diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 78ff5e5..7483b66 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -1,4 +1,4 @@
-name: publish-bench
+name: publish
 on:
   release:
     types:  [published]
diff --git a/README.md b/README.md
index 58db19c..1248a5a 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
-![status](https://img.shields.io/badge/PyPi-0.0.0.0-blue) ![status](https://img.shields.io/badge/License-MIT-lightgrey) [![test](https://github.com/deepskies/DeepDiagnostics/actions/workflows/test.yaml/badge.svg)](https://github.com/deepskies/DeepDiagnostics/actions/workflows/test.yaml) [![Documentation Status](https://readthedocs.org/projects/deepdiagnostics/badge/?version=latest)](https://deepdiagnostics.readthedocs.io/en/latest/?badge=latest)
+[![PyPI - Version](https://img.shields.io/pypi/v/DeepDiagnostics?style=flat&logo=pypi&labelColor=grey&color=blue)](https://pypi.org/project/DeepDiagnostics/)
+ ![status](https://img.shields.io/badge/License-MIT-lightgrey) [![test](https://github.com/deepskies/DeepDiagnostics/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/deepskies/DeepDiagnostics/actions/workflows/test.yaml) [![Documentation Status](https://readthedocs.org/projects/deepdiagnostics/badge/?version=latest)](https://deepdiagnostics.readthedocs.io/en/latest/?badge=latest)
 
 # DeepDiagnostics
 DeepDiagnostics is a package for diagnosing the posterior from an inference method. It is flexible, applicable for both simulation-based and likelihood-based inference.
@@ -242,4 +243,4 @@ python3 -m pytest tests/test_metrics.py::test_newmetric
 ```
 
 ## Acknowledgement 
-This software has been authored by an employee or employees of Fermi Research Alliance, LLC (FRA), operator of the Fermi National Accelerator Laboratory (Fermilab) under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy.
\ No newline at end of file
+This software has been authored by an employee or employees of Fermi Research Alliance, LLC (FRA), operator of the Fermi National Accelerator Laboratory (Fermilab) under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy.
diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb
index 17b64db..f2a5cfc 100644
--- a/notebooks/example.ipynb
+++ b/notebooks/example.ipynb
@@ -15,13 +15,12 @@
     }
    ],
    "source": [
-    "#import deepdiagonstics\n",
-    "import models\n",
-    "import data\n",
-    "from utils.config import Config\n",
-    "from utils.register import register_simulator\n",
+    "from deepdiagnostics import models\n",
+    "from deepdiagnostics import data\n",
+    "from deepdiagnostics.utils.config import Config\n",
+    "from deepdiagnostics.utils.register import register_simulator\n",
     "\n",
-    "from plots import CDFRanks, CoverageFraction, Ranks, TARP, LocalTwoSampleTest\n",
+    "from deepdiagnostics.plots import CDFRanks, CoverageFraction, Ranks, TARP, LC2ST\n",
     "\n",
     "import yaml\n",
     "\n"
@@ -89,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -97,13 +96,14 @@
       "text/plain": [
        "{'common': {'out_dir': './DeepDiagnosticsResources/results/',\n",
        "  'temp_config': './DeepDiagnosticsResources/temp/temp_config.yml',\n",
-       "  'sim_location': 'DeepDiagnosticsResources/simulators',\n",
+       "  'sim_location': './DeepDiagnosticsResources/simulators',\n",
        "  'random_seed': 42},\n",
        " 'model': {'model_engine': 'SBIModel'},\n",
        " 'data': {'data_engine': 'H5Data',\n",
        "  'prior': 'normal',\n",
        "  'prior_kwargs': None,\n",
-       "  'simulator_kwargs': None},\n",
+       "  'simulator_kwargs': None,\n",
+       "  'simulator_dimensions': 1},\n",
        " 'plots_common': {'axis_spines': False,\n",
        "  'tight_layout': True,\n",
        "  'default_colorway': 'viridis',\n",
@@ -115,21 +115,25 @@
        " 'plots': {'CDFRanks': {},\n",
        "  'Ranks': {'num_bins': None},\n",
        "  'CoverageFraction': {},\n",
-       "  'TARP': {'coverage_sigma': 3}},\n",
+       "  'TARP': {'coverage_sigma': 3},\n",
+       "  'LC2ST': {},\n",
+       "  'Parity': {},\n",
+       "  'PPC': {},\n",
+       "  'PriorPC': {}},\n",
        " 'metrics_common': {'use_progress_bar': False,\n",
        "  'samples_per_inference': 1000,\n",
        "  'percentiles': [75, 85, 95],\n",
        "  'number_simulations': 50},\n",
-       " 'metrics': {'AllSBC': {}, 'CoverageFraction': {}}}"
+       " 'metrics': {'AllSBC': {}, 'CoverageFraction': {}, 'LC2ST': {}}}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from utils.defaults import Defaults\n",
+    "from deepdiagnostics.utils.defaults import Defaults\n",
     "Defaults"
    ]
   },
@@ -144,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -155,20 +159,36 @@
       "                [--model_engine {SBIModel}] [--data_path DATA_PATH]\n",
       "                [--data_engine {H5Data,PickleData}] [--simulator SIMULATOR]\n",
       "                [--out_dir OUT_DIR]\n",
-      "                [--metrics {CoverageFraction,AllSBC} [{CoverageFraction,AllSBC} ...]]\n",
-      "                [--plots {CDFRanks,CoverageFraction,Ranks,TARP} [{CDFRanks,CoverageFraction,Ranks,TARP} ...]]\n",
+      "                [--metrics {,CoverageFraction,AllSBC,LC2ST} [{,CoverageFraction,AllSBC,LC2ST} ...]]\n",
+      "                [--plots {,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} [{,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} ...]]\n",
       "\n",
       "options:\n",
       "  -h, --help            show this help message and exit\n",
       "  --config CONFIG, -c CONFIG\n",
+      "                        .yaml file with all arguments to run.\n",
       "  --model_path MODEL_PATH, -m MODEL_PATH\n",
+      "                        String path to a model. Must be compatible with your\n",
+      "                        model_engine choice.\n",
       "  --model_engine {SBIModel}, -e {SBIModel}\n",
+      "                        Way to load your model. See each module's\n",
+      "                        documentation page for requirements and\n",
+      "                        specifications.\n",
       "  --data_path DATA_PATH, -d DATA_PATH\n",
+      "                        String path to data. Must be compatible with\n",
+      "                        data_engine choice.\n",
       "  --data_engine {H5Data,PickleData}, -g {H5Data,PickleData}\n",
+      "                        Way to load your data. See each module's documentation\n",
+      "                        page for requirements and specifications.\n",
       "  --simulator SIMULATOR, -s SIMULATOR\n",
-      "  --out_dir OUT_DIR\n",
-      "  --metrics {CoverageFraction,AllSBC} [{CoverageFraction,AllSBC} ...]\n",
-      "  --plots {CDFRanks,CoverageFraction,Ranks,TARP} [{CDFRanks,CoverageFraction,Ranks,TARP} ...]\n"
+      "                        String name of the simulator to use with generative\n",
+      "                        metrics and plots. Must be pre-register with the\n",
+      "                        `utils.register_simulator` method.\n",
+      "  --out_dir OUT_DIR     Where the results will be saved. Path need not exist,\n",
+      "                        it will be created.\n",
+      "  --metrics {,CoverageFraction,AllSBC,LC2ST} [{,CoverageFraction,AllSBC,LC2ST} ...]\n",
+      "                        List of metrics to run.\n",
+      "  --plots {,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} [{,CDFRanks,CoverageFraction,Ranks,TARP,LC2ST,PPC,Parity,PriorPC} ...]\n",
+      "                        List of plots to run.\n"
      ]
     }
    ],
@@ -195,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -209,8 +229,8 @@
    "source": [
     "%%writefile my_simulator.py \n",
     "\n",
-    "from utils.register import register_simulator\n",
-    "from data.simulator import Simulator\n",
+    "from deepdiagnostics.utils.register import register_simulator\n",
+    "from deepdiagnostics.data.simulator import Simulator\n",
     "import numpy as np \n",
     "\n",
     "class MySimulator(Simulator): \n",
@@ -242,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -275,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,13 +329,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 725.57it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "# We can do a similar thing by passing specific kwargs \n",
     "# Here we're just calculating the coverage fraction \n",
-    "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --simulator MySimulator --metrics CoverageFraction --plots CoverageFraction"
+    "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --simulator MySimulator  --plots CoverageFraction TARP"
    ]
   },
   {
@@ -331,25 +359,188 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using standalone functions \n",
-    "\n",
-    "DeepDiagnostics, if you have a configuration file set, can also be used with just the functions. Below is a list of all the functions and examples of their use. "
+    "We can do a similar thing with metrics, or with plot metrics or plots. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
+   "source": [
+    "! diagnose --model_path ../resources/savedmodels/sbi/sbi_linear_from_data.pkl --data_path ../resources/saveddata/data_validation.h5 --simulator MySimulator  --metrics LC2ST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[K12656823, 0.025117976419460897, 0.3210162208447511, 0.07601853392691404, 0.06380:\u001b[K3287, 1.0, 1.0, 0.3491858283608714, 0.11148460600402221, 0.1705053499709257, 1.0], [0.18886563228152808, 0.999801565848368, 0.06420094428578094, 0.003829460887442848, 1.0, 0.0, 0.05521830659929361, 1.0, 1.0, 1.0], [0.3723571672317456, 0.04190096758672124, 0.05917814671785937, 1.0, 0.5279380279033324, 1.0, 0.3939235158158101, 1.0, 0.3331214449554769, 0.14278821453735335], [0.16794501054145394, 1.0, 0.2836227076540423, 1.0, 0.08725893205243684, 1.0, 1.0, 1.0, 0.2898180261624975, 1.0], [3.4497582568349117e-10, 0.9999999999997726, 2.2475195038396123e-09, 0.9999999860851057, 0.0, 0.01498136204169076, 0.9999999999999996, 1.0, 1.0, 5.1250789168122424e-08]], \"lc2st_null_hypothesis_probabilities\": [[[0.2790388229755212, 0.2735488578185722, 8.397499558887578e-05, 0.002544143029782342, 0.4337491005580647, 0.5281736584242632, 0.2941875894651199, 0.8471557950221957, 0.491588056016956, 0.14904058563692402], [0.9298861402948934, 0.5799854862294562, 0.3862690266799903, 0.3032391090498666, 0.7976109456527416, 0.581394460307604, 0.6422201235768321, 0.9667699549263491, 0.4300242827109272, 0.603196699074676], [0.7458949626530249, 0.4908091805562187, 0.3295099292259487, 0.9048431566532888, 0.4475631382010551, 0.37204375915039756, 0.37192176501806684, 0.2599461309841308, 0.12187398110432535, 0.28911814717222706], [0.9715750712017363, 0.9302578076591757, 0.280485962197362, 0.7789217374607884, 0.5768391118918303, 0.6121690665788355, 0.8770606717117648, 0.5942009041559981, 0.6127097617959039, 0.9487840622625356], [0.5326295893366368, 0.7700871663694474, 0.6654461398938305, 0.8528700301216272, 0.8800724382630881, 0.6637915760733999, 0.8461285354871109, 0.8827762176374074, 0.2243871033069973, 0.6636540545834573]], [[0.049262331605416376, 0.0071008394:\u001b[K\u0007\u001b[H\u001b[2J\u001b[H\u001b[H\u001b[2J\u001b[H{\"lc2st_probabilities\": [[0.1983312341145994, 0.19218918728498446, 1.0, 0.20351917821653287, 1.0, 1.0, 0.3491858283608714, 0.11148460600402221, 0.1705053499709257, 1.0], [0.18886563228152808, 0.999801565848368, 0.06420094428578094, 0.003829460887442848, 1.0, 0.0, 0.05521830659929361, 1.0, 1.0, 1.0], [0.3723571672317456, 0.04190096758672124, 0.05917814671785937, 1.0, 0.5279380279033324, 1.0, 0.3939235158158101, 1.0, 0.3331214449554769, 0.14278821453735335], [0.16794501054145394, 1.0, 0.2836227076540423, 1.0, 0.08725893205243684, 1.0, 1.0, 1.0, 0.2898180261624975, 1.0], [3.4497582568349117e-10, 0.9999999999997726, 2.2475195038396123e-09, 0.9999999860851057, 0.0, 0.01498136204169076, 0.9999999999999996, 1.0, 1.0, 5.1250789168122424e-08]], \"lc2st_null_hypothesis_probabilities\": [[[0.2790388229755212, 0.2735488578185722, 8.397499558887578e-05, 0.002544143029782342, 0.4337491005580647, 0.5281736584242632, 0.2941875894651199, 0.8471557950221957, 0.491588056016956, 0.14904058563692402], [0.9298861402948934, 0.5799854862294562, 0.3862690266799903, 0.3032391090498666, 0.7976109456527416, 0.581394460307604, 0.6422201235768321, 0.9667699549263491, 0.4300242827109272, 0.603196699074676], [0.7458949626530249, 0.4908091805562187, 0.3295099292259487, 0.9048431566532888, 0.4475631382010551, 0.37204375915039756, 0.37192176501806684, 0.2599461309841308, 0.12187398110432535, 0.28911814717222706], [0.9715750712017363, 0.9302578076591757, 0.280485962197362, 0.7789217374607884, 0.5768391118918303, 0.6121690665788355, 0.8770606717117648, 0.5942009041559981, 0.6127097617959039, 0.9487840622625356], [0.5326295893366368, 0.7700871663694474, 0.6654461398938305, 0.8528700301216272, 0.8800724382630881, 0.6637915760733999, 0.8461285354871109, 0.8827762176374074, 0.2243871033069973, 0.6636540545834573]], [[0.049262331605416376, 0.0071008394:\u001b[K"
+     ]
+    }
+   ],
+   "source": [
+    "! cat DeepDiagnosticsResources/results/diagnostic_metrics.json | less"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using standalone functions \n",
+    "\n",
+    "DeepDiagnostics, if you have a configuration file set, can also be used with just the functions. Below is a list of all the functions and examples of their use. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# All metrics require a model and data \n",
     "Config(\"./my_config.yaml\")\n",
@@ -367,11 +558,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/cdf_ranks.py:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/cdf_ranks.py:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  thetas = tensor(self.data.get_theta_true())\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/cdf_ranks.py:45: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/cdf_ranks.py:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  context = tensor(self.data.true_context())\n",
-      "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:42<00:00, 97.72it/s]\n"
+      "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:49<00:00, 91.56it/s]\n"
      ]
     },
     {
@@ -385,7 +576,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -400,14 +591,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling from the posterior for each observation: 10000observation [01:44, 96.00observation/s]\n"
+      "Sampling from the posterior for each observation: 100%|██████████| 50/50 [00:00<00:00, 78.32 observation/s]\n"
      ]
     },
     {
@@ -421,7 +612,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -436,18 +627,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/ranks.py:16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/ranks.py:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  thetas = tensor(self.data.get_theta_true())\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/ranks.py:17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/ranks.py:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  context = tensor(self.data.true_context())\n",
-      "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:39<00:00, 100.20it/s]\n"
+      "Running 10000 sbc samples.: 100%|██████████| 10000/10000 [01:45<00:00, 94.46it/s]\n"
      ]
     },
     {
@@ -461,7 +652,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHECAYAAACp7JvEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAizUlEQVR4nO3deXSV9Z348U8WkhAxhNWAg7hUwYUiWkSmqMyAolWPddqR+nNacZxxuli1dW2tSrej49aqY2vtnKpnxqkLdZl20NYRrYqaWjTIYKHoBFMVBKUIyBKSfH9/dEiNLCZIckO+r9c5nCP3PvfJ5+aRL2+ee5+bopRSCgAAslFc6AEAAOhaAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDPtCsCUUqxatSp8ZjTA9rGOAt1JuwJw9erV0bdv31i9enVnzwPQI1lHge7ES8AAAJkRgAAAmRGAAACZEYAAAJkRgAAAmSkt9ADQWZqbm2Pjxo2FHoP/U1JSEqWlpVFUVFToUQCyJwDpkdasWROvvfaaz1zrZiorK2PIkCFRVlZW6FEAsiYA6XGam5vjtddei8rKyhg0aJAzTt1ASikaGxtj+fLlUV9fH/vuu28UF3sHCkChCEB6nI0bN0ZKKQYNGhS9e/cu9Dj8n969e0evXr3i1VdfjcbGxqioqCj0SADZ8k9weixn/rofZ/0AugerMQBAZgQgAEBmBCB0c9OmTYtPfvKThR4DgB7ERSBk48G7Z7Z725OmfqITJ+nepk+fHg888EDU1dVtc7v58+fH5ZdfHnPmzIlXX301vve978V5553XJTMC8OE4AwidoLGxsdAjdLq1a9fG3nvvHVdddVXU1NQUehwAOkAAwg4wceLEOPvss+O8886LgQMHxpQpUyIi4vrrr49Ro0bFLrvsEsOGDYsvfvGLsWbNmtbH3X777VFdXR2//OUvY//9948+ffrEscceG0uWLNnq13ruuedi0KBB8c///M9bvL+xsTHOPvvsGDJkSFRUVMTw4cPjyiuvbL1/5cqV8Q//8A8xaNCgqKqqir/+67+OuXPnts7zzW9+M+bOnRtFRUVRVFQUt99++xa/ztixY+Oaa66Jz3zmM1FeXt7RbxkABSQAYQe54447oqysLGbPnh233HJLRPzpY09uvPHGmD9/ftxxxx0xa9asuOiii9o8bu3atXHttdfGv/3bv8UTTzwRDQ0NccEFF2zxa8yaNSuOPvro+O53vxsXX3zxFre58cYb4z//8z/jnnvuiYULF8add94Ze+65Z+v9f/u3fxvLli2Lhx56KObMmROHHHJITJo0KVasWBFTp06N888/Pw488MBYsmRJLFmyJKZOnbpjvkEAdBveAwhbsHLFOx3avqmpKfbee+/4+iWXttnHtM+d0fr7Qw4+NL52ydfj/Au+Gj/4wQ9ab9+4cWPccsstsc8++0RExNlnnx3f+ta3Nvsa999/f3zuc5+Lf/3Xf91mlDU0NMS+++4bEyZMiKKiohg+fHjrfU899VT85je/iWXLlrWetbv22mvjgQceiBkzZsRZZ50Vffr0idLSUi/r0kZH3kMbkff7aGFnIABhBzl49MGb3fb444/H975/fSxatChWr14dTc1NsX79+li7dm1UVlZGxJ9+Pu6m+IuIGDJkSCxbtqzNfmpra+MXv/hFzJgx4wOvCJ42bVocffTRMWLEiDj22GPjhBNOiGOOOSYiIubOnRtr1qyJAQMGtHnMunXr4pVXXtmOZ83OqqNBB/QsAhB2kE1Bt0lDw6vxmf83Nf7+jL+Pb1x6WfTr1y+erX0mvnzOl6OxsbF1+169erV5XFFRUaSU2ty2zz77xIABA+InP/lJHH/88Zs95r0OOeSQqK+vj4ceeij++7//O0455ZSYPHlyzJgxI9asWRNDhgyJxx9/fLPHVVdXb98TB2CnIwChk9TV1UVLS0t859vfbf0RaA88cP927WvgwIFx3333xcSJE+OUU06Je+65Z5sRWFVVFVOnTo2pU6fGpz/96Tj22GNjxYoVccghh8TSpUujtLS0zfsC36usrCyam5u3a04Ky1k9oL1cBAKdZK+9946NGzfGrT/+USxevDjuuvuuuO3227Z7f4MHD45Zs2bFggUL4tRTT42mpqYtbnf99dfHT3/601iwYEH8/ve/j3vvvTdqamqiuro6Jk+eHOPHj49PfvKT8atf/SoWL14cTz/9dFx66aXx29/+NiIi9txzz6ivr4+6urp46623YsOGDVv8Oo2NjVFXVxd1dXXR2NgYr7/+etTV1cXLL7+83c8RgK7hDCDZ6Mib0jt6EciWjDpoVHz3O9+NG264Ib717W/FX47/y7jsssvjC1/8/Hbvs6amJmbNmhUTJ06M0047Lf7jP/4jSkpK2myz6667xtVXXx2LFi2KkpKSGDt2bMycObP1LOTMmTPj0ksvjTPOOCOWL18eNTU1ceSRR8Zuu+0WERGf+tSn4r777ou/+qu/ipUrV8Ztt90W06ZN22yWN954I8aMGdP6+2uvvTauvfbaOOqoo7b4EjMA3UdRev+bjbZg1apV0bdv33jnnXeiqqqqK+aC7bZ+/fqor6+PvfbaKyoqKrZrHzsiALemun/fTtt3d7cjjs3OqivW0e70ErCrgKF78xIwAEBmBCAAQGYEIABAZgQgAEBmBCA9Vjuub6KLOSYA3YMApMfZ9LEojY2NBZ6E91u7dm1EbP7TTwDoWj4HkB6ntLQ0KisrY/ny5dGrV6/Wz7/riM6Mx/Xr13favrurlFKsXbs2li1bFtXV1Zt9diEAXUsA0uMUFRXFkCFDor6+Pl599dXt2sfad9ft4Kn+rPKPvTtt391ddXV11NTUFHoMgOwJQHqksrKy2Hfffbf7TN6jM3+9gyf6s0mfOKrT9t2d9erVy5k/gG5CANJjFRcXb/dPm2ja2LyDp/mzXz44q0Pb+4kKAOxoLgIBAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjI+BAWCHe/DumR3a3scdQddyBhAAIDMCEAAgMwIQACAz3gPITqmj7y8CAP7MGUAAgMwIQACAzAhAAIDMCEAAgMy4CIRuw4UdANA1nAEEAMiMM4DQzfmRWgDsaM4AAgBkRgACAGRGAAIAZMZ7AAF6iOaW5oiUCj3Gdlm/fn2hR4AuU1xcHGVlZQWdQQAC9ACNjY3xzuqV0dzSVOhRtsu8efMKPQJ0mfLy8hg5cmRBI1AAAvQALS0t0dzSFMVFJVFcVFTocTqsoqKi0CNAl2hqaooNGzZES0tLQecQgAA9SHFRURQXlxR6jA4r9Mth0JWamgp/pt5FIAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmfE5gAAUXO2Tc9q97bgjDu3ESSAPAhAAuqmOhHGEOKb9vAQMAJAZAQgAkBkvAQOwU/GyKHx4ApB2e/DumR3a/qSpn+ikSQCAD8NLwAAAmRGAAACZ8RIwAHwIPsOQnZEzgAAAmRGAAACZEYAAAJkRgAAAmXERCPQwHfm8Rp/VCJAnAQhAj+Ynh8DmvAQMAJAZAQgAkBkBCACQGe8BBAB2On4Cy4cjAAEAPoSOxGhLS3McOGZkJ07TPgIQAHoIVzzTXt4DCACQGQEIAJAZAQgAkBnvAaTTdORHkgGQt46+f5EPRwACwHsIEXLgJWAAgMwIQACAzAhAAIDMCEAAgMwIQACAzLgKGADgPXK4EtwZQACAzDgDCAD0aDmc0esoZwABADLjDGDG/Kg2AMhTtwjAxsbGaGlpKfQY2Wlubir0CBTY+vXrO7R9cXFxlJWVddI0AHSVggdgY2NjLFiwIDZs2FDoUbKzYtXbhR6BAps3b16Hti8vL4+RI0eKQOADed9d91bwAGxpaYkNGzZEaWlplJYWfJyslBb7fueuoqKi3ds2NTXFhg0bnK0H6AG6TQGUlpY6q9DFiotLCj0CBdbRP3NNTd42ALlyRq9ncRUwAEBmBCAAQGa6zUvAANDTeRmV7sIZQACAzAhAAIDMCEAAgMwIQACAzLgIBAAy5aKUfDkDCACQGQEIAJAZLwH3ME7nAwAfxBlAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzPgYGIAepCWliJbmQo8BbEVLSoUeISIEIECPUFxcHCXFpdHc0hQt3ePvF2ArysvLo7i4sC/CCkCAHqCsrCz67lod0U3OLgBbN3LkyCgrKyvoDAIQoIcoKS4p9AhAOxQ6/iJcBAIAkB0BCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkJnSQg/AttU+OafQIwAAPYwzgAAAmek2ZwCbmpoKPUK31NLSXOgR6MEaGxvbva0/owA9R8EDsLi4OMrLy2PDhg3+gtmCphbfEzrP+vXrO7R9eXl5FBd74QBgZ1fwACwrK4uRI0dGS0tLoUfplv6waEmhR6AHGzVqVIe2Ly4ujrKysk6aBoCuUvAAjAh/oWxDSUm3OET0UBUVFYUeAYAC8FoOAEBmBCAAQGYEIABAZgQgAEBmXGHQxR68e2ahRwAAMucMIABAZgQgAEBmBCAAQGYEIABAZgQgAEBmBCAAQGYEIABAZgQgAEBmBCAAQGYEIABAZgQgAEBmBCAAQGYEIABAZkoLPQBQOA/ePbND25809ROdNAkAXckZQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzPgcwB2go5+lBgBQSM4AAgBkRgACAGRGAAIAZEYAAgBkRgACAGRGAAIAZEYAAgBkRgACAGRGAAIAZEYAAgBkRgACAGRGAAIAZEYAAgBkRgACAGRGAAIAZEYAAgBkRgACAGSmtNADdEcP3j2z0CMAAHQaZwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyU1roAbrCg3fPLPQIAADdhjOAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZKS30ANvjwbtnFnoEAICdljOAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZKS30AJs8ePfMQo8AAJAFZwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADJT2p6NUkoREbFq1apOG2Tt2rWdtm9gx+jMNWCTXXfdNYqKijr963Q16yiwSWevpe1ZR4vSplVpG1577bUYNmzYDhsMYGveeeedqKqqKvQYO5x1FOgq7VlH2xWALS0t8cYbb3TKv8xXrVoVw4YNiz/84Q89ctHPlePa83TVMe2pZwA7cx2N8GeuJ3JMe6auOK7tWWfa9RJwcXFx/MVf/MUOGWprqqqq/A/eAzmuPY9jun26Yh2NcHx6Ise0Zyr0cXURCABAZgQgAEBmCh6A5eXlccUVV0R5eXmhR2EHclx7Hse0e3N8eh7HtGfqLse1XReBAADQcxT8DCAAAF1LAAIAZEYAAgBkRgACAGSm4AF48803x5577hkVFRUxbty4+M1vflPokdiK6dOnR1FRUZtfI0eObL1//fr18aUvfSkGDBgQffr0iU996lPx5ptvttlHQ0NDHH/88VFZWRmDBw+OCy+8MJqamrr6qWTriSeeiBNPPDGGDh0aRUVF8cADD7S5P6UUl19+eQwZMiR69+4dkydPjkWLFrXZZsWKFXHaaadFVVVVVFdXx5lnnhlr1qxps82LL74YRxxxRFRUVMSwYcPi6quv7uynljXr6M7DOtoz9IS1tKABePfdd8dXv/rVuOKKK+L555+P0aNHx5QpU2LZsmWFHIttOPDAA2PJkiWtv5566qnW+77yla/Ez3/+87j33nvj17/+dbzxxhvxN3/zN633Nzc3x/HHHx+NjY3x9NNPxx133BG33357XH755YV4Kll69913Y/To0XHzzTdv8f6rr746brzxxrjllluitrY2dtlll5gyZUqsX7++dZvTTjst5s+fH4888kj84he/iCeeeCLOOuus1vtXrVoVxxxzTAwfPjzmzJkT11xzTUyfPj1uvfXWTn9+ObKO7nysozu/HrGWpgI67LDD0pe+9KXW3zc3N6ehQ4emK6+8soBTsTVXXHFFGj169BbvW7lyZerVq1e69957W2/73e9+lyIiPfPMMymllGbOnJmKi4vT0qVLW7f54Q9/mKqqqtKGDRs6dXY2FxHp/vvvb/19S0tLqqmpSddcc03rbStXrkzl5eXppz/9aUoppZdeeilFRHruuedat3nooYdSUVFRev3111NKKf3gBz9I/fr1a3NML7744jRixIhOfkZ5so7uXKyjPc/OupYW7AxgY2NjzJkzJyZPntx6W3FxcUyePDmeeeaZQo3FB1i0aFEMHTo09t577zjttNOioaEhIiLmzJkTGzdubHM8R44cGXvssUfr8XzmmWdi1KhRsdtuu7VuM2XKlFi1alXMnz+/a58Im6mvr4+lS5e2OYZ9+/aNcePGtTmG1dXV8bGPfax1m8mTJ0dxcXHU1ta2bnPkkUdGWVlZ6zZTpkyJhQsXxh//+McuejZ5sI7unKyjPdvOspYWLADfeuutaG5ubvM/cUTEbrvtFkuXLi3QVGzLuHHj4vbbb4+HH344fvjDH0Z9fX0cccQRsXr16li6dGmUlZVFdXV1m8e893guXbp0i8d7030U1qZjsK0/k0uXLo3Bgwe3ub+0tDT69+/vOBeAdXTnYx3t+XaWtbT0Q++BbBx33HGt//3Rj340xo0bF8OHD4977rknevfuXcDJAHYO1lG6i4KdARw4cGCUlJRsdnXTm2++GTU1NQWaio6orq6O/fbbL15++eWoqamJxsbGWLlyZZtt3ns8a2pqtni8N91HYW06Btv6M1lTU7PZxQVNTU2xYsUKx7kArKM7P+toz7OzrKUFC8CysrI49NBD49FHH229raWlJR599NEYP358ocaiA9asWROvvPJKDBkyJA499NDo1atXm+O5cOHCaGhoaD2e48ePj3nz5rX5n/6RRx6JqqqqOOCAA7p8ftraa6+9oqamps0xXLVqVdTW1rY5hitXrow5c+a0bjNr1qxoaWmJcePGtW7zxBNPxMaNG1u3eeSRR2LEiBHRr1+/Lno2ebCO7vysoz3PTrOW7pBLSbbTXXfdlcrLy9Ptt9+eXnrppXTWWWel6urqNlc30X2cf/756fHHH0/19fVp9uzZafLkyWngwIFp2bJlKaWUPv/5z6c99tgjzZo1K/32t79N48ePT+PHj299fFNTUzrooIPSMccck+rq6tLDDz+cBg0alL72ta8V6illZ/Xq1emFF15IL7zwQoqIdP3116cXXnghvfrqqymllK666qpUXV2dHnzwwfTiiy+mk046Ke21115p3bp1rfs49thj05gxY1JtbW166qmn0r777ptOPfXU1vtXrlyZdtttt/TZz342/c///E+66667UmVlZfrRj37U5c83B9bRnYt1tGfoCWtpQQMwpZRuuummtMcee6SysrJ02GGHpWeffbbQI7EVU6dOTUOGDEllZWVp9913T1OnTk0vv/xy6/3r1q1LX/ziF1O/fv1SZWVlOvnkk9OSJUva7GPx4sXpuOOOS717904DBw5M559/ftq4cWNXP5VsPfbYYykiNvt1+umnp5T+9PEFl112Wdptt91SeXl5mjRpUlq4cGGbfbz99tvp1FNPTX369ElVVVXpjDPOSKtXr26zzdy5c9OECRNSeXl52n333dNVV13VVU8xS9bRnYd1tGfoCWtpUUopffjziAAA7CwK/qPgAADoWgIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCkHabOHFinHfeeYUeo0N2xpmBnm9nXJu2d+ZLLrkkTjjhhB0/EB9KaaEHoPNMnDgxDj744Pj+97+/Q/Z33333Ra9evXbIvgB2JtbT7VdXVxeHHnpoocfgfZwB5AM1NjZGRET//v1j1113/dD72VHbAexsclxP6+rq4qMf/Wihx+B9BGABTZw4Mc4+++w4++yzo2/fvjFw4MC47LLLIqUUEREbNmyIc845JwYPHhwVFRUxYcKEeO6559rsY8aMGTFq1Kjo3bt3DBgwICZPnhzvvvtuTJs2LX7961/HDTfcEEVFRVFUVBSLFy+OlpaWuPLKK2OvvfaK3r17x+jRo2PGjBlbnOu8886LgQMHxpQpU1pvf+/p/w+ab2v72dr34f3bPfzwwzFhwoSorq6OAQMGxAknnBCvvPJKm8edc845cdFFF0X//v2jpqYmpk+fvs3v+X/9139F3759484779zqNosXL46ioqL42c9+FkceeWT07t07xo4dGw0NDfHkk0/G4YcfHpWVlTFp0qRYuXLlNr8e0DWsp9verlDr6dKlS+PNN9+M5ubmOPLII6OysjLGjh0b8+bN2+a+6QKJgjnqqKNSnz590rnnnpsWLFiQ/v3f/z1VVlamW2+9NaWU0jnnnJOGDh2aZs6cmebPn59OP/301K9fv/T222+nlFJ64403Umlpabr++utTfX19evHFF9PNN9+cVq9enVauXJnGjx+f/vEf/zEtWbIkLVmyJDU1NaXvfOc7aeTIkenhhx9Or7zySrrttttSeXl5evzxxzeb68ILL0wLFixICxYsaL393HPPbd3ug+bb2n629n14/3YzZsxIP/vZz9KiRYvSCy+8kE488cQ0atSo1Nzc3Pq4qqqqNH369PT73/8+3XHHHamoqCj96le/arPvTTPfeeedadddd00///nPt3lcHnjggRQRadKkSenJJ59Mzz//fBo2bFg64ogj0ic+8Yn03HPPpWeffTYNGDAgXX/99e093EAnsp5u++sVaj196KGHUkSksWPHpqeeeirNnz8/TZw4MR144IHtPbR0EgFYQEcddVTaf//9U0tLS+ttF198cdp///3TmjVrUq9evdKdd97Zel9jY2MaOnRouvrqq1NKKc2ZMydFRFq8ePFW9//eBWb9+vWpsrIyPf300222O/PMM9Opp57a5nFjxozZ5v7aM9/W9rOl/bZnu+XLl6eISPPmzWt93IQJE9psM3bs2HTxxRdvNvO//Mu/pL59+7ZZmLdm+vTpqX///umtt95qve3v/u7v0p577pnefffd1tuOPfbYdNFFF33g/oDOZz3d9td7v65aT6+88spUUVGRXn/99dbbZs+enSIiLV++/AMfT+dxEUiBHX744VFUVNT6+/Hjx8d1110XL7/8cmzcuDE+/vGPt97Xq1evOOyww+J3v/tdRESMHj06Jk2aFKNGjYopU6bEMcccE5/+9KejX79+W/xaL7/8cqxduzaOPvroNrc3NjbGmDFj2tz2QW/YfeWVVz5wvvbsZ1vbLVq0KC6//PKora2Nt956K1paWiIioqGhIQ466KCIiM3eVzJkyJBYtmxZm9tmzJgRy5Yti9mzZ8fYsWM/cJa5c+fGySefHAMGDGi9raGhIaZOnRqVlZVtbjvppJPa9fyAzmc93fp2hVpP6+rq4pRTTomhQ4e23rbpe7ppBgrDewB3YiUlJfHII4/EQw89FAcccEDcdNNNMWLEiKivr9/i9mvWrImIP71vo66urvXXSy+9tNn7VnbZZZcdMmN797Ol7U488cRYsWJF/PjHP47a2tqora2NiLZvan7/VXRFRUWbLSpjxoyJQYMGxU9+8pPW9wNtS11dXYwbN67NbXPnzo3DDz+89ffr16+PhQsXxujRoz/4yQHdnvW089bTgw8+uM1tzz77bOy+++4xePDgdj0fOocALLBNfwg3efbZZ2PfffeNj3zkI1FWVhazZ89uvW/jxo3x3HPPxQEHHNB6W1FRUXz84x+Pb37zm/HCCy9EWVlZ3H///RERUVZWFs3Nza3bHnDAAVFeXh4NDQ3xkY98pM2vYcOGdWjuffbZp13zba+33347Fi5cGN/4xjdi0qRJsf/++8cf//jH7drXPvvsE4899lg8+OCD8eUvf3mb265atSoWL17c5l/w9fX18c4777S5bd68eZFSilGjRm3XTMCOZz3dskKtp2vXro1Fixa1+b61tLTEDTfcENOmTduur8+O4yXgAmtoaIivfvWr8U//9E/x/PPPx0033RTXXXdd7LLLLvGFL3whLrzwwujfv3/ssccecfXVV8fatWvjzDPPjIg/LXaPPvpoHHPMMTF48OCora2N5cuXx/777x8REXvuuWfU1tbG4sWLo0+fPtG/f/+44IIL4itf+Uq0tLTEhAkT4p133onZs2dHVVVVnH766e2euz3zfRj9+vWLAQMGxK233hpDhgyJhoaGuOSSS7Z7f/vtt1889thjMXHixCgtLd3qZ3nNnTs3SkpKWl8SifjTv2D79+8fw4cPb3PbPvvsE3369NnumYAdy3q6ZYVaT1988cUoKSmJ2267LY466qioqqqKSy+9NNatWxcXX3zxdn99dgwBWGCf+9znYt26dXHYYYdFSUlJnHvuuXHWWWdFRMRVV10VLS0t8dnPfjZWr14dH/vYx+KXv/xl6/snqqqq4oknnojvf//7sWrVqhg+fHhcd911cdxxx0VExAUXXBCnn356HHDAAbFu3bqor6+Pb3/72zFo0KC48sor43//93+juro6DjnkkPj617/e4dk/aL4Po7i4OO66664455xz4qCDDooRI0bEjTfeGBMnTtzufY4YMSJmzZoVEydOjJKSkrjuuus222bu3LkxYsSIqKioaHPb+9/TM3fuXC//QjdjPd2yQq2ndXV1sd9++8Xll18eJ598cqxcuTJOPPHEePrppz/UZyCyYxSl9ryIT6fY0Z8sD5Ar6yl0jPcAAgBkRgACAGTGS8AAAJlxBhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgM/8fFu+ej8oSUHMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x500 with 2 Axes>"
       ]
