdS2VWDBJAUQVAdNzZawUDcQjgldNl6XuEzSmRVkf7UWOIvPYuy0BWeJx6ZY4rr0sjaGM4vW72pSGmsPJcQSj/QpypfoRSIQ0G/CEOi9FASGkTubajdA1u7IEAEgk1nckCIN2NMu8x/rTu1v1CAVHiDfDiBCCS7fEococZhYrKJTYkHBKm53CNhSEeJvMlbyBE3MljPYpazo8ukePHmefWkXERliD2s1Au/baaxGLxfDYQ/XBpGHo+I8vfQHZlUW85rXnr8S1x7mh1oXT7VsL4+jKZK7MhCUVF5FJSLhlXx8umYp5x7AETfsAI1cwfRVLNqW9KlIDm4Y1DKZlL/i7dEsM11+WRiIaTiXVl5SbHF0pdcefdQYhBJqm1QeTLca/tWPFCSbjZyGYpLQnn99o9EoyPQIhBOB41EkkNIXHTfsyWMoZeOCZRcgiBwq6ITZyrXBCYWeECY++lITJUf9hzT169Dg/EQUCUSQwrWqf91mHoOUMNFEUceutt+KJpw5CN2xPSvh3f/WH+KtP/W/wvIDXvOa1Z/Nse1wACDwHd5hhpm8QiqoFOrpaloVKuQRVi4CAYmxAxbbxSNPIhYjCQxJ5GKbdtInXDRuWTbGUNyCJHGSp8bonXotMD3+G+5SOjpdE0lwpJtRL+HdKbTBJCIEs8Z7sOSxZL5hMdnUOYWFBbm/G5Eaj9w3vEQwBeALwDWkvQgjScQlTIxpOzpcxnFE2tGsfU41RVAwb8aiA6y5NI9Xrl+zR44KCEIK+uIRyxWp5zFLOaDn4e02grYNJANiyZQsW5mbqzuG+73wDW7Zfiq/9132YnFj/vqMeFxYCXw0w2HiQYEfXcpkFEEzmSiBLnO/sPo4jGB9UkC2Ydf9OKcXMYhmzyzoGUhLKut93jm7o9pfzEb+qHKE+AWZIIpGIZ8ADMHWH2aGLWXZlGQAQj69vZdItBnQj6e2xfvS+4T0CIY5LWKsWntF+FTFNxLbxjV3lczsBdMNGMiJc9FblPXpcqPQlJZR8N7aMQsn05PnrA21r8T80NIRiMY98LgeAmVe8+PyTuPbm1+Om667sSbh6dAxLXlQDgLGJycDKZKnIAghNZSMjxDbJ4KGM4vVkutg2c3ffOhbBpiG1aSQPUwdwG3r+9PmIJJBmx1VCu5K5AvWVSaC7UTDZlSWIogRF1bo7iZCYFoXaS05sOHqfSI9QcBxpKaGIRwRcsycZWu9/ruAcDwHDshHf4Ofao0eP7gmadytJXNPGVzdsFMtWx71C/pC2EtuhIVZ5nJtjDogPPcD6J6+45rZekqtHVwg8V+fmOTo+iZMnjsKyWidVik4wqWoRAO1ljRGFB2m4Nm1KIQoc9u9INN1TdcNGqWyhPyX1qkhrjChUJc0ulLYeDRJEYzCZiUuoGJ3Z92azS4gnUuv+WZtOZbLHxqIXTPYIhBAWTDbKXKs/Z3LXjY67yFGgZ7rTo8cFTFTlW/oRWhZtWssquo3lggFCgOX86iqWYXp63GByfn4GAHDw/ruRSvdhatuelutsjx7tEPj6kQ5TW3bCMHQ8//yzLR9z4tghAEA6MwBC0LaaLgoEoLQu2UKpM5cQLNikYNf//IqOlaKBuZWK72zDHquD41hfuFUr1Se064lIzZVJIchvqYnsytK690sCLJjUevu3DUcvmOwRCCEAz6GlzPV8gucIKO0Fkz16XMjIEo+oKqCiN2fXDcuG5vTc2I5sb7lg4MqdSWybiMDsdqCeg2UDkti+p8cNJpcWZ2HbNh564Nu45rpbwXHcBbHO9jj7sEp4NQK4+tpbAAD333t3y8ccvP/bkBUVl1x2FWSp/T2REAJF5uv6fG2bQnAeJks8EhERZxZ1jPYruHV/H8YHVSQiPRXQeqBK9Z8FKJoqx2Fp7JnUlNbJuFZkV5bPipOrabUeu9Tj3NG7bfUIhICNBrkQMuaEEEQUHqrcu/R79LiQGelTkPfpi7QsNs+tLyFhKW+AUtZr1JeUIIt8xxn5RnTDDpyrNzg4CABYXpzDyy88jeWlBVx97W2OVPH8X2d7nH1YtYrzqlX9A8PYPLWraQRNLQfvvxtXXHUDeFEO5cSuSlx9MEkBoaa/97rLUrjligz2bosjogq4dk8asd4c53VBlbn6xBcha9YzqcocBA5esi0M2ZWldTffAcCmBvR6JjccvU+kRyiYAc/5v8lxB/72Nmw9elzY9CUlWD6RoWFSqDL3/7d350GS3vV9xz/f7qfvc67dGe3OnpIQOpG0SCJSgY6ULCtBXHYKp1IFFirsyuUcLnDiVCLHpiA4iYnBFRehVECCIIWJKZvEAmJDgQyyAUUSCLB2JcuSVrtaaY+Z3Znp6eP55Y/nmdnunp6Z7rn6er+qpran++nu39PfZ3ue7/P9HbrusrwKmZhOnS2rmIsp5kWUiDWOO6t3Yb6q07NllSv+mjPFzi3UtH8ytXbbxscViUR09swpfffPg8rRG2+5vadnw0bvS3iRhklybrj5Dj395Hf1nr93p+bmzjds+/LxF/Ti3zyrW269K5ghs40T9JaVybrzAi8aUSEbW/77yvG8fVKJ5sqkW/W7az3pdLqhMmlmymdjKrXo2bGa87PndqSbq8Qak72IiGBdZmvP5tpP0smIJkYYwwEMunzGk2nl1fVqzSmT8pRMRHXloZwWyjXtDr8T4vHIqt27SmVfkYjp9ExZs/Otx1X6vpNFtO53TDQa1cSuXTp7+lU99ud/qiuufIMKIxNrzqYJrCeX8bRQd6HjZ9/+Hr359rv1Vz95Qk8++cOGbZ89+mNJ0pXX3KCa79bt5ipJ6US0YZyec+reWq5DLpmINC1vtPokievJ5XK6cOFCw33pZGOs17NT3Vwl9fR65sOKiGBdtso6k/3o8N6MiszkCgy8mBfRRDHRcHItSb6c4mEVppj1dGAqrdFCMIFY3Au687fq3uXLaf9kSm+4vLDqem6lsq/xQlzxNk52Jicn9dILR/WjJ7+nm2+9U75zVHKwKZfuzahU9peP38lL9ukTH//PkqQXX2hcc3Jp2ZDpfYdUrjplUusfs+lkU2XSsXh8t6QS0aaeFxuvTObzec3Ozjbcl4xFViwFs5pyeVELC3Nblkw653T81dKKtYCXJjdLJUldeg0RQVui3mCM5ZkaS3LCBgyJSyYSml9s7KplZoqH3aTMTEeuKGosTCaDMdVeyzXWTFIxF9P+qZS8VRLOStVXZp3xkkumJif146f+UrVaTW+69S75/sWZMYGNKGRjmhpPaq5uiZvDhw4qEono5ePP6dgzP9bzzz0jSTr+4vPK5QrKF0bk+04TxfV77ASV+/puro1jJrFz8umYErGozsxW5PtOEbMNn6Pl83mVy2WVSqXl++JxazuZPD97LnidLUgmX3xlQa+cKSuZiKwYTlCpOmWS3pqzDqM7iAjaEmPyLAB9ZiQfb1jKoFZz8mv+cjIprRzXlU1HVamuHCtkCqqWZqZ8xmu5DlulGkzu0479+/cH75cr6sqrb5SjyoMtsGs0rlK5FnZBjSiZTGp6ep8e+fJn9Q/edZve/bZb9OMfPa6XXnxOe/cdUrnilE5G2zpug/83F4/RoJq+jTuDVeUynm69dkQ1P/ge2sycFoVCQZIaqpPJeHPlc3WnXnlZklQsjq653dKyMSdOl1bdJpmIKBYzTY4lVnzHLpZ9jeTpWdaLSCaxrvXWnwKAXpRJRpVMXEwOXz1X1qXT2TWrh8Wcp/kWE+w4XezqX8zGVG6RTDqnthfU/vCHP6w//PIf6VOfe0SL1UhQ5aHXBDZpKSms+U5eeHHi8OHDmjl3WqlURpFIRI9+8xG99MJfa+++g7qwUNX07lRbVa3mix3160xi56WTUcVjUZUr/qaSyXw+L0mamZlZvi/awSQZ33vsW5Kka6+/ec3tLszXNF6IK+ZFWg8l8IM1gN98/Zj2TKTUvEmpUtNIjmSyF/EtgHWZGVfMAfQdM9NoPrY8jiweM73+QHbNE699u9PKpT3NNS0rYnVjkvJZT+UW1UtZ+5NDjIyM6O1ve6tuu/laLVRq8p0jmcSmZVKeJJPva7kCf9lll0qSbrntb+uqa4/o0W99TSdefkF79h6U79R2Nd2LRuQ7t3xxxslxobmLzExT4wldWKhpM2FYSibrK5Mxz2SrDQ5v8hff+TNdevlVmtg1teZ2C+WaDu5JK5tuPZTAd1I0GlE25SnddFFubqGqStUp2+axip3FtwDawlTMAPrRaC6uxYqvxYofzPC6TgUm5kV0eG9acysm7rm41m42HZO/yqz57Uy+U6+Qi0mO8WfYGolYRKlERIuV2nJl8tJLg2TyyC136E233qVnfvKUarWa9u47KKn9YzaViOjqQzm9NlNeriwZh2xXTRSD7qAbnclVat3Ntd0JF+fmzuvJxx/TLbfeteZ2tVpwsWw0H1M+47Xs2eH7Tl6YQwaT7ATHWLXqq1TxdcvVI1QmexRfA1iXaWX3FgDoB9l0VL7vgvE2bZ6IJOLRFVflTRfHJRWzngpZT/Olld1hO522PpOMKhGPqlz15XHRHVtgJB/TfMlXIrw4cccdd+jQ4ct1/c136Y6771O+MKJCcVRXX3tEkmsYQ7wWM9OhPRlNjSfDWZJtIGZ572fpZDAp0mYmFmzVzdXz2qtLvvD8MVWrFV1z3RvX3K5UrmmsGA/WIs207tkR9M4IjkUvGlEm5am0WNOZCxW9bn9w3A3CeueDiGQS68qmo9q7a+1FuAGgF6WTUZlJlZpTvs1lgVqfXLvlE2cz0+X7srqwUNWr5xa1WPZVrfqKxyIdn9SZmcYKMS0s1hh/hi2RS3lBZTI8Fm+88UY99r2nlM2P6+Ch1+lrjz6rr377mA4culxOUjzW2TG7dyK5XLknmeyuZDyq6CbXAW/VzdWLmPxVV929aGF+XpKUyebW3K5cdcqlgqtlmZTXsmeH7zcWLq48mNWZ8xUVszFN70qv2xZ0D9dBsS4vGlEhy0kOgP6TSkQViZgqFV/pZHuT4wTd/prWOJMaxiVNjSd1+43jOjNT1pNHZ5VORjfcBSufDmaHZfwZtkImFYxJq+++WsjGZJGgK+FSdcc5J1PnXbNH8nHJSWaOSlGXxWMRJcPvuI1q1c213cpkqRQkk8nU2slepeYrmw6+f5PxSMu1el3TuPHdo0m96ZpRjeRjHR+j2FlEBwAwsCIR0/WXF1TMx5RpO5m05lyyoZvrklza0/6ptHaPxnXmfEUHpjbWgyOb8cLqAifm2Lx4LDhZ9+qqPPFYRLtGEg1ds6s1p1Qi2vH6hOlkVF7UVHMSh2z35dLepirEuVxQVWyczdVkpoallVopLYTJZHKdyqGTUomgfpVKRBWNKuwqfVGrceOTY4mOhw5g5xEhAMBAmxpP6i3XjysRby+Z9KIReV5EtVr9idTq48MO7cloaiyhsUJ8Q+1LxqNKJiKb6qoGLEnEIopHIyuqOdO7Ug3L3lSqru1qfbNs2lO5vLklKbA1ChlP0U1MwJNIJJRIJBoqk8Es/lHVVplobEmptCBJSqbWvpBmZstJYTwW0U1XjmhmrtKwje+cYsxo3Zf40wUAQJN0MqpKeCbl+27NLn3jxbhuumpkwyfWyXhEiXiE8WfYEvF4RPG4rRiDu2skoUQ8qsVycFxXa364lEjn8mlPFmHMZC/IZzxFNnZNYFmhUGhIJiUpEW++oLbScjKZXL9XRiJ+8XgcK8QVb1pvkhmt+xdRAwCgSSYZnHTXak7OrX2SU3/VfSMSsYhS8c2NewKWxD1TssXxFI0G66yevRBUhCpVp0xqY8dtPhOTmW1qfUNsjXTSU6rNXheryefzDd1cJSnhRVTzN9/N1fedopGVY3PTycb1Jn3nFGMml760rV8DZvaQmZ0ysx+1eOxfmpkzs/HtbAMAAJ1KJ6M6P1/V6dnytne/ikRMuYzHiTm2hFlwPHktjtk9E0mNF+OanavId07F7Ma6ZqdTUcU963i8JbbeaD6mQ3szm3qNfD6/ojKZTUdbrgdZ72IyuXplcr5UUza9MktMJyOq1vWjdU7MaN2ntjtqn5Z0T/OdZjYt6W5JL2zz+wMA0LHdYwn9rWtH5UVNlarb9plWD12S2fD4NaBZIR1ruUxNJBJUJ+cXg5P4fGZjpaBEjK7ZvSIS2VzPCKl1N9fdowlV2ujm6nkxebHGmayX1vadL9VUrvm6+nB+xXMzqWhDZdJp+79nsT22NWrOuW9JOtPiod+R9AGtmC8PAIDuG83HNTWe1MFL0jp7vtwwM+Z2GA8X9Aa2wmX7MipmWyeKI7mYcumoxgpxxTY4Ri0Zj4RdaTfTSvSKVt1cC9mYZK5hXGOzUmm+5eQ7FxaqenVmUTNzFd14RbHlskmZpKdqU7JKV//+tOO9k83sbZKOO+eepHsEAKCXTYwkVD02yyyD6CtrrctnZrpif1brrPqwpkQ8ov1Tabq5DohW3VzjsYgmignNzVeVSXnhRGRqiHmptNByvGStJk2NJeScNFFs3ZU6mYg2VZQYg9uvdjSZNLO0pH+toIvretu+X9L7w18ZVwkA2HGZZHR5qRBgUFwysbE1UZeYmfbt3txroHcUi0WdO3duxf0TxbheO1tWJiW9NlOWFzWN5i8mh6WFBSVTLZJJ32nPrpT2TCRXveAQXPBwmp2rKJ+JSUZlsl/t9F/Hw5IOSnrSzJ6XtFfS42Y22byhc+6Tzrkjzrkjkl7b2WYCABBUYNKpKJVJAANrdHRUMzMzqtVqDffn0p78sH4YMa1Yd7K0MN9y8h0np3gssmbX/bhnKld8VWvS2QsVmVhqpl/taDLpnPuhc26Xc+6Ac+6ApJck3eCcO7mT7QAAoB1mprF8jMokgIE1Ojoq59yKcZOpZFRL+Z2TyTV1TC2VVkkm25iZNR6LaLwY15HXF1StOvnOkUz2qe1eGuTzkr4r6XVm9pKZvW873w8AgK0WTFTS7VYAwPYYHR2VJJ050zhnZioelXMmFw6wTXiRhuVCSqXW3VzNrOXSNPViXkRvumZUEyMJjRViKpd9JnTqU9v659E59wvrPH5gO98fAIDN2jORXNG9CwAGxcjIiKSVyWQ0aspnPC1WfElOU+NJPffyvMYLCXlRU6m0oHxhpMUrurZmwF6aTXhyNKkTpxepTPYprgEAALCGRDzKGpAABtZqlUlJKuZiWiz7MpNetz+ny6azOnu+LGmtMZPqaJx5MR9TOhmVkUz2JZJJAAAAYEi1SiZrtZrOnj2rfNrTwqKvRDy6PM5RCpK+1ZJJk3W0bm4uHVU2FWVpkD5F2AAAAIAh1SqZfPjhh3XgwAE5v6xqzSkRrl2aqFvDtNU6k77vFI0GXWTb5UUjuv7yQkcJKHoHUQMAAACGVKsxk8ePH9fs7KzKpfOq+U7JeF0yacGEPIstJuCpTzw7kUkxy1m/IpkEAAAAhpTnecrn8w3JZLkcjIusVUqKRk2JeDBuPBGPSE6qVKpaXCwpmWrs5lrz3fK2GA4kkwAAAMAQGx0dbUgmK5WKJKlaKSnm2XJl0syUTXmam5+XpBVjJmu1i1VMDAeiDQAAAAyx5mRyqTJZKi0onYw2JIjZlKcLF+YkacWYyVLFVzFHl9VhQjIJAAAADLHVKpNzc3PKZzzF6sZBZjOeLlxYWZn0fSffOV0yvnKGVwwukkkAAABgiK1WmZyfn9doPt4wqc5ILqYLc2Flsm4CnrmFmqbGkkolGDM5TKhDAwAAAENstcrk/Py8Lt2badw2H1OlvCBJStRVJkuVmiaKjdti8FGZBAAAAIbYxMSETp8+rVqtJqmxMtks5kWUSwbJZjqTXb7fSUonqUoOG5JJAAAAYIhNTk7K93299tprkhrHTLaSjgXJZjqdW77PZEqyLMjQIZkEAAAAhtjk5KQk6eTJk5LWrkxKUmUxSDIrStbd65RMkFoMGyIOAAAADLHmZLJ+zGQrMzMzkqRisahSuaZqzSkWiyjmkVoMGyIOAAAADLFOK5Ozs7OSpCNXTWp2rqpyxVc+zbyew4hkEgAAABhiu3fvlrSyMrnamMmZmRklEgnt2Z2XRaSFxZoKWZLJYUQyCQAAAAyxTCajXC7XUWWyUCgoHotocjSpctXX9O50y20x2LiEAAAAAAy5ycnJtsdMzs7OKp/PS5IOXpLWSC6mHN1chxJRBwAAAIZcfTK5XmVyZmZmOZkcK8Q1VojvTCPRc+jmCgAAAAy5TiuThUJhx9qG3kUyCQAAAAy5yclJnThxQtLFyuRqE/DUd3PFcCOZBAAAAIbc9PS0ZmZmdO7cubbWmaQyCYlkEgAAABh6l156qSTp2WefbWs2VyqTkEgmAQAAgKG3lEweO3Zszcqkc45kEstIJgEAAIAhd+jQIUlBMrnWmMn5+XnVajW6uUISySQAAAAw9DKZjKamptatTM7OzkoSlUlIIpkEAAAAoKCra/2YyXK5rGq12rDNUjJJZRISySQAAAAABcnkUmUykUhIkhYWFiRJpVJJDz30kM6ePSuJyiQCJJMAAAAAtG/fPp04cUK1Wk1jY2OSpHPnzkmSvvzlL+t973ufPv7xj0uSpqamutVM9BCSSQAAAAAN1cbp6WlJ0iuvvCJJOnr0qCTp4Ycf1tjYmK677rqdbyB6DskkAAAAAGWz2eXb+/btkySdPHlSUjDL65K7775b0Wh0ZxuHnkQyCQAAAKAhmVyqTLZKJu+5556dbRh6ltftBgAAAADovkwms3y7uZvrsWPH9M53vlPT09N6xzve0ZX2ofeQTAIAAABoqEzmcjkVi0WdPHlS58+f16lTp3TTTTfpgx/8YBdbiF5DN1cAAAAADclkLBbT5OSkTpw4oUcffVRSsHQIUI9kEgAAAEBDMhmPxzU5OakvfelLuvfeeyVJV1xxRbeahh5FMgkAAACgZWVSknbt2qWvfvWruuqqq7rVNPQokkkAAAAAKyqTExMTkoLZW+++++5uNQs9jGQSAAAAwIrK5MsvvyxJuu2227rVJPQ4kkkAAAAAisfj8jxv+fYDDzwgSbrvvvu62Sz0MJYGAQAAACAzUzab1blz5xSLxXTnnXfKOdftZqGHUZkEAAAAIOliV9d4PN7llqAfkEwCAAAAkHQxmYzFYl1uCfoBySQAAAAASVQm0RmSSQAAAACSqEyiMySTAAAAACRRmURnSCYBAAAASKIyic6QTAIAAACQRGUSnSGZBAAAACCJyiQ6QzIJAAAAQBKVSXSGZBIAAACAJOn666/XNddco2Qy2e2moA+Yc67bbViXmX3fOXekjU17f2cAAAAAYPvYTr0RlUkAAAAAQMdIJgEAAAAAHSOZBAAAAAB0jGQSAAAAANAxkkkAAAAAQMdIJgEAAAAAHSOZBAAAAAB0jGQSAAAAANAxkkkAAAAAQMdIJgEAAAAAHSOZBAAAAAB0jGQSAAAAANAxkkkAAAAAQMe2NZk0s4fM7JSZ/ajuvt82s5+a2VNm9odmVtzONgAAAAAAtt52VyY/Lemepvu+Lulq59y1kp6R9K+2uQ0AAAAAgC22rcmkc+5bks403fc151w1/PUxSXu3sw0AAAAAgK3X7TGT90v6ky63AQAAAADQIa9bb2xmvy6pKulzqzz+fknvD39NtvuyW9A0AAAAAMA6zDm3vW9gdkDSV5xzV9fd915JvyTpLufc/LY2AAAAAACw5Xa8Mmlm90j6gKS3kEgCAAAAQH/a1sqkmX1e0u2SxiW9IunfKZi9NSHpdLjZY865X962RgAAAAAAtty2d3MFAAAAAAyebs/mCgAAAADoQySTAAAAAICOkUwCAAAAADpGMgkAAAAA6BjJJAAAAACgYzu+zuR2MrNHFCxDgu0xLum1bjcCxKGHEIveMQixGIR9GBTEoncQi95AHHpHO7F4zTl3z040hqVB0DYz+75z7ki32zHsiEPvIBa9YxBiMQj7MCiIRe8gFr2BOPSOXosF3VwBAAAAAB0jmQQAAAAAdIxkEp34ZLcbAEnEoZcQi94xCLEYhH0YFMSidxCL3kAcekdPxYIxkwAAAACAjlGZBAAAAAB0jGSyj5nZtJl9w8x+bGZPm9mvhPePmtnXzexo+O9IeP8VZvZdM1s0s19t8XpRM/t/ZvaVNd7zPeHrHjWz99TdHzezT5rZM2b2UzN71yrP/5CZvWhmF5ru/x0zeyL8ecbMzm3wY9lx/RYHM0ub2f8OH3/azD5S99ibzexxM6ua2c9t9rPZaQMWi/ea2at1/y8e2Ozns5MGIRZ1+3DUzC6Y2XEz+6aZXb2T+2Bmubrj4Akze83MPrbK8280sx+a2TEz+10zs/D+nw/3yzeznpkFsF29cjxtUSweDI+lpde4dws+oh0zYLG4LmzbD83sj80svwUf0Y7o0zisdg74y2EMnjCzR83syk18NDtuwGLR+fm4c46fPv2RNCXphvB2TtIzkq6U9FFJvxbe/2uS/kN4e5ekN0r6kKRfbfF6/0LSw5K+ssr7jUp6Lvx3JLw9Ej72G5J+K7wdkTS+ymvcErb7whr79U8kPdTtz3dQ4yApLemO8HZc0rcl/Wz4+wFJ10r6rKSf6/ZnO+SxeK+kT3T7Mx3yWPx9STdI+qKkXwr34RclPb3T+9C03Q8kvXmV1/hLBd+zJulP6o6n10t6naRvSjrS7eOjn4+nLYjFg63a1C8/AxaL70l6S3j7fkm/2e3Pd8Dj0PIcUFK+7vZ9kh7p9uc7rLFo2qat83Eqk33MOXfCOfd4ePu8pJ9I2iPpbZI+E272GUlvD7c55Zz7nqRK82uZ2V5Jf0fSp9Z4y5+R9HXn3Bnn3FlJX5e0tCDq/ZI+HL6P75xruZiqc+4x59yJdXbtFyR9fp1teka/xcE5N++c+0Z4uyzpcUl7w9+fd849Jclv+wPoIYMUi343ILHIhPtwpaT/E+7DS5Ku6MI+LL3O5QpORL7d4j2mFJyUPeaCM4HP1rXtJ865v1rjvXtajx1PS6+zoVj0uwGLxeWSvhXe/rqklr2qelG/xSFsQ8tzQOfcbN2vGUl9NaHLIMWiSVvn4ySTA8LMDki6XtJfSNpdd4CclLS7jZf4mKQPaO0kYo+kF+t+f0nSHjMrhr//pgVdJL9oZu285wpmtl/SQUl/tpHnd1u/xSF8zlsl/WkbbesrAxKLd5nZU2b2B2Y23Uabe9IAxOJJSQ+E+7BLwd/O8k7tQ9M275b0P8OT4lbPf2md5/e9bh5PTdtsJhb/OPy//dBS17d+NACxeFrBCb8k/bykvvye7ZM4rMnM/pGZPaugmvdPO31+rxiEWEidnY+TTA4AM8tK+pKkf9Z0dUfhQbTmgWRmf1fSKefcDzbYBE9BNeU7zrkbJH1X0n/c4Gu9W9IfOOdqG3x+1/RbHMzMU3DF6Xedc89t8D170oDE4o8lHXDOXavgquNnVnt+LxuQWPxbBSc3VUk3hW2u7eA+1Hu3+qjnxlbrgeOp3kZj8V8lHZb0BkknJP2nLWjLjhuQWNwv6R+a2Q8UdE8sr7N9zxmQOMg593vOucOSPijp32xBW3bcoMSi7vltnY+TTPY5M4spOHA/55z7X+Hdr4TdOpa6d5xa52VulXSfmT0v6QuS7jSz/2FmN9cNwr1P0nE1XrXbG953WtK8pKX3/6KkG8IBxEvP//dt7lJfnij1aRw+Kemoc+5jG9ztnjQosXDOnXbOLYa/fkrSjR18DD1hEGIR7sPvKRhLdUjSrytIJFM7uA8K3+s6Sd7SiUaLfTiuxm7SDc/vdz1yPC21ZcOxcM694pyrOed8Sf9NwQWKvjJAsfipc+5u59yNCs49nu380+iePotDu76gPuwSPoCxaP983PXAwFV+NvajYCD5ZyV9rOn+31bjgN+PNj3+oFYZ/C/pdq094PevFQz2HQlvj4aPfUHSneHt90r64jptXzHgV8E4pOelYP3TfvnpxzhI+i0FX3qRVR7/tPpzAp6BiYWkqbrb75D0WLc/32GLRd0+/H7dfR+S9J2d3ofw8Y9I+o11PvfmiUbubXr8m+rPCXh65njabCya/m//c0lf6PbnO8Sx2BX+Gwn36f5uf76DHIe6bZsn4Lms7vZbJX2/25/vsMYivK+j8/GuB4Cfjf9Iuk1ByfwpSU+EP/dKGlMw1ueopP+riydUkwr6Vc9KOhfezje95qoHb/j4/ZKOhT+/WHf/fgWD2J8K33vfKs//aPi+fvjvg3WPPSjpI93+XAc9DgquYDkFA8SX2vtA+Ngbw/bMKajoPN3tz3eIY/FhBeN5npT0DUlXdPvzHcJYfCS8728kLUoqSXpEwQx4O7oP4WPPrXccSDoi6UcKKiyfUHgyoOCCxEvhfrwi6avdPkb69Xjaglj8d0k/DPflj1SXXPbDz4DF4lcUzLz5jIL/731zMbtP49DyHFDSf1Hw9+4JBX/vrur25zussQgfe1AdnI8v/WcCAAAAAKBtjJkEAAAAAHSMZBIAAAAA0DGSSQAAAABAx0gmAQAAAAAdI5kEAAAAAHSMZBIAAAAA0DGSSQAAAABAx0gmAQAAAAAd+/9gCq59qOS+8wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1080x540 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sam.visualization import sam_quantile_plot\n",
+    "\n",
+    "\n",
+    "pred = model.predict(X, force_monotonic_quantiles=True)\n",
+    "\n",
+    "sam_quantile_plot(y, pred)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1206346375258462"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a forecasting model, one can choose `predict_ahead` differently. Choose a tuple of multiple values to predict multiple timesteps ahead. Also, the parameter `use_diff_of_y` can be useful in forecasting applications."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a603532e0ef3e672029c611f470e4ea76f9f8ba87d880bb6488c98a584e893e8"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/mlp.ipynb b/examples/mlp.ipynb
index dae3af1..7d99365 100644
--- a/examples/mlp.ipynb
+++ b/examples/mlp.ipynb
@@ -20,12 +20,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-07-28 11:56:06.033825: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
-      "2022-07-28 11:56:06.033872: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+      "2022-08-18 15:12:10.064301: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2022-08-18 15:12:10.064364: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
      ]
     }
    ],
    "source": [
+    "from sam.datasets import load_rainbow_beach\n",
     "from sam.models import MLPTimeseriesRegressor\n",
     "from sam.feature_engineering import SimpleFeatureEngineer\n",
     "\n",
@@ -149,7 +150,7 @@
     }
    ],
    "source": [
-    "data = pd.read_parquet(\"../data/rainbow_beach.parquet\")\n",
+    "data = load_rainbow_beach()\n",
     "data.head()"
    ]
   },
@@ -199,17 +200,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-07-28 11:56:09.589161: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
-      "2022-07-28 11:56:09.589448: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
-      "2022-07-28 11:56:09.589502: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (L17646): /proc/driver/nvidia/version does not exist\n",
-      "2022-07-28 11:56:09.590332: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2022-08-18 15:12:15.640758: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2022-08-18 15:12:15.640889: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2022-08-18 15:12:15.640963: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (L17646): /proc/driver/nvidia/version does not exist\n",
+      "2022-08-18 15:12:15.641414: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x7fca38761d60>"
+       "<keras.callbacks.History at 0x7f94dcf13df0>"
       ]
      },
      "execution_count": 4,
@@ -246,7 +247,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -277,7 +278,7 @@
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x7fca40071910>"
+       "<keras.callbacks.History at 0x7f94dcfebe20>"
       ]
      },
      "execution_count": 6,
@@ -314,7 +315,7 @@
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x7fc9cd06d9a0>"
+       "<keras.callbacks.History at 0x7f946dc4c190>"
       ]
      },
      "execution_count": 7,
@@ -347,6 +348,7 @@
     "\n",
     "model = MLPTimeseriesRegressor(\n",
     "    predict_ahead=(0,),\n",
+    "    quantiles=(0.025, 0.975),\n",
     "    feature_engineer=feature_pipeline,\n",
     "    use_diff_of_y=False,\n",
     "    epochs=20,\n",
@@ -363,19 +365,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<AxesSubplot:xlabel='TIME'>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1080x540 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -385,7 +377,13 @@
     }
    ],
    "source": [
-    "model.predict(X, y).plot()"
+    "import matplotlib.pyplot as plt\n",
+    "from sam.visualization import sam_quantile_plot\n",
+    "\n",
+    "pred = model.predict(X, force_monotonic_quantiles=True)\n",
+    "\n",
+    "sam_quantile_plot(y, pred)\n",
+    "plt.show()"
    ]
   }
  ],
diff --git a/pyproject.toml b/pyproject.toml
index 9927dc0..c38dcc2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,6 +6,7 @@ build-backend = "setuptools.build_meta"
 packages = [
     "sam",
     "sam.data_sources",
+    "sam.datasets",
     "sam.exploration",
     "sam.feature_engineering",
     "sam.logging_functions",
@@ -19,7 +20,7 @@ packages = [
 
 [project]
 name = "sam"
-version = "3.0.3"
+version = "3.1.0"
 description = "Time series anomaly detection and forecasting"
 readme = "README.md"
 requires-python = ">=3.8"
diff --git a/sam/__init__.py b/sam/__init__.py
index 53478d3..1d535de 100644
--- a/sam/__init__.py
+++ b/sam/__init__.py
@@ -25,6 +25,7 @@
 
 __all__ = [
     "data_sources",
+    "datasets",
     "exploration",
     "feature_engineering",
     "logging_functions",
diff --git a/sam/datasets/__init__.py b/sam/datasets/__init__.py
new file mode 100644
index 0000000..2235373
--- /dev/null
+++ b/sam/datasets/__init__.py
@@ -0,0 +1,7 @@
+from .datasets import load_rainbow_beach, load_sewage_data
+
+
+__all__ = [
+    "load_rainbow_beach",
+    "load_sewage_data",
+]
diff --git a/sam/datasets/data/rainbow_beach.csv b/sam/datasets/data/rainbow_beach.csv
new file mode 100644
index 0000000..ae04929
--- /dev/null
+++ b/sam/datasets/data/rainbow_beach.csv
@@ -0,0 +1,742 @@
+TIME,battery_life,transducer_depth,turbidity,water_temperature,wave_height,wave_period
+2014-06-15 00:00:00,11.6,1.495,0.85,16.6,0.136,3.0
+2014-06-15 01:00:00,11.6,1.42,0.87,16.3,0.117,4.0
+2014-06-15 02:00:00,11.6,1.478,0.79,16.1,0.114,7.0
+2014-06-15 03:00:00,11.6,1.518,0.76,15.9,0.111,3.0
+2014-06-15 04:00:00,11.6,1.507,0.77,15.7,0.107,3.0
+2014-06-15 05:00:00,11.6,1.484,0.73,15.6,0.104,3.0
+2014-06-15 06:00:00,11.6,1.457,0.65,15.5,0.11,3.0
+2014-06-15 07:00:00,11.6,1.504,0.68,15.5,0.117,3.0
+2014-06-15 08:00:00,11.6,1.435,0.65,15.5,0.108,3.0
+2014-06-15 09:00:00,11.6,1.435,0.65,15.5,0.108,3.0
+2014-06-15 10:00:00,11.6,1.46,0.62,15.9,0.115,3.0
+2014-06-15 11:00:00,11.6,1.466,0.63,16.1,0.12,3.0
+2014-06-15 12:00:00,11.5,1.384,0.62,16.3,0.105,4.0
+2014-06-15 13:00:00,11.5,1.429,0.62,16.4,0.105,4.0
+2014-06-15 14:00:00,11.5,1.429,0.62,16.4,0.105,4.0
+2014-06-15 15:00:00,11.5,1.386,0.81,16.9,0.116,3.0
+2014-06-15 16:00:00,11.5,1.386,0.81,16.9,0.116,3.0
+2014-06-15 17:00:00,11.5,1.438,1.03,17.2,0.111,3.0
+2014-06-15 18:00:00,11.5,1.417,1.07,17.4,0.103,4.0
+2014-06-15 19:00:00,11.5,1.373,1.03,17.4,0.104,3.0
+2014-06-15 20:00:00,11.5,1.524,1.0,17.3,0.117,6.0
+2014-06-15 21:00:00,11.5,1.362,0.85,17.0,0.117,6.0
+2014-06-15 22:00:00,11.5,1.476,0.87,16.8,0.119,5.0
+2014-06-15 23:00:00,11.5,1.479,0.83,16.8,0.118,3.0
+2014-06-16 00:00:00,11.5,1.513,0.78,16.7,0.096,4.0
+2014-06-16 01:00:00,11.5,1.464,0.75,16.6,0.089,4.0
+2014-06-16 02:00:00,11.5,1.504,0.84,16.5,0.093,7.0
+2014-06-16 03:00:00,11.5,1.481,0.72,16.4,0.086,6.0
+2014-06-16 04:00:00,11.5,1.57,0.71,16.4,0.118,3.0
+2014-06-16 05:00:00,11.5,1.481,0.69,16.4,0.087,5.0
+2014-06-16 06:00:00,11.5,1.559,0.64,16.4,0.081,4.0
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diff --git a/sam/datasets/data/sewage_data.csv b/sam/datasets/data/sewage_data.csv
new file mode 100644
index 0000000..799ec8c
--- /dev/null
+++ b/sam/datasets/data/sewage_data.csv
@@ -0,0 +1,26278 @@
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+2019-12-29 10:00:00,560.472,138.549,30.95,28.124,0.0,2.1
+2019-12-29 11:00:00,602.27,155.051,51.653,42.543,0.0,2.1
+2019-12-29 12:00:00,605.705,159.501,72.16,60.538,0.0,2.0
+2019-12-29 13:00:00,599.924,166.967,91.379,84.726,0.0,2.3
+2019-12-29 14:00:00,546.413,162.142,92.182,93.533,0.0,1.8
+2019-12-29 15:00:00,500.788,149.49,87.903,90.305,0.0,1.2
+2019-12-29 16:00:00,495.889,142.453,84.996,84.584,0.0,1.5
+2019-12-29 17:00:00,489.739,128.964,75.04,77.974,0.0,2.0
+2019-12-29 18:00:00,484.144,124.03,63.286,70.929,0.0,2.9
+2019-12-29 19:00:00,513.805,135.743,64.132,64.726,0.0,2.7
+2019-12-29 20:00:00,507.386,132.83,69.428,65.923,0.0,3.1
+2019-12-29 21:00:00,437.779,125.634,72.016,73.945,0.0,3.5
+2019-12-29 22:00:00,412.76,119.505,70.403,72.775,0.0,3.8
+2019-12-29 23:00:00,401.707,108.736,63.569,64.376,0.0,3.6
+2019-12-30 00:00:00,377.169,98.734,56.059,58.962,0.0,3.8
+2019-12-30 01:00:00,316.184,83.723,50.707,50.044,0.0,3.5
+2019-12-30 02:00:00,277.656,73.342,45.781,49.02,0.0,3.2
+2019-12-30 03:00:00,255.689,67.103,40.224,47.61,0.0,3.4
+2019-12-30 04:00:00,250.926,49.84,37.917,37.704,0.0,3.1
+2019-12-30 05:00:00,255.694,34.65,32.704,32.708,0.0,3.4
+2019-12-30 06:00:00,279.11,34.36,23.728,23.994,0.0,3.8
+2019-12-30 07:00:00,354.625,44.754,18.643,23.883,0.0,4.2
+2019-12-30 08:00:00,435.428,61.936,22.125,21.283,0.0,5.8
+2019-12-30 09:00:00,536.696,109.039,30.219,22.277,0.0,6.3
+2019-12-30 10:00:00,583.2,160.886,38.489,33.387,0.0,6.3
+2019-12-30 11:00:00,577.023,194.044,59.604,49.198,0.0,5.4
+2019-12-30 12:00:00,576.264,195.949,84.229,77.665,0.0,3.8
+2019-12-30 13:00:00,546.887,181.218,92.047,89.863,0.0,3.9
+2019-12-30 14:00:00,513.226,169.2,86.018,89.116,0.0,1.5
+2019-12-30 15:00:00,466.563,154.737,76.678,82.27,0.0,2.9
+2019-12-30 16:00:00,432.716,149.289,70.516,73.303,0.0,3.8
+2019-12-30 17:00:00,-999.0,145.802,65.114,69.789,0.0,3.9
+2019-12-30 18:00:00,499.652,136.633,62.748,65.279,0.0,3.6
+2019-12-30 19:00:00,526.533,164.554,67.938,62.249,0.0,3.6
+2019-12-30 20:00:00,513.653,197.561,76.323,68.696,0.0,5.0
+2019-12-30 21:00:00,478.834,183.487,81.702,80.039,0.0,5.2
+2019-12-30 22:00:00,474.766,160.117,82.527,84.475,0.0,5.2
+2019-12-30 23:00:00,448.407,146.601,78.958,81.482,0.0,6.2
+2019-12-31 00:00:00,395.702,133.325,70.202,77.352,0.0,6.8
+2019-12-31 01:00:00,316.641,107.423,63.519,66.613,0.0,5.2
+2019-12-31 02:00:00,289.823,77.792,56.92,53.427,0.0,3.9
+2019-12-31 03:00:00,279.045,53.411,41.18,45.668,0.0,3.8
+2019-12-31 04:00:00,246.439,45.022,28.23,36.909,0.0,2.2
+2019-12-31 05:00:00,238.292,45.787,26.05,31.444,0.0,3.2
+2019-12-31 06:00:00,223.829,37.154,22.189,29.016,0.0,4.3
+2019-12-31 07:00:00,294.715,34.527,21.607,21.88,0.0,6.0
+2019-12-31 08:00:00,420.303,58.707,18.429,18.419,0.0,7.1
+2019-12-31 09:00:00,544.107,100.598,28.13,24.245,0.0,7.9
+2019-12-31 10:00:00,614.616,166.691,47.849,34.708,0.0,7.2
+2019-12-31 11:00:00,615.356,207.318,64.03,49.687,0.0,5.9
+2019-12-31 12:00:00,600.42,194.888,83.854,72.082,0.0,2.0
+2019-12-31 13:00:00,571.595,186.641,91.406,91.683,0.0,2.6
+2019-12-31 14:00:00,552.401,172.128,94.838,96.125,0.0,1.8
+2019-12-31 15:00:00,535.996,154.113,90.847,93.714,0.0,-1.0
+2019-12-31 16:00:00,504.437,149.019,81.886,85.875,0.0,-0.3
+2019-12-31 17:00:00,528.674,154.188,75.172,78.158,0.0,-0.3
+2019-12-31 18:00:00,548.642,162.998,73.379,73.018,0.0,0.0
+2019-12-31 19:00:00,475.968,179.909,77.024,72.907,0.0,0.1
+2019-12-31 20:00:00,447.153,173.214,86.784,84.012,0.0,0.7
+2019-12-31 21:00:00,416.418,139.749,88.561,90.096,0.0,0.7
+2019-12-31 22:00:00,395.46,129.821,83.179,88.561,0.0,0.7
diff --git a/sam/datasets/datasets.py b/sam/datasets/datasets.py
new file mode 100644
index 0000000..2af827c
--- /dev/null
+++ b/sam/datasets/datasets.py
@@ -0,0 +1,27 @@
+from pathlib import Path
+
+import pandas as pd
+
+
+PACKAGEDIR = Path(__file__).parent.absolute()
+
+
+def load_rainbow_beach():
+    """
+    Loads the Rainbow Beach dataset (subset of the open Chicago Water dataset)
+
+    Source:
+    https://data.cityofchicago.org/Parks-Recreation/Beach-Water-Quality-Automated-Sensors/qmqz-2xku
+    """
+    file_path = PACKAGEDIR / "data/rainbow_beach.csv"
+    return pd.read_csv(file_path, index_col=[0], parse_dates=[0])
+
+
+def load_sewage_data():
+    """
+    Loads a sewage dataset, containing the discharge of multiple pumps and some weather data
+
+    Source: Fake dataset by Royal HaskoningDHV
+    """
+    file_path = PACKAGEDIR / "data/sewage_data.csv"
+    return pd.read_csv(file_path, index_col=[0], parse_dates=[0])
diff --git a/sam/datasets/tests/__init__.py b/sam/datasets/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/sam/datasets/tests/test_datasets.py b/sam/datasets/tests/test_datasets.py
new file mode 100644
index 0000000..cd30832
--- /dev/null
+++ b/sam/datasets/tests/test_datasets.py
@@ -0,0 +1,38 @@
+import unittest
+from sam.datasets import load_rainbow_beach, load_sewage_data
+
+
+class TestDatasets(unittest.TestCase):
+    def test_rainbow_beach(self):
+        df = load_rainbow_beach()
+        print(df)
+        self.assertEqual(df.index.name, "TIME")
+        self.assertEqual(df.index.dtype, "datetime64[ns]")
+        self.assertListEqual(
+            df.columns.tolist(),
+            [
+                "battery_life",
+                "transducer_depth",
+                "turbidity",
+                "water_temperature",
+                "wave_height",
+                "wave_period",
+            ],
+        )
+
+    def test_sewage_data(self):
+        df = load_sewage_data()
+        print(df)
+        self.assertEqual(df.index.name, "TIME")
+        self.assertEqual(df.index.dtype, "datetime64[ns]")
+        self.assertListEqual(
+            df.columns.tolist(),
+            [
+                "Discharge_Hoofdgemaal",
+                "Discharge_Sportlaan",
+                "Discharge_Emmastraat",
+                "Discharge_Kerkstraat",
+                "Precipitation",
+                "Temperature",
+            ],
+        )
diff --git a/sam/models/__init__.py b/sam/models/__init__.py
index 077d715..0c06fdc 100644
--- a/sam/models/__init__.py
+++ b/sam/models/__init__.py
@@ -1,18 +1,36 @@
-from .benchmark import benchmark_wrapper  # noqa: F401
-from .benchmark import (  # noqa: F401
+from .benchmark import benchmark_wrapper
+from .benchmark import (
     benchmark_model,
     plot_score_dicts,
     preprocess_data_for_benchmarking,
 )
-from .keras_templates import create_keras_autoencoder_rnn  # noqa: F401
-from .keras_templates import (  # noqa: F401
+from .keras_templates import create_keras_autoencoder_rnn
+from .keras_templates import (
     create_keras_autoencoder_mlp,
     create_keras_quantile_mlp,
     create_keras_quantile_rnn,
 )
 