@@ -514,9 +705,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -528,7 +889,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAI6CAYAAAAUmF2zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9ebwUxfX9qe6ZeSs7yCaCLAIqEonERAVciKhocIl7IsQoGvck7ibuxBiNMWq+RqMBMRhj4vrTxDVgAu5RwAUREUSUfXtv3mxdVff3R1X3dM/0bO/NY7NOPi/y3vT0VPd0d52699xzGRERDAwMDAwMDAwMqgprWw/AwMDAwMDAwGBnhCFZBgYGBgYGBgbtAEOyDAwMDAwMDAzaAYZkGRgYGBgYGBi0AyLbegAGBgYGBjsHhBBwHGdbD8PAYKsgGo3Ctu2i2xiSZWBgYGDQJhARVq9ejc2bN2/roRgYbFV07twZvXr1AmMs9HVDsgwMDAwM2gSXYO2yyy6or68vOOEYGOwsICIkEgmsXbsWANC7d+/Q7QzJMjAwMDBoNYQQHsHq1q3bth6OgcFWQ11dHQBg7dq12GWXXUJTh0b4bmBgYGDQargarPr6+m08EgODrQ/3ui+kRTQky8DAwMCgzTApQoOvI0pd94ZkGRgYGBgYGBi0AwzJMjAwMDAwMDBoBxiSZWBgYGBgEII5c+aAMbbDWVMwxvDUU09VbX8DBgzAnXfeWbX9FcP111+Pb3zjG97vU6ZMwbHHHrtVPrs9YEiWgYGBgcHXDoyxoj/XX3/9th5iSeQSEherVq3CkUceuVXHwRjDueeeG/j7/PnzwRjD8uXL2/XziQj3338/9t9/fzQ2NqJz587Yb7/9cOeddyKRSATGyBhDJBJB9+7dMXbsWNx5551Ip9OB/R188MGh1wTnvOKxGZJlYLCToT0miClTpmDAgAFV3WclmDFjRujD+rbbbsPAgQNh27Y32QwYMABTpkzZ6mPcHrCjkIPtAatWrfJ+7rzzTnTs2DHwt0svvXSbjS2TybTp/b169UJNTU2VRlMeamtr8eCDD2LJkiVb9XMB4Ic//CEuueQSTJo0CbNnz8b8+fPxy1/+Ek8//TRefPFFb7u99toLq1atwooVKzB79myceOKJuOWWW3DAAQegubk5sM+zzz47cD2sWrUKkUjlrleGZBkYtALupP/OO+9s66G0CU1NTbjhhhswcuRINDY2oq6uDnvvvTeuuOIKfPXVV9t6eEXx4osv4vLLL8eBBx6I6dOn41e/+tVWH8OAAQMCK91ddtkFY8aMwZNPPhnYLndl3LVrV4wePRp//vOfIaUs+hnLly8PvNe2bey222447rjjMH/+/Kocx0cffYTrr7++6hGHlpaWgj+pVKrsbZPJZFnbVoJevXp5P506dQJjLPC3xsZGb9v//e9/2G+//VBfX48DDjgAixcvDuzr6aefxqhRo1BbW4uBAwfihhtuCEQ9VqxYgUmTJqGxsREdO3bESSedhDVr1nivuxGpBx54ALvvvjtqa2sBAJs3b8ZZZ52FHj16oGPHjjj00EOxYMECAOoZdMMNN2DBggXetTFjxgwA+enClStX4tRTT0XXrl3R0NCA/fbbD2+++SYAYOnSpZg0aRJ69uyJxsZGjB49Gi+//HJF5xIAhg4dikMOOQTXXHNNwW1mzJiBzp07B/721FNPtaky9bHHHsOsWbPw17/+FVdffTVGjx6NAQMGYNKkSfj3v/+NQw45xNs2EomgV69e6NOnD0aMGIELL7wQr776Kj744APceuutgf3W19cHrodevXq1anzGjNTA4GuKzz77DOPHj8eKFStw4oknYurUqYjFYli4cCEefPBBPPnkk/jkk0+29TABqJXqKaecElid//vf/4ZlWXjwwQcRi8W8vy9evBiWtfXWj9/4xjfw85//HADw1Vdf4b777sPxxx+Pe++9N5A+2XXXXXHLLbcAANatW4eZM2fixz/+MT755BP8+te/Lvk5p556Ko466igIIbBo0SLce++9+Ne//oU33ngjNGVUCT766CPccMMNOPjgg6sasfQTlVwcddRReO6557zfd9llFy+1k4tx48Zhzpw53u8DBgzA+vXr87YjotYPtgiuueYa/Pa3v0WPHj1w7rnn4swzz8S8efMAAP/9739xxhln4K677sKYMWOwdOlSTJ06FQBw3XXXQUrpEaxXX30VnHOcf/75OPnkkwPH9Omnn+Lxxx/HE0884Zlannjiiairq8O//vUvdOrUCffddx8OO+wwfPLJJzj55JPxwQcf4Pnnn/dIUadOnfLGHo/HMW7cOPTt2xfPPPMMevXqhXfffdcj9/F4HEcddRSmTZuGmpoazJw5E8cccwwWL16M3XbbraLz9Otf/xqjR4/GO++8g/3226/i89wazJo1C0OHDsWkSZPyXmOMhZ4TP4YNG4YjjzwSTzzxBG6++eaqj8+QLAODryE45zj++OOxZs0azJkzBwcddFDg9WnTpuWt7LYlbNvOc1Neu3Yt6urqAgQLQFXTJJxzSCnzPsOPvn374gc/+IH3+xlnnIHBgwfjd7/7XYBkderUKbDdOeecg6FDh+Kee+7BTTfdhGg0WnQso0aNCrz/wAMPxPe+9z3ce++9uO+++1pzeAZlYtq0aRg3bhwA4Morr8TEiRORSqVQW1uLG264AVdeeSUmT54MABg4cCBuuukmXH755bjuuuvwyiuv4P3338eyZcvQr18/AMDMmTOx11574e2338bo0aMBqBThzJkz0aNHDwDA3Llz8dZbb2Ht2rXeNX377bfjqaeewj/+8Q9MnToVjY2NXnSmEB555BGsW7cOb7/9Nrp27QoAGDx4sPf6yJEjMXLkSO/3m266CU8++SSeeeYZXHDBBRWdp1GjRuGkk07CFVdcgVdeeaWi97YWS5YswdChQ9u0j2HDhgXSigDwf//3f3jggQe838855xz89re/rXjfJl1oYNCOeO+993DkkUeiY8eOaGxsxGGHHYY33ngjb7vNmzfjpz/9KQYMGICamhrsuuuuOOOMM7zVeiaTwbXXXotvfvOb6NSpExoaGjBmzBjMnj27VeN6/PHHsWDBAlxzzTV5BAsAOnbsiGnTphXdx+23344DDjgA3bp1Q11dHb75zW/iH//4R952L730Eg466CB07twZjY2NGDp0KK6++urANnfffTf22msv1NfXo0uXLthvv/3wyCOPeK/narIYY5g+fTpaWlryUiVhmqzNmzfjkksuQb9+/VBTU4PBgwfj1ltvDaTq3LTc7bffjjvvvBODBg1CTU0NPvroo6LnIRe9evXC8OHDsWzZsqLb1dfX49vf/jZaWlqwbt26ij4DAA499FAAKPk5pa7BGTNm4MQTTwQAHHLIId759EdZWot4PF7w5/HHHw9su3bt2oLb/utf/wpsu3z58tDt2gv77LOP92+3R53bs27BggW48cYb0djY6P24ep5EIoFFixahX79+HsECgD333BOdO3fGokWLvL/179/fI1jufuPxOLp16xbY97Jly7B06dKyxz5//nzsu+++HsHKRTwex6WXXorhw4d79+iiRYuwYsWKsj/Dj5tvvhn//e9/80hLe6Ea0UsiyktZnn766Zg/f773c9VVV7Vq3yaSZWDQTvjwww8xZswYdOzYEZdffjmi0Sjuu+8+HHzwwXj11Vex//77A1APuTFjxmDRokU488wzMWrUKKxfvx7PPPMMVq5cie7du6OpqQkPPPAATj31VJx99tlobm7Ggw8+iAkTJuCtt96qOF30zDPPAFBpuNbi97//Pb73ve/h9NNPRyaTwaOPPooTTzwRzz77LCZOnOidg6OPPhr77LMPbrzxRtTU1ODTTz/1Ui0A8Kc//QkXXXQRvv/97+Piiy9GKpXCwoUL8eabb+K0004L/eyHH34Y999/P9566y1vtXnAAQeEbptIJDBu3Dh8+eWXOOecc7Dbbrvhtddew1VXXeWJnv2YPn06UqkUpk6dipqamoKTUyE4joMvvviirD5+n332GWzbztOplAN3oi32OeVcg2PHjsVFF12Eu+66C1dffTWGDx8OAN5/24KGhoZtvm014I8yupOxP912ww034Pjjj897n6utKge5xxSPx9G7d+9QslvJ9eL21yuESy+9FC+99BJuv/12DB48GHV1dfj+97/favH9oEGDcPbZZ+PKK6/Egw8+GHjNsqw8UlSoHU252GOPPfDxxx+3aR+LFi3C7rvvHvhbp06dAhG/1sKQLAODdsIvfvELOI6DuXPnYuDAgQBUKmno0KG4/PLL8eqrrwJQFXIffPABnnjiCRx33HGB97sPpC5dumD58uWBtNXZZ5+NYcOG4e677857mJXCokWL0KlTp8DqulJ88skngQf4BRdcgFGjRuGOO+7wSNZLL72ETCaDf/3rX+jevXvofp577jnstdde+Pvf/172Z//gBz/Ayy+/jHfffTeQQgvDHXfcgaVLl+K9997DkCFDAKjQf58+fXDbbbfh5z//eeA8rFy5Ep9++mkgqlAMjuN4EcevvvoKt9xyC9asWYMLL7wwsJ0Qwttu/fr1uPfee/Huu+/imGOOKavvXyKRwPr16yGEwMcff4yf/vSnAOBFocJQzjU4cOBAjBkzBnfddRe++93v4uCDDy7ruA0URo0ahcWLFxeckIcPH44vvvgCX3zxhXedffTRR9i8eTP23HPPovtdvXo1IpFIQZ1cLBaDEKLo+PbZZx888MAD2LhxY+iCYd68eZgyZYr37InH420ugLj22msxaNAgPProo4G/9+jRA83NzWhpafFIZVuLN0477TSccsopePrpp/N0WUSEpqamorqsjz/+GM8//3yrI1WlYNKFBgbtACEEXnzxRRx77LHe5AaoVMNpp52GuXPnoqmpCYBK3Y0cOTJAsFy4q2bbtj2CJaXExo0bwTnHfvvth3fffbfi8TU1NaFDhw6tOTQPfoK1adMmbNmyBWPGjAmMx11xP/300wWr6Dp37oyVK1fi7bffbtN4CuHvf/87xowZgy5dumD9+vXez/jx4yGEwH/+85/A9ieccELZBAtQVY49evRAjx49MHLkSPz973/HD3/4wzxN28cff+xtN3z4cNx9992YOHEi/vznP5f1Oddddx169OiBXr164eCDD8bSpUtx6623hkZQgMquQYPW49prr8XMmTNxww034MMPP8SiRYvw6KOP4he/+AUAYPz48RgxYgROP/10vPvuu3jrrbdwxhlnYNy4cUXF4ePHj8d3vvMdHHvssXjxxRexfPlyvPbaa7jmmmu8quYBAwZg2bJlmD9/PtavX5/n9wSogolevXrh2GOPxbx58/DZZ5/h8ccfx+uvvw4AGDJkCJ544gnMnz8fCxYswGmnnVay4rUUevbsiZ/97Ge46667An/ff//9UV9fj6uvvhpLly7FI4884qX5W4uTTjoJJ598Mk499VT86le/wjvvvIPPP/8czz77LMaPHx+QVHDOsXr1anz11Vd4//33cffdd2PcuHH4xje+gcsuu6xN4ygEQ7IMDNoB69atQyKRCBVkDh8+HFJKfPHFFwBU2mfvvfcuuc+HHnoI++yzD2pra9GtWzf06NEDzz33HLZs2VLx+Dp27JjnC1Mpnn32WXz7299GbW0tunbtih49euDee+8NjOfkk0/GgQceiLPOOgs9e/bEKaecgsceeyzwEL/iiivQ2NiIb33rWxgyZAjOP//8QDqxrViyZAmef/55j+C4P+PHjweQ1da4yE0blML++++Pl156CS+//DJee+01rF+/HjNnzsxL0wwYMMDbbu7cuVi9ejWeffZZL8K3bt06rF692vvJ1RhNnToVL730El555RX873//w9q1a3H55ZcXHFcl16BB6zFhwgQ8++yzePHFFzF69Gh8+9vfxu9+9zv0798fgFooPf300+jSpQvGjh2L8ePHY+DAgfjb3/5WdL+MMfzzn//E2LFj8aMf/Qh77LEHTjnlFHz++efo2bMnALUgOOKII3DIIYegR48e+Otf/5q3n1gshhdffBG77LILjjrqKIwYMQK//vWvvUKSO+64A126dMEBBxyAY445BhMmTMCoUaPafF4uvfTSvOrSrl274i9/+Qv++c9/YsSIEfjrX//aZl83xhgeeeQR3HHHHXjqqacwbtw47LPPPrj++usxadIkTJgwwdv2ww8/RO/evbHbbrvh4IMPxmOPPYarrroK//3vf4tWwrYJZGBgUDGmT59OAOjtt98OfX3VqlUEgH75y1/mvXbnnXcSAPrggw+IiKi2tpZOP/30op/38MMPEwA69thjaebMmfT888/TSy+9RIceeij1798/sC0Auu6664ru79RTTyUAtGLFiqLbuZg8eXLgc/7zn/8QY4zGjRtHDz74IP3zn/+kl156iU477TTKfawIIejll1+mn/70pzR8+HACQIceeihxzr1t4vE4PfroozRlyhTq2bMnAaBrr73We90938uWLQuMqaGhIW+s/fv3p8mTJ3u/19TU0He/+1166aWXQn8+//xzIiJatmwZAaDbbrutrHPiftbEiRNLbjdu3Djaa6+9Su4LgPfjfoeVjMv/vkquwb///e8EgGbPnl3yM3KRTCbpo48+omQyWfF7DQx2dJS6/o0my8CgHdCjRw/U19fnmRYCKm1kWZanzxg0aBA++OCDovv7xz/+gYEDB+KJJ54IVMFcd911rRrfMcccg7/+9a/4y1/+0iotwuOPP47a2lq88MILAcuE6dOn521rWRYOO+wwHHbYYbjjjjvwq1/9Ctdccw1mz57tRZMaGhpw8skn4+STT0Ymk8Hxxx+PadOm4aqrrqpIPByGQYMGIR6Pe5+1vWLWrFkB001/iq81qOQabIsZpIGBQWGYdKGBQTvAtm0cfvjhePrppwMi0jVr1uCRRx7BQQcdhI4dOwJQIf8FCxbkuYQD2fJkN7RPvsqcN99809NVVIrvf//7GDFiBKZNmxa6j+bm5qLOzbZtgzEWEN0uX748ryntxo0b897rVkK6+pENGzYEXo/FYthzzz1BRG2uPAKUZuP111/HCy+8kPfa5s2bW9WPrD1w4IEHYvz48d5PW0lWJdegK0Le0RohGxhs7zCRLAODNuDPf/4znn/++by/X3zxxbj55ps9j6jzzjsPkUgE9913H9LpNH7zm99421522WX4xz/+gRNPPBFnnnkmvvnNb2Ljxo145pln8Mc//hEjR47E0Ucf7VUfTpw4EcuWLcMf//hH7Lnnnq3yB4pGo3jiiScwfvx4jB07FieddBIOPPBARKNRfPjhh3jkkUfQpUuXgl5ZEydOxB133IEjjjgCp512GtauXYs//OEPGDx4MBYuXOhtd+ONN+I///kPJk6ciP79+2Pt2rX4v//7P+y6666eP9fhhx+OXr164cADD0TPnj2xaNEi3HPPPZg4cWKbxfmAOr/PPPMMjj76aEyZMgXf/OY30dLSgvfffx//+Mc/sHz58oKVjzs6yr0Gv/GNb8C2bdx6663YsmULampqcOihh2KXXXbZhqM3MNgJsDVzlwYGOwtcjVChny+++IKIiN59912aMGECNTY2Un19PR1yyCH02muv5e1vw4YNdMEFF1Dfvn0pFovRrrvuSpMnT6b169cTEZGUkn71q19R//79qaamhvbdd1969tln87RSROVpslxs2rSJrr32WhoxYgTV19dTbW0t7b333nTVVVfRqlWrvO3CPufBBx+kIUOGUE1NDQ0bNoymT59O1113XUCT9corr9CkSZOoT58+FIvFqE+fPnTqqafSJ5984m1z33330dixY6lbt25UU1NDgwYNossuu4y2bNmSd75bo8kiImpubqarrrqKBg8eTLFYjLp3704HHHAA3X777ZTJZIho22uyCqG1miwX5V6Df/rTn2jgwIFk23ZF+iyjyTL4OqPU9c+I2qnZk4GBgYHBTo9UKoVly5YFGhsbGHxdUOr6N5osAwMDAwMDA4N2gCFZBgYGBgYGBgbtAEOyDAwMDAwMDAzaAaa60MDAwMCgXeBkHAjethYtlcCOWIjGoqU3/JqDiHDOOefgH//4BzZt2oT33nuv4ibz1cLy5cux++67b9MxtCcMyTIwMDAwqDqcjIPFb32KRDy11T6zvrEWQ781uGyiNWDAAHz++ed5fz/vvPPwhz/8AQBw8MEHe83cXZxzzjn44x//CEB5wU2ePBmzZ8/GkCFD8Oc//xn77ruvt+3555+PgQMH4uc//3nJ8axevRrTpk3Dc889hy+//BK77LILvvGNb+CSSy7BYYcdVtYxlYPnn38eM2bMwJw5czBw4MBtamHSr18/rFq1aqe1UTEky8DAwMCg6hBcIhFPIRqLIFrT/lONk+ZIxFMQXCIaK+89b7/9dsBQ94MPPsB3v/tdnHjiiYHtzj77bNx4443e7/X19d6/p02bhubmZrz77ru49957cfbZZ3sNnN944w28+eabeY2Sw7B8+XIceOCB6Ny5M2677TaMGDECjuPghRdewPnnn4+PP/64vIMqA0uXLkXv3r1xwAEHtHofjuMgGm171NC2bfTq1avN+9leYTRZBgYGBgbthmhNBLHaWLv/tIbI9ejRA7169fJ+nn32WQwaNAjjxo0LbFdfXx/YznXKB4BFixbhlFNOwR577IGpU6di0aJFABQJOffcc/HHP/7R69hQDOeddx4YY3jrrbdwwgknYI899sBee+2Fn/3sZ3jjjTe87VasWIFJkyahsbERHTt2xEknnYQ1a9Z4r19//fX4xje+gYcffhgDBgxAp06dcMopp3gN4adMmYILL7wQK1asAGMMAwYMKOtcMcZw77334nvf+x4aGho8o+Knn34ao0aNQm1tLQYOHIgbbrgh0EXh448/xkEHHYTa2lrsueeeePnll8EY87pDLF++HIwxzJ8/33vPq6++im9961uoqalB7969ceWVVwb2efDBB+Oiiy7C5Zdfjq5du6JXr15tbjTdXjAky8DAwMDga49MJoO//OUvOPPMM/N6Oc6aNQvdu3fH3nvvjauuugqJRMJ7beTIkfj3v/8NzjleeOEF7LPPPgCA3/zmNzj44IOx3377lfzsjRs34vnnn8f555/vtTjyo3PnzgAAKSUmTZqEjRs34tVXX8VLL72Ezz77DCeffHJg+6VLl+Kpp57Cs88+i2effRavvvoqfv3rXwMAfv/73+PGG2/ErrvuilWrVuHtt98u+xxdf/31OO644/D+++/jzDPPxH//+1+cccYZuPjii/HRRx/hvvvuw4wZMzwCJoTAsccei/r6erz55pu4//77i7brAoAvv/wSRx11FEaPHo0FCxbg3nvvxYMPPoibb745sN1DDz2EhoYGvPnmm/jNb36DG2+8ES+99FLZx7K1YNKFBgYGBgZfezz11FPYvHkzpkyZEvj7aaedhv79+6NPnz5YuHAhrrjiCixevBhPPPEEAODKK6/ET37yEwwaNAgDBgzAgw8+iCVLluChhx7C66+/jnPPPRcvvvgi9ttvP/zpT39Cp06d8j77008/BRFh2LBhRcf4yiuv4P3338eyZcu85t4zZ87EXnvthbfffhujR48GoMjYjBkzvLZUP/zhD/HKK69g2rRp6NSpEzp06NCqNN1pp52GH/3oR97vZ555Jq688kpMnjwZgGpqftNNN+Hyyy/Hddddh5deeglLly7FnDlzvM+aNm0avvvd7xb8jP/7v/9Dv379cM8994AxhmHDhuGrr77CFVdcgWuvvRaWpWJD++yzD6677joAwJAhQ3DPPffglVdeKbrvbQFDsgwMDAwMvvZ48MEHceSRR6JPnz6Bv0+dOtX794gRI9C7d28cdthhWLp0KQYNGoROnTrhkUceCbzn0EMPxW233YZZs2bhs88+w+LFiz1d129/+9u8zy638cqiRYvQr18/j2ABwJ577onOnTtj0aJFHskaMGBAoO9n7969sXbt2rI+oxhyo3ILFizAvHnzAj1OhRBIpVJIJBJYvHgx+vXrFyBz3/rWt4p+xqJFi/Cd73wnEE088MADEY/HsXLlSuy2224A4EUMXVTrGKsNQ7IMDAwMDL7W+Pzzz/Hyyy970ali2H///QGo6NOgQYPyXp8+fTo6d+6MSZMm4fjjj8exxx6LaDSKE088Eddee23oPocMGQLGWNXE7bmCdMYYpGy7lUZuKjMej+OGG27A8ccfn7dte7dYaq9jrDaMJsvAwMDA4GuN6dOnY5dddsHEiRNLbusKtHv37p332rp163DjjTfi7rvvBqCiOo7jAFBCeH8lox9du3bFhAkT8Ic//AEtLS15r2/evBkAMHz4cHzxxRf44osvvNc++ugjbN68GXvuuWfJsVcbo0aNwuLFizF48OC8H8uyMHToUHzxxRcBYX4pDdjw4cPx+uuvB6J78+bNQ4cOHbDrrru227G0FwzJMjAwMDD42kJKienTp2Py5MmIRILJnaVLl+Kmm27C//73PyxfvhzPPPMMzjjjDIwdOzYvXQUAl1xyCX7+85+jb9++AFSa6+GHH8aiRYtw//3348ADDyw4jj/84Q8QQuBb3/oWHn/8cSxZsgSLFi3CXXfdhe985zsAgPHjx2PEiBE4/fTT8e677+Ktt97CGWecgXHjxpUlsC+Ee+65p1U+XNdeey1mzpyJG264AR9++CEWLVqERx99FL/4xS8AAN/97ncxaNAgTJ48GQsXLsS8efO813KLC1ycd955+OKLL3DhhRfi448/xtNPP43rrrsOP/vZzzw91o6EHW/EBgYGBgY7DJw0RyaVafcfJ81LDyYEL7/8MlasWIEzzzwz77VYLIaXX34Zhx9+OIYNG4af//znOOGEE/D//t//y9v2hRdewKefforzzjvP+9sFF1yAgQMHYv/990cmk/GE2mEYOHAg3n33XRxyyCH4+c9/jr333hvf/e538corr+Dee+8FoIjJ008/jS5dumDs2LEYP348Bg4ciL/97W+tOnYX69evx9KlSyt+34QJE/Dss8/ixRdfxOjRo/Htb38bv/vd79C/f38AygPrqaeeQjwex+jRo3HWWWd51YWF0ol9+/bFP//5T7z11lsYOXIkzj33XPz4xz/2yNmOBkblKu4MDAwMDAxykEqlsGzZMuy+++6BiXNHcHw32PqYN28eDjrooIKath0Nha5/F0b4bmBgYGBQdURjUQz91mDTu/BrjieffBKNjY0YMmQIPv30U1x88cU48MADdwqCVQ4MyTIwMDAwaBdEY9GyW9wY7Jxobm7GFVdcgRUrVqB79+4YP358qI3FzgqTLjQwMDAwaDVKpUsMDHZmlLr+jfDdwMDAwMDAwKAdYEiWgYGBgYGBgUE7wJAsAwMDAwMDA4N2gCFZBgYGBgYGBgbtAEOyDAwMDAwMDAzaAYZkGRgYGBgYGBi0AwzJMjAwMDBoF2SEQNJxttpPpkADZoN8XH/99ejZsycYY3jqqae26Vi2hzG0F4wZqYGBgYFB1ZERAgvWrEYik9lqn1kfi2Fkz16I2XZZ2wshcP311+Mvf/kLVq9ejT59+mDKlCn4xS9+4TUwnjJlCh566KHA+yZMmIDnn38eAJBOp3HWWWfh6aefRq9evfB///d/GD9+vLftbbfdhhUrVuDuu+8uOZ6mpibceuutePzxx7F8+XJ07twZe++9N8477zwcd9xxBZsqV4pFixbhhhtuwJNPPolvf/vb6NKlS1X221qsWrVqm4+hvWBIloGBgYFB1SGkRCKTQdS2yyY9bUFGCCQyGQgpgTI/79Zbb8W9996Lhx56CHvttRfeeecd/OhHP0KnTp1w0UUXedsdccQRmD59uvd7TU2N9+/7778f//vf//D666/jX//6F0477TSsWbMGjDEsW7YMf/rTn/DOO++UHMvmzZtx0EEHYcuWLbj55psxevRoRCIRvPrqq7j88stx6KGHonPnzuWfkCJwm0FPmjSp1cQtk8kgFquOnX+vXr2qsp/tESZdaGBgYGDQbojZNmoikXb/aQ2Re+211zBp0iRMnDgRAwYMwPe//30cfvjheOuttwLb1dTUoFevXt6PP+qyaNEifO9738Nee+2F888/H+vWrcP69esBAD/5yU9w6623omPHjiXHcvXVV2P58uV48803MXnyZOy5557YY489cPbZZ2P+/PlobGwEAGzatAlnnHEGunTpgvr6ehx55JFYsmSJt58ZM2agc+fOeOGFFzB8+HA0NjbiiCOOwKpVqwCoNOExxxwDALAsq2ySNWDAANx0000444wz0LFjR0ydOhUAMHfuXIwZMwZ1dXXo168fLrroIrS0tHjvW7VqFSZOnIi6ujrsvvvueOSRRzBgwADceeed3ja56cL3338fhx56KOrq6tCtWzdMnToV8Xjce33KlCk49thjcfvtt6N3797o1q0bzj//fDiOU9axbE0YkmVgYGBg8LXEAQccgFdeeQWffPIJAGDBggWYO3cujjzyyMB2c+bMwS677IKhQ4fiJz/5CTZs2OC9NnLkSMydOxfJZBIvvPACevfuje7du2PWrFmora3FcccdV3IcUko8+uijOP3009GnT5+81xsbGxGJqMTTlClT8M477+CZZ57B66+/DiLCUUcdFSAYiUQCt99+Ox5++GH85z//wYoVK3DppZcCAC699FIvKrdq1SqPfJWD22+/HSNHjsR7772HX/7yl1i6dCmOOOIInHDCCVi4cCH+9re/Ye7cubjgggu895xxxhn46quvMGfOHDz++OO4//77sXbt2oKf0dLSggkTJqBLly54++238fe//x0vv/xyYJ8AMHv2bCxduhSzZ8/GQw89hBkzZmDGjBllH8vWgkkXGhgYGBh8LXHllVeiqakJw4YNg23bEEJg2rRpOP30071tjjjiCBx//PHYfffdsXTpUlx99dU48sgj8frrr8O2bZx55plYuHAh9txzT3Tv3h2PPfYYNm3ahGuvvRZz5szBL37xCzz66KMYNGgQ/vznP6Nv375541i/fj02bdqEYcOGFR3vkiVL8Mwzz2DevHk44IADAACzZs1Cv3798NRTT+HEE08EADiOgz/+8Y8YNGgQAOCCCy7AjTfeCEARNjftWGma7tBDD8XPf/5z7/ezzjoLp59+Oi655BIAwJAhQ3DXXXdh3LhxuPfee7F8+XK8/PLLePvtt7HffvsBAB544AEMGTKk4Gc88sgjSKVSmDlzJhoaGgAA99xzD4455hjceuut6NmzJwCgS5cuuOeee2DbNoYNG4aJEyfilVdewdlnn13RMbU3DMkyMDAwMPha4rHHHsOsWbPwyCOPYK+99sL8+fNxySWXoE+fPpg8eTIA4JRTTvG2HzFiBPbZZx8MGjQIc+bMwWGHHYZoNIo//OEPgf3+6Ec/wkUXXYT33nsPTz31FBYsWIDf/OY3uOiii/D444/njYOIyhrvokWLEIlEsP/++3t/69atG4YOHYpFixZ5f6uvr/cIFgD07t27aPSoXLhEycWCBQuwcOFCzJo1y/sbEUFKiWXLluGTTz5BJBLBqFGjvNcHDx5cVOS+aNEijBw50iNYAHDggQdCSonFixd7JGuvvfaC7UsR9+7dG++//36bj7HaMCTLwMDAwOBricsuuwxXXnmlR6RGjBiBzz//HLfccotHsnIxcOBAdO/eHZ9++ikOO+ywvNdnz56NDz/8EA888AAuu+wyHHXUUWhoaMBJJ52Ee+65J3SfPXr0QOfOnfHxxx9X5bii0Wjgd8ZY2USuGPzEBwDi8TjOOeecQJGAi912281Lw7YHwo5RStlun9daGE2WgYGBgcHXEolEApYVnAZt2y46Wa9cuRIbNmxA7969815LpVI4//zzcd9993npR1cr5TgORAEfL8uycMopp2DWrFn46quv8l6Px+PgnGP48OHgnOPNN9/0XtuwYQMWL16MPffcs6xjriZGjRqFjz76CIMHD877icViGDp0KDjneO+997z3fPrpp9i0aVPBfQ4fPhwLFiwIiOfnzZsHy7IwdOjQdj2e9oAhWQYGBgYGX0scc8wxmDZtGp577jksX74cTz75JO644w5PrB6Px3HZZZfhjTfewPLly/HKK69g0qRJGDx4MCZMmJC3v5tuuglHHXUU9t13XwAqzfXEE09g4cKFuOeee3DggQcWHMu0adPQr18/7L///pg5cyY++ugjLFmyBH/+85+x7777Ih6PY8iQIZg0aRLOPvtszJ07FwsWLMAPfvAD9O3bF5MmTWr1eXjyySdL6sHCcMUVV+C1117DBRdcgPnz52PJkiV4+umnPZH6sGHDMH78eEydOhVvvfUW3nvvPUydOhV1dXUFqxpPP/101NbWYvLkyfjggw8we/ZsXHjhhfjhD3/opQp3JJh0oYGBgYFBu2FrubC35nPuvvtu/PKXv8R5552HtWvXok+fPjjnnHNw7bXXAlBRrYULF+Khhx7C5s2b0adPHxx++OG46aabAl5ZAPDBBx/gsccew/z5872/ff/738ecOXMwZswYDB06FI888kjBsXTt2hVvvPEGfv3rX+Pmm2/G559/ji5dumDEiBG47bbb0KlTJwDA9OnTcfHFF+Poo49GJpPB2LFj8c9//jMvfVYJtmzZgsWLF1f8vn322QevvvoqrrnmGowZMwZEhEGDBuHkk0/2tpk5cyZ+/OMfY+zYsejVqxduueUWfPjhh6itrQ3dZ319PV544QVcfPHFGD16NOrr63HCCSfgjjvuaPXxbUswqkai1sDAwMDga4lUKoVly5Zh9913D0ycO4Lju8HWx8qVK9GvXz+8/PLLoZq2HQ2Frn8XJpJlYGBgYFB1xGwbI3v2Ug7sWwm2ZRmCtZ3h3//+N+LxOEaMGIFVq1bh8ssvx4ABAzB27NhtPbStAkOyDAwMDAzaBTHbLrvFjcHOCcdxcPXVV+Ozzz5Dhw4dcMABB2DWrFltSm/uSDAky8DAwMDAwKBdMGHChNAiga8LTHWhgYGBgYGBgUE7wJAsAwMDAwMDA4N2gCFZBgYGBgYGBgbtAEOyDAwMDAwMDAzaAYZklQARoampqSp9nwwMDAwMDAy+PjAkqwSam5vRqVMnNDc3b+uhGBgYGBgYGOxAMCTLwMDAwKBdQJQBUXIr/mw9d/kdHddffz169uwJxhieeuqpbTqW7WEM7QVDsgwMDAwMqg6iDCizEJR+e+v9ZBZWRLSam5txySWXoH///qirq8MBBxyAt99+O7DNlClTwBgL/BxxxBHe6+l0Gj/84Q/RsWNH7LHHHnj55ZcD77/ttttw4YUXljWepqYmXHPNNRg2bBhqa2vRq1cvjB8/Hk888URVJSuLFi3CDTfcgPvuuw+rVq3CkUceWbV9twbbwxjaC8aM1MDAwMCgHSAASgAsCrBY+38cZdTnofxG0WeddRY++OADPPzww+jTpw/+8pe/YPz48fjoo4/Qt29fb7sjjjgC06dP9373N4e+//778b///Q+vv/46/vWvf+G0007DmjVrwBjDsmXL8Kc//QnvvPNOybFs3rwZBx10ELZs2YKbb74Zo0ePRiQSwauvvorLL78chx56KDp37lz2sRXD0qVLAQCTJk0CY6xV+8hkMojFqvO99urVqyr72R5hIlkGBgYGBu0HFgNjNe3+UymRSyaTePzxx/Gb3/wGY8eOxeDBg3H99ddj8ODBuPfeewPb1tTUoFevXt5Ply5dvNcWLVqE733ve9hrr71w/vnnY926dVi/fj0A4Cc/+QluvfVWdOzYseR4rr76aixfvhxvvvkmJk+ejD333BN77LEHzj77bMyfPx+NjY0AgE2bNuGMM85Aly5dUF9fjyOPPBJLlizx9jNjxgx07twZL7zwAoYPH47GxkYcccQRWLVqFQCVJjzmmGMAAJZllU2yBgwYgJtuuglnnHEGOnbsiKlTpwIA5s6dizFjxqCurg79+vXDRRddhJaWFu99q1atwsSJE1FXV4fdd98djzzyCAYMGIA777zT2yY3Xfj+++/j0EMPRV1dHbp164apU6ciHo97r0+ZMgXHHnssbr/9dvTu3RvdunXD+eefD8dxyjqWrQlDsgwMDAwMvnbgnEMIgdra2sDf6+rqMHfu3MDf5syZg1122QVDhw7FT37yE2zYsMF7beTIkZg7dy6SySReeOEF9O7dG927d8esWbNQW1uL4447ruRYpJR49NFHcfrpp6NPnz55rzc2NiISUYmnKVOm4J133sEzzzyD119/HUSEo446KkAwEokEbr/9djz88MP4z3/+gxUrVuDSSy8FAFx66aVeVG7VqlUe+SoHt99+O0aOHIn33nsPv/zlL7F06VIcccQROOGEE7Bw4UL87W9/w9y5c3HBBRd47znjjDPw1VdfYc6cOXj88cdx//33Y+3atQU/o6WlBRMmTECXLl3w9ttv4+9//ztefvnlwD4BYPbs2Vi6dClmz56Nhx56CDNmzMCMGTPKPpatBjIoii1bthAA2rJly7YeioGBgcF2h2QySR999BElk8nA36VMkEi+SiL9DsnM++3+I9LvkEi+SlImyh77d77zHRo3bhx9+eWXxDmnhx9+mCzLoj322MPb5q9//Ss9/fTTtHDhQnryySdp+PDhNHr0aOKcExFRJpOh8847jwYMGED77bcf/fe//6UNGzbQwIEDacWKFXTNNdfQoEGD6PDDD6eVK1eGjmPNmjUEgO64446i4/3kk08IAM2bN8/72/r166muro4ee+wxIiKaPn06AaBPP/3U2+YPf/gD9ezZ0/v9ySefpEqn//79+9Oxxx4b+NuPf/xjmjp1auBv//3vf8myLEomk7Ro0SICQG+//bb3+pIlSwgA/e53v/P+BoCefPJJIiK6//77qUuXLhSPx73Xn3vuObIsi1avXk1ERJMnT6b+/ft73wER0Yknnkgnn3xyRcdUDRS6/l0YTZaBgYGBwdcSDz/8MM4880z07dsXtm1j1KhROPXUU/G///3P2+aUU07x/j1ixAjss88+GDRoEObMmYPDDjsM0WgUf/jDHwL7/dGPfoSLLroI7733Hp566iksWLAAv/nNb3DRRRfh8ccfzxsHlSlqX7RoESKRCPbff3/vb926dcPQoUOxaNEi72/19fUYNGiQ93vv3r2LRo/KxX777Rf4fcGCBVi4cCFmzZrl/Y2IIKXEsmXL8MknnyASiWDUqFHe64MHDw6kW3OxaNEijBw5Eg0NDd7fDjzwQEgpsXjxYvTs2RMAsNdee8G27cAxvv/++20+xmrDpAsNDAwMDL6WGDRoEF599VXE43F88cUXeOutt+A4DgYOHFjwPQMHDkT37t3x6aefhr4+e/ZsfPjhh7jgggswZ84cHHXUUWhoaMBJJ52EOXPmhL6nR48e6Ny5Mz7++ONqHBai0Wjgd8ZYVaoT/cQHAOLxOM455xzMnz/f+1mwYAGWLFkSIHntgbBjlFK262e2BoZkGRgYGBh8rdHQ0IDevXtj06ZNeOGFFzBp0qSC265cuRIbNmxA7969815LpVI4//zzcd9998G2bQghPK2U4zgQIrzy0bIsnHLKKZg1axa++uqrvNfj8Tg45xg+fDg453jzzTe91zZs2IDFixdjzz33rPSw24xRo0bho48+wuDBg/N+YrEYhg4dCs453nvvPe89n376KTZt2lRwn8OHD8eCBQsC4vl58+bBsiwMHTq0XY+nPWBIloGBgYHB1xIvvPACnn/+eSxbtgwvvfQSDjnkEAwbNgw/+tGPAChyc9lll+GNN97A8uXL8corr2DSpEkYPHgwJkyYkLe/m266CUcddRT23XdfACrN9cQTT2DhwoW45557cOCBBxYcy7Rp09CvXz/sv//+mDlzJj766CMsWbIEf/7zn7HvvvsiHo9jyJAhmDRpEs4++2zMnTsXCxYswA9+8AP07du3KDEshSeffBLDhg2r+H1XXHEFXnvtNVxwwQWYP38+lixZgqefftoTqQ8bNgzjx4/H1KlT8dZbb+G9997D1KlTUVdXV7Cq8fTTT0dtbS0mT56MDz74ALNnz8aFF16IH/7wh16qcEeC0WQZGBgYGLQfKIOt0vm1FW7vW7ZswVVXXYWVK1eia9euOOGEEzBt2jQvFWXbNhYuXIiHHnoImzdvRp8+fXD44YfjpptuCnhlAcAHH3yAxx57DPPnz/f+9v3vfx9z5szBmDFjMHToUDzyyCMFx9K1a1e88cYb+PWvf42bb74Zn3/+Obp06YIRI0bgtttuQ6dOnQAA06dPx8UXX4yjjz4amUwGY8eOxT//+c+89Fml52Hx4sUVv2+fffbBq6++imuuuQZjxowBEWHQoEE4+eSTvW1mzpyJH//4xxg7dix69eqFW265BR9++GFeVaeL+vp6vPDCC7j44osxevRo1NfX44QTTsAdd9zR6uPblmBUjUTtToympiZ06tQJW7ZsKcvrxMDAwODrhFQqhWXLlmH33XcPTJyu47syCN1KYPVgsX3Atob5qUGrsHLlSvTr1w8vv/wyDjvssG09nDaj0PXvwkSyDAwMDAyqDsZiQGwfVOLA3nbYhmBtZ/j3v/+NeDyOESNGYNWqVbj88ssxYMAAjB07dlsPbavAkCwDAwMDg3aBITwGjuPg6quvxmeffYYOHTrggAMOwKxZs9qU3tyRYEiWgYGBgYGBQbtgwoQJoUUCXxeY6kIDAwMDAwMDg3aAIVkGBgYGBgYGBu2AHYpk/ec//8ExxxyDPn365HXtLoQ5c+Zg1KhRqKmpweDBg7fPBpIGBgYGBgYGOx12KJLV0tKCkSNH5vWJKoRly5Zh4sSJOOSQQzB//nxccsklOOuss/DCCy+080gNDAwMDAwMvu7YoYTvRx55JI488siyt//jH/+I3XffHb/97W8BKLv+uXPn4ne/+11BIV46nUY6nfZ+b2pqatugDQwMtgrmz5/vOU2H4dxzz8UPfvADAMDHH3+Ms846q+C2U6ZM8V7//PPPcfrppxfc9pRTTvE+d82aNTjhhBMKbjtp0iRcdtllAJQB5MSJEwtuO2HCBPzyl78EoJ5LxTyFxo0bh2nTpnm/u8aQYdh///29ZyIAfPe730UymQzdNndR+73vfQ8bN24MbLPLLrvgwgsvRCwWC/SrW7p0qddSJhexWCzQH3DZsmWB564fkUgEgwcP9n5fvnw5UqlU6La2bWPIkCHe7ytWrEAiEe7TxRgLtGlZuXIl4vF46LYAMHToUM+l/Msvv0Rzc3PBbYcMGeI1L161ahW2bNlScNvBgwcjElFT8Zo1a4q2nBk4cCBiMVWxuXbt2rzvwo8BAwZ4vk3r16/H+vXrC27bv39/wLKwbNUa2JaFPfr1LejIblAZdiiSVSlef/11jB8/PvC3CRMm4JJLLin4nltuuQU33HBDO4+schBlQHwFWGQAGNupv7atCsm/ALMawKyuoa+nnSasbVqCvl2+AcuyQ7chsRpAFMzu1o4j3TYgygDiC8AeAMbCj7/0PjiILwakz5GbWepatjqFv0fGQWKtvt7DA+7SWQFmdwCzugBQC6J58+YVHMfRRx/t/bulpaXotoceeqj372QyWXTb/fff3/t3Op0uuu3ee+/t/ZtzXnTb3Qf0ABF5zX2LbZvbbmTevHkFSVZjY2Pg9zfeeKMgubCs4Ll/6623sGbNmsDf+vfvj3Q6nUfUEolEQeKUa9qYSCQKEj2XVLhIJpOBvnZ+uGTFv22hY8slEcW2zUUqlSp723Q6XXRb//dUybaZTKbotv5myaW2FUKAJCHNHdiWZQhWFbFTz9arV6/Oe/j07NkTTU1NSCaTqKury3vPVVddhZ/97Gfe701NTejXr1+7j7UkKA7IDQB1B1jnbT2anQdiDUhYQKxTKIlIp1cglfkSjhiCGivc8Z/EWjCrM4Cdj2SBMiDZAmY7AFpHskApQKwHQIB7jmUSxBkQ3TuURJH4AhBfAVYHwO6R/7qMA3IlYPUHoEjWnnvuiccff7zgMPbaay/v3wMHDiy6rb+PW9++fYtu64+ydO/evei2u+++u/fvxsbGgttK/hV227UBAAcQRTQaLbrfPn36BH7/xz/+UXDb3GfirFmzwDkHAJDcAuKrwCJ9wawO6NYteE0/+OCDAeJExBFha9Cjexf06tUrsO1uu+0WmOj9yCVv/fr1K9o82Y++ffsW3DaXHPTp08c7tlLo3bs3unfvnvNXAlEGDEFPp169eqFr1/CFWe44evTo4bXECYMb8QLU9dOhQ4eC2/pJZLdu3dDQ0FBwWz857dKlS+h856K2thYtSRUdZDAEq5rYqUlWa1BTU5PXk2q7AKUB2gJQC4DO23o0OwWIOICMJrDrATs4+ZCMQ4pVkDIBIVMA8kkWEQcoDqL6nfTRRAAEQBKtP0ChfqxuHpElagTkOkBuAnIigCS3AGItAA4SXwJW1zwCTOJLQG4EKLsA6t69O44//viyRtSlS5eyt+3QoUPZ29bX15e9bU1NTei2JOOgzLtQpFZFLmzbLnu/ACra9nvf+172s8Vq9dl2H7DoiLzznpveJPEVkvEPsPzLmrwJvxixyEUlLcsq2bYYYclFboQPAIiEWiSwmgBxamhoKEpw/Khk2/r6etTX15e1bV1dXVHiVOm2wiXEO+eDbJthhxK+V4pevXrlhbbXrFmDjh07ln1xbi8gmQQoCRKFc/AGFYIyAOkVPP9Sky7fy2IViJKQUkDI8FQGKAWQAyBce7LjgwBI/dNaKJLln7AZiwJgILESRNl9ExFIfAXAAayeivzKDcERyWZArNa/qffOmzcPv/jFL4pGcHYUkPgKoCa0/by3BgIA0+e9sIYHcCUMK5GdRnbWNrg763EFIYW61kyqsLrYqUnWd77zHbzyyiuBv7300kv4zne+s41G1AZQM4AYQM1KJ2NQBWhyZHUHaENgMlcT+SoI6ghHEkiEa0BAaQBpRdh2SkiABNo02VMBAmp10ZO5b+FAWwCxBrA6a+1hBMRXqoiCu4n4CkAGYHUgve/XX38d06ZNwzPPPNP6cW4HcK87WJ0BkIogbtUBCIBFAERB/Avv/IZCrgNoM8C6QBGRHZOMLF++HIwxPP744xg7dizq6uowevRorFixAv/973/xnQMOQUNDRxx22GHYvHnzth5uu0Hqa81QrOpih0oXxuNxfPrpp97vy5Ytw/z589G1a1fstttuuOqqq/Dll19i5syZAFQ10T333IPLL78cZ555Jv7973/jsccew3PPPbetDqFVUCmpFsDqpDraUxxghfUABmWCMgAkGIuBEAPxL3RKK6In8jQE6wBHRiBkoUoiTbCKTUY7NHS6sE1NfgVA+Y9uxqIgWCCxErC6QkW2vgLAwZgWRluds1EVuydINqkoltUZkHEozVJW5Jur39nRkCWQ3fTCausSF0WqmD6/axSRsvuEbKejWKweahqRgbESETjfmo2hs4hE7IqiMQsWLAAA3HvvvfjVr36FhoYGTJo0CT/4wQ/QoUMj7r7rNggZxdFHT8L06dPx05/+tL2Gvk0hhfr+TCSrutihSNY777yDQw45xPvdFahPnjwZM2bMwKpVq7BixQrv9d133x3PPfccfvrTn+L3v/89dt11VzzwwAM7Xh8lNyVldVD6HxkvWA1nUAky2XnBm1TWg1i9N5ELyZGRNqRsARHPq+wkmYAiIBxEsmAl3I4LHU1pS0SFOMAKkAWrs4ogyg0qgiLWqAiXBmMRECIg/iVgdVNaLGTAWDcQWuCSLFcI7RcR72gIEEgw5BKXrQMHgAXGbBCr0wuP7nmNnkmsAeQWwO4FNScHI1mcC8z846Nbc+Aezjj3FESj5U9t7kL9b3/7myf0HzduHObOnYsPPpiP+joGsFqMHj0aq1evLrG3HRdeJMuQrKpihyJZBx98cMGyZAChbu4HH3ww3nvvvXYc1dZARv9EAdTo9Mpu23ZIOwFIpqFnCDWpoEatzq0GuBM5l1vgUASSUloAmyOOlU0Aq4WaEDmAGHY+tC1dqNLb4eQzEM1iMSjtVk7hidUFkGsBsVwJ4q3OAIBNqTRqogk0RncSkuUnkCSQ1cNtTXB4VaSsEyBWg8RqsEj2eaNsPb4ErHq9qHDHuGOmCxcsWIDjjjsuUEm5YsUKnHzyyaivrwMo6f1t0qRJ22qY7Q43GmxIVnWxQ5Gsry0oparfGQNZ9YCMgyiVTalU86MoCZAAs/IrbXY+JBC4BazOejJv8SZyRwpIskDSJbpZKPKQAlidFtBXb0JUFgXrwSIDimzTrKue2pHYkTvRtyX1k0FR+wdXmwXLi2JlOMemzc3o2b2LJsAxkFgHwAFjdUhxjtXxBHo01qIRWZK1LdKF6p4hMKu8qjBAR63IX0zBAwSSJKElk0KHWPnXFFEGoDSYVX5FXR5kBtDRWMYskNUI8OWQ1AygBozVgCitFhe237KB6WtFIRKxcca5p7R6GGoxLVrlCRiJVEa058+fj6uuuirwtwULFui0oIrQpVJJLF68GCNHjqx4PO0FRcQ1GffuUwvMqnxeICKQdNOF1RylgSFZOwBIJrwHH1ADYDMgWwC7uiSLSIKcpQCLgFnDSr9hR4dMapGvgkqRNACUAWOq+lRICQkGQVKRXT8orX5YA4CETotVaWyUUlYG2owydBO+XBlxRnat0oeGfgq8B3lrIVNZf6wQMBYBsVpF7nUUa+Wajfj0i9X4dkMtOtbVZQmwTpNvSCTQlMqge4MAEXmr8G0SyRLrVCrZGlh6Ww3inwPyKwQjfDHvumtyODa2NKO+XiBSLm+UG0FiI1hsz7LHkQ8eHBPrAKAZEOsACBCkmtCtDiGp8SwhZIxVlLLLhbJOcJRpbTum4JuamrB8+XLsu+++3t+WLVuGLVu2qL9p4vj+wg9ARBgxYkS7jaVycP1Mcp8PhNZ62UkpvTvc+GRVF4Zk7QiQTTqVoleXUFGMqjuMy/WAXP21ENWrKJQD5JgMshzDUUdP3o4gkEwEuQKlAXCA1ahihDZFe3LhRo8ECt6mlALJjWBob5LlVhi24t1EUOe5+MOf6QgOAGSEwNpEHJvTKXy1pRkdamuV/YPdGwCQdBysScQhCF4EcdtGslQBRWVv4gCrK6itTDgZZDgPVFWW3qejr8PWIRsZyZ5DxhjAyvGlUhoyKrIoqHA02BrpxwULFsC27YATv6vR6t+/v7LOATB//gIMGjQo1Etrm4EI6ry7zzCh/1z5d+DaNwAwoawqw5Cs7RxeSsqv9WFKl0XUv2r5cyIHxFfoyEwaREFfo50O5OhigsLRQCKCkBI2s+CQrchuYAPtkMxsUJsr8HIhi6YglbeUAGQziNL5OqZqjqNNmiyu31/+tbQxkUBzOoOOiGBtPI4eHRvRzWfQuCGRRNrhiEWi2kBR4Pzzz8cJJ5wQ4ti9NdCaytIgmclFUzoNCQZZAckiTfrDCjTKHxOhbdOCO/G3FYStQbQWLFiAoUOHBtr8LFiwwBfZUp+/YOHC7SpVqFC9c6MiwTpdWLW9GgCGZG3/0DoL+Fb6YEqXBUrqEuoqfIxYA9BGwOqhyUMGwI5l2FoZ3EhW4VtAEoGIELEsZCQDkFItNnRUkSiOAHloZbQnHASVDuBeFDMIl/gk1LVgtxfJUhMdEW/lw1eoY7DK041lhMDaljhqLRsJAUQIWNXcjA41NYjZNhKOg7WJOBpiNcg4jiabhN69e6N3796tGmGbodNalUGi0HSW5hwJx0ENc7U25SKlr8Ei0c9SY6rguwqCobqkaOuQrAsuuCCvqfj111+fMw7gnrvvqEhzt/VQHUoUSBeaSFZVsbPVm+98oBSUb5A/rVUD5c9UwCCz0o+gJCBWKMLGYlCT+87q+6RBDgAqqvcQROAgRJgFR1i6AEGJ34lIkRuXABFQ3UgWobgJqE7hUVqJ5NsTpAlfq97rRuPKi2RtTCQQdxzUWTYE56i1ImjKpLFONwRen2hBmnPUR6MgxsAlrzK5bQV8nQPK2twnUg5DgjtICw6AvLL6siBTar+tvXe9isa2TAvVJEXbtlpRfU9uZG57rJzMHRMr8PfSkJK8txmKVV0YkrW9I1dsDXelwVSFUjU+gn+lCUMnnWYQ2HkdzDXKOD5FpICIbUEgAiEc3/fh/luTLIaKJtrSH+7XZIW9rgkYqwPkhqLWJm0cCLLi99ag/HRhRnCsaYmjIRpVn8gFICUaYzVYE49jXUsL1icSaIypqB2DpdNpEi+99BJ+9atf4dVXX23lOFsHtwquMqLntssJn86SGQecSxBBCc3LGoeAisy657s1yNdklYvskVTpOqStE8kqMQj93+2VdoSdm9adL38ky2iyqgtDsrZz5KWkXLBarctqm22AMkD8ErA6ZcPEBOy8vfgUiFIodfkLKSFIIsIsSCJI+CoM3ZRqIJVXTU8j13er0ITpkqxGJXamAr0V2ww90bUpOlKeSeuGRAItjoP6aAyCc0ghwR2J+mgUGSmwLtGCtOCoi6qoLrNcLZzEc889h2uuuQYvvvhi68bZaggUJcOhcK+T/MmMiLAlnUJNJKJ5RrnXlE4tu+nZVsGNnLZFi1mte2Br+4MVQjaS1X4LmcoRjLK1HX7hu6FY1YUhWdsx8lJSfrA6qBY7ibZ9hm7jwSxfl3jGlFHnzgzK8cgKgdSSmIhtQUqCIFuTXmiSpXx8iAggq6o9JT1he8FJVk/srBZKi9NeKUN9Elo9cZf3vozgWNvSgoZoDIwxSK6Om2fU+zvX1mJTMokOsaxAWUWy1Hkqt7qwrYuSfLhu+LKCfetzGkI8M0IgyTlqI+q6Klv4Tg6yZK/wOS9KFLS+rVJNDmUzTRWQwpJ79f20cg8kQTLVhu+8vM8mkpVVgW6HUJEs45PVHjAka7uGA2UTkE+yVDWZFsW3BTKhqhUDsAG0V2Rk20ORoiTAokW3k/p/EUtFsoSMaCNYCqRxVzU3Y3Mq36y0bXCjCoUmTIlVzVuwJa3aoFQrdZyLNHewsmkLZKs1erysuWpzKq2jWOo7EVwCjIFn1DmNWDZ6NnZAbcTva2bp6kIqy/GdKANyPqruufI0Z+5POSicLvT0WFxi45rNkLLcyVunCslGUZLlfKz810LROqKwOZWC8OwEqkmyqrGPtni85WqyCu1nW2hYix1TazRZ/kiWYVnVhCFZ2zMoBSBdoLrMRVtXUCEPexZVZqc7LRw9ORaPZAnXARlM0S0W0aTW0REtRQgSjoOMRJV1bK7Op8CkRQJpIZARQuuyNrbLapoLAQkJIZ3WRQRIlLU05kLAYsyLokguYdkWeEYWjL5Y0JEpEuU1iJbrlKFpiM6x9fC74VcQySqQ6klkHDAAIsPBuYAUZUYQSQnlwayC+jAiDiBZ5PhFq/hIWgjfd9T2tFo2FQZUVl2ZtycUJ0flotT1uy3aH7mf6x9b68iRlKSE795uDMmqJgzJ2p7hS0mFvw5Uh2TlXgYRABn9UN4J4RqRlopkEYEhO/ELGYWq6tS2Cfr9ac4hJKs+ySpqAirgcAEuhKoKpZY2p47DQJCQUuq0XOUTifJuKv2Y4SRh+SYJISQiEQtCCCWADwEDU6kqEiUjWURp7QPX0o7fU5mTObmeRMHJzNVjxewISEpIDghZboTEjRha+pwX2iaFosUUrZhfU9wdY7VsHPz7aCvJakuT7dz3FdgPFXmt3VDo8yofB8ngfW0oVnVhSNb2jFIrboYik3AZu/dKyUMiWcSx84rfS3tkAcrCwa25IQCCbCgD0CYV0WIxCCnBScKRACCqGE1Sk3ah/RFxSFJRBOZ+X+2gyyJS9W2tJVlAmSRLyGwUS6rIiB2NgCRBigLnlDE1jfoiWQVJFl8NyGaA1emih2rBjTZWEs0Iv+/SQiDlKD2W4BJcUvkLHXL07iyocx62jU5rFdxna0g0wfG+n2qSLP/+2rKfakSZSo3D9ZLbFsL4tlMi4ZIsff+ZQFZ1YUjWdgyVkiplKtiWB0ghbUgEatW7k9o46OMqJfCVJMF83jHSPdcUhyJqMXAplZ+WVwlWJZLlpebCvwNJAgTSfkoAmA2Sm6rz2YFxCBDZqExz5IPMBPpDFgInCctS3wdJ1YswErEhhIAUBdKFDCAQhORFhe8kE4BYCVgdVOq9mhG/QJPeSjRZ+X5UScdBRnJEtUeYlAQqM5Klole2Otey0H2rFheFCjTU3yubErgkOCRzsnrVSJ2556cKJKtqwvdS0aMdM5rlLlCyz0PDsqoJ4/i+naJoZaEHVdHW+lsivJTcaxOzsxqSlkkehSTfso6BCwkgoic0CcZscJmBkNlIVvVIlhttCI86CJFRbVdcPQyrA+RmEDk5xrVtHAZJCKn1PBWmk9TKPoNyLAG4lLBYlmSBCCxiqUhagXShxXS6EBzXXHMNzjrrLPTv3z9/HOIrRYytPgA1A5SsYo89oXRDrO3C94TjADo9zdMCkipoq0NJqPNsQ6X6Q2wziENdT4Wuf1VEUQkcKbSex080qhXJyv13pbtpK/kp1ydrW/h5Ve/zPNG7Pkzj+F5dmEjWdou0l5IqDAttS+kVLiVX9/DOGclS6aIyJn7KTvwWGDhJHQnJnhdBykuLS11ZVbV0odbKFYw6pCGJqSiaJK3LSlY9ZUgQkFDVlZVHKBwUczb3PoMIXGQ1WVJKkIT3OxeFP5eIgaTAkCFDcNBBB6Ffv37B12UzIL4CrM662XFULx6qtYBwj6+S8+NGDrLnhYiwOZVCTURdl9zh+vYsl2SlobqXu1HHMHKuvbQK2bOQA1TYr5TrdDngTvsMaHPaLJgubH0aTnr/LbWPK6+8EkcffXTOMHLJ3vYWyQKqInzX95dbVWgoVnVhSNb2Cq9/YAmS1aZoU/7EsCmZxNqWFu2VVWiC37E9YSBbykphOVxkSZbF4AiXZGUrC7mQEJK0p5YbKajKIAFECupnJDmQknmGqYzZiuBVucWOJIIA0995pSRLG2OWONeSCOTzZyKpJlZmu5WGhSNZAlTUXoLElwDSPh+4CFR1aZUWEMT1IqWCSBblT9gpzpHiDmrsCKQUEFxqAln6/iZPPxlRJMk1Jc3dTmb03zPhpIM4Kk4XChEo/1doa7qw3DRdOftxCXDxfcyfPz+kAbS/OCGcemQrIXeeSJZhWdWFIVnbK6gFcCfPQvAeqK1FvjakJeOgJZOBmozytStEDsj5sIjXDkAyDhLr2jCu9oOyIUjDJUnFEIhkMQZHCgBa08Nq9TYEQQQBC0K2hogUGqPUUQUeap0gSaULhSSfcDUCqlI/S98nQUqWNUcNG6/cHH49uHolYljb0hIyGSuo80feuZZakwXGYDHmGZLmgjEGIguSOJ599ln8/ve/x8KFC33jigNiTbC5OnTbqCpFaRXBqVTwLdGUSuPLpibvZ21LHBkhELVsSC5VGhg+HWBRuKncCFQkq5Dreyr7es5iQJEFB46Q2JgoX7Pmj2JR1byyVISdy9aTl/y+g6VJ1j777JM/DgBciiLRuSqlNquK8sdBvqIRd5FjfLKqC0OytkMQZUB8JWDVltiyjenCkFLyFHfUpM0iyqg07z0tgFwPEitDV8NEEsQ/AzmfgLZLr62MTouUjmQJkUOyhCa99q5gViMAtZIH1ENdeTZVI5LlRkUiCPowZUHkgIhBQIJ75MWNqFQRJFWUzk0th20i1hcg1cqFPOlIbEy0IFWgSlDqHpGW29VJR7IsxmDZNpxCJAsAkYrmTp8+HZdccglee+21nM93oBqq6/cwnc6qWlGHT8dUgeP7plQLlm3ehJVNW7CyaQtWx+Ooi0bBGIPgUon9JQDJS6fLvAiqmy4s4PpOKR2JDdMOqr8lHIm1LfGChDgXKc5zig2q0IJGV7QKkqCQqF+ZO0FwAVl4H6tXr8aaNWsghMDYsWNRX1+P0aNH4/333weRW2VcbB87ciRL5FXvGklWdWGE79shVLn5ZsDuXWJLC65tQNGIV+FPgp9kERFSnCNiWwBqodIKOUJqSqgJSqwF7D4A6xLcpdygTB/JAYmVYNbQVoyrHeHpcToU34wIwufdpETWyjPKsrLnIyU4ol7bHaA6wnf3AapTgDnESY2DQ4CBEcuSLGZVieT5RyJAxLQmq9CxZQAZYoug08ppImQChpVBSJ3yzArffe7TNitCspi2l3DCqwu9NjFW7hvbgWRVUvQgIcBQH42iY03+QkoI5ftFjPlsVord325LnYiK7oGQqw1UXQrSUITTTeMGxwQiCGLICGV0W1eiRRGgSFZUn19PkwXVZiaRbt21SDIFSRk4AnBsBmbZFT3f6mtqkL2H3IMsTErmz58PALjzzjvxu9/9Dl26dMH555+P007/ERYueLME0dseI1kVgHxGpKa6sF1gSNZ2BqIkIFW5eemmuq67s0Drmrq6D3AtMJYEThJMQEeyUjrqkyUVJDfrNjxCVW2xzlktDQkQ/0KNy+4GiFUgexcwK4eIbUuQ0qUUNHjV4FKtXm0vkmUhQxycKKCS81I8kvTkVg2So8vOXZ1VXlUf1xYOFsAUGVRg7UCyVCqPigm7ZRpAKqSykQPaRynNhW+cOW/XQTJ/utCFbVmQQkIKDssOfmeMMUgwEDkFfLIKnQsbRInqTCVSi8Wlg/InWQJJKhgxkEIJ/6UFnYIuFclSn51t8M6Qf+yaiLEaFY3Oe109RwQxpLnqJOA24i74sURIcw7b8h+IimQl0ins+oOzio+7nfDlrBmor4kgeN4KR+bmz5+P2tpaPPXUU+jTpw8A4Oabb8JBB43BuvUb0LFLV7UhUQH+sS3IVXU+kwL2G5omG45VVZh04XYG4qu0dUPxSAsArdlpi21AMF3IpVBCbiJke6BlV/xEjoqwsVrA6qL0LrTJt7t1AG0ArC5grA6AIl3Vb8rbFjhlPZ8IEpLIm0AsptJY0heNISJkuEDUsiChIlnVKQrITRfmC4vV5zAwaEE+gOroYYJQkSyC4jCF9q091XKdxnW0JMk5HJJem6JcuD0iLTci4jvHVkSRLFHAKwtQEUah/cKCqasCbWJYNDwV3iq4kaxKzr3UpDB8NpNcgjFAklupV2q/Of0hWQjZJ11ZyKJASKTLjZhKAlI6klUKjpRwSMK2FLFVX1vpyNHWQU4kq0j6cv78+TjppJM8ggUAXbqohaFaPLlvD9vHNopkFf2o8seRbUcFeFWvJpJVVZhI1nYEJdT9ErA6lelVoitnWtkOI1f47ui0DcGChAULEoHqRUooXYfVFYxFVHyDfwVEuwDgSkfGarJRIqsrINcCcj1g79KaAVYdJNNlnSuh+3kxnyZLNYnOTniOlBAgRC1LRXwIqIqg2tXKMVv/O3fCVCliggWLWUhz93U3bVVFkISk8BQU4JJKTcYpBaDR95oShbdkMiqdWoBkCUmBB7v0WTZYtgUptFdWLCyyorykQtvq5KXEXEQApNuQZte794oBLE1syq0ulCCwgitczqWn16Zy2vXkHaeK7gUP3dVtuRGesEiWhJA2OAkkeWmtJxcCQkjUWixPs1RfE8OXs2aU3EfeoRABlPC852I2wbLqKvJ+U+lC//iLE+D58+fjnHPOCfztjTfeQN++fbDLLj20lrCU63tb3ekrRXU+yxO9WwzEc01JDaoBQ7K2I6hy82QF6TV3Um2Lo3GWSHAplIM5VMWclUsaqAUg7iNRnRWJoj5QHk0bAauntzljURCiqmec1aWqJpmtRwL+y359SwJ10QgaYkGrDElZ8TWg0oVSVxK6EFJrtGwblu6jV52qNYk052hyUuhRVyCSBUWGbYtlXd89PUyIEWUrQbq+TRbUpQhFLiiN/HYuaThStYuRJL0WRblwLRy8332Ha1mWZ2mQCyfjQB26gJT5JCtb+ZcDL5KVAVAXOqbyoJsye9dTefcheYLu8MmMZzKwbBvErbLaGeX7vtnI/y6UrQNjtr5Ow0kW11+DqjAuDk5BLZ0fDEBDbanCnXy4xSNcAhkhURshWFZtxc+OoP7PvS/yDWgTiQSWLFnikXRAEY+77roLkyefBgIrP2pU+CttB7T9w8inx7Itlj1nhmNVFSZduJ2A5BZArFZpuLDXibAplcz5q1tN1sroRU4InUupbjqpH1KMqciPN8bNgao8xpTAlPhKpcVi9fmRAaszQBtBYnXrxlhtyGTgGDamEmhx8icUt2+hX/guEUwXCh2diXiRLKtIS5NKQMgIB5vTafUd5UWQuCYVDDaz4Ah30q7UFLOcoSjBlEqFhkU33HR1JL+alNLgQkU8bMsKRAEDeyAKzGOS87yJO6xJ9MavNiLRklHkN7StTiG3+ShUiq2thqS6mpSpdGG5aXGSipwVChjwNIdlMeWTRbJ0hIxSwWpZZoekbn3XJWMh6UIVPeWSELVspIXSZRWDIyRA2Qbq2e+wLWnrUim5cneTS7LCFwkLFy6EbduYPn063n77bSxevBgnnXQSkskUrrj8Eu/Tqaxx7ICRLJ2Gt3y6OsOxqgtDsrYTqBL4jNYy5SPpOFjX0oKMyK5A1cMtPI1THoK6kDQXsG1Lu5i7K3SlXSHiWo+VMz6ri04JNgGsY94nMGYDrAEQKwtM0lsPKrKRguuRJaVEynGQDpnApSZZ/pUvIxYobXdIKD90r7rKgvK1ausDUOoKQiUKzyPRJHWEw4ZtKUNOR0pdXVhdkkWQcBwBlcEL+f7c6kdWq66BwGsZZKQqxY9atvYZC9kFkdcjElDWGSynsk3muL7zjINUIqOCaCRw669vwvPPP4+DDz448PlhjzjmahlDKgylFEg7ZVqPeKk8V5NVXtGBctFnodoXIgnuCFi2BQblZl86XRgSyco9toBGywLlkjB9jWUEhy0BzoWv8XM4VFVrTiQHgD9yVDkUpSGo5t++/8vfkmQRYut/thUmWfPnz8cee+yB6667Dscddxy++c1vIhqNYt68V9GhQ4ectxQjgNtTurD8Agw3kmVZTEXtALBqSw6+5jDpwu0GxQ0yBREcIcGFRCxvcd5W4btCijsqKkNuJCuqIj+AThUqPZYfjMVArB6wooVTVKxWuayDoxwT0PaDrtTTFgycJLiukMqFJJk3BRIjcH8kKyDGZlAZLddtuy23FoEgIUgRFDuPNAkQlL2EbSlNlpASUYuhbenjnFEQIZNKYfO6OHjHeoRfZ/p4WQOU5UdGXRMkATjIaO05Y6xgexwuZYDMCi7BclbW3Al+R5mUg0wqg0iHWhAJfPObI8GsnGIRyqBgm5gCqd2NLZ+jKfElBvYcE/6+AHyarApIliRNskJCBoJLCCFhR23lgyaLpwvVwsXVWrmwoRpBZzVnqvmz+4EW8tOJ6otqbkqgeUMzOvbphLTgaCjScSIluLfAAHIjWS6paWNKy/2/sN24izZWE/ZiWX8799xzce655wIATjrppOyWpDSGBNfzK+v/FUw5biPhezGUfdpzSJb3bAtPrRq0DiaStb2AHIT2ENQQRHCkCEzy6n1Am0LzeneuR1bMtnWlnBvJcvQDJ0eP5QOzOhaMwClo7di2bsdDHH7PIUdICCmR4vnRpzCRNiPoCJICJ+FFYCyXZJWhoSkNqasbdWAqz5ZBggsCmLKYEJ6o3I1sViuSRcp9XBP8cHsIfbysRqWovAgJB0gi6UjYTGnHnALj4hQkWVLIAAGxbBs8E7x20skMnJSjPbzyU+bq+3QNOkNQoG1UKrMBGbGlzNSf/3oq3wiWiIMKRLIkF5CCYFuWjgeVup7cqsHcdGFuwUQKHhFjkfy0tr43uVBu85JQMl2Y9dTLjeO46cLWRrKgzX3da7pYtCr/taDbO3z/bT0JIipAOLznxtaLZOUfX+sRjGQpGG5VXRiStb2gRN8wSRKOEPm6FoY2kJfsQ8GRKqoT0Wka6UayyAHIUZqxMlzSw1HEhXqrIugpxnU1JSepW3hkQSEPTBWNyf49I4S3kmeM6SbRhVqaVAD94JbaaZpCyvGF64jOLOX6ThLV12SplCkJQIgCHlxaXK6EyY5OXbl/52jhElHbVuenQA9C7nPWJ1LEzp8utEIMSZPNCV2JpqoLn3rq/+GBBx7AihUr3L0CRZtT57eNIuJIO+sgZKa81LZndspQmREsh9Iy5b8ihISUAsy21NipBFnx+hTmCt9zrkOZ8kX1bOT2LyQSuqJWgoSETarFVsGPJULa4bD9C8MA4QBadx1WQlQUuc5PSxYiIRXsm7IqrGCiMeyzsv9uu1SgGmhFutDOCvwNx6ouDMnaDkCem3bhcnIhle6Gh4qH25Au9CoLJbgQmmSRjg64zXST4XqsMqHSiNR28tFmBCdeLpX/EhdS9SfzIRsZysJiVkBXlOHCE4xaFoPjRRXbGrGTXjsPtYLOnexkQC/GAJ2Ka0sEIQxq0pUAHOFeo7nI6evmRbIEHJFBSiqLCxvM1yYlCEcKH8mSIFBA+G7ZNgTPVhDyjIN0MgNmWyBd4XnLLb/D2WefjQULFvjGJQpHh1lEp7B9Q5fNyPBmgETB5uh5x57dYdnXtyRZMDIiuSIN6rpiRao6XbjpwhCSBbflk4RKD7qLJDfq5r+uHBUXEhIkABsMCadAI2m4izLpLcrgaqi8sZYadyHk7yVsP8HGzIWIT845rijCW166MZ/MbQ2SVeD4WrEfdzpRkSzDstoDhmRtF/BrO8JBIHAhQkiW3XpBuW+VzKWAIPJWpqo/r34Y02ZFtFjlJdlBbGOS5Y88QJEsy1KTf+55daTwKgtdWBYLkCzldq3Ol8WgNVnViNgpswMhJUSIezdRBlwS/Ebbyk3dTVlUMZKlm+MKnbbMS6MFPNoiIIrrf3NwqSwWoratzjOQFzEEgj0iSZISufsLDmymUljaXyCTcuBkOGKxCKDJqMi1cPDaERVauKjIm//eSTtNEJSGctQv557ym52WX1FHpNJxYZoXv86PQZ2bouRAE7tAgQaz9Vjc68bfQBrqv14EzIUD4bZoEhIWMThSFkwZulH1oNt7TsqwNRF2N4JUMg3nJ1i55yeMeFVa8ajIE/kiWsj5V/D3HU/4rnqEqn8rTZb6tzEjrS4Mydou4EYDCl/cXCj/qkxehVZbmkRnb0YuJKAf/Oqx5Xt4UApKj9VW0fq2rloJOmOnhUp3ECiv8i1XjA24REo9qIVONXptd2CpCrxySu5LQmnliLTrd15UxYGQzCOBWdd3N11YrfOs/KukBKRQycN87ZPPi4rFABnXaTyuJmIo4m7pPnwy59yQ9h5zj0Xq5tDMzp571VqHPBuHdFKdD2bZaqLQETfA75NVot0Ui0ClwrPn1uHrkBER5T9fVrrQTzDdPqKlJzilyQq/2/3Negku2Sh2PRXqYOCLHOtWUtlzEaKRdBuO63RhBNA9DMMXDFxHsvzpwuB329oKw/A0X3hK0P1vOZGsSscjA3sKHWmoNmprRrLCUD5BCrq9+0l660b1dUWpa8qQrO0CevIqInx3RcOZ3Eo4ZqG1fj/kRT+gLQDUxcIouJIkyrRBj+X/wG2dLswRT+tIFCE/wuJGufywYIFrTypBQQ0b80hEeJVZnq6qKFTKjGu39bxIlsyoSIj+7mzLQlpwn6VH9R70pAmfkFTAGNNvkxDT6ULVJ88fBbGYmsBzNYVCBq0ySPft80cRLduCEEKlsoiQ2JJAJBqBG2gVMqytTilNltaQ6QpDogwcvglpHgOIIMtKF+aTitLGoaobpN9fyg+eznqEWShNskg6BSrvgEAkCw6y6UI3nejv5uBAkgXBhRfgJlCovQmgikYY1HVv2aoaNp30Vyy6hL/SazFLbkprofTf8hY1YZ9Z2MYhF7nkicG9pUqRm60dySrEhsobR5ZkZfWQKLJXg3AkEkrbGS3Q69NYOGwPCPjthIMLVbKfyXvoWQjz+ykPvtSX4LDcz2fMNxlGAdncaj1WFjaI0mXfwEQCoAQAWxO8aNtLin2RB9XcVsC2GJhkeZ5AXErkcCxFFHQPPrcyMesIz5DRURk7N9ojEyC+FIgOBWOFS+J9b1A2GpK8dKHfxV1SRhk4MJdksZyIQ/XShUIIXVVpaU1UEZLFYgDFvchninPY+rpRkSzX98s/UmXwGvU96HMjWe5xCiHAHY50KoNITQQ8zZVgm6z8tjr6nip0zTBmqcicu0ChOBweB0cNJKXKSsFTwIfL332hWKsedXyyQFImk+GwItn3i5LEwFc16AfzacTINT91FwQW/G2S3EpMAabSlaQigxZYQZLFKZsqZRZg1aaxbv16RG0bDfV1OhoiAMbKbl2kIqCqcCLNdeNzx4KwVWGB3yKGvCgkA5AAs3x0zOuj6b9WNVllVsnxqHGkA+PgzFKnmVlehTV5nQ6Y/hGB18uFlBKJlmSeF1yRESLX4zD4GgNQ2utNktBmpDZSqTQE5yAhkMk4SKVSxsKhBIgIiUQCa9euRefOnXOa02dhSNZ2gdKaLEcKRCxbT/LS0wL50xSV3xTZlX6Kc9i2hURTAomWBHgH7TnEooDcBFiqeSoR4cumJuzS2IhYgYsqHKqiqfyhbQQ5i6AeGhGA2SDWCBYd2uq0pX9S5JK8dEfEspDyRQiJVI9CK+f7sC0Gh6tGM65hq5susXQgg8Ic2ikO1XaIKyJSeqRQ8Q5lyphNAbp6OQcCDC0bmsE61MOOWuBCQErX26t6JItIVRdySXpSEcFnu8xgcyoDyRLoVl8PAgcoBZIZJByOqG1BCoGNazZDNka1diwLIaVP6K0cqInyU7XqNQknmQF3OOoaayEyHCTUOXcnqGwky6+X0kejr91u9fWoi0ahUuE6kiWatYVJHQgMQub6SIXBgUeomAVIB6WjCLoiTr0pb3zc4Z5HGINbYl9Mk+WvGvRDRaCZO87QjJp7zStyKElHN6SEFISoHUHcCT8PKYcH3PVjjQ6aHAdr18JnzivAECswvtCD0feoipRJAiIWg21B78dfyShAPoKvFi8uUVeRVH93SNLHyVBTNGOQHYciT44g7y6M2sg5HrUdkZJYWEyWuf8gHIcjnUxV9J5qIhqzUVNjY/XmODJcgMc3obFukyFZZaJz587o1atXwdcNydouUDqS5QiJqGWB61SV7W3rrySq9OtUrVmICCmHI8IspFMpZJKZrBCcNQCW7REbR0rEM2l05DWVkSxmA2VNXC60HYDdTZOWDCDXAdRfEb9WwUGWZCnhbixqwyYLKScbuZCkoiu55qpuyotLApcqpedvIC1I6p6HPKjSkHEdlStXK6V1RiQhpCsg1mkU3Rwa0kImkUYmFkWspg4ZycGJEC2Z1qgApO0ESGVkyLtO/eBocQQ2ppvQoaYGUTCA0nBEEhkCIpYNJ+0gHU/DqrXzSJZ7rv0WDoUWDNyR4E42TaWiNQRJCGkQLfIW+inOsTGZQE0kokmWBaKUygXJ9WhxLEQjNiS3FCkueX783nblpQsVmSGo5t45JEsKCC68Y2BMdxgooPHLRlzC7nvfooYcBCz13eGSj2SRhJCWIuqSIIVAzKpFmnNwIT0/LBcpwX2VhWoBwuvi6N21AXW2rdKNciOYPQgs0rvEOXGPJw3KLASnGJZsbEaSc/Ssr0PfDgQWHQFmNWS35V+BxBqAdQYoARbdG8xSUVPpfALIZjCrc3D/chNYZA8wu0fxccgkyHkfYPX4ZEMTUoKjxraxW2eCHdsTzO6qt2sBOR9iY8pCihP6NGbAIsPA7G5lHa+Ljz/4BIvfX4YePbtj8B4DSp8n2QKSK9GUiSGeyaBnYwMilms6mwQQhWX3K7kfKb6EZaWxS68eiNgMv/j78/jkyw246PA9cfLhP0QsFmbyauBHNBotGMFyYUjWdgG36i2cZEmp2kdEbNX/LeD67q2gWxO9UCFnR0pwSMSsCESGA4K8dKHXFsd9BxWzkigG14W6vIib0ppYqj8iA4jqAFqN1ov8Ab8DOJfS6zvoRrXcyUSQiiLF8oTvOuUlKU/DlU2HBd20iUjZX/hK6ksOk7i3OnYkAK9HXg0AAUFCucE7ApCEiMWQchTxizK9fVVAWnytKiclBY+BtBcWl0BTKo1NyQR2qVMVhhmRAOcMDXUWEhkOzgWiyJaMu/AIrSd8Dx+7MiR1kEk5sKPqscVckgULv7/zaiTT3TB8+HA9uPxzneQO4o6D5kwaPRoaAEQU+aUk0rwJGRlDTdRGgluQ5ZIs+EmWZqNFL2+djgt5hXMJkgQrxrzjU6Qzv/uAfgcKivuZrbyxAB2VyX/Ue4sBTeIlLBCXanyCELMtNKcdZARHxM5GYKWUyHC1KAt8JAAwQm2NjmQJC7DTsKLlVSWT5CAmkJY2yBYqMhQh1NZwsJgFZmX3Ix0JCKa6N8g0WIy81yVTEa7c1kwkJFgEYJHi4yHpgCwBCQuICFiMIG2BaEwgVmuB2bV6uzTI4rCF6nBQG5NgMdt7vVwkW9JIJzPo1q0LBg0dWPo8ibUgZzVWxGsQ35xEx64R9O7QUY8JAKKwakrvR6Y3AGBgmqB9tWkzvtiwEUmeRG1tDWKxtlaTGwCGZG0fKGE4yEnFEKKWDUfkur63oUm0rgzjQkAIiUiEaeFrmFWEgpBStfcJKcUvDlez4gBFWnVkkYI/ssc0iWm9/gyBSZFLqaYVZiFiKWdyThIRWMqbSAJWbq9rZnmpQi5EQFXjviYRyRljSqULy4pyuHAF764thP+9iuhIYiChPKIspsYspFR3dBUd30lrb9TlkJu6UlEWTqp59pp4HF1qahBlzcjwJDjUeeEZDp7hiBK0g7lvD6QbcbNsdWEYLIshnVIu79FaFcm0GFNjE4RDD/0WaupGZkce0rcwkVEVj/F0WuvpojqN2wzOE0hLG/WWrcgGBdvS5J0Zz1Hen7YvVQmozplE8Nrx9ikkJJewXBd1S7n5F/w+yVH3sBUWccgualRULvc4bGQXA+r5IWQEUqqKWe4oeQInibQUqPe9021HVRvJPzeBTgm+atPyUk+KtKuenQQbluv0FXIO1DExZuljdAsYdNS7QHSvPF2oIq+CVF9Q22L68ZwrblfkVEh1/cPVoVWIRIsi9PUN9SW2dKEqwYVOka9taUG3+nrE7Ajc67DUOSevI4BP/+dpwramgH/nh6ku3C5Q/MYkIpBUNzuBciq0XC+r1pTtKz8ix7MjsCAc5UdUiES57X1KNY/NQ6g3TxFQKr+ikaHVJCv3oeL4zmHEsiAkeb31pD4fuekcF5IIaSE8PVaiOYF0QkUNBHKqPWUCajIL0WoVBM9SKlJ79c4bCUgScIQiJFIqsiE913egepEsdawg6DRozmSn2xRxCTTGYog7DjYlOUAZODIDkDrXmVRGmZrqKKwfIjdKU4hk2RaIE3iGI+JO7owBXnFAvs2FfwIhImxJp9AQiyHFBZIOh9vRgMRGZITUuhoGIAKSasIvjFyLCHexU+Lck1Tp15CpXnDV+NvVOjG40dFC43DHWChdqD3bKB2ii7LhN44FpLYBUYRWaI0iA/LudUfo/opW/vQhAxO0v9q0DOhiBWKqf6pl+Ruy59w7fgd7xkDSde93PcHConsRgMpo/q0XvQRFrmxN5KTMuYf1do6WCWSPoTIkWtTY6xvKLS7SNjIk0RCNocVxsD6RyHm9FFHievw+kiVdpaDRYlUThmRtF5BF7wmRM+nzMJLVqkiWEr5zKb2qf+7oSaCAO7cr+E6FNFUujvL7F2a1JvkTA1Ei7C1lIMftXSg9GpCNQrleWW7PwEIkS0hSHltaoLxlXTOS8RRADFK61YDq3JHrKk66+qgsSI/LONKtLssSKCk5pCRV9eerKlRGlkyv5qsBl1gpzZP0OhN4nwhAwJFKX1QfjWJNIgNHJJHIpGBZUaX3SyjncAvI61+Ylz4slC60LGXTwLLVhmBqQpASePKpV/DXv/4VmzZt0kPPBATISc6R4g7qo0p8n+CupQEHqBlpqYXwAIAIJDIobo2itVIsJ11YkuCSjmTlP3pFTiUfA1TEsijJKhBtY7YiwR7JiYa87hJTl2Sp79p12AfUvZHIaa/jSOn5n+Ui4JXFYgDS8FotlYT6HEFKk2hbTJvgAv7zqs6H35IiClCzftFBYX1qBKBUGV5ZXB+LIla2xbSOLvf7VedI2Zu4917lcgYvktVYJsnSKWnl5Qc0RGNY29KiG92Xa1WhFwm+a8edV4zevbowJGt7QG7FVg5UexVdtUPIEQ+3gWTp9zhSqFtTSt3yQ0V1wpokS6miapkCJoWFUUn/wgIrdBbVkaHWwNU1qYdKSvC8ScKNbgnKphJzQfp1RwjYjEEKjnQyDSkBYm5UxXectAnZ9Gi5UTzuFWh7EUVvouUgSEipfblch3DSZfVeWrYaUP5HkEw5sQPIm2RIt4EBQ0M0hrgjsSkZR5qnEbEjEI5Q2jECoH3G/JA5k4Hk4fojy7aQSXJEotlrQlXhKeH7T392O0477TR8/vnneujBdGHKcZARAjE7gohloznt6pQ4QAm0cBtRnR+WcO0Pik2YbjFC1hahvJQwwZ2u8/aYS7IYU9WWhb7PohO6dn3XdhqhkSw42YpRMDhcFTnYEQuSq+dA1LbRkgm211GRrfwjYGCBZ4ayMhC+iFkpZC0lJNQ1JV2NW4Bo5kSrWAyghF5chLUZcgeke7GWjKyJQPTW1VsScgmv8J7HJAnuOa0ERNS6dCEUuXMXNwkvmsUC2xT+4OD1C8C7Nw3Jqi4MydouUPyGkEQQkF5DYL/JY9aAsrXCdwtp3ehYCgJJFckSUuZNgAA8UbijLQOKgYjUzd+SwOp4XI2xLFNOrieG3NVoFEC6dZEaciNZ6uGb5hwR2/80YTq6hYAQOxcMypOKE8G2LDhprlOsXEcALe8BRpQBZBxgtXqiqCCSReq7Ju8Brs+166FFivj4Iw4ZLzrXhuKAAEg7quvJPtcYkwSEFJ5nF2MMDdEoNiST4NJBxIqBO9wjWRblO+vnOsALIfMEy4Ca+K0IQ219Vs/HoNOFIqsnsW07VG8SdzJeZLImYiOeSWu9E4FkGgkHiPpsUWSpazVkklJ/L8PCgUSBdKEIepQzFekrfL0Xu5ds9TolES6Ot/UzQ6fwidT1Q9r8VUqQFIhaNtJSYH0igaSjNF6536ELi1khMgIqO5KlUrTu/eemS3VYOXDdubIDt01QTJFql1AWLCJySXUJyYFe9KrolZITELKedbnbce9ZaaG8RWQW6VTae47W1ZdZIECORwIZ1H3XGI1hbaIFaSH0+SozkuW7fr17yJCsqsII37cDUKBKKR9+3YplhRmSApVGL8hz72ZIOg4itjKbVM9PghA+nYEPXqSLVEVeLGTcGcGxMZHE5nQKLZkM0lygNhZBtxqJWDkPIe8hmjMxsIiOZGUAVGqOmp0UpXQjUb4SdJb1ysqmBvJhgSHFuRKc2zbSaQ4nw5VGhbGsUJ24HmcSYD0AalZC6nKGqidhm1meq5Llfb/Ca7RMUpFiQLm+q+vCtRJoO1SPPZXKlkKEeIDxPDPR+mgMW5KEGEujNhZBSyapJhFLLQicnEgWF0FCK4UInSAZY+jQpTHnb9CTTVa7o8qptd5EW30QEZpSacQi6nFXG4lgczKFJHfQGIkgIyUcyVEXjUJwjkwyA9kxZ0LNQ/4kpY+gyHvU6+rqyj9GJ81h2dlHssVciVr4OFQT6+y5ywillWyIxXyGoymER4VtHylRY+ZcgBHBjuiG3IJQE40g6Tj4dNMG1FgR1MeisBiDHZKitBgLKZiJgKilTJWPkghIHblPtaRQ01ADSQyW5/kFZKNV7jHF9N9U38nC/EKTrBJ+fep5zHQcVxeWgNQCyvdduEUhwpNbMOS3wCoON4pVW1dT0gogCw5JykzXvVfqolGsS7RgYyKF3o1RlEeyZCDV7H53ttFkVRUmkrVdQN3UheAPwdsWy+9f6ImjK4EWR5IibTazspO2dnwOi1RxoVIIrpVEGDan0vhs8ya0OA7qolF0qa9TvedkrqanEAqF/KPq761qI6QmRcZsrzrK7/MTsRlSOgUqqBDFyopxXfsHJ80huYDgyiHb8QvVKQGQ+yCrpMekUHTQ8gvO3fMmdeBSpXbdhsKWe10wC60rgghDdkVMkrRWLZguVIEG8lIMjDHURjtBIAqL2RAO9y5tpu0I/NcVlyKQllWRrPJGx3TVl5DZVbgSjQf1JknHQYorryNARVwEJJKcg9k9kKFOcKRExLKRjKfQtCGuJ9DS6cJ8PVSpc68rv0Lud+7wnIpWN4JYaJ8p+O+RplQaK5u2+FJ7DG4FYX6lmSajEFCNv1VBDQMDsxlIAlIoU9gudXXoXteA2mgELY6DDdprLBeMqe8z+EdfP8uSyMAlWU7aQdP6ZjhcWykEWgC50aqs1QVcHScVaDPknQMqI5quUs3CN2a1dGE571X9Ht02PFSgpVYxVJ4qBACRXTL6zkFdNIqNqRTK0mT5e45quCQrJJBs0AaY07ldwF8Kng//lG8j6+7toaJUVHavgBIic5Kqwk5IHblgWsyZ/66MVCaEQkeywpDmHDZj6FRTi5gdUdol7YdU1jj1gyx3YlATmigd7g/dZ/Zzw6qjbKbOq9DkpdC3oUxHodO3FlItKhUihYBlMXDhRpIESG5B1ji1PJLlPrAFqe9aeuQm2yJFutVMEqrXH5GKogmhUotV6hFJPrKptEH56RJ3LP4igdpoR3So2w0AkE5ksuaasMBBAQsSLoNVnIKHu72HQ02alBvJyknlJTmHQ0KXuCtEmI2mlNvCRWmMLMZ0xaYEJ21UWvDkhFzH5P1fkfdJr5jADykVUfdHM1Q0ihWuLsyJgGckR4smlL4BFRiS1mzBbU2kW2lJQiRiKxIf0FcxxOwIOtXUokd9I+pD+rSpSFbuh1VQYShVFSS5aWpSTv6qys///hAHe11hGGbdEUCOiD58G0X2SAbJsLo1gyRL6BizumvV86kSOUPllYXQpJhpvVj2z5auRkVZ8hE3rej7i7tQMaKsqsKQrO0BxFFs+a4iRurCt638iap1FWXqRnS08ahtMa85L0hCCpnnaQSoSVGREyoYyUpyBxHfZJH1l2LFJy4PIQ9R37BbpzkS3j65Nl/1pwsj2k3fNSkttBy2NGFkAARXovdITQSSqwcyd1eRlAbkZqXHAtT3W3bTYdVSxLL8TaezPkCuRMvVjJC231Ap3EqqGEuMhHQFnY6mqXFkzz0R19Vv4Q9mIkI6mYEVcS0JsuN2waXwomCqhY/02sqUggpgBElWIJKlH28tTibQcBoAaiIRxJ0MHCE1yfIGAUgoslz0WhUhE70+wKJQertcdbHkElKIgCBGabIoJ3roQ46gPcU5Uo6DJPd/RxR6KQejOg6IqYg50/5KksIj2cVghb2vzApDdYzKdkNookvC7aBgBckN8fxjcvtm5kT38pAnog8bjAMwWy1ufF+yzEkXghwthtf3oeeVVv791+JFsiojWSpFHvTCyt5HZVg4hBRbuRkTu8z7z6A8GJK1PYCKR7Ickt4kYVsqhZBv41Ap8dAeL9rtPGKph5vLO5RtRP6N6giflUTI6/4WPbkI9zPKh3J7L/AiA6ii9jzuTrMPZi7zK9gilqp841qvVSiaoiqNlAaDZziEIxGNRUEgMJ/XFmSzmlhcklW2KFZ79JCl/a8o5+HOwQmez4QU0vNQc1v+qIm8nPRMcRCUD5eOF0Edmv8Y0poShqe/hMMhOIcd1ZEsUi2h/JOwI30tdWR+VKwY3O9IEgWE7/5UnqvHyk1v1UZspDlHkjtI8AyielEghQSEMlgtXhVXiPiUJllhixfBVb9Af/saZQOmIqPhRCsYyUpzjowUSGTc78htrVNAeuulth1wydQ51AUMgIqSVgJ1vbrebu4xlFthmC1McaO0kKQjubnkPoxIxQBqAWQypGDGj+KaxazJrB0o/FF3lVrAeN8FOcqIVFL2dZ/GrRy46cKGitKFenGD4L3CwPRpK50upLB0oXsPGZJVVRjh+zaG/6YuBC6k10A34O7toRUkS692uMhOj7kPVcoJ/SvdhoCtlBuhhqSOFOAkUWPnX1qCLJTXv7DYajQKoDU2Dn4dkMx7Brk6nYx2uy800VvMQkYq+4dMyoGUArFoVFXQQZFTFYZPAyR8zazdVEJWrBoOTZ402VAkyy+odbS43o1yqNSKZavJiXvpkGwlZWvheqUxS+n1uJCAzEmXkAVCOCl1MhzcEajRVVMMmhBpAui2i/K7vZMkIFrZ2k+ShbvuvhCxyCB0794d8HmpJXVkp7Em6IruipnjmTRSjuNVFqrIIIFzNbETcU0Uck9OIduVUuRWf7c5bxZCQAgRqKxkzFLu5zLo8QZAR66z0Tq3mCNmR7AllUTfjh3gGY4WatDsRnVkBqTThf5xhVm4FIMb5aW8qstyKgyVNgxWDThlPHsSkjpdqNNwzBXs5xkVx9TChgHFpzXlZVWYRmSLbqSv4Ii5RY46UuXqr6QbySK3upCKXBv5SMR1urBMjyxF8FQpTG6QkjEGQUy3vyr13eWnVd05xaQLqwtDsrY5XP1I4QnRkcK78C3GIALu3tBi50ojWWq14y/HFr4lqArVB9/BtVeSra0kwgxJHaEE8Y2x3NJ2N5JVRv9CShdejXoVhpWBtM4CANKCh3pgMShCy4u4vVtM+QTZlgUnpc65ZTEd1SMd8SHYJHJSwPlprAIjhZtuYDo+pH5zNVkZTbrIE35LKRHR41U8WecT20iyABVdspgNwK2c9BFr6YDIAiuQHuGO0oi5CwRIFfVySZbStgHRnObQVqUraclw7PEHoVvHg8DsRpBo9uaYJOeqp2NujySoNlVNqRQcKXXDaOjefUBGulGJcEf1sEgAtI9S8dErD6jcjaTnUZR9wa0ulH4LDw9aU2NlOxhwIjREo0gJjiTnulFzRqfswuCaZ3JIWBBSBKlRhZEspjVtQlLOpVdGhaFXUay0ocy1KCFdiUnufaFJVmhRjAOQpfoZFoSF4tF0ZbEBKwohfVYbjKn7zkvFKeIrdY9RvRGyz/PyULnwXREo6at8dKFt4+A+24sjSLLUAjpbRGNQPZh04TaHjnyU0GQFw8JBQoTcVi5lQT240zx7o0qHey09XEPAwEi1yNm2GCK28ovKRUaIUCNPxvyi8MJjzbq9F+L/UQAZVO6snH2opDiHbYefb0eKvPPth21ZyAilYUslUohEI6qvogSYVJWJKm2QAJhvdepV/ZVOJ0GnCN0RSKkImjKOdLx0oeU2EHazF5rgleqFWS6IFNFmFtORLOhxuCt3R6cyw8EzvspCxiC16a2bLiSd1rR86SmSJQh4gXEGBM2+SELcyYQ6kwNAjR1BSgg42g8K0BV15KZGi1UYhgmsy0gJa9+z3PSq5OHXhWoUHvZ9Bgk719W+tdEoHCmQcty2QsXuJRsqaiw8bZ1/WMFnTGnYzNUQ5ryvrArDrPeVcNvaSKlTyNkFChUgvl6FYUG3dxclnpU+Pz3hk2kwAEJmi1rUc1uNTerrWFK2mKBcJCrWZKn7WwTInf9lt9qxxHNGZgIRTv93ZptIVlVhSNY2h3vThl/Y0q1281/4lOv6rsqxK9PhqAdZWlcWAsonxyVZSgsRvFGFTlMqn5yQKkeoFXXYKFSazRXaFhOGug/RQimOiHpIVkqydJsVIlJGpCEPEpvZSDpcVxf6q4qyx+j+3ZJAOuUokqUF6iqlQKrknFp8eiz1Tm+VXHScPv0Nc6mwK6hV505IBhIEZjOdcvMJyQPpwjJOi/gSJDeGviZJFUK4hFlNui5RVJNeuD+7QjqRhq3Txsq9XP3di2RpzQ3zR7LcCF0FECTx//7f63j6meeQTqe9KJOUEluSKdSENDIGlClpmnO1xvH0XVIJ36Urti50nYXpKMtx21cLldzLj4eQLIupKl51j+W8nlNBmZEC2hYWDAxxJ6PvlWLpQhvKrFRCSIB8pMpiDNKprEpVaYIoRMtZRoWhTnEx12uLCPAiWZrcuI7uoQ720MrvbMQ6HDki+jxkyatqW8O8yLt6l17E6HStJOa14SL/62VASolkolKS5RodM60s8MXRmBqLDCXlWbgLJP958hcxGU1WdWFI1raGlz8P/yo4qaYaViCNEHR9b11rHVeTlZ3khCOyATXK12QoDRCyJCvExiHNeZ7eBND+Xm7lW1GiUcjt3UV5hoJ50OlCLslzaw8bY4pz7VGljiGTTGP1srXgTtZWoneHDrAFIDICkZjtRWlcLZWQFkAZsECaptK2Kwxw04UEdU4oAxCHIEUGLFsJu73KOlja7LP8Bz3JJj3Rhr4Igqr2U6lUV5ji+oAJLUoOeStJZJIO7KjbdoZ5ESd31ex6gLkrZ9dCpJJIFoMinGdOuRXHHf8jxONxuFGmFOdIC+6ZkObC1WX5I11Ckx1yqyALTcgy2BvRHU2pe5BILUJyI1k8k40iB3ZHunI4j7gESRYXEsl4Chu+3ICaSARNqTRANYDVE4zVIByacGgLB38lHbNs8FDT48JgnoYwJJJVssKQe8coJGUrnaVb1ad/yXV7D3xOLUC8qOZxxZYmNGeai4wjK99Q+kqJtZ+vVe2hAosM9SN1WpNAPsJT3nlLJdMegautK8/t3Y1SS8kQ39yCzeu2eC+52ULXBqb4MQbNnlOZ7HVuNFnVhSFZ2xwuOQr/Kjxhs59kWSxIspilb75KHoqkw9268JhIeRRZqqqNhMgjUIKk5w/lVrPlOnjn2jd4Y2YMDncfUsXalRSPZCmvLFlRJMsvEuZS5HlkuYgwpUvxV7hxRyDRlER8UzywrZN2FNGxrCwp0BxEogNg987ZuztRlIoOKKIhwWAxNRlndVYOSItuiWeLIdznKWMMGa+jbpm6EEoVJGQqK6nSwxDZysXsZCfBJcsjDIA6b9xfWciY0h35XMHdFk3e+zWpr4RkEUhZH2io6kJFsrgW2YdVurroWlePzr4JznXQF5J0tLjQ9xVyjTJXx1UMKvqXF8lyOKzcFDYDqGD6J0iyHKF6RCbiKcQsGymudFnMaigyloi3HwKD4FntJ7MAWSHJ0kPOi4CXU2FIMuVFmiURlJm69Cw6sveO2wA6LJLVMeS+yyLNOeKOABe8sOWNL6ImpAQJ1bqKuHA7KyL7DFNpO5fdeHKoMu891yOrrr42n2AXhHrOSzBILpFszi6Q3B6L3oOoIASCzc0RKGIymqzqwpCsbY7gwzLvVSmVNsD3VLatXD1UmamoAMizIlCaIpElDf5qMv9YfNU2NrMU6fI9UIvZN1huKFuKEhNRkYdoduioLJKlHyqw4ejzGabTsbVXlj+FJbiAk3GwZX2z0hhpZFIhn0+qx6SaRIPiW48cluWj5FZUqXJ6x3XKp4yyaYClhO8uN/E8olwJennpQqV/c1Bo1eu6s7uRAVVAKX2EXoBTOCniaQ7BRdZck8HTX7nFFm77Iu9cV1jN5o3Td51aluXpTVxtTzHSZjMrx3FepamkHiOFeJtleyOGpAtL3oMiYPLpgjs8L02qIqTuIiHnPTnHluIcTJC6RoUEJ4FkSGFK8AOyxq1CahKsJ3vLtsCd1lmBFPwei5q7NgGIetcEyaz2L5CGy3F7DxwOs/LuOz8SjoOkIyC9SE4Ysm15lG9Y9njUZSZ9P+RZXnhH7FZsloHK9ViAK3x3veHSyQyE9z0zHVFzx1kAOZWpAAJFTCZdWF0YkrXN4T4sC5AsyqboXLiu79kHYLmpKB9IqrQTNAFym0Pb+qEhKEf3FXx4ug85PxFz7RsiIasyiykTVeFZVhQalxPYfygYUyvfsqEfKswGlyLPX8ZFxFaVTf4UlmotYiHVkkbz5mw0KxVPIxINpiykJM+moMDAy5iEVSTKLc+2GMsKbikDct2eZbaRsjtpWwzBaFNJqBV5IbNL1fuOeQteGdB7qcmZCxZ6LrnDA5WCjAEkSGv5pN5fUNGVaxlSDlQRiE9P4vYuhF2xcFuZoeopnbTlCMKus0Ip/tLpwmwkK3vkUgjIkOgqAwOBhWuycj4nJVR7J+5w8AzP6rKKIisod7SwyF/FrDR5FTxTkPVUy4cNonjI3zWJpBaAxXThCLx0oYpsuVvqCFIrazrimQxSXGoCXeh70uSaXD0YqYpTiaz/l6fJosAzUXqVA5VFsiprqaOuBRWwJghHeos/i+l+i61IF6a5P11YwXAMSsKQrG0NKix6B9SN66boXFja9d3xHoB+e4CyP9i7IRnUKlYKTeaYesjlNnsNe3j6U4oZriqcoiGVe66JqizZv7Cch2gEQAEdURh8ImEug0exee1mJONqXzazwCEDKSzucFgWEKuLoml9M3jGAXc4Mmknj2TBW/EWWsmXOnbAJVnQKSXLE9wCyv5Cqio/6SPemtRZgdRimSSraJpZeoRUVVdBe/Bko6aiQFTByeTsU6dfGIMXyco9T5VO6Gq3LFCZZ1kuibcQZvpZDCqCqFYeimzbBVJcWleYtzBSi52CDu36va7Bqwt170mwSC7Jgr4cwqpSBUBuBFB5ZIFLSC6VN1kkguZUuoRru5supKzBbTZUrfpVVkpUwcKv/2IVhl7PwRigtU2SK9JHUlc5a4JPMlO2B5UfUkpsSaXAwSAkL7zY0ZWpXpN4qY1uibxm6VlNorqGba1F08lubRdTGol4a9ze1ffJXUNkx0Em5TNqhXulFPvesxWULjLa54+xytL1BqVhSNY2R0gqwP8q5ddvuYTFNY/LpqIqE767PdS89h06OqL8l0h3l8+Ozd/eB4A2JA1GssLsGwBk+xeCikZzirq9ex8cBWSiglRG1oPHEcIjUEQS8c0tyCSzD0V3Je5F6jIClm2jtr5GR7Na4KQV0YrEgilNNaGpB25hlJj43Qe5T2fFdW89kANBXKeQ9BcHBDyWlIZPltBkuJ/lnpfwMUk9ybmOXdmKU1eTQtq4Nf+96UTKqyx0x6aIobIbIHcSy/m8isGUlsiF6vgkVdQy53otCV8RgVSSeIBS+deZR3pyU9plRJRJ5AnfBc+69gePDXosQH66UABM/c1xm7U7EoIL8JSDmkgESe6USBlmqyGFVJEj5kWylGRA5jZ8LgELLG9xpo6lRhPWMAKS0n+PKi2clLqZuKstIl8arkTbnAJIanf/mB2FKGKl4vY+dCNqIH1/ualLL0qkzku2wbn79/KNoVvVUgdZzSAIkFx6fn2Aj5gXg0935iKjv2dj31B9GJK1zSGL3hRhzyuXsOStGCvUZElint5L6souy2KwLAskkEey/O19AKVh8ufyHSkLzu2qvFsJpYv3L0yhtEduFFkRbDlQEx9jShDs6rGcDEcm5cBxfPvJibQ7GQeWTqHW1MewZV0TkvG0ToX53LkBHVHJt74IbFRSryH1qlg9BC0wlWL1yq7dSQje3eue82Bbk3KiOFltVSi0nQRzj09qPZJb9uVaeuQQGSL14Ld9zu1MpzIsgtcsPLdHpOSF2xkVgkpt+9KFljsBqgrcSiqlpCuzIRXVFXBb9OReZ+55C0sXuq8XAAkvFeztTUgIITw9lLc3bTZLwj3nvt34IuBcSJXuEYRINIJUMoOoZYNLiWQRG4ZswYZaLDGRtc+wbJZ14K8AzKe5C0L524XqsigN6E4IUi8S3EWE1K2WvGMuZlRcBAnHASepSJZwi2vC4LYqypI8IVTUSl31+rvXES+3O4R2nEAlnoWtThcSvGcAAUglgue0dLow/9hT+joxeqzqwzi+b2uQLLrYDkt5uC1ggtV/5ehBgntWlWq2mpxldtXvGvtJoXL/7qPf4SJQeZIrwE9xp6DxoyduJqCoaJ1ShX19vJ1FAJlEcTdr/z5V1AVQbu8R3YSXpzm4w+GkfHoES+ndACXSF1xZJQBATV0MzRviSNWm8r4yZik9l1UoXaK2ApEoEVuR+oFNniZLkuojGYEiAkJKKDNSVdkoRFaTkW1rUg7JctMGhVb1AkKq79kVYbtjBHFvZZ9LjJwMB+cCsdqsCNnVqim/IaV3caQItnHhFGgrUw4YY6iJRnH9redgl/quKl0oVJTJkSK0irQQspEKFUH2GhMTz7nOChSrMAZlBlZ4gpPEQbC8KCSgNFnI0V1mx+ROmrnfUdYM1ZFCRfMIiNZEVeWrFLCZhbiTRncUmcT18XKZvYbUoShbkkrThZauOs4FYxFQgQpDkkkv9UqkrEKYhGeCqyQGNpRHVtZcdW1LCyKMoWt9aZKyJZVChKlnHad80podjC6acKUNBHUOdNpSEvTiQ3mxcZI6xablFMwuYyGl0Hrhu04XSkIkYiOd1JF1LV8obUaav7BPOmVoYQ1aBUOytjmKT4aFUh4MQdE5St5YuSBfl3mmH6ZuSETVqXAp4e9D5m/vAyBgSGrpqJZtW8gk0+COQH3H/IefIBuF+heWdnt3EYFacQZXjCQ3A5BgVtfcTwV0bzYuRDaSlXbA0wJOhntkoWNNTdbHiSt7ADuWNdSM1cfQsqUFdjTXcdpS27Lwno4K5aQSgkk0izE4PvGvRDa6YFmK3LntBN10ofSqoEp9lEsWwsfrXhtMa7KkFLpwQYBI9S0UyLbFcSEySoBd2+jz/vFFnEinhFyzR+99nFfu0cOAWE0UJ5x6OIb36APGBMiLZBV27g89XjdlpkQ52sE7jbyVv5duUddRPJ0BgXQrqeL3IZEWvvv+JgSF3eJQpNzlQbn7zFY3OlJq/ZRErD6mFg8ZjpqIjaZUGkKGW5aoj1ALLM51KCbiW2jpc1IKqZYUpJCo71iv2k4Ve09oJKvZI7ES0jPYtS0rJ0KUhN86I8kdOFyUJFkZIRB3MqiJKDd5IYpFb1Uky6t8leocSF0lpL4PAZfkCuG7ht1xlun4XnlLHXiLciEJDBJ2LALhCPAM92lEvYumwD543vWWzJhIVnvBpAu3MVwNQCFwyk/HuAhU/zFUli7UzX+lFjb7vYYsZinxr8ymJIkU6fLn7CNW1pDUb9+QSqTRvDGkkoigzSudAnqqbHPWYmCuBsInMCVyQM6nILEm5HNVaF+QNiLVk2M6mQYsZdPgHn/EshHTWiL196BxaU1dDJZlo7Y+GEFjlhJL28wK16QAKO8B7D7JFcFiDFr8yzzLDbW6lwCzdNGCFq0im4orP5Llit9DRkJZg0imI3RZTUoGpEldLpHhPCQyYwEAwSJX26VTjb6HOoniUd0wuFWLBEuJ8r2oJfOamZcLRV5VmkppxlzNUu53FrzPtqRT2JRKwlX0FfQdI5WSp5DqwkLHBhTQqlHWDNURwjNyjcQi4Fw15q6NRpHgDrakiqTnWUcIdNCdHJD3KPJXboZBSoGNqzchvrkFgFt8Ucj6IZJXYehVFiKqj1VXyxIDsxkgVOGPOlbX7V3dn1xIxJ1MjjFzPpKOgzTnqI3YsKGjvSHPSlWwoJzQpa7qdsX3pP3zVHzZTTcq7ZYFhmw7G3WPF/Thcs+bkEgl1fdSbnPo7PlSkgQSTKV1ScJJB8XvxYm+A/+N5nbBAExlYXvAkKxtjjC/nSwcIUPN4XJF52rVW0kbDPJSUoAKibv/tjSHkUIE3LlVusdX5ciyhqSufUPUtiCFhJPJH4tqmeGWOIdEdCj4EC19CL59yPUArQdkc94D3g3tCykhdcNiIolUPIOa2phnOJgLVyvjN4lkjKGhU52XQvT/XXCVOsg1cc1uZAWIYfgxyYAszEsBSgBQ34eU2UbKzIIn1na1IVRkos85QkVAC0wIJLN2AxaDjjBAv8dR7X1C0oWSizyy5I5NJbXddGFQNM+FrLilDhhDKpXG3P/Mxyuz34Z7XTlSuSGVb/IIj8AyML0gcF/I/c6C6ZaMEPpedK/tQudeib5yNVnc4QV8n1zfoxDSTK52SHkcWZ6NR7Zgw2YWIpaFNS3x0BQeADCrAcTqtcs65RWtlNJkJZqSiG9u8e4fr/gijGSFVRhSCtnKQui2Rvr6tm1IKfXYXQ+ybCTLkQJpzpHixaPDLU5GV8laniu9kGHvcQm6BZV015FXIk3kyVskQjq6ObS+/pkbgbZQThVxQrfTsSwLtbWFHPnDoC1cKLsgAHy+fVQsWe0i2HrIkdIjqkb4Xn2YdOE2RwmSJcPFuxazQgxJK3FBdydM/VB2BJhnHKkeFEJmW2S47tyxnHShIKmrxdQkGYlZkFJNtEQyh5RZSAtC4f6FJfoWBs8AiJQ2iigD4l+o91EaKsXjb1OhooX+Ni5uhWC0Lop0S9prp+KHENmJoxSYpdJheWncnDGXFsWS9z9AkRuVMlHvlbACk5TnpA63TYxEkKYV+6jimizpVp8iqzmTbjqMMqo5bkgkSzj5ujMGpv0z1bEJkgHRPGmNSSWkCFDHv2XjZpz3gxtQV1eDlqZjFUGCivblkuFiIF09yWydInLPYe7iJSfd4kiRJQJF04XaABhBx3eeEbCswuPMFhv4fvdFfFOOA5YT1HbSatLtUFODjckEtqRSBdNqbn9EhjztfUFyBqgo1pZ1TZ7xLKCuQYcEOBHy1JIsBsgUgven29NQkyypiBaRVJ5nAU2WOrdML8KEFvwnHY6OBXgKEWFLKuVFpy2tLZShJEtpDWHFsv01dYRHCtKpQje66UAQCzwTJZFaIFIKJUmWThXWNdRVqINyILQ9iaUXvZFoBKm4lmCwbKSu4F7JCeheuZRweHahZlBdmEjWtoZ0AiLYXPACuhLbYsjkkaxKIlmqB55bBi44DxhHulU1HsnSug9/oEEJ5HU0y2ffIB2uokY5ZMOyGJyi/QvVpM/KiWSxqE4zACRWA3IzYPVAeAVTtizb9cDiGdX2JRqNqLRpgUhWuXAjTozcSSKM5KhqtWI+Su4krCiWnzgBAFev+U1hLQbJg5EDSeVFNclzey8sfM9WLqoIpZpoMgC4TmEG7QgAJXzPi/TpdCFJgOlqNvJFRkm67vKVPeRVqlv927Y1iWXwopaVCt/dfWZTXkyfJz+C5yvDhW5qXCqSpeIjuZeGaqkT/g4G10/Mv0+f75uQcEgCwteNIRpBOqnGbDMLMSuC1UWiWdKVBRACzyLGGESR6sREUxItTQnUNtZCcqlTx4qshl//MWTF6xpeZaEmKshaJbiN16VnjZAtKnDNQsEYtqQLp0NTmoTVRlxdJUDEIEKc/LOLDtu7xyTXnycEXIUqZAbqPlayCl0Emo1kqZLQgmMC/JWFlYjeoT4XCNi2RKIRZPSiUUkGShRB5UhUhJReWti01Kk+DMna5igcyVLlywVIFrO8iQpAeamo4N5Bgca42fJ5T/SqtlJb6zRV3qTFlCbLb98ghNQ6huCD1mYl+hcWafGSD7ViJJkAxErAatDkTCKvgimkzUomldHRdvWEDNOecKeCQgJdCeWWevPQNIvro1TCjBXwxulC6Ga+QrKARYTr8uwRN2JearE0MnBTXGGTIrnshWXTkgSmrzOpIlnIvz55hsOyc65Zlu2rRoxUD0lkV86qKI8qfyIx5kUcVRo4DYDpPptUUM8YBk/kbVm6slZCBfuDk7hfRyn0PSqIdOVssSii7kvp06vlVrDmHR6g08MysB/XDNWNoknfPiIRW1UY6vPSoSaGLekUNhfQZknoBZEMXnOWZUEUuAekENiytgm2HUEkGtGLMBV1F/p85B0L05YYvkUQyQT8pq5ur1a1vSpA4EJrsnznQOimzHWRKFocp6AuK+k4SAuOqGaxtmVBEIMMfVa6gnjLSxW7z0UlhVLaSDetKLS+yzXr1WcNxbznXLiRrIZKSRZxSFILRkbqcyMxW1VJpx2lnSyiycy2hPJFskh6thsmklV9GJK1DUFe7j78ASsIgYnID9tSoeqA63sFTZNdfY8ah/uQzrkcfETJjQLlTloMDBkuA/YNQgiv2skP9QDWk3dopKWCSByLAuSAxBeAbAZYR++l/JY76qEiXb0NgHQiAzuS7asXRqh4SESmECy96obWrlHYQ86bKIqXVyuthw/EIKXSgkhiwbfr6jCP0DJyi7xLD5ocKBIR/lAmCJBkANPuBFLqcWQAXSWX9x6S4FzknzemdHBS94NzciKjqoULQo1si4LBq1pUCwDdUkeL2CuJjHmn0NVkSai0isztLpDVQwkicK1v9ApRCtoDqO/eL3xXFazFtGhMj8tHInwRF66rNMkR3j7siK0itV67FRXNKqTNEu7YWTA1zqwsgc1FS1MSLc0J1HWoUQJ1Uu7wni6wSHVb4P6UTfDbY0gfyVLPPQYuXU+yrDeY1Lq+2kgEGc49C4JcNDsZXUCiF5BgkMQgCzn5az89d3FJQsKO2J42TjWrVpIHVxvlPp+zwvdSC6lWVhYCimRJ97NUJM+ylITAyag0tiy6yHKvnex9xoX0rgsTyKo+DMnapijgt6MhXZ1KqCaL+UShQDmpqJy965tRaXpESKpGSspqE3TLkdxtbEtpw1z7BkDpu6RO1wTHrNoBcSKEkQCSwaqX4tA2DmIdYHXyjSsKoDm7TwpWAimtjkA6kYYd1atb2/Y0LH7wTKbs8LnlRrKkL/2SvxUKp0pd+Jv6aDCCILWC5rriKbBLTxjv1reVSBe4oIwuMiiw8pVSTSoMiswJCelzNSfJ4Gr6vNE7MpQ0qAx01lcrw0Wg8TkJV8xfetiB/VrMWyyoRYJOo7WiRY8XQbHUvzkJqOss491XSh/lI1m6vF8E7sViwnd1TrMFJ/kVrIHjg1td6P8+3UlcR7JI9ddzF0lWxIIQqsLQRYeaGJrSKWxK5kez1HUbEnmyWGAf3qcLgS3rtiASicCyLFhQKVul69LC8pzG8ZtSSV2gEQWg0/zEodpjZUmWkNkCHJeYk642dCv/oM+JsnlQn5cI6dMopcSWZMpLFQJ6oQcGKcMi6dljdVvocKH6uapIn/Sl4tQ9KX13a/b5WLrwpDXpQndRTrqIx3+ZMQCZZFpHsortRevOfJosQdlntRG+Vx+GZG1TaEFroebQUgYmIj8ilhWwWCh3BeVBi1MtZJ2dc1M8/tYnKgIRFlFT/liufQP5qoPyI1nwmQuGjTOJ8ltmqMkP1AJmNWb/zGp0BZNvwtNtVtyGxDzN4WSE5ytj2SxQAu0eO8+ERPcKQbchcduBhLeIKaPtiu6JFhyM1lkQ9/oWut+Ean8CL4WoHsAoqQkJpA3cVXHuSGS2/YabVs22RRKqgTILvs9rdpxz3vxeQpYumCBf2owoa0tRCRiYp6ezLMubQFrVoofcNBUA995iERUx5Ysg0++CMm8CcqM3SbnFIdLVCBFQTJMlvWiHmy5UpJ8Vuc5Ug3D/9+nTZEnl5SZE9lr1WkL59FQWsxC1VTQrtzCD6+hR7t1tW+GtdVq2JJBoSqK2UanNmc20x1iWZPjThQnHwarmZiQcbeoqmzVZDVYWAqpNjbc4Y0q/J1yyr41C1XmTXqouZkXQlM6PTG1Jp5HgGdRGs6a4PMNBZEFSJiTalr33pO5xSpJg27Z3T6v7UD2/pEuydMQtuLvikeTWGZGq57ubpmSAN4Pb0QhSLWk3+Y/CPmAhfQuF8Dih0WRVH4ZkbVOUimQhMBH54U5USbd8mbklzmWu4LWbt9tgl7S1QXCTLIkTpLQ0ubCZ8iNy7RtIP/xIr2wD21qWmlQKCUNlGW7vGqpsugGwu+e8EFMPY1f34VUyWuCCYMECz3AIwWHrhry2bUNwGZhM1MQlyq52U/IRqdImOZNMFi7JKvIAJoG86Y4xCKneK2CrKkY9LsuCV2IOuEJpVpJkZa89N5IVNl4luFcCfDWJqFqL7Io6b69cC9gLkQYiL7XqjwK4BpSVPuSVkFmvwr1Ilkqj5QryS8G7XrUNgYrG1ADgKmIKFX2A1QCwTup4SULoik5l3eGK30M/AUTSl/SCLiYofNzZSJZvnz4z1AwXgKC8aBiDr6xfo0MshqZMCs2ZICFRi6mQK8ByG8dTYNumDc2IRCPevcGY7krgkjcKFmdsSCbQnE6r88Oi8HoYer0MsyRIEHmHalkMDKSfGW51YY23nftsrInYaOFOoBBISok1LXHYzA50odi4ehPSLRnlqZZLRIh7iwZOqn+iqlBVOkcpoFPkaqEkfRHJ7PfknsjyNFmVpQt1Jwpi3iLSJdSRWAROmqtFDliRSFr+nJPmPFvgYkhW1WEsHLYlyK2WKRDJIulV7IWhJhLB2pYW/H/2/jVatq0qD4afPsasWpd9OZtzOOeAXMR4QQ1eIRBE4w2DF8irJn5cjAgqKsqryQkxoihNbUgz+iKNSF7USGL8RPFTvN9oHjwtqCT4oWj8NBgFxRfhXPdtrVVVc47R+/ejjzHmpeasmlWraq+19pkPbR/2XqtqzlGz5hyjj96f/jyP2N0LE2wkxPY5uU8EXPa6MKIxSUcZBwApC9REFjJZSb6h4BBozVtyGBA8GF4oyS+kSyECnXD735JkLrX8NHikYQZgH9VJJWfVI8pDaTDxNDIDNy2UgBxMn73zYRfbN8jSCTf6ibVlUogsllreRE+6StbQgOBAoOyjUPB9AFekMSguaCVZWEtlyzhZviwbhIaAJhgcfkzpPFxZQHz8XfU93td4KnPHDB1oHEjiKcO1okdeBBnCrY+4hG/+9q/BR9+xixhkzfxsZX5XGWOVhGsiA9jbO9/jOXo7akZYb4BFQVadK9amz1b7fIiJxmYmSzF1hXa0NrJhdpTVjM8B3ZgRCEdFgUfslRkUJa3Pj9kYgosZxoBimmN2lGO8V7FMSiXfeAHLTcbMOTwwOYIL3aMaJF0Lm6AZUvNJHIvoWJRMTmmzxhBk2Uem16nZvM6NY0s4yGc4KlySargym+LKdIJH7JVBjIigmBaQUczkxnJw/L1DvJ9dUP+HSNiEqTBvef9LbZNBKMvW+qVto7tQN6fKBSs5ZgCQjTLMjmbwuQPvdWxiAURyP1U2s7nziYM2lAs3jyGTdaKID3r7je07J2vF+XHgWUwnWLlcCIYTSYrizYWRjAG8BNJpEEVtuV2sMcpLifINgcAsgjkl67SwRPJ0DQUWNQH0BQWNr7LDMO5YNbthDGF6OFMNnvgZrAH7uowDe5WwWFTGaUKARLjuNInGsixT4H1UsoZElEo8PpRbyxhLswgxoI3NBeWC3jXYKLw4QieHhH3iD5HVMpDzIasiOpZmI8QyhXBhzSb6JHnQ733dIFy85Tz+xb98Dl70oi9E7LqL3/Uq4BCExwXe8fJnyaUsUDV72V0u1GJOeU/1kQnR8m81aNbrLyKYOqfWL41sWJZZ5DMH9vVgeyezuDqb1u4NJzGYriPyDKWyWZpO8ppPXhWxzFcNOC5PJpgWLgVsurhrqbDZWQjE4EYztWQQMllAc4DVTUyctyIvi5lx3+HhXBZLAm9OO3Rdy3NY4XyF8qmwbsIkBH/a+FHJ7lfH1PhXF5zzmIUs40pBVtiUx5KlVGkDITvscq1QdJ6/8Zk9Bw/coVy4NQxB1okiZjW6JBwWv9uQwU6W4d6DAzgf09jLF4a4m47lwrYFLopcRgJxwR5N0jsQslNVkqsvfce6xu/EYM6/MBnxbiC5SqQTuA4IsbRSeAaxYDbJkY0rZFhjUit9hIqprl6+Svo6nevsAr4EgLYynC5QnHghUvFLS1kEKYMsl4jsS7hf8JXy7PxrJWa4SBdOEaDwoYZNyp9p3hNtoq71Y4bsKaQWSC4MCBcglQulqoxukTu/8q6cXSxDaznG+yWBKspMFkDBWLws5cwhlrgq32+bcGvt8wEVl4R4HBeufyhRtmQBbWaDp139XtuxGaaFNqpEFJ5rPL8EY0IDS3nu6cGk85mI975uNhjOM+4/OsLeaBTKaZV5gqfQzsJR7RieBXCSvgeE9zU/YjNTPLIlL+vKVOUqLjaU1Dk4OwhHQ+zm91RKc8TAI5LrJXx3cQMTg9x4CxNVS6QmyHy0YxJKhdZajHd6GNyXnwAAIz6C8dmMIFJuKc9lPquocz4d1+U2hkzW5jEEWScKXcSoo6zBrU3ydZwfj3G9mOHybAakxbXHeUUlHAyUtNo8D5lgHBsmxq7MgGZSkLJczCWPgTuEDNv9C2MX4CaCrDEgscNQxyASOsCimWo2nzFzlUxWt2r7YsSyCy/8Htp/J5UW/+qVMaTXnwOPCV7muvfieTXrtfg8ijLD1x5gSVIxT+udSNglazChBs/19xUzt5jHFjvCpN5DuYrwaxVEhKOjCf7k3X+O9/zpXwNBdqJLX24RJJZhDWBCe/4yAn3BHgYmGHlHTlZXqTaWmSpZJLdYJiQ2HNS/Iz1PwV7FilvGqB2Gfq47cGQscnYllxOaMScvieeXjhG4aUnGxTkcXZ9ivNseGCQZAOim4OpsisNihnOjEQxMRctqBMhVqP5YGQhpIBOCOkKIoBHK1PXP2Mwi7dgMR0WBo6LAvYcHyIypZbEALalqF6t2X859T1z6QXrPQOxsDD8jSJklovB5K/dYOUaLRe4b1VLhauK7ceNUZtOq80A2ssiPNLiTro1cw63AhexezEKu2t07YDnO3CV9wxvegCc84QnY3d3F0572NLzrXe9a+PrXve51eOITn4i9vT087nGPw7/+1/8a00WGqTcSS+r2Kri3+CE0ZLBjM9x3cKA76V4m0dGHKxC2WzNZAHxJZi1cu70PAOyPRtgf68SbSo82qwUtlSN3+BcqQZ06iO8igoMWmYV2jAE5DBwLn0pbDFE1eu+TfEMVtXLhEq5MF3SipQ4JB4T5cfEiDNQp29F014tokwLmmyHSJBnPvawJIt0nXVYwqm+tEg4EMlpC8z5m4nQhnRMiLdzCjsyo6u65XlxZL4+leP/7/g7/6kWvxEu+7ocAeDjRbN6qnCzvGWTK4EJNlxe/J3cexpCKA7tY+u/+fqtkf0C73Rb5NQoQJE+4IiOhIrIudB+zm5dfSR2GDQ/R2Al3lNczWYvU9uMcMDvKUcwKZDujudeod6dLf8+dx/1Hh8iMTZ6ByWuVdgCZQLsF6/INjBDsGi3b6rM7n1Fk4Vpj69hazLzDfYcHuDKb4uLOvM9O1O7T5F/b86HlwqQmL+E88bJIop4D0GxXzEoqjS+mtRZrFq7XWQhEfqkP00RT7JmCiC4DSeKiiaYThArHViUczlxIcOpxpq7oW97yFtx111141atehT/6oz/Cp3zKp+BZz3oW7rvvvtbXv/nNb8a3f/u341WvehX+4i/+Aj/xEz+Bt7zlLfiO7/iOGzzyLrS061egu+TlO53z4x1cy2e4NlvumaUIDxZ0IvPOo5mSIKOWET7sLL2wOtizx30feABFXk4i58c7SYsmZVSMlkKasGSQe2DOv3CJkOpBnuPvrl3BUYfoYH3wKlSqlh3aCMDQTJBvMa4GQpltVv7O5W7lTEhcaAxogU4TQToD4biIUm0hJlKxwSg8maLjCuKEqqbCURV+SSYrEddL4nzliGE3r0FfWEW0XBgWI99QCBcRuNniIEsQOlKlzufyrt0kuQ/iWhObP7zEbrsVM1nRqkRvmCAquzyTZQ3BGCBPopndnCwJpsIAesmEGADg2C0Wx6I6Xc573S4V7d6H1SaPKnYyi6vTSdIt8+Geagv2lJekn2d6lHeW0Kvq8NYQcva4Np3hfNh8WUNJVVyfzzx8jjJzHf0mhVFmQ0UzvM14wTeeAUMEEuBglmNsstYAW4NmBuJzVHkOpWI+Hb/3aikwDFCpEUGGw3G9bF9uGxYHWYfrCpGG59SHLKtg/hoAAHuEe6ztPqybQ8eMfSwJr/rMDFiOMxVkvfa1r8VLXvISvPjFL8YnfuIn4o1vfCP29/fxpje9qfX1f/AHf4BnPOMZeMELXoAnPOEJ+Kf/9J/i+c9//sLs12w2w7Vr12p/toeY+m1H4bt3l1UYIuyNMjxwdITctSkZNxHS8mEx925eqiDuIh2rqryHcoO8YxTTvBaQVBEzOMYaeDe/AzWGkAswJ8q5JMi6Op3hymyKqeujah880jBLHUMuEMZnhwWsnS9JmswirwSObf57y0DGgAOfpDvIMlho5B1EX6uLuwoootKhFYno1bdViO9QWY2FgoipbBCDrOZrg5lxECPV+0HgEgnb1AyeAW108LxY9kK8hIxY/d5mN1+u6oto/0MqxJZ0q1bll/iQQdFsVih9LygXikhqCLHBR1AbG7ozWTVqdI/rRUSqjl/l2AWD31jK13t1/r02s5gdtgRZNsPUO0ycS/Y0XSk7AYLMBOPw6iFG4/ZyPoW5IY5Z+WmCLNrZEKEInaf6fFZsreK5qgFfCl6U+F+/cmjNoo6zDJdnU1zo4DlJyGKJQ3g+Gx2bQU9PRYtbeIISS6JaRvdcjqEs4AEIPrJdfL5ULjy/OJOVeKXlBwAAOPGBI9moc4SNEEu0YWo7fymkq8fSmSZmK4cga/M4M0FWnud497vfjWc+85npZ8YYPPOZz8Q73/nO1vd8xmd8Bt797nenoOp973sffuM3fgNf/MVf3Hme17zmNbjlllvSn8c97nGb/SBViEdTzLEKJ5xuelc4PPDBB+Y69iLOjXZwWOS4PD3sc+JkYkykljJzu9iwqDJz8kg0pMKPzvnWlm8AOkGGDBnLfGu4oehfWECKvwTP/hg8+2M1ee4IOHPv8eDkELnzmC4wrL08meCho6PAYxGkNnGoPpfzHsWswGg8vyIZa+BmLpVkitzNBTLLELOChhZ1pkXrlzYETpaUYpV6XA2ifAyy/HxXX1I9jx89yYN0oVwQFfOZLISSSXyFAUJ3oQvWPw1/Rc8QL52ZGSLNhCqPr55p4g4j9D4o+SQZYC5pMLpA+qQNIlIu7pV7f1GQ5UIJ2hDpBgTVILTtJFwLfKPH55zPYwVEAJhCkBXGEhTnZy6Uwju8D7NRhjwvUhkvYmwzFOwxLQow1C4rlujmzg+VmcgnBfJJjlFHAENESfzUgHB1luPceKfye5OCX4qafg1qQMxAGj1I+fllfrPmWu6XC+Md3HHuXOf3rqLLGsSp+G1ls5NEOrV8LiKguSkuSNqYS4C5OMdJLBsEgkdjR1WhT7lQ+Aji3gvh6iZfj1+lItQ24eFjex8DxJY5WopaR6fzGrAVIUAeugs3jzMTZD3wwAPw3uPOO++s/fzOO+/Ehz/84db3vOAFL8D3fu/34jM/8zMxGo3w0R/90ficz/mcheXCV7ziFbh69Wr683d/93cb/Rx1LCb7ugoPyuVKOp1N2nlJ6s9lkLsefDMJoogSymTFPC8ktuyz9yjEp12bdxzEPNvHribCAgQV6KZWlkXwL5RzUHuNg/BnAphHtB7z+myGo6JIZdEuPHh0VCo/kwk7QS1tMVh92DwDLYuasQbes4ppeqeq5St7vOhuOTMGk6JjJ7vIyDvsPptJhWi6WzAHvlU9k0UoOWTKd1pULginkmrZILUr1eADkTt1MFLUgroDHud0Saplo9RSZ1GQFWOMR+zv49y44lnn++q7zSNJBxgLMheWSp+0QbjCdUtcoHZeSzpv4LLYwB9q6oi1vCM0ZobrGYQ+F5ULiYxmcjgG4BrkIljqwGuA2rYhsJkBO57rMAxHxkGRawndKb+pLaNGQah3NsnhnUfWwmUE9PlRPiZjb5Thlt0d7FTsbGKJOH039ta55115cFFnzdR+3ieTRUQLOUXKyRIIq2Cnr5XtY2bLJJmU6saDwgaTRQA6D6IduIrlWWwAChctBG3tGevSHHpRudADfA3iP1Qfo+hzaVpuy5ht1mB2keJ7RYjUe1gyKUAcgqzN48wEWevgnnvuwfd///fjP/7H/4g/+qM/wlvf+lb8+q//Or7v+76v8z07Ozu4ePFi7c+2IFJP3VYReVAxY8HOIz/KUUy7S00EC9+URmg/uu5fRdPLwvPeaSbwUjxLyGSFgMxpx1KTdJmO7LRT0ZIJfmb1h92Y4F9I50HmEfU/1EJWFcGDkyNkxmIns5g4V+lSKlF4xkGRY1blffA1gLW0Uk6caN3pqlYWg52H90qQNdmKFi9G9axGVhfAqWvLaJiF5SQEHZwa8Z1UPkEtR0I2pLLAKOFVaq9tJ/ZWUbarl+euIpQLq58PIRtA+xCYsBhWjhA4L53q5URpMt/NstqC6P1qZs5V+PCdx2Cls+lgAUpbn5JwzeyXZLJK2ytr1IHBBcuoruBMqCwFR5eBheVCaMAsKQtWdoVGEdEu70ObWXjv4VuyvztZhmvTWSCV83zpKcAE/8Kj60cwLR25aZzhu9WMpMH+qJ7xskYDdBcDYtoDNeQbWMLvud7lps4XjSDLtUvKLEL8rDqveTBXNzuRI1pysqpzHBkTqnWxHKffSSK+o9pdONLjdVAg+gmRBo6YvxfCB/qjwC91rGr0TSQuYSwBN57p6H1Y5WTNvIM1sbw7lAu3gTOj+P7IRz4S1lrce++9tZ/fe++9eNSjHtX6nu/6ru/CV33VV+Hrvu7rAACf9EmfhMPDQ3z91389vvM7v7O3Zcr20B1k+dDJMorlQs9whcPRwQQXH9kV+GWNiaMLkjoX1S6C57rtyIaFxjMce/goNup00vYd5ULngpCgrbd/RyT/wp66SEdFgWvTGc6NR8iMwVFeYOocxo3yyMQVmDkHGzrDtDV8Cp1QTMhKIPirzZ9HM1laCrWxbLGCECmgAaSwICMl+ebeY2/U7MSyUL5GRbU9QSdGnbqrmSol1Tou1bdrwrFkUhlZX9uD+C5lu7piPsjiSqkQQPC5VKcALxq0VsfR3k1aImqvzQ1FgvbXmhN80ggLz7PvENdceAwl4dTKVJo07D6QD11oMVj0IlDVq3jt6/eoiAvZDv2c/QRYCT4s7um4woAZlUHWIu9D0dJ3EzvW4igvcFgUcD6UxrKWcmHIdPsjj3FLV2F6nSVIofdEWyhmQom4aRpfBUsoWwpq96aEZzf9O1z3Pk1BVfjCpWyqZgcrc2XYlGiTSaiUV6RtKEjdeJRBH6PUlaIYDosEZwffmbHuZ6kT76EpxH8YZD4GtXJhyyQWL5lOEW2crKpsS+QUqp6cc+UmbcBmcdJRRm+Mx2M8+clPxt13351+xsy4++678fSnP731PUdHR3OBVFT6Xlf8cLOop26riJyp1DHi1Hh3ejib41hE6ATRR55CIOKVixHPMxdwEhA0VKok7nyqZTDfQXzn4PcX+TfNhST6F3YFaU1cnU5RsMfYZsmvsS1DNA1Blo+74ehhGHen2vOsQVaH3heg2YU+GYY2pExNIJPOWvlzi5T5Q3chTC260Y5BgWffVtXTTs5KJku7tLCE+F4pF0r6T/UFyutqmIJzICHHzrR6uXBJZyu1ViWT3+W6E/xjHvMovPgbn4f/11c9G4Dyxla21EmG1ZX3hWCyCz6Ud5OBNiSIVXaVahlSyVJKjyArcPkrROaYodSNQxTM7Lp2xlBr9ntsMxTicZjPUra57bkw1mhXaV4g2+nek8dyaddnMmSCpVb3Z+aQParJJgBzemWRrL/q/eILhs0sDADvCSzVrH9pqcPhf+KQmjGURsapq49DRqtGfK9HgmgrFxZ5kTqz9xZlsuJ1olsA/yEIH4TnUe8Fass8BrcLFiQf1TrCz8I97jgEq2TSfDxksjaPM5PJAoC77roLX/3VX42nPOUpeOpTn4rXve51ODw8xItf/GIAwAtf+EI85jGPwWte8xoAwHOe8xy89rWvxad92qfhaU97Gv7qr/4K3/Vd34XnPOc5NVuVEwO7RjahhA+dNomTVbhgAlognxTYu9Dy1ZGB5yLsphY9LLE8QKHjZp58G0s+sVwYf+ty3Z11ZS3Y+dDxX/JOaseFEoT78GacZzwQFKMjLBkc5Tlw7lzttVenU2TWagAnjBGNICgAsWHX75SPgfZdYG3868IAcAL2gIWpCT4mkNESZsciDDBUi7HBN4FmN9saDohQyxAFNl3HOSIpvlo2qPdG6Ysi6btCwEfsypLQgVX/vSsWyzAQtQcWHErWGK2353vsYx+FF3zKR+NRH3EbACD3a8hBhDHUFvcWfaYqvJ//nTbVdXV2OfgKJ8stUccHQteejgYIIsL6/8pxWhao2ZHFdNJOITAInYsxOdaSGaLQzAEsznJQUofvvl4ELNxcVTPcVWmEyJeLiGT9bNUgyzmd58JGzFeJ7yizn1HolL1P2WblZHE94KtOJeERSnMvEYRnc373R0eaxRqNRhiPuzODcRNG5hzEXYX4+wB4tQQKz13zSmuThG6w5kzF0zGjlluU5xGMRzQEWVvEmQqynvvc5+L+++/Hd3/3d+PDH/4wPvVTPxW/9Vu/lcjwH/jAB2rZh1e+8pUgIrzyla/EBz/4Qdx+++14znOeg1e/+tUn9REaWJDJ4tJlHlAl7WyUwRUO+TTH3oX5XRBRBuaoZLzoAdadGiRLCu1dgYdIWTARURKtHdlWDSxAA6PqjrjJj0k7/h68mev5DEeuwK0VM9udzOJ6PqsFkrn3OCgKnBuNkbtmKVN3/VGjZ9GunxCC2aUja4chA8e6ixxlFgezvCXgNUjyFXPDULLtoivTtoipdER9Qm2fZCNiu3q8R+ok3/izphNAXPDKckn9WhYzt3jzQtS6yMas2LrlQjV0LjNsedCuYu9xdH2Cc7ecWxp0RduU6hgkZgQ70Kbqz+m/7UE0ay8BgKDFtixbSgTxEs4Vj+mDa0KPICvLUMwcvHOwWf3O3skyzFxs0JBW8jxZQn69qBlCtyF65y1T7ncLGwnmky9aYq43IHCgIXToFnef2zFslgX+HaFmfVNRQo/8NPa+9Ag1FO6HEGQFEn+ZydLNI6eK8whAQ4IBqwiRVkre5iLg/x4wF8BEiE4gzW9Lg7uuLBYqwX/kLmqVIjMm6WWt3OwzYCnOVJAFAC972cvwspe9rPV399xzT+3fWZbhVa96FV71qlfdgJGthtIFviuTVW9DL4IytM0sJtcnuOX2W1reZcHiIOLmSKWt56dyt94VeEQFd4DgC1WAzzKrHmCNAEJCOc40Wattn69HkPXg0REMqFbCGQUuycQ57IcMVywV3rq3hyOXV8qb0d7CBj2jxTBWFySayyP1gzGRXyQYjVWBOpY6K69C5yIcJsZFpbO2hoNYTtQSZzC41Vd3jDT6Fu6GA6C1tMii+uTl56Mg6Bha8RuvL5aol8dgaO6+iQKUa7YXHhwc4cEPfwjTYoJ/ePsdKDzDEiGfFnjow1dgrMH+hcXCjxw642o2KX6+q62KwlciJv2EQeICqLn3RogLynhldnoZ788AyCX6IZblQobVb3GBEwMAZCODYlbA5e1B1sFsVpL+W66/zSxMRtjZ7+ex1yntEtBtnB7LhfXfJ0J9Nchao7zMrM06JlOpFg4m0ZEbGfX09LX6HldR0jehszbSOCJvq+wuRFQ8CQPPgKbOFfprZFU3YWTOQ9zfA9gLzUqBI9b11iT50bzW9TXHMSeubaR1DN2Fm8cQtp4Y4kPQxcmqTCpe+VjWGozGGSZHs6RJU4Va0ri6/ksbkoKyLCXfqjlrlG8I4xhlgXRbz2Zxiz1HV3lgsbdfILzPZrU2f0C915x4zCq8rEnhACi3gKSSLaExIEcASuHGRTCWkM+KtYRI9XxaMhERjK3uDmdz5cc+nCwNmKaHVX5dKL+2dRVVuGDpSLwgk5XKhfHeo5bXhuxGg2AfLU6ajQvVe7QLMRhsBn8xkF9Vlyzi3e/+n3jpi78D3/vtr4ML96s2AzCmh1Nce+D6Ug5mG8esnatWIg/t7xGGCHn0PGkNon1NrL9NOqWJmvGwCKIgpYSSvy/aNbLSmKx2GDbtdYDwLDGXVlhtnCwiXHjE+d78xK7N0+T6BCQorXVa0KarFz9/jZMl9Sx/H3DsGDYmBEpRwiFeF9XTA2JGFKip4BvthPA+SCSgaZGk93aZhx4ByNG0sTk6WCGTVYW5APAVeKGQSVt0HeM80UZ8L9cc7VIsaSHAYBC9DQxB1okhECE6OFlFZbLxXlujKTPIdkZwuZYMm9CuFobnbsHOeO40by/JKKUJmEjbwb2HHdukOTP3Wi4XS0PU2j4O0MLJFgAO8xwzdjWtHSBkQwQ1vtPV2RQjE15HUgZUdAEwd2qHlPMt4oJ12JChm03zhQKRXaiWTCJJvxlkaSC8OMhiALMjzcJEcVQTVNylzQXAQAmv6ZAUysFdHzgKL1Z0shqQEKxUie/xHuDQ3VV9V/Ue7UIkBzfvubYs0ipI3YXWoPAcOv4oWbRcv3yAyfXJ4oO0PAeqQL4gk8W+FphYY1CkMm3btfe6LBOVQemS4IUoZLEEKO8bShpePvfLy6zS3mEIaDZrvEG/urbypXcOl++7CjdzKDrElIHIzWv8kHReqXI4Y5CzSnMDc+SexlJZtLeKx9WMN1Deo9VsWclRVSsjHwjwZaYL9aCcMi1BNjoM+3UWAnPyPnQeuv0a64ZEFnznEjexbZksJJ6ZF0YUwx4yWdvDEGSdGOq7iiamroCJOyvnVQvHGl3IWZC3iJIaUg0m6VQUj4hdPu0cmcZLk2ipdzrZ2yBV0ORfcKPsQxU/s/o4aeFkCygfq0tYcJRluB5ER3PvcVgU2MlKEncM4IgsyJxLmmOtPIXquKyBeAmT8fqNEcm/kQgz3x5ktht5a71BValVssMVFZHR0ALftJ8ptbGqAdmyTFZVQqLNT1HrIdUrpuXCsF8XqZkVsNNyzKJMFmIHmrQE58foLkyf2yiROwqEspe0h7n6wLUUsLYfo07ij92xi6RGcudrO39DhNwtyGSBA/E9ZH2XBKXVsZWSHHofawDoIewXX3PofZ1P2+eEizu7qey+CbQ2AxQernAQif6O7XDhGa3zAKFBQyXYZambbPdB7AyMmycl+1esvThHJHnFbK2wIArBxYyacMyktXT31ThlGTRwawZZfTSygKa8DxGB7KPA2EW9iN/yWSX9p3kR6meolLtLTtYQZG0aQ5B1UpDFQdZRUSCLu64gKRB3TTbLcHR9XqohWlcsz2TpwmRASTumDZHo7FkXk9Q5GDqJmqXGZtlH/czmx2KpJKK3wTPjYDbDbovHIACMjcVRECWdFAVmrki8J0tmLrCJmmNtKslVRK0s9n7t0hVQLghjqyT9dizOZFHIAkaPSBNJ434+GEklpST0GFTfO0VPm+emuZ+VApX1wEO8ZkF9o1nCO1XUX0gwN4AS6us/XsbjWYa4sBljUIRMmwnZIgDYv7CHwytHmLQ8M+UY6osSkRoOd5WZlRdUVx1PbgY8LwQbbU5iudA7DiLAi++zJIwaF81A0GZROyx4aXUwqMJmFrPJrLNkuqgjcBWo7db8PeeCth6FTuUueBVgq20iyBggZCfTeGW5cXcT7OsNPsIGjjWjW1XRB8pgr1pCJUOphKgctvrxEy8ydUcGa52GIGnfTFZVUqL2OeJn5+5Ai1s2Moq61dbMu1LjLWayhhhr4xiCrBNDNydLuTwumat69UlJC9hoJ8OshZdlSMmQvMRsGVB7Fl3IuZNvERdvx+o3VhQFiCiVxebKPr5e9iFDuhA0YEzkrrRjUjhMncfOqD3I2sksCqe8rKMi136ZGIAaQt6Y6DkQ8pfNy2W5r11Buw9iSQwAMmMxdS7tEhO6Ov9SM4JeR1d4FOE7JgqdWTzflUimniEyIevV5Z2mk39z1I0gq8EwieeJnDNXUbsGykl6UZAVM27NBfK4azxXMoc1Mq9TaVCbWQCCK/d3Z7Oai1IpWtn+eicCB6lzsoxRne7Iiq6PEgjdigQVZfXeLzXFLp9pvWc04xjEaUM2LFuSybKZVSusLnmSJRneviBjk0l0Fd7pvUyiwXvXNfVBMb7OA9R7xlc2Zc1SdR80G3wEMciKSu+lpIlnFWcVrhpVUyqhi2CuIYIQA6DqSQnzmayenCyuewymH4czKO+wozkmBIjztVeuPfe5cynIHyQctochyDoxdGeycq9ZmrEty4XVhTUbl1IOVegCj5bSTwPCQYROM01dnA4iLZ851m6tYlpv0Z/LZDXIw9aYsOurj8dGflHH5H7kCnjhznJhFDacOIdrsxnGpuqRZpD7ur6RD+VCackCtcF7v7Lae4QAqYw6tppZmSsZEjrKhTGTJYCQllnyUqNIeU+Y/77iLtrHXTSSvUv7IF0jUItk/MpLojhno2QhUrZ+1zoEewhrUnAYaGaulvECl6Gq+F5UZCe886Bwv+5d2MPR1SNMrrVzs5pjSITrzu7Y4DtY+S4MofTubOXDCDjkBpNC/7L7kSobGmFEOyQWgQSfv2WBWjbW4KedH4m5sv+6IIPWzLUvnG4IReACn68NTkLjTOWaEpRS7iplSBaular7oGaqDAAMtZ6KXd7iU1ATA46qcDHF7yGMv7NLsvrZglZW+StZoVzY3nmu90HwAG25d7RROHJum+VCTs+9iCD35Rwb7Y4GxffNYwiyTgoVtegmco7k3fDQNzSpYsalycsyZDQ/tqy7MHCyiOptyk2oyrGHE124itzVymjNxZKbO1RDgXtSf9hNEFgsOna012ezlMXrgoXBtdkMh67Kx9KdmIfUjh31d4R56YKkH6zH4rcA8dRZ6N6a52VRdykvdE5R4D3NjrTEZUBw7FXpuTE2U1kA4r89E9DBzYvZkMao6/8KCpV1fkzY9frSMDzCdeimVUFbKxeG4NIYOPZpaXFF6aNoMwsQcKWDmzUX8BOUF9exmPpAjK5ysqKbgUqItHHcouI7tXKXuj9fXC4ZsazFouX65cLDGnyy953k9w0lsjRz3ZLJcoVTHTcvC90eUrmwScoS9e2svW7F57N6jwkAYik3IlJvBInlwrZsUSwre66X0sssbRUZqlpZeV4kEedFQVZZvpyfA70IQJrpbvve0+agM9BXFMzwKMvVPgT9A/F98xiCrBND98KSe1/rXCnyeZHHrIOXJQC8LDeJ9rF81mEzAyDpGrkgD+Cdr41jnvjeDKYCQb4xqUYLjjZSsfOMg3xWC5zaMM4sJkWO3LmaDpUJXWXVidyLlpDAyxekLMuWlhUXQblA5WJGQE1uIrwKbZYbpVZP9FkE8mkR9K80kyXcvduMC4kJ3KBuI+qyXV0HaVoyay3fDwHidYHxXLc+KfJiuYtCWIiaF7ivj2UXPuZjn4DnPu85+Kwv+Mc1v8WmRML+hV0cXZ1gejj/fDQ3Anrvd3tsxnuq2uEW3Qy4we8KZ0DSuqIyk7UURhvA0jPE6jkp0rKpWQRCq4wDgKBsfvzFNTXENDLXiXQfNNbaOjYjkR/Nxo4QmFfnGtfgwvVB9f2GKNzHPjxzdUmTaFcEzG+24rzlOoKc2u1CI4CPUgAfs1jjnbHOM53ornLEw2uw13INwnfQJuFQ7Vj0rFnDSIuIQf8g4bB5nDkx0psHTV5MiYkrYCsPmGsReczGgZeVq91OCaMT8QIIq1EtSb1NuQkyBBOzGc5ruW1c2ky4RvAwZ6ETTaJb/As9t3Mz1OjZ49Le7sLPMA6ipCJl0CEhs6BBQKVcGD6DeG6dl6rYu7B7nBhLmwIqnzezFod5M6AyaM8ylUreIZ0F7zxc4WEyLYMSpJOdGoPcVNbzrmMXVbarh1HPBVlRh6mWVDDBF9EzHDfMoWfLNZ80gzdvvcKBO7UuPvFJH4uP/ZgnYP/xl0JDh0nik1VunbFWdd9auEnMdVFPirpI3J4t8g01fCAuyJL4U3VoZiSafy+zIKoeU2LQLZFDZLVk5frfqcbaVtkXAPBu+eaj30kIXPhQRtUfiYjeG1Qq17dZasUynAgaJVjd6FW/B+c5ZW+Bflln78pAkowJHC2AxQVZV81kabDHnXMzoEGY84x6h2Mo2dfemEG/rwLAeDWNLGGA5pfnWKbULuP5d+omM2S85j5ENcjSOTIGVYOEw/YwZLJODN270KM8T2RWEYZzPpB3S2Q7GYq8mJs4RQy4w/29fI1DUi1m6exOMkTIYHDn/jl4LzWukjEGPm+WmJqZj6DW3CwXguDAre3cE+fmMgRtGBmLiStqOlrXH7qOaw9ch0e9zBMDLtemMdUAER2Ll6AdleW5R8biqMjrWQcyc11HCgeJSs4CZCOrMg65CyVAB3C7aCRQ7qINaTm2swEiZEMqA8I8JysEbaaeqYEInPepO1Vf236PNkFUIeVWh+OX84oWHheU4lJmLRFG8cm5gFTaZQbY1z9rDFS5ZbyAktu71mHHketTPS9DgiEKEeDyUp1/8WdD6FqL2c/YBSeqGN/zXrXWYHqUt34W4dKj7zhIDTFVPmQRtPVGWeJrtQVZXkK2SeqlwKiLx5WsYsxkXbn/Kq49cK3X2LwrmwzIhEwWQie2aNcdEVW6ONu/XQn8Ti88FwzOCe3SSJ/zMB/37yz0KHyB912+gryR8fQhg9Uprht4gRw8Ruuod1BW59nokjEQ3zePIcg6KbR616l2ycx7jGJnYRE6bho3f5xcp0f1gIphIVgcZHnx6n6ViOodt4Epyb8cbHSqLc1Nkqt39d154gq1qDhbGNx/eDg3WVybTpH1MCUjItx+7hzOj3fSz/Jprrtm1LvCYiZgUWl0U4jt+REjq2TsWW2y7AqyBALl1UmsBoguyJmxuH33HDKY7q6i1GUHtdaQWKKqvEYErZmsZpCFqKpdeVW4TbScXeFoddyjTaRxN+b+qhHvOrh+/QB/93cfwoP3X0mabuz9vMVT5XzzP6uXoEL4GGyE5s/ZlsnS90W7k+ablEcj4TVdmYi24wEayOpiLQCMcsJ8T44hkPxG2zxHve83lmWIGn5VH83oEjEaZ4mv1VouDNeaUFf+j/eUugzoz1zIShXTHIfX5q1r2lDl5+nzIfqMcI5qVpklsOk6gywNSBxzW/zeeFvUytJnPZYLzy3LZImHF4+cBXkj6+rD5kY6OH3xs3EcbBVcpAC2ef+WEg5DkLVpDEHWCaGdfAwUXlWRR7GzcIFdyWic4fDqYX0hFYKwW9hhKOK0LBXalLvm6erOtNlJ2CbPwK49I9E2qV7c3cGVyQRXZiWvrPCMgyKfU3nvQmZsvWSV+9Sm7ipBlvM8p+C8LTTNmkfGImffCLLU/miOgC0u7IUDsRVQLs1MJ+kscNm6gpm401cJh5gxaGYN6u3qYdQo9YLCUFqI7yADsKDwHh7ltexjqVP/nPX7wXs+1iL/m79+D17ydd+OH/2//gs4dKXGzUFTuV/v2/lSLVfJXABAqpOl6uLz92/BvtZ5WYVjaWls0O9CRDNZqtzf48MRAgmbAISsC1kNslbYNGQhyGr97N6vK7ZfR9DPq2ZtvYuZLJu+k7ZMFkvgbvpSzFgRuHGhazOWFQ10/pkd5XBz5fh5VMuzZAzAHJ6RHFXqRiyTt0XWyiXlRHVo89qs3iu6cZCWTNbycqGIx9TLXNNM7OrlroaHyCXEkkxWY+ObbHWGTNbGMQRZJ4Z2sbkZazdf0shyoa25ZQEb74yRT/J6lyFl4ETmbAeL16x8bA3vWOGiVpB4nguokjxDJXjoMqtt6x7LjAWIcP/hQVrcJ8HoeXdJ2akLRe50wUDdtqeInKw1upJWRSwbRPJvnAjzZiarzVonBsaB0kNEyEYZpuH7ZV+38qiCAIgrd6MsBElckypCKau1XFgPsjSbVi3d6EsK58P4whEX3KNtmPc9XF7GXQSfMnhUSpP4uoBvhDE2yWLUx9AINCqE67bSTOG5tWxrgoTIPOcuXM/kFce97sV4zTUuiVwvAyd+JYExEwKgtg7D45Zr0zmqchMBcdNDodMYjJocQxpDpcRVy2SFv8ayrWd9BgxpsKyUicVBVjKuj1SHUAp2QmDOa1WFaFfUKvYZgjPXwdMjoJ3LJTGT1b9cyOJROJ5rmoldvdLcFKQh6gbDu+bzLKhurooGp2zIZG0PQ5B1Ymh4U8Wf+johnoMWTtvNb0cWrvCYVYIsggXz4iDLS6GyBohtyu2vo0omy+V5vXvMRv+1crDs2jMSXTo8F3d2cHkyTdmsqSvAWFC+XABmzaY4pzyD6g7QeYYRnWiOo+TeC6SLSXWhsWRwlFcDYRNWzfkASKInmTKAYTOLYlak+6CrZb9KuCcQGNTOy0gBePUahx13ZTwSuuSaiu8Akk1RNZO1ii1O9dpIIHAfp4ybuiqt6kdZQ6nZYi7IsujI5tQDDbWoQtBEml85c9/Oh7KGoHz05vPHkNTYgF6druEDBE4SkDSdQrkQvLxEWz8W5rSyOOjVbYT4no5Zz2QBZVYcEOQtjhQCwHG8N+bvudKYWblUCIGTm7na/Nc6Ht8QOSUCWDXnWGYQUe0xIJYlBdISeBoiiCtLiq0byrkoy0BE57f+GlmayWYAh0X9s6UyX9c8EF83R3yvdyzOvEtNISKlov4QZG0eQ5B1YihTt1VMnKuJcDbLdE3YkcVRVWCRrLZCLxQkZbAQKGzgOidYCt1gXpBPXa30YsnoJFXVr3HzHBEi6lSa1nKfwX0HB2BmXJvOEhdtVbCTxMOxpItgROFd2mWu6nm2KsjO7+ZH1uIwr5KOQyZr7jtiCKKAqyS18ljmWdSyTyF7E/+uFLS2TFZcqNuuc2VSDurWc0EK4m6/nJCX3aNzZ6meRjhlJtZFVYw07vS7jJ2NtcmmqjaGxqJFIbmnwo71Y4moPUybWK76F8asQe1NpSVKIM33+8wUCo0x26hdcIV3gKDTraEN1lrMGhxOhDLdRoOsysbLzTQYjXwt4nZ9PB+y4iTzHW5aMfVlAMSSvFPtKMPk+mJeVrSZifMXWZ0FvCBk/csgK2rqaVDWuLYUAjbh9k2FEObicRoBcgigksk6v5yTxaFiOSmKetDKJeOvs1wo6iwgtWA2NhVUg6zAd+Oy43coF24eQ5B1UuiwTTjMc4wqprFtNhVVjHdGmBxMSw0cMvASu5Da4TlwfxZ0SAFl+t97P6eRRVb5CTGY4OBOP+erZ0y3nQeACztjXJ5O8MBkonysDr/CZfDBRJtD+3XhS0X5IqokSzf/bFMwQdW8OjGOjcWMfcWv0QCNUl70tpPY2BQWfZsZOOfgcr/QY44MNYIdSryqOurCi+HN8aT18bRUVwlBYwdllmvVIKu6EiWj8Q2UC6OdkCHTqUAfy1ZV+YPkudl4HaQMKKtwQeyxLUgygS/DczIqYTzhMnPPcmFsfii/+mCH5cP3s/wI5dgyg9m03mHI0c5ug89FlUKQz3KYzCa+FrFoubmBGEC1fiAJXpwIZUXRYI29YLw3wmySo1jAy+LgExmDt/SMejUBh0wRzaFj1x238AQ1s8/dGWWav1eADJApmHmlcmG4KLWmmchHi2r3bZcqBpA6V1SfgVILTEKHcJQJYi6foSHI2jyGIOvEMJ/Jyr0SpKvddcXMLdytJimHiQosGrKB9Ni98Il4JIpHn5EG3zOqBH/GBNXpEASmlvk5Dky7f2Eav7GwZPHA0SFmzi0VIe0C+8DpEIEVFUMoAs8jikDqwrbdW749k2XgPCNPAXMbJysQowOnK/ZFxBZ2Vyzm4EQydeUnQVOnWS4s29XraHA4wKFRoHmewNOrHGN6MIVdITiuBqBNU/F1EK+1CYrrahfVfs9ZY8K9Wl57DpmR+oclLUkJzxHfOWh9tS1Iloy64c2p/AfJFP1rSEUs/2wh16DBUFWx23nMtX8ugXYYulrJMJLJN1kmil+vCKtUhTVpw0bQ69fUyOPGM1OFIaTGlZhp0oohY2dvjCJ3KBaUDBPfi6KEQ9hgCoHhQgldf5eHDKG0ZPeMMYAvS8i9yoVBxmE2PUz3/d7+Yg1AhGePiFCwT3zOmAmN9k3ttjqaMVRHgso9KGU0XTDDodJ5K6Wu4KCTtXkMQdYJQBIfp375C+/hKp2FgPJHFvnoxQd9cqh1f4qlkg61b51cVLCxr52Jy52aJreMI04cybew2c1F7d1c1x68nrJvF3d3cHU2DRPXerckew92kuy5OHguRiPfxMna9hwSS6yVBUPtjjhxUYgs2oMslJysyu+IgGLW0NpqOW81o8QxkzXHDWrLKrZzsnS9aJZudPefRumUEzMa9w+Oq8tQzDy2dWr1BVcyWXGRc4Vv3ZyQVaHSWvYtZiaqnnmRGsfzRH3X4KRVocr8BM95o3tUg9hULpR+PDQizUy4mJkI93EUzFyFk5VlmZqOV8jvElwENlUuNMakIM4XaoJtKpszhA1Q07+wS48MiG0ZWlb2wvr3ULaL3/F00p3JioF8CiAodkGGTFalfH5tOtNAvIUnSLEU5zXj3Jp0m/uJCpIeHaie1+7eznJnBPHw7HF45RDCSJksL1LKu3QE6alRQiJdICLOBWqizr7cJFRV+AdO1uYxBFkngrig1W/o3Hv4SmdhVEteZlY82hnh8OpEVasRyaxd5UKdnFhobvHognc+WLu0aQ4FjkAge88TjaPNQ2UXnhe4ct9VTIICsiWD/WyMcxXNq1WhApMlWZvDQsQi2gkXgpdNck/a0NZhFVFrmxbRMkX5gzB7Bg80prTQ25Gq+y+KsUzo9ozXmcR0SzjMDS3OzPXyZXtQWlfTL2YObuaQ7fTPZFU1fjaRyXrSJz0R/+z/eCae8o8+GefHYwAa/LXdr0mWpBqQtgR6iXAt892FMZPRxsmyRHAyAssRIFWukIQwQeI/e/MDCVQG34SQpWCAZaXgNPKiDq8elqNaIeDrd46SHuCdbs6yyvxFXo3Mm5msmVvAOSQEMVINCCg1dSiyUYbJtaPOIG3O1ivcAywUmoQ0w+OZcVTkGJNp5wmGjlMtl3cJJjfHEIKswwMAfUqFWmnIc4ejaxOw95i6GECGYDGepy3IioEToyXI5/Q5q76b4kvD66FcuHkMtjqnCFHkMYK97miW7XzGOyNMD2copgVoFBfbBUEWNN3TFA/twqJYjCt8gTYNp0h8Z8+wYUc7O5phcjDF/vldXLj1AgDgXFgc10X0XxNhwAu8VdV3w6RZh54B5abQzBIaGEyq7dg0gvBlkP2I8INQLpQQYFfkJrLMosg9srzotkAiXXgkSimQgcgM8+VC1zI5hyCrxslS8u38Ih67xPTn+bSoZRSWQTV+qqTzuAiuP7k//Rmfjn/4CR+LR33kHSlQV4Xv7vdUM1nqB9whyNmSyfKBrN/27Bgy8Mjg/QxKeD5fHigmB7krgO0GSxkEM6JwJ1YOTnfP7eLagwe48IgL2NnfCaTw43HiaiAkwVPvNJNVb4aJZb86/+/qbIqMqJtJyhXeFurk+mwnw2yawxUOo/Fo/q1dZtxi4CUYRJsxpoXDzHvsmSyQ6uvzbuw45bnScvpoc/cKhU1XGWQt6ywEABc6HwUjMjgMnclRGy1uxFrnAqpyCRvEdwiIDJw0g8Ry4zR4F24eQybrFGFSFHOdheJlaSbLZhbeecyOciXeguC5K30eJ2vqFA+de0fHHEVAEt70vqMbLXCUquWz6cEUvnA4uj6ZM5NdF5rxKyUnCJW2b0lmNTcMze62zDZlHPYBvgpJFkga5GiOU7ll8VraTLWdXF50f1+RtxJiBwOCqyzMEWoSOx84zQVZ4FYtHkukC2d4bX40Wyk7qB525b85aA5tggtSlq4Z7BhmweakLisitaC2CpH57sI2W56544MgfLVyIE6562hptUr2SMuFJXGZg/feqsHpeHcElztcf+h6GNZmOVnGGnin36mLGlnVY3vM+RfOvMfEOWTSnduLHX1aMqtz6kajTDWzOnhZXWbcIoDzZSPIzDk4YdjIH2u6bBgArI1AkPYAp3WupFXkGwCggA+8MAuDmfcogl6bQCp6XO2NF4h8s+qDVvl78/5lLufnIcbaPIYg65RARHBYFBhVFgZ22uXS5Dm1wWYZDq4egUDwYiqL99yZVIwU1CkeWgWR6mG1vY6sTRMp0kM6T3yvWm2w9zi6NsHuuV0lrC4REuyLonBpUY3lHx/0XzilEG4gmkGWsci9h4uLO+0CMgFEd7iQyNkpg9p4LU1m4L0LGqLt31fTN66UdGiWYHLMyzfEIKvyWm5ZIKHnd057C0UYR4fThkH5YsSMW3me438x168d4IEHLuMwLGTsWTM9XVk/oGYvs0jkVVpuHV7QVBJRyC7AV1A6L5TfjUgs5fQtF8bLpNyhpN21ZnC6e24X1x46wOxoujhNvQYiL0543oi7Wkqsls4nRYGZd8iYWjcRFLTnOPIqpWH4HBswmvIUAV2ZLMNUaRIyOCpcsKxRntpcFjdliYDU4tdAe8nS4uggBlnLy4WQAi7EfhkRCueRe5dI/xT/v+OrjxtMmesu1Dc4qVMGYvAKDOXCbWAIsk4JCmbM2GFUmWS4Qe5chNFuhtmRZoi0BDPreCWHLEuwf1nyTJEx8AVrG3YD2jkYSgNdiyXVrTZmkxz5tMDu+R3Nvm0qyMpdKp1oh5kKkjJX1N5vIJo8kJE1KHxpiq3kdw/xIcgKQY6ISRNg/GqSSe4i4djY9ReVmw2paGMzIJAitatX3o1md6FnDcTngmYiFN7DgFDMCriZw2jFIKumCL6B7+VN/+nn8KKvfjn+65t+EQDmdJGaMNaiqHbYJf5ZWwmo5KtEqJvAggdHAC9jLRcGXpbEhU2o5KGtsJ7pGuhCGVgzOes2C8Rs1rUHDzZy/WuIEhlRW68q8Frla1XOe1QU4Rlt0aaCzkESZDNc6KprNjZkowxH19t5WV1NEJAY7GmG8Fo+xcja8n5oaeIBS2dmrAyGm7/IcBQak3plssTBcZjHQHDCOpeh2unaff+of2adBxvL1YDSUmp8slgCxRBkbQNDkHVKUHgP77iWyXIL9KWaGI2z0MpcwIvpDrKE4cWDAgFyWcnCEMG5dvJ9tXOws/3a1IngSuDWwNEYwvSgKxjsj+SdFxWMg+VJ7nzaofUl+W8Kzd1zRmqFUlQnaNoF+EFEjSyEYoCklvMKEduQHrMrO9PIZBmKC1kzyCqFF9N704RbJb63d1CpHlc06C3gnINdRXaDUCsdbyL4TSKsYRVp6iLNDcEQfNVeZsG90Sx1A7pItZHeIwwZOM6U6xMzlVHtnerZxj4gBCuaIDXAInDHvGy753dx/aEDzA6ny1+8AqKtCwvDzfL6vBH5WhXuUuRjjW3W6WEZDbUBUZ4lCL5wtblrtDtCPi1a9bK6miAQuG0QQcEeU+cwtplmNlt4akSk929Ha586LbRlOUc4Clm2vpysqOgf75Xc++TSoSVndM8FiJmsRrkwZN9y52vPhjo4hHljCLI2jiHIOiWY+cgHqGSyVgiy4oTt8gIiBoJZR+q69NXThWjJLUDBk7AlyIqdg2pps3iskXtzdO0olZeynREmB5Ol712GZJuRRdVmbU/O2Yeyil7LGzV9GGPmvrtWD0PaC4vwFIn4Hv7WnMeVl7VAM60hHWFAoc2/HjipPtsStXeEzEsr58PAcQhiZ1GSYhVOFmqdfX1lRBYhZg1jsJg00zqulbEGrijFahdmc0QFKqso2C9cjIhIbZ3IQvhK+KmHJF2I1SIkpQIR4ncnUBmB46yH450RnHPpO9wUjNWAyBceReFrAXjka4HKcuHMe0wLh90sU8eINlqCQVLed6EM7AquzUlZeD7aeFm+xe6LoPchQ7NPU+dQeI9xJZPVRaUQ39HVSR2bOcpwdKTXef/cfjh+3jo/R9mV5O8ogsxYHObaYBIDrEXlZkJUs692xpb3cM4+bUiByOUsZVAGbBZDkHVKUPj5UlDVOb4PiAjsGD61OLcFL6H8Iehlp0GG4AvfqpFFldLAsrWSvaDIC0yPcox2tQNovJOhmDkUs34lw3zWzrnwLtjpGArXQDMNzgchP6KNmeD2AZHpUEGnhlH0LoApwAdIOlkh2Y9Gi7Z2GHbfD03pCIrK4zXy6wEgM4A6OjkbARm3ED8MEPwhgcnBBNlo1QZlqrXvd7XdrwJp7MIlkum7rpXR76faGds+UtRKKYDyhw6KfKH9kzUqIgnaA/hy6PLikIPotvzp/HwIZSg6B9CuZh02cN12z+9icn2zmawY7BdBwLiqkRX5WkaQvEUnRYGcHTKjKv1dmXUOYqReGBSI/9XvN2aZmrZB6o3Z7HAMJUgfvBBFs1hRYHShHh2iuXfLR0e7LA5zhsmR3mt7o7+C5P8Dkv9/Af5Q29EBCBwLxCm1Y2wNjoq8tNRZRlKnSPEsKQDa8KLUDed9rYtQpCxzDt2Fm8cQZJ0SHBb5nO5KMXPLhesq0FQ9qzmwRJG9JrRcKKxdKMvuAJtZ5Qa0ZbJiaYAZ3GGYm87qtfvR5Q5Z2N2qj5zHbIGQYMTkYIL7P/BAeznAl63iauOjGUEngolT2YNWL7ItwVgNCJsZuswYTIpy/BowEYSvhUVTs24qKVDfVWY7Gdhxa7BbQyxFhVCNuSo8eR+AGYja9MgiLysepr2DythgweMEs6N85SCLDKV2fEBLe8ed1psB7TKbH2OV15Re1xVkkZZuq8KZD00mmBUO+6N5qYAIG3hrwC4g05CtnCfA9wYRWACyd4JoFJ654wdZ4/EIwou7MFdFDPbdrEXAOGzKwGUm66gI3a7craKuHam6kSs8axmNGWhwpkbjDIfXJzUukrBXP8s5T1UDsMDxeTBdxPVZnvQJl15bQU0wujxm+600m5VE9d29IKsi1yHcFuBq9rnwYaslgpG1ytl1LvUgLzP1lpDJKjPUGmQ5EThpaLwNxPetYgiyTglmTndzVRS5W6nNm0y0EzFag281iQ6WFqLq6MsU1sc7I9z6qEvtmkChNCCeW82ha2f1gsn1KcjUydTWEqaHk873ATrRXHvwOo6uT1qzXrEdn4iCpYqHNZox8awBVxffYxvY2d/B5GCKg6t149qRMZi4or7I0i7ADyEuwnFibbZoG2Nw66MvzWn3NFFVblbxxsCZkwng7wXoYsc7Q5AXoGKXmNuxGyLcNtqFdQJXuJU6C4GwYFbOpR5xx/tiqgbRwHKel7Ga2YxlS+4KyowuxFFuYFIUuPfwYKmmG5Hyphg28LIOoaa/QaJgnXIhl8FZ7LLbBM5d2sf5W3p0vK2IIi/mBIzjpoygtkAigivTKXbCRm7e2khBhkAicCFgUt9B1EpeADDaGSOf5MgrJUP2mvWbI7Eb/d4L2UHBuzjMZxjHYHPJtb11vIML43lrHM1kzd9LR4c6Z+3uZbDZrSBzAcAOgBY+ajB1Zx/K6qFcWATLNao/pp0o75E4HiX3O6/X0DY4WUO5cHsYgqxTAGZG4X0tyGIfSmDLMhcVEMXMQAavDmpzr1FCc6m3c5z1Tdvx1ZhZg6zu8kwxyzE5mGC8W1+gRrtjTA5mC3lZ04MpDi4fgp16oTVRWyTDNTBxoZOwVraUY7cFYwyyUYar918DVzzsMqM70oKbvKyjRJCOlaBFLdqLUC0lqFZOCLL8A7rY07mONxJqszd7/dEc813LKbNJvpBc3gXllJRlTb+BDGP6/kPmrakm3oR6HPrkb9i1ZlHIsMSsywNHR5g5h/3R4iDLkhpVe2GAxhC+DIiHCIXGhtXLhdV3qBipX5jJ6IttOCB0dfPGTVmUVzkqCkxdgR2blTyolvtJy4zqKxhtiZjnS4DZaD4zrnSG+eCBiACvemUTV2DmffJN7Qy60zG7Mp/t99LRkY5n/1wl+0kW4LbNZeBkBbKYBC/O5BXQlS5rfDZ2Pkg4hNdyAYTsftO+rFouHIjvm8cQZJ0CFMFjrxpkec9zpszLEEtl2ojjgBb95Ng1hkh2PEaUFSdoYQ6K6+2vM8bA5R75NMeoYb8y2slQTAvkHVIOmsW6pvwxS3AtmayqGbCxmrWKExOHbqQ28+ptYvfcLibXJzi4Uk6k80bRCKW7PARaqGR51vRZTN2FBC+AZ6d8DP8hwOwvWFSru17VgmKhdmsREUwOp0tFcltBpYQBgI1kGD/10z8RX/hFn42P+geP1WPmi0vXEbFc2NUUoYRrzQ4eFQXuPzrsZf1kjQYTjiXwsq4BUJ2jGGSughi0VDvyxG8nQNoENBvb8ovA1yJWP9GDPEfuPUbGlh6KrWQnDSwK51WWxKM7IMssjq6XGWTNkM1vAGPZ2gvjsCjgKnZmC+2rQgDThTY+V8xk7e9XS8wZgKa/JQB4iPjUWV5yvAieVbZDf7a48UIkepfG92tnqmd1K6g+H+osMpQLt4XBVucUwDHDeUY2rncWrprJSoEFh7R1S7nQi0/da5tSenaOF6rHk9Hsgk6M9dcYY8CekU9z7J6bT8FPDia4fvkIe+d3kU8LTFu6h4pZ2XUXOSGxg1LV3tF67m3CGEI2znDl/qs4f2kPxmYNo+hqNsQiWmBI+E+XAvky1MyShTTA4ocAuQKYO7vfGFneAZrtnJ/MDRGcAG5arFwq1APoeeLasokM45c85/Pw+Z//jJIPVHjQAmJ6RCwXyoKmiEiiv//wADPncLHlHm3CxKYDFiDbBfh+gC6EIKtDFXzR8cICG9/no33Sjap/rwiCzl9Nrlf8foj1/r4+m8EQJeuZTg9LUvWBRPxuKclFjHdGmFyfKvdznGkprEVrsBRNDR2LlfucXXeQTqarqSVUElp+noKsaiYLFpACGvxU5wJVw3ceIJHk8zmyKmas1wpYVDNUIeLokhn1skK5sCUIZF8G8EOQtXmczqf0YYacPXzDcDT5fq2yAgWCtwYZQHu5MCSeNyhC6J3XiWzBxFRMi06StMkMJgfzJFARwbUHrgPCyEYZbGZRzIo5Kx5XVHS84jWI5ahAKF9GFN0GNJs1xcGVOjeraE7StAfIJAU66TtaJ8jyMZNl1H5ECoj/EIBxEEBdgOrixVremq8WquhpkXuMVu4sjAttzNiF8vYGgt9YUhJh+GKxb2GEi5ZQHV22FNwKBIIHJxOc72lgrv6F6p2p11yzhAxoOXvF51o10KLAh0pKMN84juGqiE4Q3aUnDUAnzmGc6T20yEORKl2jPmTCupDtZCjyAvlE+U7Sybcr58o8SDdE+AU8QdWJ6wiykp5XHUm+Yb9RLoQLgVYFEufvYIQdaBQjY0KQtTwTSiGTKhIlXOIfU7MziuDAXQUGTtY2cEof04cXnJ/vsPJORftWmoyDdx9EwpTeksliH7JcsjHdKJe7TtIqoNmq2VGO8W57R9YoGFxX+UuAZrEOLh9i97wK+NnMwhceLq+rdbtKgwBVvBINwq4zqGzfMKGsAGMIo50MV+67Bh/MoQ3qHYY66BBkIfKxSv/Flc5XEYcFAIGBlwLgBwFzS48jlJO3DzIfc84iQfTUe7eUhN8GAoVMHYPZz5Uu1sHR0RQHh0eYzZQn5h0vLWVqCVuvFXdY8BARSPT5nHmHvQUdhXPvlaoUwBiQGZhJM1l+xYA/bBwiVKjyhla/V4Ihgst96iKuggAQCzy0W27HloboXZl15cZF0+PFjQ3x/ZMgstollxHvY/bqtDG25YZhEb+UaJ6zdXDlAJfvuxLMqebPN2nNZGXQ+blJ6fCAqHQDocxMj6zFLNqb9dgf6+Mby4Uh2CL1QTSNZT9aFgFDkLUNDOXCUwBt967f3MW0WDkoMCHLoB1REnhZdXAg4HZYb60MYwyKwnVyJADlXZ2/7VxnJmu0k+Ho6gSzSYG98/qalMWCEloBwGYGzjm43CM29wh7MHOSuojXQIIC/MwV2DN24fi2id1zuzh46ACHV49w8baLyKx2GFZBlGm5MJYwogzDihPeeHeMw2tHcHmBbDwCwUC4gPK7lgUIgS8DIBoQA9SykNOxTL3JROI7kvzHKjIlbXj19/wHvOO//SG+5VtfhK//hBf0sqJSQdLoVtCue2SIQCy4ns/wyHMdDQMdENJWeQCBl3U/vGS6SLcYqS+Cficlv8Yzo7jOuP5ABqIuj9KTA3vd1JhM5sbnXYar9x9AQlkwOlx455FPM1y5t+XziMD5DH/5wBXtbXAMX3S8FgD7ES5/6BB/9xcFnPMoZi2vFQF7gvnwIYiO1M4sfCfa1EEwdv74wgaAwwN/9+HEFcunuXY2jycgAH87vobqDXX5Qd1AVYMsIquK7HNztAY80ZsxZqYzUj6nHVFNUqQN0R9Un2GBBnOaycqdm+vKZO+HcuEWMQRZpwBT52q6JSKCycF0LQ2i2P7vPCBSzK0dIj5lSjYRZxljIMtsTIgwHncv8pGX9eDfX4Yxl8M4gaPrE+yfL20oooefq5j7eq+70RiIaYlHU+7WqiikCYt6K6l2yzCGMNrVbNb5S/s1o+ispiGkmSY1714Po90Rpg8d4Oj6FBdvGwXu3WXAfGTPI9S1nJRf2+BkGYKbeeye71c6m0OFU6Jxgxw7nx5LHUQqYSLMoCVZNjJl1s93ZZaIYADcsX8O46x/FgvQJTZ1OdIeYC5AZYILFcZdKciKchz678J7TB4yyA+BXmmNG47QljoD5sdnUEzL53dSy+QYFJ2fx8BN669d1BcKVIVJu45LQBjLfJ9fo9u2gaboqb5WN08HmG/OIQIuXdqZf8vca5V7p4r+JpUfiQiPunAeBMI1WSwgS4YgvqKTlfiVZs5SB0BpWo8hk7UNDEHWKcDUO2QVwqcvHPJZvpYGEbNqqejU0WIxIdpd0qpTugYiqV299tZfLXcv7iKf1se7s7czV5IiAoqK8js7lZCI5SFVOlcV+v29MSwZSLBQaSXV3gCMd8aYTXK4wqtg4swhZ18LsshopkTkGmiJen4XiAjZKMP1B6/jwq3n4LEHj30Q9fFLMyhpu5phohYxUgA4/4hz65HedZSI9j8k3EurbRli+SjaGbFfXoK0IbBn7wLxvZ1wDcFaRswGJkl1qBr5rWA8oJmsFUvXJnFsAifLe0SN2Uc8ymL33OlaGAXa4dl2j0yPZti/sIf9R5yDMZQ2lwdXDnHtwWvYOz9/rwqAg4MjXLrzEmhkkD94BFe4OTmYKiYHE1y6/Ra43OHw2tFcU40AmB5OcelRlzDeHZWdhcJ48P95CABax+8K9Um9/bG3wliLaw9ew9UHruPcxT2cu/0iBILH33Jp7v67cHEHFy42gizCfCZLlJ/Lok1PXAuAgg7ckkyWCSLHzsfgSrNZHLopm76bRV6eY8hkbR5DkHXC8EEjy1ZIyfm0gCs8dnp0MlWRdscCeKZ5UiW07KAdiH4juxYKgo3HLceNx6OF2a4IO8owOypF/LxTbk/87FVZCUMGeyODycx1kmpvBMgaeK+L/07VKLqF46NWNuufa/fcGIfXJtpIYA08+gRYQMi9xFGAI8265Zp1cet6nSUSd0W7+o6r1QaUvBWCkpKVFL74oJQZcHjOunhhZWZ49WyRMTTX4BDHKat2VFLg6iFwED2DnR7g/CWDvQunkVrbnkm0GWHvAvDoj7xQ+7l3B/DO4Nwt7e/LmXHhjgzYyTCZHYHFYHe/O1tprEW24zDeIwi6jst45KN2sX+xFGP1zuHwisBmFuPd+fcUhcDNPG59tM7N1y/PsH+BsH+LwW2P3YNjxuNuv6VVEX4eBJFZ7XEX8YmQHzcCTSy9G5MQcSwTKi/LC+BEsFvZbIpwjQw/6GRtHqfx6XxYoWAlOWaVm7uYFUmEbh2ICAomQOYzWSweBLNUob0vjDFLxR83CfXwKw2pY6dPs/xSJbwuM3zdNkyFZNtqFF1BbCtfd6QmGNweXjnSoKN3F2m1PBI7kzbfK5CC4PCdHDcDCpRlORMcD/oE/DZ0DvoiZmHnX28CSX+Zl10bDBHyRiMHI5Q0O8636FjMZZnfVzJZdnS2FkUyoamnAhEVt13USKEkcA1QfUNJvg2j3QyzoymKvFh4ezUNyiMBvyvrbaCcT2ZWB4ppgfHeKHEYuZQN7YEM86rvRQiQlO4AiRIMlTF2iKEmRMkLFzJZoWzoWNRiqHLvRQmLiCGTtXkMQdYJw3mGY64JkU4OpzX3+pURunAgxVxqWdhBZHEHzSogu7qC9XFgMwuXlx2GXS3atTHdwPEtgkuLS9MoukQSnDzGeXb3d3Fw5RDe+bq6/EJUM1mhg0poJe7QSggBlkprHPNQFd5K7OJcBtU78srLUrpKy2tKkv6qsKSaRNXnz3PIivGKXcMVbS0vobwTSrnZYvH5UwcyBuwa16XQ78EsEF7W4DQEmry89D8aq/m8eHR2mmrgVn/SONj2dJWI4z3BXq2+sp0sCZumdGNfkAXm/Aud8mlZQMZq9rI5RlksAUKGACF4xHKh3sBO1IezWi5klpokxRBjbR5DkHXCKNiDKxpZ0Uh5fc4LgoSDhW9RfffiILQ5m5mYEbhRMJmB9y6R353rCrIq6uVrlHu2gagUnRmDo6KjM0rk2J2fo50M+bTA7GDWP5NFhFK8Vspy4ZbAFS+P4wZyqVxoCL7oFxHFc/rgodea5aR4b69RLqRgrZMENMNxBCvLVhBihkXfm0/0ezL27JV3jFG7K6kE/0Xu4J1Hli2e85xXrqWwLM24EFHQtFpMi5jfhC7OZIGMZrFCSX53f5z8OIPO7lLOVIkW1XdxYA7Hs0ilwxr84gxzysC6WOpWOSAXug1rz1vqJA7vHYjvG8fAyTphFFxfzIpZAZc77K3bvQWohAMo1OQ9gJJDo7Y6ZmMikDBmrXLKuogTROwMc3kxpywN1HeobQKBNxqaZSmFBafOtQqkbiIeJFJ9rsMrhyjueETfd9WCrHV0ulaBsIA3tMV78j/6JNx22yXcccdtKyxwCle4hXYuEoQzV8HsaIrptIA9N4IXRoYqYTlc21WePVLCsorLCmYhyMrOWKkQgHrXu+AnGB7bqLO3KGAk0WswjuXbHtdvtDPC7DDH3i3t3FbBvObVIlFUoCz9TwMv1FgLgkogRBHh3ncLZQDnqKm+SxF00ATG2uBhW38bCxZ+fgrRnko9RCFSqM1TAywClwSMY+b65OfLmwlDkHXCKLyvTfD5tIBvsaToCwq7b2Gju0VxtW0PhxKiYDO7FkM3XuiTiFInosuLucnZNMtGpyDIMsYmc+vMWMy8Q8G+JoIIlFyl42L33A4OHrqO69ePgDu6X3d5OsHYWOxnhGp3oWpmbe9LXSYquQpe+KIvBwBce+j6ysr+mvlqz1xEG5i+38f0cIprDx7g4PIBaGxw6SMfmfhiHCVTODwvKzzexhjAa1DOIsiD7MBZKxUCWkYtPILWmt77rliSmgGSVUzMFPWZu8a7IxSzYmGGbC5JFOzMlh1/djTFzt5OHJzKxoTj9c+cV1TfKX6ZDt4rcT1a6HCTk8Xz4tU1BH8fH3WyRK+vD7yxGgJPCxj4WNvCEGSdMJoaWbPJ7FhlPAodKV4InEToSjA7gHUHdyzeVwXCgB3duMqzzSxmh3lQe59X96ZGV86NzLR1wVgtiwBqFH2Yq1H0uPEVMNbrZps7nzEwBFy/crgw8Lg8meD8eFwPssJivtUq66r8lZ5gv1yINEIXbk6k5U4sGefsaIarD1zD9YcOwd7BZhnAWt6KwpHx41LKZK1QLoyLLTPEGLipfk9nMZMVHRmqAfbsaAZrFy9FBMCxB1YQwjXG4MIjzncfkwi+0ZyQH+W9Noz51OGWR+6VgwsfR1Z6fkOQFSgdEioPPlSWjTWQGeY2iUs3EiEbpZzH6GFLmPl5T0apZrKGIGsrGIKsE8a0KJJekoqQzo7FxzJE6mgvpCric5wsDxEDEbcx3ahsnCFrRgtbRJZZFIVDkRfw7OcUw1Voshpk3bChdcJYqx6P7GGMhQejaDHwZpFEaj4uxjtjHB1MUXifPOKamDmHvWyEqJMlocQgcvyuv0UoZReOjyKUjtUMuH8AY4wNz8pi8v2iIH12NMW9f3M/ppMZds/vYjzeS9loFvUvBDR4ZggoRFurZAkp/EdES0DFTBfFbHz2FsWo5RfJ1iKMfJIv3aQRASOyqfS6kbEYo7ylCvqIQBtrkVXkHaLUB3Hk3PU8P1m1vklaWbFjGgAExrZnUpfprBmKXYmi7gdwiM02c2rvLPAhcLUDH2srGIjvJwjvdRKOmawiLzS9vbO+DhEocB4Q9YjqC7nnaCWynshiG87dsoedvRtXu7Aj9TDMJ+pVN5fJIiSJB0CzdzfaHLoJCoa0cVLXVvD56Vi/s9VsV7pgrXZkOde++xcRFKG7tdyOcyXTsMVyIdTMehPJrG/+hu/C5/+TF+Dd7/6zbmHRFhhbV4tvQySdt2F6qAFWPi1w4RHnk84bVRbHks8VVl/B0qBufhCByByO6WahvHMWM1nxg4fr4gq9P5f5YBKAW7IRDG/uM5sKTxJQ6kEfEejzt+xjd7/kzEY/TgCpZNgbApSq75p1KqcuChza5nuW7xpjR6qWGgtACLnzc4GUSLVcOIQD28BwVU8Qhfgg3xDI3DMHV7hWY9W+0MVc6Zc+dpZUIKJE35NUQD8uTBD3zI9ysGOYxueIO9S4A2S3ItF4C1D1ZknBX/n91MEaY20kvLHGwjM6gyzPml1RmYcYZAVyNpaU0I4JYQ2EN8ELjFmRyKHqu1gYa5fywroWzcnBBPf97f2YTXOcu7RfC9KSlpKXWrlQF1Qtw64qnxJ9HlkEPg+ZrGPsxU4a8TtzM+0sXEZdIFJjbS7mS15rg+pdyPlMxWlXd9qI328kvq8QZRGQRKODeGgkqEfh3mYmta/MmpYL1YTaC8FL2cWeIKWm4FAu3A6GIOsE4ZIQqX4Ns0DmPk4WwxiTdtCC+SDLiQOEFnbQnHbE61PkRasZsIlGy5F07LdL4u4DsqZWJgHQOhmnLNIGgkJrNJNV+HbtKA72HXVCbORxHF9KogtaMnLwfjMdrrEF3kQbkp6LRcwu9jhB7Z+Tgwnu+8ADyKc5zl86N/e8xkwWIMHaJBLfRZMVsmIAS+UwRAT+DJcLI2JwWxSuF9GcjNHy6zGaguaPSTUJmGK6ngi0vjxKbKxKNSRIEo3WTkDvNVCvOnhU0SWeW4UJr9N7rghBVl30GtAgM5lDn9H14LRjCLJOEI65ppE1PZguJYAuQ3RgB4fFulIuFGF477XDqWeHzmmFid1G7b/U0lyYyNVm5QYOrnVIoYRUWdSbGlZlC/iS7qGesFY1ibq0xJxneA7lQiJEZWgJ3YXb4r1rViIEvxvYPVdV/1VDqd+XrbYlyz9lNZPA7PHg3z+EYprjXEuAFQaigRQLihAACmLXmaxcLiRTSkk4ZviQ+DiLxPeIeE1dvryzEIgSKAxXuI09yyZZgulY1haBDvedfoxVu4Orqu8aZBXNeaHJCewjZkukCvnCADwc63PSxsniIZO1VQxB1gnCcZnL8M5hejTD6JgEcgpWDCwCFlPZJWn3iqClxn8GYTLbqe6dbGyqmaxTElBGC4suy5sobbOR8ZKWrfKucmHoICw5WSHICgEWbWl6iBsBdpsRxI3fswldpX3L4DG7uAzVb2l2mGN6MMP+xb3O7ygG1ARCEa59XHjjs7ea4jsSJ2s282lAZ7VcWNWMmx1Nl4qQ6psQOI0b0vcL41BhVD6eCHRFT23lTBZlFdV3LRc2zSCqOm0iLYKiHRBI0krU//IcD1cEqaQ9SDhsB0OQdYLInU83fTFz8LlHtnO8TJYBQTudBUwGQDXIKktBZx3ZKIPLfesm2AQV+ujxpZys0zGBRNV3ItKW9AqSWOWGdMcMqZea67Dw8SJwUiW6AwgG4tii4nvMOHm/mZJ1HLs1RkuQPY8Zg/GFY0X5nQHafcbCvUpWhgU5VzPJAK3V6Uopuzg50jSWsafnnl4VxpjUaZtPi4V2OhFEmonx3F+iY/lBI9etFIFeZ5NLiA0rlb/3hgUwC/NyyO55ri3M1cNFjcNl37xB4LBxAYARb+E58WMuM/5nubJxmjEEWSeImS9S+jafFuANTCCJDyICFgJ4CvEPQvgqPB+GrsOz/zBlI4OiQ+0dZJI9BrNf2cZkGdYVCyWipFRviEIGqXJcxF3w5sIbA3QGWerTJrrLlZL4HjNZm+o+nQOVdiGbWDCTQbS1yiVb5btecqnJmDJYZ4+DK4cYjfulkAi6YEYR0R6n6zyOZrKAo0O9f47JKjhRRIkVV3i101nSWahv0g4+7SbezH0Zg2wROZYIdNX0HOjmWra/OQuUDofIn3VcD7IaUZYeaxmHDYBjADIDhOE7pCV84dPYhu7C7eAMP6pnHKJCpJnRCTs/mm1mZxr4IMIMz2OAH4AUDwKwapYqR4AfnTAN/PgwVmUc2hY8YwgsQWQylA2bWlrrQkRw+cNXcP7W8xivKLVhban6boiShlJ57BDmeNmYUSsByDvKqk4EHlFPByHNEjoLebu2Ot77jX0v/+ipn4wnfNRjcemWC6u/mbAw0NOSkn5Ps8Mcs8kM527Z73toeEiiBajQ7OqpLDII97GkTNaZDrJCudDl2k29e77d9qb5HoSM66a4Q8kdw/PxRaCB8ADTXDQjInj/5cu48/x5nBs3pW4s1FtQM04QBN5Z1cS50iwTAs1l6RFjom6cHrfw0tr8U+RFqHgM5cJt4Qw/qmcbTUf0fJZvRIE98kEAoMAOKLsEQPlYs+IIE38hTPSHxz7XSeOWR15Y2IUnXj3SIMsnpb7QSxs4JSsGWWQoqb5HtfEqYqGuz0619zlB3ZwsVo4Gs6jKdEUni0UgWyofRAXzTX0vd/3brwMQuSur5YrOXzqHRcWXSLgGgMn1yWrZN9FFnCWKvIZFeFUQBUsexuQoZLLOKB8LCErmnuFyB0i/MpUJ18B7vzlOVqAVMB9fBLp8buddvDwLZt5h5h3OoS3IclAtqyBG2qho1BJZccpYcgkoyLcoh4uRe6k5i0TkM5eemKFcuB0M+cETgvMM5xmZNRBhuHy5VswqEJFaOYrIImcLJ2PAn7xu1CZgrF04MXBa4DCvD7M2QrlijZKhDRIDzMrF8y1qzhKkEzZF1DdQVfc2FOxhk3AmkMqF0CBrW3cIGQpSBpv8XjSDuWr50RizODMStYp8KBWuEFiTCLxo92YK/9blQwaC8jQFWWd3QSQieGZMe1rYhDcluY1NPRsxS5lP8+OLQKMMrprlQgYj977VoJnIAkn1ncHCgVcYfo+6sHLZnbqsXEilhAMYM5G5TBV71SiL38GQydoOzv5Ke0bhmOGD2rsvvO7QNigOSlzvSgHCYhsIpHTSmgZbRiKgBimLjW3SRAPUtdZKS2DvlYgfO5JqB9JOp1VNjhfBECF3PBfMAUDhGRlZeMxnspR8v8VMFjb8vWwJ6h4gmB7NMJvmvZ0NKNhbeZEUTJuw8K0zCBLNcMxCkDU6w5kssppByif5UgubBANskqsIlMHa7Cg/tgg0UJLSm6N0rM4KXRllfYODSNCzYk6Bf5UTCJTm08t0/8ggiU5D1Ce1qYPlHCs3MgVZN/eacFIYruoJwYUurmjtwF6O/ZBHEHRSbnJ+DoscI2ODCORGTnVqISjJ1cKb68RSte6yo20VRF0m71Tp3DdU30NMCGBzWuuZMXCFa5WLyL2DjRpMOoL0R7NMGxpEAzEA3tT38s//2Tfi8z7r+fjrv/rA8QfXRMigrFoq1MURADSblbTA14gTtCVBA/LZNPjMneFMlondpT2U3mvv2Uq7K8EVZcfvsSClVlb9x/rdTV3R9i6Uqu9Fut9ihjdm/arn6OMIQYSgk+XhWE2nm1ljdiqJEYOsQSdrO7jJl9rTi6LS2u2dioRuqjU5ZgmqnB9mxqQoMDIG7P2p0Y3aJthXlO839XklGAuvkZFQXSYOkyhCyawSZCEGH9jYeA0RCu/AaJLstZxsSffEKcMiWmJl2aJKPpVcpU3ch3lRqBL4FhYJYwwggsMrRxjv9vfnNESpzON9sNKB/mzVURJR0lUrJsFn7sZZhW4cZLT71xf9gyzq8Po8PgTs/LFFoMOhlHbX5GSJik5PnevoMjQQmQHw8Kwiy5EGQVR3JdB9syzfJAex5mhMztxSLgwm5hyirIGTtR0MQdYJoXAMEy5/l7fcP76DcgABAABJREFUuiAglSriQz3zHgUzMmOCoN/N/UAZIrDzScphUxOIIHYvrj7hx6aE6NnHUs9kcczuABtLZRlr4JzM8UE8B95VmK11HFGQlDdKvp8fFBB3+5sIsuLiuzH9pAqisMVsmmO8u0KNjkouDQcBYIFqt637mQvnUARz6NH47E7dsfvXOQfbQyML0OzxJjeiVRTFevpYTajdzTwnS581rSy08bKiVhakgJp1VPxYCY0NXciGLuNkRckL8WoSH+b+KqITRLLVucnXhJPC2X1SzziqGll+00FW2CmiwvnJvUfBHhkQFteb+4GiUBZdJ+O0CBIVB4+BuFOVShCsB49+k8c/R0RGBs77eskBuvD7QIaN5aiyVBjItZsZwhzK0s9mzlAqvm9hxCEjtaqmF1H0xSMUnlPnKAuv7UsZS4WAYHSGM1kJqzR4kDaNGLv5JcvN3LFFoIG0bZjbgHEoFzrPcwLE+pag+i5OgyyWpFGn7gh1SgHShqgbJrg9qICrqNp7I/0V1504NwxB1nbQ+45929vehnvvvXebY3nYQESCRpbe1MXUbXSHRmFC8ig5PrNkVBt1Vm7uByrufDddYVjVd64N3nstHTU5WcDG1ZeNVXVt35j4HasxtLbGB4sf1QnQAoMQNscMq4MQtYQ28+XwFjNZMMBskq9UKgRiB6V2aBbh2dNOsXa9oqXHQxlkGYsz3x28qj89EYIN0+bvyU2IQANIXKn5cqHAEsELo2jd9FmoM4eHF92AxMYkMgR21SBL+m30QqacQxaLWqRDipmKOQ+K79tF7zvri77oi3D33Xenf1+7dg2f93mfhz/+4z/eysBuZqSWeYpBVr7RHRoZLRfqAxk0flwBA5OyFDc7yVHV1f3meRzHzMAYIviZ0x3qXHdhhZO1oQAnZsyagqTMkdxOMKiqz0elfN5eHE51LtpxETNZ2wg8slGG0XiMcc+uwohYDjZQ/mW0XWHuwafpQD4NGbtsc92nJ4Wd/TF2zi0XIY0gIuye312tZNsDu/u72NnfOfZxNOPklDvXaDjyzKmjtjOTFT0GnXIV462cOnGj0bj0pD8YQITAHDZYNP+8uVkBY6jiXXi2A/fTit450uakWBQF7rnnHly+fHnjg3o4gdnDrdBl0we6+9Egy7MAFjjMc9Xk8hxa58/2JL0MMZPAHZYya2N5pn4hTGZRBGsdApKaOBA4WaLM2Y19PUEpu2mt44RTCYGIKo0YsYS5PW88Mss7xZgZs6MZ9s7vLT0ep3LH5hcJQ4RztywfQ8sbIQUDomKwPNbsg/j1TLENkPhYxp7953d3jcBm/8Ia38MSbCpoIyJ1xaH5/GwRdPEYMmelpYiq7xQ6AqXWXaj2VwyypnfyVzdXBM9O7XXmjKEZRWg8iJmsoVy4HQyK7ycMdgzvPEYb3KEZY+ChZqoigtx7zLzHyFiI9xuVNDitMCZ08rnNcrJW9sZroKr6DmibdTo0tJxL2BznnKxmVJrNFcycpl0T28QJSAbR2+mXj6NKO/Mu5NMCk+tT7OzvLC3lfPpTngRXOOzsnB6ikjEEJwIKEg7Ke4nXeU3ie8hkbcghasAmEYRNLeocKkCFp4kIRgxmrfzbqPpO6iEqlWwnIZlYG4vez2UsyXtfYObNnNq7LxjsPew4K70/b/I14aQwBFknjKiRZTdZLiQCnEo4sCgnxDmP/b0R8qnbWOv8qYYBxHHqoNkkiChYw6wecKlWlk5wQfqz8tsQ3PToHuoLEzTTZkW9XOik7IEyBvVyIRjHiAWWggjqz7hAfFdEYDIL9rKUJ/7v/69XbHiEx0f0xSPRBEfBQbqB1+uoJCIUs8CdsdsMgAesg6rERJMC4IRVj06oVSuLyAZah8D5wKOtcLJidhvoX2ZXwVctYbK3c1kq7xzYC3asSRp6TbHSAZvBSit72+Rw0y/WW4Z3wWZlk6UOA+V/iIDBmHkHD1WXj912N/v3ZshAGHDObZTQGac4Aq3FK6IsBlkMknq5UASlFcamrEMstZpEqw6WQjlZ8bMIwLzR7r+5MQW+0sLoSQBj6xpBZwnRF8+IlnuKYFYeBYjXgQvlwrPsW3izgkzoCqT5eaFwqkuYGerWyhIA8IErWdIFYkYqlsT7U0w1I+Y5R+4xn8kK2oxkTAoKt9I4MmC1TNbXfu3X4hu+4RtqP3v2s58N25K/JiJcvXr1eKN7GCCqDW8ShrR2zyGTlXufooM25e+bEaohqQHDJgnRkS8RF9FVRVCsIeRB7RpEte+DIUkNfWPhDRHIy5x/Ye590mnTrBwr4V48BD6Z3W4LHDobuyAisDZbz4bmFCASliGSeDZA+G7XvB9TkDVksk4lJJTcq9lpES0XWxCMIRQh4B4318yQ5vQcE9lBINRUm2HQv1yoMRacc/AZtWSySpV7P3CytoreQdZXf/VXb3McD1tsWiMLqDyYQb37MC9gjT7U21FNPoUInCxZszzTBRENrFIaf0WQMfDew3sVHKx1IknFm2yDiu+A7qar5c3ClyVjQ4RC1L8wg9dedKGtZjuzkUU2WkwuMtnyTJZzDs9+1tfCGMJbfuFHcOHi+U0Oc22kcrIAPvlBhudvDU4VAXB5yDgMJI9Th2hjo80s5bzgWOABjAwhMwaTooBrC7JgADh4b+t7G6KkeQgAfWccfXYJDNXeymx7kAWUG+9BwmE76P24/uf//J+3OY6HLYpZDrNpJiuV9i2eBUdFjnHgfG1anPO0Iqmr9zH6WgUCOAmaY2sErGny8wzKLFzVMmNNb7tlMCB4r2rTozDZFuzTzjVdq6D47sWjmktzhetv5NsTF2+9sPgFAtjMIp/kC1/mPePoaALgdGpHVQNxOkbXpgjgA53HZg+TjdJZQugCJKoHWSycJBcsGXiJMg7Nmq8FZApmmg+ygi0OgJUaUkgAsINnmld7L1zaRA2K79vF6ZuVHmbIpw52AQF4HRhTqonPnEPBjFEI5NaxgznLUGG/zWayHnI5Ck07rXsUVa82VDfxFmylq88QwbFP/C8RQeF9IrqaoCrNgRciXHKH8rzA1fuv3fCynUhoElhy3moQcxo5JXFxVFozkofhqhCfSDobny8GHB9EFLxI69UCL6Xob8xuurbsrLkAmEtzv0vOECnIWmVUmT7rMHNq78XMJdmgQfF9u1hre3r33XfjbW97G/76r/8a169fx4ULF/AxH/MxeNaznoXP/dzP3fQYb154lW9o47RtAsKinoXe4/xYdWnYrW5Qe6axYbkKBrQTVI6nV+4dIyPUdHMEAFqUmY8LgraR+0SeVRHUqCpdWvwA6ltYIKX/QrlV/Or8s+NA+pjgArVy4qlsQRcJ6vmaWVhV6TzBhYA4w1asZQYcD5GDZ4CaFXtV9Fdfh1bVd6IdgHbg+Gr7/Bw2E6tsdgQGB8WjYHbqWTMRUZeRcB+VOlnDfbUNrBRkffCDH8RXfMVX4H/8j//Rykf5wR/8QXzGZ3wGfu7nfg6PfvSjNzbImxU+BFmbVjGO0IyEEjHjQ+7d+sTbswYCqaHzRjNZoUsTtHZ2xxiDYuYwjrpJASxhFd4wKJYLwzPrRX0Ld0JQQiB4KCcLohmvZL0kGqQ2TW+3DunXYcmnPZPlGYZU8DH6Q64T9DOHgHh082vcnUVELpbSGWOHoHIuq76BBgYz7zqPEy23mlinAkFQsWvTKE2y9/DeVzJZAydrm+g9K+V5ji/5ki/Bu971LrzkJS/BO97xDly+fBlFUeDy5ct4xzvega/7uq/DO9/5Tjz72c9GUczrgQyowzkPdtsxPQX0IfNcf2h5S072pxESynKbFHxS5QGCsbR2E4GxJnEiooWNjre+C94UTOCJRJK9Y+V4xPJA3IVzyGSBGRyyL5J+f4OhvlOl3EMH6pms03dfM2u3rxcGccxErF8uHEjvpxQxG5yM1hW+IpUCaBZ86rqDrMK33+tJg2uFCYKCJ2kzaGtqMw6crO2i9yP75je/GX/6p3+Kn/7pn8bzn//82u9uueUWPOMZz8AznvEMfPZnfzb+5b/8l/iZn/kZvPCFL9z4gG8miPfBp2o7iwOzqr2PKuVI9n6rrfmnCUkOYYMclhRkkQWke7JcBGMNXO5qGaR0B2yBk0VEOrGmiVrLhdWdq1LeAQirhlaQcFALJrNRr8E+iFpBxqgVFDpsp6rZxE0K+m4ChgjsHUZmFz7oH61rMM4xyLJDJutUggAIQELBuUFNoZtBUUaEWdDKauOKOt+u65eeP+aVpu/CM/bb1N7ZgzL9+aD4vl30npXe+ta34hnPeMZcgNXEC17wAjzjGc/AL/zCLxx7cG14wxvegCc84QnY3d3F0572NLzrXe9a+PorV67gm7/5m/HoRz8aOzs7+LiP+zj8xm/8xlbGtir8FtTIa4hBlikXKOf4YTNJkzFgx2sRjbsh2qlj128iiJY/GtCUJt5oMYzeBMioCG3MZDVLGBGaFPJBK4tSME5NoskNgEDNmY018AtkHIwx+KRPfiL+4ZM+7tQJ7FJQ97dENe7dOuOUxMk6+76FNyNKSReu9a9w48GxxqIQbuVlAZplagt2SjX5/m4dBoDzfl6+wfuayfRQLtwuemey/uRP/gQveclLer32Wc96Fn78x3987UF14S1veQvuuusuvPGNb8TTnvY0vO51r8OznvUsvPe978Udd9wx9/o8z/EFX/AFuOOOO/DzP//zeMxjHoO//du/xaVLlzY+tnWwzSDLBGudmfc4N1ZPNxEGWE5lWWUbMEYzRpv8uCyCEZH6Q67LybIEVzDEC4RKE+9tdX4aIuSu7GpqljCAtBGHSjg4SNDJOkmfSxuCrEVl2Yu3nMcbfvT7buCo+oMMwTvtLHPCsMf4fqO/t8mGxfA0Iiqzl8GV/kWfufL7ygxhUjCc9y1aWZp5am5+gIqYLaM3/cFYg9x52EYupenn6gfi+1bRO8h64IEH8JjHPKbXax/zmMfggQceWHtQXXjta1+Ll7zkJXjxi18MAHjjG9+IX//1X8eb3vQmfPu3f/vc69/0pjfhoYcewh/8wR9gNFLy3xOe8ISF55jNZpjNZunf165d29wHaKCYFVvrLCRjIcJwnpOlgrBmTh4uQRZIOWm92tR6QsL1I7t+Cc1YA/YqAcGmzF412783BgKIBTnrSu1aziGgkMmSZN4cWVlEZitlzGUwBsgyi9l0sVbWaQY7XTQ9S++ygYjgvr91mE3Ka15MynIhHiaZ6LOEqLBOUpcVKYRhKkGWDfy8tkyWiBq5t3298eWySiaLDHL2yBpl9KbLyMDJ2i56rz6TyQQ7Ozu9XjsejzGdTtceVBvyPMe73/1uPPOZz0w/M8bgmc98Jt75zne2vudXfuVX8PSnPx3f/M3fjDvvvBNPetKT8P3f//3wvltl/TWveQ1uueWW9Odxj3vcRj9HFUXutkd6N8BYDB514XwpOudFxUgfLjGWUemBjW785fiTERGB2YO9egSWQZaSvTcNtQBiFOG+55YOxtK/kMHeQemylHTGTkJdjYiQjbPOwPPgyiGmR7PW350GkNGuTmsIXnzvkuvsSHD1fsb0QNIflYEQ2KG78FRC55iqmHCZySIiPPShyyjyQudiqZeP0ztCE0wziKLA7QPiJqzf958Zg9uyHYxtPZfS3NwPBtHbxUq9KoeHh3jooYeWvu7g4GDtAXXhgQcegPced955Z+3nd955J/7X//pfre953/veh7e//e34yq/8SvzGb/wG/uqv/grf9E3fhKIo8KpXvar1Pa94xStw1113pX9fu3ZtK4GW9wxX+KTEvmnog1lPPRczB1d47OzvbuWcpw07ezuw1m5cjNQaAznG1xbHQyzgCg9LAAj7jXNujDEgx8iDlUbhOfkWVscUM1yxybDKybrRxHc9L2E0zrRZowWucLjv/odw17/+Puzt7eJn/j+vv8EjXAxjNNuZEeHWvX24vN/G0xV6rUc7hEc+VhfDfJrD7BEyMrXMyIBTglBaN1CN4rgvKNjDiODwaIrdczsYjUcABAXP39MsSkJvcrKU2xdew/304/SNOsc0UeRFbXPPA/F9q1gpyPrGb/xGfOM3fuPS13V1TtxoMDPuuOMO/NiP/RistXjyk5+MD37wg/jBH/zBziBrZ2end8buOPCeIcIwHV1Tx4Vmcbj2XeTTXCeCh8nDZAxtXIPMB52jTVQgfdiVxiDLC6v45/EPXYN+/4I8+BcWfj6Qi7whiCTZD4odU2v6NG5g5LAj25pF8051flye46EHr2B/f++Gj24ZiEhJxszYH41xDT2DrFAdHe8RLtyq88PkQK9FMeVNVr8HbApJBkXlT+LzUkQepOcymCGDWcvGgcEqrdD4ggkECVHWKs+h+inWX8/ewzuudeIO5cLtoneQ1RWU3Cg88pGPhLUW9957b+3n9957Lx71qEe1vufRj340RqNRLTX6CZ/wCfjwhz+MPM8xDoTwk4D3QSNrfzszpgn6QsIMCp//6PoENhuEdo4Dgk6SZDYQdLAAYuqZLNmCWKzRCrFnj4IZecW3MIKS+nzUyULS/jFmMfl8WyACqGMT4guPbJSlReRUbhwMACdgDxjbX607ZrKy6v4gBLzMvFGO4YDNIMnFIHiQQr8rEQaJcvM4aGBlVo2im3BON8VN2yQygA/vXYX+oN3V9ee2TQC7LBcO99U2cGaCrPF4jCc/+cm4++678aVf+qUA9Ca+++678bKXvaz1Pc94xjPw5je/GcylFtVf/uVf4tGPfvSJBliA2tvwNrNKhiAF6zmsllamhzOMdoYg6ziIU9YmMrVxwYxBVrLgOPaR6zBkQF75ISwSOpjq41eZAV0iqvbQUTHfbUGJfimI0o67mR13ziMbZ4lfeRqbOQwZOC45cH3DVJ+CrIqOGcXutQ1zDAdsBPHejIkmEYETgQdAELD3qextyaSscvWe9uy185fm52jxpSVW31x3FBlWsnwQHm0RwPaneaNyE+D0zUwLcNddd+HHf/zH8ZM/+ZP4i7/4C7z0pS/F4eFh6jZ84QtfiFe84hXp9S996Uvx0EMP4Vu/9Vvxl3/5l/j1X/91fP/3fz+++Zu/+aQ+QgJ73upkGU2i4+45n+Yo8gLZeAiyjgOBXlui5Wrki0DQkpces0J8582X2mP84b1H7r2WqZslieRfyOFPGKWcXDebIcBmBsbYuTKJd5rJSkKKp0yIFNDrLiLq+whAXD8hyVguzMaNICvcJ6eBijFgHgSAuCLjEI2dhcBeykyWMXAtHYYSNlnNxgYypf3WKpIqMbtWM6x2PCeAHfXzhiBrO+g9M/3v//2/sbu7i2/7tm9b+Lp/+2//Lfb29vD+97//2INr4rnPfS5+6Id+CN/93d+NT/3UT8V73vMe/NZv/VYiw3/gAx/Ahz70ofT6xz3ucfjt3/5t/OEf/iE++ZM/Gd/yLd+Cb/3Wb22Ve7jRaLbRbhrGKBEzTvD5JE8/H7A+CLrIGWOOJW1A1gadNEo7yVgu3DwpywCsBtG583BBjRoApodT5LMchggeOuGyGrCFGEtQWd9vKIgIJrMwluYESSO3MJULT2PgQSYsnDFT2a/9PpYLbaVceEJfwYAVUP1+OGxwvTBMkGaJmyobgqaiwctyTp/RZpcfEQEcpFVWmW/CGlDlZbWtO6VB9Cl8hm4C9E5rvP71r8ejHvUovPrVr174ule/+tX4+Z//ebz+9a/HD//wDx97gE287GUv6ywP3nPPPXM/e/rTn47//t//+8bHcVy4mduaRhYALUMxpwn+6PoU2cDHOjZiip9CNktqnjj9YYLVTYaynZuFQWsKnC48V5g8mQU5Oy0hh1LU1QeuYXd/B3u3nguEXR+4YiHWE4EhcxIyWalcaKyBOEb0ua0GopJ24acvk6VrY0l4Zhb0UcZ1ech4jOuL3sk0HwxYBSLaMSwAvGjnMAmBheEKlWGw1J7JYuG5EiKgc4VAqx+xfN8HqWGlct+0yRcNnKztovdVfdvb3obnPe95SdSzC+PxGM973vPwm7/5m8ce3M2MPHcb9dRroloudLnD7Gg2lAo3AOWDa3chmVLZeVWQUTIsUaUUgCBouKXbInpZRl81EcFsksN7CdpdEhaGIEYaJncyZUnzhoIAY+2ctQ47hrUZspHFeDTCx37cR+GjPvrxN358S5AWy2iJ4pdbPGnGQ/9e5WTFJoQBZwECIDxL8bmR0uGDSO8C18jOso/E98ayTARmDpzNFboLgwr94dUjXL98gOuXD5AfzeY2JJHyMFQ5toPeq+4HPvABPPGJT+z12o/92I/F3/7t3649qJsd3nuAt2cMXQML8mkOVzjsnNu+NMXDAYaQOE3iJWVYVgEZU1qupEyHEtO31T0mLHAs4OBb6J2Ddx6+cCHoAkT8vFhplLO+QUjZwhCQjMYZ3Kw04/bOw44MRjsjPO7xH4Gf+MkfuGFjWwcxU9BH48jFj0lATUNyqBeeDYhAQKqVFcycmRnCujmoSer4usG889xum2MAOCRJnr4+YcYauMLh3g/cXxkfsLtfXwf8UC7cKnoHWTs7O71FRg8PD0+8e2+AwrPAFYVyWE4jb+WMQfnglKx11g0+TOj8gXDq3GNR4uzWFL2lvhP2zoOdwBUOBAJDAwGudCMpbnSQFc4arsNoJ8PBlbLM4ZzH7v54axpzm0bynVtgdB2RSoWjBsE9KIoPOOWQGA9rJguIhHblOgp7kM2QGYujhoyDZzWLb87T2t0buFX9mwthDOHibReWvi5xsob1YSvovWX++I//ePzO7/xOr9fefffd+IRP+IS1BzVgcxBmTK5PkI2GUuEmEMuFSnxfv7sQhpKtjq8YN8fjbwMkZScRAPiCwezhnYqTMoLcQOouDO+jG80HEhQiuO/oUBXTR1k9OCwcxvvjs2PhFxYx34P43ibfAEQHh6Gz8LQjaaFJKMMF2Q0JmoWxw3BkDI6KonZfM4eGkwZUjFTLicDmbZXinDBksraD3kHWc5/7XPzar/0afumXfmnh6375l38Zv/Zrv4bnPve5xx3bgA0gn+aYHeUDH2uDsKRq6GRpbZHOyJkj0R1vbLUmbMO9MCAQcWNCxAf1Zy1jaGnDJ/ZVXT7ghuZQRMAECIl2Z2W2lsQRAOOdMcgYvPe978Pz/sX/iZf/q8UNOSeJ1IDSI0vpQnKjSXpXH8nT4aQxoBvxNhURpQEEux0QwL4MwkbWwgkjrxDRPbfbPSh3V8J9tPn5YdDJ2i56B1nf9E3fhE/7tE/DV3zFV+ClL30pfv/3fx/Xrl2DiODatWv4/d//fbz0pS/Fv/gX/wKf8imfgm/6pm/a5rgH9EQx86qPNYiQbg5EMESg4E233iFKiQ1B4EpF0vkWFtLYBl7NpvhChQk9c9gll4KoWwz1lkLPLxiZDI4ZmTWVbsKY6dH7eTbL8fcfvBf33ffgyQx2CQjBmF2052xZyb5aLpw7DvtGGXfAaYOUiSz1LQzZbkMGwpw2ZZmxKLyv2etwZzm5IsUgWNo8sSpKGZTh3toGVuJk/fZv/za++qu/Gj/6oz+KH/uxH5t7jYjgC7/wC/Ff/+t/vSH+fwMWw8SOJDmlOkJnEAJRnRsiGEtocsT7goJVTcweaau/tntvI74hYwAPFFwKkRZFoZ2DFS0dz6wyDieJEOSNrNEgK7Op+5F96Cwc21DuLDu2TiPU2sQnPtwyyyTXUS4E6QJOm7XiHLBB6D3qAGRgETjPKtfgPIxVGYdIDYjBV+48EJZKpR7Mb9qiFEgivm/4Vo/csaFcuB2slN647bbb8Gu/9mt417vehV/5lV/BX/zFX+DatWu4ePEiPv7jPx7Pec5z8I//8T/e1lgHrAgyBsVsUHnfFES0lJYZCx/LhWtGWcZQ4D9pGUllHMIku4W5zqhbLJwPmSEAxdQhG2caZEVh1DDRk5zcrlYgEAolFWaMrYG1FuwZ7DyysVXfQl9yWuba3k8JiEwwB9ZgFku4+jGTZZvlQkPqQzqsg6cWRAT49BSjYK8BktfgWjzXmh+IqNZh6Jya8LQeF2XGaeOcrOiaMARZW8Faq+9Tn/pUPPWpT930WAZsGGQI+TTHzv6QVdwEtLGHYA3BACBjIccVD/XKO/Kpe2hLXaAEwKvK9DhTm5pi6pCNMrjCBX9PUvJ97TPd+IlXyf9KDp4ww45HoNAo4JzH7rldGGtBVJSdiKe01KHmvlGpezlpOXGympkslIv1gFMK0o1TEHeBC+rtaqGmGWpf4XCOjMVhkad/O5aFTxt7XpsDughDd+F2MTyxNzHIEIqZGzJZm0LgjGfGapBlN0AIF+VARXVo7S7aAifLEMQDTjwsGdXH8h42BFwSPBNz5lSuCwPc+FiWQdKO3QAQGGs1k8Wqmr2zF2tmlALC05vJgpoDh+95GZ+mVHufP5BATqWy/QAFoWyEcUHzLmYyY3BdzWRlxmBSFKnk7byDWXB/CEct+U13F8Zy4XBvbQPDVb2JsbO/g0u3Xxj4WBuD+vgpd2Ez3CkSVY1PHWjRK3DDICKQCO7YP4+90QjsvPKbMp0CvFOSrjBDqK5jf8P5TiGYtaaUzMh2bCIGj3Y0AiGT1BFObfChnCxR77klPQ3KOdO/NzNZhgiXbr+I8e5AyjqtUIvQGGQJPIKzgi/LvFUfQeUcim5sABTeLxSrjYrvm34cB+/C7eJ0zkwDNgJDBLNNf8SHIWKQRUQbKVExh+7CkE0C2jkXzIyHPnwlmcyuiki0jU5OLmSyTMgAaSar9E+rdhptTRx14YC1BJjBwAsjC4EhAGRjG8ZlsLs3xmMf92jceedtN36MPRBFZ30PjSMfSoVEgGl5bO0ZEV99OEMzqwTPPj1T3nPqSPZFycHKjEXOPvGynFvsAhI3GZve9CQJh2EzvhUMdaQBA3oi6t2QMbDCx96ixPZ+pUtx6gRtg8u9cqYcr7fYGoIUDPa6gFd91BDOPyYKJtFBqTr8/43PZAX2WzCIdp4x2smQ5w67ezvIRiHIAuHTP/0f4r/8v38I4/EpzfAQgZ1X0jMvLvVUS4WntVtyQDdCbwkAzWQxCwz0OTLGgI2tbZJiAJ47BnY0m7xpeYY+4KG7cKsYMlkDBvRF4CoR6QR53IVQ/Qt10uXIyQJaa0qucMdS7U+G4aEbsrqjjjvspGAfgyyEMuMWjczbEEORHWuRkco42EzZKnZkS44hhf+cYrcZ7UDVdn7B4lJPp3zDgLOBYORsoMETQ0BB8Z0QmiAamWgCYRYyWYX3J9LhN3QXbhdDkDVgQE/EtdyQ0U6c42ayqAyyvHCy4Gib6tQU2a7tIVgVPwU0aIvlATIWvgjCidBAK1rlEeiGixQmwdHMYneUwQnDWgNjLEY7I5hQSzMGp9442URLFOeXKrYn+YYhyDqTUMFfJM8EhjY7CAtgCGQIrqh3I49s2WHoPHdaRRG2Z201GERvF722xl/zNV+z8oGJCD/xEz+x8vsGDDi9kMDFIhglDSWRzHV2gWS08wzQ3aRntbZpW4iFBdaatb0SVWepNIjOpy4ZLJNBCrK8AGAfAj3Rz3ijM1kiEKPXeDfLcGUyxdhamIywu1dpuyPCH77rT/AT/+nn8Mmf8vH4N9/2khs6zj5Ilih+cXs+0G2pM+BsgEL5LwZEFErvWi4kIDhEsPeJKzsig2lRwHmG992K/kS0NY/2OKcMiu/bQa8g6+1vf/vKpZGBUzDgZgMHtWVrCJZJyazGBC+N1XlSZAjOMXZAcAssM5LKM9HaulwUNHw00GK4mUukd2NN2mEz6w7cGEqdjjHjdcO888KiRATs2gwMhs1GyMYZskp3HRHh+rVDvP99f4fbb791++NaB6GExN5jWZhVlgtvxMAGbBoEpHK7iEBIgpVOeK4NgQuvm7IwXWRWZRwmLgezwHYFOmme2TyGTNZ20SvI+pu/+ZstD2PAgJPB5GAKO7b9iNOCYHtDYWcarTTau8GWwQRrHYh2GAraJ1F2DGvtyqUx9h6TgxznbtlLwZGIwBe6a7aBQG5CAEYsEKFEPNc5XcsccSd9g2IsbTAg1RICAGMtxjujRHoHtNwaOWanVaQzWlvF7sJFKInvw2J3JhE3QZFaGUqFIgKympl17JIuFqCCpNd4iknutPu3I2us8irbGXYczxBkbQenc2YaMOAGYXo0QzEp+r04LP6GCJZMIrPymrMfRTVo0YnOd6zBkY9lVjSkLnKP2dG0ps3DzPBBIysLGlmwYXFgwQzncZA/Iohf6oee+tj1dAPJT0Zb3kfGwsCAMsKF2y5gt+FeEC/HaV8g+ih1+wVq7wPOANIjQmAgUQnUeku9TpllzloHAKZFARHuLtmRZpg3DW2G0b8PEg7bwRBkDXhYw9Bqqu1E2oVjiCAU0i3rmkSHzjNwOEQHb8c5j/FOlsp7fVEGZNUgS5Lae8z+WGPAPhhFg8BsS2sQElwpZmAquw63jpAxIxBG1iIzBl4EF2+9MKf7JhvOZPnCY3o028ixVsVQLjzboNDBCxH4YMRe5WRp0OXBjd0UgXCU5/DSrUlH1C9QXxVVjueg+L4drH1Vf/M3fxNf8AVfgNtuuw1ZlsEG64vqnwEDTjM4coz68pxCvYyCsCCZyMlab/KLJQAJXUgMbu8sLBzGe2OVYVg1oGvuTkOQpYFM7C40OvkH77RoB6IcNIE1GojesBhLlIQb/QszQ3Ad39GmFd9n0xxH1yYbOdYqYF9Rex/KhWcSFOVEgk2WMVTeoCiftyavMrMWU+eAIP/QemxjtpJIrnopnvZs8FnFWjPTL/zCL+DZz3427r33Xjzvec8DM+P5z38+nve852Fvbw+f/MmfjO/+7u/e9FgHDNgsRGBs/xIcV4jgFHhZSVtqDWjHXygXCicu0twwAYx3xzCWUnt4HwiXBPb0GUK5sHqepKEVyhgiwQZEABaCMbZcQG4AJCmjE6wxGFnbGWTFQZ31XXjMYpEBzA3u5hywGWhXIQDETBZa5wbv6/fyyBgUzgdOVsexiVaiCvRFNcgayoXbwVoz02te8xo89alPxR//8R/je77newCozMNP//RP48/+7M/woQ99CB/1UR+10YEOGLBpsA9BVt/Xx1ZnU+43jTXH7vhDIMe2TaJJM2qUKV9jpYlWM29J4xRKoncz1zqhJkIuU8kvoeAhSLJSgHcsSN1+Zi8bdQZZu3s7uO2Rj8DFW85v5tSMhf5xax+3Q/8swqlU0pDFOsPQ+0YAIXgWGJgaHzKiWfYbWYvcOc0kd0k4QJ/PTd8d9XLhcO9tA2tJSP/5n/85XvOa18BaiyzTQxSFsjaf8IQn4Ju+6ZvwAz/wA3jhC1+4uZEOGLBhsPDKvnwmlApjkKKZsPXOn+QQROCB1kyRdwxrM+2qs6sJEorU9XXIGHjPKGZ50siqQjNZtjxHCipthQi/fUhI6cVQdneU1TqyqviS53w+Pusz/hHOXzq3qbNvXKaCQtfZQiHSgY919hEEfwGBB6vuXEuQ1byXMzIovIdqlnapkYZjb9y3sBzLaTVZP+tY66ru7+9jPFZRwEuXLmFnZwcf+tCH0u/vvPNOvP/979/MCAcM2Bb8YsPeJphL4+Q419nMHJ+QGrNYLYdh55GN1UrGmGzlkgFF6XYEhfnCqQ9iC4mevXJCGFzO5aRdUZQWkBuAik4WoG3uXZ5/xmKjZLEY3q1bAm4DxcV2wSI2WOrcBAhipGAVnzVBI63+EoJzbu5nEmRcugIdssrH3JY5NIBOtfkBx8NaQdYTn/hE/Pmf/3n696d+6qfip37qp+Ccw3Q6xZvf/GY8/vGP39ggBwzYBlg0aOo/twR7jIpvoT1GuTDBawDX1qLtnFcrGWvXK2NVqFRkgCJ0FprMNF+mBrVEwXoncs0EmbEIVJNeYBG4wi1/YQcEyleL38vIGv0cLYFP7ODaGET0Om/wkEQG4mSh+a8fyoVnHlXrGy8cuI711xhj4Iv5+UKr8903HSHIrGw4yIrlzE14sQ5ox1pB1pd92Zfhl3/5lzGbaavzd37nd+Kee+7BpUuXcPvtt+Md73gHvv3bv32jAx0wYNMQZmCFph0GYANdSU1gVSTzuFmPxMfy8yUlVzjs7GkNaeUpUOpBSMxGsRdkjUyWsRbe+fBzqZysLI32zRjNDmeYHExx7cHryKc9NcgaIFAaQ2aCUXRLa+Vv/sY9uOvfvBo/9V/eutZ55pD4YBvMZBnAs18YJA/lwrOP+Ozq80KwRPDe1Z5pMgTv5u/jDIttc4gCvWHDrCwvsXFkCLC2hbU4WS9/+cvx8pe/PP372c9+Nu655x689a1vhbUWX/IlX4LP/dzP3dggBwzYBoRX7EoTBtlxymRRUEM/NoKVDUv7rme0o6X5VTkTEnV3EidLW8p9xTstQs1rPS6Md3DJ7oZuR83cGUsr7aCFBedu2cd4Z4RrDx1gcjDF3vldjHdXiCBMaTE0MhaZNXCeQ+mwxH33Poj3/q/34aM/5iP7H3vR2IN8xSZLkH1KrUO58OYAARiTwaWLyhFkV9dwI6LWLO/YGzwiG8/9PL3PEMQDtNaK3Y1B7X372NhX9lmf9Vn4rM/6rE0dbsCArYOD3UVfiKhDoTrqKDFby1rrT1CGCOK8amU1eDsxQ5aNQ2Cxcjo/tAdWFJ27sm4xyJJQItS3a9RnoPyoVbows1GGRz72kbh42wVce+g6Lt97FdaaZOWzDERlJmtkDUZkkLdkstIisaJQaxcEKPXINiT1R4bgc4bNFnCyQrnQDuXCMw1BvWOPk1uCQruRJciklPfD9GiGbLRgOSZS4d0N3x6+Ui4csB2sNTO9//3vx6/+6q92/v5Xf/VXB7/DAace4v1KPAROxFNCjAGEFnMploGMhfccWrTrbf7sGFmWlX59K86DEvlFEaY7o2KDj6IaGZddiYaQSPJ9uU/CZUAx3tvBpdsvIRtltU6mPqiy5XZHIzg///44ok12RtGGOylNUPruynqKyJDJuolQfUyYufYMRistrtzL7D2KWYGspeM3whDNKcVvApzKhUNn4bawdrnw2rVreM5zntP6+ze84Q24dOkSfvZnf/ZYgxswYJuIwUT/10vKXMVMy3HLhWQA7z0ykJYjK+OJnoXZWB/TlYmpUdE+STEQcsftAYkhcOGTUGI8k1C0EFohyBKpSUSQQcXXrScaFcq9LINfkMnaKGm30pG5EcRST8cY1dJI/z5wss4+pJbJ4tp9bCzBFQJmSYnSYubgcjfny9ly5JWttZYhZrKGcuH2sNY39s53vhNf8AVf0Pn7z//8z8c73vGOtQc1YMCNQuRX9Q0glItV6mV1iQeucn5XhHKhSC1b5YvQWRh4SKvOgwLUjkdE8EW7fIMJmSzxgYsV6oNkCAYh4FmBp2QbPJRqsNdn4Fbtt9OPRtaiLfCJGYFNlQuBSDLeXJBlTMhkdfw+ZrGMHdTebwpUgywnNU6WCdlkrpDffeFaO36buHTHRezsdfO21kEqFw5B1taw1sx0+fJlXLhwofP358+fx4MPPrj2oAYMuFEoS2HLXysQWGOCtU6dN7SujICxJii+Y86X0DmH3cqkurIJsqjuTinhQPDOtZYljKVgHs3ps0iI0ohMML9d4dyVSZuC7EXftwsAmLq0xsja4J9YP0rZObnJHf4KBLQeiAsrOgIoHxowh1LhTYJw7zDr5qnGdzIGzFzTz8pnRc1LtAvG2i3oZOk4Bk7W9rBWufDxj388fv/3fx8vfelLW3//jne8A4997GOPNbABA24EbGbDAh1I4gvAof3PEMFLLBeWMgnrTIBEBO+9RjQyb6uRrdKR10D8RFU7oPO3nceohWBLgTcUzaNBsTwayPlkAPje567vjLXEyiu4W5tKdyEQjKLJwAsjozJI3Nkd49y5feztLSu1rIAFpc3rD3lcuc+v3HwovI+DKwIgn/udD81mC5rLBpwhpABKBMwMW+nkNVF4tMKNzKftDgw3AiUnawiytoW1gqznP//5+L7v+z489alPxcte9rLE8fDe40d+5Efwlre8Bd/5nd+50YEOGLBJxAyIqpkHP7FlyRARZCEIit2FTBSMnnu8vw0GgBPcMtrBkclSIJI6CyvdeNXSZu+ArlGmG4/bg7Z4PC2/SVRWBCiQ/FfkVJlGuRAGENeT0wWUJcqAkbWw1qDwjKwi4/CN/+e/xLO/8PNw8dbuzPqqWFTafOjvPWaTddNci9833hvIxzcD4rfMHqlDtwmfzNgF06N8YefpNlFysoZ7b1tYK8h6xStegd/7vd/Dv/pX/wqvfvWr8cQnPhEA8N73vhf3338/PudzPmcIsgacakhQezdZSMH3KhdCy2D+b2HodhgAPpDf1y0XWjJwDIAFJKXqMjuPbJQl0jtQkuwjZaoXYgDZE95L8jxE4IgRym7DZeAQADYbAowx8NJPBZ6VDFbLK46txaXxLh44OsTeqBIoVsq1myqlLCptRv7U7Y+3neW9fFogn+Yr+SmSAfYvDgvdzYCYsFWtufZSXHwmXaGk99ExMtbHAQ/E961jrSBrZ2cHb3vb2/CTP/mTeOtb34q//uu/BgA89alPxT//5/8cL3zhCwezyYcx8mkBVzjsX9g76aF0QrwSUrPMptLYMrAIMgIABsEDKC121iVKU+BCMat3WQwUXOFhM9vIZKF3abOO/mPj4KumiSxJRH9Vxu8TiWrJs0mkNSvoe8agrhlJ3ra/jweOjuDYp2xWyu5hcxJC1RJrFcpZ079fvM3CZu1nPLqegwzj3CPMwHV5mEHL7uEmYS0LtgX/7LX07mYO3nnsZrs3cpgJAydr+1hbjNQYgxe/+MV48YtfvMnxDLgJUMwKFPnpDrIYErq5TO9uMkKQI4ABSFIZTJXU1xxIEBnkpnQCC7IdmzoL4wh6lzbLw6/EH/KOU1uiIJQRVujAFN+eyepL2o+djYbmJV4v7uzg4u4Ors9yPGJP762f+alfwW/98j34wi/5bHzxsz+31zmWgqjVjzIS1ImCMfUCGEurx8IDzjyIKFEX1S5rXuaFgNRdWOQOwnJiQc7Aydo+hnTTgI1DRE792iI+LOZZEHHqESQxgCwGWRCdmKKO1LrdheG93nklzFIsCc4bCpOJZbwVTrDC5G2Mgfc+VgnD26lSLlx+Yi+swWsjqEoq6ksQT0Gxe7MCIsLt++fgxSfu2vv/6gN4z3v+HB/6+/uWH7wvOvhnsVRoR4s7wYSrGccBDytQ2eDBQdm9+QyStXAhyMpns017Pq+EUsJhCAW2hV6ZrM/93M+FMQa//du/jSzL8Hmf93lL30NEuPvuu489wAFnECuRhk4GzAxrDKw1wd+v34JoNHUFxNbsoOfArn/nXet4PM9lPpqWP6QtdyspzK9i+WOM0eATMqdS3TslxtDAs2Xs/cYdeGBErbpgt+zs4txoB4dFjgvj3dTJtVnF9zCOBlwemhGWWN/ErsxN+h8OOBsglK4Kap8zn6UyQUoFAGYHOewiO50tIxHfT/l8fZbRa2aS0IoawcwhFdr9h1e00Bhw8+BMZLJYQNaEcmF/DScysfdNYAMZPAt+ZMeBD8FN/cI1ywy0slBmR7zQ/lpDpfVNmHRTsGX6XSMJ2lzN8qAhUl+i5QcIp48y8XVk1uD2/X1MCqfzjGxhJ95B8nd99ayYg6zH5oY04GyATEkmbys5A/poeefhnUOeF4s9C7eM8vk57TP22UWvb/eee+5Z+O8BA6rQLrzNdnxtGiICm1msouEkiARRA0hYSKHB2nErQ+xdvRNJ5ic+DXhW53/1XeyTeW1YJCi0Fsbgrg+YuaI9VoJs3yAtvH5BzPSIvT3sHR5g4oqt2OqYji1CymQtCbJEgv3JEGQ9LBGDq659FxkDdoxi5sCOsXNCnYXAYKtzI7Dy9m8ymeCuu+5aaBA94OGNxOE56YEsgLAgG5lSw6lnJGITJwspk2Uze2wbFvYSElkVTtYcJ2meRLsUK6SyonmthLqldjuulukTFmQtmj9kbD/ivKBCfG//rDtZhtv29nBU5GVAuOFFoi3m9tHEeYloqITxnOb7f8B2UE3YdmW3jTHwzMinObzzMPZkhEiBsrtwKBduDysHWXt7e/jRH/1R3HvvvdsYz4CbALpOrtCzfwJg5mSpY1bQF9CylAHASuYWgc0MxK/PyUr+hRXGeVyoGy9czQMQtNJib2yF3E61/+vdJSeM1kWjfzVPUufmolPetrePjCyKcN030Z2VAtsuTlbRL5OVjjeEWQ8/ENUU31tfYpW3NTvKT7z7lBPxfQiytoW1iAxPfvKT8Wd/9mebHsuAmwSpvHSKgywRSYRTDS6Wv4chwX4uZrK0XDgaj471UTXIC2WvONm1ZrJMp4ZTOwKTvncASYms25xy+87BIhzKsOtB1SMo+UN2YX80wsXdHQgRRqMM2QayAfEqdYnLuuCIs4z4DsQ+gdN7/w/YDoi0nL+Il2xIn7N8ktcsd04CpYTD0F24LazFuHvd616HL/7iL8aTnvQkvOhFL0KWnRxxb8DpQyo3MYCTnUMWIk4sq8gimBRksU6WgMpAHGMjSIbA3CgviMzxkmKXXxehthO9SVmmxnFSjR8TB9nvVMywdv61fYnpeg0olSm7QEQYW4vv/qG78K9f9qJex15+8iib0R6YViUclmNFqY0BNwW0tK5NLOw7MllGuY/eedjRyU6QAydr+1grOnrRi14EYwy+4Ru+Ad/yLd+CxzzmMdjbqwtPEhH+5E/+ZCODHHD2QH0Vwk8SYWIxxvTnZFkKH06SYKYN1jzc0q7dB8rjCFyoqk5Wy7GMpaQ63vfYvcuFbVpYFHwEex4jBZ1tA+l5ACHp9fKMLBiysvn0QoQsWvM6sBf1okO/TNYik+kBNy9ipllYwN63zgfG6P3qCo/x3smR3oFB8f1GYK0g69Zbb8Vtt92WPAsHDGhiVamBk0DcvamG03I0BbwjIdxmBtZasGOYNXamZIxacFTKdMrJauM2rSp8ur5QKgDYSLZfYQ5uzVr1jtI0aLKGFmaygPj90Urm00vPTe1NG1G+gUw/ftlq3LkBNw1CpjmWCxc5HTjnsHdCdjoRg4TD9rFWkDVIOAxYjtWlBm404gRoLPppOEHCZBSCs/DTbGTVg9AzsEaQZSreh9XAorVC17dsl4638nASOIzHEvW2xQHaO/1Wklho8S1sgyHCf/4PP4M/+x//PzznOZ+Hz/ycp/Y/Rwv0ks1bAgF10nufz3JapUsGbBkhgymiFlUL7wM5+fukLBcOnKxtYbiyAzYOAlZSUb/RYBYYMmV3IS0vF2qWyYS1XzWQYrnQ2Aw2s/Drqr5bmmv3LkO5Onrb02ATE3iQcFiRcNa2K+5NnId+btsjmCQi/PmfvBfv/IM/wn33PbTSGLtOTtSeifNJ7b3foVZrUBhwsyA1TYgod3LBfW+O0SCyKQyK79tHr0zWf/tv/w0A8E/+yT+p/XsZ4usHPLwQF/jTusQIM8hQCgYoW14u1PVSgk5WeF+l1X9nd4SD6QpkqQoMGTiuv7drPMZSP65bVGJYce40RPAuKKkHvSqTZA36oS3rtYpBtKBf+cKAkrG2aSHbrwpJlj7zv+sr31AG6wMp6+EICg0PLAK3IJN1/hHnT1q9AQDgh3Lh1tEryPqcz/kcEBEmkwnG43H6dxciadcfQztowNlEKlNtkoy8YYgIyJAqtaNv/CClTlVANZAc7Yzh/fW1xmM6xFDbMiqmt/6YZqFWjbJMZuFdUQvSFgmDVsEc9K2OMWGLyt736kY0hHSPrZptaz13+P+2gLCvpY7EEhB1i1EOuHkRlWvEC8DS+SycFqI5D92FW0evIOt3f/d3AQDj8bj27wEDmkhlqk2RkbcA9hoMpExWzwnPEEBUinVW7Vey8TFS/xXphBrahmX76y95MK66fKWEyu75Xeye20HdqJlKQvgiqyRhGGNaAyQKAepSq6UgXdHnGyFTyWRtYJGQSHxvC7J6lwtjcIshkfUwhFa5JXQLn55gqgs8KL5vHb2CrM/+7M9e+O8BZwPMjCv3X8Mtt17Ynj5L6A47zWKk0VuOUndhjwkmqLHrYl7R1wrde23aUH3RKp2gZ5h/rcn62dOwwImA2aMnjSiNBUTgkIUmY1KABMRMTft7mUMwZVuI72b5+9PvTbd/YG2soFKxuuWgrvC49tB1XLr9ln5BWCK+z/+q1Mhaksni2KE4iJE+LBGERlXC4XjCvDcCQ7lw+zi2iuh9992Hv/mbvwEAPOEJT8Add9xx3EMO2BK8Z2TWIp/l2BvtLX/DmtCW/34q6ieBaGJsokRCj11c7DwzkR0tusib4PFnR5lqZbFsbsJqOUzP5kLtcIJ2Da3zNUh4v6Gwy6VYkmsKWZRgCVy3lkFq4GEWvr988WIh0vKYtNAg2juvUiLew5geU10YWmvA1lPtvSwXDovWwxJBjNR7BiCtG47TBB66C7eOta/s3Xffjac85Sl49KMfjac//el4+tOfjkc/+tF4ylOegt/5nd/Z5BgHbAjiBaPdDEW+HkG71zliZuEUK143TYz7LOgSYwOqSDhQWRUymVGj6A3yENvG1XfKjpfeUE+ifMsBKARWMUBa2jHqlYPSIu8F9M1uhuvcJ0axRAsNomMw3aW83TrKFrkKEelPfA9l1j6ZuAE3H/QWF4hnCG+GK7hN+AWZ4AGbwVqZrF/8xV/EV3zFV+DOO+/Et33bt+HjPu7jAADvfe978VM/9VP4oi/6Ivzcz/0cvuzLvmyjgx1wTIgE8163nBuz9jkqxN9TmsoSBmzFCqpf4klX/3L9DdNn+JxZZmGsATsGNiTi3Pb19LWniZwqYwzWCfsEAgnlUUMmicsu+kqj4n2biCqFrsxlVkuaCTK9Jn0C8H//7A+geOgIlz90Zf5YLMiyrNNDru3kFPhnVajlkf49W/LdCpfP1Snt+xiwRcTv3gdB0tOeIIqK7wPxfXtYK8h65StfiSc96Ul4xzvegQsXLtR+9x3f8R34zM/8TLzyla8cgqxThrgIZqMMznmMRpv3nExt8CtY1dxoCHvYVTNZCJ12+g4ABEOlkGhmM2Qji2JWbGycraPqGRiH6iaMIayTt4wNDJH4bhLxfcF7WEBj256BM6aX1ZJoHbZXkGWMQWYteJS1cuKEtSGhr35Zyv41FpyokWWsSmgsPQipLMipTeUO2CoIgDjWLGvv+v7JYOBkbR9r3QHve9/78OIXv3guwAKAixcv4mu/9mvx/ve//9iDG7BZCGt7/Hh3hGK6uWCgCg4dWtYez85lm2ARmKqpeZ8gi5XQb5JOFsFQPRs42hmtVJqqYvf8LnbP1y02WrWles6F8dqvWwYQlhRomWDYLAQsChxEpJPoG8uOSwU6w0bA9JiaYuDX1bggEsqFPaUUoq1R87r3lW8IB4ExBtaux4UbcPYhUP4rC596al7iZJ3yYPAsY61Uxsd//Mfjvvvu6/z9vffem0qIA04PtKxlsLu/i6Prky2dBAAIxtpTG2QBweg5oOyc6y6hll1jkZMV2FgExDVctbKurTWe8bhfjbF3iTeMydolPKpl76/IN5glNDvhBV2Wqba65LQiQE+DaIDwIz/wJvw/7/t7/LMv+Xx86q2fWPutcrLMajy5Wiel3g/95RvKbLExpqdV04CbEYsaMk4T/KCTtXWsFb7++3//7/HGN74Rv/zLvzz3u1/8xV/Ej/7oj+KHfuiHjj24ARuG6C59Z19Xi60EQbFDawMK3NtENVtBhpaWwsIrg06Wyjik7kKJpOgNt2u3cbJWYL4LpX7INc9P6dqo8Oq8/U/tlCGoaYMx6KcdJZI4YMtgifAH9/whfue33oErV652fIT+i4dILHXX74e+8g3pGIZAdigWPpxxVoRoB4Po7WOtTNZ/+A//Abfffju+/Mu/HB/xER+Bj/mYjwEA/NVf/RX+/u//Hh/3cR+H17/+9Xj961+f3kNErUHZgBsHFoHNDEZ7Y2SjDEXhemdQeiOUS057kGVqmazl8gIMTurn8bExxNB3hSCrI8BYFTGD0taZ1NuoOQQM1q5pbyRlazcBZRZrGfE965hSYnZnmUck9GS9OFmV7sL+DQGLTq7yG7GrMt4P65QLDfoq8w84SeQzdTcY7254HjwjGLwLt4+1gqw//dM/BRHh8Y9/PAAknawsy/D4xz8e0+kU//N//s/ae0572vRhAWYYazAaj7CzO8Zsmm88yKopHZxi1BdlXViVs9b++kh8hxEQWQgolQ7jWmpHFoZUvf04i3483nHsacIR1n7uqiTwKGvQR4Kh62Nr0NjPNJnQz8aHiFJ3VJs2F1BmVPt002pcGuQ5KnIVq5QLRTSAp4GTdSYwPZwCePgGWTx0F24dawVZMagacLbAIjBWRTP3Lu7j8PrRxs+hi5kJUhGnDyzz/npq8beYLySsZSxLBiALrSsp/8aFXn2TWZjMwPvjBVlARTW/gb72NALNsFlrVY5hRcmOWDqLmaxIqVqGrgxm8lHsk93pqZMFAJIMotvfkAXR2b6fn2xd3R7obw6tb4rfUY/BDxhwwii7C0935eEsY7iyDzPEZ2l3T3dum+ZlKSflFGeyeN5fj4wJnOxFnXOa5dCMSfgTykvxfTYIkorbAOG54wJW7WkWIXbK2cSnWg0xyIr8qOhfuCwTtaicSWYxpyvCVARfl4E7FonEk9vJYGzpcbgchOjZGC+aXyHIipZNAwacBQzlwu3jWEJJRVHggx/8IC5fvty6WH/6p3/6cQ4/YAuIi9Fob4zRWNXfxzsbTJUHTsrxS13bAXMgNlc5WT3kBVhEE1iq7x6CIFHbmiBEZYxFllktwx5nkBK761o4WT34Y5VXw0RbnT4vr40BgDXlOKwS35dhUdmh26OxgRXGWRpEm1q2SgKBPhtl/UViJZRHK0G3iPS21EnnNf1sgQacLLbZ/czc52k5eSTvz1M6X98MWCvIunLlCl7+8pfjp3/6p5Hn+dzv42TnN2gxMmBDCA/TaDzCeHeM2STfaJAlotmy02rTwMwqR1DLZAGqxbD4vSa198f3CizqXYmj3QyTo9mxxqjVx3aj4j78MSBYe1DZGbhqlBUXIC0XBo5UPO+C1y9qf6SeGZ6ozdVrnFwNssoEoLBa/NigxN9HkDT6DkZOoQBgX2YNbY/HREIwflo3GQNKsGdYaze+ThERhLl/k8oJwsvAydo21gqyXvSiF+FXf/VX8bznPQ9Pe9rTcMstt2x6XAO2hOoOe//iPg6vbp6XBTJwK+4S2fuyA22bYC3nxJ2bY51gl/nyRa6Z1pIsEPoKo+J7xGhnDF5TKyudCyFYaFVOX84fiwfRYPcYUgKmVAQziOdtj0RVucAs/P5M38GswMn67f/2k/ib+x4E3z9BNZAUCT6KmWazXA+/ToFoYBsbGljggrisyfrt9jWTlWGVgHbAycAzaymZN2vsTmFOOAvZzFIn6/QHhGcVawVZb3vb2/At3/It+OEf/uFNj2fAllHdsezsjYOYpmws8yQMHLFDnqvYaV/CcQz2Ltw67yKwSXAqZ+qk8uDREXZ6lHcEGpzpy2KQ5WqcLACdiuerQUpeUAN9+GNA5FRR8pFcR06AiGDBIP6gKoKZbi0x3bnTwoVK398vyup7N95y4TzOTaaYXM5rn5G9pCzeaJzh6FoPTlYIpNNllxXV3gOSJtiAUw3x6uVqvGCpqeYqIII4ORPZTB4MoreOtcLX2267LWljDThjqDz4490RRuPR0l1+b4NdAIBADIIVS3/CteDGyAppicCkDj2AwEGTahEnS0T0M6WfGACs3YaVt21CkDRmstqmvd72NIhSGnWZid5jYATiew6DAgQPWdTNGDJHi0qCRP38LMl0fPgWqIE1gGg+XQ5IM5aWkO30NImWUtU/ZjbLzsJ+4wHKkuPNgpUU888Q2PskPLtJcX6dS06/pQ5QziNDuXB7WCvI+vqv/3r87M/+7IqL74CTBHO5s4/IxiPs7C0OsvJJjiv3Xu1NEpW4UJnA41lldaftklEBAKJ6VoDyEbI4uZjF55ZAilZPvZKXZYjAtUyWgTH2WM+G8p7aa2Z9+WNAQ0pg5csavBorBPxE7WpB2/3VhLFYej9Q+tNv0v/uV7wOP/BdP4IHH3qolt2L5RpjbLfVTwOCMsCLn6PUyOq/CJHp9lM8a8hnBa7cfx15vh2v05MEe0GWWZABuKMMvg7IBK7gGYiyBlud7WOtcuF3fdd3YTab4SlPeQq+6qu+Co997GNhW3SRvvzLv/zYAxywKQT9qsbDlI1H4INuorZzHsaa3mU/VMpUy3hOEXGBNsZopmkjJbeuc5XH9yzIjIFnLSF66Q42JaSXkq2OzqQwZIFGudBmdDytrChG2nK5YwDb6xjRHmZpcbHj7UEjC2TTeTszaA2uWxtMj0yWAGWk1QO/8HO/hcuXr+H/+MLPrY2NPWM0Csr8fSUVUrmwFE5dSSMr4ibKZE0Ppzj/iHNwsy24Q5ww2HtkOyPMpnmvTctqxway8ennOZVivjfLHXv6sFaQ9cEPfhBvf/vb8Z73vAfvec97Wl8zdBeeLkjkqLQFWQu+J2GBHVmwl04179rrRWUNYrdXH6qDhC4fsgTvblyQ5ZgxMgaOHYxdXFJjVHd7QZCU57lsJlMh1l6SAYvQRf6O2aJlwYqEbsh0oBXDLOZyDGRh4JRr0vXy6Dm4SCcr66uC3r+7sGxBb5gFin4XQH/V95jJSg0OEPgV5BvS6OMmo/c7TifyaYFslGF3fxdXDq+c9HA2DgEw2smS/MemQAR49qBNcby2iKFcuH2sFWp/zdd8Df7oj/4Ir3jFK/BLv/RL+N3f/d25P29/+9s3PVYAwBve8AY84QlPwO7uLp72tKfhXe96V6/3/ezP/iyICF/6pV+6lXGddnDonGougsv89th72Mz2TqcLAKHQrWKWE7QBwHslTWcjC++Wd4EdF1Ed3AsjszZwoJZ0vglrliYJZWoGq8k7M8ZiNLa9JAO6zyUhC9ZSLiTT054mZhTRm8PVeHcoTTIAE7hP3cGdMMNkixsIlk3jSY1/he7CtBNvdC6qT6fuIa21qnzfp4RbiaNlTU6WCZ2hN4BiuFVMD6e4eOt5nL+0D2D1e+gsIMts4GRtkpQVRHfPQODi0ybl9GfdzirWymT93u/9Hv7dv/t3+J7v+Z5Nj2ch3vKWt+Cuu+7CG9/4RjztaU/D6173OjzrWc/Ce9/7Xtxxxx2d7/ubv/kbvPzlL8dnfdZn3cDRni5IB2dmmXaRIGQCfN8cRNA56qkQDoTskjXY2dvF7Oh48gd9EDMbjlmVjkNQs2ioUdyykt4BgjxncyrNxmPw9ena44vDaCsLUohAlgUMMXA0tiJGutIgotGx8s8MBLKAM6eefYvvpaXlZokSCP3rhfE6ZFlWCwDFe9hMj2Ez7Yz0LMoLWzTG8P+xfByDLLtCuTB2gJ5l5NMC2TjDhVsvwGZWG2Rm7qbz+LOZcvZcsbnNnQZtm5OE2CYGTtb2sVb4+qhHPQq33nrrpseyFK997Wvxkpe8BC9+8YvxiZ/4iXjjG9+I/f19vOlNb+p8j/ceX/mVX4nv+Z7vwT/4B/9g6TlmsxmuXbtW+3NTIIgzNh/8PqRgQ6b3LjZyalTaoB9Bmz0jG1mMd3t2gR0TMZvHwrDGBoqVWWyrw81yqWayNLtTv6bj3axf1qTrXBLVotsnPmOXZN30IPUOxVW7CxGyZqSZrETm7sxkCbLRkiBr6UQuIX3Wt1ioWVA9dj2gF5RyGsb2tzuKgaAxWjFNEg4rlAtVt1Vfv/VGji1hcjDFxdvOY2d/B9k4w87+Ti+tsbMCNXG32qiSmdU3IQtApDZOZ6FgnGypBk7W1rBWkPVv/s2/wX/6T/8JBwcHmx5PJ/I8x7vf/W4885nPTD8zxuCZz3wm3vnOd3a+73u/93txxx134Gu/9mt7nec1r3kNbrnllvTncY973LHHfhoQtbCaC52xRoOorqBAlH7UN2iI6uAm7uZ7LDLCAjvOkI2zjU52XYiBZTyVkryBRX3cgqpgn0FZLgRA9UGb/z97/xpr25KdBYLfiIg511r7dR73ndfXTtsJGL8Lu+wygq6uxgLRgu4WorGQmrSsFj+gs5FIKCAlbGMhYUO7EW4osMrGmB90GzVUV6tEkUBlk7RtDHbZ5S4XxtgFmU7beW9m3nvuee7HWjMi+scYIyLmnDHnWnuffe6+N3ON1Ml7zt5rzfeM+OIb3/iGs9dwGtOVemYHvyntPUi0qw5q6igilMmas+QIIWwXmG9lsvh6mgKkbAvVfZohRRVzCsRYy0zWDv0L0zUng+CRHhK3A+ef052UrAHeixBrfbZGu3Q4KTzrDo6X18r2XCUevPkIm2s6huDZiNQ4BlrXmQpVzSRNNC1/N4Wm2/dM1rOLK6ULz8/P0TQNPvCBD+AP/aE/hNdee21UXUhE+JN/8k9ey0ECwJtvvgnvPV566aXez1966SX80i/9UvU7P/ETP4G//bf/9qQ4vxYf+chH8OEPfzj9++HDh58XQCvGKL3o+hOhsZxSqgnbg7pmk5ky+h7vB/IdjAieyQjeo2kc95kzRlaZ168RKCdBDbUL2CZ+DQCszQYDU5os4OkHrBgjPEU8Wp/juF2OP1DvtzPcSF/fdJVJJDFhVqoM54/ZNvPDyVYeK5bi890OMQnfne23/CH0NDFN69BdzE/QBE4hP15fsHhZWCzb7FrRKbYXRnlIStW276U4e3KB5169jXa1SD9rVy1Xl95QGiyECN95dBddqhp9qu11vCiwMv49DfM8jGbhcHT78D2hcwp7x/dnHld6Wv/0n/7T6e9/42/8jepnrhtkXTYePXqEP/JH/gh+6Id+CM8///zO31ssFlgsFts/+B6LGCJchWnQVX7wARgYaUaxISA7b9Q5DCLWeu2qyYpg7yrXWNjGoOs82mdR/iwl+sYSi95h0Fhuj7NL5Vv2jKLEEJkK5/LUPctixAYej84vcNgsxhWMhraCXrVguIS8aRTsGcUpPLXnmottKYdtQCUme7DdEy2/8isfxb/97GdhT8MoHViC3aZxeLKl2jkCOPcdzs8vYI2Bv6x9gyxKAoCNtIl6r8X6fDNisQCgXbapPdFN6LKiWLtcV/rVS5WxsQ7GumuuLiS0q6dqEf+ORRK+vwef1fdKXAlkfeITn7ju49gazz//PKy1+MxnPtP7+Wc+8xm8/PLLo8//+3//7/HJT34Sv//3//70s1CIZP/dv/t3+PIv//Jne9Dvpoh1kbt1BtbW04Xa28s1DvFSdhwES4SoVTY7hLEGtnGw1rH9wTMYozRFQNbwIGsNGkkz7TLEmMIqMwnfxSahtAdQtmxnb7FBRPG4ctaiCx6t7b+mXB25g4WDIdGaXT4LWxTZAZBWM3MMWtwOLrcP5DGBrF0ByisvP497NuDtT70FPwBZ5fG4hdsN8Bvp02gInedj2BVkaWPv867D2+fnV2cQbzC6TYfl4RLtsr/QdA3rss4end8IyPKR39fryr9GH9EciI8ar7O+IGNv4fDs40og60u+5Euu+zi2Rtu2+IZv+AZ87GMfSzYMIQR87GMfw4c+9KHR57/iK74Cv/ALv9D72Z//838ejx49wg/8wA98XqQALxNR2owMg4hFwd35evwl6e3lLmlCmATXRNg1z2illLpdNXjy4OxS+9s1gmemwVqDCx/giNBaK61qtg8yzAQKwKIEPxhkiUM6wCBoCLwuHURoiI1Sh3Y7u5h6AqprupoAW7xXk/Cd8crMNmiHlNqWa5Fc2mk30MubpLxvObxkbls876ZillyLVJVJhKBM1o6AXxtMs4UJsJnxiTt7fA7b2nedwWfwAU1bnxYOjpd4/PY7p8PthY8wzqT01tNG8D6d585mtZ+Hsa8ufPaxM8j66Z/+aXzgAx/YqarwE5/4BH78x38cH/zgB5/q4Ibx4Q9/GN/+7d+Ob/zGb8Q3fdM34a/9tb+GJ0+e4Du+4zsAAB/84Afx6quv4nu/93uxXC7x1V/91b3v3759GwBGP/9CiCjMTS1c63B+OnZ99z5IGm938bSCKyICWULc0o0jhABDBlb8utrVAg/vPdpxb5eLELia0FiLzndYOQdrjHiIbUllIcKo27v+iVnzVF4fFT6zV84VD5YI1hl0NZBqt4MmBUlWDUkvGTGy3F1gowjg57eztVKVdNsT4FNKU7e159Houg4f/vBfxtsX5/g/fPAPwOg1iZLmLrZhdxUhi/A+GsCLhGtXJitELYRAKvyYuksXpxdofPvuA1khwC3q08JN6rJ8CHDOsjv7NUSIkQttkIH6F2Jkn7kvXKD5rGPnK/st3/It+OhHP5r+fe/ePRwcHOBf/It/Mfrsv/yX/zIBn+uMb/u2b8P3f//347u+67vw9V//9fj5n/95fPSjH01i+E996lN4/fXXr32/nw8RYpxczbvWVl3fY4hoWncpNkYndyPtSbahs+CZYdNUpmufodu75wmEiD2TFs5lrmeHc0x9A8U7apguTKGVZVdcdCuuasmiq1TEGdquTeGMI6VJ/0qaEyPwjAz7iVF9G7rtbc9JSqPOHHREZrO2xWbT4W/+4I/h//F3/mt4H9JxhKDNqvNGjBXTyW3XISIVe/hOZP+72jdIuhAEbhw+x/4RXaHZ0TsTtRZpgOiyWofu4p3vYxiD6CavMf2q4+F7wc/qWUXWZN3wgXwex85M1vDhjjHi/Pz8HW+d86EPfaiaHgSAj3/847Pf/dEf/dHrP6D3UJiJ1bydsBzQqr+dG+wWky0JDtk2KAYfQJbSPpyI79Vy4jojhAgnlUk+BiydAsgdNE4gMSNFpq6IOR4zmLy5vYw2nb0KaIysyTIs0B/GLqXhChwTo3MFnyxrMnDShGj1s2LUOvV8aZiyaXiVyeKfm3RP5qPUEZrGAOLjVPbCTL+30urJB2CubZPcU1hAh7ad3d5jZkQ1/TIF6kg+/26MKdbPNQ6rgwWePDx7x4XdMcRrT2lpt4unLlR5D0fWZH3hXoNnHfsr+wUUU5SwmZh0QoywC5cGoZ2a+0J75iGJv+eiTOEBDLKcE/H7NUfwPqUIYoxojGWQlFzcpyMiwiUmyyBJwit4gI3h6cpNZ7Oha12Ru8tUExBZwA1indClNVmiMUspvOlLFH0EmfmWOhpbm4bvrnnv+V5ZW7AcFeNdriSjlB6ZiqjPQySE7pLCd2TRPhcczIDbHd6NdzqSJm4GhK6OV0/XMuqKEby/NjCkIFzZc/U0+3xsG7Qt9pqsZx97kPWFFETowniAdDNVO9YyQ2HMDuXTylBIf71dJpLgA1ybJ2jXOG5I/YwG8tSrkfi8VY6x0xBD+n8KtPjfPEAXHzPColw5X5hZvNoWdtVPkKGra7J4A6mVEBHf29qWtC/mbm11ptOoqmkiWzPGqHy+AEylBUetWbVxxEB+a3sonoADAvRV2dXtPUYtfiA4TQdWdhdjvBLwfdbBli12lrlul6rLevadGcpQAHsdUEAb0qsO1GhT8HfZ/XgnQu/j3sLh2cUeZH0BhaeANx4/wtr3TRnJctuU2krOyO+ICHGX/oVEsIS0UtzOZMVe9aKx3GC5e0Ygi6zhdjowaCibbMYd3oTsk6X/JWHC+hMmN3G++so4xpiYJAczThnuMiAqYNHKuyuETaagfI5TWcfoYxL7z4Zsb/ayxLr3WC1KJqs02eVm1X3xvLZQmXJ9L1Pdhgh+k6G33VFUEQO7fAfEkfC+v6/t+rWbCF+4oE9Fu2rQLJqtxq7PKq4DBmlDemXPLzW+fZ7F3sLh2celLBw++clP4ud+7ucAAA8ePAAA/Mqv/Eqq2tO4CR+tfewSBEcGXQgo9eW2olfRSUe1LMYyYzGnMGJPrJgHrR3eW07h9UUv7arFk4fXa+Og52Mdn78zbETqhX3QOW/adkHYJdULsQlSshsoRcycciH4i6szWWQMWm1e60NadfMOdtgEH4ikbrchm+HuYwHSyqrCqXxhTGB8LozQhlOC7xgiYBlg7TLml3rQkkXjZtXjJ9U1DhenddF2SvUBAAHdhXjqNbsDohhZN0TyrETCxHVnU9wY3/m021zEEKXP4/SKwzqHxaplv6zVO3hw2N2GY1twQ3qbe1sa0VBuGd8+3yLEmBh4myQhsjB7Fy4C3qtxKZD1nd/5nfjO7/zO3s/++B//46PPPZU/0D6eWURJkQ2ZEWOzXsXKMBN1IJJ0IXSlNycCltJCMgZul9YvEm4wqLeL9tpTKTHGdD4MsgjOcDsNFVprRWDdXUBSZgCyJosn1GG6EOBJ/6rnoBN+YwwcjXVEu7xbEWzBYMgAO/Q6HH43o85cGKCD77AowcuzMuofOIps0jq5Z3ludjnH1FLH9AFemGhW7VqLUEmXp12rXxoB67V6ZF1yHNMUpTGYyBYy4/UuHB5957FYthi23hpGs3B4cv+dA4j6vDhnqs/fZSP4gPYgV033mKx3l6PGM43Sc0yZrBgDSBbW+7ie2Blk/Z2/83ee5XHs4xmGClpBQGNMEjtq1PQqXsXDzoKMYT+praJhqQ4Ds2O7joNDJ3rXXP96MqUInMU6eiwse2T5GCA6Z5lc6n5QFLW6qfxjUjWaH6jcjXk6d2pDgDMGrXM43WxGv9t1G1qAcKmD0So/Y5E8z1Ck5AaXiBt8bx+USb1cp3qRR8Uou53g88/fxv/8S/8tPv3wEUqXN04XVkBWM9c+JRbnSujW0jh315Y6YOsNa9miwpKmRmuiLABXUso924ghTnpkleGa3dzzryu0etU2NmunngZkDSQKnDr8wtNklfNAAq0x7lxNvo/dYmeQ9e3f/u3P8jj28SxDevbBMDtyPtA7GWNhrEG3zjqLWDRQJTIwzsBv0WGEGAHSBtHbNTophTd4qW1jYS17d11XiqA8n24dcZJcrSkJ2mkivRNjnoDHwnc2ZhzqOYw1TzcRyb5WTYOH532j2N2qrNgGIk32l94/gzS9hYovq0A0ogpqRpvUazhbXWh2Tlc45/BlX/YaVo8e4Rd/9Tfy4cQI68ZD25zWSNOjDJqBTWKyth5G3gRiujcE1vnV2kolZ/h3V7YQ3ns0O5ijzl7HZxDqpeca9jp7KpNfjCUKWUP5zor5bzp6tjPKZAXMpov3cfnYX80vgIghp28NGYQKleAa25sQfBDXbEkBGbtLdSHAYMWINmU7yFJBchm2cQz6Ntc36HEfRgNjHXwMWMkkTPI/UJQBvHKc+jniJFwGWww4jDEj3GAsIT5FBZZWPS7leC8bAZnF2gpsBqHPAZGAc2g6FVUgGiZAzTBSWmZyx8x2mUtyPLZEgxK1dJKz01vOp8RPrerpdm4ODQAhpMkqPSOVk03O8O/C2IVFrjWaf5YRArPQtnHXVpVZppNZQ2me6n19L0YpQ0iaLNTbr+3j6nGl3oX7eG+FloxzCqg+ujeNw5NCSBxDhFtl3YJrzPZmz6JBsaRu4/Mva5ioZrLOwjYWfuNRiiSeRusXCyPSiAinbs/CXPg5UbZkJ2rVhUCEHdgSKHh8usmARes1C4ZdG1AbSd2ylcYlJpCY5HVi4aCpXz7n4WlF72HdLveFZhkDtUCYYkBjDL1n6q237uOvfP+PIDYWv/cP/97+nmp9Otldtd4WppfCo8xkXSZdGBlwBxT6rlpuVJ3h34GE4WXfmdp1G4aRPqPvWHsdFeQ3eb9PG0MgwRrKp97seypq6cIg9hb7uL7YQ9YvgFDzvUgKC8ZhF32dRemODkCas26ZqHVANyq43nJcqWHzGEQsD9pUbr/ZdHh47xEevPnoykaIwQe02qsMrE3TvzN+YKamnuLTSksgnVRieFiE60PA6XqNe6enePP0Cd7ejHtB7hokmycIazDQ9lASqda/n9KwZg0bP4XLWnZlgKVXR5too2rBELFbComfjWkmIgIqc6uckwf8pxDDk/Sze/ce4P/yf/07+C//5o+NvlSb/LmK1nIVbWXfekuJKFcXXkL4HoHk18XnGieKC9UZ/tnO6t2mw9tv3N/J06qsvt0WxpKk898Z5kf1lFYrWJ8CZGWJwnBht8Mi8vMs+l0q8s93Adr72D32V/MLIaJMzDKBWJgRmzDURZXu6ACXbu/k+C6Mj9upOoxXTVSpSmuXLfy6w6N7j7A+XePWc8dYrFr4KzrBhxAESAYYGDgFWQlIyMHXNq92Bhh8Vpksa4QBi3jx6BBffOt2biFzhZBkHYgIzli23SjuF/W0UVPfB4wJMCBYmihzm9x/RCTAGk6PgiySawWVe8hRPj/nXYd7p6fVbc+atMbIFZE1zVm8AOgWEB6k51AtHMrPJ+uRyjbMrOu7HJOAy+5CQcflJ14FpHFmeH0nqq836w7WuUlvsDLKauJtYZ1lX713CGTFyAs+MmbUwurS29JKWDnPR+sLPLw453f4CyxdqIygNdlTLkZup7WP64t9uvA9En7j8eCtR7j94smly2tTykAmEGtIvLL6vd2GUVor7NLbKk1wZMSMcbD/QQQf2EG6pp9pHdqDFocnBzh57hjtqsGv/dKnZ4He6cMzhBBwdPuw+nu1b7CG0KgRoVaUEU22fImi82afJwWEmcl64eAQd5ZLHLgWzhrcPz8HmadrAKzZucZYtt0IAU0CozKJTwmAZbdWXdoNXarDTxRQmdOj2bR1iu0rn58L32HjPQPaQcp4HmNFRDuVRFsDtABTYY8BOk7ggRmqfgq1lnK0znBaqMJYhPTsEoKP8BuZgC5p4UBEyUzVmGlN1jvhsO27gGbhdrIm0GpiuwMjaawUpkyAkvV6g9P7pzh54eRazpNZdSvsICE+RcutTiQKytidbzb8rLt3tmLy3RC62CiNSPl27VHWdcb+at5AWGtFG7L7YOE7D2stuvXl02UhBBEcExbWsm5kMKDoJNlLSxUT5y6u4THypE8JzKmYcuK4fOilJMs4OFnh1S9/GS9+8QtYHi5TBeTc6rnrutnfG2vQhQg38PGyAoimHboZZZmhMScZIAYcNA1OFsskCDbEeqqnlGTBgJtEq4Fs+pURJmRWQc56Lqir/WUiSpV8sqwwhYVDf7+laa2GDwGts1hX7sVsZaSAj6o8Pa4BcwzY9wHhEWKM6f1huxCpEPMs1q+lC4nMtE9XzJ/pLnz64WXtgvQJ0WRgbV9lteqznNiD93DNNBjqHVMXpL/jblWirp1+F7uLjseYa+o/yufB44R72rSej+wL6Ni+xZK21rmOI31vhWqyhkDY7KmXa40vwEfr3RHW2UsJZbz3aJYO3eYK7SyS90lEa13V4NJaA2ttjzIvUwc7CVwjp5c4RcWmpFURj0QIvNKuhTEW7WrRP0a3XUzOfdX6nyl1GD4GODK9CikVh0+1wsk+YyWTNSM4B7GB61UjKiAQfZpzA5BlpkX66UPaCsfAUrwcqxbBxrW6qiXVZI378aUK0eJ6hhixdA02FeNPY+oVnLLbBD7G4UF0BHIvA+YIiI8Tk6UtbCJJMYWZFs9z1WcdZKkWrTsX8OZ0ZX+JMFFQFgF2AgdH/sxlnfivErRjleuw1cy2cG07CbKCD2iWzbU2klbmiZ7Sf857z4ymsbjYdFgWru9faJFb6vQX2NsKlvZxudhfzRuKprGTk00tgmen5as0To4xs1KaKvNDJkt0Fj4wQ2Co315jFzEk70fa6iB7K81NJJfxZHGN21pZZJ3ttVsB+joMHwIWA7sBI6BlSpStQmYei/R4p501WftvrjwXqLZNNV2rpumDLMyJ9CE3glkYgmEB9qUOgNkgnm8lXZjSpX1gUKsQJUS01mDjx88qGbMV8NWBTQRoCaIVYF4FwiN4AXHsNs/WEFzuj0n3eTPnfi+gdn0u23W4PAgik5hMlq/V0880AwSvI/QdNmR2GmeCj2gXu9udu9ZNMmQhBLSLRqqDryfS+OXsU7F/wSN5gZ37Dq1zUnDx7NO377ZITFYqpJGem1+AgPNZxv5q3lCQY7PNXSMED+u2g4xaxJBdfAnE3kuD7VjLrsexC8n8j3pMFg/Y2/ZPZMW+gQRoTDMXQF0LNhWusYhbrplzbjS4+xCTDsOHgFXTn0yM2BJMibLZcXroRi6orBLcZJiB21UmhAh2DFcav7G2D0xUIDXjnB6jMFmwl16ZsoQ/CkiWdKEadw0QUK1CNAKwxlatGgwRMMOsRAVzvfPpADjRZAHkXgLMEXz3mLepwl1hskCEqQ4/kxOIVPwZIqyFyTIOl1oIpX0QcRpqhsVVAP2sKtq6jWdTX7dbujAED9fuXrrvmnlGyTiLrru+JtKqG3pa/7ngu3SePgaWT6Sqji+s0DnAUn4Wd2r0vo9LxT77ekNhnb0Uu8CrDJrLUs1+11oDRE7jtZI2K4MMa7W894DnF62k0DUFE0PA1AwWAwCTNUnMfNRTVTUtz7YwdtrIsmTfNqf9wZ3d3kXTtYlJ9J7OXbQxU6LsKNQSSTpUvxMnmCxDAjp0kr3kAK5pK43G9FVKxsxr5JJQ3zJA2snCqvd9HXzV08CBNWkAUexV0IcQYM24QtSa+r2imXsI8HM2EkvHNYAWoCVvg5aI5ovwVV/xOfz8z/4DeAAbPjj2Q5NWUPXtTzM7fKsIGwFZzl0+M6WtHq0eTw20B8WrT6nbmwkvIKtdtQhvP9r6ea3g2zWMrXttXMYKYpfI44SaIm+vcp7dHpgR74KHg8Fh28I8OQXoGd2Id3GkYo+CySJTN/Ldx9Vjz2TdUNgrrBZcy1U9l9U6RHE7V1dzN2RGoGJWhyjpQmvH6ULa2mhYqtKQmSwydZ3TZUrGNeb0ItFzSxO3cPC+D7JKvQkR4AbXXldyU3qPVG1nh+xV3UaBDcg5XThst7NbxCTSBrjC0JS2G6pbmrwXzEXxPG8Z6E589uzJOdbnm/EveulRBlvUE/5zBB/gFjYdaxBD0QPXiGC//6zSjEmrbtmMhiWtLCxaobgXcXD0Ar7mq17BV33lBxKT5b3nZ3WCvZtKV2pLKEOEzZkwWc3uWrakZwEL952K7GufhZoD87+uGuenF/V7By6UWR60lwI7l2mXoz5Tw3c7dAHWOixW7bUI+1XzZyzhwfk5Aj29mN5Yg4vOY+EsDps2PTvPuhDh3RCnj86wXvMzk6sL+RkJPsp7vwdZ1xl7kHVDcRVKtl0tuCrmklqHCCSTREM0AhkaqnkKniuNyolKUzJhBjRo1ZRWiCU2ozIuXqZkXGNoWtrbXmRt0GLVjuatGCKa1iUA4ArWJfo3Yek06REmzSNTGqt8ZSxqkyRBqtvMFnF6dVf5GurZNpaF+qrL4kq66VSkCsiNAZKr6USszza4OOsbp8bAwuxcTWnSPoG+cHxoWsu2DdxzsbXjCsO5zGXEmMXjX6wBc9QTxBMtAXMHiGsQuJJVQa0+UzHGcXNti5l0JbO1G9Fkuaau0aseu7Bg0QgsNVMCfkj7HTuvq9sh1udrnJ+eT+xC7FEuk46/xLtotFBmcH+9DzCOsDhYcKryKcXvLF0gwBCerC+wecr+ggQu6LnoOhwvFmjE/oMXLjP+Ip8HsV5v0G06bM74nQiDdKFqMfE0RTv7GMUeZN1QXGa1oI7tTevQtg26KwxcMlUyY2W43H04gbjWIXjPKZd2IA634jE0MwipRxGnCsHpkokZXhs2XyZdqBNG9RhER9Yu21GFYfBeQFYQkFnsM57BYQ1t7ltNbSIKu5LThXJAmGKy2Fbg8i1AYlHlljRZxsARFSBrOhXF24jpOIgMDE1DvXovOBG+J1DJz4uhKALh/rUtm+2GGGGNwcJaLJwbM1k7XIMxNuHKwmF88pOfxl/8Sz+Kv/3D/0+5d7w6V3b0rNvg/tlp7xim2h3FGFOq7+JMhe/TurdxyAQlejk7A24jIA21L7P96ZgCak3rdmLMk0j+Eu+inTB25eo9i3bVwDmL7inF78EH0QmJiP8p539dgPgYcNQueEFUMFlXY57fG3H++ByHJwdJo6fFTzoXeSkasZfUcO5jPvZX84bCqF/VLh42PqQy+cVhe7XSaLEosCLKtTLQlKHmozxxDnVLkvqbO15Wj6dVvFUNWWUSyA2bL8NkyUA7sT0yhHbZwDV90W2IDBoVALhCg4AChBiDKssRBPWM+jGKT9Yw0ueIrpANimJkmc1cjTForO1VGBo7s20Bauw6byrpty1HEAEyUdKDYtqaDDbHt7M0rfUhSs9Fg6NmMaow3FoqXxG+a2XhMP7DJz6Nv/AX/y7+5g/+WHrWYogpRdZJJWmvMnNKExaRGNgEsnYvthPRMJ+AnbShkM9GZmeeXmBMaNoG3UU/PR6EKXOtm1+Y6OdlgWIulVrk9PsQlISOqxTZgqWFf8om73ytDKDFFU8BAHQR6BHgjMFB03CfVQAwxK/z0/hDvItjfbGBtQ7NoknPQun4DmiB1L5v4XXHHmTdUJAMsrusnBiQcJl8u2ivVJEUjbaG4fSTrRiSpgEZqKbxXGNn9x0BFr5LNZo1dlLnoCm8ywSXvZvqNdMG0LZx3EpksII21iYAkEEHV63xuK3pwhrLIVxO8qTQqA9IlITvu6eb+hsY/QUr17dxmLUikB+rNowICJdNWyY20oL1WOLNVQEPJVgIMTKQN4SFs+P9bhHVqqloOo5BZWEZ3utK3Mh5sk7NiEVHFWRNnzGzg11EJ8Cgaed0b4NvS7qQj4eK56Uexroturqd9opm0aBb90FWt2YvKNu42YWJRvABZOly+kgjlYsDEO19h3bZAmB5w1Afedlg6QI/e3aqseWOwfZkhHUIWFiHlXOymDEIqsn6PO1feP74HCfPHWF5mN+jpMlK6ULVne7jOmMPsm4oVBRe76PWj6hd6J3hNN4VJm9S/x5jYKmfftLomY9WQBablc7sAxBGQLZhCHFCdB06j+YS1Uy8PUlRVA6CtUE80S5W7cht2lqpIBStGIcH0IhDN4Fs3dsqxsDt+2gIsgym8j3uCq7+AN9rZqH6YGPZDHyJtq3ojVQDwgojNR2jyYXxhmS+LHLn5FhtFVMCch+ZJSBikDUyeN02htMAhw0qC8vQy2GthTOUJkoly4KU6Jd2JcPrmnYjmeCNuL3rae/6lkVEKRlkITHR/OCq1mxX1WTpO+UaOzIo9l2HZuGk39/0wkRDK0QvwyoDnI6saTSVBW8uYQkxFcxMWoSIXr/Rq1UYsg50HQJOlovEqjr19bPmqZpPv1tjfbGBaxxOnjvqd2aIeZECyHPg9oYD1x17kHVDQVbYgR1e6uA9l6WTgWssrHWXblkRJYWh7W4smZEhqXW86kVAzxG9/H2suHinfQC55QtkUJxYrYcY4Sbc3qfCiJcXakyW93AyQCwO2mRIqm7txloEBDibq/YQPUCOM5rITN9o2zFXjfURwPQkYtRv5rKTgXycNVn5x43pN/Wbc05HjGIRkKsC57CNMX0BM7MyBkTiyQHRxMiB5XTDWMsTIncVAICFdWiN7WmitjVG1pRbjnFlYdpX0VZHgc3QtdwN9s86wamCAcLmTMDLYjsD1Puu6CYjqfaxzvppGH03rhjKnNU0iL4LaJcL2Y+FsaguTDS4QtRtvTfDcG3d608F9K51T80OhcCtt4Ja2Fiq6kl3CWbhOV181GRGpzGWx8fhguDzJM6fnOP47hEWB8teul7vS7JwkIzJPq439iDrhsKSAVWEo7UIPgvRXWthG7OzLiu3hOFBWVfxC+dG+yYRopOlalXSrk1UVVNjQKyBmfjKZVfOxtpJ9i+LiQHbZIMj1rMxCxgii8hzSLpQPjxXcs86M2BXJssSTwhXwljpXuXQVXz2DeoL0EfbAFLVlKF6g+gMQPvXNErqVGqxoMJ3EppSt5+0PMXAHEJEI89Oay2agfP7NhnSaKKvVBZqDNOFMACZvtUIn3u+TlNAmlcIwOZCUoVLcznGWL5PlCsLZ0+V1HfuqpM636Pl4YJ9n4qUYQgBixWDUtZ+1Rcm6fMzPUTnwjWu9wSqFsw2GWTVOjBcJkIIsA17YxnlnK943WIEPPFCa1Wcr5OiCZpqufQejvV5ZrEAKWAi1gKPzEjl9/u43thf0RsM5+Y1ThqlEN1Yh3bhdq8wFNsCGIJFrs5tnR2tMK14YRljqvqMOQsFjVKwbROTVf9sub0u+KpD+DDmGsQqaGtal4oKkp7N8vmW9g2InTBZfBxTGhrW8+unCu+wiepCgEH0nPHldMRCZJ+Pp0nso3g4zbAs6utFYKEwUawzavJsGDu4pvp9dZeFaqUi/0sAWaj0uwsIaOXfRISDpu3ZOEyZhCrgI2GCctQrC/k7/F9msiilxK3NzX+XzvW2RqZe8aqesdq30C3NfHFBZQPaSslqSnKGGSISw9arpgs9ozrXWiwOFtiU4vfIaUQAqSpvbnEUQkCznAZZ6wld1XBC7joP67Iti2tstQPDZcNaBspGno/tfn31iIjowB0fVq4EWVxxat3uzOV7Jc6fnONEWCwgg+7oYwZZxbj3hdjD8VnH/oreYLhmDHRqEYHeSrM9aOF3bBQdxY8KEHZEBv6FdSNQw943JoGSYezkuVOwPVZW9MOBKzkNF/u4f3aG+2d1z58ybOMmQZaCNhbAS/m4j1zJ6QghZgDA4cF9+QiI05NijBHapKZysuPPhydo6C2QZT3XZaI0xSxBlhPj1i4hi20si5oKUr+asn9iApKol/bhqktlnRxKJisWOqWaaS3AAFDjsG1Sj8HZSIdGg9sQq3osAL0G0Y4knSrp6k5aSa0a1zNyZf+xOl8JA3TqkbWs25xMRZAULYhgYcQ+YwZk5T5NVwo1NCVjcHC8hJdq2iAWCsp8ExFcYyabOWtMid7XvsObT57gotIiZ1gcw0akmckiMmhXT9/DUK1jjCGRWVwtrRdDxDpEnCz7aTNrCAH8LD+lDde7KrpNB+scju/mRUqpa03vRAGy9u4N1x97ldsNhnMO0a93+my5amwX7c5pqCgTKfeEy3okW/GcITKp3Y+RXiznXYd7Z6d45eg4TdpzkROS4rdSWXVGnxs2l98MO5gG2WZse5HctrWJbMMDve88ELkCS/2R+r5BHkRsCqlu+LUQZ4oegOSYGJHiGRpag2YsOkIIPdCbv8v7GPqoWSK4QjNiaNo5XUESh4AWSYX2NOVB92X6AuaYm3wnXZekakxh2Bh8QLNsMXRXL81uW9vXkun5ajl9eeIklZD607nKQgD4X/6nvx0/+fEfwPHJa8wKGRIBN6ELHg0ZrFwDZ9jItbUmXYfRNeOzS+lCt+AJfdeI8pBw0YJcsRlvSwKqRQS7hnrnERHaVdZldZsAowUyEraxiE/mDn4aZG18rtAc3gXrDIyxkiZkINceLnvPw+JggYf3trf1mQtrDdYxZsc2Q1fzF5MX4Lht+9tPfRFtdVGk5/dei83FBu2qQbvKd67UtQ7ThcCeyXoWsb+iNxCaFsElqpfKVaNrdq8wDJL6AfVp4anUn2sd5+0lraai4azT2bJPyr3n1ORveJyawiuduUERuzgNOjfuXaatN1ITbCIsD1r4LiD4ggWkPstSiszL/w6DJ05FWT3EMHE5AohcMsisxf3PPsTFWQVgR6RVexnKbOkkMAsARKiv55M2NSSywCtX1/QFzHq+uZrSgP3EQk+TVTOtBfpmhgvrYJHTnJTYzeEx63NalOnPVBYCwN27z+E/+ebfiq/+6t8klbOihTLMZC2dQyuVh6WRa7U6TX6/PlNNFltXXCpdaJklM6J97Kc9+zHUjl06hKE2hsXvrnXoLtjRu2ldr0rMOjtZsBISm1nXR3YhYOUcupofnBiSKkvmO492kHasPR+7Rkohi90McY716tWFMbcV652HTIPMZPXPM3iP+595kFrRvJdis+5weLzqLWZKXWv2ySokEPuWOtcee5B1A0FS4bdL+4IQ4qiCi3sYOvhdKgwDoPXkrpj8GrKwZT+8Ytuq5wCkClDoeZ0g56j6co40JKum4eeLFB7/MwiztoOovrLiDl5TCQXbt2wRvEcIhbFqJLgSSREAGFBk4FC2sikjQhmwIRc09fp4GOozOL3jDZzqmUzhmNLLSw6V2KleK0Lnn5wIsnp0RirZgCFi0IpK27oRlmBNURRNlwiOB6m8oWltiAEGpueo31gL52wSvxMpOzYEymmvBRc6XVmoR5n+RgqwGCD6ELB0DawYufZAHsYgL/KlwlrShe3SzjJRw1DdXnqOaFaOKCL5aRC+dX9AApSucVgdLLBZdwidx3LV55xsO12wUhaG1GITPBfJVJ5VK6yIVjqz913/XrlGvPIuaWUCQNoP5U4T6vm21RR5anMC5IesnT6uxo3vR7f2XEjzHnOCVxC6OGhHv3MiuSgd3xMzumeyrj32V/SGotmxhDt4dUYv9DmN4/LpHcTv2pMP6OferWG/qKFXVtM2aJf9NilGVpLGyIS9ZYDTlRNbRowHLq/nlNgy1s/UQN9o25VBIIjbe0l16wo6xtxbL1K+FjwIEUBtrrqcSCUhxmK1109xTh4nmUnJTRRfoglTLgDUc1HXaIxNk/4crc/ZQgO1X7AUqnYCWtJe7VlHCnbyeRvTNz+IGHpkMRgtHdtba7GwFpvEpAhQG7GRSIxrj8maqCwEgH/zb34Zf/Wv/QP8V/+v/w4GuSE5icZmIce2dA06mSR1kh7TenwAa3F7dwsjp74ryiqfTbFLMdyIvWRdssCftvRRnI+cbuZ9ro5X8J1nC43VMB02/aykd3GCyeIUu62m8o1l1rusTLVNfztN66QDw+V1WUHZOgE/RCT3+WpVgHzNzOh50jGq9k51nU99T99L0XUernFoVjWQZbhHbSl8j6Fgr/dxnbEHWTcUWnIOzKf9tIKrnMyMtez0vCPI0smyZBisAKbh4HF09xB3XrqV9x/ZwTtGJJf6mli/LJnXSZJ9pSrHNHB79yHAwVRB3zBq2hE1ay2BKFcYWkTPaY1kRJoGkQ5sT9COyvrH6ciQ04y9Qah+fmrIOjVe+RBg3EQPPUhqtvJlZ3PabRtAz78uWgHV6Buqu/tnC4LES4pGazBBlSBLm34PJqvDtsWmUyaJtznG0jEBrJxmm64sBID//md/Hv/5R/5L/PCP/IMkNLcms776vC+dg4/KpJHsf8jqsUbNd/zzZsVO4Ls3iC4WM8rIFVq44oOcSjR0KR+uyg5774L6ZZWVhRpzKaBkdNxUFi8x5obyE6n8pnWIPiZANHyWrHRhuIr4PficEgWimOsS6KqtX0R7Nkzjajul2il2my4xP++l2Jxv0C6bEbMIqBbYJ3BsiDKg3acLrz32IOuGwhkD7vM3LWAGcgXXcKW5XLUjp+daxMBC2IDY08o0xsCCem7YgFafZAAUZTBXTdaUuaCuNCPyJDlVYeUHaSYf2bumBvqGYSqNl5OVQGHPwO11OL3grEnNoTN44cpCUCvqGUZGNb1QiMoCDs9l4vUhAGSrQElOGNbVdSUZrNaZrHR95sZCJelI2cQo2x7tjFNFjRXvnPw5Y9TvLIOs2gBcmtaGGOAwSMmCWwJ5KMgy1R5xmsrRVFs6kQk9FpCrC7WvJiinfAwMG7gC6b98GtOC9E5kN01jmEkk2jlfyAUmRnfRTz336L+i2neiw8Bu+0M/Pb5q0CwaWGc5vVXEXLsc32Wj42F0waOxFgdNO6kBbaSpfPD9ykINIsJiOe7AsEuwE30e+5wazlpcjckCa0GHr6WlmtFwcQ5bK3nffdFtOhwcrarjr74jmi60xhTpwn3vwuuOfXXhDUUjOhkWjsbJ1FLwgauHBi+LW+zWvTZG1TH1K+uICK2zON3MCzpjjKmqzYhZadWlPq3Qs0+Wakaq2ywmgs4HHKwa+MIgbyqMpAVjCIDJwvlhH0QtY+82HcgaYeRMZlnE7Z3ThQZEXdLJDLVXMZaFAgMmqxoyaE/cUx94QuqmVvdU7yPniu7M8w2Is/YI4Ek/AqPMV2IqHFd6BmlEzmeg31cGz4IQ02o/pb0GbTpa8QcrQ20zYlQn+ko7maiZUmGatlQWAmVbHSMu69zfswsB1hAa9U0TMBNj9lqKiDh/EvDmr3cIAfCdAwwvWlaHTS5M2TK5/tpbD/HD//x/xOPTC36mHWHVNLBk8PjiApvzDQNRvV8xIviIxaqF9wGb9abKJG6L0HFPv6ZI7a/PN2JE2h8vgg+4OFtX91PbjoYPfL+WzuF0s0mu+mV0a4/NepP0aIt/tpj8zGXPUxdPi1UrVY4Wa+8RNvyn1vprLnznAUv4v/3jn+8tgEKMOF1vgBixOS+OM/KC0FoGIe8lo04uQmiq19xvPNYXG9w/58IbYwhQRvO9c4rvmdiDrBsK1RARSXVOM6GJCPX2M8oE6UQ5FTFkgDBkR1pr8ehi3kKCJ1USdmNexBplBk2JpgkhOdBPM4UYsHQO5123FfQZMUwNMaamNtxEtn+NuIfhAuuzTXJ7R2JnAE4XtgAaqNFm0gUN04XQysxSowTNffU/Gz00RWfSvNq3K4ghgtqJZtQhptYso3MvU7EzTBanN10CLKm58+hzTFkZxxVH+hxGPTe593qupvhnEkwPWuo0lee4tRYNsS4rsWOjg4GAXHlmtlQWAkhO4lpVaAxr2boQ4YxJhR6N/N3HkEFciLj/WY/Th3ogWYx/+84iVyFO7p3jn/3CJ/Fzn/jMlk/t490Vb9/0Abyr4oXjlcwjRgaW9xZr926PPci6odDKO62cmQrvPVxTyau3Ds5xD0MzAdAAmXDVgXvwu6V1sz3N9DuGCBv5nHMG52fjNKXS95bQY1DImGrGpdcbkTilswvoU5+X6GMqOgshVEvF2yUXCBhr0Y1SWV5YEp50+Vgl5RkwaEsYi9TZkMmiAYjiLxM8+yWpnq34WghDv67hvkwVOJd+NnNVQAGAMVmLxw7kvO3BrpKVBq/WpUoMyqxSASQHPuyFk375s7aSbnCG9XLc5Jc3W0F8gKRz+bg7gBpMVxYi+/xYA0uEg+MVjm2DTfRYWJeesdLItTVI2qVuLaDqJQvgHPb2Cu9/+Xl8+fvv4pcfvDmfkpV46zEb6P6nv/lV/C++/ksRVw6vHTVozSn+w9sO91+/j4PVIrEg3nt0a48XXr2Li7M17n/uIVZH00ByKs4en+PoziGO72TNWggBoRu/C13X4a1ffxtu4UYM6dmjM9x+8RZWx6vRPh6tz/H86hB3Vit86sF9xMjtuMpYn17grTfeBhmD1dESt54/GW1nc7HBW79xD4vDsafaXJw/PsfhrQMcP3eMR+tzvO/oBJ87PcXFozOcvv0Eq6PxMc/F48dnaI8W+KovfbXX+aELAZ968ABx0+Hh6w/Sca7P1zDWoF22OH14iuXh5e/TdcbZozPECKyOl7NM9sXZGk3rcPeVO9Xfr8/XeOvT97A8XGLRWHztF78If76GWbhLpcj3sVvsQdYNhQFT79aZfkuMStTKq11huDkUupahTT9jpN4kDfDkM9UsV0NFulEEwcbVq+JiyURQ/u5wLBimmWLkI3DGYLEL6DOqH+p/rub71bQOzYK1WWHjU6sb3nEHmCUAbjsi3JuQLGMmi8w0yOqnF7kVTdYJEYaoLXg/qX3g61hnJ7Vty9ZQ40ZjwY72cZLJUhbItRbnp+t0vlm4n9OkPVnawIYD0ObQ4/MyqnpT5qxIe/aCVEcG8HUcp8nLyA2iLQjcpsotGpxdrHGrYH8bY9CQYed5J42QY0warMNbBt5HtM9bvPLFR1i0udXStrj3+AwA8DXvu4vf8ZVfDBy1+KrnWqzcE/zcpw0+vVrg5PAgvaPriw38xuO1r3gVpw/P8MYnP4OTu8c77auMh/ce4YXXnsedF29v/Wy36fBry9+AbSzagczg4b1H+KIPvIKDW4ej77355Al+03PP4bmDA/zKW2/i4cUat5Z9oHH2+Ay/vlog+IgXv/h53HlpfDzdusOvHf4GrLO9yuVdz/HWCye4f3aO3/L88/jV+2/j0b0nePAb9y593e699RBHLx7jd3zNb+k9pyEE/E+f/QzW5xu8dfA5LA8XsM7i8f0nOHnuBNYR7r3+No6vcJ+uK0KIOH1wmrIay4PpNPrDe4/w/KvP4+7Lt6u/vzg9x68tlzg4yc73p7E+z+zj6WN/VW8orKQi7A79C2vaA2N4wNpWtRP1+xVHczeTzsvfZ12GzonW2Yl0oQjHTWY8DDGwKz8dRfejK2ofAxxx5SNraLaAPiIGTQPfnlrLn9XxErdfvAWS3m1N7zMeREuwDxT399POOkO9UATgEuAor1iFloksqLfGwBpmZ2q4ca44kP3Ixj9XgW5pA1BnQaW8UZgo7rFY/6wyYq51g4KGCCPNoWXvIIoJfPhKsQEQ+wylftOwoYQyfjUii9sJoQC8gTVzM5Hb6kCaRPO18TFgWRRvEBGWzqGL4iov11CZLCdV7poaT0nhHcrZ7wmTdVsmPc6Yixlt+nr5fGAAJq8eu349uXwPxplhQ+fe76RQRMFIraE8wBOztdJNYWKx51qH49uHOH98fmkrBKvNm0mF75Tu02VDU2Kj6kIjlaTCcup7Ejw32jbSBeMmI4pNzcHxAS5O15PZD72+y9U0mCXLRSxll4cQ4h5kPaPYM1k3FJoWmQYtOaaqgxbLFo/efrzTvjpgxI64gk2qMQZqLlkakE4db273klvFZHO7/DKzL09OM/kQxTDSwHU0WSpehm0s4mn/Z7XJ3ViHg2OXjq9XZRYhkzj7SeVJj6otO0yVyVKQVTJVXlJs4ttlwJWdW8+qOC5QncmSdBr3rZMEYBxP1iECi3Q5RKxMcXReMWTvNNc0Pdd3O3K4Z42Z3p6hDUfe2/g+qJ9TiEH0YTRy1uazJkUpcnDzw9Mf+AP/O3zVb3F48aVXBpWsMYneNRbOwZ+d82cM4NcRweu5Ey7OCql/Ooz5ZzHGmJisO4fLfLkIAFkY48cMYsxVuk+LsnYVYhsjLt8Dy5du4xkkufF13niu+M1eY27CK8um/pdVvzWJ2y/dxumTc5w9PMfhrd3TfFq0os++UYb4CrAnIkp18vh3zhCijF0hxNSaqlk0WJ/v1vrsWYa2Rzu+e4izx2c4P73AqpK+7Nbs+F/zx9JQPzkex6UoRarQ93H9sb+qNxTGECwIppkGWSGwP9PUCsPtSL3zvDLuk2fJwJKZTNGVZfXJWmDKpV7AARWTnbY6QQHSkqeVur2HAEeExjD7gx1c312T24SkvoVbnIpDGDBZBPAaQ3VAwmSZmiahrP4cnv9wv6rJMmLoT6wfG8SUd5FWBtZW6lbYGh+CXOc+gE1HEKV5MDRdSMJHDs8q2w6UzxiD7ijbz0xWyQAE79E048l5ql0TN+HVe1XxqQrMmWbrg4hta8DXXvti/K7/7D/G13z1lwPI3QgiMAJZJUiwxiBs9Fi0SLW0kODrv+1JPFt3OBcm+faqFSZO0qowrP8bsI36NzL6uaeIS4A0fmcGTNaE7QIg9g0m22C4CS2V6vmstbPVg651eP6VO4gxXqpFjRXNKnug8Z+ase4uoWnw2gKmMdzDlKTSjptds5Hqu6GqUC0WmkWDk+ePsTnbVFnBbt1hsWqr/lgaSddaPA8xxirY3sfTx80/PV+gQTIpzPWgCz7COFNNhQFIk9w2Cj4S72+kyTIsip76vtoeODJpgpxyhkbMk7ZOHr0BLVHwHtbZ7PYeA1rLQMAZ0+tzNxVlmxD28bLT4E/PBTGZU2a3d2ayiAwM4gx7URhz9n6v96U8Xg/ASSskoUSK66upvqnUik72taMwJGawBaCdEqlmsGCyC/vIACwkJktT0goIFCDqkXAjbQEmstq3he4piIWHnZiMnTEpW2WolrrUptR63AHbiXY5vhgllcoiZgfDFimD/advWZP0WK7J91xTmalx95a5/C1hsQ4XDZaNSz5rRoG2EVBVPB5qDsyavacbfi9jHFmTJfhNQLtqqs/82gccNFkT56xNadYyVM/HhqbzfO3q5AC3XzzG+cPtacPct7BIj0MqRim/+5cN9dkbBqclKaWcu469/Fybq3RvNHRxagjHd46wPFzg4snF6GPdusPq+GB2U8psDiUXc1Xq+7h67EHWDYUTd905N+bk9j4Bsmxr4Rq3vb2OiNGHe3KGq7K6CU1YFNsDV2ilJo9X6Ow8OfN/SfvmJZAFNIURaSc95gCIjslUtR9llI7iyYh0q2dOLCba7PbOCS6xcJAUbm0CMAPQwaen/y41NwEwCxAZWMvGl73tBe0VNyF8B2arC9WzrFcJWQm+7AT2t6prvDQFAeSUdJTzIpKS7gI0mSLFxd5hfSNSQ2Mj0nzsuYq2tmhgBg/CBgHcZHueqf25n/t5/OAP/9f4F//fn03p941n894hk9WYDBKMIfgEsvrHW7Kw26Zw1WPdLasD0+as6C7HjN11jbqXmRRtMy4W8b4bCeHT74LHqmAqeayoL4Bc28A280wWwNf29ou3sTpZ4fTh2exnS4sQtWuxhMSMs1v+7CbG21SbgkokKYGwrH7DPlPG2uQ4f5OhixgyBq5xzGad99ks1TUuZlKFGq5xI5A6tZjfx9PF/qreUKTV8hzIkh53U+xR0zo4N2NqmXZmJHXXv9229A+qhI8RFtJWQ78z8SIKucJC69ghhlNuYyMmhbqLEDzsoKmwloU74pXaNkNSLcEHpPWGpiW3RNYLqdu7VJoVLM2UAaW2g+m/MppOKz/vAbDPEmuY+qxN2X6k2mw7BMBAruGj/vEnsXtI4LVWHRolzZvSfcQ2EmPuKDOT1kn3gS6ktBcp6pFzVZypZfimB7JiAsm1sMW50sScRena5n3OxT/+x/8E/6f/8/fj//5j/yS9T5vg4YiZrBhDuobOcHFFFzyMJXgp6HVtfm7UNR4AikdsMhLIStoY6a0nnvPWyrUq04Ux98GkBFivOIFveeZjjHiyZrbDufGkijhjakzoFQ80xnJbp8oCyLUN2kWdERt9tnF47pU7ILB56lRoQ3pTpAuV7Y7QMeByLvIR4/ErxgvEeCbp7ADneH9d12FxwPf13cDwsGYqSz6OhM168vZjnD464z8PTke9Z6fCNW7MZO1b6jyT2IOsGwpNb0DFlpWBNvgAt7CTgxcROzX7zYQ5qG5TdFIGHrH7D4gxD26ts9U2Ofp9AvU8ZWjieKPM4lxJ9hAIb0qaqg8GYoxwg9y/riKtkWrEicGzEx2WtSZNhtr2x0jrnLkG08mbSt3eUzrKJKhUtReIOtAO70NOV5UfJmphycFAWhL1WgDx5OEcMys141PG3qdAvI8Yz/PeZJIJ0JRTPVXMU7werxWbhgnwKKdE0vvRe24mTUZX/ab4oKS5SLVMefjwIUo16VS6MFt/VL3hIiBFkMWFmGdGcnWhpLQMYeM9lo3aNJwB4XOIccMgwbBXliGDTkCWLZisfN0g7ubzk46K3u9q+xIS2C0XyBl+7odXnSzBxyDVrOM2TtuirC6diwvf4f75Gda+q+uKaNznkLfPBS+tLZksgiNT7S26PGxxeHtsATEVBycHOL57jIuzcbpLI3aBZQVWWoLJ/XDS85VT1jvvkreJirYs3APC/TQ2GGsTdms0Hf5uAFmxL9VwjcNz77uDoztHWB0tsTpaiqfYIdyMHit/f6wFnms6v4+rx17pdkORmSzxDQoBGDBWIcSqEWkZi4PlZIVhoseN6k08mKpZA5KKaYydFr4jYmE57aFlZdz7y4zK2mKMiEY0LeKsnRimQcqsTO1F5MpAIsLCOTxej6t51r7D5548xotHx2wTIUAveI9mwa7tb5+eIiDi+YP+gJ90Rmml1oGd3vXxF/2MtDcaT3pRHBGGQEvTZ32QxcyYFeanT4koO2mdqYvsowrOI0B3gHAfsC+nXzfG4nTTSVEQVSshebehYOmmB0+dqK046XvvSyVWcb5lypFY6DxkskScXIue8L3SGDnK82RSjd+QNRxHuq9yHFYA6DKBeNV1BTjboLEWF94zyCk0Wfms8iNtdhC+33vCAPjO4RKpMrK4Tsqc9s418jX/3JMnsJuAWhsn/c7moquzEvKMbGMeQoxw0u9yqP8K3k+K1ddSWdgWxRBqg/HgYgyMDrZogGqxPGhxf4ax7jqP5eESxlqEbpNS/Ub0hZyGH7kGzwYzWbW8eb5Xxlp438GQSdWzZPLC8qZYrRgi3KCJ9+HtIxzenm6gPhdloUupf9vH9cceut5gNAJAuJy2xmT5qpN5bxvtzCAjlLsORZaiMDh5Zm6trbIh/HXWMfHKPOtphh4raV+J7VCBTdGOpvi4lnr7OBYpt8ZWV8tr79FYJ07jGZjGgDRRhBiqOZ6IKMqrIl1YtmshC8U7VTFybwAqByL9bH8S5VQkbxN2kC70Aa4xeeAe2iqAnwdmOVkzFmOe2Jzh9K4x0/q4KKBQmSji/ONEGjSv4K2xiJ6ZF5sQh55jtrlgoqavg/MxJB+jWpgCjBqLOoNnMrzi521+eMptdfjflrjp+SIxMPK8R/7c0jp0PsjEzZ9wLd+fGPn7ZUuobZWubyUma5mMZ3NxCfGaicbgSVmloL+u3JfN+QaP335crTyOfjcmS21LQoijNFm3CZNi9S54tMb2LU8ALF2T2OSnDRaUT6dKu02HhXg9hZCb2xsq7C8u33O6x7RyCyxmxdMz5AjdRQfb2DT2MhNcXX29YxFk3Luu6OmvxFZkny58NrEHWTcYztgMsipl/gC3sZndRutStdcwQhAGKzFZEbyyzwOls0NNUQ4vg1spAlafmmFaToXLuSorp11UG65lyKnFSGCRcln5NQX6Np77G4YgFZcCTEMI2UqAabTRwO3VZyelC7sRyLKU04VjYi+KZmvIrlQ0WQRATDyNkaqlYqKMIcI2jkFikUYtg1OBAaAFYF5kNkuiEeNHZQqnBn6+DXy8CS/VPkoZVLiFhZc8GldrlposOTJBQVyQkX/Hbu/TCwIaPEOjyTWEzF+lze4GshKTRcSea+l5ykwWwDYOPrL9hS88sjRFC5P7btq0YJgyfC00WQfcnilSqWuRFDRhVCqvhqj6TNWIZG3IXrP/4CpebBUqh8jmsF4rN4s0v/d+Uqy+8QEH7Vhj1brtHSJ2DddYWGsRummk1CxYwB2RTW6NVEZMdg2YiOTz17tma2jbJkOipJPr0SxcamKvXSy2mUY/y7hui4WScVZd6174/mxif1VvMJwYM1ozrvzR2Pbgu9bBOpsmnDJCDFKxlbVRPKjkfRlMt2qJkb2liHJbFGOF1al5P1FKkIAZGB4co2iPovjyqN1Clzyy8jlOtfrx0fOqHLkVTPAMskxRBVWrgNL2MVnAH0C9xsMmO5Ib09u/2j3opFuCDrVIGKEXYibLGR64+sL3ANuaBJJGflFRARIAWJB7HwBCjJxCdcYUE/W0JotBNYM9Ti3pbwaHWkykbEgaAaNAxxSpRgVrPOFYY3utgUY+ZIPIHBFAbvzMhRjTs56OaUufO31nkg2FYc2aS2n3CAa8/G5oRwEiKpgs/hjf5SLVV4jgp+by0ohUDzUtKsCVtjRAt1EqQ40BK+tMHftGYS5qqXxdrGxjsnyIaIzhhUkpSwAQuoDFsi5W74LHQa1f6jUyHa7NbcGGoc+0a7OtiEtMFvL7fxlmSSrvXCnJiBto/1LlfDVN3mtbI1XgN0hk8WFc4/VXfVeMMc0TU5WX+3i62F/VGwxrbFo9DR2wk95kG5PVWDjn6u11JB8RoSsXWdnH0tl7urVOAA9ulniFz47JLEYdVhnFAJk88koeYFAFRG53Iq0hktt7DHDW9gSXbuZ4rHjmDBtrK9vH+q5xulFF2SldGDVtmvYKQ5wyMgYoc3g6+cLI30aTEp8nb1aqFpXJYrFHX7Elwv9kqD7Wf/Mkoqwj3QbM84nNUqZQUyZTLItJOh8GglWPVfSzWa51adKzpOeWPllI0mhUkBEQqn0L0/HkWoXJ+8t92UieT4NtepvEZGlrIDHXzaA9grWHGWRF8O2NMTNZ/DmgwFV89wiYMnxlt/dCk6XgrAfCMRa2R34mrKSpaILNTELnGsMdJV27ZfSOkUGWj6HH/uq1a5b1/ndEqN7L0gbjacNYh3bh0NVAVufhnEui/IjcUD2nt81ozJyNGAFD/erXuE4peUuR7x0RXGPRFjYIRoo9rovFu2o8ra9aGcZSaocEL/6Gu8vb9nGJ2AvfbzCcZeDiGov1wDaG2Rc7aZmgQWTQrho8eXA6/qXk2kFaVRN40ikq1rR9Sq21DsnvNd3Ewk+2jujWw6bWMQns87e5L2E0vP0gHkYKHDsfe418AfQMQ3VbXZDmzmDAROKCv7nY8LEp+EK9Akp/lyhyAvqPvhFmga/XUKgMqKaodi/KSTgggwPb85XqfYNr+6spj8wWMQtDZAD7KqJUyblBEYKv9BaPEN2TlutRnRSidF4czpl0vAyW+6nR1HiGkFIpvfOaWQkbMok/HT5nysywrYUcWNRU7HR8x3d8B37Hb38/vuxLDtIxt9bkatgo/Q+FBWyMgYPBhbRJMZavYQiafs3Hpr5oU5qpR+drbKS68faqZTasTEkLsB1eEtaeaasjPtlaGoq1hnVAkywNtnU5QIQV4XvJ/kqGrFpZ6KWycFFJ/ZY2GHOp4V2jPWir45bvQk8TBeRnS0EsL7J231cMgFY/59gAWIDvlTCchsF++XyrfUssrt1NxHUK00kKXYIPSdBPxl5J57aP+diDrBsMHTics72+cQALpI3dxWQTWBws8PDeo9HPtSFqhA7+KibOb5Ilbu/D/fX6L3GEelDl5rsAO80/GaxA2e7BSCUiZGTS0mvDrSpCRLNsUwpq2MiXj4fZiPJ4Nj6gsRatdSltZp3F+mwjFUMmm2EawnrTH30DAhbWFfoa6jFZRA6aTSPbT2XFGKSiCahyMGSLSViFtOwkbyVVOwx2glfj0wEgVHsGRJBaGJg7wmbdgzHHiCLINmaCyVKASlb2I0zWMDWJ/vGRMISeIMdePHukAIufh1qZ+Fw6iVki6hmganRdx4xsq617FKzOg4iv/MqvxFf8JgP412UfbEKa05ZR7g9f48ayueV6zf9ORqQifKfCBLZMzdQusbJYx6sWjZVyeMpaLkjT8Um2UtN9ld/zPgOMbUbjAv9O+x9uYzaiaOH67G9igCsga+PZK2po5gogtb7yIW4t6tulEq9dtFVuyG88VsfLXvPxBK7kOay1ZpqLKIvAoU0BmUPEwM3cjeGWPa51wqrKZ1RH+ozzhVM9ZDWmKnfngm1t+sftZPGuoDuGCGqnrYL28XSxTxfeYCRPnoUbvQjB88A41QuuDNe66kAdfEiskdNGzQMRiDVqAFpZwkR28DaDtEfteKOkQQrlDQAu69fCnBhib/ACxo18tdVPeTyb4LGwFqumyf3nnGO7ARFsagug1rpRBVQIKFJI6vZegoSi8TL1oVQ29gSqr0tZdZT8t5jNskS9r+T+jwZvPH6Mc3RV4EMKaFQhQgZk3wfAw8DDaMpmYpJVJRKkulAd5MtdJff1YmC1LrcC4v+W90YuDKnP13jfUx5Z4KMQWwRNY+bwG8/FAI0TABYwSb+N9wp9oC0RDtrS7VpSrpouNBaOCOsL+bw8Aopz1U+Mz1avfV2Lo3qs5w6XBaiFLAzyvas5k6s/li5gajtQ/WNNFB8vWWnGpJl4yfmQexZWFnBd8GjJjtoSAVwk05BBt8UE9GyzwRuPHm6tRFS2aLhQ6LoOi2XftVwPx4g+1F4yXaiO6XrdUmqfDsB3Te6hIbSLptf8nMy0/vG64uzJOR6+OV4o87HGdByXCR8D3nxyikcXF+nPw/MLPLw4l7klg+5tbv37uHrsmawbjFQ2bEpZMIeXHn+0Q6LcNTbpLcoVeHfR4e7Lt9FBSqBjhyGKUCf46gBCEUQbkL+fVsQARGc1iLIMOLE+IflkIdabCrvBZGEpt/pRd4rOBxwetD09hWsdQhfgWifHz3Q6NwIeAFYpZefQwbUEWbmCsqyC4+/m61Sf9PMknxkYB1ILh94litI3zMhHCbEbX3ejWbtyf+YWgBUsdQmsDI1Oy2CwwulCJVf6gn6Z6IuDtNYIexBE09UHWQQDIk75lgUZal7pZiYBBaradLy8NH7jcXiykr0AmXGdf/b/1b/6V/ilX/wJfP3XnODrvv5F3FkucWtRFjQElHsiYrPUjTJZbd5b0rkV4Co7wI+vcW6ps8qMMQHG5H0aqQIcfp/n8nwRqsL3yPf3ogrAsDPIKtdo1llsLjbwHWu0XMX+JcSIpplmNZZNg7Pz+ZY4XfQ4Wi7w6GKNO6vV5Odca2Gtg+/CiFVzhT8YM3+5KIKIAFepUJ2LoPdfz0sqC+kAIIKNgZP8By2OD1c9llDBV5yphHza8CP5RQ4dN7b1Zx3Gxnus2gYfuHM3nfdnnjzG505PGXSL5CKGuFX7u4+rx/7K3mCkid2Me5yFLqARe4Zt0bQsEu26/KKuzzdolg4Htw6AJBzVyavQ4RDXoA0tIDSt5ugCFuewyLRzjV3TiSo7jfMK3ZD2GRP/pSZ7WhkYNANXb2clXVislgO4v2HZ4NqKOzn7NfHnLbhMf3h0IYbMmI3c3sGpHT5pri4M5epa3dX12g2jzPdwc2htR2PQBzHBxwQ6HRtdjTdX2jMM7SKIYKW4QO0vaqt5Fs9n4XvU1OXgU+renvbgdCCPgx6U/HcGSjGtgjWSRcbMs6q/z897jhACFisWYTOzExILNxc/+qM/iu/4P/7n+H//Nz+ZvtsH7WLs2mNuCd2FpssofU6bqFMPlMUEDIfxVtG3MLG4UbVcwiByon5svIr8FmoKtRZkDGItXRgmTDUrwdop3ge7fEf4jhdwNUuAUFTy1WLpXNXHrgwfIg6sQ4hhls1yjYNrba/3ailJAFSGQGnBYvU5GyyGtoZUFyY2KK4BWgC0gpoRk9g41PSGbMXy7JisENmapmbFE3esJh2Gl+rto7bFgfxZuiaNrdw0nJnRbdrffVw99lf2BkOp76FNQwgBvvM42rFVhW0cp8+KCsOLswsc3jpEK14zRoXvarKgqStt/IrhRMBicfYVdcydKKtjbaFv4tDXP2m/SMGHlj/nVBkg9g2jSZGjdRZePq/fW6RGrbI/a3hgFL8mZRMW1sGgD9K4X5x+twMDob7wXZmW1fESq8MFzmQSFfP63ZmsxJBlIbVeK21mHY22Bxmng3gCNmkb5TEycIupnNw4O1NdWDBZKuwvr0mI6fjSmVhNp2gVVv8aqfj9uffdweo4M0Y+cGprqm8hIFWsch1KMKbH37SuSCTupska+mRNRsnmGINOmKyULoTcY4M0mRsUvRtn0oV3j1ZAjMm6IWv3bEZdFZCVOz6M02XpsCeABPtkzbN8aREjHRt8DHDOInqP4MMoHZe+F2L1ndRoJixWhts4aBe4s1riYcUhXsNYi2bR9CoMQ+fhGpf6m+o4pO+EXrNtfRuHoeA+t9ZaA+ZY7rF0BTCUxp1hPHMPqQjuHVoBsKkh/CWPoQvc/aD3jhfvaNM4hCDC92v04NpHP/Yg6wYjrf5lstNV3PnjCxycrHB4a7d2FUSExapNxn7BexCoB9JyYkQZkjywNW5sAKppHWMiQA6WkNJw6rgeB1YHkcredQzqzABkae6/Cwx8atqPhXNJk7UJ7I/VansfWZVzX0Sb/JrUeNFZrYAqBisqK98Gbu/yAZLqt6ZtcPd9dxC6gM2mkwlRy5trE1sBsmIAl4SD2TGpAFO2Qt2+WaBvRM41sMKAaIPSfZLNCbNjRffjRbMzjCgrdr6sClTKljh5R9k4Vg+Z7Tmi3vdhupA4UekWuXgB4AndgfV706HeRkB5MKELsNYlXWEpfN8m7FafrDlwNwxLpmCy+GfMspJo4Tg4Uxj7RGUR93pMVjkBysKCmgTcR18nLW3ApCZrLrhZ8Pxn9P1tEjPMTEkEg9PSoqD3PcwzWY0pTSom9o2AhbV44fAIiHGWzVqsGnSbzMB3Gw/nbNJEpXGoAL8G/ed2l0iMWLpPHYhkfKQGEB++qVvBrPHl9nmZY2MdJKraWHX4v6xPlg8BiwF4KotTjIAsYHdmdB+Xjz3IusFIIEuMAqMPCCGg6zrcev6k1xB0WywO2rSyPz9dY3m0wPJIfHCiahEiiBrw5Fm01jHjJtEqJOd3z8LaEiixQLobdHHnOVs1KQKyDERwy0yT5v65DYutTpCtsYmJ2viAxhjpoWhEosrGlWT5WIhIjBdZsOsMjVIaCQAM3d75yIv/Bw5vHeLWCyc4e3CGENghfIajQT9dqEyWETYkV0IF72GdQSBZUdOYxYgysee0U39fRlg1rnocm3rGKML3BLJInrOsLyv3U4ppiQiucQLykKsb5epkxVJ/r16elbkGs9oQPciEkr7bebiWy/W1mjU7tc9Hbqszsd8IqGdZjJn16i40