-from .sam_shap_explainer import SamShapExplainer  # noqa: F401
-from .linear_model import LinearQuantileRegression  # noqa: F401
-from .base_model import BaseTimeseriesRegressor  # noqa: F401
-from .constant_model import ConstantTimeseriesRegressor  # noqa: F401
-from .mlp_model import MLPTimeseriesRegressor  # noqa: F401
+from .sam_shap_explainer import SamShapExplainer
+from .linear_model import LinearQuantileRegression
+from .base_model import BaseTimeseriesRegressor
+from .constant_model import ConstantTimeseriesRegressor
+from .lasso_model import LassoTimeseriesRegressor
+from .mlp_model import MLPTimeseriesRegressor
+
+__all__ = [
+    "benchmark_wrapper",
+    "benchmark_model",
+    "plot_score_dicts",
+    "preprocess_data_for_benchmarking",
+    "create_keras_autoencoder_rnn",
+    "create_keras_autoencoder_mlp",
+    "create_keras_quantile_mlp",
+    "create_keras_quantile_rnn",
+    "SamShapExplainer",
+    "LinearQuantileRegression",
+    "BaseTimeseriesRegressor",
+    "ConstantTimeseriesRegressor",
+    "LassoTimeseriesRegressor",
+    "MLPTimeseriesRegressor",
+]
diff --git a/sam/models/base_model.py b/sam/models/base_model.py
index 1d162bb..7df65da 100644
--- a/sam/models/base_model.py
+++ b/sam/models/base_model.py
@@ -56,6 +56,9 @@ class BaseTimeseriesRegressor(BaseEstimator, RegressorMixin, ABC):
         node in the output layer and does not reflect a quantile, but rather estimates the central
         tendency of the data. Setting to 'mean' results in fitting that node with MSE, and
         setting this to 'median' results in fitting that node with MAE (equal to 0.5 quantile).
+    feature_engineering: object, optional (default=None)
+        Should be an sklearn-type transformer that has a transform method, e.g.
+        `sam.feature_engineering.SimpleFeatureEngineer`.
     kwargs: dict, optional
         Not used. Just for compatibility of models that inherit from this class.
 
@@ -591,7 +594,7 @@ def score(self, X: pd.DataFrame, y: pd.Series) -> float:
         # This function only works if the estimator is fitted
         check_is_fitted(self, "model_")
         # We need a dataframe, regardless of if these functions outputs a series or dataframe
-        prediction = pd.DataFrame(self.predict(X, y))
+        prediction = pd.DataFrame(self.predict(X, y), columns=self.prediction_cols_)
         actual = pd.DataFrame(self.get_actual(y))
 
         # scale these predictions back to get a score that is in same units as keras loss
diff --git a/sam/models/lasso_model.py b/sam/models/lasso_model.py
new file mode 100644
index 0000000..837866c
--- /dev/null
+++ b/sam/models/lasso_model.py
@@ -0,0 +1,231 @@
+import os
+from typing import Callable, Sequence, Tuple, Union
+
+import numpy as np
+import pandas as pd
+from sam.feature_engineering import BaseFeatureEngineer
+from sam.models import BaseTimeseriesRegressor
+from sklearn.base import TransformerMixin
+from sklearn.linear_model import Lasso, QuantileRegressor
+from sklearn.multioutput import MultiOutputRegressor
+
+
+class LassoTimeseriesRegressor(BaseTimeseriesRegressor):
+    """Linear quantile regression model (Lasso) for time series
+
+    This model combines several approaches to time series data:
+    Multiple outputs for forecasting, quantile regression, and feature engineering.
+    This class implements a linear quantile regression model. For each quantile, a
+    separate model is trained.
+
+    It is a wrapper around
+    the sklearn `QuantileRegressor` and `Lasso`. For more information on the
+    model specifics, see the sklearn documentation.
+
+    Parameters
+    ----------
+    predict_ahead: tuple of integers, optional (default=(0,))
+        how many steps to predict ahead. For example, if (1, 2), the model will predict both 1 and
+        2 timesteps into the future. If (0,), predict the present.
+    quantiles: tuple of floats, optional (default=())
+        The quantiles to predict. Values between 0 and 1. Keep in mind that the mean will be
+        predicted regardless of this parameter
+    use_diff_of_y: bool, optional (default=False)
+        If True differencing is used (the difference between y now and shifted y),
+        else differencing is not used (shifted y is used).
+    timecol: string, optional (default=None)
+        If not None, the column to use for constructing time features. For now,
+        creating features from a DateTimeIndex is not supported yet.
+    y_scaler: object, optional (default=None)
+        Should be an sklearn-type transformer that has a transform and inverse_transform method.
+        E.g.: StandardScaler() or PowerTransformer()
+    average_type: str (default='mean')
+        Determines what to fit as the average: 'mean', or 'median'. The average is the last
+        node in the output layer and does not reflect a quantile, but rather estimates the central
+        tendency of the data. Setting to 'mean' results in fitting that node with MSE, and
+        setting this to 'median' results in fitting that node with MAE (equal to 0.5 quantile).
+    feature_engineering: object, optional (default=None)
+        Should be an sklearn-type transformer that has a transform method, e.g.
+        `sam.feature_engineering.SimpleFeatureEngineer`.
+    alpha : float, default=1.0
+        Regularization constant that multiplies the L1 penalty term.
+    fit_intercept : bool, default=True
+        Whether or not to fit the intercept.
+    quantile_options : dict, optional (default=None)
+        Options for `sklearn.linear_model.QuantileRegressor`.
+    mean_options : dict, optional (default=None)
+        Options for `sklearn.linear_model.Lasso`.
+    kwargs: dict, optional
+        Not used. Just for compatibility of models that inherit from this class.
+
+    Attributes
+    ----------
+    feature_engineer_: Sklearn transformer
+        The transformer used on the raw data before prediction
+    n_inputs_: integer
+        The number of inputs used for the underlying neural network
+    n_outputs_: integer
+        The number of outputs (columns) from the model
+    prediction_cols_: array of strings
+        The names of the output columns from the model.
+    model_ : object
+        List of sklearn models, one for each quantile.
+
+    Examples
+    --------
+    >>> import pandas as pd
+    >>> from sam.models import MLPTimeseriesRegressor
+    >>> from sam.feature_engineering import SimpleFeatureEngineer
+
+    >>> data = pd.read_parquet("../data/rainbow_beach.parquet").set_index("TIME")
+    >>> X, y = data, data["water_temperature"]
+
+    >>> simple_features = SimpleFeatureEngineer(keep_original=False)
+    >>> model = MLPTimeseriesRegressor(
+    ...     predict_ahead=(0,),
+    ...     feature_engineer=simple_features,
+    ... )
+    >>> model.fit(X, y)
+
+    """
+
+    def __init__(
+        self,
+        predict_ahead: Sequence[int] = (0,),
+        quantiles: Sequence[float] = (),
+        use_diff_of_y: bool = False,
+        timecol: str = None,
+        y_scaler: TransformerMixin = None,
+        average_type: str = "mean",
+        feature_engineer: BaseFeatureEngineer = None,
+        alpha: float = 1.0,
+        fit_intercept: bool = True,
+        quantile_options: dict = None,
+        mean_options: dict = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            predict_ahead=predict_ahead,
+            quantiles=quantiles,
+            use_diff_of_y=use_diff_of_y,
+            timecol=timecol,
+            y_scaler=y_scaler,
+            feature_engineer=feature_engineer,
+            **kwargs,
+        )
+        self.average_type = average_type
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.quantile_options = quantile_options
+        self.mean_options = mean_options
+        self.feature_engineer = feature_engineer
+
+        if self.average_type == "median" and 0.5 in self.quantiles:
+            raise ValueError(
+                "average_type is mean, but 0.5 is also in quantiles (duplicate). "
+                "Either set average_type to mean or remove 0.5 from quantiles"
+            )
+
+    def get_untrained_model(self, quantile=None) -> Callable:
+        """Returns linear quantile regression model"""
+        if quantile is not None:
+            estimator = QuantileRegressor(
+                quantile=quantile,
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                **(self.quantile_options or {}),
+            )
+        else:
+            estimator = Lasso(
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                **(self.mean_options or {}),
+            )
+        return MultiOutputRegressor(estimator=estimator)
+
+    def fit(
+        self,
+        X: pd.DataFrame,
+        y: pd.Series,
+        **fit_kwargs,
+    ):
+        X, y, _, _ = self.preprocess_fit(X, y)
+        self.model_ = [self.get_untrained_model(quantile) for quantile in self.quantiles]
+
+        if self.average_type == "mean":
+            self.model_.append(self.get_untrained_model())
+        elif self.average_type == "median":
+            self.model_.append(self.get_untrained_model(0.5))
+        else:
+            raise ValueError(f"Unknown average_type: {self.average_type}")
+
+        for model in self.model_:
+            model.fit(X, y, **fit_kwargs)
+        return self
+
+    def predict(
+        self,
+        X: pd.DataFrame,
+        y: pd.Series = None,
+        return_data: bool = False,
+        force_monotonic_quantiles: bool = False,
+    ) -> Union[pd.DataFrame, Tuple[pd.DataFrame, pd.DataFrame]]:
+
+        self.validate_data(X)
+
+        if y is None and self.use_diff_of_y:
+            raise ValueError("You must provide y when using use_diff_of_y=True")
+
+        X_transformed = self.preprocess_predict(X, y)
+        prediction = [model.predict(X_transformed) for model in self.model_]
+        prediction = np.concatenate(prediction, axis=1)
+
+        prediction = self.postprocess_predict(
+            prediction, X, y, force_monotonic_quantiles=force_monotonic_quantiles
+        )
+
+        if return_data:
+            return prediction, X_transformed
+        else:
+            return prediction
+
+    def dump(self, foldername: str, prefix: str = "model") -> None:
+        """Save a model to disk
+
+        This abstract method needs to be implemented by any class inheriting from
+        SamQuantileRegressor. This function dumps the SAM model to disk.
+
+        Parameters
+        ----------
+        foldername : str
+            The folder location where to save the model
+        prefix : str, optional
+           The prefix used in the filename, by default "model"
+        """
+        import joblib
+
+        if not os.path.exists(foldername):
+            os.makedirs(foldername)
+        joblib.dump(self, os.path.join(foldername, f"{prefix}.pkl"))
+
+    @classmethod
+    def load(cls, foldername, prefix="model") -> Callable:
+        """Load a model from disk
+
+        This abstract method needs to be implemented by any class inheriting from
+        SamQuantileRegressor. This function loads a SAM model from disk.
+
+        Parameters
+        ----------
+        foldername : str
+            The folder location where the model is stored
+        prefix : str, optional
+           The prefix used in the filename, by default "model"
+
+        Returns
+        -------
+        The SAM model that has been loaded from disk
+        """
+        import joblib
+
+        return joblib.load(os.path.join(foldername, f"{prefix}.pkl"))
diff --git a/sam/models/linear_model.py b/sam/models/linear_model.py
index 327927b..755a2e5 100644
--- a/sam/models/linear_model.py
+++ b/sam/models/linear_model.py
@@ -2,6 +2,7 @@
 import pandas as pd
 from sam.metrics import tilted_loss
 from sklearn.base import BaseEstimator, RegressorMixin
+from sam.utils.warnings import add_future_warning
 
 # Keep package independent of statsmodels
 try:
@@ -54,6 +55,11 @@ class LinearQuantileRegression(BaseEstimator, RegressorMixin):
     >>> model.fit(X, y)
     """
 
+    @add_future_warning(
+        "This class is deprecated and will be removed in a future version. "
+        "Please use the LassoTimeseriesRegressor class from the sam.models "
+        "or the QuantileRegressor class from sklearn.linear_model instead."
+    )
     def __init__(
         self,
         quantiles: list = [0.05, 0.95],
diff --git a/sam/models/mlp_model.py b/sam/models/mlp_model.py
index e687aeb..83e7cd5 100644
--- a/sam/models/mlp_model.py
+++ b/sam/models/mlp_model.py
@@ -15,30 +15,15 @@
 
 
 class MLPTimeseriesRegressor(BaseTimeseriesRegressor):
-    """
-    This is an example class for how the SAM skeleton can work. This is not the final/only model,
-    there are some notes:
-    - There is no validation yet. Therefore, the input data must already be sorted and monospaced
-    - The feature engineering is very simple, we just calculate lag/max/min/mean for a given window
-      size, as well as minute/hour/month/weekday if there is a time column
-    - The prediction requires y as input. The reason for this is described in the predict function.
-      Keep in mind that this is not directly 'cheating', since we are predicting a future value of
-      y, and giving the present value of y as input to the predict.
-      When predicting the present, this y is not needed and can be None
-
-    It is possible to subclass this class and overwrite functions. For now, the most obvious case
-    is overwriting the `get_feature_engineer(self)` function. This function must return a
-    transformer, with attributes: `fit`, `transform`, `fit_transform`, and `get_feature_names`.
-    The output of `transform` must be a numpy array or pandas dataframe with the same number of
-    rows as the input array. The output of `get_feature_names` must be an array or list of
-    strings, the same length as the number of columns in the output of `transform`.
-
-    Another possibility would be overwriting the `get_untrained_model(self)` function.
-    This function must return a keras model, with `fit`, `predict`, `save` and `summary`
-    attributes, where fit/predict will accept a regular (2d) dataframe or numpy array as input.
-
-    Note that the below parameters are just for the default model, and subclasses can have
-    different `__init__` parameters.
+    """Multi-layer Perceptron Regressor for time series
+
+    This model combines several approaches to time series data:
+    Multiple outputs for forecasting, quantile regression, and feature engineering.
+    This class is an implementation of an MLP to estimate multiple quantiles for all
+    forecasting horizons at once.
+
+    This is a wrapper for a keras MLP model. For more information on the model parameters,
+    see the keras documentation.
 
     Parameters
     ----------
@@ -112,26 +97,25 @@ class MLPTimeseriesRegressor(BaseTimeseriesRegressor):
     >>> import pandas as pd
     >>> from sam.models import MLPTimeseriesRegressor
     >>> from sam.feature_engineering import SimpleFeatureEngineer
-    ...
+
     >>> data = pd.read_parquet("../data/rainbow_beach.parquet").set_index("TIME")
     >>> X, y = data, data["water_temperature"]
-    ...
+
     >>> simple_features = SimpleFeatureEngineer(
-    >>>     rolling_features=[
-    >>>         ("wave_height", "mean", 24),
-    >>>         ("wave_height", "mean", 12),
-    >>>     ],
-    >>>     time_features=[
-    >>>         ("hour_of_day", "cyclical"),
-    >>>     ],
-    >>>     keep_original=False,
-    >>> )
-    ...
+    ...     rolling_features=[
+    ...         ("wave_height", "mean", 24),
+    ...         ("wave_height", "mean", 12),
+    ...     ],
+    ...     time_features=[
+    ...         ("hour_of_day", "cyclical"),
+    ...     ],
+    ...     keep_original=False,
+    ... )
+
     >>> model = MLPTimeseriesRegressor(
-    >>>     predict_ahead=(0,),
-    >>>     feature_engineer=simple_features,
-    >>> )
-    ....
+    ...     predict_ahead=(0,),
+    ...     feature_engineer=simple_features,
+    ... )
     >>> model.fit(X, y)
     """
 
@@ -333,11 +317,11 @@ def predict(
         X_transformed: pd.DataFrame, optional
             The transformed input data, when return_data is True, otherwise None
         """
-        if max(self.predict_ahead) > 0 and y is None:
-            raise ValueError("When predict_ahead > 0, y is needed for prediction")
-
         self.validate_data(X)
 
+        if y is None and self.use_diff_of_y:
+            raise ValueError("You must provide y when using use_diff_of_y=True")
+
         X_transformed = self.preprocess_predict(X, y)
         prediction = self.model_.predict(X_transformed)
 
diff --git a/sam/models/tests/test_lasso_model.py b/sam/models/tests/test_lasso_model.py
new file mode 100644
index 0000000..7715c1c
--- /dev/null
+++ b/sam/models/tests/test_lasso_model.py
@@ -0,0 +1,83 @@
+import pytest
+from sam.feature_engineering.simple_feature_engineering import SimpleFeatureEngineer
+from sam.models import LassoTimeseriesRegressor
+from sam.models.tests.utils import (
+    assert_get_actual,
+    assert_performance,
+    assert_prediction,
+    get_dataset,
+    set_seed,
+)
+from sklearn.preprocessing import StandardScaler
+
+
+@set_seed
+def train_lasso(X, y, predict_ahead, quantiles, average_type, use_diff_of_y, y_scaler):
+    fe = SimpleFeatureEngineer(keep_original=True)
+    model = LassoTimeseriesRegressor(
+        predict_ahead=predict_ahead,
+        quantiles=quantiles,
+        use_diff_of_y=use_diff_of_y,
+        y_scaler=y_scaler,
+        feature_engineer=fe,
+        average_type=average_type,
+        alpha=1e-6,  # make sure it can overfit for testing
+    )
+    model.fit(X, y)
+    return model
+
+
+@pytest.mark.parametrize(
+    "predict_ahead,quantiles,average_type,use_diff_of_y,y_scaler,max_mae",
+    [
+        pytest.param((0,), (), "mean", True, None, 3, marks=pytest.mark.xfail),  # should fail
+        pytest.param((0, 1), (), "mean", True, None, 3, marks=pytest.mark.xfail),  # should fail
+        ((0,), (), "mean", False, None, 3),  # plain regression
+        ((0,), (0.1, 0.9), "mean", False, None, 3.0),  # quantile regression
+        ((0,), (), "median", False, None, 3.0),  # median prediction
+        ((0,), (), "mean", False, StandardScaler(), 3.0),  # scaler
+        ((1,), (), "mean", False, None, 3.0),  # forecast
+        ((1,), (), "mean", True, None, 3.0),  # use_diff_of_y
+        ((1, 2, 3), (), "mean", False, None, 3.0),  # multiforecast
+        ((1, 2, 3), (), "mean", True, None, 3.0),  # multiforecast with use_diff_of_y
+        ((0,), (0.1, 0.5, 0.9), "mean", False, None, 3.0),  # quantiles
+        ((1, 2, 3), (0.1, 0.5, 0.9), "mean", False, None, 3.0),  # quantiles multiforecast
+        ((1,), (), "mean", True, StandardScaler(), 3.0),  # all options except quantiles
+        ((1, 2, 3), (0.1, 0.5, 0.9), "mean", True, StandardScaler(), 3.0),  # all options
+    ],
+)
+def test_lasso(
+    predict_ahead,
+    quantiles,
+    average_type,
+    use_diff_of_y,
+    y_scaler,
+    max_mae,
+):
+
+    X, y = get_dataset()
+    model = train_lasso(X, y, predict_ahead, quantiles, average_type, use_diff_of_y, y_scaler)
+    assert_get_actual(model, X, y, predict_ahead)
+    assert_prediction(
+        model=model,
+        X=X,
+        y=y,
+        quantiles=quantiles,
+        predict_ahead=predict_ahead,
+        force_monotonic_quantiles=False,
+    )
+    assert_prediction(
+        model=model,
+        X=X,
+        y=y,
+        quantiles=quantiles,
+        predict_ahead=predict_ahead,
+        force_monotonic_quantiles=True,
+    )
+    assert_performance(
+        pred=model.predict(X, y),
+        predict_ahead=predict_ahead,
+        actual=model.get_actual(y),
+        average_type=average_type,
+        max_mae=max_mae,
+    )
diff --git a/sam/models/tests/test_mlp_model.py b/sam/models/tests/test_mlp_model.py
index c6a607b..789172f 100644
--- a/sam/models/tests/test_mlp_model.py
+++ b/sam/models/tests/test_mlp_model.py
@@ -1,125 +1,39 @@
-import os
-import random
-
-import numpy as np
-import pandas as pd
 import pytest
-from numpy.testing import assert_array_equal
 from sam.feature_engineering.simple_feature_engineering import SimpleFeatureEngineer
 from sam.models import MLPTimeseriesRegressor
-from sklearn.metrics import mean_absolute_error
+from sam.models.tests.utils import (
+    assert_get_actual,
+    assert_performance,
+    assert_prediction,
+    get_dataset,
+    set_seed,
+)
 from sklearn.preprocessing import StandardScaler
 
 # If tensorflow is not available, skip these unittests
 skipkeras = False
 try:
-    import tensorflow as tf
+    import tensorflow as tf  # noqa: F401
 except ImportError:
     skipkeras = True
 
 
-def get_dataset():
-    # We are deliberately creating an extremely easy, linear problem here
-    # the target is literally 17 times one of the features
-    # This is because we just want to see if the model works at all, in a short time, on very
-    # little data.
-    # With a high enough learning rate, it should be almost perfect after a few iterations
-
-    n_rows = 100
-
-    X = pd.DataFrame(
-        {
-            "TIME": pd.to_datetime(np.array(range(n_rows)), unit="m"),
-            "x": np.linspace(0, 1, n_rows),
-        }
-    ).set_index("TIME")
-    y = 420 + 69 * X["x"]
-
-    return X, y
-
-
-def assert_monotonic(predictions, quantiles, predict_ahead):
-    if isinstance(predict_ahead, int):
-        predict_ahead = [predict_ahead]
-    for horizon in predict_ahead:
-        for q1, q2 in zip(quantiles, quantiles):
-            if q1 > q2:
-                q1_col = f"predict_lead_{horizon}_q_{q1}"
-                q2_col = f"predict_lead_{horizon}_q_{q2}"
-                assert sum(predictions[q1_col] < predictions[q2_col]) == 0
-
-
-def assert_prediction(model, X, y, quantiles, predict_ahead, force_monotonic_quantiles):
-    if isinstance(predict_ahead, int):
-        predict_ahead = [predict_ahead]
-    pred = model.predict(X, y, force_monotonic_quantiles=force_monotonic_quantiles)
-
-    actual = model.get_actual(y)
-
-    # Check shape
-    pred = pd.DataFrame(pred)
-    actual = pd.DataFrame(actual)
-
-    assert pred.shape == (X.shape[0], (len(quantiles) + 1) * len(predict_ahead))
-    assert actual.shape == (X.shape[0], len(predict_ahead))
-
-    # Check that the predictions are close to the actual values
-    if force_monotonic_quantiles:
-        assert_monotonic(pred, quantiles, predict_ahead)
-
-
-def assert_get_actual(model, X, y, predict_ahead):
-    if isinstance(predict_ahead, int):
-        predict_ahead = [predict_ahead]
-    actual = model.get_actual(y)
-
-    if len(predict_ahead) > 1:
-        assert actual.shape == (X.shape[0], len(predict_ahead))
-    else:
-        assert actual.shape == (X.shape[0],)
-
-    actual = pd.DataFrame(actual)
-    for i in range(len(predict_ahead)):
-        horizon = predict_ahead[i]
-        expected = y.shift(-horizon)
-        assert_array_equal(
-            actual.iloc[:, i],
-            expected,
-        )
-
-
-def assert_performance(
-    pred,
-    actual,
-    predict_ahead,
-    average_type,
-    max_mae,
-):
-    if isinstance(predict_ahead, int):
-        predict_ahead = [predict_ahead]
-
-    pred = pd.DataFrame(pred)
-    actual = pd.DataFrame(actual)
-
-    for i in range(len(predict_ahead)):
-        horizon = predict_ahead[i]
-        pred_mean = pred[f"predict_lead_{horizon}_mean"]
-        y_true = actual.iloc[:, i]
-        missing = y_true.isna() | pred_mean.isna()
-        assert mean_absolute_error(pred_mean[~missing], y_true[~missing]) <= max_mae
-
-    if len(predict_ahead) > 1:
-        for i, j in zip(range(len(predict_ahead)), range(len(predict_ahead))):
-            h1 = predict_ahead[i]
-            h2 = predict_ahead[j]
-            if h1 > h2:
-                mae1 = mean_absolute_error(
-                    pred[f"predict_lead_{h1}_{average_type}"], actual.iloc[:, i]
-                )
-                mae2 = mean_absolute_error(
-                    pred[f"predict_lead_{h2}_{average_type}"], actual.iloc[:, j]
-                )
-                assert mae1 >= mae2
+@set_seed
+def train_mlp(X, y, predict_ahead, quantiles, average_type, use_diff_of_y, y_scaler):
+    fe = SimpleFeatureEngineer(keep_original=True)
+    model = MLPTimeseriesRegressor(
+        predict_ahead=predict_ahead,
+        quantiles=quantiles,
+        use_diff_of_y=use_diff_of_y,
+        y_scaler=y_scaler,
+        feature_engineer=fe,
+        average_type=average_type,
+        lr=0.01,
+        epochs=40,
+        verbose=0,
+    )
+    model.fit(X, y)
+    return model
 
 
 @pytest.mark.skipif(skipkeras, reason="Keras backend not found")
@@ -151,27 +65,8 @@ def test_mlp(
     max_mae,
 ):
 
-    # Now start setting the RNG so we get reproducible results
-    random.seed(42)
-    np.random.seed(42)
-    tf.random.set_seed(42)
-    os.environ["PYTHONHASHSEED"] = "0"
-
     X, y = get_dataset()
-
-    fe = SimpleFeatureEngineer(keep_original=True)
-    model = MLPTimeseriesRegressor(
-        predict_ahead=predict_ahead,
-        quantiles=quantiles,
-        use_diff_of_y=use_diff_of_y,
-        y_scaler=y_scaler,
-        feature_engineer=fe,
-        average_type=average_type,
-        lr=0.01,
-        epochs=40,
-        verbose=0,
-    )
-    model.fit(X, y)
+    model = train_mlp(X, y, predict_ahead, quantiles, average_type, use_diff_of_y, y_scaler)
     assert_get_actual(model, X, y, predict_ahead)
     assert_prediction(
         model=model,
diff --git a/sam/models/tests/utils.py b/sam/models/tests/utils.py
new file mode 100644
index 0000000..b1fb2c8
--- /dev/null
+++ b/sam/models/tests/utils.py
@@ -0,0 +1,130 @@
+import os
+import random
+
+import numpy as np
+import pandas as pd
+from numpy.testing import assert_array_equal
+from sklearn.metrics import mean_absolute_error
+
+
+def set_seed(func):
+    """Decorator to set seeds of multiple libraries for a test"""
+
+    def wrapper(*args, **kwargs):
+        try:
+            import tensorflow as tf
+
+            tf.random.set_seed(42)
+        except ImportError:
+            pass
+
+        random.seed(42)
+        np.random.seed(42)
+        os.environ["PYTHONHASHSEED"] = "0"
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+def get_dataset():
+    # We are deliberately creating an extremely easy, linear problem here
+    # the target is literally 17 times one of the features
+    # This is because we just want to see if the model works at all, in a short time, on very
+    # little data.
+    # With a high enough learning rate, it should be almost perfect after a few iterations
+
+    n_rows = 100
+
+    X = pd.DataFrame(
+        {
+            "TIME": pd.to_datetime(np.array(range(n_rows)), unit="m"),
+            "x": np.linspace(0, 1, n_rows),
+        }
+    ).set_index("TIME")
+    y = 420 + 69 * X["x"]
+
+    return X, y
+
+
+def assert_monotonic(predictions, quantiles, predict_ahead):
+    if isinstance(predict_ahead, int):
+        predict_ahead = [predict_ahead]
+    for horizon in predict_ahead:
+        for q1, q2 in zip(quantiles, quantiles):
+            if q1 > q2:
+                q1_col = f"predict_lead_{horizon}_q_{q1}"
+                q2_col = f"predict_lead_{horizon}_q_{q2}"
+                assert sum(predictions[q1_col] < predictions[q2_col]) == 0
+
+
+def assert_prediction(model, X, y, quantiles, predict_ahead, force_monotonic_quantiles):
+    if isinstance(predict_ahead, int):
+        predict_ahead = [predict_ahead]
+    pred = model.predict(X, y, force_monotonic_quantiles=force_monotonic_quantiles)
+
+    actual = model.get_actual(y)
+
+    # Check shape
+    pred = pd.DataFrame(pred)
+    actual = pd.DataFrame(actual)
+
+    assert pred.shape == (X.shape[0], (len(quantiles) + 1) * len(predict_ahead))
+    assert actual.shape == (X.shape[0], len(predict_ahead))
+
+    # Check that the predictions are close to the actual values
+    if force_monotonic_quantiles:
+        assert_monotonic(pred, quantiles, predict_ahead)
+
+
+def assert_get_actual(model, X, y, predict_ahead):
+    if isinstance(predict_ahead, int):
+        predict_ahead = [predict_ahead]
+    actual = model.get_actual(y)
+
+    if len(predict_ahead) > 1:
+        assert actual.shape == (X.shape[0], len(predict_ahead))
+    else:
+        assert actual.shape == (X.shape[0],)
+
+    actual = pd.DataFrame(actual)
+    for i in range(len(predict_ahead)):
+        horizon = predict_ahead[i]
+        expected = y.shift(-horizon)
+        assert_array_equal(
+            actual.iloc[:, i],
+            expected,
+        )
+
+
+def assert_performance(
+    pred,
+    actual,
+    predict_ahead,
+    average_type,
+    max_mae,
+):
+    if isinstance(predict_ahead, int):
+        predict_ahead = [predict_ahead]
+
+    pred = pd.DataFrame(pred)
+    actual = pd.DataFrame(actual)
+
+    for i in range(len(predict_ahead)):
+        horizon = predict_ahead[i]
+        pred_mean = pred[f"predict_lead_{horizon}_mean"]
+        y_true = actual.iloc[:, i]
+        missing = y_true.isna() | pred_mean.isna()
+        assert mean_absolute_error(pred_mean[~missing], y_true[~missing]) <= max_mae
+
+    if len(predict_ahead) > 1:
+        for i, j in zip(range(len(predict_ahead)), range(len(predict_ahead))):
+            h1 = predict_ahead[i]
+            h2 = predict_ahead[j]
+            if h1 > h2:
+                mae1 = mean_absolute_error(
+                    pred[f"predict_lead_{h1}_{average_type}"], actual.iloc[:, i]
+                )
+                mae2 = mean_absolute_error(
+                    pred[f"predict_lead_{h2}_{average_type}"], actual.iloc[:, j]
+                )
+                assert mae1 >= mae2
diff --git a/sam/preprocessing/__init__.py b/sam/preprocessing/__init__.py
index ff0612c..6ea5703 100644
--- a/sam/preprocessing/__init__.py
+++ b/sam/preprocessing/__init__.py
@@ -9,12 +9,15 @@
     make_differenced_target,
     make_shifted_target,
 )
+from .clip_transformer import ClipTransformer
 from .normalize_timestamps import normalize_timestamps
 from .rnn_reshape import RecurrentReshaper
 from .sam_reshape import sam_format_to_wide, wide_to_sam_format
 from .time import average_winter_time, label_dst
+from .train_test_split import datetime_train_test_split
 
 __all__ = [
+    "ClipTransformer",
     "normalize_timestamps",
     "correct_above_threshold",
     "correct_below_threshold",
@@ -28,4 +31,5 @@
     "make_shifted_target",
     "make_differenced_target",
     "inverse_differenced_target",
+    "datetime_train_test_split",
 ]
diff --git a/sam/preprocessing/clip_transformer.py b/sam/preprocessing/clip_transformer.py
new file mode 100644
index 0000000..6089642
--- /dev/null
+++ b/sam/preprocessing/clip_transformer.py
@@ -0,0 +1,75 @@
+from typing import List, Union
+
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+class ClipTransformer(BaseEstimator, TransformerMixin):
+    """
+    Transformer that clips values to a given range.
+
+    Parameters
+    ----------
+    cols: list (optional)
+        Columns of input data to be clipped. If None, all columns will be clipped.
+    min_value: float (optional)
+        Minimum value to clip to. If None, min will be set to the minimum value of the data.
+    max_value: float (optional)
+        Maximum value to clip to. If None, max will be set to the maximum value of the data.
+    """
+
+    def __init__(
+        self,
+        cols: list = None,
+        min_value: float = None,
+        max_value: float = None,
+    ):
+        self.cols = cols
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, pd.Series] = None):
+        """
+        Fit the transformer to the data.
+
+        Parameters
+        ----------
+        X: pd.DataFrame
+            Dataframe containing the features to be clipped.
+        y: pd.Series or pd.DataFrame (optional)
+            Series or dataframe containing the target (ignored)
+        """
+
+        if self.cols is None:
+            self.cols = X.columns
+
+        if self.min_value is None:
+            self.min_value = X[self.cols].min().to_dict()
+
+        if self.max_value is None:
+            self.max_value = X[self.cols].max().to_dict()
+
+        self._feature_names_out = X.columns
+
+        return self
+
+    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
+        """
+        Transform the data.
+
+        Parameters
+        ----------
+        X: pd.DataFrame
+            Dataframe containing the features to be clipped.
+        """
+
+        X = X.copy()
+        X[self.cols] = X[self.cols].clip(self.min_value, self.max_value)
+        return X
+
+    def get_feature_names_out(self, input_features=None) -> List[str]:
+        """
+        Get the names of the output features.
+        """
+
+        return self._feature_names_out
diff --git a/sam/preprocessing/rnn_reshape.py b/sam/preprocessing/rnn_reshape.py
index 4f72c55..f7badf5 100644
--- a/sam/preprocessing/rnn_reshape.py
+++ b/sam/preprocessing/rnn_reshape.py
@@ -24,11 +24,9 @@ class RecurrentReshaper(BaseEstimator, TransformerMixin):
     ----------
     window : integer
         Number of rows to look back
-
     lookback : integer (default=0)
         the features that are built will be shifted by this value.
         If target is in `X`, `lookback` should be greater than 0 to avoid leakage.
-
     remove_leading_nan : boolean
         Whether leading nans should be removed.
         Leading nans arise because there is no history for first samples
@@ -36,9 +34,9 @@ class RecurrentReshaper(BaseEstimator, TransformerMixin):
     Examples
     --------
     >>> from sam.data_sources import read_knmi
-    >>> from sam.preprocessing import ReshapeFeaturesRNN
+    >>> from sam.preprocessing import RecurrentReshaper
     >>> X = read_knmi('2018-01-01 00:00:00', '2018-01-08 00:00:00').set_index('TIME')
-    >>> reshaper = ReshapeFeaturesRNN(window=7)
+    >>> reshaper = RecurrentReshaper(window=7)
     >>> X3D = reshaper.fit_transform(X)
     """
 
diff --git a/sam/preprocessing/tests/test_clip_transformer.py b/sam/preprocessing/tests/test_clip_transformer.py
new file mode 100644
index 0000000..2bd0328
--- /dev/null
+++ b/sam/preprocessing/tests/test_clip_transformer.py
@@ -0,0 +1,61 @@
+import unittest
+
+import pandas as pd
+from pandas.testing import assert_frame_equal, assert_series_equal
+
+from sam.preprocessing import ClipTransformer
+
+
+class TestClipTransformer(unittest.TestCase):
+    train_data = pd.DataFrame(
+        {
+            "A": [1, 2, 3, 4, 5],
+            "B": [3, 4, 5, 6, 7],
+            "C": [1, 2, 3, 4, 5],
+        }
+    )
+    test_input = pd.DataFrame(
+        {
+            "A": [1, 2, 3, 4, 5],
+            "B": [3, -1, 5, 6, 10],
+            "C": [1, 2, 0, 4, 10],
+        }
+    )
+    test_output = pd.DataFrame(
+        {
+            "A": [1, 2, 3, 4, 5],
+            "B": [3, 3, 5, 6, 7],
+            "C": [1, 2, 1, 4, 5],
+        }
+    )
+    test_output_fixed = pd.DataFrame(
+        {
+            "A": [2, 2, 3, 4, 4],
+            "B": [3, 2, 4, 4, 4],
+            "C": [2, 2, 2, 4, 4],
+        }
+    )
+
+    def test_fit_transform(self):
+        clipper = ClipTransformer()
+        output = clipper.fit_transform(self.train_data)
+        assert_frame_equal(output, self.train_data)
+
+    def test_transform(self):
+        clipper = ClipTransformer().fit(self.train_data)
+        output = clipper.transform(self.test_input)
+        assert_frame_equal(output, self.test_output)
+
+    def test_transform_single_col(self):
+
+        for column in ["A", "B", "C"]:
+            clipper = ClipTransformer(cols=[column]).fit(self.train_data)
+            output = clipper.transform(self.test_input)
+            assert_series_equal(output[column], self.test_output[column])
+            # check if other column have not changed
+            assert_frame_equal(output.drop(column, axis=1), self.test_input.drop(column, axis=1))
+
+    def test_min_max(self):
+        clipper = ClipTransformer(min_value=2, max_value=4).fit(self.train_data)
+        output = clipper.transform(self.test_input)
+        assert_frame_equal(output, self.test_output_fixed)
diff --git a/sam/preprocessing/tests/test_train_test_split.py b/sam/preprocessing/tests/test_train_test_split.py
new file mode 100644
index 0000000..14c6a4f
--- /dev/null
+++ b/sam/preprocessing/tests/test_train_test_split.py
@@ -0,0 +1,47 @@
+import unittest
+
+import pandas as pd
+from sam.preprocessing import datetime_train_test_split
+from pandas.testing import assert_frame_equal, assert_series_equal
+
+
+class TestTrainTestSplit(unittest.TestCase):
+    data = pd.DataFrame(
+        {
+            "TIME": pd.date_range("2022-01-01", periods=10, freq="H", tz=None),
+            "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "b": [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
+        }
+    )
+    data = data.set_index("TIME")
+    split_date = "2022-01-01 07:00:00"
+
+    def test_single_split(self):
+        train, test = datetime_train_test_split(self.data, datetime=self.split_date)
+        assert_frame_equal(train, self.data.iloc[:7])
+        assert_frame_equal(test, self.data.iloc[7:])
+        self.assertEqual(train.shape, (7, 2))
+        self.assertEqual(test.shape, (3, 2))
+
+    def test_multiple_splits(self):
+        data_a, data_b = self.data[["a"]], self.data["b"]
+        train_a, test_a, train_b, test_b = datetime_train_test_split(
+            data_a, data_b, datetime=self.split_date
+        )
+
+        assert_frame_equal(train_a, data_a.iloc[:7])
+        assert_frame_equal(test_a, data_a.iloc[7:])
+        assert_series_equal(train_b, data_b.iloc[:7])
+        assert_series_equal(test_b, data_b.iloc[7:])
+        self.assertEqual(train_a.shape, (7, 1))
+        self.assertEqual(test_a.shape, (3, 1))
+        self.assertEqual(train_b.shape, (7,))
+        self.assertEqual(test_b.shape, (3,))
+
+    def test_datetimecol(self):
+        data = self.data.reset_index()
+        train, test = datetime_train_test_split(data, datetime=self.split_date, datecol="TIME")
+        assert_frame_equal(train, data.iloc[:7])
+        assert_frame_equal(test, data.iloc[7:])
+        self.assertEqual(train.shape, (7, 3))
+        self.assertEqual(test.shape, (3, 3))
diff --git a/sam/preprocessing/train_test_split.py b/sam/preprocessing/train_test_split.py
new file mode 100644
index 0000000..ceb82cb
--- /dev/null
+++ b/sam/preprocessing/train_test_split.py
@@ -0,0 +1,43 @@
+from typing import Union, Optional
+import pandas as pd
+
+
+def datetime_train_test_split(
+    *arrays: Union[pd.DataFrame, pd.Series],
+    datetime: str,
+    datecol: Optional[str] = None,
+):
+    """
+    Split the dataframe into train and test sets based on datetime index values
+
+    Parameters
+    ----------
+    arrays : Union[pd.DataFrame, pd.Series]
+        Allowed inputs are pandas dataframes or series (with datetime index)
+    datetime : str
+        Datetime to split the dataframe on
+    datecol : str, optional
+        Name of the column containing the datetime index. If not provided, the index
+        of the dataframes are used.
+
+    Returns
+    -------
+    list: list
+        List containing the train and test splits of the input arrays
+
+    """
+    output = []
+    for array in arrays:
+        if isinstance(array, pd.DataFrame) or isinstance(array, pd.Series):
+            if datecol is None:
+                dates = array.index.to_series()
+            else:
+                dates = array[datecol]
+            train_index = dates < datetime
+            test_index = dates >= datetime
+            output.append(array.loc[train_index])
+            output.append(array.loc[test_index])
+        else:
+            raise TypeError("Input must be pandas dataframe or series")
+
+    return output
diff --git a/sam/utils/__init__.py b/sam/utils/__init__.py
index 9a8d30c..467c224 100644
--- a/sam/utils/__init__.py
+++ b/sam/utils/__init__.py
@@ -6,6 +6,7 @@
     sum_grouped_columns,
 )
 from .sklearnhelpers import FunctionTransformerWithNames
+from .warnings import add_future_warning, parametrized
 
 __all__ = [
     "assert_contains_nans",
@@ -14,4 +15,6 @@
     "make_df_monotonic",
     "sum_grouped_columns",
     "FunctionTransformerWithNames",
+    "add_future_warning",
+    "parametrized",
 ]
diff --git a/sam/utils/warnings.py b/sam/utils/warnings.py
new file mode 100644
index 0000000..53c4fec
--- /dev/null
+++ b/sam/utils/warnings.py
@@ -0,0 +1,24 @@
+import warnings
+
+
+def parametrized(dec):
+    """Decorator to make a decorator parametrized"""
+
+    def layer(*args, **kwargs):
+        def repl(f):
+            return dec(f, *args, **kwargs)
+
+        return repl
+
+    return layer
+
+
+@parametrized
+def add_future_warning(func, msg):
+    """Decorator to add a FutureWarning to a function."""
+
+    def wrapper(*args, **kwargs):
+        warnings.warn(msg, FutureWarning)
+        return func(*args, **kwargs)
+
+    return wrapper
diff --git a/sam/validation/__init__.py b/sam/validation/__init__.py
index c0f92e4..3081746 100644
--- a/sam/validation/__init__.py
+++ b/sam/validation/__init__.py
@@ -1,5 +1,15 @@
-from .find_extremes import RemoveExtremeValues
-from .find_flatlines import RemoveFlatlines
+from .base_validator import BaseValidator
+from .outside_range_validator import OutsideRangeValidator
+from .mad_validator import RemoveExtremeValues, MADValidator
+from .flatline_validator import RemoveFlatlines, FlatlineValidator
 from .setup_validation_pipeline import create_validation_pipe
 
-__all__ = ["RemoveExtremeValues", "RemoveFlatlines", "create_validation_pipe"]
+__all__ = [
+    "BaseValidator",
+    "OutsideRangeValidator",
+    "RemoveExtremeValues",
+    "MADValidator",
+    "RemoveFlatlines",
+    "FlatlineValidator",
+    "create_validation_pipe",
+]
diff --git a/sam/validation/base_validator.py b/sam/validation/base_validator.py
new file mode 100644
index 0000000..5b6464d
--- /dev/null
+++ b/sam/validation/base_validator.py
@@ -0,0 +1,52 @@
+import logging
+from abc import ABC, abstractmethod
+from typing import List
+
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils.validation import check_is_fitted
+
+logger = logging.getLogger(__name__)
+
+
+class BaseValidator(BaseEstimator, TransformerMixin, ABC):
+    """Abstract base class for validators"""
+
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def validate(self, X: pd.DataFrame) -> pd.DataFrame:
+        """
+        Validate the data.
+
+        This method should return a boolean array of the same shape as X, where True indicates a
+        value that is invalid.
+        """
+        raise NotImplementedError("You need to implement the validate method.")
+
+    def fit(self, X, y=None):
+        """fit method"""
+        self.feature_names_in_ = X.columns.tolist()
+        self.feature_names_out_ = self.feature_names_in_
+        return self
+
+    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
+        """transform method"""
+        X = X.copy()
+        invalid_data = self.validate(X)
+        X[invalid_data] = np.nan
+        return X
+
+    def get_feature_names_out(self, input_features=None) -> List[str]:
+        """
+        Function for obtaining feature names. Generally used instead of the attribute, and more
+        compatible with the sklearn API.
+        Returns
+        -------
+        list:
+            list of feature names
+        """
+        check_is_fitted(self, "_feature_names_out_")
+        return self._feature_names_out_
diff --git a/sam/validation/find_flatlines.py b/sam/validation/flatline_validator.py
similarity index 63%
rename from sam/validation/find_flatlines.py
rename to sam/validation/flatline_validator.py
index ed0e8d4..0e50c87 100644
--- a/sam/validation/find_flatlines.py
+++ b/sam/validation/flatline_validator.py
@@ -3,12 +3,13 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.base import BaseEstimator, TransformerMixin
+from sam.utils import add_future_warning
+from sam.validation import BaseValidator
 
 logger = logging.getLogger(__name__)
 
 
-class RemoveFlatlines(BaseEstimator, TransformerMixin):
+class FlatlineValidator(BaseValidator):
     """
     Detect flatlines and set to nan. Note that you have to check whether
     signals can contain natural flatliners (such as machines turned off),
@@ -32,11 +33,12 @@ class RemoveFlatlines(BaseEstimator, TransformerMixin):
     pvalue: float or None (default=None)
         Threshold for likelihood of multiple consecutive flatline samples
         Only used if ``window="auto"``
-        Small pvalues lead to a larger threshold, hence less flatlines will be removed
+        Small pvalues lead to a larger threshold, hence less flatlines will be
+        removed
     margin: int (default = 0)
-        Maximum absolute difference between consecutive samples to consider them equal.
-        Default is 0, which means that consecutive samples must be exactly equal
-        to form a flatline.
+        Maximum absolute difference within window to consider them equal.
+        Default is 0, which means that all samples within used window must be
+        exactly equal to form a flatline.
     backfill: bool (default = True)
         whether to label all within the window, even before the first detected
         data point. This is useful if you want to remove flatlines from the
@@ -45,7 +47,8 @@ class RemoveFlatlines(BaseEstimator, TransformerMixin):
 
     Examples
     --------
-    >>> from sam.validation import RemoveFlatlines
+    >>> import pandas as pd
+    >>> from sam.validation import FlatlineValidator
     >>> # create some data
     >>> data = [1, 2, 6, 3, 4, 4, 4, 3, 6, 7, 7, 2, 2]
     >>> # with one clear outlier
@@ -53,7 +56,7 @@ class RemoveFlatlines(BaseEstimator, TransformerMixin):
     >>> test_df['values'] = data
     >>> # now detect flatlines
     >>> cols_to_check = ['values']
-    >>> RF = RemoveFlatlines(
+    >>> RF = FlatlineValidator(
     ...     cols=cols_to_check,
     ...     window=3)
     >>> data_corrected = RF.fit_transform(test_df)
@@ -67,7 +70,7 @@ def __init__(
         margin: float = 0,
         backfill: bool = True,
     ):
-
+        super().__init__()
         self.cols = cols
         self.window = window
         self.pvalue = pvalue
@@ -97,57 +100,64 @@ def fit(self, data: pd.DataFrame):
             self.window_dict[col] = threshold
         return self
 
-    def transform(self, data: pd.DataFrame) -> pd.DataFrame:
+    def _validate_column(
+        self,
+        data: pd.Series,
+        window: Union[int, str],
+    ) -> pd.Series:
         """
-        Transforms the data
-
-        Parameters
-        ----------
-        data: pd.DataFrame
-            with index as increasing time and columns as features
-
-        Returns
-        -------
-        data_r: pd.DataFrame
-            with flatlines replaced by nans
+        Validates a single column against the fitted dataframe
         """
+        data = data.copy()
 
-        self.invalids = {}
-        data_r = data.copy()
-
-        if self.cols is None:
-            self.cols = data.columns
-
-        for col in self.cols:
+        # check if values within range are within margin
+        flatliners = (
+            ((data.rolling(window).max() - data.rolling(window).min()) <= self.margin)
+            .astype(int)
+            .fillna(0)
+        )
 
-            these_data = data.loc[:, col]
+        # apply backfill if needed: label all points within flatline window
+        # as invalid. This requires a forward looking window
+        if self.backfill:
+            inv_flatliners = flatliners.iloc[::-1]
+            inv_flatliners = inv_flatliners.rolling(window, min_periods=1).max()
+            flatliners = inv_flatliners.iloc[::-1]
 
-            # check if sequential values are equal
-            no_change = (these_data.diff().abs() <= self.margin).astype(int)
+        flatliners = flatliners.astype(bool)
 
-            # check if all sequential values are equal within window
-            window = self.window_dict[col]
-            flatliners = no_change.rolling(window).min().fillna(0)
+        return flatliners
 
-            # apply backfill if needed: label all points within flatline window
-            # as invalid. This requires a forward looking window
-            if self.backfill:
-                inv_flatliners = flatliners.iloc[::-1]
-                inv_flatliners = inv_flatliners.rolling(window + 1, min_periods=1).max()
-                flatliners = inv_flatliners.iloc[::-1]
+    def validate(self, X: pd.DataFrame) -> pd.DataFrame:
+        """
+        Validates the dataframe against the fitted dataframe. Returns a boolean
+        dataframe where True indicates an invalid value.
 
-            flatliners = flatliners.astype(bool)
+        Parameters
+        ----------
+        X: pd.DataFrame
+            Input dataframe to validate
+        """
+        invalid_data = pd.DataFrame(
+            data=np.zeros_like(X.values).astype(bool),
+            index=X.index,
+            columns=X.columns,
+        )
 
-            # save to self for later plot
-            self.invalids[col] = flatliners
+        for col in self.cols:
+            window = self.window_dict[col]
+            invalid_data[col] = self._validate_column(X[col], window)
 
             logger.info(
-                f"detected {np.sum(flatliners)} "
+                f"detected {np.sum(invalid_data[col])} "
                 f"flatline samples in {col} "
                 f"with window of {window} "
             )
 
-            # now replace with nans
-            data_r.loc[flatliners, col] = np.nan
+        return invalid_data
+
 
-        return data_r
+class RemoveFlatlines(FlatlineValidator):
+    @add_future_warning("RemoveFlatlines is deprecated, use FlatlineValidator instead")
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
diff --git a/sam/validation/find_extremes.py b/sam/validation/mad_validator.py
similarity index 76%
rename from sam/validation/find_extremes.py
rename to sam/validation/mad_validator.py
index c5ee211..3c80fff 100644
--- a/sam/validation/find_extremes.py
+++ b/sam/validation/mad_validator.py
@@ -3,12 +3,13 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.base import BaseEstimator, TransformerMixin
+from sam.validation import BaseValidator
+from sam.utils import add_future_warning
 
 logger = logging.getLogger(__name__)
 
 
-class RemoveExtremeValues(BaseEstimator, TransformerMixin):
+class MADValidator(BaseValidator):
     """
     This transformer finds extreme values and sets them to nan in a few steps:
 
@@ -35,19 +36,19 @@ class RemoveExtremeValues(BaseEstimator, TransformerMixin):
 
     Parameters
     ---------
-    cols: list of strings
-        columns to detect extreme values for
     rollingwindow: int or string
         if number, this amount of values will be used for the rolling window
         if string, should be in pandas timedelta format ('1D'), and data should
         have a datetime index. A sensible value for this depends on your time
         resolution, but you could try values between 200-400.
+    cols: list of strings (optional)
+        columns to detect extreme values for. If None, all columns will be used.
     madthresh: float
         number of median absolute deviations to use as threshold.
 
     Examples
     --------
-    >>> from sam.validation import RemoveExtremeValues
+    >>> from sam.validation import MADValidator
     >>> from sam.visualization import diagnostic_extreme_removal
     >>> import numpy as np
     >>> import pandas as pd
@@ -67,9 +68,9 @@ class RemoveExtremeValues(BaseEstimator, TransformerMixin):
     >>>
     >>> # now detect extremes
     >>> cols_to_check = ['values']
-    >>> REV = RemoveExtremeValues(
-    >>>     cols=cols_to_check,
+    >>> REV = MADValidator(
     >>>     rollingwindow=10,
+    >>>     cols=cols_to_check,
     >>>     madthresh=10)
     >>> train_corrected = REV.fit_transform(train_df)
     >>> fig = diagnostic_extreme_removal(REV, train_df, 'values')
@@ -77,10 +78,15 @@ class RemoveExtremeValues(BaseEstimator, TransformerMixin):
     >>> fig = diagnostic_extreme_removal(REV, test_df, 'values')
     """
 
-    def __init__(self, cols: list, rollingwindow: Union[int, str], madthresh=15):
+    def __init__(
+        self,
+        rollingwindow: Union[int, str],
+        cols: list = None,
+        madthresh=15,
+    ):
 
-        self.cols = cols
         self.rollingwindow = rollingwindow
+        self.cols = cols
         self.madthresh = madthresh
 
     def _compute_rolling(self, x: pd.Series):
@@ -102,6 +108,7 @@ def fit(self, data: pd.DataFrame):
 
         self.thresh_high = {}
         self.thresh_low = {}
+        self.cols_ = self.cols if self.cols else data.columns.to_list()
         for c in self.cols:
 
             # get data
@@ -119,7 +126,7 @@ def fit(self, data: pd.DataFrame):
 
         return self
 
-    def transform(self, data: pd.DataFrame):
+    def validate(self, X: pd.DataFrame):
         """
         Sets values that fall outside bounds set in the fit method to nan
 
@@ -134,37 +141,39 @@ def transform(self, data: pd.DataFrame):
             input data with columns marked as nan
         """
 
-        self.rollings, self.invalids, self.diffs = {}, {}, {}
-        data_r = data.copy()
+        invalid_data = pd.DataFrame(
+            data=np.zeros_like(X.values).astype(bool),
+            index=X.index,
+            columns=X.columns,
+        )
+
         for c in self.cols:
 
             # get data
-            x = data.loc[:, c]
+            x = X.loc[:, c]
 
             # determine rolling and diff
             rolling = self._compute_rolling(x)
             diff = x.values - rolling
 
             # as thresholds are computed in signed way, we can directly compare
-            invalids = diff > self.thresh_high[c]
-            invalids |= diff < self.thresh_low[c]
-
-            # save some variables to self so they are available for plot
-            self.diffs[c] = diff
-            self.rollings[c] = rolling
-            self.invalids[c] = invalids
-
-            # set false values to nan
-            data_r.loc[invalids, c] = np.nan
+            extreme_value = (diff > self.thresh_high[c]) | (diff < self.thresh_low[c])
 
             # log number of values removed and tresholds used
             logger.info(
-                "detected %d " % np.sum(invalids)
-                + "extreme values from %s. " % c
-                + "using upper threshold of: %.2f " % self.thresh_high[c]
-                + "and lower threshold of: %.2f " % self.thresh_low[c]
-                + "using madthresh of %d " % self.madthresh
-                + "and rollingwindow of %s" % str(self.rollingwindow)
+                f"detected {np.sum(extreme_value)} extreme values from {c}. "
+                f"using upper threshold of: {round(self.thresh_high[c], 2)} "
+                f"and lower threshold of: {round(self.thresh_low[c])} "
+                f"using madthresh of {self.madthresh} "
+                f"and rollingwindow of {str(self.rollingwindow)}"
             )
 
-        return data_r
+            invalid_data[c] = extreme_value
+
+        return invalid_data
+
+
+class RemoveExtremeValues(MADValidator):
+    @add_future_warning("RemoveExtremeValues is deprecated. Use MADValidator instead.")
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
diff --git a/sam/validation/outside_range_validator.py b/sam/validation/outside_range_validator.py
new file mode 100644
index 0000000..c62a370
--- /dev/null
+++ b/sam/validation/outside_range_validator.py
@@ -0,0 +1,92 @@
+from typing import Union
+import numpy as np
+import pandas as pd
+
+from sam.validation import BaseValidator
+
+
+class OutsideRangeValidator(BaseValidator):
+    """
+    Validator class method that removes data that is outside the provided range
+
+    Parameters
+    ----------
+    cols: list (optional)
+        Columns of input data to be checkout for being outside range. If None, all columns will be
+        validated
+    min_value: float, dict or "auto" (optional)
+        Minimum value to check against. If None, no minimum will be checked. If "auto", the minimum
+        value of the data will be used.
+    max_value: float, dict or "auto" (optional)
+        Maximum value to check against. If None, no maximum will be checked. If "auto", the maximum
+        value of the data will be used.
+    """
+
+    def __init__(
+        self,
+        cols: list = None,
+        min_value: Union[float, dict, str] = None,
+        max_value: Union[float, dict, str] = None,
+    ):
+        self.cols = cols
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def fit(self, X, y=None):
+        """
+        Fit the transformer to the data.
+
+        Parameters
+        ----------
+        X: pd.DataFrame
+            Dataframe containing the features to be checked.
+        y: pd.Series or pd.DataFrame (optional)
+            Series or dataframe containing the target (ignored)
+        """
+
+        if self.cols is None:
+            self.cols = X.columns
+
+        if self.min_value == "auto":
+            self.min_value_ = X[self.cols].min().to_dict()
+        elif self.min_value is None:
+            self.min_value_ = -np.inf
+        elif isinstance(self.min_value, str):
+            raise ValueError("min_value must be a float, dict or 'auto'")
+        else:
+            self.min_value_ = self.min_value
+
+        if self.max_value == "auto":
+            self.max_value_ = X[self.cols].max().to_dict()
+        elif self.max_value is None:
+            self.max_value_ = np.inf
+        elif isinstance(self.max_value, str):
+            raise ValueError("max_value must be a float, dict or 'auto'")
+        else:
+            self.max_value_ = self.max_value
+
+        self._feature_names_out = X.columns
+
+        return self
+
+    def validate(self, X):
+        """
+        Transform the data.
+
+        Parameters
+        ----------
+        X: pd.DataFrame
+            Dataframe containing the features to be checked.
+        """
+
+        invalid_data = pd.DataFrame(
+            data=np.zeros_like(X.values).astype(bool),
+            index=X.index,
+            columns=X.columns,
+        )
+
+        invalid_data[self.cols] = X[self.cols].gt(self.max_value_) | X[self.cols].lt(
+            self.min_value_
+        )
+
+        return invalid_data
diff --git a/sam/validation/tests/test_flatlines.py b/sam/validation/tests/test_flatlines.py
index b91c395..e378938 100644
--- a/sam/validation/tests/test_flatlines.py
+++ b/sam/validation/tests/test_flatlines.py
@@ -1,11 +1,11 @@
 import unittest
 
 import pandas as pd
-from sam.validation import RemoveFlatlines
+from sam.validation import FlatlineValidator
 from .numeric_assertions import NumericAssertions
 
 
-class TestRemoveExtremes(unittest.TestCase, NumericAssertions):
+class TestFlatlineValidator(unittest.TestCase, NumericAssertions):
     def test_remove_flatlines(self):
 
         # create some random data
@@ -14,7 +14,7 @@ def test_remove_flatlines(self):
         test_df["values"] = data
         # now detect flatlines
         cols_to_check = ["values"]
-        RF = RemoveFlatlines(cols=cols_to_check, window=2)
+        RF = FlatlineValidator(cols=cols_to_check, window=3)
         data_corrected = RF.fit_transform(test_df)
 
         self.assertAllNaN(data_corrected.iloc[[4, 5, 6]])
@@ -28,7 +28,7 @@ def test_remove_flatlines_auto_low(self):
         test_df["values"] = data
         # now detect flatlines with high tolerance (low pvalues)
         cols_to_check = ["values"]
-        RF = RemoveFlatlines(cols=cols_to_check, window="auto", pvalue=1e-100)
+        RF = FlatlineValidator(cols=cols_to_check, window="auto", pvalue=1e-100)
         data_corrected = RF.fit_transform(test_df)
 
         # no flatlines should be detected
@@ -43,12 +43,13 @@ def test_remove_flatlines_auto_high(self):
         test_df["values"] = data
         # now detect flatlines with low tolerance (high pvalues)
         cols_to_check = ["values"]
-        RF = RemoveFlatlines(cols=cols_to_check, window="auto", pvalue=0.9999)
+        RF = FlatlineValidator(cols=cols_to_check, window="auto", pvalue=0.99999)
         data_corrected = RF.fit_transform(test_df)
 
+        print(data_corrected)
+
         # all flatlines should be removed
-        self.assertAllNaN(data_corrected.iloc[[4, 5, 6, 9, 10, 11, 12]])
-        self.assertAllNotNaN(data_corrected.drop([4, 5, 6, 9, 10, 11, 12], axis=0))
+        self.assertAllNaN(data_corrected)
 
 
 if __name__ == "__main__":
diff --git a/sam/validation/tests/test_outside_range_validator.py b/sam/validation/tests/test_outside_range_validator.py
new file mode 100644
index 0000000..4a5000f
--- /dev/null
+++ b/sam/validation/tests/test_outside_range_validator.py
@@ -0,0 +1,76 @@
+import unittest
+
+import pandas as pd
+import numpy as np
+from sam.validation import OutsideRangeValidator
+from pandas.testing import assert_frame_equal
+
+
+class TestOutsideRangeValidator(unittest.TestCase):
+
+    X_train = pd.DataFrame(
+        {
+            "A": [1, 2, 6, 3, 4, 4, 4],
+            "B": [2, 3, 4, 5, 9, 4, 2],
+        }
+    )
+
+    X_test = pd.DataFrame(
+        {
+            "A": [0, 7, 3, 4, 5],
+            "B": [10, 0, 1, 2, 3],
+        }
+    )
+
+    def test_no_outliers(self):
+        RF = OutsideRangeValidator(min_value=0, max_value=10)
+        data_corrected = RF.fit_transform(self.X_train)
+        assert_frame_equal(data_corrected, self.X_train)
+
+    def test_all_above_max(self):
+        RF = OutsideRangeValidator(max_value=0)
+        expected = pd.DataFrame(
+            {
+                "A": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                "B": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            }
+        )
+        data_corrected = RF.fit_transform(self.X_train)
+        assert_frame_equal(data_corrected, expected)
+
+    def test_all_below_min(self):
+        RF = OutsideRangeValidator(min_value=10)
+        expected = pd.DataFrame(
+            {
+                "A": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                "B": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            }
+        )
+        data_corrected = RF.fit_transform(self.X_train)
+        assert_frame_equal(data_corrected, expected)
+
+    def test_auto(self):
+        RF = OutsideRangeValidator(min_value="auto", max_value="auto")
+        data_corrected = RF.fit_transform(self.X_train)
+        assert_frame_equal(data_corrected, self.X_train)
+        data_corrected_test = RF.transform(self.X_test)
+        expected_test = pd.DataFrame(
+            {
+                "A": [np.nan, np.nan, 3, 4, 5],
+                "B": [np.nan, np.nan, np.nan, 2, 3],
+            }
+        )
+        assert_frame_equal(data_corrected_test, expected_test)
+
+    def test_single_col_auto(self):
+        RF = OutsideRangeValidator(cols=["A"], min_value="auto", max_value="auto")
+        data_corrected = RF.fit_transform(self.X_train)
+        assert_frame_equal(data_corrected, self.X_train)
+        data_corrected_test = RF.transform(self.X_test)
+        expected_test = pd.DataFrame(
+            {
+                "A": [np.nan, np.nan, 3, 4, 5],
+                "B": [10, 0, 1, 2, 3],
+            }
+        )
+        assert_frame_equal(data_corrected_test, expected_test)
diff --git a/sam/visualization/diagnostic_flatline_removal.py b/sam/visualization/diagnostic_flatline_removal.py
index 41d5767..6fbca31 100644
--- a/sam/visualization/diagnostic_flatline_removal.py
+++ b/sam/visualization/diagnostic_flatline_removal.py
@@ -1,16 +1,19 @@
-import numpy as np
 import pandas as pd
-from sam.validation import RemoveFlatlines
+from sam.validation import FlatlineValidator
 
 
-def diagnostic_flatline_removal(rf: RemoveFlatlines, raw_data: pd.DataFrame, col: str):
+def diagnostic_flatline_removal(
+    flatline_validator: FlatlineValidator,
+    raw_data: pd.DataFrame,
+    col: str,
+):
     """
     Creates a diagnostic plot for the extreme value removal procedure.
 
     Parameters:
     ----------
-    rf: sam.validation.RemoveFlatlines
-        fitted RemoveFlatlines object
+    flatline_validator: sam.validation.FlatlineValidator
+        fitted FlatlineValidator object
     raw_data: pd.DataFrame
         non-transformed data
     col: string
@@ -25,8 +28,8 @@ def diagnostic_flatline_removal(rf: RemoveFlatlines, raw_data: pd.DataFrame, col
 
     # get data
     x = raw_data[col].copy()
-    invalid_w = np.where(rf.invalids[col])[0]
-    invalid_values = x.iloc[invalid_w]
+    invalid_w = flatline_validator.validate(raw_data)[col]
+    invalid_values = x[invalid_w]
 
     # generate plot
     fig = plt.figure(figsize=(12, 6))
diff --git a/sam/visualization/extreme_removal_plot.py b/sam/visualization/extreme_removal_plot.py
index 5b6bb2d..1747dc9 100644
--- a/sam/visualization/extreme_removal_plot.py
+++ b/sam/visualization/extreme_removal_plot.py
@@ -1,10 +1,9 @@
-import numpy as np
 import pandas as pd
-from sam.validation import RemoveExtremeValues
+from sam.validation import MADValidator
 
 
 def diagnostic_extreme_removal(
-    rev: RemoveExtremeValues,
+    mad_validator: MADValidator,
     raw_data: pd.DataFrame,
     col: str,
 ):
@@ -13,8 +12,8 @@ def diagnostic_extreme_removal(
 
     Parameters:
     ----------
-    rev: sam.validation.RemoveExtremeValues
-        fitted RemoveExtremeValues object
+    mad_validator: sam.validation.MADValidator
+        fitted MADValidator object
     raw_data: pd.DataFrame
         non-transformed data data
     col: string
@@ -30,8 +29,10 @@ def diagnostic_extreme_removal(
 
     # get data
     x = raw_data[col].copy()
-    invalid_w = np.where(rev.invalids[col])[0]
-    invalid_values = x.iloc[invalid_w]
+    invalid_w = mad_validator.validate(raw_data)[col]
+    invalid_values = x.loc[invalid_w]
+    rolling = mad_validator._compute_rolling(x)
+    diff = x.values - rolling
 
     # generate plot
     fig = plt.figure(figsize=(12, 6))
@@ -41,8 +42,8 @@ def diagnostic_extreme_removal(
     plt.plot(x.index, x.values, label="original_signal", lw=5)
 
     plt.plot(
-        rev.rollings[col].index,
-        rev.rollings[col].values,
+        rolling.index,
+        rolling.values,
         "--k",
         label="rolling median",
         lw=3,
@@ -63,9 +64,9 @@ def diagnostic_extreme_removal(
     sns.despine()
 
     plt.subplot(212)
-    plt.plot(rev.diffs[col].values, label="abs(original - rolling)")
-    plt.axhline(rev.thresh_high[col], ls="--", c="r")
-    plt.axhline(rev.thresh_low[col], ls="--", c="r", label="thresholds")
+    plt.plot(diff.values, label="abs(original - rolling)")
+    plt.axhline(mad_validator.thresh_high[col], ls="--", c="r")
+    plt.axhline(mad_validator.thresh_low[col], ls="--", c="r", label="thresholds")
     plt.legend(loc="best")
     sns.despine()
     plt.tight_layout()