XSKfC0j7zkaoSEC5NvPee5Jb6jALwd8z+v/Su5KGliDIQJqPfRpjTWFW1mvNX58g7FprbLY4KcaTWjsd2fistq4xdsQU18KSwe3FErdXKzw8n2az1NVdw28CmmWTALZWKeo95gb0uDzI0m4TvVWFjAPEBs12SpsKSGXms0FZnK4TrWbVF40XZ5e1cPAIRR9PDrY3kfRx2Zty3xz6mcX+yt5g2MR2IA2EZ48ucHh8sDOLpaFMQIwR3UWHk+eOcwk0FaXB1GDEZNlxk2gVkrMWycEVc41xpmqUGEfgQAY2MvDe80Ch6UI/HgDSdSle+E3wOGjaZCNBxCyOirRLV+jGcmViMzAkTYwBnxloBLJML/tARLjz0i0sj5c4fXzGUIU0/daPvlllAFHb246m5Li6UnQQYjdBtp7uy7UDY5AF4kkwIKKeMhG2kQhEVtgZM7J7yAL0/jZUBzjmK2R7lWRRiBHNluokLb1PHk/C3HYbbvBtrFU5Gp8DbQdZeRU+M4wNFhUWWZNl+xiSK2HlPemZvlZCmaznjlapEpDPM8p9sznNPDxuzXhhKgXNMbfI2ibPUQPexlpmPiWlqzevZt8A5MrHqagyxcNtyOeICC8ecgPjzQSbpe9vbvfTYXGQ308t0DDFfSEdNyePoHJMMmbwuygVxmkcYJDlKoBYw9hp7dzTRtflfqw1kKcNnM0ORVBlEDBiJZ2I/30MPdC2bw797GIPsm4wjLAMEC2M33h43+HkhcuxWACEgTC4OFujWTZYHeeqnggZ7CPAFTXZOwgo7B2KCDGKpSanjQzFNAAYSSv5AZNl08BXMFlK7YcIspQmRB+nQVZJaXfB40A8mdRuIoQojteUhfTIFYRL17CXDwoj0sRkYTyB07i607UNnn/ljhiHYovwvfyn3jcjTBGD6BBDqq70oh8b0v9aHGCo0AONtq1NfzGdPqC+r1cSuJcDeODfD+d4t3BFGrH4JWnacdwbMoSIdssEoJVdbPgq6dAY0XUdloc82XFja5K063bXx8xkTV0HAGihzyIAxC5my4ZG07xZO6iRWEiMhe8hxkG6MGazzLQJdkcvibBUis8XJF3ikS6vSK1PtY+ZYw11GwacjnfWICBkNsi61KR9GAT0CkyG0RjDxQMTInAuQDFpcr+1YG3Wowk2SztWlD0My6b3ickq3gVrCNEMR6z5YF2TbmcDoAW31IEsjEJevFRiTv/4tFGaNAc/Bq/6+0ttUwuXhhY5shANsd80fC98f3axv7I3GFZfemF7Th+e4fDkAIe3psuey3hwfp5WiK5xsI3F2aMzHN8+RCPAREFGn8nK/dyAur9RjEFWkBHah68cZJrWVTyeVPtjoP5RKV0GbiOUemZh7JGlUVLaAJJhqU2rME59kCHYVAkUEwPGjYC9nH/oV74JM9cPQo27WZ0c4NYLx7DO5fMaRnntUnqKt6kMWASAIMBVPMZszr31v05quVARfpMDokwGMdYnWq2iKkGa9C4Mg89p0+AyrDWpsKGPwCoHLOHL6s2Z0LZIfEgZ9KZJNSrzE7DV7RI7Ct/1eZfnYX0uwMzG0emUjvXDooAyHpxeiO8ScPtgiRgCyIrGSsE4OUm55m0oKxMJaOgRDF1wynhYdBKRUuuTmq0tzIOHGgGTyAGQUk7GUdW+IW96etvKFPtYZ6a86CVTG5wtbJZrXephqM7/ZXUfg8W+EaemM4moWo1XixD0egCIFwKwlHXOqcgp6PYseZ7gA9yi4ZR57XwiZw8uEz5yt41h6teSgSOWU5Awe8A8e7mPp4t9ScGNBaUUxSZGuIZ9a269cLITLRxiwJPNGsbwJGUb/tO0DQ57VYks+LQ9dqIBr+Y4ak7CiRmSScMReqs8146bUhvSQUon5MwiEQGuLV5qoCp6BzKlvQkeBia18FCwFGKAcTw4lxOswqTG9FfClnj1VnN7T98kSkanGkSEkxduwS4bWRFOMFmx0PEgM1mGuPKRionSGoNOANJoMouQSjXdWCVdiIDGttzUVUBB6RSt87EtLCKI3GhfUc5vmCZYHi1x5Ncqy+6dJ5fSVzvtzTaHTkcvGjL1o/Ke2ZVmkVkrk1in7UPTn/gTfwL/2//Nf4b/6GvqIm4OJ9eBn9WLUwFmDgl1MrjtvwdaehCJEAfsgrJYtw4W3AA9CtNRLjLkmTI9uwqxlCCgxSkaAoxxYxAlqRwrflVs/dH/yLZJMcR8Txpn4c8CM9DGoGkdzIyma5tT+8I6PFlvqr/zge9pmfI/WSxw2LY47zY9lgpgVrxZNDh/fA7jDKyzPcNiFeL3KuTYbyHbfewAyNlXThYVcQ3Y54qFp5Wii5nUbcXL8LoidB7twkmbm3rDbOsczrsNWmt3auTcCfvVDOYSK9Xk6xjQqhY44tIeXPvYPfYg60ZCWQrPhoPieH10+xAHJ7uxWF3ghryaFiMiLA9aWGuy4B2F9iYNUgYwDVC8zDy59PsXhiggKHoZ0IBylecaNxb0EpCl2AwIiJD7JyZ2TcrLJ8CkUtrnXQfnDFpJbRBxVd6571iP1bieYFMnyUZa43CHeWZNeOKoub3LNQFhLCNnYf3J7UMYyqBm/N1YADjVwVHSoxHllJO60xPGLJIoqIvmzMPfc7rQmtyeRj3I0rb0FMp0YYWlyymu/m/aRYvl0RJEQ3NEORfUtSlzE1Q6ehIfIGEUuouOe9SpJxJi0mTRDpqs3/7bfzu+5Zt/M+Lmfxj9rn8/HABOV10Ik2WbnMfTz5Zpx7J9y5Dd6Nk3gHfjGscgK3J6HSndWKjYop4fRB8UEGlc/BDUONOqyfAYSGxrsRJCFrA3kgZTJqtd1lOx+l7OOfcDwMq5kYZTwwduh1UuoIgItxZL/MajB5k8KmK5avH4/mOYjek1huZjiqPKN14wSsug3TAWUisiPkoQHRW/5fFpNgNb8TK8rggxpj6NtT3EEGAd4d7pGY4WLU4WQ03pOHwMaMhUW10tnMP5xQWM4+cLoc8sDxnYfTxd7NOFNxBZRB1YXxMjju8c4s5Lt3cWN7Ijer8q8Oj2Ee68dBv98vqBUSRZ8EjXTxdq/8L0vRCL1Sh3qS+jphnj9jvKgLCY1AiLxT3BeHBf+wBnM3gahlLa512HxaDFh+qRAODoziEWB23WsCjIMqwJ8TEkk1KuLqy5vQNIou7KhCdgZNaXiIDS7T1vU/vXZSNDZt60DcuQyYrFYF9jshwAAUdUMFHFMWvKsWzwzAL4iL7je+S5pTKYZkvOcv8iNqb6RLNLXzVt46G2Jd16g2bhkq6OWa68v51iEhAoo2oB0dwAwMWZpNeLTlR8zyPswBiXM6ZxRDSVeqzgPbeoaaWaUNLrourK6WKo9opEn2ZBFCaqD9kXyTbcCge1FNKWSTDGrFHk9Lt4mTmLdsoja2CXMBXM6tafA04dm9FzddCMOzGk7Qno8xuPdtX0xq8YMUp5OSM9OQ0mtzkM9f/K42AJVJQpry2z5OPPGHTYxk3qcGPkaurWGWwqTFctuhBGHlkarbNiZkxJM6rSgy4GtM1uJtj72C32TNZNheprhNk4uLWb8aiGjwGtsVgXL93BybgiMVXn8E7lT550gCwo94XzbxqkCYD4I5VRlv+mSUInYTIg0yKG7PhO1qTvbILHwtT7o+nxWDJY+w6H7XHvhXfWpFX0yd1jAOMVuLOcRuxCTA7W7OtVcXsHoKk9XqvGXlogpFLnDvWJX38mAK5MF8LAkAc5wF9IhZCALEtasZS3xH+PvSbDZbAjdWTAGAnkSJgOQDEgH3+fn+J2OIPDjhFT/lK8jaEmLKeA+8fMYGYXkOXk+BVobDYed1aL4v6q8B0AbV9s/ORP/iRe//Sv4Ld9rcOXftkro7NIQJUaKDK/OGWGzjbFcxuR2LV0tukfFnzvc6S+hUdLdBupDHMWCJ3o6RhkGfQBi4Inxtd20oogemHWLIvne90PCp3jXAQRcgPC/EROWztX71mo2y7tEqZCmeJa+BCxqKQil87BkUm+d73tCZPpNx6Lg0Xvd6HynFrDjZwB4mu1vUZCLCwMKC20iv2kDg1ITO1Iq/iMqu9KW5ya6D0fI3sQTjGIw/CBu2TUorVskUOGfQY9fHqevA9JA7uP64k9k3VToSCLLtmDS6Lz7FC8jcLWii0qJh1OxeTvGRm0ywVzQOmXIyAt5oFGRZNBKuI0NZIfKH5RtQkumWzfsPEeh207u1paOAcDSpWFGsr8lTFs69IYbmTrg08mpRwbAM04FUXMOtVeBvXf0s+NQ78lTFZRXZgsHIyFlxSnOuI7Y6XarogIxEiwRtnA4REJVDaESMKwUX8Szo2fcyqQyIy0VDFii4N0HIA8fXb6HEYAp4e2pZgAmRwR82Yj+qaYkSe5MtU5F3/pL/0l/O//0Hfg//Pxn694GCmzqIsK8ZBTJqulQmQsDGJxf9PzVCFt3ir6FvquQ7NwbGtCUigCJ0gKnGaSe5JT93z/J09TAAEzT4PJN7Kua5sxZUS2QrHGIBJ/7/D2ARYHdSYr9lLr09GQhZ3wygpxbIAJMMhaOod1N+7RZ1tOGZdps3xMY98utXIxdqIoYCI43b0BsBgwWQyKLUXUTUqwlTm8amizbufsfD/K9HzWq01H241xNlOgTLgWV+i+A8Lk9/ZxtdiDrBuLvr7msuFjYLfzOP/yB63OoWLiJtebOBwR3GDFrGyLTq5mkG5Sn6roAw/8PAoU6ULlzljPZZvc7y7EMAJPw2idhbN2VLVWtZuQxthlRdPCOXQx9ptDRw+Ymp5BJuPCpiJdBRUQqzp6FKpl8AKwbPFzTheSJQQfYCynWaOchxGmUPfJu4jSmLiiyZI+fFa0Ueqvhv5tQ9YXyetNFfo/Ts8bUQXa5a4pO76X15+FzvMtdTQY7Oc0p5qQ5p2Ux3SZ6sJa+qpcVLTp9+fKZLX5vVP5Vgku0nHS+P0sjUj9xqe+i6kaN4E708doQU8SnC6sHDWAQrNnYBvbs3iotUOqBSFbMbARLE/Ot1+8jXa5qH7HRwYZ2zzdG+msUPPKihNFEMYYHLcLXHTjdFfTOmHYzMi/q+ZAr88P7Qg49LiIAAPuWVj62fH9kvQt1VvrTD0LTxu+y0yotQaGTPJ/K/YuYytXis456OfjRVWPBfTTr661UmVc/GwHFnkfu8ceZN1YqGbJYAtOmghmb+KERiZ9KuoAUQ7+tjd/p/6FgwEkV5iptqj4juVmwuqVxS9q8R0BcoakMsdmt/cIbC35b63FwlosBqmFqrljjDKR5Md56Ry8DwihHPQ7ALXCAgJNrGGz/9YwCadfpfTJoSbLEDffNoYQuiC2E4AV0X+qPEsgi5GPM3oUdSbLkhytTLa9gV/AU+mTxSC5vyVt8VMLUs3YYP/MBPQlQqxjr1RKVrdLCasSEYuce9VmxTXeYaDvm5FOgCxSdpF/f1aArMHB9di40vl9uOV7BZMVYkS7bKRdEOReSrqQxmlaZoqY3dSJe5TJFU85vka2x9JpwcMufeyU7dKmyttc2rX1zLbUmDPZBmC8kWlW87BtRxW8ADOtzbKBa3I7neKgRgCeLWkA46ja668WEWAwgw1gjvq/JG6i7UwcmfamfdpspnudEcTt3YoI3Vgj6eLB0cs9bOx2XZaC9MnCIqmUDDFy4Ykzg+/tYcF1xv5q3lhwDbmtDLK7RmunaXuNUAilk0alwhLU+heqlQALp3lwS42enQwIIfIfoQP4XJQZ0rQLpz6M41WYIzMCT8OwZLB0zSj1ULWbqEwOS+cQEHompUNH9hxGxMhj4buyjZkZGX+Xf66Np/UzxEwjYhI3a3NoXmV2MIopelVuCiQn0oV6TUWXkqvPiiPWe50mOyt9ivvC91q1pPo/5WclB/HThhJ2hFAWFsyHUW2QMHGuGTBZnC/FJKAdRGKyTD/9LUeGfA2tbDri/IxBVrMoetEp+zc4h9RmZ3B9S+E7IJ5xURsZAxlkMYtbVhey/ItF9sbwAmv0xBUVdWV/OaDQZM1cn6STI0KMFz0DyrkIyN5ac1HrqpD2TdNav5VrYFHXFS0OltzovhmkCyusnW5euwSk4w8BfjMNQNiTMKBfWci/YfNYJAnEOMZFJtcRoQtoltJlQapJfeV+Q8bflWuwntNugQ2cnRl7ZGlYWRSFGPiayxi77Xv7uFrsr+YNhfaFM1sGzFqo0HshJplzg6c2U4b2FCQrK3vqrZCbQf/CCGVDmJ2x1C9HN8aKXsTLvMggp2S/+DwJrjEgSQesPVtPtFvElQvncNQ2Y9FrpQIoIKQ2Hhp5oIgZfEUCJkAWV7zVr6NBCVKHIWAk+v62k0M6EtWvuhNLBi0+g4U94+RbMdmzlkbTXMP9ZY2NGjFaY/oGhjFKf8OSleHS/V5WMUbUcW4Uwbayd+Upyc+K3Xls71uYjl6uMTFiR7tqR9W0Jj0/uzNZppJCFoERMnNLOD/36Rm3bQY/qkUqL3cCTAMQ5EPA/ScMsm4vW2YhGsvaRwCGorzbovMrvs99G7lwIf2u9khFpEqz0WWV13hujRJVJ4c14H8DJnYwRMnuZfJ7Pf3ifHAT90HaXtzep5iQpXNYOIuLii5ruWpweHIweubLtKeGVgFq0+YQAp48OMPj+0/w+P5pdd8sfFd2cpgutQAZWPCCoTaeKrO4I3G2c3jv0YgvmJHOHxi8z0QG0fB1OGwb+C3pQi/jwhyTZa2BDxGucUkXmb63d3+/1tiXEdxUiL7G0OX9V7rAlUNLZ7euUGOU/lWxTBc6ZNd3fqG04oS/Ix3rC/dqTUMFmcQBXsFfnG6gecHsEK/b51gsWrzw2nMwxqLbbHDQNFsH85PFAieLsXaEU3BGnNyF5g5A0/S31xiuzPIx5jRi1e0dwuiw8erwUsZYsmdTTBYAdOhbQzDQzCawGWSRUU0WX7fMdPCky/q5ygApoKHnfO/MCF9kIqgoXKDB0cc4aRdCUD+nMcijwTUKMaKZajY8/HYh9DfGYHE4vr9DTd9cbNdk8TFruvD0CRtouoXh6xYyuiUaCt/lMg4sFu4/uZAFCOGwabh4pXXogtqV6DumQLk4ohiZfSSu3szgoX+dtRIOyGCr2Ij4zk0vzHzUnqEBoBYWHewO6UKtxN0lyq4KGl0I4vZe34azBgdNi7fPz3EwkGQe3DqcrLAeMox8b5hZuji/gPcdDo8PsDq+hTc//Va1OpBQaI1GVh2WYanZgKieGeDiIFx6rN4WMcbUncNIM29f6Nai5xQmCHDOMrO/ZU3exYCWzOQYqxpcHwKO7xz1vrccGMnu4+ljfzVvLPhFt8Sl/tsGwDIYZBGWjhmmqT5igAzYmu7SNFQSaPf7FyYhcKoY0wnPFRRz3pdtLYJU8BHK/djeJGDFVRjgysKjpi683SXUPb48Dh/DaNVW0t5cXCCAcqInHpEVkfIwpZbNWmeZLHQYOS2S+IsJgegaKz0hI5xpUmudxCDq5GjMaCLI+xIIIlos6/ogmxNFCqDyJE4DBjCCV+cPL85xulmnnyvbUgU6lFsipc/v0LcwfV0E2DFGLFYtFovBvYgEUqrm0iBrqrpQWTGL0ydsSNosTQ88MWjqFxqoK/3wMN4S+4Y7h0tE79G2DaxzwmQpU2ISIFZdVIiSCjUEayRlk56p/vsbQ8gNpwdGnKUofipSIQhFcI++Ds6MNZej712CyaqxJGq3MFdperJcoptoyVMLJskHIAt87dzC4dZzx3jfl7+MVz7wEg5vrbgYp3KeAcr+Tb3HLN9wpu8XqEFq2LxjG5/LhCmq+VzTb60ToNW4xDIL6yZTrho+BCyb6cIiYwyctfCDd6bb2zc8k9hf0RsL1SyNGaJt4WPAwjoWT29ZoeYS6PGkU05MahDJ248FK8XARCuayvHLOXF9j4C6M1FiUHiSiTGwSFu+FxCxeIoSYR3Ee8AijvsgNsbCGfblYZDEmqmpR94Q66dKzSlPnDppKPAYhgzaMYBGTJYANMP6LAVEjghkGjgr6aI42FoytBwdJf8R3UvnO0kvDITRgKSglcGrC9MJwEXX4QLAQZMBotH/H02WkuIq04U79i0E0Es5P/fqnTFQoAjQ0ApjOj7ykY/g9dd/Hf/R1z9f+S2zgfzMCsg6ZTDZLm2/wXCMKnfLh0L6LPevW6nH8p3HQlItMS0ygPL5d/qjmDOY+o6wVnI8aXMqV1JIA1YhhgizqHULyJELQSJASyCu0VqHJ5t6K5zye7syWY1UyZbhA2tMp6raAGbAuNIxM9FTkXRuI60c/zl67hh3XryVniMyHd9vH3uvqoIuHgcnWFLTAF76gm76332yWWNl1B39+kCW6q1cD2QNZBueF8fRMBPYiN3DxvtUSDQMNSKdi9ZYnA6eBx+3f28fl4/9Fb2xEBfmCmjYFp2PuLVgsWRjDE4rGgeNXAIdwB5RhicdItERydEUA2OMuhLW43TI7tX5ONMKTLRExigTwcJf/sPbCjGmVfjTgCyj+qIByBq5QsuAdNF5BklRQNYMkzWyJ4gxefLIhypfVAZE06TpSIEkhGbRv7bUYW2vhZOKpVxdCAHe9bSmtmvhTgEGZxvAWDe6FjBR2g1lJqvUBqUjFANOIytjfQ6J6i13GBj0rxEwvvZTwQBSJvMKE6IQTKu9tsXv+32/DzFuENc/w8K23ld0UYG0vdMnArJW/fRiLtoYMibjY8hGpCvEGJOehdPKBiCvZwoFpSg0YwzbIzdLNwaxUm0H/QqQmnirUehcVWg681QIIkwWTtHY5fZmyjFiV9PNxhi44rkB+H1ZTLiMayydQ+scLjqPVbMFZCH3Pi0j6VgH1gPGWBgr9jZFul1rKayVv1T1fspk9d+nx5s1zjYbmKaf7r6OiF1I2tZ0FM4iFtWD2g4oEqEhk6quL3yH5YQLK9tozI+xC+cQzsYp9l0XTPvYPd5z6cL/4r/4L/D+978fy+US3/zN34yf/umfnvzsD/3QD+F3/s7fiTt37uDOnTv41m/91tnPv6Oh6QQRAV9mgeRjpnUbOxag9qNI4yUxOgOnsrWOTpRRwVCqemShPNsG9NN0aQUW1claAUkJskJqztwFL4PE1bG9M3Ic5TkP9DQaS+sKkKRu71Mgy+mppFCwObcyTxMqDVfIPMlaub/WmeTu3xABcHCW2cPkk6WpoNLjarQvnrDVlHWIb9RlnwbHUrVYkElj6RzOi1Utk5o1kGVHTBaA2fRQGWr1MLWgCIjIbXt2Heyn9l3q2lhXqJqsxWog6o/1djKqhSnB170n/crC1HMulqBMrh1xg/BITGPFIJl0AAY2AftaRZsyWEaa+Ca2Mo7ZrWFoIUhK3Ud5XiYAXboMhK3skkZjVTydt+ml1ddctNbisGl6nSomzyNKqn3w7FpDVasFY8GLi9FpshbOpfeq9j6zYW0pm/Ax4HyzwVHTMlNfqeR9mvA+wDhKbaUAwLh+Wj+EIJ5g2Vz2sG2x6aaPg4CtNgylBlcjAjuni/exe7ynrujf//t/Hx/+8Ifx3d/93fi5n/s5fN3XfR1+z+/5PfjsZz9b/fzHP/5x/OE//Ifxz//5P8dP/dRP4bXXXsPv/t2/G7/xG7/xDh95LUQYDU6nXYbJKlccjd3uGG+UyeoxLQ2G/Qt5jR1FNwRYyqyU9uErd2Wkos8HbgTN6aAggEVX81xxFBGx8R6NMVsH4rlgqwUDP1hTDle7gKQmrA6uGwAzLvPkRoarXtsCRT2X2nd10GbtWnmcAEHHLG72a8S3iz/vyCAQkgYjyqRi4rzwXe9/iKHKaphUKVcK3zOIzsGgbtk4nAkbyodQ98lSIffwaduV/VDAO/e4mgSOtg9NP/ETP4GPfvSf4M03H2I8s5YpVwfA4PSUQdZy1RcPR8jkPAJZcs2KM9Z04Z2DRfI3AkrtI1CmpKxQiJxV5/trJQ1vyUqasoJcZVvGCXMrz0gpip+KEHSSlfdX0svbRhhC/T2qhSPxyirsBMrF31wcty02YZp919BK3PExKbs3+KnKJ4aWJkGuu2orK6loTvUzK6uarAdn57i7WnFxEcSa4lLj9Hx472Gd7RU3mAHLH4O09SrA5so1Iz2VhlaeT1UWaqjrezqWGOBmKkP3cfV4T6UL/+pf/av4o3/0j+I7vuM7AAA/+IM/iH/0j/4RfuRHfgR/7s/9udHn/97f+3u9f//wD/8w/uE//If42Mc+hg9+8IPVfVxcXODi4iL9++HDh9d4BmXwalorZS5jcleuOFq7fYVKgCy1ixfPLAD/IP+T2ILBC5Nl1UjR8AQFac3SFcdpLMEYi9BFxIaAJNLVgYwBgRXTzbUPuLtabfXh2RaNs7hYF5Q66qv7xlq0ZLm6MHrAznWvN+xpVCywg2hCmBSYAlnCYkVbGbwNXxIj7SusQUAHZ8CTrGGRe+xU+M7PwnS6MLODP/lvfhV/47/5V4hdwPpikyd7HxAp4q9/9H+EsT8OgkGMG5xf3MfjU49W+8R1Hu2iQTSEVtIPzrAVAaLHqmlA5p/1WJwYL3CxeYRNZL0bIrAJAQdNsxPQihE43WxGlXwaG+9x0BhYciDz385ua32xxhuvfwYhBBwcLIVVLbepqSF9Ljy6LiAE4PDf8yQV1h2ss/CdBzUWR6t/3Ut9nm86rDcdvHwOAH79rUcAxL6hsWgWuRjA5hxfulepnVEEEAKIDJvNkoG2O6r1IVTApuxwkB59pSh+KnKXg40UPFg4zelviV1MZfn4eLHUlypEaR49H6um5cshKdCpCEK+j5gsYp51uDAl4rS8v1j3fq7pQmf1eagACXl31YbmouM+lK8cH+ONx49x7juQNdeJsRC6iNVh0zu/4XsUAxiERUpedAu5xrUqSq0838ZIWdtPfWrf2j3Iuv54z4Cs9XqNn/3Zn8VHPvKR9DNjDL71W78VP/VTP7XTNk5PT7HZbHD37t3Jz3zv934vvud7vuepj3d7SFqNuKLnYsfu6kFWHFrRtZXej6ppGE7c7DivYY0RIzwGWUtL8nsBWTBwpsNFcZjq+r5Zb2DaBuzEHJEHMtFkEa/UffQ4nGinE/1bABHITN8bDUcmpUgTECJCDPeB6EH2OT5Da9E6ywNXmHJ7T1cAFrEHdgO4wICx4lwKD/K74QRjYYSpICeO910UZk2csdO9AQB2+uZV68S+xI7jh//xf49f+A+fmbtMALb9flu8/pTff4difX65z1/DuunV24doWpcE6oiALRhP1i+aZDirSrZoM6NS3uMY+mJtBR/JO6lIKZstbFHoFSMsADgY4+trhEHsArKifwOgIyxdg4frPqDZxZR25RwW1mHjfbXPoQYz6tOarOHC9GyzwcOwwXKw5tSm5EbAbf1C2LTIiQAeXpzjfScnOFks8ebpKXvSWYNLFIFvjeC75JGloU7+CqBiDAzwCzDeWovWWGyCH/kNdoEXtdvAkiPTs8LpfGBrhx3TxfvYPd4zIOvNN9+E9x4vvfRS7+cvvfQSfumXfmmnbfzZP/tn8b73vQ/f+q3fOvmZj3zkI/jwhz+c/v3w4UO89tprVzvouSiYHmdMStdsi05WHLri3jqoqfljEqTLj2mBWGqyKPcvjBEiTO7AYExAFvUHNutYl3Hhg/TTk0FaBzIy4Ko7SuPadKpwDURmXWhCnK7R2FymzYOEDMTxrLdaP2lbNOYkpajI1BvjytmM9E3Zf0s3Opcu1IrNIog1TG7hcPTiLbi2AbqLVL1niECWEC90ApUpupqqQ7H9gM/cfwwA+KO/57fhsPNYHTGAPDs9h1lE/NYvfRGLxVcA1ADhET537/+HX/7VM9w5WoHI4OzxGW69dBvUWnzRrVt46/QUT9ZrNNaC4im+6NZzMM1v6u/ev4nPPfoVPFgvcdi26ELA2nf44mPAuTsALUDgtEsMrwN00gcSMeJTD+4jRowm1wjgyXqN144MFos7IPvFlfPP8a9/4mfx+OFj/MzP/Wt867f+Nrz22nMgZKYyxlOATmDsbb5i/i0gnuLg8Bgb5/Hpe/exfusJVkcrnD0+g7m1xG9+9aVeleVnHj/G5+4/TJ/TeO5ohfet2l6j5YDIk3iPKTHSsoQLRlLbGlbNZCarSKGm90vThRXvpG1kQzIgjgBoAVADR37kL1eGppm2gawYOwAeiOdYNouetx6QdUNz0VrLOsCumwVZ2a+vf0wk7/tmgHjOuw6w42pr7veohTz6rg7DpJSiDxFHbYuXDtlDSnVaZAyu0ycrRiSPLI3UE1ZBloz3oQCbC2vRGAZG7eBUfPRs87ANZIk+0ktVexeZkaaUZt7HdcV7BmQ9bXzf930ffuzHfgwf//jHsVxOp40WiwUWFRPM6w/VnQTW5uyofO8Crzh0pbJNaxGhq9M+yBpO4ixKNuhikOovBRcuAUIzaKBMZGCdRehCEvSybrkECTxQevFTmhS9RwD2DhDeBuyLs9eAdRM8ECTdRkrV8OqPxIX8sJWJkIC5x53IMu9WXMycdtHU00S6MFkrDLavkxkBq8M8SXPVF5sb8v0r6EEtOKiuKHk/MQZ89v4TAMD/6uu+FPHeYxzePoQhwsMHT+COPH7n130Aq8NvAlGDGO7h1z/9Fo7Mfbx86wTGWjy89wgvfvlLMAcNvvqFl/Dg/By/cu8tHDUtHB7gK174Ytjlf9zbe+x+Hb/+1ik+fXaEu6sDnHcd1qHDV9+NaJdfC7Kv8OfiOeLFzwBmCaI+e/hvPvtZrIPHcdt/x0IMuH92jq96zuBg9RpM81sr55/j0f/0KZwvjvFf/Q8/g9/1J38vfufv+E09FjT6NwD75TDNl/L2N/8z4H8VZF/CG48f4d/++us4+7X7OLl7jAdvPUL78jH+k9/yZTgu3v1ffXAfn/j0Z3H66/y5Mh7ee4R2WYD2GCXNWz77ki6mgiElwFoA1HBxQXqsCqBN1Et/O2fQrfMibKu7PiG30qIFgBaGHs1axQQR/2/VZMUzgI6BeNZrJBwQYbFbSxYiwnG7wKNCllGLgIiW6m1+uB9of+Tz0WPROPiuz64hMmbVpul1ljhnBpbO4ZXjk2RnwEUDEcZh516JOwWx5q73I2v43HyUlkwALAlbn4shVk2Dh4O0KMCV5yfD/o+VsIZbnfkQ0Rj2yFoNHWL3cS3xnuEGn3/+eVhr8ZnP9FMgn/nMZ/Dyyy/Pfvf7v//78X3f9334p//0n+Jrv/Zrn+VhXiIy0+N2EK9rdIFTATrQ2oL2HYam0oykpKgEAeLiXkYr/QtDDMJkaX87HpxcpaLHLVwqgdc0SZ5oxKZCVurO2ekSYQKIDgFExDjv51MCS3ZQl/1GgKuE+t9PxzcEQYMDIPStNPppl/qrks6XamJtW61lUiaLy9CLgVuyGT2Pq9G+DB6enuNcJt3nbx3yPSrTSQbFfeNzg4h2Y8z+PCwx5wH8sG2lNNzLvurCeyq8IDh1rVqR8ngXPLnH8SRgJxrwpieI2GpkLoIPOD/jFOH9h2/Dumlbjvz3FrpCN8XEnRkYve7FsULTuYNjle+4ss8eKflUXHdyknwqrhkUyDQwZGFNTOCHNx6StUY6Duf6aaqtQIgKhtsC5gDOeFhjJiuRVR+1FcDFM4BWAAIX3cj18IGriHe181g4VzX97O0qRmn+PQ5XWVzqGDMuLmSUxZuaGn/4vrWG8NzBAZ5blYsiHj+NcTuP09siKLs2ELobkW2Esj8hUfIp1DhsWmwq7XV8DFjt4HWlFhxaJBB2LFrYx+XjPQOy2rbFN3zDN+BjH/tY+lkIAR/72MfwLd/yLZPf+yt/5a/gL/7Fv4iPfvSj+MZv/MZ34lB3jOx55MhsHXA0fIhYFZOKMZRo33EM6fbydtvR7O9kEI6ILBKNyOBB6PbhcTYy0bDJO8kEYJBTXj6Vxy+sna8sNM8B5gVms2aibKqtlZBOU5LUVib3js936MheRsWws2/kCky/LpoiGg5SKmLXY+WUDLMbBobY1yeVjAdpHJzSGrWweP1tThUerVq04pcWk0YNnILqHa/2ZoQIg/i5UNbMGsLKORw0Dc46FqbXAWnfS8prgYSh3rlzb8IjoAKWHdXFw1pZSUkHOB2np+xV5X2HJ08es0ZptMhQ2KYHlYXxRhYDmqaL+tPBJa95iwFA6AKsdXDVdkIF4CTDjZ4pi841bUXkGAgr+6U4O/A2SjG8dQaxmFBrQvnemVPRFoksgANY+NSKqfodZdC2AriO0/lRuioQe2X55DK/25SyS3+8GDGpEbID9t9HHketFm8UoU3P2Tpu6tli7emqsXj/7Tu9dJu25bpOTXjwQapT+xvVApkemDNqwpzvzcKNF8kcuxUfEBEay5kL+cm+MfQzivfUVf3whz+MH/qhH8Lf/bt/F//23/5b/LE/9sfw5MmTVG34wQ9+sCeM/8t/+S/jO7/zO/EjP/IjeP/734833ngDb7zxBh4/fnxTp5Ai9csTH6mdSn8glHixUuEeeFRNN2oKIJtpFre7wlS0xiVGLOs2VGtkZCXY349tbAJXypjlx0qZLAZpKXU3CG5UzUwT2VfBbNaYBdEwxSTDK/AipUAtEIdpCDUinZu8GSz0Brfe6lEBZC0sWGMz/L3rfUXvh9F0IQ1FvXGWyeLDcHjjba5we+7kIBOiymRBr0efyVIHc650Y/aPU8kmAY7by6WkvTAJsspem2ysWjJ5ZRxiyCgC2Nprk491fpI4fcIg69GTR8J21I+1n3K1ie0zcl9JnPwj1FtssAXKRrTlc+E7D9fa5JGlYRTYFe8O7yszjeonx6BOfl8+z/IMlIySbfoGmXMYS13SbWLVLMi0zFJRvyl8GdklfnrjMW74uaAlQKyNVHYsiCnprnYerbFwZNDNNDvOsoVx2AEQ2XgP5yyMG9vhMIAv+6rWIo/Ho98Y/e/1TZfBBxhnRkCdSKqQe0yWAuC8/9bWu31E7AZgeRvssagLnH1j6GcT7yl+8Nu+7dvwuc99Dt/1Xd+FN954A1//9V+Pj370o0kM/6lPfar3Ivytv/W3sF6v8Qf/4B/sbee7v/u78Rf+wl94Jw99IhyA9c5l0xqlB4olTg3UVqgpBZBm18GkA4sYfZrUkucW8WpfxEGSWrLVwd1YbrbLg4D8MGmRXJq0G2N6DNzgSNPxwJwIm/U5wL5U/bQVAKHGqdygWLfRgkFVEdELCJhLQ0lfvQGIdL3qv4n7lHpBDkO3yN9Lvd2M/I4cN/HVw9SS9jRB1vf1hjBZz906QJQBuNf/UNzeewAxMZcRUargImLPaPWwXWDpGhi6mDhXQnk1QojSNmZ8/mSWqLWom0sXMtCJqOvRcpw+OQUA3L59Gz/wAz+AL37tiwAMKgxrz3taa1BKwcQgtWeDdAwA5DR7SvoBALrOY3nQjpo3m9TYOzOIJL0rNRKzTAyyKFk8CEj2sWCEZSuDF49mJvsABVnlsbQJFA1bqaTvqSh/FsGdc6qQTgBIyokMuhDQxYhjXXDtEI21cNaK5UAdVMcYJwXcjvoi9E3waIi3OXq65J2g9KxWIqX7a82hJTV5yXF6LnznYRtbrRR1jcXmPN+nSCS+hfkzC2fhrMHGByyEDQuRPQmbHVpSAcDC8qK6ExYwFzvt4zrjPQWyAOBDH/oQPvShD1V/9/GPf7z3709+8pPP/oCeJsgCMUhl3G4vcETfldcWgvXRZ0cpgHIf5aCiok9KH8ljm03/pcoAZJ3ow0ib4eofOb8QQKAtqUJp3qyiVPdFiOs3EeNatDSDfRLJ0UT4oG2DPB+jWVWauHYAllsYEjNKD0Xo6lXZuSmQVUsVQiZRnlgVEBoiKQ5oeOWfCRZmVAoBfz0s3nibRe8v3Drg7Rc91WJkpqynv5OJPU1LgmMioTfBHTYNG7hOalcMTAE6QwxYWL0uw8rKJVik73vXfSo1ntzuqbKtQSiT9f4v/WL8rv/1H0mi9v7+0//JvzldqEyP+i8lPbqhEUAgeaaTWaj83m86LA76QnjeBuQzuh2b3j1lV8goW2RgqGgSnssL+TMF2NGy/tSJYQYI+RA4bWfk/NPiwqEl4NGUJmtGZJ4ingH2FbCYnhc2rWPg5kPYuVE4wACtJYN1CJgqQwqYZrJKRhUANj7g1mqJU2MRCT0PKX4npJfj5JRnZDweW13weGPALZOuJ4IPODiuW8pY5xBCSLqtKGxoSSA0Jts4LOScuhCkndiOTJbjESF7ZO1+//axe+z5wZsMaoAYZGW9PV045crbiGB9GDkNoqNGmS5U/6vC9d3k7n1JW1GwUpYwAoPWmSTWTOmSYiWvwnftuVUPbQosAyDdAcyLk9osI5NfiEx186Ci2zgET6bFgBg7wMwZkQIKRDKAKPqmFcL+ekzpvUihIyIiQois3DIRZFoQGSkLl6sQASItsZ96NV0CWc+fHCIIiM6teULRSigfB4m5bIzZ1b5noAkG7LdXS4E4db0RFSLuGCMaI4zJqLJyIdekn/Y1tb48efPM/MykrIAMsg4OD/TIUU+3l9dQn/ds+RGJr4Fq1KrYZcAqhcDFA82if78Zhg+ff5uaYpcnSYmpdCMsnYB4777Ih2KUyXb6+oQoQI4KdphaAA6tjVUWkTcduW9e7BDDo/rGoweZ2+BraQGwaW3nuSJZF1Ex+ultFLFqm55jfGWHk8yaNf2rGmLA0aKVVlV9tjQG9p/jooo5TRahzmTR9JrniuG9R7OoM+uuZZClRRAgM2rtRUQ4aBucbjZ4cHGOBxfneHRxwVY8O6Y1dYzvQkBT2cc+rif2IOtGQzRZsvrdVrnSeVlxDFYqzUTVEAOFkmYeWDiQQ5nTSV5XUMBUTPZaKTUAgyTpQqQVeglG2DOptQ7Hi+V0h3dt6ib7IiKxA4hVDYk1JqXIAsRmQVOCdIhxhaEHJtfLxfUohu2e/xaUxZgahNyAOcrbLLVQHrEwIHUgODgbM7MTOX1HmANZFq8rk3XnkEGCycJ3Zt8AU7J2LPKB5vrUNyhW0kN3lwc4altUQVayBdB/S4os6QvLqFcYJmuBQaRHP+7CZHG68HNvfhY//uM/jrOzNbaDLD1G36ui03ShMWOmUvVJRMw8nD46w6N7j7E8WmKxyiArs3C6H32OLUBBGLOYCFHV3Bly/Eky+f5x5UIPJFNxrGVaM8Qw0uSEGOFAsOmZFSaLHBo7/nzve2SA+BiI9xFjP/2a9ViHvQXaUlJOsRRcxye8jfCkuq8YI+A/jSP3GH5GkwVgAFD7Px+yzo2xcM4hEtghvzg3Hdum2OxSIzsMK55S8ZpBiBuaXOn+ZMAOQe61QZVluts+xisHpzhpW5y0Le6uVri7OtjJqwzIVdpdCFhuaey9j6vHey5d+PkV3Pmd+waqemc+FcAeWf0XrhSsl6Ean2Rv0HuJ8speQ/sXkk6evTRQ0SKkCGuNOFIrC1ECBB60Gmvw6snJzHUQFqqcXM0RgKXoQA56n7YCfliHpmyMMlkHeXKnZdo+0RbvM+pXVunA3Be7Tgz49nlUXyUREhNIrDFEPxaZZcgmqpk9Q5pE6wMlkcMb9xlkvHz7CCH0fZT4NtM4XQhx4y/lZTROxxwtWhy6FepeQjrp5etgDbMDw8mLiBDNEeD7fUWnhNUxHROwXZPFTNZf/xt/Hf/dx/8JfvEXPobf8oHawdaY2wAi7gIQFXQCKa3bP4d8UE8enOHgZInnX30OR7dXPS1NFB2U0TMs0vNERnYbhSGWVCAJyKIIYwG/USYyjlqipB6hXlhvmezvnZ7CkMHdg/x+KJPJ72+T7kvEApYeT64TQtpvAOg58at7JX9ArRvEZkVTa071fz19X5e3YQ7HO4uPADpASw+TXrEasf9ORv8GYO6AaCE6U/5dFzwccZufhXPS6zHLILg4Qrc192w1AMbeXUZStEH1eUUq8ipRtQAp95fSwyEVqNTYqVvLBreWt2T8uXyo6/vaMyO5j2cTeybrBkObkirbsa1JdBcD2sqKY6pJdGpqOkpj6MrNYZguVKH8WMTLqQ2DfvUOt9ZhJis1YE0MwFQaZxiip6Ly+BaAYdPDYQwrpZht8kiTvTnsMygRnJqdDZ0c1TdG0y6aQpxmssjcAZmxRke3aWRgDiGmSQnEE6Axsbd25lVsthuo7A1vvC0g6+4xfAww1vYcDHLaKv+EzWblnzEm5ma65L6aOxPg4fNEQcD0Wm1cYThNBkQYRHlmdgNZDx7eBwBY27+3+V0od2YTMLAk4IcAxEL4PtgPEYuQj+8c4pUvfwmvfuBlnDx3PBIr56djaPgrnKRqv0hIRSncIOMAin2gEZE0WBpGelwGH2QBpOwyRo2CvVbkDVNj5gCWpkXNmckK8q40iOW7F88E4FgB8Dx2NNZAbSgSEIieFzi0GrFZMQYgPAbMrQR8ptg1UO5tGCO7zOt4wPpRvs8bz352rXXcm7PQKOq5JaZyDsCbBrVqDdWAwmg7n+lN7BJ+1gIE2RvMSxUwYbI4YFctb3U/Mmb7EKb9C/fx1LEHWTca/IIwM7OttF1o3Uo1iqnNEODB58AxkOszTLr7bNAICC0uqYrcsT6DLEvKtOXjJCI0UspuEAbf2fXxqmsl2MF7bOVAojvQisrkaJ9E8sfoTe4EbDO4zNcni7qT/1YFpO4WlLKMMaqGyaZJyZom3bsokz1rp83kZBALkPXqnROeQGz2BtJ0Yc19ngRUZN+gsdhbj3vOcZ73E8GeXxGYaFdEFR3clGibSTx9vnZLF759/x4flbUDLK/MaHkOudCDiNgCIC1s6posIoKxhBe/5AWc3B2Dq3zsykACGKZphUmOgT/DkiHWSrGFA7dW0uMPMY68k4wtDCqJeremNRZrn8FTjJJaimHwDBxIxeFERNU/BWar7EtAeNC7pmRuFee2AMAMkiWTxjCODqATwL5vsA0A8QFgboHMIVrDYutuxkU9M1ldb7zSik8A0sPPorEGrTZKj0OQZRPTOB0OmNBkGWIIzWa8T+f6HiYsQDRI5RBJn0fS3Lr24asjPme4aMmafWPoZxn7K3uTQVbSbEJHb3lfQghYVijmKXFoiAEHrTaCroGs/spN7SC0Eq1M4RHZpDsZgsHFagFjTdKWZR2X2Y3IiqE+UZtDQCrUhtEYFfsLIIw+sVXl5M7frQizxzvj+1BMdialC3k/l08RqOBchO8IBe1vYUzDLXZKoW7qNVl/Ne89OsNGxMIv3z0CEGEdFa7x+v0+k0WSlky3Qz5TbaMS9d6PzwfiLeVlUmaB9YTJa1FhWBxJ9aO8xyBasunr3HUeF+cMvO8JyBr7ZM0zt6kLAokGiqjvtZbOlnZ6fLNVCvogC2xjyY3XvWjg9BqwGa1mJNP9D2Hkx0SGEsjSIhM9y6VruGefhJcOEgyWmmIbLZzhI6pKCxJDxo777FfHbBZ71jWSKtQNtpwutBbWykStDEwEyDSsqyzYLGajTkHuNYBWaCyLrWvid7WUSCArdgAWCeAYScXHGNH5kDz4nLUgN/CZArjRNvE9mY7+ojOdKlHKCDBgntnEDtF1LHofWoDkY+Vxx28CyKp8o3/caVx7inCGGTpH+8rCZxl7kHWjwZffEfugTNLmGoRevzANnbh7FTUCQLhLuzBMo8mrwmSJPswmQXMGTCwHGZv93X3lDlYnq0KTpft5OiYLVOiyhuds2Z8n9/QqJntaIE/u6va+jckSwTnlnohZG7O94q0eJsHUIKIjW6YtqIVTTCFVf9bElE6qxetiRPr8yUHSURhne/eeF739tJUVO4kYI2KIMOJEPu3RVvu5SdDFB7EaIGAaZI0rDLXadcTaKqNEBjWjXA1lsay1ePKE/cLGJpHD51CPh593aygLzjVdaDACWaZOEFeD08LjdKFq/UIQ9s+o2Sql4gSifpsoMygQ0Uk3+CALHSOdDgwW1mJdgKzcb3MIflt+t6nuqUdAKvIgsiBzAtiXgXBf9FgH6GsjWwAejTFohkyWpJDJHAL2Vd4GwP+l5wDzAiBqxWXTjBo9A4XOLd0TXURliQCD4CitZJp8H1x/caf6yt54VglOg9ZhdZPeW+zcnWMquk2HxWp6PDKOgbT3XWq7M16ciMEythdMTQURoXUWzuxu+7CPy8f+yt5oGBmHtcXH9Muiv6u9DCpYH5rzqRg0gYSRODkPWhpOGamRiN1kVqZ6mNIgt8fC7DpL1dkQonZSl9VKmiGZrSLI+UAYFJncY8eD8zYmSwZh5S64caperylmZ1soCKEEIrJVIqfFnDUI4NUxT/bz1YXq9v7ynWOY5NqeLzLri4bHS4AR40uxvdDvVEEWpf+rnRGzckXfwnplJVCrMEwMxGCi4ie0z57WIts3rOClz6K1zeBwdVFRZ27V7Z1t6vg4aoweYQIQDiL0MHgfZHFaVlrASLrQ6LMmoIezmPk9HGJGtvog+M6LFQHrmIjATDWVC7TcCopKHSI1LLS3YbIZfTItVvBnXwHQMjgytzHuBcnMzsK51KQ9M7ICeuxLAB0ihvsANiD3mhwXm8OuXIPOj5lq9QTrpQuLRSEBmVWnPC5aIjhrU0++dLxGyduZ95gK07pBWGPgZawe+/BdPoYWIGUYYS5ZuyVJztE6okNZzHHVaK2FM7RPFz7D2F/ZGw1dyTLDUa6QYozogk9/NsHDUr0BqxENValt6HxAaw2WCWTV0oVDLYu0u3C6Ei471ittPZ4g0+YSW3ZZJitisrTa3EW90TA3N2XjRdObHPqTu9Lq25msEqhm/y0+viu9KoUmR+8t3z4FEo7ThcjeVcqwTIKsew8BAC/fOYIT1hEDkMU+SuWEKAxUkZaKAszrTNZUulKfh9BvZzRZFk8Y9jCcXlDEQtM0jcxLkKUTqbXD7xQq814w+wJoit1wmjWlgwbHD/W4mp9U1eDURC0W0cgtixADO3cbgjWqDzNJgF9ej1r7Ftc4hBBzeb8A5eN2gYVrcNEVKVmqvH/UwpkWDTz8BGOevyfjkrJZ5PjvvYuTe+ctncMiNTruv2/MZr1PgNrz/Kc4tqlG0dzovLAYSaBC3yN+dtee3coXsiAyRMnMs4zcAHzuPZ5+9lrDhT+l8W95rLuySQrYXVMvVgKYubTCXFppwj0uUNHrbFEWL102FtahsfZaWwbtox97C4ebDNHFsCDa4Mk64nS9xlm3qfbtWrmmWgVitXN78c6ug8ftxRLG6ERSS0HZ0Y9a6xB9h3EKz8AKxzK1sh9bOOz44kZMM03mUNI6fedwI2xE6rdGGVj07APIAHS4g55KmSweNJP/lh7gFluBeojuyQCdF/1Y0oUYEDVopCQ/sDI+6UamLBTeuM8g65U7x3zuRIjlXApJcw2+z1VhzFSoLRl/tnZdJkBlofVKdhS0TRvSrzDUYx71lwNy4cQsk8XpwoPDA3zv934vvPc4Pj4B8Hrl+IfXoElg1hkD2KFtxvh0t0MsTW0BhgZO+wKCjSFp/o0BkOTCATKDVjAVjaVrDHLLJCkUMMwiHTct3j4/T+ljq60Leu9Uw7osCjgfXHttXF5r6UT2fYjxFJy67x1RGjuWroFJ3/EY9gkl+zJivA+yX5TfYRmP2onJvQtqpFxuNy8Ktf/kxrPovRVdnjU0elUjlBXbVrk6Hg/Tb0oLh8Hvzh6fo1t3OHmuVmHcj9AFOOfw0G/w5OwJDAhL57BsmgSkiAyMY1aNnJ1YDCmzxyz+VaOxFoftFnubfTxV7EHWjUaueGIhN7NHLx0d4aRdsOdL8WljTLU1jSOCQ78UuvNeRO9AniaGA0ylb5YlBKWgexOGtIipTJDpIzK59RtS7+orMzH40SFAB6zLKoS3VvRAyuIxwiwGdjpCxG8A0QJml0Fk0HoGw75pTwOyCN26bNyrK2oS1hBAEO5QHdQn4vV7DwAAL9055NSuMeiKYT9AAVb/ehOs1CHI+cm9nNRkVX+url9R0qmqU5keRoY9DNVccZhxYQaOz30KYAIFk3W0wp/5M3+Gvxvu9cnOGHqAMO88sy9W9Ukio6pZWZCK4wWITUWMCvox2KekZI2wHQQkwgeWQVjh+N/b7yCMVBxq5aGPERZcuHCyXOKzpyIuh6YjgR5YIouIBVaO8Ph8bGBKVKZMCxbUHAPNV2LkM6f+WzHi+dUq38/EOBXvojkAmq9Gv0UWj32NQWoUXdoUnG82eP7goC+mJ0qOBSTA47Tb4KBZJv2kgeGefsVFjQCsEb3f3Hs8owUsK6+HLFm36QDibgBzbvwA9ywkZ2Abhy+5dRtdCLh/do4HZ+cAIu4e8BjntA+kpDlH72n0gGmBeCHP++xuJ6N37/bxTGIPsm40dEUb8OLhIe4erHDg2pEZ4bYwhsuo12UagFA4rOvEXUkXDkDQneWKhbHxAWDKFBunXwwRukmQhcE+dLIfCoJrX5xKObWIdAyEt8CsCIeV9iIZCA3SVrSQ8w4A6j3C+mFSWi2lJHTkirEiitgl+PwtpOqLTDZ5FYG3NYY/pmkEM5/SeONtYbJuH4EtFAjdYIRNfeuKIGJOwAMgYTZ76RjoqarAaCKNSMpkBbRp0pq7t7kIgcgys1MR67Jreh8o1yIzWeU9zYwwkXJPteto0mlZYQDZa42qt1eZvt2YLANUtHDaBoeF6mDmMrFsBgYWMP10T61a2Io/XnKqL6rvDpsGDRmsfQcLUwVLfPoHWDUR/rS/v14j+UqatW7kq611QmGPADDD0mB4H8c9SPn8G0ujRtEhRngE3F4VFiCDMcIy/sDG+1RZCDDDZZ2BH9y1XFk49x7rojCMgL6OBcYa+G7gNxaBZtGgW3dol/OyBL/xMAcNFq3D8weHaK3Fq8cB987O8O/ffis9w87Z1E/TVNPZXhagT/A0TJYxZvaK7OPpY399bzR0wA04aFucLJaXBlgaZf/CLnipPNKBjhswj1fICrzyoLtwDgeNCuL7+hLAwBrUtQRRqqto+J1dEi6YXUWSuTPSZVlxK25TJdVgsqclAAfEDmS26bHysVLhap8nu6sK33mbhlhInxv3Zk2Ok1RRUJ8swiwz9Ma9+wCAl+8cJvPYILYPev857TO+1zxvyPkZBl40Oq8J0biekbB9Pkawl6LqQiaiLEIAUmVf7Rmq+nsNQpms1WqJn/mZn8HP/uzPwnsFVVn8XZ9Q87adsSBjuRchAFMpYddKyG16G60a5E6+/UUGSVVujKw8co6Q30Wu5h1BqhrIEtZQy/4DuPqViLCSdNNZ1zEjNaFJIzrAolIyGaOymtsLD3IoyBoanHqAFtuZa8pMVksGm4IdOu82OHANjiSNlYA/LVCCaf2zdPn9Zt+n/vGzvlKe6dm0vz7LtdY6GWSVj0MQyw23cMxobYmu64CFxVHTpqyENQaHki5UvZxdOO5ralCYIvdOSqxqnk6TtY9nH3uQdZORtBnXUK0iEwYgDsgu6xQykzWM6UFlrMniY3VUL8BhPVYcgCWdrKfPj20Wtgzs5hAg2/dbEhzBurVSBKq7lsmdgO2idwUcDoSQJtU+yLoiHw+TXJUtEQyX/yGD1iwE58kes4Dzjbc5XfjyHdaZNYbzCYZzUqw/qeqRLFcjRpm0rOErNjqtKRaIz0U7VAIQywIze7zDCkOt7KtVFxrE7SDrMYMs21h80zd9E77xG78R5+cX6N8fLdoYpgszc5vurWDK2tompwtnDwl8OWs6RE6vWn1YQcJU2vT73JEhV9RV9WGGrQlMIXxXzaYxBreWC5yuN8Lw6nEMDWlbNJYLHkppAVscVPqVzgVlJqt/MTrkdlZzIQsNiqNG0WebDW6vVoU0QmxYxHdNDWUNsfVAKaFI8gE5L9bc6eJmFyarv+gstwsA1vZ9srqNh20MFqvFTiALAOAItxb9a6R+Y51WuxrDqWEi0cIOn2WA72/dQHUf757Yg6wbDQEXT+tuBy7F1VXQJngsnetXx9Umr6LR6zgGIt4k1K0L3xN2qLBf84NAhYUaHechQKuelYNVp2KrKzkzOMcW7Ivktk7caTfCOvigbubF9bsykwVYEDyCTLjlRMZCW2NzWbgtvjcM7wM+cz+DLABorEtMVvRRdF3jdJ+BhaXYKz+vT+h6fFPpQhnuI6Rv4TyTVaswVBHxcLdmcjGQQ5msxTKniLitzpDJmlpU8POerBM0NTzBcNSEzsOIUdJJNATjJNtl6BuJ9Uf5eSQpSCiNZE1V18M9QvP9CkHa4EgctS0L602tJZburuUmyuIxl48/Vp7NbWGhbYr6EUC0W3oe0sbnoGlSo2htNt0HIeIJRX3mxhpCQ7YHsiwRe0sZYlsM0dNloDunyZq2RGAG1wCmv0DwnYdtHA6OtwPLECM78jcNVgNTafUb0+tAhs8jEjJQl+jZZJjmWuaPfTy72IOsGw1RNV4Dk+WKFfHaexw1hY4iDtN4GjpQTr2kxeMhLICbShdC/bHL75SarKmYcKMvt0wtYG71QJYhw4J/Y4rzM8V3CDDH4JXebs1PjYj7fc9/S2OXFMrowKHXDGAPMhUka+scQ9SbVK0jTOmS3nz4MB3bC7d4IksTDHEKSXVEI5AkoEJvHYuBaynkOU2WnAhFGCMsGO2SXupXGDpjKsL37Zqs9XqDzYa30xY+QyZZIuhGZ553YV8SwBQQVSthZx5q6nnPEWIsgO1Yk6VsIVkBMkWFnaFcWRi9MFk18bQhWGuL6sJ+I+mDpsXSNdIKSt7D0TVo0LgWjiI2hTeVR+x7480yk3LoZMH3aqhP2s5GcuSin7aorjzbSKqwKTRc0csx9avpnDBZiwGT5SwvXkKIzO4SFeze3LmZyfHQamEDoedp5ruA5UGLdtHAWoduM526C51HcMBq2fZSnAAk7emS3tU6y2CRau9paZMx9jrcx7sr9iDrRiMPNE+9pd5KPBaid/73fLqwMjBEYKyvMnA05Xis9gNu9J35fIuwIVsGdjK3e2yIMzyYpr6FlfQI0RGzWVubQ+vn2YfHIwxE4VdNF/L5GyFNGqP3OqeLDBzbbwQV20+zOa+//TYA4MVbBwm4NdKDT5ksQAFD/3iJHIwp7l1KtQ6jrufhbRA4eaFMHwDpwTcXwx6GlsYeQZzVGRhoDuJMWKymcbmJLiBtdYrFShymrfVAMnOrbuK6DqiJzZXsmvKFS8feq0QdLzKM6KCy7inff0rpPQGaBglIlcFMlklSu4A+k7WwFqvGiXeZnv+YyQIaHDboaaBijFzlGtXqZcdpgRYYjV2jMWDiq6RpU88+TcTV0WebDZ47OBxoU8WugFq+h9paRywsSlE4KciSd0pNflk/WFtUlDE9HmtxEWy/x2zoPNpli2bZoNmiy+o2HsESbh2uqlXiS9fAS9pU08MwVDGgLm0yGuw1We/u2IOsG4xyoHnayDoETku1rnyJ621reP8TKyFK/5d/kCaEWmWYTnIDYLaNqdt1YKdDgBxi7NKxO2PkvD2ApqLBWXCaceciWgtD7GllNT3AB4ntbE31oMFsBvN8TkX6aaVv0yTLXmYQK4D68b5xj3v1capQ24sYkOEJmEvLCWPHdyDzMhwh1m0Ltgnfs7EpwVIARlVjtS+VbY6YFRj7ZKkL/fRzUFYW+oKJMT3tof63dr8yU8E4NLNZKpaOMSAKmE+M4FZNFre44XMtn0EB2YY3kJwV0vNoxG5BNFlhXpNlrS1Yzz6TRUS4vVzKe6/3cPgcNQA1OHB94+IQFbBt9ynrH1TbSxdmgfqu7xvriRpr0RiLi87DkMHxYlDNGDsgibz1fedFwmEzfv5aZxGJgCAaxQSi54+LEis7ocnSW9z7EuBaByKD5eES3cVm9F0NvwmgxuL2sp5OXTiLAAWQBtbyeYze08ImgxclT58J2ceziz3IuvGwuI6XxEq6Yu150Fr2QNYMSDBLfmknj638u7aJ6QenelhuTdXqwl3Shdva3hyA0wV5EHvh8BBHrQz0NQbE3AbZV7cyLenj5ECICaiWTNacd9PMQfNEziY/vHrtpW4lnWSB4NlHqaan0njjHjNZL9/hxtAAUoUhgRJQo4qmypAVk0r+XqRYr2SNc5osvr9GjpNTX7uCrAZ675yx40c+IqVQpyK7vR8kkMWgpJIurD7vhe2AuNUnSXG61Q8B/+li2/NMVogBARErp9dsyB6ZxGQZYwd9JXkRYlSTpf0gKy+ZMUZ62hXAanCP7qwO8OLhUTr/sSGtAdCitf2FT0iFAPXvTccCfUCifUJ3eCaApCdqDTeKfnhxgYOmwVEzfJc9gKXc5yy2v7s8wN3VGLC0zoEMp/3VbNZZ2ikNOiUkt8RlMTBZo8eVhRZNy2PX8nAx2bII4MrC5cFypMdKey7ubbtqcHTnEMbV+gpq30K34znt4yZjD7JuOgr6+2mCLQ0IF12HhbGDrurTbWuAA4zLsPXY8nfUyG9KBkyAMCg0+s48iGTqe7tZKa/Cy5ThneUqN8CugCyiFmRf3LLdHIYs1J5Ay+OfLoxsVwS5ZsD2kYUhC2cJPgaeos000NB04ct3jpF0KYbpEeO46snI/oYWDkQORFJtJeDPVfcTkcUn4yAReHNT84jdJlRJbcQsWB6mnEMMaLYwmtMtdcDXLM4zWXw/HVj43t9V8paKHbRxNMBP/Nzb+ehijVuLJW4v2V6gf/z894ZyupBTogWTBU4BqgWHmdBkGUuSLuTfEWjUb3HpHG4vl+BnYyLtahq0lp8BZRNjr6n07taJYxZFtEKXZLKMMViK+P3uwaqijwvIXl2ZeT9atD2PrLRVy2m9GCIQwPW2tKNWzLTV8dgIw1iebbf2sM7AijSjWWjqv/7ErKPHYUWPpdEYC+6VyT5dJ3ePWaVYa6mTbDIskkPrPt6VsTcjvem4JrpXq+3Oug7PrVYVgDCR/jEtYjdM/2naYLgNrlAbvtSpNUmsAQSLkn0aRx0gjY6TCJGWQHxQ+a0Hr6qfLnTy60KoNEy9uiaLKLBjNCngbNLvidh7DCHw/ET6vXFkJusYicmSHoRRejmCIlDVZAlDEiN/ZkKHlJ/FqXQhM6+GDIwJGJtM1qKvdTGDFLI+PwunTFM9nhTpwqOjI3zXd33X4Fi3pQvBz1p4zGo4SQH1xeYeDBzZVJMMAb7+foYYsA4d3n94my95GIJEvg+M//i/pnDjVxZOhfHBBxhJPY0O21gc3TpAI2aXAXG631z0gJ16p1o0lqTBuuc2WspkxTDzvUqQ648F0WPo9j4feew7cC1WTYOTtlKlF5HHCNMAlYbSZTgyMNYiXnip2FSd4i6sT10+ofKEaIaVhRaWBV9olw1c69CtPdpl/96EENEh4ni1rOqxAG5x4wxXfrZFZfjIiDSlTwF+gPfpwndz7EHWjcf1+JzoCthHNjbtRcRMGqat4AdNGQ0GA2FDai+1Mhw1YDZ7fjEAO5mFAqBDILxZ28ikjukyQcbBUGBWZWhOeYV0Ia98jeiXiCf1ofBdmSz+wny6MDFZJ8jWAyT92iiBLJ5QxqkiIpPuBJFO+MPYVsZvQSnVGGY+1993hANwLv/uMwIXXYeFa7Bq5rdXpgtPTk7wPd/zPXzE0QOl8H0uvUvcJJqIgYtihL7+bgEFWYZosm7j4cUFbi+Wwh51GC5M+LyNpFWRmhr3niVywnIZxjgTwImIcPL8iZxvdnuvxwyThRYtRThD0kSecZJJmqzLvEd2MBZsACx2fhe5lyQ/ka2zuLVc4rCtHDcB+Xy2j5fWGGF2PSK4PQ3bU+xyXNPVem7QAsl3Hoe3cl9U6xwWqwXOH5+PnN+994jW4O7RIabCkRGQFdEWw8/4Phc2Gdcw7u3j2cY+XXjjcT0gqzFsLjk058sxNek0APpGn5O2CuSqyT/tfVYVLpPdobpw10G5Us0EOaBrGWy0Mx/6jbgj/+5qYWAQYa0RtqRM3VoY4ibBMer+pwHO68JkvXL3VkppaHNwY4mJBPnf+HhFUC23gn1/auc0YeSpW5HWOK3qqna97tQiMVkDNdF51+G4kebnO2myhjocZYfi4Ge14GeIwSk0c1poDaUyrxDp155eHwO6EPDS4VFhKEqV42cGMxJXCNLg2Ax44UIEhOBTj8K5iMh9C+sxDbKIOKW1bBps5BkiaLp0DpzVNsZVnalSNHIaa/fI53pnucJrJ7dGz10yK07PWX6OJrdKBCMdMLI7PLBb1eO0RpYXXvn4gg9ol/1tro6X1QrDzbpD01gcH0z7aTnLvn++HItj5T73bDL4IY57r6x3bexB1g3H3Et9ue1w6XJrbM83hn+Z/q/yRTHs7OmypryrmMUwGFeHsYi4sp+t6dCAnYWyU6mpnpj4aYJTrgY1e4OrgyxWApXmpvxfHvyb1O4G0Ml+Il3YY7LUK0gEuVZcvE0EoT8Z8L5Y+xMQU0VdnTSpVSYW2xGVUmvd5a47NQm4DPfbBY+TxQLzDFpZXXiAi4sL/OIv/iJ++Zd/GZTAvZp6YhKsqU0HFwzIfShbzUSgfB9MxW4CAB6eX+D2UlksYO6dsQQEiknXU36GyMEIyI4hwuzQVsvPOMP3z7P2C26SfeBadGWFZgJZl1msaHpXt+OBnYxI9VhyAURjDVYjwTvQE3kj37+5sJSLB2IQFlGKTLYfk5vcvCuYT20arqJ3jcWiAQijZ+Z8vcHhaoGDdh6ELq1DV6SnI8WxRq8HGPvFAPt498UeZN14XN8taIxBUwNZs/tpZGApdVOiyRqJ5R0slXYNHGk8qQKEYvKrxpwofxDEzt51c8hrAFlkpCmrGVD00ym87dtkg1NnpXFvRO+6GnKwBERZbbNkZ3yvNl2Hzz14CAB4+c4dJFAmvdoi8YRCENAwOl4+pxggVXy0JV1YD0MsznXZYXPHC8FpOt5GTrt0wcORwYFTYFjfXowxM1lHK3ziE5/AV33VV+Gbv/mb0/ntxGSliZ0n4gDqAxYCSpE+0ZiI9THAR48XDw+zLipGvXmD/ZlU+WuMttEpmCxqoLgrhor+ZuJaGKMteqofmLkvXCixKHqdRuSU1M7vIoCxz55HvZn0VJjtr1VUTygBYAUwmwoqGsfHGMUpHzs+q9PXnysU+fn1HVcWugHIcssGrnHour5u7LzrcOfosFIp2I+lc6lzR3LiL97TsU2GPk97kPVujT3IuunYYdCYihgvejRxYy0OmqYuiJ0YYHhlqEJf3fDYQZ3DSSVUv7VOBGSSqqQLt1lUDEDHfIxZtzzoXA+TZSCVgCXIqmrNdgziikyuLtRrk7dlTAMrl9pAJ7nxvj774IG4fFs8d5w1WQDQWsO+QFEwQm2yB0RfFABhs+qanrkJmo+PmzyruHy3615WojFxxGCZ9VgOB40+O/Uhab3ewMvEdXCQqwvzs16ArDnmVloZMdthAMPVlP1G0FyBCHA6dmjhwCzWCrd7rV+mCgYMFPPaxPaWmiwr/Su5bZJx269n6nE4O3xPbYcn59ZaeY8DrADwy72LQOpfqOmtUqC+U+yyLxXT63anv8MeZ2u+ztbk1Lhq4Xaa7oY6sxxGPOnIELpNx5WFw/Y4rYjfL/opwy4G3JnRY6Xv2zxeRnDWoJ8uHNhkzLZG28e7IfYg68Zjh9VcJWKMgH8diI/Tzw5cI2mXwee2MTHmcMBk6XeG4mkrFgH9dCGvuFC1DtiqOZtJj40/y47V/dSmNpi+Pk2WMVQBqldNF7KwvTFW+tYNris10PlAU0q165ErC2+Lw3m+/o2x3L+Qja7H4moAEKuAKG1GrEFd0xPXW3Q1FtYALvW525WFzGkYMS5ARMR51+HWainsm5lM82mqsF20cI1LPlnZwmEI5qeeKX5OUjsjYRCZFFKfqMzOGFBvsyFGdEMWC/rdmiaRnfajFCdoqjYdJTWcLuQcU2oAPRchxInm3rpPYLq6kkF8YwiOLNbeF3pKjI9/NtSnyRf7vcR7uFMxyQZAW6RG7fSrGB8C/nVYabkVIOvFokH39mOqL3KAorUOCN26Q7NwcAOQRUQ4OF6iW28QY8T6bI23P/cIy1WLW8cHW3fPIIv3nyw9eu/p0CbDYL41Wj9iPEtmu/t4Z2JfmnDjcVWc2zHoKF6YF4+OKp+bd/DmWKG/Eionm/6xqqdPmULh6TYg+2LlIDI7EHWXKPnW1GbSfmgfr+tYL5gkCB8DkKuCLEJjDL7s7l0AkNY35XV1aGS2tBRh0sq0H68nkHVn9PvWWmETDaLpAFObKDLzESnCGFsRGV8AsCD70vTZGCs9IwVk7VxwkBcTJRsaYsRxo95E00zW6eO+6H3sk2XRb4g4ky6UdLdWi9lkeaET2KJXIFB6eq29x8K6itM439ex7ocNfJk5BOyAITbGctGCLGx28WbTljqTFg6zzC6zT61hHdTaeykC0PuzO5NVVo0m25dLFaDw/Y7RT6cpowdswRjOMW1xA5DlpLP0/QvBw5gWuzQf5+DrUzsmXrwwNtycd1isTqpbaFcLxAA8eusxmmWD2++7hfZkiVsnO4Asw36HPobUE7OXFh7ZZPTtUbaG/xyPnfaF3T6/j6eOPci66bhymqsDoKXjc1FnpXqHYBYDr6wJMbq4ZA+ZLEVcppqm2kVsuhtAYq+sFRDvD471MgaIc8Hu3Gq5UOwZT8NkjdKlvW071uwI8KjrqQrR+907fL2KTTqpXGQYpc2KK2krdaumSkoUAMLbgH0FoNuzZ/TK8TEa6wCaclavRb4/6lB/0TFgYcHzxZjlK2JYWahMVgYag+s8CVayjojblggAAiGL15fpFgxTqhe+w2HTiKdXGVMFHMxksUM+RgsRbVOkZve1voXDCDFmTdwgssfd1HaYfbSWwfn9i3MsrMvnednxyCwA/wQg0U5dpjoxAYS5ZVgHXgT2v1MHZuxxZikw+CZC1wU4Q5dIFyqA0bRcDiMpZiJC6OLIpkFjsWyxOlni8OQQR3cPsSYGxSNbmEo01sBag84HRNTSwn2bDAa6FsB667Zj3AC0y5yxj+uMfbrwxoMn1UuX4MaNpHW25eJ1BTcHEprBr8MEaGEafmEtNqHoWQad02rpkmnNWSrPvoxonQ76qc3U+/A6hO+UhKZ9AbKKmq8ShcaiwtYQWTSGOJ1EWhk4nS58pcJkWTLJwZyZuMq1oNx+B+i7h/OxnQNw0oZo7lkxYrVw2XQhP4NqfGkM4azbYNU4aWY+v72yshDAOF0o7NR2kJGrsRrL/IYRTRSzBFYmIn4nzUD5vvHdKCXPMVUwwL0pTRLAD+6/gN8ABtg1t/fRnqK0JqrGlvR5YkoDDtsW646ZLBO3L8bqoWOQgJJLabJ2EG1HgHo+enPVdOyFxea/BLKE6L3c336adjJIuxOMgYglrhCOBLjGjioLNRYHC7z6gVdw95U7aBctNj5g1VR6q1ZCZQWdtAQapYWrNhlZQzgbcQ2+X09fzb6P3WMPsm48dlnN1UKahG792lSlYBHUoueVFadc2Hm5fWu5xNoX4nPEgo6/TJpNmZDLpCi0bYiGpguvQ/hOIm7FTgPiblEyLLUGvLxy5QovTLbVeb1ksgY6PmMoCXLZc6k2WUpbEAqyL9NnssJ9wL4E0K3501GBdGqHtOsQUgp0+VjWXYdby6UcL9/HqeuuTNbhIF04ZrIU7EylCzPIcEbTrCpIFxBMS+jEVUp5lL09qDQlZjRQa8LuoJIg1rT3740hx88cIFWt9cMuw8dQsRjRmE+7MhAyQPRYuiaxePayGrsUUjV6abd3FKnbbZrNYixK6daJ75CDowBrDQIBXYiwVq/5LufWoF8xmcMQ2GKDIE7v0+dqigpvH3zdZLV2+ERYNg6dpAtplBb28nyWO6u3AhrHWsZ1hzjZr3Yf1x37dOGNR7mau8QAFzf8wmwdlHfQZMmLx1S0rhJrx8IT2WHTQntsaTl+HgfGaarpY9RJ7TJM1nCwCuDmsdcBiliSbRHFnX3HwoGZYDpfQVYFiJKFs0aaQytomBO+j5ksJ1VPWfNUS9sykxVBbIxpcioss1jv2+E66r53a4fU/56k6aiBATfyzdomZiGmonR7B4CXX34Zf+pP/Sncvn272H7A9uc9gwxrDbN/SRjNjYjz+8Au7nr31r5D6xwOapNrDKKFG0YupnB6XwpgqgArGrXw2A5aY4x9s9xeaPp8qprYio6qQ2MbOCNgmyIQtzHele2ZRooLWb5wOQsIeSZiqO52bFcAzGuQ+L4TApwTwE7MBNFACzd5PkSItGAR/SCsMUWvUAPX7naukcDmvTvG0jncPzsHWYPlKC3sZaFZxrRLff9ANoC5y/+N64lsxT6uO/ZX+cZjB8q8GkEGTE5rTDIKUVeoc4NnyxNL7ORjExOeTMAr0aScdx4HDZs1mhnH62m6TVfdlwVZlJyc5/u0XTa49Ywz5eqxZwJ2hSi/VwMA7HMFoNCNjPeV3d7vjK6xJYKz3DLHJV3PGGSReHYBBFsK38N9wL4KMltYrPJ8oucV9K6h5f7itk4gLJ3LBpQxAHYOZOW+hQDwJV/yJfj+7//+vHlyqfB97nnPIGMj1Z4o7CwCkjkvWSB2YBkyx0XncdQ20pR8GHPpQmYaObs0Thf//9s792A5jvref3/dM7OP89DDsp6WLEvCD4zAYEe+MlAucp2IwgFcoQongKKkHIMLQeWiKgcbkyiJCXYooEgRBxckhPwBESEBVyrWNXFMHArjXAojB4iNwVjYBksHS9bjSDrn7O5M3z+6e2Z2pnt2Zs/ueUj9qXLZ3jOP3pnZ7t/8Ht+fLiZByXAhlJiq+U86fF5kUHgAZhCwuky05vo32sPjbUSnA3S6E9TL7quvuxGdF5X+fStPb8Ywk54Z9b2pA4+rUluoqs5C714GqgPRS/mPieJKxSDw4sbQRWiJDPMzY6bGPUSIIATLh4WNMhlljawQxJdCiCnIFle9E/Eds8cZWfNNGZe5dd8Gkni8bQIRKEooBvTCUwNwWn1ia/wrJyqfEUb9AMemp9FUiyQz5JvIgxcYeKUWhCxprSwf8rsPysiSE6jfNa8Vh5++++Of4M4vfRkzbXNZtBAzgJhSE2MkjV72YFLpJ9pod07ixKk2PAZ86oEnwPg3QJlr9vQLLwAA1ixfDu1Z04amx5n0hjGo5HdmMLqV4cgiCAbVexBqwvVBfC2mpqbxyDf+H6anpq1XSIg2INRzQidB9Jx12679IIBoEnohP9NqgRHh576SIBFtgF4C0QvG/Y++KI3M5qhNUVzmZMFq7KeRifaMPEDl2TCdk8V89XvwAUxLD4iS+W5FHYzXTBW8QNGLiS6k4Dq7PXVviTgIDMTkb4GVMLIiwCIkG/8VhS8urAaEJxFwDz7nKiG7euheopX/lRewAvI6m0NzEl3tacrJyu6jRb8+bscAADfTSURBVEs9IJqCz5hsUUSAp6VRSoe268YxeSQzJsEJtUa5F4xWGMHzGIIS+mca3ZzeGBYmIPucyetYnDeiFeqBOsDGgTDvqXMMB2dkzTvq7dHiMjeRuNGbkBOONjhM9MrRULAmEJ7oHleOxCAcr9fx4tRpNR6Ax8nhppwsm7etj4lda2XpcCkiyGTOQSCNk27F/GJP1mf/79fxnz/44YDODwAnABw2/mW0Xsf6888HMIN0iJlBvvFGSo3UHLJR+WYgtfCoexGdjL1Yz/70x3j2mecrjLUNYLLC9nlOdlVFtZAY+nk4ZxhfMgYAmJqawsTEBOr1OlavXo3k/pQIj7MACENw5qtEG6h9ZHWa3KYBhKfkywZJqQkGsuRj6fOarjuLE9p9T4edU2PL6HWVCXsTLBpnALSxU5wrJ383us8pZ6Tmnx7XzTgYD/raVVN71+g0BQNC5Z2mlinZXNywT7xtHcBxcObJmLh2zpUMFwIqBGpIWZKVhQzM5whSRtap1gxaYYjljbxnqBOFCIjHhlM83PAIQB6ILc3t4ymtrCgTFrbKZJTKzZV6Y6AaiI1AdEokyjsGgjOy5h39I6qS+K6MKmoibgFinZtt+j3dEDUhdGmvVflZT8ICI76vqmBCWWocDyA7kKLEfumFqpZPpbSy4jJkAWuftqoQw3itgdGckrc9J+uFozKs8L63XI9tl1yS3yA6BtF5DsSXQUTTACKQd3HyVi1O4dTpJ/H9p09gtBHh5Rs3wKtdajzXyzdcgNFGHSLqIPF+ykXS8xgikgnMNiMLpPSYWLo3YxRP9KeVFtUFF67FJS/fYhyDiE5ChAdVnvd6EFtu3M5E1HkGwAyIRjDT6cBjPA57ieiYrGxkK6z7L1k+jnpD3ptHHnkEv/Zrv4atW7fi+9//fup6lgmPSyNDtgfi8BiHbhVFsddEVs3p51rnY5n76wH255Dgex6WrhhHraGT7rvDxYxxEGtLY6xE70IgyafLUyZXLmmy3PT8lCern8R3rfreLnFeA+QnntEcHaTlCor30V6vJoAQPqvFwq6ctOevrAFpz3X1GGH8vDGMNBOP6lSnLX+NQuTuSzuMMN6s5ec40YY0fJbmz84YPGKY6rQzYWGbTEaJ7yVU0jvVATEDEJKUC8dQcUbWvJMkBJdGtCFd43VAhTXs6MmzBxRk7CDDPqnKnobno+H5mO50ZASMbGE1veCZ3vSrJk8j0cqKZPiociuQ4qPDZ54hgdk+EWn9qjdedSVed/nLc38X4QREm4H4GggVLqPg6nhyE9FJnDg5jTpewJJmiNddcSlqzW09x5k2XIlICZKKAm8IgROXvScZded6qNCwzntaueZ8bNyywXhmER6FaB+SRlawGsRX9xhrQtQ+CYRHQXxp/ridKVCwBsTXlDuWqa2OAEC9w+O6Io4RqcozJKmIsf5QDQIRdLuomU6IsVqAwJpwHh8l9xkR0GjW1IKZraDkKrzbu+lzGquRJSKAFf+mZP6afHaWN5tq1FPoq4CElCeaOPpaTsgDIkuqRFaINN7HB6LsfNkBaCROxPcYkwYrIxCraGQp75zJCPEZBw/8OB8rEhEYCL7H0Qo7qHvd1z4UYU64Ng7dGXuwSnkOjzOITjYsbJPJ0LltRbQAWiLvPdUgfwPKu+UYKhV9w45BI3/ENt0XGx31Q/MBVjdquiTYNK+ypN7eCLAl8WqDiTGG8XoNM52OlHBg8u8mxWurJ0tE8QJfCWoi9mQRMBj5BnUwYpnJr9iT1a1fZTlmVygru8gyEEnVb8YJxErcqzgsm2qtw2V4RHpCTDpZTBmoAIjAGSnJjmRxzMokmM9NydeqvKjqHDrTcasdz6yTpY35XjmISWud5pImmmONuL9gfO3IB4TKnYZAK+xgSa1HzpHRWyyvO2dMjSj/+2Dq3oDSVbpm5KLOupoGZ7ZAz+uYCi+N12oYq9VQfp7IovKg+hYELmq7lRUi1RgSvUUHUj9K3j+uWmMxzlTUsHy4MJFxyM+rHmNxA2dAFkMEnocm9zBjCMEJAUNT6A7kdTPPKT5n8Em2nmJd2f0WmYxcSykDogXQmNpeVdCK3gKmjtnjjKyFAPXo75dFKffKxdqcpJlsK8p5emKtLD2x2EJOPDZCxoIaIghVXQiYvV95gyChxIJgGkVOK2tQDtlsRaH+b3PS7OTUFE5NSy/iKquRlTYwTd48mezMuSo1L2Uw6rfy5BoESuGaAYXhQpn+o4Ux1WSfMbK0TIIZSv6pLNCahKnSmEv1izEaWZToZBWGx5WRQUSoNwIEtXTIWh9PJ3QrCQYiNAp0keQttr2YyBwqRiYtLQZiHILLF5deEg5SO0kbf8Yt0Ns7wfLvDGIWRhYY0s9RNYoq48wFOPKz7D468V56ebRshn7mQXYNtvwJ7FpZPuepRuJJB4BljSbaUbdRFooIHjHUcpWF+neXnm+7qfu+bMPUdaNlmkj+9y0NyFjn0IQQICZ/10QeQCMooxLvmD3OyFoIkIeyDT4lbfUjgUo2Ldq3WH8oGYPyjKENvSDn0T9ulc/h+6hxDzNhmAoXZskbBAkR+qoMVO7yJBF0UJ4s01jtie/aizXebGC0YfNypPczLLLEwchTshFQvQt7kfe4+ZwBHoExATLeB1LvxUKKrYKSZGHSRla3TIL9+2hPUbVFVXqQTMa2XnTKPwv5cGG3t7AYaWToPpXJo556lsgHwMCEzDeseV4uFJTH8JtRIUDZ6Nd0/xk48bgRcK/cyUgIWQ1pNcZsOXnpc5ruW78vPARp1PG+vNJU5IURsBzT8N21Mryq1uYkQJyBPFIisFXmCK36njdapAc43QEgxJJaHQ3fg9YOTP+NeyyvaSZSkQhL0n/d8+IWX137MdM8UyzQKnRRQ7owgcacJ2uOcEbWgqCEu7cLkVTyqLCGnbIip4GaWFqQi4W5UirtmQk4R9P3EYpIvS2aE39jXRvj9+jDQKJAjXGQau9IiXiWCxd2N222HjR1KJO3QF5rzpXxU+p6JEnrGk4MHudqQbdUFzLZ4Jtzrfaur5+PMAwxPTUDoJeRpa9RH9fdWgWlc01m48lK37se05rKueFEsemZb0cjPTNEHRCAUb9HPpY1xC4lNaSMg2lsTObdkFB6VcVE6hD2nCyUeOkweT76fOEBZLVm3zlZva6pSRbD8BwRIMcv7xtT3j4Wi62WH5t8GfBhChcy1VYHUKFb5eGsez4Crztk2I4i1JiXqyxMKv08a6qHltbovs82mQydImB72W6p8yX7EmtWfLF39IszshYEJcXkYkTqDc8zrf8pyhlZ0sCoKyPLnNeTzR8jIiyt1cGJqbd00+NUUD2ZSjSuhnoLLBhr/5gmK3O1WlfTZityX+l1M90LDkYMnsfkIlzqeuQrNjmTlXJF1YVEOsGaKdHLDoAARISpMzLsyRhDrV5Uip/yZFW+d9z8rOqegRUW+VyDaNLXo8w1lN4OEpF8wWcGg12pvhOkMvx44TXR2PMYOQn11fNGNiMGRgLc7/0cy352hubeyeEs40ijv2f3c079VAcCkJWYtZ5eOOtYTFOD7mtqbe+V3lYdIPbKMjAKwbgHqFB85cpHqhkNIE4EUlbWdCeMPZwB5xj1fcyk2o21wxAjpp6FogOwUUiDyezJ8rlsKN19ny36hb1aDYmW8mKlPVkBQKx6z1xHZVx14YLAHEYxVbfETYb1pKEmFXNXeqCSxAFrAuEEZLKpzXLrnpybQYCG54GRXqRyB4U18b3UgmBAq3KLafSfcGsjM9ZYP8geLiz0ZHUl/ptCtwyMODwO1ZS693eRWkEM6QlaerKYevO13AeVXA1SOkup6q10qLA4d0XnY1VJJI5Hafm8ekuWTZs24ZZbbsHmzZu7x2Ztb5MdBwdjsuQ+VnvvakcjVd+J2qiXChXC8pIhk9plKNh0/7ncj4Tsi6eIRISpdgcjQb4yLRmzjV6J76ZFuYQH0HY4qkEwi9ZVTwz5YQASuQLTd8ne37S0gZwPOEXwOEm5kn6KY8ic6yrzvOT8MNPpYFm9Hns4l9TrOHLmTDIqEVkkP0IQNSCYAMJjxtMHjCPgmbCwNXzKk2iB8SWmBfDzM0ZwHTLM20JVEVlHNZyRtQAwKfaK6BgQnQG8dZmttfCo/rFpF7nNyAHKTp5EDZmISQV5TuR15QI1PF8tQObzmwyCbvrxQvmItXJoFAN1yKYS+yWyobHpHIeOSY2sNT09WYA2srIGrwzveag1fNQYKngDONKJqx5jqDdqqLOa5d7ptjqyslDWuoXQ1VtnTumk96JQoTxOcj0qTh+kw1QZYVoRAryakOVrXvMafOYzn0l9kg6h9goXynEwCuN8qaT7gLx2sudkgIDNYEmtjhGrPlYqcd8i4eBzDyMeg8/C3P2V3kWGoOajkcq3mWq3cWJmBjWPd8ltRIjgM6/YEO75DClPVlpfbzZSKGwUJPoVBLacU+gKPMN1V3lXyXOUDjfLhHKGDrzAg1cP4Neqh7aJ6hAGI4urFy4hBDoi7PJw1j0fjJKergTYQ8zkg9AwngOQrXXGanUEaSPLFj7tkZMl595MpwKqS4NNtNDdcFqvLyWLBBw9ceHChQCxvKNHtWIRIqtsrBtDa0+WvRKm6/ilxhEgCb9Z9skk6fucYUm9Do8E7BNZPucsqSir/gjKcnclqjfAnCx58MTIEkIA0SmArTZ6WfrzZOW/L2MeRkdraI43jH83H7a7IpURYWS0jpHxhnGs0phgANN6TFAVR/I5KldZCCTVolVFZIGkEs3UEqWXcdcL7THVXo0i5IJMSJLfjfl9rA5GAuvGx5WX0UaRbIT8fO34mGplZLj/4Kg3PYwtH4s/a0cRmp6Pdti9cEYC+VYrikSSo9fvwVP3MXUf+vUqAyC2HMRX9bWvfp5ETjNKK7ibjNtMuDMlbSCfSR+cRfA4x9iKMficqodCLbmusoU8IVQ9CdMezoaX9HTtRCE4sZyRlYQ2A+S1CRM8zrBufDz2ZIlYGzFvzMrvrF+0TQhQJmGeiMmQZSr5XYhIvtizNX2Gfh0m3JVcEGR/iDp00ZTGVhfZSiwPupmtEWtpuQFSjaILxRzzuRzrx8expF7PfY/kuCaJCu1x6NOZSk3oZN3BqhanDEJxSr6le2bBTZ2TJfsJWgeq/tGTa/66EgtAVfur6VZMetRM5lkVtb4jVcXGmQorpt6My1UW6u+D6jkueszGnLfQnGtSwNTUFF588UVMTk6mxpUK+xWiEt8RStMsLgLIPkt1mJKf82hPluHepYspBCz31wdlVttOFKLh+2hnRDejSFiNrLKq7XIBNf0mB5nbWBaLF0a0AbKFrtMGNaDnxNiQYjUQ5O8pjCIl6NnHC4FhF8bkC8qZdhs1j6ORMrJ8zjDqB5jpdNAOpZGXqyxMRyIoAMhkYBrQeVW21kVxi7HMbrFH0BQSHENXhEGcBNg4yOvTYHYYcUbWgoC6f9BiGkAdssw2Y2SJNsCa8eQjJ0zZAqTw+KXQzZeLJup8/pgci7C//WQMAknJnooWpFYWk5VNA0UaAdKLNQmwdSAyGx66urAwXJhdZI3fl6sQXpFxaxpnch88InjQiuHm+y3FGaXiO9ebUNaTVcbIYpbckDJj5vnSeJGMoyxf/vKXsXLlSvzWb/1Walwyz6xXDqI2MrTKOtP5aZlnSRp+Zap+i1r56M+j1P9ntmAmT69ctDsZT5YQJYysUrp4BmHYefFe6DkgOz901IuUAdJzlN4nzBgRtdiAjiBU5XPF7xarvnePi6uXlOlOByN+kBMaHa/X0BEhOiJEjXNDuFAbPQFi6YtShrzMnbK+jJBvrhYU+crCeJeUd0uIEBBnZKusvnpQOmw4I2tBkNVRmQLYGIifB+Pkg8zkU6T6XiUMEGu32IX7yPLGpAZi+dwkUVFhQTAORPcw7LciyoYK7cVerJXGrYQQJasLUx4W673wpSp1gYGUp9vYlaXqrIc3zAMjgkdMeU6Sir7TZcOF8ffp08giw4Ial9+XJ1dd2OUxLHENKQBIejkY00ZQ5juVLqjoHS6EbqViGBvBAyi5JpGIZCEDYwgz1yqCUL0GbePo7cmSJw3iRTmu5JuX5cBmZEXqRcqyT/rFTXS6jAiiAMSEvLeA1ISr7KVL57qmzqzU+TtRhPF6fnx1zwcHw0wnzBUtyLG2k3k2nm9LGFmipSoSi8Zrmpd1zpWpKlFGH6SBdQKg5QAzz3eO/nFG1kIgtyi2QGwZZFJ3ACFmkj8JAWLZN40ain+oZScYbbgUbW9ZwARgaxNhrp7UYZ1+jawAcmIZ9FuXCqNEJwF2gdWLdfzUaUy3pKu9OCcrvXjZFmIPHKIPT1b3pBp4XN0dm4EsW4wwRuCkcpcqCZGqYxMrX7HadX59v5OFKxGUrXa8nE5W2mNYxmuhjIykCMCklF7W8CvKLyT1u9DPf/55Zyo/TIeNZKiJYTSQz3Z3OElkVMDTf9LJ+2WeIdm/UdJvc+gBYJUfSMvUZMnuE3YbZEpHy2NSB436CRfGua7d8yon+Wmde2h4+WdW5mVxtDod49+lh06HQdV8W9gWTSEiEBsp2MBiZCnjzPjSrI0vcQYQMyDvglnIeDhsOCNrQSAnRiEi9VZJABuR7nJqdIcMCcguBtK9WxDWKJmzRMTk+YoazJqS9GPK53HJZNV+3jA1Mq+hLzHTAohY4km0eLEA4JDyYi0fG0OtoPIsXmRFBFviOxGHRyzu/1hunHnD1WdcvbWbj8HU3xnjINLCqJmWOqO9jCy1GPctm5ENU4VIvAblMTaI7qp8LDeO5Lob9MVItz7p1bxd31fTeVlqGxgNMSJf+U+VkRWFCBjHiO/DI4ZOJgxEVoOhvDhvdyi0aPzDhiErVpyTqclAcRWoui85vT2px+YxDmil/crzjGc0gBiT4rHZfCyNzxlG/MCSjwXlydLtbdR8a628VrvEXlC71IJdOT8Ekc0DFgBUA6IjAJ0HsPMLx+HoD2dkLQhkSTIQqXysBkCjqgJkufos5dbPTj4WJe3YS1DlNlMDxQueLbndHuoyt1PRnqw+H0FSeQYD1cgCpFHkA/yCgnAFcPglKd9Q7MVSxwNQ6O3QHibSXo+S48wQcF7QOBggxkFMSP2gVN/CTruD1oysMioVLuzp7SyA1TK5I7r8vr9wYXeD6LShVYzuf+fpIgCjMK69UXAXsQepKElb/xYN4ULywBDFv5B2KPWVatxDwHmuwtDqyVJGVqlCkK5FedDtqaqQmvti2uiWqTGQ7l+YKuCQyP/2GIPyZZXzbqYPH4vj5g1sjwOjPgdnIYRoq3+S8Y/X66j7HmrGsG4mDErNEp4srRBf4LUnbn/5tcxjRASwMelZ89YP/IXVIXE6WQsC7dGJlBdleZzgSGwcIhQqZKAXRkNYwzivVn9DJTYCEc0UbdHjXJZ9ckSQKtH9Vgb6atIZ8MRADKDzepakl0p6B5As/EV5Lxw+Y4iqGJ0Gj6LHOSJR4MkiDsYYAk/lmpAMI5w5I71Y3OMIgmJjR+pH2UQiy5Bp7is66Kcli/ZkdTeIruDJUouSz7lqawPkniXt6UvrSRnRN8LkpUrrxJnHxpj0vGiPRRiFGAl8eJyh5nk409ZGh/qVKYNBRMflfBGnoTGAL+/93aG+a+zImuULzyyQzxNHd5Wb6u1XVFxBPhBFKSmY1HOrEuN9RmBxQU4f8wxrAOGp3Mej/CU0/BEgOp76tAPwNQCApuerNkyWZzo1VpseVxeipa5HkWgoz31FIVqQ82SBB4yNQYjVADuveAyOvnFG1kKA9NucAEQLxFITJY1A5h3NqMneh9GTpcIa3W8jfVTwsdUgVlRRVWTU2KoLTSHGqLL3ouuQRABfK9/EBgktAXn1nhU25ZLegWRh1YusWSh0WaOBpGqqDPntVjQaiDqjsC0ojAdYtnIczRFVKKGqi9JCpGWMXuKr+77uREFmUZH92Kq+RecT39P/LnMN5flWj47KqyVOmj1ZVKQ/pOn1MsNURaV5G0YcRAyR1mcjqfgNyCbsJ6a1J1vm7TFd9i/OgLzNao5Q4S1bRV4OnvKa6q4G8+XJ8ACkX+zakC9gRfODzkFSntCu/DlptHMWyQT4vnM/81XbQnSwenQZeLA1yZESkxDtH8YdOkZrATZ6y5QuWnpf7TFMGY+lqnRbAMZ65EGaQpPTyttfkALAVoPYSqeLNUSckbUgUG+RoiMNknSCIzWR6GUR5IKUnXx0+EZPOJoiJWozPRe7oh+j9W+Gz0VUnPtVAuKDzyEgNoqcOrIBLUS6pke4MPZk6DwT473gqsKtijeB5Q7FGAPxYk8W90hJAERxUn+S9F5ugSY+mwqkzNhEB+DVhUgvu+wy7Ny5E9u3b9ejQnx9Sy0YUgeJM9UoWeQNIHnvAgCTxiMk9NJr0+FC/TLVDZH0uACqshAs9oLUPT/O1YpUcYRUHW8BqKlFsigh2kIuXDg/niwAch4IM95NvrTHTtrI6i7gABC/rDCK4JNOfK/+3YhqELlc0ilw3gTx82KjR0YKA8RhPdiU3rVGVtrrJtXVTS3UknO2AD7eY7AsfxwxpdIe7HN6UoziGBbOyFoQqIVBnFZGVTJpEhEEWwGEPwHgA9ywEKpmtvmwxjByLaTRlv4xF7cVAUwGQZLwvDg5VNqTBSAWTvTNBkC8CFQJaxQZu7bcOFl96BFTkgKZpPeelYUDIJc/GKKfCtEdO3Zgx44dyWGJQXTlZfUaR7IoJUaGYTpkdSA83uNgPfr+xV0SPONvkZRXKRJAK4zgeQyBJ7erqcVaCIFIpHW9zsh5orTnKotcXGWe52xD97MlK4xqkKnJELciM4ab5XXm6KQ0svr4bibVdzEF8DXdXiWqq21bxZ6pWBg0vY3WJtQGmGk/AWK97nO64lI/YxGILemxn2PYOB/hgkC94YrTABvPCc5J74p+ezUthL6aaLJhjWFUDZlKrksYWTksHeUXCeVa6mi0kWUrk08lbJd22+e3S4xdM0QUiymmk4XLyzcMgmzuSDRA8UN9HUt6suLnuEizzdwouJseKvNxCxtLTpYyfoUQ6EQhApI5eoDMGfOIoyNk/hFjsvdkkrvZr2GkPRjq+w+8gKQKGfkBEeXawOSIjXVpnFC2uhAeOBPgXID61uMz5LqKDogt7R4KeZDzcgvFpDSy4p2VFE22fZo+nS6q6Pkb6Z6XkzY8fXg5HQPFGVkLgKQkmaQ+VhY2CqAGiLZBI0vlJyFArgqqsOqpX/TxTEZWhXBh4ecLn6SlTgkjSwsnWu+FniArVIIaqzntCdj6cyItAEDIerJGSoYLZ0V2setD7R0AWq0Wpqam0G6nFycdjiubk6VeTAp6/hErofouRI9FnKdysvL3nzEOKQ8r0A4jNIMk9ChVwxnaYYgwkq1imFB3kPUIIRWhFM3l959dfuTsyXiytIZUIepFQYRxbmGyu9yfIZSmdJ/hQh121BIeQrTlZybDhY0h3QfQiBJN7TaMdd6frcJQFz30MjozL79iCqDGLDydjkGxeFe5sw6ufhT5HzBRXZXaFiicswZy7Up6Lrr9oBex9MLTI8G+K6yohzZfJeOzJ4oiHD52HEDJcGHsZbQZAOl8rCrhwmzbj6L2LnKx4UTgpD0v0pM4p+HCrjAVujxqVfiLv/gLNJtNvP/9708+jBfTMuHCdLPqUO1reh7LjC0JvZpR57H0BNU5M0IAoQgx4iceXs4Y6r6PtqqkYwA4m4HMx5qNl0JpncXhwvnzZKVzhhKZml5ebr2PLJzIoZp7S6+tOReuN7qKVutxTVsNF2JNGNvadNHOze+y2X3D6slKKgvLeLJSeoRiGmBLnSzDAsAZWQsF8qxGlvz7cgC1gsnH1MxWi/AN8DbHE1bVcGEmxEg98lgWMEcnJ9EJQxARVi4pk/OQDheacrLUNS1szG06puk+6L+ZINXgVmtTzVO4MO4PadF9K0G+uhCo5g1kiYdRGRnGBUklJhejXzJscBS9iBBJgUuBKO5ZmKbpBWiHoerDxwBMQ3aD6OHdKCTt+ZCJ+/MGcfnSBcCYHG7bBwSgYwk3B2AUgVMkw4X9JHfHua5qXhVTynAxGKRUB4j1aPYszGOlJqyeLNECaKSEscTk/KFfXkSYC2s65geX+L5QIB+gJdayZeKjEGIMtsmHWAARZn/gsvJmsAmtesLqbvVRnE+kJsToiNRYiiepxfn4aY2s85eMwze2zsiiPX82ocg+woXxgp29D3ZPDhFTEgAiWUCQ9mTNRbgwFaaCQOItqEZeJwspY7VsTpY2fsICQ0+FjKLjclvRQSIsqg3VVo/voMfILKXyDMQ4Om2pQF/L6CvVPPl7i4SA5zFAzAD8wln9rhO9s2nApHY/p6Se4zgkV8aTpSuy8/eOyAcngLFEIqM6ulChjOFSg5ybVdWnEXOroGKtrDZAZcLCSfg7CWu6UOFCYHGucmclzeJkTxoH8XWwa54YbqU4A/DVAxldApeLmOik1vIeyvLUAHlbIMS0dGOLGRj1vhYJ1ZLegWSRtS1k2jCoHi6s4lEkyGbIHCGAJog8tFpttNvyLboxZ4nvuhJWGVkD9WSVy7+R8gwegCklJ2JZ1KkGsCVqvDWA1ZB4jWeAaFouxAVGgayE0+MzwcAYRztso+bXc+1YaqrSMIwEfJWLR3wA+nCk+tYB8xy6TxdDdCBlanotTepljwjGuY98BJxhWa2OGu9vnpGGaADgdHE+FiCfDVIJ7AZvVWGroEJvqehdBID089xOhTVd0vtCwBlZCwTmbyz8O5FXbDBlfrwiOg2gLoUjBwgRh2BLgfAQADXRi14eFA/wNnT9NS+cuniolPQOIDaumO3nliRsl/ZOpBsixxQbWZwx1D0fNQ/xQqBDhb7v91R7HwypMJWI0I/aO2Bqq6OOXaXQg3wgOgXpqTAbSUQ1wH81ALLem6R9lfVE8h+r4SA9jO2wg/GaF1cWanzG4TOOmTCEx2YAjA5mASUfiEL1CM3jbzE9D4g2wEuoj8ceUW4x0qUBtrxZm93LHGsA4UkAU4WGC5EHQSOAOGE5UEGrIAoAlV+Z9rjFfWwLld7Tx/FlhbpoAXztop1fzzYWZ1KMw0CmmW10Qv7QWG9hzaoQW4ruflsCRb0LjcdYxBPAoYqerOS7mhfZpLq0yjWxhW1hDZcRcaweHYXPEE/cc5v0rr+rTibuQDb5rj4NGcOF8XRWtkIzgA5bFsmJEBUbv73+nhhZlvtLDAwcAhFG/LyoaY1LSYdW2EHA2gAbG5DshWpxRMD8LgXpIg7pZS23jzbQTZ4sZYSJ2cpTKK+lmALYsuJ5iwoqDONWQSaDT+egZfOySvQs7Dq/Lz2uInT6WAsIZ2SdNejJpiO9WNQYuBcrhkYA8pUWC9BbwuHsonxLnTS6gtAGrxiyUYnyuerCImNXfx7GTWpjI2t0bowsOQy1uBvK78tiDhdqL1nZ51D3UewlwTBL4lCw3ZNFTDZ/aRq8iYwxNHxfCcl2uttuzWZYlNanmudwYezdFEo2o8Q+2mA3Gi7p6tH+v5u8RlKwtpfhQqyhvPompCfLaMxTkIQa04iWMrDKGtQqJ4y4CxUuIM6NVfFcgJTeiujIxqVD8mLJc41I17mYUh8MQ/R04XKoZEudBH1dCt6oyawGbsdQ5dkjbBt/ntKmmtvKQj0M9cat8m/64ZWvfCXe9ra3YevWrclhVYuQsiHXeAEderhM3xN7Th4nDp8z+My8TdMPQCSkUTkr6Yb0abl6ZnRl63yhvVLaq9bbyIrbwVjDzdoz3Lb8vSTkI+nW0OO6Uw2grKyKQnRk6NGILkLJ6hy2lNey3LxKFBTKTDjmB5eTddagqqXECYBGhufFgs7LWg6EzwMYh1a8nr+2HHNLZU+WCgcWJvOWkgro2gHJm7qmR9hW35+UNtWcVhbGlAvTFbFr1y7s2rUr82lR3pMB4onzb6hGljZibOdgYMThM1vPO6DGPTR4B0RLBuil4EikVOY7J4uUJ8eWY2VCeqvM0ht6PhyEkcUR95AtpA5pMMkG193YWwXJ1mlNIDyS+UsbZfqoJqgXXZvMhGNeODdcD+cAUtSuliQ9Dupt13Y+No5kgdd6XOcGcXXh8rJhG109WGREVcvJonjRNgkgFnmyupOwz5ya25wsIB2CwSzzZXJHRrUFNVXVNtQcQVURWJT4zhgCTnFj6CyBx1H32iBaOsB2VPpc82xkxeefgfQYlfx+LCgIN3vqNzdbeQplrJUR9iSlY2jKyxLC2K0joQGTVlbxPtmNOUA1p4+1wHDm7tkE1QFaNlQvVnIu2epHiBlIN/+5YWR1whC/PHEcQJVwoa6CKrpG/fwUTcr7Rblx6bBVNlw4h54s3XeOgH5FMNMNypPjsopGm4fZaHWVRxsRtoIEqZ9V81hOiFQTcI46B8irkgfYAx1ym/d8Si2k2VJeuiqeLFsxCVeSBur4fSNlG8q0MCLissIwOmb4o0BRGJRYHaITqqpwIO6HWbayEFDbD6jy1DEwnCfrbIJGAH5BiY7tgzhXM8nLinOBzn5ePHECUSTbdawYL9s7rniRBWTSLFlzNmyHbRjemnvlZHWXvc91daGEAbq1T5+erPe85z0gInzkIx9JfRqArDpyBuJ+bwPuipA7j9Z0sp/DZwHGfPsiXGMd1FgdAR9k1ViqUGC+dbLApX4e6uU9TzRS7LGnGnpd997nCAAaQzlBUMht0Z3ALl9Ee7TGoQbAliJu8wQh/7/K81w6rOmYS5wn6yyCeevn7FxEDIKdB4QHgS4V97MbHSpctXQpuMXrkCfdm9CyBV9beSzEV0GEhyDEjCrp75UbpwwwlSwshJgnIyulcdSnJ8tUXUh8BYAVFY6iPTlVPWBV6X3/lzZGgXpB7ztxHOvPuxjkldCQKk06eXz+XpKkkCYHUJQcnqfnfKf0p2bz3Yg8UPDy8tuzBkQnU2EYHQP4GmWA2fZbDgRXodszbesQYIHJjiEuH2thcW64HxxDgdgYdHnzufIoHepLvqG8EnklaBnAV8lqUgBJlWevsQQg4mjNtGJjZW6rC/Wi3r9xbtbJqkrayBh+TlZhTh551vJ/IaYBeCC+drDFJXGT7F7yInOA0rWigXphaqjWD3QAZCoMpReLg/i6nveOSBbHJP9UGzeRVyqs6Zhbzo2V0TEcVF4W1CJwLlC9pQ7kRE86EXdwEJHygJGazHsZWepvrFsjK6gF8Er1YBwUanEnD4P0ZFUmNjKKNKwGAUudy0ZWWDZFdFwa0zRogUkWG5j99fYbILqX5sCS+nWBhY+5XeZqkLlXKmQYHRvSvXMsFpyR5egfaihDq41z5VGKNbIqC5EOSUeMlipv1jFICYeCc8TyDt1G1sjoXOdw6DyooG/PzMA8WUrQcrjyI2U8meZKUSGmAPiD92IBSJpkL4QXJCUqOkAjSxq2g3+5KT5nPa4wHJoH0rGoWHQr4z333IONGzeiXq/j6quvxne+853C7b/yla/g0ksvRb1ex9atW7F///45GunZDxEBbDlQWJ5+dtGf2vuQwoVIe7O48igWnUOGhbQEwLwIkQKINYz6VHsHBuPJIi1kOciF3XwmSK9RdSNLe7GG0SaFdHPlBfHb1V6nQd4L7Smdu2WOiAFsVCbxD80D6VhMLCoj68tf/jL27NmDvXv34nvf+x5e9apXYceOHfjlL39p3P7b3/42fvu3fxs33XQTDhw4gBtuuAE33HADfvjDH87xyM9eiI2qapZz402tr3BhrEs1pJ+b9maJ6R5VYqzLWzA/Se9A4kGavZE1O08WIKu+ht0Yu/f979IOU0gvVtBXUUT5oc3F9y8xDB06HuRYSFfRzvUyNwqIMxieB9KxmFgIrzCl+eQnP4mbb74Zv/d7vwcAuPfee3H//ffj85//PG677bbc9n/5l3+JN77xjbj11lsBAHfeeScefPBB/NVf/RXuvfde4zlmZmYwMzMT///JkycH/j3+9//5ICZeesncfgEAI4ZGI1n4pqamEFm2JWJoprednkYUheZtQWg2k9DQ9PQ0Qsu2AGEkve3MDMIwL5ankdvKyWRmZgadgm2bzSZIb9tqodNp27dtNONJqtVqoV2wbaPRBCu7bb0Re0Fa7TbabUtjVwD1egNcbfvssaMAgG/e/xCe/8/v5ra9aNMmjI1KleajR4/iF7/4BeTiKQA81LXtxo0bMa5kII4dO4bnn3/eOoYNGzZg6dKlAIATJ07g2WefzWyhCxCexAUXfB/LlVDq5OQkDh48mNouhE52Hh0Zx0hzJNbI+u///m/s3r3bOoZbbrkF73rXuwAATz31FG666Sbrtrt27cLNN98MAHj22Wfxzne+s3uD6JQqjw9w44034v3vfz8AYGJiAm9729usx33rW9+KW2+9Fa9+9asxOTmJjRs3WrctBQswWO+JASrjyTQsxNFxgG8YbjIzBVgYywBTYxmwJ2uYLzcWiNUhiAF8pWvU7FgQv65StFotPPbYY7j99tvjzxhjuO666/Doo48a93n00UexZ8+ers927NiB++67z3qeu+66C3/6p386kDHb+MnEYZxszRRvdHqy/AGrbHvm1JC2Pd17m7NkWwJwyZoLsaye9wCdmZzCmcmp+P9Xnb/Getip0zOYOv1iqW1nptqYmCq3bXsmxMShctsCwLLzlgKQLxSPPPKIdbvf+I3fiP/79OnThdu+4Q1viP97amqqcNtt27bF/91qtQq3fcUrXgEAuP3227vmgv6pDVBB3UYJLS7VEUCEh1IfNobrxQIgE7UXgKeFPIBGB+z10TlZc1w5SXWAVoD4urk9r2NBsmiMrCNHjiAMQ6xatarr81WrVuFHP/qRcZ/Dhw8btz98+LD1PLfffnuXYXby5EmsXz9Y/amPvuud+NGPf2L1nvhBgItSb+g/+9mzaFmMMs/zsGnTpvj/n3vuOUxPTxu3ZYxjy5bN8f8///OfY+rMGfMgieHil22J//cXv3gBp0+bjS4hQrzs4ovB1GR26NAhTE7aDb/Nm7fEGlOHDx8u9BZu2rQZnieP+8tf/hLHjx+3brtx40UIAhluePHIERx76SXrthdeeCFqNSkOePToURw9etS67fr1G9BoyNDWsWPHgDNncMFS8xvqZZddhiXj8m8TExM4+LODsjJfzORaZFxyySVYtnRZPN6f/vRp6xi2bNmCFedJDaijL72En/zkx7lthAgBIbB5y8VYef5KOd7jx/HUU8nvQ0QzACUJ51u2bMaml10Yj/2f//mfrWO4/PLL4/++6KKLCre99NJL4/9et25dblsRTQFK02fLluQ5O++88wqPe9FFF1n/1g/krcOwPR1EAeBtQKESN1sB8l+V2dFXMilDHJtXbIDPGWw5qEBHqh+INQFvQzWB2oGceBzkb3FyCg4Ai8jImitqtVq8+A6Ld13/68D1vz7Uczjmn41bNuDq1/5K6W1/5X+9pty22IArt11RettXX/XKUtuuWLECv/mbv1lq22XLlpXedmxsrPS2zWaz9LaDYK4W4F5hI6IawM+fk7F0n7f/vLhBQip0PPDjzkMfPyIm8yQdDiyixPcVK1aAc46JiYmuzycmJrB6tblX3+rVqytt73A4HA6HwzEoFo2RFQQBrrzySjz0UJI8HEURHnroIWzfvt24z/bt27u2B4AHH3zQur3D4XA4HA7HoFhU4cI9e/Zg165duOqqq7Bt2zZ86lOfwunTp+Nqw9/5nd/BunXrcNdddwEA/uAP/gDXXnstPvGJT+D666/Hvn378N3vfhef/exn5/NrOBwOh8PhOAdYVEbWjTfeiBdffBF//Md/jMOHD+OKK67AAw88ECe3P/fcc13ihNdccw2+9KUv4cMf/jA+9KEP4WUvexnuu+++uELJ4XA4HA6HY1iQEJaupA4AsrpwyZIlOHHiRKxp5HA4HA6Hw9GLRZOT5XA4HA6Hw7GYcEaWw+FwOBwOxxBwRpbD4XA4HA7HEHBGlsPhcDgcDscQcEaWw+FwOBwOxxBwRpbD4XA4HA7HEHBGlsPhcDgcDscQcEaWw+FwOBwOxxBwRpbD4XA4HA7HEHBGlsPhcDgcDscQcEaWw+FwOBwOxxBwRpbD4XA4HA7HEPDmewALHd0/++TJk/M8EofD4XA4FidjY2MgovkexpzjjKweTE5OAgDWr18/zyNxOBwOh2NxcuLECYyPj8/3MOYcEtpV4zASRRFeeOGFgVrhJ0+exPr16/H888+fkw/dMHDXdLC46zl43DUdLO56Dp5hXlPnyXIYYYzhggsuGMqxx8fH3eQwYNw1HSzueg4ed00Hi7ueg8dd08HhEt8dDofD4XA4hoAzshwOh8PhcDiGgDOy5oFarYa9e/eiVqvN91DOGtw1HSzueg4ed00Hi7ueg8dd08HjEt8dDofD4XA4hoDzZDkcDofD4XAMAWdkORwOh8PhcAwBZ2Q5HA6Hw+FwDAFnZDkcDofD4XAMAWdkORwOh8PhcAwBZ2QNiXvuuQcbN25EvV7H1Vdfje985zuF23/lK1/BpZdeinq9jq1bt2L//v1zNNLFQ5Vr+rnPfQ6vf/3rsWzZMixbtgzXXXddz3twrlH1GdXs27cPRIQbbrhhuANchFS9psePH8fu3buxZs0a1Go1XHzxxe63n6Lq9fzUpz6FSy65BI1GA+vXr8cHPvABTE9Pz9FoFzbf/OY38eY3vxlr164FEeG+++7ruc/DDz+M17zmNajVatiyZQu+8IUvDH2cZx3CMXD27dsngiAQn//858X//M//iJtvvlksXbpUTExMGLd/5JFHBOdcfOxjHxNPPPGE+PCHPyx83xc/+MEP5njkC5eq1/Qd73iHuOeee8SBAwfEk08+KX73d39XLFmyRPz85z+f45EvTKpeT83BgwfFunXrxOtf/3rx1re+dW4Gu0ioek1nZmbEVVddJd70pjeJb33rW+LgwYPi4YcfFo8//vgcj3xhUvV6fvGLXxS1Wk188YtfFAcPHhRf//rXxZo1a8QHPvCBOR75wmT//v3ijjvuEF/96lcFAPG1r32tcPtnnnlGNJtNsWfPHvHEE0+IT3/604JzLh544IG5GfBZgjOyhsC2bdvE7t274/8Pw1CsXbtW3HXXXcbt3/72t4vrr7++67Orr75avOc97xnqOBcTVa9plk6nI8bGxsTf//3fD2uIi4p+rmen0xHXXHON+Ju/+Ruxa9cuZ2RlqHpNP/OZz4hNmzaJVqs1V0NcVFS9nrt37xa/+qu/2vXZnj17xGtf+9qhjnMxUsbI+sM//ENx+eWXd3124403ih07dgxxZGcfLlw4YFqtFh577DFcd9118WeMMVx33XV49NFHjfs8+uijXdsDwI4dO6zbn2v0c02znDlzBu12G8uXLx/WMBcN/V7PP/uzP8PKlStx0003zcUwFxX9XNN/+Zd/wfbt27F7926sWrUKr3jFK/DRj34UYRjO1bAXLP1cz2uuuQaPPfZYHFJ85plnsH//frzpTW+akzGfbbh1aTB48z2As40jR44gDEOsWrWq6/NVq1bhRz/6kXGfw4cPG7c/fPjw0Ma5mOjnmmb54Ac/iLVr1+YmjXORfq7nt771Lfzt3/4tHn/88TkY4eKjn2v6zDPP4Bvf+Abe+c53Yv/+/Xj66afx3ve+F+12G3v37p2LYS9Y+rme73jHO3DkyBG87nWvgxACnU4Ht9xyCz70oQ/NxZDPOmzr0smTJzE1NYVGozFPI1tcOE+W46zn7rvvxr59+/C1r30N9Xp9voez6JicnMTOnTvxuc99DitWrJjv4Zw1RFGElStX4rOf/SyuvPJK3Hjjjbjjjjtw7733zvfQFiUPP/wwPvrRj+Kv//qv8b3vfQ9f/epXcf/99+POO++c76E5zmGcJ2vArFixApxzTExMdH0+MTGB1atXG/dZvXp1pe3PNfq5ppqPf/zjuPvuu/Hv//7veOUrXznMYS4aql7Pn/70p/jZz36GN7/5zfFnURQBADzPw1NPPYXNmzcPd9ALnH6e0TVr1sD3fXDO488uu+wyHD58GK1WC0EQDHXMC5l+rucf/dEfYefOnfj93/99AMDWrVtx+vRpvPvd78Ydd9wBxpxPoQq2dWl8fNx5sSrgnroBEwQBrrzySjz00EPxZ1EU4aGHHsL27duN+2zfvr1rewB48MEHrdufa/RzTQHgYx/7GO6880488MADuOqqq+ZiqIuCqtfz0ksvxQ9+8AM8/vjj8T9vectb8IY3vAGPP/441q9fP5fDX5D084y+9rWvxdNPPx0brADw4x//GGvWrDmnDSygv+t55syZnCGlDVghxPAGe5bi1qUBMd+Z92cj+/btE7VaTXzhC18QTzzxhHj3u98tli5dKg4fPiyEEGLnzp3itttui7d/5JFHhOd54uMf/7h48sknxd69e52EQ4aq1/Tuu+8WQRCIf/qnfxKHDh2K/5mcnJyvr7CgqHo9s7jqwjxVr+lzzz0nxsbGxPve9z7x1FNPiX/9138VK1euFB/5yEfm6yssKKpez71794qxsTHxD//wD+KZZ54R//Zv/yY2b94s3v72t8/XV1hQTE5OigMHDogDBw4IAOKTn/ykOHDggHj22WeFEELcdtttYufOnfH2WsLh1ltvFU8++aS45557nIRDHzgja0h8+tOfFhs2bBBBEIht27aJ//qv/4r/du2114pdu3Z1bf+P//iP4uKLLxZBEIjLL79c3H///XM84oVPlWt64YUXCgC5f/bu3Tv3A1+gVH1G0zgjy0zVa/rtb39bXH311aJWq4lNmzaJP//zPxedTmeOR71wqXI92+22+JM/+ROxefNmUa/Xxfr168V73/tecezYsbkf+ALkP/7jP4xzor6Gu3btEtdee21unyuuuEIEQSA2bdok/u7v/m7Ox73YISGcH9XhcDgcDodj0LicLIfD4XA4HI4h4Iwsh8PhcDgcjiHgjCyHw+FwOByOIeCMLIfD4XA4HI4h4Iwsh8PhcDgcjiHgjCyHw+FwOByOIeCMLIfD4XA4HI4h4Iwsh8PhcDgcjiHgjCyHw+FwOByOIeCMLIfD4XA4HI4h4Iwsh8PhcDgcjiHw/wFgMJbHoCKF5gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -538,7 +899,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 720x600 with 5 Axes>"
       ]
@@ -548,7 +909,7 @@
     }
    ],
    "source": [
-    "LocalTwoSampleTest(model, data, save=False, show=True)()"
+    "LC2ST(model, data, save=False, show=True)()"
    ]
   },
   {
@@ -562,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,9 +941,7 @@
     "    \"plots\":{\n",
     "        \"LC2ST\":{}, \n",
     "        \"TARP\":{\"coverage_sigma\":5, \"bootstrap_calculation\":True}, \n",
-    "        \"Ranks\":{}, \n",
     "        \"CoverageFraction\":{}, \n",
-    "        \"CDFRanks\":{}\n",
     "    }\n",
     "}\n",
     "with open(\"./my_full_config.yaml\", \"w\") as f: \n",
@@ -591,131 +950,207 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/cdf_ranks.py:31: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  thetas = tensor(self.data.get_theta_true())\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/cdf_ranks.py:32: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  context = tensor(self.data.true_context())\n",
-      "Running 10000 sbc samples.: 100%|█████████| 10000/10000 [01:40<00:00, 99.17it/s]\n",
-      "Sampling from the posterior for each observation: 10000 observation [01:44, 95.86 observation/s]\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "Sampling from the posterior for each observation: 100%|█| 50/50 [00:00<00:00, 11\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/plot.py:75: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/Library/Caches/pypoetry/virtualenvs/deepdiagnostics-081AeCAa-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/maggiev-local/repo/DeepDiagnostics/src/deepdiagnostics/plots/plot.py:91: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/ranks.py:32: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  thetas = tensor(self.data.get_theta_true())\n",
-      "/Users/maggiev-local/repo/DeepDiagnostics/src/plots/ranks.py:33: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  context = tensor(self.data.true_context())\n",
-      "Running 10000 sbc samples.: 100%|████████| 10000/10000 [01:39<00:00, 100.83it/s]\n",
-      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 555.41it/s]\n"
+      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 767.85it/s]\n"
      ]
     }
    ],
@@ -725,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +1175,7 @@
        " 'ranks.png']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/src/deepdiagnostics/plots/plot.py b/src/deepdiagnostics/plots/plot.py
index 26b81e3..054777d 100644
--- a/src/deepdiagnostics/plots/plot.py
+++ b/src/deepdiagnostics/plots/plot.py
@@ -55,10 +55,9 @@ def __init__(
                 "plots_common", "default_colorway", raise_exception=False
             )
         
-        if save: 
-            self.out_dir = out_dir if out_dir is not None else get_item("common", "out_dir", raise_exception=False)
+        self.out_dir = out_dir if out_dir is not None else get_item("common", "out_dir", raise_exception=False)
 
-        if self.out_dir is not None:
+        if self.out_dir is not None and self.save:
             if not os.path.exists(os.path.dirname(self.out_dir)):
                 os.makedirs(os.path.dirname(self.out_dir))
 
diff --git a/src/deepdiagnostics/utils/defaults.py b/src/deepdiagnostics/utils/defaults.py
index d9943ec..2412a55 100644
--- a/src/deepdiagnostics/utils/defaults.py
+++ b/src/deepdiagnostics/utils/defaults.py
@@ -2,7 +2,7 @@
     "common": {
         "out_dir": "./DeepDiagnosticsResources/results/",
         "temp_config": "./DeepDiagnosticsResources/temp/temp_config.yml",
-        "sim_location": "deepdiagnosticsResources/simulators",
+        "sim_location": "./DeepDiagnosticsResources/simulators",
         "random_seed": 42,
     },
     "model": {"model_engine": "SBIModel"},